[ { "path": ".gitattributes", "content": "*.ipynb linguist-documentation\n" }, { "path": ".github/CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at team@opengenus.org. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n" }, { "path": ".github/CONTRIBUTING.md", "content": "# Cosmos\n\nWelcome to the universe of algorithms and data structures. Before you begin contributing you must understand the concept of our organisation along with our goals and inspiration.\nRead all about us [here](https://github.com/OpenGenus/cosmos/wiki)\n\n## How to Begin\n\n`“ Getting started is the most difficult thing to do; once you file it out, they rest of the journey is as soft as the straw. Be a good beginner. ” \n ― Israelmore Ayivor`\n \n 1. You can start contributing by selecting any field from our high-level structure that interests you the most.\n 2. Once you have chosen the path, **make sure that the algorithm is not already implemented**. This step is very important because we dont't want your time and energy to be go in vain.\n 3. When you complete step 2, read the [style guides](https://github.com/OpenGenus/cosmos/tree/master/guides/coding_style) for the language you want to implement. The coding style for every document is required to be similar so that it can be processed quickly and precisely across all platforms. \n 4. Last but not the least, follow our [code of conduct](https://github.com/OpenGenus/cosmos/blob/master/.github/CODE_OF_CONDUCT.md).\n \n So what are you waiting for? Go on and start contributing! :smile: May the code be with you :metal:\n" }, { "path": ".github/CONTRIBUTORS.md", "content": "# Contributors\n\n> Every work is great only because of its contributors\n\n## Contributors\n\nThanks goes to these ❤️ wonderful people who made this possible:\n\n\n| [
Aditya Chatterjee](https://github.com/AdiChat)
| [
Vytautas Strimaitis](https://github.com/vstrimaitis)
| [
Lauri Välja](https://github.com/OFFLlNE)
| [
Keval Kale](https://github.com/jainkeval)
| [
Hung-Wei Chiu](https://github.com/hwchiu)
| [
Aakash Bhattacharya](https://github.com/abbh07)
|\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| [
Navendu Gupta](https://github.com/navendu29)
| [
Sirjanpreet Singh Banga](https://github.com/sirjan13)
| [
Daniela Ruz](https://github.com/druzmieres)
| [
aman-mi](https://github.com/aman-mi)
| [
Anurag El Dorado](https://github.com/aedorado)
| [
charul97](https://github.com/charul97)
| \n[
Manan Manwani](https://github.com/manan904)
| [
Bhavesh Anand](https://github.com/bhaveshAn)
| [
Prashant Nigam](https://github.com/prashant0598)
| [
Vítor Gomes Chagas](https://github.com/Vitorvgc)
| [
Amit Singh](https://github.com/amitsin6h)
| [
Alabhya Vaibhav](https://github.com/AlabhyaVaibhav)
| \n[
Arnav Borborah](https://github.com/arnavb)
| [
Yatharth Shah](https://github.com/yatharthshahjpr)
| [
David Wu](https://github.com/Pl4gue)
| [
Mohit Khare](https://github.com/mkfeuhrer)
| [
Saif Ahmad](https://github.com/the-saif-ahmad)
| [
Sanchit Jalan](https://github.com/Sanchit-20)
| \n[
Yogesh Badhe](https://github.com/warlockz)
| [
Garvit Kothari](https://github.com/Garvit-k)
| \n\n" }, { "path": ".github/ISSUE_TEMPLATE.md", "content": "\n\n\n\n**This is a(n):**\n\n- [ ] New algorithm\n- [ ] Update to an existing algorithm\n- [ ] Error\n- [ ] Proposal to the Repository\n\n**Details:**\n\n\n" }, { "path": ".github/PULL_REQUEST_TEMPLATE.md", "content": "**Fixes issue:**\n\n\n\n**Changes:**\n\n\n\n\n\n\n" }, { "path": ".gitignore", "content": "**/*.vscode\n**/*.netrwhist\n.DS_Store\n.replit" }, { "path": ".gitmodules", "content": "[submodule \"third_party/uncrustify\"]\n\tpath = third_party/uncrustify\n\turl = https://github.com/uncrustify/uncrustify.git\n\tbranch = uncrustify-0.66.1\n[submodule \"third_party/namanager\"]\n\tpath = third_party/namanager\n\turl = https://github.com/iattempt/namanager.git\n" }, { "path": ".travis.yml", "content": "matrix:\n include:\n #C with clang\n - os: linux\n language: c\n before_script:\n - wget https://download.savannah.gnu.org/releases/libgraph/libgraph-1.0.2.tar.gz\n - tar -xvf libgraph-1.0.2.tar.gz\n - pushd libgraph-1.0.2 && ./configure --prefix=/usr && make && sudo make install && popd\n script:\n make c\n addons:\n apt:\n sources:\n - llvm-toolchain-trusty-5.0\n packages:\n - clang-5.0\n - libsdl1.2-dev\n - libsdl-image1.2 libsdl-image1.2-dev guile-1.8 guile-1.8-dev libsdl1.2debian libart-2.0-dev libaudiofile-dev libesd0-dev libdirectfb-dev libdirectfb-extra libfreetype6-dev libxext-dev x11proto-xext-dev libfreetype6 libaa1 libaa1-dev libslang2-dev libasound2 libasound2-dev\n env:\n - MATRIX_EVAL=\"CC=clang-5.0\"\n\n #C with gcc\n - os: linux\n language: c\n before_script:\n - wget https://download.savannah.gnu.org/releases/libgraph/libgraph-1.0.2.tar.gz\n - tar -xvf libgraph-1.0.2.tar.gz\n - pushd libgraph-1.0.2 && ./configure --prefix=/usr && make && sudo make install && popd\n script:\n make c\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - gcc-7\n - libsdl1.2-dev\n - libsdl-image1.2 libsdl-image1.2-dev guile-1.8 guile-1.8-dev libsdl1.2debian libart-2.0-dev libaudiofile-dev libesd0-dev libdirectfb-dev libdirectfb-extra libfreetype6-dev libxext-dev x11proto-xext-dev libfreetype6 libaa1 libaa1-dev libslang2-dev libasound2 libasound2-dev\n env:\n - MATRIX_EVAL=\"CC=gcc-7\"\n\n #C++ with g++\n - os: linux\n language: cpp\n script:\n ./scripts/build_cpp.sh\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-7\n env:\n - MATRIX_EVAL=\"CXX=g++-7\"\n\n # Verify file name\n - os: linux\n language: python\n python:\n - \"3.6\"\n script:\n - pip install namanager\n - namanager -s third_party/namanager_settings.json --required\n\n # Check formatting of Python code\n - os: linux\n language: python\n python: 3.6\n script:\n - pip install black\n - ./scripts/python_code_style_checker.sh\n\n # Check formatting of Javascript code\n - os: linux\n language: node_js\n node_js: node\n script:\n - npm install -g prettier\n - ./scripts/javascript_code_style_checker.sh\n\nbefore_install:\n - eval \"${MATRIX_EVAL}\"\n" }, { "path": "LICENSE", "content": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n Preamble\n\n The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works. By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users. We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors. You can apply it to\nyour programs, too.\n\n When we speak of free software, we are referring to freedom, not\nprice. Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights. Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received. You must make sure that they, too, receive\nor can get the source code. And you must show them these terms so they\nknow their rights.\n\n Developers that use the GNU GPL protect your rights with two steps:\n(1) assert copyright on the software, and (2) offer you this License\ngiving you legal permission to copy, distribute and/or modify it.\n\n For the developers' and authors' protection, the GPL clearly explains\nthat there is no warranty for this free software. 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No Surrender of Others' Freedom.\n\n If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License. If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all. For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n 13. Use with the GNU Affero General Public License.\n\n Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work. The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n 14. Revised Versions of this License.\n\n The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time. Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n Each version is given a distinguishing version number. If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation. If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n Later license versions may give you additional or different\npermissions. However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n 15. Disclaimer of Warranty.\n\n THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n 16. 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Interpretation of Sections 15 and 16.\n\n If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n END OF TERMS AND CONDITIONS\n\n How to Apply These Terms to Your New Programs\n\n If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n To do so, attach the following notices to the program. It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n {one line to give the program's name and a brief idea of what it does.}\n Copyright (C) {year} {name of author}\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\nAlso add information on how to contact you by electronic and paper mail.\n\n If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n {project} Copyright (C) {year} {fullname}\n This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n This is free software, and you are welcome to redistribute it\n under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License. Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n.\n\n The GNU General Public License does not permit incorporating your program\ninto proprietary programs. If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library. If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License. But first, please read\n.\n" }, { "path": "README.md", "content": "# Cosmos\n\n> [Join our Internship Program now](http://internship.opengenus.org/) | Try this cool [One Liner knowledge tool](https://iq.opengenus.org/one/)\n>\n> Participate in [Checklist project](https://github.com/OpenGenus/checklist)\n\n> The universe of algorithm and data structures - OpenGenus Cosmos\n\n**Cosmos** is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning `C`, `C++`, `Java`, `JavaScript`, `Swift`, `Python`, `Go` and others.\n\nThis work is maintained by a community of hundreds of people and is a _massive collaborative effort_ to bring the readily available coding knowledge **offline**.\n\n> **Many coders ask me how to improve their own performances. I cannot say anything except \"solve and review and prepare your library\"** - _Uwi Tenpen_\n\nMake your fundamentals in Algorithms and Data Structure with these free resources:\n\n* [**50+** Linked List Problems](https://iq.opengenus.org/list-of-linked-list-problems/)\n* [**50+** Array Problems](https://iq.opengenus.org/list-of-array-problems/)\n* [**50+** Binary Tree Problems](https://iq.opengenus.org/list-of-binary-tree-problems/)\n* [**100+** Dynamic Programming (DP) Problems](https://iq.opengenus.org/list-of-dynamic-programming-problems/)\n\n# Cosmic Structure\n\nFollowing is the high-level structure of cosmos:\n* [Artificial intelligence](/code/artificial_intelligence) :robot:\n* [Backtracking](/code/backtracking)\n* [Bit manipulation](/code/bit_manipulation)\n* [Cellular automaton](/code/cellular_automaton) 🐚\n* [Compression algorithms](/code/compression) 🗜️\n* [Computational geometry](/code/computational_geometry) :gear:\n* [Cryptography](/code/cryptography)\n* [Data structures](/code/data_structures)\n* [Design pattern](/code/design_pattern)\n* [Divide conquering](/code/divide_conquer) ➗\n* [Dynamic programming](/code/dynamic_programming)\n* [Graph algorithms](/code/graph_algorithms) 📈\n* [Greedy algorithms](/code/greedy_algorithms) 💰\n* [Mathematical algorithms](/code/mathematical_algorithms) :1234:\n* [Networking](/code/networking) :globe_with_meridians:\n* [Numerical analysis](/code/numerical_analysis) :chart_with_upwards_trend:\n* [Online challenges](/code/online_challenges)\n* [Operating system](/code/operating_system) 💻\n* [Quantum algorithms](/code/quantum_algorithms) :cyclone:\n* [Randomized algorithms](/code/randomized_algorithms) :slot_machine:\n* [Searching](/code/search) 🔎\n* [Selecting](/code/selection_algorithms)\n* [Sorting](/code/sorting)\n* [Square root decomposition](/code/square_root_decomposition)\n* [String algorithms](/code/string_algorithms) 🧵\n* [Unclassified](/code/unclassified) 👻\n\nEach type has several hundreds of problems with solutions in several languages spanning `C`, `C++`, `Java`, `Python`, `Go` and others.\n\n# Maintainers\n\nThis is a massive collaboration. Hence, to keep the quality intact and drive the vision in the proper direction, we have maintainers.\n\n> Maintainers are your friends forever. They are vastly different from moderators.\n\nCurrently, we have **5 active maintainers** and we are expanding quickly.\n\nThe task of maintainers is to review pull requests, suggest further quality additions and keep the work up to date with the current state of the world. 🌍\n\n[Check out our current maintainers](https://github.com/OpenGenus/cosmos/wiki/maintainers)\n\n# Contributors\n\nThe success of our vision to bring knowledge offline depends on you. Even a small contribution helps. All forms of contributions are highly welcomed and valued.\n\nCurrently, we have over **1000+ contributors** in Cosmos and **over 2K+ contributors** in sister projects of Cosmos.\n\nWhen you contribute, your name with a link (if available) is added to our [contributors list](https://github.com/OpenGenus/cosmos/wiki/contributors).\n\nYou can contribute by writing `code`, documentation in the form of `installation guides` and `style guides`, making Cosmos search friendly and many others. There are endless possibilities.\n\nAdditionally, you might want to take a look at this [contributing guidelines](https://github.com/OpenGenus/cosmos/wiki/contribute) before you make Cosmos better.\n\nYou may, also, refer to the available [style guides](/guides/coding_style) before contributing code.\n\n# License\n\nWe believe in freedom and improvement. [GNU General Public License v3.0](https://github.com/OpenGenus/cosmos/blob/master/LICENSE)\n\n---\n\nOpenGenus is sponsored by DigitalOcean and Discourse.\n\n

