[
  {
    "path": "Abstract/abstract.tex",
    "content": "% ************************** Thesis Abstract *****************************\n% Use `abstract' as an option in the document class to print only the titlepage and the abstract.\n\\begin{abstract}\nArtificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node.\nGenerally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions.\n\\newline\n\nIn this thesis, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks.\n\nBayesCNN is based on Bayes by Backprop which derives a variational approximation to the true posterior. \nOur proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective. \n\\newline\n\n\nIn the first part of the thesis, the Bayesian Neural Network is explained and it is applied to an Image Classification task. The results are compared to point-estimates based architectures on MNIST, CIFAR-10, and CIFAR-100 datasets. Moreover, uncertainties are calculated and the architecture is pruned and a comparison between the results is drawn.\n\nIn the second part of the thesis, the concept is further applied to other computer vision tasks namely, Image Super-Resolution and Generative Adversarial Networks. The concept of BayesCNN is tested and compared against other concepts in a similar domain.  \n\n\\end{abstract}\n"
  },
  {
    "path": "Acknowledgement/acknowledgement.tex",
    "content": "% ************************** Thesis Acknowledgements **************************\n\n\\begin{acknowledgements}      \n\nI would first like to thank my thesis advisor \\textbf{Prof. Marcus Liwicki} who was always present physically or virtually whenever I ran into some trouble or had a crazy thought. He consistently steered me in the right direction whenever he thought I needed it.\n\nI would also like to thank my second thesis advisor \\textbf{Felix Laumann} with whom I worked in Copenhagen and in Kaiserslautern. He also made sure Imperial College, London is never far away with our continuous calls and Skype discussion. He contributed in all possible ways in the thesis and made sure we are on the right path. \\\\\n\nI would also like to acknowledge \\textbf{University of Kaiserlautern} for providing me with the opportunity for writing the thesis and \\textbf{MindGarage} for providing me with the computation power.\\\\\n\nI must express my very profound gratitude to my parents (\\textbf{Vivek} and \\textbf{Anita}), to my sister (\\textbf{Mineshi}) and to \\textbf{Purvanshi Mehta} for providing me with unfailing support and continuous encouragement throughout the thesis. \n\nFinally, I am thankful to \\textbf{Ashutosh Mishra}, \\textbf{Saurabh Varshneya}, and \\textbf{Ayushman Dash} for their invaluable comments and endless discussion. I am also thankful to \\textbf{Sadique Adnan Siddiqui} for his late night tea and delicious food. This accomplishment would not have been possible without them. \\\\\n\nI would also like to mention \\textbf{BotSupply} for providing me with the funds to go and meet new people to discuss ideas and to get some inspiration. \\\\ \\\\\n\n\n
\n\\end{acknowledgements}\n"
  },
  {
    "path": "Appendix1/appendix1.tex",
    "content": "%!TEX root = ../thesis.tex\n% ******************************* Thesis Appendix A ****************************\n\\chapter{Experiment Specifications} \n\n\\section*{Bayesian Settings}\n\n\\subsection{Image Recognition}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}[c]{c | c} \n     \\hline\n     variable & value \\\\ [0.5ex] \n     \\hline\n     learning rate &  0.001\\\\ \n     \n     epochs & 100 \\\\\n     \n     batch size & 256 \\\\\n     \n     sample size & 10-25 \\\\\n     \n     loss & cross-entropy \\\\\n     \n     $(\\alpha \\mu^2)_{init}$ of approximate posterior $q_{\\theta}(w|\\mathcal{D})$ & -10 \\\\\n     \n     optimizer & Adam \\cite{kingma2014adam} \\\\\n     \n     $\\lambda$ in $\\ell$-2 normalisation & 0.0005 \\\\\n    \n     $\\beta_i$ & $\\frac{2^{M-i}}{2^M-1}$ \\cite{blundell2015weight} \\\\ [1ex] \n     \\hline\n    \\end{tabular} \n    \\renewcommand{\\arraystretch}{2}\n\\end{table}\n\nSample size can vary from 10 to 25 as this range provided the best results. However, it can be played around with. For most of our experiments, it is either 10 or 25 unless specified otherwise. \n\n\\subsection{Image Super Resolution}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}[c]{c | c} \n     \\hline\n     variable & value \\\\ [0.5ex] \n     \\hline\n     learning rate &  0.01\\\\ \n     \n     epochs & 200 \\\\\n     \n     batch size & 64 \\\\\n     \n     upscale factor & 3 \\\\\n     \n     loss & Mean Squared Error \\\\\n     \n     seed & 123 \\\\\n     \n     $(\\alpha \\mu^2)_{init}$ of approximate posterior $q_{\\theta}(w|\\mathcal{D})$ & -10 \\\\\n     \n     optimizer & Adam \\cite{kingma2014adam} \\\\\n     \n     $\\lambda$ in $\\ell$-2 normalisation & 0.0005 \\\\\n    \n     $\\beta_i$ & $\\frac{2^{M-i}}{2^M-1}$ \\cite{blundell2015weight} \\\\ [1ex] \n     \\hline\n    \\end{tabular} \n    \\renewcommand{\\arraystretch}{2}\n\\end{table}\n\n\n\\subsection{Generative Adversarial Network}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}[c]{c | c} \n     \\hline\n     variable & value \\\\ [0.5ex] \n     \\hline\n     learning rate &  0.001\\\\ \n     \n     epochs & 100 \\\\\n     \n     batch size & 64 \\\\\n     \n     image size & 64 \\\\\n     \n     latent vector (nz) & 100 \\\\\n     \n     number of generator factor (ndf) & 64 \\\\\n     \n     number of discriminator factor (ndf) & 64 \\\\\n     \n     upscale factor & 3 \\\\\n     \n     loss & Mean Squared Error \\\\\n     \n     number of channels (nc) & 3 \\\\\n     \n     $(\\alpha \\mu^2)_{init}$ of approximate posterior $q_{\\theta}(w|\\mathcal{D})$ & -10 \\\\\n     \n     optimizer & Adam \\cite{kingma2014adam} \\\\\n     \n     $\\lambda$ in $\\ell$-2 normalisation & 0.0005 \\\\\n    \n     $\\beta_i$ & $\\frac{2^{M-i}}{2^M-1}$ \\cite{blundell2015weight} \\\\ [1ex] \n     \\hline\n    \\end{tabular} \n    \\renewcommand{\\arraystretch}{2}\n\\end{table}\n\n\\section*{Non Bayesian Settings}\n\n\\subsection{Image Recognition}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}[c]{c | c} \n     \\hline\n     variable & value \\\\ [0.5ex] \n     \\hline\n     learning rate &  0.001\\\\ \n     \n     epochs & 100 \\\\\n     \n     batch size & 256 \\\\\n     \n     loss & cross-entropy \\\\\n     \n     initializer & Xavier \\cite{glorot2010understanding} or Normal \\\\\n     \n     optimizer & Adam \\cite{kingma2014adam} \\\\ [1ex] \n     \\hline\n    \\end{tabular} \n    \\renewcommand{\\arraystretch}{2}\n\\end{table}\n\nThe weights were initialized with Xavier initialization \\cite{glorot2010understanding} at first, but to make it consistent with the Bayesian networks where initialization was Normal initialization (mean = 0 and variance = 1), the initializer was changed to Normal initialization.\n\n\\pagebreak\n\n\\section*{Architectures}\n\n\\subsection{LeNet-5}\n \n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c c} \n     \\hline\n     layer type & width & stride & padding & input shape & nonlinearity \\\\ [0.5ex] \n     \\hline\n     convolution ($5\\times5$) & 6 & 1 & 0 & $M\\times1\\times32\\times32$ & Softplus \\\\ \n     \n     Mmax-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times6\\times28\\times28$ & \\empty \\\\\n     \n     convolution ($5\\times5$) & 16 & 1 & 0 & $M\\times1\\times14\\times14$ & Softplus \\\\\n     \n     max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times16\\times10\\times10$ & \\empty \\\\\n    \n     fully-connected & 120 & \\empty & \\empty & $M\\times400$ & Softplus \\\\\n     \n     fully-connected & 84 & \\empty & \\empty & $M\\times120$ & Softplus \\\\\n     \n     fully-connected & 10 & \\empty & \\empty & $M\\times84$ & \\empty \\\\ [1ex] \n     \\hline\n    \\end{tabular} \n    \\renewcommand{\\arraystretch}{1}\n    \\label{tab:LeNet}\n    \\caption{LeNet architecture with original configurations as defined in the paper. \\cite{lecun1998gradient}}\n\\label{tab:AlexNet}\n\n\\end{table}\n\n\\subsection{AlexNet}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c c} \n \\hline\n layer type & width & stride & padding & input shape & nonlinearity \\\\ [0.5ex] \n \\hline\n convolution ($11\\times11$) & 64 & 4 & 5 & $M\\times3\\times32\\times32$ & Softplus \\\\ \n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times64\\times32\\times32$ & \\empty \\\\\n \n convolution ($5\\times5$) & 192 & 1 & 2 & $M\\times64\\times15\\times15$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times192\\times15\\times15$ & \\empty \\\\\n \n convolution ($3\\times3$) & 384 & 1 & 1 & $M\\times192\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 256 & 1 & 1 & $M\\times384\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 128 & 1 & 1 & $M\\times256\\times7\\times7$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times128\\times7\\times7$ & \\empty \\\\\n \n fully-connected & 128 & \\empty & \\empty & $M\\times128$ & \\empty \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1}\n\\caption{AlexNet architecture with original configurations as defined in the paper. \\cite{krizhevsky2012imagenet}}\n\\label{tab:AlexNet}\n\\end{table}\n\n\n\\subsection{AlexNetHalf}\n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c c} \n \\hline\n layer type & width & stride & padding & input shape & nonlinearity \\\\ [0.5ex] \n \\hline\n convolution ($11\\times11$) & 32 & 4 & 5 & $M\\times3\\times32\\times32$ & Softplus \\\\ \n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times32\\times32\\times32$ & \\empty \\\\\n \n convolution ($5\\times5$) & 96 & 1 & 2 & $M\\times32\\times15\\times15$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times96\\times15\\times15$ & \\empty \\\\\n \n convolution ($3\\times3$) & 192 & 1 & 1 & $M\\times96\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 128 & 1 & 1 & $M\\times192\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 64 & 1 & 1 & $M\\times128\\times7\\times7$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times64\\times7\\times7$ & \\empty \\\\\n \n fully-connected & 64 & \\empty & \\empty & $M\\times64$ & \\empty \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1.5}\n\\label{tab:AlexNetHalfArchitecture}\n\\caption{AlexNetHalf with number of filters halved compared to the original architecture.}\n\\end{table}\n\n"
  },
  {
    "path": "Appendix2/appendix2.tex",
    "content": "%!TEX root = ../thesis.tex\n% ******************************* Thesis Appendix B ********************************\n\n\\chapter{How to replicate results}\n\nInstall PyTorch from the official website (\\url{https://pytorch.org/})\n\n\\begin{verbatim} \n\ngit clone https://github.com/kumar-shridhar/PyTorch-BayesianCNN\npip install -r requirements.txt\n\n\\end{verbatim}\n\\textit{cd} into respective folder/ task to replicate (Image Recognition, Super Resolution or GAN)\n\n\\subsection{Image Recognition}\n\n\\begin{verbatim} \npython main_Bayesian.py\n\\end{verbatim}\n\nto replicate the Bayesian \\acp{cnn} results.\n\n\\begin{verbatim} \npython main_nonBayesian.py\n\\end{verbatim}\n\nto replicate the Frequentist \\acp{cnn} results.\\\\\n\n\\\\For more details, read the README sections of the repo : \\url{https://github.com/kumar-shridhar/PyTorch-BayesianCNN}\n\n\n"
  },
  {
    "path": "ChangeLog.md",
    "content": "## Change log\n### 2016/04/23 - Version 2.3.1\n> Commit 7e4d9a261dd4e5dbdbf75c642ce2bbaa7e87429b\n*   Fixes #124 Pagestyle options\n\n### 2016/03/19 - Version 2.3\n> Commit 665937d81ff7c561569bbec240b4fa703063be3f\n*   Fixes #118 and #119 on option processing and font sizes\n\n### 2016/10/20 - Version 2.2.2\n> Commit 7867b367fe6c9523461f5bcd91a8f4f5e92badb2\n*   Fixes issue of non-centered style for advisors and supervisors\n\n### 2016/07/04 - Version 2.2.1\n> Commit ef6e4394f3dd5a7442c2f48f3196d7d0a15d077f\n*   Removed deprecated unused function romanpages defined in `preamble.tex`.\n*   https://tex.stackexchange.com/questions/328438/roman-numbering-not-enumerating-latex-error\n*   Fixes issue #101 incorrect page numbering scheme in `draft`.\n*   Deprecated function `draftmode` use `draft` instead.\n\n### 2016/07/04 - Version 2.2\n> Commit 68c4499d170bf167d2410741d573cdf9402d6e80\n*   Support for including multiple supervisor and/or advisor\n*   Fixes `maketitle` in thesis.tex\n\n### 2015/06/20 - Version 2.1\n> Commit a5feb0732d7f13fa9703fddcee703224c00b83dd\n*   Support for including supervisor and/or advisor\n\n### 2015/06/20 - Version 2.0.1\n> Commit 0e0d1e617acb42b034870ea325271cce8d3d8d8e\n*   Fixed centering of year on front page - Issue #16\n\n### 2015/06/20 - Version 2.0\n> Commit f4723434f92eedc163fdcb74c7ac04c8d842bc05\n*   XeLaTeX and LuaLaTeX support\n*   College crest + University crest support\n\n### 2014/12/02 - Version 1.4.5\n> Commit 427230cae012194a2e433ec001677c43ffd8250e\n*   Fixed issue with the misaligned date and college in the frontpage.\n\n### 2014/12/02 - Version 1.4.4\n> Commit 7f17bc3c682df2950e4dc09cd5e71278edc3fc6d\n*   Fixed pagenumbering issue in 2 page nomenclature - Issue #28\n\n### 2014/10/08 - Version 1.4.3\n> Commit df43fcdc7308feeea8c7dfe06ad24d45d5caca3e\n*   Fixed `\\degree` command conflict - Issue #26\n\n### 2014/10/08 - Version 1.4.2\n> Commit 4f0f3394330bfca302b724ab1b3c06977f7e08f3\n*   Added mynotes support in preamble\n*   Fixed printnomecl in abstractmode\n*   Fixed issue with pagenumbering when using oneside option\n*   Draftmode is deprecated, default `draft' supports linenumbering, watermark\n*   List of contents doesn't appear in TOC but appears in bookmarks\n\n### 2014/08/11 - Version 1.4.1\n> Commit fb70181ff9e00bfb50e3e3040d6b2cf15cd6ebfe\n*   Fixed line numbering in draft mode with AMS Math environment\n*   Biblatex & custombib error catcher - bug fix\n*   Modification to the declaration text\n\n### 2014/07/15 - Version 1.4\n> Commit 330f82a439e179dc03fbbbe156f3e830ca1fdd68\n*   Option to have subtitle on front page\n*   College name is an optional argument\n*   Travis-CI integration\n\n### 2014/06/09 - Version 1.3\n> Commit 93723653eef19b8ca81d57df389b3a8efa652203\n*   Draftmode - watermark text, line numbering, version numbering\n*   Better error checking for documentclass options and log messages\n*   arara/LaTeX-mk support\n*   Option to choose `raggedright` for text\n\n### 2014/03/24 - Version 1.2\n> Commit 4b709fed6cdfd5b3796152f9c59b619c72df491f\n*   Rearranged package order in preamble to support equation numbering using cref\n*   Removed dependencies on obsolete packages\n*   Restyled the URL fonts to use the same font as text\n\n### 2014/02/14 - Version 1.1.2\n> Commit a7f123bb220bf1e207b6e49e8b2978c203582e12\n*   Updates to abstract separate mode with no page numbers\n*   Declaration included in abstract mode for submission to student registry\n\n### 2014/02/10 - Version 1.1.1\n> Commit e7f34cfd71cbe1b590d615a00d99b8d05513e5ba\n*   Biblatex handled as a custombib option\n*   UTF8 and Fontencoding after font has been loaded\n\n### 2014/02/04 - Version 1.1\n> Commit 6e00ac94c2193882dd6f42686fc455cc66d829df\n*   BibLatex Support with bibtex backend\n*   Chapter mode for compiling only specified chapters and references\n\n### 2014/01/11 - Version 1.0\n> Commit 2f6918863e3c9d0a7e95bd2651ce7ef8ae38f90a\n*   Fixed an issue with the headers in Nomenclature section\n*   Removed deprecated codes, added functionality to tweak chapter headings in preamble.tex.\n*   Distributed under MIT license\n*   Acknowledgement and Cls file update\n*   Appendices after References\n\n### 2013/12/09 - Version 1.0 Beta Release 10.0\n> Commit 973492fe1f1805e4fef60ec54060621b3e90a3cd\n*   Fixed issues with DVI >> PS >> PDF and workaround, when papersize is not set for older versions of dvips (5.97 or lower).\n\n### 2013/12/08 - Version 1.0 Beta Release 9.0\n> Commit c11f98e26566af08cb9c4cacbdfddf6b28111886\n*   Wider text area (75% of page size), support for separate abstract for submission to the Student Registry, appendix\n\n### 2013/12/05 - Version 1.0 Beta Release 8.0\n> Commit 324d1a5609992028afb109b424573cd3a5e31849\n*   Update class file to support dvips driver when using dvi > PS output in hyperTeX. Removed deprecated codes from Declaration and class files\n\n### 2013/12/05 - Version 1.0 Beta Release 7.2\n> Commit 2f397eda12ef2b81314b67847e312f688095a379\n*   Update to margin dimensions (1:1 ratio is maintained) with a binding offset of 5mm on the print version. Replaced the hmargin ratio of 3:2 with 1:1 with a binding offset.\n\n### 2013/12/04 - Version 1.0 Beta Release 7.1\n> Commit 9cb782f26cc3573f8d3077db520ba84b5f295049\n*   Declaration with automatic insertion of the author and the degree date and conforming to the statments in the University guidelines\n\n### 2013/12/03 - Version 1.0 Beta Release 7.0\n> Commit 1f695d512ae5ce765398db4dc4b6381dc0351868\n*   Default font size is 12pt and the default paper size is A4, confirming to the University regulations in terms of font, font sizer, paper size and set them as defaults.\n\n### 2013/11/27 - Version 1.0 Beta Release 6.2\n> Commit a5f49d49a6cc39209d95f91e667fd7b359ab5227\n*   Update to the Makefile to remove PS files when running Makeclean\n\n### 2013/11/26 - Version 1.0 Beta Release 6.1\n> Commit e29a99406649dcce8f23b6d9df0b87eabd09fc0e\n*   Update to the Makefile to support PS to PDF conversion\n\n### 2013/11/26 - Version 1.0 Beta Release 6.0\n> Commit 187b9324420812326e62d963afa42e26532e82e7\n*   Included a Windows Batch file for LaTeX / Nomenclature compilation\n*   Supports \\printnomencl[optional_argument]\n\n### 2013/11/26 - Version 1.0 Beta Release 5.0\n> Commit 76a733ee305ed4aae9d546492cef768512df2b13\n*   Supports DVI/PS\n*   Supports Custom Margin and FancyHdr update\n\n### 2013/11/24 - Version 1.0 Beta Release 0\n> Commit 73c8dd9ea82c21476d964ad5cdff1b71fe7327c8\n*   Author(s): Krishna Kumar\n*   Adaptive Title Page: Title page adapts to the length of the title\n*   Print / On-line version: Different layout and hyper-referncing styles\n*   Pre-defined and custom fonts (Times / Palatino / Latin Modern) with math support\n*   Pre-defined and custom bibliography style support (authoryear / numbered / custom)\n*   Custom page styles: 3 Different Header / Footer styles\n\n### 2013/11/14 - Inception\n> Author(s): Krishna Kumar\n"
  },
  {
    "path": "Chapter1/chapter1.tex",
    "content": "%!TEX root = ../thesis.tex\n%*******************************************************************************\n%*********************************** First Chapter *****************************\n%*******************************************************************************\n\\chapter{Introduction}  %Title of the First Chapter\n\n\\ifpdf\n    \\graphicspath{{Chapter1/Figs/Raster/}{Chapter1/Figs/PDF/}{Chapter1/Figs/}}\n\\else\n    \\graphicspath{{Chapter1/Figs/Vector/}{Chapter1/Figs/}}\n\\fi\n\n\n%********************************** %First Section  **************************************\n\n\\newacro{cnn}[\\textsc{Cnn}]{Convolutional neural network}\n\nDeep Neural Networks (DNNs), are connectionist systems that learn to perform tasks by learning on examples without having prior knowledge about the tasks. \nThey easily scale to millions of data points and yet remain tractable to optimize with stochastic gradient descent.\n\n\\acp{cnn}, a variant of DNNs, have already surpassed human accuracy in the realm of image classification (e.g. \\cite{he2016deep,simonyan2014very,krizhevsky2012imagenet}). Due to the capacity of \\acp{cnn} to fit on a wide diversity of non-linear data points, they require a large amount of training data. This often makes \\acp{cnn} and Neural Networks in general, prone to overfitting on small datasets. The model tends to fit well to the training data, but are not predictive for new data. This often makes the Neural Networks incapable of correctly assessing the uncertainty in the training data and hence leads to overly confident decisions about the correct class, prediction or action.\n\nVarious regularization techniques for controlling over-fitting are used in practice namely early stopping, weight decay, L1, L2 regularizations and currently the most popular and empirically effective technique being \\emph{dropout}~\\cite{hinton2012improving}. \n\n\n\\section{Problem Statement}\n\nDespite Neural Networks architectures achieving state-of-the-art results in almost all classification tasks, Neural Networks still make over-confident decisions. A measure of uncertainty in the prediction is missing from the current Neural Networks architectures. Very careful training, weight control measures like regularization of weights and similar techniques are needed to make the models susceptible to over-fitting issues. \n\nWe address both of these concerns by introducing Bayesian learning to Convolutional Neural Networks that adds a measure of uncertainty and regularization in their predictions. \n\n\\section{Current Situation}\n\nDeep Neural Networks have been successfully applied to many domains, including very sensitive domains like health-care, security, fraudulent transactions and many more. However, from a probability theory perspective, it is unjustifiable to use single point-estimates as weights to base any classification on.\nOn the other hand, Bayesian neural networks are more robust to over-fitting, and can easily learn from small datasets. The Bayesian approach further offers uncertainty estimates via its parameters in form of probability distributions (see Figure 1.1). At the same time, by using a prior probability distribution to integrate out the parameters, the average is computed across many models during training, which gives a regularization effect to the network, thus preventing overfitting.\n\nBayesian posterior inference over the neural network parameters is a theoretically attractive method for controlling overfitting; however, modelling a distribution over the kernels (also known as filters) of a \\acp{cnn} has never been attempted successfully before, perhaps because of the vast number of parameters and extremely large models commonly used in practical applications.\n\nEven with a small number of parameters, inferring model posterior in a Bayesian NN is a difficult task. Approximations to the model posterior are often used instead, with the variational inference being a popular approach. In this approach one would model the posterior using a simple \\textit{variational} distribution such as a Gaussian, and try to fit the distribution's parameters to be as close as possible to the true posterior. This is done by minimising the \\textit{Kullback-Leibler divergence} from the true posterior. Many have followed this approach in the past for standard NN models \\citep{hinton1993keeping,barber1998ensemble,graves2011practical,blundell2015weight}.\nBut the variational approach used to approximate the posterior in Bayesian NNs can be fairly computationally expensive -- the use of Gaussian approximating distributions increases the number of model parameters considerably, without increasing model capacity by much. \\citet{blundell2015weight} for example used Gaussian distributions for Bayesian NN posterior approximation and have doubled the number of model parameters, yet report the same predictive performance as traditional approaches using dropout. This makes the approach unsuitable in practice to use with \\acp{cnn} as the increase in the number of parameters is too costly.\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.28\\textheight]{Chapter1/Figs/weights.png}\n\\includegraphics[height=.28\\textheight]{Chapter1/Figs/distribution.png}\n\\label{fig:Scalar_Bayesian_Distribution_Gluon}\n\\caption{Top: Each filter weight has a fixed value, as in the case of frequentist Convolutional Networks. Bottom: Each filter weight has a distribution, as in the case of Bayesian Convolutional Networks. \\cite{Gluon}}\n\\end{center}\n\\end{figure}\n\n\\section{Our Hypothesis}\n\\newline We build our Bayesian \\ac{cnn} upon \\textit{Bayes by Backprop} \\cite{graves2011practical,blundell2015weight}. The exact Bayesian inference on the weights of a neural network is intractable as the number of parameters is very large and the functional form of a neural network does not lend itself to exact integration. So, we approximate the intractable true posterior probability distributions $p(w|\\mathcal{D})$ with variational probability distributions $q_{\\theta}(w|\\mathcal{D})$, which comprise the properties of Gaussian distributions $\\mu \\in \\mathbb{R}^d$ and $\\sigma \\in \\mathbb{R}^d$, denoted by $\\mathcal{N}(\\theta|\\mu, \\sigma^2)$, where $d$ is the total number of parameters defining a probability distribution. The shape of these Gaussian variational posterior probability distributions, determined by their variance $\\sigma^2$, expresses an uncertainty estimation of every model parameter. \\\\ \\\\\n\n\n\n\\section{Our Contribution}\n\\newline The main contributions of our work are as follows: \n\\begin{enumerate}\n    \\item We present how \\textit{Bayes by Backprop} can be efficiently applied to \\acp{cnn}. We, therefore, introduce the idea of applying two convolutional operations, one for the mean and one for the variance.\n    \\item We show how the model learns richer representations and predictions from cheap model averaging.\n    \\item We empirically show that our proposed generic and reliable variational inference method for Bayesian \\acp{cnn} can be applied to various \\ac{cnn} architectures without any limitations on their performances. \n    \\item We examine how to estimate the aleatoric and epistemic uncertainties and empirically show how the uncertainty can decrease, allowing the decisions made by the network to become more deterministic as the training accuracy increases. \n    \\item We also empirically show how our method typically only doubles the number of parameters yet trains an infinite ensemble using unbiased Monte Carlo estimates of the gradients. \n    \\item We also apply L1 norm to the trained model parameters and prune the number of non zero values and further, fine-tune the model to reduce the number of model parameters without a reduction in the model prediction accuracy. \n    \\item Finally, we will apply the concept of Bayesian CNN to tasks like Image Super-Resolution and Generative Adversarial Networks and we will compare the results with other prominent architectures in the respective domain.\n\\end{enumerate} \nThis work builds on the foundations laid out by Blundell et al. \\cite{blundell2015weight}, who introduced \\textit{Bayes by Backprop} for feedforward neural networks. Together with the extension to recurrent neural networks, introduced by Fortunato et al. \\cite{fortunato2017bayesian}, \\textit{Bayes by Backprop} is now applicable on the three most frequently used types of neural networks, i.e., feedforward, recurrent, and convolutional neural networks.\n\n\n\\nomenclature[z-cnn]{$CNN$}{Convolution Neural Networks} \n\\nomenclature[z-nn]{$NN$}{Neural Networks} \n\\nomenclature[z-mnist]{$MNIST$}{Modified National Institute of Standards and Technology} \n\\nomenclature[z-cifar]{$CIFAR$}{Canadian Institute for Advanced Research} \n\\nomenclature[z-dnn]{$DNN$}{Deep Neural Networks} \n\\nomenclature[z-ann]{$ANN$}{Artificial Neural Networks} \n\\nomenclature[z-lgb]{$LGB$}{Lateral Geniculate Body} \n\\nomenclature[z-kl]{$KL$}{Kullback–Leibler} \n\\nomenclature[z-cov]{$Cov$}{Covariance} \n\\nomenclature[z-sgvb]{$SGVB$}{Stochastic Gradient Variational Bayes} \n\\nomenclature[z-rnn]{$RNN$}{Recurrent Neural Networks} \n\\nomenclature[z-gru]{$GRU$}{Gated Recurrent Units} \n\\nomenclature[z-vi]{$VI$}{Variational Inference} \n\\nomenclature[z-mean]{$\\mu_{ijhw}$}{Mean} \n\\nomenclature[z-variance]{$\\alpha_{ijhw}\\mu^2_{ijhw}$}{Variance} \\nomenclature[z-kl]{$q_{\\theta}(w|\\mathcal{D})$}{Variational Posterior Probability Distribution} \n\\nomenclature[z-relu]{$ReLU$}{Rectified Linear Unit} \n\\nomenclature[z-mle]{$MLE$}{Maximum Likelihood Estimation}\n\\nomenclature[z-map]{$MAP$}{maximum a posteriori} \n\\nomenclature[z-sr]{$SR$}{Super Resolution} \n\\nomenclature[z-lr]{$LR$}{Low Resolution} \n\\nomenclature[z-hr]{$HR$}{High Resolution} \n\\nomenclature[z-gan]{$GAN$}{Generative Adversarial Networks}\n\\nomenclature[z-dcgan]{$DCGAN$}{Deep Convolutional Generative Adversarial Networks} \n"
  },
  {
    "path": "Chapter2/chapter2.tex",
    "content": "\n%!TEX root = ../thesis.tex\n%*******************************************************************************\n%****************************** Second Chapter *********************************\n%*******************************************************************************\n\n\\newcommand{\\ba}{\\mathbf{a}}\n\\newcommand{\\bb}{\\mathbf{b}}\n\\newcommand{\\bd}{\\mathbf{d}}\n\\newcommand{\\bw}{\\mathbf{w}}\n\\newcommand{\\bx}{\\mathbf{x}}\n\\newcommand{\\by}{\\mathbf{y}}\n\\newcommand{\\Exp}[2]{\\mathbb{E}_{#1}\\left[#2\\right]}\n\\newcommand{\\Var}[2]{\\text{Var}_{#1}\\left[#2\\right]}\n\\newcommand{\\Cov}[2]{\\text{Cov}_{#1}\\left[#2\\right]}\n\\newcommand{\\Corr}[2]{\\text{Corr}_{#1}\\left[#2\\right]}\n\n\\newcommand{\\bA}{\\mathbf{A}}\n\\newcommand{\\bB}{\\mathbf{B}}\n\\newcommand{\\bV}{\\mathbf{V}}\n\\newcommand{\\bW}{\\mathbf{W}}\n\\newcommand{\\bv}{\\mathbf{v}}\n\n\\newcommand{\\bphi}{\\mathbf{\\phi}}\n\\newcommand{\\bt}{\\mathbf{\\theta}}\n\\newcommand{\\bdelta}{\\mathbf{\\delta}}\n\\newcommand{\\beps}{\\mathbf{\\epsilon}}\n\\newcommand{\\q}{q_\\mathbf{\\phi}}\n\\newcommand{\\bmu}{\\mathbf{\\mu}}\n\\newcommand{\\bsigma}{\\mathbf{\\sigma}}\n\\newcommand{\\bSigma}{\\mathbf{\\Sigma}}\n\n\\newcommand{\\D}{\\mathcal{D}}\n\n\\newcommand{\\eqnr}{\\addtocounter{equation}{1}\\tag{\\theequation}}\n\n\\chapter{Background}\n\n\\ifpdf\n    \\graphicspath{{Chapter2/Figs/Raster/}{Chapter2/Figs/PDF/}{Chapter2/Figs/}}\n\\else\n    \\graphicspath{{Chapter2/Figs/Vector/}{Chapter2/Figs/}}\n\\fi\n\n\n\\fbox{\n    \\parbox{\\textwidth}\n    {\n       Chapter Overview\n        \\begin{itemize}\n            \\item Neural Networks and Convolutional Neural Networks.\n            \\item Concepts overview of Variational Inference, and local reparameterization trick in Bayesian Neural Network.\n            \\item Backpropagation in Bayesian Networks using Bayes by Backprop.\n            \\item Estimation of Uncertainties in a network.\n            \\item Pruning a network to reduce the number of overall parameters without affecting its performance.\n        \\end{itemize}\n    }\n}\n\n\\pagebreak\n\n\\section{Neural Networks}\n\\subsection{Brain Analogies}\n\nA perceptron is conceived as a mathematical model of how the neurons function in our brain by a famous psychologist Rosenblatt. According to Rosenblatt, a neuron  takes a set of binary inputs (nearby neurons), multiplies each input by a continuous-valued weight (the synapse strength to each nearby neuron), and thresholds the sum of these weighted inputs to output a 1 if the sum is big enough and otherwise a 0 (the same way neurons either fire or do not fire).\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.28\\textheight]{Chapter2/Figs/NeuralNetwork.png}\n\\label{fig:Neural_Network}\n\\caption{Biologically inspired Neural Network \\cite{karparthy}}\n\\end{center}\n\\end{figure}\n\n\\subsection{Neural Network}\n\nInspired by the biological nervous system, the structure of an Artificial Neural Network (ANN) was developed to process information similar to how brain process information. A large number of highly interconnected processing elements (neurons) working together makes a Neural Network solve complex problems. Just like humans learn by example, so does a Neural Network. Learning in biological systems involves adjustments to the synaptic connections which is similar to weight updates in a Neural Network. \n\nA Neural Network consists of three layers: input layer to feed the data to the model to learn representation, hidden layer that learns the representation and the output layer that outputs the results or predictions. Neural Networks can be thought of an end to end system that finds patterns in data which are too complex to be recognized by a human to teach to a machine. \n\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.58\\textheight]{Chapter2/Figs/MLP.pdf}\n\\label{fig:Two Layered Neural_Network}\n\\caption{Neural Network with two hidden layers}\n\\end{center}\n\\end{figure}\n\n\n\\subsection{Convolutional Neural Network}\n\nHubel and Wiesel in their hierarchy model mentioned a neural network to have a hierarchy structure in the visual cortex. LGB (lateral geniculate body) forms the simple cells that form the complex cells which form the lower order hypercomplex cells that finally form the higher order hypercomplex cells.\nAlso, the network between the lower order hypercomplex cells and the higher order hypercomplex cells are structurally similar to the network between simple cells and the complex cells. In this hierarchy, a cell in a higher stage generally has a tendency to respond selectively to a more complicated feature of the stimulus pattern, and the cell at the lower stage responds to simpler features. Also, higher stage cells possess a larger receptive field and are more insensitive to the shift in the position of the stimulus pattern.\n\nSimilar to a hierarchy model, a neural network starting layers learns simpler features like edges and corners and subsequent layers learn complex features like colours, textures and so on. Also, higher neural units possess a larger receptive field which builds over the initial layers. However, unlike in multilayer perceptron where all neurons from one layer are connected with all the neurons in the next layer, weight sharing is the main idea behind a convolutional neural network. \nExample: instead of each neuron having a different weight for each pixel of the input image (28*28 weights), the neurons only have a small set of weights (5*5) that is applied to a whole bunch of small subsets of the image of the same size. Layers past the first layer work in a similar way by taking in the ‘local’ features found in the previously hidden layer rather than pixel images, and successively see larger portions of the image since they are combining information about increasingly larger subsets of the image. Finally, the final layer makes the correct prediction for the output class.\n\nThe reason for why this is helpful is intuitive if not mathematically clear: without such constraints, the network would have to learn the same simple things (such as detecting edges, corners, etc) a whole bunch of times for each portion of the image. But with the constraint there, only one neuron would need to learn each simple feature - and with far fewer weights overall, it could do so much faster! Moreover, since the pixel-exact locations of such features do not matter the neuron could basically skip neighbouring subsets of the image - subsampling, now known as a type of pooling - when applying the weights, further reducing the training time. The addition of these two types of layers - convolutional and pooling layers - are the primary distinctions of Convolutional Neural Nets (CNNs/ConvNets) from plain old neural nets.\n\n\n\\section{Probabilistic Machine Learning}\n\\subsection{Variational Inference}\n\nWe define a function $y = f(\\mathbf{x})$ that estimates the given inputs $\\{ x_1, \\hdots, x_N \\}$ and their corresponding outputs $\\{y_1, \\hdots, y_N\\}$ and produces an predictive output. Using Bayesian inference, a prior distribution is used over the space of functions $p(f)$. This distribution represents our prior belief as to which functions are likely to have generated our data. \n\nA likelihood is defined as $p(Y | f, X)$ to capture the process in which given a function observation is generated. We use the Bayes rule to find the posterior distribution given our dataset: $p(f | X, Y)$.\n\nThe new output can be predicted for a new input point $x^*$ by integrating over all possible functions $f$,\n\\begin{align} \\label{eq:post}\n%p(\\f | \\X, \\Y) \\propto p(\\Y | \\X, \\f) p(\\f).\np(y^* | x^*, X, Y) = \\int p(y^* | f^*) p(f^* | x^*, X, Y) df^*\n\\end{align}\n\nThe equation \\eqref{eq:post} is intractable due to the integration sign. We can approximate it by taking a finite set of random variables $w$ and conditioning the model on it. However, it is based on a modelling assumption that the model depends on these variables alone, and making them into sufficient statistics in our approximate model.\n\nThe predictive distribution for a new input point $x^*$ is then given by \n$$\np(y^* | x^*, X, Y) = \\int p(y^* | f^*) p(f^* | x^*, w) p(w | X, Y) df^* d w.\n$$\n\nHowever, the distribution $p(w | X, Y)$ still remains intractable and we need to approximate it with a variational distribution $q(w)$, that can be computed. The approximate distribution needs to be as close as possible to the posterior distribution obtained from the original model. We thus minimise the Kullback--Leibler (KL) divergence, intuitively a measure of similarity between two distributions: $KL(q(w) ~||~ p(w | X, Y))$,\nresulting in the approximate predictive distribution \n\\begin{align} \\label{eq:predictive}\nq(y^* | x^*) = \\int p(y^* | f^*) p(f^* | x^*, w) q(w)  df^* dw.