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\n" }, { "path": "code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/Hashing.java", "content": "import java.io.File;\nimport java.io.FileNotFoundException;\nimport java.util.ArrayList;\nimport java.util.Collections;\nimport java.util.Scanner;\nimport java.util.Stack;\n\npublic class Hashing {\n\n\tpublic static void main(String[] args) {\n\t\t//start timer\n\t\tlong startTime = System.currentTimeMillis();\n\t\t\n\t\t//list used to hold key words.\n\t\tArrayList listOfKeys = new ArrayList();\n\t\t\n\t\t//method opens key word file then returns a scanner of the file of key words.\n\t\tScanner keyWordFile = openTextFile(\"files/kywrdsOdd.txt\");\n\t\t \n\t\t//method reads the key word file and populates a list of keys.\n\t\treadKeys(keyWordFile, listOfKeys);\n\t\t\n\t\t//array will be used to count frequency of letters.\n\t\tint[] occurencesOfLetters = new int[26];\n\n\t\t//this method counts the frequency of letters and stores in occurenceOfLetters array.\n\t\tpopulateOccurencesOfLetters(listOfKeys, occurencesOfLetters);\n\t\t\n\t\t/*this method sets the value of all keywords based off the frequency of each word's \n\t\tfirst and last letter.*/\n\t\tsetValueOfAllKeys(listOfKeys, occurencesOfLetters);\n\n\t\t//This method sorts the list of keys by value in descending order.\n\t\tCollections.sort(listOfKeys);\n\t\t\t\t\n\t\t/*this arraylist takes values from occurences array but deletes characters \n\t\t * with frequencies of zero.*/\n\t\tArrayList uniqueLetters = new ArrayList();\n\t\tpopulateUniqueLetters(uniqueLetters, occurencesOfLetters);\n\t\t\n\t\t/*g 2 dimensional array overview:\n\t\t * collumn 0 stores g-values\n\t\t * collumn 1 stores ascii values of characteres\n\t\t * collumn 2 stores 0 if value for g has not been set and 1 if value for g has been set.\n\t\t */\n\t\tint[][] g = new int[uniqueLetters.size()][3];\t\n\t\tpopulateGArray(uniqueLetters, g);\t\t\n\n\t\t//this hashtable will have keywords mapped to it using a hash function while the cichelli's algorithm is running.\n\t\tKey[] hashTable = new Key[listOfKeys.size()];\n\t\t\n\t\t/*max and mod values initialized. max value is utilized in cichelli's algorithm\n\t\tmod value is utilized in the hash function*/\n\t\tint maxValue = listOfKeys.size()/2;\n\t\tint modValue = listOfKeys.size();\n\t\t\n\t\t/*creating a stack and populating it with the keywords from my list of key words.\n\t\tit is easier to implement cichelli's algorithm with a stack rather than a list.*/\n\t\tStack keyWordStack = new Stack();\n\t\tpopulateStack(listOfKeys, keyWordStack);\n\t\t\n\t\t//Cichelli's algorithm is called to map key words to the hashTable\n\t\tcichelli(keyWordStack, hashTable, g, modValue, maxValue);\n\t\t\n\t\t//this array is used to count the frequency each keyword appears in a subsequent text file.\n\t\tint[] keyWordCounter = new int[hashTable.length];\n\t\t//this method initializes the counter array with zeros.\n\t\tinitializeCounter(keyWordCounter);\n\t\t\n\t\t//method opens text file then returns a scanner of the file.\n\t\tScanner textFile = openTextFile(\"files/tstOdd.txt\");\n\t\t\n\t\t/*this method reads the second text file. \n\t\tOther methods checkForKeyWord and printResults are called within the readOtherFile method.\n\t\tThe result is that the program will print number of keywords etc. present in the second text file.\n\t\tThe hash function is used in order compare key words with the words of the second text file. the printed\n\t\tresults are accurate thus proving this implementation of Cichelli's algorithm is effective.\n\t\t*/\n\t\treadOtherFile(textFile, g, hashTable, keyWordCounter, modValue);\n\n\t\t//end timer\n\t\tlong endTime = System.currentTimeMillis();\n\t\t//calculate the time it took to run the program.\n\t\tlong timeTaken = endTime - startTime;\n\t\tSystem.out.println(\"Execution time: \" + timeTaken + \" milliseconds\");\n\t\t//System.out.println(\"Start time: \" + startTime + \" milliseconds\");\n\t\t//System.out.println(\"End time: \" + endTime + \" milliseconds\");\n\t\t\n\t}//end of main\n\t\n\t//method opens text file then returns a scanner of the file.\n\tpublic static Scanner openTextFile(String fileName)\t{\n\t\tScanner data;\n\n\t\ttry{\n data = new Scanner(new File(fileName));\n return data;\n }\n catch(FileNotFoundException e){\n System.out.println(fileName + \" did not read correctly\");\n } \n\t\tdata = null;\n\t\treturn data;\n\t}\n\t\n\t//method reads the key word file and populates a list of keys.\n\tpublic static void readKeys(Scanner data, ArrayList listOfKeys)\t{\n\t\tString currentToken;\n\t\twhile(data.hasNext())\t{\n\t\t\tcurrentToken = data.next();\n\t\t\tcurrentToken = currentToken.toLowerCase();\n\t\t\tKey currentKey = new Key(currentToken);\n\t\t\tlistOfKeys.add(currentKey);\n\t\t}\n\t}\n\t\n\t//this method counts the frequency of letters and stores in occurenceOfLetters array.\n\tpublic static void populateOccurencesOfLetters(ArrayList listOfKeys, int[] occurencesOfLetters)\t{\n\t\t//using an array of 26 to keep track of occurences of letters\n\t\t//initialize all array values to zero.\n\t\tfor (int i = 0; i < 26; i += 1)\t{\n\t\t\toccurencesOfLetters[i] = 0;\n\t\t}\n\t\t// count the frequency that each letter appears as a first or last letter\n\t\tfor (Key e: listOfKeys)\t{\n\t\t\tfor (int i = 97; i < 123; i += 1)\t{\n\t\t\t\tif (e.getFirstLetter() == (char) i)\t{\n\t\t\t\t\toccurencesOfLetters[i - 97] += 1;\n\t\t\t\t}\n\t\t\t\tif (e.getLastLetter() == (char) i)\t{\n\t\t\t\t\toccurencesOfLetters[i - 97] += 1;\n\t\t\t\t}\n\t\t\t}//end of for loop\n\t\t}//end of foreach loop\t\t\n\t}\n\t\n\t/*this method sets the value of all keywords based off the frequency of each word's \n\tfirst and last letter.*/\n\tpublic static void setValueOfAllKeys(ArrayList listOfKeys, int[] occurencesOfLetters)\t{\n\t\t//set values of keys\n\t\tfor(Key e : listOfKeys)\t{\n\t\t\tint firstLetterValue = occurencesOfLetters[((int) e.getFirstLetter() - 97)];\n\t\t\te.setFirstLetterValue(firstLetterValue);\n\t\t\tint lastLetterValue = occurencesOfLetters[((int) e.getLastLetter() - 97)];\n\t\t\te.setLastLetterValue(lastLetterValue);\n\t\t\tint totalValue = firstLetterValue + lastLetterValue;\n\t\t\te.setTotalValue(totalValue);\n\t\t}\n\t}\n\t\n\t//the uniqueLetters arrayList is populated with letters from the occurencesOfLetters array.\n\tpublic static void populateUniqueLetters(ArrayList uniqueLetters, int[] occurencesOfLetters)\t{\n\t\tfor(int i = 0; i < occurencesOfLetters.length; i += 1)\t{\n\t\t\tif (occurencesOfLetters[i] != 0)\t{\n\t\t\t\tuniqueLetters.add((char) (i+97));\n\t\t\t}\n\t\t}\n\t}\n\t\n\t//this method populates 2 dimension g array\n\tpublic static void populateGArray(ArrayListuniqueLetters, int [][] g)\t{\n\t\tfor (int i = 0; i < uniqueLetters.size(); i += 1)\t{\n\t\t\t//collumn 0 holds g values. all initialized to zero\n\t\t\tg[i][0] = 0;\n\t\t\t//collumn 1 holds ascci values of letters.\n\t\t\tg[i][1] = (int) uniqueLetters.get(i);\t\n\t\t\t//collumn 2 holds 0 if g-value is not set and 1 if g-value is set. all initialized to zero.\n\t\t\tg[i][2] = 0;\n\t\t}\n\t}\n\t\n\t//this method populates the stack with keys from the listOfKeys.\n\tpublic static void populateStack(ArrayList listOfKeys, Stack keyWordStack)\t{\n\t\tfor (int i = (listOfKeys.size() - 1); i >= 0; i -= 1)\t{\n\t\t\tkeyWordStack.push(listOfKeys.get(i));\n\t\t}\n\t}\n\t\n\t//Cichelli's algorithm is called to map key words to the hashTable and is a perfect hashing algorithm.\n\tpublic static boolean cichelli(Stack keyWordStack, Key [] hashTable, int[][] g, int modValue, int maxValue)\t{\n\t\t\n\t\twhile(keyWordStack.isEmpty() != true)\t{\n\t\t\n\t\t\tKey word = keyWordStack.pop();\n\t\t\t\n\t\t\t//finding first and last g values by comparing 2d gArray to first and last letters of word.\n\t\t\t//setPositionInG refers to the row of the array where the letters was found.\n\t\t\tfor (int i = 0; i < g.length; i += 1)\t{\n\t\t\t\tif (g[i][1] == ((int) word.getFirstLetter()))\t{\n\t\t\t\t\tword.setPositionInGFirst(i);\n\t\t\t\t}\n\t\t\t\tif (g[i][1] == ((int) word.getLastLetter()))\t{\t\n\t\t\t\t\tword.setpositionInGLast(i);\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\n\t\t\t//if values are already assigned for both letters try to place key in table\n\t\t\tif (g[word.getPositionInGFirst()][2] == 1 && g[word.getPositionInGLast()][2] == 1)\t{\n\t\t\t\t//check if hash value for word is valid.\n\t\t\t\tif (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null)\t{\n\t\t\t\t\t//assign hash value to word\n\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;\n\t\t\t\t\t//recursively call cichelli method\n\t\t\t\t\tif (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true)\t{\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t\t//detach the hash value for word\n\t\t\t\t\telse\t{\n\t\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;\n\t\t\t\t\t\t//set values of g array column 2 back to zero.\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tkeyWordStack.push(word);\n\t\t\t\treturn false;\n\t\t\t}\t\n\t\t\t\n\t\t\t//neither letter assigned g-value AND first != last letter\n\t\t\telse if((g[word.getPositionInGFirst()][2] == 0) && (g[word.getPositionInGLast()][2] == 0) && (word.getFirstLetter() != word.getLastLetter()))\t{\n\t\t\t\t/*increment first g value up to maxValue times, but dont increment 1st iteration \n\t\t\t\tto see if we get a hit while g value is still set to zero. Previous sentence explains\n\t\t\t\twhy for loop condition is (maxValue + 1)*/\n\t\t\t\tfor (int m = 0; m < (maxValue + 1); m += 1)\t{\n\t\t\t\t\t//not incrementing g-value in first iteration\n\t\t\t\t\tif (m > 0)\t{\n\t\t\t\t\t\t//increment g-value\n\t\t\t\t\t\tg[word.getPositionInGFirst()][0] += 1;\n\t\t\t\t\t}\n\t\t\t\t\t//check if hash value is valid if not we want to keep incrementing g.\n\t\t\t\t\tif (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null)\t{\n\t\t\t\t\t\t//assign hash value to word\n\t\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;\n\t\t\t\t\t\t//set values of g array column 2 to 1.\n\t\t\t\t\t\tg[word.getPositionInGFirst()][2] = 1;\n\t\t\t\t\t\tg[word.getPositionInGLast()][2] = 1;\n\t\t\t\t\t\t//recursively call cichelli method\n\t\t\t\t\t\tif (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true)\t{\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\t\t\t\t\t\t//detach the hash value for word\n\t\t\t\t\t\telse\t{\n\t\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;\n\t\t\t\t\t\t//set values of g array column 2 back to zero.\n\t\t\t\t\t\tg[word.getPositionInGFirst()][2] = 0;\n\t\t\t\t\t\tg[word.getPositionInGLast()][2] = 0;\n\t\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\t}\n\t\t\t\t}//end of for loop\n\t\t\t\t/*I believe this following code is only reached if hashfunction has a hit \n\t\t\t\t but returns false at some point or if hash function has no hits! */\t\t\n\t\t\t\t//reset g value\n\t\t\t\tg[word.getPositionInGFirst()][0] = 0;\n\t\t\t\tkeyWordStack.push(word);\n\t\t\t\treturn false;\t\n\t\t\t}//end of else if\n\t\t\t\n\t\t\t\n\t\t\t// else refers to case where only one letter assigned g-value OR first = last letter\n\t\t\telse\t{\n\t\t\t\t//determine which letter(first or last) is assigned a g value.\n\t\t\t\t//if the following condition is true than the first letter has a g value and last letter will not\n\t\t\t\tif (g[word.getPositionInGFirst()][2] == 0)\t{\n\t\t\t\t\tword.setUnassignedLetterPosition(word.getPositionInGFirst());\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\t// reaches (else if) if the first letter does not have a gvalue. still possible neither has a g value.\n\t\t\t\telse if (g[word.getPositionInGLast()][2] == 0)\t{\n\t\t\t\t\tword.setUnassignedLetterPosition(word.getPositionInGLast());\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\t/*reaches else. if the first letter = last letter\n\t\t\t\tthis case should not be reached with the given set of keywords.*/\n\t\t\t\telse\t{\n\t\t\t\t\t//there is no unassigned letter first = last.\n\t\t\t\t\t//set to arbitrary value of -1.\n\t\t\t\t\tword.setUnassignedLetterPosition(-1);\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\t/*increment the unassigned letter's g value up to maxValue times, but dont increment 1st iteration \n\t\t\t\tto see if we get a hit while g value is still set to zero. Previous sentence explains\n\t\t\t\twhy for loop condition is (maxValue + 1)*/\n\t\t\t\tfor (int m = 0; m < (maxValue + 1); m += 1)\t{\n\t\t\t\t\t//not incrementing g-value in first iteration\n\t\t\t\t\tif(m > 0)\t{\n\t\t\t\t\t\t//increment g-value\n\t\t\t\t\t\tg[word.getUnassignedLetterPosition()][0] += 1;\n\t\t\t\t\t}\n\t\t\t\t\t//check if hash value is valid if not we want to keep incrementing g.\n\t\t\t\t\tif (hashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] == null)\t{\n\t\t\t\t\t\t//assign hash value to word\n\t\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = word;\n\t\t\t\t\t\t//only need to change the unassigned letter's set field to 1.\n\t\t\t\t\t\tg[word.getUnassignedLetterPosition()][2] = 1;\n\t\t\t\t\t\t//recursively call cichelli's method\n\t\t\t\t\t\tif (cichelli(keyWordStack, hashTable, g, modValue, maxValue) == true)\t{\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\t\t\t\t\t\t//detach the hash value for word \n\t\t\t\t\t\telse\t{\n\t\t\t\t\t\thashTable[hashFunction(word, g[word.getPositionInGFirst()][0], g[word.getPositionInGLast()][0], modValue)] = null;\n\t\t\t\t\t\t//set unassigned letters set field back to zero.\n\t\t\t\t\t\tg[word.getUnassignedLetterPosition()][2] = 0;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}//end of for loop\n\t\t\t\t/*I believe this following code is only reached if hashfunction has a hit but returns false at some point\n\t\t\t\t * or if hash function has no hits!\n\t\t\t\t */\t\t\t\n\t\t\t\t//reset unassigned letters g value\n\t\t\t\tg[word.getUnassignedLetterPosition()][0] = 0;\n\t\t\t\tkeyWordStack.push(word);\n\t\t\t\treturn false;\n\t\t\t}//end of else\n\t\t\t\t\n\t\t}//end of while\n\t\t// empty stack means we have a solution\n\t\treturn true; \n\t}//end of Cichelli's method\n\t\t\t\n\t\n\t//overloaded hash function takes Key data type as an argument\t\t\n\tpublic static int hashFunction(Key keyWord, int gFirst, int gLast, int modValue)\t{\n\t\tint hashValue = (keyWord.getKeyWord().length() + gFirst + gLast) % modValue;\n\t\treturn hashValue;\n\t}\n\t\n\t//overloaded hash function takes String data type as an argument.\n\tpublic static int hashFunction(String word, int gFirst, int gLast, int modValue)\t{\n\t\tint hashValue = (word.length() + gFirst + gLast) % modValue;\n\t\treturn hashValue;\n\t}\n\t\n\t//this method initializes the keyWordCounter array with zeros.\n\tpublic static void initializeCounter(int[] keyWordCounter)\t{\n\t\tfor (int i = 0; i < keyWordCounter.length; i += 1)\t{\n\t\t\tkeyWordCounter[i] = 0;\n\t\t}\n\t}\n\n\tpublic static void readOtherFile(Scanner data, int g[][], Key[] hashTable, int[] keyWordCounter, int modValue)\t{\n\t\tint lineCounter = 0, wordCounter = 0;\n\t\t\n\t\tString x;\n String []y;\n while(data.hasNextLine()){\n \tlineCounter += 1; \n x = data.nextLine();\n \n /*the following conditional statement takes care of the issue of their being an\n * entirely blank line encountered before reaching the end of the text file.\n */\n if(x.length() == 0) {\n \tx = data.nextLine();\n }\n \n x = x.toLowerCase();\n\t\t\tx = x.replaceAll(\"[,.()]\",\"\");\n \n \n y = x.split(\" \");\n wordCounter += y.length;\n \n //method compares a token to a key word to see if they are identical.\n checkForKeyWord(y, g, hashTable, keyWordCounter, modValue);\n }\n //method prints statistical results\n printResults(lineCounter, wordCounter, hashTable, keyWordCounter);\n\t}\n\t\n\t\n\t//method compares a token to a key word to see if they are identical.\n\tpublic static void checkForKeyWord(String[] words, int[][] g, Key[] hashTable, int[] keyWordCounter, int modValue)\t{\n\t\tfor(String word: words)\t{\n\t\t\tchar firstLetter = word.charAt(0), lastLetter = word.charAt(word.length() - 1);\n\t\t\tboolean firstLetterBool = false, lastLetterBool = false;\n\t\t\tint firstGvalue = 0, lastGvalue = 0;\n\t\t\t\n\t\t\n\t\t\tfor(int i = 0; i < g.length; i += 1)\t{\n\t\t\t\tif(firstLetter == (char) g[i][1]) {\n\t\t\t\t\tfirstLetterBool = true;\n\t\t\t\t\tfirstGvalue = g[i][0];\n\t\t\t\t}\n\t\t\t\tif (lastLetter == (char) g[i][1])\t{\n\t\t\t\t\tlastLetterBool = true;\n\t\t\t\t\tlastGvalue = g[i][0];\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (firstLetterBool == true && lastLetterBool == true)\t{\n\t\t\t\tint index = hashFunction(word, firstGvalue, lastGvalue, modValue);\n\t\t\t\tif (word.equals(hashTable[index].getKeyWord()))\t{\n\t\t\t\t\tkeyWordCounter[index] += 1;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\t\n\t//method prints statistical results\n\tpublic static void printResults(int lineCounter, int wordCounter, Key[] hashTable, int[] keyWordCounter)\t{\n\t\tSystem.out.println(\"Total Lines Read \" + lineCounter);\n\t\tSystem.out.println(\"Total Words Read \" + wordCounter);\n\t\tSystem.out.println(\"Break down by key word\");\n\t\tfor(int i = 0; i < hashTable.length; i += 1)\t{\n\t\t\tSystem.out.println(\" \" + hashTable[i].getKeyWord() + \": \" + keyWordCounter[i]);\n\t\t}\n\t\tint totalKeys = 0;\n\t\tfor(int e: keyWordCounter)\t{\n\t\t\ttotalKeys += e;\n\t\t}\n\t\tSystem.out.println(\"Total Keywords: \" + totalKeys);\n\t}\n\t\n\t\n}//end of class\n" }, { "path": "code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/Key.java", "content": "public class Key implements Comparable {\n //fields\n\tprivate String keyWord;\n\tprivate char firstLetter, lastLetter;\n\t//unnassignedLetter is used if only one letter(first or last) is not set.\n\tprivate int totalValue, firstLetterValue, lastLetterValue, positionInGFirst, positionInGLast, unassignedLetterPosition;\n\t\n\t//constructor\n\tpublic Key(String keyWord)\t{\n\t\tthis.keyWord = keyWord;\n\t\tthis.firstLetter = keyWord.charAt(0);\n\t\tthis.lastLetter = keyWord.charAt(keyWord.length() - 1);\n\t\tthis.totalValue = 0; //updated in setValue method\n\t\tthis.firstLetterValue = 0; //updated in setValue method\n\t\tthis.lastLetterValue = 0; //updated in setValue method\n\t\tthis.positionInGFirst = 0; //updated in Cichelli's alogrithm\n\t\tthis.positionInGLast = 0; //updated in Cichelli's alogrithm\n\t\t//unassignedLetterPosition is set to -1 as an arbitrary value.\n\t\tthis.unassignedLetterPosition = -1; //can updated in Cichelli's alogrithm under certain conditions.\n\t}\n\t\n\t/*this method is necessary in sorting the list of keys by value.\n\t * I created this compareTo method based on info from the following link:\n\t * https://beginnersbook.com/2013/12/java-arraylist-of-object-sort-example-comparable-and-comparator/\n\t */\n\t@Override\n\tpublic int compareTo(Key comparesTo)\t{\n\t\tint compareValue = ((Key)comparesTo).getTotalValue();\n\t\treturn compareValue-this.totalValue;\n\t}\n\n\t//getter and setter methods\n\t\n\tpublic String getKeyWord()\t{\n\t\treturn keyWord;\n\t}\n\t\n\tpublic char getFirstLetter()\t{\n\t\treturn firstLetter;\n\t}\n\t\n\tpublic char getLastLetter()\t{\n\t\treturn lastLetter;\n\t}\n\t\n\tpublic int getFirstLetterValue()\t{\n\t\treturn firstLetterValue;\n\t}\n\t\n\tpublic void setFirstLetterValue(int value)\t{\n\t\tthis.firstLetterValue = value;\n\t}\n\t\n\tpublic int getLastLetterValue()\t{\n\t\treturn lastLetterValue;\n\t}\n\t\n\tpublic void setLastLetterValue(int value)\t{\n\t\tthis.lastLetterValue = value;\n\t}\n\t\n\tpublic void setTotalValue(int value)\t{\n\t\tthis.totalValue = value;\n\t}\n\t\n\tpublic int getTotalValue()\t{\n\t\treturn totalValue;\n\t}\n\t\n\tpublic void setPositionInGFirst(int position)\t{\n\t\tthis.positionInGFirst = position;\n\t}\n\t\n\tpublic int getPositionInGFirst()\t{\n\t\treturn positionInGFirst;\n\t}\n\n\t\n\tpublic void setpositionInGLast(int position)\t{\n\t\tthis.positionInGLast = position;\n\t}\n\n\tpublic int getPositionInGLast()\t{\n\t\treturn positionInGLast;\n\t}\n\t\n\tpublic void setUnassignedLetterPosition(int position)\t{\n\t\tthis.unassignedLetterPosition = position;\n\t}\n\t\n\tpublic int getUnassignedLetterPosition()\t{\n\t\treturn unassignedLetterPosition;\n\t}\n}\n" }, { "path": "code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/files/kywrdsOdd.txt", "content": "jitter\nfrequency\nChapter\ncamera\nstep\nin\nrange\nthe\nToF\ninfluence" }, { "path": "code/algorithm_applications/src/Cichelli's Perfect Hashing Alogorithm/files/tstOdd.txt", "content": "During the last decade, the ToF range imaging cameras have significantly\nimproved and have been used in various applications from agricultural sectors\nto engineering industries. With the improvement of the technology to build\nthe depth sensor, novel applications of ToF range imaging will increase. At\npresent, more researchers are mainly focused on the noise investigation in\nthese cameras. However, from this thesis, we found that the jitter significantly\ninfluences range measurements therefore it is worth further investigating jitter\nin ToF range imaging systems. Several investigations could be extended to\nfurther perform the ToF range imaging systems due to the jitter influence on\nmeasurements as follows.\n\nThe proposed methodology in Chapters 4 and 5 could be extended to correct\nrange measurements after extracting and measuring the RJ and PJ in the\nlight signal of the camera. This will be very useful since the available jitter can\nbe compensated before the measurement. Since for a particular configuration\nof the AMCW camera (e.g., with default modulation frequency and integration \nperiod), the periodic and random jitter in the illumination source is fixed\namounts which can be computed with the proposed methodology. Then, considering\nthe benchmark numerical results in Chapters 7 and 8 for the influence\nof the periodic and random jitter on range measurements, respectively, can\nbe applied for compensating of the measurement. In addition, the proposed\nalgorithm may be applied to extract the jitter in other types of ToF range\nimaging cameras such as pulse based and pseudo-noise based modulation cameras.\nThis methodology can be used for jitter measurement in any kind of\napplication where the reference clock signal cannot be accessed.\n\nA cheaper SDR USB dongle with the proposed algorithm for jitter extraction\nin ToF cameras at lower frequencies in the RF signal was investigated in\nChapter 6. The high sample rates and high quality of SDRs can be used to\nexplore the relatively large range of frequencies of the PJ and RJ in the light\nsource of the AMCW ToF cameras by setting a suitable local oscillator frequency\nto get a large intermediate frequency (less than the Nyquist frequency)\nin the dongle. Also, this concept may be used to find the jitter in the light\nsource of the aforementioned two modulation types of ToF range imaging cameras\nas well as in any other relevant applications. In addition, it may possible\nto modify the DSP engine in the dongle which is a software based controller in\norder to get maximum benefit but this may not be a straightforward process.\n\nIn Chapter 7, the influence of the periodic jitter on range measurements\nwas investigated. Can we compensate the PJ amount in the first phase step\n(by determining and correcting it) before it proceeds to the second phase step\nand so on, in the correlation function within the integration period? With\nthe influence of the PJ in each phase step a constant (a fixed amount), then\nit can be further analysed with the crosstalking between phase steps, during\nthe integration period. But if it is not a constant (i.e., random behaviour),\nthen it will be difficult to compensate for the influence of the PJ on range \nmeasurements. Thus, this is a more challenging task for future investigation\nthat arises from Chapter 7. On the other hand, here the periodic jitter at a\nsingle frequency was considered. But in practice, the ToF cameras may have\nperiodic jitter at multiple frequencies. Then, it is very interesting to know how\nthe analytical model will change accordingly. Will the influence be linear or\nnon-linear and will it be partially or fully cancel on range measurements? So,\nsome interesting questions arise from this future investigation.\n\nA finite number of evaluations of the Monte Carlo simulation were used to\ninvestigate the influence of random jitter on range measurements in Chapter 8. \nWith increasing the number of simulations, it is surprisingly effective to estimate\nthe better result than the current one. In order to understand the\nimpact of uncertainty of the results obtained, it is necessary to analyse the\nstochastic model for the full range of possible outcomes, to get much more\naccurate results. On the other hand, all simulations were done at fixed integration\nperiod T = 1 ms and it is interesting to investigate the behaviour of the\nrandom jitter for various T, especially for less than 1 ms integration period\nsince now the depth sensor manufacturers are trying to develop the AMCW\nToF cameras with short integration periods. " }, { "path": "code/algorithm_applications/src/Strassens algorithm/strassens algorithm.cpp", "content": "#include \r\nusing namespace std;\r\n\r\n#define ff(i, a, b) for (int i = int(a); i < int(b); i++)\r\n#define ffn(i, n) ff(i, 0, n)\r\n\r\nstatic bool comp(vector &v1, vector &v2)\r\n{\r\n return (v1[1] < v2[1]);\r\n}\r\n\r\nvector> aloc_ary(vector> a, int l, int m, int n)\r\n{\r\n int i, k, o, b;\r\n vector> c(n, vector(n, 0));\r\n\r\n for (k = 0, i = l; k < n; k++, i++)\r\n for (o = 0, b = m; o < n; o++, b++)\r\n c[k][o] = a[i][b];\r\n return c;\r\n}\r\n\r\nvector> adn_ary(vector> a, vector> b, int n)\r\n{\r\n vector> c(n, vector(n, 0));\r\n int i, j;\r\n ffn(i, n)\r\n ffn(j, n)\r\n c[i][j] = a[i][j] + b[i][j];\r\n return c;\r\n}\r\n\r\nvector> sbn_ary(vector> a, vector> b, int n)\r\n{\r\n vector> c(n, vector(n, 0));\r\n int i, j;\r\n ffn(i, n)\r\n ffn(j, n)\r\n c[i][j] = a[i][j] - b[i][j];\r\n return c;\r\n}\r\n\r\nvoid crt(vector g[], int n, int st)\r\n{\r\n queue q;\r\n bool v[n + 1];\r\n memset(v, 0, sizeof(v));\r\n q.push(st);\r\n v[st] = 1;\r\n \r\n while (!q.empty())\r\n {\r\n int a = q.front();\r\n q.pop();\r\n ffn(j, g[a].size())\r\n {\r\n if (!v[g[a][j]])\r\n {\r\n q.push(g[a][j]);\r\n v[g[a][j]] = 1;\r\n }\r\n }\r\n }\r\n}\r\n\r\nvoid cbn_ary(vector> &c, vector> c11, int i, int j, int n)\r\n{\r\n int a, b, x, y;\r\n for (a = 0, x = i; a < n; a++, x++)\r\n for (b = 0, y = j; b < n; b++, y++)\r\n c[x][y] = c11[a][b];\r\n}\r\n\r\nvector> mul_ary(vector> ary1, vector> ary2, int n)\r\n{\r\n if (n == 1)\r\n {\r\n vector> res(n, vector(n, 0));\r\n res[0][0] = ary1[0][0] * ary2[0][0];\r\n return res;\r\n }\r\n\r\n vector> ary1_11 = aloc_ary(ary1, 0, 0, n / 2);\r\n vector> ary1_12 = aloc_ary(ary1, 0, n / 2, n / 2);\r\n vector> ary1_21 = aloc_ary(ary1, n / 2, 0, n / 2);\r\n vector> ary1_22 = aloc_ary(ary1, n / 2, n / 2, n / 2);\r\n vector> ary2_11 = aloc_ary(ary2, 0, 0, n / 2);\r\n vector> ary2_12 = aloc_ary(ary2, 0, n / 2, n / 2);\r\n vector> ary2_21 = aloc_ary(ary2, n / 2, 0, n / 2);\r\n vector> ary2_22 = aloc_ary(ary2, n / 2, n / 2, n / 2);\r\n\r\n vector> ary_tmp1 = mul_ary(adn_ary(ary1_11, ary1_22, n / 2), adn_ary(ary2_11, ary2_22, n / 2), n / 2);\r\n vector> ary_tmp2 = mul_ary(adn_ary(ary1_21, ary1_22, n / 2), ary2_11, n / 2);\r\n vector> ary_tmp3 = mul_ary(ary1_11, sbn_ary(ary2_12, ary2_22, n / 2), n / 2);\r\n vector> ary_tmp4 = mul_ary(ary1_22, sbn_ary(ary2_21, ary2_11, n / 2), n / 2);\r\n vector> ary_tmp5 = mul_ary(adn_ary(ary1_11, ary1_12, n / 2), ary2_22, n / 2);\r\n vector> ary_tmp6 = mul_ary(sbn_ary(ary1_21, ary1_11, n / 2), adn_ary(ary2_11, ary2_12, n / 2), n / 2);\r\n vector> ary_tmp7 = mul_ary(sbn_ary(ary1_12, ary1_22, n / 2), adn_ary(ary2_21, ary2_22, n / 2), n / 2);\r\n vector> res11 = adn_ary(sbn_ary(adn_ary(ary_tmp1, ary_tmp4, n / 2), ary_tmp5, n / 2), ary_tmp7, n / 2);\r\n vector> res12 = adn_ary(ary_tmp3, ary_tmp5, n / 2);\r\n vector> res21 = adn_ary(ary_tmp2, ary_tmp4, n / 2);\r\n vector> res22 = adn_ary(sbn_ary(adn_ary(ary_tmp1, ary_tmp3, n / 2), ary_tmp2, n / 2), ary_tmp6, n / 2);\r\n\r\n vector> res(n, vector(n, 0));\r\n cbn_ary(res, res11, 0, 0, n / 2);\r\n cbn_ary(res, res12, 0, n / 2, n / 2);\r\n cbn_ary(res, res21, n / 2, 0, n / 2);\r\n cbn_ary(res, res22, n / 2, n / 2, n / 2);\r\n return res;\r\n}\r\n\r\nint main()\r\n{\r\n int n, i, j, x;\r\n cin >> n;\r\n\r\n if (log2(n) != floor(log2(n)))\r\n x = pow(2, ceil(log2(n)));\r\n else\r\n x = n;\r\n\r\n vector> ary1(n, vector(n,0));\r\n vector> ary2(n, vector(n,0));\r\n for (i = 0; i < n; ++i)\r\n for (j = 0; j < n; ++j)\r\n scanf(\"%d\", &ary1[i][j]);\r\n\r\n for (i = 0; i < n; ++i)\r\n for (j = 0; j < n; ++j)\r\n scanf(\"%d\", &ary2[i][j]);\r\n\r\n vector> mul1(x + 1, vector(x + 1, 0));\r\n vector> mul2(x + 1, vector(x + 1, 0));\r\n\r\n for (i = 0; i < n; ++i) {\r\n for (j = 0; j < n; ++j) {\r\n mul1[i][j] = ary1[i][j];\r\n mul2[i][j] = ary2[i][j];\r\n }\r\n }\r\n\r\n vector> res_arr(n, vector(n, 0));\r\n res_arr = mul_ary(mul1, mul2, x);\r\n ffn(i, n)\r\n {\r\n ffn(j, n)\r\n cout << res_arr[i][j] << \" \";\r\n cout << \"\\n\";\r\n }\r\n return 0;\r\n}" }, { "path": "code/algorithm_applications/src/binary_search/ceil_of_element/ceil_of_element_in_sorted_array.cpp", "content": "#include\nusing namespace std; \nint solve(int arr[],int n, int ele){\n int ans=-1;\n int low=0;\n int high=n-1;\n while(low<=high){\n int mid=low+(high-low)/2;\n if(ele==arr[mid]){\n return ele; \n }\n else if(ele>arr[mid]){\n ans=mid;\n high=mid-1;\n }\n else{\n low=mid+1;\n }\n }\n return arr[ans];\n }\nint main(){\n int n; \n cin>>n; \n int arr[n];\n for(int i=0;i>arr[i];\n }\n int ele; \n cin>>ele; \n int ans=solve(arr,n,ele);\n cout<<\"Ceil of \"< 0){\r\n String[] input = br.readLine().split(\" \");\r\n //n: number of candy boxes\r\n int n = Integer.parseInt(input[0]);\r\n //k: number of friends\r\n long k = Long.parseLong(input[1]);\r\n String[] input1 = br.readLine().split(\" \");\r\n long[] arr = new long[n];\r\n long max = Integer.MIN_VALUE;\r\n for(int i=0; i=low){\r\n mid = low + (high-low)/2;\r\n if(canDistribute(mid, n, k, arr)){\r\n low = mid+1;\r\n res = mid;\r\n }\r\n else{\r\n high = mid-1;\r\n }\r\n }\r\n return res;\r\n }\r\n \r\n public static boolean canDistribute(long val, int n, long k, long[] arr){\r\n if(k==1){\r\n return true;\r\n }\r\n long peopleServed = 0;\r\n for (int i=n-1; i>=0; i--){\r\n//this is the number of people who can get candy\r\n peopleServed += arr[i]/val;\r\n }\r\n if(peopleServed>=k){\r\n return true;\r\n }\r\n return false;\r\n }\r\n}\r\n" }, { "path": "code/algorithm_applications/src/binary_search/first_and_last_position_in_sorted_array/firstAndLastPosInSortedArray.cpp", "content": "#include \nusing namespace std;\n\nint first(int arr[], int x, int n)\n{\n int low = 0, high = n - 1, res = -1;\n while (low <= high)\n {\n int mid = (low + high) / 2;\n\n if (arr[mid] > x)\n high = mid - 1;\n else if (arr[mid] < x)\n low = mid + 1;\n else\n {\n res = mid;\n high = mid - 1;\n }\n }\n return res;\n}\n\nint last(int arr[], int x, int n)\n{\n int low = 0, high = n - 1, res = -1;\n while (low <= high)\n {\n int mid = (low + high) / 2;\n\n if (arr[mid] > x)\n high = mid - 1;\n else if (arr[mid] < x)\n low = mid + 1;\n else\n {\n res = mid;\n low = mid + 1;\n }\n }\n return res;\n}\n\nint main()\n{\n int n;\n cin >> n;\n int arr[n];\n for (int i = 0; i < n; i++)\n {\n cin >> arr[i];\n }\n int q;\n cin >> q;\n while (q--)\n {\n int a;\n cin >> a;\n cout << first(arr, a, n) << \" \" << last(arr, a, n) << endl;\n }\n}" }, { "path": "code/algorithm_applications/src/binary_search/floor_of_element/floor_of_element_in_sorted_array.cpp", "content": "#include\nusing namespace std; \nint solve(int arr[],int n,int ele){\n int ans=-1;\n int low=0;\n int high=n-1;\n while(low<=high){\n int mid=low+(high-low)/2;\n if(arr[mid]==ele){\n return ele; \n }\n else if(arr[mid]>n; \n int arr[n];\n for(int i=0;i>arr[i];\n }\n int ele; \n cin>>ele; \n int ans=solve(arr,n,ele);\n cout<<\"floor of \"<\n\nusing namespace std;\n\nint main() { \n int n;\n cin >> n;\n int arr[n];\n for(int i = 0; i < n; i++) {\n cin >> arr[i];\n }\n\n // Bubble sort algorithm code \n int counter = 1;\n while(counter < n) {\n for(int i = 0; i < n-counter; i++){\n if(arr[i] > arr[i+1]) {\n // if arr[i] > arr[i+1] then we swap those two element\n // because they are not in sorting form\n int temp = arr[i];\n arr[i] = arr[i+1];\n arr[i+1] = temp;\n }\n }\n counter++;\n }\n\n cout << '\\n';\n\n return 0;\n}\n" }, { "path": "code/algorithm_applications/src/merge_arrays/merge_two_arrays.c", "content": "#include \n\nint main(){\n int m;\n printf(\"Enter the number of elements in first array\\n\");\n scanf(\"%d\",&m);\n int arr1[m];\n int n;\n printf(\"\\nEnter the number of elements in second array\\n\");\n scanf(\"%d\",&n);\n int arr2[n];\n printf(\"\\nEnter the elements of first array\\n\");\n for(int i=0;i1)and (Room[i-1][j]!=-1 and Room[i-1][j]==1)):\\n\",\n \" print(\\\"The Location \\\"+ str(i-1)+\\\" \\\"+str(j) + \\\" is Dirty\\\")\\n\",\n \" Room[i-1][j]=0\\n\",\n \" Battery-=1\\n\",\n \" print(\\\"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to \\\" + str(i)+\\\" \\\"+str(j))\\n\",\n \" elif((i>1) and (Room[i-1][j]!=-1 and Room[i-1][j]==0)):\\n\",\n \" print(\\\"The Location \\\"+ str(i-1)+\\\" \\\"+str(j) + \\\" is Clean\\\")\\n\",\n \" Battery-=1\\n\",\n \" elif((i>=1)):\\n\",\n \" print(\\\"Obstacle Present at \\\"+str(i-1)+\\\" \\\"+str(j))\\n\",\n \" else:\\n\",\n \" print(\\\"We are at LEFT-MOST Level\\\")\\n\",\n \" j+=1\\n\",\n \" print(\\\"The Room After Cleaning is \\\")\\n\",\n \" visuals(Room)\\n\",\n \"\\n\",\n \" return Battery\"\n ],\n \"metadata\": {\n \"id\": \"Rb-h9AQRaKZK\"\n },\n \"execution_count\": 89,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"room = [[0,0,0,0,-1], [0,-1,0,0,0], [0,0,0,0,0], [0,0,0,0,-1], [0,0,0,-1,0]] #initial condition in the morning\\n\",\n \"visuals(room)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 269\n },\n \"id\": \"M5EfYk8AcSNO\",\n \"outputId\": \"76263ab3-d692-4d3d-c1f9-3f3b31c521fa\"\n },\n \"execution_count\": 90,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"room = evening()\\n\",\n \"Battery = 100\\n\",\n \"visuals(room)\\n\",\n \"print (\\\"Vacuum is placed at Location A at 0,0\\\")\\n\",\n \"print(\\\"**** Cleaning Room A ****\\\")\\n\",\n \"Battery = cleanRoom(Battery, room)\\n\",\n \"print(\\\"********The Room A After Cleaning is \\\")\\n\",\n \"visuals(room)\\n\",\n \"print(\\\"The Battery Remaining in the Vaccum Cleaner After Cleaning Room A is \\\",Battery)\\n\",\n \"print(\\\"************************************************************\\\")\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 9214\n },\n \"id\": \"0S4JptjpbfXM\",\n \"outputId\": \"14b3c7f9-8527-4b7e-b240-ca8b641012af\"\n },\n \"execution_count\": 91,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Vacuum is placed at Location A at 0,0\\n\",\n \"**** Cleaning Room A ****\\n\",\n \"#Current Battery Status: 100\\n\",\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 0 0 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 0 1 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 0 0\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 1 0 is Clean\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"We are at LEFT-MOST Level\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 0 1 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 0 2 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 1 1\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"We are at LEFT-MOST Level\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 0 2 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 0 3 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 0 2\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 1 2 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 0 2\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"We are at LEFT-MOST Level\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 0 3 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"There is an Obstacle at Location 0 4\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 1 3 is Clean\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"We are at LEFT-MOST Level\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"As there is an Obstacle Going to the BELOW TILE\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"#Current Battery Status: 89\\n\",\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 1 0 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"There is an Obstacle at Location 1 1\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 2 0 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 1 0\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"Obstacle Present at 0 0\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"As there is an Obstacle Going to the BELOW TILE\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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uifgcobRd6q2jKQ2DHtNc211DjHs0ovEZtt4bVzVVf6m4EuZCQCwtra97Nur1OrQasrvpTdwPaSUVgg3b+ed7X+rdebPlavxVyISEXFvL+usBWcFy0k4rABu3qbtXX+r1aA2wHciEhFxaq/rrwbEG0DdpJRWCDdlfIp1mFPcIdYV/rt0IuJOTCgl8zb3XCwXHRTioCG7TjJ+K+1t99vNvX+q2QCwm5sBD31wW6g+OinVS4km7s1KlTjssY6R3Bw82Hnu16s2KEDIz2jjouZ3Jy0vnNQH8XqjwA5OIVRkaAhw/9mYEzDGB01FERStuF3iowOTmJxcVFe/U5q6oxvb29jstIxBOIdkbxvPJcwR01R6wzhsSZhONy6umUnKK7C1UeAHLxComEOOD5ufcuEIuJ+h2gtF3orQKpVMp20A7s8MjwG8Oo7vgzu1DZqWCoZ8iXuhtBLiTkwsLwsH8zcJUKMBQcF+2kIrCba05GTuLSuUsIMW8/V0IshLG+MSVbldfX17G+vu64HN1dqPIAkItXOHkSuHRJJDL0klAIGBtzvG9babvQW0VTHgJ9nnYmn8HI740oz0pyENHOKB7++kMMxJ1vr1V5XrDOLlSfm0wuLGQyYkDXy+2A0agYQHa4BV15u9BXReucpz0YH0QinoARcmXo/QsYIQOJeEJJwFYNuZCQCwuDg2JA1fDGBQxD1Kfq/BSFtIuKQD9pA/pmKAEoc00dylwjocw1EspcI2mZJ20A6DnRg4V3FxDtjLpaT7QzivmL88oCthuQCwm5sNDTAywsiO/qbhKNAvPzaqKUS7SDisAHbQCYeGsCk/2TiHXGXCk/1hlDsj+Jq29fdaV8lZALCbmwMDEBTE6K9WduEIsBySRwNfguWl2FFkGbMYa5C3O41n9N+ZNVtDOKyf5JzF4IXiaORpALCbmwwBgwNwdcu6b+MTMaFVEwgNlqGtHqKgI/pm2Fc47lx8u4+eFNlHfKjnbFGSEDESOC+Yvzrj1JuTJ+uYdOLtz0UC+XXOzBObC8DNy8KdK4ONkiaBgiLcv8vCuPle63C21UNDWm7UrQfvPNN/knn3yivNw6uec5jN8fh5k3Ua6Vsct3bf9uiIUQMSJIxBO49949V8cq62svlWUqaYAOLrzwAJCLV8jlRGpy0xQRa9e+C4RCIkIlEsC9e64N3HrWLoKvAuvr6xgcHPQvaKvIEWmHTD6D2fQsVjZWEO4Io7hdbPiUZYQMxDpjqOxUMNY3hqnhKQzGD3WjFeRCQi4sZDLiu/zKijiKrlhs/MhpGGKwtlIRu0WmpsQauhYi6Crs5ojUOmjXKZQLeJR7BPOZidXNVWy92EJlp4JwRxjdx7sx2juKxJkEhnqGgpOU1SXIhYRcWCgURCJD0xRpVra2RFQKh8URdaOj4nFyaCgwyXrdIqgqfA3ap0+f5p9//rnycnUjmUwCUHxIkIaQBwm5kJALSTKZxOLiopqgzRiLAHgIIAxxKuCfcs7/3SG/48pEpG64PumkCeRBQi4k5ELSzESknQ2fFQBf5Zz/hDHWCeA7jLEPOecOk8YTBEEQzXLoOm0u+MneXzv3fg79aGSMNfyxfhVKpVL7Xlf/FK4zMDCw73X1r1mAmIU9qEzraVrJZHLf616f0T6ozP1eU6Pf1f01HeV9st5bq7ymVnyfvH5N+70+nV/TUd+nZrC1uYYx1sEYewzgMwDf4pz/ZYNrkoyxDGPMuxlIgiCINqOpiUjG2EkAKwB+i3P+gwOuozFt0JhdHfIgIRcSciHZc6H+wCjOeQHAtwFcPNqtEQRBEE44dCKSMXYawDbnvMAY+ykAPwfgPx30O1G3j9jShP7+fr9vIRCQBwm5kJALSX9/P7LZrK1r7Sz5+zKA3wfQAfFk/sec8/9w0O94vbmGIAhCd5jNzTWHPmlzzr8P4G0ld0UQBEE4wqPEPO5SKBeQfpqGmTextrmG/PM8qrtVdIW6ED8Rx0jvCBLxBIbfGG757crkQkIuLBQKQDot9m6vrQH5vEhf3tUFxOMiuWIiIdKat/g2dt1daHU06+tk8hnMfG8GDz59gK6OLpS2S/seDBTtjKK6U8Xlc5cx/c60JwcDeTk7HmQXXq8SIBcWMhlgZgZ48EAEpVJp/1OSolERvC5fBqanXT8liVxImlk9omXQVnUE59337qLnRI9r9+lFo9TBhVedk1xYUHUe6d27IoeXC5ALScsG7fph9zc+vIHKTsXxYffhjjAW3l3AxFsTTe9KsoObjVInF253TnJhoX7y/40b4ug6pyf/h8Mi6eLEhEgJoxByIWnJoM05x62PbmExu4jSdklZufW0UnMX5rTpoLq5cPvDi1zswTlw6xawuCi++quinmNrbk5psCIXEtc21/hFvWMuZZeUdkwAKG2XsJRdwtRHU0rLdQtyISEXFupBamlJbZACRHlLSyIbgA60uAstgvby42UsZhdR3C66Un5xu4hUNoU7H99xpXyVkAsJubCwvCyeKovuuECxCKRSwB1y4beLwA+P5J7n0PdBn2sd08qxrmPY+PqGsvyAqr/+6erCja/B5MJCLgf09bkXpKwcOwZsbChJlkguJL4Pj5w9e1ZZWeP3x1HZqSgr7yDKtTKu3L+irLzbt2/j9u3bysrT1YVqDwC5eIXxcTHR5gXlMnCFXABQ7sIugc4RaT4zMfr7o8rHKw8i2hnF2q+vBS7BK7mQkAsLpimSGqoeuz2IaFRsSgla4l/NXdjdxh7oMe2Z9AzKtbKndZZrZcymZz2t0w7kQkIuLMzMiCc+LymXRVrzoNEmLlwJ2iqS+hbKBXzz0282tUFCBbt8FysbKyiUC47LSqVSSpKW6u5ClQeAXLxCoQB885vNbRZRwe4usLIi6ncAuZA048GVoP2jH/3IcRnpp2l0dXQpuJvmCXeE8SjnPAXm9evXcf36dcfl6O5ClQeAXLxCOi22Y/tBOAw8IhcAlLmwS2CHR8y8iVLVw7EpC8XtIsxnpi91N4JcSMiFBdP0dvzWSrEo6g8KbeQisEF7bXMNNe5g26kDars1rG6u+lJ3I8iFhFxYWFtztjXbCbUasLrqT92NaCMXgQ3a+ed5X+vferHla/1WyIWEXFjI++sCW+TiJR66CGzQru5Wfa3fqzXAdiAXEnJhoeqvC8/WQ9uhjVwENmh3hXyaVNgj3BH2tX4r5EJCLiz4NfFWJ0wuXuKhi8AG7fiJuK/1dx/v9rV+K+RCQi4sxP11gW5y8RIPXbgStAcGBhyXMdI7AiPkTzY0I2RgtHfUcTmccyXnKujuQpUHgFy8wsiIOOfZDwxD7D50ALmQNOMhsE/aiXgC0c6oL3XHOmNInEn4UncjyIWEXFhIJMQ2aj+IxUT9QaGNXAQ2aA+/MYzqjj+TC5WdCoZ6hnypuxHkQkIuLAwP+zcBV6kAQ+QCgOcuXAnaT548cVzGychJXDp3CSHm7edKiIUw1jemJDv3wMCAkqEi3V2o8gCQi1c4eRK4dEnkMfSSUAgYG3OcqZxcSJrxEOjztDP5DEZ+b8Tz09we/vpDDMSdNyaV5wXr7EL1ucnkwkImI8ZzvT7Z7uFDwGHAJRcS38/TVsVgfBCJeMKziScjZCARTygJ2KohFxJyYWFwUIynejUJZxiiPlVPyCppExeBftIG9M1QAlDmmjqUuUZC2Vok5ELSMk/aANBzogcL7y64vmIg2hnF/MV5ZQHbDciFhFxY6OkBFhbcXz0RjQLz80qClGu0gYvAB20AmHhrApP9k4h1xlwpP9YZQ7I/iatvX3WlfJWQCwm5sDAxAUxOiuVnbhCLAckkcJVc+O1Ci6DNGMPchTlc67+m/Mkq2hnFZP8kZi8EMBNHA8iFhFxYYAyYmwOuXVP/lBmNiiAYxGw1jWhxF66M2J86dUp5mfUO+uWf/jJufngT5Z0yartHP4rRCBmIGBHMX5x37UlqcnLSlXJ1c+GWB4BcvEI9WH35y8DNmyIVlpPjSg0DiETEMIALT5XkQjI5OYnFxUVb1wY6se9+5J7nMH5/HGbeRLlWbir1VIiFEDEiSMQTuPfevWCPVdqAXEjIhYVcTmQmN00RsJpJwxUKiQCVSAD37gV7DNsOmriwm9hXy6BdJ5PPYDY9i5WNFYQ7wihuFxs+ZRkhA7HOGCo7FYz1jWFqeCp4WbUdQi4k5MJCJiO+yq+siJPoisXGT5yGIcZqKxWxWWRqKnjZ1p0ScBe+Bu0333yTf/LJJ8rL3Y9CuYBHuUcwn5lY3VzF1ostVHYqCHeE0X28G6O9o0icSWCoZ0jJTke7rK+vA1BzgJZdgujCDw8AuXiFQkHkMTRNkWVla0sEpXBYnFA3OiqeJoeGHO/uswu5kKyvr2NwcNC/oK1ynbbOuLIOVUPIg4RcSMiFpKXWaRMEQRCSQ4M2Y+wNxti3GWOfMMb+hjF204sbIwiCIL6InSV/NQDTnPMsY+w4gHXG2Lc4594NWhMEQRAAbDxpc863OOfZvT+/APAEwKHrXhhjDX9SqdTLa1Kp1L7X1ce76gwMDOx7XTKZfHnd+vr6gWXWJz8AIJlM7nvd65MjB5W532tq9Lu6v6ajvE/We2uV19SK75PXr2m/16fzazrq+9QMTW2uYYx9CcDbAP6ywb8lASRf//8EQRCEOmyvHmGMHQOwBuA/cs7vH3ItrR4BzY7XIQ8SciEhF5I9F7ZWj9h60maMdQL4BoB7hwVsADh//rydYlseLzYY6QB5kJALCbmQZDIZDNrcwHPokzYTHwG/D+DvOee/badQr3ZEEgRBtArM5o5IO+u0vwLg1wB8lTH2eO/nFxzfIUEQBNE0hw6PcM6/A6Cp6c3Nzc0j31ArUZ+xts4ctyPkQUIuJORCYl3dchgtsY29UC4g/TQNM29ibXMN+ed5VHer6Ap1IX4ijpHeESTiCQy/Mezp2SN+TLQUCkA6LY5VWFsD8nmgWgW6uoB4XOQ9TSSA4WHPjlXwbcIpiO2CXEh8m4gMYCdpZiJS66CdyWcw870ZPPj0Abo6ulDaLu17mlu0M4rqThWXz13G9DvTnpzm5mWjzGSAmRngwQPR9kql/Q8wi0ZFG718GZiedv8AM687Z5DbBbmQeB60A9xJWj5oqzo3+e57d9Fzose1+/SiUao6KvjuXZFezw286pw6tAtyIfEsaGvQSVo2aHPOsfx4GTc+vIHKTsVxhpJwRxgL7y5g4q2Jpncl2cHNRsk5sLwM3LghTpV0mpQjHBb5UCcmRNIPlbjdOXVqF+RC4nrQ1qiTtGTQ5pzj1ke3sJhdRGm7pKzcei7AuQtz2jRKzoFbt4DFRfENTxX19Hdzc2rbpLsfXnq1C3IhcTVoa9ZJmgnaWhzNWm+MS9klpY0RAErbJSxllzD10ZTSct2i3haXltS2RUCUt7QkEnXoALULCbmw0OKdxJWgHVWcAXn58TIWs4sobheVllunuF1EKpvCnY/vKC23v78f/f39SstcXhYPD0V3VKBYBFIp4I5CFW54APRsF+RC4pYLHTtJMx4CnyMy9zyHvg/6XGuMVo51HcPG1zcCm9Q1lwP6+txri1aOHQM2NoKb05XahYRcWNC4k6jcEekr4/fHUdmpeFJXuVbGlftXPKnrKIyPi/kULyiXgSvBVUHtwgK5sNAGnSTQQdt8ZsLMm45mwJuhtluDmTeRyQfv3BTTFD9OJsCboVYT9QXxCBlqFxJyYaFNOokrQdt6iLgTZtIzKNfKSsqyS7lWxmx6VklZRzngfD9mZsQHu5eUy8CsAhUqPQB6twtyIVHtQudO0oyHwC75K5QL6J7p9rxBAkDEiGBresvxdl5VS5oKBaC72/v2CIh9BVtbznbzqlzapXu7IBcSpUv+NO8kLbHkL/00ja6OLl/qDneE8Sj3yJe6G5FOi123fhAOA4+Co4LahQVyYaGNOklgg7aZN1GqKl5jaZPidhHmM9OXuhthmuqXm9qlWBT1BwVqFxJyYaGNOklgg/ba5hpq3KMJhdeo7dawurnqS92NWFvzbm7ldWo1YHXVn7obQe1CQi4stFEnCWzQzj/P+1r/1ostX+u3kvdXBbaCo4LahQVyYaGNOklgg3Z1t+pr/V6te7VD1V8Vni17tQO1Cwm5sNBGncRWYlcbm/AAAA5GSURBVN9mOXv2rOMyukI+TSrsEe4IOy7j9u3bCu7Ev/mVOmGHKlR5APRvF+RCotKF7p3k9u3buH79uq1rXQnap0+fdlxG/EQcG/93Q8HdHI3u492Oy2gmhdBBxONit6xfdDtUocoDoH+7IBcSlS507yTJZNJ20A7s8MhI7wiMkCufKYdihAyM9o76UncjRkbEcb5+YBjA6Kg/dTeC2oWEXFhoo07iStD+/PPPHZeRiCcQ7VR7WqBdYp0xJM4kHJeTSqWUJC1NJMQxvn4Qi4n6naDKA6B/uyAXEpUudO8kzXigHZENoB2REtoRKQnaLkByYUHzTtISOyJPRk7i0rlLCDFvbzHEQhjrG/M0a/thnDwJXLok0tV5SSgEjI15l7XdDtQuJOTCQht1ksAGbQB4/533ETEintYZMSKYHp72tE47vP+++ED3kkhEJKIOGtQuJOTCQpt0kkAH7cH4IBLxhGeTLUbIQCKewEB8wJP6mmFwUAybeTXXYhiivoHgqaB2YYFcWGiTThLYMe06OmflUJ24VNekHG4kcNW1XZALiSuJfTXtJC0xpl2n50QPFt5dcH2WPNoZxfzF+eCmUQLQ0wMsLLg/SR6NAvPzwU01BlC7sEIuLLRBJwl80AaAibcmMNk/iVhnzJXyY50xJPuTuPr2VVfKV8nEBDA5KVYZuUEsBiSTwNXgq6B2YYFcWGjxTuJK0B5QPMbDGMPchTlc67+m/Gki2hnFZP8kZi+oyVZjhXOu9qsfAMaAuTng2jX1DxPRqGjrKrLVWHHDA6BnuyAXErdc6NhJmvEQ+GzsVjjnWH68jJsf3kR5p+woL54RMhAxIpi/OK/H08NrcA4sLwM3b4qlqU5OpTQMMQk+P6/HE/brULuQkAsLmnUSu9nYtQradXLPcxi/Pw4zb6JcK2OX79r+3RALIWJEkIgncO+9e8Een7NBLicSUJumaJe79lUgFBLtMJEA7t0L9hi2HahdSMiFBU06ia9BOxaL8aIHs7eZfAaz6VmsbKwg3BFGcbvY8MnCCBmIdcZQ2algrG8MU8NTGIwf6sYx9WEiVYmODyKTEd/YVlbEgWPFYuMHC8MQQ3KVitgTMDUlVkq5iZcegGC3C3Ih8dpFkDvJwMAAstmsf0Fb5ZI/OxTKBTzKPYL5zMTq5iq2XmyhslNBuCOM7uPdGO0dReJMAkM9Q57u4nJlSdMhFAoiXZ1pimQaW1ui7YXDYpfv6Kh4aBga8m4Tlx8egGC2C3Ih8ctFEDtJM0v+WiJoBxXfGmXAIA8SciEhFxKl67QZY3cYY58xxn6g4uYIgiCIo2Nnyd/vAbjo8n0QBEEQNjg0aHPOHwL4+2YLZow1/LGeG5tKpfa9rv7Vqc7AwMC+11kzYKyvrx9YpnXSI5lM7nvd62vNDypzv9fU6Hd1f01HeZ+s99Yqr6kV3yevX9N+r0/n13TU96kZlJ2swhhLAlCYP4ggCIJ4HVsTkYyxLwH4c875z9gp9PTp01xF9hrdqX+6K8vOoSnkQUIuJORCkkwmsbi4qG71SLNB2+3NNQRBEK0Gs7m5RosDowiCIAiBnSV/fwggDeAcYyzHGPuNw36nVCqpuDftWV9f9263V4AhDxJyISEXkmY80OYaF6nPCre7C/IgIRcSciHZc2FreMSjvDzuUigXkH6ahpk3sba5hvzzPKq7VXSFuhA/EcdI7wgS8QSG3xgOVjJSFyAXEnIhIRcS3V1o/aSdyWcw870ZPPj0Abo6ulDaLu17GE60M4rqThWXz13G9DvTnhwY5eWTRJBdeP1ERS4k5EKigYvWPXtE1bGTd9+7i54TPa7dpxeNUgcXXnVOciEhFxKNXLRe0K4f8H7jwxuo7FQcH/Ae7ghj4d0FTLw10fSuJDu42Sh1cuF25yQXEnIh0dBFawVtzjlufXQLi9lFlLbVrU6pp1KauzCnTaPUzYXbH17kAi/LJBd4WaaGLlpnnXb9DVjKLil9AwCgtF3CUnYJUx9NKS3XLciFhFxIyIWk1V24ErTPnz+vtLzlx8tYzC6iuO1ONpzidhGpbAp3Pr6jtNxMJgPVO0N1dOGGB4BcWCEXEl1d2CXwOSJzz3Po+6DPtTfAyrGuY9j4+kZgc+KRCwm5kJALic4uWmYb+/j9cVR2Kp7UVa6VceX+FU/qOgrkQkIuJORC0g4uXAnam5ubSsoxn5kw86ajWd9mqO3WYOZNZPJqviUkk8lXzvF1gs4uVHoAyIUVciHR3YVdAr165Ff+9FfwJ5/8SVPrKp0SYiH88j/+ZfzBv/wDx2WpnB3X2YXqVQLkQkIuJC3gQu8lf4VyAd0z3SjXyoruyj4RI4Kt6S3HW1hVNUrdXajsnORCQi4kLeJC7zHt9NM0ujq6fKk73BHGo9wjX+puBLmQkAsJuZC0k4vABm0zb6JU9eeI1+J2EeYz05e6G0EuJORCQi4k7eQisEF7bXMNNe7NhMLr1HZrWN1c9aXuRpALCbmQkAtJO7kIbNDOP8/7Wv/Wiy1f67dCLiTkQkIuJO3kwpXztKPRqOMyqrtVBXdydFSs9ezv71dwJ/q7UOUBIBdWyIWkFVxks1lb17oStFVsY+8K+TOpUCfcEXZchqpUSrq7UJlSilxIyIWkFVzYPYAqsMMj8RNxX+vvPt7ta/1WyIWEXEjIhaSdXAQ2aI/0jsAI+ZMNzQgZGO0d9aXuRpALCbmQkAtJO7lwJWir+NqTiCcQ7XQ+Nn4UYp0xJM4kHJfDGFNy5q7uLlR5AMiFFXIhaQUXdgnsk/bwG8Oo7vgzuVDZqWCoZ8iXuhtBLiTkQkIuJO3kIrBB+2TkJC6du4QQ8/YWQyyEsb6xQGVhJhcSciEhF5J2chHYoA0A77/zPiJGxNM6I0YE08PTntZpB3IhIRcSciFpFxeBDtqD8UEk4gnPJhiMkIFEPIGB+IAn9TUDuZCQCwm5kLSLi0AHbQC4+95dJWum7RAxIrj33j1P6joK5EJCLiTkQtIOLgIftHtO9GDh3QXXZ4ajnVHMX5wPbBolgFxYIRcSciFpBxeufI84e/as0vIm3prA9//P97GUXXIl91usM4bJ/klcffuq0nJv376ttDxATxdueADIhRVyIdHVxfXr121dG/jEvnU457j10S0sZhdR2lZ3BGO0M4pkfxKzF2aVrRl1G3IhIRcSciHR0YXdxL7aBG1AvBHLj5dx88ObKO+UHeWCM0IGIkYE8xfnlT9hewG5kJALCbmQ6ObC16Dd29vLVSX3bUTueQ7j98dh5k2Ua+WmcsKFWAgRI4JEPIF7791zdUwqlUoBaC5pZ7Po4MILDwC5sEIuJLq4uH79ut45Iu2QyWcwm57FysYKwh1hFLeLDT9NjZCBWGcMlZ0KxvrGMDU8hcH4oW4cozpx6UEE2YWXHgByYYVcSDRw0fpBu06hXMCj3COYz0ysbq5i68UWKjsVhDvC6D7ejdHeUSTOJDDUM+TpziWvGyUQTBd+eADIhRVyIQmwi/YJ2kHFr0YZNMiDhFxIyIWkJbKxEwRBEF/EVtBmjF1kjH3KGPtbxti/cfumCIIgiMYcGrQZYx0A/jOAdwG8CeBXGWNvun1jBEEQxBex86T9swD+lnP+d5zzKoA/AnDpsF+qH3D++k99mQ8glrnsd93rC9cHBgb2vc66ZKiea22/H2uChmQyue91AwOvHgJzUJn7vaZGv6v7azrK+2S9t1Z5Ta34Pnn9mvZ7fTq/pqO+T81w6EQkY+xfAbjIOb+29/dfA/BPOee/+dp1SQB1Mz8D4AdN3UnrcgrAj/2+iQBAHiTkQkIuJOc458cPu0jZ2SOc8xSAFAAwxjJ2ZkHbAXIhIA8SciEhFxLGmK1t5HaGR54BeMPy9569/0cQBEF4jJ2gbQL4R4yxf8gY6wLwKwD+zN3bIgiCIBpx6PAI57zGGPtNAB8B6ABwh3P+N4f8WuqQf28nyIWAPEjIhYRcSGy5cGVHJEEQBOEOtCOSIAhCIyhoEwRBaITSoM1ouzsAgDF2hzH2GWOs7deqM8beYIx9mzH2CWPsbxhjN/2+J79gjEUYY3/FGPvrPRf/3u978hvGWAdj7GPG2J/7fS9+whj7IWPsfzLGHh+29E/ZmDYT293/F4CfA5CDWHXyq5zzT5RUoBGMsX8O4CcA/ivn/Gf8vh8/YYx1A+jmnGcZY8cBrAO43KbtggGIcc5/whjrBPAdADc55498vjXfYIxNARgEcIJz/jW/78cvGGM/BDDIOT90o5HKJ+0jbXdvRTjnDwH8vd/3EQQ451uc8+zen18AeAIguOm8XYQLfrL31869n7ZdCcAY6wHwiwCW/L4XnVAZtM8AeGr5ew5t2jmJxjDGvgTgbQB/6e+d+MfecMBjAJ8B+BbnvG1dAPhdAL8DwH7+r9aFA/jvjLH1vSNB9oUmIglPYIwdA/ANAL/NOX/u9/34Bed8h3P+FsTO4p9ljLXl8Blj7GsAPuOcrx96cXvwzzjn/RCnqX59b4i1ISqDNm13JxqyN377DQD3OOf3/b6fIMA5LwD4NoCLft+LT3wFwC/tjeX+EYCvMsbu+ntL/sE5f7b3388ArEAMNzdEZdCm7e7EF9ibfPsvAJ5wzmf9vh8/YYydZoyd3PvzT0FM2m/4e1f+wDn/t5zzHs75lyBixf/gnI/7fFu+wBiL7U3SgzEWA/DzOOCUVGVBm3NeA1Df7v4EwB/b2O7ekjDG/hBAGsA5xliOMfYbft+Tj3wFwK9BPEk93vv5Bb9vyie6AXybMfZ9iIecb3HO23qpGwEA+GkA32GM/TWAvwLw3zjnf7HfxbSNnSAIQiNoIpIgCEIjKGgTBEFoBAVtgiAIjaCgTRAEoREUtAmCIDSCgjZBEIRGUNAmCILQiP8PmsnApP3+GCoAAAAASUVORK5CYII=\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"#Current Battery Status: 86\\n\",\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 2 0 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 2 1 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 3 0 is Clean\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 1 0 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 2 1 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 2 2 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 3 1 is Clean\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"Obstacle Present at 1 1\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 2 2 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 2 3 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 3 2 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 2 2\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 1 2 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 2 3 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 2 4 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 3 3 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 2 3\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 1 3 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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EZhZeo7laxvLHsSt31IBcSciEhF5KVFecmIF+mWgWWl52rz7NBO7eVc7X+zeebrtZvhFxIyIWEXEhy7qrApoMqPBu0K7sVV+t3at2rGciFhFxIyIWk4q4Kx9aGAx4O2u0Bl2YV9gi1hVyt3wi5kJALCbmQuDUJWSPkoArPBu3YiZir9Xcc73C1fiPkQkIuJORCEnNXBTocVGFLurFTp05ZLmOwaxCPNh45ttPLiBbQMNQ1ZLmcsbEx6zcD/7tQ5QEgF0bIhWRwEHj0yJ3JSE0DhoaslTE2Noa5uTlz9Vmrqj5dXV2Wy4jH4ogEI9gqbym4o8aIBqOIn4lbLqeWTskqfnehygNALoyQC0k8Ls663nJeBaJRUb8Vksmk6aDt2eGRgVcGUNlxZ3ahvFNGf2e/K3XXg1xIyIWEXEgGBtybjCyXgX4HVXh2c83J8ElcOncJAebs50qABTDcPawkI/Xa2hrW1tYsl+N3F6o8AOTCCLmQnDwJXLokcjo6SSAADA9bz9reiAdPn6edzqUx+K1B5Zk4DiISjODRNx+hN2Z9e63K84L97EL1ucnkQkIuJOm0GNt2cmdkJCLG0q3uxm+a87T7Yn2Ix+LQArYMvX8GLaAhHosrCdiqIRcSciEhF5K+PjG2rDmjApom6lN0fIppPB20AWDxyqJj60HDWhj3rtxzpK6jQC4k5EJCLiSLi86tmQ6HgXsuqPB80O480YnZN2cRCUZsrScSjGDm4oyyVGN2QC4k5EJCLiSdncDsrBi2sJNIBJiZUZNqrFE8H7QBYPS1UYz1jCEajNpSfjQYRaIngWuvX7OlfJWQCwm5kJALyegoMDYmluLZQTQKJBLANZdU+CJoM8YwfWEa13uuK3+aiAQjGOsZw9QF72XiqAe5kJALCbmQMAZMTwPXr6t/4o5ExAeC09lqjHh69cjLcM6x8HgBt967hdJOydJOMC2gIayFMXNxxranBzuyTdfwkws7PdTKJRd4US65wF65wMICcOuWSAtmZbekpokx7JkZe56wG1k9YkvQfvXVV/mHH36ovNwa2a0sRu6PQM/pKFVLDaVbCrAAwloY8Vgc967cs3V8rrb2UlV2jnr4wYUTHgByYYRcSLJZkaVd10XwbiQlWSAggnU8LiYd7RrDXltbQ19fn3tBu6+vj6dVpCg+hHQujanUFJbWlxBqC6GwXaj7ZKEFNESDUZR3yhjuHsb4wDj6Yoe68RXkQkIuJORCkk6LYY2lJbHCpFCo//StaWLculwWG2fGx8VyQrthjDV/0K6RL+Wxml2F/kzH8sYyNp9vorxTRqgthI7jHRjqGkL8TBz9nf1Kdjp6GXIhIRcSciHJ50VOR10XGWc2N0WADoXEaX1DQ+LJur/f+k7HRnA1aJ8+fZp/+umnysv1G4lEAoDag3H8CHmQkAsJuZAkEgnMzc2pCdqMsTCARwBCEKcC/gXn/N8d8ju2TET6DbsnWvwCeZCQCwm5kDQyEWlmw2cZwFc4579ijAUB/JAx9h7n3GLSeIIgCKJRDl2nzQW/2vtrcO/n0I9GxljdH+NXoWQyue91tU/hGr29vfteV/uaBYhZ2IPKNJ6mlUgk9r3u5Rntg8rc7zXV+12/v6ajvE/Ge2uW19SM75PTr2m/1+fn13TU96kRTG2uYYy1McYeA/gEwPc55z+uc02CMZZmjDk3A0kQBNFiNDQRyRg7CWAJwB9yzn96wHU0pg0as6tBHiTkQkIuJHsu1B/NyjnPA/gBgItHuzWCIAjCCodORDLGTgPY5pznGWO/AeA3Afyng34nYvcRWz6hp6fH7VvwBORBQi4k5ELS09ODTCZj6lozS/6+CODbANognsz/jHP+Hw76Hac31xAEQfgdZnJzzaFP2pzznwB4XcldEQRBEJZwKDGPveRLeaSepqDndKxsrCC3lUNlt4L2QDtiJ2IY7BpEPBbHwCsDzb9Fl1y8gFxIyIWBfB5IpcQ+9pUVIJcTqdzb24FYTCSajMdFincn97GbxFdHs75MOpfG5I8m8eCjB2hva0dxu7jvYTiRYASVnQoun7uMiTcmHDkMx8nZcS+7cHqVALmQkAsD6TQwOQk8eCACdLG4/4lRkYgI5JcvAxMTtp8Y1cjqEV8GbVXHTi5eWUTniU7b7tOJRukHF051TnIhIRcGVJ3Nurgo8pnZQNMG7doB7zffu4nyTtnyAe+hthBm35zF6GujDe9KMoOdjdJPLuzunORCQi4M1LIg3LwpjvGzmgUhFBIJKEdHRXochTRl0Oac4/bD25jLzKG4XVRWbi2V0vSFad80Sr+5sPvDi1zgRZnkYg/Ogdu3gbk5MQyiilq+selppYHbts01blFrjPOZeaWNEQCK20XMZ+Yx/nBcabl2QS4k5EJCLgzUAvb8vNqADYjy5udFZgSX8EXQXni8gLnMHArbBVvKL2wXkMwkcfeDu7aUrxJyISEXEnJhYGFBPGEX7HGBQgFIJoG77rjw/PBIdiuL7ne6bWuMRo61H8P6W+vKcuKp/vrnVxd2fA0mFxJyYSCbBbq77QvYRo4dA9bXlSSOdH145OzZs8rKGrk/gvJOWVl5B1GqlnD1/lVl5d25cwd37txRVp5fXaj2AJALI+TCwMiImHR0glIJuKrOhVk8nSNSf6Zj6NtDysfoDiISjGDlmyueS2pKLiTkQkIuDOi6SPCoehz7ICIRsUFHwTpus9vYPT2mPZmaRKlacrTOUrWEqdSUo3WagVxIyIWEXBiYnBRPv05SKokU7w5iS9BWkdQ3X8rj3Y/ebWhTgAp2+S6W1peQL+Utl5VMJpUkLfW7C1UeAHJhhFwYyOeBd99tbOOMCnZ3gaUlUb8FGvFgS9D++OOPLZeReppCe1u7grtpnFBbCKtZ6ykwb9y4gRs3blgux+8uVHkAyIURcmEglRJb090gFAJWrbswi2eHR/ScjmLFwbEpA4XtAvRnuit114NcSMiFhFwY0HVnx7KNFAqifofwbNBe2VhBlVvYdmqB6m4VyxvLrtRdD3IhIRcScmFgZcXaNnUrVKvA8rJj1Xk2aOe2cq7Wv/l809X6jZALCbmQkAsDOXddYNM5F54N2pXdiqv1O7Xu1QzkQkIuJOTCQMVdF46tDYeHg3Z7wKVJhT1CbSFX6zdCLiTkQkIuDLg1CVkj5JwLzwbt2ImYq/V3HO9wtX4j5EJCLiTkwkDMXRfocM6FLUG7t7fXchmDXYPQAu5kQ9MCGoa6hiyXwzlXcq6C312o8gCQCyPkwsDgoDjz2g00TezEtEAjHjz7pB2PxREJRlypOxqMIn4m7krd9SAXEnIhIRcG4nGxpdwNolFRv0N4NmgPvDKAyo47kwvlnTL6O/tdqbse5EJCLiTkwsDAgHuTkeUy0O+cC1uC9pMnTyyXcTJ8EpfOXUKAOfu5EmABDHcPK8lI3dvbq2SoyO8uVHkAyIURcmHg5Eng0iWR09FJAgFgeNhy1vZGPHj6PO10Lo3Bbw06foLZo28+Qm/MemNSeV6wn12oPjeZXEjIhYF0WoxtO33K36NHgMUPH9fP01ZFX6wP8VjcsckWLaAhHosrCdiqIRcSciEhFwb6+sTYslMTkpom6lP1bcEkng7aALB4ZdGx9aBhLYx7V+45UtdRIBcSciEhFwYWF51bMx0OA/ecd+H5oN15ohOzb87aPkseCUYwc3FGWaoxOyAXEnIhIRcGOjuB2Vn7V5JEIsDMjJJUY43i+aANAKOvjWKsZwzRYNSW8qPBKBI9CVx7/Zot5auEXEjIhYRcGBgdBcbGxFI8O4hGgUQCuOaOC18EbcYYpi9M43rPdeVPE5FgBGM9Y5i64MFMHHUgFxJyISEXBhgDpqeB69fVP3FHIuIDweFsNUZsGbE/deqU8jJrjfKLn/sibr13C6WdEqq7Rz+KUQtoCGthzFycse3pYWxszJZy/ebCLg8AuTBCLgzUAvcXvwjcuiXSglk5ulXTxBj2zIwtT9hjY2OYm5szda2nE/vuR3Yri5H7I9BzOkrVUkPplgIsgLAWRjwWx70r97w9PmcCciEhFxJyYSCbFVnadV0E70ZSkgUCIljH42LS0cYxbLOJfX0ZtGukc2lMpaawtL6EUFsIhe1C3ScLLaAhGoyivFPGcPcwxgfGvZdJ2iLkQkIuJOTCQDothjWWlsQKk0Kh/tO3polx63JZbJwZH1eSbf0wXA3ar776Kv/www+Vl7sf+VIeq9lV6M90LG8sY/P5Jso7ZYTaQug43oGhriHEz8TR39mvZKejWdbW1gCoOUDLLF504YYHgFwYIRcG8nmR01HXRcaZzU0RoEMhcVrf0JB4su7vt7zT0Sxra2vo6+tzL2ir2hHpd5Tv+PIp5EFCLiTkQtI0OyIJgiCIX+fQoM0Ye4Ux9gPG2IeMsZ8xxm45cWMEQRDEZzGz5K8KYIJznmGMHQewxhj7PufcuUFrgiAIAoCJJ23O+SbnPLP35+cAngA4dN0LY6zuTzKZfHFNMpnc97raeFeN3t7efa9LJBIvrltbWzuwzNrkBwAkEol9r3t5cuSgMvd7TfV+1++v6Sjvk/HemuU1NeP75PRr2u/1+fk1HfV9aoSGNtcwxj4P4HUAP67zbwkAiZf/P0EQBKEO06tHGGPHAKwA+I+c8/uHXEurR0Cz4zXIg4RcSMiFZM+FqdUjpp60GWNBAN8DcO+wgA0A58+fN1Ns0+PEBiM/QB4k5EJCLiTpdBp9JjfwHPqkzcRHwLcB/JJz/kdmCnVqRyRBEESzwEzuiDSzTvvLAL4B4CuMscd7P1+1fIcEQRBEwxw6PMI5/yGAhqY3NzY2jnxDzURtxto4c9yKkAcJuZCQC4lxdcthNMU29nwpj9TTFPScjpWNFeS2cqjsVtAeaEfsRAyDXYOIx+IYeGXA0bNH3JhoyeeBVEocq7CyAuRyQKUCtLcDsZjIexqPAwMDjh2r4NqEkxfbBbmQuDYR6cFO0shEpK+DdjqXxuSPJvHgowdob2tHcbu47wlmkWAElZ0KLp+7jIk3Jhw5wczJRplOA5OTwIMHou0Vi/sfYBaJiDZ6+TIwMWH/AWZOd04vtwtyIXE8aHu4kzR90FZ1VvDilUV0nui07T6daJSqjgpeXBTp9ezAqc7ph3ZBLiSOBW0fdJKmDdqccyw8XsDN926ivFO2nJUj1BbC7JuzGH1ttOFdSWaws1FyDiwsADdvilMlrSblCIVEPtTRUZH0QyV2d04/tQtyIbE9aPuokzRl0Oac4/bD25jLzKG4XVRWbi3/3fSFad80Ss6B27eBuTnxDU8VtfR309Nq26S9H17+ahfkQmJr0PZZJ2kkaPviaNZaY5zPzCttjABQ3C5iPjOP8YfjSsu1i1pbnJ9X2xYBUd78vEjU4QeoXUjIhYEm7yS2BO2I4gzIC48XMJeZQ2G7oLTcGoXtApKZJO5+cFdpuT09Pejp6VFa5sKCeHgo2KMChQKQTAJ3FaqwwwPgz3ZBLiR2ufBjJ2nEg+dzRGa3suh+p9u2xmjkWPsxrL+17tlEptks0N1tX1s0cuwYsL5uax5TS1C7kJALAz7uJCp3RLrKyP0RlHfKjtRVqpZw9f5VR+o6CiMjYj7FCUol4Kp3VVC7MEAuDLRAJ/F00Naf6dBzuqUZ8Eao7lah53Skc947N0XXxY+VCfBGqFZFfV48QobahYRcGGiRTmJL0DYeIm6FydQkStWSkrLMUqqWMJWaUlLWUQ4434/JSfHB7iSlEjClQIVKD4C/2wW5kKh24edO0ogHzy75y5fy6JjscLxBAkBYC2NzYtPydl5VS5ryeaCjw/n2CIh9BZub1nbzqlza5fd2QS4kSpf8+byTNMWSv9TTFNrb2l2pO9QWwmp21ZW665FKiV23bhAKAaveUUHtwgC5MNBCncSzQVvP6ShWFK+xNElhuwD9me5K3fXQdfXLTc1SKIj6vQK1Cwm5MNBCncSzQXtlYwVV7tCEwktUd6tY3lh2pe56rKw4N7fyMtUqsLzsTt31oHYhIRcGWqiTeDZo57Zyrta/+XzT1fqN5NxVgU3vqKB2YYBcGGihTuLZoF3Zrbhav1PrXs1QcVeFY8tezUDtQkIuDLRQJzGV2LdRzp49a7mM9oBLkwp7hHyq38cAAA43SURBVNpClsu4c+eOgjtxb36lRsiiClUeAP+3C3IhUenC753kzp07uHHjhqlrbQnap0+ftlxG7EQM6/93XcHdHI2O4x2Wy2gkhdBBxGJit6xbdFhUocoD4P92QS4kKl34vZMkEgnTQduzwyODXYPQArZ8phyKFtAw1DXkSt31GBwUx/m6gaYBQ0Pu1F0PahcScmGghTqJLUH7008/tVxGPBZHJKj2tECzRINRxM/ELZeTTCaVJC2Nx8Uxvm4QjYr6raDKA+D/dkEuJCpd+L2TNOKBdkTWgXZESmhHpMRruwDJhQGfd5Km2BF5MnwSl85dQoA5e4sBFsBw97CjWdsP4+RJ4NIlka7OSQIBYHjYuaztZqB2ISEXBlqok3g2aAPA22+8jbAWdrTOsBbGxMCEo3Wa4e23xQe6k4TDIhG116B2ISEXBlqkk3g6aPfF+hCPxR2bbNECGuKxOHpjvY7U1wh9fWLYzKm5Fk0T9fV6TwW1CwPkwkCLdBLPjmnX8HNWDtWJS/2alMOOBK5+bRfkQmJLYl+fdpKmGNOu0XmiE7Nvzto+Sx4JRjBzcca7aZQAdHYCs7P2T5JHIsDMjHdTjQHULoyQCwMt0Ek8H7QBYPS1UYz1jCEajNpSfjQYRaIngWuvX7OlfJWMjgJjY2KVkR1Eo0AiAVzzvgpqFwbIhYEm7yS2BO1exWM8jDFMX5jG9Z7ryp8mIsEIxnrGMHVBTbYaI5xztV/9ADAGTE8D16+rf5iIRERbV5GtxogdHgB/tgtyIbHLhR87SSMePJ+N3QjnHAuPF3DrvVso7ZQs5cXTAhrCWhgzF2f88fTwEpwDCwvArVtiaaqVUyk1TUyCz8z44wn7ZahdSMiFAZ91ErPZ2H0VtGtkt7IYuT8CPaejVC1hl++a/t0ACyCshRGPxXHvyj1vj8+ZIJsVCah1XbTLXfMqEAiIdhiPA/fueXsM2wzULiTkwoBPOomrQTsajfKCA7O36VwaU6kpLK0vIdQWQmG7UPfJQgtoiAajKO+UMdw9jPGBcfTFDnVjmdowkapExweRTotvbEtL4sCxQqH+g4WmiSG5clnsCRgfFyul7MRJD4C32wW5kDjtwsudpLe3F5lMxr2grXLJnxnypTxWs6vQn+lY3ljG5vNNlHfKCLWF0HG8A0NdQ4ifiaO/s9/RXVy2LGk6hHxepKvTdZFMY3NTtL1QSOzyHRoSDw39/c5t4nLDA+DNdkEuJG658GInaWTJX1MEba/iWqP0GORBQi4k5EKidJ02Y+wuY+wTxthPVdwcQRAEcXTMLPn7FoCLNt8HQRAEYYJDgzbn/BGAXzZaMGOs7o/x3NhkMrnvdbWvTjV6e3v3vc6YAWNtbe3AMo2THolEYt/rXl5rflCZ+72mer/r99d0lPfJeG/N8pqa8X1y+jXt9/r8/JqO+j41grKTVRhjCQAK8wcRBEEQL2NqIpIx9nkAf8U5/4KZQk+fPs1VZK/xO7VPd2XZOXwKeZCQCwm5kCQSCczNzalbPdJo0LZ7cw1BEESzwUxurvHFgVEEQRCEwMySvz8FkAJwjjGWZYz9/mG/UywWVdyb71lbW3Nut5eHIQ8SciEhF5JGPNDmGhupzQq3ugvyICEXEnIh2XNhanjEobw89pIv5ZF6moKe07GysYLcVg6V3QraA+2InYhhsGsQ8VgcA68MeCsZqQ2QCwm5kJALid9d+PpJO51LY/JHk3jw0QO0t7WjuF3c9zCcSDCCyk4Fl89dxsQbE44cGOXkk4SXXTj9REUuJORC4gMXzXv2iKpjJxevLKLzRKdt9+lEo/SDC6c6J7mQkAuJj1w0X9CuHfB+872bKO+ULR/wHmoLYfbNWYy+NtrwriQz2Nko/eTC7s5JLiTkQuJDF80VtDnnuP3wNuYycyhuq1udUkulNH1h2jeN0m8u7P7wIhd4USa5wIsyfeiiedZp196A+cy80jcAAIrbRcxn5jH+cFxpuXZBLiTkQkIuJM3uwpagff78eaXlLTxewFxmDoVte7LhFLYLSGaSuPvBXaXlptNpqN4Z6kcXdngAyIURciHxqwuzeD5HZHYri+53um17A4wcaz+G9bfWPZsTj1xIyIWEXEj87KJptrGP3B9BeafsSF2laglX7191pK6jQC4k5EJCLiSt4MKWoL2xsaGkHP2ZDj2nW5r1bYTqbhV6Tkc6p+ZbQiKR+LVzfK3gZxcqPQDkwgi5kPjdhVk8vXrk63/xdfz5h3/e0LpKqwRYAL/7j38X3/mX37FclsrZcT+7UL1KgFxIyIWkCVz4e8lfvpRHx2QHStWSorsyT1gLY3Ni0/IWVlWN0u8uVHZOciEhF5ImceHvMe3U0xTa29pdqTvUFsJqdtWVuutBLiTkQkIuJK3kwrNBW8/pKFbcOeK1sF2A/kx3pe56kAsJuZCQC0krufBs0F7ZWEGVOzOh8DLV3SqWN5Zdqbse5EJCLiTkQtJKLjwbtHNbOVfr33y+6Wr9RsiFhFxIyIWklVzYcp52JBKxXEZlt6LgTo6OirWePT09Cu7E/y5UeQDIhRFyIWkGF5lMxtS1tgRtFdvY2wPuTCrUCLWFLJehKpWS312oTClFLiTkQtIMLsweQOXZ4ZHYiZir9Xcc73C1fiPkQkIuJORC0kouPBu0B7sGoQXcyYamBTQMdQ25Unc9yIWEXEjIhaSVXNgStFV87YnH4ogErY+NH4VoMIr4mbjlchhjSs7c9bsLVR4AcmGEXEiawYVZPPukPfDKACo77kwulHfK6O/sd6XuepALCbmQkAtJK7nwbNA+GT6JS+cuIcCcvcUAC2C4e9hTWZjJhYRcSMiFpJVceDZoA8Dbb7yNsBZ2tM6wFsbEwISjdZqBXEjIhYRcSFrFhaeDdl+sD/FY3LEJBi2gIR6LozfW60h9jUAuJORCQi4kreLC00EbABavLCpZM22GsBbGvSv3HKnrKJALCbmQkAtJK7jwfNDuPNGJ2TdnbZ8ZjgQjmLk449k0SgC5MEIuJORC0goubPkecfbsWaXljb42ip/8n59gPjNvS+63aDCKsZ4xXHv9mtJy79y5o7Q8wJ8u7PAAkAsj5ELiVxc3btwwda3nE/vW4Jzj9sPbmMvMobit7gjGSDCCRE8CUxemlK0ZtRtyISEXEnIh8aMLs4l9fRO0AfFGLDxewK33bqG0U7KUC04LaAhrYcxcnFH+hO0E5EJCLiTkQuI3F64G7a6uLq4quW89sltZjNwfgZ7TUaqWGsoJF2ABhLUw4rE47l25Z+uYVDKZBNBY0s5G8YMLJzwA5MIIuZD4xcWNGzf8nSPSDOlcGlOpKSytLyHUFkJhu1D301QLaIgGoyjvlDHcPYzxgXH0xQ51YxnViUsPwssunPQAkAsj5ELiAxfNH7Rr5Et5rGZXoT/TsbyxjM3nmyjvlBFqC6HjeAeGuoYQPxNHf2e/ozuXnG6UgDdduOEBIBdGyIXEwy5aJ2h7FbcapdcgDxJyISEXkqbIxk4QBEF8FlNBmzF2kTH2EWPs7xhj/8bumyIIgiDqc2jQZoy1AfjPAN4E8CqA32OMvWr3jREEQRCfxcyT9pcA/B3n/O855xUA3wVw6bBfqh1w/vJPbZkPIJa57HfdywvXe3t7973OuGSolmttvx9jgoZEIrHvdb29v34IzEFl7vea6v2u31/TUd4n4701y2tqxvfJ6de03+vz82s66vvUCIdORDLG/hWAi5zz63t//waAf8o5/4OXrksAqJn5AoCfNnQnzcspAL9w+yY8AHmQkAsJuZCc45wfP+wiZWePcM6TAJIAwBhLm5kFbQXIhYA8SMiFhFxIGGOmtpGbGR55BuAVw9879/4fQRAE4TBmgrYO4B8xxv4hY6wdwNcB/KW9t0UQBEHU49DhEc55lTH2BwAeAmgDcJdz/rNDfi15yL+3EuRCQB4k5EJCLiSmXNiyI5IgCIKwB9oRSRAE4SMoaBMEQfgIpUGb0XZ3AABj7C5j7BPGWMuvVWeMvcIY+wFj7EPG2M8YY7fcvie3YIyFGWN/wxj72z0X/97te3IbxlgbY+wDxthfuX0vbsIY+zlj7H8yxh4ftvRP2Zg2E9vd/xeA3wSQhVh18nuc8w+VVOAjGGP/HMCvAPxXzvkX3L4fN2GMdQDo4JxnGGPHAawBuNyi7YIBiHLOf8UYCwL4IYBbnPNVl2/NNRhj4wD6AJzgnH/N7ftxC8bYzwH0cc4P3Wik8kn7SNvdmxHO+SMAv3T7PrwA53yTc57Z+/NzAE8AeDedt41wwa/2/hrc+2nZlQCMsU4Avw1g3u178RMqg/YZAE8Nf8+iRTsnUR/G2OcBvA7gx+7eiXvsDQc8BvAJgO9zzlvWBYA/AfDHAMzn/2peOID/zhhb2zsSZF9oIpJwBMbYMQDfA/BHnPMtt+/HLTjnO5zz1yB2Fn+JMdaSw2eMsa8B+IRzvnboxa3BP+Oc90CcpvrW3hBrXVQGbdruTtRlb/z2ewDucc7vu30/XoBzngfwAwAX3b4Xl/gygN/ZG8v9LoCvMMYW3b0l9+CcP9v77ycAliCGm+uiMmjTdnfiM+xNvv0XAE8451Nu34+bMMZOM8ZO7v35NyAm7dfdvSt34Jz/W855J+f88xCx4n9wzkdcvi1XYIxF9ybpwRiLAvgtHHBKqrKgzTmvAqhtd38C4M9MbHdvShhjfwogBeAcYyzLGPt9t+/JRb4M4BsQT1KP936+6vZNuUQHgB8wxn4C8ZDzfc55Sy91IwAAnwPwQ8bY3wL4GwD/jXP+1/tdTNvYCYIgfARNRBIEQfgICtoEQRA+goI2QRCEj6CgTRAE4SMoaBMEQfgICtoEQRA+goI2QRCEj/j/52ZEIcmOeQEAAAAASUVORK5CYII=\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 2 4 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"There is an Obstacle at Location 2 5\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 3 4\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 1 4 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"#Current Battery Status: 69\\n\",\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 3 0 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 3 1 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 4 0 is Clean\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 2 0 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 3 1 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 3 2 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 4 1 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 3 1\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 2 1 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 3 2 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 3 3 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"The Location 4 2 is Dirty\\n\",\n \"Moving the Cleaner ABOVE and Cleaning it and Positioning it back to 3 2\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 2 2 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 3 3 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"There is an Obstacle at Location 3 4\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 4 3\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 2 3 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"As there is an Obstacle Going to the BELOW TILE\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"#Current Battery Status: 54\\n\",\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 4 0 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 4 1 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 5 0\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 3 0 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 4 1 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"The Location 4 2 is Clean\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 5 1\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 3 1 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"----------------------------------------------\\n\",\n \"*Checking the Current Tile\\n\",\n \"The Location 4 2 is Clean\\n\",\n \"*Checking the ABOVE Tile\\n\",\n \"There is an Obstacle at Location 4 3\\n\",\n \"*Checking the RIGHT Tile\\n\",\n \"Obstacle Present at 5 2\\n\",\n \"*Checking the LEFT Tile\\n\",\n \"The Location 3 2 is Clean\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"As there is an Obstacle Going to the BELOW TILE\\n\",\n \"The Room After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"********The Room A After Cleaning is \\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n }\n },\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"The Battery Remaining in the Vaccum Cleaner After Cleaning Room A is 45\\n\",\n \"************************************************************\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"\"\n ],\n \"metadata\": {\n \"id\": \"JxRIBHZ_hp_v\"\n },\n \"execution_count\": 91,\n \"outputs\": []\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/a_star/a_star.py", "content": "import random\n\n\nclass Node:\n \"\"\" Simple node class for A* pathfinding \"\"\"\n\n def __init__(self, parent=None, position=None):\n self.parent = parent\n self.position = position\n\n self.g = 0\n self.f = 0\n self.h = 0\n\n def __eq__(self, other):\n self.position == other.position\n\n\ndef make(n):\n maze = [[0 for i in range(n)] for j in range(n)]\n for i in range(n):\n for j in range(n):\n x = random.random()\n if x > 0.7:\n maze[i][j] = 1\n\n return maze\n\n\ndef astar(maze, start, end):\n \"\"\" Returns a list of tuples as a path from the given end in the given maze\"\"\"\n\n # Defining a start and end Node\n start_node = Node(None, start)\n start_node.g = start_node.h = start_node.f = 0\n end_node = Node(None, end)\n end_node.g = end_node.f = end_node.h = 0\n\n # Initialize open and closed list\n open_list = []\n closed_list = []\n\n # Adding the start node in open list\n open_list.append(start_node)\n\n while len(open_list) > 0:\n current_node = open_list[0]\n current_index = 0\n\n for index, item in enumerate(open_list):\n # lower f means better path\n if item.f < current_node.f:\n current_node = item\n current_index = index\n\n # Popping the current index off the open list and adding the node in closed list\n open_list.pop(current_index)\n closed_list.append(current_node)\n\n # Found the goal? Cool. Now backtrack\n if current_node.position == end_node.position:\n path = []\n current = current_node\n while current is not None:\n path.append(current.position)\n current = current.parent\n return path[::-1]\n\n # Generate Children\n\n children = []\n for new_position in [\n (0, -1),\n (0, 1),\n (-1, 0),\n (1, 0),\n (-1, -1),\n (-1, 1),\n (1, -1),\n (1, 1),\n ]: # Adjacent squares\n\n # Getting node position\n node_position = (\n current_node.position[0] + new_position[0],\n current_node.position[1] + new_position[1],\n )\n\n # Checking if in-range or not :\n if (\n node_position[0] > (len(maze) - 1)\n or node_position[0] < 0\n or node_position[1] > (len(maze[len(maze) - 1]) - 1)\n or node_position[1] < 0\n ):\n continue\n\n # See if walkable or not\n if maze[node_position[0]][node_position[1]] != 0:\n continue\n\n # Creating new node\n new_node = Node(current_node, node_position)\n\n # Confirming the Child\n children.append(new_node)\n\n # Now, Loop through the children\n for child in children:\n\n # Child is on the closed list?\n for closed_child in closed_list:\n if child == closed_child:\n continue\n\n # If not? start making f,g and h values\n child.g = current_node.g + 1 ## why not child.parent.g?\n ## Eucledian heuristic without sqrt? Genius\n child.h = ((child.position[0] - end_node.position[0]) ** 2) + (\n (child.position[1] - end_node.position[1]) ** 2\n )\n child.f = child.h + child.g\n\n # Child Already in Open List?\n for open_node in open_list:\n if child == open_node and child.g > open_node.g:\n continue\n # Add the child in the open list\n open_list.append(child)\n\n\ndef showPath(maze, path, n, start, end):\n dupMaze = maze\n # General Display\n for i in range(n):\n for j in range(n):\n if dupMaze[i][j] == 1:\n dupMaze[i][j] = \"|\"\n elif dupMaze[i][j] == 0:\n dupMaze[i][j] = \" \"\n\n dupMaze[start[0]][start[1]] = \"S\"\n dupMaze[end[0]][end[1]] = \"E\"\n for i in path[1:-1]:\n dupMaze[i[0]][i[1]] = \".\"\n\n return dupMaze\n\n\ndef main():\n print(\"Started!\")\n maze = make(random.randint(3, 10))\n\n print(\" You maze is\")\n for i in maze:\n for j in i:\n print(j)\n print()\n\n start = tuple(map(int, input(\"Please Enter the starting Coordinates: \").split()))\n end = tuple(map(int, input(\"Please Enter the ending Coordinates: \").split()))\n\n path = astar(maze, start, end)\n\n display = showPath(maze, path, len(maze[0]), start, end)\n print(\"\\n\\n\")\n for i in display:\n for j in i:\n print(j)\n print()\n\n print(\"The Perfect Path will be {}\".format(path))\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "code/artificial_intelligence/src/artificial_neural_network/ann.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndataset = pd.read_csv(\"dataset.csv\")\nX = dataset.iloc[:, 3:13].values\ny = dataset.iloc[:, 13].values\n\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\n\nlabelencoder_X_1 = LabelEncoder()\nX[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])\nlabelencoder_X_2 = LabelEncoder()\nX[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])\nonehotencoder = OneHotEncoder(categorical_features=[1])\nX = onehotencoder.fit_transform(X).toarray()\nX = X[:, 1:]\n\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\nfrom sklearn.preprocessing import StandardScaler\n\nsc = StandardScaler()\nX_train = sc.fit_transform(X_train)\nX_test = sc.transform(X_test)\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nclassifier = Sequential()\n\nclassifier.add(\n Dense(units=6, kernel_initializer=\"uniform\", activation=\"relu\", input_dim=11)\n)\n\nclassifier.add(Dense(units=6, kernel_initializer=\"uniform\", activation=\"relu\"))\n\nclassifier.add(Dense(units=1, kernel_initializer=\"uniform\", activation=\"sigmoid\"))\n\nclassifier.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n\nclassifier.fit(X_train, y_train, batch_size=10, epochs=100)\n\ny_pred = classifier.predict(X_test)\ny_pred = y_pred > 0.5\n" }, { "path": "code/artificial_intelligence/src/artificial_neural_network/dataset.csv", "content": "RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited\r\n1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1\r\n2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0\r\n3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1\r\n4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0\r\n5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0\r\n6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1\r\n7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0\r\n8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1\r\n9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0\r\n10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0\r\n11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0\r\n12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0\r\n13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0\r\n14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0\r\n15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0\r\n16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0\r\n17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1\r\n18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0\r\n19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0\r\n20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0\r\n21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0\r\n22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0\r\n23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1\r\n24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0\r\n25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0\r\n26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0\r\n27,15736816,Young,756,Germany,Male,36,2,136815.64,1,1,1,170041.95,0\r\n28,15700772,Nebechi,571,France,Male,44,9,0,2,0,0,38433.35,0\r\n29,15728693,McWilliams,574,Germany,Female,43,3,141349.43,1,1,1,100187.43,0\r\n30,15656300,Lucciano,411,France,Male,29,0,59697.17,2,1,1,53483.21,0\r\n31,15589475,Azikiwe,591,Spain,Female,39,3,0,3,1,0,140469.38,1\r\n32,15706552,Odinakachukwu,533,France,Male,36,7,85311.7,1,0,1,156731.91,0\r\n33,15750181,Sanderson,553,Germany,Male,41,9,110112.54,2,0,0,81898.81,0\r\n34,15659428,Maggard,520,Spain,Female,42,6,0,2,1,1,34410.55,0\r\n35,15732963,Clements,722,Spain,Female,29,9,0,2,1,1,142033.07,0\r\n36,15794171,Lombardo,475,France,Female,45,0,134264.04,1,1,0,27822.99,1\r\n37,15788448,Watson,490,Spain,Male,31,3,145260.23,1,0,1,114066.77,0\r\n38,15729599,Lorenzo,804,Spain,Male,33,7,76548.6,1,0,1,98453.45,0\r\n39,15717426,Armstrong,850,France,Male,36,7,0,1,1,1,40812.9,0\r\n40,15585768,Cameron,582,Germany,Male,41,6,70349.48,2,0,1,178074.04,0\r\n41,15619360,Hsiao,472,Spain,Male,40,4,0,1,1,0,70154.22,0\r\n42,15738148,Clarke,465,France,Female,51,8,122522.32,1,0,0,181297.65,1\r\n43,15687946,Osborne,556,France,Female,61,2,117419.35,1,1,1,94153.83,0\r\n44,15755196,Lavine,834,France,Female,49,2,131394.56,1,0,0,194365.76,1\r\n45,15684171,Bianchi,660,Spain,Female,61,5,155931.11,1,1,1,158338.39,0\r\n46,15754849,Tyler,776,Germany,Female,32,4,109421.13,2,1,1,126517.46,0\r\n47,15602280,Martin,829,Germany,Female,27,9,112045.67,1,1,1,119708.21,1\r\n48,15771573,Okagbue,637,Germany,Female,39,9,137843.8,1,1,1,117622.8,1\r\n49,15766205,Yin,550,Germany,Male,38,2,103391.38,1,0,1,90878.13,0\r\n50,15771873,Buccho,776,Germany,Female,37,2,103769.22,2,1,0,194099.12,0\r\n51,15616550,Chidiebele,698,Germany,Male,44,10,116363.37,2,1,0,198059.16,0\r\n52,15768193,Trevisani,585,Germany,Male,36,5,146050.97,2,0,0,86424.57,0\r\n53,15683553,O'Brien,788,France,Female,33,5,0,2,0,0,116978.19,0\r\n54,15702298,Parkhill,655,Germany,Male,41,8,125561.97,1,0,0,164040.94,1\r\n55,15569590,Yoo,601,Germany,Male,42,1,98495.72,1,1,0,40014.76,1\r\n56,15760861,Phillipps,619,France,Male,43,1,125211.92,1,1,1,113410.49,0\r\n57,15630053,Tsao,656,France,Male,45,5,127864.4,1,1,0,87107.57,0\r\n58,15647091,Endrizzi,725,Germany,Male,19,0,75888.2,1,0,0,45613.75,0\r\n59,15623944,T'ien,511,Spain,Female,66,4,0,1,1,0,1643.11,1\r\n60,15804771,Velazquez,614,France,Male,51,4,40685.92,1,1,1,46775.28,0\r\n61,15651280,Hunter,742,Germany,Male,35,5,136857,1,0,0,84509.57,0\r\n62,15773469,Clark,687,Germany,Female,27,9,152328.88,2,0,0,126494.82,0\r\n63,15702014,Jeffrey,555,Spain,Male,33,1,56084.69,2,0,0,178798.13,0\r\n64,15751208,Pirozzi,684,Spain,Male,56,8,78707.16,1,1,1,99398.36,0\r\n65,15592461,Jackson,603,Germany,Male,26,4,109166.37,1,1,1,92840.67,0\r\n66,15789484,Hammond,751,Germany,Female,36,6,169831.46,2,1,1,27758.36,0\r\n67,15696061,Brownless,581,Germany,Female,34,1,101633.04,1,1,0,110431.51,0\r\n68,15641582,Chibugo,735,Germany,Male,43,10,123180.01,2,1,1,196673.28,0\r\n69,15638424,Glauert,661,Germany,Female,35,5,150725.53,2,0,1,113656.85,0\r\n70,15755648,Pisano,675,France,Female,21,8,98373.26,1,1,0,18203,0\r\n71,15703793,Konovalova,738,Germany,Male,58,2,133745.44,4,1,0,28373.86,1\r\n72,15620344,McKee,813,France,Male,29,6,0,1,1,0,33953.87,0\r\n73,15812518,Palermo,657,Spain,Female,37,0,163607.18,1,0,1,44203.55,0\r\n74,15779052,Ballard,604,Germany,Female,25,5,157780.84,2,1,1,58426.81,0\r\n75,15770811,Wallace,519,France,Male,36,9,0,2,0,1,145562.4,0\r\n76,15780961,Cavenagh,735,France,Female,21,1,178718.19,2,1,0,22388,0\r\n77,15614049,Hu,664,France,Male,55,8,0,2,1,1,139161.64,0\r\n78,15662085,Read,678,France,Female,32,9,0,1,1,1,148210.64,0\r\n79,15575185,Bushell,757,Spain,Male,33,5,77253.22,1,0,1,194239.63,0\r\n80,15803136,Postle,416,Germany,Female,41,10,122189.66,2,1,0,98301.61,0\r\n81,15706021,Buley,665,France,Female,34,1,96645.54,2,0,0,171413.66,0\r\n82,15663706,Leonard,777,France,Female,32,2,0,1,1,0,136458.19,1\r\n83,15641732,Mills,543,France,Female,36,3,0,2,0,0,26019.59,0\r\n84,15701164,Onyeorulu,506,France,Female,34,4,90307.62,1,1,1,159235.29,0\r\n85,15738751,Beit,493,France,Female,46,4,0,2,1,0,1907.66,0\r\n86,15805254,Ndukaku,652,Spain,Female,75,10,0,2,1,1,114675.75,0\r\n87,15762418,Gant,750,Spain,Male,22,3,121681.82,1,1,0,128643.35,1\r\n88,15625759,Rowley,729,France,Male,30,9,0,2,1,0,151869.35,0\r\n89,15622897,Sharpe,646,France,Female,46,4,0,3,1,0,93251.42,1\r\n90,15767954,Osborne,635,Germany,Female,28,3,81623.67,2,1,1,156791.36,0\r\n91,15757535,Heap,647,Spain,Female,44,5,0,3,1,1,174205.22,1\r\n92,15731511,Ritchie,808,France,Male,45,7,118626.55,2,1,0,147132.46,0\r\n93,15809248,Cole,524,France,Female,36,10,0,2,1,0,109614.57,0\r\n94,15640635,Capon,769,France,Male,29,8,0,2,1,1,172290.61,0\r\n95,15676966,Capon,730,Spain,Male,42,4,0,2,0,1,85982.47,0\r\n96,15699461,Fiorentini,515,Spain,Male,35,10,176273.95,1,0,1,121277.78,0\r\n97,15738721,Graham,773,Spain,Male,41,9,102827.44,1,0,1,64595.25,0\r\n98,15693683,Yuille,814,Germany,Male,29,8,97086.4,2,1,1,197276.13,0\r\n99,15604348,Allard,710,Spain,Male,22,8,0,2,0,0,99645.04,0\r\n100,15633059,Fanucci,413,France,Male,34,9,0,2,0,0,6534.18,0\r\n101,15808582,Fu,665,France,Female,40,6,0,1,1,1,161848.03,0\r\n102,15743192,Hung,623,France,Female,44,6,0,2,0,0,167162.43,0\r\n103,15580146,Hung,738,France,Male,31,9,82674.15,1,1,0,41970.72,0\r\n104,15776605,Bradley,528,Spain,Male,36,7,0,2,1,0,60536.56,0\r\n105,15804919,Dunbabin,670,Spain,Female,65,1,0,1,1,1,177655.68,1\r\n106,15613854,Mauldon,622,Spain,Female,46,4,107073.27,2,1,1,30984.59,1\r\n107,15599195,Stiger,582,Germany,Male,32,1,88938.62,1,1,1,10054.53,0\r\n108,15812878,Parsons,785,Germany,Female,36,2,99806.85,1,0,1,36976.52,0\r\n109,15602312,Walkom,605,Spain,Male,33,5,150092.8,1,0,0,71862.79,0\r\n110,15744689,T'ang,479,Germany,Male,35,9,92833.89,1,1,0,99449.86,1\r\n111,15803526,Eremenko,685,Germany,Male,30,3,90536.81,1,0,1,63082.88,0\r\n112,15665790,Rowntree,538,Germany,Male,39,7,108055.1,2,1,0,27231.26,0\r\n113,15715951,Thorpe,562,France,Male,42,2,100238.35,1,0,0,86797.41,0\r\n114,15591100,Chiemela,675,Spain,Male,36,9,106190.55,1,0,1,22994.32,0\r\n115,15609618,Fanucci,721,Germany,Male,28,9,154475.54,2,0,1,101300.94,1\r\n116,15675522,Ko,628,Germany,Female,30,9,132351.29,2,1,1,74169.13,0\r\n117,15705512,Welch,668,Germany,Female,37,6,167864.4,1,1,0,115638.29,0\r\n118,15698028,Duncan,506,France,Female,41,1,0,2,1,0,31766.3,0\r\n119,15661670,Chidozie,524,Germany,Female,31,8,107818.63,1,1,0,199725.39,1\r\n120,15600781,Wu,699,Germany,Male,34,4,185173.81,2,1,0,120834.48,0\r\n121,15682472,Culbreth,828,France,Male,34,8,129433.34,2,0,0,38131.77,0\r\n122,15580203,Kennedy,674,Spain,Male,39,6,120193.42,1,0,0,100130.95,0\r\n123,15690673,Cameron,656,France,Female,39,6,0,2,1,0,141069.88,0\r\n124,15760085,Calabresi,684,Germany,Female,48,10,126384.42,1,1,1,198129.36,0\r\n125,15779659,Zetticci,625,France,Female,28,3,0,1,0,0,183646.41,0\r\n126,15627360,Fuller,432,France,Male,42,9,152603.45,1,1,0,110265.24,1\r\n127,15671137,MacDonald,549,France,Female,52,1,0,1,0,1,8636.05,1\r\n128,15782688,Piccio,625,Germany,Male,56,0,148507.24,1,1,0,46824.08,1\r\n129,15575492,Kennedy,828,France,Female,41,7,0,2,1,0,171378.77,0\r\n130,15591607,Fernie,770,France,Male,24,9,101827.07,1,1,0,167256.35,0\r\n131,15740404,He,758,France,Female,34,3,0,2,1,1,124226.16,0\r\n132,15718369,Kaodilinakachukwu,795,Germany,Female,33,9,130862.43,1,1,1,114935.21,0\r\n133,15677871,Cocci,687,France,Male,38,9,122570.87,1,1,1,35608.88,0\r\n134,15642004,Alekseeva,686,France,Male,25,1,0,2,0,1,16459.37,0\r\n135,15712543,Chinweike,789,Germany,Male,39,7,124828.46,2,1,1,124411.08,0\r\n136,15584518,Arthur,589,Germany,Female,50,5,144895.05,2,1,1,34941.23,0\r\n137,15802381,Li,461,Germany,Female,34,5,63663.93,1,0,1,167784.28,0\r\n138,15610156,Ma,637,France,Male,40,2,133463.1,1,0,1,93165.34,0\r\n139,15594408,Chia,584,Spain,Female,48,2,213146.2,1,1,0,75161.25,1\r\n140,15640905,Vasin,579,Spain,Female,35,1,129490.36,2,0,1,8590.83,1\r\n141,15698932,Groves,756,Germany,Male,44,10,137452.09,1,1,0,189543.9,0\r\n142,15724944,Tien,663,France,Male,34,7,0,2,1,1,180427.24,0\r\n143,15628145,Forwood,682,France,Female,43,5,125851.93,1,1,1,193318.33,0\r\n144,15713483,Greeves,793,Spain,Male,52,2,0,1,1,0,159123.82,1\r\n145,15612350,Taylor,691,France,Female,31,5,40915.55,1,1,0,126213.84,1\r\n146,15800703,Madukwe,485,Spain,Female,21,5,113157.22,1,1,1,54141.5,0\r\n147,15705707,Bennelong,635,Spain,Female,29,8,138296.94,2,1,0,141075.51,0\r\n148,15754105,Olisanugo,650,France,Male,37,5,106967.18,1,0,0,24495.03,0\r\n149,15703264,Chukwufumnanya,735,France,Male,44,9,120681.63,1,1,0,74836.34,0\r\n150,15794413,Harris,416,France,Male,32,0,0,2,0,1,878.87,0\r\n151,15650237,Morgan,754,Spain,Female,32,7,0,2,1,0,89520.75,0\r\n152,15759618,Alexeeva,535,France,Female,48,9,0,1,1,0,149892.79,1\r\n153,15811589,Metcalfe,716,Spain,Male,42,8,0,2,1,0,180800.42,0\r\n154,15689044,Humphries,539,France,Male,37,2,127609.59,1,1,0,98646.22,0\r\n155,15709368,Milne,614,France,Female,43,6,0,2,1,1,109041.53,0\r\n156,15679145,Chou,706,Spain,Male,57,7,0,1,1,0,17941.16,1\r\n157,15655007,Li,758,France,Female,33,7,0,2,0,0,82996.47,0\r\n158,15623595,Clayton,586,Spain,Female,28,2,0,2,1,1,92067.35,0\r\n159,15589975,Maclean,646,France,Female,73,6,97259.25,1,0,1,104719.66,0\r\n160,15804017,Chigolum,631,Germany,Female,33,4,123246.7,1,0,0,112687.57,0\r\n161,15692132,Wilkinson,717,Spain,Female,22,6,101060.25,1,0,1,84699.56,0\r\n162,15641122,Wei,684,France,Male,30,2,0,2,1,0,83473.82,0\r\n163,15630910,Treacy,800,France,Female,49,7,108007.36,1,0,0,47125.11,0\r\n164,15680772,Hu,721,Spain,Female,36,2,0,2,1,1,106977.8,0\r\n165,15658929,Taverner,683,Spain,Male,29,0,133702.89,1,1,0,55582.54,1\r\n166,15585388,Sherman,660,Germany,Male,31,9,125189.75,2,1,1,139874.43,0\r\n167,15724623,Taubman,704,Germany,Female,24,7,113034.22,1,1,0,162503.48,1\r\n168,15588537,Robinson,615,Spain,Female,41,9,109013.23,1,1,0,196499.96,0\r\n169,15574692,Pinto,667,Spain,Female,39,2,0,2,1,0,40721.24,1\r\n170,15611325,Wood,682,Germany,Male,24,9,57929.81,2,0,0,53134.3,0\r\n171,15587562,Hawkins,484,France,Female,29,4,130114.39,1,1,0,164017.89,0\r\n172,15613172,Sun,628,Germany,Male,27,5,95826.49,2,1,0,155996.96,0\r\n173,15651022,Yost,480,Germany,Male,44,10,129608.57,1,1,0,5472.7,1\r\n174,15586310,Ting,578,France,Male,30,4,169462.09,1,1,0,112187.11,0\r\n175,15625524,Rowe,512,France,Male,40,5,0,2,1,1,146457.83,0\r\n176,15755209,Fu,484,Spain,Female,35,7,133868.21,1,1,1,27286.1,0\r\n177,15645248,Ho,510,France,Female,30,0,0,2,1,1,130553.47,0\r\n178,15790355,Okechukwu,606,Germany,Male,36,5,190479.48,2,0,0,179351.89,0\r\n179,15762615,Campbell,597,Spain,Female,40,8,101993.12,1,0,1,94774.12,0\r\n180,15625426,Ashbolt,754,Germany,Female,55,3,161608.81,1,1,0,8080.85,1\r\n181,15716334,Rozier,850,Spain,Female,45,2,122311.21,1,1,1,19482.5,0\r\n182,15789669,Hsia,510,France,Male,65,2,0,2,1,1,48071.61,0\r\n183,15621075,Ogbonnaya,778,Germany,Female,45,1,162150.42,2,1,0,174531.27,0\r\n184,15810845,T'ang,636,France,Male,42,2,0,2,1,1,55470.78,0\r\n185,15719377,Cocci,804,France,Female,50,4,0,1,1,1,8546.87,1\r\n186,15654506,Chang,514,France,Male,32,8,0,2,1,0,95857.18,0\r\n187,15771977,T'ao,730,France,Female,39,1,99010.67,1,1,0,194945.8,0\r\n188,15708710,Ford,525,Spain,Female,37,0,0,1,0,1,131521.72,0\r\n189,15726676,Marshall,616,Spain,Male,30,5,0,2,0,1,196108.51,0\r\n190,15587421,Tsai,687,Germany,Female,34,7,111388.18,2,1,0,148564.76,0\r\n191,15726931,Onwumelu,715,France,Female,41,8,56214.85,2,0,0,92982.61,1\r\n192,15771086,Graham,512,France,Female,36,3,84327.77,2,1,0,17675.36,0\r\n193,15756850,Golovanov,479,France,Male,40,1,0,2,0,0,114996.43,0\r\n194,15702741,Potts,601,France,Male,32,8,93012.89,1,1,0,86957.42,0\r\n195,15679200,Crawford,580,Spain,Male,29,9,61710.44,2,1,0,128077.8,0\r\n196,15594815,Aleshire,807,France,Male,35,3,174790.15,1,1,1,600.36,0\r\n197,15635905,Moran,616,Spain,Female,32,6,0,2,1,1,43001.46,0\r\n198,15777892,Samsonova,721,Germany,Male,37,3,107720.64,1,1,1,158591.12,0\r\n199,15656176,Jenkins,501,France,Male,57,10,0,2,1,1,47847.19,0\r\n200,15811127,Volkov,521,France,Male,35,6,96423.84,1,1,0,10488.44,0\r\n201,15604482,Chiemezie,850,Spain,Male,30,2,141040.01,1,1,1,5978.2,0\r\n202,15622911,Jude,759,France,Male,42,4,105420.18,1,0,1,121409.06,0\r\n203,15600974,He,516,Spain,Male,50,5,0,1,0,1,146145.93,1\r\n204,15727868,Onuora,711,France,Female,38,2,129022.06,2,1,1,14374.86,1\r\n205,15627801,Ginikanwa,512,Spain,Male,33,3,176666.62,1,1,0,94670.77,0\r\n206,15773039,Ku,550,France,Male,37,3,0,1,1,1,179670.31,0\r\n207,15755262,McDonald,608,Spain,Female,41,3,89763.84,1,0,0,199304.74,1\r\n208,15679531,Collins,618,France,Male,34,5,134954.53,1,1,1,151954.39,0\r\n209,15684181,Hackett,643,France,Male,45,5,0,1,1,0,142513.5,1\r\n210,15612087,Dike,671,France,Male,45,2,106376.85,1,0,1,158264.62,0\r\n211,15752047,Trevisano,689,Germany,Male,33,2,161814.64,2,1,0,169381.9,0\r\n212,15624592,Tan,603,France,Male,31,8,0,2,1,1,169915.02,0\r\n213,15573152,Glassman,620,France,Female,41,9,0,2,0,0,88852.47,0\r\n214,15594917,Miller,676,France,Female,34,1,63095.01,1,1,1,40645.81,0\r\n215,15785542,Kornilova,572,Germany,Male,26,4,118287.01,2,0,0,60427.3,0\r\n216,15723488,Watson,668,Germany,Male,47,7,106854.21,1,0,1,157959.02,1\r\n217,15680920,Marchesi,695,France,Male,46,7,49512.55,1,1,0,133007.34,0\r\n218,15786308,Millar,730,Spain,Female,33,9,0,2,0,0,176576.62,0\r\n219,15659366,Shih,807,France,Male,43,1,105799.32,2,1,0,34888.04,1\r\n220,15774854,Fuller,592,France,Male,54,8,0,1,1,1,28737.71,1\r\n221,15725311,Hay,726,France,Female,31,9,114722.05,2,1,1,98178.57,0\r\n222,15787155,Yang,514,Spain,Male,30,7,0,1,0,1,125010.24,0\r\n223,15727829,McIntyre,567,France,Male,42,2,0,2,1,1,167984.61,0\r\n224,15733247,Stevenson,850,France,Male,33,10,0,1,1,0,4861.72,1\r\n225,15568748,Poole,671,Germany,Male,45,6,99564.22,1,1,1,108872.45,1\r\n226,15699029,Bagley,670,France,Male,37,4,170557.91,2,1,0,198252.88,0\r\n227,15774393,Ch'ien,694,France,Female,30,9,0,2,1,1,26960.31,0\r\n228,15676895,Cattaneo,547,Germany,Female,39,6,74596.15,3,1,1,85746.52,1\r\n229,15637753,O'Sullivan,751,Germany,Male,50,2,96888.39,1,1,0,77206.25,1\r\n230,15605461,Lucas,594,Germany,Female,29,3,130830.22,1,1,0,61048.53,0\r\n231,15808473,Ringrose,673,France,Male,72,1,0,2,0,1,111981.19,0\r\n232,15627000,Freeman,610,France,Male,40,0,0,2,1,0,62232.6,0\r\n233,15787174,Sergeyev,512,France,Female,37,1,0,2,0,1,156105.03,0\r\n234,15723886,Fiore,767,Germany,Male,20,3,119714.25,2,0,1,150135.38,0\r\n235,15704769,Smith,585,France,Female,67,5,113978.97,2,0,1,93146.11,0\r\n236,15772896,Dumetochukwu,763,Germany,Male,42,6,100160.75,1,1,0,33462.94,1\r\n237,15711540,Pacheco,712,France,Female,29,2,0,1,1,1,144375,0\r\n238,15764866,Synnot,539,Germany,Female,43,3,116220.5,3,1,0,55803.96,1\r\n239,15794056,Johnston,668,France,Female,46,2,0,3,1,0,89048.46,1\r\n240,15795149,Stevens,703,France,Male,28,2,81173.83,2,0,1,162812.16,0\r\n241,15812009,Grant,662,Spain,Male,38,4,0,2,1,0,136259.65,0\r\n242,15651001,Tsao,725,Germany,Female,39,5,116803.8,1,1,0,124052.97,0\r\n243,15813844,Barnes,703,France,Male,37,8,105961.68,2,0,1,74158.8,0\r\n244,15596175,McIntosh,659,Germany,Male,67,6,117411.6,1,1,1,45071.09,1\r\n245,15576269,Madison,523,Spain,Male,34,7,0,2,1,0,62030.06,0\r\n246,15797219,Ifesinachi,635,France,Female,40,10,123497.58,1,1,0,131953.23,1\r\n247,15685500,Glazkov,772,Germany,Male,26,7,152400.51,2,1,0,79414,0\r\n248,15599792,Dimauro,545,France,Female,26,1,0,2,1,1,199638.56,0\r\n249,15657566,Wieck,634,Germany,Male,24,8,103097.85,1,1,1,157577.29,0\r\n250,15772423,Liao,739,Germany,Male,54,8,126418.14,1,1,0,134420.75,1\r\n251,15628112,Hughes,771,Germany,Female,36,5,77846.9,1,0,0,99805.99,0\r\n252,15753754,Morrison,587,Spain,Female,34,1,0,2,1,1,97932.68,0\r\n253,15793726,Matveyeva,681,France,Female,79,0,0,2,0,1,170968.99,0\r\n254,15694717,Ku,544,Germany,Male,37,2,79731.91,1,1,1,57558.95,0\r\n255,15665834,Cheatham,696,Spain,Male,28,8,0,1,0,0,176713.47,0\r\n256,15765297,Yao,766,Spain,Male,41,0,0,2,0,1,34283.23,0\r\n257,15636684,Kirkland,727,France,Male,34,10,0,2,1,1,198637.34,0\r\n258,15592979,Rose,671,Germany,Female,34,6,37266.67,2,0,0,156917.12,0\r\n259,15750803,Jess,693,France,Female,30,6,127992.25,1,1,1,50457.2,0\r\n260,15607178,Welch,850,Germany,Male,38,3,54901.01,1,1,1,140075.55,0\r\n261,15713853,Ifeajuna,732,Germany,Male,42,9,108748.08,2,1,1,65323.11,0\r\n262,15673481,Morton,726,Spain,Female,48,6,99906.19,1,1,0,64323.24,0\r\n263,15686776,Rossi,557,France,Female,32,6,184686.41,2,1,0,14956.44,0\r\n264,15673693,Reppert,682,France,Female,26,0,110654.02,1,0,1,111879.21,0\r\n265,15700696,Kang,738,Spain,Male,31,9,79019.8,1,1,1,18606.23,0\r\n266,15813163,Ch'iu,531,Spain,Female,36,9,99240.51,1,1,0,123137.01,0\r\n267,15653857,Wallis,498,France,Male,34,2,0,2,1,1,148528.24,0\r\n268,15777076,Clark,651,France,Male,36,7,0,2,1,0,13898.31,0\r\n269,15717398,Fielding,549,Spain,Female,39,7,0,1,0,0,81259.25,1\r\n270,15799217,Zetticci,791,Germany,Female,35,7,52436.2,1,1,0,161051.75,0\r\n271,15787071,Dulhunty,650,Spain,Male,41,9,0,2,0,1,191599.67,0\r\n272,15619955,Bevington,733,Germany,Male,34,3,100337.96,3,1,0,48559.19,1\r\n273,15796505,Boyle,811,Germany,Female,34,1,149297.19,2,1,1,186339.74,0\r\n274,15725166,Newton,707,France,Male,30,8,0,2,1,0,33159.37,0\r\n275,15800116,Bowman,712,Germany,Male,28,4,145605.44,1,0,1,93883.53,0\r\n276,15758685,Dubinina,706,Spain,Female,37,7,0,2,1,1,110899.3,0\r\n277,15694456,Toscani,756,France,Male,62,3,0,1,1,1,11199.04,1\r\n278,15767339,Chiazagomekpere,777,France,Female,53,10,0,2,1,0,189992.97,0\r\n279,15683562,Allen,646,France,Male,35,6,84026.86,1,0,1,164255.69,0\r\n280,15782210,K'ung,714,France,Male,46,1,0,1,1,0,152167.79,1\r\n281,15668893,Wilsmore,782,France,Male,39,8,0,2,1,1,33949.67,0\r\n282,15669169,Hargreaves,775,Spain,Male,29,10,0,2,1,1,68143.93,0\r\n283,15643024,Huang,479,Germany,Male,35,4,138718.92,1,1,1,47251.79,1\r\n284,15699389,Ch'ien,807,France,Male,42,7,118274.71,1,1,1,25885.72,0\r\n285,15708608,Wallwork,799,France,Female,22,8,174185.98,2,0,1,192633.85,0\r\n286,15626144,Chu,675,France,Male,40,7,113208.86,2,1,0,34577.36,0\r\n287,15573112,Kang,602,Spain,Male,29,5,103907.28,1,1,0,161229.84,0\r\n288,15790678,Davidson,475,France,Female,32,8,119023.28,1,1,0,100816.29,0\r\n289,15727556,O'Donnell,744,Spain,Female,26,5,166297.89,1,1,1,181694.44,0\r\n290,15697307,Nnachetam,588,Spain,Male,34,10,0,2,1,0,79078.91,0\r\n291,15652266,Chidiebele,703,Germany,Male,42,9,63227,1,0,1,137316.32,0\r\n292,15607098,Ahmed,747,Spain,Female,41,5,94521.17,2,1,0,194926.86,0\r\n293,15655774,Booth,583,France,Male,27,7,0,2,1,0,51285.49,0\r\n294,15590241,Chuang,750,Spain,Female,34,9,112822.26,1,0,0,150401.53,1\r\n295,15785819,Shao,681,France,Male,38,3,0,2,1,1,112491.96,0\r\n296,15723654,Tsao,773,France,Male,25,2,135903.33,1,1,0,73656.38,0\r\n297,15774510,Tien,714,France,Female,31,4,125169.26,1,1,1,106636.89,0\r\n298,15684173,Chang,687,Spain,Female,44,7,0,3,1,0,155853.52,1\r\n299,15650068,Johnson,511,France,Male,58,0,149117.31,1,1,1,162599.51,0\r\n300,15811490,French,627,France,Male,33,5,0,2,1,1,103737.82,0\r\n301,15803976,Efremov,694,France,Female,31,10,0,2,1,0,160990.27,0\r\n302,15682541,Hartley,616,Spain,Female,36,6,132311.71,1,0,0,15462.84,0\r\n303,15695699,Calabrese,687,France,Male,35,8,0,2,1,0,10334.05,0\r\n304,15624188,Chiu,712,France,Female,33,6,0,2,1,1,190686.16,0\r\n305,15812191,Brennan,553,France,Male,33,4,118082.89,1,0,0,94440.45,0\r\n306,15636673,Onwuatuegwu,667,France,Male,31,1,119266.69,1,1,1,28257.63,0\r\n307,15594898,Hewitt,731,France,Male,43,2,0,1,1,1,170034.95,1\r\n308,15660211,Shih,629,Germany,Male,35,7,156847.29,2,1,0,31824.29,0\r\n309,15773972,Balashov,614,France,Male,50,4,137104.47,1,1,0,127166.49,1\r\n310,15746726,Doyle,438,Germany,Male,31,8,78398.69,1,1,0,44937.01,0\r\n311,15712287,Pokrovskii,652,France,Female,80,4,0,2,1,1,188603.07,0\r\n312,15702919,Collins,729,Germany,Male,30,6,63669.42,1,1,0,145111.37,0\r\n313,15674398,Russo,642,France,Male,38,3,0,2,0,0,171463.83,0\r\n314,15797960,Skinner,806,Germany,Female,59,0,135296.33,1,1,0,182822.5,0\r\n315,15631868,Robertson,744,Spain,Male,36,2,153804.44,1,1,1,87213.33,0\r\n316,15581539,Atkinson,474,Spain,Male,37,3,0,2,0,0,57175.32,0\r\n317,15662736,Doyle,559,France,Male,49,2,147069.78,1,1,0,120540.83,1\r\n318,15666252,Ritchie,706,Spain,Male,42,9,0,2,1,1,28714.34,0\r\n319,15677512,McEncroe,628,Spain,Female,22,3,0,1,1,0,85426.28,0\r\n320,15626114,Pearson,429,France,Male,24,4,95741.75,1,1,0,46170.75,0\r\n321,15810834,Gordon,525,Spain,Female,57,2,145965.33,1,1,1,64448.36,0\r\n322,15678910,Ts'ai,680,France,Female,30,8,141441.75,1,1,1,16278.97,0\r\n323,15694408,Lung,749,France,Male,40,1,139290.41,1,1,0,182855.42,1\r\n324,15585215,Yuan,763,France,Female,31,4,0,2,0,0,50404.72,0\r\n325,15682757,Pardey,734,France,Male,30,3,0,2,1,0,107640.25,0\r\n326,15736601,Tai,716,France,Male,35,4,144428.87,1,1,0,134132.65,0\r\n327,15601848,Scott,594,France,Male,35,2,0,2,1,0,103480.69,0\r\n328,15736008,Hunter,644,France,Female,46,9,95441.27,1,1,0,108761.05,1\r\n329,15669064,Mazzanti,671,Germany,Male,35,1,144848.74,1,1,1,179012.3,0\r\n330,15624528,L?,664,Germany,Male,26,7,116244.14,2,1,1,95145.14,0\r\n331,15598493,Beach,656,France,Male,50,7,0,2,0,1,72143.44,0\r\n332,15601274,Hsieh,667,Spain,Female,40,1,146502.07,1,1,0,19162.89,0\r\n333,15702669,Faulkner,663,Germany,Male,44,2,117028.6,2,0,1,144680.18,0\r\n334,15728669,Knowles,584,Germany,Female,30,8,112013.81,1,1,0,177772.03,1\r\n335,15742668,Day,626,Spain,Female,37,6,108269.37,1,1,0,5597.94,0\r\n336,15697441,Hsueh,485,France,Male,29,7,182123.79,1,1,0,116828.51,1\r\n337,15740476,Tsao,659,Germany,Female,32,3,150923.74,2,0,1,174652.51,0\r\n338,15648064,Kennedy,649,France,Male,33,2,0,2,1,0,2010.98,0\r\n339,15636624,Nwabugwu,805,Spain,Female,39,5,165272.13,1,1,0,14109.85,1\r\n340,15807923,Young,716,Germany,Female,39,10,115301.31,1,1,0,43527.4,1\r\n341,15745844,Kerr,642,Germany,Female,40,6,129502.49,2,0,1,86099.23,1\r\n342,15786170,Tien,659,France,Male,31,4,118342.26,1,0,0,161574.19,0\r\n343,15681081,Marrero,545,Spain,Female,47,5,0,2,1,1,38970.14,0\r\n344,15684484,White,543,France,Male,22,8,0,2,0,0,127587.22,0\r\n345,15785869,Pisano,718,France,Female,25,7,0,2,1,0,30380.12,0\r\n346,15763859,Brown,840,France,Female,43,7,0,2,1,0,90908.95,0\r\n347,15658935,Freeman,630,Germany,Female,34,9,106937.05,2,1,0,138275.01,0\r\n348,15747358,Russell,643,Germany,Male,59,3,170331.37,1,1,1,32171.79,0\r\n349,15735203,Seleznyov,654,Germany,Female,32,1,114510.85,1,1,1,126143.23,0\r\n350,15576256,Yusupova,582,France,Male,39,5,0,2,1,1,129892.93,0\r\n351,15659420,Foley,659,Spain,Male,32,3,107594.11,2,1,1,102416.84,0\r\n352,15593365,Shih,762,Spain,Male,39,2,81273.13,1,1,1,18719.67,0\r\n353,15777352,Ikedinachukwu,568,Spain,Female,32,7,169399.6,1,1,0,61936.22,0\r\n354,15812007,Power,670,Spain,Male,25,6,0,2,1,1,78358.94,0\r\n355,15625461,Amos,613,France,Female,45,1,187841.99,2,1,1,147224.27,0\r\n356,15739438,Reed,539,France,Male,30,0,0,2,1,0,160979.66,0\r\n357,15611759,Simmons,850,Spain,Female,57,8,126776.3,2,1,1,132298.49,0\r\n358,15661629,Ricci,522,Spain,Male,34,9,126436.29,1,1,0,174248.52,1\r\n359,15633950,Yen,737,France,Male,41,1,101960.74,1,1,1,123547.28,0\r\n360,15592386,Campbell,520,France,Male,58,3,0,2,0,1,32790.02,0\r\n361,15803716,West,706,Spain,Male,28,3,0,2,0,1,181543.67,0\r\n362,15696674,Robinson,643,Germany,Female,45,2,150842.93,1,0,1,2319.96,1\r\n363,15706365,Bianchi,648,France,Female,50,9,102535.57,1,1,1,189543.19,0\r\n364,15745088,Chen,443,Germany,Female,29,9,99027.61,2,1,0,10940.4,0\r\n365,15676715,Madukaego,640,France,Male,68,9,0,2,1,1,199493.38,0\r\n366,15613085,Ibrahimova,628,Spain,Female,33,3,0,1,1,1,188193.25,0\r\n367,15633537,Nolan,540,Germany,Female,42,9,87271.41,2,1,0,172572.64,0\r\n368,15594720,Scott,460,Germany,Female,35,8,102742.91,2,1,1,189339.6,0\r\n369,15684042,Blair,636,Germany,Male,34,2,40105.51,2,0,1,53512.16,0\r\n370,15583303,Monaldo,593,France,Female,29,2,152265.43,1,1,0,34004.44,0\r\n371,15611579,Sutherland,801,Spain,Male,42,4,141947.67,1,1,1,10598.29,0\r\n372,15774696,Cole,640,Germany,Female,75,1,106307.91,2,0,1,113428.77,0\r\n373,15694506,Briggs,611,Germany,Male,31,0,107884.81,2,1,1,183487.98,0\r\n374,15688074,Gregory,802,Germany,Male,31,1,125013.72,1,1,1,187658.09,0\r\n375,15759537,Bianchi,717,Germany,Male,35,7,58469.37,2,1,1,172459.39,0\r\n376,15758449,Angelo,769,France,Female,39,8,0,1,0,1,21016,0\r\n377,15583456,Gardiner,745,Germany,Male,45,10,117231.63,3,1,1,122381.02,1\r\n378,15667871,Kerr,572,Spain,Male,35,4,152390.26,1,1,0,128123.66,0\r\n379,15677371,Ko,629,Spain,Female,30,2,34013.63,1,1,0,19570.63,0\r\n380,15629677,Distefano,687,Spain,Female,39,2,0,3,0,0,188150.6,1\r\n381,15713578,Farrell,483,France,Female,50,9,0,2,1,1,111020.24,0\r\n382,15591509,Milano,690,France,Male,36,7,101583.11,2,1,0,123775.15,0\r\n383,15568240,Ting,492,Germany,Female,30,10,77168.87,2,0,1,146700.22,0\r\n384,15622993,Boyd,709,Germany,Male,28,8,124695.72,2,1,0,145251.35,0\r\n385,15689294,Onyemaechi,705,Germany,Male,44,3,105934.96,1,1,0,82463.69,0\r\n386,15720910,Black,560,France,Female,66,9,0,1,1,1,15928.49,0\r\n387,15721181,Oliver,611,Spain,Male,46,6,0,2,1,0,45886.33,0\r\n388,15776433,Greco,730,Spain,Male,62,2,0,2,1,1,186489.95,0\r\n389,15748936,Whitehead,709,Spain,Female,45,2,0,2,0,1,162922.65,0\r\n390,15717225,Ikemefuna,544,France,Female,21,10,161525.96,2,1,0,9262.77,0\r\n391,15685226,Morrison,712,Germany,Female,29,7,147199.07,1,1,1,84932.4,0\r\n392,15785611,Onyeoruru,752,Germany,Male,38,3,183102.29,1,1,1,71557.12,0\r\n393,15573456,Cunningham,648,Spain,Male,46,9,127209,2,1,0,77405.95,1\r\n394,15684548,Demidov,556,Spain,Male,38,8,0,2,0,0,417.41,1\r\n395,15620505,Celis,594,Spain,Female,24,0,97378.54,1,1,1,71405.17,0\r\n396,15807432,Cheng,645,Germany,Female,37,2,136925.09,2,0,1,153400.24,0\r\n397,15584766,Knight,557,France,Male,33,3,54503.55,1,1,1,371.05,0\r\n398,15612187,Morin,547,Germany,Male,32,8,155726.85,1,1,0,67789.99,0\r\n399,15762218,Mills,701,France,Female,39,9,0,2,0,1,145894.9,0\r\n400,15646372,Outhwaite,616,France,Female,66,1,135842.41,1,1,0,183840.51,1\r\n401,15690452,Tung,605,France,Male,52,1,63349.75,1,1,0,108887.44,0\r\n402,15747795,Pai,593,Germany,Female,38,4,129499.42,1,1,1,154071.27,0\r\n403,15781589,Carpenter,751,Spain,Male,52,8,0,2,0,1,179291.85,0\r\n404,15732674,Fennell,443,Spain,Male,36,6,70438.01,2,0,1,56937.43,0\r\n405,15642291,Fontaine,685,France,Male,23,8,0,2,1,1,112239.03,0\r\n406,15692761,Pratt,718,France,Male,36,9,0,1,1,0,45909.87,0\r\n407,15578045,Mitchell,538,Spain,Female,49,9,141434.04,1,0,0,173779.25,1\r\n408,15745354,Franklin,611,Spain,Female,37,4,0,2,1,0,125696.26,0\r\n409,15701376,K'ung,668,Germany,Male,37,10,152958.29,2,1,1,159585.61,0\r\n410,15691625,Ko,537,Germany,Female,41,3,138306.34,1,1,0,106761.47,0\r\n411,15566594,McKenzie,709,Spain,Male,23,10,0,2,0,0,129590.18,0\r\n412,15760431,Pino,850,France,Male,38,1,0,2,1,1,80006.65,0\r\n413,15686302,Fisk,745,Spain,Female,31,3,124328.84,1,1,1,140451.52,0\r\n414,15801559,Chiang,693,Germany,Female,41,9,181461.48,3,1,1,187929.43,1\r\n415,15810432,Moseley,795,Spain,Male,35,8,0,2,1,0,167155.36,0\r\n416,15809616,Hsiung,626,Spain,Male,26,8,0,2,0,0,191420.71,0\r\n417,15720559,Heath,487,Germany,Female,61,5,110368.03,1,0,0,11384.45,1\r\n418,15695632,Dellucci,556,France,Female,39,9,89588.35,1,1,1,94898.1,0\r\n419,15659843,Li,643,France,Female,46,6,0,2,0,0,106781.59,0\r\n420,15615624,De Salis,605,France,Female,28,6,0,2,0,0,159508.52,0\r\n421,15810418,T'ang,756,Germany,Female,60,3,115924.89,1,1,0,93524.19,1\r\n422,15716186,Richardson,586,France,Female,38,2,0,2,1,0,87168.46,0\r\n423,15674551,Fitch,535,Germany,Male,40,7,111756.5,1,1,0,8128.32,1\r\n424,15622834,Stevenson,678,France,Female,35,4,0,1,1,0,125518.32,0\r\n425,15566111,Estes,596,France,Male,39,9,0,1,1,0,48963.59,0\r\n426,15784597,Lattimore,648,France,Male,26,9,162923.85,1,1,0,98368.24,0\r\n427,15652883,Chung,492,Germany,Male,39,10,124576.65,2,1,0,148584.61,0\r\n428,15806964,Utz,702,France,Male,45,0,80793.58,1,1,1,27474.81,0\r\n429,15576313,Wei,486,Germany,Female,40,9,71340.09,1,1,0,76192.21,0\r\n430,15806467,Boyle,568,Germany,Male,40,1,99282.63,1,0,0,134600.94,1\r\n431,15597602,Nwachinemelu,619,Germany,Male,57,3,137946.39,1,1,1,72467.99,1\r\n432,15743040,Kuznetsova,724,Germany,Male,41,2,127892.57,2,0,1,199645.45,0\r\n433,15705521,Pisani,548,Germany,Female,33,0,101084.36,1,1,0,42749.85,0\r\n434,15595039,Manna,545,Germany,Female,37,8,114754.08,1,1,0,136050.44,1\r\n435,15799384,Collier,683,France,Male,33,8,0,1,0,0,73564.44,0\r\n436,15581197,Ricci,762,France,Female,51,3,99286.98,1,0,1,85578.63,0\r\n437,15693737,Carr,627,Germany,Female,30,4,79871.02,2,1,0,129826.89,0\r\n438,15624623,Hs?,516,France,Male,35,10,104088.59,2,0,0,119666,0\r\n439,15783501,Findlay,800,France,Female,38,2,168190.33,2,1,0,68052.08,0\r\n440,15690134,Hughes,464,Germany,Female,42,3,85679.25,1,1,1,164104.74,0\r\n441,15782735,Chukwuemeka,626,France,Female,35,3,0,1,0,0,80190.36,0\r\n442,15611088,Genovese,790,France,Female,31,9,0,2,1,0,84126.75,0\r\n443,15672145,Swift,534,France,Female,34,7,121551.58,2,1,1,70179,0\r\n444,15732628,Ugoji,745,France,Male,46,2,122220.19,1,1,1,118024.1,0\r\n445,15787470,Parkinson,553,Spain,Male,47,3,116528.15,1,0,0,145704.19,1\r\n446,15803406,Ross,748,France,Female,26,1,77780.29,1,0,1,183049.41,0\r\n447,15730460,Oleary,722,France,Male,37,2,0,1,0,0,120906.83,0\r\n448,15644572,Turnbull,501,France,Male,40,4,125832.2,1,1,1,100433.83,0\r\n449,15694860,Uspensky,675,France,Female,38,6,68065.8,1,0,0,138777,1\r\n450,15658169,Cook,778,Spain,Female,47,6,127299.34,2,1,0,124694.99,0\r\n451,15794396,Newbold,494,Germany,Female,38,7,174937.64,1,1,0,40084.32,0\r\n452,15785798,Uchechukwu,850,France,Male,40,9,0,2,0,1,119232.33,0\r\n453,15710825,Ch'en,592,Spain,Male,31,7,110071.1,1,0,0,43921.36,0\r\n454,15668444,He,590,Spain,Female,44,3,139432.37,1,1,0,62222.81,0\r\n455,15726631,Hilton,758,France,Female,39,6,127357.76,1,0,1,56577,0\r\n456,15733797,Sal,506,France,Male,36,5,0,2,1,0,164253.35,0\r\n457,15747960,Eluemuno,733,France,Male,33,3,0,1,1,1,7666.73,0\r\n458,15634632,Titus,711,France,Male,38,3,0,2,1,0,68487.51,0\r\n459,15707362,Yin,514,Germany,Male,43,1,95556.31,1,0,1,199273.98,1\r\n460,15662976,Lettiere,637,Spain,Male,37,8,0,1,1,1,186062.36,0\r\n461,15732778,Templeman,468,Germany,Male,29,1,111681.98,2,1,1,195711.16,0\r\n462,15718443,Chibuzo,539,France,Male,39,3,0,2,1,0,36692.17,0\r\n463,15670039,Sun,509,Spain,Female,25,3,108738.71,2,1,0,106920.57,0\r\n464,15773792,Evans,662,France,Female,32,4,133950.37,1,1,1,48725.68,1\r\n465,15613786,Ogbonnaya,818,Spain,Male,26,4,0,2,1,1,167036.94,0\r\n466,15726032,Enyinnaya,608,France,Male,33,9,89968.69,1,1,0,68777.26,0\r\n467,15663252,Olisanugo,850,Spain,Female,32,9,0,2,1,1,18924.92,0\r\n468,15593782,Brookes,816,Germany,Female,38,5,130878.75,3,1,0,71905.77,1\r\n469,15633283,Padovano,536,France,Male,35,8,0,2,1,0,64833.28,0\r\n470,15749167,Fisk,753,France,Male,35,3,0,2,1,1,184843.77,0\r\n471,15759298,Shih,631,Spain,Male,27,10,134169.62,1,1,1,176730.02,0\r\n472,15683625,Hare,703,France,Male,37,1,149762.08,1,1,0,20629.4,1\r\n473,15635367,Muir,774,France,Male,26,2,93844.69,1,1,0,28415.36,0\r\n474,15681705,Fanucci,785,France,Male,28,8,0,2,1,0,77231.27,0\r\n475,15603156,Elewechi,571,France,Female,33,1,0,2,1,0,102750.7,0\r\n476,15591986,Johnston,621,Germany,Male,46,6,141078.37,1,0,0,34580.8,1\r\n477,15798888,Pisano,605,Germany,Female,31,1,117992.59,1,1,1,183598.77,0\r\n478,15809722,Ankudinov,611,France,Female,40,8,100812.33,2,1,0,147358.27,0\r\n479,15677538,Nwokike,569,France,Male,38,7,0,1,1,1,108469.2,0\r\n480,15797736,Smith,658,France,Male,29,4,80262.6,1,1,1,20612.82,0\r\n481,15695585,Atkins,788,Spain,Male,34,6,156478.62,1,0,1,181196.76,0\r\n482,15744398,Burns,525,France,Female,23,5,0,2,1,0,160249.1,0\r\n483,15750658,Obiuto,798,France,Male,37,8,0,3,0,0,110783.28,0\r\n484,15578186,Pirozzi,486,Germany,Male,37,9,115217.99,2,1,0,144995.33,0\r\n485,15676519,George,615,Spain,Male,61,9,0,2,1,0,150227.85,1\r\n486,15637954,Lewis,730,France,Female,35,0,155470.55,1,1,1,53718.28,0\r\n487,15758639,Moran,641,France,Male,37,7,0,2,1,0,75248.3,0\r\n488,15613772,Dalrymple,542,France,Male,39,3,135096.77,1,1,1,14353.43,1\r\n489,15731744,Carslaw,692,France,Male,30,2,0,2,0,1,130486.57,0\r\n490,15807709,Kirby,714,Germany,Female,55,9,180075.22,1,1,1,100127.71,0\r\n491,15714689,Houghton,591,Spain,Male,29,1,97541.24,1,1,1,196356.17,0\r\n492,15699005,Martin,710,France,Female,41,2,156067.05,1,1,1,9983.88,0\r\n493,15624170,Tan,639,France,Female,38,4,81550.94,2,0,1,118974.77,0\r\n494,15725679,Hsia,531,France,Female,47,6,0,1,0,0,194998.34,1\r\n495,15585865,Westerberg,673,France,Female,38,2,170061.92,2,0,0,134901.34,1\r\n496,15804256,Hale,765,Germany,Male,36,8,92310.54,2,1,1,72924.56,0\r\n497,15662403,Kryukova,622,France,Female,32,6,169089.38,2,1,0,101057.95,0\r\n498,15733616,Sopuluchukwu,806,France,Male,40,5,80613.93,1,1,1,142838.64,0\r\n499,15591995,Barry,757,Germany,Male,26,8,121581.56,2,1,1,127059.04,0\r\n500,15677020,Selezneva,570,France,Female,58,8,0,1,0,1,116503.92,1\r\n501,15727688,Chizuoke,555,Spain,Male,32,4,0,2,1,1,54405.79,0\r\n502,15715941,Lueck,692,France,Male,54,5,0,2,1,1,88721.84,0\r\n503,15714485,Udinese,774,France,Male,60,5,85891.55,1,1,0,74135.48,1\r\n504,15730059,Udobata,638,Spain,Male,44,9,77637.35,2,1,1,111346.22,0\r\n505,15715527,Freeman,543,Spain,Female,41,4,0,1,0,0,194902.16,0\r\n506,15576623,Outlaw,584,France,Male,31,5,0,2,1,0,31474.27,0\r\n507,15805565,Obiuto,691,Germany,Male,30,7,116927.89,1,1,0,21198.39,0\r\n508,15677307,Lo,684,Germany,Female,40,6,137326.65,1,1,0,186976.6,0\r\n509,15773890,Okechukwu,733,France,Male,22,5,0,2,1,1,117202.19,0\r\n510,15598883,King,599,Spain,Female,37,2,0,2,1,1,143739.29,0\r\n511,15568506,Forbes,524,Germany,Female,31,10,67238.98,2,1,1,161811.23,0\r\n512,15761043,Macleod,632,Germany,Female,38,6,86569.76,2,1,0,98090.91,0\r\n513,15782236,Gibbs,735,Spain,Male,34,5,0,2,0,0,71095.41,0\r\n514,15593601,Isayev,734,France,Male,34,6,133598.4,1,1,1,13107.24,0\r\n515,15682048,Pisano,605,France,Female,51,3,136188.78,1,1,1,67110.59,1\r\n516,15746902,Belstead,793,Spain,Male,38,9,0,2,1,0,88225.02,0\r\n517,15752081,Vassiliev,468,France,Female,56,10,0,3,0,1,62256.87,1\r\n518,15781307,Schneider,779,Germany,Male,37,7,120092.52,2,1,0,135925.72,0\r\n519,15775912,Mazzanti,698,France,Male,48,4,101238.24,2,0,1,177815.87,1\r\n520,15745417,Knipe,707,France,Male,58,6,89685.92,1,0,1,126471.13,0\r\n521,15671256,Macartney,850,France,Female,35,1,211774.31,1,1,0,188574.12,1\r\n522,15653547,Madukwe,850,France,Male,56,7,131317.48,1,1,1,119175.45,0\r\n523,15595766,Watts,527,Spain,Male,37,5,93722.73,2,1,1,139093.73,0\r\n524,15742358,Humphreys,696,Germany,Male,32,8,101160.99,1,1,1,115916.55,0\r\n525,15763274,Wu,661,France,Male,48,3,120320.54,1,0,0,96463.25,0\r\n526,15786063,Chin,776,France,Female,31,2,0,2,1,1,112349.51,0\r\n527,15600258,Chesnokova,701,France,Male,43,2,0,2,1,1,165303.79,0\r\n528,15573318,Kung,610,France,Male,26,8,0,2,1,0,166031.08,0\r\n529,15653849,Lu,572,Germany,Female,48,3,152827.99,1,1,0,38411.79,1\r\n530,15694272,Nkemakolam,673,France,Male,30,1,64097.75,1,1,1,77783.35,0\r\n531,15736112,Walton,519,Spain,Female,57,2,119035.35,2,1,1,29871.79,0\r\n532,15749851,Brookes,702,Spain,Female,26,4,135219.57,1,0,1,59747.63,0\r\n533,15663478,Baldwin,729,France,Male,32,6,93694.42,1,1,1,79919.13,0\r\n534,15592300,Mai,543,Spain,Male,35,10,59408.63,1,1,0,76773.53,0\r\n535,15567832,Shih,550,France,Female,40,7,114354.95,1,1,0,54018.93,0\r\n536,15776780,He,608,France,Male,59,1,0,1,1,0,70649.64,1\r\n537,15592846,Fiorentini,639,Germany,Male,35,10,128173.9,2,1,0,59093.39,0\r\n538,15739803,Lucciano,686,Spain,Male,34,9,0,2,1,0,127569.8,0\r\n539,15794142,Ferreira,564,Germany,Female,62,5,114931.35,3,0,1,18260.98,1\r\n540,15762729,Ukaegbunam,745,Germany,Female,28,1,111071.36,1,1,0,73275.96,1\r\n541,15667896,De Luca,833,France,Male,37,8,151226.18,2,1,1,136129.49,0\r\n542,15626578,Milne,622,France,Male,26,9,0,2,1,1,153237.59,0\r\n543,15776223,Davide,597,France,Female,42,4,64740.12,1,1,1,106841.12,0\r\n544,15705953,Kodilinyechukwu,721,Spain,Male,51,0,169312.13,1,1,0,109078.35,1\r\n545,15802593,Little,504,France,Female,49,7,0,3,0,1,87822.14,1\r\n546,15615457,Burns,842,Spain,Female,44,2,112652.08,2,1,0,126644.98,0\r\n547,15708916,Paterson,587,France,Male,38,0,0,2,1,0,47414.15,0\r\n548,15720187,Han,479,Germany,Female,30,7,143964.36,2,1,0,41879.99,0\r\n549,15595440,Kryukova,508,France,Male,49,7,122451.46,2,1,1,75808.1,0\r\n550,15600651,Ijendu,749,France,Male,24,1,0,3,1,1,47911.03,0\r\n551,15750141,Reichard,721,Germany,Female,36,3,65253.07,2,1,0,28737.78,0\r\n552,15657284,Day,674,Germany,Male,47,6,106901.94,1,1,1,2079.2,1\r\n553,15763063,Price,685,Spain,Female,25,10,128509.63,1,1,0,121562.33,0\r\n554,15709324,Bruce,417,France,Male,34,7,0,2,1,0,55003.79,0\r\n555,15711309,Sumrall,574,Germany,Male,33,3,129834.67,1,1,0,193131.42,0\r\n556,15775318,Lu,590,Spain,Female,51,3,154962.99,3,0,1,191932.27,1\r\n557,15705515,Lazarev,587,Germany,Male,40,5,138241.9,2,1,0,159418.1,0\r\n558,15634844,Miller,598,Germany,Male,41,3,91536.93,1,1,0,191468.78,1\r\n559,15717046,Wentworth-Shields,741,Spain,Male,53,3,0,2,1,1,38913.68,0\r\n560,15571816,Ritchie,850,Spain,Female,70,5,0,1,1,1,705.18,0\r\n561,15670080,Mackenzie,584,Germany,Female,29,7,105204.01,1,0,1,138490.03,0\r\n562,15800440,Power,650,Spain,Male,61,1,152968.73,1,0,1,82970.69,0\r\n563,15665678,Tan,607,Spain,Male,36,8,158261.68,1,1,1,76744.72,0\r\n564,15665956,Pendergrass,509,France,Female,46,1,0,1,1,0,71244.59,1\r\n565,15788126,Evans,689,Spain,Female,38,6,121021.05,1,1,1,12182.15,0\r\n566,15811773,Hsia,543,France,Male,36,4,0,2,1,1,141210.5,0\r\n567,15651674,Billson,438,Spain,Female,54,2,0,1,0,0,191763.07,1\r\n568,15689614,Teng,687,Spain,Female,63,1,137715.66,1,1,1,37938.74,0\r\n569,15795564,Moretti,737,Germany,Male,31,5,121192.22,2,1,1,74890.58,0\r\n570,15706647,Jordan,761,France,Male,31,7,0,3,1,1,166698.18,0\r\n571,15728505,Ts'ao,601,France,Male,44,1,100486.18,2,1,1,62678.53,0\r\n572,15730076,Osborne,651,France,Male,45,1,0,1,1,0,67740.08,1\r\n573,15622003,Carslaw,745,France,Male,35,9,92566.53,2,1,0,161519.77,0\r\n574,15607312,Ch'ang,648,Spain,Female,49,10,0,2,1,1,159835.78,1\r\n575,15644753,Hung,848,Spain,Male,40,3,110929.96,1,1,1,30876.84,0\r\n576,15653620,Gordon,546,France,Female,27,8,0,2,1,1,14858.1,0\r\n577,15761986,Obialo,439,Spain,Female,32,3,138901.61,1,1,0,75685.97,0\r\n578,15633922,Gray,755,France,Male,30,4,123217.66,2,0,1,144183.1,0\r\n579,15734674,Lin,593,France,Female,41,6,0,1,1,0,65170.66,0\r\n580,15658032,Hopkins,701,France,Male,39,2,0,2,1,1,82526.92,0\r\n581,15692671,Dobson,701,Spain,Male,36,8,0,2,1,0,169161.46,0\r\n582,15737741,McKay,607,Spain,Female,33,2,108431.87,2,0,1,109291.39,1\r\n583,15576352,Revell,586,Spain,Female,57,3,0,2,0,1,6057.81,0\r\n584,15753719,Rickards,547,Germany,Female,30,9,72392.41,1,1,0,77077.14,0\r\n585,15803689,Begum,647,Germany,Female,51,1,119741.77,2,0,0,54954.51,1\r\n586,15718057,Onyinyechukwuka,760,France,Female,51,2,100946.71,1,0,0,179614.8,1\r\n587,15722010,Zuyev,621,Spain,Male,53,9,170491.84,1,1,0,35588.07,1\r\n588,15680998,Nwankwo,725,France,Male,44,5,0,1,1,1,117356.14,0\r\n589,15614782,Hao,526,France,Male,36,1,0,1,1,0,160696.72,0\r\n590,15591047,Ma,519,Spain,Female,47,6,157296.02,2,0,0,147278.43,1\r\n591,15788291,Okwuadigbo,713,Germany,Female,38,7,144606.22,1,1,1,56594.36,1\r\n592,15604044,Mitchell,700,France,Male,38,8,134811.3,1,1,0,1299.75,0\r\n593,15679587,Chan,666,France,Female,34,9,115897.12,1,1,1,25095.03,0\r\n594,15775153,Buchi,630,Spain,Male,32,4,82034,1,0,0,146326.45,0\r\n595,15603925,Greco,779,Spain,Female,26,4,174318.13,2,0,1,38296.21,0\r\n596,15680970,Lombardi,611,Germany,Female,41,2,114206.84,1,1,0,164061.6,0\r\n597,15697183,Uchenna,685,Spain,Male,43,9,0,2,1,0,107811.28,0\r\n598,15567446,Coffman,646,Germany,Male,39,9,111574.41,1,1,1,30838.51,0\r\n599,15637476,Alexandrova,683,Germany,Female,57,5,162448.69,1,0,0,9221.78,1\r\n600,15714939,Fallaci,484,Germany,Female,34,4,148249.54,1,0,1,33738.27,0\r\n601,15683503,Hudson,601,France,Female,43,8,0,3,0,1,110916.15,1\r\n602,15645569,Mai,762,Spain,Female,26,7,123709.46,2,1,1,169654.57,0\r\n603,15782569,Stout,687,France,Female,72,9,0,1,0,1,69829.4,0\r\n604,15592387,Burke,566,France,Male,30,5,0,1,1,0,54926.51,1\r\n605,15609286,Chadwick,702,France,Male,37,10,150525.8,1,1,1,94728.49,0\r\n606,15814035,Lawrence,601,France,Male,29,9,0,1,1,1,80393.27,0\r\n607,15661249,Bellucci,699,France,Male,53,4,0,2,0,1,111307.98,0\r\n608,15629117,Harper,584,France,Male,28,10,0,2,1,0,19834.32,0\r\n609,15607170,Boyle,699,France,Male,35,5,0,2,1,1,78397.24,0\r\n610,15586585,Duncan,698,Germany,Female,51,2,111018.98,1,1,0,86410.28,0\r\n611,15686611,Moss,495,France,Male,30,10,129755.99,1,0,0,172749.65,0\r\n612,15603203,Avdeyeva,650,France,Female,27,6,0,2,1,0,1002.39,0\r\n613,15619857,Crawford,605,France,Female,64,2,129555.7,1,1,1,13601.79,0\r\n614,15805062,Lynton,667,Spain,Male,38,1,87202.38,1,1,1,77866.91,0\r\n615,15660271,Duncan,688,Germany,Male,26,8,146133.39,1,1,1,175296.76,0\r\n616,15745295,Gether,727,Spain,Female,31,0,0,1,1,0,121751.04,1\r\n617,15719352,Davidson,754,Spain,Male,39,6,170184.99,2,1,0,89593.26,0\r\n618,15766575,Larionova,612,Germany,Female,62,8,140745.33,1,1,0,193437.89,1\r\n619,15594594,Loggia,546,Spain,Male,42,7,139070.51,1,1,1,86945,0\r\n620,15646161,Steinhoff,673,Spain,Female,37,8,0,2,1,1,183318.79,0\r\n621,15682585,Guerra,593,France,Male,35,9,114193.24,1,1,0,71154.1,0\r\n622,15603134,Pai,656,Spain,Female,40,10,167878.5,1,0,1,151887.16,0\r\n623,15636444,Craig,535,Germany,Female,53,5,141616.55,2,1,1,75888.65,0\r\n624,15773456,Lazareva,678,Germany,Male,36,3,145747.67,2,0,1,89566.74,0\r\n625,15745307,Ch'iu,477,Spain,Female,48,2,129120.64,1,0,1,26475.79,0\r\n626,15604119,Alderete,850,Spain,Male,35,7,110349.82,1,0,0,126355.8,0\r\n627,15626900,Kung,427,France,Male,29,1,141325.56,1,1,1,93839.3,0\r\n628,15605447,Palermo,752,France,Male,49,2,78653.84,1,1,0,7698.6,0\r\n629,15589030,Ts'ai,649,France,Male,47,1,0,2,1,1,145593.85,0\r\n630,15692463,Rahman,799,Spain,Female,28,3,142253.65,1,1,0,45042.56,0\r\n631,15712403,McMillan,589,France,Female,61,1,0,1,1,0,61108.56,1\r\n632,15811762,Pickering,583,Germany,Female,54,6,115988.86,1,1,0,57553.98,1\r\n633,15718673,Mirams,839,Spain,Female,33,10,75592.43,1,1,0,62674.42,0\r\n634,15724282,Tsao,540,Germany,Male,44,3,164113.04,2,1,1,12120.79,0\r\n635,15738181,Douglas,850,France,Male,31,6,67996.23,2,0,0,50129.87,1\r\n636,15633648,Jideofor,696,Spain,Female,51,5,0,2,1,0,55022.43,0\r\n637,15603323,Bell,660,Spain,Female,33,1,0,2,0,0,117834.91,0\r\n638,15583725,Mairinger,682,France,Male,48,1,138778.15,1,0,1,168840.23,0\r\n639,15588350,McIntyre,744,France,Female,43,10,147832.15,1,0,1,24234.11,0\r\n640,15798398,Pagnotto,785,France,Female,36,4,135438.4,1,0,0,190627.01,0\r\n641,15784844,K'ung,752,Spain,Male,48,5,116060.08,1,1,0,156618.38,1\r\n642,15580684,Feng,706,France,Female,29,5,112564.62,1,1,0,42334.38,0\r\n643,15809663,Donaldson,583,France,Female,27,1,125406.58,1,1,1,110784.42,0\r\n644,15640078,Chambers,660,Germany,Female,39,5,135134.99,1,1,0,173683,1\r\n645,15698786,Marcelo,819,France,Female,39,9,133102.92,1,1,0,27046.46,1\r\n646,15569807,Ejimofor,673,France,Female,34,8,42157.08,1,1,0,20598.59,1\r\n647,15730830,Dale,752,France,Female,30,3,0,2,1,1,104991.28,0\r\n648,15805112,Pokrovsky,578,France,Male,38,7,82259.29,1,1,0,8996.97,0\r\n649,15633064,Stonebraker,438,France,Female,36,4,0,2,1,0,64420.5,0\r\n650,15703119,Liang,652,France,Male,38,6,0,2,1,1,145700.22,0\r\n651,15730447,Anderson,629,France,Female,49,4,0,2,1,1,196335.48,0\r\n652,15813850,Christian,720,France,Male,52,7,0,1,1,1,14781.12,0\r\n653,15711889,Mao,668,France,Male,42,3,150461.07,1,1,0,108139.23,0\r\n654,15664610,Campbell,459,Germany,Male,48,4,133994.52,1,1,1,19287.06,1\r\n655,15751710,Ginikanwa,729,Spain,Male,31,8,164870.81,2,1,1,9567.39,0\r\n656,15692926,Toscani,498,Germany,Male,25,8,121702.73,1,1,1,132210.49,0\r\n657,15813741,Nnachetam,549,Spain,Male,25,6,193858.2,1,0,1,21600.11,0\r\n658,15698474,Sagese,601,Germany,Female,54,1,131039.97,2,1,1,199661.5,0\r\n659,15568595,Fleming,544,France,Male,64,9,113829.45,1,1,1,124341.49,0\r\n660,15603065,Grubb,751,France,Female,30,6,0,2,1,0,15766.1,0\r\n661,15592937,Napolitani,632,Germany,Female,41,3,81877.38,1,1,1,33642.21,0\r\n662,15699637,Anenechi,694,Spain,Male,57,8,116326.07,1,1,1,117704.65,0\r\n663,15667215,Chandler,678,France,Male,31,2,0,2,1,1,58803.28,0\r\n664,15788659,Howells,695,France,Male,46,4,0,2,1,1,137537.22,0\r\n665,15763218,Akeroyd,661,France,Female,41,1,0,2,0,1,131300.68,0\r\n666,15645772,Onwumelu,661,France,Male,33,9,0,2,1,1,84174.81,0\r\n667,15725511,Wallace,559,France,Female,31,3,127070.73,1,0,1,160941.78,0\r\n668,15575024,Uwaezuoke,503,France,Male,29,3,0,2,1,1,143954.99,0\r\n669,15640825,Loyau,695,Spain,Male,46,3,122549.64,1,1,1,56297.85,0\r\n670,15662397,Small,640,France,Female,42,5,176099.13,1,1,1,8404.73,0\r\n671,15576368,Bledsoe,624,Germany,Female,48,3,122388.38,2,0,0,30020.09,0\r\n672,15674991,Kao,667,France,Male,42,9,0,2,0,1,58137.42,0\r\n673,15721024,Wickens,642,France,Male,26,0,0,1,0,0,47472.68,0\r\n674,15745621,Wertheim,640,Spain,Female,32,6,118879.35,2,1,1,19131.71,0\r\n675,15642394,He,529,Spain,Male,35,5,0,2,1,1,187288.5,0\r\n676,15754605,Jarvis,563,France,Female,39,5,0,2,1,1,17603.81,0\r\n677,15607040,P'an,593,Spain,Female,38,4,88736.44,2,1,0,67020.03,0\r\n678,15715142,Repina,739,Germany,Male,45,7,102703.62,1,0,1,147802.94,1\r\n679,15810978,Pugliesi,788,Spain,Female,70,1,0,2,1,1,41610.62,0\r\n680,15668886,Blakey,684,Spain,Female,38,3,0,2,1,0,44255.65,0\r\n681,15780804,Nucci,482,France,Male,55,5,97318.25,1,0,1,78416.14,0\r\n682,15613880,Higinbotham,591,Spain,Male,58,5,128468.69,1,0,1,137254.55,0\r\n683,15775238,Achebe,651,Germany,Female,41,4,133432.59,1,0,1,151303.48,0\r\n684,15786905,Russo,749,Germany,Female,40,8,141782.57,2,0,0,86333.63,0\r\n685,15747867,Trevisani,583,France,Male,24,9,135125.28,1,0,0,89801.9,0\r\n686,15600337,Dobie,661,Spain,Male,42,2,178820.91,1,0,0,29358.57,1\r\n687,15801277,Maccallum,715,France,Female,31,2,112212.14,2,1,1,181600.72,0\r\n688,15579334,Watkins,769,Germany,Female,45,5,126674.81,1,1,0,124118.71,1\r\n689,15802741,Mitchel,625,France,Female,51,7,136294.97,1,1,0,38867.46,1\r\n690,15720649,Ferdinand,641,France,Female,36,5,66392.64,1,1,0,31106.67,0\r\n691,15589493,Otitodilinna,716,Germany,Male,27,1,122552.34,2,1,0,67611.36,0\r\n692,15688251,Mamelu,767,France,Male,43,1,76408.85,2,1,0,77837.63,0\r\n693,15665238,Beneventi,745,Germany,Male,36,8,145071.24,1,0,0,6078.46,0\r\n694,15740900,Perrodin,589,France,Male,34,6,0,2,1,1,177896.92,0\r\n695,15681068,Chinagorom,796,France,Female,45,2,109730.22,1,1,1,123882.73,0\r\n696,15748625,Napolitano,664,France,Male,57,6,0,2,1,1,15304.08,0\r\n697,15727299,Edgar,445,Spain,Male,62,1,64119.38,1,1,1,76569.64,1\r\n698,15620204,Walker,543,Germany,Female,57,1,106138.33,2,1,1,120657.32,1\r\n699,15669516,Steele,746,Spain,Male,36,2,0,2,1,1,16436.56,0\r\n700,15736534,Elkins,742,Germany,Male,33,0,181656.51,1,1,1,107667.91,0\r\n701,15803457,Hao,750,France,Female,32,5,0,2,1,0,95611.47,0\r\n702,15659098,Toscano,669,France,Male,30,7,95128.86,1,0,0,19799.26,0\r\n703,15603436,Savage,594,Spain,Female,49,2,126615.94,2,0,1,123214.74,0\r\n704,15566292,Okwuadigbo,574,Spain,Male,36,1,0,2,0,1,71709.12,0\r\n705,15808621,Mordvinova,659,Germany,Male,36,2,76190.48,2,1,1,149066.14,0\r\n706,15580148,Welch,750,Germany,Male,40,5,168286.81,3,1,0,20451.99,1\r\n707,15776231,Kent,626,Germany,Male,35,4,88109.81,1,1,1,32825.5,0\r\n708,15773809,Campbell,620,France,Male,42,4,0,2,1,0,6232.31,0\r\n709,15649423,Cooper,580,France,Female,35,8,0,2,0,1,10357.03,0\r\n710,15734886,Mazzi,686,France,Female,34,3,123971.51,2,1,0,147794.63,0\r\n711,15722548,Fisher,540,France,Male,48,0,148116.48,1,0,0,116973.48,0\r\n712,15650288,Summers,634,Germany,Male,35,6,116269.01,1,1,0,129964.94,0\r\n713,15629448,Brady,632,Spain,Male,38,1,120599.21,1,1,0,92816.86,0\r\n714,15716164,Nicholls,501,France,Female,41,3,144260.5,1,1,0,172114.67,0\r\n715,15807609,Yuan,650,Spain,Female,25,3,86605.5,3,1,0,16649.31,1\r\n716,15578977,Robinson,786,France,Male,34,9,0,2,1,0,144517.19,0\r\n717,15677369,Golubov,554,Germany,Female,37,4,58629.97,1,0,0,182038.6,0\r\n718,15804072,Chen,701,Spain,Female,42,5,0,2,0,0,24210.56,0\r\n719,15696859,Oldham,474,France,Male,45,10,0,2,0,0,172175.9,0\r\n720,15653780,Kambinachi,621,France,Female,43,5,0,1,1,1,47578.45,0\r\n721,15721658,Fleming,672,Spain,Female,56,2,209767.31,2,1,1,150694.42,1\r\n722,15578761,Cunningham,459,Spain,Female,42,6,129634.25,2,1,1,177683.02,1\r\n723,15736879,Obinna,669,France,Male,23,1,0,2,0,0,66088.83,0\r\n724,15571973,Chinwemma,776,France,Female,38,2,169824.46,1,1,0,169291.7,0\r\n725,15626742,Carpenter,694,France,Male,36,3,97530.25,1,1,1,117140.41,0\r\n726,15672692,Yin,787,France,Female,42,10,145988.65,2,1,1,79510.37,0\r\n727,15673570,Olsen,580,France,Male,37,9,0,2,0,1,77108.66,0\r\n728,15767432,Ts'ai,711,France,Female,25,7,0,3,1,1,9679.28,0\r\n729,15654238,Jen,673,France,Female,40,5,137494.28,1,1,0,81753.92,0\r\n730,15612525,Preston,499,France,Female,57,1,0,1,0,0,131372.38,1\r\n731,15812750,Ozioma,591,France,Male,24,6,147360,1,1,1,25310.82,0\r\n732,15790757,Cody,769,France,Female,25,10,0,2,0,0,187925.75,0\r\n733,15723873,Ponomarev,657,Spain,Male,31,3,125167.02,1,0,0,98820.39,0\r\n734,15744607,Martin,738,Germany,Male,43,9,121152.05,2,1,0,64166.7,1\r\n735,15612966,Milani,545,Germany,Female,60,7,128981.07,1,0,1,176924.21,1\r\n736,15784209,Tang,497,France,Male,47,6,0,1,1,1,90055.08,0\r\n737,15794278,Romani,816,Spain,Male,67,6,151858.98,1,1,1,72814.31,0\r\n738,15766741,McIntyre,525,France,Male,36,2,114628.4,1,0,1,168290.06,0\r\n739,15661036,Davis,725,France,Male,46,6,0,2,1,0,161767.38,0\r\n740,15705639,Onyemauchechukwu,692,France,Female,28,8,95059.02,2,1,0,44420.18,0\r\n741,15637414,Gell,618,France,Female,24,7,128736.39,1,0,1,37147.61,0\r\n742,15716835,Rossi,546,France,Male,24,8,156325.38,1,1,1,125381.02,0\r\n743,15696231,Chiwetelu,635,France,Male,29,7,105405.97,1,1,1,149853.89,0\r\n744,15641675,Kirillova,611,France,Female,49,2,88915.37,3,0,0,161435.02,1\r\n745,15670755,Shaw,650,France,Male,60,8,0,2,1,1,102925.76,0\r\n746,15640059,Smith,606,France,Male,40,5,0,2,1,1,70899.27,0\r\n747,15787619,Hsieh,844,France,Male,18,2,160980.03,1,0,0,145936.28,0\r\n748,15587535,Onyemauchechukwu,450,Spain,Female,46,5,177619.71,1,1,0,54227.06,0\r\n749,15813034,Martin,727,Spain,Male,38,2,62276.99,1,1,1,59280.79,0\r\n750,15698839,Okwudilichukwu,460,Germany,Male,46,4,127559.97,2,1,1,126952.5,0\r\n751,15790314,Onuoha,649,France,Male,41,0,0,2,0,1,130567.02,0\r\n752,15634245,Muecke,758,Germany,Female,47,9,95523.16,1,1,0,73294.48,0\r\n753,15677305,Hsieh,490,France,Female,35,7,107749.03,1,1,1,3937.37,0\r\n754,15661526,Anderson,815,Germany,Male,37,2,110777.26,2,1,0,2383.59,0\r\n755,15685997,Azubuike,838,Spain,Female,39,5,166733.92,2,1,0,14279.44,0\r\n756,15660101,Nnonso,803,France,Male,31,9,157120.86,2,1,0,141300.53,0\r\n757,15637979,Fuller,664,Germany,Female,36,2,127160.78,2,1,0,78140.75,0\r\n758,15815364,Ashley,736,Spain,Female,28,2,0,2,1,1,117431.1,0\r\n759,15647099,Ts'ui,633,France,Female,37,9,156091.97,1,1,0,72008.61,0\r\n760,15625944,Buccho,664,France,Male,58,5,98668.18,1,1,1,60887.58,0\r\n761,15583212,Chidozie,600,France,Female,43,5,134022.06,1,1,0,194764.83,0\r\n762,15582741,Maclean,693,France,Female,35,5,124151.09,1,1,0,88705.14,1\r\n763,15637876,Burns,663,Germany,Female,36,6,77253.5,1,0,0,35817.97,1\r\n764,15622750,Chu,742,Germany,Female,21,1,114292.48,1,1,0,31520.4,0\r\n765,15672056,Kenenna,710,Germany,Male,43,2,140080.32,3,1,1,157908.19,1\r\n766,15812351,Beluchi,710,Spain,Female,27,2,135277.96,1,1,0,142200.15,0\r\n767,15810864,Williamson,700,France,Female,82,2,0,2,0,1,182055.36,0\r\n768,15677921,Bobrov,720,Germany,Male,60,9,115920.62,2,0,0,157552.08,1\r\n769,15724296,Kerr,684,Spain,Male,41,2,119782.72,2,0,0,120284.67,0\r\n770,15685329,McKenzie,531,France,Female,63,1,114715.71,1,0,1,24506.95,1\r\n771,15584091,Pitts,742,Germany,Female,36,2,129748.54,2,0,0,47271.61,1\r\n772,15640442,Standish,717,France,Male,31,4,129722.57,1,0,0,41176.6,0\r\n773,15639314,Cartwright,589,France,Male,32,2,0,2,0,1,9468.64,0\r\n774,15685320,Johnstone,767,France,Male,36,3,139180.2,1,0,0,123880.19,0\r\n775,15789158,Nikitina,636,Germany,Male,49,6,113599.74,2,1,0,158887.09,1\r\n776,15752137,McElroy,648,France,Male,33,7,134944,1,1,1,117036.38,0\r\n777,15712551,Shen,622,Germany,Female,58,7,116922.25,1,1,0,120415.61,1\r\n778,15628936,Archer,692,Spain,Male,28,9,118945.09,1,0,0,16064.25,1\r\n779,15797227,Otutodilinna,754,France,Male,28,8,0,2,1,1,52615.62,0\r\n780,15769974,Shih,679,Spain,Female,35,8,119182.73,1,0,0,121210.09,0\r\n781,15737051,Denisov,639,France,Male,27,8,0,2,1,0,192247.35,0\r\n782,15585595,Owens,774,France,Female,28,1,71264.02,2,0,1,68759.57,0\r\n783,15654060,P'eng,517,France,Male,41,2,0,2,0,1,75937.47,0\r\n784,15745196,Verco,571,France,Female,35,8,0,2,0,0,84569.13,0\r\n785,15571221,Bergamaschi,747,Germany,Male,58,7,116313.57,1,1,1,190696.35,1\r\n786,15660155,Lorenzo,792,Spain,Male,36,5,92140.15,1,0,1,67468.67,0\r\n787,15605284,Outtrim,688,France,Male,26,1,0,2,1,1,104435.94,0\r\n788,15694366,Hou,714,Germany,Male,42,2,177640.09,1,0,1,47166.55,0\r\n789,15600739,Galkin,562,Spain,Female,35,0,0,2,1,0,119899.52,0\r\n790,15653253,Pagnotto,704,Spain,Male,48,8,167997.6,1,1,1,173498.45,0\r\n791,15763431,Echezonachukwu,698,France,Male,36,2,82275.35,2,1,1,93249.26,0\r\n792,15643696,Young,611,France,Male,49,3,0,2,1,1,142917.54,0\r\n793,15707473,Summers,850,Germany,Female,48,6,111962.99,1,1,0,111755.8,0\r\n794,15769504,Munro,743,Germany,Female,34,1,131736.88,1,1,1,108543.21,0\r\n795,15776807,Brennan,654,France,Male,29,1,0,1,1,0,180345.44,0\r\n796,15686870,Ball,761,Germany,Male,36,8,108239.11,2,0,0,99444.02,0\r\n797,15668747,Virgo,702,France,Female,46,9,98444.19,1,0,1,109563.28,0\r\n798,15766908,Trevisani,488,Germany,Male,32,3,114540.38,1,1,0,92568.07,0\r\n799,15570134,Padovano,683,France,Female,35,6,187530.66,2,1,1,37976.36,0\r\n800,15567367,Tao,601,Germany,Female,42,9,133636.16,1,0,1,103315.74,0\r\n801,15747542,Perez,605,France,Male,52,7,0,2,1,1,173952.5,0\r\n802,15762238,Fraser,671,Germany,Female,44,0,84745.03,2,0,1,34673.98,0\r\n803,15681554,Alley,614,Germany,Female,31,7,120599.38,2,1,1,46163.44,0\r\n804,15712825,Howells,511,Spain,Female,29,9,0,2,0,1,140676.98,0\r\n805,15640280,Cameron,850,France,Male,39,4,127771.35,2,0,1,151738.54,0\r\n806,15756026,Hooper,790,Spain,Female,46,9,0,1,0,0,14679.81,1\r\n807,15613319,Rice,793,France,Female,33,0,0,1,0,0,175544.02,0\r\n808,15798906,Cox,628,France,Male,69,5,0,2,1,1,181964.6,0\r\n809,15708917,Martin,598,Germany,Male,53,10,167772.96,1,1,1,136886.86,0\r\n810,15778463,Ikenna,657,France,Female,37,6,95845.6,1,1,0,122218.23,0\r\n811,15699430,Davide,618,France,Female,35,10,0,2,1,0,180439.75,0\r\n812,15649992,Alexander,681,Spain,Male,65,7,134714.7,2,0,1,190419.81,0\r\n813,15578980,Piazza,516,Spain,Female,33,3,0,2,1,1,58685.59,0\r\n814,15775306,Ni,421,Germany,Male,28,8,122384.22,3,1,1,89017.38,1\r\n815,15641655,Black,700,France,Female,26,2,0,2,0,0,50051.42,0\r\n816,15619708,Harker,745,France,Male,25,5,157993.15,2,1,0,146041.45,0\r\n817,15734565,Hughes,696,France,Male,29,8,0,2,1,0,191166.09,0\r\n818,15806438,Chiabuotu,580,Germany,Female,42,2,123331.36,1,0,0,103516.08,1\r\n819,15591969,Kuo,497,Spain,Male,27,9,75263.16,1,1,1,164825.04,0\r\n820,15747807,Gallagher,720,France,Female,43,6,137824.03,2,1,0,172557.77,0\r\n821,15596939,Calabresi,659,Germany,Male,36,4,132578.92,2,1,0,84320.94,0\r\n822,15716155,Shaw,841,France,Female,36,5,156021.31,1,0,0,122662.98,0\r\n823,15765311,Zhirov,642,Spain,Male,34,8,0,1,1,0,72085.1,0\r\n824,15757811,Lloyd,732,Spain,Female,69,9,137453.43,1,0,1,110932.24,1\r\n825,15603830,Palmer,600,Spain,Male,36,4,0,2,1,0,143635.36,0\r\n826,15660602,Ch'eng,464,Germany,Male,33,8,164284.72,2,1,1,3710.34,0\r\n827,15660535,Avent,680,France,Female,47,5,0,2,1,1,179843.33,0\r\n828,15666633,Huang,758,Spain,Male,56,1,0,2,1,1,10643.38,0\r\n829,15596914,Shaw,630,Germany,Female,31,2,112373.49,2,1,1,131167.98,0\r\n830,15639788,Yuan,577,France,Female,39,10,0,2,1,0,10553.31,0\r\n831,15695846,Hawkins,684,France,Female,34,6,0,2,1,1,130928.22,0\r\n832,15726234,Trentini,708,Spain,Female,41,5,0,1,0,1,157003.99,0\r\n833,15797964,Cameron,732,Germany,Female,29,1,154333.82,1,1,1,138527.56,0\r\n834,15625881,Koehler,634,Germany,Male,37,3,111432.77,2,1,1,167032.49,0\r\n835,15780628,Wu,633,France,Female,30,6,0,2,0,0,41642.29,0\r\n836,15575883,Manna,559,France,Male,34,2,137390.11,2,1,0,9677,0\r\n837,15585036,Okoli,694,Spain,Female,37,3,0,2,1,1,147012.22,0\r\n838,15589488,Ch'eng,686,Germany,Female,56,5,111642.08,1,1,1,80553.87,0\r\n839,15585888,Nwokezuike,553,Spain,Female,48,3,0,1,0,1,30730.95,1\r\n840,15727915,Artemiev,507,France,Male,36,4,83543.37,1,0,0,140134.43,0\r\n841,15707567,Esposito,732,Germany,Male,50,6,145338.76,1,0,0,91936.1,1\r\n842,15737792,Abbie,818,France,Female,31,1,186796.37,1,0,0,178252.63,0\r\n843,15599433,Fanucci,660,Germany,Male,35,8,58641.43,1,0,1,198674.08,0\r\n844,15672012,Jen,773,Spain,Female,41,5,0,1,1,0,28266.9,1\r\n845,15806983,Moss,640,France,Male,44,3,137148.68,1,1,0,92381.01,0\r\n846,15592222,Lo,505,France,Male,49,7,80001.23,1,0,0,135180.11,0\r\n847,15608968,Averyanov,714,Germany,Male,21,6,86402.52,2,0,0,27330.59,0\r\n848,15586959,Unaipon,468,France,Female,42,5,0,2,1,0,125305.34,0\r\n849,15646558,Clamp,611,Spain,Male,51,1,122874.74,1,1,1,149648.45,0\r\n850,15725811,Lim,705,France,Male,25,0,97544.29,1,0,1,59887.15,0\r\n851,15572265,Wu,646,Germany,Male,46,1,170826.55,2,1,0,45041.32,0\r\n852,15794048,Wan,667,Germany,Female,48,1,97133.92,2,0,0,113316.77,1\r\n853,15677610,Chambers,511,Germany,Female,41,8,153895.65,1,1,1,39087.42,0\r\n854,15745012,Pettit,653,France,Female,43,6,0,2,1,1,7330.59,0\r\n855,15601589,Baresi,675,France,Female,57,8,0,2,0,1,95463.29,0\r\n856,15686436,Newbery,523,Spain,Male,32,4,0,2,1,0,167848.02,0\r\n857,15693864,Iheanacho,567,Germany,Female,49,5,134956.02,1,1,0,93953.84,1\r\n858,15760550,Duncan,741,Spain,Male,39,7,143637.58,2,0,1,174227.66,0\r\n859,15686137,Barry,456,Spain,Male,32,9,147506.25,1,1,1,135399.21,0\r\n860,15809087,Landry,598,France,Male,64,1,0,2,1,0,195635.3,1\r\n861,15807663,McGregor,667,France,Male,43,8,190227.46,1,1,0,97508.04,1\r\n862,15809100,Nucci,548,France,Female,32,2,172448.77,1,1,0,188083.77,1\r\n863,15794916,Pirogov,725,France,Male,41,7,113980.21,1,1,1,116704.25,0\r\n864,15614215,Oguejiofor,717,France,Male,53,6,0,2,0,1,97614.87,0\r\n865,15805449,Ugochukwu,594,France,Male,38,4,0,2,0,0,186884.04,0\r\n866,15686983,Rohu,678,Germany,Female,25,10,76968.12,2,0,1,131501.72,0\r\n867,15808017,Cary,545,France,Male,38,1,88293.13,2,1,1,24302.95,0\r\n868,15756804,O'Loghlen,636,France,Female,48,1,170833.46,1,1,0,110510.28,1\r\n869,15646810,Quinn,603,Germany,Male,44,6,108122.39,2,1,0,108488.33,1\r\n870,15710424,Page,435,France,Male,36,4,0,1,1,1,197015.2,0\r\n871,15799422,Evans,535,France,Female,40,8,0,1,1,1,27689.77,0\r\n872,15692750,McGregor,629,Germany,Female,45,7,129818.39,3,1,0,9217.55,1\r\n873,15794549,Andrews,722,France,Female,35,2,163943.89,2,1,1,15068.18,0\r\n874,15803764,Stanley,561,France,Male,28,7,0,2,1,0,7797.01,0\r\n875,15674840,Chiazagomekpere,645,France,Female,38,5,101430.3,2,0,1,4400.32,0\r\n876,15653762,Chidiebele,501,France,Female,39,9,117301.66,1,0,0,182025.95,0\r\n877,15581229,Gregory,502,Germany,Female,32,1,173340.83,1,0,1,122763.95,0\r\n878,15800228,Bednall,652,Spain,Female,42,4,0,2,1,1,38152.01,0\r\n879,15656333,Jen,574,France,Female,33,3,134348.57,1,1,0,63163.99,0\r\n880,15697497,She,518,France,Female,45,9,105525.65,2,1,1,73418.29,0\r\n881,15585362,Simmons,749,France,Female,60,6,0,1,1,0,17978.68,1\r\n882,15571928,Fraser,679,France,Female,43,4,0,3,1,0,115136.51,1\r\n883,15785519,May,565,France,Male,36,6,106192.1,1,1,0,149575.59,0\r\n884,15743007,Seabrook,643,France,Female,45,4,45144.43,1,1,0,60917.24,1\r\n885,15777211,Herrera,515,France,Male,65,7,92113.61,1,1,1,142548.33,0\r\n886,15721935,Kincaid,521,France,Male,25,7,0,2,1,1,157878.67,0\r\n887,15591711,Sleeman,739,Spain,Male,38,0,128366.44,1,1,0,12796.43,0\r\n888,15625021,Hung,585,France,Male,42,2,0,2,1,1,18657.77,0\r\n889,15702968,Artemieva,733,Germany,Male,74,3,106545.53,1,1,1,134589.58,0\r\n890,15600462,Barwell,542,France,Female,43,8,145618.37,1,0,1,10350.74,0\r\n891,15768104,Wright,788,Spain,Male,37,8,141541.25,1,0,0,66013.27,0\r\n892,15780140,Bellucci,435,Germany,Male,32,2,57017.06,2,1,1,5907.11,0\r\n893,15585255,Moore,577,France,Male,42,9,0,1,1,0,74077.91,0\r\n894,15772781,Ball,703,France,Female,51,3,0,3,1,1,77294.56,1\r\n895,15669987,Sung,728,Germany,Female,35,8,125884.95,2,1,0,54359.02,1\r\n896,15697000,Mello,728,Germany,Male,32,5,61825.5,1,1,1,156124.93,0\r\n897,15733119,Mistry,718,France,Male,35,8,0,2,1,0,94820.85,0\r\n898,15782390,T'ien,621,France,Female,40,6,0,1,1,0,155155.25,0\r\n899,15654700,Fallaci,523,France,Female,40,2,102967.41,1,1,0,128702.1,1\r\n900,15632210,Hill,657,Germany,Male,25,2,171770.55,1,1,0,22745.5,0\r\n901,15642041,Burns,727,Germany,Male,40,1,93051.64,2,1,0,71865.31,1\r\n902,15709737,Hunter,643,France,Male,36,7,161064.64,2,0,1,84294.82,0\r\n903,15792388,Li,645,France,Female,48,7,90612.34,1,1,1,149139.13,0\r\n904,15786014,Ku,568,France,Male,28,5,145105.64,2,1,0,185489.11,0\r\n905,15794580,Ch'en,599,France,Male,58,4,0,1,0,0,176407.15,1\r\n906,15675964,Chukwukadibia,672,France,Female,45,9,0,1,1,1,92027.69,1\r\n907,15814275,Zikoranachidimma,685,France,Male,33,6,174912.72,1,1,1,43932.54,0\r\n908,15724848,Oluchukwu,516,France,Female,46,1,104947.72,1,1,0,115789.25,1\r\n909,15754713,Rivera,685,Spain,Male,31,10,135213.71,1,1,1,125777.28,0\r\n910,15693814,Niu,806,Spain,Male,25,7,0,2,1,0,18461.9,0\r\n911,15599660,Bennett,604,France,Male,36,6,116229.85,2,1,1,79633.38,0\r\n912,15746490,Wollstonecraft,648,Spain,Female,53,6,111201.41,1,1,1,121542.29,0\r\n913,15566091,Thomsen,545,Spain,Female,32,4,0,1,1,0,94739.2,0\r\n914,15655961,Palermo,756,Germany,Male,27,1,131899,1,1,0,93302.29,0\r\n915,15710404,Chinwendu,569,France,Male,35,10,124525.52,1,1,1,193793.78,0\r\n916,15775625,McKenzie,596,France,Male,47,6,0,1,1,0,74835.65,0\r\n917,15792328,James,475,France,Male,39,6,0,1,1,1,56999.9,1\r\n918,15719856,Lamb,646,France,Female,45,3,47134.75,1,1,1,57236.44,0\r\n919,15593773,Olejuru,784,Spain,Male,35,3,0,2,0,0,81483.64,0\r\n920,15733114,Hay,552,Spain,Male,45,9,0,2,1,0,26752.56,0\r\n921,15797748,Lu,729,France,Male,44,5,0,2,0,1,9200.54,0\r\n922,15743411,Chiawuotu,609,Spain,Male,61,1,0,1,1,0,22447.85,1\r\n923,15753337,Yeates,555,France,Male,51,5,0,3,1,0,189122.89,1\r\n924,15601026,Gallagher,572,Germany,Female,19,1,138657.08,1,1,1,16161.82,0\r\n925,15658485,Heath,785,France,Female,34,9,70302.48,1,1,1,68600.36,0\r\n926,15636731,Ts'ai,714,Germany,Female,36,1,101609.01,2,1,1,447.73,0\r\n927,15628303,Thurgood,738,Spain,Male,35,3,0,1,1,1,15650.73,0\r\n928,15633461,Pai,639,Germany,Male,38,5,130170.82,1,1,1,149599.62,0\r\n929,15677135,Lorenzo,520,Germany,Male,61,8,133802.29,2,1,1,90304.01,0\r\n930,15590876,Knupp,764,France,Female,24,7,106234.02,1,0,0,115676.38,0\r\n931,15790782,Baryshnikov,661,Spain,Male,39,6,132628.98,1,0,0,38812.67,0\r\n932,15700476,Azubuike,564,Germany,Male,41,9,103522.75,2,1,1,34338.21,0\r\n933,15634141,Shephard,708,Germany,Female,42,8,192390.52,2,1,0,823.36,0\r\n934,15737795,Scott,512,Spain,Male,36,1,0,1,0,1,135482.26,1\r\n935,15790299,Williamson,592,Spain,Male,37,9,0,3,1,1,10656.89,0\r\n936,15675316,Avdeeva,619,France,Female,38,3,0,2,0,1,116467.35,0\r\n937,15613630,Tang,775,France,Male,52,8,109922.61,1,1,1,96823.32,1\r\n938,15662100,Hsu,850,Germany,Female,44,5,128605.32,1,0,1,171096.2,0\r\n939,15668032,Buchanan,577,France,Female,37,4,0,1,1,1,79881.39,0\r\n940,15599289,Yeh,724,France,Female,37,10,68598.56,1,1,0,157862.82,0\r\n941,15754084,Palazzi,710,Spain,Male,35,1,106518.52,1,1,1,127951.81,0\r\n942,15676521,Y?an,696,France,Female,31,8,0,2,0,0,191074.11,0\r\n943,15804586,Lin,376,France,Female,46,6,0,1,1,0,157333.69,1\r\n944,15781465,Schofield,675,Germany,Female,29,8,121326.42,1,1,0,133457.52,0\r\n945,15729362,Lombardi,745,France,Male,36,8,67226.37,1,1,0,130789.6,0\r\n946,15709295,Wall,697,Spain,Female,25,5,82931.85,2,1,1,128373.88,0\r\n947,15745324,Milani,599,Spain,Female,39,4,0,1,1,0,194273.2,1\r\n948,15741336,Ejimofor,715,France,Female,38,5,118590.41,1,1,1,5684.17,1\r\n949,15783659,Blackburn,659,France,Male,67,4,145981.87,1,1,1,131043.2,0\r\n950,15620981,Wickham,684,France,Female,48,3,73309.38,1,0,0,21228.34,1\r\n951,15630328,Bird,635,France,Female,48,8,130796.33,2,1,1,43250.3,0\r\n952,15785899,Ch'en,789,Germany,Male,33,8,151607.56,1,1,0,4389.4,0\r\n953,15606149,Wood,571,Germany,Female,66,9,111577.01,1,0,1,189271.9,0\r\n954,15671139,Brizendine,694,Spain,Male,39,0,107042.74,1,1,1,102284.2,0\r\n955,15660429,Ch'in,665,Spain,Female,42,2,156371.61,2,0,1,156774.94,1\r\n956,15571002,Yusupov,706,France,Female,44,4,129605.99,1,0,0,69865.49,0\r\n957,15631681,Jibunoh,807,Spain,Female,43,0,0,2,0,1,85523.24,0\r\n958,15731522,Ts'ui,771,Spain,Female,67,8,0,2,1,1,51219.8,0\r\n959,15619529,Ndukaku,531,Spain,Male,27,8,132576.25,1,0,0,7222.92,0\r\n960,15628034,Wilder,629,France,Female,37,6,129101.3,1,1,1,23971.33,0\r\n961,15686164,Maclean,850,Germany,Female,31,1,108822.4,1,1,1,132173.31,0\r\n962,15582797,Ch'iu,685,Spain,Male,35,4,137948.51,1,1,0,113639.64,0\r\n963,15753831,Cox,642,Spain,Male,32,7,100433.8,1,1,1,39768.59,0\r\n964,15731815,Nepean,529,Spain,Male,63,4,96134.11,3,1,0,108732.96,1\r\n965,15580956,McNess,683,Germany,Female,43,4,115888.04,1,1,1,117349.19,1\r\n966,15602084,Coles,663,France,Female,42,5,124626.07,1,1,1,78004.5,0\r\n967,15589805,Benson,563,France,Female,34,6,139810.34,1,1,1,152417.79,0\r\n968,15720893,Gilbert,637,Spain,Female,34,9,0,2,0,0,26057.08,0\r\n969,15641009,Wilhelm,544,France,Male,37,3,84496.71,1,0,0,79972.09,0\r\n970,15605926,Sinclair,649,Germany,Male,70,9,116854.71,2,0,1,107125.79,0\r\n971,15805955,L?,638,France,Female,48,10,138333.03,1,1,1,47679.14,0\r\n972,15801488,Buckner,723,France,Male,25,3,0,2,1,1,134509.47,0\r\n973,15605918,Padovesi,635,Germany,Male,43,5,78992.75,2,0,0,153265.31,0\r\n974,15779711,Gray,750,Spain,Female,38,7,97257.41,2,0,1,179883.04,0\r\n975,15705620,Lu,730,France,Male,34,5,122453.37,2,1,0,138882.98,0\r\n976,15685357,Wright,750,Spain,Female,36,8,112940.07,1,0,1,9855.81,0\r\n977,15570060,Palerma,586,France,Female,43,8,132558.26,1,1,0,67046.83,1\r\n978,15582616,Y?an,520,France,Female,38,4,0,2,1,0,56388.63,0\r\n979,15799515,Wei,652,France,Female,48,8,133297.24,1,1,0,77764.37,0\r\n980,15642937,Padovesi,550,France,Female,46,7,0,2,1,0,130590.35,0\r\n981,15624729,Tsao,594,France,Male,27,0,197041.8,1,0,0,151912.49,0\r\n982,15566156,Franklin,749,Germany,Female,44,0,71497.79,2,0,0,151083.8,0\r\n983,15792360,Clark,668,France,Male,32,7,0,2,1,1,777.37,0\r\n984,15807008,McGregor,614,Germany,Female,35,6,128100.28,1,0,0,69454.24,1\r\n985,15704770,Pan,773,France,Male,25,1,124532.78,2,0,1,11723.57,0\r\n986,15756475,Kenniff,551,Germany,Male,31,9,82293.82,2,1,1,91565.25,0\r\n987,15655339,Spencer,566,France,Male,36,1,142120.91,1,1,0,79616.37,0\r\n988,15613749,Lees,569,Spain,Male,34,0,151839.26,1,1,0,102299.81,1\r\n989,15664521,David,659,Spain,Male,31,7,149620.88,2,1,1,104533.51,0\r\n990,15681206,Hsing,722,France,Female,49,3,168197.66,1,1,0,140765.57,1\r\n991,15745527,Burke,655,France,Male,37,5,93147,2,1,0,66214.13,0\r\n992,15806926,Watson,615,France,Female,35,2,97440.02,2,1,1,139816.1,0\r\n993,15724563,Hawkins,752,Germany,Female,42,3,65046.08,2,0,1,140139.28,0\r\n994,15782899,Ginn,661,Spain,Female,28,7,95357.49,1,0,0,102297.15,0\r\n995,15623521,Sozonov,838,Spain,Male,43,9,123105.88,2,1,0,145765.83,0\r\n996,15810218,Sun,610,Spain,Male,29,9,0,3,0,1,83912.24,0\r\n997,15645621,Hunter,811,Spain,Male,44,3,0,2,0,1,78439.73,0\r\n998,15608114,Manfrin,587,Spain,Male,62,7,121286.27,1,0,1,6776.92,0\r\n999,15659557,Artamonova,811,Germany,Female,28,4,167738.82,2,1,1,9903.42,0\r\n1000,15787772,Hansen,759,France,Female,38,1,104091.29,1,0,0,91561.91,0\r\n1001,15691111,Pai,648,Germany,Female,42,8,121980.56,2,1,0,4027.02,0\r\n1002,15592089,Larsen,788,France,Female,43,10,0,2,1,1,116111.51,0\r\n1003,15633897,Owen,725,Germany,Male,39,1,50880.98,2,1,1,184023.54,0\r\n1004,15701301,Murphy,646,France,Female,42,3,175159.9,2,0,0,67124.48,1\r\n1005,15723685,Ekechukwu,601,Germany,Female,26,7,105514.69,2,1,0,50070.59,0\r\n1006,15701602,Ayers,521,Germany,Male,52,5,116497.31,3,0,0,53793.1,1\r\n1007,15739189,Johnson,561,Spain,Female,33,6,0,2,1,0,45261.47,0\r\n1008,15573086,Millar,564,France,Male,42,7,99824.45,1,1,1,36721.4,0\r\n1009,15569050,Farrell,444,France,Male,45,6,0,1,1,0,130009.85,1\r\n1010,15750765,Sanders,650,Spain,Male,71,0,0,1,1,1,175380.77,0\r\n1011,15799811,Herrera,724,France,Male,40,10,0,1,1,0,127847.25,1\r\n1012,15698442,Eberechukwu,719,Spain,Male,35,3,122964.88,1,1,1,138231.7,0\r\n1013,15655274,Bardin,548,France,Female,29,4,0,2,0,1,48673.18,0\r\n1014,15603594,Nwankwo,635,Spain,Male,24,4,0,2,1,1,70668.77,0\r\n1015,15585961,Talbot,496,Spain,Female,43,3,0,2,0,1,199505.53,0\r\n1016,15686936,McGregor,676,France,Female,37,5,89634.69,1,1,1,169583.18,1\r\n1017,15770424,Onyeorulu,541,Germany,Male,40,7,95710.11,2,1,0,49063.42,0\r\n1018,15587451,Goold,778,Germany,Male,41,7,139706.31,1,1,0,63337.19,0\r\n1019,15602010,Zikoranaudodimma,850,Germany,Female,45,5,103909.86,1,1,0,60083.11,1\r\n1020,15600583,Garner,633,France,Male,31,1,0,1,1,0,48606.71,0\r\n1021,15654673,Onyinyechukwuka,625,France,Male,49,6,173434.9,1,1,0,165580.93,1\r\n1022,15717164,Genovese,485,Spain,Male,32,6,102238.01,2,1,1,194010.12,0\r\n1023,15765014,Mai,547,France,Female,48,1,179380.74,2,0,1,69263.1,0\r\n1024,15682639,Marshall,642,France,Male,32,3,0,2,1,1,88698.83,0\r\n1025,15729279,Naylor,718,France,Female,25,4,108691.95,1,1,0,63030.97,0\r\n1026,15759805,Pinto,582,France,Female,32,4,0,2,1,0,59668.81,0\r\n1027,15767864,Fulton,628,France,Male,33,6,0,2,0,0,184230.23,0\r\n1028,15769948,Palerma,737,Germany,Male,35,0,133377.8,1,0,1,64050.19,0\r\n1029,15686345,McCaffrey,828,Spain,Male,34,9,0,2,1,1,81853.98,0\r\n1030,15688071,Collins,609,Spain,Male,53,10,0,1,1,1,154642.91,0\r\n1031,15681174,Zuev,730,France,Male,39,1,116537.6,1,0,0,145679.6,0\r\n1032,15667521,Crawford,631,France,Female,22,3,0,2,0,0,30781.77,0\r\n1033,15750243,Genovese,830,Spain,Male,40,4,0,2,0,1,81622.52,0\r\n1034,15695475,Maclean,645,France,Male,29,1,130131.08,2,0,1,196474.35,0\r\n1035,15689176,Fabro,663,France,Male,46,3,0,2,0,1,176276.1,0\r\n1036,15652955,Price,678,Spain,Male,30,0,0,1,1,0,35113.08,0\r\n1037,15668958,Chatfield,521,France,Male,30,2,107316.09,1,1,0,64299.82,0\r\n1038,15631054,Volkova,625,France,Female,24,1,0,2,1,1,180969.55,0\r\n1039,15581479,Archer,523,France,Male,30,1,83181.29,1,1,1,138176.78,0\r\n1040,15577478,Ch'iu,714,France,Female,72,3,0,1,1,1,86733.61,0\r\n1041,15780870,McKay,580,Spain,Male,67,3,153946.14,1,1,1,7418.92,0\r\n1042,15692317,Craig,722,France,Male,30,5,0,2,1,0,166376.54,0\r\n1043,15593969,Abramovich,630,Spain,Female,39,7,135483.17,1,1,0,140881.2,1\r\n1044,15570417,Chien,579,France,Male,35,1,0,2,1,0,4460.2,0\r\n1045,15779059,Timms,670,France,Female,38,4,119624.54,2,1,1,110472.12,0\r\n1046,15785980,Williford,588,Spain,Male,34,6,121132.26,2,1,0,86460.28,0\r\n1047,15644200,Hamilton,807,Spain,Female,42,1,0,1,1,0,16500.66,1\r\n1048,15793949,Cheng,726,France,Female,48,4,0,1,1,0,114020.06,1\r\n1049,15645103,Su,812,Germany,Male,25,5,54817.55,1,1,0,131660.31,0\r\n1050,15705860,McKenzie,631,Germany,Male,40,3,107949.45,1,1,0,52449.62,1\r\n1051,15623828,Akobundu,682,France,Male,30,4,0,1,0,1,161465.31,0\r\n1052,15715003,Ko,625,Spain,Female,49,2,80816.45,1,1,1,20018.79,0\r\n1053,15623471,Marcelo,607,Germany,Male,38,3,98205.77,1,1,0,176318.27,0\r\n1054,15798348,Chukwuebuka,600,Spain,Female,50,6,94684.27,1,1,1,50488.91,0\r\n1055,15743016,MacDonald,602,Spain,Female,22,7,141604.76,1,1,0,30379.6,0\r\n1056,15769499,Lampungmeiua,545,Spain,Female,74,3,0,2,1,1,161326.73,0\r\n1057,15798521,Tai,675,Spain,Male,33,3,0,2,1,0,45348.08,0\r\n1058,15706534,Enyinnaya,581,France,Female,47,1,122949.14,1,0,0,180251.68,1\r\n1059,15706186,McKenzie,640,Germany,Male,33,8,81677.22,2,0,0,34925.56,0\r\n1060,15812197,Kline,850,France,Male,38,7,80293.98,1,0,0,126555.74,0\r\n1061,15650933,Ma,490,Spain,Female,48,8,155413.06,1,1,0,187921.3,0\r\n1062,15692991,Wood,710,Spain,Female,38,4,0,2,1,1,136390.88,0\r\n1063,15631189,Riggs,613,Germany,Male,38,9,67111.65,1,1,0,78566.64,1\r\n1064,15762198,Capon,812,France,Male,34,5,103818.43,1,1,1,166038.27,0\r\n1065,15699598,Smith,723,France,Female,20,4,0,2,1,1,140385.33,0\r\n1066,15692744,Davison,512,France,Male,36,4,152169.12,2,0,0,38629.3,1\r\n1067,15688963,Ingram,731,France,Female,52,10,0,1,1,1,24998.75,1\r\n1068,15599131,Dilke,650,Germany,Male,26,4,214346.96,2,1,0,128815.33,0\r\n1069,15680303,Gibson,594,France,Male,57,6,0,1,1,0,19376.56,1\r\n1070,15628674,Iadanza,844,France,Male,40,7,113348.14,1,1,0,31904.31,1\r\n1071,15648075,Hebert,686,Germany,Female,47,5,170935.94,1,1,0,173179.79,1\r\n1072,15586970,Pinto,695,Germany,Male,52,8,103023.26,1,1,1,22485.64,0\r\n1073,15625698,Dumetochukwu,624,Spain,Female,23,6,0,2,0,1,196668.51,0\r\n1074,15790497,Ross,503,Spain,Male,37,6,0,2,0,0,136506.86,0\r\n1075,15682618,Jamieson,535,France,Female,31,7,111855.04,2,1,1,36278.89,0\r\n1076,15762937,Chiganu,743,Germany,Female,32,6,140348.56,2,1,1,163254.39,0\r\n1077,15750929,Burgess,702,Spain,Male,39,8,0,2,1,0,99654.13,0\r\n1078,15729832,Cheng,658,France,Male,29,3,145512.84,1,1,0,20207.02,0\r\n1079,15633650,Woods,677,Germany,Female,41,8,146720.98,2,1,1,4195.84,0\r\n1080,15748856,Liang,664,France,Male,32,10,107209.73,1,1,1,112340.2,0\r\n1081,15589195,Bluett,766,Germany,Female,38,7,130933.74,1,0,1,2035.94,0\r\n1082,15699911,Chapman,461,Spain,Female,35,8,0,1,1,0,132295.95,0\r\n1083,15663438,Andrejew,688,Spain,Male,36,0,89772.3,1,1,0,177383.68,1\r\n1084,15692583,Udobata,678,France,Female,32,5,0,2,1,0,90284.47,0\r\n1085,15591257,Ejimofor,796,France,Male,24,8,0,2,1,0,61349.37,0\r\n1086,15646513,Spyer,803,France,Male,42,5,0,1,1,0,196466.83,1\r\n1087,15708063,Walker,712,France,Male,36,2,100749.5,3,0,0,70758.37,1\r\n1088,15696098,Palermo,498,France,Female,31,10,0,2,1,0,13892.57,0\r\n1089,15645517,Philip,850,Spain,Male,22,2,0,2,1,1,9684.52,0\r\n1090,15649744,Fallaci,628,France,Female,51,3,123981.31,2,1,1,40546.15,0\r\n1091,15604304,Perry,539,Germany,Female,34,4,91622.42,1,1,1,136603.42,0\r\n1092,15784092,Henderson,732,France,Male,36,7,126195.81,1,1,1,133172.48,0\r\n1093,15585198,Bergamaschi,715,France,Male,41,4,94267.9,1,0,1,152821.12,1\r\n1094,15624347,Fokine,651,France,Male,40,4,0,2,1,1,147715.83,0\r\n1095,15621687,Mackay,813,France,Male,34,0,0,2,1,0,43169.15,0\r\n1096,15689081,Wu,692,France,Male,29,4,0,1,1,0,76755.99,1\r\n1097,15813168,Maslova,756,Germany,Female,39,3,100717.85,3,1,1,73406.04,1\r\n1098,15604295,Wei,543,France,Male,36,6,0,2,1,0,176728.28,0\r\n1099,15724127,McLean,790,France,Female,26,4,141581.71,2,0,0,98309.27,0\r\n1100,15673055,Sung,494,Spain,Male,38,7,0,2,1,1,6203.66,0\r\n1101,15768201,Paterson,850,France,Female,39,2,148586.64,1,1,1,176791.27,0\r\n1102,15782219,Fanucci,703,Spain,Male,29,9,0,2,1,0,50679.48,0\r\n1103,15746410,Thompson,432,Spain,Male,38,7,0,2,1,0,150580.88,0\r\n1104,15780144,Tisdall,512,Germany,Female,32,2,123403.85,2,1,0,80120.19,0\r\n1105,15590476,Onochie,589,France,Male,28,7,0,2,1,0,151645.96,0\r\n1106,15624293,Mironova,514,France,Female,46,3,106511.85,1,1,0,55072.32,0\r\n1107,15618182,Ndubueze,678,France,Female,38,2,0,2,0,0,115068.99,0\r\n1108,15660316,Stephenson,420,Germany,Female,34,1,135549.9,1,0,0,149471.13,1\r\n1109,15678886,Golubev,679,Germany,Male,38,7,110555.37,2,1,0,46522.68,0\r\n1110,15616330,Liao,595,France,Male,31,4,0,2,1,0,189995.86,0\r\n1111,15592229,Mullan,713,France,Female,52,0,185891.54,1,1,1,46369.57,1\r\n1112,15798424,Glover,833,Germany,Male,59,1,130854.59,1,1,1,30722.52,1\r\n1113,15714750,Northey,690,France,Female,42,3,92578.14,2,0,0,70810.6,0\r\n1114,15648800,Paterson,731,Germany,Female,21,8,132312.06,1,1,0,106663.46,1\r\n1115,15626147,Maclean,608,France,Female,62,8,144976.5,1,0,0,175836.03,1\r\n1116,15626608,Howarde,479,Spain,Male,48,5,87070.23,1,0,1,85646.41,0\r\n1117,15723250,Teng,519,France,Male,42,8,0,2,1,1,101485.72,0\r\n1118,15592583,Colman,731,France,Female,47,1,115414.19,3,0,0,191734.67,1\r\n1119,15759381,Johnson,617,Spain,Male,61,7,91070.43,1,1,1,101839.77,0\r\n1120,15585241,Butcher,756,Spain,Male,29,2,117412.19,2,1,0,4888.91,0\r\n1121,15589358,Stanley,848,Germany,Male,31,4,90018.45,2,1,0,193132.98,0\r\n1122,15672704,Jackson,809,France,Female,24,4,0,2,1,0,193518.76,0\r\n1123,15789955,Hu,698,Germany,Male,56,1,112414.81,2,0,0,93982.02,1\r\n1124,15596800,Hill,779,Germany,Male,33,1,158456.76,1,1,1,197000.92,1\r\n1125,15627305,Pan,606,Spain,Male,35,7,0,1,1,0,106837.06,1\r\n1126,15645316,Han,612,Germany,Female,58,1,149641.53,1,1,1,115161.28,0\r\n1127,15593973,Wilkie,663,Spain,Female,33,8,122528.18,1,1,0,196260.3,0\r\n1128,15647301,Bray,549,Germany,Female,45,3,143734.01,2,1,1,96404.38,0\r\n1129,15750258,Ann,675,France,Female,32,2,155663.31,1,1,0,97658.66,0\r\n1130,15685309,Souter,669,France,Female,35,7,0,1,1,1,49108.23,1\r\n1131,15628205,Greco,571,Germany,Female,34,1,101736.66,1,0,1,195651.66,0\r\n1132,15733974,Mao,500,Spain,Male,37,9,125822.21,1,1,0,111698,0\r\n1133,15762110,Anderson,628,France,Male,37,0,0,2,1,1,171707.93,0\r\n1134,15706899,Ma,559,France,Male,34,4,0,2,1,1,66721.98,0\r\n1135,15732660,Black,769,France,Female,27,2,0,1,1,1,57876.05,0\r\n1136,15656121,Medvedeva,733,Germany,Male,31,6,157791.07,2,0,0,177994.81,0\r\n1137,15614220,Benson,750,France,Male,22,5,0,2,0,1,105125.65,0\r\n1138,15645269,Duncan,583,France,Female,42,4,0,2,1,0,17439.66,0\r\n1139,15698510,Onwudiwe,468,Germany,Male,42,9,181627.14,2,1,0,172668.39,0\r\n1140,15569247,Mitchell,727,Spain,Female,57,1,109679.72,1,0,1,753.37,0\r\n1141,15566251,Ferrari,618,France,Female,37,5,96652.86,1,1,0,98686.4,1\r\n1142,15716134,Russo,617,France,Male,40,5,190008.32,2,1,1,107047.92,0\r\n1143,15763625,Hazon,793,Spain,Male,41,9,0,2,1,0,152153.74,0\r\n1144,15605965,Henderson,630,France,Male,43,9,0,2,1,1,34338.04,0\r\n1145,15694821,Hardy,765,Germany,Male,43,4,148962.76,1,0,1,173878.87,1\r\n1146,15601688,Piccio,546,France,Male,28,8,0,1,1,0,159254.29,0\r\n1147,15575581,Dickson,614,Germany,Female,30,3,131344.52,2,1,0,54776.64,0\r\n1148,15671209,Holden,593,Germany,Female,29,5,101713.84,3,1,0,134594.99,0\r\n1149,15616529,Hsieh,613,Spain,Male,34,3,0,1,1,1,41724.72,0\r\n1150,15773906,Doherty,655,France,Male,38,4,0,2,0,0,110527.71,0\r\n1151,15722993,Page,700,France,Female,27,6,137963.07,1,0,0,8996.79,0\r\n1152,15752463,Samuel,826,Spain,Female,29,4,129938.07,1,0,1,190200.53,0\r\n1153,15589754,Malloy,652,Germany,Male,45,2,151421.44,1,0,1,115333.43,0\r\n1154,15669899,Fitts,755,Germany,Female,45,7,135643,1,0,0,143619.52,1\r\n1155,15766887,Iadanza,538,Spain,Male,39,2,122773.5,2,1,1,58467.08,0\r\n1156,15768006,Wu,729,France,Male,34,3,152303.8,1,1,0,12128.69,0\r\n1157,15741295,Yefimova,615,France,Male,49,3,0,2,1,1,49872.33,0\r\n1158,15811327,Pan,700,Spain,Male,54,1,79415.67,1,0,1,139735.54,0\r\n1159,15690007,Ts'ui,434,Germany,Female,58,9,125801.03,2,1,0,60891.8,1\r\n1160,15690664,Liang,729,Spain,Male,37,10,0,2,1,0,100862.54,0\r\n1161,15719348,Tsao,513,France,Male,35,8,0,1,1,0,76640.29,1\r\n1162,15781802,Abramov,755,France,Male,41,6,104817.41,1,1,0,126013.58,1\r\n1163,15752731,Millar,615,France,Female,30,9,0,1,1,0,87347.82,0\r\n1164,15600997,Demuth,747,Germany,Female,32,5,67495.04,2,0,1,77370.37,0\r\n1165,15750776,Genovese,850,France,Female,36,0,164850.54,1,1,1,62722.44,0\r\n1166,15723907,Lawless,712,Germany,Female,49,5,154776.42,2,0,0,196257.68,0\r\n1167,15633419,Brooks,622,Germany,Female,28,1,143124.63,2,1,0,81723.8,0\r\n1168,15702430,Ignatyeva,548,France,Female,35,10,0,1,1,1,31299.71,0\r\n1169,15710456,Balmain,607,France,Female,27,2,0,2,1,0,63495.86,0\r\n1170,15650351,Millar,653,France,Female,38,8,102133.38,1,1,1,166520.96,0\r\n1171,15590820,Ecuyer,699,Spain,Male,26,6,79932.41,1,0,0,150242.44,0\r\n1172,15640454,Parkhill,693,Germany,Male,40,0,120711.73,1,0,0,27345.18,1\r\n1173,15697789,Li Fonti,647,Germany,Female,43,3,122717.53,2,1,1,87000.39,0\r\n1174,15808182,Beneventi,478,Spain,Female,36,3,92363.3,2,1,0,44912.7,0\r\n1175,15588670,Despeissis,705,Spain,Female,40,5,203715.15,1,1,0,179978.68,1\r\n1176,15721292,Atkins,719,Spain,Male,39,5,0,2,1,0,145759.7,0\r\n1177,15604217,Williams,726,France,Male,34,9,0,2,0,0,14121.61,0\r\n1178,15651369,Wright,626,France,Male,21,1,0,2,1,0,66232.23,0\r\n1179,15782454,Hancock,552,France,Male,49,4,0,1,1,1,190296.76,1\r\n1180,15814032,Hsieh,807,Germany,Female,31,1,93460.47,2,0,0,172782.69,0\r\n1181,15570326,Wilkins,621,France,Male,34,6,0,2,1,1,99128.13,0\r\n1182,15624428,Longo,651,Germany,Female,24,7,40224.7,1,1,1,178341.33,0\r\n1183,15755638,Mancini,673,France,Female,43,5,168069.73,1,1,1,146992.24,1\r\n1184,15600992,Madukaego,652,France,Male,36,1,0,2,1,1,151314.98,0\r\n1185,15755649,Winter-Irving,584,Germany,Male,47,7,130538.77,1,1,0,92915.84,0\r\n1186,15795228,Stewart,756,France,Male,37,3,132623.6,1,1,1,58974,0\r\n1187,15589257,Grant,670,France,Female,35,3,103465.02,2,1,1,174627.06,0\r\n1188,15719302,Brennan,765,France,Female,50,9,126547.8,1,1,1,79579.94,1\r\n1189,15639882,She,528,France,Male,30,2,128262.72,2,1,0,50771.16,0\r\n1190,15791279,Murray,701,France,Male,40,5,169742.64,1,1,1,153537.55,1\r\n1191,15636935,Rischbieth,797,France,Female,29,1,0,2,1,1,132975.39,0\r\n1192,15686909,Lung,639,Germany,Male,27,3,150795.81,1,0,1,85208.93,0\r\n1193,15589572,Otutodilichukwu,785,Spain,Female,61,4,129855.72,2,1,0,170214.82,1\r\n1194,15779947,Thomas,363,Spain,Female,28,6,146098.43,3,1,0,100615.14,1\r\n1195,15573769,Fiorentini,764,France,Female,24,7,0,2,1,0,186105.99,0\r\n1196,15578866,Hughes,676,France,Female,43,2,0,1,1,1,55119.53,0\r\n1197,15739131,Whitworth,718,Germany,Male,28,4,65643.3,1,1,0,28760.99,0\r\n1198,15813444,McIntosh,590,Spain,Female,34,6,0,2,1,0,171021.44,0\r\n1199,15678058,Ayers,584,France,Male,38,9,104584.16,1,1,0,176678.72,0\r\n1200,15769169,Trentino,645,France,Male,41,7,0,1,0,1,28667.56,0\r\n1201,15804602,Boyd,772,Germany,Male,30,6,99785.28,2,0,0,197238.03,0\r\n1202,15651052,McMasters,399,Germany,Male,46,2,127655.22,1,1,0,139994.68,1\r\n1203,15724334,Alekseyeva,529,France,Male,22,5,0,1,1,0,151169.83,0\r\n1204,15569451,Miller,463,France,Male,35,2,101257.16,1,1,1,118113.64,0\r\n1205,15650098,Baranova,630,France,Female,40,7,0,2,1,1,34453.17,0\r\n1206,15724307,Mitchell,780,France,Male,76,10,121313.88,1,0,1,64872.33,0\r\n1207,15599268,Yobachi,584,Spain,Male,32,5,0,2,1,0,10956.82,0\r\n1208,15594864,Huang,752,Germany,Male,30,4,81523.38,1,1,1,36885.85,0\r\n1209,15616451,Genovese,697,France,Female,47,6,128252.66,1,1,1,168053.4,0\r\n1210,15715667,Sorokina,850,France,Female,32,7,0,2,0,0,155227,0\r\n1211,15658969,Gray,711,France,Male,51,7,0,3,1,0,38409.79,1\r\n1212,15738174,Ervin,452,France,Female,32,5,0,2,0,1,75279.39,0\r\n1213,15813590,Vance,610,Spain,Male,42,6,0,2,1,0,158302.59,1\r\n1214,15624229,Noble,694,France,Female,22,4,0,2,1,1,11525.72,0\r\n1215,15674148,Milanesi,579,Spain,Male,33,6,0,1,1,0,94993.04,1\r\n1216,15625080,Parkin,745,Spain,Female,54,8,0,1,1,0,173912.29,1\r\n1217,15682528,Cremonesi,572,France,Male,33,5,0,1,0,1,41139.05,0\r\n1218,15696900,Burns,505,Germany,Male,29,3,145541.56,2,1,1,58019.95,0\r\n1219,15730038,Docherty,706,France,Female,23,5,0,1,0,0,164128.41,1\r\n1220,15812272,Ugonna,693,Germany,Male,44,5,124601.58,2,1,1,46998.13,1\r\n1221,15654654,L?,725,Germany,Female,33,7,115182.84,2,1,1,177279.41,0\r\n1222,15697625,Bevan,791,France,Male,37,2,163789.49,2,1,0,75832.53,0\r\n1223,15616280,Hsia,536,France,Male,46,1,65733.41,1,1,0,61094.53,0\r\n1224,15654229,O'Neill,699,Spain,Male,47,1,0,2,0,1,30117.44,0\r\n1225,15628298,Johnstone,500,Spain,Female,47,8,128486.11,1,1,0,179227.12,0\r\n1226,15733387,Pham,707,Spain,Female,53,6,109663.47,1,1,1,52110.45,0\r\n1227,15775572,Bergamaschi,531,Germany,Female,42,6,88324.31,2,1,0,75248.75,0\r\n1228,15613844,Murphy,557,France,Female,28,7,146445.24,2,1,0,184317.74,0\r\n1229,15578515,Osinachi,659,France,Female,38,3,0,2,1,0,158553.1,0\r\n1230,15607598,Muravyov,575,Spain,Female,31,6,0,2,1,1,95686.42,0\r\n1231,15742480,Igwebuike,775,Germany,Male,36,2,109949.05,2,0,1,71682.54,0\r\n1232,15749482,Zack,772,Spain,Male,30,4,78653.05,1,1,0,1790.48,0\r\n1233,15607537,Crawford,587,Germany,Male,46,9,107850.82,1,1,0,139431,1\r\n1234,15575410,Chidiegwu,667,Germany,Female,39,4,83765.35,2,1,0,118358.54,0\r\n1235,15684865,Lucchesi,771,France,Female,66,7,143773.07,1,1,1,130827.88,0\r\n1236,15600700,Pan,523,Germany,Male,63,6,116227.27,1,1,1,119404.63,0\r\n1237,15774155,Trevisani,662,Germany,Male,33,0,103471.52,1,1,1,162703,0\r\n1238,15634267,Yudin,717,France,Male,42,5,0,2,1,0,172665.21,0\r\n1239,15619626,Wade,746,France,Male,24,3,137492.35,2,0,1,170142.09,0\r\n1240,15660422,Chung,569,France,Male,28,7,0,2,1,0,73977.23,0\r\n1241,15617934,Septimus,579,France,Male,36,9,129829.59,1,1,1,60906.12,0\r\n1242,15760774,Hargraves,519,France,Female,21,1,146329.57,2,1,1,194867.27,0\r\n1243,15813132,Chukwukadibia,696,Germany,Male,30,4,114027.7,1,1,1,193716.56,0\r\n1244,15593331,Sidorov,693,Germany,Male,25,6,146580.69,1,0,1,14633.35,0\r\n1245,15616709,Bunton,587,Germany,Female,38,0,132122.42,2,0,0,31730.32,0\r\n1246,15658052,Cameron,626,France,Female,44,10,81553.93,1,1,0,20063.63,1\r\n1247,15721189,Kung,666,France,Female,66,7,0,2,1,1,99792.82,0\r\n1248,15711288,Hay,512,France,Male,24,6,0,2,1,0,37654.31,0\r\n1249,15770030,Conti,689,Spain,Female,28,3,0,2,1,1,192449.02,0\r\n1250,15803681,Sims,803,France,Female,26,4,0,2,1,1,181208.47,0\r\n1251,15702789,Carter,548,Germany,Male,32,5,175214.71,1,1,1,155165.61,0\r\n1252,15814930,McGregor,588,Germany,Female,40,10,125534.51,1,1,0,121504.18,1\r\n1253,15658306,Lo,693,France,Male,68,4,97705.99,1,1,1,61569.07,0\r\n1254,15699523,Chu,499,Germany,Female,55,4,126817.65,2,1,0,123269.71,0\r\n1255,15610383,Dumetolisa,628,France,Female,46,1,46870.43,4,1,0,31272.14,1\r\n1256,15615032,Peng,624,Spain,Male,46,3,0,2,1,1,62825.03,0\r\n1257,15781989,Drake-Brockman,733,France,Male,42,9,120094.93,1,1,0,184056.45,0\r\n1258,15647402,Wan,628,France,Female,38,3,0,2,1,1,48924.73,0\r\n1259,15740494,Cameron,633,France,Female,33,3,0,2,1,0,191111.02,0\r\n1260,15701265,Tretiakov,559,Germany,Female,36,1,104356.94,2,0,1,54184.06,0\r\n1261,15743532,Ball,704,Germany,Male,27,5,147004.34,1,1,0,64381.33,1\r\n1262,15794870,Sal,744,Germany,Male,38,6,73023.17,2,1,0,78770.86,0\r\n1263,15747591,Chung,665,Spain,Female,40,1,173432.55,1,0,1,116766.79,0\r\n1264,15726557,Lai,638,France,Female,42,7,165679.92,1,0,0,32916.29,0\r\n1265,15732199,Gether,837,Spain,Male,31,9,104678.62,1,0,1,50972.6,0\r\n1266,15662291,Davidson,534,France,Female,55,8,116973.26,3,1,0,122066.5,1\r\n1267,15749050,Justice,548,France,Female,36,3,0,1,1,0,65996.9,0\r\n1268,15781586,Osonduagwuike,837,Germany,Male,38,2,126732.85,1,1,1,79577.38,0\r\n1269,15617078,Ewing,658,France,Female,44,6,148481.09,1,1,0,130529.13,0\r\n1270,15723339,Chin,554,France,Female,38,4,137654.05,2,1,1,172629.67,0\r\n1271,15671322,Chiang,724,Germany,Male,30,7,115315.04,1,1,0,15216.53,0\r\n1272,15793854,Ahmed,723,France,Male,42,2,99095.73,1,1,1,17512.53,0\r\n1273,15756539,Marshall,585,Germany,Female,39,7,165610.41,2,0,0,131852.01,0\r\n1274,15612064,Tsou,474,France,Male,33,5,0,2,1,0,181945.52,1\r\n1275,15625916,Chien,562,Spain,Male,32,6,161628.66,1,1,0,91482.5,0\r\n1276,15683195,Ubanwa,719,France,Male,32,9,146605.27,1,1,1,77119.45,0\r\n1277,15690182,Kapustin,635,Germany,Male,37,5,113488.68,1,1,0,95611.74,1\r\n1278,15721719,Calabresi,743,France,Male,42,7,77002.2,2,1,1,80428.42,0\r\n1279,15641690,Hsiao,681,Spain,Male,67,7,0,2,0,1,163714.92,0\r\n1280,15634896,Grant,521,France,Female,39,6,0,2,0,1,27375.15,0\r\n1281,15671590,H?,741,Spain,Male,25,4,0,2,1,1,73873.65,0\r\n1282,15779182,Chia,790,Spain,Male,46,8,182364.53,1,0,0,139266.48,1\r\n1283,15778287,Ugoji,622,France,Male,35,8,0,2,1,1,131772.51,0\r\n1284,15609510,Gregory,669,France,Male,45,7,149364.58,1,0,1,173454.07,0\r\n1285,15742229,Mackay,583,France,Male,59,7,127450.14,1,0,1,67552.71,0\r\n1286,15658532,Nnamutaezinwa,520,Spain,Female,63,5,162278.32,1,1,1,34765.33,0\r\n1287,15590993,Findlay,579,Spain,Male,37,5,152212.88,2,0,0,120219.14,0\r\n1288,15565701,Ferri,698,Spain,Female,39,9,161993.89,1,0,0,90212.38,0\r\n1289,15597239,Ku,548,Spain,Male,39,7,131468.44,1,1,0,164975.82,0\r\n1290,15688880,Amechi,672,Germany,Male,40,10,102980.44,1,1,0,1285.81,1\r\n1291,15813917,Kirk,653,Germany,Male,31,9,143321.97,1,1,0,83679.46,0\r\n1292,15679611,Andrews,734,Spain,Female,37,2,130404.92,1,0,0,34548.74,0\r\n1293,15636589,Murray,794,France,Female,41,7,0,2,1,1,74275.08,0\r\n1294,15687752,Griffin,641,France,Male,30,2,87505.47,2,0,1,7278.57,0\r\n1295,15584363,Longstaff,824,France,Male,30,0,133634.02,1,1,1,162053.92,0\r\n1296,15737748,McWilliam,534,Spain,Female,33,3,151233.62,1,0,0,199336.63,0\r\n1297,15803365,Coffee,653,Spain,Male,55,2,70263.83,1,0,1,62347.71,0\r\n1298,15793247,Hancock,498,France,Male,34,5,0,2,1,1,91711.66,0\r\n1299,15572360,Clark,683,France,Male,30,10,57657.49,1,0,0,79240.9,0\r\n1300,15795166,Creswell,618,Germany,Male,42,8,153572.31,2,1,1,76679.6,0\r\n1301,15724620,Dodds,538,France,Male,37,1,134752.08,1,1,0,162511.55,0\r\n1302,15800856,Ewen,643,Spain,Male,34,3,83132.09,1,1,1,21360.88,0\r\n1303,15671097,Carter,428,France,Female,31,2,0,2,1,0,54487.43,0\r\n1304,15683930,Ch'iu,593,Germany,Female,32,9,134096.53,2,1,0,53931.05,1\r\n1305,15749004,Tsao,718,France,Female,31,0,118100.59,2,1,0,103165.15,0\r\n1306,15800434,Burgess,811,Germany,Male,52,10,76915.4,1,0,0,146359.81,1\r\n1307,15709117,Fanucci,823,Spain,Female,46,3,81576.75,1,1,1,28370.95,1\r\n1308,15638806,Blackburn,645,Spain,Male,49,2,0,2,0,0,10023.15,0\r\n1309,15662294,Bennett,710,France,Male,33,10,118327.17,2,1,1,192928.82,0\r\n1310,15690079,Boniwell,591,Spain,Male,30,8,124857.69,2,0,0,50485.7,0\r\n1311,15759317,Vasilieva,748,Germany,Female,27,2,90971.85,1,1,1,131662.47,0\r\n1312,15750497,Longo,850,France,Female,37,7,153147.75,1,1,1,152235.3,0\r\n1313,15596181,Kwemto,542,France,Male,38,8,65942.26,1,1,1,68093.23,1\r\n1314,15576602,Lawrence,809,France,Male,38,3,0,2,1,1,80061.31,0\r\n1315,15644833,Duncan,675,France,Male,54,2,0,1,1,0,149583.67,1\r\n1316,15734634,Bocharova,607,Spain,Female,27,5,100912.19,1,0,0,7631.27,0\r\n1317,15808689,Morres,850,France,Female,31,4,0,2,1,1,33082.81,0\r\n1318,15720702,Shih,789,France,Male,37,3,0,1,1,0,121883.87,1\r\n1319,15665077,Vogel,598,France,Female,43,5,0,3,1,1,100722.72,1\r\n1320,15763612,T'an,756,Germany,Male,41,2,124439.49,2,0,1,47093.11,0\r\n1321,15596493,Wisdom,687,France,Female,47,7,0,2,1,1,177624.01,0\r\n1322,15704483,Lorenzo,724,France,Male,40,6,0,2,0,0,106149.48,0\r\n1323,15598846,Shahan,700,France,Female,44,2,58781.76,1,1,0,16874.92,0\r\n1324,15629244,Bryant,635,Spain,Male,50,7,159453.64,2,0,0,54560.79,1\r\n1325,15765537,Liang,687,Germany,Male,26,2,142721.52,1,1,1,153605.75,0\r\n1326,15729975,Chidozie,613,France,Female,46,8,167795.6,1,0,1,44390.38,0\r\n1327,15682773,Hayward,781,France,Female,38,3,128345.69,2,1,0,63218.85,0\r\n1328,15688007,Liu,703,Spain,Male,20,3,165260.98,1,1,1,41626.78,0\r\n1329,15574331,Alexeeva,593,Germany,Female,62,3,118233.81,1,0,1,24765.53,1\r\n1330,15645572,Calabresi,743,France,Female,40,6,0,1,1,0,28280.8,1\r\n1331,15742854,Lettiere,640,Spain,Female,46,8,0,2,1,0,89043.19,0\r\n1332,15575417,Chou,849,Germany,Male,37,7,143452.74,2,1,1,17294.12,0\r\n1333,15796721,Nnamutaezinwa,778,France,Male,38,3,145018.49,2,1,1,126702.41,0\r\n1334,15734942,Nnamutaezinwa,539,Germany,Female,38,8,82407.51,1,1,0,13123.41,0\r\n1335,15664772,Greece,489,Germany,Male,28,1,79460.98,2,1,1,167973.63,0\r\n1336,15576683,Yin,568,Spain,Female,43,9,0,1,1,0,125870.79,1\r\n1337,15682563,Larionova,618,Spain,Male,38,5,126473.99,1,1,0,91972.49,0\r\n1338,15650889,Golubev,710,Germany,Female,30,10,133537.1,2,1,0,155593.74,0\r\n1339,15612108,Norman,625,France,Male,52,5,164978.01,1,1,1,67788.49,0\r\n1340,15761132,Capon,682,Spain,Male,46,7,128029.72,1,1,1,62615.35,0\r\n1341,15645511,Chukwudi,727,Spain,Male,43,2,97403.18,1,1,1,107415.02,1\r\n1342,15609824,Fedorov,794,France,Female,41,7,176845.41,3,1,0,166526.26,1\r\n1343,15640268,Avdeeva,652,Spain,Male,71,4,0,1,1,1,120107.1,0\r\n1344,15645778,Reid,670,Spain,Male,42,3,81589.04,1,1,0,188227.8,0\r\n1345,15691104,Kennedy,460,Germany,Female,40,6,119507.58,2,1,0,91560.63,1\r\n1346,15714567,Chan,568,Spain,Female,26,6,0,2,0,0,166495.2,0\r\n1347,15777826,Wofford,643,France,Male,30,5,94443.77,1,1,1,165614.4,0\r\n1348,15668445,Mai,521,France,Male,37,2,0,2,1,1,86372.24,0\r\n1349,15576162,King,615,France,Male,32,7,92199.84,1,1,1,2755.53,0\r\n1350,15778135,T'ao,575,Spain,Male,43,3,0,1,1,0,83594.51,0\r\n1351,15613141,Hsu,717,France,Female,41,3,135756.96,1,1,1,103706.41,0\r\n1352,15635435,White,648,France,Female,54,9,120633.42,1,0,0,5924.38,1\r\n1353,15596552,Stephens,535,Germany,Male,48,5,134542.73,1,1,1,58203.67,1\r\n1354,15623644,Frolov,626,Spain,Male,29,7,0,2,1,0,49361.84,0\r\n1355,15683403,Lombardi,611,Spain,Male,52,7,0,1,0,1,73585.18,1\r\n1356,15615029,Munro,734,Spain,Male,39,6,0,1,1,1,95135.27,0\r\n1357,15769005,Hayward,709,France,Male,49,4,154344.49,2,1,1,38794.57,0\r\n1358,15746326,Fields,591,France,Male,43,3,0,2,0,1,198926.36,0\r\n1359,15722364,Onwumelu,664,France,Male,43,9,189026.53,2,1,1,56099.86,0\r\n1360,15704954,Suffolk,431,France,Male,37,0,120764.08,1,1,1,117023.08,0\r\n1361,15694409,Tsao,647,Germany,Female,22,3,97975.82,2,0,1,62083,0\r\n1362,15754068,Judd,578,France,Male,32,4,0,2,1,1,141822.8,0\r\n1363,15683841,Hamilton,555,Germany,Male,41,10,113270.2,2,1,1,185387.14,0\r\n1364,15789095,T'ang,775,Spain,Male,30,4,0,2,0,1,57461.13,0\r\n1365,15719958,Degtyarev,850,Germany,Male,39,3,124548.99,2,1,1,120380.12,0\r\n1366,15689514,Kang,625,France,Male,43,8,201696.07,1,1,0,133020.9,1\r\n1367,15621353,Hudson,645,Spain,Female,37,7,0,2,1,0,13589.93,0\r\n1368,15627232,Jibunoh,608,Germany,Male,44,7,114203.47,1,1,1,77830.36,1\r\n1369,15745843,Kinlaw,689,Spain,Female,31,4,0,2,1,1,136610.02,0\r\n1370,15722902,Chizuoke,652,Germany,Male,50,8,125437.64,1,1,1,17160.94,1\r\n1371,15791767,Lucciano,769,France,Female,26,7,0,2,1,0,176843.53,0\r\n1372,15792722,Omeokachie,611,France,Female,43,8,64897.75,1,1,0,114996.33,0\r\n1373,15723006,Gorbunova,489,France,Male,38,8,0,2,0,1,196990.79,0\r\n1374,15771942,Tikhonov,528,Germany,Female,46,9,135555.66,1,1,0,133146.03,1\r\n1375,15774738,Campa,632,France,Male,44,3,107764.75,1,1,0,185667.72,0\r\n1376,15574004,Mancini,429,France,Female,27,6,117307.44,2,1,1,24020.49,0\r\n1377,15587233,Donoghue,457,France,Male,41,8,73700.12,3,1,1,185750.02,1\r\n1378,15808228,Tuan,768,Spain,Female,44,6,60603.4,1,1,1,178045.97,0\r\n1379,15682834,Johnstone,715,Spain,Female,35,4,40169.88,2,1,1,199857.47,0\r\n1380,15571752,Romani,668,Germany,Female,32,10,92041.87,1,1,1,43595.9,0\r\n1381,15743067,Fuller,625,Germany,Male,26,3,130483.95,1,1,0,122810.53,0\r\n1382,15714466,Baxter,846,France,Female,41,5,0,3,1,0,3440.47,1\r\n1383,15617982,Pirozzi,661,Spain,Female,42,3,0,2,1,0,35989.41,0\r\n1384,15696637,Sung,571,France,Female,23,10,151097.28,1,0,1,17163.75,0\r\n1385,15690647,Rogers,582,Spain,Female,46,8,67563.31,1,1,0,44506.09,1\r\n1386,15672756,Mills,716,France,Female,35,8,112808.18,1,0,1,17848.3,0\r\n1387,15704586,Osonduagwuike,758,France,Female,42,7,0,2,0,1,76209.56,0\r\n1388,15674526,Byrne,725,France,Male,66,4,86459.8,1,1,1,141476.56,0\r\n1389,15775295,McIntyre,630,France,Female,40,0,118633.08,1,0,1,60032.46,1\r\n1390,15684196,Aitken,627,France,Female,55,2,159441.27,1,1,0,100686.11,1\r\n1391,15727281,Macintyre,653,France,Female,27,9,0,2,1,0,96429.29,0\r\n1392,15787835,Simpson,775,Germany,Female,38,4,125212.65,2,1,1,15795.88,1\r\n1393,15730540,Simpson,794,Spain,Male,45,8,88656.37,2,1,0,116547.31,0\r\n1394,15646276,Metcalfe,831,France,Female,32,2,146033.62,1,1,0,191260.74,0\r\n1395,15582180,Lees,561,France,Male,29,9,120268.13,1,1,1,173870.39,0\r\n1396,15697095,Zetticci,705,Spain,Male,46,7,0,2,1,0,117273.35,0\r\n1397,15748797,Dale,636,Spain,Female,33,0,0,1,1,0,92277.47,1\r\n1398,15754796,Byrne,487,Germany,Female,46,4,135070.58,2,1,1,44244.49,1\r\n1399,15628947,Praed,693,France,Female,38,3,0,2,0,0,78133.48,1\r\n1400,15775546,Laurens,517,Spain,Female,29,5,0,2,1,0,103402.88,0\r\n1401,15670481,Woods,684,France,Female,27,9,122550.05,2,0,1,137835.82,0\r\n1402,15619029,Bykov,620,Spain,Female,43,2,0,2,1,0,20670.1,0\r\n1403,15613282,Vorobyova,757,France,Male,29,8,130306.49,1,1,0,77469.38,0\r\n1404,15721487,Pirogova,739,France,Female,27,6,0,1,1,1,57572.38,0\r\n1405,15797276,Sturt,662,Spain,Female,41,4,90350.77,1,1,0,75884.65,1\r\n1406,15612494,Panicucci,359,France,Female,44,6,128747.69,1,1,0,146955.71,1\r\n1407,15629617,Cook,572,Spain,Male,23,2,126873.52,1,0,1,67040.12,0\r\n1408,15600821,Hardy,721,France,Male,69,2,108424.19,1,1,1,178418.35,0\r\n1409,15579062,Chu,707,France,Male,32,9,0,2,0,0,30807.02,0\r\n1410,15814268,Franklin,444,France,Female,40,5,84350.07,1,1,0,143835.76,0\r\n1411,15710164,P'eng,523,France,Female,73,7,0,2,0,0,130883.9,1\r\n1412,15693904,Chiang,685,Germany,Female,30,4,84958.6,2,0,1,194343.72,0\r\n1413,15588986,Grant,673,Germany,Female,29,4,99097.36,1,1,1,9796.69,0\r\n1414,15797733,Udobata,503,Germany,Male,30,10,136622.55,2,0,0,47310.24,0\r\n1415,15620507,Siciliani,485,Germany,Female,30,5,156771.68,1,1,1,141148.21,0\r\n1416,15685150,Evans,799,Germany,Male,28,7,167658.33,2,1,1,111138.25,0\r\n1417,15667651,Young,585,Spain,Female,33,8,0,2,1,0,114182.07,0\r\n1418,15774166,Mitchell,607,Germany,Female,24,2,109483.54,2,0,1,127560.77,0\r\n1419,15649280,Lucchese,521,Germany,Female,40,9,134504.78,1,1,0,18082.06,0\r\n1420,15705657,Hewitt,535,France,Female,44,2,114427.86,1,1,1,136330.26,0\r\n1421,15753969,K'ung,724,Spain,Male,45,5,83888.54,1,0,1,34121.81,0\r\n1422,15742378,Swaim,520,Germany,Male,32,5,110029.77,1,1,0,56246.69,0\r\n1423,15794874,Quinones,696,Spain,Male,41,9,127523.75,1,0,1,191417.42,0\r\n1424,15589221,Kennedy,657,Germany,Male,30,1,139762.13,2,1,1,23317.88,0\r\n1425,15596671,Endrizzi,603,Spain,Female,42,8,91611.12,1,0,0,144675.3,1\r\n1426,15583668,Ludowici,726,France,Female,42,2,109471.79,1,0,1,175161.05,0\r\n1427,15710206,Larson,591,France,Female,39,4,150500.64,1,1,0,14928.8,0\r\n1428,15799966,Chigolum,792,Germany,Female,59,9,101609.77,1,0,0,161479.19,1\r\n1429,15794560,Maclean,550,France,Male,57,5,0,1,1,1,133501.94,0\r\n1430,15626485,Lu,601,France,Female,26,8,78892.23,1,1,1,23703.52,0\r\n1431,15703143,Tuan,820,France,Female,29,3,82344.84,1,0,1,115985.38,0\r\n1432,15809772,Glover,667,France,Male,48,2,0,1,1,0,43229.2,0\r\n1433,15687959,Landman,573,Spain,Female,44,4,0,1,1,1,94862.93,0\r\n1434,15585282,Trevisano,755,France,Male,62,1,127706.33,2,0,1,142377.69,0\r\n1435,15714993,Longo,552,France,Female,41,9,124349.34,1,1,0,135635.25,0\r\n1436,15596021,K?,598,Spain,Male,44,8,0,2,1,0,148487.9,0\r\n1437,15646615,Muir,576,Germany,Male,28,1,119336.29,2,0,1,58976.85,0\r\n1438,15742632,Alexeyeva,670,France,Female,31,9,0,1,0,1,76254.83,0\r\n1439,15574068,Norman,504,Germany,Male,56,9,104217.3,1,0,0,55857.48,1\r\n1440,15806967,Simmons,778,France,Female,65,7,0,1,1,1,77867.23,0\r\n1441,15796334,Chukwualuka,558,Germany,Male,39,10,144757.02,1,1,0,22878.16,1\r\n1442,15688713,McCall,627,Spain,Male,44,6,0,1,1,1,114469.55,0\r\n1443,15796179,Moore,683,France,Male,43,8,0,1,1,0,96754.8,0\r\n1444,15598751,Ingram,556,France,Female,43,6,0,3,0,0,125154.57,1\r\n1445,15703019,Okeke,583,France,Female,38,10,0,2,0,1,113597.64,0\r\n1446,15646302,Shao,705,France,Female,24,7,100169.51,1,1,0,121408.55,0\r\n1447,15680855,Iloabuchi,637,France,Male,33,2,145731.83,1,0,1,109219.43,0\r\n1448,15697311,Nebechukwu,697,Spain,Male,56,5,110802.03,1,1,1,50230.31,1\r\n1449,15585367,Diribe,555,Germany,Female,46,4,120392.99,1,1,0,177719.88,1\r\n1450,15726556,Macgroarty,594,Germany,Female,26,6,135067.52,2,0,0,131211.86,0\r\n1451,15676242,Artemova,632,Spain,Male,31,3,136556.44,1,1,0,82152.83,1\r\n1452,15684198,McDonald,551,France,Female,38,10,0,2,1,1,216.27,0\r\n1453,15774882,Mazzanti,687,France,Female,35,3,99587.43,1,1,1,1713.1,1\r\n1454,15714227,Kelly,672,France,Female,53,7,0,1,1,1,136910.18,0\r\n1455,15608653,Davison,521,Spain,Female,34,7,70731.07,1,1,1,20243.97,1\r\n1456,15784280,Reilly,686,Germany,Male,35,2,109342.82,2,0,1,86043.27,0\r\n1457,15789546,Ojiofor,639,Spain,Male,28,8,0,2,1,0,126561.07,0\r\n1458,15590320,Shelton,850,France,Male,66,4,0,2,0,1,64350.8,0\r\n1459,15678385,Lange,465,France,Male,25,2,78247.31,2,1,1,10472.31,0\r\n1460,15571778,Trentini,817,France,Female,55,10,117561.49,1,1,0,95941.55,1\r\n1461,15657085,Gardiner,578,France,Male,23,10,88980.32,1,1,1,125222.36,0\r\n1462,15640627,Wan,611,Spain,Male,34,4,0,2,1,0,170950.58,0\r\n1463,15566211,Hsu,616,Germany,Female,41,1,103560.57,1,1,0,236.45,1\r\n1464,15669293,Hovell,517,France,Male,37,5,113308.84,1,0,1,31517.16,0\r\n1465,15595067,Zhirov,637,Spain,Female,40,6,0,2,1,1,181610.6,0\r\n1466,15753566,Espinosa,806,France,Female,32,3,63763.49,1,1,0,156593.09,0\r\n1467,15650391,Wallace,633,France,Female,29,7,169988.35,1,1,0,4272,0\r\n1468,15681843,Barbour,624,Germany,Female,35,0,180303.24,2,1,0,163587.9,0\r\n1469,15814846,Ozerova,691,France,Male,52,3,0,1,1,0,175843.68,1\r\n1470,15670374,Wright,819,Germany,Female,49,1,120656.86,4,0,0,166164.3,1\r\n1471,15762332,Ulyanova,568,Germany,Female,31,1,61592.14,2,1,1,61796.64,0\r\n1472,15700223,Steiner,806,France,Male,48,4,164701.68,1,1,1,21439.49,0\r\n1473,15729956,Akabueze,726,Spain,Female,26,1,80780.16,1,1,1,19225.85,0\r\n1474,15594862,Aleksandrova,552,France,Male,36,8,0,2,0,0,132547.02,0\r\n1475,15598782,Pinto,755,Germany,Female,30,6,154221.37,2,0,1,62688.55,0\r\n1476,15745080,Griffiths,634,France,Male,26,8,0,1,1,0,21760.96,0\r\n1477,15703399,McNeil,756,France,Female,26,5,101641.14,2,0,1,154460.68,0\r\n1478,15732175,Bruno,776,France,Male,37,2,0,1,0,1,8065,0\r\n1479,15630725,Johnson,649,France,Female,45,5,92786.66,1,1,0,173365.9,1\r\n1480,15640260,Okorie,595,Germany,Male,32,8,131081.66,2,1,1,69428.79,0\r\n1481,15716822,Moen,646,France,Male,30,5,98014.74,1,1,1,12757.14,0\r\n1482,15583748,McGuigan,592,Spain,Male,38,8,0,2,1,0,180426.2,0\r\n1483,15605968,Fancher,574,France,Male,26,8,97460.1,1,1,1,43093.67,0\r\n1484,15790683,Matthews,850,France,Male,36,1,104077.19,2,0,1,68594,0\r\n1485,15607713,Kaeppel,850,Spain,Female,29,1,0,2,1,1,197996.65,0\r\n1486,15700212,Shih,475,France,Female,46,10,0,2,0,0,122953,1\r\n1487,15626710,Yudina,642,France,Female,39,4,0,1,1,1,76821.24,0\r\n1488,15716491,Akabueze,710,Spain,Female,51,4,93656.95,1,0,1,141400.51,1\r\n1489,15625824,Kornilova,596,Spain,Male,30,6,121345.88,4,1,0,41921.75,1\r\n1490,15617705,Ozioma,609,France,Female,39,8,141675.23,1,0,1,175664.25,0\r\n1491,15761976,Su,797,Spain,Female,31,8,0,2,1,0,117916.63,0\r\n1492,15634891,Jamison,504,Germany,Female,43,7,102365.49,1,1,0,194690.77,1\r\n1493,15744517,Esposito,735,Spain,Male,50,9,0,1,0,0,166677.35,1\r\n1494,15686963,Hardiman,680,Spain,Female,30,3,0,1,1,0,160131.58,0\r\n1495,15808189,Woodard,449,France,Male,52,6,0,2,0,1,123622,0\r\n1496,15580845,Chienezie,685,Germany,Male,57,7,101868.51,1,0,1,113483.96,0\r\n1497,15799156,Okwuadigbo,569,Spain,Male,38,8,0,2,0,0,79618.79,0\r\n1498,15694296,Chineze,631,France,Male,35,9,112392.45,2,1,0,24472.23,0\r\n1499,15677049,O'Brien,595,Germany,Female,25,7,106570.34,2,0,1,177025.79,0\r\n1500,15583595,Tao,461,France,Female,28,8,0,1,1,1,103349.74,0\r\n1501,15590146,Mao,630,France,Male,50,1,81947.76,1,0,1,63606.22,1\r\n1502,15801548,Buckland,661,France,Female,31,7,144162.3,2,1,1,14490.79,0\r\n1503,15660833,Flannery,796,Germany,Male,39,5,86350.87,2,0,0,105080.53,0\r\n1504,15762277,Jamieson,710,France,Male,47,5,158623.14,1,0,0,83499.89,1\r\n1505,15791302,Swift,741,France,Male,32,8,0,2,1,0,143598.7,0\r\n1506,15798975,Doherty,606,Germany,Male,48,4,132403.56,1,0,0,36091.91,1\r\n1507,15599956,Payne,747,France,Male,27,10,0,2,0,0,13007.89,0\r\n1508,15577274,Genovese,549,Germany,Female,43,3,134985.66,1,1,0,6101.41,0\r\n1509,15701200,Lucciano,576,France,Male,36,6,0,2,1,1,48314,0\r\n1510,15638149,Rowley,528,France,Male,37,6,103772.45,1,1,0,197111.99,0\r\n1511,15786199,Hsing,535,France,Male,33,2,133040.32,1,1,1,110299.78,0\r\n1512,15701765,Vincent,575,Spain,Female,37,0,0,2,0,0,30114.32,0\r\n1513,15586974,Pearce,656,France,Male,39,10,0,2,1,1,98894.64,0\r\n1514,15729040,Lamb,440,France,Male,42,2,0,2,1,0,49826.68,0\r\n1515,15788676,Riley,539,Spain,Male,38,8,71460.67,2,1,1,10074.05,0\r\n1516,15602497,Honore,850,Spain,Male,39,6,133214.13,1,0,1,20769.88,0\r\n1517,15701333,Blackburn,646,France,Female,37,7,96558.66,1,0,0,163427.18,0\r\n1518,15812071,Endrizzi,744,France,Male,54,6,93806.31,2,0,1,140068.77,0\r\n1519,15634375,Duncan,710,Spain,Female,36,8,0,2,0,0,83206.19,0\r\n1520,15738267,Macarthur,544,France,Female,64,3,124043.8,1,1,1,111402.97,1\r\n1521,15786800,Gould,723,Germany,Male,52,5,131694.97,1,0,1,92873.5,1\r\n1522,15591130,Medvedev,507,Spain,Female,29,6,0,2,0,1,94780.9,0\r\n1523,15720662,Sholes,787,France,Female,35,1,106266.8,1,1,1,16607.15,0\r\n1524,15751531,Shaw,598,Spain,Male,41,8,0,2,1,1,161954.43,0\r\n1525,15653595,Ts'ai,796,France,Male,51,6,0,2,0,1,194733.28,0\r\n1526,15568360,Rolon,569,Spain,Female,41,4,139840.36,1,1,1,163524.7,0\r\n1527,15781210,Reid,711,France,Male,34,8,0,2,0,0,48260.19,0\r\n1528,15668058,Chinwendu,661,Germany,Male,35,8,124098.54,1,1,0,86678.48,0\r\n1529,15597131,Fu,415,France,Male,32,5,145807.59,1,1,1,3064.65,0\r\n1530,15697283,Mackenzie,578,Spain,Male,23,8,0,2,1,0,112124.98,0\r\n1531,15640953,Bligh,611,France,Female,26,2,107508.93,2,1,1,120801.65,0\r\n1532,15715031,Davidson,600,France,Female,28,6,0,2,0,1,52193.23,0\r\n1533,15589660,Lamble,661,Germany,Female,32,1,145980.23,1,0,1,56636.28,0\r\n1534,15769818,Moore,850,France,Female,37,3,212778.2,1,0,1,69372.88,0\r\n1535,15782736,Jose,573,Germany,Female,47,4,152522.47,1,0,1,164038.07,1\r\n1536,15614818,Trevisani,764,Spain,Female,33,9,168964.77,1,0,1,118982.51,0\r\n1537,15794014,Schofield,838,France,Female,34,8,0,2,1,0,27472.07,0\r\n1538,15732448,Stewart,821,France,Female,28,8,0,1,1,1,36754.13,0\r\n1539,15723411,Jamieson,607,Spain,Female,36,4,98266.3,1,1,1,46416.36,0\r\n1540,15797686,Howard,558,France,Male,38,8,113000.92,1,1,1,152872.39,0\r\n1541,15605950,Onwuamaeze,530,Germany,Male,23,1,137060.88,2,1,1,165227.23,0\r\n1542,15812497,D'Albertis,654,Germany,Male,37,5,112146.12,1,1,0,75927.35,0\r\n1543,15690678,Brooks,530,France,Female,33,4,129307.32,1,1,1,172930.28,0\r\n1544,15747677,Gordon,656,Spain,Male,69,6,163975.09,1,1,1,36108.5,0\r\n1545,15618926,Nwachukwu,520,Spain,Male,43,7,0,2,1,1,36202.74,0\r\n1546,15673908,Chinweike,602,Germany,Female,42,6,158414.85,1,1,1,131886.46,0\r\n1547,15727944,Simpkinson,701,Germany,Female,48,1,92072.68,1,1,1,133992.36,0\r\n1548,15807294,Walker,653,Spain,Female,30,2,88243.29,2,1,1,96658.26,0\r\n1549,15618581,Diribe,668,Spain,Male,25,8,0,2,1,1,135112.09,0\r\n1550,15584364,Trentini,652,France,Male,48,4,59486.31,1,1,0,163944.18,1\r\n1551,15599552,Conway,639,Spain,Female,54,2,0,2,1,1,53843.71,0\r\n1552,15749177,Maslow,730,Spain,Female,52,7,0,2,0,1,122398.84,0\r\n1553,15718779,Clark,780,France,Male,34,1,0,1,1,1,64804.04,0\r\n1554,15568106,L?,592,France,Female,38,8,119278.01,2,0,1,19370.73,0\r\n1555,15779481,Swadling,628,France,Male,34,4,158741.43,2,1,1,126192.54,0\r\n1556,15709994,Gallo,658,France,Female,40,7,140596.95,1,0,1,135459.02,1\r\n1557,15772777,Onyemachukwu,850,Spain,Female,29,10,0,2,1,1,94815.04,0\r\n1558,15706815,Samoylova,515,Germany,Male,37,2,90432.92,1,1,1,188366.04,1\r\n1559,15618018,Dickson,571,France,Female,35,1,104783.81,2,0,1,178512.52,0\r\n1560,15671032,He,760,Germany,Male,42,0,77992.97,2,1,1,97906.38,0\r\n1561,15634281,P'an,720,Germany,Female,43,10,110822.9,1,0,0,72861.94,0\r\n1562,15766374,Leak,632,Germany,Male,42,4,119624.6,2,1,1,195978.86,0\r\n1563,15600991,Artemieva,694,Germany,Male,31,6,109052.59,2,1,1,19448.93,1\r\n1564,15777576,Frost,559,Spain,Female,40,5,139129.44,1,0,1,32635.54,0\r\n1565,15742613,Warner,773,Germany,Female,42,8,152324.66,2,1,0,171733.22,0\r\n1566,15649523,Kennedy,581,France,Male,38,1,0,2,1,0,46176.22,0\r\n1567,15651063,Ifeatu,524,Germany,Female,37,9,127480.58,2,1,0,179634.69,0\r\n1568,15683124,Evans,713,France,Male,53,6,115029.4,1,0,0,191521.32,1\r\n1569,15618314,Chu,676,France,Male,40,8,114005.78,1,1,1,67998.45,0\r\n1570,15670823,Hsueh,651,Germany,Female,42,1,116646.76,1,1,0,44731.8,1\r\n1571,15607133,Shih,717,Spain,Female,49,1,110864.38,2,1,1,124532.9,1\r\n1572,15615012,Fan,594,France,Male,23,5,156267.59,1,1,0,160968.44,0\r\n1573,15725141,Whiddon,716,France,Female,44,3,109528.28,1,1,0,27341.63,1\r\n1574,15623560,Onyekachukwu,668,France,Female,35,6,102482.76,1,1,1,53994.64,0\r\n1575,15693018,Ermakova,678,Germany,Male,23,10,115563.71,1,1,1,91633.53,0\r\n1576,15636756,Marino,545,France,Male,23,2,0,2,1,0,189613.12,0\r\n1577,15647474,Niu,613,France,Female,40,9,95624.36,2,1,1,60706.33,0\r\n1578,15576714,Manna,687,Spain,Female,21,8,0,2,1,1,154767.34,0\r\n1579,15585047,Onyemere,715,France,Male,28,7,160376.61,1,0,0,196853.11,0\r\n1580,15743976,Archer,618,Germany,Male,41,8,37702.79,1,1,1,195775.48,0\r\n1581,15793881,Mitchell,721,France,Female,35,6,118273.83,1,0,1,3086.89,0\r\n1582,15576517,Everingham,445,Germany,Female,34,7,131082.17,2,1,1,70618,0\r\n1583,15631072,Huie,690,France,Male,38,1,94456,2,0,1,55034.02,0\r\n1584,15730394,Crowther,709,France,Female,43,8,0,2,0,0,168035.62,1\r\n1585,15631460,Swift,671,Spain,Female,42,3,0,2,1,1,128449.33,0\r\n1586,15692002,Skelton,538,France,Male,33,6,93791.38,1,1,1,199249.29,0\r\n1587,15595282,White,735,France,Female,33,4,0,2,1,0,149474.69,0\r\n1588,15789548,Giordano,592,France,Female,37,7,0,2,1,1,126726.33,0\r\n1589,15758035,Bateson,747,France,Male,61,7,155973.13,1,0,1,147554.26,0\r\n1590,15617518,Hu,675,Germany,Male,36,7,89409.95,1,1,1,149399.7,0\r\n1591,15651802,Day,632,Spain,Female,39,5,97854.37,2,1,0,93536.38,0\r\n1592,15631813,Beneventi,621,France,Male,39,6,0,2,1,1,58883.91,0\r\n1593,15729668,Elizabeth,521,Spain,Male,29,3,60280.62,1,1,0,154271.41,0\r\n1594,15741728,Atkins,591,Spain,Male,36,7,135216.8,1,1,1,122022.89,0\r\n1595,15576676,Serrano,706,Germany,Female,28,6,124923.35,2,1,1,50299.14,0\r\n1596,15711378,Willis,677,France,Male,38,4,0,2,1,0,187800.63,0\r\n1597,15765520,Stevenson,769,Germany,Male,27,7,188614.07,1,1,0,171344.09,0\r\n1598,15656726,Ch'ien,771,France,Male,32,5,62321.62,1,1,1,40920.59,0\r\n1599,15647842,Cunningham,601,Germany,Female,48,8,120782.7,1,1,0,63940.68,1\r\n1600,15719309,Stephens,670,France,Female,42,1,115961.58,2,0,1,29483.87,0\r\n1601,15748718,Gordon,517,France,Male,28,2,115062.61,1,1,0,179056.23,0\r\n1602,15594404,Bevan,834,France,Female,49,8,160602.25,2,1,0,129273.94,0\r\n1603,15751158,Mashman,571,France,Female,42,4,108825.34,3,1,0,55558.51,1\r\n1604,15593470,Tu,576,Germany,Female,36,8,166287.85,1,1,1,23305.85,0\r\n1605,15695129,Milanesi,718,France,Female,31,1,152663.77,1,0,1,17128.64,0\r\n1606,15640865,Romano,636,Germany,Female,31,9,80844.69,2,1,1,74641.9,0\r\n1607,15714080,Goliwe,566,Germany,Female,40,2,97001.36,2,1,0,154486.01,0\r\n1608,15648721,Hsueh,711,France,Male,64,4,0,2,1,1,3185.67,0\r\n1609,15801466,Gray,574,France,Female,39,2,122524.61,2,1,0,88463.63,0\r\n1610,15750248,Wright,619,France,Female,35,8,132292.63,1,1,0,65682.93,0\r\n1611,15758726,Chiemeka,588,France,Female,24,0,0,2,1,1,140586.08,0\r\n1612,15781553,Chung,760,Germany,Female,49,9,91502.99,1,1,0,117232.9,1\r\n1613,15649121,Pinto,665,France,Male,52,3,0,1,1,0,116137.01,1\r\n1614,15674811,Kellway,739,Germany,Male,29,3,59385.98,2,1,1,105533.96,0\r\n1615,15646037,Sopuluchi,641,France,Male,77,9,0,3,1,1,81514.06,0\r\n1616,15722578,Spitzer,685,Germany,Female,21,6,97956.5,1,1,1,164966.27,0\r\n1617,15665695,Potter,594,France,Female,49,4,0,2,1,1,23631.55,0\r\n1618,15801062,Matthews,557,Spain,Female,40,4,0,2,0,1,105433.53,0\r\n1619,15662955,Nicholls,697,France,Male,27,8,141223.68,2,1,0,90591.15,0\r\n1620,15770309,McDonald,656,France,Male,18,10,151762.74,1,0,1,127014.32,0\r\n1621,15657386,Fiorentini,712,Germany,Male,43,1,141749.74,2,0,1,90905.26,0\r\n1622,15777797,Kovalyova,689,Spain,Male,38,5,75075.14,1,1,1,8651.92,1\r\n1623,15783955,Miah,697,France,Female,25,4,165686.11,2,1,0,15467.98,0\r\n1624,15804516,Builder,589,France,Male,38,2,0,1,1,0,79915.28,0\r\n1625,15681758,Baddeley,525,Spain,Female,25,10,0,2,1,0,69361.95,0\r\n1626,15630321,Hu,680,France,Male,44,3,0,2,1,0,86935.08,0\r\n1627,15588248,Hs?,617,France,Female,28,0,0,2,1,1,7597.83,1\r\n1628,15591932,Ford,680,France,Male,32,5,92961.61,1,1,0,116957.6,0\r\n1629,15810347,Todd,662,Spain,Female,30,9,0,2,0,1,157884.83,0\r\n1630,15595303,Johnston,736,Germany,Male,46,5,130812.91,1,1,1,77981.54,1\r\n1631,15634950,Obiajulu,657,Germany,Male,57,8,107174.58,1,1,1,126369.55,1\r\n1632,15685372,Azubuike,350,Spain,Male,54,1,152677.48,1,1,1,191973.49,1\r\n1633,15745827,Padovesi,617,France,Male,30,3,132005.77,1,1,0,142940.39,0\r\n1634,15755868,Farmer,562,France,Male,35,7,0,1,0,0,48869.67,0\r\n1635,15735222,Ignatieff,705,Spain,Female,23,5,0,2,1,1,73131.73,0\r\n1636,15604804,Lu,516,France,Female,33,7,127305.5,1,1,1,120037.36,0\r\n1637,15718944,Artemiev,573,France,Female,37,6,0,2,1,0,193995.37,0\r\n1638,15678626,Okonkwo,538,Spain,Female,31,0,0,2,0,0,179453.66,0\r\n1639,15571550,Dore,699,France,Male,39,9,0,1,1,0,80963.92,0\r\n1640,15723053,T'ang,504,Germany,Male,32,8,170291.22,2,0,1,15658.99,0\r\n1641,15661528,Ashbolt,583,Spain,Male,47,5,102562.23,1,1,0,92708.1,0\r\n1642,15754177,Bazarova,712,Spain,Male,53,2,111061.01,2,0,0,26542.17,0\r\n1643,15683544,Buccho,626,Spain,Male,62,3,0,1,1,1,65010.74,0\r\n1644,15708048,Burn,631,France,Female,34,4,124379.14,1,1,0,106892.91,0\r\n1645,15701109,Andreyev,663,France,Female,37,7,0,1,1,1,185210.63,0\r\n1646,15600110,Endrizzi,506,Germany,Female,41,3,57745.76,1,1,0,4035.46,0\r\n1647,15651533,Brown,570,Germany,Female,50,5,129293.74,1,1,0,177805.44,1\r\n1648,15777904,Nock,703,France,Female,45,7,0,2,1,1,68831.72,0\r\n1649,15655574,Okeke,698,Germany,Female,40,8,150777.1,1,1,0,114732.62,0\r\n1650,15569423,Cunningham,731,Spain,Male,41,4,0,2,1,0,22299.27,0\r\n1651,15718106,Kelley,625,France,Male,34,6,0,2,0,0,197283.2,0\r\n1652,15585067,Wilson,634,Spain,Male,31,9,108632.48,1,1,1,179485.96,1\r\n1653,15675501,Woods,616,France,Male,59,5,153861.1,1,1,1,17699.48,0\r\n1654,15633233,McFarland,500,France,Male,56,1,100374.58,1,1,0,118490.8,1\r\n1655,15667134,Cisneros,446,France,Male,32,8,0,2,0,0,133292.94,0\r\n1656,15659105,Borchgrevink,669,France,Female,47,9,61196.54,1,1,0,58170.24,0\r\n1657,15575409,Rozhkova,581,Germany,Male,31,6,116891.72,1,1,0,107137.3,0\r\n1658,15752342,Bradley,704,Germany,Female,54,6,133656.91,3,1,0,145071.33,1\r\n1659,15654851,Obialo,748,France,Male,44,2,92911.52,1,0,1,85495.24,0\r\n1660,15741429,Hudson,680,Spain,Female,31,9,119825.75,2,1,1,101139.3,0\r\n1661,15682356,Veltri,655,France,Female,37,7,111852.84,2,1,0,10511.13,0\r\n1662,15806447,Mazzanti,690,Germany,Male,32,0,106683.52,2,1,1,137916.49,0\r\n1663,15800229,Thorpe,695,Germany,Male,40,7,139022.24,1,0,1,193383.13,0\r\n1664,15663441,Golibe,700,Germany,Female,40,4,148571.07,1,1,0,189826.96,1\r\n1665,15791991,Udinesi,773,France,Male,52,4,0,1,0,1,144113.42,0\r\n1666,15775082,Stewart,749,France,Male,42,1,129776.72,2,0,1,143538.51,0\r\n1667,15579706,Curtis,611,France,Female,46,5,0,1,1,0,77677.14,1\r\n1668,15718247,Hayden,606,Spain,Female,46,8,0,2,1,1,183717.94,0\r\n1669,15755722,H?,554,France,Male,24,10,0,1,0,0,92180.62,0\r\n1670,15582259,Campbell,567,France,Female,37,7,0,2,1,1,28690.9,0\r\n1671,15716994,Green,559,Spain,Male,24,3,114739.92,1,1,0,85891.02,1\r\n1672,15586880,P'eng,594,Germany,Male,41,2,122545.65,2,1,1,42050.24,0\r\n1673,15713854,Cremonesi,513,France,Female,37,6,0,2,1,0,110142.34,0\r\n1674,15780835,Liang,652,Germany,Female,26,1,131908.35,1,1,1,179269.79,0\r\n1675,15675896,Gough,680,Germany,Female,42,7,105722.69,1,1,1,90558.24,1\r\n1676,15658459,Bates,784,Spain,Male,33,10,0,2,1,0,162022.47,0\r\n1677,15658057,Padovesi,812,Spain,Female,44,8,0,3,1,0,66926.83,1\r\n1678,15801767,Yin,784,Spain,Female,40,8,0,2,1,0,108891.3,0\r\n1679,15569178,Kharlamov,570,France,Female,18,4,82767.42,1,1,0,71811.9,0\r\n1680,15731478,Nicholls,712,France,Female,42,1,87842.98,1,0,0,92223.59,0\r\n1681,15811236,Burns,705,Spain,Male,39,6,133261.13,1,1,1,78065.9,0\r\n1682,15746749,Fleming,681,Spain,Female,32,3,0,2,1,1,59679.9,0\r\n1683,15662758,Watson,620,France,Male,41,0,97925.11,1,1,0,85000.32,0\r\n1684,15709387,Obiajulu,711,France,Male,52,5,0,1,1,1,159808.95,0\r\n1685,15572093,Han,613,France,Female,24,7,140453.91,1,1,0,129001.3,0\r\n1686,15713826,Ferguson,613,Germany,Female,20,0,117356.19,1,0,0,113557.7,1\r\n1687,15570205,Tao,682,Spain,Male,36,5,0,2,1,1,147758.51,0\r\n1688,15589348,Le Grand,850,Spain,Male,37,4,137204.77,1,1,1,28865.59,0\r\n1689,15804610,Valdez,601,France,Female,41,1,0,2,0,1,160607.06,0\r\n1690,15700854,Cunningham,595,Spain,Male,35,8,0,1,1,0,100015.79,1\r\n1691,15758836,Godfrey,675,Spain,Male,36,3,54098.18,2,0,1,54478.52,0\r\n1692,15772933,Mai,591,Spain,Male,31,8,0,1,1,1,141677.33,0\r\n1693,15809006,Walker,602,France,Male,23,7,113758.48,2,0,0,84077.6,0\r\n1694,15689612,Pirozzi,554,Spain,Female,34,8,0,1,0,1,106981.03,0\r\n1695,15744614,Feng,541,France,Male,37,9,118636.92,1,1,1,73551.44,0\r\n1696,15704250,Akabueze,506,France,Male,34,7,0,2,0,0,115842.1,0\r\n1697,15700255,Robson,814,Germany,Male,44,8,95488.82,2,0,0,107013.59,0\r\n1698,15669410,Yevdokimova,683,France,Male,30,8,110829.52,2,0,0,24938.84,0\r\n1699,15807595,Ijendu,485,Germany,Male,51,7,144244.59,2,1,0,51113.14,0\r\n1700,15664523,Colombo,696,Germany,Female,31,8,122021.92,2,1,0,33828.64,0\r\n1701,15642833,Akubundu,608,France,Female,30,8,0,2,1,0,128875.86,0\r\n1702,15605279,Francis,792,France,Male,50,9,0,4,1,1,194700.81,1\r\n1703,15713644,Marshall,686,Spain,Male,22,5,0,2,1,0,158974.45,0\r\n1704,15750466,Rhodes,790,Germany,Male,42,1,85839.62,1,1,0,198182.73,0\r\n1705,15739054,Y?,654,France,Female,29,4,96974.97,1,0,1,141404.07,0\r\n1706,15612771,Bell,452,France,Male,35,4,148172.44,1,1,1,4175.68,0\r\n1707,15788483,Kerr,719,Spain,Male,38,0,0,1,1,0,126876.47,0\r\n1708,15732832,Jideofor,707,France,Female,40,5,0,2,1,0,41052.82,0\r\n1709,15772892,Robertson,699,France,Female,49,2,0,1,0,0,105760.01,0\r\n1710,15713843,Kao,850,Spain,Male,30,2,0,2,0,1,27937.12,0\r\n1711,15567993,Palmer,828,Spain,Male,28,8,134766.85,1,1,0,79355.87,0\r\n1712,15617603,Mackay,850,Germany,Male,30,5,123210.56,2,1,1,102180.27,0\r\n1713,15744983,Burgmann,712,Spain,Male,47,1,139887.01,1,1,1,95719.73,0\r\n1714,15630419,Davis,634,France,Male,44,9,149961.11,1,1,0,57121.51,0\r\n1715,15738828,Milano,730,Germany,Male,45,6,152880.97,1,0,0,162478.11,0\r\n1716,15778025,Dellucci,685,Germany,Male,43,9,108589.47,2,0,1,194808.51,0\r\n1717,15799479,Coles,809,Spain,Male,33,9,0,1,1,1,124045.65,0\r\n1718,15684269,Gray,707,Spain,Female,35,3,56674.48,1,1,0,17987.4,1\r\n1719,15762745,Macvitie,648,Spain,Male,32,8,0,1,1,0,133653.38,0\r\n1720,15746970,Townsend,760,Spain,Female,57,1,0,2,1,1,25101.17,0\r\n1721,15725024,Pope,805,Germany,Female,33,3,105663.56,2,0,1,33330.89,0\r\n1722,15592116,Jensen,585,France,Female,39,7,0,2,1,0,2401.26,0\r\n1723,15624391,Thomson,595,Spain,Female,30,5,100683.54,1,1,1,178361.04,0\r\n1724,15567422,Chiazagomekpele,630,France,Male,42,6,0,2,1,0,162697.93,0\r\n1725,15612627,Ozuluonye,627,Germany,Male,29,5,139541.58,2,1,0,80607.33,0\r\n1726,15574879,Wright,631,Germany,Female,37,2,121801.72,2,0,1,23146.62,0\r\n1727,15745107,Lung,776,Germany,Male,38,5,112281.7,1,0,1,89893.6,0\r\n1728,15734491,Lombardo,676,Spain,Female,36,4,0,2,1,1,3173.31,0\r\n1729,15675320,Leonard,758,Spain,Female,40,5,93499.82,2,0,0,123218.81,0\r\n1730,15643824,Johnston,637,France,Male,33,0,132255.99,2,0,1,74588.41,0\r\n1731,15643438,P'eng,850,France,Male,20,7,0,2,1,0,31288.77,0\r\n1732,15721730,Amechi,601,Spain,Female,44,4,0,2,1,0,58561.31,0\r\n1733,15680727,Fang,735,France,Male,49,5,121973.28,1,1,0,148804.36,0\r\n1734,15752508,Docherty,614,Germany,Male,32,7,99462.8,2,1,1,51117.06,0\r\n1735,15808846,Horton,672,Germany,Female,21,3,165878.76,2,1,1,164537.17,0\r\n1736,15727251,Vincent,642,France,Male,30,8,117494.27,1,0,0,61977.82,0\r\n1737,15663489,Onio,633,Germany,Female,29,0,138577.34,1,1,0,193362.99,0\r\n1738,15683677,Schiavone,769,Spain,Male,39,9,0,1,1,1,47722.79,0\r\n1739,15596414,Chandler,796,Spain,Male,41,8,107525.07,1,1,0,18510.41,0\r\n1740,15730639,Fiorentino,715,France,Male,23,7,139224.92,2,1,0,65057.71,0\r\n1741,15672132,Butusov,695,France,Female,42,7,121453.63,1,0,0,46374.64,0\r\n1742,15742638,Wang,747,France,Female,25,4,0,2,0,1,42039.67,0\r\n1743,15578603,Alexeieva,584,Germany,Female,54,1,77354.37,1,0,0,138192.98,1\r\n1744,15726088,Vinogradova,476,France,Male,40,6,0,1,1,1,22735.45,0\r\n1745,15682533,Hughes,850,France,Female,39,7,79259.99,1,0,1,186910.74,0\r\n1746,15772995,Ts'ao,529,France,Male,30,2,116295.29,1,1,0,75285.47,0\r\n1747,15765694,Bage,584,Spain,Female,59,1,0,1,0,1,130260.11,1\r\n1748,15659486,Yudina,586,Germany,Male,34,9,74309.81,1,1,0,15034.93,0\r\n1749,15568963,Naquin,674,Germany,Male,34,2,152797.9,1,1,0,175709.4,1\r\n1750,15703820,Endrizzi,552,France,Male,42,9,133701.07,2,1,0,101069.71,1\r\n1751,15569410,Tang,601,Germany,Female,33,7,114430.18,2,1,1,153012.13,0\r\n1752,15632256,Schroeder,541,France,Male,29,7,127504.57,1,0,0,86173.92,0\r\n1753,15724466,Swearingen,744,Germany,Female,41,2,84113.41,1,1,0,197548.63,0\r\n1754,15777639,McGregor,595,Spain,Female,23,10,101126.66,2,0,0,37042.8,0\r\n1755,15802501,Onyeorulu,724,Germany,Male,33,5,103564.83,2,1,0,121085.72,0\r\n1756,15778410,Clarke,533,Spain,Female,52,7,0,1,0,1,194113.99,1\r\n1757,15670702,Smith,618,France,Male,37,2,168178.21,2,0,1,101273.23,0\r\n1758,15704763,Kozlova,523,Germany,Female,39,1,143903.11,1,1,1,118711.75,1\r\n1759,15645544,Nekrasov,642,Germany,Female,30,5,129753.69,1,1,0,582.53,0\r\n1760,15757646,Olague,584,France,Male,35,9,0,2,1,0,192381.21,0\r\n1761,15701121,Holt,521,France,Male,38,5,110641.18,1,0,1,136507.69,1\r\n1762,15796313,Olsen,662,France,Female,36,4,166909.2,2,1,0,138871.12,1\r\n1763,15815660,Mazzi,758,France,Female,34,1,154139.45,1,1,1,60728.89,0\r\n1764,15602844,Niu,717,France,Male,38,7,97459.06,1,0,0,189175.71,0\r\n1765,15636238,Graham,611,France,Male,40,1,0,2,1,1,102547.56,0\r\n1766,15770101,Millar,766,Germany,Male,43,6,112088.04,2,1,1,36706.56,0\r\n1767,15645543,Bell,636,France,Female,34,3,0,2,1,1,44756.25,0\r\n1768,15596397,Kelly,814,France,Female,48,7,0,2,1,1,132870.15,0\r\n1769,15770525,T'an,760,Spain,Male,28,1,141038.57,2,0,0,16287.38,0\r\n1770,15684267,Davila,607,Germany,Male,39,2,84468.67,2,1,1,121945.42,0\r\n1771,15689980,Willis,725,Spain,Female,36,4,118520.26,1,0,0,131173.9,1\r\n1772,15633260,Dumetochukwu,600,France,Male,37,1,142663.46,1,0,1,88669.89,0\r\n1773,15756471,Giles,656,Germany,Male,27,4,118627.16,2,1,1,160835.3,0\r\n1774,15721303,O'Meara,640,Spain,Male,34,1,137523.02,1,0,0,24761.36,0\r\n1775,15802256,Yao,439,France,Male,28,7,110976.23,2,1,0,138526.96,0\r\n1776,15725664,Wallace,549,France,Female,38,8,107283.4,1,0,0,157442.75,0\r\n1777,15674851,T'ien,622,France,Male,38,5,0,2,0,0,105295.77,0\r\n1778,15701946,Ndubueze,715,France,Male,34,4,124314.45,1,0,0,97782.92,0\r\n1779,15748947,Chukwuraenye,657,France,Female,41,5,95858.37,1,1,1,68255.88,0\r\n1780,15673342,K'ung,703,France,Male,36,2,0,2,1,0,108790.95,0\r\n1781,15601008,Stevenson,802,France,Male,33,8,0,2,1,0,143706.18,0\r\n1782,15771636,Marshall,793,Spain,Female,36,0,0,1,0,0,148993.47,0\r\n1783,15642002,Hayward,554,France,Female,35,6,117707.18,2,0,0,95277.15,1\r\n1784,15693381,Tipton,533,Spain,Male,38,1,135289.33,2,0,1,152956.33,0\r\n1785,15607691,Gibson,658,France,Male,36,8,174060.46,1,1,1,94925.62,0\r\n1786,15589380,Fraser,713,Germany,Male,40,3,114446.84,2,1,1,87308.18,0\r\n1787,15603846,Fang,711,Spain,Male,37,2,0,2,1,0,83978.86,1\r\n1788,15753549,Dubinina,669,France,Male,25,1,157848.53,1,0,0,37543.93,1\r\n1789,15725355,Morey,439,France,Female,43,8,0,1,0,1,104889.3,0\r\n1790,15773017,Todd,763,Spain,Female,37,6,0,2,1,1,149705.25,0\r\n1791,15625641,Forbes,697,Germany,Female,74,3,108071.36,2,1,1,16445.79,0\r\n1792,15776467,De Salis,702,Spain,Female,35,8,14262.8,2,1,0,54689.16,0\r\n1793,15746451,Barry,686,Spain,Male,41,7,102749.72,1,0,1,194913.86,0\r\n1794,15777922,Afamefuna,629,Spain,Male,36,1,161757.87,2,1,1,146371.72,0\r\n1795,15606841,Ibbott,823,France,Male,38,1,0,2,1,0,156603.7,0\r\n1796,15757648,Marshall,683,Germany,Female,35,5,95698.79,1,0,1,182566.76,0\r\n1797,15677173,Law,555,France,Male,37,9,124969.13,1,1,0,60194.05,0\r\n1798,15764170,Pinto,647,Germany,Male,44,4,93960.35,1,1,0,36579.53,1\r\n1799,15610446,Chinedum,714,France,Female,51,4,88308.87,3,0,0,5862.53,1\r\n1800,15612776,McKay,850,Spain,Female,39,10,0,2,1,1,143030.09,0\r\n1801,15794122,Otutodilinna,713,France,Female,59,3,0,2,1,1,62700.08,0\r\n1802,15774931,She,452,France,Male,30,7,112935.87,1,1,1,99017.34,0\r\n1803,15779247,Pai,683,Spain,Female,24,8,98567.1,1,1,0,187987.01,0\r\n1804,15707078,Kruglov,577,France,Female,26,1,180530.51,1,0,0,123454.62,0\r\n1805,15605263,Chin,552,France,Male,33,5,140931.57,1,0,1,10921.5,0\r\n1806,15607381,King,769,Germany,Female,31,7,148913.72,2,1,0,53817.23,0\r\n1807,15683471,Hansen,691,France,Male,38,7,0,2,0,0,81617.4,0\r\n1808,15605037,Ting,818,France,Female,49,2,0,1,0,1,192298.84,1\r\n1809,15576085,Stone,739,France,Male,41,5,0,2,0,0,143882.25,0\r\n1810,15770435,McLean,639,France,Female,50,6,115335.32,2,1,1,53130.41,0\r\n1811,15592994,Zikoranachidimma,651,France,Female,65,0,0,2,1,1,190454.04,0\r\n1812,15624068,Fu,779,France,Female,26,0,0,2,0,1,111906,0\r\n1813,15595221,Trevisano,850,Germany,Female,33,7,134678.13,1,1,0,113177.95,0\r\n1814,15637131,Fallaci,829,France,Male,38,9,0,2,1,0,30529.88,0\r\n1815,15613471,Wiley,579,Germany,Male,31,2,90547.48,2,1,1,18800.13,0\r\n1816,15583499,Chiagoziem,510,France,Male,32,9,103324.78,1,1,1,46127.7,0\r\n1817,15752816,Murray,531,France,Male,29,3,114590.58,1,0,0,75585.48,0\r\n1818,15804075,Chuang,628,Germany,Female,36,3,91286.51,1,1,0,63085.94,0\r\n1819,15800517,Huang,633,Spain,Male,32,5,163340.12,2,1,1,74415.2,0\r\n1820,15712319,Chukwukere,714,Spain,Male,45,8,150900.29,2,0,1,139889.15,0\r\n1821,15797389,Hsia,604,Spain,Male,23,9,124577.33,1,1,1,7267.25,0\r\n1822,15621432,Lee,630,Spain,Male,35,1,0,2,0,0,186826.22,0\r\n1823,15779390,Theus,850,Spain,Female,31,4,91292.7,1,1,1,162149.07,0\r\n1824,15711219,Jennings,788,Germany,Female,57,8,93716.72,1,1,1,180150.49,1\r\n1825,15770498,Parker,798,France,Female,37,4,111723.08,1,1,1,83478.12,0\r\n1826,15678727,Tan,770,Germany,Male,45,4,110765.68,1,1,0,26163.74,1\r\n1827,15573893,Barry,569,Germany,Male,25,9,173459.45,2,1,1,44381.06,0\r\n1828,15740104,Tuan,425,Spain,Female,22,7,169649.73,2,0,1,136365,1\r\n1829,15792649,Patterson,547,Spain,Female,31,9,0,2,0,0,99294.22,0\r\n1830,15605275,Ofodile,725,Germany,Male,45,8,116917.07,1,0,0,173464.43,1\r\n1831,15572467,Chandler,506,France,Male,37,5,0,2,1,1,127543.81,0\r\n1832,15738219,Nash,632,France,Female,36,7,0,2,1,1,52526.65,0\r\n1833,15600710,Atkinson,620,France,Male,22,0,0,1,1,0,32589.45,0\r\n1834,15804394,Brenan,663,Germany,Male,32,8,130627.66,1,1,0,47161.25,1\r\n1835,15694188,Obidimkpa,700,Spain,Female,46,5,56580.95,2,0,1,45424.13,0\r\n1836,15583718,Terry,696,Germany,Male,38,6,142316.14,1,1,1,8018.49,0\r\n1837,15802478,Spring,767,Spain,Male,31,6,0,2,1,1,195668,0\r\n1838,15619343,Mahmood,561,France,Male,56,7,152759,2,1,0,133167.11,1\r\n1839,15758813,Campbell,350,Germany,Male,39,0,109733.2,2,0,0,123602.11,1\r\n1840,15761374,Bellucci,706,France,Male,54,9,117444.51,1,1,1,186238.85,0\r\n1841,15569209,Amaechi,464,Spain,Female,34,5,76001.57,1,1,1,158668.87,0\r\n1842,15788539,Foxall,501,France,Female,34,3,107747.57,1,1,0,9249.36,0\r\n1843,15747222,Bentley,745,Spain,Female,35,8,0,2,1,1,116581.1,0\r\n1844,15769346,Baird,587,France,Female,36,1,134997.49,2,1,0,44688.08,0\r\n1845,15699634,Howard,667,France,Female,48,2,0,2,1,1,148608.39,0\r\n1846,15589076,Henry,737,France,Male,36,9,0,1,0,1,188670.9,1\r\n1847,15812338,Sopuluchukwu,485,Spain,Female,30,7,0,1,1,0,107067.37,0\r\n1848,15758845,Rocher,590,Spain,Female,37,0,64345.21,1,0,1,61759.33,1\r\n1849,15685844,White,518,Germany,Female,35,8,141665.63,1,0,1,192776.64,0\r\n1850,15583090,Komar,581,Spain,Female,29,8,0,2,1,0,46735.19,0\r\n1851,15587581,Russo,785,Germany,Female,33,5,136624.6,2,1,1,169117.74,0\r\n1852,15633640,Loewenthal,799,France,Female,52,4,161209.66,1,1,1,89081.41,0\r\n1853,15573741,Aliyeva,698,Spain,Male,38,10,95010.92,1,1,1,105227.86,0\r\n1854,15633574,Montes,730,France,Female,41,4,167545.32,1,1,0,128246.81,0\r\n1855,15711455,Kuo,740,Germany,Female,36,4,109044.6,1,0,0,94554.74,1\r\n1856,15570601,Cheng,785,France,Female,47,9,122031.55,1,1,1,33823.5,1\r\n1857,15690925,McIntosh,527,Spain,Female,29,2,27755.97,1,1,0,97468.44,1\r\n1858,15709338,T'ao,544,France,Female,29,1,118560.55,1,1,1,164137.36,0\r\n1859,15780746,Tyndall,705,France,Male,61,4,0,2,1,1,191313.7,0\r\n1860,15681956,Bailey,684,France,Male,34,9,0,2,1,1,65257.57,0\r\n1861,15778190,Onyekaozulu,639,Spain,Female,28,8,97840.72,1,1,1,178222.77,0\r\n1862,15786852,Nwachukwu,565,Germany,Female,38,2,158651.29,2,1,1,179445.28,0\r\n1863,15726494,Romani,481,France,Male,44,9,175303.06,1,1,0,65500.53,1\r\n1864,15641183,Chin,731,Spain,Male,25,8,96950.21,1,1,0,97877.92,0\r\n1865,15805312,Bellucci,607,France,Male,45,7,123859.6,1,0,1,113051.57,0\r\n1866,15636572,Christmas,760,France,Female,32,7,0,2,1,1,105969.05,0\r\n1867,15632575,Moore,559,France,Female,70,9,0,1,1,1,122996.76,0\r\n1868,15740164,Genovesi,715,France,Female,33,3,85227.84,1,1,1,68087.15,0\r\n1869,15574947,Cartwright,656,France,Male,36,8,97786.08,2,0,1,21478.36,0\r\n1870,15597909,Johnstone,652,Germany,Male,33,7,128135.99,1,1,0,158437.73,0\r\n1871,15782574,Warner,624,Spain,Male,33,6,0,2,0,0,76551.7,0\r\n1872,15734999,Stephenson,634,Spain,Male,36,2,85996.19,1,1,0,15887.68,0\r\n1873,15706593,Ellis,850,Spain,Female,50,10,0,2,1,1,33741.84,0\r\n1874,15766686,Nebechi,659,Germany,Female,39,1,104502.11,1,1,0,20652.69,0\r\n1875,15590268,Chu,529,Spain,Male,35,5,95772.97,1,1,1,112781.5,0\r\n1876,15763055,Onuchukwu,572,Spain,Male,31,5,98108.79,1,0,1,119996.95,0\r\n1877,15664754,Steele,640,Germany,Male,39,9,131607.28,4,0,1,6981.43,1\r\n1878,15643630,Quaife,770,Spain,Male,55,9,63127.41,2,1,0,185211.28,1\r\n1879,15641043,Scott,648,Spain,Male,35,7,0,2,1,1,78436.36,0\r\n1880,15768095,Yeh,579,France,Male,31,9,0,1,0,1,139048,0\r\n1881,15811314,Y?,589,Germany,Female,36,9,140355.56,2,1,0,136329.96,0\r\n1882,15669922,Conti,530,Spain,Female,36,2,0,2,1,1,14721.8,0\r\n1883,15707114,Holder,831,France,Male,30,2,0,2,0,1,3430.38,0\r\n1884,15670602,Burgess,790,Germany,Male,24,7,107418.27,1,0,1,160450.21,0\r\n1885,15713479,Ozuluonye,656,France,Male,35,6,0,2,1,0,1485.27,0\r\n1886,15663830,De Luca,563,Spain,Male,32,6,0,2,1,1,19720.08,0\r\n1887,15566958,Li Fonti,667,Spain,Male,39,7,167557.12,1,1,1,41183.02,0\r\n1888,15680918,Freeman,613,Spain,Male,34,8,117300.02,1,1,0,139410.08,0\r\n1889,15663921,Pisani,429,France,Male,60,7,0,2,1,1,163691.48,0\r\n1890,15716324,Ignatieff,665,France,Female,23,9,143672.9,1,1,1,115147.33,0\r\n1891,15796969,Lahti,731,France,Male,33,4,0,2,1,1,74945.11,0\r\n1892,15574783,Perkins,584,France,Female,37,1,0,2,1,1,180363.56,0\r\n1893,15773487,Conway,634,Germany,Female,31,8,76798.92,1,0,0,196021.73,0\r\n1894,15802486,Hayes,488,France,Male,34,3,0,2,1,1,125979.36,0\r\n1895,15783398,Rizzo,535,Spain,Female,49,7,115309.75,1,1,0,111421.77,0\r\n1896,15649418,Krylov,776,France,Female,29,7,178171.04,2,1,1,115818.51,0\r\n1897,15604588,Li Fonti,850,Spain,Female,38,3,0,2,0,1,179360.76,0\r\n1898,15735428,Talbot,673,Spain,Female,37,0,0,2,0,0,82351.06,0\r\n1899,15629078,Matthias,850,Germany,Female,45,5,127258.79,1,1,1,192744.23,1\r\n1900,15806880,Boyle,627,Spain,Female,30,6,0,1,1,1,113408.47,0\r\n1901,15754999,Ch'eng,570,France,Female,33,8,0,1,1,1,124641.42,0\r\n1902,15781034,Mason,796,Spain,Male,67,5,0,2,0,1,54871.02,0\r\n1903,15622017,Bruno,773,Spain,Female,33,10,0,1,1,1,98820.09,0\r\n1904,15705885,Smeaton,752,Spain,Male,36,2,0,2,1,1,45570.84,0\r\n1905,15677382,Miller,625,Spain,Female,69,1,107569.96,1,1,1,182336.45,0\r\n1906,15566843,Gotch,535,Germany,Male,20,9,134874.4,1,1,1,118825.56,0\r\n1907,15608387,Fu,786,France,Female,29,4,0,2,1,0,103372.79,0\r\n1908,15810786,O'Toole,620,France,Female,67,3,0,2,1,1,43486.73,0\r\n1909,15626983,Ledford,605,Spain,Female,48,6,0,2,1,1,40062.99,0\r\n1910,15773605,Iadanza,670,Spain,Female,32,3,0,2,1,0,46175.7,0\r\n1911,15811261,Alaniz,617,Spain,Male,42,0,70105.87,1,1,1,120830.73,0\r\n1912,15590606,Saunders,595,France,Male,41,9,0,2,1,0,5967.09,0\r\n1913,15576644,Lin,687,Germany,Female,29,4,78939.15,1,1,0,122134.56,1\r\n1914,15750264,Pinto,757,Germany,Male,30,6,105128.85,2,1,1,62972.13,0\r\n1915,15741554,Streeter,746,Spain,Male,31,2,113836.27,1,1,1,174815.54,0\r\n1916,15769051,Shaw,503,Spain,Male,25,7,0,1,0,1,192841.13,0\r\n1917,15812198,Chen,543,Germany,Male,48,1,100900.5,1,0,0,33310.72,1\r\n1918,15699772,Barclay,428,Germany,Female,40,3,129248.11,2,1,0,72876.43,1\r\n1919,15744105,Kodilinyechukwu,768,France,Female,28,3,109118.05,2,0,1,50911.41,0\r\n1920,15739858,Otitodilichukwu,618,France,Male,56,7,0,1,1,1,142400.27,1\r\n1921,15723720,McKenzie,591,France,Female,31,7,0,2,0,1,48778.46,0\r\n1922,15638355,Woods,658,France,Female,35,5,126397.66,1,0,0,156361.58,1\r\n1923,15805637,Hsing,625,France,Male,36,9,108546.16,3,1,0,133807.77,1\r\n1924,15629575,Wheare,717,France,Male,36,2,148061.89,1,1,0,179128.69,1\r\n1925,15586243,Yobachi,667,France,Male,44,8,122277.87,1,1,1,91810.71,0\r\n1926,15757931,Fang,804,France,Male,24,3,0,2,1,0,173195.33,0\r\n1927,15716023,Pearson,693,France,Male,31,1,0,2,0,1,182270.88,0\r\n1928,15647782,Brown,729,Germany,Male,36,8,152899.24,2,1,0,177130.33,0\r\n1929,15716609,L?,484,Germany,Male,54,3,134388.11,1,0,0,49954.79,1\r\n1930,15623791,Padovesi,632,Spain,Female,40,3,109740.62,1,1,0,141896.74,0\r\n1931,15627262,Soto,536,Germany,Male,23,6,92366.72,2,1,0,120661.71,0\r\n1932,15652693,Greco,573,France,Female,26,4,129109.02,1,0,0,149814.68,1\r\n1933,15586993,Giordano,655,Spain,Female,56,5,0,2,1,1,41782.7,0\r\n1934,15815560,Bogle,666,Germany,Male,74,7,105102.5,1,1,1,46172.47,0\r\n1935,15584930,Grimmett,726,Germany,Male,30,5,111375.32,2,1,0,2704.09,0\r\n1936,15799031,Ayers,523,France,Male,39,3,0,2,1,0,6726.53,0\r\n1937,15810457,Miller,728,Germany,Female,33,9,150412.14,2,1,0,170764.08,0\r\n1938,15697879,Webb,701,France,Male,30,3,156660.72,2,1,0,45742.42,0\r\n1939,15594902,Lombardi,518,France,Male,38,3,90957.81,1,0,1,162304.59,0\r\n1940,15799710,Wei,739,France,Male,37,7,104960.46,1,0,1,80883.82,0\r\n1941,15659651,Ross,531,Germany,Female,31,7,117052.82,1,1,0,118508.09,1\r\n1942,15645956,Jideofor,452,Spain,Male,44,3,88915.85,1,1,0,69697.74,0\r\n1943,15651713,King,684,France,Male,45,6,148071.39,1,1,0,183575.01,0\r\n1944,15737265,Nwokeocha,728,Germany,Male,39,6,152182.83,1,0,0,161203.6,0\r\n1945,15687310,Humphries,783,Spain,Male,39,9,0,2,1,0,143752.77,0\r\n1946,15607347,Olisaemeka,734,France,Male,22,5,130056.23,1,0,0,121894.31,1\r\n1947,15698321,Yobanna,648,Germany,Male,34,3,95039.73,2,1,1,147055.87,0\r\n1948,15657812,Ch'iu,688,France,Male,52,1,0,2,1,1,172033.57,0\r\n1949,15569187,Fleming,680,Spain,Male,35,9,0,2,0,0,143774.06,0\r\n1950,15681562,Trevisan,516,France,Female,43,2,112773.73,2,1,1,139366.58,0\r\n1951,15615456,Aleksandrova,680,France,Female,37,10,123806.28,1,1,0,81776.84,1\r\n1952,15589793,Onwuamaeze,604,France,Male,53,8,144453.75,1,1,0,190998.96,1\r\n1953,15781884,Knox,624,Germany,Male,27,9,94667.29,2,0,1,4470.52,0\r\n1954,15675190,Chia,623,France,Male,21,10,0,2,0,1,135851.3,0\r\n1955,15600734,Townsend,624,Spain,Male,51,5,174397.21,2,1,1,172372.63,0\r\n1956,15779176,Dike,565,Germany,Female,58,3,108888.24,3,0,1,135875.51,1\r\n1957,15605286,Moyes,565,France,Male,55,4,118803.35,2,1,1,128124.7,1\r\n1958,15674922,Beavers,710,France,Male,54,6,171137.62,1,1,1,167023.95,1\r\n1959,15737506,Tretiakova,645,France,Male,42,6,0,1,0,0,149807.01,0\r\n1960,15780514,Fuller,707,France,Male,33,8,136678.52,1,1,0,54290.62,0\r\n1961,15623647,Dellucci,655,Spain,Female,36,1,135515.76,1,1,0,86013.96,0\r\n1962,15668472,Ritchie,705,Spain,Female,24,5,177799.83,2,0,0,79886.06,0\r\n1963,15692416,Aikenhead,358,Spain,Female,52,8,143542.36,3,1,0,141959.11,1\r\n1964,15771139,Douglas,578,Germany,Male,34,8,147487.23,2,1,0,66680.77,0\r\n1965,15738318,Kung,800,France,Female,40,5,97764.41,1,1,0,98640.15,1\r\n1966,15772243,MacDonald,612,France,Female,33,9,0,1,0,0,142797.5,1\r\n1967,15638463,Okwudilichukwu,681,Germany,Female,48,8,139480.18,1,1,1,163581.67,0\r\n1968,15598088,Ni,559,Spain,Male,25,5,0,2,1,1,163221.22,0\r\n1969,15693468,Simmons,488,Spain,Female,39,9,140553.46,1,0,0,12440.44,0\r\n1970,15671930,H?,717,France,Female,36,5,0,2,1,1,145551.6,0\r\n1971,15762268,Hancock,666,France,Female,41,10,141162.08,1,1,0,50908.48,0\r\n1972,15780954,Cran,582,Spain,Male,26,4,65848.36,2,1,0,30149.21,0\r\n1973,15700174,McKay,733,Spain,Female,30,0,83319.28,1,0,0,57769.2,0\r\n1974,15635728,P'an,693,France,Male,41,4,0,2,0,0,156381.47,0\r\n1975,15679283,Parkhill,694,France,Female,33,4,129731.64,2,1,0,178123.86,0\r\n1976,15591386,Golubova,622,France,Female,35,5,0,2,1,0,51112.8,0\r\n1977,15694192,Nwankwo,598,Spain,Female,38,6,0,2,0,0,173783.38,0\r\n1978,15585901,Johnson,717,Spain,Male,35,1,0,3,0,0,174770.14,1\r\n1979,15792329,Mao,494,Germany,Male,37,5,107106.33,2,1,0,172063.09,0\r\n1980,15635597,Echezonachukwu,644,France,Male,33,8,0,2,1,1,155294.17,0\r\n1981,15775880,McElyea,554,France,Female,30,9,0,2,1,1,40320.3,0\r\n1982,15630913,Rosas,476,Spain,Female,69,1,105303.73,1,0,1,134260.34,0\r\n1983,15756680,Phillips,667,France,Male,28,6,165798.1,1,1,0,147090.9,0\r\n1984,15587913,Palerma,748,Spain,Female,40,4,0,2,1,0,132368.47,0\r\n1985,15737605,Morris,531,Spain,Female,45,1,126495.57,2,1,1,164741.5,0\r\n1986,15627876,Pavlova,719,Spain,Female,47,9,116393.59,1,1,0,63051.32,1\r\n1987,15772601,Lu,845,Germany,Female,41,2,81733.74,2,0,0,199761.29,0\r\n1988,15758606,Yamamoto,738,France,Male,54,4,0,1,0,1,55725.04,1\r\n1989,15657107,Angelo,563,Spain,Female,46,8,106171.68,1,1,0,163145.5,1\r\n1990,15622454,Zaitsev,695,Spain,Male,28,0,96020.86,1,1,1,57992.49,0\r\n1991,15775803,Cawker,841,Spain,Male,41,1,0,2,0,1,193093.77,0\r\n1992,15570859,Froggatt,626,Germany,Male,36,2,181671.16,2,1,1,57531.14,0\r\n1993,15748381,Gorbunov,613,France,Female,29,6,185709.28,2,1,1,77242.19,0\r\n1994,15787189,Tai,824,Germany,Male,60,8,134250.17,3,0,0,153046.16,1\r\n1995,15666055,Rowe,705,France,Female,49,7,0,1,1,0,63405.2,1\r\n1996,15617648,Mikkelsen,584,France,Female,44,5,95671.75,2,1,1,106564.88,0\r\n1997,15755678,Kovalyov,534,France,Male,62,2,0,2,0,0,42763.12,1\r\n1998,15624781,Mbanefo,672,France,Female,34,1,142151.75,2,1,1,168753.34,0\r\n1999,15779497,Ts'ai,603,France,Male,43,5,127823.93,1,1,1,19483.35,0\r\n2000,15567399,Enderby,633,Germany,Male,43,3,144164.29,1,1,1,158646.46,0\r\n2001,15613656,Lombardi,842,France,Male,58,1,63492.94,1,1,1,83172.19,0\r\n2002,15734311,Hamilton,661,France,Female,27,3,0,2,1,1,76889.79,0\r\n2003,15657214,Hsia,601,France,Male,74,2,0,2,0,1,51554.58,0\r\n2004,15799350,Mao,632,France,Male,41,0,106134.46,1,0,1,105570.39,0\r\n2005,15729970,Ugochukwu,684,Germany,Male,29,8,127269.75,1,0,1,79495.01,0\r\n2006,15725835,West,785,Germany,Female,32,3,124493.03,2,0,1,52583.79,1\r\n2007,15745543,Hughes,687,France,Male,39,7,0,2,1,0,26848.25,0\r\n2008,15727384,Chukwuemeka,705,Germany,Female,43,10,146547.78,1,0,1,10072.55,1\r\n2009,15666916,Lira,639,France,Male,43,6,99610.92,2,1,0,187296.78,0\r\n2010,15732917,Li,729,Germany,Male,46,5,117837.43,1,1,0,104016.61,1\r\n2011,15612050,Castiglione,556,Spain,Female,48,8,168522.37,1,1,1,151310.16,0\r\n2012,15726267,Paterson,570,France,Male,32,9,117337.54,2,0,1,62810.91,0\r\n2013,15780124,Blair,841,France,Male,74,9,108131.53,1,0,1,60830.38,0\r\n2014,15742238,Dellucci,705,Germany,Male,35,4,136496.12,2,1,0,116672.02,0\r\n2015,15679024,Udinesi,553,France,Male,32,3,116324.53,1,1,0,77304.49,0\r\n2016,15715297,Yuan,779,Germany,Female,40,2,75470.23,1,1,1,52894.01,0\r\n2017,15633612,Yuryeva,696,France,Male,28,4,172646.82,1,1,1,116471.43,0\r\n2018,15602929,Wilson,728,Spain,Female,37,4,0,1,0,0,4539.38,0\r\n2019,15696703,Dean,691,Germany,Male,27,3,160358.68,2,1,0,142367.72,0\r\n2020,15756668,Ross,706,France,Male,30,3,98415.37,1,1,1,110520.48,0\r\n2021,15565779,Kent,627,Germany,Female,30,6,57809.32,1,1,0,188258.49,0\r\n2022,15795519,Vasiliev,716,Germany,Female,18,3,128743.8,1,0,0,197322.13,0\r\n2023,15761477,Golibe,501,Germany,Male,24,4,130806.42,2,1,0,80241.14,0\r\n2024,15731890,Chukwukere,601,France,Male,41,1,123971.16,1,0,1,172814.99,0\r\n2025,15633043,Fedorova,545,Spain,Female,39,6,0,1,0,0,38410.74,1\r\n2026,15752953,Chien,634,France,Male,45,9,0,2,0,0,17622.82,0\r\n2027,15603088,Rossi,451,Spain,Female,23,9,0,2,0,1,48021.71,0\r\n2028,15606613,Samson,655,France,Female,59,7,0,1,1,0,88958.49,1\r\n2029,15635939,Fenton,458,France,Female,39,9,0,2,1,0,116343.09,0\r\n2030,15666043,Mackey,520,France,Male,33,4,156297.58,2,1,1,166102.61,0\r\n2031,15746190,Payton,624,Spain,Female,28,2,0,2,0,1,104353.26,0\r\n2032,15591357,Cowger,765,France,Male,51,3,123372.3,1,1,1,115429.32,0\r\n2033,15658716,Banks,667,Germany,Female,37,5,92171.35,3,1,0,178106.34,1\r\n2034,15679909,Pugliesi,665,Spain,Male,41,8,0,2,1,0,132152.32,0\r\n2035,15634262,Fantin,709,Germany,Male,34,4,148375.19,2,1,1,21521.38,0\r\n2036,15799825,Bentley,583,France,Female,44,8,0,2,1,1,27431.62,0\r\n2037,15756875,Freeman,782,Spain,Male,34,6,147422.44,1,0,1,42143.61,0\r\n2038,15678146,Wong,668,Spain,Female,24,7,173962.32,1,0,0,106457.11,1\r\n2039,15710743,Onwuamaeze,621,France,Male,47,0,0,1,1,1,133831.37,1\r\n2040,15595831,Shen,579,Germany,Female,64,6,145215.43,1,1,1,164083.72,0\r\n2041,15626684,Huang,547,France,Female,38,5,167539.97,1,0,1,159207.34,0\r\n2042,15709846,Yeh,840,France,Female,39,1,94968.97,1,1,0,84487.62,0\r\n2043,15635459,Shih,667,Germany,Female,27,3,106116.5,2,1,0,3674.71,0\r\n2044,15642544,Henderson,723,France,Male,34,5,0,2,0,1,12092.03,0\r\n2045,15566494,Fang,487,France,Male,45,2,0,2,1,0,77475.73,0\r\n2046,15655238,Dellucci,668,France,Female,31,9,0,2,0,0,41291.73,0\r\n2047,15733429,Chou,520,Germany,Male,34,8,120018.86,2,1,1,343.38,0\r\n2048,15814536,Conti,549,France,Male,37,2,112541.54,2,0,0,47432.43,0\r\n2049,15771702,Roberts,567,France,Female,35,5,166118.45,2,1,0,127827.18,0\r\n2050,15723008,Lo Duca,720,France,Female,45,1,102882.4,2,1,1,35633.15,1\r\n2051,15797160,Glover,492,France,Female,49,8,0,1,1,1,182865.09,1\r\n2052,15792222,Johnstone,712,France,Female,37,1,106881.5,2,0,0,169386.81,0\r\n2053,15644765,Ashton,689,Germany,Male,26,4,120727.97,1,0,1,149073.88,0\r\n2054,15610686,Melton,850,France,Male,63,8,169832.57,1,0,0,184107.26,1\r\n2055,15730868,Marshall,747,France,Male,41,5,0,2,1,1,22750.17,0\r\n2056,15705991,Kenenna,469,Germany,Male,38,9,113599.42,1,0,0,11950.29,0\r\n2057,15577078,Zakharov,539,Spain,Male,38,6,0,1,1,1,152880.07,1\r\n2058,15679550,Chukwualuka,743,France,Male,32,9,0,2,1,0,175252.78,0\r\n2059,15787655,Chu,707,France,Male,47,3,0,2,1,0,174303.29,0\r\n2060,15668081,Capon,581,Spain,Female,50,4,0,2,1,1,80701.72,0\r\n2061,15747980,Cattaneo,737,Spain,Male,38,6,146282.79,2,1,0,198516.2,0\r\n2062,15710295,Patrick,445,Germany,Female,38,6,119413.62,2,1,0,175756.36,0\r\n2063,15724443,Taylor,703,Germany,Female,29,3,122084.63,1,0,1,82824.08,0\r\n2064,15571305,Stephenson,588,Germany,Female,35,1,103060.63,1,1,0,179866.01,1\r\n2065,15569503,Yeh,765,France,Male,44,6,0,2,1,1,159899.97,0\r\n2066,15581840,DeRose,626,France,Male,33,8,0,2,1,0,138504.28,0\r\n2067,15772262,Vavilov,545,Germany,Male,37,9,110483.86,1,1,1,127394.67,0\r\n2068,15767794,Browne,744,France,Male,31,9,120718.28,1,1,1,58961.49,0\r\n2069,15629338,Collingridge de Tourcey,658,Spain,Female,31,2,36566.96,1,1,0,103644.98,1\r\n2070,15790379,Rowe,629,Germany,Male,28,8,108601,1,1,1,119647.7,0\r\n2071,15750684,Jibunoh,719,France,Female,42,4,0,1,1,0,28465.86,1\r\n2072,15697214,Korovin,686,Spain,Female,36,5,0,2,1,1,152979.14,0\r\n2073,15711015,Hammonds,743,France,Male,36,4,0,2,1,1,190911.02,0\r\n2074,15573309,Ward,626,Spain,Female,48,2,0,2,1,1,95794.98,0\r\n2075,15805303,Olisanugo,661,Germany,Male,44,1,141136.62,1,1,0,189742.78,1\r\n2076,15741385,Gallop,710,Germany,Male,45,9,108231.37,1,1,1,188574.08,0\r\n2077,15780254,Gartrell,654,France,Male,40,6,0,1,0,0,183872.88,1\r\n2078,15744843,K'ung,569,Spain,Female,34,6,144855.34,1,0,0,196555.32,0\r\n2079,15815626,Oluchi,640,France,Male,63,2,68432.45,2,1,1,112503.24,1\r\n2080,15784736,Jamieson,562,France,Male,45,6,136855.24,1,1,0,46864,0\r\n2081,15813412,Barlow,721,France,Female,55,3,44020.89,1,1,0,65864.4,1\r\n2082,15809143,White,456,Germany,Male,32,9,133060.63,1,1,1,125167.92,0\r\n2083,15617617,Stewart,811,Spain,Male,39,7,0,2,1,1,177519.39,0\r\n2084,15779738,Buccho,534,France,Male,24,1,0,1,1,1,169653.32,0\r\n2085,15668669,Benson,423,France,Female,36,5,97665.61,1,1,0,118372.55,1\r\n2086,15687477,Thompson,594,Germany,Male,28,5,185013.02,1,1,0,16481.12,0\r\n2087,15578908,Todd,725,Spain,Female,32,0,0,2,1,1,138525.19,0\r\n2088,15687658,Burgin,716,France,Female,52,7,65971.61,2,1,0,14608,1\r\n2089,15615020,Nnaife,595,Germany,Female,41,9,150463.11,2,0,1,81548.38,0\r\n2090,15608886,Okwudiliolisa,679,France,Female,33,1,0,2,0,0,69608.48,0\r\n2091,15602551,Johnson,667,Spain,Male,39,9,0,2,1,0,68873.8,0\r\n2092,15672945,Parkes,661,France,Female,37,5,136425.18,1,1,0,81102.81,0\r\n2093,15757408,Lo,655,Spain,Male,38,3,250898.09,3,0,1,81054,1\r\n2094,15806132,Martin,555,France,Male,55,4,146798.81,1,1,1,74149.77,0\r\n2095,15813022,Kapustina,531,Spain,Male,70,1,0,2,0,0,99503.19,0\r\n2096,15673578,Page,611,Germany,Female,40,7,128486.91,2,1,0,10109.47,0\r\n2097,15757916,Amaechi,600,France,Female,38,9,0,2,1,1,58855.85,0\r\n2098,15689168,Munro,531,Spain,Male,37,1,143407.29,2,0,1,84402.46,0\r\n2099,15769216,Panicucci,601,France,Female,43,2,0,1,1,0,49713.87,1\r\n2100,15593295,Greathouse,548,France,Male,57,6,76165.65,1,1,1,133537.53,0\r\n2101,15804814,Ts'ui,759,France,Male,40,4,0,2,1,0,124615.59,0\r\n2102,15778934,Napolitani,678,Spain,Female,49,8,0,2,0,1,98090.69,0\r\n2103,15802351,Beers,755,Germany,Female,33,6,90560.3,2,1,1,42607.69,0\r\n2104,15630241,Tretyakova,594,France,Male,61,3,62391.22,1,1,1,192434.11,0\r\n2105,15719561,Lin,768,France,Male,42,5,0,3,0,0,60686.4,0\r\n2106,15615096,Costa,492,France,Female,31,7,0,2,1,1,49463.44,0\r\n2107,15659931,Ibezimako,637,Germany,Female,55,1,123378.2,1,1,0,81431.99,1\r\n2108,15714586,Marcelo,646,Spain,Female,42,3,99836.47,1,0,1,22909.56,0\r\n2109,15634949,Hay,593,Germany,Male,74,5,161434.36,2,1,1,65532.17,0\r\n2110,15589224,Moore,596,Spain,Male,41,5,0,2,0,1,141053.85,0\r\n2111,15795990,Lumholtz,722,Germany,Female,48,10,138311.76,1,1,1,3472.63,1\r\n2112,15603216,Simpson,642,France,Male,25,7,0,2,1,0,102083.78,0\r\n2113,15631201,Hill,472,Spain,Female,28,4,0,2,1,0,1801.77,0\r\n2114,15686255,Mouzon,738,Germany,Male,35,6,101744.84,1,0,0,85185.44,0\r\n2115,15746594,Wu,732,Spain,Male,33,8,0,1,1,0,119882.7,0\r\n2116,15718893,Pirozzi,404,Germany,Female,54,4,125456.07,1,1,0,83715.66,1\r\n2117,15671609,Ibeabuchi,701,France,Male,44,7,0,2,1,0,23425.78,0\r\n2118,15652540,Garnsey,683,France,Male,31,2,0,2,0,1,77326.78,0\r\n2119,15774857,Synnot,460,France,Female,27,7,0,2,1,0,156150.08,1\r\n2120,15791836,Wildman,690,France,Male,29,5,0,2,1,0,108577.97,0\r\n2121,15651554,Anenechukwu,618,Germany,Female,54,4,118449.21,1,1,1,133573.29,1\r\n2122,15583576,Tai,671,France,Male,30,2,0,1,0,1,102057.86,0\r\n2123,15732740,Plant,765,Spain,Female,32,9,178095.55,1,0,0,47247.56,0\r\n2124,15723320,Azubuike,651,Germany,Female,25,2,109175.14,2,1,0,114566.47,0\r\n2125,15603851,Galkin,704,France,Male,32,7,127785.17,4,0,0,184464.7,1\r\n2126,15777923,Johnston,544,France,Female,45,6,0,2,0,1,151401.33,0\r\n2127,15735719,Babbage,790,France,Female,40,9,0,2,1,1,70607.1,0\r\n2128,15703482,Walker,710,Germany,Male,34,9,134260.36,2,1,0,147074.67,0\r\n2129,15605835,Rice,743,France,Male,37,8,69143.91,2,0,1,105780.18,0\r\n2130,15664881,Norton,702,France,Male,34,4,100054.77,1,1,0,109496.45,0\r\n2131,15757568,Bogolyubov,704,France,Female,45,6,0,1,1,1,137739.45,0\r\n2132,15792660,Gibbons,614,France,Male,38,2,116248.88,1,1,0,105140.92,0\r\n2133,15599722,Chia,609,Spain,Female,43,6,86053.52,2,1,1,113276.46,1\r\n2134,15726354,Smith,688,France,Female,32,6,123157.95,1,1,0,172531.23,0\r\n2135,15610355,Hunter,713,France,Male,44,1,63438.91,1,1,0,64375.4,0\r\n2136,15704284,Ekechukwu,736,Germany,Male,57,9,95295.39,1,1,0,28434.44,1\r\n2137,15621893,Bellucci,727,France,Male,18,4,133550.67,1,1,1,46941.41,0\r\n2138,15588219,Ford,850,France,Female,38,1,106871.81,2,1,0,29333.01,0\r\n2139,15688619,Scott,718,Spain,Male,45,3,105266.32,2,1,1,193724.51,0\r\n2140,15765518,Gregson,643,France,Female,51,2,105229.53,1,1,0,34967.75,1\r\n2141,15616931,Moore,653,France,Male,41,8,102768.42,1,1,0,55663.85,0\r\n2142,15758372,Wallace,674,France,Male,18,7,0,2,1,1,55753.12,1\r\n2143,15782591,Cook,690,France,Male,35,6,112689.95,1,1,0,176962.31,0\r\n2144,15612109,Speth,819,France,Male,38,9,122334.26,2,1,1,181507.44,0\r\n2145,15613712,Boag,634,Spain,Male,34,1,0,2,1,0,61995.57,0\r\n2146,15639322,Grave,633,Spain,Male,33,4,137847.41,2,1,0,98349.13,0\r\n2147,15594349,Streeten,850,France,Male,49,5,122486.47,1,0,1,59748.19,0\r\n2148,15574167,Fox,665,France,Male,33,2,101286.11,1,1,1,159840.51,0\r\n2149,15811842,Artemyeva,630,Spain,Male,26,7,0,2,1,1,6656.64,0\r\n2150,15648794,Giordano,836,Spain,Male,57,4,101247.06,1,1,0,37141.62,1\r\n2151,15771211,Perkins,668,France,Male,38,10,86977.96,1,0,1,37094.75,0\r\n2152,15588614,Walton,753,France,Male,57,7,0,1,1,0,159475.08,1\r\n2153,15630698,Hay,745,France,Female,55,9,110123.59,1,0,1,51548.14,1\r\n2154,15694200,Gardner,693,France,Male,36,8,178111.82,1,0,0,58719.63,1\r\n2155,15721426,Milne,606,Germany,Male,65,10,126306.64,3,0,0,7861.68,1\r\n2156,15725997,She,660,France,Female,35,6,100768.77,1,1,0,19199.61,0\r\n2157,15762138,Hu,608,France,Male,42,5,0,2,1,0,178504.29,0\r\n2158,15750649,Uwakwe,744,France,Female,44,3,0,2,1,1,189016.14,0\r\n2159,15685706,Bird,731,France,Female,40,7,118991.79,1,1,1,156048.64,0\r\n2160,15641835,Anderson,683,France,Male,72,3,140997.26,1,0,1,52876.41,0\r\n2161,15586821,Bellew,727,France,Male,28,5,0,2,0,1,19653.08,0\r\n2162,15569678,Cocci,561,Germany,Male,32,6,166824.59,1,1,0,139451.98,0\r\n2163,15793842,Krichauff,700,France,Female,34,2,76322.69,1,1,0,128136.29,0\r\n2164,15667554,Cameron,605,France,Male,35,6,0,2,1,1,45206.57,0\r\n2165,15794479,Becker,767,Spain,Male,77,8,149083.7,1,1,1,190146.83,0\r\n2166,15585041,Ainsworth,511,France,Male,33,7,0,2,0,1,158313.87,0\r\n2167,15780650,Biryukov,667,France,Male,40,9,0,1,1,1,96670.2,0\r\n2168,15780846,Redding,787,France,Male,33,1,126588.81,2,0,1,62163.53,0\r\n2169,15805260,Wood,705,Germany,Female,56,2,143249.67,1,1,0,88428.41,1\r\n2170,15621629,Scott,773,Germany,Male,43,8,81844.91,2,1,1,35908.46,0\r\n2171,15662151,Gould,554,France,Male,40,4,0,1,0,1,168780.04,0\r\n2172,15747174,Hao,526,Germany,Male,58,9,190298.89,2,1,1,191263.76,0\r\n2173,15651585,Power,661,Germany,Male,35,2,117212.18,1,1,1,83052.03,0\r\n2174,15649738,White,698,France,Female,46,0,0,2,1,1,125962.02,0\r\n2175,15633108,Thorpe,646,France,Male,26,4,139848.17,1,1,0,164696.27,0\r\n2176,15769254,Tuan,757,Germany,Female,34,9,101861.36,2,0,0,187011.96,0\r\n2177,15704746,Inman,699,Spain,Male,35,2,167455.66,2,1,1,55324.49,0\r\n2178,15637644,Hanson,667,France,Female,24,4,0,2,0,1,34335.55,0\r\n2179,15609562,MacDonald,774,Spain,Female,43,1,116360.07,1,1,0,17004.14,0\r\n2180,15787459,Parkes,745,Spain,Male,40,3,88466.82,1,0,0,116331.42,0\r\n2181,15762902,Stanley,649,France,Female,42,7,0,2,0,1,22974.01,0\r\n2182,15738605,Fischer,634,Germany,Female,46,5,123642.36,1,1,1,49725.16,1\r\n2183,15724889,Chinweuba,665,Spain,Male,38,9,0,1,0,1,87412.74,0\r\n2184,15730735,Henning,713,France,Male,38,9,72286.84,2,1,1,26136.89,0\r\n2185,15689147,Ogochukwu,652,France,Female,40,1,0,2,1,0,126554.96,0\r\n2186,15730397,Narelle,739,Spain,Male,40,1,109681.61,1,1,1,193321.3,0\r\n2187,15762169,Bergman,556,Germany,Male,37,9,145018.64,2,1,0,90928.02,1\r\n2188,15589320,Sagese,699,Spain,Male,34,8,0,1,1,1,76510.46,0\r\n2189,15799211,Anenechi,708,Spain,Female,32,8,187487.63,1,1,1,120115.5,0\r\n2190,15798310,Palerma,480,France,Male,35,2,165692.91,1,1,1,197984.58,0\r\n2191,15609998,Okwudilichukwu,700,Germany,Female,59,5,137648.41,1,1,0,142977.05,1\r\n2192,15583548,Harrison,525,Spain,Female,47,6,118560,1,1,0,82522.61,1\r\n2193,15761763,Jamieson,845,France,Male,33,8,164385.53,1,1,0,150664.97,0\r\n2194,15764409,Goodman,613,France,Male,37,9,108286.5,1,1,1,114153.44,0\r\n2195,15710161,Ko,850,France,Female,34,2,0,2,1,1,171706.66,0\r\n2196,15735246,Norman,798,Spain,Female,58,9,0,2,0,0,119071.56,1\r\n2197,15791700,Ugochukwutubelum,773,Germany,Male,47,2,118079.47,4,1,1,143007.49,1\r\n2198,15670753,Uvarova,614,Spain,Male,35,2,127283.78,1,1,1,31302.35,0\r\n2199,15573876,Chia,473,Spain,Male,48,8,0,2,1,0,71139.8,0\r\n2200,15770174,Piazza,762,France,Male,29,6,141389.06,1,1,0,54122.89,0\r\n2201,15641114,Power,701,France,Male,37,8,130091.5,1,1,1,120031.29,0\r\n2202,15682435,P'eng,600,France,Male,35,4,143744.77,2,1,0,104076.51,0\r\n2203,15751788,Johnson,850,Spain,Male,28,9,97408.03,1,1,1,175853.64,0\r\n2204,15672598,Walker,613,Spain,Male,30,9,111927.45,1,1,1,175795.87,0\r\n2205,15762803,Innes,509,France,Male,31,3,0,2,1,0,15360.91,0\r\n2206,15812982,Francis,509,Spain,Male,38,2,0,1,0,0,168460.12,0\r\n2207,15597901,Chidozie,609,France,Male,34,1,0,1,1,1,181177.9,0\r\n2208,15731507,Mackenzie,456,France,Female,33,1,188285.68,1,0,0,58363.94,0\r\n2209,15809826,Craigie,728,France,Female,46,2,109705.52,1,1,0,20276.87,1\r\n2210,15764237,Manfrin,663,Spain,Male,33,9,0,2,0,0,91514.62,0\r\n2211,15769917,Onyekachi,673,Germany,Female,34,1,127122.79,3,0,1,76703.1,0\r\n2212,15641850,Pethard,717,France,Male,40,0,98241.04,1,1,0,110887.14,0\r\n2213,15770974,Nwabugwu,741,Germany,Female,37,8,170840.08,2,0,0,109843.16,0\r\n2214,15803749,DeRose,498,Germany,Female,41,4,87541.06,2,1,1,12577.21,1\r\n2215,15684999,Ch'eng,850,France,Female,26,4,62610.96,2,0,1,179365.1,0\r\n2216,15770225,Padovesi,493,France,Male,36,9,0,2,1,1,65816.53,0\r\n2217,15627484,Obielumani,686,France,Female,47,5,113328.93,1,1,0,124170.9,0\r\n2218,15610337,Stephens,666,Spain,Male,35,2,104832.49,1,1,0,175015.12,0\r\n2219,15752488,Emery,733,Spain,Female,31,9,102289.85,1,1,1,115441.66,0\r\n2220,15610056,Dufresne,631,Germany,Female,34,6,125227.82,2,0,1,128247.03,0\r\n2221,15806049,Lee,714,Germany,Female,49,5,140510.89,1,1,0,141914.94,0\r\n2222,15736069,Hsing,767,Germany,Female,35,6,132253.22,1,1,0,115566.57,1\r\n2223,15763662,Longo,711,Germany,Male,43,2,39043.29,2,1,1,175423.69,0\r\n2224,15615575,Vial,722,France,Male,34,8,0,2,1,1,133447.49,0\r\n2225,15691723,Chukwudi,631,Spain,Male,55,9,99685.06,1,1,0,114474.98,0\r\n2226,15774098,Grant,701,Germany,Male,38,3,125385.49,2,0,1,52044.66,0\r\n2227,15750808,Ma,790,Spain,Male,46,2,131365.37,2,1,1,180290.68,0\r\n2228,15744368,Sun,633,Spain,Male,58,6,98308.51,1,1,1,132034.13,0\r\n2229,15610594,Moss,644,France,Female,37,8,0,2,1,0,20968.88,0\r\n2230,15756125,Booth,757,Spain,Male,44,5,140856.16,2,1,0,158735.1,0\r\n2231,15623277,Ross,696,France,Female,30,8,0,2,1,1,196134.44,0\r\n2232,15795954,Ndukaku,746,France,Male,35,2,172274.01,1,1,0,22374.97,0\r\n2233,15671969,Pruneda,649,Spain,Male,36,8,0,2,1,0,161668.15,0\r\n2234,15791268,Neumann,565,Spain,Male,38,0,122447.76,1,0,0,67339.34,0\r\n2235,15713655,Calabrese,720,France,Female,38,10,0,2,1,1,56229.72,1\r\n2236,15633930,Yobachukwu,648,Spain,Female,56,6,157559.59,2,1,0,140991.23,1\r\n2237,15712849,Tung,632,Germany,Male,41,3,126550.7,1,0,0,177644.52,1\r\n2238,15639077,Marchesi,622,France,Female,30,2,158584.82,3,1,0,142342.55,1\r\n2239,15808784,Hess,835,France,Male,28,2,163569.61,2,1,1,154559.28,0\r\n2240,15648577,Pickering,493,France,Female,31,3,0,1,1,1,176570.28,1\r\n2241,15670345,Mazzi,785,Germany,Female,33,6,127211.45,1,0,0,191961.83,0\r\n2242,15633112,Madukaego,681,Germany,Male,42,3,118199.97,2,1,0,9452.88,1\r\n2243,15714397,Trentino,621,Germany,Female,30,2,101014.08,2,1,1,165257.31,0\r\n2244,15780038,Paterson,756,Spain,Male,38,6,119208.85,1,1,0,169763.89,1\r\n2245,15756305,Marchesi,515,France,Female,66,6,0,2,1,1,160663.11,0\r\n2246,15578799,Anayolisa,625,France,Female,58,10,53772.73,1,1,1,192072.1,1\r\n2247,15800326,Poole,717,Spain,Female,39,6,0,2,1,0,93275.61,0\r\n2248,15785485,Zhou,595,Germany,Female,41,2,138878.81,1,0,1,112269.67,0\r\n2249,15783958,Bates,539,Spain,Female,37,1,130922.81,2,0,0,2186.83,0\r\n2250,15727546,Olejuru,762,France,Male,35,9,0,2,1,1,43075.7,0\r\n2251,15739576,Bustard,706,Spain,Male,20,8,0,2,1,1,12368.11,0\r\n2252,15631333,Wade,677,Spain,Female,25,8,130866.19,1,1,0,42410.21,0\r\n2253,15604782,Tan,733,Germany,Female,33,7,187257.94,1,0,1,190430.81,0\r\n2254,15589643,Ngozichukwuka,684,Spain,Female,41,7,0,1,1,1,138394.37,0\r\n2255,15585533,Calabrese,679,France,Male,36,6,147733.64,1,0,1,172501.38,0\r\n2256,15681506,Lane,478,Spain,Male,43,1,0,2,1,1,197916.43,0\r\n2257,15630551,Forbes,696,France,Male,33,2,163139.27,1,1,1,7035.36,0\r\n2258,15698349,Davy,686,Spain,Female,35,4,0,2,1,1,159676.55,0\r\n2259,15776631,Ma,466,France,Female,36,5,119540.15,1,0,1,80603.99,0\r\n2260,15762216,Barrera,686,France,Female,41,4,129553.76,2,1,0,187599.8,0\r\n2261,15623927,Alexander,576,France,Male,55,9,0,2,1,1,94450.97,0\r\n2262,15681402,Ngozichukwuka,763,Germany,Female,61,1,66101.89,1,1,1,143981.27,0\r\n2263,15586264,Murray,572,France,Male,43,2,140431.98,1,1,0,26450.57,1\r\n2264,15594685,Hall,757,France,Female,49,2,0,2,0,0,164482.92,0\r\n2265,15812945,Padovesi,582,France,Female,29,0,0,1,1,1,84012.81,0\r\n2266,15734628,Lysaght,623,France,Female,35,5,0,2,1,0,101192.08,0\r\n2267,15629323,Kelechi,617,Germany,Female,37,4,116471.43,2,1,0,175324.74,1\r\n2268,15666823,Nebechi,425,France,Male,39,4,0,2,1,0,197226.32,0\r\n2269,15777553,Hanson,659,France,Female,56,9,123785.24,1,1,0,99504.03,1\r\n2270,15613097,Kao,605,France,Female,33,4,0,2,0,1,83700.66,0\r\n2271,15622217,Tu,538,France,Female,38,8,88758.95,2,0,0,28226.15,1\r\n2272,15703588,Palerma,665,Germany,Male,25,5,153611.83,2,1,0,35321.65,0\r\n2273,15570835,Fallaci,491,Germany,Female,57,4,112044.72,1,1,1,41229.73,1\r\n2274,15679299,Shen,726,Spain,Female,27,7,123826.07,1,0,1,78970.58,0\r\n2275,15808044,Ts'ui,580,France,Female,65,9,106804.26,3,1,0,107890.69,1\r\n2276,15579208,Chikezie,550,France,Female,48,6,0,2,1,1,191870.28,0\r\n2277,15684951,He,542,France,Female,59,2,68892.77,2,1,0,7905.06,1\r\n2278,15667620,Dreyer,732,France,Female,43,6,0,2,1,0,65731.53,0\r\n2279,15582960,Short,473,France,Female,33,5,125827.43,1,0,1,145698.73,0\r\n2280,15590730,Hunt,745,Spain,Male,34,9,0,2,1,0,50046.25,0\r\n2281,15763747,Ricci,732,France,Male,36,7,0,2,1,1,60830.24,0\r\n2282,15778320,Teng,848,Germany,Female,40,5,148495.64,1,0,0,158853.98,0\r\n2283,15642787,Ijendu,572,France,Male,37,1,133043.66,1,0,0,111243.09,0\r\n2284,15624633,Kibby,702,France,Male,45,9,74989.58,1,1,1,171014.69,0\r\n2285,15766765,Obiuto,664,Germany,Male,39,7,60263.23,1,1,0,170835.32,0\r\n2286,15783615,Ramos,630,Germany,Male,50,3,129370.91,4,1,1,47775.34,1\r\n2287,15640161,Calabrese,618,Germany,Male,44,5,157955.83,2,0,0,139297.71,0\r\n2288,15619889,Vasin,556,France,Male,26,4,0,1,1,0,195167.38,0\r\n2289,15579166,Munro,619,France,Female,30,7,70729.17,1,1,1,160948.87,0\r\n2290,15789097,Keeley,644,France,Male,48,8,0,2,0,1,44965.54,1\r\n2291,15674880,Archer,658,Spain,Male,50,2,0,2,1,0,52137.73,0\r\n2292,15778157,Murray,598,Spain,Male,27,8,90721.52,2,1,0,109296.18,0\r\n2293,15779064,Chidiegwu,677,France,Male,27,2,0,2,1,1,20092.89,0\r\n2294,15801265,Tang,689,Spain,Female,45,0,57784.22,1,1,0,197804,1\r\n2295,15589204,Farrar,591,France,Male,33,9,131765.72,1,1,0,118782.06,0\r\n2296,15664543,Shaw,699,France,Male,40,7,0,1,0,1,152876.13,1\r\n2297,15582714,Napolitani,749,Germany,Male,47,9,110022.74,1,0,1,135655.29,1\r\n2298,15797595,Greenhalgh,709,France,Female,40,9,131569.63,1,1,1,103970.58,0\r\n2299,15614034,Martin,607,Germany,Male,61,2,164523.5,2,1,1,35786.76,0\r\n2300,15763171,Hu,650,Germany,Female,25,2,114330.95,1,1,1,25325.07,0\r\n2301,15647266,Y?an,651,Spain,Female,45,10,135923.16,1,1,0,18732.84,0\r\n2302,15757577,Odili,676,France,Female,61,8,0,2,1,1,118522.73,0\r\n2303,15736656,H?,723,France,Female,49,4,0,2,0,1,89972.25,0\r\n2304,15635078,Chiemela,714,Spain,Male,45,0,124693.48,1,0,1,187194.15,0\r\n2305,15680141,Yuan,759,Spain,Female,35,7,147936.42,1,1,1,106785.7,0\r\n2306,15576945,Clements,582,France,Male,29,0,0,1,1,0,142516.35,0\r\n2307,15602034,Kolesnikov,697,France,Female,34,2,126558.92,1,1,0,73334.43,0\r\n2308,15732020,Rutherford,610,Germany,Male,57,6,106938.11,2,0,1,186612.47,0\r\n2309,15611029,Hsiung,488,Germany,Female,33,4,140002.35,1,1,0,123613.81,0\r\n2310,15621210,Angelo,599,Germany,Male,46,9,123444.72,1,1,1,31368.08,1\r\n2311,15569222,Mendes,781,France,Male,32,6,147107.91,1,1,1,40066.95,0\r\n2312,15664639,McGregor,645,France,Male,19,9,128514.84,1,0,0,175969.19,0\r\n2313,15724223,Bronner,545,France,Female,55,5,0,1,0,0,10034.77,1\r\n2314,15644621,Mironova,597,Germany,Female,40,9,106756.01,2,1,0,151167.94,0\r\n2315,15756056,Ku,561,Spain,Female,28,3,0,2,1,0,191387.76,0\r\n2316,15700353,Evans,662,France,Female,37,6,0,2,1,0,51229.17,0\r\n2317,15624388,Henderson,649,Germany,Female,50,5,155393.32,1,1,1,87351.42,1\r\n2318,15627212,Smith,630,France,Female,36,2,110414.48,1,1,1,48984.95,0\r\n2319,15648005,Russell,672,Spain,Male,33,2,0,2,1,1,182738,0\r\n2320,15681446,Sun,636,Germany,Female,37,9,157098.52,1,1,1,153535.27,0\r\n2321,15775888,McDonald,593,Germany,Female,38,5,85626.6,1,1,1,125079.65,0\r\n2322,15749019,Wong,545,Germany,Male,45,6,93796.42,2,1,1,162321.26,0\r\n2323,15709928,Niu,567,Spain,Female,41,1,0,2,1,0,3414.72,0\r\n2324,15784676,Fanucci,583,France,Male,51,6,125268.03,2,1,0,165082.25,0\r\n2325,15748116,Zetticci,681,France,Female,29,2,148143.84,1,1,1,52021.39,0\r\n2326,15612193,Hsia,762,Spain,Male,29,10,115545.33,2,1,0,148256.43,0\r\n2327,15762984,McIntosh,648,Spain,Male,35,7,0,2,0,0,122899.01,0\r\n2328,15613713,Kozlova,644,France,Male,30,5,44928.88,1,1,1,10771.46,0\r\n2329,15664204,Meany,706,Spain,Male,29,2,0,2,1,1,18255.51,0\r\n2330,15639415,Thompson,850,France,Male,35,3,162442.35,1,1,0,183566.78,0\r\n2331,15806332,Le Gallienne,484,Spain,Female,39,5,0,2,1,1,175224.12,0\r\n2332,15614929,Cheng,508,Germany,Male,28,0,96213.82,2,1,0,147913.56,0\r\n2333,15695492,P'eng,439,France,Female,29,6,156569.43,1,1,0,180598.66,0\r\n2334,15635972,Lloyd,484,Spain,Male,36,8,0,2,1,0,186136.48,0\r\n2335,15616380,Wheeler,803,Spain,Female,37,1,0,2,0,0,7455.2,0\r\n2336,15581440,Christie,724,Germany,Female,48,6,110463.25,2,1,1,80552.11,1\r\n2337,15654390,He,640,France,Male,33,7,154575.76,1,1,0,25722.28,1\r\n2338,15660688,King,701,Spain,Female,35,9,0,2,0,0,170996.86,0\r\n2339,15806307,Favors,537,France,Male,37,3,0,2,1,1,20603.32,0\r\n2340,15647975,Vida,651,Germany,Male,26,5,147037.32,1,0,0,141763.26,0\r\n2341,15595728,Thomas,523,Germany,Male,41,0,119276.31,1,0,0,122284.38,1\r\n2342,15735388,Wayn,717,France,Female,25,7,108664.85,2,1,0,190011.85,0\r\n2343,15788535,Tan,593,Spain,Male,44,5,0,1,1,0,128046.98,0\r\n2344,15765902,Gibson,706,Germany,Male,38,5,163034.82,2,1,1,135662.17,0\r\n2345,15642345,Y?,714,Germany,Female,49,4,93059.34,1,1,0,7571.51,1\r\n2346,15641250,Calabresi,794,Spain,Male,38,9,179581.31,1,1,0,23596.24,0\r\n2347,15706163,Enyinnaya,518,Germany,Male,46,4,113625.93,1,0,0,92727.42,1\r\n2348,15746708,Ritchie,589,Germany,Male,55,7,119961.48,1,1,0,65156.83,1\r\n2349,15775203,Chia,824,France,Male,45,3,129209.48,1,0,0,60151.77,0\r\n2350,15787907,Wang,719,Germany,Female,42,5,137227.04,3,1,0,149097.38,1\r\n2351,15646764,Lorenzo,617,Germany,Female,58,3,119024.75,2,1,0,35199.24,1\r\n2352,15678284,Pai,651,France,Male,35,7,74623.5,3,1,0,129451.29,1\r\n2353,15726791,Nuttall,637,Spain,Female,45,2,157929.45,1,1,1,145134.49,1\r\n2354,15813144,Osborne,554,France,Female,26,7,92606.86,2,1,0,192709.69,0\r\n2355,15669342,Ferri,731,Germany,Male,35,2,127862.93,2,1,0,139083.7,0\r\n2356,15710366,Hamilton,569,Spain,Female,42,1,0,1,1,1,83629.6,1\r\n2357,15614934,McEwan,625,Germany,Female,37,4,142711.81,1,1,0,35625.41,0\r\n2358,15588701,Lai,592,France,Female,38,4,0,2,1,0,35338.96,0\r\n2359,15665438,Hs?,669,France,Male,43,1,163159.85,1,0,1,15602.8,0\r\n2360,15644896,Thompson,663,Germany,Male,32,3,108586.86,1,1,1,182355.21,0\r\n2361,15670205,Boyd,518,Germany,Female,41,5,110624.99,1,1,0,89327.67,0\r\n2362,15635776,Trevisani,686,Germany,Female,43,5,154846.24,2,1,1,151903.6,0\r\n2363,15791053,Lucciano,709,Germany,Male,45,4,122917.71,1,1,1,11.58,1\r\n2364,15644005,Holman,571,France,Female,33,9,0,2,0,1,77519.62,0\r\n2365,15796343,Bazhenov,707,France,Female,31,2,82787.93,2,0,0,91423.69,0\r\n2366,15751057,Douglas,701,Germany,Male,32,5,102500.34,1,0,0,106287.77,0\r\n2367,15623430,Hill,672,France,Male,34,9,0,2,1,0,161800.77,0\r\n2368,15682600,Lo,620,Germany,Male,39,9,159492.79,1,1,0,80582.34,1\r\n2369,15769312,Forbes,557,Spain,Male,48,10,0,2,1,1,185094.48,0\r\n2370,15708212,Lin,648,Spain,Female,54,7,118241.02,1,1,0,172586.89,1\r\n2371,15650258,Sinclair,479,France,Female,35,2,113090.4,1,1,0,195649.79,0\r\n2372,15604345,Kemp,730,France,Female,22,9,65763.57,1,1,1,145792.01,0\r\n2373,15578297,Ebelegbulam,737,Germany,Female,43,1,125537.38,1,1,0,138510.01,1\r\n2374,15671789,Woods,616,France,Male,31,3,94263.91,2,1,0,168895.06,0\r\n2375,15726186,Genovese,639,Spain,Male,29,4,133434.57,2,1,0,97983.44,0\r\n2376,15764618,Tseng,815,Spain,Female,39,6,0,1,1,1,85167.88,0\r\n2377,15730738,Chiang,786,Spain,Male,31,9,0,2,1,1,18210.36,0\r\n2378,15637650,Williams,549,France,Male,50,9,94748.76,2,0,1,13608.18,0\r\n2379,15606267,Wilson,622,France,Female,38,4,98640.74,1,1,1,110457.99,0\r\n2380,15625904,Wang,624,France,Male,26,9,74681.9,2,0,0,31231.35,0\r\n2381,15654463,Moore,841,France,Male,34,4,0,2,1,0,141582.66,0\r\n2382,15774151,Iadanza,614,Spain,Female,41,7,179915.85,1,0,0,14666.35,1\r\n2383,15693259,Wallace,676,France,Male,30,1,128207.23,1,1,1,55400.17,0\r\n2384,15642468,Clark,697,France,Male,42,9,132739.26,2,0,0,174667.65,0\r\n2385,15758531,Y?,732,France,Female,40,10,0,2,1,0,154189.08,0\r\n2386,15728352,Yermakov,623,France,Male,27,4,120509.81,1,0,0,142170.44,0\r\n2387,15637240,Wei,541,France,Male,46,4,124547.13,2,1,0,94499.06,0\r\n2388,15595588,Chukwunonso,773,Spain,Female,39,4,0,2,0,1,182081.45,0\r\n2389,15778395,McIntyre,762,Germany,Male,34,4,88815.56,2,1,0,68562.26,1\r\n2390,15711825,Ts'ai,655,Spain,Female,35,1,82231.51,2,1,0,88798.02,0\r\n2391,15599251,Chung,602,Germany,Male,32,7,184715.86,2,1,0,113781.99,0\r\n2392,15570004,Tsou,850,France,Male,31,3,0,2,1,0,121866.87,0\r\n2393,15656912,Aitken,649,Spain,Male,51,4,0,1,1,1,150390.57,0\r\n2394,15657342,Dawson,850,Germany,Male,28,4,147972.19,1,1,0,60708.72,1\r\n2395,15716284,Ward,543,France,Male,43,9,0,2,1,1,78858.07,0\r\n2396,15672374,Pai,672,France,Male,52,8,170008.84,1,0,0,56407.42,1\r\n2397,15732476,Ifeanyichukwu,600,France,Female,27,3,0,2,0,1,125698.97,0\r\n2398,15747724,Briggs,671,Spain,Female,34,10,0,1,1,0,23235.38,0\r\n2399,15633877,Morrison,706,Spain,Female,42,8,95386.82,1,1,1,75732.25,0\r\n2400,15672516,Wall,541,Germany,Male,51,7,90373.28,2,1,0,179861.79,0\r\n2401,15607827,Nebechukwu,711,Germany,Male,34,4,133467.77,2,1,1,42976.64,0\r\n2402,15751336,Yao,630,Spain,Male,30,3,0,2,0,1,10486.69,0\r\n2403,15646539,Liao,531,France,Male,31,3,96288.26,1,1,0,56794.73,0\r\n2404,15756901,Ch'ang,641,France,Female,26,4,91547.84,2,0,1,28157.34,0\r\n2405,15809286,Burke,631,Germany,Male,37,8,138292.64,2,0,0,152422.91,1\r\n2406,15759021,Kay,685,France,Male,35,9,0,1,1,0,167033.83,0\r\n2407,15725039,McIntyre,702,Spain,Male,32,8,71667.74,1,1,1,126082.18,0\r\n2408,15579130,Chidiegwu,708,Germany,Female,43,0,118994.84,1,1,0,181499.77,1\r\n2409,15754112,Musgrove,653,Spain,Male,55,7,0,2,1,1,41967.03,0\r\n2410,15735522,Boulger,654,Germany,Male,37,2,145610.07,2,0,0,186300.59,0\r\n2411,15613326,Gow,596,France,Female,33,1,138162.81,1,1,0,85412.54,0\r\n2412,15739502,Amaechi,549,Germany,Female,31,9,135020.21,2,1,1,23343.18,0\r\n2413,15670914,Robe,754,France,Male,38,2,0,2,1,0,180698.32,0\r\n2414,15604073,Bibi,815,Germany,Female,25,8,135161.67,1,1,1,136071.05,0\r\n2415,15806027,Niu,556,France,Female,52,9,0,1,1,0,175149.2,1\r\n2416,15574886,Palerma,706,France,Male,32,6,94486.47,1,1,1,146949.74,0\r\n2417,15707120,Cocci,850,France,Male,46,9,117640.39,1,1,0,88920.68,0\r\n2418,15800845,Artemieva,732,Spain,Female,33,8,111379.55,1,1,1,45098.62,0\r\n2419,15603914,Arcuri,614,France,Male,40,6,0,1,1,1,20339.79,1\r\n2420,15722765,Owen,580,Spain,Female,57,0,136820.99,1,0,1,108528.74,0\r\n2421,15783305,Franklin,593,France,Female,46,7,98752.51,1,1,0,145560.38,0\r\n2422,15574842,Lorenzo,653,Germany,Female,25,2,158266.42,3,1,1,199357.24,0\r\n2423,15607837,Muriel,746,France,Female,29,4,105599.67,1,1,1,43106.17,0\r\n2424,15714877,MacDevitt,662,France,Female,29,10,0,2,1,0,137508.31,0\r\n2425,15782941,Chijindum,573,France,Male,31,2,0,2,1,1,91957.39,0\r\n2426,15630167,Gibson,684,Spain,Female,39,4,139723.9,1,1,1,120612.11,0\r\n2427,15759038,Whitehead,793,France,Female,41,3,141806.46,1,1,0,102921.17,0\r\n2428,15661821,Johnstone,798,Germany,Female,49,5,132571.67,1,1,1,31686.33,1\r\n2429,15728006,Endrizzi,524,France,Male,40,2,180516.9,1,1,0,180002.42,0\r\n2430,15712176,Burke,816,France,Male,31,8,0,2,1,1,28407.4,0\r\n2431,15689351,Johnson,742,Germany,Female,41,4,92805.72,1,0,1,73743.95,1\r\n2432,15782247,Yeh,540,France,Male,22,4,0,3,1,1,186233.26,1\r\n2433,15769064,Marshall,537,Germany,Male,39,3,135309.36,1,1,0,31728.86,1\r\n2434,15718153,Kao,759,Spain,Female,74,6,128917.84,1,1,1,48244.64,0\r\n2435,15613189,Browne,774,France,Female,52,2,56580.93,1,1,0,113266.28,1\r\n2436,15661734,Taylor,608,Germany,Male,42,8,131390.75,2,1,0,71178.09,0\r\n2437,15592645,Gibbons,704,Spain,Male,37,4,0,2,0,0,25684.93,0\r\n2438,15768387,Nott,581,France,Male,41,8,0,2,0,0,29737.14,0\r\n2439,15792525,Lei,628,Germany,Female,61,1,97361.66,1,1,1,149922.38,1\r\n2440,15586976,Alexeeva,566,France,Female,42,6,0,1,1,0,180702.12,1\r\n2441,15790659,Sheets,701,Spain,Male,59,7,0,2,0,1,27597.59,0\r\n2442,15691446,Tokaryev,735,Spain,Male,29,10,0,2,1,1,95025.27,0\r\n2443,15772632,Ts'ui,680,France,Female,34,1,0,2,1,0,167035.07,0\r\n2444,15706587,Johnston,560,France,Male,57,0,0,2,0,1,116781.71,0\r\n2445,15572461,Kung,663,Germany,Female,29,4,102714.65,2,0,0,21170.81,0\r\n2446,15654409,Unwin,665,France,Female,34,5,67816.72,1,1,1,29641.58,0\r\n2447,15568025,Hsueh,758,France,Male,51,8,81710.46,1,1,1,116520.07,0\r\n2448,15715769,Hao,621,France,Male,26,2,75237.54,1,0,1,44220.4,0\r\n2449,15667458,L?,764,Germany,Male,28,10,124023.18,1,1,0,166188.28,0\r\n2450,15567980,Frater,537,Germany,Female,46,5,100727.5,1,0,1,140857.76,1\r\n2451,15679294,Brennan,589,France,Female,46,10,107238.85,2,1,0,37024.28,0\r\n2452,15606507,Pisani,555,France,Male,24,5,0,2,1,0,27513.47,0\r\n2453,15578825,Golubev,734,France,Female,29,0,139994.66,1,1,0,17744.72,0\r\n2454,15619935,Vanmeter,783,Spain,Female,59,9,126224.87,1,1,1,4423.63,0\r\n2455,15636089,Hs?,678,Germany,Female,51,1,145751.03,1,0,0,109718.44,1\r\n2456,15727490,Scott,661,France,Male,47,5,0,1,0,1,107243.31,1\r\n2457,15591766,Crawford,607,Spain,Female,25,4,121166.89,1,0,1,115288.24,0\r\n2458,15641629,P'eng,537,Spain,Female,38,1,0,2,0,1,41233.97,0\r\n2459,15813303,Rearick,513,Spain,Male,88,10,0,2,1,1,52952.24,0\r\n2460,15756920,Genovesi,576,France,Male,63,9,70655.48,1,0,0,78955.8,1\r\n2461,15726403,Glenny,660,Germany,Male,41,1,129901.21,1,1,0,26025.6,1\r\n2462,15592765,Marks,637,France,Male,40,8,125470.81,1,1,1,174536.17,0\r\n2463,15704442,Fleming,672,France,Female,53,9,169406.33,4,1,1,147311.47,1\r\n2464,15641136,Davison,629,France,Male,32,2,0,2,0,1,77965.44,0\r\n2465,15725818,Chibuzo,583,Germany,Male,40,4,107041.3,1,1,1,5635.63,0\r\n2466,15612071,Wilson,763,Spain,Female,32,10,95153.77,1,0,1,81310.1,0\r\n2467,15719809,Endrizzi,516,Germany,Male,32,3,145166.09,2,0,0,111421.45,0\r\n2468,15716518,Yuryeva,617,France,Female,27,4,0,2,0,0,190269.21,0\r\n2469,15742210,Ugochukwu,700,France,Male,38,9,65962.63,1,1,1,100950.48,0\r\n2470,15630617,Lo Duca,727,Germany,Male,36,6,140418.81,1,1,1,113033.73,1\r\n2471,15720838,Gallo,689,Spain,Female,31,3,139799.63,1,0,1,120663.57,0\r\n2472,15595537,Trout,626,Germany,Male,49,9,171787.84,2,1,0,187192.23,0\r\n2473,15623196,Morley,686,France,Male,38,6,149238.97,1,1,1,97825.23,0\r\n2474,15679249,Chou,351,Germany,Female,57,4,163146.46,1,1,0,169621.69,1\r\n2475,15693199,Shao,739,France,Female,37,8,0,2,1,0,191557.1,1\r\n2476,15661219,Trentino,627,France,Male,32,10,0,2,1,0,103287.62,0\r\n2477,15617136,Mazzanti,451,Germany,Female,38,9,61482.47,1,1,1,167538.66,0\r\n2478,15760294,Endrizzi,512,France,Female,41,8,145150.28,1,1,0,64869.32,1\r\n2479,15652808,Monaldo,774,France,Female,41,5,126670.37,1,1,0,102426.06,0\r\n2480,15657139,Otutodilinna,652,France,Female,40,8,84390.8,2,0,1,107876.2,0\r\n2481,15803790,Allen,638,Germany,Male,37,2,89728.86,2,1,1,37294.88,0\r\n2482,15764105,Milne,475,France,Female,57,1,0,2,1,0,89248.99,0\r\n2483,15672610,Somadina,567,Spain,Male,40,4,118628.8,1,0,0,91973.63,0\r\n2484,15766896,Chieloka,750,France,Male,37,3,0,2,1,0,16870.2,0\r\n2485,15587735,Chukwuebuka,850,France,Male,39,6,96863.13,1,1,1,121681.19,0\r\n2486,15659501,Chioke,753,France,Female,38,6,142263.45,1,0,1,33730.43,0\r\n2487,15745001,Kovalev,683,Spain,Female,36,7,0,2,1,0,104786.59,0\r\n2488,15651140,Doherty,710,France,Female,32,3,0,1,1,0,94790.34,0\r\n2489,15571148,Baranov,645,Spain,Female,21,1,0,2,0,0,28726.07,0\r\n2490,15776824,Rossi,714,France,Male,28,6,122724.37,1,1,1,67057.27,0\r\n2491,15633141,Robinson,696,Germany,Female,35,4,174902.26,1,1,0,69079.85,0\r\n2492,15764174,Bidencope,612,Spain,Female,26,4,0,2,1,1,179780.74,0\r\n2493,15778155,T'ien,520,Germany,Female,31,3,108914.17,1,1,1,183572.39,1\r\n2494,15715920,De Bernales,782,Spain,Male,23,10,98052.66,1,1,1,142587.32,0\r\n2495,15671917,Wade,666,France,Male,46,5,123873.19,1,1,1,177844.06,0\r\n2496,15666548,Chung,466,Germany,Female,56,2,111920.13,3,1,0,197634.11,1\r\n2497,15625623,Stevenson,567,France,Female,45,4,0,2,0,1,121053.19,0\r\n2498,15748123,Chienezie,613,France,Male,20,3,0,2,1,1,149613.77,0\r\n2499,15648735,Cashin,718,France,Male,37,8,0,2,1,1,142.81,0\r\n2500,15634974,Seppelt,614,France,Female,37,8,75150.34,4,0,1,131766.67,1\r\n2501,15713378,Brownless,711,France,Male,38,10,0,2,0,0,53311.78,0\r\n2502,15753370,McDonald,691,Germany,Female,38,5,114753.76,1,1,0,107665.02,0\r\n2503,15782659,Mamelu,527,France,Male,32,0,0,1,1,0,109523.88,0\r\n2504,15583364,McGregor,476,France,Female,32,6,111871.93,1,0,0,112132.86,0\r\n2505,15625942,McDonald,619,Spain,Female,45,0,0,2,0,0,113645.4,0\r\n2506,15720284,Crawford,607,Germany,Female,37,4,135927.06,1,0,0,180890.4,0\r\n2507,15679642,Feng,695,Spain,Male,44,8,0,2,1,1,70974.13,0\r\n2508,15628007,Genovese,653,France,Male,33,1,0,2,0,0,53379.52,0\r\n2509,15661974,Pirozzi,677,France,Male,46,2,57037.74,1,1,1,158531.01,0\r\n2510,15689341,Gibbs,655,France,Female,50,10,0,4,1,0,179267.94,1\r\n2511,15607993,Milne,625,France,Female,52,2,79468.96,1,1,1,84606.03,0\r\n2512,15693267,Dickson,679,Germany,Female,34,7,121063.85,1,1,0,56984.58,0\r\n2513,15769522,O'Connor,734,France,Male,51,1,118537.47,1,1,1,116912.45,0\r\n2514,15755825,McGuirk,666,France,Male,39,10,0,2,1,0,102999.33,0\r\n2515,15598175,Toscani,592,Germany,Female,26,4,105082.07,2,1,0,132801.57,0\r\n2516,15744327,Ruth,564,France,Male,40,4,0,1,1,0,85455.62,1\r\n2517,15798666,Hughes,814,France,Female,36,6,0,2,1,1,98657.01,0\r\n2518,15577064,Onyekaozulu,592,Germany,Male,36,2,104702.65,2,1,0,107948.72,0\r\n2519,15759436,Aksenov,758,France,Female,50,2,95813.76,3,1,1,67944.09,1\r\n2520,15690231,K'ung,612,Spain,Female,62,0,167026.61,2,1,1,192892.05,0\r\n2521,15751561,Meng,498,Germany,Male,61,7,102453.26,1,1,0,187247.56,1\r\n2522,15739068,Nwoye,638,Germany,Male,25,4,148045.45,2,1,1,114722.42,0\r\n2523,15758056,Calabresi,558,France,Male,35,1,0,2,0,0,111687.57,0\r\n2524,15742269,Milano,756,France,Female,24,1,0,2,1,0,184182.25,0\r\n2525,15726490,Kirby,782,Spain,Male,52,4,0,1,1,1,52759.82,1\r\n2526,15738411,Ho,505,France,Male,34,10,104498.79,1,0,1,126451.14,0\r\n2527,15727919,Chukwuemeka,671,Spain,Female,29,6,0,2,0,0,12048.67,0\r\n2528,15709396,Hale,801,France,Male,42,6,0,2,1,1,95804.33,0\r\n2529,15654106,K?,604,France,Male,26,8,149542.52,2,0,1,197911.52,0\r\n2530,15621653,Rice,716,France,Female,29,10,87946.39,1,1,1,182531.74,0\r\n2531,15598086,Brown,624,France,Female,45,3,68639.57,1,1,0,168002.31,1\r\n2532,15752300,Sagese,607,Germany,Male,47,4,148826.32,1,1,1,79450.61,0\r\n2533,15658693,Aksyonova,827,France,Female,60,2,0,2,0,1,60615.83,0\r\n2534,15631838,Findlay,606,France,Male,61,5,108166.09,2,0,1,8643.21,0\r\n2535,15803804,Walker,717,Germany,Female,35,5,103214.71,1,1,0,172172.7,0\r\n2536,15578809,Hao,651,Germany,Male,40,1,134760.21,2,0,0,174434.06,1\r\n2537,15752026,Hammer,691,France,Male,58,3,0,1,0,1,194930.3,1\r\n2538,15723706,Abbott,573,France,Female,33,0,90124.64,1,1,0,137476.71,0\r\n2539,15752838,Lucas,723,Spain,Male,38,6,0,2,1,1,94415.6,0\r\n2540,15569571,Davydova,584,Germany,Female,46,6,87361.02,2,1,0,120376.87,1\r\n2541,15769703,West,550,Germany,Female,45,8,111257.59,1,0,0,97623.42,1\r\n2542,15679770,Smith,611,France,Female,61,3,131583.59,4,0,1,66238.23,1\r\n2543,15791102,Mai,549,Germany,Male,41,9,95020.8,3,1,1,131710.59,1\r\n2544,15655192,Fiorentino,850,Spain,Female,24,1,0,2,0,1,69052.87,0\r\n2545,15709487,Freeman,668,Germany,Male,34,5,80242.37,2,0,0,56780.97,0\r\n2546,15687130,Nkemjika,686,France,Female,43,0,0,1,1,1,170072.9,0\r\n2547,15755178,Ramos,660,France,Male,50,1,0,3,1,1,191849.15,1\r\n2548,15634772,Mario,682,Spain,Female,59,0,122661.39,1,0,1,84803.76,0\r\n2549,15617197,Chien,524,France,Male,50,4,0,2,1,1,31840.59,1\r\n2550,15631240,Dubinina,645,France,Female,36,8,0,2,1,1,12096.61,1\r\n2551,15784301,Wang,850,France,Male,42,0,0,2,1,0,44165.84,0\r\n2552,15631310,Hsieh,537,France,Female,53,3,0,1,1,1,91406.62,0\r\n2553,15756560,Moran,599,Spain,Female,46,7,81742.84,2,1,0,83282.21,0\r\n2554,15732270,Hung,727,Spain,Male,71,8,0,1,1,1,198446.91,1\r\n2555,15739357,Moss,756,Spain,Male,30,2,145127.85,1,0,0,7554.68,0\r\n2556,15771540,Fedorova,755,France,Male,38,9,148912.44,1,1,0,80416.16,0\r\n2557,15567486,Li,634,Spain,Female,41,4,0,2,1,1,164549.74,0\r\n2558,15714634,Nebechi,837,France,Male,26,4,89900.24,2,1,0,175477.03,0\r\n2559,15727021,Obialo,727,Germany,Female,30,8,119027.28,2,1,1,137903.54,0\r\n2560,15650670,Bateson,567,Germany,Female,40,2,105222.86,2,1,0,93795.86,0\r\n2561,15711834,Long,650,Spain,Female,30,6,0,1,0,0,67997.13,1\r\n2562,15729763,Nelson,655,Spain,Male,34,1,116114.93,1,1,1,49492.15,0\r\n2563,15646566,Bell,763,France,Female,58,9,187911.55,1,0,1,35825.18,0\r\n2564,15645463,Udinese,843,France,Female,27,5,0,2,1,1,67494.23,0\r\n2565,15672144,Mao,667,France,Female,38,6,144432.04,1,1,1,73963.17,1\r\n2566,15596088,Fanucci,705,France,Female,50,4,77065.9,2,0,1,145159.26,0\r\n2567,15614878,Yeh,660,Germany,Female,29,6,180520.29,1,1,1,123850.58,0\r\n2568,15635240,Onuoha,553,France,Male,42,1,0,2,0,0,23822.04,0\r\n2569,15775905,Moore,612,Germany,Female,47,6,130024.87,1,1,1,45750.21,1\r\n2570,15700657,Thornton,641,Germany,Female,40,2,110086.69,1,1,0,159773.14,0\r\n2571,15611905,Warlow-Davies,513,Spain,Female,31,5,174853.46,1,1,0,84238.63,0\r\n2572,15652527,Champion,680,France,Male,44,7,108724.98,1,0,1,72330.46,0\r\n2573,15785865,Mazzanti,711,France,Male,58,9,91285.13,2,1,1,26767.85,0\r\n2574,15645942,Macleod,689,Spain,Male,40,2,0,2,1,1,164768.82,0\r\n2575,15688691,Lei,665,Germany,Female,51,9,110610.41,2,0,1,1112.76,1\r\n2576,15592736,Lucchese,551,Germany,Male,54,5,102994.04,1,1,0,176680.16,1\r\n2577,15673529,Lombardo,645,Spain,Male,36,4,59893.85,2,1,0,43999.64,0\r\n2578,15724145,William,616,Germany,Male,29,8,149318.55,1,1,0,140746.13,0\r\n2579,15704629,Wright,582,France,Female,32,1,116409.55,1,0,1,152790.92,0\r\n2580,15597896,Ozoemena,365,Germany,Male,30,0,127760.07,1,1,0,81537.85,1\r\n2581,15731790,Boyle,697,Germany,Female,38,6,132591.36,1,1,1,7387.8,1\r\n2582,15634719,Chinwendu,704,France,Male,31,0,0,2,1,0,183038.33,0\r\n2583,15703205,Uwaezuoke,656,France,Female,46,5,113402.14,2,1,1,138849.06,0\r\n2584,15567333,Archambault,712,France,Female,31,7,0,2,1,0,170333.38,0\r\n2585,15754537,Ko,748,France,Male,40,0,0,1,0,0,60416.76,0\r\n2586,15612030,Udegbulam,724,France,Male,28,9,0,2,1,1,100240.2,0\r\n2587,15573242,Greene,691,France,Male,50,6,136953.47,1,1,1,2704.98,0\r\n2588,15601892,Hunter,563,France,Male,33,8,0,2,0,1,68815.05,0\r\n2589,15663885,Blinova,741,France,Male,32,5,0,1,1,1,64839.23,0\r\n2590,15701096,De Garis,778,France,Male,44,8,123863.64,1,1,0,144494.94,0\r\n2591,15710450,Okwudiliolisa,848,Spain,Male,22,7,120811.89,1,1,1,185510.34,0\r\n2592,15790846,Ts'ai,634,Germany,Male,38,2,148430.55,1,1,1,56055.72,0\r\n2593,15658956,Tuan,505,Germany,Male,40,6,47869.69,2,1,1,155061.97,0\r\n2594,15755223,Tseng,692,Germany,Male,53,7,150926.99,2,0,0,119817.19,0\r\n2595,15787318,Holmwood,537,Germany,Female,47,6,103163.35,1,1,0,16259.64,1\r\n2596,15737310,Thompson,633,France,Male,29,10,130206.28,1,1,0,184654.87,0\r\n2597,15763665,Y?,833,France,Female,28,4,136674.51,2,0,0,5278.78,0\r\n2598,15668818,Chidubem,592,Spain,Female,40,2,200322.45,1,1,1,113244.73,0\r\n2599,15765812,Trevisani,587,Spain,Male,48,1,0,2,1,1,8908,0\r\n2600,15704844,Hsiung,550,Spain,Male,62,7,80927.56,1,0,1,64490.67,0\r\n2601,15744582,Randall,680,France,Female,24,10,0,3,1,0,154971.63,1\r\n2602,15616700,Leach,622,Spain,Female,41,9,0,2,1,1,155786.39,0\r\n2603,15683521,Godfrey,594,Germany,Male,28,0,142574.71,2,1,0,129084.82,0\r\n2604,15583049,Wallace,643,Germany,Female,34,7,160426.07,1,0,1,188533.11,0\r\n2605,15643752,Wei,540,France,Male,25,5,116160.23,1,1,0,13411.67,0\r\n2606,15620398,Mitchell,635,Spain,Female,34,5,98683.47,2,1,0,15733.19,0\r\n2607,15715707,Light,657,France,Male,32,3,118829.03,2,1,1,73127.61,0\r\n2608,15814209,Capon,814,France,Male,31,1,118870.92,1,1,0,101704.19,0\r\n2609,15733768,Hou,600,France,Male,32,1,0,1,1,1,101986.16,0\r\n2610,15755242,Rowe,682,France,Female,46,2,0,1,1,1,114442.66,0\r\n2611,15729412,Holloway,682,France,Male,38,4,107192.38,1,1,1,15669.17,0\r\n2612,15746564,O'Sullivan,566,France,Male,42,3,108010.78,1,1,1,157486.1,0\r\n2613,15588446,Udinesi,550,Spain,Male,34,3,0,2,0,0,131281.28,0\r\n2614,15665221,Nwebube,630,France,Male,26,7,129837.72,2,0,1,197001.15,0\r\n2615,15640846,Chibueze,546,Germany,Female,58,3,106458.31,4,1,0,128881.87,1\r\n2616,15700209,Walker,486,France,Male,63,9,97009.15,1,1,1,85101,0\r\n2617,15658360,Gregory,762,Spain,Male,35,9,122929.42,2,0,0,149822.04,0\r\n2618,15602735,Kuo,692,Germany,Male,45,6,152296.83,4,0,1,108040.86,1\r\n2619,15724834,Wilson,498,France,Female,30,1,0,2,0,0,135795.53,0\r\n2620,15800062,Lanford,850,Spain,Male,49,8,0,1,0,0,25867.67,1\r\n2621,15685300,Meng,603,France,Male,35,6,128993.76,2,1,0,130483.56,0\r\n2622,15760102,Yeh,551,France,Female,36,5,0,1,1,0,183479.12,0\r\n2623,15787026,Onwuatuegwu,627,Germany,Male,27,0,185267.45,2,1,1,77027.34,0\r\n2624,15653696,Goliwe,515,France,Female,28,9,0,2,0,0,94141.75,0\r\n2625,15788946,Anthony,605,Spain,Female,29,3,116805.82,1,0,0,4092.75,0\r\n2626,15600724,Scott,567,Germany,Male,29,5,129750.68,1,1,0,109257.59,0\r\n2627,15574324,Genovese,568,Germany,Female,29,2,129177.01,2,0,1,104617.99,0\r\n2628,15707144,Onyeorulu,571,Germany,Male,25,6,82506.72,2,1,0,167705.07,0\r\n2629,15775891,Myers,634,Germany,Male,48,2,107247.69,1,1,1,103712.05,1\r\n2630,15711789,Davey,768,Spain,Female,42,3,0,1,0,0,161242.99,1\r\n2631,15600879,Parsons,554,Germany,Female,36,3,157780.93,2,1,0,6089.13,0\r\n2632,15681196,Chikere,629,France,Male,35,1,172170.36,1,1,1,159777.37,0\r\n2633,15716000,Hs?eh,638,Spain,Male,48,2,0,2,1,1,7919.08,0\r\n2634,15766776,Sal,576,France,Male,41,1,0,1,1,1,188274.6,0\r\n2635,15680278,Ts'ai,661,Spain,Female,42,9,75361.44,1,1,0,27608.12,1\r\n2636,15688637,Witt,592,France,Female,27,4,0,2,1,1,183569.25,0\r\n2637,15591179,Skelton,702,Spain,Male,30,2,0,2,1,1,145537.32,0\r\n2638,15677435,Kazantseva,647,France,Female,29,0,98263.46,2,1,0,164717.95,0\r\n2639,15698619,Bowhay,593,France,Male,43,9,0,2,1,1,76357.43,0\r\n2640,15581036,Beyer,712,Germany,Female,40,3,109308.79,2,1,0,120158.72,1\r\n2641,15622117,Fries,625,Spain,Female,31,8,0,2,1,0,151843.54,0\r\n2642,15599301,Tao,538,Germany,Female,28,6,164365.44,1,0,1,5698.97,0\r\n2643,15581548,Kaodilinakachukwu,637,Spain,Female,22,5,98800,1,1,0,122865.55,0\r\n2644,15586870,Ni,632,France,Male,27,4,193125.85,1,1,1,152665.85,0\r\n2645,15735263,Hsueh,736,France,Male,27,5,51522.75,1,0,1,192131.77,0\r\n2646,15765322,Connely,755,France,Male,23,5,84284.48,2,1,1,62851.6,0\r\n2647,15582944,Becker,425,Spain,Female,39,5,0,2,1,0,140941.47,0\r\n2648,15687162,Clayton,461,France,Male,51,9,119889.84,1,0,0,56767.67,1\r\n2649,15644962,Connolly,745,France,Male,21,4,137910.45,1,1,1,177235.23,0\r\n2650,15612615,Graham,616,France,Female,37,6,0,2,1,0,86242.18,0\r\n2651,15813439,Ch'ien,587,France,Male,33,5,100116.82,1,1,0,34215.58,0\r\n2652,15604544,Manfrin,850,Germany,Male,40,4,166082.15,2,0,1,44406.17,0\r\n2653,15761348,Kuo,601,France,Female,38,0,0,2,1,0,165196.65,0\r\n2654,15785078,Fomin,730,Spain,Male,26,3,0,1,1,0,34542.41,0\r\n2655,15759874,Chamberlain,532,France,Male,44,3,148595.55,1,1,0,74838.64,1\r\n2656,15643658,Barber,850,Germany,Male,53,2,94078.97,2,1,0,36980.54,0\r\n2657,15713267,Zimmer,779,Spain,Female,34,5,0,2,0,1,111676.63,0\r\n2658,15737782,Brazenor,562,France,Male,29,9,0,1,1,1,25858.68,0\r\n2659,15815490,Cocci,670,Germany,Male,40,2,164948.98,3,0,0,177028,1\r\n2660,15679410,Caldwell,729,France,Female,62,4,140549.4,1,1,0,30990.16,1\r\n2661,15756241,Yirawala,767,France,Female,44,2,152509.25,1,1,1,136915.15,0\r\n2662,15688409,Donaldson,742,France,Female,28,2,191864.51,1,1,0,108457.99,1\r\n2663,15742272,Ozerova,669,France,Female,44,8,96418.09,1,0,0,131609.48,1\r\n2664,15717898,Bruce,542,Spain,Male,32,2,131945.94,1,0,1,159737.56,0\r\n2665,15769582,Kang,586,France,Male,29,3,0,2,1,1,142238.54,0\r\n2666,15635660,Rossi,612,Germany,Male,30,9,142910.15,1,1,0,105890.55,1\r\n2667,15576723,Ts'ai,740,France,Female,37,7,0,2,1,1,194270.91,0\r\n2668,15591577,Moran,584,France,Male,35,3,146311.58,1,1,1,105443.47,0\r\n2669,15582325,Jennings,524,France,Male,52,2,87894.26,1,1,0,173899.42,1\r\n2670,15693947,Tokareva,614,France,Female,19,5,97445.49,2,1,0,122823.34,0\r\n2671,15760446,Pagnotto,598,France,Female,64,9,0,1,0,1,13181.37,1\r\n2672,15611105,Castella,799,Spain,Male,35,7,0,2,0,1,140780.8,0\r\n2673,15630920,Du Cane,724,France,Male,34,2,154485.74,2,0,0,78560.64,0\r\n2674,15574910,Ferguson,601,France,Male,50,2,115625.07,1,1,0,185855.21,0\r\n2675,15756472,Odinakachukwu,804,France,Male,25,7,108396.67,1,1,0,128276.95,0\r\n2676,15682890,Woronoff,745,Germany,Male,38,5,65095.41,2,1,1,140197.42,0\r\n2677,15641994,Meng,667,Germany,Male,43,1,103018.45,1,1,0,32462.39,1\r\n2678,15733297,Sinclair,518,France,Female,38,10,84764.79,1,1,1,162253.9,0\r\n2679,15767793,Hsu,819,France,Female,38,10,0,2,1,0,30498.7,0\r\n2680,15725698,Panicucci,520,Spain,Female,35,4,115680.81,1,1,1,90280.7,0\r\n2681,15813532,Burns,625,France,Female,39,5,0,2,1,0,32615.21,0\r\n2682,15576760,Onodugoadiegbemma,673,Germany,Male,36,5,73088.06,2,0,0,196142.26,0\r\n2683,15732102,Darling,656,Germany,Female,27,3,150905.03,2,1,0,16998.72,0\r\n2684,15739046,Maggard,850,Spain,Female,23,9,143054.85,1,0,1,62980.96,0\r\n2685,15631927,Thomas,574,Spain,Female,28,7,0,2,0,0,185660.3,0\r\n2686,15672115,Lettiere,679,France,Male,60,6,0,2,1,1,77331.77,0\r\n2687,15618765,Ponomaryov,530,Germany,Female,42,0,99948.45,1,0,1,97338.62,0\r\n2688,15679148,Oliver,508,France,Male,44,3,115451.05,2,0,0,67234.33,0\r\n2689,15728474,Chienezie,558,Germany,Male,32,4,108235.91,1,1,1,143783.28,0\r\n2690,15636999,Mao,414,France,Male,38,8,0,1,0,1,77661.12,1\r\n2691,15754261,Ho,648,Spain,Male,42,2,98795.61,2,1,0,89123.99,0\r\n2692,15629150,Lucchese,721,France,Female,37,1,0,2,1,0,70810.8,0\r\n2693,15736274,Prokhorova,751,France,Male,31,8,0,2,0,0,17550.49,0\r\n2694,15627697,Alekseyeva,662,France,Male,34,2,0,2,0,1,21497.27,0\r\n2695,15721585,Blacklock,628,Germany,Male,29,3,113146.98,2,0,1,124749.08,0\r\n2696,15639946,Sazonova,597,Germany,Female,39,8,162532.14,3,1,0,36051.46,1\r\n2697,15792176,Henty,698,Spain,Female,40,0,92053.44,1,1,1,143681.83,0\r\n2698,15699450,Li,723,France,Male,48,7,0,2,1,1,150694.58,0\r\n2699,15729954,Azuka,586,France,Female,28,5,0,3,1,0,170487.4,1\r\n2700,15600103,Alexander,633,Germany,Female,29,8,104944.1,1,1,1,97684.46,0\r\n2701,15786200,Brock,564,France,Male,31,4,0,2,1,0,53520.03,0\r\n2702,15797010,Shen,649,France,Female,31,2,0,2,1,0,15200.61,0\r\n2703,15670172,Padovesi,622,France,Female,30,4,107879.04,1,0,1,196894.62,0\r\n2704,15627352,Bulgakov,459,Germany,Male,46,7,110356.42,1,1,0,4969.13,1\r\n2705,15622494,Mazzanti,718,France,Male,27,2,0,2,0,0,26229.24,0\r\n2706,15585835,Lord,655,Spain,Female,34,4,109783.69,2,1,0,134034.32,0\r\n2707,15595071,Ramos,696,France,Male,22,9,149777,1,1,1,198032.93,0\r\n2708,15628203,Pai,637,France,Female,38,3,104339.56,1,0,0,119882.86,0\r\n2709,15667190,Yuan,630,Spain,Female,21,1,85818.18,1,1,1,133102.3,0\r\n2710,15780212,Mao,592,France,Male,37,4,212692.97,1,0,0,176395.02,0\r\n2711,15766869,Uspenskaya,634,Germany,Male,37,1,89696.84,2,1,1,193179.88,0\r\n2712,15775741,Powell,608,France,Female,28,9,0,2,1,1,125062.02,0\r\n2713,15628170,Brown,565,Germany,Female,32,9,68067.24,1,1,0,143287.58,0\r\n2714,15701318,Poole,763,Spain,Male,67,9,148564.66,1,0,1,87236.4,0\r\n2715,15710928,McChesney,665,France,Female,55,8,136354.16,1,1,1,93769.89,0\r\n2716,15682547,Lucchese,649,France,Male,38,1,122214,1,0,1,88965.46,0\r\n2717,15631170,Clements,695,France,Male,45,3,0,2,1,1,30793.61,0\r\n2718,15648702,Yuriev,775,Germany,Male,70,6,119684.88,2,1,1,74532.02,0\r\n2719,15783444,Endrizzi,788,France,Female,39,3,135139.33,1,0,1,113086.08,0\r\n2720,15809178,Pan,569,Germany,Female,42,9,146100.75,1,1,0,32574.01,1\r\n2721,15806688,Manfrin,726,Spain,Female,56,8,123110.9,3,0,1,130113.78,1\r\n2722,15576824,Kennedy,564,Germany,Female,44,3,111760.4,3,1,1,104722.47,1\r\n2723,15675422,Conway,544,France,Female,32,9,110728.39,1,1,1,14559.62,0\r\n2724,15681550,Lablanc,614,France,Female,41,8,121558.46,1,1,1,598.8,0\r\n2725,15812628,Dodd,453,Germany,Female,38,8,120623.21,1,1,0,129697.99,0\r\n2726,15597951,Muir,471,France,Female,58,4,114713.57,1,1,1,36315.03,0\r\n2727,15807045,Milanesi,829,Germany,Female,37,3,103457.76,1,0,0,1114.12,0\r\n2728,15581748,Shen,754,Germany,Male,57,2,101134.87,2,1,1,70954.41,0\r\n2729,15770420,Dillon,749,Germany,Male,46,10,78136.36,2,1,1,73470.98,0\r\n2730,15608230,Hoelscher,667,France,Male,23,1,0,2,1,0,91573.19,0\r\n2731,15730339,Bell,670,Spain,Male,30,3,133446.34,1,0,0,3154.95,0\r\n2732,15712584,Liao,670,France,Female,33,7,0,2,1,1,88187.81,0\r\n2733,15592816,Udokamma,623,Germany,Female,48,1,108076.33,1,1,0,118855.26,1\r\n2734,15641480,Sinnett,571,France,Male,32,5,131354.25,1,1,0,125256.53,0\r\n2735,15708505,Palerma,641,Germany,Female,37,7,62974.64,2,0,1,39016.43,0\r\n2736,15791131,Chimaijem,551,Germany,Female,30,2,143340.44,1,1,0,145796.49,0\r\n2737,15618225,Porter,741,Germany,Male,36,8,116993.43,2,1,0,168816.22,0\r\n2738,15644724,Fan,472,France,Male,31,4,58662.92,2,0,1,73322,0\r\n2739,15662098,Palmer,650,Spain,Male,41,3,128808.65,3,0,0,113677.53,1\r\n2740,15723894,Younger,625,France,Male,45,7,137555.44,1,0,0,124607.7,0\r\n2741,15787699,Burke,650,Germany,Male,34,4,142393.11,1,1,1,11276.48,0\r\n2742,15687738,Nwagugheuzo,535,France,Female,38,8,0,2,1,0,136620.64,0\r\n2743,15576126,Young,649,France,Female,41,2,125785.23,1,1,1,70523.92,0\r\n2744,15658889,Watson,689,France,Male,22,4,136444.25,1,1,0,51980.25,1\r\n2745,15667046,Tseng,694,Spain,Male,38,7,121527.4,1,1,0,113481.02,0\r\n2746,15669957,Drake,655,Germany,Male,52,9,144696.75,1,1,1,49025.79,0\r\n2747,15655794,Hanna,620,France,Male,36,8,0,2,1,1,145937.99,0\r\n2748,15599829,Padovesi,577,France,Female,35,10,0,2,1,1,25161.61,0\r\n2749,15753332,Loftus,401,Germany,Male,48,8,128140.17,1,1,0,175753.55,1\r\n2750,15671124,Buccho,599,France,Male,25,6,120383.41,1,1,1,24903.09,0\r\n2751,15767474,Lorenzo,481,France,Female,57,9,0,3,1,1,169719.35,1\r\n2752,15720671,Ibezimako,704,France,Male,42,8,129735.3,2,1,1,179565.57,0\r\n2753,15626787,Wei,698,Spain,Female,31,8,185078.26,1,0,0,115337.74,1\r\n2754,15774491,Ross,480,France,Female,28,6,0,2,0,0,48131.92,0\r\n2755,15579647,Oluchukwu,682,France,Male,42,0,0,1,1,1,160828.98,0\r\n2756,15625522,Walker,700,Spain,Male,31,7,0,2,0,1,145151.96,0\r\n2757,15765806,Wu,492,France,Male,29,1,144591.96,1,1,1,196293.76,0\r\n2758,15566708,Chidalu,444,France,Female,45,4,0,2,1,0,161653.5,1\r\n2759,15668347,Ingram,624,France,Male,36,6,0,2,0,0,84635.64,0\r\n2760,15575214,Ch'en,709,France,Male,37,7,0,1,1,0,159486.76,0\r\n2761,15591123,Iredale,557,Germany,Male,68,2,100194.44,1,1,1,38596.34,0\r\n2762,15573280,Gallagher,646,Germany,Male,50,6,145295.31,2,1,1,27814.74,0\r\n2763,15589018,Padilla,719,Germany,Male,28,3,106070.29,2,1,1,183893.31,0\r\n2764,15654495,Potter,706,Germany,Female,47,6,120621.89,1,1,1,140803.7,0\r\n2765,15597265,Mao,660,France,Male,38,7,0,2,0,1,146585.53,0\r\n2766,15733876,Schneider,667,France,Male,36,9,0,2,1,1,40062.29,0\r\n2767,15677217,Ibragimova,705,France,Male,30,1,0,1,1,1,181300.32,0\r\n2768,15747265,Huang,598,Germany,Female,27,10,171283.91,1,1,1,84136.12,0\r\n2769,15713379,Anderson,669,France,Male,26,4,0,2,1,1,197594.34,0\r\n2770,15730433,Nakayama,580,Germany,Female,38,1,128218.47,1,1,0,125953.83,1\r\n2771,15693347,Gardener,676,France,Female,32,5,0,2,1,1,75465.41,0\r\n2772,15715465,Aksenova,714,Germany,Male,28,7,77776.39,1,1,0,177737.07,0\r\n2773,15680736,Milne,597,Germany,Female,72,6,124978.19,2,1,1,7144.46,0\r\n2774,15610765,Onwumelu,559,France,Male,29,1,0,2,0,0,155639.76,0\r\n2775,15650034,Kudryashova,564,France,Female,28,1,0,1,1,1,162428.05,0\r\n2776,15782468,Hart,850,Spain,Male,51,3,109799.55,2,1,1,12457.76,1\r\n2777,15685109,Teng,689,France,Male,39,7,0,2,0,0,14917.09,0\r\n2778,15776233,Kruglova,758,Germany,Female,61,8,125397.21,1,1,0,182184.09,1\r\n2779,15761141,Palerma,604,Spain,Female,71,10,0,2,1,1,129984.2,0\r\n2780,15781702,Brookes,733,Germany,Male,38,9,111347.37,2,0,1,194872.97,0\r\n2781,15790235,Hsing,778,Spain,Male,40,8,104291.41,2,1,1,117507.11,0\r\n2782,15641416,Shaffer,732,Germany,Female,61,9,94867.18,2,1,1,157527.6,1\r\n2783,15775234,Laurie,646,France,Male,24,8,0,2,0,0,92612.88,0\r\n2784,15659475,Chung,597,France,Female,33,6,135703.59,2,0,0,74850.84,0\r\n2785,15642202,Whitfield,821,Germany,Female,37,5,106453.53,2,0,1,127413,0\r\n2786,15771417,Thomas,640,France,Male,43,7,132412.38,1,0,0,69584.3,1\r\n2787,15585100,Rioux,511,Germany,Female,40,9,124401.6,1,1,0,198814.24,1\r\n2788,15700487,Osonduagwuike,805,France,Male,46,6,118022.06,3,1,0,162643.15,1\r\n2789,15726589,Matveyev,540,Germany,Male,39,1,82531.11,1,1,0,114092.52,0\r\n2790,15747503,Hayward,705,Spain,Male,44,0,184552.12,1,1,0,68860.3,1\r\n2791,15595883,Nkemdirim,540,Germany,Male,39,4,127278.31,1,1,1,16150.34,0\r\n2792,15663826,Brim,532,Spain,Female,66,3,0,1,1,1,115227.02,0\r\n2793,15742820,Trevisano,535,France,Female,45,2,0,2,0,1,170621.55,0\r\n2794,15624793,Soubeiran,627,Germany,Male,23,5,184244.86,1,1,0,103099.22,0\r\n2795,15597930,Wilson,646,France,Male,52,8,59669.43,1,0,0,172495.81,1\r\n2796,15665110,Helena,515,France,Female,25,7,79543.59,1,0,1,38772.82,0\r\n2797,15770719,Duncan,697,France,Female,39,6,151553.19,1,1,1,44946.29,0\r\n2798,15731327,Hale,652,Germany,Male,27,2,166527.88,2,0,1,146007.7,0\r\n2799,15576044,Macdonald,579,Germany,Male,28,6,150329.15,1,1,0,145558.42,0\r\n2800,15775662,McKay,760,France,Male,43,8,121911.59,1,1,0,193312.33,0\r\n2801,15646817,Chiekwugo,769,France,Male,51,9,156773.78,2,1,0,40257.79,0\r\n2802,15596060,Skinner,498,Spain,Male,29,8,127864.26,1,1,1,46677.9,0\r\n2803,15723299,Sorokina,774,France,Male,53,4,113709.28,1,1,1,153887.93,1\r\n2804,15636982,Weller,705,Germany,Female,43,7,79974.55,1,1,1,103108.33,0\r\n2805,15751175,Bess,648,France,Female,44,2,0,2,1,1,58652.23,0\r\n2806,15618936,MacDonald,688,France,Female,51,5,0,1,1,0,91624.11,1\r\n2807,15787529,Gray,592,Spain,Male,38,0,0,1,1,0,65986.48,1\r\n2808,15780128,Ogbonnaya,705,France,Male,33,3,144427.96,2,1,0,113845.19,0\r\n2809,15615991,Udegbulam,654,France,Male,42,7,99263.09,1,1,1,67607.9,0\r\n2810,15757001,Mai,624,France,Female,32,2,79368.87,2,1,1,145471.94,0\r\n2811,15595388,Yeh,594,France,Female,30,10,0,2,1,1,124071.71,0\r\n2812,15699550,Babbage,695,Spain,Female,34,9,0,2,1,1,67502.12,0\r\n2813,15581620,Franklin,597,France,Male,28,2,0,3,1,1,78707.97,0\r\n2814,15600934,Randell,758,France,Female,52,7,125095.94,1,1,0,171189.83,1\r\n2815,15738672,Paterson,737,Germany,Female,40,2,162485.8,2,1,0,149381.32,0\r\n2816,15721307,Pickering,694,Germany,Male,37,1,95668.82,2,1,0,100335.55,0\r\n2817,15619280,Uspensky,683,France,Male,25,4,0,2,1,0,152698.24,0\r\n2818,15768244,Macleod,538,Spain,Female,30,8,0,2,1,1,41192.95,0\r\n2819,15806837,Nnaife,669,France,Male,37,4,0,1,1,0,132540.33,0\r\n2820,15643496,Randolph,730,France,Female,34,5,74197.38,2,1,0,96875.52,0\r\n2821,15813916,Kudryashova,622,France,Female,31,1,89688.94,1,1,1,152305.47,0\r\n2822,15626385,George,714,Spain,Female,33,10,103121.33,2,1,1,49672.01,0\r\n2823,15603582,Robertson,569,Spain,Female,34,3,0,1,1,0,133997.53,0\r\n2824,15764351,Yuryeva,668,Germany,Female,59,5,120170.07,1,0,1,50454.8,0\r\n2825,15667938,Hurst,628,France,Male,32,9,149136.31,2,1,1,16402.11,0\r\n2826,15576360,Ch'iu,600,France,Male,40,1,141136.79,1,1,1,67803.83,0\r\n2827,15628813,King,693,France,Female,43,4,152341.55,1,1,0,9241.78,0\r\n2828,15584190,Esposito,704,France,Male,36,7,120026.98,2,0,1,100601.73,0\r\n2829,15716449,Fraser,527,Spain,Male,33,9,132168.28,1,0,0,98734.15,0\r\n2830,15759913,Trentini,553,Germany,Male,43,6,85200.82,2,1,1,160574.09,0\r\n2831,15701555,Nicholls,575,Spain,Male,53,1,84903.33,2,0,1,26015.8,0\r\n2832,15758482,Montalvo,626,France,Female,32,0,0,2,0,0,187172.54,0\r\n2833,15758171,Tien,582,France,Male,20,4,0,1,1,1,55763.66,0\r\n2834,15680346,Chuang,683,Spain,Male,40,8,0,1,1,0,75848.22,0\r\n2835,15649124,Fang,850,France,Male,30,9,121535.18,1,0,0,40313.47,0\r\n2836,15812917,Kosisochukwu,653,Spain,Male,35,6,116662.96,2,1,1,23864.21,0\r\n2837,15768455,Young,679,France,Male,60,8,0,2,1,1,51380.9,0\r\n2838,15703059,Scott,549,Germany,Female,49,6,124829.16,1,0,1,93551.36,0\r\n2839,15646196,Yeh,850,Spain,Female,36,2,155180.56,2,0,0,169415.54,0\r\n2840,15585451,Vigano,558,Germany,Female,32,1,108262.87,1,1,1,6935.31,0\r\n2841,15714057,Windradyne,528,Spain,Male,40,4,0,2,1,0,25399.7,0\r\n2842,15748473,Curnow,801,France,Male,38,5,0,2,1,0,66256.27,0\r\n2843,15785782,Ugonna,513,Spain,Male,48,2,0,1,1,1,114709.13,1\r\n2844,15693233,De Neeve,666,Germany,Male,38,6,99812.88,2,1,1,158357.97,0\r\n2845,15757521,Ricci,606,France,Male,35,2,132164.26,1,0,1,164815.59,0\r\n2846,15812513,Nnaife,599,Germany,Male,45,10,103583.05,1,1,0,132127.69,1\r\n2847,15674950,Ebelechukwu,544,Germany,Male,39,4,142406.43,2,1,0,146637.45,0\r\n2848,15678572,Keating,529,Spain,Male,38,7,99842.5,2,1,0,90256.06,1\r\n2849,15713608,Tuan,850,France,Female,41,5,0,2,1,1,34827.43,0\r\n2850,15579262,Shearston,497,France,Male,41,9,0,1,0,0,22074.48,0\r\n2851,15610426,Tien,764,France,Female,39,5,81042.42,1,0,1,109805.17,0\r\n2852,15776454,Hamilton,603,France,Female,48,5,0,1,1,0,100478.6,1\r\n2853,15771483,Arnold,609,France,Male,40,6,0,2,1,1,97416.34,0\r\n2854,15648489,Ting,487,France,Male,53,4,199689.49,1,1,1,24207.86,1\r\n2855,15646609,Chao,748,France,Male,33,1,142645.43,1,0,0,69132.66,0\r\n2856,15693203,Powell,710,Spain,Female,75,5,0,2,1,1,9376.89,0\r\n2857,15813067,Williams,432,Germany,Female,45,3,110219.14,1,1,0,43046.7,1\r\n2858,15769829,Cheng,534,Spain,Male,51,3,0,2,0,1,20856.31,0\r\n2859,15662434,Zhdanova,607,France,Male,25,3,0,2,0,0,187048.72,0\r\n2860,15773503,Tsai,551,Spain,Male,32,4,0,2,1,0,53420.53,0\r\n2861,15705890,Nebechukwu,674,France,Male,45,7,142072.02,1,1,0,37013.29,0\r\n2862,15711398,Fetherstonhaugh,525,France,Female,25,6,0,2,1,0,89566.64,0\r\n2863,15752375,Ojiofor,645,Germany,Male,33,8,149564.61,1,0,0,149913.84,0\r\n2864,15659175,Severson,755,France,Female,43,9,0,2,1,0,18066.69,0\r\n2865,15597033,Speight,708,Germany,Male,37,8,153366.13,1,1,1,26912.34,0\r\n2866,15590228,Greenwalt,715,France,Male,21,6,76467.16,1,1,1,173511.72,0\r\n2867,15631848,Grover,727,France,Female,26,9,121508.28,1,1,1,146785.44,0\r\n2868,15654211,Milani,559,Spain,Female,27,1,0,1,0,1,1050.33,0\r\n2869,15707968,Akobundu,545,Spain,Male,36,8,73211.12,2,1,0,89587.34,1\r\n2870,15594084,Anderson,524,France,Male,22,9,0,2,1,0,74405.34,0\r\n2871,15651093,Chien,707,France,Female,55,1,0,2,0,1,54409.48,0\r\n2872,15798824,Kennedy,671,Spain,Male,38,0,92674.94,2,1,0,3647.57,0\r\n2873,15671591,Castiglione,439,Spain,Male,52,3,96196.24,4,1,0,198874.52,1\r\n2874,15707189,Marshall,667,Germany,Female,36,1,114391.62,1,1,1,53412.54,0\r\n2875,15733581,Duncan,831,Germany,Male,32,9,80262.66,1,1,0,194867.78,0\r\n2876,15641640,Uspenskaya,545,Spain,Female,33,7,173331.52,1,1,0,150452.88,0\r\n2877,15585284,Thomson,604,Spain,Female,35,7,147285.52,1,1,1,57807.05,0\r\n2878,15617866,Calabrese,657,Spain,Male,67,5,119785.47,2,1,1,107534.32,0\r\n2879,15667751,Herrera,487,Spain,Female,36,1,140137.15,1,1,0,194073.33,0\r\n2880,15669411,Muse,750,Germany,Female,52,6,107467.56,1,1,0,126233.18,1\r\n2881,15789425,Marsden,694,Germany,Female,37,8,98218.04,2,1,0,182354.46,1\r\n2882,15570943,Artemyeva,711,Germany,Female,35,2,133607.75,1,1,1,120586.32,0\r\n2883,15685829,McKay,551,France,Male,37,3,0,2,1,1,50578.4,0\r\n2884,15721917,Chuang,559,France,Female,38,8,95139.41,1,1,1,86575.46,0\r\n2885,15776047,Nicholls,620,France,Female,29,3,0,2,0,1,153392.28,0\r\n2886,15716024,Dennis,660,Spain,Male,42,5,0,2,1,0,115509.59,0\r\n2887,15675328,Knight,449,France,Female,37,6,0,2,1,0,82176.48,0\r\n2888,15604314,Webb,703,Germany,Female,26,1,97331.19,1,1,0,63717.49,0\r\n2889,15658339,Pugliesi,795,Germany,Male,37,2,139265.63,2,1,1,198745.94,0\r\n2890,15630402,Nebechukwu,594,France,Female,31,9,0,1,0,1,5719.11,0\r\n2891,15689616,Ward,586,Spain,Male,34,5,168094.01,1,0,0,20058.61,0\r\n2892,15774224,Nixon,613,Germany,Female,30,5,131563.88,2,1,0,170638.98,0\r\n2893,15701291,Chidubem,601,France,Male,44,3,0,2,1,0,30607.11,0\r\n2894,15719606,Rivers,657,France,Male,50,9,0,2,0,0,37171.46,0\r\n2895,15644119,Sochima,531,France,Male,31,3,0,1,1,1,42589.33,0\r\n2896,15646859,Heydon,621,Germany,Male,47,7,107363.29,1,1,1,66799.28,0\r\n2897,15606836,Lombardo,782,France,Female,33,2,94493.03,1,0,1,101866.39,0\r\n2898,15664150,Holland,528,Germany,Female,29,9,170214.23,2,1,0,49284,0\r\n2899,15624510,Tien,696,France,Male,52,6,139781.06,1,1,0,27445.4,1\r\n2900,15810944,Bryant,586,France,Female,35,7,0,2,1,0,70760.69,0\r\n2901,15668575,Hao,626,Spain,Female,26,8,148610.41,3,0,1,104502.02,1\r\n2902,15603246,Genovesi,498,France,Male,73,2,170241.7,2,1,1,165407.96,0\r\n2903,15804002,Kovalev,691,France,Female,33,1,128306.83,1,1,1,113580.79,0\r\n2904,15728773,Hsieh,568,France,Female,47,7,0,2,1,1,45978.39,0\r\n2905,15598044,Debellis,715,France,Female,35,3,0,1,1,1,152012.36,0\r\n2906,15694829,Chibueze,680,Germany,Male,32,7,175454,1,0,1,77349.92,0\r\n2907,15600575,Padovano,802,Spain,Male,41,6,0,2,1,0,47322.05,0\r\n2908,15727311,Yen,539,France,Female,22,0,100885.93,2,1,1,38772.65,0\r\n2909,15570769,Kibble,494,France,Male,69,9,93320.8,1,1,1,24489.44,0\r\n2910,15606274,Lori,594,Germany,Male,38,6,63176.44,2,1,1,14466.08,0\r\n2911,15746139,Enemuo,596,France,Male,33,2,139451.67,1,0,0,63142.12,0\r\n2912,15704987,Lu,649,France,Female,52,8,49113.75,1,1,0,41858.43,0\r\n2913,15628972,Nebeolisa,699,Germany,Male,32,1,123906.22,3,1,1,127443.82,1\r\n2914,15697686,Stewart,787,France,Female,40,6,0,2,1,1,84151.98,0\r\n2915,15733883,Ward,604,France,Male,28,7,0,2,0,0,58595.64,0\r\n2916,15617482,Milanesi,489,Germany,Female,52,1,131441.51,1,1,0,37240.11,1\r\n2917,15704583,Chikwado,651,France,Male,56,2,0,1,1,0,114522.68,1\r\n2918,15621083,Douglas,698,France,Male,57,6,136325.48,2,1,1,72549.27,1\r\n2919,15649487,Sal,578,Germany,Female,38,4,113150.44,2,1,0,176712.59,1\r\n2920,15736760,Douglas,538,Spain,Female,42,9,0,1,0,0,152855.96,0\r\n2921,15714658,Yates,696,France,Female,33,4,0,2,1,1,73371.65,0\r\n2922,15599081,Watt,507,Germany,Female,46,8,102785.16,1,1,1,70323.68,0\r\n2923,15705113,P'an,685,Spain,Male,34,6,83264.28,1,0,0,9663.28,0\r\n2924,15631159,H?,705,Germany,Male,41,4,72252.64,2,1,1,142514.66,0\r\n2925,15792818,Perry,499,Germany,Female,29,6,148051.52,1,1,0,118623.94,0\r\n2926,15633531,Lavrov,717,France,Female,76,9,138489.66,1,1,1,68400.14,0\r\n2927,15744529,Chiekwugo,510,France,Male,63,8,0,2,1,1,115291.86,0\r\n2928,15669656,Macdonald,632,France,Male,32,6,111589.33,1,1,1,170382.99,0\r\n2929,15581198,Jenkins,668,Germany,Female,39,0,122104.79,1,1,0,112946.67,1\r\n2930,15729054,Korovina,744,Germany,Male,32,4,96106.83,1,1,1,79812.77,0\r\n2931,15573452,Manning,663,Germany,Male,42,7,115930.87,1,1,0,19862.78,0\r\n2932,15776733,Wilson,638,Germany,Female,37,7,124513.66,2,1,0,158610.89,0\r\n2933,15724858,Begum,688,France,Female,54,9,0,1,1,0,191212.63,1\r\n2934,15713144,Ingrassia,588,Spain,Male,46,8,0,1,1,0,61931.21,0\r\n2935,15690188,Maclean,631,France,Male,33,7,0,1,1,1,58043.02,1\r\n2936,15689425,Olejuru,687,Spain,Male,35,8,100988.39,2,1,0,22247.27,0\r\n2937,15671766,Enyinnaya,599,France,Male,44,10,118577.24,1,1,1,31448.52,0\r\n2938,15782806,Watson,718,Spain,Male,28,6,0,2,1,0,146875.86,0\r\n2939,15764419,Langdon,730,France,Male,27,5,0,2,1,1,116081.93,0\r\n2940,15591915,Frolov,533,France,Female,39,2,0,1,0,1,73669.94,1\r\n2941,15772798,Chikezie,711,Spain,Female,28,5,0,2,1,1,93959.96,0\r\n2942,15792008,Zetticci,555,Spain,Female,26,9,0,2,0,1,158918.03,0\r\n2943,15715541,Yang,850,France,Female,42,9,113311.11,1,1,1,198193.75,0\r\n2944,15639277,Lin,678,France,Female,41,9,0,1,0,0,13160.03,0\r\n2945,15798850,Goddard,576,France,Male,32,7,0,2,1,0,4660.91,0\r\n2946,15776348,Rogers,835,Germany,Male,20,4,124365.42,1,0,0,180197.74,1\r\n2947,15727696,Zubareva,592,France,Male,42,1,147249.29,2,1,1,63023.02,0\r\n2948,15793813,Onochie,774,France,Male,36,7,103688.19,1,0,1,118971.74,0\r\n2949,15694395,Ts'ui,620,France,Female,29,1,138740.24,2,0,0,154700.61,0\r\n2950,15764195,Newsom,519,Spain,Male,39,4,111900.14,1,1,1,97577.17,0\r\n2951,15744919,Genovese,734,Spain,Female,37,0,152760.24,1,1,1,48990.5,0\r\n2952,15671655,Thorpe,763,Germany,Male,31,7,143966.3,2,1,1,140262.96,1\r\n2953,15654901,Horton,733,France,Male,51,10,141556.96,1,1,0,130189.53,0\r\n2954,15649136,Williamson,650,France,Female,43,6,0,2,1,1,16301.91,0\r\n2955,15775562,Shoobridge,538,France,Female,33,5,0,2,1,0,126962.41,0\r\n2956,15807481,Peng,577,France,Female,46,1,0,1,1,1,158750.53,0\r\n2957,15642885,Gray,792,France,Male,30,8,0,2,1,0,199644.2,0\r\n2958,15789109,Watson,686,France,Female,41,10,0,1,1,1,144272.71,1\r\n2959,15814004,Fyodorova,589,France,Male,29,2,0,2,0,1,98320.27,0\r\n2960,15673619,Bazhenov,530,France,Male,25,9,162560.32,1,1,0,64129.03,0\r\n2961,15595135,Solomon,778,Germany,Female,29,7,123229.46,1,1,0,181221.09,0\r\n2962,15583681,Layh,616,Spain,Male,31,7,76665.71,2,1,1,163809.08,0\r\n2963,15605000,John,550,France,Male,38,9,140278.99,3,1,1,171457.06,1\r\n2964,15718071,Tuan,655,France,Female,51,3,0,2,0,1,15801.02,0\r\n2965,15679760,Slattery,721,France,Male,46,1,115764.32,2,0,0,102950.79,0\r\n2966,15654574,Onyekachi,499,Germany,Male,36,5,131142.53,2,1,0,174918.46,0\r\n2967,15577178,Genovese,511,France,Male,45,5,68375.27,1,1,0,193160.25,1\r\n2968,15595324,Daniels,579,Germany,Female,39,5,117833.3,3,0,0,5831,1\r\n2969,15756932,Caldwell,696,Spain,Female,36,7,0,2,1,1,82298.59,0\r\n2970,15726358,Chiemenam,681,France,Male,34,7,0,2,0,0,130686.59,0\r\n2971,15595228,Wanliss,815,France,Male,45,7,0,1,0,1,52885.23,1\r\n2972,15782530,Bruce,681,Spain,Male,30,2,111093.01,1,1,0,68985.99,0\r\n2973,15592877,Wright,641,Spain,Male,42,9,132657.55,1,1,0,35367.19,0\r\n2974,15651983,Fang,591,France,Female,56,9,128882.49,1,1,1,196241.94,1\r\n2975,15746737,Eames,565,Germany,Male,59,9,69129.59,1,1,1,170705.53,0\r\n2976,15774179,Sutherland,487,France,Male,37,6,0,2,1,1,126477.41,0\r\n2977,15667265,Cavenagh,729,France,Male,39,4,121404.64,1,1,1,159618.17,0\r\n2978,15655123,Dumetolisa,505,Spain,Female,45,9,131355.3,3,1,0,195395.33,1\r\n2979,15595917,Mackay,580,France,Female,35,1,102097.33,1,0,1,168285.85,0\r\n2980,15668385,Dellucci,642,France,Male,40,1,154863.15,1,1,1,138052.51,0\r\n2981,15709476,Kenyon,850,Spain,Female,41,3,99945.93,2,1,0,71179.31,0\r\n2982,15711218,Parry,616,Germany,Male,39,2,121704.32,2,1,0,55556.3,0\r\n2983,15798659,Kennedy,526,Spain,Female,43,3,0,2,1,0,31705.19,0\r\n2984,15663939,Arnott,523,Germany,Male,35,8,138782.76,1,1,1,186118.93,0\r\n2985,15694946,Hanson,663,France,Male,35,9,0,2,1,1,195580.28,0\r\n2986,15631912,T'ao,840,France,Male,30,8,136291.71,1,1,0,54113.38,0\r\n2987,15768816,Shen,570,Germany,Male,42,0,107856.57,2,1,0,127528.84,0\r\n2988,15682268,Steere,676,Germany,Female,26,1,108348.66,1,0,0,60231.74,1\r\n2989,15684801,Abbott,689,France,Male,47,1,93871.95,3,1,0,156878.42,1\r\n2990,15636428,Sutherland,703,Spain,Female,45,1,0,1,1,0,182784.11,1\r\n2991,15809823,Thurgood,491,Germany,Male,19,2,125860.2,1,0,0,129690.5,0\r\n2992,15699284,Johnson,584,France,Male,49,8,172713.44,1,1,0,113860.81,0\r\n2993,15786993,Lung,810,France,Female,51,5,0,2,0,1,184524.74,0\r\n2994,15709441,Cocci,745,Spain,Female,59,8,0,1,1,1,36124.98,0\r\n2995,15710257,Matveyeva,625,France,Female,39,3,130786.92,1,0,1,121316.07,0\r\n2996,15582492,Moore,535,France,Female,29,2,112367.34,1,1,0,185630.76,0\r\n2997,15575694,Yobachukwu,729,Spain,Female,45,7,91091.06,2,1,0,71133.12,0\r\n2998,15756820,Fleming,655,France,Female,26,7,106198.5,1,0,1,32020.42,0\r\n2999,15766289,Dickinson,751,France,Male,47,5,142669.93,2,1,0,162760.96,0\r\n3000,15593014,Evseyev,525,France,Male,33,1,112833.35,1,0,1,175178.56,0\r\n3001,15584545,Aksenov,532,France,Female,40,5,0,2,0,1,177099.71,0\r\n3002,15675949,Fleming,696,Spain,Female,43,4,0,2,1,1,66406.37,0\r\n3003,15672091,Ulyanov,786,Germany,Female,32,2,104336.43,2,0,0,59559.81,0\r\n3004,15801658,Summers,580,France,Male,55,6,104305.74,1,0,1,175750.21,0\r\n3005,15706185,Clements,596,Germany,Male,47,5,140187.1,2,1,1,174311.3,0\r\n3006,15789863,Kazakova,683,France,Male,39,4,0,2,1,0,171716.81,0\r\n3007,15720943,Pirozzi,747,France,Female,45,1,114959.12,1,1,0,189362.39,1\r\n3008,15697997,Jamieson,602,France,Male,33,5,164704.38,1,0,1,180716.1,1\r\n3009,15665416,Ferri,779,France,Male,62,10,119096.55,1,0,1,116977.89,0\r\n3010,15660200,Mai,551,France,Male,31,1,0,2,1,1,185105.44,0\r\n3011,15619653,Hannaford,666,France,Male,47,2,0,1,1,0,35046.97,1\r\n3012,15773447,Fomin,526,Spain,Male,30,8,0,1,1,0,36251,0\r\n3013,15739160,Mahon,849,France,Female,41,9,115465.28,1,1,0,103174.5,0\r\n3014,15689237,Shaw,471,France,Female,27,4,0,2,1,0,122642.09,0\r\n3015,15679297,Volkova,628,Spain,Male,43,3,184926.61,1,1,0,122937.57,0\r\n3016,15591433,Miles,674,Germany,Male,43,8,85957.88,2,1,0,8757.39,0\r\n3017,15642725,Madison,797,France,Male,32,10,114084.6,1,0,1,125782.29,0\r\n3018,15701962,Scott,590,Spain,Female,29,2,166930.76,2,1,0,122487.73,0\r\n3019,15811613,Voss,588,France,Female,27,8,0,1,1,0,20066.38,0\r\n3020,15741049,Colebatch,577,France,Male,29,7,0,2,1,1,55473.15,0\r\n3021,15724423,Wilson,571,France,Female,38,6,107193.82,2,0,0,38962.94,0\r\n3022,15574305,T'ang,680,France,Male,36,3,116275.12,1,1,1,63795.8,0\r\n3023,15678168,Gibson,648,Spain,Female,27,7,0,2,1,1,163060.43,0\r\n3024,15697020,Hs?eh,618,France,Male,39,2,91068.56,1,1,0,26578.69,0\r\n3025,15610801,Pan,648,Germany,Male,41,5,123049.21,1,0,1,5066.76,0\r\n3026,15745232,Chikelu,759,France,Female,39,6,0,2,1,1,140497.67,0\r\n3027,15722758,Allan,585,France,Male,40,7,0,2,0,0,146156.98,0\r\n3028,15792102,Yefremova,774,France,Female,42,3,137781.65,1,0,0,199316.19,0\r\n3029,15675185,Chuang,697,Germany,Female,48,2,108128.96,2,1,1,103944.37,0\r\n3030,15801247,Fan,605,Spain,Male,39,10,105317.73,2,1,0,138021.36,0\r\n3031,15725660,Dellucci,676,France,Male,20,1,80569.73,1,0,0,68621.98,0\r\n3032,15638963,Garran,678,France,Female,22,4,174852.89,1,1,1,28149.06,0\r\n3033,15800061,Moretti,495,Spain,Female,45,3,89158.94,3,1,0,135169.76,1\r\n3034,15578006,Yao,787,France,Female,85,10,0,2,1,1,116537.96,0\r\n3035,15668504,Lucchesi,770,France,Male,36,2,89800.14,1,1,1,105922.69,0\r\n3036,15687491,Nkemdilim,817,Germany,Male,45,9,101207.75,1,0,0,88211.12,1\r\n3037,15610403,Anderson,659,France,Male,43,1,106086.42,2,1,0,26900.63,0\r\n3038,15741094,Sagese,693,France,Male,21,1,0,2,1,1,3494.02,0\r\n3039,15807909,Rubensohn,744,France,Male,47,9,0,2,1,0,113163.17,0\r\n3040,15666141,Baldwin,829,Spain,Female,26,8,101440.36,2,1,1,19324.5,0\r\n3041,15617134,Iqbal,716,France,Male,38,4,0,2,1,0,189678.7,0\r\n3042,15783029,Monaldo,671,France,Male,34,7,106603.74,2,1,1,26387.71,0\r\n3043,15622833,Mahon,835,Germany,Female,29,10,130420.2,2,0,0,106276.55,0\r\n3044,15746422,Muir,636,France,Female,38,1,0,1,1,0,45015.38,0\r\n3045,15750839,Burns,649,Spain,Male,29,2,45022.23,1,1,1,173495.77,0\r\n3046,15749130,Dyer,621,Germany,Male,27,1,74298.43,1,1,1,52581.96,0\r\n3047,15779862,Lyons,658,Germany,Female,31,3,133003.03,1,0,1,146339.27,1\r\n3048,15767871,H?,784,Spain,Male,48,7,0,2,1,1,182609.97,0\r\n3049,15679651,Gardiner,783,Spain,Female,37,1,136689.66,1,1,0,197890.65,0\r\n3050,15576219,Cameron,615,France,Male,32,4,0,2,1,1,6225.63,0\r\n3051,15699247,Chapman,791,France,Female,44,5,0,2,1,1,123977.86,1\r\n3052,15619087,Taylor,762,France,Male,53,1,102520.37,1,1,1,170195.4,0\r\n3053,15605327,Namatjira,607,France,Male,35,2,0,2,1,1,114190.3,0\r\n3054,15610140,He,601,France,Female,34,5,0,2,1,0,27022.57,0\r\n3055,15791174,Leibius,540,Spain,Male,67,1,88382.01,1,0,1,59457,0\r\n3056,15602373,White,812,France,Male,44,4,115049.15,2,1,0,165038.41,0\r\n3057,15762605,Wall,685,France,Male,58,1,104796.54,1,1,1,154181.41,0\r\n3058,15598840,Moretti,680,France,Male,33,1,123082.08,1,1,0,134960.98,0\r\n3059,15744279,Patterson,680,Spain,Female,58,8,0,2,1,1,65708.5,0\r\n3060,15670619,Coppin,631,France,Female,33,8,0,2,0,0,117374.22,0\r\n3061,15599533,Tsao,569,France,Female,43,7,0,2,1,1,77703.19,0\r\n3062,15757837,Kao,584,Germany,Male,33,3,88311.48,2,1,1,177651.38,0\r\n3063,15697574,Stewart,582,France,Female,40,9,0,3,1,1,60954.45,0\r\n3064,15578738,Tuan,609,France,Male,32,7,71872.19,1,1,1,151924.9,0\r\n3065,15762228,Barnes,506,Spain,Male,35,6,110046.93,2,1,0,26318.73,0\r\n3066,15614827,Sun,503,France,Male,42,8,104430.08,1,1,1,147557.71,0\r\n3067,15789815,Fallaci,503,France,Female,28,5,0,2,1,0,125918.17,0\r\n3068,15579781,Buccho,806,Germany,Male,31,10,138653.51,1,1,0,190803.37,0\r\n3069,15587013,Tien,653,France,Female,31,7,102575.04,1,1,1,11043.54,0\r\n3070,15570932,Pirozzi,666,France,Male,43,7,137780.74,2,1,1,119100.05,1\r\n3071,15794661,Liu,674,Spain,Male,32,2,0,2,1,0,140579.17,0\r\n3072,15581654,Long,798,France,Male,32,7,0,2,0,1,37731.95,0\r\n3073,15644296,Scott,740,France,Female,30,8,105209.54,1,1,0,1852.58,0\r\n3074,15614420,Gerasimova,531,Germany,Female,32,0,109570.21,2,1,1,172049.84,0\r\n3075,15609653,Ifeatu,614,Germany,Female,44,6,118715.86,1,1,0,133591.11,1\r\n3076,15594577,De Luca,556,France,Male,35,10,0,2,1,1,192751.18,0\r\n3077,15584114,Ogbonnaya,678,Germany,Female,43,2,153393.18,2,1,1,193828.27,0\r\n3078,15673367,Humffray,587,Germany,Male,33,6,132603.36,1,1,0,55775.72,0\r\n3079,15685576,Degtyaryov,527,Spain,Female,36,6,0,2,1,1,102280.29,0\r\n3080,15774727,Monaldo,757,Germany,Female,34,1,129398.01,2,0,0,44965.44,0\r\n3081,15694288,Cawthorne,468,Spain,Male,28,3,0,2,1,0,170661.02,0\r\n3082,15603319,Graham,693,France,Male,29,2,151352.74,1,0,0,197145.89,0\r\n3083,15759066,Carpenter,483,France,Female,44,5,136836.49,1,1,0,192359.9,1\r\n3084,15814816,Kambinachi,466,France,Male,40,4,91592.06,1,1,0,141210.18,1\r\n3085,15724402,Tyler,770,France,Female,30,8,0,2,1,0,100557.03,0\r\n3086,15571059,Martin,734,France,Female,54,3,0,1,1,0,130805.54,1\r\n3087,15674206,Walker,716,France,Female,22,8,0,2,1,1,92606.98,0\r\n3088,15715160,Khan,439,France,Male,36,2,165536.28,2,1,1,123956.83,0\r\n3089,15730448,Iroawuchi,538,Germany,Male,25,5,62482.95,1,1,1,102758.43,0\r\n3090,15662067,Summers,743,France,Male,40,8,68155.59,1,1,0,94876.65,0\r\n3091,15779581,Bottrill,734,Spain,Female,43,3,55853.33,2,0,1,94811.85,1\r\n3092,15662901,Hu,656,France,Male,37,2,0,2,0,1,67840.81,0\r\n3093,15689751,Jones,666,France,Female,31,2,79589.43,1,0,0,4050.57,0\r\n3094,15667742,Vincent,627,Spain,Male,41,5,100880.76,1,0,1,134665.25,0\r\n3095,15738448,Sanford,480,Germany,Female,25,3,174330.35,2,0,0,181647.13,0\r\n3096,15680243,Brown,792,France,Male,19,7,143390.51,1,1,0,33282.84,0\r\n3097,15745083,Lei,613,Germany,Male,59,8,91415.76,1,0,0,27965,1\r\n3098,15708228,Toscani,476,Germany,Male,30,3,134366.42,1,1,0,68343.53,0\r\n3099,15628523,Chien,539,France,Female,24,3,0,2,1,1,198161.07,0\r\n3100,15708196,Uchenna,696,Spain,Male,60,8,88786.81,1,1,1,196858.4,0\r\n3101,15735549,Lori,810,Germany,Male,35,3,96814.46,2,1,1,120511.03,0\r\n3102,15809347,Fanucci,763,Germany,Male,32,9,160680.41,1,1,0,30886.35,0\r\n3103,15660866,Chimaobim,640,France,Female,29,3,0,2,1,0,2743.69,0\r\n3104,15766609,Jowers,655,France,Female,47,10,0,2,1,0,167778.62,0\r\n3105,15654230,Miller,526,Germany,Male,31,5,145537.21,1,1,0,132404.64,0\r\n3106,15794566,Kirsova,678,France,Female,28,4,0,2,1,1,144423.17,1\r\n3107,15800890,T'ien,554,France,Female,45,6,0,2,1,1,181204.5,0\r\n3108,15697424,Ku,597,Spain,Female,30,2,119370.11,1,1,1,182726.22,1\r\n3109,15724536,Chin,560,Spain,Female,28,1,0,2,1,1,120880.72,0\r\n3110,15735878,Law,850,Germany,Female,47,10,134381.52,1,0,0,26812.89,1\r\n3111,15707596,Chung,546,Germany,Female,74,8,114888.74,2,1,1,66732.63,1\r\n3112,15657163,Cockrum,623,Germany,Male,42,1,149332.48,2,1,0,100834.22,0\r\n3113,15622478,Greaves,698,France,Female,40,7,105061.74,3,1,0,107815.31,1\r\n3114,15779529,Grant,620,France,Male,32,7,0,2,1,1,34665.79,0\r\n3115,15636023,O'Donnell,619,France,Female,40,10,0,1,1,1,147093.84,1\r\n3116,15582066,Maclean,561,France,Male,21,4,0,1,1,1,36942.35,0\r\n3117,15666675,Hsieh,753,France,Female,39,7,155062.8,1,1,1,16460.77,0\r\n3118,15732987,Hs?,721,Spain,Male,43,3,88798.34,1,0,0,45610.63,0\r\n3119,15789432,Mazzanti,451,France,Male,33,6,0,2,1,0,184954.11,0\r\n3120,15663161,Chiu,680,Germany,Female,51,5,143139.87,1,0,0,47795.43,1\r\n3121,15694879,Reeves,590,Spain,Female,23,7,0,2,1,0,196789.9,0\r\n3122,15593715,Castiglione,634,Germany,Male,27,3,107027.52,1,1,0,173425.68,0\r\n3123,15575002,Ferguson,676,France,Male,29,4,140720.93,1,1,0,36221.18,0\r\n3124,15622171,Nnamdi,642,France,Male,30,8,80964.57,2,1,0,174738.2,0\r\n3125,15795224,Wu,760,France,Male,39,6,178585.46,1,1,0,67131.3,1\r\n3126,15685346,Chu,736,Spain,Female,26,4,135889.13,1,1,1,165692.03,0\r\n3127,15691808,King,656,France,Male,43,7,134919.85,1,1,0,194691.95,0\r\n3128,15721007,Charlton,776,Germany,Male,33,8,115130.34,1,0,0,129525.5,1\r\n3129,15794253,Marsh,832,Spain,Female,34,6,138190.13,2,0,1,146511.2,0\r\n3130,15694453,Walker,631,Germany,Male,37,9,131519.49,2,1,1,51752.18,0\r\n3131,15813113,Chang,795,Spain,Female,56,5,0,1,1,0,35418.69,1\r\n3132,15614187,Pottinger,648,Germany,Female,39,3,126935.98,2,0,1,57995.74,0\r\n3133,15619407,Buckley,615,France,Male,39,4,133707.09,1,1,1,108152.75,0\r\n3134,15646227,Folliero,682,France,Female,27,1,97893.2,1,1,0,166144.98,0\r\n3135,15660541,Olisanugo,694,France,Male,34,5,127900.03,1,1,0,101737.8,0\r\n3136,15753874,Kent,694,France,Male,37,10,143835.47,1,0,1,33326.71,0\r\n3137,15617877,Jessop,607,France,Male,44,0,0,2,1,1,81140.09,0\r\n3138,15772073,Hodge,664,France,Male,48,10,0,1,1,0,140173.17,1\r\n3139,15701537,Ignatiev,756,France,Male,60,2,0,1,1,1,166513.49,1\r\n3140,15736228,Chambers,645,France,Female,40,3,129596.77,1,1,1,103232.6,0\r\n3141,15780572,Mansom,653,Spain,Male,30,4,0,2,1,0,120736.04,0\r\n3142,15769596,Yen,710,Germany,Female,24,2,110407.44,2,0,0,15832.43,1\r\n3143,15586996,Azikiwe,697,France,Female,76,7,0,2,0,1,188772.45,0\r\n3144,15722061,Allen,619,Germany,Female,41,8,142015.76,2,1,0,114323.66,0\r\n3145,15638003,Komarova,648,Spain,Male,55,1,81370.07,1,0,1,181534.04,0\r\n3146,15775590,Mackay,482,Germany,Female,48,2,69329.47,1,0,0,102640.52,1\r\n3147,15730688,Yu,548,France,Female,28,8,116755.5,2,1,1,158585.17,1\r\n3148,15753102,Curtis,752,Spain,Male,44,6,83870.33,1,1,0,178722.24,0\r\n3149,15810075,Fang,648,France,Female,39,6,130694.89,2,1,1,153955.38,1\r\n3150,15723373,Page,643,Spain,Female,34,8,117451.47,1,1,0,65374.86,0\r\n3151,15795298,Olisaemeka,573,Germany,Female,35,9,206868.78,2,0,1,102986.15,0\r\n3152,15584320,Brennan,686,France,Female,39,3,111695.62,1,0,0,136643.84,0\r\n3153,15724161,Sutton,644,France,Female,40,9,137285.26,4,1,0,77063.63,1\r\n3154,15750056,Hyde,702,France,Female,29,6,149218.39,1,1,1,9633.01,0\r\n3155,15609637,Nkemakolam,652,France,Male,51,7,0,2,0,1,43496.36,0\r\n3156,15794493,Chimaijem,641,Spain,Male,32,7,0,2,1,1,24267.28,0\r\n3157,15569641,Sung,692,Germany,Female,41,8,130701.29,1,1,0,59354.24,1\r\n3158,15815236,Chiganu,574,Spain,Male,34,5,0,2,0,0,28269.86,0\r\n3159,15811177,Beneventi,643,France,Female,31,3,167949.48,1,1,0,143162.34,0\r\n3160,15680587,Esposito,834,France,Male,23,4,131254.81,1,1,0,20199.3,0\r\n3161,15672821,Owen,591,France,Male,28,5,0,2,1,1,48606.92,0\r\n3162,15767681,Smalley,470,Spain,Male,34,9,0,2,0,1,89013.67,0\r\n3163,15600379,Hsiung,608,Spain,Male,34,7,86656.13,1,0,1,59890.29,0\r\n3164,15801336,Ch'ang,649,Germany,Female,37,8,114737.26,1,1,1,106655.88,1\r\n3165,15721592,Barton,665,France,Female,38,5,0,2,1,0,156439.56,0\r\n3166,15581282,Lucchese,651,France,Female,39,6,0,1,1,0,24176.44,0\r\n3167,15746203,Hsia,555,Germany,Male,62,4,119817.33,1,0,1,43507.1,1\r\n3168,15583137,Pope,637,France,Female,48,7,130806.99,2,1,1,132005.85,1\r\n3169,15680752,Horrocks,675,France,Female,49,0,0,1,1,1,80496.71,1\r\n3170,15688172,Tai,677,Spain,Male,40,5,0,2,1,0,88947.56,0\r\n3171,15791373,Chikezie,850,Germany,Female,35,2,80931.75,1,0,0,12639.67,1\r\n3172,15589449,Frye,815,France,Female,56,3,0,3,1,1,94248.16,1\r\n3173,15692819,Toscani,665,Germany,Male,32,1,132178.67,1,0,0,11865.76,0\r\n3174,15727467,Mellor,485,France,Female,27,3,0,2,1,0,141449.86,0\r\n3175,15734312,Kang,577,Spain,Male,43,6,0,2,1,1,149457.81,0\r\n3176,15764604,Sutherland,586,France,Female,35,7,164769.02,3,1,0,119814.25,1\r\n3177,15613014,Hs?,722,Germany,Male,29,1,107233.85,2,1,0,24924.92,0\r\n3178,15759684,Ting,528,France,Female,27,7,176227.07,2,0,1,139481.53,0\r\n3179,15609669,Chuang,542,France,Female,39,4,109949.39,2,1,1,41268.65,0\r\n3180,15685536,Chu,552,France,Female,34,5,0,2,1,1,1351.41,0\r\n3181,15750447,Ozoemena,678,France,Female,60,10,117738.81,1,1,0,147489.76,1\r\n3182,15663249,Howells,575,Spain,Female,37,9,133292.45,1,1,0,111175.09,0\r\n3183,15638646,Lucchese,669,France,Female,43,1,160474.59,1,1,1,95963.14,0\r\n3184,15734161,Nnonso,636,France,Male,43,6,0,2,1,0,43128.95,0\r\n3185,15631070,Gerasimova,667,Germany,Male,55,9,154393.43,1,1,1,137674.96,1\r\n3186,15761950,Woronoff,652,Germany,Female,45,9,110827.49,1,1,1,153383.54,1\r\n3187,15649668,Wilhelm,637,Germany,Female,36,10,145750.45,2,1,1,96660.76,0\r\n3188,15713912,Nebechukwu,516,Spain,Female,45,8,109044.3,1,0,1,115818.16,0\r\n3189,15586757,Anenechukwu,801,France,Female,32,4,75170.54,1,1,1,37898.5,0\r\n3190,15596522,Meredith,692,France,Female,42,2,0,2,1,0,145222.93,0\r\n3191,15625395,Chinomso,585,France,Female,28,6,105795.9,1,1,1,41219.09,0\r\n3192,15760570,Stephenson,590,France,Male,32,5,0,2,1,0,59249.83,0\r\n3193,15566689,Chimaoke,554,Spain,Male,66,8,0,2,1,1,116747.62,0\r\n3194,15725794,Winters,659,France,Female,49,1,0,1,1,0,116249.72,1\r\n3195,15673539,Napolitani,690,France,Female,26,3,118097.87,1,1,0,61257.83,0\r\n3196,15705298,L?,697,Germany,Male,29,0,172693.54,1,0,0,141798.98,0\r\n3197,15675791,Williams,610,France,Male,36,4,129440.3,2,1,0,102638.35,0\r\n3198,15747043,Giles,599,Spain,Male,36,4,0,2,0,0,13210.56,0\r\n3199,15736397,Wang,544,France,Male,23,1,96471.2,1,1,0,35550.97,0\r\n3200,15678201,Robertson,548,France,Female,46,1,0,1,1,1,104469.06,1\r\n3201,15720745,Murray,635,Spain,Male,24,4,140197.18,1,1,1,142935.83,0\r\n3202,15637593,Greco,722,France,Male,20,6,0,2,1,0,195486.28,0\r\n3203,15598070,Marchesi,564,France,Female,33,4,135946.26,1,1,0,63170,0\r\n3204,15787550,Chao,719,France,Male,69,3,0,2,1,1,58320.06,0\r\n3205,15603942,Hawthorn,547,Germany,Male,50,3,81290.02,3,0,1,177747.03,1\r\n3206,15733973,Bibi,850,France,Female,42,8,0,1,1,0,19632.64,1\r\n3207,15596761,Hawdon,515,Germany,Male,60,9,113715.36,1,1,0,18424.24,1\r\n3208,15652400,Moss,667,Spain,Male,56,2,168883.08,1,0,1,18897.78,0\r\n3209,15717893,Briggs,607,Germany,Male,36,8,143421.74,1,1,0,97879.02,0\r\n3210,15622585,McIntyre,525,France,Male,26,7,153644.39,1,1,1,63197.88,0\r\n3211,15733964,Russo,606,Spain,Female,53,1,109330.06,1,1,1,75860.01,0\r\n3212,15753861,Ballard,686,Germany,Female,27,1,115095.88,2,0,0,78622.46,0\r\n3213,15747097,Hs?,611,France,Male,35,10,0,1,1,1,23598.23,1\r\n3214,15594762,Pisani,827,Spain,Male,46,1,183276.32,1,1,1,13460.27,0\r\n3215,15667417,Tao,572,France,Male,33,9,68193.72,1,1,0,19998.31,0\r\n3216,15684861,Thomson,726,France,Female,32,8,0,2,0,0,185075.63,0\r\n3217,15742204,Hsu,579,Germany,Male,31,6,139729.54,1,0,1,135815.38,0\r\n3218,15623502,Morrison,598,Spain,Female,56,4,98365.33,1,1,1,44251.33,0\r\n3219,15774872,Joslin,663,France,Male,36,10,0,2,1,0,136349.55,0\r\n3220,15611191,Scott,505,Germany,Female,37,10,122453.97,2,1,1,52693.99,0\r\n3221,15674331,Bidwill,576,Germany,Male,30,7,132174.41,2,0,0,93767.03,0\r\n3222,15619465,Cameron,555,Spain,Female,24,2,0,2,0,1,197866.55,0\r\n3223,15575247,Cartwright,524,France,Male,30,1,0,2,1,0,126812.85,0\r\n3224,15695679,Yao,776,Spain,Male,39,2,104349.45,1,0,0,79503.05,0\r\n3225,15713463,Tate,645,Germany,Female,41,2,138881.04,1,1,0,129936.53,1\r\n3226,15785170,Neal,850,Germany,Female,32,0,116968.91,1,0,0,175094.62,0\r\n3227,15796351,Yao,603,Germany,Male,35,1,105346.03,2,1,1,130379.5,0\r\n3228,15639576,Burns,691,France,Male,26,9,136623.19,1,1,0,153228,0\r\n3229,15693264,Onyinyechukwuka,583,France,Female,29,10,0,2,1,1,111285.85,0\r\n3230,15589715,Fulks,584,France,Female,66,5,0,1,1,0,49553.38,1\r\n3231,15769902,Christie,679,France,Female,33,6,0,2,1,1,98015.85,0\r\n3232,15587177,Lloyd,646,France,Male,36,6,124445.52,1,1,0,88481.32,0\r\n3233,15814553,Ball,559,France,Female,34,5,68999.66,2,1,1,66879.27,0\r\n3234,15601550,Genovesi,595,Spain,Male,36,6,85768.42,1,1,1,24802.77,0\r\n3235,15664907,Alexander,527,France,Male,47,1,0,1,1,0,21312.16,1\r\n3236,15612465,Siciliano,684,Spain,Male,34,9,100628,2,1,1,190263.78,0\r\n3237,15810800,Ositadimma,673,Spain,Female,32,0,0,1,1,1,72873.33,0\r\n3238,15665760,Kazantsev,802,Spain,Male,38,7,0,2,0,1,57764.65,0\r\n3239,15588080,Giles,675,France,Male,54,6,0,1,1,0,110273.84,1\r\n3240,15776844,Hao,762,Spain,Female,19,6,0,2,1,0,55500.17,0\r\n3241,15717560,Martin,580,France,Male,50,0,125647.36,1,1,0,57541.08,1\r\n3242,15629739,Hartley,621,Germany,Female,31,8,100375.39,1,1,1,90384.26,0\r\n3243,15729908,Allan,411,France,Female,36,10,0,1,0,0,120694.35,0\r\n3244,15716781,Dolgorukova,815,France,Male,24,7,171922.72,1,0,1,178028.96,0\r\n3245,15646936,Nnamdi,631,Germany,Female,32,2,146810.99,2,1,1,180990.29,0\r\n3246,15768151,Romano,514,Germany,Female,45,3,109032.23,1,0,1,155407.21,1\r\n3247,15579212,Chuang,638,France,Male,57,6,0,1,1,0,33676.48,1\r\n3248,15721835,Owen,791,Spain,Male,25,7,0,1,1,0,89666.28,0\r\n3249,15800515,Singh,516,France,Male,35,5,128653.59,1,1,0,127558.26,0\r\n3250,15591279,Nwagugheuzo,734,France,Male,37,3,80387.81,1,0,1,77272.62,0\r\n3251,15587419,Shipton,611,France,Male,58,8,0,2,0,1,107665.68,1\r\n3252,15750335,Paterson,850,Germany,Male,43,0,108508.82,3,1,0,184044.8,1\r\n3253,15699619,Rivas,641,France,Male,31,10,155978.17,1,1,0,91510.71,0\r\n3254,15606472,Lung,585,France,Female,38,5,0,1,1,1,87363.56,0\r\n3255,15778368,Allan,552,Germany,Male,50,4,121175.56,1,1,0,117505.07,1\r\n3256,15671387,Fetherstonhaugh,507,France,Female,29,4,89349.47,2,0,0,180626.68,0\r\n3257,15573926,Lung,735,Spain,Male,38,7,86131.71,2,0,0,93478.96,0\r\n3258,15709183,Davidson,707,France,Female,58,3,102346.86,1,1,1,114672.64,0\r\n3259,15577514,Mai,698,Germany,Female,36,7,121263.62,1,1,1,13387.88,0\r\n3260,15778830,Dellucci,841,France,Male,31,2,0,2,1,0,173240.52,0\r\n3261,15768072,Mitchell,688,Spain,Female,33,2,0,1,0,0,27557.18,1\r\n3262,15768293,Sun,614,France,Male,51,3,0,2,1,1,5552.37,0\r\n3263,15654456,Napolitano,511,Germany,Male,48,6,149726.08,1,0,0,88307.87,1\r\n3264,15807525,Bailey,447,France,Male,43,2,0,2,1,0,33879.26,1\r\n3265,15574372,Hoolan,738,France,Male,35,5,161274.05,2,1,0,181429.87,0\r\n3266,15671249,Kent,422,France,Female,33,2,0,2,1,0,102655.31,0\r\n3267,15779744,Chou,537,Spain,Male,30,1,103138.17,1,1,1,96555.42,0\r\n3268,15624755,Pepper,707,Germany,Female,40,3,109628.44,1,1,0,189366.03,0\r\n3269,15611430,Abramowitz,690,France,Male,54,5,0,1,1,0,12847.61,1\r\n3270,15774744,Lord,664,Germany,Male,33,7,97286.16,2,1,0,143433.33,0\r\n3271,15629885,Wilson,850,France,Female,33,7,118004.26,1,1,0,183983.82,0\r\n3272,15708791,Abazu,584,Spain,Male,32,9,85534.83,1,0,0,169137.24,0\r\n3273,15793890,Harriman,728,France,Female,59,4,0,1,1,1,163365.85,1\r\n3274,15646091,Frankland,560,Spain,Female,43,4,95140.44,2,1,0,123181.44,1\r\n3275,15596984,Pinto,629,France,Female,31,6,0,1,1,1,16447.6,1\r\n3276,15800215,Kwemtochukwu,658,France,Male,25,3,0,2,0,1,173948.4,0\r\n3277,15577806,Chiu,794,Germany,Female,54,1,75900.84,1,1,1,192154.66,0\r\n3278,15749381,Yu,790,France,Female,41,2,126619.27,1,1,0,198224.38,0\r\n3279,15683758,Onyekachukwu,640,France,Male,44,7,111833.47,1,1,0,67202.74,0\r\n3280,15670615,Castiglione,652,Spain,Male,37,7,0,2,1,0,68789.93,0\r\n3281,15715622,To Rot,583,France,Female,57,3,238387.56,1,0,1,147964.99,1\r\n3282,15707634,Anenechukwu,775,France,Female,32,2,108698.96,2,1,1,161069.73,0\r\n3283,15806901,Henderson,584,France,Female,39,2,112687.69,1,1,1,127749.61,0\r\n3284,15775335,Ellis,635,Germany,Female,48,4,81556.89,2,1,0,191914.37,0\r\n3285,15724150,Nkemdirim,814,France,Male,48,9,136596.85,1,1,1,185791.9,0\r\n3286,15627220,Kang,735,Germany,Female,43,9,98807.45,1,0,0,184570.04,1\r\n3287,15672330,Lear,678,France,Female,31,1,0,2,0,1,130446.65,0\r\n3288,15668521,Jamieson,693,France,Male,37,1,0,2,1,1,82867.55,0\r\n3289,15807837,Mazzanti,640,France,Female,30,6,107499.7,1,1,1,187632.22,0\r\n3290,15592570,Marino,773,Spain,Female,23,8,0,2,1,0,56759.79,0\r\n3291,15748589,Winter,736,France,Female,30,9,0,2,1,0,34180.33,0\r\n3292,15635893,T'ien,693,France,Female,28,8,0,2,1,1,158545.25,0\r\n3293,15757632,Hughes-Jones,496,France,Female,41,1,176024.05,2,1,0,182337.98,0\r\n3294,15691863,Cody,751,France,Female,39,3,0,2,1,1,84175.34,0\r\n3295,15706071,Hunt,528,Germany,Male,39,0,127631.62,1,0,1,22197.8,1\r\n3296,15654296,Estrada,754,Spain,Female,19,9,0,1,1,0,189641.11,0\r\n3297,15755018,Dickinson,568,Germany,Female,26,10,109819.16,2,1,0,154491.39,0\r\n3298,15594041,Fanucci,592,Spain,Female,41,2,138734.94,1,1,0,90020.74,0\r\n3299,15670587,Yang,558,Germany,Male,25,10,111363.1,2,1,0,197264.35,0\r\n3300,15724527,Forbes,825,France,Male,34,9,0,2,1,1,31933.06,0\r\n3301,15801904,Heard,677,Germany,Male,28,0,143988,2,1,0,8755.69,1\r\n3302,15658195,Efremova,653,France,Male,34,5,118838.75,1,1,1,52820.13,0\r\n3303,15630113,Morphett,593,Spain,Male,35,4,161637.75,1,1,1,20008.46,0\r\n3304,15784320,Lenhardt,632,France,Female,44,3,133793.89,1,1,1,34607.14,1\r\n3305,15676513,Burns,601,Germany,Male,35,8,71553.83,1,1,0,177384.45,0\r\n3306,15574072,Ch'ien,786,France,Female,62,8,0,1,1,1,165702.64,0\r\n3307,15633854,Sun,654,France,Female,40,3,0,2,1,0,167889.1,0\r\n3308,15618566,Jamieson,572,France,Female,38,7,0,2,1,1,133122.62,0\r\n3309,15733014,Nolan,813,France,Female,62,10,64667.95,2,0,1,140454.14,0\r\n3310,15753343,Barry,523,France,Female,28,2,121164.11,1,1,1,59938.81,0\r\n3311,15746076,Saunders,506,Spain,Male,50,3,0,2,1,0,12016.79,0\r\n3312,15608226,McMorran,513,Spain,Male,72,3,98903.06,1,1,1,81251.24,0\r\n3313,15605684,Phelan,664,France,Female,31,7,104158.84,1,1,0,134169.85,0\r\n3314,15638988,Fu,684,France,Male,54,6,0,2,1,1,94888.6,0\r\n3315,15628767,Hotchin,608,Spain,Female,63,3,139529.93,2,1,1,175696.16,1\r\n3316,15737977,Aksyonov,527,France,Female,25,6,0,2,0,1,96758.58,0\r\n3317,15758116,Rossi,666,France,Male,53,5,64646.7,1,1,0,128019.48,1\r\n3318,15575119,Hughes,779,France,Male,71,3,0,2,1,1,146895.36,1\r\n3319,15625126,Duncan,629,France,Female,40,6,0,2,1,1,139356.3,0\r\n3320,15567114,McGarry,430,France,Male,35,1,118894.22,1,0,0,2923.61,0\r\n3321,15672242,Aksenov,712,France,Male,24,2,0,1,0,1,121232.51,0\r\n3322,15681327,Akhtar,682,France,Male,30,9,0,2,1,1,2053.42,0\r\n3323,15802585,Pisani,634,France,Female,41,8,68213.99,1,1,1,6382.46,0\r\n3324,15740630,Pisano,487,Spain,Female,31,1,0,2,1,0,158750.13,0\r\n3325,15815420,McDaniels,808,Spain,Male,47,8,139196,1,0,1,74028.36,0\r\n3326,15711468,Tennant,527,France,Female,32,7,0,2,1,1,44099.75,0\r\n3327,15799626,Donaghy,637,Germany,Male,50,4,126345.55,1,0,1,17323,1\r\n3328,15659325,Todd,802,Spain,Male,40,5,0,2,1,1,175043.69,0\r\n3329,15651352,Tobenna,529,France,Female,38,2,0,1,1,0,146388.85,1\r\n3330,15684925,Vicars,850,France,Female,43,3,0,2,0,0,2465.8,0\r\n3331,15657439,Chao,738,France,Male,18,4,0,2,1,1,47799.15,0\r\n3332,15574122,Tien,817,France,Male,34,5,129278.43,1,0,0,165562.84,0\r\n3333,15720508,Hsing,735,France,Male,31,3,119558.35,1,0,0,72927.68,0\r\n3334,15599078,Yang,619,Germany,Female,41,5,92467.58,1,1,0,38270.47,0\r\n3335,15702300,Walker,671,France,Male,27,5,0,2,0,0,120893.07,0\r\n3336,15660735,T'ang,581,Spain,Female,31,6,0,2,1,0,188377.21,0\r\n3337,15671390,Chukwukere,690,Spain,Male,36,10,0,2,1,0,55902.93,0\r\n3338,15647385,Ch'iu,579,Spain,Male,56,4,99340.83,1,0,0,4523.74,1\r\n3339,15739223,Pai,688,Spain,Female,24,3,0,2,1,1,102195.16,0\r\n3340,15631305,Franklin,599,Spain,Female,28,4,126833.79,2,1,0,60843.09,1\r\n3341,15809263,Y?,729,Germany,Male,29,5,109676.52,1,1,1,25548.47,0\r\n3342,15640866,Peng,718,France,Female,29,3,0,1,0,1,134462.29,0\r\n3343,15775663,Otitodilichukwu,712,Germany,Male,53,6,134729.99,2,1,1,132702.64,0\r\n3344,15631800,Pagnotto,474,France,Male,37,3,98431.37,1,0,0,75698.44,0\r\n3345,15654292,Vessels,565,Germany,Male,33,8,130368.31,2,1,0,105642.43,0\r\n3346,15648320,Heller,658,France,Female,31,7,123974.96,1,1,0,102153.75,0\r\n3347,15726747,Donaldson,714,France,Male,63,4,138082.16,1,0,1,166677.54,0\r\n3348,15694510,Ifeanyichukwu,725,France,Male,45,1,129855.32,1,0,0,24218.65,0\r\n3349,15572291,Kao,825,France,Male,40,6,132308.22,1,0,0,117122.5,0\r\n3350,15603465,Dunn,665,Germany,Female,45,5,155447.65,2,1,0,51871.95,1\r\n3351,15685628,Calabresi,670,Spain,Male,35,2,124268.64,2,0,1,84321.03,0\r\n3352,15792729,Holland,474,Germany,Female,34,9,176311.36,1,1,0,160213.27,0\r\n3353,15767414,Calabresi,591,France,Male,40,2,99886.42,2,1,1,88695.19,0\r\n3354,15568044,Butusov,508,France,Female,31,7,0,2,1,1,6123.15,0\r\n3355,15751333,Atkinson,695,France,Female,36,2,0,2,0,1,167749.54,0\r\n3356,15623062,Vasilyeva,660,Germany,Male,24,5,85089.3,1,1,1,71638,0\r\n3357,15713621,Mollison,687,Germany,Male,41,10,134318.21,2,1,1,198064.52,0\r\n3358,15670668,Webb,658,Germany,Male,29,5,75395.53,2,0,1,54914.92,0\r\n3359,15750638,Obiajulu,705,Germany,Female,33,5,116765.7,1,0,0,190659.17,1\r\n3360,15747878,Aiken,739,Spain,Male,60,4,0,1,1,1,51637.67,0\r\n3361,15726796,Brabyn,844,France,Male,38,7,111501.66,1,1,1,119333.38,0\r\n3362,15754952,Su,602,Germany,Female,48,7,76595.08,2,0,0,127095.14,0\r\n3363,15652192,Traeger,759,France,Female,33,9,160541.36,2,0,0,93541.14,0\r\n3364,15681924,Ekwueme,747,Germany,Male,38,2,129728.6,1,1,0,89289.54,0\r\n3365,15763544,Thompson,673,France,Male,47,1,0,2,0,0,108762.16,0\r\n3366,15764431,Chinwenma,671,Spain,Female,34,5,130929.02,4,1,1,28238.25,1\r\n3367,15684010,Tuan,640,Germany,Female,74,2,116800.25,1,1,1,34130.43,0\r\n3368,15648881,Tsai,581,Germany,Male,40,0,101016.53,1,0,1,7926.35,1\r\n3369,15733303,Liu,630,France,Male,67,5,0,2,1,1,27330.27,0\r\n3370,15643294,Robinson,703,France,Female,33,8,190566.65,1,1,1,79997.14,0\r\n3371,15749905,Carr,698,Spain,Female,47,6,0,1,1,0,50213.81,1\r\n3372,15625175,Palerma,742,Germany,Female,43,6,97067.69,1,0,1,60920.03,1\r\n3373,15643967,Chineze,652,France,Female,37,4,92208.54,1,0,1,197699.8,1\r\n3374,15578251,Fang,644,France,Male,37,2,186347.97,2,1,0,92809.73,0\r\n3375,15772573,Simpson,735,Spain,Male,55,2,103176.62,1,0,1,163516.16,0\r\n3376,15733234,Moretti,777,France,Female,58,4,0,1,1,1,62449.07,1\r\n3377,15721582,Hale,644,Germany,Female,40,4,77270.08,2,1,1,115800.1,1\r\n3378,15628219,Benson,665,Germany,Female,37,3,111911.63,1,1,1,110359.68,1\r\n3379,15571302,Estep,529,Germany,Male,72,5,94216.05,1,1,1,78695.68,0\r\n3380,15637178,Mishina,803,Spain,Female,45,7,0,2,1,1,128378.04,0\r\n3381,15601184,Abramovich,604,Spain,Female,26,3,0,2,1,0,155248.62,0\r\n3382,15629511,Lavrentiev,738,France,Male,49,6,106770.82,1,1,0,123499.27,0\r\n3383,15570629,Alexeyeva,655,Germany,Female,72,5,138089.97,2,1,1,99920.41,0\r\n3384,15665766,T'ang,698,Germany,Male,39,9,133191.19,2,0,1,53289.49,0\r\n3385,15693732,Kilgour,775,France,Female,66,9,0,2,1,1,67622.34,0\r\n3386,15765982,Chin,735,France,Male,41,7,74135.85,1,1,1,11783.1,1\r\n3387,15582016,Fiorentini,766,Spain,Male,41,6,99208.46,2,1,0,62402.38,0\r\n3388,15798024,Lori,537,Germany,Male,84,8,92242.34,1,1,1,186235.98,0\r\n3389,15588622,Marchesi,599,Germany,Male,25,7,108380.72,1,1,1,79005.95,0\r\n3390,15724863,Sheppard,420,Spain,Female,55,4,91893.32,1,1,0,144870.28,1\r\n3391,15618213,Nnanna,674,France,Female,32,7,85757.93,1,1,1,95481,0\r\n3392,15780411,Norris,570,France,Female,46,3,0,2,0,0,820.46,0\r\n3393,15725429,Vincent,623,Germany,Male,33,8,96759.42,1,1,1,174777.98,0\r\n3394,15600626,Bradley,710,France,Male,30,6,0,2,1,1,8991.17,0\r\n3395,15668460,Bellucci,466,France,Male,29,6,0,2,1,1,2797.27,0\r\n3396,15576263,Clements,759,France,Female,22,5,0,1,1,0,22303.17,0\r\n3397,15720354,Knowles,581,France,Male,71,4,0,2,1,1,197562.08,0\r\n3398,15691624,Chidiebere,820,France,Male,33,2,132150.26,2,1,0,23067.97,0\r\n3399,15793196,Kelly,759,France,Male,41,9,0,2,0,1,190294.12,0\r\n3400,15633352,Okwukwe,628,France,Female,31,6,175443.75,1,1,0,113167.17,1\r\n3401,15750874,Onyemere,676,France,Male,31,3,78990.15,1,1,1,124777.14,0\r\n3402,15588923,Murphy,591,France,Female,33,4,113743.37,1,1,0,124625.08,0\r\n3403,15715745,Elliott,690,France,Female,26,5,157624.84,1,1,1,49599.27,0\r\n3404,15611800,Loggia,624,France,Female,62,7,125163.62,2,1,1,151411.5,0\r\n3405,15576928,Walsh,573,France,Female,23,2,0,1,1,0,122964.18,0\r\n3406,15793693,Mahomed,694,France,Male,60,9,0,1,1,1,57088.97,0\r\n3407,15581252,Dolgorukova,632,Spain,Female,29,7,80922.75,1,1,0,7820.78,0\r\n3408,15797760,Bogdanov,632,France,Male,40,3,193354.86,2,1,0,149188.41,0\r\n3409,15790564,She,832,Germany,Female,40,9,107648.94,2,1,1,134638.97,0\r\n3410,15593736,Cook,598,Germany,Female,46,7,131769.04,1,0,0,184980.23,1\r\n3411,15595937,Bruno,430,Germany,Male,36,1,138992.48,2,0,0,122373.42,0\r\n3412,15815628,Moysey,711,France,Female,37,8,113899.92,1,0,0,80215.2,0\r\n3413,15782802,Beneventi,582,Germany,Male,26,6,114450.32,1,1,1,14081.64,0\r\n3414,15627412,Ferri,605,France,Male,39,3,0,2,1,0,199390.45,0\r\n3415,15734609,Skinner,657,France,Female,37,2,0,2,1,1,7667.48,0\r\n3416,15710689,Angel,578,Spain,Male,40,6,63609.92,1,0,0,74965.61,1\r\n3417,15565806,Toosey,532,France,Male,38,9,0,2,0,0,30583.95,0\r\n3418,15815530,Chin,612,France,Female,42,10,75497.51,1,0,0,149682.78,0\r\n3419,15632272,Lung,792,France,Female,42,2,0,2,1,0,92664.09,0\r\n3420,15684103,Mellor,674,France,Female,26,10,0,2,1,1,138423.1,0\r\n3421,15654519,Hassall,680,France,Male,31,1,0,2,1,1,3148.2,0\r\n3422,15767722,Richardson,593,France,Female,39,0,117704.73,1,1,0,197933.5,0\r\n3423,15654346,Poninski,679,Germany,Male,35,1,130463.55,2,1,1,37341.17,0\r\n3424,15660147,Dore,493,Spain,Male,32,8,46161.18,1,1,1,79577.4,0\r\n3425,15814998,Bonham,688,Spain,Male,42,5,0,2,0,0,197602.29,0\r\n3426,15802207,Ibezimako,769,Germany,Male,43,4,110182.54,2,1,1,87537.32,0\r\n3427,15658668,Hunter,581,Spain,Male,49,10,0,2,0,0,41623.59,0\r\n3428,15715079,Bold,465,France,Male,41,9,117221.15,1,1,0,168280.95,0\r\n3429,15570360,Wan,641,France,Female,35,4,0,2,0,0,125986.18,0\r\n3430,15674678,Bradley,731,Germany,Female,43,9,79120.27,1,0,0,548.52,1\r\n3431,15780925,Tretyakova,625,France,Male,37,1,177069.24,2,1,1,96088.54,0\r\n3432,15688193,Graham,468,France,Male,36,3,61636.97,1,0,0,107787.42,0\r\n3433,15778219,Izmailov,790,France,Male,26,5,0,1,1,0,20510.79,0\r\n3434,15696514,Calabrese,587,Germany,Female,37,6,104414.03,1,1,0,192026.02,0\r\n3435,15712303,Valentin,692,France,Male,66,4,159732.02,1,1,1,118188.15,0\r\n3436,15719090,Osonduagwuike,676,Germany,Female,34,4,89437.03,1,1,1,189540.95,0\r\n3437,15735632,Williamson,571,France,Male,41,8,0,1,1,1,63736.17,0\r\n3438,15619436,Pan,700,France,Female,32,3,0,1,0,0,95740.37,0\r\n3439,15722404,Carpenter,445,France,Female,30,3,0,2,1,1,127939.19,0\r\n3440,15662063,McIver,746,France,Male,36,7,142400.77,1,1,1,193438.69,0\r\n3441,15745605,Trevisan,722,France,Female,47,2,88011.4,1,1,1,90655.94,1\r\n3442,15636658,Rozhkova,596,France,Male,36,2,0,2,1,1,12067.39,0\r\n3443,15784130,He,850,Germany,Female,30,8,154870.28,1,1,1,54191.38,0\r\n3444,15606755,Moretti,597,Spain,Female,46,4,0,2,1,0,58667.16,1\r\n3445,15801699,Fishbourne,436,Spain,Male,43,5,0,2,1,1,35687.43,0\r\n3446,15784097,Gibson,660,Germany,Male,28,1,118402.25,2,1,0,14288.93,0\r\n3447,15764654,Zikoranachidimma,649,France,Male,37,9,87374.88,2,1,1,247.36,0\r\n3448,15612092,Palmer,646,Germany,Male,32,8,105397.8,1,1,0,78111.84,1\r\n3449,15610903,Chukwueloka,560,Spain,Female,31,5,125341.69,1,1,0,79547.39,0\r\n3450,15705777,Real,710,Germany,Male,49,10,129164.88,1,1,1,193266.72,0\r\n3451,15661936,Chikelu,513,France,Male,40,3,141004.46,1,1,0,105028.46,0\r\n3452,15700864,Fiorentini,607,France,Female,21,0,0,2,1,0,116106.52,0\r\n3453,15722965,Yefimova,757,France,Male,57,3,89079.41,1,1,1,53179.21,1\r\n3454,15737521,Ball,619,Germany,Male,40,9,103604.31,2,0,0,140947.05,0\r\n3455,15814465,Ch'in,612,France,Male,24,1,182705.05,1,1,1,171837.06,0\r\n3456,15580988,Odell,842,France,Male,29,8,0,2,1,1,123437.05,0\r\n3457,15789974,Enemuo,713,France,Male,33,6,94598.48,1,0,0,197519.66,1\r\n3458,15713370,Hunter,657,Spain,Male,36,8,188241.05,2,0,0,183058.51,1\r\n3459,15748673,Nepean,770,France,Female,37,9,0,2,0,0,22710.72,0\r\n3460,15754919,Nwebube,773,France,Female,40,10,0,2,0,1,69303.15,0\r\n3461,15641662,Enticknap,470,Germany,Male,39,5,117469.91,2,0,0,63705.9,0\r\n3462,15813422,Lu,781,Spain,Male,35,4,80790.74,1,1,0,116429.51,0\r\n3463,15713596,Ugochukwu,428,France,Female,62,1,107735.93,1,0,1,58381.77,0\r\n3464,15791216,Mann,600,Germany,Male,43,8,133379.41,1,1,0,177378.66,1\r\n3465,15689031,Murphy,697,Spain,Female,37,7,168066.87,1,1,0,35450.53,0\r\n3466,15763704,Docherty,692,Germany,Female,43,2,69014.49,2,0,0,164621.43,0\r\n3467,15631339,Adams,791,France,Male,28,4,0,1,1,0,174435.48,0\r\n3468,15771509,Hirst,538,Germany,Female,42,1,98548.62,2,0,1,94047.75,0\r\n3469,15769586,Horan,820,France,Female,49,1,0,2,1,1,119087.25,0\r\n3470,15656096,Cumbrae-Stewart,679,Spain,Female,26,3,76554.06,1,1,1,184800.27,0\r\n3471,15585280,Kinney,649,France,Female,36,2,0,2,0,1,75035.48,0\r\n3472,15743582,T'ang,632,France,Female,27,3,107375.82,1,1,1,62703.38,0\r\n3473,15761692,Muir,594,France,Male,40,9,122417.17,2,0,1,190882.69,0\r\n3474,15627840,Toscano,682,France,Female,42,0,0,1,0,1,91981.85,1\r\n3475,15778861,Wallace,720,Spain,Male,33,6,97188.62,1,0,0,91881.29,0\r\n3476,15770554,Fraser,769,France,Male,31,4,61297.05,2,1,1,7118.02,0\r\n3477,15806956,Iqbal,746,Spain,Male,30,1,112666.67,1,0,0,11710.4,1\r\n3478,15701908,Nina,623,Spain,Female,40,7,0,1,1,1,25904.12,0\r\n3479,15736990,Chuang,537,France,Male,28,3,157842.07,1,1,0,86911.49,0\r\n3480,15743714,Ch'ien,468,France,Male,46,7,91443.75,1,1,0,10958.18,0\r\n3481,15807993,Bruno,588,Germany,Female,30,0,110148.49,1,1,0,5790.9,1\r\n3482,15644686,Kennedy,729,Spain,Female,34,9,53299.96,2,1,1,42855.97,0\r\n3483,15677377,Lawrence,543,Spain,Male,37,3,0,2,1,1,78915.68,0\r\n3484,15626412,Mort,499,Spain,Male,39,6,0,2,1,1,81409,0\r\n3485,15643679,Goliwe,784,Germany,Male,28,2,70233.74,2,1,1,179252.73,0\r\n3486,15728456,Martinez,604,France,Male,33,3,0,1,1,0,42171.13,1\r\n3487,15630661,Vasilyev,614,Spain,Female,25,10,75212.28,1,1,0,58965.04,0\r\n3488,15734044,Black,671,France,Female,31,7,41299.03,1,0,1,102681.32,0\r\n3489,15705001,Napolitani,587,Spain,Female,35,3,83286.56,1,1,0,125553.52,0\r\n3490,15809817,Ch'en,593,Spain,Male,43,10,0,2,0,0,53478.02,0\r\n3491,15809137,Sagese,453,France,Male,29,6,0,1,0,0,198376.02,1\r\n3492,15751593,Fraser,570,Germany,Male,35,6,85668.59,1,1,0,105525.36,0\r\n3493,15626491,Hughes,655,France,Female,45,7,57327.04,1,0,1,47349,0\r\n3494,15765461,Giles,632,Spain,Male,47,3,0,2,1,0,178822.32,0\r\n3495,15568120,Lacross,681,France,Female,37,7,69609.85,1,1,1,72127.83,0\r\n3496,15787161,Pisani,591,Germany,Male,46,4,129269.27,1,1,0,163504.33,0\r\n3497,15812324,King,779,France,Male,27,1,0,2,1,1,190623.02,0\r\n3498,15588944,Maughan,456,France,Female,63,1,165350.61,2,0,0,140758.07,1\r\n3499,15694253,Palerma,686,France,Female,41,7,152105.57,2,0,1,132374.41,0\r\n3500,15759566,Tochukwu,617,France,Male,74,10,0,2,1,1,53949.98,0\r\n3501,15675675,Slate,850,France,Female,32,5,106290.64,1,1,0,121982.73,0\r\n3502,15802060,Ch'ang,646,Germany,Female,30,10,100548.67,2,0,0,136983.77,0\r\n3503,15660505,Romani,735,Germany,Male,46,2,106344.95,1,1,0,114371.33,1\r\n3504,15782630,Genovese,543,France,Male,35,5,137482.19,1,0,0,62389.35,0\r\n3505,15700710,Chiebuka,490,France,Female,37,3,116465.53,1,0,1,24435.77,0\r\n3506,15742834,Liao,640,France,Male,45,1,0,1,1,1,10908.33,0\r\n3507,15806511,Berry,445,Spain,Male,45,10,0,2,0,1,90977.48,0\r\n3508,15608166,Fallaci,761,France,Male,36,9,127637.92,1,1,1,81062.93,0\r\n3509,15614230,T'an,426,France,Female,34,3,0,2,1,1,61230.83,0\r\n3510,15729958,Wilkinson,777,France,Male,37,1,0,1,1,1,126837.72,0\r\n3511,15800814,Palerma,534,France,Male,35,2,81951.74,2,1,0,115668.53,0\r\n3512,15674727,Lazarev,777,France,Female,42,5,147531.82,1,1,1,38819.45,0\r\n3513,15657779,Boylan,806,Spain,Male,18,3,0,2,1,1,86994.54,0\r\n3514,15801395,Warren,790,France,Female,33,10,135120.72,1,0,0,195204.99,0\r\n3515,15757911,Trevisani,643,Spain,Female,32,2,0,1,0,0,131301.74,0\r\n3516,15665340,Trevisano,584,Spain,Female,37,8,0,2,0,1,100835.19,0\r\n3517,15787151,Liao,638,France,Female,34,7,0,2,1,1,198969.78,0\r\n3518,15757821,Burgess,771,Spain,Male,18,1,0,2,0,0,41542.95,0\r\n3519,15600688,Liston,600,France,Female,39,5,0,2,0,0,118272.07,0\r\n3520,15594878,Thompson,661,Spain,Female,41,5,28082.95,1,1,0,69586.27,1\r\n3521,15569248,Milanesi,554,France,Female,43,10,0,2,1,0,149629.13,1\r\n3522,15812706,Mazure,627,Spain,Male,49,4,111087.5,1,0,1,146680.25,0\r\n3523,15645045,Rudduck,659,France,Female,38,9,0,2,1,1,132809.18,0\r\n3524,15766746,Darwin,835,France,Male,35,6,127120.07,1,1,0,28707.69,0\r\n3525,15700383,Uvarova,763,France,Female,35,7,115651.6,2,1,1,104706.29,0\r\n3526,15632551,Buccho,625,Germany,Male,31,4,77743.01,2,1,0,75335.68,0\r\n3527,15795129,Gallo,799,France,Female,30,9,0,2,1,0,136827.96,0\r\n3528,15650545,Tomlinson,849,France,Male,69,7,71996.09,1,1,1,139065.94,0\r\n3529,15612769,Carr,692,France,Male,28,5,61581.97,1,1,1,70179.91,0\r\n3530,15710853,Ts'ui,623,France,Female,24,5,0,2,1,0,116160.04,0\r\n3531,15623712,Coates,453,Spain,Female,42,5,0,3,1,0,83008.49,1\r\n3532,15653251,Hickey,408,France,Female,84,8,87873.39,1,0,0,188484.52,1\r\n3533,15755077,Norton,778,Germany,Female,37,0,105617.73,2,1,1,133699.82,1\r\n3534,15808557,Mancini,695,France,Female,42,5,0,1,0,1,72172.13,1\r\n3535,15614687,Tien,677,Germany,Female,44,4,148770.61,2,1,1,191057.76,0\r\n3536,15626882,Stobie,662,Spain,Male,37,5,94901.09,1,1,1,48233.75,0\r\n3537,15748034,Drakeford,534,France,Male,29,7,174851.9,1,1,1,79178.31,0\r\n3538,15632324,Pisani,602,France,Male,59,7,0,2,1,1,162347.05,0\r\n3539,15761023,Murphy,554,Germany,Female,43,2,120847.11,1,1,0,7611.61,1\r\n3540,15761453,Kovalev,667,France,Male,42,6,0,1,1,0,88890.05,0\r\n3541,15646726,Crawford,672,France,Male,43,5,0,1,0,0,63833.09,0\r\n3542,15637169,Maclean,838,Spain,Female,67,4,103267.8,1,1,1,78310.04,0\r\n3543,15636024,Blackburn,692,Spain,Female,34,4,109699.08,1,1,1,37898.91,0\r\n3544,15801218,Bermudez,675,France,Male,49,8,135133.39,1,0,1,179521.24,1\r\n3545,15642655,Savage,731,Spain,Male,33,1,0,1,1,0,130726.96,0\r\n3546,15690130,Wyatt,468,France,Female,32,8,137649.47,1,0,0,198714.29,0\r\n3547,15653753,Chiemenam,542,Spain,Male,43,6,113567.94,1,1,0,89543.25,0\r\n3548,15641359,Shao,662,Spain,Female,35,6,0,2,0,0,2423.9,1\r\n3549,15776827,Langdon,770,Germany,Male,37,5,141547.26,2,0,1,180326.83,0\r\n3550,15647725,Napolitano,675,France,Female,61,5,62055.17,3,1,0,166305.16,1\r\n3551,15648455,Kung,647,Germany,Male,51,4,131156.76,1,1,0,29883.63,0\r\n3552,15580629,Blackwood,604,France,Male,31,6,134837.58,1,1,0,192029.19,0\r\n3553,15730161,Marcelo,833,France,Female,39,3,0,2,1,0,1710.89,0\r\n3554,15626612,Yin,741,Spain,Male,40,4,104784.23,1,1,0,135163.76,1\r\n3555,15662865,Storey,658,Spain,Male,36,1,0,2,0,1,84927.42,0\r\n3556,15629094,Fomin,528,France,Female,36,1,156948.41,1,1,1,149912.28,1\r\n3557,15651823,Nkemjika,590,France,Female,60,6,147751.75,1,1,0,88206.04,1\r\n3558,15594827,Glasgow,675,France,Male,34,1,124619.33,2,0,1,163667.56,0\r\n3559,15786392,Chen,765,France,Male,41,4,124182.21,1,0,0,100153.43,0\r\n3560,15727353,Ch'ang,650,France,Female,64,7,142028.36,1,1,0,32275.09,1\r\n3561,15733777,Evans,817,France,Male,44,8,0,1,0,0,65501.91,1\r\n3562,15614302,Crotty,699,Germany,Female,31,10,125837.86,2,1,0,189392.66,0\r\n3563,15723263,Cocci,495,Germany,Female,34,9,117160.32,1,1,1,116069.24,1\r\n3564,15687270,Iroawuchi,491,Spain,Female,61,8,0,2,0,1,139861.53,0\r\n3565,15803121,Chia,847,France,Male,51,5,97565.74,1,0,0,144184.06,1\r\n3566,15598700,Hysell,676,Spain,Female,30,5,0,2,0,1,157888.5,0\r\n3567,15741875,Williamson,746,Spain,Female,25,3,104833.79,1,0,0,71911.3,0\r\n3568,15631709,Ginikanwa,470,Spain,Female,31,2,101675.22,2,1,0,45033.75,0\r\n3569,15672970,Chigolum,714,Spain,Male,20,3,0,2,0,1,150465.93,0\r\n3570,15761670,Morley,695,France,Female,50,8,0,1,1,0,126381.6,1\r\n3571,15706005,Roberts,674,France,Male,46,2,174701.05,1,1,0,90189.72,1\r\n3572,15790336,Tokareva,664,Germany,Male,36,6,71142.77,2,1,0,122433.09,0\r\n3573,15754267,Fleming,697,Germany,Male,31,3,108805.42,2,0,1,123825.83,0\r\n3574,15791988,Chinomso,670,France,Male,68,4,0,2,1,1,11426.7,0\r\n3575,15683375,Compton,541,France,Female,32,4,0,1,1,1,114951.42,0\r\n3576,15625151,Wan,640,France,Female,66,9,116037.76,1,0,1,184636.05,0\r\n3577,15635285,Taylor,647,France,Male,28,8,0,2,1,1,91055.27,0\r\n3578,15574296,Kambinachi,757,France,Male,23,2,80673.96,2,1,0,93991.65,0\r\n3579,15711618,Chang,704,Germany,Female,39,1,124640.51,1,1,0,116511.12,1\r\n3580,15670943,See,778,Germany,Male,31,9,182275.23,2,1,0,190631.23,0\r\n3581,15634359,Dyer,639,Germany,Female,41,5,98635.77,1,1,0,199970.74,0\r\n3582,15586629,Campbell,637,France,Male,33,5,0,2,1,0,139947.17,0\r\n3583,15588461,Cremonesi,686,France,Male,35,4,0,1,1,0,8816.37,0\r\n3584,15773221,Harris,577,Spain,Male,43,8,79757.21,1,1,0,135650.72,1\r\n3585,15664227,Threatt,506,Germany,Male,28,8,53053.76,1,0,1,24577.34,0\r\n3586,15741745,Lane,757,France,Male,28,7,120911.75,2,1,1,131249.46,0\r\n3587,15652626,Grave,826,France,Male,55,4,115285.85,1,1,0,140126.17,0\r\n3588,15599410,Stanley,721,France,Male,41,2,0,2,1,0,168219.75,0\r\n3589,15571958,McIntosh,489,Spain,Male,40,3,221532.8,1,1,0,171867.08,0\r\n3590,15785406,Watts,446,France,Female,51,4,105056.13,1,0,0,70613.52,0\r\n3591,15687884,Alekseyeva,677,France,Male,37,3,88363.03,1,0,1,117946.3,0\r\n3592,15621685,Davies,769,France,Male,29,2,123757.52,2,1,0,84872.66,0\r\n3593,15628886,Matlock,677,Spain,Male,56,5,123959.97,1,1,1,60590.72,1\r\n3594,15699325,Fedorova,555,Germany,Female,62,10,114822.64,1,0,1,8444.5,0\r\n3595,15578369,Chiedozie,652,Germany,Female,37,9,145219.3,1,1,0,159132.83,0\r\n3596,15654156,Marcelo,722,Germany,Female,32,5,106807.64,1,1,1,76998.69,0\r\n3597,15707199,Cooper,643,France,Male,36,0,148159.71,1,0,0,55835.66,0\r\n3598,15671630,McMillan,796,Germany,Female,40,1,99745.95,1,1,0,177524.19,0\r\n3599,15632079,Hardy,720,Germany,Female,37,8,156282.79,1,1,0,45985.52,0\r\n3600,15767921,Madukwe,613,France,Male,41,7,0,2,1,0,60297.72,0\r\n3601,15573599,Adamson,506,France,Female,57,6,0,2,0,1,194421.12,1\r\n3602,15747208,Watt,608,France,Male,50,6,0,1,1,0,93568.77,1\r\n3603,15582762,Mazzanti,667,Spain,Male,77,2,0,1,1,1,34702.92,0\r\n3604,15772528,Mishin,750,France,Female,47,7,121376.15,2,1,0,54473.6,1\r\n3605,15755798,Feng,610,France,Male,33,4,111582.11,1,0,0,113943.17,0\r\n3606,15788683,Kang,588,Germany,Female,34,10,129417.82,1,1,0,153727.32,0\r\n3607,15616922,Kelly,479,France,Female,26,1,0,2,1,1,19116.97,0\r\n3608,15771855,Yu,682,France,Male,37,5,0,2,0,1,112554.68,0\r\n3609,15601873,Bull,677,France,Female,36,7,0,1,1,0,47318.75,0\r\n3610,15657868,Serra,850,Germany,Male,40,6,94607.08,1,1,0,36690.49,0\r\n3611,15711716,Ferguson,580,France,Female,56,1,131368.3,1,1,0,106918.67,1\r\n3612,15734246,She,746,France,Female,21,8,166883.07,2,0,1,194563.65,0\r\n3613,15792151,Hamilton,635,Spain,Female,37,3,0,2,1,0,91086.73,0\r\n3614,15770159,Nnanna,664,Germany,Male,25,6,172812.72,2,1,1,108008.65,0\r\n3615,15747649,Summerville,558,Germany,Female,36,0,126606.63,2,1,1,172363.52,0\r\n3616,15639357,Allan,415,France,Male,46,9,134950.19,3,0,0,178587.36,1\r\n3617,15738907,Tobenna,798,France,Female,60,6,96956.1,1,1,0,31907.44,1\r\n3618,15663446,Volkova,792,Germany,Female,29,4,107601.79,1,1,0,18922.18,1\r\n3619,15750867,Nucci,489,Germany,Female,46,8,92060.06,1,1,0,147222.95,1\r\n3620,15715939,Wright,730,France,Male,33,0,0,2,1,0,1474.79,0\r\n3621,15763806,Astorga,773,France,Male,41,4,0,2,1,1,24924.92,0\r\n3622,15637993,Pokrovsky,711,France,Male,36,9,137688.71,1,1,1,46884.1,0\r\n3623,15720338,Mazzanti,592,Spain,Male,55,8,85845.43,2,1,1,128918.42,0\r\n3624,15627162,Blesing,695,Germany,Male,27,6,125552.96,1,1,0,105291.26,0\r\n3625,15596710,Ku,640,France,Female,33,1,167298.42,1,0,1,145381.65,0\r\n3626,15781678,Pisani,470,Spain,Male,31,4,55732.92,2,1,1,103792.53,0\r\n3627,15634968,Hsueh,789,Germany,Female,37,6,110689.07,1,1,1,71121.04,1\r\n3628,15609475,Ricci,604,Spain,Female,39,7,98544.11,1,1,1,52327.57,0\r\n3629,15573319,Azubuike,493,Germany,Female,35,8,178317.6,1,0,0,197428.64,0\r\n3630,15738291,Nevzorova,671,France,Female,48,8,115713.84,2,0,0,83210.84,0\r\n3631,15782456,Odili,656,France,Male,46,9,143267.14,2,0,0,193099.43,0\r\n3632,15794841,Kung,739,Spain,Male,19,5,89750.21,1,1,0,193008.52,0\r\n3633,15684696,Lei,560,Spain,Female,26,3,116576.45,1,1,0,157567.37,0\r\n3634,15629846,Sheehan,827,Germany,Female,47,8,143001.5,2,1,0,108977.5,0\r\n3635,15674442,Kung,681,France,Male,23,7,157761.56,1,0,0,147759.84,0\r\n3636,15571689,Kelechi,740,France,Female,37,5,0,2,1,1,27528.4,0\r\n3637,15730469,Anenechi,663,Spain,Male,31,4,103430.11,2,0,1,36479.27,0\r\n3638,15809320,McElhone,845,Spain,Female,52,0,0,1,1,0,31726.76,1\r\n3639,15684367,Chigbogu,555,Spain,Male,27,5,0,2,0,0,96398.51,0\r\n3640,15793049,Atkins,680,Germany,Female,48,8,115115.38,1,1,0,139558.6,1\r\n3641,15603665,Colombo,638,Germany,Female,39,0,122501.28,2,1,1,95007.8,0\r\n3642,15613623,Tilley,640,Spain,Male,62,3,0,1,1,1,101663.47,0\r\n3643,15569572,Sopuluchi,778,France,Male,42,6,0,2,1,1,106197.44,0\r\n3644,15698791,Udinesi,679,France,Male,45,3,146758.24,1,1,0,48466.89,0\r\n3645,15626233,Onyekachi,593,France,Female,32,3,0,2,1,1,151978.36,0\r\n3646,15607263,McCartney,788,France,Male,55,3,0,1,0,1,13288.46,1\r\n3647,15610900,Thompson,770,France,Female,70,9,110738.89,1,1,0,22666.77,1\r\n3648,15624775,Onyeoruru,729,France,Male,67,2,94203.8,1,0,1,102391.06,0\r\n3649,15691703,Shih,545,France,Male,47,8,105792.49,1,0,1,67830.2,1\r\n3650,15745355,Golibe,597,France,Male,41,4,153198.23,1,1,1,92090.36,0\r\n3651,15724955,Lucchesi,537,France,Male,38,3,0,2,0,0,141023.01,0\r\n3652,15628999,Townsend,732,France,Male,79,10,61811.23,1,1,1,104222.8,0\r\n3653,15654341,Chao,542,France,Male,34,8,101116.06,1,1,0,196395.05,0\r\n3654,15744240,Shen,688,Germany,Female,46,0,74458.25,1,0,1,6866.31,0\r\n3655,15632365,Booth,542,Germany,Male,33,8,142871.27,2,0,0,77737.86,0\r\n3656,15729689,Chan,754,Germany,Male,35,6,98585.94,2,0,1,106116.84,0\r\n3657,15759284,Yeh,750,France,Female,37,6,0,1,1,1,117948,1\r\n3658,15602124,Badgery,731,France,Male,30,7,0,2,1,1,184581.68,0\r\n3659,15661903,Hsia,699,France,Female,43,3,80764.03,1,1,0,199378.58,1\r\n3660,15664668,Zarate,534,France,Female,42,9,144801.97,1,0,1,12483.39,1\r\n3661,15736431,Congreve,494,Spain,Male,27,2,0,2,1,0,22404.64,0\r\n3662,15748639,Hayslett,497,Germany,Male,35,7,110053.62,2,1,1,92887.06,0\r\n3663,15628123,Robinson,632,France,Female,28,5,118890.81,1,0,1,145157.97,0\r\n3664,15602731,Wong,724,France,Male,31,5,0,1,1,0,134889.95,1\r\n3665,15794137,Nevzorova,751,Germany,Female,37,0,151218.98,1,1,1,109309.29,0\r\n3666,15748696,Page,733,France,Male,42,9,150507.21,1,0,1,169964.12,0\r\n3667,15725068,Quinn,701,Spain,Female,21,9,0,2,1,1,26327.42,0\r\n3668,15807340,O'Donnell,525,Germany,Male,33,4,131023.76,2,0,0,55072.93,0\r\n3669,15586133,Pisano,666,Germany,Female,44,2,122314.5,1,0,0,68574.88,1\r\n3670,15576185,Sinclair,653,France,Male,29,2,0,2,1,1,41671.81,0\r\n3671,15660809,Loving,850,France,Male,28,4,0,2,1,1,12409.01,0\r\n3672,15616666,Artemova,646,Germany,Female,52,6,111739.4,2,0,1,68367.18,0\r\n3673,15706904,Robertson,750,France,Male,43,6,113882.31,1,1,1,74564.41,0\r\n3674,15606915,Genovese,764,France,Male,24,7,98148.61,1,1,0,26843.76,0\r\n3675,15749693,Ugonnatubelum,658,France,Female,32,9,0,2,1,0,156774.75,0\r\n3676,15791743,Corbett,727,France,Male,32,1,59271.82,1,1,1,46019.43,0\r\n3677,15796480,Reilly,687,France,Female,31,2,0,2,0,1,145411.39,0\r\n3678,15790442,Wright,631,Spain,Male,33,2,0,2,1,1,158268.84,0\r\n3679,15609458,Vincent,797,France,Male,30,10,69413.44,1,1,1,74637.57,0\r\n3680,15593897,Carr,650,Spain,Male,25,7,160599.06,2,1,1,28391.52,0\r\n3681,15604576,Eiland,850,Spain,Male,22,3,0,1,1,1,144385.54,0\r\n3682,15666270,Omeokachie,676,France,Female,40,2,147803.48,1,1,0,95181.06,1\r\n3683,15572626,Mackenzie,620,Spain,Male,44,8,0,2,1,1,15627.51,0\r\n3684,15727197,Pinto,576,France,Female,52,9,170228.59,2,0,0,148477.57,1\r\n3685,15714006,Gardener,482,France,Female,35,2,133111.73,1,0,1,79957.95,0\r\n3686,15642137,Fang,695,Spain,Female,39,5,0,2,0,0,102763.69,0\r\n3687,15665327,Cattaneo,706,France,Male,18,2,176139.5,2,1,0,129654.22,0\r\n3688,15626806,Labrador,668,France,Female,32,2,0,2,1,1,40652.33,0\r\n3689,15662578,Dettmann,679,Germany,Male,35,1,110245.13,1,1,1,178291.09,0\r\n3690,15790829,Gibson,703,France,Female,45,5,0,2,1,0,131906.44,0\r\n3691,15654959,Hope,670,Spain,Male,67,6,158719.57,1,1,1,118607.4,0\r\n3692,15760244,Ives,590,France,Female,76,5,160979.68,1,0,1,13848.58,0\r\n3693,15715394,Greece,613,Spain,Male,35,4,123557.65,2,0,1,170903.4,0\r\n3694,15722246,Omeokachie,742,France,Female,60,4,0,1,1,1,13161.66,1\r\n3695,15609704,Mao,608,France,Female,33,4,0,1,1,0,79304.38,1\r\n3696,15757628,Savage,571,France,Male,40,10,112896.86,1,1,1,121402.53,0\r\n3697,15633586,Brierly,595,France,Female,39,7,120962.13,1,0,0,23305.01,0\r\n3698,15565796,Docherty,745,Germany,Male,48,10,96048.55,1,1,0,74510.65,0\r\n3699,15717935,McDonald,589,France,Female,21,3,0,2,0,1,55601.44,0\r\n3700,15577700,Rapuokwu,749,France,Male,37,10,185063.7,2,1,1,134526.87,0\r\n3701,15747345,Bergamaschi,678,France,Female,22,6,118064.93,2,1,1,195424.01,0\r\n3702,15678317,Manfrin,603,France,Male,46,2,0,2,1,1,59563.49,0\r\n3703,15698335,Bergamaschi,504,France,Female,73,8,0,1,1,1,34595.58,0\r\n3704,15768451,MacDonald,739,Germany,Male,40,5,149131.03,3,1,1,60036.99,1\r\n3705,15753213,Lees,604,France,Female,34,7,0,2,1,0,193021.49,0\r\n3706,15769645,Senior,612,France,Female,35,3,0,1,1,1,48108.72,0\r\n3707,15657565,Nwokezuike,629,Spain,Female,44,6,125512.98,2,0,0,79082.76,0\r\n3708,15620323,Ekwueme,652,Spain,Female,42,3,83492.07,2,1,0,37914.12,0\r\n3709,15679983,Garmon,565,France,Male,34,7,0,1,0,0,74593.84,0\r\n3710,15812616,Enyinnaya,707,France,Female,49,10,0,1,1,0,82967.97,1\r\n3711,15601796,Chizuoke,645,France,Male,30,1,125739.26,1,1,1,193441.23,0\r\n3712,15729489,Hyde,762,Germany,Female,34,8,98592.88,1,0,1,191790.29,1\r\n3713,15613216,Cameron,639,Spain,Female,39,1,141789.15,1,1,0,92455.96,0\r\n3714,15657937,Lord,709,Germany,Male,22,0,112949.71,1,0,0,155231.55,0\r\n3715,15815428,Biryukova,823,France,Male,34,3,105057.33,1,1,0,9217.92,0\r\n3716,15640409,Carpenter,817,Germany,Female,46,0,89087.89,1,0,1,87941.85,1\r\n3717,15699492,Lorenzo,665,Germany,Female,27,2,147435.96,1,0,0,187508.06,0\r\n3718,15623536,Madukwe,646,Germany,Male,39,0,154439.86,1,1,0,171519.06,0\r\n3719,15707551,Hutcheon,568,France,Male,30,8,73054.37,2,1,1,27012,0\r\n3720,15577999,Sleeman,850,France,Female,62,1,124678.35,1,1,0,70916,1\r\n3721,15788775,Milne,473,Germany,Male,40,8,152576.25,2,1,0,73073.68,0\r\n3722,15758362,Williamson,731,France,Female,41,9,152243.57,1,1,1,88783.59,0\r\n3723,15807961,Bruno,619,France,Male,25,4,0,1,1,0,145524.36,0\r\n3724,15710978,Palerma,715,Germany,Male,42,2,88120.97,2,1,1,21333.22,0\r\n3725,15703541,Wang,772,Germany,Female,51,9,143930.92,1,0,1,46675.51,1\r\n3726,15626474,Onyemere,686,France,Female,31,1,0,2,1,0,4802.25,0\r\n3727,15608344,Dawson,749,Germany,Female,29,7,137059.05,3,1,0,102975.72,1\r\n3728,15768367,Nebechukwu,781,France,Female,27,7,186558.55,1,1,1,175071.29,1\r\n3729,15806210,Bateman,675,Spain,Male,66,5,115654.47,2,1,1,131970.86,0\r\n3730,15697702,Lord,730,Spain,Male,29,2,0,2,1,0,14174.09,0\r\n3731,15689152,Loggia,683,Spain,Male,38,3,126152.84,1,0,0,15378.75,0\r\n3732,15568573,Graham,554,Germany,Female,51,7,105701.91,1,0,1,179797.79,1\r\n3733,15689598,Dean,722,France,Male,46,6,0,1,1,1,93917.68,1\r\n3734,15713374,Jarvis,689,Germany,Male,67,9,157094.78,1,1,1,99490.01,0\r\n3735,15679733,Haugh,796,Germany,Male,40,2,113228.38,2,1,1,46415.09,0\r\n3736,15759274,Micklem,447,France,Female,32,10,0,1,1,1,151815.76,0\r\n3737,15607748,Bennett,498,Germany,Male,37,8,108432.88,2,1,1,14865.05,0\r\n3738,15607577,Roberts,663,Spain,Male,27,8,0,1,1,1,188007.99,0\r\n3739,15813697,Onyekaozulu,498,Germany,Female,44,2,120702.67,2,1,1,98175.74,0\r\n3740,15801125,Kegley,627,France,Female,32,1,0,1,1,0,106851.7,0\r\n3741,15777855,Manna,649,France,Male,45,7,0,2,0,1,75204.21,0\r\n3742,15635396,Thompson,738,Germany,Female,29,9,139106.19,1,1,0,141872.05,1\r\n3743,15698031,Romano,587,Germany,Female,39,6,101851.8,2,1,0,7103.71,0\r\n3744,15678944,Brown,655,Germany,Female,32,6,130935.56,1,1,0,9241.83,1\r\n3745,15718507,Su,647,Germany,Male,37,3,116509.99,1,1,1,149517.71,1\r\n3746,15808334,Mackay,776,Germany,Female,37,1,93124.04,2,1,1,196079.32,0\r\n3747,15804709,Watt,688,Germany,Male,35,5,111578.18,1,0,0,166165.93,1\r\n3748,15645835,Milani,605,France,Male,32,9,0,2,1,1,55724.24,0\r\n3749,15738166,Hsu,596,France,Female,39,10,86546.29,1,0,1,131768.98,0\r\n3750,15675360,Valenzuela,427,France,Male,33,8,0,1,1,1,13858.95,0\r\n3751,15793042,Sung,629,France,Male,39,2,129669.32,2,1,0,82774.07,0\r\n3752,15630106,Lo,496,Spain,Male,29,2,0,2,1,0,55389.59,0\r\n3753,15810385,Giordano,717,Spain,Female,36,2,164557.95,1,0,1,82336.73,0\r\n3754,15578211,Connolly,777,France,Male,23,6,0,2,1,1,163225.48,0\r\n3755,15572792,Bellucci,535,Spain,Male,35,8,118989.92,1,1,1,135536.72,0\r\n3756,15620030,Jamieson,744,France,Male,29,1,0,1,0,0,82422.97,0\r\n3757,15783541,Fomina,755,France,Male,31,5,0,2,0,1,194660.78,0\r\n3758,15679284,Aksenov,593,Spain,Female,45,6,79259.75,1,1,0,55347.28,0\r\n3759,15582910,Turnbull,514,France,Male,38,4,112230.38,1,1,0,16717.11,1\r\n3760,15688337,Dixon,721,France,Male,40,9,118129.87,1,1,1,160277.65,0\r\n3761,15734970,White,835,Spain,Male,38,7,86824.09,1,0,0,175905.97,0\r\n3762,15759140,Long,682,France,Female,64,10,128306.7,1,0,1,66040.83,0\r\n3763,15643042,Han,590,Germany,Female,40,2,117641.43,2,0,0,92198.05,0\r\n3764,15773868,Belov,653,Germany,Female,37,3,125734.2,2,1,0,134625.09,1\r\n3765,15615820,MacDonald,837,France,Male,49,8,103302.37,1,1,1,50974.57,0\r\n3766,15730273,Parsons,841,France,Male,27,8,0,1,1,0,171922.72,0\r\n3767,15724890,Cross,584,Spain,Male,36,4,82696.09,2,0,0,83058.14,0\r\n3768,15765952,Milanesi,769,France,Male,29,4,145471.37,1,1,0,188382.77,0\r\n3769,15685920,Lombardo,599,Spain,Male,34,2,101506.66,1,0,0,198030.24,0\r\n3770,15663263,Collins,698,France,Male,47,5,156265.31,2,0,0,1055.66,0\r\n3771,15568953,Alexeieva,477,France,Male,27,1,128554.98,1,1,1,133173.19,0\r\n3772,15643361,Cullen,477,Germany,Male,34,8,139959.55,2,1,1,189875.83,0\r\n3773,15699486,Johnson,745,Spain,Male,34,7,132944.53,1,1,1,31802.92,0\r\n3774,15747854,Rudd,749,France,Female,35,3,0,3,1,1,132649.85,0\r\n3775,15691785,Findlay,850,France,Male,61,1,0,1,1,0,53067.83,1\r\n3776,15709004,Mai,528,Germany,Male,22,5,93547.23,2,0,1,961.57,0\r\n3777,15652218,Morrison,750,France,Male,33,2,152302.72,1,1,0,71333.44,0\r\n3778,15697127,Monaldo,543,France,Female,31,2,147674.26,1,1,1,16658.76,0\r\n3779,15658486,Gidney,579,Spain,Female,59,3,148021.12,1,1,1,74878.22,0\r\n3780,15694160,Sagese,624,France,Male,37,0,0,2,0,0,112104.55,0\r\n3781,15685290,Wall,595,Germany,Male,46,5,142360.62,2,1,0,48421.4,1\r\n3782,15701042,Dalton,596,Germany,Female,27,2,151027.56,1,1,0,170320.58,0\r\n3783,15680449,Hsing,431,Germany,Female,44,2,138843.7,1,1,0,37688.31,1\r\n3784,15599860,Warner,647,Spain,Female,26,8,109958.15,1,1,1,136592.24,1\r\n3785,15723169,Williams,640,France,Female,31,9,138857.59,1,1,0,48640.77,0\r\n3786,15803842,Dunn,752,Germany,Female,45,3,105426.5,2,0,1,89773.45,0\r\n3787,15728224,Kerr,710,Germany,Female,41,9,149155.53,2,1,0,42131.26,1\r\n3788,15644174,Marchesi,638,Germany,Male,27,4,135096.05,1,1,1,186523.72,1\r\n3789,15707110,Endrizzi,660,Germany,Male,28,2,170890.05,2,1,0,41758.9,0\r\n3790,15765415,King,609,Spain,Female,45,4,89122.3,1,1,1,199256.98,0\r\n3791,15756751,Griffiths,596,Spain,Female,54,0,78126.28,1,1,1,153482.91,1\r\n3792,15795151,Hartzler,705,France,Female,38,3,123894.43,1,1,0,21177.1,0\r\n3793,15632859,Chukwudi,444,France,Male,36,7,0,2,0,1,138743.86,0\r\n3794,15584037,Denisov,727,Germany,Male,58,5,106913.43,1,1,0,25881,1\r\n3795,15621409,Endrizzi,496,France,Male,32,4,127845.83,1,1,0,66469.2,0\r\n3796,15581102,Baresi,554,France,Female,22,8,0,2,0,1,142670.61,0\r\n3797,15578096,Nnachetam,537,France,Male,26,7,106397.75,1,0,0,103563.23,0\r\n3798,15669887,Lambert,839,France,Female,51,3,0,1,1,1,69101.23,1\r\n3799,15621834,Game,700,Spain,Female,43,0,0,2,1,0,59475.35,0\r\n3800,15655341,Chinagorom,458,Spain,Female,35,5,166492.48,1,1,0,135287.74,0\r\n3801,15685314,Noble,850,France,Female,28,2,0,2,1,1,38773.74,0\r\n3802,15653997,Haynes,699,Spain,Male,31,6,114493.68,1,0,0,138396.32,0\r\n3803,15629551,Cattaneo,615,Germany,Female,44,9,126104.98,2,0,1,110718.02,0\r\n3804,15651264,Yobanna,850,Germany,Male,51,4,124425.99,1,0,0,118545.49,1\r\n3805,15760825,Fraser,604,France,Female,40,1,0,2,1,0,123207.17,0\r\n3806,15597394,Rhodes,668,Spain,Male,34,0,0,1,0,0,99984.86,0\r\n3807,15740383,Jimenez,594,Spain,Female,38,10,0,2,1,0,58332.91,0\r\n3808,15670562,Pharr,470,France,Male,30,3,101140.76,1,1,1,50906.65,0\r\n3809,15698117,Jerger,701,Germany,Male,41,0,150844.94,1,0,1,127623.36,0\r\n3810,15694805,McIntyre,664,Spain,Male,35,1,115024.5,1,0,1,169665.79,0\r\n3811,15746802,Onio,477,France,Female,30,6,131286.46,1,1,0,194144.45,0\r\n3812,15589428,Tomlinson,756,France,Female,42,9,0,2,1,0,35673.42,0\r\n3813,15790267,Onuoha,625,France,Female,40,7,141267.67,1,0,1,177397.49,0\r\n3814,15665402,Panicucci,703,Spain,Male,73,5,137761.55,1,1,1,159677.46,0\r\n3815,15642093,Piccio,646,France,Male,30,7,0,2,1,0,153566.97,0\r\n3816,15666181,Ramsden,650,France,Male,33,0,98064.97,1,1,0,52411.99,0\r\n3817,15602554,Vorobyova,664,France,Female,31,9,114519.57,2,0,1,79222.02,0\r\n3818,15724251,Todd,682,Germany,Female,29,6,101012.77,1,0,0,32589.89,1\r\n3819,15740147,Cremonesi,725,France,Female,44,10,0,1,0,1,93777.61,0\r\n3820,15718289,Bradley,553,Germany,Male,46,3,82291.1,1,1,0,112549.99,1\r\n3821,15763148,Stanley,576,France,Male,39,9,84719.98,1,0,0,191063.36,0\r\n3822,15685245,Jowett,608,Spain,Female,56,5,0,2,0,1,153810.41,0\r\n3823,15626985,Yefremova,850,France,Female,39,0,104386.53,1,1,0,105886.77,0\r\n3824,15585823,Wilson,627,France,Male,31,8,128131.73,1,1,0,96131.47,0\r\n3825,15728167,Abramovich,667,France,Male,44,2,122806.95,1,0,0,15120.86,0\r\n3826,15762928,Venables,548,Spain,Male,44,8,0,1,1,0,16989.77,0\r\n3827,15751774,Monnier,774,France,Male,76,4,112510.89,1,1,1,143133.18,0\r\n3828,15654733,Hsieh,794,Germany,Male,57,3,117056.46,1,1,0,93336.93,1\r\n3829,15809777,Gadsden,497,Germany,Female,55,7,131778.66,1,1,1,9972.64,0\r\n3830,15744200,Ni,587,France,Female,36,1,70784.27,1,1,0,30579.82,0\r\n3831,15720713,Chibueze,850,France,Female,29,10,0,2,1,1,199775.67,0\r\n3832,15695356,Chinwemma,722,France,Male,46,5,0,2,1,0,179908.71,0\r\n3833,15653315,Kang,555,Spain,Female,35,1,0,2,1,0,101667,0\r\n3834,15604792,Kuo,609,Germany,Male,38,6,140752.06,2,0,1,171430.16,0\r\n3835,15704819,Ositadimma,734,Spain,Female,39,6,92126.26,2,0,0,112973.34,0\r\n3836,15670859,Smith,718,Germany,Female,39,7,93148.74,2,1,1,190746.38,0\r\n3837,15602797,Okwudilichukwu,645,Spain,Female,49,5,110132.55,3,0,1,187689.91,1\r\n3838,15662533,Porter,598,Spain,Female,23,6,0,2,1,0,153229.19,0\r\n3839,15778154,Kung,628,Germany,Male,50,4,122227.71,1,0,1,14217.77,1\r\n3840,15806230,Trevisano,629,Germany,Male,40,2,121647.54,2,1,1,64849.74,1\r\n3841,15662884,Naylor,739,Germany,Male,58,1,110597.76,1,0,1,160122.66,1\r\n3842,15750778,Ponomarev,653,France,Female,60,2,120731.39,4,1,1,138160.11,1\r\n3843,15717185,Udinese,711,France,Male,28,8,0,2,1,1,64286.39,0\r\n3844,15677804,Aliyeva,783,Spain,Male,38,1,0,3,1,1,80178.54,1\r\n3845,15568915,Bailey,681,France,Male,38,6,153722.47,1,1,0,101319.76,0\r\n3846,15736495,Jackson,712,France,Male,34,8,114088.32,1,1,0,92794.61,0\r\n3847,15737354,Yin,554,France,Female,48,7,0,2,1,1,63708.07,0\r\n3848,15667889,Akobundu,611,France,Female,37,6,0,2,1,0,110782.88,0\r\n3849,15577831,Byrne,560,Germany,Male,41,4,152532.3,1,0,0,10779.69,0\r\n3850,15729836,Robinson,646,Spain,Male,32,1,0,2,1,0,183289.22,0\r\n3851,15775293,Stephenson,680,France,Male,34,3,143292.95,1,1,0,66526.01,0\r\n3852,15697597,Chiemenam,631,France,Male,26,1,149144.61,1,0,1,123697.95,0\r\n3853,15639669,Forbes,746,France,Male,36,9,127157.04,1,1,1,155700.15,0\r\n3854,15631392,Douglas,654,Germany,Male,43,9,84673.17,2,0,1,82081.35,0\r\n3855,15580935,Okechukwu,687,Germany,Male,33,9,135962.4,2,1,0,121747.96,0\r\n3856,15590344,Russell,708,Germany,Male,32,3,151691.44,2,1,1,172810.51,0\r\n3857,15653306,Ermakova,679,Germany,Female,32,0,88335.05,1,0,0,159584.81,0\r\n3858,15805025,Oster,636,France,Female,45,7,139859.23,1,1,1,108402.54,0\r\n3859,15658449,Chizoba,695,France,Male,45,9,43134.65,1,0,1,77330.35,0\r\n3860,15694450,Bianchi,677,France,Male,42,5,99580.13,1,1,0,21007.96,0\r\n3861,15605666,Peyser,720,France,Female,34,6,110717.38,1,1,1,9398.45,0\r\n3862,15615126,Cocci,780,France,Female,37,3,0,2,0,0,182156.81,1\r\n3863,15726588,Seleznev,653,Spain,Female,36,3,0,2,0,0,110525.6,0\r\n3864,15645095,Huang,674,France,Female,28,3,0,1,1,0,51536.99,0\r\n3865,15808960,Alleyne,620,Germany,Male,40,5,108197.11,2,1,0,49722.34,0\r\n3866,15729435,McKenzie,623,France,Male,40,6,0,2,1,1,66119.07,0\r\n3867,15656840,Zikoranachukwudimma,547,France,Female,29,6,104450.86,1,1,1,37160.28,0\r\n3868,15659149,King,530,France,Male,39,2,0,2,1,0,197923.05,0\r\n3869,15585490,Nkemdilim,746,France,Female,34,4,0,1,0,1,65166.6,0\r\n3870,15674929,Anderson,512,France,Female,31,7,0,2,0,0,49326.07,0\r\n3871,15746341,Ejikemeifeuwa,630,France,Male,40,8,0,2,1,1,42495.81,0\r\n3872,15662091,Adams,570,Spain,Male,21,7,116099.82,1,1,1,148087.62,0\r\n3873,15620123,Christie,605,France,Male,39,6,111169.91,1,0,0,9641.4,0\r\n3874,15616240,Yeh,530,Spain,Male,37,4,0,2,1,1,164844.37,0\r\n3875,15624186,McGregor,813,Germany,Female,25,5,123616.43,1,0,1,132959.33,0\r\n3876,15605036,Pisano,704,Spain,Female,37,9,155619.58,1,1,1,135088.58,0\r\n3877,15805151,Ginikanwa,565,Germany,Male,31,2,89558.39,2,1,1,4441.54,0\r\n3878,15753847,Hawkins,645,Spain,Male,45,4,0,1,0,1,174916.85,1\r\n3879,15653222,Otutodilichukwu,526,Germany,Female,32,6,131938.92,2,1,1,1795.93,0\r\n3880,15757541,Rickard,778,France,Female,33,9,151772.63,2,0,0,180249.94,1\r\n3881,15726945,Andreev,677,France,Female,72,8,0,2,1,1,153604.44,0\r\n3882,15794276,Steele,588,France,Female,64,3,0,1,1,1,189703.65,0\r\n3883,15568328,Black,488,France,Female,22,6,0,2,1,1,66393.89,0\r\n3884,15604355,Shand,519,France,Male,39,1,97700.02,1,1,1,30709.03,0\r\n3885,15735788,Chiagoziem,709,France,Male,31,6,0,2,1,1,71009.84,0\r\n3886,15618255,Fedorov,642,Germany,Female,56,6,103244.86,2,1,0,143049.72,1\r\n3887,15720941,Tien,710,Germany,Male,34,8,147833.3,2,0,1,1561.58,0\r\n3888,15769110,Stehle,653,France,Female,46,5,0,2,1,0,49707.85,0\r\n3889,15576094,Sung,743,France,Male,71,0,0,2,0,1,29837.65,0\r\n3890,15756150,Alexander,418,France,Female,39,2,0,2,0,0,9041.71,0\r\n3891,15719579,McIntosh,670,Germany,Female,33,9,84521.48,2,0,1,198017.05,0\r\n3892,15748854,Sung,723,Germany,Female,28,5,91938.31,1,1,0,143481.85,0\r\n3893,15612455,Yao,549,Germany,Male,45,6,124240.93,1,1,1,146372.51,0\r\n3894,15664802,Chinweuba,543,France,Female,42,5,0,2,0,0,101905.34,0\r\n3895,15735687,Chinweuba,595,Spain,Male,37,2,157084.99,1,1,0,134767.13,0\r\n3896,15664734,T'ao,673,Germany,Female,25,3,108244.82,2,1,1,103573.96,0\r\n3897,15767894,Ch'ien,741,France,Female,21,9,0,2,0,1,139259.54,0\r\n3898,15666884,Su,508,Germany,Female,41,5,82161.7,2,1,0,187776.49,0\r\n3899,15750156,Yu,662,Germany,Male,59,2,104568.41,1,1,0,8059.44,1\r\n3900,15751120,Loyau,752,France,Female,36,2,119912.46,1,1,0,124354.92,0\r\n3901,15575748,Conti,809,France,Male,36,9,68881.59,2,0,1,109135.11,0\r\n3902,15714610,Alexeeva,575,Spain,Male,30,2,0,2,1,1,82222.86,0\r\n3903,15720305,Power,591,Spain,Female,40,1,86376.29,1,0,1,136767.16,1\r\n3904,15678129,Hill,643,Spain,Female,45,9,150840.03,2,1,0,155516.35,0\r\n3905,15566633,Freeman,698,Germany,Male,55,8,155059.1,2,1,1,144584.29,0\r\n3906,15680436,Hsing,496,France,Female,29,4,0,2,1,0,164806.89,0\r\n3907,15674343,Esposito,597,France,Male,44,8,78128.13,2,0,1,109153.04,0\r\n3908,15658890,Belonwu,603,Germany,Male,46,4,98899.76,2,1,1,86190.34,0\r\n3909,15599004,Tsao,655,Spain,Male,37,1,0,1,1,1,106040.97,0\r\n3910,15726487,P'eng,431,France,Male,63,6,160982.89,1,1,1,168008.17,0\r\n3911,15698716,Baker,620,France,Female,70,3,87926.24,2,1,0,33350.26,1\r\n3912,15710527,Matthews,782,France,Female,35,4,0,1,1,1,119565.34,0\r\n3913,15655590,Garcia,581,Spain,Male,46,2,79385.21,2,0,0,188492.82,0\r\n3914,15732266,Field,553,Germany,Male,53,5,127997.83,1,1,0,165378.66,1\r\n3915,15669326,Gordon,658,France,Male,44,2,168396.34,1,1,1,14178.73,0\r\n3916,15672246,Jefferies,686,Germany,Male,43,2,134896.03,1,1,1,97847.05,0\r\n3917,15620276,Palermo,539,Spain,Male,36,6,0,3,1,1,118959.64,0\r\n3918,15640258,Chou,685,France,Female,50,6,94238.75,2,1,1,50664.07,1\r\n3919,15740283,Ewing,850,France,Male,29,1,0,2,0,0,152996.89,0\r\n3920,15759717,Mazzi,763,Spain,Female,39,7,0,2,1,0,19458.75,0\r\n3921,15620268,Thomson,634,Germany,Male,43,3,212696.32,1,1,0,115268.86,0\r\n3922,15743871,Nkemdirim,567,France,Male,59,3,0,2,1,0,25843.7,1\r\n3923,15614491,Lockyer,539,France,Male,39,3,139153.68,2,1,0,147662.33,0\r\n3924,15595047,Murray,764,France,Male,41,7,0,2,0,0,134878.34,0\r\n3925,15732334,Black,653,France,Female,40,0,0,2,1,0,35795.85,0\r\n3926,15701206,Torreggiani,566,Spain,Male,44,5,0,2,1,0,66462.79,0\r\n3927,15581280,Atkinson,714,Germany,Male,29,6,92887.13,1,1,1,69578.49,0\r\n3928,15651943,Richards,580,Spain,Female,65,1,0,2,0,1,103182.46,0\r\n3929,15609545,Azubuike,548,France,Male,29,5,83442.98,1,0,1,177017.39,0\r\n3930,15658548,Ignatiev,646,Germany,Female,36,6,144773.29,2,1,0,53217.3,0\r\n3931,15626008,Miller,622,Germany,Female,52,9,111973.97,1,1,1,162756.29,1\r\n3932,15774133,Cox,706,France,Female,35,8,178032.53,1,0,1,42181.68,0\r\n3933,15763798,McMillan,680,France,Male,23,5,140007.19,1,0,1,31714.08,0\r\n3934,15758013,Napolitano,698,France,Male,37,5,98400.61,2,0,0,25017.28,0\r\n3935,15705765,Lane,581,Spain,Female,46,1,0,2,1,0,104272.04,0\r\n3936,15648362,Kennedy,728,Germany,Male,45,3,108924.33,2,1,0,84300.4,1\r\n3937,15761102,T'ao,707,Spain,Female,32,4,132835.56,1,0,0,136877.24,0\r\n3938,15610165,Hsiung,761,France,Female,26,1,0,2,1,1,199409.19,0\r\n3939,15723717,Heath,483,Germany,Male,41,1,118334.44,1,0,0,163147.99,1\r\n3940,15654611,Parry,736,Germany,Female,25,9,81732.88,2,1,0,136497.28,0\r\n3941,15659736,Herbert,716,Germany,Male,66,5,121411.9,1,0,0,10070.4,1\r\n3942,15603170,Kang,654,France,Male,32,9,121455.65,1,1,0,190068.53,1\r\n3943,15786167,Andreyeva,649,Spain,Male,20,5,0,2,1,1,58309.54,0\r\n3944,15671915,Bowen,649,France,Male,46,5,0,2,1,1,76946.6,0\r\n3945,15794792,Golubev,612,France,Female,31,8,117989.76,1,1,1,54129.86,0\r\n3946,15652789,Hancock,657,Spain,Male,40,10,0,2,1,1,52990.7,0\r\n3947,15739168,Fowler,511,France,Female,31,5,137411.29,1,0,1,161854.98,0\r\n3948,15719950,Sutherland,682,France,Male,61,10,73688.2,1,1,1,172141.33,0\r\n3949,15743818,Rowley,748,Spain,Male,58,9,122330.7,2,0,1,124429.19,0\r\n3950,15717937,Gibbons,554,Germany,Male,43,5,99906.89,1,0,0,24983.39,0\r\n3951,15602841,Lockett,794,Spain,Female,28,5,0,2,0,1,86699.98,0\r\n3952,15619972,Akabueze,807,France,Female,47,9,167664.83,1,0,0,125440.11,1\r\n3953,15796114,Phelps,594,France,Female,34,7,141525.55,1,0,0,9443.15,0\r\n3954,15633546,Frederick,652,Spain,Female,33,3,124832.51,1,1,0,195877.06,0\r\n3955,15758755,Beneventi,729,France,Female,34,9,132121.71,1,0,1,105409.31,0\r\n3956,15695168,Bruce,625,France,Male,39,2,0,2,1,0,100403.05,0\r\n3957,15754342,Green,597,Germany,Female,60,0,78539.84,1,0,1,48502.88,0\r\n3958,15756610,Carlson,657,Germany,Female,38,5,123770.46,1,0,0,47019.66,1\r\n3959,15640917,Tang,633,France,Male,43,5,0,2,1,1,48249.88,0\r\n3960,15663164,Yudin,663,Germany,Male,49,7,116150.65,3,1,1,84358.71,1\r\n3961,15616811,MacDonald,535,France,Male,47,0,160729.1,1,0,1,145986.35,0\r\n3962,15610781,Watt,702,France,Female,29,10,88378.6,1,1,0,88550.28,0\r\n3963,15600911,Mbadiwe,712,France,Male,33,2,182888.08,1,1,0,3061,0\r\n3964,15629603,Chuang,607,France,Male,31,8,0,2,1,1,43196.5,0\r\n3965,15714981,Sabbatini,476,France,Male,37,4,0,1,1,1,55775.84,1\r\n3966,15775892,Caldwell,748,Spain,Female,23,8,85600.08,1,0,0,134077.71,0\r\n3967,15782778,Ewers,815,France,Male,35,4,0,2,0,1,198490.33,0\r\n3968,15786643,Tsao,602,France,Male,32,10,0,2,1,1,116052.92,0\r\n3969,15595657,Hannam,649,Germany,Male,40,4,95001.33,1,0,1,123202.99,0\r\n3970,15743673,Wood,551,Spain,Male,27,2,113873.22,1,1,1,85129.77,1\r\n3971,15634310,Ko,509,France,Male,30,6,0,2,1,0,180598.86,0\r\n3972,15790809,Lo