\n\\end{align}\n\nMinimising the Kullback--Leibler divergence is equivalent to maximising the \\textit{log evidence lower bound},\n\\begin{align}\nKL_{\\text{VI}} := \\int q(w) p(F | X, w) \\log p(Y | F) dF dw - KL(q(w) || p(w)) \\label{eq:L:VI}\n\\end{align}\nwith respect to the variational parameters defining $q(w)$. This is known as \\textit{variational inference}, a standard technique in Bayesian modelling.\n\nMaximizing the KL divergence between the posterior and the prior over $w$ will result in a variational distribution that learns a good representation from the data (obtained from log likelihood) and is closer to the prior distribution. In other words, it can prevent overfitting. \n\n\n\\subsection{Local  Reparametrisation  Trick}\nThe ability to rewrite statistical problems in an equivalent but different form, to reparameterise them, is one of the most general-purpose tools used in mathematical statistics. The type of \\textit{reparameterization} when the global uncertainty in the weights is translated into a form of local uncertainty which is independent across examples is known as the \\emph{local reparameterization trick}. \nAn alternative estimator is deployed for which $\\Cov{}{L_{i},L_{j}} = 0$, so that the variance of the stochastic gradients scales as $1/M$.\nThe new estimator is made computationally efficient by sampling the intermediate variables and not sampling $\\beps$ directly, but only  $f(\\beps)$ through which $\\beps$ influences $L_\\D^\\text{SGVB}(\\bphi)$. Hence, the source of global noise can be translated to local noise ($\\beps \\rightarrow f(\\beps)$), a local reparameterization can be applied so as to obtain a statistically efficient gradient estimator.\n\nThe technique can be explained through a simple example: We consider an input($X$) of random uniform function ranging from -1 to +1 and an output($Y$) as a random normal distribution around mean $X$ and standard deviation $\\delta$. The Mean Squared Loss would be defined as \n$(Y-X)^2$. The problem here is during the backpropagation from the random normal distribution function. As we are trying to propagate through a stochastic node we reparameterize by adding $X$ to the random normal function output and multiplying by $\\delta$.\nThe movement of parameters out of the normal distribution does not change the behaviour of the model. \n\n\n\\section{Uncertainties in Bayesian Learning}\n\n\nUncertainties in a network is a measure of how certain the model is with its prediction. In Bayesian modelling, there are two main types of uncertainty one can model \\citep{Kiureghian}: \\textit{Aleatoric} uncertainty and \\textit{Epistemic} uncertainty. \n\n\\textit{Aleatoric} uncertainty measures the noise inherent in the observations. This type of uncertainty is present in the data collection method like the sensor noise or motion noise which is uniform along the dataset. This cannot be reduced if more data is collected. \\textit{Epistemic} uncertainty, on the other hand, represents the uncertainty caused by the model. This uncertainty can be reduced given more data and is often referred to as \\textit{model uncertainty}.  Aleatoric uncertainty can further be categorized into \\textit{homoscedastic} uncertainty, uncertainty which stays constant for different inputs, and \\textit{heteroscedastic} uncertainty. Heteroscedastic uncertainty depends on the inputs to the model, with some inputs potentially having more noisy outputs than others. Heteroscedastic uncertainty is in particular important so that model prevents from outputting very confident decisions.\n\nCurrent work measures uncertainties by placing a probability distributions over either the model parameters or model outputs. Epistemic uncertainty is modelled by placing a prior distribution over a model's weights and then trying to capture how much these weights vary given some data. Aleatoric uncertainty, on the other hand, is modelled by placing a distribution over the output of the model.\n\n\\subsubsection{Sources of Uncertainities}\n\nThe following can be the source of uncertainty as mentioned by Kiureghian \\cite{Kiureghian}:\n\n\\begin{enumerate}\n    \\item Uncertainty inherent in the basic random variables $X$, such as the uncertainty inherent in material property constants and load values, which can be directly measured.\n    \\item Uncertain model error resulting from the selection of the form of the probabilistic sub-model $f_{X}(x,H_{f})$ used to describe the distribution of basic variables.\n    \\item  Uncertain modeling errors resulting from selection of the physical sub-models $g_{i}(x,H_{g}), i = 1,2,...,m,$ used to describe the derived variables.\n    \\item  Statistical uncertainty in the estimation of the parameters $H_f$ of the probabilistic sub-model.\n    \\item  Statistical uncertainty in the estimation of the parameters $H_g$ of the physical sub-models.\n    \\item  Uncertain errors involved in measuring of observations, based on which the parameters $H_f$ and $H_g$  are estimated. These include errors involved in indirect measurement, e.g., the measurement of a quantity through a proxy, as in non-destructive testing of material strength.\n    \\item Uncertainty modelled by the random variables $Y$ corresponding to the derived variables $y$, which may include, in addition to all the above uncertainties, uncertain errors resulting from computational errors, numerical approximations or truncations. For example, the computation of load effects in a nonlinear structure by a finite element procedure employs iterative calculations, which invariably involve convergence tolerances and truncation errors.\n\\end{enumerate} \n\n\\section{Backpropagation}\n\nBackpropagation in a Neural Networks was proposed by Rumelhart \\cite{Rumelhart} in 1986 and it is the most commonly used method for training neural networks. Backpropagation is a technique to compute the gradient of the loss in terms of the network weights. It operates in two phases: firstly, the input features through the network propagates in the forward direction to compute the function output and thereby the loss associated with the parameters. Secondly, the derivatives of the training loss with respect to the weights\nare propagated back from the output layer towards the input layers.\nThese computed derivatives are further used to update the weights of the network. This is a continuous process and updating of the weight occurs continuously over every iteration. \n\nDespite the popularity of backpropagation, there are many hyperparameters in backpropagation based stochastic optimization that requires specific tuning, e.g., learning rate, momentum, weight decay, etc. The time required for finding the optimal values is proportional to the data size. For a network trained with backpropagation, only point estimates of the weights are achieved in the network. As a result, these networks make overconfident predictions and do not account for uncertainty in the parameters. Lack of uncertainty measure makes the network prone to overfitting and a need for regularization.\n\nA Bayesian approach to Neural Networks provides the shortcomings with the backpropagation approach \\cite{mackay1996hyperparameters} as Bayesian methods naturally account for uncertainty in parameter estimates and can propagate this uncertainty into predictions.\nAlso, averaging over parameter values instead of just choosing single point estimates makes the model robust to overfitting. \n\nSevreal approaches has been proposed in the past for learning in Bayesian Networks: Laplace approximation \\cite{Mackay1991APB}, MC Dropout \\cite{gal2015bayesian},and Variational Inference \\cite{hinton1993keeping} \\cite{graves2011practical} \\cite{blundell2015weight}. We used Bayes by Backprop \\cite{blundell2015weight} for our work and is explained next.\n\n\\subsection{Bayes by Backprop}\n\\textit{Bayes by Backprop} \\cite{graves2011practical, blundell2015weight} is a variational inference method to learn the posterior distribution on the weights $w \\sim q_{\\theta}(w|\\mathcal{D})$ of a neural network from which weights $w$ can be sampled in backpropagation. \nIt regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood.\n\nSince the true posterior is typically intractable, an approximate distribution $q_{\\theta}(w|\\mathcal{D})$ is defined that is aimed to be as similar as possible to the true posterior $p(w|\\mathcal{D})$, measured by the Kullback-Leibler (KL) divergence \\cite{kullback1951information}. Hence, we define the optimal parameters $\\theta^{opt}$ as\n\\begin{equation}\n    \\begin{aligned} \\label{KL}\n        \\theta^{opt}&=\\underset{\\theta}{\\text{arg min}}\\ \\text{KL} \\ [q_{\\theta}(w|\\mathcal{D})\\|p(w|\\mathcal{D})] \\\\\n        &=\\underset{\\theta}{\\text{arg min}}\\ \\text{KL} \\ [q_{\\theta}(w|\\mathcal{D})\\|p(w)] \\\\ & -\\mathbb{E}_{q(w|\\theta)}[\\log p(\\mathcal{D}|w)]+\\log p(\\mathcal{D})\n    \\end{aligned}\n\\end{equation}\n\nwhere\n\\begin{equation}\n    \\text{KL} \\ [q_{\\theta}(w|\\mathcal{D})\\|p(w)]= \\int q_{\\theta}(w|\\mathcal{D})\\log\\frac{q_{\\theta}(w|\\mathcal{D})}{p(w)}dw .\n\\end{equation}\nThis derivation forms an optimisation problem with a resulting cost function widely known as \\textit{variational free energy} \\cite{neal1998view,yedidia2005constructing,friston2007variational} which is built upon two terms: the former, $\\text{KL} \\ [q_{\\theta}(w|\\mathcal{D})\\|p(w)]$, is dependent on the definition of the prior $p(w)$, thus called complexity cost, whereas the latter, $\\mathbb{E}_{q(w|\\theta)}[\\log p(\\mathcal{D}|w)]$, is dependent on the data $p(\\mathcal{D}|w)$, thus called likelihood cost. \nThe term $\\log p(\\mathcal{D})$ can be omitted in the optimisation because it is constant.\n\\newline Since the KL-divergence is also intractable to compute exactly, we follow a stochastic variational method \\cite{graves2011practical,blundell2015weight}.\nWe sample the weights $w$ from the variational distribution $q_{\\theta}(w|\\mathcal{D})$ since it is much more probable to draw samples which are appropriate for numerical methods from the variational posterior $q_{\\theta}(w|\\mathcal{D})$ than from the true posterior $p(w|\\mathcal{D})$. Consequently, we arrive at the tractable cost function \\eqref{cost} which is aimed to be optimized, i.e. minimised w.r.t. $\\theta$, during training:\n\\begin{equation} \\label{cost}\n    \\mathcal{F}(\\mathcal{D}, \\theta)\\approx \\sum_{i=1}^n \\log q_{\\theta}(w^{(i)}|\\mathcal{D})-\\log p(w^{(i)})-\\log p(\\mathcal{D}|w^{(i)})\n\\end{equation}\n%\nwhere $n$ is the number of draws.\n\\newline We sample $w^{(i)}$ from $q_{\\theta}(w|\\mathcal{D})$. The uncertainty afforded by \\textit{Bayes by Backprop} trained neural networks has been used successfully for training feedforward neural networks in both supervised and reinforcement learning environments \\cite{blundell2015weight,lipton2016efficient,houthooft2016curiosity}, for training recurrent neural networks \\cite{fortunato2017bayesian}, but has not been applied to convolutional neural networks to-date.\n\n\\section{Model Weights Pruning}\n\nModel pruning reduces the sparsity in a deep neural network's\nvarious connection matrices, thereby reducing the number of valued parameters in the model. The whole idea of model pruning is to reduce the number of parameters without much loss in the accuracy of the model. This reduces the use of a large parameterized model with regularization and promotes the use of dense connected smaller models. Some recent work suggests \\cite{DBLP:journals/corr/HanMD15} \\cite{DBLP:journals/corr/NarangDSE17} that the network can achieve a sizable reduction in model size, yet achieving comparable accuracy. \nModel pruning possesses several advantages in terms of reduction in computational cost, inference time and in energy-efficiency. The resulting pruned model typically has sparse connection matrices, so\nefficient inference using these sparse models requires purpose-built hardware capable of loading sparse matrices and/or performing sparse matrix-vector operations. Thus the overall memory usage is reduced with the new pruned model. \n\n\nThere are several ways of achieving the pruned model, the most popular one is to map the low contributing weights to zero and reducing the number of overall non-zero valued weights. This can be achieved by training a large sparse model and pruning it further which makes it comparable to training a small dense model.\n\nAssigning weights zero to most features and non-zero weights to only important features can be formalized by applying the $L_0$ norm, where $L_0 = ||\\theta||_0 = \\sum{_j} \\delta (\\theta_j \\neq 0)$, and it applies a constant penalty to all non-zero weights. \n$L_0$ norm can be thought of a feature selector norm that only assigns non-zero values to feature that are important. However, the $L_0$ norm is non-convex and hence, non-differentiable that makes it a NP-hard problem and can be only efficiently solved when $P = NP$.\nThe alternative that we use in our work is the $L_1$ norm, which is equal to the sum of the absolute weight values, $||\\theta||_1 = \\sum_j |\\theta_j|$. $L_1$ norm is convex and hence differentiable and can be used as an approximation to $L_0$ norm  \\cite{tibshirani1996regression}. $L_1$ norm works as a sparsity inducing regularizer by making large number of coefficients equal to zero, working as a great feature selector in our case. Only thing to keep in mind is that the $L_1$ norm do not have a gradient at $\\theta_j = 0$ and we need to keep that in mind. \nPruning away the less salient features to zero has been used in this thesis and is explained in details in Chapter 4. \n\n\n"
  },
  {
    "path": "Chapter3/chapter3.tex",
    "content": "%!TEX root = ../thesis.tex\n%*******************************************************************************\n%****************************** Third Chapter **********************************\n%*******************************************************************************\n\\chapter{Related Work}\n\n% **************************** Define Graphics Path **************************\n\\ifpdf\n    \\graphicspath{{Chapter3/Figs/Raster/}{Chapter3/Figs/PDF/}{Chapter3/Figs/}}\n\\else\n    \\graphicspath{{Chapter3/Figs/Vector/}{Chapter3/Figs/}}\n\\fi\n\n\\fbox{\n    \\parbox{\\textwidth}\n    {\n        Chapter Overview\n        \\begin{itemize}\n            \\item How Bayesian Methods were applied to Neural Networks for the intractable true posterior distribution.\n            \\item Various ways of training Neural Networks posterior probability distributions: Laplace approximations, Monte Carlo and Variational Inference.\n            \\item Proposals on Dropout and Gaussian Dropout as Variational Inference schemes.\n            \\item Work done in the past for uncertainty estimation in Neural Network.\n            \\item Ways to reduce the number of parameters in a model.\n        \\end{itemize}\n    }\n}\n\n\\pagebreak\n\n\\section{Related Work}\n\n\\subsection{Bayesian Training}\nApplying Bayesian methods to neural networks has been studied in the past with various approximation methods for the intractable true posterior probability distribution $p(w|\\mathcal{D})$. Buntine and Weigend \\cite{buntine1991bayesian} started to propose various \\textit{maximum-a-posteriori} (MAP) schemes for neural networks. They were also the first who suggested second order derivatives in the prior probability distribution $p(w)$ to encourage smoothness of the resulting approximate posterior probability distribution.\nIn subsequent work by Hinton and Van Camp \\cite{hinton1993keeping}, the first variational methods were proposed which naturally served as a regularizer in neural networks. He also mentioned that the amount of information in weight can be controlled by adding Gaussian noise. When optimizing the trade-off between the expected error and the information in the weights, the noise level can be adapted during learning.\n\n\nHochreiter and Schmidhuber \\cite{hochreiter1995simplifying} suggested taking an information theory perspective into account and utilising a minimum description length (MDL) loss. This penalises non-robust weights by means of an approximate penalty based upon perturbations of the weights on the outputs.\nDenker and LeCun \\cite{denker1991transforming}, and MacKay \\cite{mackay1995probable} investigated the posterior probability distributions of neural networks and treated the search in the model space (the space of architectures, weight decay, regularizers, etc..) as an inference problem and tried to solve it using Laplace approximations.\nAs a response to the limitations of Laplace approximations, Neal \\cite{neal2012bayesian} investigated the use of hybrid Monte Carlo for training neural networks, although it has so far been difficult to apply these to the large sizes of neural networks built in modern applications. \nAlso, these approaches lacked scalability in terms of both the network architecture and the data size. \n\n\nMore recently, Graves \\cite{graves2011practical} derived a variational inference scheme for neural networks and Blundell et al. \\cite{blundell2015weight} extended this with an update for the variance that is unbiased and simpler to compute. Graves \\cite{graves2016stochastic} derives a similar algorithm in the case of a mixture posterior probability distribution. \nA more scalable solution based on expectation propagation was proposed by Soudry \\cite{Soudry:NIPS2014_5269} in 2014. While this method works for networks with binary weights, its extension to continuous weights is unsatisfying as it does not produce estimates of posterior variance.\n\n\\newline Several authors have claimed that Dropout \\cite{srivastava2014dropout} and Gaussian Dropout \\cite{wang2013fast} can be viewed as approximate variational inference schemes \\cite{gal2015bayesian, kingma2015variational}. We compare our results to Gal's \\& Ghahramani's \\cite{gal2015bayesian} and discuss the methodological differences in detail.\n\n\n\\subsection{Uncertainty Estimation}\n\nNeural Networks can predict uncertainty when Bayesian methods are introduced in it. An attempt to model uncertainty has been studied from 1990s \\cite{neal2012bayesian} but has not been applied successfully until 2015. Gal and Ghahramani \\cite{Gal2015Dropout} in 2015 provided a theoretical framework for modelling Bayesian uncertainty. Gal and Ghahramani \\cite{gal2015bayesian} obtained the uncertainty estimates by casting dropout training in conventional deep networks as a Bayesian approximation of a Gaussian Process. They showed that any network trained with dropout is an approximate Bayesian model, and uncertainty estimates can be obtained by computing the variance on multiple predictions with different dropout masks.\n\n\\subsection{Model Pruning}\n\nSome early work in the model pruning domain used a second-order Taylor approximation of the increase in the loss function of the network when weight is set to zero \\cite{lecun1990optimal}. A diagonal Hessian approximation was used to calculate the saliency for each parameter \\cite{lecun1990optimal} and the low-saliency parameters were pruned from the network and the network was retrained.\n\n\nNarang \\cite{DBLP:journals/corr/NarangDSE17} showed that a pruned RNN and GRU model performed better for the task of speech recognition compared to a dense network of the original size. This result is very similar to the results obtained in our case where a pruned model achieved better results than a normal network. However, no comparisons can be drawn as the model architecture (CNN vs RNN) used and the tasks (Computer Vision vs Speech Recognition) are completely different.  Narang \\cite{DBLP:journals/corr/NarangDSE17} in his work introduced a gradual pruning scheme based on pruning all the weights in a layer\nless than some threshold (manually chosen) which is linear with some\nslope in phase 1 and linear with some slope in phase 2 followed by\nnormal training. However, we reduced the number of filters to half for one case and in the other case, we induced a sparsity-based on L1 regularization to remove the less contributing weights and reduced the parameters. \n\n\nOther similar work \\cite{DBLP:journals/corr/AnwarHS15, DBLP:journals/corr/LebedevGROL14, DBLP:journals/corr/ChangpinyoSZ17} to our work that reduces or removed the redundant connections or induces sparsity are motivated by the desire to speed up computation.\nThe techniques used are highly convolutional layer dependent and is not applicable to other architectures like Recurrent Neural Networks. \nOne another interesting method of pruning is to represent each parameter with a smaller floating point number like 16-bits instead of 64 bits. This way there is a speed up in the training and inference time and the model is less computationally expensive. \n\nAnother viewpoint for model compression was presented by Gong \\cite{DBLP:journals/corr/GongLYB14}. He proposed vector quantization to achieve different compression ratios and different accuracies and depending on the use case, the compression and accuracies can be chosen. However, it requires a different hardware architecture to support the inference at runtime. Besides quantization, other potentially complementary approaches to reducing model size include low-rank matrix factorization \\cite{denton2014exploiting, DBLP:journals/corr/LebedevGROL14} and group sparsity regularization to arrive at an optimal layer size \\cite{DBLP:conf/nips/AlvarezS16}.\n\n"
  },
  {
    "path": "Chapter4/chapter4.tex",
    "content": "\n%!TEX root = ../thesis.tex\n%*******************************************************************************\n%****************************** Second Chapter *********************************\n%*******************************************************************************\n\n\\chapter{Concept}\n\n\\ifpdf\n    \\graphicspath{{Chapter2/Figs/Raster/}{Chapter2/Figs/PDF/}{Chapter2/Figs/}}\n\\else\n    \\graphicspath{{Chapter2/Figs/Vector/}{Chapter2/Figs/}}\n\\fi\n\n\\fbox{\n    \\parbox{\\textwidth}\n    {\n        Chapter Overview\n        \\begin{itemize}\n            \\item Bayesian \\acp{cnn} with Variational Inference based on Bayes by Backprop.\n            \\item Bayesian convolutional operations with mean and variance\n            \\item Local reparameterization trick for Bayesian \\acp{cnn}.\n            \\item Uncertainty estimation in a Bayesian network.\n            \\item Using L1 norm for reducing the number of parameters in a Bayesian network.\n        \\end{itemize}\n    }\n}\n\n\\pagebreak\n\n\\section{Bayesian Convolutional Neural Networks with Variational Inference}\nIn this section, we explain our algorithm of building a \\ac{cnn} with probability distributions over its weights in each filter, as seen in Figure \\ref{fig:filter_scalar}, and apply variational inference, i.e. \\textit{Bayes by Backprop}, to compute the intractable true posterior probability distribution, as described in the last Chapter. Notably, a fully Bayesian perspective on a \\ac{cnn} is for most \\ac{cnn} architectures not accomplished by merely placing probability distributions over weights in convolutional layers; it also requires probability distributions over weights in fully-connected layers (see Figure \\ref{fig:CNNwithdist_grey}). \n%\n\\begin{figure}[H] \n\\centering\n\\begin{minipage}{.4\\textwidth}\n\\centering\n\\includegraphics[width=\\linewidth]{Chapter4/Figs/filter_scalars.png}\n\\end{minipage}\n%\n\\begin{minipage}{.4\\textwidth}\n\\centering\n\\includegraphics[width=\\linewidth]{Chapter4/Figs/CNNwithdist.png}\n\\end{minipage}\n\\caption{Input image with exemplary pixel values, filters, and corresponding output with point-estimates (top) and probability distributions (bottom) over weights.\\cite{shridhar2018bayesian}}\n\\label{fig:filter_scalar}\n\\end{figure} \n%\n\\begin{figure}[b!] \n\\begin{center}\n\\includegraphics[width=\\linewidth]{Chapter4/Figs/CNNwithdist_grey.png}\n\\caption{Fully Bayesian perspective of an exemplary CNN. Weights in filters of convolutional layers, and weights in fully-connected layers have the form of a probability distribution. \\cite{shridhar2018bayesian}}\n\\label{fig:CNNwithdist_grey}\n\\end{center}\n\\end{figure} \n%\n\\subsection{Local Reparameterization Trick for Convolutional Layers}\nWe utilise the local reparameterization trick \\cite{kingma2015variational} and apply it to \\acp{cnn}. Following \\cite{kingma2015variational,neklyudov2018variance}, we do not sample the weights $w$, but we sample instead layer activations $b$ due to its consequent computational acceleration. The variational posterior probability distribution $q_{\\theta}(w_{ijhw}|\\mathcal{D})=\\mathcal{N}(\\mu_{ijhw},\\alpha_{ijhw}\\mu^2_{ijhw})$ (where $i$ and $j$ are the input, respectively output layers, $h$ and $w$ the height, respectively width of any given filter) allows to implement the local reparamerization trick in convolutional layers. This results in the subsequent equation for convolutional layer activations $b$:\n\\begin{equation}\n    b_j=A_i\\ast \\mu_i+\\epsilon_j\\odot \\sqrt{A^2_i\\ast (\\alpha_i\\odot \\mu^2_i)}\n\\end{equation}\nwhere $\\epsilon_j \\sim \\mathcal{N}(0,1)$, $A_i$ is the receptive field, $\\ast$ signalises the convolutional operation, and $\\odot$ the component-wise multiplication.\n\n\\subsection{Applying two Sequential Convolutional Operations (Mean and Variance)}\nThe crux of equipping a \\ac{cnn} with probability distributions over weights instead of single point-estimates and being able to update the variational posterior probability distribution $q_{\\theta}(w|\\mathcal{D})$ by backpropagation lies in applying \\textit{two} convolutional operations whereas filters with single point-estimates apply \\textit{one}. As explained in the last chapter, we deploy the local reparametrization trick and sample from the output $b$. Since the output $b$ is a function of mean $\\mu_{ijwh}$ and variance $\\alpha_{ijhw}\\mu^2_{ijhw}$ among others, we are then able to compute the two variables determining a Gaussian probability distribution, namely mean $\\mu_{ijhw}$ and variance $\\alpha_{ijhw}\\mu^2_{ijhw}$, separately. \n\\newline We do this in two convolutional operations: in the first, we treat the output $b$ as an output of a \\ac{cnn} updated by frequentist inference. We optimize with Adam \\cite{kingma2014adam} towards a single point-estimate which makes the validation accuracy of classifications increasing. We interpret this single point-estimate as the mean $\\mu_{ijwh}$ of the variational posterior probability distributions $q_{\\theta}(w|\\mathcal{D})$. In the second convolutional operation, we learn the variance $\\alpha_{ijhw}\\mu^2_{ijhw}$. As this formulation of the variance includes the mean $\\mu_{ijwh}$, only $\\alpha_{ijhw}$ needs to be learned in the second convolutional operation \\cite{molchanov2017variational}. In this way, we ensure that only one parameter is updated per convolutional operation, exactly how it would have been with a \\ac{cnn} updated by frequentist inference. \n\\newline In other words, while we learn in the first convolutional operation the MAP of the variational posterior probability distribution $q_{\\theta}(w|\\mathcal{D})$, we observe in the second convolutional operation how much values for weights $w$ deviate from this MAP. This procedure is repeated in the fully-connected layers. In addition, to accelerate computation, to ensure a positive non-zero variance $\\alpha_{ijhw}\\mu^2_{ijhw}$, and to enhance accuracy, we learn $\\log \\alpha_{ijhw}$ and use the \\textit{Softplus} activation function as further described in the Experiments section.\n%\n\\section{Uncertainty Estimation in CNN}\nIn classification tasks, we are interested in the predictive distribution $p_{\\mathcal{D}}(y^*|x^*)$, where $x^*$ is an unseen data example and $y^*$ its predicted class. For a Bayesian neural network, this quantity is given by:\n\\begin{align}\np_{ \\mathcal{D}}(y^*|x^*) = \\int p_{w}(y^*|x^*) \\ p_{\\mathcal{D}}(w) \\ dw\n\\end{align}\n%\nIn \\textit{Bayes by Backprop}, Gaussian distributions $q_{\\theta}(w|\\mathcal{D}) \\sim \\mathcal{N}(w|\\mu, \\sigma^2)$, where $\\theta = \\{ \\mu, \\sigma \\}$ are learned with some dataset $\\mathcal{D} = \\{ x_{i}, y_{i} \\}_{i=1}^{n}$ as we explained previously. Due to the discrete and finite nature of most classification tasks, the predictive distribution is commonly assumed to be a categorical. Incorporating this aspect into the predictive distribution gives us\n\\begin{align}\np_{\\mathcal{D}}(y^*|x^*)& = \\int \\text{Cat}(y^*|f_w(x^*)) \\mathcal{N}(w|\\mu, \\sigma^2) \\ dw\\\\\n&=  \\int \\prod_{c=1}^{C} f(x_{c}^*|w)^{y_{c}^*} \\frac{1}{\\sqrt{2\\pi \\sigma^2}} e^{-\\frac{(w - \\mu)^2}{2\\sigma^2}} \\ dw \n\\end{align}\nwhere $C$ is the total number of classes and $\\sum_c f(x_{c}^*|w) = 1$.\n\\newline As there is no closed-form solution due to the lack of conjugacy between categorical and Gaussian distributions, we cannot recover this distribution. However, we can construct an unbiased estimator of the expectation by sampling from $q_{\\theta}(w|\\mathcal{D})$:\n\\begin{align}\n\\mathbb{E}_{q}[p_{\\mathcal{D}}(y^*|x^*)] &= \\int q_{\\theta}(w|\\mathcal{D}) \\ p_w(y|x) \\ dw \\\\ & \\approx \\frac{1}{T}\\sum_{t=1}^{T} p_{w_t}(y^*|x^*)\n\\end{align}\nwhere $T$ is the pre-defined number of samples.\nThis estimator allows us to evaluate the uncertainty of our predictions by the definition of variance, hence called \\textit{predictive variance} and denoted as $\\text{Var}_q$:\n\\begin{align} \\label{variance}\n\\text{Var}_q\\big( p(y^*|x^*) \\big) = \\mathbb{E}_q[yy^T] - \\mathbb{E}_q[y]\\mathbb{E}_q[y]^T\n\\end{align}\nThis quantity can be decomposed into the aleatoric and epistemic uncertainty \\cite{kendall2017uncertainties,kwon2018uncertainty}. \n\\begin{equation}\n    \\begin{aligned}\n    \\text{Var}_q\\big( p(y^*|x^*) \\big) &= \\underbrace{\\frac{1}{T} \\sum_{t=1}^T \\text{diag}(\\hat{p}_t)-\\hat{p}_t \\ \\hat{p}_t^T}_\\text{aleatoric} \\\\ &+ \\underbrace{\\frac{1}{T}\\sum_{t=1}^T (\\hat{p}_t - \\Bar{p}) (\\hat{p}_t - \\Bar{p})^T}_\\text{epistemic}\n    \\end{aligned}\n\\end{equation}\nwhere $\\Bar{p} = \\frac{1}{T}\\sum_{t=1}^T \\hat{p}_t$ and $\\hat{p}_t = \\text{Softmax}\\big ( f_{w_{t}}(x^*) \\big )$.\n\n\\begin{figure}[H] \n\\centering\n\\begin{minipage}{.4\\textwidth}\n\\centering\n\\includegraphics[width=\\linewidth]{Chapter4/Figs/det.pdf}\n\\end{minipage}\n%\n\\begin{minipage}{.4\\textwidth}\n\\centering\n\\includegraphics[width=\\linewidth]{Chapter4/Figs/bbb.pdf}\n\\end{minipage}\n\\caption{Predictive distributions is estimated for a low-dimensional active learning task. The predictive distributions are visualized as mean and two standard deviations shaded. \\textcolor{blue}{$\\blacksquare$} shows the epistemic uncertainty and \\textcolor{red}{$\\blacksquare$} shows the aleatoric noise. Data points are shown in \\textcolor{grey}{$\\blacksquare$}.\n\\textbf{(Left)} A deterministic network conflates uncertainty as part of the noise and is overconfident outside of the data distribution.\n\\textbf{(Right)} A variational Bayesian neural network with standard normal prior represents uncertainty and noise separately but is overconfident outside of the training distribution as defined by \\cite{hafner2018reliable}}\n\\label{fig:filter_scalar}\n\\end{figure} \n\n\\newline It is of paramount importance that uncertainty is split into aleatoric and epistemic quantities since it allows the modeller to evaluate the room for improvements: while aleatoric uncertainty (also known as statistical uncertainty) is merely a measure for the variation of (\"noisy\") data, epistemic uncertainty is caused by the model. Hence, a modeller can see whether the quality of the data is low (i.e. high aleatoric uncertainty), or the model itself is the cause for poor performances (i.e. high epistemic uncertainty). The former cannot be improved by gathering more data, whereas the latter can be done so. \\cite{Kiureghian} \\cite{kendall2017uncertainties}.\n\n\\section{Model Pruning}\n\nModel pruning means the reduction in the model weights parameters to reduce the model overall non-zero weights, inference time and computation cost. In our work, a Bayesian Convolutional Network learns two weights, i.e: the mean and the variance compared to point estimate learning one single weight. This makes the overall number of parameters of a Bayesian Network twice as compared to the parameters of a point estimate similar architecture.\n\nTo make the Bayesian \\acp{cnn} parameters equivalent to point estimate architecture, the number of filters in the Bayesian architectures is reduced to half. This makes up for the double learned parameters (mean and variance) against one in point estimates and makes the overall parameters equal for both networks. \n\nAnother technique used is the usage of the \\textit{L1 normalization} on the learned weights of every layer.  By L1 norm, we make the vector of learned weight in various model layers very sparse, as most of its components become close to zero, and at the same time, the remaining non-zero components capture the most important features of the data. We put a threshold and make the weights to be zero if the value falls below the threshold. We only keep the non zero weights and this way the model number of parameters is reduced without affecting the overall performance of the model. \n"
  },
  {
    "path": "Chapter5/chapter5.tex",