Duca,685,Spain,Male,40,7,74896.92,1,1,0,198694.2,0\r\n3973,15668695,Endrizzi,536,France,Female,22,5,89492.62,1,0,0,42934.43,0\r\n3974,15669281,Ch'iu,711,Spain,Male,38,3,128718.78,1,0,0,114793.45,0\r\n3975,15621031,Mofflin,761,Spain,Male,27,8,0,2,1,0,63297.7,0\r\n3976,15720071,Fiorentini,535,France,Female,49,3,0,1,0,0,61820.41,1\r\n3977,15792180,Chiekwugo,566,Germany,Male,22,7,144954.75,2,1,0,102246,0\r\n3978,15813894,Bogle,620,Spain,Male,21,9,0,2,0,0,154882.79,0\r\n3979,15669490,Ifeanacho,837,Germany,Male,37,6,94001.61,2,1,0,140723.05,0\r\n3980,15783030,Owens,685,France,Female,40,7,0,1,1,0,72852.74,1\r\n3981,15695792,Ch'ien,673,France,Male,65,0,0,1,1,1,85733.33,0\r\n3982,15575676,Chung,638,France,Male,24,1,0,2,0,1,162597.15,0\r\n3983,15627665,Sung,614,France,Male,46,4,0,1,1,0,74379.57,1\r\n3984,15814092,Wang,626,France,Female,44,2,0,1,0,1,173117.22,1\r\n3985,15695225,Sun,834,Spain,Male,38,8,0,2,1,1,66485.26,0\r\n3986,15615091,Maitland,691,France,Male,24,6,0,2,1,1,92811.2,0\r\n3987,15794345,Ma,706,Spain,Male,38,8,0,2,0,1,46635.11,0\r\n3988,15726484,Pollard,633,France,Male,37,7,141546.35,1,1,1,124830.11,0\r\n3989,15650442,Hsieh,644,Germany,Female,32,8,141528.88,1,1,1,167087.34,1\r\n3990,15714256,Gerasimov,666,France,Male,30,7,109805.3,1,0,1,163625.56,0\r\n3991,15778752,Johnson,708,France,Male,32,10,86614.06,2,1,1,172129.26,0\r\n3992,15601659,Fiorentino,496,Germany,Female,59,7,91680.1,2,1,0,163141.18,1\r\n3993,15602811,Chioke,730,Germany,Male,38,0,38848.19,2,0,0,94003.11,0\r\n3994,15779414,Rossi,696,Spain,Male,40,3,153639.11,1,1,1,138351.68,0\r\n3995,15763097,Siciliano,809,Spain,Male,80,8,0,2,0,1,34164.05,0\r\n3996,15633666,Efremov,701,Spain,Female,33,7,123870.07,1,1,0,97794.71,0\r\n3997,15718789,Brigstocke,604,France,Male,30,5,0,2,1,0,75786.55,0\r\n3998,15690620,Olisaemeka,665,France,Male,39,10,46323.57,1,1,0,136812.02,0\r\n3999,15737071,Tang,639,France,Female,60,5,162039.78,1,1,1,84361.72,1\r\n4000,15665062,Lucchese,696,France,Male,19,1,110928.51,1,1,1,2766.63,0\r\n4001,15600692,West,520,France,Male,38,5,0,2,1,0,163185.76,0\r\n4002,15792064,Pai,545,Germany,Male,53,5,114421.55,1,1,0,180598.28,1\r\n4003,15811486,Tang,634,Germany,Female,29,8,130036.21,2,0,1,69849.55,0\r\n4004,15626141,Fedorov,750,France,Female,26,1,151510.17,2,1,1,19921.72,0\r\n4005,15738546,Gboliwe,530,Spain,Female,41,4,0,2,0,1,147606.71,0\r\n4006,15677052,Ko,589,France,Female,59,2,0,2,1,1,126160.24,1\r\n4007,15656454,Le Gallienne,654,France,Male,37,6,83568.55,1,1,0,47046.72,0\r\n4008,15645496,Seleznyova,648,France,Female,43,7,139972.18,1,1,0,143668.58,0\r\n4009,15612505,Joseph,835,Spain,Male,45,3,100212.13,1,1,0,152577.62,0\r\n4010,15708513,Bevan,446,France,Female,39,1,90217.07,1,1,0,191350.48,0\r\n4011,15685654,Allan,514,Spain,Male,66,9,0,2,1,1,14234.31,0\r\n4012,15732307,Lavrentiev,694,Germany,Male,33,4,124067.32,1,1,1,77906.87,0\r\n4013,15726814,Walton,554,Spain,Male,46,4,0,2,0,1,57320.92,0\r\n4014,15653776,Salier,720,Germany,Female,57,1,162082.31,4,0,0,27145.73,1\r\n4015,15597914,Evdokimov,641,Germany,Female,51,2,117306.69,4,1,1,26912.72,1\r\n4016,15631603,Ponomaryova,813,France,Male,32,1,122889.88,1,1,1,26476.18,0\r\n4017,15789753,Millar,480,France,Male,40,6,148790.61,1,0,1,79329.7,0\r\n4018,15678034,Grosse,811,France,Male,46,9,180226.24,1,1,0,13464.64,1\r\n4019,15690209,Hsiao,715,Germany,Female,32,3,104857.19,2,1,0,114149.8,0\r\n4020,15592091,Belbin,620,Spain,Male,31,2,166833.86,2,1,1,135171.6,0\r\n4021,15647453,Ifeajuna,721,France,Male,42,4,102936.72,1,0,0,1187.88,0\r\n4022,15697100,Wright,772,Germany,Female,48,6,108736.52,1,1,0,184564.67,1\r\n4023,15811290,Komarova,680,Germany,Male,44,0,129974.79,2,1,1,33391.38,0\r\n4024,15629187,Titheradge,535,France,Male,38,8,85982.07,1,1,0,9238.35,0\r\n4025,15758073,Dellucci,655,France,Female,20,7,134397.61,1,0,0,28029.54,0\r\n4026,15640769,Hobbs,660,France,Male,63,8,137841.53,1,1,1,42790.29,0\r\n4027,15606641,Beggs,762,Germany,Male,56,10,100260.88,3,1,1,77142.42,1\r\n4028,15718280,Luffman,662,Germany,Male,39,5,139822.11,2,1,1,146219.9,0\r\n4029,15764335,Caldwell,463,Germany,Female,41,8,123151.51,2,1,0,70127.93,0\r\n4030,15634218,Mancini,501,Germany,Male,27,4,95331.83,2,1,0,132104.76,0\r\n4031,15808760,Evseev,603,Spain,Female,42,6,0,1,1,1,90437.87,0\r\n4032,15648461,Hs?eh,688,Spain,Male,37,7,138162.41,2,1,1,113926.31,0\r\n4033,15593555,Chinedum,430,France,Male,38,9,0,2,1,1,12050.77,0\r\n4034,15569079,Hagins,632,Germany,Male,48,6,126066.26,1,1,0,64345.61,1\r\n4035,15800736,Kirwan,601,Spain,Female,42,4,96763.89,1,1,1,199242.65,0\r\n4036,15792607,Little,769,France,Female,38,2,0,2,0,0,75578.67,0\r\n4037,15640034,Milligan,551,France,Male,42,2,139561.46,1,1,0,43435.43,1\r\n4038,15807563,Ch'iu,841,France,Female,52,5,0,1,0,0,183239.71,1\r\n4039,15684461,McKay,469,Spain,Female,31,6,0,1,1,0,146213.75,1\r\n4040,15580134,Crawford,479,Spain,Male,27,2,172463.45,1,1,1,40315.27,0\r\n4041,15679075,Onyemere,701,France,Male,37,8,107798.85,1,1,0,16966.73,0\r\n4042,15742504,Azuka,593,France,Male,36,2,70181.48,2,1,0,80608.12,0\r\n4043,15567328,Ch'en,738,Spain,Male,38,5,177997.07,1,0,1,19233.41,0\r\n4044,15698294,Royster,635,Spain,Male,31,1,0,2,1,0,135382.23,0\r\n4045,15607142,Parkin,658,France,Male,32,8,0,1,1,1,80410.68,0\r\n4046,15738516,Kozlova,687,Spain,Female,36,5,0,1,1,0,17696.22,0\r\n4047,15806403,Hu,650,France,Male,37,9,0,2,1,0,17974.08,0\r\n4048,15656707,Ma,720,Spain,Male,21,2,123200.78,1,1,1,180712.28,0\r\n4049,15653715,Coates,602,France,Female,63,7,0,2,1,1,56323.21,0\r\n4050,15806184,Burns,618,Spain,Male,33,4,0,2,1,1,77550.18,0\r\n4051,15585734,Gouger,803,Germany,Male,41,9,137742.9,2,1,1,166957.82,0\r\n4052,15725639,Ignatyev,793,France,Female,63,9,116270.72,1,1,1,184243.25,0\r\n4053,15618401,Douglas,616,Germany,Male,41,10,113220.2,2,1,1,114072.91,0\r\n4054,15785385,Fiorentino,550,Spain,Male,51,5,0,2,1,0,153917.41,0\r\n4055,15734762,Ignatiev,602,France,Female,56,3,115895.22,3,1,0,4176.17,1\r\n4056,15767129,Munz,452,France,Female,60,6,121730.49,1,1,1,142963.29,0\r\n4057,15797204,Paling,655,Spain,Female,28,3,113811.85,2,0,1,76844.23,0\r\n4058,15769272,Clark,510,France,Female,26,6,136214.08,1,0,0,159742.33,0\r\n4059,15771966,Akobundu,557,France,Male,39,8,146200.01,1,1,0,177944.64,0\r\n4060,15691952,Fanucci,676,France,Male,37,10,106242.67,1,1,1,166678.28,0\r\n4061,15593250,Hsiao,640,France,Female,29,4,0,2,1,0,44904.26,0\r\n4062,15605333,Clancy,529,Spain,Male,31,6,0,1,1,0,10625.91,0\r\n4063,15800083,Macdonald,559,France,Male,45,8,24043.45,1,0,1,169781.45,1\r\n4064,15575691,Palerma,689,France,Female,58,5,0,2,0,1,49848.86,0\r\n4065,15689886,Holden,626,Germany,Male,39,10,132287.92,3,1,1,51467.92,1\r\n4066,15809838,Moore,697,Spain,Male,30,1,0,2,0,0,735.79,0\r\n4067,15736154,Gallo,823,France,Female,44,1,0,2,0,1,182495.7,0\r\n4068,15767391,Otutodilinna,565,Germany,Female,32,4,90322.99,2,0,1,118740.37,0\r\n4069,15704910,Rios,631,Spain,Male,23,3,0,2,1,0,13813.24,0\r\n4070,15656613,McGregor,646,France,Female,34,3,131283.11,1,0,0,130500.65,0\r\n4071,15611551,Hill,676,Spain,Male,48,1,131659.59,2,0,1,14152.15,0\r\n4072,15732430,H?,850,Spain,Female,54,4,120952.74,1,1,0,66963.15,0\r\n4073,15741865,Ferrari,810,France,Female,38,9,153166.17,1,1,1,93261.69,0\r\n4074,15634143,Onyemauchechi,581,Spain,Male,30,0,53291.86,1,0,0,196582.28,0\r\n4075,15609676,Nkemakonam,718,France,Female,35,2,167924.95,1,1,0,43024.64,0\r\n4076,15761600,White,713,France,Male,43,5,86394.14,1,1,1,130001.13,0\r\n4077,15676404,Kirillov,672,France,Female,50,1,0,1,1,0,12106.82,1\r\n4078,15659236,Iadanza,781,Spain,Male,33,3,0,2,1,1,42556.33,0\r\n4079,15690440,Stiles,656,Spain,Male,47,1,0,2,1,1,197961.93,0\r\n4080,15694601,Ankudinov,583,France,Female,31,4,158978.79,1,1,0,12538.92,0\r\n4081,15812262,Gaffney,808,Germany,Female,37,2,100431.84,1,1,0,35140.49,1\r\n4082,15762821,Udinese,721,Spain,Male,33,5,0,2,0,1,117626.9,0\r\n4083,15669301,Romani,778,Germany,Female,29,6,150358.97,1,1,0,62454.01,1\r\n4084,15672640,Kambinachi,850,Spain,Female,45,4,114347.85,2,1,1,109089.04,0\r\n4085,15750458,Hawkins,693,France,Female,39,4,0,2,0,1,142331.39,0\r\n4086,15627251,Tsui,520,France,Male,34,4,134007.9,1,1,1,193209.11,0\r\n4087,15764294,Ifeatu,759,Germany,Male,31,4,98899.91,1,1,1,47832.82,0\r\n4088,15659962,McIntosh,637,France,Male,60,3,0,2,1,1,70174.03,0\r\n4089,15788536,Armit,755,Germany,Male,40,2,137430.82,2,0,0,176768.59,0\r\n4090,15596979,Fang,662,France,Female,47,6,0,2,1,1,129392.75,0\r\n4091,15681220,Chou,503,France,Female,37,8,0,2,1,1,97893.32,0\r\n4092,15635097,Okeke,599,Germany,Male,39,2,188976.89,2,0,1,176142.09,0\r\n4093,15780779,Ramsbotham,583,Spain,Female,40,4,0,2,1,0,114093.73,0\r\n4094,15798470,Scannell,764,Spain,Female,48,1,75990.97,1,1,0,158323.81,1\r\n4095,15760880,Edman,513,France,Male,29,10,0,2,0,1,25514.77,0\r\n4096,15616929,De Luca,730,Spain,Male,62,5,112181.08,1,0,1,61513.87,0\r\n4097,15758775,Vasilyeva,820,Spain,Male,34,10,97208.46,1,1,1,59553.34,0\r\n4098,15663386,Tuan,597,Spain,Female,26,7,0,2,1,0,110253.2,0\r\n4099,15621267,Ejimofor,637,France,Male,32,5,0,1,0,0,148769.08,0\r\n4100,15720509,Hs?,696,France,Male,34,9,150856.79,1,0,1,8236.78,0\r\n4101,15693322,Shaver,635,Germany,Female,37,9,146748.07,1,0,1,11407.58,0\r\n4102,15589544,Wallis,673,Spain,Female,57,4,0,2,1,1,49684.09,0\r\n4103,15772030,Coupp,662,Spain,Male,33,3,0,2,0,1,68064.83,0\r\n4104,15693337,Perry,683,Spain,Male,41,0,148863.17,1,1,1,163911.32,0\r\n4105,15676571,Bezrukova,850,France,Male,55,6,0,1,1,0,944.41,1\r\n4106,15701392,Lucciano,815,Spain,Male,28,6,0,2,0,1,185547.71,0\r\n4107,15741092,Ingram,671,Spain,Male,34,10,153360.02,1,1,0,140509.86,0\r\n4108,15643865,Lo Duca,601,France,Female,40,3,92055.36,1,0,1,164652.02,1\r\n4109,15769389,Wan,709,Germany,Female,39,9,124723.92,1,1,0,73641.86,0\r\n4110,15807768,Cohn,702,Germany,Male,28,1,103033.83,1,1,1,40321.87,0\r\n4111,15801630,Yen,558,France,Male,40,6,0,2,1,0,173844.89,0\r\n4112,15705034,Peng,691,Spain,Male,40,1,0,2,1,1,145613.17,0\r\n4113,15763107,Little,700,France,Female,30,9,0,1,1,1,174971.64,0\r\n4114,15667085,Meng,667,France,Male,33,4,0,2,1,1,131834.75,0\r\n4115,15647008,Adams,624,Germany,Male,54,3,116726.22,1,1,0,110498.1,1\r\n4116,15584505,Hill,580,France,Female,23,5,113923.81,2,0,0,196241.43,0\r\n4117,15748068,Boyle,571,Spain,Female,31,3,0,2,1,1,194667.92,0\r\n4118,15663964,Pagnotto,561,France,Male,37,5,0,2,1,0,83093.25,0\r\n4119,15782311,Feng,529,France,Male,28,9,0,2,1,1,52545.24,0\r\n4120,15588197,Endrizzi,670,France,Male,36,7,0,2,0,0,59571.5,0\r\n4121,15610105,Shen,666,Germany,Female,21,1,121827.43,2,1,1,99818.31,0\r\n4122,15606133,Lay,628,Spain,Male,42,7,0,2,0,1,172967.87,0\r\n4123,15599403,Wu,577,France,Male,60,10,125389.7,2,1,1,178616.73,0\r\n4124,15648225,Shephard,652,Spain,Female,38,1,103895.31,1,0,1,159649.44,0\r\n4125,15608406,Schmidt,678,France,Male,26,5,111128.04,1,1,0,60941.27,1\r\n4126,15633378,Davidson,692,Spain,Female,49,9,0,2,1,0,178342.63,0\r\n4127,15664759,Lamb,675,Spain,Male,32,10,0,2,1,0,191545.65,0\r\n4128,15625545,Hussey,712,Spain,Male,52,9,0,1,1,1,117977.45,1\r\n4129,15772148,Ferrari,639,Germany,Female,37,5,151242.48,1,0,1,49637.65,0\r\n4130,15810829,Macfarlan,618,France,Male,48,7,0,1,1,0,13921.82,1\r\n4131,15731669,Szabados,554,France,Male,39,2,129709.62,1,1,0,173197.12,0\r\n4132,15738634,Yuan,533,France,Male,47,9,83347.25,1,1,1,137696.25,0\r\n4133,15737571,Matveyev,540,Spain,Female,28,6,84121.04,1,0,1,80698.54,0\r\n4134,15667602,Cheng,704,Spain,Male,33,3,0,2,1,0,73018.74,0\r\n4135,15684147,Palerma,678,France,Male,43,5,102338.19,1,1,1,79649.62,0\r\n4136,15789874,Wang,712,France,Female,29,3,87375.78,2,0,0,166194.53,0\r\n4137,15757952,Teng,651,France,Male,44,2,0,3,1,0,102530.35,1\r\n4138,15698732,K'ung,789,Germany,Male,51,3,104677.09,1,1,0,74265.38,0\r\n4139,15714355,Sinclair,775,Germany,Male,32,8,121669.23,1,0,1,125898.39,0\r\n4140,15599090,McKelvey,564,Germany,Male,40,7,108407.34,1,1,1,83681.2,0\r\n4141,15762048,Yuan,841,Germany,Female,33,7,154969.79,2,1,1,99505.75,0\r\n4142,15790596,Moran,850,Spain,Male,39,0,141829.67,1,1,1,92748.16,0\r\n4143,15609623,McConnell,637,France,Female,63,5,0,1,1,0,28092.77,1\r\n4144,15711901,Iheatu,500,France,Male,45,2,109162.82,1,1,1,126145.08,0\r\n4145,15779809,Giordano,655,France,Male,44,8,87471.63,1,0,1,188593.98,0\r\n4146,15729018,Alexander,666,France,Female,33,2,147229.65,1,1,1,56410.17,0\r\n4147,15698246,Gordon,658,France,Female,24,2,0,2,1,1,84694.49,0\r\n4148,15712409,Tang,749,Germany,Male,66,6,182532.23,2,1,1,195429.92,0\r\n4149,15758306,T'an,654,France,Male,32,6,0,2,1,1,137898.57,0\r\n4150,15621435,Davies,623,France,Female,39,1,160903.2,1,0,0,78774.36,0\r\n4151,15566295,Sanders,761,France,Female,33,6,138053.79,2,1,0,148779.41,0\r\n4152,15569098,Winifred,627,France,Male,44,6,153548.12,1,0,0,35300.08,1\r\n4153,15662532,Holmes,757,Germany,Male,31,8,149085.9,2,1,1,197077.36,0\r\n4154,15664001,Riddle,695,Germany,Female,53,8,95231.91,1,0,0,70140.8,1\r\n4155,15703437,Chinedum,726,France,Male,34,3,0,2,1,0,196288.46,0\r\n4156,15708003,Aleksandrova,587,Spain,Male,41,8,85109.21,1,1,0,1557.82,0\r\n4157,15599452,Conti,605,Germany,Female,43,8,125338.8,2,1,0,23970.13,0\r\n4158,15719793,Watson,850,Spain,Male,62,5,0,2,1,1,180243.56,0\r\n4159,15771580,Davison,850,France,Female,38,0,106831.69,1,0,1,148977.72,0\r\n4160,15732268,Cook,751,France,Male,29,3,159597.45,1,1,0,39934.41,0\r\n4161,15722350,Udinesi,627,Germany,Female,37,7,147361.57,1,1,1,133031.96,0\r\n4162,15611371,Siciliani,736,France,Male,43,4,176134.54,1,1,1,52856.88,0\r\n4163,15673584,Bell,652,France,Female,74,5,0,2,1,1,937.15,0\r\n4164,15636396,Jackson,627,France,Female,35,7,0,2,0,1,193022.44,0\r\n4165,15706170,Onyemere,636,France,Male,34,1,84055.43,1,0,0,37490.84,0\r\n4166,15725478,McClemans,775,France,Male,60,7,0,2,1,1,111558.7,0\r\n4167,15654562,Ma,850,Spain,Female,45,5,174088.3,4,1,0,5669.31,1\r\n4168,15737509,Morrison,850,Spain,Male,34,8,199229.14,1,0,0,68106.29,0\r\n4169,15690796,Chambers,516,France,Male,37,8,0,1,1,0,101834.58,0\r\n4170,15716728,Basedow,513,Spain,Female,42,10,0,2,0,1,73151.25,0\r\n4171,15605665,Nwora,673,Germany,Female,69,3,78833.15,2,1,1,37196.15,0\r\n4172,15748481,Howey,564,France,Female,27,6,0,1,0,0,7819.76,0\r\n4173,15757777,Pai,636,France,Female,35,2,0,2,1,1,23129.46,0\r\n4174,15747808,Ni,712,France,Male,29,3,102540.61,1,1,1,189680.79,0\r\n4175,15810593,Forbes,568,France,Male,51,4,0,3,1,1,66586.56,0\r\n4176,15693376,Baryshnikov,741,Spain,Male,43,0,0,2,1,1,2920.63,1\r\n4177,15579808,Kramer,754,Germany,Female,39,8,129401.87,1,1,1,87684.93,0\r\n4178,15598275,Sochima,709,France,Female,32,7,0,2,1,1,199418.02,0\r\n4179,15737080,Marchesi,510,France,Female,32,1,0,2,0,1,28515.17,0\r\n4180,15668580,Todd,716,Spain,Male,33,2,0,2,1,1,92916.53,0\r\n4181,15569438,Mai,607,Germany,Male,36,10,106702.94,2,0,0,198313.69,0\r\n4182,15675842,Pinto,656,Spain,Male,26,4,139584.57,1,1,0,36308.93,0\r\n4183,15577587,Reynolds,550,Germany,Male,52,5,121016.23,1,1,1,41730.37,1\r\n4184,15625592,Sal,486,France,Male,26,2,0,2,1,1,31399.4,0\r\n4185,15635141,Miller,598,Germany,Male,59,8,118210.42,2,0,0,60192.14,1\r\n4186,15642570,Scott,675,Spain,Male,35,8,0,2,1,0,29062.25,0\r\n4187,15702175,Herrin,755,France,Female,29,4,148654.84,2,1,1,28805.09,0\r\n4188,15677785,Stevenson,656,Spain,Male,32,5,136963.12,1,1,0,133814.28,0\r\n4189,15786153,McKenzie,644,Germany,Male,47,9,137774.11,2,1,0,151902.78,0\r\n4190,15759499,Gardiner,598,France,Female,32,4,111156.52,1,1,1,167376.26,0\r\n4191,15659568,Atkinson,850,Spain,Female,31,3,121237.65,1,1,1,31022.56,0\r\n4192,15715597,Onyemauchechi,679,France,Male,36,1,97234.58,1,1,0,188997.08,0\r\n4193,15610147,Ross,632,France,Male,60,2,0,2,0,1,2085.32,0\r\n4194,15802362,Newland,550,Spain,Male,45,0,0,2,0,1,70399.71,0\r\n4195,15660524,Hu,572,Germany,Female,54,9,97382.53,1,1,1,195771.95,0\r\n4196,15747168,Sanders,626,Germany,Female,47,2,103108.8,1,0,1,166475.44,1\r\n4197,15796910,Tsui,625,Spain,Female,57,7,0,1,0,0,84106.17,1\r\n4198,15707674,Marino,515,France,Female,58,2,131852.81,1,1,0,81436.68,1\r\n4199,15565706,Akobundu,612,Spain,Male,35,1,0,1,1,1,83256.26,1\r\n4200,15587596,Morrison,628,Spain,Female,39,8,107553.33,1,1,0,117523.41,0\r\n4201,15751943,Mai,529,Spain,Female,43,5,0,2,0,0,79476.63,0\r\n4202,15621227,Hs?eh,668,Germany,Female,46,7,161806.09,1,1,1,173052.19,0\r\n4203,15757588,Wright,526,France,Male,30,9,0,2,0,0,100995.68,0\r\n4204,15640922,Demaine,791,France,Female,52,7,0,1,1,1,122782.5,0\r\n4205,15567557,Chien,573,France,Male,27,2,128243.03,1,1,1,11631.34,0\r\n4206,15670103,Dickinson,565,France,Female,38,5,126645.13,1,1,1,168303.55,0\r\n4207,15720929,Kazantseva,604,France,Female,47,8,62094.71,3,0,0,9308.1,1\r\n4208,15732774,Marchesi,656,France,Male,37,7,112291.34,1,1,0,153157.97,0\r\n4209,15628558,Pan,447,France,Female,44,5,89188.83,1,1,1,75408.24,0\r\n4210,15729201,Harewood,682,France,Male,55,9,0,1,1,0,153356.8,1\r\n4211,15731117,Kao,437,Spain,Male,28,2,109161.25,1,1,0,152987.42,0\r\n4212,15615207,Yeh,792,Spain,Male,47,0,0,1,1,1,5557.88,1\r\n4213,15773512,Bischof,627,Spain,Female,25,4,0,1,1,1,194313.93,0\r\n4214,15572145,Ashton,767,France,Female,34,8,0,2,1,0,94767.77,0\r\n4215,15642710,Napolitani,686,France,Male,26,7,0,2,1,0,1540.89,0\r\n4216,15574213,Wilson,789,France,Female,53,1,158271.74,1,1,1,5036.39,1\r\n4217,15718852,Uren,794,France,Male,56,9,96951.21,1,1,1,71776.76,0\r\n4218,15583840,Okechukwu,587,Germany,Male,35,5,121863.61,1,1,1,23481.69,1\r\n4219,15782418,Ku,589,Germany,Female,19,9,83495.11,1,1,1,143022.31,1\r\n4220,15813504,Onyemachukwu,543,Germany,Female,25,1,146566.01,1,0,1,161407.48,0\r\n4221,15711314,Kao,589,Spain,Female,45,1,0,1,0,0,125939.22,1\r\n4222,15621064,Russell,701,Germany,Male,23,5,186101.18,2,1,1,76611.33,0\r\n4223,15627847,Woronoff,850,France,Male,40,6,0,1,1,0,136985.08,1\r\n4224,15588090,Ferri,726,Germany,Female,51,8,107494.86,2,1,0,140937.91,1\r\n4225,15735270,Ruggiero,767,Spain,Male,47,2,0,1,1,0,48161.18,1\r\n4226,15671804,Wilding,648,Spain,Male,36,8,146943.38,2,1,1,130041.45,0\r\n4227,15753215,Yashina,651,Spain,Female,36,8,0,2,1,0,91652.43,0\r\n4228,15789941,Yevseyev,633,France,Female,36,6,125130.28,1,0,0,125961.48,0\r\n4229,15691061,Rapuokwu,740,France,Female,37,9,0,2,1,1,73225.31,0\r\n4230,15808326,Maslov,592,France,Female,34,9,0,2,1,1,20460.2,0\r\n4231,15566660,Cole,670,France,Female,41,10,0,3,1,0,81602.02,0\r\n4232,15778947,Sullivan,628,France,Male,36,3,0,2,1,1,8742.91,0\r\n4233,15632977,Hsiao,745,France,Male,47,5,0,2,0,0,145789.71,0\r\n4234,15591747,Rossi,705,France,Male,32,3,0,2,0,0,129576.99,0\r\n4235,15567335,Allsop,559,France,Female,42,7,0,2,1,1,190040.29,0\r\n4236,15609299,Chamberlain,595,France,Male,29,6,150685.79,1,1,0,87771.06,0\r\n4237,15669945,Jackson,492,France,Male,35,4,141359.37,2,1,0,39519.53,0\r\n4238,15736271,Dumetochukwu,498,France,Female,29,9,0,1,1,0,190035.83,0\r\n4239,15710390,Uspensky,655,France,Female,39,6,94631.26,2,1,1,148948.52,0\r\n4240,15756481,Garcia,636,France,Female,39,3,118336.14,1,1,0,184691.77,0\r\n4241,15736730,Soto,634,France,Female,45,2,0,1,1,1,143458.31,0\r\n4242,15626040,McDonald,793,Spain,Male,63,0,0,2,0,1,27166.75,0\r\n4243,15746553,Castles,526,Germany,Male,50,5,124233.24,1,0,1,159456.87,1\r\n4244,15622518,Stephenson,768,France,Female,26,5,51116.26,1,1,1,70454.79,1\r\n4245,15684908,Davidson,540,Germany,Male,64,1,91869.69,1,0,1,95421,0\r\n4246,15569446,Tu,732,France,Female,34,8,122338.43,2,1,0,187985.85,0\r\n4247,15777504,Colbert,617,France,Female,30,8,0,1,1,1,92621.9,0\r\n4248,15677906,Owens,637,Spain,Female,54,5,0,1,0,1,150836.98,0\r\n4249,15703292,Chimezie,573,France,Male,26,8,86270.93,2,1,1,90177.3,0\r\n4250,15712938,Genovese,531,France,Male,44,3,0,2,1,1,34416.79,0\r\n4251,15631359,Daluchi,489,France,Female,38,5,117289.92,1,0,0,85231.88,0\r\n4252,15720847,Sheffield,601,France,Male,35,2,0,2,1,1,118983.18,0\r\n4253,15787830,Bailey,452,Germany,Male,33,7,153663.27,1,1,0,111868.23,0\r\n4254,15599869,Dyson,728,Spain,Female,29,1,0,1,1,1,83056.22,0\r\n4255,15592078,Davide,590,Germany,Female,27,8,123599.49,2,1,0,1676.92,0\r\n4256,15596228,Uwaezuoke,490,France,Male,29,4,0,2,1,0,32089.57,0\r\n4257,15578462,Hs?,596,Spain,Female,76,9,134208.25,1,1,1,13455.43,0\r\n4258,15756894,Onwuka,635,France,Male,29,1,0,1,0,1,24865.54,0\r\n4259,15796167,Flores,782,Germany,Male,35,7,98556.89,2,1,0,117644.36,0\r\n4260,15664808,Nicoll,790,Spain,Female,37,3,0,3,0,0,98897.32,0\r\n4261,15664555,Hughes,587,France,Male,40,2,0,4,0,1,106174.7,1\r\n4262,15607278,Romano,794,Spain,Female,46,8,134593.79,1,1,1,46386.37,0\r\n4263,15585222,Norman,515,France,Male,41,8,0,2,1,1,185054.14,0\r\n4264,15750299,Davison,746,Spain,Male,24,10,68781.82,1,0,1,47997.39,0\r\n4265,15761294,Manna,667,Germany,Female,56,8,137464.04,1,1,0,130846.79,1\r\n4266,15810454,Reed,709,France,Male,32,4,147307.91,1,0,1,40861.55,0\r\n4267,15673984,Daniels,536,France,Female,35,8,0,1,1,0,171840.24,1\r\n4268,15609319,Hunt,711,France,Female,41,3,145754.91,1,1,1,101455.07,0\r\n4269,15709941,Feng,753,France,Male,46,8,0,3,1,0,90747.94,1\r\n4270,15580252,Waters,748,France,Male,44,4,112610.77,1,0,1,2048.55,0\r\n4271,15741275,Yuan,623,France,Female,57,7,71481.79,2,1,1,84421.34,0\r\n4272,15707132,Yudin,465,France,Male,33,5,0,2,0,1,78698.09,0\r\n4273,15600708,Calabresi,640,Spain,Female,34,3,77826.8,1,1,1,168544.85,0\r\n4274,15804787,Onyemauchechukwu,562,France,Male,75,5,87140.85,1,1,1,39351.64,0\r\n4275,15690021,Martin,502,Germany,Female,42,0,132002.7,1,0,1,28204.98,1\r\n4276,15763895,Hung,536,France,Male,32,7,178011.5,2,1,0,22375.14,0\r\n4277,15623478,Maslova,670,Germany,Female,32,4,102954.68,2,0,1,134942.45,0\r\n4278,15797910,Zetticci,775,Germany,Male,51,2,123783.25,1,1,1,134901.57,0\r\n4279,15577751,Pisano,759,Germany,Male,30,4,101802.67,1,0,0,8693.8,0\r\n4280,15781777,Sutherland,604,France,Male,33,3,148659.48,1,0,0,42437.75,0\r\n4281,15740527,Lai,766,Germany,Female,62,7,142724.48,1,0,1,5893.23,1\r\n4282,15721251,Watson,554,Spain,Female,41,4,112152.89,1,0,1,36242.19,0\r\n4283,15602994,Gorbunov,487,France,Female,53,10,89550.85,1,0,1,90076.85,0\r\n4284,15750769,Padovano,725,France,Male,35,7,75915.75,1,1,0,150507.43,0\r\n4285,15740175,Raynor,732,Germany,Female,42,2,118889.66,2,0,0,87422.15,0\r\n4286,15679968,Ting,623,France,Male,40,5,118788.57,1,1,0,192867.4,0\r\n4287,15694404,Eberegbulam,781,France,Female,42,3,156555.54,1,1,1,175674.01,0\r\n4288,15657529,Chin,714,Germany,Male,53,1,99141.86,1,1,1,72496.05,1\r\n4289,15762882,Manna,577,Germany,Female,31,4,61211.18,1,1,1,145250.43,0\r\n4290,15642579,Chang,731,Spain,Female,37,1,128932.4,1,1,1,180712.52,0\r\n4291,15598884,Kent,650,Spain,Female,23,5,0,1,1,1,180622.43,0\r\n4292,15770185,Buckley,779,France,Male,32,10,80728.15,1,1,0,86306.75,0\r\n4293,15800287,Micco,706,Spain,Female,46,2,127660.46,2,1,0,150156.82,1\r\n4294,15665861,Avdeev,733,Spain,Male,44,3,106070.89,1,0,1,101617.43,0\r\n4295,15662203,Bremer,579,Germany,Female,42,3,137560.38,2,1,1,85424.34,0\r\n4296,15616454,Davidson,476,Germany,Female,34,8,111905.43,1,0,1,197221.81,1\r\n4297,15702788,Gadsdon,775,France,Male,40,9,126212.64,1,1,0,70196.57,0\r\n4298,15778149,Connolly,538,Spain,Male,68,9,0,2,1,0,110440.5,1\r\n4299,15680001,McDonald,602,France,Male,38,7,111835.94,2,1,0,124389.61,0\r\n4300,15711991,Chiawuotu,615,France,Male,30,8,0,2,0,0,3183.15,0\r\n4301,15633834,Milne,700,Germany,Female,28,3,99705.69,2,0,0,146723.72,0\r\n4302,15765266,Fleming,615,France,Male,32,1,0,2,0,0,2139.25,0\r\n4303,15791867,Hicks,544,Germany,Male,44,2,108895.93,1,0,0,69228.2,1\r\n4304,15675380,Logan,573,Spain,Male,56,3,154669.77,1,0,1,115462.27,1\r\n4305,15770576,Hammond,555,Spain,Male,50,7,128061,2,1,1,62375.1,0\r\n4306,15775294,Weber,692,France,Female,31,2,0,2,1,0,91829.17,1\r\n4307,15727059,Lettiere,476,France,Female,40,4,0,2,0,0,182547.04,0\r\n4308,15702499,Schnaars,770,Spain,Male,46,9,190678.02,1,1,1,14725.36,0\r\n4309,15611699,Tao,641,France,Female,40,7,0,1,1,0,126996.67,0\r\n4310,15654000,Algarin,705,France,Female,35,5,0,1,1,0,133991.11,1\r\n4311,15657881,Onyemere,784,France,Male,38,5,136712.91,1,0,1,169920.92,0\r\n4312,15719991,Korovina,727,Spain,Female,52,1,154733.97,1,1,0,80259.67,1\r\n4313,15720687,Chidubem,576,France,Female,41,4,112609.91,1,0,0,191035.18,1\r\n4314,15687079,King,646,Spain,Male,69,10,115462.44,1,1,0,40421.87,0\r\n4315,15582276,Greco,638,France,Male,34,5,133501.36,1,0,1,155643.04,0\r\n4316,15763980,Beneventi,632,Germany,Male,30,1,58668.02,1,1,1,78670.52,0\r\n4317,15720774,P'eng,850,Spain,Male,44,7,89118.26,1,1,0,104240.77,1\r\n4318,15592194,Metcalf,492,France,Female,28,9,0,2,1,0,95957.09,0\r\n4319,15803685,Greco,673,Germany,Female,77,10,76510.52,2,0,1,59595.66,0\r\n4320,15759456,Lupton,609,Spain,Male,34,7,140694.78,2,1,0,46266.63,0\r\n4321,15611544,Ibeamaka,701,Germany,Male,36,7,95448.32,2,1,0,189085.07,0\r\n4322,15794257,Hsiung,651,France,Male,34,4,91562.99,1,1,1,123954.15,0\r\n4323,15681697,Rueda,508,France,Male,31,8,72541.48,1,1,0,129803.08,0\r\n4324,15579566,Li Fonti,616,Spain,Female,43,3,120867.18,1,1,0,18761.92,1\r\n4325,15577970,Alexeeva,489,France,Male,34,5,0,1,0,0,43540.59,0\r\n4326,15727489,Madueke,567,Spain,Female,45,1,157320.51,1,1,0,62193.92,0\r\n4327,15764284,Torres,714,Spain,Male,27,3,0,3,1,1,129130.09,0\r\n4328,15581811,Chukwubuikem,678,Germany,Female,30,1,139676.95,2,0,1,16146,0\r\n4329,15622527,Holloway,581,France,Female,55,6,0,1,1,1,22442.13,0\r\n4330,15753362,Evdokimov,748,Spain,Male,60,3,0,2,1,1,78194.37,0\r\n4331,15666652,Anayolisa,781,France,Female,19,3,0,2,1,1,124297.32,0\r\n4332,15789714,Semmens,691,Spain,Male,21,3,103000.94,1,1,1,104648.58,0\r\n4333,15771543,Tu,507,Germany,Male,31,2,134237.07,1,1,1,166423.66,1\r\n4334,15748327,Anderson,724,Germany,Male,34,6,118235.7,2,0,0,157137.23,0\r\n4335,15754649,Fang,705,Spain,Female,57,3,0,2,1,1,34134.14,0\r\n4336,15810460,Fanucci,708,Spain,Female,64,5,0,3,0,1,112520.07,1\r\n4337,15771742,Boyle,580,Germany,Male,38,9,115442.19,2,1,0,128481.5,1\r\n4338,15642160,Milanesi,850,France,Male,38,5,0,2,1,0,16491.64,0\r\n4339,15798439,Davidson,714,Spain,Male,25,2,0,1,1,1,132979.43,0\r\n4340,15605293,McKay,559,France,Female,43,1,0,2,1,1,196645.87,0\r\n4341,15692631,Bogdanova,577,Spain,Female,44,8,115557,1,0,1,127506.76,0\r\n4342,15665376,Lavrentiev,647,Germany,Female,35,3,166518.63,2,1,0,147930.46,0\r\n4343,15772412,Corser,554,Spain,Male,30,6,135370.12,1,1,1,179689.05,1\r\n4344,15654577,Alexeeva,549,Germany,Male,54,5,92877.33,1,1,0,2619.64,1\r\n4345,15585427,Madueke,528,France,Female,26,10,102073.67,2,0,0,166799.93,0\r\n4346,15584536,Barber,720,Germany,Male,46,3,97042.6,1,1,1,133516.51,1\r\n4347,15585853,McCardle,743,Spain,Male,41,7,0,1,1,0,163736.09,1\r\n4348,15645271,Radcliffe-Brown,615,Germany,Male,24,8,108528.07,2,0,0,179488.41,1\r\n4349,15579387,Ni,635,Germany,Female,44,2,79064.85,2,0,1,113291.75,0\r\n4350,15623107,Chukwumaobim,686,Spain,Male,45,3,74274.87,3,1,0,64907.48,1\r\n4351,15754072,Dennis,840,Spain,Female,36,6,0,2,1,0,141364.27,0\r\n4352,15666163,Hayward,695,France,Male,43,1,100421.1,1,1,1,101141.28,0\r\n4353,15765192,Jones,564,France,Male,26,7,84006.88,2,0,0,183490.99,0\r\n4354,15804822,L?,805,France,Female,31,4,0,2,1,0,4798.12,0\r\n4355,15612893,Nelson,558,Spain,Male,45,4,0,1,1,0,131807.14,0\r\n4356,15593636,Cardus,657,France,Female,39,4,80293.81,1,1,0,97192.76,0\r\n4357,15693326,Whitehouse,641,France,Female,42,7,125437.14,2,0,0,164128.58,0\r\n4358,15809901,Johnstone,755,France,Male,36,8,0,2,1,0,176809.87,0\r\n4359,15759751,Tsui,483,France,Male,48,1,0,2,1,1,110059.38,0\r\n4360,15605425,Chia,545,Germany,Female,44,2,127536.44,1,1,0,108398.63,0\r\n4361,15801934,Su,678,France,Male,66,8,0,2,1,1,47117.03,0\r\n4362,15592000,Calabresi,781,Germany,Female,48,9,82794.18,1,1,0,124720.68,1\r\n4363,15618695,Ts'ui,571,Spain,Female,22,3,108117.1,1,0,1,53328.7,0\r\n4364,15637110,McCulloch,634,Spain,Female,35,10,0,1,1,0,82634.41,0\r\n4365,15727408,Koo,523,Germany,Male,27,8,61688.61,2,1,0,147059.16,0\r\n4366,15716328,Miller,501,France,Female,40,2,0,2,0,0,141946.92,0\r\n4367,15669060,Woolnough,662,France,Male,74,6,0,2,1,0,123583.85,0\r\n4368,15675854,Douglas,573,Spain,Male,50,0,159304.07,1,0,1,155915.24,1\r\n4369,15621116,Fang,648,Germany,Male,33,5,138664.24,1,1,0,29076.27,0\r\n4370,15781495,Munro,662,France,Female,22,2,126362.57,2,1,1,97382.8,0\r\n4371,15740470,Vinogradov,725,France,Male,39,4,160652.45,2,1,0,57643.55,0\r\n4372,15714391,Lai,563,France,Female,35,2,183572.84,1,1,1,66006.75,1\r\n4373,15730137,Udegbulam,628,France,Male,31,0,88421.81,1,0,0,72350.47,0\r\n4374,15596455,Mao,546,Spain,Female,45,2,0,1,0,0,197789.83,1\r\n4375,15717290,Onyekaozulu,688,Germany,Male,41,2,112871.19,2,0,1,65520.74,0\r\n4376,15616555,Fu,850,Germany,Male,41,8,60880.68,1,1,0,31825.84,0\r\n4377,15659820,Cross,614,France,Female,34,5,0,2,1,0,185561.89,0\r\n4378,15696301,Snider,719,France,Female,37,9,101455.7,1,1,0,25803.59,1\r\n4379,15771087,Harrison,757,France,Female,71,0,88084.13,2,1,1,154337.47,0\r\n4380,15808831,Dale,669,France,Male,29,7,0,2,1,1,138145.62,0\r\n4381,15812241,Udinese,438,Germany,Male,59,7,127197.14,1,1,0,51565.98,1\r\n4382,15680370,DeRose,492,France,Male,39,7,0,2,0,1,71323.23,0\r\n4383,15780561,Hay,622,France,Female,39,9,83456.79,2,0,0,38882.34,0\r\n4384,15800784,Bruce,645,France,Male,42,4,98298.18,1,1,1,676.06,0\r\n4385,15715796,Romani,728,France,Male,37,0,0,2,1,1,72203.8,0\r\n4386,15605375,Tseng,651,France,Male,35,2,86911.8,1,1,0,174094.24,0\r\n4387,15621520,Tang,783,Germany,Female,42,2,139707.28,1,1,0,2150.22,0\r\n4388,15665460,Isayeva,732,Spain,Female,67,1,0,2,1,1,177783.04,0\r\n4389,15801152,Hill,553,Spain,Female,39,1,142876.98,2,1,0,44363.42,0\r\n4390,15756425,Barnes,660,France,Male,30,7,146301.31,1,0,0,96847.91,0\r\n4391,15674328,Moreno,670,France,Female,40,3,47364.45,1,1,1,148579.43,1\r\n4392,15742404,McGregor,718,France,Male,38,7,0,2,1,0,38308.34,0\r\n4393,15757140,Genovese,787,France,Male,51,0,58137.08,1,0,1,142538.31,0\r\n4394,15570051,Gill,775,Germany,Female,38,6,179886.41,2,0,0,153122.58,0\r\n4395,15669175,Ts'ai,479,Germany,Male,24,6,107637.97,2,0,1,169505.83,0\r\n4396,15790324,Green,660,France,Female,20,6,167685.56,1,1,0,57929.9,0\r\n4397,15691119,Martin,721,Germany,Male,68,4,136525.99,1,0,0,175399.14,0\r\n4398,15743478,Johnson,659,Germany,Male,39,8,52106.33,2,1,1,107964.36,0\r\n4399,15707007,Onio,743,France,Female,39,8,0,1,1,0,94263.44,0\r\n4400,15572547,Vaguine,670,France,Female,45,9,104930.38,1,1,0,155921.81,1\r\n4401,15567063,Manna,766,Germany,Female,34,6,106434.94,1,0,1,137995.66,1\r\n4402,15689633,Toomey,845,Spain,Female,38,2,112803.92,1,1,0,179631.85,0\r\n4403,15720637,Bell,710,Germany,Female,46,10,120530.34,1,1,0,166586.99,1\r\n4404,15616859,Bonwick,602,Germany,Female,43,2,113641.49,4,1,0,115116.35,1\r\n4405,15766166,Folliero,604,Spain,Male,43,2,145081.72,1,1,1,23881.62,0\r\n4406,15617655,Holt,564,Spain,Female,35,9,0,2,1,1,105837.38,0\r\n4407,15623450,Brown,637,Germany,Female,27,7,135842.89,1,1,1,101418.05,0\r\n4408,15796612,Ch'ang,527,France,Female,31,1,112203.25,1,1,0,182266.01,0\r\n4409,15795963,Fiorentini,687,France,Male,34,7,129895.19,1,0,1,28698.17,0\r\n4410,15781598,Middleton,756,Germany,Male,41,6,149049.92,1,0,1,50422.36,1\r\n4411,15691871,Millar,503,Germany,Male,42,9,153279.39,1,1,1,151336.96,0\r\n4412,15740345,Osborne,657,Spain,Male,42,5,41473.33,1,1,0,112979.6,1\r\n4413,15662626,Feng,666,France,Female,40,2,0,2,0,0,36371.27,0\r\n4414,15596575,Vale,615,Germany,Male,39,5,113193.51,2,1,1,52166.25,0\r\n4415,15657321,Arkwookerum,712,Germany,Male,27,8,113174.21,2,1,0,147261.58,0\r\n4416,15575955,Lujan,764,France,Female,24,0,0,2,1,0,88724.49,0\r\n4417,15743893,Alexeyeva,471,France,Male,42,3,164951.56,1,1,0,190531.77,0\r\n4418,15697270,Gannon,608,Spain,Male,27,4,153325.1,1,1,1,199953.33,0\r\n4419,15644356,Prokhorova,682,Spain,Female,47,10,134032.01,1,1,0,144290.97,0\r\n4420,15677586,Romero,587,Germany,Female,32,3,125445.04,2,1,1,130514.78,0\r\n4421,15571261,Toscani,714,Germany,Female,35,6,126077.43,2,1,1,53954.24,0\r\n4422,15698758,Onwuamaegbu,630,Spain,Female,31,1,0,2,1,1,169802.73,0\r\n4423,15787014,King,648,Germany,Female,28,8,90371.09,1,1,1,146851.73,0\r\n4424,15739857,Trentino,785,France,Female,40,3,0,2,1,1,96832.82,0\r\n4425,15774630,Peacock,601,Germany,Female,47,1,142802.02,1,1,1,57553.02,0\r\n4426,15805523,Nnaife,717,France,Female,28,1,90537.16,1,0,1,74800.99,0\r\n4427,15749557,Chao,707,France,Female,44,6,0,2,1,1,192542.17,0\r\n4428,15681180,Barese,771,France,Female,23,7,156123.73,1,1,0,72990.62,0\r\n4429,15742028,Udegbulam,602,France,Female,35,5,0,2,1,0,31050.02,0\r\n4430,15686463,Fu,626,France,Male,38,7,141074.59,1,1,0,52795.56,1\r\n4431,15654379,Onwuatuegwu,588,Spain,Male,59,4,0,2,1,1,27435.41,0\r\n4432,15783629,Degtyaryov,616,Germany,Female,42,6,117899.95,2,0,0,150266.81,0\r\n4433,15751193,Nnaemeka,621,Spain,Male,33,4,0,2,1,1,40299.23,0\r\n4434,15724099,Udinese,674,France,Male,26,6,166257.96,1,1,1,149369.41,0\r\n4435,15568429,Mitchell,633,Spain,Female,46,3,0,2,1,0,120250.58,0\r\n4436,15648967,Ch'en,698,Germany,Female,64,1,169362.43,1,1,0,84760.32,1\r\n4437,15664498,Golovanov,508,France,Male,26,7,205962,1,1,0,156424.4,0\r\n4438,15779522,Efimov,736,France,Female,24,0,0,2,1,0,109355.73,1\r\n4439,15583850,Davidson,672,Germany,Male,68,0,126061.51,2,1,1,184936.77,0\r\n4440,15696539,Wade,613,France,Female,21,7,105627.95,1,1,1,36560.51,0\r\n4441,15760121,Maynard,712,France,Male,32,9,100606.02,1,1,0,165693.06,0\r\n4442,15628279,Murphy,624,France,Female,38,3,0,2,1,1,163666.85,0\r\n4443,15766163,Zotova,676,France,Male,38,1,0,2,0,1,35644.79,0\r\n4444,15566467,Hannah,683,Germany,Female,32,0,138171.1,2,1,1,188203.58,0\r\n4445,15639049,Cartagena,489,France,Female,31,7,139395.08,1,0,1,6120.84,0\r\n4446,15736413,Hall,739,France,Male,29,1,0,2,1,1,164484.78,0\r\n4447,15634815,Hunt,701,France,Female,37,3,0,2,1,1,164268.28,0\r\n4448,15716381,Greece,666,Germany,Female,50,7,109062.28,1,1,1,140136.1,1\r\n4449,15708162,Thomson,565,Germany,Female,40,1,89994.71,2,0,1,121084.27,0\r\n4450,15569364,Victor,666,France,Male,36,3,0,2,1,0,35156.54,0\r\n4451,15791191,Mitchell,633,France,Male,59,2,103996.74,1,1,1,103159.11,0\r\n4452,15621205,Olisaemeka,578,France,Male,34,4,175111.11,1,1,1,74858.3,0\r\n4453,15704788,Krawczyk,812,Spain,Female,49,8,66079.45,2,0,0,91556.57,1\r\n4454,15775756,Alexandrova,809,Germany,Male,33,8,148055.74,1,0,0,199203.21,0\r\n4455,15641312,Paterson,615,France,Male,36,6,0,1,1,1,27011.8,1\r\n4456,15782531,Chou,684,Spain,Female,31,8,0,2,1,0,188637.05,0\r\n4457,15724428,Abel,544,France,Male,40,8,0,2,1,0,61581.2,0\r\n4458,15743617,Chesnokova,713,Germany,Male,47,1,95994.98,1,1,0,197529.23,0\r\n4459,15585839,Niu,633,France,Male,37,2,0,2,1,0,182258.17,0\r\n4460,15658158,Sullivan,672,Germany,Female,23,10,110741.56,1,1,0,80778.5,0\r\n4461,15637678,Ma,661,France,Male,35,5,0,1,1,0,155394.52,0\r\n4462,15701809,Cavill,749,Spain,Female,28,3,0,1,1,0,3408.7,0\r\n4463,15676937,Nicholls,635,Spain,Female,32,8,0,2,1,1,19367.98,1\r\n4464,15778975,Nnonso,850,Germany,Female,70,1,96947.58,3,1,0,62282.99,1\r\n4465,15710375,Gibson,641,France,Male,41,6,0,2,1,0,65396.79,0\r\n4466,15579914,Garcia,633,Germany,Male,30,2,109786.82,2,1,1,139712.81,0\r\n4467,15595160,Renwick,413,Spain,Male,35,2,0,2,1,1,60972.84,0\r\n4468,15595391,Norris,538,France,Male,31,1,0,2,1,0,1375.46,0\r\n4469,15630363,Nkemakonam,437,France,Female,39,0,102721.49,1,0,0,22191.82,0\r\n4470,15692443,Piccio,612,Spain,Male,33,5,69478.57,1,1,0,8973.67,1\r\n4471,15593795,Linton,516,Germany,Female,53,1,156674.2,1,1,0,118502.34,1\r\n4472,15642824,Onyekaozulu,826,Spain,Female,56,8,174506.1,2,0,1,161802.82,1\r\n4473,15683524,Tobenna,660,Germany,Female,23,6,166070.48,2,0,0,90494.72,0\r\n4474,15713532,Wang,646,Germany,Female,29,4,105957.44,1,1,0,15470.91,0\r\n4475,15719827,O'Donnell,767,France,Male,36,3,0,1,0,0,65147.27,0\r\n4476,15578435,Langlands,640,France,Male,40,8,110340.68,1,1,1,157886.6,0\r\n4477,15723028,Smith,778,France,Male,33,1,0,2,1,0,85439.73,0\r\n4478,15595609,Sykes,679,Germany,Male,52,9,135870.01,2,0,0,54038.62,0\r\n4479,15622443,Marshall,549,France,Male,31,4,0,2,0,1,25684.85,0\r\n4480,15579112,Gibson,598,France,Male,47,2,0,2,1,1,186116.54,0\r\n4481,15648479,Stephenson,655,France,Female,45,0,0,2,1,0,166830.71,0\r\n4482,15659234,Y?,494,France,Male,30,3,85704.95,1,0,1,27886.06,0\r\n4483,15811970,Kang,653,France,Female,42,1,0,2,1,1,5768.32,0\r\n4484,15774192,Miller,539,Germany,Female,38,8,105435.74,1,0,0,80575.44,1\r\n4485,15595136,Kryukov,645,France,Female,37,1,0,2,1,1,68987.55,0\r\n4486,15630580,Y?,751,Germany,Male,34,9,108513.25,2,1,1,27097.82,0\r\n4487,15660646,Fanucci,528,France,Male,35,3,156687.1,1,1,0,199320.77,0\r\n4488,15614365,Lombardi,696,Germany,Male,31,3,150604.52,1,0,0,5566.6,0\r\n4489,15776128,Hs?,716,France,Female,44,6,155114.9,1,0,0,133871.83,0\r\n4490,15787035,Anderson,602,France,Female,35,8,0,2,1,1,152843.53,0\r\n4491,15792646,Trentino,647,Spain,Female,64,1,91216,1,1,1,41800.18,0\r\n4492,15726832,Donnelly,850,Germany,Male,61,3,141784.02,1,1,1,92053.75,0\r\n4493,15773260,Tsou,590,France,Female,32,0,127763.24,1,1,0,100717.54,0\r\n4494,15624437,Johnson,825,Spain,Female,32,1,0,2,1,1,42935.15,0\r\n4495,15717138,Watson,850,Spain,Male,31,6,82613.56,2,1,0,149170.92,0\r\n4496,15657317,Allan,789,France,Female,32,7,69423.52,1,1,0,107499.39,0\r\n4497,15626948,Butcher,701,France,Female,42,6,86167.82,1,1,0,153342.38,0\r\n4498,15758901,Henderson,713,Spain,Female,47,1,0,1,1,0,107825.08,1\r\n4499,15777759,Boucaut,570,France,Male,30,2,131406.56,1,1,1,47952.45,0\r\n4500,15773322,Obiajulu,536,Germany,Female,44,4,121898.82,1,0,0,131007.18,0\r\n4501,15697318,Ifeatu,771,Germany,Male,32,9,77487.2,1,0,0,33143.04,0\r\n4502,15678916,Kelly,512,France,Female,75,2,0,1,1,0,123304.62,0\r\n4503,15632118,Pirozzi,698,Spain,Male,45,5,164450.94,1,1,0,141970.02,1\r\n4504,15788118,Siciliano,656,France,Male,33,7,138705.02,2,1,0,37136.15,0\r\n4505,15788930,Silva,761,Spain,Male,37,7,132730.17,1,1,0,199293.01,0\r\n4506,15628583,Iweobiegbunam,709,France,Female,30,5,0,2,0,1,161388.22,0\r\n4507,15635177,Williamson,597,Spain,Female,66,3,0,1,1,1,70532.53,0\r\n4508,15587690,Madueke,592,France,Male,28,2,116498.22,1,1,0,144290.25,0\r\n4509,15627630,Chiagoziem,599,France,Female,41,1,0,2,1,0,96069.82,0\r\n4510,15610930,Kwemto,572,Germany,Female,35,1,139979.07,1,1,0,185662.84,0\r\n4511,15657747,Zito,611,Germany,Female,43,9,127216.31,2,0,1,17913.25,0\r\n4512,15568006,Ukaegbunam,634,France,Female,45,2,0,4,1,0,101039.53,1\r\n4513,15751748,Trevisani,666,France,Male,51,2,148222.65,1,0,0,156953.54,1\r\n4514,15722212,Edmondstone,696,France,Female,41,8,0,2,0,0,28276.83,0\r\n4515,15658670,Chien,669,France,Female,38,8,0,2,1,0,84049.16,0\r\n4516,15761654,Boni,726,Spain,Male,30,8,134152.29,1,1,1,147822.44,0\r\n4517,15812210,Yashina,497,Germany,Female,32,8,111537.23,4,1,1,9497.99,1\r\n4518,15787051,Georg,750,Spain,Female,39,7,119565.92,1,1,0,87067.73,0\r\n4519,15642991,Tung,850,Spain,Female,29,7,0,2,1,0,23237.25,0\r\n4520,15713769,Michelides,617,Spain,Male,38,7,0,1,1,1,27239.28,0\r\n4521,15605826,Korovina,652,Germany,Male,46,10,121063.8,3,1,0,151481.86,1\r\n4522,15648898,Chuang,560,Spain,Female,27,7,124995.98,1,1,1,114669.79,0\r\n4523,15705309,Yuriev,629,Spain,Male,39,5,0,2,0,0,116748.14,0\r\n4524,15734202,Chidimma,660,Germany,Female,52,4,86891.84,1,1,0,90877.76,0\r\n4525,15658852,Stevens,676,France,Male,38,8,0,2,1,1,133692.88,0\r\n4526,15612633,Kao,581,Spain,Male,43,9,78022.61,1,0,1,30662.91,0\r\n4527,15604818,Edmund la Touche,798,France,Male,34,9,154495.79,1,1,0,191395.88,0\r\n4528,15593900,Belousov,705,France,Male,38,1,189443.72,1,0,1,106648.58,0\r\n4529,15624995,McCane,714,Spain,Female,31,6,152926.6,1,1,1,50899.91,0\r\n4530,15570087,Parry-Okeden,664,France,Female,44,8,142989.69,1,1,1,115452.51,1\r\n4531,15802617,Hudson,697,Germany,Male,43,7,115371.94,2,1,0,64139.1,0\r\n4532,15656029,Marsden,609,France,Male,37,6,0,2,0,1,22030.72,0\r\n4533,15739194,Manfrin,548,Spain,Male,38,0,178056.54,2,1,0,38434.73,0\r\n4534,15607275,Ch'ang,850,Spain,Male,39,6,206014.94,2,0,1,42774.84,1\r\n4535,15629475,Clark,656,France,Male,41,2,0,2,1,0,158973.77,0\r\n4536,15635034,Aldrich,727,Germany,Male,37,9,101191.83,1,1,1,34551.35,1\r\n4537,15756333,Khan,642,France,Female,55,7,0,2,1,1,101515.76,0\r\n4538,15777436,Murray,710,Spain,Female,31,5,0,2,1,0,9561.73,0\r\n4539,15676835,Anayolisa,710,Spain,Male,33,1,0,2,1,0,168313.17,0\r\n4540,15574206,Shillito,718,France,Female,37,7,0,2,1,1,55100.09,0\r\n4541,15613017,McMillan,586,Germany,Male,32,1,149814.54,1,1,0,31830.06,0\r\n4542,15815131,Howells,617,Spain,Female,36,7,115617.24,1,1,1,71519.4,0\r\n4543,15585455,Stewart,630,France,Male,28,9,0,2,0,0,32599.35,0\r\n4544,15692929,Ikechukwu,791,Germany,Female,42,10,113657.41,2,0,1,139946.68,1\r\n4545,15758081,Repina,673,Spain,Male,39,8,138160,1,1,1,110468.51,0\r\n4546,15667476,Cox,477,Germany,Female,36,3,117700.86,1,0,0,74042,0\r\n4547,15738248,Lo,662,France,Female,37,5,0,2,1,0,151871.84,0\r\n4548,15672152,Grant,850,Germany,Male,37,9,122506.38,1,0,1,199693.84,1\r\n4549,15673372,Stevenson,635,France,Female,58,1,0,1,1,1,58907.08,1\r\n4550,15587611,Kauffmann,537,France,Male,59,9,0,2,0,0,103799.77,1\r\n4551,15803415,Samsonova,579,France,Female,39,3,166501.17,2,1,0,93835.64,0\r\n4552,15715673,Niu,651,Spain,Female,46,4,89743.05,1,1,0,156425.57,1\r\n4553,15655648,Bock,610,France,Female,25,2,0,2,1,0,123723.83,0\r\n4554,15763613,Barlow,581,France,Male,30,1,0,2,1,0,199464.08,0\r\n4555,15660385,Stevenson,592,France,Male,39,7,0,2,1,0,83084.33,0\r\n4556,15733261,Kung,688,Spain,Female,35,6,0,1,1,0,25488.43,1\r\n4557,15796231,Nwankwo,681,France,Female,18,1,98894.39,1,1,1,9596.4,0\r\n4558,15624866,Brewer,658,Germany,Male,37,3,168735.74,2,0,0,70370.24,0\r\n4559,15623730,Ch'iu,792,France,Male,34,1,0,1,0,1,86330.32,0\r\n4560,15668248,Quinn,528,Germany,Female,62,7,133201.17,1,0,0,168507.68,1\r\n4561,15694518,Kodilinyechukwu,624,Spain,Female,36,0,0,2,1,0,111605.9,0\r\n4562,15638028,Ifeanyichukwu,562,Germany,Male,31,4,127237.25,2,0,1,143317.42,0\r\n4563,15795895,Yermakova,678,Germany,Male,36,1,117864.85,2,1,0,27619.06,0\r\n4564,15694376,Sullivan,705,Germany,Female,64,3,153469.26,3,0,0,146573.66,1\r\n4565,15669204,Grant,650,Germany,Male,23,4,93911.3,2,1,0,69055.45,0\r\n4566,15773779,Jacka,593,Spain,Female,46,2,76597.79,1,1,1,54453.72,0\r\n4567,15580682,Tsai,652,France,Female,40,4,79927.36,2,1,1,33524.6,0\r\n4568,15768530,Emery,554,Spain,Female,27,4,0,2,1,1,135083.73,0\r\n4569,15672875,Piccio,584,Germany,Male,32,8,40172.91,1,1,1,137439.34,0\r\n4570,15617082,Sanders,516,France,Male,33,7,115195.58,1,1,1,11205.5,0\r\n4571,15760514,Sharp,789,Germany,Female,43,9,116644.29,2,1,1,60176.1,0\r\n4572,15761775,Myers,598,Germany,Male,20,8,180293.84,2,1,1,29552.7,0\r\n4573,15799964,Campbell,669,Germany,Female,30,7,139872.81,1,1,0,188795.85,0\r\n4574,15693906,Abbott,645,France,Female,24,3,34547.82,1,1,1,11638.17,0\r\n4575,15739514,Preston,659,France,Female,32,9,0,2,1,1,93155.75,0\r\n4576,15756926,Atherton,833,Germany,Male,29,1,96462.25,2,0,1,48986.18,0\r\n4577,15770984,Fanucci,697,Spain,Female,40,7,130334.35,2,0,1,116951.1,0\r\n4578,15703979,Evans,580,Germany,Male,39,3,119688.81,1,1,0,137041.26,0\r\n4579,15801821,Cookson,691,France,Male,38,1,0,2,0,0,44653.5,0\r\n4580,15711028,Nnachetam,534,France,Male,52,1,0,3,1,1,104035.41,1\r\n4581,15791842,Johnstone,478,France,Female,32,6,71187.24,1,1,1,110593.62,0\r\n4582,15746127,Hort,572,France,Female,47,2,0,2,1,0,36099.7,0\r\n4583,15663625,Johnson,501,France,Male,37,4,0,2,0,0,12470.3,0\r\n4584,15604891,Zaytseva,624,Spain,Female,38,8,0,2,1,0,95403.41,0\r\n4585,15589666,Sorokina,595,France,Female,39,9,136422.41,1,1,1,151757.81,0\r\n4586,15627881,Diehl,603,France,Male,30,8,0,2,1,1,47536.46,0\r\n4587,15664895,Onuchukwu,602,France,Female,25,0,0,2,1,1,101274.17,0\r\n4588,15676094,Osonduagwuike,500,France,Female,34,6,0,1,1,1,140268.45,0\r\n4589,15761720,Mead,422,France,Male,41,6,153238.88,1,1,0,11663.09,0\r\n4590,15611961,Stewart,615,France,Male,35,7,0,2,1,0,150784.29,0\r\n4591,15680167,Thomson,635,France,Female,78,6,47536.4,1,1,1,119400.08,0\r\n4592,15762543,Goliwe,711,France,Female,32,1,0,2,1,0,126188.42,0\r\n4593,15658475,Lori,834,France,Male,36,8,142882.49,1,1,0,89983.02,1\r\n4594,15779743,Onwuamaeze,633,France,Female,44,7,0,2,1,0,29761.29,0\r\n4595,15661532,Butusov,650,France,Female,31,1,160566.11,2,0,0,27073.81,0\r\n4596,15782360,Rogers,743,Germany,Male,65,2,131935.51,1,1,1,96399.67,1\r\n4597,15767908,Nicholson,567,France,Male,38,6,127678.8,2,0,0,45422.89,0\r\n4598,15677105,Rossi,706,Germany,Female,46,4,105214.58,1,1,0,108699.59,1\r\n4599,15641474,Hall,638,France,Male,46,9,139859.54,1,1,0,38967.29,0\r\n4600,15624451,Huddart,641,France,Female,38,3,0,2,1,0,116466.19,0\r\n4601,15577985,Chinomso,574,France,Female,34,5,112324.45,2,1,1,17993.43,0\r\n4602,15571666,Shaw,642,Germany,Male,30,8,134497.27,1,0,0,43250.54,0\r\n4603,15783691,Hargreaves,722,Spain,Female,35,1,120171.58,1,1,0,125240.8,0\r\n4604,15671172,Swain,623,France,Male,23,1,106012.2,2,0,1,191415.94,0\r\n4605,15731760,Butcher,681,France,Male,25,5,0,1,0,1,90860.97,0\r\n4606,15585599,Stone,530,France,Female,34,8,0,2,0,1,141872.52,0\r\n4607,15784958,Allan,797,France,Female,55,10,0,4,1,1,49418.87,1\r\n4608,15734524,Wang,653,France,Male,51,3,0,1,1,0,170426.65,1\r\n4609,15614103,Colombo,850,Germany,Male,42,8,119839.69,1,0,1,51016.02,1\r\n4610,15794895,McKay,581,Spain,Male,34,1,0,2,0,1,81175.25,0\r\n4611,15772381,Brient,589,Germany,Male,38,8,92219.21,1,1,0,99106.97,0\r\n4612,15710553,Yin,555,Germany,Male,48,3,142055.41,2,0,1,79134.78,0\r\n4613,15649292,Bellucci,748,France,Female,49,7,29602.08,1,0,0,163550.58,1\r\n4614,15792565,Duncan,745,France,Female,46,7,0,2,1,1,67769.94,0\r\n4615,15718245,Pirozzi,730,France,Male,34,1,0,2,1,1,126592.01,0\r\n4616,15703117,Findlay,565,France,Female,44,1,0,2,0,1,89602.81,0\r\n4617,15758136,King,778,France,Male,37,3,141803.77,1,0,1,179421.84,0\r\n4618,15799932,Iweobiegbunam,812,France,Male,24,10,0,2,1,1,156906.15,0\r\n4619,15633516,Tucker,526,France,Male,42,1,0,1,0,1,168486.02,0\r\n4620,15622532,Izmailova,708,France,Female,47,0,126589.12,2,0,1,132730.07,1\r\n4621,15798960,Meng,680,France,Male,33,2,108393.35,1,0,1,39057.67,0\r\n4622,15698664,Liang,567,Spain,Male,43,2,115643.58,2,0,0,174606.35,0\r\n4623,15703614,Hutchinson,564,Spain,Male,48,5,132876.23,1,1,0,79259.77,0\r\n4624,15699195,Shen,709,France,Female,24,3,110949.41,1,1,1,168515.61,0\r\n4625,15710543,Okwuoma,629,France,Male,46,1,130666.2,1,1,1,161125.67,1\r\n4626,15695499,Chinwemma,510,France,Female,45,10,103821.47,2,0,1,77878.62,0\r\n4627,15622321,Golubova,506,France,Female,32,3,0,1,1,1,80823.02,0\r\n4628,15715744,Schiavone,605,France,Male,39,7,0,1,0,1,119348.28,0\r\n4629,15788151,Moore,650,Spain,Male,32,1,132187.73,2,1,1,178331.36,0\r\n4630,15687153,Graham,850,Germany,Male,49,8,98649.55,1,1,0,119174.88,1\r\n4631,15684958,Amadi,489,Germany,Male,38,2,126444.08,2,1,1,82662.73,0\r\n4632,15706116,McKay,659,Germany,Female,30,8,154159.51,1,1,0,40441.1,0\r\n4633,15740557,Fedorova,753,France,Female,43,5,0,2,1,0,109881.71,0\r\n4634,15707291,Percy,477,Germany,Male,48,8,129250,2,1,1,157937.35,0\r\n4635,15583353,Floyd,610,Spain,Female,45,3,0,1,1,0,38276.84,1\r\n4636,15761024,Long,619,France,Female,33,2,167733.51,2,1,1,65222.48,0\r\n4637,15630709,Castiglione,619,Germany,Female,31,2,56116.3,2,0,0,2181.94,0\r\n4638,15639590,Melendez,758,France,Female,30,3,141581.08,1,1,0,156249.06,0\r\n4639,15659399,Mazzi,516,Germany,Male,50,7,139675.07,2,1,0,45591.23,0\r\n4640,15567078,Kovaleva,789,France,Female,27,8,66201.96,1,1,1,79458.12,0\r\n4641,15696373,Gill,687,France,Female,44,9,0,2,0,0,103042.2,1\r\n4642,15786617,Arcuri,485,Germany,Male,34,3,133658.24,1,1,0,70209.83,0\r\n4643,15657449,Chukwuma,446,Germany,Male,25,3,136202.78,1,1,0,176743.51,0\r\n4644,15672594,Stevenson,597,France,Female,60,0,131778.08,1,0,0,10703.53,1\r\n4645,15714240,Ponomarev,712,Spain,Male,74,5,0,2,0,0,151425.82,0\r\n4646,15782144,Gilroy,522,France,Female,34,3,0,2,1,1,3894.34,0\r\n4647,15665008,Sidorov,805,Germany,Female,26,8,42712.87,2,1,1,28861.69,0\r\n4648,15581733,Bates,781,France,Female,28,4,0,2,1,0,177703.15,0\r\n4649,15751392,Fanucci,689,Spain,Female,57,4,0,2,1,0,136649.8,1\r\n4650,15785815,Toscano,670,Germany,Male,31,1,142631.54,2,1,1,175894.24,0\r\n4651,15664214,Hearn,670,France,Male,33,2,141204.65,2,1,0,76257.46,0\r\n4652,15579996,Iroawuchi,524,Germany,Female,25,7,131402.21,1,0,0,193668.49,0\r\n4653,15675252,Martin,734,Spain,Female,39,3,92636.96,2,1,1,125671.29,0\r\n4654,15579617,Sinclair,489,France,Female,51,3,0,2,0,1,174098.28,1\r\n4655,15593976,Swanson,578,Germany,Female,31,5,102088.68,4,0,0,187866.21,1\r\n4656,15716041,Chinomso,622,Spain,Male,39,9,0,2,0,1,100862.36,0\r\n4657,15654489,Fomin,843,France,Female,38,8,134887.53,1,1,1,10804.04,0\r\n4658,15736302,McKay,687,France,Male,48,4,0,2,1,1,170893.85,0\r\n4659,15805909,Bergamaschi,700,Spain,Male,28,8,159900.38,1,0,0,22698.56,0\r\n4660,15572762,Matveyeva,410,Germany,Female,50,2,102278.79,2,1,0,89822.48,0\r\n4661,15724632,Madukaego,537,France,Female,41,0,0,2,0,1,175262.49,0\r\n4662,15670416,Ferri,780,France,Female,43,0,0,1,0,1,15705.27,0\r\n4663,15749528,Achebe,652,Spain,Male,58,6,0,2,0,1,170025.43,0\r\n4664,15578783,Mai,620,Germany,Male,35,0,76989.97,1,1,1,17242.79,0\r\n4665,15580719,Davis,697,France,Female,23,10,0,2,1,1,79734.23,0\r\n4666,15656293,Davey,786,France,Male,35,3,0,2,1,0,92712.97,0\r\n4667,15691875,Tsou,850,Germany,Female,39,5,114491.82,1,1,0,99689.48,0\r\n4668,15596870,Marino,749,Germany,Male,54,3,144768.94,1,1,0,93336.3,1\r\n4669,15780770,Kerr,445,France,Male,31,7,145056.59,1,1,1,175893.53,0\r\n4670,15751491,Hsiao,443,Germany,Male,50,3,117206.3,1,1,0,42840.18,1\r\n4671,15706200,Graham,637,Germany,Male,41,2,138014.4,2,1,0,140298.24,0\r\n4672,15808674,Ejikemeifeuwa,616,Germany,Female,45,6,128352.59,3,1,1,144000.59,1\r\n4673,15641411,Volkova,756,France,Female,23,1,112568.31,1,1,1,113408.11,0\r\n4674,15764661,Wang,644,France,Male,33,2,0,1,1,0,96420.58,0\r\n4675,15689492,Benjamin,850,Germany,Male,41,1,176958.46,2,0,1,125806.3,0\r\n4676,15602405,Ryrie,703,Germany,Female,38,9,99167.54,1,1,0,65720.92,0\r\n4677,15610271,Andreev,684,Spain,Female,42,3,103210.27,1,1,0,31002.03,0\r\n4678,15791780,Ts'ao,706,Germany,Female,48,10,104478.12,3,0,1,158248.71,1\r\n4679,15589147,Frolov,580,Spain,Male,61,8,125921.37,1,1,1,94677.83,0\r\n4680,15756975,Montemayor,777,Spain,Female,35,3,0,2,1,1,17257.72,0\r\n4681,15729582,Fu,676,Germany,Male,48,3,80697.44,1,0,0,101397.86,0\r\n4682,15742971,Whitehead,708,France,Female,44,2,161887.81,2,1,0,84870.23,0\r\n4683,15568046,Izuchukwu,809,France,Male,24,7,109558.36,1,1,0,183515.13,0\r\n4684,15694890,Lai,588,France,Male,38,1,124271.26,1,1,0,75969.19,0\r\n4685,15736963,Herring,623,France,Male,43,1,0,2,1,1,146379.3,0\r\n4686,15646490,Duffy,537,Spain,Male,42,1,190569.23,1,0,1,127154.8,0\r\n4687,15607314,Chiefo,536,Spain,Male,53,2,143923.96,1,1,0,2019.78,1\r\n4688,15576745,Fyodorov,769,France,Male,48,2,96542.16,2,0,1,197885.72,0\r\n4689,15669606,Chu,690,France,Male,33,5,0,2,1,0,138017.68,0\r\n4690,15737832,Robertson,771,Spain,Male,45,0,139825.56,1,0,0,170984.97,1\r\n4691,15681990,Palmerston,497,Germany,Male,24,6,111769.14,2,1,0,55859.27,0\r\n4692,15758050,Madukwe,622,Spain,Male,37,4,0,2,1,0,4459.5,0\r\n4693,15787848,Chinedum,602,Spain,Male,30,9,113672.18,2,0,0,102135.92,0\r\n4694,15713594,French,543,France,Female,32,7,147256.86,1,1,0,112771.95,0\r\n4695,15588186,Polyakov,520,Spain,Male,45,7,107023.03,1,1,0,32903.93,0\r\n4696,15786739,Clements,669,France,Male,37,1,125529.55,1,1,1,162260.93,0\r\n4697,15699467,Connor,631,Spain,Female,41,0,0,1,0,0,87959.83,0\r\n4698,15680706,Balashov,537,Germany,Male,48,4,131834.8,1,1,0,166476.95,1\r\n4699,15645717,Avdeeva,732,France,Male,62,2,0,2,1,1,25438.87,0\r\n4700,15748597,Chester,844,Spain,Male,56,5,99529.7,1,0,1,157230.06,1\r\n4701,15773709,Hung,838,Spain,Male,35,0,0,2,0,1,197305.91,0\r\n4702,15629787,Tu,652,France,Male,27,10,107303.72,2,0,0,44435.76,0\r\n4703,15661007,Thompson,660,France,Male,33,0,72783.42,1,0,0,181051.99,0\r\n4704,15686812,Jones,692,Spain,Female,44,8,0,1,0,1,159069.37,0\r\n4705,15754113,Li,588,France,Female,35,0,0,2,1,1,155485.24,0\r\n4706,15749489,Denisova,533,Germany,Female,22,10,115743.6,1,0,0,43852.05,0\r\n4707,15574352,Clogstoun,850,France,Male,43,4,161256.53,1,1,1,140071.57,0\r\n4708,15701281,Tan,511,France,Male,27,8,0,2,1,1,49089.36,0\r\n4709,15811985,Power,530,Spain,Male,44,6,0,2,0,0,55893.37,0\r\n4710,15713505,Harriman,554,France,Male,31,1,0,2,0,1,192660.55,0\r\n4711,15685653,Benson,585,Germany,Female,40,3,162261.01,2,1,0,137028.51,0\r\n4712,15758831,Thornton,754,France,Male,39,3,74896.33,1,0,0,34430.16,0\r\n4713,15618774,White,474,France,Male,54,3,0,1,1,0,108409.17,1\r\n4714,15764448,Mackenzie,837,Germany,Male,35,0,144037.6,1,1,0,145325.32,0\r\n4715,15611024,Kalinina,567,France,Female,23,9,93522.2,1,0,1,81425.61,0\r\n4716,15738220,Bennet,800,Spain,Male,38,1,0,2,1,0,51553.43,0\r\n4717,15805764,Hallahan,646,France,Male,18,10,0,2,0,1,52795.15,0\r\n4718,15580487,Martin,627,Germany,Male,38,8,106922.92,2,0,1,84270.09,0\r\n4719,15675787,Rivera,505,France,Male,26,8,112972.57,1,1,0,145011.62,0\r\n4720,15583580,Chiawuotu,566,Germany,Female,35,1,123042,1,1,0,66245.44,1\r\n4721,15780654,Sergeyev,619,Germany,Female,33,3,100488.92,2,0,1,36446.74,0\r\n4722,15695034,Christie,757,France,Female,44,4,123322.15,1,1,0,137136.29,0\r\n4723,15805671,Louis,648,France,Male,32,0,0,1,0,1,117323.31,0\r\n4724,15790658,Iqbal,621,Spain,Male,42,8,68683.68,1,1,1,74157.71,0\r\n4725,15578648,Marino,543,Germany,Male,49,6,59532.18,1,1,0,104253.56,0\r\n4726,15734987,Robertson,658,France,Female,43,7,140260.36,2,1,0,2748.72,0\r\n4727,15721740,Pai,633,Germany,Male,50,7,88302.65,1,1,1,195937.16,0\r\n4728,15641822,Barese,648,France,Female,19,1,0,2,0,1,22101.86,0\r\n4729,15765650,Chigolum,501,Germany,Male,40,5,114655.58,1,0,0,126535.92,0\r\n4730,15788556,Trouette,683,France,Female,42,4,148283.94,1,1,1,44692.63,1\r\n4731,15576550,Ugochukwu,619,Spain,Female,38,1,0,1,1,0,112442.63,1\r\n4732,15622230,Cribb,705,France,Female,35,3,0,2,0,1,66331.01,0\r\n4733,15653937,McIntyre,638,Germany,Female,53,1,123916.67,1,1,0,16657.68,1\r\n4734,15743538,Pickering,710,France,Female,31,1,0,2,1,0,20081.3,0\r\n4735,15591740,Fletcher,590,France,Female,54,4,0,2,1,1,93820.49,1\r\n4736,15650086,Uchenna,725,France,Male,43,2,165896,2,1,0,130795.52,0\r\n4737,15718773,Pisano,638,France,Female,32,0,0,2,1,0,160129.99,0\r\n4738,15615140,Corson,791,France,Male,36,6,111168.97,1,1,1,189969.91,0\r\n4739,15644361,Hooper,702,France,Female,40,1,103549.24,1,0,0,9712.52,1\r\n4740,15774536,He,607,France,Female,32,6,0,2,0,0,196062.01,0\r\n4741,15618661,Chidubem,535,France,Male,30,6,103804.97,1,1,1,125710.53,0\r\n4742,15605020,Schofield,651,France,Male,45,2,165901.59,2,1,0,23054.51,1\r\n4743,15762134,Liang,506,Germany,Male,59,8,119152.1,2,1,1,170679.74,0\r\n4744,15685279,Somadina,511,Spain,Female,57,8,122950.31,1,1,1,181258.76,0\r\n4745,15582849,McIntosh,757,France,Female,51,1,0,1,1,1,22835.13,1\r\n4746,15655410,Hinton,768,Germany,Male,49,1,133384.66,1,1,0,102397.22,1\r\n4747,15649129,Sal,757,France,Male,32,9,0,2,1,0,115950.96,0\r\n4748,15702380,De Luca,663,Spain,Male,64,6,0,2,0,1,15876.52,0\r\n4749,15759067,Bromby,537,Germany,Female,37,7,158411.95,4,1,1,117690.58,1\r\n4750,15683027,Chang,570,Germany,Male,29,4,122028.65,2,1,1,173792.77,0\r\n4751,15597487,Hunter,850,France,Female,35,5,0,1,1,1,80992.8,0\r\n4752,15763256,Sheppard,661,Germany,Female,64,8,128751.65,2,1,0,189398.18,1\r\n4753,15620111,Fan,659,France,Male,54,8,133436.52,1,1,0,56787.8,0\r\n4754,15623053,Muir,454,Spain,Male,40,2,123177.01,1,1,0,148309.98,0\r\n4755,15595592,Lai,708,France,Female,59,2,0,1,1,0,179673.11,1\r\n4756,15740072,Padovesi,720,France,Female,37,2,120328.88,2,1,1,138470.21,0\r\n4757,15778005,Kemp,785,France,Female,39,1,130147.98,1,1,0,163798.41,1\r\n4758,15583278,Greece,743,Spain,Female,36,8,92716.96,1,1,1,33693.78,0\r\n4759,15601263,Young,493,Spain,Female,48,7,0,2,1,0,48545.1,0\r\n4760,15709222,Chukwueloka,557,Spain,Male,34,3,0,1,0,1,123427.98,0\r\n4761,15713949,Woods,850,France,Male,40,1,76914.21,1,1,0,174183.44,0\r\n4762,15717706,Forbes,799,France,Female,32,3,106045.92,2,1,1,17938,0\r\n4763,15756071,Kang,756,France,Male,34,1,103133.26,1,1,1,90059.04,0\r\n4764,15696564,Nweke,752,France,Male,38,0,145974.79,2,1,1,137694.23,0\r\n4765,15657637,Ts'ui,696,Spain,Female,36,3,0,3,1,0,65039.9,0\r\n4766,15755863,Milano,630,Spain,Female,49,1,0,2,0,1,162858.29,0\r\n4767,15719858,Chao,659,Spain,Female,38,9,0,2,1,1,35701.06,0\r\n4768,15688876,Wan,685,Spain,Male,39,9,0,2,1,1,18826.06,0\r\n4769,15698528,Napolitani,599,Spain,Female,31,3,0,1,1,1,130086.47,1\r\n4770,15770345,Kovaleva,559,Spain,Female,31,1,139183.06,1,0,1,143360.56,0\r\n4771,15761506,Russell,615,Spain,Male,19,5,0,2,1,0,159920.92,0\r\n4772,15716619,Chiebuka,580,Germany,Female,36,3,74974.89,1,1,1,12099.67,0\r\n4773,15788367,Ellis,487,Spain,Male,44,6,61691.45,1,1,1,53087.98,0\r\n4774,15709451,Gordon,646,Germany,Female,35,1,121952.75,2,1,1,142839.82,0\r\n4775,15640421,Conway,811,France,Female,35,7,0,1,1,1,178.19,0\r\n4776,15580068,Buccho,526,Spain,Male,35,5,0,2,1,1,105618.14,0\r\n4777,15677123,Aksyonova,767,Spain,Male,37,7,0,2,1,1,24734.25,0\r\n4778,15619801,Batty,548,France,Female,33,1,80107.83,2,0,1,82245.67,0\r\n4779,15582246,Rowe,737,Spain,Female,45,2,0,2,0,1,177695.67,0\r\n4780,15711843,Pisani,613,Germany,Male,40,1,147856.82,3,0,0,107961.11,1\r\n4781,15680046,Onochie,711,Spain,Male,36,8,0,2,1,0,55207.41,0\r\n4782,15804131,Farmer,850,Spain,Female,53,7,65407.16,2,0,0,182633.63,1\r\n4783,15722611,Cameron,752,France,Female,53,8,114233.18,1,1,1,51587.04,0\r\n4784,15729224,Jennings,710,France,Female,37,5,0,2,1,0,115403.31,0\r\n4785,15811588,Eluemuno,664,Spain,Female,53,7,187602.18,1,1,0,186392.99,1\r\n4786,15702138,Swift,510,France,Female,22,3,156834.34,1,0,0,44374.44,0\r\n4787,15749799,Pisani,577,France,Female,34,2,0,2,1,1,84033.35,0\r\n4788,15752885,Nnonso,529,France,Male,42,1,157498.9,1,1,1,82276.62,0\r\n4789,15674932,Cameron,757,Spain,Female,44,9,0,2,1,0,177528.92,0\r\n4790,15743828,Stevens,691,France,Male,41,2,0,1,1,1,56850.92,1\r\n4791,15642022,Zito,621,Spain,Male,34,8,0,1,0,0,47972.65,0\r\n4792,15746461,Taylor,709,Spain,Male,35,2,0,2,1,0,104982.39,0\r\n4793,15809991,Ferrari,756,Spain,Male,19,4,130274.22,1,1,1,133535.29,0\r\n4794,15787322,Yeh,788,France,Female,41,6,0,1,1,1,25571.37,0\r\n4795,15575498,Gould,705,France,Female,39,5,149379.66,2,1,0,96075.55,0\r\n4796,15691387,Agafonova,483,France,Male,29,9,0,1,1,1,81634.45,0\r\n4797,15765457,Fowler,719,Spain,Male,35,1,100829.94,1,1,1,165008.97,0\r\n4798,15666173,Chidumaga,793,Germany,Female,32,1,96408.98,1,1,1,138191.81,0\r\n4799,15627377,Sabbatini,593,France,Male,41,6,0,2,1,1,99136.49,0\r\n4800,15656683,Johnson,551,France,Male,52,1,0,1,0,0,63584.55,1\r\n4801,15679810,Chapman,690,France,Male,39,6,0,2,1,0,160532.88,0\r\n4802,15606310,Birk,823,France,Male,71,5,149105.08,1,0,1,162683.06,0\r\n4803,15756871,Capon,512,Spain,Male,39,3,0,1,1,0,134878.19,0\r\n4804,15610002,Chidubem,802,Spain,Male,41,5,0,2,1,1,134626.3,0\r\n4805,15567802,Childs,450,Spain,Female,34,2,0,2,1,0,175480.93,0\r\n4806,15745452,Sun,651,Germany,Male,41,1,90218.11,1,1,0,174337.68,0\r\n4807,15617252,Lung,697,France,Female,33,1,87347.7,1,1,0,172524.51,0\r\n4808,15753248,Tao,611,France,Male,28,2,0,2,0,0,25395.83,0\r\n4809,15610755,Napolitano,643,France,Female,33,0,137811.75,1,1,1,184856.89,0\r\n4810,15662238,Davis,822,France,Male,37,3,105563,1,1,0,182624.93,0\r\n4811,15799186,Sagese,632,France,Male,38,4,0,2,0,0,192505.62,0\r\n4812,15686941,Hutchinson,575,Spain,Female,26,7,0,2,1,0,112507.63,0\r\n4813,15601172,Nelson,672,France,Male,31,6,91125.75,1,1,0,177295.92,0\r\n4814,15723858,Schiavone,517,Spain,Male,39,3,0,2,0,1,12465.51,0\r\n4815,15615896,Chienezie,621,Spain,Male,39,8,0,2,1,0,36122.96,0\r\n4816,15737647,Obioma,775,Germany,Female,77,6,135120.56,1,1,0,37836.64,0\r\n4817,15582841,Butusov,600,France,Male,29,8,0,2,0,1,34747.43,0\r\n4818,15760090,Pisano,640,France,Male,28,7,0,2,1,1,131097.9,0\r\n4819,15588587,Stetson,752,France,Female,36,1,86837.95,1,1,1,105280.55,0\r\n4820,15683157,Waring,613,France,Male,26,4,100446.57,1,0,1,149653.81,0\r\n4821,15694209,Fanucci,484,France,Female,32,3,0,2,1,1,139390.99,0\r\n4822,15655875,Thao,511,France,Female,33,3,0,2,1,0,132436.71,0\r\n4823,15805704,Murphy,745,France,Female,32,2,0,4,0,1,179705.13,1\r\n4824,15744789,McConnell,786,Spain,Female,32,6,114512.59,1,1,0,15796.66,0\r\n4825,15799357,Armfield,727,France,Male,35,5,136364.46,1,0,0,142754.71,0\r\n4826,15726153,Fanucci,622,France,Male,31,5,106260.67,1,1,1,2578.43,0\r\n4827,15713346,Panina,794,France,Male,24,10,146126.75,1,1,1,88992.05,0\r\n4828,15665053,Nixon,636,Spain,Male,52,4,111284.53,1,0,1,32936.44,1\r\n4829,15592379,Walker,741,Spain,Female,42,9,121056.63,2,1,0,39122.58,0\r\n4830,15692599,Chiemela,687,France,Male,34,5,128270.56,1,1,0,191092.62,0\r\n4831,15620758,Martel,660,Spain,Male,30,4,0,2,1,0,129149.06,0\r\n4832,15637428,Briggs,660,France,Male,35,7,0,2,1,0,13218.6,0\r\n4833,15808389,Iheatu,617,France,Female,79,7,0,1,1,1,160589.18,0\r\n4834,15807003,Jennings,762,France,Male,32,10,191775.65,1,1,0,179657.83,0\r\n4835,15702912,Ch'en,752,Spain,Female,35,2,0,1,1,0,44335.54,1\r\n4836,15590623,Kovalyov,561,Spain,Male,34,4,85141.79,2,1,1,29217.37,0\r\n4837,15728078,Yeh,609,France,Male,26,10,126392.18,1,0,1,43651.49,0\r\n4838,15708256,Chien,803,France,Male,28,3,0,2,1,0,159654,0\r\n4839,15582335,Brown,556,France,Female,40,9,129860.37,1,0,0,17992.94,0\r\n4840,15649150,Buddicom,531,France,Female,53,5,127642.44,1,1,0,141501.45,1\r\n4841,15691647,McGregor,411,France,Female,35,2,0,2,1,1,93825.78,0\r\n4842,15668270,Thompson,587,Germany,Female,44,5,125584.17,2,1,1,41852.24,1\r\n4843,15624820,Ross,683,Spain,Male,56,7,50911.21,3,0,0,97629.31,1\r\n4844,15736254,Ch'ang,654,France,Male,29,2,91955.61,1,1,0,37065.66,0\r\n4845,15720814,Warren,670,Germany,Female,36,2,84266.44,2,0,0,38614.69,0\r\n4846,15642997,Uspenskaya,655,France,Female,36,2,147149.59,1,1,1,87816.86,0\r\n4847,15693200,King,752,France,Female,36,7,0,2,1,0,184866.86,0\r\n4848,15624596,Trentini,534,France,Female,23,5,104822.45,1,0,1,160176.47,0\r\n4849,15807167,Konovalova,635,France,Male,42,1,146766.72,2,0,1,164357.1,0\r\n4850,15660301,Dellucci,491,Germany,Male,70,6,148745.92,2,1,1,17818.33,0\r\n4851,15593094,Goddard,516,France,Male,27,9,0,1,1,0,142680.64,1\r\n4852,15618239,Neumann,530,France,Female,48,0,0,1,1,0,85081.09,0\r\n4853,15574137,Ch'in,687,Spain,Male,35,3,0,2,1,1,176450.19,0\r\n4854,15614740,Walters,684,France,Female,41,6,135203.81,2,1,1,121967.88,0\r\n4855,15574071,Muravyova,706,Germany,Male,23,2,93301.97,2,0,1,127187.04,0\r\n4856,15671148,Barry,490,Germany,Male,33,5,96341,2,0,0,108313.34,0\r\n4857,15721921,Woolnough,796,France,Male,44,8,165326.2,1,1,1,57205.55,0\r\n4858,15717995,Keen,849,France,Male,27,0,0,2,0,1,157891.86,0\r\n4859,15632050,Liebe,779,France,Female,41,10,99786.2,1,1,0,86927.53,0\r\n4860,15647111,White,794,Spain,Female,22,4,114440.24,1,1,1,107753.07,0\r\n4861,15759991,Hunter,748,Spain,Male,36,4,141573.55,1,1,0,82158.14,0\r\n4862,15790204,Myers,663,Spain,Female,22,9,0,1,1,0,29135.89,1\r\n4863,15686780,Rogova,645,Spain,Female,55,1,133676.65,1,0,1,17095.49,0\r\n4864,15640491,Raff,464,France,Female,33,10,147493.7,2,1,0,100447.53,0\r\n4865,15783225,Cocci,737,France,Male,54,9,0,1,1,0,83470.4,1\r\n4866,15734438,Kanayochukwu,590,France,Female,29,4,0,2,1,0,121846.81,0\r\n4867,15688760,Obialo,522,Germany,Female,37,3,95022.57,1,1,1,129107.59,0\r\n4868,15768124,Liu,648,France,Female,34,3,0,1,1,0,54726.43,0\r\n4869,15661330,Gilbert,754,France,Male,37,6,0,1,1,1,116141.72,0\r\n4870,15781272,Coles,669,France,Male,50,4,149713.61,3,1,1,124872.42,1\r\n4871,15573888,Ponomaryov,648,Germany,Female,43,1,107963.38,1,0,0,186438.86,1\r\n4872,15575858,Bergamaschi,763,France,Male,40,3,0,2,1,0,134281.11,0\r\n4873,15645937,Guerin,790,Spain,Male,32,3,0,1,1,0,91044.47,0\r\n4874,15702337,Sinclair,581,France,Male,37,7,0,2,1,1,74320.75,0\r\n4875,15764537,Dominguez,703,France,Male,43,8,0,2,1,0,9704.66,0\r\n4876,15619616,Costa,571,France,Female,33,9,102017.25,2,0,0,128600.49,0\r\n4877,15585133,Wei,657,Spain,Female,27,8,0,2,0,0,6468.24,0\r\n4878,15573971,Mills,737,France,Male,44,7,0,2,0,0,57898.58,0\r\n4879,15579433,Pugh,793,Spain,Male,29,8,96674.55,2,0,0,192120.66,0\r\n4880,15777045,Price,783,Spain,Female,44,3,81811.71,1,1,0,164213.53,1\r\n4881,15611580,Wood,751,Spain,Male,33,4,79281.61,1,1,0,117547.76,0\r\n4882,15614778,Robertson,579,France,Male,31,6,0,2,1,0,26149.25,0\r\n4883,15771750,Sawtell,655,Germany,Female,36,10,122314.39,1,1,0,9181.66,0\r\n4884,15593280,Yuryeva,614,Germany,Male,43,8,140733.74,1,1,1,166588.76,0\r\n4885,15569274,Pisano,678,Germany,Male,49,2,116933.11,1,1,0,195053.58,1\r\n4886,15654408,Kharitonova,562,Spain,Male,41,5,165445.04,2,1,0,85787.31,0\r\n4887,15657468,Simmons,711,Germany,Female,53,5,123805.03,1,1,0,102428.51,0\r\n4888,15614213,Muramats,620,France,Male,37,0,107548.94,1,1,0,71175.94,0\r\n4889,15589869,Tang,437,France,Male,49,9,111634.29,2,0,1,166440.32,0\r\n4890,15693205,Peng,691,Germany,Female,41,8,109153.96,3,1,1,148848.76,1\r\n4891,15797113,Bevan,552,Spain,Female,34,4,0,2,1,0,140286.69,0\r\n4892,15676958,Zito,765,Germany,Male,34,5,86055.17,2,1,1,104220.5,0\r\n4893,15739592,Sokolov,707,Germany,Female,51,10,98438.23,1,0,0,70778.63,1\r\n4894,15656263,Teng,764,Spain,Male,29,5,0,2,1,0,65868.28,0\r\n4895,15636872,Amadi,585,France,Female,32,8,144705.87,2,0,0,171482.56,0\r\n4896,15589435,Davide,784,France,Male,31,7,0,2,1,1,143204.41,0\r\n4897,15640464,Parkes,605,France,Male,41,5,91612.91,1,1,1,28427.84,0\r\n4898,15723851,Mazzanti,699,Spain,Male,40,2,0,1,1,0,78387.32,0\r\n4899,15722122,Findlay,544,France,Female,40,7,0,1,0,1,161076.92,0\r\n4900,15696852,Hsu,803,France,Female,32,9,192122.84,1,1,1,54277.45,1\r\n4901,15634936,Chukwukadibia,735,France,Male,41,7,179904,1,1,1,137180.95,0\r\n4902,15575935,Baxter,673,France,Male,59,0,178058.06,2,0,1,21063.71,1\r\n4903,15634491,Kung,652,France,Male,30,2,176166.56,2,1,1,152210.81,0\r\n4904,15628530,Booth,694,France,Male,42,3,156864.2,2,0,0,88890.75,0\r\n4905,15678720,Evans,741,France,Female,44,7,0,2,1,1,190534.76,0\r\n4906,15627999,Kung,590,Spain,Male,30,3,0,2,1,0,83090.35,0\r\n4907,15571244,Tung,809,Spain,Female,33,3,0,2,0,1,141426.78,0\r\n4908,15739931,Yuan,523,France,Male,34,2,161588.89,1,1,1,51358.66,0\r\n4909,15806256,Jackson,540,France,Male,48,2,109349.29,1,1,0,88703.04,1\r\n4910,15787258,Ross,596,Spain,Female,29,6,0,2,1,0,116696.77,0\r\n4911,15706463,Yang,597,France,Female,36,9,0,2,1,1,7156.09,0\r\n4912,15691004,Yu,407,Spain,Male,37,1,0,1,1,1,49161.12,1\r\n4913,15792228,Onwumelu,748,France,Male,60,0,152335.7,1,1,0,126743.33,1\r\n4914,15733447,Gay,562,France,Female,51,1,124662.54,1,1,1,65390.46,1\r\n4915,15679062,Morrison,734,Germany,Female,47,10,91522.04,2,1,1,138835.91,0\r\n4916,15594409,Belov,710,France,Male,45,1,0,2,1,1,36154.66,0\r\n4917,15613816,Mao,539,Spain,Female,39,6,62052.28,1,0,1,59755.14,0\r\n4918,15681991,Walsh,542,France,Male,32,7,107871.72,1,1,0,125302.64,0\r\n4919,15796074,Bruno,717,France,Female,36,2,99472.76,2,1,0,94274.72,1\r\n4920,15625941,Gray,682,Spain,Female,50,10,128039.01,1,1,1,102260.16,0\r\n4921,15615016,Maurer,515,France,Male,33,2,0,2,1,1,136028.97,0\r\n4922,15748414,Chiang,526,Spain,Female,33,8,114634.63,2,1,0,110114.38,1\r\n4923,15751203,Cattaneo,702,France,Male,26,5,56738.47,2,1,1,100442.22,1\r\n4924,15662658,Grieve,651,Germany,Male,34,2,90355.12,2,0,0,193597.94,0\r\n4925,15574868,Lowell,792,Germany,Male,36,5,115725.24,2,0,0,1871.25,0\r\n4926,15790282,Trentino,817,Germany,Male,58,3,114327.59,2,1,1,42831.11,0\r\n4927,15762927,Sung,674,Germany,Female,36,6,100762.64,1,1,0,182156.86,0\r\n4928,15803456,Yen,641,France,Female,40,9,0,1,0,0,151648.66,1\r\n4929,15771857,Philipp,513,Spain,Male,39,7,89039.9,2,1,1,146738.83,0\r\n4930,15700601,Dynon,561,France,Male,34,1,78829.53,1,1,1,12148.2,0\r\n4931,15569670,Alexeyeva,627,Germany,Male,30,6,112372.96,1,1,1,118029.09,0\r\n4932,15772341,Hs?eh,682,Germany,Male,81,6,122029.15,1,1,1,50783.88,0\r\n4933,15661548,Ferri,683,France,Female,29,0,157829.12,1,0,0,129891.66,0\r\n4934,15787597,Hsu,420,Germany,Female,31,1,108377.75,2,1,1,9904.63,0\r\n4935,15806913,Bishop,670,France,Female,54,2,95507.12,1,1,1,63213.31,0\r\n4936,15804862,Toscani,505,Germany,Male,43,6,127146.68,1,0,0,137565.87,0\r\n4937,15792986,T'ao,580,Germany,Male,24,1,133811.78,1,1,0,17185.95,1\r\n4938,15625632,Philip,577,France,Male,36,3,121092.47,2,0,1,143783.46,0\r\n4939,15727703,Li Fonti,773,Germany,Male,34,10,126979.75,1,0,0,36823.28,0\r\n4940,15606273,Rene,616,France,Male,37,5,144235.73,2,0,0,154957.66,1\r\n4941,15799652,Daigle,763,France,Female,38,0,152582.2,2,0,0,31892.82,0\r\n4942,15715047,Joshua,640,Spain,Male,43,9,172478.15,1,1,0,191084.4,1\r\n4943,15784687,Simmons,592,France,Male,36,1,126477.42,1,0,0,179718.17,0\r\n4944,15615322,Jamieson,528,Spain,Male,43,7,97473.87,2,1,1,159823.16,0\r\n4945,15722072,Hou,630,France,Male,53,5,138053.67,1,0,1,114110.97,0\r\n4946,15646784,Cochran,529,France,Female,31,2,164003.05,2,1,1,60993.23,0\r\n4947,15644692,Bibb,546,France,Female,47,8,0,1,1,1,66408.01,1\r\n4948,15670354,Jen,753,France,Female,62,6,0,2,1,1,136398.9,0\r\n4949,15716357,Corran,772,Spain,Female,39,4,122486.11,2,1,1,140709.25,0\r\n4950,15786717,He,567,France,Male,36,1,0,2,0,0,8555.73,0\r\n4951,15771383,Loggia,628,Germany,Female,45,6,53667.44,1,1,0,115022.94,0\r\n4952,15649793,Lovely,658,France,Male,20,7,0,2,0,0,187638.34,0\r\n4953,15731543,Becker,679,Spain,Male,58,9,109327.65,1,1,1,3829.13,0\r\n4954,15684516,Plascencia,629,Spain,Male,34,1,121151.05,1,0,0,119357.93,0\r\n4955,15677249,Somadina,731,Spain,Male,42,9,101043.63,1,1,1,192175.52,0\r\n4956,15581525,Walker,775,Germany,Male,33,3,83501.66,2,1,0,128841.31,0\r\n4957,15628420,Alekseeva,660,Spain,Male,33,2,80462.24,1,0,0,150422.35,0\r\n4958,15600478,Watson,752,France,Male,39,3,0,1,1,0,188187.05,0\r\n4959,15594502,Zotov,655,France,Male,37,6,109093.41,2,1,0,1775.52,0\r\n4960,15784361,Williamson,543,Spain,Female,46,5,140355.6,1,1,1,85086.78,0\r\n4961,15767626,Carpenter,811,France,Male,42,10,0,2,1,1,3797.79,0\r\n4962,15632521,Cattaneo,689,Germany,Male,45,0,130170.82,2,1,0,150856.38,0\r\n4963,15665088,Gordon,531,France,Female,42,2,0,2,0,1,90537.47,0\r\n4964,15652084,Boni,515,France,Male,40,0,109542.29,1,1,1,166370.81,0\r\n4965,15574761,Lynch,466,France,Female,41,3,33563.95,2,1,0,178994.13,1\r\n4966,15729515,McCarthy,782,France,Male,36,1,148795.17,2,1,1,195681.43,0\r\n4967,15682070,Davies,611,France,Male,64,9,0,2,1,1,53277.15,0\r\n4968,15743817,Hargreaves,621,Germany,Male,40,8,174126.75,3,1,0,172490.78,1\r\n4969,15572158,Blackburn,604,Spain,Male,41,3,0,1,0,0,11819.84,0\r\n4970,15584477,K?,655,Spain,Female,35,1,106405.03,1,1,1,82900.25,0\r\n4971,15614893,Meng,689,Spain,Male,38,2,0,1,1,1,82709.8,0\r\n4972,15665963,Cattaneo,681,Spain,Male,30,2,128393.29,1,1,1,180593.45,0\r\n4973,15612524,Hunt,643,Germany,Male,41,2,127841.52,1,1,0,172363.41,0\r\n4974,15596962,Owens,617,France,Female,24,4,137295.19,2,1,1,91195.12,0\r\n4975,15744942,Steele,638,Spain,Female,55,2,155828.22,1,0,1,108987.25,1\r\n4976,15573278,Kennedy,743,France,Male,39,6,0,2,1,0,44265.28,0\r\n4977,15717056,Pan,828,Germany,Female,25,7,144351.86,1,1,0,116613.26,0\r\n4978,15795881,Alexander,776,Spain,Male,35,8,106365.29,1,1,1,148527.56,0\r\n4979,15758939,Bray,540,Germany,Male,35,7,127801.88,1,0,1,84239.46,0\r\n4980,15792250,Nnabuife,616,Germany,Female,45,4,122793.96,1,1,1,62002.04,0\r\n4981,15740406,Padovesi,628,Germany,Male,38,10,113525.84,1,1,0,46044.48,1\r\n4982,15768137,Bray,667,Spain,Female,23,6,136100.69,2,0,0,169669.33,1\r\n4983,15569120,Lucas,615,France,Male,30,7,0,2,1,1,156346.84,0\r\n4984,15723721,Tinline,543,France,Male,30,4,140916.81,1,1,0,157711.18,0\r\n4985,15777122,Esomchi,553,France,Female,31,4,0,2,1,1,89087.4,0\r\n4986,15742681,Liao,554,Germany,Male,26,4,121365.39,1,1,1,8742.36,0\r\n4987,15582090,Iroawuchi,684,Spain,Female,36,4,0,1,1,0,117038.96,0\r\n4988,15711254,Retana,452,France,Female,35,7,0,2,1,0,164241.67,0\r\n4989,15775067,Fang,606,France,Male,47,3,93578.68,2,0,1,137720.56,1\r\n4990,15602851,Ozioma,629,France,Male,40,9,0,1,1,0,106.67,0\r\n4991,15802857,Robson,659,Spain,Female,33,8,115409.6,1,0,1,1539.21,0\r\n4992,15701175,Bruno,493,France,Female,33,8,90791.69,1,1,1,59659.53,0\r\n4993,15783019,Price,794,France,Female,62,9,123681.32,3,1,0,173586.63,1\r\n4994,15728912,Swanson,554,France,Female,44,6,92436.86,1,1,0,126033.9,0\r\n4995,15585580,Chang,796,Germany,Female,52,9,167194.36,1,1,1,62808.93,1\r\n4996,15583480,Morgan,807,France,Female,36,4,0,2,0,1,147007.33,0\r\n4997,15620341,Nwebube,500,Germany,Male,44,9,160838.13,2,1,0,196261.64,0\r\n4998,15613886,Trevisan,722,Spain,Male,43,1,0,1,1,0,44560.17,1\r\n4999,15792916,Ositadimma,559,Spain,Female,40,7,144470.77,1,1,1,18917.95,0\r\n5000,15710408,Cunningham,584,Spain,Female,38,3,0,2,1,1,4525.4,0\r\n5001,15598695,Fields,834,Germany,Female,68,9,130169.27,2,0,1,93112.2,0\r\n5002,15649354,Johnston,754,Spain,Male,35,4,0,2,1,1,9658.41,0\r\n5003,15737556,Vasilyev,590,France,Male,43,7,81076.8,2,1,1,182627.25,1\r\n5004,15671610,Hooper,740,France,Male,36,7,0,1,1,1,13177.4,0\r\n5005,15625092,Colombo,502,Germany,Female,57,3,101465.31,1,1,0,43568.31,1\r\n5006,15741032,Tsao,733,France,Male,48,5,0,1,0,1,117830.57,0\r\n5007,15750014,Chikere,755,Germany,Female,37,0,113865.23,2,1,1,117396.25,0\r\n5008,15784761,Ballard,554,Spain,Female,46,7,87603.35,3,0,1,96929.24,1\r\n5009,15768359,Akhtar,534,France,Male,36,4,120037.96,1,1,0,36275.94,0\r\n5010,15805769,O'Loughlin,656,Spain,Male,33,4,0,2,1,0,116706,0\r\n5011,15719508,Davis,575,Germany,Male,49,7,121205.15,4,1,1,168080.53,1\r\n5012,15609011,Barry,480,Spain,Male,47,8,75408.33,1,1,0,25887.89,1\r\n5013,15703106,K'ung,575,France,Male,40,5,0,2,1,1,122488.59,0\r\n5014,15626795,Gorman,672,France,Female,40,3,0,1,1,0,113171.61,1\r\n5015,15773731,John,758,Spain,Female,35,5,0,2,0,0,100365.51,0\r\n5016,15756196,Tsou,682,France,Male,50,6,121818.84,2,0,1,124151.37,0\r\n5017,15687903,Okonkwo,501,France,Female,29,8,0,2,1,0,112664.24,0\r\n5018,15777599,Esposito,746,Germany,Male,34,6,141806,2,1,1,183494.87,0\r\n5019,15754577,Boni,556,France,Female,51,8,61354.14,1,1,0,198810.65,1\r\n5020,15584113,Pratt,823,Germany,Female,53,4,124954.94,1,0,1,131259.6,1\r\n5021,15669589,Page,491,Germany,Female,68,1,95039.12,1,0,1,116471.14,1\r\n5022,15632793,Wilkinson,638,France,Female,29,9,103417.74,1,1,1,15336.4,0\r\n5023,15711130,Tseng,734,France,Male,45,2,0,2,1,0,99593.28,0\r\n5024,15615254,Clark,555,France,Male,40,10,43028.77,1,1,0,170514.21,0\r\n5025,15720583,Finch,745,Germany,Female,44,0,119638.21,1,1,1,34265.08,1\r\n5026,15780432,Shen,728,France,Male,37,3,122689.51,2,0,0,106977.53,1\r\n5027,15673223,Hou,626,France,Male,44,10,0,2,0,0,164287.86,0\r\n5028,15807989,Wall,681,Germany,Male,37,8,73179.34,2,1,1,25292.53,0\r\n5029,15761168,Manna,478,France,Female,38,4,171913.87,1,1,0,51820.87,1\r\n5030,15651272,Reyes,709,France,Male,38,5,0,2,1,1,81452.29,0\r\n5031,15812832,Jideofor,562,Germany,Male,33,8,92659.2,2,1,0,1354.25,0\r\n5032,15680517,Sal,769,Germany,Female,34,7,137239.17,1,1,1,71379.92,1\r\n5033,15750569,Iweobiegbunam,684,Germany,Female,46,3,102955.14,2,1,0,154137.33,0\r\n5034,15690743,Shao,536,France,Female,61,8,65190.29,1,1,1,64308.49,1\r\n5035,15627741,Heath,631,Germany,Female,29,2,96863.52,2,1,1,31613.35,0\r\n5036,15712121,Chidimma,657,Spain,Male,34,5,154983.98,1,1,0,27738.01,0\r\n5037,15805429,Murray,699,Germany,Male,59,3,106819.65,1,0,1,163570.25,0\r\n5038,15814923,Sullivan,606,Spain,Male,38,7,128578.52,1,1,1,193878.51,0\r\n5039,15589230,Wu,612,France,Female,63,2,126473.33,1,0,1,147545.65,0\r\n5040,15775490,Downie,660,France,Female,38,5,110570.78,2,1,0,195906.59,0\r\n5041,15749727,Chukwufumnanya,829,Spain,Male,50,7,0,2,0,1,178458.86,0\r\n5042,15619238,Allan,567,Spain,Male,29,8,0,2,1,0,156125.72,0\r\n5043,15593468,Findlay,850,France,Female,33,3,0,2,1,1,11159.19,0\r\n5044,15718454,Ch'eng,712,Spain,Female,44,2,0,2,0,0,45738.94,0\r\n5045,15789498,Miller,562,France,Male,30,3,111099.79,2,0,0,140650.19,0\r\n5046,15744691,Tsai,755,France,Female,29,3,0,3,1,0,4733.94,0\r\n5047,15708289,Graham,793,Spain,Male,25,3,100913.57,1,0,0,10579.72,0\r\n5048,15790412,Norton,471,Spain,Male,26,8,0,2,1,1,179655.87,0\r\n5049,15741416,Yegorov,707,France,Male,42,2,16893.59,1,1,1,77502.56,0\r\n5050,15598894,Holt,784,Spain,Male,38,10,122267.85,1,0,0,145759.93,0\r\n5051,15663294,Kao,703,France,Male,32,1,125685.79,1,1,1,56246.72,0\r\n5052,15572728,Ross,704,Spain,Male,36,8,127397.34,1,1,0,151335.24,0\r\n5053,15706729,Hsiao,662,France,Male,38,0,105271.56,1,0,1,179833.45,0\r\n5054,15674433,Allan,636,Germany,Female,28,2,115265.14,1,0,0,191627.85,0\r\n5055,15641170,Liang,640,Spain,Male,36,4,0,1,0,0,173016.46,0\r\n5056,15806284,Briggs,739,Spain,Male,31,1,0,2,1,1,58469.75,0\r\n5057,15690958,Cantrell,767,Germany,Male,23,2,139542.82,1,0,1,28038.28,0\r\n5058,15606386,Wang,753,Germany,Female,46,3,111512.75,3,1,0,159576.75,1\r\n5059,15682322,Aksenov,714,France,Male,37,9,148466.93,2,0,1,151280.96,0\r\n5060,15579915,Glennon,707,France,Male,29,4,0,2,1,0,139953.94,0\r\n5061,15681928,Yancy,577,France,Female,35,4,108155.49,1,1,0,105407.79,0\r\n5062,15734005,Mazzi,633,France,Female,42,1,0,2,1,0,56865.62,0\r\n5063,15650432,Liu,849,Germany,Male,41,10,84622.13,1,1,1,198072.16,0\r\n5064,15592578,Nucci,614,Spain,Female,41,7,146997.64,2,0,0,137791.18,0\r\n5065,15671243,Y?,558,France,Female,47,9,0,2,1,0,103787.28,0\r\n5066,15775709,Nucci,832,France,Female,27,10,98590.25,1,1,0,30912.89,0\r\n5067,15702631,Tang,567,France,Female,26,2,0,2,1,1,78651.55,0\r\n5068,15602282,Kao,587,Germany,Female,45,8,134980.74,1,1,1,123309.57,1\r\n5069,15717879,Chen,712,Spain,Female,79,5,108078.56,1,1,1,174118.93,0\r\n5070,15740878,Yao,655,Spain,Female,29,9,0,2,0,1,85736.26,0\r\n5071,15794468,Tsou,641,France,Female,42,6,0,2,0,0,121138.77,0\r\n5072,15773277,Barnes,676,France,Male,35,5,106836.67,2,1,0,84199.78,0\r\n5073,15572657,H?,472,France,Male,29,8,102490.27,1,0,1,181224.56,0\r\n5074,15800295,Cruz,644,Germany,Male,34,9,112746.54,2,0,0,141230.07,0\r\n5075,15672397,Smith,598,France,Male,38,0,125487.89,1,0,0,158111.71,0\r\n5076,15684921,Onuchukwu,792,Spain,Male,25,8,142862.21,1,1,1,130639.01,0\r\n5077,15720676,Bukowski,700,France,Female,37,7,0,2,1,0,17040.82,0\r\n5078,15731829,Simmons,616,France,Male,34,10,0,2,1,0,25662.27,0\r\n5079,15732672,Stewart,743,Spain,Male,35,6,79388.33,1,1,1,193360.69,0\r\n5080,15692406,Gow,427,France,Male,37,5,0,2,1,1,121485.1,0\r\n5081,15764405,Williams,731,France,Male,29,10,0,2,1,1,162452.65,0\r\n5082,15757537,Francis,610,France,Female,31,6,107784.65,1,1,1,141137.53,0\r\n5083,15793307,Calabresi,724,Spain,Female,41,4,142880.28,3,0,0,185541.2,1\r\n5084,15660679,Chimaobim,653,Spain,Female,38,9,149571.94,1,1,0,118383.18,0\r\n5085,15666856,Chikwendu,774,France,Male,49,1,142767.39,1,1,1,8214.41,0\r\n5086,15687372,Padovesi,547,Germany,Male,49,8,121537.71,2,1,0,46521.45,1\r\n5087,15667289,Henderson,719,Spain,Male,50,2,0,2,0,0,10772.13,0\r\n5088,15624641,Kharlamova,740,Spain,Male,43,9,0,1,1,0,199290.68,1\r\n5089,15734610,Onio,543,France,Male,42,4,89838.71,3,1,0,85983.54,1\r\n5090,15631882,Yeh,688,Germany,Male,45,9,103399.87,1,0,0,129870.93,0\r\n5091,15642709,Feng,474,France,Female,30,9,0,2,0,0,63158.22,0\r\n5092,15811026,Norman,505,Germany,Male,43,5,136855.94,2,1,0,171070.52,0\r\n5093,15596303,White,688,France,Female,39,0,0,2,1,0,53222.15,1\r\n5094,15787255,Manfrin,650,Germany,Female,55,2,140891.46,3,1,1,179834.45,1\r\n5095,15617166,Ritchie,610,France,Male,37,0,0,1,1,0,114514.64,0\r\n5096,15742442,Udegbulam,705,Spain,Female,46,5,89364.91,1,0,1,139162.15,0\r\n5097,15758692,Kao,669,France,Female,29,7,146011.4,1,0,0,50249.16,0\r\n5098,15568238,Diaz,650,Spain,Male,20,8,0,2,1,1,113469.65,0\r\n5099,15730353,Olisaemeka,550,Germany,Male,29,9,145294.08,2,1,0,147484.13,0\r\n5100,15731555,Ross-Watt,595,Germany,Female,45,9,106000.12,1,0,0,191448.96,1\r\n5101,15582404,Miller,572,Spain,Female,26,5,0,2,1,0,119381.41,0\r\n5102,15721462,Shubin,622,Spain,Female,58,2,0,2,1,1,33277.31,0\r\n5103,15632899,Nwankwo,662,Spain,Male,20,9,104508.77,2,0,0,73107.53,0\r\n5104,15808526,Cartwright,783,Germany,Female,58,3,127539.3,1,1,1,96590.39,1\r\n5105,15694349,Ngozichukwuka,714,Spain,Male,44,7,0,1,0,1,6923.11,0\r\n5106,15718465,Sadler,671,Germany,Male,51,3,96891.46,1,1,0,176403.33,1\r\n5107,15682995,Azuka,600,France,Female,32,1,78535.25,1,1,0,64349.6,0\r\n5108,15584776,Shen,847,Spain,Female,37,9,112712.17,1,1,0,116097.26,0\r\n5109,15777772,Whittaker,650,Spain,Male,55,9,119618.42,1,1,1,29861.13,0\r\n5110,15576156,Abazu,710,Spain,Female,28,6,0,1,1,0,48426.98,0\r\n5111,15646756,Murphy,682,France,Female,33,8,74963.5,1,1,1,32770.56,0\r\n5112,15742886,Ford,642,France,Male,26,1,138023.79,2,0,1,117060.2,0\r\n5113,15586135,Gratwick,536,Spain,Female,28,4,0,1,1,1,136197.65,0\r\n5114,15616152,Pai,754,France,Female,47,1,185513.67,1,1,0,27438.83,0\r\n5115,15721460,Lorenzo,678,France,Male,60,8,185648.56,1,0,0,192156.54,1\r\n5116,15727317,Brady,533,Germany,Female,49,1,102286.6,3,1,0,69409.37,1\r\n5117,15649536,Wong,741,Germany,Male,38,4,128015.83,1,1,0,58440.43,0\r\n5118,15754929,Douglas,757,France,Male,31,10,39539.39,2,0,0,192519.39,0\r\n5119,15572051,Kennedy,721,France,Male,40,3,0,1,1,1,144874.67,0\r\n5120,15668142,Chang,700,France,Male,37,3,77608.46,2,1,1,175373.46,0\r\n5121,15701176,Brown,663,France,Male,26,5,141462.13,1,1,0,440.2,0\r\n5122,15708422,Hsiung,677,Spain,Female,35,0,0,2,0,0,76637.38,0\r\n5123,15655632,MacDonald,655,France,Male,27,2,131691.33,1,1,0,49480.66,0\r\n5124,15744606,Davidson,832,Spain,Male,29,8,93833.86,1,0,1,10417.87,0\r\n5125,15612140,Milano,721,Spain,Female,46,7,137933.39,1,1,1,67976.57,0\r\n5126,15656086,Bovee,542,Spain,Male,54,8,105770.14,1,0,1,140929.98,1\r\n5127,15655298,Lewis,654,Spain,Female,54,5,0,2,0,1,47139.06,0\r\n5128,15644796,Dyer,821,Spain,Female,38,8,0,2,0,1,126241.4,1\r\n5129,15726250,Hsia,508,France,Female,38,3,166328.65,2,0,1,22614.19,0\r\n5130,15764432,Hicks,588,Germany,Female,42,2,164307.77,1,1,0,48498.19,0\r\n5131,15631721,Millar,691,Germany,Male,38,9,163965.69,2,0,1,103511.26,0\r\n5132,15707479,Fan,664,France,Male,40,7,125608.72,1,1,0,122073.48,0\r\n5133,15579826,Young,439,France,Female,66,9,0,1,1,0,65535.56,0\r\n5134,15668104,Kerr,479,Spain,Male,37,6,118433.94,1,0,1,160060.9,0\r\n5135,15641604,Frolova,850,France,Female,55,10,98488.08,1,1,0,155879.57,1\r\n5136,15587240,Vasilyev,518,France,Male,40,4,0,2,0,1,194416.58,0\r\n5137,15680767,Sabbatini,717,Germany,Female,64,10,98362.35,2,1,1,21630.21,0\r\n5138,15601594,Ifeanacho,698,France,Female,51,6,144237.91,4,1,0,157143.61,1\r\n5139,15589969,Capon,850,France,Male,34,6,0,1,0,1,52796.31,0\r\n5140,15703728,Chieloka,700,Spain,Male,47,4,0,1,1,0,121798.52,1\r\n5141,15617790,Hanson,626,France,Female,29,4,105767.28,2,0,0,41104.82,0\r\n5142,15662500,Ts'ao,774,Spain,Male,32,9,0,2,1,0,10604.48,0\r\n5143,15778526,Bradshaw,719,Spain,Female,48,5,0,2,0,0,78563.66,0\r\n5144,15670584,Nkemakolam,646,Spain,Male,31,2,0,1,1,1,170821.43,1\r\n5145,15748069,Clunie,485,France,Female,25,3,134467.26,1,1,1,113266.09,0\r\n5146,15680597,Cover,784,Germany,Male,38,1,138515.02,1,1,1,171768.76,0\r\n5147,15628992,Esposito,850,Germany,Male,32,2,128647.98,2,0,0,54416.18,0\r\n5148,15719624,Hodgson,669,France,Female,38,9,121858.98,1,1,0,130755.34,0\r\n5149,15812767,Harvey,731,Spain,Male,70,3,0,2,1,1,141180.66,0\r\n5150,15689201,Dobie,721,France,Female,49,1,120108.56,1,0,1,183421.76,0\r\n5151,15614716,Okwudilichukwu,515,France,Female,37,0,196853.62,1,1,1,132770.11,0\r\n5152,15683618,Dyer,774,France,Female,35,3,121418.62,1,1,1,24400.37,0\r\n5153,15799631,Chase,585,Spain,Male,36,10,0,2,1,1,180318.6,0\r\n5154,15692259,Baresi,695,France,Female,29,9,0,2,1,0,111565.45,0\r\n5155,15590966,Lo,729,Germany,Female,42,4,97495.8,2,0,0,2002.5,0\r\n5156,15656426,Tyler,713,France,Female,42,3,0,2,0,0,82565.01,0\r\n5157,15675256,Ts'ui,555,Spain,Male,33,5,127343.4,1,0,1,121789.3,0\r\n5158,15751185,Aparicio,699,Spain,Female,50,0,158633.61,1,1,0,193785.87,0\r\n5159,15789582,Macleod,587,France,Male,55,9,0,1,1,0,64593.07,0\r\n5160,15651103,Sal,762,Spain,Female,69,9,183744.98,1,1,1,196993.69,0\r\n5161,15672299,Yeh,510,France,Male,44,6,0,2,1,1,175518.31,0\r\n5162,15772250,Udegbunam,842,Spain,Male,46,9,0,1,0,0,17268.02,0\r\n5163,15763922,Alexandrov,608,France,Male,31,7,79962.92,2,1,0,60901.72,0\r\n5164,15633870,Ozioma,850,France,Female,36,10,0,2,1,1,100750.03,0\r\n5165,15624323,Atkins,642,France,Male,36,4,0,2,1,1,195224.91,0\r\n5166,15688612,Campos,850,France,Male,33,7,140956.99,1,0,0,3510.18,0\r\n5167,15694644,Wood,455,Spain,Female,43,6,0,1,1,1,81250.79,0\r\n5168,15587174,Kerr,726,France,Male,29,7,0,2,1,1,91844.14,1\r\n5169,15579559,Chienezie,544,Spain,Male,30,8,145241.63,1,1,1,80676.83,0\r\n5170,15775430,Tsou,651,Germany,Male,31,7,138008.06,2,1,0,129912.74,0\r\n5171,15623695,McKinnon,814,France,Female,31,4,0,2,1,1,142029.17,0\r\n5172,15760849,Nwachukwu,537,France,Male,39,2,0,2,1,1,137651.6,0\r\n5173,15813095,Nwebube,553,France,Male,37,2,0,2,1,0,33877.29,0\r\n5174,15705281,Burt,800,Spain,Male,38,9,0,1,1,0,78744.39,0\r\n5175,15812594,Ross,791,France,Male,34,7,0,2,1,0,96734.46,0\r\n5176,15626322,Lees,699,Spain,Female,29,9,127570.93,2,1,0,164756.81,0\r\n5177,15723105,Feetham,756,France,Female,28,6,0,1,1,1,164394.65,0\r\n5178,15588449,Chuang,591,Spain,Female,27,5,107812.67,1,0,1,162501.83,1\r\n5179,15794849,Aitken,850,Germany,Male,22,7,91560.58,2,0,0,10541.38,0\r\n5180,15620000,Chambers,760,Germany,Male,34,6,121303.77,2,1,1,59325.21,0\r\n5181,15799720,Coburn,569,Spain,Male,43,8,161546.68,2,0,1,178187.28,0\r\n5182,15711287,Ahmed,661,Spain,Female,35,5,128415.45,1,1,0,142626.49,0\r\n5183,15613102,Ogochukwu,670,France,Female,31,2,57530.06,1,1,1,181893.31,1\r\n5184,15621440,Soto,694,France,Male,38,1,0,2,0,1,156858.2,0\r\n5185,15677146,Obiajulu,728,France,Female,28,4,142243.54,2,1,0,33074.51,0\r\n5186,15801169,Yegorova,764,Germany,Female,39,9,138341.51,1,1,0,50072.94,1\r\n5187,15722425,Lucchese,639,France,Male,32,9,0,2,1,0,111340.36,0\r\n5188,15682421,Talbot,683,France,Female,30,2,0,2,0,1,100496.84,1\r\n5189,15691910,Lu,663,Spain,Male,30,4,0,3,1,0,101371.05,0\r\n5190,15721779,Arnold,826,Spain,Male,41,5,146466.46,2,0,0,180934.67,0\r\n5191,15579548,Nicholson,735,Spain,Male,36,5,0,2,1,0,105152.17,0\r\n5192,15681075,Chukwualuka,682,France,Female,58,1,0,1,1,1,706.5,0\r\n5193,15607884,Wallace,663,France,Female,39,8,0,2,1,1,101168.9,0\r\n5194,15767757,Pisano,562,Spain,Female,29,9,120307.58,1,1,1,6795.61,0\r\n5195,15791550,Kelly,696,France,Male,27,4,87637.26,2,0,0,196111.35,0\r\n5196,15658589,Brady,850,Spain,Male,38,2,94652.04,1,1,1,171960.76,0\r\n5197,15670822,Palmer,719,France,Female,22,7,114415.84,1,1,1,177497.4,0\r\n5198,15629744,Tan,804,France,Female,71,8,0,2,0,1,147995.96,0\r\n5199,15660768,L?,604,France,Male,40,1,84315.02,1,0,0,36209.1,0\r\n5200,15726310,Mordvinova,782,Spain,Female,27,3,0,2,1,0,143614.01,0\r\n5201,15641298,Corones,512,Germany,Male,42,9,93955.83,2,1,0,14828.54,0\r\n5202,15625675,Clements,569,France,Male,36,1,67087.69,1,1,0,154775.7,0\r\n5203,15713354,Morrice,597,Germany,Female,22,6,101528.61,1,1,0,70529,1\r\n5204,15633866,Hsiung,753,Germany,Male,30,1,110824.52,1,1,1,57896.27,0\r\n5205,15704231,Barrett,430,France,Female,33,8,0,1,1,1,69759.91,0\r\n5206,15735400,Kanayochukwu,756,France,Male,28,8,179960.2,1,1,0,89938.08,0\r\n5207,15632826,Tardent,493,France,Male,38,3,134006.77,1,1,0,89578.32,0\r\n5208,15751022,Bowhay,777,Germany,Female,37,10,121532.17,2,1,1,73464.88,0\r\n5209,15664737,Lei,779,Spain,Female,38,7,0,2,1,1,138542.87,0\r\n5210,15681126,Baker,702,Spain,Female,38,2,0,1,1,1,161888.63,0\r\n5211,15738954,Pisano,551,France,Male,35,7,129717.3,2,0,0,86937.2,0\r\n5212,15662263,Castillo,749,Germany,Male,22,4,94762.16,2,1,1,42241.54,0\r\n5213,15621611,Gibson,742,Germany,Male,55,5,155196.17,1,0,1,121207.66,1\r\n5214,15783752,Lindsay,752,Germany,Male,29,4,129514.99,1,1,1,102930.46,0\r\n5215,15709474,Macnamara,740,Germany,Female,57,3,113386.36,2,1,1,65121.63,1\r\n5216,15701280,Romano,576,France,Male,24,3,0,1,0,1,78498.04,1\r\n5217,15671104,Aksakova,637,Spain,Male,43,3,172196.23,1,1,1,104769.96,0\r\n5218,15796434,Farnsworth,724,France,Male,28,5,97612.12,1,1,1,96498.14,0\r\n5219,15781505,Giordano,685,France,Male,20,4,104719.94,2,1,0,38691.34,0\r\n5220,15625819,Arnold,625,France,Female,38,7,0,1,1,0,164804.02,0\r\n5221,15753174,Thompson,571,Germany,Male,37,9,139592.98,3,1,0,104152.65,1\r\n5222,15654067,Koch,584,Spain,Female,29,4,0,2,1,0,88866.92,0\r\n5223,15724719,Jones,550,France,Female,22,7,139096.85,1,1,0,129890.94,0\r\n5224,15624695,Otitodilinna,662,Spain,Female,72,7,140301.72,1,0,1,179258.67,0\r\n5225,15718216,Fleetwood-Smith,803,Spain,Male,43,3,0,1,1,0,72051.44,0\r\n5226,15586300,Chinonyelum,615,France,Male,66,7,0,2,1,1,74580.8,0\r\n5227,15783349,Montague,481,Spain,Male,39,1,111233.09,1,1,1,123995.15,0\r\n5228,15725767,Milani,701,France,Male,23,3,0,2,1,0,38960.59,0\r\n5229,15791925,Palermo,751,France,Male,29,10,147737.63,1,0,1,94951.27,0\r\n5230,15793585,Anderson,675,France,Male,35,8,0,2,1,1,56642.97,0\r\n5231,15576641,Crawford,733,Germany,Male,40,5,125725.02,2,1,1,50783.1,0\r\n5232,15749519,Lin,822,France,Male,38,6,128289.7,3,1,0,9149.96,1\r\n5233,15684960,Yewen,559,France,Female,46,5,0,1,1,0,21006.1,1\r\n5234,15591286,Simmons,731,Germany,Female,49,4,88826.07,1,1,1,33759.41,1\r\n5235,15668323,Mbadiwe,678,France,Female,41,1,143443.61,1,1,0,196622.28,1\r\n5236,15608528,Munro,645,France,Female,68,9,0,4,1,1,176353.87,1\r\n5237,15645184,Graham,701,France,Male,29,2,0,2,1,0,176943.59,0\r\n5238,15702566,Lombardo,554,Spain,Male,26,8,149134.46,1,1,1,177966.24,0\r\n5239,15660840,Kalinin,723,France,Male,30,3,124119.54,1,1,0,162198.32,0\r\n5240,15750811,Woodward,766,Germany,Male,44,3,116822.7,1,0,0,197643.24,0\r\n5241,15733842,Pirozzi,597,France,Female,24,1,103219.47,1,1,0,60420.07,0\r\n5242,15581526,Iweobiegbulam,574,France,Male,41,1,0,2,0,0,70550,0\r\n5243,15662751,Piazza,655,Germany,Female,40,0,81954.6,1,1,1,198798.44,1\r\n5244,15684319,Baranova,780,Germany,Female,37,10,95196.26,1,1,0,126310.39,1\r\n5245,15702190,Fan,672,Spain,Male,43,5,0,2,1,1,64515.5,0\r\n5246,15588517,Sun,717,France,Male,38,7,0,2,1,1,158580.05,0\r\n5247,15801863,Marino,521,France,Female,32,2,136555.01,2,1,1,129353.21,0\r\n5248,15584271,Donaldson,633,France,Male,59,5,0,1,1,1,137273.97,0\r\n5249,15700366,Burton,669,France,Male,39,3,119452.03,1,1,1,171575.54,0\r\n5250,15804038,Quinn,740,France,Male,44,9,0,1,0,1,96528,1\r\n5251,15720820,Sabbatini,462,Germany,Female,24,9,69881.09,2,0,1,64421.02,0\r\n5252,15743759,Brooks,619,France,Male,39,5,0,2,1,1,158444.61,0\r\n5253,15749947,Black,665,France,Female,44,7,0,2,1,1,66548.58,0\r\n5254,15670496,Schwartz,655,Spain,Female,27,9,0,2,0,0,108008.05,0\r\n5255,15746664,Ts'ui,463,Spain,Male,20,8,204223.03,1,1,0,128268.39,0\r\n5256,15745533,Sargent,799,France,Female,63,1,110314.21,2,1,0,37464,1\r\n5257,15761497,Udinesi,713,Spain,Female,48,1,163760.82,1,0,0,157381.14,1\r\n5258,15628600,Lee,807,Germany,Female,31,1,141069.18,3,1,1,194257.11,0\r\n5259,15627002,Taylor,728,France,Male,38,1,115934.74,1,1,1,139059.05,0\r\n5260,15614635,Kepley,582,France,Male,52,2,151457.88,1,0,1,40893.61,0\r\n5261,15731281,Ozuluonye,704,Germany,Female,35,3,154206.07,2,1,1,40261.49,0\r\n5262,15814022,Lassetter,714,France,Female,26,9,89928.99,1,1,0,46203.31,0\r\n5263,15659194,Mishina,628,France,Male,30,8,89182.09,1,1,1,13126.9,0\r\n5264,15745030,Trevisano,809,Germany,Male,41,1,79706.25,2,1,0,165675.01,0\r\n5265,15691817,Iloerika,547,Spain,Female,44,5,0,3,0,0,5459.07,1\r\n5266,15707488,Tan,560,France,Female,27,5,0,2,1,0,131919.48,0\r\n5267,15784700,Chikelu,811,France,Male,31,7,117799.28,1,1,1,182372.35,0\r\n5268,15710397,Lin,584,France,Male,26,4,0,2,1,0,147600.54,0\r\n5269,15687648,Nicholson,691,France,Male,28,1,0,2,0,0,92865.41,0\r\n5270,15732281,Ugoji,680,Germany,Male,34,6,146422.22,1,1,0,67142.97,1\r\n5271,15607230,Michel,588,Germany,Male,33,9,150186.22,2,1,1,65611.01,0\r\n5272,15567630,Bruce,721,Germany,Male,40,6,100275.88,1,1,0,138564.48,1\r\n5273,15587507,Feng,850,France,Male,47,6,0,1,1,0,187391.02,1\r\n5274,15733904,McDonald,529,France,Male,32,9,147493.89,1,1,0,33656.35,0\r\n5275,15709511,Watt,622,France,Male,43,8,0,2,1,0,100618.17,0\r\n5276,15579616,Goodwin,683,France,Female,42,8,0,2,0,1,198134.9,0\r\n5277,15694852,Arcuri,575,France,Male,29,4,121823.4,2,1,1,50368.87,0\r\n5278,15589924,Rapuluolisa,577,Spain,Female,40,1,0,2,1,1,108787,0\r\n5279,15799300,Kao,510,Germany,Male,31,0,113688.63,1,1,0,33099.41,1\r\n5280,15731330,Tsui,652,Spain,Female,40,7,100471.34,1,1,1,124550.88,0\r\n5281,15694129,Summers,569,Germany,Female,28,3,100032.52,1,1,0,5159.21,1\r\n5282,15620372,Cross,687,Spain,Male,31,3,0,2,0,0,48228.1,0\r\n5283,15744622,Osorio,822,France,Male,32,8,116358,1,1,0,108798.36,0\r\n5284,15799815,Bobrov,656,Germany,Female,23,4,163549.63,1,0,1,21085.12,0\r\n5285,15759250,Barnett,745,Germany,Male,51,3,99183.9,1,1,1,28922.25,0\r\n5286,15732643,Pike,386,Spain,Female,53,1,131955.07,1,1,1,62514.65,1\r\n5287,15690540,Gearheart,684,Spain,Female,41,1,134177.06,1,0,0,177506.66,0\r\n5288,15803078,Bruno,635,Spain,Female,38,1,0,2,1,0,90605.05,0\r\n5289,15652180,Egobudike,582,France,Male,30,2,0,2,1,1,132029.95,0\r\n5290,15741195,Okechukwu,613,Spain,Male,19,5,0,1,1,1,176903.35,0\r\n5291,15743490,Zikoranachidimma,795,Germany,Female,56,9,94348.94,1,1,0,29239.29,1\r\n5292,15575510,Milanesi,659,France,Female,32,2,155584.21,1,0,1,153662.88,0\r\n5293,15732610,Ahern,745,France,Female,28,6,0,2,1,0,154389.18,0\r\n5294,15602909,Dickson,604,Spain,Female,41,10,0,2,1,1,166224.39,0\r\n5295,15734058,Anayochukwu,509,Germany,Male,32,9,170661.47,1,1,1,21646.2,0\r\n5296,15801788,McDonald,706,Germany,Female,29,6,185544.36,1,1,0,171037.63,0\r\n5297,15702462,Fiorentini,619,Spain,Female,44,6,52831.13,1,1,1,112649.22,1\r\n5298,15683416,Russo,572,Germany,Male,51,8,97750.07,3,1,1,193014.26,1\r\n5299,15794187,Young,695,France,Male,36,6,114007.5,2,1,0,118120.88,0\r\n5300,15792989,Bianchi,543,France,Female,71,1,104308.77,1,1,1,25650.04,0\r\n5301,15613734,Fallaci,640,France,Female,33,6,84719.13,2,1,1,113048.79,0\r\n5302,15606177,Crawford,672,France,Male,39,2,0,2,1,0,87372.49,0\r\n5303,15636700,Marsh,701,France,Male,39,9,140236.98,1,0,1,146651.99,0\r\n5304,15645766,Kosisochukwu,634,Spain,Male,25,9,0,2,1,1,8227.91,0\r\n5305,15671345,Piccio,531,Spain,Female,42,6,75302.85,2,0,0,57034.35,0\r\n5306,15652469,Nevels,699,France,Male,27,1,0,2,1,0,93003.21,0\r\n5307,15749638,Kaodilinakachukwu,605,France,Female,51,9,104760.82,1,1,1,165574.54,1\r\n5308,15728706,Amaechi,534,France,Female,49,7,0,1,1,0,13566.48,1\r\n5309,15735439,P'an,449,Spain,Female,31,1,113693,1,0,0,82796.29,0\r\n5310,15778696,Ikemefuna,684,Spain,Female,36,5,174180.39,1,1,0,119830.08,0\r\n5311,15624744,Tai,622,Germany,Male,42,9,115766.26,1,0,0,72155.85,1\r\n5312,15584338,Winn,714,France,Female,40,0,0,2,1,0,62762.12,0\r\n5313,15726178,Hardy,712,Spain,Female,48,8,0,2,1,0,183235.33,0\r\n5314,15794939,Chiu,783,France,Female,72,5,121215.9,2,1,1,105206.48,0\r\n5315,15788068,Lopez,743,Germany,Male,45,10,144677.19,3,1,0,22512.44,1\r\n5316,15572956,Steen,683,France,Male,36,5,115350.63,1,1,1,122305.91,0\r\n5317,15780386,Ferri,654,Spain,Male,40,5,105683.63,1,1,0,173617.09,0\r\n5318,15791114,Yegorova,700,France,Male,37,1,135179.49,1,1,0,160670.37,0\r\n5319,15708046,Knowles,744,Spain,Male,31,0,117551.23,1,1,0,158958.9,0\r\n5320,15719779,May,645,Germany,Male,25,1,157404.02,2,1,0,93073.04,0\r\n5321,15591550,Bianchi,525,Spain,Male,36,3,77910.23,1,1,0,67238.01,0\r\n5322,15639368,Pipes,732,France,Male,25,0,110942.9,1,0,0,172576.56,0\r\n5323,15699830,Doherty,721,France,Female,40,7,0,2,1,1,122580.48,0\r\n5324,15569264,Yobanna,622,France,Male,32,5,179305.09,1,1,1,149043.78,0\r\n5325,15595158,Hsu,654,Germany,Male,31,5,150593.59,2,1,1,105218.45,0\r\n5326,15599126,Russell,529,France,Female,43,0,123815.86,1,1,1,78463.99,1\r\n5327,15650575,Payne,720,Spain,Female,59,6,0,2,1,1,160849.43,1\r\n5328,15641490,Windsor,850,Germany,Female,25,8,69385.17,2,1,0,87834.24,0\r\n5329,15680234,Bray,667,Germany,Male,27,2,138032.15,1,1,0,166317.71,0\r\n5330,15592230,Seleznyov,620,France,Male,41,3,0,2,1,1,137309.06,0\r\n5331,15626212,Wark,616,France,Male,29,9,0,1,1,1,166984.44,0\r\n5332,15700627,Y?,637,Germany,Female,46,2,143500.82,1,1,0,166996.46,1\r\n5333,15782641,Brown,710,Spain,Female,29,3,119670.18,1,1,0,188022.44,0\r\n5334,15784445,Huang,717,Spain,Male,33,1,99106.73,1,0,0,194467.23,0\r\n5335,15813681,Zito,786,Germany,Male,24,2,120135.55,2,1,1,125449.47,0\r\n5336,15596649,Bailey,651,France,Female,39,8,0,1,1,0,137452.57,0\r\n5337,15700460,Allnutt,530,France,Female,55,4,120905.03,1,0,1,123475.88,1\r\n5338,15724076,Christie,815,Spain,Female,57,5,0,3,0,0,38941.44,1\r\n5339,15784000,Pope,715,Germany,Female,34,9,102277.52,1,0,0,177852.57,1\r\n5340,15733966,Johnstone,496,Germany,Female,55,4,125292.53,1,1,1,31532.96,1\r\n5341,15612667,Bird,680,Spain,Male,42,0,0,1,1,0,136377.21,0\r\n5342,15654025,Jones,646,France,Female,51,4,101629.3,1,0,0,130541.1,0\r\n5343,15589431,Pedder,807,Germany,Male,47,1,171937.27,1,1,1,65636.92,0\r\n5344,15578238,Calabrese,727,France,Male,47,7,0,2,1,0,193305.35,0\r\n5345,15566269,Chialuka,787,France,Male,25,5,0,2,1,0,47307.9,0\r\n5346,15639217,McKenzie,806,France,Male,34,6,0,2,0,0,100809.99,0\r\n5347,15688644,Holloway,603,France,Male,31,1,129743.75,1,1,0,109145.2,0\r\n5348,15662426,Tang,649,Spain,Male,32,1,0,1,0,1,91167.19,1\r\n5349,15720511,Byrne,547,Germany,Male,41,3,151191.31,1,1,0,175295.89,1\r\n5350,15567246,Selwyn,684,Germany,Male,32,3,102630.13,2,1,1,127433.47,0\r\n5351,15647965,Genovese,477,France,Female,57,9,114023.64,2,1,1,71167.17,1\r\n5352,15679048,Koger,558,Germany,Male,41,2,124227.14,1,1,1,111184.67,0\r\n5353,15675749,Baranov,695,France,Female,23,1,0,2,1,1,141756.32,0\r\n5354,15782181,Greco,592,Spain,Male,35,6,80285.16,1,1,0,72678.75,1\r\n5355,15795738,Owens,789,France,Male,31,4,175477.15,1,1,1,172832.9,0\r\n5356,15773751,Y?,597,France,Female,29,1,132144.35,1,1,0,158086.33,0\r\n5357,15655436,Kendall,839,Germany,Male,47,2,136911.07,1,1,1,168184.62,1\r\n5358,15691396,Ko,405,Germany,Male,31,5,133299.67,2,1,1,72950.14,0\r\n5359,15796958,Tang,658,France,Male,39,7,0,2,1,0,48378.4,0\r\n5360,15801832,Lombardo,684,Germany,Male,42,1,117691,1,1,1,23135.65,1\r\n5361,15661349,Perkins,633,France,Male,35,10,0,2,1,0,65675.47,0\r\n5362,15719265,Feng,589,France,Male,46,9,0,2,1,0,170676.67,0\r\n5363,15779985,Lo,750,Germany,Female,37,1,133199.71,2,1,1,27366.77,0\r\n5364,15663410,Piccio,771,Spain,Male,51,5,135506.58,3,1,1,152479.64,1\r\n5365,15704144,Mazzanti,812,Germany,Male,33,2,127154.14,2,0,1,105383.49,0\r\n5366,15774104,Chukwualuka,539,Spain,Male,39,2,0,2,1,1,48189.94,0\r\n5367,15812230,Elliot,670,Germany,Female,42,5,49508.79,3,1,1,100324.01,0\r\n5368,15742848,Gratton,673,France,Male,41,5,0,1,1,1,65657.29,0\r\n5369,15745326,Carandini,538,France,Female,62,3,75051.49,1,0,0,17682.02,1\r\n5370,15674541,Robinson,575,Spain,Male,52,8,123925.23,1,0,0,111342.66,1\r\n5371,15728564,Lo,682,France,Male,41,6,0,2,0,1,134158.09,1\r\n5372,15580701,Ma,712,France,Male,33,3,153819.58,1,1,0,79176.09,1\r\n5373,15688973,Vinogradova,598,Spain,Female,39,5,0,2,1,1,83103.46,0\r\n5374,15709412,H?,776,Spain,Male,30,6,0,2,0,1,63908.86,0\r\n5375,15607753,Alexandrova,606,Spain,Female,23,10,70417.79,1,0,1,90896.04,0\r\n5376,15705352,Yang,686,Spain,Male,38,7,111484.88,1,1,1,76076.2,0\r\n5377,15602500,Maslova,850,Spain,Male,38,1,146343.98,1,0,1,103902.11,0\r\n5378,15672437,Buccho,642,France,Male,72,1,160541,2,1,1,142223.94,0\r\n5379,15720968,Young,606,Germany,Male,27,2,130274.26,2,1,0,147533.09,0\r\n5380,15730796,Barker,627,France,Female,21,7,98993.02,1,1,1,169156.64,0\r\n5381,15768219,Sung,850,Spain,Male,36,0,0,2,1,0,141242.57,0\r\n5382,15663883,Hansen,850,Germany,Male,32,9,141827.33,2,1,1,149458.73,0\r\n5383,15589296,Brown,724,France,Female,40,6,110054.45,1,1,1,86950.72,0\r\n5384,15586425,Lo Duca,579,France,Male,28,4,0,2,1,1,176925.69,0\r\n5385,15679813,Ellis,727,Spain,Male,28,1,0,1,1,0,40357.39,0\r\n5386,15681410,Korff,813,Germany,Female,36,6,98088.09,1,0,1,26687.22,1\r\n5387,15668283,Gardiner,642,France,Male,48,9,118317.27,4,0,0,78702.98,1\r\n5388,15624072,Kiernan,669,Spain,Male,22,10,0,2,1,0,176163.74,0\r\n5389,15669664,Thompson,574,Germany,Male,54,1,99774.5,1,0,0,4896.11,1\r\n5390,15682728,Mathews,774,France,Female,32,4,0,2,0,0,114899.13,0\r\n5391,15573851,Macrossan,735,France,Female,38,1,0,3,0,0,92220.12,1\r\n5392,15733661,Illingworth,639,Spain,Female,27,8,133806.54,2,1,0,6251.3,0\r\n5393,15710012,Bowen,738,Spain,Male,44,2,0,2,1,0,43018.82,1\r\n5394,15763327,Craig,835,France,Male,32,8,124993.29,2,1,1,27548.06,0\r\n5395,15668853,Menhennitt,637,Spain,Female,44,0,157622.58,1,1,1,120454.2,0\r\n5396,15639303,Moore,589,Germany,Male,48,5,126111.61,1,0,1,133961.19,0\r\n5397,15691011,Shoebridge,591,France,Male,42,9,161651.37,2,1,1,131753.97,0\r\n5398,15638513,Palermo,723,France,Female,40,7,142856.95,2,0,0,38019.74,0\r\n5399,15648933,Reilly,831,Germany,Male,44,3,111100.98,1,1,1,28144.07,1\r\n5400,15628904,Bowen,733,Spain,Male,35,8,102918.38,1,1,1,45959.86,0\r\n5401,15644788,Fyodorov,731,France,Female,30,5,0,2,1,0,189528.72,0\r\n5402,15598161,Clements,654,France,Male,47,10,0,2,1,0,170481.98,0\r\n5403,15745624,McKenzie,828,France,Male,37,4,0,2,1,0,94845.45,0\r\n5404,15733169,Craig,590,Spain,Male,22,7,125265.61,1,1,1,161253.08,0\r\n5405,15801417,Iloerika,657,France,Male,37,4,82500.28,1,1,1,115260.72,0\r\n5406,15592707,Dolgorukova,531,Germany,Female,64,2,175754.87,2,1,1,60721.4,0\r\n5407,15593954,Eva,516,France,Female,47,6,109387.33,1,0,0,121365.45,0\r\n5408,15714431,Yeh,561,France,Male,37,1,100443.36,2,0,1,101693.73,0\r\n5409,15638257,P'an,682,Spain,Female,54,0,83102.72,2,1,1,54132.93,0\r\n5410,15690939,Howe,575,Spain,Male,28,7,0,1,1,1,10666.05,0\r\n5411,15723613,Jenkins,623,France,Female,28,4,0,2,1,0,41227.67,0\r\n5412,15813640,Shih,642,France,Female,40,7,0,2,1,0,10712.82,0\r\n5413,15707322,Nnamdi,779,France,Female,48,2,115290.27,1,0,0,98912.69,1\r\n5414,15588918,Mitchell,671,France,Female,42,6,0,2,1,0,197202.48,0\r\n5415,15600357,Findlay,495,France,Female,40,1,140197.71,2,1,0,150720.39,0\r\n5416,15747014,Pisani,850,France,Female,28,1,105245.34,1,0,1,74780.13,0\r\n5417,15809830,Belisario,630,France,Male,50,8,0,2,0,1,79377.45,0\r\n5418,15662245,Pomeroy,588,France,Male,32,1,0,2,1,1,8763.87,0\r\n5419,15651075,Ibrahimova,562,Germany,Male,35,3,142296.13,1,0,1,177112.7,0\r\n5420,15594456,K?,740,Spain,Female,56,4,99097.33,1,1,1,85016.64,1\r\n5421,15583462,Graham,695,France,Male,28,5,171069.39,2,1,1,88689.4,0\r\n5422,15757661,Trevisano,589,France,Female,39,7,0,2,0,0,95985.64,0\r\n5423,15729117,Trevisano,607,France,Female,31,1,102523.88,1,1,1,166792.71,0\r\n5424,15749671,K?,794,France,Male,35,6,0,2,1,1,68730.91,0\r\n5425,15566253,Manning,580,Germany,Male,44,9,143391.07,1,0,0,146891.07,1\r\n5426,15595153,Tucker,644,Germany,Female,44,8,106022.73,2,0,0,148727.42,0\r\n5427,15698572,Schaffer,636,Spain,Female,36,1,0,1,1,0,43134.58,0\r\n5428,15674149,Esomchi,599,Germany,Male,36,3,128960.21,2,1,1,40318.33,0\r\n5429,15623082,Ch'ang,507,France,Female,35,2,0,2,1,0,97633.93,0\r\n5430,15797905,Walker,682,France,Female,48,7,0,2,1,0,65069.03,0\r\n5431,15746028,Chu,714,France,Female,24,7,0,2,1,0,166335,0\r\n5432,15582951,Crawford,696,France,Female,25,8,126442.59,1,1,0,121904.44,0\r\n5433,15616471,Milne,599,Spain,Male,51,0,0,1,1,1,175235.99,0\r\n5434,15641575,Anenechukwu,577,France,Male,37,2,127261.35,1,1,0,56185.05,0\r\n5435,15638803,Donaldson,733,Spain,Female,32,5,0,2,1,0,131625.14,0\r\n5436,15808283,Kelly,647,France,Female,33,4,0,1,1,0,152323.04,0\r\n5437,15811200,Ts'ao,831,France,Female,34,2,0,2,0,0,165840.94,0\r\n5438,15733476,Gonzalez,543,Germany,Male,30,6,73481.05,1,1,1,176692.65,0\r\n5439,15633274,Tai,679,France,Male,34,7,160515.37,1,1,0,121904.14,0\r\n5440,15582168,Muravyova,713,Germany,Female,61,4,149525.34,2,1,0,123663.63,0\r\n5441,15807269,Milanesi,690,Germany,Male,43,2,166522.78,1,0,0,119644.59,1\r\n5442,15602979,Lin,751,France,Male,29,1,135536.5,1,1,0,66825.33,0\r\n5443,15660417,Lambert,613,Germany,Female,43,10,120481.69,1,0,0,94875.03,1\r\n5444,15590199,Temple,701,Spain,Male,28,1,103421.32,1,0,1,76304.73,0\r\n5445,15641794,Ridley,698,France,Male,33,5,135658.73,2,0,1,39755,0\r\n5446,15779174,Young,451,France,Female,36,2,0,2,1,1,180142.42,0\r\n5447,15785547,Slye,665,France,Male,28,8,191402.82,2,1,0,83238.4,0\r\n5448,15795124,Pan,726,Germany,Male,50,9,94504.35,1,0,1,5078.9,0\r\n5449,15718912,Hsueh,608,Germany,Female,44,5,126147.84,1,0,1,132424.69,1\r\n5450,15592028,Roberts,549,France,Female,46,7,0,1,1,1,109057.56,0\r\n5451,15580227,Moss,803,France,Male,33,6,0,2,1,0,115676.61,0\r\n5452,15657830,Andrews,663,France,Male,43,4,87624.03,2,1,0,149401.33,0\r\n5453,15798256,Takasuka,558,France,Female,45,1,153697.53,2,0,0,89891.4,1\r\n5454,15643819,Dawson,714,France,Female,25,4,0,2,0,0,82500.84,0\r\n5455,15754301,Bruche,704,France,Male,39,5,0,1,1,0,6416.92,0\r\n5456,15726855,Oliver,805,Germany,Female,45,9,116585.97,1,1,0,189428.75,1\r\n5457,15755225,Ryan,659,Germany,Male,34,9,134464.58,2,1,0,178833.34,0\r\n5458,15725221,Sabbatini,738,Germany,Male,62,10,83008.31,1,1,1,42766.03,0\r\n5459,15789055,Watt,635,Spain,Male,35,2,113635.16,1,1,0,90883.12,0\r\n5460,15617507,Wilson,530,Spain,Female,36,7,0,2,1,0,80619.09,0\r\n5461,15668894,Abramova,661,Germany,Male,41,5,122552.48,2,0,1,120646.4,0\r\n5462,15589563,Purdy,531,Spain,Male,31,2,118899.45,2,0,0,41409.36,0\r\n5463,15693162,Higgins,694,France,Female,29,5,99713.87,1,0,0,112317.89,0\r\n5464,15750099,Marshall,731,France,Female,36,6,0,1,0,0,152128.36,0\r\n5465,15795540,Reye,556,France,Female,36,2,134208.22,1,0,1,177670.57,0\r\n5466,15794941,Chibueze,647,Germany,Female,41,1,85906.65,3,1,0,189159.97,0\r\n5467,15611848,Kwemtochukwu,850,Germany,Male,32,3,137714.25,1,0,1,159403.68,0\r\n5468,15581237,Biryukova,573,Spain,Male,33,1,160777.9,1,1,1,149536.15,0\r\n5469,15738150,Chidozie,591,France,Male,45,5,0,2,1,1,155492.87,0\r\n5470,15678571,Barber,723,France,Male,21,4,0,2,0,0,24847.02,0\r\n5471,15736124,Thompson,617,France,Male,25,1,102585.88,2,1,1,115387.4,0\r\n5472,15623202,Maslov,704,Germany,Female,39,10,102556.18,2,1,0,171971.25,1\r\n5473,15804201,Jones,457,Germany,Male,42,4,126772.57,1,0,1,126106.4,0\r\n5474,15596863,Chidumaga,787,Germany,Female,38,3,158373.23,1,1,1,28228.35,0\r\n5475,15696277,Hs?,651,France,Female,34,9,0,2,1,0,138113.71,0\r\n5476,15748608,Trentini,612,Germany,Male,42,5,141927.1,1,1,1,43018.98,0\r\n5477,15723864,Lucas,828,Spain,Male,47,1,109876.82,2,1,0,83611.45,1\r\n5478,15802390,Willoughby,724,France,Female,34,2,0,2,1,1,118863.38,0\r\n5479,15774336,Jamieson,648,Germany,Male,44,9,111369.79,2,1,1,91947.74,0\r\n5480,15648766,Robertson,569,Spain,Male,35,3,116969.35,1,0,0,94488.82,0\r\n5481,15659094,Ojiofor,765,Germany,Female,34,8,136729.51,2,0,0,47058.21,0\r\n5482,15606397,Cameron,577,Germany,Female,44,1,152086.15,1,0,1,44719.5,1\r\n5483,15642619,Mayne,603,Spain,Male,46,2,0,2,1,0,174478.54,0\r\n5484,15666032,Mancini,568,Spain,Male,28,1,127289.28,1,0,0,45611.51,0\r\n5485,15595842,Paramor,748,Germany,Male,45,2,119852.01,1,0,0,73853.94,1\r\n5486,15753837,Young,573,Spain,Male,38,4,0,2,1,1,196517.43,0\r\n5487,15783882,Daly,771,Spain,Female,41,5,0,2,0,1,92914.67,0\r\n5488,15799790,Carter,763,France,Male,35,9,0,1,1,1,31372.91,0\r\n5489,15628155,Dike,410,France,Female,35,7,117183.74,1,1,1,109733.73,0\r\n5490,15703778,Hughes,728,France,Male,33,8,129907.63,1,0,1,36083.96,0\r\n5491,15722322,Green,655,Spain,Female,78,2,0,2,0,1,188435.38,0\r\n5492,15639278,Chinomso,580,Germany,Female,36,6,145387.32,2,1,1,169963.2,1\r\n5493,15568487,Gorshkov,712,France,Male,35,7,124616.23,1,1,1,69320.97,0\r\n5494,15682084,Chinomso,680,France,Male,31,9,0,2,1,0,36145.53,0\r\n5495,15642821,Ijendu,383,Spain,Female,48,8,95808.19,1,0,0,137702.01,1\r\n5496,15601387,Yen,721,France,Male,35,10,0,2,1,0,71594.26,0\r\n5497,15642515,Arcuri,620,France,Female,42,1,0,2,0,1,65565.92,0\r\n5498,15710421,Baresi,774,Spain,Female,36,8,117152.3,1,0,0,101828.39,0\r\n5499,15726774,Field,563,France,Male,35,3,106250.72,1,0,0,39546.32,0\r\n5500,15649078,Christian,850,Germany,Female,27,8,111837.78,2,1,1,110805.79,0\r\n5501,15641877,Ross,681,France,Male,47,9,97023.21,1,1,1,2168.13,0\r\n5502,15796496,Trevisani,631,France,Female,31,8,137687.72,1,1,0,190067.12,0\r\n5503,15815690,Akabueze,614,Spain,Female,40,3,113348.5,1,1,1,77789.01,0\r\n5504,15631739,Dunn,704,Spain,Male,24,10,122109.78,1,1,1,127654.37,0\r\n5505,15625584,Martin,786,France,Male,32,2,120452.4,2,0,0,79602.86,0\r\n5506,15802466,Donaldson,534,France,Female,53,7,0,2,1,1,80619.17,0\r\n5507,15697028,McClinton,590,Spain,Male,34,0,65812.35,2,0,1,160346.3,0\r\n5508,15575759,Bentley,583,Spain,Female,40,3,54428.37,1,1,0,109638.78,1\r\n5509,15567442,Ibezimako,656,France,Female,75,3,0,2,1,1,1276.87,0\r\n5510,15746805,Thomson,597,France,Male,33,9,0,2,1,0,49374.82,0\r\n5511,15636330,Ch'in,588,Germany,Female,48,1,143279.58,2,1,0,31580.8,1\r\n5512,15714970,Holbrook,667,Germany,Male,32,0,103846.65,1,1,0,20560.69,0\r\n5513,15653784,Solomina,627,France,Male,37,2,125190.86,1,0,1,84584.69,0\r\n5514,15693543,McDonald,708,France,Female,33,8,0,2,0,1,15246.83,0\r\n5515,15773283,Dennis,641,France,Male,65,6,38340.02,1,1,0,32607.77,1\r\n5516,15742534,Faulk,527,Germany,Female,28,2,123802.98,2,1,1,155846.69,0\r\n5517,15569878,Dale,592,France,Male,37,3,96651.03,1,1,1,3232.82,0\r\n5518,15729454,Gorbunov,465,France,Male,33,8,0,2,1,0,177668.55,0\r\n5519,15578375,Farrell,628,France,Male,39,6,0,2,0,0,134441.6,0\r\n5520,15785559,De Luca,678,France,Male,43,1,133237.21,1,1,0,111032.79,1\r\n5521,15649414,Walker,570,France,Female,61,6,142105.35,1,1,1,45214.04,0\r\n5522,15701605,Forster,815,France,Male,37,1,166115.42,1,1,0,67208.3,0\r\n5523,15686696,Brown,817,France,Female,37,6,81070.34,2,1,0,80985.88,0\r\n5524,15625586,Monaldo,717,France,Male,35,4,0,1,1,1,167573.06,0\r\n5525,15654975,Wu,641,France,Female,53,0,123835.52,2,0,1,160110.65,0\r\n5526,15782993,Pan,624,France,Male,51,10,123401.43,2,1,1,127825.25,0\r\n5527,15774382,Longo,579,Germany,Male,49,4,169377.31,1,1,1,123535.05,0\r\n5528,15689602,Findlay,698,France,Male,38,2,130015.24,1,1,1,41595.3,0\r\n5529,15756155,Fu,645,France,Male,32,4,0,2,0,1,97628.08,0\r\n5530,15812647,Yin,691,France,Male,34,8,133936.04,2,1,0,91359.79,0\r\n5531,15736043,Hamilton,638,France,Male,34,6,114543.27,1,1,1,97755.29,0\r\n5532,15696744,Miller,705,France,Female,31,3,119794.67,1,0,0,182528.44,0\r\n5533,15602572,Hsing,720,France,Male,33,9,0,2,1,1,142956.48,0\r\n5534,15674765,Mitchell,553,Spain,Male,44,4,0,1,1,0,10789.3,0\r\n5535,15678725,Chamberlin,658,France,Female,29,8,0,2,0,1,130461.09,0\r\n5536,15694444,Buttenshaw,648,Germany,Female,32,8,157138.99,3,1,0,190994.48,1\r\n5537,15795878,Anayochukwu,636,Spain,Male,45,3,0,2,1,1,159463.8,0\r\n5538,15735346,Wallace,527,Germany,Female,41,10,136733.24,1,1,1,57589.29,0\r\n5539,15687094,Calabresi,717,Germany,Female,28,9,82498.14,2,0,0,40437.67,0\r\n5540,15790067,Sun,614,Spain,Male,39,3,151914.93,1,0,0,56459.45,0\r\n5541,15605742,Tuan,737,France,Male,43,0,80090.93,1,1,0,39920,1\r\n5542,15566740,Nazarova,587,Spain,Male,51,3,83739.32,1,0,1,148798.45,0\r\n5543,15664897,Bryant,682,France,Female,35,2,181166.44,1,1,1,63737.19,1\r\n5544,15585777,Pai,710,France,Male,38,3,130588.82,1,1,1,154997.64,0\r\n5545,15650864,Power,507,France,Male,42,6,0,2,1,0,34777.23,0\r\n5546,15806709,Hao,609,Germany,Male,33,6,94126.67,1,0,0,93718.16,0\r\n5547,15633818,McMillan,786,France,Male,32,9,0,2,1,0,133112.41,0\r\n5548,15713845,Merrett,688,France,Male,38,7,148045.68,1,1,0,175479.92,1\r\n5549,15639662,Phillips,710,France,Male,38,2,0,2,1,0,96.27,0\r\n5550,15567013,De Luca,779,Spain,Male,33,3,0,2,1,0,30804.68,0\r\n5551,15777784,Tu,733,France,Female,44,6,168165.84,1,0,1,197193.49,0\r\n5552,15800251,Elder,583,Germany,Female,26,10,72835.56,2,1,0,96792.15,0\r\n5553,15651315,Dilke,627,France,Male,41,3,0,2,1,0,132719.8,0\r\n5554,15651450,Panicucci,666,Germany,Male,31,3,123212.08,2,1,1,112157.31,0\r\n5555,15784218,Mason,620,Spain,Male,38,0,0,2,1,1,38015.34,0\r\n5556,15572398,Townsend,614,Spain,Female,39,6,0,2,1,1,164018.98,0\r\n5557,15707962,Gunson,606,France,Male,40,6,119501.88,2,1,0,46774.94,0\r\n5558,15705663,Milano,700,Germany,Female,39,5,144550.83,2,1,1,189664.43,0\r\n5559,15645355,Macleod,677,Germany,Male,34,3,126729.41,1,1,1,26106.39,1\r\n5560,15729557,Olisaemeka,850,Germany,Male,36,5,119984.07,1,1,0,191535.11,1\r\n5561,15631436,Gleeson,564,France,Male,35,4,0,1,1,0,158937.55,0\r\n5562,15583073,Martin,771,Spain,Female,56,2,0,1,1,1,25222.6,1\r\n5563,15614361,Liao,620,Spain,Male,42,9,121490.05,1,1,1,29296.74,0\r\n5564,15724684,Sung,610,Spain,Male,46,5,91897.8,1,1,0,54394.28,0\r\n5565,15700083,Lai,609,Spain,Male,39,2,139443.75,2,1,0,9234.06,0\r\n5566,15636541,Cartwright,683,Germany,Male,35,5,144961.97,1,0,1,26796.73,0\r\n5567,15796015,Wu,633,Germany,Male,42,3,126041.02,1,0,1,11796.89,0\r\n5568,15787222,Ch'in,676,Germany,Male,28,1,69459.05,2,1,1,128461.29,0\r\n5569,15594270,Biryukov,693,France,Male,38,7,198338.77,2,1,1,14278.18,0\r\n5570,15701524,Ting,709,France,Male,36,0,0,2,1,0,46811.77,0\r\n5571,15645847,P'eng,569,Germany,Male,35,2,109196.66,3,1,0,109393.19,1\r\n5572,15708867,Niu,684,Spain,Female,38,3,134168.5,3,1,0,3966.5,1\r\n5573,15613140,Mellor,565,France,Male,34,6,0,1,1,1,63173.64,0\r\n5574,15628893,Power,681,France,Male,29,8,0,1,1,0,66367.33,0\r\n5575,15764073,Arcuri,503,Spain,Female,36,9,0,2,1,1,16274.67,0\r\n5576,15782879,Lang,656,France,Male,40,2,0,2,1,1,180553.48,0\r\n5577,15635964,Eve,566,Germany,Male,65,4,120100.41,1,1,0,107563.16,1\r\n5578,15726087,Ch'in,592,France,Female,62,5,0,1,1,1,100941.57,0\r\n5579,15726313,Napolitani,687,Spain,Female,50,5,0,2,1,0,110230.4,0\r\n5580,15578073,Barker,686,Spain,Male,22,8,0,2,0,0,142331.85,0\r\n5581,15786249,Whitfield,616,Spain,Male,30,2,0,2,1,0,199099.51,0\r\n5582,15812850,Stradford,494,Spain,Male,67,5,0,2,1,1,85890.16,0\r\n5583,15596972,Brownlow,534,France,Male,38,3,0,1,0,0,143938.27,0\r\n5584,15620579,Dunn,695,Spain,Female,31,8,0,2,0,1,131644.41,0\r\n5585,15768270,DeRose,579,Spain,Female,31,9,0,2,1,0,112395.98,0\r\n5586,15656597,Wang,432,Germany,Male,38,2,135559.8,2,1,1,71856.3,0\r\n5587,15699446,Hobbs,816,Germany,Female,25,2,150355.35,2,1,1,35770.84,0\r\n5588,15615004,Anderson,730,France,Female,37,1,0,2,1,1,124364.63,0\r\n5589,15704771,Ugochukwu,593,France,Female,35,6,133489.12,2,1,1,78101.29,0\r\n5590,15588372,Kirsova,715,Germany,Female,37,9,105489.31,1,0,0,143096.49,1\r\n5591,15681439,Tsou,775,Germany,Male,25,10,60205.2,2,1,0,14073.11,0\r\n5592,15607509,Ozerova,539,France,Male,38,5,0,2,1,0,47388.41,0\r\n5593,15670343,Li,576,Spain,Male,19,6,0,2,0,0,72306.07,0\r\n5594,15597968,Fyans,617,Spain,Male,50,7,0,1,1,0,184839.7,1\r\n5595,15658432,Freeman,688,France,Male,40,6,0,1,1,1,47886.44,0\r\n5596,15616431,Chiu,608,France,Male,33,4,0,1,0,1,130474.03,0\r\n5597,15796957,Iadanza,597,Spain,Male,35,9,0,3,0,1,73181.39,1\r\n5598,15815552,Ferguson,670,France,Female,42,6,112333.63,1,1,1,65706.86,0\r\n5599,15631871,Kelly,616,Germany,Female,57,7,116936.81,1,1,1,104379.36,0\r\n5600,15635870,She,579,Germany,Female,50,5,117721.02,1,0,1,192146.63,1\r\n5601,15596713,Christie,786,France,Male,37,7,165896.22,2,1,1,66977.68,0\r\n5602,15684211,Creel,704,Spain,Female,44,9,153656.85,1,1,0,158742.81,0\r\n5603,15760521,Thompson,796,France,Female,50,1,94164,1,1,1,189414.74,0\r\n5604,15608408,Lazareva,598,Spain,Male,39,1,0,2,1,0,159130.32,0\r\n5605,15804721,Boni,602,France,Male,49,0,191808.73,1,0,0,97640.2,0\r\n5606,15730272,Evseev,619,France,Male,58,5,152199.33,1,1,1,86022.09,0\r\n5607,15741988,Marino,492,Germany,Female,52,8,125396.24,1,1,0,10014.72,1\r\n5608,15771728,Mackenzie,641,Germany,Male,41,7,104405.54,3,1,0,17384.21,0\r\n5609,15605113,Sutherland,518,France,Female,27,1,133801.49,1,1,1,143315.57,0\r\n5610,15661945,Nicolay,623,Spain,Female,40,4,0,3,1,0,31669.18,0\r\n5611,15783816,Lori,733,France,Female,28,5,0,2,0,0,12761.16,0\r\n5612,15721207,Piazza,625,Germany,Male,42,6,100047.33,1,1,0,93429.95,0\r\n5613,15764072,Somerville,759,France,Female,31,1,109848.6,1,1,1,42012.55,0\r\n5614,15689412,Christie,604,France,Female,32,7,127849.38,1,1,0,15798.7,0\r\n5615,15798385,Grave,512,Spain,Female,46,3,0,2,1,1,56408.14,0\r\n5616,15775339,Lori,520,France,Female,29,8,95947.76,1,1,0,4696.44,0\r\n5617,15585256,Iloerika,805,Spain,Male,26,2,0,2,1,1,25042.1,0\r\n5618,15797329,Muir,626,France,Male,43,4,137638.69,1,1,0,130442.08,1\r\n5619,15780220,Pauley,656,France,Male,38,10,0,1,1,1,136521.82,0\r\n5620,15648951,Kao,785,Spain,Male,41,7,0,2,1,1,199108.88,0\r\n5621,15752409,Grant,553,France,Male,31,6,0,2,0,0,124596.63,0\r\n5622,15807524,Chukwuma,569,France,Female,44,4,0,2,0,0,134394.78,0\r\n5623,15766649,Vincent,670,France,Male,38,10,89416.99,1,0,0,144275.39,0\r\n5624,15696812,Lazareva,586,Spain,Male,42,6,0,2,1,1,123410.23,0\r\n5625,15581295,Ch'ien,617,Spain,Female,45,1,0,1,1,0,143298.06,0\r\n5626,15663234,Bishop,508,France,Female,60,7,143262.04,1,1,1,129562.74,0\r\n5627,15741417,Chibuzo,624,Spain,Female,35,7,119656.45,2,1,1,4595.05,0\r\n5628,15695174,Chang,654,France,Male,29,4,132954.64,1,1,1,146715.07,0\r\n5629,15665168,Calabrese,681,Germany,Female,44,3,105206.7,2,1,1,163558.36,0\r\n5630,15601503,Tokaryev,578,Spain,Male,28,4,0,2,0,0,6947.09,0\r\n5631,15706131,Logan,621,Spain,Female,37,9,83061.26,2,1,0,9170.54,0\r\n5632,15782758,Ozerova,632,France,Male,40,5,147650.68,1,1,1,199674.83,0\r\n5633,15591091,Goering,644,France,Male,44,5,73348.56,1,1,0,157166.79,1\r\n5634,15715877,Lo,821,France,Male,28,2,0,2,1,0,46072.52,0\r\n5635,15756918,Simmons,754,France,Female,38,2,0,2,0,0,3524.69,0\r\n5636,15746662,Maduabuchim,568,Spain,Female,27,1,116320.68,1,0,1,45563.94,0\r\n5637,15626679,Linger,584,France,Male,33,3,0,2,0,1,59103.13,0\r\n5638,15793343,Yeh,549,France,Female,29,8,0,2,1,1,189558.44,0\r\n5639,15576774,Stevenson,729,France,Female,38,7,0,2,0,0,45779.9,0\r\n5640,15801316,Ifeatu,523,France,Male,61,8,66250.71,1,1,1,21859.06,0\r\n5641,15800514,Kenechukwu,477,Germany,Female,24,2,95675.62,2,0,0,162699.7,1\r\n5642,15662232,Learmonth,675,Germany,Male,42,2,92616.64,2,1,0,8567.18,0\r\n5643,15737778,Dickson,782,Spain,Female,41,4,0,1,1,0,132943.88,0\r\n5644,15782096,Volkova,616,Spain,Female,36,6,0,1,1,1,12916.32,1\r\n5645,15783522,Mitchell,738,Spain,Female,37,8,100565.94,1,1,1,128799.86,0\r\n5646,15785373,Wong,717,Spain,Female,42,5,190305.78,1,1,0,99347.8,1\r\n5647,15756272,James,526,Germany,Female,35,9,118536.4,1,1,0,40980.87,1\r\n5648,15615245,Shao,660,France,Male,19,5,127649.64,1,1,1,40368.65,0\r\n5649,15600174,Walton,525,France,Male,35,7,165358.77,1,0,1,94738.54,0\r\n5650,15752956,Stanley,629,Spain,Male,29,6,0,2,1,1,88842.8,0\r\n5651,15644882,Watson,616,Germany,Female,36,10,78249.53,1,1,0,136934.91,0\r\n5652,15766272,Folliero,521,Germany,Female,61,0,125193.96,1,1,1,109356.53,0\r\n5653,15800620,Fitzgerald,691,France,Female,29,9,0,2,0,0,199635.93,0\r\n5654,15569764,Garner,687,Germany,Female,41,2,154007.21,1,1,0,158408.23,0\r\n5655,15747458,Folliero,677,Spain,Female,43,3,133214.88,2,1,1,95936.84,0\r\n5656,15573171,Liao,695,Spain,Male,63,1,146202.93,1,1,1,126688.83,1\r\n5657,15736769,Lucchesi,663,France,Female,27,9,0,2,1,0,150850.29,0\r\n5658,15763381,Chan,496,France,Male,30,0,90963.49,1,0,1,27802,0\r\n5659,15814430,Ma,747,Spain,Male,41,9,0,1,1,0,32430.94,1\r\n5660,15638607,Nwabugwu,546,France,Female,52,2,0,1,1,0,137332.37,1\r\n5661,15737133,P'eng,706,Spain,Male,68,4,114386.85,1,1,1,28601.68,0\r\n5662,15613945,Andrews,472,France,Female,26,5,0,2,1,0,108411.66,0\r\n5663,15659937,Otutodilinna,703,France,Female,40,7,0,2,0,1,122518.5,0\r\n5664,15765287,Grant,850,France,Female,38,2,0,2,1,0,9015.07,0\r\n5665,15661723,Abramovich,667,Spain,Male,71,4,137260.78,1,0,1,94433.08,1\r\n5666,15766064,Komarova,559,France,Male,33,9,111060.05,2,1,0,110371.84,0\r\n5667,15649616,Otutodilichukwu,636,Spain,Male,60,7,124447.73,1,1,1,141364.62,1\r\n5668,15719017,Donaldson,672,France,Female,34,8,0,2,1,1,16245.25,0\r\n5669,15720919,Duggan,667,France,Male,42,7,0,1,0,1,108348.94,1\r\n5670,15706706,Chinwendu,648,Germany,Male,33,7,135310.41,2,0,1,171668.2,0\r\n5671,15709653,Hamilton,497,France,Male,32,8,0,2,1,0,67364.42,0\r\n5672,15805104,Smith,743,France,Female,73,6,0,2,0,1,107867.38,0\r\n5673,15622442,Mazzi,619,France,Male,29,5,0,2,1,0,194310.1,0\r\n5674,15572801,Krischock,639,Spain,Male,34,5,139393.19,2,0,0,33950.08,0\r\n5675,15767598,Kent,540,Spain,Male,28,8,0,2,0,0,197588.32,0\r\n5676,15757897,Binder,766,France,Female,26,3,104258.8,1,1,1,428.23,0\r\n5677,15568104,Zubarev,749,France,Female,26,6,0,2,0,1,34948.77,0\r\n5678,15763414,Degtyarev,655,Germany,Male,32,9,113447.01,1,1,0,82084.3,0\r\n5679,15732265,Obialo,630,France,Male,33,9,0,2,1,0,64804.59,0\r\n5680,15621974,Davydova,778,Germany,Female,33,4,111063.73,2,1,0,83556.65,0\r\n5681,15803947,Teng,757,Germany,Female,30,6,161378.02,1,0,0,71926.28,1\r\n5682,15720706,Hsing,529,Spain,Female,39,2,82766.43,1,1,1,122925.44,0\r\n5683,15759290,Coleman,620,Spain,Male,29,9,0,2,1,0,13133.88,0\r\n5684,15651664,Wilder,615,France,Female,61,1,104267.7,1,1,0,62845.64,1\r\n5685,15795132,Molineux,735,France,Female,25,3,91718.8,1,0,0,28411.23,0\r\n5686,15811565,Cocci,705,Spain,Female,47,3,63488.7,1,0,1,28640.92,1\r\n5687,15713774,Chikwendu,644,Spain,Female,46,6,12459.19,1,0,0,156787.34,1\r\n5688,15691840,Fraser,505,Germany,Female,37,6,159863.9,2,0,1,125307.87,0\r\n5689,15682021,Lai,471,Germany,Male,23,6,104592.55,2,1,0,131736.23,0\r\n5690,15612931,Korovin,722,Spain,Female,50,4,132088.59,1,1,1,128262.14,0\r\n5691,15676707,Sidorov,577,Spain,Female,39,4,0,2,1,0,91366.42,0\r\n5692,15601383,Ibrahimova,744,Spain,Male,44,5,120654.68,1,1,0,82290.81,0\r\n5693,15662662,Duigan,573,France,Female,30,6,0,2,1,0,66190.21,0\r\n5694,15752694,Taylor,653,France,Female,32,4,83772.95,1,0,1,23920.65,0\r\n5695,15590683,Donaldson,660,France,Female,31,6,172325.67,1,0,1,45438.38,0\r\n5696,15773591,Jobson,787,France,Male,46,7,117685.31,2,1,1,93360.35,0\r\n5697,15723620,Lu,617,France,Male,41,7,0,2,0,1,14496.67,0\r\n5698,15671779,Nebechi,567,France,Male,39,5,0,2,0,0,168521.72,0\r\n5699,15672966,Cross,682,Spain,Female,64,9,0,2,1,1,103318.44,0\r\n5700,15624667,Wallace,684,France,Male,35,6,135871.5,1,1,1,87219.41,0\r\n5701,15812888,Perreault,447,France,Male,41,3,0,4,1,1,197490.39,1\r\n5702,15724154,Manna,625,Germany,Female,49,4,128504.76,1,1,0,126812.63,1\r\n5703,15749540,Hsiung,585,France,Male,36,7,0,2,1,0,94283.09,0\r\n5704,15621063,Gibbons,516,France,Female,42,8,56228.25,1,1,0,46857.52,0\r\n5705,15661626,Algeranoff,732,Germany,Female,45,6,98792.4,1,1,0,81491.7,1\r\n5706,15698703,Doherty,628,Germany,Male,40,5,181768.32,2,1,1,129107.97,0\r\n5707,15801431,Rowe,682,Spain,Female,48,9,101198.01,1,1,1,49732.9,0\r\n5708,15649451,Yates,746,France,Male,25,9,0,2,0,1,88728.47,0\r\n5709,15626156,Galloway,655,France,Female,60,3,0,2,1,1,86981.45,0\r\n5710,15606158,Genovese,644,France,Female,39,9,0,1,1,0,3740.93,0\r\n5711,15589496,Arrington,778,France,Male,34,5,139064.06,2,0,0,67949.32,0\r\n5712,15730345,Miah,617,France,Female,35,2,104508.1,1,1,1,147636.46,0\r\n5713,15572038,Chijindum,660,Germany,Male,35,9,113948.58,1,1,0,188891.96,1\r\n5714,15643439,Ferguson,537,France,Male,47,10,0,2,0,1,25482.62,0\r\n5715,15604158,Smith,554,France,Female,39,10,0,2,1,1,18391.93,0\r\n5716,15657396,Marshall,806,France,Male,31,9,0,2,0,1,140168.36,0\r\n5717,15709478,P'an,611,Germany,Male,37,1,117524.72,2,0,1,161064.29,0\r\n5718,15628824,Burton,665,France,Female,37,5,160389.82,1,0,1,183542.08,0\r\n5719,15814519,Kamdibe,648,France,Female,37,7,0,2,1,0,194238.92,0\r\n5720,15636520,Milani,692,France,Male,27,1,125547.53,1,0,0,7900.46,0\r\n5721,15794414,Forbes,507,Spain,Male,46,6,92783.68,1,1,1,51424.29,0\r\n5722,15643671,Chiekwugo,696,Germany,Male,49,5,97036.22,2,1,0,152450.84,1\r\n5723,15700650,Cousens,681,France,Male,34,3,0,2,0,0,55816.2,0\r\n5724,15680224,Ross,687,France,Female,26,6,0,2,1,1,32909.13,0\r\n5725,15784286,Wood,641,Spain,Male,40,5,102145.13,1,1,1,100637.07,0\r\n5726,15693996,Hawks,507,France,Female,33,1,113452.66,1,0,0,142911.99,0\r\n5727,15764343,T'ien,688,Spain,Female,46,8,155681.72,1,1,0,26287.21,0\r\n5728,15704168,Ting,535,Germany,Male,38,8,127475.24,1,0,0,60775.76,1\r\n5729,15680197,Thynne,701,France,Male,41,10,0,2,1,1,146257.77,0\r\n5730,15633729,Wang,488,France,Male,43,10,112751.13,1,1,1,28332,0\r\n5731,15577683,Maclean,539,France,Female,29,4,0,2,1,1,100919.19,0\r\n5732,15800746,Watson,674,France,Male,45,7,144889.18,1,1,1,102591.9,1\r\n5733,15788686,Gibson,538,Spain,Male,40,8,0,2,1,1,25554.4,0\r\n5734,15742798,French,829,France,Female,22,7,150126.44,1,1,0,152107.93,1\r\n5735,15596647,Henderson,768,France,Male,54,8,69712.74,1,1,1,69381.05,0\r\n5736,15756070,Greenwood,585,Spain,Female,44,4,0,2,0,1,101728.46,0\r\n5737,15775116,Anderson,581,France,Male,31,3,0,2,0,0,89040.61,0\r\n5738,15575428,Mistry,682,Germany,Female,35,2,117438.92,2,1,1,16910.98,0\r\n5739,15654074,Tuan,653,France,Male,38,8,119315.75,1,1,0,150468.35,0\r\n5740,15695872,Fiorentini,712,France,Female,30,1,89571.59,1,1,1,177613.19,0\r\n5741,15568885,Scott,620,Germany,Female,34,8,102251.57,1,1,0,120672.09,0\r\n5742,15725036,Jideofor,709,France,Male,42,9,118546.71,1,0,1,77142.85,0\r\n5743,15632665,Yevseyev,832,France,Male,61,2,0,1,0,1,127804.66,1\r\n5744,15571476,Kelly,635,Spain,Male,38,0,103257.14,1,0,0,158344.63,0\r\n5745,15776850,Smith,749,Spain,Female,43,1,124209.02,1,1,1,167179.48,0\r\n5746,15623649,Ogle,629,Spain,Male,32,3,0,2,1,1,15404.64,0\r\n5747,15751131,Moss,836,Spain,Female,41,7,150302.84,1,1,1,156036.19,0\r\n5748,15688128,Loggia,542,Spain,Male,34,8,108653.93,1,0,1,144725.14,0\r\n5749,15678412,Nwankwo,645,France,Female,45,8,85325.93,1,0,0,22558.74,0\r\n5750,15770291,Allan,844,France,Female,29,8,0,2,0,0,147342.03,0\r\n5751,15583392,Woronoff,747,Germany,Male,37,9,135776.36,3,1,0,85470.45,1\r\n5752,15690731,Wolfe,645,France,Male,40,6,131411.24,1,1,1,194656.11,0\r\n5753,15697948,Henderson,752,Spain,Female,36,3,0,2,1,1,48505.1,0\r\n5754,15608328,Sutherland,760,Spain,Female,41,6,0,2,0,0,101491.23,0\r\n5755,15766378,Marsden,714,Germany,Female,45,9,106431.97,2,1,1,164117.69,0\r\n5756,15600813,Hyde,717,France,Male,50,9,90305.76,1,1,1,124626.57,0\r\n5757,15706217,Kao,645,Germany,Male,28,7,117466.03,2,1,1,34490.06,0\r\n5758,15601417,T'ang,681,France,Male,32,3,148884.47,2,1,1,90967.37,0\r\n5759,15610972,Crawford,681,Germany,Female,44,4,91115.76,2,0,0,24208.84,1\r\n5760,15674620,Dilibe,679,Germany,Female,37,8,77373.87,2,0,1,174873.09,0\r\n5761,15785350,Austin,528,Spain,Male,23,7,104744.89,1,1,0,170262.97,0\r\n5762,15749119,Santiago,710,France,Female,31,3,0,2,1,1,112289.06,0\r\n5763,15756535,Chibugo,733,Germany,Male,39,5,91538.51,1,1,1,93783,0\r\n5764,15700965,Toscano,724,France,Female,32,6,0,2,1,1,150026.79,0\r\n5765,15791851,Afanasyeva,726,France,Female,34,0,185734.75,1,1,1,102036.82,0\r\n5766,15717156,Sokolov,520,France,Male,30,3,143396.54,2,1,1,898.51,0\r\n5767,15740846,Wei,556,France,Male,40,5,125909.85,1,1,1,95124.4,0\r\n5768,15573284,Olisanugo,579,France,Female,45,2,0,2,0,0,11514.39,0\r\n5769,15729083,Gorman,674,France,Male,36,2,154525.7,1,0,1,27468.72,0\r\n5770,15611612,Priestley,570,France,Female,29,0,0,1,1,0,37092.43,0\r\n5771,15694381,Lloyd,631,France,Male,51,8,100654.8,1,1,0,171587.9,0\r\n5772,15651737,Salmond,623,Spain,Male,44,1,83325.77,1,0,1,80828.78,0\r\n5773,15663168,MacDonald,665,France,Male,35,8,110934.54,1,1,0,169287.99,0\r\n5774,15643426,Robertson,523,Spain,Female,36,8,113680.54,1,0,0,13197.44,0\r\n5775,15618245,Chukwumaobim,706,Germany,Male,31,1,117020.08,2,1,0,54439.53,0\r\n5776,15717527,Ifeanacho,619,France,Female,49,9,145359.99,1,1,0,38186.85,0\r\n5777,15793478,Li Fonti,593,Germany,Female,39,8,151391.68,1,1,0,27274.6,1\r\n5778,15642248,Ko,608,Spain,Male,66,8,123935.35,1,1,1,65758.19,0\r\n5779,15640377,Goloubev,526,France,Female,36,0,0,2,1,0,97767.63,0\r\n5780,15723950,Kruglov,684,Spain,Male,40,2,70291.02,1,1,1,115468.84,1\r\n5781,15590327,Liao,604,Germany,Female,42,10,166031.45,1,1,0,98293.14,0\r\n5782,15706199,White,636,Germany,Male,36,6,96643.32,1,0,0,182059.28,0\r\n5783,15671514,Sinclair,669,Spain,Female,33,8,0,2,0,1,128538.05,0\r\n5784,15727041,Fiorentini,624,France,Male,71,7,0,2,1,1,108841.83,0\r\n5785,15738063,Shen,631,France,Male,29,2,0,2,1,1,18581.84,0\r\n5786,15711733,Rapuokwu,753,France,Male,48,4,0,2,0,1,146821.42,0\r\n5787,15652320,Woronoff,588,France,Male,40,5,0,2,0,0,100727.68,0\r\n5788,15634180,Holden,729,Germany,Male,26,4,97268.1,2,1,0,39356.38,0\r\n5789,15694566,Roberts,602,France,Female,42,10,0,2,0,0,169921.11,1\r\n5790,15726103,Tsou,689,Germany,Female,55,1,76296.81,1,1,0,42364.75,1\r\n5791,15646351,Somerville,486,Spain,Male,27,7,0,2,1,0,28823.04,0\r\n5792,15730044,Greco,809,Germany,Female,42,6,64497.94,3,0,1,182436.81,1\r\n5793,15795186,Leonard,562,France,Male,38,5,0,1,1,0,115700.2,0\r\n5794,15784890,McKenzie,763,Spain,Female,32,8,0,2,1,0,16725.53,0\r\n5795,15694125,McElhone,669,France,Male,57,5,0,2,1,1,56875.76,0\r\n5796,15565891,Dipietro,709,France,Male,39,8,0,2,1,0,56214.09,0\r\n5797,15674254,Kerr,554,Spain,Female,45,4,0,2,1,1,193412.05,0\r\n5798,15775206,Hunter,699,France,Male,37,10,0,2,0,0,83263.04,0\r\n5799,15797627,Niehaus,732,Spain,Male,54,0,134249.7,1,0,1,13404.4,0\r\n5800,15649853,Craig,625,France,Female,45,3,0,1,1,1,184474.15,1\r\n5801,15610379,Barclay-Harvey,599,France,Male,30,9,105443.68,1,1,1,121124.53,0\r\n5802,15659800,Teng,584,Spain,Female,50,1,0,1,0,1,152567.75,1\r\n5803,15716236,Milani,499,France,Male,35,10,0,2,1,0,10722.54,0\r\n5804,15672053,Mistry,526,Spain,Male,38,2,0,2,0,0,58010.98,0\r\n5805,15663933,Jamieson,625,Germany,Female,35,5,86147.46,2,1,0,163440.8,1\r\n5806,15814236,Kay,537,Spain,Female,38,1,96939.06,1,1,1,102606.92,0\r\n5807,15583597,Ikedinachukwu,696,Spain,Male,47,1,106758.6,1,1,1,80591.18,0\r\n5808,15607395,Holt,679,France,Female,33,9,112528.65,2,1,0,177362.45,0\r\n5809,15694556,Nkemakolam,684,France,Male,60,2,116563.58,1,1,0,120257.7,1\r\n5810,15744109,Hartung,850,France,Male,32,4,0,1,1,1,180622.02,0\r\n5811,15800688,Ch'en,495,Spain,Female,42,7,0,2,0,0,130404.53,0\r\n5812,15810878,Baker,537,Spain,Female,38,6,141786.78,1,0,1,147797.54,0\r\n5813,15587835,Osinachi,850,France,Male,41,3,136416.82,1,0,1,57844.26,0\r\n5814,15763515,Shih,513,France,Male,30,5,0,2,1,0,162523.66,0\r\n5815,15725882,Feng,618,Germany,Female,40,1,133245.52,2,1,1,54495.82,0\r\n5816,15788022,Sternberg,802,Germany,Female,41,4,90757.64,2,0,1,169183.66,0\r\n5817,15663917,Adams,547,France,Male,43,1,92350.36,1,0,1,80262.91,0\r\n5818,15656865,Gray,613,Germany,Male,69,9,78778.49,1,0,1,8751.59,0\r\n5819,15667971,Shepherd,592,Germany,Female,34,6,102143.93,2,1,1,102628.98,0\r\n5820,15800366,Walton,546,France,Male,29,5,0,1,1,1,94823.95,0\r\n5821,15717231,Yang,721,Germany,Male,37,4,98459.6,1,0,0,90821.66,0\r\n5822,15643188,Barnett,671,Germany,Female,47,7,114603.76,2,1,0,153194.32,1\r\n5823,15671351,Romani,624,Spain,Male,35,2,0,2,1,0,87310.59,0\r\n5824,15573628,Greene,751,Germany,Female,51,7,148074.79,1,1,0,146411.41,1\r\n5825,15698953,Hart,636,Spain,Male,36,1,0,3,1,1,74048.1,1\r\n5826,15753888,Johnston,607,Spain,Female,62,8,108004.64,1,1,1,23386.77,1\r\n5827,15737961,Miller,509,Germany,Female,29,0,107712.57,2,1,1,92898.17,0\r\n5828,15801701,Robson,653,Spain,Male,35,9,0,2,1,1,45956.05,0\r\n5829,15684419,Wallace,709,Spain,Female,37,8,0,3,1,0,71738.56,0\r\n5830,15794266,Cross,559,France,Male,32,9,145303.52,1,1,0,103560.98,0\r\n5831,15810711,Marcum,684,Germany,Male,37,4,138476.41,2,1,1,52367.29,0\r\n5832,15771270,North,635,France,Female,27,8,127471.56,1,1,1,152916.05,1\r\n5833,15607786,Mao,709,France,Male,26,6,156551.63,1,0,1,4410.77,0\r\n5834,15624519,Calabrese,656,Germany,Female,49,9,97092.87,1,1,0,74771.22,1\r\n5835,15799910,Martin,793,France,Male,32,2,0,2,1,0,193817.63,1\r\n5836,15602479,Fleming,609,Spain,Male,37,5,129312.79,1,1,1,26793.82,0\r\n5837,15617419,Roberts,618,Germany,Female,29,10,100315.1,2,1,1,32526.64,0\r\n5838,15657603,Finch,850,France,Female,35,6,81684.97,1,1,0,824,0\r\n5839,15570379,Whitelegge,669,Spain,Male,51,3,88827.53,1,0,0,85250.77,1\r\n5840,15772996,Rooke,594,Germany,Male,40,0,152092.44,2,1,1,83508.93,0\r\n5841,15729574,Lu,616,Spain,Male,71,4,0,2,1,1,173599.38,0\r\n5842,15737267,Marcelo,676,France,Female,49,1,0,1,1,0,79342.31,1\r\n5843,15799128,Matthews,608,Spain,Female,38,9,102406.76,1,0,1,57600.66,0\r\n5844,15813327,Romani,710,France,Male,21,4,109130.96,2,1,1,56191.99,0\r\n5845,15711921,Scott,695,France,Male,29,5,0,2,1,1,6770.44,0\r\n5846,15654300,Mao,530,Germany,Male,33,9,75242.28,1,0,1,101694.67,0\r\n5847,15569945,Horsley,509,Spain,Male,29,1,0,2,1,0,69113.14,0\r\n5848,15569666,Goddard,517,France,Female,45,4,0,1,0,0,172674.36,1\r\n5849,15681887,Eskridge,758,Germany,Male,33,0,129142.54,2,1,1,26606.28,0\r\n5850,15608873,Smith,665,France,Male,51,2,0,1,0,0,53353.36,0\r\n5851,15762091,Simpson,631,Germany,Female,22,6,139129.92,1,1,1,63747.51,0\r\n5852,15722053,Oguejiofor,576,Spain,Male,33,3,0,2,0,1,190112.05,0\r\n5853,15782100,Holloway,544,Spain,Male,22,3,66483.32,1,0,1,110317.39,0\r\n5854,15765300,L?,596,Germany,Male,40,5,62389.03,3,1,0,148623.43,1\r\n5855,15743570,Feng,481,France,Female,34,5,0,2,1,1,125253.46,0\r\n5856,15608541,Claiborne,498,France,Male,46,1,91857.66,1,1,0,101954.78,1\r\n5857,15750671,Egobudike,512,Spain,Male,31,6,0,2,1,0,168462.26,0\r\n5858,15813659,Folliero,594,France,Female,56,7,0,1,1,0,26215.85,1\r\n5859,15757867,Bray,570,France,Female,30,10,176173.52,1,1,0,97045.32,1\r\n5860,15652914,Ibrahimov,721,Spain,Male,38,7,0,1,0,1,53534.8,0\r\n5861,15723818,Carpenter,453,France,Female,37,4,131834.76,2,1,0,8949.2,0\r\n5862,15713819,Walsh,562,France,Male,48,3,92347.96,1,1,1,163116.75,0\r\n5863,15656484,Woods,682,France,Male,40,4,0,2,1,1,140745.91,0\r\n5864,15778515,Wu,748,France,Male,40,3,95297.11,1,0,0,171515.84,0\r\n5865,15803840,Forbes,729,France,Female,32,9,0,2,0,0,150803.44,0\r\n5866,15735339,Lynch,663,France,Male,39,4,0,1,1,0,76884.05,0\r\n5867,15600392,Amaechi,735,France,Female,53,8,123845.36,2,0,1,170454.93,1\r\n5868,15625740,Enriquez,627,Germany,Male,62,3,143426.34,2,1,1,143104.3,0\r\n5869,15663817,Y?an,713,France,Male,46,5,0,1,1,1,55701.62,0\r\n5870,15734461,Brooks,562,Germany,Male,31,2,112708.2,1,0,1,186370.3,0\r\n5871,15780142,Wang,632,France,Male,43,2,100013.51,1,1,0,24275.32,0\r\n5872,15709920,Burke,479,France,Female,33,2,208165.53,1,0,0,50774.81,1\r\n5873,15684248,Meng,658,Spain,Male,21,7,0,2,0,1,154279.87,0\r\n5874,15643158,Chiganu,598,France,Female,40,9,0,1,1,0,68462.59,1\r\n5875,15693902,Hunt,597,France,Male,19,2,0,2,1,1,91036.74,0\r\n5876,15578307,Lucchese,512,France,Female,33,6,121685.31,2,1,1,83681.97,0\r\n5877,15585379,Humphries,704,France,Male,39,2,111525.02,1,1,0,199484.96,0\r\n5878,15758510,Frolova,474,France,Male,26,6,0,2,0,0,152491.22,0\r\n5879,15692918,Hsing,604,Germany,Male,36,10,113546.3,1,1,1,134875.37,0\r\n5880,15705301,Parkes,683,France,Male,41,6,95696.52,2,1,1,184366.14,0\r\n5881,15718231,Gregory,537,France,Male,28,0,88963.31,2,1,1,189839.93,0\r\n5882,15567991,Obiuto,794,Spain,Male,31,0,144880.34,2,0,1,175643.44,0\r\n5883,15772650,Longo,732,France,Male,55,9,136576.02,1,0,1,3268.17,1\r\n5884,15574795,Lombardo,495,France,Female,38,2,63093.01,1,1,1,47089.72,0\r\n5885,15706036,Lombardo,552,Germany,Male,38,10,132271.12,2,1,1,46562.02,0\r\n5886,15723856,Gonzalez,602,France,Female,29,3,88814.4,2,1,1,62487.97,0\r\n5887,15812920,Nwabugwu,607,Germany,Male,40,5,90594.55,1,0,1,181598.25,0\r\n5888,15691287,Ford,675,Germany,Female,33,0,141816.25,1,1,0,64815.05,1\r\n5889,15804797,Gilleland,443,France,Female,54,3,138547.97,1,1,1,70196.23,1\r\n5890,15708650,Fullwood,727,France,Female,31,2,52192.08,2,0,1,160383.47,0\r\n5891,15712777,Kao,482,France,Male,38,4,124976.19,1,1,0,35848.12,0\r\n5892,15786469,Montalvo,686,France,Female,34,1,0,2,1,0,87278.48,0\r\n5893,15669219,Wilson,588,Germany,Male,35,3,104356.38,1,1,0,94498.82,0\r\n5894,15641004,Doyne,605,Spain,Female,48,10,150315.92,1,0,1,133486.36,0\r\n5895,15648067,Onwuamaeze,583,France,Male,39,1,129299.28,2,1,0,73107.6,0\r\n5896,15704014,K'ung,738,Germany,Male,37,7,140950.92,2,1,0,195333.98,0\r\n5897,15645136,O'Donnell,744,Spain,Male,30,1,128065.12,1,1,0,121525.48,0\r\n5898,15709604,McMillan,781,France,Male,23,2,107433.48,1,1,0,173843.21,0\r\n5899,15713637,Chinedum,699,France,Male,34,2,117468.67,1,1,0,185227.42,0\r\n5900,15793901,Capon,639,France,Female,27,2,0,2,0,0,125244.18,0\r\n5901,15569759,Rawling,583,France,Female,27,4,0,3,1,0,163113.41,0\r\n5902,15712930,Duncan,587,France,Male,42,1,0,1,0,0,123006.91,0\r\n5903,15586504,Trevisani,694,France,Male,40,9,0,2,1,0,40463.03,0\r\n5904,15677317,Ankudinova,570,France,Female,29,4,153040.03,1,1,1,131363.57,1\r\n5905,15664270,Balsillie,692,Germany,Male,45,6,142084.04,4,1,0,188305.85,1\r\n5906,15731519,Kerr,511,France,Female,30,5,0,2,1,0,143994.86,0\r\n5907,15745623,Worsnop,788,France,Male,32,4,112079.58,1,0,0,89368.59,0\r\n5908,15813862,Yevseyev,526,Spain,Male,66,7,132044.6,2,1,1,158365.89,0\r\n5909,15641934,Manna,749,Spain,Female,46,9,66582.81,1,1,0,78753.12,1\r\n5910,15713043,Siciliani,691,France,Female,33,6,0,2,1,1,100408.31,0\r\n5911,15700749,Powell,481,France,Female,39,6,0,1,1,1,24677.54,0\r\n5912,15697567,Bazarova,752,France,Male,33,4,0,2,1,1,39570.78,0\r\n5913,15715414,White,658,France,Female,38,6,102895.1,1,0,0,155665.76,0\r\n5914,15639530,Buda,679,Spain,Male,42,2,0,1,1,1,168294.27,0\r\n5915,15726058,Cattaneo,754,Germany,Male,27,7,117578.35,2,0,1,87908.01,0\r\n5916,15725665,Lo,679,France,Male,47,10,198546.1,2,1,0,191198.92,1\r\n5917,15698872,Brown,633,Spain,Female,39,2,0,2,0,0,191207.03,0\r\n5918,15812184,Rose,674,France,Female,31,1,0,1,1,0,128954.05,0\r\n5919,15742609,Lombardo,600,Germany,Male,28,2,116623.31,1,0,1,59905.29,0\r\n5920,15815043,McMillan,645,Spain,Male,49,8,0,2,1,0,162012.6,0\r\n5921,15640648,Howe,698,France,Male,36,6,0,2,0,1,19231.98,0\r\n5922,15627203,Hsu,508,Spain,Male,54,10,0,1,1,1,175749.36,0\r\n5923,15786196,Han,555,France,Female,44,3,105770.7,3,1,0,60533.96,1\r\n5924,15612095,Calabrese,751,France,Female,48,9,0,1,1,0,137508.42,1\r\n5925,15674368,Riley,738,France,Female,39,1,94435.45,2,0,1,189430.86,0\r\n5926,15783477,Biryukov,706,Germany,Female,39,8,112889.91,1,0,1,6723.66,0\r\n5927,15757559,Broadhurst,595,France,Female,53,7,0,2,1,0,41371.68,1\r\n5928,15591036,Genovesi,577,Germany,Female,43,3,127940.47,1,0,0,125140.72,1\r\n5929,15761241,Hsieh,578,Germany,Female,36,8,129745.1,1,1,1,143683.75,0\r\n5930,15695078,Kemp,699,France,Male,32,3,0,2,1,1,170770.44,0\r\n5931,15645744,Chukwudi,826,France,Female,30,5,0,2,0,1,157397.57,0\r\n5932,15566988,Iqbal,656,Germany,Female,46,7,141535.52,1,1,0,50595.15,1\r\n5933,15749300,Teng,556,France,Female,47,2,139914.27,1,1,1,50390.98,0\r\n5934,15594340,Tao,569,France,Male,41,4,120243.49,1,1,0,163150.03,1\r\n5935,15607065,Chinedum,765,France,Male,34,9,91835.16,1,0,0,138280.17,0\r\n5936,15778089,Stevenson,544,Spain,Male,37,2,0,2,0,0,135067.02,0\r\n5937,15773723,Duncan,588,Spain,Female,22,9,67178.19,1,1,1,163534.75,1\r\n5938,15697035,Garrett,740,Spain,Female,31,8,0,2,0,0,86657.48,0\r\n5939,15679668,Yao,850,Spain,Male,38,7,115378.94,1,0,1,162087.82,0\r\n5940,15709861,He,766,Germany,Male,30,4,127786.28,2,1,1,28879.3,0\r\n5941,15791958,Mazzi,849,France,Female,41,6,0,2,1,1,169203.51,1\r\n5942,15791030,Edwards,612,France,Female,33,0,64900.32,2,1,0,102426.12,0\r\n5943,15695339,Lucchesi,517,Germany,Male,53,0,109172.88,1,1,0,54676.1,1\r\n5944,15658813,Siciliani,645,France,Female,55,7,0,2,1,1,18369.33,0\r\n5945,15715709,Shih,696,Germany,Male,43,4,114091.38,1,0,1,159888.1,0\r\n5946,15722533,Logue,716,France,Female,40,3,0,2,0,1,167636.15,0\r\n5947,15683118,Rechner,590,France,Male,32,9,0,2,1,0,138889.15,0\r\n5948,15672798,O'Brien,656,France,Female,45,7,145933.27,1,1,1,199392.14,0\r\n5949,15680112,Stewart,473,Germany,Female,35,7,131504.73,1,1,0,189560.43,0\r\n5950,15714575,Batt,742,Germany,Female,44,8,107926.02,1,0,1,17375.27,1\r\n5951,15806808,Hope,834,Germany,Female,57,8,112281.6,3,1,0,140225.14,1\r\n5952,15590637,Ahmed,721,France,Male,41,7,0,2,0,1,61018.85,0\r\n5953,15657535,Pearson,590,Spain,Male,29,10,0,1,1,1,51907.72,1\r\n5954,15696141,Kruglov,516,Spain,Female,31,7,0,1,1,0,47018.75,0\r\n5955,15811947,Gordon,850,France,Male,33,0,124781.67,1,0,1,33700.52,0\r\n5956,15649024,Trujillo,748,France,Female,39,9,132865.56,1,1,1,59636.43,1\r\n5957,15594928,Pagnotto,798,Germany,Female,38,4,129055.13,1,1,0,157147.59,0\r\n5958,15765532,Horton,612,Germany,Male,76,6,96166.88,1,1,1,191393.26,0\r\n5959,15741719,DeRose,540,France,Female,40,3,165298.12,1,0,1,199862.75,0\r\n5960,15665629,Chiang,719,Spain,Female,33,7,0,2,1,0,20016.59,0\r\n5961,15728917,Gill,598,France,Male,48,6,120682.53,1,1,0,30635.52,1\r\n5962,15762993,Trevisano,796,Spain,Male,32,5,102773.15,2,0,1,117832.88,0\r\n5963,15571193,Morrison,579,Germany,Male,42,0,144386.32,1,1,1,22497.1,1\r\n5964,15653521,Onuora,850,Germany,Female,40,7,104449.8,1,1,1,747.88,0\r\n5965,15802220,Ikenna,599,Spain,Male,35,6,137102.65,1,0,0,76870.81,0\r\n5966,15644132,Mancini,724,France,Female,30,9,142475.87,1,1,1,107848.24,0\r\n5967,15600832,Moss,508,France,Female,43,9,0,1,1,0,103726.71,0\r\n5968,15797919,Ting,773,Spain,Male,37,2,103195.2,2,1,0,178268.36,0\r\n5969,15603743,Tai,526,France,Male,28,1,112070.44,1,0,1,126281.83,0\r\n5970,15579714,Pan,542,France,Female,29,7,0,2,0,1,196651.72,0\r\n5971,15634295,Wilson,470,France,Male,35,1,96473.59,1,0,0,5962.3,0\r\n5972,15786680,Bianchi,805,Spain,Male,37,5,0,2,1,0,21928.81,0\r\n5973,15623499,Holman,548,Germany,Male,49,9,108437.89,1,0,0,127022.87,1\r\n5974,15691823,Obidimkpa,672,France,Male,37,5,153195.59,1,1,1,162763.01,0\r\n5975,15809279,Wallace,773,France,Male,45,8,96877.21,1,1,1,113950.51,0\r\n5976,15758039,Ash,614,France,Male,44,6,0,2,0,1,104930.46,0\r\n5977,15807163,Ku,537,France,Female,38,10,0,1,0,0,52337.97,1\r\n5978,15631639,Uspensky,704,France,Female,40,6,95452.89,1,0,1,179964.55,0\r\n5979,15713770,Shih,586,Spain,Male,41,3,63873.56,1,1,0,83753.64,0\r\n5980,15698167,Kumm,677,France,Female,24,0,148298.59,2,0,0,182913.95,0\r\n5981,15781710,Carey,558,Spain,Female,31,7,0,2,1,0,166720.28,0\r\n5982,15801296,Farber,634,Germany,Female,37,7,143258.85,2,1,0,192721.98,0\r\n5983,15704378,Calabrese,655,Germany,Male,37,9,121342.24,1,1,1,180241.44,0\r\n5984,15767891,Findlay,619,Germany,Female,28,6,99152.73,2,1,0,48475.12,0\r\n5985,15640667,Yu,662,France,Female,41,4,0,2,1,0,126551.48,0\r\n5986,15702145,Edments,705,Spain,Male,33,7,68423.89,1,1,1,64872.55,0\r\n5987,15679738,Brown,527,Spain,Female,35,8,0,1,1,0,98031.53,1\r\n5988,15636634,Lindon,630,Germany,Female,25,7,79656.81,1,1,0,93524.22,0\r\n5989,15809227,Chukwudi,850,France,Male,35,2,0,2,1,1,56991.66,0\r\n5990,15601811,Caldwell,668,France,Female,53,10,110240.04,1,0,0,183980.56,1\r\n5991,15625494,Li Fonti,573,France,Female,32,9,125321.84,2,1,1,130234.63,0\r\n5992,15723737,Pitcher,680,France,Male,27,3,0,1,1,0,32454.26,0\r\n5993,15682955,Capon,758,France,Female,32,2,84378.9,1,1,1,75396.43,0\r\n5994,15758856,Kable,597,France,Male,45,7,0,2,0,0,167756.45,0\r\n5995,15746065,Lo Duca,580,Germany,Male,35,10,136281.41,2,1,1,24799.47,0\r\n5996,15783865,Kulikova,622,France,Male,59,5,119380.37,1,1,1,60429.43,0\r\n5997,15745455,Navarrete,638,Germany,Male,62,4,108716.59,2,1,1,74241.09,0\r\n5998,15583033,Huguley,640,France,Female,20,4,0,2,0,1,78310.82,0\r\n5999,15644212,Han,644,Spain,Male,28,0,0,2,1,0,119419.37,0\r\n6000,15735688,Horsley,753,France,Female,31,6,106596.29,1,0,0,91305.77,0\r\n6001,15658577,Massie,629,France,Female,37,10,99546.25,3,0,1,25136.95,1\r\n6002,15606887,Singh,775,France,Female,30,5,0,1,1,0,193880.6,1\r\n6003,15783026,H?,701,France,Female,41,2,0,1,1,0,47856.78,0\r\n6004,15579892,Doyle,708,Spain,Male,19,7,112615.86,1,1,1,4491.77,0\r\n6005,15802088,Grant,521,Spain,Female,22,10,0,1,1,1,101311.95,0\r\n6006,15589323,Law,636,France,Female,24,9,0,2,0,1,38830.72,0\r\n6007,15636395,King,529,France,Female,31,5,0,2,1,0,26817.23,0\r\n6008,15712772,Onwubiko,757,France,Male,28,3,75381.15,1,1,1,199727.72,0\r\n6009,15700937,Romano,767,Spain,Female,24,5,0,2,1,1,67445.85,0\r\n6010,15766659,Okwudilichukwu,525,Spain,Male,33,5,0,2,1,0,161002.29,0\r\n6011,15814033,Milano,759,Spain,Male,38,1,0,2,1,0,20778.39,0\r\n6012,15783007,Parker,520,Germany,Female,45,1,123086.39,1,1,1,41042.4,1\r\n6013,15654183,Aitken,738,France,Female,26,3,0,2,1,0,67484.16,0\r\n6014,15609899,Obiora,548,Spain,Male,37,4,0,1,1,0,121763.68,0\r\n6015,15747323,Vasilyeva,535,Spain,Male,48,9,109472.47,1,1,0,157358.43,1\r\n6016,15582591,Chiabuotu,615,Spain,Male,59,4,155766.05,1,1,1,110275.17,0\r\n6017,15738835,Slater,850,Germany,Male,38,7,101985.81,2,0,0,43801.27,0\r\n6018,15782404,Hughes,487,France,Female,34,2,96019.5,1,0,0,9085,0\r\n6019,15697480,Menkens,731,France,Male,30,7,0,2,0,1,143086.09,0\r\n6020,15697045,Pisani,726,Spain,Female,35,9,0,2,0,1,100556.98,0\r\n6021,15781234,Y?an,609,France,Female,35,2,147900.43,1,1,0,140000.29,0\r\n6022,15579891,Milani,714,France,Male,52,4,100755.66,1,1,1,186775.25,0\r\n6023,15805690,Chin,694,Spain,Female,35,7,0,1,1,0,133570.43,1\r\n6024,15612139,Fu,786,France,Female,33,0,83036.05,1,0,1,154990.58,1\r\n6025,15568834,Howells,698,Spain,Male,27,6,125427.37,2,0,0,27654.44,0\r\n6026,15709917,Ni,601,France,Female,46,3,98202.76,1,0,0,137763.93,0\r\n6027,15718843,Maslova,769,Spain,Male,41,1,72509.91,1,1,0,25723.73,0\r\n6028,15799494,Forster,850,Germany,Male,44,3,140393.65,2,0,1,186285.52,0\r\n6029,15673439,Sun,646,Spain,Female,50,5,142644.64,2,1,1,142208.5,1\r\n6030,15669011,Bocharova,659,France,Female,44,9,23503.31,1,0,1,169862.01,1\r\n6031,15581388,Y?an,487,Spain,Male,33,8,145729.71,1,1,0,41365.85,0\r\n6032,15743153,Singh,740,Germany,Female,40,2,122295.17,2,1,1,30812.84,0\r\n6033,15579787,Nkemakonam,686,France,Male,39,4,0,2,1,0,155023.93,0\r\n6034,15759966,Chiemenam,612,Spain,Female,36,5,119799.27,2,1,0,159416.58,0\r\n6035,15601045,Angelo,655,Spain,Male,37,8,163708.58,2,0,0,76259.23,0\r\n6036,15764021,Frolov,617,France,Male,34,1,61687.33,2,1,0,105965.25,0\r\n6037,15687218,West,674,France,Female,27,4,79144.34,1,0,1,50743.83,0\r\n6038,15626452,Beatham,711,Spain,Male,32,5,0,2,1,1,147720.27,0\r\n6039,15700964,Pollard,624,Germany,Female,27,7,104848.68,1,1,1,167387.36,0\r\n6040,15768887,Hsing,597,Spain,Male,26,5,0,2,0,1,95159.13,0\r\n6041,15735358,Dowse,682,Spain,Male,46,4,0,1,1,1,4654.28,0\r\n6042,15749472,Lucciano,775,France,Male,45,8,0,1,1,0,130376.68,0\r\n6043,15685872,Godfrey,727,France,Female,29,1,146652.01,1,1,1,173486.39,0\r\n6044,15760851,Gratton,629,France,Male,31,6,0,2,1,0,93881.75,0\r\n6045,15734588,Manning,684,France,Male,46,0,0,2,1,1,36376.97,0\r\n6046,15784594,Mazzi,549,Germany,Female,37,1,130622.34,2,1,1,128499.94,0\r\n6047,15606435,Wall,593,Germany,Male,69,2,187013.13,2,0,1,105898.69,0\r\n6048,15790247,Sims,536,Spain,Male,40,9,0,2,1,1,11959.03,0\r\n6049,15676433,Allan,707,France,Female,36,6,0,1,0,0,98810.78,0\r\n6050,15625905,Griffen,592,Spain,Male,41,0,0,2,1,0,65906.07,0\r\n6051,15626414,Russell,703,France,Male,44,6,98862.54,1,1,0,151516.7,0\r\n6052,15623220,Brown,723,Spain,Female,45,4,0,2,1,0,37214.39,0\r\n6053,15752857,Palerma,452,Germany,Male,52,1,98443.14,2,0,0,92033.98,0\r\n6054,15677908,Gilbert,552,Spain,Male,42,4,0,2,0,0,195692.3,0\r\n6055,15773013,Uvarov,633,France,Female,47,0,0,1,1,1,6342.84,1\r\n6056,15623972,Wisdom,479,Germany,Female,23,9,123575.51,1,0,1,95148.28,0\r\n6057,15738627,Hussain,768,France,Male,25,6,0,2,1,1,21215.67,0\r\n6058,15643392,Woods,742,France,Male,31,4,105239.1,1,1,1,19700.24,0\r\n6059,15684868,Cameron,668,Germany,Male,56,9,110993.79,1,1,0,134396.64,1\r\n6060,15627854,Mai,707,Spain,Male,44,3,0,2,1,1,135077.01,0\r\n6061,15669253,Gibson,754,Spain,Male,39,7,157691.98,2,1,0,133600.89,1\r\n6062,15758023,Grigoryeva,544,Germany,Male,47,5,105245.21,1,0,0,99922.08,1\r\n6063,15574558,Gunter,718,Spain,Male,32,8,0,2,1,1,41399.33,0\r\n6064,15635256,Arcuri,762,France,Male,31,7,117687.35,1,1,1,159344.43,0\r\n6065,15680399,Tung,772,France,Male,23,2,0,2,1,0,18364.19,0\r\n6066,15674720,Smith,691,Germany,Female,37,7,123067.63,1,1,1,98162.44,1\r\n6067,15580249,Lori,502,France,Male,45,0,0,1,0,0,84663.21,0\r\n6068,15675431,Chidimma,563,France,Female,34,6,0,2,0,0,36536.93,0\r\n6069,15698285,Ting,676,France,Female,41,4,101457.14,1,1,1,79101.67,0\r\n6070,15810775,Tsao,576,Spain,Male,52,2,100549.43,2,1,1,16644.16,0\r\n6071,15678173,Collee,629,Spain,Male,35,4,174588.8,2,0,1,158420.14,0\r\n6072,15665222,Lettiere,625,Spain,Male,52,8,121161.57,1,1,0,48988.28,0\r\n6073,15803908,Fu,628,France,Male,45,9,0,2,1,1,96862.56,0\r\n6074,15586039,Bergamaschi,471,Germany,Female,36,5,90063.74,2,1,1,96366.7,0\r\n6075,15802570,Dyer,811,France,Female,45,5,0,2,1,1,146123.19,0\r\n6076,15781451,Buccho,504,France,Male,42,3,134936.97,2,0,0,135178.91,0\r\n6077,15721019,Jones,687,France,Female,24,3,110495.27,1,1,0,158615.41,0\r\n6078,15738588,Nebechi,660,Germany,Female,37,2,133200.09,1,0,0,71433.88,0\r\n6079,15730657,Ibekwe,548,France,Female,41,4,82596.8,1,0,1,55672.09,0\r\n6080,15739292,Gorshkov,609,Germany,Male,31,9,103837.75,1,1,1,150218.11,0\r\n6081,15725945,Nweke,659,Spain,Female,42,2,0,1,0,0,162734.31,1\r\n6082,15813159,Hairston,526,France,Male,52,8,93590.47,1,0,1,21228.71,1\r\n6083,15636820,Loggia,725,Germany,Male,40,8,104149.66,1,1,0,62027.9,0\r\n6084,15603880,Morgan,519,Germany,Male,38,1,114141.64,1,1,1,60988.21,1\r\n6085,15619494,Abdulov,562,Germany,Female,31,9,117153,1,1,1,108675.01,0\r\n6086,15596992,Norris,482,Germany,Male,45,7,156353.46,1,1,0,72643.95,1\r\n6087,15735025,Clark,535,Spain,Male,37,3,175534.78,2,1,1,9241.52,0\r\n6088,15730759,Chukwudi,561,France,Female,27,9,135637,1,1,0,153080.4,1\r\n6089,15752912,Perkin,661,France,Female,30,7,0,2,1,0,72196.57,0\r\n6090,15711316,Ch'ang,771,France,Male,27,2,0,2,1,1,199527.34,0\r\n6091,15738785,Kang,545,France,Male,26,7,0,2,0,1,156598.23,0\r\n6092,15777896,Chukwudi,850,Germany,Female,33,2,83415.04,1,0,1,74917.64,0\r\n6093,15628963,Frolova,601,Germany,Male,43,3,141859.12,2,1,1,111249.62,0\r\n6094,15742126,Chiu,712,Germany,Male,38,7,132767.66,2,1,1,59115.77,0\r\n6095,15575623,Simpson,589,France,Female,31,10,110635.32,1,1,0,148218.86,0\r\n6096,15741652,McLean,600,Spain,Male,37,8,177657.35,1,1,1,77142.32,0\r\n6097,15738884,Hu,642,Germany,Male,41,4,157777.58,1,1,0,67484.6,0\r\n6098,15615050,Savage,575,Germany,Male,47,9,107915.94,2,1,1,63452.18,1\r\n6099,15803005,Wallace,570,Germany,Female,57,5,86568.75,1,0,1,103660.31,0\r\n6100,15743498,Winter,532,Germany,Male,52,9,137755.76,1,1,0,163191.99,1\r\n6101,15720463,Ho,796,France,Male,30,2,137262.71,2,1,0,62905.29,0\r\n6102,15588695,Su,833,Spain,Male,32,6,0,1,1,1,44323.22,1\r\n6103,15665802,Li Fonti,642,Spain,Female,36,6,0,2,1,1,97938.59,0\r\n6104,15571144,Ives,655,France,Male,28,10,0,2,0,1,126565.21,0\r\n6105,15750731,Trevisani,736,Germany,Male,50,9,116309.01,1,1,0,185360.4,1\r\n6106,15605134,Bond,617,France,Female,34,0,131244.65,2,1,0,183229.02,0\r\n6107,15626044,Lettiere,762,Germany,Male,28,3,125155.83,2,1,1,106024.02,0\r\n6108,15737910,Houghton,703,Germany,Male,35,5,140691.08,2,1,0,167810.26,0\r\n6109,15761076,Lei,507,France,Male,41,3,58820.32,2,1,1,138536.09,0\r\n6110,15710105,Stirling,581,Germany,Female,26,3,105099.45,1,1,1,184520,1\r\n6111,15577402,Grant,593,France,Male,31,9,0,2,0,1,20492.16,0\r\n6112,15803337,Baresi,648,France,Male,23,9,168372.52,1,1,0,134676.72,0\r\n6113,15654372,Pearce,462,Germany,Male,34,1,94682.56,2,1,0,138478.2,0\r\n6114,15585867,Rutledge,596,Spain,Male,36,2,0,2,0,1,125557.95,0\r\n6115,15662488,Udegbunam,627,France,Female,44,5,0,2,1,0,82969.61,1\r\n6116,15604813,Zaytseva,494,France,Male,40,7,0,2,0,1,158071.69,0\r\n6117,15611644,Onyemauchechukwu,627,France,Male,73,0,146329.73,1,0,1,43615.67,0\r\n6118,15674928,Mullah,850,Spain,Male,37,2,0,2,1,0,119969.99,0\r\n6119,15656100,Candler,632,France,Female,49,5,167962.7,1,0,0,140201.21,0\r\n6120,15764293,Konovalova,490,France,Male,33,1,0,2,1,1,80792.83,0\r\n6121,15636423,Lei,715,France,Male,40,7,0,1,1,1,141359.11,0\r\n6122,15607629,Hollis,679,France,Male,48,8,0,2,1,0,23344.94,0\r\n6123,15577313,Lionel,619,France,Male,44,3,116967.68,1,1,0,5075.17,1\r\n6124,15714493,Francis,465,Spain,Female,33,6,0,2,1,1,95500.98,0\r\n6125,15643359,Carter,736,Spain,Male,32,7,0,1,0,1,79082.62,0\r\n6126,15687913,Mai,501,Germany,Female,34,7,93244.42,1,0,1,199805.63,0\r\n6127,15790935,Johnson,535,France,Female,29,5,0,2,0,1,52709.55,0\r\n6128,15708693,Sherman,759,France,Female,33,2,0,2,1,0,56583.88,0\r\n6129,15672016,Sabbatini,819,France,Male,35,1,0,2,0,1,3385.04,0\r\n6130,15727605,Shih,533,Germany,Male,43,4,80442.06,2,0,1,12537.42,0\r\n6131,15651144,Yao,632,Germany,Female,35,2,150561.03,2,0,0,64722.61,0\r\n6132,15749401,Ko,686,France,Male,60,9,0,3,1,1,75246.21,1\r\n6133,15691874,Kazakova,687,France,Female,34,9,125474.44,1,1,0,198929.84,0\r\n6134,15620735,Chiganu,667,Germany,Female,33,4,127076.68,2,1,0,69011.66,0\r\n6135,15769781,Nucci,699,Spain,Female,25,8,0,2,1,1,52404.47,0\r\n6136,15624611,Marsden,497,Spain,Male,37,8,128650.11,2,1,1,163641.53,0\r\n6137,15773071,Serena,780,Spain,Female,33,6,145580.61,1,1,1,154598.56,0\r\n6138,15720371,McLean,652,France,Female,51,3,0,1,1,0,173989.47,1\r\n6139,15717984,Longo,477,France,Male,47,9,144900.58,1,1,0,61315.37,1\r\n6140,15806407,Wilson,652,France,Female,37,4,0,2,1,0,143393.24,0\r\n6141,15785042,Hsiung,488,France,Female,31,8,97588.6,1,0,0,124210.53,0\r\n6142,15809302,Wright,572,France,Male,24,1,0,2,1,1,151460.84,0\r\n6143,15677550,Folliero,755,France,Female,38,1,0,2,1,0,20734.81,0\r\n6144,15654096,Johnston,779,Germany,Female,24,10,122200.31,2,1,0,43705.56,0\r\n6145,15617320,Palermo,693,Spain,Female,46,3,151709.33,1,1,0,180736.24,0\r\n6146,15653065,Nwabugwu,530,Spain,Female,22,7,0,2,1,0,104170.48,0\r\n6147,15649112,Endrizzi,738,Spain,Female,33,3,122134.4,2,0,1,27867.59,0\r\n6148,15690526,Tuan,690,Germany,Male,31,2,137260.45,2,1,0,55387.28,0\r\n6149,15806945,Udobata,611,France,Female,30,9,88594.14,1,1,0,196332.45,0\r\n6150,15670066,Ibezimako,643,Spain,Male,34,6,0,2,1,1,116046.22,0\r\n6151,15625761,Maclean,632,Germany,Male,41,8,127205.32,4,1,0,93874.87,1\r\n6152,15761525,Shaw,727,Spain,Female,31,10,96997.09,2,0,0,76614.04,0\r\n6153,15735080,Cummins,508,France,Female,64,2,0,1,1,1,6076.62,0\r\n6154,15619537,Lavrentiev,550,France,Male,31,5,142200.19,2,1,1,122221.71,0\r\n6155,15598162,Saunders,754,Germany,Female,39,3,160761.41,1,1,1,24156.03,0\r\n6156,15694300,Fiorentino,759,France,Male,26,4,0,2,1,0,135394.62,0\r\n6157,15637235,Knight,794,Spain,Male,33,8,0,2,0,0,91340.02,0\r\n6158,15612444,Manfrin,549,France,Male,29,3,0,2,1,0,146090.38,0\r\n6159,15626457,Zetticci,671,France,Male,31,0,116234.61,1,1,0,172096.08,0\r\n6160,15627995,Angelo,756,Germany,Female,26,5,155143.52,1,0,1,135034.57,1\r\n6161,15706128,Zhdanov,632,France,Female,21,1,0,2,1,0,84008.66,0\r\n6162,15666430,Peck,579,France,Male,38,8,0,2,0,0,91763.67,0\r\n6163,15627385,Uwaezuoke,748,France,Male,34,5,84009.47,1,1,1,137001.1,0\r\n6164,15581323,White,488,Germany,Female,28,7,139246.22,2,1,0,106799.49,0\r\n6165,15608109,Greco,710,Germany,Male,58,7,170113,2,0,1,10494.64,0\r\n6166,15801942,Chu,619,Spain,Female,41,8,0,3,1,1,79866.73,1\r\n6167,15567431,Kodilinyechukwu,773,France,Male,64,2,145578.28,1,0,1,186172.85,0\r\n6168,15810167,Scott,657,Spain,Male,75,7,126273.95,1,0,1,91673.6,0\r\n6169,15644501,Enyinnaya,579,France,Female,26,10,162482.76,1,1,1,18458.2,0\r\n6170,15785290,Hao,542,France,Male,29,9,0,1,1,0,8342.35,0\r\n6171,15611157,McElhone,709,France,Female,32,2,87814.89,1,1,0,138578.37,0\r\n6172,15673837,Ko,617,Spain,Male,61,3,113858.95,1,1,1,38129.22,0\r\n6173,15656822,Day,568,Germany,Male,43,5,87612.64,4,1,1,107155.4,1\r\n6174,15580560,Harris,769,France,Female,73,1,0,1,1,1,29792.11,0\r\n6175,15760641,Gerald,608,Germany,Male,26,1,106648.98,1,0,1,7063.6,0\r\n6176,15587584,Nebeuwa,503,Spain,Male,31,4,0,2,1,1,21645.06,0\r\n6177,15604146,Kaodilinakachukwu,608,Germany,Female,38,8,103653.51,2,1,1,137079.86,0\r\n6178,15813974,Maruff,731,Germany,Male,37,3,116880.53,1,0,0,172718.35,1\r\n6179,15746986,Howe,850,Germany,Female,40,4,97990.49,2,0,0,106691.02,0\r\n6180,15759741,Knepper,591,Germany,Female,34,4,150635.3,1,1,1,72274.84,0\r\n6181,15734892,Fennell,579,Spain,Male,37,4,0,2,1,1,32246.63,0\r\n6182,15797194,T'ao,570,France,Male,39,10,129674.89,2,1,0,80552.36,0\r\n6183,15723786,Morris,709,France,Female,37,9,0,2,1,0,16733.59,0\r\n6184,15642726,Holmes,611,France,Male,53,3,83568.26,1,0,0,1235.49,0\r\n6185,15664339,Yu,775,Spain,Male,48,4,178144.91,2,0,0,50168.41,1\r\n6186,15754526,Walker,699,Germany,Male,36,6,147137.74,1,1,1,33687.9,0\r\n6187,15703037,Edwards,618,France,Male,37,5,0,1,0,1,178705.45,1\r\n6188,15751412,Harvey,704,France,Male,36,3,114370.41,1,0,1,66810.48,0\r\n6189,15609558,McDonald,835,Germany,Female,47,5,108289.28,2,1,1,45859.55,1\r\n6190,15572408,Chambers,714,Germany,Male,39,3,149887.49,2,1,0,63846.36,0\r\n6191,15613923,Reed,581,Spain,Female,43,4,170172.9,1,0,1,100236.02,0\r\n6192,15747000,Shih,592,France,Male,27,3,0,2,1,1,19645.65,0\r\n6193,15731781,Onyemachukwu,551,France,Male,43,7,0,2,1,0,178393.68,0\r\n6194,15727198,Teng,689,Germany,Female,28,2,64808.32,2,0,0,78591.15,0\r\n6195,15794273,Hand,604,France,Female,56,0,62732.65,1,0,1,124954.56,0\r\n6196,15804950,Onyemauchechukwu,514,France,Female,41,7,0,2,1,1,3756.65,0\r\n6197,15576304,Bailey,698,France,Male,29,5,95167.55,1,1,1,152723.23,0\r\n6198,15645200,Chiang,581,Germany,Female,54,2,152508.99,1,1,0,187597.98,1\r\n6199,15779627,Maclean,573,Germany,Male,31,0,134644.19,1,1,1,70381.49,0\r\n6200,15750755,Yobachi,449,Spain,Female,33,8,0,2,0,0,156792.89,0\r\n6201,15569654,Munro,850,Germany,Female,31,3,51293.47,1,0,0,35534.68,0\r\n6202,15753079,Chidi,612,France,Male,41,5,0,3,0,0,151256.22,0\r\n6203,15684995,Chamberlain,690,Spain,Male,49,8,116622.73,1,0,1,51011.29,0\r\n6204,15790763,Trujillo,599,Spain,Female,49,2,0,2,1,0,111190.53,0\r\n6205,15766458,Tang,498,France,Male,33,1,198113.86,1,1,0,69664.35,0\r\n6206,15616221,Wilson,497,France,Female,29,4,85646.81,1,0,0,63233.02,1\r\n6207,15776124,Mann,802,Spain,Male,51,7,0,1,0,1,40855.79,0\r\n6208,15665811,Parry,644,France,Male,33,9,141234.98,1,1,0,95673.05,0\r\n6209,15729804,Manfrin,714,France,Male,34,10,0,2,1,1,80234.14,0\r\n6210,15714062,Millar,690,France,Female,40,9,77641.99,1,0,0,189051.59,1\r\n6211,15592197,Simmons,522,Spain,Male,30,3,0,2,1,0,145490.85,0\r\n6212,15793116,Beneventi,502,Germany,Female,40,7,117304.29,1,0,0,196278.32,0\r\n6213,15638231,Chung,730,Spain,Female,62,2,0,2,1,1,162889.1,0\r\n6214,15697678,Maxwell,590,Germany,Male,36,6,92340.69,2,1,1,174667.58,0\r\n6215,15800412,Dale,458,Germany,Male,35,9,146780.52,2,1,1,3476.38,0\r\n6216,15597610,Stevens,553,Spain,Male,41,6,144974.55,1,1,1,19344.92,0\r\n6217,15726634,Wei,479,France,Male,47,1,0,1,1,0,95270.83,0\r\n6218,15670866,Chiu,693,France,Male,31,2,0,2,1,1,107759.31,0\r\n6219,15667462,Duncan,707,Spain,Male,43,10,0,2,1,0,118368.2,0\r\n6220,15662574,Brady,636,Spain,Male,37,1,115137.26,1,1,0,52484.01,0\r\n6221,15716926,Macleod,807,France,Male,33,10,101952.97,2,1,0,178153.65,0\r\n6222,15603554,Berkeley,513,France,Female,45,0,164649.52,3,1,0,49915.52,1\r\n6223,15716800,Kaur,582,France,Male,31,2,0,2,1,1,33747.03,0\r\n6224,15679429,Bell,694,France,Male,32,0,91956.49,1,1,1,59961.81,0\r\n6225,15616122,Nwokike,777,France,Male,39,8,0,2,1,1,18613.52,0\r\n6226,15742172,Williamson,598,Germany,Male,32,9,123938.6,2,1,0,198894.42,0\r\n6227,15792305,Mountgarrett,762,Germany,Male,46,6,123571.77,3,0,1,57014.17,1\r\n6228,15636016,Wreford,588,France,Female,34,3,120777.88,1,1,1,131729.52,0\r\n6229,15733138,Paterson,663,Germany,Male,42,5,90248.79,1,1,1,79169.73,0\r\n6230,15669741,Hou,777,France,Male,36,7,0,1,1,0,106472.34,0\r\n6231,15616954,Smith,592,France,Male,71,4,0,2,0,1,17013.54,0\r\n6232,15729238,Peng,631,Germany,Male,48,1,106396.48,1,1,1,150661.42,1\r\n6233,15718242,Wollstonecraft,725,Germany,Female,47,1,104887.43,1,0,0,86622.56,1\r\n6234,15682914,Bolton,850,France,Male,34,2,72079.71,1,1,1,115767.93,0\r\n6235,15654274,Corrie,540,France,Male,37,6,0,2,1,0,141998.89,0\r\n6236,15691457,Boyle,674,Spain,Male,36,2,0,2,1,1,182787.17,0\r\n6237,15719649,Lambie,553,France,Male,38,3,99844.68,1,0,0,187915.7,0\r\n6238,15778897,Cartwright,630,France,Female,28,1,0,2,1,1,133267.78,0\r\n6239,15589437,Lu,466,France,Male,26,3,156815.71,1,1,1,137476.09,0\r\n6240,15682369,Pisano,613,France,Male,47,6,146034.74,1,1,1,77146.14,0\r\n6241,15626507,Chukwubuikem,558,France,Male,27,1,152283.39,1,1,0,183271.15,0\r\n6242,15571995,Harper,775,Germany,Female,33,1,118897.1,2,1,1,26362.4,0\r\n6243,15673333,Wilson,698,Germany,Male,52,8,96781.39,1,1,1,153373.71,0\r\n6244,15748752,Ch'in,608,Germany,Male,33,1,102772.67,2,1,0,70705.58,0\r\n6245,15725302,Streeton,670,Spain,Female,20,4,0,2,1,0,119759.24,0\r\n6246,15722083,Ch'ang,591,Spain,Male,39,8,0,2,0,0,42392.24,0\r\n6247,15771442,Pennington,633,France,Male,40,4,150578,1,0,1,34670.62,1\r\n6248,15803633,T'ien,678,France,Female,46,1,0,2,0,0,82106.19,0\r\n6249,15672185,Liu,590,France,Male,47,3,0,2,1,0,171774.5,0\r\n6250,15806486,Cunningham,705,France,Female,48,0,0,2,0,0,149772.61,0\r\n6251,15570895,Ch'in,608,France,Male,42,10,163548.07,1,1,0,38866.85,0\r\n6252,15614520,Smith,682,France,Female,37,8,148580.12,1,1,0,35179.18,0\r\n6253,15687492,Anderson,596,Germany,Male,32,3,96709.07,2,0,0,41788.37,0\r\n6254,15675337,Forbes,395,Germany,Female,34,5,106011.59,1,1,1,17376.57,1\r\n6255,15721047,Ansell,578,Germany,Male,37,1,135650.88,1,1,0,199428.19,0\r\n6256,15589017,Chiu,547,Germany,Male,55,4,111362.76,3,1,0,16922.28,1\r\n6257,15611186,Yevdokimova,609,France,Male,37,1,39344.83,1,1,1,178291.89,1\r\n6258,15617301,Chamberlin,774,Germany,Male,36,9,130809.77,1,1,0,152290.28,0\r\n6259,15726046,Johnston,712,France,Female,27,2,133009.51,1,1,0,126809.15,0\r\n6260,15585748,McDonald,585,Germany,Female,28,9,135337.49,2,1,1,40385.61,0\r\n6261,15672826,Chen,666,France,Female,32,10,112536.57,2,1,1,34350.54,0\r\n6262,15595162,Cattaneo,708,Spain,Female,35,8,122570.69,1,0,0,199005.88,0\r\n6263,15650026,Barclay-Harvey,513,France,Male,44,1,63562.02,2,0,1,52629.73,1\r\n6264,15745826,Dawson,445,France,Male,37,3,0,2,1,1,180012.39,0\r\n6265,15708610,Costa,690,Germany,Male,44,9,100368.63,2,0,0,35342.33,0\r\n6266,15624471,Chikwado,850,France,Male,37,6,0,2,1,0,109291.22,0\r\n6267,15590097,Ch'eng,537,Spain,Female,33,7,136082,1,1,0,62746.54,0\r\n6268,15689328,Harrison,705,Germany,Male,48,9,114169.16,1,0,0,173273.2,1\r\n6269,15582154,Crawford,670,France,Female,45,5,47884.92,1,1,1,54340.24,0\r\n6270,15734626,Gibson,652,Spain,Female,36,1,0,2,1,1,19302.78,0\r\n6271,15702806,Martin,696,Spain,Male,24,9,0,1,0,0,10883.52,0\r\n6272,15620756,Stokes,747,France,Male,49,6,202904.64,1,1,1,17298.72,1\r\n6273,15611331,Niu,511,France,Female,46,1,0,1,1,1,115779.48,1\r\n6274,15576935,Ampt,743,Spain,Male,43,2,161807.18,2,0,1,93228.86,1\r\n6275,15661275,Wynn,532,Germany,Male,52,3,110791.97,1,1,0,148704.77,1\r\n6276,15814940,Lawrence,642,Spain,Female,33,9,0,2,1,1,150475.14,0\r\n6277,15768471,Wagner,554,Germany,Female,54,6,108755,1,1,0,40914.32,1\r\n6278,15697391,Argyle,604,Spain,Female,34,3,0,2,1,0,38587.7,0\r\n6279,15793346,Ofodile,602,France,Female,72,3,0,2,1,1,171260.66,0\r\n6280,15608338,Chiemenam,757,Spain,Female,55,9,117294.12,4,1,0,94187.47,1\r\n6281,15578546,Akobundu,491,Germany,Male,26,4,102251.14,1,1,1,145900.89,0\r\n6282,15656921,Locke,850,France,Male,31,4,0,2,0,0,152298.28,0\r\n6283,15761340,Bullen,521,France,Male,22,5,0,2,1,1,99828.45,0\r\n6284,15591135,Forster,726,France,Male,37,2,132057.92,2,1,0,34743.98,0\r\n6285,15623219,Smith,596,France,Male,33,8,0,1,1,0,121189.3,1\r\n6286,15655229,Craig,850,Germany,Female,35,7,114285.2,1,0,1,129660.59,0\r\n6287,15805884,Archer,637,France,Female,41,9,0,2,1,0,145477.36,0\r\n6288,15668289,McWilliams,690,Spain,Male,32,2,76087.98,1,0,1,151822.66,0\r\n6289,15568562,Moss,689,France,Male,40,8,160272.27,1,1,0,49656.24,0\r\n6290,15773276,Townsend,633,Spain,Male,63,4,114552.6,1,1,0,73856.28,1\r\n6291,15622801,Brown,555,France,Female,27,8,102000.17,1,1,1,116757,0\r\n6292,15779886,Munson,563,Spain,Male,24,7,0,2,0,0,16319.56,0\r\n6293,15713673,T'ien,494,France,Female,33,1,137853,1,0,1,90273.85,0\r\n6294,15783083,Shubin,534,France,Male,27,9,0,2,1,0,161344.13,0\r\n6295,15742824,Isayeva,696,Germany,Male,42,7,162318.61,1,1,0,121061.89,0\r\n6296,15621550,Hung,535,Spain,Female,50,1,140292.58,3,0,0,69531.22,1\r\n6297,15799480,Webb,600,France,Male,34,0,0,2,0,1,3756.23,0\r\n6298,15625247,Scott,807,France,Female,34,1,0,1,0,0,114448.13,0\r\n6299,15755241,Rahman,714,France,Female,52,2,0,1,0,1,144045.08,1\r\n6300,15575679,Lori,590,France,Male,24,7,126431.54,1,1,0,58781.11,0\r\n6301,15668235,Cooke,614,France,Female,41,3,123475.04,1,1,1,179227.52,0\r\n6302,15683183,Volkova,766,Germany,Female,45,6,97652.96,1,1,0,127332.33,0\r\n6303,15684592,Lamb,557,Spain,Male,42,4,0,2,0,1,86642.38,0\r\n6304,15591169,Hawes,788,Germany,Female,49,4,137455.99,1,1,0,184178.29,1\r\n6305,15653455,Smith,648,France,Female,38,2,0,2,0,1,9551.49,0\r\n6306,15732563,Swanton,726,Germany,Female,33,7,99046.31,2,1,1,56053.06,0\r\n6307,15656471,Mitchell,773,France,Male,33,9,0,2,1,1,1118.31,0\r\n6308,15598510,Colombo,583,Germany,Male,27,4,105907.42,2,1,1,195732.04,0\r\n6309,15766427,Shaw,565,Germany,Male,52,5,97720.35,2,1,0,175070.94,1\r\n6310,15785342,Shipp,705,France,Male,25,9,0,2,0,1,112331.19,0\r\n6311,15641595,Jonathan,685,Spain,Male,43,4,97392.18,2,1,0,43956.83,0\r\n6312,15798429,Hernandez,741,France,Male,29,8,0,2,1,1,115994.52,0\r\n6313,15648136,Green,658,Germany,Female,28,9,152812.58,1,1,0,166682.57,0\r\n6314,15812482,Young,575,France,Male,27,3,139301.68,1,1,0,99843.98,0\r\n6315,15790810,Han,844,France,Female,41,10,76319.64,1,1,1,141175.18,1\r\n6316,15687421,Highland,559,Spain,Male,67,9,125919.35,1,1,0,175910.95,1\r\n6317,15765643,Hamilton,725,France,Male,37,6,124348.38,2,0,1,176984.34,0\r\n6318,15654878,Yobanna,450,France,Male,29,7,117199.8,1,1,1,43480.63,0\r\n6319,15686835,Crawford,738,Germany,Female,57,9,148384.64,1,0,0,155047.11,1\r\n6320,15768340,Beavers,642,Germany,Female,19,3,113905.48,1,1,1,176137.2,0\r\n6321,15673599,Williamson,618,Spain,Male,32,5,133476.09,1,0,1,154843.4,0\r\n6322,15689096,Beneventi,590,France,Male,47,0,117879.32,1,1,1,8214.46,0\r\n6323,15684294,Chidumaga,735,France,Male,50,2,0,2,0,1,147075.69,0\r\n6324,15615828,Mitchell,550,France,Male,34,8,122359.5,1,0,0,116495.55,0\r\n6325,15746012,Chibugo,729,Spain,Female,28,0,0,2,1,1,31165.06,1\r\n6326,15615797,Hyde,743,Germany,Male,59,5,108585.35,1,1,1,192127.22,1\r\n6327,15788494,Alekseeva,555,France,Male,31,8,145875.74,1,1,0,137491.23,0\r\n6328,15793856,Abdulov,667,Spain,Female,36,3,121542.57,2,1,1,186841.71,0\r\n6329,15629545,Buckley,790,Spain,Female,41,7,109508.68,1,0,0,86776.38,0\r\n6330,15661198,Howard,727,Germany,Male,34,2,146407.11,1,1,1,72073.72,0\r\n6331,15715117,Peel,744,France,Female,39,6,0,1,0,0,10662.58,0\r\n6332,15701074,Herz,629,Germany,Male,35,8,112330.83,1,1,1,91001.02,0\r\n6333,15793046,Holden,619,France,Female,35,4,90413.12,1,1,1,20555.21,0\r\n6334,15623744,McLean,634,France,Male,34,8,105302.66,1,1,1,123164.97,0\r\n6335,15611329,Findlay,608,Spain,Female,35,6,0,2,1,1,143463.28,0\r\n6336,15740428,Wyatt,507,France,Female,35,1,0,2,0,0,92131.54,0\r\n6337,15781534,Rapuluolisa,536,Germany,Female,35,4,121520.36,1,0,0,77178.42,0\r\n6338,15618243,Buckland,730,Spain,Female,43,1,103960.38,1,1,1,193650.16,0\r\n6339,15784161,Hargreaves,583,Germany,Male,39,8,102945.01,1,0,0,52861.89,0\r\n6340,15700325,Onyeoruru,644,France,Female,24,8,92760.55,1,1,0,35896.75,0\r\n6341,15659064,Salas,790,Spain,Male,37,8,0,2,1,1,149418.41,0\r\n6342,15658364,Laney,807,Germany,Female,40,1,134590.21,1,1,1,46253.65,0\r\n6343,15704340,Fu,581,France,Female,37,10,104255.03,1,1,0,86609.37,0\r\n6344,15793455,Tien,627,Spain,Female,55,6,0,1,0,0,91943.94,1\r\n6345,15579777,Sazonova,850,France,Male,41,3,0,2,1,0,128892.36,0\r\n6346,15632345,Tuan,754,France,Female,35,4,0,2,1,0,44830.71,0\r\n6347,15814468,Wei,551,Germany,Male,50,1,121399.98,1,0,1,84508.44,1\r\n6348,15754820,Bergamaschi,637,Germany,Male,35,8,147127.81,2,1,1,84760.7,0\r\n6349,15707505,Taylor,699,Spain,Male,31,8,125927.51,2,1,0,147661.47,0\r\n6350,15699507,Messersmith,542,France,Female,25,7,0,2,0,1,82393.08,0\r\n6351,15799600,Coles,640,Germany,Male,48,1,111599.32,1,0,1,135995.58,0\r\n6352,15794472,Brookes,553,France,Female,27,3,0,2,0,0,159800.16,0\r\n6353,15646632,Reid,741,France,Male,38,9,0,2,1,0,14379.01,0\r\n6354,15676353,Etheridge,598,France,Male,35,8,114212.6,1,1,1,74322.85,0\r\n6355,15566312,Jolly,660,Spain,Female,42,5,0,3,1,1,189016.24,1\r\n6356,15570414,Chizoba,618,Spain,Male,41,4,115251.64,1,0,0,136435.75,0\r\n6357,15776743,Eberegbulam,647,France,Male,43,9,0,2,1,1,78488.39,0\r\n6358,15674637,Pagnotto,491,France,Female,68,3,107571.61,1,0,1,113695.99,0\r\n6359,15730418,Lucchesi,652,France,Female,32,2,0,2,1,0,54628.11,0\r\n6360,15739972,Hughes,650,Germany,Female,45,9,152367.21,3,1,0,150835.21,1\r\n6361,15661591,Panicucci,413,Germany,Male,39,1,130969.77,2,1,1,158891.79,0\r\n6362,15675585,Burns,416,Germany,Female,25,0,97738.97,2,1,1,160523.33,0\r\n6363,15814750,Ricci,629,Spain,Male,34,8,0,2,1,1,180595.02,0\r\n6364,15593454,Lambert,678,Spain,Female,40,4,113794.22,1,1,0,16618.76,0\r\n6365,15663421,Esposito,527,Spain,Male,28,6,128396.33,2,1,0,79919.97,0\r\n6366,15576196,Benson,743,Spain,Female,48,5,118207.69,2,0,0,186489.14,1\r\n6367,15677324,Botts,683,Germany,Male,73,9,124730.26,1,1,1,51999.5,0\r\n6368,15568742,Parkes,536,France,Female,41,9,0,1,1,0,121299.14,0\r\n6369,15693764,Mai,663,Spain,Male,52,0,136298.65,1,1,0,144593.3,1\r\n6370,15714260,Castiglione,646,France,Female,38,2,0,2,0,0,178752.73,0\r\n6371,15798200,Manna,707,France,Male,35,2,0,3,1,1,94148.3,0\r\n6372,15656627,Lin,602,France,Male,34,5,0,2,1,1,77414.45,0\r\n6373,15791111,Fink,635,France,Female,47,2,125724.95,2,1,0,63236.97,0\r\n6374,15638269,Baresi,597,France,Male,67,2,0,2,0,1,108645.85,0\r\n6375,15807473,Morehead,503,France,Male,38,1,0,2,1,1,95153.24,0\r\n6376,15708534,Afamefuna,524,Spain,Female,64,5,0,1,1,0,136079.64,1\r\n6377,15640686,Greco,700,France,Male,46,5,95872.86,1,1,0,98273.01,1\r\n6378,15588904,Balashova,692,France,Male,33,9,0,1,1,0,113505.93,1\r\n6379,15768763,Bogdanov,562,France,Male,37,2,0,1,0,1,52525.15,1\r\n6380,15770543,Lowe,679,France,Male,37,7,74260.03,1,1,0,194617.98,0\r\n6381,15642162,Ponce,603,Germany,Male,35,1,123407.69,1,1,0,152541.89,1\r\n6382,15714046,Trevisano,720,Spain,Male,33,3,123783.91,2,1,1,142903.44,0\r\n6383,15575060,Gardner,797,France,Male,24,5,0,2,1,0,182257.61,0\r\n6384,15812040,Lorenzo,594,France,Male,36,6,153880.15,1,0,0,135431.72,0\r\n6385,15812073,Palmer,529,France,Female,31,7,0,2,1,1,175697.87,0\r\n6386,15706810,Zuyeva,606,Germany,Female,32,1,106301.85,2,0,1,59061.25,0\r\n6387,15584090,Jen,621,Spain,Female,40,7,0,2,0,1,131283.6,1\r\n6388,15810807,Alekseeva,513,France,Female,43,9,0,2,1,0,152499.8,0\r\n6389,15582033,Manfrin,753,Germany,Male,44,3,138076.47,1,1,0,15523.09,1\r\n6390,15687607,Chiemenam,605,France,Female,30,9,135422.31,1,0,1,186418.85,0\r\n6391,15588406,Chiemenam,574,Spain,Female,37,7,0,2,1,0,32262.28,0\r\n6392,15784099,Clark,726,France,Female,38,5,126875.62,1,1,0,128052.29,0\r\n6393,15701352,Fanucci,611,Spain,Female,28,3,96381.68,2,1,0,181419.29,0\r\n6394,15789371,Cattaneo,593,Germany,Female,41,4,119703.1,2,1,1,109783.29,0\r\n6395,15602845,Udinesi,466,Germany,Male,41,2,152102.18,2,1,0,181879.56,0\r\n6396,15707918,Bentley,741,Germany,Female,36,0,127675.39,2,1,0,74260.16,0\r\n6397,15602812,Holmes,684,Germany,Female,44,2,133776.86,2,0,1,49865.04,0\r\n6398,15675888,Austin,550,Spain,Female,33,9,72788.03,1,1,1,103608.06,0\r\n6399,15591822,Mackenzie,593,Spain,Male,26,9,76226.9,1,1,0,167564.82,0\r\n6400,15738501,Booth,601,Germany,Male,48,9,163630.76,1,0,1,41816.49,1\r\n6401,15585907,Collier,676,Spain,Female,30,5,0,2,0,0,179066.58,0\r\n6402,15579040,Hs?,556,France,Female,46,10,0,2,0,0,109184.24,0\r\n6403,15804211,Oluchukwu,719,France,Male,36,3,155423.17,1,1,1,199841.32,0\r\n6404,15736126,Sung,850,Germany,Male,55,0,98710.89,1,1,1,83617.17,1\r\n6405,15745399,Marino,649,Spain,Female,49,2,0,1,1,0,84863.85,1\r\n6406,15760749,Vinogradov,509,Spain,Male,41,7,126683.8,1,0,1,114775.53,0\r\n6407,15637118,Burns,684,France,Male,33,4,140700.61,1,1,0,103557.93,0\r\n6408,15657829,Fanucci,806,Germany,Male,30,8,168078.83,1,1,0,85028.36,1\r\n6409,15738497,Chukwujamuike,729,Spain,Male,44,4,107726.93,2,1,0,153064.87,0\r\n6410,15690695,Flynn,683,France,Female,33,9,0,2,1,1,38784.42,0\r\n6411,15762351,Chao,689,Spain,Female,63,1,0,2,1,1,186526.12,0\r\n6412,15791172,Yeh,672,Germany,Female,21,1,35741.69,1,1,0,28789.94,0\r\n6413,15598982,Klein,602,Germany,Female,53,5,98268.84,1,0,1,45038.29,1\r\n6414,15734765,Mahmood,739,France,Female,20,4,133800.98,1,0,1,150245.81,0\r\n6415,15642912,Tu,618,France,Female,21,2,125682.79,1,0,0,57762,0\r\n6416,15769516,Shcherbakov,674,France,Female,42,9,0,2,1,0,4292.72,0\r\n6417,15789379,Zetticci,762,France,Male,26,6,130428.78,1,1,0,173365.89,0\r\n6418,15695103,Carr,790,Spain,Male,37,6,0,2,1,1,119484.01,0\r\n6419,15801924,Browne,754,Spain,Female,27,8,0,2,0,0,121821.16,0\r\n6420,15767804,Feng,729,France,Male,44,6,0,2,1,0,151733.43,0\r\n6421,15718039,Ferguson,606,Germany,Female,47,0,137138.2,2,0,1,53784.22,0\r\n6422,15579994,Shaw,616,France,Male,23,8,73112.95,1,1,1,62733.05,0\r\n6423,15595037,Palermo,772,France,Male,47,9,152347.01,1,0,1,17671.78,0\r\n6424,15600720,Moore,652,Spain,Male,41,8,115144.68,1,1,0,188905.43,0\r\n6425,15782608,Huang,743,France,Male,43,5,0,2,0,0,113079.19,1\r\n6426,15566894,Gray,793,France,Male,39,3,137817.52,1,0,0,83997.79,0\r\n6427,15749123,Sokolova,743,Spain,Male,45,7,157332.26,1,1,0,125424.42,0\r\n6428,15668943,Henderson,746,France,Male,37,2,0,2,1,0,143194.05,0\r\n6429,15577423,Mosley,627,Germany,Female,39,5,124586.93,1,1,0,93132.61,1\r\n6430,15623102,Nnaemeka,713,Spain,Male,38,6,116980.78,2,0,1,76038.38,0\r\n6431,15728012,Everett,678,Spain,Female,40,3,128398.38,1,1,0,168658.3,0\r\n6432,15683363,Goddard,540,Spain,Male,39,1,0,1,0,1,108419.41,0\r\n6433,15699335,Kuo,615,Germany,Female,33,3,137657.25,2,1,1,171657.57,0\r\n6434,15574369,Bianchi,415,Spain,Male,53,5,167259.44,1,1,1,22357.25,0\r\n6435,15703167,Rouse,628,France,Female,45,8,0,2,1,0,193903.06,0\r\n6436,15754874,Nwoye,700,France,Male,26,4,119009.57,1,1,0,141926.43,0\r\n6437,15723216,Greco,623,Germany,Male,33,2,80002.33,1,1,1,104079.62,0\r\n6438,15725094,Fang,623,France,Female,37,4,140211.88,1,1,1,93832.33,0\r\n6439,15647974,Chiemenam,679,France,Female,44,3,118742.74,2,1,0,1568.91,0\r\n6440,15583371,Artemiev,632,Spain,Male,37,1,138207.08,1,1,0,60778.11,1\r\n6441,15772559,Burrows,790,France,Female,47,10,148636.21,1,0,1,16119.96,1\r\n6442,15711251,Chizuoke,514,France,Male,45,1,178827.79,1,1,0,60375.18,0\r\n6443,15719212,T'ien,491,France,Male,33,5,83134.3,1,1,0,187946.55,0\r\n6444,15764927,Rogova,753,France,Male,92,3,121513.31,1,0,1,195563.99,0\r\n6445,15731412,Trevisano,693,Germany,Female,37,6,95900.04,1,1,1,38196.24,0\r\n6446,15719170,Sagese,679,France,Female,30,1,112543.42,1,1,1,179435.21,0\r\n6447,15596011,Artyomova,529,Spain,Male,34,9,0,1,1,1,93208.22,0\r\n6448,15614834,Long,619,Spain,Female,31,3,141751.82,1,0,1,61531.86,0\r\n6449,15600510,Hsueh,680,Spain,Female,37,6,124140.57,2,1,0,92826.35,0\r\n6450,15625706,White,693,Germany,Male,45,2,116546.59,2,0,0,23140.28,1\r\n6451,15781409,Lazarev,834,France,Female,28,6,0,1,1,0,74287.53,0\r\n6452,15722583,Benjamin,636,Spain,Female,29,6,157576.47,2,1,1,101102.39,0\r\n6453,15677243,Wan,538,Spain,Male,43,5,0,2,1,0,126933.73,0\r\n6454,15815070,Romano,566,Germany,Female,44,5,141428.99,2,0,0,68408.74,0\r\n6455,15705899,Craig,597,Spain,Male,35,0,127510.99,1,1,1,155356.34,0\r\n6456,15701522,Yermolayeva,711,France,Female,29,9,0,2,0,1,3234.8,0\r\n6457,15755978,Tseng,606,France,Male,31,10,0,2,1,0,195209.4,0\r\n6458,15722090,Tseng,615,Spain,Male,51,6,81818.49,1,1,1,169149.38,0\r\n6459,15783526,Le Hunte,589,France,Male,36,1,100895.54,1,1,1,68075.14,0\r\n6460,15632125,Blake,606,Germany,Male,45,5,63832.43,1,1,1,93707.8,0\r\n6461,15688395,Lane,582,France,Male,29,4,0,2,0,0,156153.27,0\r\n6462,15666975,Sparks,710,France,Female,36,4,116085.06,1,1,0,58601.61,0\r\n6463,15682211,Tu,467,France,Male,57,1,0,2,1,1,114448.77,0\r\n6464,15637411,Tochukwu,749,France,Male,30,1,0,2,0,1,126551.65,0\r\n6465,15591512,Whittaker,564,Germany,Female,33,2,115761.51,1,0,1,112350.21,1\r\n6466,15606855,Wang,730,Spain,Male,26,6,0,2,1,1,185808.7,0\r\n6467,15763683,Northern,678,Germany,Male,32,4,139626.01,1,1,1,118235.52,1\r\n6468,15641782,Humphries,540,France,Female,31,7,0,1,0,1,183051.6,1\r\n6469,15677184,Cremonesi,767,France,Female,35,6,115576.44,1,0,1,27922.45,0\r\n6470,15775042,Ku,615,France,Female,23,4,0,2,1,0,196476.19,0\r\n6471,15616630,Tobenna,583,Germany,Female,41,5,77647.6,1,1,0,190429.52,0\r\n6472,15800233,Okwuadigbo,850,France,Female,40,5,0,2,1,0,35034.15,0\r\n6473,15588419,Johnston,651,Germany,Female,34,10,148962.46,1,1,0,66389.43,1\r\n6474,15595557,Li,798,France,Male,22,8,0,2,1,0,107615.43,0\r\n6475,15626143,Talbot,695,France,Male,37,2,0,2,1,1,99692.65,0\r\n6476,15566030,Tu,497,Germany,Male,41,5,80542.81,1,0,0,88729.22,1\r\n6477,15701412,T'ien,739,France,Male,40,4,0,2,0,0,173321.65,0\r\n6478,15702464,Ross,549,France,Female,34,4,0,2,0,0,139463.57,0\r\n6479,15573348,Maclean,850,France,Male,35,9,102050.47,1,1,1,3769.71,0\r\n6480,15704160,Wan,648,Spain,Male,49,5,0,1,1,0,149946.43,1\r\n6481,15693704,Tsou,679,France,Female,24,6,114948.76,2,0,1,135768.25,0\r\n6482,15664752,Jack,606,Germany,Male,39,8,136000.45,2,1,0,31708.53,0\r\n6483,15628292,Lucchesi,850,France,Male,32,4,156001.68,2,1,1,151677.31,0\r\n6484,15621195,Ch'eng,619,Germany,Male,41,3,147974.16,2,1,0,170518.83,0\r\n6485,15668629,Saunders,719,Spain,Male,44,2,0,2,1,0,196582.19,0\r\n6486,15635197,Glover,640,Germany,Male,26,5,90402.77,1,1,1,3298.65,0\r\n6487,15592761,Tung,710,France,Male,40,5,0,2,0,0,162878.96,0\r\n6488,15574283,Padovano,580,France,Male,31,2,0,2,0,1,64014.24,0\r\n6489,15598097,Johnstone,550,France,Male,44,9,0,2,1,0,26257.01,0\r\n6490,15711352,Endrizzi,841,France,Female,31,3,162701.65,2,1,1,126794.56,0\r\n6491,15620751,Secombe,760,France,Male,34,2,0,2,1,0,164162.44,0\r\n6492,15656717,Elewechi,687,France,Female,30,6,0,2,0,0,179206.92,0\r\n6493,15643121,Chu,753,Germany,Female,35,5,82453.96,2,0,0,18254.75,0\r\n6494,15723671,Lucciano,661,France,Male,35,9,100107.99,1,1,0,83949.68,0\r\n6495,15752846,Pinto,699,France,Male,28,7,0,2,1,1,22684.78,0\r\n6496,15640852,McGregor,617,Germany,Female,39,5,83348.89,3,1,0,7953.62,1\r\n6497,15789313,Ugorji,595,Germany,Female,44,4,96553.52,2,1,0,143952.24,1\r\n6498,15793688,Bancks,669,France,Male,50,9,201009.64,1,1,0,158032.5,1\r\n6499,15770405,Warlow-Davies,613,France,Female,27,5,125167.74,1,1,0,199104.52,0\r\n6500,15702561,Dale,782,France,Male,32,9,0,1,1,1,87566.97,0\r\n6501,15625964,Buckley,582,France,Female,43,5,153313.67,1,0,0,170563.73,0\r\n6502,15761364,Nkemjika,679,France,Male,30,9,0,2,1,0,157871.55,0\r\n6503,15590286,Fairley,611,France,Female,40,2,125879.29,1,1,0,93203.43,0\r\n6504,15587978,Boothby,455,Germany,Female,37,6,170057.62,1,0,1,54398.56,0\r\n6505,15773242,Chukwuhaenye,621,France,Male,32,1,0,2,1,1,168779.47,0\r\n6506,15761053,Lock,596,Germany,Male,48,2,131326.47,1,0,0,1140.02,1\r\n6507,15702095,Clarke,585,Spain,Female,56,1,128472.8,1,1,0,186476.91,1\r\n6508,15764253,Ramsey,742,France,Male,32,6,160485.16,1,1,0,29023.03,0\r\n6509,15700801,Eipper,850,Germany,Male,42,6,84445.68,3,0,1,60021.34,1\r\n6510,15730590,Ko,738,Germany,Female,40,1,115409.18,2,0,0,180456.8,0\r\n6511,15643916,Munro,619,Spain,Male,46,8,62400.48,1,1,1,132498.39,1\r\n6512,15720636,McGregor,628,France,Female,50,4,143054.56,1,0,1,109608.81,1\r\n6513,15795429,Henderson,487,France,Male,24,7,133628.09,2,1,1,98570.01,0\r\n6514,15609254,Fernandez,513,Spain,Female,41,9,107135.04,2,1,1,160546.58,0\r\n6515,15625141,Porter,563,Spain,Male,26,7,0,2,0,0,6139.74,0\r\n6516,15810898,Pan,803,France,Female,65,2,151659.52,2,0,1,6930.17,0\r\n6517,15775797,Esposito,607,Spain,Female,32,7,0,3,0,1,10674.62,0\r\n6518,15795246,Kaeppel,628,Germany,Female,51,9,155903.82,2,1,1,71159.84,0\r\n6519,15795275,Lamb,521,Spain,Female,49,4,82940.25,2,0,0,62413.01,1\r\n6520,15571869,Lei,669,Germany,Female,50,4,112650.89,1,0,0,166386.22,1\r\n6521,15694143,Conti,686,France,Female,41,10,0,1,1,0,133086.45,0\r\n6522,15748231,Hargreaves,700,Germany,Male,35,4,95853.39,2,1,0,192933.37,0\r\n6523,15632185,Yermolayev,663,France,Female,42,1,82228.67,2,1,0,71359.78,0\r\n6524,15806249,Kerr,671,Spain,Female,31,4,0,2,0,1,79270.02,0\r\n6525,15743293,Waters,651,Germany,Female,35,1,163700.78,3,1,1,29583.48,1\r\n6526,15598157,Onyeorulu,728,France,Male,34,4,106328.08,1,1,0,88680.65,0\r\n6527,15700946,Kolesnikova,574,France,Female,34,7,152992.91,1,1,1,134691.2,0\r\n6528,15722692,Kazakova,464,France,Male,38,3,116439.65,1,1,0,75574.48,0\r\n6529,15696506,MacDonald,604,Spain,Male,27,9,101352.78,1,0,0,30252.3,0\r\n6530,15728823,Sharwood,836,Spain,Female,37,10,0,2,1,0,111324.41,0\r\n6531,15808851,Bufkin,511,Germany,Female,75,9,105609.17,1,0,1,105425.18,0\r\n6532,15675231,Nwankwo,518,France,Female,45,8,0,2,1,1,36193.07,0\r\n6533,15732299,Boniwell,756,France,Male,67,4,0,3,1,1,93081.87,0\r\n6534,15706269,Willis,489,France,Female,47,8,103894.38,2,1,1,107625.46,0\r\n6535,15590078,Burns,622,Spain,Male,27,9,139834.93,1,1,1,152733.89,0\r\n6536,15776985,Kung,652,France,Female,36,6,112518.71,2,0,1,110421.31,0\r\n6537,15756743,Howells,625,France,Female,37,7,115895.42,1,1,0,48486.25,0\r\n6538,15782364,Bevan,521,Spain,Female,39,3,146408.68,1,0,0,72993.67,0\r\n6539,15604093,Neitenstein,546,France,Male,34,4,165363.31,2,1,1,25744.13,1\r\n6540,15749328,Johnson,697,France,Female,45,1,0,2,1,0,46807.62,1\r\n6541,15656322,Sandover,571,Germany,Male,33,3,71843.15,1,1,0,26772.04,0\r\n6542,15685564,Nnamutaezinwa,748,Spain,Male,35,5,105492.53,1,1,1,150057.2,0\r\n6543,15785831,Sinclair,591,France,Male,35,7,183027.25,1,1,1,56028.79,0\r\n6544,15796218,Wei,814,Germany,Male,29,1,131968.57,2,1,1,147693.92,0\r\n6545,15716218,Higgins,709,France,Female,45,3,104118.5,1,0,1,174032,0\r\n6546,15572735,Chang,433,Spain,Male,27,2,0,2,1,1,153698.65,0\r\n6547,15633840,Henderson,781,France,Male,20,0,125023.1,2,1,1,108301.45,0\r\n6548,15608760,Cox,656,France,Female,30,4,74323.2,1,1,1,22929.08,0\r\n6549,15627848,Tsui,683,France,Male,38,7,109346.13,2,1,0,102665.92,0\r\n6550,15792029,Lee,620,France,Male,32,6,0,2,1,0,56139.09,0\r\n6551,15617331,Sergeyeva,637,Germany,Female,39,3,109698.41,1,1,1,88391.29,1\r\n6552,15651740,Napolitani,525,Spain,Female,30,5,0,2,0,1,149195.44,0\r\n6553,15636407,Beatham,793,Germany,Female,34,5,127758.09,1,1,0,143357.03,0\r\n6554,15607526,Lu,638,Germany,Male,50,1,102645.48,1,1,0,168359.98,1\r\n6555,15632576,Yashina,520,France,Male,31,4,93249.4,1,1,0,77335.75,0\r\n6556,15581505,Bales,641,France,Male,35,5,0,2,1,0,93148.93,0\r\n6557,15612207,Hill,840,Germany,Female,51,1,87779.83,1,0,1,36687.11,1\r\n6558,15707242,Ibeamaka,504,Spain,Male,40,5,0,2,0,0,146703.36,0\r\n6559,15721937,Romilly,686,France,Male,38,0,138131.34,1,0,1,115927.85,0\r\n6560,15773852,Hayes,533,Germany,Male,38,4,70362.52,2,1,1,104189.46,0\r\n6561,15719778,Chiu,577,France,Female,32,1,0,2,1,0,9902.39,0\r\n6562,15650538,Sun,445,Germany,Female,48,7,168286.58,1,1,0,16645.77,1\r\n6563,15797475,Brennan,720,France,Male,44,3,86102.27,1,1,0,180134.88,1\r\n6564,15780359,Storey,643,Germany,Male,25,4,115142.9,1,1,1,148098.95,0\r\n6565,15737104,Lawson,652,Germany,Female,47,0,126597.89,2,1,1,38798.79,1\r\n6566,15789936,T'ao,663,France,Female,33,2,0,2,1,0,153295,0\r\n6567,15709523,Yao,525,Germany,Female,30,0,157989.21,2,1,1,100687.67,0\r\n6568,15593425,Bracewell,662,Spain,Female,54,1,187997.15,1,0,0,111442.71,1\r\n6569,15776725,Kerr,724,Germany,Male,54,8,172192.49,1,1,1,136902.01,0\r\n6570,15604706,Blake,581,Germany,Male,38,1,133105.47,1,1,0,105732.9,1\r\n6571,15790958,Sanders,685,Spain,Male,38,4,0,2,1,1,35884.91,0\r\n6572,15747534,Torkelson,595,France,Male,46,10,0,1,1,0,73489.15,1\r\n6573,15574237,Hsueh,588,France,Female,21,8,0,2,1,1,110114.19,0\r\n6574,15690332,Wang,647,Germany,Male,35,3,192407.97,1,1,1,40145.28,0\r\n6575,15661290,Hightower,785,Germany,Female,38,9,107199.75,1,0,0,146398.51,0\r\n6576,15651883,Genovesi,794,Germany,Female,55,6,115796.7,1,1,0,160526.36,1\r\n6577,15808905,Levan,823,France,Male,37,5,164858.18,1,1,1,173516.71,0\r\n6578,15715532,Lai,687,Germany,Male,38,4,117633.28,1,0,1,88396.6,0\r\n6579,15786078,Loginov,850,France,Female,28,9,0,2,1,0,185821.41,0\r\n6580,15652401,Lafleur,496,France,Female,36,7,0,2,0,0,108098.28,0\r\n6581,15673074,Obidimkpa,527,Germany,Female,30,6,126663.51,1,1,1,162267.91,0\r\n6582,15598744,Ch'ang,576,Germany,Female,71,6,140273.47,1,1,1,193135.25,1\r\n6583,15785975,Mason,525,Spain,Female,60,7,0,2,0,1,168034.9,0\r\n6584,15613180,Miranda,727,Germany,Male,21,8,153344.72,1,1,1,163295.87,0\r\n6585,15584229,Simon,671,Germany,Female,23,9,123943.18,1,1,1,159553.27,0\r\n6586,15773804,Golubeva,625,France,Male,39,5,0,1,1,0,99800.87,0\r\n6587,15699515,Manfrin,643,Germany,Male,33,7,98630.31,2,1,1,40250.82,0\r\n6588,15705313,Stange,707,France,Female,33,2,58036.33,1,1,1,83335.78,0\r\n6589,15693817,Ferrari,539,Spain,Male,28,5,0,2,1,0,48382.4,0\r\n6590,15673790,Taylor,498,Germany,Male,45,7,109200.74,2,0,1,165990.44,0\r\n6591,15674868,Wei,696,Spain,Female,30,0,0,2,1,1,9002.8,0\r\n6592,15692110,Ch'eng,758,France,Female,33,7,0,1,1,0,188156.34,0\r\n6593,15645904,Parsons,685,France,Female,33,6,0,2,0,1,186785.01,0\r\n6594,15581332,Pan,655,Germany,Female,30,1,83173.98,2,1,1,184259.6,0\r\n6595,15808544,Cameron,747,France,Female,40,3,0,1,0,0,57817.84,1\r\n6596,15734948,Igwebuike,601,Spain,Male,24,7,0,2,0,0,144660.42,0\r\n6597,15654531,Tuan,477,France,Male,22,5,82559.42,2,0,0,163112.9,1\r\n6598,15637774,Fraser,558,France,Male,32,5,73494.21,1,0,0,136301.1,0\r\n6599,15677141,Turnbull,586,Spain,Male,29,2,132450.24,1,1,1,36176.63,0\r\n6600,15739578,Chiazagomekpere,850,France,Male,49,6,128663.9,1,1,0,65769.3,1\r\n6601,15697360,Yudina,505,France,Female,36,2,79951.9,1,0,1,174123.16,1\r\n6602,15655213,Udinese,591,Germany,Female,51,8,132508.3,1,1,1,161304.68,1\r\n6603,15580872,Chinweike,761,Germany,Female,38,1,120530.13,2,1,0,109394.62,0\r\n6604,15683213,Bergamaschi,554,France,Female,35,10,74988.59,2,0,1,190155.13,0\r\n6605,15801188,Milliner,774,France,Female,47,6,94722.88,1,0,1,61450.96,0\r\n6606,15645029,Knowles,771,Spain,Female,33,5,0,2,1,0,8673.43,0\r\n6607,15633181,Swinton,792,France,Male,31,6,71269.89,2,0,1,125912.77,0\r\n6608,15598259,Gregory,673,Germany,Female,41,9,98612.1,1,1,0,151349.35,0\r\n6609,15576000,Chibueze,765,France,Male,40,6,138033.55,1,1,1,67972.45,0\r\n6610,15766047,Sukhorukova,748,France,Female,41,2,91621.69,1,1,1,71139.31,0\r\n6611,15596339,French,422,France,Male,54,3,140014.42,1,0,1,86350.97,0\r\n6612,15715199,Estrada,568,Spain,Male,27,5,126815.97,2,0,1,118648.12,0\r\n6613,15615938,Fleming,502,France,Female,64,3,139663.37,1,0,1,100995.11,0\r\n6614,15679991,Kennedy,524,France,Female,28,7,0,2,0,1,147100.72,0\r\n6615,15626135,Combes,689,France,Male,34,1,165312.27,1,1,0,155495.63,0\r\n6616,15792934,Carruthers,661,France,Male,26,8,0,2,0,0,196875.87,0\r\n6617,15744046,Andrejew,606,Spain,Male,33,8,0,2,1,1,63176.77,0\r\n6618,15700826,Ko,678,Germany,Female,54,1,123699.28,2,0,1,105221.76,0\r\n6619,15756301,Daniels,636,Germany,Female,29,3,97325.15,1,0,1,131924.38,0\r\n6620,15586517,Toscano,647,France,Male,32,5,97041.16,1,1,1,23132.73,0\r\n6621,15751297,Wilson,732,France,Male,36,5,0,2,1,0,161428.25,0\r\n6622,15710365,Thomson,646,France,Male,50,0,104129.24,2,1,0,181794.86,1\r\n6623,15679307,Kazantseva,559,France,Female,43,1,0,1,0,1,86634.3,0\r\n6624,15610753,Cremonesi,581,France,Male,28,3,104367.5,1,1,1,29937.75,0\r\n6625,15811036,Ferri,565,France,Male,46,7,135369.71,1,0,1,140130.22,0\r\n6626,15610912,Ferri,657,Spain,Female,41,6,112119.48,1,1,0,17536.82,0\r\n6627,15619932,Lombardi,847,France,Male,66,7,123760.68,1,0,1,53157.16,0\r\n6628,15746199,Eluemuno,558,France,Female,41,6,0,1,1,1,143585.29,1\r\n6629,15584967,Chiganu,596,Spain,Male,57,6,0,2,1,1,72402,0\r\n6630,15734365,Hsueh,579,France,Male,39,5,0,2,0,1,39891.84,0\r\n6631,15726960,O'Brien,741,France,Female,36,3,0,2,1,1,89804.83,0\r\n6632,15665177,Booth,613,France,Male,44,3,0,2,0,1,136491.72,0\r\n6633,15779915,O'Loghlin,694,Spain,Male,31,5,0,1,1,0,35593.18,0\r\n6634,15729110,Lavrov,729,Spain,Female,42,7,0,2,1,0,58268.2,1\r\n6635,15575399,Somadina,480,France,Female,42,1,152160.21,2,1,0,101778.9,0\r\n6636,15678374,Colombo,666,France,Female,59,5,0,2,1,1,185123.09,0\r\n6637,15792679,Troupe,575,France,Male,24,2,0,2,1,1,119927.81,0\r\n6638,15668767,Kenenna,850,France,Male,36,3,0,2,1,0,195033.07,0\r\n6639,15761886,Franklin,740,France,Male,36,4,172381.8,1,1,1,86480.29,0\r\n6640,15583076,Deleon,588,Germany,Male,41,6,106116.56,2,1,0,198766.61,0\r\n6641,15815615,Kung,681,France,Male,36,5,141952.07,1,1,1,185144.08,0\r\n6642,15591942,Zito,611,Spain,Female,33,7,0,2,1,1,3729.89,0\r\n6643,15724924,Giordano,589,France,Female,37,6,138497.84,1,0,1,18988.58,0\r\n6644,15762123,Davide,717,Spain,Female,34,1,0,2,1,0,119313.74,0\r\n6645,15567893,Lei,556,Germany,Male,33,3,124213.36,2,1,0,62627.55,0\r\n6646,15648989,Moss,850,France,Male,37,4,126872.6,1,1,0,197266.58,0\r\n6647,15662021,Lucciano,685,Spain,Female,42,2,0,2,0,0,199992.48,0\r\n6648,15691627,Tai,713,France,Female,37,8,0,1,1,1,16403.41,0\r\n6649,15731751,Osinachi,437,France,Female,26,1,120923.52,1,0,1,78854.57,0\r\n6650,15635277,Coates,605,Spain,Male,47,7,142643.54,1,1,0,189310.27,0\r\n6651,15655252,Larionova,758,Germany,Male,41,10,79857.64,1,1,1,78088.17,0\r\n6652,15803941,Seleznev,600,France,Male,46,10,95502.21,1,0,0,19842.18,0\r\n6653,15714380,Butcher,827,France,Male,38,5,0,2,0,0,103305.01,0\r\n6654,15666559,Gould,608,Germany,Male,23,8,197715.93,2,1,1,116124.28,0\r\n6655,15799998,Cunningham,608,France,Female,30,8,85859.76,1,0,0,142730.27,0\r\n6656,15703763,Sanderson,554,France,Male,44,7,85304.27,1,1,1,58076.52,0\r\n6657,15795640,Mai,683,Germany,Female,35,1,132371.3,2,0,0,186123.57,0\r\n6658,15780056,Reid,660,Spain,Male,33,4,0,1,1,0,29664.45,0\r\n6659,15777873,Downer,628,France,Female,31,5,0,1,0,0,147963.07,1\r\n6660,15584749,Humphries,668,Germany,Male,39,4,79896,1,1,0,38466.39,0\r\n6661,15765258,Bochsa,776,France,Female,29,5,0,2,1,1,143301.49,0\r\n6662,15623346,Czajkowski,820,France,Male,36,4,0,2,1,0,31422.69,0\r\n6663,15614054,Pankhurst,665,France,Male,36,1,0,2,0,1,121505.61,0\r\n6664,15766185,She,850,Germany,Male,31,4,146587.3,1,1,1,89874.82,0\r\n6665,15667632,Birdseye,703,France,Female,42,7,0,2,0,1,72500.68,0\r\n6666,15599024,Hope,506,Spain,Male,32,8,0,2,0,1,182692.8,0\r\n6667,15798709,Gill,588,Spain,Male,32,3,109109.33,1,0,1,4993.94,0\r\n6668,15741921,Moon,622,Spain,Female,26,8,0,2,1,1,124964.82,0\r\n6669,15793671,Watt,606,France,Male,34,5,0,1,1,0,161971.42,0\r\n6670,15797900,Chinomso,517,France,Male,56,9,142147.32,1,0,0,39488.04,1\r\n6671,15667932,Bellucci,758,Spain,Female,43,10,0,2,1,1,55313.44,0\r\n6672,15795933,Barese,677,France,Female,49,3,0,2,1,1,187811.71,0\r\n6673,15660403,Fleming,827,Spain,Female,35,0,0,2,0,1,184514.01,0\r\n6674,15736299,Bell,729,France,Female,36,8,109106.8,1,0,0,121311.12,0\r\n6675,15759034,Li Fonti,654,France,Male,36,2,112262.84,1,1,0,12873.39,0\r\n6676,15724663,Christmas,654,Spain,Female,36,5,0,2,0,0,157238.05,0\r\n6677,15594556,Chuter,619,Spain,Male,52,8,0,2,1,1,123242.11,0\r\n6678,15737169,Johnson,642,Spain,Male,26,8,144238.7,1,1,1,184399.76,0\r\n6679,15632472,Scott,472,Spain,Female,32,1,159397.75,1,0,1,57323.18,0\r\n6680,15722813,Byrne,470,Spain,Male,30,4,125385.01,1,1,0,68293.93,0\r\n6681,15588450,Chukwudi,633,France,Female,60,8,69365.25,1,1,1,10288.24,0\r\n6682,15736717,Ma,602,France,Male,31,7,155271.83,1,1,1,179446.31,0\r\n6683,15680683,Simmons,640,Spain,Male,29,5,197200.04,2,1,0,141453.62,0\r\n6684,15710316,Fang,454,Spain,Female,48,5,144837.79,1,1,1,93151.77,0\r\n6685,15746333,Blake,562,France,Female,57,3,0,3,1,0,6554.97,1\r\n6686,15606861,Tien,636,France,Male,34,8,0,2,1,0,38570.13,0\r\n6687,15641285,Yusupova,621,Spain,Male,50,3,163085.79,1,0,1,131048.36,0\r\n6688,15662908,Davidson,795,Germany,Male,38,7,125903.22,2,1,1,127068.92,0\r\n6689,15814267,Zhdanova,550,France,Male,22,6,154377.3,1,1,1,51721.52,0\r\n6690,15614923,Nielson,630,Spain,Male,41,7,107511.52,1,0,1,46156.87,0\r\n6691,15579223,Niu,573,Germany,Male,30,8,127406.5,1,1,0,192950.6,0\r\n6692,15651389,Kay,561,Spain,Male,24,8,143656.55,1,0,1,180932.46,0\r\n6693,15677087,Green,662,France,Female,39,5,138106.75,1,0,0,19596.73,0\r\n6694,15665784,She,637,France,Male,27,9,128940.24,1,1,0,46786.92,0\r\n6695,15576706,Ajuluchukwu,651,Germany,Male,37,9,114453.58,1,0,1,175820.91,0\r\n6696,15615473,Sabbatini,646,France,Female,33,2,0,2,0,0,198208,0\r\n6697,15587299,Board,567,France,Female,48,3,0,1,1,0,55362.45,0\r\n6698,15655389,Leckie,638,France,Male,41,1,131762.94,1,1,1,47675.29,0\r\n6699,15784491,Ho,725,France,Female,31,6,0,1,0,0,61326.43,0\r\n6700,15809999,Gordon,709,France,Female,41,3,150300.65,2,1,0,71672.86,0\r\n6701,15681115,Iroawuchi,787,Spain,Male,39,10,108935.39,1,1,1,101168.3,0\r\n6702,15629390,Liao,653,France,Male,37,7,135847.47,1,1,0,144880.81,0\r\n6703,15792668,Hamilton,661,Germany,Male,37,7,109908.06,2,1,0,115037.67,1\r\n6704,15583863,Chimaobim,681,Germany,Male,49,8,142946.18,1,0,0,187280.51,1\r\n6705,15681878,Fan,436,Germany,Male,45,3,104339.11,2,1,1,183540.22,1\r\n6706,15782875,Cayley,663,France,Male,33,5,157274.36,2,1,1,28531.81,0\r\n6707,15732235,Kuykendall,662,France,Male,64,0,98848.19,1,0,1,42730.12,0\r\n6708,15735909,McDonald,607,Germany,Female,39,8,105103.33,1,1,0,104721.5,1\r\n6709,15653448,Duncan,754,France,Male,34,7,0,2,1,1,65219.85,0\r\n6710,15587647,Browne,850,Germany,Female,66,0,127120.62,1,0,1,118929.64,1\r\n6711,15701037,Barton,578,France,Male,39,2,0,2,1,0,70563.9,0\r\n6712,15727499,Boyle,666,Germany,Female,36,3,129118.5,2,0,0,139435.12,0\r\n6713,15724838,Moretti,599,France,Female,43,4,0,1,1,0,170347.1,0\r\n6714,15666711,Ukaegbulam,586,France,Female,46,0,0,3,0,1,131553.82,1\r\n6715,15588933,Nwankwo,825,France,Female,36,3,146053.66,1,1,1,138344.7,0\r\n6716,15763111,Niu,808,Spain,Female,67,10,124577.15,1,0,1,169894.4,0\r\n6717,15805676,Hsu,515,Spain,Male,29,4,151012.55,2,1,0,9770.97,0\r\n6718,15586674,Shaw,663,Spain,Female,58,5,216109.88,1,0,1,74176.71,1\r\n6719,15744553,Ho,444,France,Male,34,2,144318.97,1,1,0,112668.06,0\r\n6720,15776629,Christie,650,France,Female,39,4,0,2,0,0,186275.7,0\r\n6721,15647207,Onwuemelie,609,France,Male,26,7,0,2,1,0,98463.99,0\r\n6722,15715638,Ch'ang,824,Germany,Male,77,3,27517.15,2,0,1,2746.41,0\r\n6723,15750602,Clendinnen,662,France,Male,29,5,147092.65,1,1,0,10928.3,0\r\n6724,15766810,Onyemauchechi,699,Germany,Female,51,2,92246.14,2,0,1,91346.03,0\r\n6725,15756625,Crawford,752,France,Female,41,8,0,2,1,0,139844.04,1\r\n6726,15639552,Mellor,603,Germany,Female,40,8,148897.02,1,0,0,105052.9,0\r\n6727,15633213,Rizzo,628,Spain,Male,50,8,0,1,0,0,144366.83,1\r\n6728,15610416,Christie,745,France,Female,36,9,0,1,1,0,19605.18,1\r\n6729,15715208,Watkins,804,Germany,Female,33,10,138335.96,1,1,1,80483.76,0\r\n6730,15619608,Ojiofor,454,Germany,Female,50,10,92895.56,1,1,0,154344,1\r\n6731,15628697,Tung,631,Spain,Male,46,9,160736.63,1,0,1,93503.02,0\r\n6732,15643826,McKay,503,France,Male,32,4,0,2,1,1,153036.97,0\r\n6733,15718588,Meng,548,France,Female,37,9,0,2,0,0,98029.58,0\r\n6734,15709741,Hussain,668,France,Male,28,4,107141.27,1,1,0,193018.71,0\r\n6735,15723318,Mactier,619,France,Female,55,0,0,3,0,0,60810.64,1\r\n6736,15717328,Hsueh,842,France,Female,37,4,132446.08,2,1,0,87071.18,1\r\n6737,15771299,Nnachetam,707,France,Female,57,1,92053,1,1,1,164064.44,1\r\n6738,15706223,Barnes,715,Spain,Male,38,2,96798.79,2,1,1,4554.67,0\r\n6739,15612358,Christie,573,Germany,Male,35,9,134498.54,2,1,1,119924.8,0\r\n6740,15769191,Lipton,509,France,Male,55,8,132387.91,2,1,1,170360.11,0\r\n6741,15618816,Yu,670,Germany,Female,40,2,147171.2,1,0,1,69850.04,0\r\n6742,15730810,Storey,613,Spain,Male,44,9,100524.69,1,1,1,47298.95,0\r\n6743,15783463,Read,678,France,Female,26,1,0,2,1,0,45443.68,0\r\n6744,15616213,Levy,555,Germany,Female,51,9,138214.5,1,1,0,198715.27,1\r\n6745,15611287,Chiu,777,France,Female,30,4,0,2,0,1,115611.97,0\r\n6746,15786454,Moore,552,Spain,Male,55,3,0,1,1,1,40333.94,0\r\n6747,15768682,Amies,640,Spain,Male,39,3,0,1,1,1,105997.25,0\r\n6748,15766172,Tsao,541,France,Male,34,3,128743.55,1,1,0,134851.12,0\r\n6749,15637646,Rowley,756,France,Male,31,10,122647.32,1,0,0,61666.87,0\r\n6750,15653404,Aliyev,684,Spain,Female,24,9,79263.9,1,0,1,196574.48,0\r\n6751,15690546,Riley,618,France,Female,42,2,0,4,0,0,111097.39,1\r\n6752,15735636,Toscano,604,France,Female,53,2,121389.78,1,1,1,48201.64,1\r\n6753,15605424,Oluchukwu,624,Spain,Male,38,7,123906.55,1,1,0,135096.78,0\r\n6754,15568449,Fu,661,Spain,Male,38,7,143006.7,1,1,1,15650.89,0\r\n6755,15688085,Warner,627,Spain,Female,28,3,157597.61,1,0,1,34097.22,0\r\n6756,15683483,Fleming,812,Spain,Male,38,3,127117.8,2,1,1,174822.74,0\r\n6757,15659567,Ch'iu,473,France,Female,39,9,117103.26,2,1,1,85937.52,1\r\n6758,15766667,Langler,717,Spain,Male,36,2,102989.83,2,0,1,49185.57,0\r\n6759,15624975,Angelo,693,Spain,Male,28,1,145118.83,1,0,1,77742.38,0\r\n6760,15660878,T'ien,705,France,Male,92,1,126076.24,2,1,1,34436.83,0\r\n6761,15586557,Milani,661,France,Male,41,5,0,1,0,1,88279.6,0\r\n6762,15746183,Pye,573,France,Female,27,4,0,2,1,1,157549.6,0\r\n6763,15631457,Asher,639,France,Male,37,5,98186.7,1,0,1,173386.95,0\r\n6764,15754053,Chung,718,France,Female,67,7,0,3,1,1,82782.08,0\r\n6765,15645839,Yudin,570,France,Male,37,6,0,1,1,1,187758.5,0\r\n6766,15689955,Arcuri,461,France,Female,40,7,0,2,1,0,176547.8,0\r\n6767,15593510,Capon,638,Germany,Female,33,5,129335.65,1,1,1,56585.2,1\r\n6768,15654964,Piccio,608,Spain,Male,48,7,75801.74,1,1,0,125762.95,0\r\n6769,15594039,Lung,599,Spain,Male,42,6,0,2,1,0,113868.4,0\r\n6770,15625929,Trevisan,762,France,Female,44,7,159316.64,1,0,0,24780.13,0\r\n6771,15815295,John,662,France,Female,38,2,96479.81,1,1,0,120259.41,0\r\n6772,15621818,Anayolisa,747,Germany,Male,29,7,117726.33,1,1,1,175398.34,0\r\n6773,15652700,Ritchie,539,France,Male,39,6,0,2,1,1,86767.48,0\r\n6774,15636860,Ch'eng,625,France,Male,43,4,122351.29,1,1,0,71216.6,0\r\n6775,15569432,Macleod,656,France,Female,48,9,0,2,1,1,85240.61,1\r\n6776,15751455,Boyle,469,France,Female,48,5,0,1,1,0,160529.71,1\r\n6777,15800583,Chukwuemeka,621,Spain,Female,43,8,0,1,0,0,102806.6,0\r\n6778,15770214,Bryant,754,France,Female,27,7,0,2,1,0,144134.64,0\r\n6779,15613463,Hackett,679,Germany,Female,50,6,132598.38,2,1,1,184017.98,0\r\n6780,15587066,Kovaleva,535,France,Male,38,2,119272.29,1,0,0,195896.59,1\r\n6781,15693752,Reed,487,France,Male,37,2,0,2,1,1,126722.57,0\r\n6782,15714874,Major,850,France,Female,42,3,0,2,1,1,176883.42,0\r\n6783,15657809,Lo,585,France,Male,55,10,106415.57,3,1,1,122960.98,1\r\n6784,15651955,Hanson,603,France,Male,31,4,0,2,0,1,9607.1,0\r\n6785,15570912,Ogbonnaya,728,Germany,Female,32,9,127772.1,2,1,1,152643.48,0\r\n6786,15640266,Windsor,621,Spain,Male,41,5,104631.67,1,1,1,95551.22,0\r\n6787,15652069,Calabrese,833,France,Male,30,1,0,2,1,0,141860.62,0\r\n6788,15596074,Keating,502,France,Male,37,10,0,1,1,1,76642.68,0\r\n6789,15800268,Costa,825,Germany,Male,37,6,118050.79,1,0,1,52301.15,0\r\n6790,15809847,Tan,668,France,Male,46,0,0,2,0,0,29388.02,0\r\n6791,15599074,Ma,487,Spain,Female,40,6,136093.74,1,0,1,193408.43,0\r\n6792,15599591,Martin,600,Germany,Female,39,7,88477.36,2,1,0,58632.37,0\r\n6793,15776096,Halpern,606,Spain,Male,34,3,161572.24,1,0,1,191076.22,0\r\n6794,15611669,Nyhan,623,Germany,Male,50,7,126608.37,1,0,1,645.61,1\r\n6795,15694098,Jackson,575,France,Female,54,9,68332.96,1,1,1,144390.75,0\r\n6796,15713347,Reynolds,577,Spain,Male,48,6,179852.26,1,1,0,193580.32,0\r\n6797,15713094,Tai,651,France,Female,25,8,0,2,1,1,126761.2,0\r\n6798,15811978,Trevisani,693,Germany,Male,46,2,104763.41,1,1,1,62368.33,0\r\n6799,15799925,Uwakwe,800,France,Male,60,6,88541.57,2,1,1,131718.12,0\r\n6800,15692575,Kerr,760,France,Male,38,6,162888.73,1,1,0,91098.76,1\r\n6801,15743149,Findlay,711,France,Female,35,8,0,1,1,1,67508.01,0\r\n6802,15776947,Ugorji,637,Spain,Male,43,8,0,1,1,0,12156.93,1\r\n6803,15700656,Balashova,662,France,Male,32,9,0,2,0,0,65089.38,0\r\n6804,15594515,Cheng,568,France,Female,44,7,0,2,0,0,62370.67,1\r\n6805,15787884,Martin,692,France,Female,30,7,0,2,1,1,18826.34,0\r\n6806,15577988,Skinner,614,France,Female,35,1,0,2,1,1,3342.62,0\r\n6807,15795586,McDonald,478,France,Male,35,1,92474.05,1,1,0,178626.07,0\r\n6808,15677739,Dellucci,562,France,Male,36,6,0,2,1,0,32845.32,0\r\n6809,15720134,Reynolds,709,Germany,Male,30,9,115479.48,2,1,1,134732.99,0\r\n6810,15688868,Birdsall,684,France,Female,26,5,87098.91,1,0,0,106095.82,0\r\n6811,15642996,Tsai,546,Germany,Female,42,9,86351.85,2,1,0,57380.13,0\r\n6812,15771222,Oguejiofor,779,France,Female,42,5,0,2,0,0,25951.91,0\r\n6813,15605059,Mackie,576,Germany,Male,63,3,148843.56,1,1,0,69414.13,1\r\n6814,15568088,Jamieson,481,Germany,Male,44,3,163714.52,1,1,0,96123.72,0\r\n6815,15665943,Mai,445,France,Male,25,6,0,2,1,0,119425.94,0\r\n6816,15795571,Patterson,606,Spain,Male,36,0,94153.56,1,0,1,120138.27,0\r\n6817,15662243,Taylor,559,France,Male,50,5,162702.35,1,0,0,150548.5,1\r\n6818,15593128,Vinogradoff,608,France,Female,56,10,129255.2,2,1,0,142492.04,1\r\n6819,15589739,North,698,France,Male,41,3,90605.29,1,1,1,14357,0\r\n6820,15787602,Carter,568,Spain,Male,39,5,0,2,1,1,129569.92,0\r\n6821,15685019,Graham,528,France,Male,29,3,102787.42,1,1,0,55972.56,0\r\n6822,15704209,Noble,802,France,Female,39,7,120145.96,2,0,1,59497.01,1\r\n6823,15605264,Walker,669,Germany,Male,47,0,63723.78,2,1,1,181928.25,0\r\n6824,15708265,Chibugo,581,Spain,Female,24,10,159203.71,1,1,1,102517.83,1\r\n6825,15740264,Yobachi,640,France,Male,38,9,0,2,1,0,88827.67,0\r\n6826,15615477,Ignatyeva,529,Spain,Female,44,1,0,2,0,0,14161.3,0\r\n6827,15727361,Chiemela,547,France,Female,51,1,0,2,1,1,56908.41,0\r\n6828,15760216,Pokrovskaya,718,France,Female,49,10,0,1,1,0,184474.72,1\r\n6829,15806134,Storey,707,Germany,Male,34,9,162691.16,2,1,0,94912.78,0\r\n6830,15601351,Moroney,735,France,Male,43,9,127806.91,1,1,1,73069.59,0\r\n6831,15669262,Maslov,765,France,Male,43,9,157960.49,2,0,0,136602.8,0\r\n6832,15696989,Chukwueloka,469,Germany,Female,52,8,139493.25,3,0,0,150093.32,1\r\n6833,15688498,Chu,594,Germany,Female,21,2,87096.82,2,1,0,168186.11,0\r\n6834,15686964,Spence,675,France,Female,34,10,84944.58,1,0,0,146230.63,0\r\n6835,15625035,Mills,703,France,Male,50,8,160139.59,2,1,1,79314.1,0\r\n6836,15618391,Doyle,810,France,Male,33,6,0,2,1,1,77965.67,0\r\n6837,15591344,Donnelly,715,Spain,Male,42,6,0,2,1,1,128745.69,0\r\n6838,15605455,Tai,664,France,Male,40,9,0,2,1,0,194767.3,0\r\n6839,15680804,Abbott,850,France,Male,29,6,0,2,1,1,10672.54,0\r\n6840,15768282,Perez,724,Germany,Male,36,6,94615.11,2,1,1,10627.21,0\r\n6841,15685826,Hsiung,563,France,Male,30,7,90727.79,1,1,0,122268.75,0\r\n6842,15793491,Cherkasova,714,Germany,Male,26,3,119545.48,2,1,0,65482.94,0\r\n6843,15797787,Denisov,614,France,Male,36,1,118311.76,1,1,0,146134.68,0\r\n6844,15611171,Fowler,740,France,Male,33,1,129574.98,1,1,1,123300.38,0\r\n6845,15601627,Siciliano,587,France,Male,33,8,148163.57,1,0,0,122925.4,0\r\n6846,15734085,Crocker,465,Germany,Male,24,5,117154.9,1,1,1,127744.02,0\r\n6847,15809309,Longo,689,Spain,Female,40,5,154251.67,1,0,1,118319.5,0\r\n6848,15809462,Polyakova,656,France,Male,30,3,0,2,0,1,17104,0\r\n6849,15634628,Brown,579,France,Female,33,1,65667.79,2,0,0,164608.98,0\r\n6850,15775678,Uspensky,716,France,Female,44,1,0,1,1,1,152108.47,0\r\n6851,15579526,O'Meara,551,France,Male,42,1,50194.59,1,1,1,23399.58,0\r\n6852,15779103,Cantamessa,527,Germany,Female,39,9,96748.89,2,1,0,94711.43,0\r\n6853,15738715,Alexander,600,France,Female,37,4,0,3,1,0,7312.25,1\r\n6854,15593943,Chinagorom,685,France,Female,43,1,132667.17,1,1,1,41876.98,0\r\n6855,15754574,Tomlinson,738,Spain,Male,36,5,0,2,1,1,96881.32,0\r\n6856,15737814,Lo,622,France,Male,41,2,127087.06,1,1,0,102402.91,1\r\n6857,15670889,Nwachukwu,528,France,Male,34,1,125566.9,1,1,1,176763.27,0\r\n6858,15629299,Yang,546,Germany,Female,52,1,106074.89,1,1,1,23548.45,1\r\n6859,15771569,Bage,576,Germany,Male,46,4,137367.94,1,1,1,33450.11,0\r\n6860,15811927,Marcelo,733,France,Female,38,3,157658.36,1,0,0,19658.43,0\r\n6861,15785654,Ofodile,727,Germany,Male,45,6,114422.85,2,1,1,104678.78,1\r\n6862,15665524,Savage,605,Spain,Male,41,5,103154.66,1,0,0,143203.78,0\r\n6863,15736287,Piccio,586,France,Male,33,9,0,1,1,0,6975.02,0\r\n6864,15765732,Simmons,564,Spain,Female,24,6,149592.14,1,1,1,153771.8,0\r\n6865,15797381,DeRose,593,Germany,Female,48,3,133903.12,2,1,1,85902.39,1\r\n6866,15598536,Onuchukwu,736,Germany,Female,26,0,84587.9,1,0,1,188037.76,0\r\n6867,15664506,Goodwin,675,Spain,Male,32,8,197436.82,1,1,1,52710.7,0\r\n6868,15575619,Teakle,656,Spain,Female,32,1,104254.27,1,1,1,17034.37,0\r\n6869,15587394,Thomson,462,France,Male,39,4,140133.08,2,0,0,131304.45,0\r\n6870,15654457,Cross,685,Spain,Female,30,2,0,3,1,1,172576.43,1\r\n6871,15762793,Jones,850,Germany,Female,36,0,136980.23,2,1,1,99019.65,0\r\n6872,15658067,Walker,636,Germany,Female,48,3,120568.41,1,1,0,190160.04,1\r\n6873,15642816,De Salis,850,France,Female,27,7,43658.33,2,1,1,3025.49,0\r\n6874,15693088,Oliver,628,France,Female,37,9,0,2,1,1,34689.77,0\r\n6875,15793883,Lo Duca,798,France,Male,28,3,0,2,1,0,2305.27,0\r\n6876,15665283,Brookes,610,France,Female,57,7,72092.95,4,0,1,113228.82,1\r\n6877,15680421,Challis,591,France,Female,42,10,0,2,0,0,171099.22,0\r\n6878,15695148,Ibeabuchi,614,Spain,Female,37,9,0,2,1,1,62023.1,0\r\n6879,15636592,Iroawuchi,651,France,Male,35,0,181821.96,2,0,1,36923.67,1\r\n6880,15772618,Tyler,665,France,Male,25,7,90920.75,1,0,1,112256.57,0\r\n6881,15724453,Fan,570,France,Male,23,2,0,1,0,0,198830.98,0\r\n6882,15565878,Bates,631,Spain,Male,29,3,0,2,1,1,197963.46,0\r\n6883,15609160,Marsden,586,France,Male,32,1,0,2,0,0,31635.99,0\r\n6884,15678460,Dodgshun,691,France,Male,30,9,0,1,1,0,49594.02,0\r\n6885,15662571,Maclean,639,France,Male,35,8,0,2,1,0,170483.9,0\r\n6886,15606849,Blackall,698,France,Female,27,1,94920.71,1,1,1,40339.9,0\r\n6887,15670738,Mazzanti,733,Germany,Male,45,2,113939.36,2,1,0,3218.71,0\r\n6888,15662641,Amadi,850,France,Male,19,8,0,1,1,1,68569.89,0\r\n6889,15727539,Schoenheimer,618,France,Female,31,4,0,2,1,0,29176.04,0\r\n6890,15651020,Fiorentino,473,France,Female,25,6,110666.42,2,0,0,46758.42,0\r\n6891,15673877,Murray,490,France,Male,39,1,0,3,1,0,171060.01,1\r\n6892,15760865,Fan,754,Germany,Female,48,7,141819.02,1,1,0,93550.53,1\r\n6893,15705009,Cartwright,649,France,Female,56,8,156974.26,1,1,0,89405.26,1\r\n6894,15657540,Cremonesi,578,France,Male,50,5,151215.34,2,1,0,169804.4,0\r\n6895,15707441,White,690,Spain,Male,26,8,116318.23,1,1,1,83253.05,0\r\n6896,15694765,Sabbatini,610,Germany,Male,49,6,113882.33,1,1,0,195813.81,1\r\n6897,15649086,Patterson,596,France,Male,42,7,0,2,1,1,121568.37,0\r\n6898,15650488,Bromley,492,France,Female,48,6,127253.98,1,1,1,92144.09,1\r\n6899,15760924,Doherty,575,Spain,Male,41,2,100062.39,1,0,0,126307.25,0\r\n6900,15700263,Ifeatu,569,France,Male,66,2,0,1,1,0,130784.2,1\r\n6901,15806922,Bergamaschi,674,Spain,Female,41,4,126605.14,1,1,1,166694.93,0\r\n6902,15637522,Shubina,507,France,Female,31,0,106942.08,1,0,1,44001.11,0\r\n6903,15636548,Lung,457,Spain,Male,44,7,0,2,0,0,185992.36,0\r\n6904,15566891,Kinder,584,Germany,Female,41,3,88594.93,1,1,0,178997.89,0\r\n6905,15627185,Terry,744,Germany,Male,29,6,123737.04,2,1,0,141558.04,0\r\n6906,15754012,Shepherdson,687,France,Female,35,1,110752.15,2,1,1,47921.22,0\r\n6907,15627514,Short,688,Spain,Female,46,3,0,2,0,1,104902.68,0\r\n6908,15661433,Zetticci,519,France,Male,34,5,0,1,1,0,68479.6,0\r\n6909,15610653,Belov,733,Spain,Female,38,5,0,2,1,1,1271.51,0\r\n6910,15667002,Knight,666,Spain,Male,43,5,0,2,1,0,29346.1,0\r\n6911,15709199,Burson,511,Spain,Female,40,1,0,1,1,1,184118.73,0\r\n6912,15710087,Nicholls,705,Germany,Female,54,3,125889.3,3,1,0,96013.5,1\r\n6913,15679884,Hs?eh,544,France,Male,48,10,78314.63,3,1,1,103713.93,1\r\n6914,15784180,Ku,564,France,Female,36,7,206329.65,1,1,1,46632.87,1\r\n6915,15808849,T'ien,702,France,Male,40,7,145536.9,1,0,1,135334.24,0\r\n6916,15751549,H?,658,Germany,Male,31,2,77082.65,2,0,0,13482.28,0\r\n6917,15588235,Vasilieva,654,France,Female,24,8,145081.73,1,1,1,130075.07,0\r\n6918,15640418,Omeokachie,649,Germany,Female,41,4,115897.73,1,1,0,143544.48,0\r\n6919,15721116,Napolitano,597,Spain,Male,24,0,108058.07,2,1,1,187826.11,0\r\n6920,15599084,Hopwood,782,France,Male,33,7,191523.09,1,1,1,167058.75,0\r\n6921,15773394,Bergamaschi,644,France,Male,38,3,0,2,1,1,79928.41,0\r\n6922,15625713,Lindeman,679,Spain,Female,39,7,91187.9,1,0,1,6075.36,0\r\n6923,15766417,McKinley,678,France,Female,60,2,0,2,1,1,43821.56,0\r\n6924,15622578,Sergeyev,806,France,Male,34,5,113958.55,1,0,1,32125.98,0\r\n6925,15799924,Sanchez,668,Spain,Male,43,1,147167.25,1,0,0,141679.73,0\r\n6926,15618363,Muomelu,659,Germany,Male,29,9,82916.48,1,1,1,84133.48,0\r\n6927,15637138,Murray,660,France,Male,34,1,0,2,1,0,9692.58,0\r\n6928,15781665,Ibekwe,601,France,Female,37,5,0,1,0,0,20708.6,0\r\n6929,15804853,McVey,781,France,Female,48,0,57098.96,1,1,0,85644.06,1\r\n6930,15651627,White,628,Germany,Male,39,1,115341.19,1,1,1,107674.3,1\r\n6931,15680685,Patterson,751,France,Male,30,3,165257.2,1,0,0,134822.05,0\r\n6932,15808930,Mai,531,France,Female,37,1,0,1,1,0,4606.97,0\r\n6933,15570970,Han,647,France,Female,42,9,0,2,1,1,51362.82,0\r\n6934,15679961,Davidson,708,Spain,Male,46,7,68799.72,1,1,1,39704.14,0\r\n6935,15705458,Parkin,550,Spain,Male,39,2,116120.19,2,1,1,195638.13,0\r\n6936,15750396,McKissick,670,France,Male,33,1,0,2,1,1,86413.11,0\r\n6937,15679928,Horsfall,592,France,Female,31,2,84102.11,2,0,1,116385.24,0\r\n6938,15711181,Clapp,589,France,Female,50,4,0,2,0,1,182076.97,0\r\n6939,15698324,Azikiwe,725,France,Female,33,4,0,1,1,1,67879.8,0\r\n6940,15807433,Zubarev,570,France,Female,43,9,0,2,0,1,11417.26,0\r\n6941,15636590,Pisano,575,France,Male,46,1,0,2,1,1,65998.26,0\r\n6942,15628950,Coates,501,Germany,Male,25,6,104013.79,1,1,0,114774.35,0\r\n6943,15617206,Trentino,431,Germany,Male,42,8,120822.86,2,1,0,126153.24,0\r\n6944,15603741,MacDonnell,719,Spain,Male,40,4,128389.12,1,1,1,176091.31,0\r\n6945,15742607,Ermakov,850,Germany,Male,36,7,102800.72,1,1,1,87352.43,0\r\n6946,15747821,K?,554,Germany,Female,31,6,135470.9,1,1,0,107074.81,0\r\n6947,15612043,Hammonds,418,France,Male,36,7,90145.04,1,1,1,69157.93,0\r\n6948,15809558,Peppin,715,Spain,Male,31,7,0,1,1,1,149970.59,0\r\n6949,15803750,Ball,750,Spain,Female,33,3,161801.47,1,0,1,153288.97,1\r\n6950,15704681,Yeh,766,Germany,Male,37,2,99660.13,2,0,1,147700.78,0\r\n6951,15667392,L?,652,Spain,Female,38,6,123081.84,2,1,1,188657.97,0\r\n6952,15738889,Shih,658,France,Male,42,8,102870.93,1,0,1,103764.55,1\r\n6953,15598838,Greco,659,France,Female,37,1,151105.68,1,1,1,140934.57,0\r\n6954,15579109,Napolitano,574,Germany,Male,35,5,163856.76,1,1,1,15118.2,0\r\n6955,15799042,Zaytseva,611,France,Male,38,7,0,1,1,1,63202,0\r\n6956,15697042,Genovesi,738,Spain,Male,35,8,127290.61,1,1,0,16081.62,0\r\n6957,15696605,Angelo,571,France,Male,49,4,180614.04,1,0,0,523,0\r\n6958,15802274,Waters,686,France,Female,44,7,55053.62,1,1,0,181757.19,0\r\n6959,15596808,Maclean,679,Spain,Male,33,4,96110.22,1,1,0,1173.23,0\r\n6960,15705403,Seleznyova,617,Spain,Female,46,3,106521.49,1,0,1,86587.37,0\r\n6961,15732903,Fontenot,673,France,Male,39,7,82255.51,2,1,0,109545.56,0\r\n6962,15581968,Reid,745,France,Female,33,1,0,2,1,1,174431.01,0\r\n6963,15683892,Fraser,677,Germany,Female,26,3,102395.79,1,1,0,119368.99,0\r\n6964,15595447,Tuan,613,Spain,Male,39,8,118201.41,1,1,0,23315.59,0\r\n6965,15569249,Howarth,576,France,Female,55,6,44582.07,3,0,1,67539.85,1\r\n6966,15656188,Davis,584,Spain,Female,30,5,0,2,1,1,185201.58,0\r\n6967,15689661,Gorbunov,663,France,Male,22,6,0,2,0,1,131827.15,0\r\n6968,15644934,Gentry,466,France,Male,26,9,105522.06,1,1,0,10842.46,0\r\n6969,15721793,Chiu,510,Germany,Female,50,7,123936.54,1,1,1,23768.01,0\r\n6970,15687413,Sunderland,619,Spain,Female,38,6,0,2,1,1,117616.29,0\r\n6971,15761286,Fan,696,Germany,Female,66,7,119499.42,2,1,1,174027.3,0\r\n6972,15658240,Parry,554,France,Female,44,9,135814.7,2,0,0,115091.38,0\r\n6973,15706232,Niu,595,France,Male,52,9,0,1,1,1,106340.66,1\r\n6974,15583394,Zuyev,659,Germany,Male,39,8,106259.63,2,1,1,198103.32,0\r\n6975,15715643,Ijendu,662,France,Male,44,8,0,2,1,1,175314.87,0\r\n6976,15644856,Bird,556,Spain,Male,38,2,115463.16,1,1,0,150679.65,0\r\n6977,15785488,Palmer,701,Spain,Female,39,9,0,2,1,1,110043.88,0\r\n6978,15711571,Y?,587,Spain,Male,42,5,120233.83,1,1,0,194890.33,0\r\n6979,15778604,Nicholson,571,France,Female,47,7,0,2,0,0,112366.98,0\r\n6980,15751180,Adams,539,France,Female,40,7,81132.21,1,1,0,167289.82,0\r\n6981,15748360,Cocci,644,Germany,Female,34,10,122196.99,2,1,1,182099.71,0\r\n6982,15770039,Kuo,572,Germany,Male,39,4,112290.22,1,1,0,49373.97,1\r\n6983,15685096,Trevisani,753,France,Female,50,4,0,2,1,1,861.4,0\r\n6984,15669501,Kuo,706,France,Male,35,5,0,2,1,1,81718.37,0\r\n6985,15622631,H?,588,France,Male,44,8,154409.74,1,1,0,49324.03,1\r\n6986,15586699,Thomson,825,France,Male,32,9,0,2,0,0,9751.03,0\r\n6987,15702377,Knorr,627,Spain,Male,48,1,132759.8,1,1,0,78899.22,0\r\n6988,15577170,Manfrin,532,France,Male,60,5,76705.87,2,0,1,13889.73,0\r\n6989,15769451,Hayes,764,France,Female,44,1,0,2,1,1,11467.38,0\r\n6990,15811877,Shao,700,France,Female,36,4,0,2,1,0,130789.15,0\r\n6991,15648725,Sinclair,660,France,Male,41,3,0,2,1,1,108665.89,0\r\n6992,15752801,Bradshaw,518,Germany,Male,29,9,125961.74,2,1,0,160303.08,1\r\n6993,15808175,Castiglione,557,France,Female,39,7,49572.73,1,1,0,115287.99,1\r\n6994,15681342,Hurst,639,France,Female,35,1,103015.12,2,1,1,139094.12,0\r\n6995,15589210,Adamson,557,France,Female,24,4,0,1,0,0,20515.72,0\r\n6996,15696826,James,633,France,Female,32,1,104001.38,1,0,1,36642.65,0\r\n6997,15614962,Pavlova,623,Spain,Female,50,2,87116.71,1,1,1,104382.11,0\r\n6998,15689061,Davey,611,France,Male,68,5,82547.11,2,1,1,146448.01,0\r\n6999,15640074,Barrett,666,Spain,Female,47,5,0,1,0,0,166650.9,1\r\n7000,15776156,Dolgorukova,521,France,Male,27,4,121325.84,1,1,1,164223.7,1\r\n7001,15739548,Johnson,775,France,Male,28,9,111167.7,1,1,0,149331.01,0\r\n7002,15662854,Manna,681,Germany,Male,48,5,139714.4,2,0,0,73066.72,0\r\n7003,15687688,Hou,564,Germany,Female,32,10,139875.2,2,1,0,15378.23,0\r\n7004,15715750,Okeke,646,Germany,Female,44,2,113063.83,1,0,0,53072.49,1\r\n7005,15571121,Kodilinyechukwu,670,France,Female,50,8,138340.06,1,0,1,3159.15,0\r\n7006,15726466,Esposito,751,France,Male,43,1,114974.24,1,1,0,125920.54,0\r\n7007,15660390,Boyle,544,France,Female,33,6,0,2,1,1,124113.04,0\r\n7008,15663942,Hsiung,639,France,Female,38,5,0,2,0,0,93716.38,0\r\n7009,15638610,Kennedy,635,Germany,Female,65,5,117325.54,1,1,0,155799.86,1\r\n7010,15644446,Norton,672,France,Female,28,6,0,1,0,1,8814.69,0\r\n7011,15585892,Zakharov,639,France,Female,35,8,0,1,0,0,164453.98,0\r\n7012,15609356,Chimaraoke,697,France,Female,25,1,0,2,0,0,87803.32,0\r\n7013,15803378,Small,850,Spain,Male,44,8,0,2,1,1,183617.32,0\r\n7014,15599440,McGregor,748,France,Female,34,8,0,2,1,0,53584.03,0\r\n7015,15692408,Brown,463,Spain,Female,35,2,0,2,1,1,1950.93,0\r\n7016,15683168,Frederickson,572,France,Female,30,6,0,1,0,1,175025.27,0\r\n7017,15790254,Wood,741,Spain,Male,50,1,78737.61,1,1,1,13018.96,0\r\n7018,15767729,Smith,646,Spain,Male,25,5,182876.88,2,1,1,42537.59,1\r\n7019,15768600,Harris,805,Germany,Male,50,9,130023.38,1,1,0,62989.82,1\r\n7020,15699839,Hall,637,France,Male,36,2,152606.82,1,1,1,71692.8,0\r\n7021,15786237,Pickworth,651,France,Male,28,7,0,2,1,0,823.96,0\r\n7022,15694530,Porter,672,France,Male,28,4,167268.98,1,1,1,169469.3,0\r\n7023,15796813,Storey,493,France,Male,54,3,167831.88,2,1,0,150159.95,1\r\n7024,15605791,Li,524,Germany,Male,29,9,144287.6,2,1,0,32063.3,0\r\n7025,15714087,McGill,624,Germany,Female,45,5,151855.33,1,1,0,68794.15,0\r\n7026,15711446,Sinclair,569,Spain,Female,51,3,0,3,1,0,75084.96,1\r\n7027,15588123,Horton,677,France,Female,27,2,0,2,0,1,114685.92,0\r\n7028,15748552,Sal,464,Germany,Male,37,4,155994.15,1,0,0,143665.44,0\r\n7029,15618410,Murray,718,Germany,Male,26,7,147527.03,1,0,0,51099.56,0\r\n7030,15672432,Giles,594,France,Female,53,4,0,1,1,0,5408.74,1\r\n7031,15610042,Brown,574,France,Male,33,8,100267.03,1,1,0,103006.27,0\r\n7032,15580914,Okechukwu,478,Spain,Male,48,0,83287.05,2,0,1,44147.95,1\r\n7033,15583680,White,615,Spain,Male,41,4,0,1,0,1,149278.96,0\r\n7034,15813718,Kirillova,651,Spain,Male,45,4,0,2,0,0,193009.21,0\r\n7035,15767264,Lawson,465,Germany,Male,53,1,117438.17,1,0,0,74898.8,1\r\n7036,15686461,Sarratt,558,France,Female,56,7,121235.05,2,1,1,116253.1,0\r\n7037,15678882,Hay,540,Germany,Male,37,3,129965.18,1,0,0,19374.08,0\r\n7038,15789611,Lin,568,Germany,Male,46,8,150836.92,1,0,0,64516.8,1\r\n7039,15668679,Ozerova,630,France,Male,31,0,0,2,1,1,34475.14,0\r\n7040,15631685,Lambert,523,Germany,Male,60,1,163894.35,1,0,1,57061.71,0\r\n7041,15655658,Bulgakov,678,France,Female,48,2,0,2,1,1,32301.88,0\r\n7042,15753591,He,438,France,Male,38,2,0,2,1,0,136859.55,0\r\n7043,15617348,Uchechukwu,544,France,Male,44,1,0,2,0,0,69244.24,0\r\n7044,15704581,Robertson,595,Germany,Male,34,2,87967.42,2,0,1,156309.52,0\r\n7045,15738487,Leworthy,678,France,Male,26,3,0,2,1,0,4989.33,0\r\n7046,15648069,Onyemachukwu,850,France,Female,36,6,0,2,1,1,190194.95,0\r\n7047,15737627,Rivero,589,Germany,Female,20,2,121093.29,2,1,0,3529.72,0\r\n7048,15731586,Lai,785,Spain,Female,31,2,121691.54,2,0,0,81778.72,0\r\n7049,15757467,Feng,563,Spain,Male,57,6,0,2,1,1,39297.48,0\r\n7050,15597709,Hornung,602,France,Female,39,6,154121.32,2,1,0,176614.86,1\r\n7051,15720529,Schiavone,591,France,Male,29,6,0,2,1,1,108684.65,0\r\n7052,15596797,Barnet,643,Spain,Male,43,1,0,2,1,1,145764.4,0\r\n7053,15681755,Dennys,605,France,Female,32,5,0,2,1,1,42135.28,0\r\n7054,15815271,Ritchie,755,Germany,Male,43,6,165048.5,3,1,0,16929.41,1\r\n7055,15682860,Lo,769,Spain,Male,38,6,0,2,0,0,104393.78,0\r\n7056,15621546,Yuriev,620,France,Female,33,9,127638.35,1,1,1,192717.57,0\r\n7057,15705918,Howarth,725,France,Male,31,8,0,2,1,1,59650.42,0\r\n7058,15684512,Gibson,818,Germany,Female,72,8,135290.42,2,1,1,63729.72,0\r\n7059,15671769,Zikoranachidimma,624,France,Female,71,4,170252.05,3,1,1,73679.59,1\r\n7060,15642934,Mason,669,Germany,Female,35,4,108269.2,2,1,0,174969.92,0\r\n7061,15594305,Rizzo,712,France,Female,32,1,0,2,1,0,1703.58,0\r\n7062,15789201,Thomson,603,Germany,Female,35,9,145623.36,1,1,0,163181.62,0\r\n7063,15706762,Ignatyev,597,France,Female,41,4,145809.53,2,1,1,52319.26,0\r\n7064,15766183,Ferguson,580,Germany,Male,76,2,130334.84,2,1,1,51672.08,0\r\n7065,15777994,Woods,718,France,Female,39,3,0,2,1,1,145355.11,0\r\n7066,15568162,Sung,527,Spain,Male,53,8,0,1,1,1,51711.57,0\r\n7067,15680643,Lo,729,Spain,Female,42,1,0,2,1,1,149535.97,0\r\n7068,15761854,Burn,746,France,Female,24,4,0,1,0,1,94105,0\r\n7069,15730793,Russell,699,Germany,Female,54,3,111009.32,1,1,1,155905.79,1\r\n7070,15692137,Jen,759,France,Female,46,2,0,1,1,1,138380.11,0\r\n7071,15608595,Lo Duca,748,France,Female,39,3,157371.54,1,0,1,97734.3,0\r\n7072,15709459,Oluchi,698,Spain,Female,63,5,0,1,1,1,173576.71,0\r\n7073,15775750,Yao,686,France,Male,37,9,134560.62,1,1,0,27596.39,0\r\n7074,15585855,Gould,679,France,Male,40,1,0,1,1,1,16897.19,0\r\n7075,15752139,Salter,682,Germany,Male,36,5,72373.62,2,1,0,36895.99,0\r\n7076,15768295,Warner,778,France,Female,34,7,109564.1,1,0,1,113046.81,0\r\n7077,15766906,Salier,742,France,Female,25,4,132116.13,2,1,0,129933.5,0\r\n7078,15725776,Lazar,649,Germany,Male,24,7,101195.23,1,0,0,133091.32,0\r\n7079,15682576,Onyenachiya,763,France,Male,67,1,149436.73,2,0,1,106282.74,0\r\n7080,15704081,Findlay,595,Germany,Male,30,9,130682.11,2,1,1,57862.88,0\r\n7081,15719940,Gibbons,628,Germany,Female,51,10,115280.49,2,0,0,12628.61,1\r\n7082,15672894,McCawley,625,France,Female,36,8,129944.39,2,0,0,198914.8,0\r\n7083,15667451,Taylor,733,France,Male,36,5,0,2,1,1,109127.54,0\r\n7084,15636767,Yang,665,Spain,Female,32,10,0,1,1,1,22487.45,0\r\n7085,15571415,Okwudiliolisa,805,Germany,Male,56,6,151802.29,1,1,0,46791.09,1\r\n7086,15575605,Napolitano,725,France,Male,38,6,0,2,1,1,158697.28,0\r\n7087,15649160,Vavilov,554,France,Female,38,3,138731.95,1,1,1,194138.36,0\r\n7088,15615832,Teague,675,Spain,Female,35,8,155621.08,1,0,1,35177.31,0\r\n7089,15600975,Chiemenam,556,France,Female,54,4,150005.38,1,1,0,157015.5,1\r\n7090,15690772,Hughes,635,Spain,Female,48,2,0,2,1,1,136551.25,0\r\n7091,15565714,Cattaneo,601,France,Male,47,1,64430.06,2,0,1,96517.97,0\r\n7092,15763108,Davis,600,Germany,Male,53,7,106261.63,1,1,0,93629.66,1\r\n7093,15723884,Nekrasova,758,Spain,Male,40,3,0,2,0,0,96097.65,0\r\n7094,15644453,Loggia,606,Germany,Female,41,4,132670.53,1,1,0,156476.36,1\r\n7095,15655464,Combes,640,France,Female,67,3,0,1,0,1,42964.63,0\r\n7096,15783883,Onwuka,753,Germany,Female,38,1,117314.92,1,1,0,122021.33,1\r\n7097,15787693,Kharlamov,559,Spain,Male,38,3,145874.35,1,1,0,56311.39,1\r\n7098,15664793,Scott,754,Spain,Female,50,7,146777.44,2,0,1,150685.52,0\r\n7099,15642391,Lettiere,621,Germany,Male,51,4,109978.83,1,0,0,177740.58,1\r\n7100,15756538,Osonduagwuike,654,France,Female,37,5,0,1,0,1,71492.28,0\r\n7101,15668830,Wan,650,Spain,Male,24,8,108881.73,1,1,0,104492.83,0\r\n7102,15796569,Donaldson,831,Spain,Female,44,10,0,1,0,1,47729.33,0\r\n7103,15677112,Chukwufumnanya,519,France,Male,39,2,112957.26,2,1,0,97593.16,0\r\n7104,15815040,Ma,552,Germany,Female,42,8,103362.14,1,0,1,186869.58,1\r\n7105,15590434,Alexander,577,Spain,Male,41,4,89015.61,1,0,1,135227.23,0\r\n7106,15597536,Nkemjika,576,Spain,Male,45,5,133618.01,1,0,0,135244.87,0\r\n7107,15723989,Carroll,646,France,Male,40,5,93680.43,2,1,1,179473.26,0\r\n7108,15767358,Obioma,711,Germany,Female,45,1,97486.15,2,1,0,50610.62,0\r\n7109,15594812,Campbell,806,Spain,Female,37,2,137794.18,2,0,1,75232.02,0\r\n7110,15688210,Sims,670,France,Female,39,8,101928.51,1,0,0,89205.54,0\r\n7111,15681509,McKay,679,Spain,Female,28,9,0,2,0,1,61761.77,0\r\n7112,15572390,Huang,850,Spain,Female,39,6,0,2,1,0,103921.43,0\r\n7113,15801441,Campbell,670,Germany,Female,35,2,79585.96,1,0,1,198802.9,0\r\n7114,15783859,Boni,733,France,Female,24,3,161884.99,1,1,1,9617.24,0\r\n7115,15575243,Gorbunova,764,France,Female,39,1,129068.54,2,1,1,187905.12,0\r\n7116,15773421,Genovese,673,France,Female,42,4,0,2,1,0,121440.8,0\r\n7117,15788776,Landor,588,Germany,Male,49,6,132623.76,3,1,0,36292.94,1\r\n7118,15765257,Meng,564,Spain,Male,31,5,121461.87,1,1,1,20432.09,1\r\n7119,15661412,Wardell,715,France,Male,32,8,175307.32,1,1,0,187051.23,0\r\n7120,15636478,Williams,621,France,Male,31,7,136658.61,1,1,1,148689.13,0\r\n7121,15603683,Ofodile,796,Spain,Female,23,3,146584.19,2,0,0,125445.8,0\r\n7122,15651868,Clark,672,France,Male,34,6,0,1,0,0,22736.06,0\r\n7123,15815443,Lo,527,Spain,Female,46,10,131414.76,1,1,0,54947.51,0\r\n7124,15682686,Chukwuemeka,722,France,Female,38,3,0,2,0,1,167984.72,0\r\n7125,15697460,Lai,596,Germany,Male,34,4,99441.21,2,0,1,4802.27,0\r\n7126,15748432,Arcuri,746,France,Female,32,4,0,2,1,1,72909.75,0\r\n7127,15698271,Graham,523,France,Female,26,4,0,2,1,0,185488.81,0\r\n7128,15808662,Krylov,624,France,Male,44,3,0,2,1,0,88407.51,0\r\n7129,15690372,Henry,553,Spain,Male,38,1,181110.13,2,1,0,184544.59,0\r\n7130,15781875,Jamieson,850,Spain,Male,33,3,100476.46,2,1,1,136539.13,0\r\n7131,15801473,Moore,599,Germany,Male,33,2,51949.95,2,1,0,85045.92,0\r\n7132,15704509,Tan,492,France,Male,35,8,121063.49,1,0,0,85421.48,0\r\n7133,15694666,Thornton,707,Spain,Male,48,8,88441.64,1,1,1,119903.2,1\r\n7134,15731166,Macleod,743,France,Female,30,1,127023.39,1,1,1,138780.89,0\r\n7135,15728523,Rizzo,522,France,Male,41,5,144147.68,1,1,1,14789.9,0\r\n7136,15788442,Chukwukadibia,681,Spain,Female,57,2,173306.13,1,0,1,131964.66,0\r\n7137,15689781,Ts'ai,826,France,Female,49,0,0,1,0,0,178709.98,1\r\n7138,15764226,Lu,630,Germany,Female,28,8,106425.75,1,1,1,20344.84,0\r\n7139,15809837,Kent,430,Germany,Female,66,6,135392.31,2,1,1,172852.06,1\r\n7140,15805212,Black,806,France,Female,67,1,0,2,0,1,103945.58,0\r\n7141,15716082,Chukwubuikem,703,Spain,Male,39,6,152685.4,1,0,0,183656.12,0\r\n7142,15643056,McMillan,755,Germany,Female,38,1,82083.52,1,0,1,10333.78,0\r\n7143,15654859,Ngozichukwuka,612,Spain,Female,63,2,131629.17,2,1,0,122109.58,1\r\n7144,15761158,Y?an,719,France,Female,54,7,0,2,1,1,125041.52,0\r\n7145,15577515,Sung,554,Germany,Female,55,0,108477.27,1,0,1,140003,1\r\n7146,15723827,Macartney,683,France,Male,30,4,114779.35,1,0,0,183171.47,0\r\n7147,15646594,Ali,749,France,Male,41,5,57568.94,1,1,1,61128.29,0\r\n7148,15712877,Morley,724,Spain,Male,36,1,0,2,1,0,52462.25,0\r\n7149,15598802,Martin,770,Spain,Male,30,8,0,2,0,1,50839.85,0\r\n7150,15699340,Okorie,680,France,Male,37,4,0,2,1,0,61240.87,0\r\n7151,15691150,Ku,699,France,Female,32,4,110559.46,1,1,1,127429.56,0\r\n7152,15608688,Andreyeva,442,France,Male,34,4,0,2,1,0,68343.08,0\r\n7153,15737998,Cheng,529,France,Male,46,8,0,1,0,0,126511.94,1\r\n7154,15735837,Hsia,574,Spain,Male,36,3,0,2,1,1,8559.66,0\r\n7155,15659100,Lane,605,France,Male,33,9,128152.82,1,0,0,147822.81,0\r\n7156,15609070,Findlay,515,Germany,Male,45,7,120961.5,3,1,1,39288.11,1\r\n7157,15650313,Okonkwo,632,Germany,Male,65,6,129472.33,1,1,1,85179.48,0\r\n7158,15627699,Pirogova,558,France,Male,32,10,105000.23,1,1,0,190019.61,0\r\n7159,15591010,McDonald,434,Germany,Male,55,8,109339.17,2,1,0,96405.88,1\r\n7160,15798895,Okonkwo,525,France,Female,59,6,55328.4,1,1,0,83342.73,1\r\n7161,15745375,Nnanna,640,Germany,Male,23,3,72012.76,1,1,0,161333.13,0\r\n7162,15775235,Ku,690,France,Female,36,6,110480.48,1,0,0,81292.33,0\r\n7163,15780088,Porter,607,Spain,Male,34,9,132439.99,1,1,0,177747.72,0\r\n7164,15649379,Somayina,850,France,Female,46,3,0,2,1,1,187980.21,0\r\n7165,15713983,Mao,780,Germany,Male,34,5,94108.54,2,1,0,177235.21,0\r\n7166,15709252,Fuller,616,Germany,Female,28,10,105173.99,1,0,1,29835.37,1\r\n7167,15699238,Craig,618,Spain,Female,40,8,0,2,1,0,80204.38,0\r\n7168,15732884,Trevisano,676,France,Male,29,7,131959.86,1,0,0,189268.81,0\r\n7169,15587297,Ruiz,507,France,Male,33,7,0,2,1,1,85411.01,0\r\n7170,15684722,Fraser,490,France,Male,34,5,122952.9,2,0,0,154360.97,0\r\n7171,15621244,Gallo,678,France,Male,36,0,107379.68,1,1,1,84460.18,0\r\n7172,15744273,Waterhouse,637,Germany,Male,30,6,122641.56,2,1,0,65618.01,0\r\n7173,15682540,Cremonesi,602,France,Female,33,8,0,2,1,1,112928.74,0\r\n7174,15636521,Feng,744,Spain,Female,30,1,124037.28,1,1,1,142210.94,0\r\n7175,15785339,H?,640,France,Female,50,9,117565.03,2,0,0,82559.77,0\r\n7176,15638983,Jara,684,France,Female,38,5,133189.4,1,0,0,127388.06,0\r\n7177,15654625,Wilson,495,Germany,Male,39,8,120252.02,2,1,1,10160.23,0\r\n7178,15697310,O'Callaghan,559,Germany,Female,28,3,152264.81,1,0,0,64242.31,0\r\n7179,15678210,Robson,684,France,Male,38,5,105069.98,2,1,1,198355.28,0\r\n7180,15575438,Pease,613,France,Male,42,7,115076.06,1,1,1,79323.61,0\r\n7181,15632789,Maclean,794,France,Male,30,8,0,2,1,1,24113.91,0\r\n7182,15621423,Lavrentyev,736,France,Female,42,7,117280.23,3,0,0,41921.06,1\r\n7183,15573520,Rhodes,692,Germany,Male,49,6,110540.43,2,0,1,107472.99,0\r\n7184,15740458,Murphy,703,Spain,Male,36,7,135095.47,1,1,0,143859.66,0\r\n7185,15762799,Alexander,720,Germany,Male,23,0,187861.18,2,1,1,104120.17,0\r\n7186,15686885,Nekrasov,777,Germany,Male,44,3,124655.59,2,0,1,79792.3,0\r\n7187,15565996,Arnold,653,France,Male,44,8,0,2,1,1,154639.72,0\r\n7188,15662152,Trevisan,552,France,Female,38,9,134105.01,1,0,0,57850.1,0\r\n7189,15711742,Mason,708,France,Female,34,4,0,1,1,1,62868.33,0\r\n7190,15701885,Tucker,647,France,Female,40,9,0,2,0,1,92357.21,0\r\n7191,15774262,Hobson,597,Germany,Male,52,8,83693.34,2,1,1,161083.53,0\r\n7192,15567839,Gordon,501,France,Male,42,9,114631.23,1,0,1,91429.74,0\r\n7193,15644400,Anderson,709,France,Male,44,9,128601.98,1,1,0,117031.2,0\r\n7194,15797246,Terry,621,Germany,Female,34,2,91258.52,2,1,0,44857.4,0\r\n7195,15778290,Lappin,799,France,Male,70,8,70416.75,1,1,1,36483.52,0\r\n7196,15708714,Santiago,675,France,Female,33,6,0,2,1,0,34045.61,0\r\n7197,15586183,Wallace,561,France,Female,35,5,0,2,1,0,59981.62,0\r\n7198,15761733,King,707,France,Female,42,10,0,2,1,1,152944.39,0\r\n7199,15773934,Fang,670,France,Male,33,6,88294.6,1,1,0,66979.06,0\r\n7200,15705343,May,649,Spain,Female,32,7,0,1,1,0,28797.32,0\r\n7201,15593959,Travis,524,France,Male,28,1,93577.3,1,1,1,51670.82,0\r\n7202,15664615,Nnachetam,689,Germany,Female,30,5,136650.89,1,1,1,41865.72,1\r\n7203,15671014,Zhdanova,573,Spain,Female,72,8,98765.84,1,1,1,96015.53,0\r\n7204,15657778,Jefferson,657,France,Male,33,1,84309.57,2,0,0,103914.4,0\r\n7205,15585192,Cremonesi,686,Spain,Male,39,10,136258.06,1,0,0,89199.51,0\r\n7206,15592914,Fang,683,France,Female,29,9,0,2,1,1,48849.89,0\r\n7207,15770995,Sinclair,753,Germany,Female,47,1,131160.85,1,1,0,197444.69,0\r\n7208,15570990,Begley,520,Spain,Female,30,4,145222.99,2,0,0,145160.96,0\r\n7209,15596165,Degtyarev,547,Germany,Male,25,4,98141.57,2,1,1,52309.8,0\r\n7210,15788131,Atkins,653,France,Male,47,6,0,1,1,0,50695.93,1\r\n7211,15800773,Ikenna,648,Spain,Female,28,9,102282.61,1,1,1,157891.11,0\r\n7212,15690153,Sun,639,France,Female,37,4,116121.84,2,0,1,181850.74,0\r\n7213,15638989,Lettiere,711,France,Female,25,5,190066.54,1,0,0,51345.39,1\r\n7214,15623210,Smith,484,Germany,Female,55,8,149349.58,3,0,0,137519.92,1\r\n7215,15652658,Finch,721,France,Male,36,1,155176.83,2,1,1,49653.37,0\r\n7216,15684440,Monaldo,548,Germany,Male,32,2,98986.28,1,1,1,55867.38,0\r\n7217,15730287,Ugonna,679,France,Male,41,8,147726.98,3,1,0,172749.4,1\r\n7218,15720353,Chiang,553,France,Male,41,1,0,2,1,0,90607.31,0\r\n7219,15767231,Sun,757,France,Male,36,7,144852.06,1,0,0,130861.95,0\r\n7220,15761554,Blackburn,581,France,Male,54,4,89299.81,1,0,0,5558.47,1\r\n7221,15706637,Chang,718,Spain,Male,40,9,0,2,0,0,121537.91,0\r\n7222,15690492,Palermo,625,France,Male,41,6,97663.16,2,1,0,57128.78,0\r\n7223,15694237,McEwan,744,Spain,Male,39,4,95161.75,1,1,0,19409.77,0\r\n7224,15729771,Davide,799,Germany,Male,31,9,154586.92,1,0,1,88604.89,1\r\n7225,15609823,Chieloka,751,Spain,Female,34,8,127095.14,2,0,0,479.54,0\r\n7226,15793366,Humphreys,781,Germany,Male,35,7,92526.15,2,1,1,173837.54,0\r\n7227,15614813,Cocci,777,Germany,Female,46,0,107362.8,1,1,0,487.3,0\r\n7228,15566495,Hanson,704,Spain,Female,24,2,0,1,1,0,35600.25,1\r\n7229,15707602,Macleod,539,France,Female,47,2,127286.04,2,1,1,166929.43,1\r\n7230,15635244,Ritchie,716,France,Female,29,6,0,2,1,1,98998.61,0\r\n7231,15805627,Nebechukwu,670,France,Male,37,2,0,2,1,1,54229.74,0\r\n7232,15607986,Nnamutaezinwa,555,France,Male,40,10,139930.18,1,1,1,105720.09,0\r\n7233,15799785,Ikemefuna,679,Germany,Female,30,4,77949.69,1,1,1,121151.46,0\r\n7234,15699963,Scott,571,France,Male,38,1,121405.04,1,1,1,154844.22,0\r\n7235,15624595,Chiang,512,Spain,Female,35,5,124580.69,1,1,1,18785.48,0\r\n7236,15629750,Artyomova,697,France,Male,35,5,133087.76,1,1,0,64771.61,0\r\n7237,15651460,Hsieh,424,Spain,Male,34,7,0,1,1,1,16250.61,0\r\n7238,15753550,Levien,684,France,Female,43,7,0,2,1,0,131093.99,0\r\n7239,15594133,Erskine,697,Spain,Male,62,7,0,1,1,0,129188.18,1\r\n7240,15772329,Fiorentino,580,Germany,Male,45,8,103741.14,1,1,0,47428.73,1\r\n7241,15591552,Okonkwo,600,France,Female,32,7,98877.95,1,1,0,132973.21,0\r\n7242,15750921,Monds,521,France,Male,37,5,105843.26,2,1,1,84908.2,0\r\n7243,15701687,Campbell,664,Spain,Male,44,7,77526.66,3,0,0,57338.56,1\r\n7244,15728906,Ibekwe,634,France,Male,77,5,0,2,1,1,161579.85,0\r\n7245,15670029,Marcelo,445,France,Female,33,7,0,2,1,0,122625.68,0\r\n7246,15763579,Castro,702,Germany,Female,36,2,105264.88,2,1,1,52909.87,0\r\n7247,15728010,Capon,485,France,Male,37,5,0,2,0,1,170226.47,0\r\n7248,15663194,Voronova,582,Germany,Female,40,3,110150.43,1,1,1,191757.65,1\r\n7249,15736510,Loggia,605,Spain,Female,57,2,0,3,1,0,66652.75,1\r\n7250,15745804,Law,628,France,Male,25,7,0,2,1,1,195977.75,0\r\n7251,15631451,Grant,604,Spain,Female,28,6,0,2,1,1,69056.26,0\r\n7252,15746995,Greco,724,Germany,Male,31,9,138166.3,1,1,0,12920.43,0\r\n7253,15730673,Dietz,567,Germany,Male,40,7,122265.24,1,1,0,138552.74,0\r\n7254,15734649,Martel,779,Spain,Female,55,0,133295.98,1,1,0,22832.71,1\r\n7255,15701081,Jarvis,785,France,Male,36,2,0,1,0,1,61811.1,0\r\n7256,15632503,Meng,563,France,Female,32,0,148326.09,1,1,0,191604.27,1\r\n7257,15585928,Hay,821,Germany,Female,31,2,68927.57,1,1,1,25445,0\r\n7258,15648681,Voronoff,747,France,Female,47,5,139914.6,4,0,1,129964.56,1\r\n7259,15747757,Trevascus,600,Germany,Female,58,8,118723.11,1,0,0,6209.51,1\r\n7260,15718921,Ho,625,Spain,Male,32,7,106957.28,1,1,1,134794.02,0\r\n7261,15571081,Hansen,773,France,Female,41,7,190238.93,1,1,1,57549.65,0\r\n7262,15734578,Craig,726,France,Female,53,1,113537.73,1,0,1,28367.21,0\r\n7263,15579583,Hall,641,Spain,Female,40,4,101090.27,1,1,1,51703.09,0\r\n7264,15622729,Sun,649,France,Female,46,2,0,2,1,1,66602.7,0\r\n7265,15662189,Durant,434,Spain,Male,33,3,0,1,1,1,2739.71,0\r\n7266,15692718,Jackson,738,France,Female,38,7,0,2,0,0,69227.42,0\r\n7267,15762716,Chigozie,762,Spain,Female,60,10,168920.75,1,1,0,31445.03,1\r\n7268,15724851,Farmer,507,Germany,Male,31,9,111589.67,1,1,0,150037.19,0\r\n7269,15587266,Douglas,606,Germany,Female,27,6,172310.33,1,0,1,111448.92,0\r\n7270,15675926,Ardis,655,Germany,Male,34,7,118028.35,1,1,0,51226.32,1\r\n7271,15706268,Smith,697,Germany,Male,51,1,147910.3,1,1,1,53581.14,0\r\n7272,15581871,Butler,504,Germany,Male,42,7,131287.36,2,1,1,149697.78,0\r\n7273,15666166,Pettry,653,France,Female,74,0,121276.32,1,1,1,160348.31,0\r\n7274,15671582,John,660,Spain,Male,38,6,109869.32,1,1,1,154641.91,0\r\n7275,15680901,Potter,652,France,Female,34,6,97435.85,2,1,1,104331.76,0\r\n7276,15642336,Shaw,669,France,Female,42,9,0,2,0,0,135630.32,0\r\n7277,15653147,Boyle,594,France,Male,35,2,133853.27,1,1,1,65361.66,0\r\n7278,15571284,Elmore,756,Germany,Male,32,0,109528.16,2,1,1,56176.31,0\r\n7279,15591360,Udinesi,642,France,Female,33,4,84607.34,2,0,1,60059.47,0\r\n7280,15810485,Sun,486,Germany,Male,37,1,101438,1,0,0,51364.56,0\r\n7281,15611973,Tuan,804,France,Male,55,7,0,2,1,1,118752.6,0\r\n7282,15735572,Lawrence,629,France,Male,59,9,113657.83,1,1,1,116848.79,1\r\n7283,15567860,Burrows,581,Spain,Female,44,7,189318.16,2,1,0,45026.23,1\r\n7284,15795690,Shao,667,France,Male,31,3,99513.91,1,1,1,189657.26,0\r\n7285,15706464,White,667,Spain,Male,35,4,97585.32,2,0,0,57213.46,0\r\n7286,15725028,Chialuka,679,France,Male,29,3,0,2,1,1,63687.06,0\r\n7287,15751167,Toscano,680,France,Female,43,4,0,2,1,1,58761.33,0\r\n7288,15633944,McKay,644,Spain,Male,32,3,136659.74,1,1,1,14187.78,0\r\n7289,15672637,Voronkov,571,France,Female,30,4,85755.86,1,1,0,145115.95,0\r\n7290,15680895,Sal,627,Spain,Female,35,7,0,1,1,0,187718.26,0\r\n7291,15793825,Ikechukwu,536,France,Male,39,4,0,2,1,0,27150.35,0\r\n7292,15611318,Kruglova,599,Spain,Male,33,4,51690.89,1,1,0,111622.76,1\r\n7293,15768474,Clements,744,Spain,Male,34,3,0,2,1,0,27244.35,0\r\n7294,15716276,Kennedy,709,France,Female,34,2,111669.68,1,1,0,57029.66,0\r\n7295,15623668,Johnson,653,Germany,Male,31,2,154741.45,2,0,0,25183.01,0\r\n7296,15696361,Chung,648,Germany,Male,31,7,125681.51,1,0,1,129980.93,0\r\n7297,15607988,Garland,663,Germany,Female,37,8,155303.71,1,1,0,118716.63,0\r\n7298,15637891,Docherty,613,Germany,Female,43,4,140681.68,1,0,1,20134.07,0\r\n7299,15789865,Nnaife,620,France,Male,28,9,71902.52,1,0,1,190208.23,0\r\n7300,15627190,Lettiere,661,France,Male,51,6,146606.6,1,1,1,68021.9,0\r\n7301,15788224,Sanderson,669,Germany,Male,45,1,123949.75,1,0,0,110881.56,0\r\n7302,15702149,Fomin,767,Germany,Female,33,1,144753.21,1,1,1,132480.75,0\r\n7303,15708236,Wright,491,France,Female,72,6,91285.22,1,1,1,7032.95,0\r\n7304,15568469,Buckley,653,France,Male,43,0,0,2,1,0,27862.58,0\r\n7305,15764444,Pan,679,Germany,Male,58,8,125850.53,2,1,1,87008.17,0\r\n7306,15794204,Manna,687,France,Male,28,7,108116.66,1,1,1,27411.19,0\r\n7307,15807546,Chinwendu,837,France,Female,38,2,0,2,1,1,46395.21,0\r\n7308,15782159,Ndubuagha,850,France,Male,28,8,67639.56,2,1,1,194245.29,0\r\n7309,15618703,White,663,Spain,Female,53,6,150200.23,1,0,1,151317.27,1\r\n7310,15793317,Hale,547,Spain,Female,22,7,141287.15,1,1,0,118142.79,0\r\n7311,15740487,Ross,627,France,Female,41,6,0,3,1,1,138700.75,1\r\n7312,15722479,Ikenna,707,France,Male,37,1,0,2,0,1,6035.51,0\r\n7313,15688264,Nkemdilim,629,France,Female,43,0,0,2,1,1,41263.69,0\r\n7314,15583067,McMillan,687,France,Female,36,4,97157.96,1,0,1,63185.05,0\r\n7315,15686670,Duke,588,France,Female,36,2,0,2,1,0,92536,1\r\n7316,15593345,Bradbury,502,Germany,Female,33,6,125241.17,2,1,1,158736.07,0\r\n7317,15811690,Bayley,793,Germany,Male,54,2,128966.13,1,0,0,18633.4,1\r\n7318,15734008,Bartlett,727,Germany,Male,59,5,152581.06,1,1,0,71830.1,1\r\n7319,15771856,Cremin,632,Spain,Female,32,1,0,2,1,0,19525.65,0\r\n7320,15762045,Gilchrist,474,Germany,Female,37,5,142688.57,2,1,1,110953.33,0\r\n7321,15778142,Shih,850,Germany,Female,31,1,130089.56,2,1,1,4466.21,0\r\n7322,15689268,Fitzpatrick,584,France,Male,36,9,0,1,1,1,105818.51,0\r\n7323,15721507,Pagan,713,France,Female,32,1,117094.02,1,0,0,149558.83,1\r\n7324,15750476,Hendrick,742,Spain,Male,24,8,0,2,1,0,4070.28,0\r\n7325,15810723,Sanderson,607,France,Female,39,10,0,3,1,0,132741.13,1\r\n7326,15787229,Samsonova,761,Spain,Female,34,2,0,2,1,0,61251.25,0\r\n7327,15570508,Azubuike,600,France,Male,49,7,90218.9,1,1,0,91347.76,0\r\n7328,15617065,Pan,650,Spain,Male,42,4,194532.66,1,1,0,171045.31,1\r\n7329,15689786,Massie,850,Germany,Male,56,1,169743.83,1,0,0,155850.4,1\r\n7330,15648876,Sandover,501,France,Female,34,5,0,1,1,0,27380.99,0\r\n7331,15802106,Craig,418,France,Male,34,8,155973.88,1,1,0,154208.96,0\r\n7332,15773869,Onwudiwe,797,Spain,Male,59,4,129321.44,1,1,1,93624.55,0\r\n7333,15711635,Chu,788,Germany,Female,42,6,138650.49,2,1,0,64746.07,0\r\n7334,15795527,Zetticci,699,Spain,Male,43,2,136487.86,2,1,0,82815.93,0\r\n7335,15759133,Vaguine,616,France,Male,18,6,0,2,1,1,27308.58,0\r\n7336,15679394,Owen,651,France,Female,41,4,38617.2,1,1,1,104876.8,0\r\n7337,15801072,Hurst,654,France,Female,28,7,0,2,1,0,151316.37,0\r\n7338,15646082,Harding,676,France,Female,34,8,82909.14,1,1,0,91817.38,1\r\n7339,15796111,Smith,708,Germany,Female,54,8,145151.4,1,0,1,125311.17,1\r\n7340,15670646,Moore,499,Spain,Female,42,0,147187.84,1,1,1,14868.94,1\r\n7341,15578722,Bradley,689,France,Male,39,4,0,2,1,0,196112.45,0\r\n7342,15815095,Burfitt,850,Spain,Male,54,7,108185.81,2,0,0,24093.4,1\r\n7343,15730360,Mackenzie,502,France,Male,30,4,0,2,1,1,66263.87,0\r\n7344,15763194,Milanesi,643,France,Male,34,7,0,2,0,1,100304.13,0\r\n7345,15720725,Shubin,762,France,Male,28,2,0,2,1,0,167909.52,0\r\n7346,15567834,Nieves,719,France,Male,49,5,105918.1,1,1,1,16246.59,0\r\n7347,15720644,Martin,789,France,Male,27,6,0,2,1,0,103603.65,0\r\n7348,15811742,Jen,553,Spain,Male,42,7,0,2,1,0,7680.23,0\r\n7349,15813363,Woods,448,Spain,Male,25,2,0,2,0,0,95215.73,0\r\n7350,15717629,Docherty,632,Germany,Male,42,6,59972.26,2,0,1,148172.94,0\r\n7351,15713160,Lin,669,Spain,Male,25,7,157228.61,2,1,0,124382.9,0\r\n7352,15568878,Cheng,654,Spain,Male,34,5,0,2,1,0,159311.46,0\r\n7353,15809800,Korovina,726,France,Female,38,4,0,2,0,0,6787.48,0\r\n7354,15736420,Macdonald,596,France,Male,21,4,210433.08,2,0,1,197297.77,1\r\n7355,15757933,Hardy,733,Germany,Female,30,1,102452.71,1,1,0,21556.95,0\r\n7356,15623072,Shaw,529,Spain,Female,35,5,0,2,1,0,56518,0\r\n7357,15683993,Knight,493,France,Female,37,8,142987.46,2,1,0,158840.99,0\r\n7358,15570947,Bruny,615,Spain,Female,29,7,143330.56,2,1,1,126396.01,0\r\n7359,15797767,Ikedinachukwu,600,France,Female,49,6,0,1,0,1,148087.88,1\r\n7360,15731989,Moran,666,France,Male,36,4,120165.4,2,1,0,33701.5,0\r\n7361,15591035,Macleod,644,Spain,Male,54,6,0,1,0,1,84622.37,0\r\n7362,15586479,Yin,692,France,Female,36,4,0,1,1,0,185580.89,1\r\n7363,15605872,Felix,707,France,Male,73,6,66573.17,1,1,1,62768.8,0\r\n7364,15666012,Rippey,603,France,Male,40,4,102833.46,2,1,1,38829.11,0\r\n7365,15641733,Mishina,671,France,Female,34,5,164757.56,1,1,0,110748.88,0\r\n7366,15593178,Graham,568,Spain,Female,36,10,153610.61,1,1,1,54083.8,1\r\n7367,15649183,Johnston,598,Spain,Female,35,8,0,3,0,1,88658.73,0\r\n7368,15736399,Korovin,606,Spain,Male,42,10,0,2,1,0,177938.52,0\r\n7369,15751137,Lei,850,Germany,Female,36,3,169025.83,1,1,0,174235.06,0\r\n7370,15757188,Chimaijem,644,Spain,Female,26,4,153455.72,2,1,1,82696.84,0\r\n7371,15726167,Scott,655,France,Male,37,4,0,2,1,1,142415.97,0\r\n7372,15624850,Grant,850,France,Male,30,10,153972.89,2,1,0,62811.03,0\r\n7373,15717700,McIntyre,683,Spain,Male,34,9,114609.55,2,0,1,25339.29,0\r\n7374,15716347,Griffin,663,Germany,Male,37,7,143625.83,2,0,1,176487.05,0\r\n7375,15696287,Converse,682,Germany,Female,38,1,116520.28,1,1,1,49833.5,1\r\n7376,15638871,Ch'ang,639,France,Male,77,6,80926.02,2,1,1,55829.25,0\r\n7377,15765093,Coates,704,France,Male,23,6,166594.78,1,1,1,155823.2,0\r\n7378,15592999,Reid,691,France,Female,40,0,115465.98,1,1,1,60622.61,0\r\n7379,15641715,Ts'ui,599,France,Male,34,8,0,2,1,1,174196.68,0\r\n7380,15607746,Belstead,573,France,Female,36,1,0,1,1,1,56905.38,0\r\n7381,15625311,Dickinson,589,Germany,Female,41,7,92618.62,1,1,1,101178.85,0\r\n7382,15573077,Nwora,620,Germany,Female,25,8,141825.88,1,1,1,73857.94,1\r\n7383,15735106,Bishop,647,Spain,Male,28,6,149594.02,2,1,0,102325.19,0\r\n7384,15672912,Loggia,737,Spain,Female,39,7,130051.66,2,0,0,55356.39,1\r\n7385,15589881,Rowe,634,France,Female,41,7,0,2,1,1,131284.93,0\r\n7386,15660144,Balashov,660,France,Male,38,4,0,2,0,0,88080.43,0\r\n7387,15664083,Ulyanova,666,Germany,Female,37,2,158468.76,1,0,1,93266.01,0\r\n7388,15690898,Bogolyubova,696,France,Male,44,8,161889.79,1,0,0,75562.47,0\r\n7389,15808023,Remington,836,France,Female,29,9,133681.78,1,1,1,153747.73,0\r\n7390,15676909,Mishin,667,Spain,Female,34,5,0,2,1,0,163830.64,0\r\n7391,15764922,Tu,596,Spain,Male,20,3,187294.46,1,1,0,103456.47,0\r\n7392,15766734,Castiglione,430,France,Male,31,5,0,1,1,0,95655.16,0\r\n7393,15795079,Nnaife,596,Spain,Male,67,6,0,2,1,1,138350.74,0\r\n7394,15757434,Yang,599,France,Male,28,7,119706.22,1,0,0,31190.42,0\r\n7395,15673747,Ayers,519,France,Female,22,8,0,1,0,1,167553.06,0\r\n7396,15808386,Cocci,721,Germany,Female,45,7,138523.2,1,0,0,59604.45,1\r\n7397,15603565,Mackenzie,603,Spain,Female,56,5,90778.76,2,1,0,162223.67,1\r\n7398,15744044,Fiorentini,572,Germany,Male,47,4,99353.42,1,1,0,196549.85,1\r\n7399,15577771,Akabueze,453,Germany,Female,40,1,111524.49,1,1,1,120373.84,1\r\n7400,15769548,Hyde,668,France,Female,37,7,128645.67,1,1,0,92149.64,0\r\n7401,15802071,Levi,762,Germany,Male,35,1,117458.51,1,0,1,178361.48,1\r\n7402,15677395,Nwabugwu,633,France,Female,39,9,129189.15,2,0,0,170998.83,0\r\n7403,15632010,Chia,647,Spain,Male,33,7,121260.19,2,1,0,77216.48,0\r\n7404,15779492,Trevisano,796,Spain,Male,56,6,94231.13,1,0,0,121164.6,1\r\n7405,15694677,Bennetts,733,France,Male,39,1,0,2,1,1,141841.31,0\r\n7406,15704315,Teng,556,France,Male,34,8,163757.06,1,1,1,104000.06,0\r\n7407,15742009,Hsueh,489,Spain,Male,58,4,0,2,1,1,191419.32,0\r\n7408,15766663,Mahmood,639,France,Male,22,4,0,2,1,0,28188.96,0\r\n7409,15742297,Sinclair,715,France,Male,35,2,141005.47,1,1,1,60407.93,0\r\n7410,15688059,Chin,807,Germany,Female,42,9,105356.09,2,1,1,130489.37,0\r\n7411,15752344,She,714,Spain,Male,34,5,0,2,1,0,193040.32,0\r\n7412,15698749,He,626,Germany,Female,23,6,85897.95,1,1,0,109742.8,0\r\n7413,15631693,Hill,697,France,Male,36,7,0,2,1,1,74760.32,0\r\n7414,15604536,Vachon,850,Germany,Female,31,4,164672.66,1,0,1,61936.1,0\r\n7415,15802869,Ball,737,Germany,Female,45,2,99169.67,2,1,1,78650.95,0\r\n7416,15635598,Hsieh,812,France,Male,29,6,0,2,0,0,168023.6,0\r\n7417,15592326,Baker,583,France,Male,36,8,0,2,0,1,5571.59,0\r\n7418,15736533,Monaldo,730,Germany,Female,37,5,124053.03,1,1,0,118591.67,0\r\n7419,15647191,Lucchesi,677,France,Male,36,4,0,2,1,0,7824.31,0\r\n7420,15622507,Hamilton,748,Germany,Female,40,3,103499.09,2,0,0,38153.19,0\r\n7421,15765487,Kuo,753,Germany,Female,38,9,151766.71,1,1,1,180829.99,0\r\n7422,15646521,Fan,634,Spain,Female,36,1,0,1,1,1,143960.72,0\r\n7423,15746258,Wright,622,France,Male,29,7,101486.96,1,1,1,8788.35,0\r\n7424,15692430,Milano,699,Germany,Male,36,2,123601.56,2,1,0,103557.85,0\r\n7425,15625501,Wall,570,Germany,Male,38,1,127201.58,1,1,0,147168.28,1\r\n7426,15640521,Chidumaga,552,Germany,Male,33,3,144962.74,1,1,0,58844.84,1\r\n7427,15790630,Olisaemeka,619,France,Female,48,4,0,1,0,0,18094.96,1\r\n7428,15664720,Kovalyova,714,Spain,Male,33,8,122017.19,1,0,0,162515.17,0\r\n7429,15750055,Onio,503,Spain,Male,32,9,100262.88,2,1,1,157921.25,0\r\n7430,15644878,Hill,685,Spain,Female,43,6,117302.62,1,0,0,68701.73,0\r\n7431,15754578,Okeke,606,France,Female,35,0,135984.15,2,1,0,186778.89,0\r\n7432,15705379,Upjohn,678,France,Male,38,3,0,2,1,0,66561.6,0\r\n7433,15761047,H?,724,Germany,Male,31,2,160997.54,2,0,1,64831.36,0\r\n7434,15671293,Marcus,779,Germany,Female,37,2,128389.63,1,1,1,6589.16,1\r\n7435,15687527,Yobachukwu,638,Spain,Male,35,1,0,2,1,0,165370.66,0\r\n7436,15647898,Russell,610,Spain,Female,50,5,130554.51,3,1,0,184758.17,1\r\n7437,15671534,Hovell,646,Germany,Female,57,6,90212,1,1,0,13911.27,1\r\n7438,15591248,Chukwumaobim,628,France,Female,29,9,71996.29,1,1,1,34857.46,0\r\n7439,15676156,Boyle,528,France,Female,32,4,85615.66,2,1,0,156192.43,0\r\n7440,15812918,Scott,432,France,Female,27,6,62339.81,2,0,0,53874.67,0\r\n7441,15604130,Johnstone,622,Spain,Female,47,6,142319.03,1,0,0,100183.05,0\r\n7442,15700549,Alvares,721,France,Male,54,5,0,2,1,1,4493.12,0\r\n7443,15715519,McDavid,614,Spain,Male,36,5,0,2,1,0,130610.78,0\r\n7444,15707042,Dellucci,634,France,Female,24,2,87413.19,1,1,0,63340.65,0\r\n7445,15605276,Brothers,742,France,Female,29,4,0,2,1,1,180066.59,0\r\n7446,15630592,Sanders,516,France,Female,45,4,0,1,1,0,95273.73,1\r\n7447,15636626,Morrison,718,France,Male,35,3,97560.16,1,1,1,53511.74,0\r\n7448,15740411,Molle,636,Germany,Male,30,8,141787.31,2,1,1,109685.61,0\r\n7449,15593834,Genovese,691,Spain,Male,36,7,129934.64,1,0,0,75664.56,1\r\n7450,15804235,Zetticci,698,France,Female,37,2,166178.02,2,1,1,71972.95,0\r\n7451,15679801,Hsueh,712,Spain,Female,39,5,163097.55,2,1,1,23702.42,0\r\n7452,15673907,Alexander,659,France,Male,20,8,0,2,0,0,112572.02,0\r\n7453,15636562,Muravyova,573,Spain,Male,44,8,0,2,0,0,62424.46,0\r\n7454,15702571,Wright,778,Germany,Female,35,1,151958.19,3,1,1,131238.37,1\r\n7455,15627365,Calabresi,732,France,Male,46,0,0,2,1,1,184350.78,0\r\n7456,15748499,Johnson,550,Germany,Male,33,4,118400.91,1,0,1,13999.64,1\r\n7457,15598614,Lucchesi,790,Spain,Male,20,8,0,2,1,0,168152.76,0\r\n7458,15668889,Galgano,665,Germany,Female,43,2,116322.27,4,1,0,35640.12,1\r\n7459,15800049,Grigoryeva,728,Spain,Female,43,5,0,1,1,1,120088.17,0\r\n7460,15583724,Raymond,645,Spain,Female,29,4,0,2,1,1,74346.11,0\r\n7461,15622083,Paterson,647,Germany,Male,30,6,143138.91,2,1,0,2955.46,0\r\n7462,15645571,Genovese,596,Spain,Male,32,4,0,2,0,1,146504.35,0\r\n7463,15598266,Martin,610,France,Male,40,9,0,1,1,1,149602.54,0\r\n7464,15667934,Moretti,512,France,Male,36,0,129804.17,1,1,0,53020.9,0\r\n7465,15569682,Leckie,768,Germany,Male,37,9,108308.11,1,1,0,41788.25,1\r\n7466,15772941,Lane,666,Germany,Male,30,3,110153.27,1,0,1,74849.46,0\r\n7467,15586174,Brodney,700,Germany,Female,30,4,116377.48,1,1,1,134417.31,0\r\n7468,15803682,Angelo,651,Germany,Female,37,10,117791.06,2,1,1,75837.58,0\r\n7469,15627328,Millar,542,Spain,Female,26,2,0,2,1,1,54869.54,0\r\n7470,15717065,Balashov,686,France,Female,35,8,105419.73,1,1,0,35356.46,0\r\n7471,15602456,Afanasyev,850,Germany,Female,47,4,99219.47,2,1,1,122141.13,0\r\n7472,15721569,Chialuka,658,Germany,Female,55,8,119327.93,1,0,1,119439.66,0\r\n7473,15573798,Yermolayev,448,France,Female,36,6,83947.12,2,1,0,81999.53,0\r\n7474,15638272,Tien,609,Spain,Male,32,4,99883.16,1,1,1,120594.85,0\r\n7475,15799859,Lucchesi,704,France,Male,50,4,165438.26,1,1,0,120770.75,1\r\n7476,15599152,Lai,698,France,Male,31,1,156111.24,1,0,0,134790.74,0\r\n7477,15737909,Bates,759,France,Male,44,2,111095.58,2,1,0,100137.7,0\r\n7478,15646190,Saunders,677,France,Female,56,0,119963.45,1,0,0,158325.87,1\r\n7479,15711249,Chukwuemeka,544,Spain,Male,22,4,0,2,1,0,70007.67,0\r\n7480,15671987,Meagher,567,Spain,Male,35,8,153137.74,1,1,0,88659.07,0\r\n7481,15812766,Golubeva,490,Spain,Male,40,6,156111.08,1,0,0,190889.13,0\r\n7482,15778589,Collier,626,France,Male,34,7,113014.7,2,1,1,56646.28,0\r\n7483,15750104,Chan,718,Germany,Male,43,5,132615.73,2,1,0,32999.1,0\r\n7484,15784526,Chen,616,France,Male,44,5,102016.38,1,0,1,178235.37,1\r\n7485,15646563,Wright,772,France,Female,35,9,0,1,0,1,25448.31,0\r\n7486,15744423,Cocci,561,France,Male,32,5,0,2,1,0,84871.99,0\r\n7487,15593694,Williams,814,France,Male,49,8,0,2,0,0,157822.54,0\r\n7488,15785367,McGuffog,651,France,Female,56,4,0,1,0,0,84383.22,1\r\n7489,15687765,Chukwujamuike,538,Germany,Female,42,4,80380.24,1,1,0,119216.46,0\r\n7490,15789014,Scott,600,France,Female,26,6,108909.12,1,1,0,82547.01,0\r\n7491,15703177,Bell,654,France,Female,35,2,90865.8,1,1,1,86764.46,0\r\n7492,15660263,Olisaemeka,622,France,Male,40,4,99799.76,2,1,0,197372.13,0\r\n7493,15776545,Napolitani,682,France,Male,28,10,200724.96,1,0,1,82872.64,1\r\n7494,15683276,Sargood,610,Spain,Female,37,10,140363.95,2,1,1,129563.86,0\r\n7495,15599272,Harrington,795,France,Female,36,1,151844.64,1,1,1,135388.89,0\r\n7496,15589541,Sutherland,557,France,Female,27,2,0,2,0,1,4497.55,0\r\n7497,15608804,Allan,824,Germany,Male,49,8,133231.48,1,1,1,67885.37,0\r\n7498,15645820,Folliero,698,France,Male,27,7,0,2,1,0,111471.55,0\r\n7499,15659031,Mordvinova,630,France,Female,36,8,126598.99,2,1,1,134407.93,0\r\n7500,15790113,Schofield,609,Germany,Female,71,6,113317.1,1,1,0,108258.22,1\r\n7501,15652289,Williams,694,France,Male,47,4,0,2,1,0,197528.62,0\r\n7502,15605341,Baird,681,France,Female,58,8,93173.88,1,1,1,139761.25,0\r\n7503,15697844,Whitehouse,721,Spain,Female,32,10,0,1,1,0,136119.96,1\r\n7504,15652048,Thompson,563,Germany,Male,44,7,105007.31,2,1,1,197812.16,0\r\n7505,15587038,Ogochukwu,654,Spain,Female,32,2,0,1,1,1,51972.92,1\r\n7506,15660528,Niu,659,Spain,Male,27,4,0,2,1,0,99341.87,0\r\n7507,15700300,Okoli,674,Germany,Female,44,4,131593.85,1,0,1,171345.02,1\r\n7508,15642001,Lorenzen,576,Germany,Male,44,9,119530.52,1,1,0,119056.68,1\r\n7509,15580366,Okechukwu,566,Germany,Male,54,4,118614.6,2,1,1,172601.62,0\r\n7510,15657228,Anderson,545,Germany,Male,37,9,95829.13,2,0,1,104936.88,0\r\n7511,15729377,Ku,798,France,Male,36,1,0,2,1,1,159044.1,0\r\n7512,15686913,Kung,757,France,Male,38,0,0,1,1,0,83263.06,0\r\n7513,15631267,Lu,641,France,Male,50,6,153590.73,2,1,1,130910.78,0\r\n7514,15632275,Trevisano,718,France,Male,29,2,0,1,1,0,126336.72,0\r\n7515,15715907,Onwubiko,699,France,Male,64,9,113109.52,1,1,0,27980.8,1\r\n7516,15764841,Vidler,623,France,Female,35,0,130557.24,1,1,1,47880.71,0\r\n7517,15748649,Shen,644,France,Male,40,8,93183.19,1,1,0,73882.49,0\r\n7518,15771409,McGregor,586,France,Male,58,7,151933.63,1,1,0,162960.05,1\r\n7519,15779207,Nnamdi,500,Germany,Male,30,2,125495.64,2,1,1,68807.47,0\r\n7520,15814116,Castiglione,583,France,Female,42,7,0,2,1,0,144039.05,0\r\n7521,15665087,Bergamaschi,595,Germany,Female,26,8,118547.72,1,1,1,151192.18,0\r\n7522,15611189,Allingham,670,Spain,Male,43,1,97792.21,1,0,0,120225.62,0\r\n7523,15729718,Stelzer,610,France,Male,41,6,0,3,0,0,56118.81,1\r\n7524,15733602,Rubin,814,Spain,Female,72,2,0,2,0,1,130853.03,0\r\n7525,15620103,Ho,660,France,Female,40,8,167181.01,1,1,1,185156.94,0\r\n7526,15770406,Watson,580,Germany,Male,35,9,121355.19,1,0,1,35671.45,0\r\n7527,15800554,Perry,850,France,Female,81,1,0,2,1,1,59568.24,0\r\n7528,15611409,Sun,676,Spain,Male,35,0,0,2,0,0,139911.58,0\r\n7529,15646535,Harrell,578,France,Male,46,5,113226.47,1,1,0,56770.76,0\r\n7530,15575430,Robson,579,France,Female,33,1,118392.75,1,1,1,157564.75,0\r\n7531,15711299,Wilson,711,Germany,Female,52,8,145262.54,1,0,1,131473.31,0\r\n7532,15642063,Kelechi,692,France,Male,40,6,163505.16,1,0,0,90424.09,0\r\n7533,15706602,Bates,760,Spain,Female,33,1,118114.28,2,0,1,156660.21,0\r\n7534,15592773,Eberegbulam,630,Germany,Female,51,0,108449.23,3,0,0,88372.69,1\r\n7535,15786539,Olisaemeka,808,France,Male,32,1,0,2,1,1,46200.71,0\r\n7536,15737542,Davey,611,Germany,Female,36,10,103294.56,1,1,0,160548.12,0\r\n7537,15590234,De Luca,697,France,Female,42,1,0,1,1,0,1262.83,1\r\n7538,15773776,Ho,655,France,Female,38,6,0,1,1,1,188639.28,0\r\n7539,15728082,Vasiliev,601,Spain,Male,28,6,0,2,1,0,14665.28,0\r\n7540,15609987,Smith,755,France,Male,42,2,119919.12,1,1,0,156868.21,0\r\n7541,15735330,Sung,553,France,Male,37,1,0,1,1,0,30461.55,0\r\n7542,15649430,White,723,France,Male,28,4,0,2,1,1,123885.88,0\r\n7543,15768777,Wang,507,Spain,Female,34,4,0,2,1,1,60688.38,0\r\n7544,15777893,Davide,777,France,Male,43,1,0,2,1,0,21785.91,0\r\n7545,15791326,Nnamdi,566,France,Male,34,3,0,1,0,0,188135.69,0\r\n7546,15615176,Welsh,732,France,Male,26,7,0,2,1,0,154364.66,0\r\n7547,15735221,Sousa,697,France,Female,42,10,0,2,1,0,61312.15,0\r\n7548,15617991,Andrews,555,France,Male,29,4,128744.04,1,1,1,47454.93,0\r\n7549,15658504,Chiawuotu,584,Germany,Female,62,9,137727.34,2,0,1,121102.9,0\r\n7550,15785705,Thomson,705,Germany,Female,44,10,106731.58,1,1,0,137419.87,1\r\n7551,15801817,Carpenter,688,France,Female,38,7,123544.21,1,1,1,157664.02,0\r\n7552,15752578,Yefimova,626,France,Female,37,2,133968.96,2,1,0,148689.65,0\r\n7553,15781574,Ma,636,Spain,Male,76,9,126534.6,1,1,1,39789.62,0\r\n7554,15792107,Black,719,Spain,Female,35,8,0,1,1,1,165162.4,0\r\n7555,15569917,Obijiaku,706,Spain,Male,30,6,87609.68,2,0,0,137674.55,1\r\n7556,15721504,King,731,Spain,Male,41,3,0,2,1,0,101371.72,0\r\n7557,15757306,Miller,738,Spain,Male,49,3,0,3,1,1,65066.48,1\r\n7558,15647295,Chin,426,France,Male,34,9,0,2,1,0,107876.91,0\r\n7559,15642098,Cox,622,Spain,Female,36,0,108960,2,1,0,111180.3,1\r\n7560,15696120,Wallace,701,Spain,Female,30,2,0,2,1,0,115650.63,0\r\n7561,15675176,Price,512,France,Male,51,6,144953.31,1,1,1,165035.17,0\r\n7562,15700046,Yuan,635,France,Male,41,4,103544.88,2,1,0,193746.55,0\r\n7563,15782089,Mullen,685,France,Male,33,6,0,1,1,0,58458.26,0\r\n7564,15706394,Howell,609,France,Male,53,7,0,2,0,1,52332.85,0\r\n7565,15759387,McIntosh,598,Germany,Male,38,1,101487.18,1,1,1,75959.1,1\r\n7566,15623369,Clifton,708,France,Male,52,10,105355.81,1,1,0,123.07,1\r\n7567,15732943,Okwuoma,574,Spain,Male,36,4,77967.5,1,1,0,167066.95,1\r\n7568,15750545,Chidiebere,629,France,Male,44,5,0,4,0,0,117572.59,1\r\n7569,15809909,Fan,422,Spain,Female,54,4,0,2,1,1,7166.71,0\r\n7570,15642448,Onyemauchechukwu,656,Spain,Male,28,8,120047.77,1,1,1,137173.39,0\r\n7571,15791944,Harker,697,France,Male,32,7,175464.85,3,1,0,116442.42,1\r\n7572,15768342,Bolton,718,France,Male,52,8,79475.3,3,1,1,32421.32,1\r\n7573,15567919,Lazarev,586,Germany,Male,37,8,167735.69,2,0,1,104665.79,0\r\n7574,15674750,Alexeyeva,481,Spain,Female,37,8,0,2,1,0,44215.86,0\r\n7575,15778345,Stevens,749,France,Female,33,1,74385.98,1,1,0,20164.47,0\r\n7576,15687634,Glover,561,Germany,Male,49,5,94754,1,1,1,26691.31,0\r\n7577,15666096,Ibekwe,676,Spain,Male,27,4,0,1,0,1,107955.67,0\r\n7578,15581700,Paterson,615,Germany,Male,43,3,86920.86,1,1,1,150048.37,0\r\n7579,15656417,Marsh,582,France,Female,39,1,132077.48,2,1,0,192255.15,0\r\n7580,15649101,Reeves,601,France,Male,40,10,127847.86,1,0,0,173245.68,0\r\n7581,15781975,Rees,708,France,Male,34,3,0,1,0,1,121457.88,1\r\n7582,15700511,Hanson,708,Germany,Male,42,9,176702.36,2,1,1,104804.74,0\r\n7583,15770255,Onwughara,797,Germany,Female,33,10,83555.58,1,0,0,69767.14,0\r\n7584,15643574,Odinakachukwu,682,France,Male,26,8,0,2,1,0,178373.43,0\r\n7585,15595010,Huang,694,Spain,Female,39,9,0,2,0,0,99924.04,0\r\n7586,15580579,Trevisani,490,France,Female,40,1,0,1,1,1,49594.19,1\r\n7587,15748532,Dale,828,Spain,Male,42,10,0,1,1,1,186071.14,0\r\n7588,15773789,Pavlova,594,Spain,Female,38,7,96858.35,1,1,0,77511.45,0\r\n7589,15600027,Meng,579,Spain,Male,33,1,0,2,1,1,54816.57,0\r\n7590,15620832,Dean,723,France,Female,35,0,0,2,0,1,61290.99,0\r\n7591,15568819,Chiganu,619,Germany,Female,42,8,132796.04,3,1,1,191821.35,1\r\n7592,15748691,Lung,794,Spain,Female,30,1,154970.54,1,0,1,156768.45,0\r\n7593,15583552,Donaldson,674,Germany,Male,44,3,88902.21,1,1,0,73731.32,0\r\n7594,15588019,Li Fonti,418,France,Male,28,7,98738.92,1,1,0,122190.22,0\r\n7595,15713250,Izmailova,502,France,Male,33,8,0,2,1,1,123509.01,0\r\n7596,15569595,Walker,678,France,Female,50,6,0,1,1,0,8199.5,0\r\n7597,15794868,Nnonso,599,Germany,Male,40,10,137456.28,2,1,1,14113.11,0\r\n7598,15576680,Stevenson,736,France,Male,29,4,0,2,0,0,51705.01,0\r\n7599,15613699,Schnaars,430,France,Female,60,7,73937.02,1,1,0,161937.62,1\r\n7600,15609758,Geoghegan,537,France,Female,45,7,158621.04,1,1,0,120892.96,1\r\n7601,15762392,Ilyina,683,Spain,Male,30,1,113257.2,1,1,1,65035.02,0\r\n7602,15693382,Muir,828,France,Male,31,9,0,1,0,1,164257.37,0\r\n7603,15791769,Gardener,691,France,Female,29,9,116536.43,1,1,0,51987.99,0\r\n7604,15712483,Chidi,608,Spain,Female,28,4,0,2,1,0,10899.63,1\r\n7605,15636454,Fu,691,France,Female,60,6,101070.69,1,1,0,177355.8,1\r\n7606,15710138,Sun,718,Spain,Male,39,6,0,2,0,1,63889.1,0\r\n7607,15571571,Ting,680,Germany,Female,31,3,127331.46,3,1,1,176433.6,0\r\n7608,15638751,Ashton,838,Spain,Female,41,5,0,2,1,0,81313.51,0\r\n7609,15598574,Uwakwe,695,Spain,Female,31,5,0,2,0,1,13998.88,0\r\n7610,15796787,Vassiliev,681,France,Male,46,0,105969.42,1,1,0,5771.56,0\r\n7611,15615670,Kazakova,762,France,Male,36,5,119547.46,1,1,1,42693.65,0\r\n7612,15705506,Perry,751,Spain,Male,38,7,0,2,0,0,90839.61,0\r\n7613,15599535,Howell,678,Spain,Male,28,5,138668.18,1,1,1,54144.01,0\r\n7614,15768449,Ricci,634,France,Female,37,7,51582.5,2,1,1,184312.88,0\r\n7615,15725002,Smith,749,France,Male,37,7,0,2,1,0,20306.79,0\r\n7616,15611682,Rossi,590,Spain,Male,37,6,169902.92,1,1,1,128256.18,0\r\n7617,15749964,Jones,610,France,Female,27,4,87262.4,2,1,0,182720.07,0\r\n7618,15678779,Quezada,502,France,Male,33,7,0,2,0,1,4082.52,0\r\n7619,15752601,McCulloch,578,France,Female,40,7,0,2,0,0,102233.73,0\r\n7620,15758477,Tobeolisa,547,France,Female,32,2,0,2,1,0,132002.83,0\r\n7621,15629133,Black,579,France,Female,27,9,0,2,1,0,126838.7,0\r\n7622,15604963,Fraser,661,France,Male,39,5,0,2,0,0,181461.46,0\r\n7623,15796413,Green,794,France,Male,46,6,0,2,1,0,195325.74,0\r\n7624,15812470,Allan,719,France,Male,61,5,0,2,0,1,29132.43,0\r\n7625,15587443,Akudinobi,728,France,Female,69,1,0,2,1,1,131804.86,0\r\n7626,15689692,Walker,598,Germany,Male,19,3,150348.37,1,1,1,173784.04,0\r\n7627,15779586,Olisaemeka,822,Germany,Female,46,3,115074.02,2,1,0,26249.86,0\r\n7628,15667588,Arcuri,670,Spain,Female,40,3,0,1,1,1,182650.15,0\r\n7629,15624423,Liu,850,France,Male,28,8,99986.98,1,1,0,196582.55,0\r\n7630,15591107,Flemming,723,Germany,Female,68,3,110357,1,0,0,141977.54,1\r\n7631,15748986,Bischof,705,Germany,Male,42,8,166685.92,2,1,1,55313.51,0\r\n7632,15793896,John,677,Spain,Male,40,7,95312.8,1,1,1,62944.75,0\r\n7633,15620570,Sinnett,736,France,Male,43,4,202443.47,1,1,0,72375.03,0\r\n7634,15727811,Ts'ui,661,Germany,Female,47,0,109493.62,1,0,0,188324.01,1\r\n7635,15707681,Pokrovsky,501,Germany,Male,38,9,88977.39,2,0,1,133403.07,0\r\n7636,15702030,Azarov,516,France,Female,29,2,104982.57,1,1,0,157378.5,0\r\n7637,15673238,McCarthy,517,Germany,Female,59,8,154110.99,2,1,0,101240.08,1\r\n7638,15604196,Simpson,766,France,Male,32,6,185714.28,1,1,1,102502.5,0\r\n7639,15769356,Stevenson,520,Germany,Female,23,3,116022.53,2,1,1,37577.66,0\r\n7640,15665590,Moore,541,France,Male,46,6,0,2,1,1,83456.67,0\r\n7641,15572361,Chill,790,Germany,Female,34,2,164011.48,1,1,0,199420.41,0\r\n7642,15667460,Moore,797,France,Male,31,9,0,2,1,1,24748.89,0\r\n7643,15654760,Su,811,France,Male,40,1,101514.89,1,1,1,121765,0\r\n7644,15632669,Rees,722,Spain,Female,32,4,0,2,1,1,113666.48,0\r\n7645,15613673,Lung,675,France,Male,28,9,0,1,1,0,134110.93,0\r\n7646,15698522,Thomas,660,Germany,Male,39,9,134599.33,2,1,0,183095.87,0\r\n7647,15741633,Fuller,566,Spain,Male,32,10,147511.26,1,1,1,159891.03,0\r\n7648,15674583,Trevisani,768,France,Male,25,0,78396.08,1,1,1,8316.19,0\r\n7649,15665374,Dumolo,610,Spain,Female,31,5,0,2,0,0,63736.36,0\r\n7650,15588854,Wu,715,France,Female,31,3,110581.29,1,1,1,94715.24,0\r\n7651,15810716,Kerr,750,Germany,Male,42,8,151836.36,2,1,0,68695.38,0\r\n7652,15776921,Geoghegan,431,Germany,Male,45,5,83624.55,2,0,0,36899.62,0\r\n7653,15569394,Bailey,704,France,Male,24,2,148197.15,2,1,0,182775.08,0\r\n7654,15788215,Hsia,535,Spain,Female,30,5,122924.75,1,0,0,62390.59,1\r\n7655,15641007,Holden,614,France,Female,38,4,72594,1,1,1,76042.48,0\r\n7656,15594651,Milani,748,France,Male,38,4,115221.36,1,0,1,70956.75,0\r\n7657,15575146,Jamieson,492,Germany,Male,51,8,117808.74,2,1,1,67311.12,0\r\n7658,15608916,Ndubueze,573,France,Male,40,7,147754.68,1,1,1,110454.46,0\r\n7659,15666297,Abramova,706,Spain,Female,53,3,0,3,0,0,88479.02,1\r\n7660,15598586,Wetherspoon,680,France,Male,31,10,113292.17,1,1,1,122639.73,0\r\n7661,15665014,Middleton,458,Spain,Male,36,5,0,2,1,0,79723.78,0\r\n7662,15701738,Arcuri,612,Germany,Male,44,2,115163.38,1,1,1,97677.52,1\r\n7663,15650591,Calabrese,809,Germany,Male,50,10,118098.62,1,1,1,100720.02,1\r\n7664,15652667,Hampton,590,France,Male,39,9,0,2,1,1,104730.52,0\r\n7665,15679622,Clayton,602,France,Male,35,8,0,1,1,1,22499.29,0\r\n7666,15730150,Otutodilichukwu,540,Spain,Male,37,0,120825.7,1,1,0,28257.89,0\r\n7667,15813192,Chukwuemeka,494,France,Male,25,6,0,2,0,1,109988.09,0\r\n7668,15606554,Douglas,797,France,Male,29,1,0,1,0,1,149991.32,0\r\n7669,15611794,Galloway,526,Germany,Male,61,6,133845.28,2,1,1,45180.8,0\r\n7670,15672357,Sochima,631,Spain,Male,38,7,0,2,1,0,181605.85,0\r\n7671,15711759,Wilkins,576,France,Female,29,5,108541.04,1,1,1,126469.09,0\r\n7672,15615296,Rice,405,France,Male,39,10,0,1,1,0,160810.85,1\r\n7673,15699294,Pope,555,France,Male,30,1,0,2,0,0,88146.86,0\r\n7674,15788634,Romani,750,Spain,Female,37,2,113817.06,1,0,0,88333.74,0\r\n7675,15660871,Ch'ang,665,France,Male,28,8,137300.23,1,1,0,90174.83,0\r\n7676,15618258,Chizuoke,640,Spain,Male,37,5,158024.38,1,1,0,81298.09,0\r\n7677,15722535,Ireland,457,France,Female,33,7,127837.54,1,0,1,60013.17,0\r\n7678,15711977,Finch,695,France,Male,36,4,161533,1,1,0,100940.91,0\r\n7679,15690169,Meng,645,France,Male,31,7,161171.7,2,1,0,12599.94,1\r\n7680,15790689,Hibbins,647,Spain,Male,32,9,80958.36,1,1,1,128590.73,0\r\n7681,15665181,Chung,808,Spain,Male,25,7,0,2,0,1,23180.37,0\r\n7682,15633608,Black,641,France,Male,33,2,146193.6,2,1,1,55796.83,1\r\n7683,15805261,Balashov,700,Spain,Male,29,8,0,2,0,1,152097.02,0\r\n7684,15740356,Palmer,660,Germany,Male,26,4,115021.76,1,0,1,162443.05,0\r\n7685,15808223,Lea,615,Spain,Male,41,1,126773.43,1,1,1,55551.26,0\r\n7686,15769980,Singleton,705,Germany,Female,40,3,92889.91,1,1,1,109496.69,0\r\n7687,15675450,Burt,718,France,Male,48,9,0,2,1,1,72105.63,0\r\n7688,15776494,Siciliano,754,France,Male,61,5,146622.35,1,1,1,41815.22,1\r\n7689,15592412,Sun,713,Germany,Male,45,4,131038.14,1,1,0,74005.04,1\r\n7690,15777452,Sauve,587,France,Female,46,6,88820.29,1,0,0,70224.34,0\r\n7691,15692258,Thompson,569,Spain,Male,31,1,115406.97,1,0,0,145528.22,0\r\n7692,15791045,Boni,568,France,Female,38,3,132951.92,1,0,1,124486.28,0\r\n7693,15807889,Wood,634,Germany,Male,74,5,108891.7,1,1,0,10078.02,0\r\n7694,15602043,Buccho,770,Germany,Female,46,5,141788.63,2,0,0,164967.21,0\r\n7695,15807335,Spencer,676,Spain,Female,64,4,116954.32,1,1,1,91149.48,0\r\n7696,15629985,Eidson,723,Germany,Female,47,10,90450,2,0,0,103379.31,1\r\n7697,15679453,Hung,614,Germany,Female,39,8,125997.22,1,1,1,128049.34,1\r\n7698,15637315,Melvin,601,Spain,Female,41,3,0,2,1,0,54342.83,0\r\n7699,15691513,Dawkins,592,France,Male,60,9,0,4,1,1,13614.01,1\r\n7700,15622289,Rizzo,605,Spain,Female,36,9,0,2,0,1,35521.63,0\r\n7701,15715184,Capon,752,Spain,Female,31,4,144637.86,2,1,0,40496.72,0\r\n7702,15702801,Ts'ao,677,France,Female,29,3,86616.35,1,0,0,91903.9,1\r\n7703,15719931,Johnstone,850,France,Male,31,8,0,2,1,0,178667.7,0\r\n7704,15806081,Fleming,608,Germany,Female,48,2,127924.25,2,1,0,32202.61,0\r\n7705,15796336,Chang,786,Spain,Female,34,9,0,2,1,0,117034.32,0\r\n7706,15647306,Gibbs,777,France,Female,29,9,131240.61,1,1,1,163746.09,1\r\n7707,15742369,Rita,667,Spain,Male,31,5,0,2,1,1,20346.69,0\r\n7708,15655859,Munro,848,Spain,Male,35,5,120046.74,2,1,0,84710.65,0\r\n7709,15675650,Duncan,486,France,Female,39,8,97819.36,1,0,1,120531.31,0\r\n7710,15574119,Okwuadigbo,598,Spain,Female,64,1,62979.93,1,1,1,152273.57,0\r\n7711,15754168,McIntosh,506,France,Female,40,3,0,1,1,1,144345.58,0\r\n7712,15763029,Ch'iu,612,Germany,Male,46,9,161450.03,1,1,1,96961,1\r\n7713,15765048,Watt,545,France,Male,30,3,0,2,1,0,170307.43,0\r\n7714,15786215,Udinese,793,France,Male,56,8,119496.25,2,1,0,29880.99,0\r\n7715,15707559,Clark,682,France,Female,30,9,0,2,1,1,195104.91,0\r\n7716,15582129,Hsia,517,France,Male,62,1,43772.66,3,1,0,187756.24,1\r\n7717,15687540,Obiuto,684,France,Male,32,9,100249.41,2,0,1,67599.69,0\r\n7718,15787196,T'ien,692,Spain,Male,46,2,0,2,1,1,105983.09,0\r\n7719,15670898,McKenzie,740,France,Female,60,5,108028.08,2,0,0,25980.42,1\r\n7720,15775433,Tang,666,Germany,Male,71,1,53013.29,2,1,1,112222.64,0\r\n7721,15700693,Tu,693,France,Male,68,2,0,2,1,1,59864.96,0\r\n7722,15677955,Tsui,757,Germany,Male,33,1,122088.67,1,1,0,42581.09,0\r\n7723,15570086,Lynch,684,Germany,Male,18,9,90544,1,0,1,4777.23,0\r\n7724,15794875,Hung,691,Spain,Male,35,6,0,2,0,1,178038.17,0\r\n7725,15673591,Oluchukwu,842,France,Male,44,3,141252.18,4,0,1,128521.16,1\r\n7726,15631756,Tuan,482,France,Female,35,5,147813.05,2,0,0,109029.72,0\r\n7727,15757617,Lewis,735,France,Male,55,6,134140.68,1,1,0,2267.88,0\r\n7728,15612729,Chidiebere,681,France,Female,63,7,0,2,1,1,55054.48,0\r\n7729,15637857,Woolacott,616,France,Female,31,8,0,1,0,1,76456.17,0\r\n7730,15681007,Yen,850,France,Female,35,2,128548.49,4,1,0,75478.95,1\r\n7731,15593622,Service,635,France,Male,43,10,122198.21,2,0,1,179144.54,0\r\n7732,15629273,Lin,638,Germany,Male,42,8,145177.84,1,1,0,193471.74,1\r\n7733,15765846,Chuang,820,Spain,Female,31,2,94222.53,1,1,0,103570.8,0\r\n7734,15596013,Akhtar,694,Germany,Female,58,1,143212.22,1,0,0,102628.56,1\r\n7735,15722473,Faulkner,713,France,Male,41,3,0,2,1,0,55772.04,0\r\n7736,15774936,Liang,543,Germany,Male,41,6,143350.41,1,1,1,192070.16,1\r\n7737,15685640,Dancy,649,France,Female,41,3,130931.83,1,1,1,144808.37,0\r\n7738,15566563,Duigan,777,France,Female,30,4,137851.31,1,1,0,5008.23,1\r\n7739,15768746,McLean,561,France,Male,33,6,0,2,0,0,173680.39,0\r\n7740,15689952,Zuyeva,724,Spain,Male,41,5,0,1,0,1,115753.94,0\r\n7741,15725906,Hankinson,665,Spain,Female,51,8,0,1,1,1,38928.48,1\r\n7742,15634501,Wei,441,France,Male,60,1,140614.15,1,0,1,174381.23,0\r\n7743,15571940,Afamefula,579,Spain,Male,22,3,118680.57,1,1,1,49829.8,0\r\n7744,15741643,Chiang,777,Germany,Male,35,7,122917.69,1,1,1,76169.68,0\r\n7745,15806822,Myers,739,France,Female,36,0,0,2,0,0,133465.57,0\r\n7746,15701166,Chinedum,660,France,Male,40,5,131754.11,2,1,1,38761.61,0\r\n7747,15718531,Ukaegbunam,554,France,Female,35,8,0,2,1,1,176779.46,0\r\n7748,15628308,Akubundu,850,France,Female,24,6,0,2,1,1,13159.9,0\r\n7749,15585287,Sal,842,Germany,Female,35,9,119948.09,1,1,0,48217.97,1\r\n7750,15781619,Stevenson,785,France,Female,38,1,0,1,1,0,134964.85,1\r\n7751,15805162,Sutherland,550,France,Male,25,0,0,2,1,1,184221.11,0\r\n7752,15588535,Ts'ao,750,Spain,Female,39,6,0,2,0,0,19264.33,0\r\n7753,15775307,Sung,490,Spain,Female,38,3,97266.1,1,1,1,92797.23,0\r\n7754,15777616,Pisani,605,Germany,Male,28,10,113690.83,1,1,0,33114.24,0\r\n7755,15692291,Hs?eh,563,Spain,Female,42,6,99056.22,2,1,0,154347.95,1\r\n7756,15680843,Sherrod,675,France,Male,34,8,0,2,1,1,184842.21,0\r\n7757,15606232,Holloway,621,Spain,Female,36,7,116338.68,1,1,1,155743.48,0\r\n7758,15641585,Newton,850,France,Male,40,6,97339.99,1,0,1,88815.25,0\r\n7759,15684358,Kang,711,France,Male,41,3,0,2,1,1,193747.57,0\r\n7760,15806389,Walton,549,Germany,Female,55,1,137592.31,2,0,1,116548.02,1\r\n7761,15641860,Bradley,764,Germany,Male,34,6,108760.27,2,1,0,166324.79,1\r\n7762,15814237,Watkins,627,Germany,Male,30,3,128770.88,2,1,1,40199.01,0\r\n7763,15808780,Tien,850,France,Female,34,2,0,2,0,0,51919.04,0\r\n7764,15767064,Davide,614,Spain,Female,36,1,44054.84,1,1,1,73329.08,0\r\n7765,15751177,Milne,685,Germany,Female,44,2,119657.53,1,1,0,145387.05,1\r\n7766,15613427,Barling,683,Germany,Female,49,7,108797.63,2,0,0,140763.18,0\r\n7767,15647259,Barnett,643,Spain,Male,35,2,0,2,0,0,67979.35,0\r\n7768,15748660,Ellis,561,Germany,Female,49,1,102025.32,1,1,0,133051.64,1\r\n7769,15726695,Hsia,601,Spain,Female,20,9,122446.61,2,1,0,86791.9,0\r\n7770,15757473,Chukwujamuike,766,France,Female,27,7,158786.67,2,0,1,47579.25,0\r\n7771,15809509,Venables,699,France,Male,29,3,125689.29,1,1,1,151623.71,0\r\n7772,15715512,Hsia,850,Germany,Male,29,1,154640.41,1,1,1,164039.51,0\r\n7773,15614168,Alexander,792,Germany,Female,50,4,146710.76,1,1,0,16528.4,1\r\n7774,15679818,Yuan,636,Germany,Male,67,7,136709.35,1,0,1,66753.1,1\r\n7775,15609928,Johnston,850,Germany,Male,43,5,129305.09,2,0,1,19244.58,0\r\n7776,15731246,Hobler,628,Spain,Male,40,10,0,2,1,0,103832.58,0\r\n7777,15685243,Jamieson,736,France,Female,63,10,0,2,0,1,502.7,0\r\n7778,15638730,Macleod,711,France,Female,21,0,82844.33,2,0,1,1408.68,0\r\n7779,15697034,Norris,583,Spain,Female,22,2,0,2,0,1,5985.36,0\r\n7780,15699225,Pirozzi,757,France,Male,46,0,0,2,1,0,37460.05,0\r\n7781,15677387,Folliero,749,Germany,Female,33,10,76692.22,1,0,1,30396.43,0\r\n7782,15759184,Russell,705,France,Male,34,7,117715.84,1,1,0,2498.67,0\r\n7783,15595991,Hsiung,585,France,Male,54,8,87105.32,1,1,1,55346.14,0\r\n7784,15681332,Tate,437,France,Female,43,6,0,1,1,0,148330.97,1\r\n7785,15756299,Davis,741,France,Female,64,2,69311.16,1,1,1,59237.72,0\r\n7786,15750547,Bair,738,France,Male,26,9,0,2,1,1,48644.94,0\r\n7787,15566380,Drury,586,Spain,Female,33,10,66948.67,2,1,1,140759.03,0\r\n7788,15675963,Padovano,627,France,Female,57,9,0,2,1,1,107712.42,0\r\n7789,15674671,Conway,551,Spain,Male,76,2,128410.71,2,1,1,181718.73,0\r\n7790,15621466,Waters,606,Germany,Male,38,3,99897.53,1,0,0,37054.65,0\r\n7791,15607176,Kang,674,France,Male,22,3,0,1,1,1,173940.59,0\r\n7792,15570299,Martin,584,Germany,Female,31,6,152622.34,1,1,0,99298.8,0\r\n7793,15613197,Ugochukwutubelum,590,France,Male,40,8,0,2,1,0,62933.03,0\r\n7794,15798885,Burns,585,France,Male,56,4,138227.19,2,1,1,55287.84,0\r\n7795,15714883,Genovese,508,France,Female,25,2,111395.53,1,0,1,48197.06,0\r\n7796,15604497,Beale,458,Germany,Male,44,7,84386.57,1,1,0,178642.73,0\r\n7797,15773949,Cherkasova,692,France,Female,36,3,0,2,1,1,8282.22,0\r\n7798,15774164,Coles,502,Germany,Male,33,5,174673.65,2,1,0,33300.56,0\r\n7799,15774127,Potter,518,France,Male,46,3,0,2,1,0,76515.79,0\r\n7800,15619016,McMinn,660,Germany,Male,46,5,109019.65,2,1,1,33680.56,0\r\n7801,15795759,Bergamaschi,698,Germany,Female,52,1,107906.75,1,1,0,168886.39,1\r\n7802,15798844,Chijindum,678,France,Male,54,7,128914.97,1,0,0,191746.23,1\r\n7803,15717962,Ch'iu,773,Spain,Male,63,9,111179.83,1,1,1,93091.02,0\r\n7804,15691504,Yusupova,619,Germany,Female,52,8,124099.13,1,0,0,23904.52,0\r\n7805,15693893,Davis,684,Germany,Male,59,9,122471.09,1,0,1,15807.07,0\r\n7806,15672499,Iadanza,635,France,Male,34,3,134692.4,2,1,1,83773.02,0\r\n7807,15750410,Jordan,680,France,Female,25,4,123816.5,1,1,1,90162.35,0\r\n7808,15568904,Kruglova,608,Germany,Male,34,3,106288.54,1,1,1,36639.25,0\r\n7809,15649033,Echezonachukwu,603,Germany,Female,55,7,127723.25,2,1,0,139469.11,1\r\n7810,15780989,Hajek,579,Spain,Male,43,2,145843.82,1,1,1,198402.37,1\r\n7811,15771059,Welch,756,Germany,Female,34,2,148200.72,1,0,0,194584.48,0\r\n7812,15687852,Vinogradoff,611,France,Male,30,2,104145.65,1,0,0,159629.64,0\r\n7813,15695280,Hung,532,Germany,Male,24,8,142755.25,1,0,0,34231.48,0\r\n7814,15592751,Okwudiliolisa,684,Germany,Female,63,3,81245.79,1,1,0,69643.31,1\r\n7815,15598338,Mays,647,Germany,Female,33,3,168560.46,2,0,0,90270.16,0\r\n7816,15735784,Gardner,583,France,Male,38,8,0,1,1,0,47848.56,0\r\n7817,15629128,Mamelu,774,Germany,Male,42,2,132193.94,2,1,1,162865.52,0\r\n7818,15642870,Ross,677,France,Male,58,9,0,1,0,1,168650.4,0\r\n7819,15637977,Barese,542,Germany,Male,25,8,139330.1,1,0,0,54372.37,0\r\n7820,15600792,Swayne,613,Spain,Male,29,0,0,2,0,1,133897.32,0\r\n7821,15576131,Phillips,666,France,Male,40,5,0,2,1,0,147878.05,0\r\n7822,15686588,Manfrin,777,France,Female,28,2,134571.5,1,0,1,118313.38,0\r\n7823,15761018,Tan,581,Germany,Male,50,2,143829.2,2,1,0,181224.24,1\r\n7824,15616029,Adams,705,France,Male,32,7,0,2,1,0,7921.57,0\r\n7825,15761149,Teng,673,France,Female,44,8,133444.97,1,0,1,5708.19,0\r\n7826,15802758,Chinwendu,594,Germany,Female,23,4,104753.84,2,1,0,56756.52,1\r\n7827,15647838,Davison,648,Germany,Female,51,2,116574.84,1,1,0,4121.04,1\r\n7828,15735968,Hsing,605,France,Male,41,10,0,2,0,1,97213.09,0\r\n7829,15581286,Castro,734,France,Female,40,9,176914.8,1,1,1,12799.23,0\r\n7830,15625445,Parkin,572,France,Female,36,8,68348.18,2,0,1,50400.32,0\r\n7831,15600173,Manna,595,France,Female,33,9,0,2,1,1,41447.86,0\r\n7832,15635143,Fennescey,749,France,Male,42,2,56726.83,2,0,1,185543.35,0\r\n7833,15664849,Colon,573,Spain,Male,46,3,65269.23,1,0,1,189988.65,1\r\n7834,15762455,Yeh,624,Spain,Male,33,6,66220.17,1,0,1,170819.01,0\r\n7835,15797165,Bergamaschi,703,France,Male,56,9,0,1,0,0,85547.33,1\r\n7836,15788189,Matveyeva,665,France,Female,41,8,96147.55,1,1,0,137037.97,0\r\n7837,15780492,Ignatyeva,648,France,Male,42,4,0,2,1,0,19283.14,0\r\n7838,15678497,Lederer,850,Spain,Male,48,2,0,1,1,0,169425.3,1\r\n7839,15588560,Nwabugwu,569,Germany,Female,32,8,145330.43,1,1,1,132038.65,0\r\n7840,15606003,Abramowitz,566,France,Female,21,3,0,2,1,1,3626.47,0\r\n7841,15611756,Chapman,537,Germany,Female,47,4,124192.28,2,1,1,50881.51,0\r\n7842,15789563,Fiorentino,706,Germany,Female,46,7,111288.18,1,1,1,149170.25,1\r\n7843,15702416,Cecil,734,France,Male,43,7,107805.67,1,0,0,182505.68,0\r\n7844,15766288,Ikechukwu,586,Germany,Female,36,5,103700.69,1,1,0,194072.56,1\r\n7845,15667633,Allen,612,France,Female,38,1,0,2,1,1,9209.21,0\r\n7846,15622774,Kao,648,France,Male,34,0,0,1,1,1,167931.81,0\r\n7847,15755416,Hart,557,France,Female,27,3,87739.08,1,1,1,123096.56,0\r\n7848,15769915,Charlton,643,Spain,Female,20,0,133313.34,1,1,1,3965.69,0\r\n7849,15643908,Turnbull,433,France,Female,49,10,0,1,1,1,87711.61,0\r\n7850,15627395,Manners,643,Germany,Male,41,7,154902.66,1,1,1,49667.28,0\r\n7851,15679663,Chiazagomekpere,488,France,Female,36,0,0,2,1,0,136675.22,0\r\n7852,15651581,Lavrentyev,758,Germany,Male,68,6,112595.85,1,1,0,35865.44,1\r\n7853,15596379,Wallace,743,Germany,Male,39,3,119695.75,1,0,1,26136.13,0\r\n7854,15746674,Miller,730,France,Female,47,7,0,1,1,0,33373.26,1\r\n7855,15801256,Bazhenov,746,Spain,Male,49,7,0,2,0,1,10096.25,0\r\n7856,15663808,Ifesinachi,666,Germany,Female,59,8,152614.51,2,1,1,188782.3,0\r\n7857,15598521,Ma,580,Germany,Female,33,7,131647.01,2,0,0,79775.19,0\r\n7858,15621457,Chu,850,France,Male,27,6,96654.72,2,0,0,152740.16,0\r\n7859,15764726,Kerr,563,France,Male,22,3,137583.04,1,0,1,5791.85,0\r\n7860,15646374,Wynne,766,Germany,Female,28,3,62717.84,2,1,1,13182.43,0\r\n7861,15716501,Moon,659,France,Male,32,9,95377.13,1,0,1,187551.24,0\r\n7862,15589948,Disher,607,Spain,Male,28,1,135936.1,2,1,1,110560.14,0\r\n7863,15811343,Cattaneo,644,Germany,Male,35,5,161591.11,3,1,1,63795.62,0\r\n7864,15659677,Beluchi,746,France,Male,47,8,142382.03,1,1,1,62086.62,0\r\n7865,15594436,Mazzi,588,Spain,Male,33,2,0,2,1,1,12483.56,0\r\n7866,15748995,Ifeajuna,691,Spain,Male,30,9,0,2,0,1,10963.04,0\r\n7867,15677062,Howe,666,France,Female,38,6,127043.09,1,1,1,8247,0\r\n7868,15697201,Yocum,640,Spain,Female,46,3,0,1,1,1,156260.08,0\r\n7869,15666453,Moore,611,Germany,Female,29,4,78885.88,2,1,1,26927.69,0\r\n7870,15693771,Y?an,651,Spain,Female,45,8,95922.9,1,1,0,84782.42,1\r\n7871,15569867,Chinweuba,529,France,Female,29,8,0,2,1,0,19842.11,0\r\n7872,15711602,Lowrie,676,France,Female,36,3,91711.59,1,1,1,95393.43,0\r\n7873,15717736,Shen,639,Germany,Female,46,10,110031.09,2,1,1,133995.59,0\r\n7874,15750441,Lavarack,782,France,Male,36,5,81210.72,2,0,1,108003.38,0\r\n7875,15732791,Davide,641,Germany,Male,32,5,122947.92,1,1,1,99154.86,0\r\n7876,15775104,Gomes,697,France,Female,38,1,182065.85,1,1,0,49503.5,0\r\n7877,15757607,Matveyeva,623,France,Male,45,0,0,1,1,0,196533.72,1\r\n7878,15793070,Fiorentino,494,Spain,Female,41,2,69974.66,2,1,0,188426.13,1\r\n7879,15760456,Eberechukwu,731,France,Female,38,10,123711.73,2,1,0,171340.68,1\r\n7880,15665385,Gibney,657,France,Male,44,6,76495.04,1,1,0,79071.89,0\r\n7881,15612418,Virgo,744,France,Female,38,9,0,2,0,0,20940.76,0\r\n7882,15727138,Kulikova,774,Spain,Male,46,9,0,2,1,1,34774.26,0\r\n7883,15732061,Liu,850,Germany,Female,45,1,121874.89,1,0,0,6865.41,1\r\n7884,15776051,Kao,551,France,Female,45,6,0,2,1,1,51143.43,0\r\n7885,15616530,Foran,638,France,Male,36,6,188455.19,1,0,0,47031.4,1\r\n7886,15632344,Jones,792,France,Female,42,0,99045.93,2,1,0,47160.01,0\r\n7887,15744979,Fowler,666,France,Female,36,8,0,1,0,1,158666.99,0\r\n7888,15745433,Conti,716,Germany,Female,30,2,205770.78,2,0,0,65464.66,0\r\n7889,15683657,Stephenson,594,France,Female,31,0,79340.95,1,1,0,78255.86,0\r\n7890,15718572,Willis,600,Germany,Male,57,9,138456.03,2,1,1,103548.25,0\r\n7891,15665783,Ts'ui,565,France,Male,49,7,0,2,1,1,89609.26,0\r\n7892,15652782,Chibuzo,678,Germany,Male,48,2,101099.9,2,0,1,193476.04,0\r\n7893,15707025,Fang,648,Spain,Female,31,5,0,2,1,1,5199.02,0\r\n7894,15647807,Wyckoff,642,France,Male,40,8,109219.83,1,1,0,52827.51,0\r\n7895,15718281,Muir,706,Germany,Male,67,1,123276.69,2,1,1,86507.88,1\r\n7896,15660571,Halpern,668,Spain,Male,43,10,113034.31,1,1,1,100423.88,0\r\n7897,15727857,Flynn,635,Spain,Male,41,1,0,2,1,0,175611.5,0\r\n7898,15639252,Shao,603,Spain,Male,30,6,129548.5,2,1,1,19282.85,0\r\n7899,15628144,Soares,635,France,Female,72,4,74812.84,1,0,1,27448.33,0\r\n7900,15683560,Gallo,642,France,Female,40,7,0,2,1,0,183963.34,0\r\n7901,15653275,Lei,785,Spain,Female,54,1,0,2,1,0,45113.92,1\r\n7902,15622182,Daniels,628,Germany,Female,28,3,153538.13,2,1,0,110776.01,0\r\n7903,15613962,Kenechi,499,France,Female,38,9,0,2,0,1,183042.2,0\r\n7904,15618437,Singleton,567,Spain,Male,34,10,0,2,0,1,161571.79,0\r\n7905,15783338,Williams,449,Spain,Male,32,0,155619.36,1,1,1,166692.03,0\r\n7906,15764491,Greece,701,Spain,Male,35,10,159693.9,2,1,1,71173.64,0\r\n7907,15712960,Olisanugo,613,Spain,Male,37,3,171653.17,1,0,1,5353.12,0\r\n7908,15688157,Padovano,683,Germany,Female,39,2,47685.47,2,1,1,86019.48,0\r\n7909,15579287,Rossi,581,France,Male,35,4,0,2,0,1,86383.82,0\r\n7910,15570931,Grant,620,France,Male,61,5,0,1,0,0,31641.52,1\r\n7911,15615177,Ebelegbulam,561,Spain,Male,28,6,123692,1,1,1,70548.96,0\r\n7912,15809906,Mitchell,558,Germany,Male,26,1,148853.29,2,1,1,24411.02,0\r\n7913,15652169,Buckley,642,France,Male,35,2,133161.95,1,0,1,122254.86,0\r\n7914,15649450,Repina,805,Germany,Male,24,6,143221.35,2,1,0,186035.72,0\r\n7915,15777179,Ellis,687,France,Male,35,9,0,2,0,1,73133.82,0\r\n7916,15803538,Douglas,695,Spain,Male,56,1,0,3,1,0,187734.49,1\r\n7917,15610936,Becher,562,France,Male,33,6,0,2,1,0,111590.35,0\r\n7918,15590094,Nwachukwu,613,Germany,Male,38,9,126265.88,2,0,0,15859.95,0\r\n7919,15572706,Smith,589,France,Male,37,5,0,1,1,0,61324.87,0\r\n7920,15634564,Aksyonov,593,Spain,Male,31,8,112713.34,1,1,1,176868.89,0\r\n7921,15684296,Artyomova,714,France,Male,34,5,141173.03,1,0,1,98896.06,0\r\n7922,15702293,Medvedeva,588,Spain,Female,35,7,0,2,1,1,108739.15,0\r\n7923,15642099,Tsui,679,Spain,Male,39,6,0,2,1,0,12266.06,0\r\n7924,15773273,Runyon,730,Spain,Male,38,5,118866.36,1,1,1,163317.5,0\r\n7925,15613337,Gallo,833,France,Male,47,2,0,2,1,1,182247.77,0\r\n7926,15800482,Bradshaw,586,Spain,Female,33,7,0,2,1,1,168261.4,0\r\n7927,15732644,Evans,567,Spain,Female,54,5,92316.31,2,1,0,158590.66,1\r\n7928,15713426,Hancock,637,Germany,Male,30,1,122185.53,1,1,0,102566.46,1\r\n7929,15640789,Butler,711,France,Male,38,4,123345.85,1,1,0,141827.83,0\r\n7930,15598892,Bradshaw,828,France,Male,30,4,73070.18,2,0,0,161671.15,0\r\n7931,15606436,Bergamaschi,500,Spain,Male,38,7,0,2,0,0,192013.23,0\r\n7932,15751227,Ebelegbulam,807,France,Male,47,1,95120.59,1,0,0,127875.1,0\r\n7933,15812365,Greco,850,France,Male,40,8,102800.65,1,1,0,60811.56,0\r\n7934,15616088,Lucas,782,France,Female,70,7,97072.42,1,0,1,131177.22,0\r\n7935,15803886,Barber,629,Spain,Male,31,6,132876.55,1,1,1,130862.11,0\r\n7936,15587311,Dobbs,582,Spain,Male,33,6,0,2,0,1,72970.93,0\r\n7937,15617401,Thomson,468,France,Male,22,2,0,2,1,0,28123.99,0\r\n7938,15775886,Su,670,France,Male,36,3,0,1,1,0,140754.19,1\r\n7939,15807305,Watkins,805,France,Male,39,2,0,1,0,0,166650.32,0\r\n7940,15761717,Ch'ien,720,France,Male,26,10,51962.91,2,1,0,45507.24,0\r\n7941,15628008,Monds,781,Spain,Female,29,6,98759.89,1,0,0,112202.64,0\r\n7942,15583755,McClemans,592,Germany,Male,33,2,156570.86,1,1,1,37140.2,0\r\n7943,15661409,Shen,542,France,Female,42,1,0,1,1,1,178256.58,1\r\n7944,15774250,Gallo,532,France,Male,42,1,159024.71,1,1,0,100982.93,1\r\n7945,15681476,Foveaux,520,France,Female,39,1,73493.17,1,0,1,109626.13,1\r\n7946,15654870,Longo,759,France,Female,45,8,0,2,1,1,99251.24,0\r\n7947,15790448,Calabresi,473,France,Female,35,6,69617.36,1,1,0,143345.69,0\r\n7948,15785326,Randall,639,Spain,Female,35,5,136526.26,2,1,0,59653.03,0\r\n7949,15592854,Garcia,705,France,Male,25,3,113736.27,1,0,1,196864.61,0\r\n7950,15617486,Sullivan,530,France,Male,52,1,106723.28,1,0,0,109960.4,1\r\n7951,15806796,Higgins,516,Germany,Female,33,10,138847.9,1,1,1,127256.7,0\r\n7952,15644699,Crawford,850,France,Female,40,0,0,2,1,0,1099.95,0\r\n7953,15622305,Martin,746,Germany,Female,33,2,107868.14,2,1,1,146192.4,0\r\n7954,15608209,Currey,622,Germany,Male,33,3,96926.12,2,1,0,48553.77,0\r\n7955,15626898,Teng,743,France,Male,30,7,77599.23,1,0,0,144407.1,0\r\n7956,15644297,Austin,732,Germany,Male,38,5,178787.54,1,1,1,195760.53,0\r\n7957,15731569,Hudson,850,France,Male,81,5,0,2,1,1,44827.47,0\r\n7958,15582149,Ts'ui,850,Germany,Female,34,3,129668.43,2,1,1,88743.99,0\r\n7959,15802483,Hancock,686,France,Male,34,6,146178.13,2,1,1,88837.11,0\r\n7960,15686999,Nicholas,556,France,Female,40,8,0,2,1,0,62112.7,0\r\n7961,15772479,Napolitano,673,France,Male,37,4,0,2,0,0,163563.07,0\r\n7962,15778884,Jamieson,809,France,Female,38,2,154763.21,2,1,1,174800.31,0\r\n7963,15623630,Foster,634,Germany,Female,56,3,116251.24,1,0,1,42429.88,1\r\n7964,15774316,Moretti,630,France,Male,37,6,0,2,1,1,82647.65,0\r\n7965,15695097,Chiedozie,564,Germany,Female,30,0,100954.88,2,0,0,134175.15,0\r\n7966,15645404,Okwukwe,625,France,Female,51,4,124620.01,2,1,0,92243.94,1\r\n7967,15750574,Lumholtz,677,Spain,Female,34,4,0,2,1,1,6175.53,0\r\n7968,15636812,Rose,583,France,Male,40,9,112701.04,1,0,0,29213.63,0\r\n7969,15712068,Wan,592,Spain,Male,45,8,84692.5,1,0,1,67214.02,0\r\n7970,15652030,De Bernales,637,Germany,Male,49,2,108204.52,1,1,0,169037.84,1\r\n7971,15577398,Ch'eng,850,France,Male,30,6,86449.39,1,1,1,188809.23,0\r\n7972,15756848,Edmondson,633,Spain,Male,42,10,0,1,0,1,79408.17,0\r\n7973,15806929,Ch'ien,751,Germany,Male,36,5,73194.99,1,1,1,89222.66,0\r\n7974,15656005,Millar,592,Germany,Male,31,7,124593.23,1,1,0,86079.67,0\r\n7975,15722632,Dickson,716,Germany,Male,50,2,119655.77,1,1,1,12944.17,1\r\n7976,15794356,Toscani,641,Germany,Male,42,3,121765.37,2,1,1,166516.84,0\r\n7977,15659656,Pan,849,France,Male,35,4,110837.73,1,0,0,126419.8,0\r\n7978,15588341,Chigozie,647,Spain,Male,47,10,99835.17,1,0,1,89103.05,0\r\n7979,15709142,Sagese,608,Germany,Female,30,2,91057.37,2,1,0,132973.17,0\r\n7980,15627042,Reilly,555,France,Female,26,7,0,2,1,0,93122.41,0\r\n7981,15627517,Taylor,497,Spain,Male,27,7,149400.27,1,0,0,167522.19,0\r\n7982,15803032,Yen,599,Germany,Male,38,9,89111.63,1,0,0,157239.6,0\r\n7983,15665129,Kapustin,545,Germany,Male,33,1,132527.9,2,0,1,107429.71,0\r\n7984,15628272,Singh,774,France,Female,36,9,114997.42,1,1,0,75304.09,0\r\n7985,15678206,Yeh,464,France,Male,46,6,161798.53,1,1,0,182944.47,0\r\n7986,15678427,Genovese,696,Germany,Female,27,2,96129.32,2,1,1,5983.7,0\r\n7987,15678067,Boyle,667,Spain,Male,45,3,0,2,0,0,163655.01,0\r\n7988,15793331,Blair,812,France,Male,32,5,133050.97,2,1,0,89385.92,0\r\n7989,15699532,Okagbue,516,France,Male,51,8,120124.35,2,0,1,168773.54,0\r\n7990,15605827,Khan,645,France,Male,39,8,0,2,0,0,96864.36,0\r\n7991,15643635,Robertson,664,Spain,Male,32,5,133705.74,1,0,0,134455.84,0\r\n7992,15787710,Tikhonov,427,Spain,Female,39,9,0,2,1,0,28368.37,0\r\n7993,15614137,MacDonald,685,France,Female,40,7,0,2,1,0,103898.59,0\r\n7994,15754494,Ah Mouy,585,France,Female,33,4,152805.05,1,1,0,63239.65,0\r\n7995,15713440,Barese,519,Germany,Female,21,1,151701.45,3,1,1,170138.68,1\r\n7996,15803479,Winter-Irving,708,France,Female,67,1,0,2,0,1,3837.08,0\r\n7997,15709639,Wilson,717,France,Female,22,5,112465.06,1,1,1,92977.75,0\r\n7998,15601719,Fiorentino,465,Germany,Male,24,6,156007.09,1,1,0,191368.37,0\r\n7999,15772482,Iloerika,829,Germany,Male,28,3,132405.52,3,1,0,104889.2,1\r\n8000,15591489,Davison,826,France,Male,26,5,142662.68,1,0,0,60285.3,0\r\n8001,15629002,Hamilton,747,Germany,Male,36,8,102603.3,2,1,1,180693.61,0\r\n8002,15798053,Nnachetam,707,Spain,Male,32,9,0,2,1,0,126475.79,0\r\n8003,15753895,Blue,590,Spain,Male,37,1,0,2,0,0,133535.99,0\r\n8004,15595426,Madukwe,603,Spain,Male,57,6,105000.85,2,1,1,87412.24,1\r\n8005,15645815,Mills,615,France,Male,45,5,0,2,1,1,164886.64,0\r\n8006,15632848,Ferrari,634,France,Female,36,1,69518.95,1,1,0,116238.39,0\r\n8007,15703068,Nixon,716,Germany,Male,41,8,126145.54,2,1,1,138051.19,0\r\n8008,15791513,Manfrin,647,France,Male,41,4,138937.35,1,1,1,101617.64,1\r\n8009,15587210,McCartney,591,Germany,Female,44,10,113581.98,1,1,0,1985.41,0\r\n8010,15793803,Robinson,574,France,Male,34,1,112572.39,1,0,0,165626.6,0\r\n8011,15787756,Nkemdirim,467,Germany,Male,51,10,114514.71,2,1,0,177784.68,1\r\n8012,15723437,Sal,701,France,Female,35,2,0,2,1,1,65765.22,0\r\n8013,15702715,Kao,747,France,Female,34,10,0,2,1,1,50759.8,0\r\n8014,15809872,Ikechukwu,650,France,Male,32,2,84906.45,1,1,0,163216.48,0\r\n8015,15644295,Hargreaves,731,Spain,Female,39,2,126816.18,1,1,1,74850.93,0\r\n8016,15778694,Sievier,638,Germany,Female,26,1,105249.76,2,1,1,23491.09,0\r\n8017,15759555,Murphy,569,Spain,Male,41,2,0,2,1,0,134272.57,0\r\n8018,15631406,Munro,459,Germany,Male,50,5,109387.9,1,1,0,155721.15,0\r\n8019,15616676,Donnelly,632,Germany,Male,23,3,122478.51,1,1,0,147230.77,1\r\n8020,15771154,North,683,France,Female,73,8,137732.23,2,1,1,133210.44,0\r\n8021,15669491,Cruz,850,France,Female,46,2,157866.77,1,1,1,18986.12,0\r\n8022,15697691,Sinclair,512,France,Female,41,6,0,1,1,1,100507.81,0\r\n8023,15665180,Vasiliev,616,France,Female,31,3,136789.14,1,1,0,59346.4,1\r\n8024,15752588,Vasilyeva,664,France,Male,36,1,0,2,1,1,95372.64,0\r\n8025,15743051,Hamilton,694,France,Male,30,10,144684.03,1,1,1,31805.49,0\r\n8026,15571873,Sung,655,France,Male,24,9,107065.31,1,1,1,51959.82,0\r\n8027,15679743,Genovesi,607,France,Female,33,8,91301.72,1,0,1,130824.57,0\r\n8028,15769412,Atkinson,684,Spain,Male,39,4,207034.96,2,0,0,157694.76,1\r\n8029,15775124,Watterston,763,Spain,Male,37,8,0,2,1,1,933.38,0\r\n8030,15732113,Butters,671,Spain,Male,50,8,0,1,0,1,2560.11,0\r\n8031,15578141,Chien,592,Spain,Male,38,3,0,1,1,1,12905.89,1\r\n8032,15595874,Gorbunova,666,Spain,Female,36,6,0,2,1,0,176692.87,0\r\n8033,15755642,Bulgakov,667,France,Male,34,5,0,2,1,1,102908.63,0\r\n8034,15576526,Steele,850,Spain,Male,36,6,0,2,0,1,41291.05,0\r\n8035,15792489,Polyakova,622,Spain,Male,42,9,0,2,1,0,119127.06,0\r\n8036,15733705,Bull,577,France,Female,30,8,92472.1,2,0,1,126434.61,0\r\n8037,15807221,Weaver,555,Spain,Male,21,1,0,2,0,0,103901.35,0\r\n8038,15573045,Earl,547,France,Male,62,10,127738.75,2,1,1,85153,0\r\n8039,15756824,Giordano,613,Germany,Female,50,5,101242.98,2,1,0,12493.61,0\r\n8040,15773520,Begg,672,France,Female,43,4,92599.55,2,1,1,167336.78,0\r\n8041,15627439,Pickering,624,Spain,Female,36,10,0,2,0,1,186180.42,0\r\n8042,15701439,Fanucci,698,Spain,Female,50,1,0,4,1,0,88566.9,1\r\n8043,15785352,Chang,606,France,Male,37,6,82373.94,1,0,0,172526.9,1\r\n8044,15616525,Sopuluchi,720,Spain,Male,31,4,141356.47,1,0,0,137985.69,0\r\n8045,15717489,Martin,835,France,Male,23,9,0,1,1,0,19793.73,1\r\n8046,15795737,McNaughtan,771,Spain,Female,47,3,72664,2,1,1,107874.39,0\r\n8047,15693877,Stewart,811,France,Female,47,3,123365.34,2,0,0,171995.34,0\r\n8048,15576111,Reagan,734,Germany,Male,33,5,121898.58,1,1,0,61829.89,0\r\n8049,15595713,Heller,548,Spain,Male,33,6,0,1,1,1,31728.35,0\r\n8050,15808868,Nwokeocha,652,France,Female,31,3,103696.97,3,0,0,155221.05,1\r\n8051,15708193,Liu,707,France,Male,33,2,0,2,0,0,130866.95,0\r\n8052,15697801,Sokolova,605,Germany,Female,56,1,74129.18,2,1,1,62199.78,1\r\n8053,15770121,Bancroft,623,France,Female,34,9,0,1,1,0,24255.21,0\r\n8054,15800524,Nnanna,686,Germany,Male,29,3,185379.02,1,1,0,64679.07,0\r\n8055,15686236,Trevisani,525,Germany,Female,47,1,118087.68,1,1,0,88120.78,1\r\n8056,15659807,Nwachinemelu,657,Spain,Male,41,8,109402.13,1,1,1,66463.62,0\r\n8057,15736078,Ting,730,Germany,Female,33,7,130367.87,1,1,0,15142.1,1\r\n8058,15620836,Lo Duca,816,Germany,Female,34,2,108410.87,2,1,0,102908.91,0\r\n8059,15698184,Marshall,484,France,Female,50,2,90408.16,2,0,0,48170.57,0\r\n8060,15717643,Band,728,France,Female,34,6,90425.15,2,1,1,11597.69,0\r\n8061,15776596,Ferri,730,Spain,Female,39,6,140094.59,1,1,0,172450.04,1\r\n8062,15814757,Carter,477,Spain,Male,31,9,0,2,0,1,184061.17,0\r\n8063,15812607,Wilson,663,Germany,Female,46,6,95439.4,1,1,1,21038.58,1\r\n8064,15663888,Connor,549,Germany,Male,34,6,204017.4,2,1,0,109538.35,0\r\n8065,15748882,Reid,714,Spain,Male,29,9,0,2,1,0,129192.55,0\r\n8066,15690829,Sandefur,430,Germany,Male,49,3,137115.16,1,1,0,146516.86,1\r\n8067,15695819,Bidwill,504,Germany,Male,43,5,134740.19,2,1,0,181430.91,0\r\n8068,15696834,Cone,530,France,Female,29,5,0,2,0,0,121451.21,0\r\n8069,15797710,Saunders,619,Germany,Male,29,4,98955.87,1,0,1,131712.51,0\r\n8070,15700654,Liardet,617,Germany,Male,44,9,49157.09,2,1,0,53294.17,0\r\n8071,15583764,Wilkes,791,Germany,Male,31,1,130240.33,1,0,0,96546.55,0\r\n8072,15688849,Martin,609,France,Male,48,1,108019.27,3,1,1,184524.65,1\r\n8073,15661473,Boni,780,Germany,Male,51,4,126725.25,1,1,0,195259.31,1\r\n8074,15601030,Patel,777,Germany,Female,34,5,96693.66,1,1,1,172618.52,0\r\n8075,15789557,Howell-Price,817,Germany,Female,27,7,129810.6,1,1,1,59259.44,0\r\n8076,15745250,Simpson,850,France,Male,58,8,156652.13,1,0,0,25899.21,1\r\n8077,15590349,Rowland,732,France,Female,36,9,0,1,0,0,3749,1\r\n8078,15741693,Barnard,693,France,Male,40,4,130661.96,1,1,1,101918.96,0\r\n8079,15618446,Nnonso,576,France,Female,50,8,0,2,1,1,57802.62,0\r\n8080,15766552,Rossi,643,France,Male,37,6,0,2,0,0,142454.77,0\r\n8081,15668775,Pendred,757,France,Male,47,3,130747.1,1,1,0,143829.54,0\r\n8082,15757895,Martin,569,Germany,Male,30,6,106629.49,1,0,1,44114.88,0\r\n8083,15774551,K?,772,Spain,Male,36,3,112029.83,1,1,1,186948.35,0\r\n8084,15684011,Miller,576,Germany,Male,29,7,130575.26,1,0,1,173629.78,0\r\n8085,15736146,Afamefula,608,Germany,Male,28,4,96679.71,1,1,1,49133.45,0\r\n8086,15656286,Sims,794,France,Male,33,0,0,2,0,0,178122.71,0\r\n8087,15774847,Knight,593,France,Male,50,6,171740.69,1,0,0,20893.61,0\r\n8088,15619340,Obijiaku,597,Spain,Male,38,1,0,2,1,0,41303.29,0\r\n8089,15815656,Hopkins,541,Germany,Female,39,9,100116.67,1,1,1,199808.1,1\r\n8090,15623357,Onio,692,Germany,Male,24,2,120596.93,1,0,1,180490.53,0\r\n8091,15601324,Black,697,France,Female,48,1,0,2,1,1,87400.53,0\r\n8092,15715510,Eluemuno,768,France,Male,29,2,95984.69,2,1,1,73686.75,0\r\n8093,15663770,Doyle,802,France,Male,38,1,142557.11,1,1,1,172497.73,0\r\n8094,15779267,Onyemere,584,France,Male,47,5,0,2,1,0,89286.29,0\r\n8095,15597957,Rahman,614,Spain,Male,66,2,0,2,0,1,180082.7,0\r\n8096,15584620,Su,850,Germany,Female,36,6,143644.16,1,1,0,22102.25,1\r\n8097,15750772,Walker,671,France,Female,38,6,132129.72,1,0,1,76068.95,0\r\n8098,15706557,Ferguson,626,France,Female,52,0,0,2,1,0,32159.46,1\r\n8099,15594391,Samaniego,770,France,Female,68,2,183555.24,1,0,0,159557.28,1\r\n8100,15661656,Onwumelu,633,France,Male,38,2,91902.56,2,1,1,107673.35,0\r\n8101,15631217,Young,663,France,Male,40,6,156218.19,1,0,1,33607.72,0\r\n8102,15588955,Mazzi,581,Germany,Female,43,5,93259.57,3,1,0,141035.65,1\r\n8103,15758252,Toscano,561,Germany,Female,45,2,168085.38,2,0,1,115719.08,0\r\n8104,15740223,Walton,479,Germany,Male,51,1,107714.74,3,1,0,86128.21,1\r\n8105,15805413,Chiang,769,France,Female,31,6,117852.26,2,1,0,147668.64,0\r\n8106,15635116,Burgos,659,Spain,Male,60,2,0,1,1,0,177480.45,1\r\n8107,15764892,Spinelli,590,Spain,Female,51,10,84474.62,2,1,1,190937.09,0\r\n8108,15795936,Lung,560,France,Male,50,3,0,2,1,0,84531.79,0\r\n8109,15655232,Noble,437,Germany,Male,35,6,126803.34,2,1,1,161133.4,0\r\n8110,15640133,Pai,661,France,Female,34,0,0,2,1,0,185555.63,0\r\n8111,15751524,Chigozie,677,Germany,Female,36,10,68806.84,1,1,0,33075.24,0\r\n8112,15670552,Peavy,560,France,Female,31,3,115141.18,1,1,0,39806.75,0\r\n8113,15623966,Yermakov,578,France,Female,35,2,0,2,0,1,26389.92,0\r\n8114,15752193,Burton,421,Spain,Male,34,6,90723.36,1,1,1,12162.76,0\r\n8115,15607269,Costa,492,Germany,Female,49,2,151249.45,2,1,1,167237.94,0\r\n8116,15700752,Pugliesi,545,France,Female,32,6,0,2,1,1,52067.37,0\r\n8117,15777901,Lindell,640,Germany,Female,43,9,94752.49,1,1,0,184006.36,1\r\n8118,15639117,Sorenson,624,Spain,Female,34,6,0,1,1,0,582.59,1\r\n8119,15720203,Arcuri,577,Spain,Male,28,7,0,1,1,0,143274.41,0\r\n8120,15586236,Banks,704,France,Male,31,5,132084.66,3,1,1,54474.48,1\r\n8121,15676645,Parry,523,France,Male,45,5,0,2,1,1,121428.2,0\r\n8122,15715988,Cockett,793,France,Male,35,2,0,2,1,1,79704.12,0\r\n8123,15603749,Galkina,564,France,Female,53,2,45472.28,1,1,1,41055.71,1\r\n8124,15608956,Su,711,France,Male,33,1,0,1,0,0,41590.4,0\r\n8125,15733872,Marino,791,Germany,Female,33,10,130229.71,2,0,0,54019.93,1\r\n8126,15666982,Spears,629,Germany,Female,38,9,123948.85,1,1,0,76053.07,0\r\n8127,15602647,Cunningham,729,Germany,Male,39,6,127415.85,1,1,1,184977.2,1\r\n8128,15623063,Taylor,651,Germany,Male,35,8,110067.71,1,1,0,127678.95,1\r\n8129,15682928,Chiazagomekpere,695,Spain,Male,39,4,65521.2,1,1,1,1243.97,0\r\n8130,15729246,Hardacre,847,Spain,Male,31,5,0,2,1,1,76326.67,0\r\n8131,15588928,Maslow,704,France,Male,47,5,0,2,1,1,145338.61,0\r\n8132,15803352,Scott,613,Germany,Male,33,3,155736.42,2,1,1,57751.21,0\r\n8133,15607485,Wakelin,692,Spain,Female,29,4,0,2,0,0,138880.24,0\r\n8134,15656249,Esposito,720,France,Female,34,3,118307.57,2,1,1,136120.29,0\r\n8135,15761783,Shah,577,France,Male,41,6,0,1,1,1,167621.18,0\r\n8136,15716605,Chukwufumnanya,710,Germany,Female,24,7,103099.17,2,1,0,173276.62,0\r\n8137,15757425,Fleming,716,France,Female,38,1,0,2,1,1,99661.46,0\r\n8138,15603096,Lori,410,France,Male,33,6,125789.69,1,0,0,66333.56,1\r\n8139,15588580,Kennedy,584,Germany,Female,36,4,109646.83,1,1,1,70240.79,0\r\n8140,15770539,Walters,792,France,Male,30,1,127187.86,1,1,1,113553.42,0\r\n8141,15572022,Han,605,France,Female,36,6,0,1,0,1,690.84,0\r\n8142,15571843,Lawrence,486,Spain,Male,24,1,0,1,1,0,98802.76,0\r\n8143,15752502,Cooke,615,France,Male,41,4,130385.82,1,0,1,130661.95,0\r\n8144,15609058,Wan,676,France,Male,23,1,107787.47,1,0,1,116378.82,0\r\n8145,15775108,Lo Duca,571,France,Male,34,1,99325.04,2,0,1,186052.15,0\r\n8146,15708904,Yermakova,850,France,Female,37,9,0,1,0,0,100101.06,0\r\n8147,15600086,Combs,717,France,Male,48,7,123764.95,1,1,1,169952.82,0\r\n8148,15814675,Chien,642,Germany,Female,39,8,128264.03,1,1,0,61792.76,1\r\n8149,15572777,Meng,780,Spain,Male,47,7,86006.21,1,1,1,37973.13,0\r\n8150,15585106,Calabresi,492,Germany,Female,38,8,57068.43,2,1,0,188974.81,0\r\n8151,15738936,Stevenson,760,Germany,Male,29,5,103607.24,2,0,1,86334.64,0\r\n8152,15750970,Davidson,500,Spain,Male,40,1,99004.24,1,1,1,152845.99,0\r\n8153,15725772,Ch'in,654,Spain,Female,36,2,0,2,1,1,146652.11,0\r\n8154,15692106,Rose,606,Spain,Female,25,3,147386.72,3,1,0,45482.04,1\r\n8155,15791533,Ch'ien,367,Spain,Male,42,6,93608.28,1,1,0,168816.73,1\r\n8156,15715715,Artyomova,799,Spain,Male,38,2,0,2,1,1,59297.34,0\r\n8157,15785576,Mayrhofer,434,Germany,Male,71,9,119496.87,1,1,0,125848.88,0\r\n8158,15798834,Yefremov,719,Spain,Female,32,7,0,1,0,0,76264.27,0\r\n8159,15744127,Kosovich,641,France,Female,37,2,0,2,1,0,3939.87,0\r\n8160,15637427,Lu,461,Spain,Female,25,6,0,2,1,1,15306.29,0\r\n8161,15576990,Taplin,790,Germany,Female,25,5,152885.77,1,1,0,58214.79,0\r\n8162,15615352,Ebelechukwu,588,France,Male,31,4,99607.37,2,0,1,35877.03,0\r\n8163,15647333,Fleming,621,France,Male,27,4,137003.68,1,1,0,21254.06,0\r\n8164,15572050,Yefimov,768,Germany,Male,48,3,122831.58,1,1,1,24533.89,1\r\n8165,15581370,Andreyeva,681,Spain,Male,38,2,99811.44,2,1,0,23531.5,0\r\n8166,15813503,Pickering,606,Spain,Male,37,8,154712.58,2,1,0,89099.18,0\r\n8167,15769783,Allan,542,Spain,Male,37,8,0,1,1,1,807.06,0\r\n8168,15793135,Wang,713,Germany,Female,24,7,147687.24,1,1,1,121592.5,0\r\n8169,15599182,Reynolds,597,Spain,Female,33,2,0,2,1,1,4700.66,0\r\n8170,15689517,Hales,635,France,Male,27,3,127009.83,1,1,0,161909.95,0\r\n8171,15641366,Y?an,599,Germany,Male,61,1,124737.96,1,0,1,90389.61,1\r\n8172,15588859,Rowley,496,Spain,Female,44,0,179356.28,2,1,0,2919.21,1\r\n8173,15732293,Chia,759,Spain,Male,31,8,0,2,1,1,99086.74,0\r\n8174,15568032,Moore,757,Germany,Male,31,1,127320.36,3,1,0,163170.32,0\r\n8175,15623525,Copeland,564,Spain,Male,31,0,125175.58,1,1,1,72757.33,0\r\n8176,15606601,Rishel,561,France,Female,22,6,186788.96,2,1,0,73286.8,0\r\n8177,15800811,Wan,702,France,Male,40,3,148556.74,1,0,1,146056.29,0\r\n8178,15610711,Eluemuno,678,Germany,Female,40,8,128644.46,1,0,0,167673.37,0\r\n8179,15809654,Hsia,707,France,Female,46,7,127476.73,2,1,1,146011.55,0\r\n8180,15576077,Kelly,610,France,Female,27,9,159561.93,1,0,1,103381.47,0\r\n8181,15643378,Muir,744,France,Male,42,1,112419.92,1,1,1,83022.92,0\r\n8182,15566790,McIntyre,598,France,Male,28,8,129991.76,2,0,1,46041.08,0\r\n8183,15774402,Donaldson,562,Spain,Male,36,5,0,1,0,1,182843.24,0\r\n8184,15694641,Wright,621,Spain,Female,59,2,0,2,1,1,171364.18,0\r\n8185,15605916,Uvarova,659,France,Female,50,3,0,1,1,0,183399.12,1\r\n8186,15812356,Doherty,722,Germany,Female,40,6,89175.06,2,0,1,152883.95,0\r\n8187,15644179,Allen,606,France,Female,39,3,0,2,1,0,50560.45,1\r\n8188,15771674,Ma,603,Spain,Female,39,5,162390.52,2,1,0,54702.66,0\r\n8189,15623314,Tucker,506,Germany,Female,59,3,190353.08,1,1,0,78365.75,0\r\n8190,15613292,Ch'eng,715,France,Male,21,8,0,2,1,0,68666.63,0\r\n8191,15813871,Hs?,690,France,Male,47,2,0,2,1,0,151375.73,0\r\n8192,15759480,H?,644,France,Female,40,10,139180.97,1,1,1,19959.67,0\r\n8193,15587712,Chimaijem,589,France,Male,36,8,114435.47,1,1,0,26955.72,0\r\n8194,15671165,Esomchi,592,France,Female,66,5,149950.19,1,1,1,76267.59,0\r\n8195,15620746,Lorenzo,632,France,Male,42,4,126115.6,1,1,0,100998.5,0\r\n8196,15706537,Pirogov,577,Germany,Female,59,7,111396.97,1,0,1,191070.01,0\r\n8197,15589312,Larkin,588,France,Male,30,3,115007.08,1,0,0,176858.5,0\r\n8198,15741180,Eddy,617,France,Male,54,6,102141.9,1,1,1,45325.26,0\r\n8199,15733888,Sells,668,Spain,Female,36,3,133686.52,1,1,0,190958.48,1\r\n8200,15798532,Crawford,810,France,Male,32,9,120879.73,2,0,1,78896.59,0\r\n8201,15577359,Bezrukov,767,Spain,Male,47,5,0,1,1,0,121964.46,1\r\n8202,15614936,Mancini,718,Spain,Female,49,10,82321.88,1,0,1,11144.4,0\r\n8203,15747647,Iadanza,589,Spain,Female,27,4,0,2,1,0,144181.48,0\r\n8204,15588566,Wilkinson,778,Spain,Male,33,5,116474.28,2,1,1,32757.55,0\r\n8205,15570141,P'eng,724,France,Female,34,3,132352.69,1,1,0,80320.3,0\r\n8206,15800793,St Clair,477,Germany,Female,39,4,182491.57,1,1,0,185830.72,0\r\n8207,15572415,Preston,580,France,Male,34,6,0,2,1,1,160095.31,0\r\n8208,15635125,Findlay,566,Spain,Male,63,2,120787.18,2,1,1,52198.84,0\r\n8209,15636551,Nixon,711,France,Female,29,3,130181.47,2,1,0,31811.44,0\r\n8210,15600912,Gorshkov,706,Germany,Male,32,5,88348.43,2,1,1,104181.78,0\r\n8211,15768476,Chukwubuikem,703,Spain,Male,31,6,0,2,1,1,67667.19,0\r\n8212,15650266,Medvedeva,679,Germany,Male,39,2,146186.28,2,1,1,193974.47,0\r\n8213,15621004,Chukwuhaenye,603,France,Male,32,7,0,1,1,0,198055.94,1\r\n8214,15748352,Endrizzi,598,Spain,Male,34,0,104488.17,1,0,1,43249.67,0\r\n8215,15788920,Ch'ang,836,Germany,Female,32,4,109196.67,2,1,0,55218.02,0\r\n8216,15743236,Piccio,687,France,Female,61,7,80538.56,1,1,0,131305.37,1\r\n8217,15637717,Lockington,704,Germany,Male,41,4,109026.8,2,1,1,43117.1,0\r\n8218,15635500,Seleznyov,605,Germany,Male,75,2,61319.63,1,0,1,186655.11,0\r\n8219,15634792,Weston,516,France,Female,40,9,0,2,0,1,33266.29,0\r\n8220,15607560,Groom,572,France,Female,39,2,0,2,1,1,555.28,0\r\n8221,15727177,Manfrin,557,France,Male,42,6,177822.03,1,1,0,150944.31,1\r\n8222,15774358,Robertson,443,Germany,Male,59,4,110939.3,1,1,0,72846.58,1\r\n8223,15791304,Ch'ang,604,Germany,Male,25,7,165413.43,1,1,1,35279.74,0\r\n8224,15603328,Lucchesi,483,France,Male,27,1,77805.66,1,1,1,2101.89,0\r\n8225,15804937,Cambage,702,France,Male,50,3,0,2,0,0,94949.84,0\r\n8226,15804142,Tan,670,Spain,Female,57,3,175575.95,2,1,0,99061.75,1\r\n8227,15608845,Tao,804,Spain,Female,38,3,124197.22,1,1,0,74692.06,0\r\n8228,15702434,Hsieh,850,France,Female,30,3,0,2,1,0,116692.8,0\r\n8229,15632609,Burdekin,554,France,Female,39,10,160132.75,1,1,0,32824.15,0\r\n8230,15603550,Longo,588,Germany,Female,37,7,70258.88,2,1,0,139607.61,0\r\n8231,15755239,Maughan,758,Germany,Male,32,4,162657.64,2,1,1,115525.13,0\r\n8232,15670528,Franz,787,Germany,Male,43,0,132217.45,1,1,0,20955.03,1\r\n8233,15732704,Piazza,582,Spain,Male,25,9,148042.97,2,1,0,52341.15,0\r\n8234,15589019,Morant,633,Spain,Female,33,4,92855.02,1,1,1,159813.18,0\r\n8235,15677796,Becher,766,Germany,Male,47,9,129289.98,1,1,0,169935.46,1\r\n8236,15760177,Lombardi,564,Spain,Male,37,9,100252.18,1,1,1,146033.52,0\r\n8237,15636595,Loton,602,Spain,Male,37,3,107592.89,2,0,1,153122.73,0\r\n8238,15737275,Conti,649,France,Male,39,3,113096.41,1,1,1,60335.24,0\r\n8239,15672905,Sani,679,Spain,Female,40,7,0,2,1,1,163757.29,0\r\n8240,15753955,Lori,639,Spain,Male,34,7,149940.04,2,0,0,156648.81,0\r\n8241,15708504,Wong,790,Germany,Male,50,8,121438.58,1,1,1,176471.78,1\r\n8242,15592451,Lombardi,565,France,Male,32,9,0,2,1,0,5388.3,0\r\n8243,15790455,Obialo,478,France,Female,50,2,0,1,0,1,93332.64,1\r\n8244,15572174,Mazzi,825,France,Male,29,3,148874.01,2,0,1,71192.82,0\r\n8245,15656330,Von Doussa,528,Spain,Female,32,0,68138.37,1,1,1,170309.19,0\r\n8246,15569626,Miller,577,Spain,Male,35,5,110080.3,1,1,1,109794.31,0\r\n8247,15608726,Miracle,663,France,Male,24,7,0,2,1,1,166310.82,0\r\n8248,15637366,Su,505,Germany,Female,25,5,114268.85,2,1,1,126728.27,0\r\n8249,15778049,Wyatt,633,Germany,Male,29,6,117412.35,1,0,0,30338.94,0\r\n8250,15727421,Anayolisa,586,France,Female,38,6,0,2,1,1,37935.83,0\r\n8251,15688865,Wade,850,France,Female,35,9,0,2,0,0,25329.48,0\r\n8252,15751032,Enemuo,629,Germany,Female,37,1,35549.81,2,0,0,49676.33,0\r\n8253,15734737,Bruno,744,France,Male,56,9,0,2,1,1,169498.61,0\r\n8254,15746515,Greece,750,France,Male,36,7,136492.92,3,1,1,26500.29,1\r\n8255,15664311,Yang,637,Germany,Male,28,3,123675.69,1,1,1,166458.41,0\r\n8256,15708139,Brown,575,France,Female,40,1,139532.34,1,1,0,181294.39,0\r\n8257,15768574,Anderson,671,Spain,Male,58,1,178713.98,1,1,1,21768.21,0\r\n8258,15738018,Johnston,571,France,Male,40,5,0,2,0,0,72849.29,0\r\n8259,15699753,Zakharov,590,France,Male,41,1,89086.31,1,1,0,24499.97,0\r\n8260,15703199,Golibe,619,Spain,Male,38,3,96143.47,1,0,0,98994.92,0\r\n8261,15627830,Nikitina,640,Germany,Female,30,5,32197.64,1,0,1,141446.01,0\r\n8262,15570855,Leonard,670,France,Male,38,7,0,2,1,1,77864.41,0\r\n8263,15772503,Burns,737,France,Female,33,4,0,2,1,0,115115.32,0\r\n8264,15584453,Burtch,555,Spain,Male,32,10,0,2,0,1,168605.96,0\r\n8265,15710111,Clark,742,France,Male,33,6,0,2,0,0,38550.4,0\r\n8266,15618562,Woodward,618,Germany,Female,40,0,140306.38,1,1,0,160618.61,1\r\n8267,15706764,Spencer,560,France,Female,35,1,0,2,1,0,3701.63,0\r\n8268,15798737,Chao,654,France,Male,38,8,0,2,1,0,88659.44,0\r\n8269,15712608,Costa,787,Germany,Female,42,2,74483.97,2,0,1,44273.91,0\r\n8270,15636736,McLachlan,611,France,Female,53,7,0,2,0,1,156495.39,1\r\n8271,15703544,Hung,559,Spain,Male,34,0,0,1,1,0,182988.94,0\r\n8272,15815645,Akhtar,481,France,Male,37,8,152303.66,2,1,1,175082.2,0\r\n8273,15705739,Toscani,753,Germany,Male,32,5,159904.79,1,1,0,148811.14,0\r\n8274,15709643,Gray,675,France,Male,32,1,0,3,1,0,85901.09,0\r\n8275,15669805,Warren,748,Germany,Female,31,1,99557.94,1,1,0,199255.32,0\r\n8276,15737489,Ramsden,610,Spain,Female,46,5,116886.59,1,0,0,107973.44,0\r\n8277,15775131,Bartlett,580,Spain,Male,32,9,142188.2,2,0,1,128028.6,0\r\n8278,15765283,Wenz,624,Germany,Female,40,3,149961.99,2,1,0,104610.86,0\r\n8279,15628715,Kisch,709,France,Female,36,8,0,2,1,1,69676.55,0\r\n8280,15813283,Mai,605,France,Female,34,2,0,1,0,0,35982.42,0\r\n8281,15745716,McGregor,706,Spain,Male,53,7,0,2,0,1,117939.17,0\r\n8282,15598485,Pinto,567,Spain,Male,40,8,28649.64,1,1,1,95140.62,0\r\n8283,15696552,Newman,747,France,Female,21,4,81025.6,2,1,0,167682.57,0\r\n8284,15754569,Pagnotto,664,France,Male,57,1,0,2,1,1,56562.57,0\r\n8285,15701741,Williams,711,France,Female,39,3,152462.79,1,1,0,90305.97,0\r\n8286,15572631,Ndubuisi,609,France,Male,25,10,0,1,0,1,109895.16,0\r\n8287,15636069,Plummer,632,Spain,Male,28,7,155519.59,1,1,0,1843.24,0\r\n8288,15682467,Chimezie,725,France,Female,36,1,118851.05,1,1,1,102747.02,0\r\n8289,15790744,Nash,850,France,Female,34,9,92899.27,2,1,0,97465.89,0\r\n8290,15625023,Onochie,682,France,Male,40,4,0,1,0,1,105352.55,0\r\n8291,15731267,Rizzo,797,France,Male,37,4,75263.7,1,1,0,85801.77,0\r\n8292,15742879,Boni,668,Spain,Male,38,1,147904.31,1,1,1,69370.05,0\r\n8293,15757015,Davies,783,Germany,Female,41,5,106640.5,1,1,0,176945.96,0\r\n8294,15770711,Lu,766,Germany,Female,28,4,90696.78,1,0,1,21597.2,0\r\n8295,15569430,Burrows,704,Spain,Female,36,2,175509.8,2,1,0,152039.67,0\r\n8296,15617304,Ershova,722,France,Male,40,6,0,2,1,1,111893.09,0\r\n8297,15704466,Udokamma,692,France,Female,34,7,0,2,1,0,195074.62,0\r\n8298,15664681,Aitken,584,France,Female,35,2,114321.28,2,0,0,15959.01,0\r\n8299,15605534,Turnbull,644,Germany,Female,51,4,95560.04,1,0,0,72628.84,1\r\n8300,15792473,Reilly,598,Germany,Female,50,5,88379.81,3,0,1,64157.24,1\r\n8301,15802625,Hardy,733,Germany,Male,48,7,85915.52,1,1,1,23860.5,0\r\n8302,15766017,Brookman,615,Germany,Male,58,3,72309.3,1,1,1,85687.09,1\r\n8303,15762172,Kerr,850,France,Female,39,2,0,2,1,0,179451.42,0\r\n8304,15728333,McBurney,521,France,Male,43,8,0,1,1,1,93180.09,0\r\n8305,15792868,Mickey,675,France,Male,69,1,0,2,1,0,157097.09,0\r\n8306,15605698,Harrison,746,France,Male,58,3,0,3,1,1,80344.96,1\r\n8307,15777060,Olszewski,770,France,Female,33,4,0,1,1,0,26080.54,1\r\n8308,15626243,Chijioke,618,France,Male,30,3,133844.22,1,1,1,31406.93,0\r\n8309,15719898,Young,556,France,Male,36,7,154872.08,2,1,1,32044.64,0\r\n8310,15599976,Bellasis,749,France,Female,27,9,0,2,1,0,132734.87,0\r\n8311,15752809,De Mestre,702,Spain,Male,43,6,116121.67,1,1,0,61602.42,0\r\n8312,15589698,De