    "content": "\\chapter{Empirical Analysis}\n\n\\fbox{\n    \\parbox{\\textwidth}\n    {\n        Chapter Overview\n        \\begin{itemize}\n            \\item Applying Bayesian \\acp{cnn} for the task of Image Recognition on MNIST, CIFAR-10, CIFAR-100 and STL-10 datasets.\n            \\item Comparison of results of Bayesian \\acp{cnn} with Normal \\ac{cnn} architectures on similar datasets.\n            \\item Regularization effect of Bayesian Network with dropouts.\n            \\item Distribution of mean and variance in Bayesian \\ac{cnn} over time. \n            \\item Parameters comparison before and after model pruning. \n        \\end{itemize}\n    }\n}\n\n\\pagebreak\n\n\\section{Experimentation Methodology} \\label{experiments}\n\n\\subsubsection{Activation Function}\n\nThe originally chosen activation functions in all architectures are \\textit{ReLU}, but we must introduce another, called \\textit{Softplus}, see \\eqref{softplus}, because of our method to apply two convolutional or fully-connected operations. As aforementioned, one of these is determining the mean $\\mu$, and the other the variance $\\alpha \\mu^2$. Specifically, we apply the \\textit{Softplus} function because we want to ensure that the variance $\\alpha \\mu^2$ never becomes zero. This would be equivalent to merely calculating the MAP, which can be interpreted as equivalent to a maximum likelihood estimation (MLE), which is further equivalent to utilising single point-estimates, hence frequentist inference. The \\textit{Softplus} activation function is a smooth approximation of \\textit{ReLU}. Although it is practically not influential, it has the subtle and analytically important advantage that it never becomes zero for $x \\rightarrow -\\infty$, whereas \\textit{ReLU} becomes zero for $x \\rightarrow -\\infty$.\n\\\\ \n\\begin{equation}\\label{softplus}\n     \\text{Softplus}(x) = \\frac{1}{\\beta} \\cdot \\log \\big ( 1 + \\exp(\\beta \\cdot x) \\big )\n\\end{equation}\n\\\\\nwhere $\\beta$ is by default set to $1$.\n\\newline All experiments are performed with the same hyper-parameters settings as stated in the Appendix.\n\n\\subsubsection{Network Architecture}\n\nFor all conducted experiments, we implement the foregoing description of Bayesian \\acp{cnn} with variational inference in LeNet-5 \\cite{lecun1998gradient} and AlexNet \\cite{krizhevsky2012imagenet}. The exact architecture specifications can be found in the Appendix and in our GitHub repository\\footnote{\\url{https://github.com/kumar-shridhar/PyTorch-BayesianCNN}}.\n\n\\subsubsection{Objective Function}\nTo learn the objective function, we use \\textit{Bayes by Backprop} \\cite{graves2011practical, blundell2015weight}, which is a variational inference method to learn the posterior distribution on the weights $w \\sim q_{\\theta}(w|\\mathcal{D})$ of a neural network from which weights $w$ can be sampled in backpropagation. \nIt regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood.\n\n We tackled the problem of intractability in Chapter 2 and consequently, we arrive at the tractable cost function \\eqref{cost} which is aimed to be optimized, i.e. minimised w.r.t. $\\theta$, during training:\n\\begin{equation} \\label{cost}\n    \\mathcal{F}(\\mathcal{D}, \\theta)\\approx \\sum_{i=1}^n \\log q_{\\theta}(w^{(i)}|\\mathcal{D})-\\log p(w^{(i)})-\\log p(\\mathcal{D}|w^{(i)})\n\\end{equation}\n%\nwhere $n$ is the number of draws.\n\nLet's break the Objective Function \\eqref{cost} and discuss in more details. \n\\subsubsection{Variational Posterior }\n\nThe first term in the equation \\eqref{cost} is the variational posterior. The variational posterior is taken as Gaussian distribution centred around mean $\\mu$ and variance as $\\sigma^2$. \n\n\\begin{equation}\n    q_{\\theta}(w^{(i)}|\\mathcal{D})= \\prod_{i} \\mathcal{N}(w_{i} | \\mu,\\sigma^2)\n\\end{equation}\n\nWe will take the log and the log posterior is defined as :\n\n\\begin{equation}\n    log(q_{\\theta}(w^{(i)}|\\mathcal{D}))= \\sum_{i}log \\mathcal{N}(w_{i} | \\mu,\\sigma^2)\n\\end{equation}\n\n\\subsubsection{Prior}\n\nThe second term in the equation \\eqref{cost} is the prior over the weights and we define the prior over the weights as a product of individual Gaussians :\n\n\\begin{equation}\n    p(w^{(i)})= \\prod_{i} \\mathcal{N}(w_{i} | 0,\\sigma_{p}^2)\n\\end{equation}\n\nWe will take the log and the log prior is defined as:\n\n\\begin{equation}\n    log (p(w^{(i)}))= \\sum_{i} log \\mathcal{N}(w_{i} | 0,\\sigma_{p}^2)\n\\end{equation}\n\n\\subsubsection{ Likelihood }\n\nThe final term of the equation \\eqref{cost} $\\log p(\\mathcal{D}|w^{(i)})$ is the likelihood term and is computed using the softmax function.\n\n\\subsubsection{Parameter Initialization}\n\nWe use a Gaussian distribution and we store mean and variance values instead of just one weight. The way mean $\\mu$ and variance $\\sigma$ is computed is defined in the previous chapter. Variance cannot be negative and it is ensured by using \\textit{softplus} as the activation function. We express variance $\\sigma$ as $\\sigma_{i}=softplus(\\rho_{i})$ where $\\rho$ is an unconstrained parameter. \\\\\n\nWe take the Gaussian distribution and initialize mean $\\mu$ as 0 and variance $\\sigma$ (and hence $\\rho$) randomly. We observed mean centred around 0 and a variance starting with a big number and gradually decreasing over time. A good initialization can also be to put a restriction on variance and initialize it small. However, it might be data dependent and a good method for variance initialization is still to be discovered. We perform gradient descent over $\\theta$ = ($\\mu$, $\\rho$), and individual weight $w_{i} \\sim \\mathcal{N} (w_{i} | \\mu_{i}, \\sigma_{i}$).  \n\n\\subsubsection{Optimizer}\n\nFor all our tasks, we take Adam optimizer \\cite{kingma2014adam} to optimize the parameters. We also perform the local reparameterization trick as mentioned in the previous section and take the gradient of the combined loss function with respect to the variational parameters ($\\mu$, $\\rho$).\n\n\\subsubsection{Model Pruning}\n\nWe take the weights of all the layers of the network, apply an L1 norm over it and for all the weights value as zero or below a defined threshold are removed and the model is pruned. \\\\ Also, since the Bayesian \\acp{cnn} has twice the number of parameters ($\\mu$, $\\sigma$) compared to a frequentist network (only 1 weight), we reduce the size of our network to half (AlexNet and LeNet- 5) by reducing the number of filters to half. The architecture used is mentioned in the Appendix.\n\n\\textit{Please note that it can be argued that reducing the number of filters to be half is a method for pruning or not. It can be seen as a method that reduces the number of overall parameters and hence can be thought of a pruning method in some sense. However, it is a subject to argument.} \n\n\\section{Case Study 1: Small Datasets (MNIST, CIFAR-10)}\n\nWe train the networks with the MNIST dataset of handwritten digits \\cite{lecun1998gradient}, and CIFAR-10 dataset \\cite{krizhevsky2009learning} since these datasets serve widely as benchmarks for \\acp{cnn}' performances. \n\n\\subsection{Datasets}\n\\newline\n\\subsubsection{MNIST}\nThe MNIST database \\cite{lecun-mnisthandwrittendigit-2010} of handwritten digits have a training set of 60,000 examples and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centred in a fixed-size image of 28 by 28 pixels. Each image is grayscaled and is labelled with its corresponding class that ranges from zero to nine.\n\\newline\n\n\\subsubsection{CIFAR-10}\nThe CIFAR-10 are labelled subsets of the 80 million tiny images dataset \\cite{Torralba:2008:MTI:1444381.1444403}. The CIFAR-10 dataset has a training dataset of 50,000 colour images in 10 classes, with 5,000 training images per class, each image 32 by 32 pixels large. There are 10000 images for testing. \n\\newline\n\n\\subsection{Results}\nFirst, we evaluate the performance of our proposed method, Bayesian \\acp{cnn} with variational inference. Table \\ref{tab:results} shows a comparison of validation accuracies (in percentage) for architectures trained by two disparate Bayesian approaches, namely variational inference, i.e. \\textit{Bayes by Backprop} and Dropout as proposed by Gal and Ghahramani \\cite{gal2015bayesian}.\\\\\n\nWe compare the results of these two approaches to frequentist inference approach for both the datasets. Bayesian \\acp{cnn} trained by variational inference achieve validation accuracies comparable to their counter-architectures trained by frequentist inference. On MNIST, validation accuracies of the two disparate Bayesian approaches are comparable, but a Bayesian LeNet-5 with Dropout achieves a considerable higher validation accuracy on CIFAR-10, although we were not able to reproduce these reported results.\n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  c  c } \n     \\hline\n      \\empty & MNIST & CIFAR-10   \\\\ [0.75ex]\n     \\hline\n     Bayesian AlexNet (with VI) & 99 & 73   \\\\\n     \n     Frequentist AlexNet & 99 & 73   \\\\\n     \\hdashline\n     Bayesian LeNet-5 (with VI) & 98 & 69   \\\\\n     \n     Frequentist LeNet-5 & 98 & 68   \\\\\n     \\hdashline\n     Bayesian LeNet-5 (with Dropout) & 99.5 & 83  \\\\ \n     \\hline \\\\\n    \\end{tabular}}\n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Comparison of validation accuracies (in percentage) for different architectures with variational inference (VI), frequentist inference and Dropout as a Bayesian approximation as proposed by Gal and Ghahramani \\cite{gal2015bayesian} for MNIST, and CIFAR-10.}\n    \\label{tab:results}\n\\end{table}\n\n\\begin{figure}[t!] \n\\begin{center}\n\\includegraphics[width=\\linewidth]{Chapter5/Figs/results_mnist_CIFAR10.png}\n\\caption{Comparison of Validation Accuracies of Bayesian AlexNet and LeNet-5 with frequentist approach on MNIST and CIFAR-10 datasets}\n\\label{fig:MnistCIFAR10reesults}\n\\end{center}\n\\end{figure} \n\n\\newline Figure \\ref{fig:MnistCIFAR10reesults} shows the validation accuracies of Bayesian vs Non-Bayesian \\acp{cnn}. One thing to observe is that in initial epochs, Bayesian \\acp{cnn} trained by variational inference start with a low validation accuracy compared to architectures trained by frequentist inference. This must deduce from the initialization of the variational posterior probability distributions $q_{\\theta}(w|\\mathcal{D})$ as uniform distributions, while initial point-estimates in architectures trained by frequentist inference are randomly drawn from a standard Gaussian distribution. (For uniformity, we changed the initialization of frequentist architectures from Xavier initialization to standard Gaussian). The latter initialization method ensures the initialized weights are neither too small nor too large. In other words, the motivation of the latter initialization is to start with weights such that the activation functions do not let them begin in saturated or dead regions. This is not true in case of uniform distributions and hence, Bayesian \\acp{cnn}' starting validation accuracies can be comparably low.\n\n\\pagebreak\n\\newline Figure \\ref{fig:std_CNN} displays the convergence of the standard deviation $\\sigma$ of the variational posterior probability distribution $q_{\\theta}(w|\\mathcal{D})$ of a random model parameter over epochs. As aforementioned, all prior probability distributions $p(w)$ are initialized as uniform distributions. The variational posterior probability distributions $q_{\\theta}(w|\\mathcal{D})$ are approximated as Gaussian distributions which become more confident as more data is processed - observable by the decreasing standard deviation over epochs in Figure \\ref{fig:std_CNN}. Although the validation accuracy for MNIST on Bayesian LeNet-5 has already reached 99\\%, we can still see a fairly steep decrease in the parameter's standard deviation. In Figure \\ref{fig:distribution}, we plot the actual Gaussian variational posterior probability distributions $q_{\\theta}(w|\\mathcal{D})$ of a random parameter of LeNet-5 trained on CIFAR-10 at some epochs.\n%\n\\begin{figure}[H] \n\\begin{center}\n\\includegraphics[width=\\linewidth]{Chapter5/Figs/std_CNN.png}\n\\caption{Convergence of the standard deviation of the Gaussian variational posterior probability distribution $q_{\\theta}(w|\\mathcal{D})$ of a random model parameter at epochs 1, 5, 20, 50, and 100. MNIST is trained on Bayesian LeNet-5.}\n\\label{fig:std_CNN}\n\\end{center}\n\\end{figure} \n%\n\n\\begin{figure}[H] \n\\begin{center}\n\\includegraphics[width=\\linewidth]{Chapter5/Figs/distribution.png}\n\\caption{Convergence of the Gaussian variational posterior probability distribution $q_{\\theta}(w|\\mathcal{D})$ of a random model parameter at epochs 1, 5, 20, 50, and 100. CIFAR-10 is trained on Bayesian LeNet-5.}\n\\label{fig:distribution}\n\\end{center}\n\\end{figure} \n\n\\newline Figure \\ref{fig:distribution} displays the convergence of the Gaussian variational probability distribution of a weight taken randomly from the first layer of LeNet-5 architecture. The architecture is trained on CIFAR-10 dataset with uniform initialization. \n\n\n\n\\section{Case Study 2: Large Dataset (CIFAR-100)}\n\\subsection{Dataset}\n\n\\subsubsection{CIFAR-100}\nThis dataset is similar to the CIFAR-10 and is a labelled subset of the 80 million tiny images dataset \\cite{Torralba:2008:MTI:1444381.1444403}. The dataset has 100 classes containing 600 images each. There are 500 training images and 100 validation images per class. The images are coloured with a resolution of 32 by 32 pixels.\n\n\\subsection{Results}\n\n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  } \n     \\hline\n      \\empty & CIFAR-100  \\\\ [0.75ex]\n     \\hline\n     Bayesian AlexNet (with VI)  & 36  \\\\\n     \n     Frequentist AlexNet & 38  \\\\\n\n     Bayesian LeNet-5 (with VI) &  31  \\\\\n     \n     Frequentist LeNet-5  & 33  \\\\\n     \\hline \\\\\n    \\end{tabular}}\n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Comparison of validation accuracies (in percentage) for different architectures with variational inference (VI), frequentist inference and Dropout as a Bayesian approximation as proposed by Gal and Ghahramani \\cite{gal2015bayesian} for MNIST, CIFAR-10, and CIFAR-100.}\n    \\label{tab:resultsCIFAR-100}\n\\end{table}\n\nIn Figure \\ref{fig:regularization}, we show how Bayesian networks incorporate naturally effects of regularization, exemplified on AlexNet. While an AlexNet trained by frequentist inference without any regularization overfits greatly on CIFAR-100, an AlexNet trained by Bayesian inference on CIFAR-100 does not. This is evident from the high value of training accuracy for frequentist approach with no dropout or 1 layer dropout. Bayesian CNN performs equivalently to an AlexNet trained by frequentist inference with three layers of Dropout after the first, fourth, and sixth layers in the architecture.\nAnother thing to note here is that the Bayesian CNN with 100 samples overfits slightly lesser compared to Bayesian CNN with 25 samples. However, a higher sampling number on a smaller dataset didn't prove useful and we stuck with 25 as the number of samples for all other experiments.\n\n\n\\begin{figure}[H] \n\\begin{center}\n\\includegraphics[width=\\linewidth]{Chapter5/Figs/results_train_test_cifar100.png}\n\\caption{Comparison of Training and Validation Accuracies of Bayesian AlexNet and LeNet-5 with frequentist approach with and without dropouts on CIFAR-100 datasets}\n\\label{fig:regularization}\n\\end{center}\n\\end{figure} \n\nTable \\ref{tab:tableCIFAR100} shows a comparison of the training and validation accuracies for AlexNet with Bayesian approach and frequentist approach. The low gap between the training and validation accuracies shows the robustness of Bayesian approach towards overfitting and shows how Bayesian approach without being regularized overfits lesser as compared to frequentist architecture with no or one dropout layer. The results are comparable with AlexNet architecture with 3 dropout layers.\n\n\n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  c  c } \n     \\hline\n      \\empty & Training Accuracy & Validation Accuracy   \\\\ [0.75ex]\n     \\hline\n     Frequentist AlexNet (No dropout) & 83 & 38   \\\\\n     \n     Frequentist AlexNet (1 dropout layer) & 72 & 40   \\\\\n     \n      Frequentist AlexNet (3 dropout layer) & 39 & 38   \\\\\n     \\hdashline\n     Bayesian AlexNet (25 num of samples) & 54 & 37   \\\\\n     \n     Bayesian AlexNet (100 num of samples) & 48 & 37   \\\\\n     \\hline \\\\\n    \\end{tabular}}\n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Comparison of training and validation accuracies (in percentage) for AlexNet architecture with variational inference (VI) and frequentist inference for CIFAR-100.}\n    \\label{tab:tableCIFAR100}\n\\end{table}\n\n\\section{Uncertainity Estimation}\n\n\\newline Finally, Table \\ref{tab:uncertainty} compares the means of aleatoric and epistemic uncertainties for a Bayesian LeNet-5 with variational inference on MNIST and CIFAR-10. The aleatoric uncertainty of CIFAR-10 is about twenty times as large as that of MNIST. Considering that the aleatoric uncertainty measures the irreducible variability and depends on the predicted values, a larger aleatoric uncertainty for CIFAR-10 can be directly deduced from its lower validation accuracy and may be further due to the smaller number of training examples. The epistemic uncertainty of CIFAR-10 is about fifteen times larger than that of MNIST, which we anticipated since epistemic uncertainty decreases proportionally to validation accuracy. \n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  c  } \n     \\hline\n      \\empty & Aleatoric uncertainty &  Epistemic uncertainty  \\\\ [0.75ex]\n     \\hline\n     Bayesian LeNet-5 (MNIST) & 0.0096 & 0.0026   \\\\\n     \n     Bayesian LeNet-5 (CIFAR-10) & 0.1920 & 0.0404   \\\\\n     \\hline \\\\\n    \\end{tabular}} \n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Aleatoric and epistemic uncertainty for Bayesian LeNet-5 calculated for MNIST and CIFAR-10, computed as proposed by Kwon et al. \\cite{kwon2018uncertainty}.}\n    \\label{tab:uncertainty}\n\\end{table}\n\n\\section{Model Pruning}\n\n\\subsubsection{Halving the Number of Filters}\n\nFor every parameter for a frequentist inference network, Bayesian \\acp{cnn} has two parameters ($\\mu$, $\\sigma$). Halving the number of parameters of Bayesian AlexNet ensures the number of parameters of it is comparable with a frequentist inference network. The number of filters of ALexNet is halved and a new architecture called AlexNetHalf is defined in Figure 5.4. \n\n\\begin{table}[h!]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c c} \n \\hline\n layer type & width & stride & padding & input shape & nonlinearity \\\\ [0.5ex] \n \\hline\n convolution ($11\\times11$) & 32 & 4 & 5 & $M\\times3\\times32\\times32$ & Softplus \\\\ \n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times32\\times32\\times32$ & \\empty \\\\\n \n convolution ($5\\times5$) & 96 & 1 & 2 & $M\\times32\\times15\\times15$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times96\\times15\\times15$ & \\empty \\\\\n \n convolution ($3\\times3$) & 192 & 1 & 1 & $M\\times96\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 128 & 1 & 1 & $M\\times192\\times7\\times7$ & Softplus \\\\\n \n convolution ($3\\times3$) & 64 & 1 & 1 & $M\\times128\\times7\\times7$ & Softplus \\\\\n \n max-pooling ($2\\times2$) & \\empty & 2 & 0 & $M\\times64\\times7\\times7$ & \\empty \\\\\n \n fully-connected & 64 & \\empty & \\empty & $M\\times64$ & \\empty \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1.5}\n\\label{tab:AlexNetHalfArchitecture}\n\\caption{AlexNetHalf with number of filters halved compared to the original architecture.}\n\\end{table}\n\n\nThe AlexNetHalf architecture was trained and validated on the MNIST, CIFAR10 and CIFAR100 dataset and the results are shown in Table \\ref{tab:resultsAlexNetHalf}. The accuracy of pruned AlexNet with only half the number of filters compared to the normal architecture shows an accuracy gain of 6 per cent in case of CIFAR10 and equivalent performance for MNIST and CIFAR100 datasets. A lesser number of filters learn the most important features which proved better at inter-class classification could be one of the explanations for the rise in accuracy. However, upon visualization of the filters, no distinct clarification can be made to prove the previous statement. \\\\ \nAnother possible explanation could be the model is generalizing better after the reduction in the number of filters ensuring the model is not overfitting and validation accuracy is comparatively higher. CIFAR-100 higher validation accuracy on ALexNetHalf and a lower training accuracy than Bayesian AlexNet proves the theory. Using a lesser number of filters further enhances the regularization effect and makes the model more robust against overfitting. Similar results have been achieved by Narang \\cite{DBLP:journals/corr/NarangDSE17} in his work where a pruned model achieved better accuracy compared to the original architecture in a speech recognition task. Suppressing or removing the weights that have lesser or no contribution to the prediction makes the model rely its prediction on the most prominent and unique features and hence improves the prediction accuracy.\n\n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  c  c } \n     \\hline\n      \\empty & MNIST & CIFAR-10 & CIFAR-100 \\\\ [0.75ex]\n     \\hline\n     Bayesian AlexNet (with VI) & 99 & 73 & 36 \\\\\n     \n     Frequentist AlexNet & 99 & 73 & 38  \\\\\n     \n     Bayesian AlexNetHalf (with VI) & 99 & 79 & 38 \\\\\n     \n     \\hline \\\\\n    \\end{tabular}}\n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Comparison of validation accuracies (in percentage) for AlexNet with variational inference (VI), AlexNet with frequentist inference and AlexNet with half number of filters halved for MNIST, CIFAR-10 and CIFAR-100 datasets.}\n    \\label{tab:resultsAlexNetHalf}\n\\end{table}\n\n\\subsubsection{Applying L1 Norm}\n\n\nL1 norm induces sparsity in the trained model parameters and sets some values to zero. We trained a model to some epochs (number of epochs differs across datasets as we applied early stopping when validation accuracy remains unchanged for 5 epochs). We removed the zero-valued parameters of the learned weights and keep the non-zero parameters for a trained Bayesian AlexNet on MNIST and CIFAR-10 datasets. We pruned the model to make the number of parameters in a Bayesian Network comparable to the number of parameters in the point-estimate architecture. \\\\ Table \\ref{tab:resultsL1Norm} shows the comparison of validation accuracies of the applied L1 Norm AlexNet Bayesian architecture with Bayesian AlexNet architecture and with AlexNet frequentist architecture. We got comparable results on MNIST and CIFAR10 with the experiments and the results are shown in Table \\ref{tab:resultsL1Norm}\n\n\\begin{table}[H]\n\\tiny\n    \\centering\n    \\renewcommand{\\arraystretch}{1.5}\n    \\resizebox{\\linewidth}{!}{\n    \\begin{tabular}{ l  c  c  c  } \n     \\hline\n      \\empty & MNIST & CIFAR-10  \\\\ [0.75ex]\n     \\hline\n     Bayesian AlexNet (with VI) & 99 & 73  \\\\\n     \n     Frequentist AlexNet & 99 & 73   \\\\\n     \n     Bayesian AlexNet with L1 Norm (with VI) & 99 & 71  \\\\\n     \n     \\hline \\\\\n    \\end{tabular}}\n    \\renewcommand{\\arraystretch}{1.5}\n    \\caption{Comparison of validation accuracies (in percentage) for AlexNet with variational inference (VI), AlexNet with frequentist inference and BayesianAlexNet with L1 norm applied for MNIST and CIFAR-10 datasets.}\n    \\label{tab:resultsL1Norm}\n\\end{table}\n\nOne thing to note here is that the numbers of parameters of Bayesian Network after applying L1 norm is not necessarily equal to the number of parameters in the frequentist AlexNet architecture. It depends on the data size and the number of classes. However, the number of parameters in the case of MNIST and CIFAR-10 are pretty comparable and there is not much reduction in the accuracy either. Also, the early stopping was applied when there is no change in the validation accuracy for 5 epochs and the model was saved and later pruned with the application of L1 norm.\n\n\\section{Training Time}\n\nTraining time of a Bayesian \\acp{cnn} is twice of a frequentist network with similar architecture when the number of samples is equal to one. In general, the training time of a Bayesian \\acp{cnn}, $T$ is defined as:\n\\begin{align}\nT = 2 * number\\_of\\_samples * t\n\\end{align}\nwhere $t$ is the training time of a frequentist network. \nThe factor of 2 is present due to the double learnable parameters in a Bayesian CNN network i.e. mean and variance for every single point estimate weight in the frequentist network.\n\nHowever, there is no difference in the inference time for both the networks. \n\n\n\\ifpdf\n    \\graphicspath{{Chapter2/Figs/Raster/}{Chapter2/Figs/PDF/}{Chapter2/Figs/}}\n\\else\n    \\graphicspath{{Chapter2/Figs/Vector/}{Chapter2/Figs/}}\n\\fi\n\n\n"
  },
  {
    "path": "Chapter6/chapter6.tex",
    "content": "\\chapter{Applications}\n\n\\fbox{\n    \\parbox{\\textwidth}\n    {\n        Chapter Overview\n        \\begin{itemize}\n            \\item Empirical analysis of BayesCNN with normal architecture for Image Super Resolution.\n            \\item Empirical analysis of BayesCNN with normal architecture for Generative Adversarial Networks.\n        \\end{itemize}\n    }\n}\n\n\\pagebreak\n\n\n\n\\section{BayesCNN for Image Super Resolution}\n\nThe task referred to as Super Resolution (SR) is the recovery of a High-Resolution (HR) image from a given Low-Resolution (LR) image. It is applicable to many areas like medical imaging \\citet{10.1007/978-3-642-40760-4_2}, face recognition \\citet{1203152} and so on.\n\nThere are many ways to do a single image super-resolution and detailed benchmarks of the methods are provided by Yang \\citet{Yang2014SingleImageSA}. Following are the major ways to do a single image super-resolution:\\\\ \n\n\\textbf{Prediction Models}: These models generate High-Resolution images\nfrom Low-Resolution inputs through a predefined mathematical formula. No training data is needed for such models. Interpolation-based methods (bilinear, bicubic, and Lanczos) generate HR pixel intensities by weighted averaging neighbouring LR pixel values are good examples of this method.\\\\\n\n\\textbf{Edge Based Methods}: Edges are one of the most important features for any computer vision task. The High-Resolution images learned from the edge features high-quality edges and good sharpness. However, these models lack good colour and texture information.\\\\\n\n\\textbf{Patch Based Methods}: Cropped patches from Low-Resolution images and High-Resolution images are taken from training dataset to learn some mapping function. The overlapped patches are averaged or some other techniques like Conditional Random Fields \\cite{lafferty2001conditional} can be used for better mapping of the patches.  \n\n\n\\subsection{Our Approach}\n\nWe build our work upon \\citet{DBLP:journals/corr/ShiCHTABRW16} work that shows that performing Super Resolution work in High-Resolution space is not the optimal solution and it adds the computation complexity. We used a Bayesian Convolutional Neural Network to extract features in the Low-Resolution space. We use an efficient sub-pixel convolution layer, as proposed by \\citet{DBLP:journals/corr/ShiCHTABRW16}, which learns an array of upscaling filters to upscale the final Low-Resolution feature maps into the High-Resolution output. This replaces the handcrafted bicubic filter in the Super Resolution pipeline with more complex upscaling filters specifically trained for each feature map, and also reduces the computational complexity of the overall Super Resolution operation.\n\nThe hyperparameters used in the experiments are mentioned in the Appendix A section in details.\n\n\\begin{figure*}[htbp]\n\\begin{center}\n\\includegraphics[width=1.0\\linewidth]{Chapter6/Figs/networkstructure.jpg}\n\\caption{The proposed efficient sub-pixel convolutional neural network (ESPCN) \\cite{DBLP:journals/corr/ShiCHTABRW16}, with two convolution layers for feature maps extraction, and a sub-pixel convolution layer that aggregates the feature maps from Low Resolution space and builds the Super Resolution image in a single step.}\n\\label{fig:networkstructure}\n\\end{center}\n\\end{figure*}\n\nWe used a four-layer convolutional model as mentioned in the paper \\cite{DBLP:journals/corr/ShiCHTABRW16}. We replaced the convolution layer by Bayesian convolution layer and changed the forward pass that now computes the mean, variance and KL divergence. The PixelShuffle layer is kept same as provided by PyTorch and no changes have been made there.  \n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c } \n \\hline\n layer type & width & stride & padding  \\\\ [0.5ex] \n \\hline\n convolution ($5\\times5$) & 64 & 1 & 2 \\\\\n \n convolution ($3\\times3$) & 64 & 1 & 1 \\\\\n \n \n convolution ($3\\times3$) & 32 & 1 & 1 \\\\\n \n convolution ($3\\times3$) & upscale factor * * 2 & 1 & 1  \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1.5}\n\\label{tab:SuperResolutionArchitecture}\n\\caption{Network Architecture for Bayesian Super Resolution}\n\\end{table}\n\nWhere \\textit{upscale factor} is defined as a parameter. For our experiments, we take upscale factor = 3. \n\n\\subsection{Empirical Analysis}\n\nThe Network architecture was trained on BSD300 dataset \\cite{MartinFTM01} provided by the Berkeley Computer Vision Department. The dataset is very popular for Image Super-Resolution task and thus the dataset is used to compare the results with other work in the domain. \n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.28\\textheight]{Chapter6/Figs/camel_LR.png}\n\\label{fig:CamelLR}\n\\caption{Sample image in Low Resolution image space taken randomly from BSD 300 \\cite{MartinFTM01} dataset.}\n\\end{center}\n\\end{figure}\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.38\\textheight]{Chapter6/Figs/camel_SR.png}\n\\label{fig:CamelSR}\n\\caption{Generated Super Resolution Image scaled to 40 percent to fit}\n\\end{center}\n\\end{figure}\n\nThe generated results with Bayesian Network is compared with the original paper and the results are comparable in terms of the number and the quality of the image generated. This application was to prove the concept that the Bayesian Networks can be used for the task of Image Super Resolution. Furthermore, the results are pretty good. \n\nSome more research is needed in the future to achieve state-of-the-art results in this domain which is out of the scope of this thesis work.\n\n\n\\section{BayesCNN for Generative Adversarial Networks}\n\nGenerative Adversarial Networks (GANs) \\cite{goodfellow2014generative} can be used for two major tasks: to learn good feature representations by using the generator and discriminator networks as feature extractors and to generate natural images. The learned feature representation or generated images can reduce the number of images substantially for a computer vision supervised task. However, GANs were quite unstable to train in the past and that is why we base our work on the stable GAN architecture namely Deep Convolutional GANs (DCGAN) \\cite{DBLP:journals/corr/RadfordMC15}. We use the trained Bayesian discriminators for image classification tasks, showing competitive performance with the normal DCGAN architecture.\n\n\\subsection{Our Approach}\n\nWe based our work on the paper:  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks by  \\citet{DBLP:journals/corr/RadfordMC15}. We used the architecture of a deep convolutional generative adversarial networks (DCGANs) that learns a hierarchy of representations from object parts to scenes in both the generator and discriminator.\nThe generator used in the Network is shown in Table \\ref{tab:GeneratorArchitecture}. The architecture is kept similar to the architecture used in DCGAN paper \\cite{DBLP:journals/corr/RadfordMC15}. Table \\ref{tab:DiscriminatorArchitecture} shows the discriminator network with Bayesian Convolutional Layers. \n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c} \n \\hline\n layer type & width & stride & padding & nonlinearity \\\\ [0.5ex] \n \\hline\n ConvolutionTranspose ($4\\times4$) & ngf * 8 & 1 & 0  & ReLU \\\\ \n \n\n ConvolutionTranspose ($4\\times4$) & ngf * 4 & 2 & 1  & ReLU \\\\\n \n \n ConvolutionTranspose ($4\\times4$) & ngf * 2 & 2 & 1 & ReLU \\\\\n \n ConvolutionTranspose ($4\\times4$) & ngf & 2 & 1  & ReLU \\\\\n \n ConvolutionTranspose ($4\\times4$) & nc & 2 & 1 & TanH \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1}\n\\caption{Generator architecture as defined in the paper. \\cite{DBLP:journals/corr/RadfordMC15}}\n\\label{tab:GeneratorArchitecture}\n\\end{table}\n\nwhere \\textit{ngf} is the number of generator filters which is chosen to be 64 in our work and \\textit{nc} is the number of output channels which is set to 3. \n\n\\begin{table}[H]\n    \\centering\n    \\renewcommand{\\arraystretch}{2}\n    \\begin{tabular}{c c c c c} \n \\hline\n layer type & width & stride & padding & nonlinearity \\\\ [0.5ex] \n \\hline\n Convolution ($4\\times4$) & ndf & 2 & 1  & LeakyReLU \\\\ \n \n\n Convolution($4\\times4$) & ndf * 2 & 2 & 1  & LeakyReLU \\\\\n \n \n Convolution ($4\\times4$) & ndf * 4 & 2 & 1 & LeakyReLU \\\\\n \n Convolution ($4\\times4$) & ndf * 8 & 2 & 1  & leakyReLU \\\\\n \n ConvolutionTranspose ($4\\times4$) & 1 & 1 & 0 & Sigmoid \\\\ [1ex] \n \\hline\n\\end{tabular}\n\\renewcommand{\\arraystretch}{1}\n\\caption{Discriminator architecture with Bayesian Convolutional layers}\n\\label{tab:DiscriminatorArchitecture}\n\\end{table}\n\nwhere \\textit{ndf} is the number of discriminator filters and is set to 64 as default for all our experiments. \n\n\\subsection{Empirical Analysis}\n\nThe images were taken directly and no pre-processing was applied to any of the images. Normalization was applied with value 0.5 to make the data mean centred. A batch size of 64 was used along with Adam \\citep{kingma2014adam} as an optimizer to speed up the training. All weights were initialized from a zero-centred Normal distribution with standard deviation equal to 1. We also used LeakyReLU as mentioned in the original DCGAN paper \\cite{DBLP:journals/corr/RadfordMC15}. The slope of the leak in LeakyReLU was set to 0.2 in all models. We used the learning rate of 0.0001, whereas in paper 0.0002 was used instead. Additionally, we found leaving the momentum term $\\beta_1$ at the suggested value of 0.9 resulted in training oscillation and instability while reducing it to 0.5 helped stabilize training (also taken from original paper \\cite{DBLP:journals/corr/RadfordMC15}).\n\nThe hyperparameters used in the experiments are mentioned in the Appendix A section in details.\nThe fake results of the generator after 100 epochs of training is shown in Figure 6.4. To compare the results, real samples are shown in Figure 6.5. The loss in case of a Bayesian network is higher as compared to the DCGAN architecture originally described by the authors. However, upon looking at the results, there is no comparison that can be drawn from the results of the two networks. Since GANs are difficult to anticipate just by the loss number, the comparison cannot be made. The results are pretty comparable for the Bayesian models and the original DCGAN architecture. \n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.88\\textheight]{Chapter6/Figs/fake_samples.png}\n\\label{fig:FakeSamples}\n\\caption{Fake Samples generated from the Bayesian DCGAN model trained on CIFAR10 dataset}\n\\end{center}\n\\end{figure}\n\n\\begin{figure}[H]\n\\begin{center}\n\\includegraphics[height=.88\\textheight]{Chapter6/Figs/realsamples.png}\n\\label{fig:RealSamples}\n\\caption{Real Samples taken from CIFAR10 dataset}\n\\end{center}\n\\end{figure}\n\n\n\n"
  },
  {
    "path": "Chapter7/chapter7.tex",
    "content": "\\chapter{Conclusion and Outlook}\n\nWe propose Bayesian \\acp{cnn} utilizing \\textit{Bayes by Backprop} as a reliable, variational inference method for \\acp{cnn} which has not been studied to-date, and estimate the models' aleatoric and epistemic uncertainties for prediction. Furthermore, we apply different ways to pruning the Bayesian \\ac{cnn} and compare its results with frequentist architectures.\n\n\n\\newline There has been previous work by Gal and Ghahramani \\cite{gal2015bayesian} who utilized the various outputs of a Dropout function to define a distribution, and concluded that one can then speak of a Bayesian \\ac{cnn}. This approach finds, perhaps also due to its ease, a large confirming audience. However, we argue against this approach and claim deficiencies. Specifically, in Gal's and Ghahramani's \\cite{gal2015bayesian} approach, no prior probability distributions $p(w)$ are placed on the \\ac{cnn}'s parameters. But, these are a substantial part of a Bayesian interpretation for the simple reason that Bayes' theorem includes them. Thus we argue, starting with prior probability distributions $p(w)$ is essential in Bayesian methods. In comparison, we place prior probability distributions over all model parameters and update them according to Bayes' theorem with variational inference, precisely \\textit{Bayes by Backprop}. We show that these neural networks achieve state-of-the-art results as those achieved by the same network architectures trained by frequentist inference. \n\n\n\\newline Furthermore, we examine how uncertainties (both aleatoric and epistemic uncertainties) can be computed for our proposed method and we show how epistemic uncertainties can be reduced upon more training data. We also compare the effect of dropout in a frequentist network to the proposed Bayesian \\ac{cnn} and show the natural regularization effect of Bayesian methods. To counter the twice number of parameters (mean and variance) in a Bayesian \\ac{cnn} compared to a single point estimate weight in a frequentist method, we apply methods of network pruning and show that the Bayesian \\ac{cnn} performs equally good or better even when the network is pruned and the number of parameters is made comparable to a frequentist method. \n\n\n\\newline Finally, we show the applications of Bayesian \\acp{cnn} in various domains like Image recognition, Image Super-Resolution and Generative Adversarial Networks (GANs). The results are compared with other popular approaches in the field and a comparison of results are drawn. Bayesian \\acp{cnn} in general, proved to be a good idea to be applied on GANs as prior knowledge for discriminator network helps in better identification of real vs fake images. \\\\\n\n\nAs an add-on method to further enhance the stability of the optimization, \\textit{posterior sharpening} \\cite{fortunato2017bayesian} could be applied to Bayesian \\acp{cnn} in future work. There, the variational posterior distribution $q_{\\theta}(w|\\mathcal{D})$ is conditioned on the training data of a batch $\\mathcal{D}^{(i)}$. We can see $q_{\\theta}(w|\\mathcal{D}^{(i)})$ as a proposal distribution, or \\textit{hyper-prior} when we rethink it as a hierarchical model, to improve the gradient estimates of the intractable likelihood function $p(\\mathcal{D}|w)$. For the initialization of the mean and variance, a zero mean and one as standard deviation was used as the normal distribution seems to be the most intuitive distribution to start with. However, with the results drawn in the thesis from several experimentations, a zero-centred mean and very small standard deviation initialization seemed to be performing equally well but training faster. Xavier initialization \\cite{glorot2010understanding} converges faster in a frequentist network compared to a normal initialization and a similar distribution space needs to be explored with Bayesian networks for initializing the distribution. Other properties like periodicity or spatial invariance are also captured by the priors in data space, and based on these properties an alternative to Gaussian process priors can be found. \n\n\\newline Using normal distribution as prior for uncertainty estimation was also explored by Danijar et al. \\cite{hafner2018reliable} and it was observed that standard normal prior causes the function posterior to generalize in unforeseen ways on inputs outside of the training distribution. Addition of some noise in the normal distribution as prior can help in better uncertainty estimation by the model. However, no such cases were found in our experiments but can be an interesting area to explore in future. \n\n\n\\newline The network is pruned with simple methods like L1 norm and more compression tricks like vector quantization \\cite{DBLP:journals/corr/GongLYB14} and group sparsity regularization  \\cite{DBLP:conf/nips/AlvarezS16} can be applied. In our work, we show that reducing the number of model parameters results in a better generalization of the Bayesian architecture and even leads to improvement in the overall model accuracy on the test dataset. Upon further analysis of the model, there is no concrete learning about the change in the behaviour. A more detailed analysis by visualizing the pattern learned by each neuron and grouping them together and removing the redundant neurons which learns similar behaviour is a good way to prune the model.\n\n\\newline The concept of Bayesian \\ac{cnn} is applied to the discriminative network of a GAN in our work and it has shown good initial results. However, the area of Bayesian generative networks in a GAN is still to be investigated."
  },
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    "content": "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n%%                                                                            %%\n%% Class ``PhD Thesis PSnPDF''                                                %%\n%%                                                                            %%\n%% A PhD thesis LaTeX template for Cambridge University Engineering Department%%\n%%                                                                            %%\n%% Version: v2.3.1                                                             %%\n%% Authors: Krishna Kumar                                                     %%\n%% License: MIT License (c) 2016 Krishna Kumar                                %%\n%% GitHub Repo: https://github.com/kks32/phd-thesis-template/                 %%\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n% ************************** Class Identification ******************************\n\\newcommand\\fileversion{2.3.1}\n\\newcommand\\filedate{2016/04/23}\n\\NeedsTeXFormat{LaTeX2e}\n\\ProvidesClass{PhDThesisPSnPDF}[\\filedate\\space A PhD thesis class file\n by Krishna Kumar (v\\fileversion)]\n\\typeout{https://github.com/kks32/phd-thesis-template/}\n\n\n% ******************************************************************************\n% **************************** Class Definition ********************************\n% ******************************************************************************\n\n% *********************** Define a Print/Online Version ************************\n\\newif\\if@print\\@printfalse\n\\DeclareOption{print}{\\@printtrue}\n\n% ****************************** Define index **********************************\n\\newif\\ifPHD@index\\PHD@indexfalse\n\\DeclareOption{index}{\\PHD@indextrue}\n\n% ******************************* Font Option **********************************\n\\newif\\ifsetFont\\setFontfalse                 % Font is not set\n\n\\newif\\ifPHD@times\\PHD@timesfalse             % Times with Math Support\n\\DeclareOption{times}{\n  \\ifsetFont\n    \\ClassWarning{PhDThesisPSnPDF}{Font selection conflict: A font package was\n      aleady specified. Please check the document class options in case you \n      have defined two fonts.}\n  \\else\n    \\PHD@timestrue\n    \\setFonttrue\n  \\fi\n}\n\n\\newif\\ifPHD@fourier\\PHD@fourierfalse         % Fourier with Math Support\n\\DeclareOption{fourier}{\n  \\ifsetFont\n    \\ClassWarning{PhDThesisPSnPDF}{Font selection conflict: A font package was\n      aleady specified. Please check the document class options in case you \n      have defined two fonts.}\n  \\else\n    \\PHD@fouriertrue\n    \\setFonttrue\n  \\fi\n}\n\n\\newif\\ifsetCustomFont\\setCustomFontfalse     % Custom Font with Math Support\n\\DeclareOption{customfont}{\n  \\ifsetFont\n    \\ClassWarning{PhDThesisPSnPDF}{Font selection conflict: A font package was\n      aleady specified. Please check the document class options in case you \n      have defined two fonts.}\n  \\else\n    \\setCustomFonttrue\n    \\setFonttrue\n  \\fi\n}\n\n% ******************************* Bibliography *********************************\n\\newif\\ifsetBib\\setBibfalse                   % Custom Bibliography = true/false\n\\newif\\ifsetBiBLaTeX\\setBiBLaTeXfalse         % BiBLaTeX = True / False\n\n\\newif\\ifPHD@biblatex\\PHD@biblatexfalse       % BiBLaTeX\n\\DeclareOption{biblatex}{\n  \\ifsetBib\n    \\ClassWarning{PhDThesisPSnPDF}{Bibliography selection conflict: A\n      bibliography style aleady specified. Please check the document class\n      options in case you have defined two bibliography styles.}\n  \\else\n    \\PHD@biblatextrue\n  \\fi\n}\n\n\\newif\\ifPHD@authoryear\\PHD@authoryearfalse   % Author-Year citation\n\\DeclareOption{authoryear}{\n  \\ifsetBib\n    \\ClassWarning{PhDThesisPSnPDF}{Bibliography selection conflict: A\n      bibliography style aleady specified. 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If you\n    would like to use other pre-defined fonts use `times' (The Cambridge \n    University PhD thesis guidelines recommend using Times font)  or `fourier'\n    or load a custom font in the preamble.tex file by specifying `customfont' \n    in the class options}\n  \\RequirePackage{lmodern}\n\\fi\n\n\n\\ifxetex\n  % XeLaTeX\n  \\usepackage{amsmath}\n  \\usepackage{fontspec}\n  \\usepackage[]{unicode-math}\n  \\setmainfont[\n    Extension = .otf,\n    UprightFont = *-regular,\n    BoldFont = *-bold,\n    ItalicFont = *-italic,\n    BoldItalicFont = *-bolditalic,\n  ]{xits}\n  \n  \\setmathfont[ \n    Extension = .otf,\n    BoldFont = *bold,\n  ]{xits-math}\n  \n\\else\n  % default: pdfLaTeX\n  \\RequirePackage[utf8]{inputenc}\n  \\RequirePackage[T1]{fontenc}\n  \n  % If building with PDFLaTeX, use microtype spacing adjustments\n  \\RequirePackage[final]{microtype}\n  \n  \\input{glyphtounicode}\n  \\pdfglyphtounicode{f_f}{FB00}\n  \\pdfglyphtounicode{f_i}{FB01}\n  \\pdfglyphtounicode{f_l}{FB02}\n  \\pdfglyphtounicode{f_f_i}{FB03}\n  \\pdfglyphtounicode{f_f_l}{FB04}\n  \\pdfgentounicode=1\n\n  \\RequirePackage{amsfonts}\n  \\RequirePackage{amsmath}\n  \\RequirePackage{amssymb}\n\\fi\n\n% Don't break enumeration (etc.) across pages in an ugly manner\n\\clubpenalty=10000\n\\widowpenalty=10000\n\n%******************************* Print / Online ********************************\n% Defines a print / online version to define page-layout and hyperrefering\n\n% Moved below other usepackage definitions to fix PDFLaTeX footnote warning\n% Based on the suggestion by John Plaice\n\n\\RequirePackage[unicode=true]{hyperref}\n\n\\if@print\n  % For Print version\n  \\hypersetup{\n    final=true,\n    plainpages=false,\n    pdfstartview=FitV,\n    pdftoolbar=true,\n    pdfmenubar=true,\n    bookmarksopen=true,\n    bookmarksnumbered=true,\n    breaklinks=true,\n    linktocpage,\n    colorlinks=true,\n    linkcolor=black,\n    urlcolor=black,\n    citecolor=black,\n    anchorcolor=black\n  }\n  \\ifsetCustomMargin\n  % Margin to be define in preamble using geometry package\n  \\else\n    \\RequirePackage[paper=\\PHD@papersize,hmarginratio=1:1,\n      vmarginratio=1:1,scale=0.75,bindingoffset=5mm]{geometry}\n  \\fi\n\n  \\if@twoside\n    \\hypersetup{pdfpagelayout=TwoPageRight}\n  \\else\n    \\hypersetup{pdfpagelayout=OneColumn}\n  \\fi\n\n\\else\n  % For PDF Online version\n  \\hypersetup{\n    final=true,\n    plainpages=false,\n    pdfstartview=FitV,\n    pdftoolbar=true,\n    pdfmenubar=true,\n    bookmarksopen=true,\n    bookmarksnumbered=true,\n    breaklinks=true,\n    linktocpage,\n    colorlinks=true,\n    linkcolor=blue,\n    urlcolor=blue,\n    citecolor=blue,\n    anchorcolor=green\n  }\n\n  \\ifsetCustomMargin\n    % Margin to be define in preamble using geometry package\n    \\else\n      % No Margin staggering on Odd and Even side\n      \\RequirePackage[paper=\\PHD@papersize,hmarginratio=1:1,\n        vmarginratio=1:1,scale=0.75]{geometry} % dvips\n    \\fi\n    \\hypersetup{pdfpagelayout=OneColumn}\n\\fi\n\n% ************************ URL Package and Definition **************************\n\\RequirePackage{url}\n% Redefining urlstyle to use smaller fontsize in References with URLs\n\\newcommand{\\url@leostyle}{%\n \\@ifundefined{selectfont}{\\renewcommand{\\UrlFont}{\\sffamily}}\n {\\renewcommand{\\UrlFont}{\\normalsize}}}\n\\urlstyle{leo}\n\n% option to split urls over multiple lines for latex >> DVIPS >> PDF option\n\\ifpdf\n  % PDFLaTeX does it automatically.\n\\else\n  % dvips\n  \\RequirePackage{breakurl} % to split the url over multiple lines\n\\fi\n  \n% ******************************************************************************\n% **************************** Pre-defined Settings ****************************\n% ******************************************************************************\n\n% *************************** Setting PDF Meta-Data ****************************\n\\ifpdf\n\\AtBeginDocument{\n  \\hypersetup{\n    pdftitle = {\\@title},\n    pdfauthor = {\\@author},\n    pdfsubject={\\@subject},\n    pdfkeywords={\\@keywords}\n  }\n}\n\\fi\n\n% ******************************** Line Spacing ********************************\n% Set spacing as 1.5 line spacing for the PhD Thesis\n% In practice, fortunately, nobody knows really what “one-and-a-half spaced\n% type” means exactly (in terms of millimetres baseline distance). The following\n% LaTeX setting has routinely been considered perfectly acceptable:\n\n\\renewcommand\\baselinestretch{1.2}\n\n% ******************************** Justification *******************************\n% Left aligned as per University identity guidelines\n\\ifPHD@textJustify\n\\message{PhDThesisPSnPDF: The University identity guidelines recommend using \nleft aligned text. Please use `flushleft' in the documentclass option, if you\nwish to left align your text}\n\\else\n\\AtBeginDocument{\n\\raggedright\n}\n\\fi\n\n\n% ************************** TOC and Hide Sections *****************************\n\\newcommand{\\nocontentsline}[3]{}\n\\newcommand{\\tochide}[2]{\n\t\\bgroup\\let\n\t\\addcontentsline=\\nocontentsline#1{#2}\n\t\\egroup}\n% Removes pagenumber appearing from TOC\n\\addtocontents{toc}{\\protect\\thispagestyle{empty}}\n\n\n% ***************************** Header Formatting ******************************\n% Custom Header with Chapter Number, Page Number and Section Numbering\n\n\\RequirePackage{fancyhdr} % Define custom header\n\n% Style 1: Sets Page Number at the Top and Chapter/Section Name on LE/RO\n\\fancypagestyle{PageStyleI}{\n  \\renewcommand{\\chaptermark}[1]{\\markboth{##1}{}}\n  \\renewcommand{\\sectionmark}[1]{\\markright{\\thesection\\ ##1\\ }}\n  \\fancyhf{}\n  \\fancyhead[RO]{\\nouppercase \\rightmark\\hspace{0.25em} | \n    \\hspace{0.25em} \\bfseries{\\thepage}}\n  \\fancyhead[LE]{ {\\bfseries\\thepage} \\hspace{0.25em} | \n    \\hspace{0.25em} \\nouppercase \\leftmark}\n}\n\n% Style 2: Sets Page Number at the Bottom with Chapter/Section Name on LO/RE\n\\fancypagestyle{PageStyleII}{\n  \\renewcommand{\\chaptermark}[1]{\\markboth{##1}{}}\n  \\renewcommand{\\sectionmark}[1]{\\markright{\\thesection\\ ##1}}\n  \\fancyhf{}\n  \\fancyhead[RO]{\\bfseries\\nouppercase \\rightmark}\n  \\fancyhead[LE]{\\bfseries \\nouppercase \\leftmark}\n  \\fancyfoot[C]{\\thepage}\n}\n\n\n% Set Fancy Header Command is defined to Load FancyHdr after Geometry is defined\n\\newcommand{\\setFancyHdr}{\n\n\\pagestyle{fancy}\n\n\\ifPHD@pageStyleI\n  % Style 1: Sets Page Number at the Top and Chapter/Section Name on LE/RO\n  \\pagestyle{PageStyleI}\n\n\\else\n  \\ifPHD@pageStyleII\n\n  % Style 2: Sets Page Number at the Bottom with Chapter/Section Name on LO/RE\n  \\pagestyle{PageStyleII}\n\n  \\else\n  % Default Style: Sets Page Number at the Top (LE/RO) with Chapter/Section Name\n  % on LO/RE and an empty footer\n    \\renewcommand{\\chaptermark}[1]{\\markboth {##1}{}}\n    \\renewcommand{\\sectionmark}[1]{\\markright{\\thesection\\ ##1}}\n    \\fancyhf{}\n    \\fancyhead[LO]{\\nouppercase \\rightmark}\n    \\fancyhead[LE,RO]{\\bfseries\\thepage}\n    \\fancyhead[RE]{\\nouppercase \\leftmark}\n  \\fi\n\\fi\n}\n\n\\setlength{\\headheight}{14.5pt}\n%\\renewcommand{\\headrulewidth}{0.5pt}\n%\\renewcommand{\\footrulewidth}{0pt}\n\\fancypagestyle{plain}{\n  \\fancyhead{}\n  \\renewcommand{\\headrulewidth}{0pt}\n}\n\n% If Margin has been set (default margin print/online version)\n\\ifsetCustomMargin\n\\AtBeginDocument{\n\\@ifpackageloaded{geometry}{}{\\ClassWarning{PhDThesisPSnPDF}{%\n  Custom margin is chosen, but geometry package is not loaded. Please load the\n  `geometry' package in the preamble.}}}\n\\else\n\\setFancyHdr % Apply fancy header settings otherwise apply it in preamble\n\\fi\n\n% **************** Clear Header Style on the Last Empty Odd pages **************\n\\renewcommand{\\cleardoublepage}{\\clearpage\\if@twoside \\ifodd\\c@page\\else%\n\t\\hbox{}%\n\t\\thispagestyle{empty}  % Empty header styles\n\t\\newpage%\n\t\\if@twocolumn\\hbox{}\\newpage\\fi\\fi\\fi}\n\n\n% ******************************************************************************\n% **************************** Macro Definitions *******************************\n% ******************************************************************************\n% These macros are used to declare arguments needed for the\n% construction of the title page and other preamble.\n\n% Subtitle (optional)\n\\newcommand{\\@subtitle}{}\n\\newcommand{\\subtitle}[1]{\\renewcommand{\\@subtitle}{#1}}\n\n% The year and term the degree will be officially conferred\n\\newcommand{\\@degreedate}{\\monthname[\\the\\month]\\space\\the\\year}\n\\newcommand{\\degreedate}[1]{\\renewcommand{\\@degreedate}{#1}}\n\n% The full (unabbreviated) name of the degree\n\\newcommand{\\@degreetitle}{}\n\\newcommand{\\degreetitle}[1]{\\renewcommand{\\@degreetitle}{#1}}\n\n% The name of your department(eg. Engineering, Maths, Physics)\n\\newcommand{\\@dept}{}\n\\newcommand{\\dept}[1]{\\renewcommand{\\@dept}{#1}}\n\n% The name of your college (eg. King's)\n\\newcommand{\\@college}{}\n\\newcommand{\\college}[1]{\\renewcommand{\\@college}{#1}}\n\n% The name of your University\n\\newcommand{\\@university}{}\n\\newcommand{\\university}[1]{\\renewcommand{\\@university}{#1}}\n\n% Defining the crest\n\\newcommand{\\@crest}{}\n\\newcommand{\\crest}[1]{\\renewcommand{\\@crest}{#1}}\n\n% Defining the college crest\n\\newif\\ifPHD@collegeshield\\PHD@collegeshieldfalse\n\\newcommand{\\@collegeshield}{}\n\\newcommand{\\collegeshield}[1]{\\renewcommand{\\@collegeshield}{#1}\\PHD@collegeshieldtrue}\n\n% Supervisor\n\\newif\\ifPHD@supervisor\\PHD@supervisorfalse\n\\newcommand{\\@supervisor}{}\n\\newcommand{\\supervisor}[1]{\\renewcommand{\\@supervisor}{#1}\\PHD@supervisortrue}\n\n% Supervisor Title (Supervisor - Default, can be changed)\n\\newcommand{\\@supervisorrole}{Supervisor: }\n\\newcommand{\\supervisorrole}[1]{\\renewcommand{\\@supervisorrole}{#1}}\n\n% Supervisor width to align\n\\newcommand{\\@supervisorlinewidth}{0.35\\textwidth}\n\\newcommand{\\supervisorlinewidth}[1]{\\renewcommand{\\@supervisorlinewidth}{#1}}\n\n% Advisor\n\\newif\\ifPHD@advisor\\PHD@advisorfalse\n\\newcommand{\\@advisor}{}\n\\newcommand{\\advisor}[1]{\\renewcommand{\\@advisor}{#1}\\PHD@advisortrue}\n\n% Advisor Title (Advisor - Default, can be changed)\n\\newcommand{\\@advisorrole}{Advisor: }\n\\newcommand{\\advisorrole}[1]{\\renewcommand{\\@advisorrole}{#1}}\n\n% Advisor width to align\n\\newcommand{\\@advisorlinewidth}{0.25\\textwidth}\n\\newcommand{\\advisorlinewidth}[1]{\\renewcommand{\\@advisorlinewidth}{#1}}\n\n% Submission Text\n\\newcommand{\\submissiontext}{This dissertation is submitted for the degree of }\n\n% keywords (These keywords will appear in the PDF meta-information\n% called `pdfkeywords`.)\n\\newcommand{\\@keywords}{}\n\\newcommand{\\keywords}[1]{\\renewcommand{\\@keywords}{#1}}\n\n% subjectline (This subject will appear in the PDF meta-information\n% called `pdfsubject`.)\n\\newcommand{\\@subject}{}\n\\newcommand{\\subject}[1]{\\renewcommand{\\@subject}{#1}}\n\n\n% These macros define an environment for front matter that is always\n% single column even in a double-column document.\n\\newenvironment{alwayssingle}{%\n       \\@restonecolfalse\\if@twocolumn\\@restonecoltrue\\onecolumn\n       \\else\\newpage\\fi}\n       {\\if@restonecol\\twocolumn\\else\\newpage\\fi}\n\n% Set single column even under two column layout\n\\newcommand{\\setsinglecolumn}{\n\\if@twocolumn\n   \\onecolumn\n\\else\n\\fi\n}\n\n\n% ******************************************************************************\n% *************************** Front Matter Layout ******************************\n% ******************************************************************************\n\n% ******************************** Title Page **********************************\n\\renewcommand{\\maketitle}{\n\n% To compute the free vertical space in Title page\n\\computeTitlePageSpacing\n\n\\thispagestyle{empty}\n\n\\begin{singlespace}\n\\begin{center}\n\n% University Crest Long if college crest is defined\n\\ifPHD@collegeshield%\n\t{\\usebox{\\PHD@crest}}\n\t\\vspace{.15\\PHD@titlepagespacing}\n\\fi\n\n% Title\n{\\usebox{\\PHD@Title}} % subtitle is defined\n\n\n% Crest\n\n\\ifPHD@collegeshield%\n\t\\vspace{.15\\PHD@titlepagespacing}\n\t{\\usebox{\\PHD@collegecrest}}\n\t\\vspace{.1\\PHD@titlepagespacing}\n\\else\n\t\\vspace{.25\\PHD@titlepagespacing}\n\t{\\usebox{\\PHD@crest}}\n\t\\vspace{.15\\PHD@titlepagespacing}\n\\fi\n\n\n\n% Author\n{\\usebox{\\PHD@author}}\n\\vspace*{1em}\n\n% Supervisor\n\\ifPHD@supervisor%\n{\\usebox{\\PHD@supervisor}}\n\\vspace*{0.5em}\n\\fi\n\n% Advisor\n\\ifPHD@advisor%\n{\\usebox{\\PHD@advisor}}\n\\vspace*{0.5em}\n\\fi\n\n% Department and University\n{\\usebox{\\PHD@dept}}\n\\vspace{.2\\PHD@titlepagespacing}\n\n% Submission Text\n{\\usebox{\\PHD@submission}}\n\n% College and degree date\n\\vfill\n{\\usebox{\\PHD@collegedate}}\n    \n\\end{center}\n\n\\end{singlespace}\n}\n\n% ********************************* Dedication *********************************\n% The dedication environment makes sure the dedication gets its\n% own page, centered\n\n\\newenvironment{dedication}\n{\n\\cleardoublepage\n\\setsinglecolumn\n\\vspace*{0.2\\textheight}\n\\thispagestyle{empty}\n\\centering\n}\n\n\n\n% ******************************* Declaration **********************************\n% The declaration environment puts a large, bold, centered\n% \"Declaration\" label at the top of the page.\n\n\\newenvironment{declaration}{\n\\cleardoublepage\n\\setsinglecolumn\n\\chapter*{\\centering \\Large Declaration}\n\\thispagestyle{empty}\n}{\n\\flushright\n\\@author{}\\\\\n\\@degreedate{}\n\\vfill\n}\n\n\n\n% ****************************** Acknowledgements ********************************\n% The acknowledgements environment puts a large, bold, centered\n% \"Acknowledgements\" label at the top of the page.\n\n\\newenvironment{acknowledgements}{\n\\cleardoublepage\n\\setsinglecolumn\n\\chapter*{\\centering \\Large Acknowledgements}\n\\thispagestyle{empty}\n}\n\n\n\n% ******************************* Nomenclature *********************************\n\\RequirePackage[intoc]{nomencl}\n\\makenomenclature\n\\renewcommand{\\nomgroup}[1]{%\n\\ifthenelse{\\equal{#1}{A}}{\\item[\\textbf{Roman Symbols}]}{%\n\\ifthenelse{\\equal{#1}{G}}{\\item[\\textbf{Greek Symbols}]}{%\n\\ifthenelse{\\equal{#1}{Z}}{\\item[\\textbf{Acronyms / Abbreviations}]}{%\n\\ifthenelse{\\equal{#1}{R}}{\\item[\\textbf{Superscripts}]}{%\n\\ifthenelse{\\equal{#1}{S}}{\\item[\\textbf{Subscripts}]}{%\n\\ifthenelse{\\equal{#1}{X}}{\\item[\\textbf{Other Symbols}]}\n{}\n}% matches mathematical symbols > X\n}% matches Subscripts           > S\n}% matches Superscripts         > R\n}% matches Abbreviations        > Z\n}% matches Greek Symbols        > G\n}% matches Roman Symbols        > A\n\n% To add nomenclature in the header\n\\renewcommand{\\nompreamble}{\\markboth{\\nomname}{\\nomname}}\n\n% Add nomenclature to contents and print out nomenclature\n\\newcommand{\\printnomencl}[1][]{\n\\ifthenelse{\\equal {#1}{}}\n{\\printnomenclature}\n{\\printnomenclature[#1]}\n%\\addcontentsline{toc}{chapter}{\\nomname}\n}\n\n\n% ***************************** Create the index *******************************\n\\ifPHD@index\n    \\RequirePackage{makeidx}\n    \\makeindex\n    \\newcommand{\\printthesisindex}{\n        \\cleardoublepage\n        \\phantomsection\n        \\printindex}\n\\else\n    \\newcommand{\\printthesisindex}{}\n\\fi\n\n% ***************************** Chapter Mode ***********************************\n% The chapter mode allows user to only print particular chapters with references\n% All other options are disabled by default\n% To include only specific chapters without TOC, LOF, Title and Front Matter\n% To send it to supervisior for changes\n\n\\ifPHD@chapter\n    \\defineChaptertrue\n    % Disable the table of contents, figures, tables, index and nomenclature\n    \\renewcommand{\\maketitle}{}\n    \\renewcommand{\\tableofcontents}{}\n    \\renewcommand{\\listoffigures}{}\n    \\renewcommand{\\listoftables}{}\n    \\renewcommand{\\printnomencl}{}\n    \\renewcommand{\\printthesisindex}{}\n\\else\n    \\defineChapterfalse\n\\fi\n\n% ******************************** Abstract ************************************\n% The abstract environment puts a large, bold, centered \"Abstract\" label at\n% the top of the page. Defines both abstract and separate abstract environment\n\n% To include only the Title and the abstract pages for submission to BoGS\n\\ifPHD@abstract\n    \\defineAbstracttrue\n    % Disable the table of contents, figures, tables, index and nomenclature\n    \\renewcommand{\\tableofcontents}{}\n    \\renewcommand{\\listoffigures}{}\n    \\renewcommand{\\listoftables}{}\n    \\renewcommand{\\printnomencl}{}\n    \\renewcommand{\\printnomencl}[1][2]{}\n    \\renewcommand{\\printthesisindex}{}\n    \\renewcommand{\\bibname}{}\n    \\renewcommand{\\bibliography}[1]{\\thispagestyle{empty}}\n\\else\n    \\defineAbstractfalse\n\\fi\n\n\n\\newenvironment{abstract} {\n\\ifPHD@abstract\n% Separate abstract as per Student Registry guidelines\n  \\thispagestyle{empty}\n  \\setsinglecolumn\n  \\begin{center}\n    { \\Large {\\bfseries {\\@title}} \\par}\n    {{\\large \\vspace*{1em} \\@author} \\par}\n  \\end{center}\n\\else\n% Normal abstract in the thesis\n  \\cleardoublepage\n  \\setsinglecolumn\n  \\chapter*{\\centering \\Large Abstract}\n  \\thispagestyle{empty}\n\\fi\n}\n\n\n% ******************** To compute empty space in title page *******************\n% Boxes below are used to space different contents on the title page\n% Based on https://github.com/cambridge/thesis\n\n\\newcommand{\\computeTitlePageSpacing}{\n\n\n% Title Box\n\\newsavebox{\\PHD@Title}\n\\begin{lrbox}{\\PHD@Title}\n  \\begin{minipage}[c]{0.98\\textwidth}\n    \\centering \\Huge \\bfseries{\\@title}\n    \\ifthenelse{\\equal{\\@subtitle}{}}{\n      % subtitle is not defined\n    }{\n      \\\\\n      \\centering \\Large {\\@subtitle}\n    } % subtitle is defined\n  \\end{minipage}\n\\end{lrbox}\n\n% University Crest Box\n\\newsavebox{\\PHD@crest}\n\\begin{lrbox}{\\PHD@crest}\n  {\\@crest \\par}\n\\end{lrbox}\n\n\\newsavebox{\\PHD@collegecrest}\n\\begin{lrbox}{\\PHD@collegecrest}\n  \\ifPHD@collegeshield%\n    {\\@collegeshield}\n  \\else\n    % College shield is undefined\n  \\fi\n\\end{lrbox}\n\n% Author Box\n\\newsavebox{\\PHD@author}\n\\begin{lrbox}{\\PHD@author}\n  \\begin{minipage}[c]{\\textwidth}\n    \\centering \\Large \\bfseries{\\@author}\n      \\vspace{0.5em}   \n  \\end{minipage}\n\\end{lrbox}\n\n% Supervisor Box\n\\newsavebox{\\PHD@supervisor}\n\\begin{lrbox}{\\PHD@supervisor}\n  \\begin{minipage}[c]{\\textwidth}\n    \\ifthenelse{\\equal{\\@supervisor}{}}{\n      % supervisor is not defined\n    }{\n      \\begin{center}\n        \\makebox[\\widthof{supervisorrole} + \\@supervisorlinewidth][c]{\n          \\Large\n          \\@supervisorrole \\parbox[t]{\\@supervisorlinewidth}{\\@supervisor}\n        }\n      \\end{center}\n      \\vspace{0.5em}\n    } % supervisor is defined\n  \\end{minipage}\n\\end{lrbox}\n\n% Advisor Box\n\\newsavebox{\\PHD@advisor}\n\\begin{lrbox}{\\PHD@advisor}\n  \\begin{minipage}[c]{\\textwidth}\n    \\ifthenelse{\\equal{\\@advisor}{}}{\n      % advisor is not defined\n    }{\n      \\begin{center}\n        \\makebox[\\widthof{advisorrole} + \\@advisorlinewidth][c]{\n          \\Large\n          \\@advisorrole \\parbox[t]{\\@advisorlinewidth}{\\@advisor}\n        }\n      \\end{center}\n      \\vspace{0.5em}\n    } % advisor is defined\n  \\end{minipage}\n\\end{lrbox}\n\n% Department Box\n\\newsavebox{\\PHD@dept}\n\\begin{lrbox}{\\PHD@dept}\n  \\begin{minipage}[c]{\\textwidth}\n    \\centering {\\large \\@dept \\par}\n\t       {\\large \\@university \\par}\n  \\end{minipage}\n\\end{lrbox}\n\n% Submission Box\n\\newsavebox{\\PHD@submission}\n\\begin{lrbox}{\\PHD@submission}\n  \\begin{minipage}[c]{\\textwidth}\n    \\begin{center}\n      \\large \\submissiontext \\par\n      \\large \\textit {\\@degreetitle} \\par\n    \\end{center}\n  \\end{minipage}\n\\end{lrbox}\n\n% College and Date Box\n\\newsavebox{\\PHD@collegedate}\n\\begin{lrbox}{\\PHD@collegedate}\n\\begin{minipage}[c]{\\textwidth}\n  \\large\n  \\ifthenelse{\\equal{\\@college}{}}{\n    % college is not defined\n    \\begin{center}\n      \\@degreedate\n    \\end{center}\n  }{\n    % college is defined\n    \\begin{minipage}[b]{0.49\\textwidth}\n      \\flushleft\\@college\n    \\end{minipage}\n    \\begin{minipage}[b]{0.49\\textwidth}\n      \\flushright \\@degreedate\n    \\end{minipage}\n  } % college is defined\n\\end{minipage}\n\\end{lrbox}\n\n%  Now to compute the free vertical space\n\\newlength{\\PHD@titlepagespacing}\n\\setlength{\\PHD@titlepagespacing}{ \\textheight %\n\t\t\t- \\totalheightof{\\usebox{\\PHD@Title}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@crest}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@collegecrest}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@author}}\n                   \t- \\totalheightof{\\usebox{\\PHD@supervisor}}\n                   \t- \\totalheightof{\\usebox{\\PHD@advisor}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@dept}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@submission}}\n\t\t\t- \\totalheightof{\\usebox{\\PHD@collegedate}}\n}\n}\n\n"
  },
  {
    "path": "Classes/glyphtounicode.tex",
    "content": "% this file was converted from the following files:\n%   - glyphlist.txt       (Adobe Glyph List v2.0)\n%   - zapfdingbats.txt    (ITC Zapf Dingbats Glyph List)\n%   - texglyphlist.txt    (lcdf-typetools texglyphlist.txt, v2.33)\n%   - additional.tex      (additional entries)\n%\n% Notes:\n% - entries containing duplicates in glyphlist.txt like\n%   'dalethatafpatah;05D3 05B2' are ignored (commented out)\n%\n% - entries containing duplicates in texglyphlist.txt like\n%   'angbracketleft;27E8,2329' are changed so that only the first\n%   choice remains, ie 'angbracketleft;27E8'\n%\n% - a few entries in texglyphlist.txt like Delta, Omega, etc. are\n%   commented out (they are already in glyphlist.txt)\n\n% entries from glyphlist.txt:\n\\pdfglyphtounicode{A}{0041}\n\\pdfglyphtounicode{AE}{00C6}\n\\pdfglyphtounicode{AEacute}{01FC}\n\\pdfglyphtounicode{AEmacron}{01E2}\n\\pdfglyphtounicode{AEsmall}{F7E6}\n\\pdfglyphtounicode{Aacute}{00C1}\n\\pdfglyphtounicode{Aacutesmall}{F7E1}\n\\pdfglyphtounicode{Abreve}{0102}\n\\pdfglyphtounicode{Abreveacute}{1EAE}\n\\pdfglyphtounicode{Abrevecyrillic}{04D0}\n\\pdfglyphtounicode{Abrevedotbelow}{1EB6}\n\\pdfglyphtounicode{Abrevegrave}{1EB0}\n\\pdfglyphtounicode{Abrevehookabove}{1EB2}\n\\pdfglyphtounicode{Abrevetilde}{1EB4}\n\\pdfglyphtounicode{Acaron}{01CD}\n\\pdfglyphtounicode{Acircle}{24B6}\n\\pdfglyphtounicode{Acircumflex}{00C2}\n\\pdfglyphtounicode{Acircumflexacute}{1EA4}\n\\pdfglyphtounicode{Acircumflexdotbelow}{1EAC}\n\\pdfglyphtounicode{Acircumflexgrave}{1EA6}\n\\pdfglyphtounicode{Acircumflexhookabove}{1EA8}\n\\pdfglyphtounicode{Acircumflexsmall}{F7E2}\n\\pdfglyphtounicode{Acircumflextilde}{1EAA}\n\\pdfglyphtounicode{Acute}{F6C9}\n\\pdfglyphtounicode{Acutesmall}{F7B4}\n\\pdfglyphtounicode{Acyrillic}{0410}\n\\pdfglyphtounicode{Adblgrave}{0200}\n\\pdfglyphtounicode{Adieresis}{00C4}\n\\pdfglyphtounicode{Adieresiscyrillic}{04D2}\n\\pdfglyphtounicode{Adieresismacron}{01DE}\n\\pdfglyphtounicode{Adieresissmall}{F7E4}\n\\pdfglyphtounicode{Adotbelow}{1EA0}\n\\pdfglyphtounicode{Adotmacron}{01E0}\n\\pdfglyphtounicode{Agrave}{00C0}\n\\pdfglyphtounicode{Agravesmall}{F7E0}\n\\pdfglyphtounicode{Ahookabove}{1EA2}\n\\pdfglyphtounicode{Aiecyrillic}{04D4}\n\\pdfglyphtounicode{Ainvertedbreve}{0202}\n\\pdfglyphtounicode{Alpha}{0391}\n\\pdfglyphtounicode{Alphatonos}{0386}\n\\pdfglyphtounicode{Amacron}{0100}\n\\pdfglyphtounicode{Amonospace}{FF21}\n\\pdfglyphtounicode{Aogonek}{0104}\n\\pdfglyphtounicode{Aring}{00C5}\n\\pdfglyphtounicode{Aringacute}{01FA}\n\\pdfglyphtounicode{Aringbelow}{1E00}\n\\pdfglyphtounicode{Aringsmall}{F7E5}\n\\pdfglyphtounicode{Asmall}{F761}\n\\pdfglyphtounicode{Atilde}{00C3}\n\\pdfglyphtounicode{Atildesmall}{F7E3}\n\\pdfglyphtounicode{Aybarmenian}{0531}\n\\pdfglyphtounicode{B}{0042}\n\\pdfglyphtounicode{Bcircle}{24B7}\n\\pdfglyphtounicode{Bdotaccent}{1E02}\n\\pdfglyphtounicode{Bdotbelow}{1E04}\n\\pdfglyphtounicode{Becyrillic}{0411}\n\\pdfglyphtounicode{Benarmenian}{0532}\n\\pdfglyphtounicode{Beta}{0392}\n\\pdfglyphtounicode{Bhook}{0181}\n\\pdfglyphtounicode{Blinebelow}{1E06}\n\\pdfglyphtounicode{Bmonospace}{FF22}\n\\pdfglyphtounicode{Brevesmall}{F6F4}\n\\pdfglyphtounicode{Bsmall}{F762}\n\\pdfglyphtounicode{Btopbar}{0182}\n\\pdfglyphtounicode{C}{0043}\n\\pdfglyphtounicode{Caarmenian}{053E}\n\\pdfglyphtounicode{Cacute}{0106}\n\\pdfglyphtounicode{Caron}{F6CA}\n\\pdfglyphtounicode{Caronsmall}{F6F5}\n\\pdfglyphtounicode{Ccaron}{010C}\n\\pdfglyphtounicode{Ccedilla}{00C7}\n\\pdfglyphtounicode{Ccedillaacute}{1E08}\n\\pdfglyphtounicode{Ccedillasmall}{F7E7}\n\\pdfglyphtounicode{Ccircle}{24B8}\n\\pdfglyphtounicode{Ccircumflex}{0108}\n\\pdfglyphtounicode{Cdot}{010A}\n\\pdfglyphtounicode{Cdotaccent}{010A}\n\\pdfglyphtounicode{Cedillasmall}{F7B8}\n\\pdfglyphtounicode{Chaarmenian}{0549}\n\\pdfglyphtounicode{Cheabkhasiancyrillic}{04BC}\n\\pdfglyphtounicode{Checyrillic}{0427}\n\\pdfglyphtounicode{Chedescenderabkhasiancyrillic}{04BE}\n\\pdfglyphtounicode{Chedescendercyrillic}{04B6}\n\\pdfglyphtounicode{Chedieresiscyrillic}{04F4}\n\\pdfglyphtounicode{Cheharmenian}{0543}\n\\pdfglyphtounicode{Chekhakassiancyrillic}{04CB}\n\\pdfglyphtounicode{Cheverticalstrokecyrillic}{04B8}\n\\pdfglyphtounicode{Chi}{03A7}\n\\pdfglyphtounicode{Chook}{0187}\n\\pdfglyphtounicode{Circumflexsmall}{F6F6}\n\\pdfglyphtounicode{Cmonospace}{FF23}\n\\pdfglyphtounicode{Coarmenian}{0551}\n\\pdfglyphtounicode{Csmall}{F763}\n\\pdfglyphtounicode{D}{0044}\n\\pdfglyphtounicode{DZ}{01F1}\n\\pdfglyphtounicode{DZcaron}{01C4}\n\\pdfglyphtounicode{Daarmenian}{0534}\n\\pdfglyphtounicode{Dafrican}{0189}\n\\pdfglyphtounicode{Dcaron}{010E}\n\\pdfglyphtounicode{Dcedilla}{1E10}\n\\pdfglyphtounicode{Dcircle}{24B9}\n\\pdfglyphtounicode{Dcircumflexbelow}{1E12}\n\\pdfglyphtounicode{Dcroat}{0110}\n\\pdfglyphtounicode{Ddotaccent}{1E0A}\n\\pdfglyphtounicode{Ddotbelow}{1E0C}\n\\pdfglyphtounicode{Decyrillic}{0414}\n\\pdfglyphtounicode{Deicoptic}{03EE}\n\\pdfglyphtounicode{Delta}{2206}\n\\pdfglyphtounicode{Deltagreek}{0394}\n\\pdfglyphtounicode{Dhook}{018A}\n\\pdfglyphtounicode{Dieresis}{F6CB}\n\\pdfglyphtounicode{DieresisAcute}{F6CC}\n\\pdfglyphtounicode{DieresisGrave}{F6CD}\n\\pdfglyphtounicode{Dieresissmall}{F7A8}\n\\pdfglyphtounicode{Digammagreek}{03DC}\n\\pdfglyphtounicode{Djecyrillic}{0402}\n\\pdfglyphtounicode{Dlinebelow}{1E0E}\n\\pdfglyphtounicode{Dmonospace}{FF24}\n\\pdfglyphtounicode{Dotaccentsmall}{F6F7}\n\\pdfglyphtounicode{Dslash}{0110}\n\\pdfglyphtounicode{Dsmall}{F764}\n\\pdfglyphtounicode{Dtopbar}{018B}\n\\pdfglyphtounicode{Dz}{01F2}\n\\pdfglyphtounicode{Dzcaron}{01C5}\n\\pdfglyphtounicode{Dzeabkhasiancyrillic}{04E0}\n\\pdfglyphtounicode{Dzecyrillic}{0405}\n\\pdfglyphtounicode{Dzhecyrillic}{040F}\n\\pdfglyphtounicode{E}{0045}\n\\pdfglyphtounic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entries from 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entries from texglyphlist.txt:\n% Delta;2206\n\\pdfglyphtounicode{Ifractur}{2111}\n\\pdfglyphtounicode{FFsmall}{D804}\n\\pdfglyphtounicode{FFIsmall}{D807}\n\\pdfglyphtounicode{FFLsmall}{D808}\n\\pdfglyphtounicode{FIsmall}{D805}\n\\pdfglyphtounicode{FLsmall}{D806}\n\\pdfglyphtounicode{Germandbls}{D800}\n\\pdfglyphtounicode{Germandblssmall}{D803}\n\\pdfglyphtounicode{Ng}{014A}\n% Omega;2126\n\\pdfglyphtounicode{Rfractur}{211C}\n\\pdfglyphtounicode{SS}{D800}\n\\pdfglyphtounicode{SSsmall}{D803}\n\\pdfglyphtounicode{altselector}{D802}\n\\pdfglyphtounicode{angbracketleft}{27E8}\n\\pdfglyphtounicode{angbracketright}{27E9}\n\\pdfglyphtounicode{arrowbothv}{2195}\n\\pdfglyphtounicode{arrowdblbothv}{21D5}\n\\pdfglyphtounicode{arrowleftbothalf}{21BD}\n\\pdfglyphtounicode{arrowlefttophalf}{21BC}\n\\pdfglyphtounicode{arrownortheast}{2197}\n\\pdfglyphtounicode{arrownorthwest}{2196}\n\\pdfglyphtounicode{arrowrightbothalf}{21C1}\n\\pdfglyphtounicode{arrowrighttophalf}{21C0}\n\\pdfglyphtounicode{arrowsoutheast}{2198}\n\\pdfglyphtounicode{arrowsouthwest}{2199}\n\\pdfglyphtounicode{ascendercompwordmark}{D80A}\n\\pdfglyphtounicode{asteriskcentered}{2217}\n\\pdfglyphtounicode{bardbl}{2225}\n\\pdfglyphtounicode{capitalcompwordmark}{D809}\n\\pdfglyphtounicode{ceilingleft}{2308}\n\\pdfglyphtounicode{ceilingright}{2309}\n\\pdfglyphtounicode{circlecopyrt}{20DD}\n\\pdfglyphtounicode{circledivide}{2298}\n\\pdfglyphtounicode{circledot}{2299}\n\\pdfglyphtounicode{circleminus}{2296}\n\\pdfglyphtounicode{coproduct}{2A3F}\n\\pdfglyphtounicode{cwm}{200C}\n\\pdfglyphtounicode{dblbracketleft}{27E6}\n\\pdfglyphtounicode{dblbracketright}{27E7}\n% diamond;2662\n% diamondmath;22C4\n% dotlessj;0237\n% emptyset;2205\n\\pdfglyphtounicode{emptyslot}{D801}\n\\pdfglyphtounicode{epsilon1}{03F5}\n\\pdfglyphtounicode{equivasymptotic}{224D}\n\\pdfglyphtounicode{flat}{266D}\n\\pdfglyphtounicode{floorleft}{230A}\n\\pdfglyphtounicode{floorright}{230B}\n\\pdfglyphtounicode{follows}{227B}\n\\pdfglyphtounicode{followsequal}{227D}\n\\pdfglyphtounicode{greatermuch}{226B}\n% heart;2661\n\\pdfglyphtounicode{interrobang}{203D}\n\\pdfglyphtounicode{interrobangdown}{D80B}\n\\pdfglyphtounicode{intersectionsq}{2293}\n\\pdfglyphtounicode{latticetop}{22A4}\n\\pdfglyphtounicode{lessmuch}{226A}\n\\pdfglyphtounicode{lscript}{2113}\n\\pdfglyphtounicode{natural}{266E}\n\\pdfglyphtounicode{negationslash}{0338}\n\\pdfglyphtounicode{ng}{014B}\n\\pdfglyphtounicode{owner}{220B}\n\\pdfglyphtounicode{pertenthousand}{2031}\n% phi;03D5\n% 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entries from additional.tex:\n\\pdfglyphtounicode{ff}{0066 0066}\n\\pdfglyphtounicode{fi}{0066 0069}\n\\pdfglyphtounicode{fl}{0066 006C}\n\\pdfglyphtounicode{ffi}{0066 0066 0069}\n\\pdfglyphtounicode{ffl}{0066 0066 006C}\n\\pdfglyphtounicode{IJ}{0049 004A}\n\\pdfglyphtounicode{ij}{0069 006A}\n\\pdfglyphtounicode{longs}{0073}\n"
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  {
    "path": "Declaration/declaration.tex",
    "content": "% ******************************* Thesis Declaration ***************************\n\n\\begin{declaration}\n\nI hereby declare that except where specific reference is made to the work of others, the contents of this thesis are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This thesis is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and in acknowledgements.\\\\ \\\\ \\\\ \\\\\n\n% Author and date will be inserted automatically from thesis.tex \\author \\degreedate\n\n\\end{declaration}"
  },
  {
    "path": "Dedication/dedication.tex",
    "content": "% ******************************* Thesis Dedidcation ********************************\n\n\\begin{dedication} \n\nI would like to dedicate this thesis to the Machine Learning Community \\dots\n\n\\end{dedication}"
  },