Luca,555,Germany,Male,42,6,107104.5,1,1,1,41304.44,1\r\n8313,15609977,Mundy,587,France,Male,47,6,71026.77,1,1,0,57962.41,0\r\n8314,15750121,Tung,639,France,Male,38,3,0,1,1,0,42862.82,0\r\n8315,15734177,Donahue,643,France,Male,33,4,0,2,1,1,152992.04,0\r\n8316,15781347,Okagbue,600,France,Female,41,1,0,2,1,1,91193.65,0\r\n8317,15592025,Nnaemeka,651,France,Male,53,7,0,2,1,1,130132.41,0\r\n8318,15670163,Verjus,666,France,Female,27,4,0,2,0,0,88751.45,0\r\n8319,15765402,H?,520,France,Female,39,6,145644.05,1,0,0,104118.93,0\r\n8320,15624343,Napolitani,650,Spain,Female,50,7,129667.77,1,0,0,42028.16,0\r\n8321,15602354,Ginikanwa,564,Germany,Male,33,3,109341.87,1,1,0,75632.78,0\r\n8322,15579183,Spaull,586,France,Male,64,1,0,2,1,1,53710.23,0\r\n8323,15584899,Siciliani,617,France,Female,35,5,0,2,0,1,13066.3,0\r\n8324,15723658,Voronina,712,Spain,Female,30,6,0,2,1,0,152417.97,0\r\n8325,15803965,Tang,654,France,Male,55,3,87485.67,1,1,1,3299.01,0\r\n8326,15682489,Crumbley,605,France,Male,27,9,0,2,1,0,198091.81,0\r\n8327,15813645,Hamilton,491,France,Female,36,0,53369.13,1,1,1,103934.12,0\r\n8328,15766787,Piazza,707,France,Female,35,9,0,2,1,1,70403.65,0\r\n8329,15687171,Birch,638,Spain,Male,34,5,146679.77,1,1,0,102179.86,0\r\n8330,15690744,Custance,683,France,Male,43,2,112499.42,2,1,0,30375.18,0\r\n8331,15707974,Anayochukwu,815,Spain,Female,38,2,48387,1,1,0,184796.84,0\r\n8332,15673084,Galkin,645,Spain,Male,38,1,68079.8,1,0,1,166264.89,0\r\n8333,15814772,Adams,645,Germany,Male,49,4,160133.88,1,0,1,88391.97,0\r\n8334,15743709,Toomey,683,France,Male,30,4,66190.33,1,1,1,115186.97,0\r\n8335,15610343,Marshall-Hall,705,France,Female,37,10,0,2,1,1,13935.53,1\r\n8336,15737414,Shen,647,France,Male,35,4,123761.68,1,1,0,83910.4,0\r\n8337,15788480,Pagnotto,786,Germany,Female,33,0,122325.58,1,0,0,34712.34,1\r\n8338,15568519,Wood,534,France,Male,41,9,0,2,1,0,13871.34,0\r\n8339,15792453,More,602,Spain,Female,42,1,138912.17,1,1,1,139494.75,0\r\n8340,15658100,Piccio,695,France,Female,42,0,0,2,0,1,140724.64,0\r\n8341,15695197,Tochukwu,553,Germany,Female,25,7,128524.19,2,1,0,20682.46,0\r\n8342,15749807,Graham,516,Spain,Female,31,3,0,2,1,0,124202.26,0\r\n8343,15773876,Tung,655,France,Female,34,3,0,2,1,0,159638.77,0\r\n8344,15591698,P'eng,849,Germany,Female,49,9,132934.89,1,1,0,171056.65,1\r\n8345,15712813,Nevzorova,520,Germany,Male,43,3,150805.17,3,0,1,25333.03,1\r\n8346,15763898,Toscani,568,Spain,Female,46,3,0,2,1,1,29372.62,0\r\n8347,15793324,McKenzie,695,Spain,Male,32,9,0,3,0,1,38533.79,0\r\n8348,15757759,Okwuoma,807,Spain,Female,28,7,165969.26,3,1,0,156122.13,1\r\n8349,15796230,Morley,642,Germany,Female,36,2,124495.98,3,1,1,57904.22,1\r\n8350,15611729,Kerr,703,Germany,Male,39,1,141559.5,1,1,1,31257.1,1\r\n8351,15709531,Harding,556,France,Male,38,2,114756.14,1,1,0,193214.05,0\r\n8352,15650751,Butler,585,France,Female,30,6,0,2,1,1,137757.69,0\r\n8353,15641413,Crawford,587,Germany,Female,49,7,155393.98,2,1,0,13308.2,1\r\n8354,15753840,Brown,524,Spain,Female,32,6,0,1,1,1,132861.9,1\r\n8355,15669994,Greece,556,Germany,Female,31,1,128663.81,2,1,0,125083.29,0\r\n8356,15695301,Matthews,504,Spain,Male,44,4,113522.64,1,1,1,12405.2,0\r\n8357,15792004,Heath,731,Spain,Female,26,3,0,2,1,0,37697.29,0\r\n8358,15603035,Vincent,651,France,Male,34,3,0,2,1,1,105599.65,0\r\n8359,15717286,Sal,675,Spain,Female,40,8,79035.95,1,1,0,142783.98,1\r\n8360,15577107,Milne,657,Spain,Female,22,6,0,3,0,1,168412.07,1\r\n8361,15754747,Bazile,686,Germany,Male,33,9,141918.09,2,0,1,184036.47,0\r\n8362,15705676,Wardle,690,France,Female,35,9,107944.33,2,0,0,48478.47,0\r\n8363,15751912,Lilly,567,France,Male,36,7,0,2,0,1,3896.08,0\r\n8364,15677336,Aitken,557,Germany,Male,57,1,120043.13,1,1,0,132370.75,1\r\n8365,15684395,Enderby,446,Spain,Female,45,10,125191.69,1,1,1,128260.86,1\r\n8366,15659949,Chiu,850,France,Male,31,1,96399.31,2,1,0,106534.15,0\r\n8367,15812422,Ugorji,637,France,Male,41,2,0,2,0,1,102515.42,0\r\n8368,15806941,Sharpe,499,France,Male,60,7,76961.6,2,1,1,83643.87,0\r\n8369,15637690,Houghton,622,Germany,Female,34,7,98675.74,1,1,0,138906.85,1\r\n8370,15632882,Konovalova,684,Germany,Male,37,1,126817.13,2,1,1,29995.83,1\r\n8371,15807107,Patel,612,France,Male,32,3,121394.42,1,1,0,164081.42,0\r\n8372,15661034,Ngozichukwuka,813,Germany,Female,29,5,106059.4,1,0,0,187976.88,1\r\n8373,15811958,Medland,850,Germany,Male,44,2,112755.34,2,0,0,158171.36,0\r\n8374,15785167,Padovano,795,Spain,Male,29,4,0,2,0,0,155711.64,0\r\n8375,15646720,Tsui,628,Spain,Female,55,7,0,3,1,0,85890.75,1\r\n8376,15658614,H?,565,Germany,Female,38,7,145400.69,2,1,1,83844.79,0\r\n8377,15704657,Denman,601,France,Male,39,3,72647.64,1,1,0,41777.9,1\r\n8378,15567147,Ratten,802,Spain,Male,40,4,0,2,1,1,81908.09,0\r\n8379,15701319,Baxter,614,Germany,Female,37,6,96340.81,2,1,1,139377.24,1\r\n8380,15745266,Norman,434,Spain,Male,55,6,0,1,0,1,73562.05,1\r\n8381,15650437,Shen,522,Germany,Male,32,8,124450.36,2,1,1,165786.1,0\r\n8382,15764314,Reilly,550,Germany,Male,36,2,113877.23,2,1,0,174921.91,0\r\n8383,15612594,Ifeanacho,599,Spain,Male,25,3,0,2,1,1,120790.02,0\r\n8384,15593501,Graham,493,France,Female,36,5,148667.81,2,1,0,56092.51,0\r\n8385,15804150,Lysaght,755,France,Male,34,3,0,2,1,1,158816.03,0\r\n8386,15649297,T'ang,605,France,Female,62,4,111065.93,2,0,1,125660.99,0\r\n8387,15641110,Abron,708,France,Male,41,0,0,1,1,0,128400.62,0\r\n8388,15660608,Chimaraoke,699,France,Male,44,8,158697.61,1,1,0,107181.22,0\r\n8389,15806570,Y?an,763,France,Female,53,4,0,1,1,0,77203.72,1\r\n8390,15715345,Sergeyeva,743,Spain,Male,25,6,0,2,1,0,129740.11,0\r\n8391,15755521,Ma,660,France,Female,48,0,90044.32,2,0,1,187604.97,1\r\n8392,15579074,Obiajulu,619,Germany,Male,38,10,84651.79,1,1,1,184754.26,0\r\n8393,15641158,Belcher,739,Germany,Male,32,3,102128.27,1,1,0,63981.37,1\r\n8394,15752507,K?,769,Germany,Male,60,9,148846.39,1,1,0,192831.67,1\r\n8395,15597983,Brown,692,France,Male,69,10,154953.94,1,1,1,70849.47,0\r\n8396,15586069,Abernathy,560,France,Female,30,0,108883.29,1,1,0,27914.95,0\r\n8397,15655082,Pape,607,France,Female,48,4,112070.86,3,1,0,173568.3,1\r\n8398,15720155,Tao,630,Germany,Male,29,6,131354.39,1,0,1,9324.31,1\r\n8399,15582116,Ma,767,Germany,Female,45,7,132746.2,2,1,0,26628.88,1\r\n8400,15749365,Earle,543,France,Female,34,8,0,2,0,1,145601.8,0\r\n8401,15632069,Kazantsev,776,France,Male,39,8,125211.55,2,1,0,144496.07,0\r\n8402,15663134,Uspenskaya,535,Spain,Male,58,1,0,2,1,1,11779.98,1\r\n8403,15766683,Coombes,549,Germany,Male,36,6,139422.37,1,0,0,83983.39,1\r\n8404,15707219,Hopman,844,France,Female,28,4,0,2,0,1,123318.37,0\r\n8405,15709232,McKay,586,Germany,Female,47,5,157099.47,2,1,1,65481.86,0\r\n8406,15801351,Milanesi,583,France,Male,40,3,0,2,1,0,47728,0\r\n8407,15578747,Chineze,701,Spain,Male,26,5,83600.24,1,0,1,59195.05,0\r\n8408,15675626,Dawson,726,France,Male,28,2,0,1,0,0,98060.51,0\r\n8409,15583736,Shih,829,Germany,Male,36,4,81795.74,2,1,0,90106.94,0\r\n8410,15590011,Hughes,749,Spain,Male,38,9,129378.32,1,1,1,13549.34,0\r\n8411,15609913,Clark,743,France,Female,46,9,0,1,1,0,113436.08,0\r\n8412,15719479,Chukwuhaenye,619,Spain,Female,56,7,0,2,1,1,42442.21,0\r\n8413,15575147,Wall,699,France,Male,22,9,99339,1,1,0,68297.61,1\r\n8414,15597309,Howell,749,Spain,Male,36,7,0,2,0,0,80134.65,0\r\n8415,15648367,Lo,600,Germany,Female,29,6,74430.1,2,1,1,96051.1,0\r\n8416,15758031,Lazarev,760,Spain,Male,38,3,91241.85,1,0,1,80682.35,0\r\n8417,15751771,Lowe,528,Germany,Male,32,2,99092.45,1,0,1,111149.98,0\r\n8418,15689288,Folliero,630,France,Female,26,5,0,2,1,0,182612.38,0\r\n8419,15731026,Han,683,Germany,Female,39,2,100062.16,2,1,0,109201.43,0\r\n8420,15775809,Holloway,677,Germany,Female,26,6,98723.67,1,0,1,151146.67,0\r\n8421,15743076,Pai,669,Spain,Male,29,9,0,1,1,1,93901.61,0\r\n8422,15658258,Trejo,693,France,Male,43,6,128760.32,1,1,0,36342.79,0\r\n8423,15756321,Johnston,612,Spain,Female,52,5,144772.69,1,0,0,98302.57,1\r\n8424,15706799,Macknight,719,Spain,Male,44,4,0,1,0,0,84972.9,1\r\n8425,15775703,Lo,702,France,Male,26,2,71281.29,1,1,1,108747.12,1\r\n8426,15642636,Glossop,755,France,Male,29,9,117035.89,1,1,1,21862.19,0\r\n8427,15704651,Bishop,514,France,Male,26,1,0,2,0,0,121551.93,0\r\n8428,15806771,Yefremova,753,France,Female,40,0,3768.69,2,1,0,177065.24,1\r\n8429,15566735,Obialo,548,Germany,Female,36,2,108913.84,2,1,1,140460.01,0\r\n8430,15681671,Nkemjika,850,Germany,Male,28,2,101100.22,2,1,1,35337.31,0\r\n8431,15775949,Trevisani,612,France,Female,38,7,110615.47,1,1,1,193502.93,0\r\n8432,15586752,Parkes,628,Germany,Male,33,8,152143.89,1,1,1,32174.03,0\r\n8433,15582519,Seleznyov,479,France,Male,47,6,121797.09,1,0,1,5811.9,1\r\n8434,15658233,Naylor,724,France,Female,41,5,109798.25,1,0,1,149593.61,0\r\n8435,15755330,Forbes,512,Germany,Male,41,7,122403.24,1,0,1,37439.9,1\r\n8436,15605072,Douglas,638,France,Female,43,3,145860.98,1,1,1,142763.51,1\r\n8437,15617538,Nwankwo,834,Spain,Male,40,7,0,2,0,0,45038.74,0\r\n8438,15591428,Myers,781,France,Male,29,9,0,2,0,0,172097.4,0\r\n8439,15692142,Rogova,707,Germany,Female,48,7,105086.74,1,1,1,180344.69,1\r\n8440,15692931,Hsing,670,France,Male,22,2,114991.45,1,1,1,37392.56,0\r\n8441,15781127,Giordano,663,Spain,Female,33,8,96769.04,1,1,1,36864.05,0\r\n8442,15677136,Okwukwe,624,France,Female,23,5,0,2,0,0,132418.59,0\r\n8443,15677828,Chalmers,598,France,Female,34,4,0,2,0,0,60894.26,0\r\n8444,15567897,Chiazagomekpere,619,Germany,Male,23,5,132725.1,1,1,1,143913.33,0\r\n8445,15793641,Evseyev,792,France,Female,70,3,0,2,1,1,172240.27,0\r\n8446,15678333,Parry-Okeden,683,France,Female,26,7,0,2,1,0,86619.77,0\r\n8447,15630511,Picot,691,France,Female,33,6,0,2,1,0,164074.89,0\r\n8448,15792627,Reid,765,Spain,Female,33,5,84557.82,1,1,1,69039.43,0\r\n8449,15717191,Ferri,508,France,Male,49,1,93817.41,2,1,1,132468.76,1\r\n8450,15625716,Genovesi,637,France,Female,33,9,113913.53,1,0,1,65316.5,0\r\n8451,15710053,Neumayer,667,Germany,Female,44,5,140406.68,2,0,1,57164.19,0\r\n8452,15580043,Murray,575,Spain,Female,22,8,105229.34,1,1,1,34397.08,0\r\n8453,15601410,Tien,744,Spain,Female,46,1,0,3,1,1,177431.59,1\r\n8454,15684669,Parkes,567,France,Female,41,9,137891.35,1,1,0,142009.46,1\r\n8455,15619083,Yip,502,France,Female,35,6,0,2,1,1,80618.47,0\r\n8456,15692207,Ingle,609,France,Female,53,6,0,2,1,1,124218.27,0\r\n8457,15730705,Chidubem,715,France,Male,37,9,165252.52,1,1,0,85286.3,0\r\n8458,15749688,Lu,541,France,Male,32,8,0,2,0,0,40889.14,0\r\n8459,15728542,Vorobyova,850,France,Female,71,4,0,2,1,1,107236.87,0\r\n8460,15760063,Chiedozie,595,Spain,Male,23,7,0,2,1,1,168085.97,0\r\n8461,15658982,Napolitani,650,Germany,Female,28,5,122034.4,3,0,1,146663.43,1\r\n8462,15758769,Coffey,625,France,Female,44,7,0,1,1,0,4791.8,0\r\n8463,15778481,Chigbogu,817,France,Male,59,1,118962.58,1,1,1,120819.58,0\r\n8464,15661162,Akabueze,526,Spain,Male,49,2,0,1,1,0,114539.67,1\r\n8465,15568164,Istomin,850,France,Female,34,4,71379.53,2,1,1,154000.99,0\r\n8466,15601569,Ndubueze,598,France,Female,40,2,171178.25,1,1,0,137980.58,1\r\n8467,15772383,Toscani,613,France,Male,36,9,131307.11,1,0,0,83343.73,0\r\n8468,15667456,Ross,709,Spain,Male,62,3,0,2,1,1,82195.15,0\r\n8469,15672983,Fernando,678,Spain,Female,27,5,87099.85,2,1,0,149550.95,0\r\n8470,15799534,McClaran,720,France,Male,71,5,183135.39,2,1,1,197688.5,0\r\n8471,15582847,Yermakova,662,France,Male,26,0,0,2,0,1,72929.96,0\r\n8472,15612478,Somadina,525,France,Male,51,10,0,3,1,0,171045.35,1\r\n8473,15709621,Wan,662,France,Male,31,3,0,2,0,1,27731.05,0\r\n8474,15802009,Mazzi,770,France,Female,33,6,0,2,1,1,126131.9,0\r\n8475,15698816,Tuan,721,Spain,Female,33,4,72535.45,1,1,1,103931.49,0\r\n8476,15574830,Townsley,633,Germany,Male,58,2,128137.42,2,1,0,147635.33,1\r\n8477,15603082,Yashina,701,France,Male,51,9,0,2,0,0,61961.57,0\r\n8478,15685947,Henderson,556,Germany,Male,42,0,115915.53,2,0,1,125435.47,1\r\n8479,15643048,Mueller,639,France,Male,66,0,0,2,0,1,42240.54,0\r\n8480,15807568,Wright,632,France,Male,50,2,0,2,0,0,57942.88,0\r\n8481,15597591,Lung,456,France,Male,29,5,107000.49,1,1,1,153419.62,0\r\n8482,15747558,Bryant,729,Spain,Female,38,10,0,2,1,0,189727.12,0\r\n8483,15756655,Madukaife,632,France,Female,34,2,0,2,0,0,165385.55,0\r\n8484,15589949,Maclean,433,Spain,Male,34,9,152806.74,1,1,0,19687.99,0\r\n8485,15601012,Abdullah,802,France,Female,60,3,92887.06,1,1,0,39473.63,1\r\n8486,15724269,Yao,670,France,Male,25,7,0,2,1,1,144723.38,0\r\n8487,15567506,Cheatham,738,Germany,Female,40,6,114940.67,2,1,1,194895.57,1\r\n8488,15791877,Gallagher,706,Germany,Male,34,0,140641.26,2,1,1,77271.91,0\r\n8489,15794360,Hao,592,Germany,Female,70,5,71816.74,2,1,0,105096.82,1\r\n8490,15686538,Nixon,522,France,Female,41,7,0,2,0,1,176780.39,0\r\n8491,15585985,Wang,746,France,Male,48,5,165282.42,1,1,0,153786.46,1\r\n8492,15699257,Kerr,651,Spain,Male,42,2,143145.87,2,1,0,43612.06,0\r\n8493,15804104,Romani,494,France,Male,28,9,114731.76,2,0,1,79479.74,0\r\n8494,15727619,Lock,753,Germany,Female,46,9,113909.69,3,1,0,92320.37,1\r\n8495,15740237,Millar,671,Germany,Male,36,2,116695.27,1,0,0,193201.86,0\r\n8496,15801436,K'ung,696,France,Male,42,4,0,1,0,0,126353.13,1\r\n8497,15705735,Onyekachi,577,Spain,Male,43,3,0,2,1,1,135008.92,0\r\n8498,15649359,Somayina,587,France,Male,36,1,0,2,0,1,17135.6,0\r\n8499,15624892,Dennis,712,Germany,Male,37,7,93978.96,2,1,0,60651.77,0\r\n8500,15784918,Brown,498,Germany,Male,35,2,121968.11,2,0,1,188343.05,0\r\n8501,15584785,Ogochukwu,660,France,Male,37,2,97324.91,1,1,0,23291.83,0\r\n8502,15797197,Macleod,678,Spain,Male,29,6,0,2,1,0,64443.75,0\r\n8503,15574858,Page,530,France,Male,37,8,0,2,1,1,287.99,0\r\n8504,15794101,Barese,559,France,Female,48,2,0,2,0,1,137961.41,0\r\n8505,15743245,Agafonova,624,France,Male,42,3,145155.37,1,1,0,72169.95,1\r\n8506,15791535,Caraway,592,France,Male,28,5,137222.77,1,0,0,39608.58,0\r\n8507,15605215,Stevenson,767,France,Male,48,9,0,2,0,1,175458.21,0\r\n8508,15771749,Duncan,653,Germany,Female,38,5,114268.22,2,1,1,89524.83,0\r\n8509,15616833,Wang,678,Spain,Male,27,2,0,2,1,1,13221.25,0\r\n8510,15750728,Kaur,586,Spain,Female,42,2,0,1,1,0,102889.34,0\r\n8511,15769353,Jenkins,550,France,Female,40,8,150490.32,1,0,0,166468.21,1\r\n8512,15770091,Edwards,643,Germany,Male,28,9,160858.13,2,1,0,27149.27,0\r\n8513,15716420,Kelly,612,Spain,Male,39,5,170288.38,1,1,1,59601.15,0\r\n8514,15740602,Boyle,674,Germany,Female,27,4,111568.01,1,0,1,22026.18,0\r\n8515,15796071,Loane,657,Spain,Male,29,7,83889.03,1,1,0,153059.62,0\r\n8516,15811389,Padovano,724,Germany,Female,35,0,171982.95,2,0,1,167313.07,0\r\n8517,15783875,Li Fonti,500,France,Female,34,4,0,2,1,0,12833.96,0\r\n8518,15671800,Robinson,688,France,Male,20,8,137624.4,2,1,1,197582.79,0\r\n8519,15677288,Geach,599,France,Male,50,3,121159.65,1,0,0,4033.39,1\r\n8520,15633525,Payne,631,France,Male,29,7,0,2,0,1,125877.22,0\r\n8521,15634606,Chinonyelum,634,Spain,Male,52,1,0,2,1,1,176913.42,0\r\n8522,15579207,Watkins,545,France,Male,37,3,91184.01,1,1,0,105476.65,0\r\n8523,15619892,Page,644,Spain,Male,18,8,0,2,1,0,59172.42,0\r\n8524,15567778,Genovese,690,Germany,Female,54,1,144027.8,1,1,1,108731.02,1\r\n8525,15711750,Watson,711,France,Female,34,6,0,2,1,1,175310.38,0\r\n8526,15751084,Mancini,712,France,Female,29,8,140170.61,1,1,1,38170.04,0\r\n8527,15768945,Chibueze,627,France,Male,27,1,62092.9,1,1,1,105887.04,0\r\n8528,15586931,Hunter,694,Spain,Male,39,3,0,1,1,1,95625.03,0\r\n8529,15636353,Buchi,534,Spain,Male,35,4,0,2,0,0,9541.15,0\r\n8530,15623858,Charteris,603,France,Male,45,9,0,1,0,0,148516.79,0\r\n8531,15703354,Aksenov,808,France,Female,33,2,103516.87,1,1,0,113907.8,0\r\n8532,15663987,Wright,723,Spain,Male,30,1,0,3,1,0,164647.72,1\r\n8533,15780805,Lu,585,France,Female,35,2,0,2,1,0,98621.04,1\r\n8534,15768566,K?,706,France,Male,34,8,0,2,1,1,37479.97,0\r\n8535,15643229,Hou,671,France,Female,31,6,0,2,1,1,15846.42,0\r\n8536,15754940,Descoteaux,597,Spain,Male,43,2,85162.26,1,0,1,5104.08,1\r\n8537,15676576,Stephenson,646,France,Female,43,8,143061.88,1,1,0,61937.6,0\r\n8538,15800068,Cooper,801,Spain,Female,46,6,0,2,1,1,170008.74,0\r\n8539,15648030,Crump,731,Spain,Female,33,5,137388.01,2,1,0,165000.68,0\r\n8540,15668594,Diggs,620,Germany,Female,25,1,137712.01,1,1,1,76197.05,0\r\n8541,15728709,Shih,484,Germany,Male,40,7,106901.42,2,0,0,118045.98,0\r\n8542,15724181,Hudson,647,Spain,Male,47,5,105603.21,2,1,1,157360.9,0\r\n8543,15647546,Carvosso,688,Germany,Female,40,8,150679.71,2,0,1,196226.38,0\r\n8544,15702601,Wyatt,680,Germany,Male,30,4,108300.27,2,0,1,44384.57,1\r\n8545,15567725,Kodilinyechukwu,689,France,Female,46,7,52016.08,2,1,1,72993.65,0\r\n8546,15674179,Vorobyova,513,Germany,Male,34,7,60515.13,1,0,0,124571.09,0\r\n8547,15686957,Piccio,553,Germany,Male,35,2,158584.28,2,1,0,43640.16,0\r\n8548,15607690,Hsing,689,Germany,Male,47,2,118812.5,2,0,0,31121.42,0\r\n8549,15806546,Lucas,517,Spain,Male,46,4,0,1,1,0,22372.78,0\r\n8550,15632850,T'ang,731,France,Male,37,8,0,2,1,1,170338.35,0\r\n8551,15709016,North,687,Germany,Female,47,1,91219.29,1,0,0,158845.49,1\r\n8552,15638068,Thompson,507,Spain,Male,32,7,0,2,1,0,67926.18,0\r\n8553,15749345,Simpson,468,France,Female,22,1,76318.64,1,1,1,194783.12,0\r\n8554,15791321,Nwora,682,Spain,Female,58,4,0,1,1,0,176036.01,0\r\n8555,15699095,Chandler,603,France,Female,24,3,0,1,1,1,198826.03,1\r\n8556,15638329,Uspensky,522,Germany,Male,25,1,111432.13,1,1,1,168683.57,0\r\n8557,15575445,Ferguson,629,Spain,Male,41,10,150148.51,1,0,0,6936.27,0\r\n8558,15752622,Kerr,729,France,Female,32,7,38550.06,1,0,1,179230.23,0\r\n8559,15774507,Furneaux,574,France,Female,39,5,119013.86,1,1,0,103421.91,0\r\n8560,15570857,Kambinachi,677,Germany,Female,39,0,111213.64,2,1,1,147578.26,0\r\n8561,15599386,Black,627,Germany,Male,28,5,71097.23,1,1,1,130504.49,0\r\n8562,15744913,Chizoba,788,Spain,Male,36,10,109632.85,1,1,1,16149.13,0\r\n8563,15647292,Peng,697,France,Male,63,7,148368.02,1,0,0,118862.08,1\r\n8564,15728838,Leach,578,France,Male,45,1,148600.91,1,1,0,143397.14,1\r\n8565,15584704,Chiazagomekpele,519,France,Male,48,10,71083.98,1,1,0,137959,0\r\n8566,15749068,Nickson,632,France,Female,40,9,139625.34,1,1,0,93702.96,1\r\n8567,15622985,Lin,679,France,Female,39,4,0,1,0,0,172939.3,1\r\n8568,15587676,Alexeieva,699,France,Male,30,9,0,1,1,1,108162.13,0\r\n8569,15779496,Sykes,615,France,Male,64,0,81564.1,2,0,1,35896.09,0\r\n8570,15733460,Martin,622,Spain,Male,36,9,0,2,1,1,104852.6,0\r\n8571,15711457,Herz,755,France,Female,28,7,124540.28,1,0,1,188850.89,0\r\n8572,15795290,Nikitina,767,France,Female,42,2,133616.39,1,1,0,28615.8,0\r\n8573,15611223,Ko,752,Germany,Female,38,10,101648.5,2,1,0,172001.44,0\r\n8574,15794159,Highett,633,France,Female,26,8,124281.84,1,1,1,60116.57,0\r\n8575,15780677,Jackson,717,France,Female,59,4,0,2,1,1,170528.63,0\r\n8576,15690175,Ball,585,Spain,Male,45,0,0,2,0,0,189683.7,0\r\n8577,15722599,Nelson,751,France,Female,37,9,183613.66,2,0,0,49734.94,0\r\n8578,15569976,Woronoff,754,Germany,Male,65,1,136186.44,1,1,1,121529.59,1\r\n8579,15707011,Morrison,495,France,Male,47,10,137682.68,1,1,0,71071.47,0\r\n8580,15702277,Smith,650,France,Male,34,4,106005.54,1,0,1,142995.32,0\r\n8581,15801915,Rendall,529,France,Female,31,6,152310.55,1,1,0,13054.25,0\r\n8582,15580213,McIntyre,585,France,Female,43,2,0,2,1,0,89402.54,0\r\n8583,15637947,Wei,668,Spain,Male,32,1,134446.04,1,0,1,111241.37,0\r\n8584,15715888,Allardyce,591,France,Female,38,2,142289.28,1,0,1,119638.85,0\r\n8585,15732967,Cremonesi,731,France,Male,19,6,0,2,1,1,151581.79,0\r\n8586,15737047,Weatherford,754,France,Female,45,6,0,1,1,0,73881.68,1\r\n8587,15694039,Jen,650,Germany,Female,46,9,149003.76,2,1,0,176902.83,0\r\n8588,15649457,Macleod,588,Germany,Male,41,2,131341.46,2,0,1,7034.94,0\r\n8589,15742809,Mironova,712,Spain,Female,29,7,77919.78,1,1,0,122547.58,0\r\n8590,15637829,Sharpe,691,France,Female,34,7,0,2,0,1,161559.12,0\r\n8591,15633194,Osborne,771,France,Female,41,10,108309,4,1,1,137510.41,1\r\n8592,15611635,Chu,678,Spain,Female,39,6,0,1,0,1,185366.56,0\r\n8593,15638774,Chong,719,Spain,Female,40,9,0,2,1,0,182224.14,0\r\n8594,15722037,Alvarez,610,Germany,Male,36,7,115462.02,1,0,1,42581.04,0\r\n8595,15672930,Palerma,722,Spain,Male,37,9,0,2,1,0,31921.95,0\r\n8596,15668774,Chiemenam,758,Germany,Female,23,5,122739.1,1,1,0,102460.84,1\r\n8597,15780966,Pritchard,709,France,Female,32,2,0,2,0,0,109681.29,0\r\n8598,15659694,Wallis,634,Germany,Female,53,3,113781.5,2,1,1,106345.05,1\r\n8599,15624424,Palerma,678,Spain,Female,49,1,0,2,1,1,102472.9,0\r\n8600,15708713,Hill,633,France,Male,35,3,0,2,1,1,36249.76,0\r\n8601,15755405,Hudson,710,France,Male,43,9,128284.45,1,1,0,32996.89,1\r\n8602,15647570,Chung,640,Germany,Male,45,8,120591.19,1,0,0,195123.94,0\r\n8603,15684348,Zhdanova,656,France,Male,63,8,0,2,0,1,57014.43,0\r\n8604,15702541,Fraser,551,France,Female,59,2,166968.28,1,1,0,159483.76,1\r\n8605,15646942,Meng,786,Spain,Female,39,7,0,2,0,0,100929.59,0\r\n8606,15748920,Cherkasova,561,France,Female,49,8,0,2,1,1,12513.07,0\r\n8607,15694581,Rawlings,807,Spain,Male,42,5,0,2,1,1,74900.9,0\r\n8608,15643215,Jen,602,Germany,Male,38,2,71667.97,2,0,0,137111.89,0\r\n8609,15649060,Chien,727,Germany,Female,31,3,82729.47,2,1,0,60212.51,0\r\n8610,15774258,Gorbunov,678,France,Male,40,1,0,2,1,1,187343.4,0\r\n8611,15731553,Lucas,730,France,Male,23,8,0,2,1,0,183284.53,0\r\n8612,15617029,Young,596,Spain,Female,30,1,0,2,1,0,8125.39,0\r\n8613,15780716,Colombo,686,Germany,Male,39,3,129626.19,2,1,1,103220.56,0\r\n8614,15577018,Tsao,684,Germany,Female,26,2,114035.39,1,0,0,96885.19,0\r\n8615,15809515,Lewis,797,Germany,Male,32,1,151922.94,1,1,0,8877.06,0\r\n8616,15789924,Hussain,658,France,Female,39,4,0,1,1,1,147530.06,0\r\n8617,15725076,Anderson,653,Spain,Female,27,6,107751.68,2,1,1,33389.42,0\r\n8618,15672481,Ulyanov,641,France,Male,37,6,0,2,1,0,45309.24,0\r\n8619,15574115,Shaw,656,Spain,Female,41,6,101179.23,2,1,1,35230.61,0\r\n8620,15661830,Lucciano,750,Spain,Female,36,6,0,2,1,1,59816.41,0\r\n8621,15665879,Gordon,768,France,Female,40,8,0,2,0,1,69080.46,0\r\n8622,15673820,Woodward,568,France,Male,33,7,0,2,1,0,143450.61,0\r\n8623,15747772,Cunningham,706,Germany,Male,36,9,58571.18,2,1,0,40774.01,0\r\n8624,15666197,Boni,430,Germany,Female,38,8,153058.64,1,1,0,99377.27,0\r\n8625,15773639,Truscott,745,Germany,Male,35,4,98270.34,1,1,0,133617.43,0\r\n8626,15581893,Ginikanwa,747,France,Male,43,1,130788.71,1,0,1,101495,1\r\n8627,15672447,Bailey,657,Germany,Male,40,7,99165.84,1,0,1,119333.95,1\r\n8628,15777830,Hutchinson,639,France,Female,42,4,0,2,0,0,167682.37,0\r\n8629,15713890,Maclean,704,France,Male,44,3,0,2,0,1,152884.85,0\r\n8630,15577598,Chiang,651,Spain,Male,23,4,115636.05,2,1,0,70400.86,0\r\n8631,15786042,Willmore,706,Germany,Female,44,2,185932.18,2,1,0,65413.41,0\r\n8632,15753462,Godson,632,Germany,Male,30,2,72549,2,0,1,182728.8,0\r\n8633,15759690,Smith,751,France,Male,42,4,0,2,1,1,81442.6,0\r\n8634,15801414,Bitter,767,France,Female,35,2,0,2,0,0,144251.38,0\r\n8635,15656141,Ts'ao,741,France,Male,39,5,0,1,0,1,40207.06,0\r\n8636,15608701,Chialuka,651,Germany,Male,29,3,121890.06,1,1,0,54530.51,1\r\n8637,15582892,Scott,601,France,Male,46,2,99786.07,1,1,1,32683.88,1\r\n8638,15632967,Feng,520,France,Male,34,3,0,2,1,1,104703.96,0\r\n8639,15587573,Castiglione,626,Germany,Male,27,4,115084.53,2,0,1,26907.43,0\r\n8640,15654891,He,811,France,Male,30,6,0,2,1,1,180591.32,0\r\n8641,15611365,Fanucci,730,France,Female,32,9,127661.69,1,0,0,60905.51,0\r\n8642,15749103,Ginikanwa,604,Germany,Female,47,4,118907.6,1,0,1,47777.15,1\r\n8643,15810203,Manning,499,Germany,Female,44,6,77627.33,2,1,0,108222.68,0\r\n8644,15813660,Forlonge,754,Spain,Male,40,2,160625.17,1,0,1,3554.63,0\r\n8645,15605673,Liang,716,Spain,Female,29,8,0,2,0,0,78616.92,0\r\n8646,15669282,Uchechukwu,636,France,Female,20,10,124266.86,1,0,0,100566.81,0\r\n8647,15792726,Sung,470,France,Female,25,8,127974.06,2,1,1,183259.35,0\r\n8648,15593241,Tochukwu,444,France,Male,43,3,0,2,1,1,159131.21,0\r\n8649,15683053,Reyna,809,Spain,Female,48,2,0,1,1,0,160976.85,1\r\n8650,15632736,Liang,850,Germany,Female,30,3,104911.35,2,1,1,42933.26,0\r\n8651,15731865,Unwin,637,France,Male,27,1,0,2,1,0,91291.2,0\r\n8652,15760450,Rutherford,512,France,Male,43,1,0,2,1,1,52471.36,0\r\n8653,15787204,Howe,774,Spain,Female,43,1,110646.54,1,0,0,108804.28,0\r\n8654,15650454,Tran,641,France,Male,57,5,0,2,1,1,122449.18,0\r\n8655,15573730,Thompson,586,Germany,Male,42,6,126704.49,2,1,0,41682.3,0\r\n8656,15705050,Linger,611,France,Male,30,9,0,2,1,1,148887.69,0\r\n8657,15791342,Johnston,660,Spain,Male,31,1,84560.04,1,1,1,137784.25,0\r\n8658,15684316,Udokamma,532,France,Male,43,9,0,2,0,0,190573.91,1\r\n8659,15700540,Barrera,557,Germany,Female,38,2,129893.56,1,0,0,102076.03,0\r\n8660,15770631,Sutherland,730,Spain,Male,25,5,167385.81,1,1,1,56307.51,0\r\n8661,15790594,Bednall,535,France,Female,27,6,0,2,0,1,49775.58,0\r\n8662,15604020,Otoole,773,Germany,Female,36,4,105858.71,1,0,1,4395.45,0\r\n8663,15637599,Cremonesi,510,Germany,Female,44,4,123070.89,1,1,0,28461.29,1\r\n8664,15736578,Hamilton,539,France,Male,39,1,0,1,1,1,28184.7,0\r\n8665,15666332,Donaldson,690,Spain,Female,48,2,0,2,1,1,3149.1,0\r\n8666,15727291,McKay,821,France,Female,40,1,0,2,1,0,194273.12,0\r\n8667,15785920,Black,687,Germany,Male,35,1,125141.24,2,1,1,148537.07,0\r\n8668,15658987,Kane,557,France,Female,46,4,96173.17,2,1,1,116378.31,0\r\n8669,15687719,She,532,Spain,Female,37,5,0,2,0,1,6761.84,0\r\n8670,15799641,Bruno,540,Spain,Male,39,2,0,2,1,0,81995.92,0\r\n8671,15758702,Watson,705,France,Female,55,8,0,2,1,1,14392.68,0\r\n8672,15689526,Shih,542,Germany,Female,35,9,127543.11,2,1,0,468.94,1\r\n8673,15586848,Rose,706,France,Male,38,1,0,2,1,0,122379.54,0\r\n8674,15707637,Zikoranachukwudimma,765,France,Female,56,1,0,1,1,0,13228.93,1\r\n8675,15719426,Cole,529,France,Male,67,8,103101.56,2,1,1,154002.02,1\r\n8676,15639265,Isaacs,714,France,Male,54,7,126113.28,1,1,0,112777.38,0\r\n8677,15576124,Muravyova,582,France,Male,41,1,40488.76,1,1,0,128528.83,0\r\n8678,15757829,Timperley,609,Germany,Female,40,10,137389.77,2,1,0,170122.22,0\r\n8679,15633227,Kenechukwu,518,France,Female,28,9,85146.36,1,0,0,2803.89,0\r\n8680,15753092,He,791,Germany,Male,35,5,129828.58,1,1,1,181918.26,1\r\n8681,15782939,Storey,747,France,Male,42,4,80214.36,1,1,0,115241.96,1\r\n8682,15746338,Onyekachukwu,565,France,Female,40,2,0,2,1,1,129956.13,0\r\n8683,15590676,Kharlamova,735,France,Male,34,1,141796.43,1,1,0,45858.49,0\r\n8684,15599329,Christopher,697,France,Female,49,7,195238.29,4,0,1,131083.56,1\r\n8685,15783097,Lombardo,813,Germany,Male,27,6,111348.15,1,1,0,46422.46,0\r\n8686,15597885,Kerr,772,France,Male,43,6,0,2,1,1,57675.88,0\r\n8687,15597467,Duncan,606,France,Female,71,8,0,2,1,1,169741.96,0\r\n8688,15724764,Lawley,667,Germany,Female,42,10,64404.26,2,0,0,26022.37,0\r\n8689,15778418,Burns,637,Germany,Male,40,9,154309.67,1,1,1,125334.16,1\r\n8690,15684769,Whitson,542,France,Male,67,10,129431.36,1,0,1,21343.74,0\r\n8691,15756167,Doyne,762,Spain,Female,43,5,134204.67,1,1,1,139971.01,0\r\n8692,15632439,Pinto,698,France,Female,39,4,0,2,0,1,47455.82,0\r\n8693,15755138,Chin,850,France,Female,32,8,0,2,1,1,55593.8,0\r\n8694,15659092,Davide,621,France,Female,50,5,0,2,1,0,191756.54,1\r\n8695,15742116,Torres,671,Germany,Female,48,9,116711.06,2,0,0,76373.38,0\r\n8696,15801994,Buccho,775,France,Male,31,9,0,2,1,0,169278.51,0\r\n8697,15647572,Greece,504,Spain,Male,34,0,54980.81,1,1,1,136909.88,0\r\n8698,15644551,Wimble,751,Spain,Female,37,3,99773.85,2,1,0,54865.92,0\r\n8699,15709135,Pirozzi,691,Germany,Male,30,7,101231.77,2,0,0,156529.44,0\r\n8700,15684469,Hsiung,841,Germany,Male,32,2,117070.21,1,1,0,113482.2,0\r\n8701,15627637,Obioma,709,Germany,Male,23,8,73314.04,2,1,0,63446.47,0\r\n8702,15667093,Onio,673,France,Male,37,2,0,1,1,1,13624.02,0\r\n8703,15690589,Udinesi,541,France,Male,37,9,212314.03,1,0,1,148814.54,0\r\n8704,15595350,Fermin,661,France,Female,31,3,136067.82,2,1,0,65567.91,0\r\n8705,15777586,Moss,784,Spain,Female,42,2,109052.04,2,1,0,6409.55,0\r\n8706,15804064,Docherty,742,France,Female,35,2,79126.17,1,1,1,126997.53,0\r\n8707,15717770,Marcelo,850,Spain,Female,55,7,0,1,0,0,171762.87,1\r\n8708,15754443,Fadden,443,France,Female,35,9,108308,1,1,0,129031.19,1\r\n8709,15776939,Zox,778,Germany,Female,48,3,102290.56,2,1,0,182691.31,0\r\n8710,15713517,Otitodilinna,529,France,Male,39,6,102025.08,2,1,0,12351.01,0\r\n8711,15683522,Kennedy,678,Germany,Female,37,2,113383.07,1,1,1,135123.96,0\r\n8712,15673995,Tu,516,Spain,Female,65,9,102541.1,1,1,0,181490.42,0\r\n8713,15771054,Barnes,469,Spain,Male,35,5,0,2,1,0,186490.37,0\r\n8714,15578788,Bibi,786,Spain,Male,40,6,0,2,0,0,41248.8,0\r\n8715,15737408,L?,703,France,Female,41,6,109941.51,1,1,0,116267.28,0\r\n8716,15750837,Landseer,579,Germany,Male,41,0,141749.68,1,0,1,9201.53,0\r\n8717,15576022,Nwachinemelu,565,France,Male,38,5,0,2,0,1,80630.32,0\r\n8718,15635502,Ch'iu,443,France,Male,44,2,0,1,1,0,159165.7,0\r\n8719,15627298,Vinogradova,589,France,Male,37,7,85146.48,2,1,0,86490.09,1\r\n8720,15811415,Jenks,691,France,Female,44,6,134066.1,2,1,1,197572.41,0\r\n8721,15645059,Crace,711,France,Female,28,8,0,2,0,0,105159.89,0\r\n8722,15689671,Packham,775,Spain,Male,27,4,0,1,1,1,40807.26,0\r\n8723,15718667,T'ien,621,France,Male,35,7,87619.29,1,1,0,143.34,0\r\n8724,15803202,Onyekachi,350,France,Male,51,10,0,1,1,1,125823.79,1\r\n8725,15593683,Solomina,668,Spain,Female,30,8,0,2,1,0,138465.7,0\r\n8726,15703394,Hawes,633,Spain,Male,27,3,0,2,1,0,44008.91,0\r\n8727,15570289,Benson,697,Germany,Male,43,8,103409.16,1,1,0,66893.28,1\r\n8728,15567437,Emenike,734,Germany,Female,30,7,123040.38,1,1,1,76503.06,0\r\n8729,15711687,Nero,434,France,Male,41,4,108128.52,1,0,1,56784.11,0\r\n8730,15656592,Toscano,646,Germany,Male,48,8,169023.33,2,1,1,175657.55,0\r\n8731,15634373,Yang,764,France,Male,30,5,0,2,0,1,105155.66,0\r\n8732,15769125,Palerma,727,Spain,Female,41,10,0,2,0,1,47468.56,0\r\n8733,15711386,Trentini,724,France,Female,29,6,0,2,0,1,64729.51,0\r\n8734,15714241,Haddon,749,Spain,Male,42,9,222267.63,1,0,0,101108.85,1\r\n8735,15642530,Coates,706,Germany,Female,47,10,144090.42,1,1,0,140938.95,1\r\n8736,15713599,Castiglione,728,France,Male,30,10,114835.43,1,0,1,37662.49,0\r\n8737,15744770,Stone,636,France,Male,44,2,0,2,0,0,86414.41,0\r\n8738,15780498,Maynard,634,France,Male,34,3,145030.92,1,1,1,41820.65,0\r\n8739,15624397,Moore,627,France,Male,43,8,71240.3,1,0,1,127734.16,0\r\n8740,15615219,Obielumani,518,France,Male,59,5,138772.15,1,0,1,123872,0\r\n8741,15570908,Harding,687,Spain,Female,29,7,93617.07,1,0,1,113050.92,0\r\n8742,15762855,Hill,622,Spain,Female,23,8,0,2,1,1,131389.39,0\r\n8743,15661827,Brown,693,Spain,Female,45,4,0,2,1,1,26589.56,0\r\n8744,15746035,Pagnotto,450,Spain,Male,25,9,74237.2,2,0,1,195463.35,0\r\n8745,15691906,Esposito,664,Germany,Female,49,5,127421.78,2,1,0,108876.75,1\r\n8746,15793424,Tan,663,Spain,Female,28,8,61274.7,2,1,0,136054.45,0\r\n8747,15577905,Hammond,660,France,Male,34,8,106486.66,2,0,1,182262.66,0\r\n8748,15667216,Chung,579,France,Female,29,10,73194.52,2,1,1,129209.09,0\r\n8749,15673971,Houghton,655,Germany,Female,44,6,146498.76,1,1,0,64853.51,1\r\n8750,15701238,Chia,683,France,Male,47,1,0,2,1,0,148989.15,0\r\n8751,15644849,Zikoranachidimma,655,France,Female,32,2,0,1,1,1,71047.51,0\r\n8752,15635531,Boag,575,Spain,Female,30,8,0,2,1,0,185341.63,0\r\n8753,15632263,Pagnotto,574,Spain,Male,30,5,120355,1,1,0,137793.35,0\r\n8754,15720110,Oluchukwu,795,France,Male,32,2,117265.21,1,1,1,198317.23,0\r\n8755,15619045,Baxter,776,France,Female,43,4,0,2,0,1,162137.5,0\r\n8756,15697510,Tien,707,Spain,Female,52,7,0,1,1,0,109688.82,1\r\n8757,15784923,Chimezie,705,Germany,Female,37,3,109974.22,1,1,1,36320.87,1\r\n8758,15567383,Slone,678,Germany,Female,44,2,98009.13,2,0,1,31384.86,0\r\n8759,15732621,Martin,663,France,Male,34,10,0,1,1,1,114083.73,0\r\n8760,15757981,Loggia,748,France,Male,66,8,0,1,1,1,163331.65,0\r\n8761,15727819,Hartley,677,Spain,Female,34,10,171671.9,1,1,1,50777.77,0\r\n8762,15738088,Parkin,634,Spain,Male,63,10,0,2,1,0,30772.86,1\r\n8763,15765173,Lin,350,France,Female,60,3,0,1,0,0,113796.15,1\r\n8764,15665159,Brooks,727,France,Male,61,0,128213.96,2,1,1,188729.08,1\r\n8765,15618203,Tien,773,Germany,Male,51,8,116197.65,2,1,1,86701.4,0\r\n8766,15791452,Dann,675,France,Male,39,1,0,2,1,0,153129.22,0\r\n8767,15638159,Trentino,649,Spain,Female,36,6,86607.39,1,0,0,19825.09,0\r\n8768,15585466,Russo,552,France,Male,29,10,0,2,1,0,12186.83,0\r\n8769,15677310,Christie,761,Germany,Male,62,5,98854.34,1,0,0,86920.97,1\r\n8770,15646262,Ross,622,France,Male,31,7,0,1,1,0,35408.77,0\r\n8771,15656901,Nnonso,615,France,Male,59,8,0,2,1,1,165576.55,0\r\n8772,15621093,Teng,681,Germany,Male,31,4,97338.19,2,0,0,48226.76,0\r\n8773,15592123,Buccho,768,France,Male,30,6,0,2,1,1,199454.37,0\r\n8774,15589200,Madukaife,617,Spain,Male,34,9,0,2,1,0,118749.58,0\r\n8775,15602934,Dunn,452,France,Female,33,6,131698.57,2,1,0,151623.91,0\r\n8776,15812720,Hooker,807,Germany,Male,37,10,130110.45,2,0,1,172097.95,0\r\n8777,15695383,Griffin,567,Spain,Male,44,9,0,2,1,0,87677.15,0\r\n8778,15723064,Kistler,603,Spain,Male,24,1,165149.13,2,1,0,21858.28,0\r\n8779,15761606,Law,617,Spain,Female,37,9,101707.8,1,1,0,123866.28,0\r\n8780,15650322,Grigoryeva,701,France,Female,34,3,105588.66,1,0,1,74694.41,0\r\n8781,15669782,Chu,820,Germany,Male,39,9,111336.89,1,1,0,16770.31,1\r\n8782,15751628,Onyemachukwu,438,France,Male,60,7,78391.17,1,0,1,49424.6,0\r\n8783,15809057,Lu,600,Spain,Female,27,6,0,2,1,1,172031.22,0\r\n8784,15617052,Watson,782,France,Male,34,9,0,1,1,0,183021.06,1\r\n8785,15590810,Fallaci,638,Germany,Female,41,9,144326.09,1,1,0,73979.85,1\r\n8786,15801293,Ni,850,Germany,Male,27,1,101278.25,2,1,1,26265.18,0\r\n8787,15770968,Leason,741,Germany,Female,19,8,108711.57,2,1,0,24857.25,0\r\n8788,15572356,Tsai,689,Spain,Male,73,1,108555.07,1,0,1,167969.15,0\r\n8789,15603247,Bruner,743,Germany,Female,35,1,146781.24,1,1,0,189307.7,0\r\n8790,15619116,Wallace,493,France,Female,36,2,0,2,0,1,99770.3,0\r\n8791,15691792,Young,416,Spain,Male,35,8,0,1,0,0,119712.78,0\r\n8792,15783276,Forbes,725,France,Female,25,9,0,2,1,1,168607.74,0\r\n8793,15766137,Muir,497,France,Male,34,2,0,2,1,1,83087.13,0\r\n8794,15574554,Pugh,537,Germany,Male,66,8,103291.25,2,1,1,130664.79,0\r\n8795,15578671,Webb,706,Spain,Female,29,1,209490.21,1,1,1,133267.69,1\r\n8796,15716608,Walker,651,Spain,Male,38,2,0,3,1,0,67029.82,1\r\n8797,15690670,Cox,720,France,Male,33,2,0,2,0,1,141031.08,0\r\n8798,15630466,Maclean,797,France,Male,45,8,0,1,0,0,125110.02,0\r\n8799,15630349,Hollis,543,Spain,Male,23,5,0,2,1,0,117832.39,0\r\n8800,15803801,Jamieson,454,France,Male,34,4,0,2,1,0,198817.72,0\r\n8801,15647890,Su,691,France,Male,37,9,149405.18,1,1,1,146411.6,0\r\n8802,15606115,P'eng,510,France,Female,52,6,191665.21,1,1,1,131312.56,1\r\n8803,15714642,Hawkins,792,Spain,Female,40,7,0,1,1,0,141652.2,0\r\n8804,15741181,Ndubuagha,721,France,Male,41,6,135071.12,1,1,1,64477.25,0\r\n8805,15773973,Hill,765,France,Male,41,2,0,2,0,1,191215.61,0\r\n8806,15758546,Norton,850,Spain,Male,39,8,0,2,1,1,37090.44,0\r\n8807,15598940,Achebe,681,Germany,Male,38,6,181804.34,2,1,1,57517.71,0\r\n8808,15669783,Simpson,586,France,Female,60,3,47020.65,2,0,1,63241.21,1\r\n8809,15624993,Chiang,753,France,Female,36,7,128518.98,1,1,1,44567.83,1\r\n8810,15760568,Dalrymple,593,Germany,Female,38,5,142658.04,2,0,1,135337.11,0\r\n8811,15699047,Chukwuemeka,674,France,Female,21,9,120150.39,2,1,1,33964.03,0\r\n8812,15616168,Ojiofor,610,France,Female,35,7,81905.95,1,1,1,61623.19,0\r\n8813,15773146,Rubeo,652,France,Male,26,3,137998.2,2,0,1,168989.77,0\r\n8814,15770375,Fanucci,850,Germany,Female,26,8,123126.29,1,1,0,74425.41,0\r\n8815,15589725,Zubarev,740,France,Female,51,4,0,2,1,1,178929.84,0\r\n8816,15710034,T'ao,637,Germany,Male,43,1,135645.29,2,0,1,101382.86,1\r\n8817,15800806,Pai,685,Spain,Male,31,7,122449.31,2,1,1,180769.55,0\r\n8818,15570485,Udegbunam,558,Spain,Male,40,4,161766.87,1,0,0,92378.54,0\r\n8819,15575391,Claypool,677,France,Female,37,3,0,2,1,1,38252.25,0\r\n8820,15790750,Manfrin,592,Germany,Male,36,10,123187.51,1,0,1,146111.35,0\r\n8821,15714832,Baker,652,Germany,Male,36,9,150956.71,1,0,0,72350.17,0\r\n8822,15619953,Efremov,662,Spain,Female,42,6,105021.28,1,1,0,48242.38,0\r\n8823,15673929,Chin,543,France,Male,64,4,0,2,1,1,148305.82,0\r\n8824,15578835,Brookes,675,Spain,Female,50,1,133204.91,1,0,1,8270.06,0\r\n8825,15752388,Doyle,643,Spain,Female,35,6,0,2,1,1,41549.64,0\r\n8826,15797081,Ajuluchukwu,611,Germany,Female,49,9,115488.52,2,1,1,138656.81,1\r\n8827,15570194,Ikemefuna,412,France,Male,29,5,0,2,0,0,12510.53,0\r\n8828,15580149,Fowler,638,Spain,Male,41,7,0,2,1,0,43889.41,0\r\n8829,15777708,Liao,824,Spain,Female,38,3,0,2,1,0,192800.25,0\r\n8830,15769955,Onuora,683,France,Female,40,1,0,2,0,0,75762,0\r\n8831,15810444,Aksenov,562,Germany,Female,39,6,130565.02,1,1,0,9854.72,1\r\n8832,15645593,Trevisani,599,France,Female,41,2,91328.71,1,1,0,115724.78,0\r\n8833,15765345,Wood,753,France,Male,35,4,0,2,1,1,106303.4,0\r\n8834,15760873,Lombardo,594,France,Male,50,7,81310.34,1,1,1,183868.01,0\r\n8835,15794178,Walpole,657,France,Male,34,3,107136.6,1,1,0,153895.46,0\r\n8836,15589361,Chikwendu,716,Spain,Male,34,9,0,1,1,1,66695.71,0\r\n8837,15662483,Ko,850,France,Male,43,7,0,2,1,1,173851.11,0\r\n8838,15809736,Steigrad,664,France,Male,46,2,0,1,1,1,177423.02,1\r\n8839,15731148,Isayeva,558,France,Male,33,0,108477.49,1,1,1,109096.71,1\r\n8840,15774328,Boni,606,Germany,Male,40,1,144757.97,2,1,1,166656.18,0\r\n8841,15646969,Anayolisa,776,Spain,Male,33,2,0,2,1,1,176921,0\r\n8842,15718769,Fallaci,557,Spain,Male,36,1,113110.26,1,1,0,98413.1,0\r\n8843,15610226,Fenton,614,France,Female,27,9,106414.57,2,0,0,77500.81,0\r\n8844,15616270,Chao,620,Spain,Male,42,4,106920.91,1,0,1,119747.08,0\r\n8845,15790717,Osinachi,695,Spain,Male,35,7,0,2,1,0,160387.98,0\r\n8846,15635703,Chu,729,Germany,Female,39,1,131513.26,1,1,1,193715,0\r\n8847,15616365,Obiuto,571,France,Female,53,2,0,2,1,0,28045.77,0\r\n8848,15630244,Chu,457,France,Male,40,10,134320.23,2,1,0,150757.35,0\r\n8849,15734714,Nash,559,France,Female,29,3,79715.36,1,1,0,82252.28,0\r\n8850,15721433,Hixson,664,France,Female,38,4,74306.19,2,1,0,154395.56,0\r\n8851,15590201,Fiorentini,500,Spain,Female,50,5,0,4,1,1,83866.35,1\r\n8852,15590828,Chidimma,782,Germany,Male,42,7,126428.38,1,1,0,39830.1,0\r\n8853,15752097,Chiazagomekpere,708,Spain,Male,38,8,99640.89,1,1,0,12429.22,0\r\n8854,15800031,Laura,681,France,Male,43,3,66338.68,1,1,1,18772.5,1\r\n8855,15630857,Wu,674,Spain,Female,39,6,0,2,1,1,9574.83,0\r\n8856,15689953,Toscani,697,Spain,Male,43,10,128226.37,1,0,0,188486.94,0\r\n8857,15759733,McMillan,774,France,Female,26,5,0,2,1,1,64716.08,0\r\n8858,15810826,Chiekwugo,624,France,Male,36,6,0,2,0,0,84749.96,0\r\n8859,15668009,Hendley,747,Spain,Male,37,1,0,2,0,1,180551.76,0\r\n8860,15743456,Birnie,715,France,Female,32,10,0,2,1,0,60907.49,0\r\n8861,15725762,Kemp,808,France,Male,24,4,122168.65,1,1,0,174107.04,0\r\n8862,15761713,Johnstone,678,France,Female,43,7,178074.33,1,0,0,110405.9,0\r\n8863,15769246,Lo Duca,813,Germany,Male,59,2,135078.41,1,1,0,187636.06,1\r\n8864,15781129,Montgomery,687,Spain,Male,38,8,69434.4,2,1,1,66580.13,1\r\n8865,15599124,Miller,832,France,Female,29,5,0,2,1,0,178779.52,0\r\n8866,15639004,Chiemezie,668,France,Male,72,2,0,2,1,1,70783.61,0\r\n8867,15810995,Wright,526,Germany,Male,34,3,122726.56,1,1,1,46772.36,0\r\n8868,15653773,Shaw,457,France,Female,38,7,164496.99,1,1,1,163327.27,0\r\n8869,15708357,Chapman,649,Spain,Female,38,8,0,1,1,0,103760.53,0\r\n8870,15733597,Y?an,669,France,Female,41,0,150219.41,2,0,0,107839.03,0\r\n8871,15789560,Clark,668,France,Male,42,8,187534.79,1,1,1,32900.41,1\r\n8872,15699524,Howells,466,France,Female,30,3,0,1,1,0,193984.6,0\r\n8873,15626475,Gamble,685,France,Male,30,2,0,2,1,1,140889.32,0\r\n8874,15810839,Rogers,610,France,Male,34,0,103108.17,1,0,0,125646.82,0\r\n8875,15684318,McMillan,582,Germany,Female,50,6,96486.57,2,1,1,20344.02,0\r\n8876,15768120,Brown,702,Germany,Male,36,9,90560.48,2,1,0,174268.87,0\r\n8877,15712807,Robertson,556,Spain,Male,46,3,131764.96,1,1,1,108500.66,1\r\n8878,15696371,Thomas,812,Spain,Female,24,1,92476.88,1,0,0,83247.14,0\r\n8879,15675794,Hsing,645,Germany,Male,47,9,152076.93,1,1,0,121840.2,1\r\n8880,15774277,Chiu,809,France,Male,43,2,0,2,1,1,132908.07,0\r\n8881,15603764,Chang,560,France,Male,49,4,0,1,1,1,100075.1,1\r\n8882,15618647,Kornilova,744,France,Male,29,1,43504.42,1,1,1,119327.75,0\r\n8883,15614643,Chifo,731,Spain,Female,39,2,0,2,1,0,136737.13,0\r\n8884,15707696,Lu,471,Spain,Female,28,5,0,2,1,1,22356.97,0\r\n8885,15749583,Bellucci,686,Germany,Female,38,2,93569.86,3,0,0,10137.34,1\r\n8886,15815125,Michael,668,Spain,Male,45,4,102486.21,2,1,1,158379.25,0\r\n8887,15779620,Sinclair,575,France,Male,36,1,0,1,0,1,94570.56,0\r\n8888,15768233,Chukwuebuka,435,Germany,Male,37,8,114346.3,1,0,1,980.93,1\r\n8889,15637788,Schmidt,743,France,Male,23,3,110203.77,1,1,0,95583.45,0\r\n8890,15777046,Parry,580,France,Female,39,9,128362.59,1,1,0,86044.98,0\r\n8891,15788723,McIntyre,599,Germany,Female,49,10,143888.22,2,1,1,166236.38,1\r\n8892,15790489,Lo Duca,534,Spain,Male,34,5,170600.78,1,0,1,5240.53,0\r\n8893,15739476,Ferrari,680,France,Female,32,5,0,1,1,1,150684.23,0\r\n8894,15612670,Berry,631,Spain,Female,46,10,0,2,1,1,129508.96,0\r\n8895,15631222,Cattaneo,485,France,Female,39,2,75339.64,1,1,1,70665.16,0\r\n8896,15658972,Foster,699,France,Female,40,8,122038.34,1,1,0,102085.35,0\r\n8897,15724691,Gordon,712,France,Male,34,1,0,2,1,1,195052.12,0\r\n8898,15740442,May,603,France,Male,51,8,186825.57,1,1,0,93739.71,1\r\n8899,15760427,Cameron,850,France,Male,40,6,124788.18,1,1,0,65612.12,0\r\n8900,15677939,Ch'eng,584,France,Female,41,3,0,2,1,1,160095.48,0\r\n8901,15611599,Curtis,604,France,Female,71,2,0,2,1,1,49506.82,0\r\n8902,15633474,Whitehead,586,France,Male,51,2,138553.57,1,1,1,92406.22,0\r\n8903,15671973,Chukwuemeka,467,Spain,Male,39,5,0,2,1,1,7415.96,0\r\n8904,15790019,Onwughara,520,France,Male,35,9,105387.89,1,1,1,25059.06,0\r\n8905,15737735,Grant,683,Spain,Male,40,4,95053.1,1,1,1,116816.54,1\r\n8906,15661745,Browne,557,France,Male,36,3,0,1,0,1,144078.02,0\r\n8907,15797065,Goloubev,613,Spain,Female,32,0,0,2,0,1,126675.62,0\r\n8908,15710671,Gordon,786,France,Male,34,3,137361.96,1,0,0,183682.09,0\r\n8909,15656522,Sutherland,593,Spain,Male,32,10,158537.42,1,1,0,166850.57,0\r\n8910,15705085,Quesada,670,Spain,Female,29,9,0,2,1,0,27359.19,0\r\n8911,15744873,Wright,657,Germany,Female,48,5,143595.87,1,0,0,101314.65,1\r\n8912,15781914,Simmons,718,Germany,Male,32,9,169947.41,2,1,1,27979.16,0\r\n8913,15637354,Yobachukwu,623,France,Female,24,7,148167.83,2,1,1,109470.34,0\r\n8914,15717307,Read,496,France,Male,31,5,0,2,1,0,93713.13,0\r\n8915,15746695,Wunder,429,France,Female,39,6,48023.83,1,1,0,74870.99,0\r\n8916,15804962,Nnaife,606,France,Male,36,1,155655.46,1,1,1,192387.51,1\r\n8917,15665378,Shen,499,France,Female,46,6,0,2,1,0,73457.55,0\r\n8918,15757865,Powell,642,France,Male,62,7,0,2,1,1,61120.75,0\r\n8919,15578787,Goddard,641,France,Female,52,4,0,1,1,0,90964.54,1\r\n8920,15794323,Buckley,673,France,Male,32,8,121240.76,1,1,0,116969.73,0\r\n8921,15697546,McIntyre,570,France,Female,36,3,0,2,1,0,92118.75,0\r\n8922,15629519,Yen,472,France,Female,37,1,0,2,1,1,48357.9,0\r\n8923,15624703,Okonkwo,550,Germany,Male,35,9,129847.75,2,1,0,197325.4,0\r\n8924,15570002,Burlingame,625,Germany,Female,55,8,118772.71,4,0,0,135853.62,1\r\n8925,15808566,Hs?,516,France,Male,46,2,0,2,1,1,169122.54,0\r\n8926,15805463,Board,682,Germany,Male,32,2,105163.88,2,1,1,164170.46,0\r\n8927,15709136,Adams,620,France,Male,28,8,0,2,1,1,199909.32,0\r\n8928,15801605,Rizzo,626,France,Female,39,0,0,2,1,1,83295.09,0\r\n8929,15567855,Chukwufumnanya,623,France,Female,29,1,0,2,0,0,39382.06,0\r\n8930,15675141,Fraser,569,France,Female,35,4,93934.63,1,1,0,184748.23,0\r\n8931,15665759,Russell,724,France,Female,69,5,117866.92,1,1,1,62280.91,0\r\n8932,15761487,Yefimova,678,France,Female,55,5,0,1,0,1,196794.11,1\r\n8933,15700394,Palermo,713,Spain,Female,26,4,122857.46,2,1,0,144682.17,1\r\n8934,15631162,Bergamaschi,631,France,Male,32,10,0,2,0,1,196342.66,0\r\n8935,15630641,Shao,846,France,Female,37,6,127103.97,1,1,1,41516.44,0\r\n8936,15585066,Chimaraoke,660,France,Female,43,1,0,1,0,1,112026.1,1\r\n8937,15722991,McGregor,567,France,Male,54,9,96402.96,1,0,0,52035.29,1\r\n8938,15737404,Kesteven,731,France,Male,31,1,132512.26,1,1,1,185466.85,0\r\n8939,15722409,Ritchie,693,Spain,Male,47,8,107604.66,1,1,1,80149.27,0\r\n8940,15806420,Jenks,772,France,Male,34,9,0,2,1,0,170980.86,0\r\n8941,15658148,Udokamma,657,France,Male,38,7,0,2,1,0,185827.74,0\r\n8942,15810660,Boyle,774,Germany,Male,34,4,120875.23,2,0,1,113407.26,0\r\n8943,15709780,Azuka,667,France,Female,37,9,71786.9,2,1,1,67734.79,0\r\n8944,15727350,Pai,516,France,Female,37,8,113143.12,1,0,0,3363.36,0\r\n8945,15752312,Howells,551,Spain,Male,49,1,150777.72,2,1,1,135757.27,0\r\n8946,15616745,Hs?,542,Spain,Male,35,2,174894.53,1,1,1,22314.55,0\r\n8947,15572294,Kelly,623,France,Male,28,7,0,1,0,0,129526.57,0\r\n8948,15674110,Walton,701,France,Female,43,2,160416.56,1,0,1,37266.43,0\r\n8949,15662501,Ebelechukwu,583,France,Male,48,3,91246.53,1,1,0,60017.46,1\r\n8950,15649239,Vasilieva,731,Spain,Male,46,10,0,2,1,0,153015.42,0\r\n8951,15700424,Hsiao,461,France,Female,35,5,0,1,1,1,54209.02,0\r\n8952,15636388,Abrego,702,Germany,Female,23,7,98775.23,1,1,0,114603.96,0\r\n8953,15713975,Gibson,565,Germany,Female,47,10,139756.12,1,1,0,165849.49,1\r\n8954,15592925,Giordano,711,Spain,Male,42,3,177626.77,3,0,1,16392.72,1\r\n8955,15581626,Mancini,664,France,Male,54,8,0,1,1,1,162719.69,1\r\n8956,15641319,Afanasyeva,518,Spain,Male,50,4,0,1,0,0,107112.25,1\r\n8957,15723481,Wright,728,Spain,Male,42,8,0,2,0,1,41823.22,0\r\n8958,15787825,Okwudiliolisa,585,Germany,Male,37,6,152496.82,1,1,1,99907.29,0\r\n8959,15710726,Hughes,573,France,Male,52,8,0,2,0,1,178229.04,0\r\n8960,15627195,Parrott,568,Germany,Male,26,1,112930.28,2,1,0,22095.73,0\r\n8961,15657957,Hughes,602,Germany,Female,26,8,113674.2,1,1,0,197861.16,1\r\n8962,15676117,Zinachukwudi,603,France,Male,44,9,0,1,1,0,138328.24,0\r\n8963,15607874,Keane,687,France,Male,38,0,144450.58,1,0,1,137276.83,0\r\n8964,15796993,McCollum,741,France,Male,52,1,171236.3,2,0,0,21834.4,1\r\n8965,15649858,Simpson,469,Spain,Male,37,9,96776.49,1,1,1,119890.86,0\r\n8966,15811032,Gambrell,477,Germany,Female,58,8,145984.92,1,1,1,24564.7,0\r\n8967,15679963,Moretti,737,Germany,Male,43,8,96353.8,1,0,0,10209.8,0\r\n8968,15579131,Ricci,835,France,Male,25,7,0,2,1,1,83449.65,0\r\n8969,15572428,Rieke,717,Germany,Female,33,0,115777.23,1,1,1,81508.1,0\r\n8970,15622461,Ndubuagha,562,France,Female,51,7,122822,2,0,0,32626.21,0\r\n8971,15636105,Chung,758,Spain,Male,61,2,0,2,1,1,43982.41,0\r\n8972,15583849,Ts'ai,408,France,Male,40,3,0,2,0,0,124874.23,0\r\n8973,15718780,Cox,650,Spain,Female,32,4,79450.09,1,1,1,118324.75,0\r\n8974,15739271,Lei,582,Germany,Male,33,2,122394,1,1,1,22113.93,0\r\n8975,15697129,Ulyanova,706,Spain,Female,43,1,0,2,1,0,31962.77,0\r\n8976,15763415,Gray,567,Germany,Male,41,0,134378.89,1,1,1,105746.94,0\r\n8977,15796617,McGregor,720,France,Male,29,2,0,2,1,0,39925.52,0\r\n8978,15626628,Tretiakova,631,Spain,Female,31,2,88161.85,2,1,0,127630.88,0\r\n8979,15765857,Genovesi,623,Spain,Male,41,2,142412.13,1,1,0,28778.98,0\r\n8980,15742511,Gordon,514,France,Male,35,3,121030.9,1,1,0,10008.68,0\r\n8981,15786433,Aitken,650,Germany,Female,35,3,165982.43,2,1,1,24482.16,0\r\n8982,15685805,Ginikanwa,673,Spain,Female,35,6,0,2,1,0,98618.79,0\r\n8983,15627971,Coates,504,France,Female,32,8,206663.75,1,0,0,16281.94,0\r\n8984,15783025,Piazza,723,Germany,Male,37,3,94661.53,2,1,0,121239.65,0\r\n8985,15726289,Cawood,645,France,Male,25,0,174400.36,1,1,0,42669.37,0\r\n8986,15802118,Ignatieff,664,Spain,Male,41,7,123428.69,1,1,1,164924.11,0\r\n8987,15607990,Gallo,760,Spain,Male,43,6,175735.5,1,1,1,157337.29,0\r\n8988,15695932,Yelverton,766,Spain,Male,36,5,78381.13,1,0,1,153831.6,0\r\n8989,15812279,William,634,France,Male,37,5,115345.86,2,0,0,168781.8,0\r\n8990,15687558,Mault,640,Germany,Female,31,10,118613.34,1,1,0,168469.65,0\r\n8991,15729065,Mackay,784,Germany,Male,28,2,109960.06,2,1,1,170829.87,0\r\n8992,15698902,McIntyre,547,Germany,Female,42,1,142703.4,1,1,0,86207.49,1\r\n8993,15570192,Henry,608,Germany,Female,40,8,121729.42,1,0,0,61164.45,0\r\n8994,15809265,Kao,547,France,Female,35,4,0,1,1,1,133287.73,0\r\n8995,15745201,Frewin,612,France,Female,43,4,139496.35,2,1,1,77128.23,0\r\n8996,15580623,Yefremova,573,Spain,Male,28,8,0,2,0,0,77660.03,0\r\n8997,15578156,Anenechukwu,615,Spain,Male,32,5,138521.83,1,1,1,56897.1,0\r\n8998,15631063,Trentino,710,France,Female,33,2,0,2,1,0,72945.32,0\r\n8999,15692577,Tomlinson,674,Germany,Female,38,10,83727.68,1,1,0,45418.12,0\r\n9000,15810910,Royston,702,Spain,Female,38,9,0,2,1,1,158527.45,0\r\n9001,15723217,Cremonesi,616,France,Male,37,9,0,1,1,0,111312.96,0\r\n9002,15733111,Yeh,688,Spain,Male,32,6,124179.3,1,1,1,138759.15,0\r\n9003,15610727,Ch'in,605,France,Male,36,7,128829.25,1,1,0,190588.59,0\r\n9004,15792720,Martinez,676,France,Male,33,6,171490.78,1,0,0,79099.64,0\r\n9005,15723153,Wearing,708,Spain,Male,33,3,0,2,1,0,138613.21,0\r\n9006,15802823,Maclean,745,Spain,Female,38,7,0,2,1,1,194230.82,0\r\n9007,15756118,T'ao,661,Spain,Male,20,8,0,1,1,0,110252.53,0\r\n9008,15684934,Rose,726,France,Male,31,9,0,2,1,1,106117.3,0\r\n9009,15776936,Whitworth,475,France,Male,40,7,160818.08,1,0,1,169642.13,1\r\n9010,15729087,Suttor,751,Germany,Male,54,9,156367.6,2,0,1,116179.92,0\r\n9011,15786463,Hsing,645,Germany,Female,59,8,121669.93,2,0,0,91.75,1\r\n9012,15717498,Boni,775,France,Male,42,6,133970.22,2,0,1,187839.9,0\r\n9013,15718406,Marshall,540,France,Male,41,3,0,2,1,0,121098.65,0\r\n9014,15799468,Catchpole,591,France,Female,34,3,96127.27,1,0,0,30972.06,0\r\n9015,15626383,Tang,596,Spain,Male,60,7,121907.97,1,0,1,30314.04,0\r\n9016,15597385,Siddons,573,Spain,Male,41,5,0,2,0,1,14479.29,0\r\n9017,15570271,Wan,577,Spain,Male,31,6,0,1,1,1,196395.25,0\r\n9018,15690330,Efimov,830,Germany,Female,40,8,77701.64,1,0,1,19512.38,0\r\n9019,15680611,Rose,663,France,Male,67,9,0,3,1,1,72318.77,0\r\n9020,15810227,Fanucci,421,France,Male,34,2,0,2,1,1,96615.23,0\r\n9021,15807194,Iweobiegbulam,718,Spain,Male,34,5,113922.44,2,1,0,30772.22,0\r\n9022,15712199,Ijendu,655,Germany,Female,61,2,183997.7,2,1,1,161217.18,0\r\n9023,15694995,O'Sullivan,712,France,Male,23,2,0,2,0,1,66795.78,0\r\n9024,15723400,Hutchinson,663,France,Male,28,4,0,2,1,1,123969.64,0\r\n9025,15654772,Kwemto,640,France,Female,47,6,89799.46,2,0,1,13783.77,1\r\n9026,15574743,Chiu,577,Spain,Male,29,2,0,1,1,1,168924.41,0\r\n9027,15807593,Berry,546,Spain,Female,36,7,85660.96,1,0,0,134778.01,0\r\n9028,15686718,Hung,802,Germany,Male,37,9,115569.21,1,0,1,119782.89,0\r\n9029,15695299,Mordvinova,590,France,Female,45,2,81828.22,1,1,0,52167.97,0\r\n9030,15722701,Bruno,594,Germany,Male,18,1,132694.73,1,1,0,167689.56,0\r\n9031,15799635,Arbour,577,Spain,Male,51,2,108867,1,0,0,140800.66,1\r\n9032,15742323,Barese,541,France,Male,39,7,0,2,1,0,19823.02,0\r\n9033,15658435,Hingston,781,France,Female,27,5,0,2,0,0,72969.9,0\r\n9034,15586029,Davis,806,Germany,Male,34,2,96152.68,2,1,0,143711.02,0\r\n9035,15772337,Lawrence,723,Germany,Female,49,0,153855.52,1,1,1,180862.26,1\r\n9036,15807555,Chung,535,France,Male,45,2,0,2,1,0,125658.28,0\r\n9037,15603378,Padovano,768,France,Female,36,3,141334.95,1,0,1,125870.5,0\r\n9038,15792862,Blinova,653,Germany,Male,41,1,104584.11,1,1,0,15126.32,1\r\n9039,15657349,Carter,803,Germany,Female,50,8,98173.02,1,0,0,22457.25,1\r\n9040,15777614,Webb,545,Spain,Female,44,1,0,2,1,1,82614.89,0\r\n9041,15653952,T'an,581,Germany,Female,38,3,135157.05,1,1,1,32919.42,0\r\n9042,15724336,Yates,513,Germany,Female,49,5,171601.27,1,1,0,126223.84,0\r\n9043,15689594,Su,731,France,Male,29,5,179539.2,1,0,0,112010.02,0\r\n9044,15801920,Christian,727,Germany,Male,39,5,80615.46,2,0,0,180962.32,0\r\n9045,15653347,Chiu,560,Spain,Male,47,1,0,1,0,0,128882.66,1\r\n9046,15749951,Sacco,766,Germany,Male,27,5,126285.73,1,1,0,177614.17,1\r\n9047,15648178,Lettiere,630,Germany,Female,23,4,137964.51,1,0,1,174570.55,0\r\n9048,15738662,Daluchi,652,Germany,Male,41,9,159434.03,1,1,0,178373.93,0\r\n9049,15640855,T'ien,729,Germany,Male,40,5,113574.61,2,1,0,103396.08,0\r\n9050,15584288,Hung,629,France,Female,33,6,0,2,1,1,59129.72,0\r\n9051,15760988,Liu,667,Germany,Male,33,9,124573.33,2,0,0,683.37,0\r\n9052,15569624,Feng,671,Germany,Female,31,6,105864.6,2,1,0,145567.34,0\r\n9053,15597949,Gilbert,768,Germany,Female,47,5,104552.61,1,1,0,48137.08,1\r\n9054,15604551,Robb,732,France,Female,35,3,0,2,1,0,90876.95,0\r\n9055,15617476,Manfrin,546,France,Female,30,5,0,2,0,1,198543.09,0\r\n9056,15645323,Chinwenma,630,France,Male,55,2,0,1,1,1,106202.07,1\r\n9057,15793311,Smith,765,Germany,Female,46,8,119492.88,2,0,1,166896.01,1\r\n9058,15764153,Rowe,704,France,Female,33,0,130499.09,2,1,1,74804.36,0\r\n9059,15802560,Moran,470,Spain,Female,48,6,140576.11,1,1,1,116971.05,0\r\n9060,15728608,Walker,688,Germany,Female,34,9,91025.58,2,0,1,163783,0\r\n9061,15770474,Myers,685,France,Female,33,1,0,3,0,1,70221.13,1\r\n9062,15724444,Wall,567,France,Female,38,1,125877.65,2,1,1,107841.77,0\r\n9063,15753110,McKay,720,Spain,Male,64,3,45752.78,2,1,0,79623.28,1\r\n9064,15711521,Egobudike,609,France,Male,39,3,121778.71,1,1,1,138399.67,0\r\n9065,15632816,Williams,521,Germany,Female,49,2,127948.57,1,1,1,182765.14,0\r\n9066,15693637,Ochoa,556,France,Female,30,7,0,2,1,1,186648.19,0\r\n9067,15725509,Otutodilinna,662,Germany,Male,30,5,115286.68,2,1,1,149587.92,0\r\n9068,15684645,Ajuluchukwu,704,Germany,Male,41,9,62078.21,2,1,0,129050.67,0\r\n9069,15692235,Bellucci,750,France,Female,38,1,0,2,1,0,47764.99,0\r\n9070,15777459,Gordon,619,Spain,Female,32,4,175406.13,2,1,1,172792.43,1\r\n9071,15656937,Johnston,468,Spain,Male,26,1,131643.25,1,1,0,64436.16,0\r\n9072,15610643,De Luca,435,Germany,Male,44,3,151739.65,1,1,0,167461.5,0\r\n9073,15777315,Hill,529,France,Male,43,6,93616.35,2,0,0,98348.66,0\r\n9074,15611058,Eluemuno,702,Germany,Female,60,5,138597.54,2,1,1,41536.59,1\r\n9075,15630413,Howarth,608,France,Female,41,5,0,2,1,1,72462.25,0\r\n9076,15635942,Thomson,576,France,Male,40,9,0,2,1,0,112465.19,1\r\n9077,15648858,King,666,France,Female,27,1,85225.21,1,0,1,64511.44,0\r\n9078,15810732,Grant,730,France,Female,36,8,148749.29,2,1,0,91830.75,0\r\n9079,15705448,Gilbert,647,Germany,Male,52,7,130013.12,1,1,1,190806.36,1\r\n9080,15730488,Richmond,516,Spain,Female,27,1,0,1,0,1,112311.15,0\r\n9081,15620443,Fiorentino,711,France,Female,81,6,0,2,1,1,72276.24,0\r\n9082,15741078,Greece,736,France,Male,54,7,111729.47,2,0,1,84920.49,0\r\n9083,15753161,Dickson,768,France,Female,36,5,180169.44,2,1,0,17348.56,0\r\n9084,15711396,Henderson,427,Spain,Male,40,8,0,2,1,1,82870.75,0\r\n9085,15593499,Stevens,686,Spain,Female,47,6,0,1,1,0,32080.69,1\r\n9086,15579189,Mitchell,690,France,Female,42,5,0,2,0,1,120512.08,0\r\n9087,15743545,Nworie,647,Spain,Female,29,2,0,2,1,0,179032.68,0\r\n9088,15791316,Boni,714,France,Male,35,3,0,2,1,1,95623.28,0\r\n9089,15608246,Wentcher,736,Germany,Female,36,8,103914.17,1,1,1,110035.88,1\r\n9090,15676526,Bentley,608,France,Female,34,4,88772.87,1,1,1,168822.01,0\r\n9091,15813911,Hayes-Williams,809,France,Female,39,5,0,1,1,0,77705.75,0\r\n9092,15630195,Johnstone,745,France,Female,40,6,131184.67,1,1,1,49815.62,0\r\n9093,15736250,Johnstone,781,France,Male,38,2,117810.79,1,0,1,65632.33,1\r\n9094,15671334,Nixon,527,France,Male,31,4,0,1,1,0,169361.89,0\r\n9095,15574169,Trevisano,595,Germany,Female,32,0,92466.21,1,1,0,4721.3,0\r\n9096,15718839,Tsui,850,Germany,Female,38,2,102741.15,2,0,1,23974.85,0\r\n9097,15762331,Moss,569,France,Male,37,9,178755.84,1,1,0,199929.17,0\r\n9098,15606901,Graham,728,France,Male,43,7,0,2,1,0,40023.7,0\r\n9099,15713559,Onyemauchechukwu,473,Germany,Female,32,5,146602.25,2,1,1,72946.95,0\r\n9100,15768881,Saunders,738,France,Male,29,2,0,2,1,1,170421.13,0\r\n9101,15743075,Ko,659,France,Male,35,6,0,2,1,1,58879.11,0\r\n9102,15660980,Cairns,597,Spain,Male,38,6,115702.67,2,1,1,25059.05,0\r\n9103,15810942,Chiemela,445,Germany,Female,61,2,137655.31,1,0,1,29909.84,0\r\n9104,15728362,Robertson,671,France,Female,29,3,0,2,1,0,158043.11,0\r\n9105,15683339,P'eng,656,Spain,Female,34,6,59877.33,1,1,0,14032.62,1\r\n9106,15685476,Tseng,658,France,Male,31,5,100082.14,1,0,1,49809.88,0\r\n9107,15663650,Russell,698,Germany,Male,52,10,107304.39,3,1,0,28806.32,1\r\n9108,15617434,Yen,655,Spain,Male,38,9,0,1,0,1,90490.33,0\r\n9109,15622470,Yeh,772,Spain,Male,41,10,96032.22,1,1,1,75825.57,0\r\n9110,15703682,Kalinina,681,Spain,Male,33,10,0,1,0,0,158336.36,0\r\n9111,15727391,Collier,688,Germany,Male,29,9,144553.5,2,1,0,143454.95,0\r\n9112,15711062,Thomas,633,Germany,Male,40,5,86172.81,2,1,1,117279.49,0\r\n9113,15567339,Shaw,735,France,Male,73,9,0,1,1,1,114283.33,0\r\n9114,15760662,Francis,521,Germany,Female,29,2,87212.8,1,1,1,994.86,0\r\n9115,15605737,George,541,France,Male,36,5,0,2,1,0,124795.84,0\r\n9116,15692977,Ikenna,612,Germany,Female,36,2,130700.92,2,0,0,77592.8,0\r\n9117,15672082,Schatz,562,France,Male,62,3,0,2,1,0,105986.01,0\r\n9118,15600280,Tao,703,France,Female,32,6,0,2,0,0,33606.52,0\r\n9119,15804052,Scott,710,Spain,Male,23,6,0,2,1,1,134188.11,0\r\n9120,15576065,Sims,731,Spain,Female,40,5,171325.98,1,1,1,159718.27,1\r\n9121,15796838,Chibugo,703,Spain,Male,58,4,92930.92,1,0,1,85148.78,0\r\n9122,15693526,Ku,618,France,Female,40,0,0,1,1,0,119059.13,0\r\n9123,15748595,Stanton,689,France,Female,29,1,77556.79,2,1,1,122998.26,0\r\n9124,15679029,Kung,718,France,Male,33,7,102874.28,1,0,0,117841.06,0\r\n9125,15753639,Gibson,608,France,Male,37,5,146093.39,2,0,0,160593.41,0\r\n9126,15604138,Iheanacho,749,Spain,Male,34,2,0,1,0,0,174189.04,1\r\n9127,15666095,Costa,753,Spain,Male,51,4,79811.72,2,0,1,68260.27,1\r\n9128,15643487,Sal,630,Spain,Male,39,10,105473.74,1,0,0,58854.88,1\r\n9129,15764033,Lin,693,Germany,Female,43,1,121927.92,1,1,0,87994.95,1\r\n9130,15747288,Ferri,838,Spain,Female,40,6,61671.19,1,0,1,150659.35,1\r\n9131,15790599,Yin,756,Germany,Female,39,5,149363.12,2,1,1,109098.39,0\r\n9132,15737705,Avdeyeva,775,France,Female,27,4,152309.37,1,1,0,104112,0\r\n9133,15737194,Tu,635,France,Female,33,5,0,2,1,0,122949.71,0\r\n9134,15726776,Donnelly,705,Spain,Male,36,1,111629.29,1,1,1,21807.16,0\r\n9135,15804357,Loggia,481,France,Male,40,3,0,1,1,1,32319.93,0\r\n9136,15664432,Chao,727,Spain,Female,49,7,96296.78,1,1,0,190457.87,1\r\n9137,15688984,Belonwu,595,France,Male,20,4,95830.43,1,1,0,177738.98,0\r\n9138,15583026,Welch,535,France,Female,38,0,135919.33,1,1,0,80425.65,0\r\n9139,15771668,Henderson,578,France,Male,59,10,185966.64,1,0,0,9445.42,1\r\n9140,15779904,Yobanna,597,France,Female,29,5,0,2,1,1,174825.57,0\r\n9141,15784323,Gallo,449,France,Female,21,7,0,2,0,0,175743.92,0\r\n9142,15756277,Wilson,850,Germany,Female,43,8,92244.83,2,1,0,54949.73,0\r\n9143,15663312,Marino,494,France,Female,35,9,112727.06,2,1,0,183752.91,0\r\n9144,15793197,Bailey,676,France,Female,34,8,100359.54,1,0,0,46038.28,0\r\n9145,15731463,Gboliwe,818,Germany,Male,43,10,105301.5,1,1,1,78941.59,0\r\n9146,15621768,Chukwuhaenye,712,Spain,Male,45,6,112994.65,1,0,0,198398.68,0\r\n9147,15691323,Bianchi,672,Germany,Male,40,4,89025.88,2,1,0,188892.19,0\r\n9148,15781326,Ford,636,France,Male,35,9,95478.17,1,0,0,169286.74,0\r\n9149,15595640,Rizzo,698,France,Male,37,8,0,2,0,0,145004.39,0\r\n9150,15814331,Lung,597,Germany,Female,43,7,119127.46,2,1,0,55809.92,0\r\n9151,15602030,Ramirez,717,France,Male,28,4,128206.79,1,1,1,54272.12,0\r\n9152,15747974,Sabbatini,614,France,Male,49,1,0,2,1,0,192440.54,0\r\n9153,15611315,Ts'ao,708,Germany,Female,23,4,71433.08,1,1,0,103697.57,0\r\n9154,15636977,Trevisan,507,Germany,Male,36,9,118214.32,3,1,0,119110.03,1\r\n9155,15690337,Chinwenma,581,France,Female,27,5,102258.11,2,1,0,194681.6,0\r\n9156,15680666,Berry,579,Spain,Female,39,2,151963.26,2,1,0,158948.63,0\r\n9157,15679551,Colombo,504,Spain,Female,46,2,163764.84,1,1,1,165122.55,1\r\n9158,15778915,Harris,737,France,Female,32,7,128551.36,2,0,1,189402.71,0\r\n9159,15568849,Bryan,540,Spain,Female,31,10,118158.74,1,1,1,158027.57,0\r\n9160,15747762,Chigozie,609,France,Male,32,7,118520.41,1,0,0,3815.48,0\r\n9161,15753679,Mullawirraburka,778,France,Male,24,4,0,2,1,1,162809.2,0\r\n9162,15750049,Steele,621,France,Male,40,10,163823.37,1,0,0,89519.47,0\r\n9163,15606097,Zakharov,665,Germany,Male,63,7,104469.58,1,1,1,25165.36,1\r\n9164,15802368,Ch'eng,608,France,Female,47,6,0,1,1,1,126012.57,0\r\n9165,15767488,Berry,680,Spain,Male,36,7,0,2,1,0,20109.21,0\r\n9166,15669946,Jen,663,Germany,Female,46,2,141726.88,1,1,1,58257.23,0\r\n9167,15612103,Wang,627,Germany,Female,35,2,137852.96,1,1,1,172269.21,1\r\n9168,15645353,Chubb,607,France,Male,26,1,0,1,1,0,29818.2,0\r\n9169,15650018,Chen,681,France,Female,43,8,154100.3,1,0,0,114659.81,0\r\n9170,15659002,Mazzanti,766,France,Female,45,6,0,2,0,0,147184.74,0\r\n9171,15616028,T'ao,694,France,Male,30,2,0,3,0,1,15039.41,0\r\n9172,15660475,Ndubueze,411,France,Female,54,9,0,1,0,1,76621.49,0\r\n9173,15652615,Ferri,742,France,Male,39,8,140004.96,1,1,1,92985.78,0\r\n9174,15653572,Thornton,673,Spain,Male,43,8,127132.96,1,0,1,6009.27,1\r\n9175,15628059,DeRose,529,France,Male,61,1,0,2,1,1,191370.97,0\r\n9176,15703413,Montes,519,France,Female,38,7,125328.56,1,1,0,188225.67,0\r\n9177,15610433,Kwemto,573,France,Male,35,9,0,2,1,0,11743.89,0\r\n9178,15770548,Lucchese,453,Germany,Female,28,3,139986.65,1,1,0,136846.75,0\r\n9179,15645637,Huggins,798,Germany,Female,39,6,119787.76,1,1,1,164248.33,0\r\n9180,15590888,Wade,693,Spain,Female,34,10,107556.06,2,0,0,154631.35,0\r\n9181,15568326,Kenenna,637,France,Female,44,2,0,2,1,0,149665.65,0\r\n9182,15655368,Wheeler,672,France,Male,47,1,0,1,0,0,91574.92,0\r\n9183,15665579,Cartwright,597,France,Female,28,0,142705.95,1,1,0,127233.39,0\r\n9184,15676091,Iloerika,543,France,Male,42,7,0,1,1,1,56650.47,0\r\n9185,15716984,Palermo,695,Spain,Female,56,4,0,2,1,0,84644.76,0\r\n9186,15715078,Nkemakolam,584,France,Male,35,6,161613.94,2,1,1,148238.16,0\r\n9187,15569452,Butler,652,Germany,Female,58,3,116353.2,2,0,1,193502.9,0\r\n9188,15628863,Calabresi,601,France,Male,38,4,60013.81,1,1,1,38020.05,0\r\n9189,15778192,Nkemdilim,628,Spain,Male,28,4,0,2,1,1,176750.81,0\r\n9190,15793723,Ch'iu,607,Germany,Male,32,9,144272.07,2,1,0,176580.63,0\r\n9191,15798943,Alexander,646,France,Female,46,8,0,2,1,0,133059.15,0\r\n9192,15764708,Chiabuotu,572,France,Male,30,6,117696.67,1,1,0,100843.82,0\r\n9193,15791040,Vasilyeva,801,Spain,Male,58,1,79954.61,2,1,1,30484.19,0\r\n9194,15631512,Schneider,597,France,Female,26,8,149989.39,1,1,0,42330.58,0\r\n9195,15640106,Mason,613,France,Male,40,7,124339.9,1,0,0,193309.58,0\r\n9196,15710315,Chukwukadibia,529,Germany,Male,29,4,135759.4,1,0,0,112813.79,1\r\n9197,15771535,Tsui,794,Spain,Female,37,9,0,2,1,0,68008.85,0\r\n9198,15611947,Banks,557,France,Male,34,3,83074,1,1,0,132673.22,0\r\n9199,15670266,Shih,499,France,Female,28,4,141792.61,1,1,1,22001.91,0\r\n9200,15609083,Tretiakova,544,France,Female,26,6,0,1,1,0,100200.4,1\r\n9201,15567923,Barese,739,France,Female,30,6,0,1,0,0,122604.44,0\r\n9202,15788183,Longo,458,Germany,Female,43,1,106870.12,2,1,0,100564.37,0\r\n9203,15735782,MacDonald,528,France,Male,31,9,120962.59,1,1,0,5419.31,0\r\n9204,15774401,Chambers,773,Spain,Male,51,4,0,2,0,0,123587.83,1\r\n9205,15737971,Cowen,646,France,Female,30,5,0,2,1,0,13935.32,0\r\n9206,15758750,Iweobiegbunam,564,France,Male,31,0,110527.17,1,1,1,87060.77,0\r\n9207,15611767,Mai,624,Germany,Female,52,0,133723.43,1,0,0,4859.59,1\r\n9208,15643770,Yu,682,France,Female,52,5,112670.48,1,1,0,21085.17,1\r\n9209,15744717,Duffy,726,France,Female,44,2,0,2,1,1,26733.86,0\r\n9210,15570681,Chiang,560,France,Male,24,1,116084.32,1,1,0,89734.7,0\r\n9211,15792650,Watts,382,Spain,Male,36,0,0,1,1,1,179540.73,1\r\n9212,15605531,Daly,457,Spain,Female,38,6,0,2,1,0,173219.09,0\r\n9213,15605339,Baker,673,France,Female,37,10,0,2,1,1,37411.35,0\r\n9214,15672216,Uvarov,584,France,Female,40,4,82441.75,1,0,0,80852.11,0\r\n9215,15812893,Costa,629,France,Female,39,10,0,2,1,1,43174.49,1\r\n9216,15624180,Genovesi,584,Germany,Female,37,10,134171.8,4,1,1,70927.11,1\r\n9217,15701364,Doherty,724,France,Male,30,10,0,2,1,1,54265.55,0\r\n9218,15762588,Kaleski,644,France,Male,31,5,0,2,1,1,41872.17,0\r\n9219,15806318,Wright,676,Germany,Female,48,2,124442.38,1,1,0,15068.53,1\r\n9220,15712596,Huang,499,France,Male,31,4,0,1,1,0,25950.49,0\r\n9221,15600399,Trentino,598,France,Male,60,4,0,1,1,0,197727.14,1\r\n9222,15576216,Chienezie,655,Germany,Female,37,4,108862.76,1,1,0,79555.08,1\r\n9223,15620750,Sugden,559,France,Male,28,3,141099.43,1,1,1,15607.27,0\r\n9224,15623489,Tu,543,France,Female,67,0,128843.67,1,1,1,134612.48,0\r\n9225,15667944,Onuchukwu,679,France,Male,39,0,86843.61,1,0,1,159830.58,0\r\n9226,15584928,Ugochukwutubelum,594,Germany,Female,32,4,120074.97,2,1,1,162961.79,0\r\n9227,15779913,Davidson,586,France,Male,27,5,130231.8,2,1,1,192427.16,0\r\n9228,15644977,Goddard,776,France,Female,31,5,0,2,1,0,92647.94,0\r\n9229,15749679,Beck,699,France,Male,39,2,109724.38,1,1,1,180022.39,0\r\n9230,15629010,Beam,847,Germany,Female,35,5,111743.43,1,1,1,183584.14,0\r\n9231,15768465,Sheppard,582,Germany,Male,35,8,121309.17,2,1,1,28750.67,0\r\n9232,15767781,Godfrey,648,France,Male,38,10,82697.28,1,1,0,74846.67,0\r\n9233,15635364,Gray,618,France,Female,49,9,44301.43,3,1,1,89729.3,1\r\n9234,15722004,Hsiung,543,France,Female,31,4,138317.94,1,0,0,61843.73,0\r\n9235,15766044,Cameron,642,Germany,Male,49,4,120688.61,1,1,0,24770.22,1\r\n9236,15586680,Fleming,462,France,Male,27,4,176913.52,1,1,0,80587.27,0\r\n9237,15635388,Austin,640,Spain,Male,47,6,89047.14,1,1,0,116286.25,0\r\n9238,15655175,Wallace,740,Germany,Male,40,4,114318.78,2,1,0,129333.69,1\r\n9239,15639133,Ku,773,France,Female,50,4,0,2,1,0,129372.94,0\r\n9240,15799653,Fiorentino,583,Germany,Female,32,7,94753.55,2,1,1,18149.03,0\r\n9241,15723872,Buccho,589,Spain,Female,46,10,0,2,0,1,168369.37,0\r\n9242,15775627,Gordon,509,France,Male,35,8,0,2,0,1,67431.28,0\r\n9243,15630704,Haworth,612,Germany,Male,32,9,106520.73,2,1,0,177092.16,0\r\n9244,15815534,Guidry,505,Spain,Male,37,0,134006.39,1,1,1,93736.69,0\r\n9245,15697249,Lettiere,546,Germany,Female,25,3,132837.7,1,1,0,131647.31,0\r\n9246,15681316,Tai,681,France,Female,41,0,120549.29,2,1,0,175722.31,0\r\n9247,15682523,Chigozie,762,France,Male,20,1,139432.55,1,1,1,85606.83,0\r\n9248,15650244,Bezrukov,786,Spain,Male,29,7,80895.44,2,1,0,64945.57,0\r\n9249,15648638,Chia,629,Spain,Male,34,6,0,2,1,0,190347.72,0\r\n9250,15795747,Sheppard,787,Spain,Female,39,7,171646.76,1,0,1,100791.36,0\r\n9251,15607330,Vasilyev,713,Spain,Male,42,0,109121.71,1,0,1,167873.49,0\r\n9252,15624013,Maxwell,541,France,Female,39,6,109844.81,1,1,0,25289.23,0\r\n9253,15800805,Maher,451,France,Female,31,7,140931.82,1,0,1,20388.77,0\r\n9254,15667321,Cocci,644,Spain,Male,49,10,0,2,1,1,145089.64,0\r\n9255,15601116,P'an,686,France,Male,32,6,0,2,1,1,179093.26,0\r\n9256,15622033,Rapuluchukwu,847,Germany,Female,41,3,101543.51,4,1,0,16025.17,1\r\n9257,15758451,Azuka,765,Germany,Male,37,7,102708.77,1,1,0,9087.81,0\r\n9258,15688689,Esposito,678,Germany,Female,37,8,149000.91,2,1,1,21472.42,0\r\n9259,15652674,Hou,539,France,Male,20,0,83459.86,1,1,1,146752.67,0\r\n9260,15806327,Cyril,800,France,Female,40,3,75893.11,2,1,0,132562.23,0\r\n9261,15649618,Tomlinson,799,Germany,Female,39,7,167395.6,2,0,1,139537.43,0\r\n9262,15677117,Crawford,629,France,Female,61,6,0,2,1,1,133672.61,0\r\n9263,15751445,Chikwado,734,Germany,Female,52,6,71283.09,2,0,1,38984.37,0\r\n9264,15749669,Hargreaves,542,France,Female,31,3,0,2,1,1,115217.59,0\r\n9265,15656351,Laidley,414,Spain,Male,60,3,0,2,1,1,93844.82,0\r\n9266,15667438,Ferguson,675,France,Female,38,1,104016.88,1,0,0,22068.83,1\r\n9267,15682273,Burns,683,France,Female,38,5,127616.56,1,1,0,123846.07,0\r\n9268,15580912,McNeill,748,France,Male,32,5,154737.88,2,1,1,172638.13,0\r\n9269,15785183,Chukwuebuka,596,Spain,Male,29,2,0,2,1,1,1591.19,0\r\n9270,15705383,Shen,642,France,Male,35,4,125476.31,1,1,1,91775.51,0\r\n9271,15712903,Diaz,499,France,Female,21,3,176511.08,1,1,1,153920.22,0\r\n9272,15774285,Kentish,649,Spain,Female,47,8,110783.28,1,1,1,71420.16,0\r\n9273,15583138,Persse,739,France,Male,42,2,141642.92,2,1,0,172149.76,0\r\n9274,15740160,Okwukwe,616,France,Male,31,1,0,2,1,1,54706.75,0\r\n9275,15793425,Watt,560,Spain,Female,33,9,0,1,0,1,183358.21,0\r\n9276,15749265,Carslaw,427,Germany,Male,42,1,75681.52,1,1,1,57098,0\r\n9277,15623989,Griffin,435,France,Male,54,3,0,1,1,0,156910.46,1\r\n9278,15604832,Hsia,633,France,Male,29,7,0,1,1,1,130224.73,0\r\n9279,15584580,Fyodorova,443,France,Male,35,6,161111.45,1,0,0,13946.66,0\r\n9280,15573854,Chukwujekwu,727,France,Male,62,5,0,2,0,1,38652.96,0\r\n9281,15614847,Townsend,674,France,Female,45,6,72494.69,1,0,1,140041.78,0\r\n9282,15679966,Marsh,661,France,Female,31,3,133964.3,1,1,1,166187.1,0\r\n9283,15799435,Hayes,619,Spain,Male,34,1,0,1,1,0,139919.38,0\r\n9284,15752186,Padovano,562,France,Female,27,3,0,2,1,0,28137.03,0\r\n9285,15705544,Ma,633,France,Male,61,3,157201.48,1,0,1,50368.63,0\r\n9286,15713632,Ham,551,Spain,Female,48,5,95679.29,1,0,0,94978.1,0\r\n9287,15586523,Paten,720,Germany,Female,29,7,106230.92,1,1,1,69903.93,1\r\n9288,15609176,Cawthorne,688,France,Female,32,5,0,2,0,1,177607.77,0\r\n9289,15769308,Herbert,635,Germany,Female,36,9,81231.85,2,1,0,196731.08,0\r\n9290,15676810,Jen,561,France,Female,31,1,81480.27,2,1,1,65234.6,0\r\n9291,15634591,Saunders,850,France,Male,33,8,73059.38,1,1,1,186281,0\r\n9292,15679804,Esquivel,636,France,Male,36,5,117559.05,2,1,1,111573.3,0\r\n9293,15677764,Chao,461,Germany,Female,74,1,186445.31,2,1,1,196767.83,0\r\n9294,15571917,Eluemuno,771,Germany,Female,38,5,137657.71,2,1,0,72985.61,0\r\n9295,15574608,Sidorova,713,France,Male,36,8,133889.35,1,1,1,143265.65,0\r\n9296,15740868,Pirogova,658,Germany,Female,45,9,134562.8,1,1,1,159268.67,0\r\n9297,15702442,Benson,586,Germany,Female,56,9,100781.75,2,1,1,54448.41,0\r\n9298,15699797,Santana,737,France,Male,30,8,174356.13,1,0,0,31928.5,0\r\n9299,15648047,Williamson,742,Germany,Male,27,5,190125.43,2,0,0,21793.59,0\r\n9300,15766826,North,824,France,Male,26,7,146266,1,1,0,21903.62,1\r\n9301,15591628,Davies,701,Germany,Male,41,9,164046.1,1,1,0,49405.93,0\r\n9302,15583857,Siciliano,623,Spain,Female,43,4,123536.52,2,0,0,154908.52,0\r\n9303,15752534,Mironov,744,France,Male,36,10,0,2,1,1,182867.84,0\r\n9304,15741403,Amechi,698,Spain,Female,38,1,171848.38,1,0,0,16957.45,0\r\n9305,15783589,Toscano,616,France,Male,40,9,0,2,0,0,93717.55,0\r\n9306,15598046,Su,662,France,Female,39,5,139562.05,2,1,0,61636.22,0\r\n9307,15643330,Chukwuemeka,594,France,Male,37,2,0,2,0,1,95864.5,0\r\n9308,15680405,P'eng,685,France,Male,40,2,168001.34,2,1,1,167400.29,0\r\n9309,15728683,Lombardo,742,France,Male,27,0,0,2,0,1,131534.96,0\r\n9310,15621644,Lombardi,678,Germany,Male,83,6,123356.63,1,0,1,92934.41,0\r\n9311,15733032,Butler,651,Spain,Male,47,2,0,2,1,1,119808.64,0\r\n9312,15608381,Dean,585,Germany,Male,50,2,125845.66,1,1,0,9439.31,1\r\n9313,15658946,Piccio,579,Germany,Male,40,10,45408.85,2,1,0,18732.91,0\r\n9314,15757912,Bradley,722,Germany,Female,37,0,125977.81,1,0,0,160162.42,0\r\n9315,15645371,Cameron,613,Germany,Female,51,7,147262.11,1,1,1,53630.9,1\r\n9316,15653110,Chan,694,France,Male,42,8,133767.19,1,1,0,36405.21,0\r\n9317,15766355,Lettiere,550,Germany,Male,49,0,108806.96,3,1,0,61446.92,1\r\n9318,15585249,Mironova,741,France,Male,42,6,106036.52,1,1,0,194686.78,1\r\n9319,15611786,Tsui,668,Spain,Female,69,9,0,1,0,1,134483.07,0\r\n9320,15575486,Okoli,529,France,Female,27,1,0,2,1,1,37769.98,0\r\n9321,15780215,Berry,636,France,Male,31,6,0,2,1,1,2382.61,0\r\n9322,15686099,Ruse,563,Spain,Male,61,1,82182.1,1,1,0,106826.92,1\r\n9323,15739042,Bogolyubov,767,France,Female,35,9,0,2,1,0,39511.61,0\r\n9324,15815316,Kennedy,644,France,Male,50,9,76817,4,1,0,196371.13,1\r\n9325,15778489,Bolton,780,Germany,Male,71,9,142550.25,2,1,1,122506.78,0\r\n9326,15786389,Chuang,635,Spain,Female,41,10,0,2,1,1,61994.2,0\r\n9327,15601787,Greco,641,Germany,Male,35,2,103711.56,1,0,1,192464.21,1\r\n9328,15624715,Ma,593,Spain,Female,40,2,0,1,1,1,5194.95,0\r\n9329,15763093,Nucci,540,Germany,Female,35,7,128369.75,2,1,0,198256.15,0\r\n9330,15572073,Yao,663,Spain,Male,35,5,0,2,1,1,62634.94,0\r\n9331,15780256,Palfreyman,630,France,Male,34,9,0,2,1,1,114006.35,0\r\n9332,15659305,Webster,605,Germany,Male,19,8,166133.28,1,1,1,107994.99,0\r\n9333,15638882,Cardell,710,Germany,Female,62,9,148214.36,1,1,0,48571.14,1\r\n9334,15714680,Bianchi,755,France,Female,78,5,121206.96,1,1,1,76016.49,0\r\n9335,15777217,Somadina,641,Spain,Male,25,10,0,2,1,1,180808.39,0\r\n9336,15739123,Mellor,737,Germany,Male,50,4,127552.85,2,1,0,4225.11,0\r\n9337,15594450,Tomlinson,695,France,Male,49,9,159458.53,1,1,0,135841.35,0\r\n9338,15797751,Pai,466,Germany,Female,47,5,102085.72,1,1,1,183536.24,1\r\n9339,15691543,Lennox,558,Germany,Male,58,2,142537.18,1,1,1,88791.83,0\r\n9340,15722845,Meldrum,665,Spain,Male,29,1,182781.74,2,1,1,63732.9,0\r\n9341,15605804,Watson,737,France,Male,45,10,0,2,1,0,1364.54,0\r\n9342,15702061,Findlay,654,France,Male,29,7,0,2,1,1,149184.15,0\r\n9343,15694321,Su,619,France,Female,28,3,0,2,1,0,53394.12,0\r\n9344,15798749,Davidson,845,Germany,Female,43,3,152063.59,2,1,0,97910.06,0\r\n9345,15720050,Barrett,727,France,Female,28,2,110997.76,1,1,0,101433.76,0\r\n9346,15758048,Miah,582,France,Male,50,2,148942,1,1,1,116944.3,0\r\n9347,15805681,Chamberlain,716,France,Male,41,9,0,1,1,1,113267.48,0\r\n9348,15802809,Vidal,660,Spain,Female,36,0,84438.57,1,1,1,181449.51,0\r\n9349,15807239,Lung,664,France,Female,34,7,93920.47,1,0,0,179913.98,0\r\n9350,15749093,Tretyakova,801,France,Male,43,4,158713.08,2,0,0,98586.14,0\r\n9351,15689344,Montgomery,615,Spain,Male,42,4,0,3,0,1,120321.09,0\r\n9352,15606076,Golubev,718,Germany,Male,63,7,123204.88,1,1,1,100538.8,0\r\n9353,15610090,Han,667,France,Male,40,8,72945.29,2,1,0,98931.5,0\r\n9354,15693926,Pan,670,Spain,Male,37,0,178742.71,1,1,1,194493.57,0\r\n9355,15791501,Carroll,590,France,Male,43,8,0,2,1,1,143628.31,0\r\n9356,15621870,Hawkins,739,Spain,Female,40,8,0,1,1,0,167030.51,0\r\n9357,15734711,Loggia,373,France,Male,42,7,0,1,1,0,77786.37,1\r\n9358,15814405,Chesnokova,418,France,Female,46,9,0,1,1,1,81014.5,1\r\n9359,15729359,Chambers,837,France,Female,29,9,0,2,1,1,41866.26,0\r\n9360,15606944,Fleming,645,Germany,Male,43,9,140121.17,1,1,0,11302.7,1\r\n9361,15671934,Veale,552,Germany,Male,39,2,132906.88,1,0,1,149384.43,0\r\n9362,15641773,Browne,580,Germany,Male,45,2,179334.83,2,1,1,169303.65,0\r\n9363,15701972,Parsons,684,France,Male,35,3,137179.39,1,1,1,37264.11,0\r\n9364,15749114,Bailey,634,Spain,Male,35,3,0,2,1,1,19515.48,0\r\n9365,15780362,Ferrari,607,France,Female,49,9,119960.29,2,1,0,103068.22,0\r\n9366,15753229,Genovese,802,France,Male,29,9,127414.55,1,1,1,134459.12,0\r\n9367,15656009,McIntyre,736,France,Female,36,6,0,1,1,0,70496.66,0\r\n9368,15785024,Warner,629,France,Female,40,9,137409.19,1,1,0,175877.7,1\r\n9369,15670492,Gordon,737,France,Male,28,8,0,2,1,0,106390.01,0\r\n9370,15795458,McMillan,718,Spain,Female,39,2,0,1,1,1,52138.49,0\r\n9371,15732438,Cheng,561,France,Male,43,4,0,4,0,0,18522.91,1\r\n9372,15781987,Akhtar,641,France,Male,31,9,112494.99,1,1,1,32231.6,0\r\n9373,15775826,Iadanza,677,France,Male,30,1,78133.15,1,0,1,174225.88,0\r\n9374,15807457,Abernathy,641,Spain,Female,36,1,0,2,1,0,102021.39,0\r\n9375,15632538,Watson,658,Spain,Female,32,5,145553.07,1,1,1,31484.76,0\r\n9376,15641389,Shen,659,Germany,Male,48,4,123593.22,2,1,0,82469.06,1\r\n9377,15657306,Kershaw,567,France,Female,47,2,0,1,0,0,110900.43,1\r\n9378,15709447,Reed,584,France,Female,26,0,146286.22,1,1,0,105105.35,0\r\n9379,15762682,Mitchell,709,Spain,Female,35,1,111827.27,2,1,0,12674.68,0\r\n9380,15626042,Webb,690,Spain,Female,26,2,0,2,1,1,93255.85,0\r\n9381,15597109,Vanzetti,627,France,Male,70,1,94416.78,1,0,1,145299.5,0\r\n9382,15756148,Nnanna,765,Spain,Male,45,2,91549.78,1,1,1,47139.44,0\r\n9383,15665634,Campbell,645,France,Female,38,7,59568.57,1,1,1,167723.25,0\r\n9384,15739997,Capon,716,France,Female,23,2,94464.81,2,0,1,185900.88,0\r\n9385,15686242,Otutodilichukwu,771,France,Female,57,4,0,1,0,0,85876.67,1\r\n9386,15759244,Boone,687,Germany,Male,44,8,95368.14,2,1,1,1787.85,0\r\n9387,15672027,McIntyre,717,Germany,Female,33,10,102185.42,2,1,0,23231.93,0\r\n9388,15594576,Zhdanov,524,France,Male,32,1,144875.71,1,0,0,187740.04,0\r\n9389,15707138,Nagy,679,Spain,Male,39,5,0,2,1,1,100060.54,0\r\n9390,15756954,Lombardo,538,France,Female,32,2,0,1,1,1,80130.54,0\r\n9391,15619130,Simpson,752,Germany,Female,37,5,113291.05,2,1,1,132467.54,0\r\n9392,15639665,Herbert,846,Spain,Male,61,0,0,2,1,1,96202.44,0\r\n9393,15571065,Lehr,532,Spain,Female,39,0,0,2,1,0,94977.3,0\r\n9394,15686060,Chou,670,Germany,Male,43,9,111677.88,1,1,0,178827.3,1\r\n9395,15615753,Upchurch,597,Germany,Female,35,8,131101.04,1,1,1,192852.67,0\r\n9396,15800961,Ugorji,627,Germany,Male,52,1,76101.81,2,0,1,177238.35,0\r\n9397,15763065,Palerma,700,Spain,Female,40,2,0,2,1,0,199753.97,0\r\n9398,15672467,Coles,766,France,Female,52,7,92510.9,2,0,1,66193.61,0\r\n9399,15752915,Hsueh,488,France,Female,34,2,0,2,1,1,181270.84,0\r\n9400,15744695,Tu,694,France,Male,39,5,77652.4,1,1,1,25407.59,0\r\n9401,15584897,Kuo,639,France,Female,31,3,98360.03,1,0,0,20973.8,0\r\n9402,15601857,Woodhouse,705,Germany,Female,46,4,115518.07,1,0,0,76544.9,1\r\n9403,15674156,Tretiakova,810,Germany,Male,69,3,27288.43,1,1,1,110509.9,0\r\n9404,15695465,Gibson,638,France,Female,36,6,0,1,1,0,164247.51,0\r\n9405,15792232,Moss,595,Spain,Female,43,5,0,2,0,0,105149.8,0\r\n9406,15807900,Chineze,575,France,Male,36,7,0,1,1,1,55868.97,1\r\n9407,15743760,Davidson,850,France,Male,31,6,131996.66,2,1,1,178747.43,0\r\n9408,15652835,Liang,419,Spain,Female,27,2,121580.42,1,0,1,134720.51,0\r\n9409,15767818,Graham,640,France,Male,55,10,132436.34,1,1,0,978.66,0\r\n9410,15591150,Nwebube,570,Spain,Male,34,10,0,2,0,1,183387.12,0\r\n9411,15734659,Sozonov,640,Germany,Female,46,5,107978.4,2,1,0,155876.06,0\r\n9412,15796115,Forbes,689,Germany,Female,40,4,78119.59,4,1,0,119259.34,1\r\n9413,15724648,Chikezie,725,France,Male,26,6,98684.15,1,0,0,133720.57,0\r\n9414,15737732,Onwuemelie,751,France,Female,44,10,0,2,1,0,170634.49,0\r\n9415,15632280,Toth,544,Spain,Female,53,9,0,1,1,0,125692.07,1\r\n9416,15750407,Hunt,768,Germany,Female,43,2,129264.05,2,0,0,19150.14,0\r\n9417,15795370,Mazure,648,Germany,Male,37,6,131753.41,1,1,0,86894.67,0\r\n9418,15656829,Hughes,577,Spain,Female,33,6,0,2,1,0,57975.8,0\r\n9419,15643794,Bennett,639,Spain,Female,27,2,0,1,1,1,82938.99,0\r\n9420,15798605,Tien,686,Germany,Male,26,1,57422.62,1,1,1,79189.4,0\r\n9421,15637324,Kay,657,France,Female,28,7,0,2,0,1,5177.62,0\r\n9422,15589589,Bryan,613,France,Male,34,5,144094.2,1,1,0,44510.26,0\r\n9423,15778936,Ingamells,701,France,Male,33,9,147510.34,1,1,0,190611.92,0\r\n9424,15757385,Milne,578,Spain,Female,28,8,161592.76,1,1,0,177834.79,0\r\n9425,15666200,Lombardo,689,France,Female,40,1,0,2,1,1,119446.64,0\r\n9426,15683977,Owens,687,Spain,Female,72,4,0,2,1,1,50267.69,0\r\n9427,15675518,Charlton,499,Spain,Female,53,1,75225.53,2,0,0,144849.1,1\r\n9428,15584812,Overby,693,Spain,Female,39,0,0,2,0,0,81901.6,0\r\n9429,15752984,Chifley,737,France,Female,70,9,87542.89,2,1,1,42576.86,0\r\n9430,15577913,Oliver,651,France,Female,32,8,144581.96,1,1,1,87609.5,0\r\n9431,15591980,Hill,753,France,Male,33,5,122568.05,2,1,1,82820.85,0\r\n9432,15598948,DeRose,523,Spain,Female,24,5,172231.93,1,0,1,155144.12,0\r\n9433,15574142,Chuang,458,Germany,Female,28,2,171932.26,2,1,1,9578.24,0\r\n9434,15582903,Edwards,643,France,Male,39,7,0,2,1,1,170392.59,0\r\n9435,15733229,Rodriguez,638,Spain,Female,34,7,0,2,0,0,3946.29,0\r\n9436,15635752,Lo,685,Germany,Male,38,4,111798.06,2,1,1,102184.66,0\r\n9437,15771000,Powell,684,France,Male,38,4,0,3,1,0,75609.84,0\r\n9438,15804864,Chu,670,France,Female,27,5,79336.61,1,1,1,26170.08,0\r\n9439,15641175,Munro,701,Germany,Male,63,3,120916.52,3,0,0,144727.45,1\r\n9440,15692226,Onwumelu,705,France,Female,31,3,142905.51,1,1,1,58134.97,0\r\n9441,15584156,Siciliani,593,Spain,Male,27,10,0,3,0,0,94620,1\r\n9442,15702656,Yobachi,651,France,Female,33,1,96834.78,1,1,0,108764.69,0\r\n9443,15606552,Akabueze,741,France,Male,37,9,105261.76,2,1,1,149503.54,0\r\n9444,15687001,Chiemenam,596,Germany,Male,54,1,123544,1,1,1,120314.75,1\r\n9445,15781903,Odinakachukwu,581,Germany,Male,41,2,127913.71,2,1,1,44205.95,0\r\n9446,15731951,Reilly,689,Spain,Female,28,5,95328.6,1,1,0,6129.61,1\r\n9447,15580953,Forbes,544,France,Male,30,4,73218.89,1,0,1,126796.69,0\r\n9448,15810390,Amadi,718,France,Female,41,1,0,2,0,1,27509.52,1\r\n9449,15628274,Ferri,583,Germany,Male,35,8,149995.72,2,1,0,42143.55,0\r\n9450,15615444,Y?an,663,Germany,Male,28,8,123674.28,2,1,1,87985.2,0\r\n9451,15784010,Williamson,666,Germany,Male,33,2,124125.26,1,1,0,81884.8,0\r\n9452,15571586,Briggs,524,Spain,Male,29,3,159035.45,1,1,0,2705.31,1\r\n9453,15748616,Napolitani,599,France,Male,27,5,0,2,1,0,30546.4,0\r\n9454,15769402,Carpenter,667,France,Male,27,7,156811.74,1,1,1,149402.59,0\r\n9455,15739248,Lin,727,France,Male,52,4,0,2,1,1,118429.02,0\r\n9456,15603481,Robinson,689,Spain,Female,55,4,0,2,1,1,58442.25,0\r\n9457,15723604,Collins,639,France,Male,39,6,150555.83,1,1,0,30414.17,0\r\n9458,15797822,Kingsley,678,France,Male,28,2,109137.12,1,1,1,58814.41,0\r\n9459,15665064,Harvey,523,France,Male,36,8,158351.02,2,1,0,155304.53,0\r\n9460,15640580,Obiora,650,France,Female,47,9,0,1,1,0,187943.6,0\r\n9461,15581089,Knight,744,Spain,Male,35,7,0,2,1,1,43036.6,0\r\n9462,15728605,Hung,697,France,Male,40,4,0,2,0,1,26543.28,0\r\n9463,15737385,Curtis,800,Spain,Female,46,6,0,2,1,0,171928.04,0\r\n9464,15714789,Perez,664,France,Male,24,7,0,1,0,1,35611.35,0\r\n9465,15786035,Gosnell,740,France,Male,39,9,0,2,1,0,19047.23,0\r\n9466,15815259,Fang,835,France,Female,56,2,0,2,1,1,39820.13,0\r\n9467,15592716,Clarke,559,France,Male,52,2,0,1,1,0,129013.59,1\r\n9468,15734850,Milanesi,676,Spain,Male,36,1,82729.49,1,1,0,113810.12,0\r\n9469,15638047,Chia,613,Germany,Female,45,9,142765.24,2,1,0,34749.65,0\r\n9470,15674573,Gearhart,713,France,Female,25,4,121172.97,1,1,1,56268.98,0\r\n9471,15694859,McLean,751,Germany,Female,28,10,132932.14,2,1,1,46630.47,0\r\n9472,15776404,Williamson,523,France,Male,22,8,123374.46,1,1,1,124906.59,0\r\n9473,15579345,Murphy,775,Germany,Female,74,0,161371.5,1,1,1,134869.93,0\r\n9474,15690733,Angelo,608,Spain,Male,45,4,0,2,0,0,36697.48,1\r\n9475,15631481,Thomson,673,France,Male,51,8,79563.36,2,1,1,172200.91,0\r\n9476,15620988,Murray,616,France,Male,46,2,0,2,1,0,137136.46,0\r\n9477,15571529,Kirby,650,Germany,Female,48,7,138232.24,1,1,0,57594.78,0\r\n9478,15592104,Lane,655,France,Female,41,5,0,1,0,0,36548,1\r\n9479,15651900,Bergamaschi,782,Germany,Female,53,1,81571.05,1,1,0,182960.46,1\r\n9480,15596212,Yang,781,Spain,Male,35,1,0,2,0,0,42117.9,0\r\n9481,15710687,Mills,593,France,Female,33,0,95927.04,1,1,0,199478.05,0\r\n9482,15613787,Chidubem,505,Spain,Male,35,8,116932.59,1,1,0,91092.84,0\r\n9483,15599211,Findlay,707,France,Male,40,1,0,2,1,0,14090.4,1\r\n9484,15675983,Wood,692,France,Female,36,3,79551.12,1,0,1,178267.07,0\r\n9485,15622370,Boyle,813,Germany,Male,30,1,116416.94,1,0,1,85808.22,0\r\n9486,15656319,Toscano,850,Spain,Male,37,4,88141.1,1,1,0,109659.12,0\r\n9487,15605130,Seccombe,753,France,Male,32,6,177729.13,1,1,1,161642.08,0\r\n9488,15672574,Uspenskaya,850,Spain,Female,32,5,0,1,1,1,3830.59,0\r\n9489,15659355,McKenzie,671,Spain,Male,32,6,123912.78,2,1,1,146636.44,0\r\n9490,15777907,Liang,791,France,Female,33,3,0,1,1,1,144413.92,1\r\n9491,15655171,Yermakova,624,France,Male,80,3,0,1,1,1,65801.44,0\r\n9492,15619674,White,649,France,Female,35,4,108306.44,1,1,1,192486.24,0\r\n9493,15775192,Rounsevell,732,Germany,Female,48,4,102962.62,1,1,0,120852.85,1\r\n9494,15617657,Ts'ai,664,France,Female,36,0,103502.22,1,1,1,146191.82,0\r\n9495,15688951,Stoneman,789,Germany,Male,43,8,119654.44,2,0,1,148412.24,1\r\n9496,15763460,Yao,680,France,Male,33,10,183768.47,1,1,0,164119.35,0\r\n9497,15756992,Chukwukere,701,France,Male,37,1,0,2,1,0,163457.55,0\r\n9498,15617454,Ifeatu,684,France,Female,25,1,0,2,0,1,144978.47,0\r\n9499,15701932,Millar,586,France,Female,52,6,140900.97,1,1,1,67288.89,0\r\n9500,15700813,Igwebuike,522,Germany,Female,25,5,94049.92,2,1,0,103269,0\r\n9501,15645600,Obidimkpa,739,Spain,Female,27,8,98926.4,1,1,1,106969.98,0\r\n9502,15634146,Hou,835,Germany,Male,18,2,142872.36,1,1,1,117632.63,0\r\n9503,15686743,Moody,790,Spain,Male,29,3,46057.96,2,1,1,189777.66,0\r\n9504,15698792,Keldie,671,France,Female,48,6,119769.77,1,0,1,66032.65,0\r\n9505,15591724,Liu,560,France,Female,44,5,143244.97,1,1,0,98661.27,0\r\n9506,15571281,Ts'ao,651,France,Male,28,10,79562.98,1,1,1,74687.37,0\r\n9507,15661380,Walker,682,France,Male,69,6,0,2,0,1,149604.18,0\r\n9508,15728885,Defalco,808,France,Male,41,0,0,1,1,1,79888.78,0\r\n9509,15618950,Lo Duca,644,Spain,Male,26,8,96659.64,2,1,1,138775.69,0\r\n9510,15609804,Hyde,688,France,Male,29,1,0,2,1,0,154695.57,0\r\n9511,15735849,Kanayochukwu,617,France,Female,26,2,165947.99,2,0,1,168834.38,0\r\n9512,15652948,Yen,738,France,Male,33,4,92676.3,1,1,0,105817.63,0\r\n9513,15618155,Ts'ui,663,France,Male,45,5,83195.12,1,1,1,48682.1,0\r\n9514,15566378,Tillman,515,France,Male,48,5,129387.94,1,0,1,147955.91,1\r\n9515,15565879,Riley,845,France,Female,28,9,0,2,1,1,56185.98,0\r\n9516,15792922,Tu,639,Spain,Male,38,9,130233.14,1,1,1,81861.1,0\r\n9517,15770567,Ruiz,557,France,Female,32,3,123502.53,1,1,1,69826.8,0\r\n9518,15738042,Goliwe,543,Germany,Male,37,8,140894.06,2,1,1,118059.19,0\r\n9519,15714920,Balashov,585,Germany,Male,44,7,163867.86,1,1,1,112333.22,0\r\n9520,15782121,Leonard,610,France,Female,27,2,0,2,1,0,14546.76,0\r\n9521,15673180,Onyekaozulu,727,Germany,Female,18,2,93816.7,2,1,0,126172.11,0\r\n9522,15660636,Carpenter,540,Spain,Female,40,8,0,2,1,0,3560,0\r\n9523,15664504,Beede,418,France,Male,35,7,0,2,1,1,88878.15,0\r\n9524,15790322,Beneventi,660,France,Female,32,0,114668.89,1,1,0,84605,0\r\n9525,15739847,Sadlier,850,Germany,Male,38,5,146756.68,1,1,0,78268.61,0\r\n9526,15699415,Lewis,618,France,Female,46,6,150213.71,1,1,0,120668.46,1\r\n9527,15665521,Chiazagomekpele,642,Germany,Male,18,5,111183.53,2,0,1,10063.75,0\r\n9528,15682868,Elliott,850,France,Female,40,9,99816.46,1,1,1,163989.66,1\r\n9529,15584462,Liang,739,France,Male,34,9,0,1,1,0,60584.33,0\r\n9530,15661708,She,508,France,Female,41,5,0,2,1,1,94170.84,0\r\n9531,15584452,Bozeman,667,France,Male,41,6,0,2,0,0,167181.77,0\r\n9532,15717010,Yu,741,France,Female,60,5,0,1,1,1,38914.51,0\r\n9533,15643828,Teng,592,France,Male,29,7,0,2,1,1,91196.67,0\r\n9534,15733361,Davide,651,Germany,Female,45,6,86714.06,1,1,0,85869.89,1\r\n9535,15795488,Beneventi,773,Spain,Male,52,2,0,2,1,0,57337.79,0\r\n9536,15581551,Yobachukwu,850,Spain,Male,41,8,132838.07,1,1,1,175347.28,0\r\n9537,15632051,Douglas,550,Germany,Female,42,10,128707.31,1,1,0,63092.65,1\r\n9538,15780409,Egobudike,783,France,Male,40,6,0,2,1,0,109742.55,0\r\n9539,15572767,Shelby,777,France,Male,29,2,0,2,1,0,124489.88,0\r\n9540,15590337,Golubov,659,France,Male,29,6,123192.12,1,1,1,56971.41,1\r\n9541,15634551,Williamson,727,Germany,Male,46,3,115248.11,4,1,0,130752.01,1\r\n9542,15669290,Fan,603,France,Male,38,8,59360.77,1,1,1,191457.06,0\r\n9543,15621140,Nwebube,644,Spain,Male,37,9,0,2,1,1,96442.86,0\r\n9544,15613518,Bellucci,647,France,Female,35,6,112668.7,1,0,1,122584.29,0\r\n9545,15728043,Udinese,648,Germany,Female,37,7,138503.51,2,1,0,57215.85,0\r\n9546,15570073,Marian,721,Spain,Male,57,1,0,1,1,1,195940.96,0\r\n9547,15777033,Chizoba,524,France,Male,29,7,0,2,1,1,105448.74,0\r\n9548,15682454,McFarland,626,France,Female,34,3,0,2,1,1,37870.29,0\r\n9549,15758513,McDonald,569,France,Male,43,7,0,2,1,0,52534.81,0\r\n9550,15772604,Chiemezie,578,Spain,Male,36,1,157267.95,2,1,0,141533.19,0\r\n9551,15721715,Fane,769,France,Female,40,9,133871.05,1,1,1,50568.02,0\r\n9552,15688563,Marchesi,694,Germany,Male,31,4,141989.27,2,1,0,26116.82,0\r\n9553,15772009,Scott,664,France,Female,41,5,0,1,1,1,152054.33,0\r\n9554,15809585,H?,646,France,Male,38,7,0,2,1,0,1528.4,0\r\n9555,15593778,Craig,779,France,Female,29,3,46388.16,3,1,0,127939.26,1\r\n9556,15655360,Chikelu,782,Germany,Female,72,5,148666.99,1,1,0,2605.65,1\r\n9557,15780909,Caffyn,769,Germany,Male,34,7,115101.5,1,0,0,57841.89,1\r\n9558,15757310,Otitodilichukwu,655,Germany,Male,67,6,148363.38,1,1,1,186995.17,0\r\n9559,15801411,Green,623,Spain,Male,46,4,0,1,1,0,5549.11,1\r\n9560,15761706,Y?an,705,Spain,Female,39,8,144102.32,1,1,1,11682.36,0\r\n9561,15658409,Mao,686,France,Male,41,5,128876.71,3,1,1,106939.34,1\r\n9562,15810010,Dahlenburg,678,Germany,Male,36,6,118448.15,2,1,0,53172.02,0\r\n9563,15627027,Shih,738,France,Male,39,5,0,2,1,1,114388.98,0\r\n9564,15624374,Maclean,703,France,Male,28,9,0,2,0,1,2151.17,0\r\n9565,15720083,Fiorentino,554,Spain,Male,42,1,0,2,0,1,183492.9,0\r\n9566,15752294,Long,582,France,Female,38,9,135979.01,4,1,1,76582.95,1\r\n9567,15743193,Olson,644,France,Male,37,6,117271.8,2,1,0,104217.96,1\r\n9568,15696733,McKenzie,724,France,Male,29,4,0,1,1,0,8982.75,0\r\n9569,15677522,Rossi,593,France,Male,33,1,0,2,0,0,9984.4,0\r\n9570,15643523,Power,710,Spain,Female,30,10,0,2,1,0,19500.1,0\r\n9571,15624936,Yen,631,France,Male,35,8,129205.49,1,1,1,79146.36,0\r\n9572,15716085,Norris,739,Spain,Female,41,8,0,1,1,0,191694.77,1\r\n9573,15641688,Collier,644,Spain,Male,18,7,0,1,0,1,59645.24,1\r\n9574,15796834,Rivers,652,Germany,Male,35,7,104015.54,2,1,1,55207.88,0\r\n9575,15720123,Hudson,554,Spain,Male,37,3,0,2,1,0,166177.3,0\r\n9576,15604732,Milani,483,France,Female,30,9,0,2,0,0,136356.97,0\r\n9577,15723484,Hunt,669,Germany,Female,42,1,103873.39,1,1,0,148611.52,0\r\n9578,15807120,Oluchukwu,841,Germany,Female,52,3,112383.03,1,1,0,85516.37,1\r\n9579,15810891,Lorenzo,662,France,Male,34,2,117731.79,2,0,1,55120.79,0\r\n9580,15640407,Chidiegwu,821,Germany,Male,45,0,135827.33,2,1,1,131778.58,0\r\n9581,15778838,Warren,783,France,Male,38,9,114135.17,1,1,0,153269.98,0\r\n9582,15709256,Glover,850,France,Female,28,9,0,2,1,1,164864.67,0\r\n9583,15742285,Andersen,559,France,Male,62,6,118756.62,1,1,1,20367.68,0\r\n9584,15729019,Arcuri,602,Spain,Male,34,8,98382.72,1,1,0,39542,0\r\n9585,15608588,Mackinlay,563,Germany,Male,41,2,100520.92,1,1,1,19412.8,1\r\n9586,15610557,McCarthy,695,Spain,Female,35,7,79858.13,2,1,1,127977.66,0\r\n9587,15786418,Chiu,546,France,Female,20,6,0,1,0,1,20508.85,0\r\n9588,15653050,Norriss,719,Germany,Female,76,10,95052.29,1,1,0,176244.87,0\r\n9589,15744914,Moore,539,Germany,Male,42,1,177728.55,1,1,0,105013.63,0\r\n9590,15669611,Mott,632,France,Male,71,3,83116.68,1,1,1,27597.76,0\r\n9591,15594786,Ts'ai,772,Germany,Male,34,7,111565.91,1,1,1,121073.23,0\r\n9592,15649211,Fokina,708,Spain,Male,40,8,83015.71,1,1,0,101089.76,0\r\n9593,15766066,Nikitina,668,Germany,Female,28,1,124511.01,1,0,0,114258.18,0\r\n9594,15772216,Henry,738,France,Female,67,1,130652.52,1,0,1,22762.23,0\r\n9595,15619898,Chiefo,785,France,Male,55,5,0,2,1,1,7008.65,0\r\n9596,15724543,Mao,597,France,Male,61,5,0,2,1,1,81299.17,0\r\n9597,15755084,Bezrukova,531,France,Male,37,7,121854.45,1,1,0,147521.35,0\r\n9598,15730441,Dodd,509,France,Male,26,10,0,2,1,1,6177.83,0\r\n9599,15666767,Lori,508,France,Male,35,1,86893.28,1,0,0,59374.82,0\r\n9600,15690456,Yudina,749,Germany,Female,32,7,79523.13,1,0,1,157648.12,0\r\n9601,15570533,Conti,621,Germany,Female,55,7,131033.76,1,0,1,75685.59,1\r\n9602,15797692,Volkova,659,France,Female,33,7,89939.62,1,1,0,136540.09,0\r\n9603,15603135,Boni,634,Germany,Female,59,3,95727.05,1,0,0,97939.4,1\r\n9604,15698927,Ritchie,675,France,Male,39,7,0,2,0,1,36267.21,0\r\n9605,15687363,McMillan,770,France,Male,31,3,155047.56,2,1,1,186064.34,0\r\n9606,15733444,Phillips,736,France,Female,29,9,0,2,0,0,176152.7,0\r\n9607,15678057,Lombardi,524,France,Male,44,10,118569.03,2,0,0,82117.2,0\r\n9608,15806918,Ireland,674,France,Male,28,5,0,1,1,1,151925.25,0\r\n9609,15638247,Boan,700,Spain,Male,44,9,0,2,1,0,142287.65,0\r\n9610,15674833,Shao,741,France,Female,35,1,0,2,1,0,36557.55,0\r\n9611,15812534,Chiemenam,455,France,Male,40,1,0,3,0,1,129975.34,0\r\n9612,15586522,Hunter,608,Spain,Male,37,2,130461.02,1,1,0,21967.15,0\r\n9613,15794297,McKay,776,France,Male,36,1,0,2,1,0,53477.76,0\r\n9614,15737025,Roberts,635,France,Male,33,1,0,3,0,0,178067.33,1\r\n9615,15615931,Aitken,746,France,Female,37,4,0,2,0,1,171039.56,0\r\n9616,15664860,Chao,692,Spain,Female,47,3,0,2,1,0,150802.41,1\r\n9617,15664539,Bruce,683,Spain,Male,35,9,61172.04,1,0,0,82951.12,0\r\n9618,15583692,Chan,591,Germany,Female,35,2,90194.34,2,1,0,57064.57,0\r\n9619,15693131,Watts,581,France,Female,24,3,95508.2,1,1,1,45755,0\r\n9620,15779973,Gibbons,684,Germany,Male,35,3,99967.76,1,1,1,176882.08,0\r\n9621,15620557,Ni,561,Spain,Male,37,4,101470.29,1,0,1,88838.14,0\r\n9622,15639549,Jen,718,Germany,Female,33,4,70541.06,1,0,0,88592.8,0\r\n9623,15618750,Phillips,590,France,Male,31,8,112211.61,1,1,0,26261.42,0\r\n9624,15796790,Amaechi,573,France,Female,47,8,154543.98,1,1,0,29586.73,0\r\n9625,15668309,Maslow,350,France,Female,40,0,111098.85,1,1,1,172321.21,1\r\n9626,15732437,Rowley,504,Germany,Female,44,0,131873.07,2,1,1,158036.72,1\r\n9627,15665158,Chukwuemeka,813,Spain,Male,27,1,137275.36,1,0,1,115733.16,0\r\n9628,15689322,Bevan,641,Spain,Male,31,3,153316.14,1,1,0,59927.99,0\r\n9629,15596624,Topp,662,France,Female,22,9,0,2,1,1,44377.65,0\r\n9630,15601977,Burgoyne,497,Spain,Male,44,2,121250.04,1,0,1,79691.4,0\r\n9631,15801462,Yermakov,716,France,Male,31,8,109578.04,2,1,1,51503.51,0\r\n9632,15566139,Ts'ui,526,France,Female,37,5,53573.18,1,1,0,62830.97,0\r\n9633,15791006,Kodilinyechukwu,760,Germany,Female,34,6,58003.41,1,1,0,90346.1,0\r\n9634,15668057,K?,669,France,Female,31,6,113000.66,1,1,0,40467.82,0\r\n9635,15580805,Marino,655,France,Male,27,10,0,2,1,0,51620.94,0\r\n9636,15658768,Lucas,547,France,Female,49,2,0,1,0,0,65466.93,1\r\n9637,15613048,Anderson,648,Germany,Female,40,5,139973.65,1,1,1,667.66,1\r\n9638,15803654,Wei,790,France,Female,31,2,151290.16,1,1,1,172437.12,0\r\n9639,15662337,Baldwin,744,Germany,Female,50,1,121498.11,2,0,1,106061.47,1\r\n9640,15650924,Foster,761,Spain,Female,32,4,103515.39,2,1,1,177622.38,0\r\n9641,15647203,Gebhart,750,France,Female,35,3,0,1,1,0,191520.5,0\r\n9642,15682778,Fedorov,680,France,Male,34,9,0,2,1,1,95686.6,0\r\n9643,15579820,Robertson,704,Spain,Male,38,6,106687.76,1,1,0,173776.5,0\r\n9644,15709354,Tudawali,521,France,Female,41,2,0,2,1,1,113089.43,0\r\n9645,15728480,Iloerika,452,France,Female,35,8,0,2,1,1,149614.81,0\r\n9646,15641091,Onyemauchechukwu,695,France,Female,31,5,106089.2,1,0,0,99537.68,0\r\n9647,15603111,Muir,850,Spain,Male,71,10,69608.14,1,1,0,97893.4,1\r\n9648,15679693,Walker,625,France,Male,31,5,0,2,0,1,90.07,0\r\n9649,15797190,Charlton,608,Germany,Female,40,7,96202.32,1,0,0,161154.85,0\r\n9650,15788025,Tseng,715,France,Female,38,0,0,2,1,1,332.81,0\r\n9651,15646168,Ifeatu,834,Spain,Male,33,5,0,2,1,0,66285.18,0\r\n9652,15580493,Chin,469,France,Male,33,1,127818.52,1,1,0,163477.22,0\r\n9653,15726720,Blinova,480,France,Female,40,7,0,1,1,0,170332.67,1\r\n9654,15735799,Maconochie,527,Germany,Male,58,3,137318.42,1,1,1,126144.96,0\r\n9655,15773098,Ch'in,834,Spain,Male,34,5,0,2,0,0,53437.1,0\r\n9656,15668971,Nicholson,583,France,Female,40,4,55776.39,2,1,0,26920.43,0\r\n9657,15603221,Burgess,696,Germany,Male,32,4,84421.62,1,0,1,52314.71,0\r\n9658,15740043,Young,606,France,Male,32,5,83161.65,1,1,1,116885.59,0\r\n9659,15712264,Plumb,713,France,Female,39,10,0,2,1,1,126263.97,0\r\n9660,15751926,Trentino,821,Germany,Male,42,3,87807.29,2,1,1,64613.81,0\r\n9661,15589401,Allen,550,France,Female,30,4,0,2,1,0,89216.29,0\r\n9662,15742019,Benford,675,France,Female,39,6,0,2,0,0,83419.15,0\r\n9663,15660611,Gallo,748,Spain,Male,39,3,0,2,1,1,123998.52,0\r\n9664,15607634,Cobb,606,Germany,Male,40,9,95293.86,2,0,1,96985.58,0\r\n9665,15595036,Doherty,726,Germany,Male,30,7,92847.59,1,1,0,146154.06,0\r\n9666,15745794,Cocci,547,France,Male,30,6,0,2,1,1,18471.86,0\r\n9667,15781689,Macadam,758,Spain,Male,35,5,0,2,1,0,95009.6,0\r\n9668,15696054,Tychonoff,596,France,Male,37,2,0,1,0,1,121175.86,0\r\n9669,15752467,Johnson,720,Spain,Male,34,3,0,2,1,1,77047.78,0\r\n9670,15597739,Tu,674,France,Male,37,3,0,1,1,0,158049.9,0\r\n9671,15651336,Chidiebere,756,France,Female,32,4,0,2,1,0,147040.25,0\r\n9672,15636061,Pope,649,Germany,Male,78,4,68345.86,2,1,1,142566.75,0\r\n9673,15723013,Sutherland,613,Germany,Male,28,7,76656.4,2,1,1,185483.24,0\r\n9674,15784148,Beneventi,643,France,Male,62,9,0,2,0,0,155870.82,0\r\n9675,15578098,Jamieson,600,France,Male,31,8,0,2,1,1,121555.51,0\r\n9676,15638621,Simmons,735,Spain,Male,39,1,60374.98,1,1,0,40223.74,0\r\n9677,15720924,Chijioke,585,France,Female,34,1,0,1,1,1,75503.6,0\r\n9678,15566531,Iloerika,724,Germany,Male,33,4,88046.88,1,0,1,186942.49,1\r\n9679,15718064,Chia,635,Spain,Male,29,2,0,2,0,0,117173.8,0\r\n9680,15605067,Nwachinemelu,472,France,Male,19,9,0,2,1,0,3453.4,0\r\n9681,15655335,Becher,590,France,Male,36,1,0,2,1,0,48876.84,0\r\n9682,15607301,Romano,651,Spain,Female,63,8,129968.67,1,1,1,11830.53,0\r\n9683,15694628,Walker,686,Germany,Female,39,4,157731.6,2,1,0,162820.6,0\r\n9684,15607112,Chiawuotu,606,France,Male,32,6,0,2,0,1,36540.63,0\r\n9685,15635775,Watt,781,France,Male,33,3,89276.48,1,1,0,6959,0\r\n9686,15644280,Udegbunam,593,France,Male,45,4,138825.19,1,0,0,10828.78,0\r\n9687,15708362,Watson,793,France,Male,63,4,103729.79,2,1,1,80272.06,0\r\n9688,15771997,Bryant,791,France,Female,31,10,75499.24,1,1,0,22184.14,0\r\n9689,15730579,Ward,850,France,Male,68,5,169445.4,1,1,1,186335.07,0\r\n9690,15728005,Urban,698,France,Female,57,9,111359.55,2,1,0,105715.01,0\r\n9691,15791674,Sutherland,846,France,Female,34,10,142388.61,2,0,1,68393.64,1\r\n9692,15754599,K'ung,765,France,Male,42,4,123311.39,2,1,1,82868.34,0\r\n9693,15693690,Iweobiegbunam,574,Spain,Male,52,7,115532.52,1,1,0,196257.67,0\r\n9694,15728963,Wei,617,Germany,Female,51,10,167273.71,1,0,0,93439.75,1\r\n9695,15659710,Lascelles,581,France,Male,25,5,77886.53,2,1,0,150319.49,0\r\n9696,15658675,Ts'ao,710,Germany,Male,37,6,135795.63,1,0,1,46523.6,0\r\n9697,15638788,Mack,550,France,Male,32,8,97514.07,1,1,1,199138.84,0\r\n9698,15609735,Campbell,533,Germany,Male,51,6,127545.56,2,0,0,79559.02,1\r\n9699,15771477,Fiorentini,779,France,Male,49,9,106160.37,1,0,0,116893.87,0\r\n9700,15570145,Long,763,France,Female,23,2,0,2,1,0,153983.99,0\r\n9701,15797149,Lloyd,563,Spain,Female,36,4,143680.47,2,1,1,63531.19,0\r\n9702,15636912,Sneddon,678,Spain,Male,38,3,124483.53,1,1,0,126253.31,0\r\n9703,15687828,Gorshkov,644,Spain,Female,31,5,86006.3,1,1,1,73922.95,0\r\n9704,15667424,Forbes,682,Germany,Female,43,7,111094.05,2,1,1,64679.3,0\r\n9705,15759872,L?,625,France,Male,22,9,0,2,1,0,157072.91,0\r\n9706,15572374,Hopetoun,733,Spain,Male,36,1,0,2,0,1,108377.82,0\r\n9707,15754926,Lucchesi,512,France,Female,30,6,0,2,1,0,88827.31,0\r\n9708,15687431,Faria,642,France,Female,41,7,115171.71,1,1,1,37674.47,0\r\n9709,15604515,Yefremov,737,Germany,Female,22,10,111543.26,2,0,0,106327.85,0\r\n9710,15682839,Genovesi,575,France,Female,57,8,137936.94,1,1,1,84475.13,0\r\n9711,15624677,Marquez,543,Germany,Female,37,3,122304.65,2,0,0,33998.7,0\r\n9712,15646366,Trevisani,521,Germany,Male,41,8,120586.54,1,0,1,20491.15,0\r\n9713,15701768,Tung,637,France,Male,32,3,0,2,1,1,197827.06,0\r\n9714,15623566,Barnhill,714,France,Male,40,9,46520.69,1,1,1,96687.25,0\r\n9715,15681274,Marshall,726,Spain,Female,56,2,105473.74,1,1,1,46044.7,0\r\n9716,15762573,Bednall,680,Spain,Female,34,7,0,2,1,0,98949.85,0\r\n9717,15706458,Pan,812,Germany,Male,39,5,115730.71,3,1,1,185599.34,1\r\n9718,15654222,Ogg,757,Spain,Male,30,3,145396.49,1,0,1,198341.15,0\r\n9719,15704053,T'ang,710,Spain,Male,62,3,131078.42,2,1,0,119348.76,1\r\n9720,15724321,Baresi,516,Germany,Female,47,9,128298.74,1,0,0,149614.17,1\r\n9721,15621815,Obiajulu,803,France,Female,40,6,165526.71,1,1,0,12328.08,0\r\n9722,15724876,McGregor,560,France,Female,38,5,83714.41,1,1,1,33245.97,0\r\n9723,15696588,Lung,679,France,Female,36,3,0,2,1,1,2243.41,0\r\n9724,15612832,Jamieson,526,France,Male,32,7,125540.05,1,0,0,86786.41,0\r\n9725,15804295,Pinto,485,France,Male,41,2,100254.76,2,1,1,12706.67,0\r\n9726,15712536,Fallaci,625,France,Female,36,3,0,2,1,0,41295.1,1\r\n9727,15662494,Goliwe,773,Spain,Male,43,7,138150.57,1,1,1,177357.16,0\r\n9728,15807728,Ferri,530,France,Female,45,1,0,1,0,1,190663.89,1\r\n9729,15764916,Rowley,616,Germany,Female,43,7,95984.21,1,0,1,115262.54,1\r\n9730,15615330,Tretiakova,651,France,Male,23,10,0,2,1,1,170099.23,0\r\n9731,15638487,She,586,Germany,Male,38,2,136858.42,1,0,1,189143.94,0\r\n9732,15627859,Nebeolisa,607,Germany,Male,29,7,102609,1,1,0,163257.44,0\r\n9733,15622192,Young,724,Spain,Male,39,3,0,2,0,1,95562.81,0\r\n9734,15789413,Fitzgerald,733,France,Male,64,3,0,2,1,1,75272.63,0\r\n9735,15583221,Arnold,667,Germany,Male,70,3,77356.92,2,1,1,20881.96,0\r\n9736,15768495,Chidimma,700,France,Female,32,8,110923.15,2,1,1,161845.81,1\r\n9737,15644103,Wells,659,Spain,Male,78,2,151675.65,1,0,1,49978.67,0\r\n9738,15741197,Calzada,710,Spain,Male,22,8,0,3,1,0,107292.91,0\r\n9739,15664547,Black,760,France,Male,37,7,0,1,0,0,32863.24,1\r\n9740,15797293,Sopuluchukwu,677,France,Female,25,3,0,2,1,0,179608.96,0\r\n9741,15572021,Ts'ao,798,Germany,Female,29,8,80204.11,2,1,0,70223.22,0\r\n9742,15637461,Ukaegbunam,758,France,Male,35,7,0,2,1,0,77951.84,0\r\n9743,15620577,Wood,715,France,Male,45,4,0,2,1,1,55043.93,0\r\n9744,15609643,Furneaux,752,Germany,Male,32,9,115587.49,2,0,1,101677.46,0\r\n9745,15785358,Gresswell,586,Germany,Male,46,8,106968.96,1,1,1,79366.98,1\r\n9746,15603883,Ch'in,818,France,Male,36,4,0,2,1,1,8037.03,0\r\n9747,15782550,Ma,490,Germany,Female,41,0,139659.04,1,1,1,176254.12,0\r\n9748,15775761,Iweobiegbunam,610,Germany,Female,69,5,86038.21,3,0,0,192743.06,1\r\n9749,15680201,Marcelo,627,Germany,Male,24,5,102773.2,2,1,0,56793.02,1\r\n9750,15767594,Azubuike,533,France,Female,35,8,0,2,1,1,187900.12,0\r\n9751,15591985,Stewart,708,France,Female,51,8,70754.18,1,1,1,92920.04,1\r\n9752,15789339,Yen,681,France,Male,59,4,122781.51,1,0,1,140166.95,0\r\n9753,15781530,Hsieh,690,France,Male,21,8,0,2,1,1,155782.89,0\r\n9754,15705174,Chiedozie,656,Germany,Male,68,7,153545.11,1,1,1,186574.68,0\r\n9755,15572114,Shih,673,Spain,Male,40,1,121629.22,1,1,1,3258.6,0\r\n9756,15804009,Amechi,806,Germany,Male,36,8,167983.17,2,1,1,106714.28,0\r\n9757,15662698,Ko,648,Spain,Female,43,7,81153.82,1,1,1,144532.85,1\r\n9758,15696047,Chimezie,501,France,Male,35,6,99760.84,1,1,1,13591.52,0\r\n9759,15701160,Azubuike,556,Germany,Female,43,4,125890.72,1,1,1,74854.97,0\r\n9760,15790093,Aguirre,627,France,Female,27,2,0,2,1,0,125451.01,0\r\n9761,15632143,Lung,652,France,Male,31,2,119148.55,1,0,0,149740.22,0\r\n9762,15736778,Adams,807,Germany,Female,60,1,72948.58,2,1,1,17355.36,0\r\n9763,15734917,Castiglione,708,Germany,Male,21,8,133974.36,2,1,0,50294.09,0\r\n9764,15643903,Yao,619,France,Male,27,1,154483.98,1,1,0,156394.74,0\r\n9765,15569526,Morales,601,France,Male,40,10,98627.13,2,0,0,77977.69,0\r\n9766,15777067,Thomas,445,France,Male,64,2,136770.67,1,0,1,43678.06,0\r\n9767,15795511,Vasiliev,800,Germany,Male,39,4,95252.72,1,1,0,13906.34,0\r\n9768,15610419,Chukwueloka,554,France,Male,33,3,117413.95,1,1,1,12766.74,0\r\n9769,15644994,Ko,714,Germany,Male,54,4,137986.58,2,0,1,51308.54,1\r\n9770,15703707,Atkins,656,France,Male,44,10,143571.52,1,0,0,127444.14,0\r\n9771,15659327,Moffitt,520,France,Male,49,5,121197.64,1,1,0,72577.33,1\r\n9772,15771323,Panicucci,480,Spain,Male,39,5,121626.9,1,1,1,82438.13,0\r\n9773,15750549,Akobundu,660,Germany,Male,30,1,84440.1,2,1,1,60485.98,0\r\n9774,15698462,Chiu,532,France,Male,36,4,0,2,1,1,132798.78,0\r\n9775,15739692,Tsui,679,France,Male,42,1,0,2,0,0,71823.15,0\r\n9776,15744041,Yobanna,780,France,Female,26,3,140356.7,1,1,0,117144.15,0\r\n9777,15700714,Hollis,747,France,Male,29,7,0,2,1,1,141706.43,0\r\n9778,15777743,Cattaneo,705,France,Female,39,3,92224.56,1,1,1,54517.25,0\r\n9779,15623143,Lung,732,France,Female,43,9,0,2,1,0,183147.17,0\r\n9780,15712568,Angelo,515,Spain,Male,40,10,121355.99,1,1,0,138360.29,0\r\n9781,15617432,Folliero,816,Germany,Female,40,9,109003.26,1,1,1,79580.56,0\r\n9782,15650424,Bryant,641,France,Female,48,3,147341.43,1,1,1,157458.61,1\r\n9783,15728829,Weigel,509,France,Male,18,7,102983.91,1,1,0,171770.58,0\r\n9784,15680430,Ajuluchukwu,601,Germany,Female,49,4,96252.98,2,1,0,104263.82,0\r\n9785,15687626,Zhirov,527,France,Male,39,4,0,2,1,0,167183.07,1\r\n9786,15609187,Cox,455,France,Female,27,5,155879.09,2,0,0,70774.97,0\r\n9787,15609521,Chimaraoke,803,Germany,Male,34,4,142929.16,2,1,1,114869.56,0\r\n9788,15752626,Genovese,553,France,Male,32,7,64082.09,1,0,1,109159.58,0\r\n9789,15571756,Ohearn,724,France,Female,28,5,0,1,1,0,59351.68,0\r\n9790,15814040,Munroe,610,France,Female,45,1,0,2,1,1,199657.46,0\r\n9791,15658211,Morrison,559,Spain,Female,39,2,0,2,1,1,121151.1,0\r\n9792,15742091,Parkhill,825,Germany,Female,35,6,118336.95,1,1,0,26342.33,1\r\n9793,15787168,Y?,819,Spain,Female,28,8,168253.21,1,1,1,102799.14,0\r\n9794,15772363,Hilton,772,Germany,Female,42,0,101979.16,1,1,0,90928.48,0\r\n9795,15659364,Thompson,685,Spain,Male,23,5,164902.43,1,0,0,141152.28,0\r\n9796,15738980,Yobanna,506,France,Male,43,2,0,2,1,0,105568.6,0\r\n9797,15794236,Thorpe,642,Germany,Male,22,10,111812.52,2,1,1,183045.46,0\r\n9798,15721383,Harvey,627,Spain,Male,40,10,0,2,1,1,194792.42,0\r\n9799,15652981,Robinson,600,Germany,Male,30,2,119755,1,1,1,21852.91,0\r\n9800,15722731,Manna,653,France,Male,46,0,119556.1,1,1,0,78250.13,1\r\n9801,15640507,Li,762,Spain,Female,35,3,119349.69,3,1,1,47114.18,1\r\n9802,15578878,Hancock,569,Spain,Female,30,3,139528.23,1,1,1,33230.37,0\r\n9803,15744295,Hao,756,France,Male,40,1,94773.11,1,1,0,114279.63,0\r\n9804,15776558,Nicholls,673,France,Male,31,1,108345.22,1,0,1,38802.03,0\r\n9805,15596136,Folliero,637,France,Female,36,9,166939.88,1,1,1,72504.76,0\r\n9806,15704597,Trumbull,644,France,Male,33,7,174571.36,1,0,1,43943.09,0\r\n9807,15648272,Medvedeva,658,Spain,Male,35,9,71829.34,1,1,1,68141.92,0\r\n9808,15594915,Crist,649,France,Female,36,8,0,2,0,1,109179.89,0\r\n9809,15581115,Middleton,603,France,Female,39,9,76769.68,1,0,0,48224.72,0\r\n9810,15763907,Watts,820,France,Female,39,1,104614.29,1,1,0,61538.43,1\r\n9811,15705994,Udinese,712,Spain,Male,27,10,0,1,1,0,94544.88,0\r\n9812,15772421,Tretiakov,645,Germany,Female,31,1,128927.93,1,1,1,2850.01,0\r\n9813,15711572,O'Kane,705,Germany,Female,31,9,110941.93,2,1,0,163484.8,0\r\n9814,15691170,Vasilyeva,590,Spain,Female,29,10,99250.08,1,1,1,129629.41,0\r\n9815,15600106,Wei,631,France,Male,36,1,0,2,0,0,133141.34,0\r\n9816,15745431,Chinonyelum,604,France,Male,34,7,0,2,1,1,188078.55,0\r\n9817,15649508,Chin,643,Spain,Male,48,8,0,2,1,0,174729.3,0\r\n9818,15812611,Lorimer,690,Spain,Female,30,5,0,2,0,1,78700.03,0\r\n9819,15619699,Yeh,558,France,Male,31,7,0,1,1,0,198269.08,0\r\n9820,15813946,Duffy,637,Germany,Male,51,1,104682.83,1,1,0,55266.96,1\r\n9821,15762762,Onyekachukwu,648,Germany,Female,45,5,118886.55,1,0,0,51636.7,0\r\n9822,15629793,Banks,652,Spain,Male,28,8,156823.7,2,1,0,198251.52,0\r\n9823,15781298,Hughes,808,Germany,Male,39,3,124216.93,1,0,1,171442.36,0\r\n9824,15622658,Lai,551,France,Female,26,2,144258.52,1,1,0,49778.79,0\r\n9825,15658980,Matthews,711,Germany,Male,26,9,128793.63,1,1,0,19262.05,0\r\n9826,15701936,Bell,467,Germany,Male,28,10,126315.26,1,1,0,32349.29,1\r\n9827,15686917,Tu,789,Spain,Female,40,4,0,2,1,0,137402.27,0\r\n9828,15807312,Hsia,602,Spain,Male,33,5,0,2,0,1,64038.34,0\r\n9829,15574523,Cheng,576,France,Male,39,1,0,2,1,1,68814.23,0\r\n9830,15724200,Cheng,584,France,Male,38,1,115341.55,1,0,1,173632.92,0\r\n9831,15738224,Lin,593,France,Male,32,6,99162.29,1,1,0,128384.11,0\r\n9832,15593283,Higgins,705,Germany,Female,48,1,156848.13,2,1,1,99475.95,1\r\n9833,15814690,Chukwujekwu,595,Germany,Female,64,2,105736.32,1,1,1,89935.73,1\r\n9834,15807245,McKay,699,Germany,Female,41,1,200117.76,2,1,0,94142.35,0\r\n9835,15799358,Vincent,516,France,Female,46,6,62212.29,1,0,1,171681.86,1\r\n9836,15616172,Ubanwa,838,France,Male,31,2,0,2,1,0,8222.96,0\r\n9837,15777958,Ch'ien,587,France,Male,39,10,0,2,1,1,170409.45,0\r\n9838,15809124,T'ien,750,France,Male,38,5,151532.4,1,1,1,46555.15,0\r\n9839,15616367,Ricci,581,Germany,Male,39,1,121523.51,1,0,0,161655.55,1\r\n9840,15687385,McDowell,484,France,Male,41,5,0,1,1,1,74267.35,0\r\n9841,15607877,Maclean,576,Spain,Male,26,8,0,2,0,1,34101.06,0\r\n9842,15736327,Manna,567,Germany,Female,46,1,68238.51,2,1,1,109572.58,0\r\n9843,15746704,Jibunoh,638,Spain,Male,30,9,136808.53,2,1,1,106642.97,0\r\n9844,15778304,Fan,646,Germany,Male,24,0,92398.08,1,1,1,18897.29,0\r\n9845,15588456,Hsieh,658,France,Female,40,5,143566.12,1,1,1,189607.71,0\r\n9846,15664035,Parsons,590,Spain,Female,38,9,0,2,1,1,148750.16,0\r\n9847,15596405,Udinese,546,Spain,Male,25,7,127728.24,2,1,1,105279.74,0\r\n9848,15815097,Root,603,France,Female,34,9,0,2,1,0,167916.35,0\r\n9849,15762708,Chiemezie,619,Spain,Female,38,10,119658.49,1,1,1,8646.58,0\r\n9850,15776211,Toscani,678,France,Female,34,6,0,2,1,1,124592.84,0\r\n9851,15626012,Obidimkpa,459,France,Male,26,4,149879.66,1,0,0,50016.17,0\r\n9852,15792077,Degtyaryov,671,Germany,Male,28,8,119859.52,2,1,0,125422.66,0\r\n9853,15718765,Maclean,501,Spain,Male,43,6,104533.24,1,0,0,81123.59,1\r\n9854,15576615,Giordano,719,Spain,Male,37,10,145382.61,1,1,0,80408.59,0\r\n9855,15752650,Saad,681,Spain,Female,37,6,121231.39,1,1,1,146366.08,0\r\n9856,15797502,Lord,706,Spain,Male,24,2,141078.57,1,1,1,24402.87,0\r\n9857,15687329,Hope,763,Germany,Female,32,1,108465.65,2,1,0,60552.44,1\r\n9858,15779423,K?,716,France,Male,39,1,70657.61,2,1,1,76476.05,0\r\n9859,15619514,Bull,507,Germany,Male,40,3,120105.43,1,1,0,92075.01,1\r\n9860,15615430,Adams,678,Germany,Male,55,4,129646.91,1,1,1,184125.1,1\r\n9861,15716431,Brookes,775,France,Female,30,10,191091.74,2,1,1,96170.38,0\r\n9862,15798341,Victor,544,France,Male,38,8,0,1,1,1,98208.62,0\r\n9863,15651958,Giles,756,France,Male,27,8,0,2,1,1,157932.75,0\r\n9864,15726179,Ferrari,757,Germany,Female,43,5,131433.33,2,1,1,3497.43,1\r\n9865,15652999,Milne,742,Germany,Male,33,1,137937.95,1,1,1,51387.1,0\r\n9866,15691950,Parry,591,France,Male,49,3,0,2,1,0,50123.44,0\r\n9867,15632446,Allan,667,France,Male,24,4,0,2,0,0,180329.83,0\r\n9868,15620936,Warren,787,France,Male,32,4,0,2,1,1,13238.93,0\r\n9869,15587640,Rowntree,718,France,Female,43,0,93143.39,1,1,0,167554.86,0\r\n9870,15782231,Andrejew,521,France,Male,38,6,0,2,1,0,51454.06,0\r\n9871,15580462,Corby,607,Spain,Male,40,1,112544.45,1,1,1,19842.22,0\r\n9872,15736371,Kennedy,633,France,Female,34,3,123034.43,2,1,1,38315.04,0\r\n9873,15648032,Young,588,Spain,Male,37,2,0,2,0,1,187816.59,0\r\n9874,15610454,Poole,724,Germany,Female,33,9,119278.44,1,1,1,197148.24,0\r\n9875,15671358,Fletcher,720,France,Male,44,4,0,2,1,0,163471.01,0\r\n9876,15747130,Tsao,521,France,Male,39,7,0,2,0,1,653.58,0\r\n9877,15578374,Gilroy,620,Spain,Male,36,7,169312.72,1,1,0,45414.09,0\r\n9878,15572182,Onwuamaeze,505,Germany,Female,33,3,106506.77,3,1,0,45445.78,1\r\n9879,15770041,Manna,728,Spain,Female,43,8,128412.61,1,0,1,139024.31,0\r\n9880,15669414,Pisano,486,Germany,Male,62,9,118356.89,2,1,0,168034.83,1\r\n9881,15777054,Thorpe,584,Germany,Male,42,3,137479.13,1,1,0,25669.1,0\r\n9882,15621021,Dwyer,687,Spain,Female,40,1,0,2,1,0,8207.36,0\r\n9883,15785490,Okeke,771,France,Male,50,3,105229.72,1,1,1,16281.68,1\r\n9884,15577695,Zito,678,France,Male,41,2,148088.11,1,1,0,14083.12,0\r\n9885,15686974,Sergeyeva,751,France,Female,48,4,0,1,0,1,30165.06,1\r\n9886,15574584,Fang,670,France,Male,33,8,126679.69,1,1,1,39451.09,0\r\n9887,15719541,Flannagan,675,Spain,Male,31,2,90826.27,2,1,0,60270.87,0\r\n9888,15646310,Mao,684,Spain,Male,24,8,143582.89,1,1,1,22527.27,0\r\n9889,15697606,Sturdee,637,France,Female,21,10,125712.2,1,0,0,175072.47,0\r\n9890,15711489,Azikiwe,760,Spain,Female,32,2,0,1,1,1,114565.35,0\r\n9891,15670427,Chidi,662,Spain,Male,37,4,155187.3,1,1,0,48930.8,0\r\n9892,15731755,Hull,680,France,Male,49,10,0,2,1,0,187008.45,0\r\n9893,15796370,Shah,604,Spain,Male,40,5,155455.43,1,0,1,113581.85,0\r\n9894,15598331,Morgan,764,France,Female,40,9,100480.53,1,1,0,124095.69,0\r\n9895,15704795,Vagin,521,France,Female,77,6,0,2,1,1,49054.1,0\r\n9896,15796764,Bruno,684,Germany,Female,56,3,127585.98,3,1,1,80593.49,1\r\n9897,15589420,Osinachi,795,France,Female,40,2,101891.1,1,1,1,183044.86,0\r\n9898,15810563,Ho,678,Spain,Female,61,8,0,2,1,1,159938.82,0\r\n9899,15746569,Tsui,589,France,Male,38,4,0,1,1,0,95483.48,1\r\n9900,15811594,Gordon,660,Spain,Female,28,3,128929.88,1,1,1,198069.71,0\r\n9901,15645896,Duncan,646,Germany,Male,39,6,121681.91,2,0,1,61793.47,0\r\n9902,15802909,Hu,706,Germany,Female,56,3,139603.22,1,1,1,86383.61,0\r\n9903,15797665,Docherty,730,France,Female,27,7,0,2,1,0,144099.48,0\r\n9904,15778959,Brookes,606,France,Female,36,10,0,2,0,1,155641.46,0\r\n9905,15722532,Angelo,690,Spain,Female,36,10,91760.11,1,1,1,135784.94,0\r\n9906,15784124,Emenike,645,Germany,Male,41,2,93925.3,1,1,0,123982.14,1\r\n9907,15776518,Pugh,579,France,Female,38,4,175739.36,1,1,1,193130.55,0\r\n9908,15611247,McKenzie,481,France,Female,28,10,0,2,1,0,145215.96,0\r\n9909,15721469,Mach,492,Germany,Male,45,9,170295.04,2,0,0,164741.81,0\r\n9910,15773338,Endrizzi,739,France,Male,58,2,101579.28,1,1,1,72168.53,0\r\n9911,15784042,L?,624,France,Male,55,7,118793.6,1,1,1,95022.02,1\r\n9912,15776229,MacPherson,682,France,Male,44,3,115282.3,1,0,0,23766.4,0\r\n9913,15655903,Michael,701,Spain,Female,34,6,107980.37,1,1,1,119374.74,0\r\n9914,15590177,Chiedozie,718,France,Female,44,1,133866.22,1,0,1,139049.24,0\r\n9915,15568876,Hughes,496,France,Female,34,1,102723.35,2,1,0,180844.81,0\r\n9916,15813140,Taylor,543,Spain,Male,41,5,0,2,0,1,143980.29,0\r\n9917,15770516,Evdokimov,616,Spain,Female,44,7,193213.02,2,1,1,137392.77,0\r\n9918,15755731,Davis,635,Germany,Male,53,8,117005.55,1,0,1,123646.57,1\r\n9919,15574480,Ubanwa,652,Spain,Male,31,1,132862.59,1,0,0,158054.49,0\r\n9920,15798084,Murray,688,France,Male,26,0,0,2,1,0,105784.85,0\r\n9921,15673020,Smith,678,France,Female,49,3,204510.94,1,0,1,738.88,1\r\n9922,15643575,Evseev,757,Germany,Male,36,1,65349.71,1,0,0,64539.64,0\r\n9923,15596811,Mitchell,667,France,Male,36,8,139753.35,1,1,0,79871.16,0\r\n9924,15786789,Ni,725,France,Female,29,6,0,2,1,1,190776.83,0\r\n9925,15578865,Palerma,632,Germany,Female,50,5,107959.39,1,1,1,6985.34,1\r\n9926,15605672,Yuan,694,France,Female,38,5,195926.39,1,1,1,85522.84,0\r\n9927,15603674,Knight,803,France,Male,36,1,0,2,1,1,149370.93,0\r\n9928,15759915,Rapuokwu,814,France,Female,31,6,87772.52,1,1,0,188516.45,0\r\n9929,15686219,Wan,611,France,Male,38,4,71018.6,2,1,0,2444.29,0\r\n9930,15696388,Artamonova,755,Germany,Male,38,4,111096.91,1,1,1,19762.88,0\r\n9931,15713604,Rossi,425,Germany,Male,40,9,166776.6,2,0,1,172646.88,0\r\n9932,15647800,Greco,850,France,Female,34,6,101266.51,1,1,0,33501.98,0\r\n9933,15813451,Fleetwood-Smith,677,Spain,Male,18,8,134796.87,2,1,1,114858.9,0\r\n9934,15765375,Butusov,797,France,Female,46,8,0,1,0,0,162668.33,0\r\n9935,15774586,West,692,Germany,Female,43,10,118588.83,1,1,1,161241.65,1\r\n9936,15603454,Sanders,735,Germany,Male,28,5,160454.15,2,0,1,114957.22,0\r\n9937,15653037,Parks,609,France,Male,77,1,0,1,0,1,18708.76,0\r\n9938,15782475,Edith,700,France,Female,42,8,0,2,1,1,105305.72,0\r\n9939,15593496,Korovin,526,Spain,Female,36,5,91132.18,1,0,0,58111.71,0\r\n9940,15808971,Lajoie,693,Spain,Female,57,9,0,2,1,1,135502.77,0\r\n9941,15791972,Bergamaschi,748,France,Female,20,7,0,2,0,0,10792.42,0\r\n9942,15676869,T'ien,657,Spain,Male,36,8,0,2,0,1,123866.43,0\r\n9943,15683007,Torode,739,Germany,Female,25,5,113113.12,1,1,0,129181.27,0\r\n9944,15659495,Fu,784,Spain,Male,23,2,0,1,1,1,6847.73,0\r\n9945,15703923,Cameron,744,Germany,Male,41,7,190409.34,2,1,1,138361.48,0\r\n9946,15674000,Cattaneo,645,France,Male,44,10,0,2,0,1,166707.22,0\r\n9947,15618171,James,669,France,Female,33,9,0,2,0,1,107221.03,0\r\n9948,15732202,Abramovich,615,France,Male,34,1,83503.11,2,1,1,73124.53,1\r\n9949,15735078,Onwughara,724,Germany,Female,53,1,139687.66,2,1,1,12913.92,0\r\n9950,15798615,Wan,850,France,Female,47,9,137301.87,1,1,0,44351.77,0\r\n9951,15638494,Salinas,625,Germany,Female,39,10,129845.26,1,1,1,96444.88,0\r\n9952,15763874,Ho,635,Spain,Male,46,8,0,2,1,1,60739.16,0\r\n9953,15696355,Cleveland,724,Germany,Male,37,6,125489.4,1,1,0,118570.53,0\r\n9954,15655952,Burke,550,France,Male,47,2,0,2,1,1,97057.28,0\r\n9955,15739850,Trentino,645,France,Male,45,6,155417.61,1,0,1,3449.22,0\r\n9956,15611338,Kashiwagi,714,Spain,Male,29,4,0,2,1,1,37605.9,0\r\n9957,15707861,Nucci,520,France,Female,46,10,85216.61,1,1,0,117369.52,1\r\n9958,15672237,Oluchi,633,France,Male,25,1,0,1,1,0,100598.98,0\r\n9959,15657771,Ts'ui,537,France,Male,37,6,0,1,1,1,17802.42,0\r\n9960,15677783,Graham,764,Spain,Male,38,4,113607.47,1,1,0,91094.46,0\r\n9961,15681026,Lucciano,795,Germany,Female,33,9,104552.72,1,1,1,120853.83,1\r\n9962,15566543,Aldridge,573,Spain,Male,44,9,0,2,1,0,107124.17,0\r\n9963,15594612,Flynn,702,Spain,Male,44,9,0,1,0,0,59207.41,1\r\n9964,15814664,Scott,740,Germany,Male,33,2,126524.11,1,1,0,136869.31,0\r\n9965,15642785,Douglas,479,France,Male,34,5,117593.48,2,0,0,113308.29,0\r\n9966,15690164,Shao,627,Germany,Female,33,4,83199.05,1,0,0,159334.93,0\r\n9967,15590213,Ch'en,479,Spain,Male,35,4,125920.98,1,1,1,20393.44,0\r\n9968,15603794,Pugliesi,623,France,Male,48,5,118469.38,1,1,1,158590.25,0\r\n9969,15733491,McGregor,512,Germany,Female,40,8,153537.57,2,0,0,23101.13,0\r\n9970,15806360,Hou,609,France,Male,41,6,0,1,0,1,112585.19,0\r\n9971,15587133,Thompson,518,France,Male,42,7,151027.05,2,1,0,119377.36,0\r\n9972,15721377,Chou,833,France,Female,34,3,144751.81,1,0,0,166472.81,0\r\n9973,15747927,Ch'in,758,France,Male,26,4,155739.76,1,1,0,171552.02,0\r\n9974,15806455,Miller,611,France,Male,27,7,0,2,1,1,157474.1,0\r\n9975,15695474,Barker,583,France,Male,33,7,122531.86,1,1,0,13549.24,0\r\n9976,15666295,Smith,610,Germany,Male,50,1,113957.01,2,1,0,196526.55,1\r\n9977,15656062,Azikiwe,637,France,Female,33,7,103377.81,1,1,0,84419.78,0\r\n9978,15579969,Mancini,683,France,Female,32,9,0,2,1,1,24991.92,0\r\n9979,15703563,P'eng,774,France,Male,40,9,93017.47,2,1,0,191608.97,0\r\n9980,15692664,Diribe,677,France,Female,58,1,90022.85,1,0,1,2988.28,0\r\n9981,15719276,T'ao,741,Spain,Male,35,6,74371.49,1,0,0,99595.67,0\r\n9982,15672754,Burbidge,498,Germany,Male,42,3,152039.7,1,1,1,53445.17,1\r\n9983,15768163,Griffin,655,Germany,Female,46,7,137145.12,1,1,0,115146.4,1\r\n9984,15656710,Cocci,613,France,Male,40,4,0,1,0,0,151325.24,0\r\n9985,15696175,Echezonachukwu,602,Germany,Male,35,7,90602.42,2,1,1,51695.41,0\r\n9986,15586914,Nepean,659,France,Male,36,6,123841.49,2,1,0,96833,0\r\n9987,15581736,Bartlett,673,Germany,Male,47,1,183579.54,2,0,1,34047.54,0\r\n9988,15588839,Mancini,606,Spain,Male,30,8,180307.73,2,1,1,1914.41,0\r\n9989,15589329,Pirozzi,775,France,Male,30,4,0,2,1,0,49337.84,0\r\n9990,15605622,McMillan,841,Spain,Male,28,4,0,2,1,1,179436.6,0\r\n9991,15798964,Nkemakonam,714,Germany,Male,33,3,35016.6,1,1,0,53667.08,0\r\n9992,15769959,Ajuluchukwu,597,France,Female,53,4,88381.21,1,1,0,69384.71,1\r\n9993,15657105,Chukwualuka,726,Spain,Male,36,2,0,1,1,0,195192.4,0\r\n9994,15569266,Rahman,644,France,Male,28,7,155060.41,1,1,0,29179.52,0\r\n9995,15719294,Wood,800,France,Female,29,2,0,2,0,0,167773.55,0\r\n9996,15606229,Obijiaku,771,France,Male,39,5,0,2,1,0,96270.64,0\r\n9997,15569892,Johnstone,516,France,Male,35,10,57369.61,1,1,1,101699.77,0\r\n9998,15584532,Liu,709,France,Female,36,7,0,1,0,1,42085.58,1\r\n9999,15682355,Sabbatini,772,Germany,Male,42,3,75075.31,2,1,0,92888.52,1\r\n10000,15628319,Walker,792,France,Female,28,4,130142.79,1,1,0,38190.78,0\r\n" }, { "path": "code/artificial_intelligence/src/autoenncoder/Convolutional_Autoencoder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Convolutional Autoencoder\\n\",\n \"\\n\",\n \"Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting MNIST_data/train-images-idx3-ubyte.gz\\n\",\n \"Extracting MNIST_data/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\\n\",\n \"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tensorflow.examples.tutorials.mnist import input_data\\n\",\n \"mnist = input_data.read_data_sets('MNIST_data', validation_size=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img = mnist.train.images[2]\\n\",\n \"plt.imshow(img.reshape((28, 28)), cmap='Greys_r')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Network Architecture\\n\",\n \"\\n\",\n \"The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughly 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\\n\",\n \"### What's going on with the decoder\\n\",\n \"\\n\",\n \"Okay, so the decoder has these \\\"Upsample\\\" layers that you might not have seen before. First off, we'll discuss a bit what these layers aren't. Usually, we see transposed convolution layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but in reverse. A stride in the input layer results in a larger stride in the transposed convolution layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a transposed convolution layer. The TensorFlow API provides us with an easy way to create the layers, tf.nn.conv2d_transpose.\\n\",\n \"\\n\",\n \"However, transposed convolution layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In this Distill article from Augustus Odena, et al, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with tf.image.resize_images, followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\\n\",\n \"targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\\n\",\n \"\\n\",\n \"### Encoder\\n\",\n \"conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 28x28x16\\n\",\n \"maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\\n\",\n \"# Now 14x14x16\\n\",\n \"conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 14x14x8\\n\",\n \"maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\\n\",\n \"# Now 7x7x8\\n\",\n \"conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 7x7x8\\n\",\n \"encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\\n\",\n \"# Now 4x4x8\\n\",\n \"\\n\",\n \"### Decoder\\n\",\n \"upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\\n\",\n \"# Now 7x7x8\\n\",\n \"conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 7x7x8\\n\",\n \"upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\\n\",\n \"# Now 14x14x8\\n\",\n \"conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 14x14x8\\n\",\n \"upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\\n\",\n \"# Now 28x28x8\\n\",\n \"conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 28x28x16\\n\",\n \"\\n\",\n \"logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\\n\",\n \"#Now 28x28x1\\n\",\n \"\\n\",\n \"decoded = tf.nn.sigmoid(logits, name='decoded')\\n\",\n \"\\n\",\n \"loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\\n\",\n \"cost = tf.reduce_mean(loss)\\n\",\n \"opt = tf.train.AdamOptimizer(0.001).minimize(cost)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Training\\n\",\n \"\\n\",\n \"As before, here we'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sess = tf.Session()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 1/10... Training loss: 0.6958\\n\",\n \"Epoch: 1/10... Training loss: 0.6927\\n\",\n \"Epoch: 1/10... Training loss: 0.6906\\n\",\n \"Epoch: 1/10... Training loss: 0.6887\\n\",\n \"Epoch: 1/10... Training loss: 0.6866\\n\",\n \"Epoch: 1/10... Training loss: 0.6843\\n\",\n \"Epoch: 1/10... Training loss: 0.6815\\n\",\n \"Epoch: 1/10... Training loss: 0.6783\\n\",\n \"Epoch: 1/10... Training loss: 0.6746\\n\",\n \"Epoch: 1/10... Training loss: 0.6704\\n\",\n \"Epoch: 1/10... Training loss: 0.6648\\n\",\n \"Epoch: 1/10... Training loss: 0.6583\\n\",\n \"Epoch: 1/10... Training loss: 0.6502\\n\",\n \"Epoch: 1/10... Training loss: 0.6415\\n\",\n \"Epoch: 1/10... Training loss: 0.6306\\n\",\n \"Epoch: 1/10... Training loss: 0.6185\\n\",\n \"Epoch: 1/10... Training loss: 0.6058\\n\",\n \"Epoch: 1/10... Training loss: 0.5886\\n\",\n \"Epoch: 1/10... Training loss: 0.5724\\n\",\n \"Epoch: 1/10... Training loss: 0.5518\\n\",\n \"Epoch: 1/10... Training loss: 0.5340\\n\",\n \"Epoch: 1/10... Training loss: 0.5209\\n\",\n \"Epoch: 1/10... Training loss: 0.5177\\n\",\n \"Epoch: 1/10... Training loss: 0.5178\\n\",\n \"Epoch: 1/10... Training loss: 0.5266\\n\",\n \"Epoch: 1/10... Training loss: 0.5192\\n\",\n \"Epoch: 1/10... Training loss: 0.5066\\n\",\n \"Epoch: 1/10... Training loss: 0.4974\\n\",\n \"Epoch: 1/10... Training loss: 0.4755\\n\",\n \"Epoch: 1/10... Training loss: 0.4751\\n\",\n \"Epoch: 1/10... Training loss: 0.4623\\n\",\n \"Epoch: 1/10... Training loss: 0.4664\\n\",\n \"Epoch: 1/10... Training loss: 0.4572\\n\",\n \"Epoch: 1/10... Training loss: 0.4466\\n\",\n \"Epoch: 1/10... Training loss: 0.4386\\n\",\n \"Epoch: 1/10... Training loss: 0.4247\\n\",\n \"Epoch: 1/10... Training loss: 0.4170\\n\",\n \"Epoch: 1/10... Training loss: 0.4090\\n\",\n \"Epoch: 1/10... Training loss: 0.4094\\n\",\n \"Epoch: 1/10... Training loss: 0.3926\\n\",\n \"Epoch: 1/10... Training loss: 0.3806\\n\",\n \"Epoch: 1/10... Training loss: 0.3751\\n\",\n \"Epoch: 1/10... Training loss: 0.3671\\n\",\n \"Epoch: 1/10... Training loss: 0.3538\\n\",\n \"Epoch: 1/10... Training loss: 0.3384\\n\",\n \"Epoch: 1/10... Training loss: 0.3260\\n\",\n \"Epoch: 1/10... Training loss: 0.3248\\n\",\n \"Epoch: 1/10... Training loss: 0.3203\\n\",\n \"Epoch: 1/10... Training loss: 0.2970\\n\",\n \"Epoch: 1/10... Training loss: 0.2970\\n\",\n \"Epoch: 1/10... Training loss: 0.2825\\n\",\n \"Epoch: 1/10... Training loss: 0.2879\\n\",\n \"Epoch: 1/10... Training loss: 0.2816\\n\",\n \"Epoch: 1/10... Training loss: 0.2782\\n\",\n \"Epoch: 1/10... Training loss: 0.2647\\n\",\n \"Epoch: 1/10... Training loss: 0.2645\\n\",\n \"Epoch: 1/10... Training loss: 0.2757\\n\",\n \"Epoch: 1/10... Training loss: 0.2669\\n\",\n \"Epoch: 1/10... Training loss: 0.2694\\n\",\n \"Epoch: 1/10... Training loss: 0.2603\\n\",\n \"Epoch: 1/10... Training loss: 0.2508\\n\",\n \"Epoch: 1/10... Training loss: 0.2518\\n\",\n \"Epoch: 1/10... Training loss: 0.2506\\n\",\n \"Epoch: 1/10... Training loss: 0.2467\\n\",\n \"Epoch: 1/10... Training loss: 0.2557\\n\",\n \"Epoch: 1/10... Training loss: 0.2380\\n\",\n \"Epoch: 1/10... Training loss: 0.2595\\n\",\n \"Epoch: 1/10... Training loss: 0.2409\\n\",\n \"Epoch: 1/10... Training loss: 0.2446\\n\",\n \"Epoch: 1/10... Training loss: 0.2429\\n\",\n \"Epoch: 1/10... Training loss: 0.2432\\n\",\n \"Epoch: 1/10... Training loss: 0.2437\\n\",\n \"Epoch: 1/10... Training loss: 0.2369\\n\",\n \"Epoch: 1/10... Training loss: 0.2352\\n\",\n \"Epoch: 1/10... Training loss: 0.2302\\n\",\n \"Epoch: 1/10... 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Training loss: 0.1051\\n\",\n \"Epoch: 10/10... Training loss: 0.1090\\n\",\n \"Epoch: 10/10... Training loss: 0.1046\\n\",\n \"Epoch: 10/10... Training loss: 0.1066\\n\",\n \"Epoch: 10/10... Training loss: 0.1100\\n\",\n \"Epoch: 10/10... Training loss: 0.1089\\n\",\n \"Epoch: 10/10... Training loss: 0.1059\\n\",\n \"Epoch: 10/10... Training loss: 0.1075\\n\",\n \"Epoch: 10/10... Training loss: 0.1090\\n\",\n \"Epoch: 10/10... Training loss: 0.1055\\n\",\n \"Epoch: 10/10... Training loss: 0.1062\\n\",\n \"Epoch: 10/10... Training loss: 0.1087\\n\",\n \"Epoch: 10/10... Training loss: 0.1037\\n\",\n \"Epoch: 10/10... Training loss: 0.1064\\n\",\n \"Epoch: 10/10... Training loss: 0.1024\\n\",\n \"Epoch: 10/10... Training loss: 0.1042\\n\",\n \"Epoch: 10/10... Training loss: 0.1033\\n\",\n \"Epoch: 10/10... Training loss: 0.1091\\n\",\n \"Epoch: 10/10... Training loss: 0.1079\\n\",\n \"Epoch: 10/10... Training loss: 0.1109\\n\",\n \"Epoch: 10/10... Training loss: 0.1072\\n\",\n \"Epoch: 10/10... Training loss: 0.1046\\n\",\n \"Epoch: 10/10... Training loss: 0.1054\\n\",\n \"Epoch: 10/10... Training loss: 0.1085\\n\",\n \"Epoch: 10/10... Training loss: 0.1004\\n\",\n \"Epoch: 10/10... Training loss: 0.1041\\n\",\n \"Epoch: 10/10... Training loss: 0.1049\\n\",\n \"Epoch: 10/10... Training loss: 0.1069\\n\",\n \"Epoch: 10/10... Training loss: 0.1076\\n\",\n \"Epoch: 10/10... Training loss: 0.1102\\n\",\n \"Epoch: 10/10... Training loss: 0.1028\\n\",\n \"Epoch: 10/10... Training loss: 0.1099\\n\",\n \"Epoch: 10/10... Training loss: 0.1047\\n\",\n \"Epoch: 10/10... Training loss: 0.1082\\n\",\n \"Epoch: 10/10... Training loss: 0.1108\\n\",\n \"Epoch: 10/10... Training loss: 0.1066\\n\",\n \"Epoch: 10/10... Training loss: 0.0998\\n\",\n \"Epoch: 10/10... Training loss: 0.1064\\n\",\n \"Epoch: 10/10... Training loss: 0.1070\\n\",\n \"Epoch: 10/10... Training loss: 0.1066\\n\",\n \"Epoch: 10/10... Training loss: 0.1102\\n\",\n \"Epoch: 10/10... Training loss: 0.1065\\n\",\n \"Epoch: 10/10... Training loss: 0.1081\\n\",\n \"Epoch: 10/10... Training loss: 0.1082\\n\",\n \"Epoch: 10/10... Training loss: 0.1037\\n\"\n ]\n }\n ],\n \"source\": [\n \"epochs = 10\\n\",\n \"batch_size = 200\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"for e in range(epochs):\\n\",\n \" for ii in range(mnist.train.num_examples//batch_size):\\n\",\n \" batch = mnist.train.next_batch(batch_size)\\n\",\n \" imgs = batch[0].reshape((-1, 28, 28, 1))\\n\",\n \" batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\\n\",\n \" targets_: imgs})\\n\",\n \"\\n\",\n \" print(\\\"Epoch: {}/{}...\\\".format(e+1, epochs),\\n\",\n \" \\\"Training loss: {:.4f}\\\".format(batch_cost))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\\n\",\n \"in_imgs = mnist.test.images[:10]\\n\",\n \"reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\\n\",\n \"\\n\",\n \"for images, row in zip([in_imgs, reconstructed], axes):\\n\",\n \" for img, ax in zip(images, row):\\n\",\n \" ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"\\n\",\n \"fig.tight_layout(pad=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sess.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Denoising\\n\",\n \"\\n\",\n \"As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\\n\",\n \"\\n\",\n \"![title](images/denoising.png)\\n\",\n \"\\n\",\n \"### Denoising autoencoder\\n\",\n \"\\n\",\n \"Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\\n\",\n \"targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\\n\",\n \"\\n\",\n \"### Encoder\\n\",\n \"conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 28x28x32\\n\",\n \"maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\\n\",\n \"# Now 14x14x32\\n\",\n \"conv2 = tf.layers.conv2d(maxpool1, 32, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 14x14x32\\n\",\n \"maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\\n\",\n \"# Now 7x7x32\\n\",\n \"conv3 = tf.layers.conv2d(maxpool2, 16, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 7x7x16\\n\",\n \"encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\\n\",\n \"# Now 4x4x16\\n\",\n \"\\n\",\n \"### Decoder\\n\",\n \"upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\\n\",\n \"# Now 7x7x16\\n\",\n \"conv4 = tf.layers.conv2d(upsample1, 16, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 7x7x16\\n\",\n \"upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\\n\",\n \"# Now 14x14x16\\n\",\n \"conv5 = tf.layers.conv2d(upsample2, 32, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 14x14x32\\n\",\n \"upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\\n\",\n \"# Now 28x28x32\\n\",\n \"conv6 = tf.layers.conv2d(upsample3, 32, (3,3), padding='same', activation=tf.nn.relu)\\n\",\n \"# Now 28x28x32\\n\",\n \"\\n\",\n \"logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\\n\",\n \"#Now 28x28x1\\n\",\n \"\\n\",\n \"decoded = tf.nn.sigmoid(logits, name='decoded')\\n\",\n \"\\n\",\n \"loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\\n\",\n \"cost = tf.reduce_mean(loss)\\n\",\n \"opt = tf.train.AdamOptimizer(0.001).minimize(cost)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sess = tf.Session()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 1/30... Training loss: 0.6962\\n\",\n \"Epoch: 1/30... Training loss: 0.6861\\n\",\n \"Epoch: 1/30... Training loss: 0.6770\\n\",\n \"Epoch: 1/30... Training loss: 0.6639\\n\",\n \"Epoch: 1/30... Training loss: 0.6433\\n\",\n \"Epoch: 1/30... Training loss: 0.6151\\n\",\n \"Epoch: 1/30... Training loss: 0.5791\\n\",\n \"Epoch: 1/30... Training loss: 0.5330\\n\",\n \"Epoch: 1/30... Training loss: 0.4927\\n\",\n \"Epoch: 1/30... Training loss: 0.4781\\n\",\n \"Epoch: 1/30... Training loss: 0.5185\\n\",\n \"Epoch: 1/30... Training loss: 0.5113\\n\",\n \"Epoch: 1/30... Training loss: 0.4830\\n\",\n \"Epoch: 1/30... Training loss: 0.4511\\n\",\n \"Epoch: 1/30... Training loss: 0.4470\\n\",\n \"Epoch: 1/30... Training loss: 0.4327\\n\",\n \"Epoch: 1/30... Training loss: 0.4338\\n\",\n \"Epoch: 1/30... Training loss: 0.4267\\n\",\n \"Epoch: 1/30... Training loss: 0.4097\\n\",\n \"Epoch: 1/30... Training loss: 0.3936\\n\",\n \"Epoch: 1/30... Training loss: 0.3836\\n\",\n \"Epoch: 1/30... Training loss: 0.3734\\n\",\n \"Epoch: 1/30... Training loss: 0.3646\\n\",\n \"Epoch: 1/30... Training loss: 0.3488\\n\",\n \"Epoch: 1/30... Training loss: 0.3342\\n\",\n \"Epoch: 1/30... Training loss: 0.3267\\n\",\n \"Epoch: 1/30... Training loss: 0.3209\\n\",\n \"Epoch: 1/30... Training loss: 0.3075\\n\",\n \"Epoch: 1/30... Training loss: 0.3050\\n\",\n \"Epoch: 1/30... Training loss: 0.3056\\n\",\n \"Epoch: 1/30... Training loss: 0.2879\\n\",\n \"Epoch: 1/30... Training loss: 0.2832\\n\",\n \"Epoch: 1/30... Training loss: 0.2847\\n\",\n \"Epoch: 1/30... Training loss: 0.2790\\n\",\n \"Epoch: 1/30... Training loss: 0.2830\\n\",\n \"Epoch: 1/30... Training loss: 0.2707\\n\",\n \"Epoch: 1/30... Training loss: 0.2719\\n\",\n \"Epoch: 1/30... Training loss: 0.2749\\n\",\n \"Epoch: 1/30... Training loss: 0.2647\\n\",\n \"Epoch: 1/30... Training loss: 0.2753\\n\",\n \"Epoch: 1/30... Training loss: 0.2726\\n\",\n \"Epoch: 1/30... Training loss: 0.2689\\n\",\n \"Epoch: 1/30... 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Training loss: 0.1041\\n\",\n \"Epoch: 30/30... Training loss: 0.1022\\n\",\n \"Epoch: 30/30... Training loss: 0.1037\\n\",\n \"Epoch: 30/30... Training loss: 0.1024\\n\",\n \"Epoch: 30/30... Training loss: 0.1051\\n\",\n \"Epoch: 30/30... Training loss: 0.1038\\n\",\n \"Epoch: 30/30... Training loss: 0.1026\\n\",\n \"Epoch: 30/30... Training loss: 0.1046\\n\",\n \"Epoch: 30/30... Training loss: 0.0962\\n\",\n \"Epoch: 30/30... Training loss: 0.1020\\n\",\n \"Epoch: 30/30... Training loss: 0.1026\\n\",\n \"Epoch: 30/30... Training loss: 0.1036\\n\",\n \"Epoch: 30/30... Training loss: 0.1016\\n\",\n \"Epoch: 30/30... Training loss: 0.1015\\n\",\n \"Epoch: 30/30... Training loss: 0.1031\\n\",\n \"Epoch: 30/30... Training loss: 0.1024\\n\",\n \"Epoch: 30/30... Training loss: 0.1016\\n\",\n \"Epoch: 30/30... Training loss: 0.1068\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 30/30... Training loss: 0.1036\\n\",\n \"Epoch: 30/30... Training loss: 0.1047\\n\",\n \"Epoch: 30/30... Training loss: 0.1067\\n\",\n \"Epoch: 30/30... Training loss: 0.1031\\n\",\n \"Epoch: 30/30... Training loss: 0.1026\\n\",\n \"Epoch: 30/30... Training loss: 0.1041\\n\",\n \"Epoch: 30/30... Training loss: 0.1029\\n\",\n \"Epoch: 30/30... Training loss: 0.1027\\n\",\n \"Epoch: 30/30... Training loss: 0.1011\\n\",\n \"Epoch: 30/30... Training loss: 0.1045\\n\",\n \"Epoch: 30/30... Training loss: 0.1049\\n\",\n \"Epoch: 30/30... Training loss: 0.1019\\n\",\n \"Epoch: 30/30... Training loss: 0.1031\\n\",\n \"Epoch: 30/30... Training loss: 0.0987\\n\",\n \"Epoch: 30/30... Training loss: 0.1002\\n\",\n \"Epoch: 30/30... Training loss: 0.1027\\n\",\n \"Epoch: 30/30... Training loss: 0.1029\\n\",\n \"Epoch: 30/30... Training loss: 0.0986\\n\",\n \"Epoch: 30/30... Training loss: 0.1053\\n\",\n \"Epoch: 30/30... Training loss: 0.1011\\n\",\n \"Epoch: 30/30... Training loss: 0.1023\\n\",\n \"Epoch: 30/30... Training loss: 0.1037\\n\",\n \"Epoch: 30/30... Training loss: 0.1035\\n\",\n \"Epoch: 30/30... Training loss: 0.1025\\n\",\n \"Epoch: 30/30... Training loss: 0.1014\\n\",\n \"Epoch: 30/30... Training loss: 0.1019\\n\",\n \"Epoch: 30/30... Training loss: 0.1014\\n\",\n \"Epoch: 30/30... Training loss: 0.1018\\n\",\n \"Epoch: 30/30... Training loss: 0.1040\\n\",\n \"Epoch: 30/30... Training loss: 0.1037\\n\",\n \"Epoch: 30/30... Training loss: 0.1047\\n\",\n \"Epoch: 30/30... Training loss: 0.1028\\n\",\n \"Epoch: 30/30... Training loss: 0.1052\\n\",\n \"Epoch: 30/30... Training loss: 0.1064\\n\",\n \"Epoch: 30/30... Training loss: 0.1045\\n\",\n \"Epoch: 30/30... Training loss: 0.1066\\n\",\n \"Epoch: 30/30... Training loss: 0.1043\\n\",\n \"Epoch: 30/30... Training loss: 0.1031\\n\",\n \"Epoch: 30/30... Training loss: 0.1023\\n\",\n \"Epoch: 30/30... Training loss: 0.0974\\n\",\n \"Epoch: 30/30... Training loss: 0.1029\\n\",\n \"Epoch: 30/30... Training loss: 0.1013\\n\",\n \"Epoch: 30/30... Training loss: 0.1019\\n\",\n \"Epoch: 30/30... Training loss: 0.1073\\n\",\n \"Epoch: 30/30... Training loss: 0.1033\\n\",\n \"Epoch: 30/30... Training loss: 0.1028\\n\",\n \"Epoch: 30/30... Training loss: 0.1031\\n\",\n \"Epoch: 30/30... Training loss: 0.1047\\n\",\n \"Epoch: 30/30... Training loss: 0.1029\\n\",\n \"Epoch: 30/30... Training loss: 0.1018\\n\",\n \"Epoch: 30/30... Training loss: 0.1015\\n\",\n \"Epoch: 30/30... Training loss: 0.0975\\n\",\n \"Epoch: 30/30... Training loss: 0.1003\\n\",\n \"Epoch: 30/30... Training loss: 0.1028\\n\",\n \"Epoch: 30/30... Training loss: 0.1023\\n\",\n \"Epoch: 30/30... Training loss: 0.1040\\n\",\n \"Epoch: 30/30... Training loss: 0.1047\\n\",\n \"Epoch: 30/30... Training loss: 0.1015\\n\",\n \"Epoch: 30/30... Training loss: 0.1044\\n\",\n \"Epoch: 30/30... Training loss: 0.1012\\n\",\n \"Epoch: 30/30... Training loss: 0.1007\\n\",\n \"Epoch: 30/30... Training loss: 0.1010\\n\",\n \"Epoch: 30/30... Training loss: 0.1051\\n\",\n \"Epoch: 30/30... Training loss: 0.1050\\n\",\n \"Epoch: 30/30... Training loss: 0.1027\\n\"\n ]\n }\n ],\n \"source\": [\n \"epochs = 30\\n\",\n \"batch_size = 200\\n\",\n \"# Set's how much noise we're adding to the MNIST images\\n\",\n \"noise_factor = 0.5\\n\",\n \"sess.run(tf.global_variables_initializer())\\n\",\n \"for e in range(epochs):\\n\",\n \" for ii in range(mnist.train.num_examples//batch_size):\\n\",\n \" batch = mnist.train.next_batch(batch_size)\\n\",\n \" # Get images from the batch\\n\",\n \" imgs = batch[0].reshape((-1, 28, 28, 1))\\n\",\n \" \\n\",\n \" # Add random noise to the input images\\n\",\n \" noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\\n\",\n \" # Clip the images to be between 0 and 1\\n\",\n \" noisy_imgs = np.clip(noisy_imgs, 0., 1.)\\n\",\n \" \\n\",\n \" # Noisy images as inputs, original images as targets\\n\",\n \" batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\\n\",\n \" targets_: imgs})\\n\",\n \"\\n\",\n \" print(\\\"Epoch: {}/{}...\\\".format(e+1, epochs),\\n\",\n \" \\\"Training loss: {:.4f}\\\".format(batch_cost))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Checking out the performance\\n\",\n \"\\n\",\n \"Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprisingly great job of removing the noise, even though it's sometimes difficult to tell what the original number is.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\\n\",\n \"in_imgs = mnist.test.images[:10]\\n\",\n \"noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\\n\",\n \"noisy_imgs = np.clip(noisy_imgs, 0., 1.)\\n\",\n \"\\n\",\n \"reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\\n\",\n \"\\n\",\n \"for images, row in zip([noisy_imgs, reconstructed], axes):\\n\",\n \" for img, ax in zip(images, row):\\n\",\n \" ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \"fig.tight_layout(pad=0.1)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/autoenncoder/autoencoder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from keras.layers import Dense, Input\\n\",\n \"from keras.models import Model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"encoding_dim = 32\\n\",\n \"\\n\",\n \"input_img = Input(shape = (784, ))\\n\",\n \"\\n\",\n \"encoded = Dense(encoding_dim, activation = 'relu')(input_img)\\n\",\n \"\\n\",\n \"decoded = Dense(784, activation = 'sigmoid')(encoded)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"autoencoder = Model(input_img, decoded)\\n\",\n \"\\n\",\n \"encoder = Model(input_img, encoded)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"encoded_input = Input(shape = (encoding_dim, ))\\n\",\n \"\\n\",\n \"decoder_layer = autoencoder.layers[-1]\\n\",\n \"\\n\",\n \"decoder = Model(encoded_input, decoder_layer(encoded_input))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"autoencoder.compile(optimizer = 'adadelta', loss = 'binary_crossentropy')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from keras.datasets import mnist\\n\",\n \"import numpy as np\\n\",\n \"(x_train, _), (x_test, _) = mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]],\\n\",\n \"\\n\",\n \" [[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]],\\n\",\n \"\\n\",\n \" [[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]],\\n\",\n \"\\n\",\n \" ...,\\n\",\n \"\\n\",\n \" [[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]],\\n\",\n \"\\n\",\n \" [[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]],\\n\",\n \"\\n\",\n \" [[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ...,\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x_train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = x_train.astype('float32')/255.\\n\",\n \"x_test = x_test.astype('float32')/255.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = x_train.reshape(len(x_train), np.prod(x_train.shape[1:]))\\n\",\n \"x_test = x_test.reshape(len(x_test), np.prod(x_test.shape[1:]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(60000, 784)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x_train.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10000, 784)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x_test.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 60000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/50\\n\",\n \"60000/60000 [==============================] - 16s 266us/step - loss: 0.3626 - val_loss: 0.2716\\n\",\n \"Epoch 2/50\\n\",\n \"60000/60000 [==============================] - 15s 258us/step - loss: 0.2646 - val_loss: 0.2540\\n\",\n \"Epoch 3/50\\n\",\n \"60000/60000 [==============================] - 16s 259us/step - loss: 0.2440 - val_loss: 0.2317\\n\",\n \"Epoch 4/50\\n\",\n \"60000/60000 [==============================] - 17s 283us/step - loss: 0.2235 - val_loss: 0.2131\\n\",\n \"Epoch 5/50\\n\",\n \"60000/60000 [==============================] - 15s 253us/step - loss: 0.2074 - val_loss: 0.1994\\n\",\n \"Epoch 6/50\\n\",\n \"60000/60000 [==============================] - 16s 263us/step - loss: 0.1960 - val_loss: 0.1897\\n\",\n \"Epoch 7/50\\n\",\n \"60000/60000 [==============================] - 16s 266us/step - loss: 0.1873 - val_loss: 0.1819\\n\",\n \"Epoch 8/50\\n\",\n \"60000/60000 [==============================] - 16s 259us/step - loss: 0.1801 - val_loss: 0.1753\\n\",\n \"Epoch 9/50\\n\",\n \"60000/60000 [==============================] - 15s 257us/step - loss: 0.1740 - val_loss: 0.1696\\n\",\n \"Epoch 10/50\\n\",\n \"60000/60000 [==============================] - 16s 260us/step - loss: 0.1686 - val_loss: 0.1647\\n\",\n \"Epoch 11/50\\n\",\n \"60000/60000 [==============================] - 15s 251us/step - loss: 0.1638 - val_loss: 0.1599\\n\",\n \"Epoch 12/50\\n\",\n \"60000/60000 [==============================] - 15s 251us/step - loss: 0.1594 - val_loss: 0.1559\\n\",\n \"Epoch 13/50\\n\",\n \"60000/60000 [==============================] - 15s 253us/step - loss: 0.1554 - val_loss: 0.1519\\n\",\n \"Epoch 14/50\\n\",\n \"60000/60000 [==============================] - 15s 249us/step - loss: 0.1518 - val_loss: 0.1484\\n\",\n \"Epoch 15/50\\n\",\n \"60000/60000 [==============================] - 16s 274us/step - loss: 0.1484 - val_loss: 0.1453\\n\",\n \"Epoch 16/50\\n\",\n \"60000/60000 [==============================] - 17s 282us/step - loss: 0.1454 - val_loss: 0.1423\\n\",\n \"Epoch 17/50\\n\",\n \"60000/60000 [==============================] - 16s 264us/step - loss: 0.1426 - val_loss: 0.1397\\n\",\n \"Epoch 18/50\\n\",\n \"60000/60000 [==============================] - 16s 275us/step - loss: 0.1400 - val_loss: 0.1372\\n\",\n \"Epoch 19/50\\n\",\n \"60000/60000 [==============================] - 16s 263us/step - loss: 0.1376 - val_loss: 0.1349\\n\",\n \"Epoch 20/50\\n\",\n \"60000/60000 [==============================] - 17s 279us/step - loss: 0.1352 - val_loss: 0.1325\\n\",\n \"Epoch 21/50\\n\",\n \"60000/60000 [==============================] - 16s 270us/step - loss: 0.1330 - val_loss: 0.1304\\n\",\n \"Epoch 22/50\\n\",\n \"60000/60000 [==============================] - 16s 260us/step - loss: 0.1309 - val_loss: 0.1283\\n\",\n \"Epoch 23/50\\n\",\n \"60000/60000 [==============================] - 16s 273us/step - loss: 0.1289 - val_loss: 0.1263\\n\",\n \"Epoch 24/50\\n\",\n \"60000/60000 [==============================] - 17s 278us/step - loss: 0.1269 - val_loss: 0.1244\\n\",\n \"Epoch 25/50\\n\",\n \"60000/60000 [==============================] - 15s 247us/step - loss: 0.1250 - val_loss: 0.1226\\n\",\n \"Epoch 26/50\\n\",\n \"60000/60000 [==============================] - 16s 265us/step - loss: 0.1232 - val_loss: 0.1208\\n\",\n \"Epoch 27/50\\n\",\n \"60000/60000 [==============================] - 18s 297us/step - loss: 0.1215 - val_loss: 0.1192\\n\",\n \"Epoch 28/50\\n\",\n \"60000/60000 [==============================] - 16s 263us/step - loss: 0.1199 - val_loss: 0.1176\\n\",\n \"Epoch 29/50\\n\",\n \"60000/60000 [==============================] - 15s 250us/step - loss: 0.1184 - val_loss: 0.1161\\n\",\n \"Epoch 30/50\\n\",\n \"60000/60000 [==============================] - 17s 281us/step - loss: 0.1170 - val_loss: 0.1148\\n\",\n \"Epoch 31/50\\n\",\n \"60000/60000 [==============================] - 17s 276us/step - loss: 0.1157 - val_loss: 0.1134\\n\",\n \"Epoch 32/50\\n\",\n \"60000/60000 [==============================] - 18s 301us/step - loss: 0.1144 - val_loss: 0.1122\\n\",\n \"Epoch 33/50\\n\",\n \"60000/60000 [==============================] - 15s 248us/step - loss: 0.1132 - val_loss: 0.1111\\n\",\n \"Epoch 34/50\\n\",\n \"60000/60000 [==============================] - 16s 261us/step - loss: 0.1121 - val_loss: 0.1100\\n\",\n \"Epoch 35/50\\n\",\n \"60000/60000 [==============================] - 15s 249us/step - loss: 0.1111 - val_loss: 0.1090\\n\",\n \"Epoch 36/50\\n\",\n \"60000/60000 [==============================] - 15s 251us/step - loss: 0.1102 - val_loss: 0.1081\\n\",\n \"Epoch 37/50\\n\",\n \"60000/60000 [==============================] - 16s 272us/step - loss: 0.1093 - val_loss: 0.1073\\n\",\n \"Epoch 38/50\\n\",\n \"60000/60000 [==============================] - 17s 290us/step - loss: 0.1085 - val_loss: 0.1065\\n\",\n \"Epoch 39/50\\n\",\n \"60000/60000 [==============================] - 17s 287us/step - loss: 0.1077 - val_loss: 0.1057\\n\",\n \"Epoch 40/50\\n\",\n \"60000/60000 [==============================] - 16s 258us/step - loss: 0.1070 - val_loss: 0.1050\\n\",\n \"Epoch 41/50\\n\",\n \"60000/60000 [==============================] - 15s 245us/step - loss: 0.1063 - val_loss: 0.1043\\n\",\n \"Epoch 42/50\\n\",\n \"60000/60000 [==============================] - 15s 247us/step - loss: 0.1057 - val_loss: 0.1038\\n\",\n \"Epoch 43/50\\n\",\n \"60000/60000 [==============================] - 15s 256us/step - loss: 0.1051 - val_loss: 0.1032\\n\",\n \"Epoch 44/50\\n\",\n \"60000/60000 [==============================] - 15s 256us/step - loss: 0.1045 - val_loss: 0.1026\\n\",\n \"Epoch 45/50\\n\",\n \"60000/60000 [==============================] - 16s 274us/step - loss: 0.1040 - val_loss: 0.1021\\n\",\n \"Epoch 46/50\\n\",\n \"60000/60000 [==============================] - 17s 280us/step - loss: 0.1035 - val_loss: 0.1017\\n\",\n \"Epoch 47/50\\n\",\n \"60000/60000 [==============================] - 17s 281us/step - loss: 0.1031 - val_loss: 0.1012\\n\",\n \"Epoch 48/50\\n\",\n \"60000/60000 [==============================] - 15s 256us/step - loss: 0.1026 - val_loss: 0.1008\\n\",\n \"Epoch 49/50\\n\",\n \"60000/60000 [==============================] - 15s 250us/step - loss: 0.1023 - val_loss: 0.1005\\n\",\n \"Epoch 50/50\\n\",\n \"60000/60000 [==============================] - 15s 250us/step - loss: 0.1019 - val_loss: 0.1001\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"autoencoder.fit(x_train, x_train,\\n\",\n \" epochs = 50,\\n\",\n \" batch_size = 256,\\n\",\n \" shuffle = True,\\n\",\n \" validation_data = (x_test, x_test))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"encoded_imgs = encoder.predict(x_test)\\n\",\n \"decoded_imgs = decoder.predict(encoded_imgs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"n = 10\\n\",\n \"plt.figure(figsize = (20, 4))\\n\",\n \"for i in range(n):\\n\",\n \" ax = plt.subplot(2, n, i+1)\\n\",\n \" plt.imshow(x_test[i].reshape(28, 28))\\n\",\n \" plt.gray()\\n\",\n \" \\n\",\n \" ax = plt.subplot(2, n, i+1 + n)\\n\",\n \" plt.imshow(decoded_imgs[i].reshape(28, 28))\\n\",\n \" plt.gray()\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/bayesian_belief_network/Bayesian-Belief-Network.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Bayesian Belief Network is created using pgmpy and some simple queries are done on the network.\\n\",\n \"This network is an Alarm Byaesian Beleief Network which is mentioned in Bayesian Artificial Intelligence - Section 2.5.1 (https://bayesian-intelligence.com/publications/bai/book/BAI_Chapter2.pdf)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Importing Library\\n\",\n \"from pgmpy.models import BayesianModel\\n\",\n \"from pgmpy.inference import VariableElimination\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Defining network structure\\n\",\n \"\\n\",\n \"alarm_model = BayesianModel([('Burglary', 'Alarm'), \\n\",\n \" ('Earthquake', 'Alarm'),\\n\",\n \" ('Alarm', 'JohnCalls'),\\n\",\n \" ('Alarm', 'MaryCalls')])\\n\",\n \"\\n\",\n \"#Defining the parameters using CPT\\n\",\n \"from pgmpy.factors.discrete import TabularCPD\\n\",\n \"\\n\",\n \"cpd_burglary = TabularCPD(variable='Burglary', variable_card=2,\\n\",\n \" values=[[.999], [0.001]])\\n\",\n \"cpd_earthquake = TabularCPD(variable='Earthquake', variable_card=2,\\n\",\n \" values=[[0.998], [0.002]])\\n\",\n \"cpd_alarm = TabularCPD(variable='Alarm', variable_card=2,\\n\",\n \" values=[[0.999, 0.71, 0.06, 0.05],\\n\",\n \" [0.001, 0.29, 0.94, 0.95]],\\n\",\n \" evidence=['Burglary', 'Earthquake'],\\n\",\n \" evidence_card=[2, 2])\\n\",\n \"cpd_johncalls = TabularCPD(variable='JohnCalls', variable_card=2,\\n\",\n \" values=[[0.95, 0.1], [0.05, 0.9]],\\n\",\n \" evidence=['Alarm'], evidence_card=[2])\\n\",\n \"cpd_marycalls = TabularCPD(variable='MaryCalls', variable_card=2,\\n\",\n \" values=[[0.1, 0.7], [0.9, 0.3]],\\n\",\n \" evidence=['Alarm'], evidence_card=[2])\\n\",\n \"\\n\",\n \"# Associating the parameters with the model structure\\n\",\n \"alarm_model.add_cpds(cpd_burglary, cpd_earthquake, cpd_alarm, cpd_johncalls, cpd_marycalls)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Checking if the cpds are valid for the model\\n\",\n \"alarm_model.check_model()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"NodeView(('Burglary', 'Alarm', 'Earthquake', 'JohnCalls', 'MaryCalls'))\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Viewing nodes of the model\\n\",\n \"alarm_model.nodes()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OutEdgeView([('Burglary', 'Alarm'), ('Alarm', 'JohnCalls'), ('Alarm', 'MaryCalls'), ('Earthquake', 'Alarm')])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Viewing edges of the model\\n\",\n \"alarm_model.edges()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(Burglary _|_ Earthquake)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Checking independcies of a node\\n\",\n \"alarm_model.local_independencies('Burglary')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(Burglary _|_ Earthquake)\\n\",\n \"(Burglary _|_ MaryCalls, JohnCalls | Alarm)\\n\",\n \"(Burglary _|_ JohnCalls | MaryCalls, Alarm)\\n\",\n \"(Burglary _|_ MaryCalls, JohnCalls | Alarm, Earthquake)\\n\",\n \"(Burglary _|_ MaryCalls | JohnCalls, Alarm)\\n\",\n \"(Burglary _|_ JohnCalls | MaryCalls, Alarm, Earthquake)\\n\",\n \"(Burglary _|_ MaryCalls | Alarm, JohnCalls, Earthquake)\\n\",\n \"(Earthquake _|_ Burglary)\\n\",\n \"(Earthquake _|_ MaryCalls, JohnCalls | Alarm)\\n\",\n \"(Earthquake _|_ MaryCalls, JohnCalls | Burglary, Alarm)\\n\",\n \"(Earthquake _|_ JohnCalls | MaryCalls, Alarm)\\n\",\n \"(Earthquake _|_ MaryCalls | JohnCalls, Alarm)\\n\",\n \"(Earthquake _|_ JohnCalls | Burglary, MaryCalls, Alarm)\\n\",\n \"(Earthquake _|_ MaryCalls | Burglary, JohnCalls, Alarm)\\n\",\n \"(JohnCalls _|_ Burglary, MaryCalls, Earthquake | Alarm)\\n\",\n \"(JohnCalls _|_ MaryCalls, Earthquake | Burglary, Alarm)\\n\",\n \"(JohnCalls _|_ Burglary, Earthquake | MaryCalls, Alarm)\\n\",\n \"(JohnCalls _|_ Burglary, MaryCalls | Alarm, Earthquake)\\n\",\n \"(JohnCalls _|_ Earthquake | Burglary, MaryCalls, Alarm)\\n\",\n \"(JohnCalls _|_ MaryCalls | Burglary, Alarm, Earthquake)\\n\",\n \"(JohnCalls _|_ Burglary | MaryCalls, Alarm, Earthquake)\\n\",\n \"(MaryCalls _|_ Burglary, JohnCalls, Earthquake | Alarm)\\n\",\n \"(MaryCalls _|_ JohnCalls, Earthquake | Burglary, Alarm)\\n\",\n \"(MaryCalls _|_ Burglary, JohnCalls | Alarm, Earthquake)\\n\",\n \"(MaryCalls _|_ Burglary, Earthquake | JohnCalls, Alarm)\\n\",\n \"(MaryCalls _|_ JohnCalls | Burglary, Alarm, Earthquake)\\n\",\n \"(MaryCalls _|_ Earthquake | Burglary, JohnCalls, Alarm)\\n\",\n \"(MaryCalls _|_ Burglary | Alarm, JohnCalls, Earthquake)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Listing all Independencies\\n\",\n \"alarm_model.get_independencies()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "code/artificial_intelligence/src/bernoulli_naive_bayes/README.md", "content": "Bernoulli Naive Bayes is a probabilistic classification algorithm and is one of the three variants of Naive Bayes. Bernoulli Naive bayes classifier uses bernoulli distribution for the classification. \n\nThe article in the OpenGenus discusses about Bernoulli Naive Bayes in Detail. \nIt covers:\n1. Brief introduction about Naive Byaes\n2. Bernoulli Naive Bayes and Beroulli distribution\n3. Algorithm and steps on how to solve it manually\n4. Detailed python code for Bernoulli Naive Bayes\n\nLearn more about it at: https://iq.opengenus.org/bernoulli-naive-bayes/\n\n\n\n" }, { "path": "code/artificial_intelligence/src/bernoulli_naive_bayes/bernoulli.py", "content": "mport numpy as np\nfrom sklearn.naive_bayes import BernoulliNB \nfrom sklearn.metrics import accuracy_score\nimport matplotlib.pyplot as plt\n\n#creating dataset with with 0 and 1\n\nX = np.random.randint(2, size=(500,10))\nY = np.random.randint(2, size=(500, 1))\n\nX_test = X[:50, :10]\ny_test = Y[:50, :1]\n\nclf = BernoulliNB()\nmodel = clf.fit(X, Y)\n\ny_pred =clf.predict(X_test)\nacc_score = accuracy_score(y_test, y_pred)\nprint(acc_score)\n\nplt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\nl = plt.axis()\nplt.scatter(Xtest[:, 0], Xtest[:, 1], c=Ytest, s=20, cmap='RdBu', alpha=0.1)\nplt.axis(l);\n" }, { "path": "code/artificial_intelligence/src/chatbot/Chatbot.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"sQ5qZbOambNh\"\n },\n \"source\": [\n \"# **Importing Libraries**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"3mjisqmlmRk8\",\n \"outputId\": \"c28ec844-ece8-4961-f054-cb4fb4e7d157\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[nltk_data] Downloading package punkt to /root/nltk_data...\\n\",\n \"[nltk_data] Package punkt is already up-to-date!\\n\"\n ]\n }\n ],\n \"source\": [\n \"import nltk #for tokenization, stemming and vectorization of words\\n\",\n \"nltk.download('punkt') #for the first time\\n\",\n \"from nltk.stem.porter import PorterStemmer # for stemming\\n\",\n \"import json #for reading and manipulating training data\\n\",\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"import torch.nn as nn\\n\",\n \"from torch.utils.data import Dataset, DataLoader\\n\",\n \"import random #for random results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VpZlpCcqsMjM\"\n },\n \"source\": [\n \"# **Creating Training Data**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"id\": \"ItxwbvTymmVg\"\n },\n \"outputs\": [],\n \"source\": [\n \"def tokenize(sentence):\\n\",\n \" return nltk.word_tokenize(sentence)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"id\": \"dSkLLZbJmomO\"\n },\n \"outputs\": [],\n \"source\": [\n \"stemmer = PorterStemmer()\\n\",\n \"def stem(word):\\n\",\n \" return stemmer.stem(word.lower())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"id\": \"31ShRSAZmyKP\"\n },\n \"outputs\": [],\n \"source\": [\n \"def bag_of_words(tokenized_sentence, all_words):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Argument:\\n\",\n \" sentence : [\\\"hello\\\", \\\"how\\\", \\\"are\\\", \\\"you\\\"]\\n\",\n \" all_words : [\\\"hi\\\", \\\"hello\\\", \\\"I\\\", \\\"you\\\", \\\"thank\\\", \\\"cool\\\"]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" bag = [0, 1, 0, 1, 0, 0] \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" tokenized_sentence= [stem(w) for w in tokenized_sentence]\\n\",\n \"\\n\",\n \" bag = np.zeros(len(all_words), dtype = np.float32) \\n\",\n \"\\n\",\n \" for idx, w in enumerate(all_words):\\n\",\n \" if w in tokenized_sentence:\\n\",\n \" bag[idx] = 1.0\\n\",\n \"\\n\",\n \" return bag\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"74a_bZ1sqmLG\",\n \"outputId\": \"2a70a34f-14ca-4b2d-c6eb-7718a954eab5\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Words: [\\\"'s\\\", 'a', 'accept', 'anyon', 'are', 'bye', 'can', 'card', 'cash', 'credit', 'day', 'deliveri', 'do', 'doe', 'funni', 'get', 'good', 'goodby', 'have', 'hello', 'help', 'hey', 'hi', 'how', 'i', 'is', 'item', 'joke', 'kind', 'know', 'later', 'long', 'lot', 'mastercard', 'me', 'my', 'of', 'onli', 'pay', 'paypal', 'see', 'sell', 'ship', 'someth', 'take', 'tell', 'thank', 'that', 'there', 'what', 'when', 'which', 'with', 'you']\\n\",\n \"Tags: ['delivery', 'funny', 'goodbye', 'greeting', 'items', 'payments', 'thanks']\\n\",\n \"Xtrain: [[0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 1.]\\n\",\n \" ...\\n\",\n \" [0. 1. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 1. 0. ... 0. 0. 1.]]\\n\",\n \"YTrain: [3 3 3 3 3 3 2 2 2 6 6 6 6 4 4 4 5 5 5 5 0 0 0 1 1 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('intents.json', \\\"r\\\") as file:\\n\",\n \" intents = json.load(file)\\n\",\n \"\\n\",\n \"all_words = []\\n\",\n \"tags = []\\n\",\n \"data = []\\n\",\n \"\\n\",\n \"for intent in intents['intents']:\\n\",\n \" tag = intent['tag']\\n\",\n \" tags.append(tag)\\n\",\n \" for pattern in intent['patterns']:\\n\",\n \" w = tokenize(pattern)\\n\",\n \" all_words.extend(w)\\n\",\n \" data.append((w, tag))\\n\",\n \"\\n\",\n \"ignore_words = ['?', \\\"!\\\", \\\".\\\", \\\",\\\"] #remove punctionations\\n\",\n \"\\n\",\n \"all_words = [stem(word) for word in all_words if word not in ignore_words]\\n\",\n \"all_words = sorted(set(all_words))\\n\",\n \"tags = sorted(set(tags))\\n\",\n \"print(\\\"Words: \\\", all_words)\\n\",\n \"print(\\\"Tags: \\\", tags)\\n\",\n \"\\n\",\n \"x_train = [] #bag of words\\n\",\n \"y_train = [] #tag numbers\\n\",\n \"\\n\",\n \"for pattern_sentence, tag in data:\\n\",\n \" bag = bag_of_words(pattern_sentence, all_words)\\n\",\n \" x_train.append(bag)\\n\",\n \"\\n\",\n \" class_label = tags.index(tag) \\n\",\n \" y_train.append(class_label) \\n\",\n \"\\n\",\n \"x_train = np.array(x_train)\\n\",\n \"y_train = np.array(y_train)\\n\",\n \"\\n\",\n \"print(\\\"Xtrain: \\\", x_train)\\n\",\n \"print(\\\"YTrain: \\\", y_train) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1L3JKdE09J50\"\n },\n \"source\": [\n \"# **Dataset class**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"id\": \"1ecCjrFHry_r\"\n },\n \"outputs\": [],\n \"source\": [\n \"class ChatDataset(Dataset):\\n\",\n \" def __init__(self):\\n\",\n \" self.n_samples = len(x_train)\\n\",\n \" self.x_data = x_train\\n\",\n \" self.y_data = y_train\\n\",\n \" \\n\",\n \" def __getitem__(self, index):\\n\",\n \" return (self.x_data[index], self.y_data[index])\\n\",\n \" \\n\",\n \" def __len__(self):\\n\",\n \" return self.n_samples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Kej75I1M9IuT\"\n },\n \"source\": [\n \"# **Hyper parameters**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"AXx2wCnU9REF\",\n \"outputId\": \"d77ae677-9f1e-414e-bbbe-17bdbad19a2b\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"cuda\\n\"\n ]\n }\n ],\n \"source\": [\n \"BATCH_SIZE = 8\\n\",\n \"INPUT_SIZE = len(all_words) #or len(x_train[0])\\n\",\n \"HIDDEN_SIZE = 8\\n\",\n \"OUTPUT_SIZE = len(tags) #no of classes\\n\",\n \"\\n\",\n \"LEARNING_RATE = 0.001\\n\",\n \"EPOCHS = 1000\\n\",\n \"\\n\",\n \"DEVICE = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n \"print(DEVICE)\\n\",\n \"\\n\",\n \"FILE = \\\"data.pth\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"id\": \"ajFcXiv_9Gcu\"\n },\n \"outputs\": [],\n \"source\": [\n \"dataset = ChatDataset()\\n\",\n \"train_loader = DataLoader(dataset = dataset, batch_size = BATCH_SIZE, shuffle=True, num_workers = 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SkZGONQp9qEs\"\n },\n \"source\": [\n \"# **Architecture and Model**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"id\": \"uAG5MdVd9gY8\"\n },\n \"outputs\": [],\n \"source\": [\n \"class NNet(nn.Module):\\n\",\n \" def __init__(self, input_size, hidden_size, num_classes):\\n\",\n \" super(NNet, self).__init__()\\n\",\n \" self.l1 = nn.Linear(input_size, hidden_size)\\n\",\n \" self.l2 = nn.Linear(hidden_size, hidden_size)\\n\",\n \" self.l3 = nn.Linear(hidden_size, num_classes)\\n\",\n \" self.relu = nn.ReLU()\\n\",\n \" \\n\",\n \" def forward(self, x):\\n\",\n \" out = self.l1(x)\\n\",\n \" out = self.relu(out)\\n\",\n \" out = self.l2(out)\\n\",\n \" out = self.relu(out) \\n\",\n \" out = self.l3(out)\\n\",\n \" #no activation and no softmax\\n\",\n \" return out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"id\": \"jpV7X0AC-1iF\"\n },\n \"outputs\": [],\n \"source\": [\n \"model = NNet(INPUT_SIZE, HIDDEN_SIZE, OUTPUT_SIZE).to(DEVICE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5f6zchUPAS8E\"\n },\n \"source\": [\n \"# **Loss function and optimization**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"id\": \"mM84VIzq_Ss1\"\n },\n \"outputs\": [],\n \"source\": [\n \"criterion = nn.CrossEntropyLoss()\\n\",\n \"optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"vdBeS6-LAtYh\"\n },\n \"source\": [\n \"# **Training**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"ZT66QhP0AoBu\",\n \"outputId\": \"d7120795-7b3e-4a7f-f6cb-80542e71bb5b\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 100/1000, loss = 1.5947\\n\",\n \"Epoch 200/1000, loss = 0.5184\\n\",\n \"Epoch 300/1000, loss = 0.0091\\n\",\n \"Epoch 400/1000, loss = 0.0103\\n\",\n \"Epoch 500/1000, loss = 0.0011\\n\",\n \"Epoch 600/1000, loss = 0.0008\\n\",\n \"Epoch 700/1000, loss = 0.0038\\n\",\n \"Epoch 800/1000, loss = 0.0007\\n\",\n \"Epoch 900/1000, loss = 0.0012\\n\",\n \"Epoch 1000/1000, loss = 0.0003\\n\",\n \"Final loss = 0.0003\\n\",\n \"Training complete... file saved to data.pth\\n\"\n ]\n }\n ],\n \"source\": [\n \"for epoch in range(EPOCHS):\\n\",\n \" for words, labels in train_loader:\\n\",\n \" words = words.to(DEVICE)\\n\",\n \" labels = labels.to(DEVICE)\\n\",\n \"\\n\",\n \" #forward pass\\n\",\n \" outputs = model(words)\\n\",\n \" loss = criterion(outputs, labels)\\n\",\n \"\\n\",\n \" #backward pass\\n\",\n \" optimizer.zero_grad() #empty the gradient before calculating gradient for every epoch\\n\",\n \" loss.backward() #run back prop\\n\",\n \" optimizer.step() #update parameters\\n\",\n \"\\n\",\n \" if(epoch + 1) % 100 == 0:\\n\",\n \" print(f'Epoch {epoch+1}/{EPOCHS}, loss = {loss.item():.4f}')\\n\",\n \"\\n\",\n \"model_data = {\\n\",\n \" \\\"model_state\\\": model.state_dict(),\\n\",\n \" \\\"input_size\\\": INPUT_SIZE,\\n\",\n \" \\\"output_size\\\": OUTPUT_SIZE,\\n\",\n \" \\\"hidden_size\\\": HIDDEN_SIZE,\\n\",\n \" \\\"all_words\\\": all_words,\\n\",\n \" \\\"tags\\\": tags,\\n\",\n \"}\\n\",\n \"\\n\",\n \"print(f'Final loss = {loss.item():.4f}')\\n\",\n \"torch.save(model_data, FILE)\\n\",\n \"print(f\\\"Training complete... file saved to {FILE}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QwDlPLSsD9_s\"\n },\n \"source\": [\n \"# **ChatBOT**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"umtH77j9DW2P\",\n \"outputId\": \"2365e5cb-73aa-434f-8821-247bdb63c9af\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"NNet(\\n\",\n \" (l1): Linear(in_features=54, out_features=8, bias=True)\\n\",\n \" (l2): Linear(in_features=8, out_features=8, bias=True)\\n\",\n \" (l3): Linear(in_features=8, out_features=7, bias=True)\\n\",\n \" (relu): ReLU()\\n\",\n \")\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final_model_data = torch.load(FILE) #loading model\\n\",\n \"\\n\",\n \"model_state = final_model_data[\\\"model_state\\\"]\\n\",\n \"all_words = final_model_data[\\\"all_words\\\"]\\n\",\n \"\\n\",\n \"best_model = NNet(INPUT_SIZE, HIDDEN_SIZE, OUTPUT_SIZE).to(DEVICE)\\n\",\n \"best_model.load_state_dict(model_state)\\n\",\n \"best_model.eval() #change to evalutation stage\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"VJbnC_2BDcUX\",\n \"outputId\": \"2e180598-e141-41f9-93d8-261678d8baea\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Let's chat! type 'quit to exit\\n\",\n \"You: Hello\\n\",\n \"STARY: Hi there, how can I help?\\n\",\n \"You: WHat do you sell?\\n\",\n \"STARY: We sell coffee and tea\\n\",\n \"You: Nice. How long does shipping take?\\n\",\n \"STARY: Shipping takes 2-4 days\\n\",\n \"You: Ok. \\n\",\n \"STARY: Sorry, I do not understand...\\n\",\n \"You: thanks\\n\",\n \"STARY: Any time!\\n\",\n \"You: see you\\n\",\n \"STARY: Have a nice day\\n\",\n \"You: quit\\n\"\n ]\n }\n ],\n \"source\": [\n \"BOT_NAME = \\\"STARY\\\"\\n\",\n \"print(\\\"Let's chat! type 'quit to exit\\\")\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" sentence = input(\\\"You: \\\")\\n\",\n \" if(sentence == 'quit'):\\n\",\n \" break\\n\",\n \" \\n\",\n \" #tokenize and bag of words, same as training\\n\",\n \" sentence = tokenize(sentence)\\n\",\n \" x = bag_of_words(sentence, all_words)\\n\",\n \" x = x.reshape(1, x.shape[0])\\n\",\n \" x= torch.from_numpy(x).to(DEVICE)\\n\",\n \"\\n\",\n \" output = best_model(x)\\n\",\n \" _, predicted = torch.max(output, dim = 1)\\n\",\n \" tag = tags[predicted.item()] #get tag of the sentence spoken by user\\n\",\n \" \\n\",\n \" #to get probabilities of outputs\\n\",\n \" probs = torch.softmax(output, dim = 1)\\n\",\n \" prob = probs[0][predicted.item()]\\n\",\n \" if(prob.item() > 0.75): \\n\",\n \" #loop through all tags in intent to select a random sentence from responses of that specific tag\\n\",\n \" for intent in intents[\\\"intents\\\"]:\\n\",\n \" if tag == intent[\\\"tag\\\"]:\\n\",\n \" print(f\\\"{BOT_NAME}: {random.choice(intent['responses'])}\\\")\\n\",\n \" else:\\n\",\n \" print(f\\\"{BOT_NAME}: Sorry, I do not understand...\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"name\": \"Chatbot.ipynb\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "code/artificial_intelligence/src/chatbot/README.md", "content": "# Contexual Chatbot\nChatbots are interactive tools that are used as a computerized replica of a receptionist or guide. It is expected to give meaningful responses to questions posed to it. When this happens for a specialized domain or topic, it is known as Contextual Chatbot. \n\nHere, I have implemented a simple chatbot, named Stary for a Coffee retail store.\n\n# Packages\nThe following packages are used :\n1. pytorch\n2. nltk\n3. numpy\n4. random\n5. json\n\n# Steps of Implementation\nBackground : An intents file consists of tags, patterns and expected responses for a posed question/remark.\n1. Using NLTK, on has to tokenize, stem and vectorize words using bag of words. This forms the X (inputs) of the training dataset.\n2. The tags relating to each specific input (X) forms the Y(output). Thus, bag of words(X) and labels/tags(Y) form the labeled training set.\n3. We use a simple feedforward network of 3 linear layers with Relu as the non-linearity. \n4. Model is trained based on hyperparameters and saved a file.\n5. This file is loaded to perform testing.\n6. To get probability of each tag from the derived predictions, we use a Softmax classifier and choose appropriate response.\n\n# Steps to replicate results\n1. Downloading this jupyter notebook on Google Colab. Alternatively, they can also load the dataset to their own computers instead of using Google Drive or Kaggle. You have to change the filepath while downloading intents file and model saved file accordingly.\n2. Running all the cells **sequentially** in the order as in the notebook.\n3. It's done! You can go ahead and try the same algorithm on different datasets." }, { "path": "code/artificial_intelligence/src/chatbot/intents.json", "content": "{\n \"intents\": [\n {\n \"tag\": \"greeting\",\n \"patterns\": [\n \"Hi\",\n \"Hey\",\n \"How are you\",\n \"Is anyone there?\",\n \"Hello\",\n \"Good day\"\n ],\n \"responses\": [\n \"Hey :-)\",\n \"Hello, thanks for visiting\",\n \"Hi there, what can I do for you?\",\n \"Hi there, how can I help?\"\n ]\n },\n {\n \"tag\": \"goodbye\",\n \"patterns\": [\"Bye\", \"See you later\", \"Goodbye\"],\n \"responses\": [\n \"See you later, thanks for visiting\",\n \"Have a nice day\",\n \"Bye! Come back again soon.\"\n ]\n },\n {\n \"tag\": \"thanks\",\n \"patterns\": [\"Thanks\", \"Thank you\", \"That's helpful\", \"Thank's a lot!\"],\n \"responses\": [\"Happy to help!\", \"Any time!\", \"My pleasure\"]\n },\n {\n \"tag\": \"items\",\n \"patterns\": [\n \"Which items do you have?\",\n \"What kinds of items are there?\",\n \"What do you sell?\"\n ],\n \"responses\": [\n \"We sell coffee and tea\",\n \"We have coffee and tea\"\n ]\n },\n {\n \"tag\": \"payments\",\n \"patterns\": [\n \"Do you take credit cards?\",\n \"Do you accept Mastercard?\",\n \"Can I pay with Paypal?\",\n \"Are you cash only?\"\n ],\n \"responses\": [\n \"We accept VISA, Mastercard and Paypal\",\n \"We accept most major credit cards, and Paypal\"\n ]\n },\n {\n \"tag\": \"delivery\",\n \"patterns\": [\n \"How long does delivery take?\",\n \"How long does shipping take?\",\n \"When do I get my delivery?\"\n ],\n \"responses\": [\n \"Delivery takes 2-4 days\",\n \"Shipping takes 2-4 days\"\n ]\n },\n {\n \"tag\": \"funny\",\n \"patterns\": [\n \"Tell me a joke!\",\n \"Tell me something funny!\",\n \"Do you know a joke?\"\n ],\n \"responses\": [\n \"Why did the hipster burn his mouth? He drank the coffee before it was cool.\",\n \"What did the buffalo say when his son left for college? Bison.\"\n ]\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/convolutional_neural_network/cnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"training_set = pd.read_csv('train.csv')\\n\",\n \"test_set = pd.read_csv('test.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\\\\n\",\n \"0 0 0 0 0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 0 0 0 0 \\n\",\n \"2 0 0 0 0 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" pixel9 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\\\\n\",\n \"0 0 ... 0 0 0 0 0 \\n\",\n \"1 0 ... 0 0 0 0 0 \\n\",\n \"2 0 ... 0 0 0 0 0 \\n\",\n \"3 0 ... 0 0 0 0 0 \\n\",\n \"4 0 ... 0 0 0 0 0 \\n\",\n \"\\n\",\n \" pixel779 pixel780 pixel781 pixel782 pixel783 \\n\",\n \"0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 \\n\",\n \"2 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 784 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_set.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = training_set.iloc[:, 1:].values\\n\",\n \"y_train = training_set.iloc[:, 0:1].values\\n\",\n \"x_test = test_set.iloc[:, :].values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = x_train.reshape(-1, 28, 28, 1)\\n\",\n \"x_test = x_test.reshape(-1, 28, 28, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = x_train/255.\\n\",\n \"x_test = x_test/255.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 1., 0., ..., 0., 0., 0.],\\n\",\n \" [ 1., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 1., 0., ..., 0., 0., 0.],\\n\",\n \" ..., \\n\",\n \" [ 0., 0., 0., ..., 1., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 1.]], dtype=float32)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from keras.utils import np_utils\\n\",\n \"y_train = np_utils.to_categorical(y_train, num_classes = 10)\\n\",\n \"y_train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from keras.models import Sequential\\n\",\n \"from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"classifier = Sequential()\\n\",\n \"\\n\",\n \"classifier.add(Conv2D(32, (5, 5), padding = 'Same', input_shape = (28, 28, 1), activation = 'relu'))\\n\",\n \"#classifier.add(MaxPool2D(pool_size = (2, 2)))\\n\",\n \"\\n\",\n \"#classifier.add(Conv2D(32, (5, 5), padding = 'Same', activation = 'relu'))\\n\",\n \"classifier.add(MaxPool2D(pool_size = (2, 2)))\\n\",\n \"classifier.add(Dropout(0.25))\\n\",\n \"\\n\",\n \"classifier.add(Conv2D(64, (3, 3),padding = 'Same', activation = 'relu'))\\n\",\n \"classifier.add(Conv2D(64, (3, 3),padding = 'Same', activation = 'relu'))\\n\",\n \"classifier.add(MaxPool2D(pool_size = (2, 2), strides = (2, 2)))\\n\",\n \"classifier.add(Dropout(0.25))\\n\",\n \"\\n\",\n \"#classifier.add(Conv2D(64, (3, 3), activation = 'relu'))\\n\",\n \"#classifier.add(MaxPooling2D(pool_size = (2, 2)))\\n\",\n \"\\n\",\n \"classifier.add(Flatten())\\n\",\n \"\\n\",\n \"classifier.add(Dense(units = 256, activation = 'relu'))\\n\",\n \"classifier.add(Dropout(0.5))\\n\",\n \"\\n\",\n \"classifier.add(Dense(units = 10, activation = 'sigmoid'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"classifier.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from keras.preprocessing.image import ImageDataGenerator\\n\",\n \"datagen = ImageDataGenerator(\\n\",\n \" featurewise_center=False,\\n\",\n \" samplewise_center=False,\\n\",\n \" featurewise_std_normalization=False,\\n\",\n \" samplewise_std_normalization=False,\\n\",\n \" zca_whitening=False,\\n\",\n \" rotation_range=10,\\n\",\n \" zoom_range = 0.1,\\n\",\n \" width_shift_range=0.1,\\n\",\n \" height_shift_range=0.1,\\n\",\n \" horizontal_flip=False,\\n\",\n \" vertical_flip=False)\\n\",\n \"datagen.fit(x_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/30\\n\",\n \"328/328 [==============================] - 119s 362ms/step - loss: 0.5486 - acc: 0.8281\\n\",\n \"Epoch 2/30\\n\",\n \"328/328 [==============================] - 117s 356ms/step - loss: 0.1652 - acc: 0.9500\\n\",\n \"Epoch 3/30\\n\",\n \"328/328 [==============================] - 114s 349ms/step - loss: 0.1128 - acc: 0.9665\\n\",\n \"Epoch 4/30\\n\",\n \"328/328 [==============================] - 120s 365ms/step - loss: 0.0923 - acc: 0.9723\\n\",\n \"Epoch 5/30\\n\",\n \"328/328 [==============================] - 114s 348ms/step - loss: 0.0846 - acc: 0.9755\\n\",\n \"Epoch 6/30\\n\",\n \"328/328 [==============================] - 117s 358ms/step - loss: 0.0752 - acc: 0.9778\\n\",\n \"Epoch 7/30\\n\",\n \"328/328 [==============================] - 117s 358ms/step - loss: 0.0720 - acc: 0.9788\\n\",\n \"Epoch 8/30\\n\",\n \"328/328 [==============================] - 116s 355ms/step - loss: 0.0674 - acc: 0.9812\\n\",\n \"Epoch 9/30\\n\",\n \"328/328 [==============================] - 115s 351ms/step - loss: 0.0663 - acc: 0.9807\\n\",\n \"Epoch 10/30\\n\",\n \"328/328 [==============================] - 110s 335ms/step - loss: 0.0598 - acc: 0.9823\\n\",\n \"Epoch 11/30\\n\",\n \"328/328 [==============================] - 110s 336ms/step - loss: 0.0618 - acc: 0.9830\\n\",\n \"Epoch 12/30\\n\",\n \"328/328 [==============================] - 111s 337ms/step - loss: 0.0616 - acc: 0.9826\\n\",\n \"Epoch 13/30\\n\",\n \"328/328 [==============================] - 110s 336ms/step - loss: 0.0606 - acc: 0.9834\\n\",\n \"Epoch 14/30\\n\",\n \"328/328 [==============================] - 109s 331ms/step - loss: 0.0603 - acc: 0.9832\\n\",\n \"Epoch 15/30\\n\",\n \"328/328 [==============================] - 112s 340ms/step - loss: 0.0614 - acc: 0.9832\\n\",\n \"Epoch 16/30\\n\",\n \"328/328 [==============================] - 111s 337ms/step - loss: 0.0587 - acc: 0.9831\\n\",\n \"Epoch 17/30\\n\",\n \"328/328 [==============================] - 111s 338ms/step - loss: 0.0616 - acc: 0.9836\\n\",\n \"Epoch 18/30\\n\",\n \"328/328 [==============================] - 783s 2s/step - loss: 0.0573 - acc: 0.9842\\n\",\n \"Epoch 19/30\\n\",\n \"328/328 [==============================] - 118s 359ms/step - loss: 0.0596 - acc: 0.9838\\n\",\n \"Epoch 20/30\\n\",\n \"328/328 [==============================] - 115s 350ms/step - loss: 0.0614 - acc: 0.9837\\n\",\n \"Epoch 21/30\\n\",\n \"328/328 [==============================] - 121s 368ms/step - loss: 0.0655 - acc: 0.9830\\n\",\n \"Epoch 22/30\\n\",\n \"328/328 [==============================] - 125s 381ms/step - loss: 0.0611 - acc: 0.9842\\n\",\n \"Epoch 23/30\\n\",\n \"328/328 [==============================] - 119s 362ms/step - loss: 0.0600 - acc: 0.9838\\n\",\n \"Epoch 24/30\\n\",\n \"328/328 [==============================] - 117s 355ms/step - loss: 0.0608 - acc: 0.9846\\n\",\n \"Epoch 25/30\\n\",\n \"328/328 [==============================] - 116s 355ms/step - loss: 0.0664 - acc: 0.9835\\n\",\n \"Epoch 26/30\\n\",\n \"328/328 [==============================] - 118s 360ms/step - loss: 0.0667 - acc: 0.9829\\n\",\n \"Epoch 27/30\\n\",\n \"328/328 [==============================] - 117s 355ms/step - loss: 0.0635 - acc: 0.9831\\n\",\n \"Epoch 28/30\\n\",\n \"328/328 [==============================] - 114s 346ms/step - loss: 0.0608 - acc: 0.9843\\n\",\n \"Epoch 29/30\\n\",\n \"328/328 [==============================] - 118s 359ms/step - loss: 0.0650 - acc: 0.9835\\n\",\n \"Epoch 30/30\\n\",\n \"328/328 [==============================] - 115s 350ms/step - loss: 0.0631 - acc: 0.9833\\n\"\n ]\n }\n ],\n \"source\": [\n \"track = classifier.fit_generator(datagen.flow(x_train,y_train, batch_size= 128),\\n\",\n \" epochs = 30, steps_per_epoch=x_train.shape[0]//128)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"dict_keys(['loss', 'acc'])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"track.history.keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5,1,'Model Accuracy')\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n = 9\\n\",\n \"plt.figure(figsize = (20, 8))\\n\",\n \"for i in range(n):\\n\",\n \" ax = plt.subplot(1, n, i+1)\\n\",\n \" plt.imshow(x_test[i].reshape(28, 28))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/convolutional_neural_network/cnn_adversarial_examples.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"c70dacc2f9be641aeb6ba357c7a7dbb2d93306a4\"\n },\n \"source\": [\n \"# Content\\n\",\n \"\\n\",\n \"1. Introduction\\n\",\n \"2. Image loading\\n\",\n \"3. ImageNet classes\\n\",\n \"4. Gradient ascent\\n\",\n \"5. Test some models\\n\",\n \" 1. VGG\\n\",\n \" 2. ResNet\\n\",\n \" 3. Inception v3\\n\",\n \"6. Conclusion\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"052c1c3f60fa447a77cd4f35a869ed46c0332c89\"\n },\n \"source\": [\n \"# 1. Introduction\\n\",\n \"\\n\",\n \"In recent years, deep learning has led to significant progress in AI. Particularly, in the field of computer vision, we have seen improvements in image classification, object detection, image segmentation, etc, which may suggest that neural networks \\\"*see*\\\" and \\\"*understand*\\\" images the way humans do. In fact, the input-output mapping that deep neural networks learn is profoundly different from human perception. In this notebook, we will present an experience that supports this point.\\n\",\n \"\\n\",\n \"We will use the PyTorch library and some well known ImageNet classification models to show that even a powerful neural network can easily be trapped. We will build an image that appears to us to be definitely of some class, but that the model wrongly classifies as belonging to a class very far from the actual one.\\n\",\n \"\\n\",\n \"\\n\",\n \"*Original image :* https://commons.wikimedia.org/wiki/File:Plume_the_Cat.JPG\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"b1076dfc-b9ad-4769-8c92-a6c4dae69d19\",\n \"_kg_hide-input\": true,\n \"_kg_hide-output\": false,\n \"_uuid\": \"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import torch\\n\",\n \"from torch import nn\\n\",\n \"from torch import optim\\n\",\n \"from torchvision import transforms, models\\n\",\n \"import torch.nn.functional as F\\n\",\n \"\\n\",\n \"device = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"8ba650a8fb1a90c33318f8220e43e1cdec6e29bc\"\n },\n \"source\": [\n \"# 2. Image loading\\n\",\n \"\\n\",\n \"
\\n\",\n \"First, let's define a function to download an image from the Internet and convert it to a PyTorch tensor that can be fed to our models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_uuid\": \"1389b48639d9199275904fbc39fde9255fc23ebb\"\n },\n \"outputs\": [],\n \"source\": [\n \"from PIL import Image\\n\",\n \"from io import BytesIO\\n\",\n \"import requests\\n\",\n \"\\n\",\n \"\\n\",\n \"def load_image(img_path, max_size=224):\\n\",\n \" # Download the image\\n\",\n \" response = requests.get(img_path)\\n\",\n \" image = Image.open(BytesIO(response.content)).convert(\\n\",\n \" \\\"RGB\\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Resize and normalize the image\\n\",\n \" size = min(max_size, max(image.size))\\n\",\n \" img_transforms = transforms.Compose(\\n\",\n \" [\\n\",\n \" transforms.Resize(size),\\n\",\n \" transforms.ToTensor(),\\n\",\n \" transforms.Normalize(\\n\",\n \" (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)\\n\",\n \" ),\\n\",\n \" ]\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Don't forget to add the batch dimension\\n\",\n \" return img_transforms(image).unsqueeze(0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"bcc9dfd11fae98c1518acc058fb00999a0800af1\"\n },\n \"source\": [\n \"Let's also define a function to plot tensor images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_uuid\": \"d98d7825c0dd19951b13dbd92c773cce169783c3\"\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_image(image, title=\\\"\\\"):\\n\",\n \" image = image.to(\\\"cpu\\\").detach().numpy()\\n\",\n \" image = image.squeeze().transpose(1, 2, 0)\\n\",\n \"\\n\",\n \" # Denormalize the image\\n\",\n \" image = image * np.array(\\n\",\n \" (0.229, 0.224, 0.225)\\n\",\n \" ) + np.array((0.485, 0.456, 0.406))\\n\",\n \" \\n\",\n \" image = image.clip(0, 1)\\n\",\n \"\\n\",\n \" plt.imshow(image)\\n\",\n \" plt.title(title)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_uuid\": \"70cff6bd1433ed4b90d4d44e183d44b72f6bd0bf\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"torch.Size([1, 3, 224, 336])\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img_path = \\\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/Plume_the_Cat.JPG/800px-Plume_the_Cat.JPG\\\"\\n\",\n \"image = load_image(img_path)\\n\",\n \"\\n\",\n \"print(image.shape)\\n\",\n \"plot_image(image, \\\"A beautiful cat\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"79c7e3d0-c299-4dcb-8224-4455121ee9b0\",\n \"_uuid\": \"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a\",\n \"collapsed\": true\n },\n \"source\": [\n \"# 3. ImageNet classes\\n\",\n \"\\n\",\n \"
\\n\",\n \"For us to understand the predictions of the models, we will need the mapping between class IDs and class names of the ImageNet dataset. The method used to get the mapping can be found here : [https://discuss.pytorch.org/t/imagenet-classes/4923/3](https://discuss.pytorch.org/t/imagenet-classes/4923/3).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_uuid\": \"db260d4e6a0c4822f156ec0f40c0517a098919ba\"\n },\n \"outputs\": [],\n \"source\": [\n \"from urllib.request import urlretrieve\\n\",\n \"import json\\n\",\n \"\\n\",\n \"classes_url = \\\"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\\\"\\n\",\n \"classes_path = \\\"imagenet_class_index.json\\\"\\n\",\n \"urlretrieve(classes_url, classes_path)\\n\",\n \"\\n\",\n \"class_idx = json.load(open(classes_path))\\n\",\n \"idx2label = [\\n\",\n \" class_idx[str(k)][1] for k in range(len(class_idx))\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_uuid\": \"833807413d5cbae568deb16f968962ed6e1b2134\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['tench', 'goldfish', 'great_white_shark', 'tiger_shark', 'hammerhead']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(idx2label[:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"8ba62828df575ef5ea52711d9c85a3666ce844d0\"\n },\n \"source\": [\n \"# 4. Gradient ascent\\n\",\n \"\\n\",\n \"
\\n\",\n \"Gradient ascent is the same as gradient descent, but instead of minimizing a function, we maximize it. Recall that we want to build an image that a given model will totally fail to classify. One method to do that is to take an initial image that the model classifies well, freeze the weights of the model and maximize the output score of some class. In other words, we do classical gradient descent optimization with some differences :\\n\",\n \"\\n\",\n \"1. The weights of the model are freezed, so the model doesn't get updated.\\n\",\n \"2. The parameters to optimize are the pixels of the initial image.\\n\",\n \"3. The loss function is the score of some given class.\\n\",\n \"4. We do maximization instead of minimization.\\n\",\n \"\\n\",\n \"For example, if we take the image of a cat and we want to make the model predict that it's a dog, we do gradient ascient to maximize the score of the class *dog* given the initial image of the cat.\\n\",\n \"\\n\",\n \"The following function is an implementation of this procedure. It takes as input a model, an initial image, the class to maximize, a configured optimizer and the number of iterations. At each iteration, it executes a forward pass through the network, and updates the initial image according to the calculated loss. The loss here is negative the score of the given class, in order to the maximization instead of minimization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_uuid\": \"76a1193a7b46e810d04c870293f59a473e1f9048\"\n },\n \"outputs\": [],\n \"source\": [\n \"def gradient_ascent(\\n\",\n \" model, new_img, new_class, opt, n_epochs, print_every\\n\",\n \"):\\n\",\n \" for epoch in range(n_epochs):\\n\",\n \" output = F.softmax(model(new_img), dim=1)\\n\",\n \" loss = -output[0, new_class]\\n\",\n \"\\n\",\n \" opt.zero_grad()\\n\",\n \" loss.backward()\\n\",\n \" opt.step()\\n\",\n \"\\n\",\n \" if epoch % print_every == 0:\\n\",\n \" print(\\n\",\n \" \\\"Epoch :\\\", epoch + 1, \\\"Loss :\\\", -loss.item()\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"6f0b2620026cbf7a9cf8bd8ee2dee7f66f12b398\"\n },\n \"source\": [\n \"# 5. Test some models\\n\",\n \"\\n\",\n \"
\\n\",\n \"So, this is what you came here for. In this section, we will trap three well known ImageNet models : **VGG**, **ResNet** and **Inception v3**. In all cases, we will use gradient ascent to slightly modify an image to make it look (at least for the model) totally different.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_uuid\": \"65505c98891cd6faf5812ed6ed1adf1c70c16f30\"\n },\n \"outputs\": [],\n \"source\": [\n \"# A helper function to make predictions\\n\",\n \"def make_predictions(model, img, topk=5):\\n\",\n \" preds = F.softmax(model(img), dim=1)\\n\",\n \" probs, classes = preds.topk(topk, dim=1)\\n\",\n \"\\n\",\n \" probs, classes = probs.to(\\\"cpu\\\"), classes.to(\\\"cpu\\\")\\n\",\n \"\\n\",\n \" for i, c in enumerate(classes[0]):\\n\",\n \" print(\\n\",\n \" idx2label[c],\\n\",\n \" \\\"with probability\\\",\\n\",\n \" probs[0, i].item(),\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"32b70e593b8b63f73a03aeaddbfc9190aeb937f3\"\n },\n \"source\": [\n \"## A. VGG\\n\",\n \"\\n\",\n \"
\\n\",\n \"Let's download an image, load the model, freeze all its parameters and turn it to evaluation mode.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_uuid\": \"f842fa870507b92cc44f4aaaae7a4ba98eba8f57\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img_path = \\\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/Plume_the_Cat.JPG/800px-Plume_the_Cat.JPG\\\"\\n\",\n \"cat_img = load_image(img_path).to(device)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(cat_img, \\\"The original image\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"bc761e3f11123cc3e0d33589ba3a3dbbfacc46c8\"\n },\n \"source\": [\n \"*Image source :* [https://commons.wikimedia.org/wiki/File:Plume_the_Cat.JPG](https://commons.wikimedia.org/wiki/File:Plume_the_Cat.JPG)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_kg_hide-output\": true,\n \"_uuid\": \"49f3b498cdfd91d791baeb9304e2da9eddf7fe6f\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading: \\\"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\\\" to /tmp/.torch/models/vgg19-dcbb9e9d.pth\\n\",\n \"574673361it [00:26, 22057625.17it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = models.vgg19(pretrained=True).to(device)\\n\",\n \"for param in model.parameters():\\n\",\n \" model.requires_grad = False\\n\",\n \"\\n\",\n \"model = model.eval()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_uuid\": \"cee58c40f8d1ad708dad4df5b3640ff555d13dee\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tiger_cat with probability 0.784152090549469\\n\",\n \"tabby with probability 0.161953404545784\\n\",\n \"Egyptian_cat with probability 0.04843280091881752\\n\",\n \"lynx with probability 0.0024119135923683643\\n\",\n \"ping-pong_ball with probability 0.0003912121173925698\\n\"\n ]\n }\n ],\n \"source\": [\n \"make_predictions(model, cat_img)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"4a298003ac5a59bb1f1c550b1a9ee569a79b44d1\"\n },\n \"source\": [\n \"Well, I don't know if it's actually a tiger cat, but the model correctly classified the image as being of a cat.\\n\",\n \"\\n\",\n \"Let's do some gradient ascent. We will try to modify the image to make the network predict that it's a computer keyboard. So we will get the ID associated to the class \\\"computer keyboard\\\", define an optimizer and call our gradient ascent routine.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_uuid\": \"422952042dc42bf0265019cca862690fc808d68a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch : 1 Loss : 3.112983540631831e-05\\n\",\n \"Epoch : 2 Loss : 0.009192084893584251\\n\",\n \"Epoch : 3 Loss : 0.13288259506225586\\n\",\n \"Epoch : 4 Loss : 0.5330795049667358\\n\",\n \"Epoch : 5 Loss : 0.850034236907959\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_class = idx2label.index(\\\"computer_keyboard\\\")\\n\",\n \"\\n\",\n \"new_img = cat_img.clone()\\n\",\n \"new_img.requires_grad = True\\n\",\n \"\\n\",\n \"# The optimizer parameters are the pixels of the initial image\\n\",\n \"opt = optim.Adam([new_img], lr=0.01)\\n\",\n \"n_epochs = 5\\n\",\n \"print_every = 1\\n\",\n \"\\n\",\n \"gradient_ascent(\\n\",\n \" model, new_img, new_class, opt, n_epochs, print_every\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"8ef951368a38d29cde4b924c4f2398cab62a18b6\"\n },\n \"source\": [\n \"Few iterations suffice to achieve a huge score on the new class. But how does the image look like now ?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_uuid\": \"c0fb3d94882045b1e31013311522f7f23d1b2ef8\",\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"computer_keyboard with probability 0.9477413892745972\\n\",\n \"mouse with probability 0.028052568435668945\\n\",\n \"laptop with probability 0.009765724651515484\\n\",\n \"tiger_cat with probability 0.0037263750564306974\\n\",\n \"printer with probability 0.002404575003311038\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"make_predictions(model, new_img)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(new_img, \\\"This cat is a keyboard\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"0df2fb7bf54818feac8c696bc6da1ee76ad37352\"\n },\n \"source\": [\n \"As you see, the model now predicts that the image is a keyboard with high probability, so the model is very confident. Looking at the new image, a human sees no difference with the original and sees no keyboard on it. But the model is highly confident that the image represents a keyboard. I think that this shows a significant and profound difference between how a human process images and how a neural network does.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"6efbd9cad2eb2d13dbb2903de627f2ecb8022a2f\"\n },\n \"source\": [\n \"## B. ResNet\\n\",\n \"\\n\",\n \"
\\n\",\n \"The same method is used here for a ResNet model, although we use an image of a panda. I know that I should have written functions to avoid redundant code, but I think it's easier to follow the different steps like this.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_uuid\": \"7790539b798f3da2a27190b17125092790384004\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img_path = \\\"https://upload.wikimedia.org/wikipedia/commons/3/3c/Giant_Panda_2004-03-2.jpg\\\"\\n\",\n \"pd_img = load_image(img_path).to(device)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(pd_img, \\\"The original image\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_kg_hide-output\": true,\n \"_uuid\": \"5aaaae874abf4430cc66d2a491b94e64d140ef9d\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading: \\\"https://download.pytorch.org/models/resnet152-b121ed2d.pth\\\" to /tmp/.torch/models/resnet152-b121ed2d.pth\\n\",\n \"241530880it [00:06, 37683865.94it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = models.resnet152(pretrained=True).to(device)\\n\",\n \"for param in model.parameters():\\n\",\n \" model.requires_grad = False\\n\",\n \"\\n\",\n \"model = model.eval()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_uuid\": \"3fb33ad5ba0b451aca21c0ce39957147acd9da85\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"giant_panda with probability 0.9335780739784241\\n\",\n \"lesser_panda with probability 0.01059756614267826\\n\",\n \"brown_bear with probability 0.009922429919242859\\n\",\n \"American_black_bear with probability 0.005555966403335333\\n\",\n \"ice_bear with probability 0.005138159263879061\\n\"\n ]\n }\n ],\n \"source\": [\n \"make_predictions(model, pd_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_uuid\": \"c1cb222eb089346e173db80db680c4f539537d8a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch : 1 Loss : 6.201566066010855e-06\\n\",\n \"Epoch : 2 Loss : 5.7919794926419854e-05\\n\",\n \"Epoch : 3 Loss : 0.0012632474536076188\\n\",\n \"Epoch : 4 Loss : 0.027137640863656998\\n\",\n \"Epoch : 5 Loss : 0.31827670335769653\\n\",\n \"Epoch : 6 Loss : 0.8913013339042664\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_class = idx2label.index(\\\"banana\\\")\\n\",\n \"\\n\",\n \"new_img = pd_img.clone()\\n\",\n \"new_img.requires_grad = True\\n\",\n \"\\n\",\n \"opt = optim.Adam([new_img], lr=0.01)\\n\",\n \"n_epochs = 6\\n\",\n \"print_every = 1\\n\",\n \"\\n\",\n \"gradient_ascent(\\n\",\n \" model, new_img, new_class, opt, n_epochs, print_every\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_uuid\": \"3e7f859f9175fcd69c5beeb32e774ae78939fc31\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"banana with probability 0.990304708480835\\n\",\n \"pineapple with probability 0.003015466732904315\\n\",\n \"custard_apple with probability 0.0012008324265480042\\n\",\n \"corn with probability 0.000719397037755698\\n\",\n \"lemon with probability 0.0005556467804126441\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"make_predictions(model, new_img)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(new_img, \\\"This panda is a banana\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"cd5f594b23ab1c41c6044f0d2692b14c6cde682e\"\n },\n \"source\": [\n \"## C. Inception v3\\n\",\n \"\\n\",\n \"
\\n\",\n \"Time for inception, with apples this time.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_uuid\": \"8815d6f74f69f77c76200aba5d73ae8d1cc6bb80\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img_path = \\\"https://upload.wikimedia.org/wikipedia/commons/thumb/9/9b/Granny_smith.jpg/450px-Granny_smith.jpg\\\"\\n\",\n \"apple_img = load_image(img_path).to(device)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(apple_img, \\\"The original image\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"c06a0a112874f8a750d58200fd6c39b8c1e3f658\"\n },\n \"source\": [\n \"*Image source : * [https://commons.wikimedia.org/wiki/File:Granny_smith.jpg](https://commons.wikimedia.org/wiki/File:Granny_smith.jpg)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_kg_hide-output\": true,\n \"_uuid\": \"5e654073f06b0ba1944d010428d3cb70d5522bea\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading: \\\"https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth\\\" to /tmp/.torch/models/inception_v3_google-1a9a5a14.pth\\n\",\n \"108857766it [00:05, 21669980.33it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = models.inception_v3(pretrained=True).to(device)\\n\",\n \"for param in model.parameters():\\n\",\n \" model.requires_grad = False\\n\",\n \"\\n\",\n \"model = model.eval()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_uuid\": \"833210568cc01334625de9e8ee2e8e0dff915961\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Granny_Smith with probability 0.9999697208404541\\n\",\n \"bikini with probability 1.0316800398868509e-05\\n\",\n \"lemon with probability 2.941319735327852e-06\\n\",\n \"banana with probability 1.0010467121901456e-06\\n\",\n \"fig with probability 5.879896889382508e-07\\n\"\n ]\n }\n ],\n \"source\": [\n \"make_predictions(model, apple_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_uuid\": \"842e1dc14738bbc3ed7e446e3171fb6cf245ddae\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch : 1 Loss : 5.63159652244849e-09\\n\",\n \"Epoch : 6 Loss : 5.994680751797432e-09\\n\",\n \"Epoch : 11 Loss : 6.687938203242538e-09\\n\",\n \"Epoch : 16 Loss : 7.797410717103048e-09\\n\",\n \"Epoch : 21 Loss : 9.627530772604587e-09\\n\",\n \"Epoch : 26 Loss : 1.296043894427612e-08\\n\",\n \"Epoch : 31 Loss : 2.0252135968235052e-08\\n\",\n \"Epoch : 36 Loss : 4.0894814645753286e-08\\n\",\n \"Epoch : 41 Loss : 2.1704498465169308e-07\\n\",\n \"Epoch : 46 Loss : 0.01403458695858717\\n\",\n \"Epoch : 51 Loss : 0.9775058031082153\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_class = idx2label.index(\\\"lion\\\")\\n\",\n \"\\n\",\n \"new_img = apple_img.clone()\\n\",\n \"new_img.requires_grad = True\\n\",\n \"\\n\",\n \"opt = optim.Adam([new_img], lr=0.01)\\n\",\n \"n_epochs = 51\\n\",\n \"print_every = 5\\n\",\n \"\\n\",\n \"gradient_ascent(\\n\",\n \" model, new_img, new_class, opt, n_epochs, print_every\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"850c4bd228f2f5477fb8cf27cbdbdb359c17a6e0\"\n },\n \"source\": [\n \"Well, here we need more iterations to make the apples look like lions, but nothing impossible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_uuid\": \"aaf8e9abacfe29751352a06e5f2384cfd2ffe2a7\",\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"lion with probability 0.9863242506980896\\n\",\n \"cheetah with probability 0.0014698724262416363\\n\",\n \"bathing_cap with probability 0.0007971485611051321\\n\",\n \"cougar with probability 0.0007540383376181126\\n\",\n \"lynx with probability 0.0006143574137240648\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"make_predictions(model, new_img)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 8))\\n\",\n \"plot_image(new_img, \\\"Those apples are lions\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"aa4ee875ae3dee04726d5f45f34abd657e6f7b74\"\n },\n \"source\": [\n \"# 6. Conclusion\\n\",\n \"\\n\",\n \"
\\n\",\n \"As we have seen, neural networks can easily be trapped to misclassify images. The way that this was achieved shows that even though neural networks are powerful models that achieve state-of-the-art performances in different perception tasks, it's important to think twice about the predictions they make, and to avoid misinterpreting or overinterpreting the way they work. \\n\",\n \"\\n\",\n \"If you are interested in the limitations of deep learning, you can find an interesting discussion in the last chapter of the book : *Deep Learning with Python by François Cholet*.\\n\",\n \"\\n\",\n \"This is my first public kernel, so please let me know if you have some remarks or found some mistakes.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "code/artificial_intelligence/src/dbscan_clustering/dbscan.py", "content": "# Part of Cosmos by OpenGenus Foundation\n\nimport numpy as np\nimport math\n\nUNCLASSIFIED = False\nNOISE = None\n\n\ndef _dist(p, q):\n return math.sqrt(np.power(p - q, 2).sum())\n\n\ndef _eps_neighborhood(p, q, eps):\n return _dist(p, q) < eps\n\n\ndef _region_query(m, point_id, eps):\n n_points = m.shape[1]\n seeds = []\n for i in range(0, n_points):\n if _eps_neighborhood(m[:, point_id], m[:, i], eps):\n seeds.append(i)\n return seeds\n\n\ndef _expand_cluster(m, classifications, point_id, cluster_id, eps, min_points):\n seeds = _region_query(m, point_id, eps)\n if len(seeds) < min_points:\n classifications[point_id] = NOISE\n return False\n else:\n classifications[point_id] = cluster_id\n for seed_id in seeds:\n classifications[seed_id] = cluster_id\n\n while len(seeds) > 0:\n current_point = seeds[0]\n results = _region_query(m, current_point, eps)\n if len(results) >= min_points:\n for i in range(0, len(results)):\n result_point = results[i]\n if (\n classifications[result_point] == UNCLASSIFIED\n or classifications[result_point] == NOISE\n ):\n if classifications[result_point] == UNCLASSIFIED:\n seeds.append(result_point)\n classifications[result_point] = cluster_id\n seeds = seeds[1:]\n return True\n\n\ndef dbscan(m, eps, min_points):\n \"\"\"Implementation of Density Based Spatial Clustering of Applications with Noise\n See https://en.wikipedia.org/wiki/DBSCAN\n\n scikit-learn probably has a better implementation\n\n Uses Euclidean Distance as the measure\n\n Inputs:\n m - A matrix whose columns are feature vectors\n eps - Maximum distance two points can be to be regionally related\n min_points - The minimum number of points to make a cluster\n\n Outputs:\n An array with either a cluster id number or dbscan.NOISE (None) for each\n column vector in m.\n \"\"\"\n cluster_id = 1\n n_points = m.shape[1]\n classifications = [UNCLASSIFIED] * n_points\n for point_id in range(0, n_points):\n point = m[:, point_id]\n if classifications[point_id] == UNCLASSIFIED:\n if _expand_cluster(\n m, classifications, point_id, cluster_id, eps, min_points\n ):\n cluster_id = cluster_id + 1\n return classifications\n\n\n# def test_dbscan():\n# m = np.matrix('1 1.2 0.8 3.7 3.9 3.6 10; 1.1 0.8 1 4 3.9 4.1 10')\n# eps = 0.5\n# min_points = 2\n# assert dbscan(m, eps, min_points) == [1, 1, 1, 2, 2, 2, None]\n\n\ndef main():\n m = np.matrix(\n \"-0.99 -0.98 -0.97 -0.96 -0.95 0.95 0.96 0.97 0.98 0.99; 1.1 1.09 1.08 1.07 1.06 1.06 1.07 1.08 1.09 1.1\"\n )\n eps = 1\n min_points = 3\n print(dbscan(m, eps, min_points))\n\n\nmain()\n" }, { "path": "code/artificial_intelligence/src/dbscan_clustering/readme.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# DBSCAN Clustering\nA Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu. This is a density based spatial clustering of applications with noise.\n\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/decision_tree/Decision_Tree_Regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing the libraries\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing the dataset\\n\",\n \"dataset=pd.read_csv('Position_Salaries.csv')\\n\",\n \"X=dataset.iloc[:,1:2].values\\n\",\n \"y=dataset.iloc[:,2].values\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=0, splitter='best')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#fitting the decision tree regression model to the dataset\\n\",\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"regressor=DecisionTreeRegressor(random_state=0)\\n\",\n \"regressor.fit(X,y)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'array' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0my_pred\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mregressor\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mpredict\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m6.5\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'array' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = regressor.predict(array([[6.5]]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'y_pred' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0my_pred\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'y_pred' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(900,)\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#visualising the decision tree regression result(for higher resolution and smoother curves)\\n\",\n \"X_grid=np.arange(min(X),max(X),0.01)\\n\",\n \"X_grid.shape\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#reshaping X_grid from 1-D array to 2-D array\\n\",\n \"X_grid=X_grid.reshape(len(X_grid),1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X,y,color='red')\\n\",\n \"plt.plot(X_grid,regressor.predict(X_grid),color='blue')\\n\",\n \"plt.title('Truth vs Bluff(Decision tree regression)')\\n\",\n \"plt.xlabel('Position level')\\n\",\n \"plt.ylabel('Salary')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/decision_tree/Position_Salaries.csv", "content": "Position,Level,Salary\nBusiness Analyst,1,45000\nJunior Consultant,2,50000\nSenior Consultant,3,60000\nManager,4,80000\nCountry Manager,5,110000\nRegion Manager,6,150000\nPartner,7,200000\nSenior Partner,8,300000\nC-level,9,500000\nCEO,10,1000000" }, { "path": "code/artificial_intelligence/src/decision_tree/data_banknote_authentication.csv", "content": 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}, { "path": "code/artificial_intelligence/src/decision_tree/decision_tree.py", "content": "# CART on the Bank Note dataset\nfrom random import seed\nfrom random import randrange\nfrom csv import reader\n\npredicted = []\nactual = []\n\n\n# Load a CSV file\ndef load_csv(filename):\n file = open(filename, \"rt\")\n lines = reader(file)\n dataset = list(lines)\n return dataset\n\n\n# Convert string column to float\ndef str_column_to_float(dataset, column):\n for row in dataset:\n row[column] = float(row[column].strip())\n\n\n# Split a dataset into k folds\ndef cross_validation_split(dataset, n_folds):\n dataset_split = list()\n dataset_copy = list(dataset)\n fold_size = int(len(dataset) / n_folds)\n for i in range(n_folds):\n fold = list()\n while len(fold) < fold_size:\n index = randrange(len(dataset_copy))\n fold.append(dataset_copy.pop(index))\n dataset_split.append(fold)\n return dataset_split\n\n\n# Calculate accuracy percentage\ndef accuracy_metric(actual, predicted):\n correct = 0\n for i in range(len(actual)):\n if actual[i] == predicted[i]:\n correct += 1\n return correct / float(len(actual)) * 100.0\n\n\n# Evaluate an algorithm using a cross validation split\ndef evaluate_algorithm(dataset, algorithm, n_folds, *args):\n global predicted\n global actual\n folds = cross_validation_split(dataset, n_folds)\n scores = list()\n for fold in folds:\n train_set = list(folds)\n train_set.remove(fold)\n train_set = sum(train_set, [])\n test_set = list()\n for row in fold:\n row_copy = list(row)\n test_set.append(row_copy)\n row_copy[-1] = None\n predicted = algorithm(train_set, test_set, *args)\n actual = [row[-1] for row in fold]\n accuracy = accuracy_metric(actual, predicted)\n scores.append(accuracy)\n return scores\n\n\n# Split a dataset based on an attribute and an attribute value\ndef test_split(index, value, dataset):\n left, right = list(), list()\n for row in dataset:\n if row[index] < value:\n left.append(row)\n else:\n right.append(row)\n return left, right\n\n\n# Calculate the Gini index for a split dataset\ndef gini_index(groups, classes):\n # count all samples at split point\n n_instances = float(sum([len(group) for group in groups]))\n # sum weighted Gini index for each group\n gini = 0.0\n for group in groups:\n size = float(len(group))\n # avoid divide by zero\n if size == 0:\n continue\n score = 0.0\n # score the group based on the score for each class\n for class_val in classes:\n p = [row[-1] for row in group].count(class_val) / size\n score += p * p\n # weight the group score by its relative size\n gini += (1.0 - score) * (size / n_instances)\n return gini\n\n\n# Select the best split point for a dataset\ndef get_split(dataset):\n class_values = list(set(row[-1] for row in dataset))\n b_index, b_value, b_score, b_groups = 999, 999, 999, None\n for index in range(len(dataset[0]) - 1):\n for row in dataset:\n groups = test_split(index, row[index], dataset)\n gini = gini_index(groups, class_values)\n if gini < b_score:\n b_index, b_value, b_score, b_groups = index, row[index], gini, groups\n return {\"index\": b_index, \"value\": b_value, \"groups\": b_groups}\n\n\n# Create a terminal node value\ndef to_terminal(group):\n outcomes = [row[-1] for row in group]\n return max(set(outcomes), key=outcomes.count)\n\n\n# Create child splits for a node or make terminal\ndef split(node, max_depth, min_size, depth):\n left, right = node[\"groups\"]\n del node[\"groups\"]\n # check for a no split\n if not left or not right:\n node[\"left\"] = node[\"right\"] = to_terminal(left + right)\n return\n # check for max depth\n if depth >= max_depth:\n node[\"left\"], node[\"right\"] = to_terminal(left), to_terminal(right)\n return\n # process left child\n if len(left) <= min_size:\n node[\"left\"] = to_terminal(left)\n else:\n node[\"left\"] = get_split(left)\n split(node[\"left\"], max_depth, min_size, depth + 1)\n # process right child\n if len(right) <= min_size:\n node[\"right\"] = to_terminal(right)\n else:\n node[\"right\"] = get_split(right)\n split(node[\"right\"], max_depth, min_size, depth + 1)\n\n\n# Build a decision tree\ndef build_tree(train, max_depth, min_size):\n root = get_split(train)\n split(root, max_depth, min_size, 1)\n return root\n\n\n# Make a prediction with a decision tree\ndef predict(node, row):\n if row[node[\"index\"]] < node[\"value\"]:\n if isinstance(node[\"left\"], dict):\n return predict(node[\"left\"], row)\n else:\n return node[\"left\"]\n else:\n if isinstance(node[\"right\"], dict):\n return predict(node[\"right\"], row)\n else:\n return node[\"right\"]\n\n\n# Classification and Regression Tree Algorithm\ndef decision_tree(train, test, max_depth, min_size):\n tree = build_tree(train, max_depth, min_size)\n predictions = list()\n for row in test:\n prediction = predict(tree, row)\n predictions.append(prediction)\n return predictions\n\n\n# Test CART on Bank Note dataset\nseed(1)\n# load and prepare data\nfilename = \"data_banknote_authentication.csv\"\ndataset = load_csv(filename)\n# convert string attributes to integers\nfor i in range(len(dataset[0])):\n str_column_to_float(dataset, i)\n# evaluate algorithm\nn_folds = 5\nmax_depth = 5\nmin_size = 10\n\nscores = evaluate_algorithm(dataset, decision_tree, n_folds, max_depth, min_size)\nprint(\"Scores: %s\" % scores)\nprint(\"Mean Accuracy: %.3f%%\" % (sum(scores) / float(len(scores))))\n# Evaluate algorithm by computing ROC (Receiver Operating Characteristic) and AUC (area under the curve)\nfrom sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\n\nfalse_positve_rate, true_positive_rate, threshold = roc_curve(actual, predicted)\nroc_auc = auc(false_positve_rate, true_positive_rate)\nplt.plot(\n false_positve_rate,\n true_positive_rate,\n lw=1,\n label=\"ROC curve (area = %0.2f)\" % roc_auc,\n)\nplt.title(\"Receiver operating characteristic example\")\nplt.ylabel(\"True Positive Rate\")\nplt.xlabel(\"False Positive Rate\")\nplt.show()\nprint(\"AUC: %s\" % roc_auc)\n" }, { "path": "code/artificial_intelligence/src/decision_tree/decision_trees_information_gain.py", "content": "from math import log\nimport numpy as np\nfrom collections import Counter\n\n\nclass Node:\n def __init__(self):\n self.split_column = None\n self.split_value = None\n self.left = None\n self.right = None\n self.node_def = True\n self.label = None\n\n\nclass decision_tree:\n def __init__(self):\n self.head = Node()\n self.max_depth = 3\n self.root_entropy = 1\n self.min_samples = 0\n\n def train(self, x_train, y_train):\n self.head = self.build(self.head, x_train, y_train, self.root_entropy, 1)\n\n def build(self, current_node, x_train, y_train, entropy_parent, cur_depth):\n if cur_depth > self.max_depth or len(y_train) <= self.min_samples:\n temp_node = Node()\n temp_node.node_def = False\n return temp_node\n\n row_length = len(x_train)\n col_length = len(x_train[0])\n count = {}\n prob = {}\n entropy = {}\n information_gain = {}\n weighted_avg = {}\n output_set = set(y_train)\n\n for i in range(0, col_length):\n count[i] = {}\n prob[i] = {}\n entropy[i] = {}\n s = set(x_train[:, i])\n for j in s:\n count[i][j] = 0\n entropy[i][j] = 0\n for k in output_set:\n count[i][j + \"and\" + k] = 0\n prob[i][j + \"and\" + k] = 0\n\n for i in range(0, col_length):\n for j in range(0, row_length):\n count[i][x_train[j][i]] = count[i][x_train[j][i]] + 1\n count[i][x_train[j][i] + \"and\" + y_train[j]] = (\n count[i][x_train[j][i] + \"and\" + y_train[j]] + 1\n )\n\n for i in range(0, col_length):\n s = set(x_train[:, i])\n weighted_avg[i] = 0\n temp_sum = 0\n for j in s:\n for k in output_set:\n prob[i][j + \"and\" + k] = count[i][j + \"and\" + k] / count[i][j]\n if prob[i][j + \"and\" + k]:\n entropy[i][j] = entropy[i][j] + prob[i][j + \"and\" + k] * (\n log(prob[i][j + \"and\" + k]) / log(2)\n )\n if entropy[i][j]:\n entropy[i][j] = entropy[i][j] * -1\n\n weighted_avg[i] = weighted_avg[i] + entropy[i][j] * count[i][j]\n temp_sum = temp_sum + count[i][j]\n weighted_avg[i] = weighted_avg[i] / temp_sum\n information_gain[i] = entropy_parent - weighted_avg[i]\n\n max_key = max(information_gain, key=information_gain.get)\n split_set = set(x_train[:, max_key])\n split_value = None\n\n for i in split_set:\n split_value = i\n break\n\n x_train_left = []\n x_train_right = []\n y_train_left = []\n y_train_right = []\n split_data_left = []\n split_data_right = []\n\n for j in range(0, row_length):\n if x_train[j, max_key] == split_value:\n split_data_left.append(j)\n else:\n split_data_right.append(j)\n\n x_train_left = x_train[split_data_left]\n y_train_left = y_train[split_data_left]\n x_train_right = x_train[split_data_right]\n y_train_right = y_train[split_data_right]\n current_node.split_column = max_key\n current_node.split_value = split_value\n temp_dict = {}\n\n for i in y_train:\n temp_dict[i] = 0\n for i in y_train:\n temp_dict[i] = temp_dict[i] + 1\n\n current_node.label = Counter(temp_dict).most_common(1)[0][0]\n current_node.left = Node()\n current_node.right = Node()\n current_node.left = self.build(\n current_node.left,\n x_train_left,\n y_train_left,\n entropy[current_node.split_column][current_node.split_value],\n cur_depth + 1,\n )\n current_node.right = self.build(\n current_node.right,\n x_train_right,\n y_train_right,\n entropy[current_node.split_column][current_node.split_value],\n cur_depth + 1,\n )\n\n return current_node\n\n def predict(self, test):\n temp_list = []\n for i in test:\n temp_list.append(self.test_fun(self.head, i))\n return temp_list\n\n def test_fun(self, cur_node, test):\n\n if cur_node.left.node_def is False and cur_node.right.node_def is False:\n return cur_node.label\n\n if test[cur_node.split_column] == cur_node.split_value:\n return self.test_fun(cur_node.left, test)\n else:\n return self.test_fun(cur_node.right, test)\n\n\nx_train = [\n [\"Steep\", \"Bumpy\", \"Yes\"],\n [\"Steep\", \"Smooth\", \"Yes\"],\n [\"Flat\", \"Bumpy\", \"No\"],\n [\"Steep\", \"Smooth\", \"No\"],\n]\ny_train = [\"Slow\", \"Slow\", \"Fast\", \"Fast\"]\n\nx_train = np.array(x_train)\ny_train = np.array(y_train)\n\nclf = decision_tree()\nclf.train(x_train, y_train)\nprint(clf.predict(x_train))\n" }, { "path": "code/artificial_intelligence/src/deep_q_networks/deep-q-networks.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"Deep Q Networks.ipynb\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"MqLCpeuB1rsn\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"# Q Learning and Deep Q Network Examples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"DNCXqYSN1jeA\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"import random\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import tensorflow as tf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from collections import deque\\n\",\n \"from time import time\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"pZgaI7aE17NT\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"outputId\": \"3f549114-264d-4add-d57c-fb8f76e2f759\"\n },\n \"source\": [\n \"seed = 23041974\\n\",\n \"\\n\",\n \"random.seed(seed)\\n\",\n \"print('Seed: {}'.format(seed))\"\n ],\n \"execution_count\": 2,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Seed: 23041974\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0ioqscIW2DeB\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Game design\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"skRGPAnQ2IKi\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"The game the Q-agents will need to learn is made of a board with 4 cells. The agent will receive a reward of +1 every time it fills a vacant cell, and will receive a penalty of -1 when it tries to fill an already occupied cell. The game ends when the board is full.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"V8zhwjDu2CG6\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class Game:\\n\",\n \" board = None\\n\",\n \" board_size = 0\\n\",\n \" \\n\",\n \" def __init__(self, board_size = 4):\\n\",\n \" self.board_size = board_size\\n\",\n \" self.reset()\\n\",\n \" \\n\",\n \" def reset(self):\\n\",\n \" self.board = np.zeros(self.board_size)\\n\",\n \" \\n\",\n \" def play(self, cell):\\n\",\n \" # Returns a tuple: (reward, game_over?)\\n\",\n \" if self.board[cell] == 0:\\n\",\n \" self.board[cell] = 1\\n\",\n \" game_over = len(np.where(self.board == 0)[0]) == 0\\n\",\n \" return (1, game_over)\\n\",\n \" else:\\n\",\n \" return (-1, False)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"aSEtD_D63qSI\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 321\n },\n \"outputId\": \"544a5671-5e01-4c41-ec27-3f76ea52db8d\"\n },\n \"source\": [\n \"def state_to_str(state):\\n\",\n \" return str(list(map(int, state.tolist())))\\n\",\n \"\\n\",\n \"all_states = list()\\n\",\n \"for i in range(2):\\n\",\n \" for j in range(2):\\n\",\n \" for k in range(2):\\n\",\n \" for l in range(2):\\n\",\n \" s = np.array([i, j, k, l])\\n\",\n \" all_states.append(state_to_str(s))\\n\",\n \" \\n\",\n \"print('All possible states: ')\\n\",\n \"\\n\",\n \"for s in all_states:\\n\",\n \" print(s)\"\n ],\n \"execution_count\": 4,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"All possible states: \\n\",\n \"[0, 0, 0, 0]\\n\",\n \"[0, 0, 0, 1]\\n\",\n \"[0, 0, 1, 0]\\n\",\n \"[0, 0, 1, 1]\\n\",\n \"[0, 1, 0, 0]\\n\",\n \"[0, 1, 0, 1]\\n\",\n \"[0, 1, 1, 0]\\n\",\n \"[0, 1, 1, 1]\\n\",\n \"[1, 0, 0, 0]\\n\",\n \"[1, 0, 0, 1]\\n\",\n \"[1, 0, 1, 0]\\n\",\n \"[1, 0, 1, 1]\\n\",\n \"[1, 1, 0, 0]\\n\",\n \"[1, 1, 0, 1]\\n\",\n \"[1, 1, 1, 0]\\n\",\n \"[1, 1, 1, 1]\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"UYmEVU-C4CDl\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Initialize the game:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"4GYiM4ZE3_yO\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"game = Game()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"2gAEN83m4Gx6\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Q Learning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Kniwoe2u4JP6\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Starting with a table-based Q-learning algorithm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"9FN-QW9v4Fgi\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"num_of_games = 2000\\n\",\n \"epsilon = 0.1\\n\",\n \"gamma = 1\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"tVjczKrb4Q01\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Initializing the Q-table:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"WaJcA3QN4QCr\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"q_table = pd.DataFrame(0, \\n\",\n \" index = np.arange(4),\\n\",\n \" columns = all_states)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"NOnU7dG84lcH\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Letting the agent play and learn:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"yXvl_sDH4j9K\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 247\n },\n \"outputId\": \"b96a489e-8177-492b-fee8-e1816c102773\"\n },\n \"source\": [\n \"r_list = [] ## Store the total reward of each game so we could plot it later\\n\",\n \"\\n\",\n \"for g in range(num_of_games):\\n\",\n \" game_over = False\\n\",\n \" game.reset()\\n\",\n \" total_reward = 0\\n\",\n \" while not game_over:\\n\",\n \" state = np.copy(game.board)\\n\",\n \" if random.random() < epsilon:\\n\",\n \" action = random.randint(0, 3)\\n\",\n \" else:\\n\",\n \" action = q_table[state_to_str(state)].idxmax()\\n\",\n \" reward, game_over = game.play(action)\\n\",\n \" total_reward += reward\\n\",\n \" if np.sum(game.board) == 4: ## Terminal state\\n\",\n \" next_state_max_q_value = 0\\n\",\n \" else:\\n\",\n \" next_state = np.copy(game.board)\\n\",\n \" next_state_max_q_value = q_table[state_to_str(next_state)].max()\\n\",\n \" q_table.loc[action, state_to_str(state)] = reward + gamma * next_state_max_q_value\\n\",\n \" r_list.append(total_reward)\\n\",\n \"q_table\"\n ],\n \"execution_count\": 8,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" [0, 0, 0, 0] [0, 0, 0, 1] ... [1, 1, 1, 0] [1, 1, 1, 1]\\n\",\n \"0 4 3 ... 0 0\\n\",\n \"1 4 0 ... 0 0\\n\",\n \"2 4 0 ... 0 0\\n\",\n \"3 4 0 ... 1 0\\n\",\n \"\\n\",\n \"[4 rows x 16 columns]\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 8\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VvPp2xEG52gM\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's verify that the agent indeed learned a correct strategy by seeing what action it will choose in each one of the possible states:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"kEUwiq1T5yaJ\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 305\n },\n \"outputId\": \"48e0bca0-e0f3-4c8d-8eda-79ca8901d7b2\"\n },\n \"source\": [\n \"for i in range(2):\\n\",\n \" for j in range(2):\\n\",\n \" for k in range(2):\\n\",\n \" for l in range(2):\\n\",\n \" b = np.array([i, j, k, l])\\n\",\n \" if len(np.where(b == 0)[0]) != 0:\\n\",\n \" action = q_table[state_to_str(b)].idxmax()\\n\",\n \" pred = q_table[state_to_str(b)].tolist()\\n\",\n \" print('board: {b}\\\\tpredicted Q values: {p} \\\\tbest action: {a}\\\\t correct action? {s}'\\n\",\n \" .format(b = b, p = pred, a = action, s = b[action] == 0))\"\n ],\n \"execution_count\": 9,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"board: [0 0 0 0]\\tpredicted Q values: [4, 4, 4, 4] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 0 0 1]\\tpredicted Q values: [3, 0, 0, 0] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 0 1 0]\\tpredicted Q values: [3, 1, 2, 1] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 0 1 1]\\tpredicted Q values: [2, 0, 0, 0] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 1 0 0]\\tpredicted Q values: [3, 2, 3, 3] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 1 0 1]\\tpredicted Q values: [2, 0, 0, 0] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 1 1 0]\\tpredicted Q values: [2, 0, 0, 0] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [0 1 1 1]\\tpredicted Q values: [0, 0, 0, 0] \\tbest action: 0\\t correct action? True\\n\",\n \"board: [1 0 0 0]\\tpredicted Q values: [2, 3, 3, 3] \\tbest action: 1\\t correct action? True\\n\",\n \"board: [1 0 0 1]\\tpredicted Q values: [1, 2, 2, 1] \\tbest action: 1\\t correct action? True\\n\",\n \"board: [1 0 1 0]\\tpredicted Q values: [1, 2, 1, 1] \\tbest action: 1\\t correct action? True\\n\",\n \"board: [1 0 1 1]\\tpredicted Q values: [-1, 1, -1, 0] \\tbest action: 1\\t correct action? True\\n\",\n \"board: [1 1 0 0]\\tpredicted Q values: [1, 1, 2, 2] \\tbest action: 2\\t correct action? True\\n\",\n \"board: [1 1 0 1]\\tpredicted Q values: [0, 0, 1, 0] \\tbest action: 2\\t correct action? True\\n\",\n \"board: [1 1 1 0]\\tpredicted Q values: [0, 0, 0, 1] \\tbest action: 3\\t correct action? True\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"d-wNz71h6z3y\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"We can see that the agent indeed picked up the right way to play the game. Still, when looking at the predicted Q values, we see that there are some states where it didn't pick up the correct Q values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Xb0DIqqL7H0I\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Q Learning is a greedy algorithm, and it prefers choosing the best action at each state rather than exploring. We can solve this issue by increasing ε (epsilon), which controls the exploration of this algorithm and was set to 0.1, OR by letting the agent play more games.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Sgb7urf87nvD\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's plot the total reward the agent received per game:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ozl5o3SQ6mVQ\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 466\n },\n \"outputId\": \"ffcc8778-af00-4d32-ff49-88bc5dc4a3dc\"\n },\n \"source\": [\n \"plt.figure(figsize = (14, 7))\\n\",\n \"plt.plot(range(len(r_list)), r_list)\\n\",\n \"plt.xlabel('Games played')\\n\",\n \"plt.ylabel('Reward')\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 10,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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JlHbczd5GY/sx3Lq257YFQROm37Bkm3auXivPf59Kzjt+ieh/bqX68d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\\nHSEzMzMzM+s67giZmZmZmVnX+R9M3ZNmsEMTgwAAAABJRU5ErkJggg==\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Cq99Dtkf74rD\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Deep Q Network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nBb2BuWZ8iQu\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's move on to neural network-based modeling. We'll design the Q network first.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"h6f2CO378sBu\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Remember that the output of the network, ```(self.output) ``` is an array of the predicted Q value for each action taken from the input state ``` (self.states)```. Comparing with the Q-table algorithm, the output is an entire column of the table\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"vVO9DY7A7y79\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class QNetwork:\\n\",\n \" \\n\",\n \" def __init__(self, hidden_layers_size, gamma, learning_rate,\\n\",\n \" input_size = 4, output_size = 4):\\n\",\n \" self.q_target = tf.placeholder(shape = (None, output_size), dtype = tf.float32)\\n\",\n \" self.r = tf.placeholder(shape = None, dtype = tf.float32)\\n\",\n \" self.states = tf.placeholder(shape = (None, input_size), dtype = tf.float32)\\n\",\n \" self.enum_actions = tf.placeholder(shape = (None, 2), dtype = tf.int32)\\n\",\n \" layer = self.states\\n\",\n \" for l in hidden_layers_size:\\n\",\n \" layer = tf.layers.dense(inputs = layer, units = l, \\n\",\n \" activation = tf.nn.relu,\\n\",\n \" kernel_initializer = tf.contrib.layers.xavier_initializer(seed = seed))\\n\",\n \" self.output = tf.layers.dense(inputs = layer, units = output_size,\\n\",\n \" kernel_initializer = tf.contrib.layers.xavier_initializer(seed = seed))\\n\",\n \" self.predictions = tf.gather_nd(self.output, indices = self.enum_actions)\\n\",\n \" self.labels = self.r + gamma * tf.reduce_max(self.q_target, axis = 1)\\n\",\n \" self.cost = tf.reduce_mean(tf.losses.mean_squared_error(labels = self.labels,\\n\",\n \" predictions = self.predictions))\\n\",\n \" self.optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(self.cost)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"gfUsSy8k1DA8\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Designing the **Experience Replay memory** which will be used, using a cyclic memory buffer:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"zBMdWdxt1BNk\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class ReplayMemory:\\n\",\n \" memory = None\\n\",\n \" counter = 0\\n\",\n \" \\n\",\n \" def __init__(self, size):\\n\",\n \" self.memory = deque(maxlen = size)\\n\",\n \" \\n\",\n \" def append(self, element):\\n\",\n \" self.memory.append(element)\\n\",\n \" self.counter += 1\\n\",\n \" \\n\",\n \" def sample(self, n):\\n\",\n \" return random.sample(self.memory, n)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1t9m6EFk2HyD\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Setting up the parameters. Note that here we used `gamma = 0.99` and not 1 like in the Q-table algorithm, as the literature recommends working with a discount factor of **0.9 ≤ γ ≤ 0.99**. It probably won't matter much in this specific case, but it's good to get used to this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Ypu5eaS92GRT\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"num_of_games = 2000\\n\",\n \"epsilon = 0.1\\n\",\n \"gamma = 0.99\\n\",\n \"batch_size = 10\\n\",\n \"memory_size = 2000\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SCC_xpF524iX\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Initializing the Q-network:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"fZAuXwb923uf\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 91\n },\n \"outputId\": \"1c572031-b983-4dbe-d6af-6ebc1025c097\"\n },\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"tf.set_random_seed(seed)\\n\",\n \"qnn = QNetwork(hidden_layers_size = [20, 20],\\n\",\n \" gamma = gamma,\\n\",\n \" learning_rate = 0.001)\\n\",\n \"memory = ReplayMemory(memory_size)\\n\",\n \"sess = tf.Session()\\n\",\n \"sess.run(tf.global_variables_initializer())\"\n ],\n \"execution_count\": 16,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:121: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use tf.where in 2.0, which has the same broadcast rule as np.where\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hfhSAmOc3Z8m\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Training the network. Compare this code to the above Q-table training.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"cay06IcS3OZF\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"outputId\": \"14864b0d-cb6b-478e-dceb-d005f48e1990\"\n },\n \"source\": [\n \"r_list = []\\n\",\n \"c_list = [] ## Same as r_list, but for storing the cost\\n\",\n \"\\n\",\n \"counter = 0 ## Will be used to trigger network training\\n\",\n \"\\n\",\n \"for g in range(num_of_games):\\n\",\n \" game_over = False\\n\",\n \" game.reset()\\n\",\n \" total_reward = 0\\n\",\n \" while not game_over:\\n\",\n \" counter += 1\\n\",\n \" state = np.copy(game.board)\\n\",\n \" if random.random() < epsilon:\\n\",\n \" action = random.randint(0, 3)\\n\",\n \" else:\\n\",\n \" pred = np.squeeze(sess.run(qnn.output,\\n\",\n \" feed_dict = {qnn.states: np.expand_dims(game.board, axis = 0)}))\\n\",\n \" action = np.argmax(pred)\\n\",\n \" reward, game_over = game.play(action)\\n\",\n \" total_reward += reward\\n\",\n \" next_state = np.copy(game.board)\\n\",\n \" memory.append({'state': state, 'action': action,\\n\",\n \" 'reward': reward, 'next_state': next_state,\\n\",\n \" 'game_over': game_over})\\n\",\n \" if counter % batch_size == 0:\\n\",\n \" ## Network training\\n\",\n \" batch = memory.sample(batch_size)\\n\",\n \" q_target = sess.run(qnn.output, feed_dict = {qnn.states: np.array(list(map(lambda x: x['next_state'], batch)))})\\n\",\n \" terminals = np.array(list(map(lambda x: x['game_over'], batch)))\\n\",\n \" for i in range(terminals.size):\\n\",\n \" if terminals[i]:\\n\",\n \" ## Remember we use the network's own predictions for the next state while calculating loss.\\n\",\n \" ## Terminal states have no Q-value, and so we manually set them to 0, as the network's predictions\\n\",\n \" ## for these states is meaningless\\n\",\n \" q_target[i] = np.zeros(game.board_size)\\n\",\n \" _, cost = sess.run([qnn.optimizer, qnn.cost],\\n\",\n \" feed_dict = {qnn.states: np.array(list(map(lambda x: x['state'], batch))),\\n\",\n \" qnn.r: np.array(list(map(lambda x: x['reward'], batch))),\\n\",\n \" qnn.enum_actions: np.array(list(enumerate(map(lambda x: x['action'], batch)))),\\n\",\n \" qnn.q_target: q_target})\\n\",\n \" c_list.append(cost)\\n\",\n \" r_list.append(total_reward)\\n\",\n \"print('Final cost: {}'.format(c_list[-1]))\"\n ],\n \"execution_count\": 17,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Final cost: 1.1149362325668335\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"oYV9qA0R5wwx\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Again, let's verify that the agent indeed learned a correct strategy:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"IDZFlFca5k4o\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 305\n },\n \"outputId\": \"71092db7-ded6-40d7-967e-6d07f818cb58\"\n },\n \"source\": [\n \"for i in range(2):\\n\",\n \" for j in range(2):\\n\",\n \" for k in range(2):\\n\",\n \" for l in range(2):\\n\",\n \" b = np.array([i, j, k, l])\\n\",\n \" if len(np.where(b == 0)[0]) != 0:\\n\",\n \" pred = np.squeeze(sess.run(qnn.output, \\n\",\n \" feed_dict = {qnn.states: np.expand_dims(b, axis = 0)}))\\n\",\n \" pred = list(map(lambda x: round(x, 3), pred))\\n\",\n \" action = np.argmax(pred)\\n\",\n \" print('board: {b} \\\\tpredicted Q values: {p} \\\\tbest action: {a} \\\\tcorrect action? {s}'\\n\",\n \" .format(b = b, p = pred, a = action, s = b[action] == 0))\"\n ],\n \"execution_count\": 18,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"board: [0 0 0 0] \\tpredicted Q values: [-0.501, -0.503, -0.567, -0.495] \\tbest action: 3 \\tcorrect action? True\\n\",\n \"board: [0 0 0 1] \\tpredicted Q values: [-0.318, 0.244, 0.207, -1.017] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [0 0 1 0] \\tpredicted Q values: [-0.147, -0.003, -0.858, -0.681] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [0 0 1 1] \\tpredicted Q values: [0.257, 0.535, -0.509, -1.026] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [0 1 0 0] \\tpredicted Q values: [-0.422, -1.015, -0.444, -0.342] \\tbest action: 3 \\tcorrect action? True\\n\",\n \"board: [0 1 0 1] \\tpredicted Q values: [-0.006, -0.148, 0.154, -0.774] \\tbest action: 2 \\tcorrect action? True\\n\",\n \"board: [0 1 1 0] \\tpredicted Q values: [0.216, -0.856, -0.716, -0.165] \\tbest action: 0 \\tcorrect action? True\\n\",\n \"board: [0 1 1 1] \\tpredicted Q values: [0.721, -0.119, -0.333, -0.626] \\tbest action: 0 \\tcorrect action? True\\n\",\n \"board: [1 0 0 0] \\tpredicted Q values: [-1.481, -0.814, -0.931, -1.023] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [1 0 0 1] \\tpredicted Q values: [-0.763, -0.101, -0.307, -1.308] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [1 0 1 0] \\tpredicted Q values: [-0.976, -0.362, -1.192, -1.013] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [1 0 1 1] \\tpredicted Q values: [-0.318, 0.485, -0.675, -1.482] \\tbest action: 1 \\tcorrect action? True\\n\",\n \"board: [1 1 0 0] \\tpredicted Q values: [-1.265, -1.549, -0.81, -0.607] \\tbest action: 3 \\tcorrect action? True\\n\",\n \"board: [1 1 0 1] \\tpredicted Q values: [-0.628, -0.784, -0.113, -1.084] \\tbest action: 2 \\tcorrect action? True\\n\",\n \"board: [1 1 1 0] \\tpredicted Q values: [-0.703, -1.186, -1.043, -0.539] \\tbest action: 3 \\tcorrect action? True\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5MlhC9A_6vAr\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Here too, we see that the agent learned a correct strategy, and again, Q values are not what we would've expectdd.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4mhd5oY9658M\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's plot the rewards the agent received:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Qd90SDmv6pWX\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 466\n },\n \"outputId\": \"f755dab0-1386-46db-df7d-33b309f89de6\"\n },\n \"source\": [\n \"plt.figure(figsize = (14, 7))\\n\",\n \"plt.plot(range(len(r_list)), r_list)\\n\",\n \"plt.xlabel('Games played')\\n\",\n \"plt.ylabel('Reward')\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 19,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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gGLeltc+8//3gNYvfUgpnU24K6Pn6LUpxdrth0EAPz5+W024ykMw4Xr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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"eTHLJpNz7ILd\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Zooming in, so we can compare the Q-table algorithm:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"7eFgrVv-7EYV\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 466\n },\n \"outputId\": \"9c77e229-4097-4864-b28b-b33067a3da73\"\n },\n \"source\": [\n \"plt.figure(figsize = (14, 7))\\n\",\n \"plt.plot(range(len(r_list)), r_list)\\n\",\n \"plt.xlabel('Game played')\\n\",\n \"plt.ylabel('Reward')\\n\",\n \"plt.ylim(-2, 4.5)\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 20,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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qk4Y/MKTI7Ge2x0GRaplpULx3H+3Es4L6atd9zaxbnTqIpZ1XqkBLIy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\\nARk/r9ARCaCPAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"bnHs_fyF7X__\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's plot the cost too. Remember that here the x-axis reflects the number of trainings, and not the number of games. The number of training depends on the number of actions taken, and the agent can take any number of actions during each game.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"U8Rc2ksK7UMR\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 466\n },\n \"outputId\": \"0c2304aa-951b-4e13-c5ec-d07db7e1d8a8\"\n },\n \"source\": [\n \"plt.figure(figsize = (14, 7))\\n\",\n \"plt.plot(range(len(c_list)), c_list)\\n\",\n \"plt.xlabel('Trainings')\\n\",\n \"plt.ylabel('Cost')\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 21,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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fYKvb16rwd7B7ITAQgAgJDI9ulhuaDtPfQr5/Xv09qVa2iJ+nREIPMR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\\nGE6AzMzMzMysMJwAmZmZmZlZYfw/h3qA/FoTHdEAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n }\n ]\n}\n" }, { "path": "code/artificial_intelligence/src/factorization_machines/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Factorization machines\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/factorization_machines/matrix_factorization.py", "content": "import numpy as np\n\n\ndef matrix_factorization(data, K, steps=5000, beta=0.0002, lamda=0.02):\n\n W = np.random.rand(data.shape[0], K)\n H = np.random.rand(data.shape[1], K)\n # W = np.random.normal(scale=1./K, size = (data.shape[0], K))\n # H = np.random.normal(scale=1./K, size = (data.shape[1], K))\n\n H = H.T\n\n b_u = np.zeros(data.shape[0])\n\n for i in range(len(data)):\n # print(\"len\",len(np.where(data[i] != 0)[0]))\n if len(np.where(data[i] != 0)[0]) == 0:\n b_u[i] = 0\n else:\n b_u[i] = np.mean(data[i][np.where(data[i] != 0)])\n # print(b_u[i])\n\n b_i = np.zeros(data.shape[1])\n for i in range(len(data.T)):\n if len(np.where(data.T[i] != 0)[0]) == 0:\n b_i[i] = 0\n else:\n b_i[i] = np.mean(data.T[i][np.where(data.T[i] != 0)])\n\n # b_i[i] = np.mean(data.T[i][np.where(data.T[i] != 0)])\n # print(b_i[i])\n\n b = np.mean(data[np.where(data != 0)])\n\n # i : user\n # j : item\n\n for step in range(steps):\n for i in range(len(data)):\n for j in range(len(data[i])):\n if data[i][j] > 0:\n p_bar = b + b_u[i] + b_i[j] + np.dot(W[i, :], H[:, j])\n\n eij = data[i][j] - p_bar\n\n b = b + beta * eij\n\n b_u[i] += beta * (eij - lamda * b_u[i])\n b_i[j] += beta * (eij - lamda * b_i[j])\n\n \"\"\"\n for k in range(K):\n P[i][k] = P[i][k] + beta * (2 * eij * Q[k][j] - lamda * P[i][k])\n Q[k][j] = Q[k][j] + beta * (2 * eij * P[i][k] - lamda * Q[k][j])\n \"\"\"\n W[i, :] += beta * (2 * eij * H[:, j] - lamda * W[i, :])\n H[:, j] += beta * (2 * eij * W[i, :] - lamda * H[:, j])\n\n edata = np.dot(W, H)\n e = 0\n for i in range(len(data)):\n for j in range(len(data[i])):\n if data[i][j] > 0:\n e = e + pow(data[i][j] - np.dot(W[i, :], H[:, j]), 2)\n for k in range(K):\n e = e + (lamda / 2) * (pow(W[i][k], 2) + pow(H[k][j], 2))\n\n if e < 0.001:\n print(\"Epsilon's exit\")\n break\n return W, H.T, b_u, b_i, b\n\n\ndef readFile(fileString):\n return np.loadtxt(fileString, delimiter=\" \", dtype=\"int\").tolist()\n\n\nif __name__ == \"__main__\":\n fileString = r\"/home/ledsinh/test.txt\"\n \"\"\"\n You can read the matrix in many dismension\n\n \"\"\"\n data = readFile(fileString)\n\n data = np.array(data)\n print(data)\n K = 2\n\n W, H, b_u, b_i, b = matrix_factorization(data, K)\n print(\"Matrix Factorization with Bias\")\n fitted = b + W.dot(H.T) + b_u[:, np.newaxis] + b_i[np.newaxis, :]\n\n for i in fitted:\n print(np.around(i, decimals=2))\n # print(fitted)\n" }, { "path": "code/artificial_intelligence/src/gaussian_mixture_model/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Gaussian mixture model\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/gaussian_naive_bayes/gaussian_naive_bayes.py", "content": "# example using iris dataset\n# Part of Cosmos by OpenGenus\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import classification_report, confusion_matrix\n\ndataset = pd.read_csv(\"iris1.csv\", header=0)\n\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 4].values\n\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y)\n\n\nclassifier = GaussianNB()\nclassifier.fit(X_train, y_train)\n\ny_pred = classifier.predict(X_test)\n\n# labeled confusion matrix\nprint(\n pd.crosstab(y_test, y_pred, rownames=[\"True\"], colnames=[\"Predicted\"], margins=True)\n)\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import accuracy_score\n\nskf = StratifiedKFold(n_splits=10)\nskf.get_n_splits(X, y)\nStratifiedKFold(n_splits=10, random_state=None, shuffle=False)\na = 0\nfor train_index, test_index in skf.split(X, y):\n # print(\"TRAIN:\", train_index, \"TEST:\", test_index) #These are the mutually exclusive sets from the 10 folds\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n y_pred = classifier.predict(X_test)\n accuracy = accuracy_score(y_test, y_pred)\n a += accuracy\nprint(\"\\nK-fold cross validation (10 folds): \" + str(a / 10))\n" }, { "path": "code/artificial_intelligence/src/gaussian_naive_bayes/gaussian_naive_bayes_moons.py", "content": "############################################################################\n############################################################################\n\n# Importing necessary packages\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils import shuffle as util_shuffle\nfrom sklearn.preprocessing import scale\nfrom pandas import DataFrame\n\n\n############################################################################\n############################################################################\ndef make_moons(n_samples=100, shuffle=True, noise=None, random_state=None):\n \"\"\"Make two interleaving half circles\n A simple toy dataset to visualize clustering and classification\n algorithms. Read more in the :ref:`User Guide `.\n Parameters\n ----------\n n_samples : int, optional (default=100)\n The total number of points generated.\n shuffle : bool, optional (default=True)\n Whether to shuffle the samples.\n noise : double or None (default=None)\n Standard deviation of Gaussian noise added to the data.\n random_state : int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`.\n Returns\n -------\n X : array of shape [n_samples, 2]\n The generated samples.\n y : array of shape [n_samples]\n The integer labels (0 or 1) for class membership of each sample.\n \"\"\"\n n_samples_out = n_samples // 2 # Floor division\n n_samples_in = n_samples - n_samples_out # splitting it into two halves\n\n generator = check_random_state(random_state)\n\n outer_circ_x = np.cos(np.linspace(0, np.pi, n_samples_out))\n outer_circ_y = np.sin(np.linspace(0, np.pi, n_samples_out))\n inner_circ_x = 1 - np.cos(np.linspace(0, np.pi, n_samples_in))\n inner_circ_y = 1 - np.sin(np.linspace(0, np.pi, n_samples_in)) - 1\n\n X = np.vstack(\n (np.append(outer_circ_x, inner_circ_x), np.append(outer_circ_y, inner_circ_y))\n ).T\n y = np.hstack(\n [np.zeros(n_samples_out, dtype=np.intp), np.ones(n_samples_in, dtype=np.intp)]\n )\n\n if shuffle:\n X, y = util_shuffle(X, y, random_state=generator)\n\n if noise is not None:\n X += generator.normal(scale=noise, size=X.shape)\n\n return X, y\n\n\n############################################################################\n# generating 2D classification dataset\n\ncrts, Class = make_moons(n_samples=2000, noise=0.08, random_state=0)\n\nClass = Class + 1\n\ndf = DataFrame(dict(x=crts[:, 0], y=crts[:, 1], label=Class))\n\n\ncolors = {1: \"red\", 2: \"blue\"}\nfig, ax = plt.subplots()\n\n############################################################################\n############################################################################\n# Separating the co ordinates according to the classes CLASSES 1 followed by CLASSES 2\ngrouped = df.groupby(\"label\")\n\n\nu = [] # The MEAN\ngX = [] # Group\n\nfor key, group in grouped:\n gX.append(group.values)\n u.append(group.mean().values)\n\n group.plot(ax=ax, kind=\"scatter\", x=\"x\", y=\"y\", label=key, color=colors[key])\n\nplt.savefig(\"graph.png\")\n\n\ngX = np.array(gX)\nprint(gX)\n\n############################################################################\n\nX = gX[:, :, -2]\nY = gX[:, :, -1]\nclasses = gX[:, :, -3]\n\nprint(\"Classes\")\nclasses = np.hstack(classes)\nprint(classes)\n\nprint(\"Xh\")\nXh = np.hstack(X)\nprint(Xh)\n\nprint(\"Yh\")\nYh = np.hstack(Y)\nprint(Yh)\n\nprint(\"XY\")\nXY = np.column_stack((Xh, Yh))\nprint(XY)\n\n############################################################################\n# Calculating Covariance\nprint(\"Covariance\")\nC = np.cov(Xh, Yh)\nprint(C)\n\nprint(\"Cinv\")\nCinv = np.linalg.inv(C)\nprint(Cinv)\n\n############################################################################\n############################################################################\n\"\"\"\nThe Gaussain Bayes Classifier is:\n\nlog (Likelihood Ratio ^) = W' * X + b\n where\n\n W = (u1 - u2)' * Cinv * X\n b = 0.5 * ((u2' * Cinv * u2) - (u1' * Cinv * u1))\n\n\nNow, if the Likelihood Ratio is:\n Greater than 1? Class 0 Txt Book Class 1\n Lesser than 1? Class 1 Txt Book Class 2\n\nNow, if the Logarithm of the Likelihood Ratio is:\n Greater than log(1) = 0? Class 0 Txt Book Class 1\n Lesser than log(1) = 0? Class 1 Txt Book Class 2\n\n\"\"\"\n############################################################################\n############################################################################\n# Finding W\n\nmask = [False, True, True]\n\nu1 = u[0] # Mean of Class 1\nu2 = u[1] # Mean of Class 2\n\n\nu1 = u1[mask]\nu2 = u2[mask]\n\n\nprint(\"u1 - u2\")\nprint((u1 - u2).T.shape)\n############################################################################\n\nstage1W = np.asmatrix(np.dot((u1 - u2).T, Cinv))\nprint(\"stage1W\")\nprint(stage1W.shape)\n\nprint(\"W\")\nW = np.dot(stage1W, XY.T)\nprint(W)\n\nprint(\"Weight W: \")\nprint(W.shape)\n\n############################################################################\n############################################################################\n# Finding b\nu1 = np.asmatrix(u1)\n\nu2 = np.asmatrix(u2)\n\n\nprint(\"Stage 1b\")\nstage11b = np.dot(u2, Cinv)\nstage1b = np.dot(stage11b, u2.T)\nstage1b = np.asscalar(np.array(stage1b))\n\nprint(\"Stage 2b\")\nstage22b = np.dot(u1, Cinv)\nstage2b = np.dot(stage22b, u1.T)\nstage2b = np.asscalar(np.array(stage2b))\n\nb = 0.5 * (stage1b - stage2b)\n\nprint(\"Bias b: \")\nprint(b)\n\n############################################################################\n############################################################################\n# Calculating error rate\nprint(\"xtest\")\nxtest = XY[:, 0]\nxtest = np.asmatrix(xtest)\nprint(xtest.shape)\n\n\nytest = np.multiply(W, xtest)\nytest = np.add(ytest, b)\nprint(ytest)\n\n# ERROR CLASSIFICATION\nprint(\"CLASSIFICATION\")\nytest[ytest > 0] = 1\nytest[ytest < 1] = 2\nprint(ytest)\n\nprint(classes)\n\nerror = ytest - classes\n\nSQE = np.power(error, [2])\nERR = np.sum(SQE)\n\nprint(\"CLASSIFICATION ERROR RATE:\")\nMSE = (ERR / 2000) * 100\nprint(MSE)\n\n\nytest = np.subtract(ytest, [1])\nax.plot(xtest, ytest, marker=\"x\", linewidth=10)\n\nplt.show()\n\n############################################################################\n############################################################################\n" }, { "path": "code/artificial_intelligence/src/gaussian_naive_bayes/iris1.csv", "content": "sepallength,sepalwidth,petallength,petalwidth,class\n5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5,3.4,1.5,0.2,Iris-setosa\n4.4,2.9,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.4,3.7,1.5,0.2,Iris-setosa\n4.8,3.4,1.6,0.2,Iris-setosa\n4.8,3,1.4,0.1,Iris-setosa\n4.3,3,1.1,0.1,Iris-setosa\n5.8,4,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5,3,1.6,0.2,Iris-setosa\n5,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5,3.5,1.3,0.3,Iris-setosa\n4.5,2.3,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5,3.3,1.4,0.2,Iris-setosa\n7,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5,2,3.5,1,Iris-versicolor\n5.9,3,4.2,1.5,Iris-versicolor\n6,2.2,4,1,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3,5,1.7,Iris-versicolor\n6,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6,2.7,5.1,1.6,Iris-versicolor\n5.4,3,4.5,1.5,Iris-versicolor\n6,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3,4.1,1.3,Iris-versicolor\n5.5,2.5,4,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3,4.6,1.4,Iris-versicolor\n5.8,2.6,4,1.2,Iris-versicolor\n5,2.3,3.3,1,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3,5.8,2.2,Iris-virginica\n7.6,3,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3,5.5,2.1,Iris-virginica\n5.7,2.5,5,2,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6,2.2,5,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2,Iris-virginica\n7.7,2.8,6.7,2,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6,3,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3,5.2,2.3,Iris-virginica\n6.3,2.5,5,1.9,Iris-virginica\n6.5,3,5.2,2,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3,5.1,1.8,Iris-virginica" }, { "path": "code/artificial_intelligence/src/generative_adversarial_networks/CGAN.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"Age-cGAN.ipynb\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YxhGiOi5QyAV\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Downloading the dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"HkFa7wvIMG13\",\n \"colab_type\": \"code\",\n \"outputId\": \"e5c131a9-9def-43df-a1dd-1d95feec6814\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 234\n }\n },\n \"source\": [\n \"!mkdir data\\n\",\n \"!cd data\\n\",\n \"\\n\",\n \"!wget https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/wiki_crop.tar\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"--2019-08-14 17:44:33-- https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/wiki_crop.tar\\n\",\n \"Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.162\\n\",\n \"Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 811315200 (774M) [application/x-tar]\\n\",\n \"Saving to: ‘wiki_crop.tar’\\n\",\n \"\\n\",\n \"wiki_crop.tar 100%[===================>] 773.73M 10.4MB/s in 77s \\n\",\n \"\\n\",\n \"2019-08-14 17:45:52 (10.1 MB/s) - ‘wiki_crop.tar’ saved [811315200/811315200]\\n\",\n \"\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"EfQtSkwGQ6uF\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Extracting the dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n 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\"wiki_crop/99/13155799_1877-08-09_2008.jpg\\n\",\n \"wiki_crop/99/23151499_1988-09-13_2015.jpg\\n\",\n \"wiki_crop/99/2316399_1966-09-19_2011.jpg\\n\",\n \"wiki_crop/99/2316999_1959-05-16_2012.jpg\\n\",\n \"wiki_crop/99/33103599_1966-12-23_1992.jpg\\n\",\n \"wiki_crop/99/33106199_1986-10-02_2011.jpg\\n\",\n \"wiki_crop/99/33147699_1925-05-11_1954.jpg\\n\",\n \"wiki_crop/99/33197599_1991-09-09_2011.jpg\\n\",\n \"wiki_crop/99/43165099_1999-11-12_2014.jpg\\n\",\n \"wiki_crop/99/6316599_1979-08-09_2007.jpg\\n\",\n \"wiki_crop/99/7319399_1981-08-23_2010.jpg\\n\",\n \"wiki_crop/99/14115299_1985-12-16_2007.jpg\\n\",\n \"wiki_crop/99/1419099_1939-10-07_2009.jpg\\n\",\n \"wiki_crop/99/34143899_1981-03-16_2014.jpg\\n\",\n \"wiki_crop/99/8419699_1973-07-02_2014.jpg\\n\",\n \"wiki_crop/99/15113299_1956-08-01_1998.jpg\\n\",\n \"wiki_crop/99/35159299_1987-07-25_1980.jpg\\n\",\n \"wiki_crop/99/1618799_1968-11-10_2010.jpg\\n\",\n \"wiki_crop/99/2618799_1938-05-21_2005.jpg\\n\",\n \"wiki_crop/99/36118999_1983-08-12_2011.jpg\\n\",\n \"wiki_crop/99/36181699_1984-02-29_2013.jpg\\n\",\n \"wiki_crop/99/5613299_1918-06-13_1968.jpg\\n\",\n \"wiki_crop/99/5617699_1971-07-25_2009.jpg\\n\",\n \"wiki_crop/99/9611799_1955-03-22_2012.jpg\\n\",\n \"wiki_crop/99/961599_1950-01-12_2009.jpg\\n\",\n \"wiki_crop/99/171099_1983-06-10_2012.jpg\\n\",\n \"wiki_crop/99/17130499_1927-12-25_2010.jpg\\n\",\n \"wiki_crop/99/17168099_1906-11-09_1948.jpg\\n\",\n \"wiki_crop/99/17196499_1976-03-05_2011.jpg\\n\",\n \"wiki_crop/99/2718299_1917-03-24_1947.jpg\\n\",\n \"wiki_crop/99/18159799_1982-10-09_2010.jpg\\n\",\n \"wiki_crop/99/9811999_1953-06-14_2004.jpg\\n\",\n \"wiki_crop/99/981199_1954-12-30_2006.jpg\\n\",\n \"wiki_crop/99/19166199_1947-07-31_2014.jpg\\n\",\n \"wiki_crop/99/1919799_1975-05-16_2011.jpg\\n\",\n \"wiki_crop/99/29148699_1957-08-22_1998.jpg\\n\",\n \"wiki_crop/99/29153699_1980-09-29_2010.jpg\\n\",\n \"wiki_crop/99/7910899_1981-09-20_2008.jpg\\n\",\n \"wiki_crop/99/102799_1951-10-03_2001.jpg\\n\",\n \"wiki_crop/99/2020599_1977-05-03_2010.jpg\\n\",\n \"wiki_crop/99/2029399_1959-10-03_2008.jpg\\n\",\n \"wiki_crop/99/30239699_1985-10-21_2012.jpg\\n\",\n \"wiki_crop/99/40286099_1967-11-29_2011.jpg\\n\",\n \"wiki_crop/99/21207499_1982-03-24_2014.jpg\\n\",\n \"wiki_crop/99/21246499_1899-05-03_1948.jpg\\n\",\n \"wiki_crop/99/312099_1964-09-11_1999.jpg\\n\",\n \"wiki_crop/99/31292099_1974-03-28_2011.jpg\\n\",\n \"wiki_crop/99/1225199_1898-05-01_1986.jpg\\n\",\n \"wiki_crop/99/12274799_1983-11-29_2010.jpg\\n\",\n \"wiki_crop/99/12279899_1976-02-07_2010.jpg\\n\",\n \"wiki_crop/99/12283899_1967-01-11_2005.jpg\\n\",\n \"wiki_crop/99/42211399_1992-12-23_2014.jpg\\n\",\n \"wiki_crop/99/42246899_1951-02-08_1950.jpg\\n\",\n \"wiki_crop/wiki.mat\\n\",\n \"wiki_crop/00/\\n\",\n \"wiki_crop/01/\\n\",\n \"wiki_crop/02/\\n\",\n \"wiki_crop/03/\\n\",\n \"wiki_crop/04/\\n\",\n \"wiki_crop/05/\\n\",\n \"wiki_crop/06/\\n\",\n \"wiki_crop/07/\\n\",\n \"wiki_crop/08/\\n\",\n \"wiki_crop/09/\\n\",\n \"wiki_crop/10/\\n\",\n \"wiki_crop/11/\\n\",\n \"wiki_crop/12/\\n\",\n \"wiki_crop/13/\\n\",\n \"wiki_crop/14/\\n\",\n \"wiki_crop/15/\\n\",\n \"wiki_crop/16/\\n\",\n \"wiki_crop/17/\\n\",\n \"wiki_crop/18/\\n\",\n \"wiki_crop/19/\\n\",\n \"wiki_crop/20/\\n\",\n \"wiki_crop/21/\\n\",\n \"wiki_crop/22/\\n\",\n \"wiki_crop/23/\\n\",\n \"wiki_crop/24/\\n\",\n \"wiki_crop/25/\\n\",\n \"wiki_crop/26/\\n\",\n \"wiki_crop/27/\\n\",\n \"wiki_crop/28/\\n\",\n \"wiki_crop/29/\\n\",\n \"wiki_crop/30/\\n\",\n \"wiki_crop/31/\\n\",\n \"wiki_crop/32/\\n\",\n \"wiki_crop/33/\\n\",\n \"wiki_crop/34/\\n\",\n \"wiki_crop/35/\\n\",\n \"wiki_crop/36/\\n\",\n \"wiki_crop/37/\\n\",\n \"wiki_crop/38/\\n\",\n \"wiki_crop/39/\\n\",\n \"wiki_crop/40/\\n\",\n \"wiki_crop/41/\\n\",\n \"wiki_crop/42/\\n\",\n \"wiki_crop/43/\\n\",\n \"wiki_crop/44/\\n\",\n \"wiki_crop/45/\\n\",\n \"wiki_crop/46/\\n\",\n \"wiki_crop/47/\\n\",\n \"wiki_crop/48/\\n\",\n \"wiki_crop/49/\\n\",\n \"wiki_crop/50/\\n\",\n \"wiki_crop/51/\\n\",\n \"wiki_crop/52/\\n\",\n \"wiki_crop/53/\\n\",\n \"wiki_crop/54/\\n\",\n \"wiki_crop/55/\\n\",\n \"wiki_crop/56/\\n\",\n \"wiki_crop/57/\\n\",\n \"wiki_crop/58/\\n\",\n \"wiki_crop/59/\\n\",\n \"wiki_crop/60/\\n\",\n \"wiki_crop/61/\\n\",\n \"wiki_crop/62/\\n\",\n \"wiki_crop/63/\\n\",\n \"wiki_crop/64/\\n\",\n \"wiki_crop/65/\\n\",\n \"wiki_crop/66/\\n\",\n \"wiki_crop/67/\\n\",\n \"wiki_crop/68/\\n\",\n \"wiki_crop/69/\\n\",\n \"wiki_crop/70/\\n\",\n \"wiki_crop/71/\\n\",\n \"wiki_crop/72/\\n\",\n \"wiki_crop/73/\\n\",\n \"wiki_crop/74/\\n\",\n \"wiki_crop/75/\\n\",\n \"wiki_crop/76/\\n\",\n \"wiki_crop/77/\\n\",\n \"wiki_crop/78/\\n\",\n \"wiki_crop/79/\\n\",\n \"wiki_crop/80/\\n\",\n \"wiki_crop/81/\\n\",\n \"wiki_crop/82/\\n\",\n \"wiki_crop/83/\\n\",\n \"wiki_crop/84/\\n\",\n \"wiki_crop/85/\\n\",\n \"wiki_crop/86/\\n\",\n \"wiki_crop/87/\\n\",\n \"wiki_crop/88/\\n\",\n \"wiki_crop/89/\\n\",\n \"wiki_crop/90/\\n\",\n \"wiki_crop/91/\\n\",\n \"wiki_crop/92/\\n\",\n \"wiki_crop/93/\\n\",\n \"wiki_crop/94/\\n\",\n \"wiki_crop/95/\\n\",\n \"wiki_crop/96/\\n\",\n \"wiki_crop/97/\\n\",\n \"wiki_crop/98/\\n\",\n \"wiki_crop/99/\\n\",\n \"wiki_crop/\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"UpcHO--tRGyX\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Code to load the extracted .mat files\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"KQi-W_RMNZNx\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"from scipy.io import loadmat\\n\",\n \"\\n\",\n \"def load_data(wiki_dir, dataset = 'wiki'):\\n\",\n \" ## Load the wiki.mat file\\n\",\n \" meta = loadmat(os.path.join(wiki_dir, \\\"{}.mat\\\".format(dataset)))\\n\",\n \" \\n\",\n \" ## Load the list of all files\\n\",\n \" full_path = meta[dataset][0, 0][\\\"full_path\\\"][0]\\n\",\n \" \\n\",\n \" ## List of Matlab serial date numbers\\n\",\n \" dob = meta[dataset][0, 0][\\\"dob\\\"][0]\\n\",\n \" \\n\",\n \" ## List of years when photo was taken\\n\",\n \" photo_taken = meta[dataset][0, 0][\\\"photo_taken\\\"][0] # year\\n\",\n \" \\n\",\n \" ## Calculate age for all dobs\\n\",\n \" age = [calculate_age(photo_taken[i], dob[i]) for i in range(len(dob))]\\n\",\n \" \\n\",\n \" ## Create a list of tuples containing a pair of an image path and age\\n\",\n \" images = []\\n\",\n \" age_list = []\\n\",\n \" for index, image_path in enumerate(full_path):\\n\",\n \" images.append(image_path[0])\\n\",\n \" age_list.append(age[index])\\n\",\n \" \\n\",\n \" ## Return a list of all images and respective age\\n\",\n \" return images, age_list\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"67mourwwROsq\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Code to calculate the age of the person from the serial date number and the year the photo was taken\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"nMhRQSQQQUj5\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"from datetime import datetime\\n\",\n \"\\n\",\n \"def calculate_age(taken, dob):\\n\",\n \" birth = datetime.fromordinal(max(int(dob) - 366, 1))\\n\",\n \" \\n\",\n \" if birth.month < 7:\\n\",\n \" return taken - birth.year\\n\",\n \" else:\\n\",\n \" return taken - birth.year - 1\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cZx7bQL3RYBF\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Importing the essential libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"xJnhmFqeQ14o\",\n \"colab_type\": \"code\",\n \"outputId\": \"926285f7-8074-4bb4-e609-ab797ff0f079\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n }\n },\n \"source\": [\n \"import math\\n\",\n \"import os\\n\",\n \"import time\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from keras import Input, Model\\n\",\n \"from keras.applications import InceptionResNetV2\\n\",\n \"from keras.callbacks import TensorBoard\\n\",\n \"from keras.layers import Conv2D, Flatten, Dense, BatchNormalization\\n\",\n \"from keras.layers import Reshape, concatenate, LeakyReLU, Lambda\\n\",\n \"from keras.layers import K, Activation, UpSampling2D, Dropout\\n\",\n \"from keras.optimizers import Adam\\n\",\n \"from keras.utils import to_categorical\\n\",\n \"from keras_preprocessing import image\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ],\n \"name\": \"stderr\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"fG8uD912SJge\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Encoder Network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ufCJZSjQRkmB\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### The encoder network is a convolutional neural network (CNN) that encodes an image (x) to a latent vector (z) or a latent vector representation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"SEnT376ISCyP\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_encoder():\\n\",\n \" \\n\",\n \" input_layer = Input(shape = (64, 64, 3))\\n\",\n \" \\n\",\n \" ## 1st Convolutional Block\\n\",\n \" enc = Conv2D(filters = 32, kernel_size = 5, strides = 2, padding = 'same')(input_layer)\\n\",\n \" # enc = BatchNormalization()(enc)\\n\",\n \" enc = LeakyReLU(alpha = 0.2)(enc)\\n\",\n \" \\n\",\n \" ## 2nd Convolutional Block\\n\",\n \" enc = Conv2D(filters = 64, kernel_size = 5, strides = 2, padding = 'same')(enc)\\n\",\n \" enc = BatchNormalization()(enc)\\n\",\n \" enc = LeakyReLU(alpha = 0.2)(enc)\\n\",\n \" \\n\",\n \" ## 3rd Convolutional Block\\n\",\n \" enc = Conv2D(filters = 128, kernel_size = 5, strides = 2, padding = 'same')(enc)\\n\",\n \" enc = BatchNormalization()(enc)\\n\",\n \" enc = LeakyReLU(alpha = 0.2)(enc)\\n\",\n \" \\n\",\n \" ## 4th Convolutional Block\\n\",\n \" enc = Conv2D(filters = 256, kernel_size = 5, strides = 2, padding = 'same')(enc)\\n\",\n \" enc = BatchNormalization()(enc)\\n\",\n \" enc = LeakyReLU(alpha = 0.2)(enc)\\n\",\n \" \\n\",\n \" ## Flatten layer\\n\",\n \" enc = Flatten()(enc)\\n\",\n \" \\n\",\n \" ## 1st Fully Connected Layer\\n\",\n \" enc = Dense(4096)(enc)\\n\",\n \" enc = BatchNormalization()(enc)\\n\",\n \" enc = LeakyReLU(alpha = 0.2)(enc)\\n\",\n \" \\n\",\n \" ## 2nd Fully Connected Layer\\n\",\n \" enc = Dense(100)(enc)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Create a model\\n\",\n \" model = Model(inputs = [input_layer], outputs = [enc])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Oonpe4sGUdgd\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Generator Network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"BAD6DgY_R0Rl\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### The generator network is a CNN that takes a 100-dimensional vector 'z' and generates an image with a dimension of (64, 64, 3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"D9ly3Zv8T-oo\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_generator():\\n\",\n \" \\n\",\n \" latent_dims = 100\\n\",\n \" num_classes = 6\\n\",\n \" \\n\",\n \" input_z_noise = Input(shape = (latent_dims, ))\\n\",\n \" input_label = Input(shape = (num_classes, ))\\n\",\n \" \\n\",\n \" x = concatenate([input_z_noise, input_label])\\n\",\n \" \\n\",\n \" \\n\",\n \" x = Dense(2048, input_dim = latent_dims + num_classes)(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" x = Dropout(0.2)(x)\\n\",\n \" \\n\",\n \" x = Dense(256 * 8 * 8)(x)\\n\",\n \" x = BatchNormalization()(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" x = Dropout(0.2)(x)\\n\",\n \" \\n\",\n \" x = Reshape((8, 8, 256))(x)\\n\",\n \" \\n\",\n \" x = UpSampling2D(size = (2, 2))(x)\\n\",\n \" x = Conv2D(filters = 128, kernel_size = 5, padding = 'same')(x)\\n\",\n \" x = BatchNormalization(momentum = 0.8)(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = UpSampling2D(size = (2, 2))(x)\\n\",\n \" x = Conv2D(filters = 64, kernel_size = 5, padding = 'same')(x)\\n\",\n \" x = BatchNormalization(momentum = 0.8)(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = UpSampling2D(size = (2, 2))(x)\\n\",\n \" x = Conv2D(filters = 3, kernel_size = 5, padding = 'same')(x)\\n\",\n \" x = Activation('tanh')(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [input_z_noise, input_label], outputs = [x])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Av0VXOGpX_PH\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def expand_label_input(x):\\n\",\n \" x = K.expand_dims(x, axis = 1)\\n\",\n \" x = K.expand_dims(x, axis = 1)\\n\",\n \" x = K.tile(x, [1, 32, 32, 1])\\n\",\n \" return x\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4jhWzwtzW7Hk\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Discriminator Network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xVO2SCGSSE_s\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### The discriminator network is a CNN with the task of discriminating between the real and the fake images\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"HwYhR6q9W38k\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_discriminator():\\n\",\n \" \\n\",\n \" input_shape = (64, 64, 3)\\n\",\n \" label_shape = (6, )\\n\",\n \" image_input = Input(shape = input_shape)\\n\",\n \" label_input = Input(shape = label_shape)\\n\",\n \" \\n\",\n \" x = Conv2D(64, kernel_size = 3, strides = 2, padding = 'same')(image_input)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" label_input1 = Lambda(expand_label_input)(label_input)\\n\",\n \" x = concatenate([x, label_input1], axis = 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" x = Conv2D(128, kernel_size = 3, strides = 2, padding = 'same')(x)\\n\",\n \" x = BatchNormalization()(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = Conv2D(256, kernel_size = 3, strides = 2, padding = 'same')(x)\\n\",\n \" x = BatchNormalization()(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = Conv2D(512, kernel_size = 3, strides = 2, padding = 'same')(x)\\n\",\n \" x = BatchNormalization()(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = Flatten()(x)\\n\",\n \" x = Dense(1, activation = 'sigmoid')(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [image_input, label_input], outputs = [x])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cW5vfajOZu5t\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"# Utility Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ZoA4QAk0ZGGu\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_fr_combined_network(encoder, generator, fr_model):\\n\",\n \" input_image = Input(shape = (64, 64, 3))\\n\",\n \" input_label = Input(shape = (6, ))\\n\",\n \" \\n\",\n \" latent0 = encoder(input_image)\\n\",\n \" \\n\",\n \" gen_images = generator([latent0, input_label])\\n\",\n \" \\n\",\n \" fr_model.trainable = False\\n\",\n \" \\n\",\n \" resized_images = Lambda(lambda x: K.resize_images(gen_images, height_factor = 2,\\n\",\n \" width_factor = 2, \\n\",\n \" data_format = 'channels_last'))(gen_images)\\n\",\n \" \\n\",\n \" embeddings = fr_model(resized_images)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [input_image, input_label],\\n\",\n \" outputs = [embeddings])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"GgcpPMzATRLL\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### Code for the Face Recognition Network (generates a 128-dimensional embedding of a particular fed input, to improve the generator and the encoder networks)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Cacbxgzwa1a0\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_fr_model(input_shape):\\n\",\n \" \\n\",\n \" resnet_model = InceptionResNetV2(include_top = False, weights = 'imagenet',\\n\",\n \" input_shape = input_shape, pooling = 'avg')\\n\",\n \" image_input = resnet_model.input\\n\",\n \" x = resnet_model.layers[-1].output\\n\",\n \" out = Dense(128)(x)\\n\",\n \" embedder_model = Model(inputs = [image_input], outputs = [out])\\n\",\n \" \\n\",\n \" input_layer = Input(shape = input_shape)\\n\",\n \" \\n\",\n \" x = embedder_model(input_layer)\\n\",\n \" output = Lambda(lambda x: K.l2_normalize(x, axis = -1))(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [input_layer], outputs = [output])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3ozZFdfDTDyU\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### The function below will resize the images from a shape of (64, 64, 3) to a shape of (192, 192, 3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Q_Z9Yhyoizk-\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_image_resizer():\\n\",\n \" \\n\",\n \" input_layer = Input(shape = (64, 64, 3))\\n\",\n \" \\n\",\n \" resized_images = Lambda(lambda x: K.resize_images(x, height_factor = 3,\\n\",\n \" width_factor = 3,\\n\",\n \" data_format = 'channels_last'))(input_layer)\\n\",\n \" \\n\",\n \" model = Model(inputs = [input_layer],\\n\",\n \" outputs = [resized_images])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"PPjHDtrMSUp_\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### This function will convert age to the respective category\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"m5mnk1fkjftV\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def age_to_category(age_list):\\n\",\n \" \\n\",\n \" age_list1 = []\\n\",\n \" \\n\",\n \" for age in age_list:\\n\",\n \" if 0 < age <= 18:\\n\",\n \" age_category = 0\\n\",\n \" elif 18 < age <= 29:\\n\",\n \" age_category = 1\\n\",\n \" elif 29 < age <= 39:\\n\",\n \" age_category = 2\\n\",\n \" elif 39 < age <= 49:\\n\",\n \" age_category = 3\\n\",\n \" elif 49 < age <= 59:\\n\",\n \" age_category = 4\\n\",\n \" elif age >= 60:\\n\",\n \" age_category = 5\\n\",\n \" \\n\",\n \" age_list1.append(age_category)\\n\",\n \" \\n\",\n \" return age_list1\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"LB1yeYGDkHiq\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def load_images(data_dir, image_paths, image_shape):\\n\",\n \" \\n\",\n \" images = None\\n\",\n \" \\n\",\n \" for i, image_path in enumerate(image_paths):\\n\",\n \" print()\\n\",\n \" try:\\n\",\n \" ## Load image\\n\",\n \" loaded_image = image.load_img(os.path.join(data_dir, image_path),\\n\",\n \" target_size = image_shape)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Convert PIL image to numpy ndarray\\n\",\n \" loaded_image = image.img_to_array(loaded_image)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Add another dimension (Add batch dimension)\\n\",\n \" loaded_image = np.expand_dims(loaded_image, axis = 0)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Concatenate all images into one tensor:\\n\",\n \" if images is None:\\n\",\n \" images = loaded_image\\n\",\n \" else:\\n\",\n \" images = np.concatenate([images, loaded_image], axis = 0)\\n\",\n \" \\n\",\n \" except Exception as e:\\n\",\n \" print(\\\"Error: \\\", i, e)\\n\",\n \" \\n\",\n \" return images\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"dXYfNhQISuUr\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"### This function helps calculate the euclidean_distance_loss, used in the initial latent vector approximation step\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"3t7cL7NvxD9u\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def euclidean_distance_loss(y_true, y_pred):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Euclidean distance\\n\",\n \" https://en.wikipedia.org/wiki/Euclidean_distance\\n\",\n \" y_true = TF / Theano tensor\\n\",\n \" y_pred = TF / Theano tensor of the same shape as y_true\\n\",\n \" returns float\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" return K.sqrt(K.sum(K.square(y_pred - y_true), axis = -1))\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Nz_4iPHWx3-z\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def write_log(callback, name, value, batch_no):\\n\",\n \" summary = tf.Summary()\\n\",\n \" summary_value = summary.value.add()\\n\",\n \" summary_value.simple_value = value\\n\",\n \" summary_value.tag = name\\n\",\n \" callback.writer.add_summary(summary, batch_no)\\n\",\n \" callback.writer.flush()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"LihS0W34yTRo\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def save_rgb_img(img, path):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Save an RGB image\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" fig = plt.figure()\\n\",\n \" ax = fig.add_subplot(1, 1, 1)\\n\",\n \" ax.imshow(img)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Image\\\")\\n\",\n \" \\n\",\n \" plt.savefig(path)\\n\",\n \" plt.close()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"VpUYhxHQzHMQ\",\n \"colab_type\": \"code\",\n \"outputId\": \"6dae2aa6-7f82-44c3-9156-de786b277270\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n }\n },\n \"source\": [\n \"if __name__ == '__main__':\\n\",\n \" \\n\",\n \" ## Define hyperparameters\\n\",\n \"# data_dir = \\\"data\\\"\\n\",\n \"# wiki_dir = os.path.join(data_dir, \\\"wiki_crop\\\")\\n\",\n \" wiki_dir = \\\"wiki_crop\\\"\\n\",\n \" epochs = 500\\n\",\n \" batch_size = 2\\n\",\n \" image_shape = (64, 64, 3)\\n\",\n \" z_shape = 100\\n\",\n \" TRAIN_GAN = True\\n\",\n \" TRAIN_ENCODER = False\\n\",\n \" TRAIN_GAN_WITH_FR = False\\n\",\n \" fr_image_shape = (192, 192, 3)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Define optimizers\\n\",\n \" dis_optimizer = Adam(lr = 0.0002, beta_1 = 0.5, beta_2 = 0.999, epsilon = 10e-8)\\n\",\n \" gen_optimizer = Adam(lr = 0.0002, beta_1 = 0.5, beta_2 = 0.999, epsilon = 10e-8)\\n\",\n \" adversarial_optimizer = Adam(lr = 0.0002, beta_1 = 0.5, beta_2 = 0.999, epsilon = 10e-8)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Build and compile networks\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ## Build and compile the discriminator network\\n\",\n \" discriminator = build_discriminator()\\n\",\n \" discriminator.compile(loss = ['binary_crossentropy'],\\n\",\n \" optimizer = dis_optimizer)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Build and compile the generator network\\n\",\n \" generator = build_generator()\\n\",\n \" generator.compile(loss = ['binary_crossentropy'],\\n\",\n \" optimizer = gen_optimizer)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Build and compile the adversarial model\\n\",\n \" discriminator.trainable = False\\n\",\n \" input_z_noise = Input(shape = (100, ))\\n\",\n \" input_label = Input(shape = (6, ))\\n\",\n \" recons_images = generator([input_z_noise, input_label])\\n\",\n \" valid = discriminator([recons_images, input_label])\\n\",\n \" adversarial_model = Model(inputs = [input_z_noise, input_label],\\n\",\n \" outputs = [valid])\\n\",\n \" adversarial_model.compile(loss = ['binary_crossentropy'],\\n\",\n \" optimizer = gen_optimizer)\\n\",\n \" \\n\",\n \" tensorboard = TensorBoard(log_dir = \\\"logs/{}\\\".format(time.time()))\\n\",\n \" tensorboard.set_model(generator)\\n\",\n \" tensorboard.set_model(discriminator)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Load the dataset\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" images, age_list = load_data(wiki_dir = wiki_dir, dataset = \\\"wiki\\\")\\n\",\n \" age_cat = age_to_category(age_list)\\n\",\n \" final_age_cat = np.reshape(np.array(age_cat), [len(age_cat), 1])\\n\",\n \" classes = len(set(age_cat))\\n\",\n \" y = to_categorical(final_age_cat, num_classes = classes)\\n\",\n \" \\n\",\n \" \\n\",\n \" loaded_images = load_images(wiki_dir, images, (image_shape[0], image_shape[1]))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Implement label smoothing\\n\",\n \" real_labels = np.ones((batch_size, 1), dtype = np.float32) * 0.9\\n\",\n \" fake_labels = np.zeros((batch_size, 1), dtype = np.float32) * 0.1\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Train the generator and the discriminator network\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if TRAIN_GAN:\\n\",\n \" for epoch in range(epochs):\\n\",\n \" print(\\\"Epoch: {}\\\".format(epoch))\\n\",\n \" \\n\",\n \" gen_losses = []\\n\",\n \" dis_losses = []\\n\",\n \" \\n\",\n \" number_of_batches = int(len(loaded_images) / batch_size)\\n\",\n \" print(\\\"Number of batches: \\\", number_of_batches)\\n\",\n \" for index in range(number_of_batches):\\n\",\n \" print(\\\"Batch: {}\\\".format(index + 1))\\n\",\n \" \\n\",\n \" images_batch = loaded_images[index * batch_size:(index + 1) * batch_size]\\n\",\n \" images_batch = images_batch / 127.5 - 1.0\\n\",\n \" images_batch = images_batch.astype(np.float32)\\n\",\n \" \\n\",\n \" y_batch = y[index * batch_size: (index + 1) * batch_size]\\n\",\n \" z_noise = np.random.normal(0, 1, size = (batch_size, z_shape))\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Train the discriminator network\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" ## Generate fake images\\n\",\n \" initial_recons_images = generator.predict_on_batch([z_noise, y_batch])\\n\",\n \" \\n\",\n \" d_loss_real = discriminator.train_on_batch([images_batch, y_batch], real_labels)\\n\",\n \" d_loss_fake = discriminator.train_on_batch([initial_recons_images, y_batch], fake_labels)\\n\",\n \" \\n\",\n \" d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\\n\",\n \" print(\\\"d_loss: {}\\\".format(d_loss))\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Train the generator network\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" z_noise2 = np.random.normal(0, 1, size = (batch_size, z_shape))\\n\",\n \" random_labels = np.random.randint(0, 6, batch_size).reshape(-1, 1)\\n\",\n \" random_labels = to_categorical(random_labels, 6)\\n\",\n \" \\n\",\n \" g_loss = adversarial_model.train_on_batch([z_noise2, random_labels], [1] * batch_size)\\n\",\n \" \\n\",\n \" print(\\\"g_loss: {}\\\".format(g_loss))\\n\",\n \" \\n\",\n \" \\n\",\n \" gen_losses.append(g_loss)\\n\",\n \" dis_losses.append(d_loss)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Write losses to Tensorboard\\n\",\n \" write_log(tensorboard, 'g_loss', np.mean(gen_losses), epoch)\\n\",\n \" write_log(tensorboard, 'd_loss', np.mean(dis_losses), epoch)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Generate images after every 10th epoch\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if epoch % 10 == 0:\\n\",\n \" images_batch = loaded_images[0:batch_size]\\n\",\n \" images_batch = images_batch / 127.5 - 1.0\\n\",\n \" images_batch = images_batch.astype(np.float32)\\n\",\n \" \\n\",\n \" y_batch = y[0:batch_size]\\n\",\n \" z_noise = np.random.normal(0, 1, size = (batch_size, z_shape))\\n\",\n \" \\n\",\n \" gen_images = generator.predict_on_batch([z_noise, y_batch])\\n\",\n \" \\n\",\n \" for i, img in enumerate(gen_images[:5]):\\n\",\n \" save_rgb_img(img, path = \\\"results/img_{}_{}.png\\\".format(epoch, i))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Save networks\\n\",\n \" try:\\n\",\n \" generator.save_weights(\\\"generator.h5\\\")\\n\",\n \" discriminator.save_weights(\\\"discriminator.h5\\\")\\n\",\n \" except Exception as e:\\n\",\n \" print(\\\"Error: \\\", e)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Train encoder\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if TRAIN_ENCODER:\\n\",\n \" \\n\",\n \" ## Build and compile encoder\\n\",\n \" encoder = build_encoder()\\n\",\n \" encoder.compile(loss = euclidean_distance_loss,\\n\",\n \" optimizer = 'adam')\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Load the generator network's weights\\n\",\n \" try:\\n\",\n \" generator.load_weights(\\\"generator.h5\\\")\\n\",\n \" except Exception as e:\\n\",\n \" print(\\\"Error: \\\", e)\\n\",\n \" \\n\",\n \" \\n\",\n \" z_i = np.random.normal(0, 1, size = (5000, z_shape))\\n\",\n \" \\n\",\n \" y = np.random.randint(low = 0, high = 6, size = (5000, ),\\n\",\n \" dtype = np.int64)\\n\",\n \" num_classes = len(set(y))\\n\",\n \" y = np.reshape(np.array(y), [len(y), 1])\\n\",\n \" y = to_categorical(y, num_classes = num_classes)\\n\",\n \" \\n\",\n \" \\n\",\n \" for epoch in range(epochs):\\n\",\n \" print(\\\"Epoch: \\\", epoch)\\n\",\n \" \\n\",\n \" encoder_losses = []\\n\",\n \" \\n\",\n \" number_of_batches = int(z_i.shape[0] / batch_size)\\n\",\n \" print(\\\"Number of batches: \\\", number_of_batches)\\n\",\n \" \\n\",\n \" for index in range(number_of_batches):\\n\",\n \" print(\\\"Batch: \\\", index + 1)\\n\",\n \" \\n\",\n \" z_batch = z_i[index * batch_size: (index + 1) * batch_size]\\n\",\n \" y_batch = y[index * batch_size: (index + 1) * batch_size]\\n\",\n \" \\n\",\n \" generated_images = generator.predict_on_batch([z_batch, y_batch])\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Train the encoder model\\n\",\n \" encoder_loss = encoder.train_on_batch(generated_images, z_batch)\\n\",\n \" print(\\\"Encoder loss: \\\", encoder_loss)\\n\",\n \" \\n\",\n \" encoder_losses.append(encoder_loss)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Write the encoder loss to Tensorboard\\n\",\n \" write_log(tensorboard, \\\"encoder_loss\\\", np.mean(encoder_losses), epoch)\\n\",\n \" \\n\",\n \" ## Save the encoder model\\n\",\n \" encoder.save_weights(\\\"encoder.h5\\\")\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Optimize the encoder and the generator network\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if TRAIN_GAN_WITH_FR:\\n\",\n \" \\n\",\n \" ## Load the encoder network\\n\",\n \" encoder = build_encoder()\\n\",\n \" encoder.load_weights(\\\"encoder.h5\\\")\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Load the generator network\\n\",\n \" generator.load_weights(\\\"generator.h5\\\")\\n\",\n \" \\n\",\n \" image_resizer = build_image_resizer()\\n\",\n \" image_resizer.compile(loss = ['binary_crossentropy'],\\n\",\n \" optimzer = 'adam')\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Face recognition model\\n\",\n \" fr_model = build_fr_model(input_shape = fr_image_shape)\\n\",\n \" fr_model.compile(loss = ['binary_crossentropy'],\\n\",\n \" optimizer = 'adam')\\n\",\n \" \\n\",\n \" ## Make the face recognition model as non-trainable\\n\",\n \" fr_model.trainable = False\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Input layers\\n\",\n \" input_image = Input(shape = (64, 64, 3))\\n\",\n \" input_label = Input(shape = (6, ))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Use the encoder and the generator network\\n\",\n \" latent0 = encoder(input_image)\\n\",\n \" gen_images = generator([latent0, input_label])\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Resize images to the desired shape\\n\",\n \" resized_images = Lambda(lambda x: K.resize_images(gen_images, height_factor = 3,\\n\",\n \" width_factor = 3,\\n\",\n \" data_format = 'channels_last'))(gen_images) \\n\",\n \" embeddings = fr_model(resized_images)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Create a Keras model and specify the inputs and outputs for the network\\n\",\n \" fr_adversarial_model = Model(inputs = [input_image, input_label],\\n\",\n \" outputs = [embeddings])\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Compile the model\\n\",\n \" fr_adversarial_model.compile(loss = euclidean_distance_loss,\\n\",\n \" optimizer = adversarial_optimizer)\\n\",\n \" \\n\",\n \" for epoch in range(epochs):\\n\",\n \" print(\\\"Epoch: \\\", epoch)\\n\",\n \" \\n\",\n \" reconstruction_losses = []\\n\",\n \" \\n\",\n \" number_of_batches = int(len(loaded_images) / batch_size)\\n\",\n \" print(\\\"Number of batches: \\\", number_of_batches)\\n\",\n \" for index in range(number_of_batches):\\n\",\n \" print(\\\"Batch: \\\", index + 1)\\n\",\n \" \\n\",\n \" images_batch = loaded_images[index * batch_size: (index + 1) * batch_size]\\n\",\n \" images_batch = images_batch / 127.5 - 1.0\\n\",\n \" images_batch = images_batch.astype(np.float32)\\n\",\n \" \\n\",\n \" y_batch = y[index * batch_size: (index + 1) * batch_size]\\n\",\n \" \\n\",\n \" images_batch_resized = image_resizer.predict_on_batch(images_batch)\\n\",\n \" \\n\",\n \" real_embeddings = fr_model.predict_on_batch(images_batch_resized)\\n\",\n \" \\n\",\n \" reconstruction_loss = fr_adversarial_model.train_on_batch([images_batch, y_batch], real_embeddings)\\n\",\n \" \\n\",\n \" print(\\\"Reconstruction loss: \\\", reconstruction_loss)\\n\",\n \" \\n\",\n \" reconstruction_losses.append(reconstruction_loss)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Write the reconstruction loss to Tensorboard\\n\",\n \" write_log(tensorboard, \\\"reconstruction_loss\\\", np.mean(reconstruction_losses), epoch)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Generate images\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if epoch % 10 == 0:\\n\",\n \" images_batch = loaded_images[0:batch_size]\\n\",\n \" images_batch = images_batch / 127.5 - 1.0\\n\",\n \" images_batch = images_batch.astype(np.float32)\\n\",\n \" \\n\",\n \" y_batch = y[0:batch_size]\\n\",\n \" z_noise = np.random.normal(0, 1, size = (batch_size, z_shape))\\n\",\n \" \\n\",\n \" gen_images = generator.predict_on_batch([z_noise, y_batch])\\n\",\n \" \\n\",\n \" for i, img in enumerate(gen_images[:5]):\\n\",\n \" save_rgb_image(img, path = \\\"results/img_opt_{}_{}.png\\\".format(epoch, i))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Save improved weights for both of the networks\\n\",\n \" generator.save_weights(\\\"generator_optimized.h5\\\")\\n\",\n \" encoder.save_weights(\\\"encoder_optimized.h5\\\")\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0814 17:46:17.835705 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\\n\",\n \"\\n\",\n \"W0814 17:46:17.837395 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\\n\",\n \"\\n\",\n \"W0814 17:46:17.853470 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\\n\",\n \"\\n\",\n \"W0814 17:46:17.932369 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\\n\",\n \"\\n\",\n \"W0814 17:46:17.933954 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\\n\",\n \"\\n\",\n \"W0814 17:46:21.165225 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\\n\",\n \"\\n\",\n \"W0814 17:46:21.614878 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\\n\",\n \"\\n\",\n \"W0814 17:46:21.629185 139803639469952 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use tf.where in 2.0, which has the same broadcast rule as np.where\\n\",\n \"W0814 17:46:21.672935 139803639469952 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\\n\",\n \"W0814 17:46:21.839127 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\\n\",\n \"\\n\",\n \"W0814 17:46:25.817054 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\\n\",\n \"\\n\",\n \"W0814 17:46:25.818996 139803639469952 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\\n\",\n \"\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n 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heaviness of model.\"\n ]\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/generative_adversarial_networks/CycleGAN.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"CycleGAN.ipynb\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"V-On8I2N6ClU\",\n \"colab_type\": \"code\",\n \"outputId\": \"d0ee9c56-e199-48a1-a582-917a2845f9f9\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n }\n },\n \"source\": [\n \"!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"--2019-08-22 12:55:44-- https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip\\n\",\n \"Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.189.73\\n\",\n \"Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.189.73|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 305231073 (291M) [application/zip]\\n\",\n \"Saving to: ‘monet2photo.zip’\\n\",\n \"\\n\",\n \"monet2photo.zip 100%[===================>] 291.09M 21.2MB/s in 15s \\n\",\n \"\\n\",\n \"2019-08-22 12:55:59 (19.6 MB/s) - ‘monet2photo.zip’ saved [305231073/305231073]\\n\",\n \"\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"g7juGuCu7uPK\",\n \"colab_type\": \"code\",\n \"outputId\": \"042304bd-c13d-4dd1-ff9c-c15732f3664a\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n }\n },\n \"source\": [\n \"!mkdir data\\n\",\n \"!cd data\\n\",\n \"\\n\",\n \"!unzip monet2photo.zip\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n 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monet2photo/trainB/2015-03-08 11:36:11.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-12-28 09:52:30.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-10-18 16:01:35.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-11-24 14:23:25.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-08-15 09:27:14.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-12-02 00:26:57.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-03-02 19:10:47.jpg \\n\",\n \" inflating: monet2photo/trainB/2014-02-01 23:54:02.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-02-09 01:36:29.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-01-01 11:24:26.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-01-17 04:12:05.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-06-04 21:11:04.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-11-26 01:56:56.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-08-15 04:09:41.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-06-09 05:44:30.jpg \\n\",\n \" inflating: monet2photo/trainB/2014-02-27 22:35:01.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-12-05 00:37:07.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-06-20 17:24:46.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-11-19 01:56:28.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-04-19 15:14:48.jpg \\n\",\n \" inflating: monet2photo/trainB/2016-07-02 10:51:28.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-12-14 11:26:36.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-12-04 18:15:53.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-05-18 13:55:02.jpg \\n\",\n \" inflating: monet2photo/trainB/2014-03-03 09:20:36.jpg \\n\",\n \" inflating: monet2photo/trainB/2014-11-01 03:04:22.jpg \\n\",\n \" inflating: monet2photo/trainB/2015-05-28 02:38:04.jpg \\n\",\n \" creating: monet2photo/testA/\\n\",\n \" inflating: monet2photo/testA/01190.jpg \\n\",\n \" inflating: monet2photo/testA/01120.jpg \\n\",\n \" inflating: monet2photo/testA/00360.jpg \\n\",\n \" inflating: 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\\n\",\n \" inflating: monet2photo/testA/00550.jpg \\n\",\n \" inflating: monet2photo/testA/00970.jpg \\n\",\n \" inflating: monet2photo/testA/00290.jpg \\n\",\n \" inflating: monet2photo/testA/00040.jpg \\n\",\n \" inflating: monet2photo/testA/00910.jpg \\n\",\n \" inflating: monet2photo/testA/00610.jpg \\n\",\n \" inflating: monet2photo/testA/00150.jpg \\n\",\n \" inflating: monet2photo/testA/00320.jpg \\n\",\n \" inflating: monet2photo/testA/00270.jpg \\n\",\n \" inflating: monet2photo/testA/01140.jpg \\n\",\n \" inflating: monet2photo/testA/00670.jpg \\n\",\n \" inflating: monet2photo/testA/00830.jpg \\n\",\n \" inflating: monet2photo/testA/00050.jpg \\n\",\n \" inflating: monet2photo/testA/00960.jpg \\n\",\n \" inflating: monet2photo/testA/00430.jpg \\n\",\n \" inflating: monet2photo/testA/00060.jpg \\n\",\n \" inflating: monet2photo/testA/00210.jpg \\n\",\n \" inflating: monet2photo/testA/00190.jpg \\n\",\n \" inflating: monet2photo/testA/00200.jpg \\n\",\n \" inflating: monet2photo/testA/00460.jpg \\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"GWktLVd68JTs\",\n \"colab_type\": \"code\",\n \"outputId\": \"f4969979-9732-4db8-c086-0db1baeb501b\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n }\n },\n \"source\": [\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import PIL\\n\",\n \"\\n\",\n \"from glob import glob\\n\",\n \"from keras import Input, Model\\n\",\n \"from keras.callbacks import TensorBoard\\n\",\n \"from keras.layers import Conv2D, BatchNormalization, Activation\\n\",\n \"from keras.layers import Add, Conv2DTranspose, ZeroPadding2D, LeakyReLU\\n\",\n \"from keras.optimizers import Adam\\n\",\n \"from imageio import imread\\n\",\n \"from skimage.transform import resize\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ],\n \"name\": \"stderr\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"TzL3qUk4fkCp\",\n \"colab_type\": \"code\",\n \"outputId\": \"aaa90111-d001-4325-e500-34a62f755671\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 359\n }\n },\n \"source\": [\n \"!pip install git+https://www.github.com/keras-team/keras-contrib.git\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting git+https://www.github.com/keras-team/keras-contrib.git\\n\",\n \" Cloning https://www.github.com/keras-team/keras-contrib.git to /tmp/pip-req-build-s_z8f_t6\\n\",\n \" Running command git clone -q https://www.github.com/keras-team/keras-contrib.git /tmp/pip-req-build-s_z8f_t6\\n\",\n \"Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-contrib==2.0.8) (2.2.4)\\n\",\n \"Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.12.0)\\n\",\n \"Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.3.1)\\n\",\n \"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.0.8)\\n\",\n \"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (2.8.0)\\n\",\n \"Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.16.4)\\n\",\n \"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.1.0)\\n\",\n \"Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (3.13)\\n\",\n \"Building wheels for collected packages: keras-contrib\\n\",\n \" Building wheel for keras-contrib (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for keras-contrib: filename=keras_contrib-2.0.8-cp36-none-any.whl size=101066 sha256=cfc88de5fa2eac3e2695288ecdf4f8de4699b739e42455ffe03a614c72c4b121\\n\",\n \" Stored in directory: /tmp/pip-ephem-wheel-cache-pzwn1cn5/wheels/11/27/c8/4ed56de7b55f4f61244e2dc6ef3cdbaff2692527a2ce6502ba\\n\",\n \"Successfully built keras-contrib\\n\",\n \"Installing collected packages: keras-contrib\\n\",\n \"Successfully installed keras-contrib-2.0.8\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ZQW5UWxIf8LB\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nSd_diaAecm1\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Residual Block\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"UxhtAPEK-7Cy\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def residual_block(x):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Residual block\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = \\\"same\\\")(x)\\n\",\n \" res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)\\n\",\n \" res = Activation('relu')(res)\\n\",\n \" res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = \\\"same\\\")(res)\\n\",\n \" res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)\\n\",\n \" \\n\",\n \" return Add()([res, x])\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"a-RjZz0QkIoU\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Generator Network\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"g4iD-p4FebVR\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_generator():\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creating a generator network with the hyperparameters defined below\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" input_shape = (128, 128, 3)\\n\",\n \" residual_blocks = 6\\n\",\n \" input_layer = Input(shape = input_shape)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## 1st Convolutional Block\\n\",\n \" x = Conv2D(filters = 32, kernel_size = 7, strides = 1, padding = \\\"same\\\")(input_layer)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = Activation(\\\"relu\\\")(x)\\n\",\n \" \\n\",\n \" ## 2nd Convolutional Block\\n\",\n \" x = Conv2D(filters = 64, kernel_size = 3, strides = 2, padding = \\\"same\\\")(x)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = Activation(\\\"relu\\\")(x)\\n\",\n \" \\n\",\n \" ## 3rd Convolutional Block\\n\",\n \" x = Conv2D(filters = 128, kernel_size = 3, strides = 2, padding = \\\"same\\\")(x)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = Activation(\\\"relu\\\")(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Residual blocks\\n\",\n \" for _ in range(residual_blocks):\\n\",\n \" x = residual_block(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## 1st Upsampling Block\\n\",\n \" x = Conv2DTranspose(filters = 64, kernel_size = 3, strides = 2, padding = \\\"same\\\", use_bias = False)(x)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = Activation(\\\"relu\\\")(x)\\n\",\n \" \\n\",\n \" ## 2nd Upsampling Block\\n\",\n \" x = Conv2DTranspose(filters = 32, kernel_size = 3, strides = 2, padding = \\\"same\\\", use_bias = False)(x)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = Activation(\\\"relu\\\")(x)\\n\",\n \" \\n\",\n \" ## Last Convolutional Layer\\n\",\n \" x = Conv2D(filters = 3, kernel_size = 7, strides = 1, padding = \\\"same\\\")(x)\\n\",\n \" output = Activation(\\\"tanh\\\")(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [input_layer], outputs = [output])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Gmh5NIgyzyZZ\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Discriminator Network\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"lgsx2Z3EkEQG\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def build_discriminator():\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create a discriminator network using the hyperparameters defined below\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" input_shape = (128, 128, 3)\\n\",\n \" hidden_layers = 3\\n\",\n \" \\n\",\n \" input_layer = Input(shape = input_shape)\\n\",\n \" \\n\",\n \" x = ZeroPadding2D(padding = (1, 1))(input_layer)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## 1st Convolutional Block\\n\",\n \" x = Conv2D(filters = 64, kernel_size = 4, strides = 2, padding = \\\"valid\\\")(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = ZeroPadding2D(padding = (1, 1))(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## 3 Hidden Convolutional Blocks\\n\",\n \" for i in range(1, hidden_layers + 1):\\n\",\n \" x = Conv2D(filters = 2 ** i * 64, kernel_size = 4, strides = 2, padding = \\\"valid\\\")(x)\\n\",\n \" x = InstanceNormalization(axis = 1)(x)\\n\",\n \" x = LeakyReLU(alpha = 0.2)(x)\\n\",\n \" \\n\",\n \" x = ZeroPadding2D(padding = (1, 1))(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Last Convolutional Layer\\n\",\n \" output = Conv2D(filters = 1, kernel_size = 4, strides = 1, activation = \\\"sigmoid\\\")(x)\\n\",\n \" \\n\",\n \" \\n\",\n \" model = Model(inputs = [input_layer], outputs = [output])\\n\",\n \" return model\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"I_JvJPmVpnzG\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def load_images(data_dir):\\n\",\n \" \\n\",\n \" imagesA = glob(data_dir + '/testA/*.*')\\n\",\n \" imagesB = glob(data_dir + '/testB/*.*')\\n\",\n \" \\n\",\n \" allImagesA = []\\n\",\n \" allImagesB = []\\n\",\n \" \\n\",\n \" for index, filename in enumerate(imagesA):\\n\",\n \" imgA = imread(filename, pilmode = \\\"RGB\\\")\\n\",\n \" imgB = imread(imagesB[index], pilmode = \\\"RGB\\\")\\n\",\n \" \\n\",\n \" imgA = resize(imgA, (128, 128))\\n\",\n \" imgB = resize(imgB, (128, 128))\\n\",\n \" \\n\",\n \" if np.random.random() > 0.5:\\n\",\n \" imgA = np.fliplr(imgA)\\n\",\n \" imgB = np.fliplr(imgB)\\n\",\n \" \\n\",\n \" \\n\",\n \" allImagesA.append(imgA)\\n\",\n \" allImagesB.append(imgB)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Normalize images\\n\",\n \" allImagesA = np.array(allImagesA) / 127.5 - 1.\\n\",\n \" allImagesB = np.array(allImagesB) / 127.5 - 1.\\n\",\n \" \\n\",\n \" return allImagesA, allImagesB\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"C7tvhTzYs7Kt\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def load_test_batch(data_dir, batch_size):\\n\",\n \" \\n\",\n \" imagesA = glob(data_dir + '/testA/*.*')\\n\",\n \" imagesB = glob(data_dir + '/testB/*.*')\\n\",\n \" \\n\",\n \" imagesA = np.random.choice(a = imagesA, size = batch_size)\\n\",\n \" imagesB = np.random.choice(a = imagesB, size = batch_size)\\n\",\n \" \\n\",\n \" allA = []\\n\",\n \" allB = []\\n\",\n \" \\n\",\n \" for i in range(len(imagesA)):\\n\",\n \" ## Load and resize images\\n\",\n \" imgA = resize(imread(imagesA[i], pilmode = 'RGB').astype(np.float32), (128, 128))\\n\",\n \" imgB = resize(imread(imagesB[i], pilmode = 'RGB').astype(np.float32), (128, 128))\\n\",\n \" \\n\",\n \" allA.append(imgA)\\n\",\n \" allB.append(imgB)\\n\",\n \" \\n\",\n \" return np.array(allA) / 127.5 - 1.0, np.array(allB) / 127.5 - 1.0\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"KnGwFikvvR8y\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"!mkdir results\\n\",\n \"\\n\",\n \"def save_images(originalA, generatedB, reconstructedA,\\n\",\n \" originalB, generatedA, reconstructedB, path):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Save images\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" fig = plt.figure()\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 1)\\n\",\n \" ax.imshow(originalA)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Original\\\")\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 2)\\n\",\n \" ax.imshow(generatedB)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Generated\\\")\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 3)\\n\",\n \" ax.imshow(reconstructedA)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Reconstructed\\\")\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 4)\\n\",\n \" ax.imshow(originalB)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Original\\\")\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 5)\\n\",\n \" ax.imshow(generatedA)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Generated\\\")\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(2, 3, 6)\\n\",\n \" ax.imshow(reconstructedB)\\n\",\n \" ax.axis(\\\"off\\\")\\n\",\n \" ax.set_title(\\\"Reconstructed\\\")\\n\",\n \" \\n\",\n \" plt.savefig(path)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"S9r7OmwCxV04\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def write_log(callback, name, loss, batch_no):\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Write training summary to TensorBoard\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" summary = tf.Summary()\\n\",\n \" summary_value = summary.value.add()\\n\",\n \" summary_value.simple_value = loss\\n\",\n \" summary_value.tag = name\\n\",\n \" callback.writer.add_summary(summary, batch_no)\\n\",\n \" callback.writer.flush()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"bpcRW1SpyWrK\",\n \"colab_type\": \"code\",\n \"outputId\": \"b480ba42-96f6-4202-f33a-cc25030d5b16\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n }\n },\n \"source\": [\n \"if __name__ == '__main__':\\n\",\n \" \\n\",\n \" data_dir = \\\"monet2photo\\\"\\n\",\n \" batch_size = 1\\n\",\n \" epochs = 500\\n\",\n \" mode = 'train'\\n\",\n \" \\n\",\n \" if mode == 'train':\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Load dataset\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" imagesA, imagesB = load_images(data_dir = data_dir)\\n\",\n \" \\n\",\n \" ## Define the common optimizer\\n\",\n \" common_optimizer = Adam(0.002, 0.5)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Build and compile discriminator networks\\n\",\n \" discriminatorA = build_discriminator()\\n\",\n \" discriminatorB = build_discriminator()\\n\",\n \" \\n\",\n \" discriminatorA.compile(loss = 'mse', \\n\",\n \" optimizer = common_optimizer,\\n\",\n \" metrics = ['accuracy'])\\n\",\n \" discriminatorB.compile(loss = 'mse',\\n\",\n \" optimizer = common_optimizer,\\n\",\n \" metrics = ['accuracy'])\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Build generator networks\\n\",\n \" generatorA_to_B = build_generator()\\n\",\n \" generatorB_to_A = build_generator()\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create an adversarial network\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" inputA = Input(shape = (128, 128, 3))\\n\",\n \" inputB = Input(shape = (128, 128, 3))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## --> Generated images using both of the generator networks\\n\",\n \" generatedB = generatorA_to_B(inputA)\\n\",\n \" generatedA = generatorB_to_A(inputB)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## --> Reconstruct the images back to the original ones\\n\",\n \" reconstructedA = generatorB_to_A(generatedB)\\n\",\n \" reconstructedB = generatorA_to_B(generatedA)\\n\",\n \" \\n\",\n \" generatedA_Id = generatorB_to_A(inputA)\\n\",\n \" generatedB_Id = generatorA_to_B(inputB)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Make both of the discriminator networks non-trainable\\n\",\n \" discriminatorA.trainable = False\\n\",\n \" discriminatorB.trainable = False\\n\",\n \" \\n\",\n \" probsA = discriminatorA(generatedA)\\n\",\n \" probsB = discriminatorB(generatedB)\\n\",\n \" \\n\",\n \" \\n\",\n \" adversarial_model = Model(inputs = [inputA, inputB],\\n\",\n \" outputs = [probsA, probsB, \\n\",\n \" reconstructedA, reconstructedB,\\n\",\n \" generatedA_Id, generatedB_Id])\\n\",\n \" \\n\",\n \" adversarial_model.compile(loss = ['mse', 'mse', 'mae', 'mae', 'mae', 'mae'],\\n\",\n \" loss_weights = [1, 1, 10.0, 10.0, 1.0, 1.0],\\n\",\n \" optimizer = common_optimizer)\\n\",\n \" \\n\",\n \" \\n\",\n \" tensorboard = TensorBoard(log_dir = \\\"logs/{}\\\".format(time.time()),\\n\",\n \" write_images = True, write_grads = True,\\n\",\n \" write_graph = True)\\n\",\n \" tensorboard.set_model(generatorA_to_B)\\n\",\n \" tensorboard.set_model(generatorB_to_A)\\n\",\n \" tensorboard.set_model(discriminatorA)\\n\",\n \" tensorboard.set_model(discriminatorB)\\n\",\n \" \\n\",\n \" \\n\",\n \" real_labels = np.ones((batch_size, 7, 7, 1))\\n\",\n \" fake_labels = np.zeros((batch_size, 7, 7, 1))\\n\",\n \" \\n\",\n \" for epoch in range(epochs):\\n\",\n \" print(\\\"Epoch: {}\\\".format(epoch))\\n\",\n \" \\n\",\n \" D_losses = []\\n\",\n \" G_losses = []\\n\",\n \" \\n\",\n \" num_batches = int(min(imagesA.shape[0], imagesB.shape[0]) / batch_size)\\n\",\n \" print(\\\"Number of batches: {}\\\".format(num_batches))\\n\",\n \" \\n\",\n \" \\n\",\n \" for index in range(num_batches):\\n\",\n \" print(\\\"Batch: {}\\\".format(index))\\n\",\n \" \\n\",\n \" ## Sample images\\n\",\n \" batchA = imagesA[index * batch_size: (index + 1) * batch_size]\\n\",\n \" batchB = imagesB[index * batch_size: (index + 1) * batch_size]\\n\",\n \" \\n\",\n \" ## Translate images to opposite domain\\n\",\n \" generatedB = generatorA_to_B.predict(batchA)\\n\",\n \" generatedA = generatorB_to_A.predict(batchB)\\n\",\n \" \\n\",\n \" ## Train the discriminator A on real and fake images\\n\",\n \" D_A_Loss1 = discriminatorA.train_on_batch(batchA, real_labels)\\n\",\n \" D_A_Loss2 = discriminatorA.train_on_batch(generatedA, fake_labels)\\n\",\n \" \\n\",\n \" ## Train the discriminator B on real and fake images\\n\",\n \" D_B_Loss1 = discriminatorB.train_on_batch(batchB, real_labels)\\n\",\n \" D_B_Loss2 = discriminatorB.train_on_batch(generatedB, fake_labels)\\n\",\n \" \\n\",\n \" ## Calculate the total discriminator loss\\n\",\n \" D_loss = 0.5 * np.add(0.5 * np.add(D_A_Loss1, D_A_Loss2), \\n\",\n \" 0.5 * np.add(D_B_Loss1, D_B_Loss2))\\n\",\n \" \\n\",\n \" print(\\\"D_Loss: {}\\\".format(D_loss))\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Train the generator networks\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" G_loss = adversarial_model.train_on_batch([batchA, batchB],\\n\",\n \" [real_labels, real_labels,\\n\",\n \" batchA, batchB,\\n\",\n \" batchA, batchB])\\n\",\n \" \\n\",\n \" print(\\\"G_Loss: {}\\\".format(G_loss))\\n\",\n \" \\n\",\n \" D_losses.append(D_loss)\\n\",\n \" G_losses.append(G_loss)\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Save losses to TensorBoard after every epoch\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" write_log(tensorboard, 'discriminator_loss', np.mean(D_losses), epoch)\\n\",\n \" write_log(tensorboard, 'generator_loss', np.mean(G_losses), epoch)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Sample and save images after every 10 epochs\\n\",\n \" if epoch % 10 == 0:\\n\",\n \" \\n\",\n \" ## Get a batch of test data\\n\",\n \" batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)\\n\",\n \" \\n\",\n \" ## Generate images\\n\",\n \" generatedB = generatorA_to_B.predict(batchA)\\n\",\n \" generatedA = generatorB_to_A.predict(batchB)\\n\",\n \" \\n\",\n \" ## Get reconstructed images\\n\",\n \" recons_A = generatorB_to_A.predict(generatedB)\\n\",\n \" recons_B = generatorA_to_B.predict(generatedA)\\n\",\n \" \\n\",\n \" ## Save original, generated and reconstructed images\\n\",\n \" for i in range(len(generatedA)):\\n\",\n \" save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],\\n\",\n \" originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],\\n\",\n \" path = \\\"results/gen_{}_{}\\\".format(epoch, i))\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Save models\\n\",\n \" generatorA_to_B.save_weights(\\\"generatorA_to_B.h5\\\")\\n\",\n \" generatorB_to_A.save_weights(\\\"generatorB_to_A.h5\\\")\\n\",\n \" discriminatorA.save_weights(\\\"discriminatorA.h5\\\")\\n\",\n \" discriminatorB.save_weights(\\\"discriminatorB.h5\\\")\\n\",\n \" \\n\",\n \" \\n\",\n \" elif mode == 'predict':\\n\",\n \" \\n\",\n \" ## Build generator networks\\n\",\n \" generatorA_to_B = build_generator()\\n\",\n \" generatorB_to_A = build_generator()\\n\",\n \" \\n\",\n \" generatorA_to_B.load_weights(\\\"generatorA_to_B.h5\\\")\\n\",\n \" generatorB_to_A.load_weights(\\\"generatorB_to_A.h5\\\")\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Get a batch of test data\\n\",\n \" batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)\\n\",\n \" \\n\",\n \" \\n\",\n \" ## Save images\\n\",\n \" generatedB = generatorA_to_B.predict(batchA)\\n\",\n \" generatedA = generatorB_to_A.predict(batchB)\\n\",\n \" \\n\",\n \" reconsA = generatorB_to_A.predict(generatedB)\\n\",\n \" reconsB = generatorA_to_B.predict(generatedA)\\n\",\n \" \\n\",\n \" for i in range(len(generatedA)):\\n\",\n \" save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],\\n\",\n \" originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],\\n\",\n \" path = \\\"results/test_{}\\\".format(i))\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0822 12:57:49.734445 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\\n\",\n \"\\n\",\n \"W0822 12:57:49.736517 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\\n\",\n \"\\n\",\n \"W0822 12:57:49.752670 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\\n\",\n \"\\n\",\n \"W0822 12:57:50.060953 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\\n\",\n \"\\n\",\n \"W0822 12:57:50.235987 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\\n\",\n \"\\n\",\n \"W0822 12:57:50.237730 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\\n\",\n \"\\n\",\n \"W0822 12:57:53.725808 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\\n\",\n \"\\n\",\n \"W0822 12:58:08.037763 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\\n\",\n \"\\n\",\n \"W0822 12:58:08.039646 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\\n\",\n \"\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 0\\n\",\n \"Number of batches: 121\\n\",\n \"Batch: 0\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\\n\",\n \" 'Discrepancy between trainable weights and collected trainable'\\n\",\n \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\\n\",\n \" 'Discrepancy between trainable weights and collected trainable'\\n\",\n \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\\n\",\n \" 'Discrepancy between trainable weights and collected trainable'\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"D_Loss: [0.5305587 0.49489796]\\n\",\n \"G_Loss: [25.205275, 5.0539395e-09, 6.623143e-08, 1.2187737, 1.0739226, 1.2182144, 1.0600963]\\n\",\n \"Batch: 1\\n\",\n \"D_Loss: [0.4999975 0.5 ]\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\\n\",\n \" 'Discrepancy between trainable weights and collected trainable'\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"G_Loss: 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0.00473141, 0.0023868706]\\n\",\n \"Batch: 27\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0667522, 1.0, 0.0, 0.0033675889, 0.002700807, 0.0033674338, 0.0027008553]\\n\",\n \"Batch: 28\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.052085, 1.0, 0.0, 0.0022735798, 0.002461434, 0.0022734143, 0.0024614318]\\n\",\n \"Batch: 29\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.1021444, 1.0, 0.0, 0.0048889676, 0.0043968046, 0.004889828, 0.004396752]\\n\",\n \"Batch: 30\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0780519, 1.0, 0.0, 0.003395278, 0.0037004033, 0.0033947781, 0.0037003746]\\n\",\n \"Batch: 31\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0768307, 1.0, 0.0, 0.0032819586, 0.0037026913, 0.0032816841, 0.0037025642]\\n\",\n \"Batch: 32\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0663079, 1.0, 0.0, 0.0033780397, 0.0026499787, 0.0033778278, 0.0026499734]\\n\",\n \"Batch: 33\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0675365, 1.0, 0.0, 0.0030047006, 0.0031349768, 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([0..1] for floats or [0..255] for integers).\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 21\\n\",\n \"Number of batches: 121\\n\",\n \"Batch: 0\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0317351, 1.0, 0.0, 0.0010825285, 0.0018024929, 0.0010828323, 0.0018020737]\\n\",\n \"Batch: 1\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0511647, 1.0, 0.0, 0.0018909018, 0.0027604117, 0.0018909741, 0.002760621]\\n\",\n \"Batch: 2\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0320256, 1.0, 0.0, 0.0017160284, 0.0011955148, 0.0017146135, 0.0011955099]\\n\",\n \"Batch: 3\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0510559, 1.0, 0.0, 0.0028536946, 0.0017877315, 0.0028541593, 0.0017875144]\\n\",\n \"Batch: 4\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0260245, 1.0, 0.0, 0.0012029654, 0.0011628708, 0.0012032909, 0.0011627255]\\n\",\n \"Batch: 5\\n\",\n \"D_Loss: [0.5 0.5]\\n\",\n \"G_Loss: [1.0420709, 1.0, 0.0, 0.0010179211, 0.002806708, 0.0010178435, 0.0028068065]\\n\",\n \"Batch: 6\\n\",\n \"D_Loss: [0.5 0.5]\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"sDD37FBMlLA2\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/generative_adversarial_networks/DCGAN.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"DCGAN - OpenGenus\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"UXfgMQ1dmasI\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**We start by importing the necessary libraries, as follows:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"cYrqR3Yy-VpY\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import itertools\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"import torch.nn as nn\\n\",\n \"import torch.nn.functional as F\\n\",\n \"import torch.optim as optim\\n\",\n \"import torchvision.datasets as dset\\n\",\n \"import torchvision.transforms as transforms\\n\",\n \"from torch.autograd import Variable\\n\",\n \"from torch.utils.data.dataset import Dataset\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Nln7a6ZrmrTW\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**Here, we will set the hyperparameters to be used during training**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"uXkQa-1TBc9i\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"img_size = 64\\n\",\n \"n_epochs = 24\\n\",\n \"batch_size = 64\\n\",\n \"learning_rate = 0.0002\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"bAj0X_p7nK5d\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**For our model, we will use the Fashion-MNIST dataset and load it with the following code:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"-2IiQiMxBjSD\",\n \"colab_type\": \"code\",\n \"outputId\": \"6784f081-1617-4f03-d3a8-f4044305c19b\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 323\n }\n },\n \"source\": [\n \"transform = transforms.Compose([\\n\",\n \" transforms.Scale(img_size),\\n\",\n \" transforms.ToTensor(),\\n\",\n \"# transforms.Normalize(mean = (0.5, 0.5, 0.5),\\n\",\n \"# std = (0.5, 0.5, 0.5))\\n\",\n \"])\\n\",\n \"\\n\",\n \"train_loader = torch.utils.data.DataLoader(\\n\",\n \" dset.FashionMNIST('fashion', train = True, download = True, \\n\",\n \" transform = transform),\\n\",\n \" batch_size = batch_size,\\n\",\n \" shuffle = True\\n\",\n \")\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/torchvision/transforms/transforms.py:208: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.\\n\",\n \" \\\"please use transforms.Resize instead.\\\")\\n\",\n \"\\r0it [00:00, ?it/s]\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to fashion/FashionMNIST/raw/train-images-idx3-ubyte.gz\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"26427392it [00:05, 4956151.69it/s] \\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting fashion/FashionMNIST/raw/train-images-idx3-ubyte.gz\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"32768it [00:00, 514028.76it/s]\\n\",\n \" 0%| | 16384/4422102 [00:00<00:27, 157619.62it/s]\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to fashion/FashionMNIST/raw/train-labels-idx1-ubyte.gz\\n\",\n \"Extracting fashion/FashionMNIST/raw/train-labels-idx1-ubyte.gz\\n\",\n \"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to fashion/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"4423680it [00:00, 22078231.48it/s] \\n\",\n \"8192it [00:00, 206571.99it/s]\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting fashion/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\\n\",\n \"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to fashion/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\\n\",\n \"Extracting fashion/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\\n\",\n \"Processing...\\n\",\n \"Done!\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ceMIpeBVnia6\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**We then define our discriminator network in a function:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"9zg40neoCeNg\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class discriminator_model(nn.Module):\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" super(discriminator_model, self).__init__()\\n\",\n \" self.conv1 = nn.Conv2d(1, 128, 4, 2, 1)\\n\",\n \" self.conv2 = nn.Conv2d(128, 256, 4, 2, 1)\\n\",\n \" self.conv2_bn = nn.BatchNorm2d(256)\\n\",\n \" self.conv3 = nn.Conv2d(256, 512, 4, 2, 1)\\n\",\n \" self.conv3_bn = nn.BatchNorm2d(512)\\n\",\n \" self.conv4 = nn.Conv2d(512, 1024, 4, 2, 1)\\n\",\n \" self.conv4_bn = nn.BatchNorm2d(1024)\\n\",\n \" self.conv5 = nn.Conv2d(1024, 1, 4, 1, 0)\\n\",\n \" \\n\",\n \" def weight_init(self):\\n\",\n \" for m in self._modules:\\n\",\n \" normal_init(self._modules[m])\\n\",\n \" \\n\",\n \" def forward(self, input):\\n\",\n \" x = F.leaky_relu(self.conv1(input), 0.2)\\n\",\n \" x = F.leaky_relu(self.conv2_bn(self.conv2(x)), 0.2)\\n\",\n \" x = F.leaky_relu(self.conv3_bn(self.conv3(x)), 0.2)\\n\",\n \" x = F.leaky_relu(self.conv4_bn(self.conv4(x)), 0.2)\\n\",\n \" x = F.sigmoid(self.conv5(x))\\n\",\n \" return x\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"RYq70yMdn10w\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**In the preceding function, we've used the normal_init function, which we will be using in the generator as well. We define it as follows:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"s-57O395F6Y6\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def normal_init(m):\\n\",\n \" if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):\\n\",\n \" m.weight.data.normal_(0.0, 0.02)\\n\",\n \" m.bias.data.zero_()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jgtP-fnqoLVP\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**Next, we define the generator:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Pq1ePijZGSJk\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class generator_model(nn.Module):\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" super(generator_model, self).__init__()\\n\",\n \" self.deconv1 = nn.ConvTranspose2d(100, 1024, 4, 1, 0)\\n\",\n \" self.deconv1_bn = nn.BatchNorm2d(1024)\\n\",\n \" self.deconv2 = nn.ConvTranspose2d(1024, 512, 4, 2, 1)\\n\",\n \" self.deconv2_bn = nn.BatchNorm2d(512)\\n\",\n \" self.deconv3 = nn.ConvTranspose2d(512, 256, 4, 2, 1)\\n\",\n \" self.deconv3_bn = nn.BatchNorm2d(256)\\n\",\n \" self.deconv4 = nn.ConvTranspose2d(256, 128, 4, 2, 1)\\n\",\n \" self.deconv4_bn = nn.BatchNorm2d(128)\\n\",\n \" self.deconv5 = nn.ConvTranspose2d(128, 1, 4, 2, 1)\\n\",\n \" \\n\",\n \" def weight_init(self):\\n\",\n \" for m in self._modules:\\n\",\n \" normal_init(self._modules[m])\\n\",\n \" \\n\",\n \" def forward(self, input):\\n\",\n \" x = F.relu(self.deconv1_bn(self.deconv1(input)))\\n\",\n \" x = F.relu(self.deconv2_bn(self.deconv2(x)))\\n\",\n \" x = F.relu(self.deconv3_bn(self.deconv3(x)))\\n\",\n \" x = F.relu(self.deconv4_bn(self.deconv4(x)))\\n\",\n \" x = F.tanh(self.deconv5(x))\\n\",\n \" return x\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6t6wtqWZoW-6\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**To plot multiple random outputs during training, we create a function to plot the generated images:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"vcNQyBrWIj81\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"def plot_output():\\n\",\n \" z_ = torch.randn((5*5, 100)).view(-1, 100, 1, 1)\\n\",\n \" z_ = Variable(z_.cuda(), volatile = True)\\n\",\n \" \\n\",\n \" generator.eval()\\n\",\n \" test_images = generator(z_)\\n\",\n \" generator.train()\\n\",\n \" \\n\",\n \" grid_size = 5\\n\",\n \" fig, ax = plt.subplots(grid_size, grid_size, figsize = (5, 5))\\n\",\n \" for i, j in itertools.product(range(grid_size), range(grid_size)):\\n\",\n \" ax[i, j].get_xaxis().set_visible(False)\\n\",\n \" ax[i, j].get_yaxis().set_visible(False)\\n\",\n \" for k in range(grid_size * grid_size):\\n\",\n \" i = k // grid_size\\n\",\n \" j = k % grid_size\\n\",\n \" ax[i, j].cla()\\n\",\n \" ax[i, j].imshow(test_images[k, 0].cpu().data.numpy(),\\n\",\n \" cmap = 'gray')\\n\",\n \" \\n\",\n \" plt.show()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Ms3si8doq2Li\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**Let's create both networks by calling the defined functions, followed by initializing the weights:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"-8ILTwkaLRE-\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"generator = generator_model()\\n\",\n \"discriminator = discriminator_model()\\n\",\n \"generator.weight_init()\\n\",\n \"discriminator.weight_init()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"2_uH0bdJrhB-\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**Next, we need to make sure we use cuda:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"G-5MaWp1Lpub\",\n \"colab_type\": \"code\",\n \"outputId\": \"bac77e77-1106-48c9-a37b-90a829dc2766\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 234\n }\n },\n \"source\": [\n \"generator.cuda()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"generator_model(\\n\",\n \" (deconv1): ConvTranspose2d(100, 1024, kernel_size=(4, 4), stride=(1, 1))\\n\",\n \" (deconv1_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (deconv2): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (deconv2_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (deconv3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (deconv3_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (deconv4): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (deconv4_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (deconv5): ConvTranspose2d(128, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \")\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 9\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"n1vpQ6YYL2O8\",\n \"colab_type\": \"code\",\n \"outputId\": \"be521716-59c5-4a8c-e488-ee531b95526f\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 216\n }\n },\n \"source\": [\n \"discriminator.cuda()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"discriminator_model(\\n\",\n \" (conv1): Conv2d(1, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (conv2): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (conv2_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (conv3): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (conv3_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (conv4): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\\n\",\n \" (conv4_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (conv5): Conv2d(1024, 1, kernel_size=(4, 4), stride=(1, 1))\\n\",\n \")\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 10\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SAKWb3f0rm5K\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**For GANs, we can use the Binary Cross Entropy (BCE) loss:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"eKycgI5vMFEN\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"BCE_loss = nn.BCELoss()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"LN6SoajhsC1U\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**For both networks, we need to set the optimizers with the following settings:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"L6Ax_VDHMTvB\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"beta_1 = 0.5\\n\",\n \"beta_2 = 0.999\\n\",\n \"\\n\",\n \"G_optimizer = optim.Adam(generator.parameters(),\\n\",\n \" lr = learning_rate,\\n\",\n \" betas = (beta_1, beta_2))\\n\",\n \"D_optimizer = optim.Adam(discriminator.parameters(),\\n\",\n \" lr = learning_rate / 2,\\n\",\n \" betas = (beta_1, beta_2))\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"wK1dbdTAsWvj\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"**Now we can start training our networks with the following code block:**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"1HVBmoZFN41I\",\n \"colab_type\": \"code\",\n \"outputId\": \"564d3083-bf1f-4d26-e0b5-e0f206967dad\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 7437\n }\n },\n \"source\": [\n \"for epoch in range(n_epochs):\\n\",\n \" \\n\",\n \" D_losses = []\\n\",\n \" G_losses = []\\n\",\n \" \\n\",\n \" for X, _ in train_loader:\\n\",\n \" discriminator.zero_grad()\\n\",\n \" mini_batch = X.size()[0]\\n\",\n \" \\n\",\n \" y_real_ = torch.ones(mini_batch)\\n\",\n \" y_fake_ = torch.zeros(mini_batch)\\n\",\n \" \\n\",\n \" X = Variable(X.cuda())\\n\",\n \" y_real_ = Variable(y_real_.cuda())\\n\",\n \" y_fake_ = Variable(y_fake_.cuda())\\n\",\n \" \\n\",\n \" D_result = discriminator(X).squeeze()\\n\",\n \" D_real_loss = BCE_loss(D_result, y_real_)\\n\",\n \" \\n\",\n \" z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)\\n\",\n \" z_ = Variable(z_.cuda())\\n\",\n \" G_result = generator(z_)\\n\",\n \" \\n\",\n \" D_result = discriminator(G_result).squeeze()\\n\",\n \" D_fake_loss = BCE_loss(D_result, y_fake_)\\n\",\n \" D_fake_score = D_result.data.mean()\\n\",\n \" D_train_loss = D_real_loss + D_fake_loss\\n\",\n \" \\n\",\n \" D_train_loss.backward()\\n\",\n \" D_optimizer.step()\\n\",\n \" D_losses.append(D_train_loss)\\n\",\n \" \\n\",\n \" generator.zero_grad()\\n\",\n \" \\n\",\n \" z_ = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)\\n\",\n \" z_ = Variable(z_.cuda())\\n\",\n \" \\n\",\n \" G_result = generator(z_)\\n\",\n \" D_result = discriminator(G_result).squeeze()\\n\",\n \" G_train_loss = BCE_loss(D_result, y_real_)\\n\",\n \" G_train_loss.backward()\\n\",\n \" G_optimizer.step()\\n\",\n \" G_losses.append(G_train_loss)\\n\",\n \" \\n\",\n \" print('Epoch {} - loss_d: {:.3f}, loss_g: {:.3f}'.format((epoch + 1),\\n\",\n \" torch.mean(torch.FloatTensor(D_losses)),\\n\",\n \" torch.mean(torch.FloatTensor(G_losses))))\\n\",\n \" \\n\",\n \" plot_output()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1332: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\\n\",\n \" warnings.warn(\\\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\\\")\\n\",\n \"/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1320: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\\n\",\n \" warnings.warn(\\\"nn.functional.tanh is deprecated. Use torch.tanh instead.\\\")\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1 - loss_d: 0.581, loss_g: 3.141\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\\n\",\n \" This is separate from the ipykernel package so we can avoid doing imports until\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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ODxeEQgh0IhjEYj+Hw+jMdjmZNyuSywg8r3AC40L+cdq8pD/FtVIvL5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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 2 - loss_d: 0.658, loss_g: 2.578\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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0PPGwAhxRmrREVLZEVERIKYyAEAyuUyHA6H/Ov3+7IwzoqSpglRgdfr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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 3 - loss_d: 0.567, loss_g: 2.900\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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9r7NC3eqq2L9HGaJmT1PT8fjUYIBAJYWloS7bVSqaDZbELXdbhcR3oK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\\nI+cK6VzO5Vw+NnKukM7lXM7lYyP/Hwg7WWmajOR9AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 4 - loss_d: 0.449, loss_g: 3.365\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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EthpzO8HIBoZE5Ip9MpRDVTDNTrsjMrlQparZZ4fKx2HzvIPIuYYbyd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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 5 - loss_d: 0.379, loss_g: 3.801\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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fZD016vF8lkEt/4xjdw5swZPHz4EL/+9a8PHbgW4zNqhWK1WiXhjwvq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\\n6IK+MHTBkC7ogi7oC0P/C1+GyfuUV4SUAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 6 - loss_d: 0.393, loss_g: 3.857\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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UBcLhdisRj8fj+azaYE1NrtJw8HNAO3VWbE/7mmiPMRUmi32yIYXC4X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\\nRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 7 - loss_d: 0.264, loss_g: 4.188\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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O/E6XSKgUBrj4IX2D+LZouHRgwFEgDxkI4DpxwpkLioZszBYrEgHA5j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\\n0heGThnSKZ3SKX1h6JQhndIpndIXhv4/YOm8Arp4QrAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 8 - loss_d: 0.297, loss_g: 4.333\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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0U4HIbJZBJss1arodlswmw2w+/3o9vtiuAwGo2oVqvI5XK4efMm8vl8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\\nylSm8oORqUKaylSm8oOR/wcCpQeEusXqFQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 9 - loss_d: 0.422, loss_g: 4.118\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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YdqtToGIK5fv4719XX0+32JHfzOd74zttfohKJVZJom/vM//xOFQgHp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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 10 - loss_d: 0.257, loss_g: 4.494\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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VIRtamE4Tc2mw2dTkfGgcZ9rvNerycG/UqlgkAggEQigWw2O2BWOKkP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\\n/T/9IEu9bOsCJwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 11 - loss_d: 0.325, loss_g: 4.415\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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aFQEB6lVCrB6/UiFouh1+uhWq2KqZNMJlGv17GxsSGIqtlsotvtIhAI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\\nG0P/H0gNwennQ5cMAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 12 - loss_d: 0.206, loss_g: 4.772\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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Yyl3W6jVquhXq8LdhQMBhGPx3vW8GnGeBRyEiw+n0+cDnxtNBqo1Wrw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\\nSl8Y+j9BcNCvVgyaLgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 13 - loss_d: 0.346, loss_g: 4.383\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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hSH1Ol/swdTpaMpRc+Gy+VCtVqVjufzeTSbTXi9XuHer7/+utihExMT\\nsNlsqFarCIVCbRKNi5rSadCN2g1rYB84MYFAANPT023tLxaLIgFrtZpIT96T4DWZNr1svIZM1Ov1\\nIhqNIhqN4smTJ21Aead2D0r9jE036W18Jjff0NAQIpEIJicnMT09DbfbDYfDgUAggGg0ikQiga2t\\nLfFAcr5pItGxQeFFnGPQsI5e65IaKj2eXFMUElx71G75arfbxQSp1+uo1WoolUpwOp0IhULCgPL5\\nvDBpPsPn83V1upwETsb+2e12hEIhVKtV7OzsSB8p5Gu1GqrValt8HLUgi8UiDhhtAhrNQuJI3Ndk\\nXHTa6H1tFOonZrLpRhk1IFInHKlarWJiYgIejwfj4+Nwu91iq3o8njZbNJvNihv9r//6rzE9PY2Z\\nmRl84QtfQCAQwOLiorgXuTAcDocsHN2Ofsmo6Zn1o9FoYGxsDBMTE5iamkKpVEK1WpW2aulSLpdF\\nczs4OBB1mBsNQJv5kMvl4PF44PF4JPjunXfe+VhMF91nM3Opn2v152yfx+PByMgInnvuOTGD6O0Z\\nHR2F3+/H+fPnJZcvlUpJ+oHP54PP50OlUhGt64033sDdu3fb8It+yKydnEOa/H6/Xz6n17fZbMLl\\ncmFnZwcHBwdyD7bJZrMhlUohm83C5/Oh0Whgf39fGC4DWxl7FgwGRWD6fL42XNNsvE9ijqn92e12\\nHBwcIJlMwuv1wuVyIRgMolarSVgDGb3T6WwzT/UfNVSt6VPolsvlj4Tm0OPIlCqn0ylQzMeGIbHh\\nWnJ1whh4jdVqRblcxrlz5xCPx8XWpkSkXavt2HK5jAsXLmB0dFRAcObbMJ9Ix4RoUPIok9vpN2TA\\nwGG8STgcFs2F0pYMldiQUeVlNCzTDbgoKFE0MOj1ehEMBk3H3NjOk8AezPreD1bI5/P/jY0NOBwO\\nbG5uIhaLIZFIiGsdOFykkUgEwWAQrdZhTh/xF46VDsjz+XxtQqZfMs6/bm+z2ZTkWKfTCZ/PJ+5x\\nzgk1Oz6b69fpdIomQQ2K67dcLmN/fx+ZTAYTExMYGRnB+Pi4AMrUco3MXK+to86lXheMhXI6nfB4\\nPOI9pFIAQEwt4q3UkACI55jrV+9xerr5TH7udrthtVpFAFOjcjqdcDgcMkb9aoEDmWzsuHFRauqU\\nj9VoNDA1NYUrV66IJsHFyE1Ntzm9TKOjowiHwxITUa1WsbKygrNnzyIWi7Wpmjrg8DjUaSNaLBYE\\nAgEEg0HxIOnvOeg65IB91BnSBPGNKQYEJAOBADweT9tzuzHLQaiTZmskM/PVqPWSuHAPDg5gt9uR\\nTCbh8XgQiURkQzBo1O/3w+VyiUbCfgOHpgRxN3pWdY7UUcnYbmovdrsdrVYLfr9fNtXBwYEk1xIz\\n4uaiVk8zjpH4Ho8HxWIRe3t7SCaTePbZZzE1NYXx8XGUy2VJpubz9Fzo9p1E3/gZI82ZoqTNNR2O\\nopkMqxWwbcY/EtczmbTL5UK5XBYmxvuQIXGf9DuHfTGkQW5o5uVqNBoIhUJYWFjAlStXsL+/D5fL\\nJcFodrsdXq9XBtjpdCKTyeCP/uiPUK/X4fF44HK5kE6n8e1vfxsvvfQSrl+/jvn5ebRaLQmipGQb\\nlMzMUbM+U6JygdlsNsG8GMNB75ndbhdTrtVqyaQxuCyfz7fdgxubEo7tMrO9j7qIjRptp+/NcJtO\\nWgexl1/7tV/DpUuXcOnSJczNzclGvX//PhwOh6TX8Dfa68PPvF4vKpUKnE4nhoeHMTw8LCZ8v6TH\\ny6jJMV4mHA4DONyghUIBwWAQU1NTqNfrYqIdHBwgn88jEolIjiG1W84rYQjgUFOIRqOYm5vDmTNn\\nMDo6ivX1dSSTSenncYWlWV+pYXNN+v1+jI6OAoAICavVCo/HI14zDVLzHtQSyWz4GRmpkTElEgkA\\nEM0fgGho8Xgc0WgUqVRKQgmohJyIydZp8epGahBXL3xuQpvN1maPE4XXAYAEyLiAq9WqaAyFQgHV\\nahWlUgn3799HsViURUyTzSwSud/+GTec2f9sG39DCcEkRA0EAhAGRc1P2+qUJlwIOtiStjcXjpkJ\\ndVyt4Tjfc2wajQbi8ThisRiuXbuG6elpBINBSbjkvHDMGLhKSW2xPE0h0oKAGokRf+iXzDQR/Z7m\\nit1uh8vlQjgcRiwWw/r6OrLZrDAPp9MpAC03NtcwSWsLVqtVGC1hCWIrxrWu23TUudTMl21k7iTH\\nXGsyhBRoxuk8O65R9lPDHxwrzfg0kwcgGQn8rQ790GlRvYKWjwRqGyUttSItkbQayRgFFmbSoBmA\\ntlgODpzX60UqlQIAGchCoYD19XUUCgUsLS0hGo0iFouJOTco3qD7ZiSzRULVnm1nv9l+Auz0uKTT\\naQEa+ZtWqyUBZwBkg3ICXS6XJOlqdbwbNnISZASue5l0bMPQ0BCef/55XL16FYFAAFarFVtbW0gm\\nk6jVaojFYvK7/f19AJDxoHudY0IB43a7peTFcePKjGOnNYpW6zB3MhKJIBqNylzwd9xgnEO2XYO9\\nXHPUlPf39yW1hONE86XbWB9lLi0WS9uap8ZN7ZxCkFgeww6I7QBPTW69lvWY8XNqV5qJETvVoTra\\n3NX30cKgGx0L1OZn5NDsoFFrot1OxkHblRNOosqsGRy1qZ/97Gf47//+b6RSKaTTabhcLvzLv/yL\\n5MStr6+LFtYr+Kpf0v0EIBIIeLqp3G63tFkDh1tbW3j33XfFc8YAykqlgkKhgGw2K8yH40hJRClj\\nNvYfF3UyW/VY6IVosRzWgXr55Zfx3HPPSfgDzVONhzGjnh5H9plAKLUgndfXarUQCoUAAJlM5th9\\nA9pDUNjGcrmMQCCASCQigk9rd8ViEdVqtS16m0A9NYVyuSyM6/Hjxzh79qxgTaVSSTYs59VsfI86\\nv2SOLpcLoVBIigKSGTHIkZocGRYAif+jdqST1jXDohAmQE1MlPlwOmYMeFpXyu12fwQDNEarG+lI\\nkdp6k5K76pgDI2eMxWLi+uQAaA2DjIeaBhcOXZZWqxUffvghHj58KAmNrVYLm5ubYh+73W7EYjFJ\\nzD0umS0QTha/czgcotnQ1GB/MpkMtra24Pf74fV6MTQ01KYZarOHUpeqNoHBQWNwPi4ybh6aAIwI\\nHhsbw8HBgTBkerIASKEvu90uGiPnml4ZzimxQOBQ44jH4wAOgw0HITMcif3QHjUyII4zQXdGVBu1\\nBA3aEvvk5zoglnE+RhjDDEM6qnZk1J7JAHw+30dij7SXj9cC7Xiv1hx1cLG+D5kR965RcGpTl+kn\\nOgiYkEY3OlJyLTvD97pRVCN1Gsf58+dx/fp1XLx4EQDEROF92GgOBhlWNBpFtVpFoVAQrwYBRqqn\\nfr8fk5OTeOGFFzA8PIz79+/j9ddfH7Rbbe03/m8ElqnhUbpqRkO3cblcxubmJuLxONLptATV6Wxp\\napZaeno8Hin0ZtYuo8l8En3s9p3ekOzn1atXMT8/j5mZGQGqmQ4EQFJljNplNBoVrZhAKKUrNWYy\\n9nPnziGbzeLOnTtYWVk5sf6xfdSS+ExuFMbPaOBXmxl6c1NLplZXKpWkSoDL5RIPHh0aun3H1Y44\\nFzpol4C97r/2/tJDphksGbIO0jUz3TWATg8dmRLHq1AoiPbIeCiNm/bDfPv2sgHtBbxpP1ON5cN0\\noBQbcePGDXz5y18WcE/jJzR1tPqnOW2xWMT+/j6Gh4cxMTGB1dVVRKNRjI6O4syZMxgaGsLly5fx\\nyiuvYHh4GDdv3sTc3FzfE0sy2rrGvmumQ3CW7Web6Ylxu93I5XJ48uSJFOwCDg9NGBkZERWbzIsm\\nDYPo6AXSz9b/H9d8M8MBzRgewedWq4XFxUWMjo4iFArhK1/5Ci5cuAC3241CoYBMJiMagzbdqeYD\\nh2uHBfipBVJD0QApF/iVK1dw8eJF/OAHP8Cbb745UN+0JmvcADSTmWNIvI+aDXDIXCn8mBhbrVYl\\n0JHPcLlc0j/mLlYqFelPMBhEMBiUnC8ytqPgYp2I/WOoxdDQkGjaXItcn9VqVdYO+869SAxUxwxp\\nnFdbP1oBIQ7H+7MtPp8PwWCwKyM2o64MSXM1Pbm0F1utlgRZ6SRSvbmtVismJycRi8XEHCPIpz1T\\n2rYk5yc3rlQqmJubQ7lcxr179zA7O4vr16/jS1/6EkKhELxeL0KhENxuN86fPy/gab/Ur8TixBaL\\nRQk8A556WvSmpkZXr9exs7ODeDwuHhqr1Sq2vfZaaBXbeKqDmRlyVHPOCKoa76tNc4YzXLt2DZcv\\nX8b8/DwuXLgAn8+HQqGA/f191Ot1+Hw+mUsd/ctXLlZuFmpKZHgcC5oTrAAxMjIykLvcCCnoudRu\\nbDI+jSuxjVogaiyF5ppReOk4s3Q6LUC9DvblffWcneRcUjunGczxpaOE2C3jkKjhET/S42YUgBpe\\n0XiTcZ9w3nV+nw4bMOJJZtTz1BGzRWp0/WmuyY1ZqVSQSCSQSCTaAENOlNYwGAzHCNhwOAyv1yuJ\\nin6/Hy+88IKclBCNRiUlgVn2tN93d3dx+/Zt/O7v/m7fk2ocWCMD1p4HnSjscDgkj41aHvC0aFy9\\nXsfm5iaq1Sqi0Sj29/fb4q0ymYxgZXwWx1Zvlk4TehxNyWxh6Mj64eFhPP/885iZmUEsFsPU1JR4\\nv+jW57FODodD8BdqCVwnxDSsVmubQ4Pt1wxCjyvnE0AbpjFo34yMl4w/l8shHA5LEXuLxYJbt25h\\nb29P6v4Qa6JpxwBPhipwY+pAyZ2dHWxsbGBvbw/A001KJqyDEnUbj4sVstQLx56YFseTQDWZrwal\\njftXjyPNPCoNHE8G9lK75JrVmCC1M+NcdKO+K0ZqDkqOSkbAjrMTbOjQ0BAWFhbEBKGJx99xEihB\\nWLSM3JudJogai8Vw9erVNnesTj+xWCzY29vr+wwvklFD0otXS0NOstZWjMmGNOk4BvS2EGxn/JSu\\nucz28z46nUK7UE+KNPbANus5DAaDGB0dxbVr17C4uIixsTHk83k0Gg0UCgVZwPl8HtVqte2MMC1k\\n6AKm5gw8BUaZ0EonB79j27QA7OWZ6URmoLZmjow5YvuYY0cXt8ViETOn1WrJemw2n2buEwDnK72o\\nBOh1pLIWxGzPSZnhxHHIvJnEzDHmOtOlfDSRAWthQYaklRL2X69LvQeApyapDn3od/0OlDqi/3Sj\\nzYCrWq2GSCSC2dlZ+P1+2YhGjs3PqFoy3J2bmRNKVVPHAhG30bFBrBM8CHXCjbRazn6zTgy9hnrB\\nUaupVqttdYC4UGnC0WSlZKW2SWZG6aWjzvuxvwfpq8ZZ9Dy63W5cvHgR09PTOHPmjPSTjIZtb7Va\\nchwQP6M5pl35XKzc+JSa7DMXrdaWOBYUXgRKB+mfHjOjdqQFIouUEQPi2GiNQZvWOlBT1zfi9doM\\n0tqePmLJrJ1HEThGhss62dTICKd4vd62yqX8nv2kQsB2UiAaoQid/6avJzF+DngaGKqDe/sJxxkI\\n1DZyO605kWkwo/jq1at45plncOPGjbYTUzXOpCNmh4aGpLBZs9nE8vIyzp8/L2dGsfA43eTaBOQC\\ndzqdwhSOQ2a4GdsOoM3M0BoONZpisShVAMiQdnZ2kM1mpUA6cYZUKoVAIIBm8zDw0+hO1uOv23bU\\nfvF1ZmYGly9fbgOjGZ5w4cIF+P1+xGIxCaKjG1/32xjyoYPjyIyYNsDx09qtFmz802Y9k0SZnd8P\\ndWPcGtugZmaz2ZDJZLCxsSFt5QamEOR6I2mBR7xQ11QCnpb54MZmxoIO+Tgu6X3IevPxeBxutxt+\\nvx+RSET2jcfjkYBcwhwOh0NwTQ18cww5t5xXLYBp5hLcp8OK73n6rR63EzHZuhEXogbtGHH7yiuv\\n4OrVq5iYmGhjPMbN1Gq1ZKGyHAnr8bJkQjgcFmbAejMEC7W51Gq1TiQw0shwSdo252RwUwJPU0Wo\\nsnPhUhPSk8/2EoTU2qeWcEatVLdv0IVNzcvpdGJmZgZf+9rXZMFQkkYiEdE8WR6kXq+LC9tut0ul\\nAo2psdSpfhYXqzG9QmtFelw5DrVaTcICAoHAQAxJCxM9Vuyjfs91wwqdOqqZDhhqEYQHdOoIP6Pg\\naTabkjOWyWSkDA3XqcZdgfYg4uMyKc6f3W6XwwbowSVxHVHrM+4TClW2x5gtwM/I0Nl+rWnRlDOG\\nt/Tdj+MMgp50bfcDwMzMjESN6k6ReVGT4e90vAQ1CH5HDaNUKuHhw4eYm5vD+Pi4LGx9HybznUS/\\n9P9U78k8KWn1kUfsO+sk6c084hs4AAAgAElEQVTAIm3807EfeoHqUAIdhn9SWhIPQhwbGxO1mguR\\nphG1Ps1w9WIzbiLdHu0AIMPVmJI20TUWo0uV8LkEUY1HkncjM1xGmxzanCJIz/ASZvOzP3Tray+f\\nNuPJaPg5gfxGoyEF6MjwyWz5e6OmdFSTjWNurLrKgGJjoCdNZ5qgOtyG1ocRu9OQCBksr6PZzfFl\\nP2i6GrGqXv3sy+2v3/OGGpijRKX7PRgMYnZ2VirXUeJQqmoOSyBOA9jMqs/n81hdXUUmk8Hf//3f\\nS47Ub//2b+Pzn/88ZmZm2pihzWbDzs4O7t69O9DEdut322D974ZiLWyHw4FkMikh+pwwakgaNKbp\\nQ0abz+eRyWSQy+WEodHstVgsgoXp6GU9mUeRqBofOnfunIwZg/eKxSLy+bxIeQ1KkiHpuKJmsykx\\nKNzAZEh682swmVoSA2AphWlOcfPwIMbt7e02tb+fuePzOKbcQGR4BNq9Xi8CgYCYV8lkUvqkKzYw\\n8pqbnqA41y0AmeNUKoWdnR0sLy/L4aEau+o0d0eZT70Xi8Uitre3JSaMQsbn80leG1OfuP80dKKZ\\nNttDiMQYXa3xJc2MmFbFeaZpOMi67evUEd144421tLDZbJiYmMAzzzyDiYkJyXZmh7gwjBtMc3jG\\nsVitVnzve9/D7u4unjx5gnv37iGTySAcDkuaCO9NhlipVKRq33FJSzC2U5tXWpuhZsb3DKSjvU1G\\nyw0KQA7WY8Q3JRSLrmvAVGscx6Hx8XGcO3cO8/PzSCQSbYsmHA4Lo6B5DDxdoDrvUJvfGpjmotca\\nH7EYPV4Wi0XGS48lzZ98Po/19XXs7Oxge3t7oKRpraFx7IwubTJAAIL16QwBMh0tbPVBFEaMkvej\\ngGXBtjNnzkjUvi4ba8RijzuvXBu5XE7iwrSQByCCTjsOOFY6aFIzIM6L3uPAU1OVn/F+GtflZ2aH\\nGnSjnnFIZuo4v2Njm83DGkDj4+OYm5vDc889J1nedIFrb4W2XXWna7Va27FH3/nOd9oSHTVo6vP5\\n2jwEeoCPmvWvydhXDi4XKD9jcq0eMyMozQ1CkJZMjR45Mmpd8pO/0ybCcWlychJnzpzBzMxMm5eF\\nz6E5xXZT49GucQCi1XA+NQPgXFMjJpBaKpXahBHTLfQzeVZ8NpvFw4cPsbOzg/39/YECI4GP4kga\\nzzLmVlGAsaQyTW0KEa2F6E3FMSBpLahcLkuMGYWYFkZ8tqajmuAkMnN97BS1F/afJiSZkk7xIvyg\\n26mFhZ57Ci3NYC2Wp84A7bzQtb2MWKgZ9TTZjEQVnOAxi2l97nOfwx//8R8jFApJlK1uuObG+pUc\\nnEfFPHz4EDdv3sQPfvADWdQENS0WS1sJE20S0KUcCAQGchP36rtmCuFwWJIGyUAnJydRKpVQqVSk\\nomSlUsHQ0FDbRFqtVsTjcUxMTACAVBnkKalc4MSmBg1d6Ido5k5OTqJSqWBjYwMPHjxAoVCQo33K\\n5bLknI2NjckcpdNpYZ7RaBTA4eJkATKN321tbSGdTqPRaEhdI+21YnE+riP2l+Ysy74S2GYOZD+k\\n16yeO5vt8CCCYDAoqSHcIKurq3jw4IGYHRSAjJfS8WQavOfY6HQRHmyay+WkyoXWjo7rcOlElUoF\\nqVRKGCq9k7u7u6LRGrVH4KnprU1mHYaiSWu4kUgEwOG8x2Ix8bKx6KJ+hmbmxzLZdKCT5oLhcBhX\\nrlyB1+sVtX9hYUE8TOSq+o+NYeeNQVTAYb2cmzdv4tatW7J4qVlxoOg61aaS5sSsizwodcPL+L/X\\n6/0IkF0sFttUWdrWZIocNx3L0mq1pARJJpPB7u6upL7oaG9tep6EycZNRa2VjMFqfVpYrFAoYG9v\\nDy6XC7lcTvpHnKVeryMQCIg3lZuVxHZr3CcYDAqzYvInmVUwGBQNkbgh78fs9eHh4b77qLUBvSno\\nLOE46tiqdDqN3d1dqeNNpwS1DuKBeoNyTqnhMeaHG59Bo1zzOrVK34NtPs7cavyH+4v9pGlsrH8E\\nQMxPHb6io7H1HtDBq2QwzWZTSvQ6nc62hHOSUdvvRV0ZklFKNxqH5SqHhobw5S9/GX6/XzgivRW5\\nXA6FQgGxWEyYFd2mRg6tGQrt2LfeektOrADQxlwsFgtisVjbwqB5xIURCoU+guz3IuNgae+F/o5l\\nTnjiiMYidAF/7SnTi4WLltpdLpfDxsYGNjc3RaJxk7PGjln7+NlRSJuEjP1iTAlweAIKI5ZZVI8Y\\nFzeXrvzJwEXdRsa+OJ1O0Vi5YHWKAc3gYDAIp9MpJ3VQa2Lt60FIa916HgG05R9qD1SlUpEMAR5v\\nZLPZRJPSmQB6Thg3x/nks8gE+BwAIlTZLmrPRjzpKMT76SBVvueccT1qF73RSiFzYns4fpwDmnv0\\nGHMNaChFJ05rU9+IQXeirrPtdDoxNTWFyclJiQKdmJjA2NgYFhcXRYLrkHV6LQh20aTiny5vQBxi\\naWkJP/3pT/Hee+9he3tb4l+014bq8Be/+EWMjo7KMdzcYDTlMpmM5BEdl4zgKAeYkrBcLuPWrVsY\\nGRnB2bNnxZPGhFQ9odQG9MJmEbfNzU187Wtfw1e/+lU5l83v98sxzdr+NjoTBqG/+Zu/ETDb5/Nh\\nenpajiianp6G3W6XQ/4oTEqlEjKZjGhVeiETF6KrGThc1GRgRhdzKpWShFPWFuf46AhqahzpdBqP\\nHz/G3bt38fLLLw80bxq45Rqidk1txeFwCMOt1+tIpVISa0TmlM/npXIpC45RA+RcM8qb/eEYkBno\\nzASjlnWc+dRYGRkNtUwey0Qhx5xC4GkICpmnHivNLI1jylcmHVMb49mJTBHTzNYMi+1GXRmSw+HA\\n5OQkrl27hkqlgrGxMclzoveAzMcIxtIG5+bjBOu4Fg7i7u4uXnvtNTx58kRMAR0Nzc4AwMTEhLip\\nC4WCgGy85igF2swG3wjCGRkCJcPBwYGUs6DGp4//JiPTKQWUljQP7t+/j9XVVUnULZVKbeaAXnh6\\nQgddxDs7O9jd3RUTl9pQPB6XoDoC9ZSwxFyy2awIHkpGgvk6o91ut2N/f180IUafu1wuZDIZGReW\\n+6WQ4hopl8uiuezt7eHBgwd4++23B+qncQ61iUEGT5NSBzoa00C4tjnm2gOlawJxXtk3Mlcdb6bn\\nTmMqfDV67gbpI5mersSpD53gtZwz7lddH0njTBq6MKaF6TXHsAAm9jLjQF/LfcF1fCwM6Stf+Qpe\\nffVVLCwsIJlMygkS1WoVT548kSNttPqtyx7w4ZQ4Oi8NONzU6XQa//mf/4m3334bqVSqTU3XnSDm\\nMDs7K5n01CDI7BwOx8DVBY1kxI343mazIZFIyLlsa2trSCaTWFpakpwuegnJkAC0MTAdYEbws1qt\\nIp/P4/79+1haWsLu7i6WlpZEwzLDtvR9ByHttq9UKrh16xZu374tUo+APUu68HrGhfG5OgaHjJqa\\nAmOVdGClrrmjXfAEurmptLQmc9Zm8aBkZEpk8tRYm80mstmsaHRMlaEJCkASo4kdUvvRcxoKhaSI\\nIE9IodAh09aM0Uj9mjNmxHXAUBiW+aHA5JhzjIF2KEZbL0btiGPP740ZBUzv0ZHuHo/HNHapX+rK\\nkJaWlrC8vIzp6WnYbDY5XqXVepprpIOkqBlpu19LAL0w6vU6crkclpeX8cEHH4hk1DYsn6UHksdw\\n06TgdQyX57nlRyW9+Y2fb29vw+VyIZVK4eHDh1haWoLD4RCvDPEuLgRuKEoRBpJxcZNpEy/xeDzy\\nqrUz3a7jkhmoChxKfx0/lc/npR80ryj9W61WW6gDAPmfvyfD4fXaM0VTnZqSDt/QmoJOXTiJfnPt\\n6Vw2AthM9dHxO9QemJ/Iz6vValuApz61tlNAoG7HIJhKrz5pbYSaEsefAcncq/T2cR9SIAJPo6yN\\nZq42BzVYrh0wVAjYH1YPpaPEKFS7UVeG9P777+PGjRsoFApwOp2Ix+MiIb1erwC7dI1Wq1XJxq9U\\nKsJgqA5SrWMkcLlcxv3797G5uYlQKCTF3M02Ij+jpKaZBLQXyCIwehzqtJAymQweP36MO3fu4Je/\\n/CUePnyIl156SVRjgom8B9vE/xmTAhwuAB2DRI+l1+v9SP6Rvt9xyQzAN5os9JRqIUINV3tI9T2A\\nj5odZs8y649RMhvNmaNsXOOG5/3ZR3rcqInpGB4AbVouAMHAgMP1pj2uBwcHwsx4hqDNZhOziWNj\\n1D5OitGSuZJB6lAFKgfGWCg9Z5xXmmAaUtHxVnqtGIOe6XHzeDwIBAJt2Jru67FMtnw+j+9+97v4\\n53/+ZwwNDeHixYuIRqM4e/Ys/t//+3+i4ussZi1ZNFhJ0IsYybe//W288cYbSCaT2NnZkQ3NRhvB\\nMOBww/zd3/0dgMNFGo1GZZCXl5fRarVw586dQebTlDphNTs7O8jn82KWWCwWvPbaa/D5fLh06RJi\\nsRjC4XDbkTNkRGwrc5xyuRzy+Xxbwi0zpAuFwpHKqPTbN7P3GufQjJSkcZR+F5fZ92Ympxnj0s88\\nzsbVm4jCslAoYGhoCNVqFbu7u8hkMqjValIfSadDkGhiMycsl8tJbJb2Om1vb6NYLIrArtcPj0en\\nt06bPCdJDGqkdk1thoxDZxLQ5KRmqr2SWqiQgWunAL8vlUriWeYfFRUdCjMoNtYzDmlra0vUvlwu\\nh+npaeTzeZHktF0ZWEV1z+PxyO+025+5Pj/+8Y+xurralsel44u0Ksq/crmM995777Dh/2vzU9PY\\n2trC5uYmstnswINgpn2YSfatrS3EYjE0Gg2RgtlsFslkEo8ePcLCwgLs9sNTaTWWZbFYJByBuEkm\\nkxEMplKpSEIkpa+OrTK2p1ObB6FODKHTPbuNzyDP7LQRT0pjMCOuSe0dIlPY398XaZ7L5YQZEQ7Q\\ngpEeOLr7yWTIjIiPsvYXNzPxKpowJ9lv9qPROCyepzUyI5Sin0lPo2aOxghtrRjwfvo9NTD2c2ho\\nqM3MNQrUftZsV4ZEjIOxBLu7uyiXy3j8+DHef/99RKNRUfM8Ho8cWxONRjE+Pi5eIgbYlctlrK+v\\n4+HDh0in06Je0gNlNjlawmkGabfbsbe3J0FrzBc6CpltduP/rVYLW1tbqFQqIoVYY6ZUKmFtbQ17\\ne3twu9148uSJqOu8F1VmSqX9/X2ZbNr9etFQqur2dNJujtPPfu7TSWM0ft9pc/X6vfHzTmbeUUl7\\neTkvzMfa3NxEMpmUJGfteNBxOXzlHHHDES4wPktHeROLM14LHD+fTe+NTCaDnZ0d8Xpls1kEg0EA\\n7d5EOpm0Fq8j0I1OJTIe7WjRigM1zOHhYQk9KBaLAvTzGf30sWccko6dyOfzyGazbRxXLyKewRUO\\nhyX9wGKxIJVKScKhPgiQWhEHVL9q4vWlUklKqGr1Ui9YqquDUrcNRSIGNjU1JcGD9XodyWQS//Vf\\n/4WxsTFMT0+jUqlgeHhYjv8GIECo2+1GPp9HMpkUV3o0GsXw8LDkUhFs1Z4q3cajUqff97pnL4bQ\\ny7TqhAn1ywyP0mcjA7RYDoNq6VULh8NSg5qgNNd6r3XQaRz12qMGoTEYzZBOymHBtjD1hkcRURkg\\nrssYOJpWNCd1aIDuA2P8dLaEzl2kstJqtcR0ozbocDgkxIQMqd8+9kyuNcZMaIlvjK8gw0in09jY\\n2BB0Xkf66khRjeprs43P1gtXMwx+bsyROS7WoF9J+p6MTPf7/RgaGpJctEqlgtXVVXz44YdSfmV6\\nehrJZFI0QW2+MoaLHp9EIoEzZ85Ifxh0SfX3OAvWrI+d/v//k7rNl1HAHHVe9Zw2Gg0MDw+LdywY\\nDIpnjZCC8TfdzEu2jZvSKNA435xrrVV06+9x+kgAmTFkdLC0WoflhikQgUPHBSubsrIlf8/xYGkZ\\nMiUGk1oshzlrPOiBlSK4fpmFwLizQbDAvsqP8NU4SZp5GMPOdSyCZkBcHMbJ1APcCfgzLtRO3x2X\\njH3lpBMXqtfr2N/fF4Ca1zEcoFKpYHl5GalUSrwP9+7dwz/90z/hwoULWFtbw71790Sa5XI5LC0t\\nYWFhAfv7+3j//ffbcqG6tfE4fdR9O0nqp239zNdx26jnsVqt4o033kClUkGxWMTKyorEgDGqutPG\\n6WSSmmk7NA0bjQbW1tZQKBSwubmJXC7XZhYZ6Th9BJ56y6j1UWsiVMLzAXkdMTDgaaVTVmbQtY20\\n+cpwm1br0O1fLBaxs7MjB3mQ4RJHZZ/N9lIn6qkh6U7r98ZJ0tnMxmuMWpCZZNFkzAPqpi0Z23jS\\nG0ybDLu7u9jZ2cH6+rpMKuNpLBYL1tbWkEqlsLm5ie3tbdFyKpUK3nrrLdy+fVvyo2hzszTsysoK\\nXnvtNayvr2Nrawvb29uidn9c1G2DHWcc+2FG3RjtSRI1zmq1ih/+8IeCl1DyG/MV2X6z9cb/O7WV\\n3xGfevDgAbxeLx49eiSeWT4baC9xc5R+6baQuTidThwcHKBQKMDn88k5cevr6zg4OEAqlRJPMfA0\\njIZtLpVKosmzjTqMh+kzxWJRtP+RkRG43W6BGWgOEvDvpGCY9qv1f6m3n9IpndIpKfr4xO8pndIp\\nndKAdMqQTumUTukTQ6cM6ZRO6ZQ+MXTKkE7plE7pE0OnDOmUTumUPjF0ypBO6ZRO6RNDpwzplE7p\\nlD4xdMqQTumUTukTQ10jtXn8TLf8G7Ocn1brsAyDz+fD6OioHL3DMg/GqE1GQ9tsNokcZbKgsYKk\\n8Zmdor339/f7GwFAzn0btJ8nRcwf4qklANqSEjulMgCHCc/9ksfj6Zhca9ZPXVPns5/9LMbGxhCP\\nx7Gzs4PNzU05ZZdJlayjHQwGJfcpGAxKbaC//du/lfQERvR3StMxUr+nEU9MTHRNyO0nido4Dr3o\\nJPIoLRYLNjY2+v5NIpE4drrJSZFZ/me3tu3u7nb8rmfqiFnjjakkZjluXq8XiUQCMzMzcv5YOp2W\\n/C7daOa6eTweTExM4ODgQI6l4TM6pYkcN81hkH72S53aZGw3U07m5uYAHI4DSzkw3UYn2B43TabT\\n9WbVGvne5XLB7/fj1VdfRSgUQq1Ww8zMTNsJFsDTEqg6M1yfYlEqlXD9+nU8efIEW1tb8rtu43UU\\nMuuHcX67Mfh+run0Xv92kP4chUH0k+M4SE5hP0nFg9zjqNT3oVf6oZ0mWF/jdDoRiUTk2B1qSTqr\\n2niiCM98o9TXuXD6/mZtO4lE027P6OeZ3XL+uv12dHRUNi9rRLEMCfOIerW7XzJbsGZ5gfyMDOZT\\nn/oUXC6X1A+nBuT1ettys3RhLiZ3Op1OlEolzM3NoVKpSKKnLvVxkkzJrM+dyJhU20+OXbe1z3vq\\n7z/Ofplpl70EYrd76es65YvqZ3W691H73JMhmSUddnqYXly6Sl4mk0GxWBSzjMxI14dhBnE0GsXm\\n5mZbcbNeXPskNCSg+0T00k7M2tetXdSAWOI3n8/LkUHVahV+v18SQHd3d9s0iuNQp0Xb6b3WdjTD\\nsVieFpbTBxowcx6AFMRnQmssFvvIIQwflxlspH7NNDOm3Gsd9LqvrmV9kmQ2Z53mkf+brXG+soSI\\nrr/F63WZW37X774b5Nq+NaRuNzRKOS5Mngh6cHCATCaDVqsl9XcBSCZ8tVqVbOJEIoHNzU2k02ms\\nr6+31WA5iQ7328dezKTbQjRKEl5rLChHhrO6uiqlMLRWQrPOTCs9qmptPDer17jy9NlCoSClVPSJ\\nKfq8e13Cgid76I0Yj8elLg/78HGQ3pz6THv9vd54+nszrbGTJsnrjWS2PnjAKPC02uRxtfpBTKRu\\nJqnVaoXP58PQ0JCcq8cTYZjtTw2eh3mw+KLRimF7OrWv15rtypA6qWtmnTN+xlooPOeKxdVrtRq8\\nXi/8fr90jgu7UCjA7XYjHo+jWq3i0aNHsFgsbcXWzdpj1t5BqJfk6wdjMPtML3JqGCQyWYvlsFY3\\nmQ/L+bL+MhlSP8/vt6+97qHHg4yxXC7LmWT6ZAp98CCAtlo8rKFDJutyueQ45o9LM+qluXRjKp3M\\nVn2N8b3Z//p3rDnN+vP6vD6jFTAIdTILzcy3Tm0Enh7aMDQ0hKmpKXE48PRbwiecR32KiNm+1+0x\\n0zJ79bOvekhmn3ebBABtBc71qQRatdd1aci4HA4HotGoqPrA05osWgszGwT9ehJkxv37MR2N0kKf\\nuc4Ny0MHW62WnMCrT0KlpkEzl/c9aa2iGz7Ao7B5Wgy1DaBdhde/Ze1lXeKYY0hmq88A+zj6YxQm\\nxnboz7vdp9t33bRV/s8DQUdHR0UDYQ1vnlhyHMFi1sZuAtts3/JUYR4fpgWKvn+z2ZTjuXw+X8/T\\noXtZEZ1oIJOt26Y3qm9kRuPj43J2FQBks1mpY+x0OpFIJDA7OwubzYaZmRkEAgEpn8njlYzFok5a\\nuhpNtaOaRmabGjicwGeeeQbXrl1Do9HA6uoqXnvtNSkjCjw9GYPH2TidzraTQkna4zboOHRj5J36\\n4/f7MTw8LJKeC5WANBmMLk0MPPW+kcnWajU4nU4pIdupPZ3aMSiZ4T+9nqHPTuvXrNXP4yvNM4/H\\ng/HxcfzVX/0VWq0WotEofvSjH+H111/He++9h0wm01XT6Od5uv29vjMy5EajgdHRUYTDYdhsNjx4\\n8ADr6+sol8siILnfWq0WYrEYzp8/j4mJCXznO98xLW53XOqrhG0nk8H4uXGh+Xw+hMNhLC4uIhgM\\nYnh4GGtra2KGRaNRnDlzBmfOnBFO7Xa7kUgkYLFYMDk5KXWP6ZUxa59+pvF9P9RJze+nz90WUrPZ\\nlD7Ozs5ifn5ejvxeXV3F+vq6qO480UGfbqFjsGjnMz6r1Wo/Q+skyahi6xNI+UxdzZJmOM1Oi+UQ\\ne6K2x9+Gw2H5vJuma/z8KMda9frO+AzNZHmNDocw3kMzrFarJZUWp6amMD09jWg0itnZWYTDYfj9\\nfnEOxGIxjI6OYmNjQ0z1owiXbuZ2P3ACtaBwOIzh4WG43W7xgmstluuM8MvCwgKee+45/PjHP8be\\n3h6y2SyApzX2O41vv/3sS0MadLC4UG02G6LRKCKRiBTGJ3YUiUQwMjKCubk5zMzMwOl0IpVKYX9/\\nH9PT0wgGg5idncXm5iZ2dnZM7fyTai/wUczI+Jn+3Pg7PtO4kSkVz5w5g4WFBVy6dAmNRgPhcBj3\\n7t2To2Jot7PmsTaNuIFpOulTggctb9uvNNPXaCwIQBsGxLP3KpWKBHYCT5kpFzb7FA6H4fP5BBA3\\na4/ZQj7JMr6dNor+rlPbjPfhNRSY1WoVly5dwgsvvCDrOhAIiOfU4XBgYmICyWQSKysrcoTYUfsx\\nyDrX11MbbzQaiMVimJ6eBgDs7e0JYzYzw61WK2ZnZ7GwsIC5uTmUy2WkUqm2U366jVk/7e3L7T+o\\npkB3r81mQyQSwfj4ODweDxYWFhCNRhEIBFCv15FKpfDzn/8cP/zhDzE6Oor5+Xl89rOfRaPRwO7u\\nLt555522CevElLhoT/KEDt1/sz52u67Vaolk/Iu/+AtcvnwZ2WwW29vb4kn88z//c1nEq6ur2NjY\\nwOrqKrLZLHK5HN5++22Mj4/j3LlzGB4eltNvNzY2cPfuXWQyGWxubh65L/0Qx5OgNU1po5eIGq8O\\n6aC5SabEwzW1Cd6pPXqejwKA99qsZkyon99TA9SnubrdbnzrW9/Ciy++iPHxccTjcVgsFgSDQdTr\\ndTx69Ah/+Id/iHPnzuHy5cv4vd/7PXz6059GNBpFOp2Wk5ZPSkPqZKIa9yotkrW1NaTTadRqNezt\\n7QnoTmeK1tgLhQJSqRSq1Sq++c1v4kc/+hH+7d/+TY4303NFAc3wH5680ot6etm6dcr4XjMqSg66\\nDmmalEoleL1embBQKIRkMil4SrlcRiaTwfLyMtLpNBwOh+ApxgHvtKCPwpSMGJhRY+p3wVDFDYVC\\nuHjxIqanp+H3++VASYKG8XhcJBE1oEAggEqlIiESY2NjGBkZQSKREDxtY2MD5XJZvF6D9lH3p9NY\\nGscCeOoap0YEQM4do9OCi1wfeqkjuM3c7N0YApnhUTZrtzkz04Y7/U4D1HS6WK1WOSR0cnISly5d\\nQigUgsPhwIcffiiHQyaTSTm0gUcS5XI5OJ1OhEIhOWj1KNpur/73+p795uk2DGIF0Bboqk8MYmhK\\nPp9HMBjE1NQUrl69it3d3TYBZLPZUCwWZe/zdOB+2jZw6kinhWzEknhwHCX51tYWdnZ24HK5MDQ0\\nhPPnz8Pr9eL69esIBAJ48uQJHj16hA8++ABPnjzB+vp6W26XcbD1ZjRqRkfVknox3F5E08Rut+Py\\n5cv4xje+AY/Hg0wmIxqGZiY0YUOhEEKhEBYXF+F0OlGv1/H1r39dvDCjo6PI5/NYWlrC8vIyHj16\\nZHoKai/qBt6bXWexHHrGwuEwKpUKHA4HSqWSHH5JxwUAuN1uOV6csUqMZSEzzWazEiDbz1jy9agC\\nplvfzK43E0L6dWJiAiMjI5idnUUwGITT6RRc7+2338bW1hb+8R//UYQOgX2aqRQoExMTiMfjuHDh\\nAjY3N7G/v9929PpR+tqNsZuNBZk9hR+1JqfTKbmdPB+Q4QlOpxNbW1u4d+8eLl68iOeeew6Li4uS\\nfxoIBGS+qO1ns1l897vfRTabbTujrRP17WXTHTG+N5MqoVAIkUgEXq8Xy8vLWFpakhND+ZnFYmnz\\nwu3u7uLx48dy9DbPgDID5Ix0VHBQ368TXtQNQDTiLVarFYlEAlevXsXY2BgKhQLy+XzbIZDUhqxW\\nq4Q+kPFSyyD20mg0EAgEUCwWsba2Jkdwa3X6ONRpDjXZbDa4XC65lm3TbW61WoJvAU8BYeIVjNon\\nk+0HB+y2oQbto3F9dDuCSF/fah0G80YiEZw7dw5jY2MYHx/H+Pg4Wq3D4+V/+ctfylHqnMtWqyUe\\nRUapU/hSq7x+/Tr8fnVG0p0AACAASURBVD/efvtt/OxnPztW/8za3+1/TRxjj8cj4Qk8Z40afK1W\\nQzweh9frRb1eFwCc8VX0LPLeIyMjCIVCKBQKmJ6elv3eCzPr22Trtii0N4iLMBKJIBQKwePxYG1t\\nDXfu3JEIXdqc9Krlcjmk02ncv38fd+/eFWlidiR2L2Z0HGnaDRczftYJh2i1WpJU7PV65SBCTjgX\\nIxcrJ4ibltoltQ3iNsTVdnd3T+Q8LzMzje3X3qVms4l0Ot1mctEkIcOkl5AgLXEialLEXIhFUVJy\\nw/di+MdlSGZ9NLunMcmYJqrFYkEoFMLU1BQmJydx5swZJBIJVKtV7O7u4ubNm0in08hkMqINajO1\\nUCggGo2KNsE5Pn/+PM6fP49Go4E333zzRPrY7zhwbNln7mGPx4Nz584hEonAYrGI9kNQPhwOA3ga\\nBc/1SWbEdW21WoWpDQ8PCxzRqzrFkbL9dcf0ouJ74PAAukwmg1u3bmFlZQXFYhE+nw/ZbBY//elP\\nxV349ttvo9FoIJVKiVbEQeqHuZzEgu1kmupXXmd8TwnC9gYCAXzuc5/DK6+8glwuB6/XK6qv2+0W\\nrwsAAUh53DJPVbXZbDhz5gyAwwXy6NEj3L59Gw8fPhQs46ipB0aNyIw58T0P/SuXyyJMmBICHJZt\\nyWQyePLkiZieNDmpCVE7JPM1Vi7oNgfG8T8pMhNcZPJkJJVKBX6/HxMTE/j617+OZ555RnBRm82G\\nvb097O3t4c6dO8hms2g2m22JxuxPs9mEx+PB1NQUrly5gnA4LF7SUCiEQCCAxcVFJBKJE+2ffmV7\\n9GdsHxmu1+vF7OwsLly4gGeeeUaOHQ8Gg7Je6fFttVqIRCLilOF9qSVRc+IzXnrpJQDArVu38P77\\n73dte99etl5mjVHaptNp5HI5tFqHkcrBYBBut1s2rd1uh8/nQyAQwM7ODrLZrLgWj4qNHBU7Os49\\ntCZB6RmLxZDP55FOpyXknps7n8/D5XLJ4uWm5Yag9lQqldBqtXBwcIBisSgLmwGKx+lrP30m02i1\\nWnLGO0FsagAOhwO1Wg35fB47Ozuw2WwIBoNt7n/2ETg8T56AeKf1dBIMyOzenfptZMp8JUhPE83v\\n98PlcgkueufOHdy+fRsrKyttOV/G+2uPI8eDY8ONTo34pPrbScCaaaRso91uRygUQiwWk3gxarlk\\nNl6vty1tiJ8T6yXWpE0zelkJZ/Rat30FRpq9djKdSIVCAeVyGVarFbFYDIVCAV6vF06nE41GAwsL\\nCwgGgxLLoFX5Tvc3G2CjZnZU6mUedAIG9QRcuHABi4uLCIfD2NvbQ6FQwPj4OFwuF2q1GpLJJACI\\n3c3NXSwW5f4+n0+uZ2IyM/+Hh4fFLDoutmIUMmabmIxHp4Ewl40aE4FRJkLTDG82myiVSm14Uzab\\nRalUMjU5e+E5g/at273MzFW9lshA3G63ANlOpxPRaFRqVv3iF7/A7du3kc1m2+bCuA4ZCsHvNFOn\\nkKI2dlTqZyyNygOv0VkQkUgE09PTEuSqvW/BYFDMuEajIdHcZEa6TA7z3dxutxzLTQHWi/pmSJ06\\navyOk0ovi87ir9VqIiXHx8fh8/kwNjaGVCqFlZUV6YgZman4+rUbaNcv9cOMjIuXoPPIyAh+5Vd+\\nBVevXkW1WsXOzg4AIJ1Ow+PxwOv1YmhoCLu7uygUCshmsyKFNjY24PF44Pf7JfQBgHi08vk8Go2G\\nTDA9bP1McCfqtNH1piUAr6OwmYNnsRye755MJrG2tibxLAyCZR/K5TI8Hg8ajQY2NzelL4O047hk\\nxMWMm5FEE6vZbOKLX/wi5ufn4ff7sb6+jnv37kn/t7a28OGHH2J3dxf1el0y30nao+rxePCZz3wG\\n09PTImiYu8jqCfl8vmPNq36ok0Dp53qaoC6XC/F4HJFIRIJ2o9EoZmZm4Pf74XA4kE6nBdfUFR84\\n106nUzIOLBYLSqWSvHJcj6UhmXXUbAF1Mus4MU6nUzQCuv/K5bIAZX6/X8qcMlr5KHSSAGive/H7\\nWq0Gl8slkjQQCAiQzYmzWCwIBAIIBoPiheK9dRJto9EQZlOr1WC32xGPxyUYzev1IhQKicfrpMy2\\nTgxBm5HEBth2buZKpYJyuYx8Pi+4Aq/l/PM+BMCp9uuI4I9z7rSW0uk69omm08LCAhYXF1Eul7G+\\nvo69vT18+OGH2NnZQblcRrFYNHW8GIVVq9XC3Nwc4vG4CBOGh2jt86jC5STw03q9jkgkgtHRUXHt\\nE+NdWFjA0NCQxNG5XC5ps+6nbgdDfrhG6VX2er3HZ0jG92bqoZm5oyN33W43KpUKotEogsEgtre3\\nEYvFJGK7UCgIIr+3t/eR+/UyD3mNGVjZD5lNqpl0NmqB/PP5fIjFYhgZGUE8Hkc4HBbsh1oGY1bo\\nbaAXzeFwCJBtsVgQi8XQarWwvb2NQCAAj8eDbDYLr9eLyclJPP/88wgEAtjc3MSjR48G7qtZX7p9\\nzpAMu90ujIl4ADcvAflCoSABcEbvDYCPMDXgo4GuRpPquCYbGSs/N86rbkez2ZRkYr/fLyZoJBLB\\n7OwslpeXJYA3k8mItqAFKO/fbDbFKzU9PY1wOCwhADRzHA4H3G43fD6fmMBH6SNgXoa4256gkGB/\\nr1y5gtnZWVitVjz77LMoFotIJBIYGRkRzyEBbGKHZLhsO7VqAG2ldM6cOYNGo4H9/X2JBO9EPb1s\\n3YCobtKNi1fH3YyMjEgH5ufn4fV68eTJE2SzWfj9flHn2Sm96f8vyMxs4GKjVhMOhzEzM4MbN27g\\nwoULiMViEhjXarUEd6Drn5oS8DQilppFo9FANBqVKpHMX0skEgiHw/B4PHC5XJiensbNmzextLQ0\\ncJ96MW09l9VqVXKVuMA8Ho9gg2Sq+XweuVxO6qbzHk6nU1IKAAieoDEptkm3jWNurCBw1L7qygTG\\n7whI22w2zM/P49y5c1hYWEC5XMabb74Jm82Gy5cv4+zZs3j11Vfx7//+7/jFL36BQqEgDNm4Dwju\\nUjOiuVYul6WEh8VyWK9c5yoOQkbtzsxkM1MU+Dnn5/r167hx4wZeeOEFzMzMIJVKYXR0FG63W9qo\\ncw91gUUN5jNmEDhkjvy91WrF2bNnMT09jaWlpZ6aYE8MqVtmdj+gKAfaZrOJvcpEW3LQYrHYVh/G\\n+CytkejJN3u+sQ0nQWaMifl6Z8+exfj4OJ5//nnE43E4HA7BBDweD0KhkGzsZDKJRCIhUb7lchml\\nUkmidKlNUrPUgZH0sLGm0lEimLtpnp2kKEFXbkAWGdPjwTYVi8W28iScf5o22WxWStwaN4pZu8yu\\n64eMm9VisXxEk9HaQ7VahdvtxsLCAhYWFjAxMYGHDx/inXfeQTabxe7uLl599VX8yq/8CjweD/b2\\n9rCxsdFx/KkJzc3N4dy5c23VNKPRqIyPXteZTGagPvbTd/bfjOhsWFxcxLPPPotoNCo5ioFAAMDT\\n+aepbQzYJRlNeW3KkolZrVaMjo729CYOBGr36qReBHR312o1ST0AIGofQbMLFy5geXlZAE9dN8d4\\n9E2n5/baWIOQmdZn3DhkFjabDd/61rckZujx48dSoJ8gNE/tAID33nsPS0tL8Pl8GB8fF2a9tbWF\\nSCSCYDAok72ysoKVlRU8fvwYCwsLiMfjslF+8pOfYGNj40iM16iFdAuypIllt9uxvb2NUCgk5prG\\ng5xOJ3K5nDBiapC6Dnij0cDa2lrbqTPGdhlz3IhPmAXI9uoj0B6NrXEsBuuNj4/j2WefFWzn9u3b\\nePTokRzvVCqVUC6X8fDhQ3zve9/Dn/7pn2JkZARjY2NIJBLY29sDgI9oN+z/3Nwcrl27JrFYBLMp\\nWD744AM8evQIb7zxhiTZDkrGNWoU1CQ959lsFufPn8fi4iK+8IUvYGFhQbDM4eFhYShci2RKnA8G\\nfLpcLrhcLvh8PgHxGdWtiy/y+QDaqr+a0UCpI0b7vpNKSNSelRJ1xjcl6MHBAWw2G0ZGRjA6Oorh\\n4WHRGILBICwWS0fPQzcGdBztqJtkMTKq4eFheDweXLt2DV6vF8ViEZubm7Db7SgWi/B4PNIHi+Uw\\n8MzlckkiZTqdhs/nk9gObt50Oo1ms4lsNovNzU3cv38fkUhEsIyDgwOpQzMo8zViNJRoRu1Ev+cC\\nYkyVrpOjNVoC8vxdpVKRuDLiDwcHB+JxMRtjsyoAR8UF9f3JjLSJNTU1hbGxMUxPTwvg+vrrr+PR\\no0dIp9PiOaO5XS6X8R//8R+4ePGilNXZ29uTHD/dRq/XK3hhKBSS/D2eTci2PHjwAK+//joeP37c\\nswJjt/4ZP+s0XjQN7XY7RkdH8fzzzyORSEgcEoC2kihOp1NKyxD8brVaAlrzd9T2qNlTYFNT1DBM\\nr3SngcqPdGIExqCwVuswm31sbAxjY2OS42Sz2WTjra2tSZDg7OwsGo0GlpaWcP/+fQFOU6lUx7Z0\\na9tJkpl25PV6MTMzg0996lNIJBKo1+vY399HoVAQ8JpAJY8NcrlcmJmZkWRMSo9WqyUAaqPRwPb2\\ndlvsx+XLl3Hu3DnJ+B8aGsLw8DDq9bqEFgxCRvOzG+AJPC1In8vlJDfRiMmw7TTZdK4dTXX2iXFM\\nxjbp8T4OE+qEpVADO3v2LGZmZrC4uAgASKVS8Pl8sFgs2N/fFw2dG0wzss3NTRSLRUxOTsLv97et\\nefa5Wq0iEolgamoKMzMzGBkZgdV6WKP64OBASrCwZvxbb70loQNH6WsnANvIrHQpmfn5eVy5cgWX\\nLl2C1+sVrx+ZjTarORba9OJ1On9NzzfHULeDQrlXvFVfGlI3pmRcQOw0S2doN3gwGITP55P/6/U6\\n9vb2cP78eczPz+Pu3bvI5/MS36IZHTtlJj1PAjPqpBHp97SvX3zxRfmLxWIoFosYHh4Wyc+4I0pF\\nxmYEAgH4/X7RFlqtFvL5vGBP3Lj05Jw9exY3btwQ5uZyuTA6OopPf/rT+OUvf4kHDx4cq89cSNSE\\ntDsXgORhMTq5Wq0iEAjIdT6fT0wRhmvkcjkAh2klZALcqIxNM4LaxjYZzZBB59dolvKzcDiMl19+\\nGb/zO7+Dzc1N7O7uYmVlBaVSSTYimZBuB02sg4MDMUsZ1Vyr1cSJYbVa8Zu/+ZuYn5/H1NQUbDYb\\nlpaWcPv2bQkUvnHjBoaHh+H1epFOp7G1tSVC7jjUacw4xkNDQ3Ly8B/8wR/Ie7/fLwyJADsBaZrn\\nZDSlUkm0Hq5Jmm/8AyAMnP/TueXxeBCNRrv2o+8Stp3QejOgmZ+xdg4XvMfjaaudA0Ds9TNnziAS\\niQA4jPJOJpNtQGSngT6ONDWS8Z7arKEN7Xa78elPfxrz8/NybIzH40E8HpccLqbIWCyWtgqPBLPp\\n8t3b2xOJ22g04PP5BDOJRCJS7lTXF/L7/RgfH8e9e/eO1G/jZjUyAC1p2Wetduu6R7yOWg/NUS5w\\nhjRwsdIr00kL6qS1DdJPI1ZEzx6lPeGBx48fI5VKoVarIZfLSdwX6xN1u+/BwQHcbjfGxsZQLBax\\nsbEhmOif/dmfwe/3w+/34/XXX8e7776L73//+wLwh0IhzM7OYmxsTMZEJy4PMo/G/Wi2J/SpNZcu\\nXcLi4iKee+458Xbp/aUjyOlZ1elcFotF5pqMRQPcVB4IgFMYMdShXq8fH9Q204jMFoxRo9ja2hJc\\niNG+5LzcnOx0vV5HJpNBPp9viw5OJpNdB974/jikNTxNumzI9PS0gIHBYBDFYhGrq6tiS/t8PoRC\\nIRkDq/WwDjYXXjabhc1mg8fjkSRL4NAUoGcDOFxEy8vL4s2ZnZ0VVbdYLGJra+tIJ1YYNVmz/pu9\\np4ZDJwMlJvOXtISkVAUgzJqmHSPPNfDaqQ1HJZpD3IjcEJwfejXT6bR41gjAs4Bgr5wrXs/cvaGh\\nIQQCAYTDYVgsFuzt7WFtbQ0//vGPsbq6KqETtVoNmUxGjggjkKzz/QYhzUyMSgNNqfHxcQSDQVy4\\ncAFXrlzB9evX29JFtClGIshvs9nakmd5T+KiJLOTZuj0IVFg9TrwdOBs/24TpSXc/fv34XK5MD4+\\njrNnzwoSTzCWuW1erxeVSgVPnjzB3t6e2KahUAhra2uiCpuZUJ0Y5KBEm5jPZs4dtRnayt/85jdx\\n/vx5nDt3Dul0Gru7u3jw4AH8fj9isRhmZmbg8XiQz+dFGhCgz2azEhjmcDgkcjcYDOInP/kJxsbG\\n4HQ6xc3M8r3pdBp/+Zd/KQmPOzs7ePfdd7G6ujrwIh5U89BAJTdSNpuVtlSrVeRyOeRyOfGwcE64\\nGCl0KpUKUqlUG4ZkpmHz86NSJBJpcxJ4PB45wWZ6ehpbW1v4wQ9+IM6UixcvolAoSFoEU3ioFbJA\\nmW4XcSWfz4f5+Xm8+OKLUnjw/v37ePPNN/Haa69hbW1N2kCMjXXUCZ53iiLvRVp7Z/I6vV4Wi0XC\\nRH7rt34LY2NjOHfunFQXYCAuMyiMQl/jZ2Qk2mvKAwGMEfmaKdE8Z9xatVpFMpnE48ePu/ZroBK2\\nnb7rhLdQnSeGogHPra0tSVgMh8MSkwS0FwDrNFknCWK3WofR1sFgECMjI1LNkhKQHrILFy7A7/dL\\nFDbBe3oS6UHSi5iTxntZLBYBu2maRaNRyQN68803sb6+jrNnz4rqP/2/kb5Uj6lpHDXFhmRkBN0w\\nHS5KSjluVpblBdrL3GovC3+v8+B6aWhHZUpTU1MYHx8X7bxcLku4As2FfD4vUpweUZ/Ph6tXr4oW\\nSBwvnU5jb29PtEEygEQigbm5OSwsLCCRSGBtbQ23bt3CzZs3xRT3eDxtKRQ0Yxjeoj1YgxI9ZvV6\\nHZOTk3jmmWfaqmjwpJPFxUXxdpOo4eqTY0g6rYdzYXyu1WoV3E0fSEFvY6vVkrGi5gk8TTPqRkeu\\nh9QN1efnDBPnpFDNJ4BIkJicmguD3jdjvpN+tqbjakk2mw3xeBzDw8OYmZnBiy++KBOYSCQQCATg\\n8/kQiURQLpexu7srZkEsFpNcHh5PxP8pZdkHema4KLlwzp07J96sx48f4/Hjx3J00qVLl5BIJOBy\\nuZDL5cQMYn3n45KeR4LQery5cbmZtVAB2g+M1CUouOiZNtSL6Wnq5i3qRUNDQ3juuedw8eJFKaVL\\nIVEqlZDNZpFMJsUzyOe4XC68/PLLSCaTKBaLODg4QKVSwerqqtyHWGggEMDExAQWFxdx5swZOBwO\\nbGxs4M03/z/2viRGzjut+1dd+773vntrr7GzOcnMfJMEBGhGMyCkESeEOMGRI0cuHDhzRkIcYBgJ\\nwSAREDBkMkMGzxAldmI7dty2e3Gv1VXdta9d9R36+z1+6u+31m5/yqEfqVXVVW+97//5L8++/Ao/\\n/elP4fP55HDqvC5KLnQEsIb1sASJlRpHR0fx7rvvSn81j8cjKj69wDTYc51oZqDETkmWn/EZ9ApS\\nCuL37B8IoC1MgM4Rhg8wPolj6MVEjy0hmZ4Mvmo9dWJiQig2YzEuXLggdX+YNFqpVDAxMSGlL7/4\\n4ouuLmKr8QzDXb/zne/g3XffxYULF+BwOFAoFJDNZkVlogq3t7fXVi+Z+rWuEaRFWernFH1ZU5rB\\nklyopaUlFAoF5HI5/OAHP8DTp0/xD//wD1hcXITP55OSvna7HTdv3sTrr7+On/zkJwNH91pJRHqu\\nuFGB9lrWlUoFCwsL0v2E+FLlJBFmyRQW47PZbG3F3nWwbK8x6veDrOeHH34otiyWDXG73eIgcDgc\\nksFPLl4sFpHNZnHlyhVsbGzg0aNHCIfDSCaT+MEPfoBUKoUf//jHcDgcmJ6exo0bN6TY2u3bt/Fn\\nf/ZnUg2RFRVp2Oe8MjaPr/Q80TYzKDSbTfzhH/4hfu/3fg/BYBAejwfZbLbNvtNqHcX7USrXKTzc\\no5owU6KlRE/GyjQhXZ6Yn9tsNjHHcL+wFIneJ81msy9P4uAz0QFMrgo8N/5R39a6qO4VTl3Tbrcj\\nkUhIqoUV8elkd7D6rF+4dOmSuOR5KD0ej3AwuukLhYJ4GijOs1IBdXFyRoq+DHij/YXpFyRqzB5n\\nLhDd4tvb21hfX8eZM2eQTCZht9sl099msyEej2NycnIgPK2cD6bXzZSQKKUyIltvOkq9Wj1jrJFO\\nM9AG1042o26MZFDVdHV1Fc1mU+q6M8n1lVdeESmX0ibz8JjAzGRQ1jlqNo9aSF+5cgWxWAz1eh2V\\nSgX/9V//BZfLhYcPH7YxBm3b0Z/xVXuXue7DpAGRqFAC0/mTTOWhVMskbkrtOoARgDBMALLWxIFS\\nP/emNsDzrFB60l41fW9+T2bdDYYmSL0kEXqWGMkaDoeljIZuNkibCCvyTU5OSiyLlfrQ7ZnDivkX\\nLlzA+Pg4EomE/Pbw8FDcwQBEggGONhMz28lZmIPGw0WxFTiSMNLpNCqVisyF1+uV2kgA2gIOa7Ua\\nUqkUdnZ28PTpUyQSCVFpWb42FoshFosNhGc/thvzM84FN7HmhKa0Qw8MXej0mOp24cMwkEHWs1ar\\nYWVlBRsbG2JoZkv3UCgkAZ7cnwCkwcT4+Dj8fj+SyaTkVgJHa/Pmm2+KOeGrr77CT37yE+ljxkNI\\n4mBVAUDviXq9jkwmIxLaoDgCEAYIQOwy/IwEiHYmrarpyGpdlF+PWRffA54zH96L4R1U4ShN6Xvo\\nFBnu6X4kwYEIkt5EVhNobrBisYiVlRVUq1V4PB4UCgWkUilEo1HxRJG6P3nyRCopAmiz2Hdyi56U\\nwfvzzz+X9i+c0EQigUQiIQZRqlcskpbL5VAsFrG+vg6n0wmfzwev1yubk56Yubk5tFotLC4u4unT\\np3jy5Al2dnZQLpexs7ODYrGI3//935dWSPF4HDdu3MArr7wic7qwsCB9vx48eIBPPvkEH3/8MT7+\\n+OOB8LQiAJ2kTX097QLalU61iAeS80aCxMPMzahd8freVuMyJbRBctkmJycl8JbldR0OhxRV46Gl\\nZDs6OtrWhogHSbdromOhWCyiXq/j2bNnePz4sYRe6GRSq5gi3kfbX549e4adnZ02G9wg8M1vfhPJ\\nZBIAJHiVRIN4cG1JoLSkQ2mNUizx0GEbHBcDZDWT1XtAe9sY1sGz6/V6sbW1hfX1dayvr/csRDeQ\\nUdskCprjmsSKgyXhAdDmcctmsyI+0ttBSUNnQuuxWL12Gssg8ODBAwnKZA2jYrGIYDDYFrjG+sFO\\npxPxeFyK3Gv9nFIDuQY3us1mw/b2NlZWViTvjSqgtmfw/mfOnMHu7q7U3mEZjydPnuDJkydYWVnp\\n2cHBhE4qsJ4/K2JFrqi9aK1WS4g0CbD2zJCL6k6o2rjbbSy91robMBZIV0WgbY+SN4NQuX65XE4y\\nBGhS4FyMjByVYCYDokqqg0CBF3O0THVU34/BmMxrGwZqtVqbEblWq8Hr9aJWq0nOpCY+VK+4DnTT\\n62BGfs4zQJWN+5lMRptbeG8tLVMzYp5mNpsVon8sgtRJCtK2h07X0pNG/Zy1jpiLtbe3h1arJYFo\\ndK/r+2nXv7ZxWEG3sfSCn/zkJ/jFL34hHgUmBtPek0gkEIvFMD8/L8XQ2eaJ5SSYW8Y8nnK5jHQ6\\njU8//RTPnj3D6uoqlpeX5fByAwDA3//932NhYQELCwuYnp5GKBSSuCwuZLFYFBsN56VXomI/88T/\\nNac2GQslDV3biNKtZjLkkMViUbi3zXaUYKzLE1sRPdOzZmVz6gei0SjGx8cl5o3EnDhQnaRdUJd+\\nMcdDSKfTEqQLoE0i6iS9WZ2PkZER7O3tCaOiXW4Y1z9jnKgKsfwLpTugvQqB9orpQFVdoZSEjCEo\\n7GpbLBbbeq9prznHwPvTFMMQEVYTZazasZJrO20Gk5PyMwK5aqPRkHbY0WgUCwsLWF9fRz6fb8sc\\nByBEgCpcsVhsS9zT9z5JmwPQXveFUadMKHU6nSgWi9jZ2cHu7q54SjweD8LhsOjJdrtdeqS73W6J\\nCGZHlUwmA+C5Pq71aWb/P336VKoA7O/vC9fWSauaEA2Dp5Xzodc6awM2cdeGT46Nqi29Om63W4yt\\nHHsnA64pOQ3jYQOA5eVlBINBsbXx4DMEw8SZOFpJ/2bMlJbgzOtNYmp+Dhwx6Xw+bxnKMiieu7u7\\n+M///E9sbW1hdHQUs7OzYuPRFShNgzMltFarJeeNyd/Ac0mWoTgulwuxWEwCWukxI2M1JUSeHTIi\\nxkBx7XvhOXRyrZWxWb9Shy2Xy9INc2RkBLu7u9jc3EQkEmkTLXW3CkpUVl6fTuPr9n8/+DGAy/Qu\\naIkQeB6ST/VKey74ewKNjUyrANp1e/18SpLr6+vyHXG3ioAeFEegvcOEqYrr9bM6eBw/CTAdEmaK\\nBokSADkg2tvYDaxMAP0wIA17e3uiyvK3PHRaEtHEyJTC+XxTcrGSnrqNX8+tPg+MzbEKTOwXMpkM\\nPvroI9y7dw8zMzP4oz/6I9mTVFu110ubHshoKSnyFYAQKQBSP1trJ61WSxgwpSCq7yQ8tP/abEcZ\\nCrQh6wTuTnBiXjbzf8al5HI58a7l83lUq1W4XC5cunQJbrcbmUwGT58+xerqKrxer3R8DYVCbUZD\\nPsMKBt20nfDRY++Eq9aBu0kWemwmt7c6aNwEOj/J6h6dOHG/OHY7UFZ4UQJi19yJiQlxPmiVR2/Y\\nVqslRcmonrCsSjcGY3qmhgFd4sZKDTSfZTI9kwBZzbF5PeeUh1CDKfk9fPgQNtvzBpqdJLReQDtn\\nrVbD3t4e/vRP/1TSnK5du4ZkMonZ2Vm89dZbYsAnYZyenhbctfeUUlIul0MqlUI6nZYqnz6fT1p6\\neTweSSzXBF/Pi5aEKemfSBxSJ+mjH/2e9od0Ot1WBJ6iIPOMSMX5yvB8ipuduOUwC9kNTyvxXONq\\n9d4KOl3TLwEZVl3p975WYLXOxMPhcIjxV0uRXFMtwfE35Mo6381KejCff5I4d1Kd+rm+22+s7mul\\nCppEq9VqtUXzSgFlNAAAIABJREFUHwd4P0aR7+7uwmY7cuUzkHV3d7fNPuv1egEAExMTok7RxsP8\\nxHw+j83NTezs7CCdTksiMMNUACAUCmFhYaHNXkQ1EHhe5I32KLbJYgpONxhYQjIPqvkZ/yf1dTqd\\nSKfTWF5eltovDocD6XQahUJB3IlMGzEziTsRIIrUVp8PA6bKYnWfTiK51Vi1StDpHla/M/ExPzcJ\\n5yDQa470c/Vrs9mU/CyK44eHh2Ln0pINGQg55v7+vqjgFPP72UMvA7rh32sPdVuzTr+1WkedlnFc\\n3EmAWq2WMHaHw4FUKoV6vY719XU8ffpUKljW63UJqGX2gR739vY29vf3sb29LXF4GhcGAAPAmTNn\\nJBKbklowGBTDts6J297exu7uLlKpVM9923dfNvMgmLEKndQ3u92O7e1tPHr0CMlkEqFQSFyJ9Xod\\nm5ubqNfrmJqaAvC82Rxbb5v37EYoTGIxKPRzYM1nWv3e/I0mdoOMsRPOnVSJfu7XTe3V7zsdSgbA\\nMSeLnwMQqZeJyvyO0c39RFwfl7F0g26EppP03U2F0wTFSmqymsdObYAGxZf2IKuI8Eqlgq2trRfO\\nBb2DsVgMfr9fJCY6kpgaRbuQSTD5md1uxyeffCLEhxkY9MLp+CbGMFHQOFakdi+uQDAPnEagXq+L\\nGzIej0v3Vp3XBkCCCkl1OWGMf+mHSOjxDApWG82Kw3X6TP+207z1Q8DM5/RDQPqFbpu+09pyHBT5\\n/X6/bDhWaeCmZ8JnIpGA3+9Hq/W8rItOM9D37Yb3y5Kaeq31IBKrvk83xnzS0ElS13Yh81qOUdv+\\naPeiHYiGdr1W2i7E+zFLgeo7VTSaWXhPfkfnRq/56Ftl64dzWX1ntx/17VpZWZGC58vLy23tlff3\\n9/Hs2TO0Wi0JNKQhrJcKZT57GHWmX2LUCbrZiazsCIOMsdv1L0OKsGIuNttRUOfTp0+lBfrIyAjW\\n1taQyWSkBnOj0cD29jaWl5fldzSMMo6q3+drrn5cPK0Orf5cX9drT3XaE/0yzJNcs07Erxfjo6DA\\n78w8Nt2HTa+Hlog1QdSF2UiICKZ3sxf0rbKZYC6WOSF8PzIygs3NTckKj8ViePTokWRDb25uYm1t\\nDSMjI5JUy6BK7Uq02hS9ROx+oRdHtnquOYZ+fqsXsZd0YHWPk4Bec9RJAmTaRSQSkfrh9+/fx8rK\\nivyGKUDLy8vigqb9KJPJvJCcaTU2/b6THa4bWI2/m9TSiQiZ35kHs5NE2e+YrK4ZBHrtOytGqD/X\\nAZ16rrupn/p6M4Si3zH3utbWehls9hRO4RROYQgYLirrFE7hFE7hJcApQTqFUziFrw2cEqRTOIVT\\n+NrAKUE6hVM4ha8NnBKkUziFU/jawClBOoVTOIWvDZwSpFM4hVP42sApQTqFUziFrw10jdSemJjo\\nmLLQM+JyyKhp/fte0O3+29vbfT/L5/NZJn42m00ppWCVfNpqtaTdTDAYxKuvvopYLCadJVKpFLLZ\\nLBwOByYmJnDu3DlEIhHcv38ff/d3fyfJibynzh3qFqGs57+fwmeEQCDQMQq8W3SzvpaZ/g6HA1NT\\nU7h27RoikQgymYzMx71797C3tye/MSskdoqM77ae7M7SCyYnJ1+YO6scy372L3MoJyYmcPbsWbz5\\n5pvSueSDDz7A/fv3pTifVUnhftOZOJ6tra2+cATaC6kNAt1yM7tlO5jR3sc5293qag+UXKsHp19P\\nMs9qkDD0kwJzYXV4PRMCrXK83G433nnnHUxNTSEajUpXkvn5eUxPT0v5jXq9Dq/Xi3A4DJfLhUQi\\nAbfbjVKphFqthtXVVezt7UlbHbNErVVu0jD498oH1P9bpRuwTXM4HEYgEEAikYDD4ZAiXiMjI0gm\\nk3jjjTeQSqUkwz+bzUozxkFzwQaFTmU9Or3vBCxSPzc3hzfeeANzc3N4+PAhisUixsbG8I1vfAOv\\nvfYaVlZW8POf/9yyzo/VM08ST33vkzhv3XItTSJ+0vgQBspl65VPpq+xyvXqxLW6cen/H5kt3TZo\\np0Vg54V33nkHMzMziMViePbsGYrFIhYXF/Hqq69KkjC7VDC5lH3Z2OuNpV/ZLbffZNp+iXc/YIWn\\nSYjdbjd8Ph8mJycRDoellxyLwft8PgQCAQSDQZE6y+WycH7W12beYqe9dRJ49Ds3nfYxu8aMj49j\\naWkJU1NT+PDDD7G7u4tGo4H33nsP09PTuH37thCkVqtlWR6YcJx8y0Hw6Cbl9Hsfvu8l2Vn9/jj4\\n9S0hcXNqUbaTGmP1+06qQrdEvm5JpubzjnM4eyXPmvf2eDyIx+MIhULSXJAtnFicKpvNwm63S30Z\\n9ivTlfRYn/rChQvSOXd9fR3FYvEFFeVlEiMr4Pyy3fLo6ChCoRBCoRBsNltbeyNKhgBwcHAgtZLY\\nEMBmsyEUCmF2dhalUgn7+/tIpVIvFOM7KehHJbO63mSeTqcTgUAAh4eHuHfvHra3t5HL5bC2toat\\nrS1MTEzA5/MhEomgWq1KvWzCoERxGOi0ZzvhqfHrpTL3kpK6PaPf603oKSHpGiisfdOpnIBVvZt+\\naqD0gm7SwklsaE3cTPz0/ZvNJiKRCM6ePYvZ2VlMTU0hGAzC6XSiXC5L3eLDw0Osr6+L7YRthLPZ\\nrBRT572p4o2Pj2NxcRF37tzB/v4+7t+/L3XJdXfY4+LcaZPoz0ko2bssEAhgYWEBoVBIOqSwZCnx\\n0UW5Dg4O0Gw2pYYS2xKxikOxWMSnn34qNjYyum7je5lgpabSJhQOh1Gv1/Hw4UORcjc2NvDs2TMs\\nLi5iZGQECwsLWF1dRS6X69hRxFT3udeGxVWfTSscNC5Wz9afDwKDnrtBr+9JkEzOoVvDmAiZh7lf\\nhHtR9pelr+r7axw7qZ1sMfPqq69Krzb2aOeBBI5UE25Mdm4ol8uivvFebM3Dzh0ejweLi4soFArY\\n3NwUImaF/0nb6PTnHo9HVLNgMAi/34/Dw0Ps7e2h2WxKmx2qZIeHh4hEIvD5fKjVaiiVStJFhfWY\\nCT6fDwsLC0in03C73dKe3GrOj3NYh7lG71sWo6NE6/P5cPHiRZTLZaytreHg4AC5XA61Wg2JRALp\\ndBqpVMry/sOqT73Gb3WPTqob/+/HPGElSZnaSb9j1PfrZ027uv07iXBmJTkr9es48P/DbtTteSY+\\ntA0EAgEkk0mcP38eCwsLGBsbQ6vVkl5UvF633mahexIfALLJeW/aVjwej/TYWvh/7bPN6n/8zUlJ\\nEVZzHQgEEAqFEIvFpJ048WDRPI/HI33a9AYmoaXdrFAoiFclk8nAbrcLsRsdHbWUKHivlwmd9hjN\\nEV6vF5FIRCqbsuwyx1Uul6XGE4373RjGSePTiUBYSXv6c6u9pPd5PypXP1KPlTmlnznoy4bEG2lC\\nZNqTzOJbg1aK6/Xsl0mkeunObIz3/vvv49y5c1hcXBTi4vf7heBQtapWq3A4HNIJl5uD/5sSZ7lc\\nhtPphNvtlkJ2v/M7v4P/+Z//kY4Sptp2UkRJbxaGIZw7d05ampfLZWlJze+55tVqVTqKsCMsbUn8\\nvc/ng8/ng9PpxPb2trTkuXz5MiYmJnDr1q0XDNzHgX5VBKtDwnU6PDzExYsXMT8/j9HRUTx58gS3\\nbt3Czs6OEOCJiQlhUvPz83jw4EFbD7Nu9pnjGn7N+5k46WdYEUYrqaeX3anfz/V3VhJXLxio64i+\\nMd2cuiMlJQkrO5OpFllBL3GyH9F3mIXuZhhkQXu73Y555c4/PDyULq2cCxJhABKfQiLEeXG73TJP\\nJPBerxcOhwMulwuBQEA6nI6NjWF2dhb7+/ui2um5OEkibbPZpDsF62brbq+64y5LnbIqJJsO+nw+\\nIVrsaMGW5A6HQwzEjUYDkUikrVkAiZ4Jg+LY796ykir4vMPDQ4yOjuL8+fPwer3Y2NgQuxpwtLZU\\n0el5JO5Wz7ViHietXVjt4W4qWCfcrc5TL0LSjzTYLxHu2+3fyfBnSk2m+NeJW3Qy/HUCK6rea8yD\\ngJWoytdwOIxIJIJXXnkFoVBIDis7LWhizDbEwPOYGF4/MjIivaxI1A4PD2Uzs+U4Daqjo6NYWlrC\\ngwcPLAMDT1INoDQzOjoqnjHd+dTtdouUxnlxOp3iLWTLZBIothQnQbLZbIhGo9KqPBwOi60KgBC/\\nboTjONDtYJnfNRoNcTJ4vV48fPiwLT6Maz0yMoJAICDNE60CaPnaSQrvRLCGAavzqol8vxJRJ2LZ\\nTX3rdp9BVLa+U0esDFPsvdRpcJqT95JuBiEincTiQe9jBVYcJBQKYW5uDgsLCzh79ixisRharaMm\\nfSQeVuOhJKGb6FGCoNGUh5/RvmyiWa/XJTDP6XRazvNJSkh8ttfrbYsXqtfr0tzQ4XCIWs61pxRI\\nYkR3P8MYWIc7Go22te1htPf09DQikYjlOpw0WB1Iq3XzeDyIRqOIRCIolUrSJYfAxhVszjg2NiaE\\nuV8cTtoUoQUA/X+na83fdfq+1zN72ZEGhZ5GbSvqpqUBblwTeqlf5vteKkg/VPY4i9vp/pRiKpUK\\nqtWqxNx4vV6RIvinY3N4YPU8Udrgn7a7OZ1OiWOiZMHAQ1P6fBmgiac2wJvjJp7sSszGjyQ2zWZT\\nGjRoj6LNZsP+/r4Yu2nonp6elh5uVnCSqmknVcWcB93sUndvpXput9ule04mkxFi1Olwn7R6zXFa\\ngfk8zbw7MXItpenvO6l++nfdnjGMWtp3K20rUdCkynqgVikX/I794rXOTcKmr+8Uw9RtgoZV1zpJ\\nXCMjI9jf34fNZsP9+/dx4cIFLCwsSOcNblbd29zsQ8VxHR4eSkiA0+mUuWBaCQ2+drsd4+PjqFar\\nKJVKcu3LIkgadxJVqpflctlSItYxUlrqoTSXz+dFJSuVSmg0GigWiwiHw3C73VhbW4Pdbse3v/1t\\n5PN5PHjwoOPYXpa0ZLVvKakycv7u3bu4f/++2Au5Tzc3N7G1tYW5uTlcv379hfg7qz3VSUsYBj9T\\nYtbPNAmS+Txt8zXHZZ5d83vzWea9zWtM3HvhOnArbasBmQjpybAiKvyeG5jchXYVK/GzXwnpOERJ\\ng3620+mE3W5HvV6XOJxmsykHi3hqdUb/jgZhLjLVNp2Q6fP5xMbCw+73+5FMJsVjNUwyZTd89Vxy\\n/LT5BAIB2Gw2iSXyeDwiHZFA6nlzuVzSsopGXrfbDbfbLf3j5+fncebMGZw/f14CCZPJpDgNzDF1\\nYxSDQj/3aLVaom663W40Gg2JH9MOBTJjRnLzs3q9DpfL1aY1vAy1phtuJiGyIla9mHwnyaaX9GSe\\nPfM+/azBQASJE2/e3ArRTl42fQ3VFPYlbzQayOfzL3itNPK9FngYsFoM/bnNZhOjJfA8a95utyMU\\nCqHRaLS5rslNSYx4D/1HewM9N16vF6FQSMIAKGFQHTClzeNuZCvuRu8YCSBd+JVKpY0gaaDBWs8V\\nbUqcM3ZKXVxcxMWLF3H27Fl8+eWXyGazEttjpZq+jPXudGj43uv1irrscDgk/si8B1NqGFelVSWT\\nAXV6dqfPBsXD1CjMOex1HxOs1D3guZpIGqDxtDrvvZ5jBQNLSGY/d77vpUZxw9EwurCwgFgshtHR\\nUVy/fh3T09MoFAr44IMPcOvWLayvryMSibRNuhZFKbHQXkOjqna9DwNWB4DEslAoIJVKIZ1OSwpE\\nsViE0+lEIpFAqVRCpVKRgDqWL6EKQImBniYdMhGPxzE5OYlSqYSDgwMEg0GMjo4iEongN3/zNzEy\\nMoLt7W2k0+mhceuEb6vVErd8OBzG2NiYxCDV63VUKhXMzs6iVqu15aAxBout0pvNpuTpxeNxOJ1O\\nbG1tybi/+93vYv7/xfYkEgkxlpMx8f+TwqsfRmZKAQx+bTQa2NvbQ6FQQL1eb4uxoxTs8XgQi8UQ\\nCARkjbXZwcqepF/N98PgZyW9WAG/MzUWU3K32WySInZ4eIiZmRlcuHABoVAIa2trePToETY2NmRf\\n8576OWSg2pnTL4EcunOt1XVWiPKV0kU8HkcgEMDly5cRj8dFXOdmPHv2LHZ2duRwm11ree9ms4lg\\nMAiPxyNqwf7+/omoayYu9XodhUIB2WxWkmXT6TT29/fFmMs4Fbr9uVhcOKqmuhMvF5KqYL1eh8fj\\nQSgUws7ODtLpNDKZDLa3t2WTDML9OuFqxTyoLjudTvEAZrPZtufymfSs0c1NSQGA4EgmEY/HUa/X\\nEQqFsL+/j/HxcYyMjCAajSKXy8khMLm91ToMg6PGs1OZGeCI0bpcLoRCIZw5cwbxeBytVgt+v/+F\\ne/OwZjIZ5HI5tFotcXToe2vTw0kRWnMsBCsvrDmvJs4mTrRjzs/Pw+VySUoUA1tnZ2cRiUQwNzeH\\ne/fuwWazvZCbybnRkpoZGtQNBq6HZKXWWNVm0RuYf7S/LC0t4ebNm4hEIsjn80ilUtja2kIwGMTs\\n7Kz0i//0009fUFlqtZqkJwQCAUxMTGB0dBSrq6uS1GkVoNYNem2YcrmMVquFdDqNUqkEu92ObDaL\\n9fV1jI+Pt4nLmgBpG5kmXPRS6YjnYrGIWq2GeDwOj8eDYDCIx48f4/Hjx3j27BnS6bTYprqtTz+4\\nmnjrz5rNphCkZrMpnj+dDsNgT0aoU0Wj1MpwAbvdjrGxMcRiMWSzWTx79gyjo6OYmZlBMBjE2NiY\\nlPkwN3Gn8fWLn0nYzIOpuTufU6vVEAqFcOHCBYyPj6NSqcg66j1F3NPpNHZ3dyUeyePxvGBjI5hn\\nQn82LLGyUuFNZmM1Divgus/OzuLXf/3XEQgEUCwWpVaXw+HA+fPnJUxlZWVFPK+8p96fmi4MciYH\\njtTuJAmZG11PFiNbXS4XFhcXceXKFYk+DoVCOHv2LPL5PNxut1Dg9957D3/5l3+JVColBMput0tt\\nGhbJotrwwx/+EHfv3pUNMSheGswDytIhv/rVr/Dll1/in//5nyUG6Y//+I8RDoclx4vqnU621fen\\nu5vFy6i2pdNpZLNZZLNZVCoVZLNZPHr0CHfv3sXjx4/F3sLxnRTH1aI8gWq1DnykC79Wq4nrm8TH\\n5XKhVCqJak7bC/ENBoOIRCK4ffs2JicncXBwgGw2i1wuh4sXL0p4gxloOwyuWrVvNpsIhUKSl3bm\\nzBkhrgxo3d/fB3C0Hjs7O2g2m7h9+zbW19dRr9fx2WefyVqReGonzPLyMv793/8d9+7dQ6FQkBgy\\nSpbA86BTqjFkSCdhHzOJXKfPOxEsxsSNj4/jxo0bmJmZEa/v2NgYLl68iFwuJ8yVCdePHz/G8vIy\\n7t69i1QqhVbrKAuBBP7cuXNCpFdWVkTVN+1xJvRNkLQtSBMmUzQlaI5E+04kEhGxj3YYt9uNZDIp\\npS28Xi8CgQDOnDmDK1euCCWmKjE1NYXr16/j+vXruHDhAlqtFrLZrHg7rJJRjwO8F8tOMHPf5XLB\\n7/fLxmQcD+cKeF5x0kwgZUwLAAmAzOfzKJVK+NnPfialTFKpFFKplEhPVmrNcXHTG5SbU6eLkNvr\\nw8hNR8KkC64RZ6ptPHzlcln2jtvtbktWdbvdllIFYRhcOefRaBSxWAzRaBRvvfWWpOUEg0G0Wi1k\\nMhkUCgU0Gg2EQiFUq1Wp3knmQe+nSSz9fj8ajYYcSk1k9IFnqEMsFsPW1hYKhQKAIyJ4nL3aSyqy\\n2idcM64fic/8/DwuXryIYDAoXsXJyUlMTk5ifn4emUwG+/v7cv2bb74pds1isYjDw0OEw2FhLGfP\\nnkU0GhWJqlKpwOVy9SxF3DdBMvVw7RXRsSv6gNIuMTo6ijNnzuDs2bNYWlqSza29FIx5yefzIl38\\n9m//Nh48eICzZ8+i0WhI6Yrp6WlMTEzIhnE4HLh06RK2t7exvr6OtbW1ftEC8GKogv5cA/Gh1EDb\\nDxeXG4BSAsemgaKrtkXMzMzgs88+w6effor79++jWCzC4XCgVquhWCxK5LTVmI4DWoqluunz+cSr\\nFolE4Ha7AUAICyUA/s8idLwfVVR6obgZAeDixYs4f/48ZmZmMD09jd3dXWQymTaJxcpeOAyMjIxg\\ndnYW3/72t0UCuHDhghBVBrkmk0m4XC4pI+L1esVm6HK50Gg0xBTA9QoEAmg0Gtjf30cymUQkEkGh\\nUIDf78ejR49kjSiZ3bhxA5FIBLFYDMvLy0in09jc3MTGxobcd5i1M9dRf06GopkjGU0ikUAgEIDf\\n78fY2Bii0SgWFhYwOzsrQZ9cQ4/HI2YIOnWazSYWFxeFoXzxxReo1+uYnp5GIpFANBpFIpGQaP3J\\nyUlcvHgRa2truH37dle8BvayETSyVJEqlYoc0qmpKamq+PrrrwshCYVCSKVSbUmaOzs7mJqagtPp\\nlEPodDpx/fp1nDt3DsViUerukAPzwLpcLsRiMfyf//N/cOPGDfzoRz/C+vr6sGgJWInC2mvAZxcK\\nBYRCIcnWp5RACYEESXs3aPwmh4rFYtjd3cWHH36IBw8eSOa81Zis7CInAdrlXygU4PP5ZEMFAgHx\\nrJFx6IBIHZHNV9pe8vm8eKEo/Xq9XkxOTqJWq2F3dxeVSgXBYBCpVErmeFjCa7PZxB41MzOD999/\\nHxMTEwgGgyLJAUA+n5d1YqUG5hE6nU4Eg8G2NdBpQpQwaFfL5XJ49dVXUS6X8cMf/lDmZHZ2FqFQ\\nCNevX0cikUAikcDq6ioePXqEn/70p9je3j7RdaSzYWxsTOx8rOcej8cF98uXL0tjisnJScGXSdVe\\nr1ei6VdWVnDmzBmEw2FMTk5iZGQE+Xwe4+PjeOWVV3Dt2jXs7Owgn8/j8ePHiEQiCAQC2NnZQa1W\\nEztcKBRCNBrFjRs3uuLQV6S26Y0wORklBhpnDw8PEQwGcebMGbzxxhu4du0aAIjXKBqN4ty5c6jV\\nalhfX0c6nRZukkwmUalUUCqVsLW1JRNks9lQKpXw+PFjOQgXLlyAz+eDx+PB+Pg4vF4vFhYWhKsP\\nClYqp36vpQl6xWjYpQpAIsTDq12gANpUH0pTlAxzuVxbvpe5BlbjOA5oHElMdDiCtgnpgFWqYmaY\\nBaVELTHpWLL79+8jmUziwoUL4pUsFAqijuuYLT22YcI4qtWqVBDgHjk4OJBgR3aL4V7LZDJIp9NI\\nJBKIRCJSqpcqOm1lnCdt2OchdLlcODg4kDpR8XhcvFUkYlNTU8jn83L/k7Qh2WxH8XIXL14Uhths\\nHlU5PXfunGgyZ86caVufZrMptZ0oSZbLZTx48ACbm5uIx+OYmJjA9PS0FN/b29uD1+vF9PQ0FhcX\\nsb+/jy+++AJra2vI5XIiFDBOr1Qq4fDwELFYrCs+PQmSlZW+1Xoeper1evH9739fHjQyMiIGznK5\\njFQqhdXVVQQCAezv76NUKiGXy2Fubk4iend3d7G/vy9clEbUnZ0d+P1+yXWiapbL5bCzsyML4Ha7\\nZcGZfT0oWG0M7SkwgZxUV32krQWA2JO0TYuH2gzN5yKSSJl2sG5u3GFB/57zSImgVqsJYfL5fEgm\\nk0ilUrI+tVqtDV+K7iQylJJ15QO73Y4vvvgCsViszbtG+yLVdpPYDipBcG7tdju2t7dx7949TE1N\\nIRwOt3mEYrGYBOVWq1VMTExgf39fSvdyLNpBwfWu1WrSfoqSEw24NOYyyLXRaGB5eVlsU/TEkSgS\\nx2EIk/6NjiHiegLA1NQUkskkzp07h/39fezu7uLJkycSkT4xMQG3243t7W189dVXWF5exve+9z0k\\nk0lkMhns7e3hgw8+wNjYGN5++20AR/v4yZMn8Pv9UrZ3e3sb//qv/4rd3d22ssRU90wPcSfo6fY3\\nDWRUm2hjGBsbw+LiIiKRiMQFeTwePHjwAKlUCuvr61KmlOVenU4nSqUSvF4vgsGgLAw5MxNMc7mc\\nBOaRUzNoT+dNaTWJG+o4YGWot3rPcXCT0oCnjda6SqSZkMxrC4WC5Ht1Iox87kmBth9RFaGEU6/X\\nxcbicrng9XrlsJqR2bSrmXmJ2sZEvCkJptNphMPhFzw9pvFf328QvDiuYrGIUqnUxlToAdMqKqUJ\\nfs4gR0p7NNxrozYJEe175XJZqmjqvaJjzbhHeF+dzzno2jocDunuwjHRsMzywl6vV8bE59XrdTHq\\nm+E6dDIVCgV4vV4kEgmMjIwgnU7DbrdjZWVFShU3m02USiXxnO3s7IjqxmYHDM/hWeon/amvOCQd\\nnU3CwPpAr7/+OgBgf38fmUxGuBHVqEgkItyUeqrNZkM+nxfdcnR0VDgYD4XL5ZLJ9Xg8bW7ycDgs\\n1Qk1V2V3jHg8PtDidsLbfG9eoxNNTQMiD6OWaPTCAM+JAr08Vs87SSJkdV9Gk1MqoJQGAIVCQWKR\\ngOceGtoMiSeJlFatOB86l4+qt74X50OrQyYMKj0QJ/aRo/RCTyJtGzp3Uq8h1TE6UuhB1HmGlMr5\\nPDIWXsMkagBSmxs4aoHF8zEsfgCklPLc3JwwEDIMv98v+ZJUOTc2NoRxs1OObkoRCoWwsLAgMXaU\\nhufm5uT8auP+wsKC2JnoadMODauA5n6gK0GiIYtBcpw8EpebN2/iu9/9Lu7duydxNOSOFy9eFJVu\\neXkZ29vbbRSbHSnsdrvUjmYNZorz3MAsFE9Kz0ROfeApHY2NjeH8+fMDTYIpMnMi9Sa1ijalmsKE\\nVO3y5jy8MOFGp1oeSCu7USc4CU+b3iwjIyOSphIIBFAul9saDESjUQDPD7qWbLjpgefqqY6+phTZ\\nbDYRi8XgdrvF0E3V3G63SwXJk8J1ZGQEwWAQsVhMEn2pyjBqnLYhGnuZqkSjPYNweQ05PNVZqnGs\\nuc2Yq8PDQ5EyOH7iy30yaPCuCdPT03jjjTdw5cqVNjMKVVYSBuJI6Zttm2i3Y7ArTR20dY6MjMDv\\n9+PGjRs4f/689BCkh/Xy5ctSV7xUKomB3OfzIZPJiNquz1Y/altXgjQ+Po6ZmRnE4/E2dye9J/F4\\nHD6fD9M/RS6XAAAgAElEQVTT0xJE53Q6Ua1WpY8Xrf4kJFrkA56L9+SS5CY6gpd6KDcE1QFyPeB5\\nSgMrFw4C5qY33eGmDYfcklxe5zlp4mLWFOJ7EiMeZpMY6uvMMRGO44ky1U8SVZbP5fwxR80s6E+J\\nh9ISvTn6vnqtNBPjfbk5uZ90EOlxCC4PJO8ZDAblf9qsaPughKQ9oSypwsBdJtvqcTMOjfYzpj2R\\nydJtToZERqVNDFo9H0YK5jqRMJLgsUMK0z0ACIFkORgGotrtdmEIHo9HJCuqYjyv09PTaDQa2N7e\\nxu7uLgqFghA3j8eDzc1NOBwOJJNJIX5s6MCzzD3TC7oSpKWlJcRiMQSDQaTTaSleT1WLthMGPVIX\\nN9v3kFtxQ9MIqvVJHWauPTk6voe/JRXXm5iu8lqt1lNPtQJNdHRsiH62vobEhk0idXQ4vU86otfk\\nFiRcPCjaW2V1KE+SGJmvlBz02DTB5ZzogEltS9ISET8jPtpGwgOpCR2fdxK48V5cf6pROsKdKhf3\\nDtdBrzHxo42HhnqGCHDu+B3LtVDC0PMCtNuROknEg+Jcr9dx9+5dbG9vi2OHZotIJCLpP8lkUqQl\\nqpI6cwKAeCOZBsS+e41GAzs7O5IQns1msbm5ie3tbWxubgrODx8+xPLyMlKplBjrNTOmhGwyXyvo\\nSpB+4zd+o60oF12A8XhcJvSrr77CxMSElPyk+3plZQV+vx/xeFw4MLtT0HjGSaLXhgiQcBEBShPA\\nkQeIIqluJcRx7u7uYnV1daDFNcH07GiixM1GiS4UCsHn84kkp7/n4eABJ1GifYmbl0RUq4Uvy3ak\\n8dBEksbRXC4n3FXXYWKhuFqtJkxJj1GrogBk85Mwk3MeHByg1WpJfXJtMDbneRjipKPjGRtz5coV\\n+P1+1Ot1SdrmASYHZwQ3Y3mi0egLz6ddSNt/+NtgMIhQKIRMJiOqGXC0d5iIXCqV4PF42oiWnrNB\\n4MmTJ/jss8/aWrS7XC74fD5pc+71erG0tIRkMomxsTExvWhnhQ7VYNZAqVRCrVYTAkSVLhAIIJVK\\nYXNzE2trawiHw0gkEtjZ2UEmk8HOzk6brVQb9vtdy64EaWVlBdeuXcPU1BSmpqaQy+UQj8dlIUk5\\ni8UifD6fTEw4HJZoXy266wOrNzUHqwPr+L+pE1P6oPGRhIhqAz1Ww4KWgvhqfkZurwu7k+vSs0Dx\\nn/hpVYKSEfFj8q6W7PpZwGGJlsaNxJNjq9VqYvswvSWMNdNxSZx7jlfbWYB2BkLp5ODgQAgVDcZW\\n0uGwKg3nkYyNTSlbrZbkHJJIVqtV+Hw+8UxxDTkurj9j7Gw2m5gugOfeU3qPNWPRtZ6IA13+Zl7i\\noERJ42E6GeixzeVyqFarYtQHntevAp7bAKl16LNI7YfSE5kqpc5sNotyudxWikevmQbuMx0c3Am6\\nEqRPP/0UV69elWAx6sGMNeIDGPjkcrmEM0Sj0TYCBEBES7okeTC5YLRnUGqgCkjpQ8eu6INAlY0G\\nyeMQJBNM1QZ4PsG0WXFTaV1ZS3zclADaiCx/x41MFY/PMJ9rjuskjNt8llZ/tYRKPGivs+J2eo05\\nfrbNJs78rFariUTIA6GfqW1U5r37AT233F+7u7sAjmyiPFCUgID2rINarYZAINB2CDOZjBi2bTab\\nBDZyPrjPGc3Oe+m66VZxa3r+BgUSRB2Ey3vRIH94eIiDg4O2eeE6EGcSXjOFRdtFrdZCFyEkUdZm\\nC5ORn4iE9C//8i/46U9/Cp/Ph5mZGXzrW9/C2bNnMTU1JWkg8XgczeZRYS7tCmUaCQmLbg3UbDZF\\ndG+1WtKSmIXhybG4aQ4Pj4qtMyKck0s7ldPpxP7+PgqFAu7fvz8UQTJVGRIQK6rOmCf+6dpAAF44\\n3ADaNiDtE7x2YmJCAum0LcM8jKY6c1LAg8dNSINmq9XC1taWGC25+bgZaYfQ3hMz3IHSAwBMTk5i\\nb28P6XRacqSy2azMKcG02w1yYLWNJplMAjgyK5TLZbz55psSC0d1PxaLyTzHYjGR7oLBoNgwx8bG\\nUC6XxehfLBYltYS1whnYyVy/arUqdlZ63Px+v+TudbMX9gNk5LR38ex1qnah8yF1DXh6rwnabMDx\\n6QYPJK5a4gWe2wG1mqalbwoux4pDIoKlUgmpVAp37tzBxsYGkskkAoGAtLdhDILH4xH3KMU4xoLQ\\nYEZdmge4XC4L1+REMQ6EwVtEQh8Ku/2oqBspMwOynjx5Yulu7wZWHN/qc309Yy606qKJiOnBIXCB\\ntI2Jqq8pGXSD4xIl0yamCT2BnWlzuZw4JWgf0UQbeL6RW62WuMu56XVcCj8vl8uy0bW6rnEaVmWj\\nlFCv1yWSmFI1cMQQ2C2Y4wPapQKOifhQumN8Dn+v102r53yvDzBj6rjeGsdBQZsAdEkPfsY9yDXW\\nKqImDLpzTKd51hKddsDwM71Opu3VZLK91rIrQaJo12odBe99+eWXEpBI4x8DxyipcCPwoAGQkhYk\\nODyQvI66uSY2FEW58b1eL5rNplB6cgFKUOR81PcHAb0pNGc3J1pvVsZp6Kx+7T3hfJjRqbxeb37i\\n26/r+ySkI62KaqM7x82Yr2KxiFwuh9HRUcGN6ptWs/UB0RtR40xuq59PRqU/MyVB0yPVC6jiU+ou\\nlUrS+02rT1xH2smIB3/LeeCYQqGQmAY0IWPysFbVNZGlNOhyuSRhWUuRw4JmAtpQTjy02UCDXnte\\n049bXhMUk/Do95q4aWLdD3QlSHqjVatVEdX1RqEeSqMZuQjwvBkgPSzkSDQAk1prMU9zSi6aDsjT\\n7nEuCO/PsQ4adGZKRHoCtT2F39OrMjo6Ks/SUoEmQtrlzznRdhLtFdISoB5bp01w3OA6DdotnEwm\\n0Wq1sLq6KgF/VONISBkDY845Dwc9UmQqdrsdOzs72N/fx8HBgRACOj64bzTRt8K7HyBj2NvbQzab\\nRbFYxO7uLv7gD/5APFHEhdK4zWZrs3kxipnEi+VcqR6Vy2UxVXC8ANoOOO/NtaIxm5LjcW2Aeo/p\\ne5Hoa0Zoqv/m3JrXmmCugZZ8+Ko/43szhKYXc+lKkDSimhDxf+0Bs/IaUKwjslw8TTVNAqeR10RA\\nx3/wkPO3Wp8ehBprMH9jtSiacDEHipyIojOlRB5WLepaERviY0pX/diK9L0GAVNd473I/Wnjom2P\\nwX/s0cZr9ObSdcSpglNS5TywBDC9eXqdNXMxjaCDrifnmQGKlUoFBwcHUlaFsWME7hvuMe1kYCQz\\nPW9UvVg0z/Qim6EblCC12kMV8jhgJfXo/dIPMernGZ2IpinNmmDakvjZsWxIJgHSr53em9JGP7YQ\\nq+eZ35uTYxIfk7gNAuYk6ckktdfjI9e0itKmtKCNevy9Njhq6cHr9bZ93knUNnEbVJUhmJuXkkqz\\n2UShUEAsFsP29jaWl5eleiI9VXodtD2M9hHToE0Vv9lsSpdXRgybEqEVQ+mlvloBCSAJJ1Wm27dv\\n4/Lly5iYmGhTvYDnlR3J/ACInYvX0dDNHDedqsG9Qk1BS/isjMEwh2g0emzp1upsajXayq5jzmM/\\nn3c7S1bExUplM6WpbjBQxchu1LKTmGeFVCcku33eD6Hplwha/c40KJuETi8sJ5lSko7x4Pe0melY\\nKy0J6VpCjBPRUmc3kf64oj7x0q800Gr3fqFQEPugNtRqlZnE1zwM/A0dHACk8D+fp4kRvXr9SKq9\\nQM9dNBpFMpmU+DnmtdEWabq3zYh52i4Z8c3UKBJb7S01o/M5Dzrim3lvOqj3uOtp3kOvUzdJU+/r\\nftS0Tt9ZnRXzOVYCjRX0VNlMNcIKOklPJhXXB7oTFTY3NDlwN2o+rGhP0JKGSdS0VMT33HiawOiO\\npSRY+vCa46ZdTMfj0NDabW5M3I8LmrjQjmWz2STIVAeeasmH+NAGSDXMxFWvoQ6OJUHQTgyN0zCS\\nLn/P8VSrVUxOTuLKlSsSUc98SxJSPU6qVSSUrVZL4nl0wCdVNEo//L0Z7sF7auLM+/LzYYlRv2fR\\nfEY3LWTYZ1pd10m7OhZBIhc3H9Tpf6uBdXvfTffsl9B0ImjDgtZzO3keaNwMBoPC9RmuoMtoMIiT\\n9hUW9dJGepvNJqqBLv3Rz4E8Cc7K+zDiPpvNysFjKEYnKbfT+pgckzhr2xIJNutZM4TDJEyDrqce\\nU7lcxldffYWpqSksLS3h9ddflw4pOuCWhmYSMo5Le4UZe6YZJaUjRuyTcPE+NNzTyF+r1SRBVfcz\\nG4bwdmNc5rz1kpKOC+bzB9nDJnQ1Qmi3IkGL5vp/fV0/hKjTteafCebnJyEl9Hsf/WwWqKN4roPd\\ngOcHULv0uZk1NyZBotTVr23hJDYScQLQRkR1kKeW8oYFvUFJ7OgVZYa5dlubHH0YnPi7crksdXyY\\nr1coFCTJVKubHBftSyRCJFo6cZvX8DPegwSN88o8Tn7HMAQyp+NCp7NkfncSzxnmu072qU7Ql5et\\nk0GqkxrV6cG99Egrit4Jul1znEXQ4qbm7vqg6Bgsun+1h4XRu/RQ6bgQfQ05aS6Xk2JogxzA4242\\nPd+M2aHRttFovFBi1UoMN+/XiVPylXFqrObIhoTmPYdV2YD2yqMAJIWCBIHER6tt2oXPOCTGYtFr\\nms/nxdbHFuLsUsK54nrTkA48ty2SCGob0rBg7kmgu2f4uNDLZNPpPA66hj29bPqmph2I3x1XTRoU\\ntDHOCo4jAvdaVNoNAoEAFhcXMTk5KZuMMSpUBarVqlTm4+ZkTAttEaVSSUq8sLecOQ7z+cfFUQPt\\nRuwyQs5dqVTaJCXzmSbhMfcB3zebTZEyeL9KpSIGc/6RsJv2h0Fx1XuVqnQqlcLjx4+lSikrT7hc\\nLglqZA4bJRxdXI0uenoLOWfhcFhw0utKwsdW6ywBXK1W8cEHH+Dhw4fY3NwcGDcNZohMP3AcIt8N\\netmzBlnLvrqO6PeaW/KhOvipH+jGZbt9bjUmXm8l2QwCvVRL01AbDAaRSCQwNjaGer2O7e1t8bgx\\nypdpM0wXoH3B4/Egl8tJXebx8XEkEom2XEATN3MMx8VR35sEVndGMSsx6N+bRMdUG7g/dNAkAwV5\\nLbPO2RCCaq8VDIorx8J77u/vS1lWNo1giAILuOnaW9rGR8JEj6KJO7P3iSPVz0qlgvX1dYm3yufz\\nqNVquHfvnjR1OAkczc86Met+iZEpfAxCxMyzZ3UWe+HZV01tfTOTQ5uifLff9zuoQeG4urI58eZ7\\nkxBUKhU8ePAA//RP/yS1YwqFAqampiSKl0GArVYL0WgU9XpdEknJQekCzufzWFtbE2OnqSJZqcon\\nwe04b7lcDg8fPpQi/F6vFysrK6J+8Hk6PcIMbtUpQXo9aJPR6lKr1cKXX36JYrGIe/fuSSkL2mN0\\nusZxcKTqlsvl8OjRI/zoRz9CKBRCJBLB+vo6/H4/ZmZmZGxkJuxjxrEyS4HVHSjRsTpkLpeD3X5U\\naXF3d1fUP7YEo6rWarWwubmJfD4vxPdlnAVt8xvUDGK+tzoTnaCbysZxHVtCMgej7SrdBtLp9ycN\\n+tAO+5xOtq1ONpFyuYxbt27hl7/8JXZ3d6UWFL04GxsbSKVSEtEbCoWkTjVtSfRqsf3RvXv3pJ+8\\nlahrjmEYMNeRY8lms/jVr34l92YLb0oLrVarLWbIVNH0OHW4Az9nyhHVoXq9jv/+7/9GPB7HnTt3\\n2ozFZghGp3nohqP+DYlJOp3GnTt32ube5XLh3LlzSKfTqNfrSCaTIh3evHlTVPFUKoV8Pg+/3y81\\nwanysXLA4eEhisUiVlZW2ion8JVqna5d1EkdHhQ6MVN9byu1ehAwiVUn4jOs1iPXtV4WpTiFUziF\\nUxgQjufPPYVTOIVTOEE4JUincAqn8LWBU4J0CqdwCl8bOCVIp3AKp/C1gVOCdAqncApfGzglSKdw\\nCqfwtYFTgnQKp3AKXxs4JUincAqn8LWBU4J0CqdwCl8b6Jo6Eg6HLfPEuiXNdcu9soJuOWRWSaXM\\nCdLdMq0SUs3kxW6wsLDQMSPZahw6cXRmZgaBQADxeBxffPEF9vb2pKwFE2x5T7ZrCgaDeO211zA+\\nPo5wOIy/+Zu/kdwmK1zMMWlYWVnpG0/drdUKT/O9fmXf92AwiG9961uYm5trq/09MTEhtcGZ/nHr\\n1i381V/9lSSq6q4iVgm5fG+V88QOrL1gfHy84307JSezRlWj0UAymcTi4iLef/99TE1NSfPR5eVl\\nbG1twW63IxaL4a233sL8/Dyq1Sr+8R//Eevr68hkMi/M6SB1wnd2dvrCEQCmp6ctU2t65UGauOvf\\nMbXnnXfewcLCAgDg888/x+rqalvHIVb/7JRT2Sv5Y2Njo+N3A3Ud4cPMgl3mxFgtvhVx0QXgzM2j\\nf8eN7PV6cfPmTSmYfuPGDaTTaXz88ce4e/fu0LlsVknE5v/mvdl94nvf+54U/pqcnJSe67lcDvv7\\n+7KA5XIZiURCuv1euHABuVwOuVwOoVAIhUKhbZFPKs9pUDz1e11zx+VyYWZmBmfPnsXVq1cxPT2N\\nQCAAv9+PVquFSCQidYQ4N06nE8vLy3j06BG2t7fbSiLr5/XKxRoErPas1XvOcyAQwPT0NObm5nDx\\n4kWEw2G4XC7EYjF4vV5MTU3h/PnzUr+JHWkjkQi8Xi8qlQpcLhdyuZxUqHz27Bk2NjakO7MVjseF\\nfvE0wWrNSWCWlpZw9epVFItFpNNpeL1evPbaa/jGN76Ber2Op0+fYnl5Gevr6y9UNwUGS8TtBH3V\\nQ+qEpFVSa7/JdbobByeFBbHMFjEkWF6vF9PT05idncX09DRu3ryJjY0N7O3t4d69e0MlY/aCbkmJ\\nDocDb7/9NoCjhNtIJIJKpYJAIICdnR1sbm621Y6emZnB/Pw84vE4wuEwtra2sLa2hmAwKBnnOrn2\\nZUMn6Y+vrJjYaDQQCASQSCSwsLCAs2fPYnR0FJFIBPF4XGqKs20QM/aXlpZw/fp1yYLXBfHNTi6d\\ncB40GbTbtVaH0el0Yn5+Hu+88w7efffdtkL8IyMjiEaj0iRTM1BWJzg8PMTo6Kh8FggE4Ha7pYqD\\nbox5kknmw+wRq/PMGvCBQACTk5N4++238bOf/Qw7Ozuo1Wq4du0aLl68iGq1Co/Hg/39fTx9+rSN\\nIJlAXActOAgM0HXECqw4m/l/twFFo1EkEgmcPXtWCnhtbm5KXZ75+Xl4vV54PB74/X74/X5cunQJ\\n4XAYsVgMU1NTACA1bU6SAxGsNpCWEqk6FgoFPHv2DDabDYuLi7h8+TJWV1elJOzh4aFIFOVyGQ8f\\nPkQqlcLa2hri8TiA5yL7oGrvcXCzytznc/x+P5LJJMLhMF599VXMzMxgenoao6Oj0qvenHsAQpRc\\nLhfefvttuN1uTE5OIpPJYH9/H5lMBqlUSrp4dBoXcPJrqe8XCARw48YNLC0tYXp6uq27Mq8vFArS\\nzojSLw8bx6draZ8/fx42mw3BYBAfffQRNjc3pTTL1ymPXRMNh8OB8+fP4+rVq9jY2MDGxga2t7fh\\ncDiQTCbRbDYxPT2Nt99+W9pjsZQM0L3syKAwUIG2fuxA5u/M3/MeXq8X4+Pj+M53voNf+7VfE66S\\nyWSwvb2NQqGA9957D+FwWNoNmT3NaNOIRqPwer3IZrMvlHXoF6zwNDcw/3c6nfD7/YhGo0ilUrDZ\\nbEin0/joo49gt9sxMzODK1euYHZ2VhY9k8ngwYMH+Pzzz3FwcCAVA0dGRhAOh6Wp4XFLm/aDpyli\\n8zMenNnZWcRiMZw/fx4LCwuYmZnBjRs3pPIlcXK73UJUWq2WlL09ODhAqVSC3+/Ha6+9hjNnzmB3\\ndxdbW1vY3t7G1tYW7t69i2w2i6+++qqtONtJEKB+mGKr1UIikcB7772H+fl5jI2NSZcVbSpgg0vW\\nNdL3ZvMGSpF2ux0TExNIJBJ44403sLq6iv39fRQKBUvb3XFx7YdJmSYAU02mdHTz5k0sLCzg3/7t\\n33D79m1UKhWMjIygVCrh888/x7e//W387u/+Ll5//XU0Gg38+Mc/FinJqub6SyNI5qJavTcn2Oog\\n85XfUQ+fmppCLBZDuVzG6OgolpaWkE6nUa1WEYvFRBXg/ZrNJiqVCvL5PMbGxkSU9Hg8Usx9WHHW\\nynZltXkcDgcikQhGR0ext7fXVjfn2bNnqNfrcLlceOeddxCNRnF4eIjd3V387d/+Le7cuQOfz4er\\nV6+K2vPll1+i1Wq11c7pZoQdFjrZjPhKyeDSpUtYWFjAa6+9hlAoBL/f31amNxwOSyfXarUKr9cL\\nr9cL4Khr7cHBQVvHknK5LJUxk8kkzp49i9nZWWxubuLp06dtjR1PAtdu0rK5Z6k+6m4oNptNCtMB\\nzxkgibbZHLLVarUZxnk95yqfz3cc33HxHPYaSnjxeByJRAIOhwMrKytYXl4WImu327G3t4e9vT20\\nWi1MT08jFArhm9/8Jn784x+/0D1n0LFZwUASkv7MtB1ZXWulznGgLpcLyWQSsVhMNjylHZY6NYkL\\nN4ouikXvz3E2cSeiaoVzq9WSGtTxeBy5XE7mght2eXkZH3/8Mebm5qQ2NW1Ka2triMViYmdhVxK2\\n2DHH9LLUNCucWGr30qVLmJ2dxeLiotQJZ+sgFrOnNMcSrrqfGVUcFvNvtY7K5Ho8HjgcDrHHhMNh\\nLC0tYXNzE6lUaujW4FZgxRD5uSY0Ho+nrTcd50S3ZteE22yPRMmQntVKpQKfzyfVJXUnW72mJ0l0\\nB5kLDa1WC6Ojo5iamoLNZpPCglS3G42G2DU3NjZw9+5dvPrqq5iYmHihxHGnsz8oHMuGROg0IHMi\\ndM3imZkZLC4uSi1iv9+PWq2G1dVV6dKQzWbFi0OVhq2HKBUlEgksLi5idnYW6+vrUoB9EDCJaj8b\\nJhKJYHp6GtlsVuwp3//+97G3t4dPPvkEv/jFL9BqtbC6ugqbzYarV6/i/fffx4ULF1CtVmG327G1\\ntSUqH4C2rqeUlk7S7mCFJ997PB68+eabeOONN3Dz5k14vV4cHBwgEolIBUQa52lLYRVMp9OJr776\\nCoeHhxgfH8fo6CgKhYIwDoY/FAoFaQnl9Xpht9vxJ3/yJ/iP//gPfPDBB1IU3xzfcfA0VRR9DQkS\\nCbHuPMJx6EaZNptNwjJ0y3ASYafTKRKSz+dDIpGQRgCdGoYOC8PsC804yTzfffddnDt3Dnfu3MHP\\nf/5zsf9x/r1er3SG+dWvfoV4PI54PI7x8XHk83mUy+UXiNJx9mzPwEgrUayTamZ1PT8jx+H38/Pz\\nWFxcRCKREF3cbrcjFArJ4rIHut1uRyQSQTAYlBZE1O/tdjvm5uYwNTX1gnfuZYDdbpcWN+z11Wq1\\nsLe3B5/Ph/n5eVy9ehVzc3PSx318fFzwCwaDiEQichBsNptlI4B+1JdhN6V+5T08Hg+SySSuXLmC\\nYrGIg4MDeL3etpgTvfHYYhs48jC6XC5hHvo7ShCNRkMkJODIEREKheB2uxGLxTA7O3vs/m+dcDRf\\nNQSDQek6TGKpJSjgebMASne6RC3XlXuWISl2ux2jo6OYnZ3tOV7O7UmBSXiIi54Hm+3IFppMJjE3\\nN4eRkRFpxaUlQ86BPr9OpxOXLl3CzMyMCA8nRWgH7jqikTY/63S9aVSz2WyYmJjAzMyMxK9QdTk8\\nPJTGelqiok7ODU6xuNlsIh6PY3R0FMFgUHp8DQpWxl4rPDkObkKfzyd91SYnJxGJROB2u7G5uYlS\\nqSRtd0hIk8mk1LHWPd38fj+cTqd0cjVVXCs47iYgTiSUlFrv378Pp9MJn88na0BJVksOxIlrpttw\\nU02rVquyqXXDAKo/brcb4+PjmJ2dxaNHj5DL5doOz7DQjWny85GREZEASShNSUg3+OSYdd1wXstY\\nNHYwHhkZweTkJIrFYk9cTlot7/UsjtHpdGJsbAyjo6MIBALSAstkDPrs6uDJW7duYWtrq2PTy2Ek\\n/KFVNiv7kB68ea2muIzdmJiYgM/nQ71el0WkLcXkGvxeBxAyPIBipcvlku6jg0Inyc4ksGwQ2Wq1\\nJNaEh/PBgwdoNBoolUptojs7jpCY6U6oh4eHqNVqYgClQbjbvHfj+MOA1+vFxYsX4fV6hSBQ/WB3\\n12g0Km3B6R632WxtHUMYpc0WQwx34JpwbUmI7HY7yuUyYrEYrl27hlu3brV5pE6C4HaTNhlKUq/X\\nUS6XpTWT1+t9wfOnbV/A8352tP+RQVHCcLlcCIVCCAQCL00F7wdvbevVNq/FxUW89dZbODw8xJ07\\nd7C8vCzj7GRzq9fr2N3dxaNHj3DmzBlsb29jZWVFuqtY2T0HxblvLxvfa+gULar/NwfDQxmJREQq\\n4EJTHNQRyyZnogeLXhFuBnYXNZsb9gNWk2aFl/bMkIPSaE3bim65zAaEPNRUKXX6Cw83DyyAttbS\\nneCkxHxKMoxQLpVKbaov7XeUACixajuD3gdkFLpDLPEnoeMztW2GB5x7QdsjhiFMvQg3n6MJJ936\\ndrtdCC5feQ+NK43w2pDPcAiuD/dltzGeNJGywtuUGP1+PxKJBHZ2diTUptNvgaP5qlQq2NvbQzqd\\nxtzcnDDnTmd9GOi7DZIJ3SbTpM7ctAAECS1p6A2p+6rrHmXU4bmR+Vmz2ZSOotxYZj5YL+hnIjU+\\nHo8HgUAA4XAYuVxO3MUcFzc7pR9KBppDaYLE1spUgXiIO0ltxwV9WO12OwKBAM6cOQOPx4N8Po9A\\nICB5aTRcp9NpOJ1OuN1usRG53W74/X5pM02im8/nUSgU2jY302ei0aiEDfA7r9eLUCjUZr85KZWt\\n03ckfsTJ7Xbjyy+/FIM7JTi23ObamR45plmk02nUajW8//77aLVacnj39vY6qqB6foY9zFYM05SQ\\nTA2Gax6JRPDRRx/hl7/8JSqViuDViYDmcjl8+eWX2NnZwfvvvy/z14mBmhJTP0y0qxXR6gZWN+x2\\njVYB9h4AACAASURBVMnttG2CHImGQQBiSwLaxWICv6e71uzPflKGURMXLiTH43A4xKBHg5/eeBTb\\nNTHSRID35Lh1LE4vr8VxD63epJRKI5EIwuGwzCljiyiBAhBjfrlcbov5ooeKf5wnEwe32y1GfRKf\\nkZERBINBIYB8Vj82tGGB+5BE0OVyIRgMyhqSSGl1TO9B4kgj/d7eHlZXV/HVV1+Jg0JL6/00SDxp\\nPM25o/TGjIFoNIpYLIZSqYRMJiNmgl7Eg0GvVLsp4WuGre9jSma98Ox6ejVXt/pOvzcPiX7lHw9p\\nPB4XDqW9FvwfeE54uJjaJa6z6PWE6+cNAuZvur3XnhYdMKelMk28THGfkpHN9txAqAmEqQ6ZoA/4\\nsFzVtC04nU4kEgmRNDm3JLi01fFaEiTtQODG1EyEz+CG9fl8CIVCCIfDQnw4p7FYrK0ZJefxJNUZ\\nvScpjXLfac+ZZjh6j2qPIw8kAGxubmJ5eRmrq6sSzU6gqaGX+j0smPYevppzx31Xq9UwPj6OWCyG\\ncDgMALIP9ZnSZ5bveV2hUBAPMW1tWvDoJA32g+dARm1TJDQJkTkBVhQzGAxienoa0WgUoVCo7Tsi\\nqIkSYzh0YiYPNCUt2qRcLpd4robFS4/HxI3ckiK+y+VCNBpFuVxGtVoVVYsbnb+lCseNTVsYQdtO\\naE/qZFg05+u4wAM4NjaGcrks449EIojFYhIrlc1mMTU1hWAwiNHRUdmQ+/v7oo5RWmVAIA9ApVJB\\no9HA+fPnJZSA1zLUY3R0VAzmwzCVfvDUc1goFKQjLYnj//7v/8Ln8+HChQs4d+4cAoEAPB6PrIcO\\n+KSayWTTzz77TNIt6HGcmJhAOp3uaegdBtdODItxU8DzKHQSRJ/Ph1gshj//8z8XhxJVcJpCgOcx\\nWDyPXCt65xgmwYwJAG0R6qYErk02vWCoSG2r76wOtfm+1ToqU5FIJOD3+9uMmryWxEjbljTX5H20\\nqqc3+LAbuR88OR7GnOgFNaU9EiQdx6EXxhTjyYFMD2GnxTwunvqVMUQ2mw2FQkFaPmsuSU+hz+eD\\ny+VCJBIRZkHbHu1/DDDUBl7Oi/Zc8RBoVc3hcLRdc1KEydyrjKpOpVISD/XFF19IAjHXWCfaagbJ\\nsZmSYLFYFMLFXEv+3mR0xwFTHeOaeTyeNpWYZgPNTOl48fl8mJqawsWLF1Gr1cQOxN/W6/U2FZzJ\\n8NFoFLVaDX6/H9PT0zg4OJBAWO5XKwLcz3r25WWzurE+pFalJExdlNdTPWDuk47M5fek8vpZJF70\\nWnDTm8m0nYzB/eBoRWD5Xh9Oqp406LZarTZPEtBuV+K4CJoTEbdGo9F2oDutgSmlDgom8SfxpDG6\\n0Wi0FVnjtfl8XpjA/Py8pJO0Wi0Eg0FkMhmRZHkwAcDn84m0RLWBh9vtdkvaic60t5JQTxKostXr\\ndeHwoVAIt2/fRr1ex9WrV9v2tGl20LmVVDepyu7s7IjHkgnYOtDQPD/DglZviY/f78fo6Chu3ryJ\\nYrGIer0uqngymUQ2m0Wj0cDm5qbsx3PnzqFUKmFlZQWpVArhcFhwr9frODg4kDi1K1euSMK1zWZD\\nOByWnEQAbWt5eHjYlgyv56Ab9KWy9Ss96Ff9cC5Ao9FALBbDuXPnXiBU3Oz04NhsNtnU2j7Da3h4\\nyX0rlcpQ6lov/PQ12s7FeKJgMIhSqYSDgwMcHh4Kh6edS0tOACQux4w1IkfS86U37ss4nNrlzooK\\n9DKRiACQwL9Go4FMJoNIJIJQKCR5Wy6XC8+ePcPBwQEuX74slRvoKuYcHBwctDkFyFj4TH6m8T1p\\naUJ/VigU8PTpUywtLcHpdKJYLKJQKCCXy4l0wH2pzQk2mw21Wg3FYhEejweLi4vweDwolUp49uyZ\\n2JdyuVxbVQe+ngRR0sSo1WphfHwcr7zyCs6dO4eLFy9ib28PqVQKc3NzsiepFtPovrW1hcuXL+P8\\n+fM4ODiQQNF0Ov2Ck4n/szBdvV7H5cuXsbCwgHfeeUdUtlqthlwu15ZQ/LOf/Qx3796VeKVuMHRy\\nrfneivpZHaZgMChGzWKxKBNkEjBNZSkOc4K4MZgTxutoUyoUCr3Q6omn1abR4+N4gsGglD2hqK6l\\nI22Y53ckPhy7JladbHInITWYtj+qS+SwFOv1eAFImEM+n4fb7RYXMfFrNBrI5/OSuxYMBiWpVONF\\nlUDPEzc7CbxW74fF0wpvgp5HjrtarbZ51hgrpZ0O2vvHMdbrdVFlfD6feI1ZKqdSqbywF09KQtL3\\na7VaiEajOHPmDGZmZhAKhUTaZNnibDYLj8cjY6VjIhgMIhgMYm5uDsFgEIFAQAgSGRG1lq2tLSHS\\nDB1wOByYn5+Hy+WSgFKbzYa9vT2J0mfpHe0R7wQDlR8xN0q39yZXGRkZETuFzubntZxc4Dn119cx\\nYJKh+E6nE4VCQbj6K6+8gm9961tSc2hQsFKL9OeUxGhUZ4VH6twOhwOlUqktxwlAW01t3ot2CEpE\\nPLzaINlNvB12Q1sRNe1VowG0Xq+jXq9Lvh0JEsfMA0oGUKlUJD/P5/MhEAiICgM899Zxc1MaarVa\\nWFlZERWHqTOsKKD33iA4Wq2f1TzwEB0eHorniDFYZDp6T2qHCveADltgkCDXNZlMYmZmpi1S+ySB\\nhmav1ytjoIoYi8UQDAYlU4AVKuiqr9frcn6450qlEnZ2dqR6ATP+uSdyuVybKkspkhIupaRqtSrM\\ny263Y3x8HJcuXcLa2hpWV1e74tSVIFkZ4jSR4SsRIMHSE89NHIvFMD8/j2vXrsHv96NSqUgyJoMe\\neW/egzooP2c0tNvthtfrlTy4kZER3LhxA5OTk/jrv/5rfPHFF0MtsJWaqf/XCbEU2Zke4PV6RcUk\\npyX+OlueBLZarcq9I5EIUqkUqtXqCx42c3yE43JZElJuUm3rogrH8bdaLZw/fx75fF6SiPk9JaNo\\nNAqbzSaeKUo75XIZPp9P8vm4H8hINCdmfiL3lhXevaBfCaTVOjI6T05Ootls4tmzZ2IPmpubQygU\\nEuLLNWy1WmK8p83P4/EgHo9jcnIS09PT+OSTT8Sewxrr/Y57EOCZ4BkcGxvD1NQUxsbGxBzAQFdK\\nfzw/mrGQyVerVVSr1bYyvDabDalUSp5ZqVTEAUFbIceio/u5vx0OB8LhMEKhEF555RXY7Xak0+mu\\nePWsqa0nShMhbZjVnJYEhX/ccPV6HZOTk5ifn5eStLwnDys5L4mc3gycAFJuh8OBQCAg9/D5fBgb\\nG8Pi4qJ8PihYbWRNlLQurb+jmE/8Tc8acdClLPR9OIe9bHUnpc7wPvQk0SbGZ5CgVCoVpNNpZDIZ\\n+Hw+WQfG6GSzWaTT6ReIDKXHSqUi68UCepQ0AoGABBdyvvRc6cjfQfHstob6f5/Ph0gkgkKhIKoV\\nI5i1fYtrpdNd+Mf7LC4u4smTJ/j0008RiURw4cIF+Hw+2edW4zLXZFAc+Ru73Y5oNIqxsTGxAeXz\\neWQyGQCQUAsSHQaEJhKJtj1crVYRDAbbKoAeHh5KfBr3BdVZ/Xx6VbVnjnY6VoK12WyIRCJd8epJ\\nkPQhoE6tXfXcRPo7LQlwswWDQVy+fBmvvfaapHfocg90nWt3eS6XE1GQk0qj48bGBhKJhLg7eQ+3\\n241EIjHQ4hLXThIhADkkOjCThkvWhCHHIuEEIHayWCwGh8OBXC7Xlv+mOR1VVR0SwO/1ODm+49oh\\nHA4H/H4/QqGQ2OOY11av17G6uoqdnR0pw8o0CaaINBoNqfnz+PHjNhxIfKm2MRF5f38f6XQav/Vb\\nvyXF82u1WlutJBIjrR70C+b6dZM2M5kM7ty5IxLtzMwMxsbGxJbG/a9DILjfKUkybmtpaQnlchl/\\n8Rd/AY/Hg6WlJSwtLQE4aifGOLVO4xwUNEMpFAr48MMPYbfbcf36dczNzeHg4AAPHjzA+Pg46vW6\\n1D53OBxYW1uT/RuPx+F0OmUfU0oF0OaFBI7OMM8rk8UBtNkLdaVQSk2PHz/G7du3kclkerYn66my\\nEag3A2jjEtpYq6/VklM4HMb8/DwmJyclMda8r5aqKBozsI5lLLhJSI3z+TxcLtcLk8iQgkHANM5r\\n7qxzmXgd8a/VapJzR9DufBIfet74uQ5tIEEyYVgJoV+gDYUbql6viyGSLl/gaCOWy2Xk83nxlDCL\\nnbYfzgXXjAS3UCjI/Wi32N7eFiLOoEmtupupNYOqbBq6zV2hUMCjR48wMTEhqhrrc1GS02PQBNJu\\nt7d5dRkwWiwWsbW1hSdPniAUCmF9fR3AiyU9rKS4QYCEkeeBRur9/X1p26QT0ImPzjvjHqSKpTvG\\nUMjgOWPLJ86JLlWibab6zFKIoW2KgbfdoC8vmz4Y+k8fTpN4aQ73zjvv4I033sD8/LyoBzqClM+h\\n1KSN3ZwsEiRt3acRlEZT4IgbRaPRvhfWxNPcwFqk5UbUnigdHKg9gMyDKhaLbTleJG6aGGljtjnn\\nViracVU2Ao2RLN3CTUg7AYMkY7FYW/EulqYNBoNCVMgZ2aeMoQ2VSkXsEZRwGQ8DQGxLrVZLGjpY\\nSRLHASvDuM1mQ6VSwcrKihDES5cuYXp6WjyjlEC07UtHQWvbWyAQEHtiJpPBrVu3sL6+LrhaMZxe\\nxvduwL2qnShUEWkz0sGmPK8sHU3PJpk3VTbtzGC8XSAQEElRh+Zo0MIJVUgd2V6tVnu6/IEB45BI\\nDfWkmOqFJkSxWAyRSAQ/+MEPcPPmTZkkrcoRAboquVHz+bwQJF3Cgd46xs1wMlutlqQ89NJTrcDK\\nxkCiyIWjF4kBfczkpk0knU63Gd3pMaJKwixyj8eDbDYrKpsOGbAalxXXP666BhwRg0QiIfNFgkSJ\\nLxKJCDOgE2JmZkaaMPAwNBoN+P1+WQeqMbQ7cL0oMZTLZdk7JD4jIyOYmJjA+Pg4Go2GNFkc1Mtm\\nZS/SrwR6B1dWVuDz+TA9PY1vfOMbCIfD4gUEIHYxTVAoRf7f9r7suc3zOv8BQZAgdoAAKW4iJVKW\\nF8WVU9uJUntsJ+m0nSQ36bS36UV70en/0utObzrT9LYz7eQmnXHHjZs6rezasmRJtkRa3IkdIHaQ\\nAL5e8Pccnu/VBxCbf+MLnhmNSBD48K5nec5G3G1i4qxzzNLSEpaXl9HpdPC73/1O3k9twoQFRiH9\\nHL/fjytXrmB5eRmLi4sSH0eTmN1gqE0R5iBOWKvVhMm0Wi2Jw+LZ5D2j8GSpHDIYWizaIUEhRIiC\\nZ8rUFE0aKFLbPCAmA+Kg3W63dA35vd/7PayuriISiTwn4TkhhrZ7PB6Uy2UUi0W5AGYAoU74JL7E\\nZ/FC0G08LOk5k+Ews5va0unpKfL5PObn522f4+ZQsjYaDfFecKPq9boNtKUblhqTE3M0f9brOChp\\nBwKZBQFbMtBWq4VQKIR8Pi/Mh25x4KzeOYnrw7WhZOVFJjNnnIvOD+PcAcglMTXuUb2J3Z5DvC8S\\niWB1dVW61XJc+hk0dShQtYeJ4Lff78fS0pLgZQTxLxrHMKRNn4mJCczNzUnlRzJL3hUWDeQ4uNft\\n9lnlUq3pAxAhxGhz4DwyXKc+8Rn8PL9PwxCM8+L6XTT3kSO1OUngPCWi2WwiGo1iYWEBN27ckLgO\\nDpJaESfFDdZaCSUtNQdqEazGqFM1XC6XVP3LZDIXuha7zdEJsyCDIGbEfCFqE9q80IyIKr/u3ADA\\npmJTpaa7v1cMUjeMa1jS2h+DEkmUZmROvGBkGDrMQQcPcv90KgwltE6+1e2G9JqzlK+JRw5C3dbN\\naf4ARLtNJBKyJ8RSNBRAbY9YIGPQAIhpx7Ie1CjoudPlOcZF/E4AEiNETycrYQYCAdlL7f3WReeo\\nxetMf5PZavyUWpQurcL14bpqc1KHSfSzBkMXaNPmGy8nAMF23n33Xbz22mv4gz/4A8RiMTQaDWkZ\\nxKhqDZ6xwiAXgOC3rp3darVQKBSE+dBF6ff7kc/ncXR0hIcPH+LRo0f9TOvCOXJu/J+aQDQalQVm\\nBw6NiXG8BPQqlYr0vNcMmRvEv5vVCTmucWNI+oDpyogE4WdmZiS/ia5/yzorO1Gv17GxsQGfz4eT\\nkxOUy2Xx0mlwX2MHnOfCwoKcAzJuFunTwZCaIQ0D9urLo/93WjOPxyPF+LlfwHksGYWGDoSkk0Uz\\nXXYZWVlZQTabhcfjkdpBptNmHCY3K01MTEwgl8vh8ePH+Pd//3dYloU///M/lyBkpvwQw9EeXVoz\\nNK8pdDlffccpUPk57fbX5YCc8E9qUP3Me6ia2k6AK5kJgbW/+Iu/wPz8PLxer5gtTIal5qBxE6L+\\nxCu42WRgrF2tu7ySmbVaLaRSKTx58mQs/b1M0F67gDlOfbh4kSklCOZS2rZaLVv2O8fNzTdV2W4g\\nrNP6j0J0IjAKvl6vS1oIGz7SNUyT7PT0VC5ZJpOR5Esmc2pBA0A0CcbBcI1owuq6UhyPjksyTdiL\\nyGQ85v8kHUsXCoWQSCTEI6pjyniuCWjTLNUaAk0cap3NZhPpdFr2XpPTXIbZx1arJS2JwuEw0uk0\\nnjx5gpmZGfzoRz/CwcEBUqkUZmdnhYnyuwmD8EwCsM2HTEen0QAQ5kthQwbG+0ZTkM/UjNAMdu1G\\nA9dD6iax3W43EokEpqencfXqVdy5c0cyyNmKWOd18dAR9+GzeFDZEZXxSYVCAS6XC4lEQmx5nYib\\nz+exs7MjwWCDUi98hodQx9lo/IER2mRS2uTkQSZz1TlbWsPUa3KRajsOs42fp5mi8+g4XqYc+P1+\\nKaPRbrcFlGYdKAoF/qNpRwkMQIQRtRAyPuKAlOa6U/GwmqC5d71MYJrfPIcApJIBwW3mujFtifjl\\n6empXDauweLiolRg1JdRa25OezEoMfTi5s2b4s3NZDJ4+vSpYLB0s9ODqteVZ1kLV/1s4DyVyGQm\\nGsLQFoQWKjoQ2AlH60YDF/nX3JIDsCwL3/nOd/A3f/M3+P73vy89tpg9TWBMZ3nrGji8AGQ+lFix\\nWAzpdBoPHjyQanzf/e53bbWfqVbHYjEsLS1hb2/PFgXdLzkxIy483eMTE+eNBdPpNNLpNF566SWU\\nSiXs7u7KJee4stmszIvdREKhEDqdDnZ2dmSjotHoQO1/xoVF0HT2+XwiIaenp8VEe/HFF3F8fIxC\\noYCbN28KM6FUJo5E4JpmbCKREDOOeJ7L5cLy8jIWFhbg9/slUJSt0HlxdEzZsHPtR7vk2WV+3cTE\\nWaY7TZNQKCTnkS2CuK/FYlFc62QGjEb/sz/7MwSDQWxtbdnShczvH1W75Xjn5ubw0ksvYX19Hb/+\\n9a9Fc1paWsLp6SlSqRQsy5LASB1aoyPraZ202215nWdfwzFkQIzepkDTWjG1K54L5vj1M+cLAyP1\\ngnq9XolbuXr1qsQwBAIBLC4uIhwOS5NBHc1KFyBgt/E1CM6gOK/Xi0KhIP2etra28PDhQ+njVq/X\\nUavVbLlgOuqZh2hQ6sZ4NfGiUNLTnUsbmcAsgzpZHwaAAHvcPD6Hn6EW1k2COr0+zKGmGcTPa5du\\np3NWY0rXL6pWqyiVSpJYybUGztadXjhqhABsXk5qRkw74We55/TI6jAOUr9qvrkmppdOz90kHcRp\\n5htaliXjn5g4q4pJD6KGDTh31gWn+d4rL3FUHImANcubzMzMYG1tzQbEc1za7NLmsDbPdKwQIRWt\\nAelx6/PL/7V2r+elGdbIJhs3RWMq4XAYy8vL+KM/+iOJyVn7f+UHmN9UrVYxOzuL6elpW9AdN0mr\\nezz4p6enAlAXCgVsb2/j7/7u7wTw/dnPfoZEIiEYE006raWx88UwUtXEcDhvjpO/a7vaBE51BUR9\\ncbmWmiERSCTp6OR+5zDsPE0zhpeMc9DmCNeVKQRkGnREUAJrLDESich89Rw1aKzbbNPk5/7TezSM\\nydbrojuZ5TQztTZMzxNjcDiHcDgsF5xwAXFBxuUQf9LMyAlcH5VCoRAqlQqOjo4wOTmJubk5LC4u\\niubJGC/uq2mVABClgeamrgGlMSViqBQser+5Zvrcu1wuCZPRga8jm2xutxuvvvqqeFWY1RyLxbC+\\nvv5cmVKqaNrFTcmruTBw3viReNHk5CQ2NzfxD//wD0gmk3j8+DGOjo4EMNzZ2cHy8rKo1YyfoFvx\\n4cOH+O///m8cHByMzaQhccO4MVTPfT6fZLvThCX4y89o9z6LV5H56sNAyeSk3XXTjkadp76QzDGi\\n8Dk+Psbm5iauXr2KjY0NiVnJ5/PY399HrVZDNpuVAFAyGAqfWq0m+CG9UNSCJybO8hTz+TzC4bC0\\nWmIIgnZBd5t/P3PrBiCTMXg8HiwtLYmrnpo9Y66Igela7RQ6FCgMZ+D+c+912gnXVf8/7LxIJycn\\nKBQK4jTY3NzE06dPEQ6H8eqrr9ryDomTkenwvFHwaa8bMS8KTh17R0tHg9q892RUFMRsHnp8fGwL\\nkbiIejKkycmz4ktvvvkmAGBpaUkSRSORiFxGqt9MStSFtihRyHjq9bocOB5OeuCePXuGDz74QFzD\\nrAxJFfL4+FguLD0FBF8zmQx2d3dtNVuGJQ0Ya08LcO4O1VJIB0Pq+CTtceBnOVdKHK1JDaLejypt\\nTWBcB/tR+6lWq5icPC9Wz15rbBHOZGaaKVrr5aGnaQPYvYn5fB6FQgGJRELOES+H1syHJRMicFov\\nCgBtagGQ89vpdCTyHoDMm7WeyLAY3c5Cg73O37g0JYZYsCkna1pPTExgd3fXdm5pipMB8Wxq71e3\\noEVqzRy7nod25VMj5D3nmjBUpBugb1JPhvRXf/VXeO+993Dr1i2k02kcHh6K5CiVSlLCgOo9LxnB\\nUXJanb1NEJqLYVkWHj16hA8//BBfffUVDg8P5aAQcKR2QrcsAIlT4qHR1f/GSVqL0EGN3FBqg2Ys\\nBw8K46tYCIx/ZxyPPsAa8OwGzDoBpMPMiURwluYSKww2m01sb29jbm4OKysryGQyEqNkWWcxSayt\\nw7noA6oDSsvlMtLpNDY2NpDP51Gr1fD48WO43W68/PLL4qRgfSbTKTEOM8dkBJZ1VnAvEokIw+H4\\nS6WSLfiPc+E8uQ4Uijpni5HSfH3UEJRuVCqVREB7PB689dZbePPNN0UzXVxcRCQSwe7uLo6Pj6WO\\nFT2Z2gQjUfjz/nG9tPDXGCjvubnO1CDb7bYwSloLF1FPhpRIJODz+aR/E1VugqEapadKqJkRAJvN\\nydepXdRqNaTTady9exdffPGFBE5yYlp6a2ZHzIGHhQtoVhIYF2lNhwm9/G62hCHj5EEn9kKtg+ki\\nwHmqDKWSLhPqJF3HhTuQuIYUDjS52TCRWub6+jrm5+clJYKYEDUopp0QM9HxZsAZ8Lq8vIx0Og2X\\nyyXJp16vF6VSCel0WsZkpoyYONewZOJQ5vNCoZC8xj1kAbpyuSw9BKvVquToseEBz5tOvp2enn6u\\nvdc3RQTw0+k0Hj58iEQigUAggFQqJYz18PAQzWbTlgLC2lY8axoH4zPJdGhCU5tmZQiacRpfpVYE\\nQMxcLZz62c+eDOnk5ATRaNSGizAwjr3eAYgGwfgMgoK6SR4Prd/vt2X7Hx4e4uOPP8azZ8+QTqeF\\ns2pvHHBe1kB7f5gfRxOICzSqyaZJfz+BewK7LMdAc1WrxublJAPixlKy0UzQ36fjd4DxJdPq79AH\\njIdQxw9NTk7i2rVrWFhYQCQSEe2g0+kglUoJM2axL3o/qSkAEIbFfSMO6fF4kM1mbdqnxjB0Osq4\\nTDcnoleXtaD5/RrgrdfrAkVQuExMTNiKz2mnDHAeTmHGlWntbFTSWA4ZEJn60dERqtWqZDB0Oh0k\\nEgkRnEx/crnsZUSoFVGw0kPOpHiG7RCW4Ty0R5FKCgvyce/71RZ7MqR//Md/xGeffYabN28iHA7j\\nxo0biMViiMViSCQSstkrKysAzpNEuXEcvEbpa7Uadnd38bd/+7e4d+8eGo0Gdnd3xXzhZ2kD64Au\\nMqvp6WnMz8+L14PgGUtxjpPIZNg+mAAhmSVTWWi/+/1+NJtNMWloxgEQU4i4BZnP3t4eCoWCLap3\\nHNpBN9KqONVwHjY9zocPH0oIxq9+9SvMzs7i5s2bmJ+fl5AP7TZnSIjH40E+n0e9XpdwjXg8Lqko\\nr732Gh4/fgy/32/zspJJOpmnw1I3T12nc5ZVsL6+LjFU3C/OjT33SHq8LFBHdz8dOIxij8ViNo1v\\n3Fqu1sQ7nQ6KxaJoSfF4XISG3++XSHoyWmJ/1OQIR2iBy30gvgRAHEi8E1xXer6Z29hsNuHz+ST8\\nQYPgF1FPhrS7uwuPx4N0Oo1wOIxSqYT5+XnE43HBDzqdDuLxuDAemiE0QRj8Rs6bTqexu7uL//iP\\n/8DBwYFIR0poU7vhxWfhq2QyiXQ6jXq9jmQyKcF32WwWAIa22TUDMA+xlp6cM003DRDyQjNkXhZZ\\nRchSWmjSGfYAbPb7uLUjPT96Q4hzUTtgjtru7q70+jo+Ppbmnl6v19ZwkJeTF5WCx+Vy2dIn0uk0\\nTk5ObDgUL62OU9EHd1xM2YkxMcSBJisj0AHIeSY+xP3Re8w11NHajD3r5jE1xzMOovXCNlPxeFwg\\nFI0JETLROKh2rlD4U3ui6UYNScdpUVnQz+NaULMktquF8kV0YWBkOp0WF286nUYgEIDf77cVbmdx\\nqng8jsnJs7YwjD9qt9vY3d2VMqiHh4d4+vQpSqWScHcdy+A0BsuycHBwALfbjV/96lfioSGK32w2\\n8eTJE+HCw5hsTi5a4PxytFotmROlOdVT7XGg9GCxKz0eMi7tGu50OoI18QCYIPc4NQYSpRarDFK6\\n5/N5BINBFItFJJNJvPTSS9jY2MDt27cRDodx7do1AW7JuGZmZkT1pzpPc5alUWu1GnK5nJg6yWRS\\nmJjOBOe68cCPw2TrpiUxpozrUSqVRCvS+8TLyiL3NMl5ETl+uuJ1iMA3TfqMMB+R82WUuTZNW7dx\\nIQAAIABJREFUWWKFzJgCg++hg4Pwh9mFRXuFNSDOc8t1odbE9JVuwaom9WRIzEWju9cEqDhpoveh\\nUEjq3jATn8FzlUoF+Xz+uXa73FQn1ZaH0e12Y39/H9lsFl9//bWtQ4K2Y7W6Py7i+Gijsw7SwcEB\\nDg8PcefOHZEKzH1ilT2v12vLzQMgrwNndaYZ2U4PYjdm6rSZwzIo7h8z+TOZjEhAr9eLSqWCYrEo\\nFzQSieCnP/2pbd3JXHkReVFZ1YDZ5izx22w2cevWLWxubmJnZwdPnz4VPIpeSJ/PhytXrgg+OQ5z\\nx/ys1jqpyZIJLiwswOVywe/3i9lGk6zdbkuXnHa7bYvt0fdibm4Os7OzUifLaezfxBmlaUXIhExF\\nF8Cjy5/YESEOMppCoSACVHviyKTIyHjn6dCgdUONibgaGRNNxX5Sui7UkDgRPSkuAgDhio1GQwpS\\n8fLy87Q3TdCZ2pHe0F4LzgA6vmZqNePcaFMyM8WhVqshk8kgn88jlUrZ4nB4kKkl6g1gSguZFV+j\\nZ0djb93GYNKomgPjhFi21OfzIRqNCg7CQwgA8Xgc9XodOzs70hhT11LSiabadKNbmNUmy+UyHjx4\\nIJeAHU0ikYhIczPVaFxkCjvWneaezc/Pi6agm5fSHHFK+qVWwnFOTp41C2B77YvGMa75AOcBvCSP\\nx4Pj42MJ7QAgGQ6svEArRjtjaHJSwNASYFcTlsGlOcbzzDtMbzv3Ut/tkbxsfICTCcMF0QCvBqIJ\\n3nJTzSBBPrvXpeN3m4GDTiaMOa5xETWAk5MTqZFMT0ahUMB//ud/SqzN0tISZmZmUC6XcXR0hEql\\nIpsDAMlkEk+ePIHX65XEY7asMb1L/Y5tWCK+UygUpLNIOp3G3t4eDg8Psbm5iWQyiQ8++ACZTAaz\\ns7OSSErG4ff7RUOgs4FSUYeC8O8ff/wxPvvsM3z88ceyjv/0T/+Eubk5bGxs4ODgAFtbWxKFr8fa\\nLw2yfo1GAx999BGSySTi8biYLouLi1haWpLzbFnnda+YaGtZFgKBgATxTk9PS7rJp59+irt37wo2\\n1208TrjlsPMkA+C8iOFyDNRENd5Fs5LOKJ5Bmlg6SJVWCrXX2dlZSZqlk4NCm06dSqUi5YD6nZ/L\\n6vFOHU/hxAy6Lab2lmiGpH8nk9GL6rTITs83v8tJLdftWy6itbW1rn/jJtJjoXEPZnkT+2ETTA28\\naxA7GAwiHo9LIXuawwQjqYEOAmRvb2/3PU8GPZI8Hg/i8Ti+853v4C//8i9xdHSEg4MDvP/++8hk\\nMtje3hYpGY1Gce3aNayvr+MHP/iBfJ6BsrFYTFzddAO3Wi3RFHO5HP7+7/8ejx49ku6ojEVbXl7G\\nK6+8ApfrrFhdoVCwVU+wLMtWMrcXzc/P97V+GpTlWMPhMObm5rC0tITXX38dwHnTw+PjYzHHmRYR\\nCoVEEPv9fhSLRdy9e1dwOX5PP0S8tl9aWlpyvJeBQADXrl0DYMd1CA9QKwTsNZCo7fBu6tf5fBOs\\nD4VCCAaDuH79OmZnZxGJRGxgPzvMPH78WFJsAODg4KDrvHoypEu6pEu6pP+fNHidjku6pEu6pG+I\\nLhnSJV3SJX1r6JIhXdIlXdK3hi4Z0iVd0iV9a+iSIV3SJV3St4YuGdIlXdIlfWvokiFd0iVd0reG\\nLhnSJV3SJX1rqGfqCCN7eyUI6uhqHYmt894Y6axLeOiKjyz+Pz09jStXrmB6ehqtVguZTOa5pFmm\\nYjDsnRG35liKxWLfixAIBLomrzqF/ZvR5Lokw/T0NF5//XXcuXMHExMTUnEwGAziN7/5Dd5//30p\\nzs410N9nPtf8XvM1Nqnsh1hm4yIyx8TkyZ/+9Kf4+c9/jqtXr0p6CBOvWauKnS1YOz0Wi0nB+b/+\\n67+2VXkw590rwrrfSqDBYNCWlKvPqU7i1iVjXK6zQnWvvvoqbt++jddeew3/9m//hgcPHkhjA3bw\\nZTUAdmJ54YUX8Itf/AJffvklnjx5gnv37kmSq4547kZ67pVKpa85ApCCa92Shy9Ky3L6fvM1Td3O\\n40XPdnoPSwU50YVtkC4agGZGbrcb0WgU8Xgcq6urUp2OhZpYzzefz0stnImJCSwvL2NxcVFKnJZK\\nJTQaDezv70tpEhb3arVakn9VrVZtOXT9boJJ3TbADM13eja/n3k8TLF46623MDk5KRX7QqEQXn/9\\ndVSrVezs7OD+/fuSAd2L8ZkJoaPQIGkMOqGalQxYDVTXBg+Hw1IxEjhvzx0OhzExMSGvs0wqW6ub\\n3zOO+fF7nBI5uZ+sanDt2jVEIhEsLi5KVQIWKKtUKvjhD3+IH//4x7a8vHA4LAXI6vW6dC0JBAIo\\nFovIZrO4efMmgLN8MlZO3drakhSkbhUMhsllc1q/brWkeuWBmr93S8dyOqPDvN6LejKkizLNzUl4\\nPB6Ew2EsLCzgjTfeEIbENtjLy8uYmppCKpXC3t4eTk9PEQqF8M477+DmzZsIBALSkieXy2F/f18O\\n+N27d1EqlVAsFuHz+WBZFnZ3d5/LkO9n0hfNuVtip6kVMqGRjJsMNx6P4/r16yJ9mSdVq9WwsbGB\\nSqUiGdiUpkx4NNe8l9Ywaia8E8PVz9XPZyNHPW5qq6xAyPHrGswsSxIOh6UMri5GpwXJOKhXIisA\\nYTobGxtIJBK4deuW1GtKJpOSSPvd734Xc3NzmJycRCAQkNIszWYThUJBKi7OzMzgyy+/RCwWw9TU\\nFJaXl20liE9PT7G3twfLsiTDvpfFMexcnSwWc+7mvpoapNMZGGQM5uu98l+70YXZ/k6DNidgWRb8\\nfj/W1tawsrKCxcVFUZ1Z3pX1tFdXVxEKhaQoOmtst9ttlMtlKYfRarUwPz8vG7ixsSG9v0qlErLZ\\nLP7lX/4F+XzecWOGoX4XjgzI6/Vibm5OGiHotkE8oLr18v7+PiYnJ7G6uopYLIZ8Po98Po+dnR3J\\nkma9KN3ZwWkv+PuoRPNXk6ltTkxMYGVlBeFwWC4hcFZShMXM9Fh1g0XWG2+325ibm4NlWc+ZJt90\\nOqW+iIuLi7h+/Tri8Tj8fr90U6FJWK/Xkcvl8PTpUxwdHSEYDCIWi8kYWeqWda2q1So+/fRTbG5u\\nolKpIJFIyPllJc0XXngBxWIRqVRKsu85nnHOvZdmozUqbeo5aeCjCvRBxmfShQwJcB60/mJme//k\\nJz+RjSLDYVlPl+usOBT7kQeDQQAQLerw8FDqAjHznTWMmUlPqXtwcICnT5+K9tFvecxupC+lZZ3X\\nSzZfp8RfWFiA1+tFIBDA6uoqFhYWkEgkkM1mpUVOq9VCMBjE0tISEokEksmkzG1paQnLy8soFArY\\n39/H3NwcarUaGo0GdnZ2UKvVUK1Wn8NZRtWI+BySU0VG83cW/H/55ZextrYmJUbYjUI3vOReUUui\\nCcGifevr6+h0Okin0wM1Dxx2jrwArLs1NzeHF198Ebdv38bJyQnq9Tr29/dtjJTn9aOPPhKtkHMm\\n02IxslarhWKxiK2tLSntyzPPQnVerxdvv/02tra24PV6sb29Ld17x2WCW5Ylmqi5l7o5gWnSDaoN\\naerGXC6a00gakhMuY/5M8ng8mJ+fF+7LYm3ElWq1GlKpFA4ODnD16lVcvXoVlmVJnaFUKoXT01Np\\nHuByuVAul2UBWRjq5OQEOzs7+Oijj1CtVh1b5wxDep7UCsxCcsCZVkR1n6V7WYIjEomItN3b24PL\\n5UI8HofH40Gz2cRXX30lkrNYLKJcLsPj8eD69esAztsjPXv2TGokscbNIGpvrzmaz+ilqusDHwwG\\nbd1nAMilozanQVzWGtedOGjymWC+plEZr9MZIFMlZNBoNJDP57uaMsT12MZdl23l31nGgzig1+uF\\nx+ORzsRkTCz3HIvF0Gw2cXR0JJiqHu8w55YCs5vVYq4FzzBfN++O+Qz9+iBj0s9x+rkXXYghdVPp\\n9N/YZDAcDkvhd/arYs3tqakpHB8f4+uvv0YkEpFC5KwpzYYCVI+56fSkNZtN+ZnPGXf/dC6aLsyl\\nn82KeYlEAmtra1L0nvV0WA8ol8vhwYMHAohOT08jl8vZzBi2ru50OuK9AYC5uTmRvDzY3dZ/ULoI\\n2DTfx/fywrFkKUujEtgmsTYU1097VVn2VH9/r+8dlpwuEr1/CwsLCIfD0uFGM1BdTE5fVHacocbM\\nv7GWO8FulozV/feomUxNTSESiUjLeHP/hjmzToqC0/zNOmS9TLVe58J8jYK717idzunIGlK3Aenf\\n5+fnpaIgcAbkzc/Pi5SIRCJot9t49OgR7t27h2aziaWlJfG4bG1tIZVKSTU+3TOKVfgKhQKCwSAW\\nFhakZnA3bj7oZTXnqYFHLny73caf/MmfIBaLSelPVsajOcoWMvfv38d//dd/4erVq6hUKvB6vbhz\\n5w5effVVKd5WKpXkAudyOWne9+KLL2J5eVnM2P39fblApjQdRZNwMtWcCrFzLdg0klUI2Y9Oh3fQ\\n5OHltazzutUs8UuA2En77gWQDks0HyORCG7cuIFyuYx8Pi9ml1M4ge5SzFK2PBdkMpFIRBoacJ3I\\nsILBoJwbhgj4fD5sbGzg7t27KBQKjudsEHJiPPpv5l7qtTZNu34ZkRND68YQzTn1O8++vGy91C+a\\nKjSp6Npkf/R6vS5qK5vaVSoVkbpsLhmJRGzdX4nB8ALMzs6KacSDYC6QecAHIXPD+DMxEAK4NN8Y\\nW6W/j4Al6yyz9bjH48GzZ89QqVRsBf9pBnBeDHHweDy4cuUKisWiVP3TmoguGzwsOUkuPs9U5dlx\\nRWMy1A60h1FfCn3oyaB4RpxMNm0u83f9rGHnxpg3n8+HQCCA09NTVKtVWy97xsjpVulktjzPvORO\\n3V3JlC3LsnV65e+sLMq+dX6/X0z7UUw2k/QadoMxLrofF90dJybTDzkxTifqy8tm/mxqJleuXJE4\\nIuIe/Mf4IzbOa7VaSKVSUpOZRdYjkQiA82aTMzMzol6z8wOLiVMq6cs6rHbkNE9NLpcL4XAY8/Pz\\n0vyOHXM5Nw3es7ypy+VCsVjEwsICAGBvbw/1el1qD1NLAM5NHa5rKBTC/Pw8ksmkSHKneY6iOeg5\\nO5mn5vvYRodBkq1WC16vV+KvtJCo1+u2crx8biwWE++rk9nC79KXigxiWCKTp8lJ4cLywvxOMhgd\\nK6T7BVJr0h5WjkvvnW4NROcMcaZwOIxgMChdTLR2Ng7czOzeYzImE7dy+t5uyof5Xeb7uzFBU8Bc\\n1HmkLy+bfqATXbt2DYuLiyiXy3IZeThnZmaQyWQkxqNUKkltX0osdn3ggSEWQ2BYt1mhuTY/P4+v\\nvvpqZA9br7la1hmWxZ5zxIksy5JQBjbHZKF/4l6xWMzWclj/r2sYAxCQU3foBSDdOCqVynMHbRSs\\nzJyr+TzzUPKy0p2v22IR1CezMrUkxpFRW+B+mpLWCecw92MYCoVCWFxcxNra2nMaEYDnGhlq3At4\\nXnMGIOaqroOuu3TwOYFAQNaApuvKygparRZ2d3dRKpWeW4tRqJtJ1m39eplX3ZiS02e6aV3me6mp\\n9qK+uo70GiDd8zTX+BluOuOQ+Br7NFUqFfj9flGdeRBYRLzT6aBQKGBmZkbc/xrgJSA5js3UktGU\\nzhwbANH46EFimIJlWRLBS+lPxsM1oomn1XS9TvyZ7lvicQRcTVW3V9uobuRkDjlpKeb+kjFrDIVM\\nlP8ztoxpP2xLrbUKp1ZP/L5uEtY0VwclBuvSu0ZTiUKE+8t+ZTrMAzhPe+JYeAb0Z/m76ZnVZt/p\\n6anghn6/36ZJ6e/rl7Sp142Z8H29fnbSTJ2+w+nzTtZTL0vFsizbHXaigTQkPWDtLQgGgwLKApBc\\nplqthuPjY+k4YFmWNOVj5wk+hwGR3NjJyUmk02nE43GEw2FkMhlMTU1hcXFRPB+UvtolO4w97sRw\\n9cJOTk5Ky2Xda6zdbiOXy8khNF3D9KrxINNboyUrL0MwGJRLTXOWr1Eim2Me9KL2kmT6MplrQROD\\n49BeJAaz0lvo8/kwPT2NUqkkAoiR3ZlMRnq+kZyYpLl/g2JI+nMzMzMIhULw+Xwol8u2FBdTI2N7\\nI+65aQLpEAzuHV93uVyyR9oc53NoytMSoJnuNN9+yIl5m/NxEjxag9KCUf9da4g0sRjkSw+67sGn\\nn89zpJ+vmTvXvxtdeKLNifMfLxnb73q9XjGnAoGAgHscpLbHg8GgSE4OnvEpeuDT09MIBoPipSMY\\n6ff7EQqFbI34unHyfsnps3ym1+tFNBoV1zXnq6WcxngITDOuSAcJ8gAQG7Ossxgftt+ZmZkRTSkU\\nCok07WbijDpPzaScmAOZB01Oam78eXJyEj6fDzs7O3j8+DEKhQKmpqZknTSjo3aiz1EvRgTYm5MO\\nOkfgvLU34QEKMe4d94VmKeOq+BovJj/D1zh2nln9HJfLJTFXBMIpeKampmwhFKYQGHSeTmvWzQx3\\n+pv+Xlop+m9+vx+xWAxXrlzB0tKSxAJ2G6828c159TPHvk02p4dr5mNuIjdQJ9cCkANNTsxnEnPq\\ndDoCipLxBAIB5PN5nJ6eol6vi6bESzIqOV1S/k67l03xACAcDqNUKqFWq9ly0DRgR4akvVFaK5qc\\nnBRv28LCApaWlnDlyhV5DThLzWC1AJPhdtN2elE3JqY1JHOPXS6XJMNq3EX3qqtWqzg4OIDL5UI0\\nGkUkEoHP50OhULBF0pttnfndvbQ9bTIPQmQWNPndbrfMg/OiUOR38DzpfeQ5J5GJaFOS+0MtkheS\\nwolEvFTfASch0C91w9s0w3RaV1Oz0XeR54oKRSQSkf5rMzMzyGazNq+kfoZmdsQcdV+3fqivSG2n\\nn+k1YCQy/04tiB6xVqslNjt72pNpUfpqrxwZk9vtxurqKsLhsMT3NBoNZDIZ+P1+3L59G++//77N\\n9T4sc3JScfk7NTeaU8x6p/Rgd1qT9GaRWTP51rIscYW73W7cuXNHLnE+n0c6nYbLdebdW19fx927\\nd0XDNDWlYebJn801Mxkdf9aeNK4J+9tPTk6i0Wjgk08+Qb1el8RitgzP5XKIRqOieViW9Vzsj9bQ\\ntITlOaGJPMg8uX7Ly8tS0qZYLEouXqlUkuBd3eZcz50mFi8sALloPp9PziovPc85taNarSaJxwCk\\n5ThDWgKBAMrlsi3Ad9A58mfTvNTrqt+v7wvnxaBOCt1f/OIXWFtbQ6VSwfXr17GxsQGPx4NPP/0U\\nxWIRT548wZUrV0TA8PsoaN1uN9555x3U63U8ePAAuVxOmP9IXrZuHFgvAtV2SgZ6XFiPiKo9sZd2\\nu237DCfAjdX4Cg8/1Vy6avmantyompL5eb3Z9A7p9AefzyepAfV63SYxtYSiCaY9hSSuBTUA/XwA\\n8h3mXpjg46DzvEgq6/fwADcaDcGDOFamxGSzWRweHqLdbqNUKsnFa7fbmJmZEXO+WCwKTtNtHBoa\\nGBY74vM0UyOz0WtHrVBrSVpr4vt092F+ju584kHcS906XQtduvmJHWmvHuc9CJl3s9f5N801/brL\\n5RLsdmVlBcvLy1hbW0MsFkMsFhPhMzFxFvC5urqKtbU16STsxBS5FpFIBNVqFS6XC6lUymbKdaOe\\nGJKTWWBuKLUjMg+mkTCgkSo78QQ+g5JGaxLaFU6chXY/AJvqDNjdt6OQCeqZc9aHqtPpSGLt/Py8\\nLVBOHzQyWKq/pk1tWedgaKPRsAXqaZyGz3E6TMPOdZC14HzYjlknano8HtTrdWQyGWQyGRSLRZsk\\nZkQ3W1AXCgVUq1VbYKT5fXrNGfU9SBE6wG4SErshEMt0HL2eXF+65vXamg4LXVaEWhQ1HD5De+qI\\nSzWbTRSLRTHhyVB0gO2wpJmTFnjmGpt7z3lTCUgkEnjvvfewsbGBWCyGUCiEZrOJZ8+e4eDgAIFA\\nAC+//DLeeOMNSYvSygSZOplyIBDAiy++KPjdRR42YAgNSf9tcnJStB56vmKxGCKRCLa3t20LTgYD\\nQCZBxkSm1ul0UKvVJKaFkdFTU1Oo1WqoVCoCKPOSOzHNYU0ZJ/xE42G0s+m9mZubg8fjwczMjKj9\\njEsCzlV0zpnroSW4y3UWQKmTh5vNpqj6VKfNAzWshuQ0d87Tibm7XGcAbalUwuLiooQ88DIeHBzg\\ns88+QyaTwezsrJgB5XIZAEQgtVot3Lt3D4VC4UJPC8fF/wcROtrsZHQ0APHuVatVNJtNCeokQ+E+\\n8xk8m9q81Gukz7CuDEEtyO12i/fx9u3b8uyTkxNUq1VUKhUbhjrofmqTu9v5dXqu+frc3Byi0Shi\\nsRh+8IMfIB6PY2trS/DL1dVVcTC12238+Mc/xve//31MT09jd3cX6XQaqVRK7gQZ2TvvvCM5foFA\\nAIlEQpLoe1Hfbn8ugv5ZZ25rhkM3ebValYnoA2ZuvHZ/8gDSHiUD4mbTexMMBp+L5TAXfRjSG00N\\njiYaKxhw3vQyNhoNOaycH9V/MmTtKXM6yGRslDRkbjTzTJNtVOombMzvoOeF8VHUishc6/U60um0\\nqP3AOc7CtaCmo5mt+T2mYBnlonLcU1NTUqVAa6Cnp6cIh8PPAa46nIOvcQ+5f5yj9grrMWo8kcKI\\nGnWj0ZC1osam8ZxRiffHNIH13+ksokKxurqKeDyOF154ATdu3IDb7Ua5XEaj0UA8HkcwGEQ0GhUT\\n3OfzYXl5Gd/73vfEW07FIRAIYGlpSWpOMaOBwsCyrNEZUjftg5hKIBCwRWUTWQ+FQhKzQEZFdZaq\\nHhdPxzSQIU1MnGVR+/1+waY8Hg8qlQpisZh81zjNGaf5khkQ2K3X6wK0M8iTDIgeMr4GnJuVWgpz\\nc2hOcH4+nw/xeByNRgN7e3tIJBLi9jc1mFExMz1fMkWn+XM+WhulV4lxKUyZoVufJVZY94fC6uTk\\nxMa8OY9u2t9FWFc34jlipLvGrLiXZsyYxgA1jqQFrjaHTDxEB8/S3KQwSSaTyOVy2NvbQzqdRrVa\\nRalUslUQ0OPph5zWSM9R76tev3q9jrW1NSmfc+vWLcRiMfHyAhAtLxQKIRQK2byrvL9vvfUWFhcX\\n8fLLL+Pg4ACNRgNerxeJREK0rnA4DJfLhbW1NXEqfPzxxz3n1beXTS8EJ+jxeCQpluBevV7H8fEx\\nIpGIzdSp1Wpiwuha0twUvTEadyKopnEIMjwnV/UoZM7X7XYjHo+LVuZ2u4UBM9o8EAhIOQ4AkvXP\\ng6AjfbWpRpC42WwilUrhhRdeAHCWRV4oFOTAx2IxW3lYfRCHYUpOZqkem9OaAOfNBCiIAEghulwu\\nJ+8ndqhNPX4H8RmTGZnfRaYBQADpQYjPZXApcLYvjIGbmZmxjZHeNP09HCPNTeA8pknjf7z0PM+s\\nVNFoNCQg9Ne//rWkS2WzWRv2pDXiUYnCk3eJOE4oFMLq6iparRYajYYUUkwkEhJb5PP5EI1G0el0\\ncPXqVVSrVRQKBTx79kwYiq59tbGxgWvXrsGyLGSzWdFAA4GAVHTgOPx+P1ZWVrC3t4ff/OY3Pecw\\nEoYEQOr90FvEaGTGCbEWEqtGUsKaHjITVzk9PUWxWBT3KL0k/Jw2Z3QskB7boKTny/99Pp+Uk9DV\\nA8lQ/H6/gJrUFKkB6TQYExPhmE9OTqQCAr0TMzMzNuxIx/LoSzzMPM39dJqz+WxGZOuyI81mU8bG\\ny0jPkzbRyRAYX6YZtBMzNCX+sBeVkhyAzQvGUAUyU+4TYAeEaaJy/jqYkmumA2E1wKvHzrI7uqjd\\nRevdL5lnQZv6HIvf78eNGzfw7rvvilfwjTfeEM2deBfhFVbuCAaD2N3dxd7eHqanp7G8vIzZ2Vmx\\nEsrlstyHWCwmzI6Cm2uuPcvUynrRQBiSuRjA2SY2Gg3EYjEkEgnxZBwcHEiph2q1KkGE2m7Xl5Xe\\nB3qtOp2zXDbWH2K2fz6fB3C2sbTNWTx9VNL4EZkfWwexpCxB63q9jnw+L9HjjCcxx6HNMgAyT5oN\\nrVYLm5ub+Prrr7G+vi52OplyLpeT+CwANixkmMvqdPD1nE0iw89kMsjlcrh69artohIX4rwsy5I6\\nVixcx0NM/KjbmLRg0he4H++MOWaX68xZ8OjRIwSDQWGWdEOz5nmxWBQBwYBPXiaNJXJ8WhDzn/67\\nHmupVEI+n7dVFujGeAbVdglzmKZnKBTCj370IyQSCQSDQcFbV1dX0el0cHR0hP39fVQqFRSLRbz5\\n5puYnZ3F4eEhPvzwQxwcHOBP//RPEYvFEI1G8fnnn+OXv/wlvve97+GHP/yhMDKeXWZn0GqgBUSn\\ngcaES6USMplMz3kNxJBMAJKmGuv/0Hwix+UhpPTXLk+9QRpT4cISd9HqMQ+NVnVN234UMjUPbWpQ\\nw6MGWKlUxA3OTWDwoAZBqRHo6F6dcNput5HP56VSgDZbeEEAu4eHF2VUJmwC5U6XgvuZz+cFA6L2\\nRtcv30fmwX3n+MiU6aHTa+u0/uPAx6jFssMLLy+BecIHpiMFsOckkunwmVq71aQBczKoWq0mZqoW\\nvqbZbALR/ZBlWWJ+AudlVhKJBF544QVEo1F4vV7EYjH5DL2bzL+kIOF9evToEQ4ODrC0tIRXXnlF\\nLJyDgwOUSiWUSiXRMHk2iaUS5+Q91zADoQvmsfaivitGOknRmZkZWRS3241kMineJ0ayUm3TTISm\\njBnDwM/yYNBupWrodrttnJcNBTSgPA7SjI6YAMfPUrzpdBrZbFb6kmlzkgdex1kB9prGGoMgpsQK\\nCNTM+N0MLuSBGsXbpvdQ76/5s74oLpfLVlqF0dnUCCg4Op2OlKChBNcmXi/gVs/JZJTDkH4GvUEH\\nBwdyLsPhMDqdDpLJpDAZJ7PbXA/zHjAWj1qDdoJoKKEb1jnsPN1uNzY2NqRxAs8INRveGTbmPD09\\nhc/nkyyDmZkZ3LhxA+12G5lMBsFgEGtra7h//z7+9V//FY8ePcLbb78t7aBmZmZEw/X2F/B7AAAT\\n30lEQVT5fHJ3KYDJcAlp8OxqnDcSiVwYdT+0yUYv2NrammBEExMTwnHffvttKYTOnC6de6btbV5Q\\nRrHSzr59+7a4Dul61xs4OzuLeDyOZDJp0y5GJa0h6axsllkpl8vY2dnBF198gT/8wz/E9PS0ZJOz\\nswjz39hNRLv/NR4EnMcr5fN5+Hw+eL1eeT+L27Eofb8dXC+aWzdywqhOT09xfHyMWq0mcyODZgCk\\nZZ3Vmf7yyy+xs7ODa9eu2aKSncwec1ymp6jfMTsRzetoNCqtiXZ2diQ38r333sPk5CT29/eloQKZ\\nCQUohZzWRslkNL5FpkvvIuPk8vm8zaOnNWM9r2HO7cbGBv74j/8Yb775ppiNGpfk97HmErXEUCgk\\nTGthYQGPHz9GJpNBNBrFO++8g0ajgX/+53/GyckJXnzxRfz+7/8+bty4gUajgVqtJgA495b4GjVk\\nkylzfhROY6uHpBcQgDAQbZ/zfwBSnIouYgC2PC5ybR4ALii1EZ/Ph5WVFfk+SmJtFjIFZdzeNj13\\najv0FpDpVioVZLNZkah6bpwLpYTO+dHeN24Ugb56vY5qtSoBfPT6sDqhCWCOyoCdTLZuUlwXr/d6\\nvbZC+XxPuVxGLpdDOp3G9evXEQwGbQm1pvPB/A4TEhiFaIJNT08jEAigUCggk8kIvMCYL3rcaGpp\\nYcEzpRmz1uY5L15E4in8ThJNeKc5O/3cD92+fRvr6+tSkZSaEIvn8fwRcD8+PhbrIhAISF86WiUn\\nJydYXFzEz3/+czx9+hS1Wk2YWTAYlCYeNHm5nxw7zbdOpyNnmGuoo+4vor69bCZT4qTp5iY1m01p\\n5EjtgDENjFEyPSgEwmmSaSnEC0xzSeNLrJPsFIs0CpnmA5/L789ms7baOtp1y/eY5SrM9aSGSKZN\\nTxaZmzb5aMLpeQ47V/PimxfB3GdevmKxKCEXNNuY1qHxF64NBYgu/+r0XXoe49Jw+VwWitNaK7E+\\nADbPrY5B4p6Z5UHMdafmoSPyLes8d1Nn9WuB6XRWB91Pv9+PVColTIB16hmKwzOTSCTQbDbFurCs\\ns7zEcrmMQCCASCQiuGi73cbs7CxeeuklZDIZnJyc4PPPP8fKyopoXsRRKZB0I1g6uOiNJTEG7ZNP\\nPrmQKQ1ssplcnVoLbcdOp4NisYhPPvlE4nPINNiBgRurOzZQcuk6Stvb24hGowgGg2ITa4al45/G\\nrR1pHIkHm62Unz59ilwuJ4z05OREJBMTLBlnQve49jhQMyTI22g0kEwmAQC3bt1CJBIR6UWpzLH0\\n0mQGnVs/DIB4SiqVQi6XQ7lcFrd5NpuVQmxkAIeHh9jb28Px8bEIDM1Qu42l12UdlFGRodTrdWxv\\nb0uA7SuvvIKtrS3s7+8DODeViXlwr+lZpanJn/VY9frRCmC6yOTkpIQ78O9Oplq/e+BEuVwOH3zw\\ngYRjeL1eLC4uYmFhAevr64jFYlImmgKOTIWChVCC7uA7OzuLWCyGSqWCJ0+e4N69ezbGe3JyguPj\\nYym212w2xfPNyhcUthTIDAXR8Ec3GrhRJJkOmZGWfqVSCe12W4BupnvwUlJaUW2nVsR/ExMTEh7A\\nA8VJHR8f22pL66oA34Sppp9JtZMeBW4ozTQGAwIQJsI4JXqmqDFxPWl7M3iwWq0imUyiUqlIswRe\\nBA2Cc3yjahNOTKmb5KbJBpyZlRRCxAhpvmhXMM9Fs9mUg8g56YttAsfjYLjAeZUCag/AuSeK30lh\\nYIaiUNiRGfMSkfFq8FtjRPxOjp/5nToK3MSNht3LTz75ROqZ804mk0nk83mkUikJTN7f35eaRpOT\\nkwiHw5LyA0A0/YODA6RSKXi9Xty/fx+ZTEZCJLQmTYFKLZNpIzQFGYCqq1jwPjuFxZjUd9cRvWhc\\nANOOrFQqcjEjkYjgDozG1geVz6H05DMJ0OkcLj7j5OTEVo+YXjZdj2kcpLUjPUceVoYx0GNDdV17\\nZrTtrAP0eOl4OXRgGjUm5siR0eu8Kb0X42TEJoPSF0fHTXFevOwUOvycbhDabrelrxn315yD0xjG\\nMS+uMbVwl+ssSJANJXQqjBlkywtmmq06nUR7ijV4S1NNz4/P7KYhDkN7e3uizbDOGM8Qa4ZRcw+H\\nw1hYWBAHCceSz+eRyWRwfHyMVCqF+/fvo9VqYW9v7zmvtVZEOG/Ts0ZXP8+vxvEYGc90om7UV182\\nkmZOtMljsZikOszOzqLVaiGfz2NhYUG4sC7RwM9PT09LagglEQ8N/62srMhBmZ2dRaVSwe9+9zs5\\n7KyXrLWHcZB+FoFzSnvGUhHsY/4WY3AI+mk1XTNt3ZeM3xOPx1Gr1VCv1wVApIpLBq7rJGlNaRQy\\nhUy3ZzKil2Y2wVJeBvNSMV+Npt3ExISENFCw8Lu6gfOmVj4odTpnNb5v3LghZZATiYS4rHO5nC0a\\nHoBIdX35OG9eNO0x08KKr4XDYdGSr1+/jsnJSTx58uS58WlNcBim5HK5RLvh/MhYgXPBcP/+fbhc\\nLlEOGFDMhFeGRFBhOD09RSwWg9/vtzlTqCgwK4PMqNPpCGyihbAuO0OGvbu7i83NzZ7zGjgwUh8i\\nIvnahOMis9UwJ0lVj5PQposJKHLjc7mcaEHUihhgxfdrTcpUhwedm3lB+TuxIh3oxw0giEczjWPX\\nbZM5Jpp5xCOAc28lGRtNIsuyJDWF5qmp5g8TGGlqQd3mzN/5P/9RG9Tv5fprzYIHmPEv9FKWy2WZ\\nS7+a3iDz1GvD2tW6SF40GsXi4iIsyx5CoU0qfh/PnNYGTVNTz1U7N1yuszyyaDQqf3dyKAwrVPhd\\nxILoEdN4HR0PPKccKwFwc791ojgZGB1WHCdNNH43X9PrZFmWLYCSZyabzYr53I36YkhOh5SLot2f\\nOkK5UCgIR+Zm6vfyn7bbAYj6yQhmqoLktATX2u02AoGArSwsxzfMJjt5gPQYuXna/NJ2scYnuDHU\\n+vg+wF7aVkcL8zUyPi3tNOPl2Di+QcgJM+q2Ft0Yl9Pe6fEQc6FmQYyDwZT6QpoakpNGPuh+6ucx\\nDonOECaaJhIJ8f5pLFJjRQS2ybhMtz3NQXNvdHZBMBiUUi3cU6dzNgzps2AWMqQTSY+J30/N2+U6\\nD1Wh2cqsCwAiHCuViqQyWZYlThYmy5PZMjiZEAP3vV6vC0ZKTK0X9a0hmYeZXJfeFG4Q6xhvb2/b\\nLi8Xj5yZ6jK1j1qtJnE+3FQecA2uUSOr1+u2zGK+f1SwV8+XF0EzTOBMckYiEUSjUanNTCxFXzKN\\nQ+h1I/HyUtpQ2nEtCJY7mX76/0HnZTIl87nmeylQtGDRXkRTO9CuYJrkPPRkSk7gtRn3w9c0ftYv\\n0RHyv//7vwiHw/B6vZJXVqlUUCgUbIm+FCYUENqNT7Bea7UcJz/Pi8j3NxoNaZtNhjdO0vtIJkJm\\ncXx8/Jy1oJm0rtygMU1ivebn9Hv4M3FEc146MFKvHwXtRevQF0NysuW1Z4HcuN1uS62gra0tQdz5\\nPmpQ2n3Nz+kN5gITmGPtZidQ1ZTSw9rkJFMqa6mvL+fExITUDtdmjPbukCihzL9xY7lhzJGjd1F7\\ngLiGTprFKKT3wUnL5P90j/OAtdtt8ZpynbRJqx0aWiB107bN79VjG9Q05boyuI+SPJlMynnRXiLt\\ntNAMhueM504zAJ11oDUrnUbBs0xm3IsG3UvT/NM/a+bpZJbp7+sGc+h9MO+a0zj09+h91u/vhykP\\nHKnNfzqOgXVfKNlDoRAikYgEZBGcJQcPBALCnE5OTiRAKxKJIBwOCzf1er3IZrMoFApYX1+3leZg\\nTAWxqXGRXlwTtATOS3GwPRG1Ih30qZkPia/r2jv6gvN7mDMWCoVgWZakH5hNDTjWQajXoXcyJTRT\\n4iXTzLBarQrT1EyN2l6tVpOzooWR3i9TqDjRIKapvgg+nw/z8/NYXl5GvV7HV199hVu3buHGjRvi\\n+eG+6DAMl8sl5gcvsAlekzSz4RrRA6vDALQgcdq3QfeyGzNyYui9Pm++t9vYnBiq+d36/d3mc9E8\\n+24U6fQztQTiKToFxLLOizUxqY85PpQ4lLrT09OiWbFKwMnJCa5cuQKfz4dkMimxISbudJFNOizp\\nhWP5D12ClR43/V5qSOYFMjU4vpfv1xoEs+U5RzMHrNfBGYWcpBt/J4PV8+Hlpeluan/awaExFBPQ\\n5lpo05Gv6f/7Ja2lejwexONxcYzkcjmkUimkUikJVSD4qpt60qtK7Iu/8xxwX3hR+TuFKMF8U6M1\\n13pUcnqmXtthvsd8Zq9njPsMAn0wpG4D4AHSKQ2UDhqU1X3VyLh06VO6yQOBgBTNKpVKACA2OAMR\\nXa7zwmgTE+ftifvJkRmUOFcT8+H89PipARBr0Oasvsx6c/X7ycxdrjPvJANMWcLDZArfxEEwGSXH\\nq/EDEveQZUZI3Hcyaw3Y82/8pxkrn6+1JCdToB/Sn2+1WhKrMzk5iWw2i0wmI/gmBaqO99JMiOPS\\ncUt6rYg7aU1ICxqdv2Uy21GZkgkt8Ln6n35Nj7vbz+Y+d/uspnHMRdNQqSMu17mHKJvNSkErFoTS\\nrYPpEuTGUspQMvl8Pulewdie4+NjbG5uIpVK4cGDB/jtb3+LN998E9FoFLVaTXKreHicLvyoxGfq\\nSpjUVujmJ2MiU9XaETUEzpWmHU023fygUChIFDqZPA87C4hpM/Cb0pKA5+Nj+DNNdLY6mpycRK1W\\nw/7+vs1lDpwF6G1tbYmHi/Ey2rzrJon1BRhmP/Wl2t/fxy9/+UtpZjozM4NCoQDLOqskQU8QPW7U\\nTClQdFkRvk/H5TCrQIe+MBOBXis2ANWCalzYn/7Z1MB7aWa9NLZuY+v1er9mYq/nkPqO1CbX55dz\\n06vVqkgTluGYmpqSUh2WZcnl4+Zp7YNlN9iKeWpqCslkEoVCAYeHh/jiiy+wt7eHSqUiSZLEoijR\\nxk36glAy8lDSw6TTJTqdjriFiSVRoyMjBiCvmR4KHVKv6yJpSatd7dyPcc1VP8sJQKa2oYusEXSn\\ns0HvKSuEVioVG9Cte3OZ32sypFHmw+/rdDpIpVKCZWphYX4PPb4ULjpkQwdDmp8zAWS+l9/B7H+N\\ndY7zzHZbR/7cj+DqhkM5Pbvb35y+n6SZ1EVjGajriL4MbrdbzIv5+XnZUGo9V65ckXgUqu6U/gRE\\nWRng4cOHODo6srWJYRJusVjEzs4OyuWymGo008yI53FuNJkBJR6lJwBxd9NbQ6CWjJmf0zWkqb7z\\nkDK/z7IsWaOTkxMcHR3B4/Fgfn7eFqimXdTjJHPNnNZSA78M3AQgHkHzc0y4ZAQ7tStqk6Zp4DSO\\nUYnmHwUEE02pyWgtVHvX+LMO8OTF1gGO2nvGnwkpEK7QNYRogo9bkJjaqfl3p9dN7cnJ7HNiOHx9\\nFPNzZA2JD9FqIRmLy+VCqVTC9va2tO7RofM8fPRm6LgUBks1Gg0cHR2JdymfzwtIyDSDWq2Gvb09\\nrKyswO/3i6SpVCpSi0aPc1TSuIDP5xM8JBqNIpVKoVwu49mzZygUCrh165YNX9BmG0tz8HVdnYDh\\nEJZ1lrhaLBaRyWSws7ODTqeDO3fuSJE3nSP3TZlqJun4H15s5kZxH1lH3Iy/KhaLouXyYpo5X70w\\nCb6HNMiB18+lRsnxaaiBkfRshU4mSxOdgkBrw8B55j6xU+4jn+vz+cSbqIN8+VkdzzbM/PQ8+2EG\\n3ZiO+ax+TLtuZmK3Z+m97HeOA8Uh8SBxQ1jaIJVKIZvN4n/+53/QaDSkABY/o7PA+Qyq+hqwpZqs\\nVWod89JoNBAOh22Sl687SYNRiMyUDCQcDgtAzxibQqGAd999FxMTEzJv05PE+QNnJXkZtuD3+wVz\\noAcvmUxiYWFBLg5LBOs1HDczcsKLnP4OnF8CzicUCgnOos9Gs9mUCp6MrOdZoXDqhoVd9Pugc9PP\\nqFQqguuUSiW43W6pkcQcLwpc7WWjCQecZydoLxsZtq4T5Xa7JbhVm3VONOr8zN9Npu7EVC7CeZyY\\nncm0ur3HVF74vU4eaJP6qqmtDyQXv1Kp4OnTp6hWqzg+PhbAU7vEdSwOGYu+qNprZB4ePVG3243N\\nzU3BooipPH78GPv7+yIBzWcNS1r93t7eFo3v888/x+bmpkj8qakpfPrpp8J8qTVyDLqVDteCh3Nv\\nbw+WZYmZlsvl0Gq1JFP78ePHto4mbCw4bg1JMwWTqTtd6Ewmg3w+L22qAHuVA65bqVTC4eEhcrkc\\n8vk89vf3xWTRB9NJcmuGNepcOT+trWezWXz99dcIh8PSYZnxdBQm3DtqSdTKqRHpi8XQDV5ARqoX\\ni0URXtrjaK7vMBkGg5yDixi8k5Zz0Xc7fc6JtCncz3gHyvbXvxeLReTzeXzyyScC2tELQYzFnICp\\nzl0UxelyucTW//zzz5FMJsXbNTk5KWYBmxEOe3i7qayNRgNfffWVeAR/+9vfIp1Oi8ekXq/jww8/\\nxPT0NEKhEJaXlyW7ncGgVOF5sMnAj46OJAE5m83aPEOnp6e4f/8+gLONLJVKUtVv3NRNIzElaadz\\nVm9ne3tbIrTz+bx4OzURW9ra2kI2m0Uul8P29rZ4XLXGMW5y2kv9e6VSQa1Ww71798QkpkbDJFxi\\nTDzX1MR1zBEZDDE01gCamZnB6ekpKpUKdnd3JTxEr60eq9MY+51nP/M375yTCTfK+/sZnxNu1fVz\\n1qjqxCVd0iVd0phoPA3NLumSLumSxkCXDOmSLumSvjV0yZAu6ZIu6VtDlwzpki7pkr41dMmQLumS\\nLulbQ5cM6ZIu6ZK+NfR/Dwh56/voHVAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 14 - loss_d: 0.255, loss_g: 4.536\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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68pXPr9Pg4PDyWok7YTbnKOAwURBRTbsWt/GnJSH/h8s23OFREPgAFj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\\nQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 15 - loss_d: 0.309, loss_g: 4.403\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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rGIWq2G9fV1SS9hW71eryxQYkk2m00iuQlcT05OwmQyIRQKIRQKIRKJ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\\n9IOh/w/hrVSbPQj8MQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 16 - loss_d: 0.299, loss_g: 4.360\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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4PD4cDFixdx+fJltFotHBwc4OOPP4bf78fS0hJCoRCsViuCwSBqtZpo\\nQcwFAw5BL7PZjEqlAqvVKlqSxWIR82YYhnSSNGjzqwy40WggmUzizp07uHLlCmZnZ9FsNuW7crmM\\ncrks4Q293mE0azQaxdbWlq6EPW1mNIi0C81isQgDtdlsqNVqyOfzCIVCcDqdIjQcDoe0kYyWpmy5\\nXJYN/Morr+D+/fsoFAqoVqunou2Oc69Bzx2Eu6gWAMNRXnvtNfj9fjSbTezs7ODOnTt4/PixxJmZ\\nzWYJECXzoUlEjxUdBc1mE7VabaJ+cm8O2gd6zFD7epQZqR0DPeK4GAwGnD9/Hp/97GcF+zWZTMjn\\n8/D5fJiensbu7q4ECw+isbxso6SIwXAIZNIlyDwuLVEbMpvNkujZaDTQaDRQLBZRKpXQ6/VQq9VQ\\nqVQkkFDPhNFu0uMu6nFwJHVi+JphC9yA1IhcLheAQ+yl1+uhXC6j3W4jl8uJvT2MsfI56sI7SRpX\\n02RCbTAYBHCY6lKr1USbpeZEbY8mHADBBKvVqkhabkjg9MIYBtEozUg1RXmd+jubzSYxZ0yJUa2C\\nhYUFrK2twWq1wul0IpvNytqm6cv0onA4LOkVxWIROzs7E/dnkjWh1YK1oTR6WtIgpqb3bKPR2Kcp\\nOZ1OgSs6nY7sc6vVOrSdY5ls6ms99Y+DTanqcrnQbDZRr9clLJ5SwGq1wm63C+peKBSQy+VQLBYF\\n5K5WqygWi6jVarJIVG1NffakuUfjkt49B9nlVFvZHsYcsZ8GgwGZTAb7+/sCzNP9qQLCes96Fpog\\nSTvfxIW8Xi8cDoeA8OoGs9vtsFgsgiFQQ+j1eqhWq0gmk2Km83s1d0/tp8p8T3NOhwmAQdopY7GY\\nPDo3N4f5+Xk4nU4sLS1hb29PgmWJFTI2rV6vw+fzwWw2Y3d3F06nE3Nzc5iamkKn08He3h5SqdSR\\n+jQMG1L7qSeAjoNnaZkV08VsNpuYa2RE7XYbTqcTrVYLDodj6H0nyvYfph4ajUacO3cON2/exOuv\\nv46//du/xfb2NlqtFi5fvoyrV68inU6jVCpJUB1D5kulEn79619L8mahUMDdu3dxcHCAUqkk2peK\\nPWgH5CRJDzDk54PMK4PBgGg0CpvNhkwmg0qlgkwmg3A4jE6nA4vFgu3tbWxsbGB6ehqxWExMUa0r\\nnNJLC+IPatdxaRDWYTabRfrb7XZUKhWZOzIdpg0wAZq4GQF7l8uFSCQCm82G9957D7Va7alkYjUi\\nWM+MOo1+UsNTvxsUX8dcvO9+97tYXFwEAIEZGMS7tLSEq1ev4vd///dRLpdRLBZFS+50OiJk3W63\\nQBKNRgPNZhPvvPOO5HxN2h895qmlYcx2XA1VOw/q7zqdDjweD7773e9iamoK0WgUqVQK9Xpd8FK7\\n3Y7p6Wlcu3Zt6HOOnFyr5bIMmgqHw/D7/chkMsjlcsjn8/B4PJibm0Or1UIymUSz2RRuajQaEQwG\\nhXP2eoelLqampmCz2QRDUuvojKKjStdR5pNWcpNBMfiPEecejwcAxLtWKpVQq9XQaDTE3KFaS/Bf\\nfYZaW2rYhjxOP4GnN6iW2bI/NE0YD8aQB7vdLtdyc6kxSUwxCIVCMtfDTFA9zei0tEM9zVTPGgAg\\nSaMzMzOIxWKoVqt9keg2m01yt6gFEpKgOQtAMCj+EVNyuVxjxehoaRjmo87lMAxykBald92guaFT\\n6ty5c5JZwfsZDAYJ3XE6nWL6D6Kx3f7a93omWzAYFLwhn88jnU6jXq9LDIrNZpOMf3JNj8eDcDgM\\nu90Oo9EogViRSATxeBw7OzuShqEdQLVdJ6ExDfsdzVTtONDETCaT4v72+/1otVowmUzIZrNSsYCu\\nYoLEfr8fwWBQtEa1H1xQWkl+kqQ+T2tCcU5dLhei0WjfZ2ofWLGBmiz7QS0vFAqJwOEca4nMV91E\\np2Gq6jHiYZvUYDgEbKPRKG7cuIFoNCqYIIVLu92Wyg4M5TAYDpOT6dwgvmaxWASGYFDtURNrh9Eo\\nvOeo9xpk5hGgZ4Z/u90WRkx4wmKxwO129xUJ1KOJNCS1kSoR0MpkMshms3j8+DG2traQzWbRarXE\\nrszlckgmk4K+t9tt2O12rK2tYWdnB7FYDIuLiygUCgCAvb09AId1eFwu18ABOYmFO2wDaDerw+EQ\\n8JYJo2tra9jf34fdbsf+/j7y+Tymp6cFpC8Wi3j06BE++eQTGAwG3Lx5E3Nzc0in09jY2ND1ZlAd\\n1rbjuP0cRHq4gM1mg91uR6lUQj6fR7FYlNgwFmVLJpPicQsGgwLi06RjtP3W1hbK5bKUNSGwr8fo\\nRwHQJzUGemYMN5Tb7cbVq1fxzW9+EysrKzCbzSgUCmg2m/D7/VJRlZpOOp3Go0ePpM9OpxP1eh2p\\nVEowNM4pGRcx1lEpFePSpGtkHJNt0PywPw6HAy6XC06nE7VaTf4YBsGYw2AwiCtXrgxtz0RFWAZJ\\nFroxm80mkskkEolEX0Ez4ElNIOYzMUYln89ja2sLW1tb2NnZQb1el+fkcjlRf+lKHsdmPi7p4Rj8\\nXC23wJgjLiZ6lJLJJHK5nGAlBIOz2SzS6TQKhQIMBgNcLhempqaGplMM2rAn2c9h4CbNjF6vh2az\\niUKh0Oddo7OC3lBqR8SSaK5wUVKL5P31zDT1s5NgwlrSY/wqUTNkuMrKygri8biEMZBRE1JgQjGr\\nQLASpNvthtPpFHBXG91OYcN1MinpOUIGfTfJfSb5ncFgkARbg8EgmiPzVCuVijg6ABwP1NaSqk6z\\nQb1eD4FAAH6/H7OzswiFQvD5fHjxxRdRqVTQbrdRrVbxySefoFKpSH0kLnDmyHg8HiljwKBCg8EA\\np9MpKD2ZntoePVzruKTdDKpZY7FYMDMzA5/PB7fbLcXaKCHv3LmD/f19WK1WUemLxSKazSYCgQBm\\nZ2dFs/D7/ZiampI+8hkqqQFuJ7E5x70HJZvT6cT09LT8llpNtVqV2KR2u41KpQKLxSIAPs3zUqmE\\ner0OAPjSl76ERqMhiZZarOYk5m8Yo1EZkRbL4nsGPl6/fh0rKyuYn5+Xsip2ux02m02YbL1eF1OT\\nZtjKyoqA/Ax3oCZlsVj6EowZv0NP5aT9VIWVtv/jzvEgJjYMrtGO8cWLF3Hx4kVZF/v7+yiVSn3F\\n/cxms/R3GB3JZGNDGFvyxhtvIBqN4uLFi1hdXcX09DS+/e1vY39/H+l0GlarFdlsFj6fDysrK7BY\\nLBKnEAgEBIdhyADt7Gq1CpPJJKVvafboSVXtgB6lP8O+4wKy2+1YXl7GhQsXsLCwgDt37khcFRMN\\n4/E4vF4votEoisWiFGRbXl5GvV7vc4HOzs4iHA6jXC7L5h7UppPUFLTMVr2/+nkoFMLi4iJ6vZ6U\\nJGW8FQ8rODg4EA1IzTfkZj04OIDX68XnP/95pFIp3Llz56lyFMO0lqNqD7yn6sXT4mTUejudDq5c\\nuYJYLIbp6WncuHEDMzMzogEQNyMw32g0xOxijS86NKgRscqBx+N56iAH4IllQW170v4dldT5HSQE\\ntIJCj9FR2/3sZz+LK1euIJPJYHd3V2AZgv4UuplMBltbW0PbdmSGpDby5s2bmJqakhMo3G63nLhx\\n584d8aQxyU6NYGWdnVKpJNJHjV4lQ4pEIohEInj06FHfoGnbdNSJ0vMgaCeBtZzo+YtGo7h165Z4\\nYViyoVQqwePx4Ny5c0ilUgKE7u3tYW1tTepBcWOzWiY1iZPoz6T9VT9X+0vzhAGN1Ajp0qbzYm5u\\nTgqzAZCwAKPRiHw+j1wuh1deeUXWASOZVSYxqD2TjoHKaPTurwL0AMTr++abb2JxcVFMcgKzBOOJ\\nG1J4qsC0tmIDmTPj85iI3ev1ZO0TO2IKyiSk9uso4zPpdXqaNfcpT53Z2dmR0jtc2/QqOxwOtFqt\\nkeENRzLZVKwAOBx8njZBO5JBj7u7u1hYWIDX65UASEoQo9GIarXaF7dCtY+lYiORCBwOB6anp6U0\\niZ6GNMpzMinpqagEY+nGpOfA6XRKlHo+n8eHH36IRCKBxcVFwZjcbrcERzL8IR6PIxKJ4MKFC9jY\\n2JASJnr4yUkxpkGquHb8LBYLwuEwFhYWMD8/j1KpJEGsly5dgtfrFfzvzp07crKMy+VCtVrtq/BQ\\nr9cFGwwGg3jppZeQSCTkWCgAut63QW0eRtz8ZBDU4tXXZBQ2mw1zc3N47bXX8Prrr2N6elqgAQb1\\nqjWwucnoTSMjojmi5i+yPhAFKp9L3IhtUZnepDTJmhhlgo26l1Y4t9ttCe50u91otVp4/PgxZmdn\\nJVGeuLHZbBbt6Re/+MXQ50ysIWm5pvpHCdLtdmXxZjIZRKNR9Ho9iVWhRGLHWLwLeFJ7mp4a1kmi\\nt2fY4lSxlpMk3o/gNACRvszjKhaLEvz24MEDrK+vo9VqYXV1VfJ3isUi8vk8TCaTHO1EM5X3UaXq\\nafRlHKKr1uVywev1wuVyiemppvI0Gg0pucKNaTAYpOonAX1mjNNDRfOHdJKmqMlkkhgvPqNcLguT\\nYuoGzW/mX7344ot9pia1Q3VtNhqNviO/VOHB6zlG/IyBpKw/TkBbm0ozqclGmkTb0Zqrk/yOr2n+\\ndrtdOBwOrKysyAEefr9fYuu4fymQACCVSo2s8jBxYKTW/jQYDCJNHA6HSJhyuSzFmorFIgqFQp8b\\nkOqq1WoVJsX7UZ1ncmoulxNzaZAkfRbmDaUvC/lTLWdkdqVSQTabxf7+vhT593g8cDqd8p6Sm0GT\\nnU5HgGLV+6Qd89Psn14/mfrAjULtgAyHOVpMClVBYZrk/LxarSKTyWBzcxPr6+t4/PhxX8XJYW1h\\ne8alcDiMy5cv49y5c+IdpLQ2m83iko5Go7Db7YjH43A6ncjn8+KkoFnG9lutVmFG6gkhNOPUFCk1\\nr5HJ1NVqVZiPqqkxVu0k62kPIi0+qFoZekxKXY/ae7MvzHMEDgH7559/HsChxcSaZqVSSTy1pVIJ\\nxWJxaJ+O7WUjx/R4PJifnxdPC2vecAHztErmsFFlpkeCHgseQAhA6unU63VZUMMG6bQ2rWqiOp1O\\nhMNhiUjlAmeOlslkwvLysnjQVldX4fV6pR4UY3Kq1SoKhQJarRb8fr9oSHqa0bPWksh4mZPIGCSj\\n0YhQKCRM2GAwCB5ULpdRKBQkmpvMi4GB7XYba2tr2N3dlRQTbSKrKuhIkxbki8ViuHHjBl5++WVJ\\nW+C4Ei5gqRg1p5IOFFbxpJaullihAKJWw9AHmmcUmqpHqdlsIpfLyfpV+0YGxoTsSedI/T8JDYIE\\nhsEg6ljYbDb8zu/8Di5fvozr169jamoKzWYTBwcHSCQSKBaLCIfDoiW1Wi1YrVbs7+8jm80ObduR\\nTTaqbXRr+v1+xGIxlMtl5PN5cf+ybgylAyWoNq5IPdNLBUabzaYUiNJrx0nhLMOkDfvKSSGYyQFn\\nzpfD4RAsidrc3Nyc5Osxkl0dOwBSJ4raE5+tDf8/rmmjvZeeVORzaWJQW8hmszAYDHLoJTWAqakp\\nOT6cpUaoYakpQnNzc/I9i8MzNodtG+bxGZfIPFjlkGY+mWe5XIbH45H6zxSONKMZXUzhRzMLQF9o\\nBp9DMJpjQs3QYrFIYi3jcNhXMj7VQhiGoZ0EacdQb6zVNaYFzLkWeXrQt771LczMzGBqagqNRgP5\\nfB4//vGPsb6+jp2dHXzjG99AIBAQvI1KxSgBc6yDIslQVFCai5BMih4Ji8UiE8ZGUXrSfKNZQFWY\\ni4KvGaKuZpSfBI0LkPd6Pan3A0Dio/x+P1ZWViT2hFoTF7T6ewLiAGTTz8/PY3NzU7ddg0D8o5Ce\\nqj6Ims0mNjc38e6774qaHY1GEQgExCx3Op3yOpvNYnd3V+LMAoGABL+ylPHDhw+RSqVko+qRXj8n\\n6fve3p7kUXo8HilQT6HB9UNGATwpPM/1RiajHk9F7ZjtIRPjHKleNuJJrHJApsf1wLgm9XeTakjH\\ntQYG/VbbF5VJRaNRxONxnD9/HtPT02g0Gnjw4AHu3buHbDaLW7duIZvNCq4YCATkCDSGQJwoQ9IS\\nJ48xF7VaTRJqWQGSkocdI9MBnkTvejweSSkgA2ICKtXuZrMJq9UqcUralIrTInUTq25aSsSZmRkE\\ng0HE43E8evRIIs57vR5isRgajQZ6vcOyrblcTjTHUCgk+WAXLlzAvXv3dPuhdSefRH9IgxgTy8Xc\\nvXsXd+7cQafTwauvvoovf/nLeP311xGJREToMNTjww8/xKNHjxAKhTA3NwcAwshYQfPnP/85UqkU\\n7t69K3ii2q5JGOYg2t7exk/vkFqKAAAgAElEQVR/+lOUy2UsLCzIUed2ux1+v1/WpHpIgbruKOwo\\nKCkIVSHKdQxAzHVVsyKuRNON9zQajRIszDCPXq8n8XqTkh6j1oLXejRoXLXjTuCeRRS/+tWv4rd/\\n+7dx+fJlpNNp/OQnP8G//du/4dGjRygWixKFzUqwDP6l2ao6RAbRsQIjqaUwj6VSqSCXy2Frawtz\\nc3PiPlYlAzOi6TZnPIfdbu/zMpFZ0U6nV2rQQJ6GtNB+bzA8OY+MIQCtVgupVAq1Wg2/+tWvsLm5\\niXK5jHPnziEWi8HlcgmWsL6+jtu3b+O5557D0tKSANqMatWL1tWTVCdNWvWcz1G9RY8fP0YikZBF\\nRRPE5/NheXkZv/rVr1CpVJBMJgGgrwgb8Zbt7W2ZQ7WigdoObb+1r0cRXcy/+MUvsLa2hg8//FBq\\nU/HQCCZ1Ey/isyloqE2phyH2ej0kEgnxFKvlienapvBQY44MBgMKhYLADzxCntgcY3Xy+fzE86aO\\njxbfHWetDLIM1JgrMpfFxUV86UtfwtzcHOr1Ov7mb/4GOzs7ImQZa0eBogb5GgwGUThGtetYGhLw\\nBG9oNpvY29vDxsYGHj58iLm5OTgcDjgcDpTLZakUSVuZzIigNied4KB69A4xKCZwDsI+ngWpOA8j\\nyhmP86tf/Uokwblz5yQJlV6nTCaDvb09Ofucmh7NWPVYJeAJdnVSJpuWBuE1xDgACAh8cHCATCbT\\nx6TY5uXlZfGoqPNHM4UCiDl+g0qQDGrPJH03Go1IJpNIJpPSDjKd6elp2O12uN1uLC0tCZitCkJ6\\nT6enp/sOxzQajbh7967k8pGxAJCYOoL79D4RyiiXy5K3Sc8qmWGhUMDU1NTIHK9hNGp8Bo3hICCb\\n645APssHLS8vi8b3L//yLxJ/SDwYeBIQS+FNi0gtzDeMxmZIeh4Qqr9/8Rd/AZPJJGkRTqcTf/iH\\nfwiHwwG32y3JtNSUaMY4HA6xM8lpyXh4CCM3faFQQL1ex9TUFHq9nsS+aNt3GqRqEGownNVqxcbG\\nBvb29pDL5WAymRCPxxGLxfD888+jXC7jP//zP0Xa/tZv/Zbkwt2+fRsffvgh9vf38dd//deYmppC\\nIBDAwcFBX3zLaWlFg/pJyaiOp8PhQKPRkGJkrBDZ7XalgmK9XsfOzo6UIQYOzSQepVwul+H1esUF\\nn0gkdCPT9WiSuWW71A3BzXJwcCDeQyaAk6mYzWYsLCyIl5CaLs/YozBUryeGRHOt2WzKGub6VmOe\\notEoZmZmRKgxSp+a8nFJz2lBjXRQ3Jf6G+1+slgsuH79Or7yla/gypUr6HQ6+Kd/+if88Ic/xPT0\\ntOBgatwcn0tlw2QyoVQqyenNx2JIWg6q12EAIuUJXqmnaVAzYiPJYW02G5xOpyRo8kgk4kUAJHyf\\n9bYLhULfaQ96nTst1z+JRe0Z/NfpdBCNRnHu3DmEw2FJFo5EInKksNVqlcXIU0F5TFIqlRJ3tIo5\\naOk0TTYtqfPKGClWOdzb25PDGbhoudjpQVXXBz0rrVYLd+/eRbvdRqFQkMVMUrGy4/aT2psqSGiS\\nUTNRj3Mno8nlcn3eNd6LQaJkOgxjUPtPbyIAwZTUtBE+W8VdmZJD8/+kyOl0IhQKiUe0Wq0in8+L\\nUNeC1kC/qaeWX758+TI+97nPoVKp4J133sH+/r6UlgGemOYqrsZxI8NuNBrI5XJ9lTwG0dgF2vQ2\\n+iDwUS8jn1gR1XziTto0jGq1KpKTiYq9Xk+CK6lGDvMSnAaxr1Rha7UakskkarUaLly4gEuXLmFx\\ncREul0sATaaLmEwmRCIRZDIZSbPw+/3wer0C+jWbzb6cKS2uc9qkMhEtKEpGU61WJY4oEAgILkjP\\nI/E+grsqjkTwlhov/9Rr1HE+ib6QtOuU+I8KIfR6PYm1omBg34gDqsc/0fxggiydFayPxd9qA0r5\\nOdd1r9cTc/6kyGQyYXFxEc899xzsdrtgaoyv0jJqjpHWqxsMBnH58mWcP38ea2tr+NGPfoTd3d2n\\nPITadQM8UUSYiMxDO0bR2BrSIHsTeCLdKE0Zm2Sz2aQsBZkSwS6C1gS0KbHIuIxGo5x0WqvVcOfO\\nHTlGheqzGrN0XM1o2O/VvvPYb3qiPv/5zyMSiUg1QYvFIueaMwAQgOTsxWIxeDwe+Hw+vPHGG/jC\\nF76AeDyOTCaDRCKBR48eSXTrIJD3NEhP++WYUNvY29vD22+/jcuXL0t+Fk0cnmpqt9sF5GURN4Ph\\n8JBBnlrLsAc14lnbv6P2dxAORSIjZCAkN6ZWW6NGpYLdKqkalPodNSEVC+QYcrxorqnfDaqJNW6f\\nVQdBtVqVAwe+853voF6vI5/PY2lpCcViEbdu3RKGrK4xznOz2UQ4HMabb76J1dVVmfdf/vKXqFQq\\nfRaKlhGxHSoux7Qh7Xl8enSswEjtgKiTqsZdUJXjkSjESNRBVItWqUc1d7td8ULkcrm+yNtnCWaz\\nz5SEwOERQbFYTAL9yuUyut0uUqkUOp2O1A+myWIwGPqCJwmWMsI3EomceoDcOKTFA7i4yuUyNjc3\\n5WBEMiMA8Pl8yOfzou0x+JX94UJXNSh1/k7T1Nbel8yBmotKnONx7jNMWA/7rQocA0/i+SYh1QzV\\n3rvdbmN/fx+dTkciphcWFqQ8tBqgqG17r9eT0jnXrl2Dw+HAL3/5Szx48ADpdFq0eJqfqqbFdqgh\\nFcDhvmdNsGN52bQDrv7X6wgXsOptYL4T1VUChUwoJGZULpdRrVb7gO1utyundzBfSIs9aJ9/kpJV\\nS2ruEsv1njt3TiTcJ598gu3tbbTbbVy5cgU+n0/M1Ha7jVAoBI/Hg0ajgV//+tf42c9+hgcPHuDb\\n3/62nFKhelu0G/ZZknYsiSFVKhXcunULZrMZc3NzuH79utSdpkdud3dXagkZjUZks1nkcjlh5CqT\\nImk31Un3Rb33sHUyzAOox4CGaWSj8Fd1HCbtMwNTqamruXcmkwnJZBK7u7toNptYXl7Gd77zHXz/\\n+9/H97//fTE1aWFQKHKfvvnmm/jqV7+KS5cu4a233sLf//3fI5vNwuFwDGRG2n7wO+7tQqGAcrk8\\nsl8Te9kGAcnqf6rEapSrOhEApOi7ChACkBgktXYM76MCaKNU89MiRov7fD455I8BgOl0Gs1mEwsL\\nC4jFYoIb8Iig7e1tyZonuOjz+ZBMJlGv18ULcZyFehzSmr96m44MZnd3VzQ6ChcedKBGJlM7LJfL\\nI/tzmhrSMKakpxFqhYHemAyCMIYxM/X7YUxtFJ0/fx5LS0tyoEY2mxVwneEElUoFv/nNb8QbGo1G\\n8corr+Dx48dIpVKoVquSf2m327G6uopAIICvfOUrWFxcxM7ODt59910pzKceBqodG5VUM7fX6y/s\\nN4rGYkha7YiknVCV6ajnUqkgmN1uF3en0+nsC78H8BQT03qdVO+Jth1qW49Deveg5CEo73A44PV6\\nkUwmhclUKhXYbDaEQiGEw2HRClnbaX9/H48fP4bFYoHP54PdbkcoFMLOzo54cbRA6/8l6Y0xS/Lu\\n7e1hbm5O2hoOh5HJZFCtVgWfsVqtcgyUenSQ9t5qBv045s9J9EtP+x8HQxx0vZ5JNux74Hha7+XL\\nl/HKK69gdnYWiUQC+/v7cu9MJoNkMolqtYq7d++i1Wrh5s2bCIfDuHjxIuLxuOBKFPhGoxEXL17E\\nzZs38dWvfhWtVgs/+MEP8PDhQ2Emw8aBJij/1Ovo2Rwn3WsshjRMO1Ibp+INrDvT6x1WhWShqt3d\\nXdRqNZjNZvh8PnQ6HUQiESwvLyOdTiOVSknZBp7Hpk1K1GvboPdHoUFaYK/X68unS6fTSCQSkjBa\\nq9VQrVZhtVqxtraG733ve9jd3ZXSLNlsts/TwMTcv/u7v8P+/j5yuRwACOj/rGiQBqH2nf8NBgN2\\nd3dhNBqxuroq4R7RaFROnEmlUnItc7ToceH9VaJGrIfpnFTftExgXBNxFPMZ9tzTpOeffx7RaFQS\\nthcWFmT8qJUwpYteQrvdjtnZWfzVX/2VBDEDkJghv98Pn8+Hn/zkJ9jc3MT777+P9fX1vkMd2De9\\nMeVnDOthmR41OHgURjqSIWnVeD1SFxEfWKvV4HA4JOfJarVKTSBOMM0W1S41GAzSAdU1qpYj1bbt\\nuDSuVqV6VAwGg8RPeb1ehMNhrK2tSSS21WpFOp0WBkXtoFgswm6392X38x48reJZ0qRaAftN9zGJ\\nbl41+ZkxNiwv86w1Pj1Bqm3DpGbkpBr4oOsnAcT1iDF6ACSRV7UumNBcKpUkkJUYXjAYRDAYxMzM\\njGg0vd5hsHGxWMRHH32EjY0NbG5u9pWR0Soe2nare8NgMIgpSIfGODSSIY0zWFqOqZov7AgZjtVq\\nhdvtRqVSQalUgtFoFOC7WCzKxmUGOdNKBoHpJ0HjSjyCdGpxtZmZGXi9XkQiEWxvb+Pdd99Ft9vF\\nxYsXsbCwgCtXrkgKzdtvv4379+8jGAzi1VdfRSqV6itvoc3tehY06nl6pgeZDDVGmpoUJGokMgv0\\nZbNZidHRkuqo0NPOjsPIjoNbDcOJxqVBmtlxzbZkMonZ2Vkpt6seW20wGKSsMgvsMUyGsVIsE8Oy\\nMeVyGfv7+9jb28MvfvELwTsJtWjNMG0fabLxPU+sIVZMHjCqvxMXaNNqTHqNZNlSbkS1UJvL5cLq\\n6iqAw2xwxiGphw6qSbfAk3y5QSo/2zPou3H6xd8OW8Bqjh1Ba6PRKBnv4XAY09PTsFqtuHr1Ks6f\\nPy9Ao8VikXrDU1NTWFlZkYjl5eVl7O/vw2AwSLG2o1YRPA4N2jRclBwbtSgd8ETlJ/6lBg2qQZWs\\nuU7Sk7iDpO9x+jPu/OpphdrXg64fdR9uSG2axbD7DKP/+Z//wfb2tpRUYVyYwWCQGECuV1odvV5P\\nYuOY48ez05LJJO7du4eDgwNsbW2hWCyKlqPCJKoXUttPdb0kEgncv38fJpNJ6siPE9Iytsk27uf8\\njt4lh8Mh9mOz2YTT6RRsiCZQKBQSs4an1prNZsTjcdy7d0+0EXUgJmnPODTO7zmoauZ4MpmUhba8\\nvIxer4fV1VXcuHEDkUhETnt1Op14+eWXMTc3J9HK4XAY7XYb0WhUIlrdbvdT5889KxrHJDcYDGJ6\\n0mxT45G4+BlrxfEiUyNT0jOj9LSGkzLzxrmPHm427vV6n2sZjZ6rfNxnaenu3bt4/PgxjEYjFhcX\\nsbi4iEajIbmkfF6lUoHL5UIsFpP6VNR8qOGWSiXs7e3h7t27cpacGkGvp4ior7XzaTAYkMlksLa2\\nhnq9js3NTRHeo2gsk21UA/SuffjwoWSzs8wp1ftkMol0Oo1ut4upqSlEIhEpe0v3sMlkwoMHD7C/\\nv49isSjg6KiFdVQVXw+8JKmfZTIZPHjwQKRLMpnE9PQ0bDYbZmdn5YggVlH80Y9+hIODA3z2s5/F\\nysoKPB4PLBYLtre3cXBwILEZGxsb2Nrawt7eXl/G9LOiQdqu+r36ular4cGDB/jggw8QDofxX//1\\nXwJoB4NB7O7uwu12Y3t7G/fu3cP9+/ef8tRo6bjm2bA+aT8f15N5nDkYxbDU508aEEv81WAwSFkY\\nmmT0ZNPDazKZsL6+DgBykizwxLvJPcnYP71YrEHas/Yzg+EwDnF7exvZbFayFsYdx2OB2oPU216v\\nhw8//FDKMrAYF2u/PHjwALdv30alUkE0GsWNGzfgdrvlLDZu9nK5LBt12Im1xyUtdx/Wz2Qyibfe\\neks0AZYbsdlsWFpaQjweR6VSkf7/wz/8A0qlEh4+fIi//Mu/FJu6UqlI0fsf/vCHUvpTPavs/4oG\\nbWL1dbPZxP379/Hv//7vcDgc+Nd//Vc51igSiWBlZQV+vx+PHz/GRx99hFu3bvX1S0+qDnr2UUhd\\nk1oGoAfQ6j1b1WYmNeEGMfhBz5x0PXc6HckMKBQK2Nraeqq9g9qglrSh5qKO1aDfq6+HhWkYjUbc\\nvn0bwJPsDCZgj4pFMvROcmef0Rmd0Rkdg/7vE6fO6IzO6Iz+P50xpDM6ozP61NAZQzqjMzqjTw2d\\nMaQzOqMz+tTQGUM6ozM6o08NnTGkMzqjM/rU0BlDOqMzOqNPDZ0xpDM6ozP61NDQSO1IJAJgsihS\\nho57vV6srq7iypUrqFarWF9fRz6fl+qITLbk6RxM0AwGg1IPiZnzekXgVdKLSJ3kFAf2U3tP9mdQ\\nmgzz8S5cuICXX34Z586dQ71eh8lkgtvtllNF2AcmCWcyGeTzeUQiEQQCAWxubuKdd97Bz3/+c2Sz\\n2YERtnqfT3IEs9frfeqe2j5rI3O16QNqsqXP58P09LREn1ssFlitVpw/fx7xeBzVahXvv/8+7t+/\\nj+3t7aHPHkWlUmms66ampvqeMygPSyVtH7XXjErKVa/Ru35QNLb2Gp76Ow6Fw+GR7dB7pvZatX3P\\nggwGw9A1O1HFyEGf6YXcsyQFazGreUxcvCaTCQ6HQypL8ohjllRQQ9RHtW/Y+6OQ3j20n3U6HQQC\\nAaysrODKlSs4f/48er2eVEyMRqNSCZKJpdVqFYuLi3KefK/Xw9bWllQ4GLSw9XKHJu3nqPw4NUVC\\nL4+JlQ5u3ryJubk5hMNhLC0tyUGRPLPM4/HIHK+uruLWrVv49a9/jffff3+sqoHHJb0M9EHXaPus\\nx/QnTbTVpqwMYo7j3nsS0tsLgxis9rPTaM+otmhp5DFIo77Tu4YlL1mYi5XrWAlRZUo8saPXe3LG\\nlVrmg6VLBiV8aidZLZM6KenltA2SbmS6POpncXERy8vLclimwWBAIBCQEiq8Xj2BxWA4PDBAPTBQ\\n7zmDmNFRNva4v9E+s9frwW63w2w2Y3V1FTdv3kQ8HselS5fgdDr7TpRRT1RZXFyUo6t++ctfSmKn\\nnvZwEpth2LrU0zb52aBaPaPuM4yGbfpR+ZOjaJzxGnRPbX0jta0nNQ8q6QnVQXSk83sHNVodWJ4o\\nwsXJQyE9Hg96vZ4UJmu1WqLq88RNq9UqpRR4ltuwtpDUpMFJ+zPOZ2o/WVbj3LlzCAaDMBgMUryN\\nDInVDXg/Mmm18gEAzM3NYW5uDvfu3UM+nx9r8k5y4WjvpS7WTqcDk8mEUCiEubk5nDt3Di+88AJm\\nZ2flFGFqv2riZKPRkLIkgUAAL7zwAoLBIIrFInK5HHq9nlQwHCdZddL+DLuHnuY9juY4Do3T3mEa\\n0ySkZWr8bFzzUk+gn7bGNoqOxJD4EJUBcBP1ej10Oh05AohndLlcLilOVavVkMvlRCtgCVev14tA\\nIACfz4der4doNIpCoSDMa5j9r57jNuk56eNMoErEvXw+H7785S9jfn6+r2Y0jwOiNsCjkCqVCsrl\\nshwSwOtXV1dx+/ZteDweOctu1MI6qoY06F7a93a7HTMzM4hEIgiHw7hw4QIWFxexsrKC+fl5uN1u\\nGRueBEvNlhoxteDFxUU5DHN3dxebm5solUrI5XLY3NwUbO00NscgPGXYptTTUvVe69E4c3JcRqTe\\nQ9s/rTaiHnek/R2vG2QFjNu+YYJD/e7YJtuoRmhfq4PA43B4nppagoTHtrB4uMPhkILgPDKIWITe\\naZeqaUZ8hngM6/getS/az9XBVBme3+/HwsICFhcX4ff7pZh5r3dYla9cLqNSqSAcDkvVSB4ZRGCb\\n5k00GhWwWJXcR10gRyX2l7V0Ll26hOvXr+Oll15CNBoVwcHqkJxfHm/OOeAG4JyTAX/jG9/AwcEB\\nEokENjY28PDhQ6yvr4vGMk6J05Pur9rvYZrpSeJeJ6XdDjL99Jis9nf8P4hJTNK+STG2YXQkhqQW\\n5Nez2Q0GQ5/EY8F+AFL+tFKpHDZAOcGUg8nqiVarte+YJD1pwOfx/XEwJJXIfPQAXm4ynilHphkK\\nhcTzZrfbxdRk/9RxoHZEDVKtRa1Hp2Hb69271+shFAphcXERN27cwJUrV+SMeGqeai0cranW6x1W\\nC1XnneWIY7EYfD4fZmdn4ff7EQqF8MEHHyCbzSKTyZxK/9g3PWY3TDPQ0qQa6UlqsINoENSgNb/1\\nNL5BYz0Jcxl33CahI2tIwGAAUVs7mHV8eSw2/+iFazQaODg4kDPv+Xu73f7UuW18rbfAuEFYXfK4\\nxIWsvmc7arUakskkfvjDH0qoQqlUQigUQq/XQyAQkDrhPP8qnU6j0WjA5XIhHo+j1Wqh0WhgbW0N\\nuVxONr1e+VrtIjjJzaveu9fr4U//9E9x6dIlvPrqq8LgS6VSX1lTanp2u10qFHKe1SOTiavx1Fu7\\n3Q63242XX34Zq6ursNvt+P73v48f//jHqNVqp9I/AAOZ/aT4IWmQKaj3md4GPq6A0T5Tby9oBame\\nVTMIeFe/G6QQjNvOSfp5JIakTq6eTUoJSu5ssVik0D0XODUMFsbnwibQCRzGWvCARfVZfK1lTicp\\nlfQGkvfmQQU3b96UEz15wgIxIB53xNNou90uHA6HMFpuvlarhXg8jvn5eWxsbIx9HttJS1917hhH\\nRGZMAUETmWA1mT+rgtLTpmJg7XZb8DKOQ61WEzP7woULeOGFF7C9vY07d+5IGdWTOpfuKJtyELbE\\n3+i9Hvf5J0XDrIZR/RmEO2mvJ6SiCiAt7nTSdGQNadTkUspzg/I1gU+v1yvlTqenp+U0W2IT7XYb\\nsVgMe3t7fUxKJe0zVVPyNIh9ttlseOGFF/D5z38euVxOaoebzWaYTCYpwdtqtWRCqQ3yJA6epd5u\\nt+U0klgsBpvNNnYQ4GlQq9XC7OwsZmdnpf1qjWXiXqpJZjKZBMimuc5Fy1NHGNJBk5Xnfc3NzeGF\\nF17A5uYmHjx4gEqlcuKHZA7TuvQ+GwTE6mkd2nscVcObVMBorZNBTEjv/SDmpN5HnUOn04lOpyOH\\nnBJeGKR1joNfDaITAbXVRmiBQTbaaDTC4XDA4/HA6/Xic5/7HGZnZ+VkhHK5jHa7ja2tLZRKJTQa\\nDVy4cKHPYzZq0tRayZPQKJtY/d7j8eC5556TKOv5+Xk55YGbrNVqoVqtyvltNFs4DgRwU6kU8vm8\\nHPIXi8Xg8Xgmir4+LrHPJpMJgUAAbrdbMJ5SqYRut4tWqyXaH9tKJssjmYnfqf23Wq3ChIxGowgW\\ns9kMh8OBXu/QQ3f+/Hn8+Z//Od577z3kcrkTZUha7X3Q9yoNeq+uZT1hzGu1m32Q5q6nnYxLgxSC\\nQX3SMqFhvyUG6nQ68bWvfQ1f//rX4Xa7cXBwgM3NTWxsbOCf//mfRSvmycMUXFQ6KIA5z9pn6dGx\\nMCQ9Lqx9KE2USqXSF/BIxtTr9eSQOx5FTc+cthN6WBK/P465Nuq3vD89bJFIRE4ONRqNEuSoSg3G\\nYO3s7IhkCQaDcgILI7U5cU6nUyZQr12npSbzGfQcBoNBOcbIYDCIpsc+khnRBON7aogqcN9oNPpw\\nJ5rs7BNNPZvNBqfTKSccn3T/1P+D+j+O6aVnDqn31m76YftDq+EchYat20EKg572om0zADidTszP\\nz+PatWtYWFiA2+0WXDQcDmNzcxOZTAbFYhHFYlG0aQodxh02m03k83nk8/mTPShyXJCMr2myuVwu\\nMWG4ad1uNxKJBMrlMuLxODqdDgqFgvwxfieZTIqU1j53EMc/CmMadRKC2j+Xy4WVlRVMT0/DbDYj\\nmUwim82iWCxidnYWPp8PlUoFFosFwWAQH330EXZ3d3Hx4kXE43F4PB6YTCYUi0W43W6YTCb4/X7k\\n83kEg0HZ4JMei3Mc4rzEYjE899xzEsbA4M1ms4lMJiMOBvUECbvdDpvNhkKhIOfumUymvuO0G42G\\nHB/OFBn+nr9xOBzwer1wOp0SMnJcGsZoRpleg64fZI5QYHEsAfS9Z1uoUTA2jc6e0zDxtJqhum/0\\nwktUput0OvGlL30Jb775Zt9pt5FIBC+++CJWV1dxcHCAarWK//3f/0Uul4PL5RKLgSfj1mo1/OAH\\nP8D7778vYS/DaGyGpGcnq53WUrPZFLxEPXrZ5/PB5XIJVtRsNlEqlXBwcIB8Po9yuSwLv1arCSis\\nN/l67TuOxBlFvV5PJoXmCnCIu5RKJSSTSdHqDAYDcrkc1tbWkEgkEIvFROvjhqvVasjn832fqd5J\\nvf6dFhkMh8GQ4XAYdrtdtD0Acraeqk1xcdOkUwFPqvw8WJPqfLvdhs1mEwyNv2Pf6BzgWXUn0adx\\nPuPneonc6vqmqa134CMZtaplkNnQlOXc2mw2+P1+NBqNEwt30NuHWjOV11CbHcSwXS4XlpeXMTMz\\nAwCoVqsSa0aqVqsIBoNYXV1Fr9eT894IO/A3BoMBH3zwwdj9OFakttphreYCPDnplQF0zGULh8Mi\\nFZrNJrLZLFKpFDKZjMStmEwm6aQKsPG5ejbyaZPBcHgWWygUEnOGE1woFCQVhsnC1WoVd+7cQTKZ\\nxPnz54UhGQwGNBoN0axoY6vM7FmRqqrzSHNKRLvdLtULaG6pwZtqzqL6mt8zRYYR+UajUdKBAIgJ\\nTBOPAZcWi0WCKgdhP+P2a9zPAX2zRrvuAPTFuamMRmXW1BK5zqkZsKpFIBCQNCHe5zg0CjrRtldl\\nrLye/fJ6vZiamsLi4iJMJhNyuZzMKT2svCYUCiEajaJSqUgYCE+g5r2Z08m1P4wmZkjaAdTjwFRN\\n3W63ZILXajVks9m+HCeHw4Hnn38ei/8/2jmRSODx48difyYSCd1NqrV3+T0xjpPe0OqmYF4XU0Uq\\nlQp6vR6mp6clHqlareL27dvY3t5GKpWC0WhENBpFJBJBo9GQTUfGVCwWATwJODwOHjZJn9Q5tNls\\niMfjuHnzJlKplIDYTqdTXPUqxkUMqdFooFQqCUbEzalKSDJiBpJyDMjgbTabnPybTCaxv79/bE/j\\nsDUwDPTV+72qIalaBlOiIpGImClvvfUW/H4/YrEYVlZWJHWm1Wohn8+jXq/D6XTC6/VibW0NBwcH\\nwsxPivSY+CB8S11rnP93hiwAACAASURBVJtvfvObePnll+WoeJvNJtkXHIeFhQUYDAYUi0VYrVYR\\nwkwDYs4qFZBQKIRisTjyiPiJyo+oEosN1GMU/I1acoRYCRf65uYmzGYzpqen4fP5EIlE0Gw2kcvl\\n4Pf7YbVakUqlnrI5B0k4i8Ui0nZUp49KBKudTieMRqNoBtQumOEeCATgdDrFu0a3KQCZ3HK5jFqt\\nJsm2jL15VtqRlulx/JhH2Gw24XK5JA2HjgbV60lQ3m63Cy5hsVhk/FUpTGeGirVw8XKjBwIBBINB\\npFKpgczhtMZC+16LiXLuuZYZl+NyuTA9PY1AIAC73Y75+XlEo1HMzc3B7/fDbDZjamoKlUoFZrMZ\\nhUJBzGHex2g06qZIjdt2PeajfqcF0PXwVvbPbDZjeXkZFy9eFKFCrYdzTMGkCn9t7CG/M5vNiMfj\\niMVisFqtSCQSQ/szsYakZ5ppv+P/er0uIHW324XH44Hb7Ua328W9e/ckMM7v98PlcsHtdiMUCiEc\\nDsPpdGJra+upuKJBi5OqME3Do/Rp1DV8tsPhkE1H04ImDcHshYUFrK+vy5HHdP97vV6ZzHq9jkql\\nIgGTQL8UPm3SarnE9yitaVpRs6Gqr7aVjJi5elarta+MCseNta+IHxFr4liwthQB9WdFg8w69TvO\\nB/vLsAUACAQCmJubw8LCAlwuF0qlEsLhMObn50VYORwOEVZ0lZPBu91ulEqlI4P4w/BGPWtG3aNq\\n3wBIKZ3r169jenoahUIBlUpFwlp4veriJ9ygftbtdlGtVmWsZmdncfPmTXz88ccnw5CGcVY9pgRA\\nonXpMgYON3IkEkGn08Ha2hqKxSLm5+exsLAgn+/v78PlcsHpdAozG+R1UtvAJF0OxiQ0DjMCnuTd\\nkcFaLBaZLLPZjO3tbdTrdQQCASQSCSQSCYnWfvToETY2NnD58mWUSiVhtiy3ojKlZ000qwD05azR\\nzKJWSOA9l8uh0Wig2WxKYGupVEKlUkGz2USxWEStVhNmw/IylJiAvlnh8/ng8/lO3PWv199xTDZ1\\nfZGZMOOgWq1K2lO73RaNv1Qq4dq1a/izP/sz/PSnP8Xdu3fxySefCCDM8JZEIoF8Pi/maigUOnJf\\nBoHZg/am9j9wONdXr17FG2+8IRp8oVBAt9sVrV6NoeP9VYWBjgsKGKZGzc3N4fXXX4fBYMBHH300\\ntD/HArWHgcyqKcOO0CxwOBySXrG3t4dqtYpwOCzmHO/FmCTtgGufS65NM+Ak7XHtc2mSEKxlW5rN\\nJsrlMvL5PIrFInZ2dpBMJqXtuVxOSqkwcBA49GioZgz79azJYDD0aTmU5GQkKvBJZ0OlUpGIe/aB\\ni5DzyLlQA+RU7526doitnVb/9TYi3+thRtrPuLaYq2cymSQvk0TNNxwOw+PxADgsM+x0OmG1WqU0\\nTTqdRqfTkfK/LOcyKWlxILX92mv03nN+aFpevXoV7XYbOzs7aDQacDgc4pyiKaY+g4yKzEidS2Zp\\n2O12KXV8Im7/YSaEnqShZ40NJiZht9sRCoVkoVerVaRSKfFQMYiSE1cul8Xtr32eqjUZDAZhEqfF\\njPhMdbG6XC7cvn1bah9Vq1UYDAZMTU1JMFg+nwfwJNO/VquJup7P52XjqrE9WuDxWRCZAYkqOJ0Q\\nZFCqZGQRvmq1Kuq91WqFz+fri+bmvYgp8T3vw3HlgmU7TrL/KjPiBiJpGaPqfVKFnApuq2uPOFir\\n1UKxWMSjR4/w9ttvo9FoIBKJwO/3y3NKpRKKxSLa7TY8Ho+UdRlWhHBQfygQVfNJxXqAJ55u/kbd\\nN+prauk+n0+8q3RSqJVAgSflhTiXqkamemE5Zh6PBy6XS7zTw+jIgZFsGGONGAjHhrATlJrsQDgc\\nRiAQEMmbSqVQrVZFmng8HinsxjyxYaUjOPjc7EetqTPO4qeEr9VqUhrl8ePHSKVSMJvNCIVCcLlc\\nKJfLODg4wP7+vtTXpkeuWCzCbDbj/PnzEu1K7YoRr8+a1FQBFbiluU2PKRkSTVfidurnBLgpYGjq\\n0REC4KnFzY0fDofFY3VcRqTFSthPbiJ695xOJ9LptLi2fT6fOFVYgrlSqfTFw7EffAY1Jnojt7e3\\n8fbbb+Pq1auIxWIIhUKw2+2IRCLY2NgQLNXtdiMcDqPVaskhCOOSytC5t7QMARjuceN42O12rKys\\n4I033kAkEhEtWRUcarQ+hRf3HcdBdcqwLDVjzywWC+LxOObm5ob2a2wMSQ8EY9oDATuqbOyAVroQ\\nxFtcXJQTMJj57/F4JGeKv6FU5nMHtQmAMMZJAe1JiROiZvGzL/F4HFarFbu7u9jY2OgzW9nGarWK\\n6elpzM7Owmaz4eDgAM1mE4VCAaVSSVTiozDVSUldmFar9Sn8iFJf7SMPKqCZBhxuDnrnut2unCrj\\n8XjE0UDMTRvNXK/XRTPiJtULUDwpoinh8XgQCoUQCATQbreRzWZFc5+bm4Pb7Ua5XEYqlZK0JwB9\\nbSdxzTNMwmQyweVywePxiAn33HPP4fz584hGo0gkEshmsxJ71m63+4DjcYjrkIJg0P4YFPGvajRW\\nqxU3b97Eiy++KAyK1xBmITZEZqzuU1XbpMbECh+qpmyz2RAIBIbPz7Av9YA+9bUWb9CCZGpSZrvd\\nxt7eHur1OoLBoCxWfr6zsyMucJoBvK/W06ZtC4FmNTJ2UhoX2KYKy3SHF198URbHCy+8ALPZjP/4\\nj/9AIpFApVIR3InhDVSHs9ksbDYbZmZmJLhsa2vrqcV1GmabdrHSzLLb7cIwrFYrKpUKDg4OsLW1\\nhc985jMi9RibVK1W+yLyWVKmXC5LcjFDBXg9GR43sVpX2+/3Ix6PIxwOHwtPU7UjdSwtFgui0SgC\\ngYCUfHnppZekeuWtW7dgNpuRSqWwsrKCmZkZ5PN5vPvuu7h//76YZnyGHpnNZgSDQQljqdVqmJ6e\\nhslkEk241+shGAxKGV+mWUxChDUAiBBWNRTVjNLDfPn+61//Om7evInV1VX4fL6+AEhqifQoqzXy\\nyaTJsIAnJhqtIhLXRzwex+rq6tB+jcWQtBoS/6vgM6Nu1cFgI6nC53I5lEoltNttOYuNRfx5Hhlw\\n6Eqnyae2Q69dJDLE09IsVOnP+CK/349AICAqLDPhAYiar+Ik2mBCFpKLx+NoNpsCmGrppJmSdiwt\\nFoukhrRaLTG7Wq0Wkskktra2cO3aNZlHtfonsUHiEXRxU3sGnmAy5XIZLperLwVBFR78nMD2Ufqs\\nrhV1Q9LUYj10BjIGg0Fks1lEIhFMTU1JNdN8Po9wOCzau9Vq7asAqsWY1DlSTSnui2aziXQ6jUwm\\nI9VQacIfpZ8qnqeGTujtAVUbAp5UW3A6nbhy5Qpu3LghJ8dw7NV54byztDE1ZzWOCoDUy6JmpIYB\\nAIce3OXl5aH9GsqQtLay2kA2UmtSqf+73S5yuZxMSrVaxf7+vmBDTLg0m81S9D+bzcJkMklJCzVg\\ncJDGppUEJwVsayeWnL9QKMDhcIgKy0lIp9MoFApYW1uT6GX+jonCVGF3dnawsbGBdDoNh8OBR48e\\n4cGDB32R6VoQ/TQ0JeBJ6REex6QupEKhgI2NDXHjc7GpibN0hauBk36/XzQg1QTXBlJykVOjJsNW\\n53vSfusxJZpTNA8JLlcqFSkOd+3aNezv7yORSODu3bvY3NzExYsXMT8/j1Kp1DcOWmxKfU3BRQ0j\\nl8tJP7hWyKCTyaSEFBxl7oCnzUh1zFSnAZ/v8XgwPz+PCxcuIB6PS3yYOn9qegy1dpqXDPngHqAG\\nTEHNuCTeh+vFZDIhGAwO7ddQhqSWkCVgRyCM9isluh6z4ICbzWaEw2Gk02kkEgncuXNHfseSEzab\\nTdR8k8kkxeTViE9V4nIC+JqZ4rlcDgcHB0M7PQlpNwMXMA+0pDlpsViQTCaxvb2Nra0t5PP5viTh\\n9fV1vPPOO6hUKggGgxJEl06nJS7l/v37KJfLuvjJSTIjlckBh6r49PS0RJerBeR2dnbw4MEDcQGT\\nKTP4kRuwUqkIgMkFroYDqKVsVXc3AAFMGdSqaomT9tvtdsvmU/MDqb2pzoVwOIxer4dSqYRUKoUv\\nfOELwkQeP36McrmMUqmE559/Xk5aoUexWCyKxkusUMVODg4OBJKo1WoS06MKMYZ+lMtl5HK5I88l\\nx0hlPKqWyfdOpxMejwe/93u/h6WlJSwuHp4lSGGi5hwCEI8nA18ByDUGg0HWAIUTTfhutyvleVRn\\ngMViGRlvNZQhUb01m83igldjivhwretP5catVkvcm1RhWVsaOFyMzH62Wq1Ip9Pw+/3yntJStV+5\\n0amCE8GPRCLY2tqSAwSOSlrthKTG2fR6PclcNxgM4hm02WyywTix7XYbpVIJv/nNb7C/v49XX30V\\nf/RHf4Rut4vp6Wn4/X4ZT0rPcTbiSTApekcYoa1igTQtM5kMHA6HaDuUnmQ6qvnA3xIMByD9IlBc\\nrVYRiURkbanYH+dbnYdJiHXMydipyVMb5zqmY0Iti8JcSwASMZ5Op7Gzs4PFxUVcv34dhUIB6+vr\\novEBkEqZAESI5nI5eDweEaZaLYp7yePx9GkUk5KK25Axco7oMeUcOxwO+P1+/PEf/7EwbuZU8jq1\\nGiT3F9uv1YiBJ/lvXLcUKKz2QAuKjHqU9TKUIVGN1t6IHWDQIwusqZKXx+IYDAbxnExPTwvoyU4w\\nyM7r9aLVakndHcZqcLDJ/Gj+8XlqfE8ymUS1Wj22p23QJlAZLd/TBGCpV8ZaUULwWoPhMIUgFAoJ\\nfsZxiUQifaVix92Ex8XLVAyCG5AChuVRiJkxopibmSo+S9dSa+ZiZuleHmvVbDYRCARQKpWwt7eH\\nixcv9nmJOL/83VFNVLfbDZvNhmg0Kn1gXe9QKAS/39/HLBk7NT09jWq1Kg6L6elpcfurtYssFgsC\\ngYBofNQMqPlwnNrttlRKUDFEMnUm2FLDZLzaJMQ4Ia4/FUtSTSWj0YipqSk56NPtdsPr9QpDVfeu\\nGsiqYmTa9aIWUKQwUVOCuAZokttsNuRyueMl13a7XcE9VHyGjSJTUo+9UU03TiJdyqFQCJVKBevr\\n6xKBnUqlxOUKHEa1EotRVWJtXW4yO/7RFu/1elKn6KRInYhGo4H79+9jbm4OtVpNwuqZTmA0GqW6\\nHr1slDQej0c0okqlIuAqc7go2U4DLxpEZCQ0I7mx8vk8PvnkEyQSCVmQdGEDT7TFfD6ParUq7m41\\nHILqPRlAKBTC+vo67ty5gy9+8YuyUFUvEWObuFEnHQufz4dLly4hFAr1VSs0mw/Pv2O7WIfowYMH\\nkm/F8AS3241YLAYAAkY3m03s7++L65rxQ9VqtU8bo9eJQZJqrB41EjWHj/2cNFK72+1KOywWiwRf\\nAhAnEas5Wq1WKaz20ksvCU6nCpFer9enHXMdqMd0UTHhvBJvJLxCU43tU+fU4/Fgd3cX9+/fH9qv\\noQyJcRTUTLiwqHJSmmojWYEnVSOLxaJEMFerVRSLRaTTaVnYvV4P+XweiUQCtVpNaqtQWqqZ49zc\\nahgAJ5btYu7UpDRq4fO7crmMu3fvSmrA0tKSSMm7d+9iY2MD9+7dQzqdhtVqFRsdAPb29rCxsYFS\\nqYQvfvGLwgBUPOZZMSLgieOBGi3BUeZbffTRR9je3kY6nZY8NZbQoNZKc49joIKz/E7Fb2q1Gkql\\nEsrlct+GJNPnRlCjiSfRBKm98bnAE9CZNXqIi1Dj02YDlEolgSVUzxMAcYNrGSmfDTxxw7NkC9el\\n0WhEqVQS6CGfz6NQKCCfzx8pf8/tdmNqakrMrlwuh3q9jnQ6LaU+6Hja2dmR4OMrV67IuKhR8eyf\\nqqES22s2m33edEIWhFfYb5Vf8PfUuPP5/EisbChDokpIZkQOqmohqimnbiguWGa5E9zd29uToDAu\\nxlQqhc3NTTnBFoBoTOw8N6zqwuUg8j7U1I5Cw36nenyq1So2NzclKXZ5eRnt9uFpvLu7u0gkEn3H\\nNs3OzsLr9WJ7exsbGxvY3NzE7OyslIiliaKadqdNKkbG8aSpQbyOCcF7e3t9mhEjypnHxah7grdM\\njzCZTBJrpkb2EnNgyIAqyCjggCeasOrtGYco/WlGqUyPpT945JbBYEChUOiLGmebyuWyRIyrZgoF\\nJZmO6sVTPVQq1KDGGVHj53UUvpPGznW7XcEwaXkQ61UDbNVieMVisa8kNJklvadq2A69ZzQ9qfER\\nWuBeV01F4AlTUk09Xlur1WRvDKKhDCmRSPSBYirDUaWCapOri6ter6NYLCKRSAijYaVEaj4GgwEH\\nBwf4+c9/jng8Dq/Xi1QqJUm3KminmmhqG9TweWoc/4+9L+lx7LzOfkhWcZ7JIlnz1NVzqyW1JMtT\\nBCNGnDiLBIGRGEmAID8jey+CZBEgmyB7ZxPHiDMYQWwnlu3Ysiwp1tTqVrd6qqFZI8niVByL36K+\\n59Th2/dyqipFizpAo1mXl/e+w3nPPJw28L1O51GXlNu3b6NQKOAb3/iGiOVLS0tIJpPIZDL4yU9+\\ngvX1dXz3u9+Fw+GQQDt29SgUCuLFZMAh56JV5LOQmLStjweJYrf2DGazWXQ6HUxPT3cZrhl7xaRQ\\nHnhKG8x90/YNEl+/3w+/34+trS3E43GJ8CbSU+2ho2RYle3DDz/ERx99hFarhcnJSSwsLHRFRBN4\\noFldQON5KBTqCu6jqsI8Sx5KZhnEYrEuuyqJGkvN0NDN+4EjQrG0tIRwOIxsNjtUmVfg6NxVKhVs\\nb2+LyufxeDA7O4uFhQVZs7Gxo+YNL774IpaWlpDJZKSeFyVdElyqyrqZK+vhBwIBvPLKK10Bk3t7\\ne3jy5InE1C0tLQkRNteJ69mvo05PgmR6O0xuRSqo3Yv8DQ2VOzs7CIVCXXELJucqFAriekyn09jc\\n3MTu7m5XtKf5fI2oOsaDC3wWoLlctVpFPp+XNtG0E4TDYTgcR/3G2u02tra24HK5cP36dSQSCRwc\\nHCCTychYaXeiAdY8fGdtT9JSJg8VnRmlUgkzMzNYWVkRj5FOtCSTYmKzJq66BjeN3+S6ZGK1Wk1s\\nZzwUAKQDi17zQUETQuYUkvDSq6bz9iKRiNg5eGjYiZfqButf0fPLQ8a14P0AhLgx+JF/0zZGaZRr\\nzpph/SQHE2h7TKVSyGQyiMfjglP6vXQqTUxMwOFwoFQqSX4h44e06s79p3rNhFuWVllfX0cul8Oj\\nR4+kqsVzzz2HeDwuKi6lJq4JcYke914wVHItgYip3YUaiFytVgvlchm5XA7As0FaJDBU66jf61rT\\nJnHhNTsVZ1CXuQnDHHxKMcViEQ6HQ/LwgsGgSGcLCwtwOp3Y29uTZNrnnnsO2WxWgsPoneSGWYnt\\nZ0GMzLAGnWyqXf4OhwOpVArXrl0TCXds7KjqIddBq+xUAUiUyCRIjKjK8HeMfNZGXhqFvV6vlPYd\\nBvgOzoFqBj22tAeFw2GpIU6bDg297CzMFk38fSgUkv2i946qn8PhEHsT8ZNqKuer43D4LsaiZbPZ\\noebJxNzJyUmk02ksLS0BgEitzA3UrnfGUCWTSWEoALr2id5T2n/8fj+i0SicTifW19fxy1/+Ej/+\\n8Y9FCmUe3nPPPSdSrXZAMQxAJ7/3gqHqIWliYNohgGdLg+iIVKZWcML6GbVaDfV6HZVKRUR3nUGt\\niY55QK0mOKyENOj95jjovWi32/jwww8BQIyVOzs7eO+99/Diiy+KhHF4eIhUKiXce3JyEpFIRCRE\\nU10z4bSIk6myARAEZbgG8wivXbuGr3/964JYrHSgUwwCgYDEGjHHioSZ0iNwnO6QSqWws7MDt9uN\\nS5cuSVoGpY1Lly7h7bffxu7ubl8EtgKqDbR/kCCQQbCzyfj4uBRWY9UCSro0zjNWilIUjdpcC4bF\\n8BCya4vP5xOmGg6HZSymfanZbEoC7zDw/PPP4+WXX8b8/LxI7FSJ1tfXZVxTU1PihVtYWJASIDrx\\nlaEXJCaUZnw+nzCpe/fu4a/+6q/w8OFDPH36FLu7u4I/Ho9HJDBKp6xyQUnU6/VKX8JecKLyI/o7\\n8x59Tf+vY4s0lzZVQvN7q/fZSU/DwqgHXcfj0Kvh9XolYrVcLmNlZQUrKyvCner1OqLRqCADEQPo\\nzk+ymstZqG96nbXxltyOaSUM3uM1t9stqhuD3kxHAw83cJwFzro7tC8SgUncAIitZhRipNdHS2Uc\\nE9VjHe1vBtvqwE9KWDrGp9PpyKEl5+ealUol6QDMZ1MDqNVq0ryAEpX2Xg0D7GMXDAaFkVerVWmt\\nxfg+OhZoIwyHw10lhml7ZVClLrDHJOp8Po+7d+/iF7/4haja7DzcbrelU41ed2pR5l4QH+xgqL5s\\ndofBJETcaNPOw7gk7fLn/2aglVUYgZ16ZkpoZw0c697eHsrlMnw+H+bn57G7uyvcamFhAXNzc/it\\n3/othEIhlEolvP766/jwww8Rj8elOaTTeVQjiv3ZCGc9Fz6fa63jhaiSOBwO6RiTTCYlNYQqGHBc\\n8paqC8V+TVC478xlSqVSePjwoRBzfkdPHSOmRyFIvUAzANpsNAMw8Y3rpHHa6pnadLG7u4uHDx92\\nPUODZjY6kHDYud6/fx+dTgczMzOSJBwKhZBOp/Haa69JSaBMJiMeVDJKOhK06YMBrIFAADs7O6LO\\nvvPOO/jbv/1brK2tSb88nYFAfNEqmWZC9Xpdgj7v37+PN998s+e8hlLZNIExpRRzY7QnTEsSDIg0\\nN4uTZD6T3iArqUG/79MGjkVHLmtDKgv9j4+P46c//Smi0SjS6bQkd1ISYrS2novVfEaV/HqBfidV\\nRSuJ9d69e/jXf/1XJBIJVKtV4cI8TLFYrKvjC9dGx+7Q1T8zM4OPP/4Yn3zyCRqNBqanp0Vapv2F\\n8T20J1G9H3V+Jp5a/W8yNXO9ddwbn02wu1/f1+us2EnD/SCRSGBmZgZLS0tdjhWW7mFQLuN+mKfI\\n8dHwzXgtSq6tVgszMzPI5/O4c+cOvvvd70qteDNOTo+fqjuJEvefqn6ncxyX2AtG7joCWHMAq8XX\\nv9NNBc1n0bNBiqst/7zvLL1ovUAjjeYCOuK11WqhWq1K0fZms4l/+Zd/wfj4OH7/939f9GyqO9Sx\\ndUyLjvEw36/hJOqbPgT0GHFPOJZAIIBWq4X79++LakW7EpGdFRfZHpx2MBIzqkH1eh21Wg1TU1PY\\n2NgQwz4Rmb+hvUFnjA8bh2Q1Ryvpxyr/0lxXq8+mZGHipxWjNPdKfz/qHsbjcSwuLuLSpUtidyVj\\nZPiFluKBI3sYGQ8Z5szMjFRmIEEKBAJwu934zne+g7fffht7e3uoVCpSZobz0akpOryBMVG0VTG8\\nYJDmGyMV+Qf6qxSagLD2DL0wZpwQn0VvE+1MpphsbvKnCSZy8l82mxXX9djYGObm5jA5OYmHDx/i\\n3/7t3/CTn/wEbrcbi4uL+LM/+zM899xz2NnZwfb2Nnw+nxiEAQxVguIktiT+lhINVTEiEYPrWq0W\\n1tbWsL6+3hWRb0bNA8c2IuA4mNWMTaNa1mw2JcaJHiAGiNLWQBvIqGqbFdPUxMKUwIFnpR3ioJ6L\\n1f3mb01CZ0pG5ljMcQ4C//mf/4nHjx8jk8lgaWkJV69eldK4U1NTYjPSjUl1nBcZPplGsVjEG2+8\\ngb29Pbzxxht48OABdnd3sbGxIRKvVsH1PDc2NvDgwQOMjY1he3tb/rFJx8bGBiqVivzfC0byshHM\\nTdDX9WdyT7viT/pZpiHSfD8R5DQNvMM+S49/Z2enS5KjMTAejyMcDuPSpUvCGfL5POLxuOS7UVzW\\nauxpzWkQMA+U9obp+CjamIBnDykPNyU7vRbm/Rq010kzJNqvGFw4KuOxOuiDPMtOwjeJid07rYiO\\nJlBW7xp0bBpKpRKePHki6SKULoPBIPb39xGJRBCLxSSMgcGqDPCkzdDhOLITZrNZ3L59G9lsFm+/\\n/TbW19efGaOpJQBHTObp06ddBee2trakOmy9Xsf29jbK5bJoPr1gaBuSBrvB6o2g6E2rvJ2NBEBX\\nRK1Vxr7J2T5t0MjFuZHq02XOxn/xeBxf+9rXsLi4KD24/vu//xsLCwsIBAK4dOmSxLQUCoWuLrga\\nrA6EHs9J56EdCYy7Ye6ZHoOVrcPOltJLXSEwFcFMNaC3qFqtnlgKtJJAeuGgVr+sVLBeBMTOTmRe\\nN2FUCYkR1Q6HAw8fPkS5XBa1iy3dg8Eg4vG4FN4jMWBcFdeb+XRPnz5FqVSS9l0kWCZD4Wcy5rt3\\n72JjYwO3b98Woseeisxx5PP6zXPkvmx2Yq4GbiztDIwtsuI2JDR0K5rla3u956RgdbB6Acfabrfx\\ngx/8QKSLZDKJZDKJWq2GL3zhC3jxxRcxOzuLjz/+GD/+8Y/x61//Gpubm3j++eeFY+XzeeF0jPmw\\nkpR6EfKTzp0EQdfk0QZQu8Nmd+CsJGerA0zVjKpfqVRCoVCQ7PqT7LdJOO3wxwq/zHFb2TK11GPi\\njJbs7FS2kwJjpViffXV1VZ5NL6fT6ZSgRp0IrLUUXRyPmQL0cJvSEOegwel0IpvNigo+yN73nNew\\nC2EOyOpQmGoYPW20H5j3aqJkVX7DivPaje2kGz7IIec7Go0GHj58KAbEmZkZZDIZTE1N4Utf+pLo\\n6sViUcqp0DBYrVZRKBSQy+VQKpXE8DconIbKqpkApSXGitCzZLffVkxJf+61D+S6nDPTHOgUoJo4\\n6l5aqfS91KxBP1sxCStibUXEzc/696PMs9dh14X59/f3u5icVSE4jtlu3Lyn1zj19+b/w8CJvGzm\\ndatBENHpOdHXex0qOxWhH6KfNvSzHTDlo9lsSorMe++9Jw0C33vvPTx+/FiiW1ljeWtrS6oxVqtV\\nZLNZ6UgxyFxPqs4Ax9yyWq3iH//xHxGNRhGJRBCPx3H37t2uoM1e77diHnbqHO/hmv3whz+UiOD1\\n9XU8efJE4rlGKrn8RgAAIABJREFUPay9VMaTEHIr7m9XMqaXWqj/tro+zFisQMc1aTOHKeGRyfO6\\nVahNr/mY3/WSRPs9g3CiVtq9BkZwOp3C9QBYcj9yTGZgs36S+ayzIDh8bj8OasUNqXLwbxaXy2az\\n+PDDD6UODXV9JjMy/qhUKqFSqSAQCDwzlrOaqzmv8fFx7O/v4+///u+lw6iunmgnetupIr2kCPN6\\nuVzG3/3d3yGRSCCTyWBtbQ3FYhGFQkGSr62e2Q/M8ZpjsyIsVtetJHOr/eknEenvhmGwg8xRj0mD\\n9nqaTqRhtIB+3w8jCZrjtnxu5zQMEedwDudwDqcAw3dUPIdzOIdzOCM4J0jncA7n8JmBc4J0Dudw\\nDp8ZOCdI53AO5/CZgXOCdA7ncA6fGTgnSOdwDufwmYFzgnQO53AOnxk4J0jncA7n8JmBvp1rgf71\\nX0xglGswGEQqlcKf/Mmf4Nq1awiFQtLT6fDwULKSX3/9dXzve9/D//7v/0rHV0Zvm2BGiFqNDYAU\\npRoEXn755YHv7QW91uOsIq/feuutge9ltxMNVikCZkdZlkLNZDKYm5vD7/7u7yKVSqFUKuHBgwcS\\nXc22PIuLi4hGo5JRvrOzg9u3b+P111/Ho0ePsLe3J7V4gOPuFLrHvDk+puX0A5ZnHQTsooztIstN\\nnBslkrxXhDXrbQ8CrNltlbZj916rtK5PA0y60asmUk+CRGTkZ/PhwHG3UX2vw+FAOp3G/Pw8FhcX\\ncf36dVy7dg3NZhP7+/vwer2IRqPSH+vmzZvI5XLwer3493//966qib0O+CDJk58m9Eqp6Zfn0wtO\\nk6BZpUuY70okEkilUnj55Zcl0TYajSIQCCAUCnX1XnvllVekVEmlUkEkEpHKgbq1USgUwuXLl7G+\\nvg6Px4NCoYBSqYSNjQ3p7/X06VOpDWU3vn7QL0fSXIthn9vvef2I2TDfDTueXsTI7jdnDVZCTC/o\\nm8tmV3qg3+dYLIapqSlcvnwZCwsLiEaj0gVzbGwMwWAQfr8fHo8H8/PzePXVV7G9vd3V+pecWktK\\nfP7/ZV2kQaEXQQVGP2zmc4Z9Rq/fdzodKY/61a9+Veots9Y1e2xVKhVkMhlcvnxZOp5yz9jxlB0s\\n4vE4AoEAnn/+eZG69vb2UCwW8etf/xobGxsIh8PY3Nx8Jqn2pHPs9f2oibfMSTPLdFhJ7YA1rup7\\ndHmQYcBu/wb9zUkSjfu9wy4X9VT7slm9UL+UhcNZRjOVSkk7Yd4fCAREoqrVatLfKhQK4cUXX8TX\\nv/51NJtN5PN5fPDBB1K07LNOgKySTa2y3AHrJORBiJPV84cdYy+gCnXjxg0sLS3h4OBAys7qebCi\\nJ2vg6H57/J7Fvbj3nU5HCrIBx0nWCwsLSCaTWFlZwcOHD7G+vi6VBkeBQaUAq6TpYWFQFcjqHRo/\\nrJ7VD0yJ+ywI96hgl1yrtSg7GKkekuYQ+rrT6UQoFEIikcALL7yAqakpTE5OSjEpSjwul0tKaLI6\\nZCgUws2bNzE9PY2DgwOsra3hW9/6lvS86lUk6izBimhYER/z3l6SiBVyDiLWjoq8Vu8xgY0KfD4f\\nvvzlLyMcDkvhLu4t1Wh2lmAbdI/HI22iWd+o0+lIH3hKWHt7e0K0WF0zHA5LI8E333wTnU4Ha2tr\\nQpCGPWy9mAAPhd1ze72n1/2DSCp2hJKSpVWdol4wqvpvNRaTqA2y5v0kWHNcugdeLxi5HpIVhyGh\\nGhsbQywWk4Z5DodDikaxQp3u7UTC1Ol0pL8Xqy+ya0KvQ6zh09CT7TjjsOqUeUjM3+lNP4159RtT\\nIBCA3++XovAs8sVuIrpfXqPREKLicDjg9XqlnRIbHrB8MQDpsgIcl+tglUiv1wvgSEJjk8iTzNEK\\nRwmjrqGdVjDogeyluveTGuxgVKJkNza7dRpEzes1Fi3Nmw0+TBhpJVjPSBuedeF3ttEl5yMC61Y7\\nrPGrG81RCmLBLtqYzEXRYEWoTgv6SSRWXHAYbt5r7KdxiEzo9Rx+5/f7BYHY4om2If5PKYcHaXx8\\nXFQzXtNNAbR9RBMjdm3VzSLdbvcz3U2Hmb+dJGOq/MNK2SchZHbPMxnOZxU4VjvCZV7vdV5Otcg/\\nQXNLDqDT6UjXgU6ng83NTYRCIaRSKennRXUNODZac5CUktgwkHWWWUrV1Lk/bbXNVNF6vX8Y6cjq\\nPVYIehIRfZBxUd1ihxTdUWRsbEyM1FqapbTUbre7VHLdqpnqmu48w9bUfAeN36lUCoVCAdlsduQ5\\nWs33NAi6qc4MqkqaRMcqVOCzZgMaBHppAyae6kab/fbiRBUj7YyzLpcLjx8/RiKRkEZyRFxWIdQG\\nUpMCj4+Pw+/348KFCwCAXC7XZbvS77QzoJ0W9NK1rd47zPvtfmP1tzmWk4LdOOlV073atb1QczlK\\nNo1GQ5BO929nx1raSCghj4+Pi9TEfmEMLfD7/SJVE29GlTrPSnU/iQ3vrMZx2nhPsCLEvN7LbEJm\\n02q1kE6nJabx1DvXajAPlCYa+XweuVyuqxcTB0qua2U7IQd1OByYn59HNpt9RoTvBWdFjMzrwxg4\\n7QiM3bNMMInvWdmTTKm00WhI+yrN4TlmbQck8WDbdAIDVHXvNqqCmhk1m01xiLBh5UklnNM+oKMy\\nv7OUZIZlgr3A6jm8ZtV0wUogAdAVDtFqteDz+bC4uIhisdjXhjSy258DMqWbTueos8He3h4ajQbG\\nx8fh9XolSI4cVXN9IjBVAjYLTCQSiEajJzJynhRM46g+mIMigR3xGuReq7GcNqfVUgglITazBI7m\\nnclkxPa3s7MjIQG0AfI+3kPC5fP5ROKhR7VWq4kU1mq1xKi9srKCZrOJ999/XySqUQ/bMMRi0L20\\nO4BWDKKXzXPQeweBs5CeuXesce/3+7vUeTIS04YIAF6vF5FIBIuLi/B6vfB6vbh06RJSqRTeeecd\\nfPLJJz3ffeIi/1YLwSaABwcHqFQqmJiYEK5H6cjhcEh7Xn6m1ERbU6lUkjQTOzgNaWGQZ2picJoc\\nic/+NKCXuknCUq/XxdsVj8eFmQAQIzRDMehpIyHSMUtsAUWi43K5sL+/j2AwiGAwKMZrtmBiK+9g\\nMIhEIiFMqJ8R1GougH3wo3nfIKq2lUTbT4LrpY73e8dpgB3Rs7vOjsvMohgfHxcvKiVnMpNms4lI\\nJAIAso8kTrx/dnYWgUAA4+PjgkeDeFBPpLJxQqYB6/DwEPfv38f09DQuX76M2dlZBINBiS3xeDxd\\n/eG1lMTfHxwc4Kc//Sk2NzdRq9XOTEe2AiuCa0U8rKSnXs/SMKxtRP+O/w/rLrYTxwnVahWNRgP1\\neh1utxuZTEZCN/7jP/4Djx49khZFXq8XwWAQsVhMCBbTRQCIpFQul5FIJOBwOPCrX/0KN2/exNWr\\nV+FyucSBwXl4PB5Eo1HMz88jHo+LcXuYw2pFiHrZOqw+m6qxiQNmWlO/fbRjbicBE/fs3qslGZ3m\\nxTHrUIyVlRVcvHgR169fRyaTQTQaRSgUErufw3HUdtvpdCIWi8Hv9yMQCMh7SJiIBwzzefToEba2\\ntuB0OoWQ2cGJCZLdQgAQ7ws5JIPvaGvgoWIoABePHV0fPHgg3HhQo/ZpgpXtxpynFvlHGZuVkVD/\\nr3V3/j3os63eZXcICe12G7u7u/D5fGg2m6hUKnC73fjVr36FYrGISqUCv9+Per0uEi6AruaS9Kg1\\nm0243W5Eo1F0Oh3cu3cPV65cQTAYlAj9VqslUhTxgDFLp20P7CUB2REyfY/+Tu+5VQqJ1f6cppRt\\npb5bfaY9Vu+PdlBQAvZ4PJiZmcHFixcxNTWFYDDYtb8EdsXlPnU6HQkJoeDAKH3gSKp+8uQJPvro\\nI8lV7AWnTpC0AZSqG9UBHb/Ef7QrUMx3uVwSPrCxsSGL+2mrN3yvnpfd9yYR0WCHqFZc2DSMW32n\\njcPDGPv1mO3WkAcrn88jEAjA6XSiUCig0+ng/fffF49ZPB4Xj1ij0ZBASnJict3x8XH4fD4EAgG0\\n2208efIE+/v7CIVCKJfL2N/fF5WMHLVaraJarYoXbhRpYhC7it4zuz0ypVC9L2SwOi7LTiIbVMoe\\nBsxxa9CMknvD1toafwDI/ni9XszPz2N5eRmpVErCN9rtNtxut0hXjMinHUmHgDCCn/ZCnvdsNou7\\nd++iUCj0ndfIBKmfvs3vuSCM5HU6ncI9SUk5MRKoer2Ocrks1NxUTawklbMAq/dojqFVFBMBNcIQ\\nCXgv43poS7Oahz4w2oUai8WwsLAAAF2u+VHnZH7X6XTwxhtvoNVq4c6dO+h0jnIOKb4nk0npcKuN\\n1eSOdF5ookmJicjeaDTEVtFoNFCr1VCpVMRwWigUUKlUujqwnhTsVDYrKZRgJ+UcHh4iEAjA5/Oh\\n0Whgd3fX8rf9GBTXhs6eYSAUCsHhcIjjiATH6/WKikyGRamm3W7LWpOYshLD0tISlpeX0Wq1sLe3\\nh06nI44H2n35PJfLBb/fj/HxcZGUHQ6HSNWHh4dCuJrNJjweD0KhECqVSt95jkyQrLiAFmdJRLxe\\nb9eC6Aht83k8sLTs/18AuZ9V+2H+rXPyrH7P+wBImRXgiBuNjY2hWq1ia2sLAHpuEJGXsT1erxcL\\nCwtYWFhApVIRCfK0gHPN5/Oo1Wrism80GggGg3KAyGRIjGicplrAOXGfibAzMzMoFov44IMPEIvF\\n0G634fV6JWCS8Uw0cFu5mk8bBrHFcU9psG232/D7/fD5fJbETONAL1tWNBpFOBxGsVjE9vb2UOPm\\nmtMbGggEhNAw/YdEyuFwIBAISG6i1+vF4eEhGo0GotEoUqkU5ubmEAqFZG85Xp5l7j0/a6mJa8Lv\\n6HUlrqRSKSQSCeRyORwcHPSe11CrYAF2oi5wXHhLR1sTSXUksD78pNjRaFSScq1EalJ4E05qMDSl\\nIF6zMgSa79JqFTdnYmIC8XgcY2NjiMfjGB8fx+bmJsrlMiqVSpdB2GpdgSOixSoKs7OzuHz5Mu7d\\nu4f9/f2h5qalLnNu/N7pdGJ7e7tLogsEAqJac5zahU8bBHDslNCEdmxsTGwUOzs7+K//+i9cvHgR\\n6XQac3NzMj9yeCbcWq1xP7C717TPDXqvLlIXDAbh9XpRq9XEK6w9THY2R/M9fH4ymcTCwgLW1taG\\njk6ndNTpdISoPHnyROxvTNvS3rJqtSrni/bBubk5TE1NYXp6GqFQCK1WCx6PR7yklLRIePhMLUBQ\\ngtK2Xk0s4/E4pqamsLe3d3YSEsFOLO10jly5Xq9XkJVcD4Dop8Bx3AN/R5cz79GT5/Ptsv9Pi6Pa\\n6fkm9yNwI/1+PyKRCC5fvoxMJiNcZXx8XNScK1eu4ObNm9jY2MDGxgbefPPNLqJtvnN2dlYqJzz/\\n/PMIh8OIRqNDu8QJVuqEfjcz/LUBlszF4XCgXC4/w0RIwGi05L6Vy2UUi0WEw2G8//77yGazyOVy\\ncLvdCIfDSKVSmJ6extTUFNLpNPL5PPL5vKUUfVKwU6NMIk0i5Ha7EY/H8cUvfhErKyvY2dlBLpdD\\nuVzGxsYGdnd3RT1h6oxpfzIJYK1WQyQSQTgcxqVLlzA9PY1msylS9KDAA0+m0Gg0EAgEupghXfKs\\nykAm4vf74Xa7JV5oenoaiUQC4XBY7HckdlQHKTnR2WCaUkz7KIkTQ0E4zn424JHKj/ClVt/xuhYV\\n9cZwQhwo7zW5DCekr+n3ngUR0tCLk5rzBCAxNKFQCOl0WnTy7e1tiddhpcVEIiGeDL/fjw8++EAO\\nvrY3US9fWFjAxMQEFhaOCt1RChnWhmQePE109DytOD2RnIjFvxnCYd7DtSFy12o1rK6uYmdnpyto\\n1u12Y35+HlevXpVn8/BYrf1JwVSjCDo4khIGQx8uX76Ml19+GR9++CHcbjf29vawsbGBer0uxNqK\\nwFt9puo9MTGBZDIJv9/fles1LHQ6HSmIR4JBJsh30/0OQKTXYDAIj8eDcDiMUCgkVTbo8aSzAjhm\\nttqmy/PJdxIHKClpr57T6ewKeu4FI5UfsfqsgQPwer1SvOvg4EDchKTamihxcfk9DzDTDPi9fq8V\\nYp0GmAdVgykVBQIBEY8TiQT8fj9SqRQ8Hg92d3dRLBbhcDgwNTUFANje3ka9Xkcmk0EymcTU1BT8\\nfj8ePnyIbDYrdqHJyUlMT09jZmYG8Xgc4XBYbCskcCwdO8y8zLmYEp9mDvo+2hwajQbK5TJqtZp4\\nWGj3oXpARO50jhKuy+Uy2u22RG0zuVbbFkulkhizNUGyGvcgYKqidvvJ73mg6Bl8/vnnkUqlEI1G\\nJTdzbGxM4mgikQhyuZxUQbUzmut3N5tNTE5OYnZ2FrOzs0gmk4hEImK7GQa0s6NWq6FarYrtSI+F\\nsX8kEFSvA4EAwuFwV8lhZlDwHq2ZkIEQdIAzcEzQaUvSql88Hsf8/Dw2NjZOX0IaZKE6nQ4ymQzi\\n8TgymQw6nY4E3BEReR8NYnT7agNuPB5HuVzGwcFBl/5vTuqkdiPgWKLjWHQKhRb1HQ4HUqmUqKKx\\nWAw+nw/RaFTE3fn5efEmPXr0SPT8K1euIJPJIJ/PY39/H8lkEvF4HF/72teQy+WQzWbx5MkTVKtV\\nzMzMYHl5GXNzcwAgXK5Wq+Hw8BDFYlGI3KhgrqMWz4FjZOZ9jUYDlUqlKxeNh4KEJhgMwufziRpX\\nqVSQz+dxcHCAhYUF8TDSi0qpqlarCQHf3t5GtVodaU9NaYQBmFbSLpkf53zr1i0Z+9WrV5FIJAR3\\nt7e3EYvFhDlMTk7C5/Ph9u3b+M53viMxVbSxUBOg0X9qagrxeBwXL15EOByGz+cTprS9vY1oNDrU\\nPBnjxaJ6brcbPp9PJGsyB9qAKBgwqFGXkCZjAdBVMkhrNdq2yv02JTsSKJ4frsPU1BQ8Hg9WV1c/\\nfYLEAUejUTmsnIw2RnOSDIzjwpFSNxoNJJNJCQMwjWGnKRnpMqtm4KYmgO12G4lEQnTuRCKBUCgk\\nB/n+/fsSt5PJZDA2NoYf/OAH2NvbQzKZxG/+5m8iEolgf38f7777LkqlEnZ3dyWS/bnnnsP169fR\\nbrdRrVZF3aW9gMhyeHiISCSCWCx2amsAHKsTWu3SaowW4Yl8lKYoWVHK0NyUDCWTyaBQKDzjxXG7\\n3ZIvxfK1xJdh95kHhMSB+2iW4eWYyVgCgQB++7d/G263GwcHBygUCtja2pJo5GQyKWV0AoEAvva1\\nryGTyeBXv/oV/umf/kkIF5/n9XrFa+hyuTA3N4ebN28imUyiUqkgl8uh0WiIPcmqI0wvYGVOj8cj\\nJoBmsynJqwy1YJpOJBIRIplKpeD3+0VDKZfLEiCpi/HpnDVNfBwOh6jqXEd+JhFkSAHHOWhO6qkQ\\nJCvx+M///M9x/fp1xONxmYAOWycl5eC1DaVUKmF7extXrlwRyYkctZddx+r6IMAxUWzXUcI8ME6n\\nEy+99BImJiaQSqWEu/3P//yPeNOy2Syy2SySySTC4TCWlpbQbrfx1ltv4a233kKlUsE3v/lNQaCP\\nP/4Yjx49QiaTEe7GAz01NdVFDIjwJBQ+nw8zMzOjbZgCLXJT0ksmkygUCtjb25N9oa1Dcz5yYgbM\\nMd6MnJhITclxZWUF+XxeEixJ5Or1OorFIjY3NyVXblQbIeODyFQodfPAsOHA0tISYrEYJiYmMDU1\\nhYmJiS77XaFQQLVahcfjQTwex8zMDH70ox8hn89jaWlJ1uHKlSv467/+a7zxxhtYXV3tSiqtVqvw\\ner24deuW2Bepkvp8PiSTSSQSCSwtLeGVV14Zat86nY5Ifnt7eygUCjLXeDwulTjn5+cRi8UQCoXE\\nS1ipVMTmxBr3fr9fjN18rvZ0asZDrYZnWGddmASsXq/D7/cLXvVTTU+FIJHA6CjQ5eVlTE1NSX1l\\nUmNNdTUR4291kifFfzuvkH43P48C7XYbHo+ny0DNcAVye6/XK65REqhOp4ONjQ1EIhFJh6jX69jY\\n2MDW1haWlpZw6dIl/PSnP0WxWMSbb76JpaUlXL9+HYFAoKs7B+vENBoNuFwucc3qSo06xQYAwuHw\\naBtmrCE/U0oNhUJdTgXGtgCQeCO6vblGDKLTtgQSehp/k8mkcGI9J6YKlctlMagCo+0nD5lWBUnQ\\n2+02QqEQYrEYpqenEY1GsbKyglQqhUgkgo8++ghAdyAho4/p2aTrPJ/Pi5p069Yt1Go1hEIhzM3N\\nwefzIRgMit3sxo0bsg7AEb5p7YEMbRjQHj3gOPGZEjUz80lAKE15PB6USqUuDy33hP9oS+K5JZjn\\nlUyJ95pSrUm0aLfqBacmIfF/IiSt9iQyRFaN+By4jkWiGB0MBsVwavUuK4loVDsSDW+hUKgrvN7h\\ncEhAIOfWarWwurqKJ0+eoNVqPWOAdblcePLkCV5//XVMT0/jtddeQzKZxPe+9z0UCgV897vfxYMH\\nD/DFL34RpVIJtVoNDx48kDZRJHilUkm8JSRMzAnk9ZO6xU0ir0MUdHWGTucoZoaMgioJ/9F4qWtZ\\nmdHpOnBO1zsip6dr2LT3DEuUqMJEIhGRvkgQg8FgV3wY453IGDRRpTq8ubmJ1dVVCbm4evUqqtUq\\n1tfX8cknnyCTyYjzgU4GqmAsxUwGR4bscrnkcJJY9svxsptrIBDAxMQE/H4/CoUC6vU6QqGQSIpz\\nc3OIRqMS1+dwOBCJRLq8pNp4rXGC/2v1W6tufIZmQto8w2uMMwNw9sm1phsZgBjZGLXJKFyGlWtP\\nDA8WAyDp5fB4PEgmk1hfX5dn23kyCKMSpEQigeXlZSwsLIitgRtE7whD5MfHx5FKpVCpVFAqlTAz\\nMyNeiZs3b6LVauGDDz7A2toa3njjDXzjG9/A9evXMTs7i+9///vY3d2VQzw/P4+ZmRk0m01xv7JU\\ni0YMHmaqIX6/H0+fPsXq6upI87VaL5/Ph0gkgomJCcRiMdRqNXmv2+3GtWvX0Gq1sL+/Lyo4bT9E\\nbBJLnccEQKQ6GlGp1jH5WnNk4GT2wampKbz00ktYXFwU47mWzJk+QXd4oVAQaU4buAnFYhE7Ozuo\\n1Wp49dVX4XQ6sbe3h/feew/FYhGZTAa3bt0CcEQg9vf3RRqanJxEu91GLpcTZ8DBwYEw3Xa7LU0y\\nf/7znw81T5fLhWg0ipmZGSwuLmJxcbGrVLTP54Pf78fCwsIzdkEttWg1S0tx2h5IoqVthjowVCdG\\na7MHcCS5kRjFYjFcunSp57wGJkgmNzUJkZZctEdK182hqxCAeNO0OqYzx0OhEMLhsJQ3sBuH1TiH\\nBerzmUxGDgdTI6rVKvb39yUuIxKJYGZmRoyWd+/eFY9DNBoVrrW6uioI4na7cenSJTHQp9NpzM7O\\nijRCly0L7HMePLA6Kp1SjM6hGhW4VzRm+3w+hEIhIRxaUmGBNiIkx0qJiCov91P3VdPeJsa/UNrk\\nvDRXNgnTMHvKuKHLly9jd3cX2WwW1WpVIpOpEufzeTgcDkn25XprJwzXZnNzE7dv35ZUF0p07fZR\\nw8z79+8LMdvf3xcpksmkPAdsaEC7DVVfBlkOA2NjY11MjB5MhtiMj49LXSOun9ZKtENBq1g6s4Ln\\nk1Id/yZB06VmNFEiMdMaENe2n2o6MEHqZbMx1SZyVnrPtNWdCbVmoXdtV+CCJBIJoe7mOOzG1+se\\nO6hUKtja2hKOQOMedW8d39FqtVCpVATxGD/T6Rw3Qszn82g2m1hfX8fbb7+NQCCAjY0NZLNZNJtN\\nOSjAcaItOTM3kkRHGy+pso2NjeHBgwd49OjRUPO0Atr8wuGwEAugO0XH5XIhGAyi1WoJh2celza8\\n05hJ4qNxRSOzbhaq92uQve0He3t7KJVKKBaLcLvdmJycFGbSarVQrVYl+BKAGON1fSaN34ywPzw8\\nxPT0tAQOZjIZwWOqQB6PR+KSnM7j2j9UXamaUUImfk9PTw+7bQiHw5ibm8O1a9eQTqfFS8f1JXHS\\n7zfd99oUQdstcGx3okbAfdVxRzzTvF9L8Fog4fnm/WfWl42fCabqxBgQck8ugrZ76BKlOiSdkE6n\\nJaHRfLfdmEYBitTM1Ukmk5L/MzU1JYvNGJmPPvoIpVIJ5XIZjUYDxWJRElG5+G63G7VaDb/85S9l\\nrpwHOTU3lETJtEVxfYlMlDCJQP3qEw8CXPvJyUkxzvNAk4Nqjk43MjO9uW5634jQJFha3A+Hw8hk\\nMrhz547EGpkSMmGUfb19+zbcbjc++eQTTE1NYXl5WaSFyclJOXhUrXTQLQk/zQp63NVqFclksiup\\n1O12i0TN1A/+plwuS8XEcrksqk8ulwMAwY9OpyNeqGFgdnYWr732Gl5++WUhAtVqVVJZyNjIQLSE\\no5m+jujm3lH91vY+fT9xj+eX+Ek1HECXEEHiRgmyF5xJYCTFU00diXB+v18C/FjoSVNe7YXjRIZB\\nylHsSNpQ3G63hXvu7+9LKQbgODNfx0YRkXVELONp9G+4NtrYx3eT0Oisaa4ZwYqgn7TWOMdDt73f\\n70c4HJYW2XROaATXcWSMMWm322K74Py015TXeVgSiYSohFZqmTZ4m9/1AzoJNjc3xdXucDgQj8dx\\n48YNpNNpBAIBpNNpkdjNGDldWplBqPV6HTs7O2KCoPrF+u9UfShl6XkzjKHT6aBUKgneMIJ9c3Nz\\naGnX6TxKXE2lUuJV41nR89IGab2mxD9Kh3reVL+4F3SsMK5K/x6AEC7GNun3mWksp27U1oeKYKpw\\nVL1oXacox1wWLgLjNPTB0weTk7NKZTCRdFSDNp+vvWuUVshNuJGa4xMo2ZGAcDPNSG+OkXPRwZhm\\nUCF/p5FaEzCNPCcFIg0LdZH4kPNTZeU4ORftSdGeQHNcnDN/y5AGvW56LKbq3c9maEK9XseTJ0+6\\niqZ1Oh2EQiHkcjlJ3aBEw9ADqiv6wI6NjUl9pna7jXw+L3Xe2VGHtiniB+v/kCjQiE67E39PyVqX\\ndR4GWq34kwatAAAgAElEQVSWxK4Bx3XHmLKhvducjyb0Gg+1dkL8IoMhHjocx41DGd+lJUgzR00L\\nIpog9cu/PJHKZvU3cLS4Ozs7UnaA4j0pLIkQRX92uuVvGefDXB87D9tpgc5HoroJoGshubAmQeYh\\nA46zxIHj3CH+088xRWAdIasPMMdAzk3pCYCk4ZwEOHY6EZhXRRXU4TiqYXTt2jVBUCI9OTDnS8ZD\\n4kGvKfMZaZ/S62ISGpOxEYbZe71ueq/q9Tp+9KMfCaOhMZ5rq/eK18hYdeAucVHvvx63FaPU13VZ\\nD0o5ZkzRIFAsFvHOO+9I4GY8HpezpT1jWgrmP/1+nXdI+5pmoPpzIBCQeUQiERE6SBh1loUZ9Ez8\\n6Jd/eeICbXoDtIjNDTWD4DweD4rFoiycrnek9U8GpVkho2lYH5aL9poTcNxnTEsBWqUEuqU2zY21\\nqGtKSryu9XX+rVsJ6ftNjwWBovVJ56uJHyO1AQinZyQxjcJW6pg2gPMzOaGOUdJivtWeafw5CQPS\\n49DP02tvevc0rmp7Fv83mbAVblhJdJqQaaKgJQfi2jAwNjaGWCyGVCrVZZg3n0VGQqmJ543j0VoA\\no+T5PI6fzJJSpd53nvVO57hlOgkdcUSrrP3SnU5cwtYKqbgpjLUA0DVoxqJQL6U9QQdn0cCo85r0\\n+06LCNlBLwTRhMcO+tUq0gTNVEn1dfM9dq71UcCU0Px+v1RyBI6QPplMYmZmBvl8vivOhL83xXI+\\nj9eZ4a8NqqZtEehNoIYFU4rV60u7m36/frdmHNpmpu/jdasx6+v8p93tPNw6n5M21WHA6/Uik8lI\\n3BrPEyVXACLJakJFFz3tPlTPaPdlZDuB9+gyuTzHWt3VBErjg5a8SqXS2bRBstL79eZw4vl8Hn6/\\nXzwzVNmCwaAY4vx+vxgFtRh9eHjUSontU6zsSPp/OwI5CljNyXy3+T4725rVdaCbYGluPcwhPOlc\\naTui3QhAl3rI6o2pVArr6+tSacBUJzkWfkcCx0x0EnBdyYGqm56L3dyHmaeWsno9x4r4aclIP8Mk\\nMv3GZV7XAYk6Ep0S5yjMtVwu4+HDh5icnESn05G4pEAgIFLI4eEhUqmUEB2CVrcZ3qKlJs6ZhJT2\\nL3p1W60W1tbWEA6HkUgk5F1+v1/eVS6Xu7y0/G5nZ6fnvE7kZbM6uJwYKTHQHYLu9/uFcgcCARSL\\nxS7RT2eUZ7NZcZH3g9OSlvohh/m9OXe732gYlvDo3w0yxkFBG5qtCCZVGBIp7qkZq6Ofx2v6n87D\\nI9LzQFip/Obnk4C51naqnPk+O1PBoHtpZdIAjjUFk7ANO99KpYL19XXcvXtXbGAs+DYzMwOn0ynx\\nUsCRnZSGc62OMi6QYSlOp1NCWDweDyqVCg4ODsSzSIJ17949zM/PS5VK3k+PM73QWhvS59sOTt3t\\nz4VmJjUNoZR6yBk5MDPOgcmbh4eHePLkCXK5nIjIgLUkchbjtwIrZOw1pmEJlCld2T37pMRIv49G\\nbcaeMKQBONobNhMggpFQadWRaompFpATkzuHw2FUKhVUKpWeezjoOg4CvYiReY8dERr0+YP+zk7q\\nHga2t7fx7W9/G9/+9rcBoGv9KZW43W6srKwgEolIlYNQKISFhQXx/tGjzL2t1WrI5XIolUpSFYDq\\n1tramjAVr9eLl156Ca+++ipSqRQ6nY40PqCHkZVCWSvrrbfewq9+9St885vftJ3XiQiSqTbJQ/+/\\nu3R/fx+JRKIrypkdLFiuNBKJoFqtCtHR+TVE5tM8jIOCiaTa3mASiZMSSKuD0k/yGlVKMqU7qlK6\\nJxqrH8TjcSQSiWcisrWEo434/Mfn0TCqDeBEeisVyJzfWYCpavci/Ke1v4OOaRjQ46ZKSVsXswka\\njQbW19exs7MjVSl9Pp+Uf+F51E4KqluUdGjbYugC76vX61hdXcXh4aE0AqXkzJpdZG4MSchms7h3\\n717PeZ1YZdMLxL/b7Tb29vYwOTkpkhC5L3tZkZJzQTUSE1EZ/6HjfwbZvLNAnlEIQC91YZDnW0kK\\ndkxg0PHo51KsJ3PQ4rnf78fKyopEG5teNa2OaRsY91JXm9Q2CxIkrTZo26PVupwGmMSll0Q7qPpm\\npUJbfTaJoNVeD7ufdsSSTILEhrFDPGeUbvVZNRmtLmZHFRPojj2jBrOxsWEbL6jHpT15veBU2yCZ\\nC8KBMKuZ+U86p8VMHu10OuKizOVyyOVyIkENqnefhhRlZxuzer4dsltd7wWDIPlJgFIqCQdL8KbT\\naaRSKXE2PH78GNFoFLdu3ZLs/93dXXQ6HeF2AIRTkgvqILtkMimqHKXepaUliU42JV+r9RwFekmV\\nmpif9N3DGrd73T+KBGYnzfEziYlV2ILVM6xw1w4XgeNzfdo4eqrlR4DjxaYOSmmHGd4mZ3U6nZIX\\nYy7q5uam5Bzpd31aahvfaSKyqW7046i9CKfVnDRinOaGOxwO6TjqdDoljiUej0vYPyUYJhDTa8PQ\\nDHpJqVJ7PJ6u4vDa1keghMV36Dw+vbZ6PU4qGfVSqU/r8Ni91wQddW8nbZ3VGDSu2kncGqe19NyP\\noQ5KcIeZ35nU1D48PMTm5iZKpRLq9bqkJDDGgdyRSE3jF5NbWeeXDQvNxbBCrrNAMlPS0df1u602\\n245YWUlcdu81f3NSYI2pYDCITueo8QD7odFbwhzEcrmM9957D51OB+FwuCseha57piYwP5FECYC4\\nihnKwWJptVpNCuJTmtaJqua6jCI99GIIgzKL0wSt6ljBaduorM6CFT5p1dtkBv00kl6SaL/39oJT\\nMWqbL3a5XPjRj36ESqUCj8eDL33pS1Jjh9XyKCXRy8N4iK2tLXz88cd47733hEjZBQ7qd58lx7M6\\nIJoQmZszCHe2E5fN+fSa27BIrHP1KPnoXEISJI/Hg3w+j5/97Gf45JNPJBqYKh/Ls5BZ6KRg5jlp\\nQzjtUA6HA48fP8b9+/dRqVTE68p3W6kLpwWDqGunBcPu5VlKSHy+frdOQdLXde6kHlOv68PMZ5B5\\n9iRIo27e4eEhHj58KDWLX3rpJYRCIQCQYvFcJIYF0L5UKBTw5ptv4gc/+IEgvTkpKwLwaUAvAqLH\\nYSfBWT3H7n7zszkOrTIOCo1GA/l8HgAwMTEh9ZwovQDHZSM6nQ6ePn2Kzc1NYRgEnVbAEA3mqZHJ\\n6GRb1mymLZGhHGZZGW1gNdfqtODTkIis1JezZJp2YPfOXipYL7WO9+j7zXt6zXEQetKTIPX6sdWB\\nJFCU39vbw09+8hPpQcWOnaw22OkcxTNQlM/n83j//ffx0UcfYWtr65lJDzKW0wKr91ltRq8Nt3vW\\nMIS+n+4+DPC9tNsBx6VI6V1jhC3vdzgcXS2RHA5HlwOCEqzOY6Ik5nK5UK/XUa1Wu9IU6MTodI5L\\npLJZIdtCm0G1g0K/df00JCQ76IWnZ02s7HCoH/5qQtNL5dTQj5H2glO1IemXsXPr1tYW3n//fSFK\\nL7zwAqLRqNRxoR2pVqvh3r17ePPNN6WDqS7JYEWYznITh1WfCL3UuX6c0+6e0wQmV7ILRTKZRCgU\\nkjIb29vbODg4eKaujZZUdVKv7oLSi5uaf1O9Y8XFZDIJl+uo/dDu7i5KpZI8cxgCMgjB6beudir4\\noHaTUd55GkRyUM1hkOuDMl+r3/e6r+/edP6v2MU5nMM5nIMBva3F53AO53AOnyKcE6RzOIdz+MzA\\nOUE6h3M4h88MnBOkcziHc/jMwDlBOodzOIfPDJwTpHM4h3P4zMA5QTqHcziHzwycE6RzOIdz+MzA\\nOUE6h3M4h88M9Ewd0f3GrdICzCBvFv2anZ3FX/zFXyAcDqPZbOIXv/gF1tfXEY1GkUgk4PV6EQwG\\npdhXOByG1+tFo9HA3t4ePv74Y7z55pt48803pXyFHoMekx306yGugYm/vebG670+X7lyBTMzM7hy\\n5QrS6TQKhQKi0ah0WwmHwwCAX/7yl/iHf/iHrtyyQQPmzbGxIPsgEAwGu+apP/dK+RgU+qXWWO2f\\nXTqGOZ5KpTLQGJLJpO3zrXKu9Pt6gU5mdjiOO+OYqS2DJlFbwd7eXs/vNXCe/XDWah56fCfJO+sF\\nvX7fa54D57INkqfCZnIulwtXrlxBKBRCq9WSBFoWk2d9X7baAY67IlQqFek39c477/RNSDzNzBc7\\ngmOVPMzvWRbU5/Nhfn4e169fx/z8vBQ+1wXowuEwQqEQDg8P8cYbb2BnZwe5XK6ri8dZg7lmp5k7\\nN0h+nl1meK+M8mH32O755hgGmYsGVsRkXmYwGEShUMD6+rrUoNYVDPT7zgr6CQkmDLouZlLtKEnO\\nXC+zzXYv6Jvt3y/LVx9ONr7zeDzIZDLw+XxotVpS1J8Ske7/zgE3Gg0pZZFOpxEOh6V7Qb9yHieF\\nUTLEdf0eNtFLp9NIJpOYmJjAzMyMzJldG0KhEDKZDEqlEjKZDA4ODrC7uyvP04m5o0iFg4BVORfO\\n0bx+Ug5p9Vn/bScV9frtoO/txfntxtRrnbknZKrT09PIZDLIZrPSpYMdj3ku+LuzALs1HfS3dlUk\\n7KS6Xtn/vSRPk/n1a7DaV0LqRYw08vBvdrdkeQlWDuS9zBzvdI7L2LLIPGvq8B72kTcX6/8qH5iF\\nqtiQLxgMIpPJIBwOw+/3I5VKweFwSGG5aDQqKtve3h6q1SpKpRLK5bJ0HA0EAtjb25PaRCzbYcJp\\nEGGqGnofeN3q3l5/A71xox/YEYzTIL7D4Mkw68pa8ZVKBffv38fa2hpCoRCmpqbw9OlTlEqlgTPj\\nRx3DaYEdAxqmEoCdRMzvrJhfv3kOXX6ESA1AainzZU6nEz6fTypCdjod6efF4u+6YysJE2sss5Id\\nW/BY9So3J/1pAInExMSEICCL31+4cEEqYTocDjQaDRSLRdRqNUxMTAiRdblcWFtbw87ODorFohCr\\nubk5bGxsYH9/H+VyGfl8Xmpa95IURkHiRCIhhLBQKGBra8uy2L7Vu+y+6we97j+pFGYFdiqMyUCt\\n7Ha91pgEvNVq4eDgAIVCAc1mExMTE7h06RIymQzi8Tju3r0rzzfVn37jPi0YRHLp9bthv+v1vW4E\\nABwX97ODkeoh6ZKWJE5UYVhjmdd199NO57ijLQvNU9fk3wBELB4fH+9q80swjYpnSZg0Iq+srGBi\\nYgIrKyvS+pv1wMfGxqS0q8vlwubmJmq1Gi5evAiXy4VarSbdVlqtFra3txEKheDz+XDlyhXUajXU\\n63Xcv39fiFav0r2jcNSlpSXMzc0hGo3i8ePHqFQqKBaLzxTcNztVmH3brd5tJ+pbraX+bKUC6PtH\\nATvp3eq5g4xbg/5+fHxcOsiurKxgfn4ed+7c6Sn59RvHWUKvNT6r95troQUSKzgRQWJZUop67OXt\\n8/meqTpoIjf7wpuf+TdtTaYEZkX9z0rkJbFkTejZ2VkkEglEo1EhiLu7u1IhMRaLSanXbDaLsbEx\\n7O/vw+/3Y39/H6VSCe12G9lsFtVqVQhuNBqF1+tFu90W7yB7pJmS0knA5XJheXkZExMTaDQa+Pjj\\nj0VlNg8w/6dUoKtEDuIVtEN0rRZovAGOJeaTgtUz7OpC698MKlHoObTbbVQqFfh8PkSjUWknPYh0\\ndBaSfr9nDnpW7FT0XmMdRDg4dZWNapdWpTRHcrvd4jmjGqa7jFDyoZfN7J5Jgzct87QjmRM1KxUS\\nTpM4cbxXr17F9evXMT09LX3LxsfHpScZ5/H48WO0Wi3s7u4im82iVCqhVqthf38fXq8XqVQK4XAY\\nwWAQfr9fKmPu7OzA6/UiEAggk8lgamoKKysruHPnDu7cuYODg4OuMY0K5XIZi4uLuHLlCg4ODvD+\\n++8jkUjIerHBo3YpkygWCgW0Wq1niAY/a2lVS1i8RmJgNm2gqutwOKQbCefJ0rjsnjoI2EkgvYy0\\nVr/t9w6Oj4S6UCggm80ilUohn89LTfF+MIrNaZDn6XHa3dfP1mb+3u5ZJj70mku/eY4kIZlGVy39\\njI2NIRKJCEEhoSERI3HSSKu5JIvE1+t1hEIh8cDZ6fdnqa51Okc1v0OhkEgVnU5HDO8kllrtBI7i\\nfaLRKNxuN/b397s6a7RaLSnPyvgnShyNRkOIVzQaRSwWQzQaRb1ePxX1lD3f/X4/Zmdn8fnPf14Y\\nRqVSgdvtxtjYmLRJItBmwvlz7bk37EbKPaS0d3h4KMyJ/dyotpMIUdVnGeNarSaNQd1uN3Z2dvDw\\n4cOh52pnlO8luY0iOWjJstlsYn5+HmNjY3j8+LEQ4F6/1885beiHK+YZGnUMg+LkIGs8EkEybQH8\\nm0btYDAoqpzmEkRGAF1F5nmwKVYTgsEgyuWyhAZYWfI1nNamcpNoEwuFQojFYnIozTlTWqQKxn7n\\nLF7PPus8dB6PR9QgSgg8pDyU09PTSCQSSCaT2NnZkeL4JwEdMzUzMyN70Gg08PTpUySTSbH/8V4S\\n3p2dHdlL/n94eIidnR3pBU/p2Ov1CpGm+u7xeBAOh9FqtYRZaUMxbXHlchm7u7twOp3w+/14//33\\nRyJIw+LCoFKCCcQDEuCZmRlplGDayEYZ12mDORarz4RetjD9vNOEvm2QBnkpuQS5azweF2Qj5ye3\\n8Pl8AIBisSgH2u/3C7FiB5JSqQSv1yvSxSBU+DTFXofDAbfbjWQyiWQyicnJSXQ6HeTz+a6GhiQo\\nlBjYa4yR6H6/H6FQqCtAkp02uCbj4+NyyBnNvbGxgcuXL+Pzn/88/uZv/garq6snnl+j0cDPfvYz\\n7OzswOPxSOvser2Og4MDZLNZkWjGx8cRjUbRbDZFaiGBIpFpt9sIBoMyP0o6lJwY8sH4HN0U0uFw\\noNlsCqMhHlD6DIVCSCQSODg4wIMHD4baO2Cw2LJhnmd1XeNJIpHA/Py8tPza2NjA9vY2gG6Nws42\\nc5oH285xYH5v3msHVrY3k5BpVb6XFHiiOCSriVipSqbRmkRH30sJAjgSbXO5nKh4Dsdxf69ardal\\nmxNBzXGZYzoJ2ImS7NBKrx+9ZO12G9VqtauPmLalULKiOsfOrjzs3BRyUUqGlJgoYYyNjSEWi3V5\\nLU+CuAxPaDQaGB8fR6PRELvNxMSEqNY04ofDYbEreb1eQUyOT0sGbPjZbDblmdrmxB5tbKnOg8w1\\nYJvter0u0lQymUQul0Mmkxl5zlbQC2f6SQpWUnowGEQkEsHc3Bzm5+cxOTmJpaUl5HI5VKvVrvMw\\niE3mpNDLtmpqN3a/BY5NMIeHh3C73V04TYeTqZa6XC6x+9EcYNqae0FPgqSpmblR5oO1tOPz+bp0\\ndm3QrNfryOfzePfdd3F4eIgvf/nL8Hg8qNfrgrAkXkRsU1UZhKoPA1Yb1el04PF4kEql4Ha7kc/n\\nRTJgOkylUhEJg9IAcExoKPlwPtp+QpWNG05DfygUknbijUYDhUKhy6h7EqO93+/HxYsX8cILL2B3\\ndxcPHjxAtVqF1+vFq6++ilKphP39fUxPT8Pj8Ug8GdUsrYYAEAIGdKtx2kmhOSeJNdeQ1yklaltb\\nIBBAPB5HNBpFIBAYai9NsOPuVjCI2sL3HB4eIhKJ4OLFi7hy5QouXrwIj8eDW7du4eLFi/jLv/xL\\n3L59G8FgUDzSDBHRzJhE/CRghRdWhGkQFZQhOAzhAY7MJ7SjUqUPhUKIRCJyjQy32WxibW0NlUoF\\ntVoNxWJRJGw2JLWDoXPZrCavbS66SaD5O5fLJekSlJBosOWB5mGloVUH7lnp4mdBlPh5fHwc6XRa\\nbD70DAYCASHWVp4UPTbdzdW0p1GNqdVqQoi8Xi/cbrfMfXd3t+vwEkYhTHt7e6IyMtKcame1WhVu\\n5vF4upKaSVS4P1RTddgG14MSLRkJv9fAtSCucD14TXtYm82mGP9HATtctQLirVWLaLtAymg0isXF\\nRTSbTezt7eGTTz7B7Owsbty4gT/6oz/C2toa7t27h3a7jUKhgM3NTQBHqjmdP4VCYWSC1A//rXDE\\ntNWa9wcCAXi9Xng8HjEthMNhzM7OdjUGpUe4Uqk8g9uRSESuPX78GKurq2i1WigWiz3HO3KjSI2s\\nelHYedTkpPy7Wq0im80im81KciKJERG00WigXq+jXC6jVqt1NSbUxO80jdtWzxsbG0M4HIbP5xMJ\\njpuls885dh4o3Y5aG72B486uRHByR40AXq8X29vbKBaL8Pl8InFoJBpljtVqVQhKIBBANBqV0AxK\\neSRQVK81MeRec31oD9I2Ikp9DBol4eJ6aoIFQGxNOkSEXjaGgJDzDguDqikEc2/0czSec40ODw8R\\nDAaxvLyMVquF9fV1PHz4EMvLy5iZmcGf/umf4vHjx/jnf/5nNJtNbG1tSXWGdDqNaDQKl8uFjY0N\\nPH36dKQ5WtmIzDXgfVbrYt5L6TQUCkkbdOCo9frk5CQCgQA8Hg86nQ5mZ2cxOzuLg4MDCYEhpNNp\\nAJDMjXa7jXK5fPqBkZQgdCAjJ1OtVlEsFrG6uio2A/6GIvnm5ibeffdd3LlzB5OTk6hWqxIOwMNL\\nG00+n5ecOL2gDCE4TdDPJ0EJh8O4fv06isWiHFaPx4OFhQU5OAxc1OugCSivEYjw2pNVqVQQCoWQ\\nSqVw8+ZNCahstVrweDy4cuUKgsEgdnd3USwWR5YKL1++jFarhUePHokkQvtUJBKRsjAHBwddtqRW\\nqyXeTqobeq1cLpc4JhiT1Wq14PP5xBAOHEs8JLA0/lerVSHKtENxHcngRgEtWdt9b3Uoe6k+VLE+\\n97nP4Stf+QpWVlZw4cIFFItFtFotfPWrX8X4+DgeP36MqakpXL16FRcuXEC73RanBp+3tbWFfD6P\\nH//4x1hbWxtpjnbz68Ww7GxHZCgLCwtIp9OIxWJot9vY3t4WQpROp5FKpZDJZGR/SHTGxsYk148e\\n1Wq1ii984Qv4yle+grfeegvf//73e85lZLf/+Pi42Da4iU6nU1oyU0duNptShoAHbW1t7Zke7jyc\\n2vMEWKtEdhRef38SoGE5FAoJp6jX6+JRikQiQvX1+3TEsWkvAY4lJt7Da9TZm82mVEQoFApiIGQm\\n+Uk9MbOzs2i1WigUCkgkEnC73djb2xPjPW1/2v7HsXEuWhV3OBzCHLgnHLOV8ZY4Q+JDCYtEiu+g\\nE8Hn82F/f3/gWkjDgEmseuGMdti43W6kUim88MILuHHjBtLpNNxuN6LRqBi4mZtJAz0NwFpSpj2F\\nkfpWCdUnnZud5NRLszg8PEQoFMLs7KzYkFKpFJxOJ9LpNKanpxGLxcSMwefo8BVqCjq3lXvarxTJ\\nSARJGyNNC/rBwQHy+bxsADeRg240GlhfX0cul8PU1BQODg6EuPB+Ri3rg20iuFXSrTm+UcHhcMDn\\n8yESiSASiSCRSKDdbkuwIw2+kUgEpVKpK0aIhxI4VuV4SPlsbQhutVqSbqIj4NvtthCizc1Nifo+\\nCbF1u91iXIzFYmJDajab8Hg8ACAIQ/sXvX1W8+M49f5RPececE4ulwsejwd7e3vY3d3F5OSkqKis\\njsC5eTwe+P1+hMNhiWY/TRiECFkxPaozly5dwuc//3ksLy+L0VerppTyNW5zrw8ODkQy5JrTfnha\\nYKWm9ZqXOcd4PI50Oo16vY7p6WkJRfH7/Ugmk4hEItjf3xcpmwSXtl86o7xeL/x+v9gkx8bG+hYU\\nHIogaUQjx9aTqdfrKBQKePDgQRexIHJzo5gNXywWRXwn0BOxt7cntgWqUBqsKgBozn4SoHfrgw8+\\nwLe+9S1xT7/88stwOBxixNMHmQedoq8ej84DMw30Xq8X+/v7ePToEd5991289957GBsbQzab7bKl\\nnYaERM8HGUkmkxGXPlWzWq0Gv9+PRqOBhw8fIhgMIhgMdnnGzHnpoFZNpLQEyT3M5XK4ffs2Dg4O\\nMD09jeXlZfE2kWh7vV4cHh6iWCxie3sbOzs7J5q3BpMY9ZO0tb3I4TgKVXj++edx+fJl+P1+cUq4\\n3e4uBspgXq4FY+yq1SrcbrdIIzwLppo/CPSyHQ2qppqfWQkil8thdnYW8/PziEQiMvdms4n9/X2s\\nr6+LhNvpdCRFikSKOEVm5Ha7EYvFsLy83HNOQ3nZtHFTb5I2XnPB9WS1qmIa1ygVARC1pVQqSWTw\\noLai0/K2ARBKXy6Xsbq6KlIe31MoFBCLxRCPx0V64mHkOumUCKsxUvLgtd3dXWxubuLRo0ddNaFM\\nCeUkQDXQ5/NJcGS73UY4HO5SJb1eL2q1GnZ2duB0OkVa1YRIe1v0/nOepteS31UqFeRyOcTjcXg8\\nHqmGwLUgrjQaDZRKJZRKJQkwPCkM4gSxwiMa7Rkoy7HTEUDmRKJKaYCSEW1g2vNIlY6lZkbxstmp\\nZlZEtx/oeReLRVQqFWEiBwcHknZER1OpVJI0JABSrYJ7zXgznZHg9/sxNTXVcxwDB0bqSRJ5tURC\\nxDMLspFT1Ot1CSrULnDNRR0OhxQz29rakoPZz/DYi8sNCuYz9eEAjqSm+/fvy2EFjmwiiURCvEes\\n/cQxmd5BjYx8Po3/tKmQowLHEbKml2dUoJqcSCTgcDikEoG2a+h3knO73W4Jz9A5hxyjHi+v05it\\niTRwRLTK5TKePHki9jLaDMnoWNajUCggn88jm80OPddexmmg21tr9b2+h1z/tddew5UrVzA/Py9e\\nM4fDAb/fL9JFuVwWFUzHYXHtGJRKKUpLEqOA1Tz1s6zwxo4g057Jul/tdhvr6+soFouIRCK4du0a\\nXC6XlKCm6k4cpteWoQzEb5o3YrEYJiYmes6nbwlbq8+mHqoRlUTJDIjTyZNahWEBM/5dLpdx+/Zt\\n/PznPxcpzG4cg4x7UCAB1UiqbSWdTgePHz8GABFTXS4XlpaWhMjyGXRhm/YVDTqClfFHPp9PpC2r\\nOZ9UZdvY2MClS5eQSqVEVSCHI/FnPScGsE5PT0sJYs6TBmhT8tXrxznrMdPLVigUsLq6iv39ffzx\\nH1E+zAcAACAASURBVP8xIpGI1IzioVhdXRU3+ieffDLwHO2YlRXTslLzTemOTplAIIDf+Z3fwa1b\\nt9BoNPDo0SOxfWnvYzKZFI8y14xBsZSidWAk7WhWZolhYBCmrNdGz5PEcXl5GVNTU/jc5z6HWCyG\\ncrmMd955B0+fPkUoFMLBwQFisRgSiYQ05tA1y5LJpHhYHz16hFwuh3K5jAsXLiAcDgvz7gV9V8Hc\\nIL1xJiHiPbSraITk/YzqdDgcXZyBqluz2cTTp0+xvb39zLv5nH4bMKwkYXJJK87KMQMQzs5DrEto\\ncC78nX62jnPR4PV6BSlOU/U0oVQqiX2AUeE8EJTa6IBgfBBTBoh4nIP2EhK04Z7z1fumk6gPDg6k\\nCB3xRXtnaBwtFAooFApDz9XEFzuipO+3wnFKPl6vF1NTU4hGo3jy5An29/fFMK1tJTMzM2LA5rgp\\n9ZF4kVFpXBhVQrJSQ+3sqfq6/kxv2NLSEl544QVcunQJLpcLjx49EsYFAHfu3EEqlUKlUsHS0pLs\\nKcfAMA/W9WL4Dt/B898LBkqutSMM5nVSfQbFUUfWRs5OpyPufqo1WrI4ODhArVZDLpeTQ6LHYodo\\n5iIPA1rlNJ+p36X/5lw5ftN+pL1NRFbNJYHjA8gcM10t4CyAiaqlUgkXLlzA1atXJXmWDEO7bXVY\\nBj1+vdbXVL+5DrqiAw84s/uB4yhtljqmxKCZ2DBgZzuxUs+s5kO7j9frxZUrV/AHf/AH+NznPoeJ\\niQlUq9WuahYkVixMSMm32Wx2pbzosBYSfI/Hg0gkgomJCZFUR51nP7zX0hAJYbvdxo0bNySW6rnn\\nnkM6nZYmG9euXUO5XMYPf/hD/PrXv4bD4cAf/uEf4tatW1haWkKhUJCATqZ8UcKfm5tDKpXC7Ows\\nYrGYnIcTlbC1M5TZqVGcKI3C/MfNozjHQ0sRnUinD7gZr2ClrvTjeoOCJgD91CI9V+CYqGj1jN+b\\naRFauqAUQOOmJr526tppSE/7+/twOBxIJpOoVCpyELg3JEAmYdS5epTmTPuEVtv0XHnt4OBAuCrX\\njiog913HnXFdhonRMSUdu/20MkHo98TjcXHxv/LKK7h48SLy+TzK5TIcDofk+ekDPjY2hkqlItK+\\nLjaoI/lpZyHR9fl8mJycHHiOdvOwWgeOjzmTVJk413Q6jdnZWSwuLmJxcbErf5FGfBrdmfbFel40\\n5JORaRsqQ2QoFTLYVvd6tIKhvWx2UgQXgYMjMdIHSnNB4Jij8jpw7OHSyG01Fu3R0ddPAoNwGf0/\\nA+DM35sqGw+wSdTJpTh/k/hY2bVOOr9yuYxGo4GJiQm0WkctqsbHx7G/vy+HiCoIx0c13Co6X8+V\\n32mipFVa7itjbkyJUod4UIKiJ2uYOer/7cBqjak+er1e3Lx5ExcvXsTc3Bw6nQ6y2Sy2t7elnlU8\\nHpdka46fBFTXgych1mVYiONjY2OSqdDP+9Rvvubc9P8sg0O8o5eQNizadunp29jYEM/a2toaJiYm\\nsLOzg93dXTx+/Bg/+9nPMD09DYfDIeYG4MiLazLhSqUiHslcLtc3pmwoSxonZKc+ARA1rVarSeVA\\nIjY5IdUCAF1SEu8hJ7GTgHpJMSeVJMzfW3FSoLs0g/6eY9fXaHvR1TJ1xDNVOX2g+81jlHl2Oh1J\\n07h//z4uXLiAixcvIp1Oiyve7/fD7/eLZ4TpHLopA/8xUVer75q46ghuqkChUAgzMzO4e/euSBNc\\nNzZO4NqY5VoGnSN/Y0qzXG/g6JD6fD4JEI1Go1heXkY4HEY4HMbly5clgnpra0tSO4LBIBwOh1S1\\nAI4ZK6V8LfVTAqYpIhgM4vDwUFKOKIW8/PLLQ+0l56rVZOKk3++XMskTExNSOoZ7wqhyrW6WSiWs\\nrq7C7XbjF7/4Bba2tsTpMT09jYmJCVQqFekw/ejRI0kn4ZzZkdbpdIpExNrxzWYT+Xy+rz1wYIJk\\nIphJhTWykqPpYDoOWhMfcgmN4JoQme/gIdRivHkwe4npwwLHanqLKBXQ9amlJG2cNAk2CbMdgbVS\\nhe3sBCeZ5+HhoZQacbvdCAaDksbAPWZhOXpSdKE8rXbqRgymGqvVIKoL9LZwv2lboeeJxI3Eyyyn\\nOwhoXCIx1IX0GG3v8/kwNTWFTCaDVCqF3/u930MkEsHY2Ji0o1pfX8f29jacTicuXLggRFpXQ2BF\\nh8PDw64aT5SMKAXTFqYDSilRJRKJofeRahLXjmvM+LhkMokbN25Iyg5TdJhnRomOsX9bW1vwer3Y\\n3d3FxsYGGo0GUqkUFhcXMTMzg6mpKbElsgoqnR4a18motBSqzRq9YKTkWtOGoLk8X8rDSiQ30wpI\\noUnZtbeBYj1D7wmmNGEnIZxEQuJ8iDS6WaV5jzbiadVKqzHakKn/6WfqdTClIysp9CTz5LO1rYhq\\nCh0SACQKnWPTSAegi1BrZsX91gyFY2WH30AgIKETPKR+vx/1el3uIzHy+/19DaEmkMgGAgEkEgkh\\nmkwkdrlcCIfD8Hg8mJqaQjKZRDQaRSQSkf1mTiZVDMaacXya8eh9poTINdamBX1mNL7o+wYFl8sl\\nEi3PFVXeVCqFWCwmBfYAiDrKeCGmsTAuivYvej7pwp+dnZU94N4BkBg1nYtqdz45b9rTesHQNiRT\\nbdELycWmnYgFysg1NAfVRc0cDoeUsWi1WpLUms/nnxmDDPz/G8A19Dq8w4A+ZJqYmt/rkinkkkQK\\nqnP8vUmMzCBC0/ah15vvNiW1Ueapx0BpjdHbzO4vl8vCAbXHjcZZffA4RtpMeF2rbSQobrcb4XAY\\n6XQaTqdTgh+pHpbLZXQ6RyVy2ViB7cmH2bvFxUVMTk5ieXkZv/Ebv9GVG8fxEz/1Ou/u7orxNpvN\\ndh1w2kpareP63yRQ3AemPekyLlpC1Co9x8CQi2Gz/VlUb2FhAfV6XcZ0eHgo2QONRgNbW1syVzIC\\nMh7aBTn2XC6Hg4MDpNNpaZcVj8cFTwuFghBynRRN4YP7rau86rNyZvWQeEis7C2aCvKA6sOspaFK\\npSKIq3N/eGD5Lisxz4yC1mM7DdBz1MA5aiMlx8MN0dUVzTHp+fM67zfX0iRS5lhGDQ8goaSnU6sS\\nXP92u91Vj4oHTe87kdHpdEq8CVV2IrH27tDVPTk5iWaziWKxiGKxKBKRJr40rodCISwsLAw8N5fL\\nhYWFBbz44ouYm5vD3Nyc2D6A427LWlonU6xWq0J42Rbd6XSKLVQnTVPd0QSGz9d2QtrfyLyoQRDn\\nadbQav8gEI/HJfqZ46GEyax6ahlm+opuQspqlp1OR6RSVm5laoi2HeuaVoxGJ0Gi9Elc4tpWKhXZ\\n1xNFaptgHjDz0FBaMNUU07ZAJGZCIjksCzzpQ6ulMv1+jQSnRYSswOpd+n2aA/KfthlxHjogjutD\\nZNT6v163s54T90NXHCChp3NCF+znvXodNBPiXCghcF68j1HLiURCIrOz2ayoUsSTw8OjfLpIJIJo\\nNIrp6emB5zY2NoaZmRksLy9jbm4O6XRaKjISfxh+wJpWNLrye6ZHUD3hdTplzCC/er0ukgFjurj3\\ntM2ZDI5SCp/Vr7SrCZlMBoFAQNJPKPEAx5J0q3VUk4qODF1Hnfjo8/kkQ4CqG431TBHRJZW1tKw9\\nidxvrpc+D2RuLDvTc/+GWQSTg2vQf2tDLz8zWZXBcFwAxmrorH+/349YLPaMqqQJk3lgT6KuaSlM\\nP4dIq/V9ra7SrkJOSBVH69HamwYcG65NO5PO2zON+FZzPQnRIsLoyowssu9yubC2tobt7W0hUhwv\\nVTtNhMn5dUoAVXLaLXhA2eHV6/Xi8uXLqFQqeOedd+D3+zE5Odml9iYSCWQyGak6MCgsLy9jfn5e\\naviwqB2lM86X3jxtTNYqNg+njk7Xtix9QKPRqOwZvXDA0YEm/tfrdbFHUbrUTpphS6zcuHED9Xod\\njx8/7noOx0+j/fj4OCKRiBifnU6n9AqkLS0cDotwQNuhw3EUH9XpdKT9V6VSkTrZuVxOVEUSdC0l\\narUOOE6cP1WCBAxW5kPHERHMwDByD80VrURXK9uVlap4EomiF5EzpTXg2JOmXf9WcVH8WxMP06DJ\\n51NqtCNGg6z7oHPl+tNYqVsfMeI4Ho8DgLRzInJqjxLHqgkSr9HmQuM1cKzOdDodTE1NCdekykq1\\nXdvmeLgGBcbWMC2m0zmqEc3xA+hytnD8rVarK2iQe8F5UjKiaq3tJrr0COentQLgmIHpM0AixNim\\nYWBrawsLCwtIpVLPmEjIDAh8J9ea93BczMmj4JBIJERNpSpIGxVtbJSeKTlzvfReapVYq4E996/f\\nxHsRAvN7U3TTUgUHpnu0Mdxc69NEDm3Q1Wqffr+dfWlYIGGwkpKs5s3xm/MFusMfgG6Dtb5Hu/91\\nsqWV3cgkZicBbdNj7WQdL0Z3O3MOaejWAXBmB1t9uDXRpldHpwzx0MzNzf2/9r6sN67sunpVcah5\\nZHGmKImUyG61BnTattzdaXfbMRInfggcGEn+RP5A/kaegiCBnwIE7ocgE5AEAZI4jRi25IbVGkhR\\n4jwXa56Lxbp5INbmrqN7ayBL/hr4agMEa7h17xn3sPZwBLvRwCrXiB63XmoF0fxnjXL2yazzbgbn\\najNaCxyOV7PZFAbH+We/qTlxPNgHCljTi8r7lctlSWLulU5OTvDBBx9geXlZGBxNMxZBIwNhO5jO\\nRWahsUI9zm63W2pg8Z4ul0vMeDqkKDx0jh61aq5pnW5ES6AddZXLBjhnTev3Jt5Djst76Y3K7/XG\\npU2tJ47UycN0FS2pnSmqv9cMQR/2qM0wYmgmKM9FzH5rs5bf0QPCMdOmn6lxXrafs7Oz4i4OhULY\\n3d3F6OgoFhcX5ZQTn8+HRCIh7dJR99SSdBAj+0JJyWdpbYAMjpjEvXv3MD4+jp/97Gf493//d/z8\\n5z/HL37xC9RqNUxOTuKb3/wmlpeXsbu729OGLRaLWF9fl9LDPHWYXjauSY2j0dOlcQ+aOzr0QVdo\\noOag56rZPC+4pkMpqE1xLKkVsZqi3+9HtVqV00i6pRcvXqBareLnP/85xsbGsLS0hHg8jmAwiLm5\\nOTHPqNHS6XB2dl6JVAcqAxdaI9cg5zgUCok2BFzsQxYk9Hq9Mg5ay6JpDJxr2SxEuLu727ZfXeey\\n2Zkcdtfze31yqct1EWtBzkz1TqvnnHD+1myDE10VADa9VU5YlckYNCPWG9LU6kymre8HQCbTzmRr\\n18fLMChuAlaFXFtbw+npqSRAnp6eYnp6usXkILBNBkr8j9Umh4YuSvTqAEe32y3ALxkVsZlgMAif\\nz4d0Oo1UKoVMJoOvvvoKzeZ5JUve/+DgoOVcuk5ULBaxu7srp+B6vV5MT08jGo1ibm5OGAfxHWpU\\nBJYZM0OmosMDdOAuPY/UhjgfLIgfjUbfCCilAKbWpD1TvcZa5fN5rK2t4ejoSOp6l8tlRKNRMbGJ\\n+dEc1LFknOtKpSJzTWak96SufU+8iMnzTJ2h9sk5o+ZVq9Xk8AdWmux0usql3f78rzeuZlaMAuak\\naFBba0Zc7JyobDaLXC4nYejtnv82yM68ohmpzQhqL+yLCVpz4et4LH1/mmxaW3Jidk6m2mUY0tbW\\nFra2tlpwEL/fj/n5eVy7dg3T09P43ve+J8cvpVIp8cKwguPh4aEswFgsJvWcKJHHxsak7ClNwWaz\\nKeo8N//u7i7+8i//UsaZmzefz+P169fyjF7wlePjY/z93/89gAutm4GSDx48wOLiIm7evInf/u3f\\nRjAYxNjYWEtNdF0Fku5qmjqMUm42m/D7/YKPscYTT12mtsExIXbTaDSkLAdTKUKhEJ4/f47//u//\\nxp/92Z/1NJc89ebg4ABffvklAIgJTYa/sLCAeDyO2dlZWY909QOt+aQ0IYkXsc38jukkWviQ6dNr\\nzjksFApi4umUsUqlgr/4i79w7NOVqkLZSXsypkKhAL/fj2g0KmYYO6o1DUoJ4gesLknQzck0M0Hf\\nfpLWbvhMSn39XJMJc+ORGfF7bkp9Xw366dgefZ0ThmUyrW7J1NTIBLmoS6USdnd3USqVpLonmSm1\\nAko69oGaCM1XRvMGg0FpI/vDgEe/34/t7W2srKyIS1mHiujfUdvuhbSmSYbCKpWlUgn7+/vIZrMY\\nHR1FPB4Xb6+JdzUaDelXJpNpAbKpzbPvjKlbXl4WRwC1D135Qq8fCqtms9nRlGlHeh3ovWZZ50nB\\nhUIBqVRKrqMTg5qtds+TWVII07xl3JhpDTDUQWNvwEXiubaCeP921HMumwn82lGjcV6cKh6PS2c1\\nsKajf3lPHb1Nk83ObNIbnO3oJ1PSIDXbxdeaEVA6ABfVH8lYtPZDiam/N4M6TZPRJDsz2WSa3ZL+\\njXYUJJNJqaH9+vXrljHQr9l+Mk+aaHpcAoGAMCeaAC6XC7FYTILvXrx4IafTaFDYJD0HvRDHTG+y\\njY0NKU6/srIiyac83opBjAAkoJB94FljPEU3n88jm83C7/djamoK5XIZw8PDSCQSImQZaMl1z5gn\\n7dRgnhlLelymnwDe2JtkqEdHR0gmkzKPWrBqwanXghaQer3ovcBr+Vw7q8nElKmAtKNLHaWtAVzT\\nnGAnX716Je5XgntcHIzf8Hq9KJfLLRKS6rPH40GhULDdjNwE2lPVDzIxM91Pzf31pjQ1PtPE4ial\\nd5GTSKakAyJ1CRJzzO1e0wTqR781k9T5Rp20MfNz9o+v9ZjoygA65KNT23oxTc31qJ/BKhQu13kZ\\nFlbNjEajSCQSUpOIpTIYAZ1MJlsOtzSZJJlWOBzGwsKCwBMUVD6fT+ZYRzAzPuqXv/zlpeqGm+Ok\\n+0xmQkujneC2+860RJzmwA4ndaJu9uqlIrX1InXaQOl0Ws4so3pKDs3J1LEf1KCo3pleNZP0ffpF\\npomkub5uk2VZLUGDGv+xa7PpVTOlEknHIpn/zXb221S1Y3hOi8xOCJF0TBbHg33QaRvm+Hbbtl7J\\nqQ9cb1pi8/BDYjN0cWcymRaPlBZW7Ac1nV//+tdwuVwtMT+M96Ig4vgcHR0hnU5jf3//UnigXV/Z\\nPpPsBLv+zul6fV99rZ3gtSOn+zjRpUFtu83H92dnZ6IqlkoljI2NyYamrV4ul1sqStKEM5MR7QaR\\ndnm/yG6DmSqrngj9G5okOh/NnCTdB+0i52LWQXf63qZWao77ZUw2p347CRm7BWc+12kd6Pvp55j9\\nfJtkt4H0a2pqjAuiFk8Qlu9N09WEDBh4+MUXX0jQL7/X9de5D87OzpDJZCSJuR8Cxq5/5ro1P3Na\\nr07k9J3TmrcTpu3oyl42s1FswOrqKqanp8UkK5VKyOVyUpmQ6DzjPQig6s3mpEZ24va9LnK76+2Y\\nAT+nphMKhVoizU1PGjUeXRsGaK2SwH7q6Ge7hWSOeT82c6ffO2lB7a7pZbF3kpz9Ylh2z+FrCkXG\\n6XRDWlPmazKvp0+ftnyumZde0xpTpCDrhZzGxk6gmNovmWI7TVeT3V7Tmq5JJpTTTpiZ1PW5bJ0a\\nakpYgn4kemdYfoCMZXR0VDwtLEFCM0g/w26xO7WpV2rXT6fPddSpji63rNaoc82c7J5DXMKsLWMn\\n0a7az7dJnTaB3ete7tmPdnWidhCEHZmArn6e6aU1TX7z/lcRop32hp2WYqfFOpHT+uvl83b30tR1\\nYGS7m5vS0LIuSqWmUilMTEygUCiIXU7vWjabxdOnT/Hy5UtEIhHx9GSzWTkF02nS7BjV2yA7yUCV\\nm/VnGAymFyMZE3ARD6NP+OB1NFUpZdsB22Y73gZTsrtnL2PciwCx25h29JtiYnbr2GyDkxbYTht0\\nYlLA5Y8/ciJz7J3mwAkO6HRf83U315uft6MraUjmQ7RkbzQaODw8xObmJmZnZ1GpVJBOp5HJZMS8\\nKRaL+NnPfoZ79+5hcXERx8fHUjI0k8nA5/N1ZDpvkxm1I2Y+k8kMDQ21eGI0RqFTSRhn5ff7BXti\\neL8dDvV1oE6S0Emld/qtvqabfv4mx6KdGeRkdup+dHLG6Ht2em470mZ/u/mxgzk6CblOTKxbjewy\\nmr3L+jqt/AENaED/X1N/9cUBDWhAA7oCDRjSgAY0oK8NDRjSgAY0oK8NDRjSgAY0oK8NDRjSgAY0\\noK8NDRjSgAY0oK8NDRjSgAY0oK8NDRjSgAY0oK8NtY3UnpqaAmCfze0UkeoU2al/axeGbnftZYj3\\nOjo66vo3rKBnFxrfLtrU7Xbj7t27+PDDD/H9738fXq8Xu7u7+NWvfoX/+Z//Qa1WQyAQwPj4OKan\\np/H7v//7mJ2dRbVaxeeff47Hjx/j2bNnb5S10AmZOvfJjno5YFCfQdaJnNJXdPpDNzlT/PyqaT7p\\ndLqr68LhsG1bnNplftZtWgTJXDP6T59MY16v78M8t07HTGsKBoNdtdMsBcOz6CqViiQU89gkpjLp\\nkiysCBmNRnH//n2kUikcHR2hWCxK3/T17drE+W93Bl3HXDYddq47anbc6b1dzgw/Nzdbu9y1bukq\\nqSRmu53apv/v7++jXC7LUULFYhF7e3t49eoVRkdH8YMf/AC3b9/G/Pw8pqenEQwGhVHpM8v4DLtn\\n95Muc2+TOZsb2i5NxGmzv20ymbmmbtetkxBqd1QWy8fEYjFEo1FMTEygXC4jl8vh5cuXLc/gHLDA\\n4GWK/HfKP2M+JQ84uHfvnjDIsbExhMNhRCIRPHv2DMfHxwiHw5iZmUE8HpecUwCSgzk1NYWJiQk8\\ne/YMq6urcgx6Pp/H4eGhHJXUTrHoZv47lh9x6rDTRjLf2yUqOiVwdnrm2yK7yXVKkOR3XFDlchmF\\nQkHOtgLOy58y1y0QCODWrVu4du0aIpGIVAjgmXS9nDlmMsXL1EPqNLZO3/N5nXKbzO+cntFNQm03\\nSZ925DQu7YSq/t5OGFmWJRUvgYtDGvie+YqxWAw3btzAzZs3kc/nsbW1hZcvX0opY5YpYVI1q032\\nelCk2R+TXK7zkjbxeBzz8/P45JNPpHrl9PQ04vE4JiYmMDc3h7W1NUxOTuLBgweIRCIolUpyiGco\\nFEI4HJaql4lEAl6vF4VCAQcHB9jZ2cHJyYkjo3Z670SXqodkl1zXy4K0W2zttJPfBNmZpe2IbSyX\\ny0gmk1hdXcX777+Pu3fvolqtYnR0FMViER9++CGmp6eleqbH4xHJog/R6yUhs5052W0/nTTXdr+z\\n26hmLZ9umc3/K2q3tuzWIDUYt9uNpaUlhMNhWJYFv98vWhFP1xkdHcX169cRiUQQiURgWRaWl5cx\\nOTkpp+0wufz09BTxeBzXr1/HwcEBjo+Pr9Qv0yzmCcQPHjzAnTt3BH4xD+CMRqOYn5+Hy+XC7u4u\\n8vk8SqUSzs7OEIlEEAwGJQG8VqvB4/FgYWEBlUoFExMTuH79OprNJg4PD7G9vf1GIq9unznGdtRz\\nCVs7E87p2k6f96oV2THCt0X6/lR/9XFG+prt7W3813/9Fz755BM8ePAA165dw4MHD1Cv1/H+++/D\\nsiysr6/j5OQEExMTqNfryGazYsNr09XUKk012I75X6WP+pl8z0MJ9Pe6rng368BJZe8G2zHf97NE\\nRzfMSF/XbDYRj8eRSCTw4x//GLOzs3C5XAgEAnIyLs+xHx0dRTQalbHx+XxoNpv47LPPpJrD69ev\\nAZyP8QcffIDr16/jl7/8Jf72b//2Un2x2/TDw8MYHx/HzMwMfvSjH2F8fFwqStTrdTlwoNFoIBKJ\\nIBAIoFgsIplMIpPJyKGaExMTiMfjODk5EYE6PDyMhYUFKQ/Egz3X1tbwV3/1V7Z7WpvRnagnhnTV\\njXBVrcdJi3objMm8pwni68XAMqc80CAcDmNyclJOrXC73ZiZmUE4HMbIyAhOT0+RSCRa1HQ7TM2O\\nGfWjXyYD7PSdiZ/YYTTdam92TKkb7azf5DSeds+KRqN49913cefOHczOzsKyLNGK9Nl71KaAi6qJ\\no6OjmJqakuqhkUhEStRMT0/D4/GgXq8Lo+qFnMzn4eFhTE5O4saNG4hGoxgdHRWGwpLRPJ6IJ+xO\\nTk7C7/fj+PgYY2NjiMfjcmqQy+VCvV5HpVIBcM5My+UyLOu8htfi4qJgYPp4L7bL5bo4Iqxvp45o\\n0vWg+7FYnMDQTpvwbZt21ArMSn/62ZwUy7JkYfp8PkQiETHJeDifZVlSM8nu/LXfRB87maZOz+sW\\nC+iGaTrNt9Pz+tF/cy1pLK5d2yzLQjAYxPT0NMbHxxEOh4WhaKnPjUjsRt+Ph0ry0Ei32y2F/y3L\\nQiaTwebm5pX7pykQCGB2dlaYpvao6ZNPuI+9Xi/i8bgccMnjxTOZDNLptDAznlqrD2yIRCKYmJhA\\nIBCQ+5tjSqbUiS7FkOw2aC8SRze0HdktIODNynt6cfVr85raif5vEk2wg4MDLC4uymkqw8PDchwO\\ncH6aZ6VSwfb2NtbX15HNZt+QJOYzOjGsXsiOoXZLpinZ7v69ttdp7TgxjctQu/vztR1zdrlcCIVC\\nmJubA3B+VLcWyFry836sEMqKojxphNqJ2+2W+1SrVTx69AiHh4c998nJLB4eHkYsFsPMzAwqlYpU\\nImUbyWzOzs5weHgo/bh9+zYWFhZQr9eRTCaxvr6O9fV1NJtNBAIBAbz1EWQUuAS88/k8crncG2uF\\nB1n07aBIu45fxYxop9J3+p1ZVVFv2n4zJVNS6/881oacf2RkRM5Ap6TQZWz9fn/LYrXrs5P20E9z\\nrdNndr+z+2/XRgoL9t2Osdvdu91nb1MLbkd6nk2TDICsQ2pLLperJaaMYzE8PCxasT5MoFKpoFgs\\ninZ9WTLHl6ec+P1+AJB7253U4/P55DVP+NUmnd/vF5O9VquJVseqqNQIaYJaltVSN1+PYzfz2DND\\nslPdO5kc+rftcKB2C5a/pQqqOfBVjwOyo3ZagNkmmmpcjI1GQw4ipETweDzi8qcNrr1s3T7/T27T\\nuAAAIABJREFUKuQEOPbyO5KdQKIpwDE5PT2VoNN299L3s3v/NsbCjrnarWluRh6jDUDixzTDoslG\\nxkUmw81Ls15fm8vlUCwW5YTby5KdpcIjzYeHh+XASj2ebLc+XbheryOXywlmBAB+v19+X6/XW07S\\nISMiY4rH43KqkKkJdas9X+qgSN0pUq/YQbsFYf7GsiwB38bHxzE/P4/r168jmUwinU5je3sbR0dH\\nLUfL9ErtGGI7TYXf0TyjhKFk8fl8MvE8DODk5ATPnj2Tww70fczXb4M64UDdSDTzmqGhIQQCAYyN\\njeFHP/oRvv3tb+P58+f4yU9+IifQtFsjJhNg4GihUGjRJq9K3eBI+ntu7pGREXi9Xni93pZz6ikk\\nyYB0xDO9cDSXuB4ajQYqlQpWV1exsbGBtbW1S50zaLc2yTxDoRDGx8dFa6PnlHACmSvhF574k8/n\\nRZjQ6UJTk1ovGRKZWSAQwNnZGRYWFlAsFrG1tYXT09NL8YievWyXxQlIeiE7NdjEhXhMcTgcxu3b\\nt7G4uIiZmRnkcrkWYI6/7ZWcjh3ia1Nam1ofmQo1ILabzFEvAB4GQOZk1/d249YP6vZeppmq26nv\\nw88CgQCWlpbw/vvv48aNG3C5XJidnRUmTGlq3p9ETbfRaCAQCGBychInJyfY29vrS7+dqN3GodbD\\nk2ddLpcAvGQyesNbliWbd2RkpIUpaQ2fWggDansNbbATGOyHx+MRsw041+h4kCVwEYtkpzXp8wXZ\\nTn2ggMaOqe2xr/F4/I2I804WkEldM6R2i7gb3IekPVFsYLVaFZVWMyue2UaPQTQaxZ/8yZ/A7Xaj\\nXq/jq6++wubmZstJsN20x6ReTD7z3pwUv98vsSkARMKwLzwYk0Fn1Wq1hYEBzp6sfmJivF+7e5qL\\npxPoDADT09O4e/cu/uiP/kjibur1Ou7evYtUKiWmjNYWeG8yKuJxfr8f8/PzeO+99/DixYue8hK7\\n6X+717q//Nzj8SCRSGBoaEiO1+bG1eaLPrGWAkiPNRmBNnO8Xq9ohL2QkzY/MjKCSCQCr9crmvnQ\\n0JDknmnPMSPHeT9+xhN87ZgkmRWZEI8QPzs7w+zsLILBYMs+NBlep7XcNUPqZgL1d06LmEFX7733\\nHhKJBAqFAl6/fo1kMilqHgdtYmJCsJehoSFEIhGEw2Hs7Oxgc3NTPBVXYUa99NMON+EC83g8KJVK\\ngg1RUgQCAVkAp6enqNfrYntzETgxxG6lSi/khNM4XUPSBx3aaUkffvghvvOd72B2dlY8i4VCAeFw\\nGLFYDKFQCKlUqkXtBy4O3KRUDwaDiEajsCwLjx8/RqlU6im9xok6QQRO65ZMw+v1inbE66gx0azj\\n9QyUpClHJszNXKvVJML/+fPnODk56Vsa0MjICMLhMPx+v+wbMkm2n1oPAFmH2ktIQcn1oQ9ENcMd\\n+NuzszNJMbFjZN3uz0tFajs9jNeYn/F1s9nE6Ogo4vE4vvWtb2FmZgaZTEY6VCqVJEQdAN555x2J\\nCGXHfT4fcrkcVldXkc1mxY432/k2yG6xaonH5Npmswm/3y+4F4HFSqUiYCFB8E5a0NtgSmZ/urm/\\nEzOiFP7ud7+LTz/9VDYAY1cSiQQmJiZwdHSEZDIpv9Xz5na74fV6EYvFMDY2hoWFBaytrWFjY+PS\\nmCCpnfDs9r6WZQl2BEDmUDNV7fml0KUmRcCYgqhWq+H4+FjwI17XCzkJErfbjWAwKFgVI8hHRkZQ\\nq9Vs4QhqQ9yHXNP6JGXdTwBistLi8Xg8CIfDwri7bbNJl8aQ+L7dNVQRtRSZmJjA7Ows/uAP/gAL\\nCwsol8v4zne+g1wuh3q9jtPTU5GULpdLkvc+/fRTxGIxPHr0CE+ePMEXX3whp8BqrwEHth/kNOma\\nwbpcLoTDYdy8eRMzMzOYmJiQAx+9Xq94ZCqVCiqVCkZHRxGJRDA1NYVsNot8Pm8LaJtj10+zzQlA\\n72Tvm/PdbDZx69Yt3L59Gx9//DESiQSOj49xdnaGGzduoNFoYGpqCh999BEsy8LY2BiOj49xdHQk\\nycUulwvBYBBer1e0pUqlgn/7t39DLBbD/v4+Xrx4caU+2zFSs09mfxlbFIlEEI/H38BOCBNwgxIj\\nYuAgvVUUpAAksJCY2sHBAbLZ7KVizZwwpOHhYdy+fVsCcQOBAE5PTyXeiAxKryviY2b/qL2TMTG4\\nk33hfJHR0SpwctT01WTTD9A4jxNT0tqD5qyFQgFbW1v413/9V1y7dk0A3mKxCLfbjcnJSUxPT2N4\\neBj7+/uiAlerVeTzebx8+RK5XE4GgVKBqiJBxauStvu1e95JQxobG5OJBSBuUmpCXOCWZUmeUK1W\\nQy6Xa3mm3dj2G9Du9HknRkxyu92YmprCBx980CIZPR4PPB6PeN4CgQCGhoYQCoXEA8TrzNNXKXHn\\n5+dRLpdRrVavxIw7YWXma1NjDYVCkpPGPgFo0Sr0eidoXa/XBdDWZUb4n1oL73VV0us1Go0KNuX1\\neuVYe7/fL/NkJkUDaJkLfY0O/uQe01oh+8h5dwLou1nHV8pls1P/zAVAlZ6A3/HxMVKpFP7mb/4G\\nwWBQbFIO5GeffYZoNIpwOIy1tTVZ9JlMBoeHh1hZWUEqlYLL5RKsptlsSg4ZGVc/yM78tBsTnVzJ\\n8hLUktg/gqDDw8OIRCJYWFhANpvF8fGxrbdDa0j90o7a4Ue9mjEEcycnJ3H79m2ZB96L2irHhgA/\\nmRP/GK1Mc5bu5MnJSeTzeezv71+5/3aMvRssDYDgR8xLHB0dFaeE1iw4ZzR7arVaC9MhkRlx7V/V\\nJNV9oHD0+Xzwer2CF7HIms/nQzgcbqldRCFprkGuWTMHjcyG80tmRDjFZPJ2Wlw7ulL5ET0Rpgah\\nvQzValVAXQZZMYCKg8KNu7Ozg4cPH2J5eRm1Wg137tzBnTt38M///M/48ssv8fTpU4RCIXz729/G\\n7du3EQgEYFnn0aGVSgV7e3vY3t6+TLfeINP8tBtcTgYxBpYe4WtiZC7XeYZ4vV5HKBTC1NQUgsGg\\nLIR2OFi/tCOn+2syTRvdb2pu1Bbu37+PH/7wh/jGN74hOJlONCWoS3wwmUwKtkbNsVKpCPZWrVYl\\nkDIWi2FxcRGbm5tveCMvS3Yake6nfk8hyZpF0WhUNh//2C5uTAod9p3MinNMzYmmFYWXHQ7aiZxM\\nasYFMXXJ6/Xi4OAAT548wcOHDxGLxaQNZC5673KOtZauNSbGHpXLZTSbTdRqNVnzdEDZmWzd0qUj\\ntdkBJ5NNv6YnDEDLIHCj0kXcaDSwsrICt9uNbDaLO3fuCOayt7eHg4MD1Go1zMzMYHFxER999BE8\\nHo+UdTg4OGhxO16FnKSmqTWxHzo4Tv+OsR9er7flGgBiymn73a4Nb4MhOZEdxmQyZoZiXLt2DTdu\\n3EAsFpM51Bge1wcL2bEWkE4sZqKm9rACkEBEM3apX33UfbXT/Pk520PcRUdUM8hQe0qpGdPdTgxG\\na73sp45o7ieGFAwGZR48Hg8qlYp4OGmt6HnltdSodL+BN2OkdOS5DgPQ/XTSuPtqsnV7U6DVHh0e\\nHm6JWSgUCiJVOCAcTLfbjePjYynzysTVarWK8fFxfPLJJ4LoFwoF5PN5FItFbG5uYmtrC+l0Gtls\\n9jLdsu2n04LV33NxcVL1hKVSKcHRGL3tcrlk4WpJ1A2Q3M/+2DFXu88pwQmA3r9/H4uLi3jvvfcQ\\nCASEsQJoKcsBoMW7FAwGxaRhmgnNWo/HI+YvX7OkS69Bg2afnd7bmcl2mikFqmYeZhAsr6tUKsJI\\nNXbEzc5n+f1+wab6IXS4/rjWiCHxmYyfGhkZaQlBINPUILapHfFzbZLmcjmMjo5KvpxWNNqZ/30z\\n2XQDSXytGYrOZwIu4ldGRkYwMTEBn8+HZDKJZDLZoqrqjmezWRQKBWQyGdy7dw+np6e4f/++4ERc\\n0Ht7e8hkMigWi3jy5Am2t7cFIH8b5GRW0aXPEHvtFk4mkxJ5TFC0VqthZ2enJQlRT6KdVtIvTclp\\ng+pNz+fpuJmbN29iamoK09PT+N3f/V0sLCwgFAohEom8cT9qgQx05NzGYjG5jvcmyBqJRCTGp9Fo\\nSHAeTYF+kR7Hdto913EgEEAoFBJNh0xZ99XOpNHmLa+ltufxeDA2NiZe2MvksZlrRJe9CYfDAhvQ\\ndMtkMhIGYObYcS5M01z3TzO809NTrK6uYmpqCnNzczLXNOXHxsaQzWbf6JeT0NV0qcBI/Z6TwcHm\\nQtYSh+qcx+ORQmX5fB6FQkE6y4HQnDadTsPr9Ypb2eVy4Sc/+QkSiYSYCZZliU3MdIx+gcBsk530\\nZBtZVoIYSDQabZE+vIc2587OzpBKpaQ2jhkc2c3EXbU/fI42F80N5nK5UC6XUavVsLS0hAcPHuC9\\n997DzZs34fP5RNrS9OZcMCiPuCHNakY0a2an42TI2CnNXa6L6O1eyYl52wlVO2ZPrYjlaIGLrH3i\\nhlyvGvhlATa9yS3rIleMz/R6vS1lcC9Deo1Qy/T5fAgEAi25dFoL0+Y0YO/h0+tWCyy9bnK5HOLx\\nuDA8MuxAIIBYLNZSH8oc/3bUliHZqV52E8gwdU6QKd2JFSSTSdy9exd/+Id/iFwuh8ePH8uC1hyZ\\ng/nnf/7nsgH+93//Fy9fvsSvfvUrLC8viy0ciUTg8Xjw/PlzKflxFRW/lzEgZbNZrK+vo1wuv4Gh\\nmAAo1d29vT3Bl7rRftpJ8177oyWg9q6wvS7XOSg7MzOD73//+/jBD34gpSjoUib+kclkUC6XBcSN\\nxWKo1WoSXxMKhUSyJpNJWNZF5LLLdRHhTGbE7xKJBILBIMbHx+Waq5Kddm/3GXCR5sEg3UAg0JKO\\nQSFIpk4zjeOpvVLa1GPsHBORJyYmJOv/smRZ58GYc3NzmJiYAHBRkQCAeAWpFek17Xa7Wxil6TUk\\naWxveHgYqVQK4+PjqNfrcvSUy+XC+Pg43n//faytrUntJD6rm3Xe8RgkO9I3JQemeuj020ajIfV6\\n3333XczOzuLly5fCrHhPYk2BQAC/9Vu/hUAggEKhgF/84hd4+fIl8vk8arWaLHJKK2phlwkys+tf\\nOw3FBH2r1SqOjo5ksWnvivbKcMOXSiUx8XgPO/yik5bUqxbF8AhKNarsVLkJ0hJQXl5exqeffooP\\nPvhAcD3taeJvqMXQxc+0EZrrrANFbRJAy4alp43/2U5ufl2zp1sycTCncWuH3wEXFRY1U9RzrC0C\\nt9st4QB0YPD+1Aw13uj1ehEMBlGtVq8MMxAWIU6pE5W1+ci55r5he4h9aiZLgaX7TUbNGDGuHzJp\\nv9+PRCLRolxoq6dvJptJmoNSHTeBLVNTIqfmyQXr6+tSWJyDFgqFcOvWLczPz6PZbOLJkyfY3NzE\\n06dPkUqlZJCHhs7Lwubzeezt7aFYLL7h4bosdXsPTly5XEYqlUIul5PCVgCEYXKT8d5kRtor4fRM\\n08S6av94vhYrADL+hy7uSqWCpaUl+Hw+XL9+HWdnZ1hbW5NDJumUoIZA0LnZbCKfzwtWwXYXi0VE\\no1E5bYWxKszxY5Dr6emp4EblchnxeBzAuXQ3aypdhUzNyIlxcUMSM2GMlC4Zw7kjsyGGqDc20AqK\\nayYVj8cxPj6O09NTWdu99MM02YhfmRYH16OOpGY72FZNWrHQ65ZhBG63WyoVnJycYHZ2FsPDwygU\\nCqJMkJnZhRa0o74W+ad3hQNgpj6wpKfb7cbDhw8BAI8ePcLTp0/h8XgwOTmJjz76CNFoFLFYDP/4\\nj/+IlZUVbG9vSyg+6/bShMjlctjc3MTu7m7fAwlJ5qI1F3O5XMbBwQEqlYqowWQ66XQa9Xpd+t5s\\nNt84vaSbZ+rPLktTU1P4nd/5HXz88ccS8Ee3OyV0sVhELBaTqN5sNouXL1/iW9/6lqj7rG90dnYm\\nJkIoFEKj0UAqlUKpVMLY2JhcT+ZFDZbepVqthlevXglepIUWN8Do6Cjm5+cv3WfTTDDBWhOC4BgH\\nAgEpdzM1NdVyQrBOEOacss2sYuFyuSTmjoGyjUZDMLWhoSHMzMzg4cOH+PLLL3uuqW1q09RSGP3O\\nvcBnaiwIuHA2sf+0MjjmOutB57QxxWdkZATZbBYrKyu4e/cuIpEIMpkMRkdHkUgkWioaOM2FHV0p\\n4oz2MrUjj8cj6qA52WRI5XIZLpcLi4uLKBaL2N3dxdbWFkZHR8VEyGQyKJVK+Id/+Ic3Mr31IFnW\\n+blo2WxW8Ji3DQjzvx5YRuYyVYIBoGxbo9GQUAcN7OrFYdfubiawF1pcXMQ3v/lNCWRk2wFIGkuj\\n0cD+/j7y+TxOTk7EVGFwK+d1ZGQEhUIBuVxOsKWhoSGEw2GJFvb5fLI4aboRwC6Xyzg+PkYulxMg\\ne3JyUiQ8++33++VMsctSO+0TuMA5NVPiyTHXr18XrIRMSMeRmaY5tQOtMZHRUiOmdhKNRjE9PY29\\nvb0reRJ1P7Rmx9g+4kQ0l/Va04xLeweBCziG9+W6tixL6nXv7u4KIwYgJja1NFMAdEoivnIIrGVZ\\nEjuiw+TtVDSv14tGo4GdnR3cvHkTN27cwOzsLN555x2Mj49jcXFRNJ6XL1+K1Nb5QsQxKAk46HqD\\n92MTt2MGZt8okShB3G63nF9eLpdb6stQkjG0n/fpxmS7Ko2Pj2NyclJCJ3RlBUbZEpAmfgNAvGbU\\ngCORiAghMhjOPzVASshKpdKSXMoa0hwXAOKuJgBOLIS/66fb3wlTMjHM5eVl3Llzp+WIH24mvdn0\\nhuXGpjZIDUk7D7RWEo1GMTMzI4mwvfZD/4bOIc4TE3nNMBQzwlz3SWtNeo1rTyzvNTc3h2fPnuHg\\n4EDMWROjtBv3Tg6nnhmSHgg2dH5+XhhEpVJpkTYul0sayTOinjx5guXlZdy+fRsPHz7E0tISlpaW\\nkE6n8dOf/hRffvkl1tbWJGGWUkbHdLCglQ6y6+fmZT/bhcHrZzYaDTk+hsF+jUYDh4eHqFarAnae\\nnp7C5/MhGAwKZuOEIzlFcF+WxsfHJTiRKjrvrwvC8zBLagUAJPeK/dTR9QyQ47yUSiXp2/Hxscxj\\nuVxGqVRCOp1GIBDAyMgIxsbG5LhmahX8z/mm9nsZMk2xdteMjo5ienoa8/Pz+NM//VPcunVLmKHW\\n1HW0Nc0djhML8JEx05zSQDIZQTweRygUwosXL3quUGG3Vlg1gRoLcUHW6KLJTMWAe4pMlGNueszJ\\nlACI+f3gwQPs7OxgZWVFEsR1QUIydrO9fTHZ7DYLbzw8PIwbN24I3lAqlXB4eCgNs6zzc62mpqbw\\n7rvvYmlpCQCwsbEhXNnr9SKXy+HFixd4/PhxS7lTM7WCCbVmRLAdgHZV0up1Oy2Gk6oxIjLoUqmE\\nQqEgXieX6zwwjqeFAvZ1lvh5P2llZQU7OzuYnZ0Vpsk2sepCrVYT7YR/xD+4CRmExyhsLmb+6c1r\\nWZbkg+VyOQwNDWF8fByhUKilLjMAqTzI//TYkVlelZwYPxlLPB7HjRs3cOfOHTnDnt8T89JapU6J\\n0mlQHCcydI3raI2KGibxvMuS3hs0z/hctoVeMf189lsLGmq1DBugJkwcjGs1kUjA7/e31HriftV/\\nmtF2A6d0FYfE1/qPHR4eHsbs7Cw8Hg/8fj9SqZQEwnGQPB4PYrGYMKQXL16I7cn6N4VCASsrK3j9\\n+rWofGdnZy1MRzMHPoObwW6CrkImE2p3T8ZN8TUnnG3keWxkmkx9SSaTb0ya+axuJUs3tLGxgdev\\nX2N+fh7hcBjxeLxlEXF8Cbpqk1QzGQKfDPbThwYSN9EJtFzotVoN0WgUwWBQorZpVvD++qBBM7+t\\nF9Iaupby5nySMbhcLsmRfOeddxCNRmWjambMsiLEMmmecc2aADKxRY4jzWAyXOI2V0kgZt/4HAas\\nst86PIPhOVzbGhsjA2LyLK0QmtGkRqMhyca8liEvHFu9Z00TsB11ZEgEJAlkskMzMzNYXl4WUwsA\\nvvGNb2BmZgbvvPMO/u7v/k449fr6OlKplESQrq+vY3V1Ve75+vVrPH36FMViUTwwQGtQGYmD4/P5\\nJONYa2P9ok5Yg9kml8slJgk3os/nEze5PqdtYmICCwsLSKfTjgXsNSPqlzmaTCbxT//0T3j06BF+\\n7/d+D9/73vdQKBSkcB69a/Qa0TvILH62i1n61HAZHBcKhcS7pBciN/2tW7cEW2HQnNYUdCqR1+tF\\nKBTC6ekpgsFgT/3ksxm9zI2jMR4e6ZNIJPDuu+/i7t27uH//vtQ/sixL6lCzD9QogVYshMyEfWOe\\n2vT0tATC0ozj/nG73QiHwwgGg5ibm0M0Gr30vLpc5wGmoVBI5kAfTMC1FAgEEAwGpXInBbsWNlqz\\n0oyLPIBa4sTEBOLxuNRbyuVycvBGNpsV5wTnQ7e1HXVky3qz6wBGJrYyBaTZbIpHyePxYG5uDqlU\\nSlz0jUYDGxsbUmqCEqVer0sWvxnPpDugNyXryZAh6fijbtTCTv3Vz7Qzn0zmwA1cq9VQKpUkxsay\\nLDnCR4fvj46OIhaLSW0guza8DarX6zg6OkKj0cAXX3yBUqkkZXb9fr9sJG5iFlIzzVH2x+12SyzK\\nzMyMbFhGXFP1J86nKy+yWBvv1Wg0UCqVYFmWOC58Ph9ev36N/f39nvoZjUbFwzc7OytALhkSBRpN\\n54mJCalDrRN7iQ8xL4xaCDcnBSbXK5kCNR+uaZpSTETmGNA7y/VwWeL9yEy0ENcxYcBFwUGgtZ62\\nToGh9sY1y9I6tEYoWIlXEW/0+/0y5wwPAFrTU65kstmZSpSK1WoV2WxWsuupGTDOhKdY6nQBxguR\\nw3q9XmFsNM+0ixBoZTCUVlxI9CjoOJZuOt2O9PPNZ+tr9GtuVILXOo6DEbu6XTRv2/WV1I4RXqZv\\n6XQalUoFuVwOqVQKk5OTgtUEAgH4fD7EYjHE43EsLS0JI8rn8+LSpRZ0dnaGZDKJw8ND5HI5iWtK\\npVLiTeO11WoV165dk2ju69evCzjO8r6MdKbgsywLm5ubWFlZ6bqPTF+h1/bevXtoNBoi2QnAT09P\\nS38YOU8chxtOa6f0YPEZ3MR643o8HmHiAKRfNOnpWaUmRaCbnsurEPPYaEoDECeKrlOlNT0yK13D\\niK/JTDlHZiqNTo8ivkbmHQwGpR12CbbtqCsNyazZYlkWCoUC1tbWsLW1JXWJ9vf3UavVUKlUJJaF\\nv2MELgeFhcto0ujn6XwaTcRfKOH29/eRTCZlgZgegcuQHcDczhzUOEs+n0epVJLcHn6vcQqaEuwD\\ncRgnE9HuWVfpGxdaqVRCNpuVuBmmDrhcF/WOmIYAQLyEurQK70cMRQPgGveh1KVWwiRUArBkGGwj\\n1xzXx8HBQdd9XFpawg9/+EPcu3dPKiRqoUJhRqlfLBalyJ8GqbWgITPRgZD1el0wMy2QWHhQmyv0\\nLvt8vpbEa2rLMzMzuHfv3qXnlSZaMBgU3Mfn88lJLxx/jRXRrNZOBWY/ZLNZlMtlOdyU2g4dGRoL\\njUajODg4wMbGBu7evStxdrpcLsM7uhGoPSNpnLDT01M5EYSqPrlxKpUS801vZk4A3Y68Xpcs0SED\\nZrAaB40ua2po2pTsN5ltMbUarQWweh5NBAK+uv2UTCY+ZsfwdJ/6ZcbpHCViXubYmUAw283vdO0c\\nu/bye1Pb1ICqrhKgT93Q2iSldy99v379OhYXF3Hr1i3ZOLw3zU1qaXwutQZKeWJZ3MAEiTUeQ6ZE\\n7Z9rm/maNEnJCDSD0oyPXua5ubmu+2gSGTjHXWM+DDolrglcmODaqhgeHhYBxNAUwg0aJAcu4pcY\\nL0Yrh3OrFRgTP7qSyaYZhJ15wU5PTk7i2rVr8Pv9ePz4MYrFolQBNCcyHo9jcXERa2trOD4+bvFK\\n2JmIZgdCoRDq9TpOTk6wv7+Pk5MTOb6ag3VVk62bz/R3VOnT6TQ2NjYQCoUQjUZbAjsZCMhIbpbc\\nNRmbE27VDzLva/cczjlf60xwfZ3JjPS4280nyckjaprHfE1J3i199tlnmJycFA8nUzq0psvQBo/H\\nI6fEMMpaY0dAq/mm/wCIcA2HwyJo0um01ASKx+Oo1+tYW1sTr2atVhPMhYG0ACQF5zLEeeIaI9MD\\nIIep0vRqNBoIh8NvxEUxih6A1H/Sc6zxT7r5mbeWTCaxs7MjY0tGRsugF2rLkAg8s4PatKBNShME\\nOHfZ8ogf08zhRFIF58DZNdjJbOKpnH6/XwrMp9NppFIpHB8f9y251myLE9Ct20cpSM1HS156qvRm\\nNLUMJxxJaxjtxqiX/piv20kyp/boa83fdduubiSm+YxORGyOgpQaDwBhBpZ1XslTrz9eC1yENdBh\\nwvVG85baBDEkQhhMizo7O0OhUBAAmfgqn63DHBijxE18VaJ2x3Wby+XEo6lDaNhPMg2azjQry+Uy\\nQqGQYGC8XpcLAiAlo7VnlaEEPp+vJQeQ49yO2jKkoaEhSZpkaQiTaF+TYQUCASQSiZYAMa1F6LIF\\n5uABeOO/ltjadmX9l3Q6je3t7Z4jXbshkxnpz83PaKdzjKj6Msq4XC5L2VqacjpkgX01tRAnRmhn\\nMnVD7ZgNn6mj0+20KlN71XNoalhOzNUUWE5aca+Mt16vtyQO6yh+fbrJyckJyuWy4Hk0ach8yGhG\\nR0cxNjbWEpOjc9qIHdH1zXlnmgz3Dj1sOnZJm1ZXJd5T50menZ3h5ORE8g11lDiAltpIACRfjRgQ\\n00+4dqmAkAH5/X4Eg8GWk3H5n5Up28XY2VFbhsRsdc1UtA3N/yy7YVmWmCFTU1Mt8Te8lkXMdNRq\\nu4bqz/1+Pz7++GNMTEwgnU7jq6++wvPnz7G3t2drHvSDOpmAGtsKh8MYHx+Hx+NBMpmUfLxqtYrD\\nw0NkMhlMTk5KrIvOD9NqtsZvOFZ2mkiv6rDZDxOvMU0tfmc3R3y+Dmcwr7FjTJ2YjdNrMPfDAAAG\\nFUlEQVRn3dLnn3+OV69eSc2tW7duiTlCZuN2u1vSUTi+jBIHIKEPZE4aqOfpNul0Wk7kJXD7x3/8\\nx7IRx8fHAZzvo2g0ing8Dp/PJx69oaEhyd386U9/ih//+Mdd99MkOknojKAD4quvvsLW1hYmJydb\\n4sqAC6HGUAg93pwHaoE0C8lIiR+Hw2Fsb2/j8PBQNCPGQ2nXP+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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 17 - loss_d: 0.313, loss_g: 4.249\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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JD1O9/5Do6OjvDRRx9ha2sLR0dHgmWNSoM0yn5EULter4uJQ8bU7XYl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\\nhv4fs2cj7zgQCcIAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 18 - loss_d: 0.255, loss_g: 4.637\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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3JBxsjyZrMp5jGRD+fBZDIJQUunivH5GjnZbDZEo1H4fD7xthnHbVwO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\\nDd3Q94ZuGNIN3dANfW/o/wNpLtphl+dS2AAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 19 - loss_d: 0.283, loss_g: 4.438\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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mHYz3p8AoEAotGorDu6Y4yK0erXbdSuPudMC2rioolEArFYDPl8fqg+\\njhRl0x0yBZA5QGROu8115jv4vdPpRDKZxOTkJGKxGBKJBDKZDDY3N7G1tdWV0v5Z4A2aGUOhEILB\\nIJrNJmq1mkQSOFHawtGmLjUCJ5NMTAvIzsphZne73Ybb7YbX60Wr1ZKcDm41CQQC8Hq9kus0jpmv\\n50FjR72uZZvN/Vl63k3tubi4iPn5eTidTlSrVVQqFeTzeTx+/FisDTv+6XS687PGnc9eWtgUoBR8\\nOm1DKz3dNlOQMvyt++JyuWRuGHHy+XxYWFjAyspKF4Bs9n/Ufppjb/fbMPdTIXI/ppkvpOdf8xuF\\nNy1BvQ70+o9EIqKUh0lVGSox0uwIYD+Qdhqtl0tnajFOXjKZxLlz5zAzM4P5+Xk8fPgQDocD+/v7\\nEnIn6Y4fhcw2MiPV7/ejWCzKe5noxuuZmUoBReJnvfeHbpt2ZfiZzM0MV4b7iUUEAgGpMOD1ersE\\n86j9NDGafi6b3VyZli6JjMhUiS984Qvw+XwoFovY39/H9vY2dnd3UalUxNrlc3Rel9a0DJeP0s9B\\n1jsAEUCcSx0908Tv9Dyxr1QUnHsdxNCClVnc3HZkt37G5eNBikT3uZeQoiJlagrbz/5ooa3bSHyN\\n1wHocn39fj9cLheSySTC4bDsiRtkJQ0USOYA9hoM/Tc3RwJPlyjhd0y/B4DTp0/j7NmzeOGFF/Dd\\n735XokudTkc0zt/93d/h7bffxrvvvvtUu7TZfRQLicld3NMUj8dRLBYFrA4EApIjRKA7Go2KwNGL\\nl3kcDA9z4dHto2Zi29PpNBYXFzExMSEZxKlUCrFYDKVSCT6fD1euXMHdu3fx/vvvD5XTYUfaGrFj\\nUlPRdDoHuJY5n+a9TqcT0WgUV65cwZe//GVMTU1hbm4OS0tLAgr/4Ac/wMcff4w7d+5gbW1NBM7S\\n0hIuX76M7373u4hEIqhUKvinf/on/PjHP+7ik1H6aPaXFAqFkEgkpK86lYJWKgCxjDmvlmVJ4iqf\\nGQwGZaEWCoUur4DuD/mGuWamYNfjflzUiy9MF7zZbGJ5eVnWH6PGVJ7AoeuqrUNG3MjH/E4rVgBI\\np9O4efMm3njjDXz44YcoFosD2z4w7N9vAPV3ekC19us18D6fD16vF4lEQhZiKpVCKBQCAJHUTDtn\\n/g+ldafTwdzcnEz67u6udHjciIXeSxaLxeD3+0UTMhRMjcGNstSwpimv/W8KLDKEXtTsD7Gh1dVV\\nVCoVZLNZ1Go1wZLy+bxsKyEdBUfSc9HrOq359PV22MTly5dx+vRpLCwsIJFISJRRu0LJZFLGdn19\\nXSwq1pVKJpPwer2IRqMIhUIyhqMuVrOPppXAbRLaEtAYiQnA0oLTz6a2124bBRwtLt4LoGuRm20c\\np4/jkOmKtdttpNNpXLhwQUB3s02m4te5SJqP+R3HknzPIE0kEhHMtR8NBLWHwRjYEXPi7RiXDeLC\\nv3DhApaWlmBZFuLxuOR1cGFz8TPUzvB7u93GxYsXEQwGkc1mUa/XUSgUjjyxiUQCp0+fRiqVgs/n\\nQyaT6epnq9VCPp8XN840Y2lNUZhqMi0L9k+7Brdu3cLu7i6mpqawv78vmnd3dxePHj3C/v5+V27T\\nOGRqSuDpcLudZWx+1vvqXn75ZTz77LOIx+Pwer0oFoviAtBNCwQCEr3kXiev14v5+XksLi52bcUA\\nDpl+XDL5liH8QCDQ5UIzqsnNz3pMtIWkXXG2Syspwg58Lhew3ZYJs23HFWUb9B4SrfPz58/j9P9N\\nTSDpfuu26YRH7aaawprWpGVZUkus0+kIBNKPBgqkXr5oP+2sNalpMjOy9J3vfAdnz55Fp9MRkzEW\\ni3XleESjUckDotVx5coVXL16FdeuXcPNmzdRLBbx5ptvYnV1Fevr62MxsWVZSCQSSKfTso2AdW50\\nftDc3ByKxaIwNd0tEseE2lZrUT0uwKG/bVkW0uk0isUi7t27h3/+53/G3bt3u1IOKMiKxSIymcxT\\nFsso1Evzm4rFHEMuSu0iz8/P4+bNm1hcXMTv/d7vIRwOY319HbVaDalUCl6vV5JKW60Wbty4geXl\\nZdy8eRO//vWvkc1m8eyzz2JxcRHLy8tiibIYGu8fh0yFCBzgOfPz87h+/bpEARnVZZRUZ81T2JLo\\nJmthxu0VzKpPJBLictN1j0QiskdMuz52c/JZkh0fLiws4Nvf/rYIDSoFfY2Zpa2tI52drff5EV9z\\nOp340pe+hGAwiDfffBM//elP+7ZxrCgbO6WFk2nO23WeJq3L5RLLaH19XRLIXC4X9vb24HA44PP5\\nEIlExFXy+/1IJpOIxWJ46aWX8NWvfhXpdBqZTAaPHj16KodiFOp0OqK9XS6XRNWI/5BBdTiXQpKL\\n09RwpiAiBkMQlNYFzdpyuYzNzU188skn+Pjjj7tcOztz/zgYWFtseh71HGpsjn9zl/vi4iJefvll\\nXL58GTMzM1JmpdPpSGkKJn4CQCqVEsyt0WigWCzi6tWriMfjiMfjKBQKXW4wx+iofdTYpc/nQyKR\\n6Apv8zcqEtNyZJRUWwz6Xi5kZtqTZ4HDiK3Ofmabhok6fRak+8f6RY1Go+t3O2zR5HFtCZq8o5XX\\n/Pw8AoGAlNDpR0OD2sDhItOM3OsFWkDoTvp8Pinnkcvl4HK5EAqF4PP5RPqypAND3bFYDL/1W7+F\\n5eVleL1eLC4uwuv14u7du9je3sb+/r6ktQ+y3uzIsizk83l0OoeRnY8//hh+vx/hcFiEaCwWQ6vV\\nQrlcFtdMT4iOUBAXomlMC4/4hMkAtCQACChousymizwOmYzWy5rUroi+zul04rd/+7dx5swZzMzM\\nYGpqCh6PB2tra8jn82IRNBoNyXgGIK44GXt6elpyvlwuFyqVCvb29kQI6e03x+WaapyPAkVrfh0o\\n0OOkkyL1fkWNE1II6cggFZgZ1bNb7P8viRE1Ynoc717jrNe7iSHp3zk2TH1hJC6ZTEow4UgYkm6Q\\n1pR22JCdlaRxI5fLhXg8jnPnzkl1OY/Hg3g8jlgshnK5LNexyFO9XhfQ8+tf/zoqlQpWV1fRarXw\\n4MEDPHz4ELVaTYQczcdRybIs5HI57O3tYWtrC48fPxYm0ikAH3/8sWzjoMAl3gWgy93k3xRcfBYF\\nEu+xrIMyn4VCAeVy+alQ6nGSOW+m26BzjShI2e7FxUUkk0mcPn0a3/zmN3HmzBnBekqlEtbW1sTS\\n1LvAaT3wbz4zlUoJ8zJUTKuTlQcDgYAkxY5CdgIcOHQ3KpXKU4l+rVYLxWIRlnVQPZFt0tYVn2FG\\nGomZ1Ot1eaYu8MY9Yrptej4+S5fN7tnNZhNTU1O4fv06Ll68KK6WafnpdvI5OhhAAURhRjfNHK9A\\nIIBAIIB0Oi2uci8auNtfT+owYUs7/IiMEI/HcebMGUxNTUkGZyQSAQDs7OzA4XBIlK3T6aBQKEgY\\nksmCa2trkvFMgJSm+LhAr9agjGrpPuzv78OyLDx58gQTExO4fv06otEoUqkUqtVq16ZZakT61drk\\n10lneky5EDWu9FmSOUcUkGYWsXZfzp07hytXruALX/gCZmdnAUDcrkKhIFqX1h77wfnTBb243cbh\\ncMiWHN7LzGe6QPx+WOq1uDm+rFXExePxeKStpVJJAG+t9fXfOrLGsaKiYYG9YDAobqkWup+VoulH\\ndu+r1+sIh8NyAg6tQwoTLXRNt1Jb+Dpbm7/pQnbkK12Wh+u7Fw3tsg1yh8yOs0GNRgPT09OYnp7G\\nhQsX8Pzzz8Pv9yOdTkseB8107pKmUNBuEP8988wzojW1O3Xv3j1sb29jZ2cHm5ubg7rVtw96Rz8A\\nca/IyLTwgsGgVMQzSUchaK7rsKkOabOiJK27cdzOYUhrdGpyts3v98uG4uvXr0tROm4rmJ2dlQW8\\nt7fXBWRGo1HZCkOXtlqtigVME57RQQoqBgzYVw00p9NpzMzMiGIaljQf2mFBHo8H4XDYFjfLZDKS\\ng8bFZeJGfBZzkgjc8vtwOCxWIksU84SSfkmBn8V887nm2my1Wkin03jllVcwMTEhQlNHFNkfk8xQ\\nP3AopFg7yZwDKqt0Oo2FhYW+7R0Iag9DJpClG8ykv2QyiYmJCYTDYQkFsjPcOmECf3a4TDqdlkiM\\nw+GQRZJOpzExMSEF80elXpqLfdKuDF0wYkQm6Mmxo2WgQ/t6m4gGiukimJPdbw5GZWKHwyHCgxnh\\nHFev14tUKoVUKoXnn39eLFi6rNxTmMvlJN3BsizJF9KJgMRUtHVEAU0Xhmfc8TfOJxcF8UOdrDgM\\n2QlzjjE3u3LPmZ4X4OBcNNY1AtAVsrezbImJ6e+0lasFP9MB7AIg+vnHQaYQMvmJ1SxYkI7vp9DW\\n7qhur/7McdPYk+Z1/Y/uWzQaRTQa7dv2kbaO9PN1dWe4wMiMiUQC8/Pzkn+itQ8ZlOYdtU4wGBRM\\nhYNIKU5/nQXS+Nnn88k5WeOQZi47nMzMOdIazwT5NKivrUBzUnVdHYKgo7R3FEokErhw4QImJiYE\\n0KRQIHYSDoextLQklicFhE5M1aC7PhhRWxy60qDeYsFSHfoevS8MOLRI6b6Nu1hNjEaHtKlY+HxW\\nhaTgpcIxeV6H7M0oaLPZxMrKiiR2ttuHexNdLhcikQh2d3c/k5wjO961E0yc50AgIErAhBL0feZa\\nMvmavG4aJXbjxQBWPxqYh0SfmWad3qFOLW9aNWwIkfVz585hbm4OkUhEtgIQqOTz6MtXKhUx5fkb\\nF0Wr1UKpVBIMqVKpwOl0yrEsL7zwAgDggw8+6NvpXmRnmehFxr/1xlc9QaYm0SC7BomJpfD6Wq0m\\neSu93j+ovcPQn/zJn2BychJnzpwRV4vFtfiudrsteBwFhS6/wdNVdB4O+8c2UYF0Ogc7v7k9g2PC\\niCgXPy0S3ut2uxGLxTA/Py/41LCkF7u21umuMWeIc+rxeCTpVrvMpnVg9pHpD8QPWbzv3//933Hu\\n3DlcuHBBIoherxcTExNYXFzEkydPuiKsvRb+qGR3v8asOMZOpxN/9md/hueeew7nzp0Tha8tGc17\\npqDiM1h0TSs0GiH6vSZAnkql+vZjIKitk524I1hrdOAwt4YLtNVqwe/3I5VKYXp6GrFYTBKvaAEB\\nh9E3+td8LneIM/mQz67X68jn89I+/tZqtZBMJtFqtbCysnJs/rgpjIDD8DA3WOp3afeNwodmvJmA\\nqHdL69QAvsOOUU08b1Sam5vD/Pw8pqenxRog8zCrmvWaNObDtlJp6N3ddN90kTNtxmstzL5poFpn\\nu/M3nv11VJdNa3ttIdHyowvHfuuKiaarTaGoqzYQeOezHQ6HJPLSjeUCJTbF5+nFb1oVo/az1++m\\npcKSKa+99prkBmnBYdcGu++0F6SrROpx0vldvIdFCfvRQIHEHKFO5yD1m1mtOv9Gl2UFIG5YOp2W\\n/Wk089k4mnqmdqZbAKAL+Tf3lOnwI3BwpjtN0WFONxiG+pmubAvbrt0xjR1wHLWfzYVrjrVOBbBr\\nC2lcF+bTTz9FMpnssmi166mjK2wTXRdaw/ybAowuGGs1aSCfTMq5r9frgqVwwegAgtPplC05Ozs7\\nyGQycl78uKSVCttn1pRiygGtP42raPebbbSzBGhhUHjrU2Y4lrFYTMbSjGCP2zegdxIy/yfv8ejv\\neDze5XYD6GprL4WnBT3dcLp7/GcXlesXnTdp4OZahgeZxKalKl9ArQNAhJLP50M0GpUsXF3vh4Am\\nPwOHmocF1gnycp8RmTwYDIomJXMDEMyDWbHHRaZQoqWgXUkyu87L4P96gQLduAP/p8WltYq2Co7L\\n4vvXf/1X0Wr1el2OAHI6nV0FxrQFQyuZoXoKGApQnh+nXW+euMLTXynMSqUS/H6/zBWzmJmhHQqF\\nsLe3h48++gj/9V//hSdPniCfzw/EHYYhl8uFRCIhhciAw72H2WwW29vbmJqaQiQS6bJitOUOQKxJ\\n8j0VMa3leDwOy7JQKBTkfDfyK9eBHcZzVMFkChBzjYbDYVy6dAlXr16VTcy97jXdLE30VvS1JlRB\\nAcT+UdhR8PejvgKJyWI7OzsyCYyeUHNR4/AYIJrcNH8dDocIGPrbTqdTojI6MsF/1Da6qiIFE1MF\\ndKhdA6fjJkf2I9M64QK085PZdh1d6qURaaFQmwKwFaZ2941DW1tbeP/991EoFATbSyaTAnBzjLUF\\nS+YiJkSLioK0Wq1KNQIWYtvY2JCsbeZY6VrjPp9PkibpdtNSyufz2NraQi6Xg2UdVDOMx+Mj9dPO\\n8qAy4z++n0K1WCxienq6K5+EPrWwAAAgAElEQVSNc6nrXWlBrYmLbm5uDqFQSCKxFP5ut1sy8e0W\\n/LhWby8gWwsF8uLZs2fxrW99a6QqClqw8DODAQTsdQFDjhnv0fNQKBSwtbXV930DLSQeAU0zXYd2\\nKe35mZaDLsFAyagXKf+3c0PsLAwCksBh2jvxDC5mXebWzCM6KmmzVrdFuz7aLKXA1H9r4FDnI2nX\\nQQOPJm6iJ9cO2xqGSqUS7ty5g/X1daRSKTSbTczNzaFWqyEej4uFROaicNdZ6Wwv8aOdnR3kcjk8\\nfvwY+Xwe29vb+OSTT5DL5VCv16WeVKfTEWXi9Xrl93K5LMqFO8Hr9TqSySTm5uakcsCoZGJJDocD\\nkUhEjpRi8iZ5PJfLCbSgNT4FEjEy7W7rueQ74vG45CABkL2RVJim8hoXD9TPYB97fW6325iamsL8\\n/DwuXrxoKxD73c//NajPvrTbbdn2RO+E/dLbbIAD/l5dXe3bn74CiVaOZVlIpVI4deqUTAxLgGgM\\nSIOdxJ1yuVwXtkOBQhM9GAwiGo12HSfNBaDB8nw+j2azifX1dXEHbt26hWw2C4fDgZ2dHezu7uL+\\n/fsDgbN+ZMcg+jsKFR1V6HcvBSYnkcmDur6SLuegcTW+z84iGgd/cLlc2NjYwM7ODh48eIB33nlH\\ntCXnudVqiUvGRcaIHDfPMomTFrPOC2PbOA46cqWBe3PsqG2dTqcc4by7u4v9/X3s7u6O1E9NFKDE\\ncLglCTgQsBsbG9je3sbe3h5isRgsy+oqEcx2M71EzxNrvhND83g8KBaLkn5CV7zTOaitTkvP5Cd+\\nd5Q+kh/4j+8tlUqYn5/Hn//5n+PLX/6yuJTmO+3wJzvi2ne73djY2MAvf/lLeDweRKNRXL9+XX7T\\nXgEttmEMhYECSVtJurwHTXBtFupqc5FIRFy03d1dCc8zc5cN9/v9opm4UCl1mSNC14AZ3DSpP/nk\\nE4kW7e/vo1wuH7kmkqm1emkw7Y5pi0ZbU3oyuMC1gOL/ehOmBpV70VEjM5w74nQAxF3j3NBFpwsH\\noKuIPYWWTgKkpaz3M7G4HhURFQ6v1/drIcswvN4fNgrZuQzmhmgAInw4Pnr+2FbOFdvPa/gsWguN\\nRgOZTAahUEiELSN2jOj1mttR55Jtomuo3TTLsmT/5fXr1/HMM8/g0qVLYtnbgeC9SAs6vqNQKODt\\nt9/Ghx9+iEePHuH555/vSrI0rXsKSFbQ6Ed9BRJN9larhZ2dHQEw2+3DkzvNcCFN/Wg02pUNTEA7\\nHA5LNIMSkxEaAFJojWF/7dpQ+HDQiVPUajUUCgXJ5zlqlK2ftqDgpVVjagKt+TiJZGpt/moLgtaF\\ndhXs3t2rTeP0TzMZ//F03FgshlAoJLhHs9lEKBSSo8FDoRA6nY4IJR09a7Vagv1RAPAZrCtNt63V\\nask+QQBi+ufzeRFI4+7ts5tDLmANQjMhUpNWCtpN0TACBRZBfq6F/f19AOgKfPAfLcCjumlsT61W\\nk0g054Gfw+EwYrEY/uAP/gDPPvusCAwtEHVkrRfplAxaXbVaDX/913+NWq2GbDaLK1euCCbMtmle\\npgKkYdOPhqqHpEPvGjDm7m0zGc2yLDnzXA+CnlD+rcFD/S7gcL+XBqqJF5mAMPNZTFdgGOrHIKam\\n5cKlFUFm5t/sg+6r3tHOSdWlSPQ406rUpWrZDru2jUJ2feT7OZcU9BoD0wApmc3MUu8lkDW2trGx\\n8VRbNP9QYJg5XuMu3l7BCEILVH46hK3hAt6v0wDIpzqnRkdSX3zxRezv76NYLKJSqQi/6IRSjSmO\\nS+12G+fOncP8/DxOnTqFGzduYHJyUmrC06VMpVLCV+a6GHadcC43Nzdx69Yt/PznP8cnn3wia5xj\\n6Pf7u/hIewjkf31Ihx31FUiU8nagrdb8wNNuhBk9MjtoYjJ0D4g1aZOXJqje0ElXQ1sWxJtGxZDs\\n3DR+b17H/3VET/+v+8Y+UEhqd8kM67darS6hptuh22h+fxzE92s3Rlt+dm3Rwsp8lrYGzQVIIaz7\\npMeV/GA+c9T+mBYSn0v3CYBk/pO/zBNDyGt8hgZs2RfTwojH48jn89jd3e0qR0OrkX07qkCanJyU\\n6qnLy8u4ceOGQB86M15b8kB3gGXY99MIyGazeOedd/Dmm2/K+tOYqL5ez6GuBDpoLvsKpG9/+9tY\\nWVnB9vY2crmcSHw+lMA1O0eMgdKQJrveo0WrgeFXYkicMJZuoCsWi8UQi8XEBczlcmKpVatVMVtZ\\n5L9QKHRp4mGIg2fiQOZi0W4nzW87Ta8ZWi9OCnX9Hgopuke0nkw6DiE0LH6hXVDe12sh9YrY8Lla\\nMAH2aQ12C8TuGcOQea2+X1tFTEnY39+XgwW0O0cBpo+6MsuJWJbVZfm6XC6srq7iN7/5Dd544w28\\n+uqrcDqdEmk0t63Yjeew9C//8i+Yn5+XYoe02Ii5UQhRwel8Pv1OKkdzvnkd2/7hhx/iL//yL3Hn\\nzh1kMhnJR6QgcjqdkuXPcWdf19bWsLq6io2NDdvKGJr6CiROGDcEmhpEDyQnm2QeLW3mcQCQs84K\\nhYJoD53RTeYpFosol8uSdMfnEXCnhKbgY+LbsNQLSO7l4lCoaLAa6BY4/KwxCP1M09rUi1IvWv2b\\nFm5HxSAG0VHwKzv8pt/C66etO53RTnU1321iJlRkXJy1Wg2BQACJRKIr5K89Arv26xQYnQc3PT2N\\nhw8fikvO38yItN34jEIejwe1Wg0ej+epEj2EBXQWNfuuLT3dhl68vra2hg8++ACvv/461tfXJYVH\\nt5eelH6fhnZKpRKy2awc/NCP+v6ay+Xk5Au6Jfo4ZHMhsRP6f9054NB60NsutDmtXYB2uy2F/80t\\nBsxt0ZEqTsioGBJdwF6LxvyO79XbXNheXk+BrL/nb3qMtMCidWSCj73ac5yumynoTBet3/v6/d7P\\nwullKRyXNaiFOHmEm7LD4TA6nYO0lHg8LhY4FzOtHjvIgsJH45jcVTA7O4tIJCJAd6fTEf4dhJ+M\\nQjs7O/D7/ZJ6oIUM1woVtBaeeoMxx0n/D3QHPdbX1/G3f/u32Nrakox6ChX23xR6Wj5Y1gGevLe3\\nh2w2O1Ch9RVIH3zwAer1OgKBgJiEGgOwc0dIg5jQ7hmmOdlutyX/aGNjAx6PB1NTU5LnEYvFRAOF\\nw2FUKhVsb2+PXPLUPOvMToPpzwTn6C4ywqBT57UZbFpRfJa2kshQjI6sra31zOg1x3Ac6iUA7J45\\nzHv6zbf52Wz7oOePm1em+8i0k06nI4XYyuUy7ty5A4fDIUXoCHwXCgXhC50GAUDAW86lz+dDuVxG\\nNpvFnTt38Mknn6DZbHZZ9xRwdpb1ONbu97//fUxPTyMUCuH69ev46le/ikQiIXvVyE8URuQ3HUyh\\nRaOjhfy7Uqngf/7nf/A3f/M3uH//viTLAk8f/sqoKK01nWvocDhQLBaxsrKC/f39o2FIu7u7Xfkm\\nvQBqu0HtJZzszGAtkU0B0Gq1RKtxv5U+cI7aixUCWK1wHOrnomniBNNsBg7BW046/+ZvdD3M0D6j\\nPnw/IzK93q3bOSoja0HQS+jyvXb32LVBK5RB19tp42GE3agWr9k+PoOVGzlHwAGAv7q6KlUemdKS\\nz+dl4XJLFK0n8iCxP26darVaeP/997G9vS3RJl39gGvIbv5Gncv19XUUCgU5zDSRSCAQCEitK3oK\\nrJgQCoUE+9RRYMs63ODMXL/9/X3s7e3h9ddfx8rKSpe1qNvJedne3sbDhw8xMzMjbmSn0xGh/ejR\\nIzx+/LgrxaMX9RVItDS46I+yR8wObDTJ7ju9gKvVqmTtcnMt20bztFqtDtzAN277tWlKIULLSOfL\\nmCarGXXQUTYt6HnCZ79cDS2kRmXiXma5SXbCo59L3ks49Xt3r/aY15jXjUNcfNqS5fOz2Szy+Twe\\nPnyIN954o2s7CaOjdmF/vS1Cb6Hg9iVioXrnAfEXrYTHxQKdTqdYI2tra/iP//gPET7ctN7pdBCN\\nRhGLxXDq1Cmk02mk02nMz8/LnkKd0sEE5h/96Ef48MMP8emnn2J7e7ur7rhpODgcDjkmm1iZ3++X\\nZOZsNovbt2/j4cOHyOVytkaNpoHlR3TB+36gpJ3b1Q9zGFZA0U+ldvos8nOGYQw9AUwC7HQ6wgBM\\nmCQxMkMG1G4cAwT6f25WjEajsrNeM26vdh8XmUJi0Fz3m8NhsSRzUdot0nGErnkPFcjExASCwSA8\\nHo9Y05lMBrlcDrlcTnhML9JhrWYtnBhBZooHM7Q15KHX0ziCidszWFxubW2tS2BQSLItU1NTcDqd\\nmJ6eluRkWkzlchl+vx+7u7vIZrN47733sL293ZUISX63a+vdu3fx+PFj3Lt3D51OR6zJUqkkNcwY\\nQR5Ex1JT2w4b6MV8R6Fegs983ziuzKD3kVh4i1qXPjqz0nkfozgUSgTdqdl0/WxeR3etXyRiXI3a\\nqz/9XO1h2jCspTvus4fJJra73xSa3I/HMWYpDCo7U1DYKeBBwlrnnZVKJRFsOkPbTC3h53HWhlZY\\nvQIhTI3Z2dmR9pAnY7EYKpWK8CrPGwTQFXHUbbRTkoRLdnZ2RBhqT2IUgTtUprbWGGbj9LX9SPvO\\nWmj1ami/542qnY9CdtpW40EaY9NlUPW+MJqydHt1ZIbbXRgB2tvbk/PB7NpwlP7ZYTdHURTDWg/j\\nEts1zl42k2q1GlZXV6XIIABsbm7K2PNdZnCmF5mCT6+JarUKn8+H3d1dPHnyBKFQSE7D6fX84xgz\\nk0+0hwFAMtH5XS6XExeW23sYLWQKj36OKeS1ZWtm8wOH1RBGwQAH1tQ2BUm/xTGIsQe5V6bF0++9\\ndr8P0mC9qFc/7Z5HnMrn83VZRIwIlstlWJYlRej8fj/a7cMTKnS+Fs81y2azIugymQwymUwXKN5r\\n7I6rn/0+axrmertF0e+Z/XLA+P0o2KWZBc93FotFvPvuu0gkEnC73chms9jb25NtD/raUfiqlyIs\\nl8v46KOP4Ha7MTk5iQcPHuDevXuy74+Rw+NQoHbeguYdWimMopEPKZwYDeOWGm0wmAmxvTwSncXe\\nq316vHr2pXPcJsUJndAJndCYNHo89YRO6IRO6DOiE4F0Qid0Qp8bOhFIJ3RCJ/S5oROBdEIndEKf\\nGzoRSCd0Qif0uaETgXRCJ3RCnxs6EUgndEIn9LmhE4F0Qid0Qp8b6puprUvUDkO9MjmPY5uCSYPa\\nNUoJkmAw2PV3v4xTk+xOqDDLNDBVX7eZ5VAB2GbIDrO9BsBIlQ1YlOyoWz7sthCZmbvDbv2xy4rW\\nGdMci2EPi/T7/QP70i9rnL/z4MPf//3fx/e+9z08efIE//u//4tsNgun04mlpSVMTEzgrbfewj/+\\n4z921Tkad6vPKHW8WNvJrE1kN466La1WC7FYDM1mE/l8HqlUCqdPn8Z3vvMdvPzyy3jy5Anee+89\\nVKtVJBIJXLhwAfF4HLdu3cI//MM/yLl8PHVIV7not49Vf9/vKKSB5wWNuj3hOLY4DEOfxTNJdotL\\n71fSW1dY5Ao4KLzu8/mkYmA2m+06bmdiYgLRaBSWZckhhdlsVhjRrKL5WVCv5/YSUuZvdormKFuI\\nhnneOFtkhnm3/lvPLYUKNzp7PB6cPn0aiUTiqWORwuGwlBjRZWtH3cY0znybgt9u3CmwHA4HJicn\\nMTk5iWKxiFKp1HV0+t7eHiqVCuLxOF566SURrtVqFSsrK8jlcpienpYicH6/H48fP8b9+/exvb3d\\ndehGv3YN2tc21ObaQd8Ne30vRh1GO48ywaNaYYPeb9ZC5vN5dA6PEZ+amkI8Hkc4HEYwGEQmk0Gh\\nUJB6TWfOnMHs7KzsNn/33Xel+JUu9cANj/0W+jgM3G8vYq89Z8dp1Y5KdtbXsPf0+t7c62YSBQv3\\nElYqFbhcLkQiESwvLwOA1OXa39+XUjQ87qparaLTOSyt3OvYI3PsB9UJsuuP5hFtVVOh6SqQbrcb\\nU1NTWFpawqNHj6RQGw/hyOfzqNfrCIVCuHDhAoCDAo0fffSRFIObnZ3F1NQUpqenMTExgXfeeUeK\\nuXFPnN0menMvaD8auLnWHIRRrje/G8UVsrtmWMY8To1K0hPPcqihUAg3btxAIpGQEqg8E4tlQLmJ\\nsd1uIxAIdB2cefr0aTkCfHNzE5lMBg8fPkSxWEQ+n++yyvQYDBLww/bTbpH0mvNx3zkO6T6P4kry\\nXk39LAj+TaERCASkoFmn05FStm+99RauXbuGiYkJuN1u7O3t4Y033sC9e/ewtraGS5cuoVAoiHJh\\neWPuoLdrV6/2jtJPs288ZURXt0ylUohGo5iYmEAikYDP50MsFkM4HMbExIQcv1UqlXD//n0sLS3J\\ngZ6ffPIJbt++jZ2dHTgcDszPz6PT6WB/f18s/zNnziCZTGJ/fx/vvfdel/cwSB7Y0VD1kEyhMsw9\\n5vXHwcijYhzDkqmJTUHA75aWlhCPxzE1NYVIJIJEIoFvfvObSKfTiMViCAQCAA6Lq/PEV9aWYZEq\\n8/jjJ0+eyC7/+/fvI5PJ4I033kAmk8HOzk5XTenjdOXM3e2mlcT22SmDYXAi/Sy7z3b3DMKnBhHv\\nMY/2MctjBINBOcqd94RCISSTSUxOTsLv96NarWJtbQ1vvvkmnE4nrl69CofDgU8//RT/+Z//iXff\\nfRfBYBDLy8vY2tpCsVh86tCJWq2G7e1twVvM/h2FtGWlq0O43W4Eg0E4HA68+OKLYrVrvMfv94vr\\nVa1WcffuXdy5c0fGBQAePHiA9957D06nE6dOncL8/DzW19eRz+fFOlxcXEQikUCxWMT29ra4g2aF\\nyWH7PBSGZEpiE08ZxSUzcQI7gPP/NfUz8S3LEpfqhRdewIULF3Dx4kWpdRQIBNBoNFAul+WAxUql\\nIswXDocFWzDNVZrM7XYbqVQKkUgEFy5cQKFQQCQSwRtvvIG9vb2uSoakXmU7hiF9n1kAzc4FGIQl\\nDXrHMN+TRlF+g+4naWHEsYzFYohEInIsOL/vdA5OIuHcB4NB1Ot1rK2tyVHV2WwWpVIJU1NTAICH\\nDx/Ku1hR1O/3y4k5LJJGIFiPA12vcctDa8yLxBrbiUQCS0tLcDgc2NvbE7xIn6RLjCwYDKLdbmNj\\nYwM7OztwuVzI5/MIh8OYnp5GOp2WQv6sJc+TdyzLgt/vx4ULF7CysiKK167y5pFK2JoPo0QeFsvo\\n5w7YacPjouPAV3SfQ6EQvF4vvvjFL2J5eRnz8/OyiLPZLDqdjrhnNNkJChaLRXi9XsGRWJ1PF2tj\\nxclgMIhkMolSqYQvfvGLePz4Me7cudN13t1RLU5tdZhlSe0skn6uRq829FIyg/Ar8xm6vcOSybNm\\nTSkWuE+n01KDmmNARVIqlaRme6PRQKFQwOrqKm7duoV4PI779++jXC4jEAigVCqhWq2Kaw5AMCgK\\nu3Q6jf39fTlT0OyXWXN92H7q//VpIi6XC4lEAhcvXkQ0GkWr1RIIgO30er1ynhoxMrqZjx49Epwz\\nlUohnU7LEVFOp1MOcyVR8MzPz6NQKCCTyUiwx+SRI4HausP672GxDDuf3RRwR6XjEma92uJ0OnHj\\nxg188YtfxDe+8Q2Ew2Hs7u5K+BNA1xlcnDCg+3wst9stJ15Qe9CsZXpFvV7H5uYmvF4vnnvuOezu\\n7qJcLuP1119HPp/viSUdpb/ardGfKbD4Wd+jrSfdnmHGc9R7xnHZermJDocDkUgEkUgEZ8+eRavV\\nwvr6OoDDEzcoHLiAG42GHHL4k5/8RCqBclFaliUuOYUNLSwWvD937hw+/fRTlEollEol27EbdS75\\nDOKT+pDGcDiMy5cv42tf+xrW1tZQLpfh9Xrh9XrhdrvldFuHwyFwQCqVkgMyfvaznyEQCMDr9WJp\\naUlO8vH7/YI5WZYlFSbL5TIcDgdmZmZQLpdRq9Xw8OFDSUkx+aUfDRTL/UDBcaifezTOs46jTXbP\\n4CB6PB6k02mcP38eALryifTxxFrbmROgj+rW12pTm6db8Az0RqOB6elpvPLKK4hGo9LfcbEVu36a\\nc6v/6e/Ma+yIbetn7fS6b5Q2j0q6T7QOWORfl2klBkOrkZYUa6izMD6tC8uyRDixTCutFN7Hg1V5\\nYms0Gn3KQhh3Lu0gELqeFFD8p5+tD47UsAThh3g8jna7jWKxKAehejweOTqb79bKl8eUeb1eJBIJ\\nnDt3rutU31FooEDqJ0AGSb5hhcxRsAJTw49D/e5zuVxIp9O4fPkyPB6PWD1kTO2LE6im9uFpJPp8\\nNkZB+DsFGo8R73Q6cgTP9PQ0bt68KQmNR8XazPvGcYdGudbO+uo1X+bvR5nTXgtcLzxaqOZZejwn\\njwrC4/HIwaQ8F5DfsZ66zkWjoqJw0ue3xeNx26L34whdE4vSfaTVRBeRwgPoTmtgugr7zBNK+Fwm\\n74bDYfj9fsGb9LmEfr8flUoFhUIBXq8Xs7OzOHv2LCzL6jpMc9i+DpWHZP4/zIMHXWMurkGYxCAa\\n5jnDkNYaBHz9fj8CgYCAksSKaNLzHkbXAMg1ZFgKJn3ChYkj6fEtlUoIBAKIRCIS9TC161EWq2nN\\nDDOvvRa5fi6ALncUQNdnjo3OZjfbcRRs0eybufj1ySAARMlwHmkdcV4AiOJgv6j9mdDqdrvFcm40\\nGrLQmeJRKBTgcrkQDoefmufjcL9138mv5XIZa2trYsUBELeSGBnbp8fCsizJAgcO675TWAMHEES1\\nWu3K1QKAWCyGUqkkWGi1WpVUiGGV6VB5SP0Gb9Dfg56t7xuHRhWSg55lfqbfbQdMt1qtri0gWmvp\\nqAm1LTUmv2Pb9ckM+sx4Wl3UtuOc4Dqoj/3GbhBmpRe/do1isZiElROJhJjuZND9/f2uI3c07nPc\\nVqDZF0ZHgcOIGI9E16fM9nI99fww70c/n0SLF+g+qUbzyFHcb7v+dTodOSSS1hsPg2Qb6EbSTeVY\\n8IxAyzrENDudjlh4tIy0wOZ4kjcnJiawubmJbDb7VDLxsDQQ1O43APqFvbRuP6vHbjKGFWZ27z8q\\nmYLV5XIhFoshnU4jmUxia2tLruO11BTEHPTJpsCh1gEgoV9qT06wPsON1lC9Xpc9buFwGOFwWDT5\\ncfWx3/z0ElzmdxpPo9b9yle+gitXrmBqagoXLlxAIBCAy+XCzs4Obt++jbfffhsffPCB5O1QsPP4\\naW5ZAA6trWGJSae0cM3+RCIRnDlzBtlsVuY3m83KMVT6Wn2ShhZWehz5tz7d1uVyiXXbbrexv78v\\n1oSdkme7RyG7uWO/4/E4UqkUJicnhf8YQWMCp/lul8slwmdhYQFOpxN7e3sC9tMtJRxBXuVWKMs6\\nyNPLZrPY3t7G3t4eSqXSyJ7LyAKpl29vJ0j6mfiDvrfrQK/FZOIrx2n+zs7OIhgMdp2nRvPdtIgo\\nlPTxyXrh6qgbFxndPrfbLZpI7wdyOBxIJpOIx+MoFApHFkgm9RLodq6TnYvHhR+JRJBMJnH58mW8\\n8sorSKfTcLlccigmcGDSX7lyBRMTE3jhhRdQrVaxsbGBUqmEXC6H9fV1VCoVNJtNFItFAUv7HS1u\\n1x+tnbWi1O5aq9WSM+85lzy9Vs8d50CH9LWQpJWk3VMuWKfTCZ/PBwBd39mN/XHxLBUcrfFgMCjf\\nVavVLiuPsAEAibxR8ASDQVQqFYEcqCg5jsxsD4VCsmWEimRnZ0cA8X7HI9nRUALJzrS3G0w7pjUb\\nwmvYAY07kHF6CSc7PMDUnkfBHfRnyzoAQGdmZhAKhWSAteblhPZKLNQalX1sNpuSEMn2UshwMyfP\\nbiP4ODk5iampKdlzdJzUC6/Rn/sdnsjFHAqFsLy8jK9+9av40pe+BMuyRKBwTKenpzE3N4crV64A\\nOFi8d+/ela0zt2/fRqlUQqPRwNbWFnZ2dlAul5HNZofuD3lCKym7z7RAie1osuur3o5BzEhn3PP5\\nxAv9fr+4+eR1und2SmCcvWxmm7XgbDQaKJVKmJ6ehmVZyOVyIpB1zhTbp9eY0+mULHZa8XYuNaOP\\nPKuw0+nIKbm0cE1jYRANFEi9BI6JH9gJEnPAeR2jF1yMXJzUWOVyWQ6v00KK2ooLO5VKyUmk+/v7\\nXb7rKGT2iW0Nh8N48cUXkUqlkMvlBBPgPTTN2SdOrsYK6JuTKQOBgDAn+0ItGwgExOfX58Jfu3YN\\n9XodlUpF3MZxqZdy6SfE2TeOv7Yc+PnatWu4fv06fD4f9vf3BatZWFiQAzU/+eQTcev43dzcHObn\\n52FZFr7xjW+IVbG9vY2VlRX85Cc/wQ9+8IOh+2duszGFLCNFBGYbjQZCoRAmJyfx8ccfo9lsSlKg\\nxu2ofCg4GC2lICBOxlD4xMSEhM9pdYRCIbnHhCnGhRw4ny6XS/oxOzsLp9OJTCaD5557Dk6nE5ub\\nmzI23KsHHMAIwGEKilaKXq9X9sKRPwn+V6tVwQgbjQbeeustPPfcc10RR1pio9BIu/2H+cy/TUmq\\ns1fdbjemp6fhcDiws7Mju4XD4TBisRjW1tawvr4u9/BZXMBMdb927ZqcRJrJZLC9vY1yuYyNjY2R\\nBsGOiItw60etVhMAUEdV7MA7mu3atNcCVRPNXzOfieBjq9VCJBJBLBaD1+vtmpNRzfxerkE/13qQ\\nu8bPwWAQkUiki6GpaSlg+Z5GowGfzychcF6r3R2GoUOh0LGA+ewHFxoz6uv1OgKBACYnJ2VhM5Bg\\nzrFO4dDjpt3BUCiEWCyGYDCIRqMhLiiFlV1fjuKyaRyI7r4Onmjo4fHjx2g0GrLTn0KWBgHTTrSL\\n6/V6xYDw+/2ybSQcDsPhcCCXyyGTyciGcI6dHYCvvYteNPRu/36umR1p7RKJREQoESB+7rnn4HA4\\n8NFHH+HBgwdSniMSicjR0rQSaOqSQQgYvvLKK5ienobP58Pq6ip2dnZw9+7dI7k12jx1OByySZEu\\nlI6q6OtMnMiMivVyO6NMnoYAACAASURBVAF0LWBtdTATNhAIiGVltnUUGkYT9/pdm+xaENFqYMmV\\neDwuuILOxSEeAXQn52k8jceQ0/TnWI9CdhYgP2tAFoDsM/P5fJiYmBDLSS9WPVd22IsWVsCBQKJQ\\nKhQKkhmtx68fXjcOmXxIrAg4cCGDwSCmp6eRz+fx5MkTJJPJrkgxLXQmfOrxoldAHuTeNWKDLMPC\\n+kqm4NHeDdvTj4Z22Uyt2Gtx8Xefz4f5+XksLS0hFovBsiykUikEg0E5597j8eDq1au4evWqmLQO\\nhwOJRAIrKyv47//+b1mYXq8X8XgcN27cwMLCAhYWFrC8vIxWq4Xt7W0Eg0F4PB6srq4ik8mMMp9P\\nkTZfE4mEbCgk8XOn03kKr9BCQ2doA90hUo4TXRQuQmovLpp6vY75+Xlsb28jFosdqV+DyHQjNPXC\\nBuPxOCKRCM6fP4/JyUnU63Xk83lEIhGxeiiAUqmUvIdVDCYnJwXLIa5RrVaxt7eHjY0NbG1tde3l\\nG0R2wojfezweRKNRTE5O4t69e6hWq/B6vfD7/bK3TVca1VqeikLnmfG5WnnG43HZI8dyM3qd0F0l\\nVDGuMDLXYLPZFPytVqvhzJkzCIfDEnGjctvd3cWlS5ckiMK1RZeMe/iIoer0FOBAiDudTpw+fRof\\nfvghPvroI2xubnZ5AnbQzbD9HGkvm52GNBmAGigej2NychKLi4vwer2o1WqYmJhAIBDA48ePkclk\\nRGjNzMzIM3K5HC5evIiZmRmsrq7KAIdCIUSjUbz44ouYmprC4uKigJ/b29vI5XKCS4wbidL9Mzch\\namyJgsVO2lPraC3LvmmrSecekTmYVKb3J7VaLUnMZMRGt3UUMhVIr/t7ueB2VnEkEsHc3BySySRC\\noZCUUaG21cmFPp+vqyBYrVZDKpUSAUywn3vJtre3USgUxl6wuo/a/QAOqxzQPaR7SUzF7Hs/y0YH\\nVvh8jQFSKBNuoMvYb8wHkYn/0RLlv1AohKmpKezv78PtdqNSqWB7e1t4jHACeVlbSATqGXFkoIA7\\nCCzrIArXbDaxsrLSlSTZz/0fpp9jhf1NBuVkMUs0HA7jG9/4hgz85uYmarWa5DTs7+8jHo+LZcC6\\nLKywOD09jZmZGfzpn/4pCoUCarWalD6IxWJwuVzY2NjAp59+ilwuh3q9Lrk6LD41CpnuCACZjFAo\\nBOBQyJFxORbEByg86MJQWPE7Xq+zZvXEVyoVlEolcRloXdD81ou7n4AYpa8m2Vm+GnvRzyBNTEzg\\n+eefRzKZhMPhkHn2+XwIh8OyzYL/dCUECnqOAyM1tGYIQB+1jxr4bbVakgtGq9zv93fhJnq+dF/N\\nVA89/uxToVBANBrtmkfeoy1BU2AeBdTWbQAOeG5/fx/vv/++KDSPx4NCoSDjbaYBaP6qVqtdqQxU\\nzjoy7Ha7xeUlPmj2wU6AD8IDhxZIvZifExkIBLCwsIBoNIpgMIi5uTlkMhk8fvwYGxsbonmoBa9e\\nvSqJfizb2ukclGqYnJwUoDGbzUriWqdzkJq/vb2NRqOB9fV1eDweMUudTieSyaS4BqP2Tf/PSQuF\\nQjLoBNkpKIgr0W1jYhnbyWt1UhlwOFHUNnRzdnZ2MDs7K5qVWi8ajSIajYoprZ8xaj/tGMduLEja\\nIqaVSNdyYWEBr732Gm7cuIHJyUmUSiXk83lsbW2h1WphamoK8/PzUvSrWq2iUCigWCxKaY9cLifu\\nC9/l9/sRjUbFjRo3aVALFKfTKdABXUqW6QgEAl2Kwy5dw8wr4/f0CAgt7OzsIB6Po9VqIRQKCXhP\\nAJ/uv7mFZFTSfdOfHQ6HrI2NjQ18+OGHaLfbiEajWFpawrlz56S9Ho+nyx0mP1PJcq8meZS/UTgx\\nrYPv7Tf+pCMLJJOJtYnIwXS73QiHw7h06RJ8Ph+8Xi9ef/11KVuwu7uLer0uIDHzG4ADaby9vQ2/\\n349QKIS5uTk4HA5kMhncvn0bmUwGlUpFalHv7e3B4/EgFArh9OnTEt1hmQiaq0clmuLa+uFg1mo1\\neVcqlXrKUjQLqnEha7dPf67VaqhUKmIhkFF4Pzd1sn6zuTdsFOrn6tn9pt1ztodpDAsLC7hy5Qqm\\np6cBHIDSTL8IBAICnnKB7+/vI5/Po1QqyfYG4n+RSERwQC5y1qGiRToMmZACie43k/eInRDjYZZ4\\nu90WwFe7Y/xfp2pwfPg/3VGWrqU7qBNEWQbkqPsSTQ9Fz5G2RLQi4bhqS960fBqNhghNHUzSz2IE\\nWOfTmW0bt299BVKr1epqHJO9CDDzxYuLi/D5fFLcisXBaRVR2vLIFVpUZFa6ca1WC/F4HNlsFplM\\nBpubm+L7B4NBcQFoiTDHhwKv3T6oW81SIeMSLRwCgQBEmxaLRXz66aciPJjTYWfCazcAgDyP0QsK\\nLvrju7u7KJVKiMViYnHwemIwxCTGFUhA76x6rXTIeOxHpVIRQPhb3/oWzp8/j+npaSwuLsKyLLz3\\n3nu4f/8+3n33XWxsbMDv9+OZZ55BPB5HIpFALBbrmq9YLIZQKITFxUV5RyQSkZye6elpnDp1Cg8f\\nPkQymRypb72UZygUgmVZUtmTlR3L5TLW19cFIyGuYrpTei71hlFG55xOJyqVilj9+XwejUYDU1NT\\nYiEGAgEEg0Ep7HcUi1fznemimtcRI2JRObNfAKQPfC4hF3OriZmOY+JhvfpiRp7tqK9AYtHzYDAo\\n2oRofDgclkFIp9Pw+XxYW1tDpVJBLpcTPASAaDiagO32QeEqmrJ0gRwOB9bW1pDL5bC7uwvLskRD\\nhkIhMd8rlQoqlYq4D3rx0HoahUzLQE+sxoYASPEpJsDVajURLtpq1KkA2iLSaQF06+i+5PN5KXdK\\nS1P75zoZbxxz306jmv3Vv1P5EOtLp9NYXFzEq6++ijNnzojpXy6XsbOzg52dHWSzWckD8/l8uHfv\\nHs6ePSvjoQUys6R1WJzt4rvT6TTS6fTIc2nOKV1mneDJOdPF2ACIstMBCdNiokWk986RD3luGcuc\\nhMPhrix9uy0kowok/V620c6F428axGd1UzOrXQtcndZAHqRFxH5ry8oca7v1NEw/+wqk5eVlLC4u\\nYnJyUhanBm35j8KDvzE0yMnnvZubmwJYnj9/HqlUCj6fT/CYQqEgleay2Swsy5JNgny+nhCn0ylC\\niswVjUaPxWUjg1Fj0CoplUq4ffs2UqkUZmZmBKDUoXwKYH0sjPbxGeFgn/iOfD6PbDYrbqC2MAkC\\nE1uyc0sGkakR9XdayzF5bmFhAfF4HPPz80gmk1Jb+fz58wiHwygUCtjd3ZW630zc5CbV1dVV/PrX\\nvxZ3l0mxtHYZoCDf6NIszGNiUt8ofTQxQY0jmXNRrVZFEWg3hrymBRPv10meFDJ8XrvdlrSFiYkJ\\nEUC6moCuKWS2eRQy+9cP52Xb+F4NHXCN0lXl3jXTEtcBHAol9stsT6+2HSkx8rXXXhOQsV6vi0nb\\narVkwyP9b2pLlnbVFgLvKZfLKJVK8Hq9mJ6eRqFQkO+bzSZyuRxWVlbE5E0kEpidnUW5XJZoAZmB\\nyH+lUhHNRByp38mYdmRaBgxhknHK5TJyuRySySQymQx+/vOfY2lpCfl8Hjdv3oTX65W6ytqa8nq9\\nYsLzmfoaTjgXMRd3LpfrAnOJp5w9e1aOnBnHxNf95Xup5Xw+n6RWEKSem5tDOp2Wc7iYVxMOhwUb\\n2dzcxP7+Pra2tpDJZGBZlgQleKIFgWy3241IJIJ0Oi1ANwF/Xdua48RKB9z7Nmr/SMROmGnfbDaR\\nTCYRiURQLpexsrKC+/fvS1uoSGhZkN/YPj1ueo4ojDKZDNrtNi5duoSJiQns7e1JCkMsFusS3qa7\\nNSxpRWeHJ5nfcQ2WSqUuhWlaph6PR7wbziEt9VqtJiVKNLZqWqS9LKYjW0gejwe7u7t49OiRDAL9\\nR5q3rVZLtgvQZy4Wi09NIgewXq+j0WjgwYMHePLkSZcZW6/X5SRXul+M2LCSXa1Wk3OjmJFNwLDd\\nbmNvb2+kzZh2RMuIgo7RIwrKzc1NRKNRxONxYXQKGN1vmscUULpaoN7zw2JflUoFxWIR1WpVMnwB\\niCXIXB+CpOMKJTIUt08Qv0mlUpidncX3vvc9TE1NSYSF46CjfE6nE5FIBBMTE9jZ2cHm5iZWVlbQ\\nah1UupyenkYwGMT58+dx+vRpRCIRRKNROemDCoQHMepCYrSoLOswVD8qmUrGdNlYlhUACoUCNjY2\\nBKOkVWTmkOln8/kaa+JnWl1UatFoFMViUSoMENQ+Cg6o+2j2085CIRZE5UirmwJGu2sUyI1GQ7ZO\\naQHMtcz+mN6LOfZ6vI6EIe3v78Pr9WJqagr1el2Yl7vRaa6x5GcgEBCQ1+l0ygLmJkbdKIfDIRaP\\njgwkEomukDqf3Wg0RGLT7aO1QcCXQuk4agaxPbQKyZTUHjqKQubS7hjzZ3T+kp4QWpS8j+PFDOXz\\n588LU9GiYiatqdWGJQ0qBoNBccWCwSCmpqYEMyQm6PV6JYBAIahdWGrJTufgQEIKS24zCAaDOHv2\\nrGToM4hBhue4agEEHAoAWiKjKJhe1gJ5TmNI5GXm0zBhUOOAbJuJrZhYoeYdzjvdU71ouRfsqFE2\\nk+zaYBKjuTparBUOcSVaPlQIxJ4IevMd5HG79+lxHKZtpL4C6cMPP8Qrr7yCc+fOoVwuy4Kni6FP\\nUaBQIG1tbckRMawQqAUaD12kO8hcJG7wq9fryOVykkDJPVLJZFIGVVe7o2AoFArY3Nzs2+lepAeQ\\nJvj+/j4ASD4SXbBAICDHw+isbr0vrdFodEWOKJy0RtVWZzAYxO3bt5HP5/H1r39dtjEQ9GXNIa3N\\nRiW6ZgsLC/jjP/5jXLt2TQ6+LJVKErJmDpQJiLLdFFTcK/WVr3xFXL5OpyOueSqVkpKuPECQyZF0\\n1SiYtNXIcdrd3cVvfvObkebQxC2oIJkpTnyK7QIO99dpLFALNyoNjSdq95IWMV3tRqMBv9+PZDKJ\\n1dVV4VcGcsx9ekdxwfthUXo82HfyDgNMnU5HeJfuGSO7rKek91ha1kHEUme9m2THnxR6/aivQLp/\\n/z6Wl5cxMTEh+7r0YiBTksEoYS3LkmN6GT3Sae08yUOnETA1gNE7DiCArlwJr9crmbAUQoxKUSAe\\ndbc/FyFdQTLg3t4e9vb2JA2Bp4FwkugSUCjRamRGrAkUcqOi/jufzyOTyXThKBSQurg8D+Mblaam\\npnD69Glcu3YNFy5cEKvAPLdLA5VmXgotI8uyxJUEDsvDErMx9+QxKqqjh3y+5isCprRQGKYeZx7Z\\nB00UPFQU2u3SlhT/5riw7/qZbCPfFwwGUSgUUKlUZNwsyxJ3l30c18od1FfzO9N10wpG949zRr6j\\nNU2hrfPwOp2OeCqM+pqBFv1uMyjQj/oKpK2tLaytrUlNlJmZGTkKhvvGLMvqApG5EHX0iS6YBsaY\\nU8PtGUwDiMfjIp1pJlarVUlEZAeZ2EbrjOnxgUBg4I7iYYggIAVSq9VCNptFoVAQbcsi6QBkgXEB\\nsW3ZbFYmlOOlx4raiGYzkwp5rYl76P1YoxL3Dl6+fBnXrl3DwsICgKddE+1+UqDQkqACoNak2817\\nqCTYTq1ZqYn1O82oGJ/Lv1n6ZRQymd60HGjRUOmYALX5HHNR81pauPrZDGTQLfP7/XKNLmdsWoLj\\nCKZeeFGv9rO9xMk4FuQnXb6EVh4FFeeXQpwegRb6dpal/m1QhA0YIJAajQZ+9rOf4Re/+IXUIKKm\\nvnz5spxqmUgkRDtSk+qEQQoeMnOlUsFvfvObLouJ9WM0IP7cc889hRnRdfB6vV1FyTkQjx49wgcf\\nfDDUhPYiuiS5XA57e3tyhtfu7i4ePHgA4GBxc0c726e3kbDfOj2CkTigu9Spw3GwS3xxcRG3bt2S\\nY7k5Nnx/MBiUEyG0KT4sff/738e5c+ewuLiIaDQqiqLT6SAej3cBldqS0wmszLin8G02mxLU0HvT\\nyPRMZO10OiLI2Ged18LkQgpeRoIikQheeumlI82nKeiYO1av1/HkyROxwMzsZI6B1uwa1NXjQKuY\\ngsjn86FcLqNQKHSNG7PEtSU6zlwC3a6/aZ2w3/ybPKprk2lBz3UVDAYlIXdychLlclkUMKPpbK+2\\ngAcJ1GOJsnHwiSsQ3PR6vbh//z62trakGBXDwRxohjcpNAgOMwzM01/JHHTb9L4mHaLk3iNqbSYT\\nAof749rtNh49eoRbt2717fQgokDk+8iU2WwWuVxO8DJuO9CRRLYHOGAUnlvVbrcFw+DzKZTa7TZC\\noRBmZmbgcDgkqmdZlrg3nA9O6jgMHAwGEYvFRBMymsf50ZtBudio2dhuWjbmgqXFXCwWkclkBG/L\\n5/MSPYzFYpibm5MIHgMdTN/geJdKJdkI+uTJk5GrZJoLkouVGArbDkBwLb3T3U6Ts23EQbTFq5UM\\neZjAfb1eF+uf2f28Vrd3XNfNLtJm9xtJew9aQJgRRbNksw6IUCYQ9+3VHtNlGwb7HKlAG90tvfue\\nzBiNRrt22cfjcRFiBLfb7cOSnnrvEHBYb0Z3YGVlRQQSQ/3EFyiEeA+FG927o5AJjHKhZjIZKZPL\\n7HHgYJILhUJXZI0HA7CcK11WDZpSGNFCmJiYgGVZKBaL2NrakgP6GPXQKfvjaFUqC1puFEgcP73x\\nk4xDoUPFQgbk3GqLlada3L17F2tra9ja2sLq6ip2d3dRLBbx0ksvodlsIh6PA0BXsqC2TOr1OjKZ\\nDMrlMh4/fizHXQ9D/caG33NRMgcOOKxnDjwdOaOFpF0SvovP1XPJBayj0BoMJ//q9o5LdiC+3W/a\\nDdZRTO1S6wxyja3pa9lmffCCxtG0yzYOTjbUbn/dMWqXTqcjUSACsTrPRoOAvEd3UFOvAS0Wi/L9\\nzs5Ol/ai5OZAUSPZPWdUYnsoJBgZ+elPfyqH/iUSCczPz4vlR/yKlhDD5AyfA4dgLd0c7VszbO73\\n+7GxsYEHDx5gaWkJs7OzEokkmKhzskahN954A5ZlCZjOAl60cIHDUqgcW57KofNYdEg4l8shl8vh\\nRz/6kRTHo4VUrVaRy+WEuZPJJC5cuCD72gBIDSJuOWIUji5roVDA3t7e0H20w2i09ie+GQgEUCgU\\nsLq6KvPMRUWsRAso4BAM12uA40WFwaOP6vU61tfXEQ6HMTs7i42NjS4XXbd3XH61w5DsnkVLh1FA\\nejqWdbirgDzPdUuMiH2hG8qINuEZZt9ry6uXohxGgY5UoM38Xpv0ehPeqM/sBx6SiFf0G/B+YchR\\niUzJPXx0nXTKQTgclkxxvpfRPzIrsTH68Lo0K90jAOI6cXJ3dnZw6tSpLoali8fM8FEtpLW1Ndy/\\nf19wGp/Ph1gshkgkIi4xQ720Ehh8IFCrs3Pb7Ta2t7extbWFX/3qV7h//77k9FDz6tyf3d1drK2t\\n4cmTJwAOqgPQsuL2E+BAERGL2dvbO9J8mtEl4NClpkXH8i7EkmjFDOJT7XJTWVIZud1uOU1leXlZ\\nBD5J7wM7at90++xcOPadGJkO/fM+zcPEEjkOvF7XfdfBK83HvIeWr2nBDervSEX+9SDYPdzuZfr6\\nQQuol5k96Pn9zNZhyC4qQNKpDJubm8jlckgkEnLeOxcSBSaP22Y4XSefkQk1JuR0OuVoYwASpfvV\\nr36FeDyOZ555piuhjQygmWBYun//Pu7evfsU81BwUlsyQXJ2dlbM+E8//VQELq0f4oF8Fvugy4UQ\\nIwIO8tru37+Pv//7v5f+M+8MOMx01niVGcUaRHY8q101WmsEnLPZbFdRfrbFdKm4gLnYdA0v4LBw\\nHzFOy7Lw1ltvYXt7Gzdv3hSXiG6cmW4wKvVbm+Z3FCYsq6sroOoUi3a7LSfbaIuYgpwwC61sYnJ2\\nicg6WmfOQz8aGkPq992g7zUIe1R3qt977cz1UZ/BvzWuoVPpydRmBjqfwWiT2+2WMhcURpx8nbei\\ncQe+l9aEPkJHE3N9BgGEdv3UjMIFYVmWWD+6rdoMLxaLAnTqpEk7xjPfqV1puu7spz4MwBRMHM9R\\n+2jXZ/N7bU1z/DUkYApt4HDvl11aCeeSzyN+yBOHuZj5TrNfR+VZ/Z1dlK3TOUxepivGNmvvgvtU\\n2Wa6mWbgxrRa9btM61i39UgCyeyc6a+aDelnIQ169qB39gMph3nXIDItP83IDNsWi0U51kZbCcAB\\n3qT3CbHgOrWJ6c7qbRPUOlprEdQlI3Ah6KTDUfvbay6psfXCrVQq4i5pwJaMy20kZGa7udKfyczs\\nq47Q8Dod2CCNakXo/CAKPfaJc6CxIo6/tgR0YiTHma6L3lyqEwz14qaAKxQKyGQyXXlb+lo9J+NQ\\nPyVvjj35S+eTcR5oPXGOue9OlynRSvP/sPclvY1m19kPSXGeKZKSqLEGVamruuaeqtx2G7ETI44d\\nIIs4cZBVgCyyyCb5C1lkn02yyC4TkqycDhJ8gA3Dbttt91Q91dBVpdI8k+I8SSK/hfAcHd56X4qk\\n1EEvdIBCURzue8dzz3nOpC8u3Re9hx2OQ6dZ4sB6fbpR3xkjzQnUHellcq0YTDcQrFu7dkxqkGBM\\nzViHhoYkeJYbkRkd9W3KKHweSkoO+/v78Pv9kuGRQCaZFzcGD7IWf9vtw7CZzc1NSVpHxgVAPMSZ\\nAaHfMdqJ+hpr0FINP+fvtbppdUHpw2Y+w3xtZ9ww3+sHQ5qdnZVAZaYV4SGgukTAnBKvxuWobnEf\\nmU6TVE11qWj6adEPSbsI1Go1ZLNZOfiUrMnoT8KQzLnne+b6agmPpab4j3uTCRZ3dnY6qtxSEqef\\nmIYN6Hdn1Y92+9Btx+zLcWvZs5VNP0i/r5lVr9TL9wdV7ewktX5+SyZCcFqLr/w7l8thbW3tBbM1\\nMSM6NepFpNsEcGRKpt8RGRJDKziv2oGSi0kv6NNSf4/D6fQ6W33XjpHo75lt2DFFq3b6UU0dDodY\\nfij90GJE3yemaaX64vF4EIlEABxZQhkcS+LB5B5gknseZq5HpVJ5QZLQFkVKVmafT0JaMiHp+aSU\\nSTiBVlTuKzImraZb+Q6RoWrHVivV0+qiMvtkRz0xJH2DmqKg7kSv1Mv3T8JYBvmdOXHaCqJvSoq5\\nT58+xU9+8hMxgQYCATHv8/k+n68jc6auZMr36HMDHC44Q1MoGZGB6fknRnEaZCdl6s9NhtJveyZz\\nsvq83z1kR9VqVVKdaNyObhlUFVkCOhKJSJpdWlKpYrNf2neL7+m6amRcBwcHgrUBEKZVLpfhdB4m\\nqCuVSi/gjqcxblPlNdeQTNIEmemyQoamHVT1+PV+08YZO8dIuzGdiCGZt1m329FqE/faMSvG1gtT\\nsdvEJ8VWmMR/ZWUFi4uLqFQqqNVqkkiuVCrhN7/5DR49eiQpN7RTmcYgdCS73sz0IWq328jlciKN\\n0fRMtUPr6YzvY2R2vzTIwbfDhEhWvl/9Xibd9pJ+Ri+0tbWFcrksB4zrWa1WpVrN3t4ePv/8c5RK\\nJfEX0hcBD5opnWnVWa+LllZXV1cFi+HZefDggQSF5/P5DsxQ75V+yE71tiJTIo1EIiiXy6K68uKr\\nVqvY3NwUFwGqlnRyZuaLVqslkIRVjKZdf6wkY5P6trJ1e9/uc73h7FSCXtvu5Xv9SkjmoeOm3N3d\\nxfb2tniBE5zc39+XMBIT1AQ6c8HQoqLDLNgGVTntF6LVOx4qnRKCBQHsfLJOMmfdGJXdmh63nv0+\\nx+q7/YxTV2/R8+1yubC7uysGCWI5uVwO2WxW1Do+l2oYAV1KpcSGtFVOJ5hjKApwxEjpKqJxxX7N\\n4VZz0+/3KCmGw2EcHBx0OK1S8iY2SSZrVvUBILGKzM/VK5/oxYWj/2v2mIfafd5NkjmOYdnRaYi6\\nZnvk4mQU7777Lur1ujjradXFVGH0a1PE1dKJfk2rDX+v8Ybd3V0sLS1heHhYpKrnz59je3v7BevF\\noGSHD5nzof+2o17W77g1MyUtMpN+SK8L53N/f19i4mhgIJOhtY1OrtpsrdfEjD+jBEaA3JwDYlGL\\ni4vY2tqSMl7MGT/IpWKO87jfEytiHCmrPNMNoVarybjW19exvr6OZrOJSqWCxcVFPHz4EFNTU3KJ\\nMpX18vKylDnX6p2ef21V5D46sYSkuZr50ONUrG4gqJXKZvfbfsTTQcjEx8iQSqUS3n77bSn7rAMw\\nzX5onEWDn70cUKs5arVaePz4MT744ANcvXpVmOKvfvUrfPHFFwNjSHaY0XFYkvm31dgGOVx2e8hc\\nk15JR9ADRweSzqXatK/XTKt4DodDVBndH3oq09GQ80DVmpKFlnb39/fx2WefiR8bQW2H48iR1MpP\\nqp95Yz+szotul0U2tre3BcRmDvd2+7DIxPLyskiEn3/+OZxOJy5fvox0Oi2FHfL5PJaXl7G4uIhs\\nNmvZN6t+8e+u42qftphxRmd0Rmc0IA2WB/WMzuiMzuhLoDOGdEZndEZfGTpjSGd0Rmf0laEzhnRG\\nZ3RGXxk6Y0hndEZn9JWhM4Z0Rmd0Rl8ZOmNIZ3RGZ/SVoTOGdEZndEZfGerqqc3aatrrU/tRBgIB\\nJBIJSXmpM+ZdvnwZ1WoVGxsb4qHKnL6Mm9Ht6Wx99GT9+te/LrFln332mZQGAo73+Oyn8ohZ68wq\\n2NPOM9kqdGSQ2KRByOHoLNJ5HIVCIctx6vba7XZHNVLTW7qb9/lx4UE6hMAMuzmOeg2VicfjXfes\\nmYPIzsPZah/0Slaeyr20weDVXiiZTHZdS/18xt+x/iFzQvGccc2ZjI6Bw+ZeYJjTwcEBvve972F+\\nfh4bGxsSv8e4QaugWrYHwNK7m9RXkn/zQXQ3ZyL8mzdvIhwOw+fz4dKlS+Ky//Of/xyrq6tSneTg\\n4ADpdFoCDtPptFQwmJiYkARQ586dk8h7BrmasV+6L4PGBZmHzS6UwW4erPryZdOgY9UH1WqsOuRC\\nz4vVc3s5eOb3BXQS5AAAIABJREFUzef0cmAHCauw66tV/6xi9qy+OwgNGgJzHNmFh5DIeK9fv47J\\nyUm89dZbqFarSCaTWF1dxbNnz/Dhhx/i2bNnIkRQSGDoi84jFQwGce3aNczMzGBkZATj4+NS9OKf\\n/umf8OTJE5TLZREsrBL82fVVU19J/s24LZ3mc2RkBN/4xjcQCoWQTCYxNTUlaTJarRZ+/etfS5kc\\n4LDO2tTUFFKpFCYmJuDz+ZBOp/HGG28AgCQkz+VyKBaLePvtt7G8vAzAPtPgoAuvAwHNdrodnEGe\\n00tsG9DbITzJ8/X6mc+yylqgyUqCPK4/Wioy59GMlzR/1096DjvJppc4zJNcasf1oVtfBk1BYvdM\\nEqs/37t3D9/+9rcBHDKqlZUVPHjwQCL+NzY2pOgpExGGQiHJF+VwOJBKpfDWW2/hlVdekfPKrBj3\\n79/H7u6upL49rp/dqK/0IxTXdA7earUKv9+PeDyOubk5SWZFcrvduH37NsLhMD7++GPk83lUq1Up\\nrbO8vIx2u43Z2VlEIhEEAgHJ08KIe06SeduYKsCg1MsmPI2NeprPGWS8Ztt2Kko/felVuul2WRwn\\nyfQz9/3M8Wms6XHPt2JM5p4dhBFaqWpMiRIMBjE6OopkMonXX38dly5dkvJH+/v7iEQimJycxHe+\\n8x1cuXJFCoaSwQQCgY7UKsChun/9+nUMDw9L4jngkOndu3cPiUQC//iP/9hRu28Q6kll09xb5xxu\\nNpuo1WqYm5vD5cuXcf36dalewSRZ7XYbd+7cwY0bN5BMJrG4uIiVlRVJYVAsFpFKpTA9PY3z588j\\nEAhIWgQmK/P7/ZYljk9zQ1mpo3aSAOfEavP1e6MPSid9Dm/mfiQyu3b0/7p/Vt81D6IdHaci90vd\\nJP2TzoEddZN+TvOZen5cLhei0Sju3r2Le/fu4a233kIoFJILvV6vw+fzYXZ2FlevXu3AiKxwNxaJ\\n1GeBlUlYIvx3fud3cOfOHfzyl7/E1tYWVlZWOvCpftav56oj7CiJCdMPDg7wyiuvYG5uriMhuE6t\\nsLe3h2aziUgkglgshpWVFWxvb0vlisnJSZw7dw6xWKwjvwrb0WCb3eBOsoGtbm7zwFqpat0kjG54\\n1HF03I2p8Y5+yK6/5ka0UoHtDrTZL35u/saq7W59I+myUYOQ3TzZqXXHkd3adNsHdr/V89jvGM31\\n498sNBEMBjEzMyMMURf51AA0DU7MbKovVZ4BM6+RLqHEMvButxs3btzARx99hI2NDWnPnKPj5vhY\\ns78VQ2KH2u22VN7w+XwAIDmmterGNqLRKGZnZ3H+/Hl4vV5EIhGcO3cOFy5cQDKZRDQalYRZzMDH\\npOR87v+FeqXVQ/bfvCWOE831+4OI43bvnQTvIIPlb81CAXZMxI5OQ3LRz9Jt6oNx2pLLSdqz+60d\\nk7Zi/LqtQftitzYs6e31esVYRMbjcBzmdOJeZrUbJl6jSscqxfycILUuIW5ePm63G5cuXcLo6OgL\\nueW79dekvqqOmA0SS3rw4IFwSXaYtetZMsbpdGJmZgZ/8id/gnv37uH999+Hw+HA3bt3cfPmTanQ\\nwZuCaUGbzSYajYZt7mar1/0usvlbh+PFZOha9La6mXQ7vTALu5uym0R1GoxWP8eKAVltHD3O4yQA\\nO0nHaixW0oPV8/stiNmNjpOkez04Vm1azWcvfRjkctG/N/ejy+XC9PQ0xsfHpT4gUyObc0lpiZeT\\nrhfHVLX8W58Bj8cjbbG815tvvomlpSW8//77kvSt2zxYUc8pbM2Npm/bfD4vfkbktPxc5yEGDuuK\\nTU5OSmeTySQAdOQtpjjIckAUN3lbdts0g24oU8Uy62ZZFSzspgJZbTKrBbJ6ttk387eDSiZWB8F8\\nhu6LqV51Uzl6VXd66dsgN2u3Nns9EIMwhV4Y0HGfn2SMVqQzmvLsUTLS6pc+x+ybZlhkQGRW1F54\\nNsyLLRgMwufzSd5wXaG5VzrW7G/VoLkp9/b2pMgeJ4C4jwnohUIhRKNRRKNRmRCzTb7WzKnZbB4r\\n4p7kljH/76WIn2YmJhPqRcWy+67ds8zn9ktmtdRe1A87yfMkB8iq3V6Ycr/P6OW5gPX+7kdKtHt2\\nN6m9n/b6IapPZj/4Hs+bnZ8ZcOSkrFV7nkMzPXO7fVSwggUB/H5/T/NvRT2Z/a0aYQdbrRa+9a1v\\n4Rvf+EZHQUPWB6cOS8ZFz2LWrtLVPk2OSnN/OBwWU6TOl2zV15OS3WGxmwMrRtrtwNNjNh6Pi65O\\nsN4qt7LJBHRFi36T3+vL4bhDp8dnt3F1v0yy+50dU7BjyoMwPis11Ooztt/Le7rvpiRs1a7de+b7\\ng6g1du23220peqmrpFCq4ff1PtBOjFTZuN/5TzMvDXbrOeBz3G43hoeHX5CkeqUTVx1xOp1YXV3F\\nZ599hpdeegnAoRTEz8hAdPUDLVXoEtQcNLkwQTQ7R0hzc5/2TWP1nrkJj1NDtJQYDAbh8XiQSqVw\\n/vx5VKtV1Ot1rK2toVqtolqtdhSGNPvBm67Vasn//Y7JbNfqPf1am297UUmspE2zD/r7dnN4Gmrp\\nl0EnlRIHlRx6aZewSSaTQTgcfqHsOfAiA7Jqx+E4KmTAdvm+yYD072jIMss8kXoZY09mfzspgA/9\\nt3/7N7z99tv46KOP8PWvfx2vvfaa1LynRKD1UAAdYh1LVTscR7XGXS4X/H4/dnZ2sLq6KpUd7DbE\\nl6FCmO12Y4B24jklGb/fj9nZWVy6dAkXL17E1772NWnvvffew/vvv48PPvgApVKp41lUgV0uFwKB\\nAEKhkFR/2NjY6GucVhJmN4nMihnZ7Qd+z/RtMudyEKZ2EuJe4ms6/3U7ML2qqvrwmvBEN+3C6hmn\\nQQcHBwgGg7h9+zbGxsZk7CYD4h7g5yajoUGK/ee543f1OrfbR+C32+3G6OgoIpGIzLlu+8Qqmx6E\\n3fvsXLPZxPvvv49cLodPP/0Uf/qnf4pAICClYKjKmfqpLhmjS8QQh2KAbrFY7Fl375dMsbkXqYfj\\nthNLubiRSATJZBKTk5O4e/cuEokEIpEIKpWKMOUbN26I1/v6+joAiN7O8szBYBDT09NotVrI5/NS\\ndbVf0qCm1dj0axMrMNe8F3VDxzb1osKwff4zVa5eSfeZvmxut1vqsdl9l8/X/bJSYXlAGe9llt/W\\nbes9o5+nx3tSyU5rF8lkEsFgsEP9MsfL35gqqN0lYkpFVqogGRwZmpVkfdw4e4pl04O1mgT6Ljx8\\n+BAPHz5ELBbDd7/7XXEL4MHS3FXfvnoydJlhh8OBSqWClZUV8do2n33ce/1QN0lAzwVfm2MwqdVq\\nCSO5e/cuvva1rwmTZeaCQCCA2dlZqaJKCZFz0mq1sLm5iVQqhTt37mB3dxePHj3Czs6OlOPulVwu\\n1wuHkWOwqiNnJdlYzQ/7qX9H9VJvYH6mGbnVvNMgQqe9fs3+ep28Xi9CoZD4xunikNqMbeVRbTIg\\nPR/8PQPLAUh/dR/MA0jPaI7PDhPtlTTT5n6iyqbnw/wN+2+up5ZsALwwHs6BLqHN/cP58Hq9HRY6\\nK6ZoRz1jSFaDkkaUNzUHNTIygnQ6LeZ8fkb1Y39/H3t7e7IBtfMjvxsMBrG5uYmPPvoI1Wq1J8vX\\naZDVRrK61agCAUfMmhjZxMQELl26hL/+67/G2NgYHj16hHfeeQcAMD4+jm9+85sYGxuDx+PBv//7\\nv+Pg4AB37twR3xE6t3m9XkxNTSESiSAcDuMf/uEf8Jvf/Ablclm84Xsl7elu3o4aJzCZlJUPlvma\\n7WmP46mpKdy6dQsPHz7E2traC5cKGQNfZzIZJJNJTExM4NGjR/jss886Slz3Q1Q1EokEvvWtb2F0\\ndBQffvghHj9+jEKhIKlYWMGV4+X6WUkPmnn5/X6EQiGMjIxgamoKhUIBuVwOjx8/7jiAbIOOvm++\\n+Sb8fj8ODg7w7rvv4osvvoDH4xHH4n7Gp4n9jEajuHHjhggDXHNtedNnFYBoMLqKL4AXpH8+UxtU\\ndChZu33of0hQ26qfx1FPVjY70csUQXU1UIZ9UGWjyEfVjb8z9XAA4sDlcHTWG9d9shvsILq5qRN3\\nUy34Xf1885Zxu92YmJjAtWvXRC3jInq9XoTDYfj9fgHz+dtKpSJM6ODgAH6/H9FoFH6/X8Rh7avV\\nL0Oyk3i5LloVtZoXKxcN/s0D4PV6EQgE0Gw2MT09jZmZGdTrdVFptHgPoIMBEB8LBoMvbPR+ye12\\nw+/349KlS7h58yYCgQB+/OMfAzjMmZROpwEcMWnimGSqvCw1s/L5fAL0ejweCSofGRmB3++H3+8X\\nJ0Hg0GGQaxyNRiUygWs/Pz8vKvqgUiDQ6VNEg4epIpoSrql+68uBxDPIds3zQemHxM+DwaClv5J+\\nrh31FctmpWdbfV+HflBF0KI3B6kZEt/X3qStVgv1er2j/ni3zakXqB+ykn74vl3bdpICLQ1jY2OY\\nm5tDs9nE8vIy6vU64vE4vF4vhoeHxYmUwY67u7vIZrOIxWIIBoOoVCqIx+NiwiVDIyCrMyoMQprZ\\n2Kmp+rV5U2rMj6ppIBDA6OgoRkdHsb+/j/Pnz+PixYuyhrlcTn7PsuRM7uV0OmW8gUCgIx6ynzgv\\nvZ8SiQSuXbuGq1evYm9vD5ubm3C73UgkEpiampI5ZIgEcTkykUajIWqxx+PB2NgY/H4/9vf3BeAd\\nGxuTMtOxWEzUN6b2YD6vmZkZCSDn2B89eoSFhQVh1v2un0nmpap99+xgEj2//NvOh1BLSFZe/8BR\\nDiXTAbNbvzX1BGp3a9BqE/M2Z/oQ+ifxf41laGmIkgQH7PF45L1ugbVmPwbRyfXvySzsJDC7g7q/\\nvw+v14vXX38dP/jBD3DhwgXU63VUKhW4XC5MTU3h3LlzGBoawtbWFoDDxb9y5YqkXKlUKnA6nTh/\\n/rxYhDY2NlCv15HP59FsNuHxePrGj9h3rXZo/MHKhcBOIiaFQiFcuHAB6XQac3NzAA6zHhYKBVQq\\nFWxubuJnP/sZNjY2kM1mRcV1uVyIxWJotVrI5XLy/OXlZWxubuLzzz9HsVhEIBCQwOx+iCrVlStX\\nMDY2hgcPHkjA5+TkJKanp1Gv19FoNFCr1VCtVqW/5uVCOCEUCon1iE7AVFtqtZqkyzl37pzgKeVy\\nGfV6HQcHBzh//jymp6dlTzcaDUl8RsfiQYhrSIbj9XptJSQTN+P7lEY5d2RGmqlwrBqb4/d0TGQg\\nEEA4HBZJst+z2FeCNpLVjWp1SPUG1uIfv68tbFosZNtkSry1rNREs0+Dkv49maUpDdrhSlqdGRoa\\nwrlz53Dp0iUkEgmsrq4Kgw6FQnIQl5eXRexPp9OIxWLY29vD9vY2nE4nxsfHUa1Wkc/nRaXgRtIW\\njn7HSKmD82mG41iJ9qZqzcsmHo8jk8lgYmIC58+fR7vdRqlUwu7urjCbWq2GUqmEer0uNyeZKkFd\\nHvzt7W0A6LjErOKvjiNmORwdHYXH48H8/DwWFxclSmB4eBhLS0vCjEqlEkqlkqTJdbvdgmmGQiGE\\nQiG5FNk/h8Mh1rVisSjPpnTndrvF3cPj8WB4eBjRaFQMQDr76aBqKYmqpVaTuF+s5s5Ky9G4kNZG\\n9Hf1BaaxJk1O56HHdr9Ou6RjMSSribJ7T3feKpIcODoIdHrU0pE+eJxcu0U7TlLrl7qpK6ZYqvuj\\nGUMoFEIikUAsFsPQ0JCI68ViUXR73vY6xxNvx1arJblrSqWSHBam/Q0EAvD5fMKk+01ZwT5zfr1e\\nr+AmQGfwrR5ju91GIBCA3++Hz+fD+fPnEYvFUKvVUCgUUC6Xsb6+LmpKoVAQ6WZ7e1uyf547dw61\\nWg21Wg3ZbFbmgiqoaXWywg17Wcd0Oo1MJoPJyUl4vV5sbm4im81ibGwM8Xi8w6l0aGgI0WhUsD3u\\nwVAoJAGkeq3b7Tbi8TiCwWDHGmqMj21wLJQm6vU6isWiMCTGfQ4iSXCser34Hv8mWK0lIKtL3A6u\\nsLukgCPjk+nFDaAj/1K/1LPKZjZuJamYQCtBLy0OkgnpwXDw9Mrme0yHoAfbC/U7Eccxu17abLfb\\nuHXrFubm5nDr1i0cHBxgYWEB29vbAlBrS2IoFJL5YYgMAGES7733nlgoq9WqSFOJRAKJREI2eD/E\\nudX5cExJS9+ABHE9Hg+mp6cxPDyMSCSC4eFhOBwOLC8v4/PPP0cul0O9XofD4RB1zO/3o1KpoFKp\\nwOPxiHREaWh/f18yFVIS0l7q+hD0A94Hg0HcuHEDly5dwvnz55HL5bC6uoq1tTXcuXNH5p2hSECn\\nzxf3IddKB3ZT6gkEAohEIh1SPQ+g9kXiuMrlMnw+n+yBYrGI58+f4+HDhygWi9jb2xsIEzS1EqpS\\n/Iyah/5cM1ZNmoHo77It/Tu9jzTYfXBwgHA4jImJCVFN+6We049YMSWrv2mxASALaYJhOnmT1eAA\\niARFxtRLHwclu0U67llax261Wpibm8O9e/cwPT2NZrOJtbU1AJDc4noedWqHcrksG5Kqw+PHj8U5\\nNJ1Oi5NpIBBAMplEvV7vWr3Bbpx8Pueba6WZsq5QQe/wS5cuIRqNIh6P4+HDh1hcXEShUBBcCzg6\\n2MSQuM7VahW1Wg2PHj2SZ/Hg0HeHkoKV5aYflc3r9WJ0dBRXrlxBs9nE4uIidnZ2UC6XJcOp2+2W\\nqHTOhcPhQLFYFNW62WxKv9kuLaaUBNlfn88naiwlTjJf4lQA4PP5REpcX1/H8vIyarUa2u22gOGD\\nEo0dvMB1HiPTKqzPmcY/9bnVmJGV9KWtkGyHbQYCAcGx+lW3+8aQzM1igoBut1sWlLcLJ0H/nhyc\\nTMz8jh4gHex6kZIG0cX1bWyOyWxPb2DelufPn8eNGzfw3e9+FzMzM2g0GlhcXMT+/j5isRji8TiG\\nhobkVuZtWa1W0Wg0EIvFRMRutw99Rr71rW+hUqmgUCh0mGRnZ2fh9/vx7rvvYmlpaaCx6vGajqgO\\nhwPf//73kUgkRIqp1+tYXV3F/Pw8AGB7e1uktmQyib29vQ7G1G63ZcPqTbm7u/uCddVOHdYewP2s\\naaVSwcjICF555RV8/PHHmJ+fR6FQwP7+PpLJpAR7s22fzyfPZ94uXh4MVKUUp1VkLQXpUAkNKpPJ\\nOp1OpNNpjI+Po1arYXNzEysrK8I8G41GBw7VD3FuyBApFTmdTtRqNXGQ1GdH44a8ADTEoqU17Uir\\n147hJFwrrjNVRBoD9Nr2so5dGZIpNXTDk/hg+tFQqiGXNjEkrdeaE8a2qIvbcVmTewOnC26bz+Fn\\nBC5DoZAwpDt37iCVSgGA4CpDQ0Nixuft02g0OgIQycSZs4bPCIfDyGazaLcPgWLOYyQSQSaTQSQS\\n6Zv5dpsjronf78fFixcRjUbFMlUqlbC2tiZMhhkbiGcNDQ3Jgeq2HrxRTVzC/K65gfsZp8/nQzgc\\nRiQSQaPRkGoYQ0ND8Pl8Yu2lkyYPoz5M+iDpgFEtyXFP6gypwJGBBoAA4aFQCMPDwwiFQtje3pZn\\n0yLlcrn6qiNoRdxL7Ls5n3o8vOC0KmyFE+nLxNwzVLtNIUE7TLKwgJVgY0c95UPqhbhIxDookjNj\\npObkVHHMwZIBARCVgkDhcWZpq7/7Jf5eP8sEVoPBIC5cuIBUKoWpqSn4/X6kUikEg0Fks1lsbm6i\\nXC7D7/cjHA4jHo/D5/OJ4x1TjlBMDwaDqNVqctPwhs3lctje3sbu7q6oUNzgrVYLfr8fgUCgr/Fp\\nU78+7JQGUqkUYrEYDg4OkM1msbS0hM3NTbGWkWF5vV7U63VUq1XBuY6LVTPBVLMf7Itdv3ul2dlZ\\nTExMSNza2toaCoWCzDVxoFwuh3a73YGT6QtCYy9We4/SID3qtZmb88xDTymrWCxKVY9IJIJWq4Vy\\nuSzrfxLSqX64l7R0Y1qpzTOocUVTOtVSrAlTEP+i7xIA+ZvjJ1PqhQYy+5vECaDTGUEtHVCrD4PH\\n43nBQsTvmpkhdR5fuz7qSRvEHK4nmmoYb75gMCj4wdTUFGKxGDKZjIQOUPorlUriUxMMBjE8PIxw\\nOCziLcFpbsJqtYp4PI5AINBhVSQutLKy0qHLl8tl7O3toVKpoFqtwufz4eLFi32Nk4dO35CUVIlN\\nTUxM4OnTpygUCnj48KGY7MmIyFTphazTw5jOclw/vqfXi/uCILeZ6oLPs0o/041++7d/W9wq1tfX\\nUSwWJeyoUCggEokgGo3KRVetVjtwEe1uoMdA6UJjK9pMzvGZqg/dHLLZLHZ3d+Hz+aRgo8/nw9ra\\nmuSlPwmRCRDn0tKPZq78TKtfZMQkzbi0lVHzAxqcDg4OOjzY2Q9GGJTL5b4EmxN5avO1XhCCoZo7\\ncqNpXyQtunMyNCPiASWwa96mJNNJ0M73ohvpnE1er1cmk5V0w+EwQqEQ3njjDcRiMezv76NYLGJ7\\ne1uc6ci0KLmwkF6xWJTbh2N1OBwol8uSmG5sbEyYxNraGra2trC3tydmalZoKRQKcsu2221EIpG+\\nxsmCCbyxqMJQTXE6DysNr62tSbVgSq1a5dSR7RqDAjqtdGTEvGy0lYaMPBKJyEFyuVxoNpvwer0S\\njsEwjl5pfX0d0WgUzWYT5XIZkUhEPMSXlpaQyWSE0XFO2Z+9vb2Oajncg2Se3Id0vGS4DHDEiLjG\\nfAYAFItFFAoF7O7uSnl65sYqFosIh8MDx+txzslUCI6zH6b5nd/VDMkkfcbNs6QvG51vie0RP7N6\\ndi/Ul6e2OTCSdsiidKDxJ3LXRqMhaoZOPM4Dy3b1QGgJsZN8TuLHQWq320ilUkgkEhgaGkIikUA4\\nHBaVi8yGQGGlUsHu7i52d3fFa9fhcCAWi4k5WOdu0reHNm8TSF1cXJTxE0ug2bzVaqFSqchBjUQi\\nohL2G5DpcrmQSqXkNqYESNK+NclkUpw4W61Wh0qjpRaNoZAxk/GZm5lqjgZDtSGEUgrj0Hw+H3Z2\\ndvD06dOex/jBBx/A5/OJS8T169cxMzMDAEin02i32+KASeMCGYcuJa0PLiUKjonzRkZOJkYm5PP5\\nxPeo2WwiFouhVCohGo1icnJSvL3b7TZmZmZQqVTEEtcPacmFfdd+fiStbunf6Db4GkDHJaMNTbxc\\nNB5lFdGgMTSzv8dJu33XZeuGFTCG69q1a9Ih3iy8kannMhG4nkwuPGNwGPNj5aNhAqNW/e2VPB4P\\nXn/9dbzxxhsdPkOMlqalaWlpSYpg8nagahGJRDA+Pi43VLVaRbt95ETHzdxqtQRTomn5o48+ktuW\\nUf0ulwubm5tYXFyUEIN4PA6PxyPqIW/+XsntdiOdTuPChQtwuVySbQCAYEQEK3kQaS3VaVG06D8y\\nMiJlsCgh0l2AdfjoDFksFkUdotS0vb0t6l8gEBCHwUqlgkajgfv37+Pjjz/ueYz3799HpVIRT/nX\\nX39dYtC2traQy+Wwvr6OSCSCUCgk3twa69CMl/PG8ZLZcj60RE4nSX5PO3oyG+js7CyAQ0slqzW/\\n9957+N///d++1tIkzQS0ZKPDRLhuZooR8zxr9U23o7UWxvRpJqMlK0qgVpbJbtRXuLiVuqZfu91u\\nRKNRjIyMyGccFBdUT4j2hdEYE39LnZ1SkslsrIBR3c9eKZ1OY2pqCplMRsBaxpEVCoUOpzcuBoFQ\\ngpT0/m00GsJIadnhJqZfCheLbhHDw8MAIL5GBwcHqFQqaLfbYiTgsxiQW61WsbOz09c4i8WiVAt2\\nu91YWloSUzgtNARfeUNSPaODow68dLlckrkgFovJ59ykxBaYagaAgPPEw1ZXVwWXIv5BdWt/fx87\\nOzviv9QLaSYXjUaxtraGTCaD0dFRTE5OIhKJiKOmz+dDMpkU9ULDC/SPqtfr2Nraknng+7VarSPc\\nAoD0XWOmlDB5aD/77DPs7+9je3tb8MrHjx/jww8/7GstgRcvXx3vSYmOc6KZUL/GKrZHRkOLsBnf\\npvul3T76ob49tfX/5vs0hScSCQEFdS4kWhOcTqf4sVih/zyU9N3hhOjnW02a1eT0QuPj45JlD4Dc\\n0swuaIq4lAB8Ph+azaYwHt0Xjk1jEhqr0FHker5araNIc25YWoc0LgN0+oj0QtVqFaurq8LIYrGY\\nFOikhEMLFKUkMs18Pg+n0ynjpPsC8Z9QKCTMiAGxHD/BT8Y4NZtNlEolVCoVrK+vo1KpoF6vv8C0\\niVP0gyE5HA7k83kUCgWsrKxgcXERqVQK586dw/DwcIdFjHNI/yNtomafisUiNjY2UC6Xkc/nJXOB\\nlpK1pUrXFtTqEzWCSCSCg4MD5HI5+Hw+cQVYXV3tay05VvPcsE/awKNdEU5CHI/Gh7Sntimd8bv9\\nhDj1rLKZqpqpygGHEhJzzZB78iDt7e3JDaqj+HmraBCRm8LhcCAajWJ6ehoPHjw4NsK9V6ugSa++\\n+ioWFhawsbGB4eFhTE5Owu12S7UTHkpuAB0jxADLWq2GWCwm+AFxlXw+33GwKAWEw2FkMhlEo1EJ\\n8szn82K+1cm1KC3pEAu3292374rb7Uaj0UC1WoXL5UK1WsXKysoL86ZBUiszsI5B1KK83iuUcHUC\\nO83UddZG4CibpcZxAHRE1fdCOvlYu91GrVbDwsIClpaW8O6773b0Tx9a9pfjI/F7GlrgGPi/aYzR\\n82TCHHrsVOUZdjMI8XfUMjRY3Wg0JFSGGF+/Z0R/n/Oj9wSfSSMV9wNV8n4t3gNnjNSd5f8mPqC5\\nN3BkmiRIykPK1xpA4wZwOp1IpVICEtr1w+xTP1StVjE6OiopJnj4AYjDn/ZV4c1PqTAYDHaomADk\\n98FgUOaIaoLL5RIAmeqbNp1T9SNRcqPKyA3Qbyybx+Pp8Hnp5k7BzcTv8jXX1fT8BV5cB32w9aHl\\n/OgNzDU3VYpBDirn0JQgTGusJrNvui0TAOZ39fum7xo/1xZF81k62d6g0fEk7lntGKkZyKBkXlT8\\nn+1qadN++qZUAAAgAElEQVTcB+Y+6YX6zofUrbNkRjomhuI2dU8WiGRgJYEvUyWj6uLxeBCNRrsG\\n65nv97uJd3d3MTc3h5mZGcEHnE6niOyUdoiJsH80s5IZ84bXeBi9uh0Oh3htU/9utVoS0U8irqZ9\\nYvx+vywuk9VR7emHyMB0/zRGoOfPxPP4v55b3Y6WMPRnej1MUJwAqRWZB75XsvqNti7p98y+Ua2y\\nijskhEC1SEtClO7IXPi/vly0xAQcrTPn4aQMSUso+m89Zn0p9HpxW+0DquV8bV5a/Ef/Mn0RHPfM\\nntOPWDEmdrbVOoxen5mZEXCTwDAlCx4wr9eL/f198YEhtsTBEEcgM0skErhy5Yqt45g5sYNISL/6\\n1a/QbDaxubkJABgZGUEsFkMsFsOFCxdEhaLPjPYrorjNidcpJzhep/MwWdfPfvYzfPHFF4LR0JHy\\n+9//PqampjpKjFN6opc2sReKyQsLC5L+tF+ymyPzEFvdblbSgvlb/m238U2xXzM1qz3WD3V7phXp\\nPcy11MzUlN7s2jDHZjVe/c+UqgZV2di+6Y6hn0spTTPRQZ6h11oLIHyG1ox06hbN0I579sBlkKxw\\nh+HhYQkgJSiruSelAqo2WlzVt47mstonxYop2jHKfiibzeLp06doNptSYK9WqwlWoyUaLgDV03w+\\n32Eu5/Pz+byAf63WoQf2e++9h08++QS5XE5irDweD+bm5uB0OsXcT6mRliiqfdVqVSS0ra0tSSjW\\nL+n51ZvEvA37Ye6mes7n6HbN73R7npbgBsn7ZPW6W9+1dGF1CR/H0MxLkWQHJp90z5q/IyPV7+m/\\nNSSgMcDjyFTT2B7b0aTdAihcMJuBqebaUU8YklUj5gKEw2FcvnwZY2Njcmi1GZWltYeHh9FqtToY\\nELm3xiho4XE4HBKCYdUPE2wf5KZpt9t49uwZnjx50oFrABA1zel0ihc2/YHcbjcKhYKoVbFYTFS9\\nlZUV7OzsdATGapGdY3U6nfi7v/s7TExMYHx8XJKEMcyEPlDAYdAu48cIgvc7TnPdrC4W85YdRCU+\\nbk24libga8eUeqVufnK9Sk3dpL3jvgtYMyGTKZ8m8eJmRAEla/2PXujEYgGIe4qJ7+k+a2lOf8+0\\nrrEt/i4Wi0nGiH7SLQ9USlvfrNoaQh2cMVj0hXA4HHKwaT0i6TQGROw5SHJy7d9iLmi/HL/beNge\\n39PWA3J/nalRe117PB5ks1k5zEzwTtJzZT630WhgZ2dHEv5zsWlGpsRElVDnijop2R1+uwPU7Zl2\\n6pwVrmP+zoopmWvSz3jsxqj//jIYxHHM8LSfyfZ4/hgbaRolCJnQ41/Pabf5JTyh2+PZ1AHw+jMA\\nEjOYzWZfmP9uNFC0v7nxyDQYXsENpVUWMhQeZrvUBPrmJMBIvMkkMrpBVRer8QCdoq7Wy+k/RDM8\\nv0c3AIfj0IVBF8fsharVqjjgAUdxUXr+uTFMk/RJx8rX2tplZaHqpV07NcROzbZjWiYmc1LGe5x6\\nZPUMsw/Hfd/qs36Y+aDEZ9CnKRgMShUVrYLqzJe6blq3drnXeDHyPeKavCzJ4PTeZOgVz8KpqmzH\\nkfa41n5E7XZbLE+MDvb7/ZYe2yStLuisdNoap9uv1WodHqOnuehWUoKdM6KujKHLz1htbFMKMV0k\\nqOqS2I7J4Po1qVqNzRyn9rcyL4puqpfuq0lW6hufodV1KxznNMZpPttKTe21LTvVxg5DsuuLJlNN\\n7oc4b36/H+fPn8e5c+dkDzH5X6VSQS6XQzweRyKREA9u7UJCVxw9Hp4vnSWCZ41ZIOr1OgqFgpSB\\n0gIH3Xz6ob5DR6w2ICUkOgU6nc4O/wQNCGs/JaBT5WJ7HBAPoanO9YoRnJSOO1x2GIz+3CTNlPR3\\nTSzF6vtmO/163+r1s1PPrPpg9ff/FZ32JQMMPpYva9+dxhg9Ho9IJTwrdFMol8v49a9/jcuXLwMA\\nUqlUh8HJvHi5F2lUYfpfwhe1Wg07OzsSi5lMJgEAw8PDwgzJ5E7VD8nuhtfUbh9l1QsGg4hGo2KV\\n4u/piU3dk6Z/HmjtvwIcpTMgsE1Gpy1ZZh8GufG6tdHrLWc6n1kxFv25neja7f1+VIZeyEql0BKa\\nlYTUT7vdyAovMp9pSjWnzZC+DCZn0peBT3VrnwyJid9ohGm1WlhZWcF//dd/4dmzZ/jd3/1dAcB5\\nDgml0KpM5kNAemNjA06nE9lsFoVCAfV6HfPz85I88N69e2JpppTbaDSkJh3pS8OQrCYmn8/jl7/8\\npTg8tlot8UFi3h6KfPQoJZF5UbWjr082m8Xy8jKePXuG9fV1yw3LPvTbb/P7vR4m/TytYlj1RYv4\\nx2EyJhOwY5CmVNUP9TMvJtMwX1v1vZ92SaZDZq/P7JV62S/d+tnvs3u5GE+DUZnWRMYTMtaQSRDr\\n9ToWFhbw05/+FE+ePMGnn36Kq1evwuv1IpPJoFqtolAoADgsX8RCpa1WC1tbWygWi2i325IgkG2y\\n0kylUsH+/j4qlQpee+01wTmfPn2KZ8+eSdu9jvlEVjZNpVIJ9+/fh8vlwujoKFKplIDROtWIdpYy\\ndXJKU3y/XC5jcXER9+/fRz6f78BaTuNwkLQjnNkW+9XrM7sxluOYTq+SCTGHfvVzUyKxI47FSiXs\\nNr/9zj3HYSUd2c1Rv2Sn8vZLvYxtUMly0P4AnQddY0H0LC+VSmKIWVxcxMbGBh4+fIhYLAav14uV\\nlRWpwhKLxVCtVvHFF18gGAzK7xgUTW92SlO8kBcWFpDJZAAcecUXCgXkcrkX8NDjLtETgdp8EH1v\\nFhYWxBlqZmZGkpin02kBuMicdBoKugkUCgVJ9VAsFvH48WOsr69jbW1N3O/1obLqz2mJyVZMxmQm\\n+jOrTWhKRHaMWH//OJxCf2eQCG5TUrPCikwVqtsBs2qv14PbDbPS3/0yqBfGcRxc0WubVvvmtIiQ\\nB0uBLy8v4+LFi6JtrK2tYWdnR6qrsB+lUkmyFzidTsluwMwEdFnRmUL9fr+oc+Y+YQFMnmfzrB63\\nzqSBQkesJpWlfxYWFvD//t//w8zMjPgiXLhwQZLd63w0rFaRy+XgcrmwurqKTz/9FNvb25IeVifE\\nshLp7fraL1lJLt1Uh27ieC9iu933rX5n9Zp6fz+kJRLz+ZRe7bAkOxzL6n2rsdlJg91+NyiOZdeW\\n+bm5noNccnaqdT9tDELm/KytreH+/fvY399HJpOB0+lEsVjEkydPMD8/j1AoJN/X2QWY9YGwCWEV\\nwi50eNTzRymIxirWCFxeXrZMDdzPeXC0T3umzuiMzuiMBqSTZ206ozM6ozM6JTpjSGd0Rmf0laEz\\nhnRGZ3RGXxk6Y0hndEZn9JWhM4Z0Rmd0Rl8ZOmNIZ3RGZ/SVoTOGdEZndEZfGTpjSGd0Rmf0laEz\\nhnRGZ3RGXxnqGjrC/M69BHuS6Pgdi8UkdzYj+1lQEYCkfm2320ilUpidnZUCjc+ePcPy8jK2trZe\\nKKdj5nu2IqfTKVHGvZDX65X2OV6daE6n6QUO66SFQiHEYjFMTEygVCpha2sLa2tr4pav22FbdM+n\\naz5fDw8PSzrc7e1tVKvVY8dI6idfsQ4f4Hj5DI7T7/dLeSU99zr5XqVSQTwexx/+4R/ir/7qr+D1\\nevHo0SP5fa1Ww8OHD/GjH/0IP/vZzzoyOwCd9eZNMt9j5HmvWUFHRka6hr2YYQwMv3E4DktWjY6O\\nYmZmBj/4wQ8wOTmJcrmM9fV1LC0t4ZNPPpFCEHfv3sWNGzfgdruxsrKCBw8e4De/+Q2eP3+OcrmM\\nRqMhSft0+IRVeBP7yao3vVA4HO7pe93m2Azt4frq0JJIJAK/3w8AUslZlws3z2Qv1K1818BVR6ze\\nZ9Cs1+vFd77zHUQiEbjdbslSl81mkc/nJQKYSd0ymQyGh4fh9/tRr9dx7949lEolLC4uYn5+Ho8e\\nPcLOzk5H5PBx8UWDkLmRyYhY9eTixYtIJpO4du0afD4fgsGglEYul8soFouoVCpSnIAlpdkms+hx\\nIXnIWXa6XC7j/fffx/b2NorFYkdw4mkGDev/dVzX/v4+isViRwI9fpf5vNvttpTfnp6eRiAQQLvd\\nxvj4OICjogjpdBoLCwv48Y9/LDFRbMuMSey2pjpLYS9kMlyrWDuuxfDwMK5evYpAIIBoNIpQKCTl\\nrhiwGo/HMT4+josXL+LOnTtSDTadTiMQCKBcLmNoaAiXLl3C9PQ0CoWCFP789a9/jZ2dHXzwwQcv\\nZGb4siO2ej23zIcUDAZx/fp1TE9PIx6PS+xoIBCQ/GYMni0UCvjv//5vVCqVjsKjpJOM7dh8SL0E\\nN+qF9/l8iEajeP3115FKpRAMBjExMYF2u42trS1sbW1JIGc4HEY8Hkcmk5Gikbu7u3Kjrq2t4Z13\\n3kE2m0Uul5MCAfqGtYqoHyRPkN2hoBTj8/kwOzuLixcv4s0338TQ0JBISu32YWbLarWKWq0maRvM\\nwpHMBaXLQB0cHKBUKqFQKKBcLuP58+dSoPK4ANdBSDM5TWSSOkOnDrhlIjqH46i8+YULFxAKhdBo\\nNJBKpaSqhcfjwcjICG7fvi1t83m6PLeV5GCuySDradWeJmadiMfjuHbtWke6HOBIemfK5cnJSUxM\\nTEgGRvaZJX6cTifOnz+PYDAIn8+Her2Ovb09uFwufPbZZ/jkk086+mV1rvpd225n87iAZkqdLD8G\\nHGoJt2/fxu3bt3HhwgVZx6GhIZGIuJ93d3fxzjvvYH9/H/V6/djA6X7GeWy0fy/EhwQCAYyPj2Ny\\nclIOWrPZhN/vh9frlUx2e3t7iMfjUvxxe3tbqpXU63W5TQ4ODjA2NoZXX30ViUQCm5ubePjw4Qtp\\nDU4S5d/td5TiuNFSqRSSyaRksqQExNQqOr0KJSSqZrw1rdQkJk33+Xy4cuUK4vG45JLREf3dIuR7\\npW7R6GZdL1aqODg4QCgUwrlz5zA7O4t79+5hbm4OY2NjaDQaHfXiGo2GZA6MxWL4y7/8S2xsbEgm\\nB60Ka2Zjd7n0Kx3aSUUulwterxeJRALxeBwzMzOYmJjA+fPnJWd0pVKRfu3t7aFer6NcLmN5eRl7\\ne3tSBl2vaa1WQ61Ww8bGhuxzMuCpqSmpIrO9vY2trS0sLy9bHuJ+qdtv9fyaUijfY2qg27dvCzO9\\nceMG0uk0RkdHJZskcFS0lOchkUiIdMiIfz63W3+O6zfQo4Rk6uBWjfJWvHXrFu7cuYNarYZSqYRq\\ntSoVXCk1sB0WZKzValKKmM+jmD45OYnp6Wmsrq7i2bNnePjwYUdf+P9JpAc7vX5oaAjpdBrDw8OI\\nx+OYnp5GMplEs9lEpVJBs9lEIBCQem16M1ISYoFLbl5KRToVL3PQeDwefPOb30SxWMTGxgZ2dnaw\\nvb3dUUDADg8ZdLx6PTnvTEU8OzuLYDAohSpffvllvPXWW1LNlwexXC6LqsaKF16vF3Nzc3j55Zex\\nvb2N5eVl/Od//ie2t7cxNDSE58+fo1AooFQqvSA1cD3JwPvJ+6SxOz1Gt9uNTCaDl19+GXNzc7h0\\n6ZJkWdTqNkt1AYeYST6fx87ODlqtFqLRqKwXJedIJILh4WFJo8OLyel0YnR0FJlMBtPT01hbW8Pi\\n4iL+53/+B1tbWyJFsY+DSvVWe9fcF3p9mWt7enoaN2/exJ//+Z/j3Llzgv8RbmDJJFbUASD7udFo\\nYHJyEs1mEzs7O5bPHJT6kpDsRMF2+7B0bjKZRDKZRDqdxtraWkdFVw6u0WjA6XTK4pm3ISUCSh8u\\nlwvhcBjDw8OyYXiou/W1H7JiaMzp7fP5kEgkMDExAbfbjXq9jp2dHXme0+mUW7PZbEq5GUo3BPEB\\ndNR/17XXiI05nU4Eg0EMDQ3h9u3b+PTTT7G7uysb19y0p6HCsQ1dAikajWJubg5/9Ed/hFgshnQ6\\njYODA4TDYUnortUagpQHBwdoNpuCIxIvm56exvj4uMxHsVjEj3/8Y8zPz6NQKHSUkbIa10krcpB5\\nxONxpFIpjI+Po91uSyLAer2OVqslGU7JkLiuLJNOvKxQKMDtdiOVSgm+SNWGc0NjQygUwv7+PsLh\\nMF566SXcv39fnsc5HHRs/RDnlM/NZDL4+te/jlgs1iEJ1+t1wT11YQ5Kh0xbywSLbNuKV1jtzxOp\\nbCbZPYQA7fDwsPzb2NgQMZkgKQdAEJgLTK7N24z4Ct/nhASDQdF/rUT80yaCfclkElNTU7JR9aEL\\nBAIIBALCwHSJcILB+rCxDWJhZOZUG6i7X7lyBTs7O1heXkaj0bBNrNbveOzEZ4rxPp8Pt27dwvXr\\n13Hz5k3EYjFEIhHZtCb2Q5WWDNXtdsPr9QqITTXG5XLh1q1bcills1mkUinJVKir8Jq42SCHzxyj\\nz+fD1NQUUqkUMpkMyuVyRzVhXTJdW1V1nneunc/nE4mW0oPet41GQ8DtWq2GcDiMQCCAWCyG4eFh\\n5PN51Gq1Dmvql6W+6TnhHp2YmMDc3BxeeuklRKNRkVAJK+iq0lpb4SXbbDYxMzODYrGI9fX1Dq2H\\nv+HfVphsN+opY6TVgzQdHBwgEongjTfewPT0NPx+P/x+vww0EAjA5XJJonBaler1eod0QYbk9XoR\\nDocFZOTG0Ck3eTj0hPP/QRbXCmTkRp2ensYrr7yCYrHYAQJGIhHBlLjgPJC8TciM9PuUjCg1hsNh\\nkZSAw5v59u3b8vnPf/5zlMvlU2HAdvPjcrnw2muv4dVXX8Wf/dmfidsGDxdwlDKXoHy73cbw8DAi\\nkYjMiwbH6cpA8JfWK6fTib/4i79AqVTCnTt38POf/xw//elPXygNPsg4rXC2VquFcDiM3/u93xMJ\\naX5+XuaX44nFYh0FJw4ODlCpVKQcEK3IsVhMpGVKWLFYTC7YarUqap7X68Xo6CimpqYQCoUkvezu\\n7i52d3cHHid/Z6fF6NfaOAEAf/M3f4OXXnpJjEmtVguVSkUKc/DCcblcCAQCMh/FYlGEhD/+4z/G\\n2NgYarUaPvjggw5Ywao/vY6xK0Myq1HaTQQxg3Q6jUgkAq/Xi6mpKRSLRalRT0mHN2m7fVSUkBIW\\nO02Ad2ZmRlSgoaEhVKtVJBIJMT+aAx+UGdlZPXhbtNttAfkoHXGz0uzZbrcFL6I4y+9zrJQuyJRp\\nTWQVUOrvLpcLzWYTQ0NDmJqakvZOqqdbqdr82+/3I51O4+bNmx2YjekCQKlOWw/19zlGSkgApHop\\n55TMGDisERaLxTouGTtspNcxWpHL5cLIyIj475AxEqB2OByIx+MCZtPg4na7BV9i+1xrSuwej0eq\\nxgKHxp16vS7VnJvNpvyjZO3xeE4F97RbU80I+J7X60U0GsXExARCoZBclvv7+6JeUxLmHFHar1ar\\nKBaLUpKsVquhXq8jGo1iaGiow3fpJNSVIR2Xs5kbhwPm5vL5fBgfH4fP58Pa2ppIQcSUeCh1bl4m\\nIafFKRQKYXJyUkzp1ONjsRiGhoakZrgp2vdrleE4TNLtUJWiSKv1f1ZfoAOo3+8X9wSzLhULFbDq\\nZygUko3Pg845yOfzwtjNApn93jq9kNvtxuTkJK5fvy6WIT7L6/V2lFXW88PLhCZh/o4bXDMYnfyd\\nqtL09LTUCNNzP+g4rdQE3vwjIyOy58gkAYg7RjgcRrValYT2tJgGAgE5dLQqMT+82+3G8PAwgsGg\\nqHBDQ0Oy1h6PB5ubm8KIuT/MwqinsWft3mPb1DwmJiZEyjNzaFN1IxELazQayOfzSCQSAIBKpSKq\\nay/Vb3od58BVR3TD0WgUo6OjSKfTqFQqWF1dxejoqHBRinkEzDSuwo1K03EymRRfjv/4j/8Q643b\\n7UY+n8elS5ewtLQkOvogeqrVWOxuGZ/Ph1qthuXlZeRyOXi9XgwPD6NUKqFSqXTo5ktLS2KxGRoa\\nQigUQiQSQbPZxO7uLqrVKjweD0qlEnZ3d5FOpxGLxVAqlRCNRhEOhxEMBrG/v4/NzU3B0czSUeb8\\nnwYRJ6BUTOyA/2sXBI0zcDNqKYqqKVU+zYw0RkOVgIfYnHtSP+tpzou+qKrVqqhiZC4+nw/Pnj3D\\n6uoqLl26JFIvmWQ0GhVLYyKRkL7Te3tmZgapVArvvvsu3nnnHaysrODOnTu4ffu2PJO+anS0pBOw\\n7l+/ZCfVm/O1v7+PYDCIV155BXfv3sXdu3fh8/mwt7cna8q1pC8d54sWcGoDpVIJ2WxWznGr1cLV\\nq1dx//59lEoluWROQj0xJDscia8zmQzGxsY6zKXUrSkJ6RuTi6pBYFrUPB6PiJG/+c1vOhwJ6RfC\\nTX5aZCchURzXYC7DX7QExINKyadcLsPv98PhOPLWpsjP39H6xqKZvF3pWMk2qdoMIvn1QhxnPB4X\\n1wVaWLhOpvSjLTa6JDrH0263pVwOv0cmo5kRJQ0aPk5D5LcbI5ks1UbdF6pt2WxW9iDXgGq4/g1N\\n4tlsFrFYTCxPTqcTiUQCfr8fQ0ND4tZAHIZe3X6/X8ZvVm3ulfR5tGNMHFs0GsXNmzcxNzeH0dHR\\nDk2Ca8sx6t/TfQE4BPJDoZDMSbVaxfDwMPb29nDlyhU4HA48ffr0BRW+XzqWIZkDthr8pUuXkMlk\\nsLq6imq1inq9jufPn3fgBFRnTGCUzKdarYo01G630Wg08M///M/I5XLSztDQEOLxuIi/3fp8UuLE\\nRqNR2WDUsTc2NgTnWltbQy6XQ7FYRDweFzBeS03VahXBYBDxeBzRaFRwCgKsHNvQ0BDm5+dRLpcx\\nOjqKQqEgN5ZmEKcxNq5jq9WC3+/HyMgIMpkMwuEwyuXyCxZDYgR8X0tu7XZb1s3hcKBWqyGXy0nV\\nYjJ1rRYQFxwaGsLIyIioP9pCOQhZWRIJCwBH4U1khC6XC4lEAi6XC48fP5Y1oZ9Zs9kUMz7dQMjg\\n6IeVTqcRj8fx5ptvYm5uDvl8XhwhPR4P4vE4yuUycrkcEolEh4qqJbhBx2s1fjLeq1ev4ubNm/jh\\nD38ozpB0UeCloS8YLSzw+8QzeWnQMZi/S6VSePToEf72b/9WzrJZIJJjPY4GjmXT9MUXX0gMVyAQ\\ngMfjwSeffIJgMIiRkRFMTU114EZkMDqMgJu81WqhWq1idXW1Q6zm97U1x44pncZNS4ZElYKSkNfr\\nxczMjJiBHQ4HIpEIKpUKMpmMqGDEnWhB83q9YgL3+/2IRCIYGRlBKBQSnywASCaT4jqxvr6OnZ0d\\n8elhv05K5gFwuVwYHx/vsIySYVAK0n5KmhGZ3t3a6VPjTLx4NH4EQLCbc+fO4fnz58KATzo+/ZpM\\nlBig9pdKJpOoVCrI5/MoFoti7RwZGUE8Hu/wnWO8G6UKABKMzAO6u7uLXC6HlZUVcap1Op1i5meI\\nDefzJMy325gp7bz66qu4fv16R2hPvV5Ho9GQsve8eIBOpkTGC6BDRXc4Dt1ZeB5HR0fh8Xjw+7//\\n+/j444/x+eefDzyOnj21u03a8+fPsbm5iWKxiEwmg3g8jnfeeQfT09MYGhoSfdV0DKTJVUdcHxwc\\nYHd3FxsbG6jVah2WEKATaLfCC6ze74Ws9G8yQ41v0Prn9XplwSj1XLx4EaFQSAKIKRHwVubNFI1G\\nUa/XEQ6H4Xa7xUTscrmQyWTEv4nj9Xg8He4Cp4Ej6XX1eDyYnJxENBoV3EqrOHwW50abkMmUKPnw\\nwJvf56HUagAll3Q6jcuXL+MXv/iFJSTQL4ZkriH/ARAMs91uC84XDoexsbEhkQXVahWZTAaBQAC5\\nXA6VSgVDQ0OIRCIIBAKyjxl/mc1m5bmFQgEbGxvY2NjA1atXkclksLa2hmazKRcVGaTu82kT54/x\\naYlEQi4GCg+88CkM6GgJ4Agr5B7WBgoyJIfDgZGRESSTSfzBH/wB6vW6JUPqdYw9V65loyYn5t+1\\nWg2ff/45Hjx4AOBw4c+dOydesdlsVixmwJFXr3YsAyDhJI1GAz6fr8Pp6jgc5bRFX7fbjampKTgc\\nDmxsbAh4Hw6Hkc/nZSOGQiF4PB4Ui0Xk83lRW91uN0KhkMyTTkPC99vtNmZnZzssPfV6Hb/61a/g\\ndrsRDocxMzMj1UHpTHdam7jdPnRpSCaTCAaD0j8eGGJClJ74Gw2y67UDDvEGquf8/d7enuBqOv5p\\nb28Po6OjuHXrllxOfPZJrImmZWlkZAS1Wg3b29vIZrM4d+6cMP3V1VX84he/QDgcRiqVQjqdhtPp\\nxM7ODp48eYL19XWMjo6K+wrbZZ17vmZbuVxO5mxoaAgLCwsCPYRCIYyMjCCVSiEUComP1mniZ2yP\\nXvJjY2OiorFKLSV17kkAcpFQgDDVXaqplBC5x4mNTU1NYXJyErFYDFtbW9KXftTSYyUk4MWUFXYg\\nsJZ0eOMT0KPFjQOj2K9BUe2foj23zeceN8DTUtnIJACIT1GhUMDTp0+xsbGBfD4v5l4yTzoCapBe\\ni+eUsoiZ8BDSL4TzUygUxM+F4TjValXA8dMaJzcu8Rs6qAJHzAg4ykukiWus50sfBq3iadxJXyxU\\nCUOhkBxw04w8yDj5G7/fj7GxMcTj8Y5QJKbEqVQqqFQq8Hq9mJ6eRiwWQzgclhAXOq7S8kupQPuG\\ntdttwUgLhQIikYio9DzgADq0gVAoJLmjToNM3IwUDoelrwwIp3TI73Kd6L6hLxxNVLmBI1cYva+5\\nxtqFo99LpScJyWRM3Tg63+dhDAQC4gl6cHBgaanRoj4Zkva90cznNNSV40g/l06LvDG2trawuLiI\\nXC6HXC6HTCaDSCSCYDAokhHVUw1C8zV9URKJhMStlUolSWVC6YQYB60Z6XS649Y5rTnQGAutiFqt\\n4vrzAuEY7FKHaPBYMzM+h5eQ9mtyOg9j+HjYTUlsUAmJgHUmk0EmkwEAMeHH43FxwWD83qVLl+Qw\\nZbNZiVlzu90SeEonQvaN+zcQCEg+Ja/XK9ksNCOmQy3jAoPBIHZ2dqSvg5IVtMK/6StULpc7nHP5\\nTHOSnC4AACAASURBVO5VzWhI+vzzjJpChF5Prh/3j/at65UG9kPSZKpxQGfUNQ+nHqwWy9lxipUO\\nh0PCTczBWB0C/fokaptug7c/Y5Y8Ho+4HHg8HoyNjWF8fLzDh4MiLyUf4OjA6UPu8Xhko/t8PgHC\\nt7a2EIlEEA6HJcxif38fMzMzaLVaWFpasp2DXself0dx3OfzIRwOiy8UJVntPa1xBEpTbEMzKi0F\\n6efxguIBIDF9C7E6zfi6SeTHjZGUz+fx/PlzbG1t4dNPP5U4u+npaQmHoA9dPB5HvV5HLpeTS4dZ\\nAba3t5HL5UTqp6MsAe9arSZMSWetcLvdiMfjcmE1m00xjFy8eBGVSgXlclnmvB+ykmR4eVDSo4RE\\nKYnroNfOytdNazFkRlq941rRTYQXKaGIQannBG29qEkml6ZlTQOf2pSsRXwygWazKbcJ0HnTms+z\\nAkBPm+g34vf7US6XJZiUOng2m+3IUEBpQ6uwlIqYE4p5k4hv0JVBJ6/TDoeBQEBUw5OQ1XzpNaAZ\\nXH/XVMFIOgCa/eT/ej21WudwOMT5TrtG8ABbqQmDjFE/v1wud0Thc92ePXsml8hrr72Gqakpcf4r\\nFApoNpsdTrm7u7solUpyANkmGYkGf9vttqTaoTpHVZRzyUDbWCwmkEa/ZDdXtA6nUimR+Ci9aCbE\\n/amlWs6hZlB6bvVzNdZIRkdL3qDr2JMfktXr477HiaC6w5guvXF1eALxCh0b1s20f5pkqqAm3sOD\\nxJudE88bgpYb3hSaOXOTkglzsQ4ODiTvUCqVQqlUgt/vRywWQzweB3AkcWg8y2TE/ZCpgvMZxDl4\\ng+t0FAA6GIeeH33x6L5QfNfzyPeLxaJ48hM3IyM0JbhBwF7zUmT0PdeCkjgZSTQalUtmc3MTuVwO\\nW1tbmJiYkDCJlZUVbG9vo1AoCCbqdrsRi8XQaDSwvb0tFlad64jaAfFTquL0Z0qn05iamsLy8nJf\\n+bTt5kbvC156GhohA9Yqt744NEPSzJUMjJ/xstWX5t7enuClpjNpPwJDzxhSL055Wori7cKDTNFW\\nd1KrM9wolCCYApS3zJdJ5sKaqkKtVkO5XBYTfCQS6XAgc7lcYoXiTal9bjRGwDbJ1BwOBxYXF8U6\\nE41GJecQN0osFhPzNPX+k4zV3MytVkvynTNUgJkg6RxnkmY6GoznnJA4dofj0CT+/PlzPH/+HPF4\\nHLFYTNQaxjqaGMYg4+NzHQ5HBz6ipVZefC6XC8ViEbVaDSsrK/I6mUzi4OAAS0tLWFlZ6ZBweajZ\\nbx5cqm5MqUKrFn2rNKNwOp24cOGCjPfJkycDjVPPs/4sFAphbGwMQOflTzzX7XYjGAy+EDpiSsj6\\nWdyve3t7UnSBTI8MickKTX7RK8wwcE5tu8/4vgYoKZJrAFvrp9o/ieAqf6OdITmwL5tB6fYZFsD4\\nO4/Hg2g0KqoAbzxKSjzQ+sbREpYOhWk2m8jn87h//z7y+TwKhYLMAdsmZkPcgp8B6OmSMMdliuE8\\ntLlcTp7fbh96ZpdKJUkvwjHpC4X4Dy8T86LRl5nL5RJfNWYR5Vh4EEycUfe5X7L6jb5o+JppbRYW\\nFuTSDIVCeP78OSqVCtbW1hAIBHDz5k0x06+srCCbzYrbACWl9fV1MfnTwZfzxHlmPBzPBw0bJ1VV\\nOS7uXYLZHCMZCfcVSWstdADWzIvt8jzSIlsqlSSIXF+4lJyswPFeqC8/JDuywiYYAc0NRScyzbA4\\nAdrUqLNL0m9FMyj9HLv+nXRxuWFdrsMUpe32YZCk9rQmGMkDp9NYaE9c7WDI0ji0whBHogmaMUL5\\nfF42O3MqEZhk1oOTkKmyAYeHipYupind3d1FNBqF0+mUPNAa39K3KT3JuYYm8+L3c7kcPv74Y9y6\\ndQupVEqYMy2sJgPqdy21+mjeylYYVavVQj6fx9LSEiYnJ4URe71epFIpjI2NSS71+fl5bG5uIpvN\\nimXO5/NJfFi1WkU4HJaxf/rpp/D5fHjjjTfk4iUxa2MkEsHq6qrM36Ckx8XUy1euXOm44DkX5XK5\\nQ1WzYvpkPtorXWfrqFQqAuADkH1Bi6QVBmX204p6Dh0xGUA3ZuVwHJVP0Qg9f+NyuTrShBLMJhcH\\nIOZVffgGwbMGJS4ITabsK9VLu8XU+jYPrh4TF5e3MRdwb29PEnzx5mRcEKUHjSOZPkG9kOltC3QG\\nUhLfIjMtl8ui2hBXMoFPrc6T+XIj673TarUkbIHxfZw/MjxtoTsp9crIKMkwfo/Pp0pJ1xXgKAUx\\n1eZcLodwOCzR/Iws4FptbGyg3W5jbGxMnArpWEhVV1u8ToN4ITLTqdWZoVSqw0X4OS9izcC08YJM\\nptFoiBsEA+IdDodEFZhqe6/Us9nflE70+/pQ8jPGgHEz84bQ39POdlrc1//b5Vr5MlU3LelofEvj\\nWTxMjH3iTU/zKjcm089q0ZigKosEFItFcVoj0B2NRlGtVjtUXh2Jz34OMjbzb4ZQ8HJgv1dWVsTT\\nXkt8OrEX36fqqq1mJtZF6WN3d1eyh5Lp6fQm3fCRfsd33PfIPCntsmgDA43pbzY0NIRkMimBsvPz\\n83j27Jm4SlAq3t3dFcxvZGREov8bjUZHhge32y0pTHZ3dzukp5MS9wrT0+qsmCSNp1n5jmmnWO1+\\nQ4bFlDmMqNCxlpp5mWt3IgzJbMC8WU3VSYto0WhUIr2BzsRe+pbVnFUDbJROdCa6Lxs7IpERMQvg\\n/v4+stksRkdHBZBsNBrI5XIYHR2VQ0hGw9uPEp52PqOjJb289/f38fTpU3GMDIVC8Hq98jveyMzJ\\nTFxgEGzFTqpiyApzVhHsXFpawuXLl9FutztuQaqnOsbQ3Hy8JbU/Cw8+JQce1GKx2FHNdFDcSJM+\\nQPp/TewTjS43btwQj2bmsGo0GtjZ2UGj0cDw8DA8Ho9cJlzrxcVFMcbQHykSiWBjYwMLCwtYXV2V\\n2K/z589L1oeFhQX85Cc/wcLCwkASr52qxZxdqVRKLjAGaPPCZH/1RamlIy0t8T0C8gcHB1heXpZM\\nBoQcgCPzv5UU3gv1FDpiTgAxEU6G6bXLg8c8NyRtRjTVP/5PlUFjMf0M6iRMi7+lyE3gkWAgmQwZ\\nUqlUwszMDABI3uVSqSQMjbcvE7lp/4y9vT3JGcXskEyB2mw2sbm5KZY9qmrcVNxkg4xVr5u+QLTp\\nl+NmBDwZrHag43tsR5uTudH1fBIsprjPVMfhcFjm0gpzYPuDjtHu9/qwUKK7cOEC6vU61tfXsbi4\\niFKpJJZNrgn7ur+/j3Q6jXa7LfUH9dg9Hg8WFhbw0UcfYWFhAYlEAolEAt///vdx8eJFeDwebGxs\\n4OnTp5JbexAyx0ofJOYv0s6m2ulU+8sBR6mKKS3xTPNi4e8pRdOdQaehBtBRRmqQ/Tlw+hGTuejv\\n8/DxAANHjmNmfJQpcXEC6L1Lbvt/Qey70+mUml3sk5b0CoUCstksstlsRzwP54KWMTJTAvpcUB5Q\\nxlHRIqKxFJ0dgaoif2eCxf2OkaQvGA2+k+HqZHh8pjYNE+cDIBig7h/ni7+nNZKSBKUn+uxoZqf7\\n24/00E26spKWNEOmBTCfz0sKW63y8NJgjqx4PI7d3V3JYHpwcIDXX39dVJnl5WVsbW11qHGMi2Px\\nxUKhIMnd+iUr4cDpPCqlpSUgbT2zApy5Z/XZNIntOJ1OMbKYaZopoWk3l15wZ1JPddmsbpxuDVOc\\npxWJHJU3qpY0OFEayef/IyMjIkHYVTX4MogJ70dGRsRLOhgMilRTq9Wwvr6O7e1tBINB6T8lGVqq\\ntPjKGCftrMZKD5lMRtKK5nI57O/vIxKJCKCvg3UpIZ2W+krJRmNg3MSNRkMYjr5ptRMj/6evEvEk\\n3Ueu7cjISEeVWDJpvcbmuAZRZTRZqTT8n1JeNBpFOp3Gp59+CpfrsIb92NiYGDQ2Nzcl7w9TkIyP\\nj2N8fByLi4v413/9V9nP169fR6VSwebmpjCa2dlZXL58GdevX8dLL72EZDIpvnaUtgaJZbMCrN1u\\nN0ZHRxGPxzukaX3GWImZn2nJSV+s2qGXv+X/MzMz8Pv94vjM/jMfvhXM0sue7RnUNm8t/m8FaOuO\\nmxHg2lrUbrc7REKgM9g2kUhge3v7hefY9euk6pq+ZQjcUpzXt6UOkCXYTQ9n7a5AMoNRDw4Oczr7\\n/X6RvMrlshxKp9MpoHaxWJR5IYPiRumXrBgZ+0RmR9+SWq0meJW+UbV/mZUTo6m2ABAJj+1rZ0Lg\\nkNnR6mbV59MkrjPXg4UU3nzzTVFZYrEYqtWqeM+PjIwgGAxKFdtarQa/3y8FCtLptFi08vk8NjY2\\nUCwWMTMzI2Wq6UxLxqDDanjZ9EumtEsGm0gkEIlEZB+a55XpcvSa8XOtsmkDDnCk0TidToyMjEhO\\nJW2d1P5PJrNjP7tRT46RekCmhec4/VwzIb7mRibGoidLqyPxeFySoZOsRPrTkBasxkOmROmBNwEl\\nCprrmZJWm8ZZappj4gK7XC7J2KerjjBXjylpkQnyn50lqp9xmvNFBmxiP6bFBOjMKKhf69xWOriU\\nGAPHrpPbcx51O6dFdvvSNF+zxPa5c+ewuLgIABKjuLe3h0gkImWaFhcXsba2hnK5jAsXLuDll18W\\nz30GRFPtW1xcxMzMYQGAQCAgh5emcl52JmM/CbGtdDotRgq9ntxDhEL4G+5p9sl0DSFpJppIJLC7\\nu4t8Pi/rp7NoWgXG90J9O0aa0pCdWkf8gaS9lPk3J4DYAqOnGZGdSqUk6p1kBbRbvX8SYhQ4q4xo\\nvIzBl7T+uVwuyZdMhsTFrtfrHSbdYDAoBQYXFxfhcBz6G129ehV7e3t4+vSplI3a3NyUzJPFYhHl\\nclmiwgcdq9XvuJloPanX6/D5fGJl4vO0mmZ1gBi7qNVzWmY0BkZmS+93OprSWmliIv1iSHqsVuqa\\n3q9+vx/j4+NoNBqYn58XqfT58+eYnZ1FMpnEyMiIeNR/8MEHkmztwYMHePfdd6Xk16uvvooLFy7I\\n97LZLB49eoQPP/wQxWIRgUAAiUQC0WgUDocDw8PDAI4vNdbPeNvtNoLBIG7duoWRkRGZc4fj0JBQ\\nqVRQKBTw0ksvvWB00K91aXcAL1jmHA6HhKUUCgUZH8+3lgb7lfz6Tj9ixxBMop8Jv6N9UkyEn5PT\\narU6REkd0NqtL6fJjNg/Wgnz+TxyuRx2dnYQiUQAQNQtgpe0sLCvdCZkIjaWbeamaLVaCAaDqNfr\\nWF1dRTQaRTabFZyMZmjGQlGaqNfrIu4DJ1NntNrC9eEmJA5EpqHB716eybputNzoUASK9Xq+TNVB\\nU6/P7IX0OMPhMDKZDK5duwaH47BiBmuV8W++NzExgcnJSXzve9/DBx98gE8++QSbm5t48uQJ5ubm\\ncO3aNXg8HqytreHtt9/GyspKR5ns1dVV8TfTKqPOBjGIymY1Pprmtec8AHG5yOfzHeeFfnQkSsum\\nxRSAXM50cSG0wFRBXF+NOfW7fn05RuqNeRwxWZlmNloq0ptf3xIaXxgEyD6NDcyFBSAMaWNjAzMz\\nM3KDxGIxpFKpDu9rAJJegxYZSgdMy8t5CAQCskECgYBII8DhpmBAL61RDHi1qubQD9mtn67T1Wq1\\nRA2lMx/XkUGUJFN947rpkAUd+a/LOumUGxprOOn4SOZe0N7JPp8P58+fxyuvvIKPP/4YDx8+FEB4\\nbGwMS0tLWF1dhdPpxA9/+EO88sorkomBoSYPHjxAOp3G3NwcstksHj9+jL//+7+XhHrJZBKt1mHg\\nssYI9XxrZ+GTEserK5pw/h2Oo3zflJy4zoQCdEFQWobJrEzLXLVaFYMAQ444v5rB9WMMA3o0+2tc\\nxwSRtbin3yemwsEQh9FeoWyTh5TmdnPwdgtmNbjTOKwMF6D5NBQKIRaLIRqNolAoYHFxURJgsbAg\\nmQxzJ7lcLvmMC8+bhZ7Aa2trqFQqwti2t7clWjwcDotvDnCUg1yD4ych85bUADPbpzMfmQsxFyuV\\nTeOCFNV1X3ko6CPDcA0ms9PM8KSYihWI6nQ6MT4+LsnYpqamEI1Gsb6+jlAohBs3bmB+fl76+sYb\\nb2BoaAhPnz7Fe++9h62tLXz729/GrVu38Pz5c6ytrWFvbw93795FKpXCj370I/zLv/wL5ufnMTs7\\ni9/6rd+Cy+XC/Py8SLVM7kcckczpNIF7l8slAdFUtfiMYDCIWCwm1lQG+5KJ8NKg2w2Zkg514d4G\\nDotlUsInNlgsFvHkyZOOytR2sI4V9bTyJpfTr00GZQJo2r/l/7f3Lb9tntn5DyVRvN+pO2VZsuRr\\nLM/ESZxxkgYIBi1QFAXaAl1PgSy66qLL/g/tpu2mqy4KdFGg00ERFEmAzEySTtLO+KbEdmTLulh3\\nihTvpEiJ5G8hPEeHrz9SJKX0lwUPYFj8+PH73ut5z3nOTVtnNLiozYtczGR03WzA87C2aUsbLVDM\\nPFAsFuVEYS4kirAUmW02W4M0Q/1aq0ms8U5VwW63S7kcuhroHEgmbtdt/6yI0grBSZrgdaS4DnBu\\n9Xy6DmjM0FTLde0uSp1kWufVP1Ol7+vrg9/vx/DwMC5evCiJ+4PBIG7evIlAIICjoyNkMhmEQiFM\\nTk5iYmICxWIRjx8/llCfoaEhTE9P4yc/+QkmJiaQTCaxsbGB1dVVyXN08eLFBsdE4CS/FRkAx/g8\\nIAeuWzIb/V467WYyGSQSCcFHs9msxLWRYXLfEs+lIyiLWtCrPplMYmVlBc+ePZPc44xJTSQSlq4M\\n7cxrW6C2CWS3MzhcwFo/psSkT02tq9ZqNQFV7Xa7ZN0zsQW2STPEs2xWc8Nr5litHpcR3t/fF5WL\\nAGU0GsXS0hKy2azU9KJKynSmQ0ND6OvrQ7FYlDLE5XJZTpaZmRnx4C6VSlheXka1WsXt27cFLKTq\\npJn7WYBtLXnabCchHUypoueNJx+lPB0kS2OEtr5wUROHMA8pjiuT6JM4Bq1UrnaIbgtasqQUvrOz\\ng1rtOLr/gw8+wOjoqGAgDocDT58+xb179/CLX/wCAPDuu+8iGAxifn5eSqH39/cjGo3iZz/7Gebn\\n5/Hs2TP81V/9FZLJJIaGhnD37l3cunULHo8Hv/vd7/D06dMGrIhriGpct2SODdcwU+bS9J7L5bCz\\ns4Nf/vKX+OSTT/DVV1/hD//wD0VavHTpkvgTVavHSQOZz+jw8BCbm5tIpVI4PDwUQ8ve3h4++eQT\\nMVDEYjEporC6uoqVlZUGFV63+Uwqm9VpozvPv/UA6es2m60BFOPC4MlL8R54dSERd2jmm3LeQLbV\\n8/UpQUbCv/lPpwXRMV6cUOYYosWRACBPs1qtJuAxmR8XPyOpdS0xK9+fdum0DU6mo4FQbZrWKjcd\\nHNkOfXg1A6eJaVAVINivVXartdUJmRHs7IfX68XMzIw4DY6NjWFwcBDffvutWHVHRkbwxhtvYGpq\\nCleuXIHNZhNDRn9/v2SNLJfLCIfDIoV4PB6p35bL5bC4uIhKpYJnz54hlUqJpZEStJZcuu1ns9/Y\\nbDaRUA8PD5FKpbC3tyfe5P39/VhZWUEymcTy8jIWFxfFQ5tzTMMSvde59guFQkNmBB1O1N/fLy4P\\nVkJEu9Q2Q7JiAs2YkQaqtTc2v9P+NbxHv5OMyGRInYDqnZApZQEnKqdWyShFMCUpMRamkTB9rggk\\nEqzWSdaoqvD5xKFyuZyU56HZnO/6Pvx12G9KNgStySgYiQ+gQX00f6+d/rTkSwmMqhrvZyZF4MS/\\nRfuzdKu2aYuefnZ/fz/m5uYkx9H4+DgqlYrUETw6OsKdO3dw4cKFhiBSnSmSBR/dbrek12HMGOcy\\nnU6jUCjg5cuXiMfjSKfTDVgKMVKdG+w8IAbgVcsprbXMQcWYuZWVFWkP50oHbev51G3kOtClshiV\\n0NfXh3w+L8U2tS9aJ/3ryFNbv8BU46wWKRchNxM3IUkzJk6QPnlpetcnZ7P36fd2SlaSAyW7dDot\\nksrq6ir29vaQTqcb4ry4YOkBy9psnHQmYSsUCtjc3EQ4HMbU1BSSyaTo9sRcWLHX7/cL6JlKpSTv\\nM0+/bhay1SbXY0o3A/ZBq13a+KDnlDmmNQNgrifzfTof0M7ODjY2NmTuCZ5auXl0cto6HA4x0zPQ\\nmcU2//iP/1jUxn/6p3+S6sL5fF4kAVqNmIztu+++QyKRQDQahd/vx/j4ON5++22Uy2X8y7/8C775\\n5hv853/+p0AU29vbgqXoUBvtzU9ogvdor+hOieuAwddMisjvwuGwWHQp/WorGOeWh+Zp68pkfpVK\\nBcvLy5JojoVSgeZuHK2obcdI0+Klrzd7CU3nlBrIkPREAWiwHnETUA3ib6yks1YSWyek+6Ynl23Q\\n+YKojvE01yI4f8eE/yztw+tM+cpATTIyWj9isZi4DDgcDombyuVy54KVWRFPQ6Y9oY8Q/3FMtJWU\\nainboY0SWo3TnznvjGMk3qHNz1pKspqfdomm+1gsJgwmEolgfHxcGCnTnYRCIZFGq9Uq8vk8CoUC\\ndnZ2sLe3h+XlZWQyGRwdHWFiYkJS0O7u7mJhYQHffvststmsHL5kyJQ6iI3q+ePhy89nnUsefBxf\\nYnh6zE2Pe75bO/02G+/T1pwGyU9T2U7ra1sSUquN3uy0pmpideqRQfHZ2hKj89Pwme1wWhOA75T4\\nPIqgTqdTrFxutxuBQABjY2Mi+ekCglRrNB7m8XgQDocbVL9SqYRIJCIYCl0EhoaGEAqFMDMzIydn\\nvV5HOByG0+nE8vIySqVSQ6zbWYBtk8hw6exJtYsANk9DMhBKGXrcNIDN3Famd3e9Xpex1JVfdRoT\\nq/nrZE7pGzM9PY2pqSmMj4+jVCrJWgSOfZDu3LkD4NjJdWtrSzzt8/k8UqmUpAVh6SSv14vZ2VnE\\nYjHJg76wsCC5jkgMquZ4OZ1OZLPZBssyAJFWqDKdB2lLGSX3er3eYEkj6fkz90072ofWWDKZDHZ3\\ndxGPx6VkuBXPaGe9dpQPyWQ8Vig/v9c+OV6vV04FOlER3GUMFCUj+iLZbDYBezX+ZEozZvu6IVPa\\ny2QyWF9fR19fH2ZnZ2Vz+Xw+BINBCXPJ5XJyYgJALBaTtKfs08OHDxGPx5HNZpFIJMQSVS6XxSeE\\n1qyjoyM8e/YM5XIZf/u3fwufz4exsTHcv39f0p3oUtpnJWJ9PACIbaytrWFjYwOZTAb/8z//g3A4\\njLffflskNt5vZWIGIBIuGTElohcvXmBtbQ3r6+t46623EIlEhAnRomkC951SrVbD0tIS4vE4Zmdn\\nMTMzI9kgGYpEHx2/3w+v1ytB3FNTUxKhH41GxfJJz/0f//jHyOfz+NWvfoWf//znyGQyAIC7d+8i\\nEAjA4XCIaT2RSDSY+4Hjw25zcxMA5LndMiSTiWjMjvPS39+PeDwuZd+1o7G5f6wgl1b7STO0x48f\\nSxbQZDL5ioTUCVNqK/1IO9etXkrzOPNFkxGxXAqBQACi0gDHMV9cDJohWalu50EmIE9LCp0buVkp\\ntdGRr1gsYmhoSFSN8fFxWcgEqp89e4bl5WWkUik5qRn7pjc4cKy6xuNxCdsgM6fK0EyC6KSf7COJ\\nEg/9nyqVijj+FYtFfPPNN6hWq5ifn5dgYv6eTBdoTIOq8SNtTf3uu++wtraGvb09KXRAq1ArLKWT\\nOS8Wi1haWgIAPH36VHC9wcFBXL16VbAgWtkuXrwocVkOh0McVZm/iPNMV45MJoOFhQWsrq7C4/Fg\\nbGwM7777LmZmZmC32/Ho0SM8fvxYfHHMtbW3t9fgfGriqu1Ss3XANDf8npEGBwcHLUNzzP3bap1x\\nHfKeeDyOw8NDyfB6lj3alsrWSlUz/+bnvb09PH36VNIgcBGy/jlrch0dHUmMFr076bH83XffYXt7\\n2zJPDmDtkdstmQvn8PAQ6XQav/vd70Tt2tzclBNwcXERmUwGsVhM2p1Op2WT0oHs3r17Ur+9UCiI\\njs/EVplMRpgCRW4y5kqlIom/zqOP5jM4fgcHB/jiiy8wMjKC4eFhPH/+HKurq/JdKpXC5uYm8vm8\\ngKaUZJlojGooDxHg2AeG6WOy2Sw+/fRTvHz5UqxRa2tryGazWF9flzANnd6kW3M4GRtTmlCKY/Cy\\nz+cTp76dnR3JuhAOhwUnpEpJy2e5XMbz58+lPJLf70c0GsXExAQ8Hg9WV1eRy+Xkf53Xi2Pe19eH\\nJ0+eYGtrCzabTfyizmI95ThRMkokEvjoo49kzd67dw+7u7sCKVgZhdrd3ybzYrtZz4/Vnc3fc17a\\nmVNbvcUq9/l8LcHrVgypUqnA6/ViamoKsVhMsAWn0yk5hzWRe1erVSQSCezs7ODevXsNYSvm4LQi\\nnaP5NCKQaw4aT3syTm0V5D12ux3hcBgTExMNeZY5SQwd4YLhJDYziXJhmqBns/52UhKJJXrMhWKz\\n2YSp9PX1yXgQP/F4PPjLv/xLydFcrVYl+yF9VrSfSiKRwMDAANLpNBYWFrC1tSVMhxjL/Pw8Jicn\\n8eGHH2JpaQmrq6v4t3/7NxH5zY3TrhMho9w1E9DSmlYLGak/OjqKubk5TE1N4caNG5iamoLH4xGJ\\neHl5GZubm/j444+xubmJZDKJ4eFhTE1NYWZmBi6XC//7v/+LR48eIZ1Ow+v1wul0SmhRPB5vMKGb\\nTIhtjMfjbc+l3+9/ZS6JG9ntdqTTaZHsiYtqaiYRtbu/9P1WzLTV71vtzZYMqUc96lGP/i/p/L3s\\netSjHvWoS+oxpB71qEc/GOoxpB71qEc/GOoxpB71qEc/GOoxpB71qEc/GOoxpB71qEc/GOoxpB71\\nqEc/GOoxpB71qEc/GGoZOhIOhxs+W6Uj0dc7SU2iqVkYitVzTnsmf7+/v3/qe0nBYLDhs+mFytzQ\\nZh6ZVvE/gUAAgUBAMmEyhxKfyTQl2iu5Wd+sxpzvY9KtdojZCk5137eIedPX2AePx4PR0VF4MCY0\\nNgAAIABJREFUvV7E4/GG1BOMMG9nrsz+W4UE7ezstNVHj8dzat/aWVdWY67bbLa/2zAX/c5OAqfN\\nSAerwHe2SYeMMFqAnvjASekj3sNo/aOjI3g8Hok51SmIp6encfHiRVy4cAF7e3vY2dnBs2fPZFxa\\nkU5dbFJH6Ud0BQMrxsG/TWZlXm8WgtLq73apXSbY7Lck3V5dPkd/3+y3ACQlCSPdGY/H9vGzTuOr\\nn6Ofz5AVXfigW2ongpuZF6zaZLPZJO3KnTt38M477yCXy8kis9vtSCQSSKVS2NrawuPHj19JTauZ\\nWrOYp3ba2w01i+Fqd820YqCdMqbzjr+0orGxMXg8HsRiMYmvzOfzUqDz1q1biEajUnmX4UJ2u70h\\n9ITpeZlBE4Bk1fzqq68aSlqdhdoqpa0ngLE4VouM95jP4HUdCNhsAK1OSKvAvnZ+2y7pzWHVdnOD\\ntLNhKpVKQ9ZAoDGvE1OttJPrialsmcmx29psrcayVaAyPzN2irmerl69infeeUdSTzCNyu7uLtbW\\n1tDf34/nz59LAjezMKT+m0zKql+dbNxmElY749VMYtNt1fdYrY/T3tdqfXdCze7XbWRBgomJCVy5\\nckUkmc3NTYyMjCAajeKP/uiPMDc3h2AwCL/fLweOmdO+XC4jnU5L+qBkMolEIoFcLofHjx83ZKo8\\nC7WVfoQblvljWJmAtb1Zw57ierNE9O0sCvMefu7mt+1SsxO6k0VsPo/XORZWQa36/1bqEXNCBwIB\\nFAqFhnSj3fTTPNHNPltJbLx2eHiI27dvY35+HnNzc7Db7fD5fAgEApJWQ1cgWVpakoWrA4dbqafn\\nJeV2+pxWh6nV9Xaf0Q5z6nYuTWJZLe7FyclJXLx4EXNzcyiXy0ilUhgbG8OFCxdw6dIlxGIxyXLA\\nQqSsvaYPEF2dua+vTzJ0FItFyandrUCgqSVDstvtGB4eRjAYxMHBgURFDw8Pw263Y2lpCWtraxgc\\nHJSy0EytUSgUZDN1usFN0pOmTyU9CPrEOgvptlK60QUM21U3dVpbTihwvLF1qltTDTY3ZF/fcRXb\\ncDiMa9euSdQ884130zezvc3G0Cy+4Pf7EYlE8Cd/8id48803US6XJcUvc5+Xy2UMDw8jEolI5dbP\\nP/8cDx48kERuVvNpMsJmbW6nj82gASvi/Lajyrb7/lZtMjWFbt5hRcSBHA4H3G63VMcpFotIp9NI\\nJBLY3d3F/v4+pqencePGDbz55pu4fPmy5OjK5XKSEYFSrU59wzYeHR1JutparSbZOUitGPhp/WzJ\\nkFwuF+bm5nDz5k3kcjm43W7Mzs7C5/Nhe3sbU1NTCIVCGBkZQS6Xw8OHD3FwcIBMJiPVK3V1BQ6c\\nqXvrTamxC129Qydp0+Ca/o5M6qyVOdguYilnUY90ela2kxn9CP7q+02mUK/XJUfPxMQE0um0PKub\\nRWxKDVabRY8BqVKpIBKJ4O7du7hw4YIk1GMpo0KhIP1hhdOJiQncvn0bDx8+FAlap649TX23am87\\n/dN9syKTCZ43TtXqnc3U5fNgSPV6XdL3ML/Yo0ePsLi4iN/+9reSL/zatWtSiJRSESUqSkFc+7qu\\nItvJjKE0XOzs7EiOp7NSS4Z0dHSEWCyGubk5FItFSUplsx1XLohGo6jX64hGo5KX5+DgALlcrqEM\\nLysycMDMgeTCZMd1gnRNeiGZdd2y2axkd+wkRxCfpxeEtkTodp72jGafzU3eauLM75jadXBwED6f\\nD9FoFF6vtytgux38ywrfAI4Pp8nJSczPzzek7SXRYsP5HRgYgMfjwdTUFK5evYrd3V2srq42VXGt\\n+nOWBW5KSs3Uo/NQM6zICosy39UpnNGKKHlTAKBUv7e315B3iaWdXC6XFCCgaqZzoOv9qNvHA597\\nj3UET8tXZQohzehUUDsYDGJychJjY2OIRCLY3t6WNJ9+vx8+n08Q9tdee62h8gEZ0cbGhuT0JcOi\\nadHpdCISiUidK1bNpBUAOElCxg4xox9TrgLAgwcPsLKygm+//Ra/+c1vWnbaqp964MiMuIj0BFj9\\nttVGZ45mXicz14nQ+Z1etHoBkLH39/cjFothenpacJluqBXztGKIAPDTn/4U7733Ht5++224XC7J\\neEmVlNZEZgQFjg8nv9+Pn/3sZ/jzP/9z/PVf/zUSiYQkcmslKZjqZLd9PI35fF/SUat2W6nnZ21H\\nvV6XNaVBbZ0amIf4/v4+crmcSD+6tBYZGSVzXUNOV9AZHx/HxsYGvvzyS2xubuLg4KBBG2CbdPva\\noZYMiYnaC4WCdJZYCksUMR+zrjLBOu3VahXFYlHK7BwdHQnmUCgUcHh42ACS62KL7Dg7QibB7Hes\\nDcZMeDMzMwiHw0ilUmcSf7lQTMC3mZrT7BnACQ6lxXVe1xiRqX6ZbajX63C5XPD7/VJ9xKp+WTt0\\n2kY3r/PkfeeddzA7OyvZJFm1FzipOMIFyXmiys4KNB988AEePHiAZ8+enTn3citqJh2Zm998f7tj\\n0upe855mbToPNa1Ze0xVWL+Xc6PhBLqnaEmK/zMFMHEl3s9ipjs7O6hUKg3S1VmoJUMid2QRPTKJ\\narUqIGa1WsXh4WHDAiXToKmX9b4InmWzWVFDmEpU59gmJ3a5XCKh8ER1uVxSpoh6rMfjgdPpxPj4\\nuJiZz0qnPeO0E9Bq8WnMzNwU5iLV95L5h0IhKTXDhdUpmZugHcZar9fxox/9CBcuXIDdbpeKGTxc\\naPWj+s0ik/X6cW5yn88Ht9uNDz74ANVqVSq6tFNto9NFfpoU1Im63Op6p+2yakM7Knw3ZPUuLQER\\n+GZlEpNBaYyPEjIPTQoMTqcTNptNCmO22i/NDgArasmQxsfHsbKygnq9LonqPR6PVMKgB7NeoDxR\\naWUjk9Klpx0Oh1R66Ovrg8/nayhAWK1WUS6XpZ4VJSvqxjQ5Mg80B4z/a4/qTsmUiKyYhP7b6qRr\\ndlKxfWNjY5iZmcE333yDtbU1XL16FcPDw2LRPDw8xMbGhkiKf/qnf4qhoSHU63X8+7//u9RWP80r\\n2SQr9chKZdDt52Lb2tpCvV5HKBQS655epMy/ns1mxSeJVWYymYyskYsXL+L3fu/38OmnnzbkkD5N\\nemmXWh0EZ31+M2Zn9axW179ParZWKe3wIGeUQLVabZBw6vW61Byk5Ku1E+DY6feXv/wl7t27h2w2\\na1n6vNuxbssxkrhPuVyWOubav4biHhehHhQyGS5uWq3IkenwpytOaF0WODl5eQ+ABuuXzWaTckIs\\nr9QpmYyIG81KD+a9nOR2xHky6r6+PoRCIUxMTGBjYwPZbBY3btxAMBgULI1VTChVTkxMwOv1Ynd3\\nV957VhH5NLWBYzE4OCglrCjB8pDhfHIcdP0x/k/glJjG2NgY7HY7fvvb32J3d7eBaZyXGmOOv9Xh\\n0up+8zv+ttXnZr/r9HlnoVawAhlLvX5cgFQ7MvJQ5/xx/xB20de59nRppXYtle3Mb0uGVK1WUSqV\\nxBmPnscU97QZWzdYV1gg42EnWcmB1yja06eFTlkApA48cFKWm4A5AVUCb9y8wWBQwPBOyGphWEkU\\n+no7OIImqrzhcBhXrlyB0+lEJpPBn/3Zn4nPCAH8mzdvijTo8/mQz+exsrKCfD4vUlSnZCUN6b6a\\n1+12OyKRiDjSUV0jEAqcHAw0MLAuGNcKrTmct6GhIfzoRz/Cr371K1E/dfvOg0zpoBXzbXcO9TPM\\ng4rr1dycPIRbSWhnKYHULun1Wq/XceXKFQSDQeRyOYRCITkEqcGYYDbnWGtBuVwOmUzmFePMWelU\\nP6RisYitrS0MDAxI+RdyVF2hkp3g32Q0PBk5mSw2yPuJJehTl4yMg0NcSS8qXaZ7YGBA4m70pm6X\\nzNPZihE1W9TtLmib7TjkIhgMIhgMIhAIIBaLSdkejkUwGJS/KUpzEWxvbyORSMjzuqFW+Ip53efz\\nYXp6GvPz87DZjksdseYYpSe2k7XPACCTyaBQKMDpdCIcDgvWtL6+LoHM09PTSCaT+Pzzz18xL583\\n2N3qme1iSvoZeo3qTdpMRbRSjykFn9aGbvtppYLzfdvb2zKf4XAYfr9fDhAADW1jv4gVUxBZXFzE\\n/v7+KzGPzQ7xdqklQ/J6vdjc3BQOychyXbud0g4nhxKSaerWi473aLMkB03/tl6vC+bE35lAKNtG\\nTk4m1QlRNG0l8vJaq89WpJmc1+vFyMgIwuEw3G63VOjVffX5fHC5XCgUCoKRDQwMiMct6813s4jN\\nxdKqrzbbsQFhZmYGr7/+ulhbs9ks+vv74fV6Zf5ZtZRWWRpCtA8VcCzxOhwO+Hw+XL58GYeHh/j1\\nr3/9ypy3ane7fWxXZetEYtQ4Ctf04OAgPB6POIlaPUO/0+VyifMoNYnzkpKawQsmJZNJkVinp6fh\\ncDjErYRzyH1LqU9HK9RqNaysrGB3d1cMGub7uz1YWjKkJ0+eiGqgB1I7R1EaIkNiI7jZzOtm/Xaa\\nFTV+RMdEfq9xJW5cLgyWSaZfDHCi3rVLZniLlWhtDrAW3c3Fa3UiUnWli0OtVkM+n8fBwQG8Xq+U\\nHd/b20MqlUKtVoPb7Rb1s1KpIJFIoFgsNkic3ZDVCa6v12o1eL1ejI6Oolgs4unTp/j5z38uUugb\\nb7yBCxcuwOPxYGRkRMB1emsDx/Pv8/mEmXLtMJ5qfn4ekUgE//AP/9CSGXXbN1IriaUVdqUdY7m+\\nWf77Jz/5CW7fvg2Hw4HJyUmMj4/jyZMnePToEf75n/9Z1qn53HK5jA8//BCXLl1CJBLBF198gV//\\n+tfIZDJtF8Jsp+9Wfeb3NpsNmUwGQ0NDCIVC4sYBQPz/kskkSqWSQCgOhwMDAwOCFff19SGfz4uX\\n/nlSy517eHgIv9+PYDAIt9ttyUzYaR0SQjJFXA1EczNrzImfyViIE/FZJrPQ7+f9QOcisG6XuTD1\\n4tXE9jcTU03/IzJQXnM6neJOMTQ01BBPpEVmXdrZZHDdbGArUd6KBgYGEIlEMDo6ipGRESwuLkpe\\nJzpqsi0aM2TJZs00Ke7TelOr1eBwOBAIBAQPo3PteatsZn9N5mv1LloNqY75/X54PB5MTEwgGo3i\\nwoULmJiYgM1mQyAQQDQaxeTkJHZ2diT/lZa4a7UaIpGISIYzMzMYGRlBIpHA9vY21tfXJZfQeffZ\\nvM715HQ6EQgEGuaM66xQKCCfz8PtdsPtdsvzOOe0fDcbv27aRjoV1B4aGsLc3JzgPlqUs9lsguGw\\ncSYT0tcp8ulwES5YMgWti9OzVFvu9N98j/6OzzjPQTLxJavPJpkSFJkSx0t7odPzOZ1Oi8NoMBiU\\nk3lwcFA2s9n/TqmZ2qb/rlarcLvdmJiYwNTUFKampvDgwQNxZqRLB+cRgMzTwcGBLHweNlTjdGwi\\nDRKjo6NIpVJisTHbeh74inmQaSLzpETETRkMBuH1ejE4OIixsTEMDw/j3XffRSQSEQw1l8vB6/XC\\n5XIJJsiDl+4nfH4oFMKtW7dw48YNxGIx+Hw+zM7OIh6Po1Kp4MmTJ2fuZzMyD1Sv14tIJILh4WEB\\nr7U/ElVzrWloqIRqOvvXSiXulE41+4+OjuLSpUvIZrMi3hHb0GCs3niaEWnphwyMg8B7dGf4PbEp\\n/T0XvWme1KqjBtq7JZORWn3Xasz0SczPDocDMzMzuHXrFsbHx8VLfXBwEOFwGDs7Ozg6OhKnT55e\\n9Xpdcitx03Sqkuq2N1sw5nWv14vXXnsN4+PjGBsbw8WLF5FMJrG1tYX79+8LlnTz5k1cvnwZIyMj\\nDVKwy+WS/E20zNIiQ2tjMBjE3bt38eTJEwktasYwuyXNMHVfyRjdbjcikQiCwSB+/OMfY2JiAuPj\\n44JF+nw+hEIh+Hw+jI6OAoCEPvl8PglQdbvduHr1Kv7xH/8RyWQSALC2toZ6vY7Z2VmMjIzg4sWL\\nGBoaEvxoZGQE7777LvL5PD7++OMz97XV2PFwGBgYwPvvvy9pR7g/CX3o+SGz1a42GuvkOOiMAJ20\\n0Yparmy/3y9MSFt7tNhmSkXmy80gVd5nFdXP61ot5EDws5V3rx4oPqdbMhlJO9zfCkPideBY/XE6\\nnRgZGcGlS5fgdDqRz+fFx4ee7GT02qViYGAApVJJwnPOsklPWwz62VRb6JHvcrlgs9mQzWaxvr4u\\n+XMCgQDGx8dF7dQYH5kpP3MeNbY4NTWF9fV1DAwMNMRinZWazQffy/Qt0WgUfr8fo6OjeP/99zEx\\nMYHJyUnB9xwOhyQlK5fLyOfzyGQycDgc8Pv9EkZjs9kQDodx6dIlwTNfvHiBWq0m0fU8MBluNTg4\\nKDGh3R4yp/WdpA/K0dFRDA8PNwgIvIdzRN9BDcVQ8iMvcLvdgn01U4E7ncuWozA1NYVKpYKNjQ3x\\nL9FOjkAj6KwxBX42RTo2UjMyUxris6nemNY1Lnr9bJ4656nSNPtbX7PCJfT3bOvo6ChGR0fh9/tx\\ndHSETCYDn8/XMPHMY1Mul1EoFMR/J5vNolwuvzIGZyUr7EtfJ9bgcrmQSCSQyWTQ398vAHulUkE8\\nHkc8Hsfo6Kg4UGoMkCFFVOM4RszRHIlEhNmdB+nn6ENFS+l9fX2Ynp4Wnyi6Yly5cgUul0sA3Wq1\\nCo/Hg1qtJhkS6Ztjt9uFEdHFoV6vo1QqoVY7TsMyNDQkKjc3c7FYlAOUDI2Oiv9XFAwGMTg4KIHp\\nHDcefAyW7evrQyAQgNPpxOHhIQqFgmSa0DCN/l9TN2u0JUN66623sLW1hbW1NUQiETlZBgYGkM/n\\nGxwTyUTMk4mngrn4NVMzrQBkZABeOWE18+J9ZE50g/++HM1OU9+0pMQFeO3aNYyPj+PSpUtitSKY\\n7XA4MDg4iEQigVKpJP0vlUoCKlJi0k6jZ5WUTDIxFqq/VB/7+/uxsLCA7e1teX8+n0epVEI2mxVV\\nSx9EmnnylNVtp+WNqVTNg8SUajrpiwZfqS4Dx0UrotEoQqEQ3njjDQwNDeH69evwer1wOp0YGxuT\\nTUkn28HBQezs7GB9fR2Li4sAAJ/PB6fTKXGd3Nj9/f2SqL9Wq4nkQ6yF4D33DKW0SCSCixcvdj+B\\neHUtWo0Z1xfbf3BwIP/ITNPptFi+j46OJNMnXQGOjo7EYgpYq4lnwZRaMqRYLIb19XWJQ2PcCnCC\\nuJMZsLMU+UwQ2zSFas6qJSN9nb4R2q9Ii5Xc9LRsVCqVBgtVt9RsE7QDfmuqVqu4cuUKbt68KWla\\n4vG4BDjSE35/f79BJOZY0nOWkf70kGdKk26YktUiMftLCYD+KfSBonjPAGe6JgSDQYlHZGYIMgSd\\niM6UpAEIuE8pqp1xPo3oj8bDk5gIM57Ozc1hcnISDodDijHQosQDDzhJe2Oz2QTEJaMtFovw+XwA\\nIMxJA9lkvJS09Nhy/Hkv1/D3QeZ881Cg6lgsFiUdTLVaRS6Xw/j4uOw7MlDigpxL7YfI9zR7ZyfU\\nkiFtbm4iEAiIdzXNmdqpUWeUA07AQy0OWjWYJyY3oq7KoXVa8+Q2HdOaPf8s1O3vtWowMzODK1eu\\n4M6dO/B6vUilUkgmkxJvFw6HZZFrPZ4nkdPpxNHREfL5PJLJJGq1Gi5cuICxsTGsrq424Gqd9quZ\\nWsPrPLkdDodIbNo7m/5U8/Pz+P3f/33MzMxI5kEtDfBe0zigcSan0ykHylnGnhSLxfDGG2/g9ddf\\nF+mO6rxOc+Pz+RokAQBIp9MYHByEy+VqgCbcbjeuX78uv+Hap6Tg9/tFCqPFCjhxJ+G+AU5SzTLw\\nmABxNBrtus/tMHLuFzIazmUsFpPr2WwW+/v7iEaj4iu3t7eHRCIBp9OJyclJaf/8/DwWFhaQTCZf\\nyctlBQWYa6wZtWRIxWIRwWAQDodDrED8x8nSL9emfr3J2EgTNNX3U7Ihk9LP5XXgBCtiZzm5PNm6\\nCTo9DbBu9huzb3qjTUxM4Nq1a3C73RIPSHVSJ7KiJzPHgiK+BoWz2Szq9ZNMC6309k77aS7mer0u\\nm5ZZOwm2891sx6VLlzA2NgaHwyGxUJwHHQGuAXq+n5Iz1VFzzLs9ZSORCF577TW8/vrr0k7mLqcL\\nAiVAriteZywepTvtokKJkP2iNO52uxtUW+aZpsRHRqBT9PC9ZPg0anRDrQ4ZTXR6pJTDe3j4EBt0\\nOp3weDxwuVzCqIknMevG0dGR9JuuKadRu2v21Fi2sbExBAIB5HI5ifAls6EpWgO7mkyMiBuW32l1\\nTF/X92uPcO2MqF0NgBMxXb/vPEhvDCspTA8wmer09DSuXbsGv9+Pvb09UQu0VMdNQYdT4CQ2kGI1\\nRXlKm5QmrNrTDjUDsc3+ut1uMWnTRcPlciEUCqFePzZjs7SO3++XVBbAiS+YPhx4+Gijg/bwZ3iM\\n2cZuKBaLwePxiFWSzJQbnyoUgXQdv6UxMM6TVo09Ho+EwfBgJqMmQ3K5XLIWaa2iHxYtbVQh+f5Q\\nKNSVhNRs/q32I40m9G+r1+uyJjlOiUQCiUQCo6OjArnk83nxExsaGhJXDq5rzdz0+78Xle3mzZuS\\nzMnn8wkOwE1BIEzHsMmDVXAtcOICwMWqO0LGo8FQDhgHk/frZ9ANgaddrVYTfb4TaiY16NO8GREn\\nA4C7d+9ienpa0rjG43FJys/YNaoDWrKjNVHndtLm12KxKOBnMBhssGR12s9mYLFWN9966y28/vrr\\nYhEEjnNjMQnerVu3MDIyIpubC1NnHeRm0xua/WTcG6VBLSGYGGOnlEgk8PHHH+O///u/JfEfMa9w\\nOCyhELOzs3C5XMJ0+/qO80sR69TFF7Q0z+gBumzQe51qGUFgWtT4d6lUEkyKeA0tdisrK9jc3Oyo\\nn6cdRuahRcmVEhLVSTJPQgOsPDI4OIj9/X2JP2SGVuAYjikUClL+rJ12tEstGdLBwYHEsAEQXIFZ\\nAvUi1v5G5NA8AXWYgQbDdMCeHkBiEDp5lBb5uHF5yuq4Nn3adktWVgL9t2akbL/dbsf777+Pmzdv\\nYmVlBffv30epVBKLBgDZsGwnTd8cOzJSnqiUKPSmZQ0s03LZDpmL2GRQ/H9ubg5TU1PSrnq9Dp/P\\nh/HxcVy+fBlzc3MCCCcSiQZpR79HW0c5TjzQtPMr54uYpFU726UXL14gnU6jXj/OLBGNRkX9pPTE\\nUJBgMChpb+x2O4LBoBwAPEi4vimhU/Ihsy6Xy3j58qVsXm7SWq0mRScYgHpwcCAJ7OjPRI1DJ6tr\\nh5rhNOa88jPLk+VyuVciJrgXmRWW95LRhsNhiTflHvd4PBI50Gy+uoEWTo1lo15JsZeL5+DgoEHk\\no79QK6mC/gtWIKf+X79HW1/MTnNwNNjNDdspmWKm3lz6bz3ZtDyMjIxgbGwM4XAYlUoFuVwOpVJJ\\nMAkyEjIi4gjsB1W5arUqoSMmg+YGowTIRXYWMsVsMg76xZApUnrweDyIRCISIkI/HM6/uSHMsB6d\\nzI3XKEnRD8dqrjtRTYm3cXwYAkFm4vF4xCJGiYm4XjQalUOwUCi8cujQOVLHFx4eHmJnZ0f2A9Oz\\ncFPTcZJ9LhaLyGQy4tdDq9VZspy2AxbX63WxlMfjcWGy9J/q7+8XQwulJs4rD3lKwcTUiC+a1AoO\\nOBOGFI/HMTg4CK/Xi2AwiOHhYXi9XgAnZW+0hKLBWi4KvXm1OM//TZWNk0/xkpvY9L7WaiLN6DpP\\ncCfUjOHo7zRxMbpcLoyOjuL27dt47733kEql8OjRI7x8+RL1+nFmvtHRUQQCgQbXfO3DoieIp1S9\\nXpcTmBvBbrfD4/GIly2L/50XaYB3eHgYQ0NDDaok/YWIJxaLRRkfLbES8NUe/ZSgtCc2FzwtX8yb\\n1GkuK5NqtZpk3uQmoiQaj8dF6nvw4EGD5YtMlmvPSgrWhwctihqU5zrVWTV1gDjQKF1zoxPf+b6I\\n76MH+aeffoobN27gxo0bCIVCAm5TMmc/GGCs1dr+/pPkfBrHtVL/rdpwGp3KkEZGRjAwMIChoSGM\\njIyI1ym5KhvIRlAMpM6tsR+gEXOheG4CmnyGPr24eKi+aebG33FyO2VIfDcZpMmE2Af21+/3Y2ho\\nCGNjY7h69SrGx8cBQJxICWAySwIxBw1IaysL0zhQFaBTHi0/2jM2GAxienoaKysrDdVCz0r9/f2I\\nRCLwer0YHh5GOBwGAHHHYIgLSec50mOo5wZoZOjcuGRIvG9wcFBi97hutATZKensCE6nswEL4/PS\\n6bRYBbk+OR/6/Rq/5KYE0JCjS6udlH6pimsMjfNPSx3X1HkZYUzciNfYJ0IvuVyuYZ7YHkIPlBIJ\\n2bA0GYlrljm7TqNO+teSITFNZS6Xk1wwWgri/1rl0APAAeFA8bTQSdSIKWlrh+bGPK30Pfq5emFT\\nBO1UlTElOd0vjYX19fVJHbmpqSkJlj08PESxWMTm5ibS6TR8Pp+AqbTsaH8YvoNMilIRTyWm5jg4\\nOBAwlGPi9XoxMTGBbDbbUR+bkWbofDYZEzcVM0JqlwwtFWgVEziRlPTpyY2tgXzeZ7PZxLHSSkLt\\nhClxLZCJa8sk8Ts6+rLt2sqp28v+EWPRaqmWerTEwFzVlJS0uqfBfa5bzby6oWZQQzMqlUooFosC\\nKVBS1f6EuogGBQ5aW8lgiX81Y0gmU7RqqxW1ZEhff/01PvnkE2QyGfzFX/wF/uZv/kZUNG4uiqfE\\nHwA0LDYuXjZcL0oAcrpQXdA5eovFokg9XLhah+dG0Jy+Vqt1nFObJzMXD08/j8eDWCyGUCiEQCCA\\nUCgEv9+PmZkZkV6+/vprKbzHYM1QKCQqD53n6vW6ANMEF/1+PwKBAIaHhwUgZWqPZDIJn8+HyclJ\\nLC8vA4CEN+TzecnkeRbSzJdm34ODA8FWyCjy+byEPRA35FjpirVU3/T64EYmE+JG7u/vFyjgzp07\\ncDqd+Oyzz5BIJLrKiU46ODiQii36H/tJwwxwIq2z7byuGQelLB62VGvIYIkV1et1UXcxiFHlAAAN\\nKUlEQVQojdCfzHR85Zrl+tWOk51SM2akwWpWqs1msyiVSg3gOq1qPDw4P/QtI+6ncbhSqYSNjQ0B\\nyE0mqBlPpxJuS4ZUqVQkkffGxgY2NzcxPj4umf/YeE6oqbKxcaaZUUs+XDAcPG2B4wBYSUda7KUJ\\nVftGdUpkiuFwGCMjIw1exKz7Rn8TTmY2m0UmkwFwzCxoUeP4mP2s1+vSPraVJY10QnUyeTqr0fpT\\nr9eRyWSQTCYlZqpbMnEzAGIN+td//VdEIhE5TGy24yh/eu+aLh76ecCJT5mWNKgGEBtkzb1UKoXF\\nxUU8ffq0gWGdpV8cR7ZRM6RqtSpr18Q/9GGo1S0mjyPTsdlsAuhTGtIxejxcycS0Kss28Vo3HvfN\\n+q1JMwSmuCGQTlWLDpEaVqF6TsajHTa5lw8ODiQbwnnTqZ7aFG+3t7fx/PlzUUXoDEdJheKwaWEh\\n8yCoxvv1YjW5LCePzIiDYS42rSLq5GHdqGx0VhsZGREPawDY29uTwFaC5vF4HMlkEqlUShgHJSj6\\na1DNobjOxaw3CoFRMi/2hW3hJg4EAtLOvb09bG9vI5/Pdx3/1OxEYxjL3//938v4E0/6gz/4A5Ei\\nORc8QLTarZ+tXUFMJ1hGkC8vL2NhYQH379+X9WSqz52cstq50TylNQMol8uvGDFMDMZqnKyez/ea\\nh6bZ9mbvOgtTMlVj8zql1omJCXFypDe5Nv/rjJ3aIZeMiocmD39aCK36ZL6/k/k7lSFR9N7b28Pq\\n6ipu3rwJAA1itVbjOLDsNK009KWhiKytc2QsVOf4HM2otGRlxgZp3x76R3RCs7OzeO+993D9+nXZ\\nLGQkmmmkUinJpmez2SRtBU/8iYkJUcmYLkSnbSWjIiDMydLR04FAQDAomtZZqaNQKIhlr1wuSyKw\\nTqgZxqCl2r29PcFYkskkotEoPvzwQwwNDTXcz/njvGhwl5ufqpDdbsfa2hq+/PJLPHr0SCRLqrvM\\nHmAaFToV+a02gMk4NFOx+o3GQa3u0YxJ/62lxmbPbNa2bvpp9bf5Pu7H4eFhvPnmmxgZGUGxWEQ4\\nHBYJKBwOI51OS3Vornda17QQMDAwII7S+/v7DVJfK1WtXcZ0qnxMILdSqaBYLDbgNlpk1ZgRO2XF\\n9Wkq1ZIPNyUXOZ+tmZIJeOqsAlrSIgPohG7evIn5+Xlcv34dh4eHYvUiHsU4JJaw1ilB6NfChPz0\\nas3lciIWkygGs93sq47xMi1XxJa0REJTeTc5dMyTVF/n/8TReG+5XBbx3gymBk7M5noTc51Q4qNZ\\n/cWLF/j888+xtbUlpzfHwGQC50GtGI/Z91bXrf5uJv1YPfM0sLkbhqTHywqm4DMZ1U8McmlpSQJp\\nqa3Q0ZUCg475o6c51wYPHpMBtepju/1rq3ItcIwv7OzsiEqkG04JiDXdCU4TA9GWFeAk6yPFcy0h\\nabGeA6Q7SrUCOJbCuLF1HqTZ2dm2Ok/6u7/7O8EKNjc3BbCjT0y5XEapVBInyLm5OdmE29vbcoKk\\nUqkGKZH9oPsCJ5UbnI6OdKDU3r10sEyn00gmk5KhsF6vi9mWqly7dJoIrRmIVr0PDw+xtbUlrgoa\\nB6ErBBcqpUrgRBqhSvH48WPJXKCr4WqyUt877SOpkw3SrnpxGuPphrphwpQ6Nf5nFctJ/O+jjz7C\\nRx99JPDC6Ogorly5grGxMfT390sxWI170ZDBWodU4Wu1GjY3Nxv8zdqhM0tIptkdgDSMqhKlBVNS\\n0SqdKT3VajXBV8iFtcir8Rb+0yA2n8mB0yK4x+PB9PR0WwNE2tvbk9gcus9T2iHeoCOl2SZdX4uS\\nBJky+0PJklKgDm+x2WxipdER/vV6XaRIMiHgmJH7/X5MTk7CbreL3t8uWakh5vckzg1pd3dXVEmO\\nCyVj06xNFVpjhVQD2GYrP7Jmbe5ks3Zyalv1/bT7272vXTJxrHaJ+I7VHrB6nj4kj46OsL+/j8XF\\nRfzHf/wHbt26JaFBGhbZ3d1FtVqV8BttQeQeOC/JiNSSIVGkNs2ZtCTwGlUkWo54WmpLBRcmJSLT\\nGZKDwA3PThOL4TM4YJQoeD/zDAUCAUxOTnY0CA8ePJAIcfabybfYXvaLgB7brjcuwWmdnoWMlUA1\\nJSiOGTcqcBLrVq1WxcLH55GhuVwuyd7ZqaXN3KjNQFZN/Ly7u4vR0dEG6Yd911iKJm1yr1ar4rvC\\n785TNbOiZn3slpqNUbd0FuamjSO6XVZMybxGP6T9/X3813/9F548eYIbN27g/fffx9DQkCTwZ3yd\\n3+8XnFQfPBo70u04yzi3ZEh04KrX60in0/jss8/w9OlThEIhDA8Pw+fzYXh4WJKYh0IhkSxo9err\\n65Oo90qlIhKWz+cTPxzTNZ04hdfrbXDGJCOi9Y1+MVp1AE4y/bVLX3zxBX7605/i2rVrgmXRPMrY\\nKL0RNeMgc2HMGhkNGTABcMaG0VwMHIvZZLxmWhFtkaNUQmyK87G1tdVRP4FXmZL+uxXu8e2338Lt\\nduPChQsAGo0J/FvjewDEw3drawsvXrzAL37xiwZnOs0wmoG93WJK582M+Ew+6zyY0lnbZ6WeNmub\\n/l6P7XfffYfFxUV8+eWXWFhYwJtvvom33noLNpsNiUQCe3t7WF5eRiwWw+XLl9HX14ednR0JHTLd\\nNE7D2k6jtp0+6vW65EXZ2NhANBpFMBhEKBQSRJ6ObgMDAygUCsJcmNagUqkIjuL1euH1eoUhsXPc\\nmGQwVJWA403AxFFUWbSjWb1ex4sXL8SRsF1aXFzE9evXJa5H+yERM9HOYxpIpGWIsWqaoTIUgd6u\\nmoHSV4kAMsNLAEifKZazGECtdpxsfnl5GTs7O12nrGi2QJotZD33PAw4RxqMNtW2gYHjiikrKytY\\nWFjA/v5+U/C6mWR2ls1vYinnJZGdlRmdl4TVznNbjSM/U4VbWlqCz+fD1NSUHH4s8EG/o3r92PUE\\nOIFN2hnndse/bYZE1YWnOvPAbG9vY3t7WyQEMhV9CjJnL3CSSI3ShE5WBUDUFC54r9crmIXNdhKY\\nSk9SnQKWG/bevXvtdgsAsLS0hN/85jeIx+OIxWINRQLpL0M8hIyIquLu7i4ACNbE/tVqNSmRzfEi\\nRqWBfALBBCl1oi96+7I6RSaTwaNHj/Dw4UPk8/mOU1bouQSsrURWklO1WhVwkyAm262xCTJizqHd\\nbsfu7i4WFhbwySefNKh4rRbnWRmHufFOe955Mqt2n2u1kf+vSUtK/f39SKfT+OabbzAzMwOv14u1\\ntTWsra1hYGAAq6uruH//vmSkoIZgWlybSUXtjm9bVjatMzJATwOqa2trr3RQL75mz9b/tL6rAU8u\\nbn6mJEEgXTtBklmk0+m2Ok/KZDL47LPP8NVXX2FsbAz37t1DMBiU+vSUeugCEQgEpI0sfFCpVOD3\\n+9HXdxxvxqT+W1tbODg4gNvtht/vl3uIv9XrdUl1QUC8XC5LPiWqOMxPc+/ePWxtbVnmDTqNrOaj\\nGS5izg1T8NJtg9Igw4aAE/M/v9vZ2cHHH3+Mr7/+Gk+ePJGDR6sN503NJIFmpzZ/0+q+057T7N5O\\nPnc6Hu3OZat+6+f09/cjlUphf38f5XJZUumsrKxIrGY+n5f9qfHB85zLU0HtTr6z6qgplpuN175G\\nWgIhmV7X/L12yNIMzApcPY3q9WNTei6XQzKZxNLSktQjo4RGXZlZE5lTmJu1VCphenoaw8PDEhmf\\ny+Wws7ODw8NDqQnPaH6fz4d6/dhdIB6PiyrLOLcnT54glUohm81KutByuSySZjegcLP5PE2VYzuB\\nE8srmQ79lbQKx/k4PDzEw4cPsbm5+Yo01cnm7qSf7faxmXRitck7kWS6lXw6PVy+D4mKe4/ZLIh7\\nJpPJBoupFlK6eUcraktl69a60Eps039zc7U6qczvTO9Q/ZtuBouMjJY9SoMHBwcNvjYE03UkPyW5\\n7e1txONxCU4tl8tYW1sTp0AmVO/rO44KZzaFVColhQDoxPb8+XNhQNo3SUtG5wmEWn3Wv+nvP65K\\nsba2JoYGpvbo6zvJ50NR3maz4fHjx3j58qU4yXYrxp+FrNaGudZa/VY/Q18zf2ul/v7/olbYXCsi\\no1lZWcHBwQE2NzcFn9X/zD13FsnRpFMlJKuND1hbacy/NTWbVKuOWDErK6/v89L9yfm1ox79LGim\\n1uprMplsSNA/Pj6O6elpLC4uYm9vT+qZEU8CjqsAb29vo1QqSWnmzc1NrK+viwWSienpLsC26TxC\\nZ2W8us/mXDUbSx0+9OjRI6k6Ua1WMTk5Ca/XK3mzaJksFAr4/PPP8fLlS/E0N99h9f5W66zdfllF\\noDe716RmEo6ef6v2nbXd3ZB+R7Ooe6vrzeaZzObevXvweDxyUAIQiES/VzP20w61dslW/784pnrU\\nox71qA36fmpO96hHPepRF9RjSD3qUY9+MNRjSD3qUY9+MNRjSD3qUY9+MNRjSD3qUY9+MNRjSD3q\\nUY9+MPT/ADVNItqe2Hp/AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 20 - loss_d: 0.304, loss_g: 4.402\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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Nu//VucO3cOfr8f6XQa9+7dw6NHj/DgwQPUarUuLeG0tSXNjDqdDgKB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\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 21 - loss_d: 0.338, loss_g: 3.911\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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LfbLSF5aghaOMCRoKJLRiBR+9nUUjSNmWTW6XR6TEcAonWcTic8Ho88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\\nOXVEt0YAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 22 - loss_d: 0.267, loss_g: 4.615\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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N5KpYJSqQSv1ysWGdcDn0k6qeI33TPzO/253W5HPB7HZz/7Wbz++uvY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\\nvTI0Y0gzmtGMXhmaMaQZzWhGrwz9PxUGx7m4PBf7AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 23 - loss_d: 0.341, loss_g: 4.135\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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jJQAblcrp7vZjQqGAyi2+3i4OBAjqT4/X7JiH4Wbpv+XsooCs9KMeq/\\nWaEk5t8Eg0HMzc1ha2tLFLtWzOZ3jOteDepjv2f0+5u5vtxuN8LhsCRDttttcdW8Xi9SqRR8Ph+C\\nwSACgcBTCiEQCAgy4sbXkVa+b1zhZ4ngB7nh/dxu7dbyd1IFMzMzCIVCqFQqyGazElhxOBxIp9Pw\\n+Xzwer1yTjMcDste3dvbQzabxfr6Om7fvi1KfBhfNlFipClWmpbcCY+EOBwO+Hw+eDweHBwcYHNz\\nE0tLSwgEApienpbn2Gw24VDMZEj9faby0K+fZnK1AnQ6nRI9Ic9jTipzUeimme6OaV01aQ305h8R\\nKRHKcxzdbrf8TSOt0yCKQZ8dhFysUJHuq/k5bshwOIzFxUXcu3dv6HeaG3VcpTSIszD5rn6fYXuI\\nYoLBIFwul8yd3+9HMBiEzWaT+SqXy0JT+Hw+mStGjOv1OrxerxhfGrhJlC4/w3woRmip6Mdxhc33\\neTweBAIBoRe4JhkVpqtqpjToyOr58+exsLCA69evw+/346OPPhJDPkhGirJZvWY1iGwQ+RaiCLpp\\nsVgMTqcTu7u7yGazmJ2dRSgUQjqdRj6fF0Xg9/slmbLfxrNatM+SCKUy5KSYRzhIbLPfVFjm8wh/\\n+TtwcgaMk1itVntSAPhskqZEVGZk7zT9HDYG5vvM160OJJvKqts9ThQNBAJIJpNIJpNC9Fq5eFbt\\nmmRO+7kkg5S4+Rn+z2gY0RH7ykgUAFQqFYk8cc3QePB35thFIhExusxjmsS4HB0diWvo9XoFpRN5\\n6X5YcbAaGZmv04D4fD5RpDSO5Iir1apwpvweuq0ulwvpdBrxeBzT09MoFot48uRJT1pPPxnbZeu3\\nSAEIj1Iul7G7u4toNIqlpSUUi0XU63UUi0VsbGwgk8ngyZMniMVigqLW19cxNTWF559/HtVqFbFY\\nDD/+8Y+fgpNWk2dC5Ek2q+4LFQgHt1wuS8Z5t3scKSF6IkLSbaOrotujD6JqS8LIDIltHikhB1Es\\nFhGPx5FKpbCzsyNk8GlkELQfNkb8vB5n3R/z+ST2GXE1x0U/2+oZz5JnmRQ5h8NhnD9/Hh6Pp4cX\\najabQvzylEEwGBRDQkKXofJqtQqHw4FUKoUXX3wR7777LvL5vCiRccRutyOVSomLn81mpb2a3LZS\\n/FaeBYEEk3LT6TTsdjv29/fRarVkvdNQM0LI76CS7naPE0U/++wzpNNp3Lp1C8lkEleuXIHH40Em\\nkxnYr4mjbFYdAyDhTZ7bikQiKJVKKJfLMpEAsLe3h/39fQSDQTgcDulsoVAQxWYFvUdpy2lcNn5e\\n8zy0diT3OCn6WAQnlchBE+Fmghm/TyNA5q+Qg+PzAeC5554TC1ssFp9CXL8JGdVNH/Y+u92OYDCI\\nRCKBRqMhm3RUwzGuy2ZF8Jp/G/RZK2PndrsRj8dlPji3mqgmuauRhD4KpI+aOBwOJJNJJBIJ+Hw+\\nidaOI8lkEsFgUMrYMHWGa0ejn35906+T9yQ9wMi4dskoGh1zzVOZkZ7hSY1KpYJyuSzof+h6GfRH\\ns1ODLBobyo1DFyYYDKJSqeDg4ECiaQCws7ODJ0+eoFKpiM8KAIVCAblcDvl8/inUM8ri5IIfV6wG\\nSsNu/f08OqKjJ3y/2Ub63NqlIYLSKQI8++R2u+Hz+RAKhYRneu655/Dcc88JwhjEkTwrsdrY5mtW\\nm1e/12Y7DnJEo1FEo1FUq1UJB5ufnRTZmjJozVr9rsV0ufkM1rgCIIehuWmt9gQ/y3mmIWaGv81m\\nQzQaRSKRkGDGuHN548YNeL1e4SDpCun2mEZ2mCtOol4HlsyoINtv0gl8Fg2w3++H2+1GPp/H4eEh\\n8vm8GKRBcuoomxYqg06ng8PDQ6TTadhsxxGmarUq4cJWq4UPP/xQFFStVkMmk5EkOr/f35PvoJ//\\nm0QFlGAwiGQyiaWlJSHunE4nQqEQMpkMPv30U5w7dw6zs7PweDw9CksjIH2kRf/jRAMQPoGfo6U7\\nPDxELpfD7u4uNjY2sL29jXK5/BRxPo4Mc9X6kcBWfIP53fpIAd/j9XqxuLiIRCIBt9uN73znOzg4\\nOJDIo3bfOF7kKGhpTytmnwYhKD1f7He325VEQYfDIW6a3oTa1ef6J6+oAyTkXljd4fd+7/cwOzuL\\njz76CD/5yU/G6tfNmzfx6aefYn9/X75Th9at5nKQy0bXdG5uDvF4XHiwRqPRE0ihctV1ofRREz4r\\nkUgAADKZDGq1GpLJJHK53NA5HdllG+a+6c61221UKpWeUpUkB/laLpfDwcGBbPhCoQDg5MAqj5/0\\nG+BBmva0Lht5gVAo1BO1AI7JxL29PcRisZ5JMBWNzmLVilTnd9hsth7+wOFw4PDwEI1GA7u7u8hk\\nMlhbW8P6+rqk7ZuHcX+TYjXmg84iaWWlM7TD4TCCwSBu374N4OSIhXnQlmPJXCy6PcOI0H7t0GKu\\n4UHrR7+Pc0XimK6N3limwiaS0MnBAMRt4fOWlpbg9XpxeHg4Vv+AY9QWCAQkKXPSNa+Nm9frRTQa\\nlXplRPDsA0lr3Xedu6THwO/3SxmedrstZxmHycgVI8fpsMPhwP7+vljCWCyGYrEIn88nvimhHsmw\\nYDAo5LHNZpNsVtPvHHVRjSva+nc6HZTLZVQqFYls6VpHdEmZF2SiFr3R9BERtp8Lm8/WRPdPf/pT\\nbG1tod1u98DdZrMp42mOxTh91P/zGebv3FR6o1mtBd0vuq50ZRj9icfjWFpawtLSEr761a/KUaHt\\n7W2xvuy71+vF1NQUpqence7cOUxNTWFzcxOffPLJ2P00N9s448M50u6H6ZZQqZoEsumWm20CTg68\\nxmIxyWPSdaNGESr8ubm5HndtlH1hhXRttuPodjgclnklr6WJbO3O0VhTSXFM6FG0220Ui0VUKhUk\\nk0lBVINkpFEYBR2Zk//kyRPMz8+jWq1KzoauBMl8DKbQ69pHrBagLak5mL9J/oTRLX1+iZPCQ4al\\nUqmnhri5EDnhGjGRU9F+OWE9Lcx//dd/4ZNPPkEoFOqxUM8i2qQtOKE3x51nr1hqIxgMSlgbOF6A\\nBwcHePz4sUR06MZ6PB7hVkKhEK5du4Zz584hkUjg3LlzWF5exvT0NL7zne9gd3cXe3t7opiIQOgi\\nRCIRzM3N4dKlS0gkEnjnnXfG7qcVkTsqwmdfOVfpdFoibCSwqXBJIGtuhd9FJKSVNscMOOaiwuEw\\nYrEYnn/+eSwtLY3VRxptnp0zlQvb0Y+op5AIDwaDgmJ0bh2julQ6ehzNbG59zCocDmNvbw/5fB6b\\nm5vI5/PY398fiLCBEV02PQj94LD5f6lUQqlUQi6XkxPNjUZDuAF2sNFoSEkKm82GqakppNNpWeg6\\n18FKniVSYvs5EYSbdM24KNkXkrP98qXMTGQ+B+itq0wIz1R8ogyz1K1JZg+b3H5CBODz+QT18aaY\\ndDqNF154Ael0WhJW3W43ms0m7ty5I9nnzFJntCeXywlUv3nzJn7rt35LyhiznRcuXMCFCxck+MHz\\nfsxQ1/PMs2GpVEoCIZNIvyx/czwoOoXB4XBgZmYGyWRSsuV1lQZdXobut0kI0+0E0BMA4WtEkefP\\nnx+rX6yiwXNk4yRDmgqLPJnf78fR0ZFklJOI1uNPd1yPlflcj8cj7ner1cLBwQHy+byUdB4kQ102\\nPVna8vTrtEYJtL5MMc9mszg4OIDf7xfi9s6dO3jy5AkePHgAn8+HxcVF1Go1eDwepNNpZLNZHB4e\\n9tTP0e0atsDGEQ2piU54xAWALMhSqYSDgwPs7u7ixo0bPQQ1lYgmeflMfZZJP5Mosdlswu12IxQK\\n4erVq6KY/ud//kcO21rNx6iiF6LL5cLi4iJmZmYwPz+Pq1evIplMYnp6GvPz87JwyOEEg0FcvXoV\\nL7/8Mv7hH/4Bjx8/xhtvvIGvfvWrSCaTeOedd7C9vY1SqSQn2c2ziYTxdvtxzXXdH+Zf8eByIBAQ\\npLmwsDDRfA5zTbUBNV2YbrcLj8eDq1ev4qWXXhKkyjy7crmMZDIpSEmne3A96LpI2pXRGfcAMD09\\njbfeemusvhWLRezs7GB1dfUppav7qMdC7xmNlP1+P86dO4dUKtVT4ZIRcyoY7aZxbggwNKHO2k/M\\nWLfZbBJhOxWprTti5Z/208C0wGyU3X5cf7hSqQhXwFwFfUC12z2uMLmzs4NCodBzts1swyBkNAnB\\np/vKNumDk1xIRCzValVOfAPoWZD9lDRdPj0pRFjcjByXZDIpSYXvv//+M0GChPnBYBAzMzO4fPky\\nFhYWsLKyghdffFHC83RFdKa9x+PBzMwMvF4vbty4Abfbjeeffx4XL15EMpnE3t4enE6nXGNFV0Vz\\nERxT/q7PdNGAMSuea4b84rj9HObWW6F8PcacB15pxQ1FRMSNB0AQhXlEiM9l9E3zi1o58HvGkVqt\\nJmfDTG5m0L7QLhddtVAohGAwKEhYk/L6eVqBa++A7+FrNptNKr96vV4549btHp/RHCQj10Ma1FH9\\nHv7Mw4Z+vx8Ox3GRe55P48n+QCCAVCqFZrOJhYUFVKtV7O7uYmdnR7SqeQhzEDKaVMxnaVjOCgX8\\nfi4CHh42uSL2X0N3PT5clHoiOVmaK2o2mwLlmfxmWrlxXbZut4tYLIa5uTlcuXIFb775Jlwul5SW\\nIIKpVquikFgLiG0LBAL4xje+gevXr0t6B3Bs5VmEbXp6GslkUlwaRleIjviPm4JGi6fJeWqeCtTq\\ngsth/TTF3IzDPk+ek2S2VjbcfLxxhgYLgBRf02tWpzfwd600SQeMI7lcTgrfMehhog9tvDUy19/J\\newWdTqecqCDPy3WpI2hUrroWGNcux4LVPhqNhhyqNrm0fjLS4Vo9mLqz5uta87MBLKPBBEMeLGw2\\nm1L3qFarweVyiZX47LPPsL29jXQ6LVyNlX/8LLkj3W623WY7DkHrukeaQ9EZtiTgNZFo/qxhOhEX\\ncHLkhpPMxZ1KpXDx4kXEYjFJ4Z/kmAHFbrfj5s2buHXrFq5du4ZXXnlF2k9YzrvxiGboKmYyGbnA\\n89VXX4XD4cDDhw+xv78Pp9OJixcvigHSPJnuM114zSOwvzrTl68xwsWcllFkHFTU7/OdTkfu1TMP\\nyergRrfbFf6IG1xvTgopAK2I+BrHXFd/GEUODg6wsLCApaUl3L17Fx999JFwcgsLC3C73XC73djf\\n3xcFy4oUR0dHcs5sdnZW1h8pA52hzfZrpcq26/sYKTQuOspKj2KUBNCx72WzckeslAOTIRnGz+Vy\\nACA1UkiCBoNBHB4eYnd3F3Nzc5iZmcHm5qbcWEIZpoCG8UrDxFQgtHBmwhkJbZ0nY0US6mdq5EOI\\nrcXMydEHb1m2gpOpC7tN0s90Oo0LFy5IBUd9aJTP1f3hqXTmj3U6HSH3k8kkbDabKCpmIzOhzuTU\\nzHGlgdLWV/+Nnw+FQmPNI9s+iXDcg8Eg4vF4j5Kha8ZkXm3xzQqK+h8VMREgETgVQL1eR6VSGaud\\nDELMzs7KGUfW0zp//rzkE3366acyZ9VqVZRDKpXC0v/lQbVaLSkrzPIpOsCg16cZTdTrVieFcj3U\\najW5IHWUPo4cZdOKR/9vCv/WarWE/CWBWavVZCIZXfH7/ahWq1KydWlpCdlsFs1mE3t7ez0Ta6Ix\\n/Z1mG8YVcwFrCK7T5XVqQiwWE8TCzaR5JwA957Z0kiX7w8XJZzD6yMsEWFubd39xofQb/0HCowbc\\nALwRxu12i8+vORSd0Dc9PS1WnGfuiGr5s1njSUN8/RrQWxmCr/N3/RqV8jhyGuTMOUmn07h06RLS\\n6bSsBXJ82vARHWnjw42r3R32w2azyXPYR+acjSOPHj2SthGZcmwbjQacTiei0SjS6TSazaa8RsQZ\\niUTkMCzXFKts6PZrAwGc8H5MX9FpENzXfr8fMzMzyOVy2NzclJMG+Xz+dGF/yjBlYLUAGo0GisUi\\nNjc3JfzLE/9HR0dykSRdhWKxCK/Xi/n5eQQCASwvL+O73/3uU+eeTJ/Yirw8rSvHiyw9Ho/co6Zd\\nkVarhVQqhatXrwrZqVGAbi8XsEZg9ME1eUh0QUSiS5DMz88jn89jfX1dLNkkCMntdiObzeLhw4fY\\n29sTBci8ExaR0wlv5Ami0ai0l1n1XMB0+/QBUS5WnUdlolDgaURjokuO4ajSb0yGISe94RwOB159\\n9VW8+eabmJqaEoqhWCyKkuFniGAprP/VarUkbYWuP/O+6D2QIzs6OsLu7u7IfQSAu3fvIplMClf7\\nhS98QZASuU1WZmUde6aX0JA/LsA/AAAgAElEQVQ2m00UCgVJstS3RvO1SqXSc6GBrm3GdciCi/V6\\nXYoxBoNB7O7u4t69e3jvvfeQy+VkHAbJyJnaVn8bhJT4Hn0/GX1wEsK0nDzFfnBwgIcPH6JeryOX\\ny/XUdtGKx+o7h7VlVOGC40Kr1+sIh8Ny2SN9a4bn9Y0pprsD9F5HQ+tiRtnMpMlO57iwfKVS6Skb\\nSoXIfo7b11KphO3tbXg8HklaZPvZV1ps/tObz/xOKk2dGKjRID9nujI6w1lXTtS1ynlanAmbo4o5\\n/qOMEb+fuVlTU1Nypsvr9comZ6BDh/ZpHNl2nUjrdDplXIk4ue51dVHmvI0jdrsd29vbAIDLly/j\\nC1/4An7961+jUCjg4sWLEuksFApotVoSXLLZbD1GjZFuotBCoYBEItFDFRAF6moHPDzPv9H1Z9Tu\\n9u3buH37Nh4/fix8kxV4MGWsxEirv/dTDACEwGbHuOF0PWpNqPEwab1eFzLcDD3q7zZdSqA34XBS\\n0QoJQA9a0BPJs0TcYJpr0ouSzyAfpTe3viSApKTT6UShUJAol4b3+vnj9rPZbErVBbrUTMWg9dbF\\n97WrSrJZu1Qayuu/c03oz2s3QHMvVICcQypnTfiPW71hFORoco6cn3A4jHPnzmF+fh6xWEyCGPrW\\nXc4rUS3bqftGV4YuGpW+doP13hmXQ+p2u9jd3UW1WsXLL7+Ml156SfL8FhYWpH9MTZiampI5YBCJ\\nJXipsJjiQQWjy+6yv1RGOuxPN43j2ul08P777+PBgwdYW1sTwzYKYBg5D8kcDE6M/gINw+kzsx5S\\npVKRmrxMjCQXEQqFkEwm5Q51ZnaarhC/W6MK3cZJ0ZHZP81lHB0dyQSGw2GBwoFAAD6fT/Iq9AIz\\nrbMmjbm5uFgZZeF5JJ/Ph1gsJrfWPnz4EIeHh1IAjKKfOY7s7OxIbXNGWsg1aIvJzUbLriNkerx1\\nrXDtRmsOgr9TufD9OupCBc6NTSW4traGt99+G6+99trYfTURnamETCRXrVaxvLyMP//zP8e1a9fk\\nszSa+ibiQCDQw6kxHYQVMT0eDw4PD7GxsYG//du/xe/8zu/gzTfflBQInbFdq9XkCMioQt6Pbtcr\\nr7yCF154AdlsFk+ePMH29jY2NjakzeSPAoEANjY2epQIOSGiG7pglUpFFA+rR5JOqNfriEQi8Pl8\\niEQiSKfTmJ+fxw9+8AP83d/9HW7fvi2nLzTKPhVC0ghFTyJFL0xTcWlLyWxfnUJfq9V6GhuNRsUt\\nYiRgVNhtvm/cjWoqO7oPdJ04GdwohLG66L6pNPshOtMF5cbk5uCi4ZiVy2U5hmPeVTeuAtbpF/V6\\nHffv3xel+tprr/UcntSIQCMcnY/Dn6mMTCJfKxcd6rcKBHDM+TxNkNPKjyrmfFqtC1M5AcCXvvQl\\nvPzyy0ilUjIG5uc0IgROctaYB+TxeLC/v4+NjQ388pe/xM7OjnB/OvdM100fdjzKSrg32+029vf3\\nsbu7KykKOzs7aLfbiMfj4mkwz6zb7SISiQhHSpAQCoXgcDgk+s05omdDPozeAM89Tk1NIZlMAgB+\\n8Ytf4O7du9jY2JDk1nFloELSRCQwOLRuThr9bHJGzNkhdGNNXp5rY6QAOOaUSMKZuT2D0JAVqTyJ\\nsP3a9dIbixOrFQ/dMdMd0SiOiEjDeh2x6HZP6hk3Gg1x2QqFgoyXudkm6RtwvNnW1takrPDCwoJY\\nO0ZLiJiAEyWj88mAk7wTneCoyXr2m8mD2j3juPD9+pA1N4zP5xspoc5KBvGMer2yrX/4h3+I559/\\nHvF4vCeooOeR/WY/qJAYmAgGg3j06BF+9rOf4fvf/z4KhYKEv/W6Mds4LocEnLi4uVwOjx49wuLi\\nIur1Ora2tuQeuFKpJEqFa5mlc3hzbq1WQygUgtfrFaROxcNAC/tKQ+z3++V223g8jmazibfffhv3\\n798X8t8ENKPISGfZtEUfdZD0GTDmYNDS0y/tdDooFosCg8mfVCoVHB4ePnUujG0y26c7z59PI/SX\\ndSEqfdqbaMEkcs3wttU/3RduBF2gbWZmRo5qMBeLV9DointWrvQowvlst9vIZDKSGc+DjyTP6Z5x\\nThYXF8WN4/XivKCBCBKALGJmXhMZdLtdHB4e9ih5jis/V6lU5PWpqSnEYjE8evRo7PIjWvR6MY9D\\nTE1N4YUXXsDs7CxmZ2fxu7/7uzIX5I0YHWPpHK6BVqslxdZYP6jb7eLv//7v8atf/Qrvvvsutra2\\n4PF4cO7cOXHliIaoyPRRmkn7d3BwgEqlgsXFRYTDYdRqNdkP5XJZKAEGk5h+oM+cETVXKhVEo1G4\\nXC40Gg2hEDqd4yRnoua5uTmZu729PWxsbODf/u3fejwhtk+DmFOT2laIZNBD+V6dZ0LRHAKzc4ma\\nmHOjfWta1n6kpm6XleUbV8zP83eiOJ1jwr/343Ks3AZzDPkccik2m01KtTDq0Wg05OCpPvOnv2Pc\\nPlKoGPL5PD7++GNRukSuRCperxc7OzsSYt7d3ZXCa7lcDpFI5CluSFdyYH95SJrHK6gAOcb6qFA8\\nHsfs7CwePnyInZ2dsfrXT2mzpA3/JRIJXLt2DQsLC7h06RKi0ajwYkT0VLDawAKQqJqu/lCtVvHv\\n//7vWF1dxebmJoBjRTs1NdWT2Mp9YPJt4wjHDIBEpSuVClKpFJaXlyUwZD6f/eHcM/JNl5ulf1gj\\nnDWq9Jk5rlOi593dXWxtbQmIMNM0xqEZRgr7W0FfU1GZLhWtXzAYlIL/LpcLgUCg5zjG0dGRlIsN\\nBAJyRKJer/cULNckpMkL6NdO665RybAcCK0YJ5WKQSMnQnaT2DbD3ZozYVSKi5Ph91AohOnpaUQi\\nEdjtx1ckmfdZTdpHPV/8n22oVqs9boO2Zna7HVtbW+Jy6mJsOh2Bm5S3Uehrm3SfOTZU9Hyu5laI\\nIPQzxu0jnwMcH425du0aXnrpJVy/fh2XL18Wd4RzXa1We0h6RplIDHO9sq9ULplMBt/73vfwwx/+\\nEA8fPuzh0xgK59rR7jrbS4U2juh9sbGxgX/6p3/C/fv38du//dv49re/jXK5jMPDQ0xNTQFAz201\\ngUAApVIJh4eHsmd4sJocLvvHeuL1el0SlpnXlEqlsLCwgH/8x3/EL3/5SwEZ/QzmKEp37JraepHy\\nS81FQCupF5+Gp5yAbrcrSWIk1/gdOivWjObpBd1PaU4q5EeoPPRC5D+9WTgW/AecIA8uNj0OHD8u\\nWA2rmU3r8/mQTqelPaO6y6P2T8+ZFcK0WkhUJlqxMIVD5xTpubbi2Sg0LuTFNOdkIuJxEiNNMj2R\\nSCCZTOLcuXP48pe/jNnZWTlQSmXYaDQkokS0zmL3eozYfpYR8fv92N/fx89//nPcv3+/5yoiPYZU\\n2owqa6+BOUm1Wm3kPrKfwEm0tVqt4smTJ0gkEtjY2EC5XEaxWBT0w1C+0+mUyGo8HpdgSbt9XN3R\\n7XajWCxKLTNebkm0S25zf38fU1NTiEaj+N///V9sbGxYeijjrtuxFJLmbPRCtiKcdbKUhqiMFHEQ\\neJizUCj0nBsCem/90J3TC11v+EGE9zhi8jw6EsLNyMOwVIxa+H6Ta9Lt1efViAh5tszn82F2dlbu\\nRaeCexboT4uVe2ql/NhuHQUzDRPfZ/V5PS/mWbx+7vikrqleB3a7HclkEjdv3sT169fx1ltviUvC\\nOWYkkxeVhkIhCSzohEe2lQaV1TKfPHmCd955B3fv3sXe3p4oOd12uqU6qKONGtswrnBcHA4HKpUK\\n1tfXEQqFsLGxIYiX6C4QCMh5N/at0+lgb29PjngdHBxgamoK2WwWuVwO6+vr2N7elrQHKq9KpYJM\\nJiMKO5vNolKpPLUH9ToYVTGNdXSkH5dkhV7oh9O6kgfhAb+pqSn4/X4pS8K8Hl1GgaSqy+VCtVoV\\njoHIhd89jo86Sj+5WKh0qFyYpBiLxaTMK6th6pA3266RgYnqiBKp4HhTLxfKtWvXsLGxgY2NjZ7M\\n4EkmeZT3m4rEap6tuJl+Ckw/R79XGzKr91ImjawdHR0hnU4jGAzi8uXL+OY3vyk35h4eHkr0jpwO\\nXTDmmDkcx4XJDg4OxKUEIIQwXfVarYa1tTW88847+NGPfiR/M+eHxpm5QDRszEYnQtIewqii56PV\\naiGbzeKXv/wl/uIv/qIni9pmO4mWApD1FovFpEZ8q9USpVIoFCQ7nYmzJhXBPaENGWUSV40ycvkR\\n83crK8iNx4nWbo1m8gldvV6vMPf8HH/W9VbMVH0Noc1BYTsmlX795UItl8uiSIgCGZEyx0cjI1pH\\nrVi4kBhiZx5Iu91GJBLBwcHByMGEZ9Vf/brVYgOezrka9Lx+yqnfs/s9d1Rh9cOZmRlcv34dzz33\\nnCTi8vwYw9YApAQIuUAKFQbbQqTOM15bW1t4/PgxPvvsMznvCDyNGPmaThrV2ed85rhrVit3jTYr\\nlQpyuVxPpjhFp+H4fD4h8TWPySRPs3oB16fJ1ep8w2FrdRTvZaRMbfP3QVaRf9ekNYWalZnPtBBM\\nB/B6vRKF0S4ZP6efb0J9c+AnFasNxPT4TCaDnZ0d8dG73a6UWtWEL62eXsj6kkC+V0elaGG3trZQ\\nKpUkH0j/swognEaGLZpBaGyYwrCymFzgg+D8aZXu8vIyXnrpJQnnp1IpSTvRCJsWnsaC57loDAH0\\nrMNGo9FzLfijR4/wr//6r9je3u5x2a02nUZOdM31+p5EBm1+uo1mOzj+RO+8URbAU0pRnz1jn6x4\\nY1MpWrXHqs39ZKKLIq0snak5OegMlzPPZH9/Xwg8ootKpYJ8Pi8hVGatMjpHF4d/t+qYld86Sb8A\\n9LiWgUBA2rm/v49cLic5F6VSSb7bbj++aYE5VlwUzICl20rkx1IgRI0kITc3NxEMBvGnf/qnODw8\\nxPb2tjzL6rjOaaWfUuJ4jKJ4+rVnmPHie4a9Po4kk0lcu3YNN2/e7HFTeJuGDlZo15/fSeVBftBm\\ns0n4P5VKoVwu4yc/+Qm++93v4uOPP5ZMaKv+8fkvvfQS5ubmBIkx1ykYDEqe12lQvRa9D/vNHcP3\\nrFllGiGO0SC3etKjS8Pk1DfXakWgtSUnl3C50+mIT0rkZB5LIPekjx5oq6pzkjhwesGPAglH6Q+V\\nYrvdlnR8cljt9nH5jQ8//BCPHz+WukV2u12ukWG5DkbL6A4wSkEXjeiRNcT39/fh9/uxsrKCZDIp\\nN3qw/79JsVrAoyy4fgja6nWr5w4yJJMYmP39fUHZRETBYFCyjfUdYuR2dBQUOAlKMCDRbrcRDAYR\\nDAZRr9fxySef4Be/+EUPT2glNMjPP/+81CpnMiFTCoi+h93GYfVsEzGPY0j0fAz6ud+89GvTaWXs\\nekjmYrNqNP9eq9Wwu7uLfD6PTCaDQqHQA1153oU5LaypTSLNfJ4ZobFCaacZFD6DEYV8Po+dnR2p\\nWMhogtvtlqQzKkYSnVzgLO26s7MjipR+OTknugalUkkuptRIkpnA5snw34RYLeZBfE6/se63FiZ5\\nbdD39JNarYb33nsP6+vrkvvG4mUM6RNt8mZdJuUy45p11Mn5NRoNHBwcoFarYWNjAx988IGkQAyi\\nB/ietbU1qZ6qqQwar08//VQqqo4q/ZSRfo0/6/f3+wzfM4pRInLU36MRntV39nuWKSOT2maH9Zea\\ni5iTn81msb6+jt3dXWSzWcnadTgcUpCNp6Y9Hg9KpRLu378vvq2peKygowktJ1VK+jO0sPv7+3j0\\n6BFmZ2dht9uxubmJTCYjpTrospmLQ3NefK5J/llBa5vtOERcqVTw4YcfSgSSCm9SvsHs5yDl1o/P\\nMRf4IJ7gWRgH3dZxlPH6+jq2trYEgQMndZv0ESCil0gkgmg0ilQqJbfl2u125PN5TE1NIZVKYWdn\\nBw8fPsSPf/xjydUhorHadHqem80mPv30U7kUMpvNYn9/H8ViURTf3bt3J7pO25wLK2/FbJPVZ0ed\\n237v6fezKaPMo637m3AEz+RMzuRMJpDTnUI9kzM5kzN5hnKmkM7kTM7kcyNnCulMzuRMPjdyppDO\\n5EzO5HMjZwrpTM7kTD43cqaQzuRMzuRzI2cK6UzO5Ew+N3KmkM7kTM7kcyMDM7UTiYT8bLPZsLS0\\nhKWlJczOzuLGjRtyze76+jpyuRwymQzy+TxyuRyKxWJPan2/s01m+jmPYjidTszNzcmNtmYG7DDh\\nfWmjSCAQsMxY1e3TWeNmCRGewXvttdfw8ssv4+WXX0YoFMLq6qrcWRWLxfCDH/wA3//+97G3t/dU\\nPRn9vGGp/1rGua2C97b3O3eos8zHORc4LDvY6qyVKWZWuPk7s+KHCc+sDTpW1K8tHItOp4M33ngD\\nCwsLACBXSfPsodfrlVrU2WwWjx49QqlUkuu7zIOyw87r8T26TPEwicViA/9unvW0OorV6XTw4osv\\n4ubNm/j617+OmzdvotFoIJvNysUGyWQSbrcbt2/fxp/92Z/h8PAQxWIRfr+/5wwgxw04OTKjD+Jq\\nYWVNKxn5LJvT6cSFCxfw1a9+FVeuXEE0GpUjFjs7O1KKdmNjA+vr6/jhD3/4VOq/1Rk4/XeHw4FI\\nJIL5+XnE43G8+uqrUl70P/7jP7C1tdVTWe9ZJZnrszkA5AAkT3ybi1eXEeEkeL1eXLp0CZcvX4bf\\n70cikUA0GkUikYDf78fR0RFisRiuX7+O1dVV3L9/v6cfw86NPQsxj/fwgCevtGGxrlwuZ1njeZBS\\nsfq937zrtlh9vt+5yVH7aH73MEWoyw/PzMxgcXER3/rWt3Dp0iUUi0Xk83kpjcPDuZ3O8Z19LEHM\\nom5/8zd/02O0hvV7mMEZ1k/9/H5K3ZwHrlmHw4Fbt27hlVdeweLiItxuN/L5vNwUw3OZTqcT8/Pz\\n+Mu//Evcu3cPDx48wJ07d6TmGZWSNtS6BMo4NdGHKiRuVo/Hg3g8jmvXruHq1atSPqPT6Uj9Z7vd\\njtXVVXzyySd4++23n7r8rp+m5vcEg0E5U7S8vIzXX39dvn9tbU1uwOWzrCZiEjHPlA1SpPp7dSkL\\nt9uNdDqNVColN4dEo1Ekk0l0u10Ui0VMTU1hcXER+XxeTpsDJwXjObHmePVbfOMuYnNTOJ1ORKNR\\nubGU1+fwXJU5DoOU0TBFNai9VmfWRjlXNaiP5rOtXrNCv4lEAjdu3MCXv/xlrKysIJ/P4+DgoOfc\\nJQuh6dtTqtUqVldX8dd//ddPfe8wdDSJ9DubZv5dG03+TmXjcDhw/vx5XL58GTMzMz19YckcKqWp\\nqSl84xvfwMzMDGKxGDY3N+VSBCpmXbmDB5PH7eNICMlmsyGVSuHKlStIJpPwer1S4pKlanVlR176\\nSJjbz03gQHIjhsNhXL16FdFoFH6/H+VyGYFAAIFAALOzs9jb20M2m5USJeaCOo3oz+sLGbWwBC1v\\naIhGowiFQvD5fAiHwwiFQshms3C73XKzJ68k3t7eRqVSwfT0NF566SUkk0mZzIODA7kFwrwyW7dt\\n2CIcp4+0Yrw6+eDgAK1WSwrPWblcVuNs5WKaysVsu1YIfB4ND9eRLkw3aR+tvr/fOqQl51pj3XRe\\nb0R3isX0SqUSGo2GVIJYXV3F3bt3e+q7/3+Bevv1leOrDxYTDd+6dQtTU1OIx+M4f/68XGTJ+t6R\\nSERACGuNsx/pdFoqwbJ66u7uLnK5HD777LMer0J7EqMamKEKiXD26tWreP3116WxLPjNG1V5p1oo\\nFMLy8jK++c1v4vDwEKVSCZubmyiXyzKRuoyIvlJoamoKKysr0pF6vS4F0l544QW59pkw+jd1Lth0\\n4YDjgZydncXU1BQuXLiASCSCeDyOVColRbd4R3uxWMTe3p5cPVMqlfDxxx9jc3MT09PTuHDhAm7e\\nvIlKpYJyuYxMJoNsNovt7W2sra2hUCg8VY7CRJPPYkGTL2JhONasslJA/RAO505XYBz2Gf1Zfe9Z\\nJBKRovQHBwfY29s7lbExjR7Fiu8CIEXwWWXy4OAAhUIB2WxWLu10u90olUpotVpS9K3b7eLDDz/E\\n22+/3be/w1zHScRqfM0+JxIJBINBrKysiHH/oz/6IzGm3W5XgAWV0tTUlPCqLLsCHNMS/Nzly5el\\n6OD29jYymQz+5V/+Bfv7+zg4OBBuUwOHUVD9UIXEO7hYWY+3T2gLRqXFgmw2mw1vvfUWSqUSCoUC\\n3nnnHWxubiKbzUpNX1ZO5Hfw+fl8XkpHXLx4EUdHR6hUKojFYlj6P0K92z0uvznoAslJRA+YtszU\\n+FeuXEE6ncbMzIyU/iSRCUAqADYaDdy7dw/VahVf+9rX5H41WhJeglitVtFut3Hu3DnEYjGk02l0\\nu11kMhnkcjlRCqPyEcPE3IS1Wg37+/vSPqvnD1rwAMTqFgoFmQ+rm2AouvY0K2cSpS0tLSEcDsPr\\n9eKDDz5AJpMZW/nq+RuFlzLREq/44fpi6dtKpYJisSi1smKxmCB53mV2cHDQc7OI6bIM6scka9aq\\ngij7x705OzuLlZUVvP766wiHwwgGg0gmk7DZbFIOiH1nf2OxGBwOhxQp5LzWajWUSiWhV2y241LM\\nvFTh1VdfxerqKh4+fIgnT548FRwZZR4HKqROpyPuiN1uR6FQQDQafcoa6tpFTqdT+BNWWqzX60gm\\nk8jlctjZ2RFNDECulHG5XEilUkgkElK6lovTZrPJIKXTaZRKJWQymbEncJCYboeOFLB/i4uLSKVS\\niEQiYiXZl06nA7/fL7Wyt7a2sLa2hqtXr8rd57VaDfV6HTabDcFgsOd+L15Pw0147949AE8rgEn5\\nI/0MCq+D5mK02kCmVTfHye/3I51OC0Ik0akvS+RziH70Fd0+n09qEs3PzyMQCMDpdApa5DPH7aPZ\\n9n4ISV/E4PP5JLpEl4U8it/vF1eeF2SGw2H4/X5Uq1UEg0HE43GplaUJXivF/CzESlnzu8h3Xb16\\nFSsrK7h06ZJceul2u+W2HN4FyIKBVEB033i5JftBuoQXuRKEOBwOrKysiCLMZDIoFotjr9ehCun5\\n55/H+fPnUa/X8f777yMWiyEej/egE4a9tYIickokEvj93/99KfdJIpw3Heia2VwEVEiBQADlclnI\\n1mAwiC9+8YsolUq4d+/eM+OPtOjBoxUn6Ts/P49wOIyNjQ2JMOrbRrhYK5UKPvvsM7z//vt44403\\n5NnkJYiqWMJUXzVz6dIlRCIR/PznP39KCTwLpaQ/r+sq6/rTejz7IST+v7KygjfeeAPtdluKj1Uq\\nFeHQeGtrp9PBhQsXEI1GxXXgBuHY6EsLWdZ3e3tbrqUeVWhQxlEEdrsdCwsLuHHjBl555RVcvHgR\\nPp8P5XIZ0Wi0JzLV7XalyiQR7Ne//nUsLy9jZ2dHrrU2b+4Y1N7TuHF6Xdhsx0GWF154Abdu3cKb\\nb76JUCiEYrGISqXSU1SQrjZd1W63K6WWScUQxdOgUHnV63VxuRm8mp6eRjAYxNWrV3H37l2pkz+O\\njMQhUWHwWhWtWJxOZ8+983qx2+12uVbZvL+ciouDwvezc3TLOIi0nJcuXcLDhw9x//597OzsPFWZ\\ncVLRm1xzDjabDT6fD/F4XFATIy16sHlZAWtuV6tVuZyvXC6jUCgImuLFBzrnh0ShjoAQemsl0I80\\nPo1Ybdx+nId+j9PpRDgclkBHIpFAOp3G0dERPB6PRBu5cOPxuCxor9crfeU4UPk7HA6Ew+GJCuAP\\nU+LmzyxdG4lE8K1vfQtXr16V+wLz+TxKpRLq9Trcbrcob94jSPTA9i4tLeFrX/sa9vf3kc1mce/e\\nPZTLZZTLZZlHK7J7UqNqGgfdJxLWnU5HeFvuWfMOQY2QtYGlYicXaradz6SO4JVe5FR1JF63b5CM\\nFGXT9zuxpCofzoLoDA9S8+oIiY7CaV6Ez+XA6EkjciBJ7PF4hDBfWVlBKpWSBEOrSZ5ErBaG3W6X\\n5EZOFmuA22wnhfrtdrssvv39fSHkSRqXSiWpw83kPX2vHN0/FqTnRrRSFKeRUT4/DIHRmHg8HkSj\\nUaTTaUQikR7FyssY2R9NfJNnIToimmD6BPO3uKbGudbKNHpWP+s+8uLP5eVl/Mmf/AnC4TBsNhsO\\nDg560HkgEBDOJRqNwuPx9MwP3c6vfOUr2NnZwd7eHgqFglxr9ZsSq/VOYn5lZUXuWAN6Lywlmufr\\nepz5s157XA/c27zqnq459yznMBAI9FwXP6oMVEgulwu//vWvcefOHblehjc58EuOjo7gdDqFoKRm\\n1TfLsqO6A/qKaSok3p7JASSZHovFBCZWKhVBSvv7+8jn8z3czKQyiHyLRCI4d+6cRCV0rWYSi4Ty\\n9LETiQQuXryIS5cuwev1Ynt7G3fu3BG/nEiQiplK6DR3yg0Tkx8ykdYoBKxWVkQx3HBMFtS3wvJu\\neCIirodisShRWG1UGILe2NgQHmKcxLpxxqLb7SIYDOLGjRv44he/iIODA+zs7Ah5zQ1MdM7riyqV\\nCiqVilx0ms/nEYlEEAwG8fzzz2NlZQXdbhdvvfUWdnZ28Fd/9VeScKgTbc32jCtm9Ioc7vz8PNLp\\nNKLRaE+tbnJDXGd8vw5KMVoOQF7zer3CKVmtfb1vbDaboOVYLCZjqcd8kAxUSNSmvELa4/Gg2Wz2\\npLiz8eQgaAUJxblBtQ9us9lkQ5oogIqKhCkXMD9Pt6fb7cLj8cDtdkuHNYE4iZiDReXj8XiECzGv\\nx2HbNZHJfurbQniFEj+j38sJ73a7clWUzoUyiWRgcqRk8g2mDHsuP0+U6HA4hE/TlxsQ5mv3QC92\\nXlOk0wz4fI1cSLqPKsMWval0aUgjkQgqlYoYPX0cwu12ixfAtcy5rFarcjcb3XkihWAwiKOjIyQS\\nCTG2o7RxnL6az2Oyq74NmajVbDt5I9Od5V7TV47xuzTa0Xcvcl673a7kMsVisbGOwwBDFJLNZhN+\\n4PLly5idnUWlUsHe3kN9UF4AACAASURBVJ4Qek6nE6FQSDgUTlAqlUI+n8fW1hb29/eF0ScXRZbf\\nbj++Z5ycQyAQAABJQCR5qOFluVzG7u4uQqGQRD82NjaQy+XEAowjgxY8L/RLpVKSncvIT6PRkGgZ\\noTGvCGe0iQo5k8mgXC4jFApJdMZut8Pn82Fubg65XA6dTgdra2vY3t4Wl81UsM/KZTMXYL/UAorJ\\nd5DvYWYvCXpC+mKxKNGpcrkslpOKjMqIY8xgwNHREWw2G2ZmZnDr1i0EAoGxI6pW/JeVAiAyDQQC\\n8Pv9qNfrgnbNm2PYLuDkrBaTWInqGFGmMUomk+h0OpK+kslkZOz4DH3v4KSiDbHf78fFixcRiUSk\\nzeRydU6Q5oq0u0bjat6JSIqCa5q8MSkIzQXW63VcvHhRrp/n2bVTc0idTgehUAgzMzOYmZmRMDxw\\nks3c7XYRj8dRr9flaEelUoHD4cDu7i7W1tawsbEhuUckwvWdbDrjmdqdZ2G8Xq9cYcxw8dTUFNLp\\nNBYXFxGJRDA7O4uf/vSn+O///m+J1Iw7oebP2oWg66g5Lr4vGAxKgijfy/ZzogD0kLlut1uS6nw+\\nH3w+n1hmKnRmBLNNVqTsuIu4HzqahCzn+7Ty1xCeroBplV0uF1wuFxqNRk80TFt4Il8AY4X8dR/7\\n9dv8n+NP5cr2M1mQfdLuDn8HTpAwSV4iET6DaI+KwbwgFTjhdsYVK0TvdDqRSqXg9XqFu9VjbN78\\nzLk3eUrN/5hXPun+cwwASIALgOSWEYWNKgMVUrvdht/vx9zcHKanp+H3+6XjjDhw4dlsx4lWJHaB\\n41tEHz9+jPX1deFNOp2OIAibzSbRi0qlgmAwiE6ng0AgIPlHRCLU0E6nE5FIBEtLS5iamsL58+dx\\n8eJFyd1hqHIcsdrs3CwajhKy02I2m03hRprNpvjgnCzmJXW7Jzk4ACT3iIiQ6Q0Oh0PGgoqfWbJW\\nSmlcGcQLUUxFpxWVleLW+Ub673yNXAw3qia2udjNMdfRS1rlcfrYz8XV/B9/5hk1n88niEBbe65B\\nRlW1IaYiodIieiC6YFKlRibA8YbWicbaTRpHzD7yuQwIVCoVCUJp+kRTDRpdacWk0aCpUPQYUtFS\\n8bEdXNvkE/UzB8lAheTxeLC6uoqNjQ2srq4iGo3iS1/6EpLJJJ48eSInw0nuMUEsmUwiFAphYWEB\\nr7zySs9toNqX5aQyF0InpTF7WJ9xoouTTCbh8XgQiUQQCoUEbV28eBEAsLe3N9qM/p+YkQS2zeVy\\nYXZ2VhSl3++Xs00AhCcgqa5zNLrdLkKhEAqFgiivbreLUqmEVCqFdDotlnl3dxeHh4fI5/NiYebm\\n5uQWX07mafixQf3m861EKyO9SLmZiRhomLTyYUidCpqhckauiC54dTmVPy09o3PjIl6rPph9pKsx\\nNzcHh8MhY0++kLwej9Mwuddms2F3dxcHBwdyxXYgEBAPgK53u90WKkO77zTemjs7DZ9kzqHH40Ei\\nkUC320U+n8f09DQA9KA88p1Wbi3dd41s+X6dq8Qr4YnyeYSG1Ew0GkUsFpNo5KjBmqFkC9EO3TDm\\nhpiLhAc16ZOTVOMEsFH0z9l5Wg+6aNqHN60ZF2ehUMDOzg62trbkWR9++CF2dnYkCjeu6A1ns53k\\nXc3Pz8ttstFoVNwwcmCsi0MOhZ9nBivQm84AQM70MdSvvw84OUpjlVT2LEj7QYjIfL+VheQzuFB5\\nG6y2sub131RQXA8kOzkudI+126fzscbto4mSrH7mqQLOKVG/idK4GTlHOjzOzan5E5L9pmvD91uh\\nzdMYGvN57Avnh4acir+fR6D3qw5G8DW+V+cs8egQ9wj7yTGlhzAqShrqstVqNXG1otGowFmiEuYd\\n2Gw2RCKRHv+fSqderz+VC0HYDkCgLV04Wk5aSyonLuS9vT3cu3cPa2trkuPD3B8O+KSTSqXIDbSw\\nsAC73Y5cLodz58715Fsxw5VlKAi76ZYyyQ+AjEuj0UC9XpcidoyKUDFzMVPx01qNMpmj9JHzYuXa\\nmNyCfo/VoiWCCAQCT5G03Ag6i1+nfdCFp8LTSJiIgu+fNDHSahPon6mQ/H6/pC9oPofojWiQaIGB\\ni3K5LEqIBoo8k8fjEQXAtczvN5XRuH00xYpLarfbQos4nU5BcVzb+uSE9go4v5wvzinbzTlnIIK8\\nr9PpRCaT6UmY5powld8gGQol6OsuLy9jYWFBjgiUy2VMT09jbm4Ot2/fhtPpxOzsbE90gp1kVIoD\\nwP91trKOaHDT6/wUm82GWq2Gd999F++//z4++OADFItFie7p/IpJo2yaWCVXdfHiRVEI8XgclUoF\\nbrcbhUIBh4eHEgFkFMNms6HRaAgPVq/XJSxM15aubjqdRiKREFQXCAQkisiT5lZE87Ny3cyNSmWh\\nFbMVb0XEwCM1dMVoYHgkRCMRPo8bmBtRrwONKPRi15VLJ+ljP+vs9/uxvLyMeDwuhpDcD7ku8oLA\\nSZhbG1TyiHS/t7e3EQgEkEgkREGxDI2JTJ+FsH+MRkcikR7jxnXHwEmhUBDkqbkfzr8m3LVSomLz\\n+Xxwu9097logEBADDUCQExGSRs+nQki09JFIBKlUCsFgEI1GA+VyWULcfr8fuVwOLpcL58+fF0uh\\nIw/aWumIC1/X4UUODN+rN0Gn08GjR4+wubmJnZ0dACdRDg7iJBvVXLAcfPJYhKRUVDqxU4dDtXXR\\nrgOhM/+uNy6VGxUpqzYyIbCfuzRJX60slSZ7GU3ks3VtGxMlcXHyYCXnjs/QLqweW218tPuu28c2\\nMOeMBP84fbT63VQINDpMR2AfqRz5HrrUXGdUNHzdZrOJi0J0q10Y/q3ffI3DsfQTRm61m6vXid/v\\nF3Chw/xaIZlUCUX/XRssonj2getbKyNNqptzYCVDXbbp6WnMzMxgfn4e3W4Xh4eHqFarPWVeCb21\\nYtAKh3/T/rRVh9lprRg0amEUSmeJmmSrVgajihWUbLfbyOfzePvtt5FMJuUkNwBZcNqt0huaE6pT\\nBjThx4XDzF2eirbb7XJ+SiMO3T72d1K3VD/DFCIbtrdarQo/YvWsUCiEaDQq88fNy+oFGnXRamoO\\ngkoPODFUzEfLZrP46KOPsLm5KUc2JumjqXh1v+12u+SD6TVoFRVrNBqiVDQK53qlG8cNyzYAkCxu\\nBnasxn4SxKTXgh5rq8iePsdmIiEA4sJpHk2PpeZHtUunT2f4/X45z0njrSsJjNLHoZnaV69excsv\\nv4zLly+jVCrhwYMHqNVquHnzphT4Pzo6QjAYlEbzrJK2ftqf1AOpCVCNTLRWpRvn8Xjk4KoeIN3e\\nScT8HLV9Pp/Hz372M6kGmclkJJRZr9fFd9ZZy9q62O3HJVS4EMibEHXm83lkMhksLS0JOZrP55HP\\n58WtMeW0rtogZMUEUIa3Dw8PBeFpI0FUMD09jdnZWTlMS2vIqAtwopTpEnExUxFx4XOOia7W19fx\\nn//5n6jVapb1vccRKw6Jm4lZ2IeHh6JMNFfCdahzwjgv5IkACElP90yH12OxmEQMrebjWfVLB400\\nhwRAIps6YKL3nc5C11npAHpcPH4XvQaWaOl0OhIhZwCIxeGoiEfp70CF5HQ6pTBaMBhEqVTC3t6e\\nRJd4kJTkNnACybWV0KSlJjF1A6mhtaLSrpsebH0W5zchevMBkNyqX/3qV/D5fJienkY4HEYikehB\\ndFrZ8mdtOblAGYHjOZ/p6WkJIJA8NYnPZyHDnsOKgFzMOqJkKn4uQCbgUeFQeZkJoVwj2uXVlp3u\\nK59RLpcFKfJZ4/ZRc2CmC6OjmKxmGovFeiK7JKTNZzOfR0eGiSap5DhmNptNuCf97FHmYxyhMaeB\\n1xSG/m6dpKmNgUZ5psdA5KvXAt0yAGJciBo51zwQz/pLuh39ZOjREVYydDqdODg4QLPZRDgcxuXL\\nl/Ho0SPcv39frEKpVJK6KzyHxkHQiIeEtcnd6GgEO8jFz0Vw/fp1NBoNbG5u9oRQn9UkWyEILuzd\\n3V3R9gsLC0gkEj0cEieZiY9EUsxeN6OJdJFogZnxTYLcVHYcq0m5MrOPWvESmdBlY90m8jhMHNSI\\nlxnpmnciWtR8CvvRaDSECDbdzlarhVgsJqh7f39fnjeOmG6a7rP+eWFhAQsLCwgGg8hms9ja2kI6\\nnZa54JybtIAuXKajwPoMJ9cjFTsVqw62DOK6JhFt9BkJ1+1mwqZGgeyPdrlYupYGUitfn88n763X\\n6+IaTk1NSWE65mw1m82eiJy5jvvJUJeNos8e0eITltOy8ayXXrha2VBM/9X8PmpjkotagaVSKRk0\\nqw5a8UHjiNVGNxe2rhLJ13Q6AzepTvpjv/k5vXk5+SyGpTNpR23jafpH8pbcGHkwRhBp8XQmczgc\\n7uFRuHA1D2i6aiz6xe8EehE13cF8Pt+T0zJOf624I/19/JmXNNDo8QA520RrrtcykavOo+Oc8x+L\\n7bndblm/5AO1khtlXsbpM0UHTzS/RNGoUStOrgENAvhspmBo4RhxvXKtd7snFS/Y/3H6NjTKxqxb\\n5hJxo5HzsNmO84+IBjiRmty1apCGbiZpRkJUcwecfJ6nY0TK3Lin4ZGGDZxe3PqIgHZF6XdTaTNE\\nyt81IWhGRDiRZrvMn0+Dkqw+pxEbo2ZaITG8y0U5NTWF6elpycSloqHboMO+5GrYZj6PuUamFWUm\\nNyH+pH206i+VgeZAOPZsJ9cc50kHSqhM9QY2I275fB52u10y1PXnTML7tHwgRbu+3Hs6WABA3Eut\\n/Nkmck46ek7hnJvjwM9rt579YvY+AFlP2jsaJAMVEoncO3fuyDGNbrcrxzVoDZgAyAL9VlZJKw5z\\nkXDg+DO5hlKpJJpeKyerCNtpRbfZfKbJQWg/nUXXWFuZRwJ0qJTRuHq9LuH8UCiEcrkMt9uNer0u\\n/7R11t9vtvFZ9Jd98Xq9mJ+fx/z8PFKplGTnR6NRiS69+OKLstBSqRSSySTOnTsnY8F26TnmUQkq\\nIU1kUyHR9el0OrKRMpkMDg8Pn8pwn1TMOeV86fK5OvRPVE6FQ8PMRMhSqSQHpYkQeDTk4cOHWFxc\\n7Dl0rV0YPf66PZP2i8/SCsLj8Yjbrw0cjz3ppFO6eDQMGvmbe5NcmOZ0uU51WoRW8jy8PGofh5La\\n+Xwed+/exZUrVzA3Nye+pBmF0IQW3RQ2lo3UUNVUVhppsDMaDpKl1+fhzOc8q42qf9aL2cyLIp/U\\n6XQEKRA16SQ63WYuSt6FphNDddkHbWWelVhtArqYvLwgGAzKFVO08l6vFzdu3ABwPP48p0QlrJUJ\\ncMKzaJSho2v6TBXXkM6NYXVNtm8chWTFuZm/m6iWG8mcI52pTN7E6h5CHUVi21nviTwM10S/Nk+y\\ndk3XVJ9wAE7cKvafaScUnXHN8edpAqtkVa3cOH66QoImurWnNE7G/UCFFAgEUCqVsLa2hvPnz8tB\\nOQBYX19HsViUhCx+OQdA+7LsvCYptRbWi5adIQmokxH5XL3AnyVq0M8xlZGJWthvLmITzhIiEyEx\\nB8eE/8BJCJq3u+jv5vufFZnNvnFjUiHE43H4/X5RUNq1stlsmJ+fR7vdllrhTHzUCInGiEcUgJMy\\nNfr8E2E914fm3Jjzw4seNOoap4+D+gxAqi1ot5QbmJSB7puV28doGs+vseStGeLuZ0DN9p1GNE1i\\nFfZnu00kwz2rUwFMTk9zmlTSnEtd5I0KSeffMeA16tod6rJxkOkbx2Ix5PN5fO9738PKygouXLgg\\np5pZiEtHnNgpihlG1eS2ds/s9pPCbYTGjUajJ+NUP2fQ76PKsEHj39m2VCqFYrEop/1pWchRMCze\\n7XaFb7HZbKhWq9jb25NyryxTwlweEz1aKaPTWFX2hVbT7XZLQXiWnAUgC5wF99rtdk9WuT4qoLkI\\nHuUBIC4s+8b3mgm1vA2Zd/npM4nPwtjwGVTCdDsDgQDC4TAqlYpccc7Ip96UHA/9PBL2RE0aXfG9\\n+moos4zKs0D3mptjDSIGoIiAAPREDmlMWq2Tovx6nQEnXA/7zznja/pKpVwuh263i2QyKRVPqeyi\\n0agYWh0g6CcDFRKT3FjTmh3odrtykwHDuprc07BNd5D+p9korcV16FxvQvrofIb+HEVzPacVK95B\\nKwaGyfV9a7rt2v0EIDdVsO/MZqUyIsSnNbIaH/48qVU1nwOc1DNiqF9bQO3OMKtcR8k0qtVuDpEH\\n+80EOiaHmtEqtoU3EpPQ5vdM0sd+/CXXB5UIFYvmXRg00WiVm5Jjxte4xqnIdFKoz+cTRaAPqbJ9\\nzwL1Ulwul1w0wHYxqGK32yUARMROEpuohv3RKJJ7EDhJ3TH3WLfbFV50ZWVFXqcS1/tBz08/GaiQ\\nstksVlZWJIrGZDnmHJlHRsglaLdME7zahzR9Su0D6+Q5DhQ7z8Ew0wb0Ap50gvstEKvXqEw0Ec0+\\ncBL0c7XrSU5Bw2S92c1IhNlP7eaOK/rz+ncd7tXcgo4+acKXa8FM4tQlKWw2mxT4p0EzN6TmDPP5\\nvPAvVm0dtX/DXtOoR0cRSebrBEHg5HiUpg7McDY/Q1edKFm7a1Y0wGmUkv4ckxBZ+51tpNHQkUEm\\nKmqOSStNvSZ0IrLmhPn9nc7J9V/aIBMZ68teR5GBCmlrawszMzPSYZaZaDQacuf5zs6O3IVO/1GX\\nNujH8xD26UgKNwG1erFYlCzaeDwuZ6dYm6afO3MasVJGVu+h9cnn8wCOrSGTQh0Oh1hbZmMz4gBA\\nNqkuWUG32IwsWVn6STaq2T/9HU6nE1NTUygUCqjX63IEhGiBV9rovBp+jhuUc22323v+zj7Y7cfn\\n9KgMNGdE41QsFlEsFgVVTKJwh/UZOHHb6HpTAX/88ccSGdQoj+tSK3Ge7qeLynlpNpvI5/PY2dlB\\nNBqVpEGN7HVb+PNp5lL/znHM5/NSDaPRaGB3dxetVksuvbTZbOKq8T00Mpor6nZP6onraCQTfR0O\\nBw4PD5/irpgCFIvFkEgkBDX2O89KGXoNEgecm5C1h5jEpyvraVLNdKs0ZNYaVv+uLZK22tzMNpsN\\ne3t7EmrXE/GseIZ+fI3+O8VMVeCE6PwiZjwDEAvKSCVd0HA4LCVJ9Obp16fTKF+rz2q3WvMJFLo0\\nGrnwNeaZcD1wLLhwAUhpGO3Kcn1ws9OqMhdm0n4O+wzd7dnZWaRSKWkfXWiS3OatODojX5diJrLN\\nZrPI5XJYWFgAADx+/FjC3QwAmVGm07jfpjCKxxLILKFCN3l3d1c4TR3a16Q116bmiqhoCTB4TEgT\\n3dotZC6drhFGRaZv8u0nQ4+OMMuUymZ3dxdbW1soFouiKJhLog/a8fNstLb8etNrdERh5zVZ6vf7\\n0el0sLu7K1UWn7XodllBa8276P7pJFANXYkWdPqCWaKCCokRy0HtepZKlz/zf7bPjAgCJwrJZjuu\\nScVNy6MG5NI4j0RVAHoWpnbngZPEWyqCcrksZ8ImNTLD0DINBfOuiFq50fT13gw6mKcMGLxgOZ6j\\noyMUCgXs7+/j3Llz2NjYwPr6unz28ePH4g6abX1WwrFksTiS6KzJdXBwgFgsJmsOOFGCXLPakOo2\\nmuuPgIP7mhUTSN6b7+H88iDzIBmqkJ48eYL19XWBnmtra8hkMlILh5EwQnGSz3wNQI9m1NyPCaW1\\nn8pO8G8OhwO1Wg0HBwcolUoDXZdnMdH9XEGN6Ah1OZlU3j6fT25z1Vf76BtpDw8PZbOSl9PhZit+\\nTP8+KeegP68NRTQalXNIXDQk2YETgtPj8QgZrHkv3U+NgIgau92uFKDjONLt5fu2trawtbX11OYd\\nZz77IVzdd7vdju9973u4dOkSXn/9ddy+fRsPHjyQYm3tdhuxWEw8BM4xuRDW/6ILyu/weDy4fPky\\nNjc38eDBA3zyySdwOp2S5DmoT88i54zP5JwxkLC/v4933nkHL7/8MlKpVE8lCa1kOK+6xI8GDTSs\\nXLM0HlTcpCC63W5PYCIcDiOdTgOAHAnqJ0PPshGKr66uIp1O48mTJyiXy4hGoxKCJ5Fnks/8Wbs2\\nZpRNIyb+bm4c8hmEodS+VnyP6W6MI1aoSP8NOIlK0WqSHyJpy0ik5iBIDPIfALkAkeFXnVxqfuez\\nhPZWCFWfSwROXBiiVLosAAQ9cAMy6srP6wVs9kErK6Iwrge6uiRQdch6kj6aa0GPW6vVwmeffYZa\\nrQafz4fV1VU8fvwYqVRKOBZ+v0b8brdb5u3o6Khn7hlh42kGVv3UuXlmOzg+k67ZfgiebhIDUdVq\\nFZlMBvV6/alKBLod3L9cx2bmtZ4rzRfxZ+5PAE+5Z7w4YZiMVMLW6XTi3r172Nragt1ul9tHfD4f\\n8vk8gsGgdFQfsCQhphWWlSXgQjUXDy0TowKsjaNP+ZvPmQQ9DOONNLlKl4RKUk8A4TsAKfDP93Ac\\nWVTeZjtJJNvd3ZWaUlaTZqKlSZUSEQkXla65rF2XcDgM4ESROhzHt8h2Ov+Pve/6jfS8zn9mhtN7\\n4bAN63ZpV2VtxWqJBRs2JAN2gBQEcIAAQW5ykdzkHwhymftc+T65SZAEMBIECWwYcoMlW9J6N9Ku\\nti/rkNMrOe37XfD3HJ5595vhFCrQBQ+wWHI4881bz3lO70n7bCLYeDwubmStanOsugC+VmF56HUQ\\n5iuvvCJ7nc1mn5v7ODRIsPBvuVwOBwcH+NWvfgXg+Jx/5zvfwfz8PBYXF9HpdCSeiqU0GDgaj8fh\\ncDhQqVSkhhRRJuelDf4mcyaZKGWa+XEfier490qlgkKhgHK5DJfLhYWFBfj9fom74nfT/KLRLffT\\nREf6+x0OhzTHJBKkTZF77nK5pKTzVDYkPfl2uy0epF6vh7t376JYLOLp06f40z/9UznQlKgcMJkR\\n0F/g3+TOpoGbh5jGYnJoTvSs1Bh+1u41E00AJ3lPOtOdEJZzp7pKaMw8Nobta48bAAk6pW3mNPvJ\\npHOl4ZNryZihZDKJ2dlZqRceDocBQIrJ+Xw+xOPxPuMvmZuJ6nTsjmVZYkTVVQN4gHnRefBLpRIO\\nDg7EiQJMjwjt1kvvqb5gFD681JZ1ElGv45LIUBkkquewtbUl6qud2j1o7yZVwc1x6/WnR5SIj44T\\nrYrzLvE8aGZiN0aeHdqNnU4nCoUCWq0WotGonAueNd4LXV11GJ3KkDg5QlSH4zjS+JNPPhGj5ve/\\n/334fD7posGJMkCSiInwVx8GzaxoUNN6LaE8F41RqKb78CwPsPkcfcHoEmYMDpECXeW8wNSjaRdj\\n8qxlWRKnQ49EsViUFlJ2NXPsVMdJSMNyh8MhwXSzs7OCdILBoLQHZ6tw2gqBkwBIChraBLVtkBeZ\\nl4Tr5/V6pVaWrgzBy80OMrVabaB9cBIahFCAE5umNjnQSM950JtMcrlcqNVqKJfLEvQJHKNkdpPR\\nKMP8blMVn2Se5ud0DJCeszYu1+t1Ce2gUCHD0p5yzUz1WOk1JHOjYC4Wi6hWq1heXhZnB9ePai3P\\n92nzPJUhmTYATvLo6AjhcFhC/skkyLhYAYAu1Ha7LRKV6QrAidsROHEBE+brfvH0wpTLZVHlTKY0\\nDUoa9HnTruTz+ZBIJOBwOCTrm0yJQY9kLPV6HdFoFMFgEDs7O3KwuZH1el061nJt7OwNdqqp3eun\\nkfm5druNfD6PX/3qV/j+978vsSdECDr6nCoo150HjXNm/I4OErWsk0hvXdZCow46QYiQ6LLWaz8J\\nUxqkqtmtCaU5JTvNApp50wtpdic5PDwUgUsDtm5wqtf7rMnULHSqDyPGuZbRaFQ81WQ8WkXTc+B7\\nqLJrYEF0GI/HJS7w3//933H37l0sLCxgeXlZusTQ20dVdmdn59Smn2P1C7JTtbih5LK6jAHfww0m\\n9ybk00ZNLhJwUpBcpyUQYdnVmx7087g06MBq9Y2XkyqHWXdGh8kDJzVwuEZksoxI7na70sp5UN1l\\nPbdJ7WT8vPl7q9VCsViUAE9tbGbGvT6shPT8fjIwXlD+zJ51wPH+6Z91kBy/S3t1yOimYUbm78PU\\nJJ5hXk7Wx6aXSp9r7UnWSJ1CiXZEO9PEaeOchqjy0ybHHEki3cPDQxwcHODp06dihA+FQjJHCn49\\nXmoApsMDQF/nHDamePLkCeLxuKwH7wy7TJ+JyqbJHCwPrDbm8pDry6NtJ1QbtPTRXhVeCD0pMrha\\nrSY94YZt5lnbHfT/Ho9H1BoiGs6Zh8HhOOlRRVjMQ0oVplarSTQ3kzzptRyEDMzXJjHec278Xc/N\\n4Thp50TpqFUC7gMPLoWIjvzl53SaAZ/DQ8/vozTWDEgf2GkM2iZDGIU5NBoN1Ot1VKtVaZFNVade\\nr0u7eNpTK5UKotGo2AlpKzSDggfN5yxUUq4r49ro3WXPPJbQLRQK+O1vf4u9vT1hXIFAQJgS154C\\ng2hIMyTdxJUJ1/Pz89KY4tatW4jFYshkMn2hA6FQCA7HSSHHYTR+z2m1EAxke//993Hp0iW8/PLL\\nAn/JdQH0/awTZCkNAfQdZP6zLEukFeMaqtVqn0vxi4LDg5Ag7WjJZBKXL18WieP1eqXDyOzsrASZ\\nRSIRaQ+zsbGB2dlZzM/PI5lMIpvNilpUr9dRLBZRLpf70ILd/KYx3g9SY+wYnWYO2sPCyw48Hz/D\\ncWv7oH7dZBLcZxMZnsVlPW2dNGOmpE+n04hEIkin0xJUWKvVxJPscDiQTqf7zjiLF4bDYRSLxT4b\\n2GlzmnQvTftTrVbD/v4+3G43VldXhcFms1ns7u5KVYrNzU0Zt9PpFLVNj42qH4UT36977VEVY9hE\\npVLB/fv38eKLL8qaMMD16dOn2NzclBSTYTSVykYY++GHH8LlcuHNN9/si9MhxNeZwvoAawmt1SIe\\neNoxNKflc0yD7xfFmEwiVA+Hw1hcXESpVJKOoVQlU6mUdPOMRqOCLLLZLMLhsEQJl8tl6VDbaDTE\\nsDuMpkV/p6kzGMS47AAAIABJREFUdkxjmA1r0M+Dnk+iQBq0b2fBjEZVbfldVDPn5uYQj8f7zi6j\\nkbvdLpaXl8WOFgqFkEgk0Ol0JEGXOV5nPSe7Z3Ge5XJZ0M/S0pLczWKxKN5TnZPH+6WDjCkgzH00\\nXweO94/NWgGI7ZdojSVdOLb79+9je3t7eqP2aQvidDrx9OnTPn3a5XLJhtIDxdBxoiAaBgnz6SJ0\\nOBzCWcmQmA+2u7v7nDvVbnMmMfaO8hmO7d69e+h0Onj69Cmq1Sp8Ph/C4bDYGmgTarfbEuHOGkiE\\n/U+ePMG9e/eQzWbx9OlT8R6y59ygyzTo4o86T7vPacnITiKdTkcaWJrv0z+bgoTfY6cyDXqGOS89\\n3kn30u7ZdgxUj4v7WqlUJM2CQpWIo9lsSlwPk6dZ84iIhAnCdmt91sTnHx0dSUsy3cG2Wq0in88/\\nZ9vUa2D3TNNcoV/n/zrBnah4a2sLv/jFLxCJRMSs88knn+DRo0d9cU+DyGENeQet5SYasZsAmQYP\\nNfVFnccyNzcnmb+UIIzhoG2CtYUsyxIPHjl5u93GBx98YHvgzXHmcrmhE9fElIZBz5LFMv5uWRYy\\nmQwikQgSiQQqlQoODw/FpV8ulyXjO5VKSf3sRqOBR48eYXt7u6+7hjbkjnqQaccbhRhFbDcf4Dia\\nlupKqVTCs2fPnkO1JtPRP9vZqAb9rGnYe/hcu1wwOzJdy8OQknnx6Hjxer24evUqkskkgsEg0uk0\\nAoEAnjx5IhdcI3aipVarhVu3btnOzfxOu/WieWIUSiQSfc/WatXs7CxisZiUjWG8oN28B63HuOvH\\nO5xMJgVhVqtVVKtV7OzsSOyZw3GcejOIhjKkczqnczqn/0s6m4Iz53RO53ROZ0DnDOmczumcvjR0\\nzpDO6ZzO6UtD5wzpnM7pnL40dM6QzumczulLQ+cM6ZzO6Zy+NHTOkM7pnM7pS0PnDOmczumcvjQ0\\nNHVEVzUcREw6ZbQ2ozffeOMNbGxsSEIpw+xzuZyU1WSnikQigUgkIpnCbrcb9Xod//zP/4ytrS3p\\nKcVM+EGh7ZrYemgUYgSzXcSw+R1m1CoLwi8uLuLP/uzPsL6+jkePHiGfzyMcDiORSMDlcuHhw4f4\\n4Q9/iMePH+Po6Egy3fn80/K67CJmLcs6tWi6JjO6l8/Q89b1cXQxPbfbLRHnf/3Xf42rV6/i4OAA\\n//mf/4mPPvoIjUYD4XAY165dw5tvvol0Oo2nT5/iv/7rv3D//v2+tB8+08yPsltzvsbyKKcRGyWO\\n8txhEeV2OX76WYOirTXp95yWBjNONDowOCL9NOJYGDX96quvYnZ2FoeHh3jw4AHy+TwCgYDUv2Lb\\np0gkgkgkgsePH2N3d/e59tuDItLtaFhE+qlF/vVD9WHl3/m/2+1GJpPB6uoq1tbWkEgkpNogcFxj\\nem5uDisrK9je3ka5XEav10MwGEQqlUIsFpM+4CwB8Ud/9EcoFAoolUrY3t7G4eEhfvSjH8nCnlWQ\\nuZ7ToFSRQa/1ej3E43Fcu3YN7XYbuVwO7XYbwWAQiURCqugFg0FUq1VcvXoVT548wccff9xXjsP8\\njmFzm3TeZnqM2QGGB2t2dhahUAjz8/NSyJ+lOJiXl8vlEAwG8cd//Md49913Ua1WJcmYFSAB4OrV\\nq8hkMmg2mwiHw9LA8LPPPkOtVkOxWHyOMZnzHCehWNfR0mSXBDxsrwflfQ1KILYju/w9O5okYdqu\\nyYV+3iAGSWKl0Pfeew+vvPKK5IoWCgXEYjFJi2EiebVaxUcffST1s8y52QntSe7oUIakD6z+IvMQ\\ns6bN8vIyXnnlFdy8eVMyfZnpzoPOz7ClDLPfY7GYFAJngfd33nkH5XIZpVJJsoV/8pOfiPTWC2Iu\\nyjg0CuMZ9Dmd+a+r4kUiEXg8HmklPDMzgxs3bmBhYQG9Xg8fffTR0KRUk0yUNEwCjTpP/QzWLXI4\\njovVz87O4sUXX5T2zCy+z5Iz2WwWFy9exI0bN6QSIXCMGO/cuSNIcGVlRb7jwoUL0rOsUCgAwHPV\\nDcwzNi6ZzMh8zrA1M9fXpGHIfBQhMiwXdFwalA846Hn6jHU6HYRCIWQyGfze7/0eXnrpJXS7XZTL\\nZdRqNcTjcSnZy1zUQqEgJUZYWXTY2MbJx9R0KkIa9ju/3OfzYX5+HteuXUM0GpWsaNbXbTQaqFar\\nuH//PiqVCnZ2dqR9Uq1Wk3rUlnWcUU1U1Gw2JQM+FovBsixcuXIFuVzuueTZSZnRsDmbUtUOIrNe\\n8OrqKg4ODvDo0SPp7Ml6w263Gzs7O3j27JmU541Go1K69TR1bZjKNsn8zGewk8zy8jIWFxel0BfR\\nrS7p6na7ZX/q9TqazSai0aiUf61Wq3jw4AFKpRK63W5fx9unT59Ku/CLFy9ibm6ur+Ig6++Yaz8p\\n2e3bOIxhVMY/iEkNEpbD0OAXRXoddDIwk7NZNJEVM1kVk+eb+2lZlpShHkR2Ku+oNFJNbX04zGLu\\nlmXh2rVruHLlCt566y3J8CVyYAXITqeDg4MDHBwcSE1ecmHW5ObBZW0XtgsCIMWg3nnnHXzyySco\\nlUpSpXJaOg1y2y2stq8kk0lcuXIF+XweOzs7SKVSSCaTgihcLhc2NzdRKBSkdOjS0hJ2d3dRLpcH\\n9mMjnRUcNr+D/3w+H65evYobN27g+vXr0jWD+8gicvzOcrmMer2Ora0t3Lt3D5FIBHNzcwAgnUNY\\nS4gtlFnWg+NfX1+HZR23FHry5Ak2Nzext7cndkU910nmNs7ro/xtkHCiGcPUJoDRzAqTqKXjknlf\\nWe6HwpAVCyzruAHF/v6+lOuNxWKCiKjFsDLmuMJRazaDaOSuI+Yl4MQsy8KLL76IjY0NsYewIDqJ\\nRe9DoRBWVlbELkHDGhES+14RVqZSKSlrQrWPjMnj8Uj1xmmli91hG0WS8jIT7UWjUSwuLiKRSEjJ\\nCqoz8Xgc7XYbkUhENl4Xy7I70Kftx6RQ35yn1+vF6uqqtIau1+tSdpeSUDe0ZPlTSlS2VdfIkGVj\\nzDbUrBlVrVYBHNsy5ubmYFkWisViX4urSVHSNCqt+d3mmdf/gJNKp4Oed9pr0zIiO2apn61NGxrV\\ns0YZ1XG2NXK73dja2sLh4SEikUifHZglTQbVO9drNykKHAsh8cvMTVlaWsL8/HxfLWVzw9gDPpFI\\nSN934KSLBXDcFbXb7UrzvXg8LvCRnDyZTEqjRvOwTHqA7fTvQfCb38ONZolajj8ajYokYf1s9roP\\nh8N9FfUoiezGzO8YNo5JyPw8x5fJZKSHGgUN/07hoy8p95pMi+10LOuks6n+nCkdqQ6wBhPVu2az\\n+dz3TzNHO8Zkd2ZGvUB0RBDh93o96ZSiGbgdQxsm4Cad57Dnm9VZ9edmZmZEuOvibR6PR+6bbv1O\\nh4XuyGySaTua5D6OXDHS3DzdByqdTiOVSom09fv9qFarsjlENIlEQgq0ESIS2tN7UygUcO3aNYTD\\nYezv74vUZC+pubk5xGIxeL1e1Gq15y7uIC/LKGRnYObczfc4HMcV8y5fvixhDfV6XTyHPp8PzWYT\\njUYDXq8XiUQC9XodBwcHAIDl5WXkcjkx4Ot11us96LASVU07TzKFTCYjjEUfUM2AzS4hZq8+Hl7N\\nVMiA2e+LgoSHvt1uIxwOi1ey1WpJ9Uxg8v0ctHaDLvAgLUB/xu12IxwOI5lM4r333sNLL72Eo6Mj\\nfPjhh7h79y4ePHggFU6Bkw4d+t7w7gCQapSsjDoNnYbG9FxYyC2ZTAqqJVWrVUQiETFFAMDBwYE4\\nblZXV5HJZFAoFJ6zfw5C7uPczbFUNs39yIwI+2i8ZMU9tu71+/2IRqPS+ofuYS4M4X2hUECxWEQ+\\nn8fCwgK8Xq90eNCTYP8plhXlc/S4xiWTk5+mFvF1FjOPRCLSY6xer6Pb7Qoa4ppRpaO6QgYbjUaH\\ndlEZdtAmVdlIFAas8MmxU9jwYpERkRkBJw0IKQxo9Ka9SHdGBdD3LIfDITWeWVWU8VyHh4col8ti\\nS5pGJTc/O2xfTS0A6G8iyblYliWX88UXXxSP1QsvvIDt7W1sbW1he3sb2WxWQluIOrnmbMu9traG\\n2dlZPHnyBHfu3Jl4foPQ1bB5cm56bLyPqVRKTClaCFmWJdoLML4TYJS7OXIrbRP+8lAFAgFEIhGp\\nIU3PGkMBvF4v4vE4ut0u9vf3US6XkU6nRe9ma+V6vY7d3V0phB+Px0VKcqFoh9INJDW8n5QG6cKD\\nJCVfczgcAnv9fr+UpyXq4PvZJ4uF/wntqcqx0Lqp7o463kmJzJ2Sv91u4/DwUOqck2HxUFJV084E\\nzpGv6TbN3B+zOSCRNLu+0siaTCbFiD4NDWJiw4SM3R7zd622WtaxIf7mzZu4cuUKXC4XNjY20O12\\nUavV8Itf/AK3bt3Chx9+KMyXJZnZjDIYDCIej+PNN9/E1atX8f777+PevXtTzXVUlVMLbiJd/brX\\n6xWGAwD5fB4ApNMPHTZ26vBpqH4UGokh6UnoL0ulUshkMqJbsulhu92WeJVKpYJgMIhgMChthjOZ\\nDBYWFtBsNrG3t4d8Pi8G4WaziVu3buHjjz/GW2+9JQiI6kGpVILL5cLc3BwKhYIc3lF09VHnehrM\\np7rW7XalaWSlUsHW1hY2Nzdx7do1HB0dYW9vD8FgEM1mE0dHR8KA3G53HwrQl34Q2Un3aeYJQIzT\\nVC8BiMPBRDscq+4fr9tWMa7s8PAQzWZT1DzdIJTv7XQ6ElrAy+pwHMekVSoVsVFMwnhNZsLXTrO9\\n2SEku7X2er0SrsBzvre3J7bEb33rW3jnnXdwdHSEo6MjWJaFSqUiDJtMzbIs+P1+BINB3LlzR1T5\\ns6JhyJJ91fb29iS+iI0K6Ixg92IA0jqewpa2Ubt7Yrd+46DckRmSfih1Qb/fj1QqJZ4TDpYMiB6b\\ncrkMy7KkmwjTCNrtNrLZrBjNqNpom0Sr1eoztNIFHQgE+lQnc1HGIa3jmraTQe/lz7o9NNeF0iQY\\nDErxdY47Go32PY+Xe5D9Su+B/t/8eRzi3OiWZ/seMkW/39/XkVevsykB9dj5M9Vp3QmVjIyeN4aE\\nUK3hsxjPpJ83Dk2j4uln6LmbYwiHw8JMyXh4/mlD1cGD9Ch2u10xfusuPVzrSWhUdGSui2VZcrcs\\n68QRobsKa+ZJOy7NKHqd7GgSZgScwpDMg6YvhcvlQiqVwsbGBmq1mrRLjsfjmJubw1e+8hUUCgXk\\ncjlsbm4Kgmq1Wvjkk08kbycQCGBhYaGv42ckEhHDMJ9B1aJQKCAUCmFtbQ3/+7//Kw0kzYsxDtkd\\nfjspaRIlPo2SiUQC6+vr8Pl88Hg8uHjxIqrVKiqViiAqIgOqLGTEgxjOtHYUOyLCCwaDWFpaQiqV\\nQjabFVtgOByWUAvg+MIw30+3UCbjJRPmPHRrZoY2uFwuUendbjfS6TTcbjfu3Lkj68juNFT1BrnT\\nR5kfML73yk7i69fcbjcikQjm5+fh9/uRz+el0yvth4VCQZAPLzfDWSzLErTcbDaFGbNbzzRzPY00\\nc+A+UohSNQdO2oczQLLRaKDT6cDpdKJYLGJ/f1/yCu1UNrvvHfa7SSNFag/aYNoeaFdotVqSwLmx\\nsYFIJAKn04lKpSIwnVZ9GsDj8bgk8WrbAwAJ0NPIifEutEOZlvtJ9Vc7bm4iAf2a3lwijfX1dWGW\\nfr9fJCc/y8vIVsYc9zCjpN6HQX+flKiy0ebHPaE7GziJKSPipR2E6IHufgCCCmjvI5MBjtHG0dGR\\neNyoztH9T+O21+sVRjSJajruBbD7u52tEDgO66Cw1N4p3WmZqjnny7nz8hNJhUIhaRRKBHXWRLXZ\\nbq7cOy08SGxNxpAGEt/DMz2MGdnZk0a5myMhpEEPYw82DrLRaAjSyWQyCAaDIjl48Lxer8BcIqpI\\nJCIogt87MzMjn9MHXLf/1X3iR53woHmanzeZ0SDbEg2XXq8XS0tLiMVikpRKNQg46ZulVSU7G8Uo\\ndiT9/kmIzCQQCCCZTCIUCgmj6Xa7Eoui955MiHNhp1L9Hqp1nCu9sMCJp06rZd1uVw43hQ4DRvWc\\nx6HTVN9h79ffp/ec86bHOBaLweFwCHLge+mh7HQ6Yp7gxdbpGlx7y7JQKpXG6q03zjxMpqDPsHb1\\na+M2AEFrZKjAiUeWTNfscDvMzGCu7TAay6it4w5oK4pGo3IhGeX5+PFjvPzyywIHU6kUjo6OROVi\\nXAND0JvNpnSATSaTctFv376NcDiMWCyGaDQqE2ecBHX1SRmR3RzNZ+lFt/vbgwcPsLa2JoGcTKuw\\nLEtCAmgwbDabODg4QK/Xw8LCgsRymAFsWoUbJIWmnbPDcdyj/mtf+xpu3ryJQCCAUqmEQqEgkbsA\\nROAwzYd773K5RO02VU56cAD0IcF8Pi+fpyeHyAg4js0KhUJ4/PixqHpfNNmtsymIiWx+//d/H2+9\\n9RaWlpbEGBwIBJBOp6V7MfP5isWihKzQiEyESBSow1W4zpOQHYo3mYRmrlSxyXC4Bxwv1TSmhxDB\\nulwuYbaagekzaye09fefRqeqbJyAVo0I18iU6D6ORCIoFotoNBrY3NyUzaCnhgys3W5L6DrVLh5K\\nXgQGxxFVsbss32siDD3mSWxIdr+bqMV8LnVxqm25XA7Pnj1DvV5HKpWSEAUAEqqgmekgb5K+1GeB\\niMxnc91yuRx++9vfotFoYGFhQfaLBzUajQpy5R7T/kEDLdGWmWypVRjurU41iUajUl+Jh9+yLDkr\\ng5jyKPMb933DEHCv15O9Wl1dxdLSEkKhEACICQKACBzahur1utwRInuuC7/v6OgIjUZDBP1Zkt3z\\nNFMyEbv2nFI17/V68Pl8cr4p/Pk3k7QgsjNxjEKnqmwasnITubiac/p8PonCLhQK+PTTT+F2u5FI\\nJPqgfSwWkyBC2gy0msCW2bzo9KhFIpG+C8DvNyXbJAZgPkMzXS1ZBq0LcBJ343A4sLOzg08//RRH\\nR0dwuVxYXFyU3DUW1EqlUmK7IbzXG8n11Xlgg+YzCYLgflqWha2tLZRKJXz88cd44YUXsLy8jLW1\\nNVEhQqGQeJK4Lt1uF8ViURgInQ1kNmbwGwULDzzDC+LxOBYWFrC2toZsNovDw0PUajUJGp10niYq\\nGIW0N81U2YATxwvzFF0ul8Rr0R52dHSEcrksaL/ZbEqaRTwel7vCNaQjhC73adHgIHV+kLDjP332\\nqWKXy+U+NAec3HkyUVNg6nU0xzUOjVygjRPmQOiNSSaTyOVy8Pv9iMfj8Hg88Pl8+Oyzz8R4zY3j\\nxILBoGTr05NDNAZA3OVMS0mn0/D7/QKdw+GwIA19mMblxiYNesYwg12lUpHAxnQ6jdXVVXQ6HaTT\\naUGBwDHa2NrawkcffSTVF0OhkOTlmWrPIKltjnVScjgcqFarqNVqyOVy2N3dlXV1u90iYJgorGE5\\nBZBlWXKImULicJxE4JNJaaEFHF/If/zHf4RlWfjRj34kNgyHwyGxZhyj/n8UGnVN7CQ4zzfPFf+9\\n9tprePfdd3H16lUREszzovpTq9XEM0lUFAqFxGNFAz8ZOc/O4eGhaAJfBNmdE45H51syXcjj8SCb\\nzaLZbIpNVAfCUoMxz6teU/NnvudMbEim3UIvLqEeB0Avkt/vl4RJ4CRnx+Fw9E2IG64HzMvgdrvR\\narXg9/vFFanfxxKaZpb8JLlPoxziQQyKWfG9Xk8qRHY6HUQiEZkzPYP1eh2bm5uIxWJiQzNRkB3z\\ns9vMaeap0QCfy/AKMge324319XW88sorWFxclO+0LEuiqzViJpyn5OXvHKdWWQDg1q1bePr0KT79\\n9FPZbwqXQdnkk5JeP45nEHIwhQErIdy8eVPCIWhq0B5Eqpu67ArNClqNoZOGjJqv26lAZzHfYeYA\\nXX5E5y8yc8IsMcL3Djt35nz166MwpZFy2fQktRTUm8fgRiZTamnQbDZFktBYSUMabUzcVKIgBhNS\\n4uiN5uHV7tZpbC2D4K65BubvRBn0GjLbX9tY6DY1q0cyTUPHX+mxDJM25pjHmSfw/CHVB0h7yAAI\\n8qUk1/Er3DOqIBqx6rOhVTCek2KxiHq9LpdbB4hOgozs1sVuLbUANRm0Pk8OhwPBYBCXLl3CtWvX\\nxAGjUaLdBaMQdrvdYpbQ9dP1OY7FYlhcXMT29vZEOZgcOzB6uAPXudPpSF17ChAi2mw2i3q9jrm5\\nub6zQfsR99PuOzRjHzTWYTSWl01vAOui8IBykbWLk0FjrAbIgDHmpOlFIJwn96Ydgxvo8XgkZ4xB\\nhURkOplzWjIhvB3phW02myiVSiiVSlhdXRWXcC6XQ7FYFCbECN5kMgmXyyUVMe0SawcdsmHjmHSu\\nmsyId+5rMBhELpcTlYSIUAfW6ehqHXvldDpRq9XQ6/UQDodlPxuNBsrlskTcDxvXWc3PZFT6ZzIk\\njjmRSCCTyeC9997DW2+9JedZG6W1QV/bZGiiYJ0vVjpgqpHDcez239jYEM/sT37yk4nnOOy82DHl\\nXq8nKS/cGzJYy7KwubmJYrGIubk5XLhwQebGYElWj9TfYX7/pGdzLJWNk6MUZRcQDfdIREV8Lz1r\\nDArTzMjhcIgthekirVZLIprJdQH05UURkZnjHXcxBnF6OzKRC8tlVCoVQW0ul0uidrvdLqLRqMyJ\\nVTB5oXXslakenzancS+ueXgHIUN94ZjsShREZwNwElvE+CUKIxpItV2GWfH6Nbu9m5ZM4amDFwdd\\nGHNNgOMaX5cuXcLVq1cRiUTw7NkzyR7g/hGpa9REQ7BGfmTYrJ7JsbCTB43e0855kBA1z47L5UKr\\n1ZIS0cAJSiYYoMcQgOwvn60FzjAaVbBqmig23+12I5lMIhAIiLFZH+B2uw2fzyeXlWoJVRQysqOj\\nI4lS5SYmEgnJjet0Osjn8+h2u7h06RJcLhfy+bwceF06QS/CuPr4qPqtJjvbDlvw0MjL7h1ut1tQ\\nIg8mbUhmHtMwlWNaCWSnCmnGZM6Hwa77+/t9tY5o5OS+0hDNw+3z+VCr1RAMBqUKRKvVQjAYRK1W\\nk8h9u/W0m+c0jGpUVYjMy+/3IxQK4Rvf+AZef/11LC8vS1wOAzdNtZQFCMmsgOMwAN0cgfF3DCHw\\neDzIZDJIJBLY2NjA6urqxHMkDROs5hmiKt1oNEQboV2MZYKomjGAtVwu9xVMHGTfNIXdODSWDYmS\\njZDTjKWh275er0vuGiN+6TXjeykhaU8hB9YSjXYYDRFrtZoccm130AsziT5uhxCGvUfPQ6Mcqq2x\\nWEyMw2TUVGcYbc5LYIeMBiGm02xLo5B5iOzUVDJ2rapotYzv0S5k09BOmxSJHh2duDlsrSc50OZZ\\nGIQIzfdaloVgMIhYLIZ0Oo1Lly5J3e96vf7cvDQRBTELQSMmmjVYnC8ajcLv94tGwETjTCYz9lzt\\n5m33+jDi/lLVJDEliM8lQNBOqUHPn8aUMLbKRn0zEomIOsX/2U+tVCqJEVeXXWAuECVHp9MRRMS4\\nG75uqjTagKpLhdK9TBjM8Y1Dg6SIJjsUxUtMRqsRg7Y1ABC7gmbEZGZ2h2kU9W1SlW3Q4bV7tjZe\\n63QQ7ZmhPTEcDstcWRfL6/X2OSdoWxmE/kyVaxIVfJR10AyGiDqRSODatWt45ZVXpIIm427ojOHe\\nEuXwsnK/aV9jEUIyI6o/rVZLNAR+r9frFU/muKSRCuemmbH+G1/TKrlWnXmneI5532hf0l6500wM\\n/K5xaSyVTaOkVColngdy0nq9LvEYdHsyZokMKRQKSaS10+nE0dERarVaX5FxwnmWLWH0MAMpyXzo\\nPudBASZDR8DgxRtkZ+Hv2mbCYDcGRno8HolR4vvr9Tr8fr+gBbtExXHHOOkcR0Eh3CsyFc1QeRZY\\nLZQHlyEhvLQAJCZNe1Qp4AYx2EkQkh36s3P1a9tlOp1GLBbD9773PVy6dAnXr19Hu92WrAMWIqRX\\nmGhIrwHPna7/zrPBPEGmWTmdxw0YK5WKCOnXXntt7LnardFpiEWvDxEPbX+66QTtSzpTg6qrXe2u\\nYUJ1nHM7cglbc3KUALpnl85mr1aroqZpIzg3iM9pNBrY3d1Fs9mU7rX1el0WS/+jdKXBkExvnHbS\\ng+Y4bO6DJDUZoWVZEpXLzSJCYDwH512v1xEOh/taIZ928aa1o4wyN/NnzXhoVyBsByDzIdE+6HA4\\n+loZUSVnagV7gRF5aUZhjkmv86hkqo46wJHMhL9zbplMBleuXMG3v/1tJBIJRKNRfP755zg4OJCK\\nFjzLVEMpjPS51sZ82tzI+GhmYFQ7zRtMso3FYlPvJ9dqmLqv0RMFCGt18b1E8rrGl3ZqTDOu02gs\\nlY2/69wXQlhuHHXMg4MDOBwOYVA6gpWZ0FwEXlR6IYCT+sXsDwaclDsJBAJ9zEmjokkg/iBOPgrD\\n4MWkoZaqJutDMU6JZXmZfqBjsgZ977CxTUrmgbVjBiYq1BBfQ3h92LQkZbgAEQLLregYnGFzHUXq\\nnzY/uthnZ2eRSCT6zoplnXROTiQSWFxcxPLyshSmYzoHPWk6bYJj0RkCuhIC1R5d0nd/fx+VSkVy\\n+OhtZijIoLidcei0NbMTQBrVaUTH3/k+AgDOaZCqbSJe/fqoeziyymZyOn2oAoEAQqGQeJkoHfgz\\ndW4AotJQlfP5fIjH42g0GjI5MrhIJCI5TjMzMxLHxANHRKaD2XgpJiFeKj5fz3vYgh4eHqJQKIgE\\npDeq1+the3sbwHHRuStXrmBhYQEzMzN48OCBIBAd02FusD4YZ8GUTrPd6PdpoybXh6ELugIk1TZK\\nVSIAnRpEbyMrNOhsfs30zLGNiw7JbDweDzY2NvD1r38dy8vLUmqY6RI0LrMra7vdxvb2dp9qGggE\\nJI1JV3oETvIoibK0bYXe5V7vuJ4QO/k2m02sr68jHo/D7XYjFoshHo/jwYMH+PnPf47vfve7Y+/n\\nIIE2yNQrx44gAAAgAElEQVSgGallWdJcg6YPrh/vN0GFjqQfVDhPP9tubKOc4ZFVNs0sdEwQw98Z\\n28AKiRwg4SATEun+jsVicDqdkjPldDql+LleEF50GhefPXsmOWCDJjjuxdULaErsQdxdowbNLHkp\\n+axQKCSHm5/RzS9HZaBnMU9NdmtnSlHutVlQjfWjmanOz/CSck4Oh0PiWRjpbcL+QcxGj22cebZa\\nLSwuLmJ9fR2XL1/GysoKGo0G8vk8stksZmdnEY1GkUgk4PF4xIvbaDSwtrYmTIXtwYETWwsZD5Ee\\nkaLOJuB4GQndbDYxNzeHubk5BAIBsSPpLizZbBa3bt0aeY5263Qa49b7rc+7uX/AsfCs1Wp9Reg0\\nwAgEAkMDeof9PjVDsiNyTRqheXApGQnrKSHphaFRkzYGSlZCeqIiSh2iIz631WqhXC73LRJwEg7P\\n18Y1bA8zWvPvg+xIAPpaQelwBQaDmoXHzHrFg9ZYj81uzJMypEGH13wmmS2ZPz9LVYP7AvT3GCND\\nojrLmle0Jel0BP3d5lgmnWcmk8Grr76Ky5cvY25uDo8ePUKpVMLDhw9Rq9UQi8WkEQFLhTQaDUQi\\nEWnySZsgUaEer0ZDRAw6hIXMmPbV2dlZccDQLFEqleR51WoVd+/eHWuOJtmt32m2uG63Kw4nzonz\\nonChcNVOALP0j2ljPs0EMozGboOkv4wHzrIsKezP8rJPnjyBZZ20u3G73QiFQlhcXJRJk3E0Gg0J\\npKO0abVa+Pzzz1GpVJDP5+FwOBAKhXDlyhUAx2ri/Py8FMPSUn0afdxcTLtN1QtNI6kuy0s7m2VZ\\n2NnZwczMjNgx6IUhEx5l3e1+H1eVsZvjaczOjMhnKY1wOCxlY2gvI7OlusvXNLrd39/vk5KM4NfJ\\ntJMiI9K7776LP/mTP8Hv/u7vCuO/cOECKpUK7t27h93dXezs7OC3v/0tAoEAVlZWkE6nsbCwgEaj\\nIUUC5+bmhMGyhIoO1WB1VPZooxbw2WefYWZmBuFwGG+++abE6un7Qw2h1WqhXq8jm80il8uNPdfT\\n6DSbHO25OhaJQKJareLg4ECCI5n0bmfzMgU3eYNJo+zn2AiJX0YDLuEnmQFLMWjXrk4RoUtcB5PR\\ngk8ExCJglM4Mrae6Qw+VXvBpJOqoXN3uwlCK0IOkVVvOiwXIaNDUAYfDxmoyn7NgRsPInBshPONP\\niAZ4BnSqBKUohQwRL/eN9iN+h2kYH8Zsx5nrG2+8IZUmtK0qGAxiYWFBGAi7HicSCcRiMfF2MeCX\\nF4+Ij0yFrwUCAQl9odrHJpexWAyWdVLQn+iYa9Tr9aSCKgAR5mdFg86HnW2nVqsJatSahc5to1Bi\\ngb5hNOgemX8fRGMZtfWlYJYwcBKpSqhOwyBfo91AR/ty4ylhuUnlchmNRkO8bow9YjAdmSFjnEwj\\n3SQMadhFP+25nJNmSLqsiC7GRVuSTpsYhclMYhwcNs9BZBoltRpMNV0HxdFIrOep7YvcN5fLhWq1\\nKpecz9aqjx0DOu1w29Hi4iJ8Pp84PQBIXNTKykqfKYF/83g8cr70PDl3JsfycrK8DB0s5XIZ2WwW\\nOzs7ogkwgbrb7UqzC45HeyKJ/GdnZ0eeo0l252PQ+4D+fSVK0x40vof2JY6dKjzJDsGfRmdmQ9KT\\n5gCZtX9wcCA5a5VKBcViUS6xnqhlWfI+GnkJDUulkjRSZOU9Bty1Wi3kcrm+5ntOp1MiYLXePo2X\\nzZznILuG+RpdxfV6HcFgULwsLNtLhEDVrt1uS38rO9Q1iCma45gECQ6bi563tpE4nSepLlrw8GIB\\nECM9XeN6D6iW9XrHvepYAUF7MgehwHHR4N/93d/h0qVLCAQCuHjxIn7v934PoVAIwWAQc3NzfdUi\\nOp2O1L6u1WoSaa6N7gzwpNpKJH/r1i3s7e3h8ePH+Oijj8T58od/+IeYmZlBMpkUpK8LFPKSM1jS\\n4XAgmUzi3XffHXmOp5FpB9SkhWuv10OhUMD+/j7m5ubk77QlaSHLZ/KO2qllpzEnPnsYja2yaQmn\\nuT5VE24CXwP6C7RxMwhfOVBtAKU3hhe42+1K4XwiLV0Cw0Qck6AHbYMa5zlkSLomkGacumIggL7y\\nvKfZcszxTTM/c8yDmK4m7U3S79X2EL1HOuqcdgbupXZ2EDWZYxj0+zi0vb2NQqEg9d2TyaTU767X\\n62JcZjtoeg7p0tcXhuo4z3itVpPGp3fu3MFnn32GnZ0d/PKXv5T8TZbJYb0vaghaIAEnnjuuCet0\\nT0Ia1dqZGAYxCo6Nd07fAT1OEp02zHkbJEy1gDHpC1HZKClMLxjTQJj5zcGx9xqLsZVKJezv7yMS\\niUhzxXg8jmaziUqlIipgIBCQSZBZ8ZDQGKc9A3qc45DdwbdjAMMMeTp5mN5Er9crzQQzmUxfLSG7\\n0inmdwx7bdjrp9Ew6cVnmukRNLrOzMwgFouJWkPbAtUiDfG73S5KpZIIjlKpJPE/+r1ahdDjszN0\\nn0Z0VReLRfzmN7/BRx99JF7bdDqNubk5LC0t4Zvf/CYSiQSWlpbE6WLuNc95LpdDPp/Hv/zLv+DT\\nTz9FNpvF/v6+vI/M1ufz4Xvf+56oaBoRMztBh4R0Osctwm7duoV/+qd/wt/+7d9OvZ/mmp2GhNnu\\nnoyHwqTVakllBu6LvsMkU3gMY4yj7ONkrUH/P5Ep6cOsD7RmFkQPtCFRdaMXht1vdYY0bVHcUEof\\n2pxoND1L+4q5macZ5/h3rX6QQXu9XlEra7VaX997ogq77zW/Z1pEdNrY+bMpUamW67AF3eSSahs9\\nUVqFMz+vc9l0SsIwpjoJw9WqBOfC/WB8HA3agUBAvGlU47QhnAykUCigWq3i/v372N/f7yvKT5sT\\nz+IHH3wgKvn+/j5qtZowX63ikIFZloVPP/0UxWJx7Lma6zSKUDbPN50TNFbr8ByGRGjXP3BSCUCv\\nuR6H+fo4NBZDMr9Yl7Mk6UQ9nTukw9M5Iap3lmVJjy9KKrpcuTj0VtD4eHh4KMzIhIqTkDk305ah\\nSV9cbbSnUZcXgAZ+VkEgEybD5VrZjVujUa0mnxUNs1XxO6l20I2tcxM5RqIDLWBY0C0SiWB2dlbs\\nJ9p5oY3Zw9aa3zPN3CgsGLTrcrnw9OlTKSPLKHLG1enUqMPDQ2SzWVHXtGrOS0pnhdPpxCeffILd\\n3V08ffoUBwcHfSYFClyOUXvcpm0WaYcmh+0xUb1u0KD3hHtpZlCY5gaT7O7hOOaGkTrX6gvIB7Jl\\nTT6fl9Y+dG+T0TBugYvOgl90/fd6x6U0+X5KJC5GKpVCvV5HsVjEZ599hlAohOXlZUEaui+cTvuY\\nVJU5DRGZa8BNZC4b41WIfHQZFJ0xTibLg62/QxMR5rADMM08h60VI3IJ33lRWVjOsvpbIJHohHA4\\nThJMC4WCxPiEw2FRyd1ud19iJzB9SMMg4aEDHMk0aQ/SBnx9+fgzbWMm8tJqXb1eR71exw9+8AMR\\nSGRYg9QXHRpzFvtrXvxBzF2vN+OptN2WCL5cLmNvb0/2muVkdF0zE1kPYzijzPPUNkh6oryAZrE1\\nQk/2o6pWq1ItkF1QtXHU4XCIu1UvAnDSptnpPK5rTLhMKcOUEjItXVLVHPeodJraYGc7Mv9uFqIj\\nYqIU0oZ9xi1RQg7yPJj2DLvvHpeGSVFzvzUyo3QnsuPl1CgPOEFB/Bulf6PRQDAY7ENSpro6iJlM\\nMkc7JmB+l2WdND20U2NJREz6eZyf3n8AohYSWVJY0vZihwzPUthwDsOeqYW3Vr20rSgcDqNarfZp\\nPKyMqbul6O/TjFb/fRwaq/yIZkiMI/J6vSJJNzc3cXBwgEajgQsXLsDhcIjLl5vDLH0Gn1HdYX0g\\nXl63242VlRVsb29L80jGdzA4jwfdRBrjLoR58fW8BzECvsYN42GjvYXMkkGDLFdLycxAOm3UNcek\\nN1bDYC3FxyU71GuugX42mSjnQ5shx0ZvkbaT8D18Ph0QJJZO1XlUg8YzjZpqMh+SRgF2nzHJrv6P\\nnaCiQNJoS68Tv1szgWlNDXbjN80Yer9NJKjtX2Q0bAFfKBQAQCpTkLmyVIrdnpnzMZnjmahs8ub/\\nL+X9fr94J5xOJ2KxGGq1Gj788EPs7Oxgc3NToDnVK8uyJK6oXq9LS21KVHqegsGg6Ok/+9nP8PHH\\nH+Pu3bt4++23sby8jKWlpT4jOCWcCfvHpWESctBl0Z9jNC/dt7VaDfl8XgrL8RlU8QqFgtRJ0sjR\\nRA2Dxmn+PA6Zzx10kAKBAJLJJNbX11EqlUSN0+V3TaivVVEGR+pLQYP4nTt30Ol08Pjx4+fGNAr8\\nH0R2QsUMnuX7zH0ctC52SNb8DpLprdPvNX8e9t3jkh0jMsep58Xfo9EoZmdnpfGqw3GcEM2yvRSy\\nGjWbAMCcm4lGx6GRVDZNjEQNhUJyIGk/2tvbw+eff44HDx7g+vXrEnuRzWbR6/UkIE6nlwAQlKSL\\npNdqNfzHf/wHstksdnd3EY/Hsb6+jlgs1lcG1066TKOyDfrsILWCTIYMiUzX6XRKoXTdiZcxOLoM\\nr0mDLs5ZQ3u77yQycziOI4hTqRRSqRRmZ2cRCASQTqelQzHHRNVHFz/T9aqIjg4ODkRVv3v3Lrrd\\nruQ8mhdpGqZk7pO2Bw1ay2ES3USrdmqv3bPsyJzjWZLdOR7GoAgSeJ94jkulEqrVKlqtloQwUGjS\\nBGO3fnaCW5tjRqFTEZKeBHXlXC6Hn/70p2IXqtVqaDQaePjwobg6P/jgA8nbYiwDA8c6nQ42Nzcl\\nP4iMi5yYoQCbm5uix25vb+PWrVuoVCrY2tqSKFm6YfVGTKKy2cFvk+yYhMNx7InJ5XKSSFsqlXDn\\nzh3cvXsX5XIZ8XgcmUwG6XRa1u/Zs2fSMtpu3IPUuEGMcVIypRn/HR0d4fHjx/jggw9QqVSQy+XE\\nKE1hQLsC0RJRkU4fYWgIAKns2e12cefOHWxtbQ1UVyZlxKcxjEHM47T9H2UswxAY/2aq2ZPu5aDP\\nmWMg2SEoh8OBzc1NfPzxx+KU8fv9Aira7bbcNY/Hg/v37+P+/fvI5/ND10TP3QztOG2uI1WM5BdQ\\nldrb28O//du/SSGsn//85/B4PHj27JmoZP/zP/8jg9J6NQ2BOieGeUHtdlsCCbU+DgAff/wxNjc3\\nsbCwIIFq9+/f77NXnBUNWkBTsvEC5vN5PHr0CI8ePUK9Xkcul8OPf/xjfPzxxwiFQlhbW8Pa2hrK\\n5bIcgt/+9rdS5teO8Zvfaf590oM8CCmYjKnZbOLWrVt4+PChwHra/uitSiaT4pigcGJHDSIKZowz\\nJILe2Xv37qHRaIzEjKbZ22HIx+59o/x+msAa9P5hcxp3jsOQvJ06aT6fyOXOnTu4f/8+rl27hp2d\\nHTidTvz4xz9GLpfDzMwM7t69i3v37sHpdOL999/HnTt3sLm5KWjYbn7mmR0HFTqsL1IPOKdzOqdz\\nGoMma9FxTud0Tuf0BdA5QzqnczqnLw2dM6RzOqdz+tLQOUM6p3M6py8NnTOkczqnc/rS0DlDOqdz\\nOqcvDZ0zpHM6p3P60tA5QzqnczqnLw2dM6RzOqdz+tLQ0NSRSCQyMKuZxNfN0HizrXA4HMa1a9fw\\n9a9/HZFIRMqG1mo1/Pd//zd+85vfoNls9hU10zlSo6Y8MD2FFShHIXabGIWGhezr8eg5TJuLNSx9\\nolqtjvxsswMtANtSGMBJgf9YLIZUKoVvfOMbSCQSCIfD6Ha7qFaryGazuH37tlRe4HdsbGxgbm4O\\ni4uLuHTpkpTw/fWvf43bt2/j/v37ktc2yvowX3AU0mfWjphPZtYB6na72NjYwPXr13Hz5k0sLCxI\\nWRxdXYLjZSG6g4MD7OzsyDN++ctf9jU74HeOQuPsJXsT6u8YlPCqc+j0z0wIDwaDePXVVxEMBnHn\\nzh0cHByg3W5LO/KZmRlsbW3hV7/6Vd987L7T/H47GraXQxmSztQ1M7IHDYrJdHpD5ufnMTc3h9df\\nfx1f+cpXpNJkq9VCMpnEiy++CJfLhd3dXXz++eeSdaxLc5g1fIclP551FrUd2TEOO5qm8oD+rrNI\\nqLVL+NTrFgqF4Pf7sb6+Ls0LQ6FQX1NEp9OJZDKJjY0NhEIhvPfeeyiXy1Izh11t2emU1UCbzSbW\\n1tYQi8Vw48YNbG5uol6v48MPPzzTZGEzu5znyDy7OreKtbn+/M//HOl0Gn6/H4FAQM4eWxxx/vyM\\n0+nE6uqq9A+s1Wr47LPPpA61yZi4zmc5Tz5PN3YcllNnzv/111/H+vo6Xn75ZWxsbCCbzSKfz0t1\\nUPahu337Nm7fvi3zYtlbPndQdQGTTrsvI2X7kwZlROvMXvO9brcbsVgMs7OzuHDhAq5cuYJnz55J\\nvWGv14vl5WWZ5L179/oWj3Vo7A4Wv2fQYfsi6f/qezRNe3EHJVjyEPv9fszOzuLixYtIJBJIJpN9\\nBedYCycYDCKVSuHixYuIxWJSlpg9uyzLkmafDx8+lG4yFy5ckPrbyWQSOzs7uHXrVl9zAH2mJllf\\nuznq5/JnfX48Hg+SySTeeOMNBIPBvp557Exbq9WkugErKLrdbszNzUl790qlgng8DsuypC+feS7P\\nivlqxGPOz2SAuvInx8GGDevr6/ja176Gl19+GZcvX5b69ZZlSdcY7lkgEJBkdibA64aaGoyYDOq0\\nhg6kibuOmBts1rPu9XoIh8NIJBL4yle+gtXVVYHBLGHBounRaBTr6+vSrI/NH1nBjmROeBBK+r+g\\nYZnU5t+noWGMdtJsfzuJHQ6HceHCBVy/fl1KilQqFSkzQtWZ9XK8Xi/u3buHubm5vo69LC/D16rV\\nKgqFArrdLvb39+Vw+3w+EVL5fB65XO65xpFfFPHZ7JdGFLC5uYlkMillc1gbHDiu+MiSzGTKZNZk\\nyLlcDpcuXZJLe/v2bSnVodfabizTIulBApIMWZeEyWQymJubw9raGl566SUkEgn0ej3U63XZQwoJ\\n1jxLpVJ45513kMvlsLm5iadPn4rqynOhx2R2lbFjnHY0MUMy4RoP0czMDPx+P27cuIFUKoXFxUW8\\n/fbbSCQSaLVaePr0KVwuF6LRKGKxmDCo5eVlJJNJAMfN/jhx3eVg0IXk34fVp/4iyW6Rh9nbRn3m\\naQxo3LnaHQrWoVpcXMQLL7yAN954A3fv3kWr1ZKGDSwux0u6v7/fd3ipnvF5lmWJ2sYOxQCws7Mj\\nTIe2qTfffBO3b98WSWyu0Vmqc5rcbjfi8bi01/Z4PLh9+zaWlpaQyWT6Gkf4fD6k02n4fD7puMsG\\nFpZlYWdnB1tbW9jf38dXv/pVKelcLBbRaDT66nXZXdRpz6y5RrosCFHr/Pw8AoEAQqEQ3n77baTT\\naSwsLCASiQA4tl9tbm4iFotJRVAKE5aP/u53v4utrS3s7u7iyZMnUkrmwYMH0iyAcyKNyohII6ls\\nugSoJhOxsOVyKBTCd77zHczNzclmWpaFSqWCvb09+P1+YT6WdVygze/3Y21tDd/97nexvb2NbDaL\\nH/zgB7AsS/R3c/EH2UPGla6jHnoT6g8bj36P+flRvmuU95mtjEd5ph6XZuLxeBwulwvFYlHqXpEJ\\nAZDGBU6nE6FQSCpjUq3mc3RlTK1qs0QxoT7HzwtA4zHHOSlCMs+sJj3vaDSKTCaDr33ta1Ljm01O\\no9GoqJ4UmERHfCaLCbJv27Nnz7C3t4fr169LaWfa4er1uqAIzXx0of1gMDjxPPm7Podc80gkgvn5\\nedy8eRPpdFpUbZaXzufzcgfZ9iiZTPYVXmSBRcuyEI/HEQqF8OKLL6JcLqNYLKJWq+HZs2fy/Xqe\\nnDfHpJ1WdnRqCdvT7DX8Uq/Xi5WVFTGCrq6uIhgMyiFkmVq2XGH3UxbI73Q68Hg8WFhYgN/vRywW\\nw0svvYRcLodqtYpSqSRoSR9YvSGTdjsYRwLbMZlxnjHu+3SHCJPOap4ejwfz8/Nwu92iLvMgASeC\\nh0KBXWo9Ho80iNRlTll0zoTx7Gir1W12NR7UwmrSOdoxI31GFhcXceHCBbzwwgs4ODhAsVjE/v6+\\nSHo9zmAwiEAggGg0CqfT2dc1mReWhmzWVLcsS+q/Z7PZ58ai95eMb5p5moiLpZIvX76MpaUlXL16\\nFfF4HPF4XOx+AKQNFJtR1Ot1KfxPBNRoNNDr9cSGFgqFkEgkEI/HMTs7i+vXryMQCOD27dt9czE1\\nGM53GI2tspkMgXThwgX8zd/8jUjIx48fo9FooNPpYHZ2FolEQoym+Xwe1WpVpGy5XEYulxNpGggE\\nMDs7i3/4h3/AnTt3cOfOHfzrv/4rNjc3+yQsN8SUDJNAYDvbymkoZZDePkjdGnS5hqlnX7QK6nQ6\\nEQ6HkclkEAwG0ev1RC0B8FwDAtbK5j7QBqMNtmwEalmW1NCm8OFFoRBLpVKCQOzc3pPsp7meplkB\\nAL797W9jY2NDjNCtVgvVahU+n09aS/PSttttXLhwAXNzcwCOvW7b29vSrCGfz2Nubg4bGxtIp9PI\\n5XLI5XL4zne+g2KxiF//+teCRvUY9IVlFc5pic+fn5/H8vIy/uqv/grpdBqHh4eoVquihtG8whLD\\nnU4HtVpNaqMzJIceVKqpdGzUajXxRv7FX/wFKpUK/v7v/x77+/vY29vrs79pYXDaXo7cBsn0EGhO\\nl8lksLCwIL2ccrkcHj9+DIfjuM1wMpmUSbNrqfae8VADJ27aSqWCnZ0dWJaFhYUFvPXWW3jy5Al+\\n9rOf2SI0khljMioNYhamQdlkhvp7B0ln/dqoNgM7ddTcA922ehQy0S2fR1WbTgRKQgBinNbeKo1O\\nTcapOxRrTxB/Z383tt4h0tLqkP6ecW1I5iXgz+YzI5GItGV3uVzScLTdbkvPPNZ2n5ubQygUktij\\nfD6P7e1taTVNO5zH40GpVEK9XhcERbsTu+uYqj7HM4n6PQitz8zMIJVK4YUXXoDL5RKVlEyCteyp\\n1nW7XWnUCkDuKMMWuHe6c7Hu1uz1etFut/HSSy/h9u3bODg46BsnGfEo534khGS3yfy91+shlUoh\\nk8kgmUyiVCohm81Kq+JkMik2iXa7LZJAtwbSBnEAskAPHjyA3+/H4uKi/E+GNMp4pyGTIXC+w95r\\nro3+O+dqMi67NTWfrf8RaZj9vUYhU73kd7O9EQCx9ZB4MfldGjHxf9oGKGBMR4RmZnz2zMwMfD4f\\nAoGA1GZnM8VhaHLUOeo9M+2cMzMzSCQS8Pl80tqcNiMiBRrwiZJCoRAcDoeEMLDBBACkUilBfcVi\\nUdReXny2gmLbL7vzMqmDQhOfR0/ajRs34PF4cHh4KC239HspvHmedCNTquK68acWJDTDdDodBAIB\\nuN1u3Lx5E7lcDp999lnfHo6zl2OpbHYw2Ol04g/+4A+QyWTEHciOIuSyOzs70kaaBlHLsqTRI6E6\\ndVcyLvaHb7fbWF1dlTbUDoejr5voOBJ0GjrNjmMefPN1Mx7E7pkm89PMqNPpYH19HRcuXMD29jbu\\n3Lkz9vjNteKhIhGt8HDWajVUKhXxoHDtWdif0pNqGyUpGRcZAg2stCsynIB7n0gksLOz0wf1+flJ\\n1Va7SxGLxcTDm0gkpEcgQxXYkICCtFQqSXhDJBJBvV7HkydPsLOzIyoejb8zMzNYWloSuynXIZ1O\\no1QqoVgs2o5tWrXcZOButxuJRAKLi4sIBoPC5Ok04N0iUyGCcblc0qCBd1C3RmcsFlU6r9cLABIw\\nSjvjoMYVdqYek6Zy+5MpJJNJRCIRbG5uikRhyyP25uKgaLymKqD73NPtS66sY1oo2XTTOj2WaZjS\\npJ83YSghOVGHVkc5J/58dHTUp5Loi2eiC252q9VCPB7HpUuX0Ov1JIh0WiLDoMSkzYdj5t4wGhjo\\nV4210Z37ZK4PD7vH44HP5xMvHDuv0FXOz+k1nmRvTLVeG3tjsVjf2nq9XlGtjo6O5J/uNpzP58UL\\nRXc+LyoDCZ1OJ6LRKJrNplz6breLWCzWly5hXlL92qTzNM+RVkM1U+b7GF/ENaDNkD/TOM0zreOS\\nGM5Bbyptjvw9GAyiUqnYzum0eU7MkIATaRoKhWBZlhimU6mUMCTqrNw4bgxVt1arJW2a9QJrSU0D\\nW7vdRiaTQb1eR7ValUWcFiGN+vlBqqsJvV0uF5LJpKiyvHiNRkNUms3NzefUGX5ej0dL2263iytX\\nruDGjRtoNpsTSVZt/wFOcta0x5OMn/aUQqGARCIhY9NqiGnUBiDjJUPi+9jPzefzIRaLodvtYmdn\\nB36/H6urq7h9+zbq9fpUKpuep2ZI9BKlUimsr6/LvCzLQjqdxsrKCq5cuYKDgwM0m025YD6fD61W\\nC9vb22IfevTokbQJonBhqsWlS5ewu7srKpJlWVhbW5NGqnaobVKbpx3TBSBxYWzmajI+xowxSJX7\\no1uTkflQqJrj9Hq9CIVCCIfD8t5EIoGFhQUsLS2hUChI/NUoyIg0FUMiFYvFvlgKbSQlIuA/oiQS\\nbSFEW7rRIF+nJ6DZbGJ+fh57e3viwTMnOg3EJw2TyqPoxk6nE5lMBi+++CIuXrwIv9+Per0uSK/V\\naonBnt1tiQzJjJ1OJwKBAMLhsEBtj8eDxcVF1Go1+TcOmWogfyZKYEgGDyeZChkQcGJIt6yTBp9E\\ny+zVpeNQyOzoLqeqRqlLmB+JRPrsinrM06hspp3S4/EgGo1KSgizBbxeLy5evIhAIICdnR1BBxSM\\nJApICkteUo/HA7/fL+sGQJBTMpnEkydPnlPjuf76/3Hnxs/yd/bG495ogUDUxrFzf4l+tYAkIyIi\\n4r4TXJiCjIzO6/Vifn4ed+/efa7v3ih7eWpg5DD0QOhXLpf7AuIAyKGmFGm1Wn2Bb3yutuRzITho\\nWvEty5LLEo/HUavVkMvl+jbT3JxJyU7y8HU9b/O79e8ejweZTAavvPKKxGgQCdLVms1mUa1WpTkm\\n5wH0oTEAACAASURBVEp04nQ6MTs7i6WlJZlrLBYTNzM7jY47N45fv+Z0OiWmiF4ypo+QceiANn0Z\\n+AzutTa2Ey3yAhweHooBlE4Obei2k6TT7KdGDtwjIgcamXO5nFym9fV1AOhzjXNuep91xDZwoo4G\\nAgF5jQzPsiyEw2EEg8G+9afKOw0StNtPggEKAO4b0Y9Gr2RA3F/Tk0qEyDPB51B114GvZIahUAix\\nWKzvO8y9GEanBkYOel0zEUoHLvDMzAxyuZzErBAd8Hnk0hws3au0IXGxmTMVCATg9/tFX+VnNOOY\\nlhGZc+MY7MgOlQGQwLi3334b3/rWtxAKhdDpdFAul5HP51EoFAAcG1YXFhawsrIiBkiuB6UYL3Cr\\n1cLs7KwwC+CY2c/Pz8Pv9081V6JSqlFUK4lkqHYzipqubRpCqcJp9UxH5VLK8nt2d3fh9XqRTqcF\\n4THvS4d+nIXKRtJnyuVyIRKJIJlMyvm1LEvGsrS0hMPDQxQKBWH4LpcL7XYbpVIJpVJJ5hUOh1Es\\nFhEMBrG6uopoNIpAIIBf//rXAI4vLtW2ZDIppV/s1P5xVBqS3UXnOm5sbCCRSIidCIDsJwDs7e0B\\nOIk2pxAl86FgJFJvtVqYmZmR7tI0kJPh6rXiOuv7yHMyyhzHUtlMS7m2J/j9fhkgALExaJRETqrd\\njPq5+qBTTSP01G5lvSHm2M6CKY1Dek7pdBpLS0u4cuUKFhYWhIlQwjJHiL9zvNTjOT/amYgiGo1G\\nX6R7rVYTBjEu6fXRtgb9/QDEKMqxAs+7+jUDIoznmph7xbFznwj59YUh2aGkcefIz5jnxeFwyMV0\\nOBxSC4uufgpRpspoj6BmwvQI86wyTKBWq0m2AhEsg0H1xTT/n4RMhEVGwiBkroXb7UY4HBa0ure3\\n12espq2IHjatunOfOH4yaDoiuFZEUUSQdl7mUeY6di6bfighHgcFnMSpUOKTIRHCctH0s3jA+ZqO\\ne9HGV8J77cUxmSN/npbs4LB+nT9TCtF4+dJLL+Hy5ctIp9PIZrN94QuMcKb0pOQhc+GhoD2p2Wyi\\nXq/3FUArFAooFouoVCqCUMedF9eapWGYYElVjaiHKRMaypP58n/NXMz14QVm/A2DY3kxuH6crz5j\\nmibZT/Ms6PPGs0TzAlGStgdRNSGC1LlrXCfO+fDwUCoj1Go1WTc6a3w+33MqDH/m79qgPMn8tA0p\\nHA735SE6nU4Jt2DQI5ko/yfj4hxarZaMW9sTeeZoR6P6Sial32siv6kR0qCDoV/v9XrI5/N9pRia\\nzSai0ah4aXRMBhdO67I0eAOQCFc+mwdC2xyGjXHUiZ9Ggy4BLzQv5LVr17CysoLLly/jW9/6FlZW\\nVuS9V65cweeff45arSbQVzNaMp9SqSTPI5LUa9ZoNOTAeL1eieeZFiEtLy/jtddewwsvvIArV65g\\nb28P4XAYc3NzEspBbxtw4pGjoNFxLKYNkPOkI0PH+DgcDlG/iULsDvCkpJkPL7o+S1SfGLgLAPl8\\nHisrKxJbxMvF4ms0/Pd6PdEE+GyWU3E6nUin04jH44hEIuIZpnfRbg+0uj/pPEl0xyeTSUG3TNFi\\nOALtvDR+cy2YrhUKhfDo0SOUy2XEYjFR5QGICmdZFhYXF5FIJDA7O4sHDx6gUChIjNfCwoKczXGR\\n4FiR2voL+DNwbASMRqOSfNjr9RAKhSRgjO8l1yZT0hKXl1CjJhrOtA3A7/eL5NIlRQeN9yxJj4nM\\ndXFxETdv3sRrr72GV199VQL8KpWKwHoAEmBGeKzXgbYGjSJarZbEdAAnF50eFHp0xiETLbDQGkvT\\n0tbHelWMNdFCgshKoyS9l8DJ5dfISgfidTodBIPBvjKsdILwwOvPjutls7vwHDv3js4D2k3IVBgf\\nRVRPFES7nsvlElVMo0l6nHw+n6Bdnnnuk50thXtxVvZPlqTVworrS3REVYyGedYe4/mi8NC5Z+a5\\nZ2Ak00boKZ6ZmUE8Hu/TYvT4plbZTrvs/Lvf78fKygqy2SyczuNyFgAkQIrIwnyO1r950CmF6fJn\\ncJnX60Umk8He3h7u3bv3nE5ujmkSslswPVYueCqVQiwWw+/8zu9gcXERvV4PxWIRrVYLlUoFuVwO\\nu7u74i1zOByo1+uo1+s4PDyUeBttSKQ6wI3udrsSAUx0xIPGUrHjzo0Hq9frYXt7G7/85S+xvb3d\\nZ/MhI6Wqogt2ASfqhWYc3AsiIM3EuMcAUK/XUSqVBOVpeK8PuC7WNgnZnV0KN/6vC8oxw18zRn3+\\ntN1Il13Rxl/mYDK0gERkr8+/ZtjTCFBTcFPt59jpRaNtR0dj+3w+MdYTBdGZos/k4eGhxDQxtohI\\nXnsYib5Mr+w4NFXFSG54MBhENBqVUPxarYZ0Og2n0ymlGbh43Aj+YzgAkQPtMZQ2Dsex52ltbQ3R\\naBT37t3r0/1NA+E0NOhg8LmRSASJRALXr1+XujA3btxAOByGx+PBs2fPBAXm83ns7e1hZWUFiURC\\nAgPdbrfU3mFgmobRvCiUxEdHR2Kjo6cDAGZnZ5FKpcaenz7AOzs7koKyv7+PWCyGcDiM119/Hel0\\nGsvLywLntbqs7X4kbWDVAZQA5FL7/X40Gg08evRIStVQervdbkSjUVQqFZTLZVskPs48NWrTdht6\\nhHWsVbvdRrVaFSGoz6d2h9PrWK/XhWHRC0yky/QKuvnJwHjG9XpxbtMwJDtBTAM2495oxwNOHBlO\\npxORSATZbBbFYhEu13HRRD0enTxLFb1araLZbOLixYvyvazy2ul0+lCmXvdR93Bst7+pUhG++f1+\\n8QhRStDgxU3Rhk9KHZ0awhwaXkbq75Z1HMsRjUYlfsfkwCY0PyvSDG9ubg5LS0t44YUXUK1WJccr\\nHA4jEAigWCyiVCoJgmCe0+HhoUgRQmodF8LYHJ1i43a75X+qd51OR+A2VZ5xyLzkPDSHh4f45JNP\\nxNkQCASwtrYm9aXtHAdkQDqeRp8JQnu+zlCPTqeDYrGIdrstXkeqBmQQ5t6Ou58m49TPIOrjOhL5\\nMcNf2zP199sZarleGgnqqhZcB6aSmOrLNHM0P6fVK45Lh9do1EkBSE84Vb1wOIxkMinqu0aROsuf\\nWgzPMP8/PDyE03lcwE97Z8cRKGMjJFP69HrH2f6RSERgHgBsbGwAAB48eCBMSxcGZ1F/bVshA+Nk\\nw+GwGEQt69g9Xq1W0Wq14PF4+kqDapokFN+OLOvY40eG+81vfhNra2u4fPkydnZ2kMvlEA6H8eTJ\\nEzx+/BgPHz6Ez+fDm2++iVdffRVLS0sSJEZmxYNP1Yu6Ow8TC9fRC8d1op5O204kEpFAvnHnRNLM\\nglUBidq0hDPVJ6plds8y/5EomVn7ql6vIxaLiadRG/xNZDPtfpIxRKNRiUOijeTo6Ehq+LA4G6OT\\ntQqkCw3y8nOddJQ6cGIv1CVLuFaaqRNJmvsy7tw4TtqsYrGY3DUyht3dXZRKJVQqFSwtLYlgYAFE\\n7jurGRSLRWSzWfECMwc1Go3C5/Mhm83i6OgIlUpFzBgff/wxIpFIXzlj0/532l5OXVObko0SndKE\\naEC7mO06FZiRnzrmgdJXu1yp/vF9ZsE2PbZx5mI+g78HAgEkEgkkEgmsrKxgYWFB6hTrQ83aOCxf\\nARyrsrFYTJgqmSmJXh9t4CZSoqRlXWsiSbrMWdxsWtIohsTCW9wbqi5mTBL/mehBMxQdx0KPnZmH\\nqL105r5MghxMRMfnMHjR7/ej2Wz2qZRMfyLpWDGiBJ0Xpj3GPLs0GPOZOg1qkNpiIrhJ5qoZnA6p\\nYByf0+mUPnq7u7u4cOECarVaXzqTtgHyvHJf6PHVqTQMdaCDwu12o1QqiabE8Zh0moPiVIZk6rga\\nDmtmQ4M0L4v2nnDzgP7WO2RiRCI6CI3vJXPid1NC6XQDU4+exisDnCACt9uNlZUVXLp0CVeuXMH8\\n/Lwk+rKMaT6fRzAYxFtvvYWVlRWRGPv7+7I51OGpxtKzwQPLg8T1YGwTERL/8YBUq1X0ej2k0+mx\\n52nupXkRaPykzYAMn3YB4CQmTKtWps2Ar3Fueg+ZYG3nVePnNWObdj95jsLhsMxNqxvagM3X6FSg\\nk4F7A6DP4E81j95fqnt67vwuXmR9B/T/45Kd/YihCtQ+aJssl8solUo4ODjocxzQCE2TCJl2NBpF\\nOBwW+yifz7SUUqkkZ5Q97Bjmoxm0iQxPm+dIFSNP+xu/mBI8EomIeqJ1dR5aBgfqUPNmsynMiMym\\nXq+LrYRGSF7sVCqFUqn0nOt/EuO2loKxWAyrq6uS2Hr16lUsLCxgfn5ePGjb29vSu+vhw4e4fv06\\n3nrrLbz66qvY399HqVQS71ipVJILyc3k5mi9nJeOxm7Gc1F148FvNBpy8JeXl8eepyZ9SDRj0IGs\\nvV5PLqRd8CNz8wZ9nw62JNrj2vE8aNVYG/cnNWqbRBsJKz/y+9ggkkiV6jQNszynWsByz3R2PBHu\\nzMwMKpUKUqmUBCLyskciEQQCAdRqtecu5qRI0Fxn4PguxWIxUUPX19fhcDiQzWZxcHCASqWCUCgk\\nddNDoZAEchIVszsJnUp6/WdmjrsK3bt3T1KjGHekhUckEkEoFBLHDMd5Gk2UOmJyO+aiUWoSIlIS\\nav1Wq22mnUKHs2t1TOc5aRSm3f6TMCISv49lMN5++205VAsLC/K9zHF6/Pix2HVKpRLW1tbgch0X\\n4goEAtjf30exWEQ4HJY5cXxUQemZISLi/GkA5T9KOjJ8MmCv1ztWC3BzH4HnI93N13hozbQOrbLQ\\n3kKmZId0eGFor+I8dMiAVm2mJX0W9CXRWfB6L8LhsNjt6E3UwlIb3fVacdw0LejgUMaJUd1hZDPt\\np3yfuSeTzJPj5V7ROVQul2WNyQgB9N0nHVlN1MR9os2WwCIcDsv+0OmkQQmAPmeWthtzvKfRxCqb\\n3eJwYH6/v6+2MA8u/05GQubFQ0sUQG7bbDYlWpjPZyF6fdCmlaAOx3GN5dnZWcRisT6pzxpPwEkU\\n+cLCglyqxcVFpFIpUaOIcCh5+XwyFh2Cb9ph9MHXuX9aXeN8aacbh+zWyW5/aQeh00B786iGkIlr\\n1Z17y0vIuTudTmG6vEQ0EPOSAhBBowM+J1HZzHlxrDxvOks/EAhIfzYKAAByBjWS1+EYRFVETTRy\\ncx5EG9pswR510yIik3h+iC61IONZpm2IaI/3jsxJl4Xh38nY9H5q9ZPnGDg5F9qmzHXRQm9qlW0Q\\nmQZDrUvT88QYJB1IxknxZx3VSunJyeXzeenbBRwfrJWVFeRyORSLxYGRyuMyKK/XiwsXLmB1dRXh\\ncFiaIVI1CwaDAkGZ3U3VhkGPtVpNgkIZ6cz3cIPJ0CzLkox6XhTm7+naQVwn01Co1YhJyU4d0nvY\\nbreRzWbhcDiQTCYlcE7bXMznacSq45J0sS8y03K5jEKhIB4vM1LatFWOQ/rg60uj14x2z2AwiI2N\\nDaytreHx48fiNNEqGcdDbxvtSlrd097JYDCIRCIBh8MhYTBsLGlnM5oU3WsU6Pf7kUqlMDc3J6Em\\n7XYbgUAA1WpVStwEg8E+4UD1U6dx6c7RprrN/MdUKtVXc5yqHgUSURLTnkbVYsbuOgL09wpzOBwy\\nMO0ByufzEo2sLyUPmGnk1t/H9zGgjOjA4XBIZxM9HnOi40qftbU1JJNJRKNRMU4Cx+5bvqZjhJrN\\npqAEbhJLguhKlzRcc6O1vYseGafT2Zc6QklGiRqNRvtiW2jX0FGzo5JmQuaemu9h2Vadl2UaqzlW\\nfs6E79qbys/QVlcqlbC/vy+w3i5P0TSSTzJP4ISJAv0GaQCCImgz4z8KCu1I4bnUe8ezwu/Swa2c\\nr0ZJJDumNAnxPNHrmkgk5NkMoWHdJ6qmel76TPJ88XwwO4AhIbSxzczMIBqNolAoSMkaXU5IOy0G\\nzXcQjZzLZl54zahY8lPH1JTLZbHy8wJp6K2lntaBtXuZE+OBYCyL1uXNsU0C8WdnZ7GwsIBEIiEH\\nqdFoiBTUnj9KQh5qIjqW8mVdKI1oTE+StmF0Oh1UKhWp0aztZvxORqazVQ1DCyqVyljzNA3/dkyc\\n+9npdPq6uOraOdwfuzPB1ymEAIg9kBfT4/GgUqlIECmhvl4jzVSm9bJpAcDzpZE5hahmjHr96TnS\\n6jVNExSU2g6mkaBGwfzZnJNdvM64c/V6vdIIUnsHNeoJBoPSyBLov8faw8jx6Zr4rDjB9wUCAemv\\nSHSv0SyZr3ZcjJJSMpXKRqKeyYnRHU5dmpyYA9UxHtw4Siktzbh4jABnBHG9XpeKfiZTmgT+vvHG\\nG7h48SIymQyePXuGzc1NKfi+vb0tEDcSiSASiSCTyYgRnE3x2u12Xy5buVxGp9ORhGMyEV0ZkheA\\njMayLKllQxWP9ifmv21tbUlt7l6vh7/8y78ceZ52app+nev+/vvvy8VLp9PCkMhoNPPSzge+rl3o\\nRBE8iB6PB5FIBFtbW2i32/jqV78qa8B1NFWucffTTkgBkDUn4mR0MrMJ9Jh5FikINap3OBzS5BRA\\nXwjH7u6uIIdIJIJut4tgMChR0Fo94gWelBnp/aS7n+EKlUoF+/v7Ej1eKBSwurqKtbW1vooLjI0j\\nA4tEIjI+qqmxWAwAJBauXq9LUCVrw1PV5v1nNQc+i+M9k8BIfenNy68NmwD6vENENGymp6UoVR4u\\njjZca5ekRlf6UJuh6ZPq4QDw8OFDSd8AjlvycFE1zKR01/D+4OBA9HWdIU0vGp/LQ8siZUyk1bWY\\n+TleYK3rc7MvXrwofyuXyxPNV5Np09DGWqrcpmHSvOwmYiZp1MM9ZnH4Wq0mBmKuBT2Xg6o4TEpc\\nO6r/FJwUhtwPvk5UoS8Sz5v2mmpUpF3/2o7G86BrKp2FqkbSdiTeOwbocq94z2ZmZiTsgfdVBz5y\\nPUKhUF9JHKrZDEWhXVUzQO0N5u864Jfrcdp8R/Ky2f0P9Hcr5Qaxs0ir1ZJYDEok2mc0E+PCmEZa\\n5nh5PB65zAzispMqenzjbvIHH3wgOTyBQEAMdp1OB/F4XAyZtB3NzMxIEjFD6GmwptuThnjGc+jK\\nj0dHR1LIi7o+mTRwUlqUcyUsZgH1aDQqkbbjkN0Ft9vfcDgsAZha1daX0HRd82887EQXZGg87Mzp\\nYyF9pg0BkN81GuGzpyUiTY5F27S0GsFxk+nTPqhVNT7PnCPTn3hJTVMEvWB2ezANSuKl575QxdQB\\njQ7HSYMD87sJAPgeNu8E+u3FlmVJHuns7Ky0QNcMTjNyPl8LszNT2ewkltbLOSj2A6dnjQhJS2Ae\\nZkomvs7n8xLQrc3LT5sOJ25OeNA4T6NsNosf/vCH+OlPf4pwOIz5+Xn4fD5Eo1Gsr68L5KZhm7EZ\\nh4eHUohON9+jru1wHHcy5WH7f+192W+b6XX+Q4r7LlLULlmW5W28jz3jcZo06SRFiyBFUTS96U3R\\ny/a6QO/Sf6EX+Q8KdANaFM0gQNtMZpoms2U8k/FuS7b2hSIl7iJFSeTvQniODl9/Hzep/c2FDmCY\\nIr/lXc/7nF2bnrVTJKPHdYgGFwThMHUcRBkejwdjY2Nd9dNOZCPxJI1Go5JWt16vN5Um0nNGMkU5\\nPkvrzLgx6Uz3/PlzSdHCjcrKMtS/6Wcdh9hftsnj8YibBvtsbhTTRYEiHn/TY0hzv9PpxMzMDCKR\\niIg7jUYDm5ubTQzCas0ep48mIqPhIxwOi7jGIpUMnGXVHiIZ022gWq1KcjeOw97eHjY2NlAsFnHh\\nwgX09R0mD0yn03A6j2rseTwe5PN5UVFwrLTLhR11bGXjZ+DIMsaXa2hIi5vmrFrBpSG/FkdMKMv7\\nCQEJA3XSs5MiliVixsRsNotQKIT+/n4JOeAi6+vrQ6FQEMUv3fBpBaNegf3UE0BdBcU7jgGZNxe9\\nw+EQE6r28aEMT/TWq2MkYJ04S6McKjR1OASv1WRuJLaT7TY3L3UV7DfHgbpG83qzrd30T28ELWpp\\nCyk3sWa4PBDN9mtRDziyqOk57u/vlzXBdmgrox7DkxTdNBHZUbpguScq6ykamzoz7bZh+r6x39oB\\nGoDsd+qiaBnX67QblUpHCdrMweLfPK2BoyBJKpx5qtMMTouEhpbctFq21hYonlxutxvb29vi46GT\\nixOdHQficyMCh+iHJm6Hw4GPP/5YRDatB+DJSQsgmTFJ95UbgroSXq+Rpel7o8fCtN5okfWv//qv\\nu+prq/Hhe2lFDIVC8k59CBDl6dOPm9hERmRm9XpddGyMnGcpdSpA7cz/xyW2h5tTFyxl/iI6Ler4\\nLI1cnE6nBE3rGD8tjgIQyyuLa3LNcJ1oPepJkRa3qIyn6FUul5vSSLPwpV6PAJoQEnM8sd2cE334\\nUiJoNJrzQFE1oRkyYM2ErajnMkj69NYnCE9/bUngaUgFGZWZmkxXfZrOOWCVSkV0OCayatfeTshu\\nwHgq8L2MUeNpqZWDprJXb1Y9PnyHlrf1YtWmb75HW660EvY4/bU7tThHRHQUG/UYkaFysep/vIZo\\nhExUb27NvNgfLdKeNGnmQTRLpsNNPD4+joGBARGhOTc8aChONhoNMVoAh24j7Jd+H/C6mP6/Rdxv\\nfA+RDLOUsh/MWUTXB+Bo7fIfGRr1UTQosb5grVYTP0PNnMxMB9pi3il1lcJWn9acSC2GaTFFW4po\\nSdEKaf6tFyd9QzRk5sYrl8tNJXtM9HGczWn2U//N9/OkNxmw1Xs1stRt0/oD/bu25vA6PeYch5Nk\\nvroNmolw8RHRWvmTaC9fMhurseGzeLBoJKiVoDyxTcXxccnsIw8S6k0YysO+jY6OCnJj6g66JOh1\\nSx0NfZU0eiyVSoJItDMsXQ44NhzTk0KDei1qdUmlUhEvex6ojOnT7gwUl+llTXREcEEfO1b6rVar\\n8o/P5CGkk9HZ7Ss76srsb4pwugMs5AgcWcgajYbImDTzmjl3yaS0eV8H+fGdZEh0XuS7rCBhtwpC\\nK2TD9+oN1uo+q/Ey77X73q4ddu+zEqO7JfMZWuyYmJhALBZDoVBAMpmUlCkMtdCevkS6WkGqmSxP\\n2nq9LgnZqtUqgsGgFIEIhULo6ztMoJbL5eR0NxFjJ6TnTSNRAKLMpUjq9/uRSqXw/vvv45e//CX6\\n+/vh8/madIZkXET5NK0zNIJMLpvNijX54sWLuHjxYlO9OwCiF9TtbGdo6GQegSODBPNNUb2Rz+dF\\nPCVjJCIiAKAVm32JRqNIJpOivtBtIzPib6VSSUKs4vG4eG47nU5JlduNr1VHIpseNDsGoP/miUFu\\nSZShlYD6xDUhu9ZRkbiQ9e+m277Zxm6oG07eiqlYoS2799iNpxWjMq87iX7qZ2i9Bg0VdGngqc8s\\nBTwMtG8P9Wn0RdHiA/+uVCqiv6Fvmt4sRLzH2aRW9xB1U7fBQ5R1ypaWlvDxxx9LGaBYLCY+cFQd\\nEMnxkKXeyeVySYFIOneGw2GMj483MSOiRZNhHvdw4f6jxZvzSAbDdpGYDYDtoWFKlziiPorzQRTJ\\n+aTOj+8ol8twu90YHx8XsVaL491QT1Y2oDkkggxFQ0V69tK8aDqW8dTSyj7NSbn4Ka6wtplWKFuJ\\nTJ3Awnb9tGIonZ5mVmjIigno52lEYYeazPf3uojt5pPEzZtKpfDhhx9KylwtSumIfG5W9kOLAMCR\\nDsXtdov1hWlbdK4n4CgIV4uAvcyn7hc3J1EMLcAUtWhtY5nsdDrdZGHU/QKOChZw/RMBENXv7+9j\\naWkJExMTOHfunIQS8ZpWYn4vpMU/7f9GF5mVlRVsbW01xZdpJ1B6WGvphm3U4izfQYdIFoTwer2o\\nVCpiwNKoSx9OnVLXZZD094TWOrGV3+9HJBIRDtvXd1SSWZ8QHEwta9NxS6dCoMwaDofR398voRVk\\nWCzhTdHhuDK51QlGaseI7NCQFRPQTMlqwqwYUKdt6YTMTauf+atf/Qr5fB6PHz+WDc3NyXnSZnGT\\ntIUROFKS62IOzItO5KIr8ZqiZLeb1TxQ2M50Oi0+cvF4HJFIRBgiFbXmmJjzZfUOPT/7+/tYX1/H\\n2toanM7DElj7+/tYXl5uas9JoHpN0WgUw8PDmJycxOjoqDyTaGV3d1f8kTh/9LWr1+uIRCKy55i2\\n2efzSVVjhpUMDw8Ls6pUKpKxob+/H1NTU2g0GlL4olQqifhojqsd9WRlI5Hjay07s83xZGS4BHCU\\n0kGfOGRsBweHOYQ0QyoWi/L8UCgkSdNCoZDARnpBnwR1A6HtRLNWIpcVmuvkPVZ/94oE9b3ms4lu\\nvvzyyya3C+0r5XQ6ZT55j6kX0d9TbAeaGbRGh9RJAGgSD47LdHV/yXRoOaLLCtEBHU9bzYndb6Z1\\nbX//sFxQMBhEf3+/pP3QVrZ2SLXbvhG1DA8PY3h4WAK2qbym02k2m8XQ0JA431Kc0y461H0xXq/R\\nOPRLi8fj8t6trS3J2DA2Nobh4WFBTX6/H7FYDOVyWZh8p/uqLULSZHVC1Ot1bG5uSrXKV69eYWVl\\npQmy6/wqTPavJ4QTpX1UAIjDFZOeMWKZug3GkJkiz3EWst2Gb4Vy7MZIX2vHlOyeYyIYKzTV7UK2\\nEiGtiE50OvTAynlVf2+2SXs+W/3O/zc2NlAqlbCxsYG1tTXJetBqvDrpo/k+RhGsra1hfn4eLtdh\\nemGKj602jNVvJlPXf5dKJeRyOSwtLWF/fx/pdBqLi4uYn58XPai5lnpds0Qr+XweCwsL8Hg8yGaz\\nokKZn59HKpXC/v4+Hj58CIfDgZGRkSbHRofDgXg8jpWVFfGypvTDsaM4Sp3h5uYmnj9/jlKphEwm\\ng4WFBQmaXl9fx/LyMra3t5t0hJ30seNYNvOhesGsrq4iGAxie3sbjx8/xsuXLwUKa38TeuPq2BmX\\ny4VEIiFyZzqdFk09Y8SYz2V6eloyCdA0S8uGblMv1Mmi43fmNeZ42I2V+Uy7sbUiq8Ohl762pl62\\nKgAAIABJREFUQ29aFCci0j5SVqd6O1RhJ87u7e1JBWLgyBqmGd9J6Fb4uVgsYnFxER6PB7VaDbFY\\nDLlcDgsLC69Z8joVwfm9vj6Xy2F2dhbPnz9HKpXC9vY2vvzyS3z55Zfi13NSIhvbsr6+jidPniCT\\nyWBgYEBQzsOHD7G5uYlKpYLPP/8cn332mSTvZxYHt9uN0dFRBINBQbNUYOvwrdnZWaTTabGa890u\\nl0vSO29sbCCTyWBlZUV0hLqtbfvTOClcfEqndEqndEw6eZfYUzqlUzqlHumUIZ3SKZ3S14ZOGdIp\\nndIpfW3olCGd0imd0teGThnSKZ3SKX1t6JQhndIpndLXhk4Z0imd0il9beiUIZ3SKZ3S14ZaemoH\\ng8G23sSduIS3Cx7tJqRC32/3bABdVeRgJYZOPGat3msVqqB/a+elre/Tv3cSVtBNKaREItGRN7jd\\nXOjc0/SyB45yo+trGNvI4GcG1tq91/R2Nn/Xga+tiMUR7IKT7eZNUzcxZp2smU69sHWMYDuKRqNN\\nYUB2HvFWbewmIqAVmeOrYwFNb3TdvlYFTtsG19otEP5uFR5hNkLfoxeufo++xuyAvsZsQyftbUdm\\nnFirEBCzzVZtNftr1W677/Wzzf+PG3CqY/50u/m5FcPU/dOVaxleop/DBHwOh0OYla5kq/tktsdq\\nvrvps1XMH8lMst+KemVGZvtbMYrjkFmeSv9vUrs22l3X7je9TnSAbzgclrhEnaKoE2opslk1vJNO\\nW8UiWU2QVYBhu0FpNfjHiQfS1OlmsFv45r3ctHbPNJmeTpLfaDSaAluPw5Ba9VN/ZzU/+l6zPfoz\\n22v1jk7n9jjU7gC1u7bd2rJ7l7nZTNTSzfO6oZPOO95qX9shenO/MkXJyMiI5EXi/Z2u346Cazvl\\n8naMxryfp6h5ylpdp59rvks/+/8H2W1YIgLmAWJGRXNjmAtZo0ad0F+X1m5X1+q4/bHbZJpYi4/t\\nZX0uzUDN3NjmPJ0kwu2EWq0h83M31Gr9WR24+m+7tnRKraSTTu4z29YJQjIPz0bjsP5cMBjE22+/\\nLUnaXrx4gUKhYCm6taJj5UPqlMyNqzMCmv+zlIpOMG+1SZj1z2oT9bKoW01It+PQaDQwMTGBq1ev\\nIhgMSqpXXXGEuaR0wnzm5nG5XCgUCnA4DuXyc+fOoVgs4sGDB3jx4oWkjDjJftqhJU28T1dn7e/v\\nRzKZxIMHD5DNZiWvFfP/sDTW7u7ua8UkdXtOkhlZ6UzsUFK7/rZrl8kU7Bif2R7zPb1Sp6Ilr7UT\\nxTt5DkuB+f1+zMzMyD6emJjA2NgYvvvd7yKXy6FUKuHp06eS0lfnKzsWQmrHPVudenYc1ZR7zYZy\\nEVsVmDPbY1XtolfE1Grx2YmdVv0gnT17Ft///vfR398v2Sx1PTOm5aXszUoPsVgMTqcT29vbcs/E\\nxASWlpYkvw2ryp5EP1sxKH2NRjtM/+rxeDAyMoKLFy9icXFRGBJL5nAumcua/TfXjNVmNtfNcfpo\\n1SfzHfzbpG70TnbX/G+h+E7n0ur3bg4A3X63241oNIrf+73fEz3hxYsXEY/HkUwmpYjqyMgIisUi\\nstlsVwkUT1Rks7ufpBP0A80DxTJITNDWyUlmKmmt3tlNOztFSHZQn/e7XC6MjY1henoaQ0NDkoeZ\\nuWWY4nVnZwcHBwcIBoNNBTXr9Tqy2SwcDoekEXW5XOjv739NgdxrP7vZYHr+HQ6HVN2gzoApT3d2\\ndkTEjMViTWWEWE69nXhj9Xu3/WzVx0773QmiMkkjdStEZDdvxxHXrA7jVmjMau22W/eNxmGyvoGB\\nAQwMDGBqagpvvvmmiOZnz55t0hkxNa7f70c+n+9qzR2rcp0dxDU/ezweqW7ARO6mCAYcKsVisViT\\naKOfZfU+80TtpuSKXZ/MU9vsT6t73W63nBbMI81kddVqVUplM9Unq19wTNbX16UqBuvelctlZDKZ\\npgT1vfbNJBMFtUK8TNpVrVbhcDgwODgIp9OJ2dlZvPXWW3j33XcRjUalciuZ7osXL+BwOKT6cDfI\\np9uDsN0z7d5vhcJ13zt5j/k83tduTZoItBeyQpa9ioJ6/RPVTk1N4U/+5E9w5coVXLhwASMjI7JP\\nC4UCSqUSfD4fisWilF4y+9XJHuqo6ojZuVYblgtXT3oikUAoFJJ0oczja54ojUZD4J3WN1hBfLNt\\n+tpeFL92KLCTQdT39PX1IRKJIBwOC0Oi/ohFE3d3dzE/Py+ljGdmZgBAKuQyix83NHM0s37WccQ1\\nwBq6W82pVf/I9B2OQ31fOBxGsVjEzMwMRkdHARxmTPR6vdjc3BTzLzOK2o211SbqBZW3E6/brSWz\\nfe3EsFZIyq4NVu3tllqJXt0802p/020jGAxiZGQEiUQC58+fx5tvvomBgQExYnAtMB+33++XirYj\\nIyNSgom+cieCkOzk7Fbytr5PKz6LxaIkc7fSFzBvLwsEmMzKjjmSkwM4thWqnUhhkjkOTqcT0WgU\\nkUhE9EXMKU6IyxSn6XQa9XodoVBICvCNj48jGAzC7/ejVCoJQyuVSnIiHbdarymCmf00n2sW9uT7\\nA4GAKO1nZmZw4cIF1Go1vHr1Cl6vF7lcDisrK1LLq53+ptf+mO23YmpWG9cO0ditNZPMcTwOU+q2\\n73Ztt/rd/N6uT/xeO7WeO3cOFy5cwPXr13Hr1i1RpzC3NgDE43ExyAQCAezu7mJsbAyVSgUbGxuW\\n77Cjtkpt5sR1OBwibujf9SCbXJa/Xbt2DW+88QYKhQL++7//G5VKBTs7O68NFBmSrmSrT2S7Ntbr\\ndfT39yORSCCTybw2CO3I7gTtVCTV/Xc4HIhGozJJxWJRFH3hcBhDQ0PweDxIpVKYnZ3F+vo6/uIv\\n/qKpPND29jb+4R/+AW+88Qbeeecd1Go1+P1+7O/vSwL+kyC7zdqOWZDJ1mo1qXL75MkTfPDBB5ib\\nm8Pc3BwAYGBgAL/zO78jtcJ+/OMfW+o0Ws1tr/oVfa/5fzsUaCf+6Pzwer7bPdeKyVu1txeyemcv\\n/eR9+/v7+OM//mO88cYbmJmZwa1bt8TDn46Ouv/Mg05RvlqtIhKJ4OrVq/B6vZifn8fs7GyT+NqK\\n2iIkzf1okqfYoatxas9cdozMhRvU4XBIKWYyEj1ANCuyEIDufCs6ODhANBrF5OQk+vr6Og4zINmJ\\nMuZv7WA3LUw+n08U2CyYp4spUs80NDQkeif+RgZcLpdlfOn3Y7ahV8bUqajR7v5qtSrrYHt7W+A6\\n541lsACIpVG3vVNxuNtS2rqNdv1rxTz0e7kmaVkEIOKzeQj3yjx7oVbit9mXTtrAAqEOhwPJZBJj\\nY2MYGBhANBqVUuqsNsz9yz1KyYjqlkajgYWFBbx69aqr0CagDUMaGRnB5cuXZaOzoF+xWMT29rYo\\nZL1er1QBJSMql8ti2h4cHEQymZQSxITwWvRwOBxSiK5araJUKqFQKIgfQ6uF5XA4MD4+juvXrzeZ\\n2Y9DrU62Vqe80+nE4OAg4vG41FmvVqtSRLPROLRYXL16FQMDA6JPo1jncrng9/tx+/ZtDA8PyyQH\\nAgGMj48jkUhga2urKXTgJPppfqf7xd95ONAiuLu7i7W1NTmo2K87d+6I7mt7exurq6tYWVlp+/7j\\nbu5u+mj1vdkO/h4KhTA6OoqpqSlBsC9evBC3DVO10ApxWh2uvYpsrUS/Vs+zQkkApAZfKBSS+M7t\\n7W1ZazxcHA5H054HIPrQSqWCtbU1/O3f/i1WV1cBHBkSOkGCLRnSwMAAZmZmcPHiRRSLReRyOYyM\\njAjjYeFH+pywblOtVkM+n5dSRePj44hEIkilUqjVaujr60N/f39TmeF6vY54PI5z587B6XSiVCpJ\\nwUJdSkWTrmleqVSQTqexu7uLZDLZstNWZIov3Zwu5qT6/X74/X6pL0YmTAbO0tQ0je/s7MDhcIhH\\nttfrxfT0NNxuN8rlMmKxGMbGxvCtb30LDx8+xK9+9au2YoBdW4H2G8Rqk/J9jUZDdHy1Wg0+nw8j\\nIyOCJFi2mRVR8/k8tre3kclkbN/Xrs3dMF6rPnYqRpmMmNcFg0GEw2Hcvn0byWQS2WwWH3zwAZ4+\\nfYpyuYxcLtf0XCvfpVb97lVka8eUun0fD8tYLIZoNCpWXgDimc/nax9AjSQ3NjbwxRdfiEuL6Qzb\\nrm0tGdLExASmp6dx4cIFbGxswOv1IhwOIxAIIBAIiN8Ba0CxGB8domq1GkqlEsbGxsSHplarwe12\\nY2hoSCxvwCHcYyngYDCIvb098UquVqvCpc3FQneCSqWCTCaDer2OcDjcstNWZMrT/NyJSEHitWRA\\nbDPbSEsEJ9rn84l4y+qpDDcJBoMoFAooFAoYHBzE+Pg4otEoZmZm8Nlnn9mixk7bave5Xb81w97f\\n34fL5cLQ0JCclisrK9jb28PAwACAw4W8u7uLbDYLr9fbEVLR33WLHqx0OnaIz/xsJe7wgBgcHMS9\\ne/cwNTUl67FarWJ5eRm5XM72XXrcrMbxOLqjXslqrVNvyzAQHppaHcMir7yXTJ3MyePxYH19HZ9/\\n/jm2t7d76l9LhnT9+nV4vV6srq5id3dXxCwiIOpHWPIaOJIjI5EIpqencf78eSwtLeHjjz9GsVjE\\n22+/jbfeeguJRAI+nw/lchnpdBq5XA7xeBxjY2NSKfNP//RPsby8jJcvXyKdTqPRaKBcLotu4vz5\\n801oIxQKAegd7tvJ4K30LZrYrnq9Ls6AiUQCbrdbnB9ZhJGokqIcGTp9lnj/zs4OdnZ2pLjfu+++\\nCwB48OABPvroo576SWqnR7H7LhaLIZFI4MKFC7hy5YogOpqE/+u//gs//elP8fu///v40Y9+hH/+\\n53/Gf/zHf+D+/fuWTMYMJTpJ0uJCKx8oU1ENQDznb926hd/+7d/G6OgoHA4Hcrkc7t69ix/84Af4\\n+c9/jh/96EfiZ6Yr9pK0uKJVFDp+sxcyUb3uV7fPrdVqmJ6exjvvvIPbt29jdHS0KUMDVQnUGVWr\\nVSnmqg/cer2OpaUlQdHUOXGO21nBWzKknZ0dpNNpBINBUcZy4uhbw7AANtTr9QKAiGP5fB4vX77E\\ny5cvRUfCWuPAUQ6YUCiEvr4+ZLNZZDIZ5HI53LhxA8lkEvl8HrFYTK4n6hgbGxOUQc/h/f39rlzV\\ngfaT2MnE1ut1JJNJDA0NYWBgQMRZPpfXaG91jp/f7xdmtru7K8YAin/AkYw+NjaGb3zjGyiVSvj0\\n00+76if70gol2F2r9UgjIyOYmJgQ3yLm5tnd3cXOzo6IcdSjcfHaMZx2YmO3/bPrJ7+3E9X0NaFQ\\nCOFwGJcvX8atW7cwODiIRqOBWq2GQqEAv9+PgYEBXLp0Cd/4xjcwPz+P+fl5MWyYohuffe7cOQSD\\nQTidTmxubmJzc/PY/dT/k1oZg6x0XNFoFMPDwwgEAvjNb36DZ8+eoV6vywHq8/kQj8eRSCQwODiI\\nSCQCAFJunYw2FovhypUr+OSTT0TxT5GX66AVtWRICwsLcho6nU4EAgHpCPVFe3t78Hq9Yg2ik1Qo\\nFBKl58OHD/Hq1SvRHVFUY0f6+voEzi8tLeHZs2dYX1/HzZs34fF4MDw8DL/fL+7p9IUgImIkPZWr\\n3SS64oSYE9XNhqAObHh4GFNTU4jH4/D7/dIWujM4HA7x8aB4m8/n0d/fL+0g6iSEpnjHZ8TjcYRC\\nIbx48aInj219otqhQStxg+N+cHCA6elpXLx4EalUCisrK4hEImg0GsjlcsjlcohEIhgcHMTg4CBS\\nqRQ2NzexsbHRk96rl/6xzfxfHwh2zFdbiRnmMz4+jrt37+Kb3/ymGGqq1SqKxaIYZs6cOYPvfe97\\n+Pd//3c5dDUC4rO5Zi9fvoypqSl4PB784he/QCqV6siS3Kqfdqi23b283+l0Ih6Piy7wP//zP5HJ\\nZFCpVEQsbzQaiEQiuH79Oq5du4ZvfetbCAQCYhWv1WoIhULo7+/HlStX8ODBA2xsbMDhcGB0dBRn\\nzpyx9EsyqeWKZv3zwcFBHBwciPmTnaL5f3d3V0zZWonl9/sRjUbxne98B7dv30a5XBYdEpXeZGzV\\nalXMqrFYDC6XCxsbG9jf3xezOZ9t5QQZDoebBqdXaqdgtDt16/U67t69i+9973sYHh5GLpfD0tKS\\nKArpw1Wv1xGLxTA1NSUmVW6ERqPRlOmATGNvbw/1eh17e3sIh8Nwu92CQHohq/7ZMWUtxtCAMDMz\\ng9u3b2NjYwPpdBp7e3sYGhpCLBbDysqKpJ6IRCI4d+4czpw5gxs3buCDDz5oiVxOQmQzN6bH42nK\\njmCnaGb/BgYGcO7cObz77rs4e/YsBgYG4Pf7EYlEBJ3H43HU63U8ffoUkUgE7777Lq5evYpUKoWf\\n/vSnWFtbw7Nnz5BOp+F0OhEOh3Ht2jXcvHkTP/zhD+VZfX19iEajWF1dxbNnz3ruZy9Mnmv22rVr\\nmJiYwMzMDBqNBr766is8efIEuVxO9tfBwQHq9To8Hg8eP36M999/H1euXIHT6RQEzzWcTCZx7tw5\\nXLlyBePj40gmk/jd3/1dvPHGG3j06BHee++9lu1qyZBCoRCGh4cxNjaGcrmMg4MDQUOEr2QCbrcb\\ngUBABoeKWp/Ph8uXL8sJmk6nxRubsS88eYHDtLkTExMizpBhmWIP0QOREUM1eHJ1Ozm9XqsX9Zkz\\nZ3D58mW43W4sLS3JgmQ0P0VbRvYzno390noMjjGZmCaKx70gJDvFq3631nloYptCoZDE6hF10FSc\\nSCQAHIaPFAoFuFwuJJNJTE9P4+c//7nt2JntaXVdJ6SRvO6X1f88aD0eD86ePYsrV67gzp07mJiY\\nwObmJiqVilhuiXpoda7X67hw4QKmpqbgdDqxs7ODL7/8Ug5e6gsvXbqEe/fu4dKlS/D5fMhkMpie\\nnhajzUkgRztx3G786vU6JicncePGDczMzGBxcRGVSkW8sM30PgcHB9jc3BTRXKcl1iCE+kUAGBsb\\nw507dzA9PS37oRW1NfsPDg5icnISq6urYrql4pYiiYb/1H9EIhHs7u5iYWEBk5OTCIVCInrUajXZ\\njJFIBIVCQWJj3G63iGKlUgmNRqPJ+sTBOjg4gM/nE30STYzaGa9TaqVTsVooVicr5XB6tXLSqtWq\\nKK9DoZBMMpk4ZXCeQtxA9NECXvdu1UykW2KfTN8QK6UovzPN2DrWjmEta2trCAQCiEajuHPnDrLZ\\nLJLJJNLptOgN6HjH8bIbU5O62axmm/nZfAYtoIFAAKOjo0gmk+IbxgBvfXjygKVejweF0+lEPp9H\\nMBiE1+sVhfCVK1eQy+VEHTE6OorR0VHZ8ABESb62ttZ1yFM7Mc3Ui1ld53a7cfbsWbzzzjuIxWKo\\n1WqYnJzEq1evZD/q8dNrpVAoIB6PC9LimEYiEVy8eFF87hg2VqvVkMvlsLi42LJfLXfuuXPnMDo6\\niv7+fmxtbUmSLeozyuWyOEDSCY4hBdFoFPl8HouLi+Ic6XAcevdmMhnJAEAm5PV6RQSkSFYqlQAc\\nLkiNGDRC4OB6PB44nU7Z9L2SHTOyOm34mQwzHo8jEAigUqkgFArhzJkzKBaL2N3dRT6fRzgcxsTE\\nBBKJhJiNXS4XEomEiK08rYmmyDy09zNj5NopCFv1z0phrb9n//iZB4DX68XW1hZevXqFQqGA9fV1\\nrK6uYnV1FeFwGCMjIxgcHBQry+LiItLptCScM59rtssc727JfAfHkEiOa2NiYgLDw8O4desWxsfH\\nMTo6ivPnzze5Kezt7cHv9yMQCDQxDLplcN3T58br9eLSpUu4du3aaznEd3Z2BDWSpqamMDk5iVQq\\nJcUJToJMHZHV91Ta37hxA1evXhVLWTQaxfPnz5HP51/TN+pDkrpRfk/VCuM46UfHfFg7OzvY3NzE\\n/Px8y7a33LkHBwdi2iMyIXendY3Jxti4XC6HnZ0dgaMAkEqlUCqVEA6HUS6XJV0BRROiAq0E1roU\\nWp+4yLU/D3UvZGq9eC9bLXytaGyHkvr6+hAKhWTh63geRjs/e/YM/f39mJ6ehs/nEyZ1cHCAgYGB\\nppPG5XKJUyUZkIbG3FTH1ZWZoku768nw19fXEQwGAQCbm5tYWFhArVZDIBDAmTNnEIvFRFmfSCQE\\n3mvPXnMsTTHRznrUCfF5Zv4t9sHlcuHq1au4du0a/uAP/kBQDDMy8HDb29tDJBIRi7F2b6HhgWvR\\n4XDIIe3z+eRQZb+q1apcz++JkKna6IbMcbEbJ7v59Xg8YiChdZouDvTStlK2cz0S0XOMG42G+Cjp\\nuEyOC9H/sWLZgsGgmP4rlUrTSUP04nA4xLLm8/kkA2I4HJbOUNbkBNCxkqIWcwZZQWutxNbiGcNQ\\nyIFLpZKIDIwj65Tayd1Wm5XX7O3tYWxsDJcvX0YymRTmTV1WMplEKpXC559/jlKphOnpaQQCAcRi\\nMezu7goDJ3MnEtQ6MtN1f3d3V6xB3ZLdArVb0PqEpD/Vp59+iq+++gqXL18WL/LPPvsMpVIJs7Oz\\nIp5Ho1H09/cjn88jm83K+/V4myJjq7Z0Qlp88Pl8GB0dFZTDGC1aQ6PRKBwOByqViuTzoVtKoVAQ\\nq1qhUJCkdGQg8XhcYgx5MBItkFlRrNVKYa7darUqhphQKITx8fGe+8xxtRo7q/20v7+PZDKJW7du\\nIZlMisRDqeX27dvY2dnBy5cvkc/n5UCkqN3X1yfSABkN1y5FWR4Ibrcbm5ubePHiBVZXVy39tDS1\\nZEhMGVGpVGTAOcB8IRvBiQyFQqIQ4+RxM9FPifmW+TwG7ZHJaCYEHPngcDD14LPzOoCVOqhuqBPd\\nkd31oVAIY2Nj8Pv9TRkLiBzZxp2dHWxtbSESiUgWSFqBqI8gEqI+zAyqZaQ9kWQvZMVstVLbvAY4\\ncvpsNI58vrgGKL4UCgWZR+oQmXKGMXtAex8au3HuhBqNQ18Yj8eD/v5+nD17VnJ8T09Pi0gZiURQ\\nr9exubkp63lgYEAYL40pZCr1el38xahH4hxxver9Yca46UBxoglaT2n0OAmyYkx6TqlkTyQSwqxN\\nsf3s2bNYWVnB5uamiG4axdJJkoBAZ+WwCgJPp9N48eKFHEqtqCVDKhaL8Pv9goA4GXwRlZv0sSAk\\nphjG77k5yWw4yZwYyphMX0BYS9TETU6GRjTGAeFJxTi5XszhnYgtVopYppadnp6G0+lENpsVhuz1\\neuWk5cmdy+WQyWQQj8dlYZshJkBzsjYyYbfbLXFh6XS66z7a9dNEKHaIEDhEqXfv3sXQ0BD29/ex\\ntrYmYULUexEZDg4OIhQKYW1tTSxJXCPmc7tpbyvyeDy4fPkyzp07JxkVgEOGOjg4KMhcW7Z4aPJv\\nphUmE6IIpxORsboGN6Nm7BRryKR4MLHf/I1WypMKlNZjRjKRJy1r58+fx/Xr1xEKhVCpVKRoA2NB\\nL126hKWlJayurgpz5b4Lh8Oi4NfMiO/TLiuNRgNra2v46quvsLKycjwrW6VSgd/vl8WXy+Xg8Xgk\\nsptiBQecCMXr9UoiNsJ34FARlsvlJAzF5XIJXGa0OyeTneQ/IjTg6LTmyUNmxFOYpvRuJ9BU6FoR\\nFxjp3r17uHv3Lt588034fD7kcjlRhlKcjcfjuHr1KorFIkKhkPhbkfR4MtVDPp+XcRkcHBR/pd/8\\n5je4f/8+vvjii6762I7aIZG9vT2EQiHMzMzg+9//PiYnJzE3Nyc6Qc5PvV6XRF1vvPEGfuu3fguP\\nHz/Go0ePsLCw8FqYiN2JbiraO6Xz58/j7bfflnw81JHQzYLWYepEHQ6H+MsRyTNHuC5yyTnn2iOT\\n4gbUehUATRIF0W9f31HxA+qtPB4PgsEgxsbGOu5jO7Jaw1xjbrcbP/zhD3Hz5k3cuHEDXq9XDn6G\\nLA0PD8PhOHSHmJ6eRiaTEf1hJBJBIpFoknIICPS+4D7Z29vDo0eP8D//8z8olUrHE9kIJwOBQNNJ\\nQc7ITaThKSFbtVqVID2e+rlcDuvr6ygWixIrAxwp/kzFNDtJCwZ9ePgOLigyQ/7rxYRq9Z2d5Yf9\\nPzg4wK1bt3Dx4kWxLDFvDH2nWC6mXj9M3O/z+SS+bW9vrynvEft4cHCAUqmEVCqFVCol0ddOpxPr\\n6+v49a9/jbW1ta6Qg9kHU5cDNJvH9XUUQ6i0TiaTiMVi6O/vRywWk/AXRr1TsR8KhfDWW281GS5M\\nON+tSNaOGJrR19cnBxRRDdEodUFkHlzfXH96rWtdXa1Wk8/cwFRVcE1qx1xm++ReoR4VOMrKSMRB\\nRfJxyAr5cmx1OMu9e/cwMzODSCQikgfbxPvi8Ti+/e1v48KFCxIwTSU4mSpwFEhO0KCNMNTvbm9v\\ni89WOwt4y1/X1tZw9uxZJBIJCcfQMA1Ak7KVjSMn1uZ8OlZyUVQqlSbGphcp0Bz3xY1CEYbv5Yms\\nHSK1abIbshNlzA1KGToej2NgYADnz5/HwMBAU/ECmkEZOsN+RiIRORVp5t/Z2UEsFoPDcRgfuLKy\\ngu3tbXzxxReSe+r69etoNA59fra2trC+vi6yfbd91J/NPlsxYJIWWba3t7G3t4d8Pg+XyyWxbBSZ\\neeo3Gg1ZiNSRdNLm41jYeOhR/HC5XIhEIk0Mn6KWDvqkCKXXkha56A7A+1iwgnNNnRpRAS3Fjcah\\n9z0NOsCRddTv9yMUCuHRo0ddJxU0ydQTmb+NjIyIMn9mZgbhcFj0kZRYqA8sFovyeXx8XPTC1B0S\\nLfFQffLkCTY2NrC1tYWRkREMDQ2JlT2VSiGTyTQBiFbUkiHdv39fUntQkaWV2Jw4IgAyDI/Hg52d\\nHQmXoPgRDAYlz8r6+rqINvSy5qbj88jstBWDi4gLiVaNYDAoNc1SqVRXk6mRQCtrBZnj9PQ07ty5\\ng1u3buHGjRtwOg8zJnKhRiIRlMtllEolcQSbnJzExMSEoEemFtnd3UU0GkU2m0U6ncY58YyCAAAS\\nEUlEQVQ//uM/4unTp/joo49kkfzgBz9APB5HqVTC8vIyFhcXxepzHLKzalotGh46+/v7WF1dRTAY\\nlGT+09PTmJycFOfU0dFR8bb/9a9/Lb5p3Ig8RfU4mwyyV9S0vLyMfD4vllYyDVpimSua7aDBhqiU\\nJz2ZCBmxFtu4rhnETV88fr+/vy+uAgAkcoBSAHB44IZCIcmn3kvKnFbEeUwmk0gkErh37x6++93v\\n4ubNm0gmkyiVSkin05I8EDgMv3K5XJI4sK+vD+FwGIODgwDwmsGGcYo//vGP8eDBA+Tzebz11lu4\\nc+cO/uqv/goHBwf4+OOPMT8//5qhyo7aKrWJbMiQSFR2ayUVk7NVKhXMzs5idHRU6jlFo1Hs7e2h\\nUCggn8+/pksAjhJEcTFSFNRQUpsfiUaIxJiXiS4JnZLJjHS7tIixv7+PgYEB3LlzBzMzM5IfmxNF\\n73NuuGKxiJGREZHHmX0PgKQdoQ/Ty5cv8dlnn+HBgwdYXFyE0+kU5pxKpZBIJGRz9YIAzf5afbYa\\nEwCiW2g0DmuuXbx4EZFIBOl0GtlsFh6PB3/0R3+Era0t5HI51Go1UZJubm6iUCggm80KtDcVm91Y\\nN9sRmQnHdXx8XE5yjj8dT5mvi+NJpKtVAWwfDTS0FjNiX69jnbQQgIiBGulrNQWj4Lk+jkt6LtnP\\n27dv48KFC/j2t78tYT27u7sol8uiI+a+rlQqovfk+mMOd4IQSiWffvopZmdnMTc3h9nZWfErNJ2b\\ns9ms6JM7Udy3ZEjLy8tNDorBYFBECyoKtRKa6IUpGoLBICqVCsbHx+FyubC1tSXohp2m2Vubuzmw\\nlNm1+MYJ1eEH1C1R4d6tB3MrS47+3uVyIRQK4fLly+LpSwc6LirC293dXTE9u91u8TkismF+JIqw\\nKysr+OSTT/D8+XPZvLRAZrNZFAoFERPY5v8rIqrjPAwODkrYA0/Ye/fu4eDgAMvLy5ibmxMxp1Ao\\nIJVKYWtr67W4J6v3HJcKhYIYVQYGBjA0NCQhRtFoVBx7y+WyIHTTi1s78FWr1SaXEs5zJBKR9cjN\\np5W8TqdTEAdwFOTLxIa7u7uCkJiW57hkHtCBQADf/OY3xaLGfUpmBEBcDur1uiidCTa0oYh7m5bw\\nr776Cp9//rkgI6I/MmQy7p2dHclh1slB2nIUNjc38fDhQ7jdbty+fRsXL14UZsNOcFJp6h0YGEAi\\nkZA634xpGhwcxNzcHPr6+pBIJGTDZTIZzM7ONqW8pYgGHE2yad3gIuCJy2ctLy/j0aNH+LM/+7OO\\nJ9L0s9DiGcVSl8uFyclJXLx4UXQ6e3t7eP78OUKhkIR/bG1tweE49N2qVCoYHR2Fz+cTZ7hqtSoB\\niIzv+c53voNMJoOtrS1BiWwX4T7v5ynWi5m4EzRkJTIx5svhcEib3W43vvzyS3z88ccIBAJ46623\\nMDAwICIbF+DKygoWFxfx4MEDObT0O7W4fBKUz+clhuzx48d47733RFczOTkpiuRoNAqv1ysGBhNd\\n0BRPvZEWtYCjw1L7ivEwprqCa5jMzERfXO9EEH/5l3/ZcT9NyYIRE4lEAn/+53+Omzdv4vr161JI\\ngogQOEQtzM7BeWo0GpJbTOeuikajYsX+4osv8JOf/AS//OUvUSqVpH/sK5kx0zdnMhlsb29LXFwn\\n+sO2jpHZbBbPnz/H2bNn4fP5xJ8oHA43MQYqD6lTor8Hs8wRxvGEoAWCCaB0VkVtBeJgUVzhP1pu\\nCJu50Pb397G0tNTxxHJyuVBcLheCwSB8Ph9isRiuXr0q1kK6QHi9XkEuREMUXbTy1OFwYGtrC319\\nfYKOGHLx9OlTzM/PSwpUXd7IFCG5aGi9AzqDv1ZkMp524ps2XlDsmZubw9bWFnZ2diQKvlqtYm1t\\nDdvb21Kh9uDgAGtra0in0xIobTIgO91dr0yKKIAbhCiu0WhgY2NDEA3FZYortLrRMKHbqdERSUcW\\n0GhBkYV7QqsfaGXUbgGUPmjg6Ja0BW18fByDg4OYmJjA7du3cebMGfj9/tfKZlGUAyCOmdpHUO85\\n9vfg4ACvXr3Cz372M8zOzqJarYr+TEspfHYsFpN9QUapJZpW1JIhOZ1OCarN5XKCFuiTwsnUISGU\\n1+lElUgk4HA4UC6XxXObm4uKRk4iRSIuAl1i2lR2c9K1fosew91aLPSicbvdEvJw5swZ/OEf/qFE\\ngFcqFWGITEbm8/mws7MjTqScEJpGs9ksGo2GKFqp53rx4gU+/PBD3L9/XwI5rZTMuo2lUkly8vTq\\npU1Yz/9JVno0/T1wGL+4sbGBZ8+eIZlMwul0StEHzufLly+lQi1R68bGhtT0arUoTStgr/3TfWQ7\\nGo0GUqlUEyPWOklNOhKBf+tx0H5y5jt1O8x38Vn83WRk3faT+8Hj8WBmZgZ37tzBpUuXcPfuXWEq\\n5XJZxEzNqAFIDjKKV2TK7BcP6f39fSwvL+ODDz6QnGZaqtDjxrz7RIrUPXZKbQtFUqFZKpUkqlk7\\ndXEDs+EMaaDmnpY3+msAkMXbaDQkVzQDaKvVqkQOEzpzE2onSKZ6JZGxaSjZDVHsDAQC6O/vFyST\\nSqUkBCIUCgkKy+VyWF1dhdfrlSR2wOGkMMdTKBQSd4BgMCjZMh89eoS/+7u/w/z8PNbW1po2hdWG\\nJarM5/MSNd+JxcKKNOMxF5RpOtbXUR9Ar2y/3y+xYU6nEw8ePEA6ncarV68wPz/fNO/UWZgxhnYM\\nyPzcyclqkuk6oB13rd5jd7+JILXp2kRzJlOyIysRuVvE63A4kEgkJH3KlStXEIlExKBER1r6AdId\\nge4o1P3SdywcDqO/v18Od/b1vffeE2TEIhqmisM0MPl8PmxsbGBhYaGJZ3RCHTlG0hJgKpe1Uxjl\\naB0uQh0ToTDhIfUgAF4TcXSlXMJanh56MfAE4CBoptnL6UpvXcb5UFFerValtA9FLo1QdnZ2EI1G\\nEQ6H0Wg0xOOc/eZEcJwODg6QTqcxOzuLQqHw2oazEl94gjE84zhEEbcTpKKvoVNbX99RIUztaxYI\\nBMTSyWwF5ka1mhdtxdTvJnXLjKzG0qo/Vn+3Ys5601ndb4csrZhXu3s7IcZPUj9Lx9ydnR1kMhkJ\\nYKcagShM+0dRP8lDUwdxs3jHixcv8Otf//q1MlZ6rLQLDgBJUbO9vS0i44nokOr1Om7evIk333wT\\nN2/elLggAFK7HjjaMDrnNuuNMXmXrsig456IdhqNhsA8fQ07z01EcZA6Hep6nj59iidPnmBhYaFr\\ns//4+DgmJiYwNDSERCKB6elpeL1eRCIRjI6Oik8R3RoIgc+ePYtMJoNkMolkMinWEk6wGZW/tLSE\\narWK+/fvY2VlRRCm1UbQxMVNk6p2g+iFtNsEqd2CobI9kUhInNjKygrm5uYwOjoKp9OJ8+fP48yZ\\nM3jw4AFevXqFhYUFzM3NiU9Pu4PCRDXHRUbt9FBmn602G7+3032ZZD6jXV/sROV29Dd/8zfCjKgy\\noK6GEgvXIDO7sj1UnVDZz/fTUk0H3X/7t3/DP/3TP6FYLIqqxA4lUgSNRCIYGhqSODhmC9Bj3ora\\n6pC0HK6TsQGQDUIPTlre9Mne39+PWq0msJ2oyzxxiLo0o8vlcsJdqUSkkhw4qlSyv7+P9fV1KZfU\\ni5/O6OgoZmZmEAqFEIvFpE4aXRTMhcMJogxPc77b7ZbadLoAAnDIkOi7wbG1Ywq6D2T4+nTrlRnp\\njaUXlR1a0N+zEi8PE/qpsJhlX1+ftI9Il17RWo9i9R7d314V2vp5Zl+tqN0YmuKt1T3tGJNuk9WY\\n9tpPVuKhoYh+X2QKXHP0IDdFLaac1ilSKM49fPgQT58+xdzcnLh7tForAKQddPQk86Lvmd34mdTW\\n+UH7ArEWGxkSTZaxWAx+v190PxRdWBaI4SHAoa6HKQ/IwTkpNBnqLAJESdyUdAnQybTK5TJWVlaw\\nsLAgnrfdkNPpxPj4OM6fPy+euVSys+3MGqjhqfap4nVsNwNOKeJUq1WsrKzgF7/4hVjerE5e8wR2\\nOByii6GFzW5zd0vmZmt1DQBJV0tDAsMOdnd35aAolUqyDoAj6xVFWN03/Y5u2tSKTBTTSbiCVX+t\\nxAw7lNTugDDFv5OghYUFcXQk2gGOfOXoIqORtdZXcY/xvlqtJpbTDz74AM+ePcPy8rIYk9h/u/bT\\n74pzp+/pBgW2VWrfv38fjx49EoUsO60rXGoPUMZnUefATUrv2EajIX45Oq2I9uUgV81kMsJ8NPIi\\ntKSIVygUsLa2Jk5c3SKkbDaL+fl5JBIJSc1A35RsNtuUzYB6MiIcyuWsaU5iErZgMIh8Po/PPvsM\\nH374IVZWVpocQe3GXVs6qLshGiH1srjtFoeVvkP/5nK5kM/n8eDBA/zkJz/BhQsX4Pf7MTk5CZ/P\\nh3/913/FysqKeGXT54qirplsn20herLSbXUidrXqI/B6PnDzd/M+q8/md+ZvnSqkT+ogAYB/+Zd/\\nwfvvvy8e/wx8pY9VOBxGIpGQmDNav83SXAsLC5JWd3t7G4VCARsbG6hWq03qgVbrg98xsHx7exvP\\nnz/H/Py8xDu2uldTW4bE6gp0hiMMYxa+WCwmZl7mj+ZJzs3LQdO+PkQb3HDaU5XKXwZmUhnOz2RI\\nFAOZ2c/KnN0J1Wo1rK+v4/nz53La85ShXohIxeFwiOMXFYOUn6msZ0L/QCCATCYjohpdKPT46s+m\\n+MIFTJHXPI2Po2Mx/253wusxpcNrvX6YwSAUCmFxcRFLS0uiyKfIQDFdK/j1u00mZdXebvup0aZW\\nO2hE0+pd3TDAXufguGipWCwKCmeF3UgkIsrscDgs3+ncRWRIlHRYsqrRaDSFititk1Z9ogFocXER\\nL1++xOrqapOTZSf9bSuyUbQgU+AE83SrVqvIZrNNWnwyGyKecrlsG8dEp0Y2mjXMnE5nUxItjU4o\\nMtKlXyvcelkgtVoNi4uLyGQyePLkCZ49e4bh4WFEo1GxZNCjl5Hs9Ali6AdwpNMKBAKiC/vZz36G\\nJ0+eYG5urmU+GKsFQPGRorL2GLa6pxtqtdGtUBRdF+LxOJLJJDweD/L5PKrVqrTx4OBATuqDgwNs\\nbGwglUrZ+h+ZYqpVO3ohk7lbMX5zk7RjVuZze2mPyfyPg5b0PqTujrGnXKdaQtH5irRVrVgsinjN\\n3zlmpj+SlchKIkN79eoVAODx48fY3t5ukgZOhCGx83oCyYi0d6mp9LKaAE0mhNYMialStTJOuwWY\\nHrXAUb4XDmC3lEqlRHxgAcBAIIDh4WHE43FEIhFBeozNA470XmREZNRra2tYWlrChx9+KI6B1Je1\\nOzE4Hvz393//93C5XFIjrJsJtqNuRDZzA7GP2WxWMkEUi0X4fD6J70ulUmKR5D3t0MlJUrs1Z17X\\niQK8W+TUSpndyTtbkXmw6/1o7i2rdrUbH7v9q1G6RrgMm1leXkY6nRZmZIbltKOeI/rMRll1xmqw\\nrRY7r7fyteHvdOQi/DeZTrvTtpO+kKkxDs3hcGBhYUGYIbMOTk1NYWRkRGpxETkwx8zW1hY++ugj\\nPHnypClkQvfHSldi/s1+fvLJJ01960ZJ2KrPJD7HTgFMHRIrpZTLZTEX0+GRBoZ4PC4FQYvFYsu2\\nso8npVcx+2aHgvR15ri3e2a3bSBZIbN2OqtO32PFdDq53o5xWY2R+b/5TOAorYzeT1bPaUVtdUhW\\nDezkJXYMSv+mO6iV1qYDJE8DblCrDX4cstuQJmTlu9fX11GpVAQOM2Ka4m2xWBTfKx0X1IoZWYkO\\n/Kwj/K18iLrpZ7u5Mr8zx6NarUrWT7qBpFIpQUqPHj0SxqVF7nbrppNTu5t+WlErxN4pY2o39u1+\\nt1r7vcxnO5TdSfvs3tuKebZidrSmUr1iJSq3bV+jxVUUQXiJ6W3bDSe2Oxm6ub4VU9PPAdBVChJm\\nPLTqk9WC0ZYHMgmKkRQv+c8ORbYjO0Zlti+fz3f8TFYa1X1rdUqy7/o7AE1M1soDWy/eThGQXT/5\\nmYaTdsSI9V5ELN2OkyQrhGG1frXBox1FIpGWc9lpW6y+N32WOnmeSXaIyuFwNBXLfO15rRjSKZ3S\\nKZ3S/yWdXO2VUzqlUzqlY9IpQzqlUzqlrw2dMqRTOqVT+trQKUM6pVM6pa8NnTKkUzqlU/ra0ClD\\nOqVTOqWvDf0/fLa0fYVqVYIAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n },\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 24 - loss_d: 0.265, loss_g: 4.405\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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jMzA4rvGYZzTdK/SYhMZI/Hg3A4jMXFRTgcDvR6PRwcHCAYDGJ2dhbZ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\\nSF8bGjKkIQ1pSF8b+n+xc0aGU8shJQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/getting_started_with_ml/README.md", "content": "--- \n## A Beginner's roadmap to mastering Machine Learning \n---\nI decided to collect resources and concentrate them to form a basic roadmap, to help other beginners like myself, who would like to explore the field further and not feel lost.\n\n### Topics touched in the post:\n * Introduction to my background to give you an idea.\n * Why ML?\n * Essentials to get an extensive insight into machine learning.\n * \"What is ML\" and surrounding misconceptions. \n * A basic roadmap I devised, for anybody who wishes to pursue this art further. \n\n### Gist of the roadmap:\n 1. Learn and/or revise Math.\n 2. Learn Python.\n 3. Get acquainted to Python libraries.\n 4. Code, code, and code. \n \n