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141.469 674.062\n 141.09 674.273 140.621 674.273 c 140.109 674.273 139.629 673.969 139.578\n 673.934 c 133.973 670.551 128.906 666.57 124.559 662.137 c 119.656 657.246\n 115.871 651.871 113.289 646.129 c 111.93 643.125 110.906 639.93 110.293\n 636.691 c 109.676 633.672 109.375 630.355 109.395 626.84 c 109.414 625.234\n 109.551 623.551 109.676 621.957 c 109.82 620.34 110.059 618.766 110.438\n 616.992 c 111.07 614.082 112.016 610.984 113.383 607.555 c 114.367 605.07\n 115.457 602.531 116.746 599.832 c 115.727 600.605 114.703 601.328 113.633\n 601.953 c 112.785 602.449 111.883 602.934 110.754 603.461 c 110.254 603.699\n 109.82 603.867 109.309 604.07 c 108.816 604.277 108.316 604.488 107.816\n 604.664 c 103.73 606.145 99.531 606.922 95.348 607.02 c 86.879 607.25 78.383\n 604.516 71.496 599.418 c 68.055 596.895 65.07 593.848 62.641 590.367 c \n61.438 588.621 60.387 586.758 59.512 584.828 c 58.68 583.012 57.977 581.066\n 57.352 578.863 c 55.031 570.531 55.465 561.727 58.586 554.074 c 60.195 \n550.133 62.418 546.547 65.195 543.398 c 66.773 541.633 68.219 540.203 69.664\n 539.008 c 71.09 537.77 72.672 536.566 74.676 535.199 c 74.711 535.18 l \n75.051 534.949 75.512 534.926 75.879 535.164 c 76.109 535.312 76.273 535.57\n 76.332 535.844 c 76.379 536.098 76.332 536.367 76.188 536.566 c 75.203 \n538.102 74.645 540.051 74.434 542.672 c 74.242 544.895 74.316 547.094 74.691\n 549.18 c 75.387 553.527 77.238 557.551 80.02 560.828 c 82.816 564.082 86.465\n 566.598 90.578 568.113 c 93.344 569.137 96.273 569.652 99.297 569.652 c\n 100.629 569.652 101.977 569.535 103.316 569.328 c 107.586 568.676 111.688\n 566.988 115.184 564.5 c 118.316 562.27 120.82 558.852 122.672 554.344 c\n 124.125 550.68 125.129 546.531 125.668 541.652 c 125.668 541.57 125.688\n 541.496 125.695 541.414 c 125.695 539.977 l 125.629 539.977 l 125.254 539.977\n 124.898 539.801 124.676 539.516 c 120.984 534.641 l 120.82 534.43 120.746\n 534.168 120.746 533.918 c 120.746 522.879 l 120.746 522.625 120.82 522.359\n 120.984 522.152 c 124.676 517.266 l 124.781 517.145 124.867 517.039 125.004\n 516.965 c 124.867 516.758 124.715 516.57 124.52 516.363 c 123.402 515.047\n 121.863 513.957 120.07 513.23 c 118.508 512.605 116.832 512.301 115.09 \n512.301 c 114.879 512.301 l 112.961 512.398 111.168 512.773 109.656 513.41\n c 108.066 514.051 106.59 515.098 105.426 516.414 c 104.184 517.801 103.363\n 519.43 103.047 521.113 c 103.047 521.125 103.027 521.137 103.027 521.176\n c 102.922 521.609 102.535 521.926 102.074 521.926 c 102.008 521.926 101.949\n 521.926 101.891 521.91 c 101.621 521.871 101.41 521.723 101.246 521.496\n c 98.863 518.094 97.641 514.008 97.824 510.008 c 97.93 505.984 99.434 501.938\n 102.043 498.625 c 103.414 496.914 104.867 495.535 106.496 494.398 c 108.199\n 493.223 110.07 492.336 112.055 491.766 c 116.035 490.57 120.379 490.652\n 124.23 491.969 c 125.734 492.488 127.227 493.203 128.672 494.141 c 129.117\n 490.402 130.148 486.711 131.738 483.145 c 133.816 478.52 136.602 474.629\n 140.09 471.488 c 140.156 471.453 140.234 471.414 140.301 471.395 c 140.418\n 471.34 140.551 471.34 140.609 471.34 c 140.621 471.34 l 140.688 471.34 \n140.801 471.34 140.93 471.395 c 141.004 471.434 141.09 471.477 141.148 471.527\n c 144.617 474.629 147.41 478.52 149.453 483.145 c 151.102 486.711 152.133\n 490.402 152.566 494.125 c 153.992 493.203 155.477 492.488 156.977 491.969\n c 160.859 490.652 165.207 490.57 169.164 491.766 c 171.141 492.328 173.02\n 493.203 174.754 494.398 c 176.402 495.57 177.844 496.961 179.176 498.625\n c 181.777 501.887 183.289 505.93 183.414 510.008 c 183.586 514.023 182.363\n 518.113 179.984 521.469 c 179.867 521.703 179.637 521.871 179.367 521.91\n c 178.828 522.023 178.297 521.652 178.23 521.195 c 178.211 521.176 178.211\n 521.137 178.211 521.113 c 177.863 519.43 177.027 517.801 175.805 516.414\n c 174.629 515.098 173.164 514.051 171.574 513.395 c 170.062 512.773 168.27\n 512.398 166.391 512.301 c 166.168 512.301 l 164.414 512.301 162.719 512.605\n 161.141 513.23 c 159.379 513.977 157.844 515.047 156.699 516.363 c 156.582\n 516.508 156.457 516.645 156.363 516.797 c 156.68 516.84 156.949 517.004\n 157.152 517.266 c 160.84 522.152 l 160.996 522.359 161.082 522.625 161.082\n 522.879 c 161.082 533.918 l 161.082 534.168 160.996 534.43 160.84 534.641\n c 157.152 539.516 l 156.922 539.801 156.562 539.977 156.18 539.977 c 155.523\n 539.977 l 155.543 541.027 155.543 541.633 155.543 541.652 c 156.121 546.609\n 157.113 550.766 158.559 554.344 c 160.398 558.836 162.902 562.254 166.035\n 564.5 c 169.543 567.004 173.664 568.676 177.914 569.328 c 179.254 569.535\n 180.602 569.652 181.93 569.652 c 184.965 569.652 187.902 569.137 190.648\n 568.113 c 194.742 566.617 198.414 564.098 201.199 560.828 c 203.965 557.602\n 205.832 553.57 206.547 549.18 c 206.902 547.023 207 544.844 206.805 542.66\n c 206.602 540.109 206.016 538.102 205.062 536.574 c 204.812 536.258 204.812\n 535.832 205.051 535.484 c 205.332 535.023 205.957 534.848 206.488 535.164\n c 206.566 535.215 l 210.496 537.754 213.656 540.383 216.188 543.258 c 219.078\n 546.418 221.363 550.012 223.02 553.906 c 226.305 561.676 226.855 570.84\n 224.504 579.055 c f\n1 0.972549 0.501961 rg\n222.375 566.336 m 222.336 562.422 221.633 558.551 220.312 554.848 c 218.953\n 551.152 216.98 547.715 214.426 544.656 c 212.809 542.715 210.91 540.91 \n208.762 539.262 c 210.207 542.789 210.832 546.621 210.574 550.395 c 210.121\n 557.379 206.875 563.777 201.441 568.383 c 196.469 572.594 190.301 574.906\n 184.109 574.906 c 183.328 574.906 182.547 574.867 181.777 574.797 c 169.945\n 573.738 160.977 566.871 156.535 555.5 c 156.469 555.332 156.418 555.188\n 156.371 555.098 c 154.715 551.18 153.617 546.832 152.949 541.836 c 152.934\n 541.699 152.922 541.559 152.895 541.418 c 152.875 541.27 152.863 541.109\n 152.824 540.926 c 152.824 540.371 l 146.043 540.371 l 146.043 540.895 l\n 146.18 542.289 146.363 543.715 146.523 545.148 c 146.957 548.434 147.422\n 551.578 147.941 554.527 c 148.461 557.672 149.039 560.82 149.664 563.855\n c 149.781 564.438 l 150.398 567.281 151.043 570.223 151.941 572.891 c 153.625\n 578.086 156.199 583.141 158.703 588.027 c 160.109 590.77 l 161.383 592.551\n 162.875 594.215 164.523 595.723 c 167.449 598.426 170.793 600.41 174.465\n 601.648 c 178.23 602.973 182.105 603.664 185.957 603.695 c 186.496 603.707\n l 194.012 603.707 201.555 601.18 207.77 596.602 c 210.891 594.289 213.617\n 591.5 215.824 588.309 c 217.867 585.398 219.504 581.879 220.746 577.855\n c 221.305 575.93 221.73 574.016 221.969 572.145 c 222.277 570.02 222.402\n 568.125 222.375 566.336 c f\n180.844 510 m 180.66 506.562 179.309 503.074 177.035 500.227 c 175.938 \n498.824 174.598 497.551 173.164 496.566 c 171.719 495.57 170.117 494.82 \n168.395 494.301 c 164.965 493.316 161.219 493.391 157.914 494.539 c 156.129\n 495.145 154.406 496.09 152.844 497.34 c 152.902 499.613 152.711 501.926\n 152.277 504.238 c 151.863 506.457 151.207 508.562 150.562 510.629 c 149.32\n 514.52 l 149.105 515.148 148.895 515.781 148.656 516.402 c 154.27 516.402\n l 155.727 513.996 157.699 512.02 159.965 510.695 c 161.988 509.52 164.262\n 508.77 166.371 508.582 c 168.566 508.375 170.117 508.777 171.141 509.043\n c 172.297 509.258 173.348 509.758 174.309 510.254 c 176.922 511.645 178.645\n 514.012 179.617 517.453 c 180.57 514.879 180.996 512.387 180.844 510 c f\n168.316 626.809 m 168.258 625.59 168.172 624.434 168.047 623.215 c 167.953\n 622.156 l 167.777 620.793 167.527 619.285 167.141 617.664 c 166.488 614.906\n 165.477 611.977 164.164 608.969 c 161.863 603.527 159.492 597.914 156.449\n 592.086 c 155.352 589.973 154.195 587.711 153.113 585.379 c 153.086 585.344\n 153.078 585.316 153.059 585.285 c 151.652 582.555 150.426 579.656 149.375\n 576.633 c 146.824 569.484 145.031 561.559 143.895 552.398 c 143.422 548.434\n 143.066 544.598 142.875 540.926 c 142.875 540.371 l 138.395 540.371 l 138.395\n 540.965 l 138.164 544.844 137.805 548.699 137.352 552.398 c 136.188 561.652\n 134.395 569.57 131.883 576.633 c 130.82 579.656 129.617 582.516 128.297\n 585.086 c 128.27 585.145 128.238 585.191 128.191 585.273 c 127.246 587.289\n 126.168 589.461 124.801 592.086 c 121.91 597.637 119.684 602.801 117.082\n 608.969 c 115.754 612.066 114.781 614.906 114.117 617.664 c 113.742 619.211\n 113.48 620.719 113.324 622.184 c 113.145 623.832 113.008 625.246 112.941\n 626.809 c 112.836 630.02 113.02 633.117 113.508 635.98 c 114.059 638.992\n 114.934 641.938 116.16 644.738 c 118.469 650.168 121.977 655.32 126.516\n 660.062 c 130.996 664.648 138.914 671.016 140.629 671.016 c 142.297 670.988\n 150.32 664.559 154.703 660.062 c 159.281 655.344 162.758 650.195 165.09\n 644.738 c 166.312 641.918 167.211 638.957 167.75 635.98 c 168.223 633.117\n 168.414 630.035 168.316 626.809 c f\n150.004 497.059 m 150.004 496.152 l 149.965 491.648 149.203 487.488 147.699\n 483.785 c 146.902 481.734 145.84 479.781 144.504 477.77 c 144.23 477.328\n 143.914 476.902 143.605 476.484 c 142.711 475.258 141.719 474.152 140.629\n 473.133 c 139.242 474.41 137.949 475.965 136.758 477.77 c 135.398 479.762\n 134.348 481.715 133.566 483.785 c 132.074 487.469 131.293 491.625 131.246\n 496.152 c 131.246 497.051 l 131.246 497.883 131.273 498.559 131.344 499.188\n c 131.562 502.953 132.238 506.727 133.309 510.414 c 133.953 512.566 134.578\n 514.531 135.254 516.402 c 145.984 516.402 l 146.652 514.598 147.285 512.68\n 147.941 510.414 c 149.059 506.652 149.703 502.996 149.918 499.199 c 149.984\n 498.398 150.004 497.695 150.004 497.059 c f\n135.195 540.926 m 135.195 540.371 l 128.395 540.371 l 128.395 540.973 l\n 128.297 541.852 l 127.621 546.867 126.484 551.207 124.859 555.129 c 124.82\n 555.207 124.77 555.34 124.695 555.527 c 120.234 566.891 111.254 573.738\n 99.461 574.797 c 92.516 575.43 85.398 573.117 79.801 568.383 c 74.348 563.777\n 71.102 557.379 70.656 550.395 c 70.406 546.621 71.031 542.789 72.457 539.262\n c 70.328 540.895 68.441 542.699 66.812 544.656 c 64.25 547.715 62.273 551.152\n 60.938 554.848 c 59.605 558.535 58.902 562.402 58.855 566.336 c 58.836 \n568.207 58.98 570.16 59.25 572.145 c 59.527 574.094 59.953 576 60.492 577.855\n c 61.754 581.988 63.375 585.414 65.406 588.309 c 67.602 591.469 70.309 \n594.262 73.461 596.602 c 79.676 601.18 87.219 603.707 94.73 603.707 c 95.273\n 603.695 l 99.125 603.664 103 602.973 106.766 601.648 c 110.453 600.41 113.816\n 598.406 116.727 595.723 c 118.355 594.215 119.84 592.551 121.109 590.824\n c 121.293 590.449 l 121.379 590.281 121.469 590.098 121.574 589.93 c 122.41\n 588.246 l 124.953 583.297 127.586 578.16 129.301 572.891 c 130.254 570.008\n 130.918 566.871 131.582 563.855 c 132.172 560.93 132.758 557.781 133.289\n 554.527 c 133.887 551.07 134.348 548.004 134.703 545.137 c 134.898 543.59\n 135.07 542.055 135.195 540.926 c f\n132.566 516.402 m 132.344 515.781 132.121 515.148 131.93 514.504 c 130.84\n 511.176 l 130.176 509.07 129.426 506.684 128.961 504.246 c 128.527 502.074\n 128.344 499.746 128.414 497.32 c 126.883 496.09 125.176 495.145 123.344\n 494.539 c 120.004 493.391 116.305 493.297 112.863 494.301 c 111.141 494.82\n 109.492 495.602 108.094 496.566 c 106.68 497.551 105.34 498.824 104.211\n 500.227 c 101.949 503.09 100.598 506.562 100.406 510 c 100.273 512.398 \n100.707 514.898 101.609 517.453 c 102.586 514.031 104.375 511.625 106.918\n 510.254 c 107.902 509.766 108.934 509.258 110.137 509.031 c 111.176 508.77\n 112.73 508.391 114.887 508.582 c 116.988 508.77 119.25 509.52 121.293 510.695\n c 123.566 512.02 125.477 513.945 126.977 516.402 c 132.566 516.402 l f\n158.297 533.387 m 158.297 523.414 l 155.398 519.578 l 126.43 519.578 l \n123.527 523.414 l 123.527 533.387 l 126.418 537.191 l 155.398 537.191 l \n158.297 533.387 l f\n1 g\n216.902 340.703 m 216.133 339.051 215.016 337.559 213.625 336.379 c 212.953\n 335.816 212.23 335.328 211.461 334.922 c 210.957 334.672 209.531 333.844\n 208.965 334.039 c 208.539 334.039 208.281 334.895 208.715 335.066 c 209.523\n 335.461 210.273 335.949 210.949 336.531 c 212.441 337.777 213.617 339.426\n 214.281 341.258 c 215.898 345.688 214.195 350.086 212.441 354.184 c 210.805\n 357.988 208.59 361.477 207.113 365.367 c 205.574 369.449 204.492 373.641\n 203.926 377.965 c 203.461 381.781 202.738 385.992 201.133 389.527 c 199.473\n 393.152 196.266 395.844 192.25 396.406 c 191.594 396.5 187.691 395.949 \n188.25 397.195 c 188.52 397.754 190.141 397.949 190.668 398.043 c 191.777\n 398.23 192.902 398.25 194.012 398.117 c 206.43 396.648 206.758 381.855 \n209.262 372.57 c 210.727 367.117 213.336 362.312 215.449 357.129 c 217.547\n 351.945 219.379 346.102 216.902 340.703 c 215.305 337.277 217.633 342.32\n 216.902 340.703 c h\n222.145 350.152 m 221.285 358.676 218.809 366.973 216.035 375.051 c 213.367\n 382.797 210.141 390.945 204.898 397.348 c 202.422 400.367 199.203 403.137\n 195.227 403.719 c 191.547 404.258 187.895 402.59 184.945 400.555 c 181.527\n 398.191 178.598 395.164 175.93 391.988 c 174.578 390.371 173.316 388.68\n 172.074 386.961 c 173.992 385.484 175.84 383.918 177.586 382.23 c 180.516\n 379.395 183.355 375.988 184.418 371.969 c 184.977 369.863 184.867 367.598\n 183.926 365.613 c 183.152 363.977 181.527 361.621 179.781 360.922 c 183.242\n 360.969 186.672 359.879 188.801 357.023 c 190.93 354.156 191.266 350.293\n 191.102 346.84 c 190.949 343.477 190.254 340.16 189.398 336.906 c 189.164\n 336.062 188.934 335.215 188.676 334.367 c 188.461 333.637 188.047 333.398\n 188.895 333.117 c 189.426 332.941 189.957 332.758 190.496 332.59 c 192.277\n 332.02 194.078 331.492 195.879 331.055 c 202.094 329.508 209.926 328.016\n 215.898 331.199 c 222.547 334.746 222.789 343.598 222.145 350.152 c 221.516\n 356.395 222.441 347.133 222.145 350.152 c f\n205.957 307.043 m 204.117 298.086 194.367 294.562 187.461 290.316 c 183.637\n 287.973 180.082 285.184 176.891 282.051 c 173.742 278.949 170.176 275.801\n 166.168 273.891 c 162.336 272.062 157.809 271.875 153.992 273.855 c 152.449\n 274.645 151.074 275.785 150.062 277.184 c 149.734 277.621 149.215 278.273\n 149.938 278.609 c 150.406 278.949 151.91 277.27 152.344 276.988 c 160.156\n 271.855 167.855 278.648 173.164 284.137 c 176.836 287.922 180.793 291.289\n 185.34 294 c 189.812 296.621 194.668 298.457 198.82 301.629 c 202.809 304.68\n 204.984 309.656 203.406 314.602 c 203.211 315.203 202.074 316.723 203 317.105\n c 203.676 317.602 204.832 315.168 205.062 314.711 c 206.188 312.355 206.449\n 309.609 205.957 307.043 c 205.602 305.344 206.672 310.758 205.957 307.043\n c h\n211.586 312.75 m 210.793 317.797 205.852 321.418 201.758 323.805 c 199.281\n 325.234 196.672 326.398 194.02 327.457 c 192.98 327.883 191.93 328.258 \n190.871 328.617 c 190.188 328.863 189.484 329.086 188.801 329.312 c 188.066\n 329.539 187.285 330.156 186.969 329.312 c 185.188 324.5 182.961 319.406\n 179.223 315.77 c 175.082 311.754 169.551 311.348 164.926 314.863 c 166.477\n 310.562 165.66 305.957 161.988 303.039 c 158.934 300.625 154.945 299.555\n 151.168 298.945 c 149.059 298.621 146.93 298.434 144.809 298.324 c 144.25\n 298.285 143.684 298.262 143.113 298.25 c 142.219 298.227 142.516 297.891\n 142.5 297.121 c 142.48 295.887 142.48 294.652 142.488 293.438 c 142.555\n 289.074 142.93 284.656 143.98 280.414 c 144.898 276.699 146.418 272.801\n 149.348 270.207 c 152.574 267.363 156.996 267.098 161.062 267.863 c 169.051\n 269.398 176.555 274.004 183.191 278.496 c 189.812 282.973 196.297 287.824\n 201.941 293.488 c 206.738 298.293 213.059 305.344 211.586 312.75 c 211.516\n 313.238 212.047 310.398 211.586 312.75 c f\n184.957 345.637 m 183.809 341.438 181.438 337.715 180.312 333.484 c 179.242\n 329.406 179.195 324.535 176.293 321.184 c 175.012 319.688 173.211 318.715\n 171.238 318.566 c 170.246 318.496 169.223 318.633 168.309 319.027 c 167.625\n 319.367 168.848 319.574 168.953 319.555 c 173.297 319.086 175.57 322.641\n 176.477 326.324 c 177.336 329.781 177.586 333.297 178.73 336.699 c 179.977\n 340.328 182.414 343.523 183.316 347.305 c 183.762 349.152 183.551 351.137\n 182.48 352.754 c 182.008 353.469 181.391 354.098 180.66 354.578 c 180.504\n 354.672 179.289 355.219 179.957 355.566 c 181.094 356.156 183.422 353.688\n 183.965 352.941 c 185.246 351.156 185.621 348.875 185.227 346.719 c 185.137\n 346.363 185.051 345.992 184.957 345.637 c 184.656 344.578 185.281 346.77\n 184.957 345.637 c h\n151.977 307.324 m 153.684 307.273 155.406 307.645 156.785 308.672 c 157.555\n 309.215 158.172 309.949 158.625 310.77 c 158.875 311.199 159.203 313.105\n 159.668 313.145 c 160.746 313.238 159.223 309.387 158.961 308.973 c 157.875\n 307.211 156.102 306.031 154.105 305.562 c 149.918 304.523 145.523 305.883\n 141.305 305.973 c 137.344 306.047 133.488 305.082 129.531 305.191 c 125.418\n 305.398 121.102 308.289 121.504 312.863 c 121.535 313.152 122.082 313.176\n 122.121 312.883 c 122.246 311.934 122.605 311.027 123.125 310.258 c 124.23\n 308.578 126.051 307.562 128.039 307.324 c 131.621 306.914 135.176 308.125\n 138.711 308.352 c 142.035 308.578 145.156 308.031 148.422 307.59 c 149.598\n 307.418 150.793 307.367 151.977 307.324 c 153.836 307.262 150.984 307.355\n 151.977 307.324 c h\n121.594 384.055 m 119.395 381.66 116.793 379.676 113.98 378.098 c 110.727\n 376.281 106.082 374.805 104.57 371.047 c 104.203 370.105 104.047 369.074\n 104.137 368.059 c 104.172 367.551 104.281 367.043 104.445 366.547 c 104.531\n 366.27 104.926 365.727 104.875 365.438 c 104.75 364.691 103.914 365.82 \n103.809 365.969 c 103.23 366.891 102.902 367.965 102.805 369.047 c 102.457\n 373.742 106.574 376.773 110.242 378.828 c 113.586 380.691 116.727 382.59\n 119.492 385.297 c 122.496 388.203 126.266 391.727 130.82 390.918 c 132.527\n 390.617 134.176 389.695 135.188 388.246 c 135.312 388.059 135.637 387.363\n 135.051 387.438 c 134.781 387.488 134.434 387.977 134.211 388.137 c 133.711\n 388.523 133.152 388.828 132.566 389.035 c 128.152 390.703 124.406 386.938\n 121.594 384.055 c 120.898 383.293 122.141 384.617 121.594 384.055 c h\n145.727 387.922 m 147.805 391.367 152.469 391.781 155.773 390.016 c 159.445\n 388.059 162.102 384.715 165.398 382.27 c 168.73 379.844 172.758 378.441\n 175.766 375.578 c 177.258 374.141 178.277 372.27 178.422 370.18 c 178.492\n 369.234 178.367 368.266 178.047 367.363 c 177.953 367.086 177.086 364.973\n 176.594 365.355 c 176.055 365.5 176.738 366.469 176.816 366.703 c 177.133\n 367.68 177.219 368.742 177.055 369.77 c 176.727 371.816 175.398 373.492\n 173.691 374.617 c 172.035 375.746 170.32 376.688 168.52 377.551 c 167.008\n 378.266 165.102 379.129 163.926 380.316 c 160.66 382.57 158.328 385.84 \n155.07 388.09 c 153.441 389.207 151.449 389.797 149.484 389.355 c 148.566\n 389.141 147.699 388.738 146.938 388.152 c 146.777 388.02 146.234 387.309\n 145.938 387.504 c 145.801 387.57 145.648 387.746 145.727 387.922 c 146.738\n 389.582 145.688 387.82 145.727 387.922 c h\n113.18 318.934 m 109.559 317.082 105.398 319.855 103.797 323.09 c 101.871\n 326.98 101.812 331.492 100.379 335.555 c 98.992 339.539 96.332 343.242 \n95.977 347.547 c 95.82 349.539 96.332 351.57 97.613 353.113 c 97.941 353.527\n 101.141 356.309 101.477 355.199 c 101.629 354.691 100.531 354.352 100.203\n 354.082 c 99.473 353.488 98.863 352.734 98.434 351.891 c 97.438 349.859\n 97.727 347.621 98.52 345.59 c 99.867 342.113 101.805 338.949 103.027 335.422\n c 104.27 331.801 104.25 327.922 105.484 324.293 c 106.062 322.555 107.188\n 321.012 108.836 320.148 c 109.676 319.707 110.609 319.449 111.562 319.406\n c 111.609 319.406 113.98 319.48 113.18 318.934 c 113.18 318.934 113.27 \n319 113.18 318.934 c h\n187.461 352.34 m 186.043 358.996 178.875 358.734 173.551 357.566 c 170.273\n 356.852 167.086 355.688 164.012 354.391 c 163.434 354.164 160.746 352.586\n 160.426 353.148 c 159.859 354.129 162.48 355.086 163.23 355.5 c 162.863\n 355.449 158.617 354.711 159.598 356.027 c 159.926 356.465 162.066 356.578\n 162.625 356.703 c 163.992 357.023 165.332 357.379 166.672 357.773 c 170.523\n 358.883 174.523 360.312 177.75 362.711 c 181.508 365.488 182.684 369.328\n 180.312 373.539 c 178.211 377.277 174.801 380.332 171.477 382.973 c 167.934\n 385.785 164.176 388.473 160.188 390.633 c 156.777 392.484 152.516 394.582\n 148.539 393.801 c 144.887 393.086 143.219 389.508 142.449 386.199 c 141.469\n 382.035 141.535 377.664 141.793 373.414 c 141.832 372.891 142.441 368.527\n 142.102 368.473 c 141.016 367.664 140.715 371.297 140.602 371.855 c 140.512\n 371.383 140.098 367.457 139.039 368.508 c 138.684 368.727 139.223 371.234\n 139.262 371.668 c 139.375 373.059 139.473 374.449 139.512 375.82 c 139.629\n 379.977 139.551 384.43 138.066 388.371 c 136.641 392.141 133.73 394.395\n 129.59 393.812 c 125.609 393.266 121.805 391.199 118.434 389.102 c 114.703\n 386.801 111.102 384.148 107.797 381.273 c 104.75 378.621 101.543 375.52\n 100.098 371.668 c 96.754 362.738 110.137 358.809 116.34 357.18 c 116.891\n 357.039 121.785 356.027 121.707 355.781 c 122.113 354.504 118.605 355.348\n 118.047 355.414 c 118.383 355.227 122.199 353.375 120.695 352.855 c 120.168\n 352.68 118.344 353.852 117.805 354.082 c 116.543 354.617 115.262 355.125\n 113.98 355.594 c 110.031 356.996 105.754 358.27 101.535 358.086 c 97.469\n 357.906 94.492 355.855 93.719 351.723 c 93.008 347.797 93.836 343.598 94.77\n 339.781 c 95.82 335.496 97.207 331.23 98.953 327.176 c 100.543 323.465 \n102.477 319.406 105.727 316.859 c 113.5 310.719 121.707 323.258 125.156 \n328.824 c 125.492 329.379 127.047 332.695 127.621 332.453 c 128.664 332.02\n 127.094 329.668 126.727 328.918 c 126.988 329.148 130.043 332.234 129.973\n 330.598 c 129.953 330.066 128.453 328.879 128.086 328.449 c 127.246 327.488\n 126.43 326.5 125.629 325.504 c 123.027 322.207 120.531 318.566 119.211 \n314.543 c 117.969 310.551 118.789 306.723 122.691 304.668 c 126.16 302.824\n 130.301 302.262 134.176 301.961 c 138.664 301.613 143.258 301.621 147.73\n 302.027 c 151.582 302.363 155.766 302.965 159.156 304.949 c 167.539 309.977\n 159.559 320.891 155.379 326.051 c 154.754 326.832 154.117 327.582 153.453\n 328.336 c 153.059 328.793 151.438 330.066 151.398 330.648 c 151.293 332.281\n 154.395 329.199 154.629 328.973 c 154.379 329.5 152.883 332.113 153.559\n 332.414 c 154.484 332.828 155.504 330.102 155.949 329.387 c 157.605 326.668\n 159.484 324.199 161.418 321.699 c 163.441 319.355 165.805 317.008 168.742\n 315.863 c 171.746 314.699 174.609 315.719 176.816 317.957 c 180.918 322.137\n 183.375 328.656 184.926 334.207 c 186.535 339.914 188.82 346.344 187.461\n 352.34 c 187.027 354.379 187.848 350.648 187.461 352.34 c f\n174 407.902 m 173.926 407.402 172.98 407.387 172.895 407.891 c 172.777 \n408.812 172.527 409.723 172.16 410.598 c 171.371 412.488 170.043 414.172\n 168.375 415.367 c 164.867 417.914 160.57 417.719 156.477 417.387 c 151.949\n 417.039 147.566 415.863 143.02 415.715 c 138.473 415.543 134.137 416.012\n 129.664 416.785 c 122.188 418.062 112.219 418.852 108.973 410.156 c 108.758\n 409.594 108.703 408.004 108.277 407.637 c 107.691 407.133 107.324 407.902\n 107.305 408.363 c 107.207 410.422 107.766 412.473 108.758 414.254 c 110.871\n 418.008 114.723 420.336 118.914 420.977 c 124.414 421.824 129.828 420.648\n 135.281 420.148 c 140.438 419.691 145.504 420.035 150.602 420.734 c 155.676\n 421.426 161.613 421.984 166.383 419.82 c 170.938 417.762 174.465 413.051\n 174 407.902 c 173.965 407.684 174.301 411.277 174 407.902 c h\n178.5 414.754 m 176.121 422.957 165.176 425.113 157.934 426.203 c 149.125\n 427.52 139.992 427.574 131.109 427 c 123.395 426.516 114.695 425.562 108.027\n 421.297 c 104.617 419.117 102.441 415.715 102.324 411.633 c 102.18 407.188\n 103.973 402.797 105.965 398.906 c 107.816 395.504 109.867 392.25 112.18\n 389.133 c 115.301 391.332 118.578 393.418 122.074 394.957 c 125.438 396.438\n 129.367 397.664 133.047 396.855 c 136.93 396.012 139.434 392.949 140.57\n 389.254 c 142.027 394.469 146.391 397.562 151.785 397.062 c 157.094 396.574\n 162.055 393.793 166.449 390.945 c 166.891 390.652 167.324 390.363 167.77\n 390.07 c 168.453 389.602 168.906 388.828 169.473 389.621 c 170.332 390.777\n 171.18 391.949 171.949 393.152 c 173.336 395.297 174.656 397.496 175.785\n 399.789 c 177.961 404.246 180.043 409.793 178.5 414.754 c 178.395 415.129\n 179.328 412.113 178.5 414.754 c f\n131.699 277.914 m 129.77 274.68 126.109 272.766 122.43 272.465 c 117.641\n 272.074 113.559 274.469 109.887 277.258 c 106.332 279.973 103.355 283.309\n 99.848 286.109 c 96.188 289.02 92.227 291.227 88.141 293.488 c 81.062 297.418\n 72.93 303.781 75.57 312.957 c 75.754 313.598 77.332 317.781 78.305 317.117\n c 79.059 316.785 78.43 316.02 78.23 315.531 c 77.777 314.469 77.496 313.312\n 77.402 312.168 c 77.062 308.145 78.98 304.441 82.082 301.961 c 86.129 298.699\n 90.984 296.859 95.453 294.281 c 100.004 291.676 104.137 288.34 107.738 \n284.543 c 111.129 280.949 115.379 276.555 120.379 275.441 c 122.488 274.965\n 124.723 275.121 126.766 275.891 c 127.98 276.348 129.137 277.02 130.117\n 277.891 c 130.387 278.121 130.898 278.93 131.352 278.617 c 131.613 278.504\n 131.863 278.199 131.699 277.914 c 129.965 274.996 131.805 278.09 131.699\n 277.914 c h\n123.047 300.887 m 120.629 301.941 118.277 303.5 116.949 305.84 c 115.379\n 308.633 115.543 311.828 116.516 314.789 c 113.434 311.703 108.105 311.527\n 104.578 313.746 c 101.062 315.977 98.691 319.73 96.871 323.379 c 95.918\n 325.309 95.098 327.301 94.328 329.301 c 94.008 330.141 93.758 329.727 93.016\n 329.5 c 92.488 329.332 91.949 329.16 91.426 328.98 c 90.273 328.598 89.098\n 328.184 87.961 327.75 c 84.172 326.285 80.434 324.594 77.094 322.281 c \n74.02 320.164 70.957 317.336 69.895 313.633 c 68.625 309.102 70.617 304.648\n 73.074 300.914 c 77.641 294.016 84.414 288.523 90.965 283.594 c 96.246 \n279.645 101.707 275.879 107.555 272.777 c 112.844 269.98 118.973 267.098\n 125.129 267.508 c 138.934 268.465 138.934 288.191 138.809 298.227 c 137.082\n 298.285 127.98 298.773 123.047 300.887 c f\n92.98 396.633 m 92.785 396.066 91.023 396.539 90.551 396.539 c 89.551 396.527\n 88.559 396.387 87.582 396.125 c 85.609 395.559 83.809 394.461 82.41 392.949\n c 79.73 390.023 78.809 385.934 78.047 382.156 c 77.168 377.832 76.785 373.492\n 75.504 369.242 c 74.23 365.031 72.363 361.242 70.348 357.359 c 68.402 353.594\n 66.207 349.051 66.359 344.727 c 66.504 340.836 68.73 337.133 72.199 335.289\n c 72.594 335.078 73.18 334.562 72.574 334.125 c 72.121 333.805 71.012 334.426\n 70.59 334.602 c 68.738 335.395 67.141 336.699 65.898 338.273 c 63.113 341.832\n 62.668 346.469 63.719 350.762 c 64.992 355.938 67.613 360.461 69.617 365.352\n c 71.812 370.695 72.68 376.16 73.922 381.754 c 75.02 386.629 76.621 391.914\n 80.578 395.234 c 82.477 396.82 84.82 397.836 87.285 398.137 c 88.086 398.23\n 93.836 398.191 92.98 396.633 c 92.922 396.445 92.98 396.633 92.98 396.633\n c h\n106.523 390.586 m 101.785 396.273 94.984 404.445 86.699 403.777 c 78.277\n 403.277 73.121 393.359 69.945 386.707 c 66.137 378.746 63.297 370.145 61.188\n 361.57 c 59.355 354.133 57.508 345.305 60.051 337.828 c 64.328 325.27 81.418\n 329.566 90.793 332.602 c 91.504 332.828 92.227 333.074 92.93 333.316 c \n92.66 334.219 92.383 335.121 92.121 336.043 c 91.688 337.582 91.301 339.145\n 90.984 340.715 c 90.426 343.441 90.078 346.223 90.184 349.012 c 90.301 \n352.07 91.004 355.254 93.094 357.586 c 95.203 359.938 98.363 360.801 101.457\n 360.762 c 98.992 362.465 96.957 364.973 96.543 368 c 96.109 371.102 97.41\n 374.203 99.098 376.742 c 100.754 379.262 102.863 381.461 105.09 383.492\n c 106.293 384.582 107.535 385.613 108.809 386.613 c 109.375 386.816 109.395\n 387.094 108.895 387.449 c 108.672 387.758 108.441 388.078 108.219 388.398\n c 107.672 389.133 107.102 389.863 106.523 390.586 c 105.926 391.312 107.43\n 389.469 106.523 390.586 c f\n0.0313726 0.580392 0.278431 rg\n239.352 316.453 m 239.648 316.066 239.984 315.684 240.246 315.277 c 239.801\n 315.105 239.312 314.957 238.82 314.824 c 234.648 313.691 230.266 313.105\n 225.91 312.969 c 223.723 312.895 221.555 312.977 219.398 313.203 c 218.512\n 313.312 217.605 313.465 216.738 313.645 c 216.66 313.973 216.574 314.305\n 216.477 314.629 c 216.441 314.809 216.371 314.996 216.324 315.156 c 216.227\n 315.449 216.121 315.707 216.016 315.984 c 215.594 317.066 215.035 318.109\n 214.34 319.121 c 214.168 319.367 213.973 319.648 213.762 319.895 c 213.348\n 320.438 212.895 320.984 212.414 321.523 c 212.125 321.82 211.844 322.105\n 211.535 322.395 c 210.91 322.965 210.273 323.523 209.57 324.055 c 209.242\n 324.332 208.887 324.582 208.531 324.828 c 208.531 324.84 208.512 324.84\n 208.512 324.84 c 208.645 324.828 208.809 324.828 208.973 324.828 c 209.781\n 324.828 210.574 324.855 211.363 324.914 c 212.125 324.988 212.875 325.102\n 213.609 325.234 c 215.34 325.551 216.949 326.078 218.406 326.793 c 218.867\n 327.039 219.34 327.281 219.793 327.562 c 220.043 327.715 220.285 327.883\n 220.504 328.035 c 220.977 328.363 221.41 328.711 221.836 329.074 c 222.066\n 329.285 222.297 329.5 222.52 329.727 c 223.125 329.465 223.703 329.16 224.312\n 328.844 c 226.199 327.836 228.031 326.645 229.773 325.355 c 232.395 323.41\n 234.879 321.246 237.152 318.887 c 237.539 318.496 237.914 318.09 238.281\n 317.676 c 238.637 317.293 238.992 316.875 239.352 316.453 c f\n200.43 431.973 m 200.449 431.484 200.43 430.984 200.43 430.469 c 200.207\n 426.148 199.406 421.805 198.191 417.629 c 197.586 415.543 196.836 413.496\n 195.938 411.527 c 195.543 410.668 195.109 409.82 194.668 408.992 c 194.648\n 408.992 l 194.543 408.992 194.445 408.992 194.34 408.984 c 194.012 408.984\n 193.656 408.984 193.328 408.945 c 191.207 408.793 189.051 408.211 186.961\n 407.207 c 186.227 406.859 185.465 406.465 184.734 406.012 c 184.012 405.582\n 183.309 405.09 182.605 404.547 c 183.184 406.219 183.625 407.902 183.809\n 409.516 c 183.992 411.105 183.992 412.629 183.828 414.098 c 183.762 414.773\n 183.656 415.457 183.48 416.109 c 183.434 416.297 183.387 416.484 183.316\n 416.684 c 183.27 416.906 183.191 417.129 183.117 417.348 c 183.008 417.629\n 182.902 417.922 182.777 418.207 c 182.5 418.891 182.172 419.566 181.777\n 420.207 c 181.797 420.219 l 182.047 420.508 182.297 420.738 182.574 421.016\n c 184.145 422.52 185.832 423.871 187.594 425.145 c 188.926 426.09 190.301\n 426.984 191.711 427.82 c 192.652 428.371 193.598 428.922 194.57 429.418\n c 196.016 430.168 197.5 430.863 199.02 431.465 c 199.484 431.652 199.945\n 431.852 200.43 431.973 c f\n149.039 264.105 m 149.039 264.098 l 148.953 262.863 148.809 261.652 148.598\n 260.434 c 148.203 258.301 147.645 256.223 146.957 254.145 c 145.562 250.016\n 143.73 246.004 141.449 242.301 c 141.18 241.883 140.898 241.465 140.609\n 241.086 c 140.301 241.457 140.004 241.871 139.723 242.289 c 137.344 245.926\n 135.457 249.914 133.973 253.992 c 133.238 256.051 132.633 258.129 132.199\n 260.242 c 131.957 261.43 131.785 262.621 131.68 263.809 c 132.199 264.066\n 132.711 264.379 133.203 264.699 c 133.664 265.012 134.117 265.359 134.539\n 265.715 c 134.734 265.883 134.945 266.059 135.129 266.242 c 135.32 266.43\n 135.531 266.609 135.707 266.816 c 136.844 267.953 137.836 269.305 138.684\n 270.859 c 138.875 271.215 139.059 271.582 139.242 271.949 c 139.559 272.578\n 139.859 273.254 140.109 273.949 c 140.301 274.469 140.477 274.996 140.641\n 275.535 c 141.148 273.844 141.777 272.281 142.586 270.859 c 143.008 270.066\n 143.48 269.344 143.973 268.66 c 144.32 268.207 144.656 267.789 145.004 \n267.395 c 145.379 266.973 145.754 266.598 146.148 266.215 c 146.363 266.047\n 146.555 265.863 146.746 265.695 c 147.113 265.395 147.469 265.113 147.844\n 264.855 c 148.047 264.719 148.238 264.605 148.441 264.461 c 148.637 264.332\n 148.836 264.23 149.039 264.105 c f\n98.305 405.805 m 98.422 405.391 98.559 404.984 98.723 404.582 c 98.703 \n404.582 98.703 404.59 98.691 404.59 c 98.508 404.734 98.324 404.848 98.152\n 404.973 c 97.98 405.09 97.805 405.203 97.633 405.336 c 96.555 406.07 95.445\n 406.688 94.336 407.227 c 92.219 408.23 90.059 408.801 87.969 408.965 c \n87.797 408.984 87.621 408.992 87.449 408.984 c 86.688 409.02 85.926 408.992\n 85.156 408.898 c 84.914 408.871 84.664 408.832 84.414 408.793 c 84.078 \n408.727 83.738 408.664 83.414 408.594 c 82.941 409.379 82.496 410.188 82.113\n 410.996 c 81.156 412.949 80.398 414.98 79.73 417.047 c 78.43 421.203 77.535\n 425.527 77.238 429.848 c 77.188 430.34 77.16 430.852 77.188 431.336 c 77.641\n 431.211 78.133 431.051 78.613 430.863 c 82.672 429.344 86.555 427.23 90.156\n 424.793 c 91.938 423.57 93.672 422.227 95.262 420.77 c 96.332 419.773 97.363\n 418.703 98.305 417.613 c 98.211 417.367 98.133 417.141 98.074 416.926 c\n 98 416.652 97.922 416.371 97.855 416.109 c 97.332 414.047 97.207 411.832\n 97.527 409.516 c 97.68 408.27 97.93 407.047 98.305 405.805 c f\n72.777 324.855 m 72.777 324.855 72.766 324.855 72.766 324.84 c 72.035 324.336\n 71.352 323.812 70.703 323.258 c 70.387 322.965 70.07 322.676 69.762 322.395\n c 69.453 322.125 69.164 321.82 68.875 321.523 c 68.469 321.078 68.086 320.652\n 67.738 320.203 c 66.676 318.879 65.84 317.469 65.27 315.992 c 65.164 315.73\n 65.059 315.449 64.973 315.156 c 64.887 314.887 64.789 314.586 64.711 314.305\n c 64.547 313.715 64.434 313.125 64.336 312.523 c 63.027 312.203 61.715 \n311.934 60.367 311.754 c 58.219 311.453 56.051 311.348 53.863 311.371 c \n49.527 311.406 45.125 311.887 40.918 312.957 c 40.426 313.07 39.934 313.191\n 39.48 313.371 c 39.73 313.766 40.059 314.18 40.367 314.566 c 43.082 317.957\n 46.281 321.004 49.73 323.652 c 51.445 324.988 53.258 326.211 55.117 327.27\n c 56.477 328.035 57.863 328.691 59.277 329.273 c 59.297 329.258 l 59.656\n 328.938 60 328.656 60.359 328.371 c 60.734 328.09 61.109 327.836 61.504\n 327.59 c 61.734 327.453 61.965 327.32 62.188 327.188 c 62.902 326.793 63.625\n 326.453 64.395 326.152 c 65.414 325.758 66.531 325.457 67.68 325.25 c 67.785\n 325.215 67.883 325.207 67.988 325.195 c 68.055 325.195 l 68.664 325.062\n 69.27 324.988 69.895 324.93 c 70.27 324.902 70.668 324.895 71.062 324.855\n c 71.262 324.855 71.457 324.855 71.648 324.84 c 72.246 324.84 l 72.418 \n324.84 72.594 324.84 72.777 324.855 c f\n0.137255 0.121569 0.12549 rg\n155.062 339.527 m 154.559 340.273 153.855 341.176 152.949 342.266 c 154.145\n 343.586 155.254 344.898 156.207 346.117 c 157.055 344.934 157.73 343.902\n 158.191 343.008 c 157.219 341.852 156.148 340.703 155.062 339.527 c h\n150.234 334.633 m 149.559 335.141 148.684 335.891 147.625 336.832 c 148.934\n 338.062 150.254 339.375 151.496 340.703 c 152.441 339.633 153.172 338.762\n 153.691 338.09 c 152.566 336.926 151.398 335.758 150.234 334.633 c h\n145.32 330.145 m 144.434 330.609 143.363 331.27 142.191 332.133 c 143.434\n 333.09 144.723 334.18 146.062 335.383 c 147.152 334.465 148.059 333.766\n 148.789 333.277 c 147.625 332.168 146.469 331.137 145.32 330.145 c h\n136.699 336.645 m 137.902 337.684 139.23 338.91 140.648 340.254 c 142.082\n 338.91 143.402 337.684 144.598 336.645 c 143.238 335.383 141.91 334.277\n 140.648 333.285 c 139.406 334.277 138.074 335.383 136.699 336.645 c h\n135.984 330.145 m 134.848 331.137 133.672 332.188 132.508 333.277 c 133.25\n 333.766 134.164 334.48 135.254 335.383 c 136.582 334.18 137.883 333.09 \n139.105 332.133 c 137.93 331.27 136.883 330.59 135.984 330.145 c h\n131.07 334.633 m 129.898 335.758 128.75 336.926 127.621 338.09 c 128.125\n 338.762 128.867 339.633 129.809 340.703 c 131.07 339.375 132.363 338.074\n 133.672 336.832 c 132.613 335.891 131.738 335.141 131.07 334.633 c h\n126.234 339.527 m 125.168 340.695 124.098 341.852 123.125 343.008 c 123.578\n 343.902 124.242 344.934 125.109 346.117 c 126.062 344.898 127.172 343.586\n 128.363 342.266 c 127.461 341.176 126.746 340.273 126.234 339.527 c h\n123.125 352.324 m 124.098 353.457 125.156 354.617 126.234 355.781 c 126.746\n 355.066 127.461 354.156 128.363 353.066 c 127.172 351.738 126.062 350.434\n 125.109 349.207 c 124.242 350.379 123.578 351.438 123.125 352.324 c h\n131.07 360.699 m 131.738 360.18 132.613 359.445 133.672 358.488 c 132.363\n 357.246 131.055 355.938 129.809 354.617 c 128.867 355.68 128.125 356.551\n 127.621 357.238 c 128.75 358.406 129.898 359.559 131.07 360.699 c h\n135.984 365.18 m 136.883 364.719 137.93 364.055 139.105 363.207 c 137.875\n 362.238 136.582 361.141 135.254 359.938 c 134.164 360.848 133.25 361.551\n 132.508 362.059 c 133.672 363.152 134.848 364.203 135.984 365.18 c h\n144.598 358.688 m 143.402 357.625 142.082 356.402 140.648 355.078 c 139.23\n 356.422 137.922 357.625 136.699 358.688 c 138.074 359.93 139.406 361.062\n 140.648 362.043 c 141.891 361.062 143.219 359.938 144.598 358.688 c h\n145.32 365.18 m 146.469 364.203 147.625 363.152 148.789 362.059 c 148.059\n 361.551 147.141 360.848 146.062 359.938 c 144.723 361.148 143.434 362.238\n 142.191 363.207 c 143.363 364.055 144.434 364.719 145.32 365.18 c h\n150.234 360.688 m 151.398 359.559 152.566 358.406 153.691 357.238 c 153.191\n 356.57 152.441 355.695 151.496 354.617 c 150.254 355.949 148.953 357.246\n 147.625 358.488 c 148.703 359.445 149.57 360.18 150.234 360.688 c h\n148.059 347.66 m 149.426 349.078 150.621 350.406 151.699 351.613 c 152.934\n 350.234 154.07 348.918 155.031 347.66 c 154.07 346.418 152.934 345.086 \n151.699 343.711 c 150.621 344.914 149.426 346.25 148.059 347.66 c h\n146.516 346.043 m 147.895 344.613 149.137 343.305 150.207 342.133 c 148.926\n 340.773 147.566 339.387 146.199 338.109 c 145.023 339.199 143.703 340.441\n 142.277 341.812 c 143.684 343.184 145.129 344.613 146.516 346.043 c h\n142.277 353.527 m 143.684 354.879 145.004 356.121 146.199 357.23 c 147.566\n 355.934 148.926 354.578 150.207 353.207 c 149.125 352.004 147.875 350.695\n 146.516 349.277 c 145.129 350.707 143.703 352.133 142.277 353.527 c h\n136.371 347.66 m 137.777 349.09 139.23 350.547 140.648 351.945 c 142.082\n 350.547 143.52 349.09 144.926 347.66 c 143.539 346.23 142.082 344.785 140.648\n 343.383 c 139.211 344.785 137.777 346.23 136.371 347.66 c h\n134.801 346.043 m 136.188 344.613 137.605 343.203 139.031 341.812 c 137.625\n 340.453 136.312 339.207 135.098 338.109 c 133.742 339.406 132.391 340.762\n 131.09 342.133 c 132.18 343.316 133.441 344.633 134.801 346.043 c h\n131.09 353.207 m 132.383 354.559 133.75 355.934 135.109 357.23 c 136.293\n 356.137 137.605 354.898 139.031 353.527 c 138.328 352.828 137.605 352.133\n 136.891 351.418 c 136.188 350.707 135.504 349.992 134.801 349.277 c 133.422\n 350.695 132.18 352.004 131.09 353.207 c h\n133.238 347.66 m 131.902 346.25 130.676 344.914 129.617 343.711 c 128.363\n 345.086 127.246 346.418 126.266 347.66 c 127.246 348.918 128.363 350.234\n 129.617 351.613 c 130.668 350.406 131.902 349.078 133.238 347.66 c h\n155.062 355.781 m 156.148 354.633 157.199 353.469 158.191 352.324 c 157.73\n 351.438 157.055 350.379 156.207 349.207 c 155.254 350.434 154.145 351.738\n 152.949 353.066 c 153.855 354.145 154.559 355.066 155.062 355.781 c h\n157.375 347.66 m 158.191 348.789 158.855 349.82 159.379 350.762 c 159.434\n 350.844 l 159.406 350.969 l 159.262 351.418 159.105 351.891 158.926 352.359\n c 158.848 352.559 l 158.75 352.773 158.645 352.992 158.539 353.207 c 158.223\n 353.852 157.844 354.504 157.375 355.18 c 156.832 355.762 156.285 356.328\n 155.727 356.91 c 155.754 356.934 155.773 356.984 155.785 357.023 c 155.859\n 357.191 l 155.746 357.324 l 155.457 357.66 155.176 357.988 154.879 358.332\n c 154.59 358.629 l 154.395 358.262 l 153.367 359.316 152.305 360.367 151.273\n 361.391 c 151.633 361.59 l 151.324 361.863 l 150.996 362.16 150.656 362.453\n 150.332 362.738 c 150.188 362.859 l 150.031 362.785 l 149.984 362.773 149.938\n 362.754 149.887 362.738 c 149.328 363.281 148.762 363.828 148.125 364.398\n c 147.297 364.973 146.418 365.465 145.551 365.84 c 145.352 365.914 l 144.898\n 366.09 144.434 366.254 143.961 366.395 c 143.836 366.422 l 143.742 366.363\n l 142.836 365.867 141.777 365.18 140.648 364.371 c 139.531 365.18 138.48\n 365.867 137.566 366.363 c 137.449 366.422 l 137.336 366.395 l 136.863 366.254\n 136.41 366.09 135.965 365.914 c 135.766 365.84 l 134.898 365.465 134.031\n 364.992 133.113 364.371 c 132.555 363.828 131.988 363.281 131.41 362.738\n c 131.371 362.754 131.332 362.773 131.293 362.785 c 131.129 362.859 l 130.977\n 362.738 l 130.637 362.453 130.312 362.16 129.992 361.863 c 129.684 361.59\n l 130.043 361.391 l 128.98 360.367 127.949 359.328 126.918 358.262 c 126.727\n 358.629 l 126.43 358.332 l 126.141 357.988 125.84 357.66 125.562 357.324\n c 125.438 357.191 l 125.512 357.023 l 125.531 356.984 125.562 356.934 125.57\n 356.91 c 125.012 356.328 124.465 355.762 123.895 355.152 c 123.473 354.504\n 123.074 353.844 122.766 353.188 c 122.66 352.98 122.566 352.773 122.469\n 352.559 c 122.383 352.359 l 122.199 351.91 122.043 351.445 121.91 350.969\n c 121.883 350.855 l 121.93 350.762 l 122.43 349.84 123.113 348.789 123.941\n 347.66 c 123.113 346.543 122.43 345.5 121.93 344.578 c 121.883 344.465 \nl 121.91 344.371 l 122.027 343.902 122.199 343.43 122.383 342.98 c 122.469\n 342.773 l 122.844 341.918 123.316 341.043 123.922 340.141 c 124.465 339.574\n 125.012 338.992 125.57 338.418 c 125.562 338.391 125.543 338.355 125.512\n 338.297 c 125.438 338.137 l 125.562 337.996 l 125.84 337.68 126.141 337.32\n 126.438 337 c 126.727 336.699 l 126.902 337.07 l 127.949 336.004 128.98\n 334.957 130.043 333.938 c 129.684 333.742 l 129.992 333.469 l 130.312 333.164\n 130.637 332.867 130.977 332.582 c 131.129 332.453 l 131.293 332.539 l 131.324\n 332.562 131.371 332.582 131.41 332.59 c 131.988 332.059 132.555 331.492\n 133.164 330.918 c 134.031 330.348 134.898 329.859 135.766 329.5 c 135.965\n 329.406 l 136.418 329.219 136.863 329.074 137.336 328.938 c 137.449 328.906\n l 137.566 328.953 l 138.48 329.465 139.531 330.145 140.648 330.965 c 141.777\n 330.141 142.836 329.465 143.742 328.953 c 143.836 328.906 l 143.961 328.938\n l 144.434 329.074 144.887 329.219 145.332 329.406 c 145.551 329.5 l 146.398\n 329.875 147.277 330.348 148.172 330.945 c 148.762 331.492 149.328 332.059\n 149.887 332.59 c 149.938 332.582 149.984 332.562 150.012 332.539 c 150.188\n 332.453 l 150.332 332.582 l 150.656 332.867 150.996 333.164 151.324 333.469\n c 151.633 333.742 l 151.273 333.938 l 152.305 334.957 153.367 336.004 154.395\n 337.07 c 154.59 336.699 l 154.879 337 l 155.176 337.32 155.457 337.66 155.746\n 337.996 c 155.859 338.137 l 155.785 338.297 l 155.773 338.355 155.754 338.391\n 155.727 338.418 c 156.285 338.992 156.832 339.574 157.41 340.176 c 157.98\n 341.043 158.473 341.918 158.855 342.84 c 158.926 342.98 l 159.098 343.43\n 159.262 343.902 159.406 344.371 c 159.434 344.465 l 159.379 344.578 l 158.855\n 345.5 158.191 346.543 157.375 347.66 c f\n1 g\n352.762 150.938 m 351.98 149.273 350.863 147.789 349.477 146.59 c 348.812\n 146.039 348.082 145.559 347.301 145.145 c 346.809 144.895 345.375 144.074\n 344.812 144.273 c 344.398 144.273 344.129 145.117 344.562 145.305 c 345.363\n 145.695 346.125 146.184 346.809 146.746 c 348.301 148.012 349.469 149.66\n 350.133 151.48 c 351.742 155.922 350.035 160.316 348.301 164.406 c 346.664\n 168.211 344.438 171.711 342.965 175.602 c 341.434 179.672 340.355 183.863\n 339.785 188.199 c 339.312 192.023 338.59 196.234 336.98 199.75 c 335.324\n 203.375 332.105 206.074 328.09 206.637 c 327.453 206.734 323.531 206.18\n 324.109 207.41 c 324.371 207.977 325.98 208.184 326.527 208.273 c 327.629\n 208.465 328.754 208.48 329.863 208.348 c 342.301 206.883 342.617 192.078\n 345.113 182.809 c 346.578 177.34 349.199 172.547 351.297 167.359 c 353.406\n 162.168 355.238 156.332 352.762 150.938 c 351.164 147.508 353.504 152.555\n 352.762 150.938 c h\n357.992 160.375 m 357.156 168.898 354.672 177.195 351.887 185.273 c 349.219\n 193.027 346 201.176 340.75 207.582 c 338.281 210.59 335.066 213.367 331.086\n 213.941 c 327.406 214.48 323.746 212.824 320.797 210.777 c 317.387 208.414\n 314.449 205.387 311.797 202.219 c 310.43 200.594 309.168 198.902 307.926\n 197.184 c 309.852 195.707 311.703 194.148 313.445 192.453 c 316.363 189.621\n 319.199 186.211 320.277 182.207 c 320.836 180.098 320.719 177.828 319.793\n 175.848 c 319.023 174.211 317.375 171.844 315.633 171.156 c 319.09 171.203\n 322.531 170.121 324.668 167.246 c 326.781 164.379 327.125 160.516 326.961\n 157.074 c 326.797 153.699 326.105 150.395 325.246 147.129 c 325.027 146.297\n 324.785 145.449 324.535 144.602 c 324.324 143.879 323.898 143.633 324.746\n 143.352 c 325.277 143.176 325.805 142.98 326.348 142.812 c 328.129 142.254\n 329.93 141.727 331.742 141.285 c 337.953 139.742 345.789 138.246 351.742\n 141.434 c 358.398 144.969 358.641 153.82 357.992 160.375 c 357.367 166.613\n 358.293 157.355 357.992 160.375 c f\n341.809 117.277 m 339.977 108.32 330.219 104.801 323.312 100.555 c 319.484\n 98.207 315.941 95.422 312.734 92.273 c 309.594 89.184 306.027 86.023 302.02\n 84.113 c 298.195 82.285 293.668 82.098 289.852 84.078 c 288.301 84.875 \n286.934 86.004 285.91 87.422 c 285.602 87.844 285.062 88.508 285.785 88.832\n c 286.258 89.184 287.77 87.512 288.195 87.227 c 296.02 82.078 303.727 88.871\n 309.035 94.367 c 312.684 98.168 316.652 101.523 321.191 104.238 c 325.652\n 106.852 330.527 108.691 334.668 111.863 c 338.656 114.914 340.824 119.891\n 339.254 124.824 c 339.062 125.438 337.938 126.945 338.84 127.336 c 339.535\n 127.824 340.691 125.391 340.895 124.945 c 342.039 122.578 342.301 119.84\n 341.809 117.277 c 341.473 115.578 342.52 120.992 341.809 117.277 c h\n347.434 122.984 m 346.656 128.031 341.703 131.652 337.609 134.027 c 335.141\n 135.477 332.52 136.641 329.871 137.691 c 328.832 138.105 327.781 138.488\n 326.73 138.859 c 326.027 139.09 325.344 139.32 324.66 139.535 c 323.918\n 139.77 323.137 140.379 322.828 139.543 c 321.047 134.73 318.812 129.637\n 315.074 126.012 c 310.941 121.988 305.422 121.594 300.777 125.098 c 302.336\n 120.785 301.52 116.18 297.828 113.273 c 294.785 110.855 290.797 109.777\n 287.008 109.184 c 284.918 108.855 282.789 108.668 280.66 108.555 c 280.102\n 108.523 279.535 108.496 278.965 108.48 c 278.07 108.449 278.367 108.113\n 278.348 107.344 c 278.328 106.109 278.32 104.887 278.34 103.66 c 278.406\n 99.297 278.801 94.879 279.832 90.645 c 280.758 86.934 282.262 83.043 285.207\n 80.449 c 288.438 77.582 292.859 77.32 296.922 78.09 c 304.918 79.621 312.395\n 84.227 319.043 88.723 c 325.652 93.195 332.145 98.059 337.801 103.711 c\n 342.598 108.535 348.91 115.578 347.434 122.984 c 347.359 123.461 347.918\n 120.633 347.434 122.984 c f\n320.805 155.859 m 319.648 151.648 317.289 147.957 316.184 143.727 c 315.094\n 139.641 315.047 134.777 312.156 131.414 c 310.863 129.918 309.07 128.941\n 307.086 128.801 c 306.094 128.727 305.082 128.863 304.16 129.258 c 303.473\n 129.602 304.699 129.809 304.805 129.785 c 309.148 129.301 311.434 132.871\n 312.328 136.559 c 313.176 140.012 313.445 143.539 314.594 146.91 c 315.844\n 150.559 318.273 153.758 319.16 157.527 c 319.613 159.379 319.41 161.359\n 318.34 162.988 c 317.867 163.703 317.23 164.32 316.52 164.809 c 316.355\n 164.914 315.133 165.449 315.816 165.797 c 316.941 166.379 319.285 163.91\n 319.824 163.176 c 321.105 161.379 321.473 159.098 321.074 156.961 c 321.008\n 156.586 320.922 156.227 320.805 155.859 c 320.527 154.809 321.133 157 320.805\n 155.859 c h\n287.828 117.559 m 289.535 117.504 291.258 117.887 292.645 118.895 c 293.406\n 119.445 294.043 120.172 294.484 120.992 c 294.727 121.438 295.055 123.328\n 295.508 123.367 c 296.605 123.473 295.082 119.621 294.824 119.195 c 293.734\n 117.445 291.953 116.262 289.957 115.785 c 285.777 114.758 281.375 116.109\n 277.172 116.195 c 273.203 116.27 269.332 115.309 265.371 115.434 c 261.285\n 115.641 256.961 118.516 257.355 123.086 c 257.395 123.375 257.945 123.406\n 257.992 123.105 c 258.098 122.164 258.465 121.262 258.965 120.48 c 260.094\n 118.801 261.922 117.805 263.898 117.559 c 267.461 117.145 271.027 118.348\n 274.57 118.586 c 277.875 118.801 281.016 118.266 284.273 117.82 c 285.457\n 117.664 286.645 117.598 287.828 117.559 c 289.707 117.504 286.848 117.59\n 287.828 117.559 c h\n257.441 194.277 m 255.254 191.891 252.664 189.891 249.82 188.32 c 246.574\n 186.516 241.941 185.039 240.43 181.27 c 240.055 180.34 239.91 179.297 239.996\n 178.301 c 240.035 177.785 240.129 177.266 240.305 176.777 c 240.391 176.5\n 240.785 175.957 240.738 175.668 c 240.613 174.914 239.766 176.055 239.668\n 176.203 c 239.07 177.125 238.754 178.191 238.664 179.277 c 238.328 183.965\n 242.434 187.004 246.094 189.051 c 249.445 190.934 252.578 192.812 255.344\n 195.52 c 258.348 198.434 262.125 201.961 266.672 201.141 c 268.398 200.84\n 270.035 199.918 271.047 198.48 c 271.18 198.289 271.48 197.598 270.922 \n197.672 c 270.633 197.711 270.285 198.199 270.082 198.363 c 269.57 198.746\n 269.012 199.043 268.426 199.27 c 264.012 200.934 260.254 197.16 257.441\n 194.277 c 256.75 193.527 258 194.84 257.441 194.277 c h\n281.586 198.145 m 283.668 201.602 288.32 202.012 291.633 200.238 c 295.297\n 198.289 297.953 194.949 301.258 192.504 c 304.594 190.074 308.609 188.676\n 311.617 185.801 c 313.098 184.379 314.141 182.512 314.285 180.414 c 314.34\n 179.465 314.227 178.488 313.898 177.586 c 313.801 177.309 312.934 175.195\n 312.445 175.582 c 311.902 175.734 312.59 176.695 312.664 176.926 c 312.992\n 177.91 313.07 178.977 312.906 179.996 c 312.59 182.047 311.25 183.715 309.543\n 184.84 c 307.887 185.961 306.172 186.91 304.359 187.773 c 302.879 188.496\n 300.969 189.363 299.785 190.543 c 296.52 192.793 294.195 196.082 290.922\n 198.32 c 289.285 199.441 287.309 200.031 285.352 199.578 c 284.418 199.363\n 283.551 198.961 282.809 198.387 c 282.617 198.25 282.098 197.543 281.797\n 197.727 c 281.664 197.801 281.5 197.969 281.586 198.145 c 282.578 199.812\n 281.539 198.055 281.586 198.145 c h\n249.031 129.168 m 245.398 127.316 241.246 130.09 239.648 133.32 c 237.723\n 137.211 237.672 141.727 236.238 145.785 c 234.832 149.77 232.191 153.477\n 231.844 157.77 c 231.672 159.762 232.203 161.793 233.473 163.348 c 233.801\n 163.754 237 166.531 237.328 165.434 c 237.492 164.914 236.402 164.562 236.055\n 164.305 c 235.324 163.719 234.715 162.969 234.301 162.113 c 233.289 160.07\n 233.598 157.852 234.379 155.809 c 235.727 152.336 237.652 149.18 238.867\n 145.645 c 240.121 142.035 240.102 138.156 241.324 134.535 c 241.922 132.785\n 243.051 131.246 244.695 130.379 c 245.535 129.93 246.469 129.672 247.402\n 129.637 c 247.461 129.637 249.832 129.703 249.031 129.168 c 249.031 129.168\n 249.129 129.223 249.031 129.168 c h\n323.312 162.574 m 321.906 169.23 314.719 168.965 309.41 167.801 c 306.133\n 167.082 302.953 165.91 299.852 164.625 c 299.293 164.379 296.605 162.82\n 296.277 163.383 c 295.711 164.352 298.328 165.309 299.082 165.723 c 298.715\n 165.684 294.477 164.945 295.441 166.25 c 295.777 166.699 297.906 166.801\n 298.484 166.934 c 299.844 167.254 301.191 167.609 302.539 167.996 c 306.363\n 169.117 310.371 170.535 313.609 172.934 c 317.367 175.734 318.543 179.559\n 316.184 183.77 c 314.082 187.512 310.66 190.555 307.336 193.195 c 303.781\n 196.02 300.027 198.707 296.047 200.867 c 292.637 202.707 288.379 204.805\n 284.398 204.035 c 280.746 203.309 279.062 199.73 278.289 196.422 c 277.328\n 192.258 277.387 187.887 277.664 183.645 c 277.695 183.113 278.281 178.75\n 277.953 178.707 c 276.875 177.898 276.566 181.52 276.469 182.086 c 276.375\n 181.617 275.949 177.68 274.91 178.73 c 274.543 178.957 275.074 181.457 \n275.113 181.902 c 275.238 183.281 275.332 184.672 275.363 186.051 c 275.477\n 190.199 275.402 194.664 273.918 198.594 c 272.5 202.371 269.59 204.625 \n265.438 204.055 c 261.461 203.48 257.664 201.422 254.281 199.324 c 250.562\n 197.023 246.949 194.383 243.648 191.496 c 240.613 188.844 237.402 185.742\n 235.957 181.879 c 232.605 172.961 246.008 169.043 252.191 167.414 c 252.762\n 167.266 257.645 166.25 257.578 166.012 c 257.973 164.738 254.465 165.57\n 253.898 165.648 c 254.227 165.461 258.07 163.605 256.535 163.078 c 256.035\n 162.91 254.188 164.074 253.676 164.305 c 252.395 164.848 251.121 165.363\n 249.82 165.816 c 245.883 167.215 241.605 168.492 237.383 168.309 c 233.32\n 168.137 230.344 166.086 229.59 161.945 c 228.859 158.031 229.688 153.82\n 230.621 150.016 c 231.672 145.73 233.07 141.461 234.801 137.41 c 236.402\n 133.699 238.34 129.637 241.586 127.09 c 249.352 120.941 257.578 133.492\n 260.996 139.055 c 261.344 139.609 262.895 142.93 263.473 142.688 c 264.516\n 142.254 262.934 139.891 262.578 139.141 c 262.836 139.383 265.891 142.457\n 265.832 140.832 c 265.805 140.289 264.312 139.102 263.945 138.684 c 263.109\n 137.723 262.289 136.746 261.488 135.738 c 258.887 132.438 256.391 128.801\n 255.074 124.766 c 253.82 120.785 254.641 116.945 258.531 114.902 c 262.027\n 113.066 266.152 112.496 270.035 112.184 c 274.523 111.836 279.109 111.844\n 283.59 112.25 c 287.441 112.598 291.633 113.199 295.016 115.184 c 303.398\n 120.199 295.41 131.133 291.238 136.281 c 290.602 137.066 289.969 137.812\n 289.32 138.559 c 288.906 139.027 287.297 140.289 287.262 140.883 c 287.137\n 142.504 290.246 139.441 290.488 139.203 c 290.238 139.742 288.723 142.344\n 289.41 142.648 c 290.332 143.059 291.355 140.332 291.809 139.609 c 293.445\n 136.902 295.332 134.434 297.281 131.922 c 299.305 129.578 301.664 127.242\n 304.602 126.102 c 307.598 124.93 310.461 125.961 312.664 128.188 c 316.77\n 132.383 319.215 138.895 320.777 144.441 c 322.387 150.148 324.68 156.574\n 323.312 162.574 c 322.879 164.602 323.695 160.891 323.312 162.574 c f\n309.863 218.133 m 309.785 217.637 308.832 217.613 308.746 218.117 c 308.629\n 219.047 308.379 219.957 308.012 220.816 c 307.223 222.723 305.895 224.402\n 304.227 225.598 c 300.719 228.148 296.434 227.949 292.328 227.621 c 287.809\n 227.27 283.418 226.098 278.879 225.938 c 274.332 225.777 269.996 226.242\n 265.516 227.008 c 258.051 228.293 248.07 229.082 244.832 220.379 c 244.621\n 219.824 244.551 218.227 244.129 217.871 c 243.551 217.363 243.176 218.133\n 243.156 218.586 c 243.059 220.645 243.629 222.703 244.621 224.488 c 246.73\n 228.238 250.574 230.562 254.785 231.199 c 260.273 232.051 265.688 230.879\n 271.141 230.379 c 276.297 229.922 281.363 230.258 286.461 230.957 c 291.52\n 231.648 297.453 232.215 302.242 230.051 c 306.797 227.996 310.316 223.297\n 309.863 218.133 c 309.824 217.918 310.16 221.5 309.863 218.133 c h\n314.359 224.988 m 311.98 233.18 301.035 235.344 293.793 236.434 c 284.977\n 237.75 275.844 237.797 266.973 237.234 c 259.262 236.738 250.543 235.785\n 243.887 231.52 c 240.469 229.352 238.301 225.938 238.176 221.863 c 238.031\n 217.418 239.832 213.031 241.824 209.141 c 243.656 205.727 245.707 202.473\n 248.031 199.363 c 251.16 201.566 254.426 203.641 257.945 205.188 c 261.297\n 206.668 265.219 207.898 268.906 207.09 c 272.801 206.234 275.293 203.172\n 276.43 199.484 c 277.867 204.699 282.242 207.785 287.656 207.285 c 292.965\n 206.809 297.895 204.023 302.301 201.176 c 302.742 200.875 303.184 200.594\n 303.617 200.293 c 304.305 199.844 304.766 199.051 305.344 199.844 c 306.191\n 201 307.039 202.172 307.82 203.383 c 309.188 205.531 310.508 207.73 311.645\n 210.023 c 313.82 214.469 315.895 220.02 314.359 224.988 c 314.254 225.348\n 315.172 222.344 314.359 224.988 c f\n267.551 88.137 m 265.641 84.922 261.98 83.008 258.289 82.707 c 253.512 \n82.301 249.406 84.699 245.738 87.492 c 242.184 90.199 239.207 93.551 235.707\n 96.336 c 232.039 99.25 228.066 101.449 223.984 103.711 c 216.922 107.645\n 208.781 114.004 211.43 123.191 c 211.613 123.832 213.176 128.012 214.168\n 127.352 c 214.918 127.016 214.293 126.25 214.078 125.766 c 213.625 124.691\n 213.348 123.547 213.25 122.391 c 212.914 118.367 214.84 114.664 217.941\n 112.184 c 221.98 108.93 226.844 107.082 231.305 104.52 c 235.863 101.906\n 239.996 98.57 243.598 94.773 c 246.988 91.184 251.238 86.793 256.219 85.676\n c 258.34 85.195 260.574 85.367 262.605 86.117 c 263.84 86.578 264.977 87.254\n 265.977 88.121 c 266.238 88.344 266.758 89.16 267.203 88.852 c 267.453 \n88.738 267.723 88.422 267.551 88.137 c 265.816 85.23 267.664 88.324 267.551\n 88.137 c h\n258.906 111.129 m 256.48 112.176 254.117 113.723 252.816 116.066 c 251.238\n 118.855 251.391 122.07 252.367 125.023 c 249.281 121.949 243.973 121.77\n 240.43 123.988 c 236.914 126.207 234.543 129.953 232.73 133.602 c 231.77\n 135.531 230.949 137.523 230.188 139.535 c 229.871 140.363 229.609 139.969\n 228.879 139.723 c 228.348 139.566 227.809 139.383 227.277 139.215 c 226.113\n 138.832 224.965 138.414 223.82 137.973 c 220.016 136.52 216.305 134.836\n 212.941 132.512 c 209.891 130.398 206.797 127.559 205.754 123.867 c 204.465\n 119.336 206.457 114.871 208.926 111.148 c 213.492 104.25 220.273 98.746\n 226.824 93.836 c 232.094 89.875 237.566 86.102 243.406 83.008 c 248.703\n 80.203 254.84 77.32 260.969 77.73 c 274.793 78.699 274.793 98.414 274.66\n 108.473 c 272.945 108.516 263.828 109.004 258.906 111.129 c f\n228.84 206.863 m 228.656 206.301 226.875 206.773 226.41 206.773 c 225.41\n 206.75 224.406 206.621 223.445 206.348 c 221.461 205.781 219.656 204.691\n 218.27 203.172 c 215.574 200.254 214.676 196.164 213.906 192.379 c 213.031\n 188.055 212.645 183.723 211.363 179.465 c 210.09 175.254 208.23 171.465\n 206.207 167.594 c 204.254 163.824 202.047 159.285 202.211 154.961 c 202.344\n 151.047 204.582 147.363 208.047 145.523 c 208.445 145.312 209.031 144.785\n 208.426 144.367 c 207.973 144.039 206.863 144.648 206.43 144.836 c 204.598\n 145.625 203 146.922 201.766 148.504 c 198.973 152.074 198.531 156.699 199.57\n 160.984 c 200.844 166.172 203.473 170.691 205.477 175.582 c 207.664 180.93\n 208.531 186.395 209.781 191.984 c 210.863 196.852 212.469 202.145 216.43\n 205.465 c 218.336 207.055 220.688 208.078 223.156 208.359 c 223.945 208.453\n 229.695 208.426 228.84 206.863 c 228.781 206.676 228.84 206.863 228.84 \n206.863 c h\n242.395 200.82 m 237.637 206.504 230.832 214.684 222.559 214 c 214.137 \n213.508 208.973 203.582 205.805 196.941 c 201.98 188.977 199.156 180.379\n 197.047 171.805 c 195.207 164.355 193.355 155.527 195.898 148.062 c 200.188\n 135.492 217.258 139.797 226.652 142.824 c 227.363 143.059 228.066 143.301\n 228.789 143.547 c 228.5 144.453 228.242 145.363 227.98 146.266 c 227.547\n 147.805 227.172 149.367 226.844 150.938 c 226.285 153.672 225.938 156.453\n 226.027 159.246 c 226.141 162.293 226.855 165.477 228.953 167.816 c 231.066\n 170.16 234.223 171.023 237.309 170.984 c 234.832 172.688 232.797 175.195\n 232.395 178.242 c 231.98 181.336 233.27 184.438 234.938 186.984 c 236.605\n 189.496 238.723 191.695 240.938 193.715 c 242.145 194.805 243.387 195.84\n 244.668 196.836 c 245.227 197.043 245.254 197.324 244.766 197.672 c 244.531\n 197.992 244.301 198.312 244.07 198.609 c 243.52 199.363 242.961 200.094\n 242.395 200.82 c 241.789 201.543 243.27 199.691 242.395 200.82 c f\n0.0313726 0.580392 0.278431 rg\n375.211 126.688 m 375.5 126.309 375.836 125.926 376.105 125.5 c 375.652\n 125.332 375.172 125.18 374.672 125.047 c 370.5 123.926 366.105 123.328 \n361.762 123.199 c 359.582 123.125 357.406 123.207 355.25 123.438 c 354.371\n 123.535 353.465 123.695 352.598 123.887 c 352.523 124.207 352.426 124.527\n 352.34 124.859 c 352.301 125.035 352.223 125.219 352.164 125.391 c 352.07\n 125.672 351.973 125.941 351.867 126.227 c 351.441 127.297 350.883 128.336\n 350.191 129.344 c 350.016 129.602 349.824 129.871 349.613 130.137 c 349.207\n 130.68 348.754 131.219 348.273 131.75 c 347.992 132.055 347.684 132.348\n 347.387 132.629 c 346.77 133.199 346.125 133.754 345.422 134.277 c 345.094\n 134.559 344.746 134.816 344.391 135.062 c 344.391 135.078 344.371 135.078\n 344.371 135.078 c 344.496 135.062 344.668 135.062 344.824 135.062 c 345.633\n 135.062 346.422 135.086 347.203 135.156 c 347.992 135.23 348.746 135.324\n 349.457 135.469 c 351.203 135.773 352.812 136.301 354.254 137.027 c 354.727\n 137.273 355.199 137.516 355.645 137.797 c 355.883 137.949 356.125 138.105\n 356.355 138.258 c 356.836 138.586 357.262 138.941 357.688 139.309 c 357.918\n 139.516 358.137 139.723 358.371 139.949 c 358.977 139.691 359.562 139.402\n 360.152 139.086 c 362.059 138.059 363.891 136.879 365.633 135.586 c 368.246\n 133.641 370.738 131.469 373.004 129.121 c 373.379 128.727 373.754 128.324\n 374.133 127.906 c 374.496 127.512 374.863 127.109 375.211 126.688 c f\n336.277 242.195 m 336.309 241.715 336.289 241.219 336.277 240.703 c 336.059\n 236.387 335.258 232.039 334.043 227.863 c 333.445 225.785 332.695 223.73\n 331.797 221.762 c 331.402 220.891 330.969 220.043 330.508 219.215 c 330.488\n 219.215 l 330.383 219.215 330.297 219.215 330.191 219.207 c 329.863 219.207\n 329.516 219.207 329.18 219.176 c 327.07 219.023 324.902 218.434 322.809\n 217.438 c 322.078 217.09 321.316 216.695 320.594 216.246 c 319.871 215.805\n 319.148 215.324 318.457 214.777 c 319.035 216.449 319.469 218.133 319.648\n 219.75 c 319.863 221.34 319.863 222.852 319.688 224.332 c 319.613 225.004\n 319.504 225.691 319.332 226.332 c 319.285 226.539 319.227 226.707 319.168\n 226.914 c 319.121 227.141 319.055 227.363 318.977 227.582 c 318.871 227.863\n 318.766 228.148 318.637 228.438 c 318.348 229.113 318.023 229.801 317.625\n 230.43 c 317.645 230.445 l 317.906 230.73 318.156 230.973 318.426 231.238\n c 320.008 232.742 321.672 234.102 323.445 235.375 c 324.777 236.324 326.164\n 237.215 327.559 238.043 c 328.504 238.605 329.457 239.152 330.41 239.641\n c 331.875 240.391 333.359 241.098 334.871 241.699 c 335.336 241.883 335.805\n 242.074 336.277 242.195 c f\n284.898 74.352 m 284.898 74.34 l 284.805 73.086 284.648 71.883 284.445 \n70.656 c 284.07 68.523 283.492 66.445 282.809 64.367 c 281.422 60.246 279.59\n 56.227 277.309 52.539 c 277.039 52.109 276.77 51.699 276.469 51.32 c 276.152\n 51.68 275.852 52.109 275.582 52.531 c 273.215 56.148 271.305 60.137 269.824\n 64.215 c 269.09 66.273 268.492 68.363 268.059 70.473 c 267.809 71.664 267.645\n 72.844 267.531 74.039 c 268.059 74.301 268.559 74.602 269.043 74.922 c \n269.516 75.242 269.977 75.582 270.402 75.949 c 270.594 76.117 270.805 76.293\n 270.98 76.477 c 271.18 76.664 271.375 76.84 271.555 77.039 c 272.695 78.191\n 273.695 79.527 274.543 81.09 c 274.734 81.445 274.93 81.812 275.102 82.172\n c 275.41 82.809 275.707 83.488 275.961 84.172 c 276.152 84.699 276.316 \n85.23 276.5 85.77 c 277 84.078 277.645 82.516 278.445 81.09 c 278.867 80.305\n 279.34 79.566 279.832 78.891 c 280.168 78.438 280.516 78.012 280.871 77.617\n c 281.238 77.203 281.613 76.832 282.008 76.445 c 282.203 76.285 282.406\n 76.094 282.598 75.926 c 282.973 75.629 283.328 75.336 283.707 75.09 c 283.898\n 74.953 284.102 74.828 284.293 74.695 c 284.496 74.574 284.68 74.473 284.898\n 74.352 c f\n234.156 216.027 m 234.281 215.625 234.406 215.207 234.562 214.812 c 234.551\n 214.812 234.551 214.824 234.531 214.824 c 234.367 214.965 234.188 215.082\n 234.004 215.203 c 233.84 215.324 233.656 215.438 233.492 215.566 c 232.402\n 216.301 231.305 216.93 230.199 217.457 c 228.059 218.453 225.918 219.047\n 223.828 219.199 c 223.645 219.207 223.48 219.215 223.301 219.207 c 222.547\n 219.254 221.785 219.215 221.016 219.129 c 220.773 219.102 220.516 219.062\n 220.285 219.023 c 219.926 218.973 219.59 218.906 219.262 218.816 c 218.789\n 219.613 218.355 220.41 217.961 221.234 c 217.02 223.184 216.258 225.191\n 215.594 227.27 c 214.293 231.426 213.395 235.75 213.086 240.078 c 213.047\n 240.574 213.02 241.086 213.047 241.566 c 213.512 241.445 213.984 241.281\n 214.465 241.098 c 218.52 239.566 222.402 237.453 225.996 235.023 c 227.809\n 233.793 229.531 232.457 231.121 231.004 c 232.191 230.016 233.223 228.934\n 234.156 227.836 c 234.062 227.609 233.992 227.371 233.934 227.152 c 233.859\n 226.875 233.781 226.613 233.703 226.332 c 233.203 224.281 233.078 222.062\n 233.375 219.75 c 233.539 218.492 233.801 217.27 234.156 216.027 c f\n208.625 135.086 m 208.625 135.086 208.605 135.086 208.605 135.078 c 207.895\n 134.57 207.211 134.035 206.555 133.492 c 206.246 133.199 205.918 132.91\n 205.621 132.629 c 205.32 132.352 205.023 132.055 204.715 131.75 c 204.309\n 131.309 203.934 130.887 203.59 130.438 c 202.527 129.109 201.699 127.691\n 201.121 126.238 c 201.023 125.961 200.918 125.672 200.824 125.391 c 200.734\n 125.105 200.648 124.816 200.574 124.527 c 200.41 123.961 200.285 123.359\n 200.188 122.758 c 198.895 122.43 197.566 122.164 196.238 121.988 c 194.078\n 121.695 191.902 121.582 189.723 121.602 c 185.391 121.637 180.977 122.125\n 176.766 123.18 c 176.266 123.293 175.793 123.426 175.34 123.605 c 175.602\n 124 175.898 124.41 176.207 124.797 c 178.945 128.188 182.152 131.234 185.582\n 133.875 c 187.297 135.23 189.098 136.441 190.977 137.504 c 192.324 138.258\n 193.715 138.934 195.141 139.496 c 195.148 139.488 l 195.496 139.164 195.852\n 138.879 196.219 138.602 c 196.594 138.324 196.969 138.059 197.363 137.824\n c 197.586 137.684 197.816 137.555 198.039 137.422 c 198.75 137.027 199.484\n 136.684 200.254 136.375 c 201.277 135.992 202.375 135.688 203.531 135.477\n c 203.637 135.457 203.742 135.438 203.848 135.418 c 203.906 135.418 l 204.512\n 135.305 205.121 135.23 205.754 135.16 c 206.133 135.137 206.527 135.117\n 206.922 135.086 c 207.113 135.086 207.316 135.086 207.508 135.078 c 208.117\n 135.078 l 208.281 135.078 208.445 135.078 208.625 135.086 c f\n0.137255 0.121569 0.12549 rg\n290.922 149.762 m 290.422 150.508 289.719 151.406 288.801 152.488 c 290.016\n 153.82 291.094 155.121 292.078 156.352 c 292.914 155.168 293.57 154.129\n 294.051 153.23 c 293.059 152.094 292.012 150.938 290.922 149.762 c h\n286.094 144.855 m 285.41 145.383 284.543 146.125 283.484 147.047 c 284.793\n 148.297 286.113 149.609 287.355 150.938 c 288.301 149.867 289.023 148.992\n 289.543 148.32 c 288.426 147.156 287.27 145.988 286.094 144.855 c h\n281.18 140.379 m 280.285 140.832 279.234 141.496 278.051 142.355 c 279.293\n 143.324 280.574 144.402 281.922 145.617 c 283.012 144.699 283.918 144.008\n 284.641 143.5 c 283.484 142.402 282.309 141.371 281.18 140.379 c h\n272.559 146.875 m 273.742 147.918 275.082 149.133 276.508 150.484 c 277.926\n 149.133 279.266 147.918 280.449 146.875 c 279.09 145.617 277.762 144.5 \n276.508 143.52 c 275.266 144.5 273.926 145.617 272.559 146.875 c h\n271.828 140.379 m 270.699 141.371 269.535 142.41 268.367 143.5 c 269.109\n 144.008 270.023 144.703 271.105 145.617 c 272.441 144.402 273.734 143.324\n 274.957 142.355 c 273.773 141.496 272.742 140.82 271.828 140.379 c h\n266.922 144.855 m 265.758 145.988 264.602 147.156 263.473 148.32 c 263.984\n 148.992 264.727 149.867 265.672 150.938 c 266.922 149.609 268.215 148.309\n 269.535 147.047 c 268.484 146.125 267.605 145.383 266.922 144.855 c h\n262.098 149.762 m 261.008 150.918 259.957 152.094 258.965 153.23 c 259.438\n 154.129 260.102 155.168 260.949 156.352 c 261.922 155.121 263.012 153.82\n 264.227 152.488 c 263.309 151.406 262.586 150.508 262.098 149.762 c h\n258.965 162.551 m 259.957 163.68 260.996 164.855 262.098 166.012 c 262.586\n 165.289 263.309 164.379 264.227 163.297 c 263.012 161.961 261.922 160.664\n 260.949 159.441 c 260.102 160.609 259.438 161.66 258.965 162.551 c h\n266.922 170.93 m 267.605 170.41 268.484 169.672 269.535 168.723 c 268.215\n 167.477 266.902 166.16 265.672 164.855 c 264.727 165.91 263.984 166.785\n 263.473 167.469 c 264.602 168.629 265.758 169.801 266.922 170.93 c h\n271.828 175.402 m 272.742 174.941 273.773 174.289 274.957 173.441 c 273.723\n 172.461 272.441 171.371 271.105 170.176 c 270.023 171.082 269.109 171.781\n 268.367 172.289 c 269.535 173.367 270.699 174.426 271.828 175.402 c h\n280.449 168.91 m 279.266 167.855 277.926 166.637 276.508 165.309 c 275.082\n 166.645 273.762 167.855 272.559 168.91 c 273.926 170.16 275.266 171.293\n 276.508 172.277 c 277.75 171.293 279.082 170.176 280.449 168.91 c h\n281.18 175.402 m 282.309 174.426 283.484 173.367 284.641 172.289 c 283.918\n 171.781 282.992 171.082 281.922 170.176 c 280.574 171.379 279.293 172.461\n 278.051 173.441 c 279.234 174.289 280.285 174.941 281.18 175.402 c h\n286.094 170.91 m 287.27 169.801 288.426 168.629 289.543 167.469 c 289.031\n 166.793 288.301 165.93 287.355 164.855 c 286.113 166.172 284.805 167.477\n 283.484 168.723 c 284.555 169.672 285.43 170.41 286.094 170.91 c h\n283.918 157.883 m 285.285 159.301 286.488 160.645 287.551 161.848 c 288.781\n 160.477 289.918 159.141 290.891 157.883 c 289.918 156.648 288.781 155.32\n 287.551 153.945 c 286.488 155.148 285.285 156.484 283.918 157.883 c h\n282.367 156.277 m 283.754 154.844 284.988 153.527 286.066 152.355 c 284.773\n 150.996 283.418 149.621 282.059 148.332 c 280.883 149.422 279.562 150.664\n 278.129 152.035 c 279.543 153.41 280.98 154.844 282.367 156.277 c h\n278.129 163.75 m 279.543 165.113 280.871 166.344 282.059 167.465 c 283.418\n 166.156 284.773 164.809 286.066 163.438 c 284.977 162.234 283.742 160.93\n 282.367 159.508 c 280.98 160.945 279.562 162.355 278.129 163.75 c h\n272.223 157.883 m 273.629 159.324 275.082 160.777 276.508 162.168 c 277.945\n 160.777 279.379 159.324 280.785 157.883 c 279.391 156.473 277.945 155.016\n 276.508 153.605 c 275.074 155.016 273.629 156.473 272.223 157.883 c h\n270.652 156.277 m 272.047 154.844 273.465 153.426 274.891 152.035 c 273.473\n 150.676 272.164 149.441 270.949 148.332 c 269.59 149.637 268.254 150.984\n 266.941 152.355 c 268.039 153.551 269.293 154.867 270.652 156.277 c h\n266.941 163.438 m 268.234 164.801 269.609 166.156 270.969 167.465 c 272.145\n 166.359 273.465 165.121 274.891 163.75 c 274.168 163.062 273.465 162.355\n 272.762 161.645 c 272.047 160.945 271.344 160.207 270.652 159.508 c 269.273\n 160.93 268.039 162.234 266.941 163.438 c h\n269.09 157.883 m 267.75 156.484 266.539 155.148 265.457 153.945 c 264.227\n 155.32 263.109 156.648 262.125 157.883 c 263.109 159.141 264.227 160.477\n 265.457 161.848 c 266.52 160.645 267.75 159.301 269.09 157.883 c h\n290.922 166.012 m 292.012 164.875 293.051 163.703 294.051 162.551 c 293.57\n 161.66 292.914 160.609 292.078 159.441 c 291.094 160.664 290.016 161.961\n 288.801 163.297 c 289.719 164.355 290.422 165.289 290.922 166.012 c h\n293.234 157.883 m 294.051 159.02 294.719 160.043 295.227 160.984 c 295.285\n 161.078 l 295.258 161.199 l 295.121 161.645 294.977 162.113 294.773 162.59\n c 294.699 162.781 l 294.621 162.996 294.504 163.223 294.398 163.438 c 294.082\n 164.074 293.707 164.738 293.234 165.41 c 292.684 165.984 292.156 166.559\n 291.586 167.145 c 291.613 167.16 291.633 167.215 291.652 167.254 c 291.73\n 167.426 l 291.605 167.559 l 291.316 167.891 291.016 168.223 290.719 168.555\n c 290.449 168.852 l 290.246 168.492 l 289.227 169.539 288.176 170.59 287.117\n 171.625 c 287.48 171.82 l 287.191 172.086 l 286.848 172.383 286.52 172.68\n 286.191 172.973 c 286.055 173.086 l 285.883 173.008 l 285.836 172.984 285.785\n 172.977 285.746 172.973 c 285.188 173.516 284.602 174.051 283.996 174.633\n c 283.145 175.195 282.262 175.695 281.402 176.07 c 281.211 176.145 l 280.758\n 176.312 280.285 176.488 279.812 176.617 c 279.699 176.656 l 279.609 176.594\n l 278.688 176.098 277.645 175.402 276.508 174.594 c 275.383 175.402 274.352\n 176.098 273.414 176.594 c 273.32 176.656 l 273.195 176.617 l 272.723 176.488\n 272.262 176.312 271.809 176.145 c 271.625 176.07 l 270.758 175.695 269.891\n 175.215 268.977 174.594 c 268.414 174.051 267.836 173.516 267.27 172.973\n c 267.223 172.977 267.191 172.984 267.145 173.008 c 266.973 173.086 l 266.844\n 172.973 l 266.5 172.68 266.16 172.383 265.844 172.086 c 265.535 171.82 \nl 265.891 171.625 l 265.91 171.625 l 264.852 170.59 263.801 169.559 262.77\n 168.492 c 262.566 168.852 l 262.289 168.555 l 262.008 168.223 261.699 167.891\n 261.41 167.559 c 261.297 167.426 l 261.363 167.254 l 261.383 167.215 261.41\n 167.16 261.43 167.145 c 260.863 166.559 260.324 165.984 259.746 165.383\n c 259.32 164.738 258.926 164.059 258.609 163.422 c 258.504 163.215 258.414\n 162.996 258.328 162.781 c 258.234 162.59 l 258.059 162.133 257.906 161.68\n 257.77 161.199 c 257.73 161.098 l 257.781 160.984 l 258.289 160.055 258.965\n 159.02 259.793 157.883 c 258.965 156.773 258.289 155.723 257.781 154.809\n c 257.73 154.695 l 257.77 154.594 l 257.887 154.129 258.059 153.652 258.234\n 153.203 c 258.328 153.004 l 258.703 152.148 259.176 151.266 259.773 150.371\n c 260.324 149.805 260.863 149.227 261.43 148.652 c 261.41 148.625 261.402\n 148.578 261.363 148.539 c 261.297 148.371 l 261.41 148.227 l 261.699 147.902\n 262.008 147.562 262.309 147.23 c 262.566 146.934 l 262.75 147.293 l 263.801\n 146.234 264.852 145.191 265.91 144.172 c 265.535 143.973 l 265.844 143.707\n l 266.16 143.398 266.5 143.098 266.844 142.805 c 266.973 142.688 l 267.145\n 142.762 l 267.172 142.797 267.223 142.805 267.27 142.812 c 267.836 142.281\n 268.414 141.727 269.012 141.152 c 269.891 140.57 270.758 140.082 271.625\n 139.723 c 271.809 139.641 l 272.277 139.461 272.723 139.309 273.195 139.164\n c 273.32 139.141 l 273.414 139.176 l 274.352 139.691 275.383 140.379 276.508\n 141.199 c 277.645 140.363 278.688 139.691 279.609 139.176 c 279.699 139.141\n l 279.812 139.164 l 280.285 139.309 280.746 139.461 281.199 139.641 c 281.402\n 139.723 l 282.25 140.098 283.129 140.57 284.031 141.176 c 284.602 141.727\n 285.188 142.281 285.746 142.812 c 285.785 142.805 285.836 142.797 285.863\n 142.762 c 286.055 142.688 l 286.191 142.805 l 286.52 143.098 286.848 143.398\n 287.191 143.707 c 287.48 143.973 l 287.117 144.172 l 288.176 145.191 289.227\n 146.234 290.246 147.293 c 290.449 146.934 l 290.719 147.23 l 291.016 147.562\n 291.316 147.883 291.605 148.227 c 291.73 148.371 l 291.652 148.539 l 291.633\n 148.578 291.613 148.625 291.586 148.652 c 292.156 149.227 292.684 149.805\n 293.262 150.406 c 293.84 151.266 294.32 152.148 294.719 153.07 c 294.773\n 153.203 l 294.957 153.652 295.121 154.129 295.258 154.594 c 295.285 154.695\n l 295.227 154.809 l 294.719 155.723 294.051 156.773 293.234 157.883 c f\n499.48 570.898 m 505.453 570.898 528.211 573.344 530.465 585.32 c 533.824\n 603.051 522.852 613.762 518.73 617.793 c 518.391 618.121 515.992 619.816\n 514.98 620.266 c 514.328 620.547 512.891 621.625 512.785 621.84 c 512.109\n 623.285 511.359 624.922 509.113 625.051 c 509.008 625.062 508.941 625.023\n 508.863 624.961 c 508.074 624.27 507.707 623.219 507.41 622.262 c 505.906\n 621.672 505.375 620.371 504.875 619.121 c 504.691 618.668 504.508 618.215\n 504.289 617.781 c 503.68 618.047 503.324 618.496 502.918 618.996 c 502.688\n 619.273 502.469 619.562 502.168 619.832 c 501.312 620.633 499.617 620.738\n 499.039 621.219 c 498.324 621.77 497.727 623.012 497.129 624.219 c 496.426\n 625.68 495.676 627.195 494.613 627.859 c 494.52 627.922 491.891 626.137\n 490.848 625.176 c 490.625 624.957 490.375 624.734 490.105 624.461 c 489.836\n 624.082 489.664 623.453 489.48 622.781 c 489.277 621.996 489.402 621.812\n 488.141 622.48 c 487.32 622.922 485.559 623.57 484.922 623.699 c 484.047\n 623.875 482.832 624.125 482.543 624.262 c 481.852 624.574 481.309 625.133\n 480.742 625.723 c 479.914 626.582 479.047 627.473 477.609 627.613 c 477.516\n 627.621 477.43 627.59 477.352 627.52 c 477.285 627.461 477.246 627.375 \n477.246 627.289 c 477.195 625.176 478.277 625.012 477.457 624.41 c 476.695\n 623.852 475.637 623.645 475.531 621.977 c 475.414 622.047 475.27 621.922\n 474.809 621.941 c 474.461 621.875 474.094 621.828 473.758 621.828 c 471.84\n 621.828 470.387 622.945 468.949 624.023 c 468.422 624.434 467.121 624.961\n 466.426 625.133 c 465.48 625.383 464.703 625.449 464.125 625.695 c 463.727\n 625.871 463.332 626.039 462.891 626.09 c 462.824 626.098 462.734 626.086\n 462.68 626.047 c 462.215 625.793 462.273 625.109 462.34 624.402 c 462.379\n 624.031 462.418 623.66 462.371 623.422 c 462.254 622.77 462.07 622.293 \n461.898 621.828 c 461.598 621.102 461.32 620.414 461.445 619.273 c 461.484\n 618.895 461.656 618.496 461.859 618.078 c 462.031 617.637 462.234 617.203\n 462.266 616.836 c 461.617 616.539 460.723 616.34 459.539 616.211 c 455.262\n 616.52 448.102 617.668 443.824 622.059 c 442.156 623.781 440.895 625.848\n 441.039 628.23 c 441.34 633.305 442.773 635.422 448.883 635.262 c 451.184\n 635.203 458.188 630.355 459.75 630.555 c 463.016 630.98 466.039 632.855\n 468.199 633.215 c 469.152 632.809 472.332 632.027 476.715 632.027 c 478.41\n 632.027 477.68 632.148 479.375 632.395 c 481.281 632.875 482.043 633.383\n 482.609 633.762 c 483.043 634.047 483.332 634.246 484.02 634.332 c 484.375\n 634.387 l 485.598 634.57 486.898 634.746 487.148 634.797 c 487.863 634.91\n 488.508 635.781 489.375 635.996 c 490.52 636.301 493.66 636.789 494.48 \n636.902 c 496.457 637.109 498.93 640.094 498.961 640.117 c 499.664 640.523\n 502.469 642.078 503.266 642.488 c 503.574 642.652 504.152 642.883 504.875\n 643.164 c 508.441 644.574 515.066 647.191 515.32 651.242 c 515.387 652.625\n 512.449 653.406 511.195 653.676 c 510.684 653.777 510.012 653.832 509.152\n 653.832 c 508.035 653.832 506.801 653.73 505.789 653.668 c 503.574 653.48\n l 499.941 653.168 498.68 652.887 494.73 652.633 c 493.152 652.52 491.531\n 652.332 489.953 652.164 c 488.535 652.012 487.773 651.625 487.031 651.562\n c 485.012 651.363 483.152 651.195 481.309 651.266 c 480.531 651.293 479.547\n 651.508 475.223 651.758 c 466.773 652.277 464.52 652.82 462.293 652.793\n c 448.621 652.633 421.598 650.434 419.969 630.281 c 419.441 623.535 421.473\n 617.023 425.578 612.723 c 432.359 605.641 437.418 602.723 455.945 602.723\n c 466.773 602.723 474.055 603.996 477.68 604.477 c 478.508 604.586 l 480.098\n 604.793 490.316 605.238 