\n\tFor deeper insights, refer to How I mastered ML as a freshman\n

\n\n---\n

\n\tA massive collaborative effort by the OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/gradient_boosting_trees/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Gradient boosting trees\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/hierachical_clustering/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Hierachical Clustering\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/hierachical_clustering/hierachical_clustering/hierarchical_clustering.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"Hierarchical Clustering\",\n \"version\": \"0.3.2\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n }\n },\n \"cells\": [\n {\n \"metadata\": {\n \"id\": \"7lEU2B93ivBz\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Hierarchical Clustering\\n\",\n \"In this notebook we will give a basic example of how agglomerative hierarchical cluster works. \\n\",\n \"We use scipy and sklearn libraries. \"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"beCEkyHzwL-5\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"from sklearn.metrics import normalized_mutual_info_score\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"import numpy as np \"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"elEYgSyIjP8c\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Generating Sample data\\n\",\n \" `make_blobs` is used to generate sample data where:\\n\",\n \" \\n\",\n \"\\n\",\n \"`n_samples` : the total number of points equally divided among clusters.\\n\",\n \"\\n\",\n \"`centers` : the number of centers to generate, or the fixed center locations.\\n\",\n \"\\n\",\n \"`n_features` : the number of features for each sample. \\n\",\n \"\\n\",\n \"`random_state`: determines random number generation for dataset creation.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This function returns two outputs: \\n\",\n \"\\n\",\n \"`X`: the generated samples. \\n\",\n \"\\n\",\n \"`y`: The integer labels for cluster membership of each sample. \\n\",\n \"\\n\",\n \"Then we use `plt.scatter` to plot the data points in the figure below.\\n\",\n \"\\n\"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"Nxjz1FiSEl9Q\",\n \"colab_type\": \"code\",\n \"outputId\": \"3f6f6713-ab54-4250-df8a-68b7922d5313\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 347\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"X, y = make_blobs(n_samples=90, centers=4, n_features=3, random_state=4)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1])\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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FISVOqNRiJY7zaUamyxLI7S8d41Dc6A780d0gNmYxLkO+RRP3iamnj8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\\nSZuIiEgnmLSJiIh0gkmbiIhIJ/4/0hVflg12a20AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"Gd2x3DM3qiLi\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Performing Hierarchical clustering: \\n\",\n \"In this part, we are performing agglomerative hierarchical clustering using linkage function from scipy library:: \\n\",\n \"\\n\",\n \"`method`: is the linkage method, 'single' means the linkage method will be single linkage method. \\n\",\n \"\\n\",\n \"`metric`: is our similarity metric, 'euclidean' means the metric will be euclidean distance. \\n\",\n \"\\n\",\n \"\\\"A `(n-1)` by 4 matrix `Z` is returned. At the -th iteration, clusters with indices `Z[i, 0]` and `Z[i, 1]` are combined to form cluster with index `(n+i)` . A cluster with an index less than `n` corresponds to one of the `n` original observations. The distance between clusters `Z[i, 0]` and `Z[i, 1]` is given by `Z[i, 2]`. The fourth value `Z[i, 3]` represents the number of original observations in the newly formed cluster.\\n\",\n \"\\n\",\n \"The following linkage methods are used to compute the distance `d(s,t)`between two clusters `s`and `t`. The algorithm begins with a forest of clusters that have yet to be used in the hierarchy being formed. When two clusters `s` and `t`from this forest are combined into a single cluster `u`, `s`and `t` are removed from the forest, and `u` is added to the forest. When only one cluster remains in the forest, the algorithm stops, and this cluster becomes the root.\\n\",\n \"\\n\",\n \"A distance matrix is maintained at each iteration. The `d[i,j]`` entry corresponds to the distance between cluster `ii` and `j` in the original forest.\\n\",\n \"\\n\",\n \"At each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster u with the remaining clusters in the forest.\\\"\\n\",\n \"\\n\",\n \"\\n\",\n \"For more details check the docmentation of linkage: https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html\\n\"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"hrFUAgplFE8T\",\n \"colab_type\": \"code\",\n \"outputId\": \"fa9c51c8-3ef6-431b-dd36-15ba837f440a\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1547\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"Z = linkage(X, method=\\\"single\\\", metric=\\\"euclidean\\\")\\n\",\n \"print(Z.shape)\\n\",\n \"Z\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"(89, 4)\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"array([[1.90000000e+01, 8.60000000e+01, 1.30442991e-01, 2.00000000e+00],\\n\",\n \" [1.00000000e+00, 2.90000000e+01, 1.82464805e-01, 2.00000000e+00],\\n\",\n \" [2.60000000e+01, 4.10000000e+01, 2.24296317e-01, 2.00000000e+00],\\n\",\n \" [3.40000000e+01, 9.10000000e+01, 2.58407755e-01, 3.00000000e+00],\\n\",\n \" [1.70000000e+01, 8.80000000e+01, 4.46261329e-01, 2.00000000e+00],\\n\",\n \" [3.00000000e+00, 2.80000000e+01, 4.59526997e-01, 2.00000000e+00],\\n\",\n \" [8.00000000e+00, 9.30000000e+01, 4.70577188e-01, 4.00000000e+00],\\n\",\n \" [6.40000000e+01, 7.00000000e+01, 4.85796818e-01, 2.00000000e+00],\\n\",\n \" [6.80000000e+01, 9.70000000e+01, 5.52673653e-01, 3.00000000e+00],\\n\",\n \" [2.20000000e+01, 7.10000000e+01, 5.67637608e-01, 2.00000000e+00],\\n\",\n \" [4.80000000e+01, 5.20000000e+01, 5.72218127e-01, 2.00000000e+00],\\n\",\n \" [2.70000000e+01, 9.20000000e+01, 5.83346355e-01, 3.00000000e+00],\\n\",\n \" [7.70000000e+01, 9.00000000e+01, 5.88123667e-01, 3.00000000e+00],\\n\",\n \" [4.50000000e+01, 9.50000000e+01, 6.06112007e-01, 3.00000000e+00],\\n\",\n \" [4.90000000e+01, 9.80000000e+01, 6.06134706e-01, 4.00000000e+00],\\n\",\n \" [3.80000000e+01, 8.50000000e+01, 6.54175526e-01, 2.00000000e+00],\\n\",\n \" [2.00000000e+01, 1.05000000e+02, 6.64553292e-01, 3.00000000e+00],\\n\",\n \" [9.00000000e+00, 4.60000000e+01, 6.70931992e-01, 2.00000000e+00],\\n\",\n \" [9.90000000e+01, 1.02000000e+02, 6.82628319e-01, 5.00000000e+00],\\n\",\n \" [7.00000000e+00, 1.20000000e+01, 6.95405759e-01, 2.00000000e+00],\\n\",\n \" [2.10000000e+01, 1.00000000e+02, 7.00137901e-01, 3.00000000e+00],\\n\",\n \" [7.50000000e+01, 8.40000000e+01, 7.18486705e-01, 2.00000000e+00],\\n\",\n \" [6.70000000e+01, 1.01000000e+02, 7.20982261e-01, 4.00000000e+00],\\n\",\n \" [6.50000000e+01, 1.04000000e+02, 7.30097372e-01, 5.00000000e+00],\\n\",\n \" [2.30000000e+01, 3.60000000e+01, 7.30257122e-01, 2.00000000e+00],\\n\",\n \" [5.60000000e+01, 8.30000000e+01, 7.31435561e-01, 2.00000000e+00],\\n\",\n \" [6.90000000e+01, 1.15000000e+02, 7.39908983e-01, 3.00000000e+00],\\n\",\n \" [3.20000000e+01, 7.90000000e+01, 7.52488630e-01, 2.00000000e+00],\\n\",\n \" [4.20000000e+01, 7.40000000e+01, 7.72963318e-01, 2.00000000e+00],\\n\",\n \" [9.40000000e+01, 1.06000000e+02, 7.75139691e-01, 5.00000000e+00],\\n\",\n \" [1.30000000e+01, 5.70000000e+01, 8.09976391e-01, 2.00000000e+00],\\n\",\n \" [4.70000000e+01, 1.10000000e+02, 8.17317663e-01, 4.00000000e+00],\\n\",\n \" [8.70000000e+01, 1.21000000e+02, 8.37778371e-01, 5.00000000e+00],\\n\",\n \" [7.20000000e+01, 1.18000000e+02, 8.40307889e-01, 3.00000000e+00],\\n\",\n \" [1.09000000e+02, 1.13000000e+02, 8.40561182e-01, 7.00000000e+00],\\n\",\n \" [1.10000000e+01, 4.40000000e+01, 8.47017348e-01, 2.00000000e+00],\\n\",\n \" [5.40000000e+01, 1.16000000e+02, 8.53348573e-01, 4.00000000e+00],\\n\",\n \" [6.10000000e+01, 1.24000000e+02, 8.60237379e-01, 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6.00000000e+00],\\n\",\n \" [3.00000000e+01, 1.38000000e+02, 9.76974714e-01, 6.00000000e+00],\\n\",\n \" [1.17000000e+02, 1.34000000e+02, 9.87111827e-01, 4.00000000e+00],\\n\",\n \" [6.00000000e+00, 4.00000000e+01, 9.88659025e-01, 2.00000000e+00],\\n\",\n \" [6.30000000e+01, 1.41000000e+02, 9.90928355e-01, 7.00000000e+00],\\n\",\n \" [1.80000000e+01, 1.35000000e+02, 9.94563563e-01, 1.20000000e+01],\\n\",\n \" [4.30000000e+01, 1.44000000e+02, 1.01672289e+00, 8.00000000e+00],\\n\",\n \" [1.19000000e+02, 1.40000000e+02, 1.02423556e+00, 1.10000000e+01],\\n\",\n \" [5.30000000e+01, 1.47000000e+02, 1.02433354e+00, 1.20000000e+01],\\n\",\n \" [0.00000000e+00, 5.80000000e+01, 1.04742170e+00, 2.00000000e+00],\\n\",\n \" [9.60000000e+01, 1.48000000e+02, 1.05140979e+00, 1.60000000e+01],\\n\",\n \" [1.11000000e+02, 1.29000000e+02, 1.05383364e+00, 4.00000000e+00],\\n\",\n \" [1.49000000e+02, 1.50000000e+02, 1.06811164e+00, 1.80000000e+01],\\n\",\n \" [1.43000000e+02, 1.52000000e+02, 1.13528499e+00, 2.00000000e+01],\\n\",\n \" [1.42000000e+02, 1.45000000e+02, 1.13534989e+00, 1.60000000e+01],\\n\",\n \" [1.00000000e+01, 1.54000000e+02, 1.13899133e+00, 1.70000000e+01],\\n\",\n \" [8.00000000e+01, 1.55000000e+02, 1.14020634e+00, 1.80000000e+01],\\n\",\n \" [1.08000000e+02, 1.39000000e+02, 1.16825903e+00, 1.40000000e+01],\\n\",\n \" [1.28000000e+02, 1.57000000e+02, 1.17114497e+00, 1.70000000e+01],\\n\",\n \" [3.30000000e+01, 1.46000000e+02, 1.18258513e+00, 9.00000000e+00],\\n\",\n \" [8.20000000e+01, 1.53000000e+02, 1.20489168e+00, 2.10000000e+01],\\n\",\n \" [5.00000000e+01, 7.30000000e+01, 1.26106706e+00, 2.00000000e+00],\\n\",\n \" [1.25000000e+02, 1.56000000e+02, 1.26262400e+00, 2.00000000e+01],\\n\",\n \" [1.51000000e+02, 1.59000000e+02, 1.26658643e+00, 1.30000000e+01],\\n\",\n \" [1.07000000e+02, 1.62000000e+02, 1.27731453e+00, 2.20000000e+01],\\n\",\n \" [1.58000000e+02, 1.61000000e+02, 1.30683670e+00, 1.90000000e+01],\\n\",\n \" [5.50000000e+01, 1.65000000e+02, 1.31086896e+00, 2.00000000e+01],\\n\",\n \" [1.20000000e+02, 1.63000000e+02, 1.32666243e+00, 1.50000000e+01],\\n\",\n \" [2.40000000e+01, 1.66000000e+02, 1.34505705e+00, 2.10000000e+01],\\n\",\n \" [2.50000000e+01, 1.67000000e+02, 1.35109125e+00, 1.60000000e+01],\\n\",\n \" [5.90000000e+01, 1.68000000e+02, 1.43253745e+00, 2.20000000e+01],\\n\",\n \" [3.70000000e+01, 1.70000000e+02, 1.45594208e+00, 2.30000000e+01],\\n\",\n \" [1.22000000e+02, 1.69000000e+02, 1.47692245e+00, 2.10000000e+01],\\n\",\n \" [6.60000000e+01, 1.60000000e+02, 1.55016373e+00, 2.20000000e+01],\\n\",\n \" [3.90000000e+01, 1.72000000e+02, 1.57569084e+00, 2.20000000e+01],\\n\",\n \" [5.00000000e+00, 1.73000000e+02, 1.70993648e+00, 2.30000000e+01],\\n\",\n \" [1.64000000e+02, 1.75000000e+02, 3.30049427e+00, 4.50000000e+01],\\n\",\n \" [1.74000000e+02, 1.76000000e+02, 1.07623457e+01, 6.70000000e+01],\\n\",\n \" [1.71000000e+02, 1.77000000e+02, 1.31670775e+01, 9.00000000e+01]])\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 3\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"5KVO5Sb4wJNx\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Plotting dendrogram \\n\",\n \"The dedrogram function from scipy is used to plot dendrogram: \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* On the `x` axis we see the indexes of our samples. \\n\",\n \"* On the `y` axis we see the distances of our metric ('Euclidean'). \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"g5xM3EWJJBsH\",\n \"colab_type\": \"code\",\n \"outputId\": \"2006ee9b-4637-4cb3-c936-2c2e05196ab9\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 640\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"plt.figure(figsize=(25, 10))\\n\",\n \"plt.title(\\\"Hierarchical Clustering Dendrogram\\\")\\n\",\n \"plt.xlabel(\\\"Samples indexes\\\")\\n\",\n \"plt.ylabel(\\\"distance\\\")\\n\",\n \"dendrogram(Z, leaf_rotation=90., leaf_font_size=8. ) \\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"kbERWste0pfM\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Retrive the clusters\\n\",\n \"`fcluster` is used to retrive clusters with some level of distance. \\n\",\n \"\\n\",\n \"The number two determines the distance in which we want to cut the dendrogram. The number of crossed line is equal to number of clusters. \"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"vscUQI1hKYHc\",\n \"colab_type\": \"code\",\n \"outputId\": \"aeef37c7-347a-408a-8a70-0c3e23397308\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 102\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"cluster = fcluster(Z, 2, criterion=\\\"distance\\\")\\n\",\n \"cluster\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"array([4, 4, 3, 4, 3, 4, 4, 3, 4, 3, 3, 3, 3, 2, 3, 4, 2, 4, 3, 1, 4, 2,\\n\",\n \" 1, 1, 1, 2, 2, 2, 4, 4, 2, 4, 3, 2, 4, 2, 1, 1, 4, 2, 4, 2, 1, 2,\\n\",\n \" 3, 4, 3, 2, 2, 3, 1, 3, 2, 4, 1, 1, 1, 2, 4, 1, 3, 3, 4, 2, 3, 3,\\n\",\n \" 4, 2, 3, 1, 3, 1, 1, 1, 1, 2, 1, 1, 2, 3, 3, 1, 4, 1, 2, 4, 1, 2,\\n\",\n \" 4, 1], dtype=int32)\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 5\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"jXxmbM1i7cVT\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Plotting Clusters\\n\",\n \"Plotting the final result. Each color represents a different cluster (four clusters in total).\"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"VMAFl7wiOOGt\",\n \"colab_type\": \"code\",\n \"outputId\": \"23188b59-f7a1-42bf-d30b-0a30aaf7d1c2\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 483\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"plt.figure(figsize=(10, 8))\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=cluster, cmap=\\\"Accent\\\")\\n\",\n \"plt.savefig(\\\"clusters.png\\\")\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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hp7GLhiWX0PTNtPA0kWoCv0BHm1dMJAHwA0uwe9jVDXM2hf1EkKxBdO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\\nY+AiIiIiyjAGLiIiIqIM+z+sn8Ds7/53bQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"2GU4miqf-dLu\",\n \"colab_type\": \"text\"\n },\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Evaluting clusters: \\n\",\n \"Finally we will use Normalized Mutual Information (NMI) score to evaluate our clusters. Mutual information is a symmetric measure for the degree of dependency between the clustering and the manual classification. When NMI value is close to one, it indicates high similarity between clusters and actual labels. But if it was close to zero, it indicates high dissimilarity between them. \"\n ]\n },\n {\n \"metadata\": {\n \"id\": \"BirJIkyZOpfZ\",\n \"colab_type\": \"code\",\n \"outputId\": \"2c8f934f-0b98-474a-f7c5-610378c9f79b\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 88\n }\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"normalized_mutual_info_score(y, cluster)\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:844: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\\n\",\n \" FutureWarning)\\n\"\n ],\n \"name\": \"stderr\"\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"1.0\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 7\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"b_TD3pKJbBkl\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n }\n ]\n}\n" }, { "path": "code/artificial_intelligence/src/hierachical_clustering/hierachical_clustering.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define MAX_DIMENSION 21705\n\nusing namespace std;\n\nclass TreeNode {\nprivate:\n double location[MAX_DIMENSION]; //location or the center\n double sum[MAX_DIMENSION]; //for cummulative adding\n int dataNum; //denominator\n TreeNode *left; //left treenode pointer\n TreeNode *right; //right treenode pointer\n TreeNode *next; //next for a list\n bool isLegit; //spam or legit corpus\n int serialNum; //ID in the \"string of leaves\"\n\npublic:\n TreeNode()\n {\n left = NULL;\n right = NULL;\n next = NULL;\n dataNum = 1;\n isLegit = false;\n for (int i = 0; i < MAX_DIMENSION; i++)\n {\n location[i] = 0;\n sum[i] = 0;\n }\n } // end of constructor\n\n void setLocation(int i, double j)\n {\n location[i] = j;\n sum[i] = j;\n } //for entering the data of the treenode\n\n TreeNode* getNext()\n {\n return next;\n }\n\n double getLocation(int i)\n {\n return location[i];\n }\n\n void setNext( TreeNode *input)\n {\n next = input;\n }\n\n void setLegit()\n {\n isLegit = true;\n }\n\n bool Legitimacy()\n {\n return isLegit;\n }\n\n friend class HierachicalTree;\n}; //end of TreeNode\n\nstruct Subtree\n{\n TreeNode *begin;\n int length;\n Subtree *next;\n};\nclass HierachicalTree {\nprivate:\n TreeNode *root;\n TreeNode *listHead; //for a sequence of temp subtrees\n TreeNode *listRear; //for a sequence of temp subtrees\n TreeNode *tmp;\n Subtree *subtreeHead, *subtreeHandle;\n\nprotected:\n void createTree (); //create tree from the sequence of treenodes\n void dfs (TreeNode *treePtr); //traverse the tree\n Subtree *atLevel(int lvl, TreeNode *parent);\n\npublic:\n HierachicalTree()\n {\n root = NULL;\n listHead = NULL;\n listRear = NULL;\n } // end of constructor\n\n void addToList(TreeNode *Item)\n {\n if (listHead == NULL && listRear == NULL)\n {\n listHead = Item;\n listRear = Item;\n }\n else\n {\n listRear->next = Item;\n listRear = Item;\n }\n } // put the data into the list\n\n void organise()\n {\n tmp = 0;\n int tok = 0;\n cout << \"root: \" << root << endl;\n system(\"pause\");\n dfs(root);\n tmp = listHead;\n\n while (tmp != NULL)\n {\n tmp->serialNum = tok++;\n tmp = tmp->next;\n }\n\n } //access the protected function\n\n void buildTree()\n {\n createTree();\n } //access the protected function\n\n Subtree *subtreeAt(int level)\n {\n subtreeHead = subtreeHandle = NULL;\n return atLevel(level, root);\n }\n\n int rightEnd(TreeNode *p)\n {\n if (p->right == NULL)\n return p->serialNum;\n else\n return rightEnd(p->right);\n }\n\n int leftEnd(TreeNode *p)\n {\n if (p->left == NULL)\n return p->serialNum;\n else\n return leftEnd(p->left);\n }\n\n};\n\nvoid HierachicalTree::createTree()\n{\n TreeNode *goal;\n TreeNode *temp, *temp2;\n double sum = 0;\n double minimum = MAX_DIMENSION;\n cout << \"Creating tree\" << endl;\n while (listHead != listRear)\n {\n temp = listHead->next;\n while (temp != NULL)\n {\n sum = 0;\n for (int i = 0; i < MAX_DIMENSION; i++)\n sum += abs(listHead->location[i] - temp->location[i]); //CAUTION: BIT STREAM ONLY\n //FOR NON-INT SCALING, USE EUCLIDEAN DISTANCE\n if (sum < minimum)\n {\n goal = temp;\n minimum = sum;\n }\n temp = temp->next;\n }\n cout << \"\t\tComparison between listHead and others completed\"<< endl;\n temp = new TreeNode;\n temp->dataNum = listHead->dataNum + goal->dataNum;\n\n for (int i = 0; i < MAX_DIMENSION; i++)\n {\n temp->sum[i] = listHead->sum[i] + goal->sum[i];\n temp->location[i] = temp->sum[i] / temp->dataNum;\n }\n cout << \"\t\tSubtree built\"<< endl;\n temp2 = listHead;\n cout << \"\t\t temp2 = \"<< temp2 << endl;\n cout << \"\t\t goal = \"<< goal << endl;\n while (temp2->next != goal)\n {\n cout << \"\t\t\t\ttemp2 to be set to: \"<< temp2->next << endl;\n temp2 = temp2->next;\n cout << \"\t\t\t\tdone.\"<< endl;\n }\n cout << \"\t\ttemp2 allocated\"<< endl;\n\n temp->left = listHead;\n temp->right = goal;\n\n if (goal == listRear && listHead->next != listRear)\n {\n cout << \"\t\t Subtree: head and rear\"<< endl;\n temp2->next = temp;\n listHead = listHead->next;\n }\n else if (listHead->next == listRear)\n {\n cout << \"\t\t Subtree: Now root found\"<< endl;\n listHead = temp;\n }\n else\n {\n cout << \"\t\t Subtree: head and someone in the middle\"<< endl;\n temp2->next = goal->next;\n listRear->next = temp;\n listHead = listHead->next;\n }\n cout << \"\t\tSubtree mounted to list\"<< endl;\n minimum = MAX_DIMENSION;\n listRear = temp;\n }\n\n root = listHead;\n listHead = NULL;\n listRear = NULL;\n temp = NULL;\n temp2 = NULL;\n goal = NULL;\n\n}\n\n\nvoid HierachicalTree::dfs(TreeNode *treePtr)\n{\n //=============\n // This method sequencially \"strings\" all leaf nodes\n //=============\n if (treePtr->left == NULL && treePtr->right == NULL) //Leaf\n {\n if (listHead)\n {\n treePtr->next = NULL;\n tmp->next = treePtr;\n tmp = treePtr;\n }\n else\n {\n listHead = treePtr;\n tmp = treePtr;\n }\n }\n else //2 children\n {\n dfs(treePtr->left);\n dfs(treePtr->right);\n }\n return;\n}\n\nSubtree *HierachicalTree::atLevel(int lvl, TreeNode *parent)\n{\n if (lvl == 0)\n {\n Subtree *subtreeTmp = new Subtree;\n subtreeTmp->begin = parent;\n subtreeTmp->length = rightEnd(parent) - leftEnd(parent);\n return subtreeTmp;\n }\n else\n return atLevel(lvl - 1, parent->left);\n}\n\nint main(void)\n{\n int axis, oldAxis;\n int fileSeq = 1;\n char buffer[10];\n string fileName;\n string str;\n ifstream inf;\n HierachicalTree hTree; //hierachical tree\n TreeNode *tempNode;\n\n fileName = sprintf(buffer, \"%d\", fileSeq);\n fileName += \".txt\";\n inf.open(fileName.c_str(), ios::in);\n\n while (inf)\n {\n tempNode = new TreeNode;\n inf >> str;\n oldAxis = -1; //init to a sub-zero number\n while (inf.eof())\n {\n axis = atof(str.c_str());\n if (axis < oldAxis) //reach the bottom of file\n {\n cout << axis << endl;\n if (axis)\n tempNode->setLegit();\n }\n else\n {\n tempNode->setLocation(axis, 1);\n oldAxis = axis;\n cout << axis << endl;\n }\n inf >> str;\n }\n system(\"pause\");\n hTree.addToList(tempNode);\n cout << fileName << \" read\" << endl;\n inf.close();\n fileName = sprintf(buffer, \"%d\", ++fileSeq);\n fileName += \".txt\";\n inf.open(fileName.c_str(), ios::in);\n }\n inf.close();\n system(\"pause\");\n hTree.buildTree();\n cout << \"Built\" << endl;\n system(\"pause\");\n hTree.organise();\n cout << \"Linked list built\" << endl;\n system(\"pause\");\n\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/Cell-Segmentation/Cell_Segmentation_.ipynb", "content": "{\r\n \"nbformat\": 4,\r\n \"nbformat_minor\": 0,\r\n \"metadata\": {\r\n \"colab\": {\r\n \"name\": \"Cell Segmentation .ipynb\",\r\n \"provenance\": [],\r\n \"collapsed_sections\": [],\r\n \"authorship_tag\": \"ABX9TyNdgWeFZYGYDU72Dn9fiQqt\",\r\n \"include_colab_link\": true\r\n },\r\n \"kernelspec\": {\r\n \"name\": \"python3\",\r\n \"display_name\": \"Python 3\"\r\n },\r\n \"language_info\": {\r\n \"name\": \"python\"\r\n },\r\n \"accelerator\": \"GPU\",\r\n \"gpuClass\": \"standard\"\r\n },\r\n \"cells\": [\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"metadata\": {\r\n \"id\": \"view-in-github\",\r\n \"colab_type\": \"text\"\r\n },\r\n \"source\": [\r\n \"\\\"Open\"\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"# **Cell Segmentation in Microscopy Images**\\n\",\r\n \"\\n\",\r\n \"\\n\",\r\n \"---\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"xa88eltDH4VQ\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"**Dataset Used**: The dataset that we have used here is [kaggle 2018 Data Science Bowl contest dataset](https://www.kaggle.com/c/data-science-bowl-2018),which contains 670 images of cells along with its masks. Each image in the dataset is assigned with an average of 20 images that adds up to become the mask of that single image and there are such 670 images in the dataset.\\n\",\r\n \"For our own ease we have saved the created masked files into a separate folder along with the image. Such that we can save the computation time required to join those masks into a single mask eachtime we run the notebook\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"67caY_vIXU_T\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"! nvidia-smi # To check the number of GPUs with their names\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"cpPHkZGgVCjk\",\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"outputId\": \"dc4b4d5b-9f1e-4934-94b3-cc091164ec74\"\r\n },\r\n \"execution_count\": 1,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Sat Aug 20 03:09:08 2022 \\n\",\r\n \"+-----------------------------------------------------------------------------+\\n\",\r\n \"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\\n\",\r\n \"|-------------------------------+----------------------+----------------------+\\n\",\r\n \"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\\n\",\r\n \"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\\n\",\r\n \"| | | MIG M. |\\n\",\r\n \"|===============================+======================+======================|\\n\",\r\n \"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\\n\",\r\n \"| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |\\n\",\r\n \"| | | N/A |\\n\",\r\n \"+-------------------------------+----------------------+----------------------+\\n\",\r\n \" \\n\",\r\n \"+-----------------------------------------------------------------------------+\\n\",\r\n \"| Processes: |\\n\",\r\n \"| GPU GI CI PID Type Process name GPU Memory |\\n\",\r\n \"| ID ID Usage |\\n\",\r\n \"|=============================================================================|\\n\",\r\n \"| No running processes found |\\n\",\r\n \"+-----------------------------------------------------------------------------+\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"import tensorflow as tf\\n\",\r\n \"print(tf.__version__)\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"WXWfWE9EhdwB\",\r\n \"outputId\": \"bf613ed8-e2f7-45d1-f626-2249a0b2ba49\"\r\n },\r\n \"execution_count\": 2,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"2.8.2\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"## Setup and Importing Libraries\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"nNZEIV1-Mx2W\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"import os\\n\",\r\n \"import cv2\\n\",\r\n \"import shutil\\n\",\r\n \"import math\\n\",\r\n \"import numpy as np\\n\",\r\n \"import seaborn as sns\\n\",\r\n \"import matplotlib.pyplot as plt\\n\",\r\n \"from PIL import Image\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"fzTm317pknig\"\r\n },\r\n \"execution_count\": 3,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"* `Sequence` → safer way to do multiprocessing. Structure gurantees that the network will only train once on each sample per epoch.\\n\",\r\n \"* `tensorflow.keras.callbacks` → Callbacks can be passed to keras methods such as fit , evaluate , and predict in order to hook into the various stages of the model training\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"U_JiMVeBnuIv\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from tensorflow import keras\\n\",\r\n \"import tensorflow.keras.backend as K\\n\",\r\n \"from tensorflow.keras.utils import Sequence\\n\",\r\n \"from tensorflow.keras.models import Model\\n\",\r\n \"from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Conv2DTranspose, Add, concatenate, average, Dropout\\n\",\r\n \"from tensorflow.keras.losses import binary_crossentropy\\n\",\r\n \"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"JzvejX_blACd\"\r\n },\r\n \"execution_count\": 4,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"\\n\",\r\n \"\\n\",\r\n \"* `sklearn.metrics` → module implements several loss, score and utility functions to measure classification performance.\\n\",\r\n \"* `albumentations` → CV tool that boosts the performance of DNN and mainly used for fast and flexible image augmentations.\\n\",\r\n \"\\n\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"RHDP7VJ33EY-\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from sklearn.metrics import classification_report, roc_auc_score, accuracy_score\\n\",\r\n \"from albumentations import Compose, OneOf, Flip, Rotate, RandomContrast, RandomGamma, RandomBrightness, ElasticTransform, GridDistortion, OpticalDistortion, RGBShift, CLAHE\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"1dHVlF1EmiKX\"\r\n },\r\n \"execution_count\": 5,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from albumentations import (Compose, OneOf, \\n\",\r\n \" CLAHE, Flip,Rotate,Transpose,ShiftScaleRotate,IAAPiecewiseAffine,RandomRotate90,ChannelShuffle,ElasticTransform,Flip,GridDistortion,HorizontalFlip,HueSaturationValue,OpticalDistortion,\\n\",\r\n \" RandomBrightnessContrast,RandomGamma,RandomSizedCrop,VerticalFlip,RGBShift,GaussNoise )\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"XeKj0YHIBA6r\"\r\n },\r\n \"execution_count\": 6,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"Zsj37x2SRp-g\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"\\n\",\r\n \"Now, we mount our google drive to access the dataset which was downloaded and exported to the drive folder.\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"256TnjHl7Xp4\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from google.colab import drive\\n\",\r\n \"drive.mount('/content/drive')\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"twzrfgGa7Uaf\",\r\n \"outputId\": \"a6a3e1b2-d18d-46e7-cab3-6a2f2d6d2881\"\r\n },\r\n \"execution_count\": 7,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Mounted at /content/drive\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"print('---------------------- Directories ----------------------\\\\n')\\n\",\r\n \"for dirname, _, filenames in os.walk('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data'):\\n\",\r\n \" print(dirname)\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"K3PT5Iso7ku7\",\r\n \"outputId\": \"e267074d-1e35-41e8-e216-59f86016d66b\"\r\n },\r\n \"execution_count\": 8,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"---------------------- Directories ----------------------\\n\",\r\n \"\\n\",\r\n \"/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data\\n\",\r\n \"/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks\\n\",\r\n \"/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test\\n\",\r\n \"/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"train_image_dir = r'/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images' # training images directory\\n\",\r\n \"train_mask_dir = r'/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks' # mask images directory\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"vsAYn7UZ921k\"\r\n },\r\n \"execution_count\": 9,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"train_image_paths = sorted([os.path.join(train_image_dir, fname) for fname in os.listdir(train_image_dir) if fname.endswith(\\\".png\\\") and not fname.startswith(\\\".\\\")])\\n\",\r\n \"train_mask_paths = sorted([os.path.join(train_mask_dir, fname) for fname in os.listdir(train_mask_dir) if fname.endswith(\\\".png\\\") and not fname.startswith(\\\".\\\")])\\n\",\r\n \"print(\\\"Number of training images : \\\", len(train_image_paths))\\n\",\r\n \"print(\\\"Number of training masks : \\\", len(train_mask_paths))\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"6HhHwmICI8Bd\",\r\n \"outputId\": \"be302f3b-ee2c-41d9-b585-5534cd6f8318\"\r\n },\r\n \"execution_count\": 10,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Number of training images : 670\\n\",\r\n \"Number of training masks : 670\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"### Shuffling \\n\",\r\n \"\\n\",\r\n \"`random.shuffle` → method takes a sequence which in our case is a list of path to images and reorganises randomly the order of items.\\n\",\r\n \"\\n\",\r\n \"Further training_image_paths and training_mask_paths are also combined\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"BYO8PT5fSNNV\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# Shuffle\\n\",\r\n \"import random\\n\",\r\n \"combined = list(zip(train_image_paths, train_mask_paths))\\n\",\r\n \"print(f'Length of combined: {len(combined)}')\\n\",\r\n \"random.shuffle(combined)\\n\",\r\n \"train_image_paths[:], train_mask_paths[:] = zip(*combined)\\n\",\r\n \"print(list(combined[:20]))\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"7OPwNLSUJfXm\",\r\n \"outputId\": \"1f625c27-20c8-4849-a5ad-5587ae46b2b3\"\r\n },\r\n \"execution_count\": 11,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Length of combined: 670\\n\",\r\n \"[('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/421.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/421.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/348.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/348.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/436.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/436.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/265.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/265.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/18.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/18.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/472.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/472.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/388.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/388.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/440.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/440.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/15.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/15.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/142.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/142.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/664.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/664.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/328.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/328.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/618.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/618.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/520.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/520.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/636.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/636.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/447.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/447.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/399.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/399.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/656.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/656.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/382.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/382.png'), ('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/209.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/209.png')]\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"> Following is how actually the zip() works in python and it's only shown here for reference.\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"DFWXmrI-ThnD\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# example of working of zip() in python\\n\",\r\n \"\\n\",\r\n \"numbers=[1,2,3]\\n\",\r\n \"letters=['a','b','c']\\n\",\r\n \"zipped = zip(numbers, letters)\\n\",\r\n \"print(zipped)\\n\",\r\n \"print(list(zipped))\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"gx6pC4KyTM-3\",\r\n \"outputId\": \"450c4233-74b5-4fe1-944e-c5763c790597\"\r\n },\r\n \"execution_count\": 12,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"\\n\",\r\n \"[(1, 'a'), (2, 'b'), (3, 'c')]\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"RtCWuCwXTpTB\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"print(\\\"After shuffling\\\")\\n\",\r\n \"print(f'Length: {len(train_image_paths)} ',train_image_paths[0:5])\\n\",\r\n \"print(f'Length: {len(train_mask_paths)} ',train_mask_paths[0:5])\\n\",\r\n \"# Sequence intact !\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"CVi5Z9ZY1Zq1\",\r\n \"outputId\": \"7ef708a8-3839-4432-fa46-7abb09551b32\"\r\n },\r\n \"execution_count\": 13,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"After shuffling\\n\",\r\n \"Length: 670 ['/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/421.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/348.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/436.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/265.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/images/18.png']\\n\",\r\n \"Length: 670 ['/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/421.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/348.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/436.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/265.png', '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/masks/18.png']\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# Splitting\\n\",\r\n \"train_image_files = train_image_paths[:500] # First 500 image path\\n\",\r\n \"train_mask_files = train_mask_paths[:500] # First 500 mask image path\\n\",\r\n \"\\n\",\r\n \"valid_image_files = train_image_paths[500:] # Last 170 image path\\n\",\r\n \"valid_mask_files = train_mask_paths[500:] # Last 170 mask image path\\n\",\r\n \"\\n\",\r\n \"print(len(train_image_files), len(train_mask_files))\\n\",\r\n \"print(len(valid_image_files), len(valid_mask_files))\\n\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"H5l1wVfs1pPP\",\r\n \"outputId\": \"b47af288-0832-42d9-a3e9-eb035dac1d31\"\r\n },\r\n \"execution_count\": 14,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"500 500\\n\",\r\n \"170 170\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"Follow image processing takes place for both Training images as well Maked images using PIL or Python Image Library\\n\",\r\n \"* Conversion to RGB\\n\",\r\n \"* resizing it to 256*256\\n\",\r\n \"* converting the image to numpy array\\n\",\r\n \"\\n\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"Vd3GkMO1VtR3\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def read_image(file_loc, dim=(256,256)):\\n\",\r\n \" img = Image.open(file_loc)\\n\",\r\n \" img = img.convert('RGB')\\n\",\r\n \" img = img.resize(dim)\\n\",\r\n \" img = np.array(img)\\n\",\r\n \" return img\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"OaQPdHmN3biv\"\r\n },\r\n \"execution_count\": 15,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def read_mask(file_loc, dim=(256,256)):\\n\",\r\n \" img = Image.open(file_loc)\\n\",\r\n \" img = img.resize(dim)\\n\",\r\n \" img = np.array(img)\\n\",\r\n \" img = (img>0).astype(np.uint8)\\n\",\r\n \" return img\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"n-0nrBk74Mmq\"\r\n },\r\n \"execution_count\": 16,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"### Augmentation and Precprocessing\\n\",\r\n \"\\n\",\r\n \"Training data was augmented using [Albumentations Library](https://albumentations.ai/). A strong combination of different types of image augmentations were applied with varied probabilities.\\n\",\r\n \"\\n\",\r\n \"* `CLAHE` → **Contrast limited adaptive histogram equalization**: Takes care of over-amplification of the contrast. Operates on small regions in the image, rather than the entire image. [Ref](https://towardsdatascience.com/image-augmentation-for-deep-learning-histogram-equalization-a71387f609b2)\\n\",\r\n \"* Rotate\\n\",\r\n \"* Flip\\n\",\r\n \"* `GaussNoise` → Adding gaussian noise to the image\\n\",\r\n \"* HorizontalFlip\\n\",\r\n \"* VerticalFlip\\n\",\r\n \"* HueSaturationValue\\n\",\r\n \"* RandomGamma\\n\",\r\n \"* Random Brightness and Contrast\\n\",\r\n \"\\n\",\r\n \"For more visualisation, refer [Data Augmentation Tutorial](https://tugot17.github.io/data-science-blog/albumentations/data-augmentation/tutorial/2020/09/20/Pixel-level-transforms-using-albumentations-package.html#RandomGamma)\\n\",\r\n \"\\n\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"esrzkqYlXDuG\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \" Rotation, Gaussian Noise, Crop, Hue and Saturation, Elastic Transform, Coarse Dropout\\n\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"MiS1N-Ksl7CM\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"Example Image\\n\",\r\n \"\\n\",\r\n 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)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"NU_Dq5gllwNW\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"class Train_Generator(Sequence):\\n\",\r\n \"\\n\",\r\n \" def __init__(self, x_set, y_set, batch_size=10, img_dim=(256,256), augmentation=False):\\n\",\r\n \" self.x = x_set\\n\",\r\n \" self.y = y_set\\n\",\r\n \" self.batch_size = batch_size\\n\",\r\n \" self.img_dim = img_dim\\n\",\r\n \" self.augmentation = augmentation\\n\",\r\n \"\\n\",\r\n \" def __len__(self):\\n\",\r\n \" return math.ceil(len(self.x) / self.batch_size)\\n\",\r\n \" aug = Compose(\\n\",\r\n \" [\\n\",\r\n \" CLAHE(always_apply=True, p=1.0), \\n\",\r\n \" Rotate(always_apply = True,limit=(-360, 360), border_mode=3, p = 1.0),\\n\",\r\n \" Flip(always_apply = True),\\n\",\r\n \" OneOf([\\n\",\r\n \" GaussNoise()\\n\",\r\n \" ], p=0.9),\\n\",\r\n \" OneOf([\\n\",\r\n \" HorizontalFlip()\\n\",\r\n \" \\n\",\r\n \" ], p=0.6),\\n\",\r\n \" \\n\",\r\n \" OneOf([\\n\",\r\n \" \\n\",\r\n \" RandomBrightnessContrast(),\\n\",\r\n \" RandomGamma()\\n\",\r\n \" ], p=0.2),\\n\",\r\n \" OneOf([VerticalFlip()],p=0.7)\\n\",\r\n \" ,\\n\",\r\n \" OneOf([HueSaturationValue()],p=0.5)\\n\",\r\n \" ])\\n\",\r\n \"\\n\",\r\n \" def __getitem__(self, idx):\\n\",\r\n \" batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\\n\",\r\n \" batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\\n\",\r\n \"\\n\",\r\n \" # batch_x = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_x])\\n\",\r\n \" # batch_y = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_y])\\n\",\r\n \" batch_x = np.array([read_image(file_name, self.img_dim) for file_name in batch_x])\\n\",\r\n \" batch_y = np.array([read_mask(file_name, self.img_dim) for file_name in batch_y])\\n\",\r\n \"\\n\",\r\n \" if self.augmentation is True:\\n\",\r\n \" aug = [self.aug(image=i, mask=j) for i, j in zip(batch_x, batch_y)]\\n\",\r\n \" batch_x = np.array([i['image'] for i in aug])\\n\",\r\n \" batch_y = np.array([j['mask'] for j in aug])\\n\",\r\n \"\\n\",\r\n \" batch_y = np.expand_dims(batch_y, -1)\\n\",\r\n \"\\n\",\r\n \" return batch_x/255.0, batch_y/1.0\\n\",\r\n \" \"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"llTyfsF846KJ\"\r\n },\r\n \"execution_count\": 17,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"class Val_Generator(Sequence):\\n\",\r\n \"\\n\",\r\n \" def __init__(self, x_set, y_set, batch_size=10, img_dim=(256,256), augmentation=False):\\n\",\r\n \" self.x = x_set\\n\",\r\n \" self.y = y_set\\n\",\r\n \" self.batch_size = batch_size\\n\",\r\n \" self.img_dim = img_dim\\n\",\r\n \" self.augmentation = augmentation\\n\",\r\n \"\\n\",\r\n \" def __len__(self):\\n\",\r\n \" k=math.ceil(len(self.x) / self.batch_size)\\n\",\r\n \" # print(k)\\n\",\r\n \" # print(len(self.x))\\n\",\r\n \" # print(self.batch_size)\\n\",\r\n \" return math.ceil(len(self.x) / self.batch_size)\\n\",\r\n \"\\n\",\r\n \" aug = Compose(\\n\",\r\n \" [\\n\",\r\n \" CLAHE(always_apply=True, p=1.0)\\n\",\r\n \" ])\\n\",\r\n \"\\n\",\r\n \"\\n\",\r\n \" def __getitem__(self, idx):\\n\",\r\n \" batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\\n\",\r\n \" batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\\n\",\r\n \" # print(batch_x)\\n\",\r\n \" # print(batch_y)\\n\",\r\n \"\\n\",\r\n \" # batch_x = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_x])\\n\",\r\n \" # batch_y = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_y])\\n\",\r\n \" batch_x = np.array([read_image(file_name, self.img_dim) for file_name in batch_x])\\n\",\r\n \" batch_y = np.array([read_mask(file_name, self.img_dim) for file_name in batch_y])\\n\",\r\n \"\\n\",\r\n \" if self.augmentation is True:\\n\",\r\n \" aug = [self.aug(image=i, mask=j) for i, j in zip(batch_x, batch_y)]\\n\",\r\n \" batch_x = np.array([i['image'] for i in aug])\\n\",\r\n \" batch_y = np.array([j['mask'] for j in aug])\\n\",\r\n \" batch_y = np.expand_dims(batch_y, -1)\\n\",\r\n \"\\n\",\r\n \" return batch_x/255.0, batch_y/1.0\\n\",\r\n \" \"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"SvARPriWAv2D\"\r\n },\r\n \"execution_count\": 18,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"train_generator = Train_Generator(train_image_files, train_mask_files)\\n\",\r\n \"valid_generator = Val_Generator(valid_image_files, valid_mask_files)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"gytOl_cbe7kZ\"\r\n },\r\n \"execution_count\": 19,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# Finally in 3 dims\\n\",\r\n \"\\n\",\r\n \"for i, j in train_generator:\\n\",\r\n \" print(i.dtype)\\n\",\r\n \" print(j.dtype)\\n\",\r\n \" break\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"t1hnlsZdfHeN\",\r\n \"outputId\": \"889a206c-c3f1-400d-d506-571ebf1601bc\"\r\n },\r\n \"execution_count\": 20,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"float64\\n\",\r\n \"float64\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"for i, j in train_generator:\\n\",\r\n \" print(i.shape)\\n\",\r\n \" print(j.shape)\\n\",\r\n \" break\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"OtV5i53rtZu2\",\r\n \"outputId\": \"f5201ab1-0f40-4764-c589-6522c3ef1abd\"\r\n },\r\n \"execution_count\": 21,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"(10, 256, 256, 3)\\n\",\r\n \"(10, 256, 256, 1)\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"### Some Sample Images and their masks\\n\",\r\n \"> masks displayed in the next block of code\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"osrfAv4gY_J_\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Images', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(i[:10], axes[:10]):\\n\",\r\n \" ax.imshow(img)\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 199\r\n },\r\n \"id\": \"8Az7NDtmfRqE\",\r\n \"outputId\": \"e08f1b2f-6f2c-49c3-e688-24734e426ecd\"\r\n },\r\n \"execution_count\": 22,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"### Masks\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"ph_PJJMZZEx4\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(j[:10], axes[:10]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 199\r\n },\r\n \"id\": \"MlqbaQntmSZT\",\r\n \"outputId\": \"ee92dff9-90f7-4459-d7d0-24a198239818\"\r\n },\r\n \"execution_count\": 23,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def train_generator():\\n\",\r\n \" return Train_Generator(train_image_files, train_mask_files, augmentation=True)\\n\",\r\n \"def valid_generator():\\n\",\r\n \" return Val_Generator(valid_image_files, valid_mask_files, augmentation=True)\\n\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"Zzs2rzawq5XB\"\r\n },\r\n \"execution_count\": 24,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"ds_train = tf.data.Dataset.from_generator(\\n\",\r\n \" train_generator, \\n\",\r\n \" output_types=(tf.float32, tf.float32), \\n\",\r\n \" output_shapes=([None,256,256,3], [None,256,256,1])\\n\",\r\n \").repeat()\\n\",\r\n \"\\n\",\r\n \"ds_valid = tf.data.Dataset.from_generator(\\n\",\r\n \" valid_generator, \\n\",\r\n \" output_types=(tf.float32, tf.float32), \\n\",\r\n \" output_shapes=([None,256,256,3], [None,256,256,1])\\n\",\r\n \").repeat()\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"zBgsAUP8PU3V\"\r\n },\r\n \"execution_count\": 25,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \" train_generator_aug = Train_Generator(train_image_files, train_mask_files,augmentation=True)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"mNrGbkHJPltY\"\r\n },\r\n \"execution_count\": 26,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# Augmented training set\\n\",\r\n \"for i, j in ds_train:\\n\",\r\n \" break\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Augmented Training Images', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(i[:10], axes[:10]):\\n\",\r\n \" ax.imshow(img)\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Augmented Training Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(j[:10], axes[:10]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 380\r\n },\r\n \"id\": \"96mmYmGOPn-U\",\r\n \"outputId\": \"360bdc5d-2fb3-4d72-dc22-44a2b13d1d0d\"\r\n },\r\n \"execution_count\": 27,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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8gziYPiMHQMkmNkmayjLRxYOkzpTrNl+CF5pBngyWoG+4DeKGaMKBMC0MLZDHURnTZsJqwQZHi5GT4sCBVB620qnhxajZQUGvcPDi8p2vn1a953JVM8cp6MmSeaLViSSjVqT6jNTFkp7UR80JSR/OAScV9JjNg1qgMjOOSxEyrA4uVMTJw8YEHee1rHmdvdZGbh9c5PLrO0+95loPDNV4adTBGzXziJ/4rPHBun3c89W7e9eQ7sXVhsoJrok0VcsJsAvZwnxk0M4hTsiHueMtoEhyj2IasI1M1Bmk0AdKIFsWpmArSCikLsCC501omS4FBWO6teODSQzz+2OM88sjLOXdhn/W6cu3Ks7ztN9/G89efZ15XGoK1ioqxHJXWMqIJHTLjuSUXF+cpZc3RjRtUoExrmgnVKmJOE0NJCDCOA56VBy9dYu/8eZbDwNRmrl65ysHzNyhAqo25rAHFdCBlYTEo4iOr/T0ee8UjPPbYK8njkkGUw2nDwfM3eNdTT3H9+nXKekOrjZZnmizINLwKnhrqio7G1Ss37unaee9T73EsoQmqVwrCmAU8AwqyxoqDLKgOwyBkNZo5Lko16x+K3kgKYxaKZLQAqdHqAtEZk4RNN3n+5iEPXbjEYsyU7eeTA9eu3+DZ9z7DjbJh6SPDAJqUCw9eJC3PsT+M5JTwuTLoEl+BeyLj1E1hI2uODtbcmNdMN9fINCGWMd1gKJlETnvISpEq2ODkcUGuiaSwXAgu4D7irYALzStkxcVZr2+iLbE4d5EhZcwbpsaIs3ShqtJEWGRwzTSB1CppHHBTzA0bMqtSMU+Mwwrz7X3RjZs3D5Aq7J3bQ5OgWiiuyOzMNpPySDFnlIZmaDi0AVwYV45XkFmQLLTUGEWpExRVWi2MKaFq0BRShmyUOpEZcG8sWaIiHHjh8UdfcU/Xzste9rJbn5Tujojc+v1OROTWssd/7/48+fvuPnZ/3mkZM7u1zPHvJ5c7rbzHZXP3W/8BUkovmK6qqOoLtre77+Pt7K5zvP3bLXeyLCfP4+552i3/yW0d/36n87R7fnbPx25ZT5t/Wll2PfPMM/d07ezvnff9RWZYLdnbu4AmxxcjoytNneWQmOcJBtACU6s88eR7GXBKyuS2wS0hDcacKLMhyXFdYKmhyQFFMIYqMDRKW2IIS2/ohSWXz+9xYe8yQ1YWwwWGVGliIAkrG47ajAiojeQxk1igMnOwLszrDQfTIevnbrK2NaVWMhlSr0vRnMWlPR5YXWb/8kWGVYaaGNPI3lKwZjQqphWviblsEBZUrXhtDLMzLYSVJEYfaSJMsubawREH1yZcCg+e3ycvV6yGi6Q0Yy5gE6qZqWyYmlMPDjk6WHPt5hFrjmA2DGPA+mudR5xMplFyhqmipr1CZo22vcRbzpg5y9zrUrpxfISlgteEDEqpMC4yi3EEFS6sVlQ3aMZ0VDmUDdxcs6aRUia1ggm4CLVmynRv9Z1Lly66iLLaW/ERr34tr371R/LAA5f59bf+Cj/3lreg2ltaIg4i/Xff1pFp0BzDEbS/Xi6IS28C0D8HkiaGYeCByw/w6GOP8ehjj1Bn4/nnn+OJJ97JM+97BmuV5v09oCmTVRiXSx5++GEeeejlpJy4duMa733f0xzcuEGZK26NZtuKrjmalJSU/b1zfORHvpaP/Ojfg1L5zXe+k3c9+U7W6zWYcu7cOT7u4z6GV3/Ua2nzml/55d/gt97xm6wPj2i1YGb48ftUdft+hSQJ1URKkFRRSf06AfbO7fHAA5c4f+EC8zTz3ve9lzJNTJuZUgvW7PjNjqgCjogi9HrzkJTz+/s8+ugreeDlL+fc+XPMU+Xpp57kySffxWZTGYbMQw9e5uN+78eyf+4CTz7xJO944gkOrt9kMx3h1ss7DJm95ZJzF/YhJabDiaPDQ2qZad5o3t/NIGiS3kYTQyVz4eJF3v2ud93TtbNc7rmqIgnUMyKON6OlilVA+nXh2lADccGTklLGi4E6kwmDO471Nq4Iro5Y/1ww6PPEkCaQFPdeC+l1DCOZ0JKzyAvUG66KqKKaGPLIxf1zPPjoI1zc30NEmIpxeP2Ap648xc3DI7Q65r0t1RJoU6RBGyrigpjiLghQk2EVkk9YS1RzhqSIJEwbZo6Y9TbDkGgzpEHwZqjCYhxQV1xAB4XaaJZprWE0xCopJ6onknpvp+aRVVKKKoqwGjOr1T6PvvxRVhfPI0MiNefwxgFXrj7N+559ms3RTDEjuWJiWDE0aT+PDg0QTZhBwnDJYJXBE6YO2/UkZ2gN1YzZTBKhKVgVRnWqNFrTXr31xrQu93TtXLx48dSKze0+M09bRkTIObNarVgulyyXS4ZhYBiGW/WJlNKt+sXxesf1j+PP9tYaZZ6Z5pl5nlmv19y4cYNa660y7NZ7jstkZqgqKaVbf+/WdU5+ru9u52R95G7HfHysy+WSvb09xnG8td9hGBjH8dYxuztmxtHREVeuXOHo6OgD6mi3q3Pci9264+62hmG49VrknEkp3Tr/wzDcOtfDMGBmTNN067UopfDWt771roXZP3fRnUbOQpkK6glGJalRa96e4wllADFQQTGaZbL3dnEVRbIhNVNpuAir3DBLeJL+3rdCkkxaCK30j5g8gM3GBieRkCRQBRmk3z9ro5FAM05BREjeaKa4CN4qrkoyBZ+3cRzIKYELRkUEXByvGbw3L5oLOTtYpekARUhUGARc8JYgGd56O1K1YEURBfftZ3Y2tCnWDEkJofV2vymmkCqoCDWBWd+fVOktSjFIAqJQGpITmOMNUlbUoKiTrMfX3A1VZ1bQJlgaydLnKQPnlksefvlFxnGfcVzgyZDZOVzf5Oqzz7Neb1ApTM1xMWhQzJDWs5KbOuKOWEYdnj98/tTr5o4BZrhdbDJxHG31U5e9t+Dyiwqz+u1Lc+qiL5keMDg1uHzqAdzLUX2og8sgONt/p879UJdBSwMqakvcnYYhXmgp4YC1HkitubEyBYRKJmUhNcNXgk4VbxXRQq2KCgzJaC5Uy5gYNRWwhJmRGHE1xAZshNIqmcZcIHmCvODC5QsUKxwdTtSpMGhjHhSqYcOSOjuMhmwKJkpSkAFoiorQrMJSEASbjTYIuQm4ktUwmxFLaBLEhNGcYgkUrPQbl0lCq1HagKTGKIbJgMsKyZVmMyDkalSpZHGaJdogJAaqw9gaSRNlBK1A7TegpkZtjqowWsFIVC2YOZkEqaI4pTi5rBBZU8fUA99jQzyjAlObEFXyoGCQcbIlZBzISr+pUlmIUMeRJInZKtqkB/FVSIP3oLdo/yC4R4c31pgmnAmbFbGK+4CIo2UiaaZtG0M+NrQ1JA80jKElZjJujqaBUR2sMNnMqAnVzGrvPI+/+hU89vjj/Tikstxfcf3mhs3miOoNr84jr3yYqWz49be/myvPPcv6aMK9ggnJK2VMDKIwJKwUVASjsUlOrU4WSNaomlABlUStMNaEpIE8NEwUtDJ5ZqQvO/i2wd5Ax0Y1kKrUI+dqfZb15pBr165x4fJF6jRx5dkrXDu4Rltbv/5bIoshmvGW0BHUhHMXLvL4o49w7vw56rThnU8+xc3DA3zTgxkVQa1QxclAUsOqUefC85v38dzV6wwDzIBtJja1Mrgwm2PDkuwzqg0r/cO8ZUNVWK32UR3YX55jWCY4TAzjwJFPrKcNDcfLjM0FOKIx9oajGLNP6Ly652snJwNxqi9QKkNSHEi1giRqhoGRmoRxLlRbQCuQErjBdISJkIYFcxI2FXIrmI8MJeFpwhAWOpAvPMDesEfez4gvyIBRmW9OvP033sbP/+wvcFAmRlWONoeMnnnFR72GV7/mcR579FW8/PIj/b2kE6MMqPVGRBsSC1si2fEBFrrA1huSCZL22bBtLAmMY8LVaHmBupOzkIaE5gWa+v1mXWBUBUZancjJ2Rv3qSmxaI5JYcgD1ZXklaaCysBkoENiYYaYU21kNKe5IaUhnihZWBgU1pRiDHncdm40LqzOgUJL3jsDN44ulaEkrDVGT3imd2gYeFEsF2wNLJU89MavVMOWUDShOjO1Qh6WPbChkHyD+IjbwNQOWQ5LpuaMS2V19yrOLfdS6b9boPPktDs1Hk4GWE9OPy7PcSPseNruvJOB0tMCzScbZrcLbO/u507zTwta7/5PKdFau1WW3cD1vZb5tPN8L+dyd1u75T25/u62jxuleiuQ9YFlvJsmwqZltDVKuY7IktyU4gnxTLHK1BSfC1lGDqdDBnFqM3IrNHEGGipCURiXSml928mlB3lTxU3QrLgnFhRm73kEUp35qDCnmaQLZjYUz4wsOLcQDpNwbnJKErQ5o2ScSsFZDQ5TbxQW3eBHpQecZIYGaWEslxfZP7fPcPEcyzSQgTY25tpI5uQCPgiTwyCCoow5kb0xJcWWA5cWvS4xz5AkozUhVrGDQw7qmoNrzzEgDKs9VosV+4t90v7I/p7gClJm1kczhzfXuE0svDEpqBo2O5YGfHbG8wmbHGsbhnGJrwu1GckSng1WIG373mpGStLv/9YoIrgkpAq6mLEGcxWGBIcbg+rYDEd+xFQTagaiNBOcgVwdVJFc7/naKaWiSWhlwMWZ3Xnu+atcee5qvwazkGQka++oNjOaAWJkTzRr28AgYNtr2x1Jijuo0APSrXFwcJ1n3pNwq4gkrl29yvVrB70RLIJug9iaHHGhlcr1559jmgpmhWm9Yb1eU0uFJr1ZpA7GraBt0sRqb8GlBx7gZQ8+SLHCAzducPW5Z3rwNSX29/d58OGXc/HiBaajxPkL5xiSMgPV4TijR0TA+kXuLjQMXPEG1RpJvefRiHD9+jVuXL+OYyTNjMPApcuXyQ8NNJu5fu0mdZ7xWjCXXi/QhC4y6j3cW7ZBof1pJueJzbTh6KBQW1/eMdbrNf/yHU9w/tIFWqmMw4BjiAo0A+v1pvV6w1x6Xb5Zw1r/7Oy9EI6r4QaY9qCGOipOa/M9XztDduYmJDN09FttISmQRMiimDnNYDClukDq9dSKk5uTcMwzokIawMxxKlUS6j3A0MPwiqmACAvJGJXmPcDWMLwJno1iDTdDh8yCgZwzFy5f5PLFy+wvMo5yzguXV/s0r7y7PMU0F1K2ngzRGi4NRZCWoH/MY0lo3stnzWkkxHugavZyKwajBrODaUKakYaESO3BFcm9fagbsAFrvXtKtb+HEglXx1zJmkCE5SAYCadC7R02bZ44bI33tMbe+iJCZp5mjg4POFrfoE4FqmLiPVpiAqo98YneBs7iuAgmrbcnvfbOgOSoQq2OJMNwsjlOQ1yxanhOJDdKA9de328ujHbvD6WfVo+5W2f6adswsx6cb43W2q3pKaVbP1NK5JxvLXsckHV32na9aZ6ZpolSCqWUFwRPd+swu9OPfx4HTI//79qtG5ys45y23O3mqSo5ZxaLBeM43grY5pwZhuHUbaxWKx599FGuXr3K888//wHHcbc6xu3qmrfrHB/HkcVicausOecXlP20YPzJIP3dJBMsO1WdYdT+nmwVbQMkJWGY9BhTShlrRkWARhMjacK89fugVXJW3BuzJZI5pkoWJ2nugdnSm2aoM5WK6MjgBXPQue9LPeEG4gNDdmoxmhoDveok7nh1cnYa/V6b2tiT35gxK6gOpKS0IqRkeDK0KmaK6Exl6O0UcXwwvA5Iq6h4T0JtjXScdFsyZYChgCZo1uNBTStgJGR7j0y9I7gaCHhS1HuXXrXKIAOu/Z5Gc5o3RBS1DKn0Dj5LVKn9g95APWGpB7xHUWSbPJVoiCSG1cDFBy/y4IMPMywz47BHEgGU84crprkylSOsJHwspOJYSmQDGZ3WFBVDRfv7XW5/3dx76+sF+k3k3sOU26WOs4C3K5667oc+9nmqM+329P6/M+/4/b+e/SQcr3nntV/i7PJTKBXPS9psyKrgEyQdwJUkgqRC8x6ALArZICWjiCJjJk9OE2EuhYwyiOI50XLD14YPzlydjEOtyGIJ1qizMUrqAcbtjaepM64WfMSrP4q98/scHtzg2fc9w6EbzQuoYq2SmpC1N34s95p5M3rDM4NVR5IjKcPUbySaEr3v35EiZBHq3LMETDIbcXLybSYRtLEuFu8AACAASURBVFlYuTCj6Ki4FHyjLGVmFoc0kivIKHgStM64DKg5KgUpmYUaxYU2KF4dN2NI0FqCqVcgRaFaz3oZmiBppIgBjUXrGQpTmnqGAc7eWNCa2Mi2kVC3N/Wx37QkDwjab6ajU9seQ66oS290JWeVKhvJIIoe32jFwGH2cs/XTq2lB9qpVBPcoLEhzb1ya9UY1ECV7DNNe+eDl4ZlYUyJsjYWYyYnmEphkIHloOQxc+mhB3nkFY+yf24fJaE598acG3MxmjUWaeT5Gwc889w15nYTmyvNGt7AkjFVx9QZ0oB57/0UhVYrSub8IL2xZP36FREkDSSEPPRra5oa7pUswp7ChJHEKSaUpAzbzi0BdCj9Q7MpxRrT/CSLKyusTZSpUURxNbQZjYaYYjgsBHUnJXj4wUu86vHHGNJA9crhplLeeRNbKKUZe3lgLYLOTjWoCtJuImlg8oyx5mgGcNpUQJyWeoMhObSae+PUKxtXBu8VxXk+okklJUG0f/ApysPnL3Fj7xrrow00o+oItYE3LDfcBgZPL+LJEVjPoDTQmVYaWWfMEi3NtFlpbeZGNUiZJEKqB+TaO31KNVp2aMJi8J49YyNZFLWJOmQWotiQYDNjsocuFkxzb6SR14hlnn32Gk8/+QyqjZU1kiR+64mnuXH9ed537RqHV69z+Pw12kdOPPTKVzAY2IGxGBI69Pej6T56QfBZOCeVwyEjNlM9k8sRjCMqQm6O5IwtDLER1HvvuDQYhdRg6QlJCaWhdYmVNXkhiCgmDU0j4sbeKEw19QB6hjQ3Rkuk6lhJpHHCsuK1lzM5UIWWKvPsbGYjjxmpwnI8xyYBpT81UhhZjHNfh5GG05hxAbEVykzJ695A9AGbnE1WNBnmmbkYWfuTFRdWiabQJCMN5lpIxZjLQLEGk7N/QWjTISr793zt3GuF/2RA+Hi6qp6auXu7rNnT3G79k1lFJ7e3G/g9GVjd3d5xGVtrtxoUdwqanzzW0wLAu8vurr+bsbQb9D15DMfL7U67U9B5d/puxtDJwPnuOdoNHp9W1t1tv9jgMkA2IS8VzQuaDfh6ptWjnoGzDZwlep3E2sT64BCtFfdEs4QnpThkUUQKVXpDDYVqjlsPtGRGqjfAmS3DWBBTllNhrTBsNizTipQa48LxnJg1o03YDJlBHTQzsUFawk2Y25JZNz2LUDOancEmjlyxZOgwstg7z/m8zygjw0JxV5oUZNgwN6WUgdGNRcqQBtLegFWlFcMpDNnQnLGmpKFRNkccbtYcrmdEZkaM0iY2KTOvD6lHh6yH5zk/X2DeXGTc61lNgyTyucz6RmaaCk3756mJs/CGyICUidK2gY1amdQRTYgUzJS8ETwro1eSCIWEeA8eWsuI9gCXr50qlbk1yuhQBjTNCBkrG5LlHjDaH5HikBrVB3xUqPceJLTWcFNKnrl2/Rp7569Q64Znnn0WSTAMI8v9FWMawZ15M7MpM3NZ4+X4+t5e6yaIgEnPQDVxsF43sW0Q57nrz3G0PsREmKcNpcy49IxJVPrnYhp6QNuUaZ6YNhMVw2rrnd9muDq4bRv2AI6mhOTUr3vNjKPgllgsF+ztn+NlD76M/b0lR1Pj4sXzXFzuMeclq/0VKhnTbaePtPefIAXx/kyVC5jVfh/z7RN9eE8U2EbT1cGpSHWu37jOmAd6CA8WiwW2HEieqK2hWXn44Ye5dP4iaRzZ3LzBtYMDnnznE6gkSptZ3zyglgmXHnBM48jeuX0u7l1A92A9H3Ht+auU0uuEbo60ijnUqrj2hq+abzOzt/eZRq/PbrOZrfV69Dzf+7XjZBaDYgjuadv54EgWKDD3hFlElz0Iq9tkmgYZwxySKU0MqQ3XEZL3pJUkpB7WpF9U1uv7DrOwbQ8kshlOoxhYMWQxoN47pVvaZu0OA0KFYY8hK9mX+OA89MBlrh9cZzNdYa7GIAPNDfOEmEN2pAmz9gCVyrZDIdMfEaVhlf60R3Ja7QEgRJDWs/9crLc5BVwNa6VnA9OPoVnPXM6qmNGf0EoNs4qkEaSfq+aCuqDe69iVxvrIWRxusNoD9m4zpfaAfLEZHYTmjtTtdayCCdsgs+EOrtKv+5JQUaBSGICeLOIYNVlPBiXjSRhotDEhxXrGOZkhpTONeXqnDvN70Vqj1tqzkEu59fdxXeM4yLxYLGitsV6v2dvbe0GndCnlVnB53mYx72YaH9dpTvu8NzPmeb613O4xnTzG0+a9GMeB8uOA7HHAWVVvZVvDC+txOWceeughFosFV65cuRU8R/o96W7ulFm9+3fOmXEcGRcjSdMLAvvHWczQs5zdnfVmTa31Vlb2biLAnbTBcU8MpcdDGtqfwgCSOM7MaIki2/ud1v7kgwBZaXUmyUBrQkr9Xp9koNAYkvQnIDz1z2BXrPU2bKOhOuCtt9eQ2mMgBWxu6CBgDaqiKNmFpo6RUK898UGkP5mRwAZFm4ElPPXOxVy9d/Zss69b6q2X6onBFEz701Han5ZIIqjQA7+qvQ5FwS2Rm0ISVEZEJ9Qcl8TsCe+pxr0zu5WeiLONg7lnklfEM26N5BlLjpmSALTHglrddupKQ1IiUzBNtGxkF7wtels7G4mGYXhSxrTHhf0HuXTxPKKZJtID0cOCqU4wpD7dK6wr1Zz+dIiTGME3PUu6OEq/997OGQPM3a3NfkB88jYBS7v9rA/c6N3dbdSK47evn9joaUXwk/M+YKEXNUbGiaWPbwKnHJyf9uudT8LtT6G8/1hfVMz4AxcU3QYYz8hZolbx0WHu2WitKMmFsk3fT96TH6ZBkGQMmxnzod8IikDuN0jzXslxmxmqkkSh9ceqU8qoJXz23ttFxloPaiQR0Mbl5UUef81ruXx+j1mcS5f2sfoArcHh4TVYN5bJ+rAOo+Jzz5RIDKQMtJlWW8+wFusVbXWGoVdIsEwWoWnPiLRhg+eRPDdKEVDHR0WnwoJM3TSU3oBsjLBsNIQ0FWZvqKzIvqG2hmRwbTD7drgHxxo0SWjpAYK0mGhTD0gPe1CqwQwiI3mhSGtYqmTPVFFq6Te0RYbaFMZGNSVJIlnPimG5pEzbRzSGwsIH2kLAlNkTS+1DYkhTBneKHVHHBSk7pTk6VBYmtKT9Ea4X0bPerDK0DC1hZe49xRg6QZXeu2jbR0EKBfGBZkpOM41Erd6HJHDrlTtJNE0cWeOyjjz84EX29pYMwxJBSGXC5sJmMzO3Bgjijbq+iXmhVaO1us16adAEyX0YhyNzlquB1Ax15Ugbqgvm1NMoFEdsJKmRGszWK4RTnWluLHAKY8908ARmiDopld5Y9l659Fnw1pDUSJOyLhPzOIM7qWXa0DNnVEe0NBqFpMveaJ4Si/MjF86d5/Kly+zt7+OtcnjjJjefu8w0P9PPVRX2UuVwFHxdqRUGWeAOprVn2mCk1jNXxHuPqgtIrfSHfhKTJpZAWyuWNjx75RnG5YJlXrBkydHBEXnIPXiiFfOJCoxW2LjQKCysP+GgOePzdM/XTrFCLsrRfA3XzJgNU2G9NnwzU2ks8sA4CG4LrCobaSTvQ5SwHQaieUULSDLm4jAIq6H3rkupVE2IbxgaWFJ0NtSUpgZjJa8Sq70VaGK1v+L3fvxHc/3ggJwzN28e8cwTV1hK5oGLl5Dz+0iCo3nN0Iy8GBGteCsshyVFZ4Y60RZ7LBlYqDK0RFs66kptjg0DmQlnwZgKYoY2qLMie5Cb0pKgUkmyR5krsgbJGc0DgxTEEgMTpITNRhoTUGiS0VSZSeSaaK0P8+M+owVUR0apTFkwNzQ1rGyH+1BnFutZR6K4OYwTqQ4kF0wcs175IQ39k1IFtcRgRpFMEu/l721biqX+dAkVHBY50XQg+cT16zfRUVm4cH19xKVzL67Jdbvsl12nBSJPPrJ52nq7Da6TgdWTy98pQ3l3e8fTdrdzpyzk3W2ftszJxldr7dQM5NPKelpWzfH83WM9bf3dYPFpmd0nM6dPOx+7doPKu8vtBt9PCz7fqTF3J9WOGCSBjJALXhrzZOAzWRItNTAl5f7Ui8+b/ihl60MK+NjwCuaNpALeqHXE05rmAwtRNloZxGjW6zZ5dOpstAyTG3mqyDAh0h8NH3SB1Upr3juNkmJVepCiNNY+gYxspon58KhnyLRev7DkJAwdVqzGgcXeinx+wbAYyL7oj7EaTE1wbeThEF8sGVLe3uNmzNeYVPY8w3AO1YSkiQmYvWE+YYdH3JwreMVJ5I2yyAP5vLJIe6RlRjGWNqJ7++S9FRfaeYolNgcTR9MNrt84ZJY11nojz2rPJrUmfaiqVmk6ohXG1Ho2ofWsY6U/nt5GQ6c+dEJtRkr0J3oGhanRan/KpzhoMpqNOI3F2LMLa9vgJrhlSp1YvIiGTMNQh1Iaz125RpmgtQ3ztCankXN753jwwQfZP7+/zUI+4vr157l53ZjT1J+u8z7k1vEAdrIzkp0gPUMZw5oi00ydSx+ezbZDI7ijkhCEcTVy/vwFzi2XbOaZa9dvUuZ1/6y3HlD2bVax9bwJUu4BUpxtNqYxr9dcee4Gmoxps+HC6hwf+zEfzd5yxTPPPcfl8+fJqxXTtRtYrUDr2fjSt318MO69ZWNC78BGe7aXHw/psO1Mp2cQN+1Z+oWGrydsqOg4sr9acX7/HMP+Ct9Ubh7cYD1PHB3dZNCEbISjw0MObhxwdLhhbgWrbRu/7p8LsyrTNLM+usmNiw+wt1rRZuPc+fMsV3u0UtlME2bONE24N6Q1RIQq2+y3PphJv99Af7rRZdvokhcEqu7KeparuNBq669l6pmuWWXbVoHkYAqqFWdEvNFHO+z3D6MCieRzfwQ6DySr/b2CI7k/ZTlQKdtEk167bYBS1badVUKelTFth3wg49usOnJmIJOGEbPG4UHP0F8NC/J2GIh+E6QPMZQAhySN2nQ73BAojreCSk+s0H6FY9trUpwenEbx3DMXzQXVjNKzCNXG/r6rkLJBBR8E5v7UHfSM47lVWqmYDmjudV6RHtRuxZBcWdtMMqc5VHfMHbTimhDrT8G21O/rbgZqGLUHzwyETLKK58aAUUyRVpGhxy5STQiKJe2dK/RglBn9iUFST4walLTNpL0Xu0HQ23Uin+a0z9xaK9M8Y+6kW8OjbLNi5Xh+76g6zlqe5/nWUBrHQeV5m8U8TRNt+77Z3e9x1u0L3gI7007WEe7UeX6nes/tztdugPm4zrJbbzk+NuAF03POXLp0idVqxTPPPMPNmzfveG5f0Ll+XD4ROFFPOS3gLLzwqbHd+ujxMZhZDzxvh04ax7G/B+9BKoarQFKaGVoLSTNGH/qnP83Rk74E3XaSVLImrIAw9uFOk6Am1OTQjMHYduI18Pc/9WxSSWRMdPvZ0nDd3vuTIdtOydYUSUpOleYVEe2B7WxI60+oeHXIvTPOq0Gu22ErIEvPuHd6B6GMglujJCXV/tniJpTte1yT8/+x9mbPkSRJmt9P1czcIwDkUVd3dffOkD3kLoV82AeKUMgnypL867kPpMxSdldkZXaunumuK6syE0BEuJupKh/UA4VCA9nImjaRqgQC4R7uHn6offodMUo+ezQbhmOzbQp1goqHEyyoKMMH0QQZkbWHANapNWs6DUGlYGEbezuQpomf0eia96i0h62Uraa0ErTYno9FmYbTSyF0ZfJU17gopc5YQCvGPAdFG45iNghx3ITlcGA9Hug26LYQA0wU3Rq2EWlnQljiUqGUD1w7zweYPwRY/tHrH7hY/+R1/NG86A/8PR5d47Pw7T9608ehrQ94Oh+17AdH3mOeGI+i1T/rQ8LvTbR+xurmaqyusG43gy6E1qTph2J9gAZdJuhGyKCXlIg5hk9K0wnvt4whaCxISRlED6VI3+RxkmAXK2aVqcDQBNuMyu7Fnr/47X9Pezlz8gRRihX2uwsu9gvHwzU6DdY+Yxrsu7CUgUYD6wwbFAWkMSlZXLkyRV7cHis1cpJo6sToiDW05X7MTKyRBbzNJX3jzFnEsXBqNCwxS3RqKRGJhW6p9xohNNe0GHGjh1GipCph86j1kROUEg2zI8XP39jIBwCV4ZbyFfe0+ijG0kF7UKWwNAc1mCq2ACK0feBDkFHxEKaSTObLaFy+mIl5pp46x+HUIvgxWKKzq4b3SOsEEaaudP8I2V9pIBOlDiZJOX1thT53SiTYbGTxLKbUGoSMfOBZ7kbsUh5qIUQ39heCi9AEdtOOQmX0lWneEd35/oc33BzeIaRHUx+D0Y2qTvdBkR1uC1oavg5iYzUIoJbWFMOy8GurE2JMtVG00Rk4Aw9P8C6gkR3b8LReCRxaFstKMEZJFspYUK/UOiGTceojvcBLwEjpnUzZlUWSkRYEEU4fK1obZWeUWUCcUgoXl5fYOvjsi8/55utvePvDtywnw7XRTWm1Ul40Dqcb2gi6QxiELhgkwNEqomy2EEFYS0IJQdX0tVMxDqcC4x2l7ZjmHa/jU2prRMDp1JMZ5U5UYxhMrTJskwAPw0/GqPah0+Uno1hhFed2vaFEYX7xGeIrEgMimFCmKVmFZQpsGNIDnVIyZUNRrbn9REqvJkfXntd3a7QQRI26dderOyaBSxYV+3nPp68/pdvg2Duvr14SPrh++5brZeHm/YpczBwWQ7pncdBhUJFYiKniY0GjoYW8d45g1oKVI7J5CGoFqaBDiBrYIdjNO8IbZc4Jdy2W0t1EBZg1u+dujboTemSho2UCq1Ag1hVpSg0jlpT/lznla0uPZKIDoQOPiSHKpJUrlIiBTi0tV8bADPZMeGTx5/SUjzEoaPrbT4PD0pl2jo49ow6GOZM2ehxSfilK05mpbOc3J8waqwyqTsg6WCMIU07rDfPYM2lJKffPGE+xhh9aLtwHKh/728OfH7JEPjS5e+yzH27HYwzg8+fcB1efYgOf3/sYsH6fMfTUdtz//aGFxv11niddH1r+4XF8jP382KT3seXvH4eHFhhPMZQffp8fM6QWbByx3pCR/n6HE1QNTMDJe8bplAyV3p0QoVwOxEBWxaIi9UQfQvGgcCJih0gwItIGY+/IqeDNECbm2rYJj2BVuFmDl+ZU27GGoEVz4ufCMirzruFjYVXHVoF+YvjKyTuHdRB9MIoxSUPEqSJclkv22ti3HbXtKLtKaxN+CvbSCFtYfKaqMhyWdzc4wRCnqqCtoA0slg2ASeBqOS2s3qFEMpCKprfgnvSKnwTVif3+MrMN5oqG4F2oHrx69Uumi084ALeHA/1wy/H6lre319CNoYN+a1gUSklllm9M8TI6RaYE/m1AnVjLoESnOqg4nQSPBegGOgFdkDEIEvhSL/RIr8gFR7QzidLlIxpbBqHBsMHt4YbTekAQuhsX+5lPX7/ik8sXzJcXaFPmaUcD+nGhe89maCTLy++uAUAVjTNYS/7nvinbAJJtG7H5OFZhN818+ctf8snnn1NEOdwcWMZKX45pDxAbsEskcAgJkOYFRIggKtSpMXCu372j+4n3379nf7Hjk88/48X+ErPgYrdjnvccuGVdOuNuMxMkyY3M448IBU2bA01AAPF00iBg+Eay9WxknO8J4lgULnYzv/nNr/ntX/5r5n0Dgd9/9Qf+8W//K++vb/jDzTc4Tu/JvmSz+HEJRAHL+0IC5ye+Xzu317fM+z37ix0vP/mUL7/4JfM80Vfjv/ntv+L1y5d8883X/L//z3/gd7/7xwTNRrJq0k5EUWoCKme5+kbCe+6oUz6/Qw0dafkSrlAToB9ulDPTW4WwtG/ZHMNQq5gYdVNrOg33QdATRAJCNL2bzegKpUj6o0ZhhLKKZ61UCtXA+mBs2RlqK9CgGFOZudzPdFcI4+QbsHTRKCoMjM4GyFmCTYPgVGpadRTL829kvd0tbcVcnOEFVbacHHLbfbvXK0CysSW2RjdKnRXrgRrpeexj2+dkKocrjY5LSe/1taMy49WpUvGVtDPRoG+KAajQDW/g4RTxJNdoXjehbDV/1mdtKrgLbmmjOMxIR0XBtWze1Bt1KoLQgq893+OGF6VqpB1RU3a7i2efO481yJ8CWh8DPu+/18zuQOFStnmuaioaJIH1ZV0ZI5mkvXdOp9OdiurMeh5j3LGgH9ueP7V9D5vG97MZzv/dr0U+5ljdX8/9Y9B7/8n77tch92sOSAXFl19+ydu3b3n79u0fbcvD2uOORHDehvuAOz9SF4E7+5EzMH/2iL6vVLv/Gbt5dwd+P5br8dSQ2onRWGxQmuLqiDg2PPNWZKajlEjwd0hesyYVYaXGlI3IMuhmyFo2G54kkdQoOY/oFddAxBk+qAErgtSgoAwTtKWKk0jQ22MwujPKhAao5nxaW4U+EJmAtBg0DDWQlvuDWKpMNK142hqEKmqpOPAYtFmSjIck4axmjlaNVK+bOyGaz1wNxJzQyojt2JhBq/gaWAkmnfCR9jfRoHt+d6EgEahUuhhVIIZRS9mU0I6IQjV0wCpKjVT0exSKeRJ3ijE8z0PzhalNnNYT795eU8uei92O7knwOpze8d1333G4fk8/LamyVyPCGJ62YSZHShHKZvNuEuBPE8A+wqDwydPt7o8/B4h8zgcp50nlMzfpY9+3bfjT7OV/6fgzrvDBaj605p/1qY8Qtc/HRD5iZV6ESknPGa+oGCZL+uGQASJeyl3x6AOUXXa6i1Fmx2NF646IwbAZGc58WfHbQdGJoZ0xAi2OVqhreoR5nbC1M+0a9eoq5XFrZ5pryh11IKUy7YWpFaTnjUIGDFlzMidOnYQ6GhGOTIJZQShgKYFI1kRhaEon1NL3ubRC6UIvKz0GeEuGZ0Cvgyl51nm4PSUc3tIjbJjTNo9pqY3mI7tlLdmt4UKUoBEM78lAtAlq2i3IJqOwIhRPwM1lY8auhuxgiBOLsPdKr8HqYKf0CC5eKMVodUqzfDni2tIvgcK0Uz7/5BUvP/2SXd2Dr9i68H498v3XbxhmwIrExCgDCWOlIPr8zvr+4oKuHV+UkCyAQwqzOksENYQyWUr0xsQYAy8r4hMieb6xDNaolJ1SJqXHYGYPFPowDscENo/LkW/ffMvX//wVfjvSm3AIwwUbCx1JH98yKFURMUZNqVqxYNX0XtJQVh9MmoxftYJ5YG5oU8YoUHaU0RNobumtWXZzBhoQjJ6TgappZ+AWqM5U34IBZDC5ELpCKIwKc7LRaIqNgalCHVTLyYaPjkVjORk3N7dcX79Py5g6E2GUXQHJTva6wjSX7Eq2yhdfXnF4d+T2eIuMBE81oJb0WB62bt6E6TOXtomK0GFSmsDiztGNP3zzHdfHA5989imvX7xi9JV379/y/voG6ZudihdOdkQlmTE2nLqxrJ475kk5LYNX80vKxT7DdEZhni+x+UQd+dAcLhjCDsFaRTZGWiGvFRMBKwyBKpJFh3fmsTFfPHD2DIksbHwwHYEpuLi44jd/8Vsu9q/57vCGLy5f0cqe7/dfcXO45s3+PbuLmVdXr9CdMFpQXdn1vhUCK2o1izRL//q57DfKyo4yZdNIQ1kg5VSjUItSJK1lTOf0YdcGk8NpMIZSVPEurNMRhrKb6l2TyiNDJ0YpzApaZ7ytsIwM2SgpNSc6dIGqqFTCB130rsC9VGgjOISwmzdPMSlYzyLxFClNVgdmo4dQ93NSrBqAI01ZI72ZOa2E7ti9aEgMhjh0EDF2piwM8KA0YXahu+Ih7Kpi/fn3nfvjQxObh5Oypxgl5wnG/fedWT3nZe774AGPMnTOn/8Uy/nha/fX+6cmjufPvL/sw0nR/eUfru/h6w/9oh9jNd9nC5/ZNPc/58wCf/jZD5d9+Pr5WD8GcD8G2N8H1h9OGj8WYGZUFh1wuEFUWDaW5bJ2ZCjuM2VKH9yiQY0MaIs17QxCV9yzaaXWMWnIZMQ6sDHRyooWw0ZPaXtXaNmZLqPDNBBpBI2jHShiIK8pJvSwvM6loz1D8bxD7bA6+DKwMNZYkGEUWekBc0zUFmgT2iXoJOg0sZOJGo16AZQZPxWQYByPyQT0DMcqUXFLFRNumzIs8dzD4R3L9cI4rnndS2XMhQuZmXYTrVV2bc9uvqRd7HCFMVV4u9BNKZOxtrf4MlHmwotpwqaJ11cvedW/YF1W5sl4f9s5vD1wezgyUghP8yMxJxNKFOKUAV0qDSlgvlCBmWQlHTeAtq0jO3oGtQXDg6M5qjOiQd0kfxKF+fmOYIAjkShtF2P0kRN/rUzznsuXL9h9csWuzdhYubicGesL9tfXLH0Ft2zyngPrElukSFpKiGxezBEMCfDknnps4LIohlBb48WrV3z5r37F1W7H7c3K1ctXzN+/yRAhDIuRlhibPjTIYyMmSEkr66lUPnv9Cb/+8je8eHHJ3/3j33F7856Lq18jXhlmDBvsph0SxnF0rm+vk1G2geS52pyUqSgqGyuyJtCcds+aVkuWvpQEbP4YeX1vzdNalRdXL/jtf/tb/uqv/oLB4HRcEILb2xvev7+hLydGDMwhLJ+FFs4dJZwf51OheSx7GLKuuMB+XhKE39Q4u3nHy1evGAFXl3vaNG9g0pb1IIAKVQtFUpXZ9ns++ewT4mMQZoeO00zvmsUUpWhgEhQaqp4Ek40d6K5JCIkENdFk95VINrtEQRkp2waKJ+yvWhJwl5z5uAaTwFDB1pIkmlaoQarwNAGi4Yb1haJwuxxYzNnPE/t55iSwP1xQtbLYApKKszG2EKwIagRooeNAvVPTqShaCzbWbGL5wMigQ3HDUcoouG6NiLO/uEEpRliAVFzOgVuSZA8PVIPjCBoZaCybx/RoxjgZvYBqAE6Rmqq2c2On5jlcRFLVF/mdmzvFDdGC1Q2odKcAoySvPaaKdAFfqZ7zHikFNol7Ie0XVbJGTRVwIawiUdlfPh9gvjuv/0Qz+7nDzO6ep2dLhvvP6jHGo8/p+8/0+9kND2uu57CMn1KFrCk3TQAAIABJREFUPQSbz6+dfz/XC08pmB7Waw9D887vUVV2u93dZ50B34fLvX79GhHhhx9++GDz/f74o8b8g7+fj/HZguNca55B//P38LAuPNuZPHeslgBqkSQ2FRommk1hDdaw7JJJNs9KyfA+rZFzVsl5lo2SzcgCxKbsENsworTXaaaIVk5YEnwi0ADCadNEdKNrZgE0tVTVjrT1KwRDSgK+ZRC2EaI2T+KmhdDBWLc5L5IkoTBaVEQ7qtncMc88L9use7Sk2sS6oVNasq6a13DaVBnigiOoppWEmzHCaVKSaa0QZkQxoCIWIAO0EWOlbIp502R7lyppb1QLFmtuh0+YgbRsrHWzvGlXQyRwS7A6RsdbsJrjh8Hb73/guBqXFxOY0v3A4bhwuLnmdFphtWwQaiqTVAyvSX/r5si0zRusMD5wzv6LLDJyxB/9JA9+Tk+Tnw+wPr3sx9lWPDniJ//8ZPw8aPhjONNPjGfu2ofW/LOOuD9Y8t6ufNT6hqQu6+y9Iwo9qfvzFPiSLNyypGTJpSU4WMBLIcyYvCLNmIsx2G6wvUMxltUxNQoNmjKG0GTgXtF1IIWU6d+c+EF+z6tPPqH5TJ0EWwqncWQ5eIKrnl6gIcA00fp2E/YsTErJILMgO3UyFca6gbkqZKRx3YIpDLHsvFeUroUiPU3XJdmSJum7JloRSQN3GYGEUeqMW2QB1JP9XRBaKN0TOI6h9GL5gDl7rAuoCRYTQ4JCZxXDbKYWqO44EKNlR7zAgqG+ElUxGjE6U3NcL6nDmXeBcomUEzZVplAuLl/z27/8K66uPqX3FbcVi5l22KV3r524Pu2gDuaA0RWtji3PP3v2L1/ih2uETohSW2HQYWToiONUL8ngJCfoyTA2tAXzlJYZ4Yad0rpC58AmZR0r3377DVKEy4srrq+v+Yff/Z7v3x8gAqSwxJo+l1Uyj96hmKE08C04zlLyVkqgCDVpJNlJnQNK+hCG53eoIimbw9DNE455glCEheKCqlGoHHwlLKgTIMJakzXlXVE6MiZW0ldpCsGlYkuGgYhayqh1orXO6h0ZBfPO23fv+cPvv+Kz0dnv9/zw9j231yd674wSlJbrKDlj5PXVF/yb3/5P/Oe/+U+8+affwxZOssqANcNTQjtIhjAqBbFkimE1JUVqREubkffvVg6H97xpeyyMfuis1gmc0gdWhRYNtvOzxUjJlD4vfAJghCAnw1VptSDq4I1dCVZvrCWYRyVwpgvHY0K0oKeVUoOhjTDQdUBthCZbZCwrMSp1PjHtZ5Zh7PoRo2+s4cqqSpVBTBO/+OIzPn99xWe3r9lNM1cXO351+5pvv/+Gq29/gDrzi5evKLsJdUlrjjaBrDnR1sHkcIxAThBzzxtwqYQFqy80nahVMC+06YjvLvK89UpZnWgZKll8pPoj50NE6zSvmMYWWgHhwjR1uk6JOknJIm+k/YfvSR9TtZxoMaMG0MGU0VZGKL6kN7VrSc9kDCsgtJS6euS5IoFufuIRQl9PuDvT5R4Eim4hpiaYFtqUham1IJZsxKyhTLOzk4LVYKzQLi5QH9RyImzKovYjxlOA7ENm62PA6UP27Hk9Z1lia42rqyuurq5QVY7HI4fDgWVJBsB5MnJ/wvFwsnR/UvahfXjIbH44kbvPXr7/+p9i9j48No+B6/dZRI9ZUjwF0D/GQH74GQ9ZyA/35/5+f2iydh8If/hdniebHzPpSrAui2+NvqWESyqRRJFpSRaOKqMb1ELUBBUdZz7bHI3ASyXGQveJRlA54ZJKAu27LcAFtDdEFqQJRmV0ZdKV29sV2U28mIxJMyjz6Cf2VBbPRqadkm077AS948sm9/QF8wwHi2ZIuYJ5Ry0zqDAVAz+yyMgSz48ZNDg6TLCeLP0NR6O9dEqpcOjE6lg/sSwlQb3DDcf1iHnPCZdO9MWYLoSpVF7US2Q/pQzVOro2GOlpWurA1846BC0nSpSstkrK0inGq1eX1KlxdaX0V0cOS+f2+I7j0TidGqfTCTlZ2rPVzFcQr6hlsKqF4rEn/EjVQBsJ73gjwjKQdppyUqsLZgm0iQbNesrtnzkiIstuTyarlmQ4ahFarVzMMy9fXKLTRJwq6+ic9oO2m9McwDcVUfx4/tcN7BL9Uc5tEam2krQJka3mFQEtylwqn372KS8vXuTk3Qs3xwM2eiqm/N61f29eIOc5wwbyTvPMZ198wRe//pLX8w4bne/fXbMeFw437+lL5fvvv+ebVy+Zlgu++v3v+f6771n6gnlmiiCkl7EouvmJzq3Q5mT0uwsRxtozAcosQB2NDFRGc3tCBSmFy4tLdvuZHnBxcUkrM/144qJWimYT2yy2QLwtgCSyeX5mwCbRJtLTuiQLbiXt5m5PN7x7t0dr3sf//nd/z3K78E9f/56vvvmGZV3oG/NdNLNeaqm0Vmm1Me0mfvnlb/hf/7f/hd20e/a5gwjKjFlP2opmQLXbBoS7ZfaKKjqy+YGC1TPj1hAvWEnSA+JbvZthTkU3MGjjlivgfVO2iDDIOZ64IC3STq4EEwFrQWshymYXgjFNV9lgG4WLXSom3tqa35+dwyUjGdBVkFCirww579BAXYnhDHEUo5qxbCdjmqUEaALAsfkhawjqjTEFUysYaSXYalAdhkkC8lm2AhWNwQhL6xgrRF1gBGKNUiOPeRQcT8sTAAaiQbimdN9zm8CSiFS3+taDpnkNmgp1TQ/p0klAvPzIahYLogqMDLa3MCIm0DQ2YbPYycbR8xVbTz0fH3umf0g5dH+cl3voR3y/ZvhQCB/8FCT+2PFYxsNT2/ih9zy2jLuzLAun0+kuNO+8ved9ONcM9z2jz4SC+2SCiLizpjidTo+C4vfHYzXRY9u4rutP6p+H4Yr3A/7O42OPtwibfrjlPaVkI1k1w1NLOGePVbV1I6oFsWaAZeiA83WpeeXYNjkRT5/gMvJ5M2SAKG6aYK+mUmc1pfqagLAU1BIEcVKpra5pMxiJyaRVf6FWhQnGaSBRiNFS0a2GyUpEqojXkkTAgRKR9/omwqiGypTkAByRCVkdnWB0QXRTNUfiD0UKRILb4QI60bPPRfqFKOppXWiQYHgMqIWB4NUoXZDIzJxAkVgzq8aULoYUzdBildT7SDAUJjKjq8eW0TVS6S+Tc3MK+nD6seEE63rgdDCGLYQZQ2PzeK7gkb7+BZBg39JexFQxOtOfP+TvQ2df4jTnkY+l+LOTgjfXpYcf/Wy28o+s5Xu+xecRj/74jPXe9bifu9QTy/Nnwc3/LCMe/PvMoWKsG7dxDjgFtF1sD9cJSkekM6aCektLjhqsbhQ3tGSojQxoMWcooBs2IqUUc6aV44Z0BepmoTEortRS6JEy/W9vjTUGY+yR+QVOp58WbpdbVk123lSdUQQdjkowNG8svoKPlL8rBj0D/0qthK0MSZ26iFBDQVuCjKNCCYS+JYDmA2eUvA2qKlWTDRKUbPaNAt3IvlfKRsQDIeWmVCH5/DUlUiM9YbWTMgwWWmbL416yMC6WfnxK2jOoU0cCGG49GbqeYN56ZlCj0JQaoDOEviBUuNjNfPrlF7z45adczReMY+F4UvohqGFcXF0yv7/gcPsOFyHKTNE1g3Cm51N6/vJXv+Qf/vnEaVkRGl1WfFU6wOxo3yYhPbuk7iWtVbYO4hoZdtck6CbQJEF1g2WsfPvNN/R1Jdy4Phy5vb7luK40gj56WkJoSasRF6jpAdcK+eCsDff0W5MuyFTo5INao2A+8K0oLC1BhyKS8mPJwEV3wDL52tBkU7QZH8FcZk7S03e5KKVastiHE2xhUE7aPgxjCaitwLBMC69B3Sw7CE2m+1J4//6ar7/6mrWvlNa4PRz49s23nPoJ82SJFIXQZFRJCJ+9/oT/63//P/nrv/5r/st/+S/osmIhUAZ+KtTa8DIxPFCcugeVKaW3CrpJa0SFMYKxdHpxpDjYwEKodQYhk3TPfOgujJJdNhnPZ/RUdaZ9wy6CqkYsDdlNFAY6gqnmuRBzUF3AKmMarHXQ0pcDqTV9wTjBcWCtMNWKzpk2frw26q4xxKg+sZycMgteOz4KXjp6WZl64S+mid4qw+DqYma+vOTzz35Lq05Fca3EsASGRZDYQJW1ssgJGYaUQekT3iZ69LRumSdGrKiV7KDHDj16drInS6sTEVDDqdTNOqZuUuzwHfVOBlvRdmCMGfIqo0nKpydX2DlDhZIJl6x2YphyWYLBRJsW1GeEhdCafvMRzE1Z3NL/TBL0l8gQJqezFKXEoNGwBh4TXZK1ffYWqz5x9IXQzhiOSoYzek0WWW0F7Qa9YyWYW2VQ8F4QGu6HZ587T40zaHP/d3gcID2Ph4DqOejlL//yL3n58iWqyg8//MDbt295//49x+OR0+n4kwnI/c94bDwEZu9v3/2/PcZkfgwIf/j7w/17alLzGPj8GLj82LIPWUVPvfcp1tFDAB34CaPo4XF5OB7zX34Izj9nyKamCq0JuFl6hNdJWW2hekHCsVDa5MyurKtkc85rNiRRqJ4+fA6Io1o3lcnKEkCJbGQXBzNW9ogZZfKURZ6EY5wobwV/KRmUaQU5KcssjOiIBebOumY91ZeBrTnpEyztvgS0FLRkHbO6M3VhZVB2wbI6pU40hONYaFTEhNKgdkdeFnbzJd1WrATL8cRhDI7vjxz6geV9WoNoCEeDee7sdhfMF1fspsJ0OVNrI2p6mDY1hmSdZ33gIxk75lNaWijYEWKGNu0pRfEImji9BjrSGop5UK4DBEZZmbvgpYKtdMmwvGElIaGtOUwrqQZi0OhEVFQD95GesJHMxyaCR8ejbJDT80ZOCjcVgZMAKtkQlKqUXaPUAitM046ljwQlWzKuz/a9WFB0AwCKUqUy7ybavMP6ynE50T3BrxHpi68lwZGKsru84vXLT9ntrpgvZ6RM/PDmO9bllPVMpNdvbLJjC9kA05w8qwtaFQtnqNNao15c8Pr1J3z+6lPevv2eb968Qary+z98xfVy4POXn/E3f/83vL99j40fGZAEiOgG2gXTXPnks8/4xee/TM/I4Sy28N1Xb7gdgW/qSFUBFVqpm51GoWql96wnx3pimie07HCH09pZh22gXVpd3UGpkeCyRDaB2ED72O4xZZvYj3Vwc3Mg/KsMKnS4vb3l79rfcXs88O7dNdaTvemxefTrxMuXr3j1+lP2F3um4nz6xedcXFz+hDX9p4Zr1l2lbPkeI8kpIxy1BByUiqqnX2kAJjT3tLbwmnOEMaXnr3aKZdbG1rk4cwUobJ6fdMRqhhNK2hQOcm5HyaBNF8+a1ozRO4f31/zw7Q/s656yS+Xq1f5lNru84+tIyz9LG5soGcipG2BUIRnSbhlkJYKFZdNEHYmgkCQPj5pEDk+gpWzfmWMU38g9AUUdzJPBrIKS4YeysZajKoztemrJ/C7R6LNnu0E3K0kVvFZiDMyTyV3IAGX3kiFYnvJ1SZMw2sayXosiDlFANstHj/Nz0zb25caI1LRhzFvF5hWt2SxyjIjKcjw++9zJK2urD/gpkOyRgbQ/S83z8DOeAHSfauY/pWB6Lov5sXWeX/sQKH5/mx6Ocy1xDik8B+qdweP7237e/oeN7lLKHYs7zufyViM9VFv93HH2tj6D22OMn4SGnmvR83bWWn9CBnjO0NrAnDmCmALrPcPsDGKrXzyc3mzLJchnUdhKoIwoiacUiB5Emag6NnunmnWMVCJAqzDIeZ1I3XqwxtTyGPtakhLlnqphTeVYx6hDEN0aHVIwFcRWRq/gqSJQkoUtkdd2iKW/8RioCLPm5/cKtqnUCaM6dJGN3CY5n9eAKLiM9ClGtu1SQkCb4Gb4kNxfSj7LTJirEEUoXRkaaDguhvRsbKsrJoMqDqHJaj5fDwOKKmbZnHZxqukdsXcitxOFkwN9oBI0LdhIHOh4OLL2QdimyxdJNXcN8I0sFFBK4lswUctARkt1zBPjzwowywNw+f74eMj1w3DxHxtm/BSffXJNT2PJH/Hpj27Qxy7x513+meMn+/VRiPxHjgIRJROKWUETCCyuDOvphVWmZG2obEbhnpKfMtHDETYvL4fug4lgiCLhOSlwAYzSM+0zyO63zBnEhRaqBcu0crgtLIvT9CZtsk4dW2GsC6aCmVOssrIykeGBRkWrp+OvKTplQeWhuCvN96issPGVxQZ1mrLzLwuqjdJjC18piBgVRaQQ4fQlNoauo70R3qkV1lBat2RkaNC94lVSdlmckCWDLrYOv4TgiTIzZGQxXIU6Ui2x+Mibm3t2w7MGSt8xKqMXVJ1aGtY7ojcUZnqfqIxkI9cLPn39ki8//YTLOhN6QdlPTAyWY1BqsN9V6m4HvKWuzjpZJj1rFqnPHZ/96tf84ZtvOUwL/dCJxage1MmJOjMWwceClUBiJWwH4ZRqEFnQOdtkyLKDNzOlxN+V25sjvTs9VsaJLXDDWHonFFpJ+wSt6QnOGHRTVnVqKGEdcdn2SZLFroFb+tLVSLuK0QKVmebBMraiaQ5iFYqkWX4UI8iivbaJYGV1gymSFbLCaELU9J52DbTLnaxy4FTJyd9ca/rBLRA17VeKGq573Drr6YavvzZub25hhuPpyLt318BKMGXYSQ/KqPgsHA8nbm4PXL644N/9H/+Oed7xn/6//0j0NcHovSHhlCJEddoQioHvQCy9D0OVMRYmEeaaoQIuA6RR5nzAJz1vQuuRIcCa+1fdibIVDc+97bTGftq6xrJjaUHxE0MKRSZUDbvKbaUVbBZkAZWabJUCxqBQ0kZrjvQqrYKfApnyehWzvMc1KKfAxgo16BqUNf0AHegOjCDW9NgSJuYrSwDdOyUqSsBcsVCIjm6epCHKqgs6WjKmYkFL2peEedrf1EKUlWo7xBcWz7TjGgJHwS5Scj8iJ75Dg+ENifRmjlEwt5TwulDISXpfCtQFalB0ZmeRPoS2EmPQdhA1izizjaVThKntwSxlWzi7UpAidBd8BETPsKwTTGWwtpbBrbGnNqdoyulNVmrd0+lQoNY5gQ0LRkkQWj3op6DVHVUbw06s+0FdIgMA/YjZR3ih8vRk6P6/D5m6T3kD3x/zPPOLX/yCFy9ecHV1RSmF/X7PxcUFl5eX/PDDD3z73cAsGVjRfywg74OwT0lZH06eHgK3jzF2zxOhh2Dyh8Deh/t/P+zmPpj9pyaFD1nTD7ftsdefkr4+xsL+UxPRp47LYyzv5wyl0GTBpG4e6o5s2QkMIUpOUEooaGUl8NFZp0oFgo7bhEjaX2k1ll4YsWBjR91VSgiwEF4YPSXtpQ2KrSmRlopFpovrtSVH4bVSxwUazvA9uyKc9AQUej9xPJ1Yu7PGiuiRQWXyDtKQqGgVqiulCpMJsoOjG+oVO6zIzmBZOUnaYux3eygZInY6HDOQeF0YLhz6LYfDiXd2BD9kA1+VQkWmgkyNOhm7MtO0gTZ8dE7NWc1pUSml0erEaXcOhct12FhpJZjrBFJY3bFTsC4j/WlLTW7fcJoFRSa6B6In1Dp9quyOsFoQtSA1AbLaz9YQkd6JEYQL7kJrFdSz3rOKM9A6YeJ3FmjPGcHmZRzpMczGKkZgrIOxdvoISjimhYt55nA4ET0Zo0QG7yUok+B5LYWXL1/x6S8+Zd/2HE9Hvn/zHW/fvmXZ5ty68Q5iQ6gv5h2tTazeiSMsh2veXF+zLGnfktcGP3rbwo+EIknwNDywPrj+5prjzYEXl1dM0wWvX1/y7Xdf89Xv/sCIlW+/ecMfvvqKv23K9ftrlsMRxLiz3NA8CKppY3d5ecX/8N/9j/zrf/PbzLaYGtc3B/79//3v+bu//Zu0orNkjapUWp2ou5YybpTb48I3337HZ69fc2x7rk+3vLs58O3337PYgkUwgrSSO1/3mtsRkcAfIglASjYF3CGKEwNEFg69E6qIKMdFEfsBc2Mdhvee1loIKkqbJ7748pd8+tln7PY7rnYz0/4iRZ/r81moJVKNFVvQeC8DtaAhnCRhQ4kgVetO1zwexZWKE3SIutl+JDu5qCBa8W5JgAinkmo8alA8MyzCPYNBcWpEEiUiCB8omnOV0hjmHG6PfPf9H7h88YpX5SULC9Mx+P6HN3z75gf6cIYrUoWMPFciEuQPTVAp7bgq5gYbAC4U3JMZaaaUAmWT6ZulTYdqspWLBI2JwOlmtDqDnT1PDTeBAVrSRqgBvUgCKpb+2Cp5fpikdUtIKgmjF7Q06sb1diL94nUgoeltrZ51W9V068DTf18cKbmMy2Z1EwOKEJHnC1bxMLAgpgwKi9WhZR3n3uhmvL95PsB89zxlA7X54+fwzx0PweOPZSTfVxJ9aL0fO57DFH5q3RFp4XH2j57n+Sd2Z2cA+swSPtcQtdafWFT03umjZyjnvTrjY1VT52UeNtLHGHevnbfvbFly3of7DOv7+/acMdwpFVZLhq6LIz7oNlHE06ongjoUmZTiQt+eD02F4poMX9LjWNRhVKqmGtmK454qDx9JrmIqVEv1MKq4pi7AdQvp7IIzQCsRBmw4h24EJyAigzKBtBvzLfgPJWpJuwtXCpkH5CoEhpSWiiB1hgmiGVIcTpLQpGzNSCckn+bp6pgqZx+RoPxIlZGqZMC5DtQdEEwKhSDqoHjNZqY2xFZEByZpVWRWcA2Kt7QnqXkfyceTY253mWFhPdVKMeE20ChUd0Yxaq/0naG+MAKsb+GJYigVU0Up2Mi8qVLAUVoPTAZDO9UKpQpmT9sQPgv2uYMY/wTW+DOv+afW9mAbNonSBzcg37m1v+82KP+fPkbpR+Z3uRdsZvsiGVrx1KefC7e75fgwoP78of9CA5EPjEe+r3jyl6cW/HlbNvqOJooX4zQKTSrVBHxkMSJGqBItUiI+BqU7UhoWmdxsCFENj46UHYv0BF5o1MUQ6ZhWfFaGGM0Fn6APQyO7OH2n6Wk1Bb4uLGNj+C2OmWDWieLglU4QIiwutAFSjRK7tN5QzY5jmahrdpNCA6NikczA1RrNkkVao2Ynu86kDXV2koiUH0RIAsg1GLYVIqT0a1ZhzDl58eEJlK/GEJDw9D4NJWiwT9b3pFtSawiedMAs0kmpZTGI8GRIxYzRKUJK04qSSTMLMgfWDxkOosl00ph48WLi8pOXzNOe4c603BKkv5hJMp66O8hAG3RPfzzvlsF4H+GjO9aFsS4JImngO+F4GDQHX1eCgbcEi0vNTmgHqu6z++aGqeR5UCpaAy9BU2eEELUiDGykr65HypqP5szSGAjiGcKotVHKhESQWQ6SVgpNmLfOpY8Blt+l09P6pE5IVMaaTjF1lixGo6ETOOcgOaEVR9eC+YFWG9UKwwIroHULJon07W5d0sZKAvX077PIB6Wr01kRKdADq4qcAq0LUZV+VNb1wHJKCW+3zujOtGvYanDK89JnQwiuT7fc3H7Py9Oe3eXMv/2f/y248R/+839ERNOvEHIygeBiIGlXM3nKuOeAKC19xgGZgrlMzG0m5vSmEwc7dfqq2bBphREjC4Q+mD6GDaaGuEGbUFPmMlCrhAbKwuINIcP73IMpnMNQRliy8DZZkUpKTsMLCqwKGiewSygVKU5xMqyuZZE0remJHKQlTghMKhz8xK5oepGu2VyLyCKm7lOSrg6jBcWUZspBnRorJQSZgRE0SxZwNeNUK+Yp6Z3MOUyDYunjFTrQmAgdVBGGB2KG62BZFTp0caI2Jt+87tsl3TvuLVng08AiiJhh6SxNU4apSpkv0TFwrbTIY9N8ozqVDaSPQpFBrEapO5IoFdlAoND2hX68pdSWaofWQSeINYOi6i6vfwK9uMIoTCgyga6WYaJtl6YtJoziaNlR1s6YTiiFKlB0eva589jk5Snm731W82OM5vsFfymFFy9e3HnznS0xXr58yatXr+6S1A+HQwLKB7vzJn4I6D7F7n2MQfwY2/f+8vdTzu+Px8D0h8fjPEG5/96nwN37E8WH23IfRH8IIj82uTy/9z7j5v5+PQTMPzQRfYrd/HMm2RIGVrIh7Z4hUVaQJYFXxsQkPQGwKCzSqS2YWzCtyqkVnFM25CXBAykNKTMysj5SgzUq0iKBqO1aT9qqYzKYRgZVnSjw7prhl0zlLbU1pKz0dQY1Vg4cV6OfVk52YgSUAc0NE6GqJqgayeRpXjnuV2o02kkwGUhToudEZPSg7CuKs9jKWASRlTWCfjhxuwTvD0dub25ZFyPcUh0lA/UAaxQTfDSiNm6X99gtHMMTdF+d+mLHrl0wvVRexsXG5hvYIb0G2y6dVO30HjNBemGVVIC5GVIqZencRkvrJkiPRAFCWOsmvxdSJWKZ9k436uTYqlhNgKj4hI6sAYvCqE7xkWwmJcGq5547mvb6cm+OIZrb1E9H3rx5yzxfZYCcJZGgn04cTodtuQJxZu0XtCj7ywt+/Ztf88Uvf0HVwrv371nWhZvbG4YNzAXZ2NKiKX8tU/pF+oCb5YZvfv8Hfvj2O9Y+EkyNTMIRSj7rfzIXSgmvR7D0wZs33/D7f/xHpla5mPd4FI7Hhe++/obocNtvsgkjwrDO0tNjlgi2/DuIrC2qNl69/oRf/eZLXn/2KVfzJTo1Xr9e+Off/ZJ/+qffIctxA6SV3W7m808/4dWrV7g5t7cnxlj49quvuZj23Hx+RHD+8M//wLt377DVsZ7nWUQSlSIcUU1w4Ox/EFuwkqbFQUSCt0iCt4so2OYNPNIDObrjEjnDiwy9LgGtVl5eveRXX/wCNNjvX9BmJabC8fb5qptaKmaKRzaNhPTEVAsqIGWzD9nOjWJCiG1cWkGkIO7U6JTaNpBXGT1VA14q6o7XZPmpOx6BD02Si0eC7JrHp2w1gnlWhqLpDX86nfjq229YO3z2+gvqVFmPB968+ZrrdzdpkxgJtlRv6UOuI9VsbH6mDqZBcVgjrw/XVJwW0mJieGys3IJlQPqEAAAgAElEQVRIMpI9ClNtiEnyXTzDsoYNREveS8f2vGz59/CksCXj2/FQSnFcOiqKWXaAXBO0CTZ7IxWslpyLxkAjfaj1rEzQJF+EFFwUjZS/h2/qAwUTTUsOzyaQxQANYmX7vkhAOLtDSdJg0LeQx+eO+89JlZ/mLugHnn0fagjDT0HPx0DLp5RX98fDeuCpdfy5x4fA5fPnmtldQGFE0KZG0ZL3bpIhfN9n+lzLreua69nuF+u63tV4D0Hm+7XlU43+8/Y8tq1nH+aI9Fiepola6x0Afrbu6L3f7cd9W5MPjTI2NE5S2qCRYXQ+BdJbymu9bN6/nsrJNtFrTWuMkgHHHkatBbW6We8FfUp7CRHZWMA5B1aTJNC5QB2EO1WhyOaJTF53sYVtijsjGmrOpHDaSIwlRbLJ9I3YwlsNdWNsTTYEGmllGiXzj1JiYJQB0TIcXjUZz1qTVZzpVwMPQWaBnvdhWjKDRQ2ZFEZkhk0Riu7ROa0gzTN5tUgnNO+7UUraj2DEhmUgkoqYUpDhmdVTjFLapogfSKx3Le7BoBbwnl7yWCEmo3VjjGxsWfE8FhapyhpLesu3rVXuSgtDm2QItWXNGVKo5el7zrMA5scvuQp3fj/ywXfeveUpivGTY5NIkx3j+8umSOuxHYsngPDzjS47dXFOwohc5nz9/uRivlvPj4Xf/Zd/vOaf2q8Hr59//cnLPwWXf/KnM4J9z4/5o47gz77//hlu3OosDNSFtle0D0IMqTPpqFyIMOrIYh5t6GYfIaIZONcd87y5uEBo0ErZgMsBLeVFSDYM3NJrK0MrImV6vRKl00/Q9gkM1hUIIT1+M4nYwlHWzUA+AVdfgfmYJucujKjs98fN1DwDL8Iz3KWMC1QWekQ6WRRHS2VZByIJQC1hyf5YC+yMFo2hQZGtY1eViPREu/PiM5BaEF8ZpSK2S5ZCCFo6cVuy223t/yftTbskyY70vMfM7nWPrKruBjADghQpaagjHf3/H6OFFHkoDWcGjV5qz4xwv9fM9ME8srMSmbUMHAddVZmxuHt4XDd77V1IjMiGNWeOzmTnRoMtFgYXLA9rhwjkBTCXKk1mYmqsMXCPg+W40ybYacXjAvEDkp0xBPdEbEfWlRzBYo2tn4j3Z/pwWibTJkiwdMdnNZtfu/3zf/9H3l/O7Nso4DSS1GSEkQNSi1XlbTKHImvQ94blVgb408ur+jDlN9FjaqhEzMpG2p27ca7UaW2oGL0pMZ1+6sz0mvZ5AbzNdha7KSlgJEgw4rjecCaCpQGDJWFZGnOf9KUfhbDjYSxNShajJZESUWTAZTqnxRhe1g3ShJVk66CzQo1ElG0GHoMXqxXLX6yGMwE+a1LuNmitM+7Ko62noSm47gjGloGeEzeBucGl0bRBTqTVdydist3d8eO//Bmzzt3lwmKd7//we777/hXv3n0snys2yKWWKhes+9EhN9QEl8Ay0aZ0UV6+eMnNq1e8enFDPy2Ydj5c3nP37iN3t+/w/Vh7RsPnTmtGG98w1Q+Q40YbGawJm5bPe9Mbpm30TfGmqAd7nkE7S4Asjo+BRZAnIXMyL4Iu1fhOcTI2ljbReXhaZTC1I/24T01DutMj8LHh0rnpneY7e1a4jgeMLVjX9WDWFGPnxZhcgIkjZsxoqMMkOOmpbFhiEjdliwODfgwaXiTskZwjeSVJzmS2yXIpSxaXvXw3Nelu6DKR0ohgrbOda31uJsyDgRTH/SebYwmxB7IswGTKCRuO4LxorfxlVyOiPKDJI5y0NYaeaWOlS12Hlwg6Ezu18hkLwabROhCdzS+stpQELZTYg6UbmeXlmq50S3Kd7PshAp4FsLWWxFmJtRcY4l/PBntYxD8GkB83VJ9jLj9unESE0+lE750XL16wLAvn85kxBq01Xr16RWTy66+/su87Z86fNB6fA28fvsdTbKHHTOKHzeHj43r4/KeC/p47vof78Nx5fY4V/Ph9H+7nw334ErD+kPH0+PieY1M/fu7fsu2SmBZT8BTKvmZVrlGKJ2kTwokUdCmWoXBD5IXLKcu+6rJgNo57V2fp5ZOXCi5GeBJLsqogPgAlHE46uZsLPaWajXEh18kHN+yt0puz9hULiP6WOZWhE84wckM1sFHDtGj1vY/FysO01Xp4mzunS/k5DibSjDUXZGl4Jr3tIDtzS2Q6OYRdYXrw8e4jb9/dcbn7QDQHb6RO4tLKd18DvCTx89K50w9YKJe5MR3WV0aqM97BeAF/b6/KDutgiZsZZoM9Bfm4sR3D4yW9PGAzEDv8sdtAfJB3jg5nE6/rLHY8WtkkLVn2TaKENKKtqOxoOOIrGRemDGYvIKuTSOyEJjHsHuz62k0Oj8rqQ8p3GBFiDrZdePfuDevpxHe/+wFN2PfBr7+85vbuI5EVSHct2fWokb///gf++Mc/8v2rE3PkIbPn6MGOrAgtEodlEh5cbi+8fveWF+c73r17z1/+8hdu7+6K8HHtkVKKqAAHW+sAxiniTqSQvvPu9h3/93/6v7jExr/9+z/x4c2vfPz4ng+3H3GfFcwXlHrLCzAQ8re+zCv7RARElKYV7PfyxStevPqODvx4/shl9wNALRWemfHq+1f8x//lf+Xv//BHdr/lzesP/PLTj3z4+IH/9F/+K9/98mfYk59/+YXtckeUQeeD/xWwnUHZIJCoQEpZP9UnVfVF0VoDT6kReibSk6Aky57lq52H7YXw273Do8K1vv/hFXMklw9nbk4v2c63X33tvPz9Sz68v0WnEPteAX9SnqL4YbWTx/qmtb9uoFE2Ei2UVIfea3RweMeHNtIayY55q+M+gnynBJYVxrVaB3akSzH7JQtwAswHtF4WQTT0bvDL+Il3b94UEOJl75Y5kTFoWYzESYVqk0owcFd61PAoxUkvT5zICsgLaQcjUUClZhNRALhaeaNnAF0wnYfy1TCPCtgKEFMsKOsXKaR3aGXmEKVmLGvZIkWY6OEHG7hp5cJMQ/yw5jDh0M1XcH1Wvkmk1rVFMFJROYLFtCwWkxq4ZShoIOFYFjkDUcQG4Qe7GyA7OhPrViB0fkOtTFlhXK9J+A2e+NL2OaXRtW762vC6p177qXv+4yHwcwPix9sVQP0Sq/prQOvrgHvOye3tLe7Ouq6s6/qJL/PD9xpjMMZg27Z7i4x93xljfDIkv24PiQVfYlQ/x3i+AsYRwRiDfd/LsuhgU/fej/vmb+dmjK+zsWxLlGbbO9NrrQuClkF20F7kndwn7nYQfGep1KX6poiJLisehrVgBgehpPr0zKodUgKNI+RSjKYTsp6fLoRRgz+vQWD4crB2BZUBTZmzFAFpwu6CMpmeh4/7CbOsAFmpgbJY3ZcspABtQFrioTQta17rHdd+1HfVS2l63bskYCvFp0hnHgPKlo10R9Ir1DOVZCfiqmgoBcPESHM6UhBlo9S1x7kWZtmnqhFRhDptC+lFLtDsZK2WVXtmYGGEBY1Sc/gmfJg7JoHoSpvBVGWwsEoDC/YQ2lRMR70WUuoRrXwegRrU2/NrzrdZZHyC3c5Pf/F4ewiK3jN9Hz3uS7hsVSDHQ64MGA5PrGtFdf+w354r3E+jHwO6+cm/n/CGzt924CFb+bofWj3D4U8Ev5UbTx3To4PLp3/81EM++cdzzOovoM1/9evjM3nw0XzNs/5Vm0ZFglkvKoJGeUhFXmryEw6ZuNQCpCZ4y/pC7VFyIpFDVupIrw/ApW5apgfonMkugaXgUV+WroFRXnCZZ7QVq4NwhMa0S2Xy7g2RYoS00lMQOrGWFVC2QIYwe8lDXYVLLuVR3CbDCugcBwMbgVWsZB2tPL90NWxOfBjSitGdCDEpgH1RMpZKBU5gGrJMYhq5leSwyQ6ihxR9VoiQNJZM7ip6hlgDnYazYa7l1RPGJRuk0zjheoFYEBv0vTyJ0pTuk2SySyv/tSPccNNJ7tBn5+O7D/z8Tz/if3KE38NNZ83ym8w8I3eTy7Zx8TOX3MihFcCYSvadpyxtntt+/OknGBfc9zpmARUDmWhqNTHSytvZouRibR7shECmHnKTjkTHDpmLZ/mK7bkRYyeizk1cFG1VbIcqMWf50bGC7sV01lYgpFSxuGZjj5pOVrCMlc+vLqgWqzib4jFrCKJWTMvKdwJZ6JLQKnTptCx1U75e47OaGkkhuyL7ILQhWgGHFZYW+Nw5pbJRTBGTDrZUmmyrb/kwQ6t9KO+oVmtWeBK6sJMwHZM6t74DBn0Nfn33nhn/yMs3v6O15OPHt/i+l2d5LNUcRAUN8GIhR7H5UyYyazhCNpokr043/OlPf+L3f/8D63pDTKU1eHV5wYf+K29aIz68xZmEb1iuNWhpX180i78g9g164lIqBbTUDalBT0GXhrswNZhnsFWJHUQ3UArUDGB3LBqyG2aO9M5IZR9UCJZMetafEs6Qhp6COR0ZzrgM2nfKeRNetBPM8j3MFHKtwEgMbC6EBTMbuu1HcviOLifwstPIVrLnCGMbcvgUCxeVYgOe4Wa9YaFkWlOFMGGmoRNGNNIcm0LcCBw+o6iBShUhTOBE02RzWJCj0GvMk5ZyYhuolrdZ6x0dytmj1lgm4oK08rKc+2RdXuBSjK3Ygj0q3MTzjAZ00Trny0I0ZV4+8vrDB/6uGZZeQSI3goujWqCPaqC24GH4PpGXztirNqjesxpIoabx/5rtKeDzcUPzHPP3oW3EU83XNdjlcrnQe8nL2lHon8/nT1LHH+7LY8D0KeD2SyDvU/ubmfeyzqeO/fqzh/v0nEfxc2zphz9/CgT+Env8KcbUU2DxUynyzzWsnzs337pJL8rDvJRSRYbSCawrl+Dw/FT2Kcxweipit1y7j1CnmRJ6eIo3JTaKODOjPMeXDrFXgOlsDK3h1DlW4vDOQxpT676mHqx5x9wbdy/vmOcL0pYCJKPRdGIq+Cw2kUB5DOrOHMraOvPW2Gyw3G2M1bDotB7MaURvrLdVJ59NaOHENGSOkljGxmW748PlHZdx1PI+yCUwB+kFpmoqYysF1d6dwYr5yjCnReNyF8VklYn5hY9nZX3xkvWaZaBOIUiNXWHNjr4wPp7PmBsdxwHb4dYDnwJsjF5qEvXi5faDNRV7yVArjb3Tcke3wWhaAystEE/TK5TOOl0W0pOp85pv9NVbRil/ys5B0FZZCH4oTz6+vyP8z5w/fkB743K38fb9Oy7bdm9dUW2N3IOEpxcn+nrD7sLHjx+4e3PLdltrSxYdmTyaWulChPPu7Vs2nwViXy5cLmdiRHlNR3D1Bhat81V4apSNhEfpaKVqnPN58Iu84fJ//if++dU/c77ccvvhFh/jCPJLJmWnQBy+8yJHbwdIgZQcVhmXuzOv371j2zZOLzrjkvz4L//ML7/8WGBFcbMwgRc3J7579YLvXv5A2Eusv2IfO+/ef+Dd219497oCD8c28Dnq2IjD+uO6bhTMLFTNGJHXJrb2KfO+B/SDiZ0oYomPJOTKgk3uTX+PTygzme58fPOB7d/tuAu3t2/55ZdfudsG293XM5h/6N9zlgoQVGmHJZyXf+dxXxfRyoM5+ipJQZojs2pNTbBWoE6mEXpdB5QmK2KOuOAxq19TQbx6O59J0MqOrYwh8Jg0Dpu5yyxenwXEQNPZLjuRWf2BFYmoZRkNamQFm0tlqTQxLCebSaHPlFpSSthZ8u2oPiY9EavXQI5eMo2TtsoVifJxdYsiVogiowgZSlmNlBWNlBWIH4x9m8WExg5Ck5FmdABPJkLLhqnh4oVRRJFcLJSus1SR9aUuUFq9mJrMGn6FVd1oUqGDVrW/aTIiy6Yxr6OchnbH56jMl2O9MQ38G6x5RK7WAb/dk790F3xczzwe0l7//SW7hedqkX8tM/m5euIKol5rsYeD+4f1zMO/P1eDPNy/T7yUj2Nd1/Ue0H2YA3ENbt73nTlruLZt2/1jnhviP67Lnjvm57brvl1tPa7n4hr8dwWbr39+rQezew2bZvPjPlghn05lYDWXGrQcgx09lK3Di5JvzVlE6KrkrLUlCEI4wOUCR00gHbwFqoaMAwO0LIa/etnxSDuwo8I9yt6h1CKZFd5JV0yyLK4Q0LImTLz8i7OCY0WCNpWhZSCkh0WS5lEbHGF8moKOWQHLh3LHI1mu1pximCl7jLIs6gI7BxAtDLHKt2nHAGwe64smDS1Sns0aTGbZt4pRx+alnpWciFWfF+GgyjoHIwxMCJ+cmjGsFwiuxhgHecSDqVQv6gOTGpZbVuBxw2jskEt5vEcCpbL3yaHiqSW5rc9fh18JMP8GOn71Vz8ePOe5J/0VWipU1XL92QGF/oZQP3it4y/x6f4BNRl43u+ifHoPoDgfPf0hEJZCMaUfvFYeLOx47qDywXE8uX0e3n30Yp/He/O+ZKl/P3rsXz0tfvvjgNCfeNTfDi5DeY/qWt7FQUfbwpwCvpEdECugIuf9LCG2pPVWkyRLYgriwWyddZRlQ07YorJy+6jwKNUCa0QaYbUoVMidkypsYahvpFbx7XsjdGP2ZIkaVogKww9peY7y8glBY7DMlT2FnoEOGJqIKxZJ5H4EVznDQMKQvkAO9lR0OptcJSDlz6vSyhtIqxhlONGqKBK1CqzQCgSKFjBr4esIUwbqSjRlhB9gsMJ2gJqpiAToBZNe/mQTvO1oGMMEjYUhFWBCFMg99hVkw2kl41uFOMNcBqTz/m5y+dFRklfWOQ9j1QvbvuP7LT+/fcvH9x+5fa/IeQHKz+fCqKn6fP5G+Xi7O98x9oHjeDSiBek1wTSNKihzHIycBTKJNrC5VBCklceRjIkug1g6iyiude4YUoEyEsXEkkl44FZJ1Yv08lBmskxhLPVZadQNKRC2Vr8TXRh70CXqZkF5D7dRC7I3wU1YshqN3pQQZU5HraQvnl4SmQk3KwXyprCH4z5psTFTWawYxqTh+4Zwg8rkIjuW5f2nXtfQyEAySG20GMfNsRJzcxghF1QbTRzRIyU8S+qDDNKVeVHInY+v3/D23QekRbF7h7DYiYYzo1K0e8K2JyKVko14TZ9HyYUklNPNiR/+7g/87g+/53c//AAOt+d3LDc3rDcrL7+7Jf9FefPrL6ws7KP8Rv0bQm+mnkserU5zrQCthBFV7GRLyEnxjQLXRHXgSyP8O1TP5benOwudIUFXZWSw+FqstzaQbDSpIKjeV3Zf2HxnnY1oFbziq2DaEN/Zlyxf9EFJw/dkewk2K5TGZFbhdtPgMmmA+2CxxmUkbiWE1BTMEvWAZiwq+NLr+uWOU3RGTj5ugxs6MyemndUqrGISqJT0dTIOdhWotbqTzMCH0tgINYY4J53MjydmpwJ1JIpFY8oUaJLgwuVuclqUSSd8Z4xkXcE3rfCfLqgrzoaMhjVhP4AtU0h2xghe3KyMy0b2lXVZmCOYfuH04iW7wuJJa51JIK/WCoE8bGLamGTLYskJiH19QCQ8zXJ9qsm4/vspFvDDRuXhY0SEu7s7eu+8evWKfds4nU58+PCB169fs23bk7LJh+/9eB+fYvk8bvwePu8pxsxD1vBzjOfHrJrH23Ng80PA9zEI/BSD6PGxPWRTP37fzwHHjxlAn2My/63gcr2hglNs3GxkS2QIWxYbVXBWaYjthBebpMeJGUKzjWwLNo3wwYzy+m9dilW8wKLK0J3Fy95qkOgEjYUkyQHzhaMNRhqdYtddRLE2iS0JafgWrE2IVHZR8u4Y5OP0FugGdEifXETZ1Vn3M3ZaIDsxBmsqzQYhg3Ne6tjPgzt3mk6aCtvHC7fc8fbdO8a7OzykfJIzoDVIYY4kFiVGlKWN7+R0tnTamMTJGWb0Kchu7KtUY8rG6gt7CDctGbOaIqdqq74KWKLnlcU2PCvcePiOu7LlbQX0Usym7WBMaQZqA41ABWYKS57ZSBaxYojbte6rxtJakLuwL0HXCkGdaeg3rDtyVUpQnsNNG+GVpVHDpjMRzt3tLdqt/GrHpTw8D6ZSZoI0MivENkZwVeF9/PieN+9+5t3HD+w+OOhSR/VfNmiSyXbZ2cab2qfMCv2RKxhDgZVaVgUiBV5WtHyWb68c0euZ4IPz7WTsGx/eviXcuYzyzo0okNvzkCcnwMHiOkDmRBE5Gnkm7hu//PQz//Lf/5nbD3/gl9c/8p//83/lz3/+iTFn9WhSmQ77ZefD7ZkfvruDoVVHiDB9sl22srrLsnpAsuovrmvDYQ9wnEe1B4M4geoIr38/riGqL6yQ6QIEkqMMUo6/6+HRWX63c+78+uEtf/7pnzjvZ96/e8df/vIz37+75ebm622d+suF5b0wm5StDqPIDiF1/8uD4DOCaTUYD9eDXJB4Kqfey/Yh63OxOIByCUooUSDQFIhe+RWhh2+vlT2NQIWsj6zXIiEbKpNwIdso1mAmJhXI576XnZwUSOtR7CefICp0MUrUXeQfjbKgcyk7AosKslLxg3xT74kW+IcLulDA9+FPnlFA8xRBMcSUrp0xlZxlfVifa1kP1dDgGgTmZZWBwHRmKyKLRhINyHnYjkSpJbNIQ5Y1ODLX8pwNyDAEL6ayBIsmYfX6qUYeGTwuDfOjUz+INuk7HnK/L4ZUMP0Alb8tVuu5e+HD+/5D1uzj+gL+evj73Ot9K5D8Nc+57uc1hO/K2r3aTlzZx1d27xWAve73w/roS3WBu98/5/paL168uK97oFQOc8wKjx+j/n4wmp+q8x5v1/15zFT+1prluo9jjPv9u9pkXJnXV7LDFzdJetZAL1IRj5r9iNNVAEUbaBhuga+KzI62eVgFQerCzBoipkmpECQIC8wSQokoNYh5LaLh9Z5NAm3gh32ORgHxqYf1ZO5EJKpG6E62VuqDcHJKeSpb5dFIZAUOZvWKYrPwA5HCXkLKKksCp2wp2qGgEAviIEBWz66EdiblzywuZeGBs81gyU7gpHQs51F3lWqnq7PnILIz2yDdDhVPgrUjyLeUsSEGaUyCjKDbQk4nFmEcVh4yhGU5UeO/yvORURkOhhxK9oQo8kJqqXPEFLWEVIYXlrSiZAOwwvX0GKiu1fvJZ1QTX7kaPfMFuBYpT/76W4HKPP5/UJSzCpq/ehUT8CvwWmDu1Srjfl/kynaWY98eQKkHVn2/z0IVyE+Bvvkp4Fw/+tJxXSfVz52Bb6A2XF/us79+GlH+Eoz9jeOCb+Y12zqLHcIGuaJi6Iti227RkUsisjGjFs8wP4C/nc1WdPNKEA2w6ewGLbkPqNPQkjVZQ8TxbqQJuu0MU5o6PsGWLC8bAF9KfReQIrScjAHNsvywwkH38tW1qCnYAXygUkyYReih5ZO1BDFX8B3VSnPPg1maDq1lLYTpyJZAVHG0VOBHm50YO6aKOXhrZIMwwaZWcU+UMX0GMxoilSiaEcgwxMqHsGln+GRK+TOqW4WKSKWtd21s6rRwcgINdBpmk0ijGccC6YRDu214d9qoZm2LnbEbf/n5F4ZstOVEbp3N79j3YNs39u0t5xnsUg1Jj1syFW8rwtc3XPvdBVOt4EMmjFmp4FIL3yZCBSAW27RnELOYwzoHIlmptXlMaUPLMy+E3Xfm0UQ0MfBgRKBNWHThvDlDnBlC2kCswhtjNlKS2BLrSXpNAV2dZTHMlsOrbRCibDPLcymS1U5lnyGzUtylITkx6jPWVQos6Fmg2fFd66KVVC4rUIyKLUH7XqzVuBzgYC+z/XDmzHISVkH6iablreThNIHt4sjqZDT6nEQ7mOBBBWVWJ0lTgT2IJmw+WGxy3pUba7w8de7Mafssdu3F8TZYVA9WRXlcuTSs1Xerr4r2hs/6bJflxNobbW14BLe3ncUW7r478/H9O2YE7Euxyr5h3Zle9w4N5ZKOWQUy4op4eXbNFfRcPtMSnXkXtHWwx6xQuGWibuANeykwJs2SGWeavqL38vw7OLvMcyJt40QSc5acUgITQ8+DZQo+KvgiowCJOa2sHPICpoQPUhJTZXYpWaYJ0orZqgxslF8lQxlaQT3zVA2L1YCcIV6ydztuzrZUEFRXxI9GxEuuJkdATHqWqeHS8ZYQndgmy6JItwKAdOJeHs2nfkNbCtyIrMFEirKeqlBsK8ymrHdGAk0dpCNt0iUZsxifMQ1lwznAi1SW08oqN8y5kQfHYOaOLSsyE+klnY4optHCYDfhhS2McWFv5RnWMXL6Ie/+uu1rWMpfki0+Tj+/NiB3d3f3qd6vX7/m5cuXpXTweQ/gXi6Xv2IvP9WAPAaxH+/rc49/HCJz3wR9xu/w4e8enovH9iAPgeSHr/scu/qp/X4q5OZL6eaPgfCHP3sKwP6a5vdfs6WX2gipoQ4Y3jpdSlbZrEN6rQMidZ+eA43lCA0eFUA6ihmYM9isQl6idXI6Gka0CSiySNU16YwcJY33g4XUBZl65Dc4+4uO7BUQJWuyj8ojEIxsxbCpYC/wXkqp9kKQKejY2ALi/IL11Fh1csl+rKfvCoiTjh1KsDF3zsM5fzxzOzcuYxAjS9LejPNMGoPy78t7f3xRKkDHJpcMukzYGtE2RF5ABI0V2yEk2dfBd9q5eLHCmrSq3eZkk5VuC9oX5txQBuMyK1h334jdK38gjL7bAdI2rJU3sZkyE6Q5M1aaDdKlkuaZxxB2FqiWjeyBdBgXQWWn21LEga++ePJANqvBqzUZMgoEJOselBKwH7BwRIGEXFlidgCZARPubs+8efMGJHjzaw2It8tGRvVExaQ96vrDy3tm3Usian9EOBjSB9GmzF+LwUupVgBE9GjF5GgSamjpocQ2mBbEHMyo9yXyYCtXr3T1Jy6JPwexhzrPBjTj+9//Hf/jP/wHpg/+n//yf/Df/+lHfv31J8ZWa2ZSoGSK4u58ePuav6wd8+Tj3R1vfv2V81ZM3/TyzA2imqRPBk9R51dApcBSESsQXh6wnA/GtMpvatqM/I0BGnIQZ+Q41gpzVkolNsbk47s3/J88CQEAACAASURBVL//DV6+/Jnz+cz57sLbd29Z+9eTMXorZm9QNnJ+kAxcYIiw1AHi1pAQNAfDiu0nE9QMtYIBtBUYYtOJZkh0uswa8KuCe3mhAilJy5KcdyugPb1Yg5Kl2kxqCC0k6lbswZnFPLwGUnox7yKyejtXemRZV6ihFydbgSuVnZRlXYcjUgGp13DB4qFNkGL+ZmrtVy9FLaIVQkly8gPYSgeEZlGeyarkPO5Hl1KSJFKfrUFGqUt7V3Yqb0K9bLuaFKg8XGqyIEKXYjRqq4GFeSLaaHMjrNE0mbMdwwgpNdesoC4xCgDSIKRhWT01lug0UgO3VhY9lK0k+XU2B7Xs/Hbf/Bxg+fAxV3DyqXrhWsd8zsv3uTrjawDT5wb/j1+ntcbNzQ2nm1MBp63fA6lXNq+737OJrxYS1/1+7nWf2q41zhVgnnOyrivLsiDHUGuOArSv73EFe6/b1Rv5+nrPvc9zzPHH5+ZbmM1XJvaV0fxVm9T6pEFlQEgxdkWisgsYR98ya/0cgjqMpuhha+NRuEaXQQw9VKoKowY3zaJUBcdgM9LQPtBohDqZHT2G6JFgx/kXHHHI4x5lKASVJ+MNkUnqqAGPFlPYcdqBo2Qkkp20SRtlXxPDkD4Ju4YCWtm9upBS+QQ0MI8arEXDmGVJmUZoQ9OZvYgD7hShjAQxRAZO9X4x614umjhWFoBWg6wKC3W4so9DsYzfMKLwIrlFsgJOeTgjSkjCyBqOixN2/f6UDzwGMYBUIgu677ng6exRg7DUQKigZWUysoKQkee/73/buCufBh0/ASMfsI+/tBUo+hsYfP+s6/OFSlHlqkE7HnU84Tphvu7cUb988hJVvTzY8aMgenr77UhEDs/n5GCkcDT+BVbm/bg6eGDg8dQZ+Zu2x8zj5145Hj3jc6/2Ndu37v2cg0UXpkwW8fLHO/xu+7aXxIKSyO8BS3akOzqVUwZTBZnODQtDB21UQnRIp9uoYlwbeRRAmQZjUIlYzhBB22CPRqaSOFMcmULrkLNXYWqVPqqcAS1LBSlTeGSSoWzaEKLC+3ojEro5jPpOd6mwQG2BpbFzqUX4mPp7CtnLn6sD4+rxHDutG+yGLwJWbMm5F3BZhVKlgZrWfmWshA5aCG6ONdA0LGBK0MQhbxi6EVLsnzwM3lctqZqcOuF+yE8aEpOIrFTlfWAnJX2ikgwV1BtMw+LMW1e2fSMWUCogY469WLljZzbDCMQH0loBq7t/9RoA4LFDGpEGWkFdcQyUUrIYFKKgRpfJrtBcyDmZ1kue04G+llw5FcfZCC4xK8DxtGDZD6kcZDZ8BFZ3TdLL8gQp+WdXmJT5vlsxjJJaW7OBy4bKUnJBWbg5jbq5mLCeVrobu5eXUqogbkRciAC1ExkF2OkhNETj8LQT7BAcUrl5FXZCSVZVi7mhAVgw3IDtkBMG+5Tq0QKmONoVycEUYwawBdIEJZBRjCJviexyMJYhteOt83Ixfjh9h/XGy5jstx95d7uRL4PLriyxMWSiswo6l5IhiQ8mjbvLLR8+fkR+/JF9v/D9dz/QdEG70q0z18Hp5YnTeuJyd0tXYY9E+rf4HBSDaG4lZZIQRCp5XDpoJLJ3hkxEHFscHYrMYFElom6qlsYlBi/G6QA6G3tMWCcSleg9xdA0spX32Iygo8RuZXe4BLsKYwmWuVOOgq0m0ilVhKSS22Tug8s+uHl5gxg0U3RWmMyiewH5i+G7whzY0BqQbJNJIprlWiXlBW6roW4s7sx1knsSYdzNO4SN1W4Yrsw5uLEOOvGx4CRdd6Qda7AHWAHqL+gMOjG38gknyFOHy2RosLCQlsjZWQ04KSFVULectL1ksbuU5315eyk2k30x2hykrlgISz+RzdnDuJHObEpfEtnqHLaocJzZ6/oKFaY7KYcfOYca5Ov7LeCvgc/PFe7PNWaPWbgRwYcPH3jz5g2vXr1iWRb2fb9/3Lt37/jxxx8rcOoRm+YpcPdL+37droDtw8bk8b49Zhk/ZiQ9BogfMrIfA8qfYzp/rml7ivn0kLnzlJz28XOvz3kMND9+/OPtW1Pbn9vUG25HMoglnsHSgkUXlt7opmwx8Tlq6JfOVMN0Mpux7MkMR0r8g7aj3gwtX0Ldce2YSDEBpdK7g0C60XMSvdf7BnRgdiX3QD8mYZB7HverZE/HLnAgu7V+ATdaA/Gs3FraDLhzft1e84Lv+d2NEZzYXyyItgrv3Dd8Bsnkcrdxe7ljmxuXy3Yf5jxsoiOOcDQpAGuWwgMmNpzRe4URb0s9b9yhp/J3z1MiY2cLI/iOlivnmzsirADxpfFChNmViE6MoMdk0hgenGVj3iZ3+yTHpE7IZKIFGvZJDiX7AZL60To0r7rTFGuD9EZr0GTBfWeO3wInTcEPizFZvr7eSSibgoM5q1aKL1UqTAg9fFoPH9iD+CIKGlLNtHAo34SZk/cf3vPnf/xnpiYf377j9nxXQ7wD/K33zQomklovS9NT/J10rwE+HPYuNby9F9EfrKXeCqAsq8qDOR3+W1CeZA0YEjL8AGWNgzqB3LcgR7MmZbnCgTcgSmudP/7x3/K//8P/RluN/Efln/7pz8w9Di9KyBC6NJbWaarcXQby82v2/cKH9x94//4d81IhW65Bzgfy+ANAR+Lo867rjhapgSMQ8bB0EOpz4Lo2H2zrYpL/xmASqc+lWsY6c5HFjtdUtjGYv/7Ku3dSnCkHl9uDE/11W8xxMOZqTbEs4CJTy1v72PfUJA4msibF4lXjJEGGM5ugAyYG7WDgyWQOJy1ZSEQ7SAG9HkGbUn5vUrkbzcuoJJSaeIszM9CjfqvMjjh6NgOpANQ8LEo0atwwREt9NpNsCgcjXyRKng6lRBTHzZgkPYOJ3IPVXVv1KGmwKfRal9WzmNYGKlp4QCjjkNob5VokMhAz8KzzaBUwplFqw0g5mPcF7A+pa0L74WkeNYyozL2rXUeBXgiIGKdmWDNsQszJfmRumDl7Bn2UUrW+p4ORZdHRzMCULiD3vfXEpt+T7f4125dqnetmZtzc3Nw/53w+I1I5E+/fv3/2dZ6qM/6WYe9zNcu6rveZF711lmXB9Lfvde+t/NEPgPVhLQbFTH6uXnjq2K6ALVQNsm3bvfXEtY67gtkPh/0Pt+cA+8fvff+e8FerxOfUa89t18fu+86+f11OkowokpZK5RuF4b28zN06U3ZyCi0M6w0I4rCxsmwMmeW3Lo5fGf2ZwE4zq7BKFTqzSGEcNmKHYkhFYfdjmFUkLSdoVB7VbhBqSDomrYZOXthTnRNjisGYSCjQ6/spgAnt+HziwPuiGZrQq7Wsn7viFnQaI4vEVZ7sZdfpIjTpyAw8K4eLcMBKQQpoO/rtIXXviVaDyeOma1Suh0QwJMrWYyowUYPw8olfotTYQadPWDK49EBDaCORbpVdY5QXczYWKyqbHlazEYUtDJ2UB+QN2YSW4Af5gFwPJf5B0mylYM3P1NBfBpivFcD1iv6K9eAT6PMTqvBf/faT7cpD/uSLdg9Qy/1NO/Nq8sA90PvbSz/g9H5iovxgN/LxP5/YG6Uqj/u9viLYdUxXJ4xIfjvGZxfLp80o/mp/nnzQpz/MB//lqYc/8d7/ut/9bZtEZ89JN8Vn4Lqhe6PbZKrXVN8XZI7yyE0h6bgLKWcyjW5agXSLIJfyAGstiGgVuJWBhLACQ5Wwjug4wF0nNFkSQiv0TtSZI3h1o4TvNUGWYIbgstZnng3zvULkDqsFkzKcD1POA5Y+ikEqDVWYYWjupAszBqpWFhgpsE2WVSpp+Zieq2d5TnNIMMyZXmOJmAXGqtV0iVm+ZpnCUFjGVtKt2JF1obnhMthysphwEUEZQIUXylqejrEIYzrQCOYhv6rUZ42F2RO8LCNkS6Y1dAt6CyyDvSdDOvgd+wbr3miq7LHQjqY4oiNjBy+AsyaJNUcM/YaGy1dyBVKQ7rAfUvOumCuuSeYsY/pmmFq1K22gXqxZ92qm1lXZxWFWs72k0k8CRzr2Lkb6xJm4lHUCXcp6AEE0UMrMX3VhxMTUEW+IUJK6HWzpxbpwgz6wFHY1LKXA3wXGZVJ9zCzGgjUWPWSAKRUgkAeTLAENTPIA8AWxBjqICyz9hLTErBM5KpTRDcMraMUKNO0HZO0Uw82W8uoWS4IFkZLo+FbnVXuBEyF+MEAACXo78Q//4X/g96++53SzEsDPP/2F8Y//H7fnnUX1+B4oGYOc5Vs8CZZmbCP5cL6Dn35l23bGfsfdbfmxWysW7WW/4+7DLTEHFkr6YBH9ZB3+0uZcSAtsP8DtVoFzvYGqcclg7gPLifaVyElo0lywPoGSWrsKN23BDpB7TmXlhobjsxKGZVJNTKNYwQ1mVFMgM9AdbA2WAxDapmItmfNM0xvSy3LkFAuXFW7WFQPEFiRmhVquoGdDLfBtYCrQVsYmJANkIq7s2endyP2CtgWzYpXNrMK6WwFLL21BDt8+G8KcwKkTKug2WCl2k0lH6ltBdwgtCx2bzvpKyKkMd9x3Wi/W1NbghRs0ARvM6OQEaQFeafa7VyiEtvIlzFBWnXjulYLsg5RaD3MIvXVEhZaG79XI9R7orGEPXp7knh2zgaYytbzgdG2s3wgwf85+4vr7+3XqCaDzqee4O+fzmb/85S9Hg9PvA1V++uknfvzxR3766Se2bbtvPB7/+RRo+5w9xuOfPWb+PD7O63t8yUbiMdv48WMe21Jct4dhiQ9/9tx5frgPj0Hjx+fhc+Dxl5rXh2D54599K/DcZMfl+F6JkqK8WuAkRq4dVaOdg5MYlx6cdmUI7Fp+zapFlKg1q9A1yag1NCCblRQVIaIf4POGasPC8QaqXlZSuTAy2D1YbwQ2rzpDkumKrEZPY+SkI2w3ik6hD8VzRzVRW1EfqDkfTWk+kQ8fabHiODfnC/LyBs2BT+XiF/bd8cvkHJN53nHdyhYtgX1nphS41hvkoC3BhWLj9Vb39MCIOZCl1u4Yhtsdfay4VV3nNhB/z/DOoivZBm0vf+u0LMDVS7Xhd1sxVz24jI9kbHXOLwdbyWal3buDNCRqzJsaxWKdCb2BFEtxihwWXYcazQANTr7gupePYnfm/HruzhWw9Cgv3Jhe99CDSax5MKNcq0S90nAiSm0XlIUX9Xck2LeNn375uWT6swJXCYqtnFS4lwrXrJr6rh1gN4nLlQEa977IIuXxXEMpoVunL53TWiz8OTZ2irFeDXoe+3gg9ofkNaPkwagWI1Mo9iaJe7GGiwF8fZ0atkQPbl58x3/89/8T7398zc+/vmbeFTjflsaLFy/5wx//jj/88e94eTqxvb/wy08/8/7DOy77hWtUUfoDkD2z6nQ5jk+Kwd6sIcdgP7SaPTmgvuOZR+85qx9UvW+n6nWu5zkoQlJ9D+rcFUApDgOnPEcN8arFvkWz9fF2Lx9rINLKWxMvn+zQqgdbXUd9agHZAdIMG0mueoQTBs6C5GTM8mgGJVpiqewiNKnzFqpYBHPN8uycjV0njTxCNytM3Q5feIlECGYapoLSQOVgcirGkQczAmEiWuFYKVZS9dwhswBeO+5Vh4w7YmK9IX5cpJ4gyn743CcJbRIZ5LDDqU/Kt5WyuilCjIA56QciMA20BiR+hHA1Ld/7nscERErGnmJ0NqC84vcwRLLUoodntIaUirdXLSfLitnCaW34nOTekXE+siQUDWX2QIYf6sIVWhxM72JFl592EZcyywYkviGv5P76f2Lo/HB7amhb4cQvUTN++fkXgHsm8MPtoZrp4fM/N1x+bvtc7XP9fW+NdV3p/bcwu6V39PAYjsN/3g7rm4eqqSs4/BQA/NQ5e7w99J2+2lE8fM7jWu4KTD8+R4+P9fF7f469fX2db6lfniJTfG6bekwcFTLK2oJRbF4F5iySGa3UQaV4oYZ6vQLDUwJxo3kBxWGJHLWCUD7EKRXcZ3kc0yxFS6gQON3q+xte4XWkM61VZpckx94UeGtlk5JApLA47CZ1DHOrcF6RY5TTao3mOM/uhb9E0rysN0SdJkkIyAzS2hHad9zPcyDYYT85qq7LTh6hpbkkOqOCSrMjegzIzGgCEkL0whl9lqrJCGLRygSicm40rHIi3Bk+yd4KJ5udbHoMthyTYKQfNh+NFKnhtBz1pQeqC6sAy6muI6ICE03plKpuBogEjcQOjAx5XiX65Srok4vuAdj5KV78aHsKtn3URDz5DKrggU/A3OszPgGTH73+X2Gzj4HkK05dV/D9756FfuPxy9R/S5b/3Js9/wV99jcPd+dJEP8z4PTnts9+Pp8+LJ/519+yhdSUHhE8rsBrlt8oSjpVvCGcFmPORGUUoaYJeilfrWiDvEgxZbIX69J2pAd9U6KsD+n7rEmMNsIhj8A2xwh3VHYmsKxJ7MUUDms4O4sIsSUsZRzv0VFN1iwAcSeK+XuYmzOMSUPmJNosGwVRLMElquBIKY/kdoDcoWxL0GZd3zkqXMvFK+RPq4yeNsmwQ5LpdAxpWRmCGgQVWIGsLJHsQA8qICu93iuPKZdWs9QlIY3UTj98fUUbElaTctnRTGCp4Y04qxjzRZJ7I5lYCK2VVDRC8R4wwPpkZqf7wLIWao+FkJIfqnkVVN8gVbel2ETiZdhvWMnQxGsKeASTgVajMiapQRuCLMnMjlixKLsoc8K4bITu1ej3F6QdgK8EzaSuGe0s1piAS0dilBRfBNdJiLP0hmw7U5KUWU2nTXrUdXdajpvWAF1KdrLPHZklj5xjMl3oi9C1hjDiE10MomxNJBovNYlcmDloCouW9CWyoUvUNW0lSTZVyE6oQhN8BHmZtAahNfHVPPxvI8tChiC2UXYOcyBtOdgkZW+waEMsihVqN7y8OfGnP/4b/s2/+XuW9Ya7jx+xZrx+/Z7LfM2UgY2aotIUpjAQ2iLsc0NisN81fr2dnD+e+fj6JT/8cWNpC31ZcJ9s2877tx94d3dHlggHk+XhyPCLm9wFflxvLRvEmT1WWjgzt7puZllLtLFTicANxBgTegQsWTLp6bjXlL4ZRxqvsreSdXoqxkCi0ZrhNCyDMbdiXQ8hfRIz0aUUE5yPMIy+0XtHDG7Z0YORLBSjcGix6XMHWYohpsNrVGCga1YicRwBD1iFDqrhmfgsmdPcgnWpa0d6Sb+HCDc0RKG/WA+v814WDvsFXQ+QOAVhYZOJRknz0xLfO9MHunTaCHQK1hqtDaAxWAg6i4DlINxY1Lg7goWMyZRWRZImW1Ric7bDF7M1bHTUnNZBDmsZM2UFZJYViDXBohhpKlv5oKnRvRrmmeOvaR6fu3aeYO8+Bdhef/8QTP0c4yezksR//vlntm3jd7/7Hb13bm9vef36Nefz+d6T73E4znNF/0Nw+Llm7XPs6qckl58wZPJT/+jHDehz7O3Hjc318Y+bp4fv89AG4zH75uG+PWYzP9dwPgWSf62E9Lnj+tLm2ZHRoZ0JV9qLhsj3LN+t5buPcRkb0juvErbc8VXRgCYXhi+IC21dmdtOdK/wWrSCMBHOVPBNkyBUSLtBxqVAVWnERY8AWdCcrJRKRaUaAxa48RqGjdbQxYi9GMYlDS81hLrAtjOwYuN5+eWe4z377YLZyp02bNvItLKQugs2m+x5Kdbv0uACMp1AaRmwLhVm40lOYW/GEoHbZLcj6CaFbnFk9hm+OzKMPfYCOrXB9pF9wDJe0uQjN7bgS1kEvWwLru+JoaQ5c79w3iZ3lw/FerrA6AlZjGABMqyyN8TLBsCkwNXmZSkxBiJKOzzasu2oV0gXlN/tLJoo0ssebLFvGKhfwd5DmXntJIp9WkGlBXFWyjxXVmRSkKfI0ThXv1SS/sHw8Rv7ObMsNxBSCwgzOYY8Us+p/qqUc9dguryCwnKFWOt+aK2xLCs//P0P/Ns//IlU5c2bn3n961vOcUdmoMVZOLhBcmUDwXE9ax7rDEn69fut5UCSgB77FJMP79+xfzjTv/sj3//Q+eN/+He8/G//mbfvBNQ4nU78D//u3/M//8O/5/TyJXjw2t+TCnuUR6ZGDfIPLvERXMgBtAdCfQ+W1v9/2t60SY4kSdN7VNXMIzIBFOruu6eXS37givD//xDKCmW5S+7OcLp7pqu6ULgyI9xMVflBLbIS2QkU0DPrIiWozIzLPdzNVV99D66vr7k6PmWMM7e3Z/a+gAwrUN5ngeGigWcUoYWyv8koUEUu+5vAusakdbrqcpwo9q57sV3JYHF8P/rceXtzw3kv0Bfbl90OqINaIr5qvzCGQjBpGDkD0BqmzyLHqOwV9tmAYfVZlHVdlCd5SNZjUyiBRZA4m1Dgg0b1cqo1pGhJjqDRaoiW1dGGJ63VgFvljGT5kYa3Uod6WUPMLJUksnxjPRfPHrxRKqXzJNuFQVjEEBOQNLRlWQ+FEPtevtHa0CutWjAnO4lstsCqdQ/MrB41WeuAMlYQNgTusHWvdSuXWsqzZOYyEdWy/JDqY8wSsaBbX5aHdU/b91nMeJ1ok6r9rIYPYlQfqIK1uQIQ1/AxwHVZ1KiRXmSQuX/iuvPIwPzhPfZhfeHuvHnzhojg6vqKw/HA6XTizZs37zzv/ms+VFLB39pq3WcC33/cw8/8vr+JCK13mhmtd7a+cTwe2bbtDlgWs/o8ZsWWHaPOK9O7uuh+7fPY9qH64KHy6rG66vLvwwH/z732w+NwfwB/f7s/0H+MKPDY/nwKkzyzk2Oi4og4kb2GT7ERnEGLuBTixAxa39AUmikSWoHEWtY6rlby2mG4CKRzEGXKsqGSshllKGFUPsIse5yM6nlEg0FZ1JCJNiNm3SdX9h3ZkrknvScXlytSabmTts7rdS8STmQ2UmrwnpJIT7qzBnGBqrCH0GIWMxotwNmdmYpK2QMRFAnAAyVIc5RJjEboqLXQRt3/hPJ7VydEkRBMhVzZTiKKeSPMOXvxITfKZm9qkLpVuKtUoKiP6mU7dV/1UQNUWtkZVczusiKRsl8UNVwC9UDUwBrEICVIKzU04bCVFWFGMa3ft330mP1dcJd3cOa/ffn7kOzDrYy5f3ruOzF1ZJZU6f2v+vDnepX8CUFm3dnX/16A6ntPknufXbJuBr7Kug+g1hVwcfF6+tAne//22MX8cy/1d0G+jzxJ4G/spquAvByf9yyoy/nj5zNmf9p6ONEEPScjQXrjYFXsCrqSqCddFPcEiiUSDu22fFsvvpy0Ak1z1k5kGDYrdVdDiE2gJfsw2tyR3pBZ0jB8R8yI1sq3eDeGRBmaxwlh+RpKMpvQ50boLZPydLbdSBQzQZdES7SDJjMgQuhk+ddKLxDK25p+lWRjeHl9aSzf11SyCeGGmJN5rv0Updsyr5+UxxaOn8urKE3ZJZHpWGuMhHYY+NnIMfElgbA2wWAOhWyMVgX1MYwZE6Pj2XEJtqxU6PJ7r+ZBgJGJDlv+cUbuynmUjkJV8Z2yhvBA1NGM+l4OhofDXAWzwOZH5iecxXN2eo7yos24k8aMOvi1YoghrdJqJUoOIt2YK429mYFslcA8b3GpyWkV9hWe1CUqRdvW0EOKcat7lNeRdXoEg1HWJmulOndB9mqUDFseujsWjWQv1ugWxYJXwcNKdrPWuS5lL5LiqGs1WigcDD0PVGbJOHUge0C3Sqen02cFyKlQrNYsurMKeA7ynOjWUHFaWEl5BWbukHWsJBtpjlhjj0q9bs2wLNZKi2L/97C6iamzHTrWG8+ff8mT50/50QzP4Nmzp7x89ZKzK/gGdoMPW/YUTt4aMVqFE0n5oZ/PJ/Zx5vX+GmtGax2L5BzOfjsYsXxoYzUIfLwnYaPVUADwy1rRSlc8T8aGkt2I8+Q0z1wdjlU0aH3+PFYBo1qA6VSj8jerqcxuNCaandGcaEdkOLsnMkcFiWbHxl7e5to5t4kPx8RoFsCEcweZzA00N7p6qbYXtKAYU4V2djYbOMqwxaRj5yCtmLoWFR56rBC/lE4bZ+QMHBt2MN68uaVvynGrNa05xSjWpLkj2tjUeXtzpm9Js9V6hqLmuDrmtrybD0zfFwBf985owtGcIUfGlsjw8p2W8g6UuTO4Ra6uYZvIWTA3VIqdcKuTtoaRB1U8hcGJrh0B3r655eyTrV1zfXW1ZG3KtIG5Vtr6lswUWtG2URVIsH746HPn4fawKbg0W/dtID4UvvIQ6DydTuz7zsuXL1HTO2++i+/yw1Tzx97/0rhcXvN+YN79Bu4hYHu/4fk5Fvb9fXkfk/nh9hgD6n6D9b7HPnacHoLPj4HbD9k/D7cPMbA/taH6mE2PE5/GNq5InRzPhhwSDcV7o9NRC7QpHqP8zcdEaCBH3IOmipydzZJTltf4rhUs7DOxcGwz0p08gx4CV4jsXMUgWgW1ROyc1Uo2nXWtXEuF0t2Y0qJAAjPYt1gsvFJZTRcOW2cfO603iEbbS9lTFlRKhnCynSZKm0LYXEE9HdsHY9/p+gTT5GxR4VbtgLDyKLRATo8k+0Y7T3wGU5IWA5kVkMrMxYKC5oPcFVkAPFABpptXsvtNoJacDjcF9IiAOrlP5mnydkKfk6FJj2Ja7m3iGHowcp8rcEsZEkhoJdV3CiVVq2ZKJvtIwtpaYzZmTvryv7WpdCu/6Y8+d1R/6oKyhrwV1lxgryyAVlYQjxPISnG/WDSTZTdR4FjJ8ItxXTkQJQFOuljd66LWbllg7owF9IrU4FIu96JVm2Q5CGPV9PbNePLZgf/lD7/lN7/+D8wM/vVPV4SDfz+qWZ/FHBVZoBi5/v+nBktUqimxCt5jrdu5mNOZwhiDH1/8wL9+/yNffv0LTBvPnj3h6ZOnFe40nKsn1/zil1/zxdMvkauN29eveHvzirEPejeaNXIOTqMsO0qFXP2eZIGtqNCs8eTqCb/5ze/48tuv2W/O/NM//SOv3ryGVK6ujhwOndevX/LmzQ3ugJSs2SQXc672FpVitPZpQwAAIABJREFUxq61V9W4Omw8e/4cMnnz5i376XQHCtZg4Kfj/jHbzbhBcDTWOduKcZNZNl4hzqSCqUSrloulWlOVyoBZwVF+d66VFUOIYzOqrxApID21CiuRCpvbiuTTPcsPNLJk8y2Q3HA/E5mMKNbt8vkrq8tMRFqplQyMJHQyZtXKDcHE6XrAfcfnwNUQMTLOWCgRy27HC8BN6rOGGj2EGEnrSoza75Hl7+qxelAFOSceg9YPmO2cIisYSwUf0GNZfCwSELlY/utfsSAXaKUUiFa9nrJT30sck7YY5hmVtWI2GQ4Zt8y5/F01iiPfEqJSPkJX2ODqzcrHOoi9etJCeta+9E+7rz28R0YWoeb+7x4DKi81SzPjHMmrl6/esZa4X6u8D7h+n1LoPsj7cBh9ee5jn9/MKthv+R9fQuwudhXRKu9FgG3b7j7fHcCb3HkT32cc33/MzwHAl7rk4We/X9t9qBb5kCLrsfe6/FvzuJ+eF5nv/Pwpr/tzW7cVTJ+NaIlPJeYsBSGd0Fk2XmYcWy8FO4JgoOUlPrVCSAnH96UeWtjciRMSW9XIOmEa0wZNBZ8CvdYg8VKXWFd817KQSshzkC0xaXQVnMqMGLKjO5V/YUUG1FGs94ki7GwDJkrKuLufhhrpBWarWllQOwXCitzZU+4ZmIFFkXg8nZbQsyxQ1YWT9MWUrrVUxUohwrg7H12s7rUxGToQBM1SaYSNGrRn0nrDpwJe62Q6M4UhlOLMGjoEpzI72M6ECD1gZH0jqUaIrF5vMNf9MPoadU5fbHJKkb5VmGFmkSPMZ+3we7YPAsz3scj7YO797dPL9AUurwnjRe61YGLuwOAHQK8g90DdYh6oGUIZqZPrVWSxAbRYsplxFxohl4n95cKEuis8Kof46RNdfs64XNCfvNN3290ic7cX9xFsPvmACnVz5h1M/gOw/9/8+uffMO/W30/4cCGIlwdnryhm9iWVa2Ho8lqbXv52ihND76Y3IROVTpriCltUcuhYYGYE5Appy9zq4pfJADgntAJlq1AKMgVz2KWKBFGlBzXD8Q2kQJnJTsvyW1VJ3CoxdZ8g0jhkMoqSgVotGoETE7oOMrXYzp54q4RfB2xWU5NbK2mqyQKyhDkVJ4qNhxIWlThtAl6LHYdgH62KsOXvnEpJUCn/P2JdT9KKKZ2Qi8lcRUlWQaaBxI6SpC1Zx5BSfRG4VfOCOI2ssDZ1XIWmgY4dupI5q7AaB7zDtimnfa/rbcktfXamOc0/XrJToYsXyUoS4ZV8XbU6yMTU0FHXuuuScFpgIzDrmBjJ5O35tAD0KvhEDGKnbZ3AsK6oNWSf3HjCLJsQJ1ENnEFMYy6nhtZhmytQZzhIJat7OxCW7HOw9Y54Ffs2q3mPVlel7oq2YqynCylRsjaBnANWQclY3lSt1QLuk8RIO3DQIJhl2E8B1KHU4EIDZoWxnVXAB902mgnewKKBTyQbYkIDtLXyx2tZDI6ZxV4xYDhpUQxwd3rrsFcw2Uynb40mSg/jpLfljxcloXVPJhOifItr/imc50BMyBvHWudWbst/Tyjrh1a633Y4El4Dho/dck28VXsBzZN1HYEdBNEK5zOCvh2QNM4RHEwZq/kxVbIHGobNJJuQLrQjJBXi2DIrsMEcCeWajbkNMoUmjksW62c6/RIOsQfzuNW117Pk1F7+19l2upa/eo6NaV4MxNY5jzf03pkHYctEKfVBc0MimX2iswoOSGKr46xTmRIc+kaKcvaGySiWujRmlKS+JYSWb7ZpI+joah511nftugrWNvBzENvFPsiIcHaAs2OSaHNidmDHdEOjledrGDkNVsr6ee7YNgtQggKxspg7/dDJEN76mePhCc2p0Ewf5PGATWXuxpQz/eoZPs70jGIasViZs2xyPvrceQDUXn532e5LDz8EVD5sSO6DrPdZOvfB5McC8R4DYC8/P2RPP2TLPNa4PcZKfggGP3yf+z/f37/Hfn7IyHnIfrrPRH74u/vg/fua0offx4cA6ccaqoef87Fj+vC1P3qbIEzo0ORIM6V3kF4+9orQemOTI6e45Xx7i/S2shA6nUa2wZ4Tw5AWSFTid9ogptIbq1p2okEXx93QLpwp3/e6mBuGF6t5FDvypMqWvvC7i3R90vJQ67OPkhF3XwPjBrFX/eRKJ5hZNlDeDAlnV4Eof9JclkZKeTLufmbr5Y+qWVBLjqu6L+zFCLYtydyLdCAN64Gdk6ENGRNtA5cDkcHuRiRsY2cI9BiovGWGcuOQ4WxbMEcdoV1qnd9nlvd0OmMXRI1hO3QhWmdzKVsydSKc7lYsVA08+ip+lTaoWtQh2RAZkEF6KYw8hWaTyA1VOHy8YIsqvJLMIFwKFIa7ZHddQ2wky26glVtyRjHByn9XlxLAgfLbv7B0dbn6KpC2ZMfasVZ9lUdCTsIvxtPrvZJilwBCK59/UWxrbIcDz559wbdf/47f/ep33PoN7JPzzS1v3rxkHzsjy+c2F2M4lyr1rnFff6mwv7IzkLi3FqyGxSP58eVb/tt//c989tkTvvjqc4Z7hZpF0lrt4RzBaT8xb0/8y/d/5Pvv/oqI8M2X33B48oQYg+//+j0vXvyIzrkYa9yB3Kbl9/z88y/4wx/+gc8+e87NzS1vb24IksPxil/95jeoKH/+5//O6TTwOe7sRiIFLoPB5M5vXKR61d46zz97zje/+BpCae0Hvv/+O8ythi3ud0zxj95GGX+4FwAwLyBfjmIquyENRGJhw4a5kT2qmJ6OCxXmqKWyw1dXuMBzEy1AdGWguAaEoH0jc6LphNjKf6xcG0+QnMsfXGt9LCNvTGGYMKPQGFEt3DnLnzSjoVoDhn0HbxOlCCXqQcgEOplCzgKCrQk5iziRQmVhSJ25N7sTC+httoKO5yh3m9Ri/3lUSHoAVteeRDGPXQzNoElWsBeFtrhYeX8TsHqcIeXDX/qxGhqmgeRW9nROkXEo1ubIgLEzTegutK7srmjOmh7pYlpHoFFh9mXbAT2LAXnnI69CzE8HEUvFUGedPagbLjXPwyH706dP+erLL/EI3rx5806w3wUouwC7Znb33Mv73b/3PxyqX+qHh2GBj9UDl9e7gMuHw6FsyFqntXb32S9g8wU4vtRIqlrgcwSHw4GrqytOp9M7A/r32Xt8aND+mLLssfri52qtj9keAvWZebeCfOzr/lyY8v1tH1msXLEK0U2jNWEAm4HJxoydgyQj5grKE7atgIHoUPKAXoqQSIjF2hXYcrGcvXrUCCmf/wWat12g7YVHmOIn5bDU2k0Dz0Tr4l+WQF73ktZx9fJO30upNKSsHnJC0PFe6q9YyyPeMalBvkQjKwyqFLEiuKxh9gpzdansJCvZxd0aNQLCHJtSnsYyEDFElBml6IkFuKc4MzfUCxxudCydmQWYiRgbDctk5CwlVi6AwgNpG8QkrSwzdGaxk6PTKCzE6pKHS3ifVf5YmSsXHjG8rNuEYqlbGTIv1HhCaHlBx/G958oHAea/Pd3WBfV3AKF3myzgNiHxAoJjiYKEhd6+iy7Lg/cUaqHZemdMvwOUu2147KQnT66P+Nm53XfUbCX8lo/v3aZAVOFzByfnvTe7v48PFxOBRRtkGatSkuKaiv/0cKkbxYODmXf7ev+Xn3YY8/IR/6aYzXenAz/3Hv8GwPx9W+Yk4oBoZ4aSesYkEFdmTLBWNj4KzJ3UhuosH+IUDmp4q6K7RfmLVbplI2MiDtEakSfE95KXbksepU4OXemdSvqOtGqGjCUTOCT7rMavFq6r8gWS5avrNdWRCGY6ZspO+ZEpA3arsLaWkFpTIPEiY2SCdciB0RdoCdMbzRLYSIIRgU7Q5ncpyy1BywGfcXbCDLSz4zSiGgR6HWSD8Cq6dgk0DTkqdjLOTKIVS3cuCb1LVpqxgEtyRHEmmhu7BaqJeRVGFYJccr8pxQ5qAtGTqUqPXgwTgYxJG8pwSn42J1Mcn4A6vgZJH72JL4lYAx9oOmIdkYbmgBQig5MoLYSIhjbBR2IYewfLqODFIt/Q2gJmbUNb0lWLoaCNrZWUeAPmPNflFCUNmashGot5FUOYMuv/rwyLWUGGMcnUWgM6EMY4JU0rIberMm1fQQRKqOAi5W+bFT45Teo7TCUPDXynb40xBqZl6yHR8DiDSNm0jIFvrZioOXANxkmwTSpcADjHQEdNbVs6oc55H+gmyC7oTGIrplwcGhFOy72kM70jktye3vL9Dy/44dWPCMlh24goNwwIpk3UlfSNZskQIdnIOfAUDlkhlNmKKX+QTkwQT87dUfdin5oxs1j6tB1c2drHL1CxHrunIVsQ4vSRyKboubGrkjJp2ZkyCUueaAGzPuvamGkch+G206VAzzRnzGIm6HZk9yivLKlBj89lCwRU7DmQyvBbem6olW+3Un6Y0pzIVgzg4Ry4Ym7JPB+rGM4F2PgOZjCV7g3VvTzhY8en0OgcTEqeGoBMNDZSs9QQsaxG7EBEycnO05HmHGhE3yp53hrXmxNjApOMjo0CJOpLLlY151sst7uZ7IhJDuHQorzCbDCig5Q0q3l5BhKGEOzjjFuvZs0Un8rWjJhGmwuYn4Fa1JRcDJ8Tu1aYh1IaxWACYwQ5Jm/3l3zey7MRpTwd970awfmJJsxr+5C08SEAfR8wfex14N0C/iGr96E0830A9+Vx9xu+x97nfSzlx4Dpx/b3Icj9oc/08Hc/x8p52CQ+9rj7x/p+0/MYA+h9x+F9DdXDz/LQ4/myPSY9/dDmaahmqQrSySdHWuvF2tPyxbu2a/YOT4ZwNOF2n8T5jA9nmqLW8SlINGzsJQEfjseGHJwxIIviiHbDZ01jehQY50SxTHoy9wKFRcrHP1F8FAjhDXo6gtF8MLOV0mAF3PjZcTWaC2eSA8oZsI2ykIiKPR17sfi2Xms6Y3CWrHXXhTGrvpFWnvUtzgWkiVKh54q5V1hO86pLuhI5S3ZpQp87MTu+fB9DBsTSQfpEZmPXYPMzN1gNZnMyQ6vJtFED1UgEQ1r9f5qio8AsQSoozLaqEz3RluUFzU9S5Kr0gzJF7SXZbSUT1tyYHLCs+2y0jz936jstIE5k9RS2LDP0IvuuNSaICreLAaZoFBMcijHFoutEFhNSU35qaUzLO7l1tsOR4/GaLsbbm9fk2xumejH8LgCpJJfmQFqiZhyaYe3I9dVTPv/yK7785hd8+fXn3N4e8G92Xr58QbetBg3L5oooD8nMxdySAsV19UYp1dBaUCC0LnuQXH3TdPbTDf/4T39CD/8nf/j1r7g5DV6+ekWOYMbk9ZtX/Pc//g9+/OsLzr7z+s0rLJXf/upXfPvtL9CtcXNzi6KcT2+5vZkglZOSUmy3FOiHjc8+f8rT6yuunx7YTPnyiy+Y+4lf/vaX/PpXv+bNqxte/PAUte+KBb6A+VzntmQuNp0gxgLUwbrw5Mk118djFQtffs7N27eMfQC+6u2ynPjodcejSCMSWFdyDAKKFZdAg5bG1GKiQRGuGFL2aAkeldlh3Zi7IxI/9bC6VKdR1iCELgJIGVVUb171fW9SFiwzMGtkgk7qmKzMGVvMX5bsXPZFfJHqUSIrN3B6kFPpTanTIBFzCCVG1Q5G9fK5bBDLpKSVTaDEAkZW7ZKUfzHJTKVb2d+Ele0GUhHMBZhU8CiixBRygemRirTzavXLwlCzFFcWdc6mJGSgBjIa9LIKy260vtEO69qMJHZnP81ad11rCOAgWbhIZJL01QMXU1HOhrRRwyK5XNuJtChfVf145cSDRWhhM+9ul3vjfQBz2zaurq64fvKE77/77s4a4wIsX3ImLvYU22IUR8YiCuYduOvhZd2zhuz7vjNnKbtE5G+G8e/bLgDzBdBurd2F7D0cYt/VKPf28fIaV1dX3N7e3rGQL/v1PiLBu4fw3XrxYc14v1ZU1Xfe49/CLn5Ysz2s2y6I2sPP+HBr7eMMDcwKWDUZMAVd2UPSKujX13v4IlcZDczxJuXlG2vtyHV95jrGUjZdYXY31IkAbF/3kFL0RO/IKJKXJ2hTYi4g1BqmFRJINCLPNerRrHu3VPjp1KBLY0RHIggGqDE82doEDpCCt8oEUCn/85As61ErIlVGEcBkWS+JG/SJOyu01HETNpE1qxa6KTkbYVIWPFlqEpvCmjuhVgqBjaXk16RZWeGM5bGPQLfA1e4yHOg1rOty8RwHWqlqPWYdYytAezBoGfTLeiZ1701JPIVNkzlLGaHL7ifnqEHqGRCvrIEP3K4+Poni/nYfPH0fOvm+P91nj7CKq3u/+UkqJoXNrpfJvLyxcHV1Rc4yV4kctNbwWfT0w+GKiOCzp59zaif6Ved4deSHH37E52WivibnGF7C4sudcr3RYx/8JysPkUqLLGsH4CLnuvz9naf/Lbj87jH8uWO2JsAPvsVH19v7z/8EPO9/xpbHjkxlxBmTjd6DWfPxuo95JWF670tWVD6BrRU47VohcbZ1ugu7OE0c0lEzRpYH7vStglo8kVli+kQ4UBKOXWYVtun0DWQ6rq0e6ycCo6UyNyenY96JLThsHR+jvA7ZUIxOlKl7JLuUx29ixCxT+bA1pggrP7TYMC2/nhQlM5geSAY+Bdui5GSzFbAChAjzNrnqWfIFyl+xRwWz6TDcRrFcdgNztEMLKXDrDNOqAWrjjLRqGjOdoTCj0/xMW68d2snhdLOik2uxIsny4JGQsoFoirpzOCennpgU2D594K2xC/QUVBu7UYCSODmVYzp7//jCR1xxNTadxYxwoaJeynupQvEm1rJkYlbJ0c1aye5FOI+Bj4mkMSjr/l0bh6jQxGnG4UrpduBJU0ZTzrc7u8CeZdPi4sXaWjcFyblAq2In9wRJYadAH41JTuFmOEcztAVpVvJFtJhtm3HsjRmDPSBmAEHEhKwmYPpea81iURyakrtgqvgBZC6v7DUFZnl9pzSI4HCsc8FjYwg81clI4bQPwoTppQY5ppE28VR0JMMdZKu0d0mGS33HIujpln/585+42g5cP3nCYVNe35z47sdXvNrPBRZbeW2mJX4L2oKz1Q351h1POHpg/YjPJHD2MTEF17rRuR4wd9iirBj0UF6JH7m5HhHbUZzrFG5UCQcJwW2gGthQ5ApCGqQwXWk5UR0gG9fNmHNUkWNSEseodPARQZe6NpUgTolIKQ7IZErJgJu0Kmqi0XsjR3kgEokcyuqijbIi0UMBNLIveWcs6Z5UgKW2QXpNSqQd6rsZO+gKmbCEmMQ8whUws2zNVogn/Qo5Jkww7xwOC0ifju6KHQVmErqRElgmOxXKpzPIXUGsglrzCsfpU5BmNLvFtwIkXIM5jU4HK1bJDMoqhonNIKWX9D+KJZ9pBaCrE2x4nImYbGH43GlSoZt5SjxPPBFjekdGsBlkv0b2HWdDpzL6pLeyO5Eof+ePXnfuFeaXn9/HNLm/vQ/svA9cviNnfNB8PHzO/d8/ZL08/Nv7wNY7UOzB+zzGJH4fC/jhez4GPsNPYOzDZu7ha1/+fYwR9Nj73W9q7wP0l/16+PkfOz5/D0D+8Lh/zJbmqGx4TnQ6Fp3gABiNE/1w4NwbuZ+wDrRrRJ0hxvAdmY4k+FbZDple/vvZ6JIFdNiZmJ3SMASpjW6LNGFV2+JK7MklMA8pBrS30gj0cFqWhYPMqo8OKsim5f28GX3WYHSmYQbnuXNlnQglskOeGFHsUbqS4exj6dtUiGkFsF0FcTZ0CuRkitAzOGlfXrDJ3muoGqNjzStrgQOj7WyRDNmYxx07CSKLxRPGuaQyZO7YWdgVcii6OyffUd1QneCNzIZb1jFDi1UdWk1uJGJbWSe1aorrUcULsVS8leJtpNObYV5AvUaSBtOLdCB6QJ8YJGxt+6Tzh0VoCYrhCR015dAPPH32jOfPn9BNef3mzKvXr3n7+jWTWcD+5ZzlAprI+q/YoVz8G6WAn2dPn/LVl1/x/LPPcFG+++4vnOfOuBmFM1GM3FzSe7V67sE6V9fXPH12zeFwzfVhwyQZc2c/72hTzueSR89ld6GpCwirQX2WjqzsoLIAcDQLnKXYniwzsWTde7U87F+9ecP/81//b77/83cQg7/+8ILd92qkb27Y/+S8an+te2JrfP3ll3z17bccv/gMnzt2W0Tw6isbbVMOhyMZzu1+qhqt1T62fqwaSne2p51v+RW//93vuepHzrenYvDKxfaDUlnZOp/WcDm1rElESu3TsmHbxuH6KeKgzXh6fcWbN69xv5jIlU/2x24RRVAwDXw4GdCk3rdleQdjs8KxxemhTBlkGh7lWSzr9GNk2YWoFAAuxfwXF9IVtfIaz2GYTc4BtkkFGkeQ3cAVQRfwENBs+YtH9T9G9Ra57k9qpQqVTmYRgMKj2HtbQz2XOrUh3upc3gyd8yewp0POYis18/JyDsj0InMArIBRmkHUeySGZTC9Qi171LnpMRhChf9ZkUaKxZXvejyTuAUNJbTgdnVIs2rbe2fbOseu+OHAllqe6hKYJqdxqmNMYBY1nPRlwyFlSZMxl+qkwPUURxdOMizvlMulbkzs/PcDlR+jWGqt8cUXX/DkyRPevnnDv/zrv75zP1VVDofDO/9dwF5YQXjCUsvVYOACgHo4p9OZOSdjjDtLsftA7GNg8eV9W2t3wHbv/a5muM9kvuzPdGczuwPF74Orh8OB29vbd+qaj2H3PlbzPEYQuDz2k1VSH7E9Vgt9bC3zsZ/FsoYrcVGmRDIv2JMC50E7troGgxrQNKWL0dmYYmTeFvnHk5GCSIXV6yJf1SpSdpNOvZClYSbMmNVbR8NoaIwKtEPLFmsazEFvMGMQus61rMyanaRXDCGJL7C8LeYzy8deSkUeRrYEz3LmwJAzpe71dU7sNbj2y7EJBYuVjWCoJTEckQpR9aAQW8+lTAqaN0SS7BXcJxGgRWzDKJb4jHv3TKXlIPOqXt8rI0pTMSprKbPjkUtAklirXCVGAfu9qh5cgqnOFq0IguE0g+FOqtXv3PHWSKvPik40GhNH23z8ROGjAeb3osXvf+z60+NE2nu+y/fA6nL9uNd8iNBEsd4Zc8fD2VrnD//wH/jnf/knbm9u+Oarz2Bu/PXVD5g1/uN/+gdu3u5896/f8atvf8FwR9x4yVtSdvrVAZ/O9PJejbECknJy/77+t3t83yc6L5nI945CTXgzHz8qH7WtIeLlOpfLu75nRPDTY5dfyr/bWvWBwcFHbu4d8X2FRU0Y/c7nr4rOKB+bc/kXlxrImWlIB40NPwxkJjsF0rksVrF2Qs9YbqR6gRQ9mJpsB2Geg6GViC6zw3HHbhq0xLOxzyw/xE3okaQlPcsDNTSxrOlPmsGctbBo2QdoGmQlp8452Jb0DJOSqw4vf1sVmk88Grp5XZSU6b1pgDbm7GA7e9SioCooTg+rYYj6mpKXjGOkkFZJ5eqTZoMZRpou2WhZMSg7qx7BU2nAhuA+MROabuWPa8XeRhUJp29JZCXN5yqwXAXTiaGcpLxtZe+YeQG3tqE5ayrmFQhk6aROdlFMEnahnz8BJMwqbHcPbGX6RUCzRlixokKM9CxfJg/YWknolxxnjEFkFZAC9fmyChrRoLdGQ/jqy8/5+suvKoTr++94+eJHOJ8ZLcur2vdiUIuRuqFjMq2kLdrBdydHsYekKUEnzoOpQdsowFWq4D4ernn++XOO15397Q03+5mbt7fIPBdwHY0h447hEzRMOs0SfSKIHkvCfJ60U4HqbDsSHZ9Jsx1F8U3h5NCF45LZoFSYQgYHDnUtzkmEosc6wKLUMRwb9PKW2scN0q8gGy9e/cDpv9zy9PkTNjFub078+Pol7IG4sweYSvlzCzVp88WYCamQJGuwmNWtldQvRvm+3Wpj40yEkFo32sSZnxCYRMySG2pWKGerhq0ChYTcNzQDpqJdyCmkTIKORWfKmZXZQ+9GxC0hR/JKkSEcojMduk+COue8O3kqf7FjBkOdzJ2wQw1wtOOWmE0yO30Gebhm9sFhSaoE53zOCq7rJfmMmcUolE424RA12BR2JpWEHgoH2bB2qpCMkUQrr+Sa81QTnBNyN1InYlYpypoVAjnOy1IEzBTfDR3BsIa2oGZDZ3wIarWmuQSdG86+oZRNDJHkdPSghCgzOpk7ZwSysbOjG5zOzkGVtnWmCHHasdbYuaGCJ1aYpW2M/UzfGqadGc5tCuJZDEitUMW8bgTKDOfm1RuePb0qZUDbOJ0//ty5D1o+JnG8PObyu/vNx31v5EdPy0calMfYNfebkIeA8cNG7jGA9v72GMPmMUD7Iaj+2OMeA8TvA8YPge73HYeHr/+QOf3wWDz2/g9ZQo9JeO8f24dN1/3Xe8js/rs3Ax/1/N07MyYHErkyMjviifatGol+ok2jEbwRRbyR81RD04AJyITNNtykwmZn2VDEZujemHMSLVDxpZzoSDrZwFzLu3hM+hS8KQwYQQ2ks8LIhmTJXFugUfe1tpf1l1ugsleDAYwcOB1RZ+oGEhztcKeccHZak2U+pIzzJOmES1lXZX1HewssBjGDOFz8DPvKgKjbVMkygzEASTJgAIdQ3IXWioWtXoya6dWsRQ6MwK47MhIfQTs4uQciSeX+TDI2Dm15qLZgZwe3YjLideyiwsGkGT7KakdbKTF2i8qISAft9CzGZBPYQpGrI4er98tGH26ZeZdpHpmINUyTQ298/dUX/O63v+X58+fcvLnl2ZNbROF885axF3GgGoE1gJSq4ZAlroxAlm+y9cazZ5/xi2++5vNnX3B8emT3yeHNhi5QTFceRy7Cg2kFBmnvPP3sKb//h9/x61/+jvM+uLl9xY8vvuPZkwPnffD9ix948foFZz8vks2FKHSBkCjgmixmf1TDI7nUpRQgmOt9HQFZAW2Z5Gnn1e68fX0CJvvyr6/dFebYeR1Jo1XAbOuYCufXL7k9Dc43bTOAAAAgAElEQVSnW16f35ApbFcbn332nC+ef8mME3/+y3fM805DeXr9lKfPthpSnCe9HbBnB8w6wyenm53z+W1ZilShg/hP63tSGO1FaWvWUK3BqqTy9NnTqqffNq6vnmLtBeqTKAbUO6/1c9v0xIejhwJokKrZM6VyRbQ6anNBi32DhRAmpPvyh7bKuJGyohhRQCrmtOxoBkMmJwlsWaWEdK5bBZI7lBorqQ7eajgt2KrjoC0gHi7EMoEUTJNxyRARStK6L5XXGIgW+1i0gJkmFeLr2kioUCsSu0jph1X9hZcqg9rfXKD2jCiLtL6BjVKtQZEEZKvegxVIthIqExbIAuReQPmakIgsdrWUqjS1+sGuB/rxyPFwrGOeARGcxpmIshvZR8As257I+m66CFMhswg6ogsDUC0P6CwgDCu7AFGtHidgzsb8ANjzsdv7FEciQu+dw/GAu/PHP/6R8/l897cLc/kCKttluCBy54V82R4OulUqiBKqd7NmNdgyvQOdf84H2RZgfAG077OGrTXm+EnNlhGljDPjcDhU4LYIb29u2LYNM/sbhvHHArCPDeUfYzP/e4PLd/u29u+xYMX3fd5PAaIvvuBTyiZQRp336kbMpQjLIJtgrkT6uj83djmxtYpXUC8VVtMkUtk08C7k8LpHhKG97DLUEg+hrSDPVAEWwdSS2RKmwpSyiLRcI6EjZIXUlV3qxLKBJhKlmp3LgEBRsnmFg5qQUqrNjIRIpglujsU6V1tZOoVcrJCi1MsEGtXzz4TulU+RnFE1WrtY7VSNUhlna1JkiUgrsoBJ3b9HLmQ0mVZrZSlBSiMkI5nL06NUMCy8Zu3rDFpOQnplTElll6Qv21DKZz7KTwRti1ltUu4BStk55gLNJcveLaouKsXH49tHAsz3EM97P37wsWt7/Fb5mJvv4v8KkELbStCjonz71dd899fvmUyujgde374h7IDI5Fff/pZXb0589uUVqo3r7Svmmxc80WvGPPPy9VtmGk++/Bw/n/ny6TNevH7LzX4D08ntUE3trhUy8cheVO33EysA1gWb90Dne6D632zv4LXvAW8vr3fvTz+3/LwLRP97bo80q5/6EnOnrkFDGAUc606cG4em3AKDCVrM4AL5CwyxEFzrwiF2YnParKnMbOV7Z2IE55rcarA1Rc/G29uSQTQC0wrJ6edisOBJWLKJlMxDWwWPDanJezPUvTxzbeBLat4yaaIMRl1cJrSK8eUsgnmrSVAz2laycTkLIU7TyTgL0bJmZlp2H3JJDU5BexWVSmNKNT6pDdvLMzat/G5dAIIuE2/GPoKDBVOVppOctQhcAmEsdIUABW+jbtZlzl/S+S4V3lNIVAHLMytJVIApVVxOHZATVWWXDWvJvjv0hjj0uewxLJicabYR7phmjTC3xOLjw7ak1c1dwkhtuAxcYEQBhQcvG4qo9RczIAaxQvUsEmsbponswfSayg0mRNIPB9iF49MrfvvrX/D1t79is871kyf8o/y/+IvXYDvztoZThw18GiPrOGGD9I3bOTFJGtC8MfuGHZzD8tU+i68C2cirxje/+Irf/+H3PL1+Rkznrz+84E9//kdefP8ju0xMBjoVTy0/O22YBIfrz/ji88/prXM+3/DmzRt2bjnvA6IzVVANYCXtTilfwgA3BSmp2HUzPCab75xlyYl7B5RdKzhJ80QzISZMrabBZRC7c/DGzbjh7emGQ8Ktlxe0zyBkYGzMeUNrB3xEJc8Oo3UYuubdw9AmJcVs2ypgT9iSOeZeQKzJgRODSCf944U229GYZ6W3YLeGRZTf+9xwq+ZCZk2Ez8NRq4GSeqJ25o2Wp9bBjCnFhOtADGUKDFXUBbJD8xoMuS32mzC3Cm6cHOkBYY19DA4ddinf5NBAc2Kt5KfpQTTD3Dn0AgrCk23CtKxpttSqvJ/nYrwYYlYWQzkZA+xYYRk9lUznNvoajjkyg9aFoOEuixUw0FZ++Od9px/AhzHUaFfKJhMmxeYJI7Y6N0UnmJGzZGtKyWIVQZ50hpSHoskZH0AzzKqQ8glta6sIdsyFYRt7D8yv8Bjk3JEOduh4K2aTMnjSjF2c6eUTuVmw74kdFT1Vs3l93GrIe4Az+wen6w+3h8ze+7+//PsYEHmfSXu3hj0o2j/Emn3s58vnuN/YfEhO+dDS4UMA8fuY2Q9T3e8//uH/XxjS9z/PY83MY8Du/fd6eHzueyi/b3usGXp47B8Djh9jen/o2H5SAziA87kGQ4fJ7ldce2LTycM1GlGheEIF1SBw7FyF0ZuRJ2OMM3GzF2OtN6YYV3sN7uToHN0YS90VU7G4BOwqcpgFTO9LTmqKshEofZ7YN6FTbEVJI87QdMf1AKMaIu2jgEk2CGWPCgpMiQWY7ThCZ8I5UXG2rXEKrwbNIBhVM2kj5xnNTvpG6CzN6Cz/YDErtce4+Lsq4kI0RXB0FBvJmTVQ6kXg2LLj1CA1RtVBV4wCv7wzVbFwxhTQYJ+JWKdnNU++mJlTA3NnIMVmsjMze4FKWRkF047ledgE37Ma1Bb0bEwbyGxEjfoqK2MTtDX64VOloeucS1bzKJg0Dscjv/39b/jNr3/H9fUTXl/9wMsfN/768gdyNYa2gscjC5y8vFw5xS4mslQw2tY2PnvyGc8/e871s84Yzhyj1KBZHU7J8gv8VbXFNlSO28YvvvmW//0//R/8w29/zxiDP/7x/+Pt7Ynvv/uR837DD3/5Ky9e/oiPQRJ3oX1Clm3E2sekBqVIMZYzyqf4jlAjeQf6RYBJWdDBjoei83x3HXtRVQFF1YihhDjn8+DHly/583ffcdyUMYPXP77g5YtXIMb102t++e23PPn8OeO08+KHl7zZBxWrPeGw8Vm7Qg1efPcDf3n5HRmTw3HjxdtX3Nzu5PLIFS7ZPoAofeH9hCAYusC1AoEdmw3pNUSnVV6FpizRrj7aHb9vS6p1lDlRZeUaOYkwUuleHsLSvAItxXErJWm0IN3padWfRAU+mWQp0mhMccgKg2xiK7RRcCYRRtNSbCGlpqpwRsHU1mA7QaPWJi+p+Qxd+Srg1HEwgoMUwzVNoF0C5y7eorV2OZTvuC/Zu8CG47QaPO1e5BopfAbAs4BEzQlewFDmJE/FYlapvsmliDKSrYYdOCpKYwXaqTJTEWmYZ7GxCZaAhGyJaGX2iAp2KMZl18DTGFIKx3EenPdkp/xTQZc9o+O+iGoL2DYVZji2AkihLDxMxrLpKCINKjR8+Zn//dvDIfZjmRPuzts3b3l7c/POcy82FRcwubdOb/0dr+PLa5lZXbtysUD9CYTuvTPnrOdKEWnuq7Eebg+Hypfw38vvxxh3f4vMqq/vqaDeUVZl3gHUYwHSP1fHPXb83ldj3f/M/9MA5nsEhY8ZnH8KuAyXNUdqmDqDaMv6CPAIzAUfnfRZGS+bwSiAVLSs7comEhzHBEwHU3oB4xKkBlNBM+hmpASegY/ApZEkVwtkrQFV1nCorwF1VI5SLp9jvDz2Kwy3LMByNLQH3SqzQMTpUzmnr4FTJ3ytaQ3EG+aF28w5aMdy4IuZdKue0GdZaImsAWoAUoP7cdzISHzPGvQ3XYOOug56Fnva02hxIjRpbMy+fDNaMbxtAeyuIFlZZQbILMY1MtfgCaStYa4pMs5EGG4GUV7UUyiVhitoliprgLRWQz0o4pBTdqsEaOKzkw00GqrvP49/pg56AF3+e10PUq+rKYTcA2ZFOPTOdOfXv/wNN29vOO1va1pnxtfPP+Prb37Bn/75zzyzzvbV17y4+ZHb1zv/8X/7X3n96iWv/vovXD99xvXxN/xf/+0/8+TJN/zmF8952o/8jz/+M5OarHz7/Bt+/MtfaHpg39/i4u98jvv06oQ1hc87jDnzIoWCn6Rp+XAXH7C07/3wEBX+Ny0279qM/N1f1P0Bwr/xqzdtFWDQnAgr/2XKZ/bWa1qzYewbJVeUVTj08plpvXQIadDYwAocFE+QjrSamBEVknNaBu/lcdzxvCWnIDpgGmKNUw6aDHArxulmdxIqQ4jcOWh5EGcosznhjYhlI2AVmjHnwPrGZlEAkU1kJjITT0N6J7vjK/Qj1dAzqEV5CbJCnVyLqadC6sAlmdloR5A9kW0FVXiUdDIanlXAqhbwySxZp9BpIjhZ/86JkxUu2ED78gRKx8SgzTpvUwgXLK2Ox0xwwxhMnQWo+8aQScvgHIODWaWkzvIdjlZyGXVDtaM5mZpoKurFji6D2E84D0NoqoQkLTtIYC3w3XmTkyuFEeVRWAVikjjWYR8Q+063jujGpgUsuhcztIURHaR1DldP+eUvf0mTzvH6KT6dV6/+C34aqAid5OSwbWDTAa2QFBUaxkywzfAddP//WXuzNjmuI03zNbNzPCITAMFFJCVKanX10z0zPf//h8zVPN3Vz1RJVZQoLtiRGeF+jpnNhXmAyVSCAlXtF0BmxuIeHr6YffYtK7qAu6I9wJMZk6MaBzM+evqEvnQ+fvoRh8OBpx8/ZenKtv1PthdvCfFKsNdiNSytPAG/+PxLvvrqC8SEm1e39P6Gt/YD8iY5e2DDGbOYwmolFRpSYY6qxeDazz4U2/01tcJBCBaFuSXBQtuDLVOhza0GIpuQUqmzPooaf9oqwTekFAWLFKMjrOM5GdHoAbIkuQVqBzImaZXOmwLMCVLH4Mhah7cOGGMt2VNQqoUPXTSEbNu7MAW3QHyhL4rMCUuSvk+CLWCU15YtkOYcohWrLoRnP7zho4+vOFwttAD1zsxAWyd8K0RFO3ne2DRRmbToZEseT8UXOG+TRRuSwmKCxJmpJQWVaVg30oSujbgOwnaJuwfWwKRXE+4bOaqI0n7kGIm3YIRxDujHSiOu7+PA3BLrsW+3IQeBUQDCksVuOA0jpDFEsWXSYzJVWTKINdCDkNaIqHBPqVRUmgceB+iOhjLbBjNZptZ7RcCxJFahB7rs4WJeFCqdnU3r+hRS9jCaCqMUFrIcQIXJhmoVl5uAiO3A9cQl2KIhy4JO6Blsi6JjYeTGFlJs0V8A9TwEbr4PlLwPRL6PKfJzbOC7y/0E9ve9/n0s3PvPfZ89xH3W8/0wwvvruP/8uwD8zzUld7f5wvC+b9nxECv7/jZffr68/n3S0vt/fx9z+e9JXX/uu/y5JXJBlgALDODsjGXjvC08MmF5dEWEoe01rR8RUYYbiw1u3bnujU3BYrKNAt3aEM59w2Ynxyxmnjdcndbb7glYjVGshrWJu9JEsJMQVt6cVTJ1TJxtQIsopkxCejFWt77fRyPxQwW0qgxSrfIK0mgKNgqkbT3Y1DDtKIGbcjJn2ZR5Tg7H5OQLYUm3UfVTGKY7/NWg+S6Tt0mThmsQXp6M2ZQlG5aQbBxdoZUK65wd8cFC+bTOLrRRzWhYNWiLwUzFYmOqM3YP5UMq0aRCzbKOK4lq2ITK2pBMEIcGvil9K1721pxJx0Kw1cv3Vos52VoNTgfJIYRcfsE9S+pYc0lECizSJjx69IiPHj/l8eMDy7Fze7oi4zXnm9NuGSa7dyJAgXzCzh7bwWWooYYQNDWWw0IizGgkzrpt3JxuibWGCyqyN8Vtt30S1IyrqyNf/vYrfv3V73j8+cf4aYOAr7/7C3/+6zfcnt/ww7ff8vb1G+YIImvbijlawLHsvpCiZSkhCGQNLwgh9s+i8WM0a7G79pLQq7l9ZyER4OkFVkbUcaWGE2jAzZvXfP1vf6S3BXdnXW9Zw3lyeMRvfvVrfvXZFxyujzw7f1/Xixl4h+28A3qHKxrJm/WGb//8Hd989wOP2qEyIiQxbVirvJfcg6R1x0Ntt3bqZizLgeOjR3x89ZjHjx6xbhtH6bU/pFjonvkjk+0XsHnSi8karsQyIAQLRVMYIu/AA7ckZjBNKK7NrHwJqXAtHVn1kgadxHQWELFd2sHcAc86N9oUXJQBqGQNoWag1plFf0PnVgMLt91TvAC+tnuBe4Lu7McZ1BFtCVNpvYJ/Ay0btwldCpIu75ICsMWFc0AzZZuQWopkyVYEB8+y6Yiyagl1GsY4e4H7yO5AuexB4Ln3PALa8P14tJ3pWIBoWeT0JsxLg++596cln48MYnW2pew2OisZZdU4I3APxMp/OK0ISmxtt2JPIircc8yJiTKyPJqxpPmsYRQBXudNWdn5P+7BfP+4unf/vAvY3t7c8t133/2kdrjLHL4wiS/Beq21UkjAu6C93IFeVd3VjPW66V7nsnv9PieHw4Ft236SU3F/cXe2bSvixb6dF0D7sp2tNZZW6p9L7RP7uk6n0zuW9AXkXtf1QTXZh+6/9w23/xFW9Icu98HsnyMl3N+GD613TIyYWdZGBileA8mmBAvCwHQgHPG+7WQpyF2Jv1Kgp2ui3coGV4sYZ0kptSTq+mh7mG2WrVFYkHTKQbhsZcopJuvc3xp7A4ekFPgZshNEL/72tn/PFYA7s3AR0Art00OprDLruoHvNYLvyQJOI+Hc6xojjciN6IFMCtROJ7EKHYwJSymC6wPW/gutYV5KqUvq6pYstjLTCIchRfhbZLJm0KSjJUgrdvEs3+tLzSA2mTN2QmcNy4Qi34Eg7jQ5MKJ6LQ92ZvJutwSwBCMqayOpTDE3iCFgHfca/AU1zLPD+685f6f7unPw/8O45Z0XvvtRCoygilyR8qtq1umHI357oh+OWKzkrXI4PuLJJ8GTpx/z6Sef89133/K73/yWm9szb9684GhHXr18yZvbt8yx8l9+9Tm3L9+yHK/4wx9+x7atvHz9GhLevH7L7377az77+Nf8P89fEr7SEFIabrMCETJ2X5N9Olz3jnfg8ru9k4BKUejv76N7IO3fWIX8zL4UueDNP93p7/sK8md++0VLPvyzym6l8AsWswERtGl7sFarwt1m+WpZFYrNJy6NZsoyys+W2CBL4iIt9sRfo0syo44ZxSpATJ103fdZJa2v01EWXGIvnHoV2NFo6gwC00oOzUwmRuqGZTl+bzii5XmzIkge9uZqH4iYQBuczhDWMS7BN4q0krTmziSeUrLRXJxVd7CnU+tWRaSTc5bfcnNsralSS2dyhcSZXmJBaE5qcJ7KkQMTZ0ZW4aWdidJtY85ar+zeRyTVOFK+z3Iu2VpKfR7bi9xAELG9ATRgMqLTfKKaJFYN5KEGPjIVXyYZZc+RLdFt4OZIOzKn0pdJTK3gxA9cPBTVwcxiBwvFRB1zl9tqZ9tmhdbt00ynrDV0dtqsABczyFkAVu7SFKgiOgTOGazrxrqt2LGxHBtf/PrXfP2nf+Pb01tchTiUTEay7YzsZOQoFoNQ7A7r0PcQRjEYVAhPJo0j6cF6Xrl5/YpfffopYwyur6548vgRn//mK56/ec3pzR8Z64E5zrRFWFCIwSJXPL4+shw64+wcjgu/+fWveHmlPDPw17cIZyatboqehI19mFCs9bBGawOZRlKFfeDIUgV27qyZmWfMFmTft8X46hATba18z/eE2dCk91rHxFlxTCZtCKGAORFOWB3/Fk47LAyr62VY0IyyyplJMip4yYvFEzRMi1mlxw/vuLatPNq33Z9zySQ74KPYcelwZeUffBZSCzAZMiE6phMQODiffH7NsXc2GcSWFeTSlYiBtWRukxSvc2prHJYOOkiMaQONzqPWWSVoXtuQKaQca8gRTlp554lEyaK2PSy0l9+VeE3CRQzdB06N8mlGBrYXZqGGpjAZhC/vbG08Esm9YDFFt2RN5RAJ+7mvJmg2IipQJuauPqACt3wqsUBuTpvJXARylB1QW+rciAZLojNpOZlbY90BsxHBIhDtimMMRhNEFMlShBCNmMKYK0epoBpJQZyaqmcFmOGD0faGNYw8BDm92OXdybWCMMQaGsHIURP9f3C5X/w/xGz+EPD5/ntdGqVLA3L39/e9/qH133/O5b0ealzuWn+87zPeB5YfaubM7G+Yyx/ShN0PMXwoRO+hQL/7j70PYL77moeA9buPP9RI3f2sH8r8ubtcW1YArBW77iTBYc9csMVpXbiOyWiPqMSpYs0cZ9LbkWaKnVeW40ec2socwXm5Qc6tAkXTiyWrdR1Spex9bNCtlU1Glu9764PBgfSNriU73nzSZnCVwjwucE60C+iE1rAAd0EpiXqPJJuRKDoCyWQ9VmPVpuzsx4B0LIrhV0FXSSi8JbGlYadq4EUHaZ3MWeduNCLODGvkPJCMnW1dKfOpjRlZ+Ram3HrVJNoSGUm4MHsvz+YJqSVlTa9wrswKW9TWKpW+dPYYwhZCl4UpEyw5M+lZjWqpSWpwarFUwFAKwybqDYYzllKINatSVbqUp9JZOBwbfSqLfrgHs+wgi+1B1IWjVY0214317KynV3z/7BnffPNn3rx9S1KhXpGX2nw/ZgFR+7H9SHlHoEACIRnh9G2ynlfePH/Bq5dv2fawrUtPA8Va0mZYCteHR3z80WeIJ4sL8uhA+BH5Nvn+22/5/ofveXNzw7adSak8CVL2Oti4BAZWeDRoVvO640z1WBYY4Jp7aFIBbYV517mpUQDcnv31jnUtlCxYs4CJdKrvm7PyRqIGeMth4ZOnT/ny00/56Ok1EcnYyn5xEjSfvHn9hnFziz96zM3NmWcvXnNzfks7NeYT+M1vfs/Tj5/yb3/8V954BbNFbMUYS+p+KmBL4/rqCU+ePuGT649Ynhz56OqazW9hHJmnM6ebW9yDJAj/5bL5rsqIqovFy8pPgAr3VLahmAVJo1t9xgJS236da3gMxIy2tzZB2Q7KprB7gs+sQOqkbCcqKDnLJk/292GSXr7F6kVScKkAZXbgPZ09tBisO9FnycXzcrxSSshktyOc++CnV6Uq5dEaxySjkaIsOrCsmm3bcmdwyruGu8UADmijrh3VLlWdqgZZfRWZWGQNCqghX4rjdjmfCqgJgZ7FrNXL0NVq4OdtYnKpb4JxOpXFT9N9IATaFB/bnnHj7+wMkSyCR9Q59843XpIKEhWaF34W2B5kFpXbIVV3vlMx/IPLT2wl7oDCh8OB4/GIu/P999+/C9+7vOYCJN+1wYAfrbyKJxXv/nbXVuwCTr8b7LbGtm0/AajbDgzDw3XGBSj28HfWFpf7+F0P6Lugsqoy5+R8Pr9jOd/9PHe38wJI/z1Q+H21zd+rCf8jy0Nqrfs/v299/0it41kkqBlZ17xQzEBCqu9TdrA4aGEQlO2TD3I2ZElaRg3cpYbkOgXEyzqmDTSLYSsuhBsHgdF2f3apa1IMClcQWFLw6DvTWTCr/kW2IqZMjbpnaAelMBcpJU2RkYrUkxb09CKOaRn29X7Ao4JYkU5EQxVcBQnHtI4l260VUdDdqqgTDKTmv5GMdA5pZcO6j4lLKFTYU9/98wtqCJos+JpsGkQmrqPsiPYhbHZFhpPZyd2aKC9Q9RCkzVrPhMXKiidrrWSU9afmUkMC3f0w3GpAkIIsQg6hK6WSorKmdFEO0jmlM+f7SRsfTu/533EuvANf7zQSFwRXjIYi1GTyr998TVNlOXZ+9dmXtKVxe77l2ctnZZJtim+T3//u9zx7+ZZvvvuabgvXHz3m5tVzjsdrvvriK9Thzdu3SE5+87vfEjmZ58n55pb/9Pv/zJ/+/C8VTDZvaVKBYT1LlnWO8oNM4OKvfH+RffJcg+fLnHf//w7I/Av4mz/ebB/c6b8E6f+Z5/6Ct/ml4DLU/ugt2VJQn7u0vJh72So4rqkhcuY8lY3B0qUk7b0aotCODdkZOoG2hkSQfSBDcTMM0LZfCHLicyDtQBuj+jg1pkwsS6qflHzUN+ewCJ6G5UC1YNwp0CaMSQEycxa75QxjofwLHcatoi2wy3RItHzMohgfDuUPHCB5RqKVt08uxHpG3Fk08WVUQaxegWCHkqXTy1KkZXknpgcqho59Or9sF31DfZcClgUuJ4JJ+Tk7oE3IYcS+f8cyyy/JIFcQM2KpqfsVyikHsXW0d3AnohHq5Xcq4NlRmajtdgMHLblZgPbOlKD5QMPwbWKapH/4DaznwLPTTEgL5iwmp5DorGRytQ4q+HTEqCFECs6KmhQzMwSVDbVWXkhmJSkbNUg6vX3D11//O08fP+HqyRVX19dcHR+xPD6QPwyYs+SbGnisxepp7KnP1fKETFq2svXYgnRDlqoYVZKwjUCw0fjrd99x9fgjej9wvDqytIXjcuSzjz7m3xrEeqZrhapsUVYCQ7KCFKeTqahWkN/HH3/CdDhvfwafHJfJyAPiKz0NFyGxYpnvSe5mQnCA4fReAYjTAzJwGpbJ6mc0jUUb7srWg2MT5qwhh/Ug8oAsjTEdJcrTOCG8vJxdBKXRrDF80sz3kEtHL0GSrQq92CWl0oz0RioMnwxLzIpZc/gFlx9rtts+HrB2g0ejqxNtgRnlVSoKWyIHI2yiQ8itmrIURVpgcw8sJGljYY03NDvQadUM2rkClCyZW6KxIrJguUBMtmYl8yboadVIH66QdUV7VOOnxSxLmWQutKzrmEgiPmiU1HJLwel7AQbESrQjOZXhg4wFs5LFWlhJmEZdK6cIuhxAHc5Bl1Ehg6MCH7QlcwJZQ5fRhUNvpUWN4oodpKG5ojSyWUnQhAqpHCUptGMFQlwJFeDnNYAoNopw1kS3W26krv2LJ7MfsLkhtnuQXi1V0Esx10ewp8ifaWnc+AkJofGItmx1HCrl845h5uWTJoZFh3am33z4wfM+8PM+8PjQa+4zhu///1CRf7fJuvvYQ57Gd5f7bOIPlW0+9H73Qe37oPH99cKPDeJlO+/bXdx97kP76aH13t+2u4zlh7bv8l6XZvIu+H5X+vpQ0OHd9d3/+z+6xCwmT+xpN82S0wqtT9rS2ahmWRj4CXJpHLQ86K+Wxhjg9nGdT0uSMZB1QdtWw+DcsyoCcsxis3UIWThb0LJk64sJyRGVgBnQGx6KMEkOzKUsJPQgbC3JUUA3MqEJ2ww0BLjVmgoAACAASURBVFpg3ggrQI9I2ilIdTZpkIqJs27JUSpgd4u1WDoUkHOx3m1aQ0yJtZQP4tUdYmgkxgqt74NFJ0zJLVjTad0QE8SEkYNmAmqY5V7rKWGCDdikugf1QFjYnSvILWm9atK36aVyWaQYyN7QaNgx8JyV9RALIZPQwWGFVYwWjrcB0TAPpMueibyzD5vQZKn7hyg7tfHDFrnIUy+kgyroTtuJv373LSef3J5uePniBa9fvmKMrfDbnaVsu0WDqu6gVUluc28kcg8FGtvkxavXhMCNveHtmxOvXr5grDvTNGtITxYBo9iaUvVLU8In67pye7rBTvD8+9f85etv+OG773l984oxB2P4Lvbc2c9iuyf0TsTZT9OywAB2oBnRXYVW8ukUYM+WqRt61Zel6AOSdwF6JFUba4VMi5XHuE/HZzDTUQ1EGldt4dGTJ+ijhW0Gt29vef78OedTMSR9DF69fMEfv/mWL+fguxfPePn8e3wLlqPyxa+/4rf/6becTmfevHzB29tbch11oO9qut6MZp3leMWXX/6aT58+Lh/sQ0cEXjz7gbTG6fbEm9dvmVHBZ+9k1SYPHCTvOXTmLDsKKeZyZL6TLFdbnahG2a6hWCukV7OYy+J1zKvVQMY9Sase/MKoil0p6Du4H6KQrYK+vZQSla1jMAOjCB4hRtd9aAOkdkKViBo+iO/KFtmzITLAYbbKnFGrgHSPhU5Cb4QrCxSLHKkhzw4a+SgToNRJzizCi1vVnjjhddxlDg40bATYRpaJPR6OSg3VZnGny9eZLEBnr0sUCPViD9ehuPuHC20K2SY+FrbciDGYnvRjr+uOGTFm2QhEkevUdn9YlVK9UgB+fX+6hzHurM8s9SouiJYXbg1pduVY/scA5sx8Z3VxOBzegcuffvopy7Lwxz/+8Z11xN3XXEDZy+8RUV7K9wDn4Eev5Lu1z933GWP8hE3cWsPu2YXdX9ydMQbL7O8A5Lv1yuX/y+e7/H5hVN+tKy7s6fsD/YcUbg8pvO7XNHeXnxuQf+jyPvD6fi32UA32PjLALwGZJw3LWVfnrsQsix/XWUCxthqaCsjqZK97VUir3IisbKgeF6XLLNWJthp+UgrJkIlh+BIgR7qMGlipMMaGSKmyrAlzQFPHsoKNHSuyzJ6bEBlY9LKMQMkWYMqcUbgMBVYLNXC6XAFbL6suiyiSyx7ON0VQ3/MDLJmeLKG4Z9mVeiPNcdVdHZV0U8QN8SB1V8H4JG0B2ZV6XlkESZZSWsC0/JMlpchYmmWHJIJNYzRDWhLeEWbREXuxlRUpRXVO5iyFseqOR2YN/2owmGhXxEbhT1Bs66j8hjlHMZVVkCbVL/oOIOvDGXHwS63C/oHlArn+7d93fDMTtc7h2BjudB90bVwtT/jy80/54eX3zLFyPt/y8uUb3ry6xefG85c/cFo3jn7NyW9ZFuPJ8QnXx2v++tcf+K//12fc/PCc1+cb3r54xX//P/4baY22POX/++b/5ek/fcojb/znP/w31nHLP/+Pf6apFuuiN+bb8vlSamLzN5/h3UVkP1l1J6ZwDxjOn3zaex/+/TvmoYfzzr9/d1F22vV7Vvq/Z3j23mWaIcOw/fsNL68dJMlN8T4xMdZYyjdHyo9mZqOPCsI7qhOWrKHIUWEvAEQOTD9DXyFbhXpR7Az2sImJ4AYphm0VPCequAmhyXJdf38XzqblnZqZeAPGgZEb2o947LKKGSiTtTUO02qa5LJ7C9X0DFN8ncVkzvLB0a6M0DLEz0FblM0P6ASTIHJWUI4KMw80LXGYqRIjOWhnZAHmKsXmm7Om7Y1gts5BvKw/3EvmKCVvr44vcd3I6GxSXs+G06Vz1p1BOgpInSqIGbsJLyrFgmwJLrpL7dfybLS6OMkwVMoawSVZopOmRKspnE9F+CUezIlNqyabYEaiTdA5OJjgGRwS1qA8gAdY7/jJyaVk9Lmd6ziRugHoCHxbqPRTYciKbhvffP9Xunb+0z/9AU3l5atX3Lw5FcuqQawbMYRFHaHYruK7lNWcNrTCfnDISgKXLXbfQOWYEC68jcl4ufL1v/47UDLRw+Ga7bxW07hOSCWnl2ROgzGdOE1uXr/leHXF4XANTegHRabw+MmR0/oxL0YFxHV3tuhY8VTQDeSYBdYjlTTLa6YbLbU85ETKviEmYUK3K24Z1QzuLOY5yl/FpYKmWs0/GVFSwpkCPctOptU51ZuzTt3tPsqyg+FkryBMWieyYbkS2UhPTOp873vQ0JwlAcz48IuV6Jku12wxkVjoktgWzMOF6dXpGYTC3FaiNRoFZksX1tOg09FGDRSGYi2x+IgpWzFqtKOj0Q/GHA1vG1zDjNIuhRYL95aVgy00lInhGfSudGqfZW9IDNpc6rqDMH2wifK4JwNBttofvg18MawnEQdkJi2EGA3tSRmEl89ppCK2gJ/p9Ar+S6XZhNnwAJWNFooPaJFETHo/0kcQXaspWjpmiW0Nl0qfH1F+77F7qjUZGL3Ae1HOrtAGmWdsbXTKB3+klfogNrY1UQPDEZZdYus8smCTpbzyQ7jWiaZwmo1s0O2A+EJKKWNSIXzlPCb2+BGq5WPd0kCc3JRt+WUezHd/fggYvi9xfAhUfR8wfHnt+4DOC+B8//l3Jap3vQPf954/t9wFbX8uHO99QPTd7X0fo/ru8lDT8r599lCIz/2G7e5z7rOLHmpY7277/fW+D+S+/PxLmsDZlZaJTIdjQz0IVpgNOYFeO9IOHKdx2r3l3VeWw6GaLAfpBrxGo9HM6erczAZWCeDkmXgLc+mYOhPQPMNcykpKGjHXOi+2sm3YZoFzfUgxBj3QxZixoWvpg6w7egubKvSBzk6KMftWgHlCX4AByqGk5aKl0mnJtkYNeeQKizPSg4yOOnU/YJKrlSpsKVUK0atE9bpXuieI0+wIcyLqkEZH2UYNikVa2epl0rIxNNgcrrSk6iKVWSHSKxPAFYlitM5QbKlMkKbluIYVg9kk6BOaHSr/QpKuvodO1WeLLIZiQVgN0gkctQqH8iw5azctmzf58GOnL6XYcCk1nlaPzO35zF++/QvPnv/ADKtBs/vu27qfx7EPQfd7jqmCWRFeJPAoYDYjGNvg9avn3J7e7mDbxNet7j3hiJazc2UayY4F13rmCLbzLS+ff8/mN/hU/v1Pf+Jf/+1feHO6qcC9bRIeZAbseSoVBLQzIC6gZeY7tmrm7jqcF9/l/dqyM7jKjbZAtAKTd+/iBLsAK/vxeFgO9KXRjgcUON2srOcT0/cgXUvCnbkO3r68Yd1e8+zV93z/7AfO60qks4Xw6uVL/uVf/5kfnj/mxfcvefX6NQl8+eQj/ukPv+Oz3/yG29c3/OvVFUXJrXySRDkcDzy+fkRrytVy4PNffcSTR4/Zton75DTPvHjxkrdvT6w+Oa+3uDuXjCdkD9j7wCW6kb77aU7FNImuZMwaimSrHAsRFrVSqsWArAGCaysQX0F2BqAB0bJ8iaeiPcmd5beYM30UE1CklBNe2Q/FVBQGNTwPHyCtjr+R6K4GUDWmOiKKZR0TQQ3hi4Zd36loWf6pKHv+FIJX4HH2YlqHQS/roN5qSBBnavg/KUl9llLPov5XU+buHW3uTNFSgnnZnJQdaVERVRVLLQWnS3kti1SNN/3HwDEJEGNimFduiYxSy6LCtjojY689k35wJFrZS88sRS1K5kC1smRaCkxBzYnLMaFOaNQgag/2zKgBfYiQ45dQ2R5eRISrqys++eSTn1hd/PDDDzx/8fxvnn95vPf+k3A9d3/HFM4sIsKlZphzvqtrPIJ2hzG8bdtPBsd1XX//IP1yzdi2bb+v1/tcAgeh6p7Ltt2991/sMtSshlL79t71YX5fPXd3ua+O+nu1w0PPe9863ldLXvb9/fe7gP3LstB7f1fLXID3Mca7z3W3hvuQeg4K05te50yzCiEPr+FrMLEZ5BLIVvktEgX2qgWZSngpahrOaKUyFO3ggUmFJIvvQ6rjxZ5m0KVYuJ6Bo3v+jJZPv8IMIXKQ0ugbpCmuCrsdhOSAdfd8F0NxRhgiNcrsWNUh0TAtgktI0AYENUieM3fMsnzaVVr5O+dalhfHKFJg1jnpXiQ/k8agyFVYY/NBDqcdFIlR/vdZqgSJGgzHUkO4wGj7/XhIKdhoWgC61vUxZxb+oBUGGiF4a3RtILNCSb1qnYx6L5HKu5CpuFA2O9F2BZURTGSbSDfM9HLg0dOKrY6gB4j5frXWzwLMD+GgQl1g34uC/s2heH9SshcWFFiWKjx58oRPnjzmxZuX/N//53/nL3/6E19+9Xvaoyu+f/ED3//1h/Kd8+Rw1eFwxYvvX6DdePPiJV2UX//mn7jx4HR6yxgb3/z1r5DJ04+foGacMpBXN2xXzvWTj3h1PvH25Q/85rOvUBykJrw5qrAMKTsFl3eo8U+X/caIFKs1orzlHvyw9/9+F3S+v5M/qC79G8ONv13+5uEH3vjngO4Pf8qDSxuTeW3ImAxt5bUVNWlVFZodmQxMCizrHUIOjDyjUinlI7IKodh9f3ZfuBHnmuDMSiiV1hjDaWhJsdrEN7CoYKu5FJUls3x8dRoZyiZZ3+/sHBzyGOCTmQp6iwEe5a3q5hxCiaxCZVs2kAI0zIO5B0KQrVgwskuxBESP9ZyRNHPGeeEggZsXq+BYvsFHKY9jj4VFghwbwqEMEBxSnCFaFyAtj1kRpXklhbusVfBF0gw2gkYwRklZ5nGDMHQIG1bMU92l+EHtOxdIK0aJUOyKXtMTSaF7ktdCc2EbE+37BUnBZmfg+DRaTloH1qXYPR9uSVge3DlIvYRfCMyBKJwjWMTYSHQWs4nK7YBuzHB8JN1apb2r4VuxY5QziTF1ZW7KI71ivFn58/Yntjn59unH/PDqO968fEWasI4oJoUkkcaQPSQuGzN2iY0Kpp25jWJZ6yTHAksiDHI10hoZN6gnL94o41/+yPm08uSjx5xuT3z312fMdcPnqMnl3AgL1BrnWHn+8jXHx1eAcsiFMZLwSdL5+JNPePPqlr6urFJMi5RdjrckslXiNrqfT1PLekKVmHXNyssNSyoEUhOaBtMCXcsapEIylQ4wJ2cVYls52HVZHayBNyrMaqF8zzNRq/Tv9AqEyWhginVhjmqordm+3eXruGXuwygje1YI0Qcu0wWWDfMkD41kMKIhowZQFgm9BijWFBhI9mqeIrCWaAxWtwKY2diQ8iY/lE9mIwktv+EuzoyOnaWCdDKIBbbzDY+yE5lsAXlIzL38Tn2D88TEEb2qgs2SyKDnYMOIqILLl/L+spZsazVAjYVtPWML6FLfbWyCL0lsA0Wxo5BzIV3QpSNxKtn3khxdyKUTnvh4S1+uOdIZCaqzvMtskmLlC3qYVUidjL4DtqEHJAbSjfPphFw3GsrttmIutN7KI9UaMh2LDXLhFAttqUY/sgJdRcpXbdsmeZh7sSOsDqHFYjBVTIKb9YbDcmDagsYkbAGMGFVttNY5KyxnYWFwu314w/UQW/YusHyXHXIfvPw5pvL9ddxnHd8FM98HXsNPweGfA0wfYthclrvA9H1LivvA9d3Pd5fVffezvk8O+hCT++667oLmd/ftfRb33c/w95q4u0Dz/c/+c03az/38ocvYh8TpSr8J6MrCZHVn5UzEgcdmpCa9VXDdjVxVXdOcwxRMA7Ujbd04HxsMw8Rhc6Y7PjrNZtlGjaBpL/IrQSS01pFc2EayiDEvWQtqhAs9BiFKzkZTI48KW0kl7dAwEtVr0kYhfC4VsqWQm9Z9QkuuV81csVq1XeT2p2IzhnJlwVgaEjD3+7Q0GC41kG5Jzg29vmZEVj5CONOckORxV0YGMZRFldEP6Jhkdg7N2UYgahy7E2t5t+rOtlHfGzRNjERMWXMl7Iq2Ck0Ha5SSQk1ROaALNJRYcg+6q5wOb4JvGzTlwMIqwRITF0HSkNzwPEAE8biA2Oi3ePtwa57Hjx+hoZxOZ8YOVqo03JNtODlXrB04XB0wueLt65cM3eBiaUKdS1dX11wfHyEtWLfBHBtzTMaYZQGRgo9Bm84O5+KzwGUiC1S2arbzUrzuOSI36xv+/M1fmO4sP3Sev37Od3/9lpcvXrGuJ+bw2ge7zyZZINylNiSlarmkaktyl30KoIhkPS15x8z+UfUW1fHsNhuXliooYoq1zrIsfPb0Kb/64ks++vgTbGn8r3/+X3z/zTcFVu0+mufTyl+efcvLt69Yx8qbm1vWm/M+uHM8kptTMP79L3z7l1YKMBEOy4GPP/uMz3/1FY+uHjPOK+4X08r6PFd94enTT/jVF5/TmiEEzY4MD/TQGbdnnj17wcs3N6ynE9tY35GsZLfJEMqC7EOXmI5b0bpl2TNg3llDRXmtxwFlMCKqLpKFTRKJwSXIURPc6riQKQWEiCOHvgMTc68PlTDFHIYV6JpWqq4UJWZdIgRYsAqiQknJAkSiWOmtF8jkoaUK3MpmIyLpLZFYikloBaLSd1WCKeIFMrddpZBSwHnGzrrfPdZBd3A5QMBzfx6lBNOo/JCeUfY3ViF5mXvYldR1RSIqPDu0FJ3UOTGz7EEsan+pXGxdgJwMKeIEUX1Mk6x1hDCnEVos7Gw1YEImSMOzjvlQAy+GtarsTGuFGQW072oJFYemEEr0/zjAfHdofT6fWdcVd+fVq1dVM+SPdQzwEzaymWHN8L1mFxG2sSHIgwDmBYSWfb13rTd+sj13apWH7uUX5vH5XOfyuq4cDoefANXu/s4q4wIuXwb2ZvYO5L4Pht9fz98Dmx+qT97HZr77vLs11f3Pf/dz3wWXHxr4t9Y4Ho8cr44cD8efWIRcwOWbmxtevnz5UxD/AxnMaJJMsnXcg5inAiFjw6KT6jRV8lCMYgHmrOGlpJOSuDbSyw5TcicANupcCcdMsAbl22xkyA6uZt07pKOzwG3GJM0wm7CVp/pmXn1lepHfKLBYcqIXApMnolFD6KaMDSTL7qv8kVvZ0EjVGhmO+ETbUs/bh8iigy327IsQiAXVJBi41ufz84b2sr0hBERpre8s6GAVw3DSjdgDUsuPeiN1IdiKJCTGnNDYQ0a1ZpzZAh1a128JundCSpUewxFJxDoi1YO2TMTKvmprNWAcsf14/4n98y91c1haDfkW7UzfCBOYyqI1OHvf8rMA80On0OWGeFl+HoC8PPIjIPrjS4sZodlQgbenGx5dP+LpZx/x5782vvnhW/rNwpTg7XzLbz//ksT5+JOPuXl5y6rG1eERt6cTX3zxOcMb4/YF59sBIhxaw66vuX70mMOy8N13z8n1zJePf8tXv/0dX//5azrBn5//G+vbtSaBcyK9AsU+/via16+2umlppRmrFG3+xwtH7oWO7I/ds9F4d2GAv70e5ft38p2lJD95DzCOv7vnPwgYvvOEu89/97PCT46dX1D4aF/gXNsqUqaBGeWFp1km7o5jUwizSsDMRBchhpMJ7oYqtF7eU8MSvE6W3OUQHp3pFUI1qOYplhXTjoSTPkveYI2uPzJ/sXX3+pxIFmskvHM2RzxoHJBwQpOQTp9JmLJB+RNSfrO6lMSGaUwS60rTagy3GZUgPBvLlAKRFkWHM9LRKFZw+SArm6xoM9i8vAfbodgpsU/swtBeLJ0thGW/eC1ZAUFzE5ovqEzCEx1ZF20ViE4byRDHY3JlNR33XKuYDKkBi66w7cVSF1pf2JQd7A42VWwkKx1kK7ZKFMNkysSmY92ZCiMMb0rfm5cPXcSKlZEWDBfwWb5O0ct/T0vEVnLShRzJXJI+grUIZFjruK9MN8znLotJAqd5o7UJwznZxHHOX/+Z9s1fCCbMYmJfCWymiJc8ZlEjfWBNGWI0BnOlZHaXc1SVPE4sEvdixss+hLqdjthbeH3mT6db+nIg1uBVBGxbsVBQpm20tZFLsaRON695/u2CfC7w+BFY4/zmliQ5HjrHbtzMsZ8XgUvdDM1jnxUEEV4ys2OpCcZuHaJu2JJka8wbZ3OnWcN9Fvld9lgE6YQ4k5Ln2NBqsDuoG+us4yNi96eziySo4XuInexenZHBGJOITrNGzA2zzvQK6MlG3dRC2TIw6R9+3dmPz+iNcRIsG3EsSxN2pnWG4zlrvG7Jm9sXbFsjYsW2JK6F3q5Z9tdmq2b5SqUkxFORRXYG0KCLcrSFMZwgmEMxX/De0AYHYPPAzLHVir1tNaHPNhDvyD7oaio8anXuhDk9FGclt8bSqtEecVsuWplwpWU7Qm1nk45vhnqFvpgJ2W7L1uW2cgV8cWKAxkZvV4hNgmMVOyTuQW9HwoOWG2kHYmaBUL7RpeNzcFIrtuHhUKPk6SxHoZ2VLsJJBc5nlr5gLOWvbWeCQOQaYSuQWxPvURN34DiT0QI8WMjyWN0amzX0UBL7nIPTgCdNmVrDwozgvNVwyQ1OU9B+/OBjB/4WHL3LCLnfJLyPdfs+v+D7zcNDIPHdbbjPUr6/nvug9N33+blG5f767rOW7/98/7l3t/E+IH9Z7jc/d5lDl8fvs7Lvg953Gcp3G9qH9t3dv93dLw8957L+hzwVP4St9NDSZqMnYIOggQTbphwOsJ4mblcQk8OV0FwZ0mgSsLQa2h6PzHWjbeWI/3oWq0XGAG8/+ug2JX3ipsymqJd//CFbBUeF0Om7ckfo6swhNK1zQsUQrYwJpWGHZPUjLhPLQcQZb5UMnpuSCnM6qhM1Y0rdDys0LnBZmOa0qWiUd2EzhSXq+uRBbBWcKlEMJeklcXcTGlsNyKNUHDKDbsI5immoC8RWYShNOh7BmIlfN2x1LBb8MCvANTt9l30iWRZdGizqPJJOzL6Hyy5cLc7RFqQpah1DmT1IX0q2Ks4WyVhXtHeGD7pWja+LlMTXISPKT1IncVbGsTNzIuv2wcfO57/6EiV58/olb97c0A8LM9iVO44ZPP3oEb/56reQwp++/iPn81qKpH3ovVwt/OEPf+DLL74kpdQ5N+cT333zHd//8JzYTlXbuDKtQpOE3OWtu50JSe7euT9yiAo0Pr898e9//ppnL54hqZzXM+d1MLZbxjYrV2FnXmUUWCy590skQinrZH9PrzQzRAo41tyHAoDmDtJJcsmAFxGIRFRRglTBxLCmLP3AZ59+zH/5p//Ml7/+PZ9/8Snn08qr75/x7PvnMAbpgctkncn8YeVVa5Aw5iS8WPV+AQwd1vNaXtKSHFrHmvL4o2uOx0briodzPp/2YUSiKvRD49NPn/LZpx/R+hXr6YbEmV75Di/fvOX5yxeM85mZe2jZzkDPYm69r1l876LdSW1IdHwGNkuFyM7crX1f7y9zV1flDui1Cp7yTGZ4EX/Yewav3kfCa9uiESTWaxCUFYRTva87kkojGTseHFpkhZgdLT0p+c7SYn/dJfcFeRdQLg3SlMxJp0LYRYI5Fljg4NCWjpixtAPL9SPAefv2tjJaTlGEiNpoDprMkBr+tYHkAQ0n5kS6FlPZagiGKTKr6c1WDMdK9qtZSGqUrVgUaJ1Ur+IhBBX4mft3aFm1IER5q1OKgaRsMlpE9bK2ZwFZAeIyonxTs2ExWa2ja5BWA5lUaI0KQw1BdZC7pzUSlW/2H1wyk9PtLSrC4Xikt8abN29+vK+SuyqBh2sUfqx7tm0jMt49f875Lojv7mvHGMVSvme7dXnNfAB4vru9d3++MKDnnPW/T5a+sCzLTwbQPxl0w0+sPx7K24CfDv4fGvQ/NNx/aAh/9zl3h/g/t9zfhvukBihw+fr6mqurK5ZleWd1cjgcmHPSe2fbNpZl4fr6mmfPnr1jfn/okrpn1Y1ENDEocFYaLQOaErO8ykOVLnVRcKhwzCzVN2p75s5EPJiuqFYgeHE6BcskTWlKDbMyGZmQ1cuL7d9fllIgdGfWArkN1Bq0GhiFKhlGBkxxuuwBqdkgclfElMVTimDqdLT60ii7HsKwXeVgI5mZZFNIwYQasjGZAWaKZSsHhFbnzdUOuYbU/RFgSNBppRzP2O99pb7CelkGZl3XRIs1HSrENhEt9YdOyvfZS7mFJDMmTRfUWqlcpIIHPYoEdcm2kQyMVn/Yc5daF1oq7bhwuHrMsXdUgm1M3q4ryhlMyr70ZwKN/0GLjB/hyA+7Ff7tiaNaPrFiwnkbZag9J3/841+4vT1zXlce317z6dNPuH37hr5k+YCtK5HJo08+Le+62Tlvk9Pbv3A+rXzx1a/oN8Ynj57yw80zzi/go48/ptsNy5MDEpNxuuXLP3zOzXdvsJtb+nVjvT6hpvz6q9/z9u1zvvjd7/mX//k/uD2vjBUi/EEvYpHyrsq8sy/uobuXl70f9L2c3H/7aJJ1c3zgkZ9b3vfo+7YhH/r5wj6IB570d5YmWWzW0u4QWje/FsVCblK2EXTo4QwxoMINTINRA/SSzkeSXYudGHWSdxNmJqa+s3gaU5JOkKPYq+QuHaSxSOChpBeY0bIh3phhZA7mUh54yyxQXdrGsE5zL7mBBeZCF0AHLXoVlqMYBEIxNFm38sOJxkFst4nYGHRaT3xP7MxI1imYlAxVw7DF6BkMW5Cc4Cu+GN0UxgUArstnY5BjI45XpJX3qgVEbAxVmh7K/7cVII4ISPmrEcFZsl6zm+xL29DU8i81J1VI2m7fkWQKTSqRvmXHmcyssK1DK9mLR6BHw6NuOtOk5JozSB9/54j5cQkXohtdlLSNxWp9GgOh0rfnJRnWJyrCGMZsThdltuCqJ5mdyQSDMarcuzIrtoUFoaO8z2Yw4y3nLMaWWXk7CdCtQMURTndhqCIG6sXw1J41KNkBoZj7zV8mIcJUI4raVr7VQzhdwipv9wZRG9suyesxWAPWLNsXolQAr988Y0vns23j6vERt2S73ZBwxnAGhudA3UADNZh0lCz5nxgis3wjJ/XdirAbZ9PHZKTU5HkPbbFtEl33lPaEKYTUt4egKwAAIABJREFU+WAq2CLEuK3hhS8MdUzbhTjDOoIr6Xhz1IJ1Js2hizBjqaCFpWw21nnC2oKmYh6oWXlwJRzkw+nvOiCyvuNOwyPp62RyqCCcPPP61WQ9vwXpnLbk5u23xFCibegmTAQaLCHIlfC7L/7A1aMD6y7jpm8YVTiddCEdujtpClNoKsjVY3JUuNWYIK0YXpUNbtAhsRpAZMmWkPLaDi0f8rE1sge6dqYasjWGTXoT9JgMT/pN2WxoD3LVvTAK3Mtzexo0tWKU2yC1ERlcK0yHCMNFd3DMGRzpSLG8ZeDeMIdcQGcVPOEbE7jCWCfI8QZZO7238gw8tvJUmxD9Gs+VyA3BcHXibMBGb1lDPnH6SNqhfMPOIzFbGOsteX1g3Jz+f9retDmSI0nTfFTVzD0CQJ68imx2SdeM9Mj2DxjZ//8DVmRl51Nv71b19LKKRybzQiIi3M1MdT+oBwiikGSyusZFSCSAODwc7uaqr74Hu9nY1Y5TYSjvT0dqLfT07klpq3dEdPMdh1lA5OPXnfuSwZ+tSe4PFvT3pYoPgbr3m5Bzw/EQI/g+O+X++9x/vYde/+5r3n29+43VeXuI6Xv3+XfZxefvz5/zLgv5ISD4PqB8ltPO88w0TbcN4V3fxYeO6UPs4/vH6H6ze98C5P5n/BBD+qHn/NpWWTlR0gOYRtQsYLqsvDm+ovoRdleMpsw2p1JS07dQTgMtjpTGqGtewzeDJZwZODEwMUQbvRkWitVgtAzytGrY8AQdNJBpRUeCHbIaZVJ0SoCu2gwh7MuUDV4scMy1xxHaCtEHZkFHkChUI5UI2rEG6QfXky3XejaWUhhzw1pBNAh2SIcVYbbBkE6jEB2sdqoWdK2MI3hJ9rRop0glRsFxWiwQhtQCS2Caze1QobgQsyAssJDDzjJotvkSLzlox3T7D8yTABCm1DJzWS0ZRsCog53OjA7TqDTpXAxnKRMnX2nvjyzR6K7UtWxy1YG6Q2nQpvSrPkJbDbv8+GL5+ZMnDDZU1yY+/fQZUgo/fvcDp+XIo8tn/P73X/Hpp19wWBbm71P2nQFjghWhlsqjyyuef/4JVRStyvG0sNvPHE7XrG+OiHPrK2shSU6OLeQMNqWW/MRfgQyHHoNVVsaNsxxOt6BxWwfuKTGPDRJQ2VRRsAFiG2AaybQWS/numRzNBoImnpyOz3kRAtt3ygboKbDVsmdPVtOJR5cX/P7rr/ndl1/z+MlTbJoYhyMtHKHDxg0mcp/cPW0dSKDDU1qZgLoIrQ9MHWTchsqpKhfTIyhCb413b15xfH+zAfX5YWyamec9l7snyOQsJ+P6cGAulcPNDT++esNxOTKIVNzcAinnNYsE1X8Dk6dZRbqn0nA7cOfVd2zhcUUagm1eoCkV76rUiK3ZMaptAJcp3ZUhjpH3465CEdCSAApDoXhekwheFGMwYsI8MhpGclA2pG2Dgc0PWdN/2YbS0bTaGIFH2+xtCiWEVQerOzUicxZUEB356dSYr2a+eP41V5eXGPDqzQu+ffWatkLzG6ZNjRc9FQxCvn8v2zEKw0lLQRuaJIo2NrLYIFqhFGWUgVPTum7kGhMoIgXRBelkjTbIdU8EbSldJzbf8OhQKtELVoIWQpeCq1K6I0XolByyzEqMDCxtmvY8WjXBak9laCfJHSoOXnJQtJ1H+p8INT5v7k4fg+v371lb48mTJ+x2u9v78Pnr2U7i7v3//POIvK7kPOSQ9FY+g5m11p8BzWapeLw9r3vDR9plnEP4zq/7oe3u/fpcS/TeWdeVeZ7Z7/e3dYeZJZi9EQfuhvyd/ZsfqmMeGuo/VJv8ltrh7nt8zGD7IbD7vL93P+fd392vS3vvzPPMl19+yatXrzgcDh+9n+HgXrGSQ108+5sWgdegDEWLUotiHdoYxOahn+PBktakI7EQiSRyuSepSMVhGFiGuWpA7Gxj5wa6DIYFrgU25VhTyYDxkgzoiBlkIfTsK1AQ8bSPKGBdEz/qaUnRRNCNVSklh2fdJT2bd5ul1mYb6Th0pWmSBNMwMmiR+aXhJCmtlGR1p+aMEpo2WKLQMschRqqsRXKoOiIoqrTRCWUDlxcq6WkvPghPQmWobnaHyTIeVqBnDphLejy7LLn+eVIUIhJHpQtVU32hJWuA0QJTo1ah7Cb2V1c8v3rKxaMrLi4vcQ/eL0defP+Cm9fOqmkFyy8ojH87wCx81IR1E+jcwSRlm4rn5GJ3kaEel7sn3Bze8fjqKWUSvvvLDzx/+in7y85yfWA/F169OvHtq1e8efWOFwSfP37O6M7r6+vNR8V5dHVFKcqxNa5fvmZ5f8NNO7KfJt6/P3D15BG7acf3L/4MWvlq/oJlrHz6xWd898O3PPndMy7LzFe//5I//us7oq1cTXu+/Pxr/p8//dutbCXGz9nDsUnCfnZE4vYjc7ZiC86T+O37B5/ws5d+6Lf/6e03vdZ5p/+GrfnAN79fID0GAXY5/pJQJk/rgZCRICngWhi+El2ZpmD1QFwZ3ZgsDdDFO13SHqK7oJ5BahHOWhSjMTVBpLAKzAgtjFG3dGAGqxeKn4CSjJ51YpSRjAIt20U60l5gHdgETkOV2/TikEaXgkRgZEJzxekDpsgGzEohek67T65YNAaKhTHtyBCsrjCTnkVzMj1jUnobGTioQHHEg9qdplP6tM1G1cEYhnOCKVNT3QbBQkimRY8YDHMmDcqaErcYIDoY6kQ0oszoaaWI0zVhsFVWZGh6muK4lEwb7btkgJOWDMtxYaoFq6AtmwcUyij0vqS0zj5euqWbBG3txwzdqANpZQPnNzlYeIYdarYJ2sstsMsQGhlIWDQtS+ZpA/F6kLZtO6oUltZYLTLVGmf1TpWBzGULq2sUqexMWbsjWpIZTXrg0i19hs1wNlDD5TZ8I/3vZ8Y4JXt9CKad5ZTyPy/G7I1dBG7BGgYIUw2cSOaNDm76YLx7yXq65vLiEbvdTOvB21h5d3Ng+JpBJFXwFgkYyla0dJKJPNInd5T0j7MC3gp4Z+lOnQp99GTPjo5U8KTZM0ZgUdKbUzOtGSuoKNWFqJ155GNDU6apk+AMzFOyqGWbtqIgC5MURgird8xmTDvSU+qsa6Q0s6bq4aPPnQJrFEJPVAbNA6fkwGZVVl95fTjAaDgHDq9PLMsNIBzfDU5tpUSnXl2xjJXjO0djzx9+/xWFiZgrGumLNVrPa4CUNXUHIzIoK06YwlhTAhUdmASbgrGmz+GigbSKy0JxSXB28/3rrMiwtJ6ZjjmBr8IYIDKhmlJL4gTV0uYmYFASVC1BG4VpQGjHorNu9XJdlT4ZzHtkSVaTx6aAqKCzQ8vGrtVpG/IZagORyqgBpxWdgn2dk71G0IZTpFBM6X2keoGBjGyqagRrnzDSA/L6NJj2C5MJjCBWQWpACWQs7LVmQ1kKdarQ4WZ0ShWmsoNZWLwTXRkR7Kc94IxYU0bmyer+2O0htvGHgMj7gPB90PeufcR98Pkhdu/9fXgIaP4Q2/Y+WH3/9+em7yEZ5F2J5IeYzHf37e57PMTQuduUncN0zgzw3W7H5eUljx494rPPPuPRo0f03nn16hXffvst7969+xnI/BBQ/EvN30Pf32UHPfQ5H3reb2UvA/jmn9stWYC4Y+bUtbEeYZVO7DvzlbHfTxSbkFqJmLDwDeQ1XCvH5cS4OeFr1kWqRvMGXZJ5HKdtCK6pmIlkuKg5rTpGJeiMmJgZBJVpdezRFWUqlD4z7dOuw1e43C0c3k+0m4bt+kbXc6xNnCVs3RuyRloWqRBu2KiccweaOcaOsEF0o7GCB9MwRhFasfRmdJBe6DqgGhYdH6BzwVflJEKdToxF0WlOtcnaEKuc+iDKDpt6SqZj0Hpl4FSrSQKIAd6zZmDK+/IO6BO7WnIINsGOgu5KhvHFBGMhuuOrMiZhrsKqBS+D2nb0OfBjoNFZaWjN+mbrjglNT9WYT5zYIb9hoH51dUEXqPUZ//xf/yv/+PXX3LQj/4N/5fXLF3z19T/w+NmnzHNhWdrGuM2sO7Vkg44Ibk5HvA/K48usUw8r3jOMRyJBeESo1dL6xJV1rERPhuGIBAYlEnR3kilmAwad0VMd5Zo2A7iQuGYyzJ20UYuNsYbJZjkgyNYIuW9rYWzDbTZmzm1vyC1grUgyomPzhWbzayZrLKtGqYXHT5/w+MkTdvuZOk346rx+9YZXr9/Q/U5+znYfjoAhjupGNnAysDo2Qg9kWJ4kWjA2EP3UDxyOR5bja/7n//yGpTcIkLNNwxYEFaVjTIzovH71CvFgWVf6aFStRIXeGt7ZwhWz0craMY/Bx24ynNh8m93JIXfSbfGSYaDRU0GAeyoTHMqW2YI6IqSnKorFyEHMpkZzLEEgZDt2G1kFwS2zXM52E00aZoWyqZJchDJyeNAEoiThJ/07Ixm/Y00Fa2TOx9leo/o2mEi+AtU6I5JYJMW4vHzMkyfP2M8Jqjz/NI9lPx6JBksk+93DsDQKRAebTVuGeklzXIQWafNTurHSCApq0KRTvdJCMXOcDEKumqoRtrCznGFbkohxmqdFXQlwSSA4Zyk55Ev7RKf0oEugPVmaThA9MIydOS4T6bMqG9MzSCpkSvvzessAR7H0b2b8HSjM/DTU3e12PH32lFKzvzocDj+BvWze7SK33r4icut1fL5v3wLIyO39+2yNgSSIdr+OOVtdnAHmXwOXP1QXqeqtN/S6rqzrmvYRu90tqH2uD9L+I5Lhef6ev67Jfo1p/Ev1yEMM5196zN3f3weW74PLtdZb1vKH7DMgAcn0vh6YGk+ePkFEOJ1Ov/i5bjdLwlFvSrWxBcoXyuYP7CoUE4YHq2cfLprKqxT69sQI8PT9lbRsGJstVUQq12UAkiqn3irZTYJpqiV6pBIHd8zT/mZs2Qlig0EGa5r0DLM3zViy0UCT/WzdGNUQ79SkimVNJdvwbc1MBY+RmVAieE9PTiNpwKpp1UqAeALZrqAj91eiZ0ZRpMJYQnJQ6aA6E+HESBIrulmA+EApyEi1mZulNaorRRt4ZtAYwugNxpoqUXItCYUyJrp0yhhpO0kSYrucjx/ZX3qkV/UEuDJfXvDpJ58yP73k6e45jx/NTPUSLZWLwztu3r7lxgcRzvAcvH1o+1WA+a/xzg/zcLfbEEEWPz//zU/2EbMVLucn7C93/P6f/pn/+1//B599/im1CqfDn/n8+XP+8uP33Iwbvn/hnJbGzfc/sisTHsar63cpsZb0wHr++Dky7bh585qwPT2CqVQ+e3zBqxdvOPQj826m9+BivuLHNz/yzX8E4OwpzDZTJ1huGt988w0vXr/h1dt3PH3yGOYEjowkz/40K5R7Xx8+Mrffn5nAZ6uAs/br/lH9qN7m4b+BbRP5vycg/Uvs6l/aWs+0ULEOkpKFmYkheRDDFY1ClrETNVY6SzIr2KVxeTkxnQxKQbszemOUZL6rgvaawWRFODUlxspUG3KaGZo38MQOnWpO9Jxk2xC6noio6C6Qk6WfcSdN0sNBFMOBDlKJWBIYa5marNHoVjPcZmzeZ+L5mJgIXVKKtSiD9ECbHLBpC85ZMZ9YvYLm9NTFszgTxce0MUwCRiYsh1gaymtn8U7phREN8QpRs+jQ3O/RGxVlaLJ4nGAZwqyCdiHmZIdqA8TpPRe3GIaeA4sky3kvefJGBLLCqAfwiShb4aQTrZ9ysUMxK/hYCV/At6nmb6iavfdkuVtJqWjP4WHHmYtti5pQrbFusr8oCzNGYMgITsWxATRBamEqFcFYxsokJRlNgEcOCCQ8U18jCBrWnRr5WWISJi94aziN7oGZUB28dtooEI4Op4cjsyJ9oF5SOugDrGYAXE+pkJQMyduR/JoWjgxF2djjq9OLJFPfB/UgrDtYW+dweMW0M6wHJ4Sx9vT/dmf0lOysDWrVDGGsHR+Kq2FkqNISBV3S21ckmU19BD0U00ah4J4MkNgp0oOBb2EDyoTRY03/4tKRMjFQSs0Jcos0iPIS6CqED2YTeu0waoLV08jzr2Rid4uCihO1MCKQsiV//4I87v7WcUYsTEsecnVhLmkztHY4nDqFDBM83ZwYpdGPQTve8HY98M2fvqEt8PnXT5nmyn7ac/PuFYt/wa5CREF6z+vCKipBHT3Pm+xasJHAiJpkcjg9PZh1Zl2glVTslLGtqVI2n61sUU6joUMo0fHGxpjJ8KS5rHQqzQtVgjZ3oKFhGLCT4MQmiy05lS6j0amEB1KCKMYag4ph5kwsxLRHR8pkfR1E2dh9bcWZ0h5wFoRORLKh2q11jDIV4dCO6fcGlLKj0fO9pTKKI70xDUkbqpDNIyyPgWnLsMe1UCaQ3jGFFbiYJ1w6J4Q6D9RTpSBLQbWzdueqTiwt1QQCuRZWpf6GdechZuuHGgJ4mEny0OPvg9b33/M+O/q+/PE+oHsfCD7/7JdkmPeblrss6vvNzX2WzkNs4fsNzkPH4/yfmXF5ecmXX37J7373Oz755BNKKUzThIjw1VdfMU0T//Zv/8bxePwZKHxXOnqXYX53/+8fiw/t0/3Pd3f72J99aDMsA6WKJZMsEhweLTInojkHC46vhOOuMO8uEBNmK+z3lXXNe2mPhXU5cRxL2oiF4GTzMNwxOwKwjoKFMFlmPVCd5o6G5vvrRNm8A3dmKBXTid2YiV3N50jnYqosTOjxhMzvOI70fg/NIVWPQfQNsDLHFzaP/6APx6ZC70HoirRkD/vw9G53NuoP2Ai8SzKM1iyHxQQbhdWFaE6oMavTulKmSt+8Wyd1mngybH1FYiXMCDQZfkyb/3JBZU35jFRkKFUK4yTMpUI16jwx16DWmb5ZPBFCj/RALN4ZvhCtZt6GRrI5q1JPlVGFqXfGaLgHHaMmNTg/Z6vorhC7jx9s7XaXWKm0i87v/8sfeH55ib6/4aLMjE8+4dMvP+PpxSVLW3j19iXX76/pjJT6RlALtLbw8uV3TBc7Pjk8w0z48cfXfPMf33A6Hgl31HIw9/jxBfPVY9blwJtXL1kiGbVnn2Qk7RXO7BiPVPeFDNxjU7QlcGiiGXxMyo1JN7ytbUhw+Ywhn0HkkCQhnIV1+NnIgTxnkmfN1jXegnNyux4BllZdu7nw9OkTLi8vmTRtYl6/ecGf/vhHrt+9hRikaUrKp9OSIiWhuYaAaIYEJkjuCTq5M0bPoOwQ2mnhxxc/8PrzZ1y/OfH+3VsuHu1Zl5VY0o833PGRNiNdgvXYOR6PFAqXjx9zsZsYzXn95hWv2uDUE2C7tSGMtNs4S6c/ZvORYIFHpOd1GVnfD1ATMtc3FRBp4yg/McfrQFGsR9pAbmC+WafFObck0rrN8m85aR5LPHuKvNcqkVh1niNixCpo3dbskiFt6U18Zr+mx2raWaSNonagGC16DkCiE0Mzw6Z3xBzUmLRwdbHDZmEtqbAsNnF1dUXdTZyWmooOzWD0GAFqDMsaxDVzY4YN6DUDAcVZTVCdN0a1pwUjA6Mn1h1KK0bWW0kOSqm74pa10dk/QDTVAGIZHjii0wVmt+yzHJgSkMrzPtn/aIIyrnksU70sFNsC6kcy1FXT0iWDD3MwMHwL7v47bO7O5eUl//RP/wTA6D8Ndt+/f5/X/3DCI1mZEbcAM0Cd6u3jbfOj162fNP1p8Hy+x99XL50B6zN7+dfuxffv/eeQu6urKx49ekSt9ZbN3HvncDgk8FwLpj+xmM8e0L13+va+d+ugXwOXH9o+pC77W7a7Ndf536UUaq23zOW7YYbnIf8ZrF/XtLV0982Xv996bH98yJ9vjFhNha0lviIjvcRnFURmBieKn/uFwC3xHh3ZcwZKU0Hc4BxuR65TpqkC8C64CLIMosKQBHK1BGIF77rdW3Ig6cMRGiUmzGLLs8kB4tgsN9gIerG2zILwnn2zGecsgnAlYlDoOXAYkniVbMMcz4DPtilH0u4LGAm4i7CpdzyVDW44qRzyZYAKtSh9caJmToaM7BeHRlqTrkHRwGrWa0GjSPrKt0iCkRmIODLNjB5UBbcMVezek6Annir4obQqVPIYhiUpM1HMQg3BpsKTp4/57IvfcXm1z5gEBGpkSOLa4bCFt3bP/lI/PEz/VYD5p8JgAzXvX+g/wzrP8PJDJ+VmgL89p7Uje3Zcv3lJ4HQdvLs5cBwrr96+YD29h9a5ae/ZXVzQ+sLFtGeqEy9fv2AuEyOEOu8Y7cS710dqBJ8+vuQvp/eUaux3ex49UebLyvfff8M8P+LR86eM1lm4oWjlx/aeR58+Ynl3TYtBHOHp/gmff/6Ul9ev+I//+SfcE5RBQIskGMe5Ovrp2Nz/5LcY8gb+5PFMbsDdY/bQcz/ir/JX21+FDN45+rfP+aX5wIPP/NsA67qrjDXZvAaoBUs06AWTBDhK6YjNKBkSY2s2N3VKlq2OCrPQMaKciFNBRuL0BXDJQKseApbNlaPMpaNhLEWSLWfGaVHKtE0nQzftWFo7SO3gG5i3RgbhTAPrytCCzQMaoEqvQmmxBcikD46LMVl6n7VZ0vd5GGNkME6ZApmMSWeGDZq2BBujU8sG0C4LWoMegi+B1uNmkK8onVJzn9sYeCgilWiAFUYshBRYO14ypCO95AsRTiUynFlIab4EuNOHoF6JuqIRlJH+zj2zm7Oo0JpDgqWgFmBBC0NLw5wMEoogpNC2cMFYnKbJQjbZgKTfcO6oGYMMKgpbkVB6gFWlt2CmMMqa3kibZ9FONQG9ocgMgiU7QBx349gCs5QN6gQ1CkMc2bU8MJEMhymCdQsJcckFXwWGBLaDaMq8FbpZJVZqWZBWGWrb+zmNSLsTIWWhUZPBJZnKiqTyIm5Wym6PzRBLBxuYFCjJIorToBHYVGltMEVjqUo/OIJRSkppvAtqyUxq3TEdyd4pYKdkPDAENG2GqtaNbebEME6RVg0yVfppZOjmMFQXos+4J8NkIDlp2wnqheGDIoJKY7PfQqzkxLRaBvxNuqXhBhFZoHfLkEF8pDTWlHlKdksMGOZ4T9bsbyC/U2Wz1yBYQ6nVt+mz4N7p6wk8QevJjSFQpgM3bw98/+fvefnD94zlxLv1Df/tn/4b1Imb08qpLUxjoqK4V9QE0YY3J5jxaaRX+DB8S9xdY6A9sKkgvWXQVZX0vRbBvNMspc5LW9GLVGtID0wrqsmuoivdFJEcgLAOpp0yekBN2xqNzvBKzEFZJ9QLwQo26LEBLXGir1mYWSg2Omvr9Aq2Oh6B1JlWFspIRtulXCQ7T3KwIqoUnaDuEdUM79NkcM8W1Dnta4RBrMHQDOK57NkMnSZhuBBjsLd9qldaAy2UEnjLKX2I0ESQ9cRbYNdmpn16wEds69B6A1vzeBOdCVgU1AcahdVJP8mP3O4Ct3dB31+yTrjPWr7/uPts2fuA6EPyyruve5d5/CEQ9EO//yVW832Gy0NA7EPA8/2f3T8W9xsgVWXezfzjP/4jf/jDH3j69CkXFxecTici0h7j+fPnfPXVV3z33XdbA5T7cg7mOe/rfauMh/79S0OBh473/c97/+vHbqeSWGqMoFuhjgbdYC94d4oYp76mudXxxOl4oszBIoXrww5GSqWX5khbkunSBLRjXli3kE5xoUd6q7t3Rq+4bvkVAiqVvgpTSbuBUshg0P3MZMrOhdWCYpVaBUfS9/jxTFw/YToeWI4H+pShUunTO6c9lVRiN5gGjCXlxWU4qnkP7UARtgbLN8Zs4Dh6cupUM+CnOGVULAZNK2FL7jtGGw3RiVGUsgSqG8stwHYBBHEUZCpJYBDFh7BqEGPFZqHLnhKa/opTUHyHXk1cTYGXGQooO+bhdCMlrM0ZGGU3Noslw7oS7URvC+agkxDHVMX0mlZOpul3zegkbjIoNMT3H33ujAj2OmFl5tnVc4jB9eGad8fXqVZS5bQsvHz5im/+/c9c3xwTyNwASR9O743Xr15zODZ+eLTDXTmdDty8v2Ed6cdQrPDk+WO++uJ37C53vH13zfvra9YlB4ts98kt3ZrYbDRi+3leD4OIBHuFZKqyAbRhsQFgSowMTj/7E2czFOcLkSDtyFI+v123G2k12LBtkc0uItJmQQAyxEnE0o5GCpcXV+znC7RMLKdr/vzNn3n5/Q+MdsIH6UkcZHAbsoWjbRi4GNWMWg1CWMcgRtpUyRjgOfBfe+Mv3/0Zj2A373j26Wc8Hc/4xv7Cd99+R6yZLZCfDUZbOR2PGMKTZ4/55PPPuJr2hDpznbi+OXBaF7Rv6sBIj2K53bGP3LRjPS1DBj2zW6Rmt+0F145E+o42YBKIcJAkOyQfPAEMwWkj0gZOztB+EjhsdZppBhsGhBhOw0j7BhsFYsHDsgdxIXraRxqS4exRiNgIR3UDpJHsccZmK+GdCKUnB4SiuimzoFAzxBMIjBqg65b/UfIR4oNiwcp2bUCCPCMXEZGyEWFyLZGy2ddtagT3gWzDDS1KC4XhzESqmN0hZGPqCxr2kx8zBXGnlGRbRwTFlTO7LG3IHKKg0umW7ErZ7FKkpG3k8E3FqQajYzHosBGA8hroAWVoKn8l/4qOZ27K32EzM/75n/+ZZ8+ecXNzwzTPOYAZTmvtJ5sMT6/jYuUWYBYRrBjFNr/ZkT2RFctBDnlv76PfArrDk9EcmyVPa41lWT7KGuO83a0/zIyLiwsuLi5uGcylVvYXFxA5QDjcHHh//f4WND6/15lhfa5F7jKc79Zb8HBNeN4eqrs4D95/4TP8Up13Pnbn/86BfhcXF+x2u5+FGN5Vep09qXtP397zsno+7r33n3lQ/9LmkedqdaVJnqsxyAA/NUQzHLM4jKngTcDS+kJG4KVkjR8LFhMiRkgn9DzIFJbRqKNixRlhqMSm2rIcNrbB0AZoBmDC1g/nvaZtVpBFIodUlgPsEZKhfkIG2LVtLahG10CaIluQ4JC0S4yRH1rK2ZYqB8qdzGqSEZQHJMxXAAAgAElEQVRtOJnZYApRiB55zZJWNqFJurRpovmKdqXUIETpvUEKjAivFFek9FxHE11GxkTThsrmA10cH4WYMkx0mGXWUtuOhySOISXPN7Gex1uNbvm3qAIeikpQ6h6bCxf1gov5EaZBW07cRDCfBGnKi9evebceOY4Tva2oCGP8JzyYt6Hz3e/unfk/O/W2R/w1oHkeys41mYQ3x5UWL3j75iURyvd//pYyFR7vLvjh5Uvmece027Mcj+wvZ3ibqPvNcsSmfU7FvHH17IIfv31FNUGt8MOrF5wO71mPB96++ZEpZm5uYFkHbVzz/uY9qoZhfPL8CevSeP/uDbVeYXrki8/+gdPphv1ux/VfvueCHT6njdvSTrTRUxGWVRiI3d6w86+afiY/O4I/S8q7fwf4pYXzw2jwh34j8tczgJ89Mn71wR+9d7/4vJ4WESFGqUrXwujBLFsgonr6IKUoHFGnW0qRhIbYhEyF9Zjposac4IqNtEVomh5ZTnoGqqc0oMGKIXVC154Xpnt6iLqgMuHzSvTC6EbQweb0Gt6sIKT2BEY1UBaUwtAZH4NCJiprJHi7UlAZrCh1OCqVRaH0QC8cOXVWKWTv0XARqnba5nVmwxE5IbPkPg2nGEg3IGilEZbpzRLBblJ6GgrSawNRug5KgLmwNCWqU3RChoMGvQXFBBfFXdKGAKP5QDQtJaIq3YPA8HBm0UxVZ8BQQiOneZSUsXsavM066Kq5yAynSaMWRdqcxe18Dur6+Kp5eEdr5dQFtGw2FZryQ01/LrMdVZYEiYUt6flsk6HUqOiUk/G00FhR7Yj1tG2xBPHmRgL0FHp3ZMpwDXpnmjRZvJH+b70LrgUVY6wnQlIGOMmc7DUi5V7hWNRswsySjSoD3c0MJ//uU1A8wXxZF2pMIJYgvRo9EhReGag6bSgxFoZZ/l1MsJqjKfX05h2evoJFBQnDY6VSGZaJ9DnVHMCE1BzIKEZTRVuKUstYibI1iThudXu/XH9NHKcTYwJv6CgUE7o646KmvLIIZZW0ZthukOHb5NcaoxnSG4W8xuiWaeFD8CYMGZRacF1xV4p9vNx4jZqDJqtoaxwj2IsRcaIdV05j4JJsf8yIgGUYMl1yMU1Q4PC+U16/4fjpe+qcYZGvv/uep7XSJrCazNkeikhei5nzu7Hso7PMnfBK9JVhW2CLVug9G6Q4D6MSsDAKsnRKbNLRcEYU0qtY0qN7KBoVihNrsPYVMbiohbYYMRbclajTZnExEFIGmGGJF1ROWXlF4ehO95Wd7pkRTlUYWyBrG4UULrQMYRJl40pl410zGVqiZCFpQtE5m7ktUEM10mu/ddTSNmZGEYRlUmJZcDNUC6sZRjBXRxR6C3xyxlKpoyF15dgNb8rOIErNUW0PLsVYyLVAW2eqF0gkoB/68ex34GdF/IfA3/s/Oz/nvgXGh6wY7ls/PMQifuh97zObH0pcf2j/7jORH3rN+2Dz/UbnIVD8IRbz3Z+dpbEX+wueP3/Obrfjk08+uX3N82Nvbm4QkVtvxLvy1HO44pmxfP79+evd/foYC4z7LPK7YPb9x/4mBvMwdFVKXTn2mZhyXYtlzWFtzTBPihAHpc9Ou1FOtqalUVs3f3TFWQkGYoNuSg3Hxoyy0n1CWIGWA+7NzkCDDMEbgzKlemRxRT2Qyz3TfkdUow9nDk21zGQUBJvAqBz2C6clqFVR77R1U9PYMUGmMBxlFLJeA8oYOXh38BF00QyP6TBtnr69D0qZ6C54CSzqNvRzupwoMtENZHTCbWvYglFW6LmGhUBZyOAwnSkOXgxdBUZjmgZOIRhMXSkaXNQJijJbQaeJfZ1pOMhEALuaDVgxp2hlPS1ErSzeE/CqSwKTWtEmjD642QaktCRBFDltdVXaNvSinMZgPvSPPnfcG+9Ob7m4uOR4fM/7d9f8v//6R96+ekeRys3NEUR58cMPvH73mtYXAk+2sUrK1Lux9pXh7zmeDkCqzfoWxCxq2Fx4/vQpn376CaVUxjqoNhNygq2Oik2mGSRw4JHAcATZyG/ob5BNtYilR7IJIEhJGtFQNsnn2PofAZIVhubgQogMVtvIcOdeJPy8nmyNNZrWZ3EGaHL/uqf+cV2dvjbe37zj+x9e8uc//4Wb400y60USiJ4076mdDBvemFPTXLnY7/niyy9py4kfX7zmsByJFlsWRQIqbXQO10fe7d/xu//tH3hy9ZTBwqEvvHn5I95XvHf6unI8HFibczzdcHGx5+mjR9SAOk+UIlw8vUJ1A9/S3C8tTHQboPwWkNDzmgOlRJI8MBJECjbm8Hlthr71Naa+WXClCklE0F6SmBCRdc0IoiZYLDUVV+lu4eC538OyuRd3gnJGSFIiHgGS18PZdmNYTWWnbEy7keixW/59JBxJhkH2Vz3QujBJpW/s8sVXTseFUz8xz3uqFXoHfKF70LqB9BwcRYMquCdgVNy34UZkILyA2Haeb8Qa0e3s9MzdSQuxPJcmzVwPJPdFy0i2sxRcN4Y/qVr0SEu4PEsV0bQAsL6x/9sALK1EGPSzDYY6MVLzqRQWyZ7XPIc5RGNgWafLNvxBMPG85v4O2x/+8Ad+//vfczqd2O126Dbk0q3uiDuAq3MO7s46XVVTXasbMCwbC7JzC3oOH4w+bm0a3P0WvD57NZ9D+n7Ldn79UgrTPFHrT0zqu6CsSPrWl1J48+YNx+Pxtu441wd3B9p3//1b6oK/2r9f+f2vvfbdzzBNE/v9nqurK/b7PaXWxFYibocA52Nyro9uAWiLWzA/iJ997l/b5pE5CEOhkgBpnrfJpHdVfKwQRjEYrUMfG0M4MR+JkQSwccqjEpvNRJ0Iic0ipzN82tQEmXMmnkNxRDc/53H7+IGjIYweuDVCa17/EoQWavRNUZBEGW9bcN8IRDvRQdQya2ooqo5QM6QvOjLKptSI26EdGgx1tG8WP7XSN+BWbRChMLZg5QC6omQPuUrcDmpDcqAVCr4GTZxJJyCvAVEoZmktBbhIMog1A1bV8+9oAYGjOggc8/R/7jHSvtDz3j3JFoioQkXyb2PJTD+ME9c3b9mtO7CBr/DucODmeOL1t99yff0WeoMRGRj8CyjhxzGY7/7roxiwfw1oRuQ08smzZ5zWRlsWLsqe4wi+/vIr1tM76m7iyaNn/OmbP2JqTBq0XjheZwPy+nANEeynynJyRDuvXvyYhta18v5wwzRWRAbdB7EUerxnrnseP7ri09/9jtev3vHj939BYvD6XeH6+g3TXHjy9af0Q9D64Hg6cLo+MJbGdDVRVuGrf/ia//jTH1lO661BPGS40M8+8va9kBObgf4ccNbtsX/7GnX/CP/857/ldf8TC+WvbQXBWgWCvgaUlHk6KTmMVfA5qf3hgTVDqlOwnKx1x3VmN+UNeCDsbOVUJloL1liJtVNLUHQmRk7GV4da25Y6m7IuF0d7MCRy0DEyLcalJyvQBOmFZfOMrSEwkiETNmgdYvMzG6Eb8EMCaFbQnh587pUFR7xzkgk55GRokmBFaAMknBJ70AGjbcWBJnMRRywYBnHqlDIjssKYgED0xI1r2hT4oGhOrWWr0LsqWjqxKmN2ioJKhkV0hFomYl2QOrO2NRczyGC8Bi7KHLD0QS+Oz0bZFsURsGQyGdagljm9od0o06C40sjCtGug1jEtWDRMjVE/XqoekzDWQejWgDSl9+0mUgQpSh8NK4aPwNzxzfdMNMeATTpVLX1/o1DmijcHn8DSlzmKYMyERrKUDKQnk0J1JtagW07nQwulZiE3IjLksaTstJ+ySGcyws8e1DlFDF2QnkGSaltjJYH2DCUQCiZsDOJCqT1z5JqzzpVZK92FVluG40lqCnR03GTTow7K8CyMR7KJRh34mlYbkE1Z15Q6q2bQjntgJPMrgMlThpdnYjDRCKn0HswAE4QZ1hJkLMVYJ02faIS6hTuqCGVWlpbFYwFOm3ti1pmFWQcuCTZ0HbRulDqQKRvWGs6YFHrNNfQjtyFkI9pXahWkKyHOWCvvbq5ZlgOsGZI3Ao6nI2NND/Hnz56zRueH3RsqRrnccXx/Q5+FH/7y/3H1+DmfPJ+JGCyqzPOUjbd3FlE0EoQJHF3BpLNOkkwTZqSvyXhjIB7YMFooMU5IOYFO9BVGIQsqcUoYx0hvY9fAllyzm3Z2u5L93AAdg5iMCKO60WtDfMYDSj+glowkNyXMYRkUcSgpzToJzJIWMr4WzFLW2jWoAKXirkgIulnJqI5Mft4Gc8RAhkIxYl0yoX1rnNsYTDLl8NkaFk6phVYdjhWTjk0GGyNJERhQSlDN6EtHdsIAbtYjO2Zq7TQs16EojCXw2RljzfCMapT1N6w7H8Fe/ZAtw4cYtPeBy/sBf/e/nhuZc5DeXVD47uvfB4s/xOw978Pd/bzPpr7Pgr4LEJ/39/5n+hB7+S5z2cxuv9ZacXdevnzJ8XjkyZMn1FpRVdZ1vWUr/cSb++k17/ou3z02Dx3v+4D63eN6d//ub3ePw9/SQMYIRsmwGPHAR6F4o9XCNIR+igwm8wNjqkxmDHWaBvPI4Oq2BGorEVmTTDJjqzCkk6Pw/L9bJpQLM+Edx5gmtoaJrR5wpFQGsQ3OjEkMF2UVp8wJbox9QVtnRGHGMlxLGt4rNnsGSrmjMYEoU3fYwr6KT7ivEDDpxuqNYFVLJmIXmJyqmoyi6OxEaDoy7JOg9EIvneLQyHrKJ8GjM9oGZg+heND2IMugxIKqUSJoYtSLwHxmnImyqpQ9yFyo88wwYz8El0bYlCFA/UTTiXlKZh42YRcJSPWePtS9aQ5BRVl8pMOdZhivWHrre5sopAe1qqas2keqvT5y+/6Hl8xWqV8qP7z6gX//47/zxz/+ifV0pNSKfFc5HK758cVLTscle4+ziLJnM9EhG9S+plc3CYyGgIYRJuxt5vnzp1w8eYJ4x3ZzekiabLZtyYT1zRv53DqewcrYgOLYcK6EoFPyb1KoU9ZSjM7SGsN9AxwVIsHUs5eIsg2PiC2AOJn2ItvjtnCw7FHydWLz1RBPpZ0TLOvC9z98m+ebO9+9eMG7Nz/S+0BF2O9mbJooKrR14XRYaD2gB7oNH776/HP++3//37k5nPi//s//g7/8+VtOvadHswdFx1afFz795As++/wTdmXPuma4pZaCWKHjvDscsJdvOC4Lh+OJZ0+eIFYIKciUQNrpeMTHkv6aBGEbsL+B7vqBe8/DW8W9ZQCfChYFPC1uJEiGW+Rn8ADpSt0AeldBQjHTbInM8eLUoWlbYUKMQkzgXTfGd2Fopw6yh2qDUVOpIACxJiHDagZZGZQBIiW9V8PQ2OH9hNCzVsgkPNySPRiiyXTVmt6kQxnWQUFHQYZzff2GxxepaHYL3h8Xfnx9g4+BkJYSbYCjRE/vZxspeBIDlS1grGToJZGKwDBPYDkMalp5TFtocqAJAm/AUIgwRiGkp51iz+PjG9FOIRUbAjYECW4l8HlhKtozTHVIIcYWYmiDkEIdW5CedAKjj6zXJQQdOaxTt60PhIGgfwcK87Nnz/iXf/kXrq6uOKuMYAOOt3u6u9N6epMzwMMZkUrfu8zZ89AX0nZCN3WfiNwym31TfZ79ls9M278VyL1VTk0Z6FdKuQWVgVtGtKry+PFjTJW3795xPB5Z1/WvgOYPKc1+SeH0IVLAf3Y7B/mdQxLvspjP4HFr7WcBiuf9MbNbwN7UMl+HtD/5LUB+KHgLpGTOEAZdnEz3LEmiEcVFWFYIEeJskSPBbiQ4rVEx2wBaCWixeY2PtJxxTXtQ6/huorSeWBLK2JTWpSp1rfSS2Ss+ssdQT7BbIu/7TtmGYpa5oi0DenvJENfiQR3QLe2OdASlzHRd0GGoJXg/QjLjSn0b7iiilUHLbJ51YMUIdTwmigy8RmYVeOAl39vbACmEJ0heRBijY1SqddZSWJaOSbomhAeuDZea4ZSkHZlG2ew38r6Rw64c7Fc3mJQVZ7JNkWYZ8h5ALQm8S3SmWjc/eudwc+I7/5aLeZfWIn3w7nBK67bTO5buqAdaDY8cxn9o+3UB6X1A+Veu+Z9+/fMnJgJfuJj2RMAnz5/wZHrGi7ff8ez5Y1697bTjiTfHG9Z1sMaR8MFUjL44oiu7ueBLcDgtOTHsTqkVfGVZVvoI9peF9abDcPZXe9porMc1J9Knznp8y9I7gyPri8EXX37K6J3l7YH3yzuu1wMcOjoXnj9/yvX1S/Y2M9lM77CbZ5DCcsqJV3LPuZXMnMfPQfrn/RVj+awD+6jtfx0A/L98K0EsmkyUyACq3sFKQRk0E0ZLKYVsCcJme9qai+hUd0zWWG1imiqMngBm75hOLA5KQyQYzRlSmB2g01dFJzLwAs8sizDcU643UIo0VKZkzMTKTKWvufD25kyWYNgIQ+iMSEsAE98uyrx42saU89VhSs9dZSK0U0TpFLp3pOcC+LgoY3SWJsyRvmAxZRD5GJkkHARpRJq+vFG2ArsVRBTRnguKJ2vbuMB7z8l3gQil0XPCPgwVyynz6rSS03XTinbFijJKsh60KW00tAja0rd2RA5IJgfZJdDcw1l6emFDMLqAHpDNVH9EMgO0gFNYe7KxPvrUcWHUkd5usclYJRc06QlgrgAjmFxYNwmanDLkSPueMgfaO4uAyMaMmoLCzDQbOXt1PE6IKHhlRE8GSE37C19yIe4RyMiGFsshRVPLtPaRKa9Mim+kpdAgZMVDKSRbi17xpKbDWPP89Sxag2D0zm4KfMkipPWOFgUTYDCNyvCWEkETNBxrkjd3lfRekvSbOvu9Z+B3etaKd6pPyTwTh96REMZ2DZkoTZ2JSmjaLpQpAXw0G37ziSVIu4W6SWi9Z4hM3RgXVhPo7unDVnrQIsORVCZWBpMNhhciBt2yYSzuuZ8lz0ki2dSrRHrvfeQ2dWHRPKptKBGdMQymwfDgcFy43FU6RqMzCYxywckPjBJcPnnGf3nyDF86y7qgc0GmPT0Kx5vXHPaP2F/sMOnEyWESljC8jbRxMehSc1IsyqRGabAUUrIqAmum01M9G7eieAObNhVH64SlbcjogWrQ+0jvrElp0jGZCXHElaZQS6XFSkEIWdGYMMnE9iYVdKLZFpPUVoo7lHmTqqdnoDhYGHUX9DFYfXBp+fqFbIRcTkwh9JGACqr0WLAhW8LyyMFqNaoUTjEwL0x1Zj00VHsGBiL4XLL5vIDRNH1YbSCnil52vBXMjVN7n9dJF/z0/9P2Zk2SI0e25qeqZoDHlltlLSyuvcgV6X4amfn/P2NGpvtS2NtlMVmVeyzugJmpzoPCo7yCmcVM3h5QSpIR4QscDhjMjh79zgoy5wJAtw4By7CPcqHghbGxHpcWMNdPPneO28+hIB6Klx9CTXxo8XEahHf688dcwadu6A+hLh5uH0JufEjI/tDneyjCPhTaP/TYD73uw/05/Xy99/u21eOi77iYW9eVFy9epJPo7kcn0XGR9KHj8iFx+0P7cPr3h9/Zx76rU2H9U7eivrViThCZMI4Xigo9cryuBdxLMkmjE02w6oyehdwyjSyCVsugPBWCgZrRemGyRpNIdFFzejTUjCqBty1sVp21K6INJmNYZaqNqcwQC0Ul71PucLljboqb4TaQqhsn8TwXeQiy5P64BbocqLOyDslKnjTydpVFXhOlDcFkEBQagA5kzUR41FglkVkyJHn7dKJXujZELfEELXBJtnEf6fhpBNMoGbjl2XniNMRWwgu99nQFxsTFNLHbTTyazpA5nUY+Ol0UIXnYvQlj12Fk0axLUKuwiFDWXZpU1gN9l4Jr2fAl2kcyJEmkRkgWVwNHouMhNP88ceTFH1/w7NlTpteF3//Pf+Pd2/fs1z2jDV6/fcWbd2/w0WlrumR9M09IVu9ySUFsgW9Chl/n/BVP92SxyrSrCIXqwirCsl/BU2jTEpsL27clTY74fmQabqWfY3d1PinFyTobl5dXXF09YjLj7u6ON2/esvd9zrE8UoCM7VrUFJyPzy+WznoaCI5vWBXZlk/Hzwdkdw9BbI62ZV148f33vHv7ihjKoa1Eb4gKZZr46qsvefr0KWrKzfUtf/7uj7x+f43o2IKxJp7/4mueP3vCV18b1+/f8ub9G9bWkL6kYz7qJqQ489lE1YrtCrHk/BTJor535/btW5bbfbZAi8GjR5zNZ1w8PqPf7Xn77h3fv3iV7GZkCxnj3uU5YnOxfuImdYAbOpxmGcJt4nQFGwr07JqIgvXsJIqSgddFEudjkUYClcSa2GZiwLNzsUYaZEYRZO25hhKHIpQuOA1PqwRDbMOeBbEFa9uU1wstw8XdVsyTG10956+DRICJ7WD0nNuMnGuLbQ7gkeNAcOAQzp/8zxyWAyYTt7fvefvuPd1H8qgd3DwZxkqKupLuQxPyXKeks88S2yUxNmd9Sfa0g4mx0JnJEG6PvL50cx66bALaGIRBiZG8VsuOBlWhRLBqYNHpVbCeXZG0jUlcRiL47sPXS85rsgkfF0MCqidn2zXAUkg3Bl0NaZn/I/+bDOZ5nvmnf/onas1umKN7+ejwrbVycXHBuq7c3tzcF4VPnb7Lstzf12utWUi2rcj0wAl8nBscEQ334X//m1viKH4cb0TkXoA97t+xY+rR48fUaeLm5oa7uzvubm9ZTvAYp6/5c4X8+/f9K79LtBF/UUj6kJB9uh3F5HmeKbVkpspJQR/y+JZS7vFixwL9UVz+Sdjf5iQ/Mq4/1cEcnq5/2TCSRQTfQudieOI5LcXaKgvqE5TCkIVOXu8tjEnbFgifBjs2/WaQiDyFzA8SxZZN21BQSyyGtQxFHebbsZgZm0HQQyGcXsB7zd9Juv+3Rl9inpCRpqggWDfxuPQKNe/naRQSmjZGJEP5mJHkaBb0Y92CRAsrPQ1MRTHLgPkYuf5FA1cYJSgjhWIkzV+uQhEDFwxFl0GxShipiZH3hxiBaRaWumTmTNUs+vcxGFHxmqa2UQtC6lQRBpr4LysZauWjUgI6CmtHMMwqd3cL+/2e9yYUnRit073Ru2eHXHdElTYcCWX8TFHr5wXmT3Irf+zxDyo+Hnhxbu7e04bz+NEvaeuB1hs/vPiedd3zfr/H377cgrYGU5m5PJ9p1ni/zyqky8pkE6qFQaOcVfy2Qfec3FzfZaCFC7d9ZawL6sKyrPzhP/4Ll87ZdLmxkAKrM++v33J9WCkCz68esfc9X339LbTGkyfnvHiRKeePnz5mudsjLrS2z4nTNo+L7UT56fgQPz0mwn+Da/jBl/KXb/rfu33uObBta1fOS+dOlBiFaWgmXseytdGD+UVWsaVSJF2esENDKGfObn7ERZm5PD+jiXP95prl+j2r36BbovCQbEWaLSfIJQwxAe3IQeilIgQmwZidundGNJwdWjsuAx15kbivmAti4FSQxFJ4bHJkBESnWGcJI0KJIXhVouZgN5H8z+QfC+IwZsHIMJaFxIbMNV2bxNiCwLYihY8M5nBoNTDPwD314LC5c0tA7yvDcoBwGiKpXXZRgkHZUr57EUoPiC2IY6uAd7K9qvfIO8ahMOaOaU4sm1Rs4wINBDdLiFttSINdydTqgUBbKbOkKykmVLdWlbVjBiY1gxM/cRvRCalIVyQ6RXIRPSQ7A8JHDt4NyubMLCMxDd5JDIYLSwSlbkSAENDK2aMrHp1foVpo6zXLatze7ZFJmSSI4SwezBgdxUdnIrlyjZZptuopannJoLsiTDrRdM0J4QrKvCU9C6ihZ57uaMubc1Ewneh0ikwpOvZgRWlAsTmxEZGM7+LJWs4ZqGXwzih0CybV/G5KIHVLlRXDTMAlGZ0jcQshBVel+p5h5wzpOVHuilljicAib+BtK0BYaxDKOjwDGGalL0qoU1QZu+BcKzIZ81RQmxmtMcaagSkbS9glOc8MIcSTD2VKCaVqipwueU1RBJ1magyw808/d2RQTbcb/qDUHXHbWEfn6vKc69cveXd9y+WTZ1wMpZ/PRDT8sGOZOtz+wL4tiBuzFIKCSk6I3r4/MO9eM51/mQWraOgKkxn7aPhdUHYKZ6DNtrC8YIhl6NeAiIZoIUxYWsu2qp5Fq7GJNWE1XbkDdj3bSZmUIcbonXmtyJROePeBlom17vFeia1gZ0B3Q61lMGbr28RP6H7OOjfOFmffVrTO9JYuTDXLa1hG8qvX4HC7T37+0ljbiprRD53dWeX9/oBLsOxXzibj/Pwxlxfn1DLjs+a5iW7/ywVoDMGmlba159maIRSHMEYPinU4JNdxLQt2dsZo2SY6nVnyo9ugGbTR8GLJAXdJzmaA7c4RWzaH36dtp0Ll6WT71Dn7Iaeru98LwR96/vG1T/89bqev9yEsx/Exp/8eX+dDrumH7/Oxx3wMIfGhfTwVWx+Kzg+F3lPnkkoutMcYvHv3jufPn987mXvviAhv3rzhX//1X/mv//ovbm5ufsJbbK3dH9ejW+ehI/x4/E6P9+n+fQjncVx4nb7G8XUfitKfugmdbgWxFeWMuTmuQnXJMT/q1r3SkJGTep8VmlI0iymjHdJdEjuMxBlFKRSt9L5ATeeyhyCtbknlMEbya9U8BTlWyiED2KwKNnaZpTAiu166E56uTh3ZrkoR7Ca7GUwDLxvnMJLxuhagKnvPTANTQ6QxRaFHo5DdYlHSLWlhrBFEV8ZcqAEt0oFXVrJwbQGTc4bT2uZMHeAlBSwvJGbEO4gzDuk+FBHClzQe1wxInc2ZfKaLUK92nM2PGeZMmszerhXiLlvYS6GWHcLKGhBrp9tKVwPbRB8OyKSYXTD6npWFvh+0YohsvNwNB0UzZBg+J5JtF8ZnEMG4vrlmmiuO8+LFnwkfW4hfsO6z7d1HhwjGxuoNctovcoxaIxeNAiLHzpG0qgbb+qQUdEp25O3797x/95oAimYBQSTRBoxENMXWy5QBTORaI6URTAwieX0AACAASURBVAUzYZoLT5494Tff/pLnX3xDG413769pw1nWRIzI5qyS7V4gkkFfZTdzcbbj6vyKw+HA9c17DsuKhGdQneQ8QSLQsDwntmAb2YK5Rm+s7vTFCNnClwimMvPsyy/45hdf8ezRFznWlLfcvnvL++u7xC2oYlV5+vQxj55+gWjwq1//it//4ffcvr+lD6U4GQCNMYCb/Q2HtsdWY7TE1OzmM9Z1YW2N/eGAH/aEwDRX3l5fM79+zTLOefvmDa++f827m3esrTHuA6WSRHzPo/6rjfQ/brpuYErJ+QAxELctLN0Qy+tbBKIMOoqpJmkhtvFBLB22umH5fKRJQDzHm86W9ygpZkbHMKI7bpHvJXpvrlJRinTwmk7pLUwsufEFtG/85ZKCMCki48bQFVNSsCKdjUUm2giiN+ocEMZ6GPT+juv9eyQqsSypMVjgDPrIkPfQvD+UrQszahKWuwXFx5bbkxdTTr8SDzRkQxjGQEiHtxhoaxm2HoOBMqSj8uOxw/I6GWvOYQ2laUfXDHHfmjITETLSIMRIIVtkZNP/SH4+koYAWqAWjCrQU2oq0cGy+9WRTcD1zypOPNxEhN/97nc8efKEdV1/cj8/hsUdhctaK9M8c7ff/+R8Pb13t9buUQ6nDuLTe/aRt3x6D/9bncsPBdqfOHMj7kOFSyk/KaarKtM0cXFxQSkFM6Ps9xwOh59kQvzcfn3uPn/G7eHH52zzoFIz1I/gvgvs1IgA3Bfnj4X608ccj/e6rhz2B8YYf5Fv8bNbU2Q2tG8C62jZ/aqRwcAe4L7pA3mf95F4y6qV7oOinUZiHmoteO/bujq7aDKIRwidcXVsQy5VEXqDbhnuFR6JY4pK8S1zItNK8diu3wLuHY80eIka3sBbo24BpGPriikIVgRx2zrcS84XzfPas3x907wfYODdQTdkBoaWwMLz/RSUnrlXomhk2GBomgeRjV9NIr/ugw4jAw5DMkR9uDOIxISKMkUHT61lHQ2ViohhGohnGKFLYgfrFkZoU0UitTlVwBwfoDrwBEyjMZBhRHSWg3AXPbE+7vRtLLVNwxKRDP4bH5eRf15g/oxrZlsbf/gpW9W7YNwuB8xh//Y975cb1v3Ku/U1X//il6BveXedaImpCrt6xr6vrOOAWlBQDsOZzo0audAUy4rrMMmbRTgiljykw5IBUrVkW/hY0tynwugrYoWX716z3nZ++9tvuLu+5enjJ/TRKNK5Xm45e3RF2GtkDX7z27/j93/4f9mvd4gUSoXecsLHZjt/eExiuzHn8Ym/fliPY8RHHxQnL/7ja/7kdx/+8S9++9O/f0RJfvB6n7qZGk7HIugqrNK2iromYNydhUbtJYPhcCarrBFZrumVi8c7ri4uoBZmK5wX439tPK+QPcMNxfAy6CMnoaLpCnRTKJ1pgA/JEJ6x8V01RbcxBjIgDMSFiYkehWoLDvjaiAlGTIQuGIqUiaWtWNH8u05gndEzOKRr8siGGHVzWSOWzjzJdi+RnLHVKfCY8VVQa6x1YGtaT83YGGHZqrd4iqhhCwsDnTYOoa+4FrobpTakZ5usyy5bcnVBZONXSgWX5PGqoxZUPDPugNUGSmWEUWJFS8FHIDFwN4oJaxdUjHUlAfx1ZLtmS+eraQo8scjm5hTKcOpnpKq7BqVDl0YZipd0qMtI/pJ7z/1H6VGBkWm0lq0xbC6CqEZ43uCqKrvzc/7H//h7vnz2FbUmHuePf37BePECuT2wIpzVCVtAWJEZdChdWjoWRLDo6Q4uGRhQAmLWFAt9gBkiSvWASKb2IJAuDFuROjFc0T7Std8GIwbizipChGb1VwvhnaLBFClUezhDBF09CyI0is9Ey8VFjGzziTpnm6sGFkFrugX2DYiOroNBZViKB2iGMkUTZIrtfStikWnqKFZTAGAs7FvhsiqrZ3ujMTFNhYuLRzx5doXVyt3dnnfvr1m5QdZtUdwHPhQiqEVpZUbanigpLlYXinky1zW55VOZtobDTzx3AqrAKk51oa2NMGG2GTVHq9FfHiiP68YsNkppzNFYNNhNO7pnYUHmoExC7wu9CS3OeHt3w8XhkjJn++DYGJi1VvR80NhCQEOwOuN6oAVIFOTMqV1ZVmVlSbHJAZ9BnL52tJQsnq2JPKGmSKNjD17RgLsCOzUqTgswWjp4xSkuNDUER3YGd0HbySY8D2xEBmvddQ6q2HxGp6SY7Ino8VgzkKYFN2+vefnuNdf7uxTsl47LxLLc8O76ljYyRHD/7jVXjy55/vwbnj19xpfffoVNO2bdEQyaODI5kK3Z3QrqK5BiVRfPttspW+l0FbwGySUfmEiev2yC5zxjmvcC6YVhgdugasnAnt4JKu2BIPlXz58HDtmHbLqPib3H3z1kAX/ICfwhR/TD/3/6uqc/fwhx8fBvp8L3pwjXHxJjTz/Px97/VJQ9/e9ebFfNeUA4r1+/5vLyMgOBtsXmn/70J/7whz/w8ocfOJwkmZ8e46MYfPydmd23vB7/fhou9DF39un+PzweD5/3KQvKh9syKiKDchCChpTKJZ6hNHNFojBWKD4zZKBVIRILpsBSjIZx3gJfA6+OrA2NwUpDbeBdaSEUKVAHU+Tcps3B1I64A2NaJbmjKLMYZSpMU+CHiksQtaA7x2LO4JudogdjNx8oYnRriTrQSE6/V9Q7Krl4rJNs7f0bZkwNmm3HcuNrRranay3UaODplHQtmBydVOlEPmxu2+rJcpcS2BCsG92SjFpGQadGD6iqaM8g4BhQbOBjZjHjale4mgs+d3yFNik+KrtaoYP3BTTzBWIEY6fUGvjNwKpmoLB1XGZCGqO/JZqj64rvHFkGPSYMIWxlbUFxg7pgq6RxRdJJ/qlb7wvvbq45LHvWtmRhgGwxdm+0vm5T/XTtetq3CUlRPydcbLisTWIqQmImkml/ZCz7EN6/ueblqzfc3N5yfnaBWUUPe/roGxd75LzP5UfxTdjCocE27q6qcH5+xq+++ZZf/vLXXD66Yh2dWpTvX56hrxLBEBaJttj43BKKlcqjq0v+8Xe/49kXX/D21Sv+5fd/SDReS8OER7JqI0A1tlVUJH7MM5TWSgY9D8+OKgGiGLVOPL14xPnuMoVNK9g8s3Yn8C2DIxfz0SvE4OrikufPn/Hs0RP+ZN+lmYG8l2sAo/H2h9e8fvQ9j/7hguffPGd3sePRk0v+n//7XzgcvmftyyYOCqsP3o43LIdDurD7YFkW1p6dG+mWlU00z26jTfn95HMHFI1BE0N6MjeHJsJBGZsgoAwiUXElg7DVNpbpcBZxZIAGeMt5Kd3okagd3b4HulBGQA1c+nZeJM89c2KyUzWk08cmrqgzJNAhxGb8wY95MJu4LGlAEO2YKrEEWjZMSyTKTdQR8wyL9p7dYkx5/UdjSGaI2Jp86ey+HKCWhfieXXl1JC852+yz1Ts/c8lrDOibEFXiyG5NE4uMsSEv871UyPE05aHs3iwDXwvVyFBWyM8uzjBNc5CkKKabSzPEiM3oFARRtjmHGCGB1oLaCj1o5P2ik8ajDpQOnlZE/jbpMrdvv/2Wf/iHf/gLYfL0vn7K9jUzzCyRQtv28B56fPypWH3aPfRZwuYnbMf7+On8ANiME4HCvZP5+BmOxe4jzuvi4iIFXHIOdRRk/xoW4/Rzf2iud9w+p3D98LXdj25duZ8LQRbNp2n6SS7F2Pb96JQ2M/o2nzo6zY+s68/5Hkq1NJ2VLPyW0DQ39UC6I3UTgFfJ8Lw+0GLQCjqzcdwjiyqaiE9BMysonHIMstyYysWziNIJKIWwkdek53/0vHcjKf6KG1QgnO4GkTgkawOmgrUMIo6SOMgemS1k6rRWwAYWsI6NTU1ATJhKoiXNCR+UUHwElWkLSAeLDCX04Wh0QlOjEs3Op1UGNkAsNSFCGCURZCFbgWXjqrsulK5ESfRY686slTGCgeF1oMMxLI1bUUkApWbXt+Y9sTtQBY1B96DgOd5pGt2qJVZkNAgfHGKhu1BOkD0Dg7URJY18kN072rNr5qPnyt90pt9vP4qSD0/NrUs7260inRcXl5fcLXvUg1dvX/H02TPOyxkhzvPHz7g73PD02RP2yx0mObittx21iVKVta1MZULaQKdEHuxvDqgZJoU2VqYpuTBtS3dFsrJQZKJHT34KneJZiWu2Z6pnXB/uWA8H3r655u3L91y/X7g8O2eehMfzGb4Tru/esY52X50eh0xRjq1yKycHIbKofH9cHoKwP2oMPtWP/+JBJ+rzJ4wFf/GQe8VbPrBHf/0FP+c24L1vrQszVEHW2BI488JqsIXGCVMIHsl6pQyqVc7nSq0TSiV6JdZBiHF5fsa4ueNOnYhMVTcHeqGpM0vQQpCejJtmgQ5l15wxDYYqJhk0JVI3kHvg1lk9Fw2JDgCZMwwyq1SJOsiKXYZ8uQWqK7rOSDS8OKoFi6xEywhaKUwdhigxsgXRSobiTOp4b1mlFkm+oSWLLmf0pIOaAxY5CIQINso2pcnphvfAYsV6ycWeZoCcyuAYPpMOjAMR2WYzouABh2GUMhjSKaMyPNuurDg+1nRNAj6NXESWIHoC41uZkN4YoxIqzDg+OqMUbM4JtbtwkECXTxd6ps39291ToBYnTLaBOqtnVQ2dlUPPIDk36N2pDlFAKOwIhgelVIrAxdklT64e8/U3z5nrzBfPnvL4yRNiCb47/C+k6ZZHE4hVvN1hQ+lUvDV0SpeCe0PXmVoH0fNzqmu6ljwLAlvzLzXSLTFGYG60Q6Zcqx3TwxVxo0/5GsXTedYdhiWOpPiglYktbo/oTrHA6awjmMs5UtLB7lMurNWU3pSQwRDFtFNRFvfkW0mmcDcJNHKhMULy87qjdFwTSSKkaw0JBjCNwq13bAjz1cRslYuLS5598YSrR+dombmcL9HhvDocaJHstbY2YmTRBrG80etEc2dXg8kmkIlag2m3Y6c7rBru/edOl59sxUjmXuvYVLIt2mHt6Sg5m89Zn6xcPD7ncKvIckfYGb0eKKNwfvWYcnZDv22M7rR9hxmWvmc93GDzOd4CPSuYFUY/ZMimaS4GGljPbokBlFEQc0YbTFvLIzojXTizHXcMom4BkR1kZMhoWpgLjYrXDnuhnA1UJ0oDAkYz5jmZ7ItEFll8W1g62f5cwZrRIpESh7s9+7tr9OICHXdIP2PaBV7B12xNq+NYhFWu15Wbd9e8ffea296gryw9WPd33LVBLROXNjHNO6bdBRGNdRw4LI2r6RwfBd0BW+KzDyWiMQ6SY32ZKTKI5hxEtuSmGZMDGkGxQXjJIkhkSI6aoQNoRtR0Wwk5rleUYQOKZWGu7z/53PlYy+OpwHkUJ09dvQ+D8z6EVzg+57SV8VSIPn3ch97zoYv4oXh9umg77vcxIO/08Q/f73QfHqIjTt/79Oefcz5/yG0cHtzt7/i3f/933rx9w27ecXNzw3ffffcT1/Lp5/gYu/q4YK1bKrxt+/MpITUPheOHi8CH39vDffi5TSXv8cNzZjVNUOsVVhvFLxLTdeYglTYWiiRLdBgpYtwNpCvM6xZe51jNu3wihwZSg2mtaA+8DqIrZQrqEkhZcy4wAoqjXhAdzOWMuRbwmXre6R4pKIpQpwkcJoSDOMFAZ6Pegu+2AJ11wqNRyQyLogUrkQuxAFiRIQxNMUV8pUf2j2gRbPTkDvae4nEm6TAY6ZyMAAYRlW6JNKqt0OSYVXAc+yMLsyW/74MFpaerhoBJBPHOsMqQDLYpDrIuFCNTB1GiGuYZzFWmnDuOAmW3I1iyuGzQXClqjBG479l7w/tA1Jmr59hyMIq3ZEFGZF6AJl/f49OXVr07dzc3LGWiFOVsd8bF5RPasufNm9cMMqTHfRtHYuPHhmQRmETAocdrL6f5w7PrIwLCYVka767f0dfg/dvXnO3O+c2vfsXt3Q3/8V9/zMWgGnc3N1zf3RKxpJD+4yJmu06EUhSzypNHj/nii6+4enRFrTusdN6i+Jrt05uqnTi1CLAcp3dnE988/4rf/fbvuPriCpsrF999z/XNNWI55nhsorJu0+HNrBMj0vkFifTYQtkgC8zmnt2ANph2M1OZ2fcDy90tS0sxiRBGOB6N5e6O0Ves7tg9uuDi6pJajFW5F+9tE+ejB/PlFd98+yusVh49fsRunnj55i0/vH6JtDzVgkQshB9Y2pJt4JJOvubH8DPfPlPyNCUkCwafIUC5bNz1CJCNX1wKxz7wISC9I6bbQjzNK2N4Flx0xTHUwaUxTEEF9RRB1KHTUkzVE5a2W6IyPJ22gVNE6AgaiXEQBj1k6yjNeZibIz1RXCpOk+QSY9uqdGwICs8gaxjpbrTMxBlkOJVGCi1s89vRPY1m9C1HBWJkJ150CAzTzsgFHBqWHV0aRE+jxEYu3843ow9JYcizq6wPoZTj9aSEtAw+laDrxkbueS5G0cRlRH7no0hymocSYokvIzFKJuDqeD1yaqcU2CPAB500f8BAbUJDEGsMUq8nFFkVKZ+H5jkVP8/Pz/nHf/xHzs7O7gu9x/+Ozt8jxuJ4r/3U++Mp8uJh59DPbR8qmv+17WGx+yh+S2ssh8O94/coKB9F5YdIsqNT+0M85k8RiD+2vx977qd8vojEiSzLkuLybgsK3XAkp4Xz7PLJ/R7b/O2YHTbGSBTI3d29eP45mxchulBNYZ7pa0vkjRxjdrM7lwLHkHBGuvBjaOIcm0HdkBMtiyqFwhg1We1boEKu3RyZj3zvRGiKGSYwSqfEjG9hoiqROM5OFiKP40vbzCmrop5zq5BgHUHtOfhEya47d1jJqC5RKCQL2qwS0ohqyH4b45StKyrwkTjAkMaQHL+kK6N26lbs3QZBpGRguobgFIRO6YmNTERqAnAQQXrmcllsr+mgEYyeY6jiKSvXQHuai3QIRAc2HTQMjzWNlxgWBY/ENHmTdCdLwHBEMpBw1JxTgWZu2hb27q7oSNySSB7nj22fNgu6b395qHhuJ/OD3wL35OHYWn+KKvV8x7kZT64es/rC19/+knfvb7h5e8OfX73lbn+HTBVZHWcw6sbacmGsaX/vmhNk2VXWuzukB12CebItTMDoo2U1NrZB3fIkMM2zpnlHdjP0Ti2VZb+nvWzszmb++MN3qCm7WXAZfP/DC75+/gU/vHnFyz+/5uryGbfX71n7IV/H8gZ2/Kx5QLLh6d7TfKo0f+IWPx7evzjeP/+kj0rXJ7/+yOD319/hkzcrM0vst+TMnhewa6Y5e0kmjRUmUVQD6AwX1AwhE17n3Yxbcl4LQRfYaeVOTluKB8LAd4J5pcuKWVbtJIQxKkNy8hFDcXOMgFB0W/gUT/5YaLYwdZ0Rcax32MTBaSd5Uo+gXMJYDczwdUsi1QmLjrpTmOixMCZNh2gEpaRQPaSkg0krzQeqiXPoS2foxkhVw5bIilZJkbgWKFJYFoEYRA90EnIhVVk03Qb0HHxXH+gE2j0Ds+pKG4bbjPiSfEMR1Btu0FZhWhtaaiZ2V6PE4EBBWiBVCVmZzGgtmby6BngFbbg4zQPXCfWerDAHK8YcWfn61K1JRzfe2UUJom/u5JL6U123hOnw5MFKYwgZ/DMba+9bIrPCFOzd2XUIzxbGMYKzy4mz83N6Hzz/6jnf//ADI+5grIRqJnOLUXaZCOtmdLIFWXW7uZGTbPUE8fsIRkROlivEMuhRsOZMprQaSFPKcfEUA5EZJJjHhtMoikdnqsmFG6J0TfajzZ0xJqSSDqMNFYQvyKpoBdGZWhqdjlqay7WlY71tvKtpzerwKlty7TIgBjpd4K1TVbOTYE2khdjWsqgpKOJbe6kVJowyV77+6ktq3TFCmMvEkMb5+RVv3r3dJuEdtcG6OWoYMKSBbW09VpJZf9aZZcfF5Tl//5u/Z5rOuL29/uRzp4gnexll8QWhMjQygMGNcjYxjXPOL854Nl1yKIqvKy/Ljrv3f6avwpgrh7Jwd9inU89h2ph0oJwXRctg6Y2qhZ0Jw5PbdxBPESNka7W0TZxfGN3pWrPKb8q6bOfzLHg3pjpoYdy5MAP79cB8AXaY6eLEYWUVYVdrpm/bDCNYdNCLUpcMBNOAYQaHFa+Vqp3Jje6KXsBYr7l99Rqvgy+f7dgxsYZvvEAQK1Rdk7sYa7IXdeJw2DP2B9Dg5uaWw7Ln8tEzFpSvvnzM7mwmJOj9QCx7/OlVtozuE+PByDDK4QfWu2C+mhnDMTGoyuTOJAPXle5G98Ekyto6tRS6pBuiaM8k96lQRSgUln7Ihbam8B/DGN6YPr02cS8yPvzdx5zBx+3054+5YB9iJf6ayxZ+FHdPBd6HruWH+/pwHx6K2n/hMj75+bidOotOH/Nz7uBTjMjDz3hcqB0OB67fvyci7t0zH/psp0L3h9zG7s7aGgFM08Q8z/fYjQ+1e56Kxz/nKPrY9/ApW85zMzW8hHM1P2JXFeoZ0gtS0lW8s4l+mBBbwXaM0ehtwmfnfLeyNoPbxqqd0id8N4iwrJyte4TkGJsUxA7JdNbE54BmCVJS4B6zElYZE8xrQ8+M2meGClOpTCYwG9JW1nZI/FEJ7iZlao0eifxoPUUYnfK+GD1wGrUYboKvlu4+XykTDJ+BRPvoKMnX1YHJjGgjRseQzYRQsj2/BC6DKikOos6QDEEuWAbWjRwPRg+KDLoOZOzQMPa+sLNC9EFfG6XkItdJp44ghMygsB5WJgprKUwu2JB0VmfTKy16OjhHMly7d8Q7vibbcWhHPag7pa9ZFHYDjY5YBv+ofI6DOeekqp3zi8f85te/5dkXz3j38jXXtzesvSXfWzahQHLhnCLCJrwaVDOmqbLbXbKOA8t+ZXgKUfjg9uY9//6fnSKFqRb+7le/5le/+x0/vPgzt3e3XF08pUjwx+//zN2639ArKTCLRM7hI7YANmGaZ54+fcrjJ484250BsO4P3F6/Y7+/AY90/gpbcNsWPFWU3XTGF19+yZNHT9nNExfzGdNuQkWTmRrpuAJSnLz/oDn/4r4otK26ZFvAqxAo61h5/+aad5dv6PMZ7+/u+NOL71j3t5uLL5e0fQzevHvJ65fv2e0u6fsDJmymjBSyVY6t3soXXz3nt7/+DfPZOaN32Arwcy2578cxJHLt6lKgB8PHBhxJ95vHtkb0bewzTac56ar71C3NWzOqkusgz67aXGMkSXvsCrutO8A9tecUEQvetvcqx7ltskUdAe+MQeZimCAyUrcJRQoZnO4ZHq3DWG2k+KPJOh/dUBtIGIbQN1ZqGjC2/BiPZHED9EHTdMCHpzFoyBZs7cGseZ1kkHbDQxijYTrjZujS6dsaKkO/B7IEmGf3o2UIMtIJGbgoeGTnQgvQDO0TbYTkWlEoKdxHJIZCExumKpRecM3kQA1BpGCxJp7RJ4pnSPJwS5FbsghQJbM2EnmdIaqjW+ZgSCJFZKMEqJBrUeGe9axW8GEp9kcnaqd0RVRon6szRFBr5Z//+Z95+vTp/X3yGCL3sLh8ZCqf3h9PrWofmyP8LV1Bp8X8z92O7+uRguqyLPTeOb+4oJZy/xlOC+HH91nX9d4RfHZ2du/wPYrVnyoyH7dPFY//2nZEihyF/nmet2JZ3DuSj691dGb79vhjJ1jvnZubG969e/eT+dfnfKYgKJPmNdJlc0hvIeKaXF5aBlOKJYM8vOORXVaC4MWwMbKQW5TwFZcB0hgdVAyj4gW0DRipC2lrdBuoTzRgjkiTlEd2DdfsKDUX0uYaZFUoO4C9R6KwAqILET3dvgMicp6h0gkviDrWt3PPIKITVmEdWdQRIZpnKGeTTRNoyZkvua7xKUXfsXUJ24aKiq2QlWYvQVrPrlOys61rop7cI41fqlvhDXCly6BIwgcF0HQq4NoZAUSniyHr0a1M4omiARWXZUNqGXeb7jXoaOT9J7Rn0OmxMJolRHxplKkm6z0EClvW3Ie3TxOYT0vYHzzhPrylCzdQSSfA/s2BL7/+kq9/9y3/+R9/xL1z2F/zbn/N7fU1TVamPpLjshq6QMW2hNIFrefQBrvJWA8HfElX66w58WsisCYXK9QwOnG8YUdwNqVbLnqiBVSCvqwwOmfnO87mCfSS88uZL794xvcvX6EBWgv7w0oEm2qfixhM01lyrx9ny1AccRlbu5NiOD+1kf93Cbmf/m18bPtRVv7v3CfpB0pM28Va8UnxqSO9p7gLmbA5hEMIExCzQ9/RtbNaUHdTDkqs6Vxt53DW0fMz5KYxRmTWiKTjL6TTwzdBCcroDHeKTccRJCvCUjKtVNItuwrgExKDAKQnKH4tBfVglqCPYBKnhzAGVC046RgwEVwbQyYqjRbgMiXfVh3vg+FQqIgPbBiu6YSPsiUDm2d7WBRKF0LTsTR6VuHF03Wg0YmajqbRhUkMLStIJaIk76glE230PCa6E/paKZYhcO6SbRijbDfXgaltKaRChLH0gkSw23Wiw+hLOsikgm4hFVI2N4kxzdAWhxjbgm0wi4FkmAafwUI1NcaSzFgo2JzVwvB0OvZBurMlXaMegXRy0rjx4zxAR8caUIRV4Pruljfv3vHdd3+k7fecnZ2z7BdmJRcwKFZm6IMlAh2Oi6FSaNGyrZIJ95WQdJ02AVNl6MIErBtnqRwGK4bKhNma/OStXaebM6ImckJWVEouOIoQa0fnQXi6gZzAVhjheW8QZ0RHzJDRYUs4b5IIoTrBEpkU3l2wCEYZWE/GuelE7ILwhq0ZvNMlz7FKhu5ZD6IK8wRrJ1v41HIijdANZqscw4Senj9iPj9jKpVaZ6ap0raFhvsmPEUKrzoGow2GAVGTma4gCGXKiXm9UH71zS/59a9+hQ/n4mL3yefO8F2eg+YYhbUEdTXEJprl4rq4ZKdmUYqBXypfl8fczM6r1+85hDNpQWxL9pWJJ1ePuPjikmdXj7DLM2QkiiMMVitQnHIQwHymsAAAIABJREFUigXFAtyJNsAGrgYlHVA6BmPkdKx6LrZHOC57WjPcFBvpNj7XicOho2UTjZ0MYFwrerbQJYsvw+B8bXSE68V58/1LsJXJhfOLcx6dXyBTQTWZ7U8ePWGdG/t+S/VAilOAOhUiliy2UWmHlTV6FqjN8X5gWff0nrzOy+kC3x+QxxeMfRA7uKjnhCg37Y5nAlPprAISd7imU0nXHVcXyX/ene+IolR31i50K1moHUBzFhOYFdGRE7R1zQWkFawbzYKhyiQFC0/nf0k007tXbzkse377m9998vkDfynifshh/BDlcHzs6WucPv5D73H896Ez+UPu5IcLtYcCN/ATYfZ0e+gIfih6PxSZHwq7D1tbP/ZZ7sVlfipSny4Ul77cJ8Q//Fynru5TQfh0X0+P+dF9s9vt7hfErbUPtnyevtdDvvbp8Tv+/XPbWHVrWw1Jl/JuqgQVrdDVmEsywk0a09QZUlldMHemXWEsE6splcadKTE6zLGx9p1SeuYiMPIev/H+/Lj4YKXIBb61tHedmG1m2gnmwuFMuepKmWASJ1pPM0YovTs6V7Rd4KOl6zgOdAKPyrCRmIOeyKacd6RYJZrt5pM5S+R4jwVtw9cdZDBZhqDK6EwqrCaMZqhkUPHQxNsRQR9TeqBsQhKCgRAUg9YLPXzrWBAkCloCaSO9hwLLcotNM2O1NLl4CjpSJ8Q7Zb+gZUYIpgikryxeYLfQ18yvqM3pfWHsnR63tHbNbRsMN3aj0ydLtFEjuQLmG7bCt063jTH9iVtoMLpzcX7GN19+wze//iXPLq/wVSj13wFJx5L7vZM3RrqXlSy+myllrnz5/DmPr55xe3fLdz98T7tr4E7TQJfOGLfU3cSzp9/w9dOnVBOkOk+ePOPRxSM6jau7M+ZS6evmPiPXcacGFA2lVmM6mykmLEti3w53B3549Yq7je0ZG6ZA1ShFwAW1wu5s4vlXT7GzyroGS+uM1lMEiA0bQWI4PEA5thRn7kAq7MDmsD66umFjj4qzLI0/v/ieWY275cC76/esbfyk03RdOy9fvuG7V6+ZzwuHw543799nQcMTuaiAilNq4fLyEkVY93uC4HZ5z7vra96+fYePho8UwkPS0eojHfvhsc1ht++PrdUdNkSZYKLpRv+MzgmZMkDNNixctppnDoOSAsHOE1vjAGNsGUcpRopomnoGEClCWQdhA4NOG6rE06giIvleLeilEJYYwtC4L24NQEbB1CEEHc5wNmRizbE+wCIllSAd2GGCRaINovuGgMlQPibZutFGdjIOoQvZPUEwKfiUbr9wz06OmuvDIPI6LcHUB+ETIzL8EE3nISW/b7ENlzCyE0ADGh0TY9aJEQaa4ZejOB6FCIcCZYxEJJpmZ4QofQyiDIQc74TNeBMdN8X7gaFTrqc23I2GIOGMSMej9IluDUXvhTIJzaDp7ZzptnFf/fNT/r755huePXu2mdp+LCofO6COAuXx37/ochLF5cOIhc91x55uR1H0s4u92/na+7GYNt2/3nI40Ld5gpkxTdO92Hzcju93DNQ7FsM/9rn+u8Xmn9uOSDAzY11XSi2YZIDf0cV8Oqc5fpZlWWitsSwLd3d39yL637JvWieKDVpLVnho0DaMTXihqye6TrKbhy1EftCwqAx3tCbqq5eBR0usBWSw/RDCyOtKQS0QtS3QVCmuRBHm7oyY0PDEaWKJiYqOVaG7Z7jf6kSRrTAbKB31gptuRedEJomTHccidE8XdQxB51x74YGxoCSmY7hQBJoqRs9uzQRiUjSLRDKy47kNYa6FHpZ6lWcBXmibUcxy/LbkxmtkGLNKYl6iF4Y6Z8HWGVTu9dVExDolemoKJcX1I7KrW0OoW5ixYt4wDI8MPmYMINe7HhAhWZwTI9j2NxJrK6rQYVgigZwtdPQj2yf2cX3+RXE/GYn8Ykc4t+2Gp+ULXr34I69fveCwf0udJoqBGDw7f8bt+3fEpEw2ET1TJr0BVonWmSmMLogXzi3FDvcBLIyWzDhMKJau0jaynU27c7u0bBdUz8m/BLF27PyMucyst42LZ8ZoK1WU84sLmC750w8vuLw45xe//JZ/+/3vGWtPoeCkfewY0hCRzLMIyX4tuBeXj4/5m4/l/c8/vs5JtsLf9Fr/f0ndB0seKEXAG3UYrShuCyGFMQTtgdSeQl45YwyYTKE5ftcYTZCyCVCunBejK3QveE9XoEeye1xGoiMsuxB0JCqCyGpM2NayNkpOOrrjtSbH1ga9G3VWOg1ZK5RCjWylDBVsS4bXLlnZUYcxttCInEAy1ixy5MoDcwfJ9rRpKMlez/RXdyHmjrYckozKEEN8Y55Vy0HJF5pURJwm648O8EnTSepGkE56G8lpDFkzlNBywdcPFasQJI5DtSSfeO0ZgjEq4bJVBp1QZVcazQtjzdCQrLkJ4uky8hE063Ax0CVoo4DAVDsDY1qNvk2m3BXV9snnTlb+snIZui0w68S6ZEm/lCA7bJy1rUgU0EFxw8dgDIWd0n1lbkZFGMW4O3T+9L9eMK5X+i/vOH/0lN4GL16+5G5dMc/BdiiIJwakhxBl2ewEFR0DfMWjIGJoW3CZqEi6icdAizBmMA80VnAYa6dHxWouVGiwyGCyQo8AJnRrUzFXlujoKqgJOgQvA7WK4OkI7omX8bXQSqMyZSsRTsVxTX4bKDYGvQVYLngk74po73TSARLhEM7onSiGAIfIZG/ym88WwhoU2NwWgRSYp0KIUqaJ6Wxm0kJfB63fssaeTk/E4IDQmYhBlaBbOv/EIaQyVscm2NUdz54+ZzfPmzPp08eoIsLaB31KBFgGKaQ7zg+OVXL87x3Vc7y1PCYUHj/7guli5vXLt9z5Deozo85MU+HZV7/g8tkFF1oSdaPBJJIT/55V7WGKycQsg0OHsB0mjTJWwrOKDKAlGVrLzhmtg0KJmg5IMgyyN2hzQz0n+WlumtBo9LpHvbKzDIuoHbpm6OLrFy/57j//jVd376HfsC7Bk2fP+PYXX/HF1Tc8fXxFLYPL+Zz5MbnvCLMLTSGGUVG8CPvDHaM70ZV1DV5+/5K3t9dIOF88e86kEzf79+jtS/ZnzziPK3YXZ6yx0PtgHCJ5rgEqO2JAbzeUUhkK/V3Q9lCmRrMdypKLKM/ulqGazMc+aBZElOwYGBWmFPAL2cESIljJa0kDhgT7dc///Jd/5//4v/7PTzp3TkXIjzmFPyQYnwoCD0XQ0+f8f7S9aZMkSXKm96iamXtEZtbR1cdcWAC7wiVIwf//DcvP5EIoQlkCXMzO9FF3HhHhbqaq/KAe2dnZWT3VGKyLdFcekR4eHuHmqq++x2N27hlI/RQw/HAf50brMfj58PePGT8PweunvKWfaozOYT5EgpsPm8lPMYken5M4g23b1+dG5tz0PQZ+Hx7/4/08BqEfn6OzZHS3290H26jqT9jMwE8A88fv0fn7v/Rcv7RpWPrjS6FME7VNqE7ErMxWsNGpu8Y0hKUpjI5iDIdSKm12lJlSG6YL862y+Al126wAdpQSVAY9DC/ppYo6UgPtgXOklIkClOHMTai6S0ZgVLRkmJ9IIQRWguKDWAQxpcuAOQGx7hWmIGyk4gYnmuIjPzdWJMN2Taj0XDdkR+hAwhgIYb4BwySLSYylFmIURBXtwqADA4+azWUNzKescEvQYjB6I9rGqMSZZU+RlP6aJchSUUZ3lMqpn9iJMGSz09HOnRs7dTqSbhkxNnuOlizYJdKajJUIp1vQ7cRhPbL2hqydSYS1VbQ7qoMeaXtWChvIHxCdqI26fn69I57etReXz/j6t9/w1asXaZcgK0XtRw9cJMv1SGmuRDbeqdyaePXqFb/93X9gmmd0EnYf3nHKLhdiszMYwhTKi5cvef7Fc7ob/WhMc2O3KywWtGlHLZtnQR7gBpJuDKtNehwShHWOd3ccDp1l7Xz//Z95++Y1y9I52w3UUtnt9+wvdiynFQ9nbnOCjT3l3Xe3dxwPJ8JyeJlDBc3nEzDZctllG1xtALSUHEwIyYRVsum/mq/48ssvefXqK2pV3l+/5+7ujltxYLM4IokhHz++5//5p/+LZfmP+GHh9fc/MPpCMFAiGWexWZmcFq4P1/ibwjoW3t+84Y//7V948/1bVtuyJoRk9emmNNtUYPfg8jYNkXuGdEbgokrTwrz//IF6lS2Ej4ZLZ/WFdM7cGIAC5oOTe4IPAFv4VcbblZRBS/qSJqifVjXphzptZAqHIjSrRFUkkh/okoHFRbKvaSg9Op4pflBbMuVKoF7ByDyIkuuDJj+FYQFlU9UHSEmrQacQZ3WmZx8YktY47kJhs4JBCE/VKu5IrYgEoUJIRctALDNtcjaXzGEXNt9okGJUiyRThIFDl+3zKw0ToQEH38gdJYlKWS97Hqek9Ua6zuWwpBSQ6Ek887T6G5YO3yrT/WPoGxAuvoE8JQds1TJMnvREF/czNzOZ5hvrUn2i/oIf6lPbs2fP+Oqrr7i5ucHcmbZBrYjcZyWcweaHDN4zmzatbP59fZT/mu0hIxcSKzkzss8/772ncnl7fefHjjFQ1Xv/ZviRtf3Yzuzx631K5fUUKeDx1w9rj7+0PTzOM1hcN+XEwxpLVSil3gPNvXfWdeV4PHI8Hn8ygP+3vG97KbgIV5NiKvRj2oG65Oe1uDAk8tNqSf4cEqg28J6Bs2t6s7vm8Y5tfXAtaBuEGR6Kjm0EZUDopoQxSqSHOsPRsyVYy2BBtCHuySAs0IdT3IkKFN2AXdJyKzK7yFsyrcdmjzNN6WM/KnikR3ooqYjyoOiAOuMSNAlsSdSn2eayMBIzsTroKuw0VStK+ldrDTjla5JZ819dcS+oVXzqBLm+6VRxZmYMU0uwt4B10sO6b6SmKHlOeiCFtFjzsg2qxmafdvbLBzxthksRTDrqZ7KJ06Ol1KVGho6q5D1MKmneoTjph7/Tf2vI31+xnS9vyORD31DQN99+z3SxS2nQKHz92y958/ZdvjhND6KxCrsmdCx9XWZhrJl+izhSjCaFVSsNY7GcjNdWqTpQKwxVqqYxPy4ZOFSCdUmGI5YNkLaJ3eUF17e3jGEcXt/R6sy+VOrljlOkncGzly8oCKe1p1waqMNyehcJDaAJ+uVLfYLp8wAq0e3GZj/5/cO/+jSz+CeSlF+pHon7Xf9b4e7P2yoVscAsGxvX9MZRn5FV0GopZ/f0Gh1Ynk9bCAqnWLk7nbi4dPoozHOAnLA+GHFLKU73SvGOWYXJ6L4Qvf0YfjWCaVtnTltxVYA4Oj4FtS1ppWKbrUTPC1A1weIhkQyZIUR0Skt7iCIVl1N6i5EsR9msEyD9tGzAVIMRO6RlSJ5IBmipCkWCIQLVcc9lZxLo52LPodWKtUpBaWOkDGwr/EUzqdh0oWhg63EzwAfVhjBQV6wk0NiHpD9pTVP4WA1XRTxD+bKuqVCciYZHx9XQdfNB3aQdJhtjpxZKD1gNZULFGbVg3vLmRR6jlr5N7y8/+7Ozrp0oZ1lxSybuCIoEtsn0ZGSwn3tJn9mojKrJANZgrGsWfwInWRkmXEbj+u6aZencLbdMu3cMW3j3/jWnw5oSYXVKRAZOjp7MZs8AkDI6LgWRKUPJCLpOFC2sEagP6gikNnp3pAirp99aaSW93DbfOicD57wPQjV92DwnvUR6KEkk82FcGBJBXw2p6dUsZYcOQzUZ6zpX8DUHcdZAFyxAvVCK4bPSQjO4wIQmOdWlXqADjC1MSrN0jcjXtxrUeZfBL9EoJFOW7jA1IKgCkwp18/g6jQUbK3eHhXU5JZNGHbVGYdBDQAt1e59KaRmeWCp1Y0aVKszzDqkZ8va5W0hgF0qLDNr00cELuyIc26CusKxpD9NYMzX3aLT9BLrjqgn19xdw2zmNI9p2TPuJacpAk+wSswHpG8spQ1eARal1cOxgtbG3lJouIyg7WM2YKDlIMKePDGkYrpgqarEFJjhs7D4teaOIWmhDGVYpkzLHjyqH1UGiEF05Hd9xe/2a0Tvj0Lm9u+HDm1vefvctz1/8C1cXz3j1/AW/+9v/zPMvL6jTlB5oUzDTWXTGFHxKf8jx8ZQWQyp5XR5PME90NXY75TlXaA12+xfspgYWxDG4+OYCqYXMm+/ZkFanTTO9VwonLi9zPRvhyOophZaV1ROYbpv32Uki/RK75RrYgvBCLSslChZrBorWmRFGGQviDZ2Vm+PHz/7sPCz2n/JHfsw2Pv/7FGj7S6D0k4AsP29APmXZ8NRjzuDzw+d+zEB+/Lc/A6IjfhLsU/ixYTsD4Y8ZTOfjehjk45HACdu/D5uZh6/h8bn9JUb4+edP+USPMVjXld1ul8etSjxIvX9oM/LUPp9iSf3axivzlRJQaK8qTEJoZdKJFgq7AE22UeiK+ESxzkmF0pVSd9Q4sqJMRfF9BuettxOwEPPAFk1Ux9JHWEgwBc0BX6u5rliBtitMNKayI1zYK5hdoCfHWxB4ykwt6DJYcLr1ZAbWzXLKKr4OIgpeEiQWT4n6mXXZcXYybfX9iqzZ9BQVpGlaN6yDIQl+FC857EOgjpTZ6/naSVZjZc3BvVuq0TQoPVlOS41slnBczr61qWJQcRYx5HDH3VjYTXvEZvpSaPXArcxIKEvtzAbmkkQBRg7nrXCy3DfrIPqJMRZyRhybvD/BLBehSLKYjAzhQTRVPmYU/RX3LPJ8Pr+6ZGpX2Op0yxyQiGzmIVDZmOvn60QrKhnU82y/5+tvvma/n5jbnmUstKaoaLKmMEycCaWWyovdC0KcvnROpwP7/Z6y2zGNNW9xFETyMhbfAn3YwDhP+7b12Hn38YZ6sUdW4e2HN7z9/i13N4dMuw/Q0tjv97z64hWXVxfc3HzkeHcEUUZ07o4n1sOJ16/fcFzu8v7qed1qKUw1c0XWPugWYHlPNE9QWcLz640dKpIsuFevXvLNb77m1VdfowHPLnd8/HjDx9uP9DUZtFjasdxy4I9//iNvPrwhHG5vrhlj85ndXq81x09H/vznf0WKczU/53i65f2Hd7x+9467uzt8OC7Jmq2SFmpURXr2hKrJqisbwzrVrtyf51IKz14859mzZ5/92VHZUUpn+JrKMAtaraybB7KsxijpdSpkyHng+IbklsjroJQET7sH1I5HQcRZ1GhuxEaE0JJsXjnbT+qMuDFckGFJDIlUxplvIdiSzMOIDOCTaLh3iMQwRCSHA5GB4+5JlhkKSvqqMvJ4o2p6IQO0kYrL7fexeZJrbB2lpW2QeNopuUgGKJcc6g8HIvs9BcRTDbCSuS942hqKF6wGrsIKWx+y2dOoZ58Rvg3xKjIZPgouPdeMkUrXnCdk1100fbJN0uu+9ECqUJQM+BInLIcqIKmCM2Mugo2AmizrKkb3muSpllYgn7u11vj9738PcG+X0Fpjmuf7ey3wk3v7/ZoVPyqIdPPUh1/H5n1qezhofnwPfsy4/Zz9aPnRS/q8T7P0xn5os/Xw2M/WIGcG9af2//j4Hj7P48c9rDMe135mdl9XfWrfD78/D+pPpxNBsJyW+8C/h1ZlImmJcTweWdeVdV3vPbT/moHA1csrWm3ILKx3g3enI6smW9jEEAsoaZ1REKoWFkkVUvQEdQcD3eohN6W2leELskT6xoemoneQmQZFKXQIxWuqad0y7DiB30CZqGqbt7wj0mhaiToyUNMF1cC8ZT96Jk7VHFJGSbZxZdruL8rZ9rZY9qyhgxz05vqobkDZ+u9kXhfOarW0vZQQOjtETlRPlbh6KrHGyLUOIetDjNAO0WiyYpK5SLV0IgyibflunpgZpIrBgoKjmzXZZMpafZvWbazumtaeLpokWXFEk8Tkmr28MSgoShLAxDLPK60/NQdYWnEbBJEBo78QaPwZAPPPOa+/dgtJBuJ+nnCcr199xcfrj/TuhIL14O5wQ1kqva9UFFsDxxAtdIKpPJhWS85ea6tbKEAWtdoFKXtCEhgaa1BLZYyFy31jXZ3wlVYSKGulJqB9PIIF+3mXi01r/PmH17x89RUvnu149tuXvP7Te4598OzZM453B1QrF8/33N7dwTBsjL8I2Pqjr+XRqX20pPx8B/poJ//WLfJ//1NnjgbUnHx7bOyhJSgeeYEMwVUpaluRkTYUS92MyE/Bxw9vMH9GRGM5KKfTLcfDe/q6YuppgZLeA8gprRWqOKZG1UB6WjqIJgsom4CeTNSSXj0xlIWg1IVihViDWiVlTabI1lyJBL4KRRrVBiLO0IlGJvnSEhBEzoEZgaGonw3eA3zg1LRmsWyc6kggdhRJoFsmugStKsKg68xYB8MShBxd8GY0SzlICHRbEamMnrYtlfT/8Gr3ixJtzfA3q0hJ5pSIMELY1ZSvjTLncCZGBmNGwRqcXCkWWCnoqqysaCVZCF7xVkE6xVc88vULg6FKswaitPr5jJ7a0lRGSqXuZmoP8NQB5GfmyGp5o64ieB9bSMpgYgJNBqOvRi+ao0WCu9opp5WjH1juPkAtrK6ILYxxotULRnF8jGSYhyVoH5A89TT+V4MyX2B9IYYjO9vA+ZQvRwFsxseg6NhSu3Na22xOFnI/UupEFN+CdAzJaD2EFSvTJlEDXUcyQaoQlr5WGlkYLR5UHYivSICfTqzzhJgyaUv1hBVUHUPzb31N9YU6zTd/pWhosZQvGdRQoqStxegrrTaUQbeUrXaE4gsyZsy3Yu3ukAzTVnn//pp3Hz8SrhxtUCwYPaXWUjMC0cWZp12yV0QpVfEJ1h70MHo4cerE+PzCMsdTpLXJMEadUJSTd4LGu/c32OmWaWPeyHRBqRM6bQyYqTFLg5dXvCgvCEnZUE6YHTQoYwVvRA1wyfCrmIjZWKUzDisyLliqZPI5QV83ppKvrFapJaFXxQl3VISjHZinC9poSBtY2xipYpR1Yi0LrdZck6i5xpkw28B3gbkw+sqb2w/c3Z7QZbBqoTWjzs9gK0a/vXuDzq94cblDG7QCJ3MaE1VTceSnzvF4x1CBbizHhd998w3HZ89ouwuYBh/fnPjhuz9zdXXB5bMvefXygi6Ol61pq8CqWKtE60hMSWpSB5kRDtweP1DZp4okGiIV8UxjjnDohTIZRdNnbUiFdVAmJeaGueKrsFpnXhb2ZU9XYWinTZc/svB+xfZUE3D+91PMk4fNw8Pi/aFc8eG+HjOIH4LCTwHWD/fxqeM7f/0pQPncMD1mTN83XhvbpbXGNE33j7MxEuza9nlu9J5iat9bWzxhUfFLjJlPMYMeHt+nLDPOza6IpC+hZhNzBtE/dU6eep6nGOyftU2KmCCh7GVHqQoyMtysNkIbQiPEaAhMxghlV3Z5rbiDbLJyE6wVuhbieMBcYay4Oe4NWjZqXjqFDNsrLoxQKBWvwfOdMC4KrgujNqo39nVwbAvj1qlV0DJBSbYzfcFjhSVgCwn1jBUnRrLyWs2QGZ8bdHA6DMWrZcjsFPhSs+maJ3QYPrI5mcpCaGP09HmuqvSRLCSZCnWdMDqy0QljyYWp+EpR4dAlJevbQHkMQ3tLaauk96GZU4awitFccVtZ96kuGw6hHbGOhXHyggyjaEtG7irYWOm1Y3fOiMFYg7GmXF9qwTBq1WxM1VhdmWqSJFZzSitIlM2G4Fdwd8KTNVgUs86btx+43FdKSeuqZBRsuPJ5diPZN6ooUgrPnr/ki5cvqdNFetDf2AZQJxMrRGEEMeUAphfPHuzUuT2cmPc7Rl9ZTieOyx3DUnm1yRDYxNAQhpJJZ8vSef39G07HlbDOzccjp/XI6FvS3TZ0fnZ1xasvvqTuU42zLINug7u7O0QrH96/5c2337Isp6w/RCmaf/fi5QtEhXfvPnJ7CAYr0oW0hBZgU4z6RnOWbaglsuUcrMxlQucLdrt5G2Lmq/FIoNqXvLZOx+Ue8IltPfFt2FsJlt75+PYDtzcHalOiZ2Df0lfGMhL8TmoFSHqLirApCRMwREg1QC4yCZIjFC3sLy754ssvuLp48dkfHSWVGksfub+ysaF9xUKyxiGyT1LLQGepyMYCdyF9hCNt2qo6thZicoiaxI3iCZjnbGer32AYKUGXSm2OamNxZ5pq9ho1EJckhGzAu7ukt7Gnp6hM58A4cEvv5iZbgJbkkNBMcd18iHtmKWUrVwm1hEM2tegYmoQODRgJ6FcVhpbtnhC4Zph3bBZyAkgJBnIfxhWSbVONZF6e85TCnabphz4YCVx7IKHE5DSzVLPSkTVoZRBVKaZEZOh2SAZlCkqJFZGzXVCSnZBNqVkkgf6IvAYVfAwoaXmikyJDKCUZQhKBf767Cr/7/e+5vPw58eccDjfGoJRyb5XBtp6cVUPruv44xJafKoTOj328feq+emZM11rvh9zne/dDD+HPuSefmdWlFGrJdVi3ffbtmM/D8mVZ7r9/CKifLUEeqq6eIhyc/z3XTGcF2OP65MwCfwxAny0vPmXt9dR5PNc7D8P9Hu9bNS8S247//Br+WnAZ4MuvvkR0Qs344eYH1j6S4MK6ZRZAk5yyeXGkj1TDiaDzRIRTKcTItXoisHQTxKX+2C+vaSukTfGzbz455F5dqBqULttwOa9v21ZUVGlSkahU9VSdhlDmoA7Fw5haWjH1ELC0mlRtaV0kkQQ7CdwUag5aMUXrwEypfWNca8dWJ0d0SqlsA6jMq6glld8uLTMnImBjRdc9m41SSW97FES3dbORWRYdjUJ4IalgucZPJQd0IZGYjU4UI5nTkr0pmnY8fTi0krfH0RmFtOJJXvRWUAgtYA1PAH4UanVss4FlDaIoWjoeQZWG1QS1P7V9RhX0yx/GX4afz4tyUKZKrTt+85sv+cPXf+B0d0vvgzc/vCGGQ6mEwctXX7MuRw6Hu0xx9pTm+BTQjVomYqQ/q1my+XTKJlNqpG2BZKhUWsAWtFZOy5pWBCW9m5oIpRq2wvGmc3lxQds1/uY58mFGAAAgAElEQVRv/sC7d+/59u0B/eENu93v+fjDG4ooz/eXvPrD3/BP//RfuXxxSSDo3R1FkxYfweeDwPE5sP1Zv7N9+4n9/uw9OOtn/qciyL+8qRqjFNQCdWXUoEWKzrzlVEY3vzITQbdpekROhU5jcP0xOC4LUxWGN/rxhtUMM2doofaUeBVLFgoxcapLWliYIaUyRTDozFKwUYgyESUoa6AKvShFOyI5GUJHJq0GtEiGbwa+KVJT9mEDwtI4fjhbanvK4UKCYKLWQVjKStEBtWZQSGRjKeK4T+mnbIaskV70rWxMCE/was3CtWouGFKNKNBHFoeDlNWlODAD+twcxBg0Cg2V9LZDC6PklL6Z0j2zgyRqglxuZMJ7SjnwLN6R9JEtxZHu2GVhnBytRgQUO+FihBV0MkJaMgOmwDoQnpLLz9xEZpoYrc6UWphK5bR2rG7BgrbDbaVJpH+TVFwq4oOujlihtcKpbhmqDkWDZslSLwwW39jJtqOXtMNYx5qFs1daCYoqZTUW1bQw8JT9CQWxlaGF3RSMkDyeqWEe1O40CqtFSnNEOHmntD0xVkpxxrzj1AOJRq2aVIUtUECZmcyTSUEk8NYdKwOVtNfoJQv0XQvG2GGxMGy5v+6nAqM5vQYTyag2z3AlsQwKki1cxShYdQSjiOIlkBGsGFVryjAp9K24XjFazQJZgdNp4XBn1DlDPe+ub3j74Q3r4ZA+jsS9b7SHYj0VKFpaNlsGrgXvTtHKuiy8/+4tz+ZLyty4/vjh8z87cUJHS9AX0FUymLIYOoz1eI3sn1F3M5MG0gKvDUYyUspuYnSnVBgqNA16F2oZiBboCRoznKGbXKgHgXHybHmZJsoYeMCwgkijFKMuYAWGkjYdayEb5IpRqRcTrA1rm/1NTyk9+5nYQzlVrDqzCWUXRDGKNWKaqAQug/VY+PDmwHK8ZqoNKhyjcbyb2LWXLEtnrMZi14T+LqViFZrMEKk0alK3kIsGZaUfB1V3TNMFN9cHrq/foHvl//gv/4W76yO///03fP3b3+L6m43FMCg24GCU51tgFjsmG+l5SKOXFRuVvcKhr2hr7F2xKExhLGwVZ2lgDcYKm3e+Bah5Svub0EqheUVRTDreQWNmX1b2Zfrsz86P689PmczwcxD34eMe//4x+Pspv+anwMyn5JUPv358PI9//9SxPAa4HzOXHz7+nCAfsaWOszVfW9P2kLH8+Lke//1TQPwvNU9PsZoffv2pc3k+nnND/JgB9fAcPBXi+NT2FNv5l7dAh1KasG9zWvNUwWPCfKVYw6tBUfbTDD1YtzAzJ6AHR60JyMohbeCGMk171qOzrkZUqN23cBrSKqYEIYNVGs0c5ITITKxK3ZP112jUBsML2iu0IyozE+Br0LsSvaPDqeFpS0PBFcwLZU6Q9ejBzpUw39Qm6Qs4NoCn9IKRzSNHY9SODEEbuNdtcO1Yh1bWDHeVYAxwOYGX9EqMgP0JiYosyiodbS0VHhH01bHI1HRTZ+rgs+CrEAqDSqw5wJRbx6cJkWR/q4B2IWwFVZbSKOPIKYKTdmRJ+bGtg4iN7bNPGSrM4Gt6tTqUNoEtmx/qhESGD/rGFP3cTUuDAstx4e7uHbu2hzFxd3eHuSASpEcrW325gauSrMc2z1xdveDq4hkvnl3y+v171iXtJkrZpPW+gVBbs3C8u+VNOMvpyOH2I1+8eM5pGVy//sDN9Q1rXzYrinz+DKPLv5cIDEPMuL2+5nC8y2wGN2wYPrJhVRV2bcflsx27vVB0x2laEYTD4Zb//s//nd1uz/WH97y/3ljDWzBdnQrPv3jBq5dfUltJ+6M/dQ5jEOQ65JG2D6FnZmiypsOdu8MdH69vCDPWtqN7qnm8JyBokexlE7LWHVs46L2t4Y+s+iBfvyjcnU7UkaDgWRnhdvbLzeFIKZrhoxd75rnR14W7uyPrac39eXLjgiRLIQKtcPHskhdXz7i4+Hy1n9tK7wPIkMBGQeioSVq9aVCGEJFKSSJJXRJOlKwxpCYhpJSGe9p/BUEMUHVmVawnGWFoYXhQDGoLTDMEGVVAExwWp04QTtp1eKowUTb9W0q4bcu6cSEVoAIbkgOb7ZVE+pGy+TVrzeHAkEYjsQWLQGRsIYvJ1PehyUIuhWiBWt9Y+YGRgczFepIRiLQvjADbOIuSCgXRGUoSALCKhCW2oBMzg9EtAxoLaDR0qpitCSiVkcztsb3HZeAl+7iIFMALkVV0IftCz75Oo2I6ELNUf2ik/WEJTHOIJ2t6v4d6yv1LUOzz+6yXL14k8LqBu+cAv7Mv8XnYUmu9t2Q4nU7c3t5yfX19b5kByfq1s6qGp+uY8/eP7/GlFFprXF5e/sTq6gzyruvK6XS6D+r7S9sZgF3X9SfPN22heA99iocNxuhM0wxwzyQ+s3/v7u44nU6fBGZVlXme2e2SnHj/fJJr9MM68mFA4vn19dHZ+Y7eO6fTiXVdfwIG/9JrHGPcv56H5+VxXXl+H3/10PwT2zcvv2KY0Y8LHz7e5PpXksleIsP9QgJM0xYvlKKkBehIj/Yi5+wIgLSECQrSIkP0RLY5Xe4jRDebU4MoSYQxo9dNWSO62R4poj2xk9gwQDGiTqg5usq9okJLspd9sRz+VSir43Wzd3OlFWEtiljDdcBwfBFKTUsc03xckRzwWDhuORSSyOFWkOqVvq1x3gpKT9X4mgNISDLZ0FSIzdrxooktaKERmOUOVDyDY6sSzZABOyqn9axyd87jV/OBapLRTEaq5QtUbWAL4RULRWRQirBqhqanSsy2sNuBSoNdwUYwXKgqZPSt/2K+1l9tkfGTXT9EOmVDWrfvYwzWfsdil/zzn/8b8/MLXv7uK37412/5+jdf8/b6HdWMF89f8vH1ibUpglBFoWgChGI4yq7tWWUlNi+1yR1vBT92uqWFBUzJbLBO0boVFIU2JXV/BIwRIE7dNW77HRe3xg9//pbD8cQffvc7Yjnx5+++Y1ca/+t/+jtu1uBkh7xJHm/pAbUoixlwbmr8/iTIg/Mi8W8kH8ejE7t9+fBU378H2w9LSFLaP7E9bY7xAJX+d2BKhzZgYJIylZLJbDntkMoohvgpfZA2cr8Em6dMZ3jlxju1H5irgddk1xRgk/x5kQQ2OxvDYs2CLXJCTlWWkcxmuiLVt4IHfA44zRRdqb1gqYXDrWFtUHpK2Yd4foYoxFqx0vFwSmsI0DSTUCVSUlFxZE5wOL3ElKUrbWRqspimP3gUqkIfpw3MTt/btTulpKRjFWhjC7irjYiUrqmlD3Kn4SOn/+GDqIKGMCqZOm6eII0F1MaoA42Rx6aKyshgCMtiDgtKs2SX105HkV5weoKRa6NKwDLwNE5Lf2Y1qqXnsg6HME4FZN2KW4KT/woP5hY02XF1dcmr3/+W0gs3H37gww2c5MBxGtRDoQ+jacNVCF2gKI2KdMPDUIGpOsMEkcqg481hgRrOoNKkw+JEa1TrDGtUzyn30AwmkBKwCrqbkGXBS6NaZNFZChadqUysPnAvdHUmC+qUQzPRoKng5kQdDCYwqNoRgRE1fZbFU9IFeNNktXkW+wUoZY+N4CRGaQtSFLeG+4rMM6U4JkZZ00NJF0Ub0NKbuJUKwxg6IS3lymMYFp05lKOVTPX2/NxLpNzRDJQTbk4n19IqGcbm1Tnc3LCas5sbw4zj9ZHr9Rq3SH/poSgV0Yqwpke1BbauVKlZcMTWyPbOSuNPb/6MS7BrM999/91nf3amccHQzTO0NGYBUaOqYZF+hf/hb7+ibfeE6prDD4WYBKwwaQZ1ljDMSoKaPShTbLJkUuLVYZQTHhkiWjSwRdlVTya1C4MTe5Q+anoHehBRKO6YrDgNMUOnleGNqSaD3XWX0/OWzYR2ocg+p96tYEWotEz7lUq3E+8/vOXjh++JZYE1/chOpyPIDQedUoYvzn5/gawp78ZrgkB737zLlGiFYmlRsdfG2+U9766/ww6wLAtHjOXDRz68ecvV1XO+/N0XXEyXYGC2MpULjpbS1TEEr4H2Y67/msfsK7RSiOJMlgFHS2lUz7yGwNInHKMVJ8aE9yPTtMPngkZNqxYcako1S01mkAKn5cASI4Njf8X2FHv4l8DlM+D5kGX7OGDl4d8+fPxT+3vMlHkorXz4/cN9Pm7mzsfw+LmeOu6nbDXizJiSZN2j/MSD8Zf8+x43M7/ETP5Lr/EpgPipYz5vZsbonfrAb/FxY/gpi4zH782vTa6PodCCtp+RuqfEwJiwCnJMuyfFmRWIQqekrFQVDPY1Q1hNVqwIXgrdOqPDyfqmfkg2zU5grR2xDJhpVigTaQdXG1WUITWZfgjCwLwQNWuGKRSKYQwOtuIM1iH0Lqx9ZLPnPUFNEWLzJc2rrUIZ4J3VhKkpvoULD1aqL9Syp7vAGOimyMJzVuSaa3CPyHM0V+oaeE1bJt9CfTkJdXZsV6mnbMaGJhvZKjQPwtJ7uTOImGllMMaaPodq0GfMjVO/pSFEgyudWSuUIenrbk6zwRKG9iOrOX0NJiwDR3s2kKGGSqdVZQ3DuiIno0/5Orp4sqZVqe1HyfjnbMl2F65vb3n95jWvLl/yQYT3H96znE64G+egdlElfAuqM8ksgNZ4+ew5+90zXJRaGvPFnqvLZxyOd4ytJsNTtXc8HXjz/Rt2c+Pm9pbeF5DK5Ysrrt+/5+PNDTY2GxL3DZhPNpTZ1iNsDXy4IJuCzIj04owEEFWENhX27YK5zLCRDEwGy2Hlz99+S2jBxspyWnKA75EZOrpjt9tzeXHBvJ/oI3izf48cj9t1u60BadGMS2zhfUEfxu2Hj7yeGuOL50gotx9vefv6HcMG5+ZGpGzhzmm3oh7pJwsJxD5cOz2HyB7BYpbh5dvjzJ10OVG0FnbTxBdfvOJ3v/kNu6tnXH98y//41z/h3XFPEopDvicCITkk3bdGbRNXF5/vwXxaR4LWxTPsatg2SBgUV2KkbVlGEwkiGSocmnUIBWQUqMmC0802RjzrHLG0pIvIfJgmW5BfUVTT17SMLQRLAmZJFr8GMtIqsZQMW5fEb/OzPJKJPHwbSIqSVIf0bw0vCSzjaXfkG8iEI2YUASk5jFhr0ESxITTR9FyVZGCrJJAtW+ioAM02RiXKqAn2j8jzMyqbWiq9l4sWpOrWT2eQ3ghJuy4Nis6gslniCCNG9qKe3t2QnqZDQSL9ruvI0M0Qzb6XwGKz+8EI3UI9hyClopH1LFEIKsVG+rKXDA0vnvcGC36Vndw0TamI2wDmUsr9gHi3292zZM+M3t47d3d3fPjwIe0ZHt3TVXS7rn5enzxVS53B5f1+z9XVFZdXl9SSx8N27157z2OryaQ+Ho8/yVZ4uK+H27quHA6HnzyPPQBkz/f7Wmu+bxt7+GwVYmb34HLv/ScWFg+fs7W0ANrtdznYZlNNPcBXBLlnGZ+VYZC19DzNiAqzzffD8XMg3+Pz9dT2sN56vP0S8eCv2aarPSwLP7z/geVwQFSZfVMNu9IJmgK+UrSlDaEpogXH0KJEGF48syNKwUKodau/dE01d06moSZYS5wVaYAUQtIeVcQJ7RTZ7De8EuoUG4xswEFWOkGVdDZHOitzqpPEYQaWBJeDSlUYA4ZlPTTqQkGJBmUow0CmrTbyXOPWUGSzE42yee+bZihzUYgcK2KDEkbfhlJSkmSloixYrjGmSFWqWoLBOBq5Ftt5kD2mrM80AfJSgjobZTjDGiLGkIJX8GGbKjSdf1oYNhpMI5V323B00iRKYiD5tBsIHZRhCeafCSRlex/+OgbzX2IpP9gePmgzoj5jzMOM6MF33/3AVGf+4R//EXXj8mIHbUe4c3M4cDr+DwRjt7tijE6bGsPzxe+rcGtGl0gAcQysQOlJz/eSYKKTkwMJoxPolMBKSBbz6yZJ9jEo00RooYax9gXKK15984LnU+HNOnj1vPHx7sCC8/r7PzE/v+Ty6gps4KeOlwkpeYMbo/+cobNp2z55/j5xcvPHZ1A59/GpU32/j+2HvwQu83C/P9n8yS//0nF+ausyoR7UMzNRlD5N6XtmgxoCJSfUuzAMT9ZaV2ZVphhEF5gHo1earCwaIFOyDUxwTtRZM3BGgzil5cbo0JrCYsn4cPCSzPaGEbpLb71pSeJoEayAdNmkm0Jj0KNkSHip2HBKMaYSDGsQCeJVjbQgaB2tjvsOW0batDalnZJJHVUQXyAmiEwO7RVUJ6rl8VmF6KcsfFahFIedoEYGH24M6oiUl0Rzyjb502a4wyoBJpliXBME9diyFiPZQWeJWsygNzNRDTzDVcYQZEsn1qrgxiRTAvpjpGe0TmgYKo5r0C0nWitBY4bSCU8JP5tUQ5bPb9i1wtXlBf/Lf/4Hvvn6K7otvP6ucfh//5nj4ZTJqhNMooQq1XqmwXYjxBghaXwvJT3lisCa1/9+a+ydfMwQz/A8V6Ts04YES5N8T7ZDDMdlRpcVtKTnujhDnL4okwpU0kvNA61ZKGqphEhKCz1N32QuSDcsUhZZcGKs2+Qx0Kj4WAiZqZqfCyLB0NnSBkSrgEM3pVahtTmlo3h6QlbFe7Db5aVsi6WViCrWgL5gDjYylMrEsKHUSIbzyWvaE01puxAkU12KME/ZiMGKak4/b9dOkYXr68rqjow8hy4pGS0K1jsdp4TTJcEG7ckuV+34uhJzQ2JmGUfefz84fThhMram9jM3G5SioCXZTS44HWPPTb9hGSd2u0vqPujHlPLrvlCnmt6xAcwFPzpS9vmZKjnYchtb2vhAmqNiVBOkntKyhVQmIClVPUVHtDC6M1jQUjAaUw+GF4o2rAg2GXoSdhLoXLAKOjpSSg6frCImaDFG2wYaopSheCh3p5X/+7/+n/yPH97w8f17DuuRjiM+iNL4Yv8CH8bbN9+zuvObV1/z+z9snoTNUdcEvWujlAX3kryaVjgeD9y8veaHN285HI74odMrnG5uuLi65G/+7m/55uvfMO2C4ziy3p1QP/CyPAOc0pR+AG+CtEBXJ+q2FiyGu9H2OxavTMUY1lmozFpYJWjiDJsoxTIALdLzrOBogRjQm1BqYR1sTbAwtKUv5a8MKfglsPNTjOanLBw+xVp+6u8eP/dTLOfz94+P9XHA4GNw+zFwev7vDKA/BqdhY/luTVmtBdjYgvpTEPoxyPxL7OTH20NP5Mes6PPvH4L9D7en3p/z92dfxYcA+sP34VOA+2NQ/+F5/ZytyYTEym5TTUlRqkYOTIrTC1zMM7oaEQV3g0h/Y5HOHZtXNBXlMi03gH46UFoyagyBmtXb0ARA6hIUJoSV7sJMUGalNQhVugc1jJNPyPEWQVh3O1QLx+XE6jk0UAXRwUKSJnrPAS4t7ZhCgzUcicpUgh6pepklvQuh03pg7Fl6+iIGFY2BLHlvE5cM7lJHitJHoVmH1iiyQ9cjQ+Led9mWwLVn8TIqc3SOkcqv7iMDvS2BI+0ZvDvroLcOVuinQSWwqXMKY7aJ2zmQI9QQ1q3ZPVlP1aN5KjescyxKDdAtzAZPn+WTZbp6KzBiRbWghVT+bAG9NkbG0X/mNrWaXpmnAz98a1zXj7g669oZfU1v2M1lUgmQsgmBHaFu4K6havi68OLVc66uLsGE65v3rOsCnuFGw53TaeH7N9/loHLNMLN1fMu795XltHBaFsz8vnfBk4UVnsz5c/8QpIeshKQKlbNvJdn3VYGiaKs5iCcDncKCZSyE5XN7BGabxYNqWlJpDrotIq0nqrCblLsijJHM32Qs5z07Ns8Qj7wHHJeVH75/w8f3H9FSOJ1WlmUD62Eb5GdfeX4OF7nn84TkPi1y4G9kzezFN2/6bInO1kH5KGUqjYvnz/jbv/87/vCbrwgqV7vK4ebAcV3SY3gD7tieT9gIe1IB/dl690tbMtISEO0SWd8HmNasf7ZzXu4/jwl6snmphxZqGclii3OQd4VN+u0CsZE1RDM8WkPT0kHSVsPMmAuZXxNJDnn28oLpcmYsynK85XBaWJe7BGkiGJqs61oDi7zWglTK5b3HN+a4ZO5MyaCu4hCl4C6MDSTWTU0GnlYa5CCrntsPr0lyCscsfY5dtn8DXMlslHBaEZpMyWiWBMIS/CVZxpYMa90BHlvskmc2C44MS7C6CIO0xav39hygnrYlIUF69qcHamULc22SIfAk2zE85fVSkgSlnnYno5ERgGta5JTqFOms4/M5gw89g9sW7ne2vzoDq+da4awQ+vDhAzc3N/cs28fD9Yf34k8Ngs+PPbN/Ly4u2O127Obdvf/xGIM2JYB29lI+P88Z3H5cJz28b7s7x+Px/mdnm48zUOzu957F5pbAtRt97fcA732I4SfYy+fXME0TtWQwYK31Z3Zm5+1sAXK259BtvTuznefdfA96Hw4HlmX5Vb7Tj7dP1V8P36/PefzjzSPtKr57/R1Wch3zvEUCMLujksrNvCcWdM4gObGRa4hpKhHSVTSZsCUtS9GGxiAEikqCrZp5RFJKZl7JIJgpvdNFiJrncIhTFUCxfSrVh5PXpjoRg9FmfATqndjsWz1y+Cal5OB2OKJBbHZQunaUhhVPRbPkEH6SlgS9muzrsIaGgw+ipOd7xGC1QaEiK4k5DkmSWxuI/WidU4P8+6nSGjiKhuTADU9PDC+4TltcoFOKYgST5nrcGZudY8k1b/XNpzo9pRvK6GmZFB5oKCHCUEW2QESTvBeoCBGKRtZ0GmfsIfv69Kf/KwHmzy2VHmOQicr/yOQVF672z5mmxuH4kTffvWaqBTu+Z76YseOJu+WGZ9MzMENr3g6rB906iwwcpUpFI5gkOElgJehLMM1THoBnwTwsk7ndc1I6lZIS6OOKq1A83/xYO/tdgkv/8X//T9y+e8vNcUFK4fkXz3l7feRPf/wTRvDbr78iloWXz1/x7fvv0y/0+9M2Ec6byLloiHjqrHzeyf3Jj8N/3M+/zxDq12+/8nkrHfNAh7BKR8QpJwfZ0j9NqFqJqbIsCUrJlK9voaHuKc0/BUIndIe2DmPFdGLYQhkTqNOjp+esjAzOEyW2wL5SRsrElonuHaegMmjDGaWkEftQKrACTketMmo6paX0aGxSDWOJLOhCNBkzUnE3eg/QSjSjhKDb9MqmgpI7lyisLe036Ml2vShBqOJWiCWLQaFuTNfCzhy8JAtopAAuNAjN0BcJRZvQpaBhTA7dldIAN3y3EKeWwKeT6asimA/sjix0pgRbqY5HRWymkUwUKNukfSVqYJJVWZFgQSjeQIQuK2jefMo6UauhLnQZ1KmhrX32Z6dJ4+tvfsvf//3fZsNwKzx7/py2m4h5GySUwKUzSPlNIsIQIxJstxSfuRgZcCO0poyIXJAt8HqWvhk0Td8hy4bPFnLCmoJXohishpVB05LgrIHUtEJgCIy2eeQmyD16FoRaKs2EVTPl3kvKXIRgHdkQ1MhBint6y0+VzXojUHVkOOu6UFpavUh0JhWkWEqNyswY6W9rI5jm2Fj1ngyfEUxNsS6ZECvJjO91QbvSdSAxkNIyFTfyXGisKbWkMDXoqyVnbNpRkU33UMEK47gSujU45mibGNZxTYaWuKO7oJygdAXpREz0LuhUmEZDdaDThJmzHG7pCkv//CJrTJlkrrXlDYgCPVkp86LM8wvm/YzWxrN9pZdBGTM2L/lZbuCxMvog+pHd7ooqwrCUdro7IwolghKFrkaJSi2CjpWulSWWZKasjkvDtdNkZrVB9YEXoTlgM1WCUa/Q+QYfafOiPmhtBndmr7gItWUzo8Oy+RstAaRivP72W/75//tnro8n+octKMnSn/7q4pKri2ewc9bjCjdHVu94SauYVjMVuZJFWokdos5yOPEvf/wj795+QApcThN2Mj7EiRqFl1cv+If/7R+5vLygAne3nTYtLMdrpBXaXrhdD+zkiotLw6MnA0ELY7FkxJZCeKeNwU4LpZ84amxepiDWMMkgS4lsvPohJfcxCYThnucZ78S+pRVRd+aLgpwukP75FhmfYi0/BIuf8kL+JdDy4fYYAH742Ke2x4zkx/t9Chz91GOeAsAfvs6Hj1FVVIR5nnj58iWtTfTe+fDhQzJI3ahRPwk0P3UuHgPRn2JqP/z6qdf3cB+PAfjzfs9p8Ofz9rgZ/dR5/jUA+ePNxZilIhcNEd0ASOXYj0yTcyGXiAX/P2tv9iTJkZx5/lTNzD0ij7oAFBroBrubzdnhckRmn+aPX5GVXZF5WtmRXQo5MxQOh0t240bdWZkZEe5mproPapGVyM5CF7bHRQrIIw4PT3dz1U+/42otaF6gGtstqCv7buQOPSlmCxQP1uwSCr4VwVtH+4ZcOm1dIv08VYSJng7BvDsoqzulLqieImLkqUBvtOWavTXmHmy+NGVsv5LI9Lqgvqe6xpB7XbAS9wG3zloF904qkPrKkCeRPWFFkdZZpZOZSPOKrVHHFQ2VQbIYjns3zCemEkoey9DrRLaEpQgrnkOARu1QSkhL3SWserpBioGkm2CThS1Gb2SZkVypAnYIdc1MYU0NF0d6woBaW6x3WenVsaXHvbIHC8iniqkgM/RrR6ST14BzPXtkPfShbCORPUC6YBRW3CoqiRP/8AHFg0ePePv6Nb131sPKmtYR9jvYbzmsgVaNv0c0FlFvItBq4+LFS17MmfNHj3l6cs5O9qQ0AjeHe0OsobGGrYeFukqAxwLrdR2B4XZjOwADAPEIhRx+aXHtMSwBfYSliwRjK6YRiMbrYuGHbMnoew/QpBmY0eoRYB7XqIavpovgPqxKaKxDzqxSAniWaCrV3wHCQgQPhpWC0VvFzajrEucPkXVwtDlLEuuD4kf7SeJXATqLhZ2dKgPUH57B6qMTGwMJD8ZahF5GcPPTTz7m46efcHJyRhLISXj48JRXb7ZYM1py3DtDiI2qDPZkjZDinxHUBhG2hBBsOSXsJk9V6M0AACAASURBVGAorWI4ZC1qfdGGuZKQCHwieksVkCYghksOcD+lCJrWNLKOMjaABU+ZZE6SCJB2D6at5szZgzM+e/o585kgTDRz9q/e8N2r57y+eEtrhwCx+2DqAj0b3RNqDZXEZGElWG9C9KCV43mcUG0hTU8egV+EPVLrQTJzd9oYkghyE5BpLlTv4OEtaqKkDiUgPjItBkge5Kimo0ewhPoCGvlP1sJ7urUF1ejhpEfNjbZQTTowyBuu0celHJeueJSn2RNI2BEFEdDJY/ggI1C0DZa+BO0jPGUtYwp5SkDU610l1JYfuB3vi0eLipzf3dOPvsBHMHS32/H8+XNevXr1R0Fx9w3CbzOFb99Hbw+2j+zlzXZDyukWo3hI+gcYrBpe06rhzQ4BgN9mMt8HNpsZy7L8CEQ/sn1bD9tIEWFd1pvPextYPu7vTwHMR4/qaZpunlNuKah0AMgqevNYHUqOzXYLQG2VZo2SS7DmBxju7qzr+meBzHf39+7X76t9fmpzF168eMblxSV4qEDMC9l72IJZip6vESxXG3aBGmQs7Z0kgVl4Bnootr0RYZUaw3FJhroPlfUI2E0FUUc7LHRkiiQbIWyKcksgHo9ZcuB7w/kg1USblDRuIYFvKGIWCivp9DWCRtNg9ELYvYrngZ3EyNmPXu3qg4gVgaPiGUuAh899R8CC2ZwSiCu9t5G7E8r1KkHgyFmZ0gmtL2SPgF7vjljCtLJqI3I6xhBlDQ9pkYRLY98FrRXN8d5GECT7CGDEM54V9zYUPwv0ObI1cMw7tYfSq5CCUCCJlDuWQ2Xs3VFvaEoky6y+RBj7e7Y/2yID+CPs8wZSHRNCVJlKHJjHnzxCXPn6X77lsC48ODnjdF74/Be/4aUX+nfP2bcdqQq6KbScaGvIogpGyRmasO/7AF5MqD7hugZzzYIBYSRKjsU3priFpE5KE2V2fF3x4aFZWcI2QqFeV77/+gX7wwK1kreJs9NTpBmn52c8//YF+9p4mJ1N3mB1TyeYWD6KMuQ44w+viZ9J/r3/+P4UuPz/68Xv2as/e0ffbS4VJEe+miRII1ykOYkUQQUeHnzCGiwMc3JK5NrReRSZTWikKPrbCC3jgAig9SaxlK54VmZfOOSM9AA1xB1ZS0yHzGgRDYhLRlswjTRDGz6pqI3gQadIMPlsyky9cWjgmggPOCe7Ud1I2akKpUVgWpbRADisfUzepOEKkybUwvstaaeaIH1BSkI8g/dgwhNJqeuUBoMWSgqpa5LwDIuJ6co6lIUhoY1pYHUhJ0cOJY59kMOjKTCJJmCEdQTRuHBMlY5JmY9CqA+RLUiOilVowRLWREoNXxQmJzUlCySLkJylxwS6UpmQ958sd7bTacvjx49xD7CXsy1za2iGYsHkqrqC5fBidUPLiuNoEmrtJI0wgdQhTdHseG/0LJThVNc8rBoyGr6RdJhhJmMbxTN466w1vJ1sVnIfwx7rcRPUjLQI2Us6sUpnUsNyxrWSu1AxWg6WTN0rJQdbPCR/AaY1UdSgp/DpbL3SScES0WDlWhY8G2URXDM+5IjdwhqjtUZPiotRWniHuxs5J9oM1Yymwc6mJ3RqeCtkN3rq9CXsTPJE3Cgxaq9DoqjBCMsJUqFIMH5b70wt7BiaN7zVAEAdpFfUw7PSpce1tJQRngDuaQxrUjQFdFaFjcc6vlfD2whs+cCt2jU6nZNqDDXcDD1J1O6UjfPJxx8xlZPwK9UI5yMnJt+QqSwl05bEP//+v7PuGn/z7/+KLBvEQJKQVPAeDKA6X9F3CpqD59YjCEpTpmA0IEmj7TMya0gZO4Nd7NTUUJ04ejGmSSLRV8JPsWuwgxIbvFeSZEiKUThoZ0qZbVI2m0yyxPLmkqUv9DIx6cSbi9e8fv496be/5qQ85PHDh1znEx4/PiUTw6PUoJZE7gUxYd+j2f3mq+/5x3/4e063WxYzNJ2ynYzdVMgqZGYezxNJ4fLtwtvL73j59g1zEaaTRH655cn5Kx6ePoathkJAM23p0dT7kAASUjhJK8hEajFQ6cek+FSoLGMGlEhnCW1OaxpAQGpk66yWyT1BNlo18klCDsbHv3j0wefObSD5Lvh5/P1dK4nbVhT3MXLfB/gCN8+9/f7vY7/cxzS+C84eAdW7r3P78e9jTd/+l1Jie3LC7373V/z1X/81m82Gi4sL/umf/onf//73N16lt5lBx0bs9mve3sfbrKb7wOW7+3P3mNwH5t8FmY+Pu/0ed4/D7Z/fZjvdZUXfNyD4qU2SUy1xOp2xL2ERcLLdMtcZpyJTxl1IU9jyrKPyTgXyPmPSKCZMecvBoa2X1LYLHLHFjVtLjYBZzWgS0JW9VGZP5N5JkwVTNBnWD/Q2D3akQ6/ktdLLxLw0qjesrwgVUoS29sNK71ckEomFRQx8ZporK0qxzpo60hKaBTOlDY/grBnBaQePBjortYeP6JQN7w5WsGSsOGlNEawqsRaYCaDMTanZySmAv96C4agWWRluSk+gxSkmrKWTcKwZrB6JpR5e9wdZ8CSkBmTFaidbNMht7QEmqdBWAUo0gS2CfIMQEJ8tzcpSY0DqHpYgmuoAKRUbSq6pJqpUvMrPAgn/5n/+t/znv/97Xr+5CgskTVHLlikS6GvjsCwkWcMTeljyuRnWhWVZeX7xkobzVyentB7BuLVXOo6NIOxAtcKiofWEmt10KNKPpf/wG5ZQ9wSr9QhOG0WH5aLL8D4OQDnWAELKLKGUC7nyQj3sWa8n9nXh4uIty2GPtT6sAd7VyhBe0wL0aqzrIZQApcBhT/cegYWEFaFbgNFx5oQa0ocVmXEc7o9r2o7rULAhS043w1VtLQCno0WHMvKRgr0PoUjSFP1jzomO05aGtCC1mDtJM5sy8ejBI+ZcImzz7BGb7ZYHTz7is7Xx7OUbLi9eh42Ig1lHLPw1d4dLdpfn2PrhDObIeOnQEjIsPEKVLZhaBIi2HF7JGL0KXizO5ZRJVQI0dxsB0JGXQ5IB6nlYuKkjnphzR0nUoURTj5wdF8iq5Dxxsjllc3pK7ZXTzYbNXDidT/F5g/jXPL/oyLqSk8bw3OJ8EvoIBSss2cCE3BM9OWJj/9yJTCoBPTLAQzmmPez13DK0kLYHEzqCAiOzIo82N4+gq46UDN2ZszCVkwBuc4DsrpBsotNpVoiMnlAp0g1NUQe7DatCzWDQbHhNa0KPTGwcWtgopGx0dwzFNMy+NOUIH5QKHYyMaCi3ZPjPZ3W66nAOUFAZeT0htx+R8x+03Vb5HC0ZjizmI9v3aBfx4sULnj17Rq31R/f0u+DysSY4gtXHgL7bYbvH5x8fdwwI7L3feEBDWEgcwW8RwZMFU5h3+3a7Ljq+HryrM9z9xrt5nud4vxG6uS4rrTfqWn/0WeH+WumPrr07w/nje9/8ThiWH7HuHBnMEADqsf6I4YEcn3jDqm79x8fufdufqlPuqx3/nM0PlW++fk7vw3oh2bC9cLSH6jkLWBgHYGaUpHHOtwHCOuDtxupJtWOaY2YqRxWsRzZOCixEhg+7CxHsVxc0zfF4C4sZJdQRBaXSYx2zWP/jvcI2Ag+SU8s1CIECPQW6pCaITng/YBb2fUzEgFtAa6dPStGMNqjEcDUTw9KjF78TyipPkLqEbemw5Yo8JaV7CXXDsOZJ2RHd0JcOFhkAXaO/Ft1ifY1cJxxPEUZafUEto8kBC+uqLGQ6WCXxDu+JFbOHhZfkIM+ZDEgjk8sCJJo6iYx2p0sirfFeuOPj+omM5Qlb33+u/AyAWfhR4NyPzrj3fBs6oxB2uWOt8+z1GzJwsp15/Ogxr5+/4vTBlsvLN+x2O05OT7jY71jrSp4hLZFkrVpoJpAS6/XbkKqnhFQgWyQni8QQ0CKxtvUeDAgRXFa8bLBDJU+ZqivzPIE3lp1zuS6cT4V//td/5OriOibSvgKJLI0HH33Ci9ffcPLwAbJ3/vAvX7KsB6xJ+LJ1Ykp6XAgcoqgboN6fs933/D8bDL7nyT/1ej/z/RLhESMpJq+skfZZZmN1YG/B9t3lCKnqTvIeLARTWrcYvw+QzL1GYFYGCMlEt5BpzeYcBjPgIJnUlggd0QieMZxN7qyzoHuFqaNtwqXSCPZb9mBeRmUZzn5WGjKmaq1F0dFsRVwpHfYuJMI0vTjsNTyTu1o0Hj3kTyINmWJRw1e850hHzTHOdgnGpoqgTJS0IjVkXmGNAbiO6V54QuOZrgk0k7JjNUzfk1eqOxvNaDMWCVC2ug8ZRnh+WQlfYJdEr0ZvhhK+v176kMwZqGGamUxY1nh/HIIgqrEIzo6vIDkSYEuBlc5UGtIKqxjOhzOY8zSRknBx8YbHT55Q18rl9RXLWjm4s3bIPY4vJZaZvgYFQIA8Jlu9C+YNl4TkHMBdSzQ9kC1RULq1WKh9JdlMTkrWkOkpymodn2Ct10CLYrfF9DNlHcwmIjXeY78cJ+UFaRJMy7UxTQlLwWaNBFlCPixR6NtKGPbnhFcjiA0KTSkpkSSx9ob2RPOOZGPyCcZQhr4Gy10Mtcw65LTDYQNrAqqDydFJ6xJhBCOtu5AxXbBC+FqlaB5TV6iDLZFjzQ2JaESlJMthA2HDDsEmjApChONJXCNVhSmHH2BqEH6bBeuNuThVKnOaOUh4f6cCyypkyUj68KL5qy/f8Jf/5jF5jvXEUiTyzhvD/ZyHJDQnmkd4zFSi4e4e9hPeA0x/8ewZV/sdT1895hdPvwh5rAu5ZbqEqmJdGBzuBeuKl4z0YExbivulNGeaeniONkGnmMiTgCxM3mltyE1zxZmwLlRNWK3MtiVN4akqKSEaa9+kAbCyGE8ff8ovP3/Kv/7+S3ouJOm02vj6q9/TpfPZF7/gk9NzJM2cP5r45PxjSsrUdaHlRJ4TvhToK94Wvvz6K/6P//1/5R//6Z/5+OE5nz39nEur7HaXiPdg0Uin+sIigvfEpDCnM+ZUWPZ7Lp9f8HX6mqdPP4H5IV6FrcGmRGpza0rvRppmmhl1idEfmsmmNFtY3ckyUVJG7BBDAg1v7bYEK2suGTaJTR5Ssp1waY2+zOgkbKftB58772MVv499+z4Q9S5AeRcQfd9r3g2nuxvGd99r3/79XcD27ue4K2W9/f63m8WcM59//jn/4T/8Bz766CMOhz0PHjxAVXn79u1NEM0xMf02QHsbaL772e87nvd9f/dn72Mz38cQvwu63/3/bSntfX+324/5OaweeguritwoLiiPIGWSGqyJ3itrNXxtEZA2Qzk5xx1svyCs4GEx1KvgtXOolYOHnYRqxZpTZaLnUYw1kGQho7Q8rhdHrbDfV0ruiFamVECVcqJUV9YpQonREnLXrsFusYrWmR0d6RnlELWANcQz3QqFGOQ2HEmN7kQ4sxhiM6nksFDq0PIeTQk59ABpxMgWCSDmnbyWIGdoBDU3S+w0PoO1RHZHciW3hOeVQS6lmJAJWW4SEM0xSHVl6Zkp95G1kWjdyD1jacW2ocLQYZfWPWHDNzKl6G0qTs5Cr4YXY/JMbyERVc8wVdQrNUl47lsLILpKNLkyQg3Lh7dWX/zqd/zTf/tn8LeQR+DV9pSPnjyhzBP73TXfPXvG7rqFPZOEbkgHuNqtRe80b1FJpKYclpXD1Q5MSCnieLyHoYOZkFLHXIJD60d0WQcTOBjrA/oNuwvxQdUJew50QCIGqu9CAI/8XsGx3ln2K5cXl1gzDuue3eXbkfsQj46mNewp0ghq0qy4OLV2bN9wVg6HSl1aeP0er0sJ0J+jTisOC+rBaPNktB77IsNCQzSY9ednDzl/9BBx4+LVKy73O9wPuKRBrnCEUaeJkFNi3kycnT7k/OyM1jqvX75gt3R6DTBRBfKkZM2UkjnZnDFtTmGe+OzTz/jo/CGbzdf8y3KI+scc6eHpTevsr3Z8/e23zOXDPZjLEXiZHKsFazHUdrMBWYVlpLgPAFnwGp7Dqk7PHscxaOCUFgHp9EYXwcuM1ornIACIRwBbMqcNybSXBB7XcU6dso36eDtPlKQsy46+GvPpzMPzB+yuLtlnZwHKYFmrShBaTIZlX4A+rrFeaI50ZLcaQEoKXF1ckBLM/KoGq1JkqE81zsSEj/Dz8TmTBmOxOyaKdiGVmTRNTJvCyek529NN9DY4a+/sL/fs1vAz79WDcFBarGMGq2vYpImN80Uig6OBFNDmg54ZaormjLDMhEhI352OZwVPdCUUJN2DvZkV71G3io9Bkb5jX0bIs4SC9wO3dV05PT3lyFg+3r9ba7QWCoKj7/Hr169v/ImBm3v/7RrnyEDebDacnZ2RUuL6+prLy8sbJu7d+/ER1DUzWmo/uufm4Q9tZmGdIUJRZbvd3lh4wB/XSbfVWaUUVJVaK1dXVze2ILeH4rfrldv5ER9SB9wwnPEfHZsbJdgA3G////h5jvsv47i11m4gllIKU5moI1/kPt/p28f/vu1u/fk+4sGfep272+Vhz+uLF9HzeUOkID0AWo0FOgh/JjgHfOAMWqNuUNVQxo6bSyoaAZoCyRKmKcD53mjaw24jC9Y1cp9E47Ea62QfuUEKETDqc+ArPQaGLSVKNroIolH7BEEibG1cgkDma8VVIsPMdiQd7GKc2VIQxAbG4O6sLlH70PFs4XefYo1UG9d7KYFVOSiN7pGTFfeWQrYYALsJa5OxFjskA5QiyiI91A3d0VSwNrLNFGTtUcNoeCR7+OqgXePvk8Mmwy2sYp06eiwP3MIC41EHtAGhIjFLw8d+KKmyINVCNbtCzkrqTjenpfcjzD/Dg/k94PI9yOM4RO9+3odJfVKuXl3w0YPH/E9//W94c/mWizeveX3xhjcX16SkISmaCjIlajU0d7QI6sqhrayHhlOZXbA2AghasDWxhCRB2vCZ7eC9k6cNsimoCFUWJj/hkGYObaWvRilRiM7TOYfdnpOTM6xVJG1RrVxev2FddzRTPn/6GX+4/D2pGMtux6Ozc653oHa43+r6A3uUD8FvYz4zGJR/Nmr9M7ef+XbSEpsc4TEi0FImY9Teccvo1uirDgaEx9SoOJIVvFDcqDlu3qhi3ug4Gw1ZpKkgCZp01Bwp4TGnFmmmVjI+RivuThWBllENm4iWoggpi1Fd8aR0cRKGzA2VTG9R/EwtcbCVrIms4TnXkzJLNFi1KyWFAToa0nRXjcRgV6hK7k6zHnYIyZmBXXPy2uhe2CSoaaFJppiyJsjJyL1xaDNijVqgeB7H08jVIlhEBE8rzgSSKVbx1TCHouHLy5CvmVecQpEJHUE/YJSN01zx5mxcachINE302khmbC1zmMJ+pq49mMxjkZyysWsN3QjLYvik1A5Sakz5f0bozdJWDodOmRpv3ryl1cabVy+puxWv8UepopRNiaLRO9mElhJCGwGGYQCvSclJ6Uj4PxHSu5C5FCSlSC0nvBRVMtuzLdvphGaVw27lcF3RNGHiISNue8wzvTXKFEVxDAoMnadxcymsWcMyRA33HEFwS0gC3QxKR5tQ3SAbkp3iieTQLcXn7A0piUTldN4GyAb0lNDccZlo1qnNQCdoI5TQjPD4LIS9YfjTSW9QJloewHRzmsgorLZ4vQ6Q3jItGb2lSNsdEiQOg/EyZWpvaJlQYfjbKVDRNmSY5sMPWUgSYLIiWG54i+ZwAg5rDym3wEQiSaxvScH7gtUPb9afv/ie33zxGensIeIK2pisEImsjV0Sar9muVphdR4/fcImg6QczZMf2Guj1QNtX/nuq6+Yc+GjJ6dIL1SPRN2Kk0tmTfFZRRVferCf1Eg2GKxN0CTkLNgmx1CmtGgw9gt+En7mjpP6HCyblskHh5xgNqyvtJwpPpgrZRRIFpLkspl4/Nnn5KIs+zeYKhf7a3aXCw8enzGVDaVs2LqgU6GcbslpG+tmO0CduO6NF89e8frZD/z93/9n/u4//R3XyyWXFzuWdeXxRx9zUmaWLJEoTmeTJ7bTGW1W+rLSBM62mbk/Is+Fta18890P/HLfmD56QJ0U1mAfFTFcHLVGG8WiqTPjdM9RfHmwypSO+UQGcupQwscu2TEoJBQhTRyZhW3O0A+oJGb98MHW+8DJ48/eB4Te9Vu+Ddbex9KFd+zl+5jP9wHdd0Hl+1i/973Xn3oOvGMyHb+f5onf/va3fPTRR2y3mxtfwScfPeHs7OxHAPURkD5+ptv/jp/t7me4zdq+zWy6bx/vymxvH6/b+3ybzXT3877vb3D78bf/Hvc99k9t3RMnmzJYLo1J96S94sXxaQnWyio0qdCFqRc4FJIulDJh1VjcWA6dpVeW/Y51v9KlxZAwKdVWshzItolALpkiVDfnsCBAAqgVmBAWDmz8HMFJ2cgyU4C11xgyerA8az9Q20JdagjK14oNv9WiAzRsjrIG+DgFc1alQcvkSfC+RZJR20rdZHILYolVQhlma7CQKGGRJplk4cNaPRo/zwV6AJ8yAapkD2KJEvfSIgF2k8MhIvWEeWR01KJk6aCdrUnYPkl4NlMnzFrcE9YU4ceE36KKRTBwiWbSl2AWIoVVDfEWTWhXZFiiabMIx9GK+ESWiRgdTLEP/cNDjbebmT52J+fEw/MzPv/lF3z85AmaMhcXb7jc79ldX/3ofJWcornuAaR2Mw77HVeHay53l7y9umRdFroFs1UlrD6QEcvj0dN5YIyEpRRRv7oGcAw3TOWjTZFoKENFYFB8B3HhCM4Y/YYpXnn1+iUX12+xVllbo7dOP4aRGbgGicR6RyW8x1UyZSqUzUQTOOyuaK2G/YPE+9kIycMZIKqAdfrwiAib3wAVw8E6IPQ8wdmjM7744pfM08T332345uuveesdqQPED6QaRUhJmbeZxw+f8PSzX/Dk/CH7dWGtB3bPdzeABwPQXPvK2o25bMlJoU98+slT3l5f8GS/44cfnqEpAIC6rvSl0q3ivdKvr6np8MHnjotjUtAlbEdSatEbDSZ6Fh/+wBLh5pHcTCEFoaYblgSXTLI+gsEdJDyMWQwtOmTw0GfwHmG6uTvkFe15nC/D/q9lxCpJTtjvg8FHycj1QspCmYSdQekdZwrJtSjSCbBdAbMxpI+BPx5KtvDJ1kgV7I4XAe8DSE547jQb/qCiWFM0VZwM4rgaVoHkFFXcoMyZciKcTJmPP/2YJw8eUeaM+xRepG2l7w788OoVr1+9QlILdiWZ5DF4oStJOrjSk4WNDiWYijXuTb0TrMJBgFGFKjVy611JKhH4nAK4FwNPc1jXLX2A6uGVnaZQjKwNBGeRTJY4Ch+6vXnz5oYxfFuddQz0a62xLAuvX7/m+vr6j4bTt+uHI7icc+b8/JwnT55wcnLC4XDg2bNnvHjx4sY7+QaMHozjI/B6rCNaa+ScmeaZPsDu2/flnDKbecNhf7gJHn4fk3q73d7UKIfD4UeM6uPr3VebyeiJfvLaG8/tvdNb/H1ug9y3j8/RgkRVb/bhqP46Pub2kB4CZC6l3ID/d7efAy4fP9f/iO3bL/9At07BYNII1p0E7SVIQzgLLQZtknDPYWVHRyW0wm5hK9hoNIshXndDGaFzyXHPKBPJa/SwtFBwSgzXC8QA0QyVQmMo2Ibq2jzu7RoO+pAayoRgg6zqQ6UUYaFd4w7hY1bZWdE+hp0eQbtZNEiOXZlaqLM7Uacc11zaGIz5IDOYksTxnLC1UVSoFiQl03izvgbw7GsLdTIG3lklFPPejgNhw2kUmXAavSSOdYxlJ3mme4/7dgqbT9YxoDsO8bpAUczjuIWRkA2lRRlD6bAMdQlCnXjYjRo5FFtMKPUmZPd925/twRyi6ihQ3pF3/UeEZ8OG//JE606lcr0/cHlxxadPH7NI5/zhp7z45jsu3r5kTnOky5rSUIo7JoabUjymdK4aBaNKnOAq4bPmmTRSWEspLNKRET7AGuxL70ZOTu+OWtghbDxzqDsabYRZNMqUeP78klaV5p1JEi+ev+D07Ixf/PIph/96jWj8TiWmsD1G84wy6T3A2pCs3TpuH9LOjOzhP/EX+dDtLqR9/P6noO4Pn3LhGZMMudOLkXtHaw8JOJFmOWmkESPCjkoWp1jCqGEFkaBqMA2thW/WasMzrSkbCSp/6yWm2tXRLWgdHqpDHpdLAPOrRVKwjWCSYHfOmAbIml1wUdpBorHKTs5QW0Kb0qZKEY0wix4+zC4NslO703sKUEwFaiMk3IpwoEsGFXIT6E4IzWCSaITWGBSFF6oqU4feEzVD6hUIPxzx8AgSMnkyah2eOsHbJXXDfaImI9kwZHOn0pm74lqQBrigWUM20jpalZQzVoxOBKuVCVYMXePm14uhPeQc+EzRChKNZBelpIq1kI601khTANZiQq8fzkKtVwsX1xdMc6buF97u3vLD99+zPxyCPZpjaFFrC8lrKpArycJXWNTCiyhlTDM0Q6VhOZEXo6nQR8q1tIKWNrycnU8envH46cc8OH1EawvffP99HI/9jFeLeLFiiHSmkpDBxPSsnCTBp2Ad7wdAWlrCUo6gjpbpOVLgs4R6YpWO5UxqK7YKizt5gLOulZyVTRZMJlycacpj+p5opoiFVUjx8PXzFrLTSYMt4NoxiXVR9508TUi3sKlI4fEsOYEJ4o1iM55shPwEAGLibLxQk8b1oylY+ppx6zTrNxYY6uHHJ9nDW7MK1Z3MEt7TKCYF6RVkpaUcwS29sreJlJxC3EzzRDT6P8PPkp3hfY7QltTJdHopcawPlXxoyHbL8+fPefPyin97NjE9OYMWzA1SJteVapWDr7x89ort5oyTk7+kzELJM8iBdkjYXiALSXMEI6QJ3EhaOSyOSaakGgELRzm+hMxbbIU5UVsiWYXsdGnR+I3CDS9oDxl5cseoZDJUQdKEl0gfV4PN6SknZ6dctWtOTXmxc9I088tf/oqkOfzNNplH5w84OVe2ZzNG4/vvX/Ld8x/44btvuLzec/7oz/lFLAAAIABJREFUnBdXr1gnYepbHp6fYg36AqefnHIqZ1zXyonPTFNid+g8VGHNhev9NYeUOX808fDsjCcPn9Ba58vXL3iaEg8/LVAatkYqfCcsfUTCsENMWTESg7XgG1SXCNfKKdjLGv7p6p1FjalLgHCyDdsSM7JFiKcV4fzs9INPnfc1UMevbzcDRwbM7a+PrJ7bz73dMNx9veN73n3/49e3LTbuPu++59zdt9vf35W1Hp97l8WrqpydnvHJJ5+MhPQTHjwo7Pd7Xr58OVg377wbk0ao13E/jiE9vfebZPa7n/P2e99tDG9vt4/dfZ//vmN4+3n3SVzvAv+3P/fd1/85IPPJVNhuFGmK6IwdnB0HdCPMPuF2GPOiM/ocqqnlcE0vIenPHpZhy9pYDtesyxKAGxA+h0KRgo+mRhB87mGbUxXbZCatZMuYVOqqlLbS6oEpaVhwrcZkwiowN2VJMVw3ErveWL3R+4FIOoFpTWG5ZFE3t0TUXj1+5pKYZUJrxcoKJhSdsWV4CnrGktNs2CKlWOOsh7/iwRXXTiaxErVUyp2UBVuDAdjJTNbxlOgnih4M6YasiTZBmqDVjmQhrZ0mYLlwsGiyVBPQwkfWUgBRmpllobY4tp7ANo20CwWZbBp5DaWPaY61OkdD2kxJEvc9TwHyiBjiu5C4a6LWQs4fDjAvfWEZAWjb6Yy/+PVv+eWvPiMh7JYlyA9HW4whgb1h4iPggrfG0heulx3T1Vu+//4HLi8viaoyAFC3FiX8kQWsYzDuIfF1kcFBDuDWjkRhYNgMBwtWCOanDMA5XgVn+J6HZhFwmhn90JDawKEPK0H3sK/wYcWWZAyY8OgXcc63J5xMJ0gRNtstOWuUAo1Q18EAEYaSyI+WGY73kFKLB3AhEoSPLIJS2JSZB48esJ0KrX3C5e6K3e4QVgg3FCmPIUdOnGwf8vTpJ3zx2WfMmxNevb0Itdnw/4zdN1qtLEtl3e+5urzAvHL24AEnm3M8GeePHvIXv/0NReFQOy+efc+zH15wtevxN7bK+jOCaasoWSPfoHl4CQ90nyREgykdqUJLhKVgFioOpsEu7CmUD5roiSD0SMDyUd9Hr4MHMcLQUBuNLA8nIZpZrcPBeXNxgc+Fk3Wl9c75JtF6pq4rl4cD+w70wYTHbiyxRBOTGWsNVR1iI0ZjsL3FR+hX9GmeBmk9uBTBItR47eN5DU73FGuJBGiiOfAEPPRnATIVzk7OePzoE7ZzQYuCFSSqEfp55uk0oZJ48/IVul5THapJ5OHQaJKQZtTDcH7WYSWF4i4BdGnU6ckmegpvVB9hXb0HMUaInul4HSQADUUuxDBgY0LzYM6SfVwP/rNa9Ddv3pBS4uHDh8zzHMFzAwBtrXE4HHj79i1v3769qXWO7Nzb/47WFCkndFhBTNPEycnJzeteX1+zLEusJ8d77vAuPoKvN/YRHnYTZZqo4/sj+/mYrzBN0w3r92ijcQwqPNZhx/0QkRsm87IsrOs74tndf8ftfSqwu1trjXVdKaVgZuSSf+QdfTs48e4g/vjax69TSu/AdOdHx/tD9uW4/VRN9aee9yHbD8+fxzA7TISYAG2JdYCw3WLQ2DUCt3uKQF5vw1u6y+hblJwLTRrWIHcftjAtFA1IrAEtoxrD4e4N6R1hQ5cYLOkUwbpJ45o3BaqEPWFvZMlxDzWgr6EcV6OvIU8XEoc0GPNrCzuMpkhvyLAEaj2u37UnEjHkCFwzRf86soeSBD4Sn9OQHDaNIpC60kk0IVjOtACGXdBUEAl1fK0NTz3CDZOH2kIFGUMpPA/7xbhJJ/HwXydhXinJGcYYqCeYhY4jFmotpAdJrAk+BuHmBZGOa+QiiLXAyVIE3bZmiE8InTmHcg6XuFfq+8+bP9uD+Xi6/9F5fwc0dY/QKqFzfXXJd998xa9+/TuQyusXbzidZ15Ix0ms1rFDw7KQSshpxVbyPBOikoQ0mEU4dB/etYJVA+2YVqRm1gk0pwhpkk7VFjfKtoZkSBLdDyAFT8Lh+iJo6jpA8y7kOfPpJ5+EJ3PqfPXVN/zqV59je6Nb5WJ/gYnR1iF/OVo6jFLpNvD+brMbHPfd78YFxZ2GSuG+euNDGM/Hx93+O/3oD3Tv9z/1qh/ecIk41TqaPaRNUtGkrDhZDXoA82lMoydJaFUoMVXuZWUyZaPCAig5fKzWAylvMfUxl0pYqiRPASodwhu3N8iSQALkNAs5dpaFvoInD5+g5uSgUSDqmA6frR4J780Ut5VUItBlTeCeyNkDzOsJzSuJRJHE0hvSnSIWJvZdmJJiVUhZaBKWGmYR1CabeL3SI9QvsDShWwBLvRlzknhPnMWDue3aMRO8VHptFCm03o/eFYQbl5JLsMaLCr0pUkL+2qVDVzISq4Az0k9DaSAqeG3RdCQJqwHtyPA+TZ6orGRLHLKTujNL4SDOSgufbROoQUIoPwMkPLSFH779jrY70Jvz+u1r9vtrmhm6dKwprTjaOk1DuuKakRYBYd1AUh03o4KpYr1hq4AIsySqwTR1koEWw60iecPm9CEfP/iIh08ex01EEl/332Ntx1rjZpHIrFMjHRxLGddOqkZPheJtJJl7pNFqgChdKiJ5pHGHLf5iETTaxZC8JWtI0LSGmf88Bd+htQQpMc8T07RBcXJrWO24FzpObY1VLBrqNAICMbI28AjO096RolALKnCoETDjvTIzx+BHndaCCVRosdpKo4rjHnYzyaOgcPEIW/Ia9iw1gjOSCFo1BjwizLlhkqCOxnJSLCtYDWKxJrI6NlJ9D5ooCN7D/3qTP/zcOT+d2JSVQqaK3TBFzJS6HphPBN93Lt6+5b/+9/+CTAun//5/4asvX3B9dcHnf/EF69JY9gtSnWtbefn8JZ99/gVn8ymrC36YyBN0OzB1pfYeQUalws5BMpKXMWvOZHdyVeqJUWtm0xs1ZXJ3uhh9K+gahYuMwaikA1od0ox4SMJDJzWSb/savvEabLH1cIGWiU8fPOW6Vn7x+Z5p8xs+fvpLNvPEaVbynNCTQjsor9cXfPnVH/jb//S3fPkvX/H48Rl/9e/+hvV6x8Wr5zycC4cuvHz5Ah9BY/k08eSjUx6fnVF84uBg9RK2hQcTnOwy123hZPuUMj+gpAAbpCVai+FcZ0bTOoZSCZdKlz25JjoTmLGWCAyz4pgn1h72QBF8bKArJim83zzR5oz0Gitej2tWkwWrf/rwc+cuwHufjcTt7a71wvFx92132Sn3AdY/9fw/td+3/YdvM2juAuHAj5qb+5g/JycnnJ+fc35+zuFw4Ntvv+Wjjz5iv99zfX1Nbx07SknvfJZjc3Vf03Z877tg7n2A793jeVe2+r7tp1jjx9/f10jdB/T/nCatzLHeeSqQwjoJb/hywipKlxkw1k1FtKP7YBz3blA71/XArq0cDit2faCZMZHY55CRa620lMEqk0HX8IFVJMBdLdEwqJHrxOrK/tDJtmeTTpAU/vstzchqrFoHjOyYdMoC+9bwxSkITXxYLgWbWSSyClZvsaYbeE4BivcCE5S00M0oqyAnio/QWRFn7UbyYNsk6zQySRONUEKpGFkk8ghWJdmQgeZM9QChWDPVBS+d0kBrprUYRqXo7lgRShdyUa6XsBhDow4Pr9YahB8ckwbemSj0Jag4IjJqhmA/Z6/0FOsPBrI1bJE4Fq2Sig4g3PEsTIeKWPtZuQFf/+uX7JcFJHF6suH0wQlTSbRq7PY7Xr18zu76GrORJxNcKdxyDHbdaeas+5XL6x3L7sD3z75nvz/cyLeFUNUpgstR0h0sZEk+GGYDGINgNalwFEyKxPcBlgWRIqvieYDMtYUWzuEYjhQgvOBSkS64x+tGMF74a0cTNNYRhJTT8SLEpqGeUuHswUPmF6/JaU+TPtYdi14tMHTUw8YBCTZYtwF1CzA4rkgAf0NYRcmF0/MTTranSCo4SzCRTVANwCyVmSePHvD4yRMePnpCLok3b97iS41bsTuY0N057Cvf//At3lZ6q/x68xvssOJD6v35px/xq1/+ipISva786x/+wN8u/w/7ZUfvAcjrzyAaTkQ9Lu7kFPV5yhJMYquDFVgwCf9qFaEO4kkRcDVkkFPCP3T0nD2RerC+ewHJMd7wDuIV13EAO2iKkD2nsZjjV29Z+sr5fELWzF4zXuDqesfu+g2HXQ2gpAYLzUYmSVKhipG7BfGoHz2UHdcUvUkPD+OcoY/wr8QAQpLhLYYwprFudgcfdh6YBVmpK1kVVKOXKkLKyvTghO2m4N3pS0e842eb8D+WzKlm6uMzrq6vONQDWCOJRXCmZ5I22rAV7EPVOHlksXSJoE7QmOuw0i2TskUIqguWfAxmYx0qbu8whBqqS7SjZqwpvcMOGmQdcY/24fXOfr/nxYsX1FrZbEKp5EAfoOlut2O323G0mjgCxrnE2n3DXE5xXfXWqK3dsH/3+/2NLcNdNvBd9rOIIPouCE9Ewmok53fPFSEPP+Yb/+bx/M1mw2azwfEbX+fjax2B22NddASae+8/8ly+b/upWuMIfC/LQs755tgcE7huD7Nvq7qOP799TI7HYFmWG5/rY/DhbTD/bp10d/uQuuW+WvbnbHVdgbCciPir0cNaHioKw9eEluH/b3WQ9iPssnWBHEepWx8M2nAq6EPRQo//5+RY6qTkrKsDiZSE1o00+sZg3AlOg+pIESRHfYqlyGFAcRTGupHUw2KU2M/JofWEJ0d7KCxXSUymZBSdwNdQfFmSYCprA40+WMXDY9kynhyRjiTBbIkwUk90VnqSMdQO9CpLxryjGmrmnno8tqXApOh0iR45BpDjPm01Bk7D0SmzGWtfZdUgjqXcwtXBwlq2W8K0kxBWSZThDW0eDGYXJ3us590hkWltJWelNSelyBdacaSFz3YicIH3bf9jQv7gFgH2FqJ6CwUV4HBY2ExbVCOBtbdIJr3aN15eHZBufP7pL3n26lvOnjxibZ3N6SnL9QX76z2FhdqEIomuPZgds2O7GpJrkZAq5MScHKMzWcK9gddgdN7IbAI8QzJlSiQpeHXS3JAWcr2Tk1NSDgb2g/MN2RTOlRevXvLSnTmfBvciF94ur8OH8ygzG/+9WZ+EUXjd0LzvbP5HPzwyB+4e4nuf/p7N4TZh+o+2DwWqf/bWLBJCLcBcqeBHg/6o9oPDaz2mNwZNndpsSOMLa6xDiLSoeoognbgYNcVwgUKWcN/NNDxVvBc2KgFQrEaXSrIZWgtpxEA9TZRVw1aDkqJp0fBCtKIYglYhidwwd3RyVDreIlwhpxFglaHXKDhFFSFTrOG5xFQtV7TFZL0K5Nzxlul0sveQWeTBA2mGTQXtHShBHySFXCIFK35yaBbJ86kU+irDDLEBsc9WEq2u4UGbC3taWJGkCLKRrpgWZDKshuSza4BiKkLVALwmD7/D3guJSnHwWeiWqOqDQqAs9JDLiaA5gjFkqrQ2ZGEfuLnD1fUF9Xqh+0oH9r0yNY2BgYZU1yYJ83xbiYRvpeWO14lFhWKKaoCeWYQkzoKy6gCWXZBiwRFQjb+vCmcPznn69JMoYmZhud5Td884pDfg0bRMHpYZDA9AZlisU2xCUoDf3gXJCe0Vt4Kn8EWm9bBzcaAYqRneQkIz4bRkaGqYzCQPH+N5W3j62Wc8efAQeuflmwve7t6w7MDrSmqnTLKCN9bqWFKSJIyFYpnejTYLuQuuje7hAS4u6ApdKzonbAnXPhSadUQFs4xGS45rBDtlzQE/q5Os0pbwKzQiZ6k18BTMN5GZ2ROrhr2RHTqeYSoFs7A2AKLZKEqx8HDvrSGWbmRwH7KV84yez3TNTCmmxiYFiALgD9+94Lvvv+W//MM/8M1X39B6WCH93d/+n7x89oLf/dW/4/Nff8HVbqX2hdLh4fmvePj4AT3HUMjL8JFOJUIQzXEtYdlQYDXDTZi80TVR10TZJsoa0vKWE8iKlU0wh/ZCE4s1TjO9GjTCV0vC89hTDqDdgkUvEn7E9Mxud8U3X/1ApXNyNlH38OjTz9ie7Hl1dcnHn/ya+XzDyXYLmviP/9t/pPYrPM3833/3f3H9fMdf/OYLfvuXv+Gr11eszTjZPuLtiy/5/sU3uDnTGTy53vKqHTj54rfUkpi0YJp4fLbl9cVz/t8vvyTnzC8+/V0MFjczJ9sC0oOXvXdSEmTO1H6IcNAejUVM9iPEJVmcH+qO1Vh7XZzw3Ak2UtJCX1tIjG2iTAmxCXrHN45XyJtCytMHnztH9u1PNRt3LTFumIT3MGCPz7n9+NuPud003NcY3AVU7wOn4Z233t2fv4/9ex8L+Pbnm6aJzWbDfr9nt9vd+C5/8803XF1dUQcz2czo43jdtsS4/Vr3AfB3PZr/VBN3N1zxruzzrr3G+47nTw0B3hfw+KFbnrcsdUHawkSlzlOoRtYds2bcjNWFcgBxRYpQ14W+wKHu2S876mIs1y3uVZqRvpIlsSYNpY15kC4kxbVAsP5y62g2kB7BsKmTpMI6cZ0axa45a+eYGuX/Y+3NnuRIkjS/n6qZuUfkhbtQR/d09+ySnFmZ4QopfNsXUsi/nlyRFXJlerfn7GO6qoACkGcc7mamygf1SCSyEyhUy7hIFTIjMjw8PNzN1D79DqmhOG+Z5JVWodU56iAHxmA0Qosk9WbokKFGMygx0qXRU0eKkWuKBm03XEvUX2MsLLtAEaVOSsoe/oa14YNTeo/gsa4092iUWizskgqeoxbM9Ai47YaljqSEpoS3jKhhUhkJz/vaC6rh59tFA/xJSnEi9DkJEAs6UVkskQIcySZAotLofQl3U8FbAO6WBE+d3MA62FAQT5g29hY5CVTDiqKzU/vn1zv/79/9V1qtlFQQgbpvXF5ec3G15c3r73j77h3zPMXCMr6aALsXcLV7qO+maeLtq++xDtebG+rcwH1JnV9Udxhdw49RDsW/QwCwyzhrsjRlQRw0BTtYSISAL1PGUC/gxr42FEVbsL5FQSykxrLIWIONSSB+EguSsO2I684WADtWtiHf3d7ccH5zzdnxMaux8PjklMvzN3EPswDnh2O3eEw02NqGLcwqQRd2tBIqU00JeqNtJmR9wpjXrIYRLY7so8mxdCSQJJSSWB8fhZ+/OdfXV/zww/dspwmR+Fy9d1JWapu4ur4mk3j+xXMQYbKZvgSbP370HCyYqi01Hp0+Yn10TNZgFZsY/aesyIRoxEh8f3kUauuUBEowQHFBU4CZvUNe/FK7R1CmLY1rs4SogcHgHo0Y9fBbnhrRXigIbZmfgxHfLYUztwpox+uO2Stvdze4ZrJFU2PqgtcdWS1AiWFpZvkKk44Satae0kKMMswi6Fo8bEtqakiTaFARZKSW4pi1GaGGuuWVBwgo3F7MQsIXFnG1AFmKZPIS/b2bZkorrI4F0kB3DwIBCZ8aQy4MY4JtoltbamyPscYz0uI+zZ0AgLJgFvd1ypnaDY0LDFLDPOEewFchxi5PUQt2giykJlBATcOTOYV9TWqLv3SOULjcgqX+uVtrjd1uxzzPt2CtiNxaOBzmwHEcefToEV999RWPHz++DcuTBQB2jyC9/X5/24SepondbkdrjZubmw/8m+F9HXDwPF4evH3u4E982FKKMHNVhYWxPAwD+/3+lv2sGgGeKnrLZgZuGc+wAPcLm3m/39+eh0/eYp8AYw+s6t1uh6gEYzp92OA/7ONwXu/u8wAmH8BvJPZ5Nzz5Y6q6Tx3n59QwPwZWf/yNgCShruwFUkdkxHIwcLUlfJCwl+mVQRIuiUGFyXsEH6KYBUYXQKosALQsTRsPZn9PSO/0HveFeqg2RJYMLsB7BOENWrBUo2ZJOaxykpKs4H2il2jqJc2oO1nDarX1SpYRTUFek9ToFcbov0cYaHWcFNYRAT3iHnjUoGA9rjnxjmmPTKXsFDmit46M4cAQhhcR5t4kxkVV8IWolIeBdADpe3g2J03Y8lkakSnltgqGdp1JQM+GpBYgN0rOYQNyaBLnOUd2k0yR92NBTOplCRnE8SSIHxTWgW+pFwQYNOHaw+LKCNtPoomb5ONqrR8BmKMCuYsZf3S7RT7v/uEd+FIDPFuVTFPjzeU5l1fnnJw84uuvX/D61Wuef/GCnBPrm5HTJ8ecvzunT1vMGj4QYUg4vTXUQYYj1hmaz5EqGeQ91qlQrYNqfEE41mPidjOyl2DT1hYsxRma78irDBNoKTAUplaRyWntiuHll0y18fLLr/ndv/wzT5+f0U9H3r55Db2FHL/Od0Dke9Dt3SLux0/5B6c0Hj6YkXz6sU9+Nx956r23859uCX6Ce+77bc4hoUt0CmFJUYlOVBRkZeGiGyYgUjENtp8XQVtCeiL1TssWN3Xp0BP7pQuTSCD78OCzhonTyhGpzjQ1eg3/3dQjpKxJMHaTFqYW4SxDCaaMGFRNdIxRGorRmi6MiAhZ6KOSJkMzTBIDJ55xEjbHwJWkRQKpGDZ0fN6TEVrK9ALFjNZkYaIAbqhnmia6V7I67rrYgkQnrJXgIydTsoZ/674qmoRhEmpuZBrZlFoiSK0h5DzTPGNuVAXING9ki05Y15nZgt1cHCxNDDIi3aldokOpsYgyKZgYg3c8DUzd8dYRzQFQk3AR8jDDPNJ6RbMCsZD7SUVzNELZ25ZkIb1aDWGq11XxXUGsxyKICOzJGhNd6jmsH4rgc/j0tTZQRGgqKEZWI1mj2UDqIKJ0NVYY24trNtst6gqaWI9nvHz5Bd+9ecV6r2xnSCUz73vIBHuna8GbIVmYFfIWkmqws+YpmJolMYiGD7c0TBPWO4siMdgV4ojkkDp6WlgJCXLiyfPn/OUvfsXTp08Qc55dXPKv3/6B12++p24NdUNrLKKzK3PfASVk1Sk6+lSYtWNThZG4N0j46BHAMIcU0WTxxLIS4Zsp5Ddq0QWuqmE/EVokRDNjXrrIh/CKIgeCC70a4hM2OtKJCVacOof0p+iAeQNT8qzISphaMMHVe8gZP3O72nauri/58uxL3BNjGWg4U+28fvct//d//s/85h//ju/++IqLd+dcvv2Bf/jNP3H+7gpNxmbX2W423FydM7WZx4/PePbFz0AzJYVPOd3C/1gc8YRDdNKlYpYZlqtdSmICmlbSPIGUYAJJR/vIlBrQYD1GAnFLaEmhmEgDk0UIhVpYSrBIn3wWVllos3H59jW/f/V73vzrK/y6sv56RUK5OL9mlQsnJwMF4ejojMenx5weHfPv/uov+P/+2z/w7bffcXF5Q523bGzm21dvabUyPjri8uqGTdvQd4lcZta6ZnX0BGnOtNtR2jGeGkfjETvp/Pdf/z3/z3/5Lzw6e8Tf/u1/YF1WWNvRdCCT2E97us8wrJHuwT5Uox/G1paCuaSVVIWixqS6pEM3hmPo+xRMw0GRRY3kJUMOX0ZJSkrBfuoYucHjJ48++9r5GCB6d7u/aDj8/hBb97BouA+23v/3x6we7i44HjqOw+93j+ljx/vQsd/9+0P4zPfff788F+DJv/zLP/PrX/+aq6urWy/D+z6GD33Oj52/zwVx7y+07r/mvn/0fbD/Pih///PePwd/Lshs0yLDnm6YbYXOwjTuKLompYnmiTmDzYqKYVWZW6fvJjZ9x7ztqHdyieCmKVW6LJ4UTEtQTIoAzB4AhLqSS8Nd6JaRLIweYNMkiskMXbjZDkjboTnhVJAhfJD3Qut79vOWeTODgE2NLMZMpKJXF4pPGOsIRKaTXIGCtfA0bTLHYosCPZLRI1hGMXVsFeny0hLGDJ7CMsiFIhVYrLx6gF1WoDmkLHh3Wuokjfs8S4pkc3VagsxAq+HXmiXhPTJcXCurXNj1Rk8lQK6D/6pLgKgp5j3UcQuW5yplusV6AtdwAfAlHMnCqsQWOnOi0muAR+Z9SauPvBCRz+fu7DZXUe9552qz4V//8K+IJrabG2721+z3NZjuWFhZSczRbh3RFGxdF1ptvD0/Z9EpIxK5G6JKyUrbVSbrSBdUHRavRlg4KHJgOgfILLrcBxZM02AUFoZh5PT0mJPjI+beuby4YLfZ0BawwPHDDumdJbg7agoVDQuo2CWOLPVG2F+oReBS752b6y3Xx1eUksMCMUeotd/evxp2aCILYC63wDCkBSh23I1UFkBMEymNDGOEuM2tMtUdc2uhpvTlMxPAuAtkFTRnGo2Lq3e8+uEHXr36jrrbU5sTvGywJnTv1GlmN+yZWmV7c8PKj+ilIpNwdDxSpz1XNzeQhJvdFfM80SyaBe6fs9h+v8XHbdHoQ5lDvgfmzOoMKN6MmiRsbRbGkQIiEVismtEWtlPmhCpLenxfc6xUPEdORJNKWg6vu5JckWLMOMkKWkPtNrSZWRRSZwZwxzSMI10VmkRgdVo8l5NQPaOzh1eqhZWdLM0H94NMPIKOIeZ/TY72gaa7AJV9+d66hEg8EWCVhNViR/DFlsIk7h/pFdGwdNPqlJJIqyAD5ZpJqxOs7ZnMYQ9t9rCkIzNLrJ/MlmvHYw0pKewtej/MWwJuJI/xAfFF5WkRRlgdJZNKBJyaRnPGyOHt6w4pxp7iAq0FSc+jEZ/cF1bo5ze2DiCymTHP84Ms2VIKxyfH/PKXv+Sbb77h7Ozslsl8mGcPXs3b7Zbr62suLi44Pz/n/Pyc7XbLZrO5BVLv1jx3A/6A21rnrr3WXZD47lydFnuNu8F5EIqEg5+zqt4CuneD9u4GFO52uz9pjL+/tz4PeDUzaq3M08yUp9vPp8t4dGBNH/72lk19z1LNIfywJY57t9/dnrfDOTgc44+RBz5W6/xbbKqJoYU6tA8zvSqeZ6RFg5istA74EgS9nIfmHiHA7vRZwwJSC8l9Gb8SilFnI5VEakbPIFIwJmhC7Y2chlBTpCW4VpVBCMW8Cs2cbnNYOqTAWowRavhei/uizAkFrqYVrVUQibFmyfQSzyRXKh5Ucfh8AAAgAElEQVTWnAreK0kysziZjKZOk1BSQuRf5EaAwDhYZ0hhndQ1ow6ujVIFK4XuE2qQc6PmUNvPS5MvGg4O7rgUqGHtEqBxg9wZpNC8RtO2xno0fAc1MFIzRBruwah2X/zOvZOk0CQsNjUYnlDAm9BLBJRifbF5NZIoKp1cljnBFc9B3PrY9iNV0OGm+/DRT4Ok7+my4u/hT5EcF4VXrMLZ8SN2uxt+9rOvePbiBZubHc2d7eUVZRy5Or9GqrHzHUWCtbA6GthdbPDFN9llS+spGPI96Ntmwl6U2vaxaE9C0gFSxqghZ5GQmTSUlJWp7unVGCF8yjRzpJnNPEHrXE+dWoXTJ2tKzbx4+RizxHYf0v3j4zW6D4HZ0kq/d4ben7H7EPGtUfWHp/xPzvNDQPJngcuf2Of7pz8O4Pw54DJAssV/SA0vCq2gGKkaU4puCw4Fx1rHJAc7OYPNHZWQ8M0qeGqMZLp6MBNbpOy6NaSALP6vmKN1QlTomkJO3Ro1dUpWsrcAUskUL/jQaT2jJZJINTmDO81GSukM0pi3KViEbWEtJ2dVG5qd3hIyOF4tghmAphmnkVtjcd4ghIkhJTQJH6G0fBHWWORzAg7zzkmLw5GXHIHWpsEgXQonyQ0lLyxqh5xpfcRTBGZJByvhXyZLYd+7IVpJopS0Qm1mQkh5gkmYcgqPu2nx85KMpkZunZ4MUkUEpjaQrWKSg5Uk0QxQDefG5CEzQYwyQ08ZT5H2/rmbuaButG5hdXEohJyFMaxID3uMlDNZerCZbElF75Vkwb7oVUkOpsHkdnFKz0gusdipnZ470pyaKjfTJe/eveHdFy9YrdZY7UgOD6iw84+uqzGBenSrgYPUVh1YKWppaZoIZTHOj4FUYjLo4eWYsoLtMQWVAe8h9TSNFHUdEk7DrNKt0ZtxdnLC+nhNTlBbp07fcWUTeCeLslcYUglykgtqjXn26FxK+AxT5wgRHIPVbhqM2FbjnpdkpDLAPmR/ymHh1sNru2dI0QWVbsyuZCwYdpFaQjJBNMZqSkZaozdhlUOCk4V4jgDIpWQwxVrIOiW3kL7+BKHNahiYLva040TJCUsJdePdu3P+8R9+y+XlW3bXN7h2brYzl+f/xNnrV6yPTtBx4OTyhv/+978BzazU+Nnzb3j69ITVSkg1wF/BmFcJ5sqQOj0F2zt1peTF/zclpMVCJGdQWoTSSuFYDIbK3IyYgjNuQqMiHlLa3o3UhDYaBSI9uMf41LMyW8fVeX31jv/6j3/P9XzJrm+43A68/u0fadPMlz/7mjUju1RRqSQZGU5O+du//l9plhlzpk5bNtsb/vKXv+Do6Iirqw1ejdOTFfXl19C/5+TxEX/9P/4VuQg7rXz/+hWb6x16lHm8OqE25zf/+A+QhKdPTinrIywNIdvfG+PRY2Bisj3rllBXTBRvnQKIjojt6Tkz9sw8GK0IeTYSwUa0Obx+8wlI6gvTPvwxXRZ+/dKIKM0YUserUsbx88ede2wR+LBgv7swOjz+kMfwx2wa7u/7btjdXSuL++99//0fAkQPP9/f50MLj/vvcZclnHNms9nw61//ms1mQ0qJP/7xj/zud7/jhx9+YLvdfuAheHcf94H2++fhLtP6IXD7oc/zqXN5n4Hz0N8/BHA/9Pr77OefymBGOptWGT388ckT613Gs7AvI60bvRnVIuB0sonJKrKr0cilU8WQJAwCw96RPNBkj08FH53Wg03IuGSKtIRWkNPEOI+kvPjRmpCYmAvY1Jm3E5sKx+sVPiwhs5NSd3umXqlzWE0kq+RBsClCAWt0yegqGHMwi2vkSnh0CmOeb8qsCbGGFsFaRauHf2HtNBRtUFYWjWu18EWcKzRHhoLTA6wxRa0zekJcaYPHoswXGkRrmHToI06jMZJkJjdHUscTdJ+RGrYWg0TKvRVlDewHB8+YT9TqDJJICzv6sHLpBZgFaxktFWcieYzRkpexScDn8IEVCb9Fz4JaEBt+ytVji92AdWd/s+H72oINWxutd6w3vB/uLWJRLBrBgxJjUJKom3qNxmeSkJAfH51wcnZGUeHi8px2cRWMrwNAIQt4RWJBq0AWENujVMGjdjh4nT578pgXX3/BydEZ02bCujHt92g2Qi263GMKYRe4kGE8QP64tRZV4HLPGRH0bRJKs27Odr/n8uqSYb1mHAa6zXfGnYVe40AKMkuchMU/+XYdBkiAzaqFYbXm6aMnPHv+gtMnzxnHxGazZd5vaa0jvpwPCZBccOpcuby4IJmzqxPvfrjg6vqGqQWLutlhbRdzf2udeV95+/acs5MTxv0Jm5tLhrSmWmSp7G4mprrl/PyCq5tt2IkcxqufcPG01iOIyQzJGjV/j/aQF2Ou4ZisdvBRblgG8ZHqQbbwFp7plEyWyjwLPTl5seMTEQYWD1MES32pXwHinlYB752WiBwiE1pR0mJT4ugCFgteHdO2hNulqJk9IepoiqZ6N0jJqR5ycKcEY52w9NHmIODJyGmK9WDPiwwj7CO6d7yFV7wJMW5VUDriQW7yhXndrbKpW07LKXkluHVqV5KGI3fvEVp+eXFDm7bU1kkSLiHWnW4NLIeyw1mUfwNZKt7DVq4XxVNYFNoCFagb2YPNaCzLm0hBA5EQpQIiYT0nJNKQqDXR6x4lIZZDDeIRfPy526GWuTuX323OqirDOPDk8RO+/PJLXr58yePHjzk5Obm11IBg4e52O66vrzg6OkJEmOeZq6urWz/n+/P64T2CAd+Xa7ndeisfmuqHn3vv5JRoy1rwAH6nlEg53aquDp7Lh/c4AMuHnw8g9oHRfHfev88u/tztcN8ewhEPoX4BA/ltbXe/jhG4/XxO3D9CgOe1Vab9dHvu7pMZPvZ9fnBcy3vcf+7Pqm/ubDYnTGcYC9JDxRrXqGK0aDyTUEYydQlkDeZrmTMiRs+Gdllscy2sKaRBh0GDzZtyClXCIGAFsQalI1Q8JTys50mL6lo8474PQhVh6aPNmVwZ0kwZCu4ajudqt7ZA4oamRKLQ+g6yYu7MNlN0tcwxnVqNMUeguJuhFuB1s47mZTysndmglMP11KBn3BJ2FDWSNKWqIdogoitCQZ4DP5BEYGIeDdHeBMmVVBytkVFTqbdhpUkGetuT8uHaP1hcZVC9xT0XKhRWjVh5thAUSaNbRofwIUwOZk7pFvO1RFj9Ya3eJZNIEUwriU/dLZ+/cpf3ndVPX5oHsPI9u1aIbrurcLPfc3R8Qk7Qbebi3QVX2xveXLxhNa75+ssvefP992ynHc+efkneXUJO+K4itQZwIZmp72mayBBsVgkquFsnMy3VTUwm+2nCLCYDRqFOM1bDENtThK25hIxildesTx9zsjri/PoCyYn9bqLNnau3l+y3W/7mP/zP/OF3v+fm3TVJlGE1YNvOw4F+d8/E/e0TxcQHjewf5T3/yWs/9uc/bVj5ie/7wSujm6OeSTsDqXhTqoD1RDZBtdNTFD8T0YVNhP9vtUohASXM3THoRiqGNYGmSBrAK0iBNIfXNh7d8Ryslq6R7lurUbLTS8F6+GT5AoBpF5o6uRk+JLIYdW+IOsMYQEUviayVPi+s5T6TmPGmDNmYPYV0DI+wBgNLYQJfs2M1rjWRjJSEdw/vYCmYFEbbhz+OCk4JjoG18N4qhqZCl8TgkRg+Ztj38OcJZqmhFq/sKQY1COa3DGEkn3swKKtMSCrkqeMi9BwSN4md4Z4jjXr2SG5HoXWaFqR1vGiAlU3xVKMzb1FAtkEj7dg6TcEWCnz6KWywNodliEjYtOG3A2daWOYQSfWeOjMdmUBWy8LbQzoXE0AE1iSNjhveaWWOMEKxWDh6SCytKZu+4+2bN7x584onj55S58bF+Tl1XoKrk9B6Q8xIKXybQu6Z8RzprkMyUndohUpYT/S6MBLcwoO5d5QZmSBppveJOsyUPJBaMJirtIX5U9hcb3j75geUQsmZ9dGKkycv+Gqauby6puzmCD51GLphNiNDoS6f3yWOLznoyqh1DFBhVvoARcbwDZRIHu8EUyOrgRtVgq2SFvacW4M5wIG7ra7cDWalrIYY90zC27wLrQebyDyHYmApznOCxIB6pbe4zyUP9AoqTpOfUDTj7CaHVhlPctgrANvNllY3pJM1R6tTNhtnvbri+/PXyFBIOeFqfHf+Lc94hMgxfnREOT5lOFmjEl3ekmGL45NRTSKTuAk5kFLUDLMebHtJ5GWyRjOGkk3Y60DGWcs+FDZuGCENFxlpvYUNUBEKwdzprbFCIr+mz3hWLi+2fPv6e56uj7HzLeXRE+puSz45odd37PYTzx89wfKK5sJwdMzL509Z/ewrjl6c8vKbl5yenrK73vLX/8vf8Iff/pa2nzh7+gXebvjlz38Of/sfufzhnKlHqvLVruKtQWv4vrE6fczU9rx88ZJHrfGf/tP/wRdPvqIMhUGCRdfahiEXctMIM8sBkKTmmAo2VbIY0sFzj8ZEV2pSJFfcG64rigs7ZrQqowmThAdacsjmtOTURgQr+ojkTmf92dfOB2PQPbD3fljeQ+zju4zjD67Je8ygw3YfML3Lxv3UQuL+aw+vv3scd4/n7vMHIPn+4wcAeBgGeu/87ne/49tvv6Wbsdtub6W0B0nrfduKj33eh/yWfwzkvfs57u/3TxZpDzQA7p+Dh/Z5973ug+V/zgKs2z6wjUGpfaLN4Rme0kweDhJco1VBbI5a0SKs1qcJ64k0KFahlvDGxyutJ9ISsjdYjJtiKzzvGaSxWp2xWo2U4xWMmdyDibyfC2Xv7JkxadQ6ceOJVGfE14tn7o7WLRr1WTAqNMVy1BqtzwwaNlSaJOZOiSwG5o6lkUZfgMIFcJ6DUWqDRtNew8NQcqfR6Ar0RNcA8XrWmG8M0hhhzqUJuXRmN5IPFG9UjFUtYcOlIGWmUAJQaxldOWId14QMoeIxU8QKFGeahYrRLSFa8TkzSgTZROSBL17EjtWC54qYLen1Si9E6F+WCFz1hI5CqSGV7zqSbYoGcQofyM/dhjTQmDGP+bHNe3RSxBdFEeFRGyr/qMlcNZqx1hFZFC7Sw06gC3lUTo5P+dUvfsGTFy/odc9vf5+5ud4tlmXEeun2MIOpdHtPiCxMLw/yhEAuhdOzJ7z86mu+ePqMNBZu7Jqj9Qmq5+DzstgMJqnDe7sJDX9nu8V9AywMzpqQVDEWCwvNseAXuLm+Qe07jo6OuNzcLIBLNJ5vD96VlCIoSZcVbfO2UAGAhRWds/Dk6Rlfff2SR2ePGU+OSdIYS2EoA5nFPk4cbYJ7kBl2+4nvX/3Au7cXsDCUp14xFnDu8Dl1sepQZ5p2vP7jK+o+1qL73Y71uObi8pyTR4/ovXF1dc6rf30VvvY9FL7uP4WDykKYMsCh9VDqJYfk4XvdJQgfGjQizeV2iZ4kvH+TdCLsigh2GiD1HMw3zdCUmhyXTiGHrZ4Fm/kW4MwexAJrQIrwQQfRmNe7sPiqGpLCz93d8TYHOUjk1n4t8nAI9pxGAJ92X8KSAzZzT7RiEfbbFG1KLwZI1P1muIffsYiTPSE9FAAkxzKUJfjT3KiTc3Oz4eZ4Q2aAKfxfx9PM3GDqW643V5yfnzO1Hmx9EdwSKqEiEWkBHKvgTaKeXRo2ZCJcXaFrQ83Da7tkTIVkDYtcw/j8RLPAF/Z2ZBKGSlsa4Y8oOXJ8lruupcRPGHYebNweWMMH1u04jLx4/pzHjx9zenrK48eP33sx3wGYc84LM1jY7/fc3NwEG/eOh/BdRjFwa7NxQELv1k93930AwYOd/p6Ze2AjK++tOkSElDNted1hXj8wmiGY24ffD37Ph3Nw3yv6p5zLu4zseWGbHvZ/13IEFuB3ed/e+8I0jb+b55ntZnvbzP8cYPl+nQNBLLt9DL+9Tu6+7s/axj3FMl5jXSgoSZXa90gtQeKCWKdrokmjt1A4d2k4mYEa5LMeSvTwWA/FtSRHew8Ft+fIT8q2NM5LDF/q4Q2cEz4T5CoNa7yqM0IhFafWyESzLnE8EtOGWFyXY1aaE41wqYFZJSP1FGuTspzPOVE8I9LRydDkwfZ1Jc0LgbA3sksovSz83ZMJbRCkadhm+mE9buTeEIXJnY4is5NSWFt4JPKAdVwiM6n3sI/tyzylPrD0aDCTUDpEdyswLlmuHZSmlbyoncwElciZ0Jbx7EtQ6OIPL6CW6GUhMtLwISNWSVLoLhHaLBr5IJ/I1/p8gPknFtzCYf4PkNKXE2QGu3lmP+0Z1yvOr6+Y3+x4fPKMkycn7HZb5qny5NkTygjbnVE3M63OmBGFSw4vUM3KeqXsdy2+ADeaxgRdWwAsVqGo0w5y5ilYoHvp6BIoZxKBFSdHR6Q84p6Ya+fp2VNmha+/XPPqux/YTzPzvOHtxQ/s9lvOnj3h5uqSm6sdPrPcwH8KJt+1Xv7kOdNgJ/wp8PzAi98Txf9088N38BC0LR/+0Se3P7/L1YlFP1KZuobHrQueICdHu1N7ovpElkSShafYDdRJFFSDzdYnY0jR9Y3J+uCDXEEzkhKJTPcIV5AUkgIRIyuYDCGP6g2h0pNTRDBfh4dx62R3xJS2X4r0IWSe4sacM4MarREgdcshT5WInDSLVPS+SCOGAlXjeTQYTUUGxBvWZ3IOWacPS1faKt6Fas6QwDvYuF+8zZzUZjwVtAlCJpfw+s49vA+xxZoCxeaKSIm/00qTjnlBe43CxwrKPhLb1+H9K1mRfQx8FUPSHMFkJeF9QMSoaVnMqCIVhjLRciSMukLLGeaG0xm7UHumZqe0hdWRPt8LVXzxl0wgNoJVzKMwpAqalWYzOTlNM9mCMWQ1CtE5twDr3CNp2oLF4Lnjs5MaYAo50QZj6ME+8Qo2C+/e3fAPv/lHvvrqK3LOvPr2e7abG6zOseDUTFGne1haeG8M4mQfKXSwARPogyBbmKqjBYpGI6z0wsyOxgIsdgFL5LkhpWDVaasoyo+ao25c7za8/uEtxycn7KdjynoIqa4DbY5uZE7ILjOz5xAF5Jaw3sjJ8S14jmVXHsIXW8RJ1RYJZCxUnIRnKNVIHWbJJFHcK1Iy1cLbMmVBa0WGYKq0SdEhISNApdqBWQTNC1hcg7LIYrtNiAz07nQPf80DeyNLSMySd2Qsn33tpOGYVitvduesHq9JqdOnys2b13h1cuucrE/Zrq/xbnRp7LczGwWuL9FHz2gna24ur3g1Nf7P/+t/p5BIarRBaTsNSx6CnT73YPThla5gnqA43hPFjVYytBlPCc1C8X0kFBeh+cBQYtYQjBXObB6AjhZwoTeoqZK7sddC8gHo5O7oANPVjvPLc+aV4TeNoRRePMvUs28YTHjy+BlHZ2d89fQLfvb1C46OTimrxC9+/iseHR9x/fYSivDi6SN+/xvliy9e8uzFGZfvoKyO0excnF+xb5Xp4obVeMzp+gQ5PuFy3iIDPEmnjP/uL2nSORlHUmpozgxpZO+d7dXEeg2nx87xicIM+9RJaQy5e1aqNIYOfW6UXOK70ZD+uyjTwqos5mSgD47OYSHg0iKgAydnQSyHrJ1G250DZ5837twDW+8CsfcL/E8xhe+Dnoef77/XfZD37uN3AeH7C8G74Op9VvV9wPZjx3IfRFVVxnG8XeS01ri6uroNwjk8dvd4777HQ6zkjzGwgQ8++0Of766k9D6Y/NB2n4UNHy4W757v+6+7Dyo/JC/9sS0ac5m+r1gPQEZ7Dbbofh/ASjecilDCX10TPRl5kf17t6VGCGafY8gCpJQa7LaUQXPDysiwHnk8nHK8LjAeEWu0FYYzDsJ22KO7a+ZtY5oa27ohTR1pc5A2pLOfHMlEMnsFO0p4rdCErCPZG3WyqP9HJSHU6uiikLHmpBK+rrUsjFoGUnbSDPuUEZsCQEzCWKGakg6ydxJzm0MlX6GmRNcI5yUnpENdR/O3izEkQdtA7YYXJ/c9LSspj6Sk6GrNSKKXIYJ1a6ZPDc1GbYA4qYfEVSUhKQJCW2qoFUAQC8mrl6hDUo9QIbNYiPYipCSwU2oJNqU0C/ldXxK3/POBiuP1mv12g3l4T+riIOsadnLBkjxcyCFRdrPFKkICfBaNZi5OEihp5MXzF7x48YJHj57y5uItYBg9bIfipiDWGkJYSRy8a5fwP3x5D0U1czQOfPH8KS+ff8F6PbCvlb10TOJ1IhK2RW6LejWQIwHclwX+AUSVA8Tst+QlEQ+WcRk4OlrhhG/p5cUN11eX7OcItHYLCbFI/Fc0QKWjoxPWqxXdGtv9hj37yIIRJQSOzrSv4QcrFaszU92znbehMDCIwLwIo8I11Fvm+G4TGSge5yWA7iBOREbC8j05SG/MOBfXV2ynLbKQnXK64t3lO4ZxxLtR55ntZh+hWR7gOvZxu8KHNlWLsMSUYt17SzuPr9bSohjx+E5b7aRkC4gZbGoRCcM+DxY5C/M8GjTBkKZb3LOLm01zQyWszbL2pT6EuKM7s1VImdwVSwq9wnJuctalsRWsR5ewihATkIap4rOQcwIPdeSBVViw8C7Nce7FEt0D2NRqkPrBcw5voW4Sj2ugt3jeRUkSUvqM05qRaew2O77/9jX7JzPr1REiifUusbENl28vubi8CMY+FmOF9di3Rx2tNLJZAFZqcZ1KQlG6O55agN6E93OXHuegL+udhVTURSLkujipLzV6VrpnkIT5HE287rSUSC0Urwkiy+PP2O43d28bzqVwfHLCer1mtVoxjiPjON76GB/+LuV0a7VxqBMOf7vb7T5ooN+fh80iANYsrAQO8/ef/I3GemKe5/fvm2IsjEZEzPGZ8Lu2O83wQz1xF/BOh9cvBKbNZgP8eSDzwSbjoPJorYXKxA/ZFNH8uK2RUqL3UHHfDRqc55nNdhOEmOU8fqymOzz2WZu/R6YeAqN/yuY2EqGehJuAOyOdLkOs31IjudAtRrLUBSud1KNx0bwtApOOCHQUNSWJIraQmzh45keIqswFspMlbLuoRpNEmlvkLnhYQsyaKBqgd+09woMtQkAHnFkHcqsoTktC9o43peXwN9ccrGxPGpOuzkgPjMM9UaelSUbYcCAgpZDdqbYArynmw7bw/MSgW4sGSyLIkSrR/PSwh8oCjEYzxc0wDbJcbQktHWkZkxxN7mR0F0Zd1s0mmENyQQ0m9cA8yVB7NGBSkD2bVySHQjaTkCRkcaa2zP0aWQjmLVw4tIMMYFF/dg3bVlqG7tjY6f7xa/DPC/n7CEZ5ADRvYdalhe0Oy8wcN9FuT87KVy9fMO0qU0k8efqEy5t3TNb42V/8Be+urri+uGC3C3P/Tmc9RBiAe0LWBVQ5OnlEb1dMVHzXQIWaOhXQHv5ZkgqpNmpvpBHKak2aG6UM7OYN47BmSIXddqa3XQzeCb58/gX7VsmSScBXX33B9z+85rvvXrG52fPzX/2C7WbPfne9fNawQbg9Rw/cu4eH498PUeII9Lvzok+ByJ8xjxz29OGhPDygHArLf6vNPWj32ROrlSyFmtLEw+NF4sIvrBAqksN7uDdZJnBnKp2hZ5JH6JdIo/Yl1E+UNDRKynFDUFCfSR4BWOYNeqOmhFQlZzAKzZ1RieRx2ZFrZgn3pbui5uQ0M09LEnwOHxypUIrSaqZTGVyoFje/oWgHbYk2KtZaFIs9vMBlSEvCcAGf0B6s5+a6jKQxwBka4WjSKS2hvqRwu2KTU5iQIQYZB7qE6Xw3Q3tILQdNuBs1z1hb7Cq8B0MIxZNhKSOTI0N0wEcLMGdyyFLo7OltwExRacwtwKxBYkE86oBLQ2YP5iElfAyXArmqkXHmlGhd8aFS+vzZ187cINGYzVhJeGG3pNBsSVxdGBA9pKkRZ6qINVrYvpHc0RI2FNV6NC48Lc2HYHb3Shjnp0arkNjR54xzSf1+4ubygrwauLmZmfZTWI+kSm+KaoJslF0w012DNYFGoaeHQNF1eFFqWsBDVSqV7lHYJAnvcNdFeuNTFMOTIllpyXGZSX3g4vKc3/0+grXONmfst3tevf6ezXaLO3RThpLChy8V6L6Er4BrhVXGe8Jd0GkRLiqQlOyHhOaMU2nN0G70HEyLpIrkMXx0HYolBoeqI60Z4yhoFrKWmLxcFhFJSPdclmRvFayG37qkAhodlSwLG6cLOQkygQ8wkzmyzwd6jseB1TCw31T2mz1nj064uN7y9u0FOY0c5WNeDW8hZepcsb0xbW+gHlOOMt8cn+BSeHv+Lc+ePeMXX31DXq1QDzmjexT/ZKFPDWREJBZHmhOpNapBMqMmI1kEXtkciehNB6pkvE63rDSTjFvGSmfoFcsZYaZzjKU92sfoMifH5w1mhXktHPnIVz//hmrG/uINp49HynokpxTyboWXL7/h6198wxdnT1gPa+aipBneXL7l6u2Ov/ir/4HN+Q/83d//M9f7N8jxc/74unH1w2uOHj2jMbPZvuZyAz470zCzEUVLYbVKvD3fcDZmMplxNbCdb/Da0dFjEdgS5cio0rAx0dAoQFvChw5XxtqFruvoyss+vPhTIc8BcPRBOZaZpnG9TE54p3qwFkkhDy4YbQ4VRRhWKdv2+dfO/aC3u6nfDzFmD78/BIA+xKy9u90FWO8/d9dv+SE27kNs348xXO6yX+6zew+vPSwkSynYAiQfmDgHxvLh98N5OTBzPsYUvsv4vh9CeJ/F/LFjfujcPPTZD48fFo933/tw/J9iPt/3cX7I9uTHtsbCvLLFKorGjFKqYSkWTYMCDMFK8RSSwxRBfegMOw8Pv+4wBJtXTZDZsWL03GmuZHPGkjgZj1itT0jjwLA2fKfMWUAqrhnNicc85hrDWl3sj5yadhSLRlAyaDWjJPoIzEswbm4kLzRPSwPNqLMiMlN0oIuTeg9GTCdyCHpdrA6MipBLYlVnZg+/5dEjC+WWdaPRtCaFMiZ5XogpiURFPMPYES/0HrY+dYsAACAASURBVCEzbkazkZaNMjjzUCKgq2TKyRFnRyNI1GM5C22emacb3k3bxX7M6JseCy4LVqYVSFOi5krK0czs3pAeIHNtIEXDcq07RQtiQtWKViCFCgXvSJYgwPTPv3aePHrEu/N3C4tUA8jOShkSxWHfliAoD9sKlYWxJMu91TvinaQFcUdT5ujklLMnT9BxhN5ptXJzdR1AuQaZIuKG4CDz8QX01WWd5ghZg52VsrI6OuH586esHq/ie5stGvrbm5DLLqn379lFoV5y7P0YEBXJLVMKj/WipAivzMPIo6dP+fKb50hXfnj9ivOLS/b7Sq0TzSLE+gC8iyqaEuv1Mf/+f/oVL569YLPZ84ff/55Xr79nb7vIoMbptXJ9eckfVNnMM48fXeDVuN7tub6+ClDUHbEAvc2W/CEJS6+omDxA0WUVZx6WYL7gEXgA2WYBouyshWVCWpi4+x1orJStvZfOq4AQNSWfYIT9yWZBWlBzXDtVIr/lAO3rYU2ZBlzC5q73HG4iFTR6KlEHeiIyjsKrOEl4+moKpaP4kisCZDrWWoAP3TGvmISMXSWjuZMOVh0BZd8q2WyO600cbAhmXxej3J57hWILaLJ0EgxSDqZfNqf3Zc16CJI20BS5MWICJsv7tmiolDnCQSWF3WCXyAXKlWyF3mamDG1T2e9uyCnTVZHWMYVeJ+oc9R8pwF0tcmsVJAs1UjXWVDgkSVSL6ycMMCKIXJYx00WX+yMsENOyhtPkUKMp45aQ4vTlnpEeADa9I4RffysproEe48e/xXZbU3C3VmH5V269hM2NOoedwzRNt7WDiNzaVtwyle/sF8Iaw7ohZalRLNY2h9fnnG+tJmTx7z4oy+4ygs19CYV7XwfllJBSommyAMwOH/gvH5jD4zDi5h8EEX4q7Plj24F9nFJCNFR63UKFGA2m9EGN0etiDdIDjJ7nme12e5tx8RDD/C5A/1B9dHj87r93z/tDj32s1vrY5j18h8ObV8ktiF7SE0gnyYq+2FZ6A7UcRLrkYRdjHc+Lw4ATQXdN8KEEcJ0yuYInZe4BgJJjnTxbWEmIw2CCeaJ3R9UjpI4F9NawwzQZYo5fGspmAS4HAzcFQc6WkFqBqu9txhyQSWgahD7NjvYM0rEYgEJRRmQqdXoE35qxlyBJNVMGWij1ZRmTdMkUId9mCJh5qMP6Qqyqsc9MjIlVgxAWeRFGQmkS7gu4YqnS6AEnWWQymNcY1zVF4PQtmS0U/i4JcYtwUC14F7ApMCdJkZPkirVdzLeqJKlU0gKIB0Ymn2imfybAfAeifPjHw6V3+/94LjrTH+xHYiLImthudlxdbvnZNz+jW/h4PXn+nNOzU95cvuXR48esh4mUR968e8OTJ085P/8ByLS+Z1Rh3lRqN9o8c+CCeVPUo6MrAs0skHsNOdYqDexo1LaHZAwlOtIyz6xO1mz3Pd7z9Rssdzb7iS8fnTHvtvziFz9nc37Bt/MrNlfn+DwxSnj+ivU44fIeZr9zWj74cRkaP33aP3j6I4j1J5/zO///sbf6twOXITq4ipAO3RKHZjOqQicvaZedQZyegvmn3WgKQw+JQOnQ+hTyCTGohZQiDKUM4ZElbgwpCpGphulwJF9OoAVN4fs2O7EQmBU3pzGRO9CC+ZkloxmqRWGRden4Y7gO7KYdZUzY2MjbChJFmtgy4IvSRkeqL+BZovWJNABdyW7RtayZlg/s3OW8yx6ZFBlztGi84y1YTUrHbCAVZ+o9hBPmh353JICKk31gbjXkFYMvoYVGJuNitMHpYqS8JLnqSO97xIW9dyCRSifNGaRg4qQhmOYuSzK4S0gaffFZGzIuPRKMXaMgQw/Rjegc10AzhfkniCVmRwdlRtjZAtanDnUJC0opFmMuWIvuZZJGT8E8SknwZPQquAaIam2R2CigEW7i2sIr2YMtYS0kgI5Tu3Nzs8OuKiJO6xHklkyCkWHOwBBsYQnehpR8K/VM5YhMp/cV3ivUiZgWKp0cjDGF2mISIaWQvy2NKiTsIWZ3Sh+QFpLmy7eJ64v/xvHRESad/XZirqAGxRVnJg8DE+ERZa0FmUo8Ah0z5CoMMjB5o3p0ybNrABoeVozZahS+nqjBJQJxBsvMaiTr2FBwjNGHWHDlRipG6gObeR/i1zm8n3oCqTDnHkoGYsGs7iGz6kbuQh8XuXRuS8s04ePnF806dtDOmA3P4L3x7avv+Kc//par7QXv3uz57nf/wg8Xr9hsr/EerP0iE2cnZ0hRnp89Jv1c+I//29/w4quXcc23YGanvGWuKdjQ2mm9kkr4KCfrdM8kN1Lu2KyQA3i3lhd/QWH0ZaL3YDy1VEFC4TNKMNKwEdKMi1LUmF3Ie2MqguZokJXjzJfffMFkjdXZiHqnZGGVTzk6PeXk9JjHz55zdrzGaqKKsZZE7YpNxlz3HKmwZeCoDHz14ufMXtluZ/J4ittE2+05efSMub6jl/CIM6nQEp4SR6uR0/EJDE6dKnM3zjdXPD0+WQLHtmDw8skXnKw1Eu9nFmpCx1cxnlBiUe99wD0xW0NXmbk2mIRZC3gle0YnR8Zgu0/VydXJqwCjBQtGUCmINOZ5+vxx5x6oe5dF/FCoyv2i/f7P9/d3933uF/P3QeD7Fh0/xmq++9intocA4XIHXO6+BK7cOwf3Pw/wAcicUvogjf3uAu0+YPsQQHw4tsNj91PY7563jwHQh+M4eC0ezuHHGMkPBQd+jIH9Y5vtYz4xiSC+6hHCF/2NDq1guUEP6yCTPS4ZmaJZJVUwBcSirkFRHTFpSA2wdRTBiiI5MZJZ50IyoYwZMaWW3f9P27vvSHIlaX4/MzvHPSIvlVnFItlsNjmzs7uj3ZG0AvQAgt57F9AjCJB2IUAYaKe3u3kt1jUzI9zPOWb6wzyyk9lV7OKM5I0GKzMjPCI83I+bffZdqGskzVmEeqzcTZ1ilflwQR8HXIW2eKaRj1SSpC3WSParQ7PC1EF1IUxZohC1M61G+ERzwSAbxkigZAyHOdC14LGxgSXwiHtfwTWDM8CcVUH7QD1tw6qtjOY4QZ1m1JOBY1KI6GkrRtZZu67MExSbmfcXVBlcnJ1T5h1aEuzxvjLKzHSxp9xUlqky8Y53tweWXbIGG0oZwZCGThXzCV8Ek0YRAwFfkmyiI9Pe1YV1DIopMRSbDV+coZ5p9WrUnoq0j92abB645LmitbCbJ55cP0k13Zt33B1uCHGstU3+ytawJgMqkWInpKCWwz6NoC0LL5cDP3z/gpvbuw1Fjo15mSzoLI0zHDIkthok+wZHqJaB0vvdxPn+jGkU7vqgjc7heMPdcmR4ej9DAqWigXvbAgKzpFEtD/pBhRMJ6cStM6HWwm9+8zlff/nlNviaORz/MYOD+TPILkKqF0UptXL99Jqvvvxbnn32nHG4Q2Rwe/eW1jo+GowkFhyPC+PVS27vbnlRZrQIfQxub24Yvd0fEyIfH65guf7raTKSiHX2FiIbIzY9g3s4MpIZNGJAy8fgIOGIpLQ42dskSGiGqeWlEf6rerGBU9DMH5EUVscG5GAJ5osDY00ASAcFT0XnBEMS1HOBSsUZdAmKZMAujGRgu6bP76YwdfcNnXbCEkA+WVoQQYy0NYwQTAdFN5sAz4FAj4F0gRj0IAdulkSZKJ4DkAiInmomIr2kUegtSfdaMO/JNhRnRA4wTqSGUy5MJtVbDrKCrOedJA5JglXdoI6xWaoE6zo2ewpL2Ma3kYIHo/i9ZXm4YRb4WNICZOR3ETXBdBS0Z/AwkSFkppG+9n2gXnLfJccXppaqEMvxTxRPy4fuGcJZEozLYA8l2kLZAkJzWPMv6+Mf3vPGGCzLwtu3b3n39i2Xl5fsdod77+NTEF/vnXVd7y20ToBurZV5Yz0fDoe/AE176/TRT1c/rXc8/hxoHBH3VhZ1A4tbaz8LvhORe1C7eGF4DshVM8hyRGxM2BMY9+ehjjz4vGbGNE8/CyP8mHrq8cD6FHgYEYyeVhzuTrGS6+Cjuqj3PAZtbRyPx599tsf7f/x+fqk++RDw/Hg/f23Y//59b2QDBFXbhjwOpK2FOfgWOBmqjNIQL8nMBUqp2ftox4egFPrUYSR2piPJKRkSOOX93J1dFXovjLVhml7wbThC3/KmBJnSLifHOYozcjA9EuAuoUkIkkqsEDWggJOAs4wcZkT3HMCapXVopE2VbpYWNiksqeQV/TNTPjqIBjsRujnFjVWVakJfBxqVajkcWkenYBkKrE5dKtBwK8Q2Z3NJRYZ3QUvDtBI91+gayYJWcWYxYgy8KAzLgOTWGaRdieE58BDFNb2uVXQjfSbbTkuu8xpGeKOJMspIcoDsGCyoTFgMCOgxqJvn/Ye2X89gfgiW/gWu+edfnHCS+xri9NvYFoUx+Pa7H1GMV6+/Z97teP7ZJxSdWfrgydVTzuaZb15/S2+37KaJw3qACufTFdw0tE40X7g82zHmynE50IcgqgxfUrJ48sQUx8Soary7eQc48zRhAcsyeP7knKNWrp4946InK3Vd7jiziflix/LulneHA9O0Zz0Onj//nLevvsfOjdvXC22sWziEkglj77syHx2v0/X8V9axfNovPegX/vZLuPT/j5tHRUlGqfegj00iX2zzm1JChB7gXigBeEp/uhpFk4VcDToNj0qRoG0SrLYWqJKMSAYMaCOwKFDTUmMZQI+U7Q2HMahTXtAeihdNo3U3hghKR8LQ5kTtDOZkpfiSw4ktWGJMdUvHhoJl0JT1rN0VzJxoitlEtEaRhWE5pbKtwOzLAK30EpgrOo8MuSgFH0Ej0mpjKGIZzDG0Ej4wCUwF87Yxm4UoQS3KWBXpgYuwK05Eo6+SRXJNQFZHAA2T9A+Wrqg2ogmrCKYJhPWlMVs2GEMq6Z8L3Qa15WQeHUipsKRUznvNUMVIxkZIXksyfbzsyCS9hsynzVZgYR2Rx03BtaFR8nhNgWkjGpRVoA60GD62UmvzeGISoqe8xkaAFmom6pGJ0uAlQzkwpa9pM0FxtAlaJdmSLuxlwz6H4yjTNqkWUco0U9U4u9jBgLu7W/qd4SWLpr4KjUzrtlNQEhWh4ZFFfHHwKb2OikA1o0Wgq7L6geGwrAe8dawafZSU+oTl4KO2BGt7Z0hQNa0EZjTXbFOOKmgo+4AuhaGZ0G6xpvWATtCPNN3lhDYEHclO1shAzGKFyZTZlBYlj5UEh9YoGAwFWVlcMB806VSfWGVg2jEy1KV4sJozbEopphWKBUwVXSPDWz723Kk7zqYzLq+u2O933CyNNz/d8OM3P/Gnb/+RZam8vbvj1Y9vKKbsdmcsY6FWtkHDxNd//zv+Yf4f+Z//p/+e3dn5JoEVSh/IqIQuuAXu+flFCtEXZErD8d4dKYMqRvHBOkiGmSWruK1HqDUZzUOxDmK6hTtOxBp0yzAIoadUWpVGsnOqkLYQPvH0/JL6xW9pX3xJJzirBdWJcnbBXJI3I6swRSBzNn26dz57fs16944XbxZ0wLw7Rybhs91nsAqrLNzdLXz73XfM045Pnk+8/uF7YiKbsl3l6uyaT66fEfPmfSpwc3vkuz+85PrJU2RSjkO4KDv2Z+eUXvBYSS3JmoOROdcV3GhxS9CwcYbIntEbdUQG32yFVrigFSIGvk7UkqETkOv5sIXaSg6LppLgy6/YHoOip3//UqH/8LnvYxp/CER9vL0PJH74uqf/PwSbH+7zQxLUx/t6+HqlFEo51Ul+/34fMn1+xjraGshTs/g4lf19TdAJMD69h4ePewwAP/77w308/MyPP+tjCezj1PlfGgY8BqI/dLx+aeuSdmx7Kbg15lE4RMWsYc1ptd3LyUtTDppqkTKcoStVK5mFKnQdKTOP9M/byRmrB25CxSlRKFroc0WnKYeovTMx0feFsvmXKkI5BNNq9NmQvPQy/dwXjrpDzJli5RikjVlpaFQwaLGxWL0j3Si6pB9gEWgd14rWwEdg4oxhuHZKc6KthM6EzkhbIDIwJhPQK5WODFjMmWSlS8Gks0zJeGwOLk70gaqh0rI5lYKeC3O95PLymqnOyD4ZTBoVkQwdKsyM6Rx0pZ7P7Nugy5GyD7g9gBZ6y/vaabhXPcf2CZgLshdEnTkqa+SAe5hjboT3JCE0y3sDzrBIL9p1GyZ85Pbu7ZscsgWoTuxq5dknz/mbr75iN+/44x++45/++I/4csyAxg0AdY8c1kogmcaW9b5mXYYGy3Hh1ZuX/PTjD4y1bXJ8GL6hvqQPuD8Ap4YIjNicFpwRyrRdK+u6cnO8oa2D5XDLq1evWY7LdhyzdhyRlogiRoLCgsiJnZ52K0GyDhMoFhClqDGXytXlFU+vn3J2doHVystXL7m9vd1AWd1ISmVTpxbqrnJ5fsZumtkVIy6f8OnzL/nu4jvevbmjS9uOWYbx+rLQmrPokkOEcLytyYLcWO1wWleDk7YzPTFjYyfmsbNqmNgWsjjSZk1zP0SeV6rbkRVJwEs2cAalzkotO872O4jg2I603j763BGEwSC0MEyZerB2p5rTJTafzE1BNjabScmw8hHJLkzSXXpOj1wGUh1I9mhpk7F9byJoGL00LDQt9gAomCVzNzz9PEUHGgMfhpOEAVPP9xsJbttmLzHIkO5QySBEAxspB1dP33N8bLYToNt3MuipEkOwEEIa6pZ08ropJpCUdjcy/Dy2kMxIBZSpod0YhQyYHCNZ/gHhnQ7IcEbkyIWjUSbwEKp4epD6BDJo0dASqdQiQ8rQiupI73xPtnaEkwkeINbTX7kUNHoOGlUwrQw8VSab930kxxxQYvQccET2hC6QT/512+P76sOf13Xlxx9/5Orqit1+j5Ag6tnZ2T0Q3Hv/Gev2xD42M8pWHzxWEYkIraVdzTzP96zkZSME7Pd75nlOkHmz71pbQ3qn1ErZFFW6MZGXZUkQukMtdQNu83yRzQt6bAD2afh8+uxm6cO93+1Zlz8Hib6PWHB6zqkOe3z8IuLewmOMkYN79wzXHeMexwn/s2fzsiz34PzD/Tz+fh7Xgv+c7X01za/el2hmQkVLR+To9BBMC5UktomDe9qNTiNt/lSEFp6BsZo9eiVD7GNI1h8dSgQ+Z7h3RA5WHOHYIEeeSgO8ZSYOMUGNxP5iI8T1VFIElv1DUqXxsTI00JhSPRY5TO5udHfUBi02m1SZ8BHpI18Fabn2FN8+n2nifQLuaXcSlvcFtw4bOzpqIVokM7mwKUy284uWwzFRbAJbC8MDNiAc9wzvtFNYa2bYVB+ENEYvpK4DRFN1IqXRQ9IyzRW2tXeVvHdNvgHNkjZcvo7EeDxxI+lp2YMKpWsOKG2wKxOrO6qFpp2apvj8kmjiFzv3n8PF79ni8Y9//oWeypX3gKhZTDm7koFVL1++4vlnn/Pk8nO+/eEPvHn5iovn13z3px94d3jH1199ydt3d9y+e0exMwYrV88/px0PhA/66OwvL/FIf8YeA1/TQkM8pSpILnbL2lhaJmLvbE/0YF8L09kZt7dvuLs5cnW14927t1w9fYau2VDf3r6k+eC7P33LF3/7OT7gcLPHmzOWN3lDzjtS2h58xPH6EDj/1572S8/6i7384h9/Yb/ygGzwz9jEKqErijDcE7ibHW8CE/QOc/FkdUZOfsOS5Vej08qKmsJqSGShgibTJUSRhVzYJmc3dgw6pVRUMrCmddgEUvm/mmPq3g0tkYW2wfAJLY0ohRGV8JVaB8fIdPFiU6aC4qxiabWwTepUc8LcCKTnVVY2OV0WLx1mGOvIyTzk6wxLZmN4gokSaJnRZSEkAXcpQhsV0ZUQYR5C6JKLpljaSEju56jK3GEwoG7+f9oJn9AOGkqVlT4AN8QV3xl2gD4FOGhPD6RuPW+e2tHZEpA2o/SVRt3kHGNjFAjRKozG6gUbgWilbloCp6EDag9i/niQ0CssXSlV8HakizJU6D7AnanOsGY6/bBgFs3ztYAXoVAJgzW5NCgdRJDiOBXbZJabyAwtM3hKSnwpMA2q5HHp0uma1ikiSle7Pz5t7ZSajI7dZIjB+bzn+afPubp6QnR4ffuGn358wdvXgcSCRIXemUvLxT/JZtiq9NqwHsRuh46UsdioaYGiRtvkMR1YlvQX7x3EV0Jh9JysSgAdppKy5aJ5HehmQaFk2u5kM73viHZAinNmZxyXTqhgYbR5tzHmk1HKBhSFKeyCahNaC5fne6xWWnOO45CsL1noaqyumfrtwjAjRkfV2EVhRIYeasAkSvcMqRE1NBx1w0uCiR+7XV9e8OzJJ8yXO3wE3377LW/f/UTrOYSIdoNG8MnTa9ro7PfG21GpWnl6fcXXv/sbPr94zv7qmt88/4rdvibzyKFJ4GWlLMJC4M2BjvfGpIUlOl0GOoP3mZCVUMetoo0EQVixktyltVesZME1ohGRAZLMUCNfI9rMKit6GMhsWFVGBzsara6cI1zuzxj7AptqoS6KjDWbThmUXhiXwbTJqkSFqVSunz3l5cs33L55y82bd5yd72g9ON4tHPuRF9//gf/2h++5+O1zrs7OCIW2OIt2rqZKa0eWNRtzHUHTSt115l3n9e0r7KqABGefPGG2HSErrNCmnJyvPdiVGTWnemfpBe2O7ipdlwyWcnAzugt+OCBlQiZnqMJ8am1TFuxHwTa/bhlwaJ159+Hp+uPtMbj4OHQPPgwwf6gh+GtMkg8Bnw/Zy+97ncfPed97OAGw903U9u/T/k/soFMz9JgV/NgK4zED5sRgighqrZRSWNf1Z89//Jz3sWXeBwCfttN7+NDxfwwSPz5OD1/vlwDsXxocfMw2t0aI0+IIMWGjUWOzJzJHZSCjINrpCDuM1gbelFHzJiA2MujSg4XCcE2mbXGY8vlExq5M5ZxJLtDa6L5DayVaWvI0HcTQHPqWwjJP7LxzN88c25Lsn+bQM5RtzSh2Sgms7RNIJdhtBIFhoJE1dJ0a2oI78j4go1Fr0LqnzDSSFdhKZKBsz6aml8IsDs25pVNdE8RBaRSqOy4CkQqEiA77c+pm7aXTDpvgbJqwOnN2vqPOF8wJO+OSuQyTFAin7oKUsiZ5YL+bUX9K3L7mzjq+LlSvNO+4bjJ+i7yXOlSfGMeUwR+sYz1N5HyA1GS2jkgfa9NKiFBWwDeiwfrxlmBv377eACehmnB+dsZXX37F55//jrOpcDje8cfvjcOynb8IyAZiim8AbqAmaccQynpo3N7ccRsHXr5+wbubO5a+2Vd4srECSM9hz8HzJvkNYmsat2t+u57W1nl7eEvrneOy8OrdW96+eYOvHY0EIF2SsQ4psyUCqbL5SgeihnswRmBbgHBStZINr1bRakz1jN3FjmfLE66ePuXbb/6IiW5SZ8FMKcWYppnz/Z6zsz37/bwRWIyLix3n108oP3yHrkLLgJQEtvH0Rt+CB4MsmT08Q5gjFYZjZM2XAMXpkYAkc0+tMM87zvYTkyh3fXC8PXBYT16uuab1kUBUBnjnea8ABnW/4++++ld88fVvcIcfv/uR777//qPPnaGKDUUJoicz18jgSttq+1YirRXU8vyQ2Jq7QC2vn+JO0BOkIGiRzFyLtOQ7hRcChAUWKQVn8+yUPjY1hCFkGGZs7ysv65K9OWld6JEg/ejx53OMLYhQE4SPAAnDi2Ri6qakHCPDzLs5JSyDDEXoDDQ2hWPCVgTQy8C6JRtw5CCky6BKhglGKelKMoJQI8isleHpz6yM7T0KoZ0hM/UIull2DBvJ7nYhSsrObXgGt7G5IK6BW8M8sg5WRxibLeYWhKgtZeuaVoVoDmZAmXUQboTL/XEakmGmLbawSa3oP9ODGf4SXD75Cr9584Zvv/2WeZ6TobuuXF5ecrF5M58e+9C24uGA9wToPry/nsDZdVlZ5/U+pO9kM1FKuWcin/yedbtP11Jom39ynaZ7sPqhndfD11mWhX763YMa5yF4fALFp2m639fjGuJ9ddpjcPn039baPbP6lF9xsgo5fc7e+8/syN73Gu/b91/bfg1g/PgzfsymRQjv+PAMnEOYxOjRGGSw+Yi00CpF8Wr39xWTXIuaZOYCyqZ6cMQNNWVE9ovVhN5HZhmR4Z2K4up5XSqMLlR1mhgqg9adSTSVubZlGlQgnOZKlZpDpN6TsVxrrkFjUIrQ+sDIYY5vSq1OJzpMprSk7lKKoKTNqcZ23nkOqCCtk1zBRLBjg2IYQmfdwGOBwpa5tuEPElAjGcKSge7Da4LHm19u90gyGoEOoxJpD2qOe0UjWB1Ex5YBloHA1i3DTTdf7HDPIMYtkLiFYuIEK9iE1mAesoH74OoJvHsOjlVz3VUke7EPbL+I+jziovzFb/jg37eZb5x+faIyb81A76gI8zwzmXF2ds7Zfub27hWvXrxkXyrLy5Xqhc+ePOFsmnjRXvLpF5/x9vWRd7dvKMVY+nELCzBoa/q3qDAOC0VtC74YeRFEcPSGhzPNJQv5ZYE6IaXw5s07btqR48sj7W7H/vIcH8HNyxcMrXz2xWesTZgvDI/BTz++4u3NO3pvRBGKVXoauX7Qf+2XweB4/4P+yuH/M4t8W5Q+8B3J9uD3//0vdx7x3l9//DYib5oWUOb0bulZXDEEjUJrK1KUEkaXwRhGoWYBcMzQnF4irSxkR9eBMuFLpvWqO7EE61nfzrWguNF9pGea7SBWZATawauhUxCeSaQyOi6Cxg7tad8hlGRJe8N64aidvWXoh3Sn4iyU9EONAp6+ad4hKlsQX4LB3ZypG66k1N2C5o63sb0PwUbFi9NpFFFCGmbgo4A1vBeqCgukBMLBLdCefr6rGnUstFLAKyZCE5AuNB3MNb10F5nQkeD8MKGsTpNBHSW9jRCkWPqtbUV2kjAiTdxNEpyOnozpgNCBdqVZpbgzRAg7oF7RAC8FI/3bevsVwQlmEE6PdQu40bS92Vz/15Fm/wXHng99hwAAIABJREFUXECMZnC2L/SRFgzDoEoCtFL2aKyELxwiQXiJgTaQaDn0KIVq20DOBalG66A9mbkxBKwwS+BLyt+mzVsz1oBdAt9XT6/5/PlnXDx5Qu+Dab+jL43jcaHfDnxamYERO8JXEvLOJr5uQZYljF4WGOnrpLrZxwj05pmqSyXmZJ1MEvSoSI1MED9uYRd9MBfFR8o31xYwp1VN4Cy9o2PkDW8y1tMkGmGVBB/SsW7BQrKRqIFQUCaqTXzxxW/5/DefMs8Tx+b88OP3fDu+53C3EmunjLRtaZLeygRobTB2yRxX8FD2IVipWYSoICboVPM8/RVr0KeffEqZZ5Z3Cz/88Ee+//Y77voCReihvFsaN8c7bIU3rcGAy/0Fz55c8+yL3/Dk8oJpf8Enl1fsLy+xVmm+YAG1zkhR1vW4hSNM9LUl0G9OjGSRjeHU3qCSUnkHLM/hLmf0aaGEwyowNIMXu6AagBJ95PDibJfyV8lQu1BnPQh1chid6MGdaErL1rQLko2FFH2h6Mwsiu2cWAuHKS3laaTMvexY2sqr4x3r+o79vrL2lVeH73nx/Q/8H//7f+Z4vOH81SX//t/+A2fnZ2DpJXc4tvS4XI7sr/Zc7Z5zzsCeXlJs5u4Iv13PqRfCznZEHXRNy6TZK1HWZCexIBirCEV26JnlPc0HbkIzS9BGjagVq4pgzB16gJQcrIknGi2L46bIXihjvm+KP2Z7H2D7mD37IYbuhx73GBj9JQbzw8c9ZDM/BE8fNwIPWTYnFtBjwPYhw/lhcvrDZuwxo/f0Hh4C0o/Db07spdO+T+nzjwP8HgPqD//2vqbtIeP58X8f/u3hPh7+/eFneHysT6/1UIb7/8W2qlMlwTOPDG+VyfHIa1rWAFGazmlDtEIpyrpXpGeyei+BKZQy8LVQ6YQNEGOsQDSQyqibz38EvlSmeWIulRZHhPR8DxdCClFWdj7TgHntHNtWQ0jkADOUkIIfM/AXHbh3QgtddtkA9kiQJgZiFbUJ6U7xI05nDE21IE6xYBGn+qDXQVFLWfzYgrk1LZN8JJOvhKBjYZQMDLNm6UtoQXhjkon9dM58fc5cjVKEs7pHqqayQ9J/NBiozagEVSVZojGwrpwN6PsdNTrHZUcbzjIywKeUZCLVYzKQQajaibaiJbLZHOnZ6GEUEYZ3asvgcD9ZYmlwLIpFKqKkfvxgazk2sqAVpBSunz7ls99+ztn5nuOyZo6Hl3tv4IhtvC9bea4boSe2HIAiuHdevXqNMzguR3q0e1A62LycNdlXMQwR3/yxT8wS3XoG3Z43uL295bvvX7Db7ViXhXdv33BYlgT2DIwKoozRE1QNR0qyRadSmOpEKBwPK86C97R3c98A10jWW28LWiPD6zDOdjusGqhSCdSUWiam/Y5Pnj/n6dUTnlxfc7bfIZrXdZ0nznZ7zHJ4AJt3vJ7M5RJAONl6JOiedhcqCYALGzs8JI+9JUxuJFtvtz/j2afP+Nuvfsf52Tk/vnjFP/3X/4f+qtG947TNinpb37Jp4UT5EjOuzi/5u3/7b/i7v/1bIpw3X7zkj/909dHnzizK0I09P7YabRvao2lYFxFIFDSCUoWxbnkzRbZawVNNJ/n4VAzpZoGTIV5DFWKrzXonvGysOkdkZpD2SsWdhiE9iFB6NNxXZk1f1pwibLYQPcBIkDsSZB4jgaQxCdDS6zg8z8HQzL9gU6U6+EiPeHVPiXfIdo4DYVAzZEw0MMD7SOWpJOAT27BCZNyHeQ7thCs5CoROMinzmFZKH3gx0kM1/d5lq/s8UmOrGwu5l4ClbfdARyTXQhHyM4mhmnW/SKpFRQq1pmpNRrI+PSS9hrVDWLKgRyp3NXIIb8SvJoU9HsCe/n26N7bWuL295ccffrgPr7u7u+Ps7IyrqyuePHmCmd4zcE+s5HVdWZaFm5sbbm9v79nNj0Hmdc18m3lKFrOQYHHrjb6F5p1A51MdZKUwzzPuzjRNmNk9oDtNfw6Tfzg8Pw2qT78/AeLAZt2S7+vxQP3hMXp8zN5XCz7cHoYengIAHyqvHoLYp/29T631L6lT3geSv68e+jVbX0FNUe+ope1FWtFUtOW6WQwW2RQqfYAV3HP4qVYSIyuWWU5sGT9kHSRTkqFaZO8j3bfhgtFpmWGVOG+aYMhAw+9xPpdkubPVoaMrRiqUYpckJysVlUgPZz9SFBpbcKQao6/p++yKeaoz25r7DoyKbHYfAp7vfJLM89JIfOYEgJrMjD7QOjBmuvTMJYqgihEVWhtYKB0hhjCFpgokBiUKq3UKGVroQ5KIqB3xiuOYZxbXYMUo2yC34VGwNS28GJVeM1MpLGuwEolXLZKhfmKKijPWLWBSyHVu9M0+MzDtGQbrQauCtA/DyL/CIuNDDVHeM07T3UB+Tn+N9zGhc7F8++6GuRhf/+5rfnx9w/LtC4hgN03Mesanz6/56fUP/Onb7/HeuX5yxc3rI/vdBCE8uXzKYTlytp/46ccfWZaOmYJO7Pc7lnVhFVh9ZaeFAGw4PrYmaJq5vn7G2zeveHr9FHrn7rBSL/Z8cv2MH1+9zlO+GmdPrpnvbnh5c+CnFz8gUVhbpHm2rPTRs1j6hXCPx3jtz8HmDfz9ELAbf0kGz2CBeHRs37/98mM+8PsH7yW/0o9fjIZNmGThUKzRe8q18JYuvRYMNaIkq2W4UkRwa1vIWzYs2tN7q0uHcGJ1xCtldmqbWCPQVtKTuY2U6i1KsZEXW1VGXzGtmAdxcKI4paSsKQM8VupQTJ2wLGTVhW4GPQPJhgjFdJMsBNoFLZ3OFjiRFoQJEE6BrJXRnDEHuhr9AWOWaTCRC0zfDWjgt5WhgEXKvrsh60Bt0Lc0dRswdpKhhbXTdJOiRSEGlBhAQ2SXvj0WxJqTfolBEc30dC+IrVnn0RjdcAumETAycVnEMF2JEGQYQ8nQwRIw0ux9jAI4JdJr2UfeaAIn5kK40jTZjf1X3B9jrJRIO5DQlPfVkPQtYwUXxAtj8zhKZkKhLbkAyzwxSQbTKOnbN5hQqfhxRRg0F1DFJP0Cw4PeA+RITPPWNA7wPTpGhrhsScAhyfjyYWh0qk2sAhdVuDy/5Or6CdO8x8OpVTker3nz9ob10BjryhIt25SpwNoZPYOgVG2TFK7QKzJvwI7nRTiWA6tU9pIs5TWCWQojNl88dTge8FHSN7IWwg0v6QWodUBk6riWYJxSabDN/H8gU0FH+p1ZcY5uKDsomXpsYZTdxH6e+Pz5p/y7f/9vubi4xIrRWmc2eP3yBf3WWPtKF4MSVIeOEZohMuskRBNoAzOgavq2R9qZzFJRgzuNlNB+7KZn3Lw78NP33/Cnf/oTR7/h5tVLrs6f0r8M/Lv/yt3hFXfDeTJN7KbC7uIJT59eMu/POXhj1cHVJ5+w2+2R6hTJcEjairecKtvoRDuiJuDz5sOVAyh3ySBNHZQetKpoGxwlmDHq1jSY5QCnDMXCM3RCkk1WC4yx4LWmT2kofWQoV7Sg4XQ3VIOpGOtoqA+8G1o7jIqgjKlinpZ9o3fevV05rnfUac9ZnfAJWl9Ag76mzPByfsJ/efFfePHyVQ6AjyujBbJzqsyUmPjH3/+eV29f8Luvv+Z/+Q//a8rd24FJDJHKxdkZ9bLwybMLdlNBBLRXRBUiZfhTOMvhDa5G3c/4aFgIDGHeTQxfiUWIuWKejH2RSkSnG5k4rhO9rxkiEgkALJHsiFIVlX+GC9gj0PMhgPn4cQ+39zUij8HPxw3W49d5yPq5P6UfAaoPt1PTdWIJPX7Nh83SQ3D5IbMI/hx+c3q9hzLR0/PSB/Xn4O3p59bavZfziQH0+Di9D1T+JRbQ6b8P3/MvHYvTvh9afTx+3uPnPvz3QxD9McPpr23TPpVYx2MwGbjtCVuJZqgkS7aOIFrBp5XojWVjj446Iy39SZ1bik/UCdY7x2zgTdjpTJs6RSpKqhf2umAXZ5QNtBg+E9HZe+c4ZTr7bkyM806/c2oxJs96oLlhNe+Zpp1Ss77QcLplPRB0yjCiBtYHYRU/NLpJMrHXgkxQOxn6N5VUIUiniRItrUOKCaIrzTujKKVnUzpp9gtN0wdVq+FiSQiYJnYG8zxzfnXNbjbOSkW0Mu93rN7wtaF1ZbW0xphVmaTSemdoQVsQli61pTmHJUORZQ5sFXqkHF9HsibXblR1YhRkG16HOYOCeqS0VTYlSMmBQQknfbMDGWlT5icf44/cRktmZimVeT/z6WefcXl5jvjK67evefP6JgHLiI2jc1JW6MYWzr7BJIN6z88vuHp6zTzviXBevnqJ3h6zVziRhiWH08mGzn0m2Lop0yLtUZyBhNGbc3v7Dh89w9IC1j6QNqiloFbZ73ZoMY7HI8ebW1pSf6nTxNWTK84vnxCRwPd4PYAEzvT0ucLx0bg73DHaSt0/4dYS/BE9gXbKtNvz7Mkll9fP+Yf/7u/58vPPWDWBwsmMNjYvX/eN6xkbgHyyW0qjAdmIEiKRDNY4reGn8MOttw0QOzW6CWjW/cTzT5/zD//hf+Dv/+5fYVRe/PQ9vR+5u73dGJNls2/Y5NOSALBGsvbMhKLC9cUTzp9cUEhrgFp3H33uaDV0CFXS4kEjWNcgPZ9PntDBqIF6Ajq9GAEUka1NT7KLG1s2SMUkgyOlK7LZTIWnF3JVw9N1La0G+6AgIDNjNEI6SbLPe4eH0EbmTIRDsc2rOc9kmiQn17pRZIAK0Y0OFBOikcrMPugYbMHu2rbMI0ZKu0eqDEbkw0VznYmNxCDu6ZvtlgF9CFVJhaMrTYSIRnPAs6c5+ZuHWCpSPfuFEU4xEAxXR2LzOfWZhTXZjBkVQydIi5hTr97xMECxkqFnKhNmhTor5/tz9vuSJJ914fWh0deBxEqEscY2+IrskROkE+Qkh/8V2+P64OH98HQPHWPw7uaGb775htvbW66urri4uODq6orr62v2+z1Beirf3d1xc3PD27dvefXqFW/evGFZlp/dk4ePe8jhZK8xTROlZv6ND2f0QR8D21i+D4foKkKdJtyd4/F4Pyg/HA7Zh/RGLfW+pjnZej1WOZ0+vz0Y5p/qnQ8prR4/90Ng7cMa5wQkP8yoeFz3PDzeD5VzpzrkQwqv932fj7e4X+segNnksv/PAZhVAQ+GSdooDUM1FdlRgt7yWisTeE/7ndPZbyjNBPWFWiZ8CMMDlZLDeAnamuvvpOnh7KsSNjBxXA0tm5d7OM2dSYxYNm/l4UQtKAWXBaSm77UWbErgOPqGjUiqios7VgV3Q2sOOWBCe6R/s1qG4iFUV7ZGJJU/rERMmHTUhB4OobTRsZqsY49UDDUcGxk2KOI5IEOhr8xmjEi8C1UcJ9ZUm6KCNSeqYrFZYpSa697oOTjzVKEgM10H0tfsTQliUw4bwUQqtaLlmnnCdTBNU3nZ1AaaRKJU6SaY35UM99sciEwUNrD6Q9uv774ebQ9wZO7/FfF+di4noDJ/Hr3TAn589RN9PSBROL+45MmzJzy9ekY/HjmsjeefXnG8Wfjhpx8IW/jq0895cfOWdjzw9PKKMQbPPnnO6xevqfsdrS+4Cxe7C9aps9zcggX7sxnzM17fvGOe9kgMjq/fcbbf8+n150y248JW5jKzhnK33PHVl1/z9t1LvvnmG+5u3tA95zW//e1z/vDfnKXdMUbd6PKnhVNgKzIfHvpfald+xiz+0Pf1ALf/i+f84vYvoCKfvt9f+fTdSJ8tas/Qi5GyJ7XACywemAbSnNFIJnCF6pEBCS0ldhpKlAErWClIabBJM1bNIK9jPxB9hhF0NbSCIbSiWBtMRZAOMlmCaz6QLfyvaDJ1vZBBJS0zchYT1NJnzHtgo6B6pMWEWUufOu8Z9FcVrDN6gtMuE1Z6FnAtS3aVgvbl3ns6YgUqtgx6GFUHjZ6zMU2p11ozwKM2YQzBq1E9GDpgKNKcsMGoad2Ri6sCK3MTlihgGbA4qkEfFO1QI32mpTDoVBGGVbr3DNdI0go737PEQGygGoQqRMGJtBMJRaMjnowCVbAutACThehgUu/lgB+9ecEt5bbRBSeLuTIcD6NL0DUL6NDAIlCJLYQtvflqKewxprOZHpXjeqQdGrqPe8aOGtDWjQ0kmAxGqah7MsF6eutRnOKDYQKujNFQM9wLFKOXukmIU4o+YmNqlgk149n1J3y//5E3bwc2C3YHYY55yordgkowIoFGC6XPW3O3gfnunkCkCEcCq87UBM/THu1OyKCUKX3HRmPNxCNsZNq29IZPgrmgNVn8sWaq7ghnkoJYpUVDGIwtiDPoqIBN+RnDg2lfefrZM3ZnZ1w/u8K7c3N3R5nnDJKylBq5QkRjSEBUZs2AghiDSRUpOyI6hmFFiShklGQyMqw02vLxi4+vb/npm+/40++/4ce7l7R1Zajy9Re/4ZNPdhALf/jDN8zV+Prrr3j+yRd89+IPhGdZsRMjQrnY74D02G7DUVtzMCMp9GxbYEu4chjOmW3Tcza2k6afexFFDsm+0WqUGLRWMEugSKaFdWl5zm1BDSbgoqzhlMiJffRAyoTWlinEoSgrfqw0NZyRA4ZiEAUpg6gZZjnEWFvjP//n/5N3r9/y9u0dkzl/8zf/mtKDly9vWFtjvX3H+WHl4NCWwdm+srt4wldffcl8sQMx9vMT7tYjN29ec+zJMJrPjIv5mna84a45Zzvji8+v+c3TK+bdDhmFJoVCyyKwpAzVHPRsz2glh1RnO0oLHMWPHSjEHnQLNBjA1EdO312yieh5z20qqO9wPTLhqaApy/Z9fdz2sMk4/fy+Avx9jcapEXjIFv6QxPF9zN737f+xlPNhU/I+gPTxPh82jvBzYPhDwOvj556efwKOHzZJD/fzMDn+oST2fd7L73vPDz2QHx/Hx8fvQ/Yhp78/BOgfg9mPX/fxc98Hgn/M1g8Nd2Haz0R3ujTKks2AiyJd8JNlTlc0pTn0CqUPohs7W4CZZh3pTlRjtBxKx5T37e7BvhS0zNQosBrMnble4Bw4+oG7UplspuigvTswFqFGAn2y37Ee30EJWIPZNZuSckj5eS7YFByPkSnusQXN0JFRiBhUlBENGRnwZeocewJ4k2ZAaVXf1v4ZRZg6+JKWSRnktfm9FiOWwLQCIz3kq1CmHU8uz7l4cp7vqUB4qidEO75ESnLdsTJTpYCUDEYk/XnH6MgQDi2HxFEkLYmKsxwDqRDecEqqDz1JAqPaBoQrbnkuTxRGdIYYZlN6eqJJXCiBxko3Sab2rxBsqeb/RY3z3Y7rJ88oarx9e8ObH37gzes39HYCkRIYTmA5w9lMDFOl1MKzqyt+++WXfPHF51xenhNt8Pvf/5HleKAtC4M8r52TlQXJBPfID48SuvUvQoLS4ukM14PD4S6ZlZFEAjVjV2eunl1ydvGMqs67d+940Y70JahmPLl+yifPP+Xpk/MNuO7cvnuLd9mw3wTyRgRrDN6+ecfbmwPnFw1CNlAgEIxSjc8//5x///f/jqfXV/zuy99x+fSCdQSvXvxEa2v2BtE4roe0nErfkAw7JiXarorqxk62kiAEyZbVyKY+c1s8Q59EuScjF9jPM3/z1Zf8zVdf8PTqimKVoZ3fPP+S3+/+G0trRG+cFLyBYaKZ07NZw/hw7pYDL1684Iuvfst0vqeocHG2/+hz5+ziDJM5hw994bgeqSrZi3ZQ7ZjrxnpzopWsc8W2kGW22n2zXNFk7LkGzQMr5HHbQs+LbDYbBL0ZpW5AblGidyI15RmOJQ1IOxHxBJOlBF0UiwyzGrblwITRbTCTqr3wkRXg8O09ZVaI4Pcs/KhCXeM+yC3uwxNTgq8jPV8Ux123zypQLAGwkt/H6Okd3r0xes/eMAQtmj1sz37stG4MZFsrUzpek8SYWU8k6DxJ1ogMsEko4YwhIGkfUsJzmCaKVEdlYjcZ59dnfHb9CXWaYATr2uhvX/Pu9Uv6KIT0DDHvCRuGZu5J9kODj79jbeCqGWVjBNdaObGW13X92UB5XVdev37N7e0tr1694vLykqdPn/L27Vv2+/09mNt75/b2lpcvX/Ly5Utubm5orX1QURSRnsW3t7fsdrv7/bSetYZIkhHLpjh6aOt1qo1ONhN3d3cA7Ha7e0uKWmoG/41kUE91uv/sp9cX1fvwvYdA+/sG3790LB///Jio8JAA8Lg2O20PbcVOP39sLfLBWjV+/v5F5FedJ3+5z8ClpNKgb7agI2uCMM0BUzF8ZP6ZYjRWzHRjvmav6p7gq1ZhRMd9UCnp/asF1IlFwFK10nRkGDLCiEEfQVVl7cpUNy/gtdDDtoyfSpNUl1pLy64wYy6wCOjIe2ovFdmUMRLQIpUGGcZZ8BZ491y/pwLLplxWQVtFS2yDtI5t+VdWhRiZm6EyiBaITtnfxoLISGKgblYxm8pCagLyqiBFKEUYZD5PG4KPoBSlaYc1FVzbBC1Bb43NEtSIkOwlGUhRfKS3snRFrJAe+z1VI0CvkmGUehqmpQ9/2hptLFOSpDiNjpQp+wH/cJD6L3df/0JcMp/+c1uGYLv4SIBIVXj15i1GsD8z9k/OGDjt9sD3r1/z+W8+Y3d2wfH2D5QRROzpHa53F7ySA8tYWN4t1OsZF6e1FUcyZOSs4sfG/uqKsR7YzWesa6PUid988Rnff/s9N37Dvp7z8uYHzs8v8EPw8rsX7C8qak73lTc/3XLsd+z3M599/pQff3jBT29eclhu8O6wmcn/+ZglsvxzxvFfP5g57/iFYxof/5V8COD/+O0DNOqP3DwGMQU6NP0zt6kNFKors3jKn9gh08ro6a/bzaHtmXTdpuot2fGahQMlGBq4r4QZ0guTDlzuCN+nb2c5JgC8Jnuh+0TVtk3TCla3G11R4mhgnRgjWcYSKYETgd5pnv6wTRbEDC8OR8Vs85RF0m9rFCIKk3Uag+iSgWUMRs1iqUWleKQvYJAS2iXlqkOhMFNLsB4LYp3qluEcqlsBlym5Rie0IkWQMZiHsBiEFbwnWHmUlZoEcsIgtG/hNdMWqAg+AldJn9s1gWVXT6mVd3yatmGRIaMj3rLZ3ZKQJQprUSbr9J43DtdIi4eRxvjRnKGxDWY+butBso/UUOuIVLpncVZrSc+12BoghD4qJk6JHYXCrlaunjzj+dOnTGfG3aGxLAdev/qJ27ctA+kY+Eh5fhGjjYT31YMqQqeiJRu51StoUJtxjAyMUU0fN0PRfkvZzUzlCWVXEZO0PDBNlqp6gosUlrZSqsIBuvb0J98p0Q29dfpMBgpEyWCVbSKLHlhLUKQxzTtsKN0ahaCYZJAcQRsLCpRa8a1AG4W0V5GBdrAyEd6zkfKc3I4SGI22OKa2XepKoecgQsHUsJH+S7v9OVqU5XDgeFhhdA7LkZevf2K9fUcM0ttuHOBYmHWwEimPInKQNG2+izW9wocal2fnVFWowuLBfHfyCfy47ff/9Ad+/PEFb27fsSwN9ZVZDKlO5YJyfsZYOqM32ursz3dcrU+5fXfL5TRx9ewJhYbXCWbB3FJQu4EJud6vOfGNgKJUXVhXY6cJhbYpJ4E1oONEi2QNidM82TdiPT3BR2AieN1vbE3JsJxRmNzTN4vOOiWfTFTQpeW1DMBKa85Ut2DbEYhlAnz3xn4CtPLN99/zH//T/8az8x3l7Jz97oz/+z/+J9wb/9c//Z4fv/8Tz66v+ddf/xtsCr7+7W/47OlzZDdTbNCOjd31zP5yZi/1/6XtTZskN5I0zUfVzAD3iMj7YrGKVX3ObE93y+yK7P//BysrI7PTPS09dZJFJplHnO4OwMxU94PCk8GoSFayttdEKIyMgMPd4XBA9dX3YDg55awNfP6rL+iT0Ah5l3lFhxOePHxMTqeYK1lhHKDVFI1+yvQWPuiKcJrTOoBy8ErtjupA94b6Zg1z6ejUqVLQwdGizMuCppHkK0ukL5ykRsvhG9lN0PTpjJ67Ng53f3/8+TaYfHf7479vB9/dBXaP290H6N59nvue/y479/b+jj8f122g+z4w+/Zruvsabm+bc2az2TBN072eisCH3x9lrcd/3xe4d/t57jZVd72Ub+/jLrPovvfz55jld0Hnu5/HbXD6U5clQcZgdnYhZNCekdFRCw8/LwNVYlg7t8qgQqpplTsG8KJqdCtgC4MmOmEzULyEL79nRBLbccBzx22I+iF3sjvDfBJe8L3BUrESwVGukFth3Ah1SfQGNYOlitOR7sw2UsxJJcALTPFk1NTJSyHLwqKZLDB1I2uiqDNnAYQhQa0JZEZqDgWOO8kmllpYrDOUCD3NHjZUVh1LRsrB/kme8JwYWuL05BQ5GZEGDIU5C40DehO1SvM1mC89As+0vmDzjjKMSAVPe5aUkdLIeoL0Gzam3ORKo5AFWpsYdFjzCxLFw9tULToYJXDXITlVG+4hzZe2x8krmCyIC0U73QVdhJ/Swbs6RqK4kXMiDY4vncurK66uL9jdXGEW1mWhogowDSzy8QQ8KcOw5Ref/5wvvvics7MHbHJh5zNPn73g9Ltvub68WnuvlWGt63eRlayyZlkEITo+U9fV15kj2CEILewiVNgOhcePH/Pk2RNKOSFnZznMqCS0ONvtKc+ePOHZgzNOHz2A1nnz7Yho+JG7SQiUZJXkNwuA+eKSJ08fsNsvXF3u6d5JWdicnPB3f/O3/ON//ScebLecbR8iA+i8MB32nF9esR23vHt/wXffvaPVuoLkAQoe60YV0JwiNDLHYLfWGs068XrQqO/NQ4UnGjTn5JlHp0/47OUXnG4egATjDHEO046SB8ZhAI6DtyHA2bQBb/Ga1oyPaZr57Ze/5sXPX/Dq5WeopPCL/sT16ukLzp48AE/M08z19RUX5xdM04HWwt+6IxFQLQChBpTuMAToruJ44kNIvfkCZmF7U6OHSgieNOwyWrB202hx7xVdAxI9XUqWAAAgAElEQVR7MJo1+r9g9Xr4mWqweN2jb2i9o0lIGCEa6UFUYSUWmYUdRIYIy1l7QMtYDgOXZKsFi3V61giVzkJqnSYlPmNfcEtB2lk9jV2CXuysijEs5OUW1oDegrjkNWNrgFYRWBSwjvcS9iHJSFRME73GORZM+AYWIdqSHTFoRnzLREjmNC0xbG+KFWW7gQcPNjx49JTtw7MVlG8wbHjYjPkw023CanicxbCj46vJnvaFMHX9dCaPqjIMA6enp5ycnHwAmOdlZnez+zA4PjJwj77By7JwOBw4HA5cXV19COk73qOnaeLy6vIDuHy7FgC+V2vz/f39WFdsNpsI64MPQ2tVxUQitM9/aB82z/MH1ZGZsd/v6b2HhUY3VBRNuvrVf6/O+lC/rYzp25YV99U/H1sfA35v7+cu2Hz777drxeNncnwvt7f92HMc93+3Rry7TQz2WMNJ718/Nri/vUwcsTWrB4J41itGhCUmnF4tglilhR87gkoM2dwaSA7ltmRsqaQBshbEOrmBaI9+VwWzAha2EpJ97Ro6og0kiGg9ddISWEZpjeYRhpnMKSUIjs3AvRP6YXApsKo+ouewNWtJAniVhC8JWKhd4l6FknNcE2MbKCQqOQKRAqUKXYW2sKvwtKopAlhGA8cx6eHLfyQCVsVL3JzEoPtC8hIYkq3XjRwWP26JkjNLb6sKO4g2Zh4kgfWjTEkp1ZjwyMZarURcnGxBnkTkewDZg9xn1kjWUDJjLyzqiDR6ygxd6CnsT4WE/ohc68cB5h9h0n4K1hhk3js0Xj9ehGMnkV7u4M40Nd69/o4hJXiRmOaJpXVuvnnNq599zuFmZl7eMk+ZV7/8GdM333E2jny7+5qLNwcenp1xMzd+8cXP+Or3X3J9fo27sXl+wjJl2gzLvEOTcH55jarw5OEr9ocr3r9/y7J05usppi5ywqvHj3jz9TecPTrjUTllmhqH6wPzobG7PseoNO+4x2SBH3x5/QNYLPKnF4n7DuHHYJTb235qy3N3uz8HXn98D3/hlEFBLIXkaiAmSAYmQvWRwoS7M8lM7pmNKX0A8wXTYB0iEmESquHLx4TNQyTmuoZvLj2KMs3Y0JDWqLWQrIc1B5meWniOia6pv2GvgApD6rRqLKrklBnopEFoeyNrIeuMCKQqOA0vCU9hmq5EGrBheHJyWXAZSZbxIQA8r5swZk/TOiAxSEPccLrjKpy40BpYXpjmmOBJAalQutFTQ6yhOaRpra5hDqZkrTQP1q1LwzRBkwARexR/ZYFealzctdEcBkuULNBXoFRglER1Axko3mAxPEVoJkS4iq9SL1I0B6QoKPH2vQeaCy4FaUpJmZ13Nj9BulW0fGBLWIp6LSm0ltGeUW2oOUnDyoLcKDqSk7LZbnj12UuePXzGdtxQtiPb7cLu5gK8sLTvaFdXpGIRFKmwiKG6ys7CPR4ZGzo5VSKso1ZIWaBGE1i7Qe54EvqQ43uigi2V3cU19jjhnpjmPdfXe6alUnunLj2kdGVAyOAVSRuSLyzZEMtxHOkkVcyhzUZJI5vUyXXAljVl28OzvNUD7gKWqd5JCslDlhl885CvIZnmYD1Sp7UbVhSfhaQNsxEpTu+GaI5Go2aaGrnXYBEMAbraoTHvD9zc3OAom9OR3fkFX/7+t8zTnjZ3ltpZbCbrho5DSqtFwkyTxGCCa2fQxDBmtg8e88UvfsbjJ4+ZW+Pbb7/hwpQ+nX/yufP6/DumumNH5fryJqbp1ima2c2ddjPz9NEZoomTB4/orjw4PeNke8LjZ49xS7QlMd3sYwjTevgBi6JFWSyaw42tgPnSySUzmbJvjTwYiQhU60lpKDkrJcX3lRxhL4WMNQ3pbRKKL+EdTMZ6pk8dNhVjBIxNE2bN4TXoUD1k6Ton0LB8SXLCIpVhWkibDZkR1wS2cP3+HU+ePubR2UNIGp5f25Hr9xc8yMLN9ozzyyv+7Q//k7/65V/zq1/+Fbrd8tUffsd8fk1dDH3+jNPTx0hr/J//x3+FIvzyi19xerKh9wPDJlNb4Ref/ZLHj59QsyHW6R3SMuMqkSregt2Qkqz+7xN91xk3I9NizHTOTrf4ruL9QNIBSY22Xf+/EOqSUnBaSOpRfBDMlQljLBn6jPafzmC+rxm4/e+PWSfcZQXfBi/vMppvM3ZvP/ftZuE+UPr26/lYE3P3td5mI9/e3/Hn2+Dtx1jVwAeGkfWQrx6ZQ7dfR62VYRg++DUem8uPgdi339fH1hH0vWv7cbcZu920fey5PnYsb//8sfT4H1u5h5Tcu0WSuCcoC54daQlKwtKCLAmRTtECTanJA1gUo3YQCXaPejCGXRKeOq0uZEBPE+TOfOPkMxhyRdNATz3sklKizI63Ss8BZnd3fCFCoLpC7fQp4aMy2oDliusQ93aZSVU4pARr2I4kxYfO5ELxSpMNeXQWs/BrFqU2RfKaMYzi3mhd12bJwTulCG2b2R4EzJk6iDZKFywvJB9xE06kMQ5DWC/MCqehwrLaIignxxBja4lRB8oQ0lw36DogrVO1Ij3hZox6gvhCHRtTdQ69R0MFwMBsTk4dbZ06JNQd2gru94YPiWYFmiMZNLUYQGtnaJlaJOovImQHkxWM/LRl3cl6vE8bh/3EW254f3nD2/Nr5mUJ9lEUWtHABh0z7vsSDXMZM9uTEZFE7xL2JN7pvUFrK2gcCkzxyKdhJfuorGzNleHsFn2NG/SUEVlZom6h3OmhltqenfDw0ePwRM6JtszMc6V5DE6HsuHkZMvpwzM2eWS33KwqCAuJcgh9SQJuTl8q+90NX375O/K2sL+84e35W1oLcsPj0zO++PnPefDgAQ/OHrFNmWoV7ZV5gt//5td0d87fv+fi/P16DTLWQJEY7q61fsqFR48esd1sub68Zr+/ZOkBvotFPkscC1ZIbKXci6ODomNmac5cOywHvvrdV1xfX/Di5UvOllPevXnH/rCAd7ZnD3h09hC3ysX1NYfdAbdGr43vXr/lv/9f/439f57YbjfcXF5/8rnz/MlTTh6cMuaBQ18Y84axJN6+ec8yH4JsNTjW1syL1Ye7uIUSMTnJwnZKZO0R3UJirroy4mK4bau/seTw/AwQOFHMwm91PTulhwer9zUT3eP4qRsLENF4Ceu6ko5uPY/GedFsrTO9r8deghmoQvI1kNASiNJWKwrNRiMh0mJon8MWj6UHVcLC/iXnzNwrUqIHbNNqv6XxvGQnNaHLGjJcgqskSEj03dAeoJlwDJ/rtEUDByhhK4SFN3vviTXlLMKIBaQ1pEQ410kZGIcND54+4vHDM07HDeoZSw2GyrxskIuEeycno/eEi9CbMQODGpIK5r6GjH3aKqWw2Ww4OTn5wEI2swB416H9PM8/8E8+AsJHv+Wb6+sI6OOHdUmt9cNjj4DucTh2G1JwfmhpZWaM48iRpDcMw4ffH99ZugV2H4Fp1uc/WmX03iml0HsPn+acflALHNnRt3++DT7/ufv/fWDu3XW3zjo+7vbP7v6D5//Y4PtjNdKPAdx/8lp/5HUeQ5o/ZXkVNAd4SXeokIdEX4y6tjlKWD6aGGod7WDJaCljVSKbIcdwMZGhOdaclgQtgZNojeErOmNdIEFkkhruCeuC9SlAzppoJSPNgilNXKuSZdrseCFqFTQwJQEkhlvZhZSc5hmTsGtNWjGvuEyIDORkWI37vCQJAmNLFFUciPiGqP8CRO6MLUJE51Rxok7NOUIKJUlYnvbIBcjWWdTRdrwmCoFZd7JE+LsgDKZ4ifN0Zg67oiju0NW/Inko3bAOtYe1hW1oPkMPhbf3GdHwvu694TmR3WLgt4TdlmuKAY0YOQmpO42GpQDbGwkZjtY896+/zCJjPXd/HHr82F/jwnucYR3TPV0ErDHPjbNXP+PB9oSbmxvevX6Lq/CiOe1Q+fyXf81+2XF+foFk5fThCQ/nJ2zdySmTvYVJdio8fnrGYV443Bx48OSMxycPOX8Xz3szHdiOJ4xnhe34jMkq/TBx9nDLs2fPMVs4GR/xzZvvePbiOcv+wHff/YHnT59xenrC5cUloYgpuC4ga+l3z1u+e234KZBtgNN/5tB+wg5/Wrt0e/0F4DLQRyX1GdTQZaSlmLQYFsyFHg3PRlr4zmTADe8hd+wfAhZAeiNbwRgjaTwp9I6sEsZsQrIeSZiMpFxZzEgUsjcSW1wNF6O1FBdazXg3Fq+UpBG8VjtIp6OMauExNyptqShjeAG3hHdHtdNFyd2xtMraTVkoDAo0ow0gNDaaMM80hWIHTEEkyixdgCKUlqguMRlMmbYURo9pV6vgWUhLeAp3bXQvgNJ6RnOlUijuqPS42s1QNSPS8LQyUXCkOWIBULv1CG9jjHTk3mMoQA12dFGogrQeA4METqU3JY8Z7QsDjpiGn6NY+DQvqxce0Hwi90Qvn07pGVe5nHuntijItq5ozrhXsh+JJh1aIpdMJlLUHz18wvPnLzg7O8NaBH8UCshAThN5UGpfrzdZaB269wAH0spwp9Lm8HGtIh+kjQ2QIdO8YS38aGUN1lGENi9czwvogbFuOL9eMHUOS2WZFhYamje4TzA2bDY8J8xmbIFhDPuULoZQEJtJGaQLkoXOQD0ZUK8RUqjB6kYTHGpYXjSLKafHhEK6ohlcEmUU+hwNcCN8DQ0lpUZyobMa/WshWUUtsSQNN3QZCCuLghbY7W44v7xmM5wxL5X564XXX3/Ju4sblgWaTXR3kAGRgizOmPpqX+SMZcSlBsjOwObByN/9p7/jf/+nf+Txk8cstfI//uVf+L/3/0LbXHzyuWPXnWW64buvv+PyzVt0EFoLFnHKmdZm/uZ/+2fKKDyQMzYPt4z5GZvTDa05w5A4Od2wGQsijmXBJtagSqdpgwSNjleFIZr8TRaWlkkY1A4kzIUiimlHs8aQOBfEC8JM7eGJmFC6JIooQ3eqrUoJH0PCOSbqwTCZ6CxIC5/FkiPUimx0Oq0vJB8iWKc5ZTRGE16/ecu79+95+OgJeOOwuybpCUvvvLu65OnzV7x49XN+84dfc3l+TimnbIYtZ2ePOf37/8K3v/2Kd9cX4celTjkbeclnpJIZHp1wNgzsFgdL/PLVK3728jFJM0Kl15AXV4NhKCGPp1N7i9DWtiHlRB+FucKQNvgyMU8LSY2WQyHipBiqHRRKRqe6JtX3sFTyGASpKqdAx0klrcyhn74+xpD9GBP5duNwGwy9b/u72973t+M+7/oGfgyk/tjrOv7uPvD7boDefeDrh/1qNOaPnzxmu9nSWuPy8pLr6+sPLKLbDdORDXVs+u42Sn+OcXO3Obv7Xu6ToN49Pp/SmN23/U9hLh9Xx6AnqkSgVabRW4HWMVOyRohxcl8VjQFkKSnYg90pMuGypdoSA3BPJIdMQhDmnNhoIpPYZkNOIgQmmTN0obmz9JBjNlE2aYMpFFuYRyW3zpJkteoxvDsV2Kw+iO6dqpme4QTDRKm5B4O4ABJe0eJLMLIxei6YJ8Zka9aDo4Q1iEvHu7JYeBDSMkyVrhtEG2leMDE8C/TwjE7JYBjJj84YTk8iLKg4WR2Whnll8IGeGuOwgTEChRsV9xZWCyNIG6neKOLMXul1orWE7Wb0MAMLtno/y5GJY1Ab0ST6gowbhhbXLnOnl072Elv3Dh6KN7UI/lOPELR5leV/6nIL1nvCmJeZdxfnbK73vHnzLYfdTcjFLfITjg8QD2YxIsFoAurcuLy8ZrM9YZkhDTOt7fj2zbfs9lOAxnJkEBqy5mWIR/i2BM8HY5XergxpWHWoK+hjgGqipEwZt7gEuL+0PfvdjpvdDbU2iiaGMVFybFvFuLnes592dAszaJGQLotEM269s8wH/vj6G6ZpYZonLq8uaEt83o8eP+Txs6eUzZaSC64W3q/zTMsTV9dXfPfugnl3YJqPwPzal8n3nvIlZx4+eMRf/dVfk/WU7/T39LqQhqgFlzrTl0av0Dky0tfrTndubq745o9fol559yZzcf4tr799H3YgL54z7Q78Rv+dP3zzDZs88vnPf86z5y9Z6sKjd2/5+puv2e0PmCn7eeKrr77i4vKczfaU3e7TAebx9AHDaWbMG7RvEZTNA6X1zut3MzatBJIUTNq0srBVEtCpfuv4p6AhSSo09wCCPfiCtvpph5qxIsVRT2F9JhFEqqsNFtmRLsHWzRFgLa5r9seA+RznUwou7iAW1xOP+7baCtymFlRzi+tD9vBBtdRXubvH9mvf1C3hRK3gCZAZPDzRs4eHt0gQP4p2DKesoWStdjyBpgDMQlHVcXJYy6mTcGjBgPbiZO8IGRGhmyKpBpvTYj+u8VyqwTjubivAJXhJ8R49YbmwPTnj8dlzHp5EX5dKwruiVmjjQs6OSryi7sGrDmVLwtZBnbpQrXzyuXNUJQWgHAS5I3v45OTkA3v5rmLp+O9j+N7de+nxPno7xM6Ja8d9tp5Hj+Buax7RmumQp4lSyoeB9dHOQyQ8l3e7Hbvd7geA8LGeug1uAwwyRMjorSFysHq/ryGO7Oz71Fn3rbvEg9vrvprm9u/v1nm3t7uvrvnYc9wHYv/Ya7nvb6WUDxYpn7QkAvfEOtUangp9ccQbJSkNUHQ933MEcXv4jqsHMO00JKdg0PYgx6kmhpRCoe4WdYeEHagSmKGIhH95h1KgLhnVgqQhBuIKDaG40SXu7S4GlFBd2+rFnUF7BUm0pmSH1Co+5NXTOHClUCqvN73Eh5pc1u84LdG0knNYYzkSAfWEhWuxIK7hTu0jnmBAqbQAzdWpVGgZTRlJATqP7rh0uhrVB9BC8Uj0DGVIkPkim7KsDHJBViAcjJQzZg31HCoVCfJTW8mWVRwxA11tqjzRuq9e0rL65HuEs9NwgdSd2o2SEhXDu5LS8tFT5f+TB7PDj4Cb/oP/fVjr9j9wab7F8G1dOByu+KYtPH3+Bcthx7S/5s37c07GLWkrtMXJ5rR54cuv/8iT08ewn+jM9F3lUBpPnjxGkvHks6e8++49SYyb6QYZYnLwy5cvuLzZcXl+xedf/IxhaYzPnrIslWVqXO13XOYJzc53b9+GvAzYPjgLr9FhZDlEAIW3KKx/0nH71G0/gtF/2g6PxziKiWNp6T/46f+fVerEglAEXBobK9FIYBRiwpupLD3o+knmMI2XjNLp3nHL5CQrkF9BQU1QqyzrBaa5UhWaN3KKJOCpK1IyNAtJFY6aQSasAKQzYkxklqbYOvlRIbz1/MCBCImTNZQwJ6FaomVBBqdXp4jHFC4JtQcgKWnBtWAaQYRKR+qMy0BmZcfU8CEdPBqLZuFRK93JurLyLApbV4/pvmrIINCw7HDIwxwBg2UMcHlxaCUuzkXZesL7jlY24emoUWsnb1QPewwMBs10UUT2WA+w3j3TWiX76k2oOaaK3Sg5PPtUw1qh9Yj2cxF8yeF1uE7jOgkrAZJ+6ppkRquDZkqWtTiLgLNOhJBYD7vZmo2NAKOCFGSAddqDCNSpsZ+vmeY9N7sb9hc3wIJoobVgxydfiFAfDZP+QZEaIQajwyLt+4BEN4TwaOq5hnxk6cgI+zqxv7hik5V+WOhLZfbKfHPNtN+RV0Y4XalTSO/yMfU7D0hWWOZIcm0RttRtRnWIJHaNQEpfHB8BMnWZcYnQN1on5RJsAquoJ1w6MR9I1OiU8SLkVkgYUwPNiskaINANekhtdBC0G6olPtNeEWtUq/SUOX/zhlZnDOWwu+bmZqa2mdoM98KwpnNjig9hW5B9QEuE25gWBklsTjK/+OKX/NM//iOff/E5ZycP2beFl+/f8eB//Yab9OmhNze9cn2zsN/t0GGD24R3aLpwc9gxjJmHD5+gKgw6oGNi0EzOmTwoDzdh7/DswVPSwhoUESGb3iqWgBahOJLW5icZDI3Uc9hoENNmTUJgMxkrCVkmvHU0baglwq50DbXqORqyKtDTKsutBq74XKkiSCv4fPSNNLw03AfASLp+/3OnF0EHYzanzTt+84fX/Pp//Y7L9+dk4MGjx3z5x99gY/g+t2ViZ/Di88+ptXO5Oyc/+k+cbk548OghLx494WI/sdvt2ZwWputLSh54dHqG4iwG46BYU16+eoYO22An+UgqLeRhntgvB1SVnIVhHGmL0apTMPIQtjWYIcO4MpALzXerrQgMKeM5AlkRReaQxBmZTIQDNXG6OYjS5zWN4ies+9isx5/vY7Wk1RvwtlfeXbby3X3fXreB6COD5s8xqI+/u68xuQ/w/tjP9wG33AGCrfcIuJXM06dP+Yd/+AeePHmCmXFxfs7/+vWv+fLLL9nv9x/8mY/v4diw3D0+t9/D3eNwPM63t7tPunmbcXTc/93jc9/x/hij+b7j9FNWPMwiNMpkvQ6sTBT2tCGBDauXqYavcWkY4eueU6PZyCxrLeIEU6/4avUS7JXcOi6VmpTREzqApAAyllZp+/A5xwxL4TVqBtkLddkh88xcAVkoIiweJCSXAgbFwY+hL2vwV0oSDLw5hqyawyZi25S9OSo17hfmNEa0N5JE1kFrxpBi2EbRlQHZyBb1qEpBLGwLZp/JJQXInKNCxQqjBWs3ZcVtxN0jpyC3SK4XoS0NSmeUk7DjssPq69upXZinyrSf2S0H2uw0PK7FCDVJKHcMWAwtUZdlwi4DNwYB07DCcy9BgNIInXYamQh2dQs7Ev0J508wZp3ejN1+x5e/+R1SEof9gWWZAyT1dbCiIBI2U6ESPHrnGrVOfPPNN8zTzOmDB5CN/W7m3ds37Pc7ercVyhF0DUATC5A5BfIcsLMQ1wEP1qR7D4LCasthHn62AZfF598W4TDPXJ5fcjjsoTsdo3Wj0pkOM5NPXJy/4zDNK4M8R3iuxNBbBEyC2GE3Ow43E+4t7AfoEbA4bCklkSTFIKZ32tLo9UBfJg7LzOGwY56nCE/ujvkaRrUCxDkrw/aEFy9esj05IaNsTk85OTulnA5kCufn51y3q/DzvnX9bwZI5/rqin/9H//KH/74hyDCtMrZsOWzn7/i9OwBj05PuXj/lm++e8vZdsNnrz7jwfNH7HcTKRVu5onp8E0oowg59OUNXF5dscyHTz53lMphl5DtguSRzcMz8j5zc7bn3bt3TFjUsiPkFD6hpIx3xXIPICgZpYeHcvMEIxGS2MLT01t4/bpFL9kRUlN6WlUDrMx6b6GWSMro0XFaiyGYaLCFzSXYIWqoGkUyrYeVh2pYsJgEo725xmuQhZ6JPBIgV4UkdI+aKUyf47pdLGqD3oOdbx6KM8kabCVzPFdEBa/Re5r4Gmxeoy/2HOe/Gl2DYVjEoSsmYUURwd8J88itwSXCA3UJRrMNoRZMqw8zQpchACEl+g1JlCLkIbM9PWWzSfSWSQrZA5RabI63Z0EG6U1wTwgeQdOECszbajuq9ZPPnTIEa1WTIhr5LsMwfLCYOA6LjyAz3F93fGzdZiWH7YD/AKu47QR8HF51OubfM6SnafpB0PCR5XtkMN8XynsbIJ7n+cN9PWn6UGcdt79dmyzL8gFg/pT1Y8DtD94Xf1qPfOznn3Js79Z5H3vuHwOeU0oMw/ABxP+UVbLTrKNYkIfEAkto3wfFmgfPzS0IOHa0fIxPOIiBfc2CGhteU+RTudMXY8wpwoTXwYMWXU8dpXtkChUVyrChe1jylGZYFlQivI7u0aMR4HRqSiPscawtJM9QQAfFYhIfr7+HiqJnofUwlRdXUl4vIa0zUmhUNNfAaiogEdxu5lgqZCpV4z6tPYL5lAS1hr3r+j0Pi/sY4oYRggQDOWnUR/WI3TiZyHhCLKzPZCBpsPt76mFNKU5mCDVdNzwZk04kA2kzqWxRG0DDvjTJzECCvA7SvQcRos0RSCaRv9U9k6QRhyjsWSEUFR9bnwAwH0/Oj5zw32PEnwZX+p/88OHB1qOQ2F/v8dF4/NnA6/kdZy8eI7Wy273n6tcXPH/2CkY4fPeWzcMNb6/O8ekAWpCcePXiKd+9fsPcDzw8ecBYTtjf3PDw0cjVYnTvnDx8xH5pjKeF9xcX+KHx8PljLt6fs19ZA56Uly9e8v78PQ8fPmS3v+L87XsuL3eYdHqv9NYRIYIx7EemTZ96fP4D1/cQ8rHJ/h5S/kng8l/w4hcSqTqWCzY2Gn0FzqD5Ej5XxUESPsDSY7IrScjqmGUWi3AaK0afg2m6EaWakHOhH43jW4BiKZc4B9ohCnbpkAuDRJFRNSOt40OnzeGdN2zCd8+603WD6kKXgi5RA4cM26kyh1aqtgibyY6voYLSowCrSeIiNyzhMbcUUjaWRdgUo5nTmiCa0aHje5BUSSpY9Q/hKd1SFPvayaZUgVydnlalnyuKUXtYKAxzZxoqGxTXTnc4yRaMpCrB5JV4XGbE8hwM715IonTZgSitjagYNlrcGDRsAaQaKkZLjU4mK/giWG5xTNJ6/FffJcWxVjHNgJHdaT8io/iT1RTfTtTWSU0hDSTrtDVYMNVGKcHqchNqN1KGLAfa/sD11SVuFTyxLI3D4Zr3by+4urngcLhGW6LLgtdOy1HsWQKvFWGgLhYgokBLUBCaE0GRqnQDLQqkdUq6JmvPCzeHG072W67KHnpjbjNvLt9T5xoNaq149xhIJYUmaNqEH3GPSWKIWGe0JVI+wQdha0IfE9oUtlBlobeYZrYWDUFLHjYebogprVdyGpFWg/XgKYY7PZoBU0GHCJCjNUygpMRilZ40BiEkrDdkk/A+0rIz5gAT9/OB6f0UFi3znqmHPLJpRRsRFqAxOXXvCCXsapCw6GhCHhOPTs7427/5O375y1+QkgazRITT7RkpZ7x+OhvstGT+eLMjJyXlkDZLntFF2dNo3bi5vGGzyZRHhU3akIcRpLM9OeX0yYYnrz4jPRyoGNI7LolGsPh1rpiGvFh7TJl1EZbiSFqiEUgKTUMalgYmmyk3M6UkXJg+uaoAACAASURBVFMMR+uA6QSWsTEKxbSG10g3JBmtKYM7mo3SghXYUngFegdrITvMKZOsg22ovaK54T1hMrG7uGI/X/Po4WPqzQ3prPDd2z/y9e9/Tz7dsN9f8eXlgQePn3LycGQYz1gOe9hXNi8HPG/YdHj86AmbBw8ounC5n9nd7MPL2iam/QGGxM9evOBsexaNgi4UPQXt9BaeYpvtQF3WAZtA6+Ft6D3j7ZYUFlgWQ4KLgGwE92AXZt2Ed/Uqc1MTdA6AuhZBWyOTMAuZ7k84dT6su8X/XQDzx4r/uwzl2/u4T+54u9G5C3DeZTff99jbDdbHWMG31122skgU5EdJv9/ZV7dgho/jyF/96ld89tlnH1Lk53nm7MEDAH7/+99/kKYe2U05ZzbjCMCyLD84Frdf8+1j92OsnNvAfVobziPD+whu/zkm833H+fZ29wHxn7Rc1uDNjEjHUwILSwd8S186TRrb5nhO1CHqlozQDQ49k0vcF4rGQFdqC0BGQ1llqdATYAO5gntjmbckb1wPB3rt+CIMpxlBGUfYV4/gm+wsfaLPjkinW6GrMPQePqaEvRfu4EpTcFtIsiVHqgRSCniltU6R1VrBgkloLmQTegov1eadJImcRzoVyZBSj7T4lLBUUd3SvZHXvA3UMC8MviU3o/VGHoGTwpDCl10s0uT7XEjWosZxwQy6Z7I2mjmLN2qHbR2psnDTd9TphrZfUA9VU02NLopSsGoMeaGmFIFyQ/jFNjfKaq/oS4maMnnYq7VEyxYNVxdUHfXIaug/ITcg8kUij2Cal7CDkIRb3K/cg20ZdXhYjriGjQWkFVtzWu9c3tww1YXh4h2Ys7SF5dCwVqPZFILlefSYXutCd/nA5otG1xEP314RENZhjlnYD0gw/vb762A2tWt2+wPXuwO1LdCD1Xq4vuH87QXzdmauC+fv3nI47D9c25RQgMX3xGkrlbXNCyZEto1E4C9mdI9gqWk+cFga0/VCnRv7wzVXb2+YdxPWegDLxzA4Yj+qYWdQxpHnL57w/OUzhjJSdztSSjx5/pJxW6A12jKz3+/i2tJ7HDuVyCdxmOaK2Tk3h2tUhFQSJy834B0zp2WJ150lQuskrPy2m4zXLdvxhDREoPHZ6YYHpye0JeT9s396rfz27TllM3I4jGy3YWux3OzY7fa0LrjVqKnMqd4Zk9Gb0FMNG55uDCpUNAYkQK6dfrwX2+pPZ6vNYm+UlMLuSmFpAgnEWzxWFGtH/9xGRqkkIADGABJjgASJttr6CREQqitA1YPruwb36aq7dxb3ALZcaOoMkqAnFgvQxbKtvvYB1PqReb/a26SucS3zyNmJbBinyEI1iED2oCvKCtZQhZ6DNqBJI7y9SbCavWCrpN491BvNMy7xHdYllCBdhWyOD7YeWY2g0zRSvHC2HdkMI9WNVsF6ZXTFtFGnjrUWx00basdrBrSeSNKDvGWO/oQ+K6ccoK1oMIM1gPkgAWRKKWy32w8KpR8wku/UQPephe4CtSr6PZy0rj+5T99iOt8GjI9/v/3/4+Pvq5VuM6iPf79t33WsHW7bfdxmPf85gPfHQN2PDbZvv9+fVFv8yPMfj9ePDdM/9ppSSozjyDAMbLYbNuOnEXlcbkXQSFgpST4OIxM5xcAYj4GWIqhmVEON1YFMWOwwKtOqXNIW7GUVx1qMrVSVlkKF4EgM280ZUo7vU19znyTh6lSH1BvC0aLD40oiYSXoKRTZnjZhQYYx+Oo+lTTscVMOS1LveE7I0VKjddQ07Gty4+gpX3StkCwCCOlOtwhX1aIBVquQ6XTtK5Gjg2c6jS4ZS0SWWO/IEplb6onePYLePRQL1eJ7JDiLr/kPGsokrY3ZExA2RrpaOrkFc1lX1p32FmGMbmiSIP9laGuQYSYG103XAa2ONHMGic9QlxmXFES8HtjZx9YnAMyfhiz+peCpSrC1YPV6FSFJptbGv3/173Rr/OrVI653E2cPH/HN69fw2JivF372i5csy0SXLRPKZjylPBSyDpg0Cpl3VxecnJ4icsbLFy95/PTAN6+/4c3XX5LPTvjrv/2C/+e//SvX+2uuvjnw7MFTxpx58/6c7Wbk5y9fMC8zm9PM+zeV8+mC7oa19YsFH/6/EoD+Q4/P3fWD5/gzwO9/2HP6T99X6lBToXQlFaCBVUU9WJTdougQr7hB0YR2ZWkhckKJaUmNNFGKkFqia2fMhckT2gbUAugt0vBcaMuevE7CKk7xhsmI5wXrFdEcYXkQlg9dWNJ633OjpkQ3Q6WitjIQV0sLl0gb792JHLS+SiUT5kMwh3IcqUyj9ghVyzIw1/DmcY1gjDZLyDBKIZmRVulX64nBo/CpGungyEK1MCDyamvBl4LNlISmRmrCoqu9hecVhEpYGfAegRPBiTaMALnEKrZ6F6cuyNCQGvILc8Ms4RYNaO8Z1qTTuoTMZTBYPIIqVNN6bq4J3XkA7WTveF9N8D/13BmAPsSNwSPB1FdmuyOUMlBzMKYUo9fOMgazwt9dc6iVTSm4DTSZ2e8mlv2BbjX8QyWOAWrkFKwZTUPcXIimqmYBbyTLwcZE8Y3GxRmNCzNG82A2tHlPSadcX12GzxINXHj77pyL/QW116PKkpQz3Ro9BWOziNBrXPxdNoAHs0UHyihskyCbU9waaZvptrCbBGlzDDo8PGhHk5AL10IellWy43hNSBJGqcwNchooMjKbRBAUnTGNGEt4OON4GXFbopioirqQS6FLhGKkFTTu80il4i3jDmZT+JyXTJsrwyavjN8UoYQuaGl0GxgUUt7w+MkjTk62TIcDOQcjvPaFadkxT2sS/Ceu56+e8G+/NfbLjqxbNrph8k7LxjZvGPPAdsg0coR+pmhkHp0+4OnLp7x8+ZJXTx5DMyz1CH7oHViwnMmeqbuOZMcJCXvVirtS1u+odUBrFEgr+wstmGswhr3jdQkpqMy4FnIqZK1Yhy6VNEGT+K83Z/BKS8FG9IPjYzRJ3RZcGrVs8E1USsky1aLIutgfePuHr5l7MOIvLva8u9qx79Cvdrx595qbiz3PmHjmr3j+8jm0mf/5b/+dh2cbHj37nPEMXr34BQ8fnaJeeXBh7B9eYasfl3Qnu3L6ZEORFN/bTUKWSqrKvoHnDr2joqRipJrJySLgKsUAoK9yaXpDvNOKklOhTYZ6xUumS7CdDCObIDnT0sLiivdlLaIyQ/ZIZi6f3nDdtpy47e97F/i8y7C9Dxi9u+4DnO+CvbfXXabxXSbMfX+7r6G473UdGTzHRvI2i/o+P2MzQ5Oy2W55+fIlz549Y15mcs788z//M+7O9fU1r1+//kEgUO/9g6wV+EGDeh/T+zYj5/b7vI9pnVapbBATv/9cjg3jjzGV7/ssju/5uO5+xn9uJRPQ1ftTOkWMSiVZeGMWDZsMkxgMyTzg6ox9zV8ogBNMfC9AeI6qVrpmUhnDqqotuEaojU1bNtuJQ220pZAIBdRsA2e5oGpkdkwWoHCdV0k8Eg1Md/oY8nIVIWipA+Soa5IPzG3GVPClk0RJ2eOxSwTcaCFYjxpBOVYraA5wyDukOUBMSSEHF1CMLCOokGUIT1GtFI+U9lkrxYMsQi6YJaqFV/9mDOm44zTXyFEokNXJvsWtsxz2dAsbs+vauLnacTNXtHaazsxacAl7MFMlyRFsG0EaPQupC4hTMkTxquFBq8GgNA3rpTQeFQ6Geyi2uho/YZyOu0VNLx1RqIsHE9FjsIatbO61sQz+V1hjYAF41uqIG9Znem1MKT4Tq6EGxI+g8ZoNo/HYuCcdYdjwzjQ/5m1I1LSrB4cLa+AWQRpYKpdXN+z2B0JA1Wnz0crD6c253u9ZXn9LKRH8OU8z1sKGQhIMZeTsbEtOmVqd/f6aZZ7oCH31JrUe4bZmztXle66vrxjGwjQd+P3vviQlZ+7GH19/w3430eoxsCtee6hLNXz6XTk9OePJo6eh3OtGF+HkwZbnw4YuiVonLq6v0KQBEq7AqKZ4a806iDP3TmoNVNn0wn6aOVzNbE/2MCWmWrFq3MwHvvrqNZoTJQ20NtP7zFASJ5tT/v4f/o6//8//ha//+BV/+M1v+OqbT2eh/var35NzYjOMlLGAKX0+cHmYMb4HM8yNjOJNw9fYVjCvathQJSNZKAHmZOHlK0GG8dpiwKAGKWTjJkKrQho8PMQtUeX4XYlLWKhHV7AQRayjHvd8aQkkVKqFkLO7BpgTAOoKZOF0C0sdsbCR8VVBUQgrsC6guUd/sgYFssrztQteIkA60sMziQC9luNxEXAGRDoxz1i/0y7xfumYRqiguWDSVmtG0EEi0L7FNc7F4nwxJZnSs63AL/jgYOs9ywjGSgpALqcIhc0iGDN1qZiE6vDmcMNcp1BOmmBLD0/ZnuJ7XcIPOpnGNeEnrtusYAhv5iO7dbvdMi8ztdaPspg/pvq5ew+HPwWibyuW7gKht4Hljw3PP3b/vv0ajqDx0QLj6Dd8VEAdrT6WZfkTMsFPWR+rA2/XND95cP2R9WEfHwFm7gP8by9V/WA/cgx5vB02+ONrDfv0jMnCxjNY3LtUBGthwBTHouAGKRsC1G7xb3UW8zW7QFcnnIbRyV3D6zwbXgEhrBsQxGd0GEhdUQ+vfzMJsNQd7aGa7evw9FjnzHTG9XDl1NdeldhWvmfXWwN0Bo08nGadGH9GH6cGnlNcE9Wp2imeI5dAWW+SLWw1cwhctTmrBxheOk0GREJZPxABgJjSm5EpWHK6GiOhVJJxQHri6Oker8nAHdOGpBRm91ZWdjlkiyE4WZEl2M8uSkphu9G9ISvZLGx+DdUEFuq4rI60geqVLBpZarnSloR52MKWnqlmP5o38edRnx8BMT+Z2HrvhlHA2dHf5NYKyniEGM3TzO9/8yWff/YZTx4/o00Lb998TTPn2Yu/5f35ez7//Be8+WriyaNHvLs+Zze/4fmzF1ztdhwOM68enzLdXHJ1M/Hq+WO+6W9oI9hN5fW3b1ha58WTVxzmHZuh03rm8199xuHmwG9++xXvzt9y8z4zL9N6c17fj9sKGvlHgeW/ZEkoju7sMw5iFKLH5/+J+73zkD/5WFaGxN0d++2/f+JqEgFjWuaQWuK49pUOkdBZsB6ToVQSVgNg1aowBElkkYLl8NQW9yiKSqI1kNToSkioiHTP1oLaL93oqojkCG7wRp4lJJqpM1iOq0up2FRw7biGj/LSojHR7LS0wejUHKyJLAvZnYxRyWvIXUO6xYWyBLMjVYOSQ2KpSsmd1C2kIT7QtCFLJUuJgsECkOwmFGloCdCmmCEpg2/JVumzUJrgY4riMfQYNBG0GT5A6lGMNY3CUjwKIR8dqRauYlboGrYuHWOZY3KlHoVzJ62FveBrUV2JYDhJa5CRLPS8QRYjaWJOC9IbSU/IbQ6pijvLMoTMLM+ffO4EwJ9JveIyRGL1ylJKpVNJyNypq1xUUmeuSzTO0tm/nRlS+ANV63ET6QYSoSS4hKF9gd5TeBF3XWU/Cc8dtzDX704EJwponSHDoIl5boisSbUCfUpI6YgZ5++/4+r8gqyFw7TQekXccM80aWGanxWqRnBj1ki97x1yJKNoEkru5LEwPnzEk7NnbMchQOvzb+nLW3bWWDw4n5JieiqW0DzRekx03XsETHUDTejQI1G7ZEptZMt0LcypwRRBKSIaQ4QcFgOFzLI23JmOJlhmgqmbIkAo+TqUlUpdEraBvEl0LwEufrjeZAYDR3CNc31zsmWpleuba3IuYM44DFxf30SIR/v0dv2zF/8vae/WJMmV3Pn93M85EZl16TvQwMxgCA7JXS45lNYkrfSyfOOn5UfQi8z0sksZTQ/UkkMOd4bDAQZo9AXdXV1VmXEu7nrwyEKhpgpocMMMhq6qjLxERsRx//v/8hP+w58u/OLv/5aX5xf0XUV1cLSZ0PmYo8nZnj4BOtpDJrjZHPHoww/52U9/zMnDDyh5UE3IsjBGo17umKcTsB4T8jlYgSZGmxK7LsxmVKJ5GKG8IjeliZEUdHJSB1cju9BwUlZIRyRi6FGlMLqhsmCzUMygC0vK4IPRWWUVkHt4nPUs4BO6CCPXsOvx8HzGJnoVXp+9I28T8zTxu2fP2Gjh6Fj5/MvnvD57h1djO53w6KMP2J4ek+uGs3d7fvXbz/l322N+9MGnnJ5OUWDJxIOHmXvMAQCkhPZo0tGEd2OkSqqZCtTdgnfj6GRDH5miA5XGXteppUWgR3JoHnI0T2A5kTwhFuwI9WCPCUZtAVza5DFMbPlqQMggVCAaCeIm72+RcWDD3mTKXJdfHrbb7B5ugtDXQeSbjzs0N3eBqje3u9g7t8kr79quP3fJmXmz4eTkhFIKrTUud5fR2N5ga48xuLy85O3bt1eMl4uLCzabDSLC06dPefLkCS9fvvyW7+EB6D2AzAfQ+fC8N1/negN5+C6ufx+HxxwaRF/BMrmF6X19uw5k32xkb2OG3zzO77ONWFboYshQhkTj0bNHanlv5JywPKNjROSEZ0w7XgJMxEIub7qgTBGM2kMR1LEIfS0zxyLgmZoUvJPNOE4J5sLusjJZwyawlMBL+OxdXuJjF9LQrDG4Vug5YXvFckJ6o3nHRydPytKDvY5PUCZsOF0PddjKIJKBthjSCWvyOkCKPIfmBm1mnmP9y2ROtpmOIUWwCnUZeJ5wb2hPAcB0YVMGBYlcAXdMF7psmYshG9BFsRH8gzE7bvuwIZuE/WVlaY5e7Oi14vsdu2UEeFoGtEJemaUyCkPDG95XaaosUFCWqRGcHgsf7TV0UcTYTDNLGxQXhsf4m4P/cnp/L9QDc89xGD3YxCJAeD3GqSlX5BLNEV4YcoOwvUACXBvukH1lk4XkFT8EbH0bNI6mOvynwUBWEaYIXP12db8U0DVQUPBgoyK0faVLrOfCN/c9Xz+XjY7vOpeX0dSID9AgJGznicdPHvHgwRNIxnJZ+fqd8ur5gNqItx7v1ZLShnN2dsZnX3yGlsTL5y/4h3/4B8wqBnz9/OsgHLF6cVuA8fF+43imBNM8IymxmDGVhGZ4cP8Bm1RYlsbX52fsLnf01lcfUY+1ZISMGbf1uw5f4fDddt6+OePF0QtkyrReefn8BcvSMOs8e/4lqPHowQN2uz0X+0vmzZYf//gn/OVf/iX/+//xn3n25e/427/5L/yf/9f//d7nzuVuTxJ4p7uVBb6SPMI/JUKFa4vAPBaGFJCOW1ynSQyzAO99VVfEqKozZJDaFCGMwRtGXKkW+4UfENA8rE7GxKChOQgtjRVQldVaRoIspCmYd5qhi9MrpLSyTlME4plHqCQDXFvYt0kwf/HIyTHLUAapBxNcpOEjMUr0C8M6qvGe+9LJGpLuBmiPIZVIgC/DR9jyrMpPX9nTRoDKYpBkrAPDCPhM67qRXFmw9VwNn1Qtio1QHgQLc0R2DOEfO1Iir/DLIM6nIQ3xBCVTPLHsKruLcy4vzll6EH3cBJkn3CKUkdxj4GQJVBB5fzLG4d5zWGNTSlcgo4hc+R2fHJ9Ql/qttf1QF9y1nsI3wPVdIPChBrpuQ3bYrv/+rgH6baDzzfX88P/rdhi996v3dn2Ifp1M8H211M3t+uNvfpbvGvb/0O379r9+PFJK3xrqX3+OA7i83W7ZbrdXtdb7bEpFrcDUyF1pGFoNVcdlgTxFqKwXkibG6DGAHUFMawQJR8XXLKAC3uNeMiTUXwitDTR3bETuRNLwHO5DEev0FP2AkkkFbJkxrWgqrI0AucV9RM0YophVFp9IJPCOM2N0igt9rDjMMIooQ2KwdhiQW4/BXCqrf7RFZTZoaAr7nrYOk4KJrRESr0J0hDFYxQOydhnfrKsedjdJfLXGgI5GoGUXRGKIHXb0ErhZCnssoeMqDFEmcSgZWg9S4WiEFZUBOUh26xBPDqQCBOuKt0FJBR3CUEcsMsAYEvlpPZTVpqGYNR8kT7jfXet8P8Ac1SJwQFVv/Omu7TpO6Td+5jApZw3ZuI4xC23Zk1OB2mGEtOG8nbP/fM/xvKWP8Fn8b//4S1IqnL59w4vz1+STbZyANthstlzsKscnE/ul8fLNK95cvEbnH3H06JTHx0d89ut/5flnX3C8mTi+v8Uvnc9/9xIX+OM/+iO+fvc1r85egcZ0YWTBqof//+FCXqUCd4Lo34cC3/IQt9se6Lf+872eeP3nzd1+D2z2W5788DTyg/BlJhwPRAX3aD9qjmCSPAaSCtXC3zWq5gjl61kpMmhjiXTRAfPsVNPwUu3rtLen6MkA985RioIja4TnzRIjqqEFZ9BKhNpMoowaBQA9wEEMeg8ZhuRCUmNUQaSRCMsOSYaOCVOnrQxhTWAyxTRpAukjWAIOy34hpbjBaplosse6k0YDUXxluqQ+0JJIpvEdmUMSrIXE0M0Y1qJosRR+0bpOqPrM0A55hEeygNMDnAZaSUivoBN9dIqEd+syYiplazgW5uiIoDwvYCosPiiqCIKMzFwcHwF6NUCThDxFHI72+D6Tp034F01x0pjnmEK6xY3qfbdhYbuRoYxB642hEyThEM6ISLAZvGMpM2pMZFQMb3t6joWn5Uiw1ZFDBmOQtKEjxYIiAxsaTPcMyTqjWTRlPYOGbc9GZWUyJyoDyUpva9BJjuHJ0g1yxqpSWUh+yfAUQKpJFObeKAncZjINGeFNPDy83iZTXDqkmZLh/v0H/Id//+c8efiINBfevTnj8t0bnvdEzUaqUE0wXa16iOskT04agkuJY+EDQ0lemNOaZKsaDF6PqXMi4SPCKbM2ehc2KowUYXV4xWzCxbDcI0jOZA3sCbb/MCeLQS9QQLpB8eh1cqKNFCzxAX2Owi6XDUiiLo3aOyVlzl6d8+tf/ncu3p3T7e4QgZvb9viE//jzP+fTnzziv/3d/8vn//0F+uAhmcTRVHBbmO+f8JOPP+L4+BGIM20SR/fv82BzGtKLmq/kuEYhFUe1U4djKdPbDkkrG++iMaUMU/h2ydIDbCLAd5O0KtwVKdHEdTU8zYwhSI9rDveQRw0PHzwd7C6FLIniPdJ+ZcFSJg3F80AtUcRR72SNhk4qSI4g03f7C96dvaDMGR1Oq8aD+4+p5xEi1M4Xxt4pDmWeeHB0xFdfveD+0Smf/ugT7j9+wpPHH/D4ySOmOVNXJYFtgmXAKGCrjYoYiRFhoR3MGzIJzMKowsWbN0yPT/E0szRQ0xi0DUdLqEImItleeiG1jiXHU1pZOY57gAJ5ylE7DKW1Ts6hYphzCTlur6ApFAnfId+6uX0X6Hhohm577HUg+TYWz819b/795nYbc+fme7ntvd3WzN0GcOecOTo55qOnH3Hv3j3meaa1xps3b/jqq6+uUs2vN2fLfuG3n/2Wzz77LPzKc76SnYoIR0dHVw3pbdtBjllK4fj4GPMI5zo0fPv9/luJ9TfZ3Tc9rlVjOG3129/Jze/wNmbUbYyrm7+7y0f7zk0G0lMoN7SvdhkwpTmG0FWAYN0MovAfWaORyQLNY1BpA6+JMS8BBDKTstG9o0xM0kgoiw7uY2SfmTYTR0cbzDo+Kq6dzAZtgmtizM7lnrCcOiocJSUfHUXIXTP6ds8YQl8G2i5xDWClYNSsDG/IbpC2E6mDVcenRE+HGj5shMSUPhm5F8QbZJA2odIZKTEnyFmRkw33coYGY+vs+w5ZGvt9ZkyCm9PGJYPMrk8kU/ImbIq67tGRGV0ZtkfEUc/0JlivLKNTuWB/sXBZnVEX2q5SRw35uQuTC03DeiEy4Awfhex7JIW/vk0Sg2ENtRESTKM8jOoKKdNbhzTow4IUMnJI+if9QbWyewTuiYV6T4WVXUwMtkMfuUqT09qURgNsfthfrtiLYwQSrQSg1+1QG61AsCSSW+jZZK34VSOzhINNxspwTbGvO7FGhep2ZX+FpVfss7LQPHx5nbBBwqFFmiXeo+lOAptt5tEHH/Dxxz/hwZOHWB28efOa5sbl2Y7zERLpAEw96uBReXf+jn/6u7/nzfMXfP36LV+9eEkbDTDq0uijgQV4Lkrkz60AfFgtQNsv1ItzRIReoWw2TKkgKVPrBS9fveL87BK6Iatpridfc0RW5k8ivvdC+BlT8fMzPnvmvLu8oPXKmzdv2bd9zD13F3z5xTMu311SW1h4nNw74S/+4//M//a//meePj7haPPH9Nr4+1/+8r3Pnd481FRmGAOTjFbI2alpIISsOtZmQ92CyZ4kgqE8IV5IIuzyEqCEx71K3EEGiiPq5HW/4es4ZBjZEkmEOoKdbBqRmWOsZAWi9h8WBA/RYM51Xc+xvtaFhJWaWqcQgchJfe2B1tdLPUKl8FVSLoxFsBIepkMTWTQAXPHwFO0OAzQFoD4In2dSqP0EMDlYtAjJApBuGlZu6mtgsGaGHUIxQUrGyUCPYHp1bKxAlFrYp2VlmDJGIh/W/xRZLypxrVURcjfO3y1s5z3zdosNqNapfc/5u3MudxeYNdIYSIv+bAjBmuwavq4CdsgFec/tejjeYe2+WTu4+xXL9WAj8XtWGRLX2G37Xl/HbwLFcdv5ffXU9TX/+s83/30bo/m2rIfrf7u+3+H1r6u1rr/Ozfdx13ZVJ61Dkbve783H3/U5rtdy17ebNcr3Ada3HZ/Dz4d6bLPZXLGZ3xdgtk54j++VngS1DtMc4Z0jPIYVDcsKDY/yuo8hVKoHoVRah405yGo9LEJTigGuAkmc4QnNEj0yStFEMg97GiN6WAlrUaOGJeNqs5QlBa6YBVr0DFlmPMcQVyWsRlUzdTRUnJRyhC8vQNbwWU+BB2XN9NTwEfZUEh4eMYpVpy1xv3WN/ktM0MmRASFoVrQJmjtD4v11cbJ1hoeafRggDW/EIDuv9xKBNITqFnhGipDQ4WG1dsgZcDK6hMq9J5CeEBU0pbjfZyWNUGQYEuxpOt4UTXq1bmtTlhQZSHyhXgAAIABJREFUaoIjvYdVSQmlsvbAyaw3+nfIJr4X9Ykl9TYW0KEEuQNE/Q408zqgfJh0wzdNXUwUO+fvznFxZp148dmXuBQ2m4knTz7k3bu3bI5mxs744tkLRh28+uoVn/74E57XS756/iUyzWznmd99+RnTBK1W/uH/+yU//aOf0lvn45/8AV+8+IIPHp7y2e++4mK34NrxOvjs898yb7bMlxtaazSLRgoJ/GutpiKr4s4+5D0alB82KPsB27Xv5U4A/D22a0OCH7K7Z8MUrIKqI97QXkCMxTNZaljQesgCZPU59ezszRHdggYvwlaT9LBOEMQFz+HH3IXw1xlhX9Ak40ljJmwx+SEpZQ2scXfSJIyUScOpHgBkVmWUROmO9/ClbN4RVXpXRMPDxmSE1KBL3Fy8B8tYBjpN9LaErA5BJTOk0VNGLT6jJCM1oZmBDigTIlH4mBIeyE3XlJlIhddM2EMkjcmSB8tUdfU0JBivqhJytRSF/+SDJtGMlZJxr3EDKQlGR5vSNcIKI2wlB4PqElQ7MivdOwVn9EbWVUKTg8VQSqfUgbWQWfYekrZuwVZq1pklk7PEZ3rPLSfFrWBqdHW6ZVITNA+qsS4EKeR2a8jWnANY9aVFMn0PdrGY4GI0G2TvuAj74Ww1ApbMRrAcgNwNx7B+sIOIRSF5+DpKdtqATBRVmfCVDCqI4i0x9opOKyshraEHLlH8miNpwn0wm7NIYbjTekckRTgBA4oyA0dHp/zRJ3/Ijz7+mPv3H9Bt8Ob1a872jdYa2jvdMtPqIz1MYTip5AgCVAMW5gZ1Zdx0C88o6zWai5JJvTGnwdKUJIaOFHYGmrGcwaNZsO542ZH2YfzfvEMPfzpRMBHclSkdYQmkO1YGknKoFLyRZDCaYNnXZqZSlwtygt1+jyPU/SVffPUFn332JZf1gh+AEZJkkDeZJx98zP/yP/0l3v+WkQfb7RFjAdLgkw8/4JNP/4Tje/eodfW2m1YvKcmoBvurjUgWn01ok2Otk/qIpj4ZLJkqkCUhw6A1elbEM2KNZh1NgqUYTvRq6FYjDb07UxmIJ3oK31as47WxbxNJowkxH8gwTDI2Tkm2R1On9oTME/Q9okolIWNP8RKWLz3x+vMvef3qa9pSURe6dT58/IjPdxfsDB4+fYzJ4Oho5vGjD3F1StqQ58LbZcf09Wu2eYrnt2Au1mGUJuHrZRNVdng3yJ0hMagoKcDoyZT5CNQXyEfhF0ZD0qAL0DOY02qlt8FGC5YH5krSuP61GmUahJutkTUxWqcvg7QtjAEbVdxahJV1R3MJ4EQb4wcwCQ/bXRLH66DjXfYL1/e5brNxeNzN/a8H1d32HLcxnG8yfW+TqV4Hh683earKdrvlyeMnHB8fk1K6AogPAT+11isv5cPztNZ4/tVz/u7v/o779+9z//59eu8sy8L5+fmVd+P143FoYIVoYD/99FM++eQTfv7zn18xpZZl4e3bt/zrb3/LP/3TP/H8q6+u5LjXG7/rxyfn8I4cNr7FDrptu+t7uq3J+y6p7fdtnsLLPtvqQZwKiT15D/OmsBzOhWpIF6xMZG8BIo5EHk6bO0MKW4AudKl4Ct/cWVMAd2cJ23Y2bkxFyNuwFnM12q5hJszTCdMm0Sr4qCtJY8FH4vT+Cafbe9ybEpWM94pxn7E3ztIFu4tCbxfsbME65M2qHsuJPiLQa0rQbKBlJtXBmJzkGRvLFTOHmpHh4IOcAZ+QDEdHBS1b8mZLzpXkwpQLVQ2dO5fne3xasKHUSyiTQemMNCFSyGlwXgdLWwI8zBlJQuoLYxnse8dqo+1ioL+Xc2oHbXtUMpOsQT8eQ9d5ZEZOmDTSOkBGItRHsoNVIlfesSFoVhJKBxYfzN0iG4OM9U5AczDL+wfTsvobB+gUIUaSgoyhEuCtaAz1bZXYmwZTPnn40YsGMC+r76xK+F8Gw2nV+Gl4WDKihkUDmIuAsmBT+cEndWX/RMBd9DxyYJk4AYT7obWIX3ZfQfGV5RtNfNS1MuLXZCHlwvHpPT58+oSnTx/x4OEHXFyc05fKbnfJPGd2O8VGKPXWi5JhxlIXfvf8OS9evoiBdh+M4aAB2oqWUNqJM1xIKsFCdkANw7jc7Xj97oLNbsFV+ODDD/HeefXmNZ//7ktevnoZ90CC8Y5Y2MytHyFUE0TdUMHVQYTaG29fn3Hx7hKXwWgjjqOCNWPPgqR3nJ6cMpUNTz94wr/78z/j8ZN7iE6cnMBHH3zElN9Xqh6D3IMHZjPBqaQcdnGjDbx5eIQHKoqYgApWO6JpZRD2UAfK6oWsITtPawg4Qxh9YAnUW1j0DcEjiQ9LEj6kHjYW6gGCmgSRRw6qhvVeF0MGi3NKdR2YOGIrW5w4h80dciKPAJvsMORI4cntktDi0SeO8H9OQ4FgT4b6Ls4gF0GueNgraGwe17kE0GJKMLvFcReyB9s/iWAtwHOXeC1voTa0lOPaGwkvFekeVhU2YtifQFaP+oQgi6+qqsQh3HIsjfOXZygzx6edJIXGBW9eveHN1y+pbVmH5es7lxhIOb7m9ghDw+Yn/YCl63qA38011t1pLaxaDkzXWr9hMauGjeIBFDAPxdhhuy3r4aZFxHV1k7v/3vu5rQ67XlPdHL7fNky+vu/1/x+220Dn6/tc3+/7wNzvA7+vP+e3fi/X4w7jmrjtlb6vLrn5/d32t0MNePDYPgQn/pCBumkE644upDRWS87VtT0ZWGY4zL46MSW7sptxFbwKaSxhg0GAvykZow9yyowOOim1KZoiCDzWLwUd4dEcrByaOFkii84I7+RYBzPDwFMMRFWFyTo6EoMCGM09AosHTDrRh+MDGkopsd4mi/woT2GTpVlDHeKKZg9Lixo+HmmKtXBUVnW405dgQ4OT1WkrMVJEODS2LnFP7h6hwzaMVCbEV/VbDlJr83VQXMKKhJaDsDCCpJSKr1kUEaNYPIhSQXYYdBXKSNiAJAK9oy3R1MiyZqS500mQlGnNABs2IndIlNw7WSOLIhQnxpzuVk18L8B89yl3cO36vpPy95HN38M7b0zDYtAsV+xKWy6QFB6stQ6WHv6W9+4f83V/w5w2caOi0JJy9vaCkjLzqbBcVsZwPvn0D3jx7HN667x+9oJ6csrjJ0/wMRiq7JcFXxY++PgpL16/pfXBdgvugz5q3MTHuPrMKmt03hV6f8dxuA3Y/Z5d/m3bSs+82m5M3b7ntb77z0qYXPyAbWgUYQLQw8YB8CDwBovXF1RBRDGOyHXHQBGNSXd4z2mE41hMxikRTKF0ao8E02HQi1AATY2FRBnrhWJClkHvhuaBWg4GsDekOZMIJQm7sTB8Q/gGx/Qp6yaautyBypCYGpWDFJyFZCl8mr2Hob3EBSxZqFbJXtj4HrGZRcJ/TKeE7AIwGdLjRmiAJLJP9BRy9yIZKwkbFTxjKfzJXZzWBpNXWgmrBU+D7hlVZ0EggVbBc2dSo0e0eTCqawSXbNLANyX0vWNQEbpnkgYb0Vowz5KsHoTakZGjccDxxRgpQxWmKYpdcsG6MmssNGY9vvMfcOp4SrjtEEtIhiIhXfFcyD4QlJw7rSWkC5Q1BCfNIekV0B6SUnGLdOeVRaXemaUEV1JC6iqSqJqwWiPZNYWfU1dBRywMHQ32vcJoynBn1kED6OFjmRIYg50H62Iagk+GNEWS4engT7hlb2BTJ49DURQMemVPIQYaxyen3Hv4iM10HMD4fsezl895dfYSsiGWKMRCYTZFKvimgyWmJHRN1J7i3NXExhNkJbtT08Q0y1VQYPKMu5E0wu3chHnKpC4xmEgTasayOKtRMFoKUjpTiu88J0E8ohOyZvI2GM1pZTyZOaLhBZxHp49Ezc7vnj1ne3SP09NjdhcLb998zZdfPefrr9/EpLy+/3DCUg6mfi7c++gR/+kv/xNVBtKdi9fvmI42nGzvIVPG8iCrYTul7zqaDKuVXe+0ZWE+PmKMRjPH+gb1TpWQOOoqdzrKyr41iiojzRgtmDE6IT4zJWfpDskoqjRTPBVIHqAEmbGA5ri/WXbyELpvQC4xGTRzsu2Y8ky1UGJIH8jmEtcJaGhqyGUMZLIp1RoXuuNiv+coF97sz3m77NCUOHlwxJ9t/phhzmIdFUdN2IiQHj8gzZlHHzzg4eljjk8f4trovWDWgsWVAvxNdY+JMbqSkrCsPmW5LRiZ2gxtSpKJ0ZbwjW0BYOTsNHPcG3mEp3m3AJcdo0mneGVMeWVEG5MY+z4421UKjZPpGE3Til5MjK4weTAuKSTJUYi953YbQ+TqnnSjMfq+x91kNN9mgXET/LwNSL7uBXyTYXKzybrOlL5N5nl4zIMHD7h///4VuHxoNKZp4kc/+hG73Y5nz55d7XeQwu52O/7xH/+R+/fv8fOf/wVmxn6/Z7fb8e7duzuDdg5y2z/90z/lr/7qr/jZz34WfqzLwsuXL3n27BnTNAHO+fk73r55e7X/wSPx8PkP7Gn38II/POZmg3zze72LDX79e7jZoP0gOetIcQ30aAASg1oTngWpDXENCysULOE6MDU2faJ6w6YEfTCL0laxjvREw+hjQN9QkpAz6L6F/dCAPOIcrxedLo3wHx/0muljz6gxlG3NuPdwy70H9ynleGXU5bhv5Ym22aFHhXk+Z//GUTe6VfYm5JrwlBE6iURf5aBme0QKokLtAyuQ13Bcth52P+60OXNUnKkckaYNx5LZCuxLSEmn4UDIVqUbl6NS9wN8RxpOHkbqWyZd6Bchta2t0aWhnpkmZVimWmXs94xdZ59Ba1/DiiOMeYVvmftEk85YfZ3MPRQp4evG0EzeWzSRKazCbGXhdjfSZEhNWIz9cSdC5PIErWM9MZ2+P0goxHMPP4CYDuZBSNCwM8lzYpO3eHJ6HbT9jkasrz6CwSoi+OCqWZWD3d2BSoxGQ78m2CePNRmPzyDClUOer4Nx+IaZZysJCP/GriP6m2CDh3Va+MSuHyxedX1S0SBBlCnz6NFjPv7gY05PH7DZHJEELi7eUc5SsNx1vVfaysxcFYl1afRuVF0x8OBXIW6oR2h3yoVeG3W3MLyvrOpgbMd97JKXLwLMKCWTknB5/o6vz97w9u1bdpcLrS9Xme1CgGmuYTumI7JAOKhXLWBRc8NbpfWwZZNrGT3qoEV4eP8hf/SzTzk+vcfx9piNTuwvK5yGBHzX97x6++a9zx2tg5ajh8LDNq/bgKahHNO4zsUjgNJS1MNpZRgqMSwOY7xM90Nn71G3lhFs2Q6aHDzC82QF/sfKWA7mW0jYDwCy9LyeQhYsW21YXm0EPQJ+m3c6hewJV1Z2Ygx5hjnZomEMax5diQzB2NceXsg0IAU4tPdBVkXo4cfqQVgZOJo07DhKBIyZC0OhrHYvbhpds1uoVj3IIyaGSUEsogddDhdLJrdOF8VFsCbkHH6oLTl5HcKYhPokWaWrBPPZJULOWgRCvlneUV823l5scEkM37N/d8lSWzCmPUhMrqsf6lj9WD1yaBBnpAg2e9+t9/6t/67nMeRSrtbVgxezmV0xmK+AWSL/5bC+HqwnSonB/vW19Tq4qqrkktlutuQSJJabSqbbgpXvAojTSjT6rjX7Nsbvzee7rTZz9yvM6vrf7rLBuPn8N2uim/vJjdf7Fth8C3HgfVjRN/e5/v6vA8wHksJ1IsL3bcnDt1hz2BCF0qQzZVgswj+TRLhlAJwp7DM02LWWhSIT44DvEQCBTFPAf0OhJbJWkEwuRu22Dq8UL2voqzuaBrqqejZzoi4wUtiqIpEfYX1iQhlZoUsQr3rcp9wUEyFjmDawibIG4PkK8B7chpoK0lYliESQuGRBtOCjkygM07g3mq+WPGE11aaBdfCsMbDyULFjS9wPNRQ2w1qs29Jxi1wfd8NzDmXa+rPYwFNBXJkmpyJIJ1SwEtektWB/SxfM0goed9AgxuYUqnTVTNJBb6tlsSiqTvW4x3YPsqObrYTaULSYKxMJ7O57zv+AB/P3MJiv8E6/uRvw7ebnW1DoWoy4r7IzopHIMpi2W5SJ58+eMc2F8exrBlCsMs3HnL/9ms/+5RLzzoMPnvL8+Vf0VjnantL2jZN8ghwLi1VevHjB8xcvmY9m9l884+hkw1mvvHr9ml3boc3ZXV6ytCWm56xryupfEexY1vf6HY3IbcfuW7+78wD//kNuYshXmwa79ruf5fu3O79K+8HPLRoLvpDxnCk1LkwmC089hOBpAgZeBp4zyRwa4SmTDKSQ6mApFkCoJKZS1sJeIQfrNA+w4qBCqh6S7Y2E3YFlBKPnRKrhbSMWIGwegyqCykzqQInpv+aGDCXj9JHQJGvRryTptGSkkehDII2QaQ7F55giiXeKQC+GV6FPDbGMjEFqHjdgDeCzSUbKgrrjvWFdo+gxJ2mL4skTapHqXLIE8GzOJE6VgcoMq6zLHfAF30zYkmO6507viZGcpANRoaN4B7NGDP6V5G0FkBNdIhhluGB5IEPCq9oi1GggqCnVB9IVmTM0WwsyQVMw0q3F8X/fLfUoaps5xZWxgrBRCAe4XXuGdfhQeo5FKTfEG8OFrBEo2UiRXpvKyn5OVBqzCPHthnn+0AtEEz4ynRoekiSyDJbhyCS4d7RGZ5ZqLBJshJQVHUob4XFUPDxpB8GETaokXW/mIzyecZhc18lvwV3w1Cg+0S2RS+F0u2E7FUbfc/b6gufPv+I3v/kVve8ZPSTP6jBSsECmzYxUcKmYhldTygK9kDXOl9JhFIVhLF2QfQ2mdhqgHQOSJZyEjcHAgnVdg9EmU2FxyCmsY2xomA6nEYuWxt8mnJwncA2vXgs50rDwBIdOdmGpwd745/bPbI+3+LLn3W7P27dn2CoZ3vP+FhlpGYzNlpz2FDcePHwMYthSOTm6x2bKWAUpmTaM4fvwrdaY9JobkhKb7SZaqJ4x25OXRlubKB3CbhjTaIhuKCqoRHJwdtZFnGCKm0S4S48RXTdjGgYlB4tGHJnBbY9cgvqgqlN6xdUYq0XE6FByJyfC11QzVMWbU4qyNJB5E8WWKTo5J5tjHj864kwdvxCO7t8j58SRFcbivHj+kgfHp6TjRF6UYZXcjCcffMTHP/6IR48eMt2Le8+oncIg5xR2IS3uCalkcqrYAOvBWvOiiHZUQM1ZPIZ6NJjmjlPoXvG18W2z4TsnbxxtjlBwWsi6LNNsUCjsdY+eZJ7MG6wO6rJQpn2kOktCciQ3dwpzG+wnobf392C+i4VyHfD9Lqbxbdt1oPj642+ym+8COW8LArwNmL7+GtebnZsM5lIKp6enbDabK7uKeZ5xd5ZlYZ5nPvzwQ16/fk3v3zATDk3l27dv+a//9W94+/aMTz75hFIKX375Ja9fvwa4ajCvg75mxm634xe/+AV/8id/wtOnT6/ew2azYb/f87svfkfSRE7f2GxcMaCvAezTNH3D+ubboYQ3gebbBgLXgea7vsPvYjXdtZkpaTamBL4PW6GpOGkN7lWcMQLwsTSQOWGL0hmIKtpbcOs0wahho0FmTo5Zoq/hllkbYzph0FhaZd6Apw2jSeROZDi/7Jh28hh0F3ypTEl5+Pg+edowSwHJlDHIm5mqEeI6T8JmmrhUxc4Gl0VILYZp5sE4klFJXkJ1UTTSyYewYzD3TPOKEuwmRWECEJIIUypsUqTVJxVsDEpSNjJj7ZJiA99kvG3Z906vxmLvsJ2wzUYLFzH2DuKXeHdGdWTjbOWYXd3j3hiq+C4UN6bh6y4Ys03sCfAP12BCTS3oE9KZ8hS1fl99giVFUDORxK5ThAh1E3Jy+jKQKcWw1RpJB6NkVIz55PT9z5216Ba+AZsh2JNZJ462R5w8us+94yOExMXZO168ekkbe5y4137Th6116rVewbFgrZqt4ZgawTzmDGvx6iprTkMA2weP5+gnQ5IftJ8rBBrXb1jNPg45C7IC0etwf+0ekoJoBoTtPPPhk0c8fvIQzRlvl5CcedqEMLQ7SVLU/2uvqKqMEf64ECDpodEVCqKFzdHE0ycf8vDDR7w7O+ez3/yWi91lsLNV1vtSZzRhZ46mRGmJ8eVXuCqjVUZrsWbYml+hoZjUpEDk7/iKnAehLO5DAboLQ2KIEbcQC5JMilDNe/fv8/M/+/f89Gd/yJQj/Op8f8FXr55zvD/m/r1TfvWbX/L1s5fvfe549jWsPIYSKYUthYmsWUah5HOC2SY9oVmRHt7LvpJpBrZaYhxofwPPhI9797CUGNGee7eV7S5I6uQ+hxWLB4vYbWAbJ3VHPMI+lYLngoyGjkE3pWSip9HVXzSD1/AZdsJ6wwVkRKhYGlH3qI+wQdFgyCuZLkHCScnANICfYbQUNmNZOx5ON6EkcwvgSAjruhTKxI5G0FcK0JwRD9I0cFPyiMDCLNGjhtd1MBZ9DcAWk9VS43D81+8g+Wq7sYJQKaT0Jo7Vyrtm5Hc7SKtmvHdIRifRtVN71O+mHoHICYJ2Hde9jMN1+n7bQa10AJgP6+I0TTF0Vr2ywDowXI+Pj69AZgjLnusD3FLKla9vKYXe+5W1xoGhDFzVHjf9fy8uLq6G2ddrmpueydfX9puA7W3r9vsMi78TnL72b7/x883XvEtN9l2vcVd9d/M5btYyd22/B5zfeK3DMb9ujXL4br5vG5LQrlRt4GHtJZ5pPYZ9SAxLM45LCQW0N8ZuIDnjmqhUxDOeHXoJJQYtQFXVIB72TB2DKWsouYbTARkLZoqmiADSLNw7eUA6yvT9nvN37+gG1QrJg8iio0MOWy8849pWlS2IVnpLaMqkHPfylpTcA/eSNWfJaKEYV0Osr2qMWAiGGIOOjRx4jUoMg1arptwV7z1sw0yiT7K6Dk4dLIX9hhuTZLyF5U/S4HNhUcMMLTDARNFhkCTUq6p031M0SJduSpfI2JLZSC0xRkNLqIw1hT2a5o5RaQppErpLWDDqYB7KyMCBpJc7SfMaJmykrHQjpo93bO/pwXz7H/wH8RIBCTR/+GFPiadfExxFuFZqEX60AOr0YZRqDM5j6nnRGN1hDC6SME97Ls/fce/ohHkubKeZjRT2yakXF3y2f82nP/1jmi9MR0ecv3sBY8/56wUfg2kzcbnfI7JEmMkwoIXcz9aF1YnJo67ynatj8/6NyG3H8b0f8i2M7lpB+T0A8NXk7d/4Vv4twLU2p4rGwu3hxWxC6KcI+b10AY3GxcxwnUmjYiWFv9QQTBtZBG+JSRPDV0DMAvjWJVCt4QbNkCnCyfpI+M5BI6hGJa3epYZXgxLfa89R2GQrqyTyMImPYJdkIQUbLYVEio43IwFdhUGP1x+ZnBrVIljQhgfDpBudmTQIU3dYp+YZaRaTbxRSwj1AVVEPtrSHN8+kq63I6IgJe3G2MtAstF6CoUOjjChY1AkWMI6mCB/zrqQ04VbxFFIxDJzGNKIIGqJQBtp9ZU2GD5v4IFWllWAkjuRkEeiOJifNU3yuAbk0pCVaD0laGSPSXW1673NnrOdiKcaOYKaPFDfUtLJkdUQDUnLI9zzH5M1QVGbEK0My7gdrEfAWRWDOIa13GnkE6Jc0M4oFS8BhstVWwi0K2WZoLngOr2SSxrxjbyv7MsJMLBnNlZQc6zCZBkt6ZfBgUfiSwX0T7Hd1sE7fwy5BKZmSJranp2y3R1zudnz96hW//NWvOPvqIu6fFiGK9BFylklDyjuFfCglsMUjTG5O4DCGoRuhoOghwTfPjLFA10g41x4LIop1Cf8rbyQtoC0aAYQihZEmcl44BAaJCJMkJBXu3zvl6N4xl+eX7PeX1GVQK4g1Rnbq0pglPPNaNt6evWV/fhZhKXVEMaoxqS36/jYHYx7MU43Qmlgdw0InbdhKJ83OognbV5JPdJ8xv4gCSyOtV3SVGZlhHnYnIoYchovJ6NXIs9JkRMCTVbp7HBcqmZnRBbarZCtFQ79VRzQFCGAR7KJeIhRHFrokMp2eIpRi9PAa1zm8LcUykxotZeYpmDJOZnOUcAaLJZIMVAsfPv0YdaVs99y/POM3n7/g3csXnNw/wTXz6R/+Cc0rR48ewv6MF199RR3w5MPHHN875dGTU8rKELK0gG1otaNTolpHJke7I2smwiGYwlIMoERjiJd6SNZ0rPY/EmwiFQFRpDXyFInNi8PcJYZUI63+hgmVQdYprG40vMlynQK4V6GhHLmQpmBJuwiIxH3hPbebMsGb8s7bHgPfBqDvAjOvN2l3STpvMoDvYkzffM2b7/Xm/tf/fWgqNpsN0zQxzzM5Z968ecN2u2WeZ/b7PdujLZeXl793LMYYnJ+f8/f/8Pd8/vnnTNPE5eUll5eXVzYZqnrVtB6YRfv9nl//+tf89V//Nb/851/ykx//hGVZ2G633L9/HxvG27Mz9vv97wXTHI7dvDa+h2boNmuR2xhJtx2nuxhRdx3v79tSMdhXmDO6dXwJyWFrzjQZbhPDdjQHd8UWJ1kJL2AzsjgjF3IfYTXUBdFO7olMw3Qb/scJvFfa2HCkgi/hhS8pkaviuYZ6yTtdCrrbQ12Yt8cclS1eNqHIESXZxJQHJUVzWEtnzpmz/cyxPcKXZwGASzR3IQ2NMOZhxjQSTQfHVFKD0RM6KclgeIPthCzGpE4WZcrKPCvlXmFvnbQc1hHC7qcJyiCXTGrvsKUhHoPtCz8jryqYcTDXHdHQ2rmx+Bskhx990w1KeLN77zGgk0xyZ3KhisZ63IE50VqE1Qw6tIalHJZXkhCt5NrpTFhLkKewfvJKLhp2eU6wLFMwI6Uo8w9gv4trrDGwsr5WJqBGCO3pg3s8/eBDTrYzpESaMxfLjtorVkf4L/JN+R5Y8Npb4SQJeauqklMiTyUsq/rAL42hwa6StQFzLEA1Dr3aYaz/DaXo8Pe4Z4EkDTY0EXpnB26xBju6jyBVbGRimmcenDzgaHMcstvkzGnicrvgWcjTxNG72DybAAAgAElEQVTJKWm5pC4NaJit4IdY2HwQyiiXYLOVrDz54AN+/hc/58HJPV6/e8OrFy+4rJfQo0sXCwYvImRbFcojbKiMeMzoBgcQiwA2DyxN8wiBDC9r/4bUIwGqGI7awZ4j6lHcURuoTNy7f8qHH33Mw9MHDBUu357zj7/4Bf/yq19xPE98+PQj/ua//D+8fPX2vc8dJIK+B5BzKOhCCUmoDqUHq9UicwP3AHNy5Ie4BBkoC4yDwlMCUHANhmEOptAauN2jJwBwQ1xBYxihAt3CiMIXj5DpZDAClNAalnadsPlrNSxNIhzV0RGAsolFNoN7BEk76/XoQI/LbaXQmye6dJKXcJxQxTRq8VFWULyHRd6BOp3CewYj7DizxLoc3tQDnaJXi+/UcFeKe9QUeSW/eUJ6x9XW6y2H1V6X9QJcVYkjLPiSCN0ymqKmlbSSnHSAaQBSvdIp0Se6xGhmgFNxS4gNLB0GlfEYl/X4joau6rr33Q7qILNvznnkG9uKQ71yGFLnnNlut0zTRGvtClwGrnx879+/z+np6bcA5oOV1n6/Z7ffYcOYpulqyK2qQSZwZ7PZXLGXReTKLgu+vR7fzMP4LgXT9ce9z+9vA3iv//uuO/t3vX7cN79do33Xc9wFWt81HP+u7WYtePj/4bu/i339Xc/XaRHap+t1OeL7UxmktKr3TJAcpLeimZKM0eN6hISmwEnEGq4z2VpYcWExTBLIU4A9aSSWQ1aRpDUML2GjcHSy5Uc/fsrTHz/l/M2Ozz/7F15+/ZYynGExqvcc50fK0RekFt4ZLoMm4WWcMIaADA9CWBGSG7WGPWZWX4lyHZhh9V03sVBSYqtlSIpcrAS1DlRiPXTZItpp3XDpePLAbHJcz9IinNRkzUYaYOEPFINMM3I2mgeldEiEn2IgaqRR6BK4WCb8pHU43lmtGyeGO2OENVl1C+s1EtqdIYLTEFkih0Jz3ONNYA72d4uGOIhtFsoU+Y7T7wckb92yyXWQ9cZ2GyrpBzfnNUDisLNf/fnqhyho4mdZC4p9WxCJPUuKhOJ93aMaXlyYsVCptfHbf/0Nu32l94WshZwSX335BcbgiAfUXpnKHDKdVrnY7cMEPAWgNojFleul27qoYd/8brVi/mGHjbsP2/vucziC77Pd+rjff8L/MZz8xnaYiDCc0UpoPr3idcJW+ZKL4TUKTIqSxhL5buL0fUKLoWp0iWmQWcN9PS/MMWZEdjGNGSGvyl2oXlEpaHaQuEiGhsRTkHXiFtPe5Ir3DJPjLcJ5kieyOjULnSiQNUOxkGpbmhiykHqwFPsI4LDlHBeqK5JLhBEmRdpAJKMyhUStdnwSVKENQZcaNZQpiyqphY+y5sToTl0SyTsqwuLh/1znDLUylcglUzSYB7OgNhhkbNeRMgMdT9DV4rEmdI0CTkSxScP7l/D96zkARhCG1fBqloFZBMeBUCfC6xkjO7TuUKBVRT3kzpY6zZwkjfEDLhIhIanDCKbASMHx8RTXn2te7VM63UBLiYKyOyQo1bDiUXRpCi/IbkjKdG+koN6sXoca6dGjM3liaKZ7pkpHRoQiuhuWE310kq9sUhVgROiJBFhrqmQiPKCbkYrSemeWCLdL2Rh5kA26K4s0NiUe36qgpQNGyQ7S8W6cXZxzfnbBr379z3z57AV72+M2gmXQEzYJk2VyEqo7KgFI0sKXtkw5GjLrFIVCCmKEGBllyGB4NEqlJcTCpsF9kIoESyU6gEi3zhPZhZKFKa3J4N6o+ykYtnPm8cN7fPLJT9ncO+Xi7QVfvnzG16/ehAxp3/HRQBNlDoa8NKN341KF0fvauHlIDnU66Bzea0sSk+LOzCSdLp1xsAnxxMU7I5cRlj19oY9+leSbRqW6k3tcT8YI65h6GfeOuYAUcu6cWEGyUDD2kvExMemgMUIOr8roC1odTSV83DeC2GBIMPORzBAL78KdQRamJIyR6dbJKUXBgVBdmcdgaHj5FcI77UhgeIRRDMtMGPMsAXboMZ989Ad8uF8427/l5dmei7dfI5KZc2Z7kvhgfsj25AH+6IT7m3tcLnsePHrMjz58ytG0pVVhb515ErrtYQ62+lYzzReqV6x1XBIkIzNDq/8/a2/fI8mRpPn9zMw9IrOq+o3sJYcccg97OuEO0gkHnA76HpK+tHDQQcAJC9xpZm/FnSFn2MNmd1dXVWaEu5vpD4ssFovVze5ZOdCo6nyJjIyK8HB77HlhGYrXVJYgGb7pZQOrZ0mkzIVR045ljMEYHauFm7VncVmcWgd0Y9VgtGQrdA/OVFiroiWLwRJGi04byVg6yIFKhm596HjXYvv+wvs+cHsqwt7FQrn7nvug8+m1v+aDd59NfX8bdwuPh8Dp+7+PMW4B4PPzc47HIyJym7C+m3fJyLsD5J6Yw2MMbq5vWJf11kv5VOCdWMan8L7TcRljcHNzwzfffMP333/PNE3EVlB++eWXTNPEP33zzS1z6S6gLyK3rCrgNgjwrlT3Id9G3UJj7ocm3g9E/LWC8kOGelpfRYN1n+wbl8BUOSpUH5hWekvA59yDZkHvTrVk1Npwohg6DKmpgmhilIWcj4tgonAsxHky7MLOENvOG1PoGSpcp3OON2844nQv7HUiorIfM0gCfnM1okzZuO7Z3D7cdM6L08+UfjxnvXnFqoNYJiwGGIQatSez1wt0M6okU5Gm9LBUPPXBPiRBYRSbBWraKckiWNvUHSpMZZ8s07ljY1CmytUjWA4tlSZLsPFx6JK+894TNDZpWS+sSp8FemdMkusoCY5k4wuRzE2gU6tmWHAURDOoeYTCfpC87IJ36H0C7VTvhBWII5NO+MZuNlLx4gq1d9bNnsrm8w8+d9D0WIzbQLoEKdUqF48e8eknn3JxdsFunhjR2JXKXJNtvRk6brkcCWScGLX5lBIhFDWKKRf7C549f0IpO968fU3ffHrZmqoxTkBI3GYfqAknuwxBQIOTmlo180rCJYtyT4BN40QSyn1KqzUFTXWc1sK03yVrvAzCM5leUL747W8J77x6+SOvfviR6wjWNZH8QHItp5oKLQItwnw287df/S2ff/IFu4vCGJ15mjDSBgHPIp2QZJApiIwEUj3wkQzOzS4zoWO/AxZmGb75E2f9dXr6BHbmj7QnIXK/sFzfRQTXb6958f2PnNcJm2devfyBv/8v/4Xj4QbVAqK8evkjx5urDz93wlIpJEIfkfklKK3kX0uj4NExL1t97XQLatms8WxleCe6Ig0anUJJhq84Vuf8jqasrVOaYSUSJKnZegjJ4DvdmlDDdGNRZ8NHXHKNIoG0jlrmznQZFOmIl6z2h6OlIFuoHyapSLKUjafVSNma/QMplg0e3wLOLckE3QZIxXpPUtAG1pzCl2XDHkSMYhAkyIul33yiukaYYT7wsDw/bAvSVt1ggEAiIacg6d2uW0Oma86BtW52QoMgCTrZCNsa4Fsj/mS7odFpKGVkTjZj2+dYMYPhhlQlRpKPTueqqIKljcCHjtO9/GT9KSK3lmKnNc3JRisVBIN5npmm6Wf36dO9dL/f8+zZM3a73e3rco7QW0usw/HAclxuHz/ZXeXkIreBgvDTmuakpLr/eXf3873s43ss519j//7a+BgW8u067LY598u137u2+VBj/F378DFEg9PffV3X2zyLjzomRaFlrRkGIgWvnhkGJa9164OB0bcAcinKcV3zHmybrZSBjoLaYOgCw4hu2aTF0+ZpaDaXVNNPX7Z8ARpTKHVfeXy+54uvv+Lvvvqay09vOPQDl9cH+tJZPT3LwyN/Rta4VgSVSpuEwtiaZqlSFrI+F5TYcmMy7BNmz6ykVRWRlvfekt7vHkEzR1pnbPY8VZLo11UT1yKYSGshPBiaHtG4/4RVMCij0rWjkblaoxjmBk0SXN9Yrz4E0bYphyy9/z2Bb9WWdZYpqkdGFyYmQDHdApd7x1TorhQfOEavlvOU+LbiCpRObO/LTClBnWwMvee8+ecBzA+svz8Mp4yfwNp3vVG2BZPzkx/YGLdM4uYbxVwtwzG64x6shxVsYrl5A2Okp8iuolJ4+eoSYvDm8gjeWA6Dec5EZdXtYo6RZtp+x/zjtK+RgHPc2ddfnc8fOCC/tBX5kKP289c89NuvfOzPH7v/5P+P4DJsk/oARGkmMFaKK7UKXhUOA6aSsk4XtAsxrYAmK9Y6aJ7w1ZVFg6GyAcwDmcF6JyiwNEYRVmAaEy6SnWgvt2FrxQNraUouAhKWNg/F05KDzYfWfLupDcrmBxbRMi9YktU5jY5Mhheht1zcxZTM19Bk2dSaXXwdjWbGTqfNdydBb/eGFCG6byE4A60whkGBLh1FTgSFjTGtaHRcCiLZQW4xoIA1x7RmyEUZ1AFr3aM0dDjDS4Kg02DElN13hPCCy8jjb8mutXBQpWlD1PMmgmCjMLZKKLqzRqHIQFmRIngYRZU4NsokLD0DVgRLW4YPHO7barBU0KAwEgxmYxR06JZSDiWbD61JBp5ZMgD6KMxjEE2oVek1QcviM0MW+kiWR5ikpFeyEImeEl8vgkpLBm0URnNMFddkviINDWP0lLGMSfA1qEMR26TtLRfIbU4mOWM7/zinSMMkWLpTEFZviMJOzlCC47Hz7Z+/43i84tXLN/zlzSsOyxFCqJv/U5Ep5Zg1mx5zCGuMLIWKUkRZXSixolpBjJBMf21r3libGo30vepBFn8+iFJBFIsA1QyN1GQ1aJX0W7YEpr3BvF9TMjRN/ObLr3j++ZeIwfn+DLfG1fUlrec1FKvgZrTwDCWRwCv0tTM8Qatg5Nw/Cy4ffpvqx4HvBGGhuRFDWUdn6oNmQfONWzXIbnt32kibF9X0knQViIEPZ/UVojJqYQ5ltJ7BCwrCwtJrFqPW8S7JcDJhaGenqYIQEXQH6jm30dILu9Ogk37GVRAqfSzp4WwFJJOWm3XOh7K4UsxxTioJ0lesGhOaigIEkTmZ4gJlD7t5Yv/sOf/LXPjDF5/z5i8vuLx8xTSE/fknnE8VKRd8+ref8fjJE8q+MBfFF0EqqDcYhZgrZQJtlUEjvDKNTqNzWIM6d4asLIcjEsJu2iFa6X1hGWk3Y3vDeypKsPSpVA9GVOLQcAEtuaDTnlYyMTI1vrDDVmXEoDcjtNGcTD8WQ+ZgLI3iC3U3ZVDW+PCK6z475H2L/7uP32cmP/TzoWLhVBjdBZbvg6Dv28e773so6fxuQXG3yBgb8DvG4Pr6mv1+z2634/Ly8hZgPklkHwo2vPtZ98P9ToDLqXA8/TsBwCcfx7vf9cWLF5gZrbWfsWpO/05sqj5Gygz5OfvqflK9bCysE0v75ubmZ2znEyj+LiuMEyD+MUWouWAurGNgNzVBwpH3a23kuUgGJhnK4iulD6oazQfTMCygp8EAMRxl3tiSHZWaXnghHMuRs7HH22Bow4cx7TaFQ4FJQaLRo7D0G9Bswq7m7Eyo1pFSQBvTGhzFCBpOwRCWKGmxsZuJVuFKMgQvOq0rdQsuRoM5wFs2wlWF1QObBzaCWAtNB9NWYPaxo0cwliOiMNWa4VDAXKF5glzDJ8buBh0F05mpH9Nz141oR8qsaUshuTZqXtGyNdGHYBZIE5oLXTqFieopy2fa0udxTBQpe/YqiC8MNfqYYfT0Nq0rpunDrxzQGERJYNm3sFcVpREbo8cAIXZGyLtlo/dHSNrJ5X+cIkaEYqZMu5mp7KGmjcHSg3XptGVNXq2lzzToVqJkM14iwV+RE00nkFp4+umnfP7lb8g9dS7fXNHXzIaQ0I106xtySoYMss0BsoFQW0OjyEbSQKAIYyuMfFPHcYcEcwJvzQptNF5dX/HVcEwzKPr15RWvrt7w+PwRda54Dx7v9lgU2ovvaD2LaPqmACLLMpX0An109ogvf/MbHn/2CRprAsG61Yue6/KfRt7ndZt7NZJNF4ytrsw1uhkICTZudTXmGQh5W+BvxyEk67qcA5OR7oONwe2MEVxevuI//+f/ixcvPmfa7Xj94xv+8pcXjLXhmqDs4bgybk+GXx9FNzuPLrfrJlrgqlhzRu2IBD08aylNFr+3gbKx4pjS7qGvSM1w4ROgkh7l2bg3NYj0QNcp1x/dk50rChq2sQCdk/FLjJKghw60K2CYpcRaVRg9AYywZAgiqepUy9DwE1A/gOKC44g5LulZXR1WyRM+5eQJ3DI8QwCTmp/2Z5LswkDQ8A2UyWCtEPI4eoYHigCezW/VPPdPOMSpCQEbI1wSlDZPu0xNZ68N/O8/gVQlA9lFs4mYQYXJBIyNOLfqVpcCraeCVkibMmdr4HhgnrZsobaBep6M9I9Y70QErbfbUN8xBmZ2uwaAn+6V3LkfT9OUIbubpcLpnjtNE3VTRZ0a5qfG8N11Rdu1nzV9T6qlE6A973YMHz+B3xE/ux9/DBh6H1y+//73sZfvPv/QWvB96qcH94Wfv/ddr80G4baeufP7h3zG/e0+pMY6rZ1aWzke829yYpJ/yCgrHIdB7Uwj7SFsJPHLRfE1vcKHdapYBuWtmf8UkvW2hrDQKZ52O943FY8fUcuAdXSiWIaTZ3fI2Ws2t1QMsbEpMAwRw6px9uicp5884Ww/8+Z4xalRmqJjpXdhEkFrWtxOTBvWt1KARQP3vJf2nMDZW8m8GA/cRgoh6HSU2j3V7TUYrSFab60BQwJDWAfMNRtogmE1a/4hCSivzalaEsuyNRuY2lKR3JMJbSODDPt2eZeccAlJ26sYY6tZI4MS6bfZT+KCrYqYkgKL9MRuQzEp2DYnt5wZYaQlSN+CSTNElyQ5MOFA8U5s+RSlvnvO+ecBzA+MD8YpTye8CYz4BRk6ssnIzy+l7EILGUywRqZTyvSTKbePgVnKUtYRjOHIcoTSMR8sDGQ95PognN5T7qcitLWnty9kd3HbiRSD3Vku3XbcPuALP/j8u9707g2ePvs+Lv/gu2T7/y+A7V8ZSZ54PzD9gWMiO1lCMkk9Bjql6U3r2za9U0ORqVO6JouGgbdtUReBSzL40qrCMQvGMMo66B1ChDDFvCNMuI1UbI1g1U6JQQ3bPJ1LhoaU5PhPnszbooOmgm0+x6jik7COnszgE7jtgW5BXhEDuhPqGcoF1AZNk7VUVt2YrZmG3UqnzsocRt+UTCNgJ8loPERJKaxALQJUiJPnW7AKiA+0FNTB1w5WmVRYJcAmgoUmyuQdL8Ykmv6nZkTv26UWGc4zFUZ3bIykZs8JfmuxVIA0R3xOr+gIqk40y04gFlTLwgrS/3WqzkV3Vpso5xCrU2ZHhhFj5cPh5e2rb6ySmmUUSNBGMly8GyUaTJYVdWu5aKsGKHSnlM4ypZxxjUoZC7UKukamQE+aYXDhHCIDP6ona8U88Ej/SouJKMLs0DXoXkEG0StuTiUSFOi5KKxzssAjPBev4UxeaaQVQG3p24xvDQFdkVaopowe2NxxyfCE1z+85NXLl3QPhi+UbfHr9PTFmythhZ0ZJpUhHVsMl8LQFcKYfaSNQEkpYtVgrINiirf0dVJRpppBAr11igSoY1TUJcHOSdEu6JS8Id181HPBvEtAtRpPn33Kl199zd98+owQZV0a69L5J/kj1m9ygrGKEkTP4MWyhQSMcLRneJJGoLsZk8A+IlW9mxPNmbRkCNgIVFe6Vo6xIkvfpM4pCerLYFVnKg49bWCIDKJpLSA6pZ5RLBnsae2g2TmOKSfGaUVGnnsFAVnRULxCeF6vJQRfB71u7PqA0hstKowVmydGA+t5A58jGNqQOZi80H3F5kL4hEhn1E6xGZk2mWtP2wiTHWKCiXHmivZO1ErRiemzwvmjc66+eMpwONvvsEePkOuF1gbijaKDZelge2wSVFaGaCoaRi5OXQcRSo8OKL0YY+Q8qDLYTYWhM4Gy2EqLQangKO49QWMZaAuiTOnPLZ1yMRE4puf0nn5k0TfWUnO8rIRmQFGvR3wIzSpGgxGUNQtYr4pfdWzKkK6PGe+SJt4f95mvJ6AY+FnxdH/Bfx+sfReL9qH9uPv4/efvB/ydxl0A9sQqXluj1spulyzlV69e3RaZwC3z+C44dHfcBZhPAPRdQPi0X6WUX3gxPwS6n/wf7x7Hu9u6W2g+tB93Hzcz5nnm8ZPHPHn8hIjg1atXHI/H22NxKqaur69Z1/XBY3Z/u782hsLahaozq3iCbjKo6jTZ02NFQilFiJYydlGgxtYUh2gpm/QWSFFiHOmqlCr06FQvaYGzGr07bsKInPNsEUYFiY73A7Ioy1Unjiu2g7LPImbYDRKKSmFsYXlSV2qtXKwdPSuMNzOigz5VrBQWbfRVMJlQ6zAqorZZZkAZDa2GX2dzSBwoAkujVzDZURCCA7YaqFDKxLwrua5hofeGjCC8g6zMY8KnI2WtHPYrY3X0AG0yYgjhI5mo0hlqVIcoeY5o24Eq1SB6epV2lLqpJUIEU2M+O2PeXTDPg+HnRAva6LTjES/O8RAEK26gSyWsJ4BOSdaiBl7A1rRhcxFmgcmmzTjxw0ZsIF1IeveePIzxwFtj7ZdwmYDg9eHAjz+85u3N9W0mRQJVye6S0FsrJw/P4ERRLISzaeb88TkXF+eZNTyVbITc6kq5LS7Se1Ig0n4ur4+N/SkZDCRiVIkNi9ZE+8lQoJCt4SFb10OymFOMWibOdjvEO+FwvXZev/6BH1+85uLigrPdOWWaWfZn3DTn5dvXHI4roR3ffJ/Dkz0aWzPpbL/n2ZPHXOz2tGFcXR7oxzxHRDQ9M1UQklFctGDFUEtgQkIZ3mHJGjBZqfl60WS2eqSdhkja90VEysFV8uCNE/gomOpWY25+1GOwrI0fXv2Fy6vXENkwG1vDrNTKVLe/x/syfe4NwRk117HY5laxAbyZKWL5dyz5V22SzMMkX02ED3pqrDFLxh46CKnZrB5pvxhLAoReHHfSk3yz2OgCxTN7pYtReyBSiOjZMJbAYmtWijJGB0v7PUql9GAM0lYlJPNATpePDjw2IoVsipbRIVKlNStp4UGC2SaBt5wTxQtVE+AaI5DJiWFoJLvaYmNSSuAMzPJ8F/fNLqPgGgSGOEm2EM+18+bjPkRBkzUoqsno36wrFKc5adMIVDIbKCutQbADyWwZIwnzGmseuw11CBOsy23+TRKnYmNi5z26B5mH0j2JLB8670TQW9pXrOt6yyg+3Y+tlATQtyyEdV2zsVTKL9Y0p/v7bdNm+3d67917713G7En1dDgcWNf1tqE81Ym+67dKq9P9/6H10/210O218Y61210g9SE28UPjodfc//1dFmvvGqem3S8+686DJ4/7u5/3LpD7rg3H+z7zdExrLbdrvo8J+RtDUfVk1Gb25DZndDSmXOMICQJvzbfMgBKqOD0UaIx1e00oIh0ZgdDTfsaMKqSdRsmafFg2SUsEw2Cgee0PZxwO9LZSSmW3u0BrYUjajjIc1Y6Q9jhRjD4y86KSQYdTTYyIMfAC2gYaQtfBcMU3X2h8a6yaYqNjVYlYiVFRmRK3UL+9L8QWUDtGz1DamHAvWTOuBdFBrWml45rNdtFCDVh7ZhioQETmBwgVC0GKE00YGluv07amOQyHgcEERYLWO2YFJEllw4NoOQ+e/N5jRDbwxFHLe/7wvvnUG0MdlTmVEwLKtHlAO729+7r56wDmD0YclYc5vtsGhNsbyYObO4GpG1qaUqQT2JpIaCjQPZmNJNXblxXRU9CM4N1Z+4LURNyJzRNVhOW4hfQZtx5iP/uCG406+GXxeXenf3rHXwPHxr2f7xgK8cDh/DAM+z37dXrqAXD5A/bqwTF0R8i6sQE6SE3rhSWQJPAxRjJ1Jw+GZufLdwV8ypv1OCZ7tjXctolrFeo+NpafQhuMbohkx7qOvkmzBEnPE2JVTBo9NKUJAAssNX1qVhnJDNa02HBJYMNiIGJoyXrBLBl1pUCyQZVYj+nbU5TWc2FXsVwoymA1hWVFxsRkDnLGmU3UPawNDvGWdrzCWBmjMkVsnsLJ3g2DXRQyaOWYi+StQ4+MDOYr2+INRRfSp0+zw1csiINg2tOcXwQUPBrqg1r3DM+Al4KzhlGsMqJjdU0mzdjYY5b+gtYV0cLUIpnkm192K9tkyEiPN4KIhVKE/hFAj4WlfMbTm23qHSzDadShyCAGRBu0SJZlpjsPZBW01pxM+6BhWOlEz9vvEAGTZD5Fw5DNM8ooMRhiKYmVSvjCkEJRJzTBwpmV5gWbgtKc1QY+hH2prJFetFK2tPahyGzJ7HEIqUjNG6VJzdC/Xhl9QUSpNmdxM3I+G8NpfSWspieVlVyYTpXJhdkqYspcH6HVGd5QGtLgupOfr+CWHmfeO6t2aglWTz+pEkELoSEJYhfoqxAlRaG7vVFkIsypU8kudfjmzzdBd3qN7FQz8+zJI84enbE7u8hFal0ol5XOwtJHMiyGoOUkT022h0dHfSDzjMWSciRxVAo2fbhJRqESuuTtxQctJiQ60te8mcjgbQS7XigxuD4eiZr+5LRks4wZJheoE+p5DoVUMN1UE53qdWtwZbe6egYwRF2QVqkorh1vHZ8riwi2D6znPGdTBpXqcNwK6yD9tEUomwdXeLLCiig3KJMnA2CyHR7L5iO5hWmoMc87WA2ho75D9oJ6YfKZIQNvE88uCue7R5SSQvDjWPjT69f8P//wew5vX+MiLH3l3/0P/5av/+5f4EOYIu0u9nXgPiHmjGPP9OWAWFbGulLOzvARYNncan1wWA6MITx+XPP8jUERyeMS4K0zIjB3dK4srcOUbH5WyYWNOZ2ghtx20qsYrcAUkRL3yTc/zb6xfUCKUfTjCq77Fgt3F/X3rRbug8PvYjw/xHB+F4PlIabzfTD6XfLHhzye7xcNvXcuLy959eoVjx8/5tmzZ6zryrIstwVh753D4fCzfbi7X/eZyafvePp5n0Fz/70PyVVP/07bOW37riuldFAAACAASURBVD3H/e95/1iLZMjQF19+wZdffMnjx48xM16+fMm3331LscKTJ0/49NNPGWPwhz/8gRcvXnB9fU1r7RfH7mOG7IT5KHhbMFfqzljNaIega9sYcL6pRNhAwe27eUVkUyYhhDTqyGt7tgReVEDcmEYjZrjpyu5Y6efZGO+axcup2bQcGr1fscSRx/IUNmm5D4j9tDH10l8/ItCi0DolCvs5uOpzNudWgyjsQrFYOcpEl8YUjehb4WNKGYOynxnSiS64J1iha+dQGrqC9JnVFsSnXI8NGD3JB4wJYmW2CSZYKMxNWc4GdTWGHzmWFTlWrB4YYumTjzCVDZTLmHZgUHGGD8oprAsHJlSDuRhPzh5z9ugp5WKibuuhcVPoZSH6OethQetKWxf6esSLps/1CPo6KO50m4gGeEdLNl9VznhE/ajmhER6wYreWYg7eB+8vbxCRNjVa5beOV5fc3PcAHmSDYaygWK+lVUnUCLXOiAJktvE00cXXNQL1n5ELeW2Y2NS3rI44wRyZtbMSTafdhiafppzQbVgAqqWCoR1TTZiz/m7b77NQoAngFlm47PPnvO3X37F+XzB1brwlxev+e4P3/HDq+9BB/uzicdPztjPFxwOT5inaSPWSDLe43R9psUbIcy7HXV/TpmF5Th49foly6Ft84snyCz5JYsp0zwx7/fs92ec7bMBcnM8cHl1w83hGhsDV6WaMZXKOhptbWkVN8YWYq23x8pEkHpS2WWA5Sl23WOzXRyDsRjHU/N0C7grZjx/+oxnf/Mpr17+yD9cfrgHc5eAUdCSNhBqgz4264StEazC5tGc5AtioJLnaKgQARaedlKyAU1BBkkp2Ai6JkOOkd/ZMFbxDLsbwUltJdYRURot2XWec0Gc5rHIv0XWHJvF1TQwCr07tpFywgcmaUkSsWADKBuo6oKabOujTietxEwipeYnz3EXxkgYNpBsACQ9IoFs7RQR2ihpc6lpdYdkvklE+onjI9V6AjIKMoSuW0CfdySEKSxJKWNK72kMxJnU6UO2wGKhSkl7ts2SZtb0bW/uKWXv2Xj0YDu3Le0LtZN218aIlkQRsh4yk1SoioN+hAkzeT9d1/XWLuFuqJ+IwLbuuW9B9RAz+LSOmO5kJZwAzdO42zwWkQ3orL/Yfinl1r7hbsDfx473qc/epRL7VSD33n7cX/+d3vu+ddxPbz694f37+9Dn/2K77wCXH2rMpzf2QKTdsstv2eq/MsRGXn8I0oTQbDATShudiiJiWyMwyXzRPcFGcWDQPaiqrO5ZXwNU6K5pl+MCmg294grqlB7QBlJTmSAeWz6ToLWgZizLynp5hfeOe4NoSKTit0YhtDMiC7daYgsGn0ArtRolKtc3jbBUqQTG6kE1SRhL0jDC24YtUhBJ0l1fOrUaEsEgVVwesvVsgwKIdIoVvE0JDmuS/wbB5EZ3ZS4JfhdRrAqrd0DwPhL8JdncKkLRdAqQSCa5etpXiaUKbkjBpVFViC7Z2JOOizNiJPFJlEJ6zktNwl4NAwzvAVUpTBQS8E7v7fSRd0k8713jrwOYP/g6f1dxF3d+xB3sUx54/s67HqDXisdmiZzdY/eORyZHszEBsl8oGapGnuOim5QpVkCIfuomyT3gOHuFgzsF4vacSkqkfr6rJ2D6l/v/q+PX3uMfuNEPQ5w/4Km/5kvksPVIq5v/UikJjPdcSEcI1M0TzDttNdgFUTPgaZhTxOkmqBphAd1Z104xZSyJxWhpafcwL9CF/TywQ2OxmVE64smK1mJ46xkAJZJJvPVkYD6YIy+DVXMi6yG5iC1pkO7u+JjQcNQaRPrcrjXALeVkQ/Ki7jNRO3EUfJqJNUNWKEKn8uTpEz779DPqmbAejvzlO8dfHjkeoHdh1E6Wny1ZPwqB4WsDKYhniMVklRjpORrRcHEwY66AWHrKRmXyQVPloHsiOtIGJRSh4KVBi9RfxETfwuaiBVUSnGJAlJ5BKZFsZIDRFJHCaEGRhjNjDKLnIsqjsepEpdACJv/woDaXvFZzoVdy4pOBqNMoTAaxkCwaG4hMyHCGG1Fy0SIlu4G1QIlgLWQ3MSNZkeGb713b5JMJmCGN5hmYJzZnoeGdtU/MNZCo7CZhrLGxIE6S1k7dghpVwVfDtBFDsBLYMFaBoo46yDC6Do4l8IB9tZRaAs0HEtCWNYszViSEvq55UzFhmnZYmfnk2TPOH50zAq6vXiOXN7TWqSK0WvCezATxBtoZKKNlh7KYMSIoOEOCWWZ6H3nj9R3TXNCqTFv4VwblwLo2VoQM3AlkDbw6UoKz3URbGtdXlyCpLHn94yuWQ7LfFSdMqKKApJe+BDGUoQMZI7uxPehTsJuEx08+3M9SdYePRqcn8xqhR8p96iS0gyHrAT/bs1LYPd5RWgbeLKMj8x6RwDiyoluXPAM3e18Zml6eXgZjbPcRhR5G1fRzDhW6gQ6n14mqgvUBUwb3zVo4HjZ/9ro1O1tABW9B80FIS+b7mmGeU0nZZqozBlqT5VLCGWLsphkbkgnHUqlnlRFCt56MZjVcMzDMas7qoxsvf3jNf/w//w/+8Y/fQA/20xmHwzXH6xtsnvjs88cIewhYuoEs+FqTqeJw8CNWdux1oksBXZLZVAZTC466T8l+21F8ZCL6esSmzZvV+y2D3VmZTFlb+ulrRjwTocxzsHYAQa3TmIjeiLoxKkZQVZii0sWZ5hlGsjo//Nz5CcS8O+6CoPcfv//7u1jLDwHVd4Hm++9/6LMfssF4iFlzHxS+C4S7O2/evOH8/BzZGEUXFxes63prUXF1dUVr7faY3C3+7n63D2FqP7S/p+fuFpt39/u07bufe9/m4/7rT8Dzbrfjyy++5NmzZ+z3e87OzjK8cDfz+NFjvvrqK0oprOvK8+fP+eafvuH3v/s9r1+/vmVu/5qn40PDegZvFiuwJqQxVqFOimkh1gScenPYPP1LT5mzR6eNZJKOVZg2H1W1Ja0aRlou6OhpVXEUnM5BF3RSXI6oF0oRVm2sayGmhZu3jdGc/We7bGi2jmAQgzgzDktjbgPf71nXQaEyyg3eDXqCO/OusvrKcXSaKN5uUjUWe6hpS2expVusnWlfOXkT9u2Y1ABk0GZl7imylFWJnhZJYltexmTp5d4mau0Eg51v536sLDcdrR2NtBcSUUozsIluKxo7zDtFOsE5UtKuQcjmbZgwoVycFy6enrHf7anzJpEdE+O84ZxDHSzzBbM31vXAm9eXuCRgXyjJpNqTapjNc1ZdKTpTdZcszuXDSyvhp6W9Aq5ya3GyHo+8HiPXsAHe1gwsOwV8iaJk9Jl5IUj1Vdo1kFYrmszhcrqHTAItMgtk+8ysrRISFTHg5G1K5jCIguQ1uz+/2K6vmVJsY6nC9dUNr1++or19m0U8pC/z9iVVhMdnF/zd11/x7JPHTFZZ3/zIDy/+xDfff8fx+gAou7MznsZT9mVCLaXNokGMsTE9N1awKCJsgckD15WiQrts/PDyR7qvbMbW2xoygT6bZp48ecpXX33Nb758zvnZOUUmXr19y+//6+/4/s9/5LAunM07nn3ylIuzC25ujvzwlz9zdX3Y7KiTuZ16aKVYoUyVoulV3MgwcjZLLtHMm5hLZX+xZ+nO4eaARGe/rzx9/pzffv23CJXf/f4fPuLc2cgsI9dwXcjAKIdOyRA+kjEcrSEYLsGwrANGh1IykBqEcFiQ9B6WIDa/03Bj0s36w2EQybofIK4pk7fIbW5NCWdGNVWH4U530iM6+yAUzeNISdB2qhl2Z1RkBMeNyKBjIaTgEZulWoLcKqlgrjIoZUqfbUtbIobl3FQDomye4alPDgvaiJSgb+eGJT0RNcngLsucF2IwLG00Dc2MnsIWbtjzfDydYroxvmUzVnVFomM6tvCuYKSmIIMw1Vm6pt3cSFDdUVzThsREaNGS9alCGcGInNEG5DrUPEH7yDrZ/cPVfvAT2LgsC7VmVpVv9lZjDEopaWuxAc/31Ugn64qIYFkWLi8vbxnOJxuN3vvPHjsB2Kd77bIsuPutrdbd+/4J+Dz5OD803sVe/rV7+PuUZQ8xm9+3pnlonx4iAvzsNdxBvO410R/a1t3tfQxj++52Tq/rvf9M1aWqt43EXxvqmZHkW4hnUWM0R6slKU9PfsaWtrYuW1NmoK6IZuCmlwx5T01uhrpXSjZ3al6/4XIL1oYJHU+V+QgQYaUia+PV29d8++cdx5uFb7/7ltfX1xvhKxtPjrBwIPoZRmZ5uRSMQdHg0dPHPHvyCQS8fPUjr16/YaVhx5WQzXomUgHcTfHRUI+0UxRDY0FqpYcjEpSRhLcQw3RTpliB5rTINQkWjOgIhWLB2qBop7d8juKsEcSmMo5aqRIZoOwJNNeWaxCZYLhSLLGoEqkmGdKpUojRidjsn0K3+czp1PSRFyE0tkajcBiG1tgsUzMryFtgNedEL5tCRzQthN4x3r8KehBffBcr+ePGXSj5Fjd+X2gg/OSLfCpIUq2+GeFHSm4CYPxEyg3oG+03ENgMxjM4cPs+J6D79or/OUg8Th7Q/PLCf+f4KFxWfvr894K6v3Rv/uhxD8N///jrP2k1GC6oN2LKBWsZgUehiOOjgRfEyEWEb35W3vFSWAFdNo8q8022UCguUIXeA5VgWKOYJVszBssu00wlNLvKpH+yCxmEMoSObmAxMDojDKjUcFbrTFEYGvSuVF0ZGDK3ZF66466MWaGCeUE7xGaC7qa0UKYKXVsmma97aMF8Fjza7bh4XFEK83lhfPYpV9dHOm/xm05HKFOyIkbiKzRZ0dNja/qP4Zb7LxkOiHqa4+/2eBfGBFOp9DUX51Ob8WPQoxPmKSO/VqICsUMnsKXRSiE8WYWDkpYjkVe8jU7XyjoCmdJyQyQB9BI9ZWw6cSQBTos8JsWNsvuIk8eNUWdKJLN8lUCKIWsCSVEFmRNY7wjl0JAqqCTrV0XwLumTzcqhG4Jt0sECNCZrRM/9lZ6TaIw1g4YKDM/QUMWQngGRuNJHZejCVJMJ0TtUc1YEb0ARdARDIsFlq4gHpRpKFgA5hFXaFrqYkrd0FO/E9n1O4U3DQYtS58B8Qoox18rnz5/z7/7Dv+df/5t/ww8v/sJ/+k//kW/XP7L2hdJhtM7ogVtQa00/Wgd1x8y3cxmGRgZMiTKpMkuhWR7j3XzG58+f8+WXXzDVyuvLN3z77beMH1dG7xl4iAONHjveXh04HK+Z68zxeMPV9SV//H//yOHmSJcVQTkunYtZoBR6rLQDbCaGWSDUwW4/5WxXnUdnFx986oQsCJJ+ntrAOro6SMe74iwpxexC1QxrGEXoGDyqrK1ROlx3Zdor/ThhkZIskcrqg90AbWCzEFGJ5qkCRuhNKZ7gbwhMZkgXmg7mZtgkjC7pwb0Y60TanpTsiFNK+hj6YLVGqLHglAZughdHulLHHmehlGxwSHRk3mH0lHohTOpMUSiqdIW6sfXXPujrwKyxthu+//5PRATzDmwsuAQ/vPgz//d//Xv+5/jvef6b3xBRSXOKZM6sa4d+pM553o41KPtOWKcvwex7MOVJBKsqMW7yfoAQpcA6coFpnR6OhkJP+xIrhugRphkbMKQxoqCb7U+zQu8rU7GNtZyMqhoZAIgaYxqUHujxwyeehxby8BOQep8h/L5tvAuUPm3n7vbvFyDvYih/COh5H/y9+zmnx5dl4cWLF+mRuLGHeu+3DKQff/zxtvh41/7c/773P/P+594H0k+vPxWRdx+/Gyz4vu/+kDzWzCilcHZ2xvn5OdM0oapcXFzw+eef8+zZs9tCeF1X/tV/969QUX73u9/xww8/APxifz5keDg7GQkYZPothc5ogZQGo6ZsuigxgqoDj0KPKSXjGng1rDtmwuogRZGhqYApyopSSmUdcLasXNsBboR5GGpH1n6kj8bqO9ZXB9Z+xcXFE4rtqVXobUYM6lw5LoO4umGxShxuoEGbMgTGZphb2nUtJAgltoUqSkVc6TqYvOT90gWkYzR8NWJn9EMkGMMAcaJPVF9RHqEXSvWJkFTSmCa7UNvYmtwpD5dImfY8PBlIahw9/WyNVAvFrEjPNQo1g4pdCyI34IJYpVZhcqg6Uy52nD865/x8opdEhuZaaTqIKIgPvEzsGFRgniaqVm7evuHm2jjogeIza294OGXS9IuPiX1RbCq0dUXrxzEJIbZMcUV6FnkCrDjdD0gTUMtmpCQrtcpMSII0JSpd+ra819tayTSBYRdY28Lbw8rFzTVrW7k5LLS2NUUTsdpUZ1nQZoidgMiWFC9Mc+Fv/uYTfvvVVzw9e4zshMPNws3xhqLGzfUBvb6i98i/u5MemSrIVNk/ecz5k6esrfH26i3ffv8d3/zxn7h8fUkQvL2+4ocfXrKbZuZ5x6sfLlmPxwTwZLOD2nINsmxL8OJmueHtm2venF/xT3/6b7x+/SPu6R98srqABLnP9md8/fVv+Zf/4l/y9NPH7M8uKKY8ufkE753Lq9eMt/Do/IKvv/qas905l1dvubq65Ho5Yj3Bbd3YBKbG+dkZnz3/G3bn57x6+4a/fP89x+Mx9zFT+JjqzJdff8Vnv3nO5fUVf/jHP/L26hIZxsV+zzzVZN090Eh857zTt0ZDye8ZkYt1iUiGrgkRYwOac56CQCPrJRupHtVTIS5JvHJpTDip8UtibHiCreKeTRaS0Z5ELkmghMHQaasZkr2bgu08h1a2MDCJ9AeNBLW9O4ilspUFl3yNKsnqJddNqmlfFuLUrcHgk5HeKY5L+qcXSdZy4IzI0PKOJiFNjSIdj4FpeizrFpwVBCFptREEUcB6Au1BQM37v7L9mVy2fY5NvUl6tUdDNtsPJOfwFoOqjYgZI3JfSNvCBF8LrmyMz2QxTxK4ZuQ6PihimENIkotsCz9E0v6Djwg1Po0xBofD4SfWcGu3oOMYg7JZSv3k3XtH7SM/rQ/GGLx9+/Znfr6lnJilmbtQa70N/zsB1CcP4NYabfvsE/jcavtFxsKvjfcyhrfxayzljwWX76/37q6J3rnPkQqRh9Yx97d/9zMe2r+HvtuD34OEgE6WIz8DmD/QIiOmtLmjy6Ys9Lz23KjMdB9YwLBO1IKsA62pCpAs8jKcNSA0LVHVDHNN8ymHaOkPb6GEGWNErgFK2pjKKPgkxFi5Pq786dtvefnqJX5cubq8xg8rsvasrbeAv8JML0ciIsNDB5hU5mdn/Pbrr/ntF1+ytuD85Uv0H7/hh9c/oNayoTty35dQzD1rqtIosfkp2AZfihFNiKL0yNf5Bij31rb7oSDSCddtHo4EzWv62A9VonpaJg7LdZSOVPWqpXLMDGFljS2IM6DISh+ZzzPIbeG5jsOFMqW1Ixq02HzcbXBS/zuOjiSdzda2OS99wEXTbWB08n7RnakogRM+vfNceT/A/NB1If7X4Y73kNG4g3R+8OaEW7o5AdK3m+oGTCtb0/G0xbxfcrresgueG8rJcWyv2byftqXILVP6hPkGeZjlp/3g1w7DPxsJfnj8Nbj13V9PYOHHvPevGS6FUtb0KQVkjJTwmdMpqMvmX5sBLYolqOaRAXTF6JqenUZ6k9rcWYcz2Qz7BgyKOCGVbgqLJKNCM8CuFU+D+YBRMozE1dKT8DhoHaKCrk7oSshMbZI+riHMohmuNwzVgZNgicwD9x3TTUe0EH4kDQdmpB8JcwYz5tmpEz3ipdJHYDuhxgRSQOFs95Szp69Z/nKAeqQ2xc0ZQymS8r4mBRkF9c03yDI1uEUC3JgzVCiWEgl0MM0TZ6VSdjsC4c11MnOtD9ogQzYmo0RJGUvvxAzaOqixujDF6fsZSkeqUnxj/g/SjygGTEZ3mKvSSkMXOGLYlFYBCPgH3ryAra9meB1EM2oosQUxTlPQJJCeFiJ1DLxmNuugJOBMo0yRKada0c0jz8NxUvrS3Cg1CyANwW0QTOnZqM6ITmigFIYptJU1JlScghKashvDWT2gFqymL7fMFTsIqk5435hrYLsJ8U5zMO1I74hAKZUa2UFcbgYQafWhRmgGb5jCHMYoyt4qnz79lP/p3/8H/vf/7X/l888/5/LtW169fsUfv/szHkJbC8qAWlFJFoEoWKk0GYzFUx5ZB0G9lczsxOg7ofRK1ZnffvY5//rf/o988fxzxISXr19xc1i4vjrgrVNJtlprE3285bsX39Ja4/mnnxAO3/35T7z4/ntkDTSCzkLR7L7KyNC9iAyx9CrMtWPdaJNnyOZRWI/rB587bVmY6o4LDRaOKc9UYyqCSsnAv6kTZdAPjXpxhrSVVQezCH7sxK6Ar/QG0gtRwJsxl/Tjau64V8o4pOcyytoGzJJz1W6FUZNNlNUAKsraO6UoqyjzZjthAsuxISbobMkkko72lHqHDmAH+80nfOR5JmNBdUpWgFiGQPRBzJUYJZuvWKYwuxOF9LSPTao2V0Yz/v73/403lyvTpLRW8DpggnFwrl59z4s3T3j66VPKXHJe6imJEk1FAwFTOK1KsrzHhMSgC3Q/4iNl44fX1zx5dp5dclPW2JQaUSniyDyQxYhdNmgWJuog1StuTER6e46FRQtVhe6SE/je0WNwDJgIxsishiZKkeWDz537gOa7JI8fu72727j/+H3w9C579j7ofHou7hUlD1lQ3GdF3/1sd+dwOPD999+zLAuPHz++Lerevn3L5eXlOz0P3/U97zONH2LhvGsbd9/zkBz37jF46Pvf3W5rjau3b3n69CmPHz8GuGUxP3/+HIBHjx7dpqirKr/5zW/405/+xKtXr34BbH/o8HaGzI1JHY9s2kXKj7KAkJUjBR0Z/NJaesDjiupgMmBY+tvJwGKgN6nq8klhBZmCWJMN16UQNze89c5h32hnM35sdFF0XblZjrDNL91XCo+ZLxr45krXHLdBH40SUxaaN0LfDaDiLQNnokCE0kegauwiYK/syo5dEa5GR9aGNOU4TagEfQyom+9th2oVEyOaYaWxa3t6EVx3VDphB9ZjpI1VHRRboSi73UxrGXyo/YyyrugIWsCwiULB6T95F8qMlZFglM5MtqNYMONo2XE+z9h+x/nZU+Y51y5n84QyIWH0doMOY7ZKjAPdYdeCqZ7D45bNxUtlscEO8Kh0OtNcMA12+x29DdbW0PbBp87/R9u7PUlyJOd+P/eIyKyq7p4Z3BZYgHu4vEiiTKaLmf56PchkMum8HDOaJJp0yENyubtcAIvBYGZ6uruqMjMi3PXgWY2aRg8wWJ4Thkt3dVVl1iUjwj//Lt+zhmVtZEH4wlpbm47RsNAVWM1jZpMH8jCi5hyOE4tMWE2rT2MUsXYGYGDONB+5efWCISvztPDm9k0Ajn6aa2LeFF1ZqITHsay5N7GNUvLqg9wVihRygTIv1MWoHskc5muFJ/F61BPuwmY3YO588+IFr1685Hf//BtevnwZqquSKaq8fvGSelzIJfPq+jV3xwPdwtohmKshOxYHN6PXmbtXN/z2X37H4Tjz9//xH9jv71hapyPhF7zOF5KEzVB49vQZV08v+fDqGeXiIpi99obd0yvGceR4OLK72PL0yRPG3QYEtrsdw+0tcwOJBHNEE9vdhi/+7HP+8q//isvLS15885z5eAwVSA/QMqvy8S+e8ee//ks+eHbJ5s01N9c3HOY9CLTWmQ8Ttze32M8pvCQ8e1v3CEuXsN2wniid1c87h3VVTuReETKtOU0LuvEggqxreXajVpCcwk7wxMjNHesaZC5VdA156smjqW0J72vonEPVBVkKSQkVpjTMlRJUDbI0el3ViF3wIa9+0IpYEDxUKr4kThafssq4kyQ8BUu4IBHyjdFtCR9XEaqEjL9bgFniNQghKTzZXIJ52Ikay13W4GeNfI6VzZy6oNkxg+aZbODZSTUsRAIPib+bgWonafgiuwpuSl5Ja4NkjI6mNdiPuMbTei6BNRhSMy4zSZQ+OzY0aGnFOgSTTiaY290kaslVxZra+ze2ztfPWivzPAeLeM1fODGQU0r3QX3LyQan96inzGLvujKN3f0+LPjJkydst1uGYfjBXuJ07BPAfD8XriB2SolhGGitsd/vH90L/NR41x7iMeXY+d8ee57Hjn1uC3auSnuMifxQofYYUPy+jfzH9j3vq7g6v9fJV/vU5H/foT3AzkZBJSMSygBzi7asNBaJkPZiQvOw6nGPZgqExR3So3EyRgOmiGOW6RrKmZzAPZG6U9UDqxCjSl9VBB1bMlUWXtuefHcXmWrTQm/HUI0kh0XwvPqqY7gPNHGyQB4zF9uBjz/+hA8+/IhpmsAWbq8vuD3cMPcaALUStZY7s0Fx6DGRkVVRRqxXEoku8Vpl9U9OHl7J2o2eFGlOLzmU4zURIpQEPQhzgyq9BnbQLeZi9cyQlNqNLuB9IesKLvdKFqg9hBPeBWMNSi853ns18FB/hf1oWJGYaVi8yhq8LoLIQK8NX5vT4kuQ5cRpq8e8rMfRMgRZ9B3jvXRcb2GNfyro+IPHPdKtccIY+wxEfvQhJ38wixtP59fvFyL5IcgMq39x3Dv8jWz9RPz+yf0Rk+P7178e93to/Ge8XpHHX9MP7vzgDucI+c8d/sMf37s3/ugh328SA6C1YFVYsI/RhK4eupKis10TEdZRCeTbDbwgksi9RzhLSev7PmEWDlphGbCyTEVJRdl4ZU6JYQzQMLtidKplSi70Hty7JEKdhEYldUWJlGNLCfpE0uh24yApMVuwHhoF1QlrYYae6dS8BgTmAepM8ZDQKw20kqpgOkRXvXa8V47HCftwYpM2TAKtwlCeIsOeVKf1O9zZirFIoS8dTR5eiWkk5Ra2F6aIJ1JaVjN7x2anbZxt2rBNlzz9xTM+HC/o2bm4vuPbF69oNwWksjRfr5GOqZOAurJopBqSAmht2CpRHMjNqcnpeY4QM4FqieSKpDUtTBVxbAAAIABJREFU1aFpIo3gVZDesaHj8v5MwuKO24w0xzUA86ZGbZBr+CsPXvHs9B4M4e6nzW3MsrWOJOaY5FsPCxHVAP0FKIkuEuGTYkiTCNTQhtDIWmiqCDG5agLrLWxTHDTFQuvZ8K5o9biNFFYwHmwsxSEZqgZ9QWgMZNoc7OGcM7msrL3FqdTwBUcpvZPFsRz+jNWFnDKyHdheXfI//Hf/LU+fPuU4T2w3G66efhhp6BbeTS5OLkY7EsVFXaLJFNAzrYMtjknYe1yMW3ozsoV8cbPZ8Plf/DmffPQxm4strTYudzs248DS59W6JpGkhS+zG/vXdxxuF159+x2tOdO0x6QzWQ2fOQumh+iAaDSWphKSpZQEajCechdmKlk23L25e+/vTndHPNgfyoYhwcyyhnnCdtwgWUkyUfOGWjtFggW0tAiRSmMnLxlvUWwYwa51aTSH6o2CoWmIAsqUMYUk2TJUF7oN5H4kUUA7Io3rQ+VpuqBoxa4UWsKqhTy8rIw8m+kGQ4p5LNDtjpow+Mr+SWBiyLCya0oLmSkZaYalACjEByyHrZBgkFcmkIb89Td/+B3//P/8HWVsLM3YieOLcDle0n1ifz1zcytYHRg2GV2cG6uMJcDuvB1os0E/kuaCbZ1kKa7ZI2gWPG8pCuXjJ7iHN7nPTpEhmDrm9KR4a8Fgsk5Xw3W8tyfSHOw89yOLjujiLEvlYuvU7UiuIaU7JdsnJew6lh5hPO85zjfZ56En5/YUP8Y0/imGyY+xmk+PeVdx8a7nOf3tXazbdwXamBnTNPHyu+/uZa0QAO0pzOd0jIcWFQ/P7/S303v2vgzv88efs8MfvgfnRdtDxvND2e40TXz9xz/y7IMP+OUvfwnwlrfgSR46nKXduzvb7TZ8ZFd21s8dwxiB0o2REcc2HQ4N9ciIiMhUp/VQEyC+7jUXBg37g2aVoRQkRwMYT6ES6tBSwqqDhA1CtznChI+N5nfU+YhXJfnE3EE2oL7Dq2MkRq3YUshpwIvjWmERKo06w8YHeoGtFxZN5IsRu74DbwzFmDrQEqV4hLRdXrEZjNETt9Nr7I2xNWdyIS8DxoxrNNFaq7iF9U1/Ek1glwVdIpTnWCtOo3thLMZGhTYPyEZRm2ILPAYrfep3uEV6/GyNwVus+y2RK5AL46ZgY+ZpvqRcZNDOxrdsNyO2FbIPKBpz2inQx4xNisZc8gKlo4vRd0ZujTxsuVicu9mpNgGClWiGbTxRhg2iwrJUmjV6/TlS9ViwRILEYoQa81RpWLeVIaqkMvBke8nHv/yUDz76kDpN/PHrb7h+c43ZEVi/V71/Lwi12Om16rx4+ZLDNDHPM9PxGKAgq/euKOQxZP621kMnJiexPV9q49sXr2kmPLm4Ig0FvHI7Tby+fs3+Zn8fmu7dQTXOQWPt398eePXyO6al8vsvv+Tld98yHWZUDBVnXgpeF/btiFaYpoVlruHZayCyeuGecnPcMGvs54l//Md/4He/+Q13dzdMxymIE9bvg9cMBxOqNY51pklYVmmv1HrgcPeam+uX1F5RUcZh4GIzoLmQ1BmGDdFdnSJ7QUMRtLu44NPPP+MXn37CbnNJWzrb3ZZ0ndc9g1NS4ekHH/Phx8+4enrB0hpDGqLmrZ3peOR42HNz8zqaCu87NMfah8VaJ5m0rv/9FFhtsS/1aphGo9lNQBvWSyggU8ckY11Iarg61cPz0yz8QRcJVR8OKjnW7h4gu0oY+/UWJKJQVYKlAKbNjKRCb4mSHNeCpI6TwkOU8AbtrdN8BZ48cm7Qk9WlRiChCtnDXqjlsNnySBILNFudzMm+MkwuSULyYEGeAhFZg7HEnKahaO0I2gVNES7u2mk9PE+zR7aMdKOpoj3H3GMdkfBCXgmYAfZroyu4BZjmrOtVr3QdEHU0Bbmt05BFgxiQ1mu3B5CWXLEcDG7rvn7WAY6TgsnvXUniSH43m/DhOF/PT01nd6eOI+Z+DwyfwOfD4cDxeLy/nxD1SM6Zi4sLLi8v70FpgMPhcK+OOh3rZLVxApVP629bwy7d/X7/8WOKqB97TQ/3Ue9iFwP3pMKHx/mxPdr5XuQcbH6s8f+wuf8YUPzY63sIHj96PmdA1GNkgsfO/bFxAvrfd8w1vuspGSJ5DccL6y83I5WMWqc1wwukPMQclFKsbc3p0oPdGwKnAIvV7tUuXiM4WcWpAqUluhhSK5JK1K3JKVmY2xx5TSmU7W4VqULOSvWGFsKTORewdX50uQ/GE92GHWlSNDubcWSz3UW2UxtCiWETWVh95CtzCbJPtwpS1oZqwlRxawErmjP3zqgFtXgdyRumKWwlKvf7Q1LYXQUOE972Qy406qpIdVqLYFJNvir2w1LM88zCqtJ2SAQxzVbrHIjAQu8ZXHEWkIKWYCwvqeMr8Ui6gStzMgZ3MtG8Sz1jCYY1uBCFrBkpgv8I/+u9AOY/FVP+6XGCbmXdcK+LxE/d/cHw09/WX8450c7Dx6zbNo9QwHtDqHXoCk77+pvKSVZ0XvCwAp2PXZTvOsn3eBcfe+i7HvZvYBi/BcD/rPN5/wMWDXCwRZpBhDeMQlvCg0t7eJKhFTIRChN0H7xLAMvNKEPFZwl5RFI6gi2NQqHlgiYnH8PyISfIyZGW6RK+XSUF8zebRur4ED68rpm+KqtyLXR1ZA1iGHuirem+1iNdODXFKMjaGeqSIj7ZG0WhW6GlBfeCEKDHgqEnbyFzZps4vDzw6snCh7sNtXcKle0IWRKLZeZ5YjMkWnGsBVoSsshKEqFV6JaxwVHvYVeRnNSja17U0RE2aeDZ5gOuLja4CpvxgtaM58cpwLdUVyCuRMiMCGotNmg5TNyrFUaTlXVuHG1gM1R6L+t11ckYRqHPDSlreJ1HwF4zRYZMcmUYdu/93anSST0hdNxlDb5LlEEoDnXueFa8xQQ994LoRBFW1nBC+kJLibTUcMFJUeTrmNBjA7EAB11ZPDboBaMmRbXEprf1MM4nozVBAUkZp2GzIJ5RbVRCgqhdgnU/VVSdbAlRDeWh5vh80oalTsysYQWqa+EUvpohhYvGRSxWA7k1JIdFwHYslDKw2+y4evqEUgqbzYa7/Z6+zPTZkBzSuSGNaDKmvoQBh0HrCzIksoavVCsFaRXrheqNoSiSlKbOZiwUhP1xYrPZcJwm9jdH3ry5oc3zCsYkWhvJY1y3opVlqby2I9IV6457xVvIIU1D5uiyID2FPYkqg2a8ZIahBHurN7ooRYS7m9v3/u4kF6Z6R84j6pm5RoNL8kgnMSSBlGhL+F1RNmhfyC7Uwx2zVLLsGFMUFV0KrjWY+LpjtAkp4+pX1qldyUYUerVCKjAYLe2RLGiyILnIwLPLTEoDtVf6tAYJClBg9iiuxq6IlGh8uK+AcaEvE5JHRINhJqLUmthKIFBZJOxueoQYqeZgvPXVp1LCykM1gh2+++4l/9v/8r8y5C3Xt3uW3nl594L93Z4vvvglQmEjyv5mz6SVXXLmqdGmxsXVDpcZ5gYYPSV0I1hTtEgwksQZJDrnYgvoBb01LAWjijJGdw1HjGBctmA0MWzIo6Olx7Vhkdie+45KZ1mObGWgO+gUgYY9HUltQx8s3jdxXHPM6e85HisKTuO8gPipQuccbD099nwD/zAY73TbQzD4XcdJKd0zfR+e+8NC4yH4fA6Wt9aiuFj9D1U1WHdnQO+5jPIxlvTpOc/P+THg/fyczv92Gg8LnPPHPpRwnoPeJ8bx6X5mxqtXr/jtb3/Lr371K3KOpPTLy0tUlXme2G53vHz5kru7O8zsLYbUYw2E9xm2MsmSN6wDx4nUNTx61ShKNIB6R7pSUqYqce26cqBTBqUSgZepBHigNSwhGEKG3ZOTvCAaHnpVoTTF2sTSB6Q7JXXmo7IZezBg3JgQSjbUOsvcOcwGtoBlTAzTGpJK75TuNHFqEdQLfrhDUlh+7colT59uKJuRMo5cuZJK5yjQbm+JeC9hSiMcw+rEcjQMrXayR3DPaCNIpVVbwXVjHAcG1SD17JxajZqdtgSTpiqIKH0RxkEDUPNMsoxsGylveLK5YLzcUXaJMX/MIEYRp5WMuDFqNH/TYOEn6+EV6e6k7EiKMJ2cB8bs3B2PtFbDDiQ5ddxwWGbqErY+aok0jmiOotFrI3Vjmd5fObFGlyGikVFAAFUiBBM5tCiICttt5vNffs6/+/Wfc3m5w5qx3W34l3/6HW2pK9gW11uk1jsph61h65XDIUBbrw1zP1VfpKShIkwJ1oZxt2hyuq3Vj0Jvnf3hwGIzL1+/InnUR0s1rDZabWDBaBNdwa+TFZc3vvvmG+p+T3XnuD8wTUd663Fcjz1vGTdIr8zWWHrFCMA85gRgVaDKGlTZTZjmSm3XwTJyZ+mxf49r08JaQxLuxmE/8bt/+VfmpfHtR98xloJ65/b6mt99/Uf2hwl3oy3Gm2lmaMI0LdQWjRDWQLmkQkmZy92ODz/4kM3uEpowYdAbSZ2qSiKA9nSyL7CMt868zIg4TRo3d7d4Uu7uDj+HxkMHbA3EUw1w1XqA+rlHhgtZMbFQBDHg3iF7eAqnjiuIZNSj0dLXelwtB+gh4QOaXBGN76dpQ3tGNMBYb0LVjGtHJawooCMec3XRqBTC8g+oIR+XDJ5tJbYonjopKdZOimELr2RNQQrrhiKIhELRrOGScQyxvNrMhb2hQihKzDBXglphK6EmGNMWJvC4aORgqCBWUVN6TsHYlu+bLWtgC744lhrNJfZ5LnRfgie1hgw3U4oHGcBSMDCVFL76ZvSV+V004xrKMO1OT4lOBChbZlVqWzQ0zLGccAu7x6zQaoTIikVa1PuOh3uD3juHw+HeqmIcx/v1/wT8zvN8v2+A2IvsdjuePXvG1dUVIkKt9S1G7+FwuF/PT0DyZrNhs9m8FQZ48lo+XbMnn+DT872rAX8aD/ck79UoPk2AjzzHY3uw8/3h6XxPWRIna6/zfdnDhvu5UutdDfvzz+cxsPyE3ZyA8WDC//Tz/ecaaaO0Xkl9IG+gdsO7hU1E6bTusX5pCiUA67ztgTEMKQM5rB8HCyIJujZ+wl5DvEMXZnHG5GEPSKMuRvEgRQ2asR74iEqlTwI5muhLXlYpUBBeumkEjopC7yQtoUjvnXqcePP6mu3uEgempTEtC24ega690l0QD7tRpFFqeKS3Hs2fJB3I2NpsEk8rEBwgsLXIP3KVUL32mBOkOqJOk05Su2cyOwPqTjalSszjokIirCyiLRLWjy45cCodGGDNHxJSE7o5mgzPgvSw/JE2gEZAHypkC4vIxQFxijglh7/AguG9rL7asUaYO/R838yS/G4Y+U8L+XuP8dP45/lFEK3278m6scA9Drb+CMqsmdUD43TDunV75GnOjnt+Pt/fe2VJ308O/tYLCz7s6Vf5wd//JPz30Qf83Gf56SP7W2f8I/f/t3QWLFOHNcDKo0DoLZPw2DKoYh4gZ5Xw6rNF6QlG7VQ0Fm0TZGWJNtewucgF68bYHNctvmssFQaDvu90DYmlesNxFluLJ3NoERTRPIeHczG6S/iN9YzLQk1RULcOUgxviWKNJgG+gUa6tieqd7orw+D3MjGRxlxyyNPjH0QGrMPr/TXbuyd8OAzoWKhN1pAUZchx0U69gnl4LauCVwZP9B5p1eSGFFk9jwi7lrGjNdGnCmmg7BKbLGgacElsUJ5ePeUwvAGbmRFaE7AF0YFEY25rKms1orlieKQkIrMgTLRjwsXRjaMUfI50aV+1t546yRtFHBkEPMUCNL9/wYVkvAzBFLGE6kxpTusDCwuSQ7JLUjIlLEMk3xczKvG5qeQAAq1itWMqJDa0nEjJMI9AwzSEtywa3mpSBQOyGLWtbGwzehc6M6KZUReMSACvi9PVsKykllAztCi06CImC4Z3TonWGt1iwUhdSLlAb5gKs0fCbOonOc8qUdGEpIwWobbGVS58+PEHbMaR4/HIxW7H8XDgu2/+yNwmNmPhk4+fsnvyETZXXr56wZubO45zDauXaog0Goa1Fdgywz3RPaGLYoMwTUdevLlGNNHmmdYWvv76j7x8/Ype1wU3BXO7q+GL0xuoJnqPorC7MqTwwnOFwRxyxj3YGEZioK8SuwuGIaPWmLOybRWvC+1nsFAlhyw6DdArbFSxVLAU9jDWMktdwT3NpKq4ZmhHmmR2445SBFk65JCrt+5UFXbZma0EAE/MbYMOIJ3UY8NnQ0d1g7rh1pEOXXtY9aREZgEtGAsVwBW1ujKCltWDb0GJBGAZhJxqsBOz0pozGlhzyjhFEbT6A1pfIlhMYURxMZplJK22GCUh4iyL8u2rF2x04Ma/pfaZ6Xbh7/7D/8Xm40uefPwBT7edQS8ipdgN08x2HBg3W0w7OmcWWbAGJUezyxBmhzEpLgsLhe4tEonVSSk25nOteBgUBjC9KGO+wraVXLdkATk29tYYckh+xTPiM1k8mBAFioBLpmVj8IJS6XXAfaZIwUuEjLz3d+cRxstjTNl3yTUfY8+cs39Pz3UOVj883un3HxQVD4qNc8bvj0kr3zUeFjkPi81zxvZ50N9jxzln8Tx2nPOfHxaAj53v+W0Pw4HOX/O5FPUhID5NE9M08fHHH1Nr5Xg8cnt7uxbIYY+xLAv7/Z7D4fAD3+efO8bLHW05xJwH0TzLGdRQKtIivLM0qFawQcJrWBPHGXRoIAPFBDr0ZcDSslr8GOoJDVfnuI80jISg1DVRvGsU/skHsE5hRqTTagTHlB6++rZUhIWeCtoiiNBLJ3uh5whY7XXGFoMm4Q09d5IqKSfyZss2jRQd0aRseYbMM7ZTDm0ilU7aTxxTWJVpN3QMH1zXsHmyJHgeYZkoDlq22JCxNaRArJE1WKuppLAr8ggAInVmEjmHDUkWRfKWyyeXXIwj2w9Gct8yDp0lG6SR3MJyy+YAdxYFbUbaxJyoOBuEPZBXBlEjmnxJQPNIZQNpQnMOwlGtsB0ZhghxW7WmSEkB+rznEAvADCc8rfEoCD0sK3CQJKADw+aSjz79BU+fPGOz3ZLplKIsh8Y8xXda3VdPWF8B6thLCEbrDenr2iQajUlVxryhjKEeXFql+4LPDfMIS199OwgxT6MdO1Us9n3EvrpZANPWPRiihL8kGuqeXjv7fuC4TPHaejDnVKMmGIaRTz/7JR88e8JxXvj6+TfMdaZHcl0wb52z+iogloahdaHXWINlJSu5RwXYIULVnJV123j15hWH455vv/qKvNkisqo6jvtg33nnzeENr16+YFM2HA5H9oc9rTdQARRNSslCkrBYkWXh5eGO7778ksO0kCSh2kLF2SqvXn3HH7/+Ize7N7x8/pLDYQkbtA6v725oCscp2NzvO3S1rMpEOJyZhEeprh7GVsJSToO9HJYoHk0DaainAFa9I2mtDAUSI11rvL+BF5FzIS2VuobZaUT9YR7sYE+GeOw7pDs1rc13sZXxHdJxrwIlfU/yAsQqPVfECqnFWlQ9PIeHlKEMeOtUC6WDdugahAzra0OZTk6O9ZOXegBL4dERxBMQwhLDVtB8ne+7QgoFpA9ByOy9kiyRS6d7eFH31vASezGRYCD3Hk08VKMBqLr6MEfItWiQUTpKt+Ve6WgWioliKdSO5qFusaiLGxW1DdUrA0Kz6J9o6wGcSad2x1ejtrAiee+vzqNr8GkdrLWy3+/fuv9pbT23sCql8PTpUy4uLu6VQqe1+jww93SME8P5xIRWVXLOjOPIZrN56/h3d3fs9/u3AO3HxrtUX6fXeH7bQ8byj4G7D8cJTD7Zd5zsP063AczzzH6/Z57n+wDEdz33Y+D1u17bWyD12mBzvt83/pTFxQ+e48HvPyd3Qj0xqNClM9UgymgWahZqXfMQVMl0RklUOmop5geB3hslK7MT9pequC1gA8HfFZJm5t5ROh2liTCYY7uENyXJiElHUrBzxcMbOPlCtwZqAWBrpnuPPCNTJDtdFOuNHJMSd4c9v//6K26WhYtN4fjmwOtXr6jVsGrQlKRzzG09mNgzRhZBNQeELhVZm6HeE05YcKBOrxVBKd5D7YzRssbzlQbWVvAzhdqj5lj/JfK4khleg7nciXB01/Ctd1lzoih0bTQxco/1kizQBetttZUqdGuxh7NK7xmxRE99tQcyetA8EemYRwaInppcDi0rRjDLVbhnPr9r/OcDmCWdgbunxtDJ15jHMvJ+ACKH7IK32LVvAbjCKh17xzh9UJzufu+qvE4pGt3w75/47FwCNL4nUMv6yPvfV/T7tB6dvc41AWP9xb//8b/keOsA5y/mR458/zaeM5j/y5ypbSFXpdMwScF0UHBfkKRUj3CYgdNF2qi5hozKohsNhphSu5M80waP7jsaXSkRpFYWncnlAnGLYL4u4XVjR+oQjFRrxKbAjQVFc0Vc2MyZoy/RaVenWBQWvWSKO60JzRJ5ZQ01dQbWYAo9RqeZCIkzy1gSepopouEp2GCThCWv3rvHxs0fX/BcMtvdBbPPTIcJWkweyhgXb4tOtnqjijOH4RwunZRHeptI1nHZBjgwWUxWvVB7Zu5KVYU6c7n7EAMut4UyDLRbxSZCopY8GIaWkUZ47EiA151KHhItOWkMc4dSO1Ugt4EmFVNHcyd1pZCpk5LG8NvOc6aq0U0ZfgajJ0kksFpNCA0807rhmsmDU6ewSSneMDoSemCkOjpEl28mwP6N5Kj7tDOa0+VITiO1d4SCZI/gQE1YD8/smRLM0zaQZJU5awcSaXGkRJKz6IbaFrQkdIG8OM3mCEh0ZzMUpK9puz2YEdaN1hZyFvJGWfrMSKLWhWQRCLDQEINxHMkFJIeXnYiQxgtKGfj8iy+4fHLFsiy8fPWKv/3bv+U//v1/YvCRP/v1r/ji3/1ZBAdIYvvkEvnd7+nLdyzVEVvlOLlHIJ0W2tKxttDLiIb9L8fjnuvnL7C5cr15xX6/56tvvuL1yxt6nWmSSd7pdU/ZCrO12DRYeEubF9DGYQJ8BbNKClZJTsFoGxMiWy52IxeXl+w2A8uxcne8Y6oG60L33t+ddbsdlhv9vovc9jNLq+iQGDbD6uld6KIM0uhpy+5qAZG1G9/xPJB6J+tAp9OpaC54ayQV8ihQc4R5umDjBrFgMJaUUQR0DUFQD3C9GmkU1MJKxRWsF5pMHO/2PNnuYDNgk+BDSD+HJdGSUZeFjWcWTQyDQQo/SkSZ6szdywPPX36LkfjwwyeUsmUzChd1x6ZskGFApGHmHO4mGgu//9c/8NEvPuHli+/w7cCnH3zCxTBCHnAST55esBsvGatTd5CqQE/oOJOtROaBZ2ZdQBK6zNiYQy0ClCGDgM0TnhPZHSmxEpMSmJE1WEyZxLDpoLGJLFNCO1DCzzr3giFshopJ+IR6z1iu1CVCr5AGNtKSYx6hkj93vM9G/xzo/DGp4/ljHx7jHHh+KAl9yOA9Z9P8GLv5/Px/DMQ93f4uGepjBcljrKB3STIfFniP3ee8KDr/+zlj+RQYdNrhnAPBD8H182OXNbxwmiZ2ux2qwt3dHfM8cXt7y/F4ZJom7u7ueP36Na9fv/7Rz/CnxtXVSDs6x+Mc9hZoBHICughNCqKNJpDyyjy2kd5mKIZIBHzFFjL8yUUTPRkwoBZAVenKUjqDJ7wJa84lrVVyKVhO9BbAiy9jRHT5gtiBNOxoS8eJYDxfjG3JzFooVJoKKYMvFZWwF5vKDGlBpIMKKSvaNzAWNEs0VV0Zx4FuQsuJJgupO8t+lTp7DYAwK0sXNl5ZZN3TZaf7jqEYvRpNDPWFxUdone6CzQvNHLfGIImNbpC2AuRZYIQn48jVxSWbix1DLqQNkAbSvKDqke/hsZ50FajCYkrusPHwgDUBOTRIQtNGVQ+mpw/QhZ2M3OZG0SjyltQZUsF1QCXTvGMuzBpN1PcdXhRvPWobO6GnK7tYdQVEwrZtHAZ2u8K4SbFPKpncG5//4hc8/+o5rySCKmV9rLOy+/GV/LBK+EnhESuJoRR2V1d8+tkvuLrYcntzzfOXr7ltt/RuiDiCB1M2r3NQgyZ2D3Q44L1/X1VYgJas5AlcMe8nBlEAfCvQ6S7k5Gx3F3z62Ud89smnTC32ptPhjjbPIY8+1VlIMEUh/tNsZWSFV3T4bOo69xtZNABn1rexddoyM1tH6OxEubzckfqAbgw3YX+45fbuwNdffcPF9orpsGd/dxeh8h4qoXHcMJQBJST+t8eJ7779lm++es48T7Q1X0IJKfjrl6/5Q/kDF9sNU1vYjJmSP2CeJ+ph4nqaaXUJNcR7f3kUNY89bhdEeijAvERTQGNPm/DIvQh0ACzs7vIarkcC6x0ngztdlgjvJBoywdCs6GpL6dmDCCKC1hy2FNVQjSyEfPI+vrc0iU2lZqNIeKkmM6zH++MpwCaTyMnBOpo1muc6klWZmONVGDgBQBcRklboYBLqQkmsDTdotkrKBVwbqXrYqwiIaDCnRVaVmcBa/5AJn+XU4rGEjZdr2IuYWDAcPREtDoeuJAk2J2SSBMDY0uo32+pq7ZgpOfaqZsrUlvUzDFW0NKHlYPR2j/1sB8hKNqP31S5A1mtHIFhGwpJ+xneHH66bp/+fFEUP9weP7QtOTN6T/cWJ0Xt+/9NtD5VH5vE+TtN0b41xArdPbOrzxu9PNaTfej2s8+B5A/4MFbq/7T1A5hNonlJiu91yeXnJdrdlKMP9axzHkd472+2W29tb9vv9fc7DQzD39PyPNbUfA8tPr+X09/N947vel4fP99he7Xwf+r6jSkMZUAsdv+UAgb0aa4hEkOuS0qxirmH/VxLFnB7udQH/NaOvuW7JO94CF6IoiYr0IazBigd+0gSshQ+7BrBMC5WmpDUYskvshbzHXI1iulo2SY91MYVdTrfKYTrSbkNRMwzK3Jz9fKT1ioiRxrZ63BOsY0vkFQfqbkhbcNUIMc+wYDXJAAAgAElEQVRyn6MR+nUlq9HFKSf7GNGw3WwNGzPJIwCxuQGGJqeykLphaYioG+sBJHunmoMkUil4GFiALJHHpWGhhnuQ5zSBD3gG6IgF4NzxyCrLTupCkyCCZjPsZCKfC5IE9wDsk63fW418hqU1cjNafndD9E8AmE8yReOt6+IMXL6/6Rxs/cF+/W1w+QF+e//D91uKB38/e8zbT/vWI9YL8TTZGJysLe674N8/7v5tEgFW9sD5+dwfMPEDN+N/E177GPr+c+7xnge9f08fvvh/y5k9PkqLMBazTBpDmpaYWIZCciM7a5BJwpKvyeCgWWMCWGLTX5cGpSBGdOZ9iM9BAiDsxUhsMZ9IFrKE3py0CC0XfAK0kyUjVmmqkeQ7Cz1VFukUNNKEu1OHBDWRlgBJvQ1ocpou4ets4bHq0sDXYIucaZYQqSGH80xrIJKj69ONXjw2zNXh9gb/Thi3T8lqHPd7qh9otobTzRH0p2JETyzY1k54WPdqJMt4TliaSa7UAdTDJqTaQl+OuO0pwwckXXBPzIsi3jAXLA+IG54WssIydyRrHGM0tEVAhlUn9bjWtwl06IzNaZbpImTv9F7AZpZk4WWtRpERxSkKOoQa931HMqhLDdaFp7AD6Ql8JsjdiVTCu81MEG3BTs3hzWar71CSzqyxCOQseA2J2eJ9nX4kDO+TkZa+tqGEQSvVnT5kxmVhmhTJOZ57EwugWqZLBJ0Ug07HipBbfH+7VazWYLOVkC3jhWa3eE6kdSGQtTB1SaR1EfA6wujBZi1bRBriA1mFoUAaEiknnn/zLW7Gv/z2N/yf/8f/zqubN3zx+Wf89a//gu3TJ/RaqfPM08srnucBVQLU9IrlTKvhhyXjRFktRHxqzCiZysyGL//wDeOLV5hWrBrH+RiejhI2IHVlQ9kUISXZoiiwZgyp0zxYG6SCSmPMA5IS7hFOp1IoY+Gjjz/jz774nGFI3L6+42Z/zbffPGd/nNCfw2AujoyF0htSBhZfONzc8vr5d7x4c8ef/fkXfLq9YM5hnTOmaFyoL+HP2GFwpRddNcbhU52ygBRSrvSVkYt3LN8gU8cvr1Y5ZQbpJIyuUL2zwaiSwn1mDEC4acLHSllikdeW2Q4bRC8wq2g2WmjCaWpEan2iZSevnoQyKWyg1pmX+zd89+XXHKY9vS4cpyvGiwt25SlPpyPbzQ7dJLwqd/uZ/fUtX3/9NcfZ2djAX//lX/Hphx/TXcEyQxrYFMVFqL3TRyc7TCJsvIe1ThfEK94cHUa8NkQME0O70jPovCAqtKqMmhDJDNlo2ulVsbkhdMomZKYzQhYjF0gUREBbCk9FMbRUYusZLISkBnMj54JRKS5M3UmL07MGIP4njPdhxTzGan4MuH0MmH3IBj6Xhj48xkN56Pl5nJ/DuVTzPHjm/DinAJ2HDOhzRvO5vPWta+tnFCOPMWIesrQfe19PrKjtdsvV1RVXV1dvva67u7sVKJ7fCiI8B+lFhHmeubm5wd3D4mAY2e/3TNMU703vHI5Hrq+v+fLLL3nz5s0Pgg1/ztgMIz0J1YBWOWoE+6UawSkGkEc0TXRXvHZIS1hYqGN0xpaYJYqm7ARg2ZSioSLKpWKtRwiTJ1J2Ft/hdYaSaX1tantF0xZXo0uiDDGXzcuBoWzRBZInelEkC4OWUAPkQlk6BzG0F9Rn/OAcl9jJb8pABnJOZN0htaM2RMhxKeQWvn0+CZYqSTK1OwyGLUq7NIpnQsiekBbBybrmU4jEml7d8b7Q+oEuYzCIXfAxwQLMnWYhXVVPbKSQdzuKjoiPdAvmZLeFbkarCxtpaHaqhCrItJGcSJkvIOYcamduM1k3SHNSXxh0x+JHNFX6KAxkLjeFNht56Wseh6I5cTzegM2ogNr7N7bcO4KHyoPItuDsWgmwGCQ5zReOxyOtOjq2KOi9MRMBxiqsXrQnNWZ8drYGmQXZRgGniFJK5urZU/6rv/kb/vv/5m94srvk5ctv+X//6R/5T3//TxGatLKSA+wO4AAP0Nm7Q1uPtB7ze4ag4xKB0J6i2YJHsewIXZyksNKjEbWwhckjF4Pz0ZMnfHdxxeHmwOINJ8DILmFtImeVSV9rOnDEPYIJVdGgiwYD1wCPYjq3KPpFR3752S/4/JdfsNuNHOfO65cv+Zff/jPfvXzF9atrbtI1rXXqEknTJRV2FwMfffwJm7Khe+P67jWHZeHLL5/z5u4aX60cIr8llLC1zlxfv8DsAz759DM+/euPGYeR2+tb/uGf/oHnz78O3+ifMf1YgcE9QIdTw9OAVDEGtCvuDbOwJHTrYVtVMslCJo4u92w+NVuZhisorxE23gOZpubwJkbCYi0lB1e6SDALtcfnvDLnUSEpEVCevpfCiwu1RRAfrsENQ0nFgmnoHiG+Cl3mldkXdoWBcXfEMu4dC2PpAPKlxB5EjbSC4UKiA9micdctBZCsjZyE2eKckizQlC6ZTMdyD/shU5ywWiuew75Mg3gTAZiQJYhRvStl9dC3HPun7KF3wzKinYQzu1Esk0SwfGLc95Dg5wgP700QaWuYMZQVwVBZ61xLqPRo5mCIJHL9eRYZjzXH3wVCnvYY57efbDU2282adRTHP7e7OFl6qSrDMJBySPpPzWKIxnFrjVrrPfP3oVrpMZ/g8yb/D1i5j7yOeNAPn+PHxrlCa7PZ3IcXjuMYGTqryur0Pp3up6q8efPmrX3J+TEfEgbOP4/HFGKc3f/8c3mf/dj5+/PwGKf/P2wmvHN4InkPq02NwMuawhJVikIFW737cy5Ir7QkZGk0NzIR0B3rOqBhXxpAnSEaCnXVDM6aX5RRiQaUpsAfRTKqAjrTdcBpoTjShLmTzdew5Ya70k0pFhatksGLR2Nqbhx9j+kcFlkm9Bbfz1ARKTlFyK2Rw76yS1hKeCVLigYxBa+JPIb/dCbOxSP3kKU5qpmltZVJncnmeIeZWOMT4MlRCbMLETCPeaRpRXpCrSOlkFcMqva1xiD2eW4WzSsX6tLQTYI+R6O3nRociZIkVBPm5PAGwEpe12lD5oZnDStJX5WyBqkInU6qnR6T/Tu/Kj8JMP8QYDwVH2/f6TFHi3c8wVs3nX72R+56+j0kT6dboysdC+CJjRxym7cR6wAHojN5fn4xSX3/+LeP+j2795EzcVkx5gcT3Y/MT48BtD+87ad3FH8KyPsoAP8zx/k783POYnbi4jdjsImpZ/IwkDSj8y2eghEsNYVVQ4+OdQC/sdBmS/QEsjhFC1MRikiAnqlAhnFN5kx9y0KH5NArtRjZItTNElRrwYZriqeK5wHrBUkNTcASLAjqKXHaoiOegyVokljUQrbpFqEhPbxytTlKp+UC1tak1Egv96ETe8YeoDCFuVVev3jDRVk4JudCo9eVPdN0lZrRIxV0ZY4IASYzZLDYZKdmYeguoNUpEmBq8wOHJXNz85QsjTQc8do53N2xp1IUcOcwz5RV6ObjDnxh6GBLeGNnF0KdWDAPL0Pvp4TxGafQUcYGlhOLOEUqbRHKWOklrVdSMIXfdzRpWIpiYiiJvlRciPC7Vkky0V1QGcklGKM9SyR2j0KbG5mQwgiNLEpbIkBAq4X/sEQIpJrQ+yoDbx7yvlVmonRmSQFO55DIWY+Niw+NRBRa3TwsGDyFHNHDRwoqIgOiYMVptYWoWQa6OhkoDRZv4XFkkfiaUmyKN2kTJBQdyeSVKWO8fnPNf/j3/57/7//+O/b7N7y4uebmzR2Xmw2ffPIZF8+eMpSCbjZ8Ox3Dy5voALtbhBliYbnh4BJMahWNUDZbIIP3TpM99TCHDygg1lgMkpU15bYjSZjbKoEuDTl2VAstgXRbu6iKMNJaCqWJdYYcMtNnF0/5y7/6Cz559iGH/RGuEsN24PWrV5R5po7vDxKWHhsKySP7Q+Obb7/m9uY19c4pu4yZc3dzgMsNQ19Qyyxzp7YIWBTxkLabRNHQoOdOGcZIE04J1QKEv6m3vG6AHPOZ5AM9N7wmyPmkUkJ7sNMtKX2uuENKC0sSUgtPwOHigsrKjLFYo9oqe6c6ooUiQhOlmTMOTpKBeZ7442+/5vk331JKYjNc4nOYcOhmD6Uz1wPLHxvTsfPixTf84Zvf8Zvf/ytujcNy5LNnv2ReDrRu5AEuxyuGlJkmOB4PXD65QnAGT6QxRcGaawRaWmPuRvECUsLvO2WKdUgJz4UhG1YVtKO9rT7J0TxjVEw7hYQtDmMBWz3S+xGSBIu/FbrFNa0SLPzweMy02kljYWnBxmq0aGCm95eqPxyPFR7vYrk+9FA+B1MfFkvvAqofgtTnPz9kn5z//fTvuSTzXYXHQzD5YZHyEPh+yGh+lw3GQwbyjzGDzp/v/P4nueyzZ8/47LPPuLq6YhxHROSeHbTf77m+vuarr776Ps3+ASB+8lV++fLl/TlM08zvf/97nj9/fi9tPR6PvHr1ipubm/twv3cVeT81tqXQhwElM3FLn2Z0Cc95r4kyTiwmYQuDoBasfNUeQSuFYAO28BhcUiet6/yiEkVFSqxIA20umFdy7TTVCKASqHm1A5NOK7omn4cx2XEZgvlTVp/AuWLDJQkjeeJYE0hFGVlyZWFBbYEeeykH9GJEygbbOO4XFLsNWwsVfJMp0xL7ujKwHzIje5oVfEhohkGcsq5zC4I0ZZlvyT6SRsU5QAtQbvBEc11VIEZZ0prhJZAbxUda0vB7v0gkDZlq1gj3oQecmoYNpLDXGLOEishjDu1Dw7xiB0Dj829LZTNm3BI9VzpxPmmAbANNB2AhJyhiyGBIzsxLxVIoVvxn2BxIDyDN10ISAoCTlfQSXsnhoTgdZq5fv+GjJ0d66aQmNHOm4x1zm0/VWfhDelhTwckS4XTtha9uksQ4jHz+2Sf8zX/9l/zyi095OlwybIUZePXtdxyON2G7kBOimTIUxpyoy0LtLc47BQPb3FFdz9eJ12AnMGCdN3SdM0+gAgoS691xPzHdHrnd3VLGDChZS9RvqngPltcpJ+d0dZoFo8p1rSWNe4BesjLkgrBae/QAQjqhIL7abvj8sy/41Z9/we5iQ6vKm4+fsq97rq9vOC57+tTp5gHyS2IYMh8++YgvfvXvEDO++uorfv/b3yGmvLx+Q68LpAHVUOx4D6s+V2UYt/z613/B//Q//498+MFTxJWXL264aUeev/wWN0HeXa//cFhnWa04XITkSpeOphLgZ05oS7HlUoJdWIINhzRcJfatLhG6JWtwHwQs2/n/WXvTJkmOJE3vUTUz94jMrAsoHH3NdPdwljLcXe6KkDIi3J9O/gIujw8rshzu7Bw9jcZRQF15RYTbocoP6pGVlcgEqnppEABVGR4R7p7u5qavvgcmsgZoQzDSZW3SJEYP79AjgN2PjQ2HnIxmHjUKYU+mEsz3qSupbMB77FeQfqkOap0EdBmoZ2iOj0ZV2CLR5LBIxJG12dEVxmoYLiN81U1Xn1J3EmFd0SSF7ctQMimsxnI0QiIfxJERag0/CDVBEgNTNE2goXZVAhAXieaUDWI9IlEfegLMmcQZPRjaOXr3YGGZ0TjmRYT9RVo9md0c13XdPGJOMB8MG3gxpHX6yuJWH2QtQQwgfi8fPO/cWn/cx0yG9wNy4cfNYBGJ/bJ3WQm3QeGjfcSx6X3MerC10T1N0w0AfQwTPG5zBJ3vAz5/ChS+3Wx/6Ljvgr43+NNPrNGO7OXNZnNjjTHGYJ5npmlijEFrjZwzpQTYviwLtdb3AHcItcBYmdxm9l5z+16QOXbi5vjM7IZV/VPj7trxod/50Rv7Q4Z6NI1IjnXBtZCB3h2RhuqEpQEtLCGYhDwc6YJIurGZ8DTwbpglyKHW9B7MfhjRTCRUL6odkLD00bDDwXt4KrtGXlEddCRC003xbEGiI+qJUiLroaS4X0cXColFBl5zkBSzxDGJhGVQdbp2gv3VKWuQqLiTkq376JAT3hq+3rPZPRjVCr6EiqcMDzurJFh3yGGDOBgBNLuQPTGSh33kinu6eeTTeJB3xuQka6FEypHPkEZYpGEeyibiudsnpwwLW8Eec6BYkNead/LIdO9hxehrslzvaNKw2wAaiVISPqLRpj1U0PH4jnXJQ+NnAeYfvfUB1PLuT47PjPswyfXW5dhdP/7wwd289UKWgIjfm2BWKRbcApKjofuua35rvyRFt/H94zput26s747znaGE33fo937O8c/3bf8euC53fnjnI/5bxk/u60d/30fuUdLV78XogKYIgcIHxQstw0RmTNEFnidBNFM7SM9oGnRJuEzRMcpGsWCtYQnfdqRCHcEUZErk7qgUSJBFMRtYmkE7acQNz1gnMkJuKQ5VdJU0DMwT7kLKkSYMhg7DUiyY6RHuYq5MxVhqwqYUixtbyJRgZyQ4SIOWyQ6yE3zrWBqk5oxWuRRFUuNqcbKG33Jy6GXQPRZ42gFCLpgocalL+IkNDaDQdO2AAyRj9Mz5q4XUXlAXY94X/NA4f/2WsVuwDMOFmUiNbtkjqTwPvA9cCiaCmJKTo2PAZLQatg75oNhk5Aaaww+2u6xSwEySTmsdFSVbBInY9OEPsOHR3TQnAvhcoKzhGApeNbzSNh2qMIaRXQLQXiBlWVPHRzA4gGKKlh4yQTq2GDopojkKU1c8CfSKMUVYuIWnHCpIi+bS5I7rAEuQVkkrSpJVLjSvIWw24b1HV3LkCNjzTkrBGtMKpoZT0aHB0NFgrE46mJJC6gjK0L76OIN3OIzOH7/+luXqD3QdAexK4uQXn/DFF58hwFzm6DLmxO6wZ7e7jq7wNoJLqg1oEUCHhTVKMnDf0a2gdUNRY6ggY+DJGNUpq8C2WTysNQ/E0nr9QB6xn8E0UtK68J8k9sVwlmokzVjZUHLil7/+ks8/+Yztdhuys23hxXdLhPe0fp845uFhRvaJ2js/fPsVry/ecnj1lpETm+0z+sH5L1//V371F7/g0clj3uxecfV6z5dPn4bPZZqYp/A7z6MxujOn8K9sKmRSMIqbUrMyFY8ryoJ5hXeK55CEEQzu3IUsg9bDh2zSYKlpypEKnh1aZVh4oYsmmjoiHe1h65QngTZW3/VIN/fq2Ozsr6754c33XFydU3LBzgZjesxcF+qy8PbtYL9UXn7/A7vzhZcXP/DDD9/w/TcvkG58/unnvLk45/WLN/zDP/wTf/Nv/xWfffKcMp+ymaHvGslGKENmpY+0prgnag4QIzmIVFJS2uiUPDO6U45PzyZhBaCDwwFaG2xPHwVzCEf6hjQ3vHgkM4+JrIaMLTUH0EZeAYsR6z33tdmnM0kXRldIFVBylgjq+QiLjPsKC3gf5L3N8r1bBNwGZ+/72d1F/l2GzrEwugsO32bQ3P7e+wrC43Z3geH7WC/HIuw2OH27uPmp73ioCLt7/D817gLyKSU2mw1ffPEFjx8/vik0j1YZ0zQxzzMppRs/5buM4+PfD4cDX3/99Q3reVkW/vjHP3J5eXlzfMcgoWMRe9t642OOA+DRyXNav0I94z5x1oyL6ZqxCMyDUZWkhnfBNGSSqRuLwjQkmFDWsJRwTySDNNnqPRzrFOuVNGbcOk0OTJbpm4o1i2cWHk10ooHqNhiLYc3JqmSM5bCDWWiHTBYnmaN5YoyFJFeYTWFvUyv90NjJYEpC08Hp9oRtnklbIeWZs0mxWhhZactgAfZrJoKNKOzqMa29x3O86UL3DbOkOB/eET0hiVHqgb7es5odZAYqQ8NBWNvC3gXPgtaQo+OGTidId5YtzL6w18ypzBwkvAI3KZM2hbpveO0RWuar7/8AH85hVEQr2Qr5xGkkvBm5hOe71ERXOMGoU4Gi4SedJg7NKUOwZQ0jFQ/m9gcOObIyVwRPV3jP/MhmlQhOc2c5LHz/3Q/Mectn/VM0F+py4LsXLzi/umb01QRwJTmE7m0t2wxyWRmlpVCmzHa74csvvuT07DEyTbRUmOYzPn268PjpU/K339CkI0TI9vNnjzk5PeP8zVsuLq5pvgTuoR6FqwVJJ8BdCTBtZSlniJCjlZk2SCHTHY7ROLQDr16+XvetcHV1RasLvnqMuyZk+A3bO3jQgqYgC5lpkEUk/HE9wbyN+cSsc/7DG673e7oZq+MczTt1DEopzPMJc04cdo2tzgge/skjPIQxIZXEfHLCb373G379q19zfXXF1998w+uXr2mt00dnzplHZ6cc6h6GBLvYQHPhL3/7O/72b/8Dv//9FzTfshyueSrCo5OTID2p3WKA//xIKdZtYhu6WDT1Cf/Rkp3uI673TDR3I/EYIdYiKThA4MroICnC1iOU0dbwwGBqicSaj6EUcaoGyWIgFD36es/xfo3cBjEPQkKS1SMxLEusRMNdiawXWf9JAqhhXsijrmeiIymRCPWoZAurOg2/VkED5DhioQ7Jda1l1utRNYg4KdQFsdZ3hoxYj7mHzJ8Azn1EQ89jqkaTMbxHcLXCWOcP646SGGprKF8ALyk4AcHel0HyFPfgEIQgDAkGk9KHkiUYm2PN8kB0JWzYSlUKtYVYgOaqM2Jh9xERR+uzSz/82rm7RrgPiDz+OaV0A2zefi6WUjg9Pb1h8R7HGOPmZ8d1zRFkvQkXXkHVlBLT2kg+fv7R43i32z0IMt/ev7vH83PH/Z4Nq8h74PJ9nx/s63IDiKeUbtYnNzZeEjW4rUzrk5MTHj16xPXumrPTM3JZ570RGRBLXahLvbEWeSgH4r59uh14fN+4DbDfDai++577mOE/OcxpCCZheWMjkUkUNQ4uKMF4DVDYGSOt+xM4g/KOHBo14ojGigzcS2QlWRDzcI810xRBeslGZOYYkBN2MFwUloWiYb06upJSNJiVsOERLKxWCUKVr2RCyw0nIz4QcVpbbaTWeljSCAxgBHAroowRanQwmkfwXTdIkihJ8W40YIxQQjgNkRLvWdVKSXNkaq3nCIn5YOhKyBsDLdHAs5zJRfChZFU8kKFootpArIcdqgj5yGgyYDQ2KeYSH2HDIdlxSZQEozuWLcJH19wCEydJwejr7ywxrWG/ycC0MDDG6GhO1BbNuIfGh1tkHFHIn0JMb1+DD37QEXp+aAH2Du50iQfS7S3HLbD3ZuvjW0TWpNd3qK3h7yGoTjBP3z3DV4dnv4HE4yV736X43as/M/yBP//o6N4B4Xe38FvH9sDH/Bnj2Pf/+ebAw+PDH17mNaIHPIfcMBuSUsj0ZSJJPKxKmqCHxC31iuaM0hiS0HkFEzXOSpaO5C02dbwncgrv3DYNUo0JqWn4cGsb0dUfCvtKlRGdtqQB9I0AJ0yJULYkWA+mT0qRoq4S0q8I4wnwd5062EjBXIMR5AN3yBRMQvpdkoFr+HFJyAtYBkxxU+tIjLowWYTChE+asHhDmpBSJndhcYvFXU+kSbA6QsZQ1lRni663uNFSDslndka75vx6sIzB9HLCrDHageEVJDMJLImQkIhC34GP8NWVAX3Qc2Z08BwF1GYWenOyKK2DpwFSwMIyRDweLN0MV2ES6HmH2ga5Tj93ydyMDBQ3GkpRsBSL1rExajt2EhVdAW5YLUW6o1qo7khu0VVdIGdDS2YZzgxAgnlEuIBGYnQPuyZ6UTaScOn03ELibCBF6E0o00DYRIiHOcOMnApawIazlU0QCUSQTXRblyrB5nTFEbw3uo140BFhACU53Xe4Jxafgh1PZ5O3SDeMZb1RE66d/TLw1UKFkdhOypwzvXdqbRxypdcDvRnnb865urhGbNDcsN5RnIMqc5ZgJ0tiSHSIR1dGqliOhop1oSTBtbE3JfnKqvdg3EGOItIk2O0lUXJ4by/E4qnNUzT8RicVgQTaO5unW548f0rOiZOTmdo7aZdoy57D/kDKfbU8+bBRD0qenNfnr7i4OOdw/Zpd29MWSGniUmF3+YZ6eMaLt9f8x//7/+Jyd87vf/17/v2/+RvmzSbC6Cyxbxae5JMjh0bZpGBlNWFKsRDR2bE6kaoR+iNhVCXnQe9R2OXs1O6MAmUoIxeKNGpPjDYiREEKeHTNF4vwndaNJFGOqK0SzQVGrgyFOXkQwKbC+dsLri6u2GwzWhw67Cd49cNr/ukf/44//NM3yKjMZ5nr84W3F5X99TXPf/GMLsbTR0/458N/4dXbV0x5w6dPnpMfnbI9nShzpksmy8APFgWrCkNqNORE4px5xnsA7vGcDiZVMo0MnZKwKpxOgqaZUQRhwjmgk+K5oCOD17ATkYSUzpZY8FVbPd4oJG9AyJ2rg+uEuKM1mPnDYQwl5w9vbN0H/B7HQ8zlu2ydu+DksWi7DabeLeZub3+bJXyfVcXPsYLvyiBvf9/dwuU2i+f4vfeBtQ+do4f25z729d19uAuCH8/f6ekpjx8/5uzs7D3paSmFlBK73Y6cM9vt9r0i63ZhZmbUWnnx4gUXFxfknDGzG1uN4za3geYPloc+MMp2i/TGsEqf94zHp0wXe6o2WC/BSAh3LDldDRNfFXbBwm0Wqq8hQp6N1lZQ2pySlG4TdYYkE3ltLONOLgk1CaakJ1SNNBq+gLDn0AebaW3u5wS+YbvptNGxMZg2YQG1WzLdL7EGV23Hru8YLTLLc8qoZjRnct+jFBRI08yh7TlBkZHpIuxSZRkOvdMrFNmz3TxGTNgvg08fG6rB7jaZ2cxG7QO3xtANhjGnNf9kFKQuuDeWMtBlMNNZSOxxNjrhJoyDwtZZgKkKfups2CBTRzcTLIpIqBqaazTzccbBGF6pS6dUpWelnzt500L2fyhI6YyypbWF5gdGX7DW8X7A1JBWOKTLsAhzXS3XPtwTzD3ktyIaKishgpQtDLZdwq7PDWpdeH3+ljo6by/eMGnhuu959fIlbQkwVlaGoIcwH5E1fKsIWZQyb9icbJnLxOnZKduTU3LKjKpc9HOW5ZrlcB1kjhXERYw0JbZnJ3zy7BM284Zu33N10XE/ho5ZFGkGurIAACAASURBVPQehT12rOECuFRVNienPDo5Ax9cXh3YL9eYdHCl1s53L77ncnfN6ckp+92O84s34e3sca2rrnPkTT1z5AOt1Y0JKr5KmpXTzZYvP/+ck9NTvpI/8qfvvsYOB0QCdK+t8vr1C96+/ZzNZsaGcL1/w+vzV7TWA7j2IB1JVqaS+fz5E/67v/4rHj17hn0dv7c2jNE73QbTFHZqyRMnmzPgmqU2NlPhN7/4LZ8+f87Z6RNUt/xgld2LytvXr7EaNiDiD14qPxraSwA3ecR1NCIYb/FoTmk2TGbQQR8wmdFbWr20FZPwBS7I6oUaIYGOQVJcw8dYLNaG6EBkYH0mpR5KIRwbSiLdsKZTylit5C4wdXKaAMc8MxzEBjkn2lDMhZJlrbdWwEccZYIeAdhYR8mIKskETwNL/Z1k3iTC9Fb2taVo1eCKrHk7Jo66IhYZK10NVegugJBEGd3DHgMhpR6g0lrCGQIr2HxkRSZCEYgkhtka7ryqShl0IohdJeZnU0c0UdwiKLrCvH5BJ/yqS1aGa+zFcLJ2VDoygmQjElaHYorksKBh9W/W8THWPO+vUY7/HgHUI0ipSeN3MwZ9dHp7B/iWUpjKBLxvsXD8jOM4gq/HcN3jz27sum49g29/90Pj7vrGPSx8btti3Aem3m22323837feyTnfAMo32MzKxp6miVLKzWdPpTDWtYyIcHZ2xjzPnJ2dvUcQGGNwOBzY7Xcc9hFI3Fp7D0x/iEjwIWvA47idZ3Fb7XZ87efA+/uGq4MrRcLqRFWo1sI+IufI0PKoC10HOhqJvNpZGubQU0dbBMgpwvAW97dB04oWBRQbEoC9rvWzChzZ+s1IJXKedAjNB5LyalsTmQs1Hg5xH/sI1XGXeG4nv7mnJ4chgmmLycMT1geKkDQAWssdZ82I8YyohyWQSMynbivpyqOhhIeXccoRMggsvdFMmEsw6MLSFTALwhEDd6PksMDBc1iBSuAt3ZQypai7PRqIRadQEq9znKweyeF/X+IBaSsxrsJIgrRYh4oKatAPDtlpOCKD7WqVOMSxJGsT2QIHtbXhTRy3/8T18xFJFD/x2kdRbn/6Yo7u9HpTuQVY47c+/jZefLz51hdlpSzfgKjCe1Kz9w7j1v6+A6zXwuj2l0h8kH0Im+XueZAfU5h//lM+dvsPHfeAyx9Nlf7wjXVAnwTpTpmCCRChTImSFXVlSEFWD7xsBU9KcokAieHkSgCMWcgjUU4zpMyQmXzo2FjouSBtIQ1hWu0rzIwqznBhpI4wkfIhrOkWC09oYvGeyqCNThdh1g7MwTqsjpZYmHQi4bflQR6ZgTBU16TxkNFPedDHWoAJuFYkCTIKdMgaHS9p6+IrddLIjOzYCLma0dBR6L6gi1Mj5xDTzJpVSpuVbAPvoGJ0UoRu0ULir6v5/ST0Xrlu1/SxR2uhF2WMguZVcqkTYhWVHh00iXMyPCMqTNYQL3iCNBJ17SYyNZIo2Q0no6mjOhgYM0swoFwwPdBHYhKj64dL1VWVugL04e7Qqd5INdPHYEaRXGIyFWXaRuiKtWAMazO0W7DKtSAURhJyL4CQktItha/jKHFfTCNCR2oK8C8Zsgu/Y9PV7iUJkDF3JA3MCu7cPCTLVjFLJIyiCU8Zk0YpA/GwDjAHJVFTQ7ozgptAqopLCoBhQJrCD7sRKetYZtc6SVukSY+KkRATksTD6urVW169fMmnnz2nLgdqa3z7zQu++f47hoavU/LKIJK6k3YsCdkkbDQSUAzN8fvLEqx65lis50h4QTUhI9Po4AVLHgvgSHPBh1LdWbNa0Mnoh0HSRDKniZNlAzmCMTZljsZPa4hCrQde//ADrS3Ymrb7wdfOFtqy5+s/fc2bV2+5Xg5kF169ecP14cDu4hFo4euvvuH/+c9/z/X+QOeK84sfYP4f2agwPKGnTlJh6tEkqGXGewMRyiT4BHkYYzGQymhGniW6y7qjyDYWJgitHjAxTtMJ1ivROlBOMQ5DaBLyLe+d2huFTKchmtHk5DrwnhBriErIjLth6QRFVy/RTsfYXVWWwznXWjnsz/nT11/zD3/4Z3Zj4awU2uvGm2XP6I3pZMPJ5hFn8oiTs0f8T3/7v/Dl737L//yv/5anzz9le5o4m7Y8ff4pGx+4JnIauDS6bJCeKXNHD4XaDOeAo+ScYoGXNCbwJRahksMPNo9E90xOhgxwLcwI0iYOWUg6I0PQ3OL+lAgJKRIBryNZBP9YJzWNgrh3JAktbyjWo9jNmTF9ONBzXIR/9IL7HsbrfaygH8keV2D1aK9xuwC4W0TcZQwft7sLXN/3/rtM4Yf297b/80PHcrdIub3N3XH72G+zaO4WQbcL281mwzzPPHr06GYfD4cDy7LcBPcd5aH3SURvg8a1BjvoWGDd3e74u7593A+x2H9unD1RXr+ZcF2YppmDG+nqjOKXOIrPPUJw5gjqkzQ4ENYXqo62zco+G8HqWW2aOomRw5u3iKC+p9uMFkd89U52aKKkWTASzUMuL2zYXTntdEcqGckJE9j2Rp5hODiNpSsuE82vAWMx2F9cs1wcqH1B+0zeJCZVNEkoudKCMFFKJu0ztRll7IPB0xK+XDG8ogJ5ymzmiXRyQkkZw7Ex6Mdg2xRWSb1vkGHxHlE8K6MeGIcIXpMs+C769GhYSS1dMKmMsaP20wi/S8LSG/OY2VIC6B6EEsgjdCtJRczoxTnsazzjU6H5juRC65lkE54aNCGXQdvXCIdbGmPUmOsceu7Uuta9A9Qb/SOaokkDTE+aEBVKTitoF1YOwy0Ulys7uB32XPbO9fU1CaV7ox5qMAMt1vkiRy9kQYmGoKqQp5nnn33GkyePeHz2mDIXqhtXb694+e33VOtkV16/esnLV6+xPjCx1dvREUk8efqYs7NTaj2w1D3jsGNd3gagxwi7F+LaPPphSsk8e/KU5198jnhnenXO61ed6ytjONTW8NGp9cDFxTmjGaM38ISIk5IEg5ZVrbeecCfO3w0JSFY/ShLzNPHo9JSnn31K21deX50z6kL38KSe8sxms2Vf93z3zSveXrzm26+/4bsXP7DUJaT/ImhWci5Mc+bTp59xun3Go81TLrYXgDNai4B4Eeqh87K9RnOGXGgrqFRb48Xrb3l9/pJPnjzmZNPJLlxevVkJB77Wqx986TAlpRKewFhHSwJZ7WREcDXUDDxR6HHKsABMgKJKxxnHjBc8nq+esDRW+XMYRw6tJEu4KSMtqCSSl2AemuB5takQYYwe9+jkqAXrLdaGkNVC1TBCQSEpWMiGk6yQc/xufBjdQSzk2ZJWMpCudigpIzUIaMMzOiKfgQyeDHVuVIgDwXxl89mI0C0XkgkDC3UoAy9AD0ayWWJC6dkCXMeRlbGpvoaTqZCGoTmwCl0VU24CUpikk8hU7Mb2L7us63EJCxJCkq+jA0oF1A28o0lwaUHekLGmPyWSBnA1Rlg7HgNCNX/4vBO3yrtn3BFE3Ww3bOZNgE9HBVEOEPUIjO73e2qtiAi9d6Zpeu9Zfxu0ba2FDcYKoLr7DWh7ZO7WWm8YzcuysCwLh8PhQVbx3WO4z7rrY4//OG6vbY7HknMmp/jX3aOZtLKXj+uw47GoKpoSuq5njiDz0QYEAow/Wmnk9TzcZYjfBcN/aj/vNv5vn4+U0soyT4xhXF1dISJM0/SjcOMPGYmMW6WnxPBEGgvZJ3oaqyVFJomSRnggiwf5KrtTTTB1SlPayuw3cUwLxYxeBhmFoaEotdi31KIpHMHmYa0nCO6VZNFYTJrBcnhB6/pkGEt8loY1UuOoAnDUhTGCwNgmYHiA5tlJAyQZzRWRQU5G9wl1AgSWaHHWlanPiPd0NVxKNIqt3qgqbAkS45AgzY0e933remPnMTx+f8Mkcjk0MilUKozMSIYWXefYjqxh8s1Zn9GDREYl0UViXu5O8hHWVRZzRsaRHBZB+Dr/FKWIkUcOix+NHKJMxXWzriXaqsoOEHuYoiOCBx8aHx3y9y5a4UcI7f8vIxYOtxBlf//j3wNJ/YgA39ojl/dev4FVRVFfGc0/Gu+cl2+O79b3f/AB3sVv/T6+8IePm/d+BJX5Ycz4np/e+tFHY80/M4psWWrHu3E9JwojwGRPDFtIZcOmd1jZRZriZvMkTG1GZ8Obkoowycx8Kjw+fcxctnhy+q6xr9dcLzvcCiM3ektUYlGhAtKclJ2mHswSdbrk6Li4YzJAopuT1IDobJv1ldUuq/tKwm1Ceg8plQpZOz0nXAfSjS7OnAbNFc8wpYk+PHLCLIoxSROO00QwyeGfMwSnxuQyEpJ6sA3Ia9iLM0vIt6wm0jTI3lGJRZulDKPiUtA0Apw8gKcAu2ypWNkgqZPcGTKB7QOcVgkmsKbwPZYczGELv+IuissgL41RVplvLrgZIyVYWY1xjyY2q3zMzSkWst1NVsyc7h8x1QyQ7jCDjY7OCe+KjM7pvAEIhrQLyTNShNGEvMmMMSgjh3etTcFkH06aBZ0nzDpmg00qtNFIrcNUcAsmxZyURRbo4R8bljpRJIkGi6UMxYbj1sglzkfXsAeZZebp08LJvKGb0lrl8nDNqOFnhwbInE1p6RChPhYFYEkaD4nsmCpNAR9MPdG8ot4ZBl0TWTOjRgiIoIzUeX15zT/+87/Qatxrb85f893rFyxXhk4CGL5E6YVaeNAtAZZnD6Z+sUwqiZScOU1oKhx6p40GXsAa1sGLU3yEN5QA3ckJqhqpD5wZWa1Les2kFCzUpjBGWGWU1vDdoB4WLvc7Dm2hNeNPf/qOb96+DO+5nlj68sGXTjNjd3nBxfk1B+/0w54f3rzmzesLKMLFk+fkrJz/v29ZloX59DFXFwu//P2vOTs5o0yK71aZVbvAUiEjTGWwXxqF8OLzpUVDwAwTIc8TY3S6hFKj5o43YUapJTOhMGKhIKVDh0XBp/A0sw4ig2YzW22oK1OH2hMyKq4Nl4wUB3F6i5Cceerkk8I0Pya3czQJtToXV19xfnXJ+dU5J/OWrc4sVzuuxjXXry9oLnzxxSO2WZlnY9+vePbkMX/xi//A58+fkTaJzWbicXnK2Vx4c/6WP/zpa/Tg/Pp3v+LseaYgUTznsJ0pmliarJZISm5zdLlnopsOCAWSMQsMTRG2oZk6lJQ1GGpGAO7MWF5wF+aD0VPB0mDqURybCrZxeo/FnepEGuFt3nVPIkcQ4geO+8DK26/dZQTftZ+4/b7bIOptS4q733dXuniX5Xvf9neB5of2/77ju6+YuFvE/BR7+/Z2RyD3IenlT8lN77KXU0qUUn4EPKeUmOeZo5R2nuebtPm7IYY/YjTdOv8PHfNdSerd/fvQ0a87GWGbzzj4nqkvTJPQ9uv9OxLWhTQri3dsZEoupGIMX/DRUHdYrSxoYCkUeTqcaguZiZYSkzdcNBjTRYJRIwMQzlqmCUie6G0gGQ5LZZobqVg8V5PTLGSOBoz9DveG26AMYXe45HK/x2pnwmlpZarNE5oyU5mYpxO8JJoYyQMYr+4svocq9Op0mcJCSQpenMkHU8q0poy2Z1RhYiDJGRqWZnNSPGVCCXWgpxbWWDLoe0daWuNWWvjdS7BM975Qlg3MSkLIRmQIdGGMRu1OyUbNIOwwm9BrY5MCwDw0wQjrqyaN3I3ZlMNQPB3QBntrXFxd0i6vwnd6WCS6lEHfBwik5iyeI7DuQ4cEQygl4eTklHmzoeTMftlxcbHHekck/BU7hndjDMNbj8mSWJ8ECWYt6IPHGcCdrsAliZPTU55+8glffvacT54/YzTj1asf+E9fv+D8/C1Lrbgqddnz9s05tUbxqERw735/wLvz7OlTfGm8vbikt4V+DOaScROHYxJMJxcJIkCZePbsGY+fPkU97Ox2+wt2+wUZBj6C0WqNNur6rWF1kUkMwlvaRgTJmdiRgxT3tB7v7fh+UaEPME8IypPHT3j66DHXF5dI6ySFs9MNZdqsakvn5csf+OqrP3F9eY35iBpOhJInnn/xGU8enfHkyVNyFvb9OjyaWzTPVW0NOQ7rDW2Gt0ZtC+C0ZeEPf//3bDYz29MN/+pXf8Gz58/4/e/+iu9e/MDf/ee/QwkW2geP7MwEO3gh1IhqgqQIAxUXCo6lsHcAKGjYfFnCllBhiUVTwizswMSDpGFrY2tgK9MQ8rE2cAvZ92pbYoTtj7mv4V0OKjQ1CglcIuPEIvhKTaO5lhXvsb2LE3diBODlOYcveRtBtslHRn1CLaTmTZ1sq7WFwXAPX/MU9wx5wn1Qhq0BzcGgdslYzoCv129cS0OMuH3DBgALpqNIQha/UdQmS0CPjA3X8CVNrGAxmA5GX4F6ZSXeOENKzF+EsquOaN5VV0qCZHH3jqNtiYSyTYljdlLI9Eecr5TWOrboR2iMj1NPrDnmeWYzz+RSmDfhKazyrjF7BITNjM267WFZbiymjuHBcf+9a3wfG8FH8PjY0L291jkqi1JKNz7MFxcX7Ha7B5veDx3LfWuxn9xOfnrbY/M/mNwBwKdbDOUjoOzuN17Mx2PsIjdhgMfm+H6/x91vPI+32+36Pe8a9/eRHB5akz207rr9MzPD3Nhsz1DRG0uO6+vrj7fHIK7rRKLhqAXjNjIHBJUSTSn3IFiO1aOYhutElggNtzSiSSKCDUjr2gcNVbAwEBvYujaMpkz41biCr8GzbkrSjvpqG+iDZFPUxR52UylFcHDxQpMgw7llhkYzq1tDemSAHToUUZaVhe0pws0NZaihFrVHymHzp5rp5mh3TDO4YT0xp8HwRJewxJBNIvew7UxpgjFWj/mEcWyk1WjiS8Z0wemkSVf8aQQwngBL9N4jI2jNhUuE0mB0gAVJKeyEkzMOA8kZF2Ves6dwR8l0A/HGlHMQeSePsNakJIGRt2GRatGMrITH/VgtErU4Wh+edT4aYPY/B5Vct/tgEPNHG8U7bwjB7wGu/mPi8Huc5ISmuAmiGXJ765UeLX5cp906vj8fco13RrDBf9M47sK954P7Xrh/jz/mUN7Rw28+8c85E14a2geSlNQitM8npdRKH4Uujd46ykyWzJDKpAUU0om8k8AVkDLxm1/9kk+ePydP0A4LtjTeXu7QNy+5OL+ijbCSyGOEb+E00eeGVQNNZAcdQgFMOr0IXqcIdsstDOvNY+HSB2MN90OCfey5hh9qalDfmby4CdoVnSLsDYGBcdBEbkYZTi+FbJkhg5wi8TlLwrRiQ9F5YhwcxygjPHtHGeSm68QGYzJkp0gbVJ0ohHxRfUBmXRiGobtPgvpEHwNaomdbF+aw2TYOhwmRAW4MAhRPEKZhQROgdKVMg6qKWIpCQddJSENK5gRtxHIi5fBREnFGNiQ8+WmjBVb7EQ3mooPaBakNHQmrGS1GJTE3I2WleCJPW7Y50ZLCNmP9wEEbszj7NtDkmBvdY2fysJDqSBSGpo7Mig4lqbMQC8FJM9aVhnPwRBaj90pJBR9rEIoXVA0RhyZoUXI15qeFL3/5G043Z+z319Slsl1OePv6HDgEk98GeOPQhTyFpUBKHg9jdbQHqzifRrCfSUcaWAqLgeGdKgEWTHmCNcyg2YHXL19wuLzENBY/wyvTlPEpLF3GaMGS9kLxJVgdDuWk0Gt0JU+LsDl5zOmTJxgDeXOJ70cAqJ6jg9oraVZM4uE+kq2xDAVIIOHNrUnDYmGpZEYUKOaoNCQr12a8fP0WRxFVXr16yR+++orlulO9k8QYH2HCrBMsi3C17Ll884ZXr77l1ctz9vtzrvYHfvnLgXjj5OQJnzx9TD0sfPr4Eb/8xS8oecJzQdIF3kHmTfhPS0dT5pFuGb0hrP7wTXBRkmQWW5CUydUilA6njImlDMrKSR4GLTveBzmB91gUyOiYC/vD4Hz3kuYT27MTqgquAyOz+J7iQmtRdE9FgkXARJomPnn+Cd/+y5+obXB5uOCf//6/si1KylvmpyfUq2u0CN4Hh9pIPni6PaWjXOwP5O/f4DunPkvIifBUN/Q2+Obye/63//i/8i//+EfsuvPX//1fMabB7x//NZRHsIDbgTwUyxJBoDpINaxp7DAxJqNMW4oORlLwTB2QKEynwrI7sE1Cw6IRmXMkw0uFMTHRGEnCN6wZwxrug9En0uzMyenWsBK+8V3AOEFM1uT4Dxs555vF9302E/fJKR8CUe9uexz3sYfvvnb3Zw9JOR9itdxXaN0u1O7u5+2/3weaP3SMD42H9uE2U/lYcN4+JlWltsqyLFxeXt4ktj9+/PiG0XRTvPUebH7uB5fvsrvvAuE/df7/HAbzYX/JvD3DSqNfTWzLCe3RwtIOLJcetgeT06xHYaWNsQa5xANe6RpAqpky504bsV6AmaqOJiM1oWnGGWSE0RoqkApYUQ7WmbOFCmoKQsUyJrpVZJlQoJZQlvVVTr64IaNi3ti3zuH1FePqAGbInJitr7y8CfEJZ4NMJ+FYao5vCuwGJWdGDTXFIobXGoVNTlQvbHRQDcrUaTtjcmekhChkjbBdS/E8ogzafmANmITlAIYxvIEYg2CRpdlwm+nNuW4NPXQ2lLDfyZ3ejGyDZfW3LeaYJ2xUdgLaBodh5I3QhpCScapnDPVQlx2Muhh9v3Dond3hmsWNxSrJYu10YkJPndGchEEZIB9eWmkKOW0uE598/pxffPklw53z169pyzdcjgoeIXriAQI64SO9Ql8Buvpav4iEnZunYIFBZESokEvm5HTLZp5RMjo5bQy++tPX7FsoW0Yf+BD2y4FBX60uAkJcdjvenl9y9uRTTp4945NnT7i6uiQtdQ14W2s1C3DRbIRlx8oC3p6e8vT0UayrRXn19jEXl1fYIRjawXSFpBnXaL6XuUTEsQ1qbTS3ANgsasK4TVcW51rSxf89AhzFmVKB0y3b7QlJI7x6iNDcKMA0K1B4dHIC4qHoGmAu5JR48vQp//rf/Ft+9atfUxB0ylzu9nz3/Q9cHw6Uzcx23jDPmeVQud5dcTg0Rm/YWOXM2rm63tGWyjbP5M2WvE18cvaEk3m7ll0D+YjcgDQJ1sK/eM7BkHMRioVlyTFwUbugyWjO6qdrkIig5gYia7AkErZzpqhB6yXs5qTgYzB0bVxYqCCVgeUVuHbwpJho5N30WGurKQ0LG5ZJkBrM+nCmaMH4K4KPFGsUswhjV1nJQhlLuoafE2D0vOJXBMis0oOljJADrcZGiWtx9Vd1CqzHmdYwbesrYK2CDmd4hHyrZJCwz8j2bl8GINIYFux5zQIWpmBhUjoRAfG2qjNWVu/q4+o51v5pDEzBUr6xQZpImENSx8cIBdzqClbyhqmEX3NKg35oWBqoOWYSDZIsfFxf693zeLvdMm/mG1axDUOz3mxzbCYHWBlgapkmWq3BIF8D+W7efxuYXVVaY4xQrvTwKtf6DqT1lUm4LAvX19fs9/v3gneP46F1FfDeuuKhcR9p4Dam9RBAfbM2YrXiuGU7cVuBdjxms7AqSryzqTiyvHe73Y0NyBiDzWYT58fGDVj/EHHhp/5+ez9vDzPj+uoaM+P09JRpmri8vGS3291s81EMcLO17on73yVUDNkS+CC5kNypKYEkcuuBIaQgmagSJB3teGNl6YMPC7KNR/5V146WjGNYD9ZvElZWcNgyodGMGuLkIZH1kDyYwQlE82o1EWqDARRPAehqp4uCC11XXWkq8VxNQuoFMQMNaIQOYR2UkOaRieRh2SOlkbszCLJez05v8TzOAtkN8ylq4H7Ac8IdhtVVGbwhlVAkaAoLVVa7IUcgDcQzVZzZLTK7ZIANLEOq0XyWnLASmIZogm5IAVLgBd3CO3l0cFqo6SVY0TnScNDJ8QopxxymFlYn1RoNJwkkjftZOnSZHrxUPgJgPsKMt+DGD0Ud7273E4ilwDtP5TsfcHM/+Tv484Z7vLIA3Py9t7qsZuH37sxqkn/nRk1KgIV/5oh3/jeCy+8+6GNe+OnNf+68w62T/+5Nfw4PuzWP4IcMVqPjWgaQHWoPL191jIqliaIZFSEXCdDME6MszNOWJ08/5S9/81sePdqy2+2pkmhl4rRcYQ77q8bcnTZs9e08MKxTVPAChxY3eR/CyCErkC6kDIWGe6K1Ej6hHkEonUg/7jkYRGbK0IFaeJ9Wz1ATqYQPz8AxdbT2CJJYHB/BLpqsY6nhnqPbrUa2StWCe8eXZS2WDfNMEyGbY7kz+YQMp2H0KbyHiyaGO2MYRaFqgOJaNSSCHte9WAR0ZAvGJOq0FpOB2CCPkIw4RqjonC6GD2OvDW2ygutKtwwMusbDoKSKmDJ8ptBoPbrobkJWYJII0KmKaIc0f/C1szAYTIjUkKfIHhuJJHPIMySTZnj+/CmPn3xK3mwQnFav+OHNNYerN4xrx2qnecjhxBO6yaQO4hNjNCYp4WO5MsWzK5AZHimrkwuNTutCkkTyKMhHWE2vnl8W3rE5FryPTwtnmy2pZGab2Dw+5VF9zDxNvPj+LfvlknFYovursdA3CwmWSiKL0pOHIX9r4RPtaS0sjS6V1ivSnTyVNSRAQCKUprrSrq/JqbDZJE6fPObx6RO6K1dvLnCU/fUeKcYwIXehU1BJzBsgKWeffspvfvmXfPL0ES7wL3/6lm+/fcH+8i0bh1acPibEOiVt8W7kGay9k0gN7SCdoROTGVNSOmFjMm9mRA1JBasHvvr6T1yeX3LojavLc66urqKKGDWuq/Hhj6m0dJwDvR24uLzk1ds3fPvDd9g+Fr//5//+f3B58Ybf/PbXfPn5Zzw6e8y//3f/jqfbT0hFMeuMKSOWmHedQ1boK1vGnD5F9xZpSJnJPiNpUDSRu7JPNbzZG6A7ypjx1KE5IwtzGez3g96NKRVkCaCJycAqqUNLsDGj5oZJ+LCWWsgqiA76mCIBOQmpO3ODp482vL16zYuvv+H73Tkvvv2KJ2eP+MXnf8FWu0GU4QAAIABJREFUTjm0C/b7PdkmPv3ic07niZNnT9gUYXe1ZzkcmL8/Zyf/zOY/BQBzvrvi8u0VJSuffPqYX/z2d1guXF9cM84H+dNOk0H1CJ6ZF2FJ042ffUtCKjGpeq/0klCLhUvxxBDDW6KkGTQCh5w55kQqzRMbHFDyKnv1JngpvL2qbLZQVILJr0JuTpti3i1Z6BWSffhz+NmzZ7x+/fpG+nkXjPw5hsxDxc/PfcZdeeNx3C5cbhd5t3317guFub0fDwHRtwvA29LM277Gt+0m7itc7u7/Tx3fXUbNkQ11fP2YwL7f7bm6ugovvzGYp4nTkxOmaaL3HsFfrXF1dUVv/Ufn/yFg/C54/BDQf/ucf8xYJAejxBKeK4gwHSZmKYxU2I+Bpo7X8BWETlEjMWhkXDrFoKUB4gzLqA1QizCuXQIqSSVkmqbkbIhJpI6bkDy4QgccGcrUHUuwXxrTcsHGT5jklFkaRqeVFNZNwxjN2Hnl6vwth9pYlkF3J+1hmmfYKGkymDI5Q0nglsKXeH8A29MxsifOR0fKgJZJbog0LI9Qca3VWspgyhpeCKO0kPCiSIemxnDFipOWmC9VPZqpTbEMtjjeM5N2aEI7LLTNxLJKbDPh+98xZCyIJ0YR+lDmYRxQWotQ4NyisB3Z2I/Dmtre0d65uq4cdgdq21OXwdKDLd5zQQkVj7TM7D1ChhjBuPzAoRr+qXOe+OTpMz7/7AvchSLC9y9+IB/A1rwG8dVrGA+QkFg/iHsEM7Pea0lIBEDlrM0VVbxXlqsd+8eP0HaBVef8/ILDcs1yWGijBzPbPJR8gY1Gor05h6VyfnnO5/trTJWT+YwpzSyy4F7DplDXFPvjf3Sdvxx8wKzCyIkkiUm3JE+s3OyVnRogYxJls91w9uQxp/MZ19eXvHlzHg16PIBMeBdQqiuD8/hdOMtSefPmDacnW2gra99WlnAf7M6vubjec7ZfojlAImtYybhGcNNUCmfbmS8+ecYvfvkFYrC7vuDlv3zFN199zfDOLz77kv/hb/76/6PtXXskObL0zOccM3OPiKzKuvPSvHSPdmZ6Z4XVChD2gl1BWO3ld0tfJGCxO1iMsAPNaLqnp0k2WWRdsvIWEe5mds5+OB7JrGQmmWyNDCArKysu7h4e7mbvec/z8tFnH3H67pL/8B/+it/+/T9QW8U9jmFvndZqrKaa4t2Z950/fPc137z8ijrNuIWL777jaPOQ7eUU2TBLAHiwzSOjJGmc/5pBZURao0sE0WUWnuYyD0U73RrWwigyu0KeQAlEhTlJovgg2WAuuBpJ43gn70wJkqdlgtzpQM4NtUBd5QVdJzVazz0JPknkcvQarUt0UgrGKT2ciSLhwJYeAg8tRGHRQ2dyBpkWRUNxU1RjTaJe4r00zEDdW9xzUKRLcKiRKMjkwG8wWXSZxsoi1k956Qs4iHE9OlzVLRzcwSrDe5iTRCIUuxLnvatg0feF65JH1J1kYcQKiVyZNQS/1AMHknKO0OIeQZmzL+x+wrWcW6wpuxGw1nuOg/g7DgPD+H14XdIwiFwXiHsP1+PhzEw5R3fnNRfvQWQ+uJ0P2IvDvT5EVKNeC9U9YCOuO6EPXODrRf37uJeB77f1HuNqDsB7RuY7XzeaHPwKv3XFmb5WlO+9X+2DL3OJg7P7eqjhdWf4YZs3bUOrjXme78yFuI/R4LZ568GlPpSBy4vLWGNd+7fbOuzuGrKsg9WVWaM70xpISnSJ64vkFJzwHmiXrEoVX879kSRGqw1ZMI/RGR4FRZpFKKgoYpGFoCJkDfZy9+hS7K3H2oLAS0Wxpkax9fAVrcFeFoU2d4Y8YtZRiY4fnw1ydHWYV7LkRV9csBqS6T4vobCxnqm906zhPSM6UwZhbpmW4jqAhAvYZQlR7YEjG1dKr4W0zD1SioIcHgV86Y524ZB5IGRcQmh2ySSNe1p0mIcxTTNkg5qUoOOUMCwRZjGkkcpCCegTOY90T3jqzLWxWtj3mKBDim7k1AOpZgldkIWNTj09i6BeU0wMSTHfYbgbf/ozHcyH8vDPe9Zh+A9+uOMx93j977Xma6rzUoG9/lsOmyuynDg3BOhb1O/u3wcV3finn9629x7zR1mHb3/eH2+o/n78yHbdXfj8Yz9sw1uGocc3gByplXQ8S7SAEk61VPfBNiqJ0cdofZNE6UccPTnmoxcfMz4eyeOKwYOxN28rRzLgm8LqoTDNNao3DlIK1SZkXlEUsld6UnqqSFNqzUQIqDOLkXUgZ6VFKZ+K0DVaXVLg5zAaaGbSxMgcfC8Fb42uM1pHXI22agyu9DREG6VNTONAbg2zGqGAQJOM1AaSUTNkBdJy8I/HaJPCCt2n5YseTvw2pagzLWJ46xZV+LygPxpYcsbS6CmjHfZdKdoXiH6wkXCL9vymMbGs0YSYEgg5KlW90k2wrMEnTkqSvvAFx2UC2Sl5pHhj7sKYJvqstHXB6hSORTVM7s9gLusR2cFsidUQrgTrho5GLxLbnzYcP3nC46ePKEPh+MExwzDy4eu3fPvdV3zx+z+wq5c0FXLLkEFqcOJ8rqTBqS1RHFJSTFcoDRLMc2JIOYxUTSIgkcYsirIjlaUgYkqvMzJEmEfyzDxJFKdapTbn0YOBRuPB8SMuLvdsp8u4QfdMLtB7YRAY04CPMO3maDHKndajQiwSN5jaoxAgVhiHpf2UjCXIybFqpGKUkhg3ic8+/hP+7J/+GZ9+8gkXFzv+8i//b37391tSjoWnGog1BkLYKGnkwYMj/vxP/pTPP/sVZSzspi0XlztevfyGXoIT16xGiIAmFKF6XTAFgdDwYlgzdEn+7hbc8GoNcoThyDrRi2F1pp526n5H2zVqnSF12hRtppSM+v0nzXtXpv2M1Zhknb055e13J6RxTZ8vePX2O1ozvvz7l/Sd88/+xSd88umnrDcPw33fOloVxoZlWBdl18DNqJpgEnScwUdYAoCUAclTBGbJCCaRpL7Y+GUPVSt5zOSujGOmVsM1eIWTJhItQmgwrFesrxmt0LIg0SdJzZ2xKaWEG8eXGBmVzEcffsi/+J/+Bb/9zTOevPyW54+fUCdjVRLjWKIKXgrHmyd8eLRiJSBj4nJXef31V+Cwb5W3p69Zr0cePnrCJ7/4jH/+T3/Jk4eP8EEowxGjQMvO1CuBZ+wUFE9KtYomI7cUC8IAm6ItCnUl5egCMacs7XBGRz3j3YONmpbCWBc2prQcE8Hi4dKz8N3xYEzRKlfz0rK1jmz2VpE5wijEwYb1vc+dX/ziF9Q68+7d6VULI9ziYrnmkv0xR83159/mDL5LwD78eTMp/PC7m3iHm7iJm693EIivj5uLk5uveZPtfH17r4/bFiU/djwO23v9+B62r7WGinB6enrVOvrdq1ecnp1duZMuLi44PT3lcnt5p9Pnru26vo/X9+fHMB/3HYVM3Xe61QgV9CH4q1KoKZFsptcQfkWFQzBuGNucIiW6q/BgEi8CV+sJspA15im9hxiSpEV7ow7QLNBLcwpcU1dUOmqdvTu6XfihDzN+NNL3A6UkFKPXPdJnttPMu7ML6n7LZJU+hssvW5wLRWIvTTqkHA4km2EKV23TAbML6jSDV8J0O1NFUU+MAuE6KngNV6l2JY+O9BTOwCZoWfBls4TDskfWhJMoVdi3EHWGPOBDRXVG+gZTmOY95ayidcNYHqKDw3hELz36CV0CG5KcqoJtJywZMkera687dpeOacO5QD0EyrPtJd4a1QysBSsyBX7CawTZNs3kUik+4FM4S+87xry0qpZEEsgirFYPORvPSGUgUGRQCaSJu12JU2EFsQVFEYYbkoRjUgTNsSbq3YDGbjfz7vwd49sV027Fbtrz+tUJu92eWhu2dNM4i2Al8f1KkhZhpXJycsJ3L79l8yQK0FlLvNciEIiEa1aWoGw4sFaN87M3vH20Io8b5nnH3GssPnqEOLEEV2rKjKsVz1684Bcff8Q4rLk4e828n5jnfTjJDv+PpV8s7j1YwjhIM6b9zOtvvgknIcb56QmNFu3VRKHq62+/Jq8K6zzw9vSM3X7G6eHQ08LRw4ccP3lGmwyrjXUa+Pr0Hf/wxRecvDph3Kz5i7/4C/7lv/of+eD5Cy4v94xlw+tXr9ldXi6CODjK7MZ+u+fk4g2btyvMjb//7e/4zd/9HfOS8fBzaluf//JP+cMXf+By/465G1kE7eH87EtB2iWQFZ1GTvE9SK64phBoFvFTXUgswXeHcHLTBRNRw8XrGhpmC3MNi5DdBwJj58EQLtpJPcLbQujopAotxzkqKohnkIaXxTmsitSGoWgxkgmWHHdB2hDt86okA88h5CUdEXTprg8Kd12CHg/nXFUni8eaLwWvXqdYBUcHn2Lal55ZQWaA6E5MIghl4a56iNvWkSRXjzeJYEvpC0s3LTgRi+uMm1Poi+auwTkdemQESYSNuYVInruEYL6gX/KQyBpzvbbMKdu8x1NCydAsQhjpWFdqvv/J84NOHSfCu66JjvD9vdQ9upkPOKvWGn6NtzzPM7v9jqSJYRiunnMQUQFaa1cYjFrre8F2txXKb4rFd92fD2L44X5+lyB9fb5zuN/rjwjY7wndyJWAfMh2SCkh15zZOWd0QX0c3MkQ4nLOIbcdgvCWDbkS6IdhYBxHttvte/Ojm+PmfO/mv12ffx0e8/jxY548ecJ+t+Ps7Oy9/b3Omb7PkBxufLNKcqWljiUli1EbjGWItU9PmFqgv4i5SRzvCMN1G5aicpQWwcIokoi8FwnmOsXpkonEKzDPobuILMVLD6RPX+6GIrgKuStSWqDJXGgKWSo1BYc59ahTpZ4YkmH9KO6wIlj1pSNBSEBKOULbuzO40xacZe6ZeY4tU5xsBhIhoVoU6SFWGxI6Uma5XsVriUfByqqFKU+is1ptOR7ScQsR09Wis3b5HiJxLW5Lza3OTiqdlQqTg2ugPwSh9cpqGDAVpHcgirJTM4aSSDnEZDenyJpxvWF9NFJ0YBbj/O3b6Po2Ce3IIuegu5Pa3decnyEw+3t/3HHqXT3gbj30H0MpvWU4S7DJ7ZTlw4N++p3jw+yhRi93oWWrf/Bk+f45/PDHO4/FrYfA7/j57l/98eP9F5OFe/mP+UaqGgF5vjgSlsAGF11amADLpOw0KtveGVTYWQ+zrTWePnnI0wcPOH7ymJENOsF6UGZTslVmnzEpcdMrI22KiWPtoJLAZ6zPZB0iVdjHaIXwsFJYVtwKWKeXcBWTB6RFxV/a8oVKjjSllU5Ro7a4+DlzVHu9oAWs5cWBr2hrEXwqyuCdlBStHqLLIMzz9+1oTYXBBPdGFYmAKk1kN2Yp6GpZFNeMl06bnWFwpHgwwSRBq1gSNOVAOkwLkzkJkoN9LSSKdiYqPq6wNmNYiKzamQfBmuJeY7E7FMQapilC/DyjLUQwP4SCSA4h0INVhA7oEBdI04wiqASP7r7DayKNRtorvTckOUNaGLd5QL2T+8xuu2W13iCurJ4UymrF8aNj0F+w3c18NVcSRmOPeQRz2JRormjt4ZrQmAgmPdyQM2mdwSDVyi5D9oI0oWF0SVftK1UO51mCLuhoTH3HdttYrRVNjpDQ7JQ88OjBEefbc/o0UdaFfZ1xb5F22wp92kf6uwpuisxbZL2KhZeFm7wJUDuqI6QcybYqiCUGdVqG1WrD55//kn/1L/8XPv74Ex49esRU93z38mu+/Iffg/ZF0Asn3DpBRhnSwGcf/ZJPP/uM5y9esJ93nF+cs9/uqNNE6kqTgvYei46WMJ0whGSO9YqIgwXL2p0IRxGjGWAt2oHFGWqORfIUwSo1O9UqM5CqIdbpAqnLrZX8u0Yvjcu257e//Rum8y1K5mhY03JjzBseHT8npc6vPv2cX//613z0yS94+PQ5Emo/jkSopY+IdqbzCQahiZJd8dKjC6DtySmcTlanuEaIo2o0i4WHiuAWDHHSiM3Qcg9umAhaMtUcZQ4+Vhmw4SEeCRPMpaMzeAq0xJBSCExzxtOMp45u1+QNPH/6gv/jf/4/+e//hx1vXn/HyclbLk63nJ+d88XXX/Dm3WuyrTg+fkS2ipeGzcrF69d89cUXuCpjEdoejp484p/9+p/y8a8+5+NHL0LkyBPTbkfdzZyfXtKt0b1T+0DWKZLUXZi6UfpAL42hROuaiIVTzSKEsxHX+RSdrOQhs93H97nvYyG2eXjEeiixKFSoTcgtha7fDF09IHmlJQ2hLFcSibl1Uk4UdTwNzNw/5O/BgwccHz/iYmkn/Kl2y6vr1eJKuc1tc5vz9y50xk2B8zbx+q73gB+6km9zAN10G9+GzgCuFj23bdtPibA/5vS++Z6HBeR1N/br16/pvfP06VPW6/V7Lqdpmnjz5g11fr999vri8uZxuWv7rv9323b+HBfz1CcklRB0ZkXbHCgccVY4lxSSGOoV6bCVKJW4dVwM78FkN4/7v/Vo+R9Egz0q4VzJnkgGsxS8x/3IvFM0Eu5tiEDAbE7LI0NP7FqnbvecWmNQ4UgTTQq2T3TZolXZXkzM7ZKpTog1REswQYuQxHHPiDdGDd6s+8TcW9z6mlFtotaZqc9LCl8CcTQ3nEJD8TYhVPaiFI8W8T7BusTcp0uhVUgpHIw2OVSlCvTc6c1pNkcY2N6Yu6NJSMy0vZOysvNMt8pWt2w2A2N2xh5hNpdpT+kZr3vaaHRpzJODOTugmeJU/MLZ+RadK9Ps7H0XraAekhIkvHk4GpMjLZGl00tkXqBRVLzvGNcjNoF1o/VwjjdmECVlAQvXZ4gcvrBcl0W5soTgejBtcziiBSXnjIjR2iKkdag+8+7tCX3upDxwub1kd3kWbsNDNsmC41Dk++5Pi/kC1tlu93z18hse1x02dyYLpyI9WJzuclB8r33nou3/7GLHw9Mtw6pxeXHObnsRYc0aC3ZnQXkMA4+ePuJXf/JLPnz2AvPE+qjw3et3nF6ex5y9EVkrSeI9uy3hgrGGMzPqtOP1yRsutltElf1+Gy7icI2wmydevfyGViceHB1zudtysT0Hgrm6Xg189uln/PrXf8p6vcL2lXMa24sdtc90bzxbb/j1f/1nfPbpL8llzWX9jqMnK8bVOkKnJcQV606bZ15+/TV/9x//lrev3nB+fsJf/dVf8+1334XV7WAZv+f48IMXXG7P2H5zSda4J/vioktIGAk0zAiRW+BID9EPFWpXhiQ0i7VHZqSqoRKQR7JglkjJloJ2mIXc61XAnCUNRJ9YhJHWBGWia7S/G1E3MDdyUlIPfjZJEVF0wfslSVQ6OfuCw1mEcW+Rr6DxGUuK73NZGNsydlq1RRwt6MIBVw+ucnJDvdB9phFsYwYjEeiiZoFgwb/HaTQNZ6UXCQOP98jL8YV3b4RpRofgw8oVMZzcovNCNZGWvJEmc+zrcs0WIxyRFlggSbpgTYL7Laokz6hnJGXEZybv9GYRApsd5pluOYoqEi37dqcX9/bhHszb3uK6bh4O7INScihYp5SunL6llPcYy3Vx6wafODFN0xVr+CBwxrVImKbpKnz3EPh3nct8fc5xmBvc5z7ce7+1kH59P+H9udfN4vpdyInrcwS37wXhg9h8fT4zz/MVb/nmXOIQ8JdzxpYuuQNu49CxVUoh58w0TVdzop8zDsfw+n4egpOHYeDly5dXHXrXBehxHK/E8J8a3h0sEDvdO6mlCBdtHRGN63AO0dMRvCqUQCRJX/BGGCJQF3dxyVDnwFjMKGOK65RJhCqK2VKUUpQZEyFpFCWTaZje1EilMOSRVBKK0urELBVrleRhVjt0DatKBPEB7omeO+axf56d4kpfeEu9dnxhRvfluQmLOQ6BjFBNlB6YJjeHSZAioIZYicBkCsKAt4qKAzlE3WJ0LRxKkQmlkQNL1CeSRiELSnR9iAEaAfDL91ZLILpqB5OEWJh2BGVMeck0Mqw3RMKo6SiVtgShKqsysH50xPPNQ8p6RHOi7huvz7/AvCGao2jQDmuCzFX4wS3jZzOYf+LUu+Wnux9z6/jP0Z/9fXH5vZdyv/mbn34v/96w/f7rHnzQPyWm3/eNfmr8l5Xr/eddw+73msmZa8eqUlTI2eg90n5xwVLHaTGRMaXuCZdIGUKoLJXVeuR48xBV6FYpKeOpsTeNmWUOAbfkNd4ukNbIPbHvLQrNKDUncguuLWZxY19g7Z5qLJa6I3O0lbUqZI2qZBVBxJBeIDXyTITwJAPLyFzDYSjh8OlilKqgDfGCe4RieBNamumDUAzUnJKUbiHuWs9UKqUrK53QNoQzY0zozkiTxUKuzNA3aOn0aUYWfqEykXVF7UZLTtYMrdI9072TzVngt0zm+FBIs6E107xTx5limdFSTOZWitfoBZAmJDySWdNE0pG+ixt5TkrVfbBOJTOhEW6jkVw6zglSY2JF0/uzUMsQ7SBjMtqkzOxxn1FXSlvcknXm25evcDIfflzCxdJqFDIsWjyi8TK+HyIxYXWL9q7aWxjrRXmwyeQ0MJQ1iFFbVGf3LmgLPmVNID2TSqTPWulI1SWUJG5GTMLeJl6ffMfH4y948vARSTTOg97RMVFSAUkkr8t5khfAf4TnpJ5xDbf7UArVG0VWIWj3WPDlcYBVIQ3Qm5OnFh2DZWQlhY8//AX/+7/+X/n888+Y5s56vSbnpb1oipCW2jveOq4dWDGK8fDRhg8//ZjV0Ya5z/TeOD8947tX37HfzcwtmKBdBauGaMN6R7vQC0gOTpTHmn3BFXXUK04hiVC6oGNBixDklMzUO/t3xr7uyEOEKTRgsPgccr4/XmWoI2VQdmcTc5/Z7xsPn6zJ44hV4cHxA54df8Avf/UpT54/5fjpE54dP6MIuMciyZMjrQZz/AhWrvS+nAtjBvbUFt2IYkrvjublRqGJnD0SlZsgFqnH7oIkC6HGot2rXk5oSYglSNDWTmKgyxR85mUSJ8nI1Wi1Y80YRkE0kyxDqXRZodbQVee4b9gNA5eaWA8DF6VxeX7Kq5ffst9NfPDiAzabR+QB8mZN65fIFxlNwqef/JKUBz759BOOHzxh+27L6dE7Ply/oFaJdHVgJcbFm1O8OQ8ePYAxmOHuISrVYWZkCFdi76Ab1Nvi5pmpe+fl65dM5zvSasBL4t3Ll5xOMzbvOXqw4vOPXvD5n/wZmjdYr8EazDXcC6Jkd0pf0BmrFC6kJmgRenMmy6TBUL3/FCfnzDiOt7KYD+Om8+P6724uXn4M33D43U3B9/pjby6Cbr7f4c+bSIebz/8xsfc29/RNnMX1511fvNy2D9d/d5sIftcxue5i7r3z6tUrzs7OODo6YhzHq7bTeV4KETfaaK8vZg9//zG0x81jcrPt9OeO2ZzsFiF2BXZV8IklDVwjoFUWoS8J9ET2QEfkWqLDRmakx7yB1EiWGbBoLRXHtWBizBIZDpSK1UKShTXRE0kaZtEN0SREoSTO3jJ22aj1BBkKuSSqCrU3bK60eWb2QCLgmWI1Oqg0ug9WZvQKOhk+ClprtMWPUKZMnnZMHjkXvUXXGHRmi5CdXmdaOWJe7UmyBg9kRvPG1AzLiaaLE8kVpmgdVxybB5rMTNRgNVehrkGyRwCeR43Xxek103wi5ylEcs1s+wpRYaZHkT8pOjXcEmVMZPFAcKDMtXCRTvHLmT5XrGV0EDw1uhsatl5MC0GWHZDUMVeaO3k1ovsWi9F7jkePHlJfzZgYZ5cXbC8uqHVivrjArNN1meO7LGJs0G/FQ5ROKRzxoo5KhDBuNhtW40DHuDzfMU87Wre47293THO0/ZpHy3pvfVlLhAAgEgYbJ9r9A88Rc/TZtpz0xrx0Hk77fTjxF5EuWktCY3ZfFuCulFJYjwNlcWzu9hO1zUv2ySI6kJE0sN6s+eyjT3n+9AM26zUpJ4SZzNK274fvql2txQKR4VfrNlNQhHlq9HmLu1GJ4LcI+g7j9H43892rN5y8Og3jRI/jmJIzrtY8efyYF89fMGzWXGwvOTu74O2bd+x2lWaNrILmxG6eydKYdzu2ZzO1tQW3YRHYrNBa55vXr/n3/9f/wzhmLs4vuLi8YLff0r2HgLK01N9ndAuDiRnMSSg1Ck2dKBCQcgTaEffM5gJZYHGaa4HePUIKew98xASWwpDh7nQ5uI57zIE0QqA6Mc8ThORtmfcZquHoNY+CWNDTLcI7JeHZI4y8C2SNYHV1WveYL4uEq12Wjg7y4urtqEjMu4Daw0lHjWydyhKQZ4YnoVbCHKZC8wU3140kS/eBCZ051izuIe5boFfcE8mDxW2WIJdYB3ldDB/xHiFWxZrQCHTFrFH0FnMSDcdDIHKjZw9BzuOabTkybnzhO0fH4oJ6yVFYlNboDZobSmR6eDsUnCJTxVpwZNPPuHcdRNyDSHr4L6UIET885sATFpH35OtxjHm5TBHyefjdQWjdbrdXHOXrr3X4+frfr89VDttxW1H8tn247e8/Nve4ay7yY+NwnA5O38P2HsTaYRhwj8DCwzG86sq6NidRjZC9eZ7fE5bdr7Gdk/7oPt/c5h8rpA/DwNOnTxnHkVevXvHmzZur5x72ZbVaMQwDZSg/+Z4ALobmHCgKczyDdILjm4nrqxOFvp5oRYPVjIIWpDVUQv8w8VgzthYF997C+IVTNUwbK4eZ4PkPOeEe2VS5exSAeqKkhJTE0cMNL46f8fDhUxqdi9O3vHv3lsuzHXOLroFYl3qgcwajdWEWQyVCft0aiULUbir0FMiP3imutCFQO9BDh3KHJfyuJUimdK2gK9w6mgOBhvUrsS6m8jmuQ9owDdwh0hfhXhnUse5RsJbApxagLgGpzUN/0BLYNe9C13Bwp+TREdZj/dhsuWzhSJYoutmyXiKRFEoy1g9Hnj99ztPjR8FBa5U/nHzFZW10YJAW1+gs0BVLjdLv/u784wrMN/XbpcXtzhEdLT/nJX8BxF5UAAAgAElEQVTGP/608Hp4+o+9jP/g0fzAB/1T7/MDGfqHkOkfbNT7j/ij5fq73+C/6DMIFqsLqNJdGKrRc2Iwu3IPePYIuLEUwRJtT/UONlJyYjpXLp5DnrcBKmckabQRliEx10SfZ3yu9NpwyWiqrBKRRDqAaIRdeIu3StpxHbG+R6siBaRk6jST0hCNSB2kp0BXlAxiFIzmKzRFwmnrlSyCZYHmVJ1JTbAUic7WG9oSNibcW4TDLa0SvQmuCe8RHKcLW8iys0CaoRtp7tRBEBNWzbncJ7REm6SkgWzCnAzRwmQdhoROCZNFuGJxTHtlkjFC1zST6bHdsyJDxLLJ4PS2MP32KSZw2RlUyBnEE2YDXTJSwHyKkCATNGdaN8YsWIVSEmhBVzv2JJKXYAndc6w3D9EeDnWA3pVejVKMViXE8t647Of84Q/Cfr9jnvY8ffYMmjNt99Ga2CrN9lHZ7506NXIuWBJGRtRgvVpx9PAxH374nAfrDb0b+/mCk3dvEZy6bVHw6I2eO9rCdSYezG2GcJF3D+5RSQOXb894Nw6k5AxSoMRNrU7h/xCpC6JxQFoseKfWGDzjqYfLXjOUgTRPVLnECQfHMBZQIXnFmzKbMKZEIZOT8fzxE/71//av+Yv/5i9IKQSz2iqvv33Ly2+/ZWIOdpWHOBqMPkU0c3x0zGaT2e8vmS62nJ6f8pvf/ZaTkxNa79RmdJmwrhGsouFU6+5Ys6XqGkKLkxcUTogcWcLlLiU4VS7hHt9bZ64Ex1dHWtsv7EZFGUjlmovqPtcdARa+7946TTs6O6Q902QMDKxXA2U9MqwHPvn4F6weR9hdngM9oz1hdGaM7iO1zhHksHKm/SWbzRGQUKC4RVDVKjO3yzgOtcTilYTbnp6EXAo6RUtdLw1zxwYjWcd9QMzI4gT2MOMaC4beO3numBRsEmQIQaAkpabEShO97jg93/L44ZrVRtBhxTzvePfujG+++op3Fyc0NS6mHdI6H/2T54xDYbV+yPHxmm2fOD8744Onjzh69oDj9QPKWtEhszvb8nr3lkdPHpJWK1483ZDzEV/+/VektfLxrz7mww8/JQ0ZcWFwjWtsd+be0bRiyJ3dfs+7s9f8x9/8A2dvzjk/v+DRWJg0WGCDFFqCYs7+PNPmCS0P+fzTz9m1ylqDDZg9JlPNY+JmauwvdoybECH7NLMuiVpiMf8z0O9XzpM4j+S9/w4Ln5tO3sOi4+Aehh8ubn5sIXDddXOXY/n6Y28b7zlsrr3GTZH5piB9HQ1xm+B622LtZgr8bc7qH3NY3+Ucun78ROTK4bTdbn/Air65zze39/rv7hKM73JjX/887rPIO4yNCVISqKMXoWyYhhjRq9O6kSTjCtUrLo0qhSIJRmd2SKmwTpnKjEq0qM/q+JzQoZJ7D+yVVFKfaWYMvsNWBa2xYPNBSGnE2hasMPWKuFJ8og2Oz43zOYdlqCe6Nloy0tTxrMHzzdEWqr0xt5gjTSasXGhW2cvMIGs8Ga0Z1WasQa1w2WqwSPedJjCmTpkbltah4bSYT6gUJgO3yqQJrLBOM9UGRCs7mxZUg9Ck07eZ+SICbntO0Y6/oNSaFMQnvK5IOgHBWb6sDZkVtQlJkcZOEUYbGdIIg1KyoLaE4fUEXDJOygSYZXpqeB2WauIe17zM4sJxyKpiPUdrrymJHnbN++EsAXj07Bknb0+ptXF6esHrd6eshhUnp2dM2z1qTidUteXMxBbuq2q0qeYUbrhhGHjy+DEffPgxR8cbWqt8+/W3vHnzHfVyAo9wROuN3kM4shaoJzzO/SRKpwcmYekJdY/7e19EJmuV03cVSSGYuEXXEYeyvmi41yRwCJoSj5485MVHH3F8/IDWjPn4mIuLcy7OzpfrRqAOBhUeP3jA82fPeXR8hHtmP11ysduGMOPG0hgJIuGoO1zHhJhfOEQAUkatMUsPZq4v7kyVWNR0D57kHN8DzcJqHJGUAWFzdMRq3FAdioXT/x9+/wVvvv2Gk3dvsd45u7jkqy+/4vGjh5jDl7//ir/967/BaiOPQ6ArzBFNiAu9Tbx++yrml3Olt3Z1rH1BhNx3bM/POTu9AK/LQl/D4e8egn/0DqBLmLclXT7HjmrHpkTWvvBPNTqqSvB/zRIytBBaLZjC5ExfuN8sRQ40zDwijmDBAp89ir8eTGZJi4vbapBQJDi/LhG8pc0RcyxZFIo1XLluIXzjjg3hVhQW1q5EO31CqG0p0OkY+DaX4JvmjtfosOvSMMkUjz6EvjgLSYSobB44QocsjeaO92j9pyumS5ZPWjoYRJAWHXrSliKfO6JRrAoGXYquDAn9Ojm0FOGcqgo9RaiXCV0igDSpB+KD4DvPGsHUZiWciw6elmwdcUTAs1J6iFD3HdeLurVWUk5kD8eoitBtcaQS6yeR+NLJ4uA93F+vurcWsRR4r6Nrv9//AGd1W/fU4fE/hrj4Y8ZdJoH7vMdhznE4RqV8L8IehPmc45gd0CEH4f6AzDjMba6LzhC4kMP+ighpeYzK9zePnyp435wD3TQA5JzJObPb73j58uXVMT9szzAMlFKiqHBPRAZ4ZEVkIU9O7U7xCfeMdUjZUU8oPdz2zSLQ0i26E9ywlPECqRPfG5ZLcpiCEWDwYDpXJNbgKeEsiB7pmCYCERmdPscPHvLhhy/46MlHPH7xCLrz+t0x61XhS/2WttuSasdbuHUNpyBIFWS0heveKVrouSEtIyxMZle0CG35XpIa3p1ukDzHtUp8CRQEUkF68PCzSRQXe8GsoZICJeSGjs7QQ+RVdXpPaApUVPVAgqRUwjDeo6ils4SJTyqI02sEm0rR4Ep3Q1ump7gbi0msxecKWq5CbtWU3CENcVwThbGseLRZs1kNeIHtbublq9ekZBSUtuQlWu1oSeROZE7cMe55Rt1TYrzxEO8/8tTItfjhu/gtv7tji5Zi9T038/t/PPx0eC9ZihA/vpf/ORe85S589Ro/rYq//4h7KPE/a1uu7+lPf7Z/zJ63WZbWIUGS07zg1sAjnRYroCPYFF8esxBnO+y9Ybs9TC9joeUvKJ7YroW1JWwbrW/z5cT28py3+ylaloBqEmD3cYY6YGmmtxyOB5xeFLc5wilKVIFSUrwEEF08XDSWQZrjyVl0NDzXSPlFyetE3xm5K1oq3aCnCJWhZboYqwGyK10LpTdcDcsZsSWQoy/VMOuY5avWKOkSTr06A0KTSIDWopgCk5G6UAFzZ0BoPVpI0GhJdRVKb3SDJonkwe9Ne7CxkDK0Tcd7o08jnhdfvkQbXLao6Pci4R6vmZYaSkNyVMYakRxfujFJRTSzkszjx4/Zzhe0/cjgUYW3n+Ho+eyTj9lNldPT1+xsZlhCieY2Y2kmN0c8Mc3C7GfUry84PXvNZr0ipZG5dy5PLthXw3TEpz2tJVKLwKS2nxnWY1xsVyMfP/uAD54+52i9WpwPT3n04Cm/4wvq/g3TsKftjcFjwUQJLhw9bnxdO9IyoxtWOxdcwrfRdrbZbFitRvZ9y+V+y25/Se9KSo08R9q0m5DdUW2IROiSl47tKpoEKasQ1Exo5oyyfFZzMJuaGVIaOjzhk1/9il//+Z8ByjzP5Jz44vdf8G/+7b/lN7/9HX2aqLXjLojEia1irNOKxw8est3vOD/dc3pxxjdffsWr16/Zz/vghatQraNTglypvUQrlHXGJJEUz8Lpy3G8WzKaN4YmWMkkGSkkPAVb1/pEE4328eTgwdhsIrQmkJ2u9y9OTN7YlIFPf/ULvvriD8zv9pxeTvgMre14fPyEpJ1a92yOMh98+JRVNuiFPgYGJKlSq7Km0WhMowaqoTfWOmLbaH1qJliOY9mnytAzPWcaHTdBU0MX1qDvOzIkegrXd2+KakZEMYzUw/2X3Jh1j6dyiBxij1PEGbHobEgDk3WKON4zPiq0aOHqNvD4eOQPqtT9nv1+5vmDJ/x3/+0/4/XrM37x2WfIWFgfP+Xjp09YnRa+evwND4+ecHycOH7wgifrx6yOMnMeaRd73u5OONtd8PzZM3pZM/sls82ks8b4urB5+Ij16jFDT9Sky4S6sfHEVCfe7S75wzdv+M1f/w3/71//Bx5sNjw4yrwjFqZPHqy44Aj3yuvtOYbzeH6Ou7DejDx5/AyyUAlubMqLUy7Wj3F97DPVZpoOrFLCW5DdfDvA8f3One12exWwctPZe12che8FzttaLg//fpd7+KYz+qY7+fp73uYOvj5uOqDvWpDcdDnfJaLe5W4+/HkVqHWHmHyXmH5zv67/7jCuC97Xt/O6wHzXsbhNJL/5vjd/f/hdusUxeNux/7GxeqDMU4TlzUNj1YTJI9+ABEUL1SpNQ5BYd8UTmDneQmQQiWJdXmVkFpBw5tho7OvIIGBthyawLpSS6AJU6NKZcmdFRnoU6aTPqGWyeoSuqpInYRocn8Jp2FsjeWYCdPZoga8LfiEHLgugtxn3LdudMqydKgY9FpfNDdPOiLHucC4zlQQ5RK3SBB0aXmf60aHFPIrHY4kAaNe+cEWF2hLSBWkzLUfNHd1iveIIadWxvSEeSemmDTOFPCMCPStphkkbQy3MdR/FqbHwID/gwbBhWA0xX/JOVyEPCa/hlhxszYyjaReF9mSIG4MVag8hrXinu4MpYyq0eYpQIJdYIP6M+bpmcBX63NieXfCHP3zJZrXh8uKCy9208JN7YAY03JMq0e4rHn4EJ+al42rFiw8+4oPnz1gdrSJgqDrb7QW73YR1j3NuWShbCxzGYRHk19ZU4ZCMtYt7XKtVLOYiYsFf7QtLEq6czkoK9zKEsgakofDkyTOePDxm83CFNcHUuLx4xMnJCVOtwc1HyWXgwdEx43qNloxo4ux04uTVGy732+BoY+Ei9Xjfg2M2JYn3Vgl8xrIz6uGjRQ7icxxDSYqkQEaUMrDZrHn+4gOO1htardS5Upl5d3rCfnvOu5Nz3r76jpcvv2W734MJb/2Ef//v/h1f/v73mDhnZ+fsW+fpB8/ISfD2hrkKJWXGcY0maL2x30906/F5mBHJVOHZve/48u9/y267hSk6FoS+hHXL0o0Qx2HyjtNJvQRaTwO9k5ORNEUnnCo6GrWCaEZptJkIvktCahnTRD50IzqLAOO4RJHCCe6yJCcT3/XmihjowpZXiM9VwJaX6pZJOfjDeeFCm+hSROhkjUwUUcetYzU6R0015lNqJEm0HkF5Lg11iS5OHFIUz7IUhLYYnBRPjWSK0xBRHKNpoDmyQlMPREj4IQiKTI05Xe0RZJZSOC9F6ZJYGCCIC9ajbT0E7oyzXKvEaQYl78MQkYCuiAYY1oqQNNHmyGSpy5pBNTjy5oE7MYnnoS2uPfLzdAJ3v0JPpRziZibTrrGYrxd2cynoEtp3wDgchl27x6oq6/X6CvOw3+9/kBkBPxRQ70J23TXu6i4zj2vSdQH3jx0HgXmaJkop7831rgvtwJXYvNvtmKaJzWbz3utcn88c/iylXDmhb+Itbu7XbeO6keDm8w+otzev31whOg4jrnnfi8vXxfMfG8oA1lGPQGJxp2JRoHTFWzj721JVkRQmrJblCk2RDoxmcYqHw9aykXqi9bjPZWm4dzTlhQsvSAu0FiWCO8V7hIbLyNHRyEcvnvLsxROOjo5j/q+O1cbp+RnzxczcgiWP9UDq9ARDp/fAOaou9+IW37/udWETz+HMZsB9jhBVUlzLPc7hIinMkxpF2JycWqHnujDV49pkshSIiCJ59yiaLbmleK+xDhYiaNaCS++AzYtLvDk6JHLyQGClCKI3ErLqITI7dMmYR3YSRUNz1ULWQHR0M5SYG7kkNEXoq+YwVl2cXbI73yGkpYCrkZHjkZ+USiSH3DXuKTD/Z4irdz31FtH7B1Lne2zge7zsj23m1YvHD4fLjRNtPj/19HuNZV753n5c/eWHO3L/dsx73DR+Uie+/oDrD/zHqxReHy7R2qea0Wa01MhFcEn0uUdbD3u8NVLKccOlojmqw/uaKO2Sd/p1hLRoplyMnJsx+47LbeXidMfb8zPqfM7sTtaOzB1Tgy4M2SlzZs7hSCbtYY5GPGpCdCCZkbSirdCHCjVEX3EnrRT2SssN6RHE1W3AiiLW6NmwbqxVsJppOHQnS0XSENk72YPBc+D/7Hu0MSQj5Y7PjlaBFXhuiBWaOCodq4sntCmWM2jFu1LcIVeEQqZTHTSncE3jZDWaBLKjS0GmmV4EEHKZ4ybYw3GoezBveBMsKbTMyi3aKZa2CNccIs5SDes7WYIbE/O8IPo1sdqs+JPPPofVhtcnJ+xfnbD3d9ienxXy96f/5Fdc7hu/+51j8zfReoPSdKTVEP1a8XALzU4dEvsL5+J8G8EbSem1BpdtkOAApw6imAE5ghF1JGaMOaqxZT2SS2G3m1hb4oMPX1C3Z/hFxZOyn+ZoyUudLpmcO/NcSZrJPtM8BzeuO+e7U9p3M+OwZrUZaB22ZxN1t0Po9BQhZZFiHW5MekPVSZaoNXjLKQ0whwM2Z19ujoRDWMCboqlDSgx9wn1iP295/OQJFxcTJycn/H//8W/4T7/9T8zbiwWX5IhWkLIUWxN773z77i2vL86Yp5mTV++YanA2ba7IMCDznqQjlqMFMg3RUuiphAggMYFM6iRxVIVqDjLga8iWouV5EEpLmDf67IypM+eG1QhVsQGKCC4zfZZwl91zSHPGMvCnn/4Tvv7iW2RMbKYVZ/stc01cponff/0102T8+uP/ipyHYGpZh95okpgxtNjifI4Qg9QS3Udy6rh3photqJISPR3cx4p48B3HnNnVzmhR7VeD3pRBGzkVBnH6RLRICiR3dgY+G1VGxA1StD0/6MKsjUbBpJFqp5ROdiNLxwyePHocwV1JyfkBm80jzre/QVCePfqIRw8f88uPIUnhTAyzmTdvX/O3f/s7aM5qveL09TsKjxlTZTgqbF+f8O70LSfnZ3zw4iOevXjG3BtanTY3enLOzy949+o7PnpwjOVEt5mhON2Vmcz+4pwvv/mC0zdnfPPqSzabDccPNpSS2FW4PHtNPduixxesVDk5ueT0/IR1XvOVf4v4X/Lnf/HP+eTjDwInJI4mJ/cCNKo46/Uar40iTu879vsNeRWIEdX7M5gB6pJ8Dj9cvNwUSA+T+pvC6F1i5/VxEHxvcxtfncs3fr6NL3yXIHxT/L75Xtdf46ZYfNsC8Pr23hY0eNv+3By3OYfuEo2v78/N43vXovK24/BjrbbXt/XmZ3rXPtw1ZDVQW2BVVmmENLAZG7vLi2jLdKFqYWgNyznceK70CpIa4p2SBNUVm7RiODZaLsjk7HUbITQXcT+I9PRgo6qHf6S7k0tGzaGFY1S70nNgolQzPhlY3AOnubEWDVSZW3T5WLTM55pwT3RzTPa0XUcejsw7Z7UWxI02dyRHIN1sMGPU3qhu2Bws2YzA5FHMb419X7OahCoNFUhpDexj0dmcWjMmFasTlUaV6CqhVXb7GXK4hloNRqGqYhLdTVhFU8JTYUYWA3Hw30sqDOvCarXiwcMNQ15R6JiA5lVkRnhGmBHL1Cpshh2TC9u5kS3jqnQPjqP3hUeaGt6gaTiaVwg7aViH0e9fFK0XFZsq3WrMb04q57wLZ2sNnqy7hgBrdoU3VkJcNpZughQFudVmQ16tQpRTODrasFqtETnDl0glPDAVfsVKjnZlN1tyJQAJMQA/fC+W768dwvUshFEOncLhdlY/YAzCmUkOl2m4hZ1p6qSFNZ40hEJEw4AijiRh2KxIJUKqbZrYbve8OnnHftrTMdoifS/tTqh4LMI9FHcFRDTwIRLhg6qCuZPMcYv3jPvlwPpozYMHx3zw/Dmfff5LNsOKPI6cn59y+u4tb07eoB0uzi84v7hgv5toteJB2OXrr7/m3eu3lHHNg0fHfP7LTxjXI2+Ojtm3yuX5JU8eP+XjTz5hzMJ3r9/y5RdfYC06EpFgJfd++DzuN16fXzC36A4zjQW9eXRAIo7QmZZ9TaLfh4AuniepSpUOmgItJyGEIEZZToNiUP3A949zPlzLi+iqDhMw6CKs9mU+5IuI1CkqOIGT8BRYj0E7rBNSRtIMVTs6d6qC9baYIMB9xFRxQhTq6dBqvjC7s2B1oOaFW7sEYdniEu7uwZV3AQlkRRdDLRoTXFMYfqLCsXR9xierFseM7NA0LMjmEdjnCWnQe6xPRAW3vrBbBfPI2ug5Ia0vhaDomvC2FD0kkQhWerUW6IyS4u+1RnggHuHHLAKWBPLRiqDEPDq+hAm7/2Xn6n59EIsPDts4HeUKd3E9iyKlFKiqacLdGYeBsuAh3N4nQJfy/7P27j+WXdl932etvfe599aru0k2X6JmhhpLlqOXZfiHBIEsRbBkIYD/3QBxAgSI4xgxkiAWYmk8Hg1nyCHZbHZ3db3uPefsvdfyD+vcYrFY3aweaQMEq+ve86zz2Ou7vo8C7rT1+nod+0C/V71jf13m8l1zCr/1+avmP/cZvfdr24/9Qa5Wq+vmdGvtGqBtrXF1dfUtn+n9dveWGWkB6SHU1vtQwHmer8/Rm/gv39WU32/n2bNnnJ2dfeu49/YcKaVvQPL7PnhagxXXCmY86n9PAhiTsSiwlGEWINNSJRNBm9adphp2WHv2MgSzVsG1I5Yiv8WjEVWjHYrlAbyhbmETWJdzK6HgYSgkHUKl0xw0ghNJhZyUKZcIgM9Gb4ZgYTmlkQ0kEIH1ZMri4e4GXmRRNyw2GskQJBTw1Smp4LWRNZpeEOGokhr0AHFrTQwFEInGqIFIYF8mNRRWOI1EyU7yxuxhLaIdPDXU9NoD2rvRm0BuCzm3kF2QOeYjAliSUKOIUcRgUKwu1iZdyVmCiayGlo7VRmsjZhNuxheff0ZXJ9KWjUSiS0dKRl1wYl2vGv+gFhl3Y5w3odw3XJl9L2r6BuMbcPXVa3yD7d31VftmS9cfv2Z1/2AykHs8F+S1x317ZX///RJfReSBQ18esg1hsAQ94wg5GT5oMA9aAJkm4QOY0ppp3ZjqzPz0RXTmbY3k8Dirc6XazOXW0Snh2ui1IQNkcfCCmIVs1ASTCXOlJBAvSxBdZVJFKiQ6OgnNB9bScZ/J3plTSIc6js6KprDQ6ItEilVm6+FDlDw61UKKh0h2PHVSKzAYMs9IyjEZ6eE7qNJwG5Au4WvmHemRUprF4jzkGaOHVFYbnjNzjdA3ZcWKvCTSd6QZrpnUGuKJgtNysIjEM/Qc5wkFF9J6wivBBjTHUgtfNRekO5KVZAISHkhJwEnh4aY9GAnJefjwIR/84MecbI7JOXy3n9nIdFrZMmL9/gDzyfEDDo+cs7OXXLw8Y1Vh1s6gE4mOzdB72E407yRrMdHqweyqGtIVbzMiKUITFxaB1WAC7ZgYJmfUxMXVOSfbY/rRMdI6ddqCdw6HAzQf0qYrvAcdRm2kEwFwUgdMKp2Op5AVhrVDxzpcbq+42lWO5kxVZ64jaEZzQQ1UG7qYx7XWMRWG0phciOT1eNG6N0Q2OIL3YMG5K+Idhor2KMSaKr/89HP+j3/77/jBD3+EmfHJ3/0df/uTn/Ly9IK5zbRxhppgSFhxsq8QOnXqPPnqGdYsWCe9Mk473Aq9QfEW/TMxkgRzTHsPtlluEZIiYBKsMLGFYV+c1Aj515CRyZAWs+s6N5p3RHLIkhL0FkwbkUYjwTrYIfd/7gg9w8vtjpcvnuAtsT5aMfWZ1SBIgxdfPWfIhwzHiTys0LHDUNC2YbMytu7o3GgLkzkt7OAmCaOyWgm5hj+y9UxKDZlDlFHdKdbp5mhZYa7oYEhNJDPYKr6OwIYpRSPOU6XvQp2gqYW3GIpaZ0KQbhEsqYZ6pnldEtol5JMp0+rMkDeIOpMbQylsVpmDo0PKqjCkQtLMvJtI2yvaxnl2cUZS46Pf/AE///Q/85O//SkfvH/B+x99SE8fsN1t+ezpFzx/9pRughd4ePiAo6PCNDu7l1e0k5nf/fh36U3pQzQuTBVmhTRzevmM3fklX798xunLM2qd+bsnX/LBRx9xfHjC8+48ffqE1csVx0cbmisH6yP6NDHXzE9//glffHXK7/3R7/M7H/2Q9YMT5hTSUSN8QQedqSVYVzKv6IOhVZC04g0eO5yennK13X77erqDUXubIfKda/DW57eTvuHVAOxNwPrmz7e38yrbh9vs4FcxlF8197iLuXuX9cer/n0bbL4L+L4PO/gmuLyXmd7+7Ob6bgPjN/dh/ze4C3S/67NfZ16WfMNqnUg7o7aJ1iawTlopZQdXq4n17FgGWoR2Kob0hm2UZMGg3Kw3rIYVZGGlhhwoZTpgnM65WjVaq6iHtYWbkyQm/dqU5oKRaOJkF3ztdHHa7ORqZCuMOeZaOfdQWlBI2kImqoW2a9Q8k3uhZxBZBQizrdgDwVkHaGgzdVwHOGFGHSe2c2WqRjeNZ3+DzUpwM2p2NsmZ+0zeADYg2WHJ5EhN6XlmFkWyYLNhozJmZTfPWDPGaiRxkkemg7DFetgZKA1XpbRMlYqKwCzYJjOsCuvjI9aHA4MmbNXpHKDW0SRkWTPXHWQouiHVSxiUPmuEo2mjmqA2kN1C9aYN7cIsAcSnDGNaWNXi2HD/a+fly6fMVoOA0A1rRtWYb5k5Yam3NNdUlgT3xSrGQk4rHn7DbkpvE5dnl7TNmmEQ6jxji60XCO4dWNYrAYohSmLPGo2gvdZDiSP7bbGo5GypcfbLu0TmgreYo4ijrgsAHP+vvXGxu+Lg8pJBI8Dyah55cXpGm6coYIVglQUvljY1LnVkW6948uxLXp6eBnsTJXmEAMvi92y6PHc9LANUE7lkcsnfPEfNab3RHEjBfi664ujwgN947wN+9I9/zDsPH3OwLmjecLzZcHa0Ydpu+fSLL8Gccbvl8uVFeJdb+BPTDLeZ5sahwHtH7/He4/fZHB6ySqM1aDEAACAASURBVGuePfuK3ozH777PBx98wKpk0rrw4sXXnJ5OYckuEn9rYv/vO+Zaww6jWzjr9YVsAFQ6ZorUOVSjgY0iEkqA3AuTjOBRu4iHd3MG5gZdFPVEReipLuy3uGySKt4alhrUdM2gcwvWu1HCy1otbK0U3NPiOip4NXyVeXB0zPHbJ5S+otrMbrzkxdkOHLS2yD7J4GpB9TcjWbDpkziNCPVUARAGjNk1qH+5BsicOtYlmMoZvPfl8s8k6SCdZk5AuZ0VShfFs0WgsDreMmhYg7g6WCd7gFKoBxC8SNhb7ziJJe8abWEl4xYkBGuOkiMQtlvYgvQgFHgDrdA9YbmydP7wIWMWYPZ1AJ8HmO/u4dvbjWZv4M1zY/TeGccx3qMbv2bW3rTTgoVh3FoAp2bXlhB7lq+bkTSk+iLR6NwzZOd5vnP+cX+i3evH7Ub6zd+/atyl6rr92X4e03tnmiYAbP3dbIo9MeHq6oqzszNSisDDYYiXwf4c7X2X9/vbl9yJWus1KL1f7/fNlfafpeU874Hjo6MjDg4OuLi44MWLF985zv3f5FVzu9eNWSoyCckSmi1C6cXoKCrhX49nvHWqN7JEM7HrjPkQVqWeMYtgWFktDYoe4cG4kTxh3ZEU6izxRHONeY0mvHdkTdwbtsKKM15uef7VC6Qq6+MNOLSpcnF1zrwLNXqanSwez4OUgLCZoIbS3j1CN4tx7QV/bakkgCspR3NAkkeOkBAWVkWJaFSHeUZWikgotCidMhhmkaMxJAuiY0gRkEHwprgF81msUT2RUkZGh7XTuyCDUGqgeSadOaclHFSW5mCEyca+O1knpGSkJ5pDN0G0oQi5OCaFLILPhqaECMweeMTZ1SmnL07R1qPBmOJ6zeKoFpoZ2nOcw1eMXx9gvgOH9Dt/ff8L91sl1Pehs284ZNmx16/xDbb3ra/+QwLh37PGuz64x6bvDS6Hlux1e3CvMWjFTWgegVYmK7SOVFXQyjoJYxOKCXMx5jWUXUitUlHMZ5iMnmCsI59/PrFOAzoIFcWmStLoRDkG1RlTIy8yI1WhNiMtqgDpICY0TaQeXSETo6hDzdTUya5kq0xVKTkzmkBpaNUIl5Ec0kBJaF4czrrjw8yqKhMOKSF01EIaNS3pw+YddUel0XAsQVah1QRlxqcIEvQsWMtQGyZCKzN5igciMoAb1QyRDb3PER7BfG3rPYswLAQG3BEzLIVcNZHwYiTNNIHcG1kyrYDVSk8OCtoVNaAIUsOnz5NDl2BeS3T4vUNV5eThAz76+HfR3tn1Giwbh5PjI+Y20W3HPN4voRbg8XvvM9eZx2ennD9/xpd1R27GZIqkDZ6nAJB9B5rpNcQnLi080RzwiTQkwjQoLDomdTwpSmMwxVWwaeTrL59QpDCUFccnJ8xzFFK1dax16HWvjUOGDbk1pprwAdABmw1ZJSZmkvgSPFLIuZEwLqcryAPaI/HVPdhig6ZgoaURcUebM7d4UafBFq+oTlpn1DveHEuZA5S6WK4kj5AhekOlcnF6xr//P/8D/99//Gta71xdXeK1Un1m7sF2S2mRY3ZBhk71YOzXeWSySqqRgM3sdOmkEt6G3ZVsQrhN50hH1wZdgkVfEiUbbglviZQNGXMwvmuipIrngZSE0St9jol3ThoVIjMiwQCfJTwA12JMrwkRuD1W0rmcG6Ubj959G+nCkyenIJVVWTOXTq2Nwwdrjt96TGqdvlmFj5+OpB7y4ak5mhtJHU/xHBrmhnlizhGuY30mJZC6WrwDr7AW8s9g8IXpns75mmHV8ow2yNkoHXwJh6olJhqew8PYukVhVysNWQK7MkUIFmJ2ZlHWJZNaYjhKuEZohW6jobc5PuREhMODA2qLoJuUlonG2US9OGezPqKslCEdMDPzcvecD/UDdpeVy/GSozzQHjziJz/7//nFz3/CH/zeH3L89lt8/eVz3v/oPf7Zf/vHHJ5sODt/zu75zKN33+Fwk1AdwCeyJJ4+P+V/+1/+V9yEv/vVJ8g48t6HH/DorQPa/JBPfvq3rPOGcbtCRFk/OEGScJjXnI+Js4srPv3pf+aIwrulcLzeIOZIaqgajRXeK12MzeHBUiRXhJm53N/A++nTp9cFBHy7wLgZ1HIXm/Yu8PJV7Nq7bBvuWs/tYuwuBs6rgNfbYPPtcZupexuQvn3McLc9yF2g7F0/3wUuv66ouVlU7Qu325/fPL6b37n9t7i5rdtM7bskqa9qHLxuHKxX+A4Ytuy2hH9lhvVwxMGmsTuvmDfUwcUp7rQ0wCYyt/KgrI43DOmQVRFE1xEkO8MwGIkjql/Q2wodGqllvAhFhXncQkpkc6qMiGTGZJS2IruQVUCmkHEv8x4MZFCoAVdkUXZlgS5kg+caz+KmeHaSNS7mmaM2UXuiiuKc01yhzVg12jjRaqd1idAYFqC4QK6Nqi0swCzSKFILaWrGqLmS3CgSQGkdjRnHRkPHialNFOs0L/Q0kQZo2xSN3uxozhSPAslbDfaWK6UZh8OaQ1lRcmalA03XbHAayugVnVM0hy2x65cUJqQtzoJi4AMrMZoYkxoRFWeIOGnIDE0QW2F1S9rkJeD6/tfO8+fn1BYBa1io3YJcs1g6iIKFLyoSqrjI9JDYv8VcwoA6zlxenCGSkNRpc2Z3uWPehfWPNV/UdMs9oYsk3UPZ5yyNFwfXkAHvSwH1AEBdFg9oC8lvAJvLenTPLJZlsVD6WDPOnr9ADNbHR/Rp5Oz8jIuzS+bWF9A8lHutVaZxZDdfsd1tef7iGU9/9QVXV2c0awFG758dusjhF/B0b8GQRDhcH/L48WM0C1OboMLLy3O2Vzt6r6goq3XhBz/8Tf7kn/93/OEf/1NSLjx9/owXp+dcjhdcXlxydnHF06dPmMaROs3Mcw0v3L3trhndwaTjDpvjDevDgc1BoawLYsJQCuvNivVmYLXecHR1TC7l2jlRVenWg/39Bo+e7hMmgqcEc8dySKOn1oMEExcMtcFqiL9Fr4JoWEG45ACEPQAP8wip0jWYddw8muUNSLrMOYVaPRRWFnPlyCSxAIAWD++wR4jcF6XFcWkES6koB0cb3nrrHY4PH6HamVpnnTasyo7z3SmXlxNmW2hELdmDmOBii4LDcW+L9UXUd3NVqhuRUyJhYYFiItQC9M6QJKJuejQrBEgSpJCUMtUM1Yr2gkm/ZizT43yKBbgdXrDB/Exi+ML4F9uL4T0IUywXpQd4P0ncd1lBumL08F+1YPNXjcBNsQCu1Yew4ygpmNhLumLuieqApMVRlmja/Zqj1vqN4mdZTRkKwjdhdXuLiJRSeOK6f8PUNfsGWF4A19tBvK9TNr3peB2QfGfzm3iUvQ6w3TOLby9/M/vhZuDwMAzXYYcQ7OXdbnf92dXVFSml6+/cJhG4e4SsLuscx/Hbf4fvGe5h77PZbNhsNqSU2Gw2PHj4AOvG559/fudcZs9w3p+LPYv9PiPLgFnDS4YEap0mDenLswGHLEgCRKneyZJwKyAdzQZVI8MnRe6NdaX2KZqfmlDpeJZoqLS2ENkElzGuf1V8Evpic5VbZmyVJ0+/4OrigoP1ATJk5u3I2dUF235Bn3sAgYkFFXYSiXD5EcQTnmBwIBvSEhkNtcNiA9VDuM0qK7PNmKTlfWcRqucR6i6ZULBrCrW3Cynb9XOzYYvXtIRyZw5lEiWUD96UlDq95sBhWiJ5qGWtSDTZlFClNIGFUd2tY54DBPaw93FxECGlTDKhplB2oZlCqGVbMkoVEmHt08x58uQF4zQjXclJot4nCE3ewymgY6xec5n++gBznFWun0Q3f/194zuLvRrM9Fd85/vgz6WZ/c16XvHSDlYe1yyBXw9UvbnMflr19xuvXP4OFP/2sf69tmo3fr453nAjllfQJtLS4SrSoYJ6jTR1ots206ElcgIrQpFCNV08+XJIMJix2Zk8OtW9r1irMHdQq8wVfAkeSz1u9FYaVSM0QSzsCJIHi6HnuOGkZ6RagLoC1JlZFjaM5JhMiaBloPcKyZlnR1IP77IegRep5yiaLGRukojgt1VBxegswKdGd0s9IWp0CyaJzgLSmXGGGrNWLTFlsDFTF7B6NRh9XpPSCNboEsEQ2pXehSE1ugzUMPzBJSG6D41O+DwDA00bLqBkdjlAa0mV1CwkKeq4Riq0pMSUG8MYkx+VHDVXisn1YRdOGCitsV4Hi/iyj0geyHIA/RTpK4a0u/e1IwJDSTx89IjNyRH+4hkzUUsJjbwuwbae18zdcCrdg51KmbEmZB0oCLsUXmwrjZfHzidIGyaMbJ2RTn/Z+Fy+wpPwnr/PelgzTZXt7ozt1Tkt7KKw1ikSjHulM5tirVHMqbvKUArMEhQEAiAlGd4KWg1sQIdOa0u4CAnXSu81rlHppO5kL1g3igmaNsFKp6OWyBSmuWE6Yx2ERBo2qFRMDJtHprblcsxYNZa5Nq22CPNpkFMnsQpwbm5QMr0rsh1pGGlI9DoiK1j3YMSqO0mVZhVS+ERXwEVJoVoC7+zMIuDFgQF60egGe0P6BheY5kqfKkkH0BbsCw3vynEEHZQ0h5VE7Y2U70/psRxsk49+47c4354zbbdsL3dsz+Hg4QnvPTzk4mTLWw8eMPnM1W7k5OAQ9Ym0AreMtIrojNmG7XRJ8oTXK9Kwhp6ZDdJa6VcrUjJSMdhV0jCQSgQota4UcXQOH7+QZTrZMiUJLgNaGh1hmBKtBYtMNC1e9UQRkVKEMcVMjllgYzFxyEnobmiu4AVTpZKRoTGUgeOTdykHV/Rp5HBzSEstfCPnytaddx58QKudcZ547/F7/M4/+n0ef/gWb5885sXT51yNI28/fpdjOpfPL/jy6y/4xaefsPnyOb/zh7/PX/zZn3N4csTPP/2Mn/3spzx7+oS/+Kt/ze/8+EdhMSIrTh68zTuPH/Hjjz/mk08+odbGyYMHvPfOA44Pjjh/tGO1OWT0znqtTKOzmnacb7comYPjY6a5Mlrj/PIUfZ7ZPP5heNNajhd7mkMSb0KjgvYo3Az2su77jPPz8zstFe4Cgu8qbm6Dlq8DkoFrf8Kbn+2ZMjdtKF4F4t5kC99c/vZ3brOJbwKwt+WqtwHh/e9fdxw3P79r+/tjvb3u296Itz+/C3S//d3bY39Obm7/9vru2u6rWNv3HUojJ+N0hCnXUFSzIlmj17CyqQWYYxolEg1nyWuyGat1Ye0bBjVSPiDpEn61EVrPpMFZ+0A730GNvIo2zzQNoXiRjqUI1F31UIV4bvjkIA3vwc7DDesrcjHylKipkhDmlMneMBKmYXNk3aEHUcCHgnplVy/gysmsSEeCV2Vuld24Y+qVnmZoI40h5kLirCwHG7pN2C48hPN6YJJtqDXSGpox5460YDMxONRKs8rZvEO9UQgZPxnaaOGfKx2vCtlIaWG6L96z6XCDrgbycczhhpxYHxVqT+hsC1Bf6BXyUEjThPUMs2KtYH2Hu5PyRGsJzyAtkyWCzDICrvTkWFvsEqrRckftfn6WANtpi5ksfsELO3lhUPky2U97MCArggV7aBE1uzmmAUzu6pbzq4EhF9Qb3ZUXL59ztd0FgH3NfY4ywvaFkQmSffG1lmioI2HVhi7N0iimkwT4GCu5dY/tf2YfXBWgM4Qty4vTF8jly/CC7sFo770vEl+nNkNr48snXzLVSu2d87NLtlfnzOMesFru/+UZ4qILcBT77B6WMA8fv83HP/oRD986Zpwq43bkV19+ya8++5RxhpLg5PiYf/Hf/wl/9pf/kodHJ6xXB6x+8UvOr/6Grz75kl98+imff/aE7ctLpj7GO8WFnEpkqrQOHp6juF8rEOdxRlrn66dP2e6uAtfIQhkSJRm76Srmbx42IbNFIGgAkfdHmDNCawRIYA2djJ4UPEXYLo5LR1TpvoRqEfelaQ/bEm+LH7kuc2zDOyRLoarTTlzdFfFE7ZEvYTgsjDxJiqJhZ9gN9WDUygLE04WcGqQBdUVZpNmbsgAwQFeGB2vWPTHsBrCvOb9qAX6bk3G6NSI71bAE3jqaEtVmsI57AutIEkTBewpbFARv4UNvoiQLD+TsHgFYZFKK+XLSBIlooAhBsBHHU8KIMDbpdQmblOWeWppC6hFoLoBPFHIAbgZi+31xzKCVb3zCzYLYExyvUMS65QUkd6REIyNJCuANp9VosCX8GyuNev/31l3vvP2cZLvd0lpjvV5HYB8etfkCJl8zmnsnLQqjm6qj/ft9rvO3lrkPW/mu79ye29z2Pr6tZrrLYuIa4bk1j7m9rT04fhcQvV/3NE3UWhnH8TshxDe3e3V19S1v4z3TeH+eWmu0xR5jmibGcfyWrcZ9hkjYmZycnFyvd9yNPH36lN1uh+PctL/YW2eUUmLfhMUu6X7blF4DC/AWrOWecHUSEWKXktCbLd23RSEliaQGPaw1WIhEoPQ5Qi9pgg55yWEIVnRLMIgTkQ9BDhMNq8UmHVEn1Yp1YZ6cPjfG3Y6sA1mFbQtmeG8z1htIjueVOSoJM7AqERSblmyDrhHSVxYbKTOqh6pH1OnesNnxJEH8wwiLqVBf0eL51xtoaWRR3Ba1ry9Woz1Fn1R14XrZEmoKaWksV1eEhnYidNYtlKtJGWUmYyhQdCE3VmM1RMCxYVhJqFa6JZKEF7ctypWyKuH3TthFZQRKIeU1qsJuN/LlV18hreAFnI41I0vGRZlSJ9Uef6vXwMjfCzC/Hsj1X4/oev39/cL3WcFNcPn7DR/ue3/a9ar3yO09xyuP+81Oxq9z+r7DHH/TFfxDbPT7NtEhZ6U1oZeO1GWCaoqq4V3ourB9G/TcQy4qxEWMRFGUnLE6mjuWoIwDbDpXdUXxzuiZ4tBkFx3jlEPO3YWkvviHTdfMDEkJdaMyUlKCmvBeyQkkD2i3xRTdyAK1K7VG51zSTF4PkW5uzi47mgXZdbontEQSaCOhQ0HqRESmK1kbplCkMPcWDzh3yDAv0o+yvKS0GckzM0ZmRmskF/fFQ0w0wNRZ4EA7TQTreelWTXjLpKxRqpiF53NXNC/SR+skySG57JEWrrrCZEZ0oC+BAz0ra5xSB1Si60YDz4YVZVCh5MxV3fL1i6c8evQ2BzmxWq9pc+fKJnYmdGZqv7852Itnz0grZRpHhpwXPz2JcCNtJD8gJeHR++9TbcsvPn1CLobkjrdgFJga4xwTaXFjygPFg8GFdlIPoK8btFS5PD/n01/MvDw94+DoAO/w4tk528szOoneKiKZWkOuE75JjZTAC2h1aBNuULNSBPriOTiQGMXRlZNbBGV0HMvh59hUSF4hD0y1sy7CgWZsyJh30tzCV3rVwDqNYPWWnCgJkEr3Ql3SqGmEd1OdcXWkl3ghilHWEdKgloKBo85uN4dPnQSLy7tQZUDoVDqJgiRwN8SEZIJasHDnxccxa0J7pY6AZFoOm4zsHR+i+dOto5Kjk4wxtkpSWHmk3rYGOacl7HKFDYJVp/n96WA1K3qY+c13PkTWlV988jkPnj3n6nzHajXw7vsfcHxwyfrkmCIJzZlOxZqTupG00PsYTN8uFEKC3C2IUzNOsoz2iY0ajRRBNasE0rFJaC3RW8eSkdXYW+QN647lxGSKtrbYzRizOEWiqPcUE5WmBekjTRKSQ/6KR3DiSKJ4Z/bMZlfRTSIvkwnvUJuSgUcnK3ZzouZCN6fkwrSrvLh8ie8axycfIMeFYTrn/Grkow8/4vHjt8iaeNo+Zz0kclrR6fzWj3/AybsPuXh2wY9/8AH/+l/9JQ+OT/j081/wf/9f/57/9z/+NQ8PT0ibggwZydAn4cGjQ/7gn/4BIsLF2Uv++eYP+dFvfsi7jx4zHB7wtj7i5OCE59sXWFMOi3K5m7FnL5gfzrytxsH6ANXMbp54q4aPm6gjHVpJ5J6Z+xThpD0AiDk11gj5DXroe2kifLug2RdMt4uRu8DYV7FqbhZXdxVP+/XdLNJubmcPnN4GpO8KsLkNKN/cxl3behWT+vY6b37nJpD7qu/tj+Pmft8+hzctQF4FUgPf8oe8Pb5TSL6iMXAXe/mu5V7179eN3S7CO6UkViY0icKqA8UN6cG+1WR4HrCWUOthGXS0Zhg2HD/YsJtZKM1rsIYyMtCjwZoTabOm9l0AWa1TrYMpzRIiM9agZqG6M7TwZe01I0o8x9mR2GGSqanRTSCFZDfCqCIczHICFzoT1gu72RlKY/tyC2tBpFKuOj0bl6Mg0442XSFTC9sCnQlJfGdKUEhUKm7GVo7JbcRSp5cSYKUJg61wa0yt0XcN6g6f4n4fdEWvSvKGWKd5vD9zjXBkaqbSaKaR6ZGEjnKQE1IKIorImjZl1ur0VFjhZE3Mw0wVaO7gKRrHJSzTriisG3Q3iieQCbU1pEoXoWgPax4DSQPuHZEB3qA/EY1i+4bkIrqwjuKfCchJw5O4DCGfpVNbWB14vKLw3phH5+LlGXXXWQ+Zap2Lq5FxnnAUZPHcXkAphG8AZYvKisUSLSwngq3pGufZ3UIhogGW7cMC90mD7uE3KUsxLimRS2HIAykprTu9TxHsWANgwW0ByOKeq3Xm5dkl2+1Ex+ktfElbWyxDdA8s75s1jrsGM4Hw8iSF5/DJowc8fvwYS8L2/JypN5599VWEwaXM6vCQR++8z4dvP6alxIrEJgurVeHZ6Tmf/+ozTs/P2c1TNMlFKWXg6OEx61Xh5ekl26sreu1Iq9Rp4ukXX7MuR7Q+89mTT2lzJeeB7bhldz5x6Tuevjhlt9sBhucl1FMDNE9v0NyqZlhVWmpgGuzc7uHXKQlaxmhogrLMX1z29ip1Ifo4WAq2ui/n1RbQvPcAclywlNG5k4rQeolsGfHAjxugjhL2EC7xNygeYIeLQg+g1ATEGtPo1KmxypXLqeNiHKcVRubBwxNIif5V5arvAuCw2I+ewgZQLTzAw6+540UgdWSKsE61AIyifqh0zWARiiUSDMouCxlikbZH8CpIC6JP3IZtmfMvmTiudDXUE14TbCqYoD2sz6TEs7f7wLSoNqwJSSpuAUKVDM0yIh1zI9yCok6zTFjyoRGeiKEtgrCreHi6SkKkLmGaYacX1jFvzjTbA477oLc90Nxa4/z8nDKUyO2x8BCWxZfd9r7cfLdxuwdJe+/B/K/13gDmXYDvnjW939eUUtwncv0QuN6HdoMRvPd+fh27+eZnr7I0u73sfk54kwxwcz0iwfq+vLy8Dv7LOTMMwzUbfO+5vAeqLy8vr21K3kRBdQ1UL/9N08TXX3/NxeVFKE2I5/ztcMH9zylHvXLfv49p3BuGUjDIc2AiXWnJWIniSfAWJD0Heu3xZNDAIL55d8S50xTvu+QZ9RAea3LWXelWIBmDw2yhfhDpaDN8jjnX3CasS/hKzwY6k4BmTiNsh5JHTpYZSLL4v32Tz5Q9gOac9sq9CPPLKVGbg8uibjBgBb2iUpno5K6LZV8ie5CJ4nmuzB777N2XeVjkYgQz2ZZw44qnwBZ6UyBRPJ5RtTmJFjW0a6gtPIiK1o2qnd41JgoudG90dwavVBNKtiVUPkiF7kabhdWQQUZSKkgTJHeGg4GcV3z91RehjMuGeonneYie0RZq6UYmm5HSq6+b11ZfyjfuW69EJa/nQYs06o2AyO8yf+8e3/7sNrj8xnjpIkuKhZRvaLsL2yZ6uK9f8Ss3eP/j+M5q7n0gN7541zLf+t3N47tro/fc/Bu+t5LXiMXsIF2gZFx6BMqZQAoZ84yih06alU74zUayt8Zkx1jsKBR6ormTJkd0wpNHMWeOUlAvTCWWlx4dHSVTchSwVhLJYRKJAJSWyQMk68xzTFw2dFrOCI2meemqLROs6rTs0UWuTt4MaJ9oSZl6Z7MwlY2E9BZyhxqJzU07YkrVGfVOSpm+gESDZNrG2ExKNSGVisqOXAumS+p6qphFaEm18GgezCN4QgVJYVo/lMJeNuaAqSBdcM906ZE+KgHcz01IifAUcmc9wDhvSatgSApOdUM6eFFkGLCpod6RmqEnugrTbJw+O6dL43BzRJ4zzRsXVxOtbfHaGab7++g+e/6cvEqcn1/x/MUZ09W8dBGN5Bs8K0hidVD4Jz/+fR689Q4//Zuf0DwK34agPfyqq2eG3CkevmuiMcNNKexLxBKQUWZ2F41pVxfPt2+6l+4xCRSPdHRRcDHyTJjoV4WstORQl/Rqwn4EEiNhom8dEKX1EfEB8U7dzWSUlAqNTioZG4gOpAM0Wo7zqRYhi147RQGNSVZtkJnwyaiyeEBVjU5xd7pFUnXc4bo4UgfLs/YJ6R2hRqGcGg0lm8b9mzJM0ZSRHGwPx2k92P2iJXybC9QuSGkRQqlGkjWWOkInlwHtAWabKlahSEfSQPWMpI6SGVJhpcpORlqfUVKA4fd97rQBlWkBIQtqnXfeeZf3f/gjbHfFaM7JoyNODg9YHWwogE4dFqnT7A21zNw71S6WIANF+4rJobeRbpnVIMx9JEmi98qQNuSSQlo8hIVMcWfAmAYBl5BNqzG4kwaoluhTQ/OWSdekGh35nkvIm9Ka3DpYRrsHA1FZPNyFpAXWSioJS471zDonfDAO3zvBXZF+xjZDnWaYKpdfP+d//zf/lvXxij99+1/x4aMHeL/k3bePqY+OOL3cMk8jhw8fcbCO8JvLXUyIt5c7Hj445l/+1Z/z6PEhz75+yX/66c/4T3/9N/zqyef8/p/9N7zz8C3MHLE1JTWqC8NwxMc/+if81o9/xmdPn/Dg8buIKH615XBdePfxY05/eY6IcXY1M/SJz5+/4Hz3mEfvvMPmYGCtG8pmA1LwYrhnpuSsaqeK0KWjXrC0o/dMkcTcIjztvuNOWeUdrNrbAOm+IHkVIHkXG/emz/Lt7b+KcfsqJvVdLJyby9/FBL5tB3ETNH4VCH67gLrr3N08htvg921W9F3L7rdzfZXGSwAAIABJREFUF5B++3jvYlnf/GwPbN8+1/u/123/5dexnL9vzK2SV8JqFroMpDJhJPpVyAj34VfuGUlGzY2CoFLYDANHmyPSsOGdbNTklBJy4zYmLK8pY2VtM5NGQ9hM8aKUmbBOKo4ykHLkXiRL9GyRSJ4HkrYomLqgS2iqtSjyqndSKVjrpKGjbQM0wrKokHJDmpN2ynQwoRcNI2MrRdrMXBuzRahO80g8WBnxrtO2vIujQV5bY7O95CIfshIhHVZ8cNKUufKKyEi/mrkaJ5oZuwruQhXHpeKDkEmLl7Wimmi9UjB6S6h7zO1yRjWy13MfWG9C6i0ZqindjC5pASYqSQqTg+sM60Y7DUZqdoNs+CjUZGzWka5eRcNXEguJcAadMiDklNF6/3BRkShek4R0Pxim4N5JGrL99bDh0aMHbI5PmHcjL148x3ZOW7Zj157McLWrTPUllwSY16rRLYLgHF/edX0BpqMhv8e2XSWsONiXR8GiLhLzbFsYl07MOa/Jtgt72Z3wyyXOz6oUDg6OODl5RC5wfrZjN15EoKFNdBb2MgsTGAdTpnmm9fkayHILAD7msMQc1hwWP+ewEgkFIWTEjYvtBS9ennJ88oDhaE0i7LjysELnLZtcOFwNvPj6KU+++oL33/9Nmgq7uaFJWA1rduPEPE50C6Aw58zJySF/+qd/yg9+8DH/8//0b/jZ3/2XUIg51Nb44tmXXO4uYp7TKuvViodvPcSr8/TJ51yMO54+ecK0m2g9WLRC2Pn54l5272HQ6BSHikfDqRNArESQuFoAlRN+7aEtmhA3vDvdCgXCUkXCsoYe90oHRBI5vH1oySkeSlTrQtJg85vG3JS+DnAbx6XRU1m8S8AkIzNomlHJtHnH6ekLNL0bYNK6kMoxQ56o/YqNrnhw+ICr7YyNLSztLPiYfR5RDYa1tx7MYQuChUtk26hnfCExzT3qGUUCGNcAn0QMNY2AeVVSj3BtoeESsnTDSRZq0SxE6FWP2tZLI3eAhkkKtqEtxjDFccsoTnKj5/B0D8AtyEJOhJPJ8sR1rWiFUqA2p4lTkqJmNAlkwiSumZ4A65gICScpaLt/ob5/Rw7DwDAM1+AyREDfarVit9txenrKxMSYxm8xcD0kYt9civt3tnsAiAsAa2avDE7+vv27CXzv9/O62bwAFo5H8O2yTLdOnet1YN7e3/jmem+O2/Ou1+3XzfnGTR/lm3OR/bxDRJjn+VoZt9lsrpfbn69pmri6uuLq6opxHF95rl433D2Yyh6B16UULi8vSRqhkHuQ+TvqLhbG92tC2u4a1TLDEKe/WSfZCqRHXWzxzqclEKPNRh5yeLV7WMgY4JoiqFsjgM61QLFFFeKoNqyXsG3KAap2C9xErGEutNRJHvdjEyiueI17RupiRaqQFnuaKiCmiMW9Yh7Bod40bBAtgpNT65DCorT3inrMq6YWjZ1kGVILchWJ1KPFkgegR4ioWkKLhN9/62jJePboXvV4bxXviBZcOlUGsIgydA3nqi5BNvIhwo27OlhFSQyS6b4P++tBvoPFY9/InqPxoro8/8PaSryRciJpi4aZr6A5lvNit2H0NvPVk68jS0ESLH73e6a2WwQkrzKI58U66+7xWoDZvoUqvuam+57Pr8dtBPNb/37d8nd/todO7/z0Dlw1Im89fn/9fPpup+j6uO9Y8eKsdMfq9wezDwvw7xzuMjO74/fXi95z+LeXkW/+L84tRvN9OmGxAv/OjnHHzt5vzObRGUfjbPWFXZmU7g2vhVwqSSRe1gVkauSeaB4eNt2WC1RkmXm1oPs3QdKEWYlJQA+mpaqSq+JpZtUHZmCmh8Q9J7w5niMJPM8RljATwS8ydMwLV9YQN4pH+J+mTu8JzYAqKQfTkdyos+E9AnuKONKUeS/NkoKnRikhTe0dkofsFQbmCimtyNWYzSjVMS2YRmr5iJNSPFg9r1GbUQyKkNoQ8sZsNBqtrhEf0SWMUEjY0t0eEHa2eIwlBWu4Dqh0BGEgUlOxzi5lLHcGNJjYraFWSLoEw4zROWsJVCPJvUpjpeBe+frrl1yuzlmtN1HozJU2jcy9Q7n/RfTZ579ilYXzqx2nz76m1x1BgM9UMbRH8mvrRmLgn/3RH/Po+Jh/9x/+H6RV1gLNw0NxlaMInaugaWK1MMyTEmznAagLGF8d8xFBMYvCRTxT1PHaqAzkNtLXzkoSVTpqGdNIP89VaCmjXREPGclQHbNOXxjAfSjYZLQ0MXTFXaKA9Y6aLfIipQpAGO8bjdIUSSzMLQ8/JgvPv4Rg2cMaZslYzz1B73QN25MlnwUl4d6DTbYz5hxTWwMGd8TXeO4R/NM7KaWQPXfHvIQv6KSUotScSG0mawJs6a6meLz6EB7oxYE13oT5aowGU0qkdbzQ0pARJ5jDyUlpwAoUCW9tEYuE8vuO1lmt12HD0SaGjfDW+4/ZbNaIHTFNjZcvThkOjtms1+EVViUYVCohB6pQkgKFWpaCSjJTr8y9s5YhAmimRk07QBErlJPMKmVmB11HAIbUzNpagKA0WAogW5gwaQOyW1PEoSi1JbpVigvdJ/CB7J3qIZO37lgShpRxBaFEEFNLlCQUSgQqPnhA3Tlte4lPPQCGajx5ccrZ7ozDx++TzXh7c8zR4RFXJxPPTr/kYrdjnhuHJWT6u2mktc5/+dUTDpLwP/zFX/Lhu+/z9NMXfPH1F/zqk0/5+c9/jhTl9/7wjxiSMpghNkfQ0hz3ydvvPOKv/sc/52e/fMJ4dU45PMSuRkSMd3/jQ5589RVrVeStQ86+fsKLpy+Z+syXbx3z3tt/RBkyIgrE/WVIKFCyo0xkk1APtAF0ok5K8x7+3m8w9oyUu2wW7hp3sVdujld99jrQ9y7w9eb2bv7+VQXbq2Smtz+7DeTeBnlvWlvsv3MX+HqXrcXt47sLoL8ZjnObcX17+7e3ffNvcxM8vimVfRUr+ra89mZxuS+832TMZxf09QbN4HJAqw6MNHG6C2lw8g66JSQZIhXzQh4y+Wjg4FjJQyihkgguHqzkkhFpWIrwGcsDMlfWEnZHuxzAXhMLdZIr7hW00kwpJcJ2PHpcgNLEQo6dQLSRGahzxTTCYrEZccgOXSPclbyitREdM2fdYViTLho1OdS+qKUCpPKSqX0ADLGBrJ25EVZIi4KjTzW8+HcbSqsUg1kr484Qa4zM+BQhPCGs8cjMMMdtiJDPFA11iAJJK0hJUb+xzPVdw9PxAFbNUVOsG94SlnZhgZZWdINVHlDr7KYUVmwY2QSsMKyEVWkoGRUhr8IOLNsEZFKr2GrxC+9gw/2bokJmT8bLwsJgk+V5J5SUefjwAT/8+GPefvtdtrszfvpTY3wyYU0iyNod3WdjtEZtQlUBWUJ/3AmmMIAtIXx2XSeJBMvKFrBWUbQoOeWoZYK6FPM/Xxrvtth2eFgciCjCEtZHBHwdHhzzj3/3t/lHv/0xKa/45S9+wd/8zU+Y5tNAv3vYPCWULhI1QnRIIsjZLSqqZX+TCd3Dl1okGKr7WDlxwT2AbyFjU+PpF1+xWa151z+gdqOPDQhP0IPjI370Wx/z0Q8+JK9O0JToVGobmc7P8bbFe6Mu/rKaEjkV3v/wA/7Fn/wJv/3j3+bJl5/z2WefMI/CvLAmZTdx1jqrgw3HmyM+/I33ePedx1xeXvLLzz7j9Oyc8XJH60GccAK4QMAkWOL3fu7Mjmin9yjU9v6kkqLOde9gCYHIk3CP2gThoAyhdmgjvS/PRF8AGvHIXZSwHOkKKp0Boc8xZwXoRBNaBSQJ2sPD2fdAtiuSjCy6ADoNb4m6dlbWuLjYIfmMdx+dkFjj0w5L0JugCikPJI26yrvReg9rMOI6T6IIGbE5AqYBJFRmnkAs8lqCuRegb9KwmEk9ITljugAn7F0u4t5zcQwjI9g+gH6p4X0B3MOewrEl+NwkWMTuPezLXMhJaSW852P/WoSoewZvaHMsKe6JrJmeO3OPMK2cHLOwJsTCyjEpRN88WNXZQ/oe4fD3V2yllFiv16zXa3LO5BzL7q0T9t6+6/Wap0+fMo4j8G37qFc13m834W+O+8wV9vu32Wz+K21v1iNJluX3/c6595q5h0dG5FZ7dXVPz4w43dMkJZEaDaWHgQgQFCDoSa9aPhdf9CkEEaAWYCSRojSaVZgFM93DrqVry6yszIjwcDe7956jh2OeGRUVmRXZPbpAApHuZuZm7rac+z//ZQGWCyllxmF4bslhZs9ZzfM803ojp/wcUBUJRmutlVBG1Jfu49XPfxl7+GXN8esN8KvrH8ITzYx5nlmtVs9rlIMtxm63e+67fFvv5avjwGDe7XaYGffv32ez2XyjxgubGfuGPUdvnZ7CRgf/bsD/MEp2qODZFp/fhiQFaaQcCqI+d0SFNCQ0UrmDyKagKKkFs9iXBqHNThZ9/izIUvCmUYSI01vgDhnBwnw44LwqSG1LoyPybFKK8MzugpMwbwxhtEw3peuO1NbEE14WK1QHW8Ia0SCO9YibTRphqd47DEsMqjmFDBJNIbwvapEXgFn4kUed1RfQ0TuRqcMcuFQCJxjfqQnJM0hYkETwKwxqzKWg+0rWQlue80LHPO796uEY4JYiD0gh97J4NVdssfiIn1rp3WgdJCnrFM2z5I2L/ZazacvF5XlkWrDUnYTVbTLHS2LUAQxqEuKhc/P45T2Y4fXBx+vLvi6Yebj3vBz/fbGZm+4RVy8g/+6Puun9AyD97c1fuelchdwT8MKu7NqS31r19uMb2P+BfnCV2y03LPjinWu31pfvxy+zb4A3kBzhVCIhkdIccqQmQdP3BVjuZlHAMiKDxQVrwQ+JwIhESoXeJiQ19pJJPTzx1DNSEsx7epvIMtDIzGKkrOHDjGOe0dTJMoSFhixBe14jKKEVinboK4RK7ylaBB4BMN7BU8ZnJZliWRYQCpIvxudLOJp4j85YH6i9UrIyNGVKSqIxIOxTwmxGNJKPew+geUhKy4DFQyATkzyZLZgnDSxVtCdyV5wCqwmfwwupiSHdGVJmSorPMwmjeGbyYDKoRZqoEuEsltvCbAoQPoqfGW9Czk5L4VmENooRstsOXWeyOVShZlitYDsLk1/SLkMq16ZKGzzY1rccv/j8I4orz3Z7LreXPH9ep07vjaIDHWFXt5ztL9nUO/zkJ3+fcrThD/7Pf8PZxSWSAxSMKhWSz4gpvYe/NRqT1lF8Mc7PaFACcFVySRQSYhMNYVRHa6OtQAZh3oPmEV26+NoinTV+PIfUkB4hl5ItfLSz0eeEeYdJmQfDc1r8Y0HGIc7xnpgXNkLJGr5VOTwaundKWZFVyWZUwgKkzw41PKYgmMwAuWf6kNFqiDacyr4a0HE1Bl8xUwJctgBBmztjSfEQ78Ygyk4joLJoxodg7KGdLDBrgOupGTIsDA+LyXhJI+6wm3YYMSGwHH7sVSGZkRswKuOYGbKSy0DLQr6cOds38vAavnK54GnG93v2c2e1vsvdh2uwylRX2P4pkoVRczSqLFGZkLyKLvF2kRDr0jXvhTpfktLAZi2k4SjmVpYZGZnm8K7UlS0hNEMEebowzJEyP6NYM47G1eIVD61nrPew18hhxdGaoVYRVlTfIR4P9DnH5MG6R2AXAtkZHAYJr2ofwJMzSSXZAOPIalSONmC7TJELtj3zox/9mLffvsdQ1tw53TAlZRwyYgPzfMlnn3xB3e3IeWC7bcxMfPbkU3x/yW/943/ET37r3+Pr7TlfPTnns0eP+PjTDxnLmv/yv/mv+Pf/4W8jRdlh5N7RFozN1jpDyrz9xve4/9Y77Fqn7ifm7Y5n52fsd8bdB/dJdI6L8ubxDxhL4fL8nLZVaNAudtiDGR0qusrPvUNl8XrLGeYUYRy9A6WglNd6fh2AzqsSxZcBxy9j0V5d5ypT9jq4exNAfNjGdVbydZD3+rovmyhdB5Jvmxh+WD8tIT7Xwdmb9ucmIP3w+Vf9DG/6Pq/u13WQ+aaJ6PXPuLrs9WO4/j3d9L0cJl2vw1i+PnaXM1mNZCuwc2ZvbLcXXE6X7OY9YqGS6brD54yWRMqJYSxsZMDrGHYug2IJxJ3BYFqB7RNO2FaNZsw+4NbwoaE1qjltGRs6qIbvqIZVQTDkGtYCyJSSSbaPCYwvXoF98bFtE3NOpAS0CHFJXfESgEzOC2vUDG2dvTqlSXgbpgCE3AtyOdMGZ9SFNUVBtSGSGcwwj0DOiy7kyZhrRnpl8om0qLyYFEkVrIcXsUF2QXMmjYbLaqkNhWFYhb3YkKKOkWBNVw3GX0myqHWEXOcA0/KepJlVSaEsq1B7WB41iwCn2hwdlcLARpQ+HGOSGdfC0I3zvqWieFWGEtkVuyiucNa3PndEDE2Z1Tgi4kxTjbAeAdXEOK64/+ZDTu/fZb0eSHLKvbv3ePLoMY1pAQcCnIw8nwXwNQnAsgch5UB6gWWO4haEGY31EF8A94wmpYwjR+MqFDLNuNzv2O2ithIOYWDBbFENnw6xmGSnxdLjrTff4Me//RPee+8DjsYjTlanPP3qKefPnjFLWHXowpIO5vQV2o8Fw14tnqO+3JPD0jnOC7OFbIMSfJaFka2CJWV9vOFHP/lt3n/nff7N//1/8PjpV0zTjKTM22+9xU9+67f47d/+Ee+8+wZ+OfPJx5/w0aeP+MXjx3z+5SP28+JjzGLVUgbefft7fPDBD7hzcsK777/PuFlxdrEN1SKO9Q45c7w65vsf/BoffP89yjhAycgvPuHy/II6V1pfZPbqrJYGam+d555atxjqGvW6TIiNeIrGitty/Zsh2jEW9V1OrLygOZFzorYJFNSCfS7u0VwxDa9xNDI8VFGfcV8aU5KjyWME4ikNSHSpi6pSGf1A9BDS8/tDhFz36nhSVr7n6dOvqXXi5HTL0bjizskaTcJkje3lJXWaqW0GUcSECmEJSFjQaYqQFDkQmkTC8nBemGTLGZ9SZ9SMjAmtnX0CagRMpuTBvF8AoBFd6q0FzPUJZYiQbu3hWZriviSEEtQNRDp4zAGLg1tCk4VvufvSQFCQ+F59kc67hq2ZSbAXzR0Vj/wWDTKR1TgXVXT5zQRIwWA2oSclvYa1wjiOHB0dMQzDc3D5EEh3YAkfWLHvvfcejx8/5tmzZ8/ZwDc1b2/7DH0Zi/gwbgK/U0p0MxIBgveFAAPEPFqD3VxbZRgGNCl1rs9rGVUNP94bGtpX9+OmY3hVzXQTIH29xjiE911eXj7f/lV296HmvG6Ndtvv81BjPnjwgJPTk+e/33a7ZZqmeG7Lsq/yApQWEXLJJE23bqq7hvUVvliFpHiutC6hEO9GThrZU1JgCA90PNjSilC1xzVbwy40ZQ+V9BzPLDOWBhFk7XQTShijYzQgIx10uR7MQ72RJOxocinRmK7h7+yHfq+EGt4tQk6HEmF8eMLK4blpSIvGasoeGIk5KRcSkbGji2VULK+YDGGPkXuEvkrDJmEYO92X3KaZaKgmwAoGJIu5Yu+KpsjT6Giogz1Yx3POpOZMORik0h3xmUo0dUuB2j0siJZ8rex9ySZQPKWoASxcANxqNGY1IQ5dWgDTKPtnOy7rs+XYBNdQeyURfO5LPcFCAtNoyPLyZvrtAOaXo60vxg2MYSEIBAfG8HdN9uQG0Pcbq11781ubWxb2w9/feOG7P/+V275x3ESTvjaugPu/JFZ7i/EdP861796/9fLy1y/JVr5ppJzpImAzVlI8yJMyk0NuBiSHNDW6JnrPeImTtTSllXgo9LZ49kinqOJIpCZbJGxKrtEVUwkpADUC7XA8Od6PomueGq6OtcXqYGEO+JCW0IUaxQ/Rec7FyW2kqjFo3KjwDGbs1VhZoraQj6ZUcW/UIYf8CqeYkbTResemtARiVBCYS0X7IgnUSG6WliiSsN4p8LyLJi0mZjYs5HszxpWE12IPEF9aQkQxDaBexLnsgsxR3IknGJ2VQ88JkYa2AU8eFh4t47lDq+FRnJRehaSL5L8FiKlFERI0AkRP4b0mNHReYQ364HibFwbDniSCTS96ILcZX391Fp6NreI5WIize4TxZMGSk7RyeSY8fXzGvdNTzi8z773zDvzO7/Bv/+0fUKeY5CMD0gKA1SExSFpYvcpQhLSAyuKGMZC0MIxKLpkyrMmmNJ/Z7rbIYEibab0vk6EGJby02wTeBighPVbJJO/MGEMPWS9tYawsPmLu0JuHT2/OtKmRhkQGkgkzAfYXFWo3ZlPKoJSkZJQJgd6QrkALmXQNho6nxRLDNBLA3fDZ8LnQs5NkwHtcF5oSZhEil81J2kJijTO50zy8gdFEF6fjDFpAEntrKCnCOldKXjj07lDGjFOo9QK3kBfO5miNME6ks++JvIqC7EgL7773PnnYsN9tefzkSySds30Nm4M6bpE9nN454e7mgq/ahE0BUq9KZj+ueJjvszkZQYR5mklZ0bxM1DNEBniiNWeeLmjWKVkQC/C+Z0GbklcjaYQ6G2kYCe/MjndhWFKAxT2k8EOmuwMT3TQm6c2WEM5gNKOQWqL6Hs0NmZRaOoWCCPH7irOacjBekiAlY5IpWaP7rY7kvqQJC7sne55cnENxVqORVyObzQ+Z5koeC8mh9c5UL/nk40/5q7/6S46GQhkKloRREu3pjpyF//A/+Md0TZw9fsLFxSPOv/qasydf85P/+B/wz3/vnzKc3iNZogQZgEqiaY9mTa5AAV1zVGB1tEbv3mVvb/Dw/hsc313z6cef8ejxF4yrxHu/9j77p5UHb91lfbRmvtyyzpnj1RFtCRnxKqgYs0NqivWFh6QD2jqzOpZuP+G6icl6fbyM+XvTMof3brJvOLx32M714v5lE5qrIOzVdV81UbrK1r26T1f3/eo2rwLjV5e5zjS66f2bQNyb/n81lf06wHx1f26SrN4EHl9f/lXg9HdNCF9nYncYT+ol45lwfBxJ6pMZZxc7tl+fxXPZjSZD2A7lTmqZ9R3hqGRWayGXhpcxAmAMmmeyVDZTYbdM6DMd87ClMKkIGiE3KmgLOSUWVjHSJ/CIYFF1NMFsirQ9g0C1CnSKZaoISsMy5Bae+qTFNmVI6D7qj5ZgMAnpP2EVpGQkrWOfssfkJSlqneoSNZfA2JV9q+wERi24OVU7VncMquHvWsPTv9tM74mSI0hHCZZ7yolhlSApg68Iv6DIjcBHzCuDQq2J6pVUDC0ndGZ2+8xmGOg5rB5SBx0GXBTzCiKYDzTbYfs9SYyNKmMBG1ZQRoZkrPKKMcNuvqRuY2ImMuNtZJKGpAFp86Lcud3QnFiNG956+yGgPHn8iO3FZXTxkzAejZzcucuqrKm1cVF3EaB2UAdqNApsYRaDhhTZg93OQVG5SBTdwvsypuce9i1hVBuNVYWjzRHHd445Wp2gg9BbZ7W95HFr7Oew5QhpbFiuBYU1/KAToYYsOXNycsLJ5pT7x8fomNncPeH4zoZSFJ1SAISiYRMggqQAxL0t3rOWaB5Bk2IBRncJUA8WsHkBz10UlbAXUVWO1yMf/OD7/O7v/C5DSfz+//Y/8/TpU6ZWyVlZr0dW65Gvn5wzlC+5nHb89G//gj/9gz/m5z//mE8++Zg2T4SvNDhCGjJvvPkOd0/u0rtz+XQLLTjUScO33B1Iyub4mDv3NqyGEVnCsSPYz2kW8wwREFVOTk5ZrVacb7dcbi9uf+NJPeTPvljliYWyycE9cmsG1wjtXGxd0iAMJdF98Ud1CfagCegUdUzuQY5Y1IzJPVjSi52LdBDti0dxoUtBe8NyIiVFcigFmoXF6eQtLHwKYbfiCbeZeblHnV88ZXf5LGruxytShzbvON/vqFNDKhiHgHiPAENPqCdU4jkvOFmW89AFy4rRQo0oQSo6kJla7WQNhYC74JahBVt+JHzVIUGuNBWkKYmKaVksWSQa2XKwJJKF26F0DfKGmAbQUzM9hQWKdqgpZPqeluYJijfwYcZbzJtVlbkFaJ4trmNrCjmUK2KKZ2dpHWDV0Zx5HX+VcRyfW2McANhhGL5RLxz+Tinx1ltvsdls+OKLL5imF7LC2zwvX9ZQv/ra1fcOFh0HS45SCgdl0QFsPoDLtdYXVhCHZcbM4ANzehEyeGh2X83UuL5/r3NMLzvO6wqpA4Ds7s9zPq7WW1droV92uDv37t3je9/7XoDZYw0f/EOtVeOZb740Ubx9wzYkjbcHmGXuWAlLTk+JnHI0pIewLxp6qNQD3FyC82hoKqgteAcdz4nUE5aV7Eavc+RymbOXyP7R5FgryzYCHE8MtCTxfFRoLPkkBJtWlqZQk45JoqSy2Fd6NCHFQnVMZu4SFldmaApCZBZwq2Rijq5JEe8072hf5sBmJFcQo4sGPrLAgQc9hEhamqDLZekS8+ikJAsrDbOB5EbzGoCzdDBQcTqLisOd7krpjeSCIfRRSbPhSUmEF75ZAzTsjDqhdkfDc1477h1Ncb9TB1ELG5EulBQNg+0+FKyN8HgWXxRszYO85Z2eHLwz0VCHYi+fn98OYL7NeX/DPM6vvv7KbQTN9xtg52t89I0LO4uZxa84nmOv8pyZ/GJ8e+s3Qs7fAm7/DpHcW2xN/KZ9v+Fb/jtEwN0cz0oYKTRgwNQY3ZmXEAPvjaoD2Q3JGQeKN5pEoWRtAUQnR3KlW0a84ZoXn5qQQWhVTEYkNYKIEcyK3MO3T+hYavhU6FkhdVq1uAHvG5aj25WLLu2sTDcDrZSUsSrM2VBdutzmdM+MxWhe6LWhnsiySEYERFc0mXDNS0qzk8nYGP7FmYb3jlGYW0bFAxARWewnCr03GiUUiKJ4jVRj3yqao3t06HKpBJsbNWoPcCu5BPvHM612VMJmQuaQfnUPX8jZw2vHLZEHoVdbzNege8WL0FsmK9icEZ0oOWMd5upocch7PCLRkFzo+4rYiPUKCr3cXizh3Zl0hl6RLlgpFFW2KbjGAAAgAElEQVToUSwmDjftmc8//5yUhNOTOwylsN9NHK+PeXRxQc4O1mPiORRyLnibwxxfMqk3ukTYmumKlRbWd9fcPb7D0WbDMB6xypln23O+/uox+4st5x43/Cp7zFME2SUnlQhE0pKYJWTNCBTtqCVcK9JZJEAOJZgSxYWWBK8NGx2xRFv8pYo1emtIGRBzympkkE4zZdcmkit9eThKFtwHZu2kxVtPRYKdFk7ndHW6TCTP4cutirdOHh2zCbFE9sxMeGomh8FznM94eIvWypAiZTu8h1PIr7RTGLEejaRo0DhtnunmeO7BGDOo3qnZoUVXtaXMunRO7p/y7vvvcnp6n1Ybf/vzFR9/9An7i+2tz51kA1USVeDNt+9T0kBXGJKwWmcePGjMPrMqK7RkaBU0IZPQqpIGsCRk6ag6AzCOx0g3KgmVzohSrYSHTxnIo5AslAFVo5qosjBaBqFUo5OxpgxE0ENrRh+crAWvjdZjAlRSgNd9GmmDkOghSTVD2wBUtDiSBE+Fpk7JHfOMlwjTmmfj8vwZ/9e//t/5V//rv+bZ+WPee/MdyrjmaBi498YbvPPgHYZk7M158vVTvjw/44//8I+4nC5Idhdk5Dd+44d8/uGHrI43/JN/9Lv88Ac/ZLu7iMl9S5w9e8KwWfGf/d4/4zit0NqRYYUsPpDdZgbN6GCc7408huehm1KTczQObPqK4Y0ND0/v8fH7H/Ozf/dzPvrFR2RxTt55k6NN3EO2ruzmxFDWZEooNbxSxTFXdFTcZqQKXqGmRkvO6K9x37nGLP4u9uzV16+zjW9iKV9/7TAZumlyc9jWqz77ZWzo62GAV5e/ur2bJlgH1vKrjvWmbV49ppsA+uvrXmcPXQfsb2JHXf89bmJMX///4d9V6en1z37Zeq8TtLPbnzPJwLS/pAxrqu/ZXW6ZezSRIhiqRQAdSloZYz+ipCOENcqS0WLhATukSs+F2reL1DLRqzP3mV4cbQHSSJtpnuk6M/TM7I0BsJyWgFSPHAsRiibwjDYBKpID4MM6mjq0ABw9C9rD6xCMrgmqx3PLI2Avm5O1R4NTgmGaDJCEtY4OiVmULC0aqZLJC+Ows4SBdaGmTLLMZQ+Wn5pQe0ZkwrrSJaFk1iWjg7Mqa/rGyLtjpLSQ5c9GM2EsI5oTirEqylzBU2WQQk5GzjnYftmCLR5afPbWcGnU3Ji2F2ybo254dmQoDFpYrdYMJbEeEw3YnV/G76WK26LUs0ROjg8Dlm9/7qSUuXN6h+998GskB+uNqXX6foe6083p045pt6f2me3TC86e7cLaNgEtvfDZXH5Pt0Be+2IdEYImXRjtC8is0QmM6+EA8CrjasO9e/c5fXDCKq/CNrJ11IXVeMY0T8G+1BcszLSEEtrC6BQBVLjYXnD27Gv2b95H28jTJ1+xPdvSu0bIrQSRYRgH1scbBONyuwt7CsJezBeWM6LL+eocMhOwTkrha+vmqMf2xrJis9rw1htvcrre8PjiKXUy5v2M10b3zNNnF2wvLvny0edsp3OyFB6fXfLzjz7ko48/4eLsnLnVYOl6AOrqoSSzlPjsww/5m5/9NCzgdFF7WIACalDrnvPzM1a54Fp4/NVTtpc7uh8YVwGaH22O+d4PPuBkc8Lnn3/Gp5/d3r/bJOY54omag6k3NAk2Xg8wuIlTBIoKmgrj5oTUZuYav1myBbyxTNdC9rbY9ATDTkTiHEIxC5AmZaEvFMPWDSX8S7OBJMf2TiqgKZMJn2ZJREPcnFQt/JM7+NSXOY4wX55j/iwA2hr+8GYsoD3x+QjNgqWMtGism0NOqA7QjHlR1zcrEW5eO14GTB3mCe1xb4wbV7RqDKWI4YQ9RpJg8YiGzY45mCQaxrAAWeYEkYCEiSJqZF9CDgVMOpJjDoBYcJTEw9KiCqIVFYsGW/jo4amH9B9CPVBDCSpEkKPLYngnhmompliOWAtC3y1HzvkbAX/jOJJzfg7GXh2HRvDx8TEpJR4/fszFxcU3yGq3BZkP42XMZ1WlDOX5/hzA7wOT+Wrt9NweY2HjHuw9blJOXQWYb8qHuA70XldUXR+veu+mGuiwj9c/87vqqtuMzWbDj370I1arFcBzdndv/bk1iJkt88VG8sQ8z8+/x6ts8O8aJSs9CcWIxkiyADMrQShLHg01Fs93wLUsVk1pUVGnaOhkgWZ0Dd9yhIWhrwuuVWg2IwnUEqJQW7CQkxjSM7NXZIgrWNCoB1Lc/pJUKkpqBU8RLi89gG9phjenZShZFmJMeKlLUiZAatRlYWNlRDxfQtMCMmtCzdHM4gPuET6qUbuYRX6NWGeWOJfFwibEtdPNMXXUc9hKCbjE+ix3JWtp0aLFNpPFfqM50DU3RI3OEJYYBEhdpo4Ni2WrE2Y/NexMolcYmBYyUHvYijSvdPNoJKaKWqFrozmUg6Voj+O2DljnVfSvX80i4yXjm4DnbcDUq7vo31pFXrzzynEd3H0l6HqL7V3dyE0A7Te2J4cf8YZP+Naqf4dILtFZf9U2r8PLf7fw9s0jJEM9bjwWEm5p0IiCLfUxurBpKTQIYKy6MiLMtdNNwSGT8eZoWVIsfWFIdAHCB1lzJatSWwMZQRxrkHKnJ2OeMjknxIJV6JrCjkI7ScIrqxvRFVfINuPZqHVP0RE8U5Mz1IxoXIQhU+2klrDc6ZaWCYFRegDsskjVqhtzj+CvqkaflSFleq9k0ZhAaieTmWuwlZIHmJikokkwaeAaoHuPCah0RYE2OHluVIhOmUmwBxXEjLKcmK3CCqhUrA3MpgwpLRKaiWTC3iGXHN+HpPiec8gkZUhoK3jrtDExmtIdBiNiqk1ROiqd2Qn5THb0NSof7yAL+6I1waeOJmNsjo2F5uF1t1Jl62f84tPG40dHpOycb3dcXm4ZxyOwFvIWBB2iy28MJIfSWzATvJBHBRdWd1Z87713OTm+z+bO0dKsMNJ6YLVe8fWTp9hXT9jvz0DGkNCWRpGMY0y2xmpj1AhomlVIVaLON5AcRWcEp1eMgaaLrxJO38ckvpcWE1xVzAutt2BnqDD3BK0ivdOp2BL0x9yQEoxOkuB9pvQCSWgdms64Ke6Ka8J0DluOoiwLoAPMLYrlSKoFLzFBQKLhoSRmc4rE5Mh7D/lqgckaOTmrJLRKAOfSUC10V2YzSI5o+FimbCRZfKwENA0c3bnL2++8wzxV0qqw3+/ZTbc3YZbuFCo0Z3PnHseb+3FPbpXt5Tl3727QrLQ+s68zw7hmVqFYog0e85SamSVCq5gVxgY5sdYZay18Tq3hSSELuQpuBSWM48dUaRqNldyUvRhZBR33SzhMoasxGGQ19nWRnfZOZQ7vVTFSE9oQEzUmo6jRF6/qQYRAcQV0RHv4Xk7d+OLRF/xP/+pf8qd/9uf84uOf8+Xnn9KnxhsP3mM6mhg3R9TTmd3W+cuf/gV/+Ad/xMnpCV9fXJKsw6pxcnLMUUlcTHv+u//2v+Y3f/wTzi6+Yn5S6d7Z72c+ffKMf/ijf8CPfv3X0FUOX2i5oPqAqIV/LI3uxvn+ghUbTlJZGDiV5iWSi8nI0civ/+YPeed77/HRh+/wJ3/259TtGa0csd4c83AMhiJaqdnju1FHKNGM7I73iVYHJDvFQsImrxEQ+crz6gag8ybG7kF+eVjukNh9E6v5VZ9zE9v3JvD28NpN3sMv2+/DuBrud32fbgLXXwX6Xn39JtD8umT0puN+1QT0+nLXWdk3sZdvGi9jb9/EQn+dSd50+RRSYdbEdH4BvYY3YTf6bHSHYoYWwbwQPJTE8aD4IPii8trt57B3SkdI71jPkfdQJy49nqVl6uxaw1pilYQpdboUWuuoZ+a0RyxUXHnxdXccq3Pcq6WhQ8dMmEmUBL2FpLYlp+QAJbuH16F7X9g4a7AthciXUFNazIMwDd9maY5l6DS0jagKZUjMiyc7KVN3RsoVzYlUoE2dAagWnruqipaMNkiqDNkpsgoP5s3IQCIfpagX0xq6M2RYy0gvzjB2agUvjteA7osJl7NRNpHO3k0RWgQm1pnehboPEDcxoS3jx2uGtbIpG1IeGXPURCRoydFkzNnp0wjsSDnRa6F3eWWy+vWRRDkqA6cnJxyNR+ymM86257R5xrrT9jNnlxekiyNa3fLlo6c8Of+aaiH/Dsqu41HAxjlOMCtVFJGFFb8E6tnz812ezwVkmXdlzdy5c8S9+w84PdmQJFH3lbO2RUrc09PhvqCxhZQzQ9Fga7UelimiKM7l5Y4vnzzinfP3WG/kkI0W16AoJSdW6zUP33jI/QcPaX3iq0df8dlckeoLEBDgcezzAZh2WMBpiLqqFCGnxGpzxL2Hp5zev88b9x+ST9Y8+fBnPDv7mqk33CIE+/HjR/zsZx9ydHRCr06nsT17ytmzC7b7C2brUQMsWReeOrt55q//9m/4oz/+I/7k//kD/t3PfkpvnZwVTQPeI0+jeuOrp0/JpbCfdqgUvnr0FdvLbTR9PGZt5s5mXHPv7j0ePLyPifH111/d+twxsQiGXICc1I0uHp6bJApgGdwS1YTRBJ/mIDpYgKFIp1Zf0JTwEw4bwMVuarG3c+2B2pQl8BHBNSPN0SxkdarHfFSK0wxyTlgOZ9KUI3PE1SG4MLh2qgnaKu6GtGDihxoi5moclEnhAIR0pWR9bgGDKKQRlYK2RlUndcdUSN6gafg2W/g4d43vSy3k6rLMJy0tXtO9Qx3o2SiWA+ax5ZyjB4kovnQKuni9sgTY5+W8VDw1xDUaQbpI31FyiyQn1Wh0dO2oRyC5iiMtrlEVxeaGdwtv/xZM7WA0NqQmUvbFskTIZJrd3l7lANzepDqCF8zbAwjt7qSUODo64v333+fJkyc8efLkGzXPbcZtnq0HhcLVGuRQSx1ePwCiB6/oQxjgIaxwmqawmcwZd2cYBmqtz5m712uFwzFebdq/ilX8qvdepbC6uv6rAOrb1h8pJX784x/z4MEDtttt2IMsx3/4fZ8zpRvPQe2rbOpDY+E2o2cLhUEOUEe9hElCSqiFnUSEuysWFxTJlIaGjaR1XDKaQtXQxFApGD3ObYucK9eCJCMlQyUscbwS9UZvkBaGrQFzJ495sVNqiBUygkghbDUMsQX78lD3oD0a+8KyfxHkihNK0NlBC06FFNlgmXj+5W5UM5yMeajmUzbaZGgq0J0xEd7NKtAyJXcic0kwN5Ivvu6aEHHUG60Jg2hkcRBWsb0vGJmBlkqVHPO+oTE3IbksgdEOawlGck5AI4lF8HKK3I2Wohks4qGeJyPSMavUHvhYgM4aRCCrEVyK0ktfGNUJ6QldaobxFTk3/78AzP6K/8G3Ac74/8sB2RvD527Y5vVpi39riRev3mab3+2AsfCaF3D5VZ9+q3FL5Pd1AeJXfy/fseFfEo1WEdT7AqiGP3HDkBTyqr4ydB+eb5YdqT0kfAZ1DKayaCN1BakImWSd7oApkhZ2xpIcbiJYM5IVdOjQjFYCBNK5kMWpvbHWpfAxDwawKNWNjJKkUQehmzKIMLdgNecpAmbE4qJUgqnSOmh1cko0HSNR2TrujZo05H0acnU3jf3HScnoaQksMqFnB6+ULtHJSyGJ6hIyhWqNjFKGMQq7LowYOc1IUxDBmyGSySkhsqTa+0i3YEyaNnxKSDb2Hl21dQp/tr0poxldwt9sYKDTSVkRTXiH3htZhJz2WPf4LuaOWabl6DaOdWHNSoEMRaITbg7O7VkZVQwRxXpCioGHt1gT6ESYQzKleQEmzp7OiFxSikQFWjpCIQ0F30/PmdrdjTQunUGThakwUdvIkDOb9cC42fDgjbucnpzSDXaXW47WK/qdIzZHK9yNL7+YSBZhRJZjkp2Gwlg70wy5C9WVQSuSjbkLJa3wDH3aBxuCTlEHgk1kKZjRWBSd2hfLjuTQwx/KuuJt8fkTAjwzA+3BgPCG1wx0JK2YpDMS3V5b2EtDPJ2jIDalzxJWC5pwEzwvYEhLaBKEPZMJUjNJHU3CYDAL5FwwGuKw6pGYWzRuhkmiK+4FcnVIGkwboLpTPEEXfDB6bbQ0st9P7Pc7+jyTknKyOuatN97iiy8/e437DnTpjD4E8Eilzo2xZNbrFdaciuM+M+Q1kp0MYX/Rd3SJhzwak225A/Mi0U37mDhoUO0YaPRa4jfIPa6dvlg1pJFa91GrqGBTJbzARmSYkX1c21Pr7NVYu9IVpBciRFYjOIoczhL5iGZx3ulYMFHUounQvbLSTG+dp1+f8T/+y/+BL7/8gnfffsjl2Tmtde7eucfd+6ecnB5z5/SU2QKE381x3/169wzVTts7T8/3rMbCZ59+yvvvvMsPf+M3OSqJXzw756PPPuTJl19Rysjf/3u/ye/+zj/h6OQEHQo9VagJK5DmuiSnhw3H6WpDnTMyhBdk64k0JEQ6PrYo6CoclZFf+8HfY3N8j7Nnj7g4m+htxrOTVuGTLbUvNeAc10hvVFFIA1kavYckVRW63n7y8zJm702A5cskn69a9vrfV9f51nl8BaS9yULiMNn7rm3fBABfn9hcl8TedMwvA7Svfs53TbiuL3NgWr+MKXzTOteP6/rf18HilwHKV5Perx/bLzOmfaelhtYjkIbbTCOF5B/FmuMyBBN0ALXwmedYGcsaL1DN6KMyomSd6DkxOuwvhbqDft7pDVwzeQSvRtVODmIbUsN7v7SMi5F8wFoli2NZF5Cs0q3ghKXOCmG2XeQvWEjh1ZWaEjYFMJnpaHGyd2ZZ/EGXOqY49CLobqDlhmdHfUBbQ4YGfcB6ImtC3PDFbskXgMRmkGJMHhPfLE6WIbyUs+OpMBwN3Mkb2gQrcxjGRSI/hO5xhFwWH+FuGIkia8xnGpXdHGGAg8ZEMrFG0gXNVrTesG7MVtnvO3OtKBPpzoY7ZWAYjzjSEVmNrEtmXyf2bc9cK00cX+rP7BlLoNojYM1v39gSAVIwwlfrwr37b3Fn/SWX5+eYG/vdns8/f8z5+Y7ZKvN2R+0VFUVFcTXcJQA7dGG72II5Ggdng6X9E96uHjZhC774YiRntV5zfGfDZnVEm1vUydaRJngPdvThc1SVo82ak9NjxlQ4e3bOs4uLuLZc2Ww2vP3O29y/f5e0Ktw7O2W1GjAPpm/RwtvvvM0Pvv8eeVxxfnbB5cUZKSl1BhFFU0DgiL/wWRZBjAAXg/C2+AqPvPnGm/zg+x9w78FDTk7v0fcTP//rn/Loi0dYqzRzdG5cPDvjL//6L8irgffefpcuM3/74SecnZ9RZ2g1JOW+BMC1ruz3Ez/76d/w3/+Lf8HufMuzyy0qzvHRUdgCtsZ+atR9/E6f1s/56klkgczbiWm3p801AA6ABOv1wNHRmpOTO3RvPPrs7q3PncGUnhJmnaQawKeEL7P1FsHFTWgYRZTaOrKbKKsEWejVmcyRnMGNNINg9BK5Dy4eHs5TMPWTBIARHGxf2Lfg3mmysAibR/iursAMcSij4k2p4mRfgvH6HIHvHXoKFp8YdHFoPaxSUsJzRw7sXolnf7JgFafemHOoQ4u0xSpjoOcJb52kwuzB1re2Q1LcB4tLBK76DEj43h/Cs5JGbS8Zzy32KRmJkNNHU7th0uN6kLCG8R5sQZXle6MEscWN2XvMC8TJZliqEVivjlcDXRiUiyezZEgSoZopK5ODp0ZqulzFTpfwjFcJW7qewp7mtuN6kHHv/Rs5FHFv+ubz9MAKFhHu37/POI48evyIefrm/M6WBsr153I0lr7dGL76OYea4Grg8oFtfXW5Qx1yCCQ8+Egf9v0Amh7sNQ5Be1eb8VzZx+9qTr/O93o1KPrqOIC931UvXd2f7xrf//73+eCDDxjHETP7hn3J4TvrvTPP8zcC/9wcGuz3++fL3GZYN5L50jCKxouIIRa4RniMtyDhiaEmtN4og+JuGCUUD9hCvMp0q5FjNGiQ2DyUub0LRVJM7FLsu3im5AiT77mGmtM9uKme0RzqDSdCBTGeq68MRRfFi7pTtSIMoE7kfgG5IK3Sc3RDXaKBVATMOhVZyFdKoTMvTSQRQXKEfPYcgctJQvGCKuKRt2RL9gWyPN/ckJSi6a3O3gzxRiLecxe6NEoaMe9hE+WCmFJ0CQbs0ZLR7tH57UEeUBy6IXm1MJOhqaA9AlcDcxMghdmOV5wIL3UPslrYLUUek3vDZARrJMDSYk3ykvErAsyvgT7egB8fWL/+jVdvGLf4iO9e5JdArb9T5bbcoBaj6V/ttsTiQf3t/XrxSvx1w3Tr5dt8nXdvWvCXPKhKQ5IxN8eKgGRkqX2TVtrUwqOZTp8TWSvdCz0p1makCzmH9+hklaF09s3pQwTDZOJG5qLMuYELZUkzNnNqFowBaItMIyM2RfdKlCEr3iQSg11xOl6czEGGJ+QlmX0q4LmRbaBOjgyJWY2sC7NBQExwU1pxUs0knGbRJbOloFHNNG1giSFDq44Xo3TFZQmpanG5Ss5BdRVhQJgaUTyJosQNJoobJ1NIZGhx/L4U70JHNNF7i5tIasuNrmBemTWSi9GZ7hlSiQC6xdLD1Ug20gok7WTLWDV8ZbTuZCrJG30OQHnOMEiwu7MJEw0npDCJ4dbnjjTDh2ATrF0wn0IyxwolwjNUnZQ8bqIS3kXVCqtF4tkKMM9ozsG2bpmSlOo9OnYkMEF7hBBUN+YJ+n6i1UpvxvpoFYBpEi7On3FcO/dO7nG+fcb52QUJXbzPGrM2xlzIPaMFtBlpYe5qigCP1mZyEpLC1EGsEw67naLGXDuJHFYD6wGZooC1ojCBpZA7kxSVGWQVftI9vHcndYZiSLeQ2Ygwu1AlQkkkFWabFxnfiCUj94b5EBMBcaT1EACgMYmQRFZFhwiwbGbUwFdRtSW4NNj3Ui2ClBya12BNdaVKJ3UJoF1BvFM1fAF7FzR1zI2vn53x2acfscqZMowcr0c26zXhF3m7oTlxuVNSgWQONTNQ2PWOmTOKkn2ipSMGCm1alAHDJXmvsE7RDKvgI4jByhMVR2vFk+JFySa4ZNpuj2dnenrJUDboZsQybC+3rCRTxjXmHZeK5oJIp06J5OGZbXMEJboblIURliqtD/io9NlJXdDcqBmsTZQpo1nopSOu5NxJw0gjU7+eePP+Qx7eu8tf/fXfAp3TB/c5OTliNSptntmfX7Iua7bTOV9+9hljzqSuNN3z1fYJ07PHfP7oCTDzn/+z/wgdhP/l93+fP//TP+GnH37I9uk5v/ef/if803/+X/DgNICDkhNWJQI0W6Vt57AHOUqwNnKLoNYk8QTTIa6/akI3I6+GCFsUkDHx1tsP+d7bD3i63fPs/BnMjdV6ZLU6JrVDoTTidPazUrrhq/DgbK1ieSC50O32jS24PQB8E8P4JtbvYRL0su1dn8gdJh+Hda6DrDexdq9u97q88ybw9Cb55k2vvex44Zss5JcxoK8f703fzeGYXzYZu77eTWyrm9jah3FdgnodfL7KinrZ599mTFMAi90maE4qTuot8iJUSTojvSGyotLJtovnwz6SxNc5M/fK2BsyrMhlhUwV7wPdLrF5wrWyx9A0wz4ABu+hdpA+08kM2VAKrU14Ck++lJy5h4xzkDWeI2/C3JgKYTHmca8vqdG6oqkjcYk+D4MRcXwc0Z2TFiVMbYuX1mqZtHiEWjWMxArU2fdK9oSOglRwT6Q00iu0AYotNmoaChtrYJqxLIx5zShKUyVvDHJmGDNOxlHG+YI0DtRyhPcZfGCVI1PCqbSmNPZUXS+hvTvmHOqdPl9gKtQO+31jqjUAsnyH1eoOw50jVrKinBYSFlYVLvQLhSlhNoTdSEm0OST5Q+5YNabbkc/iHO2N3VTZns9s1jNFC3nIwOLTSeNi+4xpfxm2bprIZSDngXmemeocVinKMgFfLCqW+cTBu9iJ2lBsaXp4BNd5YJJIWjx6U47gpyRMakw9QLh5nmnTHGojFTQJ42bDwwcPeOvNh6gkxBMXu8uw3xBnc+eU+3dOyDmz3e2orXNxsSctlgSbk2PefesN7r1xj7oTzu2S3XYKIoksjPYUzNeYL4YbsqqHX3ROZC1oitDKkjL37p7y1jvvcvrglMv9BX/xZz/lT//s/+XZ+Tl9rrTeETeEiU8/+Zw6/yGfvPERWpTPPn/E5f4C9RYgghPntwtundYrz548ZXe2je8rjdx/+JC333qTXBLnF5d88cVXfP3VF8y1UafOtA8Qxhv0Fs1q1WCYCwLmtKmCwZ31KW+99datzx1XWfJcPKypNAADPwAElvBipDmCs3Jukc8hErkyLfJgxIO007IvAHXHtSM+Rj6JZpCOqS9Acyc5kBNVQXr4evoSHm4uYZ3Xo/HRm1OlwVIbMjlNA7wRnO4D2TspE6q4bqA5XMLbQiaSOF9z9rC4a0G80IUYYYfQSub4zCRYh6RG77oAxYtfq0XgdFNBJC1AjsUxmmCueLO4bgSS9cVqEjx1chK0FkxqeJhLI2miqNMXYMfcsCXjRW3x0+hRl6sVTBfUQBVPEQQtC0NbvNI8rAbMYs6XROm2kHYkAP5EQ2qmqeH7Ccu3f2YdnqUHG4rrtccwDM+fgVfD6K4Cv0dHRzx88JDHjx8zzy9qrVfd/q6/d5OCqLUIWj3s48Fz+Wpju7X2jf8fwO+rVg8HAPpwDAe/6VeBqddri9uSCw6v5ZyfByder78ODOqr3+evMk5PT/n1X/91zIxa6/Pf7Gp9tl6vOTo64uzsjLnOLzAdJ7JaZntOWLjNyA5WAmwNl5oWeIsu4XTSF/VTQlgIVwrVDdGCVIu6Auja0A6WMj0l3Bu5dVokjS6h8DnwX23YFNlW3TLeFFNh0EQ1KOLMfSJp1ERLuxVRCXXXEqwZ54hjs5H6iOfl3iwd6UZJKRRYXWgL3oJZrK/QfcJQsg8BXqdQQ2XS83rCWg0LD81xvafDPSBUWb6o73sSfE6IWUJUH08AACAASURBVLxOqFpnUUxmpAZ2wGK/k5rQSuBWyeLYqszkFI2b3gRUsByq9SRGLUKVXdRdqojNOAnpJWyH3BcMoeFiVDQYZEWhh8e0ehD8Bhlxg0rgG9CxVzRBvgNg/i4Q9iXvPV/tBlT56tqve219J6P42me+cvyqcHB8VsC9v7LTM/ByoN1v+Ovbe3Kz4/R39MheoPy3Ga9RNHeL0AF1Rzr4IosoVvEldOL/Y+1deyTJkjO9x+yc4x6RWbe+95Ac9gxnwR1ihV19ESDoP0if9GcFfREkQCtAWkhaAbwsZzjk3Poy3V2XzIwI93PMTB/MIyc7O6u6iqQPeqoqMzLCPdLD3c5rrz0vAKVQrea4PnlByYJXMYu8CQsMV1qpqEOZB90dbdlxmcrGJ57ARDO87gQtFJGKtTwnKjVHE7QTMmfx2oSxDFpThmU4hUWgzWBMWATSQHoka6xaFiLDsdDs4nhPLpcWKkHULJ9vR8Oi0IEqwQ7NIBspWDPqkM3plK5cRNE56INkIfpAC5SRqaJiKawOE0bP0Ye+OVUyBNCokCnHKNWzoDVNF0D1IGwglQyEiSBUWWML+JmEagLeiDht42MdicQqBIIdB1UrQSFGZZ5zYeMCqwuZ7qzULvQgR+fkHZKxFdZ1UEvbuGdZaJ7kSI05R41rJWIBFYpMtNLY7x7z5NkjHl3s6Db49utviesrJlGOA04qSE/cRmmVgyebrgrgB07LzMuXV4ByfX3FkydPmKZ9LnQsxY1WU+Z+SdB9JBvJMkjPdWyBN/m7C8kbyuRCF09eIhXVxizGGIFEJ5gyUVxXwhtejckWRmmgFfdC8QVKMIpTZQKreK3AQkQyo8rWR6g10RxLZODURSu4DUJOeROL5EYVz0Wl2MBV8NJyzKYbywhKUYYnykJNiSa5cPCFosoY6XoRBbOK1M1NR36ubHTUS44JIVCDZQBaqbFmYjCKzMqyDNyP/NOvfot1+MlnnxHmvHj1LXY6vvW54+vKLLD2gq5H2rRjNedwOrDbVcJ3uHdmUXKUdWW9GdSnE60WwiSLphaYKjWyqG+lM3ZOPw7m+oiyCCcM1ZHXEpQhCyYzsSqTCk3zs4Nk+FI/LdT2CA2ntR1DOzEruigeA1sFHQs2gpvTFZf7PWUSyrbwaTXDLXwEUa5zxGkB0RlfC1aMx48e8V//+3/H//If/y9++/lvuD4O6pwYm2u74pd//U98+N4H/PlP/pxf/PXf8PuXV1zUwiSNeT8hLZgsePXiBZ98+iEf/umPOF2f+MV/+S/84pe/5vrmitGcRZxHj2ameUIjGeK95IiuGrDfoXNNIcAKUQvNnJUUiUI8i60BoYZ3Y4pgkZyCqOEMLTzaFy72nyDh2IhEvgQMBq6NcXSmmiNkOmANaFNGursH6u/GYL779/uC6N3tIYHz7vfuCsN3Hb9vElt/SKh9nVB6Xxy+Lybf36e7+/I6gfo+o/D+az7kdH7IUXx/3+++3n0H8fk5HhKF74vSd4/p/v7e/3095LY+/+x50Xr/vXzXbY3kkBYfxE7wozPVLN5rLKgEp9JoMqg+sHkmYsrwGDswjjvmcLrsCINFVmoTVl8gTiyS2KVdEY7RmG+DAx1sECi1JINVyNwFi8yx0JKsYTXwORdOYrlgiZEorzKCVaH3CoysnWogJjSZkZJj/zKgloaMZLs6ZVtkSgbNGQxRagj5P2MScOvIUsAqGhlwTHjeNyenHCTzLSyRB02DsmvMu4nShFYCkT3SNMUVY8thmAkK9dqwOTJkrzq2DvriyBjo5Fg/cNJGs0asHRuBh2GxYn6i9wN2MGKeeDRdsLucuNztKaXSSoVudIEpCkutjC7sZSb2Qm3Qd8md7ZYc1CJv72D2ENbDgT98+xX1stHHibUvOWq82Y/z+mfs9jt28yVPnj1haoXlZLx8+S0vXr2gWyaeeGQIcWQpsjVocyz3lr9cNOvyiE2TzGkod+dwuGE9LlzF4HhY6OuJ1QaH07I1FFI8btOe9997jw8++JCL/SVmTp10Q3kF7sphOfLi+ob51XOurw786h/+hudf/4HeV1wzCNBFWY/OzbLwzTdf8+LqGjdLh2zk+1OKUKjZ5NDc71omdnPj/acfMu0az58/p9uKF2GnE1Od+M1vfsv/9qv/nV/84z9wOB6wSNYkCKuvxCH48vPBt8+fZ8hTt8xH4Dwyn5NjQjbTbXXWsmKjU+eJZ5c7fvrTn/FnP/pTQgbPn78EE66vXrD2FRtBGNmpjsRKqEa6ZUsgIdzcHPjq22/YXe7SjTm9g+/LKuHJ1aQY2gOXDAMtmqGN4uksLlv+hcTAraTDsJNTUDZwnKowrEJJNnNITja6bChByjbBIFjJxVmNTliucYRcg6Uo0/Fe8EnAtyZBGGZCFahuDDGIRiMzcbqnOKySDRCTNOcUgdBGI27d+FHyFVstBMkVHSTGQyXrchdAg+KBSQYPquQ6uZcUkBkp2Jom9ieEnDxRo6+bmyIEFWOgVDN6shpzgiAy/FFDMqDPwS2jooMcK6+ihAxq08yVCQgLmJJvzchcjREpfoU6bLgQVU8zhkBRySkWgRrB9kRsbXvGO+gah8PhFn8Bf2y4nv9+drWe/373e2eB9NwcnqaJMcb3gn/vb2+qKe5uvffvvN6ZE33X2XxfKL4vLp+3c21w3lct+r3a4VwT3BXPXxdy/Kattcbl5eUtsuOu8/rsoF6WhcPhcMuOflPWw5vE7Hme+au/+itaa6zrmnzf7XF3G+e9d1prTNPE8XjMr0eeMRICwnfQIT+46TnwPE2RDoRt+RKe2AUIGp4I0qhoKYyYiJH3/oFRNBE6mGBiNHeMTkwtJ7OrUotitmbdoJrZW6VgBl471St9GWitDEBC0A21ynY9M8nmeUTilboF2slQTJThg9Bc46JTmhuLYl5ozROTpEIPz3C8ECg1JzE30Vo9nb5l+9Tn537OCQ0ic3V0QvRIkYI1GGuaAIt6rlNU0CG4eF5TM2gsX5OaTcoS2TyLAdIYZJCfjxPMlbOLW8zpMojt91ujZC7AFq5VFLxnrkJUCDltDWilbUxn1oJoZYhRChRL/AgmVC3Jz+6JMnrd9vZ3sjfqtt/95h9D5f41RNw721vpuP9ar3k+pjcd+Pb1txK+3/Q6d57rn/kj/+w4w9deNB847nfYxaoprsVOKANMg2ILQyrWQBmUXjCCVnI0JwTG6DTNrpCjaB1gWwGh+XEKqRS2i+TqjOJEA/VKlxQr2GDqoYJIByuYQhPFouESTNOemDwTOyVHHUVTeJIx4V7Q0pGRxUmpgWgjOhQtGNC3IqNsPLAlMtXThkJxdM3iftLAS2I3RkmXaTVLp4fGdpGtlKqo5cIntDBQxkiUCKHYdgFTh5jk9nWKZtozKDY6slPc2pZC7Jmm2rLgGVooCMUy6V0icB2INGSAqVPsBAGrdtQrVTNEpp8UWrIVe8mQwcJWTHmyiSSCKAs2CWoZtLF7FydhlQzn8hxzKVtq6+QKLfDWtnCTyhRKFeHi8pKf/PQnfPjRM3b7He7Bbx99zj/9wy+xYyDliFoQFQhlRKd5uj2GNFQSh/H5V19yvLnm9P4zbg4Hdrt9FgBurMvg6uoVy9JzjE2hSsuwEJFET6hCOFNJ9rJocrbVKzpN6S4OgchE1iIGsTCViWVUVG1zOBfm2kBXhmxp9V6YJX9XhhCxEjajOLYAmtzmIUoGW9a8+fugRaU3p3RlSCRvSjbnWoBHYVhHfKW2ihajsXUvLZ3zYUL1zloaURZ0OERlSEOko13yOuTJ8BOHVZwilgGSHnSpGMYypk3AGBlWZSdaVV48f8Hog9PNibYvfPnNH7iJt7+2RUkcj7hQW8MJ6qQ80ZnRArNO6R2RHSdKBvldSDZwpsEyNDEzGFOQqeO1YqJUAX0s2OJYsyxY5sfUMPTxs+SBSUEqtNgjLRnVQuPYD8TJaDsjSsPU6F0IJsyWDMdpihdNlvVUiNEppRJFUyRYO6oTEUYwsasTeLCf91RVpIDNlVeHa/7T//Ef+fUXv2MuFYsMJLz+9ppvvvgD+7rj//m//xO/+OWvmC92TB99xGXb4T14+uwTZoHj4SW7+cdcWOHi2QX/4//w3/Pf/Lf/Hc+//BwCfvrvfs6j/QU6R4b76DauSyekMQHTKiy6pRurImXCi6N9zc9FDIoGIwozK6fTecRsMIrSlmDxAnNwXB3Ek58aTovATgtoZdLGygDr1KJQlOg5yVLKO5w7D4jKDy0u7oqQ9xEUd8Xe+1zk+4Ls6x77OnH2/tcfSkN/aLv7mg85he+6p++LvfeP9/4+PSSa3z/GuwLv/fHWN72P94/rTYGID+333cfcfw/Ofz87jcJTwD2PCd91pr/N1iIxVcUy+0HazFoGZcAaeT0pFE5SmCuIBeJH+niMLo6VHIuu2+TQGlPmDVsnJmFy5RjOOC6U1GlZuzKVYB0CqnQqrJ6BxtLAhUIySItmSJe6YZbN7YsZ3CusKwsZVFrwbJAa0CphStROq0qMfTr9WNCpUcdAt4kwYjC2NPgeBddKi5ENxJL1S0SApNXXyLAaRyhWM5l9C9ab1dFJudSJ2tlQXTOlCq6BHwdlLqA5keJ1opbAtOK+4jHjxwzKDQvGqeQIartGxp5YlU4iHJbROZgwrjMr4HErXD5+xDwrVWa0KP0mHdZlrvTJiVcH2kXQpgumXbIRl9GRGizLRO9O5+2bomMMluXEH77+KrFTo3N9dc0YRuCYk1MsTdlfXvDTz37G+x88A5R+PPKHF5f0Xyz0pWNbYLZGLsSDFJDNQS0dYrE5suL8WRFQd5DELF29vOa3v/kNF48vqVXp3bl6ccXNzQ3dcmy/lcp+2vPB0/d48vhxhr6Z4cOSU+2Oj5Uvf/c5//mv/z8++vz3vHz+il/+6h95frhKZJcEh+XIy1fPsWXh+atrvvzqC8bo6RwtJetXEaZ5Zpp31KZYH1gfzNOej//kUz58/wnhjWWsvHy5bGHYQrfO4XTFH776DS+eP2c5nNKlbln7yChIzaaA98G+FEot7PWSVjp9WThICnhhKdBjA9/cecWC2iZ28475YkZ9x7IfTPs5BdbY3LhbjXVr5pEMq5LN8XdaV7798iv2c+XJo2ccltPbX3cmZUkHTqICSzZuJFJYlhY0N6xtkw3hnE5HjuvKrC0FVktMGlExC2r1bfUuYIkHVNGctZPNUrWNS6ehXDJs0yKdfhXCKusq1M3FjI7ExBHUMpPGiJxeMDyREKaY7zb0BrStkZGOPHIE32TjqaYjMJrQo+Mjr+nKwLQQUfIIRGA0vCa7GAmKgBHIECDrOCLXdK7kxS80RygFRLf1VlHqFhCmnmGAaDoJNWqiRXohdMVV8XGeJBAkOk7BRzrOVQVTRcwzgFPrZuBJHr6NiUpkJ60Jowc19hiDCIMgR9MlpxqbKjTfpoLfbjudTrfc4rP4evf+3Hv/Tm1yFl3PNYS7p5Tm9r2APPj+ffhN2/mnbqWN7fXPYm/vnVLKLSf47LqGPwba3W2i995vBfC76A9V3Ryu3w8Svl9zPCSGv67eOr9P0zQx72bm3Uyr7RbPcRavW2vsdtlIur6+5nA4vFVT+6F9+9nPfsann356+56c34Oz+H4+9rOIfYvtuLP77/I7Om8eOS3QvCZypkCLmsgYkWT8eqITimgidCSQsGyqueW0glWkBEtk49aq4ZZGKchgQqJiI5ssjiE1EaXOgqIgRsb4ZMMnConwLM4kJTFWns3/KEF30BqMaDTN45iBIRXRxPuNsTWUptQ0CMmaruR1Norma29ZPyKNITkFQgThisacAr5qoqWKkNgQJXpivOoWbKhsDd41oAWqFWVlhGxhzo2inT4yq0stJz18BAXLLLGq2TgVReOUgfRqDBeaN0YJakk2tZwRGy0D0kNyQn112+AYOb1kIowIytiQSQUUpaOIOHUEQ2JDWD28/YDAfG4Vvubbsv3n3wU7PARxeJvtjRr2Oz7qX2PLV3qL13rtOiRvAN95inu7/7Dv+Ae2N+7Sv9b78y99jg6xIy00G1O3Q/iC2I6CUrTSujFaIK3CCFpVIEU5pKB9bA5QtsAEhzIROtAelCkLP7McYasC3QSJ5ClXJRc5w2+DYNYycGmMSZiiUqsybCGjZhKsYc3Q04p3oUmKx+ZKqZ2Yphx1QlDtTDEzqmLryvZpherpFkYzaCMUtYWM8VO8DNRbXoSkE1op20iDiqCyw33NYiaSr9PjHBpn6dijJgheDPMdk2WwCm2iDMneQQlG7fi4yFqNwAJ0J3RvOQpIS0h8NcoyUBd8XxAPZGTn3SRTVLNxudKHZhBFU04WMAatVhYtyOiJMPEMnVNVur39WW4rSDGGZ1hQ9UAj0vW5sYk8FCTHR90vmHczT5484eMPP2L36BEvXrzgk48+5ptv/sByPFKYqGUhrNKHYVVwzcKZzeXtdHq8oh8Xrm4WLh5PPHn0mKKFw2lBI3j54hXXN4d0XwHOilvytr3MhK/IpExDUHUOkTdEnSYQR4rSRnBtB3SqFJ1TjO8poIcMhmRI5vBkroU4UZ3SfXNFF6QaeEdKZfQOMuVIYSgVYzSnmZGpDkHXHOuNWrIr6oYXhY0fNZfki0eZCC8UX+laMoQPw9QztGQI0QUdM1EVGBQfmKZbT6sCetvcwQsqjTob6ymY1KE761QyQGCqRBg1grUv1AGngF+e/oFpasRYs9B/y22EU4qiVZI7qo6sC0Md8ZbBLjJh0Zl6ZdVMA9cI1hMUOSEyg8aWbJwoHglB6kB8otUMexBOuJUc4dKOLi0XSZrunUplaArqF/tL2Bs2nBELOXQNIh0vE87AYmC9UllYyLCIdJDLlqCcacSx22d335NPeRrKZYU2Glcvv+Z/+l//Z/72H/8OW439/JR5v8d1cDE/5ud/9e958e03fPPVC3Q4tgSnqxsePZ1Y3eAYnEpnt1/57M9/zOWjC6aLSz6cL3nyZx8yrv+KMYzSDJozqLSS3NWxrMhcEFlxqRxja6RJLuTNjdZheFDqsoX8BWqF0XNxrAVOlsEVQ/McNRsJOxqZmKxTI9aBhaBywsZM30Z8qyg90pk/18ESbz85cd7OI4YPOWIeSv2+G5Z3H5fxQy7ohxy4Dz3u/sjmQ06h+4LofdH7vqB7//lft7h63c899Dr3v/bQiOnrju3+1+8/z/2ww/Pf7zuh3yS63z2OUguPHj3i0aNHlJrX0evra25ubr6X+P5DWxGhi2KUxC9oOvlNghgD10oQ7CblbAKNkqPaodMWzKU5VdQV6Td4CYYItmqObGKZSCOKrKBjQFeqpPtvkYF3p4ZmWKwmIqlgaC1IN2oVxFNMulkXpECtuplCDLNsJM41m8bTXJiloVKpewGcMSp6gDUUqZ3oa15vx2BRhXVJRyWVYDvYujkbmwCKWqanxwgoHUjRrZlSKsx6QS2CT4VSpnR9loH0iV3dA8l8HZLTZ24Qy5F+ZkA2pzq4F0KDYwzUlOJrVsgjxYfhsK4naM5umnhv94x6safVPTs6XYKTnKhloB3KFOjU+Oijj9G6Q2fHu/PodGBdDJmNcjpSxjtU9e4sa+fbb6843fwjg87N4QbzLcBaJBu0u4k/+/THfPbjn7C/bCyHhT5XtFY+//x3yPNXxCaglVLR1tJfLolec8nfrxFgsYlfkTWFBvhgSLD0E9/84WuuXr6kTA2J4Oa4sK5LGgoUUGV3MfP40WN2uxRUr5YrbpYjw88ic+f61Qv+9j//Nb8sEyOM0+mafuqYZzDr1atr/unXv2UqjdE7qxl4jmurbKiFUnjy5Al/+uMfc/F0z8tvX/D8q29579kzfvzjP2duEzfLkaotHcGvrjkdrtAJ+rIyLDj1lWUkb/vMf87OulNK4fJyx8cffsR+ytDhPgbffP2Cr77+A8d+SLe+B0FiGtxSrDidTpyWlX7qdDqn5YabmxvWPhgWjPAcKyNS2FJFtVCnlvUNzrp0Xr54SWsTV5fXLIe3F5hFlCItA/lYEY/MJymClkpD6DEoZUq38dQY/ZQ14brklBaOSzaCJCA8MYGFdM0KQnNJRJutlKhEzRDBEKOUtmElkukpDkO2bAtNtNqkExePJtrFJZdTYx1wdfMth6uFEomTAEXnjQa6eBpBtjBHxmB4Osl1OKFlm0LI7JLQDqURlsJNslMTQSAB2gUpBZeCOykgRX4OztjEwBFx1FNUB4hGYmRIYc7JJmJoZI6PJG4kxdh0jzdLZJ9GBhGD5mStOnUI0YAeiX5x0iASjpfCFNl4CPct+LmCDWqAYbmGOLucRSAKWrcmSKRJ6m03M+N4PN42WVtrOTlwJ5/gbvDdWUCNiFvRcoyBDfvO/fjcoD3/+4dqAIA41z937rlnbjD80ZF7dgWf9+Fug/5ubXS3Tjt//7xfZwfz7Wv/QH1y9zFvOpYzGqPVRqvtO+8rZCNxjHF7DNM0ISK39cbbiMznffjkk0/47LPPbvnUZ/QHcBvyN8a4bRKc30PYxNh/waaagcQieXdvHvTyxwytNYJaB+7pTs7z1TMnwg1tiljQjTSkSUljStRNoHWkCWGSflvXnKDQyhiSU6HbW+UGpRZWi1zvVsXCaLI1qlajzIPRE4WKSGYXSU6DpwNbaUGa1+jJGNY0y4WCtfycNjQniD3X1xoVrYqJ52dGlOIpGVs4rkp0wUuy4KNAeIeaAYTBxFjXzJ+pC7ICQzAtIDnxaTVgyz+QkqVSCaF4Y1FDAvomqiuSQbslJzYilIIQElu9JYhaHntUUMM6CM4SgpQNMbhNe0jJa09MBfEMRRUJWkn8SKhRIoNVX7f9MCLjfNI/dO7HH7/+ryJp/oA2ehZ87xt4Rf5o1X/77c0vdvZgf++R72Q4ju8/7t6//zX3+cEXuP25P37ve8fzVsfybsK1yC6ddiP5pXPJBYtoY4TRp4r3wayaHKuhFDIptAosCrUYPhouiSJwaVgR1FJkrG2wlnTtXDiYJkS+SeDaYHVGu2TnN4watDKxqBA600hOsrTADZCZMkghdEMKlDbD6ATZHTTxHOsX0GhMkjf1UENDCalUiy10UKkRjArROzY13CqN5M0WV3pNPrGTCaCSxHvCV0oYvURalWxlCOnI9AwKa5YJ6KYleWgEoyazx0SImPCqSFdCLtASOaYKGRR2yG5g97K5aBvDwam4DooZqwhVKx5G74Npgokc9UODHg0dRrRMR7ZuxJT6+rRhN8LBelDaO4TeUFAJJjVCG5x63jhkA/NbBhBmETEhU4YxzlPj6bOnXDx5umEbnP2jHUhlDufgxlhS9CZAumxYCtB5xsfA1oLXheXVymlRrq+uqVboo9Mt3Zheeo78DcHnFLV0zvE+HCYRujplZAibbFiJUQvWjRIDLTXH6zxwS7d2ESeKoNKQUFY/4iG0oinsWyTT2yVvKAN8tnRRq6MjMmAxdAt/MxDFZtlYi54CaNmCEgrp9imWaeCaaBnqoNTspGJkGIwrZrmgUYcRUD0dZ7GmkGga4BPoilu6mUV1a2IITMroiQmssjJM0FrokTgbtcCjcLMckZGjwioV4t343WsxmoCOXRZDCFFWZB0sZojuiHWw6poLoVKzCFryfKpFOErnsiujNWwcs00kBZ8Wmk/o1OgLuK60qIwxs9SOjIU2NURmVjdKcaoroxa0B1YLk3WWQoY+rJLJ7xGAM5WgLxMXkxFaEmPSK+uu4BZc1JI9by85RloyKX3VyhQrv/7V7/h//8+/R7oyS8OaIpeF9ebEyYJXL2948fwbTn1l/+gynTR94frqGq2Vpx885dF7Oy6fPePPP/sxMgeTFJY64CTsfXDlndZmRgiT5bXAdaHbYMeEl5oBf1RiUhyjLgFVOKkkLwyAYFmFnaysQ5AKdPAqyFBWWZEVWjGO3am1bWzOlRWSJV5nrDs7wJZg3WUXf1JBRJlHe+tzB75bxN9fXNwXLu+KyW9iCN+em/ecvq/73kPi8ZuE6PuC6/2fufuYH/rZ+89zX7S+v2C862y6f3wPCc53/37/+w/t63139evE5NeFIj7keL51L7eJTz75hPfee+920fry1Su++Pxznj9//oMLvrtbKYW+OrEfySP1dLq1SD6maKJ3bDg6FmQ/s7gzufCEiphSO2gJlpqjjeakaEMG5qxjSZdOBKcxEBfWSiaRD5ikYKqMrQYZ6mgVxAdhOcmx9HS/1I3/X5YMwywb57jUAM/FUNPKvBeePPuAfamM1enhHNaFtSy0tXPKdMIctS8lhSkphGtehyO5glFKToksiecyKXikr1ttpraOSCNWI6wS6nTfU6tTixCt4pIMTd3eY07QRVHLybSp1ETF6ZFVBPfgZINZ9hiBHmqKcDtQmegDjqeFvg4uH81cXlwiO6W2iuwCHcG65DVGp40F67B/9ohLSZPjuhZiHRwvdjTtXKyD0Sr96u3vWR7GMMFPV/R+yJrJB2C5+NSCFOFyt+PZJ+8zXxR2tWG7jvU97dRRqWjRbZwW5nnHs6dPadNMuHF9OHC4OTCWvHmLJiZC2AL/JBsXAGPthBiLreiNQIE+nBi2oY2EUlM8NE1H5WEdvHjxLS9f3aSI606PIE5OXzta87zELcXlLeCpR+fq6sBOC6WlgCVTw63nCDnBxTzzk89+yr/9y58zXxS+ff85/9h+zftPn7K73Oc02dbIMYPnV6/49Ze/50P/hJcvvuXq5gpbB3FmzJZAtunU0iqPnz7j3/zsL/j4/Y/STRrCut6w2+059RPrmnVY3uryJ42st66ubvj7X/wtV1evKDV49fIVX37xJWPt2cjxraGiZ8FOmeYdj55e8PGHP8J98Hd/97ccTyc+//xL5ouJvrz9tF94MFGwmu40imQgYxR0Y4Cq5++8ThOrd0KyIW4mIIkESdRcBsyVDSkougFWBkgEujqhE5AYK/dC9UbIQBUiw1E2EH+LLgAAIABJREFUlNWKysysBZ2U9x8/5v0PP+bxB+/n+m4oz58/4ff2T5wOK1NYZnWsQSkpzJoJMTreoHpFxLepB/Dts1FKCriNmUVGpnVEistiQhTDtWWYWATIJqZr5Oi3l81Unk5IjMT+SGIsVMDsjCPNML9CrmkTV2ZI28bYvdyGhY3i1JoTnCI5Qu9VGAXEHVquw3pLhahKNgt7iy1MuyRTu6Y5CzQbhZq4PvPAtVBKEp+7ZQ2r30ns/IHrzsbtvbm5QUSYprblHuW99Sxanu+3Z1fsGOPWEXx+jjNP+H4j+qEa5Dvnbz74tc1cM2NZFmKbLmqt3e7X3f/ODuezkOzut/f0u/seEYluulerPDT1dP7z/uTU60wDdwMHzzXI+WdLKUzTdHtMZwf2uq63rOm3ZTJfXl7y85//nN1ud7t/Z97z+XdzrtvOaJHv1Gx8t+56VxdzjbIFeyo+jDi7W3sykYsngqtJrvvC03FP8a25W5AqzJphnxNOWGMtg7kpY00ntIcwS+fUhKIZ8lk00aRIBsoPTcRjk6BIo5tTdEdsXGgmpdOomgjR7GONzLAxp0o2l8aaXPlKZa2Z27WuW9aAQrWBRaUBSEPDkjkveT2Ike5fLYnHyLfYqQVWUpz1AUEjeuFYDB2DMinRc7oXzcTdKGcHeEFwQiPXO+5pNNDKSjbGkGywVQqrRMYnhiIKYUGnoQHDnWITMlkWCObZtA6SNT/lpDywoalGCtrljAhRmPL3PSQDBmsI5qkdvfZcefOpdOdk/xdhIL6/yaasf0ezfNNnSzbJ9yHN9oGv/fD2UJzeW+zK/W/8swzD2wf6/vH/4Pa2D76/Uw9c1M+Pe+tF1LsdpEbHySCwSLwbuLLqms5NcYo5o0CMQKOnbd+ctWWhHAFendXyZI4SiDlueZEzE9oIliIssoIWWq/026RyQePIQkVLMFqGyEwULvaXXDzZIx0Wf8nhasWrwSqYZRdqQI5ck4EmIY0+OqUaUp3iwohpo+B0pIKLMyyL0VBJ5peCLCultnTaIZgE1QeN4EhBR7qqq6bAuFqgi+PFMM+OtZpvgtLC8OxI1ZGOpCrOSjpRPAajBNpmCKF6ZEFG8nLzgqrprt4vqBXUOn1NwU9wVBtBx2o6yUtPYdkErEO9CHSMZIsRmCmlJCOpeGXBMmjHK6LGyd4lT3QkEsA8wz5KoZZMrA5vhJ2w2Oe4DYZRONqRJbKrfnN9xelw5LgeaSPdV+t0Qk9CmxpFkr8YpTDGSpeg9BOzKxEnxCe87Fi70+XEdMqbhMuazQsT6M7cphxJVcN7gHSkTqwuG/OtEcUgCqYOq0MJDiNvbDGCzmByp5eSCwGUMAMZTJ5ObfGCnxypBchwkthyeHUBFKoUbHLUM/VatG7Nifw3oWj05OTpzNCF4kop6c4Pi+w2l4BStk7xSpVEfsjm4q0WW5CIY5qjgaU1rN9kZz4E61sKLRkw2RVKJDql1olVgtILTQpBZ0fJcWxx6IYi6Gr5OQ5h3h3e+syxtiE9RmDR05lnWxBMaLo+cKIY46S5mPJMiqcOYuxZPShlx6FkV1xVMhVdlN0pMIWlD6gOpSbXUWHvygq4GTq37IBveB3WTpjTdoUR2dzyCKIOhm4uHNnS2acBbhTJoCuKUHvJRPMYSCiTGGspTEWZSFfLAL54/oJ9Ud7/9CNO1zecjjd0h5tvrylTwdaVZT1g64HuhXmvlIvHMBc+ev8D/t1/+A/85C//ko92j/nsz35MrRcsbnRzprajy4HWd1QrsDNYg24ZbnNxsbl87Fz4QVRhRMVKBkiEFuykyY5tnVkVyT470zKQqTJOOdUh4YyyUc3KBD5oPW8M0TSbX6OgPrAerDEoVnBRzBdUhNXf3v1+H/fwJjTE/e0uruK+cHpX8Lz//YcSze+7a+7//EP78pDoel84fki4fcgJfN6f+/tyf7v7fA+Jum8Ss+9//f6+nJ1Q93nRD+3P+TVftxi8L96fv//48WM++OCD7yz+ciwzXVAvXrx47bHf3yxKYqQMJHJRgy10TSdmiDFwmgsyzXRR8BWNBZdHTLYxTWehDWWxwO2GSSd6iRRKRooNxbPh2iXyM8GMMRiazW+60UtimVqB0QSJwiCw6qhns7dIYGrpqCSScUmAVKY96Lrj0cXMk2fvM0WBsXIcHZkKVeDYDVgy+d0Uq31bZDq1WI7NtoqfTpS1UURYNWu84smJvWiBccwFdhdqLZRmNFEuJ8OlMIamYKQFaythhTIUjSDWyOt3C6IZkxs9jDEO2GqE5ZSMRTZ6zZWdOYwjy9I5jRukKrtHH/HoyQV1CwMSNQZCDGUqjkjDtSNeqWXiYsqAadXTxrWdmKZ0S+/NGU/eXmAGxSyxBO458u4+cnmgGXBWRSh15mLas5tm2rxn58b1qxd89eXXHK6vMy8Ep9U9n376J/ybv/wZTy4eo034/Iuv+NXf/5Iv//Bl4mA8m/UekQqYcxsQh2SApPdIQXUMiERaOHHbGD8djtxcH/Bl5cWLV3z9zXPWdcljcU+n9OYSjo1RyfZ1FSG2+zve8dbYX1zy6Sef8OzpMw7Xr/jN579nuTlw+egRH330IY8eP+Li8SOsKx9/uPD46WMihO4nllNnHT3F7sOBX/79P/Dll19zWm54/vxbhq0QdhuCDaChTK3x8ccf8cmHn/Lo6UyRCadzcwW7i86+7fCo2SxnQ0dYsntdg7EMvvz9l7x48ZKK0n3heFwzyCuMvKMnc1mLsrvY897T9/jsz/+Mj//0z/jmi6/5G/trOivdVo5d4R2GbkoRUMspOemJylk16zbVdDRrMpWHdOLkCMEokfgRFFPFi2OeoWk22u24eZihVExGIr82/ElsgsDwnui0qnjPdYjUBgayKyAzl/snPPvoY97/6CkiDesD4sCT/Z6r/SPG6RtMlHFy8Mh6ytmCIguyDozkw1oJqhS8ZQi6RTqAQxak57V+3fjulZxakwApQoSBCQVnFEGG59h7CTQSBVOQzcGrG3s5r+dGNjGapLBTJEOvkM0hW3M6FAtcbWtIFHoZGTYdyWqt7oRMmKwsajnlqpsTGcAMi7xPFVXmeYLh7CbFtbCIEUuakbyPW7d4cmA9uahvuZ1FyOPxiLuz2+2YdzO11FvR8rydsRPn8L1lXRKZuGYj6CyO3g//vXUN38NkvW7a6f52fo5lWRhj3ArMZxfw+b+zgHzGaJxf9/zcZ1fzGZdxXwi/HzT8unrsddvdGuMuO/qu+CsizPN8K86bGfv9nsPhcOtufuj47z6/qvLZZ5/RWvsOH/ssYgO3gvVdTMh3HN28nav8ddu68dJjBEzkJLClSc59oOT0c61K92TDE4U6HCewKUghT3PawitFBlUz/4qS5pCC43GBhmdjPoJoUC0wKpgi1bZJH0FqpZoR7tmkAiyC6J74iH2hWkPoEBXUGaRQrBuuogv4qCg5SREu0HNSOO9lOVFKAVkVi2zGizY4Z3FVwVZHR043TJL3AcOoGwqseE6VFW+IeHLfETxaXp9d0FiRXql1oo9jImPbnE1a6cly98zQEq2IG6MMokvqC5rrcAlLxbHmPR0t6AhEDVOheM3A6Mip9wiBmhNt1Uri2yxSJKcwAaFxm3dQ34CwfLPqc1en9LtfhB8SaH/oef94sr8lJOLuouGf+7r3n/IHn/HsmX7D4+78Ux7Uax967nvvnWz6/T/3oAT+aP9+l3cnXr+L/8JtpeQNXWuOYJVAe2ei0HtQHYItREwFtGGD7CLHSlFyFF/IwAOt1CgQOZKwetCi4GXN0JpWEArEoLSVkB2oIRuLWTw7Xg1nf/GM9z/9hE8//pCqxvXVNb//6muef/MCYtAMRhV02Uwdq2e3WxVt5Ajsxt7RMjJ0S7LDHhoIAxR05NhBbC4ADydqQS3Qlgu9AbSxsta8KMgatAvF1kQ2aMlONZKBPcOCpslNDoQxB1Wc0VPMFAvCZkYZ6HqgdWWdJiq2jfZNmHZKGYg2yigwTRCDilB0bMiR2AqeyJDDmnwxQZF5ZXTJ4i+caWximUR+lk2Q4viiIB0dwqxv31mnG+uyoLVic45hrBbIiLyxkZ37qAIjk16vXxlf/PYLPnjvGVWEdvEoCT5N6NrpPZnWapm0bWagOQpYS0FDWKtRlgnZV4rnIklORo/K8E4p0CMXGEWVzjHHCTUT7YsXPBZcpnQ/RKd6yRHpXSTFYuv2DRmY+eYC0nSZKchYsOFErZQIeq00ku2UvKd5cxEVZALW7UbhQpln8CPqWdBUASuVqhnQ4mvFimdBvo1BWgStaHKSpeFTYhykZUhKhONemcibxVBBYk03lxZKK7hnkmwdlpcRHYDm+UGGCvbeKepAp4RS512eqyIgOc7pBKeyMg2IywqLIW709e3d77GFYdVaUV8xJsrcYAx8GMfDc+bLC6Q47XJKLl9Jt4r4nIXC1BGBXTOW0wStsRtK7yvNFZ08QyfWoNcgpKDblMIsjaHZ+HA1iMocyjKtlBF4rNSp4V4Y/YSLIV4yBNUGrSg9Jkw83TmRY7kuCyGVPsFU01VTNrHZNoHIVuM3//T31AZ7n3mxvOD46grnFbKfeL89QlFCK0cXdDE+/dNP+OlPf8rl42fU4rw6vOLw4hs++K/+At3tKTZxzUo1yetY1Bz3CqOG0ksK+t0rzVeaFMSEXoSqK+KVZRiVjpT8LMs0WFal1sYwzckBHbyyYD4MIpzecuzLo2AOOw20OqMqTsNjyQWVrgyy8Cl1j69rXs58Ymknisxvf+48ICbfZw7eL8zf5PR96M+HHMIPicYPISbuC97nRdWbjuF1x3VfSL6/r3cXZ3cXMvef500C8H2H9+sWlXdd23ffk/uC+UPvH/C9fXjoGB/a52fPnvH48WPWdb11QNVaefz4MT/60Y/o/e2dhPsnjeoTMk3gxriBQyyorVgT3CvDDBkw66B6YYzK6WAsuwNymfoeMW0J64OpTAjCEgVWRabGQuCrgEemuZeCjYYzwCvFAy9QbXPdloGtNUciWVCdGQOmkvVAKQVqQ6PQdgq90uZC3Svt0Z5nH33ArmWoy3E05pOwEOx2NZtdJ+F6rCleLZW5BavktBc49BNME+I54h3WGDpyv7fQr/DBKMIkbFMvFebCWhvzWtGpY1ERWTcX7cz1UNCRTSgrjLXDrDStrGsmVfTIbIKTGpOnQDWmCosTS+d4c8Oo8F5rPJsK+1pRuWCEb8nqK2VWju7Ua81siqobM3ZmipmYC7Xvqaz45GgvBAf0+u0bW+Y5dRFjMyNE1oxZ4SmZRQE+Fo7XV/QPnlGrclg7z198w++++A2H0xE2AXSqhfeevseffPoJl5fPoMDF5QWn0w0vrp6ni/WUzy6pkCFC2rtKOscICLfbILccYWcLfQ5sDG5uDvzu179Dpsp6PLCeTtjoEJugLJHs542DaUC6pTO8LM5BkCpMVfmTH33Mz3/2b3j87H2uj9d0nC9+8zltczbvdpW6hbjNbaJOOw6vrlnXzskXypTTW906z1885+X1FWOsLGvH3QgpiRXbrhmcRd95l+7NaWbfdqy98IqFsSxpKCnCum5CPGSQ8yZ85UBfYLbiKNgWAr4N+jmCRiqRbZ755JNP+bd/8Rd8+Kd/wuNHj/jqi68gYHTL2lYCeRe6SoESKYhmiHlB24qMFPeDLVBOc4dcsjZTM7ROmGdQaDEYKM2dzqDRYPIMpBorqbRq8pprskHTPFSTXa9Oxqa0DDstldoKU5149uFjPnz/fS7myqulc7q5ASmErwzr9BCi535I5L6Fe7KrDUKEKJoBqiEMVzitaEkDhhMZ8FXzvNVQSFM2Q3PXazgujYiOb8hBb4Uk0284P1NkCkJSEFNXwjpRNF38pkwkGtCtEMWpNW4xaeaGktMBSoZJehSqW/LjLcfWEYde0Gr5GrG9t2K0aMRcts/bRIuJ/ZPGk6c7pnminwbXxxOHl9dcGyAjA7sijVEy3q2hfhYg7zpo9/v9rRv2/LizM9jMbgPh3J3T6UTv/Y1i5f064zv1E9x+rt54nm8iafLql1vn8llcvvvn+cp29zXv/vxdHMXbiKzvIsSeX0ckGxV3GdfufovFOL+HrTX2+z3H4/F7oYXn175b63z00UfsdjtevXpF7/0Ww/E9l/ad2ur8POff2d1jelf3MgCWGAa2a5VuMqJv0x82OpNUzAdewUdyyqMkMiuxWKms1SgU9Vx7RlBjhVpZGdTRcDmhljgHpKLheb9SA98QjyT64tw0jbrdLyNolA3bmOeayyn3wzoNoWhFzIjJKTLRu20GNDL8mDQEYAYoXh09m74mEB+4KRTP69Q2lVV1C7C31G8sJPdLAjFQL2ghP7M5aJyfExlIlM24plkFSKe2xI2EQXdDN/SQlRyRMHNqBLGWbR+he+JCSgya7GAYXrfzowC1YOvGsHZByd+BMDaBO4V0j5xuJhZMSuI6zGmt4hLpbn7N9maB+XWfq02QjAe++FZaZdz9897C7C1+/q4w+3bC6A898HXfi3s/+tAR33n0O+i7cf6/7fE/XFNsN6aHnvv2ye7v40M7s33tj32CHzZS/3ME6GrJVPa88ZsFXgvDQM2SeVWVEoJ5RaoiEkgVog+MCbcV2Snila7GCEPaxFiyyJM40SND4NQT2G6TZgdqGNRMbEYC2kSzwlQb7338Pv/2L37E/sljQoJnH3zM5eOn/KP/F/7w9cIJR5etyzg2d/JakCnHkWrNELNhRtdG6Ilmhs/JVBPZCoaSLu10FoN4odRIMagra3WkJ69ZSDFpNEO3C3gTZXhB6iDW7AoqheKDtRVaQIx0V46agUJmUMVpUli9InVFbBBbAGJezbLjJmGMUOQ4IJxSs08RkXzYqQiuE7hvzQ9P9rNsoTGiuHWiGVIbowZjKEShnNKxKE1wKYi9/QlkmiEboluxPVZKa6gJ1IEv6WwsUXDpxLFjtfHbL3+HFnj09JKLXePVq4Xff/4V/XBCtn0IGXQPumaYEEEiJ3p2IksthBXW/5+2d+uO5DiyNT8zc4/IxKWKZFGiSImtPqfn0r3WrPn/v2IeZh56rZ41mtOj7qYkslgXFDIzwt3N5sEiQRACiiidPv7AIoDMuOQlwn3btm/3lVqNtvELTfN+pitoLXiQKaya1b3ImCQiKviKFUNGuhyGdziVDYHRiDDUB41BbemM8UlpQzDbMWkjIg3IdTgxFsRKtlhLvhdFIlukDWIUKIq0NRcZHuCFmEZeMsqUx7jLZWsKc3m+2pw+QCy5UGPNz05Xo0Qy5qpmKFJl0C2wnh0JWta8twwDUZqcq57gXpGajX2lBGETo6/Z2qOCjIUiE70rZdJtApt8siGdclqpLhwI6ifMgaYWmX7uzpAZiXRBugQxG9f2EtsKWyEZFlHCkz1qhkWjlGAsp2wvAmxJbmMAY95RW8PprKxMekWLra20Z9p6NUXF6Q3OwYsSlfDGoDCWA2E7ik14D2qsLD2wKBzpuJABi65gM9IGMsE0Ou4TEh10lw67EKynCPrD6x94/eM7puuJUz9ydXHBhcOPtx+Y6g6xPZeXM9+8fMXl7pKvf/0r/uEf/4Evf/MVn+2usJri8xTK9NkFVpMpXreG4t6zGCPS6Q3qCKp3tOb1pLtyrELEioVuxwdWCuoZsjokqGRLexwEnwZuOVGroSyaSelLCBNAP4Ao66pIg/CO1ZbtZOGskpPEUqBrQ3xbzGi2vxLLsz87Dyfk98djC5KH7Z/3H3tf3LwvgD7m6n24n/sumPu/f7jvx1o0HzKiPybSPvz3oUB7/vcx/Mdjr8tDsfqpEL/zsT72t6daVR97rc6/eyjE3z+Xx4Tp8//v93siglorrTXmeWa/39+lqb94+fLJ8344LC65vKjML/eUIky9spTf0tqB482Jw6Fz8neMkycWv4PsO4NbjkdLR1KdM5OiTHkNK40ezrg9sMqCtYLNwkRjDKXVFQnLBRKJLQgdhE6swymrEhjVnFVyQeW9g1heWyK7YcwGlEqdL3nxxcRUCrXssIs905iQbW6lYZSpsx/BQZxpV2lhVAZGZ2hwdGcegRelhnK0QKIzmXLqmoHI4ljKOujIXHNbldDsoNJh+C25ACqBFUn3c9cMS17SQVtQFml4X1BgXgteF0IHywrrsuDSqctg1Uoxx04rhz4YS4oMFwQXU0VLilW1eqLElsymUAmGN5ZewYy6L3wxXTFbujx3q3F8Udh7pYoyx4Hjj8rt82uioBn0kyaT7EDLSXcaCQTBu3N7PPJv//7v1DpxcXHJm3ev+eMf/z/evntPX1su9gZ0Ou9v33P7/gMX+6sUHtaOe3KExe99l2LDHWwCYnJ1HfHAJfCxtTXL9v1G0uHNYF2C9/6GEMV93LlPk+ftiXxjQ+ORBgs8EMn7qZw5yKpcXV/zzW+/4auvv2YuFxSF33z5az68uSF6Y13TpTza4Pt3P/L27Q+4DN69e8v+YsfvfvNbZiaOHz5wujkmJsZJp+c4L7S2DjEBV8UkEh9y+57bdsPUZnzKcxjrws3NBz4cbhltzTBLUiBHN1E+cpvhOYeK4YRt3RQ+NmtSnqeYsN/N/P63v+c33/6OixfX7HXmcLxNYULgvMiK52uEiHu+N+64BJ2fus0ikusutOyKizRnxNZK3ccADA1AMnTcN/E8C98pCFlVdLXNsT8YZNC0lUrvjSDQloVn1eSQUieqB6XCtKvU4khUdtGInaJWePvultYWRgQjOtq3CHOBsIAuiKZpiGFgiTwoZHcBLhRNd7i40zcBV3DEnGGSOAq1fDydaJHdqD3/7SLUcISCaoo7jKDXZEDnGkcQMdRWVgchQ+bFwTUzfHJfJXFFkaigcGFSg4AWkYVDTUa21oAOIsowpYZnoLPq9s036lR4+dkFn734gqvrF1zuKm1xbm9v+WF+i7z5nkNbGAtMEkjZ2vM/cUT8xFQ+i7CXl5dZ+A2/E2tbb/TW78TRs6P5IdrhURTGvfnBL82BfulY7wfxnucZ52A7M7tzON/nDwd5LTsej3dC6/3Op4fzq/v7e2o85cS+fxxnk8J9QRx+ckyLyJ04fs59eGrOc3V1xVdffXWHJTmLxvM8370ODzu2zts6h/196uv92KjuObfXvKaPbS6e4H1Nh+9YCamEr1vmlKFnkUsytDZq3g9SKxGqA1FwVeaoDE18hVpsPHKHyND0TAV1VM8dWYHECVRYe0eYKAqigclglIkYjoyt86lkYckjGCaYFLp21BJTIUMQK4gYkyo6FZoZ1ZSxQLCkllUyCC880Aqjb0U40gipCqXAcEVkooxETGgJ8I7qdo8V3e4DhnjHtqKvaGeMHaJrdg7p5qBu0H1gkgibNAIaIWQnTRtUyzyr0MJQT+TOMCiD1p2pT1uWUIYrhmZRO2JkKpdF3iebMCznjTWEoYKIg+S1rX9E2vmUvvVtPKVGxr3/8sBV+9GtffTnT3ryg9399OMvb3Wjkf3yPn7pT3/1y0cP6Fnb//l4Qlz+2NBNuRZ+ml/9lWj+CQWBTxiTGFG3L8KSopiZItaxUpCR7U4G1LWxjjldrz0DSqQHUhOF3hVYg1IMouEGKobHDNETFTEKLRYYmqJGzWCs1ZWigUmwUtjVmaurF1zUC9YjrLZyudvxxYsXvP3i1/zw4YZyu7CWNUO0JoNF6Kn/or4mVyhgaIb8SU/OYF+OW6VHs43MLLEAk+GtpwsxZpYRGy+rpQvVS97IpaG9UEulW7afe23I6vioqCX/bogwu9LZwO1ZXKN4stXM2FhEhR5nxEcwKsjqyA5kEQoKaybYVzNGg77XdFLGYIkBNphDMMtzLgGnTgYfmjNqoXbNUA0PJjO85OSqBHmxVaV8Qt9fmY1ouRgqVVnEqSO51k5BywAaowfRGqUWWA4cF+WPyx/Y7XboVDksJ06HJSfN4snNI0XPuWTiecjMHM5aYC4wTkrICpsY1pbNiSyBdGEd6RgPTaePDcNLZ7gwvCPmKDMtNvbudrO4a0etAizJk9ZMZ6UJo3e0zASDtRjEIPpWmEhoBC65WBKUUoVojbqx2tYeWD1P4II2GrH2LDQ0gE71yrFEtq7i2caogvlEd6cHTKUk4mGkW3xIpIjusLhTRVEXWjXEghhC945OBWJFW8I7ek2x2GLBRsWrZquPB62d6MPwekTqHiRdP+aCaKHT6ey2oCbHl0+ZgCpdlNCGBSxuaDjHDkigLVhLA4TuIx0pmt0NwwRk0I5QtNJ1MI9Kj3SbYKB6zIq1VkrZ476JrdOEzx3vBYtGtJphLSO43Q10OFM3ZA82dvhIfqBrEJZBjkKKxdIVLUGxSmsnRGu21veKlkHxwqTOrQxgT5mcday8Ox359be/5XDoHA5/5PKzIC6vuOIFk73g1asXfPb551xfveTv//73XL96xatXL3ix33E5v2SYY2NGawoJJb16aDTEjEG2ZulqRGSgWZ3YkpE7YskPlL0yTgEUJFbcCrkshBlPIT4G5kLZPndDFZ0nSgi0wWyN2+bUIkxeMjijDqQFFKH0xBKoJDtSpFMwdFYagpggPeFFzx2PoSjOC4SnhMqHIun9v93HXzy1AHtKmL4vmj4UZ+87fh+2gd7f1n028n2n8ENh/Ck3z1PC78PHnff7WIvpw/08Jcrff85Tj3tMgL5/fk8J5Y+5p82Mi4sLaq1cXV3x8uXLu0WbiDBNExe7Pc8dF7vgsmZyfKESl5WLIdTpgmUPGk4xZZFO+/HA29sfuT11/HjLgRsolcsroVGgn7CdJWpgQPcjPk70MvCWHSYUoYQRna2LK+cDpoqx4iIMBdsCoyZqhtvWlu5EzQBZU2MAVzKznyemaY/uJ4rULDSVQdPsNJnCaWIMLcw7h+OORRemAstUGaeFooGORDAdpVNgOzanFJh1Zdnawq0HoRXvkWakECxWFmaoTiXQnRJjQrsnX3RUmigwLJ7TAAAgAElEQVQ2L6wDInaonYjGnfv2dhX6aAjKaJ3jGOiqtMtbdAQxnLYG5sC+sjqsbc0CMRNekueokZ1GEWCsBMIlezzWzaW0o+ycyaG6IbvBWC447VfqJ4iEeGwCr6Tr1M8CZhYntUDIYF0br394TQzn4vqa29tb3r59T1tPdB94DHBhXTp/+vOf+D9649WXv+bF1QXv377ju//4jt7WjQuci2uVdEsXnTABVPAz3miMu8CzCD93CBMjES0CjDa2QLSc0wpb9gPCiJwLnJ036hBmOQ8iA5Yjkku8v7zki8+/5OrFywwq7JWL65nr62u+//F7/vQff+bF1TWh8Md//W+8f/+G19//wPXVNb/7+nfM8471i5Wr60vevH+Pj8ZYj7lvS4dqEYGSxxgaiBl9dX74/jUvXnyOlh3hcFhuef3jG777y184HE5ESLKwI8V1iBRBU0YnGIngkpwHpwsMEMPCNwRCokHWthIjWPpCb433H27u1mfSE1cmPH++owFjKJPEnWjueetNU8WaBoeBJF5vEqqC94kqkUzu2PAzmrGcslmo1Utq6TYhFdyVqJFrNO0wyLb2AVYSq4coIgO1zbHbgrU1jodGuzihAlb2fDjc8ub7H7k9rqytZyflhgoT38SZkq7EcEvzUQJaNpe3bu3d+V56Se69aHY6Bp4oO7GtSwLwDWuD00dJrEUE3ZVZBZeShoyqTGMrhOiE6siW/ygEQq9GUd04sY65IZIFhfCOs83nTCjROFnuRyJASxZeYmPLC1RxUCXccA20F2yGOhkvP/+cV59fUe2C6+sLaIFMNR3d0Rjv3tBHp5DYk5BPqWz9NO47mM8oiXmemec5xc+Rzt/7AvNjwvL9UMCn9nP/8U8Vw+///LFjvj8/W9f1DklxFp3vz0nOoYH3XbwPXc4Pj+XhMT817gvd5/2fOyXuH8d5PnLmNffef8a5fuo1KaXw9Tff3AUG3udPn59zFt3vO6HdnXVdORwOP3OaP0SWPPc8geygLZnboOdgPhRFGOFp9mVK8F0oLrl2iG0+Urrl/GMYoo0xtoq7lgyX2zphiyaOMizxWSbbfFzyO6eaWkjeYpQ1MrBUy/QTmpCB2EQfgzmCKOWuM3cBZiePvDo037qJlVINNWOqO/a7HZdzgakiZUbXzrIeubm9IdaBVMA6vWfoHtpBNTFgJqyRc42yoSbDEiXIUGQkK9986/apAj3nA5MooymqHTAazjQGI6BgGfjswuIrqgU9BUwpdmdwqmG1k1AgT4TpEEYorjknm7QkDihaIk56rsFDts+pbBlb23vSRXCF0iZgQDWk/c0hf+dPOZ8uMsbdf+5t5OHv/kfs86cNxE+q6jP29TeoqJ86ntjFk+L2f+84n3o82PcvaN7/GaO7U4bi3akerJbteyaCLEavnkJMOAfNL18sJIKiTrRxzKAF9xSowpK1Q35xjQSfm2VFZRFPUWgIZewy3RzFOmgpqK1EG7gUylWmky83J1YfXJric+Xisys+//6aP3844C2D1XQ0RFOUpDpjTY6zxYKgaIfWJ4IOpkhMSDTmGWJNmSa8J79Gsv1LVPCxoC2IQYLha7q8I5y+CKYN31XcC3MPpCjukkFsIrSu+NyxHqgEiwyWIsxR8OKYb6EUk1KOkaykkcdT1o7LSAH9Uom10XVrseh7XGZcVnaR7WIjFG9Zmewj0N1MNE+naScn1qXhsSExxOhjSzOPdAi3+vywLfGeCawAQ6kiyViaAzkdWQeZeK3rJtQKywplWjmdnGUNQm/QMVgCxug5sdVkHCq5wExHSkMksRFCAVk3ltPKaJYsuykdDyeHUp0WcOGFQ6w0XzCfsXrmJkGTQ35mjVxgaacLGydt44AiIAVZFoaUdHlKci5XBSuVMpKb7VM6kIt3vArSbQsbzEl535wy3ozYtWy3HJrhQASFRJ4sslIKaFe6TRRd6WRlt8gWKbAJ4SL5+peWi39EkbB0/Uq2Q6oGa3dqTWePUrP+qGuKBt0puwnxDJ7xeY8dF6bdBd3Pk+5zqrMiNRhS0CU4bU7/LHc//7oTFFxk46AujHlr8bdNXFPJC557hsQxM9B0AapnQcySl8daWWo6vhGlKnSP5LIXwTxdMgWY1OndaQI0ZZVGtAxAqN1hKL6DiAyiMbugR0c50Vwy9FMcmNFJGNGRMphM6UfnJMK0fUdPIzanVN1eO+g2aDdH9jpxdX3Br+TX/PjDO27HBz5/+SX76YpalXZYOdUDF1cXfPvtV7zYX3AxX0FJMfxwWmm9Z6hPDJD5jt/ex0Cl4Kugs2M66DFTIrsiBkb0hmmFmkgSxsYY05VYlcWcWipa0ymzjm3x3ZypBt6cWibUCztbWXq2gKIThUBrMhZdA8sSGZTOWIJBTxeeg41s31rHJwAt+bkQ+lDofWrB85RIe38y/zC47zGXz8e293Ax8NQxPURUPCZUP7WNxxYf98fD1+Wx87v/uPv7eMpdfP+87ju9H76WD8/tPB7jWz9WBLgv+rs78zzfhQXtdru7dt/za1ZK2YSw5w1DsZcXTFPdEhkCLwPxyqwdj8J+vmB3udDLBfvDKxY/IK3RaxZh8UGPdM611mgx+LA0ju8XTrcplozhxBSJ2/E9Xg6M9CVmt0ZEprS3uvEHB24rgUENrBR8WTJE1sDUuZxmrBpVMkynjmCeghiDGwkmv6CaZ0FxubhDafRYKDEofabLIfnOQ1inBZFACaJneGCJyHwNMpRveKdHonZMspWULW9jV4QpVvp6YmXiYlfRKuzkgqELSzT2LbtLqjqnecek2aJKaxlQ7NB6tuM2D4KO3vZ0nUrOCUwrc1PafnA4tSwSx4ldrVgMwvYpQrty7CtKBhmGZKAWbaG701hYbSLeD9wDKxNTe/5nR8ggISLT54NBogO378CAyVKwQINpqry4vEhh9/ugbQu8GJHYr9FYDrf8qa388OOPzEXpq3NaT4zmRKQYJkQGuUnJ4EerWDFMC2N01rbQ140FTd43VQRF8eHASPEUITwL1xJsj9+cz7GJZxunMTx5kLI5tLYVOXWauN5doPsCSzAGTNPMfrejr4Pv/vQX5t3EYVn4j+/+jdEHfTS++fZbdi8vGKeBRdDbSLeuQ5Cdkho591NVZCqUUIpmUJtEcPP2PX/4v/+Fm7dvKVa5XY+8ffOam3c3DG/E5kTzba6CbyJLKMRZDt6c3dpThXfNIMMQokMUWA4n/p9//QOjO7sXO9yD199/j7KFtm0u5k8xGKooEp0hkNXeFNTdHemJXRuRzrNIfwqrgBTH158C8IYISmIpQoSLksYUqmShwUoWnrvhFUYkjiRQagWR2FrdA/EUdVHlOE68/fNraitMxytCO8vNLW9u3vL2xze0PpDo9FBMPTsWLcgTUtQaQwzFYetWTd04MhR6E2yl5zU4JE0RiGxtIjn3AkfD6R4ZUlUVJXKNSXadRcnCiphQLipqQmudPpJdjQGWc/y6FWL6KIw+8OEbQi+7EdAK3mmWnbwRlmgxEtEnLrjWnGduHR0hjnWgbt8eCYoUSr1IkdcTzfPZF8LFrlBq8P54w2F1lr5tx57PV3lMyD07mO8Lk2cn7tnpfD9E7rF7+8PxVCH5J3fxhp259/enivAfO5ezyHp2Aj8UbR8W+59TXP/UkS7iB6gMOXcTZAfged9ncfjhOTw2vvjiCy72+7sgw/sc6nmemabpZ89trXE8HjkcDrx7944PHz78rCBgagw/95Z8oqu5GNa3Yo8ObGTxCs3voER22iKBZlBPFi+HgI+7bhhF6MUwDdxzpRruVJ3orHBeh4UiEzRfIbKIhkQidHrKXHG+/kSiHVwySwHJ+Yz3QZgjMeVxORmMG6c0wDigE7Nmh0smw1T2+8qXr77gYn/JXCe0GqNDa0fkhz9z8+NbSh+0lh1bo6x42845sjuka8U1NaCMAsxOcrFkNMdI57GlhZtiNY/dE7UakQY0TNJQiNJb0MKpCFV3eX+q4w5V6j0wU6xXXEfe5yPNZxrZgaRmdOkZKBiGqoNFFuxGIC2NTsMHboq0NFmZg86D4YFuesWTH5VnfaD+BgXyr4XLT9zIkw9/riT6UFX9G3f3yePp4/vrv+SNLz76rOdt++k/fXzL/6Nk9TChezpQbCQXSwlGGFGcOVZ8qYySHF88ENuuFktlEhhNGKWyJ1EFdEFKZXgyVq0tqEomAXcnJpKpgxNtC7eoTsi6BZ+BymA6VMZpsLuc2cfmHjkMLmXH/sVL7MMNxW/wkiKtdQHpRK+s0iir02UGa9Qod+EaIk7rHcqgnoRuiuuCIxuXKye0bZAsOze6GxErpWVVy0wZ0hml4ocTVoUB24XRqZBQfGvQk5umEZRQQjpjGN3BWqBjyQVWKHMUimfrGKZYS1bj0hQbhdAgakLzZeSxuQSjBz5A60BXcKnosiIGPiDME7MxarK03XEZ2KiAEVO6sEo8n2fptkPGSpR0R4gIowSjBZM6Uygegq/KNMDGwtBCRKWtDdN0cg0LTAUfSm0gHrSirCLbRLYjHVqDmCdoitVGH4OFwEpkd95o1DGxKwEjK5uLZ6K6MoFVVu/UOhjNMNmc7lYYkWzMES2rvZtIZzS6FJSJWIOIFRPnWBwoyOK0szuzwtp7Il8iGNoYbAu3oUxFspV6cmIkizyKg04g69Z+FVTLCWyLklXiC6W0RiSQIREsUZPtlPEHOZFHkMgF+aKNEZ7dA1KpCL0P1BvztMPVad2gntOcg5gDix3aBm1yojuzgNcMi9MyUaZ0ssgwFgqzZTFnxJHpEyY/JumIcjrtoExOBjyEUKcs4NQNAVNF6OqYdGRSypZOrJau7ZBgikqjEBxofQKcsIk6kkEdGlQ11nWgxVGEFnB7e0DV2M0VGWBjcOpCRCFCkJKhohHp0D0By4cTNlVEg1lrsoxJ1t9Ukwc2LN3ko2eacLamC3RjROMwDtlatnb+/Od/583r14yvv+bbv7vks89+zTdfveI3v/0t3/7ud1xevWSeZxie1y4R5mosI4MoTgOKpnPPMcRgdIgpne87V4Y0josx1YAK0hIfQxhdGrudIkvBZTCRnSctOhqCSGG2TrRCD0dWQVk2x9gO9Ykr2t0EsK8CTIw5sAalOAcdaAhaCtFWzFfayAmUzIrGJ/C7H1l43B/3f3+fzfyU8Hqf2fzUNs9/exj2d/9v94/tKVH5OULzWWD9pXM/j4eP/Zg4/hgG5KG7+qGD+rFF3f39fIyt/Jh4/thr9piIHhHM88xut6P3zuFwuHMVnZ1ZZ7bkc8cyjAs5ZMbBKNg0J2evDtYAdLDGCT05YStSjb0W4gp2kYvGOSa6dI7Hzu3hlvfv37H0D6xtQURYnMREuRJ0Qm+xUShTZRmN4U5V3RZcLTuTpkKcpixCTVA8hZ/qgwVnJ5XwCdOSiAczbAsHHhpb+HDD5sSFlZ1yLcYxBmMNdkz4tLKcjPl2ppUlxSUqRodieFRgyYVbOD1k480OxghqGclYHoF6AYK1ZV6GFc8wrnnO4rUHU7FEHHny6ItClQyqOTVlWY/4uiBrsFKxqeFjQSQxTKhjrlCdkwzk0Ch+wltkp9q+U7jIjrNTvo64cTFdcAxP8fl0yk4wXTkcg7YeOfWFzy4L8/SS+VNm1hKZe6Cb81X1zvWbBq38zJoaL66u+dWvv2a+LJxOI8PyNlc2kmzEMKX1kfPv1jhBlgjHyLyEGOnaknR01mLsdzuur67YvbhiX2aOhwN/+uEv9Ha7eW/O5obEP2ShNt+DDZS74RrIRbOfn8Nm8xHEUkzfABo4gUkKsbfHGw7LET92bm8/sPYTEkF3Z4zOu/XIP//LMYWCvjKKcG3XXO2ukQ7r4cAPr99wOB1y+z7YKsmY1Swa1Jn91Z7PP3vBbpr5y49veP/6R1Zv3Nx84PaQOKbeGn10xsicDNjQD5JGk1DHAfeeorsqVqe8Xg4jlsSVKCm0iAQeyV19++OP/PPtkTpN9HBOx3T1jpAtXzES//TMMSKF0QFMwxkOwubed6GPE4TRSnJQfQSMwEKIoaDpuC1lEIui50BLieTtjfyuMiLDJnVjhy5kQZ6gdyiWQYKlCyM7p1naoAzhxm9YvxuYku3k64nDWBnOxq4mi9AAZ4widQufnrHz+rCAc17HZFCwk4HutcMiI1miYZuL3NBIwUg8xSqriRkMBkUtEUO1QlWqKfVq4osXn7O7foFOwfrhwNvX73kn74lwYmyGDRFctyJLlAwkd8F0bPcnSZE7DKFnm3843nULYwvG6ISMRMVthhyPQPsgpkJbgrdvbthPO/bzjPuBKlcpjksaNJQCY0E0mDQYH1N7HoynRNSH3VH3ucAP77V3n8MHgXPn539s3N3DH/z+zlEc8TPh+ZfGw3nCc1zUD39+bP7wKftXU2TDdZznL2fB28y2AEl/NGjwMQczwDzPXF1f3RXF4SxkJyd7v99TSvmZO/u8j+xyecu6rj+9RmxIB9En9/mxMcLvPqsyG7EmitQjjS/iqfew4XlQxVsnShpuEtEIrlsW0Bi4OcU6sgrDT7hIFsW2zl1FKZJroyGpo4whiZWgYapMQ1gjg4HPOQL57RuIGC1KdkUZeS9qQbqjNLMPPHMWRGCoUa3w8uUXfPnZr9C9sZ8nSslMr+Oxsnz4wC03NGsZnieanfsltvtfBxWKQ4/E6LgHNpQhYCM5y2rJkTcXmqycVIBc1whppqmR4vSI7CaLMlLTMDKANYCN63x+fUOCFp3JSwafm29aFlSUtTeKVkCx6PQBxYQ2tuus+N18wixNUJCd3d0FHwMoTPr0Z+dvQGQ8b9yraf3sp/+0Lf93bPavnvqfqrI+LQD/7DsswF364nP8y4+fsOi2mXjq0X+7bP2z33/i6+2kM00cTnOGz0Tv2dIUQo+aoQx9UMNxNfoomAXmjTE8kRBAjx1iTouSbQd4Tl7MEEn2VnFBlmBM6YrZlOpsFWgZ2DBECBssnDgtB+bpkrns0WnQV4WrK169+pLvvvsPXC9YYoWNMRZt4GULrzOD0SgBqpnePrpTd4LJoAu0OhG9U+egr0LElOD2CESCGoUoSvFBj4nQwcS0iTNktUiVOZyYzonxnm2IJeW/EkJbNibcCLRU8IFESYYZ6bxABKxjI3BXfBS0pp2hBogaXSFGz8RwycCQ0MLEoJWAJjQc0QWrSleSFRR5QZVhyMgWu1OimMBGitR25BN0ni1RPUMdS4nkII9cMHQRSggiE9MMy6mTBtNcUIll9bLpVt2MvLl3ddAUD3qdmSR5bVPJpNnZhSFOG1O6mbVgmzBJ2JagrogURDoahmnFpFAjObIxCjZZMq/PbrMwhmQrH+H4nMWA3gTRJR1d5pRSOY1Am2KTbW6goGPYmliEwSCCZCIH1JEuh1UFa4J6T8yDBgVL94xnAnop0E+OSmUqgBreoEenO0ySIl4bHSuBW34n69ozcNALq0P0bI2qct5H3wJkCk7LtG6HkAwP8OFUq4xoRGi2JxVwUawUXnxxxdX1Bb0H67sDh7GgAUs0tAVTuaD35ws9Ekpnzfdvqng4VSrDwBffKveO1T0hnV0MHKNFZAFnmhBxtKd7Z4SjIUSdUC2UZdCaU9SZ/JJFGrAytuA7lx0xjlQGZXeVTJmp0NaFSQsxkvfqZOpvMiIHtazsJoNxBNtn8rEaqgOblCDbaxXYz8mtx1eaCcouW2FH4cOy8P79a7QN/vF/+Seurmb+93/63/jt73/PV5+/ZHd1jWjJ1PBIJ17oROuK9EaPlSLkRDicwcykneYV8ZVEzWSb2alCEahTR4ZCH1CFvqabxxDWFdQ73tLBs3ZnN1UYwtgW1qaJP4rhSN2joYxIt77JHmGALECFcGrJoqBbpQwnmqHSE0VkOyZf8SJYwCdk/AH8TACFvxaVz+N+a+LDVsv7zzs7Zs8C5kMn8/m59wXmx/jE98dji6fH2jvvb+MxAfv+eOikeaxV8+GxP3zuU2L6Q9H5MXfzY4u8h4vFx4Tj+wL9/e0/ttC8v49zmvuyLLg7l5eXmBnTNHE4HO4cXM8dEkdOS4ae1pjSjWbTJkr45tLbmKdDqBfGhc00X+kOsay0umeOSt8tTOMdfT2wHg4b4y6dgWeR1mO7vpvi6ylDdZhxa9lmbopHpzOwMuhECoA+UdbOMkM1o0/GToQ6C4PCYp0XUbL7xQJrSkwDXyo+VpoGFUG8Ml1cM8Yt0hUriRMrUfDek2W7JMdP7EhHUJ2JZcG2UFVGLhxbC3ROSmoYGRg4wGqKTU1nvHekZqcITTBx2hQgF1QfOe86pQOz9xQt1r4kFqB1BoatAVbxraunfQiEE8uuwOjsDw2pl/RTR+YVlgM98jqyjoCiFJ1p40gdE2GC7CpVF1jXbG9fFZ2F4yeAdCMtfHdNnyEgG+dYMDADTaf9l1//ihdfvmAsgyVWlpEO2xBJ/jznlvG897qwwck0ERpkfkIxAVWKKhf7HV99/Rt++5vfsr+4YGkn3r55x7ub9yyHQy7kz2KPR65lPHmvpnkd9JH7yHRj0oEdGUCUJxXZ6bY5odkyJEKcGJ3bNwf+/V//lTrNHNrK6fCB92/e8ubmLafDkXV0jn6AAiWEKnuUGWxwc3vk7dt3fPfnP/Hh9kDv2/xf8p5Ty8TlZ1d8+cWXfP3Nb/jis89Yu2Plj9y+fwejs/aVaA6Rlp+kGKSby6jUuVJLoWiGWC1rp4WAKbv9Jb/+1RfUWnn343ve3Lyhn9YUU7frjqL04fjSaKOjxxQgi2bvnG7ub9SwT2jZatYo6libE8u3fYZQwUve28VbugdFM7RPB6v3zYmvme0xSLyGCtMg27Qtsng70hzUuhDmeMv7X3iybIVCHyeGThTbEBliaM3wqbEo3W6g5cdh9Ox+HC75HfItbDKyi0zIjs3YOlaRdD0mzg2iCqWBSDDY1OwC1jPAUDQLLxrpqs85hqTL0YxqM7UkT9XNGctAqnG5v+Srr3/Lr7683pA9M8vuQHhlXY+c1iNdbesMHmgzBk6MgYjipVM3RCPbu2oRmUEjUFSwMmUWRzgWlkjHrRDiYsRIoc6a0+zI6+//wqmduKiF/bxn9+IGN8Hbys3r95yW00ak1uSAx/MZzB8TT+9jr+7PHR4r9j7WPfTc8bH5iN57zMM5zVPH/kvdT49t67Fjeq6w/HDe5MMJ/+u5zPk1bOua/24c6Ptu8Ke2X0r5GTZHJBFeZxRGRNwhMEopdy7u0+nE69evORwOP9tPBrv+fN72KSKzu+GsiVFYFXzj2ZsjPYWuTqJ+VCqiPTOJxPDuW2dv4quijA1LIRDJAe493fmtJcd/WEFxtINPUB3aKkRxDEu0DUGTjlll9MTPyMi1MJMi3akk+9wj0FHQKbvBa1726WQOGBFMYkxFudzvufziIgvuLaiSzuAiymjJIu4rDO30NeMOuzSmWlkWKEVwVqoawYbwwTcNQ+gIRdnyeSbEp2TYA0U74bqt4217z/yuyClqhA7YQuLZBGFiyiLr4ok27SPDA8W2DoxE85jWzCoaPZGMJP7JVLJgbUL26VtiM8VoY6CyOZ1RcNm6mB4fHxWYn68pfuyR2+/TrPuzxz72rI9t6Wd/+1RR+N6T/1P15Ofu9uFOf+kgzu/ZL5xwPH1tevb4hXfu4w96Yug6yMRcQY4NKYH3TAONqEgk8mGwUAQmUdTBVehC2lIC6qSIZeVYSydcqFqgpZAqalvVH/oY6Sqogg02ITvb406S/M/TunB7c+LddeP65pbTRbAf2R6qVVn7AbMgYiHGmtxfEYxC9IFt7fIU21rHQS1dq0tPDtlYF6p0Zjpt2VrGZNBnyUTtxnb5daoJRRzc6FgynLfHuxhrj82RmiLOKp5M3HVmSKfWoG+prPMImmUlrwdQM0rH1myFbBL4UCZrjCGMPjK5fapMEvQiWCSVfnSYorK0rduraDprvLGOxM8UmTaHh8PIxYYXo7ScIvdIxwIy5fvyzFGLUgdEyZARHYPA6TbftRGbJ//PLKtoOoIeHSPDd9QaMtIpoJop0+EFkWAGpMDsM6uvjCmDktKV3VCfCA/WrZ1FbLu9O0Ss6WzXgejMysIQxb2m28sHpnrX6hwKE07XfN+tB6ukA0jCUFNKsC2+oGoQ64DJmVphROAm1CIEhnph7Z0iBZsCbz1F77kyUTLAQBKHkrxpT2E4BpPke9hkIN2z2DALe8vWvfDAdaSTNRR3MN0j7gwxPFaipNOmhsGaC9uOJcaAmRiDWVZWT8dV4g9a8p/iHKQH1MqrFy/5X//xf+b65Qt8BDev3/DfvvsP3vEGXzrjpeNLVpOfO9ZY0FaQ2nELehQiwCSrydUUUaH7ymTJXZ7WnBy5VGJDvDQcOxkyOzZ6InNUWLxyUXPx2eXANCvjtDGyTREPZFbwmXfv31L3hQu/xMSgKKMFSoMIllCmKMlZj0LdBb05MqAvjbqbMHLB60WplpOLnjpJBmVIwU4ZGLmrlf/yzbf8/ptv+fz6Jd9++y3X+5lpnoiyS1EBiHBG6xkm2qFwYLjQhtCiI7Fm4INPWFk5NkXLkRIF1CnF0NGYJCcyfQ5ufnxNqTOzXKNV8S6EZotX3RWW1qjdcdujdTDWZG4PmQjr+FgZUhkNaklhP7bPaXaSaYahRmAjk+p9wE53tJLue295n3CpiAcTlbU+/8b1cGHxlLPlPM5i9ENB+OHjH1sc3d/2x9AQDwXn+9t7eGwPHcSP7fvh7546zsdE2fuPeWpR8tjC7P45fMzN/JgT+7HX6zFX8sPf3f/5LD7fdz231nj79i2vXr1CVe9EZUg31s3NDbe3t391LE+Nt+3I7nUglyu7i4H5NXNveHXmqrSh9LZQoxN2xRTkPUEL4whRUoRaOGJ+YuKSF7sb1uVDdiX1AS37E2MGOyQjPsaaaKUR6LTCmGGcaJILyYmgmbGLgMVo5USrU2YKINhQpr0jr2gAACAASURBVIspQ3Sr0U9K32WRrkyFmNO90vo274kUY2wMRnRGyXOAgkdnWCbCi44M2xuF2SZUCqULLhNwyq4ehalvAaqnLZTLVsrIDpx1OLsxaAuUOUMG6TMm0K0yxQnvJ9YOzonR4bgcWJcTflhRnOJG1EqJDmbo6owQWskK9HGBIacUFM3ZmWO9ctucoh1dhUMVah1MUZliSlF5n9egaThtdIQZLZ1eK7HcQn/+fCd8K2Rr/KTHsvGRJdt1qxWurz/jq1//hs8uX/LD4Qfe//iG02lJ1u09kcJj67DYuts8QMWJNFnBJgiLBCrG5599xjdffc2LF9fMux16Cm5u0rmq2+foLINtnfspYJNOyiBRdXEWZs9t12Zbin2Km7qdXLqes8igEbjD8fCBf/nDHzitg8uXe25vTrx595q/fP89S+uJBBNHGyxbt95xueX1D68p5cDbN9/zw+sfaGvbFkUCIagULvc7/stv/45vf/93fH71Epsq3/3wA6dlYXi+5sk6T6EP961lOhfj07zj889f8ve/+zsuL2f+3z/8N/70w/e4O6UUvnjxGX//P/0D1xcXfPdvf2H5w8rtCEZbtiX7+U1N8ck31rJKxS5mWDtsyRgaeb977rBIBMiQtgktndiCP6UM6IpHpZAGkq6bK1kskTJRUHXMs/0ZD7zkSyhS00y9duIcOG0G23xJzJCoKMkxHn1lePbDxZR8dUEYrW3fhxTZRTNk1MjPlokTnmgz74pIS3GoJI4iXADdChId8QlKJyJDx1m37CpRgnM4YRpNGOT2C8k7tXSbX19ecnU900bj9sOCGHz9m6/43TffECasx1N21ClcXex5t9txXJcMd7dODCFizftLEZo3CiUxjlt7PDjDLd3GOF0U9UbEILZ29+K+tb8r0UZyWs1pYyAUIlZu3v/I+5A0/fwgmFdEGrE6fWzmi+0Swp0s+8vjOQ7jc0Ddnav4Hhrj4T33/Pu/ZTw1vznzzD/FSXx/nO/398/hflH7IUf6qeP6pf1H/MSu7iP5wveD+3rvd7zl8+Pgp9f4sXMHNrf7z4/5jMY4M5jvb6v3DK99//493333He/fv787x8fmhffnR88dqonnQ3KNJSFEd8YQEi8cab6jb/eLLGppH2CWBhccPItRVXJNki+DYprXLYs0ueja8UoWJWNi0DIkr5IdwSML7SI1rxVFE/FnkegZtw3dMwivKCMNeCNQLXRPB7RoQ6kEQQ/NNeHo+OLUyXBzFg8Yxpt3N7w7ZNfNumZOg7aVpRYsglPvqKQ2YyXoHhiNUOgIdYAXB19xne64x+JG9MCt0dVw3+4boqBORCF8zTuy+RbeB1i6tF10Wxc2kKCGsQopGPvYsCQZKCqxzQkKeduTbc4viWwLL4g2EM979/CcDyh4l4yTskA/IkJ+VGB+6mt3pxXDpqA+46Jy94R7i6dP2CfwnMzAx8ff+LyfzvNv3fEnPuvebp77sj71/Hj818/cyMMtfPoQV4rCokqJQYhRLNtFQxekGyYFEyMGeLVME95a8NTyi1EIisHoFesro1peuGqiJGRJkSVKOhEZBRuwKwugLD0vgHUPeGecgrev33D5csfFi1fEu1tib+zmwofXC+/+8p5DbwzPyZt40MWJWClq4HtWaekGEWXywZgck4ENRWJs2IxsOfeilJZMYQuQsQWFbIY/rekcaMgmvi34LplG8gG6wayBq9PXrMRrhzUaSmGykei1YvRToAxWFCm+Td6Nap1m6XSOJatSJoptYXLuawZhhBFRM4k+jEVXWk2UQEZ6waLJfJ4beMkbabghtd99Xp1swxxqSCno4tRPmDX7qdN3BR0LfRhFA+QSdcdquhbEoI2sOpYu6R6wmm5dKtKW7OdXwVvHQhmZ7ZYsumKIrenmOaVzIgSmmBjWMAxlZEuhw9CKSeQCbUn0i/uaPELPG6tatr508XRhSlYnxR11Aan4FgJplknxPrKdhYAqClRGnKhotkxahpcMnIlsh6m6fd574j5qFjFxU0ySqU1k66OZYJILAJ8M3BldiY1nZyNgDuwYrMMwg4lkQkoIMWWoy/AT6p0+ClImKNk6M5jRccBMs/XSg7Vk5bpHh8hJBFqJ5tiUyI3JjKurC66urnn1q18xlYnl1ZfMn73gn//P/4vunbGuRF+x8vwq2nQhyGm7Y/YJlYGXRmi6SLsaMTpjbYxaQAqj5KK8KgSVLk4tihfBQ9Gp0qdOWZyxS1a6mjAYSCupu4hgqvn6DoWotNsPOMH1ThAzdGy5DHZGpSTXtHrN74t3Skw0S3zNEKcPYdaStrNpQqLgKxtSyIlodBdmg//6T7/j23/4PVUcpVCnCa2D7iUFAg+WAXo4IldCv03hfRVLTEwoIQvhyhSWwk2xLDA1SUd1gRLJ/+o9r9PRA9lfUJmwiExF70HLNRaxOrM4zYRJk93VmyMlW/ZlgveHE1PtXOg+XQEF6IFWZz0F+wnohTYU31V0nGBIis8O2pSmDUQRg2nskgPXnu/ouY+8uD8+5pb5JbfLY66fh3+7v4/HXMAPFzlPuawfE4jPC8SH+3vMdf3QpfSU4PvU6/Dw3D52/g8dyA9F5MdcUQ+39dhrd37cQ9Eefs6WPh6P/Nu//Run04lXr15xcXFxlyz/4cMHvv/++09yMI/ujGi0IrjNTLaiZcYFhjvHdWGnoJaJ76fWiTGYpkoNoUUWRdPdtPKhddYerF3pvuJ1l90Y68g28tKooWipaAmCOYuLVGyqRGsszbM1nEB9sJxZzxvvV5rRCdq45dhmXvz/7b3djiRJkqX3iaiqmcdf/nR1VfdMzxBLEFheEASWfP9Lgk8w2AWWM+TuzjSnuyorMjIzwt3NVEWEF2KeFRUdmZVZ7Lkg4QfdiIp0dzNzCzM11SNHzmlk8cZn6lSwoYyRqpnZKjKlZ2lfF9wGfXGsB2N0AqcoRFHGajBAJqFOwmQr6iu9KawZ5NNL0Eakx2sEeMvPh6cfcw9GW1lKYe57Rr3IdtJqSaqZ05myqKuO+cqhCzEGXYxRVyzS1oDVKGWConiBshFHEoBVfB1IVfoSMBaO80BmoY5gjkL4gvXCbr6EWZglo0OHZlBr0DjqHhbYy4HZhKl+eUBkErdB2kyQ/r6yEZ6quHdiON//+c/8wz8Ir1+85v7hPbdvfsDWVCaJgJ88NSVS9STbwlSyff/kI5wEds5JpGbA3osXVxgZwLseBvsPe5bjQrdNeRypLNWIbYlwUjbmHEU8iSDXLdxNUvEskdS0nogINpJ7U91EwBiDRYI3P95y/3BgnicijP2ybArqkfTrMKSkT3Vfj7x7e8c//h//mTbPvH93z/7hnuwiAkirrKk2rl/c8M3f/B03r75hqoWH9YHbH3/k3e0duikEDUc3kuR0bIpSS+Ply2v+w//yP/Mf/qf/ld11pVy/4Mf//X9jWEcpXF+/4NX1b7jeZdDgn/90xcP9HolUrJ1GtdP61cLS2qzpFlCbz+aQXL3HV3BprVSGp8emGHgRtAJWtu4xAQuGBl4UGIgFQzP8WCXHnBJgI6DWVLiFUocRvs1dqrFuZLbXgkp6DRsjuxkliV0txhiSfytWShRcbPMZPeV8eKrkPIiwXBfVSI/SSF/pqOnBVzQ2YUUGwqe1ueMj6GQ4V4hSxPBakZ7zb9kKI+BEFVqPj/7i81R5/fqai8srOh3jnlkqVzff4GZEH7gETqfvg2ErYYqa0ssgLPMqNEoSS+JMVMaawX54UBoZWN0LUtL6TYen0CUHD5Qsqo1I0QMYa/wUjCmWPvUcO66FzqB0ZWFFquGbb3/biCwjEPnywtYvzVtOeK5z6fEz9zkF7q8hhJ+dn/CXBebP4enz/hS6d3FxwTzPH0nYdV0/kr4nz+mn3+u5+cynCuin8zDG2K7rJK/N7GPw4Wl+cdr2qYPqRAqftn8ijU/zIndnWZaP89PT51prqOpHdXIfg6LKuq68efOGu7u7j+TzpzrIRITWGm1qP1Nef/4P1QkpGWw6nFoixwAyoG9QKJLPH6+BnrpetrA5tWxxbhEMT54gRqqafVPGhgVeslPSxDZlcKWZYSgqnbJWTHMdhFYk9SzYSFsJUcEixXoiynBBRk8BVskJwGTJUQlBDMFZcMlOnMWveH/3nqqN6eaGEgMzOO4feH93y/3DPasZpa5pn9qSB6ij0sOTtI0ciyIkQzjXSlXLTJ4qKfKRBaIwgF0EXY1i2VUiJe2CQgwfeX/LZgNrYaRxoqaH/YjN+j0L/VOr6XFfsqg7CEILo2cXkEv+RYJGJwvBJspUHNTxJe3Ecg5hNGZoGdBOH0jL69ri0zTyL1pkPEdO/mw4+VIe8pkN/Xra9kvwiAb/VTuRzR350Qb+bQ/4Z9v+VYXAv8qxPaokPtnk19S5pAiGUtbNc2bUrHr7SpCVEw9hKpYDChm6JSWSoHQhXGEEgw5akKLUkoueFo3QIzELuqkQtOR2bChHE3bi+XvNCfAYF1h13hzfcXE7M8sOmQrzQVjW4O7tLbcP71mPzqYFyLYjEUrbUXqhT5s31BrIDG6aC5YqqHsudnSruI9Ks4FVyfCyWAlpxJpBFVqNsRrDG9OcvnGenQnE8GzfUMXckZgpdU2v51EodaFVZSyBiMPiOIGMTQXkE1YDOLJEo3ij+EDnweTpX4ZBtJZKQg+k5nCTrZaK9kGt6YnYBSZbUnVRFCgEm/dPMXzzpPewDOlQCBWaD0JgLdOXXzueanQJ3fyW0golSrBa5ETaOkOM2hTMEM+WOg0YGGOwFTXS/9ocZDK6BlNEVkMHaUMhhYlMdy1oDuiTse6zEkolr8vFiZZp67EOynSqigrSMijFRqeNCUpeH9bJtiFVyuzI0uk1FwWTCNXAMwkxE3F1gUiVPyqEdaTsCIFjpidA2f6OdoEcHZ0qbmkboyEUdVZ1dJRUlwlEZGsWW3jiZBBRMOuMpcOklDW9omrRJE2XgZSytX2l2rRUoYRlC06ZECz9v11RcUZJv3MvGfxWJ837xTtFJnChRCVcWEaGsBUKF5dX7NolvXf++OIlt3d3WdEVQ5cvJ5jNBFXHRiFqoWtQDZoLjmJ9QbQyT0KNzQ+4SHoHuqMSTCLZHcHKMoS5Gq0rWmCYp1+wKjEKhz64ai19t45H7kchujFf7Hj9u2u8d8oEYo6sgYhikfffHGkGFroSAet+MEljKkKdrpBamXRgw0ArSEk1dct0d8eJZUKLbISs0WoF2bw5pSCLou2Y94NJdk/swF0YMXJ86Q4MIhTpFeoRoiKqVMsgnzIfGbKivTKKo674FGisaAxezDM28rqui2V4B8IhVlR3qSJcgvAMiZIqVAGrFbMjL9s1qOXEyVMRLltgKS9ALQuQNKXImp+f8271EUwKY9pREIi2hb867Vc8FJ9T6T7XYgk/kdLPEZ+Pt/f0tdPC4FMLp6cez8/t+/G/PV6MPLeYOP3+eCH1S4vA55TDT/f9+Odzn326YHuqYv7U9/3UQu45QvpTQX+fU1+7Ox8+fOB4OPDjmzfcvHjBNE0fPZnfv3/P+AoVqnQH7xz3MLVgtzjzhVAuKiKOrBMGaX0zL0g0zJSxLNgaZOpMQeXIGoqFcVTHdYIFRFMhxwWEZtdLLTXtjWaoZWK6mLi6uOLl9Utaqdwv71nv97w9PPDw/kB1hyWLquFJ/IkZYgXzzj6EC7nkiNOGJul6PCDdaLsrijSwwEfn4eioGtEzEIYeuCjRlUanK/iYqazbc1yR1emMDDYegtcJSs/kctmyHrYi8SrCrMrS064iutMiNjV1o+HZoWJrtqrrxGHsieMDbU0rnjCHqaClki3tMKLQNLM1PCqqwtDB++NE8c6uCtVGjnlqLFIYe+Hy1Y467TKQ2TvLXqhV6LMQdVBtm6usgk6C21fIUCXb+dPpbhsnIlv6iYCAIYPbdz/y7v0tKg2thYu5cXl5hYuxrJ11PTJ6zoHS5ViSdNbNQ1iz0858JIGlhSDoNjgcV7RMvDvec/f2lj+/+Z7juuQqKLJLzUIy9LhoqqYAmWsGOJ6ITuvZhbcdN2xzwRz1EVGcfD4hObdN32PD/cjwzmEv2zwuyRSPtAtzOa2LHLdgWY7c/pAhTYyRilNJIruUJEHrVKm7xugrd2/f8Of9nj/96Q3f//A9y7Jwc/2Ki+uZ/cOet7fvWPyQliKiUITdxczf/93f8+///f/I5ctLrm+u+O9+9x3/MO847ve4ZIdkq8J0MbHb7SjzBVoki6++rW5OBY1I8l82pcPS17QygQy7kiRgvxSrpY1FKZbWHsPpKpQBIamcqxI0Cw4R7JpiTSi2GTO4gUI3hSqIZqZD1MJE5FpNp43ssJznVVA7BUqRNoJbpdc2zXbzXAeNcFxThDFLziudAM/ATQ/NGnCk9XTWVgIsBRJuKfwoVbbzVgmCJQR0szL7qOK2rRNwbNaRkh2uw/FS0xpnI90lKrUUpmlm2QdVnIuLVNQ/LLlvcyHE6DawWD7aF5aNCHfNebDZplIuSaRTawZChiCyku5+wYpCAbEMy24RsFbmaaTAW9gC0EAt6DUyc2doBo1LEuvijmqh9FTEZ4dihnfaZ9rV/3LYef69zxWen84dTjh5AX/KPutr8Fw303OF8Mfzh+f2dzomgForNy9uePXyFfM8f9zeGIPD4cDd3R3Lsny0rPjcnObpvz83Hzpt6/G+ROQjYRyP3nuysni6r1OB/PT9TwTz8Xjk5uYG4CNxfSK1e+/03lFVPnz4wN3d3c+2XR4JvE7n7RQWeHV1xYsXLz76O/8SXFp2XSP5lAmhumCkJRM2YG655vZcf299zllMJ/LfRNLiwoTSUhWr4siY6DEoMoja0v5UkpegGPiUXU+RYq2qBbdNsFWCKprZS1uXI8VQH6kObvl8kJFFm6PlenB42kKMYlQGEY3lfs8775it7N5/QMvgsHSW/YHDcuSwbN0IssPFqQGYpZCrp9CGmjZB6kb42J5hAfVkQ6FpYYFSJe00ibQiaiGYCBIdiUedkp7uAFLKJgfNQMO2BRTGbmBSyPz03FfVibAV8WCWDLitytZR62hVxHJ0zf8p0VJQMCwQlEXTzmpYFsTCMli9yaeL6b9IMP/V+NRnNhR8JWcbp/d/yac+42vzRfv8xAH/fxDycWL2dfhsYeEXUETo3WAOVoWiK15Shdt0I2vc6ZuSNivCJLmwWUa0KWAj5zKVeMKroT0nIBFBlJLqIU1FXNtCnM0KVlNBM4YzUC7Lmuq/dc+t39J6ML+4QIqyHld++PFH9u8/pC+yBLGC12DaBk/DsJ5pv4FTTDb/oAwZ7CWr2Yz0yzEZhFdqK1iP9OhNN3aGNsKc6tDLSNbHFlSS0IojRLGN/KloWJKloemFPGr6627+ZBqgejpXMEoSeq4TjIEvQlxm69lqmUhuEpSxIio0LRhGXbZEWAlKNRh5UsUVIhNfVYylOZX0NVocSmQIjFnFbKEWmCStShwhvkIN5tOmvIv8LjHntdfXBfWspnkY1ScYjSpr+pd5EArdDa2BygqW6boenhYYo6TCRzaii6CWJF61WyqQfcAxJxtad6iviDpRNzWZKOKV0tM/Sit4T0sMLdC3EB0OBS9GFU+VehSK6pYm66ySnn5Ekr0UYwqFuaAqxCrERUXYvHJJv+lWBHpDGrDbAU6NnzzthhRkpFqrdcMY1GmGyPbIi1Ml+SLPWbVI1W0baVMTjki27RSCbiUV4uFombLgUSqxCNU6owr0BauVqUz0WDf7kmzRHduC11goNuN14Oa8e/8jf/7X11SUm4cHpjaxHJctTOgA98aYpiSrv3Tc8chrQwsmg9oNtZoJu5vdSRkFnTVJDvWs7FYlFuOeTmmVqSbZ6tbxVnDJgIVWlBg5dlVJcteqUwhCLynh9LEyK/SpUeoVsgpuPava84r7oC/KVI1ixpgqsggXN0q3QMwzaVgLIpWYhFmdDPaqLKFcNWE5wrSusKu4N6oPkkbXLE7E4GjpkWg2cowaAwlj7A2vxv22CNOmxKb4t0Nl1M40pZ+2A2pK2RVKFZZ1UKZMHXbfZaCYphLKV5CLAi7s3bH9wG8GpUOIZpCGpRI648hy0So18F6ZdnmfmBekKtIHc81FVEOYXIipYBb0PtKPk7QfKdFhVbytrLKiayPal8dMPLdg+FLy9NQO+RzJ/Jzy9qlS+Dni9Sl5/PQ4n1PYPPZ5fuyj+FTle/rM5+w3npLVj8MCT9v91Hd8fOyPlTvPncOnBP7jn5/6rqcF0uO/0VPV8tNz/vj4T6ofM2PtnYf9Po+XLCD/UuvsU9haWS8sVYFjDzFx7Mo3MaGjMPQt1o1FBd4PdMpQ2naRLZXU9ISWXgl6qkCXgTSj9c4aTq2dZpXeg9EKKsFVa1zoxMWLK6aLa6Zpx+7igl275Hq+5jB94EUY96/3+DjiQxjRuX93z3E94qOwDKHsV6I2uHRKc6Tu4LjncpqxGbRUwiOtdNzx1pBYCI5JbByVYStSnHUr9MrciRW6BCZOCdmsFNKDXr3TPb3oy6YGNrP0la9Z7JOY6IsRLUUJETOlHbf+okCqsyy5UPfj4LAKixttpGIKb9Rm+Jqt7aqRhTYXwoxwp0wQYyCyIrUSMcH6wDGUVnaYDy7JIOTDIH0YXdIi7WHK63squHUmFZooossXXztCihjCB5EpeaQXZBLOQpDGzHmNt1K4urng1evfcnV5yeortz+84b2NJPvCMM8CvyCUzb/5D3/3B3737e+Zpx3djtzevuXN7Vvu3r3ln/5x0KbCcljZP+w59CPYtpA95U+QpEJEBmhrK1xe7KitoVpZDgf2+4cMNsZx3+7T2MIKW87nlJLqbLONBM+tj+12c4EidSNaI/cfp0DAk9pXN//yTLd38VzkyPa9FXa7iZevXnFzdcP9u3f88b+95d37dxwPC2bGxdUlf//3f8OL337D29u3mP0Xbt9kwJ+QQdGX8wX//f/w7/jdN3/D5dVVLvRFKK0AeQ+uttCHcVwPHNcFrFOAKFvHgKTjn0cq/iUiCUEJrGcbuUpgWxAd8uXjjoYhEvQ151hKZDC1gnqeN4sUjIg4Y1SiBeorUIj2kwd0kKriIoJ4Ta9fBjJG/v3K9kywtAvDFZX0ai6iWJwUw9nvKG7IyZa7ZNHBLTLLJhStBbXgVE0oQhJJmtZw2rOoobp1p2KolcyT0YKV9BxHB2abl3MRXAolhLXkukgsyWKRDORbxuBhWWjHii4rcwSo8P7uAXuZ2R3reuBwv+fh+IH72w+sDz2FT1unl4ihNFYGreR1UGROD1k/CT2ykA9BbB294U5zYRTDm+Z3285TqnjSOpEiqRDVVInjZJdABCEVek7MPgYtakc8A6X/3+Jzc5fTvz3++W+BX+qg+hIiW1W5uLjg5vqGq6srpmn62fzg5uaG3W7H29tbPnz4QB/jZ8/9zymnnzsn7s7xeOT+/p7WGrvd7mfk++mYip78xR/NceSn7Tw+v6c5zeFw4O3bt7TWmOf5L+aMYzv2/X7P7e0ty7J83N/j83WaN53I5doqV1dXvH79msvLy188p6dtqqeorpug4dgjb/5SCjICbQWGYThFG+4ju3vJe3LMQl1ANYPfRQSssPrY7mlFY2xdFdBKsIYAK3ULpq+SRVIpya8ostlzVWQLD9TIri0pnR6NJtv9FpHxBuap2yqASNo/aDqbL0taedjI7/awHunHI6ttgi0XwlcoFdVK94H3oKnDyG7w4QHFc00uubbLQmuurUR22QFNrkW1aAoQfCR/UmQrxqWfeynJJ7hnMcprIAFrgSkghrArBfORIk+dkNj26SODCiW3bUXTitB0k0sLjCwEioCrZ2cKTliqnqs4pSY/xqSs/um5zl815C/zgT8z6Hxkdn+ieL92iPp1n3puG5/GX1Wo/IsbO73hK/b67FtPA+5fbuP5zofHG0lC8WdC7V/LSn9EhXnPNJRDkArTsbUyRRI6XjUn6mZIqXQbNAnCFJtsS2LOULRuNQeLY6EVZ6ikX5XlZGNIo/WBswV7TYZbQUcGKxScpTbk2JEW3D+85WEcubpNJ7BOLpx6HFk8K+I1gjpsM7VXpA5qZJugTA0hB/hhTiM9abxqqlo3D1GfBR+ODk2l74DayMGvCloyAR0bGdIXwd4nZBpIdFSnVO5J0KNSiyEGaoJF//gX32zuqF4oTZN0b8KMs5YKR6GOwmhg6rCCtDSPD+sMrRATowQrK1MseFS8rknCes2q/pqDc9XAN8I/VdMNNcdnQ9YgoiDDcBpSx1eNNC4NHWnbIZMSI6gllQGYY4dCnSLJfXWG5EIshhPDcjAvF4hv3gdlU3QMmKjp5WrOGoE3YdKWbf9HEHWUHWJ9U02k4mIcs+Vk0rL5MjtG2oV4VXSRTfnF5lWdqutaBurCkNgmm0IrQZU577SR1cJKbK3OhSqpvmhzetVpANNAyNbR0lqGskX66HagFqdIpQKrWfrkSjDWtHqJkYvFSQOvSV6OMKSmr2b4oDupjmiVsRqum3dUE1x3iC1QKzMBlt0G2aKU4VUhwWI5mUaD7nysxkZvjGlFypyTrIBlXfnX//tfiDB+c1zYXVywHh64u3uXgY0zlC0o9EuRrhxOaS2LUBL0KfBQJoRZhWWObF/SFRm2kSdbunc01BdkTVuP3TSjrPSebdEqwqgzLJb+8VeNOjQTkQ3aZaFeXDAcZEmbE5pgpVIlFbwNgSp0X1GtxOEAMm0epUpRh2jpG10bxfM6MAJ1Ren40dm1hlXHGeia16GMpG8iCpM/sOhEHLeJFiPbx8ZApoEeFXaFfliZmhKSgTnTFITM1FFYdcmEeFHayElLUaWUgIMy7WCJBmNQVJinQg+jRNBMKFcZPKpS0VLxaHS7p5WZ0ism6UntS+DVWdaZURqVTnjnuByZ+kTZVVw7dMGWkiE5NrBa8cjJYBGHpkQfUIKB6nDpVAAAFn1JREFUU/ZfERD5zMLq9N/Pvffx6ycS9Smh+ZjAfazSfexl+JSAfW4B9zhs5/TzRJB+KhTwOUL7KXn9qcXUc2TwUzwmoB97NT4l5j+lRn56HJ/7Dk+39XTxeTofz5H/n/rcc3+303ueKqx/CWVyxtLpVihXxrADbWncPwS17pjmyqpAV8yc8cHRq85yb8wnte/kHBbDHyoy0iYhSOXNFIKFsM7ZgVJCuNQrbn57w7y74nKqUHdUcUJXemgWqaZL/HDPVb0gfKK9aGgR+m+BJYs0y9jzoS8Eyj46uw7IkSJCqxUvg7IVHt0z52Ang+OaXrW2H9h0QMdg6RkSYyNtdozsboLBslPmLmhUYKSCqQ9MkzicyLBcHRWJJKftRvEj2fKvwZgtO89oVBUOh4LNK+P9gT6OyEhy23SGWFK4IIIyM9QpHdBB6Vm88zpwApsgtLF4p2nFR85ND2NPjYrOBfUVtQmthVGUuk6s3ZmrQGtMuhFp6vDlw04Wkz1tl9xlm5knqSwbQYnngo9QtDV++913/O13f8flixcZOkhlPf4z932kuksCIn0ZVZXr+Yo/fPc3/OFv/5abFzeIKre3t/zH//Sf+D//y3/l9vs3uG8CiY9049atJ5phRXZS4QrSCtdX13zz22+Y2wVmK8vllPZT5tjmtZv+kUIthdoaU5swh+PxgU7gY+u2Iec+Ke6qlNqoIqz9mMeycTAnr07ZngUn30ghoBQiHBVlN838/rvf893vf8/Vy2tiNX744Q33D/ssTBbhxfU13377Lb/77nd8981v6ccDH96/Zx3beCrKNF/y8uYV01wwCcZyz5/f/MjhsOYc34KHQ7ZM23rJ3e0d+4cHhkOhIHNhmmdqK/RlcFj29EOm3YUZpmBuCOm/TOT890sxPFJ1poVCxRiUIfS+UihYdEohu64ki/3igZRca7gbGhOIb51nsglJ0v+00IgWxJqhgKhkGKJHkufRwQumngS1bURyyfNXUcpqWCk5DhSjuTI8C/6nYdeKo8ZWxMprNmsqlqQHKSw7zYFcUy0pUdGeN0eUjbKJU6FxI/IEVPK+8K6Mw8qd/MDxYc/u+oqpCbZ07j7smd/dcXl9xXF/5P3bdxwODyyr4XHETaAEtaUASBAmnTOjJDbBU2S4ZcRgGUkoi2ydkJp+sKZ5b4IyRFLm4unlHJ7dghGR+TLGJtxZ8U0B3bwnsSfbcysUEaXUxvgaFdgX4Gkh+ITHz+p/K6I5ItmkU+H3dDyPf37qc6ei/8kaY5qmJGZPRWh+mrOUDPngw4cPH4vPT7ukntvHXxzr9v+Hh4fsoFD9afun97mnCEw2e8TT+TtxL0/2+/h47u7uUFVevnzJbrf7KBwwM9Z1Zb/f8+7dO/b7PcBHr+bT3PGxqru2mlad/NxK5EsgDAYtn0USRCyUWlOAGKS6NZTahR6S/sIjkpxUZUSleSAjxxBTNovOHORrdWJAL5UmyVcc3CmelkSn8HipwYi03KukRaMVo0UQo2dRqqQ9VJEAz65M7/aRQB2SHZpqnVJaimbCsujmwbE7de6MzQ7HR89OHU+rJlHJcaoMxkgLHsEgkmeoAoJCpKgsF/ONKIZbdmCoroxRs7AXAuH0qtmdqUL0jEykKKojVeO2dRqXRihUlNUs7cV0mwvjWeDzFI4pgc4FsUYhsrMkjGKO6qaWtuzCyLHZkFohRjoKaMdNkKIZNF2h0LK4+wn8VQnm06Rku86eecNnX30WP/N7/lLIaV/x5N/+4j+f/f3Zo/tVB/KpjT33hi89J7J9t58fTJpDbK9/cluPX3vyjWOjqE+88mnh+fGdX0e791gpVjlqQUpWfosWxoAtS50yDI9CIR+4PlXE0lNHpeBrVocNh9myHUoKvdRsfdBK9FQrlGL0beGS1lRGlSPdhCKZhdmHoK0TDmrQRVm70DYz+ZgEtcq8JQ7bMKTODM9wv1SRrtu+F8SFWnZMbUsZd0WOOYHzSVCZqcDRBrWQ5NU2KKp7VsWqgDmjOmo7Kunr3OeZsjg9nEmCNRRB0ktvysA/lqzWI1tQRihDO0hFatC2JNBWBS5G+hyGMNWK0/HQVDaG5NRUMlWZUcG3ylYr2Q5HtrTQlBgroTPVsy00BNxXIIhVkZhRBJOOSE6YpH/5zVPcoQl9Fer2EOt+oKD4ZjlSXKAGLJ2h6R1bdZcPb1+w4bRWMAaxBbwVV8rkeCg+Oq1WilZcjNVqKjRiYGVNQ/tRQSMH7rrD1XMiHjUtRrpDrdSeHnhzOIWJRbKCadGzGgigQYlgUsE370JnUKaJmZxYSii+A3fNRW0ItKBWAduhJcNZTIOpQCzKaJmk270yuRLVEStIDCiNiuFaUqUqRsx5vD5WvLSsEIem/cyaBHixDE600vG1pRomDJ0vKBSmSIsXCqwxsJHtfqP3LJxoo4hRSwN3qgSjHamrso6VUjpL3TEP4+1yx3498O72B0Irh+PCu7fv2I9BDQUX6vTl5jw6V8w6vjiq2fpTPahN6b2zeMetpl+2C0VK2i+ooHWh2oS2XapHyAr8aDuUQdksVSortShow5Zg6QNtBWlKC6HojlGD6AvFB12FiUJfjlgJWlV0yRZNMWVqOxZVWAalKcUbxzXtdGTNuUrRhshKj4VuxkrlSgWZCvSSPoEOsg5kElQnhgvGA0FFjlk4CxUIY/LCKPkckcuGadCO2YotbK13RREZsKbdyLp0vEQqEpozSnBc4eBGmDPPjaI9VegyUa8mGiUDb0qS6gxDB4wYRLdU81/WLOxIoa+WIWWkn3ITxVtB3fAhoEnI+JrKa48tJGzkIla1Y6FEVKipbPgaPCUoH+Np6+ZjPFa+PCaCHxOVj9seP0WcPt7XY+L4sZfeY1L4tHh5rN79nAL6Uwrfzy0SH+/vKan79Lw9VR4/VdqcPveUKH+83afWIE/J5qehfU/f85zq+lPvOf3+eFF5wumcfwncHWrDo9P3cDClTRNrFKQ2QhoXUqlT5XBdsfeb/78UdNdAjMlgj2N1sD92VCplDdaSQVUna4hSCvM8cfObK35z8x3RsrW+iNHaFRJgkUSrkkTTdFFYWlpYdDPW2Zl2jcuLmVknLo/Bw/099+uP/OkOdnNld/GKq2tDeknroSKMIkwe9P0glmD1wd473dNrsYZRWiGk01XxUrNg13ZUM4iNSFyVqAN2hvYkIJc+suOsKdF3iDrr6rS4wIch8yBGYcQRU7gcqXLsS2dZVx6G09woU4AVVGfMDdVOr1k8ZgK/D2waiKXv4G6dOO4si3RS0s7qUmD0DAuKSq3X+DoYzXG5pE6FeRKmi8qQHXNMHI8H1jBiLGypt18EiWwhxuUnf+IoebzETyS5ZPbE7nLHqxev+M2333B9PdFtZlkXvn/zIw+HAyGei1wKqile0VoJLdwfVqapQ4Xb9x94d/ceXwcBGdpoG1kXpLtjUWpVWp2IefPfjsDH4LB/4PZt5cXNys3Nq8yLaO8ROW4tykmmaFHm3SUvXt5wc/2CMQY//PAnDofDFmKdZCAYqhO7i4mXv/kNUyu8eXPLYf9AXzvd7SPpdBIFniRNoZsPtOQYu5svef3qJRe7meYzixy3jptBkMXjy5sbXr9+xYuXr/C3txwPh+zeitNKNrB+ZOyP3N99IMqeH3/8gf/6n/+J47pn0NFRuL/b869/+p7dPPHmh7fsjxnu1y4uePXihhc3N5RW2R8O/PDmex488JEKNI0Mr/QthBEBsS+vToSmZ3kgGRbXAiPHOhNLMtYcddIruxgxUtVMcUoXTI2p5tyx0sjhdd26lypqFaYUzqTwoFK14zji2zOZvEZ7pMxMNfAeqa6rSsVQS4/ukEg/9U1tOIZSyvpRqRjbuiUKaC85ZkQGSYKmDUesMDRJruJJOJWaxG1stiqDjZKuiG02ImVhsKPvO673dF+ptSHWeVgN3gbT1PBYOR57qgQh59SS60qGkTRPS0LGnaFZIBgIGg2Tka3l0ZASyJQWggFpTyRBG44FyGbfkdfzqUg7UGm4DNwmAtsyVoMuFXQQqpTwJKkjC2CVr7Dm+dx1dSJ3eX5ucsLnivB/jWN4tKMv+szjY22tZcZBaxmKVytC2i+eCNVaa4rERv+oAn46X3mKpyKBx8fr7vTe+fDhAwhcXaZyOq3rNj/l4zGvg20e9NSa4ymRf5qfLMvyUZ282+3S25lg9PSUPh6PrOuKqjLPMy9evuDi4oJW218Q3ifbjZ7hK/Teub+//6Jz7OpAPifpPT2LIwuKRRrWnOgpZhGJ9BuWQKUSxZhiC5h3zWesCNEm1DIvwCnUltaf5tn1URVC5hz4Lccc2UjToinMcU+R2dAU0qklYapFqF4z3H6zjCDILgnPThKPwjJSDFZQ3I0xOmWXIanH7kg43TpjGLF13tQtOwHLQEHTzfZjMaJmgKczgeYYXdSQEcjYbD9bSc/6kh0m3iOtAsOz88canoqzzHqqDY2gXVxwfXnJ5dUldTchZtwfHth/eOB4LLhZiiLHyAIYGdjrVjA69IJrxzS7XUvdOrYle42V5Ckscr7pW8F70ny9W/JRbrlO+xR+gWD+OlKRT7xbJIl7+xW64F9VkIufjkR+/itP/nObTH3BcTl8Hcv89efuyxDPbtd/9jp50v/iez2h1R+plj++Gp/6xNd9lzly0kcD05GkgKd/VSaMQrdUnOEk6duNQYZfhEHVirVMWVVrTFVZTUi9GkitjCh4rOn74wa+w8rKLNOWiiwMXdGuRIOwVMGWJlhJojYomVg6FkyUtg9WDYhCYOg2UQsNwmCqwtGUudQMgBbBKVzIyuIk2eSAG6MpVYVYB6JJELpEhr1JYazCtAtkKGGKN8EoqA3CApFOj0oLWL2jDfCJqCQ5WALZ7nGJHPg7gZbKbIYxZ9u8zHgUJjsQYwVRZFexcHYU1iLIMS0YdrNgayBFKavQy4SrManRrdJawVUYBoPChQyGbi1xAlM4gdK1QGRAoOiXezBHZGus6oqKUWLOhbul8linrNzJwYhpQsOI0ghx6hAwpbXKGHmcTVPpYl1QbWns3yrFM3ikWSHo1N2EHT298FoQ1vGhVKat7THD12ILBXDRDOCz9ETGJxZzqGR4x8gHCloIWhYBykT3oKpQmHBfKUWZS2MdncKcrcG1Ed1oNCYRrFWmnaY39+ZDbrpSF8tW4wZdnDl3x5CKD6eUfAADlFIZI1hrUBehjFxQapkoFtsiLP34rlwpNBbS75MGk0O9yIovNojREZsQFlbLiUYtSq0QXTNYQJM4lxEMnbEmYIMxFkopLGNhsc7+/sNWgRakB7Iq9aLQQ1iPX070jOHUaU6iPQqlgi8Fv+/odE3RBzrpdyjiyDRx7NBka+2MgZQJKUrvA1pHtKIDbCK/SwShxmqOa6AVyh7qlUIdaB/pj15Lqr5cc3EsJYMOpeBFkaaE5b1SJDhUYed5rV7uZrQMvKeC3nywE1hLYRxWiMbdsvDyum5pvxNLXxh24EZf4CMYsSAWeF9xXbFRwFeGV1ZZWVyYBBacqTixCj+8e0ffP6QHtcBvXr9murmi7yvzFFg3dsVZ45qMWxUCz46LXlmKUqdLWkl/09C0pdElwKCHEFEZdUabg3dYMzRkVuVYbLMwKfSjM2rLAlNAq9u6cu15P05XRAxM0gNzRKqXUKFQ6WNh8S+/dj5lI3EiSR8Tvk8XXZ9bhD3dztNtPLV6ePz649eeI0ufvv8xAf5YtfJYYfPY2uI5Avi58/IptfNTdfRz5O3TY3z687lF1dNz/vhcPIdPeTg/JY1P5/vp9338++f28ymUUuhjQSRwbzysCh1+21e6HigIl/MObZXLaIyrmWH3FCt4m/A4sJdCnSr+Y4bZLj+mKka057xIUsU8zY3d62suLmZkFpoO0JwnrLFkaNYK3gVtRr0UGMpuV9mXQRnKNE3sSsX6Aj29V1usHO87y+HA23dOiTt0Cq7LNXq54/WrG1pVFp040lkPncN4QMagoBCBSbaTTuyolq2powpKZ7a0ZDq6UUsGzsSkeM9wNZe0HUA7oY1RAjAijjiDvirDhMtJKbMQA9Y+MAb7ozEJrAqtwyhOlVzc9Z5hNRrQRajAZDODTvPCID2xRQBRhjl1ucBbLkTdYRcGmkraUip1cnpMXLTG5TxzCKEcB7s64UNY5i8fd0I3BeM2tw/I1XNEdoVqWieIJGFLpPfqxeXMNF3AslJlU8rFSee5+Vpv6+k1jNEX1vXIapX7uwN//uEH3u8/sPjI55D5pppKpRlAmyauLm/45ptvGDq4+/GW48OeMYzeFw7377l5dcU3v3lJ3w++bz9AgTEiczQ2K7kjB6ZaKQht12jTzOG4bOuU/NZaMpT45sUNf/i7P3AxN8KVf11WjtoJO4m5k5yVss1FJBfjyWcHqoXW8rUmGTxHrIxC2jiIULTSdjUtRsz4/scfuLt7wE8S0DDolftl5Z/++f/i8sVLfKz8wz/+R/75+z+yHlfo6ce9Pxr/8sf/liqwYRzW9OadL3Z88903vL66xqsy76+4P3zgcFjwWBGyOApspDaQurMvv3Y8iQQtQqCMlE4jQwi3zZ9CU9iSf40kERSqGlFTeDBWR0uhXubay0YQtRCqdEsBSghoq7kKdtBN+VnGiqCsDrVIKoUjj8MJREZ2aoWgGliFMgRzZdIMY8xsxVzbaYkMQY6CSaHImr4pBMMHUvI8zVoYDt2F2rJj1EZaTIg44gWXmiR7geKVoGxBX41VV/rBKfaQIWUmdOmsVtACq/NTUOMYeFVchNXycHTzEvei2elXQEckQd7ymixAtyx+KFm4MY1UfMagYHhkpkUWhk73vBDhGA2lIyXXyVVJEZGfPFABTRI9xLei1F8HT6/Czz3X/9p4rpvrc6rlx/OIU+F9miamNiWJrPqRLBf42TxomiZubl6wLEnSuvvPPJ6f7vskHHg6T3i8/947H95/IDy4urpKcpfkaU5War7ZZJz293gbj88B8LNOqxOJ/Dj073S8qvls//bbb/nDH/7Ai5cvudjtPqqTT9kf67qyLMvHvIl1XZMU/xL0mp3oRD5zi0Aons6mtJ4FmbRCTIslJUVKRj6jtFj6DdfKKZ0vbOCbfaqhhBlFHY8UTEUZJKkjuA9aJLE8LMcyIrsavAsFoQzFWRAawx1pQiELxmagJbsxWY0Rks9NyVDSIsG8a8lnr7BKR3rPYHcF6ysiNT2MLwvFLe0ufMWkMtXsxIiSAjypinbBR93Wj6BMScwXRTv4tFntlbTR7BghhuKUrkjNMepmvubVt9/w7beveXF9Q5tn3OD23Vv++Md/we09i0V2aumM2ZqdouLZgWtCaytuaf86imwWGc7oWzhvE9QCH9vTKJTSCq6DUrbwQCUFdJ+xdJJ/qwHijDPOOOOMM84444wzzjjjjDPOOOOMM84444z/f+PL+7jOOOOMM84444wzzjjjjDPOOOOMM84444wzzjjjEc4E8xlnnHHGGWecccYZZ5xxxhlnnHHGGWecccYZvwpngvmMM84444wzzjjjjDPOOOOMM84444wzzjjjjF+FM8F8xhlnnHHGGWecccYZZ5xxxhlnnHHGGWecccavwplgPuOMM84444wzzjjjjDPOOOOMM84444wzzjjjV+FMMJ9xxhlnnHHGGWecccYZZ5xxxhlnnHHGGWec8avw/wBnzzTyBADorwAAAABJRU5ErkJggg==\\n\"\r\n 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \" valid_generator_aug= Val_Generator(valid_image_files, valid_mask_files,augmentation=True)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"zwYaKwTHPr6G\"\r\n },\r\n \"execution_count\": 28,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"# Augmented val set\\n\",\r\n \"for i, j in ds_valid:\\n\",\r\n \" break\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Only CLAHE-d Val Images', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(i[:10], axes[:10]):\\n\",\r\n \" ax.imshow(img)\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Only CLAHE-d Val Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(j[:10], axes[:10]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 380\r\n },\r\n \"id\": \"ctS5UNYPPwOe\",\r\n \"outputId\": \"fd427d82-370f-4ab2-93e7-6ef155bd0b3b\"\r\n },\r\n \"execution_count\": 29,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def dice_loss(y_true, y_pred):\\n\",\r\n \" smooth = 1.\\n\",\r\n \" y_true = K.flatten(y_true)\\n\",\r\n \" y_pred = K.flatten(y_pred)\\n\",\r\n \" intersection = y_true * y_pred\\n\",\r\n \" score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true) + K.sum(y_pred) + smooth)\\n\",\r\n \" return 1. - score\\n\",\r\n \"\\n\",\r\n \"def bce_dice_loss(y_true, y_pred):\\n\",\r\n \" return binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\\n\",\r\n \"\\n\",\r\n \"def iou(y_true, y_pred):\\n\",\r\n \" thresh = 0.5\\n\",\r\n \" smooth = 1.\\n\",\r\n \" y_true = K.flatten(y_true)\\n\",\r\n \" y_pred = K.flatten(y_pred)\\n\",\r\n \" y_true = K.cast(K.greater_equal(y_true, thresh), 'float32')\\n\",\r\n \" y_pred = K.cast(K.greater_equal(y_pred, thresh), 'float32')\\n\",\r\n \" intersection = K.sum(K.minimum(y_true, y_pred)) + smooth\\n\",\r\n \" union = K.sum(K.maximum(y_true, y_pred)) + smooth\\n\",\r\n \" iou = intersection/union\\n\",\r\n \" return iou\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"5EdWl5yDPzGp\"\r\n },\r\n \"execution_count\": 30,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n \"---\\n\",\r\n \"## Network Architecture\\n\",\r\n \"\\n\",\r\n \"When it comes to medical imaging, margin of error should be almost negligible. One small mistake can lead to even fatal outcomes. \\n\",\r\n \"\\n\",\r\n \"Thus, algorithms designed for medical imaging must achieve high performance and accuracy even if the total number of training samples upon which the data is trained is not enough. \\n\",\r\n \"\\n\",\r\n \"To solve this issue, [UNet](https://link.springer.com/article/10.1007/s10462-022-10152-1) and [UNet arxiv](https://arxiv.org/abs/1505.04597) was introduced in the domain of AI based medical imaging world, which gave unexpectedly good results.\\n\",\r\n \"\\n\",\r\n \"The model architecture that we tried implementing is UNet++. It has 3 additions in comparison to the classic UNet:\\n\",\r\n \"\\n\",\r\n \"* having convolution layers on skip pathways, which bridges the semantic gap between encoder and decoder feature maps.\\n\",\r\n \"* having dense skip connections on skip pathways, which improves gradient flow.\\n\",\r\n \"* having deep supervision, which enables model pruning and improves or in the worst case achieves comparable performance to using only one loss layer.\\n\",\r\n \"\\n\",\r\n \"\\n\",\r\n \"`DenseBlock` → module used in convolutional neural networks that connects all layers \\n\",\r\n \"\\n\",\r\n \"> Explanation on Encoder Decoder Model: [Encoder and Decoder](https://towardsdatascience.com/what-is-an-encoder-decoder-model-86b3d57c5e1a)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"E2KbJHBbm-sV\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"source\": [\r\n 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)\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"MN-O57WmSF9m\"\r\n }\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def nested_unet():\\n\",\r\n \"\\n\",\r\n \" inputs = Input((256,256, 3))\\n\",\r\n \"\\n\",\r\n \" A_conv_0 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_0') (inputs)\\n\",\r\n \" A_conv_0 = BatchNormalization() (A_conv_0)\\n\",\r\n \" A_conv_0 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_0_1') (A_conv_0)\\n\",\r\n \" A_conv_0 = BatchNormalization() (A_conv_0)\\n\",\r\n \" A_pool_0 = MaxPooling2D((2, 2)) (A_conv_0)\\n\",\r\n \"\\n\",\r\n \" B_conv_0 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='B_conv_0') (A_pool_0)\\n\",\r\n \" B_conv_0 = BatchNormalization() (B_conv_0)\\n\",\r\n \" B_conv_0 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='B_conv_0_1') (B_conv_0)\\n\",\r\n \" B_conv_0 = BatchNormalization() (B_conv_0)\\n\",\r\n \" B_pool_0 = MaxPooling2D((2, 2)) (B_conv_0)\\n\",\r\n \"\\n\",\r\n \" C_conv_0 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='C_conv_0') (B_pool_0)\\n\",\r\n \" C_conv_0 = BatchNormalization() (C_conv_0)\\n\",\r\n \" C_conv_0 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='C_conv_0_1') (C_conv_0)\\n\",\r\n \" C_conv_0 = BatchNormalization() (C_conv_0)\\n\",\r\n \" C_pool_0 = MaxPooling2D((2, 2)) (C_conv_0)\\n\",\r\n \"\\n\",\r\n \" D_conv_0 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='D_conv_0') (C_pool_0)\\n\",\r\n \" D_conv_0 = BatchNormalization() (D_conv_0)\\n\",\r\n \" D_conv_0 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='D_conv_0_1') (D_conv_0)\\n\",\r\n \" D_conv_0 = BatchNormalization() (D_conv_0)\\n\",\r\n \" D_pool_0 = MaxPooling2D((2, 2)) (D_conv_0)\\n\",\r\n \"\\n\",\r\n \" E_conv_0 = Conv2D(1024, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='E_conv_0') (D_pool_0)\\n\",\r\n \" E_conv_0 = BatchNormalization() (E_conv_0)\\n\",\r\n \" E_conv_0 = Conv2D(1024, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='E_conv_0_1') (E_conv_0)\\n\",\r\n \" E_conv_0 = BatchNormalization() (E_conv_0)\\n\",\r\n \" E_pool_0 = MaxPooling2D((2, 2)) (E_conv_0)\\n\",\r\n \"\\n\",\r\n \" #-------------------------------------------------------------------------------------------------------------#\\n\",\r\n \"\\n\",\r\n \" A_conv_1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='A_trans_1') (B_conv_0)\\n\",\r\n \" A_conv_1 = concatenate([A_conv_0, A_conv_1], name='A_concat_1')\\n\",\r\n \" A_conv_1 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_1') (A_conv_1)\\n\",\r\n \" A_conv_1 = BatchNormalization() (A_conv_1)\\n\",\r\n \"\\n\",\r\n \" B_conv_1 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='B_trans_1') (C_conv_0)\\n\",\r\n \" B_conv_1 = concatenate([B_conv_0, B_conv_1], name='B_concat_1')\\n\",\r\n \" B_conv_1 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='B_conv_1') (B_conv_1)\\n\",\r\n \" B_conv_1 = BatchNormalization() (B_conv_1)\\n\",\r\n \"\\n\",\r\n \" C_conv_1 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same', name='C_trans_1') (D_conv_0)\\n\",\r\n \" C_conv_1 = concatenate([C_conv_0, C_conv_1], name='C_concat_1')\\n\",\r\n \" C_conv_1 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='C_conv_1') (C_conv_1)\\n\",\r\n \" C_conv_1 = BatchNormalization() (C_conv_1)\\n\",\r\n \"\\n\",\r\n \" D_conv_1 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same', name='D_trans_1') (E_conv_0)\\n\",\r\n \" D_conv_1 = concatenate([D_conv_0, D_conv_1], name='D_concat_1')\\n\",\r\n \" D_conv_1 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='D_conv_1') (D_conv_1)\\n\",\r\n \" D_conv_1 = BatchNormalization() (D_conv_1)\\n\",\r\n \"\\n\",\r\n \" #-------------------------------------------------------------------------------------------------------------#\\n\",\r\n \"\\n\",\r\n \" A_conv_2 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='A_trans_2') (B_conv_1)\\n\",\r\n \" A_conv_2 = concatenate([A_conv_0, A_conv_1, A_conv_2], name='A_concat_2')\\n\",\r\n \" A_conv_2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_2') (A_conv_2)\\n\",\r\n \" A_conv_2 = BatchNormalization() (A_conv_2)\\n\",\r\n \"\\n\",\r\n \" B_conv_2 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='B_trans_2') (C_conv_1)\\n\",\r\n \" B_conv_2 = concatenate([B_conv_0, B_conv_1, B_conv_2], name='B_concat_2')\\n\",\r\n \" B_conv_2 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='B_conv_2') (B_conv_2)\\n\",\r\n \" B_conv_2 = BatchNormalization() (B_conv_2)\\n\",\r\n \"\\n\",\r\n \" C_conv_2 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same', name='C_trans_2') (D_conv_1)\\n\",\r\n \" C_conv_2 = concatenate([C_conv_0, C_conv_1, C_conv_2], name='C_concat_2')\\n\",\r\n \" C_conv_2 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='C_conv_2') (C_conv_2)\\n\",\r\n \" C_conv_2 = BatchNormalization() (C_conv_2)\\n\",\r\n \"\\n\",\r\n \" #-------------------------------------------------------------------------------------------------------------#\\n\",\r\n \"\\n\",\r\n \" A_conv_3 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='A_trans_3') (B_conv_2)\\n\",\r\n \" A_conv_3 = concatenate([A_conv_0, A_conv_1, A_conv_2, A_conv_3], name='A_concat_3')\\n\",\r\n \" A_conv_3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_3') (A_conv_3)\\n\",\r\n \" A_conv_3 = BatchNormalization() (A_conv_3)\\n\",\r\n \"\\n\",\r\n \" B_conv_3 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='B_trans_3') (C_conv_2)\\n\",\r\n \" B_conv_3 = concatenate([B_conv_0, B_conv_1, B_conv_2, B_conv_3], name='B_concat_3')\\n\",\r\n \" B_conv_3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='B_conv_3') (B_conv_3)\\n\",\r\n \" B_conv_3 = BatchNormalization() (B_conv_3)\\n\",\r\n \"\\n\",\r\n \" #-------------------------------------------------------------------------------------------------------------#\\n\",\r\n \"\\n\",\r\n \" A_conv_4 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='A_trans_4') (B_conv_3)\\n\",\r\n \" A_conv_4 = concatenate([A_conv_0, A_conv_1, A_conv_2, A_conv_3, A_conv_4], name='A_concat_4')\\n\",\r\n \" A_conv_4 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same', name='A_conv_4') (A_conv_4)\\n\",\r\n \" A_conv_4 = BatchNormalization() (A_conv_4)\\n\",\r\n \"\\n\",\r\n \" #-------------------------------------------------------------------------------------------------------------#\\n\",\r\n \"\\n\",\r\n \" A_out_1 = Conv2D(1, (1, 1), activation='sigmoid',name='out1') (A_conv_1)\\n\",\r\n \" A_out_2 = Conv2D(1, (1, 1), activation='sigmoid',name='out2') (A_conv_2)\\n\",\r\n \" A_out_3 = Conv2D(1, (1, 1), activation='sigmoid',name='out3') (A_conv_3)\\n\",\r\n \" A_out_4 = Conv2D(1, (1, 1), activation='sigmoid',name='out4') (A_conv_4)\\n\",\r\n \"\\n\",\r\n \" outputs = average([A_out_1, A_out_2, A_out_3, A_out_4])\\n\",\r\n \"\\n\",\r\n \" model = Model(inputs=[inputs], outputs=[outputs])\\n\",\r\n \"\\n\",\r\n \" return model\\n\",\r\n \"\\n\",\r\n \"model = nested_unet()\\n\",\r\n \"model.summary()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"Une5A_IdP5c0\",\r\n \"outputId\": \"2c2ae4f8-6f58-4444-abef-006897c1e653\"\r\n },\r\n \"execution_count\": 31,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Model: \\\"model\\\"\\n\",\r\n \"__________________________________________________________________________________________________\\n\",\r\n \" Layer (type) Output Shape Param # Connected to \\n\",\r\n \"==================================================================================================\\n\",\r\n \" input_1 (InputLayer) [(None, 256, 256, 3 0 [] \\n\",\r\n \" )] \\n\",\r\n \" \\n\",\r\n \" A_conv_0 (Conv2D) (None, 256, 256, 64 1792 ['input_1[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization (BatchNorm (None, 256, 256, 64 256 ['A_conv_0[0][0]'] \\n\",\r\n \" alization) ) \\n\",\r\n \" \\n\",\r\n \" A_conv_0_1 (Conv2D) (None, 256, 256, 64 36928 ['batch_normalization[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_1 (BatchNo (None, 256, 256, 64 256 ['A_conv_0_1[0][0]'] \\n\",\r\n \" rmalization) ) \\n\",\r\n \" \\n\",\r\n \" max_pooling2d (MaxPooling2D) (None, 128, 128, 64 0 ['batch_normalization_1[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" B_conv_0 (Conv2D) (None, 128, 128, 12 73856 ['max_pooling2d[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_2 (BatchNo (None, 128, 128, 12 512 ['B_conv_0[0][0]'] \\n\",\r\n \" rmalization) 8) \\n\",\r\n \" \\n\",\r\n \" B_conv_0_1 (Conv2D) (None, 128, 128, 12 147584 ['batch_normalization_2[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_3 (BatchNo (None, 128, 128, 12 512 ['B_conv_0_1[0][0]'] \\n\",\r\n \" rmalization) 8) \\n\",\r\n \" \\n\",\r\n \" max_pooling2d_1 (MaxPooling2D) (None, 64, 64, 128) 0 ['batch_normalization_3[0][0]'] \\n\",\r\n \" \\n\",\r\n \" C_conv_0 (Conv2D) (None, 64, 64, 256) 295168 ['max_pooling2d_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_4 (BatchNo (None, 64, 64, 256) 1024 ['C_conv_0[0][0]'] \\n\",\r\n \" rmalization) \\n\",\r\n \" \\n\",\r\n \" C_conv_0_1 (Conv2D) (None, 64, 64, 256) 590080 ['batch_normalization_4[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_5 (BatchNo (None, 64, 64, 256) 1024 ['C_conv_0_1[0][0]'] \\n\",\r\n \" rmalization) \\n\",\r\n \" \\n\",\r\n \" max_pooling2d_2 (MaxPooling2D) (None, 32, 32, 256) 0 ['batch_normalization_5[0][0]'] \\n\",\r\n \" \\n\",\r\n \" D_conv_0 (Conv2D) (None, 32, 32, 512) 1180160 ['max_pooling2d_2[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_6 (BatchNo (None, 32, 32, 512) 2048 ['D_conv_0[0][0]'] \\n\",\r\n \" rmalization) \\n\",\r\n \" \\n\",\r\n \" D_conv_0_1 (Conv2D) (None, 32, 32, 512) 2359808 ['batch_normalization_6[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_7 (BatchNo (None, 32, 32, 512) 2048 ['D_conv_0_1[0][0]'] \\n\",\r\n \" rmalization) \\n\",\r\n \" \\n\",\r\n \" max_pooling2d_3 (MaxPooling2D) (None, 16, 16, 512) 0 ['batch_normalization_7[0][0]'] \\n\",\r\n \" \\n\",\r\n \" E_conv_0 (Conv2D) (None, 16, 16, 1024 4719616 ['max_pooling2d_3[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_8 (BatchNo (None, 16, 16, 1024 4096 ['E_conv_0[0][0]'] \\n\",\r\n \" rmalization) ) \\n\",\r\n \" \\n\",\r\n \" E_conv_0_1 (Conv2D) (None, 16, 16, 1024 9438208 ['batch_normalization_8[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_9 (BatchNo (None, 16, 16, 1024 4096 ['E_conv_0_1[0][0]'] \\n\",\r\n \" rmalization) ) \\n\",\r\n \" \\n\",\r\n \" D_trans_1 (Conv2DTranspose) (None, 32, 32, 512) 2097664 ['batch_normalization_9[0][0]'] \\n\",\r\n \" \\n\",\r\n \" C_trans_1 (Conv2DTranspose) (None, 64, 64, 256) 524544 ['batch_normalization_7[0][0]'] \\n\",\r\n \" \\n\",\r\n \" D_concat_1 (Concatenate) (None, 32, 32, 1024 0 ['batch_normalization_7[0][0]', \\n\",\r\n \" ) 'D_trans_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" B_trans_1 (Conv2DTranspose) (None, 128, 128, 12 131200 ['batch_normalization_5[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" C_concat_1 (Concatenate) (None, 64, 64, 512) 0 ['batch_normalization_5[0][0]', \\n\",\r\n \" 'C_trans_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" D_conv_1 (Conv2D) (None, 32, 32, 512) 4719104 ['D_concat_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" A_trans_1 (Conv2DTranspose) (None, 256, 256, 64 32832 ['batch_normalization_3[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" B_concat_1 (Concatenate) (None, 128, 128, 25 0 ['batch_normalization_3[0][0]', \\n\",\r\n \" 6) 'B_trans_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" C_conv_1 (Conv2D) (None, 64, 64, 256) 1179904 ['C_concat_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_13 (BatchN (None, 32, 32, 512) 2048 ['D_conv_1[0][0]'] \\n\",\r\n \" ormalization) \\n\",\r\n \" \\n\",\r\n \" A_concat_1 (Concatenate) (None, 256, 256, 12 0 ['batch_normalization_1[0][0]', \\n\",\r\n \" 8) 'A_trans_1[0][0]'] \\n\",\r\n \" \\n\",\r\n \" B_conv_1 (Conv2D) (None, 128, 128, 12 295040 ['B_concat_1[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_12 (BatchN (None, 64, 64, 256) 1024 ['C_conv_1[0][0]'] \\n\",\r\n \" ormalization) \\n\",\r\n \" \\n\",\r\n \" C_trans_2 (Conv2DTranspose) (None, 64, 64, 256) 524544 ['batch_normalization_13[0][0]'] \\n\",\r\n \" \\n\",\r\n \" A_conv_1 (Conv2D) (None, 256, 256, 64 73792 ['A_concat_1[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_11 (BatchN (None, 128, 128, 12 512 ['B_conv_1[0][0]'] \\n\",\r\n \" ormalization) 8) \\n\",\r\n \" \\n\",\r\n \" B_trans_2 (Conv2DTranspose) (None, 128, 128, 12 131200 ['batch_normalization_12[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" C_concat_2 (Concatenate) (None, 64, 64, 768) 0 ['batch_normalization_5[0][0]', \\n\",\r\n \" 'batch_normalization_12[0][0]', \\n\",\r\n \" 'C_trans_2[0][0]'] \\n\",\r\n \" \\n\",\r\n \" batch_normalization_10 (BatchN (None, 256, 256, 64 256 ['A_conv_1[0][0]'] \\n\",\r\n \" ormalization) ) \\n\",\r\n \" \\n\",\r\n \" A_trans_2 (Conv2DTranspose) (None, 256, 256, 64 32832 ['batch_normalization_11[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" B_concat_2 (Concatenate) (None, 128, 128, 38 0 ['batch_normalization_3[0][0]', \\n\",\r\n \" 4) 'batch_normalization_11[0][0]', \\n\",\r\n \" 'B_trans_2[0][0]'] \\n\",\r\n \" \\n\",\r\n \" C_conv_2 (Conv2D) (None, 64, 64, 256) 1769728 ['C_concat_2[0][0]'] \\n\",\r\n \" \\n\",\r\n \" A_concat_2 (Concatenate) (None, 256, 256, 19 0 ['batch_normalization_1[0][0]', \\n\",\r\n \" 2) 'batch_normalization_10[0][0]', \\n\",\r\n \" 'A_trans_2[0][0]'] \\n\",\r\n \" \\n\",\r\n \" B_conv_2 (Conv2D) (None, 128, 128, 12 442496 ['B_concat_2[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_16 (BatchN (None, 64, 64, 256) 1024 ['C_conv_2[0][0]'] \\n\",\r\n \" ormalization) \\n\",\r\n \" \\n\",\r\n \" A_conv_2 (Conv2D) (None, 256, 256, 64 110656 ['A_concat_2[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_15 (BatchN (None, 128, 128, 12 512 ['B_conv_2[0][0]'] \\n\",\r\n \" ormalization) 8) \\n\",\r\n \" \\n\",\r\n \" B_trans_3 (Conv2DTranspose) (None, 128, 128, 12 131200 ['batch_normalization_16[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_14 (BatchN (None, 256, 256, 64 256 ['A_conv_2[0][0]'] \\n\",\r\n \" ormalization) ) \\n\",\r\n \" \\n\",\r\n \" A_trans_3 (Conv2DTranspose) (None, 256, 256, 64 32832 ['batch_normalization_15[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" B_concat_3 (Concatenate) (None, 128, 128, 51 0 ['batch_normalization_3[0][0]', \\n\",\r\n \" 2) 'batch_normalization_11[0][0]', \\n\",\r\n \" 'batch_normalization_15[0][0]', \\n\",\r\n \" 'B_trans_3[0][0]'] \\n\",\r\n \" \\n\",\r\n \" A_concat_3 (Concatenate) (None, 256, 256, 25 0 ['batch_normalization_1[0][0]', \\n\",\r\n \" 6) 'batch_normalization_10[0][0]', \\n\",\r\n \" 'batch_normalization_14[0][0]', \\n\",\r\n \" 'A_trans_3[0][0]'] \\n\",\r\n \" \\n\",\r\n \" B_conv_3 (Conv2D) (None, 128, 128, 12 589952 ['B_concat_3[0][0]'] \\n\",\r\n \" 8) \\n\",\r\n \" \\n\",\r\n \" A_conv_3 (Conv2D) (None, 256, 256, 64 147520 ['A_concat_3[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_18 (BatchN (None, 128, 128, 12 512 ['B_conv_3[0][0]'] \\n\",\r\n \" ormalization) 8) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_17 (BatchN (None, 256, 256, 64 256 ['A_conv_3[0][0]'] \\n\",\r\n \" ormalization) ) \\n\",\r\n \" \\n\",\r\n \" A_trans_4 (Conv2DTranspose) (None, 256, 256, 64 32832 ['batch_normalization_18[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" A_concat_4 (Concatenate) (None, 256, 256, 32 0 ['batch_normalization_1[0][0]', \\n\",\r\n \" 0) 'batch_normalization_10[0][0]', \\n\",\r\n \" 'batch_normalization_14[0][0]', \\n\",\r\n \" 'batch_normalization_17[0][0]', \\n\",\r\n \" 'A_trans_4[0][0]'] \\n\",\r\n \" \\n\",\r\n \" A_conv_4 (Conv2D) (None, 256, 256, 64 184384 ['A_concat_4[0][0]'] \\n\",\r\n \" ) \\n\",\r\n \" \\n\",\r\n \" batch_normalization_19 (BatchN (None, 256, 256, 64 256 ['A_conv_4[0][0]'] \\n\",\r\n \" ormalization) ) \\n\",\r\n \" \\n\",\r\n \" out1 (Conv2D) (None, 256, 256, 1) 65 ['batch_normalization_10[0][0]'] \\n\",\r\n \" \\n\",\r\n \" out2 (Conv2D) (None, 256, 256, 1) 65 ['batch_normalization_14[0][0]'] \\n\",\r\n \" \\n\",\r\n \" out3 (Conv2D) (None, 256, 256, 1) 65 ['batch_normalization_17[0][0]'] \\n\",\r\n \" \\n\",\r\n \" out4 (Conv2D) (None, 256, 256, 1) 65 ['batch_normalization_19[0][0]'] \\n\",\r\n \" \\n\",\r\n \" average (Average) (None, 256, 256, 1) 0 ['out1[0][0]', \\n\",\r\n \" 'out2[0][0]', \\n\",\r\n \" 'out3[0][0]', \\n\",\r\n \" 'out4[0][0]'] \\n\",\r\n \" \\n\",\r\n \"==================================================================================================\\n\",\r\n \"Total params: 32,050,244\\n\",\r\n \"Trainable params: 32,038,980\\n\",\r\n \"Non-trainable params: 11,264\\n\",\r\n \"__________________________________________________________________________________________________\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from tensorflow.keras.callbacks import ModelCheckpoint\\n\",\r\n \"from tensorflow.keras.callbacks import LearningRateScheduler\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"KjnJ9R6DP8Fu\"\r\n },\r\n \"execution_count\": 32,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"def lr_schedule(epoch):\\n\",\r\n \" lr=0.0001\\n\",\r\n \" # lr =0.001\\n\",\r\n \" if epoch >15:\\n\",\r\n \" lr *=2**-1\\n\",\r\n \" elif epoch >10:\\n\",\r\n \" lr *=2**(-1)\\n\",\r\n \" elif epoch >8:\\n\",\r\n \" lr *=2**(-1)\\n\",\r\n \" elif epoch >5:\\n\",\r\n \" lr *=2**(-1)\\n\",\r\n \" \\n\",\r\n \" print('Learning rate: ', lr)\\n\",\r\n \" return lr\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"6lASddrLQDBk\"\r\n },\r\n \"execution_count\": 33,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"import time\\n\",\r\n \"\\n\",\r\n \"start_time = time.time()\\n\",\r\n \"\\n\",\r\n \"# Prepare callbacks for model saving and for learning rate adjustment.\\n\",\r\n \"lr_scheduler = LearningRateScheduler(lr_schedule)\\n\",\r\n \"\\n\",\r\n \"lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\\n\",\r\n \" cooldown=0,\\n\",\r\n \" patience=5,\\n\",\r\n \" min_lr=0.5e-6)\\n\",\r\n \"\\n\",\r\n \"callbacks = [lr_reducer, lr_scheduler]\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"BIsi4Ek0UU84\"\r\n },\r\n \"execution_count\": 34,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"import tensorflow as tf\\n\",\r\n \"optimiser=tf.keras.optimizers.Adam(\\n\",\r\n \" learning_rate=lr_schedule(0),\\n\",\r\n \" beta_1=0.9,\\n\",\r\n \" beta_2=0.999,\\n\",\r\n \" epsilon=1e-07,\\n\",\r\n \" amsgrad=True,\\n\",\r\n \" name=\\\"Adam\\\"\\n\",\r\n \")\\n\",\r\n \"# optimiser=tf.keras.optimizers.SGD(\\n\",\r\n \"# learning_rate=lr_schedule(0),\\n\",\r\n \"\\n\",\r\n \"# )\\n\",\r\n \"model.compile(optimizer =optimiser , loss = bce_dice_loss, metrics = ['accuracy', iou,'AUC'])\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"VvVeRzNXUYNG\",\r\n \"outputId\": \"e006cbcf-e035-42ca-abac-dfc80781d83c\"\r\n },\r\n \"execution_count\": 35,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Learning rate: 0.0001\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"history = model.fit(\\n\",\r\n \" ds_train, \\n\",\r\n \" steps_per_epoch=500//10,\\n\",\r\n \" epochs=20,\\n\",\r\n \" validation_data = ds_valid, \\n\",\r\n \" validation_steps =170//10,callbacks=callbacks\\n\",\r\n \" )\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"WGy0P_NMV3bz\",\r\n \"outputId\": \"72eb4ced-a066-40ea-aa17-3d4bdcc946f7\"\r\n },\r\n \"execution_count\": 36,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 1/20\\n\",\r\n \"50/50 [==============================] - 286s 5s/step - loss: 0.9906 - accuracy: 0.8211 - iou: 0.4617 - auc: 0.9286 - val_loss: 1.2632 - val_accuracy: 0.8536 - val_iou: 0.3295 - val_auc: 0.8952 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 2/20\\n\",\r\n \"50/50 [==============================] - 80s 2s/step - loss: 0.6935 - accuracy: 0.9165 - iou: 0.6279 - auc: 0.9671 - val_loss: 0.9368 - val_accuracy: 0.8994 - val_iou: 0.3914 - val_auc: 0.9120 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 3/20\\n\",\r\n \"50/50 [==============================] - 82s 2s/step - loss: 0.6048 - accuracy: 0.9340 - iou: 0.6805 - auc: 0.9729 - val_loss: 0.9129 - val_accuracy: 0.8884 - val_iou: 0.2181 - val_auc: 0.9148 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 4/20\\n\",\r\n \"50/50 [==============================] - 83s 2s/step - loss: 0.5530 - accuracy: 0.9446 - iou: 0.7150 - auc: 0.9748 - val_loss: 0.8115 - val_accuracy: 0.9109 - val_iou: 0.3839 - val_auc: 0.9256 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 5/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.5286 - accuracy: 0.9473 - iou: 0.7251 - auc: 0.9740 - val_loss: 0.7327 - val_accuracy: 0.9225 - val_iou: 0.4745 - val_auc: 0.8973 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 0.0001\\n\",\r\n \"Epoch 6/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4984 - accuracy: 0.9512 - iou: 0.7392 - auc: 0.9728 - val_loss: 0.5312 - val_accuracy: 0.9597 - val_iou: 0.7489 - val_auc: 0.9765 - lr: 1.0000e-04\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 7/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4790 - accuracy: 0.9540 - iou: 0.7517 - auc: 0.9748 - val_loss: 0.5254 - val_accuracy: 0.9557 - val_iou: 0.7111 - val_auc: 0.9680 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 8/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4704 - accuracy: 0.9556 - iou: 0.7575 - auc: 0.9759 - val_loss: 0.4076 - val_accuracy: 0.9667 - val_iou: 0.7850 - val_auc: 0.9821 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 9/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4601 - accuracy: 0.9569 - iou: 0.7622 - auc: 0.9776 - val_loss: 0.3757 - val_accuracy: 0.9673 - val_iou: 0.7927 - val_auc: 0.9856 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 10/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4576 - accuracy: 0.9559 - iou: 0.7585 - auc: 0.9766 - val_loss: 0.3815 - val_accuracy: 0.9704 - val_iou: 0.8088 - val_auc: 0.9840 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 11/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4442 - accuracy: 0.9574 - iou: 0.7649 - auc: 0.9765 - val_loss: 0.3742 - val_accuracy: 0.9670 - val_iou: 0.7940 - val_auc: 0.9833 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 12/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4347 - accuracy: 0.9586 - iou: 0.7703 - auc: 0.9753 - val_loss: 0.3418 - val_accuracy: 0.9726 - val_iou: 0.8228 - val_auc: 0.9864 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 13/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4251 - accuracy: 0.9589 - iou: 0.7710 - auc: 0.9748 - val_loss: 0.2958 - val_accuracy: 0.9724 - val_iou: 0.8225 - val_auc: 0.9874 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 14/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.4095 - accuracy: 0.9584 - iou: 0.7686 - auc: 0.9759 - val_loss: 0.2983 - val_accuracy: 0.9721 - val_iou: 0.8211 - val_auc: 0.9882 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 15/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3971 - accuracy: 0.9592 - iou: 0.7731 - auc: 0.9755 - val_loss: 0.2903 - val_accuracy: 0.9738 - val_iou: 0.8292 - val_auc: 0.9884 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 16/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3771 - accuracy: 0.9608 - iou: 0.7796 - auc: 0.9789 - val_loss: 0.2514 - val_accuracy: 0.9741 - val_iou: 0.8314 - val_auc: 0.9904 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 17/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3664 - accuracy: 0.9606 - iou: 0.7768 - auc: 0.9795 - val_loss: 0.2474 - val_accuracy: 0.9738 - val_iou: 0.8311 - val_auc: 0.9911 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 18/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3522 - accuracy: 0.9619 - iou: 0.7837 - auc: 0.9789 - val_loss: 0.2346 - val_accuracy: 0.9761 - val_iou: 0.8409 - val_auc: 0.9918 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 19/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3348 - accuracy: 0.9621 - iou: 0.7848 - auc: 0.9819 - val_loss: 0.2356 - val_accuracy: 0.9752 - val_iou: 0.8385 - val_auc: 0.9919 - lr: 5.0000e-05\\n\",\r\n \"Learning rate: 5e-05\\n\",\r\n \"Epoch 20/20\\n\",\r\n \"50/50 [==============================] - 84s 2s/step - loss: 0.3279 - accuracy: 0.9631 - iou: 0.7902 - auc: 0.9817 - val_loss: 0.2373 - val_accuracy: 0.9756 - val_iou: 0.8389 - val_auc: 0.9911 - lr: 5.0000e-05\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"train_loss = history.history['loss']\\n\",\r\n \"valid_loss = history.history['val_loss']\\n\",\r\n \"\\n\",\r\n \"train_acc = history.history['accuracy']\\n\",\r\n \"valid_acc = history.history['val_accuracy']\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 2, figsize=(13,4))\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"\\n\",\r\n \"axes[0].plot(train_acc, label='training')\\n\",\r\n \"axes[0].plot(valid_acc, label='validation')\\n\",\r\n \"axes[0].set_title('Accuracy Curve')\\n\",\r\n \"axes[0].set_xlabel('epochs')\\n\",\r\n \"axes[0].set_ylabel('accuracy')\\n\",\r\n \"axes[0].legend()\\n\",\r\n \"\\n\",\r\n \"\\n\",\r\n \"axes[1].plot(train_loss, label='training')\\n\",\r\n \"axes[1].plot(valid_loss, label='validation')\\n\",\r\n \"axes[1].set_title('Loss Curve')\\n\",\r\n \"axes[1].set_xlabel('epochs')\\n\",\r\n \"axes[1].set_ylabel('loss')\\n\",\r\n \"axes[1].legend()\\n\",\r\n \"\\n\",\r\n \"plt.show()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 295\r\n },\r\n \"id\": \"NWpoOgTjWHdC\",\r\n \"outputId\": \"5f670e41-d5d6-4049-df7b-0f6cebc29522\"\r\n },\r\n \"execution_count\": 37,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"from sklearn.metrics import classification_report, roc_auc_score\\n\",\r\n \"\\n\",\r\n \"test_generator = Val_Generator(valid_image_files, valid_mask_files, 100)\\n\",\r\n \"\\n\",\r\n \"for x_test, y_test in test_generator:\\n\",\r\n \" break\\n\",\r\n \"\\n\",\r\n \"y_pred = model.predict(x_test)\\n\",\r\n \"\\n\",\r\n \"y_true = (y_test>0.5).flatten()\\n\",\r\n \"y_pred = (y_pred>0.5).flatten()\\n\",\r\n \"\\n\",\r\n \"# U-NET basic\\n\",\r\n \"report = classification_report(y_true, y_pred, output_dict=True)\\n\",\r\n \"\\n\",\r\n \"Precision = report['True']['precision']\\n\",\r\n \"Recall = report['True']['recall']\\n\",\r\n \"F1_score = report['True']['f1-score']\\n\",\r\n \"\\n\",\r\n \"Sensitivity = Recall\\n\",\r\n \"Specificity = report['False']['recall']\\n\",\r\n \"\\n\",\r\n \"IOU = (Precision*Recall)/(Precision+Recall-Precision*Recall)\\n\",\r\n \"AUC = roc_auc_score(y_true.flatten(), y_pred.flatten())\\n\",\r\n \"\\n\",\r\n \"print(\\\"Precision score: {0:.2f}\\\\n\\\".format(Precision))\\n\",\r\n \"print(\\\"Recall score: {0:.2f}\\\\n\\\".format(Recall))\\n\",\r\n \"print(\\\"F1-Score: {0:.2f}\\\\n\\\".format(F1_score))\\n\",\r\n \"print(\\\"Sensitivity: {0:.2f}\\\\n\\\".format(Sensitivity))\\n\",\r\n \"print(\\\"Specificity: {0:.2f}\\\\n\\\".format(Specificity))\\n\",\r\n \"print(\\\"IOU: {0:.2f}\\\\n\\\".format(IOU))\\n\",\r\n \"print(\\\"AUC: {0:.2f}\\\\n\\\".format(AUC))\\n\",\r\n \"print('-'*50,'\\\\n')\\n\",\r\n \"print(classification_report(y_true, y_pred))\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"52lufQECWBMG\",\r\n \"outputId\": \"49b65e85-436c-4109-ca4e-8db6ae929539\"\r\n },\r\n \"execution_count\": 38,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"stream\",\r\n \"name\": \"stdout\",\r\n \"text\": [\r\n \"Precision score: 0.91\\n\",\r\n \"\\n\",\r\n \"Recall score: 0.85\\n\",\r\n \"\\n\",\r\n \"F1-Score: 0.88\\n\",\r\n \"\\n\",\r\n \"Sensitivity: 0.85\\n\",\r\n \"\\n\",\r\n \"Specificity: 0.99\\n\",\r\n \"\\n\",\r\n \"IOU: 0.79\\n\",\r\n \"\\n\",\r\n \"AUC: 0.92\\n\",\r\n \"\\n\",\r\n \"-------------------------------------------------- \\n\",\r\n \"\\n\",\r\n \" precision recall f1-score support\\n\",\r\n \"\\n\",\r\n \" False 0.98 0.99 0.98 5707370\\n\",\r\n \" True 0.91 0.85 0.88 846230\\n\",\r\n \"\\n\",\r\n \" accuracy 0.97 6553600\\n\",\r\n \" macro avg 0.95 0.92 0.93 6553600\\n\",\r\n \"weighted avg 0.97 0.97 0.97 6553600\\n\",\r\n \"\\n\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"valid_generator = Val_Generator(valid_image_files, valid_mask_files, 1)\\n\",\r\n \"\\n\",\r\n \"x_val = []\\n\",\r\n \"y_val = []\\n\",\r\n \"p_val = []\\n\",\r\n \"\\n\",\r\n \"for x, y in valid_generator:\\n\",\r\n \" p = model.predict(x)\\n\",\r\n \" x_val.append(np.squeeze(x, 0))\\n\",\r\n \" y_val.append(np.squeeze(y, 0))\\n\",\r\n \" p_val.append(np.squeeze(p, 0))\\n\",\r\n \"\\n\",\r\n \"x_val = np.array(x_val) # Image\\n\",\r\n \"y_val = np.array(y_val) # Mask\\n\",\r\n \"p_val = np.array(p_val) # Predicted mask\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"XVq7JzS0WJWF\"\r\n },\r\n \"execution_count\": 39,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"fig, axes = plt.subplots(1, 5, figsize=(12,3))\\n\",\r\n \"fig.suptitle('Images', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(x_val[:5], axes[:5]):\\n\",\r\n \" ax.imshow(img)\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 5, figsize=(12,3))\\n\",\r\n \"fig.suptitle('Original Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(y_val[:5], axes[:5]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 5, figsize=(12,3))\\n\",\r\n \"fig.suptitle('Predicted Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(p_val[:5], axes[:5]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 608\r\n },\r\n \"id\": \"qA54ck3ZWzFH\",\r\n \"outputId\": \"877f6322-7fd4-4bea-f576-ac4da96bf43a\"\r\n },\r\n \"execution_count\": 40,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n 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\\n\"\r\n 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \" test_image_files = [os.path.join('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test', i) for i in os.listdir('/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test')]\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"NTguCc1jXChf\"\r\n },\r\n \"execution_count\": 41,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"test_image_files[:10]\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\"\r\n },\r\n \"id\": \"6bUmm7GDYFd5\",\r\n \"outputId\": \"fc2c4b6d-226d-471c-cc81-2aa37c56a048\"\r\n },\r\n \"execution_count\": 42,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"execute_result\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"['/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/57.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/50.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/44.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/46.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/15.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/60.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/55.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/11.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/12.png',\\n\",\r\n \" '/content/drive/MyDrive/LSM and TARP /Cell Segmentation Data/test/43.png']\"\r\n ]\r\n },\r\n \"metadata\": {},\r\n \"execution_count\": 42\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"class Test_Generator(Sequence):\\n\",\r\n \"\\n\",\r\n \" def __init__(self, x_set, batch_size=10, img_dim=(256,256), augmentation=False):\\n\",\r\n \" self.x = x_set\\n\",\r\n \" self.batch_size = batch_size\\n\",\r\n \" self.img_dim = img_dim\\n\",\r\n \" self.augmentation = augmentation\\n\",\r\n \"\\n\",\r\n \" def __len__(self):\\n\",\r\n \" return math.ceil(len(self.x) / self.batch_size)\\n\",\r\n \"\\n\",\r\n \" aug = Compose(\\n\",\r\n \" [\\n\",\r\n \" CLAHE(always_apply=True, p=1.0)\\n\",\r\n \" ])\\n\",\r\n \"\\n\",\r\n \"\\n\",\r\n \" def __getitem__(self, idx):\\n\",\r\n \" batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\\n\",\r\n \"\\n\",\r\n \" # batch_x = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_x])\\n\",\r\n \" # batch_y = np.array([cv2.resize(cv2.cvtColor(cv2.imread(file_name, -1), cv2.COLOR_BGR2RGB), (512, 512)) for file_name in batch_y])\\n\",\r\n \" batch_x = np.array([read_image(file_name, self.img_dim) for file_name in batch_x])\\n\",\r\n \"\\n\",\r\n \" if self.augmentation is True:\\n\",\r\n \" aug = [self.aug(image=i) for i in batch_x]\\n\",\r\n \" batch_x = np.array([i['image'] for i in aug])\\n\",\r\n \"\\n\",\r\n \" return batch_x/255.0\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"6EZ_gFk-YIIL\"\r\n },\r\n \"execution_count\": 43,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"test_generator = Test_Generator(test_image_files, 1)\\n\",\r\n \"\\n\",\r\n \"x_test = []\\n\",\r\n \"p_test = []\\n\",\r\n \"\\n\",\r\n \"for x in test_generator:\\n\",\r\n \" p = model.predict(x)\\n\",\r\n \" x_test.append(np.squeeze(x, 0))\\n\",\r\n \" p_test.append(np.squeeze(p, 0))\\n\",\r\n \"\\n\",\r\n \"x_test = np.array(x_test) # Image\\n\",\r\n \"p_test = np.array(p_test) # Predicted mask\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"DvAyPErfYMSj\"\r\n },\r\n \"execution_count\": 44,\r\n \"outputs\": []\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Images', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(x_test[:10], axes[:10]):\\n\",\r\n \" ax.imshow(img)\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\\n\",\r\n \"\\n\",\r\n \"fig, axes = plt.subplots(1, 10, figsize=(20,3))\\n\",\r\n \"fig.suptitle('Predicted Masks', fontsize=15)\\n\",\r\n \"axes = axes.flatten()\\n\",\r\n \"for img, ax in zip(p_test[:10], axes[:10]):\\n\",\r\n \" ax.imshow(np.squeeze(img, -1), cmap='gray')\\n\",\r\n \" ax.axis('off')\\n\",\r\n \"plt.tight_layout()\\n\",\r\n \"plt.show()\"\r\n ],\r\n \"metadata\": {\r\n \"colab\": {\r\n \"base_uri\": \"https://localhost:8080/\",\r\n \"height\": 380\r\n },\r\n \"id\": \"OHvVPB-oYPL0\",\r\n \"outputId\": \"09a6cec7-7ea5-4920-b040-abfb99b11911\"\r\n },\r\n \"execution_count\": 45,\r\n \"outputs\": [\r\n {\r\n \"output_type\": \"display_data\",\r\n \"data\": {\r\n \"text/plain\": [\r\n \"
\"\r\n ],\r\n \"image/png\": 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\\n\"\r\n 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\\n\"\r\n },\r\n \"metadata\": {\r\n \"needs_background\": \"light\"\r\n }\r\n }\r\n ]\r\n },\r\n {\r\n \"cell_type\": \"code\",\r\n \"source\": [\r\n \"\"\r\n ],\r\n \"metadata\": {\r\n \"id\": \"UAYtdRGgYcID\"\r\n },\r\n \"execution_count\": null,\r\n \"outputs\": []\r\n }\r\n ]\r\n}" }, { "path": "code/artificial_intelligence/src/image_processing/Cell-Segmentation/README.md", "content": "