491.82 605.238 c 498.863 605.238 505.906 604.488\n 509.828 600.918 c 511.59 599.297 514.336 594.949 511.562 591.902 c 510.414\n 590.648 507.785 589.984 503.98 589.977 c 500.059 589.977 494.973 590.699\n 489.27 592.051 c 485.098 593.047 481.688 593.949 478.691 594.754 c 473.633\n 596.094 469.961 596.633 466.406 596.953 c 459.199 597.594 434.18 597.125\n 423.438 599.262 c 420.664 599.812 417.465 600.875 414.758 601.883 c 414.719\n 602.375 414.641 602.898 414.508 603.531 c 414.352 604.188 413.582 605.16\n 413.371 605.816 c 412.418 608.719 411.59 611.066 410.684 613.453 c 411.664\n 613.5 412.637 613.516 412.602 614.598 c 411.48 617.656 408.582 619.434 \n407.637 622.988 c 407.109 625 407.492 627.254 406.867 629.855 c 409.113 \n630.543 410.453 632.121 410.684 634.816 c 410.664 635.25 405.789 639.59 \n404.594 643.203 c 405.191 643.738 405.559 644.527 405.711 645.496 c 404.641\n 651.035 399.121 652.117 393.109 652.75 c 391.211 658.344 389.207 665.004\n 381.289 663.434 c 379.133 665.852 377.668 668.98 373.273 669.148 c 371.145\n 667.719 370.652 664.633 368.312 663.434 c 367.117 663.152 367.184 664.086\n 366.008 663.812 c 363.602 661 360.828 658.57 358.379 655.805 c 357.656 \n656.227 357.098 657.434 355.691 656.559 c 353.352 652.922 348.852 651.449\n 347.668 646.645 c 346.23 645.621 344.832 644.145 344.254 642.828 c 344.18\n 642.695 341.801 636.758 341.578 635.965 c 340.805 633.293 339.398 631.344\n 338.504 629.473 c 339.543 627.648 342.742 629.66 343.477 629.094 c 343.398\n 625.867 342.387 623.23 341.578 621.84 c 341.328 621.43 341.492 620.809 \n341.184 620.32 c 341.039 620.078 340.797 619.844 340.535 619.598 c 339.977\n 619.078 339.246 618.555 338.891 618.027 c 338.109 616.836 337.434 616.086\n 337.754 614.211 c 337.57 613.012 339.508 613.93 339.66 613.07 c 338.715\n 610.832 338.965 608.582 338.504 606.203 c 338.137 604.172 337.184 602.125\n 337 600.098 c 336.848 598.738 336.77 597.609 336.527 596.691 c 336.172 \n596.875 335.797 597.062 335.422 597.242 c 334.641 597.629 333.832 598.016\n 333.043 598.496 c 331.992 599.789 331.422 601.629 330.875 603.398 c 330.141\n 605.691 329.398 608.059 327.637 609.473 c 326.559 611.207 326.422 613.254\n 326.277 615.621 c 326.238 616.219 326.184 616.836 326.125 617.445 c 326.086\n 617.902 325.941 618.777 325.738 619.883 c 325.371 621.941 324.594 626.23\n 325.133 626.816 c 325.711 627.473 l 327.355 629.301 328.457 630.496 329.332\n 633.93 c 331.027 640.363 328.945 643.051 327.559 644.664 c 328.293 645.828\n 328.465 647.309 328.098 649.031 c 327.715 652.555 325.883 654.379 325.027\n 654.379 c 324.938 654.379 324.844 654.363 324.766 654.328 c 323.91 653.918\n 324.305 652.699 324.504 652.031 c 324.543 651.926 324.555 651.852 324.582\n 651.793 c 324.516 651.738 324.398 651.664 324.324 651.617 c 324.055 651.43\n 323.859 651.301 323.793 651.094 c 323.523 649.871 323.262 649.008 322.984\n 648.383 c 322.426 647.184 321.777 646.824 320.738 646.492 c 319.332 646.02\n 318.504 644.004 318.012 642.801 c 317.973 642.684 317.934 642.586 317.898\n 642.484 c 317.695 642.801 317.52 643.059 317.395 643.297 c 316.789 644.332\n 316.527 644.801 314.988 645.664 c 313.668 646.391 312.625 646.543 311.723\n 646.691 c 311.434 646.73 311.133 646.773 310.844 646.844 c 310.547 646.902\n 310.207 647.121 309.785 647.391 c 308.793 648.031 307.281 649.008 304.352\n 649.098 c 304.062 649.098 303.492 649.008 302.992 648.938 c 302.723 648.898\n 302.512 648.859 302.375 648.852 c 302.184 648.836 301.887 648.836 301.578\n 648.836 c 300.457 648.824 299.812 648.809 299.621 648.43 c 299.602 648.383\n 299.594 648.309 299.594 648.254 c 299.707 647.223 300.641 647.055 301.316\n 646.926 c 301.711 646.852 302.117 646.781 302.211 646.598 c 302.301 646.465\n 302.262 646.223 302.156 645.902 c 302.137 645.828 302.137 645.738 302.164\n 645.664 c 302.48 645.008 302.934 644.512 303.379 644.031 c 303.984 643.387\n 304.609 642.711 304.918 641.598 c 304.883 641.098 304.785 640.953 304.707\n 640.809 c 304.602 640.598 304.504 640.395 304.484 639.914 c 304.43 639.332\n 304.727 638.699 305.035 638.027 c 305.16 637.75 305.277 637.469 305.391\n 637.18 c 304.16 637.395 302.723 637.121 301.469 636.449 c 297.828 635.73\n 295.777 634.5 294.832 633.254 c 294.418 632.676 294.215 632.113 294.207\n 631.582 c 294.207 631.359 294.109 631.074 294.031 630.805 c 293.887 630.301\n 293.754 629.836 293.996 629.488 c 294.141 629.309 294.32 629.207 294.59\n 629.168 c 294.602 629.168 294.699 629.164 294.707 629.164 c 295.113 629.164\n 295.238 629.461 295.277 629.609 c 295.344 629.777 295.422 629.969 295.941\n 630.082 c 296.008 630.074 296.191 630 296.316 629.949 c 296.578 629.824\n 296.867 629.703 297.078 629.695 c 297.414 629.676 297.828 629.664 298.234\n 629.66 c 298.762 629.645 299.34 629.633 299.738 629.602 c 299.602 629.078\n 299.68 628.504 299.957 627.902 c 300.863 625.961 303.695 624.066 304.668\n 623.793 c 305.055 623.68 305.461 623.629 305.922 623.629 c 306.152 623.629\n 306.383 623.633 306.613 623.652 c 306.867 623.668 307.105 623.68 307.355\n 623.668 c 307.492 622.988 307.395 622.004 307.301 620.977 c 307.117 619.207\n 306.934 617.203 308.051 616.246 c 307.684 615.66 307.414 615.02 307.27 \n614.34 c 306.703 613.102 305.258 612.199 303.648 612.09 c 302.742 612.031\n 302.164 612.012 301.77 612.031 c 301.172 612.066 301 612.199 300.699 612.434\n c 300.363 612.73 300.043 613.102 299.359 613.438 c 298.965 613.539 298.168\n 613.453 298.07 613.672 c 297.887 614.066 297.406 614.609 297.059 614.59\n c 296.723 614.57 296.434 614.289 296.258 613.969 c 295.844 613.191 295.738\n 612.312 295.988 611.633 c 295.988 611.633 295.883 611.473 295.891 611.02\n c 296.152 608.77 296.797 607.504 298.453 605.996 c 298.609 605.875 298.773\n 605.711 298.965 605.516 c 299.891 604.613 301.461 603.105 303.418 602.461\n c 304.195 602.199 305.207 602.305 306.145 602.461 c 306.598 602.531 307.039\n 602.594 307.414 602.594 c 307.656 599.543 310.641 599.27 311.617 599.27\n c 311.539 598.312 310.547 596.875 310.055 596.148 c 309.938 596.012 l 309.312\n 595.07 308.367 594.062 307.473 593.199 c 306.941 592.684 306.402 592.23\n 305.949 591.875 c 305.922 591.84 305.895 591.793 305.863 591.75 c 305.277\n 590.379 305.699 588.867 306.828 588.281 c 307.191 588.117 307.723 588.043\n 308.418 588.043 c 309.16 588.043 309.957 588.125 310.613 588.184 c 310.699\n 588.199 310.805 588.211 310.895 588.219 c 309.516 586.195 309.812 583.469\n 311.691 581.461 c 311.758 581.406 311.816 581.371 311.875 581.355 c 312.223\n 581.285 312.57 581.254 312.945 581.254 c 314.234 581.254 315.516 581.648\n 316.742 582.027 c 317.098 582.129 317.434 582.238 317.789 582.328 c 317.965\n 581.562 317.859 580.852 317.734 580.18 c 317.617 579.516 317.52 578.914\n 317.684 578.289 c 317.859 577.691 318.223 577.234 318.582 576.797 c 318.984\n 576.277 319.379 575.789 319.41 575.105 c 319.305 574.891 319.109 574.695\n 318.926 574.48 c 318.465 573.98 317.879 573.336 318.359 572.176 c 319.109\n 570.809 321.094 570.562 322.656 570.367 c 322.945 570.332 323.215 570.289\n 323.477 570.254 c 323.477 570.039 323.398 569.941 323.223 569.75 c 323.078\n 569.57 322.898 569.344 322.762 568.992 c 322.25 567.512 322.301 566.305\n 322.965 565.406 c 323.637 564.492 324.922 563.98 326.578 563.98 c 327.539\n 563.98 328.582 564.164 329.516 564.492 c 330.816 564.988 331.953 566.047\n 333.043 567.074 c 333.602 567.605 334.141 568.113 334.68 568.5 c 334.871\n 568.207 l 335.355 567.516 335.969 566.441 336.547 565.238 c 337.879 562.523\n 339.109 559.156 338.641 558.059 c 338.57 557.93 338.457 557.742 338.07 \n557.672 c 337.965 557.656 337.887 557.594 337.828 557.512 c 337.773 557.418\n 337.773 557.312 337.82 557.215 c 338.602 555.547 340.914 554.09 343.148\n 554.09 c 343.84 554.09 344.574 554.164 345.355 554.332 c 346.422 554.555\n 351.434 555.145 352.09 555.801 c 352.551 556.27 353.426 557.039 353.34 \n554.773 c 354.254 552.555 l 354.16 551.926 353.957 550.207 353.582 548.266\n c 353.352 547.102 353.062 545.859 352.703 544.711 c 352.051 542.688 349.797\n 536.363 349.719 535.969 c 349.574 535.203 346.598 526.414 345.363 523.148\n c 344.93 521.984 343.898 519.859 343.82 519.617 c 343.531 518.781 342.793\n 518.918 342.32 518.414 c 342.137 518.227 341.961 518.02 341.711 517.754\n c 340.383 516.102 340.77 515.047 338.457 514.531 c 337.953 514.418 336.77\n 514.41 336.238 514.297 c 334.109 513.844 328.918 509.84 328.59 509.84 c\n 328.332 509.84 327.742 510.215 327.176 510.336 c 326.848 510.422 323.031\n 511.926 321.586 512.492 c 320.207 513.016 318.793 513.656 317.262 513.676\n c 309.812 513.77 308.852 508.789 308.852 508.789 c 308.852 508.789 307.27\n 508.676 306.461 508.211 c 305.672 507.754 304.602 506.211 304.195 504.84\n c 304.176 504.746 304.148 504.652 304.121 504.559 c 303.812 503.543 303.617\n 503.109 304.023 501.887 c 304.34 500.93 305.711 500.855 306.797 502.02 \nc 307.156 502.414 308.359 502.734 308.734 502.66 c 309.09 502.602 310.094\n 501.793 310.738 501.801 c 313.176 501.875 313.176 501.043 314.016 500.234\n c 312.973 499.508 311.316 497.984 311.711 495.777 c 311.5 495.516 311.258\n 495.254 311.02 494.977 c 310.371 494.293 309.66 493.504 309.535 492.414\n c 309.43 491.406 309.68 490.27 310.219 489.238 c 310.488 488.703 310.836\n 488.203 311.258 487.758 c 312.355 486.555 313.746 485.984 315.082 486.191\n c 315.227 486.211 315.344 486.34 315.363 486.504 c 315.422 487.996 315.949\n 489.105 317.105 490.062 c 317.434 489.988 317.723 489.941 318.043 489.941\n c 318.859 489.941 319.574 490.215 320.207 490.457 c 320.73 490.664 321.961\n 490.852 322.434 490.852 c 322.578 490.852 l 323.32 490.777 326.672 488.711\n 327.137 487.582 c 327.355 487.02 327.406 486.379 327.465 485.758 c 327.531\n 485.043 327.57 484.305 327.906 483.641 c 328.023 483.41 328.223 483.223\n 328.438 483.031 c 328.59 482.871 328.746 482.711 328.859 482.562 c 328.938\n 482.438 329.004 482.328 329.07 482.223 c 329.996 480.531 329.949 478.492\n 332.887 478.219 c 335.391 477.938 333.512 481.039 333.602 482.5 c 333.648\n 483.383 334.977 484.254 335.738 484.555 c 340.594 486.039 341.559 488.918\n 342.02 493.402 c 342.031 493.539 341.961 493.672 341.836 493.73 c 340.941\n 494.125 340.43 494.688 340.383 495.328 c 340.344 495.797 340.574 496.266\n 341.008 496.578 c 341.309 496.578 l 344.254 496.625 347.609 497.039 348.609\n 498.129 c 349.398 497.488 350.691 497.188 351.945 496.887 c 353.727 496.457\n 355.422 496.043 355.547 494.855 c 355.547 494.82 355.555 494.781 355.555\n 494.754 c 355.555 494.387 355.395 494.16 355.219 493.871 c 354.969 493.484\n 354.66 493.016 354.727 492.188 c 354.602 492.113 354.469 492.055 354.312\n 491.98 c 353.805 491.754 353.168 491.492 353.051 490.715 c 353.023 490.609\n 353.062 490.508 353.137 490.441 c 353.398 490.195 354.219 490.027 355.113\n 490.027 c 356.703 490.027 359.719 490.531 362.07 493.953 c 365.066 498.297\n 360.441 502.828 357.957 504.82 c 358.406 505.195 358.824 505.609 359.246\n 506.004 c 360.449 507.207 361.02 508.637 363.09 508.945 c 363.562 509.02\n 364.273 509.094 365.113 508.957 c 365.863 508.824 368.023 509.082 368.898\n 509.164 c 368.977 509.164 369.035 509.191 369.094 509.238 c 370.527 510.336\n 371.027 512.848 371.25 513.535 c 371.539 513.602 372.156 513.73 372.84 \n513.895 c 373.988 514.164 375.344 514.508 375.863 514.785 c 375.973 514.828\n 376.027 514.914 376.047 515.012 c 376.117 515.461 376.281 515.801 376.441\n 516.156 c 376.625 516.57 376.867 517.039 376.973 517.746 c 377.793 518.289\n 378.707 519.371 378.891 520.426 c 378.996 521.027 378.852 521.609 378.465\n 522.059 c 378.359 522.191 378.168 522.211 378.023 522.129 c 376.973 521.516\n 375.43 521.121 374.004 521.121 c 372.945 521.121 372.02 521.324 371.289\n 521.703 c 371.586 522.734 372.012 523.336 373.012 523.336 c 373.504 523.336\n 374.102 523.188 374.816 522.906 c 374.922 522.867 375.066 522.879 375.152\n 522.953 c 375.719 523.434 377.289 523.641 378.812 523.848 c 381.23 524.172\n 382.57 524.523 382.84 526.027 c 383.012 527.152 383.465 527.461 382.879\n 527.707 c 382.754 527.754 382.617 527.832 382.484 527.887 c 382.734 528.848\n 383.516 529.504 384.332 530.188 c 385.488 531.141 386.789 532.211 386.172\n 534.16 c 386.145 534.246 386.078 534.312 386 534.359 c 385.922 534.391 \n385.816 534.398 385.738 534.359 c 385.527 534.285 385.297 534.203 385.035\n 534.102 c 384.055 533.715 382.723 533.172 381.656 533.172 c 381.25 533.172\n 380.922 533.246 380.684 533.402 c 381.406 534.602 384.238 536.867 384.738\n 537.078 c 385.008 537.195 385.344 537.219 385.691 537.25 c 386.395 537.309\n 387.262 537.375 387.707 538.289 c 387.734 538.336 387.734 538.375 387.734\n 538.41 c 387.762 539.039 387.551 539.449 387.359 539.805 c 387.34 539.898\n 387.293 539.977 387.234 540.07 c 387.184 540.191 387.039 540.266 386.926\n 540.258 c 386.348 540.227 l 385.922 540.223 385.461 540.195 385.027 540.195\n c 384.023 540.195 382.938 540.289 382.164 540.805 c 382.648 540.898 383.141\n 540.977 383.629 541.062 c 385.691 541.379 387.82 541.711 388.621 543.422\n c 388.621 543.492 387.512 543.578 387.484 543.645 c 386.828 545.711 388.371\n 547.953 388.918 547.469 c 389.352 547.094 393.215 547.516 393.988 547.469\n c 394.621 547.418 398.254 548.59 399.219 548.715 c 399.516 548.758 404.93\n 550.27 405.414 550.871 c 405.664 551.215 421.223 558.93 423.613 560.25 \nc 424.66 561 419.402 552.004 419.383 551.977 c 419.383 551.977 417.707 547.656\n 417.82 545.348 c 418.109 539.859 426.879 530.668 426.887 530.633 c 429.316\n 528.395 430.113 525.773 430.73 524.746 c 429.285 522.453 427.57 521.082\n 425.625 519.32 c 424.441 518.234 423.438 515.648 422.352 514.426 c 421.734\n 514.598 418.719 515.66 416.762 515.707 c 415.848 515.723 410.48 515.172\n 409.91 514.973 c 408.902 516.344 407.418 521.195 402.398 522.848 c 402.184\n 522.887 l 401.281 523.055 399.227 523.488 398.516 523.488 c 397.254 523.488\n 396.25 522.672 395.238 521.551 c 394.102 522.23 391.492 522.98 390.027 \n522.285 c 387.977 521.324 385.105 518.797 384.949 517.98 c 384.836 517.34\n 384.949 516.777 385.277 516.383 c 385.539 516.043 385.961 515.836 386.559\n 515.801 c 387.07 515.855 388.766 515.941 389.555 515.949 c 389.844 515.266\n 390.527 514.992 391.137 514.746 c 391.781 514.504 392.184 514.297 392.359\n 513.871 c 391.781 513.574 391.066 513.355 390.395 513.129 c 388.477 512.527\n 386.316 511.852 386.328 509.145 c 383.754 508.48 382.484 506.363 382.926\n 503.398 c 383.051 502.789 383.246 501.926 383.523 501.098 c 383.996 499.688\n 384.738 498.363 385.973 498.363 c 386.512 498.363 387.109 498.637 387.695\n 499.16 c 387.793 499.238 387.82 499.367 387.793 499.48 c 387.438 500.762\n 388.254 501.98 388.668 502.508 c 389.527 502.32 390.074 501.801 390.645\n 501.285 c 391.098 500.871 391.539 500.461 392.168 500.16 c 392.465 500.027\n 392.871 500.102 393.254 500.176 c 393.41 500.215 393.543 500.242 393.66\n 500.254 c 392.438 498.102 392.387 495.723 393.555 493.539 c 394.805 491.117\n 397.359 489.348 399.863 489.121 c 399.949 489.121 400.027 489.141 400.086\n 489.18 c 400.887 489.723 400.887 490.684 400.887 491.527 c 400.887 492.32\n 400.887 493.051 401.395 493.484 c 402.156 493.336 402.898 493.242 403.582\n 493.242 c 407.492 493.242 410.125 495.648 410.375 499.387 c 411.164 499.406\n 411.82 499.152 412.543 498.879 c 413.379 498.559 414.285 498.223 415.23\n 498.336 c 416.031 498.398 416.684 498.754 417.32 499.105 c 417.859 499.406\n 418.359 499.668 418.938 499.789 c 419.285 499.312 419.797 498.945 420.27\n 498.57 c 421.117 497.941 421.867 497.375 421.762 496.344 c 421.754 496.285\n 421.754 496.211 421.734 496.152 c 421.645 495.703 421.156 495.402 420.664\n 495.082 c 420.02 494.688 419.305 494.238 419.219 493.402 c 419.18 492.848\n 419.324 492.406 419.652 492.055 c 420.422 491.266 423.668 491.152 424.875\n 491.887 c 426.242 492.715 430.258 495.855 430.691 499.531 c 430.723 500.27\n 430.691 500.441 430.078 501.117 c 430.664 501.406 431.262 501.676 431.84\n 501.945 c 433.477 502.676 435.164 503.449 436.434 504.574 c 437.09 504.293\n 437.785 504.172 438.652 504.172 c 439.211 504.172 439.789 504.219 440.336\n 504.277 c 440.895 504.32 441.484 504.387 442.051 504.387 c 442.805 504.387\n 443.457 504.277 444.066 504.07 c 444.172 504.031 444.297 504.051 444.391\n 504.125 c 445.242 504.766 445.578 505.836 445.906 506.887 c 446.09 507.527\n 446.301 508.129 446.578 508.609 c 446.984 508.711 447.406 508.824 447.852\n 508.945 c 449.055 509.277 450.289 509.633 451.281 509.633 c 451.637 509.633\n 451.938 509.586 452.195 509.492 c 452.273 509.465 452.379 509.465 452.477\n 509.52 c 453.121 509.879 453.188 510.566 453.258 511.23 c 453.266 511.363\n 453.293 511.488 453.305 511.605 c 453.305 511.699 l 453.199 512.309 452.352\n 513.383 451.125 514.82 c 450.664 515.387 450.098 516.062 449.895 516.402\n c 449.941 516.402 l 450.605 516.402 451.957 515.762 452.602 515.453 c 452.84\n 515.328 453.035 515.254 453.129 515.215 c 454.547 514.766 455.945 514.828\n 457.137 515.328 c 458.219 515.762 459.141 516.551 459.75 517.633 c 459.789\n 517.699 459.809 517.773 459.789 517.848 c 459.664 518.523 460.066 519.352\n 460.461 520.129 c 461.078 521.363 461.781 522.766 460.316 523.602 c 460.059\n 523.809 459.672 523.902 459.027 523.902 c 458.738 523.902 458.391 523.883\n 458.055 523.863 c 457.609 523.84 457.137 523.816 456.938 523.848 c 457.352\n 524.824 458.293 525.707 459.199 526.57 c 460.809 528.074 462.445 529.625\n 460.676 531.523 c 460.605 531.59 460.512 531.637 460.406 531.629 c 457.938\n 530.699 l 453.562 530.699 451.676 533.484 451.578 535.219 c 451.57 535.258\n 451.57 535.281 451.57 535.309 c 451.57 535.391 451.281 537.359 451.203 \n537.406 c 450.328 538.164 447.418 539.371 446.328 539.973 c 444.68 540.891\n 440.828 541.297 441.195 544.512 c 442.562 545.457 447.418 545.867 448.844\n 546.734 c 450.5 547.758 451 547.801 452.602 548.938 c 453.727 548.527 454.141\n 547.707 455.559 546.484 c 456.328 545.824 456.945 545.094 457.543 544.391\n c 458.547 543.207 459.5 542.098 460.945 541.445 c 461.223 541.328 465.926\n 539.539 467.949 539.164 c 473.863 538.98 478.227 539.188 481.543 538.117\n c 484.883 537.051 492.988 529.598 493.547 529.008 c 495.414 526.965 498.219\n 523.883 497.543 518.129 c 497.496 517.699 495.723 514.984 495.414 514.184\n c 494.922 512.941 492.68 509.266 492.121 508.898 c 489.441 508.41 488.66\n 509.09 487.543 508.93 c 484.449 508.449 483.074 506.645 483.074 506.645\n c 483.074 506.645 482.004 505.152 481.348 505.152 c 481.039 505.16 480.828\n 505.395 480.48 505.77 c 480.211 506.062 479.914 506.383 479.48 506.613 \nc 477.719 507.586 474.27 508.297 471.465 508.297 c 469.953 508.297 468.766\n 508.09 467.879 507.641 c 467.312 507.199 466.965 506.57 466.648 505.969\n c 466.34 505.422 466.059 504.895 465.617 504.527 c 464.449 504.332 458.805\n 502.16 458.258 499.613 c 458.148 499.098 458.121 498.684 458.121 498.191\n c 458.082 497.664 457.938 495.965 457.996 495.91 c 458.391 495.336 458.844\n 495.062 459.344 495.062 c 459.973 495.062 460.566 495.457 461.215 495.871\n c 461.906 496.324 463.844 496.82 463.844 496.82 c 464.777 496.82 464.875\n 496.773 466.32 495.992 c 466.773 495.391 467.227 494.988 467.602 494.668\n c 468.16 494.18 468.547 493.844 468.68 493.016 c 468.344 492.656 467.977\n 492.32 467.609 491.992 c 466.648 491.117 465.637 490.215 465.137 488.727\n c 464.961 486.832 465.152 485.188 465.703 483.793 c 466.426 482.023 467.734\n 480.664 469.664 479.668 c 469.738 479.629 469.816 479.629 469.914 479.648\n c 471.562 480.07 471.273 481.492 471.078 482.637 c 470.914 483.465 470.781\n 484.328 471.176 484.895 c 471.504 485.324 471.906 485.512 472.496 485.512\n c 473.332 485.512 474.461 485.07 475.492 484.625 c 476.465 484.387 477.266\n 484.273 477.977 484.273 c 479.125 484.273 480.098 484.594 481.039 485.25\n c 481.453 485.422 482.031 486.246 482.102 486.34 c 482.129 486.133 482.129\n 485.879 482.148 485.645 c 482.227 484.406 482.34 482.262 483.266 480.336\n c 484.074 478.613 485.5 477.074 488.082 476.422 c 488.41 476.34 488.758\n 476.301 489.066 476.301 c 490.59 476.301 491.707 477.328 492.109 478.352\n c 492.121 478.438 492.121 478.512 492.109 478.586 c 491.996 478.859 491.676\n 479.188 491.281 479.594 c 490.762 480.117 489.922 480.965 490.098 481.359\n c 490.156 481.508 490.48 481.848 492.062 481.902 c 492.215 481.887 492.398\n 481.867 492.594 481.867 c 495.559 481.867 499.461 484.023 500.801 490.043\n c 502.902 490.156 503.863 491.359 504.797 492.508 c 505.105 492.891 505.395\n 493.258 505.734 493.578 c 506.176 493.371 l 507.168 492.891 508.285 492.355\n 509.527 492.355 c 510.066 492.355 510.59 492.445 511.09 492.641 c 511.195\n 492.355 511.195 490.852 512.141 489.969 c 513.133 489.031 513.75 488.961\n 514.906 489.648 c 515.434 490.039 518.5 491.793 518.902 492.488 c 520.184\n 494.668 519.73 499.113 518.586 501.363 c 518.535 501.438 518.469 501.492\n 518.383 501.52 c 516.852 502 515.867 503.016 514.836 504.086 c 514.547 \n504.371 514.289 504.641 514.008 504.914 c 515.195 507.715 517.344 509.867\n 520.762 511.605 c 520.828 511.605 521.234 511.289 521.445 511.289 c 521.629\n 511.289 521.793 511.363 521.938 511.523 c 522.062 511.625 524.539 513.707\n 524.238 516.023 c 524.238 516.055 524.23 516.082 524.223 516.113 c 524.078\n 516.457 523.027 517.273 522.996 517.605 c 523.055 517.734 523.191 517.793\n 523.469 517.906 c 523.824 518.035 524.289 518.227 524.383 518.77 c 524.48\n 519.164 524.223 519.465 524 519.738 c 523.691 520.105 523.488 520.367 523.652\n 520.781 c 525.395 520.855 526.156 521.945 526.262 522.969 c 526.398 524.234\n 525.656 525.762 524.223 526.281 c 524.383 526.777 524.25 527.191 524.184\n 527.531 c 524.105 527.707 524.066 527.887 524.078 528.059 c 524.191 528.195\n 524.328 528.227 524.578 528.262 c 524.828 528.32 525.176 528.383 525.578\n 528.734 c 526.234 529.578 526.195 530.387 526.129 531.34 c 526.098 531.711\n 526.082 532.098 526.098 532.531 c 526.156 533.266 526.445 533.957 526.727\n 534.633 c 527.266 535.922 527.844 537.258 526.293 538.523 c 526.234 538.57\n 524.48 538.637 523.961 539.039 c 523.961 539.625 523.824 540.816 523.789\n 540.859 c 523.613 541.082 523.422 541.121 523.266 541.121 c 522.988 541.121\n 522.766 540.934 522.555 540.777 c 522.523 540.766 522.516 540.746 522.496\n 540.734 c 522.844 541.945 523.363 544.438 522.129 546.008 c 522.102 546.051\n 520.504 545.785 518.961 545.504 c 517.844 545.289 516.766 545.066 516.34\n 544.938 c 515.406 544.648 514.297 544.316 513.094 544.316 c 512.266 544.316\n 511.523 544.465 510.801 544.746 c 507.418 546.367 500.953 549.949 497.812\n 553.91 c 495.801 556.453 497.574 557.645 497.391 557.98 c 496.531 559.586\n 493.785 560.199 493.324 560.852 c 492.918 561.441 493.008 561.555 492.957\n 561.891 c 492.918 562.398 493.68 562.738 493.461 563.047 c 492.496 564.484\n 490.645 565.055 490.59 565.148 c 489.973 566.133 489.441 567.012 488.949\n 567.812 c 487.891 569.543 487.07 570.898 486.176 571.969 c 490.289 571.25\n 493.305 571.129 497.707 570.957 c 497.707 570.957 499.27 570.934 499.48\n 570.898 c f\n1 0.972549 0.501961 rg\n513.824 616.266 m 515.445 615.633 531.176 602.324 526.062 585.391 c 523.199\n 575.895 498.082 574.395 490.914 575.113 c 489.992 575.188 486.898 575.297\n 485.75 575.379 c 483.766 575.492 479.816 576.629 478.227 576.496 c 477.352\n 576.43 479.422 573.426 482.844 567.93 c 483.613 566.688 485.492 563.465\n 486.301 562.172 c 488.863 558.082 490.23 556.492 493.508 553.68 c 495.664\n 551.18 496.996 548.699 499.98 546.699 c 501.773 545.484 504.008 544.648\n 505.898 543.68 c 506.031 543.262 506.031 541.895 506.148 541.457 c 508.875\n 540.84 513.008 540.516 516.156 541.668 c 517.207 542.055 517.707 542.984\n 518.789 542.945 c 518.844 542.945 518.914 542.938 518.98 542.926 c 519.402\n 541.363 518.805 540.422 518.211 539.453 c 516.695 538.316 513.93 535.707\n 514.645 534.188 c 516.418 534.52 519.934 536.969 520.539 536.898 c 521.426\n 536.801 521.871 535.906 521.801 534.949 c 521.695 533.434 520.184 532.398\n 517.168 530.555 c 515.465 530.031 513.035 529.844 512.93 528.148 c 512.824\n 526.898 513.988 526.695 515.598 526.93 c 517.09 527.141 519.422 528.789\n 521.59 530.418 c 521.91 527.426 518.98 527.426 518.211 525.141 c 516.754\n 524.484 515.117 524.023 514.461 522.598 c 516.754 520.793 518.613 523.84\n 520.078 523.789 c 520.59 522.793 519.586 521.871 519.297 520.18 c 517.523\n 518.977 512.457 520.488 512.266 518.066 c 512.227 517.113 513.613 516.414\n 514.797 516.414 c 516.418 516.414 517.746 518.094 519.297 517.465 c 519.133\n 516.543 516.891 515.676 516.215 515.215 c 516.129 514.625 519.105 515.723\n 518.961 515.16 c 517.883 514.012 516.051 513.523 514.867 513.086 c 513.895\n 512.715 512.438 512.398 511.512 512.34 c 510.859 512.301 510.281 512.809\n 509.645 512.805 c 508.605 512.754 508.102 512.902 506.984 511.891 c 507.141\n 509.914 509.219 509.82 510.887 509.332 c 510.164 508.223 510.117 507.547\n 508.863 505.875 c 508.652 502.977 513.094 502.547 516.023 498.484 c 516.301\n 496.438 516.727 493.906 514.941 493.203 c 514.461 494.359 514.02 495.742\n 512.93 496.812 c 512.168 497.551 510.211 496.961 509.105 497.621 c 508.102\n 498.191 507.785 500.27 506.984 500.27 c 506.109 500.27 501.707 496.203 \n501.531 494.105 c 499.875 492.969 497.758 493.41 495.715 493.672 c 494.914\n 492.281 495.531 490.715 496.242 489.895 c 495.598 488.41 495.809 486.398\n 494.664 485.664 c 492.562 484.293 489.469 487.488 487.535 485.531 c 487.504\n 484.574 487.535 484.105 487.254 483.52 c 486.512 483.539 486.039 483.617\n 485.664 483.984 c 485.539 484.105 485.426 484.273 485.316 484.461 c 484.152\n 486.527 484.75 487.129 484.855 488.238 c 485.02 489.48 486.418 490.609 \n487.273 491.645 c 488.082 493.297 487.254 494.332 486.418 495.309 c 484.047\n 494.398 484.355 492 482.609 490.477 c 481.598 489.723 481.965 489.449 481.223\n 489.238 c 479.172 489.449 477.043 488.629 475.664 487.918 c 473.469 488.484\n 471.492 488.617 470.387 489.895 c 470.461 491.078 470.781 492 471.617 492.445\n c 472.504 492.922 473.594 492.434 474.566 492.609 c 476.996 492.996 478.363\n 494.988 479.723 496.379 c 478.188 498.336 474.469 497.039 473.16 495.77\n c 469.922 495.328 467.641 496.387 467.609 499.238 c 467.754 499.98 467.969\n 500.949 467.543 500.949 c 466.965 500.949 466.965 499.52 466.418 499.426\n c 464.383 499.066 462.938 499.707 460.395 498.066 c 460.328 498.711 460.801\n 500.867 464.285 501.438 c 466.637 501.832 466.676 501.297 467.523 501.758\n c 468.121 502.082 467.648 502.566 468.949 503.449 c 473.41 505.441 476.723\n 501.691 480.828 501.043 c 482.574 500.75 484.066 501.758 485.316 501.043\n c 485.859 503.184 487.227 504.551 489.547 504.961 c 490.695 504.293 492.168\n 504.832 493.266 505.41 c 495.031 507.734 496.734 510.141 498.18 512.828\n c 499.309 515.422 501.32 519.344 501.223 520.93 c 501.129 522.398 498.043\n 531.254 492.812 535.551 c 492.43 536.086 491.109 536.566 489.664 537.461\n c 489.402 537.637 490.5 539 490.309 539.199 c 490.02 539.492 488.586 538.055\n 487.535 539.199 c 487.465 539.699 488.68 541.109 488.43 541.402 c 482.91\n 538.23 486.387 542.344 486.281 542.703 c 484.18 542.367 482.871 542.352\n 481.129 542.324 c 476.906 542.25 467.59 544.305 466.926 544.438 c 464.238\n 545.145 460.906 547.836 459.074 548.969 c 457.129 551.156 456.262 553.293\n 454.461 555.289 c 454.652 558.383 452.785 560.168 451.656 562.664 c 451.156\n 563.832 450.973 565.055 450.27 566.133 c 448.602 568.641 445.934 572.406\n 444.027 574.422 c 443.602 574.891 441.973 575.793 440.664 576.328 c 439.535\n 576.789 437.523 575.285 437.062 574.23 c 435.113 569.902 411.051 558.051\n 397.023 551.512 c 393.215 549.742 389.766 550.156 387.762 550.52 c 388.09\n 551.523 387.617 551.664 387.762 552.035 c 387.906 552.355 388.688 552.762\n 388.918 553.172 c 389.449 554.109 389.883 555.516 390.066 556.227 c 390.703\n 558.656 390.164 560.52 390.449 562.719 c 392.793 562.402 395.02 561.973\n 397.32 562.328 c 398.254 562.473 399.082 563.098 400 563.098 c 401.625 \n563.098 402.734 561.949 404.199 562.328 c 405.344 562.078 405.527 560.859\n 407.242 561.188 c 407.945 561.641 408.543 562.387 409.027 563.266 c 409.797\n 564.723 410.23 566.555 410.309 568.059 c 410.402 570.898 404.422 575.141\n 404.961 577.59 c 405.691 578.27 407.477 577.875 408.773 577.969 c 411 579.363\n 416.164 582.578 416.801 582.93 c 418.207 583.691 419.895 584.043 421.773\n 584.457 c 421.684 586.312 419.902 587.051 418.707 587.895 c 418.168 588.27\n 416.953 588.641 416.414 589.035 c 415.77 589.52 414.863 589.93 414.875 \n590.945 c 416.262 591.102 417.32 591.559 417.562 592.844 c 417.156 594.109\n 416.086 596.105 415.488 597.652 c 420.047 596.359 427.445 595.039 439.391\n 594.309 c 447.551 593.805 461.859 594.094 470.203 592.75 c 475.281 591.953\n 483.043 588.832 484.848 588.551 c 490.984 586.93 514.645 580.637 515.953\n 593.738 c 516.215 596.367 515.309 600.594 512.055 603.77 c 510.57 605.227\n 507.371 606.746 505.117 607.398 c 504.152 607.688 497.43 608.836 495.445\n 608.82 c 494.191 608.812 494.18 608.727 491.098 608.547 c 491.098 608.547\n 489.797 608.566 487.707 608.379 c 486.039 608.219 481.676 608.02 481.676\n 608.02 c 480.531 608.027 461.398 606.34 456.301 606.574 c 450.762 606.824\n 424.238 607.344 424.277 627.664 c 424.316 646.297 450.133 649.137 458.402\n 649.109 c 460.512 649.109 463.352 648.461 463.785 648.391 c 465.75 648.043\n 465.82 648.105 466.176 647.762 c 466.859 647.074 466.137 646.598 466.465\n 646.609 c 468.863 646.758 466.176 648.664 466.176 648.664 c 466.176 648.664\n 467.438 648.879 468.016 648.508 c 468.699 648.086 469.133 647.742 469.086\n 646.918 c 469.066 646.5 470.027 647.164 469.77 647.879 c 469.574 648.234\n 469.344 648.551 468.961 648.664 c 477.07 648.137 486.859 647.441 489.191\n 647.902 c 489.902 648.066 493.785 649.125 496.531 649.418 c 499.711 649.73\n 509.5 650.926 510.887 649.859 c 509.828 647.98 502.758 644.973 499.672 \n643.367 c 496.57 641.945 497.469 642.309 496.242 641.727 c 494.336 640.805\n 487.188 638.824 483.266 639.305 c 480.211 641.184 476.957 642.512 473.691\n 640.223 c 474.961 639.914 476.223 640.984 478.199 638.871 c 479.305 637.676\n 479.992 637.875 481.242 636.602 c 475.125 633.805 472.457 635.715 469.594\n 637.973 c 469.316 638.188 468.312 637.973 468.082 638.105 c 467.477 638.418\n 467.418 639.117 465.5 639.895 c 461.609 641.488 460.422 641.129 456.07 \n640.664 c 456.848 639.117 459.539 640.137 463.488 638.035 c 465.211 637.133\n 465.086 636.688 465.723 635.539 c 461.781 634.5 461.184 632.766 457.812\n 634.402 c 455.605 635.488 453.129 639.031 449.41 639.082 c 444.238 639.16\n 437.93 640.043 437.523 627.195 c 437.129 614.926 456.262 613.109 458.527\n 613.027 c 461.398 612.93 465.586 611.73 466.688 615.352 c 466.887 616.039\n 466.289 616.168 466.031 616.859 c 465.637 618.008 465.211 619.441 465.723\n 620.789 c 467.988 620.285 468.332 618.094 471.648 618.516 c 474.691 617.203\n 473.766 613.645 477.793 613.852 c 478.477 614.336 478.535 616.133 478.277\n 617.316 c 478.113 618.129 477.359 618.66 477.332 619.422 c 477.312 620.023\n 477.543 620.465 477.863 620.883 c 478.152 621.293 478.516 621.676 478.805\n 622.184 c 480.078 621.43 479.684 619.32 484.238 620.121 c 486.176 618.438\n 490.012 617.102 490.426 616.723 c 491.148 616.574 491.82 614.316 493.43\n 615.051 c 493.188 616.211 491.168 617.344 491.398 620.488 c 491.492 621.547\n 492.754 621.961 493.43 622.895 c 494.441 622.121 493.469 621.359 495.609\n 619.727 c 500.445 617.109 499.887 616.016 503.863 614.906 c 504.723 613.629\n 506.254 612.324 508.246 612.633 c 507.699 615.152 507.09 617.074 509.008\n 618.516 c 511.262 618.379 510.664 615.492 513.824 616.266 c f\n487.969 480.258 m 488.844 478.785 489.875 478.445 489.875 478.445 c 488.188\n 477.602 485.676 478.258 484.758 480.656 c 484.539 481.246 484.414 481.922\n 484.414 482.711 c 484.414 482.711 486.098 480.609 487.418 481.566 c 487.555\n 481.66 487.543 480.984 487.969 480.258 c f\n468.508 489.512 m 468.508 489.512 470.066 488.223 470.781 487.715 c 469.305\n 486.547 468.266 485.914 468.66 482.293 c 468.023 482.844 467.57 483.539\n 467.266 484.293 c 466.328 486.625 466.938 489.438 468.508 489.512 c f\n457.871 521.496 m 458.527 519.484 455.242 519.238 454.973 518.977 c 454.211\n 518.262 457.188 517.352 457.188 517.352 c 457.273 517.059 456.938 516.945\n 456.398 516.945 c 455.23 516.945 453.16 517.473 452.668 517.828 c 451.254\n 518.836 449.77 519.309 448.777 520.48 c 447.734 520.141 447.582 518.918\n 447.551 517.625 c 445.75 517.633 445.258 516.391 444.422 515.516 c 446.32\n 515.066 447.734 514.164 448.633 512.805 c 447.156 509.859 443.383 513.504\n 441 513.242 c 440.289 513.168 438.602 512.191 438.496 511.289 c 438.312\n 509.438 441.195 509.734 441.309 507.809 c 437.66 506.363 435.039 509.445\n 431.945 508.582 c 432.293 505.977 427.621 504.293 427.621 504.293 c 427.621\n 504.293 427.707 505.949 427.129 506.316 c 423.141 504.859 426.02 501.043\n 427.445 499.086 c 427.168 497.789 426.375 495.008 424.008 495.246 c 423.668\n 495.504 423.707 495.836 423.844 496.18 c 424.027 496.699 424.441 497.262\n 424.316 497.734 c 423.832 499.688 421.934 500.965 421.051 502.547 c 421.164\n 503.59 422.426 503.184 422.312 504.219 c 422.168 505.215 420.441 505.027\n 419.496 504.352 c 418.648 503.719 418.758 502.715 418.102 502.395 c 415.066\n 501.605 412.543 503.035 409.988 503.605 c 408.832 503.223 408.977 501.711\n 408.332 500.27 c 407.754 498.844 407.176 497.168 405.328 496.812 c 403.324\n 496.438 402.184 497.863 400.344 497.863 c 399.488 497.863 399.133 496.566\n 398.738 496.492 c 398.332 496.457 396.48 497.883 396.48 497.883 c 394.699\n 503.703 402.648 505.59 402.891 505.805 c 402.84 506.906 402.648 507.094\n 402.031 507.215 c 399.16 507.691 397.438 502.414 393.699 502.789 c 392.078\n 502.922 390.992 504.914 389.672 505.082 c 389.008 505.18 388.602 505.461\n 388.41 505.828 c 387.879 506.82 388.746 508.523 389.277 509.332 c 390.816\n 511.418 392.543 511.25 394.805 510.461 c 395.895 510.102 396.887 509.184\n 398.148 509.332 c 399.219 509.438 400.848 510.328 400.633 511.289 c 400.422\n 512.379 397.832 511.832 395.645 513.406 c 393.484 514.934 394.324 516.621\n 394.324 516.621 c 394.172 516.695 394.027 516.777 393.883 516.852 c 393.727\n 516.938 393.574 517.016 393.43 517.086 c 390.992 518.336 390.434 518.582\n 388.004 517.941 c 388.004 517.941 388.504 519.879 391.426 520.012 c 392.754\n 520.066 393.188 520.18 394.266 519.598 c 394.711 519.352 394.285 518.684\n 394.922 518.395 c 395.047 518.324 395.164 518.449 395.703 518.988 c 397.457\n 521.121 400.18 521.383 402.262 520.234 c 404.227 519.164 405.566 516.301\n 407.492 513.824 c 408.504 512.301 409.441 511.082 409.66 511.344 c 410.441\n 512.422 416.637 513.129 419.805 512.039 c 420.941 511.645 421.82 510.668\n 422.609 510.836 c 423.168 510.949 424.461 512.613 424.941 513.242 c 425.184\n 513.523 426.918 515.742 428.391 517.305 c 429.719 518.75 432.379 519.922\n 432.734 520.18 c 433.707 521.176 435.656 522.152 435.691 523.188 c 435.75\n 524.223 430.066 531.121 427.07 534.863 c 422.918 541.422 420.953 541.48\n 422.602 548.664 c 423.562 552.852 428.148 563.445 430.258 564.387 c 431.203\n 565.238 431.453 564.824 433.785 566.793 c 438.66 570.883 440.422 570.574\n 440.723 570.73 c 440.723 570.73 444.816 569.297 446.801 566.414 c 447.582\n 564.266 449.828 560.621 450.945 557.672 c 451.328 556.605 451.551 555.922\n 448.527 552.91 c 447.785 551.859 445.105 548.809 444.797 548.809 c 443.16\n 547.18 441.918 547.18 439.441 546.355 c 438.379 546.75 436.242 546.953 \n434.68 546.852 c 432.918 545.484 437.457 541.84 437.59 541.578 c 439.461\n 538.301 449.633 535.762 449.941 535.051 c 449.863 530.07 452.938 528.66\n 456.898 528.148 c 456 526.047 450.887 523.281 450.191 523.039 c 450.441\n 522.117 452.832 521.281 454.488 520.461 c 456.02 519.992 457.121 521.195\n 457.871 521.496 c f\n397.457 493.391 m 397.484 492.238 398.168 491.992 398.09 491.492 c 398.051\n 491.004 396.445 491.625 395.559 493.41 c 395.113 494.293 394.805 495.648\n 395.172 496.004 c 395.52 496.359 395.809 496.473 396.445 496.172 c 396.445\n 496.172 397.84 495.402 397.898 494.949 c 397.977 494.293 397.426 494.613\n 397.457 493.391 c f\n387.156 504.922 m 387.156 504.922 388.062 504.5 387.859 504.098 c 387.543\n 503.516 386.934 503.562 386.27 502.527 c 385.652 501.551 386.066 500.965\n 385.73 500.582 c 385.527 500.363 385.113 500.75 384.855 501.504 c 384.68\n 502.039 384.574 502.734 384.699 503.543 c 384.816 504.52 385.449 506.109\n 385.934 506.293 c 386.551 506.512 386.723 506.156 386.867 505.723 c 386.945\n 505.449 387.012 505.152 387.156 504.922 c f\n381.289 553.93 m 382.59 552.566 383.898 551.199 384.457 549.078 c 383.523\n 548.113 382.773 547.059 383.734 546.395 c 383.195 544.621 381.656 542.852\n 379.535 544.285 c 378.852 543.492 377.551 542.699 377.359 541.734 c 376.945\n 539.617 379.344 538.117 380.961 537.344 c 380.316 536.09 378.852 535.668\n 377.82 534.789 c 377.309 536.363 373.156 537.562 373.621 535.09 c 373.727\n 534.43 374.363 534.531 375.016 534.031 c 375.973 533.305 377.309 531.254\n 378.293 530.727 c 378.988 530.332 379.891 530.668 380.16 529.973 c 379.613\n 529.117 378.852 528.227 377.977 528.148 c 376.184 528.02 374.199 531.254\n 372.215 531.629 c 370.605 531.891 368.785 530.949 368.465 529.223 c 369.238\n 528.715 370.277 529.391 370.758 529.672 c 372.184 528.562 373.668 528.262\n 374.238 526.363 c 372.762 526.738 371.422 526.629 370.047 525.754 c 369.258\n 525.254 366.828 524.164 366.762 523.039 c 366.723 522.086 368.957 519.238\n 370.047 518.828 c 371.461 518.262 372.84 518.676 374.391 518.375 c 374.391\n 517.152 373.504 516.816 372.977 516.113 c 372.898 516.113 372.84 516.102\n 372.773 516.102 c 368.359 515.867 366.387 518.02 363.641 519.43 c 362.234\n 519.164 361.656 517.793 362.379 516.711 c 363.465 515.16 367.223 514.371\n 368.312 513.242 c 367.195 512.98 365.402 