Cell Segmentation

\r\n\r\n\r\n

The study aims to determine a solution for the automatic segmentation and localization of cells. I have tried to utilise UNet++ architecture over UNet to detect cell nuclei and perform segmentation into individual objects.

\r\n\r\n\r\n## Image sets and experiment description\r\n\r\n- **Dataset** which was used was extracted from the mentioned repo data: Shubham2901/Nuclei Cell Segmentation\r\n\r\n| Dataset | Images | Input Size | Modality | Provider |\r\n| --- | --- | --- | --- | --- | \r\n| Cell Nuclei | 670 | 96x96 | microscopy | Kaggle Data Science Bowl 2018|\r\n\r\n

\r\n
\r\n Block diagram for the overall process\r\n

\r\n\r\n\r\n## Results Obtained\r\n\r\n

TP, FP, TN and FN are the numbers of true positive, false positive, true negative \r\nand false negative detections.

\r\n\r\n| | |\r\n| --- | --- |\r\n| Precision Score | 0.91 |\r\n| Recall Score | 0.85 |\r\n| F-1 Score | 0.88 |\r\n| Sensitivity | 0.85 |\r\n| Specificity | 0.99 |\r\n| IOU | 0.79 |\r\n| AUC | 0.92 |\r\n\r\n| | Precision | Recall | F-1 Score | Support |\r\n| --- | --- | --- | --- | --- |\r\n| False | 0.98 | 0.99 | 0.98 | 5707370 |\r\n| True | 0.91 | 0.85 | 0.88 | 846230 |\r\n| Accuracy ||| 0.97 | 6553600 |\r\n| Macro Avg | 0.95 | 0.92 | 0.93 | 6553600 |\r\n| Weighted Avg | 0.97 | 0.97 | 0.97 | 6553600 |\r\n\r\n\r\n## Final Result\r\n\r\n

\r\n \r\n \r\n

\r\n\r\n## Future of Cell Segmentation\r\n\r\nWith the rise of size and complexity of cell images, the requirements for cells segmentation methods are also increasing. The basic image processing algo developed decades ago should not be the golden standards for dealing with these challenging cell segmentation problems any more.\r\n\r\nOn the contrary, development of more effective image processing algorithms is more promising for the progress of cell segmentation. In the meantime, comparison of these newly developed algorithms and teaching the biologists to use these newly developed algorithms are also very important. Hence, the open access and authoritative platforms are necessary for researchers all over the world to share, learn, and teach the data, codes, and algorithms. \r\n\r\n---\r\n" }, { "path": "code/artificial_intelligence/src/image_processing/Cell-Segmentation/img/blogdiagram.drawio", "content": "\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n" }, { "path": "code/artificial_intelligence/src/image_processing/README.md", "content": "# OpenCv\nopencv in c++\nopencv in py\n\n# Install\n`sudo apt-get install libopencv-dev python-opencv`\n\n#Run \n```bash\n$ g++ -o out_file file.cpp `pkg-config opencv --cflags --libs`\n```" }, { "path": "code/artificial_intelligence/src/image_processing/canny/canny.cpp", "content": "//\n// canny.cpp\n// Canny Edge Detector\n//\n\n#include \n#define _USE_MATH_DEFINES\n#include \n#include \n#include \"canny.h\"\n#include \"opencv2/imgproc/imgproc.hpp\"\n#include \"opencv2/highgui/highgui.hpp\"\n\n\n\ncanny::canny(std::string filename)\n{\n img = cv::imread(filename);\n\n if (!img.data) // Check for invalid input\n cout << \"Could not open or find the image\" << std::endl;\n\n else\n {\n\n std::vector> filter = createFilter(3, 3, 1);\n\n //Print filter\n for (int i = 0; i < filter.size(); i++)\n for (int j = 0; j < filter[i].size(); j++)\n cout << filter[i][j] << \" \";\n\n grayscaled = cv::Mat(img.toGrayScale()); //Grayscale the image\n gFiltered = cv::Mat(useFilter(grayscaled, filter)); //Gaussian Filter\n sFiltered = cv::Mat(sobel()); //Sobel Filter\n\n non = cv::Mat(nonMaxSupp()); //Non-Maxima Suppression\n thres = cv::Mat(threshold(non, 20, 40)); //Double Threshold and Finalize\n\n cv::namedWindow(\"Original\");\n cv::namedWindow(\"GrayScaled\");\n cv::namedWindow(\"Gaussian Blur\");\n cv::namedWindow(\"Sobel Filtered\");\n cv::namedWindow(\"Non-Maxima Supp.\");\n cv::namedWindow(\"Final\");\n\n cv::imshow(\"Original\", img);\n cv::imshow(\"GrayScaled\", grayscaled);\n cv::imshow(\"Gaussian Blur\", gFiltered);\n cv::imshow(\"Sobel Filtered\", sFiltered);\n cv::imshow(\"Non-Maxima Supp.\", non);\n cv::imshow(\"Final\", thres);\n\n }\n}\n\nMat canny::toGrayScale()\n{\n grayscaled = Mat(img.rows, img.cols, CV_8UC1); //To one channel\n for (int i = 0; i < img.rows; i++)\n for (int j = 0; j < img.cols; j++)\n {\n int b = img.at(i, j)[0];\n int g = img.at(i, j)[1];\n int r = img.at(i, j)[2];\n\n double newValue = (r * 0.2126 + g * 0.7152 + b * 0.0722);\n grayscaled.at(i, j) = newValue;\n\n }\n return grayscaled;\n}\n\nstd::vector> canny::createFilter(int row, int column, double sigmaIn)\n{\n std::vector> filter;\n\n for (int i = 0; i < row; i++)\n {\n std::vector col;\n for (int j = 0; j < column; j++)\n col.push_back(-1);\n filter.push_back(col);\n }\n\n float coordSum = 0;\n float constant = 2.0 * sigmaIn * sigmaIn;\n\n // Sum is for normalization\n float sum = 0.0;\n\n for (int x = -row / 2; x <= row / 2; x++)\n for (int y = -column / 2; y <= column / 2; y++)\n {\n coordSum = (x * x + y * y);\n filter[x + row / 2][y + column / 2] = (exp(-(coordSum) / constant)) / (M_PI * constant);\n sum += filter[x + row / 2][y + column / 2];\n }\n\n // Normalize the Filter\n for (int i = 0; i < row; i++)\n for (int j = 0; j < column; j++)\n filter[i][j] /= sum;\n\n return filter;\n\n}\n\ncv::Mat canny::useFilter(cv::Mat img_in, std::vector> filterIn)\n{\n int size = (int)filterIn.size() / 2;\n cv::Mat filteredImg = cv::Mat(img_in.rows - 2 * size, img_in.cols - 2 * size, CV_8UC1);\n for (int i = size; i < img_in.rows - size; i++)\n for (int j = size; j < img_in.cols - size; j++)\n {\n double sum = 0;\n\n for (int x = 0; x < filterIn.size(); x++)\n for (int y = 0; y < filterIn.size(); y++)\n sum += filterIn[x][y] * (double)(img_in.at(i + x - size, j + y - size));\n\n\n filteredImg.at(i - size, j - size) = sum;\n }\n\n return filteredImg;\n}\n\ncv::Mat canny::sobel()\n{\n\n //Sobel X Filter\n double x1[] = {-1.0, 0, 1.0};\n double x2[] = {-2.0, 0, 2.0};\n double x3[] = {-1.0, 0, 1.0};\n\n std::vector> xFilter(3);\n xFilter[0].assign(x1, x1 + 3);\n xFilter[1].assign(x2, x2 + 3);\n xFilter[2].assign(x3, x3 + 3);\n\n //Sobel Y Filter\n double y1[] = {1.0, 2.0, 1.0};\n double y2[] = {0, 0, 0};\n double y3[] = {-1.0, -2.0, -1.0};\n\n std::vector> yFilter(3);\n yFilter[0].assign(y1, y1 + 3);\n yFilter[1].assign(y2, y2 + 3);\n yFilter[2].assign(y3, y3 + 3);\n\n //Limit Size\n int size = (int)xFilter.size() / 2;\n\n cv::Mat filteredImg = cv::Mat(gFiltered.rows - 2 * size, gFiltered.cols - 2 * size, CV_8UC1);\n\n angles = cv::Mat(gFiltered.rows - 2 * size, gFiltered.cols - 2 * size, CV_32FC1); //AngleMap\n\n for (int i = size; i < gFiltered.rows - size; i++)\n for (int j = size; j < gFiltered.cols - size; j++)\n {\n double sumx = 0;\n double sumy = 0;\n\n for (int x = 0; x < xFilter.size(); x++)\n for (int y = 0; y < xFilter.size(); y++)\n {\n sumx += xFilter[x][y] *\n (double)(gFiltered.at(i + x - size, j + y - size)); //Sobel_X Filter Value\n sumy += yFilter[x][y] *\n (double)(gFiltered.at(i + x - size, j + y - size)); //Sobel_Y Filter Value\n }\n double sumxsq = sumx * sumx;\n double sumysq = sumy * sumy;\n\n double sq2 = sqrt(sumxsq + sumysq);\n\n if (sq2 > 255) //Unsigned Char Fix\n sq2 = 255;\n filteredImg.at(i - size, j - size) = sq2;\n\n if (sumx == 0) //Arctan Fix\n angles.at(i - size, j - size) = 90;\n else\n angles.at(i - size, j - size) = atan(sumy / sumx);\n }\n\n return filteredImg;\n}\n\n\ncv::Mat canny::nonMaxSupp()\n{\n cv::Mat nonMaxSupped = cv::Mat(sFiltered.rows - 2, sFiltered.cols - 2, CV_8UC1);\n for (int i = 1; i < sFiltered.rows - 1; i++)\n for (int j = 1; j < sFiltered.cols - 1; j++)\n {\n float Tangent = angles.at(i, j);\n\n nonMaxSupped.at(i - 1, j - 1) = sFiltered.at(i, j);\n //Horizontal Edge\n if (((-22.5 < Tangent) && (Tangent <= 22.5)) ||\n ((157.5 < Tangent) && (Tangent <= -157.5)))\n if ((sFiltered.at(i,\n j) <\n sFiltered.at(i,\n j + 1)) ||\n (sFiltered.at(i, j) < sFiltered.at(i, j - 1)))\n nonMaxSupped.at(i - 1, j - 1) = 0;\n //Vertical Edge\n if (((-112.5 < Tangent) && (Tangent <= -67.5)) ||\n ((67.5 < Tangent) && (Tangent <= 112.5)))\n if ((sFiltered.at(i,\n j) <\n sFiltered.at(i + 1,\n j)) ||\n (sFiltered.at(i, j) < sFiltered.at(i - 1, j)))\n nonMaxSupped.at(i - 1, j - 1) = 0;\n\n //-45 Degree Edge\n if (((-67.5 < Tangent) && (Tangent <= -22.5)) ||\n ((112.5 < Tangent) && (Tangent <= 157.5)))\n if ((sFiltered.at(i,\n j) <\n sFiltered.at(i - 1,\n j + 1)) ||\n (sFiltered.at(i, j) < sFiltered.at(i + 1, j - 1)))\n nonMaxSupped.at(i - 1, j - 1) = 0;\n\n //45 Degree Edge\n if (((-157.5 < Tangent) && (Tangent <= -112.5)) ||\n ((22.5 < Tangent) && (Tangent <= 67.5)))\n if ((sFiltered.at(i,\n j) <\n sFiltered.at(i + 1,\n j + 1)) ||\n (sFiltered.at(i, j) < sFiltered.at(i - 1, j - 1)))\n nonMaxSupped.at(i - 1, j - 1) = 0;\n }\n return nonMaxSupped;\n}\n\ncv::Mat canny::threshold(cv::Mat imgin, int low, int high)\n{\n if (low > 255)\n low = 255;\n if (high > 255)\n high = 255;\n\n cv::Mat EdgeMat = cv::Mat(imgin.rows, imgin.cols, imgin.type());\n\n for (int i = 0; i < imgin.rows; i++)\n for (int j = 0; j < imgin.cols; j++)\n {\n EdgeMat.at(i, j) = imgin.at(i, j);\n if (EdgeMat.at(i, j) > high)\n EdgeMat.at(i, j) = 255;\n else if (EdgeMat.at(i, j) < low)\n EdgeMat.at(i, j) = 0;\n else\n {\n bool anyHigh = false;\n bool anyBetween = false;\n for (int x = i - 1; x < i + 2; x++)\n {\n for (int y = j - 1; y < j + 2; y++)\n {\n if (x <= 0 || y <= 0 || EdgeMat.rows || y > EdgeMat.cols) //Out of bounds\n continue;\n else\n {\n if (EdgeMat.at(x, y) > high)\n {\n EdgeMat.at(i, j) = 255;\n anyHigh = true;\n break;\n }\n else if (EdgeMat.at(x,\n y) <= high && EdgeMat.at(x, y) >= low)\n anyBetween = true;\n }\n }\n if (anyHigh)\n break;\n }\n if (!anyHigh && anyBetween)\n for (int x = i - 2; x < i + 3; x++)\n {\n for (int y = j - 1; y < j + 3; y++)\n {\n if (x < 0 || y < 0 || x > EdgeMat.rows || y > EdgeMat.cols) //Out of bounds\n continue;\n else if (EdgeMat.at(x, y) > high)\n {\n EdgeMat.at(i, j) = 255;\n anyHigh = true;\n break;\n }\n }\n if (anyHigh)\n break;\n }\n if (!anyHigh)\n EdgeMat.at(i, j) = 0;\n }\n }\n return EdgeMat;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/canny/canny.h", "content": "//\n// canny.h\n// Canny Edge Detector\n//\n\n#ifndef _CANNY_\n#define _CANNY_\n#include \"opencv2/imgproc/imgproc.hpp\"\n#include \"opencv2/highgui/highgui.hpp\"\n#include \n\n\n\nclass canny {\nprivate:\n cv::Mat img; //Original Image\n cv::Mat grayscaled; // Grayscale\n cv::Mat gFiltered; // Gradient\n cv::Mat sFiltered; //Sobel Filtered\n cv::Mat angles; //Angle Map\n cv::Mat non; // Non-maxima supp.\n cv::Mat thres; //Double threshold and final\npublic:\n\n canny(std::string); //Constructor\n\tcv::Mat toGrayScale();\n\tstd::vector > createFilter(int, int, double); //Creates a gaussian filter\n\tcv::Mat useFilter(cv::Mat, std::vector >); //Use some filter\n cv::Mat sobel(); //Sobel filtering\n cv::Mat nonMaxSupp(); //Non-maxima supp.\n cv::Mat threshold(cv::Mat, int, int); //Double threshold and finalize picture\n};\n\n#endif\n" }, { "path": "code/artificial_intelligence/src/image_processing/canny/main.cpp", "content": "//\n// main.cpp\n// Canny Edge Detector\n//\n\n#include \n#include \n#define _USE_MATH_DEFINES\n#include \n#include \n#include \"opencv2/imgproc/imgproc.hpp\"\n#include \"opencv2/highgui/highgui.hpp\"\n#include \"canny.h\"\n\n\nint main()\n{\n std::string filePath = \"lena.jpg\"; //Filepath of input image\n canny cny(filePath);\n\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/connected_component_labeling/Connected-Component-Labeling.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Importing the libraries \\n\",\n \"import cv2\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"\\n\",\n \"def connected_component_label(path):\\n\",\n \" \\n\",\n \" # Getting the input image\\n\",\n \" img = cv2.imread(path, 0)\\n\",\n \" # Converting those pixels with values 1-127 to 0 and others to 1\\n\",\n \" img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]\\n\",\n \" # Applying cv2.connectedComponents() \\n\",\n \" num_labels, labels = cv2.connectedComponents(img)\\n\",\n \" \\n\",\n \" # Map component labels to hue val, 0-179 is the hue range in OpenCV\\n\",\n \" label_hue = np.uint8(179*labels/np.max(labels))\\n\",\n \" blank_ch = 255*np.ones_like(label_hue)\\n\",\n \" labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])\\n\",\n \"\\n\",\n \" # Converting cvt to BGR\\n\",\n \" labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)\\n\",\n \"\\n\",\n \" # set bg label to black\\n\",\n \" labeled_img[label_hue==0] = 0\\n\",\n \" \\n\",\n \" \\n\",\n \" # Showing Original Image\\n\",\n \" plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\\n\",\n \" plt.axis(\\\"off\\\")\\n\",\n \" plt.title(\\\"Orginal Image\\\")\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \" #Showing Image after Component Labeling\\n\",\n \" plt.imshow(cv2.cvtColor(labeled_img, cv2.COLOR_BGR2RGB))\\n\",\n \" plt.axis('off')\\n\",\n \" plt.title(\\\"Image after Component Labeling\\\")\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"connected_component_label('C:/Users/Yash Joshi/OG_notebooks/shapes.png')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/contrast_enhancement/CLAHE.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## CLAHE of Black and White Image:\\n\",\n \"Contrast Limited Adaptive Histogram Equalization(CLAHE) of a Black and White Image is fairly straight forward. The image after CLAHE has Cumulative Distribution Function(CDF) as approximately a **straight line**. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Importing the libraries\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\\n\",\n \"import matplotlib.image as mpimg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Reading the original image, here 0 implies that image is read as grayscale\\n\",\n \"image = cv2.imread('lc.jpeg', 0)\\n\",\n \"\\n\",\n \"#Generating the histogram of the original image\\n\",\n \"hist,bins = np.histogram(image.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf = hist.cumsum()\\n\",\n \"cdf_normalized = cdf * hist.max()/ cdf.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Creating CLAHE \\n\",\n \"clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8))\\n\",\n \"\\n\",\n \"#Apply CLAHE to the original image\\n\",\n \"image_clahe = clahe.apply(image)\\n\",\n \"\\n\",\n \"#Generating the histogram of the image after applying CLAHE\\n\",\n \"hist_clahe,bins_clahe = np.histogram(image_clahe.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_clahe = hist_clahe.cumsum()\\n\",\n \"cdf_clahe_normalized = cdf_clahe * hist_clahe.max()/ cdf_clahe.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_clahe, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('CLAHE Image')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_clahe_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_clahe.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_clahe','histogram_clahe'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Histogram Equalization of Color Images\\n\",\n \"Histogram Equaliztion of color images is a little complicated. OpenCV loads color images in BGR color space. With this color space, it is not possible to equalize the histogram without affecting to the color information because all 3 channels contain color information. Therefore you have to convert the BGR image to a color space like YCrCb.
\\n\",\n \"In YCrCb color space, the **Y channel** of the image only contains intensity information where as Cr and Cb channels contain all the color information of the image. Therefore only the Y channel should be processed to get an image after applying CLAHE without changing any color information.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Reading the original image, here 1 implies that image is read as color\\n\",\n \"image_c = cv2.imread('lc.jpeg', 1)\\n\",\n \"\\n\",\n \"#Generating the histogram of the original image\\n\",\n \"hist_c,bins_c = np.histogram(image_c.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c = hist_c.cumsum()\\n\",\n \"cdf_c_normalized = cdf_c * hist_c.max()/ cdf_c.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Converting the image to YCrCb\\n\",\n \"image_yuv = cv2.cvtColor(image_c, cv2.COLOR_BGR2YUV)\\n\",\n \"\\n\",\n \"#Creating CLAHE \\n\",\n \"clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8))\\n\",\n \"\\n\",\n \"# Applying Histogram Equalization on the original imageof the Y channel\\n\",\n \"image_yuv[:,:,0] = clahe.apply(image_yuv[:,:,0])\\n\",\n \"\\n\",\n \"# convert the YUV image back to RGB format\\n\",\n \"image_c_clahe = cv2.cvtColor(image_yuv, cv2.COLOR_YUV2BGR)\\n\",\n \"\\n\",\n \"#Generating the histogram of the image after applying CLAHE\\n\",\n \"hist_c_clahe, bins_c_clahe = np.histogram(image_c_clahe.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c_clahe = hist_c_clahe.cumsum()\\n\",\n \"cdf_c_clahe_normalized = cdf_c_clahe * hist_c_clahe.max()/ cdf_c_clahe.max()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image_c, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_c_clahe, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('Image after CLAHE')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_c_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image_c.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_c_clahe_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_c_clahe.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_clahe','histogram_clahe'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/contrast_enhancement/Histogram-Equalization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Histogram Equalization of Black and White Image:\\n\",\n \"Histogram Equalization of a Black and White Image is fairly straight forward, and can be done using the hist_equalized function of OpenCV. The image after Histogram Equalization has Cumulative Distribution Function(CDF) as a **straight line**. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Importing the libraries\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\\n\",\n \"import matplotlib.image as mpimg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Reading the original image, here 0 implies that image is read as grayscale\\n\",\n \"image = cv2.imread('lc.jpeg', 0)\\n\",\n \"\\n\",\n \"# Generating the histogram of the original image\\n\",\n \"hist,bins = np.histogram(image.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"# Generating the cumulative distribution function of the original image\\n\",\n \"cdf = hist.cumsum()\\n\",\n \"cdf_normalized = cdf * hist.max()/ cdf.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Applying Histogram Equalization on the original image\\n\",\n \"image_equalized = cv2.equalizeHist(image)\\n\",\n \"\\n\",\n \"# Generating the histogram of the equalized image\\n\",\n \"hist_equalized,bins_equalized = np.histogram(image_equalized.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"# Generating the cumulative distribution function of the original image\\n\",\n \"cdf_equalized = hist_equalized.cumsum()\\n\",\n \"cdf_equalized_normalized = cdf_equalized * hist_equalized.max()/ cdf_equalized.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_equalized, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('Histogram Equalized Image')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_equalized_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_equalized.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_equalized','histogram_equalized'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Histogram Equalization of Color Images\\n\",\n \"Histogram Equalization of color images is a little complicated. OpenCV loads color images in BGR color space. With this color space, it is not possible to equalize the histogram without affecting to the color information because all 3 channels contain color information. Therefore you have to convert the BGR image to a color space like YCrCb.
\\n\",\n \"In YCrCb color space, the **Y channel** of the image only contains intensity information where as Cr and Cb channels contain all the color information of the image. Therefore only the Y channel should be processed to get a histogram equalized image without changing any color information.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Reading the original image, here 1 implies that image is read as color\\n\",\n \"image_c = cv2.imread('lc.jpeg', 1)\\n\",\n \"\\n\",\n \"# Generating the histogram of the original image\\n\",\n \"hist_c,bins_c = np.histogram(image_c.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"# Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c = hist_c.cumsum()\\n\",\n \"cdf_c_normalized = cdf_c * hist_c.max()/ cdf_c.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Converting the image to YCrCb\\n\",\n \"image_yuv = cv2.cvtColor(image_c, cv2.COLOR_BGR2YUV)\\n\",\n \"\\n\",\n \"# Applying Histogram Equalization on the original imageof the Y channel\\n\",\n \"image_yuv[:,:,0] = cv2.equalizeHist(image_yuv[:,:,0])\\n\",\n \"\\n\",\n \"# Convert the YUV image back to RGB format\\n\",\n \"image_c_equalized = cv2.cvtColor(image_yuv, cv2.COLOR_YUV2BGR)\\n\",\n \"\\n\",\n \"# Generating the histogram of the equalized image\\n\",\n \"hist_c_equalized, bins_c_equalized = np.histogram(image_c_equalized.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"# Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c_equalized = hist_c_equalized.cumsum()\\n\",\n \"cdf_c_equalized_normalized = cdf_c_equalized * hist_c_equalized.max()/ cdf_c_equalized.max()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image_c, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_c_equalized, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('Histogram Equalized Image')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_c_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image_c.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_c_equalized_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_c_equalized.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_equalized','histogram_equalized'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/contrast_enhancement/Min-Max-Contrast-Stretching.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contrast Stretching of Black and White Image:\\n\",\n \"Contrast of a Black and White Image is fairly straight forward. In Min-Max Contrast Stretching, the lower and upper values of the input image are made to span the full dynamic range. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Importing the libraries\\n\",\n \"from PIL import Image\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\\n\",\n \"import matplotlib.image as mpimg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Reading the original image, here 0 implies that image is read as grayscale\\n\",\n \"image = cv2.imread('lc.jpeg', 0)\\n\",\n \"\\n\",\n \"#Generating the histogram of the original image\\n\",\n \"hist,bins = np.histogram(image.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf = hist.cumsum()\\n\",\n \"cdf_normalized = cdf * hist.max()/ cdf.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create zeros array to store the stretched image\\n\",\n \"image_cs = np.zeros((image.shape[0],image.shape[1]),dtype = 'uint8')\\n\",\n \" \\n\",\n \"\\n\",\n \"# Loop over the image and apply Min-Max Contrasting\\n\",\n \"min = np.min(image)\\n\",\n \"max = np.max(image)\\n\",\n \"\\n\",\n \"for i in range(image.shape[0]):\\n\",\n \" for j in range(image.shape[1]):\\n\",\n \" image_cs[i,j] = 255*(image[i,j]-min)/(max-min)\\n\",\n \" \\n\",\n \"#Generating the histogram of the image after applying Min-Max Contrast Stretchong\\n\",\n \"hist_cs,bins_cs = np.histogram(image_cs.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_cs = hist_cs.cumsum()\\n\",\n \"cdf_cs_normalized = cdf_cs * hist_cs.max()/ cdf_cs.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_cs, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('Contrast Stretched Image')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_cs_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_cs.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_cs','histogram_cs'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contrast Stretching of Color Images:\\n\",\n \"Contrast Stretching of color images is a little complicated. OpenCV loads color images in BGR color space. With this color space, it is not possible to equalize the histogram without affecting to the color information because all 3 channels contain color information. Therefore you have to convert the BGR image to a color space like YCrCb.
\\n\",\n \"In YCrCb color space, the **Y channel** of the image only contains intensity information where as Cr and Cb channels contain all the color information of the image. Therefore only the Y channel should be processed to get an image after applying CLAHE without changing any color information.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Reading the original image, here 1 implies that image is read as color\\n\",\n \"image_c = cv2.imread('lc.jpeg', 1)\\n\",\n \"\\n\",\n \"#Generating the histogram of the original image\\n\",\n \"hist_c,bins_c = np.histogram(image_c.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c = hist_c.cumsum()\\n\",\n \"cdf_c_normalized = cdf_c * hist_c.max()/ cdf_c.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Converting the image to YCrCb\\n\",\n \"image_yuv = cv2.cvtColor(image_c, cv2.COLOR_BGR2YUV)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Loop over the Y channel and apply Min-Max Contrasting\\n\",\n \"min = np.min(image_yuv[:,:,0])\\n\",\n \"max = np.max(image_yuv[:,:,0])\\n\",\n \"\\n\",\n \"for i in range(image.shape[0]):\\n\",\n \" for j in range(image.shape[1]):\\n\",\n \" image_yuv[:,:,0][i,j] = 255*(image_yuv[:,:,0][i,j]-min)/(max-min)\\n\",\n \"\\n\",\n \"\\n\",\n \"# convert the YUV image back to RGB format\\n\",\n \"image_c_cs = cv2.cvtColor(image_yuv, cv2.COLOR_YUV2BGR)\\n\",\n \"\\n\",\n \"#Generating the histogram of the image after applying Constrast Stretching\\n\",\n \"hist_c_cs, bins_c_cs = np.histogram(image_c_cs.flatten(),256,[0,256])\\n\",\n \"\\n\",\n \"#Generating the cumulative distribution function of the original image\\n\",\n \"cdf_c_cs = hist_c_cs.cumsum()\\n\",\n \"cdf_c_cs_normalized = cdf_c_cs * hist_c_cs.max()/ cdf_c_cs.max()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Plotting the Original and Histogram Equalized Image, Histogram and CDF\\n\",\n \"fig, axs = plt.subplots(2, 2)\\n\",\n \"\\n\",\n \"axs[0, 0].imshow(cv2.cvtColor(image_c, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 0].set_title('Original Image')\\n\",\n \"\\n\",\n \"axs[0, 1].imshow(cv2.cvtColor(image_c_cs, cv2.COLOR_BGR2RGB))\\n\",\n \"axs[0, 0].axis('off')\\n\",\n \"axs[0, 1].set_title('Image after Contrast Stretching')\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 0].plot(cdf_c_normalized, color = 'b')\\n\",\n \"axs[1, 0].hist(image_c.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 0].legend(('cdf','histogram'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"axs[1, 1].plot(cdf_c_cs_normalized, color = 'b')\\n\",\n \"axs[1, 1].hist(image_c_cs.flatten(),256,[0,256], color = 'r')\\n\",\n \"axs[1, 1].legend(('cdf_cs','histogram_cs'), loc = 'upper left')\\n\",\n \"\\n\",\n \"\\n\",\n \"# Hide x labels and tick labels for top plots and y ticks for right plots.\\n\",\n \"for ax in axs.flat:\\n\",\n \" ax.label_outer()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/erode_dilate/main.cpp", "content": "#include \"opencv2/imgproc/imgproc.hpp\"\n#include \"opencv2/highgui/highgui.hpp\"\n#include \"highgui.h\"\n#include \n#include \n\n/// Global variables\ncv::Mat src, erosion_dst, dilation_dst;\n\nint erosion_elem = 0;\nint erosion_size = 0;\nint dilation_elem = 0;\nint dilation_size = 0;\nint const max_elem = 2;\nint const max_kernel_size = 21;\n\n/** Function Headers */\nvoid Erosion( int, void* );\nvoid Dilation( int, void* );\n\n/** @function main */\nint main( int argc, char** argv )\n{\n /// Load an image\n src = cv::imread( argv[1] );\n\n if (!src.data)\n return -1;\n /// Create windows\n cv::namedWindow( \"Erosion Demo\", CV_WINDOW_AUTOSIZE );\n cv::namedWindow( \"Dilation Demo\", CV_WINDOW_AUTOSIZE );\n cvMoveWindow( \"Dilation Demo\", src.cols, 0 );\n\n /// Create Erosion Trackbar\n cv::createTrackbar( \"Element:\\n 0: Rect \\n 1: Cross \\n 2: Ellipse\", \"Erosion Demo\",\n &erosion_elem, max_elem,\n Erosion );\n\n cv::createTrackbar( \"Kernel size:\\n 2n +1\", \"Erosion Demo\",\n &erosion_size, max_kernel_size,\n Erosion );\n\n /// Create Dilation Trackbar\n cv::createTrackbar( \"Element:\\n 0: Rect \\n 1: Cross \\n 2: Ellipse\", \"Dilation Demo\",\n &dilation_elem, max_elem,\n Dilation );\n\n cv::createTrackbar( \"Kernel size:\\n 2n +1\", \"Dilation Demo\",\n &dilation_size, max_kernel_size,\n Dilation );\n\n /// Default start\n Erosion( 0, 0 );\n Dilation( 0, 0 );\n\n cv::waitKey(0);\n return 0;\n}\n\n/** @function Erosion */\nvoid Erosion( int, void* )\n{\n int erosion_type;\n if (erosion_elem == 0)\n erosion_type = cv::MORPH_RECT;\n else if (erosion_elem == 1)\n erosion_type = cv::MORPH_CROSS;\n else if (erosion_elem == 2)\n erosion_type = cv::MORPH_ELLIPSE;\n cv::Mat element = cv::getStructuringElement( erosion_type,\n cv::Size( 2 * erosion_size + 1,\n 2 * erosion_size + 1 ),\n cv::Point( erosion_size, erosion_size ) );\n\n /// Apply the erosion operation\n cv::erode( src, erosion_dst, element );\n cv::imshow( \"Erosion Demo\", erosion_dst );\n}\n\n/** @function Dilation */\nvoid Dilation( int, void* )\n{\n int dilation_type;\n if (dilation_elem == 0)\n dilation_type = cv::MORPH_RECT;\n else if (dilation_elem == 1)\n dilation_type = cv::MORPH_CROSS;\n else if (dilation_elem == 2)\n dilation_type = cv::MORPH_ELLIPSE;\n cv::Mat element = cv::getStructuringElement( dilation_type,\n cv::Size( 2 * dilation_size + 1,\n 2 * dilation_size + 1 ),\n cv::Point( dilation_size, dilation_size ) );\n /// Apply the dilation operation\n cv::dilate( src, dilation_dst, element );\n cv::imshow( \"Dilation Demo\", dilation_dst );\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/houghtransform/main.cpp", "content": "#include \n#include \n#include \n#include \n#include \n\n#define WINDOW_NAME \"Hough Transformation\"\n#define THETA_GRANULARITY (4 * 100)\n#define RHO_GRANULARITY 1\n\nIplImage *img_read;\nIplImage *img_edges;\nIplImage *img_sine;\nint *img_sine_arr;\nint threshold;\n\nCvSize img_attrib_dim;\nCvSize img_attrib_sine;\nint max_rho;\n\n/*****************************************************************\n* create and destroy gui items\n*****************************************************************/\nvoid create_gui()\n{\n cvNamedWindow(WINDOW_NAME);\n\n max_rho = sqrt(\n img_attrib_dim.width * img_attrib_dim.width + img_attrib_dim.height *\n img_attrib_dim.height);\n\n printf(\"Image diagonal size (max rho): %d\\n\", max_rho);\n img_attrib_sine.width = THETA_GRANULARITY;\n img_attrib_sine.height = RHO_GRANULARITY * max_rho * 2;\n\n img_sine_arr = (int *)malloc(img_attrib_sine.width * img_attrib_sine.height * sizeof(int));\n img_edges = cvCreateImage(img_attrib_dim, IPL_DEPTH_8U, 1);\n img_sine = cvCreateImage(img_attrib_sine, IPL_DEPTH_8U, 1);\n cvZero(img_sine);\n}\n\nvoid destroy_gui()\n{\n cvDestroyWindow(WINDOW_NAME);\n cvReleaseImage(&img_read);\n cvReleaseImage(&img_edges);\n cvReleaseImage(&img_sine);\n}\n\n/*****************************************************************\n* display a message and wait for escape key to continue\n*****************************************************************/\nvoid disp_msg(const char *msg)\n{\n printf(\"%s. Press escape to continue.\\n\", msg);\n char c;\n do\n c = cvWaitKey(500);\n while (27 != c);\n}\n\n/*****************************************************************\n* main.\n* 1. read image\n* 2. detect edges\n* 3. hough transform to detect lines\n*****************************************************************/\nint main(int argc, char **argv)\n{\n if (argc < 3)\n {\n printf(\"Usage: %s \\n\", argv[0]);\n return 1;\n }\n\n img_read = cvLoadImage(argv[1]);\n if (NULL == img_read)\n {\n printf(\"Error loading image %s\\n\", argv[1]);\n return -1;\n }\n img_attrib_dim = cvGetSize(img_read);\n threshold = atoi(argv[2]);\n\n create_gui();\n\n cvShowImage(WINDOW_NAME, img_read);\n printf(\"Original image size: %d x %d\\n\", img_attrib_dim.width, img_attrib_dim.height);\n disp_msg(\"This is the original image\");\n\n // detect edges\n cvCvtColor(img_read, img_edges, CV_BGR2GRAY);\n cvLaplace(img_edges, img_edges, 3);\n cvThreshold(img_edges, img_edges, 200, 255, CV_THRESH_BINARY);\n cvShowImage(WINDOW_NAME, img_edges);\n disp_msg(\"This is the edge detected image\");\n printf(\"working...\\n\");\n\n // for each pixel in the edge\n int max_dest = 0;\n int min_dest = INT_MAX;\n for (int y = 0; y < img_edges->height; y++)\n {\n uchar *row_ptr = (uchar*) (img_edges->imageData + y * img_edges->widthStep);\n for (int x = 0; x < img_edges->width; x++)\n {\n if (*(row_ptr + x) > 0)\n for (int theta_div = 0; theta_div < THETA_GRANULARITY; theta_div++)\n {\n double theta = (CV_PI * theta_div / THETA_GRANULARITY) - (CV_PI / 2);\n int rho = (int)round(cos(theta) * x + sin(theta) * y + max_rho);\n int *dest = img_sine_arr + rho * img_attrib_sine.width + theta_div;\n int d_val = *dest = *dest + 1;\n if (d_val > max_dest)\n max_dest = d_val;\n if (d_val < min_dest)\n min_dest = d_val;\n }\n }\n }\n\n // scale the intensity for contrast\n float div = ((float)max_dest) / 255;\n printf(\"max_dest: %d, min_dest: %d, scale: %f\\n\", max_dest, min_dest, div);\n for (int y = 0; y < img_sine->height; y++)\n {\n uchar *row_ptr = (uchar*) (img_sine->imageData + y * img_sine->widthStep);\n for (int x = 0; x < img_sine->width; x++)\n {\n int val = (*((img_sine_arr + y * img_attrib_sine.width) + x)) / div;\n if (val < 0)\n val = 0;\n *(row_ptr + x) = val;\n }\n }\n\n // find rho and theta at maximum positions (within the threshold)\n int possible_edge = round((float)max_dest / div) - threshold;\n printf(\"possible edges beyond %d\\n\", possible_edge);\n for (int y = 0; y < img_sine->height; y++)\n {\n uchar *row_ptr = (uchar*) (img_sine->imageData + y * img_sine->widthStep);\n for (int x = 0; x < img_sine->width; x++)\n {\n int val = *(row_ptr + x);\n if (possible_edge <= val)\n {\n float theta_rad = (CV_PI * x / THETA_GRANULARITY) - (CV_PI / 2);\n float theta_deg = theta_rad * 180 / CV_PI;\n printf(\"val: %d at rho %d, theta %f (%f degrees)\\n\", val, y - max_rho, theta_rad,\n theta_deg);\n }\n }\n }\n\n // display the plotted intensity graph of the hough space\n cvShowImage(WINDOW_NAME, img_sine);\n disp_msg(\"this is the hough space image\");\n\n destroy_gui();\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/image_stitching/imagestitching.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nint main(int argc, char* argv[])\n{\n// Default parameters\n std::vector img_names;\n double scale = 1;\n std::string features_type = \"orb\"; //\"surf\" or \"orb\" features type\n float match_conf = 0.3f;\n float conf_thresh = 1.f;\n std::string adjuster_method = \"ray\"; //\"reproj\" or \"ray\" adjuster method\n bool do_wave_correct = true;\n cv::detail::WaveCorrectKind wave_correct_type = cv::detail::WAVE_CORRECT_HORIZ;\n std::string warp_type = \"spherical\";\n int expos_comp_type = cv::detail::ExposureCompensator::GAIN_BLOCKS;\n std::string seam_find_type = \"gc_color\";\n\n float blend_strength = 5;\n int blend_type = cv::detail::Blender::MULTI_BAND;\n std::string result_name = \"img/panorama_result.jpg\";\n double start_time = cv::getTickCount();\n // 1-Input images\n if (argc > 1)\n for (int i = 1; i < argc; i++)\n img_names.push_back(argv[i]);\n else\n {\n img_names.push_back(\"./panorama1.jpg\");\n img_names.push_back(\"./panorama2.jpg\");\n }\n // Check if have enough images\n int num_images = static_cast(img_names.size());\n if (num_images < 2)\n {\n std::cout << \"Need more images\" << std::endl; return -1;\n }\n // 2- Resize images and find features steps\n std::cout << \"Finding features...\" << std::endl;\n double t = cv::getTickCount();\n cv::Ptr finder;\n if (features_type == \"surf\")\n finder = cv::makePtr();\n else if (features_type == \"orb\")\n finder = cv::makePtr();\n else\n {\n std::cout << \"Unknown 2D features type: '\" << features_type << std::endl; return -1;\n }\n\n cv::Mat full_img, img;\n std::vector features(num_images);\n std::vector images(num_images);\n\n std::vector full_img_sizes(num_images);\n for (int i = 0; i < num_images; ++i)\n {\n full_img = cv::imread(img_names[i]);\n full_img_sizes[i] = full_img.size();\n if (full_img.empty())\n {\n std::cout << \"Can't open image \" << img_names[i] << std::endl; return -1;\n }\n resize(full_img, img, cv::Size(), scale, scale);\n images[i] = img.clone();\n (*finder)(img, features[i]);\n features[i].img_idx = i;\n std::cout << \"Features in image #\" << i + 1 << \" are : \" << features[i].keypoints.size() <<\n std::endl;\n }\n finder->collectGarbage();\n full_img.release();\n img.release();\n std::cout << \"Finding features, time: \" <<\n ((cv::getTickCount() - t) / cv::getTickFrequency()) << \" sec\" << std::endl;\n // 3- cv::Match features\n std::cout << \"Pairwise matching\" << std::endl;\n t = cv::getTickCount();\n std::vector pairwise_matches;\n BestOf2Nearestcv::Matcher matcher(false, match_conf);\n matcher(features, pairwise_matches);\n matcher.collectGarbage();\n std::cout << \"Pairwise matching, time: \" <<\n ((cv::getTickCount() - t) / cv::getTickFrequency()) << \" sec\" << std::endl;\n // 4- Select images and matches subset to build panorama\n std::vector indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);\n std::vector img_subset;\n std::vector img_names_subset;\n std::vector full_img_sizes_subset;\n\n for (size_t i = 0; i < indices.size(); ++i)\n {\n img_names_subset.push_back(img_names[indices[i]]);\n img_subset.push_back(images[indices[i]]);\n full_img_sizes_subset.push_back(full_img_sizes[indices[i]]);\n }\n\n images = img_subset;\n img_names = img_names_subset;\n full_img_sizes = full_img_sizes_subset;\n // Estimate camera parameters rough\n HomographyBasedEstimator estimator;\n std::cout << \"0\\n\";\n std::vector cameras;\n estimator(features, pairwise_matches, cameras);\n // // if (!estimator(features, pairwise_matches, cameras))\n // // {std::cout <<\"Homography estimation failed.\" << std::endl; return -1; }\n std::cout << \"1\\n\";\n for (size_t i = 0; i < cameras.size(); ++i)\n {\n cv::Mat R;\n std::cout << \"2\\n\";\n cameras[i].R.convertTo(R, CV_32F);\n cameras[i].R = R;\n std::cout << \"Initial intrinsic #\" << indices[i] + 1 << \":\\n\" << cameras[i].K() <<\n std::endl;\n }\n // 5- Refine camera parameters globally\n cv::Ptr adjuster;\n if (adjuster_method == \"reproj\")\n // \"reproj\" method\n adjuster = cv::makePtr();\n else // \"ray\" method\n adjuster = cv::makePtr();\n adjuster->setConfThresh(conf_thresh);\n if (!(*adjuster)(features, pairwise_matches, cameras))\n {\n std::cout << \"Camera parameters adjusting failed.\" << std::endl; return -1;\n }\n // Find median focal length\n std::vector focals;\n\n\n for (size_t i = 0; i < cameras.size(); ++i)\n {\n std::cout << \"Camera #\" << indices[i] + 1 << \":\\n\" << cameras[i].K() << std::endl;\n focals.push_back(cameras[i].focal);\n }\n sort(focals.begin(), focals.end());\n float warped_image_scale;\n if (focals.size() % 2 == 1)\n warped_image_scale = static_cast(focals[focals.size() / 2]);\n else\n warped_image_scale =\n static_cast(focals[focals.size() / 2 - 1] + focals[focals.size() / 2]) * 0.5f;\n // 6- Wave correlation (optional)\n if (do_wave_correct)\n {\n std::vector rmats;\n for (size_t i = 0; i < cameras.size(); ++i)\n rmats.push_back(cameras[i].R.clone());\n waveCorrect(rmats, wave_correct_type);\n for (size_t i = 0; i < cameras.size(); ++i)\n cameras[i].R = rmats[i];\n }\n // 7- Warp images\n std::cout << \"Warping images (auxiliary)... \" << std::endl;\n t = cv::getTickCount();\n std::vector corners(num_images);\n std::vector masks_warped(num_images);\n std::vector images_warped(num_images);\n std::vector sizes(num_images);\n std::vector masks(num_images);\n // Prepare images masks\n for (int i = 0; i < num_images; ++i)\n {\n masks[i].create(images[i].size(), CV_8U);\n masks[i].setTo(cv::Scalar::all(255));\n }\n // Map projections\n cv::Ptr warper_creator;\n if (warp_type == \"rectilinear\")\n warper_creator = cv::makePtr(2.0f, 1.0f);\n else if (warp_type == \"cylindrical\")\n warper_creator = cv::makePtr();\n else if (warp_type == \"spherical\")\n warper_creator = cv::makePtr();\n else if (warp_type == \"stereographic\")\n warper_creator = cv::makePtr();\n else if (warp_type == \"panini\")\n warper_creator = cv::makePtr(2.0f, 1.0f);\n\n if (!warper_creator)\n {\n std::cout << \"Can't create the following warper\" << warp_type << std::endl; return 1;\n }\n cv::Ptr warper =\n warper_creator->create(static_cast(warped_image_scale * scale));\n\n for (int i = 0; i < num_images; ++i)\n {\n cv::Mat_ K;\n cameras[i].K().convertTo(K, CV_32F);\n float swa = (float)scale;\n K(0, 0) *= swa; K(0, 2) *= swa;\n K(1, 1) *= swa; K(1, 2) *= swa;\n corners[i] = warper->warp(images[i], K, cameras[i].R, INTER_LINEAR, BORDER_REFLECT,\n images_warped[i]);\n\n sizes[i] = images_warped[i].size();\n warper->warp(masks[i], K, cameras[i].R, cv::INTER_NEAREST, cv::BORDER_CONSTANT,\n masks_warped[i]);\n }\n std::vector images_warped_f(num_images);\n\n for (int i = 0; i < num_images; ++i)\n images_warped[i].convertTo(images_warped_f[i], CV_32F);\n\n std::cout << \"Warping images, time: \" << ((cv::getTickCount() - t) / cv::getTickFrequency()) <<\n \" sec\" << std::endl;\n // 8- Compensate exposure errors\n cv::Ptr compensatcv::detail::or =\n ExposureCompensator::createDefault(expos_comp_type);\n compensator->feed(corners, images_warped, masks_warped);\n // 9- Find seam masks\n cv::Ptr seam_finder;\n if (seam_find_type == \"no\")\n seam_finder = cv::makePtr();\n else if (seam_find_type == \"voronoi\")\n seam_finder = cv::makePtr();\n else if (seam_find_type == \"gc_color\")\n seam_finder = cv::makePtr(GraphCutSeamFinderBase::COST_COLOR);\n else if (seam_find_type == \"gc_colorgrad\")\n seam_finder = cv::makePtr(GraphCutSeamFinderBase::COST_COLOR_GRAD);\n else if (seam_find_type == \"dp_color\")\n seam_finder = cv::makePtr(DpSeamFinder::COLOR);\n else if (seam_find_type == \"dp_colorgrad\")\n seam_finder = cv::makePtr(DpSeamFinder::COLOR_GRAD);\n if (!seam_finder)\n {\n std::cout << \"Can't create the following seam finder\" << seam_find_type << std::endl;\n return 1;\n }\n\n\n seam_finder->find(images_warped_f, corners, masks_warped);\n // Release unused memory\n images.clear();\n images_warped.clear();\n images_warped_f.clear();\n masks.clear();\n // 10- Create a blender\n cv::Ptr blender = cv::detail::Blender::createDefault(blend_type, false);\n cv::Size dst_sz = resultRoi(corners, sizes).size();\n float blend_width = sqrt(static_cast(dst_sz.area())) * blend_strength / 100.f;\n\n if (blend_width < 1.f)\n blender = cv::detail::Blender::createDefault(cv::detail::Blender::NO, false);\n else if (blend_type == cv::detail::Blender::MULTI_BAND)\n {\n MultiBandcv::detail::Blender* mb =\n dynamic_cast(blender.get());\n mb->setNumBands(static_cast(ceil(log(blend_width) / log(2.)) - 1.));\n std::cout << \"Multi-band blender, number of bands: \" << mb->numBands() << std::endl;\n }\n else if (blend_type == cv::detail::Blender::FEATHER)\n {\n Feathercv::detail::Blender* fb = dynamic_cast(blender.get());\n fb->setSharpness(1.f / blend_width);\n std::cout << \"Feather blender, sharpness: \" << fb->sharpness() << std::endl;\n }\n blender->prepare(corners, sizes);\n // 11- Compositing step\n std::cout << \"Compositing...\" << std::endl;\n t = cv::getTickCount();\n cv::Mat img_warped, img_warped_s;\n\n\n\n cv::Mat dilated_mask, seam_mask, mask, mask_warped;\n for (int img_idx = 0; img_idx < num_images; ++img_idx)\n {\n std::cout << \"Compositing image #\" << indices[img_idx] + 1 << std::endl;\n // 11.1- Read image and resize it if necessary\n full_img = cv::imread(img_names[img_idx]);\n if (abs(scale - 1) > 1e-1)\n resize(full_img, img, cv::Size(), scale, scale);\n else\n img = full_img;\n full_img.release();\n cv::Size img_size = img.size();\n cv::Mat K;\n cameras[img_idx].K().convertTo(K, CV_32F);\n // 11.2- Warp the current image\n warper->warp(img, K, cameras[img_idx].R, INTER_LINEAR, BORDER_REFLECT, img_warped);\n // Warp the current image mask\n mask.create(img_size, CV_8U);\n mask.setTo(cv::Scalar::all(255));\n warper->warp(mask, K, cameras[img_idx].R, cv::INTER_NEAREST, cv::BORDER_CONSTANT,\n mask_warped);\n // 11.3- Compensate exposure error step\n compensator->apply(img_idx, corners[img_idx], img_warped, mask_warped);\n img_warped.convertTo(img_warped_s, CV_16S);\n img_warped.release();\n img.release();\n mask.release();\n dilate(masks_warped[img_idx], dilated_mask, cv::Mat());\n resize(dilated_mask, seam_mask, mask_warped.size());\n\n\n mask_warped = seam_mask & mask_warped;\n // 11.4- Blending images step\n blender->feed(img_warped_s, mask_warped, corners[img_idx]);\n }\n cv::Mat result, result_mask;\n blender->blend(result, result_mask);\n std::cout << \"Compositing, time: \" << ((cv::getTickCount() - t) / cv::getTickFrequency()) <<\n \" sec\" << std::endl;\n imwrite(result_name, result);\n // imshow(result_name,result);\n // waitKey(8000);\n std::cout << \"Finished, total time: \" <<\n ((cv::getTickCount() - start_time) / cv::getTickFrequency()) << \" sec\" << std::endl;\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/install_opencv.sh", "content": "sudo apt-get -y update\nsudo apt-get -y upgrade\nsudo apt-get -y dist-upgrade\nsudo apt-get -y autoremove\n\n\n# INSTALL THE DEPENDENCIES\n\n# Build tools:\nsudo apt-get install -y build-essential cmake\n\n# GUI (if you want to use GTK instead of Qt, replace 'qt5-default' with 'libgtkglext1-dev' and remove '-DWITH_QT=ON' option in CMake):\nsudo apt-get install -y qt5-default libvtk6-dev\n\n# Media I/O:\nsudo apt-get install -y zlib1g-dev libjpeg-dev libwebp-dev libpng-dev libtiff5-dev libjasper-dev libopenexr-dev libgdal-dev\n\n# Video I/O:\nsudo apt-get install -y libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev yasm libopencore-amrnb-dev libopencore-amrwb-dev libv4l-dev libxine2-dev\n\n# Parallelism and linear algebra libraries:\nsudo apt-get install -y libtbb-dev libeigen3-dev\n\n# Python:\nsudo apt-get install -y python-dev python-tk python-numpy python3-dev python3-tk python3-numpy\n\n# Java:\nsudo apt-get install -y ant default-jdk\n\n# Documentation:\nsudo apt-get install -y doxygen\n\n\n# INSTALL THE LIBRARY (YOU CAN CHANGE '2.4.13' FOR THE LAST STABLE VERSION)\n\nsudo apt-get install -y unzip wget\nwget https://github.com/opencv/opencv/archive/2.4.13.zip\nunzip 2.4.13.zip\nrm 2.4.13.zip\nmv opencv-2.4.13 OpenCV\ncd OpenCV\nmkdir build\ncd build\ncmake -DWITH_QT=ON -DWITH_OPENGL=ON -DFORCE_VTK=ON -DWITH_TBB=ON -DWITH_GDAL=ON -DWITH_XINE=ON -DBUILD_EXAMPLES=ON ..\nmake -j4\nsudo make install\nsudo ldconfig\n" }, { "path": "code/artificial_intelligence/src/image_processing/prewittfilter/prewitt.cpp", "content": "#include \n#include \n#include \n\n\n\n\n// Computes the x component of the gradient vector\n// at a given point in a image.\n// returns gradient in the x direction\nint xGradient(cv::Mat image, int x, int y)\n{\n return image.at(y - 1, x - 1)\n + image.at(y, x - 1)\n + image.at(y + 1, x - 1)\n - image.at(y - 1, x + 1)\n - image.at(y, x + 1)\n - image.at(y + 1, x + 1);\n}\n\n// Computes the y component of the gradient vector\n// at a given point in a image\n// returns gradient in the y direction\n\nint yGradient(cv::Mat image, int x, int y)\n{\n return image.at(y - 1, x - 1)\n + image.at(y - 1, x)\n + image.at(y - 1, x + 1)\n - image.at(y + 1, x - 1)\n - image.at(y + 1, x)\n - image.at(y + 1, x + 1);\n}\n\nint main()\n{\n\n cv::Mat src, dst;\n int gx, gy, sum;\n\n // Load an image\n src = cv::imread(\"lena.jpg\", CV_LOAD_IMAGE_GRAYSCALE);\n dst = src.clone();\n if (!src.data)\n return -1;\n\n for (int y = 0; y < src.rows; y++)\n for (int x = 0; x < src.cols; x++)\n dst.at(y, x) = 0.0;\n\n for (int y = 1; y < src.rows - 1; y++)\n for (int x = 1; x < src.cols - 1; x++)\n {\n gx = xGradient(src, x, y);\n gy = yGradient(src, x, y);\n sum = abs(gx) + abs(gy);\n sum = sum > 255 ? 255 : sum;\n sum = sum < 0 ? 0 : sum;\n dst.at(y, x) = sum;\n }\n\n cv::namedWindow(\"final\");\n cv::imshow(\"final\", dst);\n\n cv::namedWindow(\"initial\");\n cv::imshow(\"initial\", src);\n\n cv::waitKey();\n\n\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/image_processing/sobelfilter/sobel.cpp", "content": "#include \n#include \n#include \n\n\n\n// Computes the x component of the gradient vector\n// at a given point in a image.\n// returns gradient in the x direction\nint xGradient(cv::Mat image, int x, int y)\n{\n return image.at(y - 1, x - 1)\n + 2 * image.at(y, x - 1)\n + image.at(y + 1, x - 1)\n - image.at(y - 1, x + 1)\n - 2 * image.at(y, x + 1)\n - image.at(y + 1, x + 1);\n}\n\n// Computes the y component of the gradient vector\n// at a given point in a image\n// returns gradient in the y direction\n\nint yGradient(cv::Mat image, int x, int y)\n{\n return image.at(y - 1, x - 1)\n + 2 * image.at(y - 1, x)\n + image.at(y - 1, x + 1)\n - image.at(y + 1, x - 1)\n - 2 * image.at(y + 1, x)\n - image.at(y + 1, x + 1);\n}\n\nint main()\n{\n\n cv::Mat src, dst;\n int gx, gy, sum;\n\n // Load an image\n src = cv::imread(\"lena.jpg\", CV_LOAD_IMAGE_GRAYSCALE);\n dst = src.clone();\n if (!src.data)\n return -1;\n\n for (int y = 0; y < src.rows; y++)\n for (int x = 0; x < src.cols; x++)\n dst.at(y, x) = 0.0;\n\n for (int y = 1; y < src.rows - 1; y++)\n for (int x = 1; x < src.cols - 1; x++)\n {\n gx = xGradient(src, x, y);\n gy = yGradient(src, x, y);\n sum = abs(gx) + abs(gy);\n sum = sum > 255 ? 255 : sum;\n sum = sum < 0 ? 0 : sum;\n dst.at(y, x) = sum;\n }\n\n cv::namedWindow(\"final\");\n cv::imshow(\"final\", dst);\n\n cv::namedWindow(\"initial\");\n cv::imshow(\"initial\", src);\n\n cv::waitKey();\n\n\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/isodata_clustering/isodata.py", "content": "# Part of Cosmos by OpenGenus Foundation\nfrom math import sqrt\n\n# measure distance between two points\ndef distance_2point(x1, y1, x2, y2):\n return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n\n\n# estimate volume of the cluster\ndef volume_estimation(cluster, center):\n num_of_points = len(cluster)\n distance = []\n for i in range(num_of_points):\n distance.append(\n distance_2point(center[0], center[1], cluster[i][0], cluster[i][1])\n )\n\n return sum(distance) / num_of_points\n\n\n# defining of new cluster center\ndef new_cluster_centers(cluster):\n s = list(map(sum, zip(*cluster)))\n length = len(cluster)\n return (s[0] / length, s[1] / length)\n\n\n# measure distances between each two pairs of cluster centers\ndef center_distance(centers):\n D_ij = {}\n # offset coeficient\n k = 0\n for i in range(len(centers)):\n for j in range(k, len(centers)):\n if i == j:\n pass\n else:\n D_ij[(i, j)] = distance_2point(\n centers[i][0], centers[i][1], centers[j][0], centers[j][1]\n )\n k += 1\n return D_ij\n\n\n# standart deviation vector for cluster\ndef standart_deviation(values, center):\n n = len(values)\n x_coord = []\n y_coord = []\n for i in range(n):\n x_coord.append((values[i][0] - center[0]) ** 2)\n y_coord.append((values[i][1] - center[1]) ** 2)\n\n x = sqrt(sum(x_coord) / n)\n y = sqrt(sum(y_coord) / n)\n\n return (x, y)\n\n\ndef cluster_points_distribution(centers, points):\n centers_len = len(centers)\n points_len = len(points)\n distances = []\n distance = []\n\n # define array for clusters\n clusters = [[] for i in range(centers_len)]\n\n # iteration throught all points\n for i in range(points_len):\n # iteration throught all centers\n for j in range(centers_len):\n distance.append(\n distance_2point(\n centers[j][0], centers[j][1], points[i][0], points[i][1]\n )\n )\n distances.append(distance)\n distance = []\n\n # distribution\n for i in range(points_len):\n ind = distances[i].index(min(distances[i]))\n clusters[ind].append(points[i])\n\n return clusters\n\n\ndef cluster_division(cluster, center, dev_vector):\n # divide only center of clusters\n\n # coeficient\n k = 0.5\n\n max_deviation = max(dev_vector)\n index = dev_vector.index(max(dev_vector))\n g = k * max_deviation\n\n # defining new centers\n center1 = list(center)\n center2 = list(center)\n center1[index] += g\n center2[index] -= g\n\n cluster1 = []\n cluster2 = []\n\n return tuple(center1), tuple(center2)\n\n\ndef cluster_union(cluster1, cluster2, center1, center2):\n x1 = center1[0]\n x2 = center2[0]\n y1 = center1[1]\n y2 = center2[1]\n n1 = len(cluster1)\n n2 = len(cluster2)\n\n x = (n1 * x1 + n2 * x2) / (n1 + n2)\n y = (n1 * y1 + n2 * y2) / (n1 + n2)\n center = (x, y)\n cluster = cluster1 + cluster2\n\n return center, cluster\n\n\ndef clusterize():\n # initial values\n K = 4 # max cluster number\n THETA_N = 1 # for cluster elimination\n THETA_S = 1 # for cluster division\n THETA_C = 3 # for cluster union\n L = 3 #\n I = 4 # max number of iterations\n N_c = 1 # number of primary cluster centers\n\n distance = [] # distances array\n centers = [] # clusters centers\n clusters = [] # array for clusters points\n iteration = 1 # number of current iteration\n\n centers.append(points[0]) # first cluster center\n\n while iteration <= I:\n # print (\"Iteration \", iteration)\n # step 2\n\n \"\"\"\n if there are one cluster center - all points goes to first cluster\n otherwise we distribute points between clusters\n \"\"\"\n if len(centers) <= 1:\n clusters.append(points)\n else:\n clusters = cluster_points_distribution(centers, points)\n\n # step 3\n # eliminating small clusters (unfinished!!!!!!)\n \"\"\"\n for i in range(len(clusters)):\n\n if len(clusters[i]) <= THETA_N:\n print(clusters[i][i])\n item = clusters[i][i]\n points.remove(item)\n #del clusters[i]\n break\n else:\n print(\"else\")\n break\t\n \"\"\"\n\n # step 4\n # erasing existing centers and defining a new ones\n centers = []\n for i in range(len(clusters)):\n centers.append(new_cluster_centers(clusters[i]))\n\n # step 5 - estimating volumes of all clusters\n # array for clusters volume\n D_vol = []\n for i in range(len(centers)):\n D_vol.append(volume_estimation(clusters[i], centers[i]))\n\n # step 6\n if len(clusters) <= 1:\n D = 0\n else:\n cluster_length = []\n vol_sum = []\n for i in range(len(centers)):\n cluster_length.append(len(clusters[i]))\n vol_sum.append(cluster_length[i] * D_vol[i])\n\n D = sum(vol_sum) / len(points)\n\n # step 7\n if iteration >= I:\n THETA_C = 0\n\n elif (N_c >= 2 * K) or (iteration % 2 == 0):\n pass\n\n else:\n # step 8\n # vectors of all clusters standart deviation\n vectors = []\n for i in range(len(centers)):\n vectors.append(standart_deviation(clusters[i], centers[i]))\n\n # step 9\n max_s = []\n for v in vectors:\n max_s.append(max(v[0], v[1]))\n\n # step 10 (cluster division)\n for i in range(len(max_s)):\n length = len(clusters[i])\n coef = 2 * (THETA_N + 1)\n\n if (max_s[i] > THETA_S) and (\n (D_vol[i] > D and length > coef) or N_c < float(K) / 2\n ):\n center1, center2 = cluster_division(\n clusters[i], centers[i], vectors[i]\n )\n del centers[i]\n centers.append(center1)\n centers.append(center2)\n N_c += 1\n\n else:\n pass\n\n # for i in clusters:\n # \tprint(i)\n\n # step 11\n D_ij = center_distance(centers)\n rang = {}\n for coord in D_ij:\n if D_ij[coord] < THETA_C:\n rang[coord] = D_ij[coord]\n else:\n pass\n\n \"\"\"\n # step 13 (cluster union)\n for key in rang.keys():\n cluster_union(clusters[key], clusters[key.next()], centers[key], centers[key.next()])\n N_c -= 1\n\n \"\"\"\n\n iteration += 1\n\n return clusters\n\n\nif __name__ == \"__main__\":\n # if file called as a script\n # cluster points\n points1 = [\n (2, 5),\n (2, 4),\n (2, 3),\n (8, 8),\n (7, 7),\n (6, 6),\n (-5, -4),\n (-6, -4),\n (-9, -4),\n (5, -6),\n (6, -5),\n (44, 49),\n (45, 50),\n ]\n points2 = [\n (-0.99, 1.09),\n (-0.98, 1.08),\n (-0.97, 1.07),\n (-0.96, 1.06),\n (-0.95, 1.05),\n (-0.94, 1.04),\n (-0.93, 1.04),\n (0.94, 1.04),\n (0.95, 1.05),\n (0.96, 1.06),\n (0.97, 1.07),\n (0.98, 1.08),\n (0.99, 1.09),\n ]\n points3 = [\n (-99, 109),\n (-98, 108),\n (-97, 107),\n (-96, 106),\n (-95, 105),\n (-94, 104),\n (-93, 104),\n (94, 104),\n (95, 105),\n (96, 106),\n (97, 107),\n (98, 108),\n (99, 109),\n ]\n points = points3\n cl = clusterize()\n for i in cl:\n print(\"Cl\", i)\n" }, { "path": "code/artificial_intelligence/src/isodata_clustering/readme.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# ISODATA Clustering\nISODATA stands for The Iterative Self-Organizing Data Analysis Technique algorithm used for Multispectral pattern recognition was developed by Geoffrey H. Ball and David J. Hall, working in the Stanford Research Institute in Menlo Park, CA.\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/k_means/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# k Means\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/k_means/k-means.c", "content": "// Part of Cosmos by OpenGenus\n#include\n#include\nint main()\n{\n int i,j,k,t1,t2;\n int a0[10];\n int a1[10];\n int a2[10];\n \n printf(\"\\nEnter 10 numbers:\\n\");\n for(i=0;i<10;i++)\n {\n scanf(\"%d\",&a0[i]);\n }\n \n //initial means\n int m1;\n int m2;\n printf(\"\\nEnter first mean: \");\n scanf(\"%d\",&m1);\n printf(\"\\nEnter second mean: \");\n scanf(\"%d\",&m2);\n \n int om1,om2; //previous means\n \n do\n {\n om1=m1;\n om2=m2;\n \n //creating clusters\n i=j=k=0;\n for(i=0;i<10;i++)\n {\n //calculating distance to means\n t1=a0[i]-m1;\n if(t1<0){t1=-t1;}\n t2=a0[i]-m2;\n if(t2<0){t2=-t2;}\n if(t1 // Used for debugging & learning code\n#include // Use for sorting\n#include \n#include \n#include \n#include \n#include \n#include \n\n#define PI 4 * atan(1.0)\n\n// Point structure\nstruct point\n{\n int x;\n int y;\n int I;\n int radx;\n int rady;\n int area;\n double avg;\n double r;\n};\n\n// Kmeans with weight option\ntemplate\ndouble kmeans(ByteImage& bimg, const int K, double weight)\n{\n\n // Create k random points (centers)\n point * centers = new point[K];\n\n // Totals for updating\n point * totals = new point[K];\n\n // Initialize k points\n for (int k = 0; k < K; k++)\n {\n // Initiate centers\n centers[k].x = rand() % bimg.nr;\n centers[k].y = rand() % bimg.nc;\n centers[k].I = rand() % 256;\n\n // Initiate sumation for average\n totals[k].x = 0;\n totals[k].y = 0;\n totals[k].I = 0;\n totals[k].area = 0;\n }\n // Create label map\n byte * label = new byte[bimg.nr * bimg.nc];\n double shortestDistance, presentDistance;\n double error = bimg.nr * bimg.nr + bimg.nc * bimg.nc + 255 * 255, old_error;\n\n // Weights maybe? Set these to one for original kmeans.\n double wi = 1, wj = 1; //255.0*255/(double)bimg.nr/bimg.nr, wj = 255.0*255/(double)bimg.nc/bimg.nc;\n int closestK = 0;\n\n do {\n\n // Update previous error\n old_error = error;\n error = 0;\n // A. For each point in space, find distance to each center, assign to min center (map colors)\n for (int i = 0; i < bimg.nr; i++)\n for (int j = 0; j < bimg.nc; j++)\n {\n // Initialize shortestDistance to max possible.\n shortestDistance = sqrt(bimg.nr * bimg.nr + bimg.nc * bimg.nc + 255 * 255);\n // Check distance to each center\n for (int k = 0; k < K; k++)\n {\n // Calculate the distance\n presentDistance = sqrt((double)wi * (centers[k].x - i) * (centers[k].x - i) +\n (double)wj * (centers[k].y - j) * (centers[k].y - j) +\n (double)weight *\n (centers[k].I - bimg.image[i * bimg.nc + j]) *\n (centers[k].I - bimg.image[i * bimg.nc + j]));\n\n // Update shortest distance\n if (presentDistance < shortestDistance)\n {\n shortestDistance = presentDistance;\n closestK = k;\n label[i * bimg.nc + j] = (255.0 / K) * k;\n }\n }\n\n // Update error\n error += shortestDistance;\n\n // Update totals to compute new centroid\n totals[closestK].x += i;\n totals[closestK].y += j;\n totals[closestK].I += bimg.image[i * bimg.nc + j];\n totals[closestK].area += 1;\n }\n error = error / bimg.nr / bimg.nc;\n // Update centers\n for (int k = 0; k < K; k++)\n {\n // Compute new centers\n if (totals[k].area > 0)\n {\n centers[k].x = totals[k].x / totals[k].area;\n centers[k].y = totals[k].y / totals[k].area;\n centers[k].I = totals[k].I / totals[k].area;\n }\n\n // Reset totals\n totals[k].x = 0;\n totals[k].y = 0;\n totals[k].I = 0;\n totals[k].area = 0;\n }\n\n // B. Repeat A until centers do not move\n } while (error != old_error);\n\n // Extra credit code to find and \"average\" distance between the nodes, which must be less than user input\n std::cout << \"K=\" << K << std::endl;\n\n // Make label image\n bimg.image = label;\n delete [] totals;\n delete [] centers;\n\n std::cout << shortestDistance << std::endl;\n return shortestDistance;\n}\n" }, { "path": "code/artificial_intelligence/src/k_means/k_means.py", "content": "from random import random\n\n# sets and datas\nsets = [random() for i in range(10)]\ndata = [random() for i in range(1000)]\n\nparameter = 0.01 # This parameter change the mean changing speed must be Min: 0 Max: 1\n\nfor x in data: # O(data count)\n nearest_k = 0\n mininmum_error = 9999\n # At normal condition it must goes to infinite.\n for k in enumerate(sets): # O(mean count)\n error = abs(x - k[1])\n if error < mininmum_error:\n mininmum_error = error\n nearest_k = k[0]\n sets[nearest_k] = sets[nearest_k] * (1 - parameter) + x * (parameter)\n\nprint(sets)\n\n# Algorithmic Anlayze-->O(data*means) it means Data count = K and Set count = J // O(K.J) \\\\\n" }, { "path": "code/artificial_intelligence/src/k_means/k_means.swift", "content": "import Foundation\n\nclass KMeans {\n let numCenters: Int\n let labels: [Label]\n private(set) var centroids = [Vector]()\n\n init(labels: [Label]) {\n assert(labels.count > 1, \"Exception: KMeans with less than 2 centers.\")\n self.labels = labels\n self.numCenters = labels.count\n }\n\n private func indexOfNearestCenter(_ x: Vector, centers: [Vector]) -> Int {\n var nearestDist = DBL_MAX\n var minIndex = 0\n\n for (idx, center) in centers.enumerated() {\n let dist = x.distanceTo(center)\n if dist < nearestDist {\n minIndex = idx\n nearestDist = dist\n }\n }\n return minIndex\n }\n\n func trainCenters(_ points: [Vector], convergeDistance: Double) {\n let zeroVector = Vector([Double](repeating: 0, count: points[0].length))\n\n var centers = reservoirSample(points, k: numCenters)\n\n var centerMoveDist = 0.0\n repeat {\n var classification: [[Vector]] = .init(repeating: [], count: numCenters)\n\n for p in points {\n let classIndex = indexOfNearestCenter(p, centers: centers)\n classification[classIndex].append(p)\n }\n\nlet newCenters = classification.map { assignedPoints in\n assignedPoints.reduce(zeroVector, +) / Double(assignedPoints.count)\n }\n\n centerMoveDist = 0.0\n for idx in 0.. convergeDistance\n\n centroids = centers\n }\n\n func fit(_ point: Vector) -> Label {\n assert(!centroids.isEmpty, \"Exception: KMeans tried to fit on a non trained model.\")\n\n let centroidIndex = indexOfNearestCenter(point, centers: centroids)\n return labels[centroidIndex]\n }\n\n func fit(_ points: [Vector]) -> [Label] {\n assert(!centroids.isEmpty, \"Exception: KMeans tried to fit on a non trained model.\")\n\n return points.map(fit)\n }\n}\n\nfunc reservoirSample(_ samples: [T], k: Int) -> [T] {\n var result = [T]()\n\n for i in 0..\n#include \n#include \n\nstruct point {\n\tfloat w,x,y,z;\n\tfloat distance;\n\tint op;\t\n};\n\nint main(){\n\tint i,j,n,k;\n\tpoint p;\n\tpoint temp;\n\tprintf(\"Number of data points: \");\n\tscanf(\"%d\",&n);\n\tpoint arr[n];\n\tprintf(\"\\nEnter the dataset values: \\n\");\n\tprintf(\"w\\tx\\ty\\tz\\top\\n\");\n\t\n\t// Taking input data\n\tfor (i=0;i arr[j + 1].distance) {\n\t temp = arr[j];\n\t arr[j] = arr[j + 1];\n\t arr[j + 1] = temp;\n\t }\n\t }\n\t}\n\tprintf(\"w\\tx\\ty\\tz\\top\\tdistance\\n\");\n\tfor (i=0;i= freq2 && freq1 >= freq3){\n\t\tprintf(\"\\nThe class of unknown point is: 1\");\n\t}\n\telse if(freq2>= freq1 && freq2 >= freq3){\n\t\tprintf(\"\\nThe class of unknown point is: 2\");\n\t}\n\telse{\n\t\tprintf(\"\\nThe class of unknown point is: 3\");\t\n\t}\n}\n\n" }, { "path": "code/artificial_intelligence/src/k_nearest_neighbours/iris.data", "content": "5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3.0,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5.0,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5.0,3.4,1.5,0.2,Iris-setosa\n4.4,2.9,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.4,3.7,1.5,0.2,Iris-setosa\n4.8,3.4,1.6,0.2,Iris-setosa\n4.8,3.0,1.4,0.1,Iris-setosa\n4.3,3.0,1.1,0.1,Iris-setosa\n5.8,4.0,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1.0,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5.0,3.0,1.6,0.2,Iris-setosa\n5.0,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.0,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3.0,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5.0,3.5,1.3,0.3,Iris-setosa\n4.5,2.3,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5.0,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3.0,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5.0,3.3,1.4,0.2,Iris-setosa\n7.0,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4.0,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1.0,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5.0,2.0,3.5,1.0,Iris-versicolor\n5.9,3.0,4.2,1.5,Iris-versicolor\n6.0,2.2,4.0,1.0,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3.0,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1.0,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4.0,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3.0,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3.0,5.0,1.7,Iris-versicolor\n6.0,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1.0,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1.0,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6.0,2.7,5.1,1.6,Iris-versicolor\n5.4,3.0,4.5,1.5,Iris-versicolor\n6.0,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3.0,4.1,1.3,Iris-versicolor\n5.5,2.5,4.0,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3.0,4.6,1.4,Iris-versicolor\n5.8,2.6,4.0,1.2,Iris-versicolor\n5.0,2.3,3.3,1.0,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3.0,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3.0,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6.0,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3.0,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3.0,5.8,2.2,Iris-virginica\n7.6,3.0,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2.0,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3.0,5.5,2.1,Iris-virginica\n5.7,2.5,5.0,2.0,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3.0,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6.0,2.2,5.0,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2.0,Iris-virginica\n7.7,2.8,6.7,2.0,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6.0,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3.0,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3.0,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2.0,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3.0,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6.0,3.0,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3.0,5.2,2.3,Iris-virginica\n6.3,2.5,5.0,1.9,Iris-virginica\n6.5,3.0,5.2,2.0,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3.0,5.1,1.8,Iris-virginica\n\n" }, { "path": "code/artificial_intelligence/src/k_nearest_neighbours/k_nearest_neighbours.cpp", "content": "// Part of Cosmos by OpenGenus\n#include \n#include \n#include \n#include \n\nstruct Point {\n float w, x, y, z; // w, x, y and z are the values of the dataset\n float distance; // distance will store the distance of dataset point from\n // the unknown test point\n int op; // op will store the class of the point\n};\n\nint main() {\n int n, k;\n Point p;\n std::cout << \"Number of data points: \";\n std::cin >> n;\n std::vector vec;\n std::cout << \"\\nEnter the dataset values: \\n\";\n std::cout << \"w\\tx\\ty\\tz\\top\\n\";\n\n // Taking dataset\n for (int i = 0; i < n; ++i) {\n float wCoordinate, xCoordinate, yCoordinate, zCoordinate;\n int opCoordinate;\n std::cin >> wCoordinate >> xCoordinate >> yCoordinate >> zCoordinate >>\n opCoordinate;\n vec.push_back(Point());\n vec[i].w = wCoordinate;\n vec[i].x = xCoordinate;\n vec[i].y = yCoordinate;\n vec[i].z = zCoordinate;\n vec[i].op = opCoordinate;\n }\n\n std::cout << \"\\nw\\tx\\ty\\tz\\top\\n\";\n\n for (int i = 0; i < n; ++i) {\n std::cout << std::fixed << std::setprecision(2) << vec[i].w << '\\t'\n << vec[i].x << '\\t' << vec[i].y << '\\t' << vec[i].z << '\\t'\n << vec[i].op << '\\n';\n }\n\n std::cout\n << \"\\nEnter the feature values of the unknown point: \\nw\\tx\\ty\\tz\\n\";\n std::cin >> p.w >> p.x >> p.y >> p.z;\n\n float euclideanDistance(Point p, Point v);\n for (int i = 0; i < n; ++i) {\n vec[i].distance = euclideanDistance(p, vec[i]);\n }\n\n // Sorting the training data with respect to distance\n for (int i = 1; i < n; ++i) {\n for (int j = 0; j < n - i; ++j) {\n if (vec[j].distance > vec[j + 1].distance) {\n Point temp;\n temp = vec[j];\n vec[j] = vec[j + 1];\n vec[j + 1] = temp;\n }\n }\n }\n\n std::cout << \"\\nw\\tx\\ty\\tz\\top\\tdistance\\n\";\n for (int i = 0; i < n; ++i) {\n std::cout << std::fixed << std::setprecision(2) << vec[i].w << '\\t'\n << vec[i].x << '\\t' << vec[i].y << '\\t' << vec[i].z << '\\t'\n << vec[i].op << '\\t' << vec[i].distance << '\\n';\n }\n\n // Taking the K nearest neighbors\n std::cout << \"\\nNumber of nearest neighbors(k): \";\n std::cin >> k;\n\n int freq[1000] = {0};\n int index = 0, maxfreq = 0;\n\n // Creating frequency array of the class of k nearest neighbors\n for (int i = 0; i < k; ++i) {\n freq[vec[i].op]++;\n }\n\n // Finding the most frequent occurring class\n for (int i = 0; i < 1000; ++i) {\n if (freq[i] > maxfreq) {\n maxfreq = freq[i];\n index = i;\n }\n }\n\n std::cout << \"The class of the unknown point is \" << index;\n return 0;\n}\n\n// Measuring the Euclidean distance\nfloat euclideanDistance(Point p, Point v) {\n float result;\n result = sqrt(((v.w - p.w) * (v.w - p.w)) + ((v.x - p.x) * (v.x - p.x)) +\n ((v.y - p.y) * (v.y - p.y)) + ((v.z - p.z) * (v.z - p.z)));\n return result;\n}\n\n// Sample Output\n/*\nNumber of data points: 5\n\nEnter the dataset values:\nw x y z op\n1 2 3 4 5\n10 20 30 40 50\n5 2 40 7 10\n40 30 100 28 3\n20 57 98 46 8\n\nw x y z op\n1.00 2.00 3.00 4.00 5\n10.00 20.00 30.00 40.00 50\n5.00 2.00 40.00 7.00 10\n40.00 30.00 100.00 28.00 3\n20.00 57.00 98.00 46.00 8\n\nEnter the feature values of the unknown point:\nw x y z\n40 30 80 30\n\nw x y z op distance\n40.00 30.00 100.00 28.00 3 20.10\n20.00 57.00 98.00 46.00 8 41.34\n10.00 20.00 30.00 40.00 50 60.00\n5.00 2.00 40.00 7.00 10 64.33\n1.00 2.00 3.00 4.00 5 94.39\n\nNumber of nearest neighbors(k): 3\nThe class of unknown point is 3\n*/\n" }, { "path": "code/artificial_intelligence/src/k_nearest_neighbours/k_nearest_neighbours.py", "content": "# k Nearest Neighbours implemented from Scratch in Python\n\nimport csv\nimport random\nimport math\nimport operator\n\n\ndef loadDataset(filename, split, trainingSet=[], testSet=[]):\n with open(filename, \"rt\") as csvfile:\n lines = csv.reader(csvfile)\n dataset = list(lines)\n for x in range(len(dataset) - 1):\n for y in range(4):\n dataset[x][y] = float(dataset[x][y])\n if random.random() < split:\n trainingSet.append(dataset[x])\n else:\n testSet.append(dataset[x])\n\n\ndef euclideanDistance(instance1, instance2, length):\n distance = 0\n for x in range(length):\n distance += pow((instance1[x] - instance2[x]), 2)\n return math.sqrt(distance)\n\n\ndef getNeighbors(trainingSet, testInstance, k):\n distances = []\n length = len(testInstance) - 1\n for x in range(len(trainingSet)):\n dist = euclideanDistance(testInstance, trainingSet[x], length)\n distances.append((trainingSet[x], dist))\n distances.sort(key=operator.itemgetter(1))\n neighbors = []\n for x in range(k):\n neighbors.append(distances[x][0])\n return neighbors\n\n\ndef getResponse(neighbors):\n classVotes = {}\n for x in range(len(neighbors)):\n response = neighbors[x][-1]\n if response in classVotes:\n classVotes[response] += 1\n else:\n classVotes[response] = 1\n sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)\n return sortedVotes[0][0]\n\n\ndef getAccuracy(testSet, predictions):\n correct = 0\n for x in range(len(testSet)):\n if testSet[x][-1] == predictions[x]:\n correct += 1\n return (correct / float(len(testSet))) * 100.0\n\n\ndef main():\n # prepare data\n trainingSet = []\n testSet = []\n split = 0.67\n loadDataset(\"iris.data\", split, trainingSet, testSet)\n print(\"Train set: \" + repr(len(trainingSet)))\n print(\"Test set: \" + repr(len(testSet)))\n # generate predictions\n predictions = []\n k = 3\n for x in range(len(testSet)):\n neighbors = getNeighbors(trainingSet, testSet[x], k)\n result = getResponse(neighbors)\n predictions.append(result)\n print(\"> predicted=\" + repr(result) + \", actual=\" + repr(testSet[x][-1]))\n accuracy = getAccuracy(testSet, predictions)\n print(\"Accuracy: \" + repr(accuracy) + \"%\")\n\n\nmain()\n" }, { "path": "code/artificial_intelligence/src/lasso_regression/lasso_regression.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"lasso_regression.ipynb\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"-BknsqHEEqsI\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"sns.set()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"83xnU_AyFGT9\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 212\n },\n \"outputId\": \"1501ef32-7099-4f1b-b853-e829c9c0c701\"\n },\n \"source\": [\n \"from pandas_datareader import data as pdr\\n\",\n \"import yfinance as yf\\n\",\n \"import datetime\\n\",\n \"start = datetime.datetime(2012,1,1)\\n\",\n \"end= datetime.datetime(2018,8,30)\\n\",\n \"yf.pdr_override()\\n\",\n \"df_full = yf.download(\\\"jpm\\\",start = start,end=end).reset_index()\\n\",\n \"df_full.to_csv('jpm.csv',index=False)\\n\",\n \"df_full.head()\"\n ],\n \"execution_count\": 5,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r[*********************100%***********************] 1 of 1 completed\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdj CloseVolume
02012-01-0334.06000135.18999934.00999834.98000027.86434244102800
12012-01-0434.43999935.15000234.33000234.95000128.04085036571200
22012-01-0534.70999935.91999834.40000235.68000028.62653938381400
32012-01-0635.68999935.77000035.13999935.36000128.36978733160600
42012-01-0935.43999935.68000034.99000235.29999928.32166723001800
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\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close Adj Close Volume\\n\",\n \"0 2012-01-03 34.060001 35.189999 34.009998 34.980000 27.864342 44102800\\n\",\n \"1 2012-01-04 34.439999 35.150002 34.330002 34.950001 28.040850 36571200\\n\",\n \"2 2012-01-05 34.709999 35.919998 34.400002 35.680000 28.626539 38381400\\n\",\n \"3 2012-01-06 35.689999 35.770000 35.139999 35.360001 28.369787 33160600\\n\",\n \"4 2012-01-09 35.439999 35.680000 34.990002 35.299999 28.321667 23001800\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 5\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"SE2zgaEFFOUV\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 402\n },\n \"outputId\": \"92b4b3cc-45c8-4eef-ce8a-5bba35e0b87d\"\n },\n \"source\": [\n \"import pandas as pd\\n\",\n \"df = pd.read_csv('jpm.csv')\\n\",\n \"df\"\n ],\n \"execution_count\": 6,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
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DateOpenHighLowCloseAdj CloseVolume
02012-01-0334.06000135.18999934.00999834.98000027.86434244102800
12012-01-0434.43999935.15000234.33000234.95000128.04085036571200
22012-01-0534.70999935.91999834.40000235.68000028.62653938381400
32012-01-0635.68999935.77000035.13999935.36000128.36978733160600
42012-01-0935.43999935.68000034.99000235.29999928.32166723001800
........................
16712018-08-23114.959999115.150002114.430000114.730003109.7779249265400
16722018-08-24114.980003115.220001114.449997114.680000109.7300878845700
16732018-08-27115.220001117.279999115.169998116.709999111.67245513768000
16742018-08-28117.000000117.029999115.970001116.139999111.1270608302600
16752018-08-29116.349998116.370003115.360001115.760002110.7634667221700
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1676 rows × 7 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High ... Close Adj Close Volume\\n\",\n \"0 2012-01-03 34.060001 35.189999 ... 34.980000 27.864342 44102800\\n\",\n \"1 2012-01-04 34.439999 35.150002 ... 34.950001 28.040850 36571200\\n\",\n \"2 2012-01-05 34.709999 35.919998 ... 35.680000 28.626539 38381400\\n\",\n \"3 2012-01-06 35.689999 35.770000 ... 35.360001 28.369787 33160600\\n\",\n \"4 2012-01-09 35.439999 35.680000 ... 35.299999 28.321667 23001800\\n\",\n \"... ... ... ... ... ... ... ...\\n\",\n \"1671 2018-08-23 114.959999 115.150002 ... 114.730003 109.777924 9265400\\n\",\n \"1672 2018-08-24 114.980003 115.220001 ... 114.680000 109.730087 8845700\\n\",\n \"1673 2018-08-27 115.220001 117.279999 ... 116.709999 111.672455 13768000\\n\",\n \"1674 2018-08-28 117.000000 117.029999 ... 116.139999 111.127060 8302600\\n\",\n \"1675 2018-08-29 116.349998 116.370003 ... 115.760002 110.763466 7221700\\n\",\n \"\\n\",\n \"[1676 rows x 7 columns]\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 6\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"RoA46lkJFRRH\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 218\n },\n \"outputId\": \"aeb7fe9b-a0cd-4f57-fb67-d5fc3659d6b1\"\n },\n \"source\": [\n \"close_px = df['Adj Close']\\n\",\n \"mavg = close_px.rolling(window=100).mean()\\n\",\n \"mavg\"\n ],\n \"execution_count\": 7,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"0 NaN\\n\",\n \"1 NaN\\n\",\n \"2 NaN\\n\",\n \"3 NaN\\n\",\n \"4 NaN\\n\",\n \" ... \\n\",\n \"1671 105.630344\\n\",\n \"1672 105.676721\\n\",\n \"1673 105.728722\\n\",\n \"1674 105.801820\\n\",\n \"1675 105.858816\\n\",\n \"Name: Adj Close, Length: 1676, dtype: float64\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 7\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"vk-AL5u4FU3r\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 448\n },\n \"outputId\": \"7bf22cdc-6f47-4b94-beeb-963d9ee9f486\"\n },\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib import style\\n\",\n \"\\n\",\n \"# Adjusting the size of matplotlib\\n\",\n \"import matplotlib as mpl\\n\",\n \"mpl.rc('figure', figsize=(8, 7))\\n\",\n \"mpl.__version__\\n\",\n \"\\n\",\n \"# Adjusting the style of matplotlib\\n\",\n \"style.use('ggplot')\\n\",\n \"\\n\",\n \"close_px.plot(label='AAPL')\\n\",\n \"mavg.plot(label='mavg')\\n\",\n \"plt.legend()\"\n ],\n \"execution_count\": 8,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 8\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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TUbbxyCfyT4mV5OrviCJ6Zq1LgxXXoUeWcthBCif6r1dYeK1Oq7Nbbw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\\ny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\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ZDCC88yEFYYM\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 448\n },\n \"outputId\": \"cafcf6ad-67e9-4b7d-bae6-7008acbf50e8\"\n },\n \"source\": [\n \"dfcomp = yf.download(['jpm','AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],start = start,end=end)['Adj Close']\\n\",\n \"dfcomp\"\n ],\n \"execution_count\": 9,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"[*********************100%***********************] 6 of 6 completed\\n\"\n ],\n \"name\": \"stdout\"\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
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AAPLGEGOOGIBMJPMMSFT
Date
2012-01-0350.99490713.639091331.462585139.93400627.86434222.020796
2012-01-0451.26897013.787668332.892242139.36314428.04085022.539021
2012-01-0551.83816913.780239328.274536138.70220928.62653922.769344
2012-01-0652.38005413.854527323.796326137.10977228.36978723.123066
2012-01-0952.29697014.010525310.067780136.39622528.32166722.818701
.....................
2018-08-23211.06111111.8745581205.380005135.556732109.777924105.249374
2018-08-24211.71733111.8366801220.650024136.181503109.730087106.071335
2018-08-27213.46073912.0923531241.819946136.787628111.672455107.245552
2018-08-28215.18457012.0828851231.150024136.694382111.127060107.891388
2018-08-29218.39717112.2817411249.300049137.580231110.763466109.613571
\\n\",\n \"

1676 rows × 6 columns

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\"\n ],\n \"text/plain\": [\n \" AAPL GE ... JPM MSFT\\n\",\n \"Date ... \\n\",\n \"2012-01-03 50.994907 13.639091 ... 27.864342 22.020796\\n\",\n \"2012-01-04 51.268970 13.787668 ... 28.040850 22.539021\\n\",\n \"2012-01-05 51.838169 13.780239 ... 28.626539 22.769344\\n\",\n \"2012-01-06 52.380054 13.854527 ... 28.369787 23.123066\\n\",\n \"2012-01-09 52.296970 14.010525 ... 28.321667 22.818701\\n\",\n \"... ... ... ... ... ...\\n\",\n \"2018-08-23 211.061111 11.874558 ... 109.777924 105.249374\\n\",\n \"2018-08-24 211.717331 11.836680 ... 109.730087 106.071335\\n\",\n \"2018-08-27 213.460739 12.092353 ... 111.672455 107.245552\\n\",\n \"2018-08-28 215.184570 12.082885 ... 111.127060 107.891388\\n\",\n \"2018-08-29 218.397171 12.281741 ... 110.763466 109.613571\\n\",\n \"\\n\",\n \"[1676 rows x 6 columns]\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 9\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"2A201lhUFc_3\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 225\n },\n \"outputId\": \"4cb7bc13-c5a9-4d51-b050-200148706a16\"\n },\n \"source\": [\n \"retscomp = dfcomp.pct_change()\\n\",\n \"\\n\",\n \"corr = retscomp.corr()\\n\",\n \"corr\"\n ],\n \"execution_count\": 10,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
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AAPLGEGOOGIBMJPMMSFT
AAPL1.0000000.2319330.3582480.2844890.3028230.361851
GE0.2319331.0000000.3110840.4131620.4697520.313180
GOOG0.3582480.3110841.0000000.3387290.3652400.486786
IBM0.2844890.4131620.3387291.0000000.4286980.408897
JPM0.3028230.4697520.3652400.4286981.0000000.423728
MSFT0.3618510.3131800.4867860.4088970.4237281.000000
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\"\n ],\n \"text/plain\": [\n \" AAPL GE GOOG IBM JPM MSFT\\n\",\n \"AAPL 1.000000 0.231933 0.358248 0.284489 0.302823 0.361851\\n\",\n \"GE 0.231933 1.000000 0.311084 0.413162 0.469752 0.313180\\n\",\n \"GOOG 0.358248 0.311084 1.000000 0.338729 0.365240 0.486786\\n\",\n \"IBM 0.284489 0.413162 0.338729 1.000000 0.428698 0.408897\\n\",\n \"JPM 0.302823 0.469752 0.365240 0.428698 1.000000 0.423728\\n\",\n \"MSFT 0.361851 0.313180 0.486786 0.408897 0.423728 1.000000\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 10\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"emt_IgD8Fges\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"import math\\n\",\n \"import numpy as np\\n\",\n \"from sklearn import preprocessing\\n\",\n \"dfreg = df.loc[:,['Adj Close','Volume']]\\n\",\n \"dfreg['HL_PCT'] = (df['High']-df['Low']) / df['Close'] * 100.0\\n\",\n \"dfreg['PCT_change'] = (df['Close']-df['Open']) / df['Open'] * 100.0\\n\",\n \"dfreg.fillna(value=-99999, inplace=True)\\n\",\n \"forecast_out = int(math.ceil(0.01 * len(dfreg)))\\n\",\n \"forecast_col = 'Adj Close'\\n\",\n \"dfreg['label'] = dfreg[forecast_col].shift(-forecast_out)\\n\",\n \"X = np.array(dfreg.drop(['label'], 1))\\n\",\n \"X = preprocessing.scale(X)\\n\",\n \"X_lately = X[-forecast_out:]\\n\",\n \"X = X[:-forecast_out]\\n\",\n \"y = np.array(dfreg['label'])\\n\",\n \"y = y[:-forecast_out]\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"-WeqmxZMFpnr\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.linear_model import Lasso\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"3lUYeEpQF3Eb\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"clflasso = Lasso()\\n\",\n \"clflasso.fit(X_train, y_train)\\n\",\n \"confidencelasso = clflasso.score(X_test,y_test)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"dlSop_ESF61C\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"outputId\": \"d4fcadeb-8b01-4c8e-f841-2c7f56898d6b\"\n },\n \"source\": [\n \"print(\\\"The Accuracy of our Model is %r\\\" %confidencelasso)\"\n ],\n \"execution_count\": 16,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"The Accuracy of our Model is 0.9806857462238061\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"WQEzaQ8NGH71\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 101\n },\n \"outputId\": \"5db69305-95ff-4dfa-e1d5-4e0b5d023aee\"\n },\n \"source\": [\n \"forecast_set =clflasso.predict(X_lately)\\n\",\n \"dfreg['Forecast'] = np.nan\\n\",\n \"forecast_set\"\n ],\n \"execution_count\": 18,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"array([111.34130043, 111.5623028 , 110.72433671, 109.66539761,\\n\",\n \" 107.97107304, 108.67090898, 107.79611405, 108.78140282,\\n\",\n \" 108.78140282, 108.64327267, 109.28785802, 108.96556167,\\n\",\n \" 108.74456664, 108.6985306 , 110.56779948, 110.04292987,\\n\",\n \" 109.69301924])\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 18\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ToRQqQbgGMRX\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 448\n },\n \"outputId\": \"fa4b99e6-83da-45ed-8867-4dff5cd6afbc\"\n },\n \"source\": [\n \"next_unix = datetime.datetime.now() + datetime.timedelta(days=1)\\n\",\n \"\\n\",\n \"for i in forecast_set:\\n\",\n \" next_date = next_unix\\n\",\n \" next_unix += datetime.timedelta(days=1)\\n\",\n \" dfreg.loc[next_date] = [np.nan for _ in range(len(dfreg.columns)-1)]+[i]\\n\",\n \"dfreg['Adj Close'].tail(500).plot()\\n\",\n \"dfreg['Forecast'].tail(500).plot()\\n\",\n \"plt.legend(loc=4)\\n\",\n \"plt.xlabel('Date')\\n\",\n \"plt.ylabel('Price')\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 19,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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p8Q0r6a90wx3dRh0A/djf2z74V1zwjNDVBUYia0VcyB3m7sz10WsPww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\\nEgEU+CIiIhHg/wCRSwDydCeP4QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ahKjg6KyGYnd\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n }\n ]\n}\n" }, { "path": "code/artificial_intelligence/src/linear_regression/README.md", "content": "# Linear Regression\n\nThe **linear regression** is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables.\n\nIn order to provide a basic understanding of linear regression, we start with the most basic version of linear regression, i.e. Simple linear regression.\n\nSimple Linear Regression\n------------------------\n\nSimple linear regression is an approach for predicting a response using a single feature.\n\nIt is assumed that the two variables are linearly related. Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x).\n\nMultiple linear regression\n--------------------------\n\nMultiple linear regression attempts to model the relationship between two or more features and a response by fitting a linear equation to observed data.\n\nClearly, it is nothing but an extension of Simple linear regression.\n\nApplications\n------------\n\n1. Trend lines: A trend line represents the variation in some quantitative data with passage of time (like GDP, oil prices, etc.). These trends usually follow a linear relationship. Hence, linear regression can be applied to predict future values. However, this method suffers from a lack of scientific validity in cases where other potential changes can affect the data.\n\n2. Economics: Linear regression is the predominant empirical tool in economics. For example, it is used to predict consumption spending, fixed investment spending, inventory investment, purchases of a country’s exports, spending on imports, the demand to hold liquid assets, labor demand, and labor supply.\n\n3. Finance: Capital price asset model uses linear regression to analyze and quantify the systematic risks of an investment.\n\n4. Biology: Linear regression is used to model causal relationships between parameters in biological systems.\n\n![alt text](https://statistics.laerd.com/statistical-guides/img/pearson-4-small.png \"Linear Regression\")\n\nThis article is obtained as reference from [Geeksforgeeks](http://www.geeksforgeeks.org/linear-regression-python-implementation/)\n" }, { "path": "code/artificial_intelligence/src/linear_regression/Salary_Data.csv", "content": "YearsExperience,Salary\n1.1,39343.00\n1.3,46205.00\n1.5,37731.00\n2.0,43525.00\n2.2,39891.00\n2.9,56642.00\n3.0,60150.00\n3.2,54445.00\n3.2,64445.00\n3.7,57189.00\n3.9,63218.00\n4.0,55794.00\n4.0,56957.00\n4.1,57081.00\n4.5,61111.00\n4.9,67938.00\n5.1,66029.00\n5.3,83088.00\n5.9,81363.00\n6.0,93940.00\n6.8,91738.00\n7.1,98273.00\n7.9,101302.00\n8.2,113812.00\n8.7,109431.00\n9.0,105582.00\n9.5,116969.00\n9.6,112635.00\n10.3,122391.00\n10.5,121872.00\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression.java", "content": "// author @Yatharth Shah\n\npublic class LinearRegression { \n\n public static void main(String[] args) { \n int MAXN = 1000;\n int n = 0;\n double[] x = new double[MAXN];\n double[] y = new double[MAXN];\n\n // first pass: read in data, compute xbar and ybar\n double sumx = 0.0, sumy = 0.0, sumx2 = 0.0;\n while(!StdIn.isEmpty()) {\n x[n] = StdIn.readDouble();\n y[n] = StdIn.readDouble();\n sumx += x[n];\n sumx2 += x[n] * x[n];\n sumy += y[n];\n n++;\n }\n double xbar = sumx / n;\n double ybar = sumy / n;\n\n // second pass: compute summary statistics\n double xxbar = 0.0, yybar = 0.0, xybar = 0.0;\n for (int i = 0; i < n; i++) {\n xxbar += (x[i] - xbar) * (x[i] - xbar);\n yybar += (y[i] - ybar) * (y[i] - ybar);\n xybar += (x[i] - xbar) * (y[i] - ybar);\n }\n double beta1 = xybar / xxbar;\n double beta0 = ybar - beta1 * xbar;\n\n // print results\n StdOut.println(\"y = \" + beta1 + \" * x + \" + beta0);\n\n // analyze results\n int df = n - 2;\n double rss = 0.0; // residual sum of squares\n double ssr = 0.0; // regression sum of squares\n for (int i = 0; i < n; i++) {\n double fit = beta1*x[i] + beta0;\n rss += (fit - y[i]) * (fit - y[i]);\n ssr += (fit - ybar) * (fit - ybar);\n }\n double R2 = ssr / yybar;\n double svar = rss / df;\n double svar1 = svar / xxbar;\n double svar0 = svar/n + xbar*xbar*svar1;\n StdOut.println(\"R^2 = \" + R2);\n StdOut.println(\"std error of beta_1 = \" + Math.sqrt(svar1));\n StdOut.println(\"std error of beta_0 = \" + Math.sqrt(svar0));\n svar0 = svar * sumx2 / (n * xxbar);\n StdOut.println(\"std error of beta_0 = \" + Math.sqrt(svar0));\n\n StdOut.println(\"SSTO = \" + yybar);\n StdOut.println(\"SSE = \" + rss);\n StdOut.println(\"SSR = \" + ssr);\n }\n}\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression.js", "content": "/**\n* Author: Daniel Hernández (https://github.com/DHDaniel)\n\n* Description: Multivariate linear regression that uses the normal equation\n* as an optimizer. The normal equation is fine to use\n* when n, the number of features being worked with, is\n* less than 10,000. Beyond that, it is best to use\n* other methods like gradient descent.\n\n* Requirements:\n* - mathjs (install using npm)\n*/\n\n// this is installed using npm: `npm install mathjs`\nconst math = require(\"mathjs\");\n\n/**\n * Solves the normal equation and returns the optimum thetas for\n * the model hypothesis.\n * @param {mathjs.matrix} X - a matrix of m x n (rows x columns), where m is the number of records and n is the number of features. It is common practice to add a leading 1 to each row m, in order to account for the y-intercept in regression.\n * @param {mathjs.matrix} y - a vector of m x 1 (rows x columns), where m is the number of records. Contains the labeled values for each corresponding X row.\n * @returns {mathjs.matrix} A n x 1 matrix containing the optimal thetas.\n */\nconst normalEqn = function normalEqn(X, y) {\n // computing the equation theta = (X^T * X)^-1 * X^T * y\n var thetas = math.multiply(math.transpose(X), X);\n thetas = math.multiply(math.inv(thetas), math.transpose(X));\n thetas = math.multiply(thetas, y);\n // returning a vector containing thetas for hypothesis\n return thetas;\n};\n\n/**\n * Trains and returns a function that serves as a model for predicting values y based on an input X\n * @param {mathjs.matrix} X - a matrix of m x n (rows x columns), where m is the number of records and n is the number of features. It is common practice to add a leading 1 to each row m, in order to account for the y-intercept in regression.\n * @param {mathjs.matrix} y - a vector of m x 1 (rows x columns), where m is the number of records. Contains the labeled values for each corresponding X row.\n * @returns {function} A function that accepts a matrix X, and returns predictions for each row.\n */\nconst train = function train(X, y) {\n // getting optimal thetas using normal equation\n var thetas = normalEqn(X, y);\n // creating a model that accepts\n const model = function(X) {\n // create predictions by multiplying theta^T * X, creating a model that looks like (theta_1 * x_1) + (theta_2 * x_2) + (theta_3 * x_3) etc.\n return math.multiply(math.transpose(thetas), X);\n };\n return model;\n};\n\n/* TESTS */\n\n// test values for X and y\nvar Xmatrix = math.matrix([[2, 1, 3], [7, 1, 9], [1, 8, 1], [3, 7, 4]]);\nvar ylabels = math.matrix([[2], [5], [5], [6]]);\n\n// should show thetas (in the _data part of object) to be 0.008385744234748138, 0.5681341719077577, 0.4863731656184376\nconsole.log(normalEqn(Xmatrix, ylabels));\n\n// test values #2\nXmatrix = math.matrix([[1], [2], [3], [4]]);\nylabels = math.matrix([[1], [2], [3], [4]]);\n\n// should show theta of 1 (which forms a perfectly diagonal line if plotted)\nconsole.log(normalEqn(Xmatrix, ylabels));\n\n// test values #3\nXmatrix = math.matrix([[1, 5], [1, 2], [1, 4], [1, 5]]);\nylabels = math.matrix([[1], [6], [4], [2]]);\n\n// should show thetas of 9.25 and -1.5\nconsole.log(normalEqn(Xmatrix, ylabels));\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\n# A basic implementation of linear regression with one variable\n# Part of Cosmos by OpenGenus Foundation\ndef estimate_coef(x, y):\n # number of observations/points\n n = np.size(x)\n\n # mean of x and y vector\n m_x, m_y = np.mean(x), np.mean(y)\n\n # calculating cross-deviation and deviation about x\n SS_xy = np.sum(y * x - n * m_y * m_x)\n SS_xx = np.sum(x * x - n * m_x * m_x)\n\n # calculating regression coefficients\n b_1 = SS_xy / SS_xx\n b_0 = m_y - b_1 * m_x\n\n return (b_0, b_1)\n\n\ndef plot_regression_line(x, y, b):\n # plotting the actual points as scatter plot\n plt.scatter(x, y, color=\"m\", marker=\"o\", s=30)\n\n # predicted response vector\n y_pred = b[0] + b[1] * x\n\n # plotting the regression line\n plt.plot(x, y_pred, color=\"r\")\n\n # putting labels\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n\n # function to show plot\n plt.show()\n\n\ndef main():\n # observations\n x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])\n\n # estimating coefficients\n b = estimate_coef(x, y)\n print(\n \"Estimated coefficients are:\\nb_0 = {} \\\n \\nb_1 = {}\".format(\n b[0], b[1]\n )\n )\n\n # plotting regression line\n plot_regression_line(x, y, b)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression.swift", "content": "import Foundation\n\nlet cosmos_size: [Double] = [1, 2, 10, 3, 20, 11]\nlet cosmos_contributors: [Double] = [500, 1000, 5000, 1500, 10000, 5500]\n\nfunc average(_ input: [Double]) -> Double {\n return input.reduce(0, +) / Double(input.count)\n}\n\nfunc multiply(_ a: [Double], _ b: [Double]) -> [Double] {\n return zip(a, b).map(*)\n}\n\nfunc linearRegression(_ xs: [Double], _ ys: [Double]) -> (Double) -> Double {\n let sum1 = average(multiply(xs, ys)) - average(xs) * average(ys)\n let sum2 = average(multiply(xs, xs)) - pow(average(xs), 2)\n let slope = sum1 / sum2\n let intercept = average(ys) - slope * average(xs)\n return { x in intercept + slope * x }\n}\n\nlet result = linearRegression(cosmos_size, cosmos_contributors)(4)\n\nprint(\"Perdicted £\\(Int(result))\")\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression_scikit_learn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing the data set\\n\",\n \"dataset=pd.read_csv('Salary_Data.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
YearsExperienceSalary
count30.00000030.000000
mean5.31333376003.000000
std2.83788827414.429785
min1.10000037731.000000
25%3.20000056720.750000
50%4.70000065237.000000
75%7.700000100544.750000
max10.500000122391.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" YearsExperience Salary\\n\",\n \"count 30.000000 30.000000\\n\",\n \"mean 5.313333 76003.000000\\n\",\n \"std 2.837888 27414.429785\\n\",\n \"min 1.100000 37731.000000\\n\",\n \"25% 3.200000 56720.750000\\n\",\n \"50% 4.700000 65237.000000\\n\",\n \"75% 7.700000 100544.750000\\n\",\n \"max 10.500000 122391.000000\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X=dataset.iloc[:,:-1].values\\n\",\n \"y=dataset.iloc[:,1].values\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1.1],\\n\",\n \" [ 1.3],\\n\",\n \" [ 1.5],\\n\",\n \" [ 2. ],\\n\",\n \" [ 2.2],\\n\",\n \" [ 2.9],\\n\",\n \" [ 3. ],\\n\",\n \" [ 3.2],\\n\",\n \" [ 3.2],\\n\",\n \" [ 3.7],\\n\",\n \" [ 3.9],\\n\",\n \" [ 4. ],\\n\",\n \" [ 4. ],\\n\",\n \" [ 4.1],\\n\",\n \" [ 4.5],\\n\",\n \" [ 4.9],\\n\",\n \" [ 5.1],\\n\",\n \" [ 5.3],\\n\",\n \" [ 5.9],\\n\",\n \" [ 6. ],\\n\",\n \" [ 6.8],\\n\",\n \" [ 7.1],\\n\",\n \" [ 7.9],\\n\",\n \" [ 8.2],\\n\",\n \" [ 8.7],\\n\",\n \" [ 9. ],\\n\",\n \" [ 9.5],\\n\",\n \" [ 9.6],\\n\",\n \" [10.3],\\n\",\n \" [10.5]])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 39343., 46205., 37731., 43525., 39891., 56642., 60150.,\\n\",\n \" 54445., 64445., 57189., 63218., 55794., 56957., 57081.,\\n\",\n \" 61111., 67938., 66029., 83088., 81363., 93940., 91738.,\\n\",\n \" 98273., 101302., 113812., 109431., 105582., 116969., 112635.,\\n\",\n \" 122391., 121872.])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=1/3,random_state=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\\n\",\n \" normalize=False)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"regressor=LinearRegression()\\n\",\n \"regressor.fit(X_train,y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_pred=regressor.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 40835.10590871, 123079.39940819, 65134.55626083, 63265.36777221,\\n\",\n \" 115602.64545369, 108125.8914992 , 116537.23969801, 64199.96201652,\\n\",\n \" 76349.68719258, 100649.1375447 ])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 37731., 122391., 57081., 63218., 116969., 109431., 112635.,\\n\",\n \" 55794., 83088., 101302.])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'Salary')\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Visualsing the training set results\\n\",\n \"plt.scatter(X_train,y_train,color='red')\\n\",\n \"plt.plot(X_train,regressor.predict(X_train),)\\n\",\n \"plt.title('Salary vs Experience(Train set)')\\n\",\n \"plt.xlabel('Experience in years')\\n\",\n \"plt.ylabel('Salary')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Visualising the test set results\\n\",\n \"plt.scatter(X_test,y_test,color='red')\\n\",\n \"plt.plot(X_train,regressor.predict(X_train),color='blue')\\n\",\n \"plt.title('Salary vs experience(Test set)')\\n\",\n \"plt.xlabel('Experience in years')\\n\",\n \"plt.ylabel('Salary')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/linear_regression/linear_regression_scikit_learn.py", "content": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n\n#importing the data set\ndataset=pd.read_csv('Salary_Data.csv')\n\ndataset.describe()\t\n\nX=dataset.iloc[:,:-1].values\ny=dataset.iloc[:,1].values\n \n#Splitting the dataset into train set and test set\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_spit(X,y,test_size=1/3,random_state=0)\n\n#building the model\nfrom sklearn.linear_model import LinearRegression\nregressor=LinearRegression()\nregressor.fit(X_train,y_train)\n\n#Predicting the results\ny_pred=regressor.predict(X_test)\n\n\n#Visualsing the training set results\nplt.scatter(X_train,y_train,color='red')\nplt.plot(X_train,regressor.predict(X_train),)\nplt.title('Salary vs Experience(Train set)')\nplt.xlabel('Experience in years')\nplt.ylabel('Salary')\n\n#Visualising the test set results\nplt.scatter(X_test,y_test,color='red')\nplt.plot(X_train,regressor.predict(X_train),color='blue')\nplt.title('Salary vs experience(Test set)')\nplt.xlabel('Experience in years')\nplt.ylabel('Salary')\nplt.show()\n" }, { "path": "code/artificial_intelligence/src/logistic_regression/README.md", "content": "# Logistical Regression\n\nThe **logistical regression** algorithm build upon linear regression and is basically a supervised classification algorithm. In a classification problem, the target variable (or output), Y, can take only discrete values for given set of features (or inputs), X.\nWe can also say that the target variable is categorical. Based on the number of categories, Logistic regression can be classified as:\n\n1. binomial: target variable can have only 2 possible types: 0 or 1 which may represent \"win\" vs \"loss\", \"pass\" vs \"fail\", \"dead\" vs \"alive\", etc.\n2. multinomial: target variable can have 3 or more possible types which are not ordered(i.e. types have no quantitative significance) like “disease A” vs “disease B” vs “disease C”.\n3. ordinal: it deals with target variables with ordered categories. For example, a test score can be categorized as:“very poor”, “poor”, “good”, “very good”. Here, each category can be given a score like 0, 1, 2, 3.\n\nHere are some points about Logistic regression to ponder upon:\n--------------------------------------------------------------\n\n- Does NOT assume a linear relationship between the dependent variable and the independent variables, but it does assume linear relationship between the logit of the explanatory variables and the response.\n- Independent variables can be even the power terms or some other nonlinear transformations of the original independent variables.\n- The dependent variable does NOT need to be normally distributed, but it typically assumes a distribution from an exponential family (e.g. binomial, Poisson, multinomial, normal,…); binary logistic regression assume binomial distribution of the response.\n- The homogeneity of variance does NOT need to be satisfied.\n- Errors need to be independent but NOT normally distributed.\n- It uses maximum likelihood estimation (MLE) rather than ordinary least squares (OLS) to estimate the parameters, and thus relies on large-sample approximations.\n\n\nThis article is obtained as a reference from [Geeksforgeeks](http://www.geeksforgeeks.org/understanding-logistic-regression/)\n" }, { "path": "code/artificial_intelligence/src/logistic_regression/logistic_regression.py", "content": "# Logistic regression implemented from Scratch in Python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef sigmoid(scores):\n return 1 / (1 + np.exp(-scores))\n\n\ndef log_likelihood(features, target, weights):\n scores = np.dot(features, weights)\n ll = np.sum(target * scores - np.log(1 + np.exp(scores)))\n return ll\n\n\ndef logistic_regression(\n features, target, num_steps, learning_rate, add_intercept=False\n):\n if add_intercept:\n intercept = np.ones((features.shape[0], 1))\n features = np.hstack((intercept, features))\n\n weights = np.zeros(features.shape[1])\n\n for step in range(num_steps):\n scores = np.dot(features, weights)\n predictions = sigmoid(scores)\n\n # Update weights with gradient\n output_error_signal = target - predictions\n gradient = np.dot(features.T, output_error_signal)\n weights += learning_rate * gradient\n\n # Print log-likelihood every so often\n if step % 10000 == 0:\n print(log_likelihood(features, target, weights))\n\n return weights\n\n\nnp.random.seed(12)\nnum_observations = 5000\n\nx1 = np.random.multivariate_normal([0, 0], [[1, 0.75], [0.75, 1]], num_observations)\nx2 = np.random.multivariate_normal([1, 4], [[1, 0.75], [0.75, 1]], num_observations)\n\nsimulated_separableish_features = np.vstack((x1, x2)).astype(np.float32)\nsimulated_labels = np.hstack((np.zeros(num_observations), np.ones(num_observations)))\n\nplt.figure(figsize=(12, 8))\nplt.scatter(\n simulated_separableish_features[:, 0],\n simulated_separableish_features[:, 1],\n c=simulated_labels,\n alpha=0.4,\n)\n\nplt.show()\n\n# Running the model\n\nweights = logistic_regression(\n simulated_separableish_features,\n simulated_labels,\n num_steps=300000,\n learning_rate=5e-5,\n add_intercept=True,\n)\n\nprint(weights)\n" }, { "path": "code/artificial_intelligence/src/minimax/README.md", "content": "# Minimax Algorithm\n\nAccording to wikipedia , Minimax is a \"decision rule used in decision theory, game theory, statistics and philosophy for\nminimizing the possible loss for a worst case (maximum loss) scenario.\" Put forward in simpler words, it is an algorithm \nwhich is generally used to analyze games mathematically and to find optimal moves for a player so that if the player takes\nthose moves , he/she is likely to win the game , given that the other player also plays optimally(does not make mistakes).\nIt is used widely in 2 player turn taking games such as tic-tac-toe , chess , etc.\n\n![Minimax tree](http://mnemstudio.org/ai/game/images/minimax_move_tree1.gif)\n\n# Further Reading \n\nTo know more about this algorithm , follow these links :-\n\n1. [Minimax - Wikipedia , the free encyclopedia](https://en.wikipedia.org/wiki/Minimax)\n2. [Minimax Algoritm in Game Theory - GeeksforGeeks](https://www.geeksforgeeks.org/minimax-algorithm-in-game-theory-set-1-introduction/)\n3. [Minimax - Brilliant.org](https://brilliant.org/wiki/minimax/)\n4. [Minimax - Umichigan Teaching](https://web.eecs.umich.edu/~akamil/teaching/sp03/minimax.pdf)\n" }, { "path": "code/artificial_intelligence/src/minimax/minimax.py", "content": "import operator\nimport time\n\n\nclass IllegalMoveException(Exception):\n pass\n\n\ndef new_copy(in_list):\n if isinstance(in_list, list):\n return list(map(new_copy, in_list))\n else:\n return in_list\n\n\nclass GameState(object):\n def __init__(self, board, turn, move_count=0):\n self.board = board\n self.turn = turn\n self.move_count = move_count\n\n assert len(board) == len(board[0])\n self.n = len(board)\n\n # Once a move is made, these will record the x and y index of the move most recently made on the board,\n # as well as whose turn it was when the move was made.\n self.x = None\n self.y = None\n\n def switch_turn(self):\n self.turn = \"X\" if self.turn == \"O\" else \"O\"\n\n def move(self, x, y):\n if self.board[y][x] != None:\n raise IllegalMoveException(\n \"there is already a symbol placed at this location (x = {}, y = {})\".format(\n x, y\n )\n )\n # record move (for indication of most recent move when printing board)\n self.x = x\n self.y = y\n\n # make move and switch turn\n self.board[y][x] = self.turn\n self.switch_turn()\n\n # update move_count\n self.move_count += 1\n\n def __str__(self):\n ret = \"\\n\"\n for y in range(self.n):\n for x in range(self.n):\n if y == self.y and x == self.x:\n ret += \" ({}) |\".format(self.board[y][x])\n else:\n ret += \" {} |\".format(self.board[y][x] or \" \")\n ret = ret[:-1] + \"\\n\" + \"-\" * self.n * 6 + \"\\n\"\n\n result = self.check_winner()\n if result in (\"X\", \"O\"):\n ret += \"\\nWinner: {}\".format(result)\n elif result == \"draw\":\n ret += \"\\nThe game is a draw.\"\n else: # no winner yet\n ret += \"\\nTurn to move: {}\".format(self.turn)\n\n return ret\n\n def check_winner(self):\n # check row\n for y in range(self.n):\n win = self.board[y][0]\n if win == None:\n continue\n for x in range(self.n):\n if self.board[y][x] != win:\n break\n else:\n return win\n\n # check column\n for x in range(self.n):\n win = self.board[0][x]\n if win == None:\n continue\n for y in range(self.n):\n if self.board[y][x] != win:\n break\n else:\n return win\n\n # check forward diagonal\n win = self.board[0][0]\n if win != None:\n for z in range(1, self.n):\n if self.board[z][z] != win:\n break\n else:\n return win\n\n # check backward diagonal\n win = self.board[0][self.n - 1]\n if win != None:\n for y in range(1, self.n):\n if self.board[y][self.n - y - 1] != win:\n break\n else:\n return win\n\n if self.move_count == self.n ** 2:\n return \"draw\"\n\n return None\n\n def next_states_and_moves(self):\n \"\"\"\n\t\treturn a list of tuples, where each tuple contains:\n\t\t\t- a state immediately reachable from the current game state\n\t\t\t- another tuple with the x and y coordinates of the move required to create that state\n\t\t\"\"\"\n states = []\n for y in range(self.n):\n for x in range(self.n):\n if self.board[y][x] == None:\n new_state = GameState(\n new_copy(self.board), self.turn, move_count=self.move_count\n )\n new_state.move(x, y)\n states.append((new_state, (x, y)))\n return states\n\n def evaluate(self, symbol):\n \"\"\"\n\t\treturn an estimate of how desirable of a position this position is (for the player with the provided symbol)\n\t\tfor each player, this heuristic looks at each row, column, and diagonal; if the row/column/diagonal is not obstructed\n\t\tby pieces belonging to the other player, the player's value is increased according to how many pieces they have on that\n\t\trow/column/diagonal\n\t\tthe overall value is the player's value minus the opponent's value\n\t\t\"\"\"\n other_symbol = \"X\" if symbol == \"O\" else \"O\"\n\n player_total = 0\n opponent_total = 0\n\n for total, own, other in (\n (player_total, symbol, other_symbol),\n (opponent_total, other_symbol, symbol),\n ):\n\n # rows\n for y in range(self.n):\n benefit = 0\n for x in range(self.n):\n if self.board[y][x] == None:\n continue\n elif self.board[y][x] == own:\n benefit += 1\n elif self.board[y][x] == other: # this row is obstructed\n break\n else:\n total += benefit # row is unobstructed\n\n # ccolumns\n for x in range(self.n):\n benefit = 0\n for y in range(self.n):\n if self.board[y][x] == None:\n continue\n elif self.board[y][x] == own:\n benefit += 1\n elif self.board[y][x] == other: # this column is obstructed\n break\n else:\n total += benefit # column is unobstructed\n\n # forward diagonal\n benefit = 0\n for z in range(1, self.n):\n if self.board[z][z] == None:\n continue\n elif self.board[z][z] == own:\n benefit += 1\n elif self.board[z][z] == other: # this diagonal is obstructed\n break\n else:\n total += benefit # diagonal is unobstructed\n\n # backward diagonal\n benefit = 0\n for y in range(1, self.n):\n if self.board[y][self.n - y - 1] == None:\n continue\n elif self.board[y][self.n - y - 1] == own:\n benefit += 1\n elif (\n self.board[y][self.n - y - 1] == other\n ): # this diagonal is obstructed\n break\n else:\n total += benefit # diagonal is unobstructed\n\n return player_total - opponent_total\n\n\nclass GameDriver(object):\n def __init__(self):\n symbol = input(\"Choose your symbol (X/O)\\n\")\n while symbol != \"X\" and symbol != \"O\":\n symbol = input(\"Could not understand your answer. Please enter X or O.\\n\")\n\n self.player_symbol = symbol\n\n n = input(\n \"\\nChoose the size of your board (enter a positive integer, this will be the height and width of your board\\n\"\n )\n while not n.isdigit() or int(n) < 1:\n n = input(\"Please enter a positive integer\\n\")\n n = int(n)\n\n first = input(\"\\nWould you like to start? (Y/N)\\n\")\n while first != \"Y\" and first != \"N\":\n first = input(\"Could not understand your answer. Please enter Y or N.\\n\")\n\n print(\n \"\\nThank you. To enter your moves, enter the index of your move on the board, with indices defined as follows:\\n\"\n )\n\n col_width = n\n indices_str = \"\"\n\n all_n = range(1, n ** 2 + 1)\n rows = [all_n[i : i + n] for i in range(0, n ** 2, n)]\n\n for row in rows:\n row = [str(i) for i in row]\n indices_str += \"|\".join(word.center(col_width) for word in row) + \"\\n\"\n indices_str += \"-\" * (col_width + 1) * n + \"\\n\"\n indices_str = indices_str[: -1 * (col_width + 1) * n - 1]\n\n print(indices_str)\n\n input(\"\\nPress Enter to continue!\\n\")\n\n if first == \"Y\":\n self.game = GameState(\n [[None for _ in range(n)] for _ in range(n)], self.player_symbol\n )\n else:\n self.game = GameState(\n [[None for _ in range(n)] for _ in range(n)],\n \"X\" if self.player_symbol == \"O\" else \"O\",\n )\n\n self.start()\n\n def player_move(self):\n move = input(\"Enter the index of your move.\\n\")\n while True:\n while not move.isdigit() or int(move) > self.game.n ** 2 or int(move) < 1:\n move = input(\n \"Please enter an integer greater than or equal to 1 and less than or equal to {}.\\n\".format(\n self.game.n ** 2\n )\n )\n\n move = (\n int(move) - 1\n ) # easier to calculate x and y this way, since arrays representing game board are 0-indexed\n y = int(move / self.game.n)\n x = move % self.game.n\n\n if self.game.board[y][x] != None:\n print(\"There is already a piece in that location on the board.\")\n move = \"\"\n else:\n break\n self.game.move(x, y)\n\n def computer_move(self):\n \"\"\"\n\t\tpick first possible move from list of states\n\t\t\"\"\"\n\n delay = 0.5\n print(\"Thinking...\")\n time.sleep(delay)\n print(\".\")\n time.sleep(delay)\n print(\".\")\n time.sleep(delay)\n print(\".\\n\")\n time.sleep(delay)\n\n # TODO: how deep to look? maybe base this on a difficulty parameter specified by user?\n move = self.minimax(self.game, 6, True, float(\"-inf\"), float(\"+inf\"))[1]\n self.game.move(*move)\n\n def minimax(self, board, level, comp_turn, alpha, beta):\n winner = board.check_winner()\n if winner == \"draw\":\n return 0, None\n elif winner == self.player_symbol:\n return -1, None\n elif winner != None:\n return 1, None\n\n if level == 0:\n # TODO: normalize this between -1 and 1. Also come up with a better way to identify player/computer symbols\n return -1 * board.evaluate(self.player_symbol), None\n\n states_and_moves = board.next_states_and_moves()\n best_move = None\n if comp_turn:\n for state, move in states_and_moves:\n score = self.minimax(state, level - 1, False, alpha, beta)[0]\n if score > alpha:\n alpha = score\n best_move = move\n if alpha >= beta:\n break\n return alpha, best_move\n else:\n for state, move in states_and_moves:\n score = self.minimax(state, level - 1, True, alpha, beta)[0]\n if score < beta:\n beta = score\n best_move = move\n if alpha >= beta:\n break\n return beta, best_move\n\n def start(self):\n while True:\n if self.game.turn == self.player_symbol:\n self.player_move()\n else:\n self.computer_move()\n print(self.game)\n if self.game.check_winner():\n break\n\n\nif __name__ == \"__main__\":\n GameDriver()\n" }, { "path": "code/artificial_intelligence/src/missionaries_and_cannibals.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"Missionaries_and_cannibals.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/drive/18FiKma_HxV25I73fzJk6HItVVk8Yyfqu\n\"\"\"\n\ndef get_boat_at_left_new_states(game_state): #generating further steps to be taken.\n new_states = [] #A new list to generate all the future states. \n for missionary in range(game_state[0] + 1): #traversing in loop for m+1 times and c+1 times\n for cannibal in range(game_state[1] + 1): \n if missionary + cannibal < 1 or missionary + cannibal > 2: #at max, only two or min. 1 can travel on a boat. rest all cases to be eliminated.\n continue\n new_state = (game_state[0] - missionary, game_state[1] - cannibal,'right') #If we have 1 or 2 people on the boat, we will consider them. \n if 0 < new_state[0] < new_state[1]: #if Cannibals outnumber Missionaries on the left side, we skip.\n print(\"Future State: \" + str(new_state) + \": Missionaries Killed\")\n continue\n if 0 < 3 - new_state[0] < 3 - new_state[1]: #if Cannibals outnumber Missionaries on the right side, we skip. \n print(\"Future State: \" + str(new_state) + \": Missionaries Killed\")\n continue\n new_states.append(new_state) #else, we can consider them a valid case and append it the list of future states. \n return new_states\n\ndef get_boat_at_right_new_states(game_state): #generating further steps to be taken.\n new_states = []\n for missionary in range(3 - game_state[0] + 1):\n for cannibal in range(3 - game_state[1] + 1):\n if missionary + cannibal < 1 or missionary + cannibal > 2:\n continue\n new_state = (game_state[0] + missionary, game_state[1] + cannibal,'left')\n if 0 < new_state[0] < new_state[1]:\n print(\"Future State: \" + str(new_state) + \": Missionaries Killed\")\n continue\n if 0 < 3 - new_state[0] < 3 - new_state[1]:\n print(\"Future State: \" + str(new_state) + \": Missionaries Killed\") \n continue\n new_states.append(new_state)\n return new_states\n\ndef get_future_states(game_state): #a function to generate future states. \n if game_state[2] == 'left': \n return get_boat_at_left_new_states(game_state)\n else:\n return get_boat_at_right_new_states(game_state)\n\ndef bfs():\n state_stack = list() #maintaining a stack for the states.\n visited_states = set() #maintaining a set for visited states. (allowing only unique states to consider)\n initial_state = (3, 3, 'left') #initialising our initial state. \n visited_states.add(initial_state) #visiting the initial state. \n states = [initial_state] #maintaining a list of states in consideration at a time.\n all_states = [states] #maintaining a list of all the states taken in consideration throughout the game. \n print(\"Initial state: \" + str(initial_state)) #initialising our initial state. \n\n win = False \n while not win: #looping over until we win\n print(\"States: \" + str(states)) #printing the current states in consideration.\n new_states = []\n for game_state in states: #for every state in the states taken in consideration, checking for goal test condition\n print(\"Current State: \" + str(game_state))\n state_stack.append(game_state) \n if game_state[0] == game_state[1] == 0: \n print(\"Win! Last state: \" + str(game_state))\n win = True\n break\n future_states = get_future_states(game_state) #if goal not achieved, moving ahead and generating its future states.(children)\n for future_state in future_states: #for every future state generated,\n if future_state not in visited_states: #ensuring, we have not visited it before\n new_states.append(future_state) #only then we will consider it as our next state. \n visited_states.add(future_state) #visiting the state. \n print(\"Future State: \" + str(future_state)) \n else:\n print(\"Future State: \" + str(future_state)) \n print(\"We have visited \" + str(future_state) + \" state before. let's move back\") \n print()\n if not win: \n states = new_states\n all_states.append(states)\n\nbfs()" }, { "path": "code/artificial_intelligence/src/naive_bayes/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Naive bayes algorithm\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/naive_bayes/iris1.csv", "content": "sepallength,sepalwidth,petallength,petalwidth,class\n5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5,3.4,1.5,0.2,Iris-setosa\n4.4,2.9,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.4,3.7,1.5,0.2,Iris-setosa\n4.8,3.4,1.6,0.2,Iris-setosa\n4.8,3,1.4,0.1,Iris-setosa\n4.3,3,1.1,0.1,Iris-setosa\n5.8,4,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5,3,1.6,0.2,Iris-setosa\n5,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5,3.5,1.3,0.3,Iris-setosa\n4.5,2.3,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5,3.3,1.4,0.2,Iris-setosa\n7,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5,2,3.5,1,Iris-versicolor\n5.9,3,4.2,1.5,Iris-versicolor\n6,2.2,4,1,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3,5,1.7,Iris-versicolor\n6,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6,2.7,5.1,1.6,Iris-versicolor\n5.4,3,4.5,1.5,Iris-versicolor\n6,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3,4.1,1.3,Iris-versicolor\n5.5,2.5,4,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3,4.6,1.4,Iris-versicolor\n5.8,2.6,4,1.2,Iris-versicolor\n5,2.3,3.3,1,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3,5.8,2.2,Iris-virginica\n7.6,3,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3,5.5,2.1,Iris-virginica\n5.7,2.5,5,2,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6,2.2,5,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2,Iris-virginica\n7.7,2.8,6.7,2,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6,3,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3,5.2,2.3,Iris-virginica\n6.3,2.5,5,1.9,Iris-virginica\n6.5,3,5.2,2,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3,5.1,1.8,Iris-virginica" }, { "path": "code/artificial_intelligence/src/naive_bayes/naive_bayes.cpp", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nunordered_map count(pair, vector> &feature, vector &position)\n{\n unordered_map d;\n\n for (int i = 0; i < 2; i++) // because pairs only have 2 values\n {\n for (int j = 0; j < feature.first.size(); j++)\n {\n if (i == 0)\n {\n d.emplace(feature.first[j], 0);\n }\n else\n {\n d.emplace(feature.second[j], 0);\n }\n }\n }\n\n for (int i = 0; i < position.size(); i++)\n {\n d.emplace(position[i], 0);\n }\n\n for (int i = 0; i < 2; i++)\n {\n for (int j = 0; j < feature.first.size(); j++)\n {\n if (i == 0)\n {\n d[feature.first[j]] = d[feature.first[j]] + 1;\n }\n else\n {\n d[feature.second[j]] = d[feature.second[j]] + 1;\n }\n }\n }\n\n for (int i = 0; i < position.size(); i++)\n {\n d[position[i]] = d[position[i]] + 1;\n }\n\n for (int i = 0; i < 2; i++) // because pairs only have 2 values\n {\n for (int j = 0; j < feature.first.size(); j++)\n {\n for (int k = 0; k < position.size(); k++)\n {\n if (i == 0)\n {\n d.emplace(feature.first[j] + \"/\" + position[k], 0);\n }\n else\n {\n d.emplace(feature.second[j] + \"/\" + position[k], 0);\n }\n }\n }\n }\n\n for (int i = 0; i < position.size(); i++)\n {\n d[feature.first[i] + \"/\" + position[i]] = d[feature.first[i] + \"/\" + position[i]] + 1;\n d[feature.second[i] + \"/\" + position[i]] = d[feature.second[i] + \"/\" + position[i]] + 1;\n }\n\n return d;\n}\nunordered_map calcProb(unordered_map &d, pair, vector> &feature, vector &position)\n{\n int sum = 0;\n unordered_map x;\n sum = position.size();\n\n for (int i = 0; i < position.size(); i++)\n {\n x.emplace(position[i], (double)d[position[i]] / (double)sum);\n }\n\n for (int i = 0; i < sum; i++)\n {\n x.emplace(feature.first[i] + \"/\" + position[i], (double)d[feature.first[i] + \"/\" + position[i]] / (double)d[position[i]]);\n x.emplace(feature.second[i] + \"/\" + position[i], (double)d[feature.second[i] + \"/\" + position[i]] / (double)d[position[i]]);\n }\n\n return x;\n}\n\nvector finalPredictions(unordered_map &x, pair, vector> &feature, vector &position)\n{\n vector predict_array;\n vector predict_array_prob;\n double temp;\n int size = position.size();\n\n for (int i = 0; i < size; i++)\n {\n predict_array.push_back(\"\");\n predict_array_prob.push_back(0.0);\n }\n\n for (int k = 0; k < size; k++)\n {\n for (int i = 0; i < size; i++)\n {\n temp = 1.0;\n for (int j = 0; j < 2; j++)\n {\n if (j == 0)\n {\n temp = temp * x[feature.first[i] + \"/\" + position[k]];\n }\n else\n {\n temp = temp * x[feature.second[i] + \"/\" + position[k]];\n }\n }\n if (predict_array_prob[i] < temp * x[position[k]])\n {\n predict_array[i] = position[k];\n predict_array_prob[i] = temp * x[position[k]];\n }\n }\n }\n\n return predict_array;\n}\n\nint main()\n{\n\n vector weather = {\n \"sunny\",\n \"rainy\",\n \"sunny\",\n \"sunny\",\n \"sunny\",\n \"rainy\",\n \"rainy\",\n \"sunny\",\n \"sunny\",\n \"rainy\",\n };\n\n vector car = {\n \"working\",\n \"broken\",\n \"working\",\n \"working\",\n \"working\",\n \"broken\",\n \"broken\",\n \"working\",\n \"broken\",\n \"broken\",\n\n };\n\n vector position = {\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n\n };\n\n pair, vector> feature;\n feature.first = weather;\n feature.second = car;\n unordered_map d = count(feature, position);\n unordered_map x = calcProb(d, feature, position);\n\n vector predictions = finalPredictions(x, feature, position);\n\n for (int i = 0; i < weather.size(); i++)\n {\n cout << weather[i] << \" \" << car[i] << \" prediction is -> \" << predictions[i] << endl;\n }\n\n // test cases from naive_bayes.py file\n\n return 0;\n}\n" }, { "path": "code/artificial_intelligence/src/naive_bayes/naive_bayes.py", "content": "import numpy as np\nfrom collections import defaultdict\n\n\ndef count_words(features, output):\n my_set = []\n\n d = defaultdict(float)\n for i in range(0, len(features)):\n for j in range(0, len(features[0])):\n\n d[features[i][j]] = 0\n\n for i in range(0, len(output)):\n d[output[i]] = 0\n\n for i in range(0, len(features)):\n for j in range(0, len(features[0])):\n d[features[i][j]] = d[features[i][j]] + 1\n\n for i in range(0, len(output)):\n d[output[i]] = d[output[i]] + 1\n\n for i, j in enumerate(set(output)):\n for k in range(0, len(features)):\n for p, q in enumerate(set(features[k])):\n d[q + \"/\" + j] = 0\n\n for i, j in enumerate(output):\n for k in range(len(features)):\n d[features[k][i] + \"/\" + j] = d[features[k][i] + \"/\" + j] + 1\n\n return d\n\n\ndef calculate_probabilities(d, features, output):\n x = defaultdict(float)\n sumi = sum([d[j] for j in set(output)])\n for i in output:\n x[i] = (d[i] * 1.0) / (sumi)\n\n for i, j in enumerate(set(output)):\n for k in range(0, len(features)):\n for p, q in enumerate(set(features[k])):\n x[q + \"/\" + j] = d[q + \"/\" + j] / d[j]\n\n return x\n\n\ndef final_prediction(x, features, output_set):\n predict_array = []\n predict_array_prob = []\n for i in range(0, len(features[0])):\n predict_array.append(0)\n predict_array_prob.append(0)\n\n for k in output_set:\n for i in range(0, len(features[0])):\n temp = 1.0\n for j in range(0, len(features)):\n temp = temp * x[features[j][i] + \"/\" + k]\n if predict_array_prob[i] < temp * x[k]:\n predict_array[i] = k\n predict_array_prob[i] = temp * x[k]\n\n return predict_array\n\n\nWeather = [\n \"sunny\",\n \"rainy\",\n \"sunny\",\n \"sunny\",\n \"sunny\",\n \"rainy\",\n \"rainy\",\n \"sunny\",\n \"sunny\",\n \"rainy\",\n]\nCar = [\n \"working\",\n \"broken\",\n \"working\",\n \"working\",\n \"working\",\n \"broken\",\n \"broken\",\n \"working\",\n \"broken\",\n \"broken\",\n]\nClass = [\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"go-out\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n \"stay-home\",\n]\n\nd = count_words([Weather, Car], Class)\nx = calculate_probabilities(d, [Weather, Car], Class)\npredict_array = final_prediction(\n x, [Weather, Car], set(Class)\n) # Testing on the training set itself\n\nfor i in range(0, len(Weather)):\n print(Weather[i], \"\\t\", Car[i], \"\\t-->\\tprediction is \", predict_array[i])\n" }, { "path": "code/artificial_intelligence/src/naive_bayes/naive_bayes.swift", "content": "// Part of Cosmos by OpenGenus Foundation\nimport Foundation\n\nextension String: Error {}\n\nextension Array where Element == Double {\n\n func mean() -> Double {\n return self.reduce(0, +) / Double(count)\n }\n\n func standardDeviation() -> Double {\n let calculatedMean = mean()\n\n let sum = self.reduce(0.0) { (previous, next) in\n return previous + pow(next - calculatedMean, 2)\n }\n\n return sqrt(sum / Double(count - 1))\n }\n}\n\nextension Array where Element == Int {\n\n func uniques() -> Set {\n return Set(self)\n }\n\n}\n\nenum NBType {\n\n case gaussian\n case multinomial\n func calcLikelihood(variables: [Any], input: Any) -> Double? {\n\n if case .gaussian = self {\n\n guard let input = input as? Double else {\n return nil\n }\n\n guard let mean = variables[0] as? Double else {\n return nil\n }\n\n guard let stDev = variables[1] as? Double else {\n return nil\n }\n\n let eulerPart = pow(M_E, -1 * pow(input - mean, 2) / (2 * pow(stDev, 2)))\n let distribution = eulerPart / sqrt(2 * .pi) / stDev\n\n return distribution\n\n } else if case .multinomial = self {\n\n guard let variables = variables as? [(category: Int, probability: Double)] else {\n return nil\n }\n\n guard let input = input as? Int else {\n return nil\n }\n\n return variables.first { variable in\n return variable.category == input\n }?.probability\n\n }\n\n return nil\n }\n\n func train(values: [Any]) -> [Any]? {\n\n if case .gaussian = self {\n\n guard let values = values as? [Double] else {\n return nil\n }\n\n return [values.mean(), values.standardDeviation()]\n\n } else if case .multinomial = self {\n\n guard let values = values as? [Int] else {\n return nil\n }\n\n let count = values.count\n let categoryProba = values.uniques().map { value -> (Int, Double) in\n return (value, Double(values.filter { $0 == value }.count) / Double(count))\n }\n return categoryProba\n }\n\n return nil\n }\n}\n\nclass NaiveBayes {\n\n var variables: [Int: [(feature: Int, variables: [Any])]]\n var type: NBType\n\n var data: [[T]]\n var classes: [Int]\n\n init(type: NBType, data: [[T]], classes: [Int]) throws {\n self.type = type\n self.data = data\n self.classes = classes\n self.variables = [Int: [(Int, [Any])]]()\n\n if case .gaussian = type, T.self != Double.self {\n throw \"When using Gaussian NB you have to have continuous features (Double)\"\n } else if case .multinomial = type, T.self != Int.self {\n throw \"When using Multinomial NB you have to have categorical features (Int)\"\n }\n }\n\n func train() throws -> Self {\n\n for `class` in classes.uniques() {\n variables[`class`] = [(Int, [Any])]()\n\n let classDependent = data.enumerated().filter { (offset, _) in\n return classes[offset] == `class`\n }\n\n for feature in 0.. Int {\n let likelihoods = classifyProba(with: input).max { (first, second) -> Bool in\n return first.1 < second.1\n }\n\n guard let `class` = likelihoods?.0 else {\n return -1\n }\n\n return `class`\n }\n\n func classifyProba(with input: [T]) -> [(Int, Double)] {\n\n var probaClass = [Int: Double]()\n let amount = classes.count\n\n classes.forEach { `class` in\n let individual = classes.filter { $0 == `class` }.count\n probaClass[`class`] = Double(individual) / Double(amount)\n }\n\n let classesAndFeatures = variables.map { (`class`, value) -> (Int, [Double]) in\n let distribution = value.map { (feature, variables) -> Double in\n return type.calcLikelihood(variables: variables, input: input[feature]) ?? 0.0\n }\n return (`class`, distribution)\n }\n\n let likelihoods = classesAndFeatures.map { (`class`, distribution) in\n return (`class`, distribution.reduce(1, *) * (probaClass[`class`] ?? 0.0))\n }\n\n let sum = likelihoods.map { $0.1 }.reduce(0, +)\n let normalized = likelihoods.map { (`class`, likelihood) in\n return (`class`, likelihood / sum)\n }\n\n return normalized\n }\n}\n" }, { "path": "code/artificial_intelligence/src/named_entity_recognition/NER.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"q4gSqDnOaJ7W\"\n },\n \"source\": [\n \"

NER using LSTMs with Keras

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 52\n },\n \"colab_type\": \"code\",\n \"id\": \"oLK7Y1jiNXDa\",\n \"outputId\": \"0f319464-ee49-4035-f6fb-8396f488c41f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tensorflow version: 2.1.0\\n\",\n \"GPU detected: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\\n\"\n ]\n }\n ],\n \"source\": [\n \"#Step 1: importing modules and project\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"np.random.seed(0)\\n\",\n \"plt.style.use(\\\"ggplot\\\")\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"print('Tensorflow version:', tf.__version__)\\n\",\n \"print('GPU detected:', tf.config.list_physical_devices('GPU'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Essential info about tagged entities*:\\n\",\n \"- geo = Geographical Entity\\n\",\n \"- org = Organization\\n\",\n \"- per = Person\\n\",\n \"- gpe = Geopolitical Entity\\n\",\n \"- tim = Time indicator\\n\",\n \"- art = Artifact\\n\",\n \"- eve = Event\\n\",\n \"- nat = Natural Phenomenon\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 363\n },\n \"colab_type\": \"code\",\n \"id\": \"mCKmz4SAbI_m\",\n \"outputId\": \"a03b1aed-dc60-4f03-cb06-19e440fa6367\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Sentence #WordPOSTag
0Sentence: 1ThousandsNNSO
1Sentence: 1ofINO
2Sentence: 1demonstratorsNNSO
3Sentence: 1haveVBPO
4Sentence: 1marchedVBNO
5Sentence: 1throughINO
6Sentence: 1LondonNNPB-geo
7Sentence: 1toTOO
8Sentence: 1protestVBO
9Sentence: 1theDTO
10Sentence: 1warNNO
11Sentence: 1inINO
12Sentence: 1IraqNNPB-geo
13Sentence: 1andCCO
14Sentence: 1demandVBO
15Sentence: 1theDTO
16Sentence: 1withdrawalNNO
17Sentence: 1ofINO
18Sentence: 1BritishJJB-gpe
19Sentence: 1troopsNNSO
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sentence # Word POS Tag\\n\",\n \"0 Sentence: 1 Thousands NNS O\\n\",\n \"1 Sentence: 1 of IN O\\n\",\n \"2 Sentence: 1 demonstrators NNS O\\n\",\n \"3 Sentence: 1 have VBP O\\n\",\n \"4 Sentence: 1 marched VBN O\\n\",\n \"5 Sentence: 1 through IN O\\n\",\n \"6 Sentence: 1 London NNP B-geo\\n\",\n \"7 Sentence: 1 to TO O\\n\",\n \"8 Sentence: 1 protest VB O\\n\",\n \"9 Sentence: 1 the DT O\\n\",\n \"10 Sentence: 1 war NN O\\n\",\n \"11 Sentence: 1 in IN O\\n\",\n \"12 Sentence: 1 Iraq NNP B-geo\\n\",\n \"13 Sentence: 1 and CC O\\n\",\n \"14 Sentence: 1 demand VB O\\n\",\n \"15 Sentence: 1 the DT O\\n\",\n \"16 Sentence: 1 withdrawal NN O\\n\",\n \"17 Sentence: 1 of IN O\\n\",\n \"18 Sentence: 1 British JJ B-gpe\\n\",\n \"19 Sentence: 1 troops NNS O\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Step 2: Load and Explore the NER Dataset\\n\",\n \"data = pd.read_csv(\\\"ner_dataset.csv\\\", encoding=\\\"latin1\\\")\\n\",\n \"data = data.fillna(method=\\\"ffill\\\")\\n\",\n \"data.head(20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"id\": \"riOztP-8NXHT\",\n \"outputId\": \"200d251b-17c9-451b-9e96-523f458be47e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unique words in corpus: 35178\\n\",\n \"Unique tags in corpus: 17\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Unique words in corpus:\\\", data['Word'].nunique())\\n\",\n \"print(\\\"Unique tags in corpus:\\\", data['Tag'].nunique())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"words = list(set(data[\\\"Word\\\"].values))\\n\",\n \"words.append(\\\"ENDPAD\\\")\\n\",\n \"num_words = len(words)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tags = list(set(data[\\\"Tag\\\"].values))\\n\",\n \"num_tags = len(tags)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"VdJst_g5NYY_\"\n },\n \"outputs\": [],\n \"source\": [\n \"#Step 3: Retrieve Sentences and Corresponsing Tags\\n\",\n \"class SentenceGetter(object):\\n\",\n \" def __init__(self, data):\\n\",\n \" self.n_sent = 1\\n\",\n \" self.data = data\\n\",\n \" self.empty = False\\n\",\n \" agg_func = lambda s: [(w, p, t) for w, p, t in zip(s[\\\"Word\\\"].values.tolist(),\\n\",\n \" s[\\\"POS\\\"].values.tolist(),\\n\",\n \" s[\\\"Tag\\\"].values.tolist())]\\n\",\n \" self.grouped = self.data.groupby(\\\"Sentence #\\\").apply(agg_func)\\n\",\n \" self.sentences = [s for s in self.grouped]\\n\",\n \" \\n\",\n \" def get_next(self):\\n\",\n \" try:\\n\",\n \" s = self.grouped[\\\"Sentence: {}\\\".format(self.n_sent)]\\n\",\n \" self.n_sent += 1\\n\",\n \" return s\\n\",\n \" except:\\n\",\n \" return None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"nMUQLppspkPj\"\n },\n \"outputs\": [],\n \"source\": [\n \"getter = SentenceGetter(data)\\n\",\n \"sentences = getter.sentences\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 434\n },\n \"colab_type\": \"code\",\n \"id\": \"GhiSTt2UdzYC\",\n \"outputId\": \"95d275f6-f386-46d8-a583-2861d64c6b72\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('Thousands', 'NNS', 'O'),\\n\",\n \" ('of', 'IN', 'O'),\\n\",\n \" ('demonstrators', 'NNS', 'O'),\\n\",\n \" ('have', 'VBP', 'O'),\\n\",\n \" ('marched', 'VBN', 'O'),\\n\",\n \" ('through', 'IN', 'O'),\\n\",\n \" ('London', 'NNP', 'B-geo'),\\n\",\n \" ('to', 'TO', 'O'),\\n\",\n \" ('protest', 'VB', 'O'),\\n\",\n \" ('the', 'DT', 'O'),\\n\",\n \" ('war', 'NN', 'O'),\\n\",\n \" ('in', 'IN', 'O'),\\n\",\n \" ('Iraq', 'NNP', 'B-geo'),\\n\",\n \" ('and', 'CC', 'O'),\\n\",\n \" ('demand', 'VB', 'O'),\\n\",\n \" ('the', 'DT', 'O'),\\n\",\n \" ('withdrawal', 'NN', 'O'),\\n\",\n \" ('of', 'IN', 'O'),\\n\",\n \" ('British', 'JJ', 'B-gpe'),\\n\",\n \" ('troops', 'NNS', 'O'),\\n\",\n \" ('from', 'IN', 'O'),\\n\",\n \" ('that', 'DT', 'O'),\\n\",\n \" ('country', 'NN', 'O'),\\n\",\n \" ('.', '.', 'O')]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sentences[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"SvENHO18pkaQ\"\n },\n \"outputs\": [],\n \"source\": [\n \"#Step 4: Define Mappings between Sentences and Tags\\n\",\n \"word2idx = {w: i + 1 for i, w in enumerate(words)}\\n\",\n \"tag2idx = {t: i for i, t in enumerate(tags)}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'Bychkova': 1,\\n\",\n \" 'victorious': 2,\\n\",\n \" 'struggled': 3,\\n\",\n \" 'ex-President': 4,\\n\",\n \" 'Rehnquist': 5,\\n\",\n \" 'Uganda': 6,\\n\",\n \" 're-tried': 7,\\n\",\n \" 'Welfare': 8,\\n\",\n \" 'intent': 9,\\n\",\n \" 'stance': 10,\\n\",\n \" 'Depending': 11,\\n\",\n \" 'pro-rebel': 12,\\n\",\n \" 'Catalonians': 13,\\n\",\n \" 'stalls': 14,\\n\",\n \" 'gored': 15,\\n\",\n \" 'jeopardy': 16,\\n\",\n \" 'epitomizes': 17,\\n\",\n \" 'Bernie': 18,\\n\",\n \" 'Price': 19,\\n\",\n \" 'sets': 20,\\n\",\n \" 'Cayes': 21,\\n\",\n \" 'however': 22,\\n\",\n \" \\\"'T\\\": 23,\\n\",\n \" 'immorality': 24,\\n\",\n \" 'climatic': 25,\\n\",\n \" 'touch': 26,\\n\",\n \" 'vivid': 27,\\n\",\n \" 'Shaffi': 28,\\n\",\n \" 'Sudan': 29,\\n\",\n \" '3,600': 30,\\n\",\n \" 'carmaker': 31,\\n\",\n \" 'runway': 32,\\n\",\n \" '(': 33,\\n\",\n \" 'due': 34,\\n\",\n \" 'Masorin': 35,\\n\",\n \" 'except': 36,\\n\",\n \" 'consultation': 37,\\n\",\n \" 'usually': 38,\\n\",\n \" 'rebuked': 39,\\n\",\n \" 'connect': 40,\\n\",\n \" 'defrauding': 41,\\n\",\n \" 'cease-fire': 42,\\n\",\n \" 'Husseindoust': 43,\\n\",\n \" 'hockey': 44,\\n\",\n \" 'hay': 45,\\n\",\n \" 'Elliott': 46,\\n\",\n \" 'genes': 47,\\n\",\n \" 'shareholders': 48,\\n\",\n \" 'foot': 49,\\n\",\n \" 'quails': 50,\\n\",\n \" 'decisions': 51,\\n\",\n \" 'Seche': 52,\\n\",\n \" 'Kane': 53,\\n\",\n \" 'representing': 54,\\n\",\n \" 'Nuristani': 55,\\n\",\n \" 'Monument': 56,\\n\",\n \" 'welcome': 57,\\n\",\n \" 'disappointment': 58,\\n\",\n \" 'Sunni-dominant': 59,\\n\",\n \" 'pegs': 60,\\n\",\n \" 'VanAllen': 61,\\n\",\n \" 'conspicuous': 62,\\n\",\n \" 'detainment': 63,\\n\",\n \" '454-seat': 64,\\n\",\n \" 'Luzon': 65,\\n\",\n \" 'burning': 66,\\n\",\n \" 'unintended': 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790,\\n\",\n \" 'operate': 791,\\n\",\n \" 'mon': 792,\\n\",\n \" 'hill-top': 793,\\n\",\n \" 'struck': 794,\\n\",\n \" 'Sagues': 795,\\n\",\n \" 'drug-making': 796,\\n\",\n \" 'winding': 797,\\n\",\n \" 'Villaraigosa': 798,\\n\",\n \" 'desecrating': 799,\\n\",\n \" 'disinfected': 800,\\n\",\n \" 'whereby': 801,\\n\",\n \" 'furnish': 802,\\n\",\n \" 'Ajmal': 803,\\n\",\n \" 'Kahar': 804,\\n\",\n \" '87-run': 805,\\n\",\n \" 'firestorm': 806,\\n\",\n \" 'Preparations': 807,\\n\",\n \" 'opium-traffickers': 808,\\n\",\n \" 'Santos': 809,\\n\",\n \" 'port': 810,\\n\",\n \" 'Joey': 811,\\n\",\n \" 'Sarfraz': 812,\\n\",\n \" 'treat': 813,\\n\",\n \" 'Lutfullah': 814,\\n\",\n \" 'Salaam': 815,\\n\",\n \" 'poverty': 816,\\n\",\n \" 'VSV-30': 817,\\n\",\n \" 'Zurbriggen': 818,\\n\",\n \" 'Apure': 819,\\n\",\n \" 'NHK': 820,\\n\",\n \" 'Bradman': 821,\\n\",\n \" 'capitals': 822,\\n\",\n \" 'loans': 823,\\n\",\n \" 'town': 824,\\n\",\n \" 'Zahir': 825,\\n\",\n \" 'Bolivian': 826,\\n\",\n \" 'overrun': 827,\\n\",\n \" 'insulted': 828,\\n\",\n \" 'trigger': 829,\\n\",\n \" 'detriment': 830,\\n\",\n \" 'deficiencies': 831,\\n\",\n \" 'owed': 832,\\n\",\n \" 'blind': 833,\\n\",\n \" 'shies': 834,\\n\",\n \" 'backbone': 835,\\n\",\n \" 'Monte': 836,\\n\",\n \" 'dependence': 837,\\n\",\n \" 'clarified': 838,\\n\",\n \" 'Goats': 839,\\n\",\n \" 'sole': 840,\\n\",\n \" 'traditions': 841,\\n\",\n \" 'selective': 842,\\n\",\n \" 'military-run': 843,\\n\",\n \" 'approximation': 844,\\n\",\n \" 'competition': 845,\\n\",\n \" 'extracting': 846,\\n\",\n \" 'Tamiflu': 847,\\n\",\n \" 'king': 848,\\n\",\n \" '1930s': 849,\\n\",\n \" '124': 850,\\n\",\n \" 'umbrella-like': 851,\\n\",\n \" 'OLIVE-TREE': 852,\\n\",\n \" 'Paulos': 853,\\n\",\n \" 'land-for-peace': 854,\\n\",\n \" 'Al-Badri': 855,\\n\",\n \" 'circa': 856,\\n\",\n \" 'resignation': 857,\\n\",\n \" 'Colin': 858,\\n\",\n \" 'tyrannical': 859,\\n\",\n \" 'Mohaqiq': 860,\\n\",\n \" 'plant': 861,\\n\",\n \" 'revitalize': 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898,\\n\",\n \" 'Bashir': 899,\\n\",\n \" '170-meter': 900,\\n\",\n \" 'peninsula': 901,\\n\",\n \" 'disbursing': 902,\\n\",\n \" 'criticizes': 903,\\n\",\n \" 'IDF': 904,\\n\",\n \" 'expletive': 905,\\n\",\n \" 'Karadzic': 906,\\n\",\n \" 'Khushab': 907,\\n\",\n \" 'Loyalist': 908,\\n\",\n \" 'eagle': 909,\\n\",\n \" 'fodder': 910,\\n\",\n \" 'Lashkar-e-Taiba': 911,\\n\",\n \" 'breakdown': 912,\\n\",\n \" 'Homeland': 913,\\n\",\n \" 'accurate': 914,\\n\",\n \" 'Gode': 915,\\n\",\n \" 'Thuan': 916,\\n\",\n \" 'Soviet': 917,\\n\",\n \" 'Pandas': 918,\\n\",\n \" '752.2': 919,\\n\",\n \" 'Farms': 920,\\n\",\n \" 'Jones': 921,\\n\",\n \" 'biased': 922,\\n\",\n \" 'stimulating': 923,\\n\",\n \" 'co-chairman': 924,\\n\",\n \" 'Matambanadzo': 925,\\n\",\n \" 'Of': 926,\\n\",\n \" 'Bei': 927,\\n\",\n \" 'Film': 928,\\n\",\n \" 'Verheugen': 929,\\n\",\n \" '17,765': 930,\\n\",\n \" 'year-ago': 931,\\n\",\n \" 'restraint': 932,\\n\",\n \" '81': 933,\\n\",\n \" 'outgoing': 934,\\n\",\n \" 'prize': 935,\\n\",\n \" 'genders': 936,\\n\",\n \" 'Marthinus': 937,\\n\",\n \" 'wavered': 938,\\n\",\n \" 'emitted': 939,\\n\",\n \" 'Vocalist': 940,\\n\",\n \" 'Benazir': 941,\\n\",\n \" 'undertake': 942,\\n\",\n \" 'Dhahran': 943,\\n\",\n \" 'poverty-ridden': 944,\\n\",\n \" 'Vidar': 945,\\n\",\n \" 'Guenael': 946,\\n\",\n \" 'Vuk': 947,\\n\",\n \" 'Haruna': 948,\\n\",\n \" 'misquoted': 949,\\n\",\n \" 'Yuschenko': 950,\\n\",\n \" 'strongmen': 951,\\n\",\n \" 'about': 952,\\n\",\n \" 'newly-energized': 953,\\n\",\n \" 'arc': 954,\\n\",\n \" 'takes': 955,\\n\",\n \" 'Kutesa': 956,\\n\",\n \" 'life-spans': 957,\\n\",\n \" 'anti-pollution': 958,\\n\",\n \" '12.97': 959,\\n\",\n \" 'dogs': 960,\\n\",\n \" 'towering': 961,\\n\",\n \" 'Mexican-American': 962,\\n\",\n \" 'decades-long': 963,\\n\",\n \" 'Abdurrahman': 964,\\n\",\n \" 'Raziq': 965,\\n\",\n \" 'Peru': 966,\\n\",\n \" 'classes': 967,\\n\",\n \" 'left-wing': 968,\\n\",\n \" 'ATP': 969,\\n\",\n \" 'privileges': 970,\\n\",\n \" 'eater': 971,\\n\",\n \" 'Weisfield': 972,\\n\",\n \" 'disabling': 973,\\n\",\n \" 'insistence': 974,\\n\",\n \" 'Iranativu': 975,\\n\",\n \" 'Columbus': 976,\\n\",\n \" 'reopened': 977,\\n\",\n \" 'saints': 978,\\n\",\n \" 'After': 979,\\n\",\n \" 'Dixie': 980,\\n\",\n \" 'Undersecretary-General': 981,\\n\",\n \" 'Mekong': 982,\\n\",\n \" 'mad-cow': 983,\\n\",\n \" 'plebiscites': 984,\\n\",\n \" '28-point': 985,\\n\",\n \" 'winter': 986,\\n\",\n \" 'Siraj': 987,\\n\",\n \" 'Generalized': 988,\\n\",\n \" 'Then': 989,\\n\",\n \" 'assassins': 990,\\n\",\n \" 'unprepared': 991,\\n\",\n \" 'Aruban': 992,\\n\",\n \" 'cutthroat': 993,\\n\",\n \" 'Knives': 994,\\n\",\n \" 'phase-out': 995,\\n\",\n \" 'sci-fi': 996,\\n\",\n \" 'north-south': 997,\\n\",\n \" 'Apple': 998,\\n\",\n \" 'Edelist': 999,\\n\",\n \" 'Bears': 1000,\\n\",\n \" ...}\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"word2idx\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 265\n },\n \"colab_type\": \"code\",\n \"id\": \"R44g5T7NYp_H\",\n \"outputId\": \"135a85a6-7a85-4b16-a8eb-3ad7bd4da5b6\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Step 5: Padding Input Sentences and Creating Train/Test Splits\\n\",\n \"plt.hist([len(s) for s in sentences], bins=50)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"FS4u3CRkpkc1\"\n },\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.keras.preprocessing.sequence import pad_sequences\\n\",\n \"\\n\",\n \"max_len = 50\\n\",\n \"\\n\",\n \"X = [[word2idx[w[0]] for w in s] for s in sentences]\\n\",\n \"X = pad_sequences(maxlen=max_len, sequences=X, padding=\\\"post\\\", value=num_words-1)\\n\",\n \"\\n\",\n \"y = [[tag2idx[w[2]] for w in s] for s in sentences]\\n\",\n \"y = pad_sequences(maxlen=max_len, sequences=y, padding=\\\"post\\\", value=tag2idx[\\\"O\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"q7VfnnkXpkfS\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"Y2vM7IkXpkiH\"\n },\n \"outputs\": [],\n \"source\": [\n \"#Step 6: Build and Compile a Bidirectional LSTM\\n\",\n \"from tensorflow.keras import Model, Input\\n\",\n \"from tensorflow.keras.layers import LSTM, Embedding, Dense\\n\",\n \"from tensorflow.keras.layers import TimeDistributed, SpatialDropout1D, Bidirectional\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 330\n },\n \"colab_type\": \"code\",\n \"id\": \"Aee3mCZ3pkkv\",\n \"outputId\": \"b7fb911b-21d1-43e6-adc9-bb2d8bdfb921\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"input_1 (InputLayer) [(None, 50)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"embedding (Embedding) (None, 50, 50) 1758950 \\n\",\n \"_________________________________________________________________\\n\",\n \"spatial_dropout1d (SpatialDr (None, 50, 50) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"bidirectional (Bidirectional (None, 50, 200) 120800 \\n\",\n \"_________________________________________________________________\\n\",\n \"time_distributed (TimeDistri (None, 50, 17) 3417 \\n\",\n \"=================================================================\\n\",\n \"Total params: 1,883,167\\n\",\n \"Trainable params: 1,883,167\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"input_word = Input(shape=(max_len,))\\n\",\n \"model = Embedding(input_dim=num_words, output_dim=50, input_length=max_len)(input_word)\\n\",\n \"model = SpatialDropout1D(0.1)(model)\\n\",\n \"model = Bidirectional(LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)\\n\",\n \"out = TimeDistributed(Dense(num_tags, activation=\\\"softmax\\\"))(model)\\n\",\n \"model = Model(input_word, out)\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"kOBpQg26pkqh\"\n },\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer=\\\"adam\\\",\\n\",\n \" loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n \" metrics=[\\\"accuracy\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#STep 7: Train Model\\n\",\n \"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\\n\",\n \"from livelossplot.tf_keras import PlotLossesCallback\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 536\n },\n \"colab_type\": \"code\",\n \"id\": \"Q9HWH06Ypkxh\",\n \"outputId\": \"e83ba281-0c14-4bca-dacb-cb708edacba5\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Log-loss (cost function):\\n\",\n \"training (min: 0.037, max: 0.181, cur: 0.037)\\n\",\n \"validation (min: 0.048, max: 0.066, cur: 0.048)\\n\",\n \"\\n\",\n \"accuracy:\\n\",\n \"training (min: 0.958, max: 0.989, cur: 0.989)\\n\",\n \"validation (min: 0.981, max: 0.986, cur: 0.986)\\n\",\n \"\\n\",\n \"Epoch 00003: val_loss improved from 0.05093 to 0.04835, saving model to model_weights.h5\\n\",\n \"38367/38367 [==============================] - 228s 6ms/sample - loss: 0.0368 - accuracy: 0.9887 - val_loss: 0.0484 - val_accuracy: 0.9856\\n\",\n \"CPU times: user 15min 48s, sys: 27.2 s, total: 16min 15s\\n\",\n \"Wall time: 11min 41s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"#View Loss/Accuracy graph at each interation\\n\",\n \"chkpt = ModelCheckpoint(\\\"model_weights.h5\\\", monitor='val_loss',verbose=1, save_best_only=True, save_weights_only=True, mode='min')\\n\",\n \"\\n\",\n \"early_stopping = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=1, verbose=0, mode='max', baseline=None, restore_best_weights=False)\\n\",\n \"\\n\",\n \"callbacks = [PlotLossesCallback(), chkpt, early_stopping]\\n\",\n \"\\n\",\n \"history = model.fit(\\n\",\n \" x=x_train,\\n\",\n \" y=y_train,\\n\",\n \" validation_data=(x_test,y_test),\\n\",\n \" batch_size=32, \\n\",\n \" epochs=3,\\n\",\n \" callbacks=callbacks,\\n\",\n \" verbose=1\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 52\n },\n \"colab_type\": \"code\",\n \"id\": \"6euqX7UHplG7\",\n \"outputId\": \"7222c24c-52c5-454b-a5d4-03d6df4173f0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9592/9592 [==============================] - 12s 1ms/sample - loss: 0.0484 - accuracy: 0.9856\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.04835232572507321, 0.9855588]\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Step 8: Evaluate model\\n\",\n \"model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 920\n },\n \"colab_type\": \"code\",\n \"id\": \"Tyg4mKOVplJ-\",\n \"outputId\": \"59e897c3-cb77-4dc2-a239-dea2c94eec42\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Word True \\t Pred\\n\",\n \"\\n\",\n \"------------------------------\\n\",\n \"The O\\tO\\n\",\n \"United B-org\\tB-org\\n\",\n \"Nations I-org\\tI-org\\n\",\n \"has O\\tO\\n\",\n \"been O\\tO\\n\",\n \"under O\\tO\\n\",\n \"fire O\\tO\\n\",\n \"for O\\tO\\n\",\n \"failing O\\tO\\n\",\n \"to O\\tO\\n\",\n \"stop O\\tO\\n\",\n \"ongoing O\\tO\\n\",\n \"ethnic O\\tO\\n\",\n \"violence O\\tO\\n\",\n \"in O\\tO\\n\",\n \"Ituri B-geo\\tB-geo\\n\",\n \". O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\",\n \"Surrey O\\tO\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = np.random.randint(0, x_test.shape[0]) #659\\n\",\n \"p = model.predict(np.array([x_test[i]]))\\n\",\n \"p = np.argmax(p, axis=-1)\\n\",\n \"y_true = y_test[i]\\n\",\n \"print(\\\"{:15}{:5}\\\\t {}\\\\n\\\".format(\\\"Word\\\", \\\"True\\\", \\\"Pred\\\"))\\n\",\n \"print(\\\"-\\\" *30)\\n\",\n \"for w, true, pred in zip(x_test[i], y_true, p[0]):\\n\",\n \" print(\\\"{:15}{}\\\\t{}\\\".format(words[w-1], tags[true], tags[pred]))\\n\",\n \"#Padding value of \\\"Surrey\\\" at the end till max length is reached\"\n ]\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"NER.ipynb\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "code/artificial_intelligence/src/named_entity_recognition/README.md", "content": "# Named Entity Recognition\n**Named Entity Recognition** is a key task in Natural Language Processing. The process of detecting a word or a group of words that can be classified into known categories like person name, geographical location, date, numerical values, units of measurement, product names, etc. \n\nFor eg. in the sentence, \"Rahul is looking at Taj Mahal, which is one of the 7 wonders of the world.\" Rahul is a person's name, Taj Mahal is a monument, 7 is a numerical value. So, these 3 known categories can be detected as part of Named Entity Recognition.\n\n# Packages used\n* Tensorflow - for Named Entity Recognition task\n* Numpy - for scientific calculations\n* Pandas - loading CSV dataset and data wrangling\n* Matplotlib - plotting and visualization\n\nOne can replicate the same results by following these steps:\n1. Downloading this jupyter notebook on Google Colab. Alternatively, they can also load the dataset to their own computers instead of using Google Drive (as done in this notebook).\n2. Running all the cells **sequentially** in the order as in the notebook.\n3. It's done! You can go ahead and try the same algorithm on different datasets.\n" }, { "path": "code/artificial_intelligence/src/named_entity_recognition/ner_dataset.csv", "content": "\r\n" }, { "path": "code/artificial_intelligence/src/nearest_sequence_memory/nsm_matlab/main.m", "content": "% change as desired\n% runtime approx (i7 @3 GHz)\n% 100 episodes -- < 90 s\n% 1000 episodes -- 2 - 3 min\nnum_trials = 1000;\n\n% gather data -- may take time\nnsmAgent = nsm_agent();\n[steps_learn, ~] = nsmAgent.Trials(num_trials);\n\n% plot nice output graphs\nplot(steps_learn);\nylim([0 500]);\ntitle('NSM Trials');\nylabel('Number of Steps until terminal');\nxlabel('Episodes');" }, { "path": "code/artificial_intelligence/src/nearest_sequence_memory/nsm_matlab/nsm_agent.m", "content": "classdef nsm_agent < handle\n % The agent keeps a history of all past episodes (max. 20 steps).\n % This information is used to make decisions about the current action,\n % by comparing chains of previous observations and actions.\n \n properties(Access=private)\n episodes_stored = 0;\n world = simulator();\n STM = zeros(20,3);\n LTM = zeros(20,3,0);\n end\n \n methods\n function [steps, ret_LTM] = Trials(this, num_trials)\n % gather num_trials many episodes using NSM action\n % selection. For each Episode reccord the total number of steps\n % and the last 20 visited nodes.\n % Input: num_trials --- the number of trials to be run\n % Output: steps --- 1D array where steps(idx) corresponds to\n % the number of steps needed to reach the\n % goal in episode idx.\n % LTM --- A reccord of the last 20 reccorded\n % observations, actions and rewards.\n % LTM(20,:) is the last observed state.\n % If an episode requires less than 20\n % steps, the top rows are zero padded\n % until size(LTM) = [20 3];\n \n this.LTM = zeros(20,3,num_trials);\n \n steps = zeros(1,num_trials);\n for idx = 1:num_trials\n [step, foo] = this.NSMEpisode();\n steps(idx) = step;\n this.LTM(:,:,idx) = foo;\n this.episodes_stored = this.episodes_stored + 1;\n end\n ret_LTM = this.LTM;\n end\n\n end\n methods(Access = private)\n function [num_steps, ret_mat] = NSMEpisode(this)\n % Generates an episode using previously observed experience\n % stored in LTM.\n % Output: num_steps --- The total number of steps until the\n % goal was reached.\n % ret_mat --- The last 20 observations, actions\n % and rewards, stored in LTM format.\n gamma = 0.9;\n num_steps = 0;\n \n % make sure the world is in a random state and we have no\n % current memory\n this.world.reset();\n this.STM = zeros(20,3);\n \n % traverse the world and try to pick a good action in each step\n while ~this.world.isGoal()\n o = this.world.observe();\n a = this.NSMSelectAction(o);\n r = this.world.take_action(a);\n \n % after each step, reccord what happned\n this.STM = circshift(this.STM,[-1 0]);\n this.STM(20,:) = [o a r];\n num_steps = num_steps + 1;\n end\n \n % calculate the discounted reward\n this.STM(:,3) = this.STM(20,3) * gamma.^(19:-1:0);\n this.STM(1:(20-num_steps),3) = 0;\n \n ret_mat = this.STM;\n end\n \n function a = NSMSelectAction(this, o)\n % Select an action based on actions and observations \n % (STM) made in this episode and past experiences (LTM).\n % The action selection is epsilon-greedy, with epsilon = 10 %.\n % Input: o --- the current observation\n % Output: a --- action chosen\n \n if rand < 0.1\n % pick a random action\n a = randi(4);\n \n else\n % select an action based on past experiences\n \n % find the 10 closest POMDP states observed in the past and\n % average their discounted reward with respect to the first\n % action taken.\n kNN = this.kNearest(o);\n rewards = zeros(1,4);\n for idx = 1:4\n same_action = (kNN(:,2) == idx);\n rewards(idx) = mean(kNN(same_action,3));\n end\n rewards(isnan(rewards)) = 0;\n \n % select the best action. If tie for multiple, pick random\n % witin those.\n max_val = max(rewards);\n available_actions = 1:4;\n available_actions = ...\n (available_actions(abs(rewards-max_val) < 0.01));\n rand_action = mod(randi(12),size(available_actions,1)) + 1;\n a = available_actions(rand_action);\n end\n end\n \n function score = proximity(this, episode, step, o)\n % Calculate how close a state in the LTM is to the current\n % state.\n % Input: (episode,step) --- The state in the LTM\n % o --- The current observation\n % Output: score --- The closeness of the two\n % states. Higher is closer. \n \n % if observation differs from LTM's present observation,\n % score is 0\n if this.LTM(step,1,episode) ~= o\n score = 0;\n return;\n end\n \n % local copy of LTM stripped from everything unneeded\n past = this.LTM(1:step-1,1:2,episode);\n zero_rows = all(past == 0,2);\n past(zero_rows,:) = [];\n \n % local copy of STM stripped from everything unneeded\n local_STM = this.STM(:,1:2);\n zero_rows = all(local_STM == 0,2);\n local_STM(zero_rows,:) = [];\n \n % compare LTM's past to STM past to see how close it is\n STM_size = size(local_STM,1);\n past_size = size(past, 1);\n common = (min(STM_size,past_size)-1):-1:0;\n past = past(end - common,:);\n local_STM = this.STM(end - common,1:2);\n \n %find out how many (o,a) pairs match\n matches = flipud(all(past == local_STM,2));\n num_matches = find([matches; 0] == 0, 1);\n score = 1 + (num_matches-1);\n end\n \n function kNN = kNearest(this, o)\n % find and return the 10 nearest neighbours in the LTM, given \n % the STM and a current observation\n % Input: o --- The current observation\n % Output: kNN --- A list of the 10 nearest states and\n % their score.\n \n kNN = zeros(10,4);\n lowest_score = 0;\n \n for step = 20:-1:2\n for episode = 1:this.episodes_stored\n % this entire for loop could be vectorized. But this\n % would require to refactor and remove proximity() thus\n % it has been omitted in this implementation.\n \n score = this.proximity(episode,step,o);\n if lowest_score <= score && score > 0\n [~,idx] = min(kNN(:,4));\n kNN(idx,:) = [this.LTM(step,:,episode) score];\n lowest_score = min(kNN(:,4));\n end\n end\n end\n end\n end\nend" }, { "path": "code/artificial_intelligence/src/nearest_sequence_memory/nsm_matlab/simulator.m", "content": "classdef simulator < handle\n % A small simulation of a variation of McCallum's grid-world.\n % Initialized in a random state. The world is only partially\n % observable.\n %\n % reset() -- reset the world to a random starting state\n % observe() -- get the observation of the current goal state\n % take_action(a) -- execute action a, returns the reward associated\n % with taking this action in the current state\n % isGoal(a) -- check if in the goal state\n %\n % (c) 2016 Sebastian Wallkoetter\n \n properties(Access=private)\n s = randi(11); % current state\n T = ones(4,11); % transition matrix\n observations = ones(11,1);\n reward_function\n end\n \n methods\n function obj = simulator(~)\n % construct a POMDP based on McCallum's grid world. This\n % routine creates the world by setting the transition function\n % T as well as the observations for each state.\n \n obj.T = [\n 4 2 6 7 9 11 7 8 9 10 11\n 1 2 3 4 5 6 8 9 10 11 11\n 1 2 3 1 2 4 4 8 5 10 11\n 1 2 3 4 5 6 7 7 8 9 10\n ];\n obj.observations = [14 14 14 10 10 10 9 5 1 5 3];\n end\n \n function r = take_action(this, a)\n % execute action a. This will result in a new current state and\n % a reward\n % Input: a --- The action to be executed\n % Output: r --- The reward obtained from taking action a in\n % state s\n \n r = this.reward(a);\n this.s = this.T(a,this.s);\n end\n \n function o = observe(this)\n % Resturns what can be seen in the current state\n % Output: o --- The current observation\n \n o = this.observations(this.s);\n end\n \n function reset(this)\n % Reset the world, placing the agent in a random state ~= 2\n \n possible_states = 1:11;\n possible_states(2) = [];\n this.s = randsample(possible_states,1);\n end\n \n function b = isGoal(this)\n % returns true if the current state is the goal state\n % Output: b --- True, if goal has been reached. False\n % otherwise.\n \n b = (this.s == 2);\n end\n \n end\n \n methods(Access = private)\n function r = reward(this, a)\n % the reward for taking action a in the current state\n % Input: a --- the action taken\n % Output: r --- reward for taking that action in the\n % current state\n \n if this.s == 5 && a == 3\n r = 10;\n else\n r = 0;\n end\n end\n end\nends" }, { "path": "code/artificial_intelligence/src/neural_network/keras_nn.py", "content": "import numpy as np\r\n'''numpy stands for numerical python library. It is used for scientific computing in python '''\r\nimport keras.utils\r\nfrom keras.datasets import mnist\r\n''' mnist is a data set which contains images of numbers from 1-8 in 28*28input size'''\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout\r\n''' Demse indicates layers of connected neurons'''\r\nfrom keras.optimizers import Adam\r\n# Adam is a special optimizing algorithm\r\n\r\n\r\n\"\"\" To run this code install keras, numpy and tensorflow.\r\n\"\"\"\r\n\r\nbatch_size = 32\r\ncategories = 10\r\nepochs = 50 #epochs means no. of times data will be trained\r\n\r\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n\r\n# reshape function is used to reshape matrix in python.\r\n\r\nx_train = x_train.reshape(60000, 784)\r\nx_test = x_test.reshape(10000, 784)\r\n\r\n\r\n''' The below process is called noramalization. in this we try to scale the\r\ndata so that the process could be fast.'''\r\n\r\nx_train /= 255\r\nx_test /= 255\r\n\r\n\r\nprint(x_train.shape[0], \"train samples\")\r\nprint(x_test.shape[0], \"test samples\")\r\n\r\n# convert class vectors to binary class matrices\r\ny_train = keras.utils.to_categorical(y_train, categories)\r\ny_test = keras.utils.to_categorical(y_test, categories)\r\n\r\nmodel = Sequential()\r\nmodel.add(Dense(128, activation=\"relu\", input_shape=(784,)))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(128, activation=\"relu\")) \r\n''' relu stands for rectified linear unit. it is used as an activation function when output is expected not to be 0 or 1'''\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(128, activation=\"relu\"))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(categories, activation=\"softmax\")) #it is an activation function which returns the highest value\r\n\r\n\r\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=Adam(), metrics=[\"accuracy\"])\r\n\r\nmodel.fit(\r\n x_train,\r\n y_train,\r\n batch_size=batch_size,\r\n epochs=epochs,\r\n verbose=1,\r\n validation_data=(x_test, y_test),\r\n)\r\nmodel.evaluate(x_test, y_test, verbose=1)\r\n" }, { "path": "code/artificial_intelligence/src/neural_network/neural_network.py", "content": "# The following is a from scratch implementation of a neural network in Python without any deep learning libraries\n\n# Import necessary packages\nimport numpy as np\nimport dill\n\n# Create a class \nclass neural_network:\n def __init__(self, num_layers, num_nodes, activation_function, cost_function):\n self.num_layers = num_layers\n self.num_nodes = num_nodes\n self.layers = []\n self.cost_function = cost_function\n\n for i in range(num_layers):\n if i != num_layers - 1:\n layer_i = layer(num_nodes[i], num_nodes[i + 1], activation_function[i])\n else:\n layer_i = layer(num_nodes[i], 0, activation_function[i])\n self.layers.append(layer_i)\n\n # Function to train\n def train(self, batch_size, inputs, labels, num_epochs, learning_rate, filename):\n self.batch_size = batch_size\n self.learning_rate = learning_rate\n for j in range(num_epochs):\n i = 0\n print(\"== EPOCH: \", j, \" ==\")\n while i + batch_size != len(inputs):\n self.error = 0\n self.forward_pass(inputs[i : i + batch_size])\n self.calculate_error(labels[i : i + batch_size])\n self.back_pass(labels[i : i + batch_size])\n i += batch_size\n print(\"Error: \", self.error)\n dill.dump_session(filename)\n\n # Feedforward function \n def forward_pass(self, inputs):\n self.layers[0].activations = inputs\n for i in range(self.num_layers - 1):\n self.layers[i].add_bias(\n self.batch_size, self.layers[i + 1].num_nodes_in_layer\n )\n temp = np.add(\n np.matmul(self.layers[i].activations, self.layers[i].weights_for_layer),\n self.layers[i].bias_for_layer,\n )\n if self.layers[i + 1].activation_function == \"sigmoid\":\n self.layers[i + 1].activations = self.sigmoid(temp)\n elif self.layers[i + 1].activation_function == \"softmax\":\n self.layers[i + 1].activations = self.softmax(temp)\n elif self.layers[i + 1].activation_function == \"relu\":\n self.layers[i + 1].activations = self.relu(temp)\n elif self.layers[i + 1].activation_function == \"tanh\":\n self.layers[i + 1].activations = self.tanh(temp)\n else:\n self.layers[i + 1].activations = temp\n\n # Create mathematical relu function\n def relu(self, layer):\n layer[layer < 0] = 0\n return layer\n \n # Create mathematical softmax function\n def softmax(self, layer):\n exp = np.exp(layer)\n return exp / np.sum(exp, axis=1, keepdims=True)\n\n # Create mathematical sigmoid function\n def sigmoid(self, layer):\n return np.divide(1, np.add(1, np.exp(layer)))\n\n # Use the tanh function in numpy\n def tanh(self, layer):\n return np.tanh(layer)\n\n # Function to calculate the error (mean squared error, cross-entropy)\n def calculate_error(self, labels):\n if len(labels[0]) != self.layers[self.num_layers - 1].num_nodes_in_layer:\n print(\"Error: Label is not of the same shape as output layer.\")\n print(\"Label: \", len(labels), \" : \", len(labels[0]))\n print(\n \"Out: \",\n len(self.layers[self.num_layers - 1].activations),\n \" : \",\n len(self.layers[self.num_layers - 1].activations[0]),\n )\n return\n\n if self.cost_function == \"mean_squared\":\n self.error = np.mean(\n np.divide(\n np.square(\n np.subtract(\n labels, self.layers[self.num_layers - 1].activations\n )\n ),\n 2,\n )\n )\n elif self.cost_function == \"cross_entropy\":\n self.error = np.negative(\n np.sum(\n np.multiply(\n labels, np.log(self.layers[self.num_layers - 1].activations)\n )\n )\n )\n\n # Back propagation function \n # NOTE: This is the most important part of a neural network by application of calculus and the chain rule\n def back_pass(self, labels):\n # if self.cost_function == \"cross_entropy\" and self.layers[self.num_layers-1].activation_function == \"softmax\":\n targets = labels\n i = self.num_layers - 1\n y = self.layers[i].activations\n deltaw = np.matmul(\n np.asarray(self.layers[i - 1].activations).T,\n np.multiply(y, np.multiply(1 - y, targets - y)),\n )\n new_weights = self.layers[i - 1].weights_for_layer - self.learning_rate * deltaw\n for i in range(i - 1, 0, -1):\n y = self.layers[i].activations\n deltaw = np.matmul(\n np.asarray(self.layers[i - 1].activations).T,\n np.multiply(\n y,\n np.multiply(\n 1 - y,\n np.sum(\n np.multiply(new_weights, self.layers[i].weights_for_layer),\n axis=1,\n ).T,\n ),\n ),\n )\n self.layers[i].weights_for_layer = new_weights\n new_weights = (\n self.layers[i - 1].weights_for_layer - self.learning_rate * deltaw\n )\n self.layers[0].weights_for_layer = new_weights\n\n def predict(self, filename, input):\n dill.load_session(filename)\n self.batch_size = 1\n self.forward_pass(input)\n a = self.layers[self.num_layers - 1].activations\n a[np.where(a == np.max(a))] = 0.9\n a[np.where(a != np.max(a))] = 0.1\n return a\n\n # Check model accuracy\n def check_accuracy(self, filename, inputs, labels):\n dill.load_session(filename)\n self.batch_size = len(inputs)\n self.forward_pass(inputs)\n a = self.layers[self.num_layers - 1].activations\n num_classes = 10\n targets = np.array([a]).reshape(-1)\n a = np.asarray(a)\n one_hot_labels = np.eye(num_classes)[a.astype(int)]\n total = 0\n correct = 0\n for i in range(len(a)):\n total += 1\n if np.equal(one_hot_labels[i], labels[i]).all():\n correct += 1\n print(\"Accuracy: \", correct * 100 / total)\n\n def load_model(self, filename):\n dill.load_session(filename)\n\n# Class for each layer used in the network\nclass layer:\n def __init__(\n self, num_nodes_in_layer, num_nodes_in_next_layer, activation_function\n ):\n self.num_nodes_in_layer = num_nodes_in_layer\n self.activation_function = activation_function\n self.activations = np.zeros([num_nodes_in_layer, 1])\n if num_nodes_in_next_layer != 0:\n self.weights_for_layer = np.random.normal(\n 0, 0.001, size=(num_nodes_in_layer, num_nodes_in_next_layer)\n )\n else:\n self.weights_for_layer = None\n self.bias_for_layer = None\n\n def add_bias(self, batch_size, num_nodes_in_next_layer):\n if num_nodes_in_next_layer != 0:\n self.bias_for_layer = np.random.normal(\n 0, 0.01, size=(batch_size, num_nodes_in_next_layer)\n )\n" }, { "path": "code/artificial_intelligence/src/neural_style_transfer/neural_style_transfer.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"neural_art.ipynb\",\n \"version\": \"0.3.2\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"metadata\": {\n \"id\": \"mZVJPJpBemu8\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"from __future__ import print_function\\n\",\n \"\\n\",\n \"import time\\n\",\n \"from PIL import Image\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from keras import backend\\n\",\n \"from keras.models import Model\\n\",\n \"from keras.applications.vgg16 import VGG16\\n\",\n \"\\n\",\n \"from scipy.optimize import fmin_l_bfgs_b\\n\",\n \"from scipy.misc import imsave\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"OwiKLclGfrxD\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 399\n },\n \"outputId\": \"49e81638-f90c-4076-d783-e115fdc2c6e9\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"!wget https://upload.wikimedia.org/wikipedia/commons/d/d7/Green_Sea_Turtle_grazing_seagrass.jpg\\n\",\n \"!wget https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg\\n\"\n ],\n \"execution_count\": 21,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"--2018-09-05 13:21:22-- https://upload.wikimedia.org/wikipedia/commons/d/d7/Green_Sea_Turtle_grazing_seagrass.jpg\\n\",\n \"Resolving upload.wikimedia.org (upload.wikimedia.org)... 103.102.166.240, 2001:df2:e500:ed1a::2:b\\n\",\n \"Connecting to upload.wikimedia.org (upload.wikimedia.org)|103.102.166.240|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 3170828 (3.0M) [image/jpeg]\\n\",\n \"Saving to: ‘Green_Sea_Turtle_grazing_seagrass.jpg.2’\\n\",\n \"\\n\",\n \"Green_Sea_Turtle_gr 100%[===================>] 3.02M 7.97MB/s in 0.4s \\n\",\n \"\\n\",\n \"2018-09-05 13:21:22 (7.97 MB/s) - ‘Green_Sea_Turtle_grazing_seagrass.jpg.2’ saved [3170828/3170828]\\n\",\n \"\\n\",\n \"--2018-09-05 13:21:23-- https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg\\n\",\n \"Resolving upload.wikimedia.org (upload.wikimedia.org)... 103.102.166.240, 2001:df2:e500:ed1a::2:b\\n\",\n \"Connecting to upload.wikimedia.org (upload.wikimedia.org)|103.102.166.240|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 2684586 (2.6M) [image/jpeg]\\n\",\n \"Saving to: ‘The_Great_Wave_off_Kanagawa.jpg.2’\\n\",\n \"\\n\",\n \"The_Great_Wave_off_ 100%[===================>] 2.56M 6.96MB/s in 0.4s \\n\",\n \"\\n\",\n \"2018-09-05 13:21:24 (6.96 MB/s) - ‘The_Great_Wave_off_Kanagawa.jpg.2’ saved [2684586/2684586]\\n\",\n \"\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"_24fT3LkflcQ\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 529\n },\n \"outputId\": \"20cdff29-6fcc-43ea-a5eb-b14b54d8451c\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"height = 512\\n\",\n \"width = 512\\n\",\n \"\\n\",\n \"content_image = Image.open(\\\"Green_Sea_Turtle_grazing_seagrass.jpg\\\")\\n\",\n \"content_image = content_image.resize((width, height))\\n\",\n \"content_image\"\n ],\n \"execution_count\": 22,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"image/png\": 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YJYiudxrNe7EcwGgr7JylB3752I6BNhu6MBTHYBgN3crfcHQJR6F0sg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\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Xl/MBm31q6OTlGShOBlWqQFM1FTghLJQ/SmsxtrKSFEa00o51w3Ls0k\\nth6sduUYW+t3z+784Wd/NZHsaLGw1qdpulgtg/9vwAbCcEQII6AAhiAIyBgTo1WaGmMwpr2JGJDz\\nPisL3/fWRutQVmZlyWZGaY0CAAYEFFlLMbgHf49FtEuWDMoEkWCDARQ8EBQcozilyFoXfCwTDggH\\nBIQjp2zEyAUfg/v0c4d/7H3nV80MoYj1lKKou4DBU4gYeWt1kgprvIEgInYxIER4gherpZSyahvG\\nWLBIMB6CpRQvl4utgXzk2bfd21NvTunNF74+zBPCEbLRGDPIE4NDIrVW3ujIWU85Md5ZZVMZjKo5\\n58PBWlsvFlWzPdxgSZltX/nPH39xQE9Xy0q13ZlRgpOpns7wIvvEvfkP/plv/jv/vKvqnQ+d/55n\\nzpqT8jM5NLeuf3Y3GfLRh5f20YcuXYgwd5+9fXBw+vyLs2vv/Z4f+HPXlrfeevH1e8rC7vaOmh4t\\nTQ0oCcZ+8VOv/E9/86/+6n/8NfAA0PFhoatjhNBgnI0zeXhYUYaJ58iSV1/s3/nNH+Bjupzdqtlj\\n6fv+zz/7I3x7azbont/b/9q5d3xHG88P0+TN//AL/+6/vHh0AscWW4pQ3zx2bXt6f7+ZLWOMxrm6\\nUZhL6jnG4KJjGCcs9ivbPzxKEX985OXG5cQ1hvgY46pyxDkpadvqD723/Pih9NYRmhqjR1SboLfP\\nFMctUBGqDiUSmsatb6bLpaI4+EAQRBzDkxuBjxM/tci7JGEROcQZFaSv+jwTMWKtPME4Ek1xiNbg\\noqeax6B6FRBizhkIWK+aUca6Hqdeka3t0/uxQC4MpehjwAgAEUISDDyZKM/M/VNjCyQjbYh2JuU9\\nCdRFopTZGKhZPeZW+ahd35sASutEDFW0hAbMvAVe92gV2vt9Gm0y2U0rtt4v0dLqCVCRQqd0ljSY\\nYseZs8Q7nOQroyNBiTUBx+jS7PCeSkeHPNlylhzeo6BurKchT8j+YjIesKPTgWoOUrQgsgXMQhR1\\n53fHIg6T+hTTRXfojJBenLqub0Rvj6RQk1FyMQ+d9iFAwi3FQmstCTltcr7LzzLUd02SSW/zroq7\\n22edWx0sGxsi0uASygmPBFFAJkqfDC6W6uBeNb3RNnqDpt3hVHcecYZijFqpowCtVhlQn+TRVk5R\\nmWRggfgMhM+MrQEH0zkRcaU14Sh6zliHJuUW3u0vpQOnZnZo9l5YjEZn+44MR2J2/836qHNi9MzV\\n4Y36ODoUmVrW+bZDloPjiOjIIISuZYIggp23zvX53Tstz/RpO+zaGU1GkwRrC1nklLQQMImIpcMQ\\nbBvQTr4k7ckbN49ONacyu3IJ7t1WDgVPIoTYVbGcjLDtHDACFLdhltYrFbJk5S1KhHI+Z0lJQCnV\\nB1OS0YaEvTENBEHk42CAC0wxezi/sRjNbx+NkMQ5MjVjRVGkvaGJr048wSRhtNUmYpmmaYi2N3qc\\nZU1rRSp055JcCgaLxaJg7u79QylTo8Oy6SlNA5jxaNT3QAiyxnFBordpllIs0jTVWjMhvEfGtYIT\\nE4Az5mnatx0qxm9cf9EZnyYJqmpKkRCC99QEHwKiDDvjgSDJeKQQwgMFOQLAfd8TjI21jBH/oFGP\\nnjFmIjyI5je1oQgz5CJGANQ5sNbFGBBCIWDv/drW4NXX503fPri6SFPAeFyG29ZSSkMAjGOAEAJi\\nFDDGxtnog+DUdYdHzaM7IwTYVr2q6qZMs+gDwmRjY2M+nRkHG+uT+bzVWAOKbae2dx8CBgiH3qx8\\nZ733UgiKuXNOUL6+ga4fFWLoDm43m1tD2i4X7YpR8N5rrdMkaRuVCdrbQGjsuo5wVg6H3gegTPVh\\npSyhqZTo5GD/6bd3C/++zXJ1cGe/EIRHi9qOqvmsx5wKnglSfsdY76XQHHziN1/l4bs/9me/8yNf\\n/pt/+fXjeShG5W/9718EUCHJ/vR//70/cPbTd7/x3L1fefOVMPzBH/vjrfqj2weL9cvn00u711/7\\n8nAwnkyKO0f1/+fv/8Mf/bGf+OLnPzebHa+mR2m5xXEY5CO5nuVmbnp1YZjevHv3TJb7No7dpBPl\\np353/5FLw73rr2l14VX3vf/u1959/+8eJv3r5x+5xE7+2NbDP3DtT67/xNn9R7Zv3Tk4/Jf/6AsD\\n1zzv0yzNMERkII8qEsQpQ8ISBIznz1zb+tybptTTxz+S//qn+ROX05dvZkAaFARCMUaHAnv5+eWQ\\nrAM+MkaNmAaIMpOjs0X7ZtxcX5lXwS9XUrK+h7RMnIoxYOfC7hANzhQ391PJbEoEJYiKJMY44Z3d\\nGCDvfQSaiWiiSMCjNEbkGyUS1Lso08zMNctlvWqkRYpURhVUhqRuBnKiTDP0llBS1TWKAUVEGQ3R\\nzQ9wgBon3FU4xV4IYe0KhQ6rpizQ4XEzyEfGAIIeWSuKECKxpkPRO+sIocETb+JtZYM4WYNjixV0\\naUTIGYLoDEe8OSpnS0uGAmyqTXCmkwOP+cD0oVeLocAIsDVpghptTqfLlGrPBF76Wu9rFM/fOSR9\\n02LPPfNpCpYTc8Q5cyfzeRGk5SlWSiEekb3T3j9QthkOCUsHigiU5QhnDlKPKI7gdewVtl54EHWr\\nCBUoUlQlUYqu7jfWp5qwIgkYkMBatcG4gDBzIYb54uCkiWwf6xPKDl1XHRxD79KiQJJ7HlvkFI4o\\nT0EEBdBwHBECzoB7sJEqH6lbaiNWnWUURcCBUgTM26Cna7cBbryGZ426O+UN662BEFxv3gz1krAN\\nbqY37ksdLVZhZeJqYRBOOyO577w1KGZkDY5bwgg1vavMLIRQJradHvped62uarWRcsqzJBEJZ0lk\\n1CsOPkUmgcXJ4SurKqxoSmLpg+tNn3BP9ALbVRfSNRb6vuYi6bFl5vXDaStw0JGVRWad9tpKSjrT\\neGMJYU0fehiOh7iZIQRCUOZiCpAa+ojZ2rp6vrVKk2iNLmO6s3FO0Eiw12tliQlkEjPsVd9QFlPB\\nleoZQ0Y7TMJ4mAGOnNHTvk6SbJhne4fHzbIJkSNgy+VSa+2szjI5WZdSACMhSclyuWCMOmMQ8kKw\\ntrPBu6peZVmWMHR2E+0d3AzBvfD68arFSnXRWUoQBOecIwQhHBGKIQRvLKU0TRhnCOOY51SAw4Ai\\nAkox55IgSgV3xoYQGIrGaYQQxsT4CChQDNGHB9+GGCPEEAIcLw4pwyH8N3hc6FaUD2JwPtg0L6hI\\nGCEIgTaBUEQQ4lJA9Na5l7/ylTf25xYBIjiTVPW1t9rq/nQ2H43L4bDslZIJrfvWAxJJYojN8qRu\\nFQBEGgFw26nZsiWc5PTw6Q/+8MWHt+a9DPZouVyqfgEMIYQ4IwFQ21QUUx8MoaFTLSFkMV9FhKqm\\ndhbavnnj5VdXqwUXZDwqvun9j3/q+aqd3dksi+ChVS2jYVkvSq4SUpl2fu+t65PNrc0B07B3sTj9\\n+L/497/4n8j/+i9/+m/8+OjoePHsuzcmk/jK85/5//6P/7gbvf+jP/4j57/t4Wc/sn3vZH/7scf+\\nxA9+dPXWiz/7j3/7znT3qacfDbl5/Fue3j4/fvkbH98o+OzoyAa2mKuD4/pottq/MydRbG6s9027\\ntSUvn1+/cHH05NPp3fbSeKu8s6iuH47+6DOvfeHf/Nb64W89WX7h7PgldPpJG37Lr177wr/7j5/8\\n15/7x/8q/Mt/9fhg57/7sb/z0//7x39tCGGtEPViORnGNGFOaYySRDJj3Muv3c+S/MLI3cmk3auc\\n2R8UutbMGBWjfzCaP151yeDIR0wIC4BxOV70oJr2TApESEEpDhgTb4yKAfOE9RaocxnyixWPLhrr\\nTYiTFK8VaUAQQqBJbknuAy5Sxjr6xKUc45JQyUQyQvWlC5O2QcCQ1jbNWGCZ7QD7FSZgraVcpZJL\\n7K3V1hsXbDQheIgBrY8GVArnplGvnFuOR74rLllriW8nw8DOPuYD8zFYiAmHaAJilOCICPIxti2K\\nVoPHVvXd8kjmpw4YDpgah5xprabLsLkOlu2mjlWtqldtZtvty+kRnfTaRXB1o7XubfBGyMO6hEYR\\n0UWwaUabnneLW32YEaRirJF3rusK8OmYL5ewFelw4IPHmPDIxEnfIrS7feaKCJHoYAjLBAohRO2I\\nsWCdTjB4hD2gEOOgSEPwANjqSkSutblzw7MsshB6hNd3kpxTTIlxQCiWou9U13euyJhTLUVdxIhS\\nqhViCaWUdsoRIU3wdRdxQh0g70OtbaP6UUY8RIpbaI9VH1ute+WV061pEF1w2NerW6vuxNr7tkyj\\nYCQ6TzQCLAkKUK2W99tqPiot6SrMBYQYdZekOBEUhwjUqumyqXXfzLplJYV1FsVQIaMoE1LKtBze\\nX1UjmdFIWicfPrdxdj0ZyzAkd830zsokCNNBJiPWx3tzjGmWIUFOPKIualW3QiQmGJ5DmlJMgDEx\\nHq8v5qsQQiFL7D0hCAHGyKQoGAfVouciUcH1iCDnAgohAODL2fpkRE3d+YAQsdTxta2NWBYDZRXB\\nzAdgBMqSC8YZcSGarpl1uo5gqmreNopg2vbGAj2pVZmmOrjZ/UNjnOqQUrqu26ZpgwGMKWMsyynB\\nXPWWESI4QGSEEOcDZ0mnQ7L+UK+qQfQpi1mCVLsgKFqjYgxZyjjnWmuCsPfRQxyOyge7nkUxkAl3\\nFgICH8N/iwtxWsi0aRqZMILBB3t5Lb80xEJijGLwgBCiD3SylGIQhCDie8l4jFFIUiQk5yhiFHF8\\nMFw2xjxY+HmAHqIUhwcFMRDUunb/XnWjQiEAQCAEad1jzrzX2jXKaEyJMQqhGIxjVGTFoG1MKgTn\\n3DmjrMGUadPjiB9+z9O/9M9+5bUTcnRvTv3pRgGMOAkIgkcodn1b1b0PjhBS9Z6znBA0Hg61jYks\\nYoyEsKeeuDocDlAMKZnvr7bffHmPh8p7z4JhAMYYJnhRFC7GyWiwf2f/Qx95d5ELSfzxtBrnUJ28\\n/Gv/4cZjH/1rEomHttZo066jJWWHf/+n/icaS1pefeP47Jeen33id+5/9uvm8W/54F/80Yc/+mT7\\n5vW3rrzte+58/egPXyqzS+8OT7//HX/8j18+s3XuTHrpwrZM+HhStqabVVUyzNc3z6xNyIWrF77R\\nX/ry7872X7sp7lxfM3d3k+ptZ0Oja48IQdF7ray6fvOVqE9fuHv80vWDu/NX2v72v/+FT5HW/Nzf\\n+dP/9icv/Mrfsn/q2eof/PnzP/2ju88UbYox8igrcljWl64MVuocIHMwW1x7KOsbJKUUgkNEFydy\\nY3cryVLJUUABYeqqmpOUme7hR9KMEY5FmScEYyGE9zGEKFhMSeAy044kWQiReBMSQcAHBIXzqCAG\\neZQmzBpHohwUWbn9DudyrS2iaDTa7stH18ZlCEAQUtF0rQ/BSQEmCh1qyVxbmVp1BFFOGUaR0EgZ\\nMNBE5BRJSYmkTEiSYD6rMm46RJ3tA6YyRN9V7izu6Fpad5FijAkFiILg2ujWK+ItxS5a47oG+0YW\\nkYMKLp5UM6BuuH6mM0ZfXWeMJalvFEIrKjGmmMxb762xJNy5d9geH/GwALRiGGynKAYTYOgWNJli\\nYpFTDUpOuqASrJOdxlOc8aZexy4ab32I3Pbq4P5i2XccMd9X89Y7rS2KZ3f8ZGAA2WADIjIiXLcK\\nCFb1POSst0uEVhBLonsSMEQ2P+o63fStMta7oIe+5Yhq4AlRw4IyQgO4RLgsxX3jY1HsZGVWYFX3\\nZR5xCIFw77QY7Gye27T1MRAavQPkGQWCQHJfpAJQJMGFuByJ47A4POhddVTrqFZLoymkZZKAxl57\\nRYsBrXrPUhI8wZiKJCGmvlWjbMClqLP+yGIjSFMOhEyDywo+IpuS2uAR2PnsKBleKiZhPfeRX6YZ\\nH+VkJ5tLs394mB/3eB61aVqboK0MIzGABE5XiYnMKF3kaZSI1lY1EUOMWFMJsVuORutluUGJfvDa\\nyqXEFDHkJdQ+oLY+tgrpkHMuCRaMI+TxHG2ePWcExwFJylMiHyqG46TkeZnnhYjBpglzzlmjGA0p\\np1tbO2fWi2I4cB4B4Fr30Nq+71erlelmgwkWA6Grvm4UZzgRcphTYxWJAM6TiPMsQRAQwrgcEsld\\nnHKR8Hw8GEqBmjdfea3rm1ev37POEMwo4c5HTqhHlFMiKUYoAoAxRlsrGXcWABFvnVYBEYwi2N4F\\nFwOgzvaCsuB8nieUJDg7M9naQN4QHAmKnEnGMaW05PiRi8VoUMRAo3UDAedGyfn1YrKWMozWNiYA\\nABFb03mjnHOUwQNeEEIo+oCASAyEWKvuvf7mnZeOam06TiliYF2HGQ0BGCUQQ5olkguZiqqftcum\\na5q2bedHUwRBd31dryaD8kKpbt0OP/5TP/zCl09heeBNrZangAgE67rKdX1KkBDEh+BBJJI2fYMx\\nxRj6pq7ruqqXRZFFxpfL2qju2rWdwfbVd50tVbeKqvbWcc5FkoTg9g5PMZLTZdXN5pMhguZkS7ZZ\\nDHeffxMfzh/a5TdeuPXlW7P04u63vHfn6ORmX7U+Nr7+9RdeCIvrr99+/hvHBzd/4V/9Yhb18/by\\n15qHb+3R+y/cOPfw2b/+F3e/8C9+9cPn4FP/6ouPvuOD4zO7k3ObTo4VUMzyMk0HSTgzgsk7H3+1\\nu3Ln66cXsld2hqeINnnutnfSnfEg5gOEQkTM2bZIeZrJ1sdFYNXpqVneXJnFqiJZuDMlj97Z+kuf\\nHPzXT7U//8uf+KavvP5O/8RP/+2//f/+Sz/8zfmooNqeezy9RocJD86EtrKTsQQAyZCQNCVkKPpU\\nlFvrHIPNJJGSX9uGyTB7/rYyArTWmLhMuBCCNREhmIz4ZEPsrcDpCjmTCJQmfFkFQtHWOKUM2rZX\\n0RGw8wqFrH3jnkf9UsoxcmmgYlXjFR9VFc3TAmMsMUmzoeQ0gieCc0E8eAgwyoclkxmnkic8wRFo\\n51Ga5Z1Lo1dMxr1jlNveRjGQipccu6G3fW+8QOyURqVE245M0wWCmjbO64ZGhpva4aaunCcaMEJc\\nM4wyKcbDRMrszo37HL+aF5PBnQ5zVG4n1VTS+q6yRnC6VdA7C5jdn+EuJv6QkppT8BAppa4ofSRl\\nmeIyoy7grOx7QiL3+3vc3W3auLxxNMlPME8GSVJgRgAwISjn2FE8LjE1OCLJJ6O1MfN4kKSEYYYQ\\ncsiLhAfr2TAZic7HQLE2QfkIK28LvtJaCxoThj2hlKUdkZxRQqWlhcdpVo58GBkd284EntRVf69u\\nsXOEFz4E4hpqVgBp04BcC03lPSDAyBmPsAGmtV8qY0gIgtrgVaeV7yujWqMbmZAsyQPn7bzRyhHA\\n53ZHmSgng4HVkRBGMKeCqjYUiegXU0w7iDpAwXYf7hSjsSRChEU4CUCCYUgl6Tbl2QwVKZKY0lTk\\nZZnlGbGeKmmdqRuvtAXGk/0mSXi7uHddSFBdu7k2qGvbi3EmKg9RG8+o5AI12jtH18bZemmiXWGS\\nAMYxIgQOeTfeGJwv3fSwwcFZaxGOzoZ4Oq1WpGabw3WbMNt23gc4PCHbu0m9WBrbbWxsJJwkDEtO\\nKS7OXRwhTDGStoO2sxah48NDFUIIIcsyo701OAaaj/l4LJOEZUVepixL0qIsR2VmrTbOYkqY4KZ2\\nRofodNPj4PJGj5zqEKEYYy4SH+ODuQ9CKCCEKIXIIyExPtjpJE3rYjAIR++M5JgxluVScso5o5wF\\na9q29RC5oNZa5/uj2amOkTGGCRNJBoJSwXNOMU82NieLRay79uoEX9ldSzJhbGgawJju3V7lUmCM\\ng/PBGW91MMAIDcGF6IIHrTWjmCDMMOTdfPbW7fkJaN1JDnlCvHdJkoQICLA1jjHW91rw5AHyLCkS\\nKhPAMS1yzilCYf2pJ595zyO/9ZzBaKZdQ7mcZDHnmjEWI+Ik9n2PUTTGBq+DdYPBAEF40PJKybtK\\nNVXdzGdFlm5tDZ/6trffXw1GE6m07bVpmma6mB6tbNNDrUzbaSF4tVi6YB/+4Ls3v+W9O29/+of/\\nx3f9+N94Cu79m6u78PoX2i/+5ovy2tt+8Z/+9XPo4L1j1nHSdce2n8kt/tw3vmZW83PpzcXRwBzf\\nfdvTO9WomB62f/j7TXLp2Xu3v/H9P/StXfdcMrzyxNuePD8Rg3qplntXLtGH3vuU33rkuc8HvX9A\\nuY/W6EZ5QlxEbx243ocPXJ48e2V8YT3KPNFYKhsY5jHGhMLW9ma3ai+Lxc6kWY7P/e5n3ckX7z65\\nu3d2F/TucLX3tV/92d/sq8kufPOPvevg29721Ice5z/+/vMP53x23DxzUU7KjGNEMO8DfOS9CWFY\\nG/rYQ4Om7xhBBQ+myVUABbsiHymLhlnhWBpNFJIF5wfjhMlILGCMuYhDTvJRRikNyFBRYpaOchyj\\nN3kOPqrer4/n+SiJYq3Tcv/23StqXzkdzClhygJHWCEUTReoWxJvY9/TNI/ARuMCpxMVaCGJ0rRv\\nbYRKpgmKhDHCSaaD8QKUCytLsWrGOeEcJFt6p+1qLuTSOeXVUmBNBFHKcOIQRbhyBQNronVeq44h\\nyAXfvLghs51b+/Xt/bblqfXh8E5vUTUaYwNd11SYeIKli6pv73JmrXPa+aA8Tum+8cjz29NufjwD\\nSkgpezs+PaHIGBznIi6TgbFCU4zJg+bc9jYdMY6Zau09FwvEovNtvdxHHiLy1nrPYyQikGUVsChL\\nMmuxHCcmKghYQySRURQjCUDCSrdDWJNZ164iTRmVoW37JJs0cbpkcc0bTwvXtDGljkiGEImAffRy\\nkkTjUukWjbr54is6ptD0aZnmhaK0Vx1J8okebjwOy5tYrLVmZmKWEkVhROKeqkPA0Rma8s4zR87s\\nsuqwttu5Op5Tb/Ta2KnGKUxwBFKub0A8XqAa5ODeAUFRdS2Q015w15N6OD6HaWBOL2CWO7XH+YX1\\nsmCSrvp5vXcqHGu09rkU3nb1/vUknzCvitGFIb7dkvOFn3d8ULWhQcmYmIWhLgKA5zwjpDzt6Jmi\\nSIUOnrTaCJlSoppYFFEtSDdZT/DsYJac28iRDR2KNWeFoNmKZ4z5ZrE6Fc3quHzPJVY+E55/ee6j\\nI4gBJq0ygtMb11sXPGfSx0B5slrNqSg4bldLZZ01AT26WT9/5MaTjb5xdd3mKVTBDIdD3ZuNjWHX\\n9M6CSNlqtZLlJHrEogcCnNuSr1azI7Dt4Wxl/QNTB0KYOOe01zFGbQxhJALESGJEqjdZKjnGMUaE\\nhLImBhIxEpzG6J3REVNvNE9S1du0EM73wSHnwigltfJW2Qg+Y8gZ7XyLIJZZ7kZk1VuCSeNCq10a\\nAYNLJFn1fQQcAhDMYgzOGYRQmnLVIYoRQYEQBgDGKCnlreMDUTyUR8XBpPmgrnohk945QnHbd0mS\\ntSstUz4qB8cHRyE4q/q2aZD3j13Fd+6lZbptj5e42Q+oy4S7+O4PL6an08/9oRSs79tyUFptuk5Z\\na7lInHOMy5TzxbyJ0UuZBBt602vjRXT7+6Ovfu1r7OT5c2NcLdu0lMbSu4v5mdGoVYmp5xnvHjpH\\naoM/9ZkT6MXhtPrVrz8XTfimp9//ro9deP87qhe++oW/++d/+V//yj/96/9k4sjrVXxv6N6cTCaf\\n/fQ30hyJqA/a60X5WO7zl1+80VfdWPohZYvF6l/+c4Z3rvy5H33Xr//sxz/12/73D87vXLjy1z/4\\nxJvL9t4nVmWeFrHq21UqQCZZFLxeHIWIcSB9AK5NX6mUJJu+bZF13GcZOlr2jGV+Nv2my5sf+z70\\nyfvXpl9xw9lbxYb9jeebo5tvrY+zzpgqoJMv3vnHv/hTX/jqs4supuvw6IcvfeD7f8jn5ku//r/x\\nZXVzfX1+z57WZnZPzRvnK1sK/L73P/T6a4uqMdlZrjxD3nEvuhBmdVOk68a5YN1YsrrWGpLNJMB2\\n6lpfjsR8pkXvR5tFSsWishpTgSP1MRmIpovuqDK2o0CUos8+mX/pjvR6EP2JhjRoT8FzgRBGiBHX\\n62FW3grFet+X617Vow5qxivG14Kdej0HGHKWW3ualawyyKnlMh3628shtcVI6dNJDo1GCSGYctpU\\nXGqrowWPOIHAfQh4uIVwBpwNvTXGGoST1iE9qxHBqadETuujfUB06TW9e7pYYLYpKcZa20FOYJWu\\n5Q4NZFunyxPVmJAuG54mNgy9scMM2oMZtoLSzEdurbK9Sc6EbLh2cJxR2tWryBOBXMTKBSmcxKHW\\nuLUhEDfgaS5QwdR0ZucMM4LUolmiPBOx7dJkxJ3vGQ5NdDF4BEhZJxPKgyQxBgiMxJVh54r0NLgY\\nCIQsEMcFYC6LLMr1wSCY5aKveic4o8K1q7ZIEm+7LDmZzRnj3Mo+EBR9UDaJIFTY8pY06Xl3fLMv\\nSVCIImsVHAWejBg5rWWWB6cSyVer7rUVPHJtEOdVmZaJpK6rVestZlnBBSnr5n6ekEq3KFjnCOI9\\neMoYNVU6rTbY+fn96fGw6NeXdCYfOUsgYj9E8+N9c29WrJ3VWSZMdUxxQtiAhRZinE/t/fml8+87\\nM6xeU3PNTGjZ1Ul+G1rvHGIkEflGkWKr5qcBsZSvjuaYpEDAWCCYtAYIz2s1fOLC4vab+2/a9YfX\\nDSLIArIOmYWPocFmkiAULl9utaZZvHZm9cp9LygJPkhOEfgQIJXZfLGKxk9Pj5xIhkPuLBoNs65v\\njB2ceerR23dedTZo06Z5UhaEMckIalY2zWjfEa1dPkyKwcg4HUMoMnqHnHn83Froqtlxt+zaqlE4\\nIiK5VyoCCMl01bvguaBaA6OACdjeQ4zRWQ8UC2K1JYwrY6Tk2jiZEALURe4AjDEkAsbYKJQJgVFk\\nhBHfWI/OpWx315XjnVnfdp69cHsZIAZtHj2f98o0raPAlqfgY0ylaJSjlDDGlLW9NhhjwMR5BYQS\\ngr01ylrOeQSPnH/tzv2rF8c5wfWikVL62AaA0WgMPlhrMSWEsxCisz4vWK0h5SQLy8nl7xiN1z/5\\nhVNTvSUZprFHLHnl5Tea6ZQMMmI8IaTruixJ8zw1xsQYvY8kRO99CM77KIQkOBKeo9i++90jJgcf\\nfp/NyBOScYf2fGsPX7rz1DFpaPf4ej3MycbVhwbnH3eUfcc7Hv7sp3+f5+KJ/HQjS9DNjzv/vqXc\\nvfa+j167qP7tP/qxD/3YP/jLf+8Wd3/0f/yvH/uNX/7nZ9byAuTJarF2pl0ulzLFuu8HRRRUnsym\\nBDdFOfKzN/6Xv7f3j/63v7i6//sXf/W145Ov/8rP2+/+098+8vsDXI6SbCWF7npVLzCjlGKvvBCk\\nqeqdNXrh7FpdtXi3WFRegCozdXCa9PVye3v9ibed5Y9n3Suba+0XgE9RyJv58tzGWLtThnVvWZuw\\nv/MTf+Mv/Nwv6Bc/n2bpkpnrvnz1y8Ha//lP/QTv8e2f/qn/X1ng5677D7334u/851ud0Qd7rSNu\\nqczytPMImsrngy7EFPn5iKzsEBvLQvSAaF6IZVVfkMm8ljdfq89eSBOGJMNL5RBh4CwwSFZLWjLq\\nZPSeEQ2AN4vwxTe1ba2IDRHjoAHZBsBiBCxnq5XKBMWAvEMc+XrmhxvDpaERFmJilyeEjoJkYeV6\\nRm3bWwMikrzuZuvSMKn2lzlEn+ZZF4I2HlxHsA7eexSw7TkhgeCINPLIRRu9Q4hwio22imdJzNJE\\nrxoV0JhCl+VzSk2Sr2UbcVkxjC3lBHkfpDTFQHcBgAiuAOnBgHmkjFkK4n2I5fmcUIF15KnzaNiy\\n0zpAetwOBj1usLARp4VDFDKeuuAt4yShGQ9ODXp4+ivbjyqsT7wpE0+Teuy7XNpJGSzwZt6hAF0s\\nZDJijHkfMRWIcWNcWkKmZ6vGCQy3T6cO0YBAqXRtOEIxn2ydm/cXNgp6OF8p55vG9da4gGDtDM+7\\n4Nr5aZPJzFpHWcT9yusejNKqq6Y6UJqKO0vllivrve21HfOA65RTFQAZLzEk41EcWzLY2EHguB+N\\nzm3mzFM3C0pJsb2qYTlrlC5Uh7SHJKXeRyYHZ84P8+i73j28aVarhfAeRdPMGM/QplADdHD0xus3\\nwcUyWRh6UgHLuUNjwWjw1mmVy17I3cWUgRwP40w1PoXj0yWy3gtOKCQi4IiAs4giqEVFpWAch9WJ\\nd5EhtWrxGR68o8oPBzt5sbh3dBoiTTXQgUQBWwK96+81ihCCloY1HeU728VAYoyFENZ4TAIi3el8\\nbzgm89NbCEdsKq81Rcnde2RW+6ZfrW6eBNSOMrSzzXc2pNZ1lpba2Z2dqyfHy6ZppEiO96fWO0YT\\nzFLEx2Q1feu4u3Xj1VW3mp5WiBAPcVXX3gdrreoDEMyZRAi8c1r7PEmAwCCTk4FMOULO9cYHZyN4\\n0yvvY9tYjgXlDAWCEAIAox1jkSYiAozHyXZq/swT/r0feqjJzn7jjsLERtcnYGUwg9R2xvfGEhy1\\naRetxkJSShkBDCRgijEmnFHKu1oTRlAEbQERTAAo8f8NStys7u+ZmaXG9jRaBC4RbDmbe++7rkEI\\nVX3NM7l/9/54veRIZBwuvPuRm6/dXYbzvnkd2VMwjXMmYwM/3c+50xpVVYMwBYAA0QUfY8zzHKLH\\nITilGSc+qDPbaeRu4O9/1/dv+jMf6jDc7ZI/fHPymy+hP3jh2nN3tuqdb/PPfIQ++UH+TX/avPPP\\n7bF3v9o90tfd0fLLQOD1V15Js0Rm9NsfMr1bvv7JL/3Uj//td739e77tr/1kbZY/ejXyg9/58ic/\\n/8jDZz/4KN7bex2r5LZ6yAarrSdgI8XX7x0AIpVh81V3fdbefOmV93z7X7k3+/Cf/Wt/4crDF599\\njD336d9LN9ZIxl45nAeDQggJx8QqHDFmwameU7zqmtmybXrQBgAhRISnW9vnxpeefSrdPNu7dMGe\\nWN246/R+TF1Q1dMl9O2swLzprRDCBWjCkL3x99KHHr/FHvrEb7Rf/+T9L/3GF1/6wpd+9ef+472X\\nDn/m53/xf/nJHxRN3OT36ebDTWsWbfPuJ89u724oS0Vs5pVv/cihJKERpVndlsETgCCExL4rR5ma\\n1WWOJgPkGu3HQDoWcEYA0RACROLbdCMSTgiKl8+MEBmeNgJIRsPNYJfRdGMW3vEshsnQBEwIurwr\\ni0Hx+LV848z65iCF4AmaqsiXq4yY2mSTZubBnOKcRJVEPcd+mqBucYRXne4ArEJOEWY1K7DH0sUQ\\nMQKMGRaIScAIIkKeEYIwpiQ6FDyGUBYkpbFVyLog1y+o+eiRDYm3LpFkS1VNsBhjmiQCAxCBIo6z\\n04566kLAjHNOY+9NH2JELnoUA/jQdL3SqKlN6VbWbHg3XETiW0UZTzFyx0ckIh+RASGgU5HQRR2w\\n6JLZc7tfb2+iQXW2OO+8EkongZP5/EiN89J6pGoydf0W7RuPULAIA0APEHxQpjGCgHcmEzjYlsQc\\nS9QDj5gd7Z9eO8/uv7qfFFJhfWXT79fCORD9tIu+aYNIUm09psNhiXA3jZF6b/MEtXb8TjH77HN6\\ne4sW2BooLWcRNQ6Dm9tVQ4lwYHizUBFzMMboLI6Er6tssIY9G9DemRuBa2rYqWpwT2iGTmrDiKxX\\nW1CcmpXJhzuxmpGRzMzSqIDQGvaIch/NQfS5qbvZaj8vBM19O03WdoWuFEDkmahnkI1VSsf3V3yL\\nk6AqpXlBTBAkOgMROad7jUuBfdMRHEpGKoIJQT1xyuKzowkxsxGD45MFBPHw45PlvD7e7/qd9Zb5\\n8TA9Pu14gjvfjZb35iIpBV60ZtX4s1vs5FgDQFU1oshKgKY/Wt+ZVKu268Nsflpk+WZRzxubZenB\\n/XsXt0cOERyEcX4yXrfWE8x4WlGaE2qN63lCm2qVpdjzDKc4278flpm1brVs6raLKBKEH2zoIxyC\\n7yCECD6VMsY+WoxRxJhunz13cP9OKqXpujRNvdcouoS5SglCCEuF63shAQI2IRBCCHgVFCVkRPnV\\n73zi9z6znF+/feXamrNBZtL3+uEzWGF+5sz28y/PGUYmACGkaz2hPhFcaS8T3Pc9JaTvdCo5IQRj\\nLBgEAEKIUR3GicTYBEMICX718uvVM9fSVd8WIdExMioJIVmWVa0uSVytdKfaiEDKkJ/dvvlKuPqB\\nZ37vs68Ufs5RAK8tgNG19V6tqoQlHUGcc6U7ZyxCqCiKEELCMESrnY9ARoOh6v3Z3H/gxz/wlecv\\njSboK682y3nTHry1feZsZGZqu2NmZg31vuVmPtga1q0tZPXbB6d/5oe+d7b8xI9OHvuvv/7J7z3v\\n3/lXf6iPZ77vw/W1YlvzT/+Lf/3Oz3ziU5/5tT/3wR/cXLz25fyxH0cb45/5wXfHl3+1q7bOjFTf\\n+MMVbJped30DD06jsenmo1GSj8JP/uR/n6499l8+/pe7+eu333xD442XPvt1TEctHCKRI74G80Pw\\nwfReJty3Ped0NBpUVYd8HA3TpurrepUW3HtHKUOtx0q1fXd2SG2MHOylS+Hhqxtv3u57cqaqZ8zJ\\nh7dcf6qnu+yVP9SPjZb71a2dwu81B0WG9++u+n/5S9/ywz/4V/7R+3Wz+sr1X++L7dNl98pLt89f\\nKGWXoiwRBPfLrkzchHJ+fnN+PGckIIS6rsvyEgjxBEW7EgVTS0MtpqQocrKa9SEa8EEUrDkk2zQ7\\niQYXzezGKCGG2IWPOJcROzUcJHvddqRnEL3e9QvHgEbsNd7Tjz6Zo4euXLlXJSNf++OYjiwvTHd9\\nzM8f+xDbiIYIgYeHN7OgV4iXTdvY1q7ltgld7LHVLOXgQ7TAUQTwvccPmImMQBscMBbSgjUN0h6q\\nlYkRNcc+6r1sPZsF3BtH3VbVo0x0KUSEaYgo4ZwBqRs9P23yIWY4Og4CmwCi9yg4lJCIcMRB+Bgo\\ngZPalZvSHmEsZopK3DW9DzoibE0MNlQmyoTGtlktWoP6mM5DMM0pxofoBcfaFgWBkFWCyc54F6z1\\nBlutjPHWDZJEUBI0jtFbh8UoQQgxRrmgQJixBHyomg5hGK8l03tNS7Z0NprkSVU5BC4qiyEa7Qjx\\nieDOIpG2uGv6UGKcYi6h3xyx2eJkWaynayLhnMdgo+29dWJoN3IfEbWy3N7JEyEpNRojicDXVmtZ\\n1SjIbcJIa/GyylamZ6gHYoTpGCMYkyILycrGdLJWwrmzT1QmObYisMJAwBY3qnfdfNZPo50xDtZa\\nYtql3zhcdR6B9cHoBidJIqmazofEvLEXBkXSNoKKjTNbk4JTG6Z9NGnq5vWpx2m+to1DWC1cpRPk\\nXJJPSEYP9WYqK8E4Y+z01KM8v3YuTu9VezZSgrU13plDBWM5t9N+fmhVJW0Y7R1DpyEvgDESQnDO\\n4VCwtGxaixBd3xhGi1cNSlJD/TGhedspcL5ve9P10XVWd5xlhFPMqFItij5GlGSp6W1tEE838nKq\\nm/3o1cHRoXMOMxqiIxS54DBGnHMAoJQZY0qRFImkRFDK6+UixthrHQCMMd57HygRkmII0TWd2Ujg\\n6avFeMg8mOhhJK3S3XuemuCNya//9sHR7DhgH6J99OpGjKK12b4a7E3jm3eOjA0AEKKTUjKK8jR5\\nYB0hmHHGAAAAXAwAEELgUiAg0bssIUUSlfEhBO+d04aDe/3AdiZED8F51bV931tnrDM+uojkSnUn\\nd4+A5/MTePu3Pfb686usveM60zStVw2OYXFyYvTKGIcxOOcieEKI915yobVW1jTtrLcGYSwI0dpd\\nO6u/8//xQ1/6o8l4e+0rz5+G1V6yuj3OemmPq9XdYiK8qgo2f2g3P7OBsFlwc3Ry94VNMf/dT37t\\n2W/5zm/96Dv+4T/88Hf+w7/0u29+z7/6zaN/8w2x88N/q2bvf0a8+rF3xa/85n+8WT3yKv8RE5av\\nPVf9/C+zf//Wf7eZvO9Yu4R7HL22MStKLhIw1e3btwkw3bftstnYGW3K4x/5vr/Z+EcsfvRWlX3o\\nB/44AX34SpPIYVicRIKTJBGceutkTlkqlaWNDSxlTdOlZVqWOcGMCwIQR6MBySSOsDf3e8fk3onp\\nuzAqy8cu5x99Gz63s31Rn5YbbvNdT+/fo2M0m56+IW1rMmsMHNXtS69ddwj18vRwL12eJt/66De9\\n5zv+bOF53bgi8RnTfePOpOPdrUEAslTMg0Ao5zxa6zNJADrvfYw+EuxCK1OQHjeq97YHwLkUCGOE\\naYimwSecrFUVTrhgCfV9JWnnvZ9ssIqI2QHYk7so9Jhy5LCz/YuruFPfXui+h6zcunRcbxt+btUa\\nFBWVvUh174OIBmPnjFmFRVE+Zsm4Nxl4U4SjbERt5AzbEAKEiKz1Vj8wKqMICJyzJB+PrQ1V1WEU\\nAjBJSYIUERZTAhE3xrJZpdq7G0NHE+qcw7JhaWaV9UEFmQBGCFyexHxNKnzGeGq8QxGnRA+yZCCL\\nzc1NURQsy/V8GkNzdjLc2R7Q6HvGKXAlLeoB82Y5Y4Fhin00VlERGQ60UCOeKVc71apoGIaMk2lr\\nEPYG8WHCOluOExZcw53qbYXKUii8Ul3uiZQkem80RNJzm9d8Q9j705sAeCDX8KpOytFdEtjGBkfB\\nz1qPRcFtt1pU2hSTRK9O0nQiMEYQKClswr1uEBtuBzSdV15whqjzHhunXY9inAzF2v3m9hozWBOL\\n5P5C7u44Mk0F9/1sun94yJNCgu6dphRkQbzCug6Q4wjzlXPl+lidzud3vrIdxB2ezGe2GIsEprFe\\n6SjqYAYpXVVOuRoZRAozDEqZ0JqkkKAQL7GdbOC7t/dllhJKBR0AY9Z1hLosY7P5dCESF7NSbAja\\nKn3gzUaRJyGwBPMXvvjFixfPGWMYE15HIUXnZZ7cPifIal96pyYpWwIeVo5u+PnsTpLham8BiNJR\\nwikxjgCOrutMa0VCq7ouSmGDDz7WXU0zbmy6fXa7X9k0EZhyymKacd3a9a2dxXxllI9YEMKd0wFJ\\nRlk6RBltV/cGNEYL/njvlAnR1wY5IigOFgtBtNaq62WaPAhnMcaYQI21zqlWBSG4tS4A5pwQwrTp\\nvYOIAwJsPFy8crHDV6rq9ziTPM1VP2NAX7nVYzEjoDkFHyMGdPu+fvSyWC4PCEGPPzZ88fl91Rnv\\nIyeUUoISsE6jECV14H30wdgI4KMJGBBEHJwP0QhOGSa6V5gxqy1laYyBy6Qc8FsnKtmkOWKYsAgh\\nhFCWqWD8+P6tM+O16ObVEn/7Ry58+W7fzV6l0dm+lcQCMN3XFuuMDCKQvu+109Jxxlgqk/l8niQJ\\nlSLYJk3SqlEihacuZJc++IHPfabhD1185YUTzpu6WuUoWqd8xNHHk7u1TJOg9cm9pdKtiK7I09n8\\naIE2RER/8Mu//6f+h+8jSfrTP2uhvTlhb+hs+w9+7bW58x/4wAd+YLt77hO/UI42//7//f/57Hu/\\n66f+wgf/4d/9J5+/7p5d2y+6bu+No93HL0W1d3LcVbXeHcm3ve1t927f6pRnNHXWv3X0xkA8s1Ee\\n327n3/+BR9/40te+5Vsfv3yhff65/dfvH7iQU4YjjWXGjAvee4hquJZpF5O1kemWkMjAgNg4GaC6\\nV2VIM6gRYyx0xNMyDYuVSouimZP3XJUbj++oMA6Bm1XfTvexc51Zpl4I04yLcHF7JwutYw61s5Pq\\nSC2m6zj+yMd+zKrf+pXP3dncIHywoU1dbox0l+v+ZGxmexlNBHXU+BBSmmLKI/LOm911f3KSgPOt\\nN2kkecqNCQKLdAKmErWD0db81lGR5e1qISTuQwiU4iayeiVCt+TecKEQyZx1QiTVUg/R9aW2J1+f\\nRZhu8JHqDOiuYC57Otl7aSxHYEJMAdKyODxpIz02ykFkjMRSmtG5jcX+OK5uA6IQlBRJkvngsj4i\\na7qIAgOuGsQ5R4QEG1xoMKXBezH0LgDh2oDBviU0Lwbx9hG6vL512Gnul4ImadIqBZbgEHHdQ+xb\\n62gipBCeYjxVga1cKkOwhvJki6cijc/ve7W4fwoDTAgjjJZnEuOhUy2KEBbAKJYZFhxbhSjJEqaU\\nbVK3jKHSyoaIKqso9xhFHDylwGhedfHM1ckjZzimwnvvl85iyfOcMRIMig5VTUdkV8TbyliMIQTv\\nm0U73T+db5/K3ZuyMOZAu2iM0WApZ1vbFKY+lQQ3up/3EHDgSUL53HKH1NFJzxjzVunecsoEi5y4\\n3qCEdtg/sXNhY+3sABchsurkcGrNEWGzlM/GeZ6z2vsWg25DoSEE72kujFYIW4zzvlpgnjTlNtlS\\ndrUQgnGMd87ZS3DjqDm78chDi74P0dBgnRdDCRCtsagcdIhxQXBn28X+c1yQPLUYd6FvFzgEYwJk\\n3sWrVxg9nTampCR4u3/Qo+EQrLVtp27fub597rw2bSIkJw4hYhPRn+ztH/tIu365N3MToASs5ol5\\nc1VLtgLsHUZYykC9TK23OgCS6+sbE9l3LaOx7bUPYbXsCUJ5mpTFgEUSAiKEcc6llJzzwWTkbHP5\\nSpGJDGwvpXhQwaFlOyZ8fTRe1i1QWCAIWkWMMfigjKWCAQqUMkyJMeYBsYdlg+GYMmw5hl45BOGB\\nFN45Y4yhlIpEEoI4NRY9dOHha3m4AdEiHDeT8OwjO7WFoE4ccEopJZAlJOFiexysoSFC8Lo3AXsi\\nOCeEIKAIIadV03ugDKV8lFRPPnuJUpoJOhlIxkgETynNktToYEzgKXvy2mRtlI9KQSl1zh/dr6pF\\n++r9uosyxhicZ96VSaR+US2PF/Np37mdYb9fbcWlHeGa+oojE0KwrkXYOdUiRGTCgvODYdl1XQih\\naXtABFOymq/WclSvYDgqn7i6fu3tT3zjxbh27sydr91jUg+SPMWCUFA29HUl3TLjvj2+mZHOtgcp\\nt1yoxixNONqaQDi53ubm+TcrzS5vnNxdZy9F1IxMTdqTdXX0ymff/JVfO776kb81rk//xPsfPnn+\\n53/mb/+ff+Mnf+wjF2/+7M/822/97u+58NTF+vA1yjOC8GPXrvRGu05nF88SfNZFYARjPMhG/eHv\\n/D00vvgrv37y6VfkZ1/lv33jmrz2x973Z37sB/7UI9cu4Mu7yfrWJJFDTDLGCsTKdLIdcWQpSxM0\\nyvIkz0i2cRJY7G4WOce6cdUyOGVC7I1e1goICBpP1BaNzNPE1JhR7JFPhJgk6pm3j9bW1iL0CKGI\\nxZ1jmO4vlNfzqbv8KOxdf9dHP/SxSRbQJo7p+Piw5mlBWBaQJYgEQIWkBEvtvI5+tvQx4KM5nH/8\\njDIppUCjazoTnTmzXpou805hqATBtCtM02DTOq8BOUxJt0JRr0i0mAWHPPb9qKCSYhKwdZ2gdneX\\nZuw07Z/fGRxnowEQYzpnp2JCFwlXMRhnDaEpw1DNlXRzoLgK6RsvH+q2i16GEDHGFPngkUcYII2h\\nRIFHihEG55yPAZDPZBwMw3Ajt5AYDaaPDhcRT4yj+3VaoIFytVXax3UMy40SPfTQqC6u4MqkAnOW\\nZWlAyEcEc8WRHqeBmqgT3w/8qhgRub2eXXz89dNH8LzFJBba4O7YsAIlhEQcuED7ytFcqJ4zMRHl\\nxqVLa8PMOd8bb/OBhOi99zGAxJlE1HU4E2uDtbffXg5f3G8YE9GqrAhA8s2sd0p3LvKcnt9dN26h\\nTGQUhaAYP/W2KUpAffDRbS3fWNaSEUChD5FQihHopS+894QisUHDVAOz1tos5+b2zXFBrDeEBsFj\\nUzM7TkrQUSTasYH099Wo9fNq70CtloCzGOOiWdvaHVEBVvdkfbSWeObEKM+cITGg0bowrY1ema5p\\ntGfY3joKRZ4DXR+MtwfM0/TMuS22euPGogve6ZIF43ExTI3zFktCwVgPqtaObG5NeJ4SjKU0RSZd\\ngzrtQjCIJV3X01TiaNpq72TR8jTD2KTd4QT2uZ8uZ9PaQG98pwPnIrhFxEhKqE+PEEACVgcqJGHU\\nH+KEU9KulgSjEKCaxY4UNnhrfFd3rQFCyOaIFJnkHPIizcvSQcjHdFFbbwDYKIRAqcSYAybao+3z\\nZygTQua90VRIhsBalGdCZIVqOU2IM3ZS+Mk4FYwJThMhKaWEkBACYyxGQAjb4MFhE1LGGGVSJrwY\\npFwQjDETNITgbHBWIxwJTa9sDA7vHBnnBinNkvzRh+S9+4u29fnaZtd1ATmMmPfeWZQM2PJ0Jjh7\\n4urWGy8fvvPJ9e/+wFBgT5GJJIkxkhgYodGF8w89Pt07ZiwgSlhCBMdFwrXWq1Y/wOTRdIwiHY4L\\niBQjBBAJCoTGAPDirQVQfOHi8B3vzN/9vrNb57aXx8coKO3d7vu+5XQ2f+v4+vr5mfOGMUqRiTEK\\nwYbjQdu2D+LKjHImudbKWIVw1Mq6GK69/0KS6ratR2e25+iZsw9tvfLGgSQ+1LN+MU9ojAFi3Qa3\\nFFK2zYKAdrZPsXL1acpZUZbjQSkziXBcHS9vfvWPMh43LxMLtj49deoEm1OJ9Brf9+bgX//S744G\\n5bvfs/NUevzdz965cf0bP/xX/vI//elveeicUWyCy/MHJzUl7Oj+/SzLJJy6W0ePPHVlsVjYiDhn\\ndbs6t4tmUzh5/cU16drjN299+nO/9e8+/fn/euf/+Bf3zr/3xx/76Pc99Z3v+dYfeupbv+faxcfH\\n4zHPJZlsXyjWrxi+A2kqyvJgVreNSdyoFSgatbE9LEuaJAmllDIEyPsASSoaxqLtjUNz5xFljbHg\\nECWcZSUAoiIkwuvgipRsDuDSWYYX+8/8iScuvvPijRvvfPYcmXdzrWC5PNDOLU5tjDExcVSyQbae\\nZVmRMuvdg75qdnxTwiTfKBkznNNB6RbLOWBCCCEUT/engxEtGQhiuXgA/DAQPOAek0CZB4KVDSq4\\nHiRjkhKugN7dDyKsyWykObU4b/rULvrJRXm66Ix2D2i4BLu+Ps1T1/UrhEwfmcOc9VMuTIwOA1HW\\nawt1b+pqav3KB4uBNHUPRheJ9yzVVpzO/Py0woghwm0MRPWq65WD1bx13ZFzbneQbqcWCbFoVJSD\\nbLS9arcx8oaKAExp39aO9X1Cm8giQiQ6KweiQeXxHPnWWV+2p4YG63lKo5dpFrGqlwZhhIYG9VEH\\nUgp8oV/Wd0+nglrGGGVgPKIcMYfSUiYibZdt3eRtdUpNQz2Mi13tkxG9PmvzIs3SVPmZRTRjjPrq\\n2GDcdW2a5jTl0RvBRZaadkrSdTd03IjUBRoIG6feqIi9lYNiPWG18rFyuIRcSlO7U+u2LpytVqcP\\nmCGS2toJ6kIkRT2P2XpzCvvtvBlnhBeYN6ixaHdj+3jff+GFGeM4EVe65kBrjlNGFvcZHnc4TeMq\\nIMMR8mBTUXGlkBxMkvDWvfjkuwZnRpUwm8dHry9cPxyNnOp9vw8hPb33FtKISOd1IAyzWIJMa40u\\n5nR/gamUYXmUk0GfyZxHHyN2tjG4QPemjR4UUkjpVbtzkePO9Mdez7ynzHjDGWZQdVqVElAiZIK1\\nNl77ZdMKkVi/TFpsGLW2QyhIHnOBSV1hhhBgGm0HUYoseKtNH5ygArquU7Xe2jqjGEbUV8vZzmbO\\nMXYeCYk6T958466gxEXgTEQgmCEMzEb60otvDlN8uvDD7Ucef3T+R1+/Jzm1PnqIAC6EABAppTYS\\nAMAYG9vwILwjmDinrcc5xg3m2BmPKcEoYhyHuVy1evNMUnfHMeDB+vp5AcQ7CMgDQ842TTNIOcZG\\niNQxGq3mCWEmKKXOraXrj334v/zG52MMFHMG4DmmgmvTR2dPm/C2Jzc++4X7+TCngvbdSgg0W7lC\\n4BCCTLipV2q0keflan5KEShjMCEYY2+11d2bd+14vHZXPTs5He0fftk101mvhvnoU39wK1d9bKqj\\nExWsMKFJpHDBG0c4D5xziDhA6FUjk9R0fQAAhOu2ToS8fuP8E0+Ppns36PBcVb35xu3ddroC2/aL\\nwyIfcIb70Epa7+5cWcxrgsNgY71dLYO3CCPVN1H79fV1Y+zaOAsCddVb+zdf4ONzyfxuEFKrioZp\\nicq7t/uCis1i+E/+0c/8xF/6ifx//umGJd4kvcdfv7H9wWv1+95+7bP/5WtDhJtqJtYH83m3tpZe\\nOo9+/4UXx+Ot2B3F4ASm9MLGjf90VKQau+luCauq1dXe0fQwW1tzB299+g5dVCJN6NPPPHP2WXYB\\nx9AdgTKvf/4rBwcNfWx4+Fp/7uKGoKHrZouuLJzrui5JGaIEi5BkUlujTV/3xnl091PzySPfwdRE\\nzw8m5U7obiNi295vbNELF9d1w6+eIxfXJlysLRL7u/VDH//55d5tPVTo9O7Z7//Yuz7y1Or26Ut3\\n36r72qxngTHfu0kM2NnQuzAqkGQgBK9rOyz26tMg5Bh5QzOM3JBSZRpnUcCI6+b1ELygngyJXjDA\\n3hoD4MoBtcrKtVQtpPPGdC3hqNUR6faJy7TnTdcXx8dOlrr1DhPaqgMzxWjYs6menHU9GfVcUO6s\\nzUEtRpvieN5o6zhgigmApxQhzE3dcBm9s4ihYGFtmASBQ3DOB619iDiXxDhDPBZDMMtEFHLeK4Q9\\nisgrZ6AZSIEuXNp/dSiff2Ej35qvr/X9KZeV9oQIjC1Dzgu+5ElJGZ27zFemHJm24+pguiaa9UdH\\nlNFE1/14ZMdkvoc5ROQCy7O8a5UjKYk3nHMoeB+wwCGGdRfjeDwpUnLv7mJqGhMZWZ/hw0EFdMsR\\nTxLnW+UzgvIkSU72+jaOMoKbuhOYM5JtjFbLKmicpOk6QsiT+Twm6ylgC8yR3q3Tq+Xk5OZpIlEs\\ns8Ibg5APAbG1Meqb+bT2HvjpcZsXGTI1kOABcekZ4IhCsqY31pyb9Rp4V+tApOOBNgS2bm+P4sJu\\n4/KoPXqVa2755oTWjUoAuRCRTIzhHOOYEOaDCgDR6bnfzi9uVddPZhcnRdYValGrBFyyu1Od3ona\\nNKNCDHhcBrVaOJp4jHpf+ezsdm9XhEBXdbSQRUKcaq2mC4eSzGY5dqrjonS2SRNBJb59r7h4JT6V\\nHz9XO+UWGvJhWoTgB/n66e0jtBMpd0gj73yRpMY57ztAdNG2Qoo0yTFFngLTQWxOrp5P4M7NL87D\\nhR6ur6qiyOomxOjLPMlLdu/2SZoMACIXqaCibarOkCubOcZYMKQdIEIjxOnJElNpdH32QriwTW/f\\nue8CehCt6toaEYkRscF57wUVkWHMCHPKGA8IMImqa9umixQViewbX2ayrzSjOLroYsAYIoXHdkex\\nP0nyvPX9qJykOV4bqP0j2zaN8YpgxAUtB+PptCLGWuiG6+PhZIeY48cff293/BYxR1mWeA+chGSY\\nt431LGUi1MuwHJaPXxod1igQyLIMA2KsSVLiI8aEUMesoU2llVJAGSEEoehiQMgzLIzyJwtysnht\\nVhBwK6Qb4rWPXKi3lGfE1YupKIoK/CA4xTgRgjOZYhydUSFSjGnfK2uNoLxrO84I+FDdet4/Lq49\\ntX3jyCNZnh7dLmPlTZXnBMD0SgNAMsqC01a3iWSz02kiCONcEkK4AIR9BAfEUJZxvjo9CnX79Duf\\neOMbn5isbeDQLqaqC9YHNRiK2Wx/d2McMdxbPPQHn/ijS7u7bXXnR/7Ut7bzF/jkmfd9//ec3Pid\\ne68PfIxd17YdBsAIedXN6WjIiBZUIca73pbQqUY7rCd5oTRMm/ZTLx4P3/zCe/7WT73wpdfvv/z6\\nFw7v36/i1XOPXLtanH9s8Pgfu/LIbC9hZn/zzV77pq1Kmj70cDGvAROIIvUQhWRN0xmlEdajQaac\\nrRZua7NbESGH5d03711Yl8OzQ9ksL+yuTXbPLf0Wf3z8XH/p1Zf1S7/76uz+K2fzg12+R+UpH/Ov\\n/tYb3/q+rcsXrly58Ngrr349QgsAHCOgq/G43D9eATgAOuTYiyJ6nyXpoISjBesqGpzVprc6EhQD\\nchQBI5QVUPlxpN4rRaknFIWIMOIDhkRZd3QQVzDIOpdpZfLjRsnBlYL1fVbM6zfTLIuUxrqKnGAM\\nDEVRxtlxTzHvbcRgKQEGWmdnSGUBVj4AZzwGqBrNGHOuoRgF7x3DnfYi3eqaJgZjHUiOADlrQzAp\\nVBZsQBQiAHeR88hCx1AuRLSLLktyC5vRHOLICE6Atghj76jRTpDAvOSYCMkzttkerQbyHuDNQnLt\\nF/U9Tz0Iize2rtyYv8Uj6CJPZnM/X1VlmvchzpVmCBIqMGmsC4BwltPTqWpoCB4DBEpL7yvLkwsb\\naez0atkiwosNcvzGXme9TRIcVdAMGdDJ1jjpnYqYIO5t6FaL1Wptc31jCLAMaLwuuzjr1rKuQ8Ao\\nTbsO/BRZ8JiEJJOn1RRTBoRh28WYBjJMMwvOWq85hzCvpx45o46XfoB9mlDcOeOS0bZY3WKvnaSb\\nzrowZWEKQST50HmMCQIqCuwjqY2WmAHGGAUEnmUT2i2Naog5o6RMMPfRms8f45Lr+WQHm706WhcG\\nLts19Q0UG4sGCUXOmpKnzjmwfqkcWFGm3AgxiLPDykmfgcME+3KSi5408ko0p6oRMsezo+X+iorE\\n77L6rVZa8FXtk/4t79MiLT2fVtF2HlwIKHpMsTM6n5S2M4RC35Hy4rms218cDLvH3vPwlUFXTfyd\\n39cN1caNysQYYzxsbG7p/qRtV5SRFJUe4claTquknncGBOXUGCOkIxhvbBQo3TKYY6fv3b/eW4sC\\nMypQbjbHYtETBExrhzG2wRCEtbGM4xijckx5KzEQihFFKcfBe8ZiRoVF3iBEPGAar567gBFa1f2y\\nRS7Cs4+cv3tvqiK14VTpan2YKB2zdDydLSiVSe6jH5t542P1vg881RzVL9ycIy7f8XiKs9FVGT/x\\n1fuG4p3UnZqQ5fj+rH7s6kV7685JrXrtACAfJMEohAlESrHB0SpdYRqt9z4ggj0CFCGmafDY3Tm4\\ne35t0rduUBaeDqTqouu8jk3VdRYmo8lknR7u6eDiMMu1neuqEIwjTPqmE5xSJkmSdk2LIaYyrZo2\\nCjwemU68mwf/h7fadFFZZKzrBIlYeELRwWH9yKWtxcl8NBxq78oc931NuACIWmvG8fT4pCxDvraG\\nMd3cODM93N9+gr/zyqY3qxvHZlXpWQ0za567f3p2gGfd3vvr56d75y9P+vn9L6eCf/0PvrD9bY/9\\nxs//EeHFx37k//a2x1+5/trL66PRq6+96R1/bF18cQnd4ZRLAThDEhOWmtoOU0lRVK7KmNYkl2r2\\nmRduf4Du9QaX68OSHku0MAc3vnSbvfC1h2i++dizD128SEdX1/LVcqPt02F8GIat/Oi9176Y53He\\nEazUcJKmoxScigTApXeW9TeX0+vJmjr96jAPwJACf+XyMyoNr7KrL3xjffq5l0s+d/XiWXxYn1kw\\nAsO8T/hIddaVa1U9S9IcCHvqqW956dXPj4sBBZmM0tbzrfWsQm7S3R8malpcxaf3N3NzqykCLaK1\\nFC+GgrOMt1y6ucfWjjbZvC9J56y1NKJB0oMstLKBwKL2GFHhAxsmi6a58I6n7r/eJ83tycbr1w8o\\njmtb2ZnaHPSmSzLfpbm+rZphP+iGwQ9S0kVMDo430rQ7rnOql9aiNO2ApM6FiHjs2sAsAvDRYeLB\\nstq3Tt1m4w0GDHVd37skp5TT/eOmSFWaWmU5oTxghMFHigJ4z5FJdtUiJjGpTcwGQVVskrOAM+UM\\nJwyhoKMfY4aySX1jybBcNecCUmu0bdfWzQmnED3zR69/uaaMA0+C8+tjvOpwH+xAHiPPWxPICGeM\\nWR9r40TQgmdO14Sn/bJX/pCGnFKTGRsRbgXbGuJ62SYEtRRHUMb6iDWl0sQuRIMx5AnEaHTINsXE\\nRUwIsrW/Y1DGRDbpBBiKDPUz1VGSb3Oqgjdtr2JAHDAQLBlzHptm3jMrWYCAMLU62qgdAk2xoDhO\\nsL5nhoTw6f5C0FN2ymA9I9QW8lxt77WtKpKs1zwrQ9typLsm7jz7UH3nAEXlW++cjp1SHJNCsbKM\\n7f2jE3e3cqXuZMJW1RiPqa1YiXXdRMDYZVkiKUKYoNjMT6nuFphnxRa2U99V+nS2KAfJdimjq5eN\\nWyxY25mzV5ALpp7TnXG6mi/KIrYq7jztNg+qV+6Z3moieC6TkzuwxjKWVqGyGJiDSGJ0zng8wixY\\nE4oEI1Bkcx0tQrNqZmxnLG71WwjtMUwTYBHanqf46PBUZlTkSV8F6zogeddViZTa4WK39NMVw8QG\\nAMQ4Q6rvwVQnbdeqnhBSd/X6Wdr5sdbXYyQYRyFEpyzCBDAkVFinIGIMOmjceFtmqUMRExcCpiQB\\n1BndOUCCsu3ttdYgiZy3TjKqNZpVLfLu5v1OULQxHCHCJFPT6RwhZEyHOU/RZGdcbG9ffvk6dKat\\nFnMMhGW7J/MRfs/u+MVPaBt7WeruCPUtx+TV10/f+ewTB597znoVAxHIcwKDHDsbp31QSllrATBj\\nBEcIARgnFGIk1FqHsb03Pd1aG0lp27ZfNU1RShY9AJiunh+xNEHOBcbEom6iP0myNJFUay3SJLrQ\\nacUREVluurZVPWAUDL5+M4phfYpGcr5ak51q5pT63pjlyfzS+Z1zw1q7wrS1owAxeIA8yxCVTTVn\\nUoQQBnnhgrYnKwC0Pj6Ty5phf+07H5aUvS8ujl679YWPf/YzB8hU3VvHwxRgLUX3D1ZDOx/i2YnC\\nX/ji3Y9+4F1Z6kV341P/8Z4tH/2OD3/s7c/McWF0S+bHs499+Op//dSLfXDOd8ertXy0KdFgtlqM\\nE5xImDbVxpiPh952axfyu79r13PJE27qpQvabmSQhDf0fO/OZ1/d+8JlNM43zmy+5zG+ShDGOB69\\n9eQ73pGkbV81+/dmigu/8il0CyNHWbz2tkejrdbyZPzsgKHB1tlyXrFsY/z8wfDoN5swfSU1LxGv\\n1zfT6BbCIdV2nBQh+CxhpumCz05OGsbJaBLT8m0xvoywpQRLtnP71r6cpGsZJ1vX/JKWrgjjgm9d\\nrr568+oFdQIj1fXWWIo5RmxtAyq63fcKxxkmkQRNuChzfgA8dlorBEGMi4Clt2jzxpe+vrbxtr4b\\n7Ls8R6PhaBpDs6xjVFxDb1pDcJqNY2cUIQRjwSFIqatZHKzrjCGWdUqh6BQAD4EiHH1wCCHOgRCC\\nIngfacJ73ROMOWWdt865SAkmBcItjgo7T4LyniAWnccS04PWoPYoaQKj9ZktcbAiWvU6mN4LSjIP\\njaAMwM4096tmsLETemX1yhuEk1ArlGiDuZxxugwBOJfIhcg44d4b5QMI4aNVDPmUdabtK43HGZkk\\nEoIPFDhxRlmeUpnGhNdJ0VhNswFVehVIjLmWhGXMDxiCaCO2PmprfCQ84oiYkNykIjinOV1KqGJg\\njiQEY+A4R9E4novE9ZVMACJu6pZKrNsmeCAIJNWcM8KZ8x6D6dUSoRgx8knIUNKkhYueCYfRTDAU\\nUZisJ8Fb7/3h8W2EkvUJRaHHqRhwNK17QwoUNk/nISJmjBFCBNtZHyOzANTkR4U83D9dmnruI7j5\\n7NYCy0mRpH2Be2c1JakJSBC97EK2Y6NdEowxy/NkGLGb4OkgH2VFvqynvQp5meXEoxiYX0bn1jfH\\njBodztF0Mxfstz6Trwp+cdcJr9sWt3iVisZhQDSG4CIY7yL3fYhEryqtvLYmz1B10i9m6nip5svq\\n3rxzbduYLgREKa16JBMcXRSMU8IJRjFGQAFjIIynaXCdM9PWBQCGMKYYE2ds1AqFdr48NMa44POy\\nMLMkz0qnUwQEE8CAKKWMIowxQRhF8N4LyiBEQpCyTrKMEBSCiwHlkjEeEuKLlJ7OUd92VWsQwZiS\\nPC+ttT76POUR8RAcJWiYiQeWVwQBYzxOS5aO39r3xwd7e3fudc6lJa2Xy2bVvXEv390pvCOHx3OM\\nGEE4Rl9XJycnnYj2gZQYIF45t7a5ll2+Vg5zNJ3O4P+qJE8CghhjBAjaUwLBaWfU/YPDaV3PljNn\\nQ6fBaIsQGg/SXtXzE9O2vdYaRc8w0p1erVprY1U3vdFaq1p1y+Uyy7IkSWSajwqIod45J7ulGia9\\nUioGxwjlVFx+aOdkegMjEQkvRiPrHecSCO77vm3biBH40NQd4aJ3ClBDXI3cMsuHr58sn3vzzO98\\nae333njSnPngE3/hx//+z/3JX/qJJ6+gWVG3yvQJ6zJEHJiNM+Xs9NZaQNXxkfGtqefjcPjp33y9\\nM2fe+b7v2hzK7Z0Ji/p7nz23K825MX3+U4sL7DrhwgdibfQ+njmzXTf9U5e2DFIM3mCIpxxbzLJC\\nbm8Xpmubqo52tZifHrdv1ocvHtz4RnXvrTdq/k/+6d5Xb17sNp6AwWN0eG730cfOP/pklo7G63fe\\n+e4nH3vn0xbnCTszTLrxeBwnjx3Gi7fg3H9+YfLaf7qf6sOS396a+HJIoq+GRY4pSsoUkGWU1l2L\\nWbz4xMaZzVwp+vXn7qWh6iFb302T8Qjz0XA86g+WxkYIa5SfjyQfXl7jgUeQh1UAO4gRMQQpsTh4\\nmkujmDE4sAQoSTM2yIuMep4NOstCH4TEDpDRQjKVZKGDF1YOo1bRsBwSVpGNvj1LEHbao7afbNjl\\nwmM1ZMi5gDhhiDEmBXKLvlUhCMYYxYwAAq8ichEhxkiMAVDY2RaDjWFAmBCKLAoQE0E9RBcioxoj\\nriMKAXDwZWYt4RphZRUY59tDlpwEYQqCy/E6lwwRDAg5YwQlCCAwFiTWvfHzmcE1DpEjS5NO8BCD\\nocgaa/na+kD3dYhGtwbJkHO+crHrPKWEReZjE2mO+ZoZDp9Zu//Sq3BYt43pZcGQoEHhheQYMuOs\\namhHE7sglg2Gu0f6vtXaIyq9CBmPJOTa+GsPp3feahnX0UZe8JyczCQXnVo1eDRYI3QVKe77jMs4\\ncHxZOcnkZCyNmyJakEBwwsekOm1pr53ANOHeR9Q7RxmzPltL+5un2VFa4mg5Rda5RGYWndatG02G\\ng+HZhfJJODF2LZ0EszpkbDOnVIW7qz4JRhPCQnCdy4aFj2DL7rh0y6P7N2uTBaoYvu4BBo08MjEg\\nra2RHAAAat2AEREf3iWLuTlzLjtesOR0H9NQta4xJi5NFyddO7OreiJkuSYXyx4nG4L6o70OhlmP\\nE+I99uwbz4WrT4onzte3jtWBzosiuX/j3togZRQb22Msdnb46o4jhBjjKOQnraDOHLxxzOXmcm9/\\nc7tRzlKeMewAo2Ry7erO0ddePBY0Mc5LQkLBx6OttTFa7HtAbT5k1hvnnMDCI6As4TFYozvbAcJS\\nSsAexQhdq7WUCTMBW+MRpjSGtYGfzQggTxFmqWybCmP8IH9rnSYRUwqAqHUmzYbjTK56oL53XirV\\nJyldNorKiVjVeSqd1c7Rh7Y3T2dVBP/AKb+zOXYurm8PD07a23u32uoIIyQZBU+bqsHBn+7xS5ub\\nD+/s3T1Z9MCc93lCr10c183JhScmyxdnSgfjzWh0BhFy83AhimwSyaJXiCKEQl31wTltfDEsXAwE\\ngfe+rldbm5t90/Za4ciOlnYwWdvIdNtra/umtmcvjU8PlUPOQ6ASt20tpWSUSkodJdZagmlnbNP1\\njDHdjy4/etWyHRTuELvSdgXBIQ9KKW6Ho8FEu5wbDlh5iNbqAD7GiMF5bxFl6yPhvZUkOqOpXWWp\\nzC48/tV9Z146IHGOy/yPXjU2K4tkY/vRy3/1n72n1C/ZsPbhb3voD3750yL4r//6N9J8PBq7nHQc\\nDMPEL+/0dfVvf2nv/MWLP/SxP/m55194ZpNEXz/9rZv9nelJU63MlzrYHW9txPbUU7ZcLWk2wW31\\n7e8661pNhmVf3UPKoIg649/zvsc//ckvYVY3Tbxz93hv5QiH77rw2Ouz9ac379Z3bn713+eVuGAQ\\n/47vefRwYXv1+mD4+DQdj8ZnXq1O+s+r6kh8e/HQSx25+du3RhpcWD17uZueLv//LP3Xs65bdt6H\\nzRze+MWV19p5n33y6dM5g0QDDKBJiqIkSiZ9YV2wHKpcLt24ShcuVanskouWXZJpiZAs0aJIgqYI\\nggCaIAASudEJ6HTi3mfnlb/8vXHm6YvWzfgPRqoxnufHhDLKAK+kzBpl80wsVxsCcde1MEQh0tMz\\njcldoy/kqBwuz9YH8mzeEDekaRQEl3t51ywOJ+Tpwx5RNH/6Yv40IZB4H31zSallUlgoWS6ulo22\\nG8lDX1V7B0T10AWM8uFs+xZqPgnkY2+bE572dw8WjxXILIqNChtpbJLdCJNhVDfQJxdM+h4xkdSd\\nayObXldGTmmw0Sgn8mRzmqapHg7zAKk1bYwh+oBRlDlS3mEMvAc+wrNZZwkMbTbkIfoosmFTrQgB\\nRvWUgBiDtwEEhDGMRRoajlwFTTPMIx+Li6boK9ipNgxVLAZzLa03vq0ThiMi3Md90b7fJ83FKsru\\n7v4Njt30ZoEu7l7sP0PTfECx2Gwq450LgBCEMS4SQAgJoCwGQ+tw24keJrGZbFbyvWf1erukQHjn\\nuEDQmRBCAgJkHQZb0Op2M7dOYwrtkjCaUwb+Z5UQhBhSIcTV0/McOwJcDA4D28OcUhpYRJAbpft+\\noKy4/+A2T3QEJkIqAFpvGmBjr4HzoNe2aW27bTkmhHGMaIyR0ODqAHhpoJHqQqsQcWQSIhwhXtmq\\nTfhtSKTqYoL68yvMqcK6KsvdDEfK4DhTQXsKEGEYBI8xpDhCLOheM8HbKgTkPaKBsoCjBagy2z7l\\nFgFtrXcGZ7ytFcRsG+3VJLF6XeVgvd42VVUhAgMMvYnQVwSCm0cPWm2ttetN1zdLBM9NQEfjQemD\\nh1laohjXH/1os2DJ/WMEIVw9e/m1b7xzIDrjQpoPIJSbPkZqle4opZ0zDOimWWAEnO8pqGxXIdDV\\njThKojWB2YurcwUdWG8aQikTIyFLBCOMiZTp1VYr9VOoMIYwRgCqdpXkRoB6f4AbZQOIzjllLAbr\\nRRt9YMaYAFAIocxGpk8CADHGnzKEOedCiBhjjL7pWogiwkCZ0PamkLgN1Dvtbed97awaSyQhyBMt\\n0ix4vO0cZpgxhkhkkv5U2YsSR8IwywZON0114awG0WcCeO8DMYhG1y9fLKwY53s3b0YAICaFSAh/\\n5/RyE00avZcslpw7RObbqq4qCChNWAghRgghxtGhYEG0kCAukEhFgCGEsKq2ETOMkTb9utk+f/ni\\nw6vegAAw8CHw5DJ4YowhNG43NWbUOqWUssE75zCAxlnnXJ7n0QcJz7bz6+/98NSYjtGIgReCweiH\\nE3n+9EfdOgDX234lkoQxhjGMzhNKEYyEEKVU3/cAQcaYpO7W3fHXfu6V7z8b9Q9f6Pb9GNeb9ROZ\\nmmkOzz95+ey76h/+cvr//hdvR3ErRerf/T/83Df+wy/8n//LL/2f/iYK4eXeeJfv5AAE532lzcdP\\nXzz+57/4w+uLT1YH33yY/cnshNz++cnP/5XHlyeifmLmi+XVhbW4Vy7hiSBepoIXnBTvYLHXdQWI\\nGHHsfH/65Nm7P/9VEGqOwu1xf1y4I2nOPvpxQVHCXZI7StspfXpEnnzv1/7Z9Q8+GMj+40f1x//6\\nxa//jz+p3u+mzUViHj38wY++eF+UDO7ub0Q4XVw/h6ilEiORQCxihIxLpwOltNcuxhgR7E1fINfM\\nZwggWLfjyfD149Hbt3duH5fWLe/fggeSZ7cOV/PFKJeU0tgqiB8j9iSE4IMi2ANtoYPKRe+5t1r7\\nhhCCaj05lL0Bz8627smpBMvbByjLshbCquasHeV0ZG1W5sFYxIU5r4tnj7oCxwYLo2FRymJSGtcv\\n13W/uYLqito5sBcRYQBAr2rvosxuuJCGEBHBEROOCUQa+OAdUg0sU4BjIGAbmNsuFrptrbUoRIK9\\nh7inFEdFqQ1Gw67bHzI6livHz2rRatoYcb222DeeUK28rzaDJFxtu8sX2+D7pu4SMRZyLBFZte93\\nYL6MU9yz6f4herlYaLclEGMkCRsyVsSA2h5ZhBG2Wq8Z07Yf0Lxj5JlUH15thDweeBvK3QnnXDCc\\n044HVbkAoXfhEkYTMDcYR3orGeLhEAUQYYSCJjzHDJZgvDvYjSfMIeyB6o3qI8CIxQmvQjQZwkbu\\nnT1/6AyjzAMf1us1ITZAgABEEGYEaUUkZYJxLiSE2DnXqvGb97gAvuo0Ky0wjlLbG50SB7KI8dBr\\nj71AGCpdF/koRhhjDAkbJZlqzWbNKSYRB+R7rUykHPECIULkvqHxyiKNnIcEAYyxkzQEmiWEGe84\\nIx5qmsEyQKOj9fLoBkDYYrLUXe+DsE4REPePJbfu9q0HV9cfbptaCMeS4HS12DZdegSBSxLTKkAx\\nnYyHEaCry34W9AgvBsN7v/khq7gYFKm1lnOIguU+RAt5rF2otamtNwhBF0N0toI7DniA+GQnCd5m\\nEilDIooAhuVsOZtv9iZyMkqd6QkNmFJKZJ4NuJQuIOzcDp48uwzP2t33zgprNQCAEAaRwayFXkuy\\n5YRiGBFCQsYAUZYwAIAyTlIkZIoQEhQQgtKUIy5TzoYJOhjx4ZALv3XOAIAwdNODnaTIIaHRhMV1\\nY13XdzWIlmAopUQE5hm+f4RGEo0Hw4Bir+rgYlZmk6HgicwTKTkFACi1qrfdpvZAawbjWBAK0Hbz\\nE4rCbDbLmEEwfubze84CbYzvO0KCdY4QjCmKMVIcBacME6u15FQy6ZwLEPkArpbrEMJGh2BbGjph\\na+B8byDjZDXDEW9BRD4SinzV1ZQwITLjvXYOUp7mmbLBOA2R+zP/1tdoxselCv1aK69NnyRJCCGT\\no1HJhBAh+uj1drt13v8UahYjlFKkgsmEc843m4219t4Be/2z9//g0WTzbF0QG30DXdMvTtcrdfnJ\\nw2naNN1VQZZjvvrD33iPMvx//7vNN7919C8/+Oz/L/41hl7nRzfdRaTe13WbDydArf7j/3x8/oyn\\nVw4+v3753sWv/vLs7/7iYjMrDo72/9pfOXnz9c9Agq1RjOIQIWTJtT76Z3/nwwJ1qmmA7XWjgBXP\\nL9zJdGDcLo5ORCA5uDy/+N3feujlrF41g0HR963tNnW1cW11e/Lx5sX3EM4lmpXqx3H+B/Orp6A/\\nXfWXJ+LZyc4tTuzB8WScDiTjxgEIIU8GSinda+ccQ5BjRCiTFBMON2tGQYPcZshCVXfDV4+urz7X\\nntLP3rs5a7mwMH3zwdUWJfhMJuTwBklpiVCNoU+RJQDKYh8EzuE2EMWg82odSVs5u5lFo3JT0yE5\\nJVlnEIlk0qAhqOchXRq+a+sxAABAW1tl57VQ12v1SQK07amzNcKAkilERQoMcV2WIQUwRAhDTBi9\\nefc46j6Oj6MBzlnjIsJEiKSYpCTa8VRYBwjWAUBlA8ARMxyMSZjxgJYpHGBLhQc8AOu5dNab3kin\\nhF1b1KyTsPbAg2YzxvOCEUllBGQwSPo1G/RnadZNZU1HgJNkPBwtWnb2+IVWL4TpEUb8p/mgem8D\\nTbkraPDAIhgEM7rTzjkkal851a+dN4QCf90kXFSLhSdyOtrLOOfeAtsiojnVwCFBRgMZ80j7NvSa\\nonySy6RXsVZkazCJbze7d1ApEMr3bjtvrLWRMSaLbMIg6jaX3bWzEYEIYATRTveFs4AizCXDGPbK\\nySwhEtfaGqOVUlLKGPBZ1ft6AWPiIkcImTjJKGvdoFmPIEZZrkA7w7EaFgMSthhDU+NZbXMRC6a9\\ng9YBBGlnIxfE9rY1UHKQh/ni5Ud5gqk2LOLxIGIaTdWBdgSj994jmubJEYqj6SEh0YJsdLW0xQCr\\nrseMAhBcgBCmly9duf/mo4ff79duZ5ReLvtRoRAlaZ5Qs/URL9YKAhxsUHqLCewMfj4HD95Imb8o\\nFh9vtj8t4jAphy0rjVF5IQGEEeZ1axlDqu8FVn3XtP2VNgmS4+HhYLeAHz08NdEjgikDXITodNvr\\nzjQR4WXVK6U0ANpqH1CvPEwzE8/azcw77fRzLhPvPeccRhSsTTPWahQhNsYE6FXfOKsRQkUpJEPO\\nAdXUIYRBzhiyGEFlERkMYzDj6UjwkhCacXxzT44mKaGx6k3bdMMhKwZIWUUR9C5KQhIprIqDbBj4\\nToAAB1g3SnV6d0/2ARpnlTaYAAgBBJ4zlFKFGC/GfDLIpzt7o3EBcZtlWd2H268XSSYBfH3TVMY4\\nRDAAoe1tCHGztQAEjDEmpMiTNGH1RnVaGWej8zGE4O1i2/9U4WWM4jGIVBiIf/jharNK9/ZTa1QI\\nwfs22GbTLFfbqwiCsmZbb9pGJ6nYbrdHR/mTC79YPW/nNacBwcg5X8znuu+3q/OdybGQLE0yQgjC\\n0RinlKIMx6B7rZ0HIYS270eTHU7orfu7L8zxXI9Cf47tyhl9cf58PChAtxqkXAimtgtnttVidf7w\\nTwfD8d2dSFbP7MPH6Mn6//F/+Qd/9mtfGg36vcH1SPhjXn1tZ36a/UKzRRY+MU7Z+WPZfPD6ZPb6\\nW/3L56d08MYbr+6MS8Qgk3KgOn16Va/msx98+5MHrw12RkPTr6zVmISiBN/8lV934sGoTCR3d8d+\\ndzT5rZf8jX3hKOqaDcbQmn5nxPZGSPvZaKc0MFZtk4pIqNN21fazru+/9yt/8tmfn3A+7o2+mlfI\\nL5E30dlM0DzNfHBKqeA9hMBYLRLpA6raTkcbgYlQMQCt1QLiQW6kfnF8dJcLOPLFkXj98vSiKUGS\\nnkSeUjqCGK8aBKmoHcyS4eRTU1WP5dQhKhmCaUn6voFoW0gCau/SaDrsFKNyx+muQyuxvHjtHoXw\\nUAcKOSUYsqJPctrFzi+WkBHb+pz7XIZP3muBKKqNY05o7QQ0SUoff/SJS6btDEI4DCD11mlnL1oz\\nu2oBCRY7G0pZ+LoxXdOhgECEWwMAgg6h7Spi6rCMSiGniWDIRYYMt85zxKYlJjzQPACEExLwiDqU\\n4oiGCeIHw/NtZmHvoAkRDiTpkdB05CuwoY9f1kvkIqAM7OWSsQEjFBHmfQQAZLgzugvBCo77tvZV\\n5zwkWREI8piwJOa791GZq7qlg8lklwknIIEYY06DD4gQjMNq7SgSewMJtVYEtbDVGFDf935hTmdM\\nh3FVRWs9JpQS4aOLYg1tG6zz3rsQdUdlOVQNFhTGCKKz3nsMwbbqWfQQxuCC9x76MExjNXcARasc\\niJgj4HV7Nb9K0dZ7m+QpCPjkKProvMHZMEPBhwAaXcicIsJTiZLclrTNUnky7AsWLBQgUp5Npkf3\\nQ2DJdChEojujAN/bH+dEOqs9xFLyrtYApsADSOBY1L3KeuVBiARFBG30FgK7uze6eO9bN2/cTwZs\\n1WwRyDcGx0gpsmlab9efUEGTDLoAikGJCFEQIEivV9v7rzYUawcgYZgQsZ5ddBsjEum97XtrXc8Y\\niRFSSoNzfbvJRWwbs7mWHz3cFIW7d2Nf6xbCiKAHACRZbr1bzpvVetMbTTkDPmCIYwgUgs06fXrZ\\nAhTb6onxjmLERKK1jcBDSJbberqbwOgxhju7QyYowjTGCILknDAaMYZGu1VNBwWPAXjVrc63LCln\\ny061aroj8rw83WBtk9FA2gh39g/OLtVqtQ6RMOjzQgJOCEEEwXGBKI6QsPEoqWrVGz+7Vjyi1bqn\\nMEIaAADHY3CYAgC87WsAcZqI6SgbTAtvStO1zoG+JcOEX12/rDu/rQyEUWvrXEQIYeggxJggIiTl\\n7Ph4BGHsm9Yaj2DEGHSqDwAQFBlGqRCv3JEJASBiClXUWqbeOeeMxRDhGDmhnDKte4pwBMHaWiuL\\nbAdjUBsgy1d3Jh77XqmGICw4SBPx8vFPjIbrukEARO+M6ghBEcGmao0N3lptTZZlWZYxAo9GLj15\\n5aIp1OksoXHbLGWaHU8nCIFm8aTvl5uLixR3upnXsRY53cw/nhwNo/BKPaLg5TDvfvDb//Bz/8Ff\\n/Ut/882/8rfe+IW/fu9v/adf/MOHx8v5yijEvCHQBNstTk+5XR/dutH5Zy/J6NWvf+bf/hufuveq\\n3pfmpHT7hXr7q6/lo2r/xhQzjoiwVrddxXj2+bdDVW2aqqYY3B7pz71dHsd/Kgf7eTb2zqRp2ta1\\ntVsMyfGYrAZvZEmqnQXQj0cD7yCO4Vt/+mgP/UmN2aCQFAeEJaWYiyTFiHImBGOM+eiV6kC0SnWU\\nSwCA1hpjbIEdoA4xnmRheliKWyMqiv1P/1kQMWV9fvfP7DocN0tKdnZkGQE9GFGRWQLR7GJ+NU9s\\nvde2MhEpJrKqjZSp9iaAfmcvnezuNTY/zDYInCPCJUXL2VZXVzoUlO6dz/uBOMXZcJRARuPeoUIg\\n5sMRjPV0kBy/Ol68tIwQFDuIiOuCVaHzg8XqnLeeT1fJAEjJCZXQp9E7p4LZNjxWMikZlZhn1pvg\\nDYHxf/4OIsxqUFUJRhwCDCOw1kMUgfLWqXXnAiWRSO2RI7Tv42pOO2W6Vg3HPkDaLnSte2+9i9YH\\n1G2cb9a5TDKEUEzeOrl7IBgmBBjbRmsghB3mglsAoPGurlvCCQeiM0m7VLBzlEtjfFTGLKii5dbT\\nk10O7dp7KUclj8PhAGHTvagYJjdV14SmQQiXkmcZKUktkks/u9I+kDgLKzqdjKhgfVt3TWs8YMKG\\nACvVsa4g+aTrOsg19dEoDAAqM+r6brxfFqkRPIsQlaM0wdpV5yKDQhCWGmw9jhvcVwev3haMCJKU\\n2HOIH9U3Q3rj8ObuOCF7t8spg7aN0VjM+DjjxWjamJF36czsu4ikh5JmSnWza2wV8tbR0BoNQHoU\\nELOmaV0wYc/Hyrt23bYbs/FAX1/PXQzOuRgjYw4BiCBBiELcndw8WC1PdTR39ycJs8Cw/f392RzW\\nfXdxdfVsqSCrSJJZA4KzRAwdAOvKeNocZwA6pFVn+otRkaYQSCml0ARBgSmNEABQ5ny7rTwCY+wh\\nsNxfoOBjvkeYRTg6q2MMSZEh7BlvMYRGK4bJT3G1bb3p6mZdh3b70hjTajcoYIwRw4iCD84LksAI\\nAAgAIABCDGF+XQUIIMRZKuuu1j6GCGOMFGNMgIcIRkegOTxMq7rOBLvx4A4WE+Bd6PrgwfWsVU29\\ns7Pn/CYGL4grxmVAdLlYIwR2DnMHLYaukKOkyM4vrwMIjAMXg/dRpEKSGCA4uvvKG585zrMEgvBy\\nQcthKUfFar0ACKdpIgXZzmM5Sr1RZnlOVMdRVNrcGLJBLn56lEKEDssdhguI0yzbwRBB4NKCIhB8\\nMBhGCKF2BhGyf+et127tDaX6mTeG9+/uLjeGJnnwXikTQjRWtW0bY+zaxjmHgtf1/Bd+9qQUhkUH\\ntXGADPYBASEEHyEC1B8fl977pBzW1vO8RIThGIxxTBBKqRwNAELPX1xtNmvV15/50p33++PVw5qL\\nDmHgo9su51fXjzvnh4MstDNj1LAgUwklJVRAUH94fDwEyu/fu7e3IytTPb84W4Qb3/9h8ys/fPXX\\nHv+cZ39r895SVrPN8rxZnjeK1nWtlEpyh3j6PB5/8qfNb/xmNydfSe/9wjf+V//LX/j3PvWXf/aV\\n198Zkvr0eIyKYeFCNLZHgXLKrk4/FGU5ORyzlEMg7tw7uv7XT6q5MQEwAhlBnCEuqIqqjumnX3tl\\nVbVpKmNAm21LEOZcDiY3fuu/+Z3Pf/VNi6Z8Z7huuvlihhDqdLeua+V8UpQIQMIhY8iEWLdN03fW\\nuxgjYXRnjAaM3Xw9b3ePvvmDz3zyB+rJd1+eP2IbPMy3qmU7NEPRoMYrdOvTjA2cmzQKgwDr883O\\n/jNirNJV9B5F2jdtwnxwDhXE9BwjwVlN9va7hruuK6YYQAsT23d11xVn17KMTYSwLKaD/RMfTuZz\\ntVrgvtWpgA5T1XWY0QbKaomj1yEBrtp48N1mpVwnEAhWKxydYDgroJC509u+byjlDlKCaHCG0DjT\\nIU96RXTbIRiRsk6Z3tjehcQ7RCSKNBoLfNdHCJRj16dLFNxoXAUIrDa76HzrU2Ui7fssbgjD7HCC\\n5U5P5FXX180aZcCt1mbTIectJwwJkJCcYY+4Dkb5QJ0T0eLO9pJ5HB2nRmlLaCSgqtcr5faI2Flv\\nW8ym0B4ZQ4xNMbg0tg3RlmRrmCwlRM5vemlxZKQV3UK3xhnXt0oOir5vVdtRQRAMkTuELUVgL9sb\\n7KpErZSxR1MuHSAA8nz3IJOv3LnNAHIxsS2QZGo9AAIVEw5gsDYvhwfHx5iLDCT56tK3MZmMS0Lc\\notK0Mf0y+8lDenl+ZAu86t1nX90GT42L2tJnjxbKmgi8bXU24Ds5r9omH8uBSHkyyUvCSVd7MRki\\nH7cuvQIeiySpV53I+f5wtq2U1RTDDPtetX464AwGAGCMWMrxasYV7AJEo6RoQAP6SmayXp9zYu8N\\n1agUoLvsmj7azfWitsFWW5OOjxUoP3kpjt6AY7yAoSXsAHJZDgrEKOE4QsCQ7SOwDhjbp2keAqBZ\\nQo6PIO0I4lHFvMSMAEEQAIBg0TZqW5uNUrVFgJdNLxZrvTPE7aa3rqHYIcYghFDAGKMDOHgAIPbU\\ndQEAADyIeyW6cbLfK2StF4JVTQ0h/KlfE0YAUcAwwQjt30gHKW40c1GKJJ9f6YC4ZL3kjgvNkBqN\\n5IdPrjJhxiNz6/5N4HuCAsDAeTMoUSFp5Gp/esNG2FYLisIoJZz4MqMhAK08xrjz5VldxugJAr7d\\nNjDRKgAKKCNApBDG0SjxThGw/cwXD4+/cmf3JEEhjl95EAiCgPnwU0/pKBO0mtccGx/DblmWEsTo\\nY4BCAGMUp3Qs+eJKu/xWCsXOrc9GlDZbdP7wMgY0a1xrda8148RqE0IHMPBavftKwvbu7p/c2D+s\\nojLDcnv/lc8jQKzro+632E52RzYGawzCdL1ee++pJKmklFKAgW6UcV5IkqbpK0dM773x9MeRiUqg\\n1fX1JYIxoUggQLvTGD2OUanN1cVCOw+d7ttrc764M0n/vb8w+Xe+fvgz7x7+7c/uve0XwdXdEqPL\\nD/X7v/WP/t5vvfPu/uQQQVPVbdtsTrV1N07y3vgf/mR5+Z1HF48+ZtvHf/RP//mv/aNn3/1A1vEV\\ndvsddO/1xQrXEN1++807x3nOUJmQruusA6/eGdZd6z0/uXugjPrxJv/MX7hDkoQSsVptmqrNGLi8\\nXq627st37N7u5+tmk+UcYICBHuc0L+jSErr4I7GzU+Asan///qQoGBtPGCYE4ut5BxEmWAAiOxsB\\nZowxQIQJYQzj5EuHL9XRb383/973WPXjj3X/o6p96S7e3zz+oF6eI7v48XcgDn2l02L7tGoFBE50\\nxAnjV9dEbA2MiCBOXFHKYI3vHCJYKRWNV0Z5jxFkCKaUC0zi6Ypk3FoHB2zVd7N+dQlF3Db6Yua3\\nL+P1mYLIa1Mx4QZHw9kqgboHqAYARBSv60qqMBwyytP1+lyymJdMUAWhcRZACDkpvOkQiABSTqEs\\nEohRBimzMMOakRidN9oSRJEst1XrVCMYQSjzEftgvesohFQmSG0l9hghSmUgRJd4uZ6SfgMLWxN7\\n9nwJtg1jRaghqLYI0nWmXWNcDEAI4TU6v249RMEGbUCIMWeNVhXEIOG4KGVWTkGMnClON8NcqnrZ\\nL89f1JQJp8C8UyQr18Y47ZMsoTEGaO3GQqeV6kgz8YLY68pTShEUyZjF0PgAKIvMG44B5okyQ0pG\\n0J3AHQoCR3s3uPSRZbf2jxScdHtHPvqEs6EkeeG0rWqTKc04FxyE4KSqed9gpRsSewhIvQ2b1fbl\\ncvnaXoTM5eWzvf2z20TXF9tT9IWAclpHOkJBvRxPJ5MB9poifpxJQsJ5kuazq/66EzFyLHa2LQsA\\nF1W7riiEMACfynMTqVF9xA6AARcZgx4EPDp45ea9m84C5xyKbLN4EXQbrYsBQeJBo9AYM+yQ26o4\\n+mSOv/za4f7tG61iMTTTkcwSB/v48fdeWgB1oy5qc3J//yBNYgTOwvVmq00gVEKWIIYxdQAjEBET\\nzjnpXNaunEdCaeshCAT1DlhAWD7qdMcI1RvMMDHGGd36Zr67e+vg1hFLaYQ4xggjgC6AIBmCwFqr\\nWxR1mrHx6AAgfHJjPynl+fl5ITTwgco0BhiBo5RGHxhjiSwsCADC7UaVQ+HUOnozKCSKm+rqfGWT\\nwYiiCJiUg4RKv0iSXNCDDGxGOWs7H2yACGmD33j9067R2sZnTy8wilmW7UyHMUaW5vWmkQlFAa+u\\nrpYXi145G4Hg7uyT+baqACw8Irpr00Sm6WRHAlre+O6P6eXH/WwGCMGPPzrdrkIMFkYUQohO7R7m\\nEdg0EcNMDoYwUswolZRZazkjFrj9wxEk+ns/eLK2ww8eKaXYYqaHh1E7b0z/7LytO99b551y1jvV\\nf/q1gu+9+vST9dmCX1Vlo6qnH8Tf+ubviJ2hc4Zj7NahsZhlmZRSWYMxxpgGHwEAddeCEJxz3nuZ\\nsBSGw8+//rsf4Qxop1fW9pJCpjeZlMVwx5Dx48ebiDmlOJquzETiO43EaDQ40+gZevfb+sZp+rb5\\n4r/92v/uf3MizLbqYrPZXr2Yffzh5z5342LdvXrvaFCo4ahIJTu/2PgI7t89HOyWjKt6fdp1T2P3\\nJ3/4r371X//qj4eTezP2xh8/gTbfK3Zv7k3Sb/zFtyYnuoQdQ1DH64MbQ+L8ejGXIllc+Nz94GrF\\nIkqzJMUAGlodHN54+Kg/++4PX/3q3ZO336wahI0v00Rpa9suAPhHv/qdr71dxOH05oNX33up6iAM\\nYJRLgxzFHRslUXBAckhkZyOEMPqQ8PLVm/RqdPM7PwCuWvZX7+2U5xZ0Xb1Yb+b11ZNBGcN6g7JU\\nkKfjSfR6DYRutoqwbUcoJaCut9GahALGqW8tkXlBAwp2OHAAgHzAqh5v5huInHa5sT6VTK2eeJsQ\\nKhjHvekvF42tomR9eVi/9qoLHBvnjW8mUB0ej86fIBcBgGTTW9xiYNZJyiBLMr6LIcIAa7TvPeMY\\nOQ2Ct4aN1psmQV2AkaEAQEAcN4YphQJikGAAgI9hPu8hJko3KJpIkEhHGCLvo+2bEELQHsIwGgmM\\nM4jQoPRVS63TMOjoLMMpkgaBJQotgh41gkPpKCAQYhs7s2wCiNBWxrgQRFEwE0E5yDEXAUDVx8Ws\\nopRG4BCkLC4lPCO2ihFK1nq4Jahr28rAjEqZk0BEzIgyTscYfUxJhepttTUQFQoRGCO0dW/xhKQy\\nRo+B5Qp7zwgGBn+8Oc1IliHNIuJ8b9LpKvZh9cQtbUZ2b+yMEUE694YqQ+jQOBvj4M0HKvWnujeR\\ncJn4lFVl2bbd9u6nv8JAERCu+nv8lS8ff2FBXmqI0YLetDIDsSXi2NQ2OM0DwbIIxs63eZ7Mu4se\\nImpsf311Pt/qVIptd+1NRBh6p0iwINJRArwJHDEAgJTpQO7uysHHH77QDmKMgleJEIzzyZAY6wGF\\nkPOD4cg3S5ZABYgYfuqDM7w+a8Y82y2iNcoYJ9PF7sSOhxQhND8lHZajCUPBIQ5QNMbY4AGKMXhn\\n2z46D1GkJOyOkdSzWD+umkvrcN2bCCCtPAgQOucUBAjGEFJJBikdTeTPvzP483/xHdGcYRKCUwDF\\nGCGNsWstY5QQkqQ8+C5lcXZ6WtfWWttpyQXeu7EPANSq9c5RHENwCKEYo/WOUVEOUgzwIBGCuoR6\\nQhhB1nuHQpxmlBOila9sRwRECJapfOXN1/teERAZRyTbx+TuR6fLbDC2ulmtrwsKjvZ2Gu0zgkEM\\nBGGOIXC1NZUxK+OdjzGliIu+316fv9xen69gRJwlScl2j24idXV31LWbM2CaN9+8ZW2NsaHcEQqI\\n8EnmpOSTnETUSxaFENBGQuC9/WEqEyGYhKCB5aMXs83ywvjVsn66rWfA9pPDvLXBakuBaxv7ydN5\\n1XaEcWv1HBSdjW3bvHzxyersdFCkYowl8wntZZpYGDtz1uBofAiYSE65FD66rm8JYxTHEEKMHkPE\\nBXz1zeJZfeP6E+J1JUF02iEMIAOdXQYATFefnCTWWu+hC/ri7HkI4ZDZC8IevaR/8OtPfu+ffPSt\\nb89+85svv/mb6oUfs6b78lfwX/4L5f/xPz5YXH/3r/7lX7h3l945ubsv0CDDnJAky9br7ZOHs05V\\n3hlBooB6v1zl+GW4+o3r846cL//ol/7NP/h7v/MD81l9+Av3P/c3bn79teGYGViO94yBTmb547Pr\\ndz+90y3Uz/y5T7caBeChACyj11dnIcZvf+f7d3cWm8WeZIILSjgxAAQKAAC1FX/wT/6rn/nG6wFk\\nO9mkn6/X15cagkhGMZt6h7JyEjEnkkMICaEIgdtiPf0z2XuP3tg1BFUvhtJtuhoGj2GIkGaZXJ89\\nHQ8kzdFidlatFzCU1sxB1BpXxHjIEulIImPfGogHIt3Z38s8SW6+eTTfDjMRRUa1RYlrk6wWTDPB\\nCY43MurgWrcLDzyAODhv+isYWkvjVS1lggmVqg3WX7HiYHBrX58vEHKOSB46MoBN70yNXAyEEBNE\\n8DZojAnk0o93cuuj1dL3K0QdIS7PcauxtVZKaDyy1lJKIfKTJO4UvUOorasIu7puo8acChu87TsU\\nrNX99fWWwBhcJkMWODENHE1az3LUtSBsCUEyTxyTKGxCs1murFEWEIp4TqPTjCNvEZbIeSBF4fuk\\nlII44x1kaYJFFsMYMUk4oAxAb22wxlhMQretCGeEcghwp2Oz7SvVWac8tBQ+9bOFA3EkIFPaBA+i\\nIVzACISqewc8oJ02rERE1YQHqy7rtsXt5vmcAwht3xzsPUabZSveccpvHEoSdiA1p27b6EQMh3uH\\n7WbmdESIeTyFLCLou65ryecfX1UmXl9vHVOH1HHMWoJi0q5XDxcrf7E848HDkisKkQ/KrT+q1iDQ\\nsQd0bzeZz5+67sKsTMpZMDovJPCMYCpS9uI8fOmVSUFtYVEIzkUG0NSNRy8uPlbWxAi99xBRERGI\\n0cYMoNiubQcnTYM9TqsWDWLn22eLzZqnKUV43cimQzEywsqDo/3FRU9I1rfgR+9fYmkOZey2wCEq\\nKNW9JoxyGUZDLgSQGW/6we7OaFQ2I2EiGDa2d5Aao0dDmxRiQHUpOhTC+IhqaxxORvf+/YOv/of1\\nRjsoBEecYYQQjoEnUkCQpAxCaK1LOGEU9qYNNssT7IwbDMrF9UYZx6jFGEeIAUA+AIIRhcF739SK\\nMbbSEgFMBV9eX2BGvfcEh4hJtAYjsF7bTGLOktFw3/oH287u3yybZXd9etZUi6p6XGTipxCYfDQ5\\nn2+9s0KI+NMYIyLYahODASDECAej0cGhQAhJuBkWnlIaQnh2Oeiy/YSGNeBHg2QymSTDyYBnDIEy\\nyaJ3MGBv8Pxl+2JTzLsM8hEAIRumXLJsMqQYBud3j4Yfvf9sPV9Q3FMKte/bvhuPS8b3F1dba62x\\nHoTAUcAMd13HOVtv46MnV9eLWvXd6enp4O6tRz9WIYCr88cUM0MCCIaDEsIIEA4UawgJohjjCEKR\\nD1wAESLG2KDg6Z3XZ6tyzPsEzQc5QjBy0BOMCEQhRIyRoKir1r1SAADvY9/oz70TFn169fH7PH64\\nV85p8xTry371yZ/84Y+EBoOTt/DxZ57Rv/Lw6s53H83anS89+MqX/8K//7XDMbh7f7jZNMf3Jgwn\\nx0UcD9JN1QGEnA0wmgDbqEH17OMEPD1Ontrn3/rmf//b//kvPm/QX/rcz/81NJ2cfoDSROquzxhf\\nLdq1b4ZmXuwdXM7qGJD3VK8kQLK26Jv/339y7wuHMC0q2zssjPbKxBg9wWi24ssPfnvvrbeyvYMk\\nF5QAGmxX1SiQAHIXqOkpAhgS7iHaE/6drw6+17x7+Ukd0fMssVopiKLqemOMUto7AzyonpwBzV95\\nZUpEH7oXSZGpNmCMU2Jzu/YkCCEkkb3a1OtFEradeXVx1Ykki6GLJbE+56hvaqpt8BE671c2Tfce\\nRHoEnA26dXqTJAhAlwnGICZQAQKDhxjjy9MrEv3hrf0mL/vaERpaZ9saUBryDHWtyUTooCI0BNNx\\nykIEhAvmc0KIqTbIR0wJxjQCt2naqLWgTikHvYdGQ0o8LQOW3gaB20iU0jVCgElGCBnmCSVZsJ1T\\nLm82RRbXgTHcODh0hvsYWo18p5zyKA3PFtdB64h25IMbbcqsj6FrDYhcac8QMcYQATDGIJJg3KLy\\nLCUHuc8FhQQKAgmHOfFa2WZrpCAhOOSst009u+j6pfGdMQoE7RHfG0AjeJnNEULM24qjCSHDJAZP\\nRpIRGLAuF+vaaBRCY+uF1QsSLkJA0cdyb7dbA8h9uf744aWZ7h7Q4xujobfOcIps4GY2G4x4micE\\nKr/1VknTOSnz3fLFzuri4dK9+rmDfWaeXiWW8L7bFoPHObm6vIaUMRvBaEpCMIg0HihDIIurba2q\\nsHHBJ8Ph5HD4xitlTvj5ptvYrqmN0VlT/rvh/v3p/qvGIh8gBAxiCLaPO9UgTAFAGKJUQJ4XBPu6\\n14nkWZYlwPtuZXVrnAe0M/WcYhhNjVCPASIoQuCUBlV/AbB33jRVh2182bM3P1XT6iH0gTFCCEso\\nCKGwBDCoq40vedahQbn3KRHcat4HD10bMcZgQNMhfe0wTEZpJEEpiAkPIQVan7+cr5czXIzTVDIh\\nrAMYp8oxiEPf64gDxuC1T92MwBirEVIIIQRqHwPFME8ZhggAj4F2vicU+egQ8NA7xpj28eCoPJyg\\n452Bt8EqfVSQe1k35vBE9ntMHQ5QSgmPYTyafPSnvzkcpV3XgQDSFMP4Mpg+ODoo8ahMPGTQO04h\\nZlRKuX80jtHLZCJS5I3lnFOEr+bVYJCvmnD8ygEk+d7eqOmsqc7f/xEcnqB+NTu5v1/30MCIkSOE\\nUYJSQaFDxRDZ4Ki9jm19dbl0AM63um/tYlWR0A0SUK38oIiINF3TVo3eNu31tt4dJFcfXXKe5Amm\\nBHlvx6MkxeTGpAABXF0uF5fXzbrpjdk29dP3Wp42rWo7q5N04AMIzrneRmQBAABJITPEkBAMYKbh\\nGedUELQ7TibTcov2mpXFfqu7ddN0EcYYo9aaJykGlkdrlS6KnBKEEGIiORnocPSV5XqUwCrBgYQt\\nNDOj1qtqmW8eXfjK2N2Zvv3t317+8F//cPvJk+/8+nv/42/MmvJLr/2lv/a1n/nqwa2bRvHYdq3u\\nEYm37ty2uu+bzWKxQoANd2g+kKqudIBqfX0yvDzA3/7eP/9v/6//yfduvvW3EJd7+zEb66PbcXQ0\\nLuT0+9/60Z/9c5+bHN/GjAtODt7IBCUB+LVipf1Bx1/JynK5WDtrB1mKKAModi68/+OXrx9e58P7\\nXe9j9N7qLA2ENM73Z2dNG3H2+hciZDkln/nM3kfFm0+/P8r8Il5t+lbpfmt65zpnjYcQtMZ1xjWX\\n21F5Na92+sWD3ZMbL84ZRRYBzDGapnlycoPEHEJJAZ/kGd+9ld6ewG0awtYASG2IhPmI8kmWDktM\\nqQ1y1dRh9axpQwhhOORFghMEOgtQ02RJhhDiHIKAFxutvNdhg0ms1wF3PMcNFXzb9CjOQ2gYB7qF\\n96RQgWkTCYiOQFw3xchRnME0sxG63kmqs2kCIYrAQGzSjCAUmqhXK+sdaWobSdAt8yqQSCASPpQU\\nEBT6UWIh4S74eztq8IC0+ujJpcLmoaCGYmRDZMAgYpHaLqAjuj1uYVmZMuV2ujvOh9Q4QBD2HmKa\\nQWS6aqudDZAf7R260cHw5ngiUyyIdhgg4lvbaJgRotsugFi3yio9GmNsQ9f73ckwNEWw6MqIDuyK\\nPdz1USE5SnaWEfdGe8sDponMM7PJKIBSYUzTjGa8iaYtGOxCu7qeV0bmU6s3Ya+8r/UUmX7eYFEk\\nKR1iRl1ZlBFFKFiZ5GLbqpCmKfBxRF0fPbRfqNebWs7Auq2b1ZWHRyeguqgSHknk6XjHecgFVbrW\\nPnAeA/a2ufZqm+e0aUELDxbbRTqcu7YSyEoR+kZNV/V3f5TogVh5QDCaTvO+33Rdb5yVLBgfEN9N\\nRrvAVHUdIcmTJAMBKusAJhZAQCgILgaojIPAZTyDJAAQCAXeNMh1yPm+s7tlklPkrhObx69+ZfBT\\n8G9EaTYpm1V9WKZ3Mjt969Ywd0cMUrZ66+dvE4YZgVZprYCkxXhwHKavCB4lQapTqt2iqE13SuLS\\ne79ZbYJz0QcAAKX89s3DCFgEgCcwzdH7H55FgCajIkYIIAZEtBqC6HrtjNMBAIh4JnMppWA8BCcz\\nmWVFHtDLx9GxW5nAX/1UijB48Prgxmufzqf74mY5uEvLnWzvVnayS0U66c0Lay0AESQWwzYAe7A/\\nMbp3fix4ovstQojQnOJAcDw/byiDqeAQYsIJAMj4MJoWfd8Hb68uNab65dm1URbDlsVn+ehLgxGr\\n6poi27eZdsj7wDDx3mKsVaSQBBit863gzWJed7UC0aGgp3v7b33mrneGEkQICSEgHIEPXa8+Ol3f\\nensnxiiKDGGMGAeIDvZJeWvcJHKznkcSVn13ebEkiB6NHwMAutZ4k37zX/waIExmpO6i00BrjRDS\\nynvvnY+BkK5zRHIPoUft/r1XX85oe3Udu02MHgJMCAEg8OhN3wkpo+AuhrbvBhkOwSndff7N4uMV\\nM1WLXbVezRECPhgEupRBprfpYGc0lM9e9mO+ON5B24sPON+Gl+9/+5t//Ku/Za6Se7N49/LRT6B6\\nMR0ghMN6doZjSNN0d5xwVnkD6OHJ6Nbtvp0zYdfLq0me7u2Ek+mj3//vfmk4vYnI88/+7Bt0cnO1\\nZo8+PO/7Hj353enxg4jo8rmJK6fdT4cE/Dv/9Pf+F3/xBOlcFncQzxrVQUQQ44jRdUt/5f/zzx68\\ns88H+8s2BoqFYJTC6N3+KGZgtfnBH6SSHIxjfS/70YcTOW9luLKuCf6SI5fxFicBYBRCiCEAAA4P\\nY28zyuTwFv/BjHKbEmQZizqQrcuYGyWwIFjsBX//7dH6uh90NSRsMBoFO/LGogzPNzHNY+0nooM4\\nbnlBqN8cDa6Gk0gzPpkMozjK0nHTh75bBe8ghNFFzojdbuC2brsNgr1SKyo4hiwVPKFBSho8sQ7W\\nnS0HxAXs4mrdYISIjjZCgAMJiDvn6vXGNCpCIAUinhFd7dxKVZwghHK0Nr7n7UWWuOCjcYrxoJWF\\nAQ52caDAgGQCOB1in+7B/BVzPXRqbc06Ri+wbTxkziCapliEIXf4DM7UaLlNnVbdqgcg4bnMNdz0\\nPUYGRG+qsOzLdcPS0/xhPahst9roQaas6wYjASKJCWYSUdwTnCZZibK8TPh+utP2ze6xgb6JJki/\\nPX/OdEAwotiYvnNasRBlY3l09Sqs2kq1FhqbcMkkF5jtW1Dp9rJahUhCUGJyt9y187N61Z2drixN\\nHCOSWk08HYlEQqcuKyS8FqmAfFryybLt6iag9NnuepvLBizee/+7LR090LNndGc0GooY675bEQyv\\nL68wiQDwhPSLanP7pqM+RM8K6Udo5kyiPBoOS0YpjGa0B0v53n348sn3v2v6ePvkxnI5V+0KRSVS\\nYWw/2xAI4XqpAmwTiUyLEYreaMaAM5W3OiGIwCCDcc5oq5xzDlQu1HWlE4m2TUSC5Dsnuyd7w1GW\\nlvz85bA8pJ+9lzlvGEZn5yuTFa+9c++dzxw3T91VvifK8kX14HvfC0mqMbbQ2t4BJkpd6fmSpGV2\\nODKDnGWSZBJst+vz84tmVTlPuEwppVRgC6Q3GiHAKA0OJMUw59xr1rQRE2CMpYSnjCCEnAsI4LwQ\\ngwHnhDhjKaVcJJzQvtKMl3s5TCCxPn+6EhHwH3/snr5YzFftehaaOe03aHUWi1Js2rrqNRSBFRoJ\\nxRiKwKH0SIpcuYZxlHAEQPDeJpLujAej3HjvQwhtowGkSqnofJqjy/OWURichQA5q32wu7tZjO3F\\nk0YOM+PJ+Pjg+ZMLTvwwh5OpnO5nw/Gor91q1RgHQgi3bomm1VZrhBCkzDr08qVjHIfgMMYh+hAc\\nwMh62znnbQhGJUkSY6SUjovEuuGLs5hF6r1/9vL88voiYOuDavitbbUGKCplyglpm76eI+grFJz1\\nPkLog+YJBQAY247LgTF9UaYOIIPKFxcdi5fOzIzuUegGzIs0IYTgGPq+18ojhCilddOZEN/ekdNP\\n37p4BFQ/JxT4vjWNAhFOR8Pgti5ssUDnjz88GmdYnU8KwXG7PxVf+NSOa39Ctx//+j/+Vnc5uvnK\\nF//6f/DGZz5NXj2C94fw7TtpPqrpgBvESoad8igd7N95x7mQTyYh2LauCaImnLfrtZj47/zJRyj5\\nFABgl+Iv/NyeLjxE7p0vfHZ1ujzcL29M6W7qp9Qk6eT3/of/rHz1ARsxgAhCKGJkHUxZonrb+fHF\\nj//R+NYbtx7cw4Oxi6A3lkouijxGmCSJcKv9V9mHj++xVRHsBfYtjTWCEKOIMYwosJS74Lu6o1aD\\nQ2JUmkvaez/7E5GTSwvHTW0QJBgZ0T1XFmCQxoRBxLPdrycZNrx8+kR7SwEiTukI4fXDR0l7tnPC\\ns7tvxS5lSVmmpKfl1bnqWk6R84FIkXptnTbOOQcxY4LDZG8vCkFgRIS4+WyNouBc9i6vW9yr0lvH\\nBSUoiLwgTNfb2rre2N45470HPmBECYheeYJw27noe14Om3lHvHGRWh8pg12DMOlG0wwLorXl2Fho\\ney0BvaM6OJIUHdzYp0d+N+maiYuh1wsQ1zKhlMnBwCEi0mzAc2Id8+jiXAfCGCuSFAJhd1OGUJox\\nrJoYI3J0Woh8Upzc/nD/enk9Y+O9kTImSwgXuqtSF+BwFx8QQiWF0SkDI6VGOSmEti0NtbXWqKVp\\njQ7OqxgDlTLNKY7AJNT2TnNKaLBlKonFacKRCxjD1fX8zgjTfG80TTCcQofyo4udzZOtP3jl0/di\\njIr5QcISCLcb6IkbUVTRUtms31xtZtcMIA+G8mBclcNnFy+Pjkyt9phbrux9AETXBYbF8fD45fNr\\nVCScoUKGq2W4c/Cu9dAhgtioyNjeEToa+j2tkZOMMaUEk/RnPjV5sXyxacH0zhfPrz6o1hUXWZJO\\nrYs4XivGo6tVv5yZRA4kF9p7jzCoN21daSDFg73kYHd0si8SYijDnasoA4wCq5zzJniw3Tamp2eX\\n4ZMVP61HRzviBx8PD/bnr5RxKDTGpLC3f/gh0qLcIWrphiSsTh8+/ODZIhU4T32WR22BtZpAU1cq\\nJHsnd0a3j8jn3jhE0Ug403pV6/Vi2203jXEJAODwSF5cvhhlhfXmwf1pvdru7OaqjQAAwPNgtfPG\\nOYcRpQiHEBgbK8coB3lOKQ5SIozicJBO90ZNV1/X223fbpo2OEV50LZjRTEc7e/s7eG0LEdTvnfr\\nxfM/DagMVnVVTym3zgzy0cUTo70YF2Sc84FEw4KiEGi5D0A42C0w4kopED2EGEOPsF2v14wiDGCZ\\npylDBY+jzC+Xa+htWlDVWN2bq+fnHM7LMacir2rcd6aqNiWvPXSUI56JnX2KMQQAcM6NjdY7Ah0X\\nECHAGBuWaUJjjBFDOimKYLbTcaGUAzEyaNMBe36x7bf1o+ezxoBgHSN0tdZ5XlzNrDIGht6Zdjgu\\ns6yZ7EEI6xir6G1fV9aorvchuq6Zras+xqiUGpXTRZ+kBmFki6JwpoUQ6rbpqq21VtIwHORJSnZ3\\ndwEIEEZJySSvP9ruCLC1ELaNxrGz/bxbXLRd5ebrnPKq6XzzsQXm3kHSLv9kVCbnj76z3Fyl+aAQ\\nehBPwfIPFxdXPdxTu3+VPvjL8us/N3zzC9/4mT93MkYVkCJ2WZmtN8te8vTgeFtvAEiGo3I8Eaq+\\nXpx/8Pi9t+zmNFw+L45vHr8d3ye3Z+nP4Ae3tpPX7717Y3A4efCNV1/5yo03fmbwlXeSW0c7b74u\\ngE3K0YRxwjgxxljkiaA+mh986+KrbyeOD6b5zmgwGu4fUC4B4dOdgkr0+bfGT+TN7jlP4rnu1i52\\nDnqEcYigaRpKcdd1ESPCWIGhHZLFJltdVcLiV1Orke36KvQ1YfUwtwe3UJ1n1JulRo9fAu/gfAOK\\n8fTmyW63WYF+g4UUQiLGPawfntfLx6cxsN5EHTBwrxaDk97EzXaOeRojRIwEADnn2unZqr91p8h3\\ndxSZMEh3By7Lh9ZEH1m11gR6wesA7Wy5pSxYpwI7hCEG5yIkWjtojeqNdTpQTKDJco4xpQnz3neK\\nQclChNhrjiGmBAGHGY1cYIg4sTJN+q1Fw2tSjC5U/+JpH20AKXpub6qVhzAihPxPG5UbIWOddQFn\\n6yKsNx0alYIi3auMyDR5oa4AgFDnzFHKOzkYlqVDqEax8y2kFKOIs3HwHsNEWxHo7mgAVy6NwCir\\nkDPdNmYy2s4knHHvnGsxjM4ChhkAsVr1kFiKMCQ+gSuEWT4ChWzW1oVBEqJG2GO0BKicbx0itlrS\\nYSK3DQ4cc5vfGE26x08jTy6XGAabxBXGuMegatYgdNRqGVE5HCwrTXjqqnbx8vzmjVsvn7xAQ6dN\\nAwNqNepiNpwcnZ5/+3hXMpThbMRgIwY3ysIag50ftj5Zr/dqVqST/eQIMQCLCJFP5u07L/wKtnAw\\nvbO5eq/VQAgBIAckIib3BxNsG9XPY/QACK+ND6E3KGBzY8runBRUfu60PbR+mJeRYJ9QCBBeVjxJ\\nktE4BT4QDDnCKG4paH3Tbp7lH1QcdPaZvfnK64Wt2kKYbHAuBDh9Ue/tNa9luoqb2J4hhDhGk5Qk\\nKCAfKdQEKhzA1TmZmZ3p4ZtPn82LkqJobN84bSDAjOOmqxkhfbXYmYiIqtGAP3n0glHkHUkzShgk\\nkaQ58AFAhEIIklEMUa9qhgAhQpQcc55LOsyZi+R80ewcZ96GTVVvNx1B5UAMY8TVygiK2i7R2gAf\\nrp6tETDKApnngvAikSgGY8z+LkZufXSY7I/heI8g6mKMiBCFJ7MWxBg7bRBCMUBlwt5BvpzVmeCj\\ngmBYHQz00YgcFegkrd86PlTL6+mAUAYIil6HWiXbStvQ8YSGCGW5SwnXJggqeoMKySmlDCNjrNXG\\nWksJhwgAGF20xmkAHBcIIyEYuHVnkmccE3L3QXGx0h645XLOQ08RohRba2OMCJnzq23VIaUMQggR\\nyjk3gCzbufUQEhxCsAr0bRCCyAQgXFLKh8OcY7LpotYtjL5rt4JTpxrvTZYIxhgirGl6Tvjp6Wld\\nt5u6mo7KW599sK73ogeuU6q9Mk5N9/Mb9/YFp2KY7e4XqfTz03lO4umqbQ3pjPnC199EEKh2E0Fw\\nepNl60SQNujvf//FB4/VVXcc0/2P1D5/8BXSnljXLLtljDFiGuLR8Ojd1nRN3T15cr432d/WsRev\\nLR53zl5VV/XDR/r3/5sXv/z3f/zo18+/+2uPdA5/ckq+/fyr4t7Pkgd/Br/+mez2bn35yadfO0T9\\n5e3pZJKwkjlvQyoF5+k4y3/zf/h/vv3uu+PJMEtFzkA2nDRKjwbs3Xtpd2dycXqwUW2zOvNYQxRh\\nBEZZr3UiJAAIQBxjxAmxRMOkxFKCAFOpd7482Tu+kQkCUaRSLXt2XU/CapSPxY03b/rWLWbfrTVX\\n9Xmn1fho2ijiPQgBEEa9glBr5hqAOkbxi0seUGVsSJPBZDghcbtaVy4EiGS1jZOsaJpuvnnUVYqS\\nIUcsUsoER9gRHKFDEMYQLEJQ0tB2W4JhX2NEkhCdUa01XluPEQKMVT03SjebNQIWY4SCTTMetY5G\\nUwKyFDrDI0btppMsbVqYSpQyA4rczpBbbuY1QlXjNi8LtzGw9bM8HwXrQLWuRAyqrVDEJEYEIcwg\\nCPKQJEEpZQKQzanernvgKbKLylhN0knqkeOgac+cPEnHdPPi0fa61wXWIvQUloANHv1EGQS8AsCD\\niOT0IMECjcZTL0d37+sBQJbI/REqcNzd45PhAEXWqR4yU1XZMM21csh4uOm4UL7rIsKSuqwUPkqI\\n/buHvK630xT2c/0EFZRdTvaNWtYUUeU0lxkSaL3cAEAw5tqEyc0Ri2tBPbBXzC9MkGL73iAd6Mbl\\ng/xitmwbL4WYz07j6/e2dUc9hhh4lAMfHp4+7Rtj2x4lLcuyaTxYn9mPT+8VJ3cPS81RAkx1cdXH\\nwaRtlxH6rvUuOhh9sGhTGx0vQxF3JAQwJkSJsOl9a2xfbaczXWz1ALo+6IW1VFFcSuyN3vYI2My6\\niKlJEikYEfk4hp5SM5C6lOfE7x7dHX/nByBO5S60fQtgEherOuV8dOwp2fhtlRAAibz31muv3KT7\\nO1yIEGP0QR/sO+a21XK8VvKLXz+MoNFaSwljjAFCiHyWkVwgRoGHKEuJTBhOeCJpCK5I2cFuiQgG\\nDmMEMCQ6GIIihpHGGKKCtLVKcy7L8aTq4dV8hZwDWKYwEuiKzFuDbIoBcMQGpQxyW8HgrRPc9tdp\\nCvcyoBRNy4zTxKlYpINuew0J/rC6/1IfzhpMqUwTsblcQLsBzmIiQEQQQmNVcMYHOx3JwYhKLvZ2\\nd0/eemN4/7B4cDR98MaGQo9WdDzx1kLqAwwZraY7GJPQ9z2MGLmUIIAxRMCuV3T/aDTMhA+WC3J4\\n8x0XrGBkf8ScMxBRhFiZ59q4i6oKOJlvGs47ht17D6M3GGinnQ4wMN8lKQXBAQAgjKXU3frx1WYL\\nhUAhXF1daGvmj+azZQW9BTBYXxEKqj4mcqm0jV5vu46Mi+fPN5QhQiPEyHvdtbVzvuu67XatlCEE\\nBW855yj4o9GIbs/OSHpxnWC1pn0TbSulBBHVnQfBswQsNpVVku8W1w//1dnF+Y9+8FEmkx/+xIZg\\nus2irq85Q2xC+f7g1q5iNJMcPfzWh2jdPvmx/pVf5S/SdyiDb9w5kpxDE7t1t708Dx4iDA72dzfb\\n5eH+7ruvX37tKzstX7/2htsEJ92P99GH7uKPWPW7nVq+/N5L9/i3//Hf+ea//K9n/+XfaX77J298\\ne/7ZZn/42T//l7MDMjggr33j9ttfKW/sxy+8Xty5Yd79+sn11T9Hx3dOvvZF+eCdm6+NPvvayXCY\\no9Hud9ovxPmwCE3C2gHqrdXWm65TxhjtlFLKew8hzAegHY/O36PO148W9Pf+qLv6aAFwkRVHIivb\\nRQQY1z1J8HUL8IuPqnRyD9EdAOrF1TZEJ+AGwwjNpde9tTWTWE7HVCS29cbZ/R0ets9RnC+qi0Xj\\nkjwwjFBTI91kEkkB9w5v3svp6dWK+wuAaIRpQIgwDiAkJdfaIoQ4lSBoIXgfimbZhuBczAghmIAY\\nsPfW65hETZlgGCVJ4r2NQLXdCvEEk0IKzKWjeQ6p2HZId84a4oxxkHZcwnY8PlzAgmzb3pu69Ns0\\nWT07h3bRWst5QiOyxQgjG1EMuLbeOTdArW5rF3ayo/0Hn15R4TPYdb0qszIYDIgz9mJ72YPhK4TD\\n2tWlYA7CmScYSY+3RH349gGTBGQFgXo4HQ4p2clHd0HY9/DIIxZ6Br2nDEIYjTOSzqB3QlBkJc+E\\nNW2az794FyaegHXH8jw6CyNYXqg+7j+4d1ytqzxT1jxHCo7osRzvvPfxyiKD1VmZeKurj56fQsRE\\nXiKcoqSkzfPrbUUIzoHxOqRpfG5o1S76usHq7AaSIwF4vAg6QR9ddPLQA289olLh9uUgZxQC77Wu\\ni5tv3SrY9bbdFCJrdADMsbgkdNkvQ9Mueu+hb473sx0JUm6r2RVFdvZyzPhBPmCC8GE56BxwbQNs\\nnyadqtdGwzReGw1Nr5jd0ZhRylG0RtdJ0tUN8wB0Pt9Yjpi0BL06isfHOoTs+vx0l3W//xPw2s+0\\nb92JtlN2tWydDVt6b+r2D28yiCMsTi/TJrmJKSq4xIQ4MWnp+JU3Qp6GVTv+qDu0zllcOMYrG/o+\\nyASVQ8Y4lFICiKd7ZVdZQUUAUSajGNB0UFoDjTHAh/EUIOAM4oRTgB0BDAKfcMG5vL5aAb+BwWal\\nQMCVO1AZDaKnoq+2ylkUkF1sLJfgcCwWm0YrB2gKEBUIdh3rTB8hsHSn9346vI7n7zfLFQqx7/uj\\n4x3gOxRMDB0hnjAaQqAU372beKeLkkcYkpTdPPms724sL+rnTzaPn1xD5CImVmFIYHRuuCtTSZsK\\nIAAFTWRCBkkLQeAEjyQKBnOZI6YJZt6B2ex507c7ewcnt4q9XFodooMIhhiQ7lWAYHnVhRAG4wEK\\n29l8jQD+1GD8H/1N8B/9J5NkVEJMMcZ37o9XKwMhJoS898HjLsAQQpLS3UNKAWmbTde1hBCl28CJ\\n7Y6SJAEAYIwpZwUP3tTRB+8MpZRzBqCNQQMQkzyBEAAEY4xCEgeyO3fS0+5ge3Vaq5YyyLMUBd81\\nLacYs5iM90J/cXQve/7Slq4GUN6+eZIkSLhNkqVZlk1TAQn9+GN0+d6lWURWTprTs4JUZ+ffN/2m\\nhE8++hf/5snHm5TKnfFUeHxrDxKhIgRVFzvVOgRM8P/sVz5u9IFff/LL//g3HUgR6Q3oaNKM87Zt\\nVDHg/fpsdwTK7MXO5BN3+b3+e7/7u7/4R+//6bXY/0K7/0U6ejc/+vStn/ty+ql7k6//xX7yDd+9\\n++3/6dv/5r//4L1/tdx+lGmyh179zKI7unexnvCXk0njgUM89L0uS5IMmBhInnDI4DQLd+8FIVMD\\nyrNn803zxF433HTEnAp/zjNm4oSIrLW23zZVs8UoSIyW5nF1roGtCHN68dCDFaUkOB+BokQ0rV5f\\nQmCHMdkznihr5PQWhCRjZIT7WPUyF5SgKdfvPEhb65Mi0TB/8MUvqz4iP8PEg0gAhCB6ihFBkEsJ\\nqMizyfRQNI1jjAyZw7nwQSAE0jxDWLStR9gCGHzkJqDWJ84DAHMYre43TUtuHkw67JB2WIjlqkXQ\\ndMZSFsJVb/wy43A0TBs3siqyvppOOSikqfw4v5aZ9TJebjWKJuhtQJRvBcjR+XJbWFxAaz96vySE\\ngQApSRc9iMB4cwGtd/IEp5fLp6c6GTK2zupVxMg5AvPXhztHFy4DUS667MG7Nw53hkqjljGIO2TW\\nWRRcGhhixBDGCRomAGLEgTE+GtA5YvFXDj77159tPZM4T3jdxHnvrdn4WIhEfvx43ZcSuMoBxQqY\\nCBsuv8sRqVqye5SbZtubOQSMIKzr1qnlp07OZktFk5wCj0HAblE1dHdvE6LeyVI+KcUBGgjrFBRJ\\nRdIUccg4BI57wAfJCJAwPOADEGt4H9m+X3d0KiduMatyS/NRmeFupVyrl1tno0JDLOg41avejXco\\nhBEQ0lzXVy71kQca6v5C5pxjgHANeAzeRgAGJYEW965T2z5AgFHYPWA+AgDiT99OKCAgcirugBt7\\nRznSs/PQQEQgWCaP9DsT1HgbWZaZZArgONpcNT4rIIt9vakfPXaQZEIAiEEMGG6TP/nJ9MnTGalf\\nzj9+f1Dm2LFPXpB16zMRFUpTSiCEq9VqOsqfP12WMBjnEsFrTREZNZulwB5kCJOgGqs94kyaXuWl\\nObyZgFgGzIytAfSUYoAgS9IIiuBhtvt6mqb7o841qNjhILhMQq11mvmmpZB76iOkwGNinMUEDicS\\nur7MBza+cntftd2q7VS9rjZdLguEMcYYCoYRAlnOOEMm+KKYaANcANN7b1uYe0iDc8TX05384nze\\n9OH6/NpYDxg5vZAwfaC1Pjy5AUk+HE2WZFjrPk/Rm2/teu+bzhbDIkR3cKu83hjCZVakVCY43bPO\\n3zkeEuApQQUFCG0C8l3tKMYIAhrcTdT+tf/9m1Xyv/3b/+k3Tp+NEAiC0NV1r2OUaaKVFZScvzy9\\nuqit69OJiN56q4zum6bRTrfLl70HEEUXAxM0IqlNn3AAIYxANV3b9o0shgAgLnDdGud8jBEhRLnk\\n7urOz90xXb4brylB1fYs9JvhKKurlXLWQeJCCCF4OqieP0t3t8UwFQB//IM/9jEEZXg+3K5nX3tl\\nPgbXm+23FxubIOW9D93s8YvtcLQ5SgABAABJREFUeKi522yWPzr9+E+Pbk+IZBTEThtgsTHaWxc8\\nEIhYo/aB0d59+Uuf+tmvHY0JyrJMMCkFU10bnLW6osyzsDl7+r4wmyw2IV4OiuXZkz/E6HJ2Ofn1\\n/+n6t39D//qvwV/7vcN/9Msk2KOTO0df+vOvfvnr7s/9DXjni2t5Ul6qxO/c3X9zfO/tMs3ocL/I\\nxxOa5woPiSC7e9P9w9293cN7b+zgd15v4S0D0WRg15VFYV6kFYY+ROe9p4ynCebEMtR5aPrtOZsO\\nVE95phAyrOBpmWQpU0qFEIZHRaNgcAHYme4vKKVaCWsJCkuISIbA6CDpQCE4JkkCsvzjqws++KLe\\ndjraj56f1v0AOnAyRc72nDGMMSIU4szZ4KwGCJ2eJTB9FTgfXMd8pzTrWt90uldAEpYlIR/kCONo\\nN051nXICGdt7RMm4CI+ebr0lmE4CgjGAGJWO9sXF2UAqNjHzinZbIwB8/Ai2uslRlxxG5USMkabc\\ng4gdRdRrljireJKJqw4CCC3uAriEEHvvsffWIJjkhwcOrrXRTd69//LHZ7N1EdtgPBZCEGJWKwHZ\\npqssobyzd4a7+71eXV6dGetEu2I0Q8XB8woMCmMMmZuy8aTfeusBgY3AbpC41CeA9pvffz+djgKN\\nhHS431IGtQIjec3tS+Cuh+5FNtIY4xDLg5vVTnIQI8yLgwx4ikIC3eEgfmqPvH6TvPH6ntp2Gh4y\\nkYr94aC0EHARZk9esNF44Hlxa7QLEm5A8MG0HjHf9YvGR2NMB7IjxNuu24sYSU8j42dPHz18aW4f\\nHR4exEQtZhu0cHPleqVbD8G6siZMTu7drLxkKWysbZRTQ/wqqGrT90bPXz4ZZwOPWcpQ1xKMRASA\\nikHbVA4u16s5w0EknLLg+q7vmLOkrR1jLEORUN6vyUdXDw4PXIn14R7lGOVUXZ665AbD3nMcUd1o\\na6xBLpAuBqcb4866drtQLEkyIUSCPSAvJvIxQ+0rt1Zp0BDKMrmWdhOCBJBcz7zkUAo83mEINMPs\\n1vBBwjgpctM1DTSmVjDqS90jJpmyajKA3rUAI5Gwqt0Y2yqlnY+EoCQXieQyoQgFH3HcXgQowODe\\nzq4nIjHGiEEBEVVdbLam7Y/l6JZIhfMGUpWnjJBjzmSWTTDMGj/azzvvayJgXwdMoYsOYrbeVhBC\\nhCDjMAbc9xpiAcEobig2LXNuLwNFmnk9dqrru21wvdX+8Agzt2LMQAhfnM9N1y+ucPUiwBgwLzp4\\nYlxwm3DreDfbyZ++3ESgJIJnT2fbrd3O+0DYuckRwMMcJgwbByXhP71DYAC/cGPzjb/99q996/Xv\\nVMcH7ncS+NRaG73KTtz85TPvPYgxxkgZjMFBF1yQMZhgWww9CEbbNhpHMNNaO+dCCIuN9tHovttu\\nq1wKQZDkSbddURIBCGVGCSPeWgBA19vdm/Sj2WTx/iJiRyBKBB5maD5fYIxzmURMvUOQFj/64TVL\\n5d6Nz0RXQm9Pbh4jIGfz88vnpy7g3zH/62Ln68Xg+PSjj2AaME+itRjjT39OLOZXu4NKYPdLf+/v\\nf+XzX6I4dMbp9tnB4aTIhLO2SGjC6KtvdXOD3vvkkZJvAgzLfFBVm2bbCMmq6sqsF6RdwBCGeR69\\n6ZqZra9NewYAz2Y/NHBtt5+4q4/j8iM4/6Hcvvj1b37/N37jk9/7sS9vff3Ry5Pv/LH+vd9+qa/z\\nP/qt01/+x8tvvTdsd//sna/97Btf/aII48NXqMj3pm9M8zu79O5Rf+Mb3/mD15pVgbxbu3ISZwBc\\nxWAjCiAqjAxGXjd2MLq16nBOAwBhe/W+b5XzS0HNgHRV3V2etZJxnCXz8y0ABEFCaKAoQLfCEBqF\\n+lUnOC5GGRYMFzs2cJonSc6JK13zg+gsoBCIJImNjQDa7eTO57rOWYuVhdZL3TsAMYawqw3WTzrj\\necoghEmKI6QBRKs0CV0HctIzno2BzzECAOFZbRjqUxFNiFUgOu4+fPwi9hXBgGHcOwKQXC6vNdBB\\n50CpN26tHnz53nbtudGYCxeyqPt2G7slCd0CYWogwcGu2ov1sDC9mmuzMAp6EFNBjQkWDyb7OYAE\\nY4h84/SFlDxNMABoOEQEIYaQ0XBZGUz1nz6MAGqv+whmwEZBEaNys+3q+fPj27ezXcCj9ZX2sfLb\\nVntg41gMijyHUkLD22QyrRrLc2kNBJFAMbnxxtEggyiV6dE7YXAXMt60kBd3opQX8zbwIk3CkPec\\nk8Ljo5NBKArHUxIWMOYT7AZO5hu07AJigdj1MCujITDIxJ3DVeOsxpgE20AUMmpBdFwO11dG8aKz\\nqeKvHu93YfPJzWPAR59V3YkroCPaQ1C1JgCNUBeDSli26Y7+5S998IMf6EcfrZ6/sKZGvsriJMqq\\nScHV4QgnzueSVchHjceTbJzHBG8Rdoz3lKU+Rs5gmsQibXSNECGUoRgcJRgHm5RYrtGjeOfwM0ef\\nnOmiINM9OvHxhZ7ev+nGQ+uspVIUogH9WVbATLDoger7SiPvvVHWdisIIeFuNE7PZjXkFNqotJvs\\nImcMQpAH3XcBEcxZ6lFOI3jyMToZZJ1xFNZbvfURJGWqlKoUALDMYHz3NZkXEsSIoCXYOwV08Kmk\\nxPnDo0m75fX2HADCiTEGzi6V8qlVYHR4bPowLNF8WyPuQz07P9V122llU4ohwRCa6vRKB1I3mzQh\\nJshciixPyhuWEsBERAgyxiiNCAMEPME4Att3VhCcpDkEePdgOD45mhzste3TGD0AsG6NtUjmt9ME\\nRK0zYlOKGOedndvwfJgPfLBnF5cIkWQYKU/6Kpim9q7zxmHSXV82RdHmlOF5e7MU9/fHX/vshIFA\\nCBsInif45JXpd3d/4f/1T2//5OGP/+Af/KrAK8o9Qmia2ZXedWoJonLOAAB8ME3vlOoowy+Xm20d\\nm6Zumjpni71x13UbbYLzsNNts3WJVSBGKqIx2iNAE0YIitEjgn0wAYI0z5wzwcP7J7vP53Q8jSw4\\n1285p5TynZ3pfsHVZssQTgRPjzZDvOzBsNsiY7qsSJuqVYuzkicJUrkg/E9/idqPs/IuxqWzCADQ\\nqjYR8t/8+o9RkU+Ho9nZthyA/+rv/uKX/txfCn2n+tgbHZGc5EndmKZtndhZzT6T0eLFwx9sOxRQ\\nxBhTia6uVoOi9MGY6Clyvm+d7qxWCCHo3PXZw9nykcupC16pK1+/tM3pZLxM+2e8fwlm59/8735j\\n/Uk122a++ejlH/9zZh/u5c/10+8+/u1/+Uv/9Qf/xS8uP56982R259GLmz/8+8/fv7j7ox9OH/+e\\nzS/fp/4R5wFUfj+/kpQ4bwCNwUVrAgguwni56ky1m8BAMkqSRLIrIWcE1wEzLndD1E1j+uxQX2Q7\\n2SWE2mqXSikwgAFYA3aA3y/XEfVn5xJolECSprzqLU5vaSud72sVrh7PoI1BxEbRcXuJBndVTEMA\\nzvcAMt2r9Xqe5KSzmku8uJq7QBH2IUQAPcaeQDfKhINQA0/FKEmJHI9xZDy2OwNb9bay4exPn1PO\\nAlgDM3OhoRT5TqOExlUTiXcR6zYwosb7n5qtzKRQp3PQ1cI2HSGIiQxRTCJQHObZtKBFQNRBE1xk\\ngCXWxqLcL0VBdd10RAiQMkQpZowRFKajTLXWOZPLEFxb4NN6tj0RGoPtpj3rAY3Tm3tjWM9OYcwG\\no0+/eKY2S8gFiIBEIRHjqomwLaxKu14C5On25bp56WIebYze9jAnHrp1b0ghA+laU9sw3+JsOMVA\\nRD3zEQ/z7MFxtB3oLH4R2G99z1/NVD1Xy36aHLya3twdvjnC5AyACAAYDMfHkxidf/WBFF0FAsQY\\nuugpAxTZtg8mxGp9MWW1WxjUuov2cIUMLQcfXeRIEIZUglranrd2HqJjzPnoKCa+vR6zPznO1e09\\ne9NzWlM1S6vTFQr8+GZ6/8GDvYOdtz9VfHm3+kv74c8c052qY2AiCM2zIYQEIUC5sAYEQAMkb71B\\nKSOAFZRAigymSJKz8eQ8XnTf+72Hab4H0OBsBVLpm4dkOki8am4NCGIcAp8QvbMTDib2YKBGEpXY\\nW8+ByIO3AeE8LZKksgrM+7LvnJUkhvYgVxi4g72U8Riich4pjWnZ748ljzHBHmJ0dG/EGezrCnrD\\nYgh4ePP4ZLHoIAaSkOBtxMxbZzWSmUSDw9WyCuGldYAwnPDoAPCmB95b13tjQwggAOtNPohpYW3/\\nYbOsKUTD/LjeBm2a6Qk+e/IJpJTKO9ZGzvKjsb66Am1nOc9xsACAECGh1HoQA0IIWOUwhLFdur6p\\ntmw8vLvcOuBdnkoAAMbUe//kpdof8ukozSYlpiQEAIEK0XhvswzYfsMYgTL78LHGEI9ZSKC680pB\\noEFejY8YDWFUmre+fOPuu7f87c8FsJcP8HB3V+bls48q8KdX9bMf6osPJvwRoyhLZZLS/VcLO++x\\n5NY7RHCMfjygGEYAQ286HMlivXrxcm5N9+4bk7c+/+5gvOeMhgAl2D89W676ZltvOI8RQUq4MQZC\\niBlv+k5pY63ebLYhhEEKdZE16zKa3kEnqYpOWeu8qgAAwVhcbeD67OVHZnl2hYL/zu//m8Nbt+Zt\\nnzBZDLh1PaJBNTVgRtCKoEvSnwfQIttDzp8/Pk3k8NNf/Or6+oKAzPbdTm7+wX/xfzu6eVskuFlf\\nh27eqtbGwJiYOXEX/MH+yT05vLp9xH7qjOAMFpImwmAMnXPWW0wRIeSnlgFSpllafPQ0v7UzGQ5O\\neFk6axEL7XYJzQeueibCo+k07tIXGOMiYRB0HG3qzZXpL8z6yb3y6dvlHw/D77hnP+T2nOx07cf/\\nOtl8sL36vjVnnPanTy2M4OmjEecmetCbAGOct6ppOmsa4FQq2ra6VtUKQ0C49QGt+3612t46jKLY\\n4ZIlZ+f5wO/tUs1HELHltqUwYox/uqiR8Uly61O3J0cwrvMDoZTbtON2+zREyjhGKOJVnRcrH+xE\\nVMcHNc8ChUWCsQ/aGEUEwXTs65ZyAtUMY6TMkgHFUwIRLQZ5TDLfur7v4apipI4+qeYb6jxJQGUD\\n3b9TN6nQnbcvojcIA0xc23at6YBzCBGr6k3VXFb2enEaXD8YH9S1pjL0syBYVWY20hSFQYEp3PaK\\nmdnVOUYoEJr7DgUAyaBAlBFQVUtfeVbTg50hhlTo63pBhyhcqd5bH9PiWoXOaFHeH71+3xQScnJX\\nNTeOCkTshWAshCp0HwnqHIAkgBAcgd2UbA3seXLFSSVSwojeG9kQApJEih5CnGaMsgzbJt+FEzFj\\n8NR3q9UCBOBt97yf9zemYorb5vrjYPRiviZeEZpAAr0D/QY2F8sU4+7lQlufS0FQFiHfLAHae/DK\\nbYqCh8BhEL33zsJVh4eTIJPhn31wELN+B59HN1dXz1ZBD9RTBNLBZHdvN9UVSfOu6xAlXNUOIO69\\nlxJ4FVaMXQSx88YGlHq0Xx3cZyCqq+f8gzP1/sPxb7539BvPDn5f3/3k8Lj89P4493kicx9yLhin\\nKYXR4qrBIByfXYohbyTmXDLCAYEGRWDMJaJXwTIsYOgqfHy30UzkHJdDxoblXow2YBKjI9y7VUMA\\nAjyLQniIETAtJsB6gmAcZFh1KnU2LxE1IU0GWc7vvJ2X1AbktbMUKRC6szVgEouC3HhtXyCwvWIQ\\nxpE0BJK9Uji9qfAUIDDZEVxQFEGwTjAzLoISw9jOKPHBW8YIcoaJSBlSuvax9UExqAgxm3UTIl1u\\nV0lBYWI9igDS86uKEhbBYLbqD0Z8KPLQtc5GF4eNK5l7xAXQsa21xhSzJHHAQMQQzWIMnGiNEprC\\n6FqzvFq1nPQ1ZwwAxDGOzk6mjLvNwdsPTq9b3wVGIYwd5wzCmEk1KKK1hlC0vZr3y2sR13/xL3z2\\nndvl5nq0X7J8LCkuD/bog9ekL999eD589N15oxBiqYkCM5pnRMrTDDyJYent2kaxrh0lSZGPK+Ob\\ntvcQwwh8UGU55gwiwgRDTWu01pyFdOC+9SP24092bh7vJLDCYO2cA+12e/oS9MZaW/VWhdgr0zio\\nXLhx8yYkmFOcpUxyNMxXZ92AQwhxZBQ7bwGIxui8SOu64rDGofLqMqfZZHcqCJR8eHxS8HzgIqir\\nGQg2lQmAEELo+eajJ8+72mzq3gFPoB0OedTrH//BP9wdjHrKfK/DapkL9OL8KbK5zPPQzGXcjFBn\\nYCV9vvP6/idPlRTl1z7PjQOSMOPWiMDtwohEAoSqpm1V37Y1AIAyoULQ3h5Kevlb/9mA1cH0600V\\n+xmKGkoNrI7RB795dvYEos1i1tKhD40eFmLvVpGXZLm6iigiWkHdcPKhDor3V/36ExjmEC02/ZpZ\\nKKiLKL17j/DEx+gIDhbjfum1gdG1AFRgIp1Ws6v5cj3QHccaMXs+X+k8u78BdGTIaz87/qQaYjsA\\nIDEhLuotJMg7dOXs0xdXiz/WjJ9PDj51Med9P2SEv3lyiEivNi5AeOeI0BIhn1xf52Z4i/X3dyd5\\nbejdI0EpjTEGCIzz3tSbkPBskOR3AvYIh+BRp8y0HKc7g1TIw+MBjCFEjPBumnTbCJ7M2PWTVVeJ\\nEAKG2HkDQOgt0J3bS6QPIUZ/Yw8NXrl1ucUM1mH7ySB302lOUoEEThNqMYsiR9VsXXeA0tY7x7Aw\\nlgbnCzxOqAxd3DZ9SrcgRFWPhOAXdaN040HKwOZ63jMRCbYEcO9GAAz0Nlm2QKuVd8nu/o6NSY+H\\nrBwTigDlyqrNrEHI6e1Bxw8D2hVi71oVPmYBAsoKCGzf921lCbJWm82mK9+9yeFxcJO5dhBtQfDF\\nyL5oW8gM4l5354tGB4qxIHnJKLIZaYF32hEIZp1dA3ldlNeYFh5iiFEMAOAjLHf+/yT9Z49uXYLe\\n96281s5737FynVMnnyd3eLp7enq6J3JIU5JlyRIF2oAFw9D38Ru/sgHBhi2ZMCjJIglzOJohZ7qn\\np9OT4wl1Klfd+d55Zb+Yj3HhAv6/tUTFfk6xA05ba53qexlsrodq8OF1K2lNZNxSvMhJ70GQh4KY\\nMYK8LqvAKddS2YkUeIQQ5wFjzAHvWml1zzguO2YN7j2fdX22Q4Zx7b1Ndy5G8M04jcy87j999fO/\\nXN+AQY+L4X60ywBzfjeqsdMcx9gDCMLx3qSgTcF10xqolbXVaEcjVE/2MIK+dQQQ74ECAPSdT7LI\\nNqJuQVTwKBHGmGKAOsWdc1eXXdkobT0JBYHAOQesGU5wHLRSgSzLIKaD3UPM3m+8t66DDmojnceg\\nRaNMbBfgxUeWUhoG9cGYWq8fHI12RmQY8mZttUIBZattn8a7gtM8F4LwsLsxRo/HA048gh5hIEEL\\nERmkkMJqnIyzmCei1CBmLPTOeFcDJ2lILNBKV/WqjjFgJnDJHgnZ9eLLQDAEWWP2mYBt72xvGCMA\\nOAdD0zvKoDddEsciy24uti8uy5VqLVzDejNKw3FRRCGTSgOIgRKy7c5eeO8UFX676kZTFkfUO5dl\\nO1L5/UlijAFEP9jHz95+YH2W7b+XR27v8bv1qk8jc/RwQqbP/uZ/udm+OvfylIOy6tzpad11rQdc\\na42oh9ABaBBA3oMARha52wsMMNLG/MOrP19VQcy6EhhluZUC+ulQizjZbPqLTz779x/Ndn76/XwY\\nlw0L4IZC69TqZrFxwEttYSic922vLm9WVVVWbQUQDInZPXoAQBi0te1LIYigDGOcRInu2v1x9P5/\\n+b+uTSX7S2y17g0CcrtpQo6O9u6zIGw7TxjvPWBFttls5h3uov96vLNLoBeBj8IkwKRenb91uHfR\\n40f3Jg45bW0vl7iaxXF+crLzn/6zd3/6H4Xf/4+etykdFaa52EwOinINbl9dHR0doAAVwwFBVCt0\\ne73FADtv23rLCa3LNUTeO+WccpubvQ+/E+b1/iE/eTJhmDi/Hg+51xvULu5OXych9QANJ8x7O9qF\\nry7PX56/aUvvrARdOx7W280mzxKljLZ2MozCuBecI2MwnNVdLRg/u6rz/aajoFI6Zxo5GSQIOkVs\\nbZ2eTkcA5l35qq+us+EmHHBteFedK7tngtVnL9qzr34YYMkpM97pXhl5S3jvsQOLBu58e9OXlnjd\\nHKYCKNvfvpifTB4HeYxRr3HUhUHd5nMTnr74hro+yMPh4V5V4mJ3v2+Y9+AfRI2u11qVVXm2Xsu2\\nks4rB+CrNzd3X83TwajrfZoMOxRrbZed6E0WbHmhF8NihpgEACjt17XUWgbU3CzK3vm6M7ObWbOZ\\nb9wOIQTl5Obuuq3aOE+6IFq1oGvE9qpGHBkGPELjtjLffnSBbRLGQT6YbxbLugN6kPqwA77PwpWZ\\nfwu5Fa7F0WVqF4DHlEYYWezCuNA8B+9Prog12I2PDvctaK9Or27vtG51yGqklhj12TDembaM16Ca\\ndbqJgUO9doR0gK5VWzVkUnBmHYI1xU1jHyy/eNUR5+QFxjhiyAOCO3gSkxiUanZxtTnwgdAu6V0c\\nhjyOrYOq6pQHNuZYILAsN+veWeABANY6AJDwIHG4s+Omh5h4j31IzFHhQ3AjIsMWn2BGA6y11gDz\\n93fcfOPdSkVHxVjU0IZNLz97USML4v2GIARBpZoKIttZOorBfqDbjd7JfWorrzNpYjGhbb/u9FRH\\n++d35xSXeWbCguum0qaeX23bbHT/yXthgAkUyGhrgHeh95wGEDnAvNSYFOO9rg885QHzzlbS2wxI\\nHjCErdaakqbttgBFd4vAEvJmudt0FniKIQlSu9hUAIfQa+6tsXCyQ6XxhMmtRL1iFqfjycDX86a+\\n5Swi1HNOTa+LrNV9KX3DWA+Rb3ze9TgKxqP0cRaAYQh9dwtkva3aXjvnTMYRApoTzHgyHI+WizLi\\nppjsjtLIeuyb9e74QLBhllkSxpjE0hOMIaX87naNkFJKBaJOAw+x8MJFg8htpdMYAopC0bVttVlg\\nFiRZ2Gxr5yVl2Pc3CFqjAURIqd72Jk/LdlttJGjaasTteHJ4cO+YURdgdX+fKBtaB2V9rXUfpwNr\\nbVPiPAHFdNTLMhLSx3nIQUL8g5PDp/d3Hdvv1Xj36cmLb9sktxY044P49LNlSj4zeG4gJdHQdRCD\\ntVMdJSaORCgCwTn0gAAXUDja686vvVOvikgziqTuKYu0QpxhyrBTsNgRO7sBzePr0xW07e/9bC8x\\ns7//H05b+fBoCIVqBG6VXqVJqJwHCCKECKLaGm1NGIZBEDR9s38CWS6WJadIWtlKLZ1z2nRaK4St\\nmxSf/KsvZGVF6EF8QBEjFCrgfv3pLxu16XUfhkpqlcSBtCrI8CBKnsT/7bK7LFvQtrJZb0NGB8Xo\\nlz//67MzycRWaNB1HexMKzvvtm89mbrph9vxP9eDPwmLP9/KnfFb4+CtP3b9wYvTLyeHkx//4IHt\\nXRRzBUgUsrqskNNhSCDwDGFbrbWSAQsQFW8f4Y15sFqDstk8ev8YWEQLiDECUBvk9sc6j9HOYDDe\\nmzZaT3f2Yha2dc0h0G1py1pMH97OFzoaY89SNpPQYCyt7ZyX3nQ6veCpBcgjgYxUutmyhMqNBwg5\\nK4HFlSPpkAG658x6tW7n7X0iAkPiEOoFYPVpn8GPtZv7GBCcWOuhXhHRM48dRq8vF3fneHb6Quzy\\nsxu7k2KfpWDQWvsj7+9Zlqo1jTJ121C3nrHs12dvrrn3Vo/K2Qxj6gCTjnivCZNV01EGMEOUai9b\\np7osxiKD6+2VR6DuuzwQEoaqBno+z6bXoIgJo2KQWWuRJ8g74mXE0GAYgV4CqxCgkVoJWt9e1PPZ\\nWZbkQCnhZ1YoBIDslth1CGHgkTTta3Un77/75L2HMUd2vqoQxABit5KIj40xq6qb3t9DHuNQFImH\\nHlkfpgnJIkQsu92M9vf0J1/T2ow4z1bLX7d33zBk+146CBxkmUAhTuKEA4MDNS/bHvreWj1IWGi6\\nruxsvUaQKwestdYDCIMBOb+flrYtjcMYQye7XJjehJhLjpGzQ5ZGMWz6llKGjSXaeowcIfEkDdA/\\nTO4Vq1qOgAHAUoqCkFniTEc8j5ng2DtncBl/94ffc0VYANgTESrZLpTZqMOf/smPP39xwYN0jYav\\nN7pnZrBze7HY7h+mWBkfMAKVcZBxYCTwfncySpzsA5FAKh2GozSUlZutF0U2aLfNzfVn3gciHx3d\\nLwoGKLZCuGf7cnM9WNy+CgR79mD09EBARDxmfW84ho4NDo5JxMF203rklU0RpBC3sgGRayIGojSj\\nCE4mpkUMozYU4WiyL41PoxGEFGJ0f6yfjacxXE7HsTMGZsnO0PYSOskjarWUykxoPHr15d/GQYwB\\nRghACHd2qdEoDhBigFDQN5rU26YCWX4gYlccHMWZ253i4yd7TvUxwyzYowwV+ShIUguxd6gy41YH\\neTqioRK03tkZV33OWL7d4mrrvOsQKFtlEeaEujhyIYYx3xNxSknMsSTUZ1MMBTQWaHm4enktt3o2\\np8t1eHu1sqALIwogZDxyzgRChOwwChEWftMuVLPlRU7idFjsg/hB3aaDaaJ0QrhDEMQRiOPM4t0k\\nRpgIxjkx5ckDzKLs/LOZhsH93TTeedzjHzVNd3Fz++vfzaxTrYmtDL6+ApwuLLC1AtXdrKsrjHES\\nB0HIiUdxyAJOKMV5EYVFKDiZHOyZSgmO8sEojgKvNYQug22RAAg9JPjgJPDMQYv3d5PvHqd/8Ys3\\nd5vYu7vX159c+sHhB0fZfkUprTdLK/u27ZW20mpGKIEQYlTV/fRo1Gi9qChDWNtSm/b2dm6Bp5QC\\nALab9n/+7z4i5EsuFulguLgqHJDaGKO78mb1/IMP0uG9OBoSQhZ3szwfyM5N9sM4//4gG8lShoSk\\noZDWG0D27r399P1HcRJ4QDCBXhu9udFelsv24083v/t5+G//tvv0d0at5d98IT/6n+6M+M71ZvPJ\\n53W4/9Z3f/K2ZTDCjkaYYui9996zAFvKnHPb7dJCfbMwXs6vljEJJwgffv3bJYlRddeaAHkHAs5g\\n2WxWX55fvOo7rJV3oAujpBh06/VGGQkQBqDlHonmUjveAj8+fE4IyBPy4B08fsygR/1VL28VBT4B\\nEqOWCZ8MAwDAP2S0my0yvTVV/+CgKOu+XL+5uzurtg1ut0uFYHcr6V3TKWAdxt4j76DwTgfYmlAQ\\nkDJWK7kG118wTLq+bg1bXQ16NceYBYRJxYtc8QjK3pgmHMRRd1t2aguwc6QMWIsxRiCjNiABb7Zr\\npxvOOcQCQt/2HQpCrSzmzDK2bj3qNQBOpAxStl0quag9bBFijJC9UTEoxtKBXlmtKSXcOYcZoTE3\\n0lPMytVVsacpZs7CqysvOALQoV4bC22RjOPJeyoJSnmr6lvgFQlMmlQBltevbiEJeRRqVYGGdTbF\\nDBPP/CCPhNXGeSOmO3L5+oIHzMVhElwgaJqt9ZQ+O8wYEnQ8HRfUgbTugTQ2jDkcJ7r3mAecWetr\\njOmDZ2PVlvtjE4iWQLEZHfzsD94bJmg4dVOnAj6aHL0FjEEkZT6TEObHbz880E2TDQ5S0JfGG+sB\\nxakiE5wRhhxsjfcAagqpCJDB3uh+2SpNrFlfV5UCGGNgcwmOO7YPneJBcTRl3abXCr398Pe/+t2/\\n7FynexkNrps70Nvo1dlsNXcjRp8+5YEPqt5hQK1zVgOQbY73jAfIOGwIG++JMGqQ3xSjB0xKp9sg\\nCFOVVNX7p5I72QGS9OLY7e0n4GJTFwv3jokPVuXWKM2wHYyM86Ts84oMCe6hV9umM24Tobno/P17\\ne62OpkfHCnkEWuCMcvEki6l3srsEqga6gd7l4QE2GRqO2EA4YIHvsXja2cQpazHdmcCyrg/G1dlX\\nX9JAZEUq4JawCALmrT45zpcVxixSGooAAwcYC4Eb6ja+udEVDCXNVxXFgqXFrlbLJEl29goEMHIW\\n2H6YUKktAsdVWceCblvTdS1wRJW+3awgGWmPEJCL7XIw8N4jQnGvIUKIJyAf7vUaBDldbmTTkMXq\\ny/CejlLJVBvLVRD2CKcSpkYJ42CSDSDhCFsJpZaGctnr/npmXl+WpuftpszT/uGTcRwGCCGMMWYM\\ngJbRmySJKdIEw8fPJk1Dz151xm2t1N/9gz9PMHp9/gpRwILW2ettewOMBgCsX1/zJJASbNeNht6Y\\ntu97SilFGCKAIGSEQkCtQ53kJBQQZ+Odw6bFN7eSBEkchx66FtInxwQig4H0TmOM14tmtVWDP/lB\\nW3cHJ2xTkdtV+tmvtm8u8kz8pOyoUrLvWwitcw4AAHjIAyG1oxR/9cX5IC9aRZrb3ig1WywGqDba\\nUsKtUZzTJ/fSbfdmdnW78/i7x9OF6S2CnnMeQQ91NRoPPGB1XVPK27bd2d1d6Gg+v0IU7O5yAlVd\\nVW1dmb5hgn/7xScvvvqVCGQa4VGBuADzxbU/+3JZKmHP8e03z4/vmruPMhaJ5isRKaDyCLz6H//F\\n30aH7yXHP8AIZgPuAPIAAeuur2aDItfW5PnA9dIHTpXVeJiEcUKDCACIrRKU3L8/FVFYt92LmwYg\\nyEJa3Z3GIQ6YLefz3m0e/eA+TmNEWDakGEZChDzsPMKD/fNSk2jEb89dea7BmiOLGMEEWRJzRO81\\nygDIHUTew/XKGWur1SKK7GytD3/0h9A9++aT69mbN0DfeAsxxpOdzCGsujXgHQmFdFCpdlEqg0jb\\nGdksDnYd48tsDCjjwKrN7FPIZLnu+7rGDnRSIQq2DRoMgepuEF7RkMwWPdSYQj8a9oyjHsVdB2kQ\\nOYAZJ8gTQSgjFEFgQLhadWs56FvDOMSCasU2tx0DLWY+iDuEnDGm7zsXF2K4Ky1AGEgpIUPK27b2\\n1hDmLEFaz7wQAqHcA1Gv75yfI4pjBCKLPc6q3HatbhkjLBpiFikjna7DAGpKk4HQZU95DJyGBt6t\\nooiYZdmyMCTUEvWGJWORDneEXdcqsqqgRTWcaOgX9S6z1Dm4eLNALkjGLBkG5ZdL/mAnChxkCMUJ\\nBHh2VcaTbFVxgIhWhVk8WvRqW9/vJzvhZMBm+dVCzeeMdWXdRJV9tg13TrdehXuCIyCiukcaJiFU\\nQtrlvA0GLYA1Icw5lwX+mHmMrbeS4G0pZ3W5bSnSzmDMrUoGw5zTwZ/+wdObi9vK0OnRu/Xtr+Y+\\nV84q7wmohu3q5u6l3LYz9tY38+HrGcKJDQMcCs+Y42FQpB+c3SYthyF31hUSRlvFaZJV64U1C2kw\\nSYrhlOV+/dlfuzXi1uvtCs66ARG9UfXNDeTBDBKMGXK2JygxGjHbLjustQauRdZzNskff3fDY+sW\\nfYtOT8/2c/P0oDCWKlM8fbJPXS99xEj01iP3/l4Spdmj7x4hOe/aQbcGISMaLNbrZR7D1RoK0Sfx\\nYLmujP4mHpPXV6lIMudxL2mnY+2c1tA77JwQIoooocjfO2xgW7Zb3W2g1ppzzgnVUmFSaKc9LYyz\\nFnnHWV8u0sn314tvAOoxpgAD5FfSGjEyYYaBVb5fWVuFIkDJu1VtCGPWuqo22ouqbMI0q1cb1G6S\\nsN5c1IKP4xhT0ZG0PzyM0mSgt1cHx8j4AcQEatn3ihmLkSoyTJADUEepr5pFUN6N8kz1Y5CEqm4T\\nCpzqAXIQUIr546N4dz+d37V93TnPIUbY9qv6cemw66+/eXNVLZcUWOgtY0xpMBnprvZVbQkhcZZi\\nDIH2faellIEglHCIvGCEIR/D/tH9yWxVfv6NxAQiqKr1qmw7p3xno9bm98ZFq4ExyhMSJem2Yv/t\\n/3l9eLSvularFrVvrLr7/MtvPnm9hkitb0vvfds0pmv6ZuuUlFJCRCD0MWMom9ZLCHxT1zf3H56Q\\nMKauV13rnMOQjPbRci4ssn/3779YdXXT6brpVN/Xrfz66y9LF5Pp4eMPfzCbX3NOabZb35Ikn951\\noNQaIWRUiyFAEABvBkyNj3NasM6Dez/bZekQ4NDG3d7Dh/PlnRCLIpj/8PdGXivoy8X8CyqWzfx8\\nwJaf/MWnL8+C3QT8+A8fHe89yKJwZ5o9eny/LEtIMISQUHB+6c3tHQ62l6d3VW+SJFYaz67Xn3z8\\n64vrU0bDm1Xr+w5jrEwl+43g6NGTveF+3FSrZLJPWWAphjRs23Y4ADfb7sUvbkIaVZfWS2osds5B\\nRDhlsrfX69obCklW1X2McDKNnZJSbqGABPVKgfXvPgns+vBwenN6Wuq5UOsgBJBsHdkzGoiU1p0F\\nABhNHXJuezOIa8bI1WwDCETolmn47jOT3L+nQOuhrppFKDQCUGhHRLi8+3h4UIAIUeICzmPaGC2V\\nE5QPMY8oTeptDRFWVT3IvQEQMW4Mah3qfLQ5f0W6BYYzYL3UHiEEsCGhQRhA7zFGFoJq3csSpyz1\\nWgLojFEOeM5530qMYTwNULrbrOW0gCxkEEItOxTYzkODeAfg2vX1ds07zZBR27vKF8OH8UYgwiHc\\nlkse4zTwxQjHIoqTYNTP2hZg0W/LjqA85DucJ/PTeW9FGpEspsPVdlGPRjtSbG8//rTdfXZAvLxa\\noldfnY0fnSilXStlB3Le70aVsyRgumxY3SbZRMfwQinTeoI2ubdgbZasPQ2DOgEmoMq32/nZG9AN\\nKBlfrK7r1QZbBsNpSNgkngNPqLAWEqusNYcHbxeSKuwdxtyqUi/WfaUIRITSmJFkECVwvXvw7Bd/\\n/79sKlCCtx4+Mber2myblOcBIb0m6vZN31Qvz24pu+LhEgMmrQjjEBMIEAyDQpCbRbWBaOQjh6hJ\\ncEtV51XHhB1kwZODduCpr6u/+8tfPHqburveWU1M/+pVPXdGm4V3i28rvxsAjPHuLlxvqPVIqxLc\\nLGsdM2HePXEA4O35F4JzitscfvvO6LgY/YSJwNskev7nV93UBNjp9uD7f3705M8fffDovb2InHdK\\nrYOsOLgfOGj0pl5udvsWp8NqpZsoXsj6wpOs79nOSDa2DzijUKQRC9nGo8o557xBmAOAPKX5xCfD\\nIUQeUq+lWc8bY2LnAAB9lj/KaQRIwjm31mJMcWnCdL7WoNh9CC1DUEqnVv0QAutDsV+MMCUBR8rZ\\nWhkJtoxDqOqudRosrfWq6ynrOec7I1MueoTDkOfAYhSFEJZFmtV6iu26q1U+cZAMRZiJdFA2Lk5B\\n09UlZCVqPJCET28r7mWfjf30vR0sAILaGJdN8nDnRwjokEW1nlhDsiFLwtEXLzfnp6jYRWRzFQrC\\ngIoib32FUVPk8es3S8YpY0T1sGxcEqFIYIIwxtB6RwghhI1HoyKBIMAiZNjNZKcc8B56AID1Vvfb\\nr662MBAZJfUWXd6slmsjOBnYX/gQzt6cCeryAkO3ruvtzbe/cZbuPqW17BbbprceU2ad6rTSVhnv\\n0gH57S9+c7Pelu3aebn86le9Ng7JJPKjIU9Z13ufJUyBAdJwsV09/MEBAJgg3G2Xs9Xi+fPntcdv\\nXr4I49Q7PduuF9dXSDXjyYhZe7vsGcHWSadL5FWaxuOdS1kbb+3Xv9LGQts1gxSqfl1w5oG1tml7\\nu1z4vllVtytOZBgw11XVzSd75qPP7tAn6vHbf/bDt7+ft6ulVjYIw97ivq0ZY/eO91ZqERSoCEO/\\nvtN92Ru+PtvspGOe2yiTkGqvFovlUpCICg5EE2X5alM0TfXlp+c3m4byUEFEx4wXlzv7sTEG9pVU\\nRvUSIWS1g9YA23qHMHQUXUirOuRaeW1dlxRiuo+6VtfSiGGGwpQVN2Ji6fihM1cimGsimy2nssMQ\\nBn7r8S5lGAAGnYkdHpAmHg8wol3XQAcqa26Wy747Ai7AmNtW1XpBVP8wK9e1gr05EMtksNP1pUMd\\nT4mJWFcSa3qvjPcojgqBNASm7Brjk1Yi55V3ZHO9zogDoAS2TVz/3Z0+mgiHGca8kxQ7lWQEkcD0\\nrTG1891kJyCUK205hMxUQcRqYxeXup1f5pN4vexBuEspZcUeYpg7HLDQEdGbrjYSYeDrlbH2ibTC\\nj7Ij1HDi43BI9/fDqMim6aIOwrzy3tM4pcQzMfEkjChI7Rk2GJMxjNnK1UV0RlhAYXl+PVcq9Zu5\\nqcT2pjt4MJ7qa+mkiHAYDIPcy94RArYVMBpoKoAY7x/y7e1Kuh4jcPmykn5brxtidRhJgS+YOU8w\\ndB427U3CpY0IRN72sEZJeBAiRK+ugAUeaoHJaDXDDRoACqy1nAqRwjT2xAGBCET5oyej02rPbH4j\\nzdoj/9YPj7/66K95UIXpsGPIOqlt//jBHHYvDXCI6ILTQdxJ1xsFrPHYIac7VV41HRwNZNNaLHAS\\n1EkkCGDOTctkGCcsn8qsuLFOGdREPPS65/BN3L0OfIkx9qaqLsJsSBikQcC17CDESTRLYwt05yfP\\nV9uGsM1i69OQEBGOH/xpunPCcXw1784+HdxWerW4gkCR8EErH3T+IYreEpPi5P2bZzssFm/N14Iz\\nEIA1wRY6Cyzr64TzHvjetcZp57FBHhCEWIit6zuFAUIQakqCNQvf/4BRFrBAmKqU2o4TRzAPGNKm\\n9h5yTn3+7HS2bJutrLtycXuwTxG8WM82UAVtWULOqWisVdTc3gsbY3fZaKQdFZHom9Y6jzwCwDVN\\nw0UHIezbngoOkaXU0hHDQdVLDCHy9NDCKA8IxRlSS8g6q6VC2EBtvEM+hxBajyAiy6tqu1atca7R\\nqN0ygjzD119zrT1BaDweMqzXy88HeaIcsqpHBBKKw8GhWv/KgtdX1c7T94Fpu/uPpwZA7zkCarmp\\nKEQIIecAJsBLCry0pqHcHcV+b8CYoBQjypyPMunB1ZvGGY0QIghzRihBQRCGAbKq+fab9b2TXUTB\\nMBs2m4VqSs8NbfrhJNvfjVsJOuVVubGuns/fQMC6srVe3t5d3a5UpxVxACOAsGcU5KP7MY8Y8Nkw\\njwYRIXy0Ew0GhfTJ9Hsn3HGC8N7uI2eV1vjn/+HiR9/PlstGcC7q6tsv/i72wjaOhdF2cdXWG5rE\\ny5vZy4tr2XqnFSKwEL1sG28kcKpcifm1Crho1rdyM9faz5cGQohZxWCHqdQATRNLsQMcWB3b9pwT\\nL1Xp/TIMxdVffvWXv4W73//nj//s2WiIZbOKGOg71ZT99Wru0M7q5prGIee8aeogikVGtW6+++Gj\\nbqujEPf9ejgcIgal7tM0Xd01YZCH1A+StlLt2d99zGLUOnl2lqSDYrEgleyAVRhjCKH1zjgEISSc\\ncRbuPQSEYquNMbWytfOk6hIDIwBcv9Vo20VIt8s2CxvVCYsMDolWLSALFvrOoXQk2tYhyqDzgwT2\\nYSocXG5SLBCkIAj5epW48sY6qHSdRVG9AQO2tYiIJz+OxA7hK5nuJtOHCPNysQk8BRoj5zjDEUXY\\nKgKlgdQhok3nPb1e2b5pKTAQWYCFQDFNENhjDbnfumS99pgwh0lVqyCICGRJ4LSFq5pYQDsXaUni\\nqJaCEoziADx+mFTlIk14W961LY5Ag7w3Qm2rBva9AdonGbK+5CAfhHZ7d389yfwwCHnKbN68drOW\\nobbWgEfIzHuPnFVqVJahYdNOLq2v4wEPyKYsc5se132s7QyrF1Gy8/BJHBcVxnByMsFhm4ZDKHZM\\nmimP26qViFnZw65DSDJPb1c8VGfKSthXm6s5IQQbByPBEeuHKYkNY96ovm51HAG5gXHTeQnUVnsb\\nqi5ebNYIYWstgQSYLSEzqx2jnPKgUxBi4ik/yFHgKEv2my9ffLPhRm094lDsvPgP/3Ilk1KGcnPX\\naBzEkYn3xeGatEvEiGkbxSgUQNS6VxZ6LyKxu8OfZMsg0vNy6SBhUDw4TJred0bVlalv2JZOjgfp\\npvry6L3cWp8NNy2Qra33HoYQKOMsMDIfpGGB7j3il9eVARDHsOAtA2o4yN31G+PGRvuIKYFBTx4W\\nujgL0/Vm7vVdO998Opt15ZW+81rGem0aEys4MO7hbz/facV4s/hFGENrbSsR4UQDk8V0ytHyFiTT\\n3VhA2O8q7YJwGDK0P1rfG7ceMgItQcaYkq6OWjDcGSaXp+DZhy3j0AUZxnMeGAf8vaEkHl+/MPb6\\nqpgWaewHWTg8HAFwiyCEEL69L2Jht5tWykUeUbX/9mI5Is15HicMUl3NQi5CHmOMhuMCAr3dOmek\\n6jEmjHPurKck6qXpNWybLfICiPthRjpdjYudNG7lptO2wjGFYAN8763xtga+tt453/flTKA+oCj2\\nDvtr72AY58AZD6Fx3pAHCXfDIeFYyl4Z2wMzqzayPL+ez5/cf/7ozYzJmtIA9Vt0M7MQId2VAACC\\n2tB03/txAsMBoO7+h9/JRxFhoSbweM9wVp6/WgG4ZIJ77yEk1oA0CwFyaaYAAAB2s4U8Gvpp7pIs\\n9sZi4gjqDvan43E6zOkw6AUtIfAZg4xgQbBp+/lan13efPTJ7K7sjfRcAKXBfKkAJBi3ar3sLSK4\\nffHF+vJO3d9TZ1/fdSVJE3w3f9O2tenKmEhydPiTn/Kuntd1u11d8AzxIEmiB15Q7H2zXNkoGRTJ\\n2ecft10FQ1dJFeV5nIdKqXaBDu/ZncMgjHKEUBayB49s11plBASWoWhTFxg5q+TRGCsLAPFGVZT6\\n1bzuW/ni219efvzX/+L/8cUd+d9Pvv+TP/7zx7u0fXgwMEZtq7badLGaKd0hQax169vLwWSaJMPr\\nj2fSSdP7PM+8qQPQsVh8/MtXNzdnwBrZxzRAq+X28Hmjms/4rKPabK8v45Dbcgmwc6bXssfAcgFY\\nIizim4aef20ARN4hxwarWcJ8repZFgKr3c6hu5OdjkYuvTca6FE6pdGw266dBk61fd/fXIFmcYrJ\\nRBpEKFr6rgCH2ridwx1paF/b21ldt3iQVQGrhFru7zaHB3kl4eTxoZbq8mZwepslUpVyEqI8ywfO\\nyigmhFIFYKsMR47ywuAIuIBw0XYGWxehJgwagC2QEvkWMvDZl97fNdJ4AAxyFYmplLjarIuUQx/y\\nQFjtjKy9Uduum1c1bmpo3HEB6mq99/RJ66ZhPtI2IhSiaTJjyCuP+d7ox2/3fQsZHwZjJ4A9QRfi\\nAmz6THcdIitBHSCvN7eaClj1JYUIQu85wIqqjoYiBlGBeQOpbutqqr9eLIwH/bJJGaedimqdn2Sl\\nX5sQcCVbPW/nV1tV3rz4Sm8aOcjTYsRERIB10yNL2hsPtODVMNt0qy0UJICwMglBWLbeaqdX0IJt\\nXc9//3/z+IMnpWDO26V2F7PLzw+mmvguJMj5XnDfbyFANiDIW83CBKEoODyG4TDJIuPOVH+3vO06\\n2RNCdPdxlvbqTkuncg7U6Wa1kE1rzhZNYzcI2iyjIey1te0ajhJPWez8fuNiJVpjSm9d1zx+/+l3\\nP/5cQmRjagHchowv5287zH30ZLDjGTQsKEGVNM07X71KojzyHqbFQ2N2Onu8uNsqQ5rWhcmBzp52\\nxm+rzWQn8UADiuOEVgvRZW+lwwZdb3C4csiFwavkk794+OSpd4iLc4fseUMXa+JQf3LycFu2g8HA\\nyJoGJgyVtGaUp/u773ZY5gMMTO/iuLRnrXIc9NneCIvHhCFIwmIyldpPIvjoqe77uCo7ypM5/J71\\n7P4EnOwPB6LEXt9//l6AAwp/Xjy/By3SOHU2uz373b1Dn4m6yR/9ch4iChlTWYwtEsuri+Go/+Ef\\n/KTVfrHdxFGXBBmgibUVi7j2DfOQUwZsKxVTkoWMRxwkYRcxEJCtALSfybKGmDgDoqIoTN8nooF2\\npCEPGfUQSasr2bVKKSG0N8h5CMDOXkEEhMQhjEko2s4ahW/etEWxG1D63nvB0ZiEYjtk5eZmZt1N\\n015fXVeymkcCjot8vJdCJGioo5B46PKEvf1B8JvbP9ItoRJXZVHyhxYRQWiP/oBQb2WzXm211oQQ\\nSqn30BsfB6KYZgSSaHT44rRj4d5oyLWjxgNnNEDeWRCEI5xOELdpRk+G7jDuo26RmNnzAh0xOQRl\\nRuq784uvX11vSw8wcV4TtHTdRvu1iIR2UTYoyu3qrA6fPi+yHQ0JThIMAEhiFsXBv/m//fov/+02\\nzqK26RYXLx+8+xQlw6vLj+8dP6hXl1pVq6rbdNnTR3v5gHpN4jiWXd9VEiHUd55M1dmFRAAbbSnz\\nJVyvartoyeBoXJYl7CkrLIZN07k4GrZNY3X36B73cBEy/GCSp+gMrj7+/F//v//qP+Bv83/67P/0\\nf/zgP/vw8Tvs935cHER05944oMgBK9IUeW8I5IPc2+DgYRQI4VtnjS4y2MHBIHn72QcTqNcIOZE0\\n9x/Ftr3AlCGuQmFFFBYF5wFHAGKMDfTTkc+mCUQkZLxXrOs3FN3EUO8Nscg1CVQ0GFlArfJVZZ8c\\nPVqu7lRd1WrQWYPxMUOPtvrW2M465XTN/FyqmQNeyx4F+HZ7MTl8cLvZdjJsS4hRua2udH2uUjzA\\nfhqTqm1ctP9Xf/m14FHniu22cvYVRBujQdsS6wadpt4xZwGUvTZQSs0xMRJ1ZWmNNp1yrkUBBxx5\\nCJyD2iWJ2A2DhjKAgXWgq1Z1kYaY+La6NsArG2vLlOPAW+cMQoCSjdK1Zz56cnL+rYxiRuguAGI2\\nt2jRp9p66E7yo+zj1zQImeCk6+34UXJ8WKfixhmtMVJBFwWXAZYb2XO/SIkKiDXOcoJpHO9P84C4\\ny5nyJCU8itj24nqZh7BbtjDMCGy3TX2XjEueplPEgUyKVxE4nxZ9MV5RjPPimE4daE0aJMaOEKjv\\ntrHDZLEyjVmNxgmnhCGMGWzL7aIljpQJXyI3UOy/+vXL7JefBjmT07zbjesoFMOi/e7zZhI3XKBR\\nPuOYs8AgBDQIOiMgzO+2DIqJtA2jd4SZNI1JSvu+j8MQMBXF8t5uOJjwRwON9QbUC9k4jxTl0FNO\\nlMUGijjzEIugJbpXZj3rpTWMQjF5/FZqP9Md1SbhzA9DHEU2tt3L8/Wb5Zh6KHwQcjIqboH5CmIo\\nawkBbowbp/3V7QUyKkOr3vDb87JaW4wxx7tKKQRN6NQgTUIYqEX2leFAb79dTis4GBzr8fhD23/t\\nYKBlb9avFjeVJG5R4b/56ItxItIICR567zECfemmh4829a907xljSARVvc4DwCxYN+OLs1XTy0UZ\\nehycnt10hoymRy3BxFYBAVDjj39bbeTqtsRm/N0i8sX0/Zv6ZFXfHid8voCdJ81SxaJ6/GB/vP/n\\ny86JhgWLb6uq8SQIWJHnx1pVvRl9/W0NW5Wxe22LBU3vLvu2q7XTIWYYmpNEMU6QB0q3EAaMsTBE\\nnkE+IEFxNNqXRjXewyFtnKNCMGsrh3Sr9CCJPKJOK1mvkO+koRvCsYECRa3ilAQ0GEgFreq8D5w1\\n2l+bFkASrdoHAI/6Ft8/dpnoQ47DMCS4ZQghyHVfWgedqwkFlEc5oaPUvKJ/svn8k6NEJhm5uLy6\\nOttq5Z1Wd2++9jaKC04xxRBRSiHACBJn8Xg6wixijLXVJePoL/66HuRUOFdkYRGLgGFEcFlLZ/Sb\\ny+7g0Q4tpntPYlgUx+9P3vsD/p//1/k/+6+KP/vh4sHQDSN9cyMtZ2WDWEIAOrUQxdPd9XLWtq0H\\nYLW8+OVfShbhtmrHw1ppkKVRyPFwSCYjQSLEY5iGwfmrr6Y7h0EcvPriTXHwcDgM8yKwyy/qxcV2\\nddO2q6ZpMIZaa2utMz6OCuhB02wZY2nIbCcHSeC8XM+UNtlgAvT99zu4Iw2PIkgol9r/1c/Pnv7j\\n/wJaE0VAMOTNfEw25vJXn/+/fvHLfzv49393vPfwvzl8vlOTUZaOty1DjiCEaJyImC7XdyzIgyE3\\nnVzcAoKcSPF4tGdFzDzwxGRTkE+pky1AhaGIQkGjSK9Q3zUgiBFwGJgnO94LDFDsya4lCeXMGEu8\\nDpm2mvVmWoNsucwcHGR74e1caVse7P3AW4lhnSRkvdo4ry0cO7vVViFOe6m1VUq1jYG3F2Un84ub\\nv5EuhfMLo5ZchFq7bVvqppnbwXk2NpMPHxzq3eN0/vFfKbi2DbpZbLfzM+t0LW3ZbKwx3hnuJBGB\\n1NYDFDLF0JZjg3UTBcZoWFfWWwZoWG1AwmrIuCZRr4nz0DgXBEHfLC3ya5W2Vdu3pbM9Qghh6i1I\\nXdN5ACD97Lp98+U6oZHrVwzUPRk4KJClIuOpgNvZb95ovo8gZ2aXFw/YgIkhHuQ4Zn1tCZV923Zc\\nAMZjlsRIcBZx6IVFJybKOZjp+RuXjihut6tb52yc54SzqPC7wVWCVrjqh8vVZb9btRQIHIjdJw+H\\nnWZti9IiWWtaNcpy0czlySM+RjcoKYLAcIEt3ONFEETxKDQhk3cvXw/aNRAQBUprm5K/p7NP3vrg\\nvZ0j9e73+e//WKdsSEXx+jqaq8IDW9WuamU4DhO+7Suk1pjF1ahsPnp93bGobZGBLOC3RPmAAFn3\\nyDhE975V2bZjMlGm7ofg3CqccGEkWVceDrgQIgpZwWmAKQpPTb/QMoiFUz4dL65+e/UNyaKDiB19\\n59nbb+2wZgvBp+3is3IZHj4/Giar0f4Iq6brFcmCsksFtMiQSi57EXAGWOSOoq4QyywqMRhhTNs6\\ngSiw+4VDk+Khhzf/7uu//e3p5S26/l0UkGgg9oSFPNFYJDFJ+M0QdtWq/O2v/g0L0oN9v1xe78db\\nDLDvnaWD350uw4wayDtA277RWo8mxXQ3sequ24z6/qbrcN8YAjy1g91pEC0L4wDEAId9f/Xv7u88\\n4jBZLQbbXiT7P9guPjoa7/lYQzePqlUc9Ntu9rv2f/W3n3OLQME+Ofmje5gCa1DnIRM2TCay2SL5\\nMo7N02cTZTkmbjDMCKPW9WEY8jBawYFz1nlFEIYQxlkG2KHWENqx64PzqqAiCQibHP3MQaG1GIRC\\nbt9I6zpAu3obR20QQdU72VqnnYF9KAKn+GgMbLelYLWebSCUhBoAkPNbCD3zklPnrUY04KmDvlos\\nZtu1nRa+7mWRsrZXUNOYQIa8CLDe/U7124sfvyvzD996scqvZjfInPd1E0SIjVf54TvISYQIY8wZ\\n65zDxCchANBzjO7vHzJSnd+ssZl/9dntn/5JXK7rJAo8C7XjRtaP9kQRBQe77s3Z4jefGuXs7aV6\\nTR/9/Ys/fnl+8v+9fPr8p+Ef/tN4mmybpnLddn7zKk+hknh5PsMMW2OWmyWw7uj+12XF49GutI4y\\ntG0aqywENmSkW6o/+cfPLPLd688vri8ISbMk4XZb12VTVpu6lfL0vZ/scU61xhBixpgzmgg0f3ET\\nDxhF3moQMP/p67x2VEF+dv06tuSrF9+Wv7tuOsYYW5sSxiYE8N7B5Opf/ZvRnuuVjAoCPWrqGepP\\nQ/e75en/0N19/td/++n//a9+MDv+T6+vtuMc1W3FISSMamM1cGKfvvjtns+4hV1TL8++0RHc2EJ8\\ncapxBjeavfxN6ZyrVsj31hkpCbidXwchQdhR1O2OfBWS3t27reRdZZuyIZi2jQhww2IEMYiKqS4h\\nkHNqt0kgnMVVf7PabhDbtR5spPMk8rDdKe5BGFGgAPLLMtDSuLZXxkGPxOjyzVUO7r6MU/zkobRo\\nQwPWm07o2XCyffHppnrRyPVgZ+c7b7/7dumvFGghRMgYbbe+bYjSzFmBtXHAGUcYbdqtkaX1HhNC\\nBYOY0jjENOiWPWGSYF5XJSZeeuABxt5Qho1R3gPbawSkajYCLyFQFLbe9yGqteZJiMu6s5roqm7b\\nT7iTGZ7DWHltEGTEhR2y1wyECMow9tYg5EZVlyOCeo3SgUzwQm6a8SC0XZIA2tay7nivGk5jg2KE\\ngW03Z5uHsFx52Bco4CRhPJCGBMm7O/cOKWfHOyWGAJp5Y4YkOgA02VyvEcAGx8oB2OuuQa2L6uSH\\nq9q2UsGAY+sDPqIk9YQ/fFp4p5az/uS9Qwfy1SY9Xa+cvbl5+aYi78bTJYgGr75OvvrqoPVHa/8g\\niqI4aCF0hMm6xTUExbAfJerxO0tGURJ/GoG6bJyj2JpmUfPvfCgSUWGGrAJhYGVlx97aiIx3eJCD\\nw7HTUstmgSHCwRCKHQXHyc6EO4cREiIYpKHq04N37x09+y3tBpgXNdq/uXjehHCEZTa43W7AcHeI\\nooFK/jhP7bDoIYrt3QxxCchwOOVyuZ4tU02PNToWQ5wx25YCAACR8dqaTQRmxtrsxWuZgJe7bK7s\\n9WhKLTtMhkchmhtKOudBtmOgK+L6/PN/uT/2e3t/1PScpTso2osx8NpgaB7uNvMqAxgFTuVEHw7i\\nqrVdtRoViIALZDvvetXcBJgef+8f5cWjtx+ch8XAGQ/0db4/th6EYZyrRWez09/92zTfazFdXC2o\\nkSxs+/L83uip/OIzhv5/29YM7n1w8VvJbA80JCh1Sl4vao7q+Hg0Gh+/akWWDzAlGKquMRGGtZSE\\nI+fc0URk4T/U/7GUQ92XHrbee4atu9u4rjq5/+yLr75UTT0YDDhNswwOxKYsy8cnIkmyMAwZtL5f\\nJ7hhe3m52nKII87CUO2z7k/eG41SRzHC0N42C9n1cc5ZuM944mgc4AR5pY0cFH549DgvuPfeEbIX\\nRrtROEx8EouLz2dvPbvNHj/97DPpjXW0ZQQJ4WhCN9teCNaUSkvHTZ9mQZiQySgocgQ8dobXFjEa\\nuR7k48SK9E2tv/8djhC6uu2c1MPITd7/KSI4zzQEenqUvf6yf/7e4be/iF5++sni4Adv+w3Ag3/9\\n76AM/O/96XhK17orb0rGOd5WjfMYIjTci+R6DWjhOvnt58vx4e+/917wTkE5YDwgOMiw159/8c0f\\n/8GzbdX96GdvuyC8my+1adpGD6fFwcgjALcL0pYaIocBbJoGEWyUrhsKFds5QHvv79qq/vLzK7E9\\nDTyAVZ0/7OrSE9Tk8fV6fqsXWBunA+i8iqZ83dDpJE8ylhCa2Ju3/zTBA747akF5SeVX0+7TT//H\\nL2kw1W4+GRSb21fESGt0FsbQm+F0FAzZbFbdXlY04iejbZEfDifHWNvupk7iECDHObZ9iRMcByhK\\nAyt75ByJOb4/bewDADWEkMANRlutS4PSbQXWZlp37vZ1H4djgDCy0juCUbDYwtnNLezXIRcAQFm+\\nWa23IdvyYl8b730FENdVBZ3CxFmrFxvJpH920rNY3W4THB3U/tg7oDpTVSUjYky+qvs1jZXvd5R6\\nstpo4CrvrcMUI+YB0Ko1vRSMYYw8gkIIGMFKYmUwRFTDwFMKMHXOIeCdwBYJ2M/zYYgh0tpC56WF\\nrZIKIAYAjxyEeOcw0oBRgMLJTokjIiJKM9upIt3GWbpubrxTSYgZ2UUhRoKgUREGjEeCeodJXBkz\\nI7bRLm/UuKqcsjjJE8ySfBQQU/WlsCYmOASeIoDzjG6WrhpkT959EhW7uyPEoAFG82Bg6ursa+4D\\niLGmaM1hyBxsUYxtVWrSN6uurDVSSXqjG7uYa+DPRqy6fQkghIPxQRKREDEjdm/PLho95qIAW8XE\\n9uX//JEjbm93OxyHKT69uYTbyzQNY94J3G9L6VE4nWahA3jTd9qivjRitHPvadBs7NXL+esqT0wN\\n3FZg7rVt4E/njgAqQsZhPHBdP7TBprMUTklM0nzYSaQV5gQHNJY9q7vMmUw5ncSrmI4CIqzTPThR\\n8U6rOA/TzlaD0Gb9amlIEguCpEGDATe3r7pe+ll/hNLYYmjRBrEQk0kYS6guYN9UbV212vqxd4x4\\nHAYMITx8ME6SKggZtldDtOGFSIZdno/uli1yGQ4PRoXZVisT7umW+D6dLT9992l6tP8H3XK2XajQ\\nD3Q7+M6Pvv8g7xLRE2KYlLrtiiQZjMZFymMGxjliXRoFNSPS9l2cpfkovqvDhTyEg2lSlzzVjITt\\nGjbSuSCJ4bKSdhTW11+1CXizO8VRoNbtavCjf3ZbrXn461gsOdwP61vvGEfOKO21vrpdxJxgazer\\nd4yW+OojhKfWAIJ9FNHtumcYaK0jHgX3Hx0/nFggg1A05ZmTGtkuEvx4uooTExF/MR/A7uVk94iH\\nOU/DIJrksUB5tGhJnOD9HTLYBQcPs/3HJ91W87Bxy+1+PhLYvf+Hf5bvPh7Ese76aYaUzkYFUTLi\\nJEmCsOuzwz1GCY+op7j+9hRQZHvlgacsH6Yn02R3/96jXYRfweLo9Nzi9o03TUh1FFJGGkQVBCmt\\nto/G4XS41zKmIbTa95263mbOqk3ZN5gZ6ANuQgrnZ1iteX585NKoXMqvrlb5/e/Kj74JORUohph8\\n88m1CPDn1WHV337wI/6bf/FX+OQf375E+9mAdun/87/b5Q+m2S5bXblQWGNbY0yj+qYqda9a2aeT\\nHe79q9/cXM02ye8/f/K/+xMUQGMUjoQ2/OPX89Fk6Dbrqq0A4fUavPXdR4kIPavTYqdfrxBSba9n\\nqzULQqUU8L6cV3Fko4E4/fpFniYEjjzQ7frmweNOKUjI0aq8UT3bv5+B3veNgySi0UBa1JbJ5bk2\\nwGe7gP0gv/xsas7DN1/P9390IqVstperi5///b/+/5xe2dLVYcJE5gQJlLe9xrWfY8sPD+8xSya5\\n+eI3p9R+0yy/3Hu4a3tnlY5iunMSMQLqpulKF0WRV8ZaaymUuNiss6ohHhjiJUXWuY4TCaAU5rLv\\n7PHgNQo3UisKV42EjLEo4lavUP8mCToDaBhQ59teNsC4aPAQeQR8g5wJyNZ72EoiKju0lxulaHDo\\ngntdTQEA0McBBABjhksC3AqYbz/62kd2+GDSyb2267WVsm+tUxhj64BxWmvdSxMhazWo1pRSC0Hd\\ntFWvyrp0tF+mQ+IBwRQoE3Qt7uczgoeEDf8hjssQLqgKQoNE2hs8u+mPBqO9xEKglIXISUrq6Q7b\\nlnXVuzgaG00CVHYJRpYOEI0j6Duc8TRHNECIJCNH2nnb9sAHgwTqJmYsv1uM625Tadcp2xrEAEIi\\nN6u72dnHt3fjZGenqXkT7MUZM85HJHG4tLAPktzgQFKKMCuy1S4LiL7BsOSgh65xHmNvgJrpcgmh\\nZ6y+K2+iyYiKA+sDrbX3bn94blUH4RaLBcxUID6JaRqkaScxSVOsI93VeQx9SI6evngyQXv1eFaP\\nZjOCrYGAyN7nyR/drefnr2uzloMCLL9ppYjSDGB8HkFrLr883wxEUgCvKYOHw/wtNnuUsedFlUf9\\nfEM7qcJw9XwCRxguy5WUN8iv1mV99DhAzmNnaCAmxU7/qpVLK80NHgQkYlvT7iUDjcHtqd22o2HY\\nj0K5unxhu21dOo7WBhHlPM2KkPpYeAZnBN4m3E5cOYjNUYicVsAF1iMHvGBcqS5Mw0GacBM5c5UP\\nY9JtkENVNX/xcoMGe2X3CIFPkBOT/Z+kYXNyAq7XJj0+UmK/xRMbkdGAqhIEETi5d8D3D43SD589\\nRDA4fP9HP/tJPR2MKecshs6o9dI1zfbifLOoH7BdTaBzBDayzwI4SuMwKyfj4TA3g/zvJkUkQVrV\\n625DFy+ugpBEUbDpyeMHYnD/XVTPE0JpchDjOWUYoNiR86G/heFbLOSCXjbNvKnWARKDIrVyg0lw\\nt1hdviq+uIyw07VE+yd7e5OKUgtE/NXdW02tH3zvB2b1Yvfwuen4apsv3fO6LHzHeGvaDixLhCBM\\nsa773csrbX2d7IR7ey0wj7Q0X10+uJYPw2E+yqGVXUHc6OBRrXro0HAc9p0O4jgknHpo7G4ob9NI\\nKcmIkYmwQDGCpufXXUDScsaauwvOujxRgUBS9/nD/b7XVb3aNNvxO+943edxZBoNnMd0gKXZbktN\\ngm5RQ288VuMI0qAi3H356fzJQaqbjeiaVy9eQnH9B48pwhXkgTXaRGz2mw0vZ1//avXWyXr+6W8x\\nhoeTeLwHjotTvWoYZo+fm9n5UvBYWSXN/J0n8OiRQEg180vGw7RokE7qmT/9ixceCuccRbQIg6uv\\n765nl1eXn3/v/Q94mkmtf/OLj1ulVzfQScOdzHZob93hdNpsSh6HkYBZPBL5V63cTMJAef3g3Xuy\\nmh3eOzRqzEgOPM5DJ0KxKuWqM1981DKCVHvdbmu7fRNm6+p2rSf57Zd7ZrUoBv3+pHjzV5/tZZz7\\nTUbUOFKifgnD+7Ua1HNNo2FbNzsiTrNdCmEYilGEfvlXv/z7F6du8+ukuH79osfUYYQA9CpgABwA\\nCjpbydUGI8C51852s/UwWAG88kgnqeh0po0QzHtACJFhiGjIAMCcJV5rLw1DdLO1Cgcc6UqFGN9X\\nFRI8Xy6XvSm1ap16gKI+CKxIOI4EhLyX+unT9Pjhe5vVVW+WWluGa203vWwbqRzCwPu9+4FBu735\\nEnvgCLMbjG0DrHW2VbrvjFey010rsAwYLcYjAEVAQJbqMEDQ2siWcdAj2AEADIDbrXSqotglYoUQ\\nstYS4BmG+TBpfeqUxwhhTG82y43H3uEx3bNLM8hEudE+fWiVDnGfpCQgom0XSGk+LAb3JtlOpLvV\\nIi/4CbKh23gnF9LucnnzuhgnPaxNTi4vPyXCO9SaVrH5dljRdRreKMmCdPjcX4OrvgKFTff2isJ7\\nTytvWIj4FYVIykCi3QePHoax7+u75VYC1MUxQq6xoULQhDlz1pp21bQAwgy7zqyW3mCyO754YRqM\\nRFhDKjRIQiqGDwJnsIc7lBrOdsO991Zy09dN0++XsTzaLRGlR/eUkRI7c7Qbtnd/HYRoAAmRpqsX\\nk6Cr65og31MasRfc9S8WB1EogixLk7dHT7+znf6MPP0ppIDNFS7tn/30vSc/+m9GJ8dX5xeiV/t7\\nwUrfBR252bBpITmnZRei5MUkvzw++SoAA9UfsmgmO/bqVukIqLojozEMe199ExVFrzdDtIh81WkL\\n3ZRaFWYo4oZhoxGcFtlwun//uz/I9jlGwFi8fPVCet1UftEXBCDsjRMBIK6utFDK21dBejFwjsyv\\n/cVveBTdlo82OizZvS2sgWlmN7eMFeXmMDDTKbkpcKi3wWmL+tm2Z+/86pvuSo3W9fdfyN+DwBLj\\nikgHQT2Zsj37VbN9fXU9O13sGOBlpw/HYWmDr7d7n1/eYn+ZjLLv/eB7af6MMYOcy6JBxq4c0AAI\\nJ/Hu4Q/PNtOK503Vuc5TbqmX1NeCNWLa+/aL+aZu+s56l2VFFKcU7+8XQe3HPhDUfi7k7Wh3QHSt\\nJavgYT56FgJY0Ff7+7t/9xt7PLFa9cDrP36vDq6+7drL73xIdnNsu1Zp+snL4um7caBfjAuwwbuT\\n6YOnP/3RYvZJAdLVWXNxUdXd4HsPhpPhHgjyL1/2zfwuLpKNHilPROtG+8Nnj4uDZJMPGsFC4PXJ\\nXjbcCZyJLe4IF97Ltr2799BTrwWnRgPoQTs/s014NI6tvjz9ZCsS0VW1UX0Y4Lrpj6bx/ccCNeuI\\nrYAlxzsTlxQQ48Wd61t/tbj68DshBD3Dq998umIPT8r2KPby+z8e67VuVh8/OzAf/nF0fVc9+YEc\\n72ZocFz1w9ma59NiVOOySk7eSYrRy2xok1F0sRxqPxWjt9O9EApczbFstOx/+9b79/rm/N6J7Ts1\\nny8AdhGILZ1dvr6N8qGg/fvvjIKwASBHiCyN3hmwAU+u53fv/vH3IV5Ktdn2ze0589Q617MxAp7L\\n/nNt/tr7cr3onj7n4wwWQ9EhsTeaxIFfLZZKYwNNlIB3vv+86oj+whQUvvf7gcOXPei4Xa/uzooi\\nSALXV3d9tZTf/g2jMKZLVZUBh1/fnS6rqMUjjxXZFeHx3rN3wOnffR6P36YkEIFVfW3FzouPO2V6\\npwvXSmstApanyKBwsVpWeJXngnL34s2d6jxhOiQ9JFh12nkI4dCB2KEoDsn9d0eGURAUgYOIoDxV\\nzEsPMKYCeGrUsppdAdQKkKAAaUScMtyTtAC//vq8ceOZ/cNmwyBuresRBFYAAzA1jbTqxVeb87l+\\n8Sa5ff05E8oYpeXaqa1RrXZ922urAMVmnKJiz5dKuk5wRKoNwYIBLEwvkzTwKDCeQeh75aAxWktv\\nfIphyHjICGThbE4JzZAXgsHeAo+9U4aDTTqoEN8LklDhsF7W3prQNlGIiIiG9x6ie0d7ugmXbFCw\\nET34cbIT0YPQK985DyGkcc/303iEuwQZ7uM45qHGFGCnKFfwcnF3Ezu3Fz4+SYM5DJy9sedz1S3O\\nTLO03tlKrtFwQrcM5Ijeu730Sl4CigHh2gPVt5RyRhjG1Lk1hRVgYpiMndkSPwt8a9kU9F8551qZ\\nB2LCQLg7SB2Me99TwDyw3mSzm0s1u8RIhMmBcXp56b/d+KsF49MkAYP9xyMPNnvHiKose2s42KtG\\nj+jjewsG3NV5JNdCMM9MyyDcrKzixzF4qqS0czi71Wj84bPnB3/0s//i734bNa/vbq+udyc6YPzy\\n9lR4nInVvAlGR5C7Lfe+l7Kqu9f183yP7MpbSMWEnO2U1sBt0w4madZVUGK22d7dlqRz4WSAJhiN\\nBlntdtbbtAam6TsR7YYHJyj/zt2y73WYx2gyWOY5ZhnBWgke6mJ/OB6Z7fV4sH+U49FOxIITyykS\\nICq6eJceHkS76abcgNXrs/PP/15DLmQNGV/Pf3l4tH+cnByNyYMByMwHzpYpbnh/NeSj1dXs5utv\\nW5Sj4H4cI62ZlK2SVw6U7eKL8qbe1rzvQc0jv5mbl39RZLvx6Id2y7fuj/v4eHZ9R55+R+RyVAhG\\nEYs8MNT7yG+W27UgJBEZXyw9FRRhibB7NQu3y3PjkVSWkdTqHenIvQNM+K69uVFWAWj392lO/d6D\\n7377zWd1W2kIgpA2XZszHnS/RenjSSzyWPz26t07KgdB/D9/+5/4MNyLeuIAE27mP1yDzEJIOn1a\\nvvXJ5ZGNZZ4DXn/jm98tKmUe/BMqdneKODQ3+2/xqmz0ZmUsGO0R4fjw8P7uySFExjrACFoseTG8\\n167basOU7DBixjklgzSLY8GiUPDAMAA4t5x7hAjlW6VUFIliEBqjtFF0LAS1QUS1kY3UwSDbrhWE\\nqeoMFX5xS7JJkI2T+V0TMae6SZA9/id/nKIteZrSP/oZfonwf/9vdmzN4mAQDfc9QVkSgK78za+6\\n1/mj0fFwuQbeMh44zBnoq2qr7l6r1TofR+m9/eqtY1uWrq7aH//pfyy728P7liDMQa+t65f20eM6\\njSzlaCO9loyHeF21B7t5vW5mhMV59vN/+VG8//j7Pzs5mLBQcAbZYAA2lYKTSd+r1XI+Gidew8V2\\nkR7dn21MhMXF5ct3/qiI0yjMBtOTA4vxx59+IdAJCWrX3r34egYO36GeO+spw8p57H0WqiLsuqqE\\nfpWm4XZtRVx88KBP+xfLm9Ojk2I0HUC4G5Lh6Mnko7/5a6ji8eM+HU3husaN4WnjnLHSUEopcR5g\\nSGAgsDdV3S67mhTpQVG0lEfU2CwfU5g5gM5e3oKehoS1MPn0Rbi6e79dFn3FCMcEAl0zzqP1pjGe\\nmdomcTSkJQdgWQMMSSS8sh54o4P7s5e/oiwa4rTtVh60ECJOGu8DByynkEV+914f9m8i3OvqFADg\\nHULUEgopRCFBzksIfWf1C7kr+5MigmFO02JqpfTaiohXtUriAfS0bTQn3BjTI1Z3JXBKDIIa7m2l\\n2JTSd0sIt60EURwA54HvEaaqrfIBCaIizsIAU0KYsWp4APhwv61jZK43bY1V6wkaiDn88tPs9aLu\\nlOsd6Rr9m9esLrfLeQtsCxBODhqAVRCsx0WbJC1qQdMq1YzU/ONPTtm2PLemtUTduycEZR6UOXqa\\nD3dLGUi5du6OkBlkRAyjhGrkFEDEQ5J7qZXbOx4cDqJIZIA5hJmzNitEEdwQRY0VHTpGzD99n1f9\\npmsZDsIAgZApQb9961GRC4Lx4bZCjURN5crzOW+WC41EOtA1jWjR9q0pw/Vc4/QksRmM3ka8OoqF\\niPYUlFm6xu1msotQw986vFpcvh5Pr85+57fyObn/v/3iN3+73NRvzl/PtrQYiiK4gGSYBzkJoFry\\n1rLDYzTiy0DXXFWLW5BMgr1pgMiwHxRcX9jtOhjtJOFMkIYRfDAMZZ9P730g4nxv74BgNqbzsrxc\\nlTYKp8Acn18OXl+cLt7cMkxO3i0Qk5tKxvFgFKnI89rci/rtw3v3bjd08vRnXd0tqqqXHaTM9qWx\\nuz0XyOJOvbDwzPmFovuZLdZvfp4JsLW+EcXDk/Fb9x+esG+i5KFWLwVBEalg/xFPd3opHUuuS8Gz\\nTDCJEUWoBGAF5C0XkFBoNYfkIkkvZV9uN6vDd/8TRsNf/s3f5Ad7/d3lW28fUDgQqGnQwaodAES6\\n+WdcMKV73XhEiVY+n+5j1F+9viYKEWtj7Jg4aXV7cjDqi3e2DRvGd9b1kLKQnTz44M/Oz78ZTPwo\\naEBbA4gZE7uPpv22T4K8ODwAIhtFM900iBXxm79ZXh6TeJrbG9zNPvvlrV9verSf49u780+++OjX\\njS3WLdiZzg938Q6X5692ZDyE1HNuL1+JKGNpThFkTS8mg52rm6iUaRBSThmPUBxE7R1NA0jterI7\\nKEISQt3Mbnd2KcYEY4/whuAoLwJr0HzBAgYZdAFkMU0FTxHwd9dbBPmoAAHTSrIw4nHIIwFHEZtM\\nA4xx1aHDSSgYGwzQ4vYlpflsO3l+Mv7ePz7s+ofoGu6ocwTKea01SktFrq/WcahCWm+/fv3yY3s1\\ny5N0B/ulwBAyWXc1BZewNdaXvd9d4R25/P7d+q+++LaEwQ+vm5E4Cr77013YNaarWlUjtpyMOIFI\\nhHERJWFg1+uLpXbf/fD/IKUcHdDz191f/Lt68gzFB3y5WYyf7w+Os/ablfPCe9hr0/VVu9Ln5wtn\\ndYRoGDzgNCXQtIsrpVal1NTy578/qVuNMSbUqpffGr8Ks5gICiDWAASjdPzkxzuj6B8A5VGB6rLS\\nvvj69iCLgtubv0e0Xt7OVnfaYpglg6b/BMjk4LGxMcJCnzwehzEKCjSYBvkjZhSHlhEAE9YAABhj\\nxjgDIJBw96AjbFQ3x6qze7sM118gt+36cD9cFcFrqzvn+/v3Q+NQlDEL+ygSsqsxQsTKvu+fPNjR\\nbEdXW2PVNtjVPSeELFfAmXNOJCB5V/cAgCIDmOFOQYIBFkPXiI0PEYA8oBAZyhCiXjvqEKIEhAlH\\nAd5W0F5vuZtZ2DgcVX3Y6rGzHGOsAen7HkHCGAMAGmULUgaCQaRS0mMCiEUJgIPQKwMx4FAqTwLj\\nWVlWQZLzkCzqkWsPjR5CxBta3M1cWSdFuIvYPtkdr/SWvNp+Q+lXE/GtbeaCaN1c6c0lNgvqWuGc\\nqdbOKh7a4RAeDBOrErXY1JUdZC6ffB02l0l4k4q+653p85uFqpsegWkRXONff1mtGkwQQhXQFAMa\\ndsQjITi1DjFGZO9C8d7Fm2EJR6xvfNdYPzx48tR7r73pbe+cGAQvgwR+8du+ksbVIJl6ChjAkUX3\\nQjZlYWb1Spq57KooA0++N8Jmc/dGb9p+8mj41nFQKIypQDIE6bPjt96D8IyLo8l0Sy6uSjlRChE2\\n3MK95w8fvrr7OY0iKeWTe+D879u++6vcLpNyVq9b7QMQFUHkARliAgm2lOXeZD4a7x1Fz8TVDq4G\\nId+sOh4N4WJ9D7u+V1Kx2jAI+x7027KIeZUFe03ZQbY/nsZtfVsk327nF2EylThqmtd681tvXqRD\\n6NN/9MtflQ7DUMTbmst4PBVl0+DsOw+I6Ybhh3XnNJGbdRdwL+C6Vx7F8aLDRH2lmorTW4w49omY\\n3IWDAS/ujafmzYZ+2x6RHf3B/inrlxBwEeUOVJCFZTMXRAr+oKnRdr1m4QQ2pVc1YoAwDTDXEun2\\nUx4hAGnM4cXm4VmX/eKXP0dELjeWusHHryZV+eaDD/9QXn+9G9gWxDxS0OtKYgs0ZLkBZLMum1pa\\n43940Nw7KIp8aJvXEdIr/2T+zSvGwKN391AvQ2Oy3fv/6u83zr9BPHn8/s/yUdJUPWH5xRyuydun\\n7PivuqfLLf3o69NRwmuL2cTysNlWQXZv8oPn7uSBImJz+crxABXgkwx9Wm/PTJRf9kvU2SgtbH+z\\nnLPzbe4wHwV1WfV9t8ZYb3tYRNpK2ZULiIJODmTvKKIYn/GMxAnTDR1NWJaG9+7l1RaEgciieJSY\\nMKAIwzTARwUOiP/BSfLTJ/jDid0Lk+MdlsbOSLNXTJ2lDHK9qUPOlGcPnw0HOYSAXV6oIq8SYmHS\\nN6q+evW3JgSX13erG/HeO8Pf/072cFTePzCiVItX53cX1wjr2FVPHydR6ju6KRerXgXG+YhLHhBg\\nQWvZcNdDzhlHh8PR3tG5bNrt7Xxzu2wvbt1V/PGXl+/8bBdz+eLNXHcLFod5QrmodFOLIhcizLPx\\n1x/9X4b5MMjZYKizxJ6+wE1voQ+/Ocsv7t6aL8+ePsWEQKn7kGawKb28EwS024VVWgTEyP6P/vOT\\nujFZXoB+8+J3/5PuLITQexSHWbwDJCz7XinZdV2HgD3/6pXsLbOurWoW6UdvPetLvKg0EcqRfLNZ\\nvPOzP2vvTMAFxCklwXytfveb7TgHVulvP3+TjQAWKSmCbTve1gmkhIuYRQfJdAK9DzMPEOae5JMR\\nwocKYUNSGti9oxyGMiROEBllajgBwG8IatngecAJDPdVSwfJQFtY5Jin2eXr2d7JD6WL8r0AtYum\\nDXvZAgR5+8XV+noo8lF+4AHabJNO9lKZ1tSlNNc3kBkKXaOMR/8gwNhQSqwara1qK8MBUSDH3hKm\\nMWFVKR00xGviN46atjcIeue2xllDvfcA8I7HmYeU8fhkqMZjTA7zvnf7WZIRTXuFGdaK6H7w5uzy\\nZrHRqrEOIs91ZyUKNktWXr8M+xXSdUQT2M5OFYVKN05wMeBt+9q32xhyvUTIIS11QBhskWoiBbO+\\nNU712tvBUctI35ULzmyMoG4sARX3S2NpGIYmT+Lng3d+5jMCIEIAWhrJPG6Adc75vrMkHgML0lAA\\nfRWxECI3FCvYAKXp2ecvvzm/XiwWVeX48A1Bze2y4Bhgq3wQxnEkMUUoljq7WaE3X5XrBicRESIb\\n7IfbuxecQRaTlOhvvky/0gEZjDDiNHov4Edff3bWup1OHZ7XABy4Yt7QHu+lI3336Hbxa1nuv/wM\\nlWkcHiwGR6vy7jB4Mt6/b54+OMyL4fXSWpfyVs1rKPHowb1Hq6VzNJvLKXj4MMzCrkzfnE3RUBvv\\nTj4gjm5lu07jljORhsH4wSQp9rvt0ims/cnr19c+uRe7L8b5gZOa+j4nc1mvAtFim375m/8+Yk7r\\nfjIGuBu/WT9agJBvyDdng2g4mows18vzmw4DSfhOSKN7Ezu0cnVpwqChcAXQrQmea5mV9YDzEw0f\\nnV5CZeBy2X30AslH7364//kDlshOeaDam/Ouabfb6PXZv45TOsyPt+UOC7iUihBMgghTQmObBGRn\\n78elRv3++EitXv3dvxT671gcE+OdxoS7bPrHi0XubDXY/8HHv3395nZplOyFjyihDGDQQb1inHBA\\nDt/6yaoMCUmT8RQmw6uvLgHE4ejZzWJ/KrZv/f6PTy8hqP4Vxr6pyVl97+4Sadsq2ZyfvXg2hZO7\\nN7+3+fne3je7UW21i/wtbuuAtIKFL9f3b+GPWvUBQgSb33kSbHoVhaHu5t71vUXbbmnrrm7nur3y\\nzaK2o95DB8VqeRuKgAhi1CZGN4x0RR5MQj2IrdRaZccACwBpGNQdO8pGOReH7zxT+w+nDdDB8Pl4\\nJzONHO2Oj+8fhMj+5E/+4Pv/9Pe+809+8h//kweP99LdHRqyAIkxg+FOmog0o6nd2wtrCQPKGu3b\\ntd/2wfFjmoeTvQF9/DSefbn9/h/ub0j4L35+uol2v/tHD5689fDw3lREbaJ0QNrRg/SyHUKtaN3H\\nkfjbX800utd3m65Pd4/HrurKbmsIXFXt+aw+v0VAHU3Gd3F698//y71nD6J2ie4WgYqfpyzvaxCN\\nsi8/XjsVUOwjFmJMgUGDIey5Vsw2ysYBQtimUfzgKOnvLrmfF5PRxRk9PtlVTWf1CgceI0DCjRA6\\nZkQqFofyb/79phhSAWAQBwKOvvvdtyZHSbNUm3XZ3TrmoKDGOQMRwPGcY8Q4cNZiVhgdvnqx/fwK\\n7r8Vo0Dd3iwn+9PZ3W+hfzjcGaRpSqgL6Oj+yWi5mh0+DggiSjaRKxZ3dSQGaex908pV5ZTbXjch\\nrRCqoZaaNJ+cktndMC+mWippdxZr58zUUarklMBDTaIkGX/5tQwjQaLC5XtdOepqy1F6s4FVLyzg\\no/7XCD8/fVF7twt8D6yhpqEEQOcn+HR6X9BwqJQQBCLnBYFJs92nG263wGgMYC+NJbHsvFEG0w7C\\nLk4wgnK4Gw4ZILlyACknet0SbHkYaN2EYViX25D68cRh6B1nvga2bgZxDAE1TnuR9ZsCuoOI9SfP\\nx+l33ocsDBjfn+jDw33ZbUJ3PRgsYl56K6vlurpZ5s3dGH+GqHOzq00jPHGKcrO6Wy7XmvNkmKWg\\nsRZDiEkURXGYMIpl1yW4rjpAucJBFA+Ec4bv7uV7ZNNYJII47QNoLSSy3tu0T9dm2lqoSVAtvXV7\\n6cP3g4BCDCAiAR8EUiOXUoER9FopScYeMoHbkJxavcLEUtRkScuw6apYqrbbSOKco8z2PhkdKGmM\\nWpXrX0G8KmBp7kpgLGSX6SgokrxFee+7XFyIq/ntzC+7AKqNXL5iBOgOe1iPA53uFodP8PHjYwEX\\nO6MXvLtdXn7Vnm51hdLDfJhYQKGI4uHk6cGzaLs8H7Fxxq3pXvIU7Bz+6fr8W6Cx3Cxd6V5e73Us\\n4kEbGNfeLaPx06ZqtejKehjnGUSCw3HdGRabfrO1QcH8myQc1uJRHD6XzkAIVd9upckKNZ1kF7NP\\nHDbaSELYTlwn+lXQnOr2em/3bHPtS/K27E6z8M0gm1Ajrd23/kH88P39vU/HsIKiFaZcrqBwZrzT\\nN+VaWyxXX0e2hoD2SNe1P73c87vvvvdue5IvQ9TwoPLqetG3DIH1tlWaCuqmhwVWJgs17Etqt8oT\\nRx7f3XyEW/fmbH/LvkmDbylnuJlhQCmufP/Ckp2+b+M42m6+bDcfq0Y5tdnxXbvVSnZChEmac1x4\\nQdlIU486BQEImzXcHa+dtbJx29ur9//sP/u//it99+Jv4iy3HiA1ff3FLYf1ZBpB1yW8muw0LmJo\\n/8mb0+L+fnb8cGfvJMpSNocegM1+v55dfbtcfSk9EYm0HQkI3BunnEYeGID8ynDEoNlc6v4rY5d7\\nUbubJVjOB0dBQInBibY2H8acUK1HIM/zNE+O9/0cR1FEKZ1vHKoraHETjq/lP/r5F3YvKu5e4eU2\\nHKcsJenJ/t7k+F4Njlr/MyN+zyXvhvceeAAno6FCfBgvjx8cJAxjr46OnDPGQ5Jwb5y9vZXbRR9y\\nVByNKx0c77SfzI8//cK72n7yu7PX5xRwhKAnME6H62zMmz6Aq+10oH//x1m06zBsZdN6y5qVUzUY\\nnky/eCl+8Wu571thF9VWCdYt7q7SYmfZvmMEOXnEtL4kcoydWs/MbHOZP4i2EHWAL7e6yEcaKA9Q\\nNrJZGnCMPIKEEO+7s8uK20Xv57LXCAdN00A0IRTo3hOHGEeIQByScrGthU4HyMnu0WGv1KzU/a++\\n/lcLHeZiyIJOa7Nal73rKVcIObUZe4Q99NpqEyPrNxia4Xiwk2wvv2mzPFzNN7ybcUpPvyi3swu5\\n3WipWJCWrWg2clJYLpyWPcZBHpcki4RgOA5EyBIWkVD0YocLgjBgN21of71anBIAcfvGcuiMCnk8\\npfLevVqBCDrZK397/ru7c4K1GwxSChCFoG+crFfaaQx8eBhYl3f2uoKy3EoW5UCEAADIG23YND9x\\nIoQAjAOQBCAdsrc/LOAoQRgAADwCppNQKco1IhgCjYBVlmwW/eVt5HrqHWslxz0gsLcOAY+c7xkz\\nm3IjlRdR7n1AKMQIOF13dqjtIWpsQJV1/dqQizu3uiXl9nDZRpQHng2i0XtaIVUtKVH7JwwJliXO\\nJ3VLKALu3PYK8cR4UquN1tJiFkVRFnkRkzggRuG6zyrrViv0+FnYrjxNgyQOvfemITwsMmTffLuJ\\nI+IgHx/F1kjsAKEwVn27Xpx+K7dWJ5QSdOirS+lMs+BK8zwLd/fF4ch2EBtnQZ+EvFMYBKkHvudE\\ne0dHE6N6qXu4XRtmWy48RhQQyOCwn93JVeud6WrIpulkZ5vvUGeb81fTzQZlqUKLunNH7t7bYaig\\n7GXbpKNbU5XLVd8DBwPcNg+Go+d++MPaEs92xP0IWZmJlmOfOD+b+zyLl93Byj+tTXA3L08e/TCM\\nd67b5Q5CV/PivPqdbl0uQlij/R3Ue1pEuBgMH9wb7mQijivZzomte0mFEAhhY8p2Hnz57VlSWOCW\\nUTpQlhIlNImoq+/ftwG+oxjEcfz65bdCtGFYCoYxe28tPhid3AzTRRoZj+e6umvL9SDBi2UYxmE6\\nPGBGOL2rl+lV+zZLZVzwHs2ZmAJYKz8P4wJsboYDCgjEZNss5oDzrQZnN4dteu/Zk8m7+2OOQSb6\\nAC4Y6k7Go/08YMS1xo4f7WvjYLBACG2XqGmWlAQha/bWl4DSqlxLzaPR/nBod8bs2cFJr/Hm8msi\\n3qJo/vZTsDMgHgq6i001x0h4H5zf6cM97qRZbjKMqeCHt6ffDkYdILQr28R+8s533/v5z6VY/ocX\\nd6ddXVGkw9hF5gUdDqsyjcKQ0aIFWUTZNx/7gCNUPMT8KPA7u+PiUJCr2yE+LIpBWM0+azrnvet7\\n5V1+fbEfxbFRLcYUcv3q4mJ98/e99h6Y2pqasGQkMxJ2u4/WS9U7BkmyO4yQMV0Fffadm8vC9HNK\\nVIy9b7eeiGnKty+W22/uRuXGU3n0wLZtv/f4pA93FtPvTPafzW7p2RzWLfJgJDdTjAKtg4VKdid7\\nR9Pdg4Pw6GjHmn48jq219/bQdlWNd9hgb1fJBQTettpNd66vmevuGG0isGyv5wR5ALy0HfS9MaC6\\nXXznAxwOpnf9VJ/leQwDYnw4tariUaU6wNfLE7FWbx/5vZ9EHUqJLwbDLM8dqqv4fsh3Hh+xxcXf\\njg6S/ZPs9am/dzR6/dlNdadeX5bOdKNJiAW7uOwqkNcr1W9syCJMEcDxwcMUhOl62eGCUWQ8XNFI\\nhBxwgmkQEmKGIx4XMSFEYWRUudZGJv9oZzyZPniG65tWbvbeHVlLhuOJlgQFWukeEkzxbdW2GCIA\\nAHQa015WHe5O6XB0t+pevFwQph1546Teu4cGI8pYb2qtJIy5ZxnCdCgxJSJaL1djnsSDgWVJt6qT\\nAksyQZVfXilnSRXAbjcaD613PRJ9WRprOsevaYj7aErB2/v5ZDwYwnKLgy9y/pFSjoSxtE4wBpzv\\nWq21rqqqkwOzkaxvctSJ5ka2SjsKeuAhqBUjxSOCEo0hCwsCR7/7tvb0p17uAQCcB9ZaggzCDiHA\\nKQ2SrtfEWJAPVb2tt6a3fXUUNTRWGGiCEEVymHiSgKbssa+kNUhDgmFnuWksqPBwlA8m3HOPnDYe\\n0H47BGvI/enLRi4cWKkoPIAWOrNer6FIs5zRg+ODs7VBXsA0IgihQM+avgMgTXksFbUa0oiPBhpr\\naRXsuyw++iHwHSQBIMJZJCWVYJjk/PW31/eej5wfxsN9ddcRzBCnJNAxugGbU2nTwU5KQ4Lt1zMf\\nedhGQ5Hm+4DkzqO67eqqj5gY7DS47VoaahLxKI4guvcoM8ZA1zmls104GgbZOIIQE+g8XjKogQNI\\nWYAmCO/A8XuGJMv1hqBzBDYerTFDYbp7dyavzTQcBMDLrl2X2xlliIPAILHy+8Bse2mZtqAKTr/o\\neZIQQkIKoIYgCvpmfXe53l6v6/p0vY4EgEDa0c70KCtjOIc9VVGBOeOGsVyLLc4PHwCY0LRQXrrV\\np9Xd+dPnW4SxBbrtS4xxEXVP7x0hoTzsV/N2tfTQQYld37jtdk6oPzgkViuIOVA18MuE64CV9V1X\\n3u1EWB5NE9nd7u8DYCjiutxMq6qyGHpfchGPmEQtQjRuemtUq2zKKQLe0hDsPtpPJocaACe8kQtV\\nnxupV506uwotP9lNYCiCLFUUtsXwmKT3EbYUO7vx9fxWO+/wIcNGuBtne0rCnQl08Btd3gTpqO9H\\ny4VqTC714Gyh/PIX7/7gYbVg862fLVvZ23F0tFm6YOTy9BFU/cPx88vrCOJ+cYURZOXdAiW+1ypo\\nZMTV8GT35UtE9adFsbSksg4gSDCNjVmUdWScQcgbq0Ew6Rx8eHxRLUocBPNVdjCxeRKmAUB6fqfS\\netmkgWLcEwooA4R6EfVhVLBoZLXqqs3txSYdtnmgBap019bl/58g/Gq6LU0Mw7w3r5x23vvL4eTY\\np3NPwgyAEQmAYDBBWpRMq2TJJVdJunD5Vjf6C5bLQVUuWaFYFCVBJEGBIIABOLF7pvPJ53w57JxW\\nTm/w8whFGsAGfKoYq1LOEMLm/v3mhitwVc10lc+wpueZRBQbZlWmuNPvWfCCavMD/43ORcOBqI6U\\n7GOOjv+qmpWt7U4pJ8PFMlaEaY0EqLoW68UoDrRqvrYA20uFLqoac+GalsNALbhp60VaSaHVeYRQ\\n4bkOm11s8PkOUXc3wO17mm40SsiklA8eb7WaaudQlsqz7O16fHbwI9cLiGE7mGgYIqXExg7bvVns\\n3gbjp2L2+eydxwd9293v3p+FX0lUp5eXcbhOhE9N/dtny4tzfNCFZXLV78ROM3QJi1YvwgqMJ7XJ\\niKn4/oZ1eD/wtvxKoJLDybAypbWeL8rsOlyvOg2mu1O7kSlcK0k0VqktR0AghAK8wFg7P84ZOm9t\\n7wNJ0pLXAhZLTWIcZ5FiXNWkPbCErGpeQgiAUJSxsqY8i6CSQoial7apm0xEUYUpszXKAakrBBV0\\nfNnQ2e3e5MU3JcAijSIgueAmhGXTcjVsmxp12gpAQZVyO1jIPM4BTsq0MHTLV1wDiimRlyEkhKzy\\nplFUVPea23eUFigEw7mpVM3rHGCclZlmEkhkFEWcVyXHEHAEpG6K9rZVK42LMk8qoklUor5YK2Vo\\niAihxuvabzTS8XOpEFdACQAhRBgCCQFAWVqLmgNmZ7mqi1zTaJoVTKwRjo1gk2PDbzsI06Sqmp0b\\nClAAJUAwzmtmYkKIDUvDLDIq14KUpYtNO4kEy1amTHRKamDmi9e2WttmWTa218LgNaZlUSpBCDN6\\nd5HgkFfKGCzMvCiU5zleeFJRaNiwC4nDPK3hO0bD6rU2DJavM7skzDOjYj0jwkGmtnhb6u7dNCmH\\nY2MdTbBEFNCqFBxWRA91DiZDNVkA3Sk1KlUhJqGGNabqGkrM6wpJzTY7ddUAQCSZSK75JGGYGGbf\\nDRfrqtKwUE79rtZ7nCqWF5wLGjSRZdWalmk6gACUTqfm5quTo7RemI2WZTPN1nC+ZMQx1JEswk1j\\nIAhznTWqcogxV6nvCW21kp5+dJY3g0qzeb/1zTYxU+zu7NADJ9UEB+EcWBees8SroyXXNLMPQbZY\\nLwD7aM5LzaxVVq1Tn7QapFkpSR7c3pxfQ+k03LQUgPl9IIqPpGQ5cTCfMm1DQCQNUPFJWSwgauH8\\nNaHulnMkIfcbhFeO42wt1jCJL+eJ1trRgs1OxisdnyM4ghwV2uHlMmrqG5IZsxgOQxznSNObZT1Q\\nSAcEpw6tC8UVzuOJoftYhEgteb4TlrtRhi5OL6NCraYX1CSUYb64LhJRrLVvjzJ6iN/t5RSz/T5B\\nvInqtmlRHVUNDwy67qDldlST6IrRmBlbHNyocKfV1zFPKJO6VgNRxyEDsGmZqnHj+19+Exl0jAHF\\nvGOZRN9qhTCudc2n0dY2w54TFWPExHC9GtCL1l6gI5sJsPNO3/TMovhQ02ZxOGYG9Ekmstoy/TK5\\n4rwCxSyuUgUhhPnoaiUhjWvdsKrX39bF/OoEfD/oGg2/NAOnGh0Hfm07GpRqc+D6mwwjWlFZSr0W\\nPEqgUvLgERTIBCAyGfHssssiBp3ZpON7sc6wUAqmxbdf9WK4zcB6ozt/cqNyXLCrcwIznboPPKeQ\\nu0Z/W3B58Ht/u0DJq/Dm7Q3nYLPhO/JR51/Q1eqZGrQOIJDxLy/y0WtkmBrEygGXd+6+Z3ju82Hg\\n6uj+htVpJQIC2LvbaXVNRm9soIa/Fa3XZbxijJtO1Llnvfsj4/Dhjrf/gUY9TJEqy3XmXh8nVV5l\\nRapbvLsDwYz3XJthlCQYqRoJNT2b9fb3CPAOWurDjyvXWAZbnt3sc4BWeXJ767rlCFagbrPfwnG/\\nJSdTQZFo77XLov7+x40YGi2HdT3XsbngSaaJSdF89VZKEegG1vJFnJA//N3NhlkijGsum+0bsWGO\\nM8xVMlnUYbKRJsixOecSU8/UHjsNPj790jUN1zf2fscxejSC20JI3dHiZZwsC4iQ7TY/6fuffEh2\\n+0AIjWqVAHm8WnctwSAluGH6mNfxwyemQBRiJAXQ/cro0Gfn3Xf/1r8/X8nDW7bTiApEOCxCHbRp\\n29tsH8/0OtSp5IZuQoFtvVtFl7wGskxInfT7FlG14ddfnl3ESwFQGuewED3celyXtuCYo3EtOFDK\\nNE2lJNNJe3MTRTMPRYxV1K44r8drutHby3hrkchlmgBeu41c2oeiRhDUQID0aoJlWKM5BhAhAKCE\\nUEkguOIEAwUQBlBiiCkvywLUguF1JLmESLF+FPFU2AB6VbTWaRtCJWWd5rTMIwURBprbhrUys1zH\\nZVlDTUrmMN7fp77bRNhQ1JO4lEoA1FSwx6rEIKIEYDhZJpGOTCXrar9eryKoBcxbHOd+c3tRqhEV\\nUkoYhlNheW2tEcjNnevmCmgOLvI0hQzpEaNZp5k15PVqEVK8IMLR2n5eJwgRHUVJOY3qBCoCbD/j\\nxiqcX39zvMz2I6j7Le6RrKs7ukGBFIgt5um83bq0WUapRrUyyzgveU3uGe98MkdFfn6KOCCYS9BV\\n0MKMUlNhG3uGDsvrsjrfsRyy3K2Kx2k+IIbX3DbZVu4xrJadE36NeGQEDal4XAsn6EOystgqWF1s\\nHnzy4koA1Yy8hpKj+WKj+d7O/kdMp+tOyueht9mcM5w48cD0rNHVQu88cGSSk7aFsFcVQRGtoobd\\n3alyY7ma6hUVnd7uQQHXcKvRmdHty29whoAOmUEl1RXQndlsRuHWdPW25deYYhNWJYeGTQOHeGxl\\nwsqiq++8S85OnOw6RawKl2W20BbERMNhmW/WxkEyPqLVt0a1QtWQgvGGf9xuiqxWyTrE5KLAoKyr\\nuq6AVXRaJSq/IZg3tLgePjXJRJSZAqjTcYg2N4MbXpOikv/yhdV5Erx32O0EHQKywOe6ZkFkL/BA\\n1igxvl8SoFO9Kn1eS5umWSrKVN65DVvSQSJ3fcdq0NFyqko4eftC8BPbChsWrYplWvpXo2d6DVQG\\nKxEUZU8pQFGNURGD1f3d3fFZ5rO8ycDxy/q+M5tevQaM66YBhGNRUlRaVnCIFhRnHjy18oxSSpkO\\n6su3C55W0u7c0eQZ0OTFqfz84jAytL4nUTKBdZcRryyrkyFIypbZU+vZetM6F/EE1V3dsdMoWyc2\\nAdhyrZKnrk89M3GS0xfHy+3bVpomjF7b6OXVi1QpuFKd4+VN0tztvP/7poyS0vjgxw+29xiwG9+/\\nvUxntwt1Z/Z0dV3Q3zxdJgmr776nt+P06Z+/nAVvTq+C4580eq+lrC2qNYzpnNz9zZHarF/Z9CzY\\n+3uF3NZUy8hWH3zUIHa/NXhisGa7dWu5Thfjeb9vdHub03FfyM4w1oKmzyv9jpsUqWx2Nri/l5TR\\nL355QRFxW2WMNoBex1HhNSKqaxBNEZBbtzoSIqez0b1z0Nr5Xp6XpvbdbH4xL1rSvjW4c7DVTd95\\n0u800SBoKonrKAeQlQCgK5OHM9fljcZukeVV7Io5M2uwms+mmbu27Gxlkf2HBNq2ARnF0/lU5t2t\\nWxu8kIZO2egIq6zbSYqiSOJQFdfrJYZmvIzWiNDJURAtrIB6TUMjGG4dNlxH79DsMBjf+4eDb/rf\\nOa7uK63DYGkZ8VbLIln68N2Dk6/zIGgZQTqscd//AVCVZsJ54WJF37/ff/6Lr5j6aLlcVrwHqq3Z\\nsgzPIqYvMmugI9tWs8BfayrrdvsNWyIAIYkqVQmMiV5T3CjyZsN5KFZvuJwUZTqdX8bruuQarhLf\\nJToTSOQUSyABxnSxSJHVMGDmGDyPTWabYVIjktdud8WdIPoC0m+jlcmjWACvrAtEUSkq3VlDUgIs\\nARQMI84rhJCoZNMzg66nakgpWcVrQ4sNUlKGhDLr60gkZVHpZZauw7pM1oxkQgHJDAhVnmRROAFG\\nGUZpBlxVOBYVCnIhoeSizkrmbjSDW5bdBpAqwFQhupoNNCFAnRJrMYXG8ilSSgn5TMaYSFBcpVmG\\nkuuTbE0RbLZv9LzGht4YuJvdbWzPlnQKdFmtkogSRsqclFAWIqPhsqoF1IRSkMeZ4wbUBFWdZryO\\n00LD49mbbyR4ukmPW3XRqNZoovGyVVF6FWFspLKMFeFM8wpsWlqI0TIPMxswYt2fnIDLv3rtGUvL\\nwLa9zq9Yep2MVlVR1kWSQ6iAyonsOr2DjQO8f2jtbKLtZhvEaD6/WYXdOXgOweeQm2FCaxAJEdaU\\nOHaoCbl93+xv+csX//xmvwl4dX5ZXUxLSQavr7aXXsfadGbVoiyxksd213j1ZkWK50zpNHzTt45u\\nbkmH7gwOUP8GJFLMV7ZWZZ6PetzC6V5h9rodkl1/s3v4VY11gT3AOF/OHNeg8ReWwbEqb+886Rxs\\neOYm1ULDWOWZXPNW0dy+M1g8ud0Nn1+4H/8AOUavpVsm/e4HJtFkcJN2TMmKE8qWgs4BfGl3/Slv\\nJGYb2WUpwGKy7AYrMn+T5QrIVHItSrDXqqGan4U/v/3edgWkRlKoG4tFrBMnXxfjsrfOQx7lvzju\\nuDfu7QTMQCAt+XqVJ0Vlirft9gOWzAEAUV4DBkRJ5vGkzNdZkib5/tbt6MC8gtmlSCgBoajLbivG\\nFPpBAEDhNyrHSrV6BGvga5CavmG14/Wz7uYmVBGuDkx3odEXzO5J2ytpOfhb31P1hYgTiglRkgO7\\n4fG4JBIgi202bxy++4QTnvOqZjINT795saJnFyqGsChDff11eDFazKvBXmpTz/Jlt+MaJlP5UuQn\\n65WvszByfsuwid6IeS2lYgd9z27sJosrTUcU6uO0++jHW46cff1vd7JaESoU5go/reuSZ5GAw8l1\\n8sVZU3R7oBQZfDSEW8XFjDz8x6eXICBOoB+VeYXDYbFcZK/Y4gpKuhx/9nUHfQp7sS42HapRoHU7\\nm59/G93vXFvdOkrab1c6BmbgakFPw+72b/7i7bKgrf6W5Q18m+gUYiPIVAt4h+MZ0ZJsDBwqcev9\\n7+z4FgJSF8Z6Xst0HC4VqaLGclwuDBFXuoU0zWDUiNbnKRfU3i6X8vRtb1o1i6BrMYDNUhN+p7eX\\npTuD3d8ygnbQsvbvd/zmBsQ6tiwjufzD39cce206m5TGQdeKV1OEZ5a/0lSVno4xxtHiy2ef/SZN\\nmEOgksQx9ECFPR6LqPA8TJ0wq3AW4/f3WxoFP/yR1Wk3eY0MvLQsqfgRgte9wbE9oNuD/vv39B/8\\nOw82bu93f/DDr6Pvh8e3zVmIiRIKY1onZe7t7/7F1wqhO+vF1cbhQXZihXwK0j0cbBYRgaqeFmX3\\nw/cb8MJxWLicQjrSNcdzFvP4PB2BKlJ2U2E9aG8fVMLT/UDROzJL3UZQCLWaZ6YeWK1BXkQQ6DqI\\ndT6nMuPppecWJkl0AyIELQNhXGmGVlQiiZTIs1pxomtc0aqohNSns6EpRVGTNKJFDRazEyxPhIKc\\ncyFUXGnpRAEAuKgVBDUvKIFS1lLKdZJeXVzlpJ2lQipLVZLREiCtBqwy7AILmUoTV5YVMQMJFbuO\\nWdamFCjwBWNkXYwzCb1w5Gs5Zhbk2MMUUlDaDRG+rizKhYUoyUvWUisNLds7uwJossg8rbRpgqYg\\nNYAGrKbvJ53B6j/4P5v/+N+ffUjb3d5+Fonl2JxF+0IjHA0Xz+dpFooSKgEIr6TCWUw4lsIQAume\\nZQ4cpEMdQyJ5iNG6nMdVmATtK691h7V2rL0P3IEb7Ou3u2Uagxq2iW5LpXq8JARgJSUXFaOTbMDe\\n+1vDhYivv7h3szrcMjSDb/YYSLL2Frr7ftGkuYQVNNyNLVPq3DFxMew9gx+E3o6yBsE269hClFeS\\n/7pnjBmto3C1PKNPn02TwnKbFgVosTCeneHj6eu9Rx857tDj37D8CASsHQyzS15BDSfPKzNnBghh\\nczHnOeICsI2bqNFAutlzWbCtBVS1lcr7TqTnyt3uw8FD41HLjsp10W5uNeaxv2NfZGu06wHGWLdv\\n50IvZaWhqhQNYQdpphD61fDl9PlfRVlkLH+WXbx8F/T/LgGuaT68+OmrmYjPJ77Qfvenv7kG9uN1\\niPz2tsaMOk3TbO3oDqgWWuYaV/lqXsr6l4JzseF4jWxra4NRIapBm5WNRmtzYKyiratZPBoCZm2o\\nilNmZbVS6IpWo16QBGY0Pna+frFjbNv39rFdrJJkAsUMGT8aLghUx0gbGbhizDfJmPDRbre4v+c3\\nXMf1ftfdNkrAfe+1h3OI89H1GUOEmRhCKIQANGv3bmm63LjXzdOzZJG0G9ar12nJa+I0bj3OAch4\\nXOwc8Bbe+M1PtwiWBEtCsaVRybWy5ssorioOVB20713N3uFNf6ejIwRMegrOv0rlOJoem2jVcS86\\nxtdvhu2vv1xobAVAT9G2koVllWUZN73pjf0gnq6o3tfxrJqUk7EI6wYzd9r9jSrN2Y4nlyPx+vCD\\nA9uzz6ZpOOL9LBGaXdfEOJpOpFr7W1aUrwv5sN9O/sUF+2f/y5l+UPwP/w018NuXE73VVIONbvOx\\no3eLZWPK1YXI0u3OzzTbvb+7f/s20YxOya28bNHkMi1KXvR5vVG/HTPInPbmRv/dajxeTmdVNW9t\\nOkUcEOvQsPZmsWsmbwlfUy1heoyu8xykr4/KiHYCvwOxBmTdbbpFqhjz9r4bIH2uZK0xV9eU1/yo\\nFNHwSGpB4m0HXL158c3l/DjKs3leVJqDszJ489U/LzKyfHO4b+006risDa97WOKgiNZvy626+XfN\\nIsO4zJfj9x+wjQG3G8zS817f8X2z0dVthC2cua4NkGXUa2wujd1N1HPvPmwTS/cMT/E6t2nJHlfe\\n4TvGvmM3eF1AjDqNlmkon2SberHRa04WvcVVv8wehKvNV7/5vEffRmAMYB3HgtepRV2nBI877TsH\\nq0HLTtNRe9cL41FaFvMsKAtJiZBl7KZr32gU3Olud2R8bdhlIQDwt3K5dMmMC9LpDkxtc8/bJIQU\\nEkkJsjjDtKOqBPnh9EqJglKQYAJWShdl4Zpc5KMaMokw0toAgDQpQVVIBeOowHxOHVIBZNp2mNqy\\nSPIwsslFDcg6hRBXGhKOHldlKBHhQgSOAUREVORYgkKu6ZAx5VhO4LpQ2TqgJnyDECiqvMxEuyOR\\npSOseB5rHCCoEeJCYtYlqqo6WS4xTusKKSFNS6tKl3Fe1FEF14IoJKXXlJnZj8YYowaMzzS6lADq\\num4bErTdJNJLuA9wh1CBbYhWwi28dhPZtw++893v/peh99v2jffv7k6W0wlzhKYPmgbLXjy7Oj0f\\nPOgSA5W1rhTkXLb6pZGWZSY41gHuuvsPHQ0wzZQMKljEsrq1z/YPmsv4wEzlajh4O27L4BGz21GN\\neMGKXAhZmJu7GOmJKDRtTezUTAPG21//yT9v7rXe+WjPZlGrHzDnPYYzJXvA6mW1FVYi47sZsEZD\\nTbB2psG6mmbf8uNfoDzuhLgnLV5VLPafEGx0g9SWF5B+vW3oSDC7b9hmzOhpdD4CVgdSfaSRRJ2Z\\nWkQSmsQjwXlawGzVx/4OAcf51efbi6XfcwLjfpp1QKs5weYCBGaHt5zywGozY7BMG/zCL4bGq1MV\\njqJQ6GXFp3zAhVeAbaSLzf5mUa3icd2yVbOVMFkNRy+BXFVy8d6T6cZGunF4efg9vtWs3la27m+h\\n8LNWfbIXOCoZZPGRYzjV6JJsfzBZJlo7C+AJtqDrb9tGCo3WWXGkayOM1hpW3z7vTWfdogS2VQLs\\nz2SnJrdO375x6PvJ+tgLEC94u9mwdZthCwvVN8pWYHiY9XG2BG8uL4G/CT56FN3S1odbN/r6uNc8\\nNzowLZQAuixTJWUt+JtwS3W2Eufhv/k2O58BlWZA3/G8lZRpYyPIzcfnVw4ElFDZb7pdw/TttlL9\\noozy5ddnxZaGlwGhJ28vS+s7PrI6m41XxwdRPc6jX7m+0wwokBpCKHCNfFaSMtecXrMz+PQLo93D\\nMvFQ+5HrWEWVN4yxWR8TDUo9WLDDxOjhq7lOcHOXAds/mjHCKMVEFelozp2DxyI9ZXWuQTzY8gJL\\nWq4xmYp83b/VUMffGhwV7E5Xae85mzHK0sm1noUZYXSxNN2qzGejYkjCL76If33hBnL11z9tl/9i\\n/NLpmn85TaesnB5sBNm6wdbmmqPw20TmI0IWnsM2nPnF23AC/lABUymlIYootCwjKxYYrB6/c4R0\\nwMvNi/ncd1PdbRoVjNEOcmudtS2t4dYrexAo/g3TC8YYBEsoWJXMp3OkA2Sajg7loI2coOy1AMD7\\nrm4SvTJcw/MHZh11u+9enXyK2WBZpOzmg4B8RRe/YJgUpZvOLiYXzzZa2ze6ySe/q7z97Y2HP9ro\\ndc0kNDQzzeTF1AvnW71ORoG8te88uLPN7Ru6GViUqgbtBhNLKzZ7Xqfdx0qjzC0A2P3kj4RmuNWH\\nr8bmdKFRlt3p65fLlgnIr/78G3YIjIf/MbPvdhvbu/26yKPDe4O9Rzd6ftnyrMZekaKiluJw70my\\n/tzVUoqEbhoYSoagt9mm2Ny+0SxjBUF5PkplgYmbqegMQMFrzFxlY732jar13Sr3EfVNVwtTJ5zE\\nerpy3cTd2EvXmWBdsL0VNj8RJdQ1I6uiNE2kwqtlhGWt1QnklaaJdmBqtMJ1nCQFALrkoirzGjjN\\n5oZACAKa57BKKlXURMoyLxDUsCYxVJSntuvw2kyzNSMadJoUEwVBUYVFvG5vIpMhxGun06SaJSQA\\nACCEqG8qw6KgMHFMCKoSKUqEid6inVpAUqwwqjzAaeBzABWCCCECaowxATlxOYFU1lmjGxCC8mpt\\nO+EaIcqxr2lIcmS5tQASkipZ6xqBlCULZRWhhnhW4zJTCOabtb5vPvkP7Pt/y+1l+4U/GrcXLL6n\\nK1mbwgKm8XT38IYWeOvLUZwXQHHHxCXHGENo1MDwva0gyG8Ox/gyt58veVQsQYV1vKOwUsmU2NQI\\nih0704SzvCqmo7MaiYaW8Ewr9EeBQ1ZCeIzKsi7X8VwcM/FTzXhnKj/OO7vd3YNGyYhbg5pQp1/h\\nbL38Mi9sWVyk83wdTlg5boNrG39O1WXniRE0S3w6no9VlECSxdjWMVhgklWuZhl0OmdAFq6xgFVt\\nb7QCo0LzY212bBg6J77fOGdrHuOCAKUwLC9OTNd69Pv/aLTET570PO3VwMrYtC4XzXTr8XXkr/1b\\n/lY/mRw9edLTzBBGX9fjcw0NzdXw5bNpgu+aAnNg1NrNxeIq0CihRwqnxbxW6pqYzfE4i6q/PSop\\nHDyswH5p7M7LKL1uzeBu+3H7oFc3keHYF24TxdMyF3fOvzob5oGoGx6iPvwQEDqJSDn/jY8pwWbL\\nWenktYgn+zcfM2k1mkqIJJG9CsWMTor5f4/V3LUqhOtKWovFma7NzP4d2hhE5H4YtHb32YbyC9I7\\nHQZP1ebBh4fN+rqoIyHNs6NccQWFKLI5D08NHjZbP/rs5W599NrP37T0dXdbd269Y3UDXQ9OX0o4\\nvpTLBSGMwS5rdqkbtvadOLyqcR7oA7b6GQ8xsRqBH41m6t1H+6L00tGFgZaBQyDywN5vZVwkXHz8\\nSQ4QbAbOapbPMhXQp9dLR4DyzSvZ10FLL6xdpuGpbTplRtOUpst5vzce7DeWizp6tSwv3yCmAZEz\\npXwYPX3WDDql3WAKqfZGx9HtxeuqWhfNgRDue4v5F6kAWWFn7cEvz295XibUvF5cgbTukMhvEupo\\nCP5s986brX9YZzU42Ptq4J58cOPVXmf8aHfV8K7JnY9n6+RFiMhVqDk/8zab2HI6Nx5bN3948IPN\\n6PWfIT7XNEVJKiHMU1SESVlXJfqe4+9U6+Fkfum0nKoiXMOzEVrMkMI2sJjRNxfDGXQOMEXp4oQy\\nsdkGUIt9+XWwFezuHN69jRoD2zNNzqgO251+A/LK1c14vZqy/f1Bcv9D6+jpX82mL5KrLPD5jd96\\nZ/MAbwd33fbMsbJoUYLOQQLvCbhf4lvTZfP2u53AKKgG9oO0mP4PfPPJ2XlbQvCTZ4vx/G6Vrrtb\\nUBnvWc6gFez7DnXNDd/tdvYff/j9H+bXf3X9zJf9teegeNGUyKzMDWf/h/Ey3+7iacbVFVGFOVmu\\nUfeuMr737KJzsfjuLPnQ6L5banuxfigVWU7G95obmsuZmVKNlzKSNNEI3nvcBXTQ2X+4kE2kpbjM\\nNad09bHNlnmZTtemyHje8gEXRlRpfnsV1a5rmjTtudeaY8GajiI3qUG8hKtjSGMbGp5KAaAx51wn\\nmIs4Jblpi6BNgrZuBU0cSNfxNFzkYSZrwBiTdeZark5YoxvEdSVFxkVJTJ3SWkgJkS0FAKQGkKU5\\nmaVwelYVlSG5oDwH5RJjgonSDRaNFut1pSCoeVbXOcRKYCMtoEZh0zWJbqVR7epcYX9vZ883uxaB\\naw41xHXaE9COs5pDWRclR2w+T5l5kEkVRZGUwNRbWY5aVHg6xnq+2WsH2y3D2MiiCME0KTjnYqAb\\nBy1qBxqBBDIdOe29jzfezU6txSQ4Qt55+rUGAq+/6VgpLeuS4xJ6dnrpt15nsjRNW9M3wmmEKBqd\\nFiCj88JPisrBV2b+bLr4Wi5H64INmsqXJJNaKLWQN0LTXYgYlc8kOCOkiSE0mNpjXVqmb96e9tqs\\n5tNuI6si6Xa6ntF1sTcaJ79++77wu2/49oEbUi5Udq3AacUlQTMlakcqwwJms2w7o62u0hMwmiyS\\n5Err540BIGghk5x5sQKZ5ds5+PA330Zc1g1v8+qiaBtNyZsZMD3zTZQcm72Jk46BbffbAJoO1hFp\\nCYUdwX/087/+Stz9we09u6hvaoFhN5KbZI2uR0kSRUXjxWiydfhbQlbzuMRwivA82Ja97mWjUW/1\\nirHEiMAGOul1KIdvIcBQ78FKOOq2v8habKueiRI2LVoWkX01rRAYxXH67MR+nu3UgxaxKMvh6PJI\\n9Swmf/bermhnSbe/6d157MN5JRIHhZJmGtEIwxIWNZ1q9TrLa6sVwNi2ql8hWXXKSUo0p0EB2APA\\nKEs+nFw3gh7Hjy0ZXKwa6Zqvh3nttlk5kku0HManX4hfrPb33kfvDA6rq2eaWtuWb+oXFLFgv/PO\\nDeYs/vLJ5nmjlSJGkOeaurP+q9PReLcRYNseaW3pdFZZMUKIzN/2X71Mz85RVSQEAl5dwBy++/hG\\nVUKus9nKPh2aSVFSTfhbtyFhujKz51MCVFEUv367O9jdWk7SOk95fl5xw/TL5PxtIz8uBgOvq6aX\\nTnvL41WWcMVPXzt6RrRimt80sdPdfM2cUboK235Lt1rU7NXTL210AHjfYb31VN/a0fpbC0sOny8H\\nUt+TVZ6H559+efxn/8NZL39ZVM2mme7eyNM0ld0g0julJHhnW9bo4tN+UQnKwHc+Oli7T9zm1jH+\\nBzf2jItxw6bQjb/tfEAXcnNcPqlk4y9/3vzZ0eGv/3qntsZcVgR5jEBRI8lTXtTLKPxmbAjc93wl\\nVqNmt7e56YO6Xp6dkuSC86qIUpHTnYOtyflyOks5rnWzL6z95dJkVjrw1oWAAHVty+v4m/nI1Mgq\\nlJuGjGngW7qxz/jRye07j9+NVy9WV2c9+lz3d0bTrXLoPbiXTOezTt/wdt2/+jcvlid0dAW+PsEN\\nRC/mG8nKxyQo63Jni/38l7/gyCm4VtGNPfMM0GxR2WkU9PwtkzlV7ZpGB1k3SnNQV8V5sS/RJLsq\\n3n6O2fqK5/Xzo/L6iw7DHaG1JqdnPru2UI1A/O2vFquTxs1GvtE767pMYPNqCPPLRIiqJeI7+8Wd\\nJ/+OjrrItHjpc7Eos/LFGdO4A1mn0f6eqguN545pUYS4rFs+zSs1jZ+HUx4Mx/OKB432alakxSkC\\noalbCbxHKm60e4vJN+t01TQKzU3KWMDKoswEiNY8k7xUVZ5V4uqazSM7R7sy6jsWM01bt4DpFDVX\\nWGPUAO2+3+xgr7vHec15VStwPazKOqzF2vV0hJREBEuC6zUEmZClkhrTa90mQgmIIabAdL2GjYjK\\nIZFVDSiqRCmoYwshSiEAYYDZ67BY1uOiVFbQdwg1KDJz1fAkQiZRmCgkANREsdPQq/I0lziJQl4W\\ni/morO3FKpaoSC17us6yHOpVa/vORlznWsNKQxVYIu0aUbGL5E0uDAS7juYtD826R8+1T39T87vP\\nkQlsXokkSQqHufY7Qc8/nUxohbqYNkSlSqcHSe23PYWRJmHDlpZ3MV++TNPE1v3CuPngsZ/KOJtf\\nyUK1ihM1H3ORUyMSnCiTKS+4+dFdm7wA68nW4ffOwnx7e3s4RA1vY3YuCSla5q+shUDL4defzibr\\nw9PESwTLOZrPItfR2gHz3LZpKBP1llkPtnb9ptzavNTW4/MXJ8uhHE3WrR1VxrUUCFIvL5aHxV/o\\nDErcO395fOP7P/bMvENpDHYXRSLqVlg3gy3HJNToR5s1CKMiLDPHNqr0z1vGO6h18/yssA9tVneV\\n857zIAvgScsiy+kySt3r2WUy+VzWic4qxvRc1pPUtvxWuTz+9ttECtHp8CTPsMYEbg+jTevgbzLd\\nNW97ZjtOpn+KVZYvR6RKcPy5WR/B7Ko+OTt/a4VxgyB6cPd6V58aMnIbdPOWvNnaK8bby6vgdJZL\\nAX2v3t+qqWhF63Ae+uFyb3v/QGZlVuruIDDh1Ln5oC9e4TIskB5jKBJjq2XvtnuECkbrTXTajf9t\\nPn4NyywygkLXoBEZLLGtPHszPFp+pB9mHz+SPZIMPNkdUAnUcPkJYLu2UcZFW1Bzy0o1dxcTxdon\\n12f5Olkrnl1Pcogq34UAAOyTJV8oIADKGKXSSKsUjpYlYUCL6p/8rM4QLLO1paNsWY+Oc80pJHqt\\nGYJiGBSv5rNQ07BNM6HWVXk1erv80Y/rh++A8G1TlC2KluvpNkLQkbP3vh+pvKhqXy6uSmgZ/tzW\\nakoqbt9Qqk6yisBnnT2/qNeGEWjaYjQXhIBHD7vdpPj6TeWYlkCZVv/lxs6nwARb3fRGC4XQ0yjj\\nZ1Behjcco7n2lR50u89FXWKkx+QPivw+ad9586vXr7TvTT8/+/s/urxxA77+fIsv6/XR10WJTf3t\\n4vPTwp/O57rmtDGrod3xzJjR2LWQSeiXf/nqr08I7fQxBqls39g1ozwX4DPHSk1UkHxt6s5Zcttp\\nm2GxsunMNNHlxLGqpJLw1P7B9ckLQnhVlHrPo4MmdYIy1zSLRLO6azRuH6BPDi+PvrEef+dvEzr8\\nZjwb1aS6fLt7ixXGDzjoMpKmcfGD+xnSQ6sjxPg6y7KM61brgCBEMITC7tBQB1Zvo2FWtaafqQpc\\nXazVcAT0dmPbBwAslnjFb8ol/c0LNRx+b74O82qiiUvdeQ20cXNTC/j/Ei6Oq7Xj+D3Nop3H73T7\\nNz1Y7bS/PJuczKruWKhXsb6+5KZTrYeT1iCrNpufT+4tppXefKJkg5dRtI6xEIgsq8gm0nJZrTU1\\nhKUoLazDTPFKNBpNH1//RgUhtnS++tbtb+UZr3hpdZgUtWFoueUuhwhm10U+BMTzDaG1eRQlENui\\nkIahFKhKJWAl88XUSJZdVzMdO1NuhXrMDbhi3aYuHT9Z65g0gEQSMltDtCoMm8CaQcUX01imZS0s\\nIaVOK+oABAmGNSS6btec+ZCooih4ZdbKQdQ2KGx3GGSkhm1VLjCiWCnDJLXAStKqLBazi0pcMaIF\\njiKQQlQ7/h4gjbrkUADJsaSOqbcRsHBdIThirAQgtXQrLmfp5HqSwzIrKxED2u7vfHh9VRVcvF6W\\n0xGXiZA8RYKgRkjOhAdxdnY1Yrd3Bqqov4EXcWutCUvvX8GdlpFN8vmo9HoBx7IIr8OWnziWyWXd\\nsd2wtuZSHiULCKlhtNaL/c17G//kjy9XMHG63nu3R5o1dJzZwJhpmJMyGM5vdx//DoVJrDzYvlEP\\nX+lO++Xbq83BjXgWbR5qWJtXZdRsfuGI55d1G+kX8+urkmc1SBy/S82Gz4JVZdza3RtgMZB7i/ow\\nhwOsZA9NWJuh4pe4inA6JTh7PbJrSDW90+i9oiZtbJeG/lF2OXsD9psga4u3F+fKY0LOSai1hOJ1\\nLTz7VM+vg3ZHtyWkTAeeB14rth/GDeH4VXj7+NIz+h3JrpswZ2BZ5+OcC6/lE9tGTMvTQkg9Kk7K\\n6GlDQaGaBdCQ8QhJiqqVITtnOUyEGF7by6ykFUKU33743RtO+PGDR7uP7a2N+WbwTYe8KZPH50vn\\n24tu56Zp13O3+c7bNw8vD11rd0XZyG66vLRNZ7/yn5TyTNcrhAivc89tDfTF/Kr9Yn5gu82Xx/10\\nd7upTdNRVkLK/EZWZRY7V3W0sJvavs9uv/fOnq1Qu1rYdsILgf3WJkVOZ/tAAv+nX8+Gg9vf/3jL\\nNHtZaOgIERx/Gflrvbevya5OmtudMtZoxUEtfCOJIowUYnphBV1Q2/l6afFRz7op80WUVYzNc0J6\\nO/uQ6KtZAklI0hOYzBiSWQ5JmW41VFbkVRYrpWwt93YHTXnpGkD3EZJCFIntJVfm/+Uy9G37xPbs\\ntp53+7zbQO/tvl7DP3CbGtKQbmQIxVcTZZhYp1hPV1QvNVq2nGA+bJdZU2m2YW/pwLFNbwa6rFP1\\n1Zt+1yRR7puRQUPFc7ux5x18kgEVp3NojN+7u0Yb7RPa8qENeL/f1de5cZ36+fL0r1+jpvN07/qL\\nDz6cHBn/4f/07KHCY0svLX2KqtmDZvnjg69Xn761tUgKp6w7UPaR0adMGnYBlbjRewb+9J//q2+7\\nynj/+enFhPRWs5KLhdcISOve3Q8fb247H6uJI2/smXxj93tO20fDEBFclhvjn57ehMtBE+gGldrm\\n0Wv9OjQx0imCsuJBX687j6db/6B9E+M4/fA7D4vx6fhkDQI8KfbyooWKm2nJEfMLtsesVVmT/U51\\nZ3Der1865kRIjCSCiFnM4uTX61U/y19qu7qreVrjNpXPgbARvmdYhwgrUryYL65xc8epjrKoROHZ\\n9u7KbO1EOQjXCwhVu0eujj+PszjPy9dPU99oezvNg/e7JdumBMpR2F8n6XJO1aTjyDlwn7/dTc6y\\nu93m5O2XUjUFMHIuw4znYopYJTjMdT9SQRpVDOkWbVQp5vkkmV09fP8gC1OG2eWssHKzrGzbdJ6/\\nuADCXifF6nhSZnoYHXFeYRhLHHDQV4AW+RQQTg2x0+3Vy0LkhaYjKK8Lq+DmHUkGCrJkvYIyPx7m\\n15dGmZSynBai1DFiCG3vm9QXSqmyLCVQZbKmYIYwKAD3fJNoOkJoxb0sVbICVBIn0KXJIISIdmYp\\nm82LLEl9I2Ya1EgK6kpBCbkGpIJQVVXliStlKaLryGqUtFsWBaMNw7SRjmogyzSRAgaNARYWAhBA\\nwzKqolqs40L3t1h+5OWjTM1Gl2fjqzrXDjRpGQyoycilw1qVslConufhLK/RujS3kwJdrd+0tGkd\\nRhZshbEpWZa/eXZ+rSGv53kGX0tvQyt5SbHvdxjOQ7eORieXi1pS4MqKMT+7+Ov/TjO9oHFT8+7F\\nNcbS11ptrwU6bEIzIxrtPD2Vx1O+Rq7IQ0hhE/Kg8THPknbLj6KQ2rLjKctbYhF+V36VTN9CCiGE\\nshRljk17fz3sXbHvZptd47Dvd9Uo7iRka7WosEZdmcfYj81NWISAvzSFKsUgF91KPAFVn9JA8VOZ\\nhQt539qLWm402H+n7Xdb1NRmkandYu5WIni33dEqnKapbnad1uhgE9o8BiU+XVWQfL4etyEvxvm+\\n3iKuQaUmbW+gNSwJ9RrmjaDK1qcw0Tbueq1mluVmKRpUPS1FtuuCqhqT4QtKrqF63WmuDbZ3Ov+D\\nM35AH7z/7dHB6uRwVpLNrtHebaTpJJ2vLHP3s89X9z+4fVcbHXTmxpSfnV6jAFFlUOCma0/Mp8hz\\n8gI21Oqd7/y7tu6G9LxnPHXLwrXFajR/frbFm7cH/Ut8vZiWFWQeApZpdM3LzX+zvLWUOwWbdqoF\\nMCRpAsN+EMvm5IRdXLWuzl/siezkF9UR/PHuVpsJGfT20+VI4EaZVkWvjYz9dYihQCA49N2KGWd1\\nMaoQAmkcrurtXZ25bJW/abcyU689HTu2vl5CMKmT/Mq3BGTFxh50DdpuMR2uFCkyguosRAgVZc1J\\n8c1vrPvv5L2dm2WFuALBtsXj8tWfp8nuD68E0wyfNTYn68bruEVu/8dv3jwDZF9JRO2uAnpdEkIw\\nYF5M3LwmXOROt4fZdLClFzXSNZxX2TQf5LW3nIdbjxy3//7te6zZpjHPLDcHqP3l2T7mkPOi0/LY\\nwe/kfPtAvLS7MKo8ojEeL4dnZ0u5pqOvDJw+xf2fzLf/159UW3zMs7jMgd9iECyn/nb23e88uHHU\\nbgFZ60CQPBSqPrDcXbN12/McYqN7/4HbOvvL5VlZjl73tNmbb0cYw7brRud0Ad9ZGffZ+7apFe+8\\n/4jKzUK2/K23EFqGTtreL4JbG93Bby/mU2bfkuXrZMHLurQ1QRutyWwVFcHxhXN+cuPWncHZ6/f3\\n79wvhz+VCs7WS0iU4W+YOkwE/PLrLzDcZvFbs9+SOw87D79fIiVq0vPgzbbJGPPt5Oj0581u58tf\\nX4JaiQhY4Ozy5a+xqjNop4QiwyGwCM+fR/G3TvVmsz2GGqDNTYcIWAthS8eWAMg8nSzD5d4mmk+P\\nC+v945VFeOvi+hki1G+X7/5um6JQNyADGNOyuLqeicWjhw8Rq0FeQDp35RHHcCmKMszKOihTZUgK\\npQF4C0Oto7O8rl4fzSq2o0BmmY2W89WtD95dR6EUW1n4VPqAANEdLDMhVLyAgtcA12UFgMOIglhk\\nSV5V8oPvfqT0tIhLSMUqs9bHS76Wrp5pJOo2IwhqC466myHCggBAAdEd43KkK3oLA9M3qKi5UFKI\\niIICIpwuIgpSIGh0BNYn1FBDhUSS8DrSqhjViTQdT0KUpvlqdqlqDmCtmxRJTqhiCAc2NJBoeLk9\\nwHnZFGUFs6plC4GkkpJixLBOqZKI4jg19YajYcBYVRCLoc0Wr6PPyrJE2kppkKQKxheN+MLMMhiv\\n3MAGFGo6grBCGki1Mrtc8OUiLNdTZBsESbtpg3RiiinIv85mYcTfHTSgkVUyBRBVBPXKsgYVVDSs\\n1ZdJwulSAZQaKKrKTwE4cJp7VYbixWqaDUzmaYbT6D8YdEHQqXZax/Lo05M3YZTFTLPDYlBZzU5D\\nEWpowbQuDMg6pkWWIZF7N86TXzTQWRMuymzdMPUbNmnJ2cUrg17l0dopK0LqZC9U4XWy2bx5Y9/r\\nRPN51khnbFghZLV2fGqGLJmsr8dA0vJtuZCw+OybooAsH3vxqA+CXTZ4Utm3e7t3UanG19ztPSKB\\nfqCdaVjLcqs0PzpPZdB4sw9/slu/1cikruvjI26AMgy1KBsi3J/NPFRMDT4O0CyeDhHyBsHNrD7w\\ne1DWSxCvSsFS8EFGJhB+djOIsFbmfFVLA4plcnk0PDI+e5UPgqvhdQr8xwYZ1UVp4Lc7DeyGr++8\\n+x+lzgfw4J0FL0v1dhqGCWfNrpuQyG1VDnuN9ZZlPnDhhxrrTgsRa3f7Dx3quBXg2/BXdXLBYyAl\\nuHkf8ut8nnpQu5VGS2dn0XuRPHtqrvTfbtzqqtr64mUCuBnPynz5mpTXmqW0LWNvO18cH5/mhdOw\\ny3S8a2RodvXgJhkPabZcoGVecSQLDSrV6SDPqy2nQMBi0MkKiqsDRM1SjCoF0xIlpew0iWNEAQgZ\\nMQAs315XiMCiFM1OW0CGeS1waQcYqLKMrSwvm/cewARAqTQKVNoNYH7b+Qv466c0T+p5ORpmVJxZ\\nYfcv/qzAYnY+zUyHxLOJVLXCNArLOsfheqbptBZVKrqa1uIK2DCPsv6g2ZSlSKbjhoHmk+35aY3t\\nm2/jezJLs3A1yfT18rzT0kpRHX7n3vwqqJfL2j1cZXd5XczjLauRlPPPbtSXqn5556FrcC5fje5V\\nX9zaWRuGuGmVdV0HbdImq2//rdZ/1EX1Jk9SUIbhZD4fp+ukU4mOZg8whMM3+e47Z/vdL5guXo2N\\ndq/OF6hzeNjyTnA6m3/91bPl3ccHmyP6R9BtrRKzqFZW9+iGM4LEENp9Rdsf/ug/Gc/mnLMiH2GS\\n6JSXdY8Z8NUKJ1erXfBmPnxjbe63edncYGcnLy5Hb6KampvbxWJiyvDO/dumjYK9u2+/+fpsuHm9\\n8PNs23T8RNb2rcc79z5kfldHBPK456H2VqYZDsaV3j4Uhjyf8GVclmW9iqxZVNLlsOks/FYL4+1p\\nARFWmbQm61DWMtj8aHkuMMhehEvLAUF5KpbKogYrHMivKjFNoobjeEQvDMskwGbsZXn5ViwyXW9U\\niJXZyEJxiTMwzV2wgFA19Znr1l4AhxdhGiFqIwZcrV7ZJEcAAKIlElYplmqv4mEpwtnsGpZVnGIR\\narazSJNFskhdzTB4IlUFIDOBwjCseNhrPDSaBq80b8U3dyT2EynHOuC8ThQLDA2mRZRLU2FsEYWw\\ng7GXz6caoD1P9xrO/nYTikrwUnACeG4YK83g/UNi9kylKIdalRMiLnw/QzpXUiiADc1nzM6KFAGo\\nAFdKKcw0SiiBgcun3JzEXVVtiKRmZciw1FqbFbApBGVFqrySEDCC9B6O6Z7mmxDoGMMc+8TULc8r\\nK4nkwvWKZqNq+HGwsb77rrFxQys0uxamIgaCTg60lVmVMi5nEV7njSnqrMZhZBnYud63LrAMhHbT\\ncbyLC653Zm2LM513A8vSodRGENGtrW6/520HkoFhWdua4coyxjCCQhK9hekHcfij18XBWdhi9hjy\\nn/t0NZxEFQBJVShlaeaNPIexgCIWmvJWUbMEQEft4vJ/w/3DVpvqRpqRffvWg9ZuI0nWfW8eBNd2\\nOo/zOqSE9ZdG59D63hNrVzHrNAmTVnCVF3338I/MplTV9bY/qQFy2wtznqXqGwMZFpxiD1Nl0MR4\\n8Yy39QfnS+N6RiXeuFg0y/r+KXk3SZFIUcu+FGVLuXem7Xf1LXujvepZ1xRoCKEyeqXIhs4Kt9sI\\nrXfLze8qZHWDDbr3CfNirj/MOEK4vrM7evFzDDHU8eWtx/++1rrJSZ6lWlnIjf1iJ5ju6j/tukJ1\\nqFBWWzZOuC2wZe9+uPfdvx26N9OL6PJt682v51EeMvmFWb6IClRNn03XTCMwCkmp+qcTI7j7d96c\\nj94sJuH40XjdyhcLzErX/ZrgsCixvf2HwLjR7JZGvbbNDKOGT8Bg69W7zYt4vpSkqcd/sdEZCLGa\\nHX+JnLhjvqbEzNUNKEzTiGF2FYYC2M7j91VgDF7xFs5e1E5uNRDmUlaZUqAWQNMUpBCydVhqnEun\\nf5okWbqOtty/aWmg63TKMs/kWnHebhUxx3U+59SGEGLmIAArXgOFEJWuNdcZa7Prn3y61QouqMDL\\n3MW08m8q76OO+yDybLx1SxpWmsa5Dn7R9H6FaQlJ9mYKd1oSKoEk5wARtLLQZZ5yoFCaDkcX42w1\\nqpVVp+eGSUh95NK57wS4XFlelS7s8mjp6VCURTGdUPGcWPtSFc9/bR+fHqWgno7Vej5r+LisCVRd\\nTI7VhzuMkdcvD7Phqx3zuOGNc3BfKzz93Q8Pb+7fe/D4Z6cNWf5kPe5/+BB3vVWNFPWy/OooOk/S\\nGKZk129twfpqOt0ku9tZXlbno8naajW2ErEDHAebxJVvqi8+vXA2q6larT1c87Qwsji9trfyehea\\nW1VBjxb7yylQJYNqlGRqVYhkno25aw8nbv10b++yd+ePbhmp+eg/4uhmq5s9/8Wzb3/505rDnING\\ncBMg79Of/avlmj766AfHn71Iubq540lZKWqMl/6b4gMy71UV4hLleXo6XEJKdJ2lbHN+etI2lyx9\\nuRJX8+sjcTE63B0fPN5udm4RuBlQiBUvMgWxrpupZ9RJlAJWfXJYV2nustV22z68ybsBWefXUelo\\nRereupMoO+PN12NAMvD7vzO8c+PE3vMx0YSqJCxyveex49Ye1w1fw4iTIOK65ncJAuE0cW1re4fq\\nprQ8QjXEK9IC1yTYg8imCqr1RG9dx5N1A0zvDa6/f/ukjN+W0Wqnp+/t9wTQihrGRI7WCxYYZvDA\\nLFH7AEAL2qyplFNhRVxgOxRSFmZGWsk6EUrTyqwUgmpCUiPmnAdbG6MRsr33mGUgjCmBCGNAKCc1\\nczAlNtQbdeVjKAAqKQMGYZCZWZ4qgAVXZZUCCTHEtRRhklcYrLgFdcLDt2k9yjmq5LquYgxWGW8p\\nopgGbNOoykRwVRW0jGyRlVKCWgDBUZw3qipXkEHKoYVy4mPH0ewBdnoJO1DGfl5rktiokiGjNSJx\\nFwmIqoFWbMDrPVfftmXHLS2jKgsn2OTh69TabOiuHZXKbjc0jwFxOYsd0x5Y2q59b2PbhUVBEDUN\\nL2UEYIQcSMvUjcsFXn4Tnb8EssM0saGrfHgqyzSWRRElUSVW67NiHdtt1O8wA9dFrimECb9m4jCp\\nb0VmH2ZBVTw+XXZz0G0dwu1+6XqK2COZF8y1RGH34Y3zL9zR9XhaCZMVVc6Dxt8XMU9jkxv969XY\\nkhHhQ42fqSxj9noZs6yguK2WL3+GD7cyJSev89V87euLLo1B9i1cXvvLhFfF7NlQB5aTcOvahdmd\\nWbk9vFr02SIgF65WpdNlBQ1VTobn6/o8Cm4+KCu3WHgp7IjRG6OOqtYDaVKcaZP1ebD7n71dNMdV\\npOqFI3Sk7ris3Oybg61WXN3ORteQMsf5V0row7PIEA/+9R//vzzNJU2GxVdxNM6WM8IWvrkgOwOD\\nhLVUBFz0N3V0cuLW/nz6tFX9Lxvwqw33mSfnTBvbxtzTEahnqODay3k5vnRc3Wm5wfYVNOw0WuaV\\nSMu3Fdl4lVgSTTlo1Vk4HU9tp9u70wHLegJbhi5pls4iVKmqKDbX2Lh3tyBnkuPLLD9YYyRVYSjp\\n+x6lBiDMlEo3U6Lmjm9cDKODG7vtXsc2/yUUDClW54Vk7MYnv70mDqkTQyOa1JhmiSqmWJkmkwKv\\nl7VlCyw3Oxty+oI/+aTGABpZEYf8YqT9xS/Mq9enGW19U3+c5ZwXJdtyKlEkHKpq5ME5M5YBeLG1\\nJRQXG42SmRZPr6GYW2rC88vK20mKLONZDWS3axgWM3QoQSxUZBia6Zx5nt4OdEK/JfByMm8jVNTF\\nyJAXVbaoqzMOp1ecVrPhwI2FMmefdl3FxPTNxw/9Rx9iy4Lf6645TNBTfvQrviQ/DNZffdQ/8rSr\\nt/j7Gw/fA7yseWy5YyDfhhF/8aKehO1W616ArkevSODvb+5nnUDFpvEmy3t5djWHN377483D6xef\\nHRMM1vPQNRWMjk3Tjk+OZ8r65aez88lILGfV+WJnwKtqBYuYw9qFy3JpJMbUY6p5+5NiLdbufp6l\\nqtgzjc17D7T0+H8bvnzNuYthkMjWnR/9jeX8mhB88F6Hzb4ym2nOgZS969N/MXm+Nhqk7XXTCNtm\\n7QYDaMUEWKCUPhk6bQqoE81lfj48MMZGYCbwgxp0G5v76yVkBFuWd0tDWTpWumNs3pdi9vWXo1Gs\\n5rxp7BzqZre9+z4w3TJeIVnVw0IvNrT8zAUzg66rzvdn1v9BmB8a/B2mkYzKZFm1HFfKFmLtGvpR\\nZawjD+cTm5pNxzYQT6vBRXwLsYx4ZiXowvRxgTWd1FXmEGbLeNA23v/YvRgRcfdvtL/zN8ps7bTQ\\nHD6u+L7paFkGpmdTi59jWOuumyGryFuIWqtyWyoCckPXWUFagmOZRLZWpdlac4w6X2CQQygzqTq4\\nHDg3kIwp7ivkIEKqQltHGEuGpMQECAk561KAoJJQ8awsINZs3YEUEGwxxpRSWAHCIEWwKjIN8RJQ\\n4jcUSZOqyFOFeNj1QmJRrVI15MtlSGGW8BAR6DkJpgZEVVgBWQAkOamBgQpIWZJRqOoiLQhmCXeu\\n0+7x0MkStQoBQkgHphr0o7ava77X3e7u3r3b3b3hbXu+1yjTzhw88MLwcuG2aFSL2nJ3NLIlsvFi\\nhuyyzfCeQj4Jby81QEzHUwhE6CzyQeBtDVKYJoF3QsSlobVKY5f7rMJjbzftWKktMgKHaTRbZ1qJ\\n++v0TmJ2GbSSSq+BBZmFZeJFC4o5RlazfY2XWgL2pf/I9uw6BPPXTHibs7XV3O23Nr7Y3zjeaH0W\\nKcfUKx/vd81C5YsM9D0QSu/g5iHr0BqixHIA1amNZQXMWgyvRofVdfXy2ed9u6C2CYmh08tg27Kb\\nZGNHZ272aNdM9IDsUucQQS/nxy8++f2+5gQerSkKN3uOItFq9mmfTl27W61D4Ob1+Gt69XO/sV3m\\n9OaAZ7ktqkkh/97R80l9+ps8HZJWx9a3CwKA/gjd+e7kemLzv2qb835XxSWkoj4Idi5f/hPm/3A5\\nf3p98ps6u9Lc1GsfOPY28FpqzFccuHohBb6c6d07NaWLuMjyInKoSxwTSFSgLcO7YTsYAN1t30hv\\naiVMmthOL+aXJ6Dg6zTJTYCIZTiTS/L6MyG3KKXx7CRoOdy0fzNutLXL69+gevu91vbO6riRxiLP\\np7PTdOXhW/tHbfuORGEFPaVphKokSZp2Q8NQsxsM6prnxaXGUz65vlTND/Rdv01JkUxbjeZSgrOv\\nyOj1uqnVUsQ1ikpR1jzTnBxTLhTAVCe6H8V1CdqeN311tS+iyvIqwtc8rxtsrApz8fQo+ewIwlxz\\nkmiyhOJKA/OdpgiC0Bu0t+/fyye9WgoJW4K3mkZbN7u1xNS4NJcrUGd1ESqiL3KXmIfN5pamaSkC\\njDWEpFC7g4m0LaCKSMVveWkBfMWc+skdrmCVJkl5OQLm6Gh+W0rNNP9tsOV9cGt5vfRS+P29u4/i\\nez9crKm85/Xuwr/8k29MbYy2/766/VF7vAB2I+WYGZy2KXWFXF2Q1ZvleT5LrYRu39+5hulrYnJC\\n+Oocn32bL7CeXI3+H59vPB9ZBx3so+M4mZ8vsx/+/o8klDdvIiv6piPOihKXq4ued9KyhW4VyfJM\\nQDbYSkj5Mh1OHt4DL69vBp2BJ+A6tKGQuuEGjbbVmQvxp7zGNw9xJ5i8+MUwPP/s5ShfuPd2emeT\\nS5Ubj7aaC6qnG+TrFqlpRQpZmQY0UrFO0p5fyuV1Xag5aoSiOVuhTW/x7g/EzYNtucoFs4S+1dz7\\nLQyRzDWtuWrvDlZzz/C2xkfRRscdbGvUzNOSAtZ4dVFm7T8oc9LcLnbusEJPX18cTApNoOWbF/M3\\nz1B1QYhzKGTGpVCLMFlfIkhrQda1k9Wkg6+aTtzYq8drJXUfaw3TEOuxqiska5K+OLGNTEHdsZtE\\nkYZrnsW58namh//47MJuy0Fn47HhRiRq6AXO0iqJ9U0scnKStciC0lkUxEqcrTgAOpTeZF1HMktX\\nJSgAQjWvSyC1vOa2aVOGNQdLyGbzYfdesOItoJuGZlZcL7K8jkQaRrJMqEmK2uJFhrSGSY2qFIhg\\nViV1VdiaxUtUVgaUgCBs20RSvS6EYaxKGdZp6jNDMymAkiglFNawmUbI9gyns2+yGYHLIj6rkplk\\ngSJAwVwjcSokqwtLC1mzBVJHJ9l1aj+fZr96WYw/O789ensYTPvuEEmIDKPR7AeIs9DYWbFuhFyF\\nNFEWRaQuVoGzW/z03yy272hFVWHiSVQuRqfjxbmCfM8hJgYGIjZ6e31ir6nm2kovKjEEKx5kNuru\\nrXix6hk8z0Auc60CUGPEoO2N0lNTjS+rkstYlTnR0kJkelYBB+SXZ/OewV3b0XTCqBWxNkirLK3x\\nFVzWuMCSuPCjQdKfTUzWz1ibw4M0difXVGF/Z+9m2/bGizert+FClGVdbXUfsbZvoQgBG+qYUGUm\\niyLPX/1i3twzquuvN++6kax0w2m7uiGbae4w0tWon+EnoGM3RV/Cm8t1982rN4Pf+Y+BcShZc7tl\\nMHZQGLN0NqJQ8y2D4mucXvHyurMztN4ZoLJ3fNa/e+OS19DWOxZ5tbUzd/W56RmAdhQBRd2QaKc8\\n+rkJwv1776xH9p572QfJ41bnnfY6IKmZpgpeOY4giGQ8CtfHc6VVmXLLzwmu86jQyPX+Vh9IQ/eh\\na167VqtEBwIEWNpm3RlPHQwM19lD3A9fO2Wxk4tqu4dEXNFynlUXqpymE0/byYlTiLyR5TAJK82m\\nNqpvK4M2S3P9/OKqw6nF6JVGTYXzisvTrxLkoo5TofDCXIYJ6ebuICkyQ+NNm0AITUIX82odjQyZ\\nFhyU0+vpfM/bKpm2hBBnUa2BV1hJbHgEcCGh4nUMWM0bacUxojWXANq7t2gSl5zzN99UuzeXSDOk\\nA6oy5dkldaDeGnZ7x1JKRKgsCp0gDGDO3Lymb8YH41lLcxcNZ2t6UiiZfPR9W4AcG73ewRY3MpNM\\nLTyXEsBKUoYqZAhQ4aVgmiEkWUQ0yQmvGMLUNK/ymCAsBCfTVTcpyjyZNK05c3ZRPcV4WvEaIrNx\\nt7HPymDz1i+Ow3/y5eZ+v5PN9pIQyuKzWvBkFSxf3SB7+KvJIK8gNnWFoK4zp8l1vCqL8/mkGE3d\\ntPHd9mAfCp5JWJaxSH41xknJKu3TaR2HJXOlixDlJJuu4C0MatH6sWGNB/crmWZZellTbjafKPZH\\nXXupEwtTf+PAp9kYBhhHJ5HSco0kSabJ3GpuEYAaQV+JUZ6nueFkiuB8+uD9x1p+Ai+m47Vv9FuM\\nbEkRplnT1U/7e53OnZuy8bGUctCcLFc3Nw4bhw9sZiC4wmnJ68nCMFbG9g9w429uP35YhSrJaBxr\\njImidoWwFPgk8KlO8mwZGTo7nsaGY03fnlRSGo3B6Uq3bXsh7SXceT65CWBYTJYaEA/2HF2ckvKS\\nmglPMKpWtl2tRJpCElecpznjC0ZLJxgww9PMDVlJXuF2jyIuB3iBkDRNSfSKag0hasd3FKBB//FP\\n//XXJemtfnEcgGWs34BZq8qzYJtxLkAJhCxy0o3CchaxfL6eHi3VaKH4ZYYxwqzOSswroZCohKYR\\nRWm4Kot8oTQjWshKtcK0uEoKI9gN5wWAZLfpNBp1oyF1ZNYFyKoaFAwjlOXrqK6TCLIlBxhQjQmZ\\nbx+avFRUZ5xzTccJ2QjXiDGdwIaUPCtH0g10y4QQZmlt0tLvdOOwEnntWRuu7VsMCJXB5cssZgTr\\n47jG0jVNE7cGy2khAJhIGOVYXWTO6O1j8s3tJ6+7B2XacRGvD3J4P2bN1QonZ2gYarGkEkZhHJXJ\\nvMBi+Odf/c4fvW+roQYprWQRXYniOqrvfPI3fq+3Ixuu1duizAqjgnatfpVzyEMroF4M+exGEm7S\\ndlv3lFMLrCxK9GhJJtGWubVv+JIhauiZx0pVCGgCmUvPnhridOPd70hg6MCSAq9Py7zMMs5bHxib\\nrTVZ5CfZPg8elU92epu1tSAyU6UErea/xSg18BbBj1brcwdPd/cKOskXeMs2gJA6ARBwElcE1aHT\\nziSfOZuHWXq8c/emLjOI0Wa3GVPa7uVBWWM/sXQwf9MIjRuDTf7rz17nxz/1B/9w9BbNno5Y4y7a\\nulmXX8uUWbDqHj5xTCXgiU6XCg7W7f8E0I/6G38yXe0NZyrQ6cfdM4+sE7NbiJQrX6qyilKjwp3G\\nN1X/o1A8ZM5B7P/vcN9u3f3P5KP/0+v86buP7+xtDAm2mo3ebitre/Mmc9XsTGLV129TTAgqRfO7\\nNT50HMdr1gwzXR9gQDV/Z140Mg1gsq+Ebuim3V29u7VgvcP5YlNyLZmXTEJDomXLsFC6HoeCY6ns\\nxdlREmYtH1NturEfJPzO7bvV5CibrqIHtyOdcpwpYhEHnr95QTtb60aDkO2FkYpqWUHnXoXa3Y2b\\nZbRi3qarzWpKbh0aFS+4eBFP0KTYMwysmelOv+S2gFARaCMMFLOhSCAA03I7CtcKKUJIElvInZkW\\nVErFUbV7n61y7mkcqiEmIoqyVtvJM+5pxLY0R7cNp4OhHE+lQXfWw/JqXDYGju/d8BrHDIxfzP89\\nxzZq1bw67qyXNfMGHIAoGkJYLlb6LLIl19qtDGAbQsiLnOr9ipscNaWUTruQWBmueb2mIJ93gxhC\\nWGVglk4oaxQVLav8av2j/Y/vx1/w0zdr/S8+d1rm+fWLxGp6MKGu2eiy0/jVPztpr5+PKZ7KVCKk\\nIwI1hw0OkUbieLi6PB4+e0ET9R6yNs9O4rvBqUjOL46G4dmXD29+rlu5FV5Oq62WEaP6lRynnv3h\\nT//4Jzz48ZevXsUQujTHbov2gzu0qQUNr6kXko/KnY8/duiCA+P9z376gksr0KSmV3UWh+us07nR\\nCLY5Of3lFy9fvtE2N/B0Xl9fvdx0jimvGpXYML54OV43mu951uVZ3s/JB01DX8/QSt9k0IjLfD7p\\nhGVVdxrhMt3B4/0Dev62ltrm2ermRmsjDa/saomx52kYynI9nvgb3aJOltMkimcRvzN7e5ImYlKI\\ncM7xF5+enV3+7M/l//v/F11OtqSYEXxdwOQp71UbN2p8ZbbCitwWWeI0srXwL4bjOlzZjvDtnFnS\\nbGhxZAKsp+V0FsezYaJKjXTdDGwrwBpVucWWmILxPIcC28awqNqLz/8pQVDv/qLgYp43E24kqpTx\\nluOv84zT9v7Rl6PV9VWHH79zb4HYSdMXGp9gQpbXZc0TAjJLJxTVnFGsQDyPFQAK6FmWxbkfnryl\\nxQIjUCRrDaxW1m29E9geb3W9Ki9QHUFUE5JZBm93hMQLU8sgQZTp4YJbhg0JAYSmuYLI5dzIktJk\\n2NYsSbHMogp6lSSsyKmcFhXHjFK1bvlxHvgA7gTegUaNNJuX8UpJpCZXq7J4ewEy2RyWYj6s7Ovo\\nxk5y5518+57wb27WjZshOUQ2qsXZX2enLwN/PdCK5IqPMvH85Zkwl+scg1nV3vtBha/u3PL8tvJl\\nIqWRFvfsvX8UzZeD+1uWYcr0Krxer6Cja680GDua0PkycHKtXGtlq4iDITQJhaieePgyK1G+EqvT\\nCXU1TIuqiriTt51SKgGRxmFJ5P3Ji6yE+WodA8qZWZblEpmH/BIugvPg+mwR7qApfv3Fxq9Ek/sL\\nXMplbkfG1tnVb5P2e+nsOKXbOxsubucO5g7Sj6Z4ayc1msGGjlLuAyIrFf38fx4S/dLvHPLy5fbg\\n1Dvcy8vls+kt0LODoF6DQ2WYW7R+Oe58+ot/rqyI7B82vJNmcyTa78wLfzT8C2K0ouvSv/19mNqF\\nYdUrkTJWhAfro/zbeP96WdaIGO79qPPj1ke41WuGJ0fjeRAXOlgNu93ZrQcNQfYGeLSYvTN6/Rz0\\nO7PlP8xfGElYBY3f62w353Fid+9QK7jmJSbi3UfnFnbNwX/SvmUbZMSYO0z9MkeVsMsIM7vnswsM\\nzTp+pkWX6WiV1VSonkJhpH1ANxtm6mJa3d9iZHfzwV3Qq0vtvJbBUoHECA7ralXW3xqOu/3BXYPe\\nqzUzitWkkNX116tys3Dfw4ZVRLKG2La5bcNn13V/G8LI1+0ZA0M7lL5xO1rh3Z0egpbV1OLVLvBj\\nqCagyjqdVzZpCNpgruP3rKyG7Uaz02ruNQwUziGoW86knP7G0aip4VIWcZacvBAEJr6ONaxGq20P\\nVI4e2Z5ZcmXrcVk3QF0CpOUFIdSI0l3LQL4NpawC8jTA07Pz1PXj7r3ve2Zno3htMC09GXbt5V4f\\nrYcGljrga4jmQibzVRbXdF41SilNViIyyTIAIOc1wNTngjJqZ1GexK+JngkOwgom0SpcprjwiSht\\nx5tOk5+tHtdPaqta7R6cuC3L9UD8LDvYBnjnw7++GIDL5Dsnv3LRs8ArgJVSW6sqLquEUzgYgI47\\ngfE4vT49u8CKbuyY+OaTgDXlBj/S2KnhnVcRAJt4MZzfvOke7hFdn7ebpGOfR/MrlI/UNLYb0DA6\\no4wU7Suo7SvsmnajHrFJIkH79lZjfdObvBpRbAJsGi4hv71pbB581OvdaDSd6OJ/vr6yW125XIR7\\nN78/W5ydzljabHY+eDcLm9PTyxn5/vy0ynhUScGBNTwfLwCxHbV3YGPnyeffzIMQ9A+k3rtx8Pjx\\n6/Pi/EgrMTFA126dFbUpat3ZujusqK1Bo+N2um65unjoJJBMPJxeHA1XT59v3wJbt9CPt//s//q7\\n3z5Q/+MOW/e7y55GL/7b5Wf/1QWdcj6e5tlGVUGiYd2uVXhigKrkMUMQs16Re0Xeh0S6pqGKlFZj\\nqcnZ6QzVtYsYNCL3zq7QbytAGcwI4crdHOjzgj2/ilWSZUdzinjGo1KR2nLLyLQn11eIdyx2Rb3L\\nYdZtBg9FoRCwMCSMICVnqI4NC0DHU7UkADpNCyghCklhIsqKoRWsnzY9H2N8XSZ1pKJpE+FukiGg\\nbVZ1oqEEEU1UpaYDo+lnuZA8i1YcAx0SriRU0OSJcPm6F1h1JYt6bkJR5YTyPMnyopJIph070lwg\\nK6QE4Lrrmrt61ep6SLZ2mo2bGtN1tbaaSYkWHPBqeobXsw5c6t3jUgt1tb9wHyzKvUW9Ga4PkWRf\\nv//Y7d7aNBupXpx0y0+7L/+CGIe/+aXO3Z4097uuwffee7vcDzmKQ1lFIkncbQdtBA+vL41ClUly\\ntQr1nrWV43v772yXpr377s4gMOGA77gXqooY8RWCSLPPJmcGjWi9ktTNk5qxeAPCNDGmprjjpBDx\\nINAAXdUA50XZ6lhpvC6RSgtbbx4H6XI4XXFRUW26fRcNX5x3lkZ1GiUvr8zF5PUvvxKcweTcEGbH\\n+/joORo9s0LdVWBZZ5/86WdbRYMNerlWmVUpxez5ziMtj3u97eLwvX/v1Zud2SS6elU9ad4E4G8A\\nr9LQ/q9//uyTvcnVn/1XVs1EVKeiUZb4+s0pqLMqflHBv/36Odg8+OH4OIRkkitLwtUycbE+h6sX\\n6s3Xi6nQu2Z01Z+v3uOZSvKlKa91yOdX6brYvfvJ752rfpSbw6u6034tvhnrw6da3+odTH7+J/91\\n5+HfWUeTim7VypT5YlvHfP8/TenvptV3P3vO/ulPyILsrS7bq9dDSAwBm9TqltreDHqsTGVaod25\\nKPJqTQFeX13nw0V/lrWe/uJf33rvD4JG6ntP4tbf9e46h71ZdErmsQtkXRXjMi8Mpz1Z7ZWgnfMw\\nT0FeFl4jC68TIJBFdFzPuXKpvomwVCm+nA1398Yp7Bb8Et/vFdiarUcU6Ztw6cVxq5ZvLyTVaaVY\\nFKmyWFe1U9T7y8hiau60evFyo9b2bLM2dNc1r3w91DUnLjNZz8t14qJIlBk2EBdFErqeLQp9P1sp\\nXK1UmW7ASf9gV2ADCPbho7xXDw0DMaYDVHb7HeQ4qpLKhIszF1KvdeCvV86tB2WyrFjQJHwhgdjw\\nRZgkefS0jI+zWL5+UyopIaVC1JSGBGFi2IVgEqvRdZyWa1Suw4jNSwPJmILYR+l37q15FaLSZvkb\\n4/jyf/0f7UGHUGd4sthMrks2GD63fn/4MjHe/mnn4NuLfjwLU1lkTNhQFUAkpl4HBNkubh/gdmNt\\ny5hevaXYvXO787q6PRwPug90xwEGxo4Ozs6VGS6xub29/3dLgQz/BiGNAH3LsEmbFxeqvaTtoz/9\\nyWmUQ6txfb08v76+vRHzKovw1oVqwu2b1at/SXgMasM129WDH1U8KAuHi0HfF2D+fz9bzNwbD3kx\\nX5a5299+foy+fu5CJAL3DV4qmJ6quihOZg3XALhqTS4n6OYcdlb+XvLVl4582z3Y33jyd4Dq2sy3\\nJs8TXLnl/KLexCrWUT6e8TzyqvUIwg2J/ft3d+68b63CymDad9/Toviku78dbHYXlXMZ3MOP72xv\\nr7LD7+uY//A/ffTv/hezDz44PfykjTWAsJScK9TyzJmrfUEwRIyGCSamb2BsFSuDwbYXOKaRhym1\\nWwyulard/r2zU4OnLhS0LOTGxjuHRq7KsLUlX367hPWFGU0xf2vaiHnJ9Uqu82b49sRxrxwNIizg\\nCsqsVgU0qYGhTvQ60PSgyQsEy8I0Q6QTgLFFkUZJZRFqOzUACMPacjJFnbzybLLAMIKrFYN6uMoR\\noKKWCCsAAICs27SN5sZuj2IJiF5ACLgCXOSihlLlXkMx27YtPy3yvX1XMAcqCaVATEMgoYxXghSc\\n1lkVOETogdI9O9hbZBZgTl4TgAhXrIwiHSSmNTf0yrD0ZCIRu44xejNVXy/1ziRE5PB3oe2n0/Bq\\nPlS+5dwWbjMuj3/t4O1XX/ccF1gsw5+9nK/Xl1cs0SrptPr3HixrOlqL0/NqEb/RwGJF2vsOd/TN\\nyyPZ23g3yM1MeVX/Zk1yhSyd60wDRfxlibdMZhsaKoaiUha2m9CtelzPsr0jgxaVN64aQg6VyFKj\\nSetzjZZMZJ3q9jyJq9VrHvsLS0eSnP/V/zYIOMDD7kfRg98JO40/7m3eoNh+9DDvDsydm3/9sHPG\\nxAqERVipLfRPNrLeYv2DsIJQJS0b5TkZX0beZgKjD3/2l//dZDnzFz9rvPeot69z6Cv9zvG/+ev7\\n/9F//vnkv99977eGfDFgGjmZOenPtjvtaP5zDdujr/6y8+jH11cjCPHbub4a8Qh2k6tUW361j96g\\ndOTVjWmJ2j7Xzl6TTiZmo95O1m1fi1EyE3/08g1d/utvdvxrbefe4YftKbHubhrl0P/611/e3vm/\\n0TJ4fXIZhxGsRdDpV+F9/6x5+elo8ln826vzh42nrr73v3/vzeOBrVPIoyqKN9Cqni+M5qHeQjK9\\nEL09bFrnaRJpKuGn56b83FxqpulmwHGBD651w/1k2dre2fqqPTnLUppM3yLEnBbxsnUZxz//k0+P\\nXn4r+DHzs6Aar3OD2A2HpMsI8jKX4RhW0TxUa5Ftq1iKDbnaDJdvJ8PVHG4vCri7hVuNK0ZrU4ea\\nVkGtFYiXEANZDVUVNhtFybHbKGclosaSW/4qs8x2MBjYRVUSAEBabwyY41CICj1g0NrsbWkijCld\\nWIZ03PnN792dXFEd8XfuNM68/yPvGV7L2Sbxhuekxn7N9ZyT1dgwmzOr4b9Uezdby1XZbOyl16OF\\nv5lprpa5TwxX42q41Z77ZHgjWC8nSnKKykwJW3GFaqoTjCQnZA3UApGSQmRWfKufBY0WcczV7j/S\\nbdrqt3S/meBvHlp/rOGidPeqi29NZ1bXNZkmh+RpCl7Vib4VQ5hfbm81TVqzKjFRGo7Fre2SYmJZ\\njc0HbaeXGRvx7JKPtQeT6x0cnQ/HRpxCkWykdk8vjgP9C2L2MmmxCi0Sl7Qe7X/nP8/TzN3eFEcn\\n11+/hvbcTI+IOC9zPp2cXZbF2rl/djK/HPO0cJubrVEWBttmrDBGuxpzB7292n6/NXjS7y+uz//6\\n6EQ8fTuma2958at++K/62b9Ea+64Acfr9qDoip/rLHcMqPd+QLW1rNlvfhH+8pdVW73qHY5bW31d\\nLKbTZbpm3dtVkq00vajGGQNxRlJRya53Nam+diwTqYZpwM9++qLQ/71sXXB80H38EVn8+ttfDaV/\\n6+oc5m8te/OwWuyarFXTzVHxozx4768WHzO8pyBfzKa8mCPKKEXUMNdxpmV6k2dbu87u/l7Dtv1e\\nuVhHzY3O+nIuc+i4DWifU2XYeh4CuCxyOnp788kNY3CjkgoYvWr1enczL7pgNp2nOSMF2+mP67xQ\\n69cbhsiz3uEt4PgpRJhRjAESiLMatTY7AvnGMm93o5YHbabrkGlaFyumgYaGGYS4KDKMfIU9jAkS\\nFdRzaqwZ9oAqsSEERxTrLsPXZ5zgwVXo6J1HWSbrWmJES4UBEKbOrs7qdnN3urJ0s+PrcLN7p6JW\\nEhZZxTAkrhcxC9a8zMPY4ClptiaLJuZca2xlSUNCfTErEdRwJTBLbV8hz7HaXcNsY4qAht4sKXg6\\n3EVvEY3rF9+8wmXt2D24TFJkHue53k5RWUG77Lf6yp7I4mLy8kquc050yjZYfNqqvk0nT8tsRHUC\\nq21t60arjTvTaOZ/19vt7N7SzSvn5PlVXmlMN7isZXaZrTevJ2RjoO82S9+uMTVEZWXW3Xt7xYf5\\nVfZmuZ6/qKKxEUi3DCPKiJkxS1SkR7WrXibvbdYPnChfhbj5/Lc+SVp0yOLbf/H190/zjYvF74Xj\\nxevyA7P/IbQeSnp3Sf3mJxsYnA62XHzz5vZ2dUDnVoZ9Yk8jObkON240wpBezP6/pDqX+nz3vb9r\\nNh4DgDAov/xnf3b7w7/3V//0v96+886d9b/YvdfefVg6H/ho94eX/GcFbq3mw8bhH8hp3FeTB+iX\\ne/S83zsDghs3N4sb31kb2uGtPSNcbfQ8rSNMUtSFF5cwzVt5YQ8OKVmdNpyXvYd3td3gadROyfiq\\n++BpdrBKfr1YXex/0sLsz4Wos0uw3d/iuL39wN7b/rq3Kz78e2bj+wJrxprdBXf/EIiVDE/qeFys\\nr2D16Xbw4ZtLuyQJ1Xpc1nm9xqjyzemG/6cAaXu/9UkiwDjXjcDaPFiUQqxnEdTow7/7jo/DOB46\\n/h6v1BQlf/LHXz1/ddq9GTK8mc2w0ieghGUlrLYoVxIBvnlz23e4r6EoMvp33ngOwNlPEH5LkKqz\\nmPp2jnWLXDQsZUFuEJBPa/bk3TpfIjFOE2ORaeF8Dd1mvay1tkPUrbxkw7ldyCOQ5baudT2yTHCW\\nSQ0VaZGfHw0zdmMV6xJA07fn2j/46U8efdg/fucma/iPw2dmvqrr+BE4+CHR7GQI2+acCy3h1be/\\nKUWq3v5k5j66L2vn6miQzQpbLgxQOQlHAlPXz+Q1NmLqKaM8rqrEbdUQ1Vghiw2h7gAABK65rKSs\\n++38YJ+d5k/CyhJIH33RYBJfrhrTcarJOdBSaTa04aXhPocOrMvKWP/q3hOtZw+doBh5DiMLnrGm\\nTZSCon3D7Lc/43+kVMFcLyshwJJCAMfD5JqF41m3HTElRKFKTeOTTaqB7q0bF6URcoo3969HkxJs\\n/PVvINMG8xdo0HtrmL+yHNTyI90EWMmyXJ0evY7emun4uXP9s/MXISoqF63neTOs8cW1xMx1OjtI\\nBOtVvlR/MNAEuv5/2nT9gyfLzZ2cdkX/IC4BYK12MqcLuXFsfa+jJ57fOZodIp5uVbLZfqUPP9Po\\nUgbuvNqqZ/MEbu2bUVhrxSi1NnCt9EpVbrcFS25Zws2rZDFSEipJ2rf/cXOPwXJeStK9c2scD8XW\\n91gTdoyTx93zUi/nF5oyVFjqiwm95H76VAqwAqCBMQawjNKy3YYgj1Vi7z+gkHINN8+jLXvrjuje\\nBOoh57zZbG5uzCaX+VV6jyrVsBLLoMV8wbVc8zTS/8NZ1mnalKgoBrmFn1g6rfiK2mgZ1cTybTMr\\ncXL44O7zL57l5p1a7kEumw3lOI5p5ENuVqtex1LNbZ/0blgDRzMUEYQIx3fNZrupmBmlSQ3LrGbl\\nksM87W8R4rR8A9oWNkytpn6VRJ2W9c7379fZizzTs/CMGpsIY6hqznkSLmQ8a/W6y5cjHGzn5VJB\\njrvmje3vIWwJhKuKMigM3cLELssqTRcKZ1Wt8nlksbLINzRMIDNAVuw4gCC1WldU0+tKKcebit1w\\nqhTQHRGO4UsEV+fB1nbFNWfXZbo4/vS6JnsQZNUs2t/z8PSyyqc5WSOaoGCMdXvLIy03lemiihYA\\nBlUtr9b7XrNl9cdp2lyZB5y2ChZfi1/1QZ1UkbnIKaoASpWsH/74D2HzjuU5VBeirlXpTNEheeje\\n/Z3PbtBQWX1FW2Xl55Vew7Zw72O2sw5put+3d38XvPPOMpwbgQ+1dwrUDPbTcnZ+ezhavVwN189o\\nuT8Fj4ZjHbRdLP03Vx9dP69V/wfZ0JhdBf6tx+0n6MaBGRCQzvPt/RsIx47+CqEjsd7s+v/h619t\\nRAWYReDkF/+t++7f//yL//LgvTtuX1kfvSvY33z+9eB1svnys3+ZvW5uZZZOtl0+371tOL/1oGjt\\ntDd20nVqOiaYY7OY0bqlbD5cFe8fbnaFAr4FZK5JI0z6ADQXcfloM/5G/5uG6s4KcPUl/5P/z+nv\\niuV77W8G4Pj27/0XCuqXv/if7KA9zm5IUUN4s3AeX5Q7i2F/wzX79tWj9x4r7beB1qgro6ctAFwz\\nGAthJ/mFXdPLuc1LwIgCCPd6LcT0xlaDWLi1be3T0dXqxmgZnOfvXrz8lqnbUfTxdBbMFufMaFjY\\nlUD8/L/5WYeGWrJMoLGK53Vd77fqLs5W67UXQJjGhexh90m2cpnVqbL826drSGaGWWnMNp24qR+Z\\nJlsRbwHzYNB5/yNrp1nd2swuf40MfaJU2e7hlpW5Hnv59dvBNjk94mm6AGlpU3GRyEY3K1NoPbrh\\naBWEeJlzXeMmPp+P4qZ2CZS7FHd66/CT78a3fvAP2nf/yKRYK3/e8auWEcdXbWJQ1kA12a6LPE50\\nwpdhvmiS428+vy0Vf+c73W6bu7fe6bY1gS49J4WcLtNunqZ1lrcOFSFoEbaVpE2XgVryJLUYTqYp\\nqJdAZIWxM6M309PV+HoIZRypz5F9+O1vvgawrkGgW7godOfGhmL2KnVonlsgO0NP5vmNdOnbcfZk\\nK+7vb+zfe3yr3zHRISCw+uwphuuKx/MsMQi33LjZn0n5bbqKa/guIsAws9U69rpTitHR1J09nc3f\\nfn19IfLwOF7MzdXnDc8v+M9Q70f93scGXVXZqkg5ROzHP/h4tfiGihcGe+rvYhX+BcWV1vDXJyfr\\ncRKHJ7Pp8pt8MF1yxiI7y+7tpY8PTpX46gpuz+Th8dsozW+tQ20+ei65qMtZ9Ppoxe3Mer9RjBp2\\njh4F59kBzl9vbmUE64C3rpZ5gs3G4NTvtDTEL7Mqn0pmNjXouPalFxSq6hxsjMsq5Uy1Xb0YX1Ic\\ngGhVQ/Ds6VYzTV8+O37+s7h9K38x8mQaEY3NV0VZpbOXhV8fYzaspCYVJ4rAOquT5UYndZw6Lrhu\\n7jQDbHR7n/5GXia3ygQXWckT0N18d+PJdvXsm/NvFtLrM9xLWPNKef9/lvqzy7YtMczz5pxrzpXX\\n2mvnUFW7cp187jk39+2M7gYaFEjAhEiIpEzJkUP2kKyhP+Ahy+Gb7TE89EG2qSHZgkkZJEGCIIFG\\no7tvd9/QN517cp06lat2DmvtldMM/mC9P+J5T2JpkpqrizdEPIeNncsrPj1/WZC2qjcAQGUCVCK6\\nBotCmLur/jvfEO7jMgG6oYZSR0V2ogARyQ3gZwr2yNp1tD47IZw39/ZRa0sRQNUNB+obAAAaT+Q8\\nhxBSI08kI2Z7CFK75jBS4QKUpQpYMrsOndYfyrKMeJSlCyHhLCsUQQmRCua16quXlxpenKfp7nxJ\\no1jkMV/bfg8VtGRawSxMA5WrFcUWtNTViSaTMlY0maWmBITsdFuOIoUsrPVvtzb689lq5S01x1gs\\ntdUE0DDiHAgMURDlF7EZg/7hJ1+tvGOMWMtYCah0bjXM5DxThxqaL0MPSgkxt+21+721VJdDxZ9h\\nuY01WYrlRbGphdlvfsXHgV1h5OpV/PknL3d3ku+sp2t37jRvrqt2UqtLavJbi5kUkzplOIdRPEVS\\n2lzM1j+ZfPAvX//45SoHo7KIEc+ZIJY+F4Pz63g2icMOStf8ef3x4elaR0lGLSFhwHYwRPrNxc63\\nj1gykSgJFpVG49I9VCsGmH/2Ty3pcyvJ42UIWAykbw2U94H6O/NwvSjUSq0r4aWEPZYPqhuVG7fZ\\nuvpLOnajBMy8L5SNveD6v97cuRXNqmn1HwH1njh6NBsg59kfb+rxjd/7rfM4aDSNzsM1lW6uxjwW\\nb83nh+0okyjVQSIRtCrlPClere43txqgU9YTHEs3fXc5z/GFvxpE20GkhT/HH786P/z8+Nvwv+3v\\nbqPbHbC577kmLGgM8xFs+7Pw3Q82GxA10djyp7u9y93v3YXb711f69zj7S0bQDgRWqNV6DotWU60\\nKsoePeBH9WCx2+0oZYLl75RylxW5KufBkji9wg8/0vOps5hPnv5bZ91eX7dKShbBKA7GluZsrsfh\\n5fEH3zj8D77/9Ld+F0p+TQEZl8hx57dMB2tyVzVbFqJhbHiFYikElizK34iXdQn6UHWm7oojQ0b2\\nfO4tLr0g0K+OKy+edkfz/YxphB7X6xs8iwB2kdRFLO/0SiVHRRl7/lX/YGHX3cUgj3JJK2FxrFBY\\nCE1NWRnlSyFFKlWrXSPlaTmdOWuXX1/d9Yr1hX+L1vad+plefYt0Oox5drUjAkzLmqIwUnrdXblI\\nAr06QeEMaJGfdUxZffzsJtj8O75e0WwJZNNK6WkSlCH92r+V5xJCkSRJSY6cqtUwBo4kKYQ/6Ne6\\nZsECdfoVaOPlu83GRtvCcTGYAAtfQ9UpV1mWFRIN8rFCSbsqXfTW8re/tXHyUbpuyQoMpbq2cf8t\\nrXkvhm+V9TbP5MANM/CKSXnKHVmkqrJ0GvXKplRbB2TxfDUUs+At2X4HCfdyrEAVlH4kwmfuYMRo\\nZpiSyq8FnSIKbE26fv2xKHG10pPVOlV2GUSLxFYVXIJDAZNCqu33ne4+C6NOvb7UxMlw9NFZ5L36\\ncADG0f4aE7oHa98yDv5PDTn/6JOP57Nvm7qRz66F3A19RgGEuKKAlzxbLg+vNzofqG350y+lly+o\\nVgzraxZWWnz2/PhqCa/jJGKdVlcI0yTIpBcAOBqABJuCrkMI5M59hYDPH49WOYhW8iI3NEcU7jhK\\nqnH8hKw9rDCKyunzsz7Ox5ykWSLSVWrT1xXjdVXKVNVECK1yIBELyzWt0qr11lgpX2fi86talPba\\nMhj85Re4cE2nDqTVMndDvWtY/RsPZs9+/vH2xmaGNrTo889/9vXFl4GqlVfXsJyVSZLJeB6cPIKM\\nAlqqlqQTr2eRO997OPTCElqaua1W3TQGwxNtNZZXsQKyUrZpCYF5nd0KZ+/d4VxQQFnU3Cvw5ozh\\nRKyXoE1kIKEVNNVSgOkyz3IPEOhLJC3rGGoEKwsvUVWVsBEsm1wxERQQUIvIMhIQCMFhHi47D24G\\nLxaNbrBgFX90IpPSMMkSrJdMR8iQOGq3Y6tVkZSqN/QYK5RCksIY1aXlQodRAgBArS1UuNO03qw/\\nUHF6/fKTgoKSQuJecDqKIoq8qfb1T14/cz9uN2lTcOnGbS7IVdGX1EI1WYHGSy/asYrfbVVWxfcF\\n2fDT5n6P3r8H79bmGjwtI8ycrKs/1qhwlLQ3+RM+/skH7//NzTf/oLJ3dz2tlQyNZQPzrfXtaXip\\nPXuN//xXcMGqhjnXFa82fp7+5F9Ej5/2bmjrt6ipT60y0MEY00sKfcogAtIkWAXlr7T1d1ADN7DC\\nHd3csEiz3qyvT4ZXu/0JyT4aDQKCVhPSi6e/zNbvCiJlebhjIUAOupEcTeyry9nFjLpRhK2AgjRI\\nCDTfmA/emlTepH0LKX760cejJ+j54xeY7XdNxZK/u3ydTT75s73+hzfe+WjjVlvs/a/h9IVkfKu1\\nc9dzd1hVMoDDwtcbb/1IedfcaYG9nagZ+h0Lnx5+9dL7vWO+A3Bbif/ks8OndNlZHB7tKFEz1Wp7\\nvFf8l87kr+mNLU6U1gd/YNUfoNnR+K3v+y+Tlz/93wOwFZyT7R3fXnMqmw9B902g/R6AGyC8WMkH\\nUmbbJRDs+dT5UZEqBUMyVh3nYu3+j+B394Asde81TCJMIg2vznRNLtDampazfHMWhPb+HaX9y3u3\\nkprYPk1JDEXhDxgvZbVboHG1NbR++H8hv/WtgOVlGgKhuEsnfGW9Gg04kdzcbtQnbspkpIl2G4ET\\nnRhCMKzZTfmowpM87WsEodXSsNK6Y2hoVnJWcD2MZ+vdzG40iIxQaQrZJuoySqNEsxnNuKqM3R4G\\nRhqMFYAMx975ZjeTmhqWGwqTJVQxVBNnpqq3VbK3aa417lmL85iWiVsuQwtrUukugVQL3ZMg2NLw\\niULEdg/0u6/WuijPEj9W6/Ux8VZufPvWN/dx9tlP5v0kWz+evW3VTMrC6lqt8Ev4+NhSgSrMkoqk\\nZKNlRlRlf6vRMGbNO+/ffPv3y+TU3hy99UOrvVVpqNQ2B7IXtbY1xV9srtmOaqgqq2wgIbRaBTTa\\n/SD7VpNMC6xUujtPf81j/u3c7RweBfOyPplDlbjfefe2X8oLt+hpUbV1sFymE8lISm5vcR0Ns9OF\\nN6kL+aZKBwx3F+FSb6aUBtUazlJZbrQpMBhPLAIB9RnImfXGgm4uzjAmoPRq33z3b3vziyhQzmY1\\nWQ8Fa+IoNjo7SCfrzaYxevRW4yNT+9yF76ezmqAbqjDfee8/teEgH/xpsBp0+jJo7fLAl0kIBY9i\\nJWeRIb0g5FK17kbhuTp51lor5HqnUHrDnCgkq+p/NVzmkYBlaMm6KpMpoKxa1WRcJyVeaY5HGUjN\\nbr35/Od/KnXfSqU4LJJ4oRSmEUev9dPX1dZLKnmGUr9lvfIl6KYUR2NZCnTmYbUohUqpLEoooF3k\\nDEd6mbtJXPgj2pxOuvwo1IRaz4lyFE4WUVI7GmvTX66YMGXDeffu7YsvP+xsfPvVI6DL7MD4bwzp\\nRdXSVXMJMCE46O6DRfBKlfOSFxBKo4Vq2LcS9K304igBephUSwwtOYkKmixNK10YBVozjEpNcx6a\\neKtJuttZXubIvKbW4FjND2cGUhgDEZUFhwCYZZRK+QVjBeES4qhMWKWMLBOFyakpgUbdkKEMoaRo\\nQFORassSUdMCzibx1sZq3r8jl6O5r6ZeOJ+9dEN/RWqjCY8zxljpZ14mdZnabDRvMH/VXFumLAyS\\nKWSWDBaRieHcXS4gYWV0Vezt7dlrdxYXP8nDp10cGI4ngRx9QD+rHPnxZ40IdT22w6ajq6cxhTW9\\nKenQTQLx4Ju/853ffXiWr2NWTnzZ3rqpdt7Ie3da/bRT9zk2NB0nZ4vqgX7/+y8xILe//T/h2hsR\\n3KfYqvdeqeMMlZXQMmel749/WT/96d6asmFJakUI5brdfIR17mw0t9U1OZAw7Df22i0HsRIUUkPC\\nVJJe1dmwuWxefwFfsO0F3R3pexHfLqy3vPHY6t4p3JlE9CRD8ghy8/rxE1EEXpCvGs3bvlyPQHMk\\neYvDn149ORzFQaUuSxIk2CyzJiqZFFiTrytf/flZfeOLH733tO38BJfc6t5ayrt14/nNvb/qvPW+\\n0r2P1R9fHt2b0mg0XnRv7tEwcI+ys19/Nly+3Hnw7wPwLST/voS/A1sPHd3cvHcmD1M0z7/6eXnK\\ntOfudTb3/8b3Ptu/sS47MHcpu3xRf9f+5ptr4IW/SurXLj1b+I8fy8tni9a9w1JyBCEzcBNvfiv0\\n73JoAmACgATSloOPbzy8HyySrQaDy7xZ1WesOS65MITq3B6P96feprH/B37Zx/IGS8/r+npVuTsv\\nbpROq7/2cqsiTX1jwR7O5O+CjXINHRridDz2EWkYdi2KKJJqq8P0yy/lZx/rZnUDY2Tqixb4GCKa\\npR4RAjYsRnNOQxyvskgrMh9CA9s3NAsQGSiworfvtokb+ZGuUN1kipyWmSuZ9yDgT7xbJUMUoCwt\\nSxYQxKLElDHUZDRfJQVdrfc0SeHdTXLm3h2PYyGxN99wbBXN54xW9QLWBd9I0jrvPHR612eBVaBw\\nNvhSsb6bhpzG861thNBzpi1ny6ezuZemYOmWi+vYffT62afIak2V8NX58B7Efu+rP62sLtbFKYFp\\nu22VKd94KBr1j6Eka1Ywn8+FgIKG05EXpkDXuv/65/pHXxeIJN5omSze6m5t3brztq6v3bkryQje\\neGfd7t4qicyU6smynoSFD25M0p1Yr6cmwTB/6TZK9kLStUpT3Nzk7kT0OhMo+KtpkK+kYrzsPdi7\\nAEYpRt6izctZWGZmC9nVWToO3EtNonIR+xUcumEvRuVxYJaKnRK1yBM/JQHvV5R+4LreDJcJ0jRX\\nkkTPmvc3txlFjc1tefZMloJcqkFQ5oUF8lqTU7s2nhhVKEdpCAXjo1guMczk6v3v/m8y9ik2u4qx\\ng9JwPJKxaW3ABPlLtapGRZIqDMqt8QU2+WG7u/DcdrlEdStjoh8VoyiFaRQpJkwLBmDTwlpGub3+\\nTtfQQ6M7Gl8iYEJVv/HmPr3+E1BmTz7/a3fqybWqIibV6ovN7+6fXJSOLGg1i8Ueur522LyQdLul\\nc6NZsRs5dViZSJjmDFZaDaf/Xo6Rlhe6PipxmGbUoLldMx0N2ha0g7HVnZbFEJW53jGdd+4h7yOe\\nt6vSCFFVb0ZGZSFAUW9JgTehWWFq9+vtWEgaDLzem3IaHqfWXuzxqpghwv2wiEEB09AmMbHKg3ed\\n1k5LkWqzV+D8cL2Qdi+jvdPrfDphSrR09POufm1WjIzlYbiEmkowt60y43EYT7MoF0JgGa5vGUrn\\nZp4sW307FV1LcywJSzI0zRpQ6kJoKeUwnm7a+vHnydrm5oZqZ9ybzha0SEQGAEsIIUWRr8bPyyRH\\nCLW3+9fLNHHWTL/mmDlRDcZ1LppKU8ZplBvh0r9C5oFWf5+zaQZOkBYxgVDQTv/mf6p88/04d5NC\\nO6PuMTKNm7v1hqpTXqnc+8+ZvfflL49Po8yYI81rjMbqbNg7iXepjmZTo1SiWxurrfvfxvX1yaDd\\nbP94jqvTOfJzeYVuJUgqch948fDL4+Ds8M49r3KjfX+3Is9lxpSYcgjozNsI5W9JG3LIVAdvyLHG\\nhcdzxgI9zBYAC7CO3vzgqwfZM//L+TBU8dQ8u8inrw8rdlMwSgoHSeLNNz67UR7Pn/4rSQ7d02cK\\n3Cg0I1gaYjG47fxUSj6/HlxYVRXAHEKMMca0lUqbgA12O5+/8f5bxc4flGvo9MxX77/DE7KI69nm\\nOwL/bUHulOY9fvpVu3W5PFueRT9KeBAPnmzf+2w5oXsP/iGQEAQEiAPR2KDwNrDv+FzOiHzvx4fv\\nKyef/bP/bpYEb/3hf3Gtb5cli2Zpvc/3b3Q049uJ8dosj1ceuvr8+vXTpyv3cqsiisO/mCybgipK\\n7bs41SBQEYDg/x/z59FvK0BainupKrFCckx5cTpPPmXDp9rVuPLZ0/zk+BNuN7xrj8ZsG7tOgjqq\\nyl+fuW4VV7alLtbnn7nXlnHGyFnZEoWuqoUnyahn6TKCdYj0Yvjx8NnQVhSJ5UII2RGd/byj2bDM\\na7oplaKMwyKeKZgDgHg0lOXq5TGKedcyeb/hPJ48OHMJzbO1VgEgkVBRVSfeKqg4ZmNykaecs4eY\\nBqam9kwBikf9elyXjzRUpMXM3ujEuexnaPYXf72rxqtI+3r8faSRlqPCqJFEXaddS2h0ctFxXX/2\\ncjhezmU2CGKbEz8tX43Ho4vL4WLKizzr7O6XJQ5T6mwaH/wtV6s+G08oLn528tmvg+V1r/dUrcSW\\ns9BlK0mcJbbc0kIdVYjSDyqycDXDVnWZ8SIu6WjFlOGxQerVVlXThtl2PwP3VuLdasux+nugFCfp\\n3S+OTJmuTV9laP7yVj/CHu7Hnvd45V+QYCbQ8IWGivMTMWLV67RaTC5c/4LX1JJW4iRX9aOr6Le9\\nq4QJswLODY0BkDkkVGwKxAWdXr16NAriLbWiFNd/BfNF8fxTMSXsakL0ZVkEtPS5HDPEk/D1eodI\\n5IpgSW22Z1Lv1jt/8+NfP241Px5mYoVNRSpkzDSxqt2rXc+s6HRpS3AweCVJ1vgyHg+D61AenXed\\n5oMiev30+U/NDYK4B4oolceodzM3mpOiKflKkeX5gjScoaVXmEq5BABmMj8Nl35ciNy9hqZSwBLL\\nKca45mirIh/FTFbvXD//QtXmZZKnYYAMB5FC121V+tIWr4JF1tm6vZyarvv2Kj7xgo1gEjcbbqU1\\nk4DirRqM9ho6xKTGGFuOsqRQcibmcYcV0Nko8b484abMuK5EwtQFLyilWCZpnFV5plQgF3IRb8d4\\nGzfJ4Gq1tWcUBcJY1rRKmWSW0RFE6fUWqwmk0j4zGguhXr4gu9KsLK1hGatVPaKcgIUlJxWnjBka\\nj1AEDsztvf3392udmGUDKqur5zNzdGIpQ6eTyxbq7e0YtY4fB/nqUkI0WDHHsi2iSlIJACsEFapW\\nxDdS1r4YzGiynieakJucrLlhUxRtyisRNTz3ZLPr492DxeO/8H304N39VFe1eGHp0LEzIVUQMfN8\\nYYtzhEpMhLp2L7tIWFkwWJimXFJHIGjAPIjlJE29c0v3n1NDXqKHZSFyzxV5jtYbP7wcGicDN8uS\\nRCwEqMnVb0ez+LPXEtf+RhVd32Afzmfteseurc02rEkUug3OrWX268/DRWh58HcL0j98Obir4rfu\\nbd5Uw+zwTM6yOpcMKTPNoKgcScl1a189eKcPwpui+7DW8g9apWNUZAIVFVWazUqHPPr5cvsb36hu\\nVm+sHW2yYqPp1/DEUeui2Dov7z7LNu03JrX81ODX2uLDYjG29m/pgK2ZJcaR1r1/8/ebneo/uW8x\\nWw20mw8UobFxsElcad12lWmyeFbvNIqicGl3FjWilUOFXNLs7n6vr77hRCGeFp//7JFBbnVopgga\\nL7ejsXTtgbSAqyef9W61KMmLVSglIvevR/n21fSosP7ANKT/QWdoQ8XCSXMRF2RWXkz1DVR20D+u\\n0tMWee8Xf/30aHAg8mrVssDaG6zeYtfsagq1bgJ5bCQfEvGXtWhBjUU8WYBVls6Vb9xUSiK7pikA\\nAAAIwWD+11sP71MRT4pbvz7mj4emEf4qE9e//T8jv/dB1pw+uTN+WYb76eT1xB0cfL9R3XG0t99X\\nuxdk07bpt66OveOzoF2btzYicUvUb62Dzb145nLOVQhtndKYEgVl2UuWIb2xS8pSymiYb7uugcG0\\nAeyGGW40MxilI0rMLgEA5DRdzhdKuUyGNSRXieW2o1/FpY8UbVbsqChgQLEbDZLFmXwnSDPIFZYw\\nhWcVZz20d0y9VHqbEFwjXnIgIg+V3L46TQ/eOgzX3/WCZREcjd12q1elslSqVWzJKpk0i79UW6aa\\nPrPzl46FI/cl59Cbu2ZdByCBENaqzbNBzzRNo0B1W3famyJnRh0rWw1kPCWy6/K6GwqjqoZFjLXc\\nDMp0DAdPfLtmFSu3t+3kJfRDFBREKCQDwlp72oSfRyshk/LTn0yRYk2j+pW3fTw9yKn0Zj7ZIV+7\\nFdXeyvb2yoTW7Pq12Ns01/P15vnavhnyxc3603Q+HX/hr54Pt3dpmFzG2v3R6Mtq29JMScw/bMqZ\\nbVmKNEGSUVW8vfWh1aR2v6hvPanh6OinL86Ce3LTkVXJbpZ2fSgrAUQelXyNSATnAABA2CUwKnre\\nMsS4xLOV6i7WaH4Ry/rZ1fL4U88HKc3tyXK6XL3T37hvkGGrQmzrDi9oNL6enp+NF9ns4tX23rsQ\\nxHEc5vNGTksgzQYeCqM7U99YTpbS+NfIbAhONS0L5TdB9oEmVvOToVKvC1JV8fjy2edhGK0SG8I8\\nYiiTqvOBE6dwNcoAbTnWtW3blAbxQlVUnKbEAsvbB69RGBWn59XqHSFVknCBwNBhs4uTT4Cw2xWk\\nqwBKqKKGKWsSWdIknyB4NXzeKPN2c4vx44tLNbnKBfcBkUQBhW7nMiFVqUyTehvM89oqN+I4lZAs\\nYaVZUY+eHwnWyzPKiJ4WsNIw/FUoAAVKMw8uFrGRnhRxQELpotlrIW8xPDoFvKEW4uHtqNtBBOum\\nPODJNFyAq7k5jHay3Miuizoo2s5Sq0qCGEiqxOOJB+/oqCaVCS8yrpuxtwK5IhlyRMUwU568LmfL\\nSMUS5tzk10Ug4mEAMiiVnItUYMIYDvyqNz+zjIaltdHafDq+ULs3otISgFKuY2eLwYcAWNfeaMHi\\naQkuAzyNdFR4GYlCN2w0iJdQd4GqNgOCLeLpPFkADstQSyO9SDJSSVBn54hNnyrtOMwCKXZg3IxW\\nHwWLlBi7ckXV+nAxuEyqbQU3DcdEEhNWZ9p66V3+M5UVDtjoymfs/EX7+78Pt7+J6k6uuVZTD5Fc\\nGqYvwPmR1dpsKGVjFe8KQy9BS6eNrH0PdNtJCjSMDEPjTJodf2Rv/oNw4czsm+r2D/rfOIDQ6m+o\\nopQVRuHhcjZb//Qo798JKX7WNgQuO5dxge/ekjY6c+/H8fp/AAM9qPcevLFCV9WkNNJq/eZv3Y9D\\nsbe6HlyNWPeAQRqXVTpfSu5c1UqCV5vbvxXfvmNuE2lTjuf/H0XKdjcqKtQEBmwWxas5Gy2z40fG\\nWz/MILk8PPSi4X7jfDO4MuLDmvmjBawtEjSJ/ocFAKQB8grd0qOzfwZp9PaPm19Pz26/8U6eXmwW\\nPj54OAzFrTtbLFBLxFb+k1Ba47gUlS+U8tUCWq3ddhGkUfNgc7cX5krrZqy+WswncBLRechDjtwL\\npmAu8ZfY+w0/+tBPlMXkI6d/+wr9XWtfItug+d1rYnuoeH700b3/x9O/809OfvvTV3cPRwUINHnr\\nuiFdpi7n1q0bUL4824xwv1wuwuRUVhWnIum6HrmrNEtollfMso0Hb6h5lLu6JNUc+Nbb5u1uQ9p7\\nr6I0zNAbTySiVzgVnOa9dqKphrFt1uSWolEknzX7hazYIuUalBECklzZ262IItKZxxUKoNfsba1W\\nyF6lXNLOjt+EELUqkUKFlA9lFhs229+/5X0x7xghXJ1Ji2jpd0uPzb6aFslpEIVjXf7isKJUR/Xe\\nbOpG2TzKEyWLMSox4kwjRTpjNWlAnI1739itG61whSuSYEvt8gQRUmrEzhdooyfKsl1kuaZOLetK\\nQcPbd6mG1UYHR6EzHawAF2UMJ/FukcSSak/jq2qtNAwJJ//2w7H89adDcjq7aS8Ap8XButxtzk9P\\nzVx69vEqzOeoehAVTjE7+saPbt2u2SKc6hv727vjJbvc2A/G2V0tT/IRLlLPK5tZQaMyEVKIgoFg\\nAYsT26Sks7HCP5C1wurf3LgBdu1Px3999PzQ5JVdWUBYpMuA6OpWEi64KHhjHcvQNE1xcRixNclZ\\nO3maPv9sPJtO292Dy1czGc6r5I+L5Xw+PJaMziCUfGsTy27z1pZqIwEn2Gp4x8+XRyd15Tw9CpYj\\nHkZjkf9M6BgKruuWRo8UOq0VdO+D7PRkIENXUgVMUasSKf29G3f6PNtLqd6pQkPJExhkoafIPEyz\\nsFhlgc8QrYpDBWmamcQC5dQyrQFL55U02W+MGz24oXT2vtuvai4HspK/hHxqq6vvfe8tKM6Nbk9q\\n9RUIbYvs3rgtGMV6UV1vllGei+Nqu13CtYKius6qm7IlwQooFaeVyJVVhtRu1dUSd15buSGIViAK\\nDF1lkm42dKIsJclSJQ0IJQpWHLSH174b15K8IOViFs0W1pwOrlk+ste2eKWQg0uBljNrQ+29jzbe\\nNUysdwxRYyWS4mEm+bFF+MM1t7NHsZAhMn3dXhmmzWMTtiG2sGomDCYBlPmUFjEtalDgcu4a9MXQ\\nc5eBlMLcaJZ2G0HgVutZXgYA+moFcVomLIV8XMp6lkiDuR6Mv06LOkLADfPFLC64Eol+FgHv4uTy\\n2Qy8GvVY2NIXmJRhWGwZl/Zao6SQef52Xwgkja4X4fy1xeaarFCWC1Sgr19ezHLmTYTVtTYaIS6+\\nTiaeO9cvk/U5luD1z15cr2tWG2O5Jmfz0wV+9fXRR7H95oF1s28ZCwOEl9M/cEWXSnpCsV8IMZUY\\nlEYXgXs99pyb8wsJopx7wWAC2TregNWZqH/4dHSniRQRynLUgk9M/QF3vL5jm9IO0N+Hgt60bxFD\\nMOZj+ZCo5yR74dR/eDbLaNm+tRZtlulo8M3HfufZ4dUw6sN0kefrRe8/Ae0t3VnUfe45P4rHw1s/\\nqt/88SAeg8KbF0HMlleqTJstaOq8Ib0xW6199mQ3MreQ9zUmrN79LmzuqCo2ULpXP02HA1I7lzbe\\nOjyHH//VzzTTg/cfko6db+rNG3urq0E9Gp185X/xnA0zIQQQQPYnsvdXv1i724id/+TyYthc/w9R\\ntvIl98a9rvjJv/TzO1mFqaB88mJWGq4AeDCKLJWut7g8+t0Pj2VZQZvU3jHjhB8Igs0bj9ozz8ng\\nx0/Q6DQ6id4HAED/r+Po8yZ5LUePOUKdnW/UZBWrNZGB6/EIkaSoNX78dy7/3aN/1frzz4qPPhrP\\njDQLP1vs/ezVIVAORt5NtBu+V47O4url5JWQsKJo/Ru7YXDBtZwKblY1py0ab2zYPzYkJVcx7Nz4\\nbVD995w769zfFfb2rd1xmfHAy7CYESk26+3liL768G3X3AdUSoOpoBVNFpap7t45aMMKxxVAKhqd\\n6ttblKkJjUr4jsmXt+5d9Eiwrn1WpEyt/QCwnChjxQSSDArDaTfOKxqqNdE7ewkuEIRTJJ+raFaF\\nfvnZzBk9VVH0+Muxm0iKtNCYt7WllzTHLLaUqt6gCMCicAajb7LmnfnSaOwwy0oIO2RARbpm1oqY\\nt4fL1A88z9Nqqnr/hiQbVUYzS5MsPaxXhKGalYqkpyET0KlatGCD4UJXuK2fZF990mk+RpvJMrFr\\n6tb0SM3BWlULpJryzo/vgsAYL6LhiyKVN/7s6d/zdn6Xo6CzfXAYP9wRR6FonnxOe99+Nw3+cq0t\\n2Gq4vdHnPmLJ/INv3wRIklQg2e2rbD9aybLCipyzqrb1R+80O58lJ8fHz1FIC1FpSekMY1M1rRWr\\nDo6q8UpbTM08INFMylYsnD8t3UfJpatKlcBfQmzIcmLqh11zrqOwPHs0++SoSprcM2u4qynpbTO+\\ndZ93jceKM+O10Gg2ZNSx1ZeWReP8KpOqRKehm+D89CJ/wy3afTO2nQqgvmZnyIVnl3JhRhzIskPW\\nDu4uJoPgYkBBisUyzDiOPFVmLXMaU5oboAhrG7aSl0zieNN0zYPNifGtTL65zLZCsJsrtkksp8KI\\nlJLqJnW6sJQaakMnClSsVrPOi+2Ko65OpwG9TxFYxkC0v71Bzq0WLHKt2nGoYZUtmSYoy9yJNzm/\\nRDxMiSjlSsDojNNlDgkLbFkuozhIk4IB4qgh5XFVV8PlMyxySUrtds0fDFezhbwNZiu3RVpODTf2\\ndHcmxWOZyZurVbvAcpBVLlYwSdwyWtS0+fZNrbF3u965I2lKAWng6hvxqmpyTcVZlilxKoGSybFW\\nxbNJmCQJFzBOI0BDlI9KHobxNEnkQnGyEphbm0mGyiQnKmRA8aMJxhBlEi2kyM2M6FjHK4wRD65S\\n97yIqQShga7qtVOrddqtnW3vqLZmCUqC2IVQaqrVXLDBYFHKD0u9qiazBrpoVhYVa02VFHQ5Sh7u\\nI1upYksnxrgUrqLm6yAiF/ORd7VazEq0hgRRdC1yh7u357Z8auGuN6hwynLT4BySUjp7BY59Onch\\n1cjmBlpDruqdX58SQROqaKal0nxGkypQKiUOvv7Tv7bufMPagW2rLPIUkqqSkknZOIwrY9MJKMn1\\nd2sHYnaYQlOlLOaU5aAO/NsrmOoAAQAASURBVL/eTOQAdVp3a9Vq3Hh+MX/6ygsrKc2LHIzON7i2\\nT1PQuK/VEbezS3HwkFvffvTZGVKgxAHjqa7YulG5uVfV9YZRcdLD5/mp9MjLf3lMsvYPYtSPEpRV\\nIG4qmTzMVuWsOJhdTMWrn/S6bah3xdiExv7Lz9F4WdQ6/OH3J2+pJ+SM/eoX8JcD8HWAfnr66+13\\nvzuNtio9OLpcIFt+8fWwKv1B9NW/+PGD875mffxYzuU0nOeXx6l/8kRdykC+K1VrO7WX4ZT6q8vK\\nO/1aU2xvO7moStob9Z4r+R+ef71Ihn9hdfpQoGwE9G6myr5hRSzpxtMTqWIB3e4Q16jAKM4y3xpI\\ncvUPRv/Of/z4R42PQQmCySh79iKeNmNfW07y63xTvTOwT93RZRmkUmtrE1Sa3vDE0mGxLCVJUkg1\\nSh7+2Z+kmHTLDE3j/bxsQ6OqKLBLqrWGlvueuyrDWGgKcAyrUiH14Hr0PObc0gwjL4WiWVw5uCre\\nrLVTyFG9vgbE3J2FRVEIDlaLqWXbPlurrefVdZ+nVna1AeI6wescWQzw+djU6yqWlLLYsft1AgoI\\nU4EER9LeG3n35lG7f00BwgrIsgxbWAJ5Fm4QgPc2dKLUVc2AKadFJvJ8MdSCcLuQN6BKIeEQKWWu\\nEpQvJsvldNJty0pd6b35QVT/QZq2/SCCAsvE4DQ2TK4oMqKBROQF2M04pemi4MKpxAb5LCxoXBgQ\\ncauqI2W6YlKPRHoRH132guWCL9J4fqkr/Lvi6P/7xwG0nGO3jY/m2gEYnhwusqujo4d5NiiwFGeT\\naaRDacGKMiMHGDY4Vxlxzl5HajaF2JBJCIHmTW+0dtpAHHmvXy49KfBjSLI8WiEqx/OiWL6m/oB6\\nr8rgAvgjmnqonMDBc6k4BRx2mmZJUypUJOdeEVXaqIqP7caniOekBoRKkrJmHdRpo6WZeXezkRel\\nrhxAxJN4adlMt/B4NGcAUneO29l42YwHkZBCff12VizdTNvoSNcX5wl9U9fi33z5wti6I5jFogsF\\nE6Ln8SyoqKOqHKKqLCsNWYJNPte7Mo1or8LrG3Be3p3G22WcllDEvrKMTSTkOC84kAJmk9r3suWk\\nvq5BXGXIQQiUfE0DJUyI2dAVEsmwLHwaArW6ZsoBS3kJ9FY5VRAmFCR5tCrjpQxPAL12syKnJYVc\\nUuQ0ElEAdMUB3IcSLoUq17sg4TtbXUZdjHjor7a25Ji4QSIwNy1pYVr2aF7PU1QWY9cPZsu2m0QT\\nV8RukmcRgfF6Y2XVuNCbVGmhyl4iTN1LnaoHW4VeaeUFN1So4ryghGa8UlEQlNIkasiMUU/VhaqV\\njk2iJI79tCwQzMpue0uCKI0LWGDIFTccAoGcXom5TsRK0lMkKQQbCGVQBEXOCxayMkUSp0xwCKmy\\nVq+0cl/W6ViVSGVnS5INvxjzRGMpkyQPaRlXDMIZ2vrtd55c547TB0D4EiFms/lGY2PnSUMZ5vGC\\ninuU68HUpXnQubMXeqlpcUlMPQ/KSxYiSVCprQzq08nlp4vfnKozqx+olBjDijWqtpIspaK64XPO\\nMY5XcBqsvnz2zxX5R5zvuGkLQSJJJM1lqx06g/ngc3jyy+SjQ+fz+d55MTX3dIn6sEgAmC6CWePu\\nVmXr5unF9/7Lj2Tt3aN+7eeWd5qN/bDw1eRiEawzd/yzRztSdU/e0mCxthrs/Nt//t9dziERJRIS\\nkpqWUxdr3x/yHZe3rubPm2uxnf969vP/92rV34Gz+3yyleVg2Hv1KvALSCBOrw878HHT5kYdr4aE\\nx4Dkn9YtC65EulSHqlncnnxz59Pm5Ojl/+sy+/DPYeUffTEtP/skhtm/LpN+isxQaG8fTHf/wT8S\\nD7cc6TgfmMOXn8DVSae9fPf7Va0rc6IPFsbi+k+IKqroR2Py1ux4eXDbqCakiMyshQj+qDZ/nkN5\\nKgkhzpOyGjUPZJ1Kclxyo2ajfFmf0TtDb0HTOFpFqFjOLhpPJ99gt/7jjf/pYqc2M6XHa+P/Q+Pq\\nWaeyAsOnLLU+D/aR8mkerKKlLTs3y/QJz0vAGVxFK5+oapa/+uWWkdFSMTGYX0GAMGDOSupL+zfu\\n732Jx5dlMjRMrtsPh5eTagXi/pmUXCXllItSEpEKlRWtw0UekTUKNsK0FpQVpTi2TNivB7UmEsbb\\nK3fLXb7nBh2VYAC/0izHrHDCJxwDx2JppAjtoSRXnl5uaG2+tY+aTV/WOotlO8rjTLLnU4AlWRQB\\nVAxSq4GYQU5I922RM8ykm7dib4xW6Sr1Xil8TDDyY5nzAiO/ZAUtCgRzWaJa64eroPr541vPXnYn\\nuZXmBSOZXEqGZbhzOYxx2ajRhIvTChXO3lZ4dZ1lRVtiQxa/DMLlPFwkaqPU4Fc/u3zn+w9rOzcM\\nMb77TovHI0ZfjcbS9U73budrszQuHw96t4KQ3Ur5QIufV8aPDCmbzUtWAH8eKtVaBuWPvHuyJBcZ\\n98ZALoYy+doxMALQPw90MrNr6u4HbVN7Hhwvg4kotbZIpjAZ4viZrr6sWC9ajUGrOevfmDAjcVql\\nvb1Yu+111/Odg1vz2XSVTCCQs9jTyMYMzYi9C1UcZ3cUyzZFNcg1IN2/0TES5X5Fwk0nj91AM96I\\nkiDPknx5HXqjel1JSzQaRFJwDvX06rJTwGZ07Q+ErDfeh/OnboRZHL74/EhCLcQWWJU0Xa+wsSLn\\n3XVl7vE00xWFGFvkMm7J+XqzNsY7b8/Ttj1lSZIqRebHiRIXOhLVzm1EmukwBvNgmpJRhgp5HSFU\\n+tTH9yNfp9WDVtP3R8OLk9OWnFC2LokJLXSpUyVECt2BBBd5QjBGCFDKV8iEapGbEq3IQgoDTUVZ\\nCQDnRCVlmQUJIoU/ja0ky7GlC+6bNf3oIsfqWng5ViENBAEAbfRuB1MegDKazOJ8Vcxn7mJJ3IGR\\nTkgxBrwAtS2BujI0Sqbn5zU79iLdL8MdQ7LN9p04zG4/0KlAeRDZljAsW1eAWR/v3m+slgEhpFox\\nGMSCpjwPNKkoCl8CdRkDkBZYB4jmU/f1dHwBgaxKJQN5uipEWQBIBUxKgIFgFhk19SKXaBDCJMIF\\ntTy2A1SyMpSV0liWeyhLWeRDEDitrJBqCJuJ76Df/OySO/dLxZWvo+G4H8F3cga8gqnlxBpgwvN8\\ncaJWbyLDmT8+1Ou9vds2Y6VVz3UKcZkZKqg0st3W8wP92pgpkzE9jyQP8cs5Z4J7MyN8ucAFRJQK\\nfp69/owqm4gAlqWpaKK2CdICN7S5Owfek4r++ofy4cbj1/Hj0fyoPrwGAmdYirIknQbvuPLbT79W\\nbXHS8d/8+txJelHTPLLzlTBiC1zZ1aE1/rrU3/r8l7XjoWp1w/DFfyXCqdNbk3UjT4TW7WJ9szgq\\nV78e1VdXjUYrDR91mx/XsTBaN8e3fsjfedN8D4DVnznFAiuGYp1oBgiAynFvMTytgLgJonneJq3M\\nUOLptRK+UK/dXdE/Wqv8m2bjz+fJ8s3Df3rj8P9eXgwlWSziFXV/3fmd/2hp/MFPJ+9++Mn34rRQ\\n2GtdL1pbTKj35my/RGv5vDCb+vrdN/Sq8sHNZPI0/heHfeI4yEpkNdICJYHNm7vnHDrz15Cj4vpi\\nZbqyUDQBgSR3n55qDfGqEYm6rW3XPU24spbpxWWwuAvFm8C+3TEX3bXwG//Z3/jhvycM6/m93VM6\\nfimdX3vBtMyzXttWi6W6MgvUxZA2bVctA7MSOZ1na7dqBc+bHQafvj4GktDL/LAMVrf4ukPkAuuK\\njGugekMXBGsoL4HjJO4slTAt1U5F5nbsRXGQuERkLHJ1zgDEvGpWKmYrSTJvNEm9BV09VeXY6CLc\\nKklNlmBBpEUS537omS2LTjSn4lUt4SiXWJOARUaXUFUjRntxCmVVhSiSCaMZVhtqRhdISPPXiSRT\\npVpzrR9LYlwuBvXOBGsuwzjLY1UmiKeCgSi3OecAy2eHqX/NYXxOBy/ZKqW0iJJKfwdwBDnLZE1N\\nQ6zp1G69UKH6pfhfpWl8MQ43NguNXOTBF6VKv/p68PhoVQt/czl+e3bGZ9GT8cquVFJixga61o7j\\nbEU292yTnHz+Ur4ay3DFt/Z8dWMCtQNVnam6gCLkkchL0DkchpFOCNaUlapEgi9W9O5gVWuuwSgu\\nfWYExXZtfx2Wx8npKDizOVEr60lnk/e77Z1+ndhaswILsZ7xmmY35VadO41M1AoOBDX8eVxfv1OB\\nDrUqwrhtUlDZOshEUZak4WB1w6mF+UB/mI9taDOS1u7f3RRyhfEdxkEalSI75iirtUx//EKAK4GZ\\nnAYAqC30tXq01JmcJB8aRDAh6fN/pgmmWKa3WlFxd7szp6CIo7povJXOU4qlGbw1mxuYBvK2Pec3\\ng6TWMgNj3aBolWWZLl9TGhWs6sZFc13d2c1KpBz9+sPscvrsFOmaotr2PLF1Qzo6HJ7MOlpaMfRB\\nvWIOT09GvM9i1TEhQBZCXDBdgaVMAAciXcGWmteMrGZrm7u2kIGi4sR1JSBJihYFrFgN5FqQBBOJ\\nYyq1GNSkvOnNZ3HIl6tTXgIpAwKUZrs/Pj9GMU35cBl66WpC3UsZLVUtRWYKoIaATWQ9zeUd3axu\\ny1A1HR4QSwWlAUHr1aUrWd8TTJZQzGAhAF7F9UG2sdV9e+UFXkTlXhcpEi1oGaR7fUEswTRjgUiw\\n4pLeUxRSLIJ49VgnERUFlnOkRpTlAHBuGmlapUnA0mvSspeRbJdJx+RpTq+e4uF5ln4eKsvMwBFR\\nY6SgZawz3IoLkmAbteSKyFZYXpRoIOUaBDzhcgh7Yh3d2ohEvtyorSvu61BMlc1vbL2xG8Rhojd1\\n2yCShEnXqHXbtwytb8RFvNE6M2f+8KI4OYleJ/azZ0+3zYXZKKN8iaQSgYmGOUQFwEka5o9/Uxyd\\nXMsS5cnTYORX1kGqhFs/vLj3Wx9+q/u8rp42mwRxCXEhMU0rwstP/mKAz4Uh7OVPl0/kEq7bVfVG\\nJdUIvQoVYS+mF/gefHRXWfQyeTn4SZR+EuDW0tclxgDVYzcV0dHW9q93f6cbYdivuDWzKSvIkltk\\n2Tz7uvPoZOfwuWtv990E2rzUNjfB15epbOTeI1N1ijW5KuI417LSad4AlcJf0wbaMB+6lctk0LQ5\\nrvPtP+rVfxSa21vy9WmrNM+/qD6/bv3lr93rP/ln3V7u0yMTz+qtKmMaSLTl2XnGQ6ujlEOiogSu\\n/zszXanGv/CD965zDiRzLurASIxm1ckOKVali9VHj6/M3beC5KpcRViCLI0TPzWbbS8/dO7gCtlE\\nCCl0BMBk4kHGOAC/DeXGU/GHv5r+vY/k//kU/ZZ60JCzLxRtOhp7UCFWt+rR1FBmIhNxYYqG0t1U\\nCceNG98P4iLNyBvvdO9tPj/7SHoV3VSLc6tCAPp+UZjjSaOitvFkYG83wlCUZGloFM641lqP0j+w\\nmoUFvbYlug3TgVeyNJPxNoeORqy+6qdojK1x1bHf/VZRpHpL15OslsXeYqZDaNyqDoHsJi7E5lKT\\n/bZ11erQq/PhxVcSwCSKa4LO17SVTgrAKaYCqWJKDY4pZ0CCnAFtsui8+lVpObrRmwvYKWnkzwIT\\n40YtL2AXwRjmCAisQ01BV/VqGvNRp38lqdOGamaUutYP8oSut4BUzEE8pVCahtbaZpV8MWy2EMTl\\nlY82NnHfHuv+U0QnZHz5zXenDl4Yjmao22Xg3rnz0MRCUebX9DQrBizRLVLR8qvy6iW2k50NGatY\\nLlGejm1bdqcyTTLbklYbV6NJTHmJGDJJXSZq6BbLRwtqUAbpauGIxEdMbN2xjNoRyl+mbjVLDjjd\\n2919w9n5cUK/VW+sVeV2lmCEEKa5DPMyWbnh3lqts/KWL86uC9N3zxw93eloL/Ns25t8RWCcAnL8\\nRY3cDpILbWtj5uqbJGTOG7+tsljVlTABgmpNMVQrfikSTUw07KqYrG/aJoKhlra+Vbe3g3Z77eJy\\nKilaa9ei3WVU1DxPx9eflcbNuv1mWgrY2Hf4VVTCw09W01eZiqbTi/Znz1v4LLIbvls448BusMSs\\npY7OQJFn6kYUGYOiPy3WdhXe7/1SDcrr168bLREuq6l/RGBXA3ln9xLhVAio7Hy/or6sDJdSrRIV\\nDcYLgoWCCVFUhBUZxFjSNNbsaVhCCW00kNFp3t0KwylCqCaBNVtW5TxhlDDGOZ+dRZxT1SgUdE1U\\nyNPhNFgqVS6IVan0NXCZi7iMSiMeauC6cKYRF6VjTCK2ouishO4CUqtwyRrNdmm1FlBbRhXdasUz\\nfUd9JTmaioCmJPstzVsW/OqiBMHa9t3xNAGLkBZMMolkroIsKhr7RV6HpaVIIJyex6FUFEhiIVBC\\nTVMBJlxAWZa5kGCQAl5nQsdKBmRaZlWappoWG/e2JLPf048b9una7TFVgCCsZDROQDo618IVCmKE\\n8pnOqT9KKGkhianSXD6JvbCn3LqBe063gUx77DhONr4Rb2/7g6V7Yr6OunpbDv0WJTtmtVt40sWz\\ncaVVgWqxUX11o3y5PD7KR8e797ce/QYKLi0SMU6XfqJCXPgBLfMz7n3Y789atoGlWJY3ddDS7K29\\n6vc+S9/ivd8y3xW7zXYxzVKMoc5UFOngr+X4cD7VjcahpLlY56ppNG7eqN7N11bj1VTR0Vn5llm7\\nWTTe4Y2DL5rxSMtJkecyN5KwoLFXZFG+to+qPz57qVTe/PEytKyKXFGKN5rRHj53glfF8c9sqzl4\\n8qVlVrututy0O3fWQjfcubNjo5ah94o8ppS6izWuocoNJ0jSPJqWx48Ac7p3tEL+g8Pl+z/5F5te\\nKe3d5N9551NiOPq/+ae2/6edB/ef/erE6O7Zt++FyzJDVlV+3K2QxK1Zy+ze7W5fw7fXefn6i2T+\\nar+Z++fo9UQ6GpPX08Zx+YbeUh6qxzd2n8sXnu+OJUlCEo3DDqkyKr+bFLmy8SPJ/I+QmBGFMUBk\\n4uVMTHJYgt6A9vFJx/vkK/+Y5tMWY99QJMQzXxQjyd7CVH1+dfOT45VaDeKVPJoYil1zM21VtjMK\\nS6g8r/67bmdmLg4nL/JQbyXA4tJDGaWmBoVEklCOZ6siHUgSKgT74Nbl7NFY0YJ58YP23lr9rdtO\\nZx+qguoQFAtZtdudbv2GZhecaA8Ykp8u/qFjEFq7w7OBgImm5aXYqKz384hScNVpW7UuY1qaljVd\\nNVbXxxBgQJnpGIqBIMxrdt7Tgzzq5JNMae9xznGZK0hqSr5jPMtjyFic57lEcon7MpYyIWOe6LLu\\nWLoq1F7Xsiu01ZyUoV/oVY4SvbvOEuX1L5yCM6ttmBoQdIGSQgLqPG41Nx5blVsSBLN5OPKkTNG3\\n36uz9FTrDfxbf3Oq6FwRvnsOi6Gbt1XFLAGo8FHHcU1dYBPNLi56zWtC4ZC88/JoLc+WRN4rKaXR\\nquaIJOGvn56zq1jGcas5hc2tiLPQL5xGiez1kcvo4nVDPL3dDWubrcbtDWLHlE7DFacQw+43S+kt\\nRSGKvLbz3o+RwKsAQtsSoFCNWj7PGzsds+b0N9aARLblIVOnol0RuZX5YpCyoWc01fFzd42VxbFS\\nC15I1Vvp4lp1RxEsWVFACCW7aZdwO4nqYZBCuCSO7ibTKGNpSdyp4uRr2Src6RMzV9ccgsN6WkiI\\nB2ZrxcwK0oUHlCcvIw3KBZRyf/JO5zc1e35nV35z47KJnl7hCgt1PA3WdhIm2aptENvUgZiMP/Tz\\nvBEY6/d4KDlprezuLFmyENiJgiPDlEo2G1ydP31Kl5G8SAwklhl+Ph9hU6/TItN1JqDEBJKQIiC0\\ncWFoubXdVLa+Z6/fiBMU5wXR7rEijoVgvd58uZdltSJbKppqacJQVt7FsqRAhqWqAMnMLz/5NFmC\\npunUelZZCJAlRMwdM4QQYib8Sbw4pWyR+NdUDSIsz4OZVhZ8uSqvr3jil4aAQHbmeJAblZwxp4cy\\nkT787psLL1pGV95kqaI+869VEjVaNGGVCNiJ52dMBhzGfrnRNBQpZZxKkEqEplGRJJwxwDlHkBEk\\nEUMUtBblKIsXCEg1mS4zlroSZVatXVE63bQkQl9HwpFlxsuoZH6t7muNAilq1u1O1yolZxIVuW4F\\nKIeYNoSrrpTK7u2dutUtIesJeTQ/h87Yu/AmvszH82l+M8V2wRbzywujsoOhiojdaKxs/GvAoZDX\\nzj5+2T5QuvTXSTgpEhi5PPbmZT7HyIViI1WrUqMPhVZSQe2qUamsTr3svHcS3QbKb0fV1d62YqTJ\\nemNcxVOIPc6mCE+LbEbjxoKuj/0HR+G+MFJt43RTZo+fHmR0O8PfBuq9C+/AvidlGLVafRT7d3dx\\nVa/AeZem+7bplmS/mQ4K2fQmbOM7f4Qf3Fl/OGbBXwHdeHX2tN2746zfzjlOB0vZvm6T91KrZ2oO\\ngixZ8tRDmkCfn7dOrifuMoiKa05B3bxz9MSGs+D0z/9vsjTGqOK89794fVJW7b9659uPOpvO6PCv\\n6uubeubQl8JgeBmj2dbN4bWHDaP3o+8oduvluVozRmtta+mq3/lbi032Qr/8Ar54xc+n9RlGG38b\\nGnoeHOZFXGTzoiRx3hdlFSzLxFp/cSWfLXUGLIIjyDjEpCg8OUIf/6T86c/+8SqNJfwhhKd5knLO\\nOfN1+0ZZFqCgWLba1fOHi59qhyaNLEFDAdTBOZKiduv1bxzGQNZdfvLzLFVMekSXp8nKvYpRPA0A\\nLIG+MZ6jelOX0ZywFaeJZOxbb32PFl4J3CasXY7jD59/9+Mn0A9NKzns1HmRQbO+Wzjvy/B8Swam\\n2qKLJdAtb+a1WwOMJQCAKZHF4qY3nbbbpdFPFsNyfq270ZtA2Bv7hFK5IVLH6pfyHpZzq/o27UHW\\nHCJ2TqK55ADOVoBCpnckKaHpgGA9iSeci50DS1Nr1+ctIol+v1/VbSLBxaop16tT8sHd2qE88wwJ\\nz6J2u6/bvYuCp37+lm3WaqZvNfBO76SKTjlhaaZIWLASj6/5qjB+9eV3IAXJcnD4FfzkK+182NZk\\ncfCNm34sK4ZVt2UGpiUUQeSXVNx8M4aNrbUOGbye42Da7VACnSCKtY2gvfVGo93PZguzdlVvEW7c\\n8Vw749356Viq0qszLRxdI/l47YbVfvgtKappqq1WCNZKAGZ+mD1/noyW7bIYUIUFU9204ZsNN7gM\\ng7DGRKKBoITMadz55V98nZSZeq+oCOvscKyi0mptvngx6pFsVifnH/0yyGavnzG4mq7yWgt5jZsP\\nRK3DuYDAnYQa125B1ckiyXYIB3KWCioQkHHi/bJxZ7zAnYZtU34+UndzjDDyLR5ixw7T3byIbeeh\\nM3jVM9w01dXVoP82qrRvF9rtAa2SpvTqS7NDVu3KjBElFT2iqUxokCuksZ2dzDaaF7G6GaDv2cFW\\nfoqrYKDraVrEgRsliYnk3GhlSn5CLocJ1V0qAS8GYFbRVAszWdYZhUKIkkOo0f7bN5Dd16T91cuF\\nhp3lKBXpAnAtLtj0zOVpwqhUlgxKQjN0RFbmOkQ4hzAQMOmpvF/jrfInCTgtC6QaBssS1RFYJQQ4\\nFQRVKHD8vBj8eh9d2do0zMs0YsulJxJRKYLUu8Jisf6d3pNHVcUNZICHF+AqaZ8kaWZ9kESRzM6q\\ntVFzS8eGWLhlDnrXh6mUpiT3BF8SkSIlMA+2BalIGCJMACZYggSVXIicY8okziBUmnGklF6YoxGV\\nqFJT/TQhuccojnF1OWkp81IBjiHruo41RXgZ41BHTr0FSAcBhLFqyQSnZSFKs63RU7HdvRtHmCiK\\nZmoyWoSvpAt/9Sq39pRxPoZZVsrRKpzNEbF7jqAJVTXIYeZFFKNUL151N28bvXmW5LKs1uqwXp9j\\nwGUNMYhWvnL9Kh8cZzgtCdiy9pzRaDQ02B2pcKfycp48G77B1v1Ifk9ZMwVLgQLSuUQwUqW6Xng/\\nBPPw+eJkrl8UO9nmDYVfG+qaYFWfql8/Oju/7j36nLjZHowOv/GdmxWB3rxB//YPjX1bPnlyZPfJ\\n8NXF5gPb2v/3c7gj0O5lcLS1XsWLM0QO5uVGqVdenLiCR7Wtd3o9MPdqkYXkxbBSdS0KuPeYTS+i\\nlSf7k3yVLOe9YPiC+Z4/+eO9N+1x1rjz4G8l7ur05dWdG9+cga3Zallv95K01Lrq+nv+WuXMv1S9\\n4Sqh5ip98+mkLZve8fWDylqVdZKLxbtBtTm6Wrns1R772dq9qdO86O7mBozvbdmdulfHaTqbGpgi\\nXXASKvFvDB6xE/ByWoYl6dhALUMVS9XqXz5Y/5gvzoYvYwGnjF1VnVnDCqLGvdksTgqKZMXWbdz/\\nTv3B6Y/+46869RPCfEu3+OJSNSL37/5RWroYTxzjS8lqtO6dhK/mMHn94mkyZUtZYkBMyyKUq0zG\\nVlcG0RUO8c2j1fuFKSkePrx+aVdt//GX4fTzajvO2384jGVJ7hnAeP1KM9p/I6knBIRpLCGpRdOx\\nLKtcuESVTMtFOC/ptNlvXp+OTXOnW19Dq1feRWr3zHgRV2+/1WzGWio52+9IZC3ymiSKKlvOjFZ5\\nRHQtJOWsnK9MY7m5X0GSYpCMEGRairC2muY89aKp2G4fVHW1LnIi8jVpmJPGnVRMLEJqyfXqQgkG\\n5wUSilIyp5YrWyXVXHVTq9ucc0QTArCpA1Tmo/Ox94s/kyHf6zvC/Usr/sIgl2i1evSRXiZHUKuk\\noT9ezUospkEeyf2dg54/XJrmhqEM3v4gYKJJqUTLdHtj9/Pno1A8qMiX5g18JX5wNQhNhvI51hqL\\nKKVi9YzIU1PDs/JWPLx7+04lMBw/TpGlyDwghetdHJ88+Q1P+cjtEntJk8xca33/OxuIB7Hv6Y7g\\nsG9C31AGmGwN3RvrNty79W2tSoz6vpnMJuKrq4/0h7tmCY/ExYumOXXKYKaLq+AguPAkolgVTzJo\\nyYS7KnWyUnSJFzkCUwln8de3vWXwk9e3lmebKI23vvGj04+e64iU0No0klVWDafXnS317OknvQYJ\\nEJOrd1kRnkQNxnsJc1yvyZkkxbO1g6uyWU1LDcgNDkQJYS7EzK9Ks5/RDRB5jERmOj3bfX+7/+au\\n2mK2Knfl1+1KlC9L4Q1n3nVFnRk6WqWBLl/TLCppXnFAo2nIxECcEbVKME4SmfINKstm9S5DHKeG\\nLua6zASCtlaqWqRqEACVsJBRTqwaAHIUBfFqlkeurKb1u3fT5p6XiXgVqAICok0DD2iOL+wGUZvN\\nNu01IEJO80UpjhmgMU3LOFWKQV1bbdzyhTVejRfEejdcntasuNnWvdXV8OOzWuZiITGWcGmBsBBa\\nc7mUQD5FBIFkRnChqyWQEYeqH8Kc7VRJP17kAEo5RQwoeUmzQh6tYsgYBjGMe5wBW1xlyTJyi5xK\\ny2y19Bicr0wuFJADuipLvdWqWZWmn2CDesi02025SEMNV/BGFytEr21spsOi9u67IMk9UY3UgvLA\\nK8+q4bOr3zyKSfK73/5qQ0ylWqGQpFqz33ir1+jRas20ZC78Y5+brfa8Z/divUZAdlZqqm2LJAMA\\nyLpuIcrjpVyeVqtzpgO9t65SHQ1O8nOU1Y1A8qXh+IsvnyZx6+zRdpF0plNLIAgSnkRxmtTzmlH9\\nnTvm+xcblQvz6NXwI3p8HMmdsm2B8QJ++vNfTEZHaPU5VaPddf2d9/6+btUqb/2h/vb/kty4X+fP\\n5M63jGyWsb85eN5AWA1y4+WjT9XaN1UQNKuoOlmNPe2LD3+xyO3G3R8v007oL7QTaXAuMBUKJpJ4\\nba1NHTZMivAbv4XM+LrSma+1RmZ5vKtsHT95PE2+60H4r/+r/+b+f/iff3JWDOelKed85K6XZhJo\\nV/ytl8sEiWfzKR5HaubNrgbG8WA50L47euFcf1ak9v3Lf/PVvb3jrUat/YMbUVYfnICocltUt4SD\\nmmt3KPGrHWb0rETIwejVGjYA8oh9BFcL0r4LKrqlS7JY6pZyPvgXiIvermRsIA5BEYd5zwkOVxnF\\nq4hxotuykafNuPl/1nb+IcJ5Xua8cNu7yprULv4LN0thrZ5LFE3Cdio2f3jn2MzOt/2fetO5XHiz\\nRWE1W9wDNpI7+2W1AZMn08+PAIVIMebY0l9dLnrSH3fM1fwcFS4FDDnV3RWty34hyjoJ1xCihhLH\\nRdzuNLzgHRl7KrKJoq+CCICEjJMdkU9mNxJ4JzQSYp4hGQOGP/304YJ3br6LvFPsrzayILFNaqZb\\nmnDX7m00tvqURxo4AWiNwn7m54pR5GUAhdHTI9VkWx0DXC9/9cgh/R0qVAV4bWtxMquoxl5UQmfX\\nqjSX67Vzg8KzMDk/0+IQCU5OXwp/qdqaKVgiEykHCYNYlWK1+ZWqyim/27JXjv48LZ9XDxpV84uc\\nLVaLowdvlLMV8TKj0qoKJo+lgzfvZlF7X3B4vuxuffuNtaqqChD4aRkcm4VLqsywMLo8s61VCOZJ\\nHGASonxW14ZMaLZT6QOx0Z+RnTdz18aAywg2WnpF93L3qUwPS8CzPJy8DmtOtcydlf5HRVGIUnXq\\nqCPKg3sHW1t49vrjTF62DqC99n2Ycm5alnyu8eJO57liSnI2tuG433NJlfiBI+YzEAWUi4ZURMGw\\nDZ8zXghQtHXcwkArhkA6u/X74yWgGy/+bK01eHJ97lsPm9W24aBonK+1rO3t97KVlcW0dvdhmKQF\\ns0kYIZ1lnlt3YIzU8jzRtCQrWMST1erG6jTnNFWFqyORFKTmuf2bNZbZyrzec4baVusyCi6vOix5\\nO077vR3KkZnIpuVAYShu6BOm6mqTSWMVcEOR5xOJ0luQNRUky4JdXK+AhpKM5innirxcRIrGgSwb\\nmV5z7PkiZ3wlyrBmEwkWWJJgkknAkCSEIFcJyLE6y+04OihCQ+J+pY2YqUnU4EluGAZlWbx28Dq/\\np0QNXq2jG++eTFi0KG2a9ZvT1m2P6bu0sjNbqhI/k7ieAM+TqiXZFDQ2K59oFWjUjTIjczdGAFvQ\\nFv4JwoWqM10JAOUUmJ4PcoEqLJaU0GnpEJXTmbKYsSQVlDNawsSfyTjXu0hwB8EEGizTYDTy1FI2\\nyqtKe1HfjfS21GxaJSLjobqMNFndCmMDIaUyD6HWeNNBDa5qmtPS1NhoPiRFEgzz4RW7uo6i5DgO\\nz7TGJyDviJ13qaoqpiU4j2HHuvPN3HKCUBMW1pXTy6lOxU538/b2DrylBpPpsctaRVEUUTXyq4ZN\\nDGniqIUq0SQlEeuFop2jz2Rppum8ki9R8QKmf9xgME5ewKhE/ImKlXjMp08TsuX4Sctdvhmltj/H\\nnF3WG0eqHGPD/vz1WkGm6sUfl6tTYpxP2aKz8TsbN/6GbPZS8UZa3ixYj+XCC7Edx975efuWZ7Ya\\nPSMLDv8sS6vXp5Pueo2gGKkz9+wnNcOA5Y3V2aIenpg7ZW/9idVTeX/TL6J4yibMZsP0nP4Wt5Ld\\nGx1TswezFbMgAkdWwiuo+ulf/vfbf///+t//Px+/fPVC02kBvQ9+3CbbhVKYn/7ZT2JoOSJTLy7V\\nYF5XLxrHn4+eDd78nr/3xjD0Lt/Z/fLuTidcf9+qPHDLKiXUm2yPTxasspOhbaNcaVJb0ZQkGOBy\\nUGuie7eP9/FKCxfX1+DKpQuxY3ZUs/Xw8sVLbTWtakwIEbqpJoWqsuGfDwm4KjmnHCq43d1u7chF\\nsYKfnn4/83tYrlpKoZjrt/5W63v/WQYYYGpLwFhKU1bR7B82q62aHz2u6tCs8Hju0iJTxLFdyylQ\\nS4Q2bpxa538t0kSthxcvzoPR8Oa704Ibb38v1bLPU6SaRLOD8GY7aPJFXYFdp4qThaJCpDoiXrcN\\nm0taCPpJMl3rrO299+Cy9w2kR5xHsXdbMlHTatUNUTPHy+v58Xg79E6k1deNGpERAnoqgHV1tTNj\\nN7gKOJzmeV3CmlULMCKK0iwASNUPNNxav/1ezTlfh88Hj6dZMmOkp69V02hpkxSqOAdVSdGL3rZu\\nS2K+CIJXgEWW4gl/4kYYCFWVtTyXIITe0tMasrKxz3EaTifcWLN6NYPPk1QwUUrpPGf86+D3HjSG\\n0TDJfZYVHgqs0+naZ7+IVFOlhf/xxwZW6Z0Hu74/xnA19Ud5xoIl48ooiOMo4HoVED637bDz8H5e\\nEoCN/o1aaTRnwW4NCr1mddSgEGt7b/1IkSd58tx1FzkHijyptDATwWoo2c3vcY7cMrW0NPP7JpIc\\nexadBbG0wQrlcPCvXp5kFDdUpFf2FBOgtf69zc2yftNZ1d5Sw8wqn/U6ghmmvtFee/i7Q++0JH0k\\nOJPZ5g+/t3WzqpnefCQAfhAbp1ozr9WSk1+cArg+XVwLxqsbc1K9I4p5DooskbxRruoKAR7UJcoN\\nVAnGmb4uu35DRpECKGoqtHcgAEopAv164hBYA5cq0rX4KueZ3DJgpTH5kvHBpF6X4lIsJ8te+wNZ\\nrcwHQZa2Vv7YkBa1Rt8hQa+Tq3Jq2pu8WGlWhRJTCCE71SdPT7wVGfkgKIqEVUq45FDr9KHTvkFk\\n21ZKjaSAxZpBqjrEQjE0XQI6l/QwBtcr7EYykExJIkEWe1GRwb5ARGJekuWKZZ15HIwsSUbLUp2M\\nnWTeqCpTwYKiYTyiu4PF+sVZRWY9JiIhOC/yrpfbcQEB1UWIiLxK1LK0QKHWYm+rS21HV0gKoGBM\\ndAwJUOZ5Wjoc19io3pZ91LwOlNG0IKWPcZSlAS4TdzZnzHcMoFktgJSAeqOriRGDpjXobYuCgMu5\\n6SZOgg2s9Ci3BC31MkAaQvFEEfhtV5Lra40i6umVOii2Y7wsoit/fKz7V0U2y2nZ7CFcmhm9bVM7\\nEDcypcJyh+Gbp8e8nId+WtU0iLIzXhwo1dtIvqVt2Dvtz0UCq1JUppKhYVxWICCKKRNT7q15rWxl\\nGzS+Oi04K5BCjLRm5IvpS7W+yVaLOIVM8UUhRueyKGWC6eAMZ1zlCk5Y6ktWqG15ufPxT1hwdkpk\\ntAx+UpTHCKSnr6de/J5Ze1NSHAliU9MVlagElOFk7c31u62rgNzPwlBbqwxG/wbrF5ZWrvV/PIsg\\nlrNMvd68qb29ETYrC9WQRu2DgPcv5/pakODhfL0zbaBMjvBqBAvWuFje4Rstvriod5uISJCtVEG3\\nqn/hTdDln/8fTfyvrUo79ZC88z96Hj/4V0e9o8f/9Vt2eKNKjTq9v+eqXU1SE1mbXIx6/+QX7Z+9\\nTA7P6m+8tdboy/5sS9QpmuHzMdLJKksa87HEmIFbZVsOYLoIAoq0WFv7B7zxLtqVerXTbfaU+ZE/\\nbH61/NbLXx3dugFvfO+OpAqQRApPFb2ehK+aN/VMKsM49N1YYANVbjP0pVo+KsfPVnnEgJB0p1Pd\\nHcl/72KxkWI+nwFCysL7+tXJ9gz9bmt72ZeCg1sNSGBLR1m8JHLCsSTXdlr64sU07nR+RTFJg6hr\\nnzd4GGUPINZeftHe33qZB7AhgvW7VbS+03qwrylArRm79dw9M1fp3n57iNVmlsqimLHctbr7n3x9\\nu7YoEz9ZBYMmetqAhSCi2oAgeeHQiIiS4JjUUkp1Bkg5FVUiYDhhS1eXVKIT5mWrCbNUu6JWZKlI\\ncj2gXaA5if0BqJTqNuk6l+/edbPj4fVkRxJ+KVVlEeOyksvtcCQEsTg7X6+NdS1PeKu/Npgt3QI6\\n7jK8ekkAR0DG7pUsF5OQQbl6VHI2m1sQc5YMkzKtHzRlXTt8Mu+8952M+CUR4Wz4+Pj06HpKZ2eB\\nV41maYNelViTxNsAzGTVRNyDTLVyp+NIcQZyfySnQaMj9LWN6XhP8HIRWHN+y6P9xQjUrJQrW0ZD\\nhGFluLzv7O9A4sP8ajGLCwNX2rsDL99SQhKppoSiyWiryTa2tVfP62/+nf/t6bN/ucxsSlGt/z9O\\nz3zf5biq0aIqt9c0Y2uRrk69g3Im+ZDftv033y+QL558Zcy+vpeEjIA4L2ONqK+O7748v8smG9H8\\nuap0ViULBltk/beb+r+cR97uve/ZJmO1TT8zgXV3dDkczWOUeiWLEIaFRViSn3uLl88Ad1bD8yZh\\nY0OWFDMJii4qJSChOI3bUqWzX7T2Krm+WbRaEx+rafV2V+sejEC0UMjBdCGCCg1gK9BqaXYswWTp\\nL11cYw5cZRWz+WZYgDwtVYwMSavCYqPbIoUIBy9Xl2E69S0RAAopD66jxUxugLyOgVAwJAp1E2tZ\\nttq7u2GeE4VIECqYsEhMD49FdpSkIQXAn8/z5WXdYHKlSBPZ08nlWGukI657aYjFsnRKINjEAPPi\\nJGhcUj0Z7NdLszhRDA5BwlipNEdoU9MsO+UcbVWSSDGF2K7OFTMum3phvw15VQI5J4aE0cHdDaHb\\noqApiNM8gbDKJ2a99LHkQzmCrBAolCy8nM9lGOJqJ6e7KgEYhZZ+GIDx8UQXlX0ONVbERRDRIhSi\\nBIxXjMCuSUgHYa20DLu7HGd2syrRKByM6sFF7k2sGjMbHuGrRrVe69hyUomx8XCLGhM+WEoKLu3B\\nGdddxlipyQoYnJ93S2VDV5tp2WJq9fjkstvYMlsp1GwZK+trOhcaVs1W9159c3OjhatIRFzlkOd6\\nRasRln+u43WrnCo8lyOaIcEkUETFyi/JViiGSTQxM47mr8OvnvPRhXv25SOzdQ2plIVDdzwHcibx\\nLwntKvZ7KdCEBiXV5EQFkiYBb3zd16ttxnNiyhP4MJ2eVarWYt7OjINodB3kQu6lyxnv7L2pGZZi\\nQeyi5DcZfPZXzVsHxn2qtu8kzb1cjhr8p+v3bUXNzwb9RVJR233Py00NQIwSrr24uNyoZlYtiBig\\nGYzLzuFXeuXwr6Rf/OP2e99C7V6tpSitd9YPFq4PAPZbybHsSx8cfr5z9RR1d6XKLWh01xL2JG24\\n4TSbXRo9RhcuRLRaw4g8VG9aulzxRjMsfddLNybL3vC8AdPZwf54TSpEchh/+hcPH2CFbZXLOyps\\nmmVadfKcCYC8V7++EEIq04kEqanW56ucZmENfFF6V1dLyJCiG9WZ9M38hVeE1xmlhrEinDbbkRRH\\nK0/S9t7ob2ov+dsxR60NKaK5R/FcWWf0ncq6ynw/hIYsqyIZbtystGzhXh/asKHutAcRzaNx/we3\\nQOMNVNavwwOaF1p1t73brdRyvtLkfkWS1xTicX8MQa6JCs5jyRisWY+q5MyqFNVm2zZQo+4YWDct\\nrWpfdtYdxCAqSbwkTctVNd/Bo6oRcnNb0xuIXLnLNARbQl4jhNC4sGJvHvbGC5MCmHhi8/5mLqwb\\nD/I79pEmKVcDpMvNHIFgKUkySOOUICYZHVXRQamDxnsyD+K0VCyD0UCFsmUAEfqGSkQJJtNQESss\\nZkUKHm6FyaJYRnICdJQ8fjrakQQfHUd2kdOrZfp6pCazOF0QVshgphAe2zbnNjH2s+QcAr5+UwdW\\nLVopLD1vNGZceZintUxiWGLu5erSo4cz+fzJ6/auxcH6/LJWlUCSuyC+6XS2EUpg8vrrZz6X6ywf\\n8+q8W1PNNdu06qTdStm9qr2VzUYQLJ8++/mSgyFr6PDTVlfFYagoytW5lUqGtfa9aLlyk4kds9pO\\ncSn+EITOzg5dV5/V8Z0weq1C1m8JJ/sXezWvU9nTnYIt/hRxCvlv6Mm8rWQq/3r4+FnOwOFLsRgB\\nHdja5r3h65eynGEMKgRj2RicXc0uIzN+JgdXG3SukXGjIsk6iwpYk3BGlXARdLdk3Py+z9/MK3vn\\nX5+ai3BzLbFvWFp1c7PXaaypEVVmscd82bteqiCq1bksD2anZ18+d5Cwg6hIcXsZMigl2KTKvlPa\\nttG+l5ELKA95cOFAKoQAiEU+hscXMoAKUWt1w3QcRPRwOp2dB+3OdwUTlNLmpk2lIlvMIDvR1aUq\\n0bIsCTvDKJa3ewSpiwxV/UTFF1TJAh9K6XVXH2x0xdZBVrtba/UypBQ5W3ETFTBTKcAEXq6sy3xz\\n5bVGrjZ+5IG8KolybReI6kYpkBcinhuMqpqkLaOYIkT7N8K8rQAeLXKzWDVrsLGBjAYUgEsYqBZm\\nBDBElu4Elh4tTQUaGDJJo7KGkjBeHD2XQ1eX8iJdafJSgimSBTAIVCy0dac2nxWp6zsbWw1nPl3E\\nBz+E/XpGkF5iMfXlt75zy9aaaZHOZmZq3DYbQRuf5Bzs7S4dUEqyTAu13uHx+JUP2n4qRbmwDRwf\\n/8pv/7BjzCWvA2EdKzaETAESlH8XW9sF2S2NThLOSuazoqIHdpRd1Kq7hYP8CHZ6Uy31s1mZsKTG\\nfVQIVfFr+wJ6MRl+mg9/NXj96yn+2m4n1Z5nKdeWFuk6llCx3pFIfatH0nAiTwNjnpKwJByki8up\\nceM25f7JeVCZTWK2On82Hl9PUq+frCaC09K1j79cIuPtAekvvDxzvd3ts603LvT+N3VUE/4GKoqC\\nGuJ+L8q/SWnUiiaYpfOr84KHUWJ7pR/Y+mXmMSa626PZ5QDgelkOleRqu/7loPj5w/d3J7/BsvqN\\nlXwHL3cffa0oUPL33xmjo/u7k7v/O3s+SLZ/8NtRhh6PsQtGzs8eleJsu9CaWwmZjT3cBEYHWWvA\\nfjAZu63mu3Ip1oBHwg/7myNy44e8+8MVdeXy2rm/o958D9rFxp1S1N7erusaluIVZVLPULI8LrMs\\nq1QqBhF88DLT3q13cjoPr890KCyddBplUt8dld4pzVbEbGecCFSt69MXX/4kg33c23/xTw5jjjPw\\nbRyiLKCp+/1rhn9zrlhGe75Iw2C5trURznfkvT2Ib9zYu5Dmt66GslXbFPxtJNUtG+lchVgS6g7Q\\nlWhOgSKuLkmcTrgkZzloOJ1mU26TdCf1qmtus5YL+feKHOzfv2NWtlJe6FyWFZ3FoFedKEhuthJe\\n25WwWW+gIk7byMAakRslvx7TpQ0gFmwPsITUgFUkV09flCUp2MXwsqVUegPwpnp/u2fzTeVy7B4s\\n3FjFMS1CVRUVUuR+kssVVcqaCTD1FadZWuD+DsGyIqmo2YfRSjGUlJU4oh6lAyizM+vvy7b16pPF\\n9AgUQTr6i6PkGm2Xl/dvE1IZ2FsZ6oxr1bDVcy1b1onChkavt5PGh5wWpu3X1w4mSY2HuWVyrbOL\\nqSVBuBidSEBq9MvJpwG/jN/qD8+Ce6LEAq9yXUa8arUcb07q9V0ifBFdRJEYTOFoqXbvGCeneY7l\\nV0tnlWrqne9Pc353f215+pOTlxeXvwwwcEHFyGS0kjqGxPLFoi+IO/4QTGdSffYMb05PN0pktUj4\\n4D5vHKzzkhciF3hbdH73bJpJchSC+4LOEVgwfKxXJMql3ls/SMiXWQ5ubA8b5fCNrRPv4guLCa0y\\nB1xigAHcbLnLKA5NnK4by+o9KrF2YRgh38RIUkQ0yRqZ75c17tKtEdX912BD2+biMlaUeVxV0QEt\\nDdJ/v4BNsvhlnZxvt4pcjMLQWm93d+uBnIWxeOylmOUCMKzByDTqmO8A12bU0LQ3Sn+lZsPe7hJC\\nSIWKRWHJR2bfywmJpYOL5IYfGgJzgYeR98R0buiG6s3cILBYnMtwhgHULINRQQUv9OXxnER5GvlU\\njy+wE0m8BEVepVF9I++/0efWLbv+UEE3m7gRTFcJVVBYg6isd+sLryNfzaqycBQsF5Oaei6BYj5W\\n0+rtxcJpl7mqZkBoiQBz0T06ng3PQgd1ZVXoGol5KBTXrEscQYQMTZcooKIsC1bEJQT+lWOGcWkQ\\nDAl2ERQI8moxUcCZrq9whQORCwR4WaAib6IFWl75l4mv2H1Nix99Gmv7D/KgMmGgpOlyZew8/O0s\\nkymlcnIxK/tErRSG9WrUJNU7WaMJiE6DLAGKVlxNBkY2ooEvJZio9Ivxi+1+r445UKBkAoxNDWNc\\nbX6gQyeckcFAnhYFwUFDhwKOLO2TKX87oQYKKKht5UpeszwFhrDZuv27td1t1yCKbotK89wpH9H5\\nkV1N6OtLDZSWIhFrPS5zhABG0nxiaI7RqU22wBidz5dX7GLFc1EO0gMAy+mLl9pb3VgaS+d/MeQr\\nlSsyHW5bQdtx+xvXbtS42Whrz35ZbTG+fztW+oOkn5htWBPIkTXqh4/8wIXFZCmchaLOVsunK7PZ\\nWJMh8t05Gw1eYTWrNfdm8bWqUxRPu61KpUqy1Lc3fxzAYrfGw5yFyY4f/6qqlEg0apf0PGrEo51/\\nfsRfL3uXZ/hf/1v34i9n3Y3Z9gevO8660yTXwly+8l58psaZfumap6eQK5auxRkQwHKBfH+cvDeK\\nbx0/ilAeNoTXIHZ8ujYHDwOy07TXLU3WdcDTKPV9gjfy8JJTgQi2rNCpC8yqkq1GwS+adqG2O93b\\nSq0VFbJdGHNVrmWrWAKwYEW1mvohfDWKX7uKXV/SlPg/++eAOXanSdkyPzrTqeyy0zDLt9erRdJ1\\nQ2PwPOwLc1a5Z3dOy3LF9TchAAACoSmYlsI8ENxgCfcWHmNMQ3k5w4nX5aXYeeN9i+6+/XDQ+fH3\\nTLr95r1mvrKEPM+MtzMhaVhWb9wPvVVTK5e9HzAOJNERQqJl7EvrfuSr25VFVFH1zd7eBQ1nUF7D\\nRMWIRDMmWdySjwSXnApblV8LbY+vlM8+u1Xptd797p6UXReuH7hBVHp+LF6d1aBOsrkGZabuOqqq\\nMsaKJOGqHoQSDdWyqOQhQ0CnS/v6y+LqJT97Iq7+lJPJ/W86pyq4xE4A1p+q6+Obf+v2Z/kbmUjK\\nzDMrHAqmypKqt1Vtw6eLEt1XWFQwLDXWXob73ou5Knu7B2Q8Vps1TVaWtCg7ddLb6DHlVyZ+jG9l\\nH301h2BwsUAi9nPuBd5EUsGmEu706yy5GH31kaQ1p4+HgVwtSzVw4bPHP0kzxpfEPXldbd3PMgC9\\nf74lH6GKnU4T4XRXI0pZYqqB3Jg57YeJuHp1ofFXoGkuSRWmkn1p7C7iO5a1plAu29BAltVTT+Ya\\nH5u99k2B6hCKxXSC3vgDUtlrVdaIduWzrZk7Zo6yFGtt7srq0l3GRrUqq8YkjYx2Y8caACNdshut\\n0jb1ShyKMuGJKDTJkAU8OxXhTCmPWEOs5L6n4Gj+9LBITW/BZFzhQMnTAzl2b1vH/duSUW/QYJIm\\nvLr3jlVzgsVF5E5gMlG5S0WiEIh1g9imUzFipAkGEKKTEKhyXUEFFqVq61kCeaGQAimZgigsyjSJ\\nyzSZFJTrlduKavWc0uxqVMhZlikKsI0KkeEqI95pKRIPJSuBJwnDY25ITJEqstFCM2/PsL8RTWzQ\\n3decjc7t9eVwAosYqGTosUJu6GJEiyMgIRngbXm+0UsbtXx2dhWMGlI4aewbMQJpjjWCJVE22HNZ\\neqzW9UzW4gwmJaKMI1wFAgUxpQHBmBQ0pzxP/VyXPdPWJawgVAhGFUCMqiq1zXRVZMhYpRZBsFEj\\nXBLEAujo8dKp7xny9NUvztrNN+XFcnLquYvUz6XuzgcqzEgGqh3uhcTn1u/fTZrnp1F5wwTo/NPw\\nKPWwkAsUXz77orXGO3ZcwQgAd3VV6m+8LWVFUmpcYNjgqlYKQtIMM2m58i+QO75+eV5rGQImlUYE\\n0Jrvle44VfWWxqRI2VcRtp0NiO8Fvq20JQKBJNFKP+50WLhiHKsbP/r2+nohEjgbniEoKIONqp4W\\nTVraJaCV2qy77q2jqTWOBk+GSV3N0uWr043J5StoDLcakd1dmzPJudHSe7eSyu7nv3z1zpu3tm+c\\n6sbDq2w7OZYnnnjylXrN7ZDJQFbnOAVKXJevNw708Rmiil9E6oLvP3o1psqkUx3rTklUJcivKM1E\\nEqwrNTeuLOaerirjJ6PRBII1fnYyFcXnpei+XkjdbVjXfynX/+jweNT+7AujVb0z+bffbPzpt7+V\\nJdZDYN3UYKcUCJ8t975fboLV7PPL9MnLHfrfqhVoQYngXkabAsiPn5DRnz261f717hv3kdwuiUXq\\nZ9LKPDs5htv7zAZ5qbUtWq9ThiIKh6Zt2e2+0+3JWMU65Xm77tRbfYXmB59H35iOE2/GAapQWiiK\\npspUV9QkeYkLdn5MP/3pp7EX63pyZ3twv5Gb9/+uHJ/f/HYGcMnpQiM4KO82OnHJvFv3FvW9RvqY\\nrCBhgltGK+MCMA6MnoUHTq0de/nlJHD6EqdXaSpZhqwYtsB6Hh2s35Ki1j969er9B3u6yP6GTMI0\\nlSajph9o+7fq56NelMy4WcFfWL1uiaAGy3O7ptClUPT6dNWWJRlyZ+vOflXz5meZkNQSEadyuRIF\\nIoFO9DIVuGIspe3J/AKPzl4tHvx88UNalMncA8MxP9aSV7yWsPjJBpuOymDjN399e3LaAnOHJ1Jy\\nIeg1wTEO5sxQYlmWNGNVacVrW8naTb723nFjwyf333/nGxFlGUJcIHj4ZD14fcVBcmtNz6WmIMBq\\nPiCoPilqq6kXz16u4gIhJBe29/XrdvnVRj/e3v1AShOtswa4kqZsbQNw/W7jxg/Oz46enNyi48s0\\nKpOUVLeqk7NLd3FVr+831/XN2z/a3KlLxXEumoU+dl1Vw8ACuSLnZ4dn9zZ9TaQinld73yzcwO5/\\njDHSjTiMgSJNynIKNOkM7HH8DUa8DTba2XEn8bAE7YmPhyNdWUQKuTFMxTPf+ovL+vWLxiSY0Kyv\\nNTYZ3oE062iL44+P/s1Pf7ooeg0nPXr0mCjrGe9Utn4oqZOo/h7R8XgcWg5cJpbFHUHmSrVf5rjR\\nTF8uUECsNI5IXTaCzHKoQ3KnuL63O7t5NzRTnnG7+rB/fvwIeO6kiGJoFcARjMvtduj8cLq8CWWd\\napUUWVPvNpG6MH0pg5luJxTnAjOdKISYRBUsz/cqcvcWAAGGZWmqULVUgvVwAG1D5mWsgwQAJgFB\\ny9QxRJ4ssyQoqc3KiEgElcA0EaRJxmSIyygWssja1aHsLFNWno1X/mVSLBYNyYtDm7tEEklcbc/m\\nmSzMmN40qw8ByjRZFgEEqysh1NpGHUO0aZvNA1LZvsdqv+M7W1ycw2IghSOirceF7Pu+bFEJcohT\\nL469RM0KpSzLIkkJT7KEgRyIAiGMVVUvy7LALIsoRhiUaxquKUrOeIlkTeK93OiiPISggCDgqCjM\\nzniJUPfmnf310D/7+q1v77bX3Uq+UASjUNGNm606RKYWmNV49eHFeDP5zoPOZryYcnJbrGePWJFj\\nTcP5uIkfS8YDW1cwYtUWtfn5dH4HET5bkJRjNyaJYCyN2xtVJ/fS5SFixyU6a+10k5RSEdlqLQoY\\nKFwdVCkVDEhSYRebVrUhOWopSXZIqkyqUeogS1su3QbQ0fG9OK9lGvREBNS2AqBm3XTFUiClKgQU\\neiJVsYahlcj2SbAqpeFy/Pr/R4J/ddmaHoaB3hu/HHYOleOpUyd3RjcaAAmQIAlSVCKt4PFoHORl\\na7x8MTM/wHe+8p0vHJc98vKMFciRSEmkRIRGd6Nz98mpctWundOX4xt8Mc/PeD7T3R7w81blIAiV\\nybPe83CzZ7/dP2P93/xruv/3lIMfAqWr5st8wYOZ9+psIR/Gry/E1dg6Ow+moQpaGyB+j7DjtgiK\\n6LRZpObgyypylb0/TKhRShxmCRMLCMBP91oOM0g86awWGrzEytxg919+8XJbX+aLKMsfh8bKMm30\\nRsSmT7j6ivs9f5uj5qD77od2+yZP9YzeWuaB286QyCWMNna+XH3Hq3e+gI2qvfN9LVwy7BMMpyff\\nbk/++bv/QAPd78HCLIJqFjE/JTX8BTlTjyfVqwLpWktTsapvgkIIDhW5ZXPN4paCFWjUMZGWrdUa\\nTtjLRv/9yV59vhpcGkjNkUYcB8jSqLUzvuyql/Ov//2Ng9tpcu2o67KhVtaSo//0SbN5/c2jJJaD\\nGilNjBygXp+Lip7OrX9EjLUbO2Hy8pUURgUuIZMACMBrVHVJpYZVkCx7REoH9dymWqt2s0VPwNZG\\n7ZvM+vsXlxU3+was/ZG9t1lRnmiqgaYPNyuFWt/Mh6FprfWuyZsfXq/vNYhiqLYLsOI0ryDfKr3n\\nrBxut2yrfn+tExqyh4BeMG2w0MqBH3p0Ebj+DE9PxUd/na9ki1vVyz2Q1Z9d1VtWt90/fM/fvxdv\\n7ZN3/na5/3b/7j1SWEmr9mxzO3xwc6JUyOobI33Xdzr22iFr7OeaA02HqxZngrEsDBJedlZpvGI0\\n120lUjVFIsbUUNUKwODKm3dBGUJCT/qdPlAfHuOyvDThwCCFoleWUy9bfrpz+ArYP+7HWz/7O80x\\n6uagpBJWmisTPx89vQpGw+XJhZ6e160JLTRVg1qZr7g6U+H1PPL4zdT4ne56ZTlns2h+dunZlQbW\\nAs5zkv53nR0eEWNAjavow0l8OS+gNHjKknAwGU8LQmWQm05crplDniececC+78TUzIqiRElv2VrJ\\niKZKKMTFRe3Lb+6v8W0oTOXfLf3MDwrTUtbWjHdr3+3R0+Vlz7XWS+RINHz92UcvjotKR/gzjSpa\\nli2AyHH2ds0diMIg5naIXEkAc38KnvudbLoYHBdiGOh3W0SrrTNsVXFld/3B/saa63oD120txWWB\\nfG38CipomZTX2V5/vMYLOwB66KyE3jLPHCQblBgQZFIHs4XhpTIoLYgVoUDX3tKtJbe2q3qj00BS\\nEMtoQQjbOw0FFxhCReGWRV2T1Csap1wzVCo8JMuiYFQKgjkglAlc0ThGKkJGMBjExQIZMscYcQnL\\nnl4ZhEY2dTbOp/nxgs1K1r/Gw9F8mlLMsAktVnJK0Eoj4CU7fxYhZPjCGvLb5+Les+WNdLmNs1ar\\nY1VphuwyY4QCbeHFaWaGiSzyVElnNT1QqGCQFHlqtV1EcqnFZVkKDgpOSl4kSarIJdR1wRBGgmPo\\neUWYl0WJhTQRwUjTpLDjEGSihbr7FYX0Nt78A1qvG3qo6IlCJMhXwUq9INblyZANvpA5jWdvHKwZ\\nIB3E7EaGYlJbEgA1HHAqsgRp6y2umGv7nabhdQWiDcDChRcAL89iIgEjasM2m6pljTQUmBolmgFl\\nlgHUOFwDUYok42aDbgBDU31GrmaSw3YUBUThycArvHbgaSWrcIEqm+0P7wa3mgGOsotrWZaOBZ00\\nW4+vpzjQqI0oxZRqqmIWJQhLlgcTtZHp4Vc4TgC+TtB6wj0qF3oRj1+3x18+298+7/dZffsHWUg8\\nCf3MK8fnlfiRefpwe3coT/tddm1O5k14vtUlW/Ve7S7jxlDS7Ptveesu3F634NkrM6BWisq0RAit\\n3P5gLs154QXxTNZrXuBxKerrn7ZqklpTBFjKU1a8kR4N/IFSQEUgBnlCku0z/YcZ2QEa5Fz6vaju\\nYEPFgKTuqls2N19+/LR56x/wceS232KaOi8pUQst/E/b7z0A2i2g3RGMG1qOxJQGsxu7BSS+efqF\\njF0oqEo3uKhlWRYvOLZg40ajugZJmaiTSayaUnKN+03tcnM9VtpVpz2vYyGFIyUXQksKKFkJ7AtQ\\nXKC6nWPkCFFqe7WtW6a18KpvlumxFXz84XrWqW+pGqKgMN08jmpYMkVREJ4I6y0hpzNWSkwAADFs\\nj7n79VK5iK3ValAzScHahd4AYME4Zs7bg1BZW3y6d/fNkrzrgRtz0EeKRiqzetdOw4aFzxA2XN1P\\neO1s2iGqokX17cYKqOy3zd76Xq1GF53N9SRq72yut/RBEsQa1GsgsVFB0mg3h3UcbVYnq+LLD3/f\\n3HynarbFxuHYdFNueNr6B4zYxMXno9tTBdIbBzU9ipBvdStyYx+zGFZvCog554qhBykGAECtQ3C1\\n3qzNTpMw5AWze4ROvIahch3HFMDFYqHLhU7F5/NdUeTBwk2nl+fnOPjq2DUmjhZC1ZZWQ7c1BQek\\n86YX7K3H0fXpanwyzMvKalNivRldDxFcrqwbeXiqoSxKE0KygG4SMuvUo2UsJwmNIwWIterNH7J4\\nXuazfDIvhWYaDuNIM0yf1vzssH+m3UqmZYw05GCQxWGEbDZ49VIyyQpe+qHRgFhW6jroskW3CqrO\\nXJVw07ikOiFWVZZqxmbrb+XLVqFrZO+9tenZmSVVVspgrq++87/XtUQm3079MVA7lsprb/1Ymc+j\\nXLV4VLKsKIog04BULdXK7Nspta7H7ihP1bG4ufKxeTgVqCCgFNcqWsMBqklqcqCWtAEbu7Szpjby\\nwdnz0AdAuUBsQRA12ESfjTQVFxn3zoa9S6DAUjc5E4jDRJYJFSKZxYvhkgl9wTV/mOVkLZ+1FEUx\\nWnWE21kqAXQtQ9Xder1rG1S1FAUznUKgqjoHJmbQoSUiJmMMUQSwVjKN2kDT6wg0VcN0FJrME5Ak\\nsgwQSXNR+IU+ezkuJnj2zXD4RQh6xWwwiS5mQQLrKK04JE5KBVOoIKpzXGaNjvP6YXn9bT4/Senx\\nVBEBatFzdR0ABAuChIRcOGKxWikMDJtWqSoZ1SViCYQYAKAYTYqhohNMJJciSuJULPIkcWsyyrej\\nlLEyTVNp8gDhMktUXrI0j0shMc95USJe+jl4RxGdE0mxXVEoAIqjqjfLfjl9/Zf3tnNbBv3Llafw\\n7UN9cvbd1Au5is0p7jZtIAiikI+KOyZQQm1XNuj2ISCqboMpm05ZHl0OohJCDCBUG5pqUFtomkaJ\\njbkCtHZatAaX1YQVMi2AsK+vyeujIJ5P8lLaWYyXCsN9KSEs5hUpHSZ4QkLtt5Pfe7d9N2XHzwIf\\np0yXGPHidVWfydI47GyFqF1sdBmR82W5CBa9HnRrMJepYgGebbG84cPoYnF1s35dKx/1b3Q+/eSb\\nrP1PdZyAmF8/PNtenVEwO7zxsNrmm+9dNbc0UckGmqlqe7K9krXufHf948Wk0fzBPxgYhl4/PEmT\\n1qHZ2U/dysx1My3dZb2Ds1AZavOu7S6vZxZhW27eXq9WHcdTzAX3eAYeqC8d8RgyIqcXdjPevH+n\\nnqXXk/ooNs6YMb28sutS2psa9QP0VphvL0Jt7/0fApzPFvf17FeFoaZXVsYv9Z3fylbegZBICJaj\\nZ43WJWLXt25uoE63Vo9XtV9TRCudfQFX4oUAqWchqmLy2ttguGPrkftAb+huHfO3d0c726e/9ff/\\ndpCswe4NQ5OgzDBkGCh5sozh4atn143VzasL7peitcq9ePuK2SwHk5e/hPHyT/63/4VR28fWroKg\\n4ezCWMgwr+2/G1qNML/i8drxpBxFqlfCy4x9cUb+9b+YB5/8SixSpX3bbRzqJWMR4aJYtbvHR8Xw\\n8aj9ky3U3J8mjei7eNcCK4114IOr1oN5tBhEYe9qZDj7+toHbHjWBBxKwq16NrfWf3AwfNy/8723\\no7Jj8FTZ/+3GtkazAWNSNQNayaoNb/33tlcORGW9XL/55cj5cWq936mbfHNLz8sfvuFeX9xyqgRj\\nvavybLJ2etRmPK0yP5x0vfDtW/dXB+emCJfQnaYlm0xVqHRLYTuVqh8vDt+EBpj2T06jOb8ew0qt\\nCUiu6JpqBrxpKkb14q9fITBHLODFMH769NbdM8pybfttw3lnPpqdHyeJsvn4/KYNp+37RTjyZ9PP\\nxnPD3Fk5GtY16NdqWYlEEb7Y3X42m4UMOA9fVxXVb25ty/5gtBAK9sqlPHtVqRulTE7D8Mt5uAwC\\nB/HmYZ2cnaSQOXR00dh8bDSrEPSkqGpiGeMgDc8KUggOEz2/mpiArV37L+X6LrQ3utt7W/U+VY8s\\nC1BcKXMlAbzZEoNnipLXrvjP1jbA2qqqV0VZUpa3mvf+DxWQu2Y0HjqpRS+vvrC977QVpspL3RSg\\nVPNh0nSK7c1as1WbceXqoXfXHN5r/4K3bz8DN635WrudSqe4LleSE4wFSoWSMa0ETU9uh9pba+3N\\nxeAbESiaUaRMluBbBgJMQAyqdeLryNOKV4rJQXWXKNhVoOddEOjx4rJIr7WEFdlsufDzaME5x9it\\nunfq1EExDYY8iTdm5T5obGO9qlU2DbOGwAZKMgLL3S4w2zagGMoyigSCSpkxCA0ICVY1AyvU1KmE\\nhPkwC2SZ8yyo0XRz/dnOm4P9rYd31z6ubo4Fe4rmR94i1JDZru+miTRtA1Jl5M0XRVbrVm3lyEbP\\ngHmcV5ePj9WL01YyNyhmgFBZQhOFN9fiatOxdb56sCKhiYmiEZBzkSFDKJoQAsgCI44ViBAqZI7Z\\nrFTs+XyVMyw4jOK5YMsiT0FaUCR4kQteUJKhmbdrNVwlSeBI8QsVIj0vgCJ/taoE/tpv5fwKeT7O\\n9mo3htGLTzsbDoZG3pdm13VRSZgfDmFO3WmopWUrUHfSDFgOSbKl5Sbk+VNjVAGiq9lVx17Pc1sS\\nC2GlorEqiVy1LzxVliLNoKIoennFZ2erXb+VLVotteKkUAHiKpgNhEJtnWpFqdZEu/+Vmw82r+b9\\nZTIEIcNIQdlLKWNic6DeoTBZgWrp6ePJXKZTJx447k7a61HVZDz15zMtmcvX33jxTqXLLN5Qvjk5\\nu25uN6UQ5ejkr2/eXtFwpqRG684h5TtGpWkOwXyW2G7tanTARe3Vi17j6ldrt35XnGZReTibTwJj\\nw/MMab9NDaIgbWWnSeTnUeBVzfudg4Uuo9WDtrt62ChTXlOuX6cswlJx2Nu3YxhLFta3Krl9M5my\\nqnKMR+SXn2Xnj3oBNge8veROyjEoWMETefJFTDcAmF0snfFiEoHOZtSndD30zHmAJMRydt7Ye7d7\\nWFFWfhbz23GI3MO9hlUpwM3F0iyEhnheZHl7dU8hlA2pnyiI7IBC0dbf7e7/nvXBe7Vuddy1gWyc\\nTG5igniaVCxNCiHKYtI7DSZaajre5JGukcJVWEH7QyPPc5ukjUr8MPw/XkabZYaXkcUQdVTVK+hl\\n0UKhloOy6hyLxeDRr+NP/0qcf5zvz754y/nvd7YfMsG59j6kFbMSELsY9fPquo2Wk4z9eiHeXITV\\n/OnF2ls9pVB2bn8fN6qf//8cNrlYVeyK2Vi9Ue+/gO/uP6+ur2KwOkkUnl9eHn2v0770khsKMKoH\\naArfUbT3K2YcBHmQagS5TXN9nryP6HYpbIrFxcsKQoS1bqTz6HDN0zb+EZw+Wt+8U5a0uWVtdZab\\n5LXNa1Qv7t+dZBFc275pi8vuVs1Q1PEiYmW+9DAvY+i+DXELVH8gi4GpP+m05kZ65udakLhFKZIg\\n9GbzuDQV7UuAqAQMwHDnzoBq6Z09DSw2gKxL/tJSL1ho6UWaE3/J1tX2nr1SF9mFd1rPRl9yFjTX\\nrOl8Ud2sdO5uUJUZljAn5xoCX0UHjW7N0HDLBUhOLK3fUYr9/Xo4vYiDacaBW/l90lhOl58F48XK\\nygTo6HD13fFgRHIYKsgfG0GWLNNoPA7JMoGTycq9u9htFlqT6g21uUJUYwIbWN2rmGoesyznn3/9\\nmRENpHuRFXrd7i7G8+rW717NIbAqmlOpk+69e62iOLs6fhmC+0E+uBjXCB/ZCq4YyJKJ2X4ZoCpr\\n3HpyMa2lQeutsth86xytnH5ZIaXn3rg9nte0XtxZXeRwEUcgnmSBzwee7D9O3Lax0tWS6ZHEvBDa\\naBQwgSiIDSCfnZxLvqi2PVjmRChZRLnevrO5LpOps+ZcXJ/mgEjbFCBRwYyiMI0TTESzU6+t1iwz\\nDy6PyuuTfj/i3NWUClAaGFkGAsTQBrEm1z4s0gogOOMwGnu8ZCxJIYeUoITxrrWha5Zbr9QcpYYj\\nlZZBEaNabVHWU1zNmmZmtxhuYm1U21hQ7dTdWWkeHEZlglQsgcFnI1WWnKEyYW6Zwjwvgtjwn2bR\\nSCtGCusBIbHIpyFU1t6alIclrULrNkGOn0EAUC4Jh1gIQdD/iCCiZIWcTZKqlWFchykAaClByVkI\\nZESAZHkmkSxEkOUxWu0Ag+TNdtBN2CTlZyMfVvPDezsVoPl+ngMWFVqJlZv2ldJ6kAexqi47dr0A\\nciYEwmHN0lYW/qgHrwfqLKK+0+JGXNtSnBXvzT9xui1QI1YxVxGEuc/ixIDN+0uL4FZOqULVUhEx\\nhDbWMw0HbsturNhkZV9vrBdcZqWweSIwNLGyYlC6SjdqYOdDU8efi3xomkslgJJCyyqFvVLgzc39\\nlUVkSi1A4zMvF3JNGy5Q+2YeJCbgUeKN1qzo5mZy2KrY9KYoYcP5yF78Wf13fqQ1zXLw5c69d5m9\\nSdpra3/8u4TQ0uyqltHRLoeXxZqzBNwdfvxXmy0Zwv3js6WvJZPLo+OzS99Tp2XzIqymyLL0FZ7P\\nOJirCm7Uu3IhzJU3pufbF50D0uqeL17/+GfHZVnWjT9+/h9+4+ccUNXveZCB996p2h33DesX75BP\\nD+l4pRcunorBtbyObFHGfvrQbd6LQizj77DC4cCtcv7W7+Dxs+ukrHtH+XAq+72novu7oLOWhwjo\\nanDin14RorhI1kiO0zwViFjOptrstLRZN51ZAFBTZSV+8fw5UG4B9Y/z5P7oETkahXVwpeqzPGep\\ncu/4UfLtR6cyuZ5cy6PjyuXFs12gTI4qYe4trl4BFbQ79Y77weTTcS6tMC2OX03FMjXaDPf7v/gm\\nP168zhhV1UivMrh82Nl/Yok/Q+9eO5X/1K5ZQggRyjjTS97oTzRZmtCtW5sg97d++Qk/u3zUvZUU\\n+M1xaZ4O9/yT6U+7/6361oNb308zplyN77Tdf1Pf+W+SfM1crcf93iwucPyo3vxpHuWaRUFmX76S\\nmt416ivlNJvNdb3mNjZNY3lVICRSSKiGiifL3AmWvrK4ar79vzh5toXs85NwTzAgrTvQWSGu6TS5\\nQdyrwQ1r+uiX8QdWbSYB8P3CoSANEi6vLS0v5iMgzPEoX29Iy8iPc41JhqhThDFPS4XPVBIbYAhJ\\n6OcYIWDXdKVShYhlrR8wi4yWoSyZWqmpIIGkJBx88l3yN5ONyakKxTBYvNI1K2XxMuveul9Tiq0X\\nl+1GZdNdlQq9ePBmAV58VawclIK+LFhZlus3u1BZdg7+RFWCaPJkkcxpdDksrXn8bqU8XmKksOaD\\n79mLUS4xfzGx48HQrnE3f+2gSWO1eHOvJ6aDCqg8enYxzGQuugBUgP2PK0oq3aZmQCml5H1SVcfg\\nxuTl5OEobNH7p1/9BiFy+nIeSIeDeln52y2L4/iXF8/+Q7eRpf5RkpdUgfvv7yQs0G92htPToxm6\\n/Mbbzh9JZ/fI33/2ckVfXpP6xbNw66ZirK32QkOLMpcUhVsJmVoaI2+nPjwLFybtUqNEokRI8VOa\\n+j2ZZq6BK3YlWlwkwlb1CgGSMxwshKLXbr7//dfffEzstYcPP1n0kzxZqCSmap5DiEWWE0M2NrX1\\nB84aLbJzPT5N+q+zYAakBaijmJZEZlQY4fGY4k7BKYISQ1kWKYQQilRXKcVEZ+HKrW0DOkVhaus3\\nhdNNQjHsXweRmXhs4te4h1RVJRIrLon0/d5lfj1cqdQeKIBV7IoBi2p9oVTsMi8JyBoNXaGMFx5Z\\nHFOSSQiQBgDBaWmNX/YSemd+lEGk6Eo3B7XCZ2VZcr+AvECQQqQUAoZpXJaccahJb3WdIOJIUGBF\\nciCFLDKerbRBloSCQ85LRCyLmI7UgYKj7OgZV9b17UOjmuezBQh5IXpzprmuv4rtjmQYmLe/t7au\\nxcpJMoi5z01lf7FmX9XluJp68Ar2L615ubmQq7799mXP4Ssdp5psVHhDmRAgy7KaTwKWq1Gs5ylx\\nzch29IrBEGQKo4HWjhI7jC2W+oMeFEhFlLgVa/N+Q5jppHFLFsLyJxfXiwlb3Pq9KmfZ/EIMOJcc\\nxXJ1dvIU2g4E1936stHXi/MhvPHe7Oq5zBJiHQNY3nj/h52D7e5ey6pogWAePVpG33t8DEDwXWXj\\nfSAbaer6+f1FqSPTFU67FLZu+f1xG+mYul/ef7tVV5PtRpEdhadPgmH0uoRp7/HraBIOj1/3RtuJ\\ns8WhsgiC9tod2zp7454TLpiFzOB681dPHxmtD58/1A9sUlH+vKGx0dhg7ZU0RjrYvC5qQW/g3vUN\\nEydrtP3O7N3D4/uNYzdmnMUQv5NZmhOPzi8eKnq02nHX790HdtaOryg137zr18LPlNbff3VVGSfv\\nxClE/rliGSvqu57UpYRpGqcljb1Eo60C6s2bhoSJWrOFhkDKbq8eaGbGU8jSYNU9P3ux5M4NuLAQ\\nw24waKwGJh24ddxmyaF+tLsNfus98PYu2MLnKsgKpm2v7nVa1c32R2FZxLNpp17ubizbxmbDfaof\\nn48Hz+LMLITI46jLH0WfLcy7lezku4P77wNE8oJkaT+JS5FQNn6Fauo0gv0l4+pVePLZ0TC7ALuv\\njkiB1lfCb6pba+0Pm8effvPl8FYRmDJ+oa/9fo5vYOqqGFguL4N8b49Vundreg6RWyWWsvgib++G\\nkeKQLLgehlljgb6vr8oilKqaUiJd6/MkmkRluLHX6GUPlPK81lgt/MKx3Fdfx8CXeprHadRVtcH1\\nsv0Tm375XFHr8zkoGUbIMaq5rSFOca2eOsRlcd/cfUvJMmO4SEi3yKVtU0vJfvuDSQWOOlVRJGG2\\nSPMIQwjnY6Ej7+UT9UVvM096iKI8x7oLVQVUm8yMBp3ZZS05OtjWBQys1nYspeRKEO03m6/LZQ5s\\npcgagOXLygdoqz7+4utgGrz8T48NlwxQQ9MWyNy4/VbbAp43O4+U+KT3hrjKNw+XFOldM3zsHQa4\\nOss80MvbTU+IZK/LK3ToLdqz5jt5tYVJHwx/nk9ehM+SarWeDLxg8qnPqjWnRrGgpGjdzuE8aNTi\\nIpA1elzfeBAFPE0++uUJr212e9+9jIrbu7e/R+WvBT81iU/UqZ/GU9aIy6qfV170i1dfLQqfK9qr\\nBWiFZ9KeT1TlSDTLs1+fG4YXuPfCuM7mwjZTzzQvemLNGlYOgvD8u2l/4uy8z7nMpSlATPlLKn0E\\ncoRtQijMLvNSAiBN2+B8+ujisi8qe2//k/7TX2i55sifWzqgKpYISiZH4fwiyc/HxnSAEn7DdiuO\\nFVvVsBC9uJgFSQBgE4MaUi2SD3V9aClVAgRjDAqkU0UjsCzLoDRKS/ECvdI52Lxz25sDxegkok4M\\nEcev43jKg7HKQipjBuUs0JLChGWhjV+WvbGlrpqmp1XSkgZprhKs8HLBQc7Uuq6RhrtI8bwkEiYF\\npdTWXCWXfHKUzbHi9UoOVQTyTJR+DAtkV7WykFBCjCiUEHBRFCTxCttNNbeGqIGAhAATggEUJ+fc\\nqaxTqjLGECGORBgS/9mT/6jrXWi8UdHrWelFMHWny4sza0nXfWW1ecOQjmvv/RA4tlt/Xcl6hFug\\n3Al0hWzkdV0lB3nVmlRQSscFG9b8i3AQdlHLKmG6ut9S9bCi55LFXFzE89d7tZqBmyXfBIqjwByr\\nmq44DRkFi7kgRTq5AhzFDIdms3Xv7R13mZ6vo1OvscvC6Jvhklv6n4wvNYF1gGG0dHi+KJIRrdzO\\nxdKoVYldHvzghTsni+uno8FSr5OXR7TzvX/Ga4eFe1vohoyyq/PQ43pehGG0dDcfYEwBgKpEisSC\\nq0lWjVJRFqSg0mi0WBCijbektRcGJbgpAvJSFx9B76StPV0xlg36pY16DxTthzSy2ai19aeDaYQO\\n/9nzZyaLVXcPsed/vbH7FjyZNw9v3f391YzHDklZYvWPL9PM113bmL24++Y9kd0UsHE1WWfGPqiu\\nyeqqT0qqVvIEzlK3j0eDgbXe+gB1PxC0BSpos5NAC8yIEQYXMAnQq9n465xCApskiceN9VmaOlGa\\nMFGmkyEEpeKYGrKAsqMJNQMutCjJ5sRxQIthHJZleXwyxNA+mxPtXuBoDWbNofQ0p9Bh5Xs/i3zx\\n4vbuD9D3/jcRV+7/3a1lHAxjcqreFXaDBGcoHwXLM6crWeu3nRVLK168s/rvAOtpyA36mYxGB7cH\\njfbV8xdwa1/NyH82n0VZsYzSQJeRa6Ww7BNY4aNTlgyL9DWhvyqn82++WAQXTyp1g65TE1w/OltV\\n6Hhxdn6wxpIsnMmNkDqW5q20WX+ptKyx236gaM12y5W5gVpdVb08P7UbWLa3CExHV5fQnhd1XNmq\\nMsPYi2HbceHL5TP/u8Wx8uMv/v2Cu1B3mzqblSBqsM8sOrn5fej7/PZPV9+41/vq823L+i6NMJaq\\nQm3D0A4NATEtPFl3W5JhXSnPz27oTdM2TmESZXlYqbmurVz6/6zSbfV4y6lWIABFUWQ5A+VEbVRS\\nMYHxqU6vTaoASaUCEmkPw9WePh+lz7pbc6q0gEiefKvgHMLJsLlee32aYmxnaXQ9Gu9uF3DB1Gz7\\n7ns9Tvj+rdc1NO4fsYzo54MKSJXv/15XpFfPzkazHqrUvmzrQnAG73amp0ldXR/1nu5owzu3PAgs\\nqdCdt94L/NOLRXt0ZMr637P3f1Jbudja+ZzY7641+OOnpxwRiLCh1SRix497MxoFcqErkHYuAvjT\\nH+6x3ssvKy//OVj6sFxAq5Wnmub+ab35EpQhASEBwezl86Igk6Nj/8UST74FWj8EfXmlrbUGzL4k\\nThqcLtbVixC+GPjdZI6hls8xGMwse5ywFj+NVhT4gOGXUT8ZR2sMuos55RJTHeQ5TmNcQoXKmMJE\\nIRogVFVIVYu//eXXz8dG7cb7pnZqGWVejoIym2O1hAgUkViM+eULKxqZ6LoEBjSq3NKJZoMw0sLB\\ncnEWlYhLR7XbCAHBuIY1RAiPQZ5mFJWWKss4fXk09SZ2UPAjXk/kNgxSA2GYcbWEKJ6U2RiKa0dZ\\nrNYLpYjE6MTgy8bmvNI6IupRWRZYLVOIFiFL4mWGC6/wZGq4Cq52m0rjZlhqUMpqDXAKkF7tdADS\\nEtOMVW3JOFAAcpIQYBZ6frdRlxISACVAEkEJySLnvSsvQ3XCK0LmAAAF5VQlBMNw/KgsJFAbKBY8\\nLbNPP/1o550PGnZbr0gQL6eXkTDnDn8dPcdBUuXm/S8+r/lybRK4v1wIX9FtZ44QcqAy6u+dqR3C\\niTdkk1GCKtn2Vnar/rx4PDRs7fplqW3dk8aagLUSFKi8FDjU7//9ZtUyNZMSBCEEZpdSG+nAxDNQ\\nFEUy0VkGYCDLNJEbems70CKoX9mN5nH/kQr7te4tAUVRsEJywHoAZ0LGIrXTqK/KeqvWps6dOSPT\\n5KjRe0Zufv/46dmdm/8zkLVnXt3PDJ6OVfoMsgku0wN14Q9vXwprmSNBoCAISC4FxNHc7MhiNvd9\\n0wDJ65ea4lQLIAbhh//yX34S0D7mvf3N/l5dXd8AQsG8eo/+wYeV9+vAbQT9J7trP6v2Aj5ZmNii\\nlGoIBUevfvx7HCV/yw8tgYVWBeFyiegUI1Wrj1fu/5A4blMU/nA4vwKnuRsydzyRuViNktIbJd5Z\\nwJ8+tuw/4K01yaphpuV8T9l0R5cle/oLSTedyvjm/Ze3q1ddu326NN2DP81S7gWCMcbKXFNyy7Eh\\noBhaBDkqj3nBgNI5nnaBUoW4C5SCA1UPPkJowOfFYnEnxOtZqYymR3s7q9V2WzrvhILAnZ+8/oTI\\nvR//5a+acRYryx+xHq20m9pqGUdB15YzVh/57UJpECNuHVpAKVTNlfnC2XTZznuZtbwXf32J/ms/\\nLX3/0tAgKFOJhdLci9hUSgS1ZRzOVGWom7Ou/t1q8t00vyKV33n+cjHMNuLX3xHg1xxn7R2DZ+zi\\n82Q2J5X1HbqyXo7HBzdbDK8ohsbVB5aZzouVo6Gul/2VNUh0p7WbZ4PBoK7AStNprq/UHIcZ3eYK\\nvVxu/2h58blcb34xCtByrKD02zLx7BvmXN2/Fv9EEO0ve3/vLP6RA77MUk/Tia0be3WZ+vGD37nN\\nc9bdik6zNqT5xu6GP/mioO207BvGdZEu5qHZqLgwg4V3Ry69rFDyrKRmWrLMqYP+wmVSy+JvFSqo\\nUhWSSUB1BaUlmr4CO8p51V7d3PpjAMswPRPQ39ofvx65e7s1rNmShSyd5vUdXzUCUB2Xvw1kcZLs\\np02qyuM4NSfPLpex7Tl/iJGWTl4n5XHN4K11Mizcs9Nte+69c6++uPJuNvsH7/8EKfDYm/fpHVhv\\nXj47rWVjREA4WX95lqHNtWgUaDdubG82e18/tBVRs4BGhWXzUgIVyjicLZZJE4nMVN/74Y81VR3m\\nL+sHP2BGd4Zltqjjyv2WTg1CUimhlltWooJyFnl59LxRPOvSi7pzntUoyUQ06EmDAH0dIcL7oQKC\\nMMtPe7w8mhPlMtJ07zXftRAoUyGORCC8QcIEYUmAQY6kspiAdAoRVKsthFlSJL6Uql4zKb9Cx//G\\nmxVM6+SlIqFVMp5PE302qJCM8AtdOWXKhbQ0LksuZCItjDSqY7vCMRz73mNv/DKJfKFqAkggpKJJ\\niARnMQeyZHmnyxyzwOwz/3osXy/4LFBltNMBWMYKEns7RCdzCCcqTDBRnEq1XjNce854WmRwPAvj\\nhcizsmQm8SM1n2VyEY4WKgigipFS00lVpg0NYD8vEmGQUrRIgeutMIQYFxhxTUVmzaBWhhAqsumm\\nQ2xZaiohlCOYQAiFEEVyChGmhOsot1QCkEKIpZiKpczC+QQ9PZo8/NXPffx3oa5wUROC58UQQdWs\\nBRX7KhXEcOvxF0c1xfJfhKd//sp8Lr8L615Xl3ai4MF+FuILOC+vqDcPaZVpXdRQnM6i3SJw9q1j\\n7ONezV/Ew7GYBIuojDXtx4hYfkIZ5pJxhOuaaiIBy1FYUS/0Yky0Rbsh19eHFMNuYy8ZlD//0rwg\\n3XJ0macY8MZ4AEfBZbyMjepSU6FjL0RP8DS12lDVN0eye3V6+fKz7x78IYLEhk//iqwdrm6pDRio\\ncQT8bDgNwUroS3Fjrba2ZhmT9HgRXxyJGaQQQhVLiKRJ3Xo9SZZjJDM6OB5MtuZSfvFl/6vP/s/F\\n5LxVJ2921O67/3nSOoB8EefWsnfr5y/2/+yT4mSROwDxdVYHf6M3ze7B5qvf/NWu09v62R/3h5u3\\nnPlgEHC6NowiuxsaFjKgW+7/4a+Kmxxpykq6241aIDp6BhcReHlxqpYJ9K5RMsL0pdNWm2RjcNXM\\nOc8TlkXN3HDUdF7UWqO8G4mVXNuHO1Ui/eVVDClZ9sIkFwWDaRRJYmDFlJITzQoWxGgVgII8FgW4\\nCSABwghSgmFk4KxaCcvx6XLC8kSXqGC0WRrdZVmXQWlb3ZPXK9ot+Mlf2XF/dHet/GDt5z17+4X3\\nzqtjouVKcx+5eZEPJpfxSiZ2rxfGIhBUi9cPCg7N1/1NmL08+Gm7eiZHQVAURSodiDhGWhwiRScs\\nSYGA6/UcYaGZKW7Auw9O6+Uy11vjaSEu4/b6pGaUb1aevh7+llMEuzcvrl/MQ7rmyQ0/PHNW/w6D\\nCoMGQkahW2fP8j3V7+BBZFRGfq291qhK76tfkl60I623yca7B93Tl7N3mu7rh6d7euXTCANjPtDB\\nVWtXUZxq73KLeJb/1ZQxSzx+SeJrXT2r1aCu65bDx2h3dW3t297PVCMNi3fLq0SrVZejRs1axpMa\\nJbqCmWMWScxzdeVhilSFYcu0VMEySFRDyhhpFpXF0gshHCqGYhjW/5jruGJ4s2U1Od3eGmfK3SN0\\nQ0BDTUcEynLjtn9xQo0Ozc6g4IWIzy/Y02eNh8/CbJAAAJ3jb/PXfSvl9bKskXNh6vNJo7v9QILl\\nbPwqxfEJ+xAt371TPNz7Hn7IbmiatvKj6ucfrUh7K+97s4c8CnZwMS3l10zRgum3yRU7O14twMl0\\nYWV6x6Kf6ChQZOSlrUKtCGW6mFxRWaQSzl3nuqiF7T8U0echhzDXHGPZP51U0xeMNdv17dXDG6XU\\n5oMZoWNFyTjgS28UwfnuAaSdxtW0gRGv60yUkCzHVeq23QtsFAFGpBesus+rNXb9zFszmdAn9fUD\\nQpiVjkEwEaxQ86u6FuuuWeS8IYYVHSqmRnTbad/wYh/iyvrOnt5cavDrMgogwQAgio22NS/LobFG\\nuO0ClRYEsyiCEnHOaSkgz10HIWJ2WptU5yW7LpKnkT/mVEdOBSpNICQAUgLm1qysRCWERASN5kWr\\n+titnuflPDBrSucAIDSbAXtjv9Ze0RwsCYAISWjlvJJltaykVGtPL7FKKxA6Cslv3DOAyQXLTLsH\\ncRb63uRyIFMGALAcDIrAy0re0JJuhchVHScEIpUICAtLqRnWqiyVjX3hrKw4ra7EgqiRYjKJoJBZ\\nUS6lgAQlUsqqrgioGaRKFGzXAKJP/waFNYLVyxGbLn2vPxlNkNUWroHHoZdVMrsyrDatVfqkbXz5\\n4EeLu/RKeVZ+/UKkWZHVaA7H8eJciiLPRMwqk7xTaI2Zh6/Ia9uo7r+R1LuhE7wm4+FyGAL5RizU\\nMk6uYqsEqlRUgWiZR6oGbn3P3dySVWKpBzdq3Xq+lDlSm9Z19M3HTz6OS1lWzDGSyJQ5yhaoLFds\\n2agToikG9WpKBpNGZL0xDuzLj3+1vvKdsvd3Ms/Iumnip9R88yoWhZYTNSrSq2zWCIfDWqOzU3O9\\neNBgr4PLcTzDVyHlCBScCBNdXqu6HMShVmuOXHoxmgZf/g+fXjz7/1RaL+58706hrKz96H/+839X\\n/+xR99VZEfsgjk/p+UcPP3tdJuztH7xdLARtGpr1Zjh/3t3c3Lv3e376XrL1XnPz/PokuJ7N88zB\\nNs7SMuHVj/8vV//P/2+WClWirrthtoupGIfXn/wbG9pOda7QEMNJGYPK2kFra9EknhoymDDIGRZo\\nx/KXszIPZSk1qjooWyfhw3imDi4n08ncj5MwWZZRoCmGJlEeIp4JQz1PLJBxfD14HXElzqCUEgWy\\n7Sx1O1XQUkMXmZcBPFqOzyTaib1KMfOX0ZlWaRbTf/M//Hddw/lzs/6c3vypcTO4/Fx5+smFZWJF\\nX/HCNYT9qnWdPC5j8LuvLp4AWQjgZnCNoMyM/3JlteToT9funjonrxBCgqOi5AVQAv91kidEZEhY\\nDK1ACKW/igKpVd+ktYvzowEgub01Ph/5u3a89sb3GsNZd6PJofaWffLdqfpqagPMsLld5JQLBKkS\\nFu747Ivt+11nO3o+d3V1AYC7s5+Z/bOvnmRXfP3bV5X4/v/61aO+qGjl9UkwLpSrk4572qhBwvdw\\nmW0YT1c7/d3dlxws7eonQXalqJCqJoZinLXn12FZrCWjge4c+uPlytrV2AeLKKo7Nit6ptrkRYwR\\npcrk2VUUfxohwwVAC30v8FOAiKpSXuhlwVF54agBVmwmBUDIi0HuuTTr3dxZ1jbfPQveefiXvNty\\nK40SU/Bq/M49ZdlomhgtLJWaJgvn5yw5ocGTtvy1QbIHv2tWVjW5ZVFTru2i0l6bPPVS0MCYWnzw\\n6NQ/OTIc9bw8IB8Vh97Lo4yb896De7ttg1FZLur2rzSXrRPvjVvHSjLjbNhYhezyE1DZNKfXarwO\\ntbUm8t94540G1WaDQZbCuP8k8tPWvffDo5PoDPXOltP4HwOsPXv874LsCkIItaNFkHAqJsn3CLyn\\nKiElCy6hqel1IwTx8Ow0mMKtPMjjILRWKCHqdnewus2h3cohmIVKhy4LNaSOaebT2Tmb8Vq1sV+W\\nZftWoVqxFNBGX+/vQMdWVUxQkXZ3jUlAWUizcqYaKy+OF1PhFkkdCZWKMSuWQJQICUkMa3VjeNwH\\nfCMPU4YqIi0QBoIzVSwUsiCqU1dWW+sVbD/QaYOzNI16/ecXyUCWUOVqBQNKAA0zAZybnHWTkgvK\\niyyncqrijPuXNObVqkM0zsOI5W1W2hAokgPTNBXFQZASbECp1Kq2BCUvmLvSeTWySv0PqNJZ+j7S\\nXWK217qUVvKsiPOsTIocm/6jl73ggttYU0wCNC44q3arFCslhyE3ctq1nTeTtN7S7wKuCj7lkCEA\\nIRZAQF1jAAUUFcCp5zFNUoyxg27dTCtQOOMX+lUceRPPY6q9zbWGwONCcowKhGr6loBtu1i9K9pV\\ndWe+1/obJ+JIYBEv/OMrSxdmGI/OwOwqHy7hxSjsvZy2qhuo9j3b5Anhblv6yVVnI+ecu0Vsno7i\\nBfVyPUEax0TWb6j7P1zU94GxrSoHRbY+Xs6l0IxmNU3/WqBPXFO6TqqT0DBh1bgyaerubEdkPcz0\\nEkbELG9+CEG5NvjkNUj+xeF68/LzDZuhr38xvxp6kfPGjpXGYxmEqj+Lk2wRs5PQc7e2fqvAvlVN\\n15rPyLyfRN5sLmOBEAOyTPMYSJRbwJr54cq7ArNfbcg/22txnG8cmnHa/mff/uuX3w8f3TJfvtHJ\\nV8XIBs+W4/9WwcD5/j9daqBZmNPGngq4H0527/yppanpz7/jqB2BaBbSMguD0KsLlYx0b0mOv/zN\\n+aczjA1Emrlmru2NcPYqMKueao0hIsRvr8c8sSNlKygNvWJQJzdsJjWaubtGe2m5bN2UIFSus6pv\\nQY4/rayW2vJFlnmw5GnpUQcrSNQsZAdepaGUhZvm66PLzIxtK57pjENEprzpa+vXflc1crtltmsj\\nHE8s5QyyPIQjd3P/KuzZ7KKmJSvq/7Xkr83uT/3Rpidr7dlHLvkuPPedjuqihhBL1E1t69vSSzQ+\\nVs1tgvMs5yoOHPoVqf43Ra4Hcn3vnaFJsSw9iCTMimz2QlMb23s8zu2CVHIuqmshLqSqd8o8xNl/\\nLLK0QPfWu5W1D/5IyPfqrYy69auihd8wjZeP57/+CwOZsawKJpcxTiANZ/6GdjytvHkarde9a6IO\\nnVpl7w2tXX80v3j++lhWyctPvnug8u+W8R6LLurgxfpadONGY/3OIbF2u826c2MnM7Z61mGlERHJ\\nq0lv3XBs2Z3lblUsm254EWEhZzKt+8EsVbadfGq7AKzeqW4aDIi11U0FJgYRxWzS6lwCFbqK1Kgg\\nIPPmQ855kYUQMSKHRsOmWFERkYIqMMtYz+CptVF5On/r6mW6T76yas4yV4RUOv2L9tvKoHyHyxVT\\nc6VSQfkUFhdV8/HGDzYdgsT0Q9+Xi4s6V7iy/obSC03lU5YKKLBdi8XyIpg9Mw7cv/hKGfzbXKU5\\nKNNOU1vbRkRVFXOrv7zh5WbFZs2b/zBOztNoXY9b9ebibMpWyFFnA7ntO8scJfyNuz/+RwhzTWNU\\nBZWa/5u/vjw//og3JoP4xVo0BKpZ1a/r6nu0WFTbal7IcCl0rY0L6sddyDIJSoDL2o5quRWyqZwF\\nlPoejfqlbGX9SqWzklXe1oDNcxxchtfldSq350FRWA906zzumWlQZmQH0bJ+sCUBB0BV23uielfH\\njnTUlz2VJ8IvYoqqgnMNsuLiNSiQbumKqxICqCKLMu3aHVXUne6BDI4MnSoiyvPcMLGmIFosDBNn\\nBRAqGqN1Xb2lW+uq0qkZpOpMvdmTaL4EACKoqhq1Aaz6sx1dXVOtbsRuVWnXTYAoVAWxaChlhlSU\\n5F5aDIVMACBJzIPAo3pD4DqltKJA5Mr5XMUY55IjsmLMexXVWas28/GCBSrS9NDeZIqaAl2tryG9\\ncC1sLz9X+SuhGULWqKxiKaBmAqgwYj38zbnOosO9m0WGN/buQ1I3yIyisswFhCRJQCFLpKJGU2DS\\nlqSR5BIlbFGo4872kcqvC38BbNtSgMqAH86BxDOyxiKdaq4DujYjPCVQFs8nNjWkq4QqGFYPd9qr\\nWBcDPVkUo2Dw/KJ/dJE0Hmjrt9JKbXQemLbhTV/Alr67U63ZKmlPibnkUd5isRsoud8cjHaOXpKr\\nqTGCFWwXtcksp7qAFaRIUMZKHd06YJRSAjcUu9ZpUMpq2ZxHY+gNGERGLP7pK3gDVT6m6atlpa6u\\nnuU026V/Np68djOj23D0ou+gLJmOMPFpNlp41s27/5OGmkNoYEIgj5i/TPlw2YejqBxMopwnws8h\\nMSsNP2Y7H/3Vz+vVb1cOmhzIOw8OldqNNeOz9/6wVbuz1JVW2d2CBm9YI9O0K8r66DH7fKqkhri4\\nMvrXr6yVfxKoDmme8tjvMfzP/x8fCfnUIss0TN/53eKN/ahqPjpwp9uWEXMjgwoHDbIZ2fmLq5ET\\nX8v++aS+ta2oTbVw5pmd49oiVgpscaIvlubF5erYI1eJ+cy0e3rh9+OzI+ZjhwEc5stCFEnqiThX\\nsFKt1qGq67e2Cln3pr5ejFDyTWdX3doV0OBASmcyuVlLYrSSQi0QBmjXocu6TatbjaJw5fz1U0zN\\nelslJGbmSRzKRdAp9Jp/BZudvynnrt7W/Wiqm6nBtMf9/WcXJ1u1jwsRQuwKWcjSM51Qa/9D0GnF\\ni0QtQKH8mBqaSiSQzGwb88VZq74H+N4y4LNxKhl9fd7YeecPGNK8+WB+OrHUvN87M/QgAX8nQ06x\\nutVsicEns0GwJ9XnbqdfQns+yTPGGeOXC/T1k5erb7nhS1cJklKZQyIC/RBUflzZjLToM9H75HFY\\n5d/8K6Oq8n60Y/RbN53WW++z1b8deO+3hbK/vsvs94toqzfewALI/tVP3/9oZ3fLdbtNVSFWzMrc\\nmM2ng9iC50IEr46GSrWkSv78aWUcrEqkJ/iBFDEBaUfwaje+mKppWUjsUoNopmRCFqII/UhRgpR1\\nuIDQqEtICJVcNHe6i6B8Z/RrrQpPysazZVaPeikEeH3/xen08PoJzBJZcG2/WtMoF2Vy0F48vcwW\\nolnpMuFNXZYOpnp0bub5ZYIyrAzjPEEr+wrOgvnDi3Owc/b876x/rMcX6032yy+f9+kND9Ag1V04\\nmzxOoA2fzHfg4T92+NRdRbKMDD9XHZm1VuYnFwUgGeYLer9u2AoKk1xwUpj4wojtuCjR5MXO5qO2\\nHsV5Ecz+rCz5kjOQIVDmBJYQMSzDvNBdPaciDhlcTgt7dUNen1aNhZAxZASWVBIDRarh1laaSh0O\\nMIuK4YUhS8ri6o06ocvhty+FcjsIfRWajNWCsulzs5ca89gqVWU+HflDB/AkSyMpULUGsBhRshwc\\nz1nRKDJXcNTsuFKP9LaKgFkz3KwMIIwALwTzXGyZFtVdO86qg4gF15qNhSRrkFscqOpqzWwptJiB\\nfFFksSghhnTFAXv3CmW1Zd2+b986qLzxx3rnTipNqgGL503oC1SGfgQSiRDg0owWy/nySgAAJJSA\\nuTWpKNTMfUs1pZR56fm5TynZe1Bj5pUXhRQostTm0/h6HCVhJY4lVSxkR2GaAYkIwmsNQ7HbeaQR\\nTktYHQ2fmepiYb+9SNHEfD8Ld8rSxxgCSBFWdQW+PgumJ8FStgC2DOKgwayS6CtLZFI7lopz0NYc\\ni8PstaPGqCgQ1BhtuQaSLuEmkQrzpqfAMsscqxhZ7GCBW6leKNXw5uqjN4xnq/7L4YD4ZWV8JuTg\\nK9V4p1nze8NCbf6Q5V3JRejh+bys77vVNrbsRcca7OOzg2RunvHpy/qF7veTwSBLg1zXAK/Atm5s\\nEMsuYaVjViuVTa5BQvsUnxVwiRWJiJMNzrIXx4Wcy6C96N/8/HFROHujwecra3XdRLub3k432dVO\\n60XJwkIg1svfcpoVV9PyOQVSOvrYZgOaLeOrQX/uRWM/FEmuOD6s8haMR59u76W2tR/laX33T8zd\\n3xucJKXaZaiLa/vVOZlf6txY8USwCtIDunSGnw2+k68nk+X1cQScXQVcpOar82ljTcu//humBY0K\\n6ZhQ198Ru7+P3fr6rdnmHnesEqdlkoMktz/79Gj7jUAPZg31447x4SLu6rZatcun3y5fDUkg9EUC\\nk3mygWY3neP1tiyfxc43k/qzfiU6WsPPdU9o8TLPSyZgwTwFAUujkCpawPRTHD7pE1xyfoRpVugt\\noLoQUUAwPjik95xbMONcmw3Lzx57wlFS0syJpYtLR31VczegbCAyYQkQ8WF28aJ68YlrHrXqxFy+\\nzutbs5Ax6LoGdU69Mp1t/+CQR0WH6gQrVI/CsPziUfxq2PaGPg/bYBD50uK85JIxVA0iv7uyx8o8\\n9ZemtRS5rDghrB4OllkUhA/e8pGgavza0/6n1/PKeaANzooE1N+53x9dSyTmRVJS2nz86uwyJaez\\nfBkCpTwf6z+uOZd2bTdPiiTncWgdT1qtloHxU8F64OmzXOmdn3CjcrX9441p1j2L3hxedRv1uH7D\\ncTZufP7r2fUy909+IQX93R9EtcP/F2n8pNHdhMzkbLVR7/zpT1+v4qXYe8s2VK2YpuZPc2bI4Mty\\nGvBS8cZFq3GgE6bfqOt6icvrNBM8Q45jQSRLxhBCBkjbTokBZ4We+DjLMsEomvtg/e2rZ5W7nW/r\\n+2MCyuEIUc0WnEJ3z5sQk32tGibHRr76TgZVhOL6djt4PYPQWbBq995vG7XlosT1/SwtSsYY4znC\\nAFzRqpUp+JqHR7WdK+N3TQWA995yeLo8/ijFcuvlS05X3d314+9eFP1J7Zu/0rq3xqdXVXv9zns/\\nuW3rs8nA5YwlBAs0NM96Xr7XaO6rKp+NoNrU9MYTkR9Fs4GzFzvdd2CJuXVvuUgWU0HFQlGILHlC\\nbKLYFMZ7u9WBJ3Npc8QvLwZHX3/na4mUsGklZjNaBiVVQYkwJw3aWckEqzU9YRCpZFGBVLPT3sHJ\\n66c8assioEpjzGong34yk6jgohAUWBifKCAw5EzQUkoh1ZpKy+aaAPETBCWXaB6FD1/3vbxj6l3H\\n0s21G/NQKBrCRIR5XzNbkK5K0MZpqUYDYVxzTReiAESBxK40moaNuCilYIwxLgBHQChbpPr94Rns\\nJ3vP5wf9/Ic80m1M682ssVbl7QcYOiuVJVEBUgyqVREbsSTigpkGosTQDZtiouLMMDWdaGWxGMXJ\\nyaiy0P94ONLx4MSlGqGqFDNe9BVYUkRhhZY5gUWuUMENxa51VNKE0lcxiPJw1H/OFJqPjSbjsqwi\\nYHLOBeNSICBxZ13Lcs+fH4XTKItSVIIWh0gWFis0srK1WTEtGi8iZ3UVMmwY1m67VSNInS9JfyQn\\nwez0as1GmmY1FWbVTW1+Ih4P6tqNjdX3sLLxJWIDhIEiBkXc29/o6q7de/wf1dbvCn07SQsLBv5w\\nLip3kFFXDMeyN0G1yzd0fdMzqn0GB8XLHp8swl45Qs1yY6W76mowg0qLOusrB+vNTq1/pbpSIpBQ\\n5ENLcSpZ03q6KQO7LA09bPBpma5t7AYXV+v3fmdTb39I3Tuy24Fd2Fj1N6te/vpIKO8Pp+oSsSAl\\neVEqZmoYI1DM1LLPZAwRKgoyYUa0AIMsmJfcufmP/YKHy01hvzlfZoOR2ajeBKiOndrBG0lDAFoS\\nzqw3f7p/eG+w7ZyhxZPp8IlgS79+r7CSxfNgCPZ80p+f/r9Ns620HsyWiY12woxGgeaxTmLD3Vug\\nzMJgkvrzi0bl5uVpdoiubdmRWkq4uSwrhtuDZ33r8sheDMpRjByBW5Q6TaNdd3G+/Xujzgcy5iNz\\nvbt919FxCEqepDmAwjRtajlEKWvfqzXfmdmVi4bmcTGHaCtIzSxGSQklUzN/HWR3Kp2hqoqoHxkR\\nnUxlyI00niAs/XlGtDVz9c1hcKYa6kp9eGPz1c3frY5Hs1hAdTONeibNLcOKjQ2ybf9Fp9kZe/Ga\\nE64YPheZyMU8vY1nmfPZyd5KbOxckZsWixGlKsKKny1Hc7qy3qV6JvkgyJQMySirXI/s6Xdf6DVX\\n694VVNxYZ8kL0x9MwPXcot4sk7i6SEbD1X2w19UAAFtkMnnlJ9+dXX15bDXo+UdjWetcRbpaseNE\\nID8Fk1St78pyORpMdf188XSmpCclWPvl5R/Neunxi6Cg7Gy58ii5e84O69mniXdh6M9kytU3/ksf\\n/U6Gt2dlG3KziHwom736/651qzr7glCG5fhq9vG3/vUCBzEtFmCeh+NUwptxwliqyfV3yywkabi5\\nSqihCY4gxGlSVvX5+tZNAxaIMUuWPCuKIkB68uKj1u2u796CIdjK08KfBLSTA0SmfDUKp3rlGigk\\nKbPXD68kMATEr6ZbUUKBLCNCCn5rGq0jxP7jX54LqlZc05umDFUqjZHWvU+kJ4tn/bz+5PLBulWr\\n7R+aqvvg3q9FiTnPz2dKarT3nLB4Ocfjf11vrE36f5mgN6ehTjbu3DWXN9aVJQNPny8y/sILeIYr\\nOcO1mjJcKry8iY0GksvHL8Kj4QYQH4r5S0BgyVIbJY7CVFcphWsJTIgiVEfjN8ejOVG4ECKRi+l3\\nT0oJeKXV6K56npdz0Y9wVpIka9mV1YVqHi2t2VyfzEmUQGSY+925uozzItaadSbss6df+Ze9tjvU\\nTFAUZV6kCkr1iqA8DGJJJOW8bFb0ahuWcklxZMjYtmrexeegpFbjTiH3clHNs0gwKoh5PVnxsgYB\\nkusKt+aYKJJDBmwGsAk0WUjF1G1LB0AgmQlehgWaTYDg9bl+OOj7k2GZXZ3rSWG5gdNcTax3guJQ\\nzdpGQ8fOBpSaJKZgAoIjygVgQGDIhYaAaVgaIAom6lq9wrIp88fa5FHbOLB0htSR41RMqVMiVCIs\\nlCYJ0wjEOFEUHAizyGXVaUqmcwm4QDnDMhr4hQ1EQOtEos3ttuPlAEIooSzjZe5aJR7k2QCzGSrL\\noEzDdCL64ebqTg1W0vlxv3F7M1n0w6LpVNc4RBej+Go458mQjObErWDmhEvQeKNaU59Vl3MAt3z4\\nIMDtEDudNWigeB4V+uYhVUAa/zzNbhigjgcTrtBCHdtuSzFdS7Wx6RZQ95dqqTZxWaLxsBwfOe5i\\nvXJ8Ry7o6OYwPziHZcyNMNk+VX5YrTbt/KzYv9VtTJAIVDcrgG3aWaDUD3/SQMGQK9yf9jz7zuXl\\nQ3T/n2kTIMwDizYFWCPaem63AjyeZmsV43qzskhf94lbRdhVCLE0WXNf2fnZYhovM9A/Cl+F0SSF\\nv/nFp9rmH/hPvqm2WpJ/mPtheP5sNLzdVUtDrVD9BmnL9QYUrF/d/0fQ/oHe2S384dXZWRYOJ/0l\\nU/e//OZUjB5fXILji79wTAXn3UXUMZzuO43x8omvqXbU17KJ8r27augHmsxk9E3u+atrxsFPai1b\\nC5dloWbLeR6bA9083dgvWt1gpZJO5xU/tTPgSGq2HPMsuSOKalzuZ2KnMDcQLhPOmRRRQWrNWs3U\\nV2vNSdQdjWljpaY2KrlkWoZhFqe5YCWAkmNKuFonqhKtrEL5WJo9A6VGcFRzllUGF7zm59uPTkel\\nIMFi/sbesnL4D197Ox5buTqChhBu8vH6RtdHH+ij3rs/vSwbPz55/cXO9/9PRtNhWWpRrkw55tPK\\nDxdg64DjWwg3dYtX3RlFxuL6pU5pgawoySG/iiMPZBkfaPL617k2tLizmAd2uVxtHnyw/Wg+HK9U\\nHm5WmvNr5dvTDohfz+dtYK1srlWpNu+wr9b2r9L+X6m5cvON6GhBT74+nfmSEZmZs150/Gp+x2k6\\nIhwWLN+tHr/dInoosy+v2+LaHD+3x2Ozd82/XZ58FOlbJbFOWOpjnT6/fnB0pT18pcUngVlXXEUQ\\njNKeloSVHes3O6qwquX6rdBuTFq3iJBpc6+5tpZZ0Kg6JhWjq0cu4jGFZKi8SXTDtBGQZTBnZqvm\\nyR/VVX3DLFSdAAQJiPVxcatxYRyuTuhK6FeoAhCYYogZz56cUB56JVDyMitYaigeZIKKcr18QURD\\nMImfP9eMS6wPIc+2u98Rj6RBVtVQheaG7VgcfG/bVXjw4nV0di1I3ZiGH4Zp/ULekMoyhVZ6fZxe\\nEH21Wtv8rtvJYoHM6p3rwfHLRx/NypWFvYXXNiChaDK6vGJp2Bqen7rqxnTJ5+NcNV4I3KFYMWS2\\nGQ7SxHS39iyD8yL0ysXY6Lyaa8Vyeqc7Vm10dTnAjqYjbumoyIFUUAd+vd8NFycTnpmxc+c3v3m+\\nGGqoQKIEGVOvn0fjZ/PR2WU+iAp/slwI/ea23F4pPStdjLNCIflcSX4tUKg7FiRUSplhKTWtcLd5\\nxhAqVJ2neTNT7puGWqSZUCqL5TRTFTP9iutAlIqh2ACgIsrCvK4kWXTcg2XBpFYkVjj0kZQasBXN\\n1gBr1jRKbWTYCrUJUhiQy0KwPJqGEy5h1Jub/a9dcCbIxF5xRz4I1BbnBAtQUhXCPaTUgFQkbAJc\\nUHWm6oVBGKGQCyiENIgJ1DpgerN+sxQpgxcS9SStUzVzqFCxATFlrAR6gZEQnGNolQjPLnLmFcxU\\n6o03VVUlqjIZLhU5L5OL2ayMRyFjRcqpYuymqQokFgAryaWOLYojy44RAgUsYxYXaPtQVdUyPgeN\\nB6YYzPp2gna4gL2v5/6pT0VatRdQSJ1AhVNFOYCyyOiYEMR9Nc/zbz4ba61djoCTplN/d2HqGp+x\\n3MOVLSQXxZxnBZhEKueY6qaKaLyUGII8kcjzSJUXfMKUaF6rwRvrxlbVaHhrFxfepRjH5tTbjCy7\\nSNBs0MH2VogpYCUQJjGwZI7R/KOnx7jSMgD0S09m33wzfFyxW/DySbPRUBA1OFaCsraY06B/7eqw\\ndWO/UAJnm1KIal3X7eqMakD3NC0RYT5IR2ToX15F0y/+OcA75fwr2NhYjMAKYLo1Z+oQGTa2qoph\\nSNKQ1m4BFxI0GfvwfLLtQ8vRT3DP54tpHijx9RczPi/gVfLql5IVgtG6Zc1eftV6891GewIRqtoh\\nL5ag0bSon7Nwnl96Xq21WZTu29d4M1Bzi/nB1RWJs2DMAHRkq6KZCiXczctMUgIUSN2KJUFYITBQ\\ncEdyp8jqGMAgLVkmEKqYiuk2OlUHtwSQGaCNdpkmJTGq67oQyusB45wDACQmWNqa6kXHI9UhIJcA\\ngMZqWQVr+2slBESkMJ18W9Ng18XKxt/qpwdnH31MRp2NfWRtY1BdcrqiX022f/uPpPWnSrAVpGWh\\nb+YKJaKAyhumMavWPQXfY4WehiTz8znXFRsDbBXJGdD05XIZFgzKGIOSaCpCI3XDyuPA2b2pyvDg\\nxu1Gd99+UN9OxxN90+oq6qvw7Ne+vuPjyvaLkyhBt0QWoE5EKpVYnc874hdh69vfXMjZd1HA4yV4\\n/EyGZ0nzalgSFVJWRMA40G78gQK2odV5snLLcOrHZSVWNwp3ZWx0R3miS5bgspRlfXwus8ls0++3\\nVydpnJkQWJgUuajhsnKzdXhY7TQJtTQgsjQHWZHKxuFBA1QNqWBNotDGn1VwbOpKdKJhTSlFjjXM\\nvTy3NoOpbGnr+3ea6w4wNVVHxdsHw7330jFbuZ6pSnQZF2KzXaqYcy7x4AkuF9M5SWCxWEJRAiwT\\nxRA3vn+Hyygs2M4bSTDnV5d+HPDR5BxXYx7FhgoQ5iXUccwOfuvHazXM5FXx4gnUIxjjuagcn8GC\\nurqCnK1ST55zd39g7Kkd49svXqbW5qT/CfFiPkrRPLLjqpQU5SPHPbNwBTC/bkuQM1QUNlkC7xgW\\nlgMZJ0+s9lMA29VKxEvmZcnFTM1fhTCZWTdWnc3fv3n4hkIRQohxaRhGQyxDoVR2le19stucu5zU\\nax2Io4vhy2GYEaUkWqHBc6L2RHm5CMaFv/DmHBhrkgPEZFkAiSABE8UNoBQaMSDPc17ECZIBQczh\\nkhEIABc8bgCA6u3afLR0nTvJfJHJAdfLMlehZLqWqFTqxQybieomAbzKUgY50xUERGSj1HGUEkBA\\nHYAtlTqaWqEISQnDjC3CpWClmIwVajrmhd26ohTHISj47bmXx5ElmDR1RQE5AFIIBSMEoZuUUQSJ\\n4jpUNzlEiFNEFZOqCGqU0pS0bFQHADCIwrIWFNy0iaJUINCQqjNO8hRwhqTUUInKcCRDH8LUdrtp\\nwqq1RrZ8AtA858us9DUjnsyQSGZhDLMCSik1TcG0EABziRGlZZvC7spt11Ahn5xMNmiLoeSz3nxP\\nAkRf/srSeiubs1U7QiSzqvq6k+YeMm1SBlJATcdLLfaPn/yKrr89X3rrq1EHZHliJoIthK8AA3C4\\nvA70mgzOTlq2UnEa9aoBGOcQRfEiFrTiVAkv6kVoBlWRvjca3j6JUrU+JcZrHASJFw16Wt2ivZK9\\nvnK9QTpnVQwRI3VCddV9Q738GuS6ZpUYLRRyvHlwPE3uWvrZUbhhVvWM0/4Uh6cDM/la0x27sZ17\\n2eiyPhXrdG0FCzMpOimqBUBfa8Pi9GGeH/en//5B5f9mlCe2mqdh4/o46dy7rWnR8clCU2murc6Y\\nXgAN4gozdo/HVIUHRnIdLEGsdIxWdHtvueekajILFk+KSa+oZaW8KHJ9bfdOopyXuAPBW3S7noZO\\n1goqDbW2tVWpZDIDw+spU5RxfP/6sTo5knFQ1mtDEwY69SEp97TGcbDqWY7m8A6ZTVNYIJKSlrs+\\nnUVSKhAamChYVXXEsiwPg2hoGBq0qqJyUziKbqSQ1qBR1Y0AlkxR9cnV8OLrZxEnAHAAqbQwsMt0\\nOqc6WXVjHWVDz37zg1WtVS2BojtfNfUZyGXr3n85fV0ff3JSc6etboLMe0V6O8msos/f+uGTa/53\\ni7zTxH/FGLCxvjwPYSGD5Yy0NMYKFRMIhE5LjbJiQbCmQsI8L1ir7OV5PpsjwdTQVyI/U5qiKHSW\\nXc8m+40GcW78DLUPif7m+p2H7FnlUt3SlSfO7PNw2X555vSPXujTdGuLHfXXvp4SzODpvzPpn7/a\\nCv/697a+ek/12lp0MHh0Y+8lWjvlJVpZgywfZuTOp9F/PXtSXVwH8eCGVuRmachcDzPBvWnVCVky\\ndXQTY5rPj1bxaefePLZqFlbe/smN1k47i3ylmRO4a+x9v9u6eT3M122VRR6uta2xvvHG3c5+A9sH\\ndffAMC7shk01aohrVyW2W8lDQ60FJ9907GxqN9R59mPn7Qe2CnNc6O/+CNV/Tz7vlQ+n9dpLVVF2\\n7zSoATDKFDK26pcqj3BUGMzvmgNLLUuufnO+bZElVRtq5UDm+l7TMRy1ub4jIFI1TNSKRaplaO49\\nqAyDd7V7/6uyEBn7zSm3TgACqJM/+xKPsrL0z79cVrfL2QRdfK5FMTY4K4ZBVzOwCp3qwOnm7pbC\\nMysGUa7tJezK0KCsSdRYMbTcLJIb7Q4shka3Sl1BN/7h6MVnXtkmhBDIFmcPoXgKjNHYa52cd16d\\nX1+NriBpCiGzWDYbYLzMx/lWHOyiFSo3KuWoJ3t/Ob4aoslynDC6eo8XfS4WOOvb6BqCWbKYZcvc\\nrCthEoVFFGfQNEiRx5ASxVA4JhDidOKjKFQJVghlAuOujYIAkWokKNKc5aJfWas/6+NpStOFrKo0\\nL4hjG42aw2URSYCAVc+WLZQiAKVA0sSA0IjpmVQFUxkTmFBqGqaqQSi9uEgHZyrwVPCsJFBAFyGQ\\nJWXJknQUo3ROKPP8FNk6dNcFh0AihOsIazLxQ08muRbm+mLKWAJYmgMuyiLDoMUSgLBvgEmaiCRg\\nV6N5lECIjDSXEKgaRBQwkGX1KgK4YMUcwRQjw63dXY4ja/cmRXlVhhhmnC9jRsMwAJAXHCuKwvNY\\nU0PDAhMvQ+2DtdbGdq4be5VhfHWtuA0NJMvLrIiKeuP09kFtc8OoNa1WC9CoiZoVJNK1w3bdSlIB\\nM1JzusuW8hWMNH7xqtnYdlSkKAwEy6Q3DomGUIV5oWq5jFzv3dqx1PVZXoclHwdZEE1Db6lXaqqb\\nVaujnRvXN5rw0Pt6ZfkpyWgScj8kk3lUCG5hvvz54Jvvhkua82jJi5BgrSIyRXWlnNy817CsVpkX\\nAPqGRgsP04T86ku+PHy7DMvJqx6Yfl1fndJmPcnW5kJV4xGkStirs0RqmmykacvjuWzG5de29mp5\\n9aRSS5orE2Ia8cSzi3ER2skkT9Tx5GyRAjdJ+WQuFxkNgRaG40Febh7wIjoJRnQwVdX1d1feXjXu\\nryL11ALHNZtPXn1KiaE3bixC7/j1orvyIM2VUbS6mbOBbxYhT9P8WlGvXn9GZBpmnSTwTesashkn\\nKFdUUeZSehld79yIX30hH42MmWLIRrA4YScLZR4rgMJwWgQJzC0jFSTJYJ6FcTikMKmbtQKten49\\nQgfQsAraJtSGrUOuPCBwoBSfYfYyScEwVUspIrj76jJcaZLVDjY3ughzlP/Rw/Q/G/SuVarA4tX2\\njbYoLX2MV/Z7Ev65LLrtzR1ZIigxh++/cfBkYv5XxZn87NOT0n4+XHRmIWXYt1cbuhVnHsr5SpLH\\njDEuhEQGzUJAjYLALGerB92kKBzVslWJWAAJB5menT6vdVea6i8/ftp5mexc5Gs9r2Iefn/9/uAv\\nP1W7HyZvHR7Hr18oy1MTj4qd3rfWncvXLwd//fH75tF/9cOf/9b7/6q2+XDv735QOWyourb+03yC\\nbp1NYT1LV4it4qRI3OzLy7v3ed2ht94Kbt8ljpsDC0ierDjHsaiAQlSMtomrLXBVtJXjcmd4otRs\\ncD76/jC9JeVpLg+haefysLbxjqkH0K3cbEamCpRqLyc/nCjv9GZtS2kYqR8ETCjVokw1hEIvR3m0\\nsuZazkC1joL6LRCp4ycftIyOgM4x/56EN/Z3cmflchKbcUTOB7umpptKwmWSFa5LYwWkUMbc1UGt\\nUTKcnQ+///s/U5RymjvhgnS37iDF6I9qaZoCAGyX2E6TUuvl7MZffDyegN+huJ4Vp7Pj8NkvJ1i2\\nqs5pt/mqqpMsfm50bFtV28Evs2FPr+eO+kWWllalH0zS+RCMA1ekZgFay2u7UeUFjudlveQKT3xL\\nzvb37brbyvgC8A04+43gZTzncaqqGd1Wr0cvv/RjcHL5XJsXkDdQvqPOF2kBFxHjytrqVufo2XeD\\nC6VAa+dHt1i2un7gttqeLn/eghfR40cMt3ExRWBouYXuFiXwi3yUJWl7o8kYy+OI81Ix7NnAY1IJ\\ncy3MFFaWTIxVRVg25ZDFHolKgqiYR9B0VqHgs0sf4fL0N//eZSOVplRRMixT3bQ1VwVIydJKjTf3\\n0caegaDIAORc6ooaBzEvORScSZ6VRCBTUYysTOLsWFVeqB1ZFEqREdV0hNoqSt+WKQ2HkBZJbueZ\\n9BKnyB0AAKUw49W8SFg5jqKICxCGYTI7JlTmEOUl1tBYMiz4XCeiu2mWeWETqaGJqi4wgGVZunWX\\n6FgqKM8io9tABIZ+4Y3L5TzX7H0xWMj8LmKKbuSiRAYODAWzIhFJwsuMEsCyiGLpGAhJvHoVmKhC\\nF8FLod+o48iU54pudDdAd+NNa72p07qgtlEh7UoDAXMkb+CaNDGCKYYlJrTsL3uNWlFbeXf9dqtR\\nzbA2qcXfXF/llrLJslKWhTBEffWmYrq0SE0Tt4pRLU1E6CPRcVs2gRVh3MebP7Xvy847/e0bM9sb\\nJEkSjr0oVynRNrYfv9H+ZXV8HhYBJoIJfXUHZjnFRDbat7ylZZrYUiuAV+rbGk9mN7uv2FfVbHRJ\\nk5dEGdPd3ULZDoA1GYSKhif9LJgO6w7jzCgsx95crrhDFsswW0KwMCuy4EUJq16frzt6y4icSluH\\nQRmdatwWXn9j058fxeMAn3rh41/8S2L8/mA21zThRufp1DyavOnDN64e9512VbetOHjcqlWg1MJ5\\nHsULjXZ0lKQZD7CNtOsNM6I8s9Ji+vmxUKMkQmdHo2XM05QncTnzq1dP41pdlIWs5I0hWKxeP/K/\\nyUcjKy6NQziohkE844HBkxJfD4KZD59P4cWsmC0uiyJzrCqlKlsisIhYiPO8NFtrRJJ0NiXGnSI6\\ny+lrBow1EVQuQz8RXiQHi0xYwDTfTz2JkWivNMdPag8v521naTTeG1/dd6trcBVOZpkGQktzM44E\\nYSWYdchj886fBs+Q3Rl4s1Z/6LfIbx+/+HPqun6gQ0yqlbKqtydCZxzkXCY5rMO43y97gcJyAu31\\nef+kW8ErOwsiEwS5U3W6jUdFL3f3f0T6+PQ36dlL+OTl61+OvvefjvTq4NN05XdWbkNbuyj5d7QK\\nn367//n/PXLA00P35eHffRd/8Af6G390c+XOw/l/0YN3c1H389v5UtX98A8+SG8f1lp65k2HOn05\\nx6t6U+/D3541/hBXnIu5acBB2N4cnWVrW0pta6tladUbvMzM5KqsF2dOY6WmqSorDBqEhXHp2b2i\\nnsKbGubjcffgBw9ULXo9Vsai++SirRejte3u/hsHFZqyvJDA379JC9+y1BAjiuDyam5Nn++0kbfX\\nGgZcg6xYfjMdps6xdgPSqpWlpMg0uoCCUEONgtLSpN1ur3RqmBg87ySBGZbi6fXxsXonT7ynz3uz\\nZZ6oqpAUBBcUgryEqXBKsk8wttPjegguPn0KyKY/CfzwbMX8hLInl3PSN+o3frKxoczH4z7vmCsb\\nM5hOBj3SrLvLxSXW27PwY5MUPpNIt3wPhZe/2dhWzYqyvDxigeAAzQq33PidBO9XnB1VJ4a5qSKC\\nYIFJmXB+6/ud7ft6PLnuvYAAvrTqXUMpbRLaupoktD+1R0nV88D5/OrJKAlOF3G6OAvqnk8lERdL\\nyItAlSMCM4wTbFDidBBaQRkYj6KUlwR35zNYsBBjiiWS0UwnKozCIikJRQDITKlyxQ6Caw5SxaQC\\n2EV0VqlRx0kViqpaaliPKAkSWU9TGcYWUZuNNQOVU66EwGhxsqPUDjGSWGCo0jJLBfIZzyCUkiEh\\nmEoVR1eh7KfprJR6mit5nGSRL6CWZ5WCFYYVZ9q1n5epPy2CpYQIIZSlPGdUQpLnaZb6SeJJLvLU\\nwzhBiEDANJhDJcAGigSPua4qXYtCpyqQkmKNYKL0Z3MfuCmgidT8RcwExZQJMAEgTKL+WsNYXz+3\\nqWWaJmNCgFhRUaXi1B2dEkVTiW0qWVTqGkbZ1YIUkC1PWLLa7ZBmDYJcmw7v1BvvqPYeE41Scajb\\nNd2OaeSXv0nN9V2DYiTKagM6KDl9/YrUWjX1XmXvroYqeXFptF44kHXrppVGAFJJ64q1bmhVyUXF\\ngchEWoUrhqeoqmRtA5qQNBla49YdaP6OrPwxbm1Bm8Bo4p8OXFXoiHE7HybRQg1p1o+XpyPw7udP\\nXWDaQL3rRSJLHGl2jUZV0/ZoPbcbhr3zm7crx2ZyFWKl923nF2eN0SBl8wFpryWzuaskWzsatVTA\\njWjZkfW7pSyVxZyYWimZaSkKrGTDnBr0zTc7HQtktFxtxqUYtlqT0eU47+TLUfHN19PlJ/+ypK17\\njUkDqyY8dtamq/GA+2OePHQcC2m66ZRc72pq086pDkdJtDTXbkisKH4qONZ2XFJpdlu4ab5CyYtw\\njpeXV20RiGAhs1SMA/6qt7JLcaWlAXcNzAoA7r1x8db2lTlfLgDG3YBXvBqNP38M9eXS6aW35scb\\nr07kYJKWMxVjvbrGgQR0rDlZxS1Nyu0WhgqZXH9lNTU+GKSjaSJyWc+0G4O6F14/+mWW3bNwfaed\\nq8qGIWopUduVkyjJOmaNDxoJONt940+9Y/9q8Gi9TkSqiNiYB3Qwf711/8ev5nek8EZ5qaNbEUc7\\nuykFnyvAcLAUwhxP2Hqj6D2LLhfsMgZxJhJ7DsJ0+eqxVDQ2FxfPvtm6c+0YuxACXTezYh50ftTd\\ncLwz5SdvDh+sPHSy3lp2cfpXp6Ojz/Py1//i481X2r1ALiCaEKXdAZ/+aOdf1q3TD77vLq7eHSRv\\nPPpO23j7D8tBfP68CmVTEITJ6YPGM+P2P6MHf/TmgVJFvTmDGswN1lw8BdOn3cugI+aDc7rx629d\\nnF2p2pvxpCGTcubtX4WqGL26fT9o1vZVR2O6MNvdOiWmz/yzojdCDkhbZnIk/3MurPEw/PxCX37z\\nnWJmL663l8UfVFdusmTOwfwVekfwTGnIomjX7VjBoSi+Ktaa3KlosgJJ3e1GX33Sf/2FflipGua8\\nVk8wZetUAKGCMlWI9MpWYbxVdZ0ayVtOTljOk+jkLycAQpFdBLl/9FgYqrGqjDWCASzK0oqLwveX\\nUbVLGvO2+8to6Xdbbj5/kRS+Xc94EZLT5Se/5MsMa5tr3/4mWnn3sIBcVWQYLSwVaM31cNx6GqLx\\nIoCC0bwA+mCEG+Ned7PqGnBebzi7H37/4786Mo03S7IPajuc2261gokGyljTtBdHFVauNtwApYNg\\nOh3NSgmKui2auztRVsThUI65CS0LPUseP6mVk+r6+PLFE8+384KJgOcsF2iBVEu1K1BVY2ZJKQXP\\nkyjnKShyCBmkInds7Lg6wsM8LxH3CfQgTxNWeLk2HutZZJJyEgdhwYXvJbieC8cBACBFL2RYYAAI\\nXXoZxkXghQpZW1k5SHJQLDIp7UwqmlZTEQE5gLwppZRASFQAzCGSEOIK4q3ulscX3uyqFJSnmUGY\\nApkEhJWSQqASE0jqZUsDTSmecZFhAhSZSkCE9HWQgmJKaIgAkyLFFECGoOAShRgiyUsUjE0dWCpW\\nKM1Sp8jyLFYQqiZBEiVQ5MKWGi0YgKXmWAAKAFB/OKl0zL03dy2jqSl1wVNWBpIbkDpIaTJZQVjR\\n3CovSpQW+WZ3rCK5eXjTqhgi9gZHAe1uO5WVVZqVvgxyXTdVhGhwdWk2dkMsIZSJ4iZKctR/3m0d\\n3j58c+7vuxWbQLGE36/UMqmuFG2TIRznhHO1td62FeC6nVHakrohJA0KRdeU0LQRkwAbAtZLuJqw\\nLmNNmecA6wpZbt2zGzsq5jwZTuAsXOPjhjFGhW5Nn+17rdhTJeJekZa6xZmzKMAyVLW02LGg++Yu\\nqY31MV9Me+p88OzfnH+9KBaFKH0fSm/jThNkyvVJCAU3qZ7loL7i3d8PSDA0zCbnEmS53sC3f/8f\\n0fbuyMvR9tpsdmJv3kbBWI5Wpq8ftstfE//fQvJ1feuwulPj1pILnBgNWD3jfGraOgAGz0kwh/m8\\naXLwvYMB5RFLNFRyCjLHYbIwJ5OtlHftulqr9WDYk3ygNpm+Ig0TLoc9d+9U6YgpuFdYsnPXrm6E\\nWdaobLS0JqyAftIXo9wYF05cjrXBset8q98q+d2lsnqR5te+H1RqXRUQIDLXrC0zGEFdEJBRJMpC\\npTVqKSBPuUiA7DBehcrGvHgael/qbHR39x0PNyYLF6tGry8/f/qv6lQ1u2ilNh0MgjlzL9TtLKSk\\nfTfNEz85g+VCSQoEb+tLfzR7Ofn1r2r7L8tc59bTMFY4W4cK0pBqWOm0274eDH7xzfSTR9liunRs\\no+IGmnK+Wd90zZ5lNp9Ofpxp2xQtl1l9FqbeV19brZ8UeAYPPsS2g9nDvc28lf1bVL7OgY9fPLoe\\nb4ZEyfgyiVLaHq49aLpG3bn5v1w9aJwPTRM/eTy9OR1P1kEPqSv98UQNL9746Y/G8v3F5crB23+C\\nkZcniygVlgo1ckHgdfA4UsY99sg3hw83V4I84RxdeFlhUV9e9JX6ubfywytrbURqo0vvq8+bPkPV\\nVlJpTcJ4XL95aG7WT18ryyU2lUX+6UcW+Cj3Qn02klIGvKs3G5iS40fCtKMi79RAvN3V7Ho1yF9D\\nYfNYaVQdzo1klN6WT9r823mnhSubgtT0AlUPt1nRMFXGmJEtCyVCLTOhNK1zvQplxQhg+RdS5ieX\\nfR2cg/6TIjXefgd2urFpFkG4iIIpgHR81jKQxWmSJIle7SpmmmTTube0XF1Vrkz/KgHu1fC9On3x\\nzTPKlDd1p1aUflK+UeWnlfXdeNE//9LjIq5tMt3Vrx4+I8KQWlNpK0ZZjMfD4elTmIUlWmO8mhV1\\nxdpzNKOp6ygXFTgtyMHqm9t6nTE20bwrRPKcwaW+EkcOTAJFTLp22q0JSo648l2AMEFQlkGJGM9G\\nEM3jMg0LWsB6WuipBwqiFJIBxsPhrGEzRS+yIvWWIdKadmWH4gkXJSQSwVJSAXxPk4ESzgAWWGLV\\nLzlni+EkDmIAEYSQAZwnade1orRk7FISMRmcctJ0N97q9a9LyItYKQSgmDCEAJJ5SpI5U7VCQsol\\nLMsykWp9/RZAm0k8lYXHeIJQgmGCTIUXZRIHAmHDMpQSExADXGACIVYU1RW8CqGUyMeICzCiCgqD\\nTEosBYKQq0hAiAjWLAxcZ1wa3PNAnsRllkNYSJ4CwVC2xOWM8YHp2khRclYSrKgYUg0OrmZSMaBx\\nm+grtrFGNMHEsCxDABS/7CaRBSVBpIb232oVhNt7PxSKEczGvcvFUqy2111hVOxWCXwYoJpCMY0H\\nCWhXXGnGZCgqWZZcv35orn74/vvflyFq7rZI4rNwVKnXtLSR51Vi2ksP5JnT2DysOLZquCJN89jB\\ngvoBkUi1uVbtkLolkZBpqhYZFCWDMOVeMFvqmb6r2vuBJ0M25nLY3OIHPyCIFpwubn/IDz7wm7zU\\n5YgsSFzw89noakyNG3Yqd2rrb168ZJU75h6YBFrhvPX0t+IXvVdXXhhprljd2G/SpMX6eybvaNIh\\nWMerRf0HvDlGccNBFRDBdXfl9ckf91DrumQXs1tenLB4rRaO29tDoZ41gF7yjzqNBOnvJMZ709QN\\nB3oOLBj6kT7wp9UsFjVbgyKdjqTB8p2D+2or1ajWVhERwFlTBcAyHoM0G3u1rLVrN8IKDlUodafO\\noiD3nplNK8lb0EDR1fLRL+GkPFzSGh/bTFWXQc3sBi02Wo4Cf8KqzrHbqclkvd/vxsVqkJbBcgiE\\njbWabXFIUHuNOWN82SNTDxgETC9/kQVaQ8sYgDnKiVErAfVT6/zql1zjWpM6bUVP5wYpSoGDy1/B\\n8vjN21aY/y25gRR6++Szc/7w27W1n3l9iKTHUSLF8637P/NyEWQ9UApYPowyxHkB4xSlm0FhOAts\\nampObp29WrDpJ/Hr/1ieHIOS25qtmQnERWe11dr9oNG9JWYgSYCE2nLCaDE4eFsdp9ryIp7MbpRs\\nvlp5pbXU1bUXWAllwXTrF8n09WJSIF4qxSQrVi8Hb5qmlMpOAOvDL3+doq3hf5jV/v8k+MfTdXli\\nGOb94snp5nvfHL4cOnfPTE+ewQCEGMBQBESThG2p7LK18dKhXOW111q47PJCZZZls0QKpEACBDgD\\nTO7u6fzl8OZwczr5nF/0ws+f8dSfbzxAFav18gg5Muv816snYndjHbvf8SKgq1G8WNJ2aLp1rhZm\\n78w6XFbkCZSfefvfrxJ1mhozZj0fKa6eaBj97q+aL/4GX/6W7Vpnd6OLAz4mpWes6iA5np7cuji6\\nU54+xlI65Mog/2Yjutjt1xpdHr1C85ExnLQVVvDsU9MCVVV1B3MQ/UtZxNTVwwLAMJPdXsKStvts\\n6/46tK5ef0rHl5vU6jpu82p50yR24CEENK6nbni6+2DDbAT9h97D77SoAXrR0jZ0neZeOGw3H3Me\\n571/xKyH2t43YE6N1PIpyoazONf5/YACrHir3QPsimJlW8Dz2O6Dlr9tpvPP51MvHz9ODBs5+yYg\\nVEm88ePXL4fjom6ijwXNrqZVlhUNc4nJuZLrPO0fBLPjJxPH6zLycjQT2RIWa4aR55uNTn8zioIc\\n2ai8GF67wG2QrkOs2HXTGdTzUyuym0QkQZu5Llys850bt7gbZ7UW2uRVDHIG64WUqVJAgayIK9fu\\nK8ni6zUCCjo+LJZhmLaaoekadV2x5Urz0HX6Zc0hzBUqMdZYx0gmlpHYYFXzxCSp71GCJZBXGlwp\\nCSTXgvGkWCjczdIxRKLTserFbDm3+833jr76T5IrLDSioNIDKSpqChuXvqukYfNcEy6o7bz67AVq\\nf4+LQPETCHIAa64tkHOIPIpqIRiragRNCFMCSsYq5Bi8hBgjDAOIBMYQIoGcWJYlKzPLpoZvei3b\\nsD0LQMdxqNPWlBZMSyHqKpM6lSKuRG0amDTrEsurdTodOaomEOky19LyioKsp8/MtoNQj3ODwJZr\\n21WVG6TigGQrissEgwrlcTIDH8QCnz5/fPLl2XIsDw4OEPeLsIFVUSwgBLTMx2dflsFOO4hyK1lP\\nzl6+mj0OvH/y4N2fGHg5vzD6O5Vv5OPhwA5DN/ClIsUyGY+Vu3E/2OhTSAgq9fLaUlLF6SLhToSz\\nrI87ESIYS0FYJaoaQijYbL6yZlca0a6UqWSrWjHtvtV78++m58caEGy8+5j/4ZXjFCgNjFNZDOuL\\ns+X10SS9VZhh1Hof1S/zV/bGvbbVmKqvqkW76n/wu7sqL7P9xOyFO/vethPd72y+E/k9B1suQEER\\nN1m5bXubuoROexc1Ardz6/o36utfflUpD58MjR170b69SK0S07fbP912/VyOTfGufMJBPLMBRkJA\\n/nLxJPKUQrDG4MrUJ7sDe//DbxThJoKtdsNob6kUdaZs62Qx5U5e17Ec06vyjazVi/bnzX1jy18e\\nRse7vSIKzy1jHZhzz3tlBHZeibls1ExUXk/EYGk3/eiVHr3OZ59M7e3xzAudKczTejgsypWQM0JD\\nRFzPET1vu7DaXvP1Lppnr9OUVcrrTVbbhNoQmaNzi1O/EOqTX/4sT17qillv/F+uZH+lx1xRIOem\\n8QUQuNr65/Wk+vRLrUFSZ59B9MXl+ckiHdYko+ZkEEyj6AHI02o+k3YqAsbmH1km6Ng0qtNai5s/\\n6IgcTK7Q6Oo1IB974ROgflGZRNMGFxElBd384NWL615DDvQYVDSwFC/ng86rM/Utp5p97/1+kdTM\\noo39H2fge7MSS62FWEoxrtLHjYjZUG6403qdZ5kaV40VcF6ufeE8hyvzH/3hZ1RkqXFnvjjFKqH+\\n3c+nA909e+K++2frOyK6RYyYVasjuTFlbhzXaZGOs37MRtRW41FLS0VG1MSCrF9AnNtM/vDm4w7+\\ntNN95m5Z926N3vyDDjXV1huqu7nqtZKNaIzlp72g3mgVJkmim3edpt57E1n6sWstpZhKobF5Fcde\\nwyHtOz/kcBPz5eDw4fjp0fksWogWFsLttGZoC7ecvf6wgxe48JAg6XCmIOMggBLkRTynjWv4gxp/\\nayLuns2+j3TAWcmJJLwCetbvykqzsziC/DBPNwRLJERZXiM19EUS0bUXCss1veA9KXnBriqmjagx\\nK1pX9YEDJo4+phI79StOL6RjCnPy6MuL9ru/11hPXD8pVSY0swhHqLDCVOpiEFYMqP7+j+yoxXix\\nGh2NLlf1OjWxhlZzTjdr2F+XLQmFFitENpKFu2JCmQ5SEk+eYV27HVRqXXGvdfD2TLqKuvlqUcUV\\nSxZArKlVW5bheRaWBYV5UU2sJuLpxIOpE7omx75HkBEgjDVBGmaVTEsUEjeqJFZaSKC1BlrVbhsG\\nW67pctPnbhMBpaWUWhbQFqupCbShGAfYYjWu6qt8JZDlBMZ0Mo437r4l0t8hyCtdgUphQ2ILGl0v\\nK6iEAkvdDbVtwLvbIZx97Zp3EBASJJzkxDTaRKNQRZFl4xJliS2XCClk+FIBWQvD8TSECkWSC16X\\nEGCMMadWWRY50gXHhRpg2nAt3+14ObMLbkIIIQZCybJaKy4cmNRsHc/0JHZWJ8tqOdesIHBtuWw9\\nL8pqsFiCyfFpmZO+5RlQQIxt267KvGNpA5tACwIVmh93Hz+S1ce/ba1+c2+/2H1wL2iARdJ2KMKV\\nZkBbcrp+dV0c3PA9ZpKMlb/Lz0bT7G7nzQ+pAa4mdXXrDcdXYr4C2zchokqZpJ6p49fC3g+6PaAg\\nhJqiMq0L2VxTHmszzBF4WvV7BAEAsCFtX5uUVKUYPx69vNLztEPMFQNDrgsld3Dr95Lq9Frv2Iav\\nFmb+rEznKnUY1qPQ/NzsfBbqqaIGJtaZnSn/ukQfFAUawsX2oTOd9nMrCEjKX2RH+HBNI4AOFbqp\\n3YE2aFKgVaWT6bGPdp1WUGE3i8n5Ucynz/zlb/JlUprnt9vh6TNnPhGmOC+cA/TBB6ocmpV3uDPp\\nmOcKTXGUYPOUQBY1oNuqb775pgvBdjgIBvts2R6v/cdrg+/fmC5Dc1Hp+WWzStPrDMpLT63lo/Fy\\nGCTGbs7t2OHenYHlBNLtGg0lWAnGV/b8Sr565q1qqvjPr+1xlj25brzCdLX+enEenp6aHLQAMQor\\n0XLMqphDG1mBHQ6g5fQGsKwiZnZhrzZIic9+OUkHl+KtXIgUUVbRhg+nX1745X+2LLPO1s0rcHai\\nTxZ3/KKy6BBrtd/5cPEcF9UrOJ9IOK7gY2o6xXqo8xSH/t1OWsltYRFfjeL5Z6gRBJ0/SPQCWsGD\\nB93D1oUo3RV9L1LDIj8HfIgtLnFpBYPffVrn4zIwBEL2TO7Pv/4b6jiprZfzsdlwRVVj5+8nz3F3\\n2zJ7uyaalpMjrm/kxK+AxBBKyQEAhKYS46h/4953DyN+Vixnhdp/ccrnn56002lHPp9ufSP49oNH\\nL2Igp2ZAncaNX//7r//2cfQX/1ad/j+usoXGiuV1slOqAedYk4xxmWcsX+8MmiAZtdtob/cojJYN\\na+jbgm7Qo2jT2EIpG5VJ3bv3bgG/tb/nXAy3BGyWhhtHHcu6bnrJjVsPBz0NrQcmCgzrGwzNJF8Q\\nlGokJFgFofAb8KvPuqMjsfX+d9bXvUB/LHPmxoKp/Gjh6qq1qnqYyGZ0adqJ40qDr6mioECGiUng\\nLq+d1y/dar5jnK1uUCYkW5WkgIRCtbsDvG4fKH3x6XGZxYZeybpUiq4Xa6gFhqXh+wibsv9ACO2g\\nFqpHTNRD+2ajSgbr4uLqJWyIdFVCWp88uTwZOqtMLY5/Ob1uVHyIAwW0VCyLPCdqUoxpWixysPXo\\nrBXe/rur2XNGMOMZUC/T8kSTSqJWnUSrlU31psQaEpPnK0IroSbJtPZ33Si6RjiruQdgZ1FWq9K4\\nfpEkyaDOBK6umDyp2Ty065bHy0IBUBKdKFWUmWsRR8Jl5NYZA0BjBS2MlNKkJmp+ucRZA4OWlkAD\\nkle1EhJhWjFWJoY2+gXqp7ILsCcEgYBozmxe+4i7phmQuWA4l4VhUCRXvkG9Frp4MhO6i8E5ggZV\\nEmCBMY6MNlLUlhBhgBFt721hw9/ZMW3njFotAM1sVjlqDXxueCFhvgvMoEmxH1umQryUjOoVhzhU\\nRhPDtlYII4kBA0wQB6wzsBjFoshAtpbrcRhZmNecoXRJKCYaYkxN0zYQohQjpQUu6g2xeuctcHCz\\naNs5EJVGyre9JD0t6wLJiYVfur7T27traFcIXVecl1PSlBSCJp2ixfWLbf2L3W+CwdvfV/6D/qYB\\nyrUZOY4ueZ6VYoHk8Wh5WzhuauhJ/ckyfQzi5l7z92Ot5GqxGN3u26hm7Hi24xjYUAqL6XR1ia1o\\nv7vhCK4VFEpKmVe8kwNRZrpaV+TluLXjGrLQHAJKtNaL8XK6fHq1Oh2PynBQ2/W5ztao4lg28Go5\\nenqxTttxDtzoKuLPksWERKgoAIB1pZzZ0vZ70fzRuj2q5ewmc9uvPzk3776bvT5qvTq3gLi5n73b\\nEuVX9mPm1qaniT/PydUcvfgsm51+ZA8OSmWvpYHKS8tfEKoj/kTXp8Y8hjf2mZcV5Xi+XCRsLx++\\n9+i5R7uJ6H7LO2wMV/NOFGxs2BJWiJlB61CQgySD3YdvN8INi4e2PW8Wr68n+yePXlCrtOHrLKmw\\nW6nsGENcs2TTP8XLjMUBBE1RvS/EHWrc68mtQG65nrV3W+z1sxs7q+3+swNwuff0UWd8efXxeFyY\\nrFbW+gScfO4HhdKxyoXKFlU6ZrJtm02XmDnv2C2YLHRZGAr5tW9DUvvDL8ywf3bBZ/myYgyZ6Rt3\\n/rskOdMw6zdub944evzZxeLlrwdvei6qc+Bvv7HD6uO0fgatIdFjCVS+Xoty1g7InrHof+//vLP1\\nR5Ovlv5dlhY5FXnQ7Aqu7ar/Bf3j7s3WNiufHPfDG1QuVlBUVZ7woi7i0678OnwQARJqDS5P2b33\\nduTEOMRBVHq0cg1SOmY4oC+W4FYNmsIspGJL6PzFfxoxARGCEJYQKAndQGUrfOOS/NFWP4n0cwwP\\nwOw5tJ/+8O3qw39w62x847H+1vriIw0QB/hyxaP8t+TZR9tX//m/2v5/3sTnHxzaRrXO8Gr3gTr0\\n4fmXhTLzjWgZhQGuV4J6YOuehtj03Y5bJENy/BmulG2ZOcAI+t+B0LUaB/fI89DdOD83x5fIVXN/\\nq5Phd4Le9qvnjJl+YjaJQanMtC4RVACWwKJj52YCr3n++urk+4vJr2/9nW9dz17Q7twyvddfjm0r\\nKK9OEtvyb92KeZsZTcMzbI+AAEOoA98hy5Mo+zVMf+7tC9F3+s1NSilRPrUo3vn9WdLmqKuy38h6\\nwuqiqgUTidQVoUBDaLoBg/blp/niudzxNqSUUi3I8VVjkFJzTWQ6PKnW14ZrN32+8qyVRZiA9ezZ\\nX/HCWY8IqKVMq2VRz9VDA4uOXdeL0orccc3LBCa6IAC4dMHyF3WacOZbUMMqhmpBsJ2vKwIB4msA\\npoi8vrow07JJXMPEmnMuZR1frxRTph6rtFQ8Rz43KO9HeGNnmxBSMGS223VR5um8qmqMgGfK7S0b\\nAGXbLlaeH/nr9cqMMNYvBC+l0GVR6VoCISSUShuWchohqFOJkrWJS8lFxTDT3G2X2i6waQwOdpmq\\n4jJ7evLM67YaPWJEjW7HtFcTXlZCSKC5UkprAMT5rW3XQgmAdS2oLKFUd7dv/MS13gTUkMDMmVGj\\nRWGa5pT7DDe6PYao6/YEspBvNZsRpoxXEyIYYwzStgbMtqBt+S27IgYEVQ7SDKCF7U6UuUBaey3C\\nhQYAOa4BACDQMQglGFJEodKBv0JuYQSublmo4ZkUtyKx3bPlegyrFGE1u76MHESshy1v026GQleV\\nEhxCz4XI3mjr/gfIeXOZdOx2F+OFgL7ta6254qWNy6sXwkCELqtXx89PJiUR9ts/uO+lBV/Pr86W\\n0ZthA9bp4lo3QiwlgIJlRWp18I23wz6FUEstKp4v14vhjBXX8wG+BOQFDLcakTBMRUxQKnW1mI5G\\nXy2Gv0xX55NR3miO++7RljvxpAUpt+Cfq2xmF5OWyWw9FvooXhfdCANjwFNgzZ5r7bL82LTrrdvx\\nVUyT9Ss9uYvXrw6/E2+8Zadi230j2P7u/MZs/vLMOS7s8xk8fTRcnn8O9MdQHw6X9ipVgK9cj80n\\ncWXNOrtZ5E2II65fOl//9rPxZGXHcT53df7l0Zy8TH7/vHr4+MXk2ugVnZtns0L73zJBmCcr9+Aw\\neZWa9T2Ii3sH55aznpXP2OLIVNKw+wwZYJVJkZpB1wnKwQG1tnEU5iyPRYoA6ZRWG3V73lZjq+Vt\\nNnZI733rxm17720U3G1t/eLmD1+98/6vbmRPFhfPNt9jW7cvmw2CQW14lUUSpUvOVgASNwhcO0uy\\niOP2LkuwqUpN0oXdbTa2wk8/7F9srJ+VvGIi9cNnY/GQYhsogQdvPU7U2+G/vj24MwHba65H8+Zx\\n1n11NKvqGGGL8dJxDLNRb2zzjbus9/YPOL4R9Xu9aHiWPpCkmVXnBF7nifzW9s7Fn8Ni+yeNznT2\\nfHFSPxDpvOnFtqU813LhJGzmrye3FtLBCJrX/y4cvNPpy63vdt96s9xvjkwiXXu7FanRtfXkhK7m\\nL8b02z/9ZXLx7H9sHphAEljJuijve+x+v2pMxLNP6t4P/55jnFfZ4+Fq4rDEffvvqsaPql//YvKL\\n5zU7s2ziK9qbvdqzvvjB3q+++4NfHPyLgbvVtG9/mxrF0alEjXfcDt/xhromUWsP200NsyC0Fq+l\\nrq1UtFSvofRJT/9Kj8+z9cUywa/y7gI2CqMN7hxWHOQowfOX+7stqt7LZs3R8feGn31eC3Md+xiU\\ngx1L8jkAwnbMPK3ZVcXzGNpX1fBRliWfjd9Tyy9WC4WUhmKyzqTVY9On8WX9sMrycpQh5QLlcmlT\\nkxjAq6pptCe6jXR22bkYbg7Dh1AqnxaeT599NZ+cPQcSeGRCSWE7LoQUslzEdRQZ2pbXk7iIm8bq\\nvBmMBw+bftBiRYrFEDX7HG4jYsj1qO2vZ9fHECMlsRsaaqmM9Eu7nrvVRNa8YQ8b6XrnSuMybNpz\\nJK7e24+yj//VSoQyW+F6ZpvLzVtBrY/yxbLVCNwoKeCi0MS0DVHGlBQ8IxLEuBzZiCqlWg1u2QbG\\nPqxmql7LYkGAhEauEa25yEJ73n5DBXcVsOZrJgVmPGN1AZGwHDfa7Rs0kApITSwCNDCrapyWU4oz\\nCh0tAVCSi9p2DclsyeNaocjpDujCJcK1aa3s5TKivkPsjsb4/DRH1gOCEOeri8efMRmwYsvsbg4i\\nt7fRUBIAyC0KYXUtpUAOH+zdRZSkZVEWTBFapAt358emd4NSMxX45NWkHed7UXb4pp4UFYObaWkh\\n2W5pjKEU2kJaAJ4DrZXShBAmdC0oMZrA8ascUAIIwQAaZVkqphFsOPYOBhohELU8DYjnAi40IURh\\nXQEoFZciTRdmlUd5jlfLzPIaG9ubYWTWBatNs7h+1t/sc7EJ1cBEXQCUhnCZSxTOwjt9F5XQCNsU\\nQV7wEjaRkslyZRm1Gk+aA/v+rTktf5FfHIX2dvut/yVTOXfSRnl2vtrnphRg9OgLB9vaMzHWi/OT\\nzsvFvWvaLOtKKBlXID+frV68aJDpnQf81gYPG/0sdiwL1dQ7W6GXj14vT39hoP/kq2NUJP3tHd65\\n4e//Q+vwASFWbS44XgBNuh27YdUlG+lyhqT2TLsqF9qOF8yyAsEuC0kOj17J+YurTfblzTfjZu+t\\n11dvURhu0/uP2fusb4Xhmfn58uKr0cmzvxXpJ2I9dF0XisxnqaXSTX9yo8sDbPqkpwjlxB+nrpj8\\nTRqnKl0jp7o4mYAyqR/Fq0kw/+rJs18/nvP3nn82Ox3CSWHyQErUjI/+w7vvbAyCL7Y2v1c2D6+H\\nC7V8urW1dHA4HfG6WPXtqYk9YrjA3M3M3UxFxAkCj1ILV8msHBrLOEReG7V7ztaGpztJvHlZ7xCv\\nZ6M7M/MPdffmRvdjvV7MVhLaHHkGUjPpVJgypVZSUsP0DSPAZlkkrtJ2EK5WnJbpOlk+pa1ubiVk\\ni0yKIQAAIgLU7le/+o+9nVZr5+9/+lm7/O1//9Z33+i6xmpCkjmoLqyzp8vxiyVnWopGtdZllcKW\\nt3nzhr31x0J9OJnPVraqgu2Tz0+l8lUpxskLy9S9G9aH3ddPL2/Vvujgr6+f1422YXl9iDQCGWli\\n6Nvq1VWZWVqkSzU5T6m1cbBM7qLNb7e3b9UMQAZb2xSyKRuOKP8iP4Y0/gsqnh6dtSDKHNdwHN18\\n605G7rR3zktw/uyLbzXa0IJnyWqq6VeleEurNgg+Sa//vw6thci+2z/7x9/97K33m3d+Mpim30zB\\n/4xYbz1e70FHV/HsPO97vc3BZjF5KUp4dwK+QQY7qyvjXueo3fFNgGcXDxkPcVtT89yFWLPiy0/i\\nv/lKf5xYX6xvHhlOvL5qhY9177uOITeaatta9RrxeFWNX4yMOpX+FsSmAU3M26SsPLQy5Fyyie+N\\no4Z99FfPkUt4OsPExkTB9GVv780MPk+u83416tixY2rX8o06hZgQDBEC66RvhYbZMU9fX5Vnvomk\\nZSaeiQfmE73/JgJaQQkGu4zndSHrdRmwldnrLuYtNslhzDe218GeUTnt5uH7eV6WYpTkhjPYefOt\\nv2cKZocjAMaMbGvE18dcK9x2U6VeQfeaGBL0elv3Bw//Tmm2uA4GrfbW5rs7B3ddE7GGXJlBvNLl\\nq3UDACWNiaGraPt2VTpUQM50v5U1A8GUCRTUdW2HM2wQESDgbkOIJWNUjm08QUaCXAIEZ1Adjbq/\\n/Dfg/LgzW/YWZxMllBbSNVIISC24ArYGLYIwxGgQ6c3tHQgk0pVNSwi1aSGopZS1FFgAmlI+rywp\\neRSydkR7qG9oiJKEVWVZK8fxtnc2LKk551rrdvti+uoziEGaIOo2cHOAzQ2gJdYgajf8Lnv5+iwG\\n+xl8o2a6TnOWTpOVzvOSrW2HdKnpzZdsXf+u3FAzY5CGb7IEeLrEeWIZ+fZh6QTANAwMiQYV0EgD\\nxsuaqDpLlGcYUJIiqwkhSuO0EjRCoML9hstqTChUSlPLRwYwOj2ioe1aiuskJmWSUXTp4WOKMyGS\\nZHVVZX6aNU3TlEgtKtdmr5udW0bBXIUCq1lrJ2YAbf1oA/tosqKaKE2yekkExuVsHjYD276cy0Hj\\nrVatfjq7/swlDy3w7ZrFr/+aXbCkWFi21z6dT84vPgX9zY6LlKjy5OrVoxYr/OYyu6jQ6GXiHv+6\\n23jZ22q27t1pbN9OqE0Kvop685Xx5Ovz1eW/byT/rw3z+e3+dsMhYW+zubUdog9kcpDooOaKSl5W\\ndqd30Dl4mMPcDmSjAW1PaT6SQnOsM+pbrnBw21i9hstfV1iYH/79M+PefFmHKGhsDhTAs2H/48tA\\nBSd3Wp+Uo//g1p/k05XJecsWkmgbjm528eDed9dltunLBgoB8Wp2LsoZQJeNYBEX5YvhJKUrSoaN\\nxjGunm2Qv0DIecv9qZr9rWaAltbL64kbJs7GO/n2W+v2H8zdm7/4m8lN8sm7+/txui+ANNVy8/3I\\n70m1YgYGGLecNawSq5AGNvxFfuU18ioduthfg56gB9DatppmB5Aqdq9UWzSak9lmAvfam+uIybYZ\\nhjSSyTTslEauJBd5tiprwyYRVqjUSGCitWn43C1LVs20c0Op/vnrQV22Pnn0AlaXjHmGxSPbyCq2\\nWgY76t/cf+uuMO5bA0bT57Yre11hpJ+5dGEanqgTXqJ4orQo3MM/VJkvgPP6lffkRe/58wWsnhse\\n6AUbk+vLzVv/SDfvtW6t2MvMwGg7eo3TlbtlF2XXtBDwwvlw5+mkOpmeESgM07PUbn01y+Fmrayk\\n3NfkxwNbubs7mXiI5xeON4kC113+lU1Z062M8diABTPsZhCOrX+ZmN8d1lIt1hA9cloPcrlW9cvX\\n1Y1xFV6U5tM1svCTwGZ3u4vDP/gT+xv/VyHemZN/cacfTeOIo/b089Rzeq757NOv4VR1Z41mp72Y\\nLxrTVxNWtiP7ePuDdyBxWYUIzr9/t8ovwFx0SWiK6qoNnzaufzf/y8sv/2Z28bkaBGPkk1HWTe12\\nZQTtN93b73aykyxJXni+hPaW0gRjQ3Mz6AdCzJFRuq5ggBi026CfGEIqvTJIqCExQrYEG649D6vP\\nOg9z//5mVlXEcaCs18xNaWj4Pl6tMjqIVcfj0x37S4oFhiby7VSK+HMGjQT63Wp8m9VK4VYU8trV\\nV+mNfI6QGu/dZoKqUpAiO3TRjaazxwFbX09k0J4b/YgqbOS2v+so5tYcGHMjpGZX0z4TFi+N1WqC\\nV+bhjHxXdP5klhAODkpg8K1/YtJEkJG0+wxu6cWK1WiVlONpZtuHLdvT5VmdzxPgzf1eBh3FgQYK\\nIUPg9nwcxJmfFSFQTChOXe1Hjqq5rWHDM+xkaIFH5uQiuGItaGJQYyQRQgoKhFTNAYOuAC72vaQC\\nrLlFiWNQaNkGxI5WRCMIoFoldcYIB4RW69A8N+waNH9wu+tv7D3oRNKwNCZsXXkaNXEvsi2ilFR4\\nHe37OZxk46Wpi5VqLSuvgfJ+WCvQE3ULDz6ML44x9FTqygpAKpFkVE0IHQhDUy8ghD49Gs3nAo3B\\nRmU4kHt+1dpYzxm/5g+kvlnVtdYSASylRNgwrdTyZtaOaxqouWfBGuoqsU1XSjmalFmFXUqJuWMD\\naBqYE7IEnpIUOk1AqKCdKk8QEBoWAK+gNSOkcMwa0lVk11gjrmCV4fOTc99Zdbu7Wy3PVoZJ2hA3\\nUJ2CJ485MBGUkvCsxK6Jaq/b1QA/+SQUh/dmF796/fp0sP+Drd5221nRV58nJ8v6cjnODaJPDmeT\\n9VUXuxbDlarHLz/+Yhqjyky867F3/Tebg6F/bw96e3H1juU/EMzMBViMbRbPk9N/G/J/e6/96Z1v\\nvLHxxj8zeztKmEGrZ9iNTgg1r4qlKRCQCDSDt9q9dxZVnUgg4YBQXwKWzBPGy8USA9yeHC17ndei\\nOhHEbrXfpSsxvZwJW3f2Gy7EIpuoL5/yF7/Szgkt/kYVvHjrDdOj93+0p6oE6qKxuREe/IBCPErf\\ntrqhH1rL5TJsVQJNo4YLTW5ilg2vhWDcydeTJKvH1JPEicLBSEugNTLIE6NE62pRr755/bz5u8+3\\n/vO//3dN5/M7P/6R0cNWXgtZb+9Fgu2Who0hEaWjCLH9lYNtbA9wVSguSu0sJAKgzpbWJLMYtpDn\\n48aygevlpZFgj3AldcfY3mw5iDXfb7S6DcCk3dBS1XVSFqnWWtthwxYwq3LuJMpRAfGNCZ+dV849\\nBFJl/3j2649ubd/xTEYhcMnrzvv/Iq8OzeXPGxuYBz9csuga7Myy0eEuoV1g2+d33hBACySBY9FW\\ni0tsXaWW1XBOX51dPP5tcvxELz4V2cREFmruAyZm0T9N4S3QvLUTHIGdD6G9RvCclU4+zyrlVVPk\\nhVU8YRBXDAQEYdMaH7yB7KXWyxTlJm4PlCyWYMMS0mjMoHmeql0SJt764jCSu/sX9w78GxJ41C6X\\nLjJvJllHi5PZ2tu81bdM05DH5WTj8YL8f/5GVdfnDZd2HfHONz9Ysv8SwG+zKijSvn/7reR0wd2+\\nBV7nheoHy+tPPn498odnxeCmU1+8IsVIiLF9e/d0/k0D2lt9k7yIT+xvA3DiE22Tm9QwQ2uInWc7\\nhx/vFz/9duNnvooVoPGVOj6iJ4n/IrtTtW8wkBv2RT249eVoFygtC7q+npZlQIkNtSpQwBFdx5ng\\np81OLPK5SYHnqZMRv36cQ64V/fxF/M7T4x0FNMI2sbw6FkBgYASCr6uqsZzJtlPcvs+5hEVtLZdI\\nLPT93WdcriFtxKsZdLagCiDgtm3iL5cRXkpUtLY2mx0CGVjXy9LoMLIPNVqOX4haTX/3M++G5Te8\\nnUZTquLdD/uQyhobdX93KN9VgpkY1PXqelF9+VxNxxurGeIB+dVT97PXTdPoLhY6ObfTDGA5qotS\\nVGWZXGaJDDbveU3PD735Up0fdafDgCCNia4ZKxd6ep1UF0u2ig2dBQMAkSEVtkyXGlBjr5Zl27o4\\n7D3v3zjq7CZBBxoW12aFDAsAJISos5pxpIQV18ZqhCRvUuTbtimFrisLaIKwCRQASax5LXmq0WS5\\nNpjojM3bc/qQBHfD1lZWmby4bJXngbEtuGVE7bouChSUNSprXJOpAo7ORNusu7sIk8DAHuFlvRhF\\n7jU0KUKKlaXQCgHheqZBIghtiG1s0fGrn9t4Ntif+z4pK6TtPdJ9kL+8BFJFbtOmnABNDay15hrk\\nhZHPBKJWIwLawnmhUu0SGpaFYGVxOl2XKKqSDewgqVA+F/OLmpWlaVKITaVDAlXYCBXQQpZSZ2kx\\nj3OhVe3fbGU4QEAaXlWuP6VhIJr7tLkptZFVAzR9NS1T1G7Cps/ydYHCFjAswHO1eF2zthX/1exs\\n2L35xxUcGAH1vcvQPN12z3G8Uro2/MurxeeO0VKpMV+vFtPPOIS84o3W5eHecdi/RRu7Gjiae4V3\\nCA1SlfJ8HCNwbsm/vRP++u57t1q3/htg/wMNeyy5Wsj92vCA0Qk2UMWzWhmCooRv1u4NlqfTx18H\\n+o3JsFXXrumhdUJLpivuDORXftdyG6tBb+C3+7QB46vPD2QeTA+em50Jrcfgd2H/z3k1Qe7y4S2u\\n5ju/+bPnnf/iH16DVsHk5uZh98b3kecX1Qnwb2v3MGBPocZM81pDAokqFiJ/5RBl+yCrUiALiHQZ\\nU8H6q3SsCa35ilUTqGIP7mKZTUfn15/9t57x8+2H/8df19/4/FWNZB00bmizpWZyETdroyznWtQA\\n0zxAmBoWdiaN/jvr1xdZbQOjaK6up3M8rp2SeRwHup7t01m9VhU069pLjPtAD85nDd7e6Oy4QWJg\\nVSFdCFkR6plmw96hCNWmSC8LI5Yt15yPr2b9XRfJl43dvauLTyP8Rde7xG5nvfzq+edfuXDUaEnP\\n+951Hjw79z/52/N6VW11LZKMDUeZA8zypUFRtGFaHoUyTEbVyTpYDP9dw/6tC/4mvI3juIaUGfka\\ngzZ5wS8L/7S+Ybez2df9OfC7PY2rTKSVUoYdsNX8BazOo5ZaTho0OqRqPoJvdgfLh9FnrptfzvYT\\nCccviWi4iYSjig3L7czr3ru9/s4PNl0rxIX47o9QEGwkq+tsNIJ5bodmPCxek3eLKqdUuPnZZ3/2\\nlfHiL1068zcOuvs3cuOPj49IpnwnIslwFFdRf7coYy1IWtdi/5bXhR+vz84baG0S584bcwONTb0+\\nu3gwvspJ4IHQefvvjs9/3T58q+VguGEHb9y467oIqnzNoN38YusbQTTwOmGjLZ/eks+C16/Uq9H4\\nKEJEyTIeD8Pi0RdSStuGQUS1jCsmNazSZMWyHGOJ3GJdBA3XpagGQBSXp5Z8TmgRI89J3O3syHON\\ndF1TMlBF5hQrWVRVflHq3JxNdw5iiW+m2s/LAMiK17r1xkOHKMZz/vLz+jpz5QTlsmM2Nu69vPFN\\nU5FqNGfKvMPy2inj9UcjwDYwaCms09PPUB0L6wADb/9WJFSaNvYvq3bK4ONHOpubQjhQexAwJxvh\\n6Ws9eWnTACyO5i9X4otR2N6mwDfQ2IA0lCVVFaV5Up5PppevR1D59yGxS17X7CXILx2vNh3Ia1kn\\njF+nPHkK8JkEzAp9JikyKNA24ybjUGut5ap1Y6GbDm9uSGdLkT1mNCCgjAnTpGVRqzqr86IoGIwv\\nKDFc13U95UQtJCEhRAqNMAWS6TJHGvIVdw92Nsh8Ir1y3S5iP1mhrZv3JdCdjXp3l0vSLRJ9nfbH\\nZ6skIZKXqyQdjWdglbrtWW3dxGSrHTq+6xlBi8mES0tLBAnJ10wyCkRhYg9KZVGLALMolyfL34yq\\ndt16N1bBdCnKsiGUyavzmGmza+FGw3JNjE2tMCCVpFLwEtfYbBh5ilFVAS4t06egUIqpfLlOQXpp\\n9JHTjAwLcQMmUpZYK89vVNBSMOoGb3u4DTRFCEhxmrLxLC857isJJBLzDA4Xx2kKRWFJ2RLCQoDL\\noGEhRKTITWfTMilRDOnZ6eevq9ZkXa609XeXC9/WDafBqU71DmrcKAkyE0CLLGcgaLdAY3HGi4ur\\nyyPO61jqyiKS7tYkqoRSkMxXFrJ0osXR6YuyWkM0bu1x487/vsZ/j8EtoQ2Ny3iiqiq0QAChE0t+\\neT2iWhfKs8AOAVmx/G13I1KixBhrXRuWE8/gbFb5AbLtTtQMrdYNAZUZtBfZFbHhwfvwblfkX4Kz\\nx6+QsYKrFdDNdP9HdcduoE81/P4XX/lPf/Mr1GkE7QOFbaCL40dJWeXtMN65M0W1uBoWlmF2gjPD\\nLoJ2ZYW1UoUSFdRAaQ0AMHI5OYNpkQcmYXUleOHawlMv66u/PHCft+/8ry4mneynV6pkWkSSuYR4\\nRjWx45J7DEBlEuhthIajICMa+kU5a5qrJrYQtWxz7KdFtYRFrQEgAtR2SxCJ0pU2IErTnhJgOTFZ\\n0cHwtmPnBCKKOJcMQFNLJwYhE3bTjadXKmEmxrHIKIAiXc55gDbvR9t7JXLaNbekDEz1uLm1xcS2\\n0Mbocf3850/q0dc7d942Oy5GlSTd8ZpTq0WMlmG0AMRNwEWRX50+xeYZxXM3SqQItEUFQk7v3ED9\\nm63Z6FRdPF6J1h2qJ+sxp7vvNKIFNXLNUFkp30cQZ0oSzHhabyFtTpZ93MXw1kHBQfJ6piROeHaa\\neMNlscisyXFJ69C9+Uba+JGjG7t7t1PvX5ieBFfPs/wpNkWVG5hfPX1uUAKR4yLrec/9LQk+Cnp0\\nvXZq58FqtlMlaVwTp+dTsKoqfFy5fLUQnAMArM1vE/siq06JW1EN3cFWVabUFEbJKl1B2E/qRtX+\\nsZs9vRRvSKqbuxvN/k3a7jOls2UKDHuyeAfZb0jUpG7af989vHW63z0LjSEmipKyLlaEPCNIWjYx\\n24AayDAhUA2IGKSlhMq2zKtZ1dg9ABBVFaNOVsthaW2IlOj6HO9oZgcKpMihUisnrE0jEVBjqSI0\\n1p29GreBDJTIfZtZZlLkO0IRUcpW71UYjfsDalmW2RCwFa6Cd1zPrsqkrvq2aXZtsrt75fHU1J7k\\nQovXxMBpvLY04kGb8cZ4aBHY4VXspadNPSvXmdbQ8sK0Xja3p55xBJQmaAlYEuKfLmanQRDZzsTx\\nhdfw3I5SkhOs8+Jk9mwaLy0gLUKIyVaOPYFKaxjEK1OWtWmwRphDJAyXzOcCQlcqLnkCRIlkjhC0\\nLYrclkDbReIj4FPqsxovykJwzTSRUgJdZUW1jrkLGIUlIUSIQBi7RSWUUqZpaqVqXmjBlBSslEgL\\nbc0q7slizllhOG5aVvMZPzubSrsjMGGsTgtYpyORTxjPhUg8NSNyYriw1kFREkyQoaP+ZhtIwaEu\\nljUgFAiJ8pVlcIRQpxFhAF27DYm7WM5mjz+Rk9wggzpH6fkxJhBCrUCxXjCiDYh8iTyAtBBC81lV\\nrhFjrU0FiS14TKnWkvmmtJDQCkOgENQSr1zXNNtQQW1bynY1wsA0ouV4WkMgWofC3gQwwMRUek4W\\nxy24IF6IEQLIqFdVNZ+CnGMkCFWoe8vtBDaDGvBA2BFSDAJeXb1MSff8YgjNXbgQi0pfY9sO61U1\\nGFc7WUDTcf34xdVyuDajfcs932gcF4tniiI9s+IVabS3OkbLECbFhq7FbOpdpuLpr/6mqBYW1hjf\\nGat/Poy34tKtS1LUqEqG87lhRLutYMsT1vS3H4UHA5ON3Vrv1XOLfRK1Wzs9BESMqIhj/vLLuYvi\\nttF5o79H9aHZuquX0imc5bN1PKl09O45vjFuHYfiP1nV11127bBO+eq9q69ufy22lkSpxfri/H+A\\n9HDrnf9CuBuVRBweZ2lkdBv9e+8p6lfgDJVKWyCRzdS4QT2KsCFWCgIOJbMINfoNw/vsJl00G9rx\\nJ7rKHrz7e+eLS8v88zs7n91+88Ppace5/A+H7cdu8HZSIVv5WZJuHLKNJgd5XWsJnY1abBOXUFCV\\nsaXg3LQzAEUmHBjVu51JWC7SXCjfcJy9XGG3WaOaaTzjc1CLxK+hdBxldrRFpeJJMlHItl3PIJZc\\nOAacW+bUnk8mUzW1ilhRRujzR/Hq9FlruwP3//TlS6okZXJKSHAm/xE37qzXV0b68577l451Vtvv\\nxvmmFLiWG2WaEB1w0Y8roLW+f8tk5cgVXxKyrJnMvQfJOh1sdArc/+qstgeNwF+vT8+BMz272orj\\nL/Q6PzlypRVqV2Gcm1SEoenRTMq031iIieG20cUxe5F9+1n5d2ZDuke+bDisVc4vfjZZn54uTi9c\\n/dLAaFz9ZHTSGTStMvgn10d7qLZDdxzocV5zHVdUnlef/KqWcnbVpDbD7Dnl0wrelsX8+OLGyTBR\\nEf/qCFT2Nt7v5KUY//WcqrXDMoRQPn3Td07V6sQwSHTgPToL+7dCodo6e7leJQvTOBkbn64Om7+X\\nvfyf4DABMdzPyM7pC3+00KS+HnQOKQzKOIgXmOONcfKttf+Nud/Uwdq0gRYpUjHQJxgzhLEXdm0U\\n2cRG2qqynBoFBIxg6oPl8cRViBomNEwos7IaO1DptKnP1r3h0BllgmmqhMxNG4UNgazVfOZs60r1\\n4sqt8D5BEmGtHY8xZIDbySVr7TawA4jRCMOwBK2aP5h8lAE3kqLIVKwI9Ta3wrffMoI4armm6Wb5\\npSQTCy+PsuK8aKfkxsVwRLiCdUFx3grrtpuUQNacVnk2ZW1ppEaAiYaBU1vOk4g+tgjDlnLBBbRq\\nQPoYIiiViROy/hiOH6kqcIC/3QGOkTJmxdft+pQEsI6amREwL7ChSYuiSGuVVwsOltpA0sCFkFR5\\nl2MKVT+dqVUpoG0jIbAW1MRJXAOkCZYa0KrQJpoH/QIAJVBLS0trR3KigQs04kpqwLWmzGrlJbpM\\nWyNFZH2e4zxWi2wce96ttbLPJyeifiuwsJIwW84xeIn5BIDcNq6RHxemlTEEmSZIBU2jthowuMd8\\nV3EleWW4BgGJBJXkQkFNsQ81tq0NqTWkFy74KaJIK+qApWmPpBYUy6IQgVFWnECxgXCgFTRY7psr\\nzmOBO5xs8hRBQj0zh6Da8EnfFkqXea7qCqU1zVi34uGqEKVAlmMyzjUOr5brr68O0pN9VuzxikoI\\nRH1FxWsNC6ARJQwCIPRS0iFBudYaEd81dVlVoAAu1YJSTfHrqy8e1eVvXYmwzrBe+lP/5Tp6Poer\\nEk2v4JTVrehqV8bS3e77thG2UnzOJ0PHKNYxToyw8yByLOX5mMrqepLMkxUa/yvFX9/f1DvA6/UP\\nyEJej7jGzEClZ06M1Udrucn8fsG5hB81ulsW1dLK731ns7sx6TrvMPseCCSXnkwzWKyjqNx8wOwb\\n9x/+UK3FjSJmVisI2kucrYzg0NT25RNxfvK5a3z54Lb18P3G/ca44cXr1y9O/vqksud7xi9Qbmzf\\n/P3FtShXCiXpy5//JQr2GtkbT5n/24/Whs5Nu6er3dklljmXNVVCU0ebDjKbyPM5KoW5IXa+fctU\\nN4erbHfrzXQ4pskyOrSC7/3vXowdD/zF7vvEijbgcmQ3QmVUHPWWzQ0xWGAMC9o8PvXLGitsALhg\\nTHiVW7ktpFCcyMLfNrqo58/BhMfczOKqNiMKjHxWTUqC6rXro6a7Hs3NpTIKZgAMJEw18rHlaiJN\\nWzEMBFww5xLOV9ezcZV4r0/ipHDD8Dox3r3OetNlpeBSs7wWd+vTF0omuHiiwTPBZpa3XzxeCUtZ\\nJi3WZ6YR8qBxcp5Y9Krtx18OG4Z4xfk5B4aw948nZlnFkxz25WZ6NYGN74iB1cLP5mOjfPVLx7Jq\\niVR8LspOvmJV4WMtlCiiVlNhvbMLPbbksJ+dXE2fk+FvxIF7/uD76ds7sd1d3m19bprLtj1GFsAA\\nlmsyOv4i2/jh5VEw0K8GW13Xmh8MPI9lmFpOMGtGv16vKw8vDc/X1VioNZIsCh2PT0Q5v3y+OP3o\\n9MXT6nSyUYwmNw6/tm2zZXGLZ9vWotsLAR+Oq+bXwzfK5QgyJ18i4q7c/OzLR3pxPnv61/WXT++2\\nus+ryd7F2j/Lg+OnL6Js0WiCgt4QpivTgvohT4wkNi/mYR27ld5UAAgJUVVQW/uuYwe2gSjB2Gu5\\nO+H4ZpjXZWXijAAf0ErHr6qK2G4EtPTCxCMTXVWrkTE9Xapiurg+z6uc2IZeCLFybdMD8dXxyj1L\\n3WXV8JQlq6ysu0JHL1/KWX4T8lroKOzsYIO2DjbXM0skILyZpLlnGAabnszqnTlvjfIbudlactdy\\nt5CEhokhdRLGsjHVbCtbFhqs+660nMLxRb+/Z2lQxSU0nFCc1EkFmDBpZAQRNbntgt4mni5rWq1t\\nxOp8Hoa5AhJwuXkzCzYuVHWGtTCC0LcPlfR5VbtGYZiTsOtTYhWX9QERtkasTgSg0rCmtVrXUJS1\\nZeNWY4vIHBAqZiuVxA7uaKVKdqokJ6qqS4Kxr4FZS1XiTcUDIgklwjA6LBeaKSklAKoocco9pv0y\\nB+dXIbzIcLnAaL0eXazySuOkLFk5fKJBhaAtNOUaMT7hspYwBYJ7Def5k8VqGWGZW0Jywx8nrVnS\\n0WVeAwjEinNeFBXFAmiGagGMLuJeSDYA2VjPF2nN13JpSYKwghALACtWQKAX+RmEGECpNJRCeLrc\\nttdRyJKJ8hBVBZI8QwSbDUND0Bp0+ltNxwGOVfO6QnKFBLYso8xxumCGF2RFm61FN/3atwqbrm3X\\n0DqU0JBwqsGZBqSuKtdjrGKS11yVBkWIGqrTqXJFK8vKiZ3DdHj88dIGEvquA7Rk0gIiKu5k4+tX\\n6ej4sr6eijVzNlcBaZu9Tek2L6/H8fTM96gs65w5xLnRIBb0faH45csXR8efGu6/J8XF9uGH7lY3\\n9bZKPcajJeC2gkRTCNT19TBFfs/iKQAZBmaGQiRUCX6CmaeM/R3PI6X98UszLU+6nXnYQ/3tO/Hy\\nQ9z/Z5/9ZoVJonOcc+aRSzk3mdWpi2tX/w+snjjv/9Ny+0+rje+I22vQfX6z83GklyFdDzoFW9tZ\\nH6YzzniWgOOsvm937EGDPf3zTzMlQ9fJMib4yHDSssj8lq2FtnyrqFhZyMhhXp3Uh398Xe4eTV5w\\nss03btvJb7/5/l4N/0/D3yFUnR188BPLuK8WaYATRzs6N5ypqZ9srY9bqFa2rhjLTmPrqjLnMUVB\\nBCGTpS3LlC3hqOhUugX8EleL+RzPEzBLDGjXUTlcXHLbAnYv8my2XukyUWUJcpHxvLRI08COMuGy\\nTrKJjapc1QDXvwNlbkHmj37ecFOa3E2OgX71UcVf8Wpl27tGujD4BV29aLoJVmvHVL3cvdU8jdKG\\nTycETRGwJ+d26E5tc67pe/OzpecuEOKpCATRbloW05Sur2HrdxIsmiv35GmMrBysX4oeTBcIwTYQ\\nc6sdawkAExwYWGsj7GgE8c1vtcLxaNL0/PmAnu9Zv+u8fVib/zDqp9f1ztbt8yhcA1Jjre7uO6Rc\\nEDO6+vqgTY+t+4NZ3gKQ2ds3ogZFChZ202pbGy3HC0rOXSfkABRFNTWcvq4XNH7VYo8PzL+olkfx\\n5y9ZNBeOHKVQOLTUlnd7hzYe2CSZTWn65MSzsq2AHnSZaQQ7m+uD8nHkfXmz8buOvN7cvx7wl7/6\\n6+Vvn9CWN925tQJyswL+4ornxWK58CEwBCvVeGIYdDKrHItIp6HQDEMgNTDcBkI6CKms9rdv7O68\\nd0ezETTw1oHHc1ugCohY1KwqJdaWaZqhjzpw7MCXUF42SamTpdAo9PhGVLX9ledlG3o6+qwkr0/3\\ng+t2qBirGMb8+izSledyIYkY9Ziiyr2PHG+xfC10U8u1pedA54hPLo4W569kkrVkik3mOaYdr4dS\\nA8dvy8kXHi3NZGTVw9u3QdM3K0crfx/TpgayyPliXhtGgS1I2pbAbpUz1/CSugdBBCDxuu6aJ6ul\\n8BoeAEBZXl1JYiYYIlFBJgZyzZtkfXN34bcBJ3gvArud2Gsle3f241GKKeUyoAgikVEIjCaBEAvO\\n26Elipmoc2jSpulCyYjkWgihNACaIFjwlcyJVB5BSiOJfQg41KoQkhEkhQZ1CRWDFSesmFP2mlqp\\nZqlBsKrGRbFcJUY5rRvmM0SIVLAqudI1pTOASowp5+rGnQds/tuqqsa+dy3Iag7V1QLUVsYYl0xC\\nSWyvKGMAgGC1AkJxgmTRdAZSeOfnq3gm8yQPupaSWmuYlQBoSSFQYqGZIABiKKQCfp9sv3NDdwUT\\nMTQwL0FdSAdVCOD1Ks3LBjEHTLiEGLzKEz2v0xJqBHRNau4bzMbUd2rpXilTGaZLiEFJW0kohEjK\\nEUAa2IWz1eU1M6BWSiHsB8yyx2P65BI9fZbPX331+re5DYwQGwKUVY5ma7qsV2n2vLp6nOirQW/U\\n2Oauq9vRJljLk2efuN4ybIf9oLCrWCGkAiwBXQs2Hn40z39pgK88Dm/v/6Th7E0vw6Ww49lVr7V2\\ndFYs6kyo+eWXl+stJ9COwSqJ03ILa290GjgbW1v9AT34jtpqZidPmsvf7W+zu3tmY2vLoQ1r9fYB\\n/cv5FcWAY+m09/3+GxXU2ua/bpH/2IHg3sb/wpy9k18cXp/efnR2KwaYNHLL0wTsVrjc41u//s0r\\nTFR1Mnn9xdereJeZO0fzn7rxF8HgXs3NoqgtcwayglBzOSlrCWGVy1QZKHj4jXaT7Fz9Tn362Zdl\\nmWL+Xn7y2fbf/99eWe8kq69d/NONvX9Mqh3DUZ2HonfDGGxP24fF7TfWd3aeEHMabGJHLTvZafrV\\nlTydWMMrLBVWDNeMEAbTdD21a21y2uL12B1fnE7n82mVE8vyV5SvUddXIoR0Fl+tl+NxmrMquQLI\\nIzQiIkPDsVWMB73CIusWe+wdpKTOneY5MX5l3L+NGrNW+KsgPEGQRV2FDN2krzrG1c4WvvXunX4/\\na26E7jci803PudHTgGGMAGQuPrt19wpgbzTBW92iUgIQWkx0PU2DMO92YG9TrMevWcFhZ2oC4erR\\njnell1Emp/2m5GnORNsGtd+S1Vpz2opX9XyMV9eHpO3aywXF82AHt/u5ct6TeNtu/ng+rBbBQ6t1\\nEwFfSHzkHWTyWQOD9+6f0jCcLA/Pjy8YundZ3eVGnziGzjs8Dw3qM+JXVQUJhvWqroq04ABeY+uV\\nuzMC/aWH56H3UfLCB9rA6Uw7u8iwX5V7T6c97THKZv3dsQkKcfNueydEmV+7g1bvbKNlK6+m1sR3\\ntnffdrvT38Df/MYkk5S+k+R2voKyXDW7Pa8ujZCIdNoZFEoDHl8TO+DAUrrA1ADKTblRQryu9gZ4\\nZTjfOC//GCFk0mLEbnTawoPzMKgRmCk1p4YGAABNt9o6BBcYFd0tEkWZgfIMmHl3GzV2KlkXjXYP\\nf4zs43vvZgipbMnq0RoaT1FvDZA0uzuKnQrkLdcUkOZ0ehwvBDQQxrVGWug5XJ5hFpejFNZlx173\\n2xsSiKKu9nzTLX8GwHQ7anW9ldN7sPPGP7g6nWWoI+hbEBHHyHxjqrDZ9rzlNE1L1ws3gOVyODDs\\ne7XiV8utpLrHYu75ptYmrw1WaGoKTaXUJiqK0Ob7O5P+g2jwnQ+kQobXeusPv2P1bzc3v9fuH47P\\nY0IIVJJo3UA5IJrKlu072oLKcJheGA6WADrRHagL11SWYzFdI5RhIkVxxHW5LBDVsAKaM80V47xm\\niBuQm2xS89UwlmJVOsbCtSWiBAFtGtiAk3y18F1b1Wm5xgghpWuttRACAMCZRsSqKuVs3anZ65NH\\nSX3EOyoz5QLihGuQxPOszJKiYoWoaqmVEZqyElW0Z3mOC6MtqaVavpR6NVqu5mgrKxXiGoC11gLD\\nSuu1lFoKwJGcZ+F4usHB9yj0LZNrqUGlDVOjfotzjuoC5BICggxTIV8rZZgam1kpqoylgQldpNu+\\nSQjhtdCKtEzYMACAvar0AQBcjVk8X10tXd+TLAa6RLNcHz9dRi9ehl88qcij5eQj1GzsvxF6pNSo\\nJqsLeXqBikfUfO5YhOxsIX+/Mjo3PtyL6OcPqo/7jju7CurBHaeBPHFBAYKegZ4vZmcn09lnhTja\\nCTbffPjD/uYNJx6Pz67r9Bg3I2YIpFmJg/Oz4eOP8tNs15QKjQp9PrOKhEyPYdWJWgcXy51T2Xn+\\ni3/3rTe/+PaP3iLd3wv2kO25uLI6h31//pxi6pJlu9NL6f2rVJMbJxE96Xe73Xt/ZO31u42VSa7j\\nya9Gkwj4h8latjq7GjZa3Y1v7DwLV0blTzF4TtL6zVsunY/Gw88Oox0yYatKB+0VJDEASrOqWiuq\\ntUlqrRVw+kvY3exlg9X/WK2+IBQa5Jjt/skvnt19/rNHrn2pnR84PvUDKxWtOb9XN78NQUebB8Xm\\nHu+0O2GrLd1BY3lz93ivcRTurhs3eN+ZDzzWJLVWEygn1ZrNpbmsCLMrlX61KNLRZRwLk2COHPsi\\ncxY5KZKkT06zxXVeJnXNNfANCFxStsOXmnQrkwizhcniyTHrerHj2TwPi3Jnucz9VnmddQB2bIO7\\nBrzztuXcCO2Hf1iSG4YTNND2avphkt9f1G/nhquVEQTKbV45NqXVTstNQgcn11lagm6wfq8d21jC\\n9o2odzvNnhj4DjsdR8XEo7rdQDfMS0Vs2Gt3TMPRNPJ8BSSAdZkZgM0Ar22YbAVBLziNr+MhGJj9\\nPqYeV9RrbGexmA83Llf7tRTYsJ99gsvl0Oh6r6zdU+29WOWgWDaYrx4PezzedVYRLYoylgIoXS6L\\nNXSbQeQ5Qo7TQnk2N9tT8WA+D2Qjq2HR3xlWueXaY1E7dZ59duTVqzyIGoSfzmWjtUmmT/tVcG9j\\nw6izVvPmXlWKtCoiY+a2tqnbu/ngODB+1nvYwqznuGY+SaMmcg1ieEOe4obFTcPFmFF4pogLBVAa\\nC4YrIXXN0wVsiGRwdzcnN6cTkwjXbw7q4xgCp2PpTuA1mm7oSS0lpdQxQHt7Y7Dv+ngVcyeLWgiy\\n2YwvsvuTdY+p3SQ13V6+XJ5+kv5BAVqWgVvhUhoTOMm7DTpfC0UqrUgxmqpCt0KCZxOCNEor6h8I\\nIUywCGfHN/qVR3PedFo7b2gNRDW2fatx/02Moavt2+/0sHY5vouAn03G6aKlta5qZRvYdev1OLYO\\n28Uyh9S4ZO4qdlwvKBjJlguzriARuQRx2iAwwKZLbCIpEknp0dgO1vtvB87eh5P0nSpxLB+l+AMQ\\nfR+g0AnfLjKYZ8Ag1JJ1v1v7rmlRSWy/AhRjN00zzmQm22XuIaxtQ4cmNRxbAw7A//+4C8mLKlkS\\nDoBB6rrGhiSCU4qJUVegzsaZ5OeuM69VbLuOgRlSqVYqihgAGQTCIHPbKOuyVJoppaSUUGig6lqQ\\n1RKG5i1YnaPk46g7tUJgqtSzUVkIUdUacQVVVRdxJjPGAPUW0wRCvxXtE9jh4kKpIUQ5uF5gDgmc\\nUqOo61rLmoGCKw5xnebVbA6Oz2JnllCzQxxEHSqYnM/niyW2upt2wEwfKgVUxRGiBBIJGCQQE6Ik\\naLjx3v0OJsSxOkIILpMSatprgf19DCPOIBc5BqtGe0KMJXG9PM3Qq49+3t17dPPDVw2wlOdPr4e3\\n4/6bTAElKkKvzeC62RoygpG94dFmewXHyzDufvfy9XDn+1bnfWMvFA3W1UuraLqqadm2bjQWt+78\\nxpn+a6inW/139z/8L0XnUOFrpJ/rauYBL1TGlqxomi8ffW1ffjx+RbvdINpcRYO5t1H1tuPoFjbu\\nHayu1yevK/7Jf//9P/iR/+D/gOzdLG5nS0Dsrtl/L7rNl/GJ3ya93fsQDmbPno8XqUaF2f8xj/54\\nmt+cgNZprUf59NXr6x6VYPji0Lst1xUUZffmu15v2JqvXi9STk6j2z1zK768/LO37r/f3pJAnQpj\\nSAjiOcxWBZJrwzCw0ZOQGlGz2f6jx0/XzvvmHD2FwKRWg5uH6jWMP/2/Hdw247VvdroqbM8KlM4Z\\nVH2MbCwblhnM0+31LOvd3PM3+9vvtTbuiKYLL/jNFzMHtrdoGCC7NlCeqwIs0vNj9eRqzaPd8YtH\\n2YthMmVnx1XOslWh8vNES7CFJ03rXLO5YmnBIDTalmFiApnq45pK0tNBL1WmnrwOb//Q7RUl6V69\\nmm/6p4X7k9H0misTalEXZG78sMC/F1cPxXzVbr9xeKN/071GlEyny0PEqWlXsmPZwnNsr9MkhHX6\\nC6LWMs9ql29+u4tRP9XbXCZAJQG5d+NGdfOWcdButoPNjVtkF7ol29x932cgrwiClTZtoywZAMBw\\n/QqheuO2fTPW1ep45I7kjbzEjAOu9pCYX8asmJ9qgBmiRnqReeapfGN20iqfFPXTp4fdea/P9m+8\\n3nlrJ+wYzC5dt1NQspS4zGZnc+L7PjZiUpbTRaClKxMLsiROb1lWP3P3kzzBgSerzDD15Jd/aaDz\\nFjE9L5m8LHLyENdnet6B4YDH5WjyEKASzmPd3uTGlnT32jsybFWXXzycVEBSzIorZBAcRFYzxLgI\\nAuQ3/LU5iBzG6UaZpUgzrZgCKS9YK6zDHVjIu2XWSB7P+0bVhHt+OLVM0bvhtnY3LdK0kQ2RhF7b\\noR60d+Lq/Vs3Hzoon73KkWEALo3ZuhpeKuWSZMwrlzjW878eVvUHtYFmg7ds09XW6427bT9PXZOA\\n9FqrK8NM2xv9/PJLxXQhIUS2bRqr5GzzcNxor3beCRYTD9K7G/6elnWp9FX9bi0h1gVq/954qJaw\\nKdFbuBqa5jmGRGq9WLWo8+bhfQKW24RfFNVkPvGHS1NBVcRhwy9sqwh7tiP1/GyZFaXQXs2b+QQL\\nzpAlnG43xTe0883JkYNpt6KbJbOzfIuJCDntrb2D9eWRRaFvAa9/QLx7YWQDqLOVtrAKPcrm0xqZ\\n2TwmwJbSrajPoCr5knGJEOGshIoDmEKdSRtwUWIiIFBCAtf1ObUFO9dgQUjlOoEGqOJsc2dTKiWl\\n5LUCShDDNKlhmlTLSjCJEJBAE4IqTm1RQFYedvcwuixVjhoeBSLqMs4l10Ok4grkkjMKcgKBBFws\\ni6Ka6RJiHFZcAFRqBRVZq2pFqamYBlRXDDAhlBJay07PpJb01aksP/acoVQWgkEMus122MDLaj4W\\nwFbUkiiSjBmOxIhKbiXrtUZY1lUhJaW0sb1vm3Y7CAwKihpOzmv1wjTgputsmLRZK6T1SgHphE40\\naKN1/52xPiRo2Rn8hkym6cJdr/mwkCow7MCJY3g6taoMxzkSrnJZUS3bdPy8dfgPE/luBnfnTHUO\\ni25FkmORRbeEpNC+MMDnVbWcz++0Dv/XYtlJ1zqGYB1PSKOJfN84pIuB4tknwPqM2L8J+7cbvRZR\\nTVED/+CQdppg3MXGOp2PLue/+e4f/UPa+pZAAeA+6XjzISmqQ13YS71g+huNrW+mwuPqE1p+JROg\\nqx8sk9uTa3V1pSZPkqPPzk8/+bPBwDjcudrc824/XEFaf/jGD8UwAE28LWYqXpXMlRsfPv/Fv2t0\\n9/p3H+aRrWRqc1CPdDGtbA+bhl1xhKBRC9+zN+Llr6Dzz/7yzz4GhJpSuSIEkILqX92+v4+BK0uc\\nya3FEs2m6wyaru8jJYnlKaV1loLND0r3BiB3pvh7lXsnNRwwzSv9bmZsaRDatm0SDVHiwXNw9HX2\\nbPlqudS9pQ0vcPF6GZ/Ret5QJ2+3HgfF2LuH6noG4FLyKwQwJgZt91FkCGxyUZbrcLXCqrzw2ncn\\nQzweXiBZW+BjtfWPX331BaqHrhfg1l3T+uHrJ6vVWYbjC3vQ93r3/f1bgzuwWFiXRx93dl8DiCw4\\nxIim0Y9WOabEuvT/kDahT60m/JNPjn60Wq8xryV5IcmDIBj6/QPr4C65cQd29s1gJ9zlbAweZ9+C\\nulkJvNWmshaIzZSmSnUL4U2yW7VxYAUnH//15RfnQVwrpZRCxDafnV8tKrYASHNpF/zx9Sgkzy4P\\ns8c3979y4ZODt35kt7aN3kNm3lPNb4qqVhwhEvAsbTgVz5KS9AY9t4eOVV6UAi2Xr1pRHwxTYlrr\\nI4gUj5qbRK044J3wF5G92Nsy2htRnn0RV14nzKyoll5bG2sS1yS3y+zV8UnnZOXW0qWtD5y+5osn\\n1dlacqvhxohYytp1rV3DX9Mu4Lh5dS6U581nqhbSdpAQDCGkJd7bQAhsUugwITeMo3d/2EWGQSgC\\nyM+qtw2+2W1Gg8E+K5MaB5tdw6M+qSyLdu3+jilHGGAHJpi93m0sWvoCiSmhyC7SfeuXNBn1Iss9\\nKhCIlcRz8RAi1W4rQnPDMBQoWcV79zy2WPW3EEbaNEgtUndv22l7VbphcrpeLDZvvQUkOTs+qlM/\\nUGJZJ6OlPzo1xERuC4nVRMJCCIYJJGqYn4sR/RYzDa6B7VuknHvihS3yZtiZxel1pQgx+90ycIlQ\\nawZomWMFMoUyzwua4RuX51SM4Ru9nvTu82WCOQYKp0II4BlODyCa68K2u3bQ5bLZ6gxu3dm0KaIw\\n7TWdiidElrUuAKGFMMvUqkoHYalAWtY1hBojxZmEMEeQcO1IwYitOZBKQ9/agAAwuZAyrngCoSbY\\nLpOlH3ZNTCSvTUMjBBBCiBhaQ6YklwIhJIFktaqL0kILv4vvvv2eWj8FkCsUAQCRtYOxrlUNGLNQ\\nZdNaCIERKZjkYplkme+iwAzn+TivtNYV1Nw0KtM1FKspzhEGEGJMoBACAIRdAU1CKBeK5rXExHjx\\nbFTSiJutui6LqqqqkkuECJFcy0xYmA+8wrJxVedcFIYXdDrbZns76oSG38eQAXqq8DUACKq2YLQs\\nANBMstxzQ8Qn9YwYecN7lU9pA2tZR+K6XE4ur/1K5cUKoDI1IIbErmDlbixaXmz0vzeeEFEby6G0\\newdWhN3uM7JCyxm4rCfCOPeBg8j9ovnPc7O26FDNRvH1apERq3GQghvnX6Gr12fayqxmNI63gsOt\\nvbbRDnUjeufqPLBbNkrAcvhV8vJnt7713xDvDYltJYDKfSQWZ7NDCzfTwn799Qz2vzVZVIvlCas+\\nGs4Tg98qzsL8YpwtLnurT7vq52L63+3e2xp0vGXZv/mNfzmedYODD1ubYsPhhgcHO69zjkf4m6uv\\n/9sF3nDf/q++vLaPHz9q+akDIEW60XQMQrmAFScAJ5TYSon1Ci+G/2/boYyLW9/cMqApkpnAbb2z\\nT7EgjNYsW76+nL4qpquw1gYiEaBGxSGYp1nSWa77y6KZLnvDU3s5WoPlq3Sqz4fdghAFNAOZkjHD\\nz5v9ryLwpTF7bLvAESOgLxWLiXMd3mw6uzcM3J/mm0pzw0BKVABAy20r+07OPK4NqRVLF+XkBTJ9\\nqe8kybouiWBX2P3xqxObz7+20aySHez+fi3Cmmw0beS6Xpptmt79HNziwfbw+M/vH/yQkYdLtGuD\\nSafZPb8AFqxW5CeXn0rdfteDRWjBqFhDNu+j5Wpup3F/6/43WfhGim+Oi5tr1VmY/aXe7LYFfg7i\\nJS7jkHq7VHGb1lUuNOeLAhczbsBd5Ghn9tXpF8s41QghVcsGGweTZwZkUlMspiY53y0ubu//qnur\\nlN4A6FlJ3jSNbQR3uexcFZtYZKLMs0TJ4sJ1Q1VfLvJN4T3s9iSGi9FoFKcnEJkHe0vboUieN4iK\\nbN/AsUYCgDGWGdn/Pce+aZgLtsqC2wcJ9q9jWyI0WV06e9b2RmlW08lRdhEbk2rf770/eACc2XmV\\nC2HS5VpniSNp5JhRWd0YCcrmz3PSxYpTnTLWkdxjjBkknztvXsfhJY7WDKLW6dz4JxX0oKIYm6rk\\nuNGm4duOt9mIPMR06+a7JGggLaLNkPDbXK2kYo5FNUgqu9ne3RAsR7ZrNqgTsf29K8tXrveyEXHD\\nrK8frYkw3c17kHqas9CC9fJyc3MTi0vptHVd2iak7d4i6a/BrqxZv+d57BUTNzyvL1G5Op1QwoyO\\nFrkUQiBjpMz55uGhqSYIayAVgMhoLiYnan0d+lEhK+qbNSZHlVhvfHM3KRmsRblUue77g05VwtHR\\naj1LtKypUxBjXuQVFE3Nho1dq341cmgODK51zgpQlFwIGXT616/GSd4RdghpIFTO7FuWf0/qDGDX\\nciKWjDCx8jzVyBD1nOgCIKyUghBasMawUErZUGhZay0lk4BAqXiWFYRqBGypQMUVwS2MTQSNrOBK\\nQgi1aSBMAIRaa2g5drK2oQJIa8YlQG5RYcGZba4bbc2tQevNP6nrY4JklamE0jKDUnEgFZAZ9bjg\\nQPFKKgXq2tPxVjC5eXtgYqvKpkoIhJBFiOPZWgWWjSmpleJaKin19CpJMs5UWFTcs6RJakDKIkXJ\\n+GXNEEKIWtgk3LAk5BUAFjCBYyJASv+gxZU7nqTnqVpnLSE3V5MBr0wIbWppphIIa5tCx91HwC/z\\nmeCrKl+jG4nMX/GPfzNceHvE1kF0YoJzjdwyV6wWpk1QS8nVCOWL2ghwALTaWV/PGxZ1eUJhO3Cb\\nptmgQV2YC1O/zBNJovvzVXf66qCvzPSyWGIJ8ShbXrPO/fURR9XTlncWOkZI5E71HIhe+/6g0W0o\\ne4eAFfQ3XbPKjdNy9LcP3vh7fnsLaEMJxhTPdbE4vejt3i/TMl08FquNVbFIhl9Z+HOPVZboB618\\nMzxu5cdAXYOd8fX88+6tD2Tebrc/8Lb+GAj28llr+963ZSOKDjxr43tWI7GPj0bTj6jVmwf/m+PP\\n88Wv//X3f7A18BLLRK4rqWkDRrIUNRtC47KKy1rkNpnqMkFSE/2Nk8nhiueiSlez5tUsSrHV9DK4\\nekzmTxZXI1ErIRDkElsM5audtqcLWFeKc8mWq8V8gsRc8bVZTeCUKQ4KJiTRJkpKLgQWki4GxtPd\\ndtFRsyzLUr63KG7V9WHm7Eb9VjbMNeBKKSQkNVtUNnleSuZKAJUSbP6RY0COfJehMk55Qr1Wt67E\\n0dc/o/ikvb0NmZjBwKTkYVodAAA64ElEQVSZzGZod1MpyzI9YQRau3nV7TqvDzcaMEWqjHoDp4Rb\\nhD0X0dZqVNwVf9VJ7m1t91DDBybtWmaDVPmykoqmcv/1lf/y2IonCmeWXpnr50RKEtkZiafdKIKZ\\n7XimZRuCF5VZrS5XCGZ5ZmE5c4KP6fjRq7OiYLXniYrE0Z1raiYYlASMKJ1tvTHqPfwR9+6URbB/\\nuF/lFgqjqgLrVKyeHWHHQAxU2RwApDUnQKsVydY3m7e+7RpDAydNgyg31Nub0Iq4fu7veTjYVSZh\\nSlo49VrdV5e3ShX4ls6uT4fJVpIYL46XJZLCWJTiZjgYeMZRVD6ePjp+9Fhnahfbg5u/n/M41ToS\\naZHPi6pyHPNgNS/zpTaC0WhurmZThBMmCaXYNilX9PpqM2WNR0fpk68WpqWWE1sJkxACtarZfJX2\\n66JB3d7W9jZLL0+T5lgPljmdZZsBbQJqEEKEtpqtTlU4Zb7R6nYBB5kMNlvBZi+cDcs1bKGth9RQ\\nNj3pWTNd5ma0r6C2kLQjFgakN/CuzxSWZtRCqi6Pjx/JNNiK7A7NvK0tBHWw8T6vapn/zPRGwOgG\\nsLL8ulZrabjUfdN11qJmlNilxnlaDT9/5MhTC0ZEVV5QI84FH51lA8dqB3Sk4LhmuNReMceomIJ6\\nQoyaIru/WSktADSz/BFW8bf+3ptrnq9qGBv2pOJc5IXUQSuiWs/iZ3EeLFL/OPZOYzhZHADk1pw3\\nu32gNAZ1zkBSlq5TuGTERE3oCNZ5w7Nam4FvUWJIz+GNQGvFMZZB5BNWuDJznABjvFxnWpecW0Ib\\nSkMiiWMWFmZACYwxB4JzKQVSUkBdclbxfCWURroCdJljP5a94TP0YOMeJqoqQWgZvCZAaQAKw9RK\\na2pYSJMaKlnznV6elhN3cAu5D4AstVzZcFHXpR/03cE2V5hJpAFFCGGotg9Cqnm2OuJVTgiCkFqW\\npUySjK59sEwSlK4MXktZFFTkJkmVxAjAgmOxpFHzUFXKvDpdr8ZsnCHdbMA0iIhlwyAMKZYICpeq\\nZnMHYqrkyNArdND8Xaf8z0Zu1ut6Ms1IqFoeUQuO+oICo7uX3jtUW5sEqlppM07bUCcdJ7BCnpUg\\n2BxAw1aWO7qqGX0NWU3gw47ipOKs3TjcPO2XZTqv40qlcU+cnWlyVKPFVAA58NVuT3Zm4+IBjQYa\\nmFrW88UE9lxeVcfTF2/+8E+B3iVIZkU5i3U6yU6OX/tOK/DL5fmnjj81yzEuf+r5l117st3JBlHH\\n3DS91sSgisfB1VGRZV2sgjtvfffZ42G0sXn86b/d/t6fUO1X4rCmd89fXlsbL12wbIVq+SrfW3zS\\nmv7fv/m93/PDO5q0pPYKHiJEAJG2T/IKJiUiBiNkqYxLjEkudSJb6vwYCp4uAVRmdro4LpuJM1Ts\\nuURPCFgR5EqIiGcbpMR+W3fzBuKSMIHrZZawIvUbdlhcbZGkVaWhYrkWEmtsZYDrfJUSyPThXbT/\\nbQhLORFylK9L5xy1JkVvLmdUFQiaGiQSUIP2hJJUzYBGGirJ58htQmBi2Hfd1NDL0FUCvV2Of4ay\\nR6T7d1xEA7vWs3lWfWzh2hZNDbEijpIIAHDy5NONN/4x7lu+W3j1bIj/YD0c9WO6frHaVj/94O+3\\nd9yTnda7AllQrQ4ebnrd65TXLeuby2EtL+dwOvecBDd4jRKqZkI32q3Vfq/wDaW3uogSRElaLT1d\\n+vBrjy275SogK+2c++4X56f5pCRLclCx6vHxhgIa6SVAOdRO7v6dq2o7qYPp2Qu7/z7TUoAwXom8\\nWOnifLp4kDJEy3HX2UtTigG3QEyZOFPfQW7F9TI4jGavwdGynWSuZeTSvllIB6kWRlJJLODtfJiM\\n59R2tUlP1zk8H8dieFxjmuXr4dmM4VtRRELvmY9eRsvjy0diIc1z+8NmI2UpA2pB9Wi5IjmyTLy+\\nfnUu6tRaTIlYYFxjqBynHTpYs8KoY55n5mrUql40on4lca0ApT6VbSnt9WzNMMJ2i/ofbEb89Yv0\\n7MKkYJUmLOwQqAgkMFmn0KWLpFpoVISHIheBFrv3vK391qYBQ283OetbNoKUbNxp9XdCYtyA1NS6\\n7HQPG73WG2++6ZfTBhAE95my2fTrUPHmlhfdv31+ujxK3dlly9AmNk8Ct7Bq3yHM2rrDEWCgGuaW\\nYfYcBwMhuRQ8n1Fn7sAnm1Ey6OhmhOsq8wPWOf/I1WHDTR2XFJVSMTelJkFiehXHkmhsGrfscHMZ\\nw7iw07Jam4Pn696rhfrr3+JXryVLU8QLAvJGY3OdL4fTahbr0xdofaqlmmjcLGvOFTTtruJA17gB\\nweFGRSIfasQrrbEBDQTdDeRuUbMpkcWhjahDuOCKa0EpFC7FvqmRqMt0hcwsY3QyxNvbvmWb2vCJ\\n6WHDpNijlNoeJWrFlQElxZL7HS5Z2tpzh3PyaibtjFueMlsdy3YMXACBlRIAAGRJCKlnGNA0pYKC\\nFa4lG7c/HB4voHlf23saAgpY1Lcno3hZ2CWPeC1ZrSHECEqis439ze12y0RC1AnQWEpObM91ZQ2e\\neB6g1YgoCZk2Tek2TGDalkFs6Ol4AuxCmZHRqF2vDNzsILxotxoFqIpSI6wNlxhty+653PVQ54EC\\nUcFGqGh+oeuJ0mcbeqF45G+0aJQTwhyDE6B3B3CnW244+YBWQVFqqYijvEE4T0va2JbakAXM5rPT\\nyymh2pI28Oe9dowTvzR7ZcdmVoz5Wq+uXHIe9qnpYLQW7EhdvkajUfXyq965ulMDWDDMy/xi+raD\\nq6effzRXv596W2nu60VuTpeD8rhvfxwGDbc9MMRLiIRQ59R8htjcsDa33v7TcP/N/mCbGgfWgd/Z\\nLDBfZsV0q7l3981vx6vL7v6Ps/HPr65vDToEVgpBPL08bW9sFNyyw1U+v9zdzA7vvb5xa6N78M4i\\ns2rTYYwoaClCmaoV16tLeP2ysRKmFzpCMg/EBgB79q+2uklDLWSRQTSV86SYSFkVjM9qZ+XReDpe\\npIKsSiJtC2TtHDWzbLwalfziqLN+1g88Z7vd/U7/9rf5+z9m3gZUmmtoYYygklrWCG2k6YdV3g9C\\nzuIEo5N6aV1/WcyGgrMcSo2xp6s1hX1IMDYglAxCKEAtNEBGRDC0jVatVyTow6CbLD9xwF/e8Hmn\\nWm/t7CAqGvy8Li92dt4wYI6poZQhhFgnGq5/DaUz5INaeQbk04tVr7F479tV6F59+EdvZd6fdj78\\nhrnVmV83n5yndf/BfA2r0nl3d074Upjngs1W3H18RK4fPcV4TCiEpmv1onZkJccAAAcgQ/KEgFEF\\nCKClvc03nKGUqtk5E6+fvbgSj8chrgtwNeWsAAD5JBCcIrXPclMyZBSv47qVLcR6AeoyqUeXbrRu\\nGJgT3HRxd7DnARZ4vhlopurXz8gy2wIqv1p0t3YL/FX68iVrDcJcNYgTASOiEOx1bhJ8YKuzfJU5\\nRhPB7OJMro9eM3k2ORFWCCWOx0NfGr3u9rall8HO81br5dkX2fCpDTrtrl45sgZgJfOTUWIJM4jE\\nS5Nx06wBqYjGiNjAaAOAfVc3KezDVce4jsyvm/0NXAFUcSkwBkIKVRRHWVbYVlPWdqN9oNMvisvT\\nqOnZZN3tO3XuAq6ZKDPucw0uLy/nC7dIp4cP6ILdxOGPW3tvROg6yKrNAfK0w62dnBwygbWCmvNw\\n40FSRSr6YOeNgeGddMI0cruuZyF8xcj2Am7SnRvO9aoBloPdFhAcEonb3uae6wcdtcqopYdHkKtt\\nqqFJIIbItNausyRk3OxQJ9qCigbOhtR+a9Pw/dr3HQ3t5Grp6DwKVzRAzcAXJReIjFBvKSKl+eyq\\nuBqvJqMi4d8sP32+c/nk8Op1Hz6x5QSwqemHbhg9++x384vzcnaVnH1FqjNRK5YZ2SJFwJZMKi1M\\nNO/faVrRA2qFEPra8mvJMh5UVbNy/Lj08kTUta+l8txACqSBajkDbDoGaspiyMvURnqDrNYXZ9p6\\nmxIHaQsjWymghIxc2moBA9XE4AzpNF5Rp1mWsNINsihdtdB2Jpp7CIQQQg1xnudE88B3MEQKcm44\\nQhiYgBgbl8uoKJ1QHzvI1xrWgGCvgdwtwqRgQGsotFCKKc2AFlxg6RyYXiRIJBlFAHPkrpjDE7Nh\\npc0OMK0cI1BXMskApVQCE0rR6bRLGK/KYLoyJReGxVXXSpsugpsYtZTkXOA8N0ZDp5g34NpT6kCh\\nEB3PWCWhuRkN+rlT+LOhMVrawnAsx+PmQ9R90428aCtrbenDRkoYXen2ajSvYNu0RCXqAl8vr/6W\\nunqncdB3uSa7KnQJbXiE5nOdQSBp0gp0aDkQwoFf3jKevmm/2smOguXJ+rilKnQ2BGdVdfL6KHjY\\nmjz5yxdP4WZ037meImO53Z26uxxuuKPULvmhNtRyJiEQiieWl2Lzje0bf1oWW6X8VtDrYLWJnRvE\\nilXxpWvf6L//bUDKF48ug045f/Ji487v2ag0TCR5AgVY5fjV2DWCcsXd6Vv/NL0gW2/9z1kpqrkx\\nXFvrTEuF8jKrGM+rLGonh7tLU2nKY8rU3p7Y9rMP3hk0Q6fXWdmg5mDVMK8MW1R+lGVAG8rUWZ2P\\nZ+f81VEx1J1Zoa5OWL2c9vnXW3fT7ZuW6fpy3bq8unvR/+EwfP94kpZ5VlZCKUVBzqomwnsst6lT\\ntG9eWnpJLG7mk3B+JtYXjAmAMEIIKEkMUwiBENJaa62RVkAjRVyFTQA1xr7gzTI+DvTveoNe7/1v\\npKalNKGYanqKkdGKeo6NMQRQcQ3w5PN/f+tbfxJnfnIJUtClRorYJd/94Kr1B4XUsfdfX3xxOl7c\\nX4ntvnocimgybZwv5qaab7wZuPFZeTRdVmjxuzWZXza9td+r1mvL8BtFeahMROEV1E5VZ9Q0uJI8\\nbmZOb5W/2brZpZgc3G5vtL768lfj018/pv7Mbg8psbeJ2ul1AmwaQtd1DWRx8MY9OxOEFyasla7W\\n6bz2WorUUCLZfUAs9+aD3cCTmiNCqZ9eF1OFNVoMRxfU9m6fOatnT+T9VWaumLGCVGkR7h+qnGE/\\nN1WmFLRMvxw+UfVrWQ8xe8nWmqdrmYwq1U3lptfli5XDQOY7R73y43pidb/Vo7gyCLVxjrJ6vTQC\\n83wz1JFd20ZCgNLSUWVcC7+/NZD1bO8tpUT84KHO1U0E1oJXnkoNM21vGIifisWTGAaWH3pkyzdS\\nNv9ZqT1g+tDwsKlrkUoT8jrXZSbjY3f21LWdBbh1/uRyyTec/g9vf+snRTaa1O9RBVTQHSWNihPH\\ntCbLBIQ30vG4rA/w9h8zqnXzHSfyMZTPz56/EnsXy2j+Kgo2XtmdFe19lyD/aHI9Wg8kDN3QyxcL\\nLlCTHEMGImoSBBEGGFPHsagBaruZ0wcQRGajv1xcL7QPwneKKixKT/O1E552H7oeDkKraDacNGYv\\nfgni07ZZQWxW8fg5Wowbi9Ft+8t3bn1xuP/1xt047MTYRJizAPU9ssLVfwzpha2fWea5aSFe5Zqv\\nNF8SgiCQC5EfZf1ZfYcLr/j/FQRfzbZch4GYV16du3c+Od4M4IIQSICmaEmkhtLItlylN1f5xS/2\\nH/B/8X/wvI3LYUbDKY5GFEERIBFvPDnus/Pu3L2iv090au3P1iifFBAhVRsIVFnU3OaR52JYYoQM\\nQrAqdjeHDtsZPdlv5G0AxOPnxe7BDEPP2kMIIcEu9wJMCcFWO302OGpBUrXIlMY39dX1miY7pqlQ\\neGOxhcGW0hsI+DyMaA2oaSk2iIeNUUVtjCYKgJvFoliXtkHQ5AgIhcLKsPUsr4yv9AITI6VUoiUY\\nQoswJnW91DBIxXZbBrKAwFoXmn5QB0wQuA46HS8ZDkaBtsYCYo2SUs4yspTBYrklalXN0ipbFVZe\\nic7ZXQ80gxa4Wqq2KmFW+23B2ymXi8SFDA8Rgz5Gu60+OGld1MuDusjmjiZ9BSLGHzd2T7NjyT/2\\ntx/xDuAKrWV9dU9WzJnmZlI0p9e/L6pJsvHXvaNnjeCUdbKVMQy7vAKVdVZCux16zPYiisCgcWH3\\nL5PNn9082fwulu8xUSN9mn335v7NDyfLT2/LN2c//ItBw4ODN24gymEPspEFvfFUlno7bVlZnJf1\\nFDgXnOsYPo12/t7lbsA6TmcjCp3QdLBDpM46YX9w+AtZhl/+8xftk18t21c6+GDrWQgRkRDfnZ0U\\nnV72+rUUHjAwWfzim1//evdv/xelschSWU4mUwckCvvGGImwFaAOumK3W3Aos9zEwb7Cw2Tnf2T9\\nR4AeBEnpy6WWMqRiuCrsTRYZTRFGLEdqzfR7um7S1yu4GkPnlj9qNnZiiQ6baBv5FRuumBldf7l9\\n8ceLbHrBnRDmCEEItIGGtKIQazRTz51t8+EjMCSiN3zVS1ZYlFRLypxGlzXgirqcYIgAAIgQTil3\\nKOQEU+Iixqxh0NQMFkny01Xwbxf24+0X/939tPVZppuc+E+x62NMAYWEmknd8hHQzlBUpV29Mbgj\\nTSPFelL9w7vfnx/s/X0zxfsvHnU4tNanwz91N4L27p4A1YkO8dL/qH86pBf1xSrufuegsXP02BI2\\nOZneoz2/d2CowsRAaLVaK+FoRCN2OymSyVpK/CuK0ffwCfO/31v/58D+AABqNNrpJUc/OSbuM4Jw\\n17V5pubj62D7RbdjjSoKXJlK1zeXYpVrlQM5Wzxscy8U0Y+cYKupldSaOw9JBLfcequ9vpt6Nzfx\\nwYtcf5OWWXG6AM28MtYuzSFxlgZFnNeu24njTaB+7fUyjyhqKlWXyOR+WIJGVyWON57Th+zJodwc\\nFM9+Aj5Kzr/9/gAf9pEVDtZYpk1VO6N+uDna3o0gbB1ENl2ReApBQvxdF89O8EdlXbCNn6qyUFYT\\n02wMgt7eIxrsMFu5XZqfjZGTsP1dy7udcJyvlw8PeF6FDLPCVJSHa5FaUgVOGUfXPTe7/a6ItqLL\\naynR7vm4K226WB2kokltP703uFIO51Lb7F4tClMKM7mNs1mvVnvUPCtFlex3z/5pcvdK+XRSA6oQ\\nuL/3IB9gJLG5KUotQdI03CJFyByh6WiDMAp9F3EiA8o4csrau2mGU+E2MkCaT2c6b7yi3S6XWlQF\\n79WF21Gg68csevoJh/WhM43K7yi+b9oc66uN4PRo56x7XI5+GvpHvgkeUa+vlRc6UeC7vY2dprwq\\nwXUDJgrkGkjMHaNrIVM/dmqdl9J+96dMrGqhrJDAGV9SC9ViDGhLBOyGAlNl4DqHpOVuZUm1blng\\nYDZIdjfTKiLDn0shmugDPfjvYRhA60DoAwgdHmEyVBoWeS9PY6tDYyGixmfjjz/uVWf/xLrdWWmv\\nJljXbS6YqoBGXNPED9qyqaWzmTYOVEuLpNSyzYQtplpfa6ghwBCw0uLZvG7WN0YCg7BWPgJQa+sQ\\nxChxPd/UWZ3BJtcIWwsARJpj209MpQBvQTeOknjYP3gELdRtaa0mHpzcTm2pla61LqE5k/Y+/yGF\\n41k2ndh8BaAK3MB32+Heuu0JzLQlxvN8xBjpRtC5K1c3nruZwyQdOMjUWhfM1LbKw0YMCP8zqY8a\\nAct6Xt2+VRltr1Z2PJl9/VtV5DT5663N5zJNy3XXyIeYDpq8gWC9uTM+2EJhGc/Ak/VB4LH4ZvE3\\ns+h/Qt2/w483eSjd7t3H3T/skP+CFm9x/ab69v/wky4efma78fhqaIO+AVBX64tvFvM0X0uzbhuD\\nF9QhBD96fPBnwjkAnk/DLQR6hkcYae1LrdzOxkvNbSD/uV89ojfij/912e5+LgzXjN1eXJw1B9U3\\n/2m48S0h5Ol2mHi/Xgz//rp1pCh0PVvMzgsZZWWU1dJoXqWG4L43+mD0knjW9+CzImXNamfefDiv\\nmOLHSd93g7ZuSe8gH3UWW4dmr1dDG9K4RkW6Wi+g+NbxZk78Di2KzNmdeKNJNRyXSQ4GRnV99yGx\\nr6Lqd24l9PTepyVHlMO1bhqgFSzbYg2N2t35ubP/qBttDEisoVnItrXWynppwRBDDAGXlgkMFHUM\\nSUi0iZ3I8E0AnFoahDPH32et9+Z6b3n6u3yJ6rPzaIChLAv6162C67mSxhqEF//67zs7v6wrk6sW\\n4Axho0UlS4zvz1F+rUefrCb/2JKDVtnbO6RgDJJYtm8V5Puf/+9kTzs//7jbuXna/1bEuhsU6eoR\\nCVWxWMg3dwI4ikTSIgxkWyvbeAiFXnd5++3dooIrsBFQOP7XNg5uto7+r3mZIR4e+SweftK0f9fn\\nHWS2gUfmK7O6+VLojSp2xxoVb+XAvXO8uXEl5jqOjGmyGsWTK0PdF40BjaytWR/43qNPdzvxSXP2\\nJssXhvU+Pn737tvLyavToLtmIILrThBu2kUTdhOGAwT8yJlYIRzYbPfbDzfbMBIGimZREY0wYk+e\\n1feLJPLorNh3P3r8fOOyfOjRkesSxGFFivHitjc5V3fkGYPNzla0udGO+h1ATFaRvZfD7BvhIr2G\\nj1shlQYusXwz6Q1ezk8fki5+fTlzw2/nC3EjtspFF/uO0Ser2awtYBzsYgwBtc1qrQ2wCBZt8fQT\\nzy/Pc/Kjq5N/tG2z+vqHlz89EreXa0SLpWeqFME03IqcIGF6oczu7aRZLUuD8HrWGuwSN25hFOH/\\nGNev0kWjZ45VfGTvknBLq1as38z0aikM40MoWmutggr64fD4UweJJLDhiOIuE9gr19XkTBOrHNZv\\nl1mbPpgiN1L4UQswaxYGlU4AQzl75DnMO1qy4xuVSOj7GsqsTsHmy/UKTu1L434q+GbebJmahx0a\\nOUiUbJA8KpvaWqibBgOKIOYEIkpKUTlhLDUI7czcfcGJhKYexK+Gm6+0nmmRupiHfeI4SttUGZHl\\nyKKByIC0uL4ujerkc6bS2BBnPq8Wi83M7reKQ0CB8dsSQethFlZVkxYTJeuYGF/mEKO00BolA5Rv\\nhjyaLvn9BYXAihoBiBByogiG3RY4SlJCGLcZQK5SBIo7RhpsZRBBzgDGNh2vArjiaF1kS2s1QDBr\\n6w53fA8Yi0XeEDWnJCOsdahmqAVWdw887o2muagoKWzHYzts9AGCvMxUqw2gGou7HlVu1Bi00O0S\\nszNGXgF645MxULJu5/4wxl5319lnKpS5JxqKnGiYhNmePOuaZJ3ByhKIIQHAhRpDpYRCNtGN4Wqh\\nRVssHkJmmL73/MVk/DtEmrrtJ9sfJz406+8FsgaFHBqHwtjleDQgfbLhMm8ymq2Swi0cdFSuenfF\\ngXE+XVrIt5vtvW838SvQLA36F0wdUW+YKLnPmndnWFFtWq3Lh1XRAimnk7t0OTac1CLy9v4yGoQU\\nIgRCrZCFTAiEMVlMTSEerfAWoXK++Co5SJ/RrxO7n7XhvcZlmd/88eE4/79/9Fm3aXytMNsdeNDT\\n1/vvbtS1LFJWvb0fG2WrdVXMmqpsjXKxs5PJl8L0Oo40UvT6CDTbcIXrK9zikXG8pEss8Mhg5AJs\\nosfEY4x6EFbIzqFckG5LOTbGYCqyC//6Bj6My8sr2RhXkjxhV7Q+7SZ5CLQT2O6gy7AB0EAEDFSu\\nyDWUpP954z6VsW/50OrKqhJSgrBQsgGQQYgtZAogDbsSOLVkpQoqHbbKbzJNIQDAsmg76Pso1Yhs\\nqKuvkdfTkrVKqoxyjFzUSIsqaSGZSUFWaWGhFcoarRg1WgkH/76VfP6gBhsv69Yp2rZYVA0aVjbS\\nYDVb9ZXcXIOPa/oi/uCQbLdUsyZBy7dXMzt0iOAbtFrmFoUaUGAsAAYR4kNLEY3M+XlZv76vtYJE\\nfZ9sbfc6kpRKN+Txk35ojplxvP0kdGBRBXq18rforKJXWQiXbHfrBD1O3DgEKoCAQd5xYF3VUTpe\\nAhOZlYBWJ5vs4DE5af7W34+ZOgGekEWBup0N/BblXxkgAFTGaMIZ5beIhkZpj9HYkdk8SzY5jzob\\nRwPEqNEU6qnRrWUR7W3TQtF4CxTmUhwin+06uRB9uBExqDxHDobB88cIrn2CmUc3R1vHjXfU8ZBV\\nJG2Gcn5hQJmljgZQSq2sqcBIoIjTevTi3zBZ+5s7YnU9m7gdTNJl2d8Py+n1Ms+jzQGhVpmcgNJa\\na7DJBQDdnw+fbC4vzj0mgdsykPUOjo28jpibT1ZIplqVytmg0XaOaycC4vYOV2mjeL7MS4EYipZX\\naylLRK/D6toVmatU4kskG2OGur26O00froW2gS0qmhkX09lNo2Z7LhgBZ1Syowu5V+tNtsh9lDu0\\n8Ps+0bWR97JZ8mDphR73h6QGoVfAxBpcbO2NVlXb+tvWYAuZMsr1XbmSDX5Z1MmijKs6UhoTZd0O\\nD+LNwaA3CH0Pr4wxUsosLaW0lDgeR0AbDImShuG2vz2TcEEoxBGLjn5GY1erS6lThgkJHhFONERq\\nvkC2qut6mRfWZfniDsMILU9r02/nQJVZc6+kCI0x2lqttbFKWEqR05alzatBDPlG1LRp2aLO6HF+\\n8XXY8TaOwWb/ehiVjdLINoBiW+cw2lrlFbQIqhRhK5WpSttIIUSjAHIIcZgDIUac2uqGul1jpDKl\\nMYLTYN6uUMxbFgAAQqq5aywQjEiehIg5spbWP2zScjVvRU5MC6Dplc0m1FzVLYSQ2hLjKdBEikip\\nvNfjPjZBnPmeCCIDrZWqna0A0nhrt5P0w7psEGL93kFy/Gdq23trVysDAdC1ExES9qvSIoSAZZS5\\nWbuqizNoUy+R1k+LxX8k3t0oMV74V0l3aMw6Nzta+E7CB/2m3wtdslObbYkCb6ve4TfBpbwYgyDI\\n4KW8WnVaOV9OntPNn7P9AxbdNeCcNTcHTIY45uvL6uxtSeOsbJd2dvfDdyVfze5/i8xbJOuqCCvz\\nN2XwsgGcKqAN1EADBAkFFEyrhzIhW4ZavXpbup97Q7bUX73co4PT2fVJ/c2//PvkxdXen/+MuPF4\\nXn/6yd89vPn+o+1wcPJ68nXx5lvy6rv3ntNZZ9dNVba1F1AvIf3I7qRvG+EcHz7CgaswARoLNxyb\\nGmMYSbLPeG3qniTbrLu1yp9cNv28VCRoXXSvpcB2aJR2mNAipM0Erld2fsHXc5hmWnU6A7Ux9Njh\\nk+0nTsf/oMExIJAzRJAgsHKC+2bpzNLHbbu9So+A5EoV0FjCqVRLLVuIsbVWa53mMM/xeg2qCqcZ\\nTZewriUExloJUXh70wn7gMwWAIT9l9uKHbokN4L2qbYEWbRqBL1789XhR/8DNLJ+uDbGSKut1Q4T\\n/aERVj8/+kxmZy3oQavK+awor2TVcdI3EjyoRjtNdXs1fJjKOfrLd+skq60s49Fwfn+iAbhv/O66\\nrutcWKVLYQi1IYfx8RPFe5LeqNsLdX8i9FxZNal+qfHjjWCarbYm3ueb/YTFzrLdjZPErDIw+c/u\\n9uflVKOTh2F33nb3TH0MIlurZY+SCI9CX4jMhurSQApsruB6Xju30U8W/5rOxM8RvJinBvS7d+UO\\ndk4CciprZRDWTAq3LwBr0gLFjvU7e4dbSqwbtFmbvYvmxwZFVpvBwKfENFWYO483juw87XV30fQ2\\nGYtg+Dw8MGXT9niiOTAPs2IdvUDiBgIH0ZdO+LkLejzomCpbLozvnQpZLqa3RVnzak0hrOtOm0Z7\\nm0er/CCi6dX7DepeyMvTgCkf49b5VIjLh9nDfA1FaylmBFQYrACyBrE/vXJM8tluvO42+Q/f/JA5\\n3rx50jRNGFMf3GEqm2q6uqXGO2rv7jthErhvPXTe5nZ+8bC4E0IMR5HA2EKU9fdrb9dKH9HBLgua\\nwHER10yc7RSnpllDCDceGW2K473/uvdoFm1C7Lq31272nU+um71wjP28adOpcPK85HLssGXkudT3\\nIYu27DLoC+u6kriiUtx/kRX+ogyctgp8enb1NSewJVtZWj9clatrg00dcevH0MAO6R3QKIx3XijQ\\nSqmbNjeysEAhzBhFwNaMOdOH05SEWstV2Y4L9830qOUfYCiIs86zSqHEYN8o4LoOMmtAZV2vsgbZ\\nRtgmR6D0bNbWYr06ScBDwJbWQmWMElKrihDEsXIQknqpnOlSdaQerouiKmq28ylzNIuO3cGuDGvo\\ndEEt3J73UMmxMEZ6sizD2DLHMRAAK5TRCkhmTeCJKOYej3zmlDmopQWIM2QBaOpGSg2uT+rFbBdB\\nHiXA5xQhpKS1AAgUCKG2nNuNo5d2OsFqrlQmmxIKX1vsIevCAiILTUEhoBjq0tFCd3uBMiWFAXAG\\nrdkZL6um4Fws655L3STafoF850NAnvnbxIELF6GIaE4CLTvVJFOiBQgSD5js1CHLoqn3nnWwmjLy\\ne4aXw71t7m54u7sWoYvXE5XlWsU26GUTzQ4G0vimhoZtaidAwxkkD3w5ap0ZxCs4E9fv228v/0Ko\\nR8agppGmyT758PHOUx7b7z4k/2HfTrp2vX4/e/X+7bv5GonXQr53/IlsizqLrHy5X0MkDWYIQ0QR\\nRcAahIviwoCOjgBxnPT7PPcegaqp48/YQdo9nHbf/4cPqKfCX1ygvZv7L93Dv74/+3L44leQvv50\\ncIKaN2b8+3j8lbNc2TmyJPAdOtpjo8fHN+/PDnZ6YnFIdlC/A6y2APteggmAU21zu8UCD8j4pt7M\\nEc9fT/Es3YpYf8scRgI3RmiMMUMUAZh27R9Z+lsXfjnojnvxnCO32HgZ90d59bja+6V2Y9t6xjLq\\nu15IANSCSFcu2xyKAoD50uqKKEWQNNq2bWqtpYhSRFvBs7GY3NTNIm+mdXq7atNSNK0yrZSWQs8n\\nfN6u26ZGDFcKKP+oqHqIOGTAm1ZqaabrQox/q61PUC1kYQ3SAihlQpe7HOYXdXfP2SbRMtNCmXx+\\nIpq5aC22t3lx3fM9Axb1LD25ILcTvzjNtDQK2LksYb70t9tqWlWrBUMQWaCEJkR5cb68S/KGKTUf\\ngi98ZhirjGXVAnP38cG+jI15/3p3Evcrm5Q5TvZ6ehOX+OtsNhxFNxtHFfA8rUOEpALIa+b7z4Y4\\niBMHwPp8c4vb9Qo6oNHy4jV49X7XceZ4RqKodspX3012r1cOZNOj/VOaVUo2ixLfZrxS/YjDsvaF\\n9ZzR58+6s9Wi22bs/jLuS7YdejVnlthiVdkykeSjtkkz54W7PK0Lt2bHnRf9LbsuyTF2Vdg2D98z\\nRmoKxPxyqcgu1jjAw3o5WaUG8JpQw+pM15K6hd/BbWWFEMHW0fz0YvvZo9dv/7FNXnTYP7lH3MPB\\n+Osxir108qeinnPqqXYdRQgjYLUSQqxff4F4OJs0/WPM89+1ZXw1tmFycDltTpevmg6BOnXLe17o\\nxWSsYt5YuxBZLeX87qSdnllVQggtMlq3mlpKeXOHb061df6COkNrcN46en/FhnXF8WKwpyG+k//m\\nxHl2kx1elD+Sq5ajccPf1gPodfqVRs3MACEgmjtdnziuxE654uHIRUkHEGwq6Dkk7jxp7y5Vw5Jg\\n7QZd5hgvYCFT5fg9aCZqcmlVE7nC9bewG3Md+4N9YJ57fgSRVroKSM2xhQRyZH2mMGhlJexyhg0i\\nBKXlfX5VmbQ1lC+yedZ4GLSy6q/TXANqqaFUM2Tr9fuenbrgRsHUDSaDDSqaO4snkDdGWwyBtgZT\\n7OKWRD3GCSJgNrFSHcnWsTqcXb5utXj7UC9QUqi9+MkLYagUQhf1YrmxOpvBRYuZbYu1BlwZCRnS\\nBmotpW1rI9qgb2FMmS9QXNyMoXQxpghIoduqAnVe0Nll5AhAodPzDRsUUtatKFrOkGv63GBlo93U\\nLiusTJNZI7TWSpT90IaJNMZE1Ikj3+nEUjQSwp3hh9j1tXQgdQl2m2K9hGNh7bLp5iuNfG/ohnta\\ntmHcG/ouBxQRW6+ua7XmDFHCaViwkM/meXT8y7w8Zfyh45hB8qMkfpKDJ4Cys2++HoyKg8cUaobW\\nN5vBFg2waQRDkbFurRLR0Mavj3fbraKtywkp3t9+KTRBHb7kXudmFn/8i/+5N/xUF2zzZ3b0EzLc\\nWXbqr4LJb/DDF9JPRXnVaFmkLWXYEtA5AmwoPI6NwzlFjGII4XI2v74XrjPS1LTj7zl8bpS5vM3I\\nxpO53b67+u2Pf7ne+2D0LCX06urr72RL+iT4pCPd4eY173wTki958sedT7Y2+inJVID87maPsGd/\\n+OKt57p5nmPg4eC5z3HMI2XcDKo72f3DvztXhrYWQKubKWkWY8e8O/pvf9T3Yt37gCcIKFsLBTAi\\nnDqO8Uaq069YFJXhqI09KPgy31+HYfk2yO77OXAozBBhkDKDDYSw1QKAytFrYMYOz/pJ41KJCAUA\\nEFlwt8cZMcbUs1v2cOKsz4PmjteXcfZgmrnJay0NBchq4fj09uvMYxKWGOXTDvMLUVs7KBsOW8GJ\\nzF//P4ef/QppWGcFAKhV0rZSN4Q6247jNNEzubw+2NR6Vt4vb1V+my8L2VbEAF2krdlYpo2pZ2LS\\nzt5dbGyVDqybuUD377Y24MbzQC1uEaldKqVWppoTEnM3Fm2TL5aH3uxoNBHGVcZ6jMi2KtWh6r0I\\nuxW7X53cda/uxeL8uqa746aHdIOcSuKOFHHoJZr5CJLlqvSDtYyO6jLGrvJd6SeP+37FIR0/lLK5\\nHaYXjzb1o4HxUYzt6Q+/OX/zfhb2d+L9nz7ds8SwyfmtvFdYu0lo6LqtBVzmL4KjR6vJBeXtFnpF\\nhybrDDPZVTgwbSlXsmo9zzGTV6p/rOl4NS67KdgdHG/4iwXY3o0H2mvOpeRGy243OX/wMQi5P2Ko\\ndmHlRsz1Eo9rkj4YN3TjzWaV5TafwAHT15r8NHJOb66jUUTznDvPDon6srpERX5RFjklDjK5BhBj\\nTAixyHJ3Mj4Z12Wd0sPoUDL07vZsEjtxYsp0umYVZAQlnTIZ5lmzmMytknCRtkWu2nK+zl6Bdty2\\nlUUAkrrVOPaCZ48rmr+ZThuDOeK9dZpdLvF0AbL769UXjSu7kxv77a9decfwCgzxos8ucDN9OO+W\\nslcVmsoHl899ByC2U7aoViO5Xtut/cL52Sp1Qw+jKKCUuAFnNiVBCd1h3njn11/R/d1qPnN8ocEd\\n0Ibwmnk9ZQPN5GSSt7VvYAwJtsBAJAGvDOQRwY/2vH6wjl2KmjOic0J5U8Wx/BbJ+6qVyqgqW4p1\\nCgTyFaH5NHIq7mpKIPEcElX9nobWGFT3N4IWBuX6AqvWIwrgpbRGmZRzChmiLAAAYNoG9raHRays\\nA3x5/2pV+qdvbs4u8fV7TqMNhAAE0kJg5/dKnmqvwJBIURNCDAQIEilbg9V4pVYT2Fau1h4yFBtB\\n2hkCFtMAE7cuEcbtKLrz4jSMAccojDpadfOVBVZNUnlfMYoZ5a5YQ3m3IO2Y+xnhAADTNJXf37Sw\\nq7CmUY8mQ8B5WUgiGnew3+LIqBirECI+XcnFZQVaKdsCWRQiK6x6EhzwTody4rRFhqBlvgvCmLNQ\\nts7d2fkM/GR2+13ctUkC+slP7PBZeW0KxPXr/8+BeHDw4/HlRYtSvPuEeFth0PoBh5BKBZDW0wmY\\nzTr+MzMM3myDBdffNJO697nvOAf3J9+5R/+r631iAz5d/+Qd+t++Xf39LHex83vm/KZYX1bZOwOX\\nBJWe71BKCD62pufSSJUOw5QxAoCGVTp//d7CLQOrsbpaVmt/u4SlOameF/dWXX+1d/TY2/2V6UXJ\\nk8tO+u+0enr95suyu4WfbJc7P17ABw0WovNzOjxKhsGHx87QdfqdJ9/++l7WLeO+VsHVLFzqrpdE\\nYdyjMzS5Ryf//O1f/Td9WFelQh0CCbzkMB2NPj5r9wkIpnaf9xruKtFAw/p+3GHUQ8D3wiG1e7M7\\n7/WdMx5L/uCws6vjzk11fUOQUR70NzpI+8QDGmiEgVYPDX5o6tzZ2HOjHoRWAdfCUkiKWYyJcWHW\\nj84OX9yOhlOezHjvhnXGIVgxsWQqN9C0yg+dDK1vvcCEQTNPr3RQ1KCRjUEOY44WrFEsNWSH0KbK\\nFwCTVjbICkC3axWlYrda7m6//AXeoz2S6/m1AkLXKTAVgMjBz2m7CuCc2Tfbznm3+gL5fQCM6y+d\\nXm6CUaOPZXaFGddlFVrD3bnRqCkTld5ycz58Cjg6iqCCmiHZBKBJ18715AXw05qNZ7+7W149LJfL\\nYoEvvlJRtHM36y0q11CGEryoybT1VL2AyZ8vFj0rTNDxZ9O7Ah/kwwTEAEznrjPdOq52dva6H+1g\\n1PgOGYE/ZW//CeGDMn25/+yT4ygv1qdg+R1Y3ew92o/DuZfljjKjwf6wc4dwxfurVb45u/HzmdFr\\nzHRWVjfrewjBNsRfL0u682S6vCrXSz/N+zsfuvn7vBmMoo2yqE0YxoORb9/fzgso2SZykjDIGIyh\\n4UA329u1WjPi9De7d65Y8kUTBEl2tra0xdMvvJ19TO5VtpPsg5Cdq1XFmyvXqzELAAAB70GIiwto\\nDLDZlw1yHy6aevG3LpsE9dtefG68pTVzg2+loZLSBjqERGJ+VRcctE1e143JMHwwth4dEIxxzxEq\\nG1fe9rz3gr/kOr8BbUyAi3HOLm+CxZyxJuzdArbiiD4Fv/H81HMuvf4q3IHJrsfBVTGraCm4nXqO\\nsohZ3GAkvKzoObme4Tjlq2tGKz6fkwkf8fg50a1Wm6TsM+guls33b9dN2wH5koJS1hWPWKVd6MS2\\nYQ2PtRcpy6SkRukGoBxxjNlGXyDHGR580u25WmvOLcGQEML6D9opjRDIKtAssRobsHK8wrI3O09o\\n0gNW6+2NXiFgEm1GLk4XfFa0xO7beu7g+yCRJe2XwtaVkrXsOZ1ejAwAZW6ZVFsjPDqwmBIcYlv+\\nkN28mj+M5XppltBajhB2+kxb08A5qWZSC0yE1m0rKgsbzkCrFsxyt71idAaJQZwAh2IqILKt8FTt\\nKwmRMYItcc9pGZCeazd9hdwAI0zStKyak+9xoaJuQqOBhTVAhcWl1BpZBABYz63Bz1OxLVetrrhB\\nB7KxBpRaWym60qKmNH7AHE6xGhv9FiOEwoRyQqfVMzs4dBlfr6YGCk0UxriBjrVldv1uOkXp3Rva\\n2dfIYd2fDMmWt07vFpgUbwtg+88/Q/LUEsc0W4W3AZHhEFJOOdEYCCjmQCBDP3L8Lh5AZ+NbDK7S\\n6BkRgzx/d3P1jA8+Wa2dswv67k9H/r+m7G5M3fu8g0u0Fnpp1QzB3EVxSDTVXQ63qUtKKJcpKAnW\\nAFpjsrvvWhgIOJzNFrPJfS0Pc7Ccnn3t6rrT/q6FePjkrwiIAd7FfvhQ1S54dYT2/Xb7Pz28/M1v\\nTkajeFM9W+utVvRRZzT6KNmKh8Cc59lt5AV5ky1nZX1S1SUWvY1sNQkGzeQPX+w8obwP16tVbkNL\\nJxgSd/eg043M1BfQK6ejOxFgWkNLWuMY2wMkUHRDOsemGu+uLw/uzlxv4m9ckicYHiIkpYVc1J4F\\nHg58ozjUCsoFglWRy8Fg4NNNWcmmEQghrQplYowcDKANdP+gH4x4PESWQ0tsa7Ojjnw8yBxQQ2Qx\\npl1vsve0qOoGeKFedV2G2oyqWYoQaSy+vL/X8d8tl3VJyTJbN6WiFgPVGsWtdZu0cvDpuE4m634J\\nVlCMtZFazo0BxgAsPaia2mkVk9HB0t+lwPgGIkVcEhyWIslnmcFuWW84nru52w08qTVVShGwVAap\\n4B+2d6MuLRisGc3dWCI0VetMy41I32zvv+rLC9yudb5yJ/+vY53i65v5iVhd2tWqvzxvxzdNp9Ph\\n4Hk6WzC+Ms0QQfb6Ifr+WyaVxe7CEDG2m/nGs4vZ3t7zXhJTit9tOQ+nVThT0Tz5q+7HoxZez8Cl\\nCW+L7ock7vjesg3QffJjSmVRlhXo1AtJ0kuW3QS23O2ue0E7YlPRKJ2503d50z/k83dNXjNCkd58\\n+biqx1T1+xDMhSZyq3ewOQXrh+mDdOORiNxWBoMgtqIsw43STh8yjQYvDEDQbXqdLoNXvc6hsn+a\\nq6Az+rhcn0rWpcP2SZQxNJtMMwM8oyykjGBJIWIORyAguTaTU9vmcbiB8LsC1bj/Kaf47vwriO0A\\n2gHgLsV29YMoU5HWqJ4BVYSmlTI1vmuMQkAFvc60MLfT4fjk4xFnNL/p+l0GGuSe9Y4n2EeT1IHW\\nNVrj+CLuZCE1LHFLdHCRPq4U9MUDRWnHaYO+s84gCp5aSDq82PuAUv/iMLx6nNBR527okdXr92DJ\\nfR9nmfI3ASFdaFPn7gukASO1S922KS+XvTeLcLKiWjLmxyo7M1oT7BBMtdZZG0Jlgi6xtmfhj/jg\\nRcmHTWsQphKwbEWzzLftiluDTCH1WoHFusFl8OfQbMYbTyFmSex09x7PhUuDXT+gy9naWh0yLNTa\\n29s2eASRlHWp9RJqOdzqOElfSKibJYpNFTxxfMfWaMBGPjGJ+aYZv7ZypZQCZYsgg3ygdEtJRvna\\nDzM/AJRCDFbcaRAyi/UYwRqixoLGGEsIQwgDAKzWQBtKPGEAxjgt5P28//6aXJ/JRdExEGBEgK48\\nInJ6ls8fXBwgRAEj2uYIIYSQgYK0mQtWQFdKSwAynRVSeI2s2lYS0xipECKozgf9ntIC00qhCnGu\\nQWFStdXCOBNQGSk9hFxXwyCt25U4G5+/gb46TjbVwjb6s9rZ6gzv+fqyRte9EIb9v/D99vJkUogt\\nADcsBq0TeS5XCBJrMdIYV3k16h7uOcDP7XbZ1LOUkxAl5dXyhz8g/y/sZKqu363/+DV7fBOMrnVs\\nb4toOkur1pMypVT6th1ttM+DB2a5y8LLG/H2Vvz+nCiB15VaP5ws56s0J+3qbrX8DgnP19PV6i6b\\nXW0/KpEbJ0f/UBlnmpOliaf5+UrlYR9tf7ZdTafpb/7PGO9DzqVWTeuutJfJT9HgueHSlncHH0ZA\\nNAi0oTvnZv5wPyiRD6LozduvY57pJy/S5VzM1o2m8ZAVwjPRxwirqonOmra4/EYJQkMUExBAiiGy\\nKFQywdl8b+PhxV9ebz1qgvCDy2lcmY+rTKAGcKYc/qhqvbw2rJyO2HXMHa1zaerBxj73UVtlUmJM\\nrGhr4nQoJxaSmnd5tEecbcwDzJhSROJ277P+9lOcRBohaBBOdn1C9gzq1FLS7uOyuue+Hzs2l2Td\\nyHY9q8c4V87lzMq6kVWRZ6tGCtUI0fBKKaP3fjhpp6d5hukqvVUqQwhYqxsJyyYg3Ko88fFGZR6v\\nq1DKFllgtZebjnwofbJ0qS2vtQlGenQIPWuBRqjhTDFFTi4P5PaHcdd3iON4owygur2B8obUbdef\\nLO7unQ/SLXdRNJlk/6XzKPHdP+wML4d4SlYzNj9D6TvKHptG2/qBByHEgAdedX1rbr6hchV3HNjo\\nb96b13ej7P2qDH8BTNhxJtFwfPnF+uS2vXi7t67+JsDTVnp3amux3rsrdnO3vb6FV69GwFJVFO3c\\nQfbSAXeRc+/tic7Tg8BpgyA7TkQf5G5x9d3rXe9pdX1+IRyPB0m49eFHo/t6vml0gwG+fBiB/QDj\\nd3p2hXFveT20rSSOHAy66coJRtu359OsGOhFW9W+mwwNrTg5wOR6fG/EPGbk9XoyaJvu4NjrernK\\ny6YQjRBtTSHytoawT/wIpB6Zq2p6GC8Db6jkltXC3D7yeeuRDEuQbJu470QxsOiq0XVb5kIUHm5x\\nUjg+qadMGruStgk+QHfL/unVn8GLDw6b0YEUre33o1V+21h/WfLVTDDUe9G1ncDBnAVdVLLhso1X\\nY1RNAUZrFkqSeF78QTzaWZ3OFQowgt2tD2b3AMRduPVI972UdZDLQvvN4dFeXmXTzLXoIPaiyP3G\\nZbc0TAFprDHZTEy/ml9dTMfrNsfxanoLjYYAIWBrYEVTM1n4A6rdn6xbkLrHLv8gL/18LYEJdZVv\\n65sEaQjbuMO4JyEktbLTH9DN90vTHzbu46ZVzt6nt/PaVHIwxFYqTif9uBs48nKsW9KFyDhum+Z3\\n1Xppcb+JP29UrxCXNbYCPOXJPuu7fh8dHiVeeBnga1W9ZjgDUFuLAQBQGwMVJsYa0aoWAoAxwNhS\\n30fEaZo1JtZYZa0FVhvAIMAYIowMoEAb1CjmAlg31qSzdnbDi6XKGwdiYpXWeSu1krm1D17gAAMw\\nxMRW0pYQwngYx5vVI1/4HmkthFAi3jZ5q20bONpn0hhVt1WrSyc+VE0dN8v/H6Ygnwk4u0YSAAAA\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 22\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"MxRx46nkfy8S\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 529\n },\n \"outputId\": \"17f1e0a5-82c2-4778-b30c-3c2e4b6693f9\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"style_image = Image.open('The_Great_Wave_off_Kanagawa.jpg')\\n\",\n \"style_image = style_image.resize((width, height))\\n\",\n \"style_image\\n\"\n ],\n \"execution_count\": 23,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"image/png\": 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tBOqZvG2W8DiRBxpo+O7n1ITgAwN4LHqapCbDGIklknLOE9IoZ24ZKW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\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jn+lHaj+9x7G+9+5PVkPPzSW2/adrsr9xAiQSWtqN2JzVu6zc2OYQpC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\\nEhmHnu1b3bHB/OaRYZIjCUvdWJL+H975XNdsr/6xAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 23\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"bjmMEMNFgBdj\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 52\n },\n \"outputId\": \"5c7557a8-ff26-46c2-db01-b59830e38dfa\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"content_array = np.asarray(content_image, dtype='float32')\\n\",\n \"content_array = np.expand_dims(content_array, axis=0)\\n\",\n \"print(content_array.shape)\\n\",\n \"\\n\",\n \"style_array = np.asarray(style_image, dtype='float32')\\n\",\n \"style_array = np.expand_dims(style_array, axis=0)\\n\",\n \"print(style_array.shape)\"\n ],\n \"execution_count\": 24,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 512, 512, 3)\\n\",\n \"(1, 512, 512, 3)\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"0p-FU3TaFPIO\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"content_array[:, :, :, 0] -= 103.939\\n\",\n \"content_array[:, :, :, 1] -= 116.779\\n\",\n \"content_array[:, :, :, 2] -= 123.68\\n\",\n \"content_array = content_array[:, :, :, ::-1]\\n\",\n \"\\n\",\n \"style_array[:, :, :, 0] -= 103.939\\n\",\n \"style_array[:, :, :, 1] -= 116.779\\n\",\n \"style_array[:, :, :, 2] -= 123.68\\n\",\n \"style_array = style_array[:, :, :, ::-1]\\n\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"kr_IF8T2gWCv\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"content_image = backend.variable(content_array)\\n\",\n \"style_image = backend.variable(style_array)\\n\",\n \"combination_image = backend.placeholder((1, height, width, 3))\\n\",\n \"\\n\",\n \"input_tensor = backend.concatenate([content_image,\\n\",\n \" style_image,\\n\",\n \" combination_image], axis=0)\\n\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"2jmGHrAJhNwa\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 347\n },\n \"outputId\": \"0ec43315-bd93-46e6-e610-40d2f9e0d65d\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"model = VGG16(input_tensor=input_tensor, weights='imagenet',include_top=False)\\n\",\n \"layers = dict([(layer.name, layer.output) for layer in model.layers])\\n\",\n \"layers\"\n ],\n \"execution_count\": 27,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"{'block1_conv1': ,\\n\",\n \" 'block1_conv2': ,\\n\",\n \" 'block1_pool': ,\\n\",\n \" 'block2_conv1': ,\\n\",\n \" 'block2_conv2': ,\\n\",\n \" 'block2_pool': ,\\n\",\n \" 'block3_conv1': ,\\n\",\n \" 'block3_conv2': ,\\n\",\n \" 'block3_conv3': ,\\n\",\n \" 'block3_pool': ,\\n\",\n \" 'block4_conv1': ,\\n\",\n \" 'block4_conv2': ,\\n\",\n \" 'block4_conv3': ,\\n\",\n \" 'block4_pool': ,\\n\",\n \" 'block5_conv1': ,\\n\",\n \" 'block5_conv2': ,\\n\",\n \" 'block5_conv3': ,\\n\",\n \" 'block5_pool': ,\\n\",\n \" 'input_2': }\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 27\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"i9xjfp0YjBVX\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"content_weight = 0.025\\n\",\n \"style_weight = 5.0\\n\",\n \"total_variation_weight = 1.0\\n\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"aMkDK-1KFexb\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"loss = backend.variable(0.)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"W3C_2q1DjLrf\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"def content_loss(content, combination):\\n\",\n \" return backend.sum(backend.square(combination - content))\\n\",\n \"\\n\",\n \"layer_features = layers['block2_conv2']\\n\",\n \"content_image_features = layer_features[0, :, :, :]\\n\",\n \"combination_features = layer_features[2, :, :, :]\\n\",\n \"\\n\",\n \"loss += content_weight * content_loss(content_image_features,\\n\",\n \" combination_features)\\n\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"6IvMNw7akZOD\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"def gram_matrix(x):\\n\",\n \" features = backend.batch_flatten(backend.permute_dimensions(x, (2, 0, 1)))\\n\",\n \" gram = backend.dot(features, backend.transpose(features))\\n\",\n \" return gram\\n\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"7IaIQADfm2Ai\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"def style_loss(style, combination):\\n\",\n \" S = gram_matrix(style)\\n\",\n \" C = gram_matrix(combination)\\n\",\n \" channels = 3\\n\",\n \" size = height * width\\n\",\n \" return backend.sum(backend.square(S - C)) / (4. * (channels ** 2) * (size ** 2))\\n\",\n \"\\n\",\n \"feature_layers = ['block1_conv2', 'block2_conv2',\\n\",\n \" 'block3_conv3', 'block4_conv3',\\n\",\n \" 'block5_conv3']\\n\",\n \"for layer_name in feature_layers:\\n\",\n \" layer_features = layers[layer_name]\\n\",\n \" style_features = layer_features[1, :, :, :]\\n\",\n \" combination_features = layer_features[2, :, :, :]\\n\",\n \" sl = style_loss(style_features, combination_features)\\n\",\n \" loss += (style_weight / len(feature_layers)) * sl\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"hx1ZLxVUn5Fe\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"def total_variation_loss(x):\\n\",\n \" a = backend.square(x[:, :height-1, :width-1, :] - x[:, 1:, :width-1, :])\\n\",\n \" b = backend.square(x[:, :height-1, :width-1, :] - x[:, :height-1, 1:, :])\\n\",\n \" return backend.sum(backend.pow(a + b, 1.25))\\n\",\n \"\\n\",\n \"loss += total_variation_weight * total_variation_loss(combination_image)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"z7Jbn9nzp6nj\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"grads = backend.gradients(loss, combination_image)\\n\",\n \"outputs = [loss]\\n\",\n \"outputs += grads\\n\",\n \"f_outputs = backend.function([combination_image], outputs)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"KBIkgD-dr-A1\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"def eval_loss_and_grads(x):\\n\",\n \" x = x.reshape((1, height, width, 3))\\n\",\n \" outs = f_outputs([x])\\n\",\n \" loss_value = outs[0]\\n\",\n \" grad_values = outs[1].flatten().astype('float64')\\n\",\n \" return loss_value, grad_values\\n\",\n \"\\n\",\n \"class Evaluator(object):\\n\",\n \"\\n\",\n \" def __init__(self):\\n\",\n \" self.loss_value = None\\n\",\n \" self.grads_values = None\\n\",\n \"\\n\",\n \" def loss(self, x):\\n\",\n \" assert self.loss_value is None\\n\",\n \" loss_value, grad_values = eval_loss_and_grads(x)\\n\",\n \" self.loss_value = loss_value\\n\",\n \" self.grad_values = grad_values\\n\",\n \" return self.loss_value\\n\",\n \"\\n\",\n \" def grads(self, x):\\n\",\n \" assert self.loss_value is not None\\n\",\n \" grad_values = np.copy(self.grad_values)\\n\",\n \" self.loss_value = None\\n\",\n \" self.grad_values = None\\n\",\n \" return grad_values\\n\",\n \"\\n\",\n \"evaluator = Evaluator()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"gqpbYDWaAh-Y\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 5227\n },\n \"outputId\": \"986a2c48-8b5b-47d4-eef1-9b02bca206a4\"\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"x = np.random.uniform(0, 255, (1, height, width, 3)) - 128.\\n\",\n \"\\n\",\n \"iterations = 100\\n\",\n \"\\n\",\n \"for i in range(iterations):\\n\",\n \" print('Start of iteration', i)\\n\",\n \" start_time = time.time()\\n\",\n \" x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),\\n\",\n \" fprime=evaluator.grads, maxfun=20)\\n\",\n \" print('Current loss value:', min_val)\\n\",\n \" end_time = time.time()\\n\",\n \" print('Iteration %d completed in %ds' % (i, end_time - start_time))\\n\"\n ],\n \"execution_count\": 36,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Start of iteration 0\\n\",\n \"Current loss value: 133723770000.0\\n\",\n \"Iteration 0 completed in 23s\\n\",\n \"Start of iteration 1\\n\",\n \"Current loss value: 90257430000.0\\n\",\n \"Iteration 1 completed in 18s\\n\",\n \"Start of iteration 2\\n\",\n \"Current loss value: 72950540000.0\\n\",\n \"Iteration 2 completed in 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\"Iteration 28 completed in 18s\\n\",\n \"Start of iteration 29\\n\",\n \"Current loss value: 55195734000.0\\n\",\n \"Iteration 29 completed in 18s\\n\",\n \"Start of iteration 30\\n\",\n \"Current loss value: 55181140000.0\\n\",\n \"Iteration 30 completed in 18s\\n\",\n \"Start of iteration 31\\n\",\n \"Current loss value: 55168463000.0\\n\",\n \"Iteration 31 completed in 18s\\n\",\n \"Start of iteration 32\\n\",\n \"Current loss value: 55157387000.0\\n\",\n \"Iteration 32 completed in 18s\\n\",\n \"Start of iteration 33\\n\",\n \"Current loss value: 55147467000.0\\n\",\n \"Iteration 33 completed in 18s\\n\",\n \"Start of iteration 34\\n\",\n \"Current loss value: 55138525000.0\\n\",\n \"Iteration 34 completed in 18s\\n\",\n \"Start of iteration 35\\n\",\n \"Current loss value: 55130440000.0\\n\",\n \"Iteration 35 completed in 18s\\n\",\n \"Start of iteration 36\\n\",\n \"Current loss value: 55121723000.0\\n\",\n \"Iteration 36 completed in 18s\\n\",\n \"Start of iteration 37\\n\",\n 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\"Current loss value: 54986945000.0\\n\",\n \"Iteration 80 completed in 18s\\n\",\n \"Start of iteration 81\\n\",\n \"Current loss value: 54986146000.0\\n\",\n \"Iteration 81 completed in 18s\\n\",\n \"Start of iteration 82\\n\",\n \"Current loss value: 54985273000.0\\n\",\n \"Iteration 82 completed in 18s\\n\",\n \"Start of iteration 83\\n\",\n \"Current loss value: 54984516000.0\\n\",\n \"Iteration 83 completed in 18s\\n\",\n \"Start of iteration 84\\n\",\n \"Current loss value: 54983790000.0\\n\",\n \"Iteration 84 completed in 18s\\n\",\n \"Start of iteration 85\\n\",\n \"Current loss value: 54983033000.0\\n\",\n \"Iteration 85 completed in 18s\\n\",\n \"Start of iteration 86\\n\",\n \"Current loss value: 54982240000.0\\n\",\n \"Iteration 86 completed in 18s\\n\",\n \"Start of iteration 87\\n\",\n \"Current loss value: 54981560000.0\\n\",\n \"Iteration 87 completed in 18s\\n\",\n \"Start of iteration 88\\n\",\n \"Current loss value: 54980956000.0\\n\",\n \"Iteration 88 completed in 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54976463000.0\\n\",\n \"Iteration 97 completed in 18s\\n\",\n \"Start of iteration 98\\n\",\n \"Current loss value: 54976078000.0\\n\",\n \"Iteration 98 completed in 18s\\n\",\n \"Start of iteration 99\\n\",\n \"Current loss value: 54975740000.0\\n\",\n \"Iteration 99 completed in 18s\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"metadata\": {\n \"id\": \"KmZ1AjTrDc3K\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"y=x.reshape((height, width, 3))\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"rer4Gj-HDrMd\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"cell_type\": \"code\",\n \"source\": [\n \"y = np.clip(y, 0, 255).astype('uint8')\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"metadata\": {\n \"id\": \"t99boHPiEgKv\",\n \"colab_type\": \"code\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 529\n },\n \"outputId\": 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F1+fHpz/+r1N1/98MfP/+qf58fv/vjd3fnXv/nl67dZdWl9d5eFot5X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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 40\n }\n ]\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/number_recogniser/python/driver.py", "content": "import nn\r\nimport numpy as np\r\nimport os\r\nimport matplotlib.pyplot as plt\r\n\r\npath = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"mnist.npz\")\r\nwith np.load(path) as data:\r\n training_images = data[\"training_images\"]\r\n training_labels = data[\"training_labels\"]\r\n\r\nlayer_sizes = (784, 5, 10)\r\n\r\nnet = nn.neural_network(layer_sizes)\r\nnet.accuracy(training_images, training_labels)\r\n" }, { "path": "code/artificial_intelligence/src/number_recogniser/python/nn.py", "content": "import numpy as np\r\n\r\n\r\nclass neural_network:\r\n def __init__(self, layer_sizes):\r\n weight_shapes = [(a, b) for a, b in zip(layer_sizes[1:], layer_sizes[:-1])]\r\n self.weights = [\r\n np.random.standard_normal(s) / s[1] ** 0.5 for s in weight_shapes\r\n ]\r\n self.biases = [np.zeros((s, 1)) for s in layer_sizes[1:]]\r\n\r\n def predict(self, a):\r\n for w, b in zip(self.weights, self.biases):\r\n a = self.activation(np.matmul(w, a) + b)\r\n return a\r\n\r\n def accuracy(self, images, labels):\r\n np.set_printoptions(suppress=True)\r\n predictions = self.predict(images)\r\n num_correct = sum(\r\n [np.argmax(a) == np.argmax(b) for a, b in zip(predictions, labels)]\r\n )\r\n print(\r\n \"{0}/{1} accuracy: {2}%\".format(\r\n num_correct, len(images), (num_correct / len(images)) * 100\r\n )\r\n )\r\n\r\n @staticmethod\r\n def activation(x):\r\n return 1 / (1 + np.exp(-x))\r\n" }, { "path": "code/artificial_intelligence/src/particle_swarm_optimization/gbestPSO/Gbest2D.py", "content": "import numpy as np\nfrom matplotlib.pyplot import subplots\n\n\ndef f(x):\n return -15 * np.sin(x - 5) / (abs(x) + 1)\n\n\nparticles = 10\n\npositions = np.random.uniform(-10, 10, particles)\nvelocities = np.random.uniform(-1, 1, particles)\nbest_locals = positions\nnum_iters = 20\n\nw_min = 0.01\nw_max = 0.1\nc1 = 0\nc2 = 0\n\ngbest = positions[0]\n\nfor p in positions:\n if f(p) < f(gbest):\n gbest = p\n\n\nfig, ax = subplots()\nfig.show()\n\nfor k in range(num_iters):\n for i in range(particles):\n\n c1 = 2.5 - 2 * (k / num_iters)\n c2 = 0.5 + 2 * (k / num_iters)\n w = w_max - ((w_max - w_min) * k) / num_iters\n\n r1, r2 = np.random.uniform(0, 1, 1), np.random.uniform(0, 1, 1)\n\n velocities[i] = (\n w * velocities[i]\n + c1 * r1 * (best_locals[i] - positions[i])\n + c2 * r2 * (gbest - positions[i])\n )\n positions[i] += velocities[i]\n\n if f(positions[i]) < f(best_locals[i]):\n best_locals[i] = positions[i]\n\n if f(best_locals[i]) < f(gbest):\n gbest = best_locals[i]\n\n ax.clear()\n ax.axis([-20, 15, -17, 15])\n ax.plot(np.arange(-10, 10, 0.1), f(np.arange(-10, 10, 0.1)), \"-\")\n ax.plot(positions, f(positions), \"ro\", markersize=5)\n\n fig.canvas.draw()\n print(\"Iter: {}\".format(k))\n\nlowest = np.argmin(positions)\nprint(positions[lowest])\nlocal_lowest = np.argmin(best_locals)\nprint(best_locals[local_lowest])\n" }, { "path": "code/artificial_intelligence/src/particle_swarm_optimization/gbestPSO/Gbest3D.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef f(args):\n return np.sum([x ** 2 for x in args])\n\n\ndimensions = 2\nboundary = (-10, 10)\nparticles = 20\npositions = []\nvelocities = []\n\nw_min = 0.01\nw_max = 0.1\nc1 = 0\nc2 = 0\nnum_iters = 20\n\nfor i in range(particles):\n positions.append(np.random.uniform(boundary[0], boundary[1], dimensions))\n\npositions = np.array(positions)\n\nfor i in range(particles):\n velocities.append(np.random.uniform(-1, 1, dimensions))\n\nvelocities = np.array(velocities)\n\nbest_locals = positions\ngbest = positions[0]\n\nfor p in positions:\n if f(p) < f(gbest):\n gbest = p\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection=\"3d\")\n\nfig.show()\n\nx = y = np.arange(boundary[0], boundary[1], 0.1)\nsurface_x, surface_y = np.meshgrid(x, y)\nsurface_z = np.array(\n [f([x, y]) for x, y in zip(np.ravel(surface_x), np.ravel(surface_y))]\n).reshape(surface_x.shape)\n\npositions_x = [p[0] for p in positions]\npositions_y = [p[1] for p in positions]\npositions_z = [f([x, y]) for x, y in zip(positions_x, positions_y)]\n\nfor k in range(num_iters):\n for p in range(particles):\n for d in range(dimensions):\n c1 = 2.5 - 2 * (k / num_iters)\n c2 = 0.5 + 2 * (k / num_iters)\n w = w_max - ((w_max - w_min) * k) / num_iters\n\n r1, r2 = np.random.rand(), np.random.rand()\n velocities[p][d] = (\n w * velocities[p][d]\n + c1 * r1 * (best_locals[p][d] - positions[p][d])\n + c2 * r2 * (gbest[d] - positions[p][d])\n )\n positions[p][d] += velocities[p][d]\n\n if f(positions[p]) < f(best_locals[p]):\n best_locals[p] = positions[p]\n\n if f(best_locals[p]) < f(gbest):\n gbest = best_locals[p]\n\n ax.clear()\n ax.plot_surface(surface_x, surface_y, surface_z, alpha=0.3)\n\n positions_x = [p[0] for p in positions]\n positions_y = [p[1] for p in positions]\n positions_z = [f([x, y]) for x, y in zip(positions_x, positions_y)]\n\n ax.scatter(positions_x, positions_y, positions_z, c=\"#FF0000\")\n fig.canvas.draw()\n print(\"Iter: {}\".format(k))\n" }, { "path": "code/artificial_intelligence/src/particle_swarm_optimization/lbestPSO/LBest3D.py", "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef f(args):\n return np.sum([x ** 2 for x in args])\n\n\ndef distance(p1, p2):\n return np.sqrt((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2 + (f(p2) - f(p1)) ** 2)\n\n\ndimensions = 2\nboundary = (-10, 10)\nparticles = 30\npositions = []\nvelocities = []\n\nw_min = 0.01\nw_max = 0.1\nc1 = 0\nc2 = 0\nnum_iters = 10\n\nfor i in range(particles):\n positions.append(np.random.uniform(boundary[0], boundary[1], dimensions))\n\npositions = np.array(positions)\nbest_positions = positions\n\nfor i in range(particles):\n velocities.append(np.random.uniform(-1, 1, dimensions))\n\nvelocities = np.array(velocities)\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection=\"3d\")\n\nfig.show()\n\nx = y = np.arange(boundary[0], boundary[1], 0.1)\nsurface_x, surface_y = np.meshgrid(x, y)\nsurface_z = np.array(\n [f([x, y]) for x, y in zip(np.ravel(surface_x), np.ravel(surface_y))]\n).reshape(surface_x.shape)\n\nfor k in range(num_iters):\n for p in range(particles):\n c1 = 2.5 - 2 * (k / num_iters)\n c2 = 0.5 + 2 * (k / num_iters)\n w = w_max - ((w_max - w_min) * k) / num_iters\n\n distances = list(\n zip(\n [\n distance(positions[p], other)\n for other in positions\n if (other != positions[p]).any()\n ],\n range(particles),\n )\n )\n distances.sort()\n\n neighbours = [positions[i] for i in list(zip(*distances[:5]))[1]]\n neighbours_best = neighbours[0]\n\n for n in neighbours:\n if f(n) < f(neighbours_best):\n neighbours_best = n\n\n for d in range(2):\n r1, r2 = np.random.rand(), np.random.rand()\n velocities[p][d] = (\n w * velocities[p][d]\n + c1 * r1 * (best_positions[p][d] - positions[p][d])\n + c1 * r2 * (neighbours_best[d] - positions[p][d])\n )\n\n positions[p][d] += velocities[p][d]\n\n if f(positions[p]) < f(best_positions[p]):\n best_positions[p] = positions[p]\n\n if f(best_positions[p]) < f(neighbours_best):\n neighbours_best = best_positions[p]\n\n ax.clear()\n ax.plot_surface(surface_x, surface_y, surface_z, alpha=0.3)\n\n positions_x = [p[0] for p in positions]\n positions_y = [p[1] for p in positions]\n positions_z = [f([x, y]) for x, y in zip(positions_x, positions_y)]\n\n ax.scatter(positions_x, positions_y, positions_z, c=\"#FF0000\")\n fig.canvas.draw()\n print(\"Iter: {}\".format(k))\nlowest = np.argmin(f(positions))\nprint(positions[lowest])\n" }, { "path": "code/artificial_intelligence/src/perceptron/perceptron.py", "content": "# Function that essentially involves the calculation inside the perceptron\ndef predict(row, weights):\n activation = weights[0]\n for i in range(len(row) - 1):\n activation += weights[i + 1] * row[i]\n return 1.0 if activation >= 0.0 else 0.0\n\n\n# Stochastic Gradient Descent\n\n\ndef train_weights(train, l_rate, n_epoch):\n weights = [0.0 for i in range(len(train[0]))]\n for epoch in range(n_epoch):\n sum_error = 0.0\n for row in train:\n prediction = predict(row, weights)\n error = row[-1] - prediction\n sum_error += error ** 2\n weights[0] = weights[0] + l_rate * error\n for i in range(len(row) - 1):\n weights[i + 1] = weights[i + 1] + l_rate * error * row[i]\n print(\"epoch=%d, lrate=%.3f, error=%.3f\" % (epoch, l_rate, sum_error))\n return weights\n\n\ndataset = [\n [2.7810836, 2.550537003, 0],\n [1.465489372, 2.362125076, 0],\n [3.396561688, 4.400293529, 0],\n [1.38807019, 1.850220317, 0],\n [3.06407232, 3.005305973, 0],\n [7.627531214, 2.759262235, 1],\n [5.332441248, 2.088626775, 1],\n [6.922596716, 1.77106367, 1],\n [8.675418651, -0.242068655, 1],\n [7.673756466, 3.508563011, 1],\n]\nl_rate = 0.1\nn_epoch = 5\nweights = train_weights(dataset, l_rate, n_epoch)\nprint(weights)\n" }, { "path": "code/artificial_intelligence/src/principal_component_analysis/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Principal component analysis\nSolution to Principal Component Analysis Inspired by [Sebastian Raschka](http://sebastianraschka.com/Articles/2014_pca_step_by_step.html)\n\nThe PCA approach may be summarized according to these steps:\n1. Take the whole dataset consisting of dd-dimensional samples ignoring the class labels\n2. Compute the dd-dimensional mean vector (i.e., the means for every dimension of the whole dataset)\n3. Compute the scatter matrix (alternatively, the covariance matrix) of the whole data set\n4. Compute eigenvectors and corresponding eigenvalues\n5. Sort the eigenvectors by decreasing eigenvalues and choose eigenvectors with the largest eigenvalues.\n6. Use eigenvector matrix to transform the samples onto the new subspace.\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation\n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/q_learning/q_learning.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"Q-Learning_OpenGenus.ipynb\",\n \"provenance\": [],\n \"collapsed_sections\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"accelerator\": \"GPU\"\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Nrcytm9HnDkI\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"# I. Q-Learning - Table approach\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"fC9d2GXQnAvp\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"import random\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import tensorflow as tf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import gym\\n\",\n \"\\n\",\n \"from collections import deque\\n\",\n \"from time import time\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"x5TH5NPrnfyI\",\n \"colab_type\": \"code\",\n \"outputId\": \"6cc3d02f-29f4-4b68-aeb3-2daca6d4e95d\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n }\n },\n \"source\": [\n \"seed = 2205\\n\",\n \"random.seed(seed)\\n\",\n \"\\n\",\n \"print('Seed: {}'.format(seed))\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Seed: 2205\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"PG6dN6trnqdF\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"##Game Design\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"DjE-BXUPntvI\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"There exists a board with 4 cells. The Q-agent will receive a +1 reward every time it fills a vacant cell, and will receive a -1 penalty when it tries to fill an already filled cell.\\n\",\n \"\\n\",\n \"The game ends when the board is full.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"J-i-4Vw7nogO\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"class Game:\\n\",\n \" \\n\",\n \" board = None\\n\",\n \" board_size = 0\\n\",\n \" \\n\",\n \" def __init__(self, board_size = 4):\\n\",\n \" self.board_size = board_size\\n\",\n \" self.reset()\\n\",\n \" \\n\",\n \" def reset(self):\\n\",\n \" self.board = np.zeros(self.board_size)\\n\",\n \" \\n\",\n \" def play(self, cell):\\n\",\n \" ## Returns a tuple: (reward, game_over?)\\n\",\n \" if self.board[cell] == 0:\\n\",\n \" self.board[cell] = 1\\n\",\n \" game_over = len(np.where(self.board == 0)[0]) == 0\\n\",\n \" return (1, game_over)\\n\",\n \" else:\\n\",\n \" return (-1, False)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"VhMs6jaPos5M\",\n \"colab_type\": \"code\",\n \"outputId\": \"8b3f7745-001c-4466-bbf6-655c3b776e99\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 321\n }\n },\n \"source\": [\n \"def state_to_str(state):\\n\",\n \" return str(list(map(int, state.tolist())))\\n\",\n \"\\n\",\n \"all_states = list()\\n\",\n \"for i in range(2):\\n\",\n \" for j in range(2):\\n\",\n \" for k in range(2):\\n\",\n \" for l in range(2):\\n\",\n \" s = np.array([i, j, k, l])\\n\",\n \" all_states.append(state_to_str(s))\\n\",\n \"\\n\",\n \"print('All possible states are: ')\\n\",\n \"for s in all_states:\\n\",\n \" print(s)\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"All possible states are: \\n\",\n \"[0, 0, 0, 0]\\n\",\n \"[0, 0, 0, 1]\\n\",\n \"[0, 0, 1, 0]\\n\",\n \"[0, 0, 1, 1]\\n\",\n \"[0, 1, 0, 0]\\n\",\n \"[0, 1, 0, 1]\\n\",\n \"[0, 1, 1, 0]\\n\",\n \"[0, 1, 1, 1]\\n\",\n \"[1, 0, 0, 0]\\n\",\n \"[1, 0, 0, 1]\\n\",\n \"[1, 0, 1, 0]\\n\",\n \"[1, 0, 1, 1]\\n\",\n \"[1, 1, 0, 0]\\n\",\n \"[1, 1, 0, 1]\\n\",\n \"[1, 1, 1, 0]\\n\",\n \"[1, 1, 1, 1]\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"13rUr5OVpTge\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Initializing the game:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"b3oYifvnpGAU\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"game = Game()\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YTcYkolgpbNr\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"## Q-Learning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"FCBsp5cgpW_o\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"num_of_games = 2000\\n\",\n \"epsilon = 0.1\\n\",\n \"gamma = 1\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"My1XivF3ppli\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Initializing the Q-Table:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ArHwI7kEpowo\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"q_table = pd.DataFrame(0, \\n\",\n \" index = np.arange(4),\\n\",\n \" columns = all_states)\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nXwaRzbUp4Eu\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Letting the agent play and learn:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"yW--SweBpzuV\",\n \"colab_type\": \"code\",\n \"outputId\": \"e775e65f-0dd5-4a6b-8ead-0c03b96d5c04\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 247\n }\n },\n \"source\": [\n \"r_list = [] ##Stores the total reward of each game so we can plot it later\\n\",\n \"\\n\",\n \"for g in range(num_of_games):\\n\",\n \" game_over = False\\n\",\n \" game.reset()\\n\",\n \" total_reward = 0\\n\",\n \" \\n\",\n \" while not game_over:\\n\",\n \" state = np.copy(game.board)\\n\",\n \" if random.random() < epsilon:\\n\",\n \" action = random.randint(0, 3)\\n\",\n \" else:\\n\",\n \" action = q_table[state_to_str(state)].idxmax()\\n\",\n \" \\n\",\n \" reward, game_over = game.play(action)\\n\",\n \" total_reward += reward\\n\",\n \" if np.sum(game.board) == 4: ##Terminal state\\n\",\n \" next_state_max_q_value = 0\\n\",\n \" else:\\n\",\n \" next_state = np.copy(game.board)\\n\",\n \" next_state_max_q_value = q_table[state_to_str(next_state)].max()\\n\",\n \" q_table.loc[action, state_to_str(state)] = reward + gamma * next_state_max_q_value\\n\",\n \" r_list.append(total_reward)\\n\",\n \"q_table\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n 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\"\n ],\n \"text/plain\": [\n \" [0, 0, 0, 0] [0, 0, 0, 1] ... [1, 1, 1, 0] [1, 1, 1, 1]\\n\",\n \"0 4 3 ... 0 0\\n\",\n \"1 4 0 ... 0 0\\n\",\n \"2 4 3 ... 0 0\\n\",\n \"3 4 0 ... 1 0\\n\",\n \"\\n\",\n \"[4 rows x 16 columns]\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 9\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"edwSIGCoryCL\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's verify that the agent indeed learnt a correct strategy by seeing what action it will choose in each one of the possible states\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Gwo52TJ7rtPJ\",\n \"colab_type\": \"code\",\n \"outputId\": \"27e7bc3a-e34d-4455-ad7a-a9647acbf90b\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 305\n }\n },\n \"source\": [\n \"for i in range(2):\\n\",\n \" for j in range(2):\\n\",\n \" for k in range(2):\\n\",\n \" for l in range(2):\\n\",\n \" b = np.array([i, j, k, l])\\n\",\n \" if len(np.where(b == 0)[0]) != 0:\\n\",\n \" action = q_table[state_to_str(b)].idxmax()\\n\",\n \" pred = q_table[state_to_str(b)].tolist()\\n\",\n \" print('Board: {b}\\\\tPredicted Q-values: {p}\\\\tBest action: {a}\\\\tCorrect action? {s}'\\n\",\n \" .format(b = b, p = pred, a = action, s = b[action]==0))\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Board: [0 0 0 0]\\tPredicted Q-values: [4, 4, 4, 4]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 0 0 1]\\tPredicted Q-values: [3, 0, 3, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 0 1 0]\\tPredicted Q-values: [3, 3, 2, 2]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 0 1 1]\\tPredicted Q-values: [2, 0, 0, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 1 0 0]\\tPredicted Q-values: [3, 2, 0, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 1 0 1]\\tPredicted Q-values: [0, 0, 0, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 1 1 0]\\tPredicted Q-values: [2, 0, 0, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [0 1 1 1]\\tPredicted Q-values: [0, 0, 0, 0]\\tBest action: 0\\tCorrect action? True\\n\",\n \"Board: [1 0 0 0]\\tPredicted Q-values: [2, 3, 3, 3]\\tBest action: 1\\tCorrect action? True\\n\",\n \"Board: [1 0 0 1]\\tPredicted Q-values: [-1, 2, 2, 1]\\tBest action: 1\\tCorrect action? True\\n\",\n \"Board: [1 0 1 0]\\tPredicted Q-values: [1, 2, 1, 2]\\tBest action: 1\\tCorrect action? True\\n\",\n \"Board: [1 0 1 1]\\tPredicted Q-values: [-1, 1, 0, 0]\\tBest action: 1\\tCorrect action? True\\n\",\n \"Board: [1 1 0 0]\\tPredicted Q-values: [1, 1, 2, 2]\\tBest action: 2\\tCorrect action? True\\n\",\n \"Board: [1 1 0 1]\\tPredicted Q-values: [0, 0, 1, 0]\\tBest action: 2\\tCorrect action? True\\n\",\n \"Board: [1 1 1 0]\\tPredicted Q-values: [0, 0, 0, 1]\\tBest action: 3\\tCorrect action? True\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"qFYAnBc1tteR\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"We can improve the performance of our algorithm by increasing the ε (epsilon) value (which controls how much the algorithm can explore), or by letting the agent play more games.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xu13o5eduKvC\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"Let's plot the total reward the agent received per game:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ex2SsbBvtK2k\",\n \"colab_type\": \"code\",\n \"outputId\": \"50e81ebd-138a-4440-9540-aa6e2c109846\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 466\n }\n },\n \"source\": [\n \"plt.figure(figsize = (14, 7))\\n\",\n \"plt.plot(range(len(r_list)), r_list)\\n\",\n \"plt.xlabel('Games played')\\n\",\n \"plt.ylabel('Reward')\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": 0,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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27VksmucD969T1dd2Rrnfg5lKTTNq5oV4TOPXa1Ttu4QlJ3OtywvJmO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\\nf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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n }\n ]\n}" }, { "path": "code/artificial_intelligence/src/q_learning/q_learning.js", "content": "// Q Learning Introduction: https://en.wikipedia.org/wiki/Q-learning\n// Algorithm: Q(s,a):=Q(s,a)+α[r+γ*maxQ(s′,a′)−Q(s,a)]\n\nconst TERMINAL = \"terminal\";\nconst TOTAL_EPISODE = 10;\n\nfunction State(stateName, actions) {\n this.name = stateName;\n // Actions of State\n this.actions = {};\n actions.forEach(action => {\n this.actions[action] = 0;\n });\n}\n\nclass QLearning {\n constructor(\n actions,\n learningRate = 0.01,\n discountFactor = 0.9,\n eGreedy = 0.9\n ) {\n this.actions = actions;\n this.lr = learningRate;\n this.gamma = discountFactor;\n this.epsilon = eGreedy;\n // Q Table Structure\n this.states = {};\n this.statesList = [];\n }\n\n addState(name, actions) {\n const state = new State(name, actions);\n this.states[name] = state;\n this.statesList.push(state);\n return state;\n }\n\n static bestAction(stateActions) {\n return new Promise(resolve => {\n let bestAction = null;\n let bestActionReward = null;\n Object.keys(stateActions).forEach(actionName => {\n if (!bestActionReward) {\n bestActionReward = stateActions[actionName];\n }\n if (stateActions[actionName] > bestActionReward) {\n bestAction = actionName;\n bestActionReward = stateActions[actionName];\n } else if (\n stateActions[actionName] === bestActionReward &&\n Math.random() > 0.5\n ) {\n bestAction = actionName;\n } else {\n bestAction = bestAction || actionName;\n }\n });\n resolve(bestAction);\n });\n }\n\n checkStateExist(stateName) {\n if (!this.states[stateName]) this.addState(stateName, this.actions);\n }\n\n async chooseAction(stateName) {\n this.checkStateExist(stateName);\n\n console.log(`Q Value: ${JSON.stringify(this.states[stateName].actions)}`);\n\n let nextAction = null;\n if (Math.random() < this.epsilon) {\n // choose next action based on Q Value\n const stateActions = this.states[stateName].actions;\n nextAction = await QLearning.bestAction(stateActions);\n console.log(`pickAction: ${nextAction}`);\n } else {\n // choose next action randomly\n nextAction = this.actions[\n Math.floor(Math.random() * this.actions.length)\n ];\n console.log(`randomAction: ${nextAction}`);\n }\n return nextAction;\n }\n\n learn(stateName, actionName, reward, nextStateName) {\n this.checkStateExist(nextStateName);\n\n const qPredict = this.states[stateName].actions[actionName];\n\n // γ*maxQ(s′,a′)\n const maxQValue =\n this.gamma *\n Math.max(...Object.values(this.states[nextStateName].actions));\n const qTarget = stateName !== TERMINAL ? reward + maxQValue : reward;\n\n this.states[stateName].actions[actionName] +=\n this.lr * (qTarget - qPredict);\n }\n}\n\n//\n// SAMPLE Command Line Environment\n//\n\nclass RoadEnv {\n constructor() {\n this.totalActions = [\"LEFT\", \"RIGHT\"];\n this.resetStateName = \"0\";\n }\n\n static render(stateName) {\n return new Promise(resolve => {\n const road = [\"-\", \"-\", \"-\", \"-\", \"-\", \"-\"];\n road[Number(stateName)] = \"0\";\n console.log(`\\n ${road.join(\"\")}`);\n resolve();\n });\n }\n\n static step(stateName, action) {\n return {\n \"0-LEFT\": {\n nextStateName: TERMINAL,\n reward: -1000,\n done: true\n },\n \"0-RIGHT\": RoadEnv.normalTransition(\"1\"),\n \"1-LEFT\": RoadEnv.normalTransition(\"0\"),\n \"1-RIGHT\": RoadEnv.normalTransition(\"2\"),\n \"2-LEFT\": RoadEnv.normalTransition(\"1\"),\n \"2-RIGHT\": RoadEnv.normalTransition(\"3\"),\n \"3-LEFT\": RoadEnv.normalTransition(\"2\"),\n \"3-RIGHT\": RoadEnv.normalTransition(\"4\"),\n \"4-LEFT\": RoadEnv.normalTransition(\"3\"),\n \"4-RIGHT\": {\n nextStateName: TERMINAL,\n reward: 100,\n done: true\n }\n }[`${stateName}-${action}`];\n }\n\n static normalTransition(stateName) {\n return {\n nextStateName: stateName,\n reward: 0,\n done: false\n };\n }\n}\n\nconst update = (RL, stateName, totalSteps) => {\n return new Promise(resolve => {\n setTimeout(async () => {\n RoadEnv.render(stateName);\n\n console.log(`stateName: ${stateName}`);\n const action = await RL.chooseAction(stateName);\n\n const step = RoadEnv.step(stateName, action);\n\n let nextStateName = null;\n let reward = null;\n let done = null;\n ({ nextStateName, reward, done } = step);\n\n RL.learn(stateName, action, reward, nextStateName);\n const currentSteps = totalSteps + 1;\n\n if (!done) {\n resolve(await update(RL, nextStateName, currentSteps));\n } else {\n console.log(`endReward: ${reward}`);\n resolve(totalSteps);\n }\n }, 100);\n });\n};\n\n// Run Q Learning For 10 Episode\nconst run = async () => {\n const env = new RoadEnv();\n const RL = new QLearning(env.totalActions);\n\n for (let episode = 1; episode <= TOTAL_EPISODE; episode += 1) {\n const stateName = env.resetStateName;\n let totalSteps = 1;\n console.log(`\\nEpisode: ${episode} =============================== `);\n totalSteps = await update(RL, stateName, totalSteps);\n console.log(`\\nTotalSteps: ${totalSteps}`);\n }\n};\n\nrun();\n" }, { "path": "code/artificial_intelligence/src/random_forests/Position_Salaries.csv", "content": "Position,Level,Salary\nBusiness Analyst,1,45000\nJunior Consultant,2,50000\nSenior Consultant,3,60000\nManager,4,80000\nCountry Manager,5,110000\nRegion Manager,6,150000\nPartner,7,200000\nSenior Partner,8,300000\nC-level,9,500000\nCEO,10,1000000" }, { "path": "code/artificial_intelligence/src/random_forests/README.md", "content": "# cosmos\n> Your personal library of every algorithm and data structure code that you will ever encounter\n\n# Random forests\n\n---\n\n

\n\tA massive collaborative effort by OpenGenus Foundation \n

\n\n---\n" }, { "path": "code/artificial_intelligence/src/random_forests/Random_Forest_Regression_Model.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing the libraries\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#importing the dataset\\n\",\n \"dataset=pd.read_csv('Position_Salaries.csv')\\n\",\n \"X=dataset.iloc[:,1:2].values\\n\",\n \"y=dataset.iloc[:,2].values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1],\\n\",\n \" [ 2],\\n\",\n \" [ 3],\\n\",\n \" [ 4],\\n\",\n \" [ 5],\\n\",\n \" [ 6],\\n\",\n \" [ 7],\\n\",\n \" [ 8],\\n\",\n \" [ 9],\\n\",\n \" [10]], dtype=int64)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 45000, 50000, 60000, 80000, 110000, 150000, 200000,\\n\",\n \" 300000, 500000, 1000000], dtype=int64)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=None,\\n\",\n \" oob_score=False, random_state=0, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#fitting the random forest regression to the dataset\\n\",\n \"from sklearn.ensemble import RandomForestRegressor\\n\",\n \"regressor=RandomForestRegressor(n_estimators=300,random_state=0)\\n\",\n \"regressor.fit(X,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#predicting the results\\n\",\n \"from numpy import array\\n\",\n \"y_pred=regressor.predict(array([[6.5]]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([160333.33333333])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#visualising the Regression results\\n\",\n \"X_grid=np.arange(min(X),max(X),0.01)\\n\",\n \"X_grid=X_grid.reshape(len(X_grid),1)\\n\",\n \"plt.scatter(X,y,color='red')\\n\",\n \"plt.plot(X_grid,regressor.predict(X_grid),color='blue')\\n\",\n \"plt.title('Truth vs Bluff(Random Forest Regression)')\\n\",\n \"plt.xlabel('Position level')\\n\",\n \"plt.ylabel('Salary')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "code/artificial_intelligence/src/residual_convolutional_neural_network/.gitignore", "content": "data" }, { "path": "code/design_pattern/src/OOP_patterns/__init__.py", "content": "" }, { "path": "code/design_pattern/src/__init__.py", "content": "" }, { "path": "code/design_pattern/src/functional_patterns/functional_patterns/scala/build.sbt", "content": "" }, { "path": "code/design_pattern/test/__init__.py", "content": "" }, { "path": "code/languages/python/web_programming/__init__.py", "content": "" } ]