512.113 364.363 512.039 c 363.785\n 511.992 364.16 512.207 363.496 512.398 c 362.734 512.629 363.043 512.586\n 362.484 512.805 c 360.719 513.43 359.891 515.93 357.984 515.348 c 357.512\n 513.375 359.93 511.219 357.82 509.773 c 356.309 509.691 355.684 510.422\n 354.246 510.387 c 353.391 509.719 354.074 508.367 353.293 507.527 c 354.391\n 506.699 354.641 505.969 353.918 504.652 c 355.395 501.363 364.707 499.52\n 357.801 493.652 c 357.656 494.211 357.531 494.727 357.414 495.195 c 357.02\n 496.754 356.684 497.922 355.684 498.75 c 353.93 500.188 351.367 499.465\n 349.383 500.855 c 349.273 501.605 350.488 502.695 350.035 503.285 c 350.055\n 503.355 348.32 503.777 348.004 503.668 c 347.484 503.422 346.086 502.582\n 345.77 502.02 c 343.438 497.613 339.18 500.215 337.117 498.637 c 337.23\n 495.855 338.832 495.309 338.859 492.969 c 338.254 491.941 337.539 491.266\n 337.215 490.57 c 335.266 487.301 333.648 486.297 332.395 486.473 c 330.488\n 486.723 329.41 489.707 329.188 491.453 c 329.957 495.383 332.414 497.258\n 335.16 499.293 c 335.102 500.234 334.719 500.871 334.062 501.25 c 333.262\n 501.23 332.281 500.723 331.711 500.648 c 330.391 499.906 327.848 495.836\n 327.426 495.309 c 327.242 495.121 326.348 495.57 326.059 495.355 c 323.07\n 493.035 321.797 492.215 318.504 492.73 c 317.578 492.875 316.75 494.645\n 315.91 494.02 c 315.152 493.465 314.582 493.234 314.168 493.234 c 312.762\n 493.234 313.137 495.785 314.371 497.039 c 315.777 498.656 320.816 501.992\n 324.957 500.035 c 325.805 500.234 326.008 501.031 325.914 502.152 c 324.832\n 502.762 324.148 503.562 322.781 503.371 c 321.777 503.195 318.148 499.465\n 314.371 502.641 c 313.445 503.262 313.117 503.469 312.336 504.277 c 311.797\n 504.688 311.297 505.762 311.02 505.875 c 310.68 506.004 310.75 504.895 \n310.449 504.934 c 309.871 505.027 309.188 504.895 307.984 504.352 c 306.836\n 503.805 306.836 503.398 306.547 503.078 c 306.145 502.621 305.207 502.133\n 305.074 502.867 c 305.027 503.223 305.043 503.777 305.207 504.387 c 305.469\n 505.328 306.105 506.398 307.445 507.141 c 308.129 507.527 309.621 507.734\n 310.422 507.566 c 310.516 508.812 311.75 509.719 313.195 510.629 c 314.023\n 511.137 316.172 512.098 319.648 510.887 c 320.777 510.516 327.742 507.238\n 328.551 506.918 c 329.055 507.301 331.289 508.812 331.926 509.145 c 339.371\n 513.906 339.18 510.594 342.176 513.168 c 345.16 515.66 345.047 516.758 \n348.457 523.395 c 349.18 525.312 351.402 530.934 351.73 531.777 c 351.992\n 532.398 354.68 538.301 355.684 541.887 c 356.23 543.773 356.23 546.043 \n356.039 548.203 c 357.125 546.91 358.484 544.754 358.766 544.406 c 359.004\n 543.871 360.594 541.426 362.184 542.113 c 362.84 542.742 362.473 543.555\n 362.551 544.754 c 362.945 545.254 363.352 545.93 363.715 547.066 c 364.883\n 553.953 368.938 554.32 371.73 557.746 c 373.062 553.992 376.848 554.793\n 381.289 553.93 c f\n351.453 560.867 m 350.055 559.75 350.258 558.25 346.711 557.746 c 344.266\n 557.242 341.914 557.426 341.672 557.746 c 341.559 558.793 341.164 562.566\n 340.574 563.32 c 340.18 563.848 339.391 564.551 338.852 565.531 c 338.359\n 566.449 338.082 567.605 338.531 569.066 c 338.531 569.066 335.961 571.426\n 335.73 573.781 c 335.941 574.375 336.98 575.375 336.809 575.957 c 336.77\n 576.09 336.703 576.188 336.527 576.277 c 335.461 576.852 334.594 576.027\n 333.898 575.215 c 333.957 571.828 327.992 563.07 325.363 567.848 c 325.363\n 567.848 330.805 572.641 329.832 577.035 c 329.621 578.039 328.582 578.852\n 328.242 579.188 c 327.906 579.516 327.754 578.988 327.754 578.988 c 327.355\n 576.59 325.277 573.117 322.492 573.055 c 321.527 574.297 322.355 577.855\n 321.895 579.285 c 321.309 581.023 320.316 580.957 321.277 583.211 c 321.434\n 583.629 322.367 585.719 323.293 586.059 c 323.648 586.488 322.965 586.863\n 322.684 587.125 c 321.145 587.027 320.875 586.711 319.227 586.688 c 318.188\n 586.082 317.105 584.906 315.594 584.828 c 314.082 584.754 312.281 584.25\n 313.137 586.234 c 314.234 589.766 318.484 590.719 319.543 594.223 c 317.992\n 595.082 315.816 591.07 313.262 591.117 c 312.754 591.117 312.465 592 312.066\n 592.062 c 311.434 592.16 309.93 591.34 309.418 592.051 c 309.477 592.121\n 309.516 592.172 309.562 592.23 c 311.344 594.176 315.844 597.141 315.199\n 600.238 c 315.383 600.973 316.258 601.02 315.969 602.191 c 314.949 602.98\n 312.934 602.801 312.066 603.699 c 311.277 605.227 311.074 607.418 311.297\n 609.133 c 310.246 609.574 309.457 607.309 308.359 606.703 c 306.145 605.477\n 304.246 605.152 301.961 606.402 c 300.738 607.078 297.375 610.543 299.195\n 610.41 c 299.918 610.355 300.785 610.113 301.605 609.875 c 302.867 609.512\n 304.062 609.133 304.738 609.273 c 306.18 609.574 309.168 611.125 309.582\n 614.555 c 310.352 615.195 311.711 615.867 312.828 615.309 c 313.668 615.793\n 312.066 618.734 311.453 619.527 c 310.961 620.145 310.094 620.359 309.883\n 620.734 c 308.445 623.305 312.82 625.457 309.738 627.359 c 306.684 626.246\n 303.656 627.297 302.117 628.988 c 302.047 630.02 302.895 630.941 301.566\n 631.523 c 300.719 632.035 300.488 632.797 300.969 633.504 c 301.25 633.91\n 301.77 634.301 302.559 634.59 c 306.527 634.445 310.219 632.754 311 632.477\n c 312.5 633.711 311.074 634.828 310.246 636.254 c 309.016 637.457 308.32\n 637.75 308.07 639.457 c 308.223 640.91 307.973 641.625 309.938 642.969 \nc 310.777 643.531 311.895 644.289 312.898 643.812 c 314.102 643.25 315.383\n 641.727 315.785 640.863 c 316.344 639.68 315.719 636.707 317.52 636.707\n c 319.418 636.707 319.688 641.195 321.125 642.652 c 321.48 643.008 322.203\n 643.234 323.004 643.234 c 324.844 643.234 327.184 642.078 326.875 638.66\n c 326.566 635.57 325.344 632.918 324.16 630.375 c 322.965 627.852 319.594\n 627.047 320.016 624.348 c 320.121 623.73 320.605 623.875 320.949 623.293\n c 321.344 622.629 322.086 620.121 322.355 618.461 c 322.473 617.695 322.25\n 616.773 322.355 616.059 c 322.473 615.227 323.156 614.184 323.137 613.5\n c 323.078 612.477 322.184 611.641 322.355 610.488 c 322.395 609.98 323.898\n 610.258 324.016 609.824 c 324.18 609.086 322.715 608.914 322.695 608.379\n c 323.281 604.711 326.848 604.004 327.965 600.992 c 328.188 600.391 327.465\n 595.211 332.637 594.656 c 332.754 594.246 333.281 593.895 334.109 593.613\n c 333.602 593.113 333.215 592.555 332.781 592.09 c 332.52 591.801 331.5\n 590.938 331.258 590.562 c 330.855 589.93 330.027 589.668 330.488 588.27\n c 332.125 586.73 336.125 587.543 338.504 586.746 c 338.062 583.836 337.84\n 579.438 338.379 576.234 c 338.496 575.453 338.668 574.758 338.891 574.156\n c 341.703 574.023 343.57 577.082 345.008 576.828 c 345.035 574.941 345.461\n 573.457 345.77 571.875 c 344.871 571.008 344.758 570.078 343.859 569.203\n c 342.918 568.273 341.48 568.168 341.184 566.906 c 340.613 564.379 343.602\n 561.316 345.008 560.801 c 346.402 560.293 349.949 560.84 351.453 560.867\n c f\n332.301 485.719 m 332.414 485.629 332.465 485.484 332.395 485.195 c 332.25\n 484.555 331.77 484.836 331.789 483.617 c 331.816 482.469 332.309 481.059\n 332.25 480.57 c 332.215 480.137 331.25 480.797 330.469 482.562 c 330.336\n 482.871 330.191 483.238 330.055 483.641 c 329.766 484.594 329.18 486.223\n 329.535 486.586 c 329.871 486.953 330.008 486.191 330.844 485.945 c 331.434\n 485.777 332.02 485.891 332.301 485.719 c f\n325.199 649.102 m 325.449 649.406 326.238 648.672 325.941 646.832 c 325.836\n 645.918 325.75 644.961 325.402 644.832 c 325.082 644.727 324.805 644.875\n 324.266 645.113 c 324.09 645.199 323.891 645.27 323.688 645.344 c 323.109\n 645.562 322.539 645.711 322.539 645.711 c 323.012 646.289 323.793 645.43\n 324.359 647.066 c 324.477 647.461 324.641 647.887 324.785 648.27 c 324.957\n 648.648 325.113 648.953 325.199 649.102 c f\n314.109 492.168 m 314.738 492.125 315.305 492.148 314.805 491.43 c 314.477\n 490.945 314.023 491.043 313.695 490.027 c 313.359 489.055 314.023 488.41\n 313.812 488.016 c 313.668 487.715 312.145 488.258 311.488 489.625 c 311.309\n 490.027 311.191 490.484 311.211 491.023 c 311.219 491.906 311.566 493.352\n 311.961 493.539 c 312.395 493.73 312.531 492.852 313.234 492.406 c 313.465\n 492.238 313.793 492.199 314.109 492.168 c f\n307.5 646.48 m 308.195 646.09 309.594 645.387 309.555 644.984 c 309.516\n 644.574 308.359 644.695 307.695 644.371 c 307.012 644.062 306.566 643.344\n 306.566 643.344 c 306.324 643.789 306.25 644.324 305.594 645.023 c 304.969\n 645.668 303.879 646.289 303.656 646.605 c 303.445 646.918 305.613 647.543\n 307.5 646.48 c f\n299.602 634.062 m 299.746 633.938 299.602 633.789 299.438 633.594 c 299.312\n 633.449 299.168 633.266 299.09 633.055 c 298.945 632.543 299.805 631.719\n 298.812 631.902 c 298.379 631.996 298.629 632.16 297.801 632.121 c 297.012\n 632.074 296.723 631.543 296.363 631.582 c 296.145 631.594 296.105 632.293\n 296.559 632.93 c 296.816 633.285 297.23 633.617 297.84 633.805 c 298.504\n 634.012 299.34 634.266 299.602 634.062 c f\n381.426 626.43 m 380.723 623.918 383.832 623.68 385.25 624.516 c 385.711\n 625.305 385.672 625.98 385.25 626.809 c 384.16 627.695 381.586 627.016 \n381.426 626.43 c f\n377.059 602.312 m 376.715 604.09 375.633 605.379 374.324 606.359 c 373.426\n 604.391 371.531 604.266 371.441 601.59 c 371.316 597.836 372.812 597.816\n 373.188 596.012 c 373.41 594.918 373.562 594.035 373.516 592.957 c 373.477\n 592.012 372.898 589.43 372.898 589.43 c 373.246 589.098 375.094 591.234\n 375.914 592.609 c 376.559 593.672 377.02 594.832 377.137 595.824 c 377.254\n 596.785 376.684 597.539 376.551 598.516 c 376.316 600.145 377.328 600.539\n 377.059 602.312 c f\n364.621 623.754 m 366.309 623.605 367.215 624.219 367.289 625.66 c 366.875\n 626.773 364.719 627.43 364.074 626.641 c 363.688 626.152 363.57 624.379\n 364.621 623.754 c f\n402.203 603.402 m 402.242 603.094 402.398 602.777 402.57 602.43 c 402.859\n 601.816 403.215 601.105 402.906 600.375 c 402.688 599.902 402.707 599.41\n 402.727 598.914 c 402.754 598.145 402.801 597.184 401.609 596.762 c 401.414\n 596.711 401.32 596.711 401.203 596.711 c 400.055 596.711 399.238 597.848\n 398.293 599.191 c 397.891 599.77 397.457 600.363 396.965 600.957 c 396.867\n 601.047 396.84 601.199 396.867 601.336 c 396.934 601.691 397.215 601.906\n 397.582 601.906 c 397.801 601.906 398.051 601.828 398.305 601.754 c 398.609\n 601.648 398.977 601.52 399.266 601.547 c 399.441 603.25 398.785 606.117\n 398.305 607.211 c 397.773 608.539 397.367 610.328 397.688 611.359 c 398.109\n 612.312 398.754 612.391 399.172 612.434 c 399.535 612.484 399.691 612.504\n 399.863 612.855 c 399.777 613.906 399.238 614.473 398.609 615.133 c 397.773\n 616.031 396.801 617.047 397.195 619.332 c 397.242 619.562 397.457 619.742\n 397.688 619.719 c 399.441 619.574 400.133 618.102 400.684 616.914 c 400.836\n 616.613 400.961 616.32 401.117 616.059 c 401.367 615.625 l 402.051 614.422\n 402.801 613.07 403.051 611.711 c 403.016 610.398 402.051 610.281 401.48\n 610.188 c 401.27 610.168 401.047 610.137 400.922 610.078 c 401.539 607.137\n 402.051 604.328 402.203 603.402 c h\n398.109 593.195 m 398.016 592.668 397.801 592.363 397.59 592.086 c 397.418\n 591.824 397.254 591.609 397.195 591.16 c 397.117 590.711 396.953 590.281\n 396.84 589.973 c 396.801 589.867 396.742 589.773 396.734 589.715 c 396.703\n 589.543 396.184 587.68 395.887 587.164 c 395.559 586.629 395.48 586.461\n 395.309 586.121 c 395.055 585.582 l 394.863 585.176 394.68 584.41 394.527\n 583.73 c 394.305 582.77 394.18 582.266 393.957 582.012 c 393.477 581.555\n 392.57 581.844 392.242 582.062 c 391.934 582.277 391.828 582.59 391.75 \n582.832 c 391.605 583.207 391.578 583.254 391.328 583.254 c 388.477 583.004\n 388.082 581.922 387.07 579.316 c 386.926 578.918 386.762 578.484 386.57\n 578.023 c 386.105 576.648 385.559 576.312 384.922 576.035 c 384.719 575.977\n 384.594 575.957 384.496 575.957 c 383.141 575.957 382.07 577.891 381.23\n 579.426 c 381.098 579.641 380.992 579.836 380.895 580.031 c 380.75 580.277\n 380.578 580.555 380.395 580.828 c 379.16 582.816 378.457 584.062 379.016\n 584.742 c 379.312 585.113 379.652 585.191 379.863 585.191 c 380.594 585.191\n 381.191 584.438 381.895 583.562 c 382.707 582.555 383.621 581.414 384.535\n 581.59 c 385.422 581.77 385.867 582.727 386.328 583.758 c 386.723 584.656\n 387.156 585.562 387.918 586.012 c 388.43 586.266 388.785 586.238 389.168\n 586.164 c 389.383 586.105 389.672 586.043 390.152 586.043 c 390.605 586.043\n 391.211 586.105 391.914 586.238 c 392.254 586.289 392.668 587.727 392.871\n 588.398 c 392.977 588.773 393.082 589.105 393.195 589.402 c 393.602 590.527\n 394.16 591.234 394.738 591.977 c 394.816 592.086 l 394.961 592.27 395.152\n 592.469 395.375 592.684 c 395.742 593.043 396.156 593.457 396.25 593.812\n c 396.27 593.828 396.801 595.359 397.562 595.359 c 397.688 595.34 397.938\n 595.219 398.016 595.145 c 398.109 595.008 398.305 594.738 398.109 593.195\n c h\n372.223 586.98 m 371.953 586.98 371.684 587.055 371.461 587.18 c 371.098\n 587.441 371.098 587.938 371.125 589.418 c 371.145 590.188 371.152 591.07\n 371.098 591.754 c 370.922 593.641 370.586 594.434 370.375 594.754 c 369.285\n 594.703 368.273 594.66 367.328 594.66 c 365.152 594.66 362.926 594.871 \n361.375 596.48 c 361.328 596.527 361.297 596.574 361.27 596.617 c 359.707\n 600.242 360.508 602.648 361.375 604.621 c 361.551 604.965 361.809 604.992\n 361.906 604.992 c 362.656 604.992 362.879 603.492 362.879 603.281 c 362.898\n 602.895 362.867 602.473 362.84 602.047 c 362.762 601.027 362.688 599.965\n 363.391 598.965 c 363.785 598.574 364.996 597.82 366.828 597.82 c 367.617\n 597.82 368.445 597.957 369.273 598.254 c 369.613 598.395 369.457 599.855\n 369.363 600.812 c 369.219 602.309 369.055 603.988 369.641 605.16 c 368.68\n 605.715 367.848 605.574 367.543 604.559 c 367.367 604.16 367.348 603.957\n 367.328 603.852 c 367.59 603.852 l 367.957 603.852 368.812 603.852 369.055\n 602.785 c 369.074 602.707 369.074 602.648 369.055 602.566 c 368.641 600.793\n 366.762 599.625 365.691 599.625 c 365.488 599.625 365.277 599.672 365.133\n 599.77 c 365.066 599.777 365.027 599.828 364.98 599.855 c 364.508 600.43\n 364.508 603.105 364.969 604.992 c 365.23 605.934 365.047 606.949 364.902\n 607.121 c 364.266 607.551 363.832 607.797 363.516 607.98 c 362.703 608.445\n 362.59 608.52 361.809 609.711 c 361.211 610.867 361.086 611.109 360.988\n 612.402 c 355.863 612.766 354.273 618.066 354.293 619.594 c 354.312 620.305\n 354.699 620.379 354.863 620.379 c 355.449 620.363 355.711 619.867 356.027\n 619.297 c 356.402 618.605 356.914 617.664 358.195 616.676 c 358.891 616.141\n 359.594 615.879 360.355 615.879 c 360.844 615.879 361.316 615.984 361.762\n 616.074 c 362.59 616.262 363.398 616.352 364.016 616.016 c 364.785 615.492\n 364.633 614.348 364.488 613.254 c 364.43 612.793 364.371 612.297 364.418\n 612.035 c 364.785 610.23 367.984 609.602 370.191 609.922 c 370.055 609.984\n 369.93 610.043 369.785 610.125 c 369.574 610.23 l 368.555 610.727 368.465\n 611.652 368.562 612.066 c 368.562 612.457 368.734 614.797 370.094 614.797\n c 370.344 614.797 370.559 614.598 370.559 614.336 c 370.574 613.254 371.5\n 612.781 372.418 612.781 c 376.262 612.781 l 377.07 612.781 378.031 613.164\n 378.09 614.27 c 378.102 614.5 378.293 614.695 378.523 614.707 c 379.266\n 614.707 379.672 614.023 379.863 613.328 c 380.086 612.656 380.113 611.957\n 380.113 611.941 c 380.199 611.637 380.113 610.727 379.094 610.23 c 378.918\n 610.137 l 378.871 610.109 378.824 610.094 378.773 610.062 c 379.477 609.922\n 380.125 609.848 380.75 609.848 c 381.84 609.848 383.746 610.109 384.18 \n611.867 c 384.277 612.254 384.168 613.004 384.062 613.691 c 383.938 614.5\n 383.832 615.211 383.996 615.68 c 384.152 616.152 384.719 616.184 385.867\n 616.043 c 386.406 615.984 387.07 615.91 387.762 615.91 c 389.547 615.91\n 390.789 616.473 391.578 617.648 c 391.742 617.883 391.914 618.234 392.109\n 618.605 c 392.715 619.809 393.043 620.379 393.562 620.379 c 393.793 620.379\n 394.219 620.242 394.266 619.25 c 394.324 617.727 393.34 613.328 388.141\n 612.645 c 388.031 611.711 387.598 610.883 386.867 609.785 c 385.992 608.434\n 385.105 607.906 384.43 607.52 c 384.035 607.289 383.734 607.121 383.582\n 606.84 c 383.062 606.055 383.43 603.281 383.785 601.727 c 383.812 601.59\n 383.785 601.461 383.707 601.352 c 383.648 601.242 383.523 601.168 383.379\n 601.152 c 381.73 600.957 381.645 601.816 381.355 603.188 c 381.242 603.77\n 381.098 604.477 380.828 605.391 c 380.25 605.562 379.449 605.379 379.293\n 604.68 c 379.219 603.93 379.582 603.68 380.18 603.281 c 380.848 602.836\n 381.645 602.273 381.73 600.887 c 381.73 600.57 381.703 600.191 381.473 \n599.965 c 381.145 599.641 380.633 599.797 380.16 600.059 c 379.961 600.18\n 379.699 600.336 379.547 600.336 c 379.41 600.336 379.324 600.117 379.266\n 599.949 c 379.168 599.598 379.188 599.301 379.312 599.082 c 379.547 598.699\n 380.18 598.5 380.797 598.359 c 382.328 597.973 384.797 598.574 385.258 \n600.086 c 385.422 600.613 385.336 600.973 385.238 601.41 c 385.086 602.031\n 384.902 602.816 385.383 604.141 c 385.434 604.281 385.5 604.418 385.547\n 604.586 c 385.934 605.684 386.184 606.352 386.762 606.352 c 386.867 606.352\n 387.012 606.301 387.09 606.273 c 387.707 605.73 387.34 603.539 387.328 \n603.527 c 387.09 602.25 387.281 601.676 387.473 601.074 c 387.707 600.395\n 387.957 599.691 387.598 598.117 c 387.184 596.277 385.422 595.387 382.254\n 595.387 c 381.512 595.387 380.723 595.434 379.777 595.523 c 379.699 595.297\n 379.672 595.059 379.621 594.785 c 379.527 594.215 379.41 593.492 378.824\n 592.574 c 377.242 590.332 374.188 586.98 372.223 586.98 c h\n367.348 583.73 m 367.195 583.668 366.656 582.867 366.25 582.277 c 365.664\n 581.402 364.902 580.32 364.035 579.531 c 363.465 579.023 362.445 578.625\n 361.934 578.625 c 361.789 578.625 361.664 578.656 361.566 578.715 c 361.184\n 578.965 361.262 579.438 361.328 580 c 361.367 580.199 361.395 580.398 361.395\n 580.555 c 361.316 582.895 360.395 584.305 359.352 585.457 c 358.879 585.285\n 358.496 584.992 358.117 584.684 c 357.742 584.395 357.379 584.098 356.887\n 583.883 c 356.453 583.684 355.633 583.516 355.066 583.516 c 354.68 583.516\n 354.418 583.59 354.246 583.742 c 353.996 583.941 353.812 584.453 353.504\n 585.316 c 353.379 585.672 353.254 586.02 353.137 586.301 c 353.043 586.523\n l 352.414 587.949 351.762 589.445 351.422 591.234 c 351.375 591.449 351.48\n 591.668 351.691 591.754 c 351.887 591.84 352.059 591.867 352.242 591.867\n c 353.234 591.867 353.793 590.77 354.332 589.715 c 354.746 588.91 355.211\n 588.004 355.77 587.75 c 356.742 587.766 357.398 588.043 358.082 588.34 \nc 358.859 588.664 359.66 589.004 360.809 589.004 c 361.152 589.004 361.52\n 588.969 361.887 588.91 c 362.012 588.883 362.125 588.801 362.195 588.695\n c 362.762 587.816 363.266 586.195 363.496 584.484 c 363.984 584.516 364.402\n 584.961 364.863 585.414 c 365.375 585.934 365.953 586.523 366.73 586.582\n c 367.598 586.523 368.371 586.555 369.121 586.582 c 369.516 586.582 369.863\n 586.598 370.172 586.598 c 370.652 586.598 371.387 586.598 371.434 586.02\n c 371.434 585.133 368.832 583.816 367.348 583.73 c h\n350.605 596.223 m 348.602 597.016 348.859 599.965 349.109 602.801 c 349.285\n 604.816 349.457 606.898 348.785 607.656 c 348.406 608.07 347.715 608.059\n 346.684 607.688 c 346.605 607.656 346.5 607.645 346.402 607.672 c 346.098\n 607.766 345.895 607.949 345.758 608.23 c 345.43 608.969 346.039 610.152\n 346.684 611.406 c 346.961 611.941 347.215 612.469 347.359 612.855 c 348.34\n 615.754 348.918 616.461 349.555 617.215 c 349.91 617.664 350.285 618.117\n 350.797 619.117 c 351.133 619.883 351.684 620.84 352.367 620.84 c 352.578\n 620.84 352.773 620.75 352.898 620.59 c 353.109 620.363 353.168 619.996 \n353.109 619.5 c 352.703 617.629 352.059 616.43 351.453 615.277 c 350.836\n 614.133 350.266 613.059 350.035 611.488 c 349.969 611 350.172 610.898 350.73\n 610.637 c 351.184 610.418 351.809 610.109 351.945 609.34 c 352.051 606.715\n 351.945 604.816 351.559 602.742 c 351.5 601.742 351.664 601.488 351.691\n 601.445 c 352.02 601.426 352.395 601.633 352.734 601.816 c 353.207 602.047\n 353.668 602.289 354.121 602.289 c 354.449 602.289 354.727 602.168 354.922\n 601.906 c 355.008 601.801 355.035 601.66 355.008 601.535 c 354.477 599.133\n 351.809 596.203 350.809 596.203 c 350.77 596.203 350.652 596.203 350.605\n 596.223 c h\n357.051 636.66 m 357.289 636.223 357.637 635.641 356.926 635.176 c 356.801\n 635.078 356.645 635.023 356.473 635.023 c 356.027 635.023 355.652 635.41\n 355.25 635.824 c 354.977 636.117 354.688 636.43 354.391 636.539 c 354.371\n 636.539 354.312 636.504 354.273 636.488 c 354.141 636.43 353.984 636.352\n 353.785 636.352 c 353.543 636.352 353.312 636.457 353.062 636.688 c 352.773\n 636.887 352.668 637.242 352.793 637.645 c 353.09 638.648 354.852 639.816\n 356.027 639.816 c 356.27 639.816 356.492 639.777 356.676 639.668 c 356.711\n 639.648 356.75 639.605 356.77 639.574 c 357.27 638.816 357 638.465 356.664\n 638.215 c 356.625 638.184 356.566 638.148 356.547 638.137 c 356.531 637.52\n 356.801 637.059 357.051 636.66 c h\n354.852 643.352 m 356.414 643.352 357.957 642.547 359.16 641.066 c 359.246\n 640.961 359.371 640.836 359.516 640.695 c 360.035 640.172 360.684 639.547\n 360.539 638.816 c 360.461 638.367 360.121 637.996 359.496 637.707 c 359.438\n 637.676 359.363 637.645 359.273 637.645 c 359.004 637.645 358.852 637.875\n 358.641 638.137 c 358.426 638.465 358.09 638.938 357.367 639.512 c 356.691\n 640.066 355.914 640.34 355.152 640.34 c 353.629 640.34 352.34 639.25 352.125\n 637.801 c 352.02 637.043 352.598 636.148 353.062 635.422 c 353.543 634.68\n 353.957 634.031 353.562 633.59 c 353.312 633.328 353.012 633.176 352.676\n 633.176 c 352.223 633.176 351.742 633.434 351.27 633.91 c 350.355 634.855\n 349.688 636.414 349.707 637.691 c 349.766 640.867 352.02 643.352 354.852\n 643.352 c h\n363.398 629.566 m 363.688 629.566 363.984 629.566 364.266 629.551 c 364.891\n 629.535 365.539 629.305 366.145 629.074 c 366.926 628.781 367.703 628.535\n 368.273 628.766 c 368.609 628.906 l 368.898 629.012 369.41 629.23 369.496\n 629.352 c 369.785 630.203 369.727 630.77 369.641 631.449 c 369.613 631.711\n 369.574 631.984 369.555 632.297 c 369.641 633.004 370.086 633.379 370.738\n 633.379 c 370.922 633.379 l 371.684 633.379 371.973 632.992 372.078 632.664\n c 372.484 630.77 372.137 629.941 371.75 628.953 c 371.664 628.723 371.559\n 628.473 371.453 628.18 c 371.336 627.871 l 371.125 627.289 370.902 626.688\n 370.828 625.969 c 370.73 625.391 370.73 624.727 370.711 624.094 c 370.703\n 622.352 370.684 620.535 369.055 619.531 c 369.016 619.516 368.969 619.5\n 368.918 619.488 c 368.785 619.434 368.641 619.422 368.516 619.422 c 367.57\n 619.422 367.551 620.41 367.551 620.871 c 367.551 621.023 367.543 621.258\n 367.512 621.387 c 366.906 621.32 366.367 621.227 365.855 621.137 c 365.113\n 621.012 364.418 620.887 363.766 620.887 c 362.492 620.887 361.598 621.336\n 360.828 622.34 c 360.758 622.414 360.719 622.523 360.719 622.629 c 360.738\n 622.953 360.719 623.312 360.719 623.645 c 360.711 624.637 360.684 625.73\n 361.184 626.391 c 360.98 626.391 l 360.539 626.426 360.055 626.438 359.766\n 626.809 c 359.594 627.008 359.527 627.289 359.574 627.629 c 360.055 629.566\n 362.484 629.566 363.398 629.566 c h\n364.816 641.074 m 365.094 641.164 365.5 641.129 365.863 640.93 c 366.145\n 640.797 366.492 640.781 366.906 641.164 c 367.223 641.438 367.145 642.121\n 367 642.77 c 366.875 643.402 366.605 643.059 366.492 643.988 c 366.297 \n645.645 366.27 647.102 366.828 647.484 c 367.02 647.637 367.859 648.348 \n368.195 647.434 c 368.266 647.301 368.484 646.035 368.371 645.766 c 368.207\n 645.418 368.082 645.23 368.246 644.52 c 368.484 643.441 368.812 642.199\n 371.152 642.301 c 372.637 642.367 374.168 643.832 374.758 645.434 c 375.43\n 647.27 375.27 646.652 375.539 647.559 c 376.117 649.426 376.875 650.309\n 377.531 650.969 c 378.043 651.512 378.648 650.355 378.648 649.645 c 378.574\n 647.527 377.289 648.082 377.117 646.266 c 377.051 645.23 377.145 643.504\n 377.762 643.012 c 379.285 641.797 379.352 642.582 381.5 642.336 c 382.223\n 642.242 382.379 641.734 383.418 641.812 c 385.238 641.938 385.723 643.879\n 386.012 643.504 c 386.117 643.352 385.77 642.457 385.547 642.039 c 383.891\n 639.094 379.363 639.547 375.848 641.453 c 374.652 642.207 374.449 639.594\n 371.336 639.434 c 369.707 639.359 368.891 639.945 368.246 640.27 c 366.953\n 639.004 365.449 639.094 364.871 640.328 c 364.738 640.605 364.562 640.996\n 364.816 641.074 c h\n386.945 629.168 m 387.215 629.012 387.484 628.859 387.727 628.766 c 387.965\n 628.645 388.254 628.594 388.562 628.551 c 389.332 628.41 390.355 628.242\n 390.422 626.84 c 390.449 626.465 390.355 626.148 390.152 625.914 c 389.836\n 625.59 389.371 625.547 388.969 625.547 c 388.352 625.559 l 388.176 625.559\n 388.082 625.547 388.031 625.531 c 388.062 625.281 388.109 625.062 388.148\n 624.848 c 388.32 623.945 388.496 623.09 387.957 622.016 c 387.906 621.922\n 387.82 621.855 387.742 621.812 c 385.883 620.945 383.766 620.766 382.359\n 621.441 c 382.281 621.441 382.117 621.473 381.965 621.473 c 381.434 621.473\n 381.309 621.211 381.047 620.656 c 380.809 620.121 380.488 619.359 379.43\n 619.359 c 379.188 619.359 378.918 619.406 378.629 619.488 c 378.418 619.543\n 378.285 619.742 378.293 619.977 c 378.371 620.777 378.32 621.277 378.266\n 621.84 c 378.207 622.23 378.168 622.629 378.148 623.164 c 378.129 623.402\n 378.148 623.633 378.148 623.895 c 378.168 624.555 378.188 625.297 377.977\n 626.316 c 377.676 627.516 377.359 628.23 377.137 628.797 c 376.875 629.414\n 376.656 629.91 376.605 630.703 c 376.539 632.008 376.637 632.652 376.992\n 633.023 c 377.273 633.348 377.676 633.363 377.867 633.363 c 377.887 633.363\n l 378.371 633.363 379.043 633.363 379.266 632.406 c 379.285 632.359 379.285\n 632.332 379.285 632.277 c 379.246 631.695 l 379.16 629.566 379.16 629.473\n 380.375 628.102 c 380.828 628.285 381.242 628.535 381.676 628.812 c 382.57\n 629.367 383.496 629.941 384.871 629.941 c 385.547 629.941 386.281 629.52\n 386.945 629.168 c h\n392.926 637.574 m 392.629 637.938 392.656 638.215 392.734 638.398 c 392.859\n 638.742 393.227 638.891 393.391 638.926 c 393.516 638.938 393.621 638.957\n 393.719 638.957 c 394.93 638.957 396.172 637.691 396.684 636.504 c 396.992\n 635.824 397.004 635.27 396.75 634.965 c 396.262 634.547 395.363 634.578\n 394.93 634.988 c 394.633 634.895 394.363 634.715 394.102 634.531 c 393.738\n 634.266 393.34 634.02 392.906 634.02 c 392.609 634.02 392.348 634.133 392.109\n 634.363 c 391.492 635.051 392.078 635.66 392.531 636.086 c 392.852 636.414\n 393.188 636.75 393.188 637.121 c 393.195 637.227 393.195 637.242 392.926\n 637.574 c h\n390.547 640.715 m 391.461 641.797 392.859 642.441 394.277 642.441 c 395.164\n 642.441 396.031 642.199 396.781 641.73 c 398.102 640.945 399.082 639.648\n 399.461 638.742 c 399.75 638.059 400.211 636.441 399.863 635.098 c 399.461\n 633.523 396.848 631.957 395.828 631.957 c 395.613 631.957 395.461 632.02\n 395.344 632.145 c 394.805 632.832 395.539 633.457 396.223 634.047 c 397.039\n 634.762 397.996 635.551 397.609 636.613 c 397.312 638.047 396.145 639.633\n 394.246 639.777 c 394.16 639.793 l 393.32 639.793 392.465 638.742 391.762\n 637.902 c 391.172 637.168 390.75 636.645 390.27 636.645 c 390.133 636.645\n 389.941 636.688 389.777 636.887 c 388.543 638.352 389.711 639.727 390.547\n 640.715 c h\n415.672 586.598 m 415.605 586.723 415.48 586.797 415.344 586.824 c 411.992\n 587.383 410.578 589.809 409.191 592.668 c 409.121 592.789 409.008 592.883\n 408.871 592.914 c 406.395 593.457 405.02 594.168 405.039 596.348 c 405.973\n 596.637 407.215 596.422 408.504 596.152 c 410.191 595.805 412.07 595.566\n 413.555 596.246 c 413.699 596.312 413.805 596.465 413.824 596.617 c 413.844\n 596.973 413.824 597.328 413.707 597.73 c 413.535 598.293 413.246 598.793\n 412.957 599.27 c 412.668 599.719 412.406 600.133 412.273 600.598 c 412.137\n 601.074 411.887 602.047 411.637 602.945 c 411.414 603.836 411.195 604.637\n 411.137 604.852 c 410.645 606.102 410.23 607.316 409.844 608.492 c 409.219\n 610.434 408.613 612.27 407.734 613.906 c 407.812 614.148 407.965 614.336\n 408.148 614.547 c 408.469 614.891 408.852 615.336 408.871 616.125 c 408.871\n 616.203 408.852 616.289 408.785 616.371 c 408.254 617.215 407.484 618.102\n 406.754 618.945 c 405.73 620.102 404.68 621.293 404.227 622.402 c 403.121\n 625.188 403.562 628.27 403.988 631.25 c 404.062 631.773 404.141 632.297\n 404.207 632.82 c 404.246 632.82 404.297 632.82 404.344 632.805 c 405.289\n 632.785 406.242 632.762 406.828 633.348 c 406.973 633.5 406.984 633.719\n 406.906 633.879 c 406.395 634.84 405.375 636.555 404.93 637.043 c 404.324\n 637.66 401.289 641.965 400.566 643.75 c 400.711 643.801 400.887 643.844\n 401.066 643.891 c 401.453 644.004 401.848 644.105 402.195 644.352 c 402.273\n 644.434 402.34 644.52 402.367 644.648 c 402.727 646.746 398.766 649.309\n 398.727 649.32 c 398.66 649.367 398.602 649.383 398.523 649.402 c 396.059\n 649.625 393.746 650 391.406 650.508 c 390.973 650.863 390.586 651.262 390.18\n 651.715 c 390.008 651.91 389.816 652.109 389.621 652.312 c 389.547 652.383\n 389.449 652.43 389.352 652.445 c 387.957 652.574 387.406 651.754 386.984\n 651.109 c 386.637 650.586 386.395 650.254 385.883 650.18 c 385.867 650.906\n 386.281 651.465 386.762 652.109 c 387.281 652.805 387.84 653.594 387.859\n 654.641 c 387.906 657.109 384.902 660.758 383.156 660.836 c 381.289 660.836\n 378.535 657.633 378.246 657.211 c 378.043 656.965 378.012 656.609 377.977\n 656.285 c 377.977 656.121 377.945 655.832 377.898 655.777 c 377.801 655.777\n l 377.688 656.09 l 377.02 657.707 376.492 658.984 377.465 660.512 c 377.551\n 660.617 377.562 660.742 377.531 660.867 c 377.504 660.988 377.426 661.098\n 377.309 661.16 c 376.75 661.504 376.461 662.172 376.125 662.918 c 375.684\n 663.953 375.172 665.137 373.727 665.398 c 373.609 665.426 373.477 665.398\n 373.359 665.324 c 373.273 665.246 373.195 665.137 373.188 665.012 c 373.012\n 663.539 372.117 662.75 371.152 661.93 c 370.711 661.547 370.25 661.148 \n369.863 660.664 c 369.805 660.605 369.777 660.531 369.758 660.453 c 369.621\n 659.543 369.93 658.801 370.199 658.141 c 370.488 657.441 370.664 656.984\n 370.469 656.488 c 370.199 656.734 369.98 657.016 369.727 657.293 c 368.949\n 658.152 368.09 659.141 366.441 659.328 c 366.336 659.344 366.223 659.312\n 366.125 659.234 c 364.594 658.152 363.602 657 362.926 655.625 c 362.609\n 654.992 362.484 654.297 362.348 653.629 c 362.156 652.652 361.992 651.789\n 361.297 651.309 c 360.461 651.398 359.883 651.66 359.305 651.922 c 358.59\n 652.246 357.859 652.574 356.789 652.445 c 355.969 652.34 354.863 651.57\n 353.273 650 c 352.031 648.797 350.035 645.527 349.719 645.004 c 349.254\n 644.598 347.059 642.703 346.527 641.953 c 345.199 639.961 344.43 637.617\n 343.609 635.148 c 343.426 634.59 343.234 634.023 343.043 633.434 c 342.992\n 633.312 343.023 633.176 343.09 633.07 c 343.148 632.941 343.273 632.855\n 343.398 632.82 c 344.941 632.406 345.895 632.066 346.066 630.941 c 346.887\n 625.098 345.047 622.504 343.098 619.773 c 342.609 619.051 l 342.012 618.234\n 341.547 617.48 341.145 616.723 c 341.113 616.645 341.094 616.566 341.094\n 616.492 c 341.125 615.547 341.953 615.383 342.434 615.277 c 342.676 615.227\n 342.965 615.168 343.023 615.059 c 343.043 615.043 343.07 614.949 343.012\n 614.676 c 342.801 613.484 342.484 612.312 342.145 611.066 c 341.762 609.68\n 341.367 608.246 341.094 606.656 c 340.895 605.344 340.777 603.973 340.652\n 602.664 c 340.383 599.938 340.152 597.363 339.246 595.324 c 338.852 594.488\n 338.312 593.852 337.781 593.238 c 337.145 592.5 336.461 591.746 336.152\n 590.68 c 336.125 590.547 336.145 590.406 336.238 590.281 c 336.336 590.156\n 336.461 590.098 336.605 590.098 c 338.273 590.113 l 339.234 590.113 340.191\n 590.062 340.895 589.809 c 341.855 588.383 341.566 586.426 341.289 584.516\n c 340.988 582.418 340.699 580.445 342.012 579.168 c 342.09 579.086 342.176\n 579.043 342.289 579.023 c 343.984 578.863 344.812 579.625 345.527 580.289\n c 346.113 580.809 346.625 581.277 347.57 581.324 c 349.707 580.211 350.586\n 577.559 350.285 573.227 c 349.219 572.133 348.805 570.453 348.406 568.758\n c 348.148 568.621 l 346.992 568.078 345.672 567.445 345.305 565.871 c 345.277\n 565.734 345.305 565.594 345.383 565.488 c 346.172 564.426 347.215 563.902\n 348.602 563.902 c 350.035 563.902 351.605 564.504 353.117 565.059 c 354.035\n 565.395 354.91 565.734 355.711 565.922 c 355.469 565.043 355.527 564.133\n 355.594 563.238 c 355.625 562.699 355.645 562.145 355.625 561.602 c 355.594\n 561.074 355.5 560.57 355.402 560.074 c 355.25 559.195 355.086 558.309 355.238\n 557.305 c 355.469 555.789 358.543 550.875 358.824 550.559 c 359.246 550.012\n 359.844 549.266 360.555 549.266 c 360.586 549.266 360.738 549.281 360.789\n 549.301 c 360.961 549.348 361.105 549.5 361.133 549.691 c 361.5 552.844\n 363.465 554.418 365.52 556.066 c 366.473 556.836 367.465 557.625 368.273\n 558.594 c 368.32 558.641 368.359 558.703 368.371 558.777 c 368.484 559.211\n 368.555 559.602 368.609 559.969 c 368.891 561.387 369.043 561.973 369.961\n 562.266 c 371.73 562.867 373.168 561.09 374.266 559.691 c 374.469 559.434\n 374.652 559.184 374.824 558.98 c 374.902 558.887 374.988 558.844 375.094\n 558.812 c 375.848 558.691 376.559 558.523 377.273 558.34 c 378.465 558.039\n 379.699 557.734 381.172 557.672 c 381.656 557.473 382.02 557.117 382.406\n 556.746 c 383.043 556.145 383.766 555.449 385.066 555.375 c 385.23 555.363\n 385.449 555.516 385.527 555.668 c 386.551 557.438 387.281 558.723 387.109\n 560.457 c 386.945 561.879 385.797 563.082 384.719 564.254 c 383.668 565.367\n 382.57 566.504 382.504 567.707 c 382.406 569.164 382.648 569.883 383.09\n 570.301 c 383.09 569.562 383.195 568.926 383.629 568.492 c 383.668 568.465\n 383.699 568.438 383.727 568.418 c 387.523 566.262 391.027 565.262 394.816\n 565.262 c 395.277 565.262 395.742 565.273 396.203 565.301 c 396.742 565.352\n 397.215 565.555 397.629 565.75 c 397.957 565.895 398.246 566.031 398.543\n 566.059 c 399.016 566.141 399.488 566.164 399.93 566.164 c 401.754 566.164\n 403.004 565.594 404.207 565.059 c 405.066 564.68 405.848 564.336 406.781\n 564.156 c 407.004 564.133 407.234 564.254 407.293 564.469 c 408.832 569.035\n 406.539 570.441 404.074 571.934 c 403.688 572.145 403.305 572.398 402.926\n 572.645 c 402.523 572.898 402.109 573.152 401.715 573.395 c 400.297 574.246\n 399.172 574.926 398.602 576.051 c 399.027 576.977 398.938 578.312 398.863\n 579.508 c 398.832 580.031 398.785 580.676 398.832 581.012 c 399.613 581.188\n 400.375 580.844 401.164 580.473 c 401.848 580.152 402.59 579.809 403.484\n 579.797 c 404.199 579.797 405.547 580.031 406.211 580.199 c 407.82 580.582\n 409.699 581.629 411.309 582.527 c 411.625 582.711 l 413.312 583.648 415.047\n 584.641 415.703 586.195 c 415.75 586.32 415.75 586.477 415.672 586.598 \nc f\n1 g\n488.66 340.703 m 487.891 339.051 486.781 337.559 485.375 336.379 c 484.723\n 335.816 483.988 335.328 483.219 334.922 c 482.719 334.672 481.281 333.844\n 480.723 334.039 c 480.301 334.039 480.027 334.895 480.473 335.066 c 481.273\n 335.461 482.023 335.949 482.719 336.531 c 484.199 337.777 485.367 339.426\n 486.039 341.258 c 487.648 345.688 485.953 350.086 484.191 354.184 c 482.574\n 357.988 480.348 361.477 478.871 365.367 c 477.32 369.449 476.254 373.641\n 475.676 377.965 c 475.223 381.781 474.5 385.992 472.879 389.527 c 471.223\n 393.152 468.016 395.844 464.008 396.406 c 463.363 396.5 459.441 395.949\n 460.008 397.195 c 460.27 397.754 461.898 397.949 462.426 398.043 c 463.535\n 398.23 464.652 398.25 465.77 398.117 c 478.199 396.648 478.516 381.855 \n481.012 372.57 c 482.484 367.117 485.098 362.312 487.215 357.129 c 489.309\n 351.945 491.137 346.102 488.66 340.703 c 487.062 337.277 489.402 342.32\n 488.66 340.703 c h\n493.895 350.152 m 493.043 358.676 490.57 366.973 487.793 375.051 c 485.125\n 382.797 481.887 390.945 476.648 397.348 c 474.18 400.367 470.965 403.137\n 466.984 403.719 c 463.312 404.258 459.652 402.59 456.703 400.555 c 453.285\n 398.191 450.355 395.164 447.695 391.988 c 446.34 390.371 445.066 388.68\n 443.844 386.961 c 445.75 385.484 447.602 383.918 449.336 382.23 c 452.273\n 379.395 455.105 375.988 456.184 371.969 c 456.742 369.863 456.629 367.598\n 455.684 365.613 c 454.922 363.977 453.273 361.621 451.531 360.922 c 454.988\n 360.969 458.418 359.879 460.566 357.023 c 462.68 354.156 463.023 350.293\n 462.859 346.84 c 462.699 343.477 462.012 340.16 461.156 336.906 c 460.934\n 336.062 460.695 335.215 460.434 334.367 c 460.211 333.637 459.809 333.398\n 460.664 333.117 c 461.195 332.941 461.715 332.758 462.246 332.59 c 464.027\n 332.02 465.828 331.492 467.641 331.055 c 473.863 329.508 481.688 328.016\n 487.66 331.199 c 494.297 334.746 494.547 343.598 493.895 350.152 c 493.285\n 356.395 494.199 347.133 493.895 350.152 c f\n477.707 307.043 m 475.875 298.086 466.117 294.562 459.219 290.316 c 455.395\n 287.973 451.84 285.184 448.641 282.051 c 445.5 278.949 441.945 275.801 \n437.93 273.891 c 434.094 272.062 429.555 271.875 425.75 273.855 c 424.219\n 274.645 422.832 275.785 421.82 277.184 c 421.5 277.621 420.961 278.273 \n421.695 278.609 c 422.168 278.949 423.668 277.27 424.102 276.988 c 431.918\n 271.855 439.625 278.648 444.922 284.137 c 448.594 287.922 452.551 291.289\n 457.102 294 c 461.57 296.621 466.426 298.457 470.578 301.629 c 474.566 \n304.68 476.734 309.656 475.164 314.602 c 474.961 315.203 473.836 316.723\n 474.75 317.105 c 475.426 317.602 476.59 315.168 476.812 314.711 c 477.938\n 312.355 478.207 309.609 477.707 307.043 c 477.359 305.344 478.43 310.758\n 477.707 307.043 c h\n483.332 312.75 m 482.555 317.797 477.609 321.418 473.516 323.805 c 471.039\n 325.234 468.43 326.398 465.77 327.457 c 464.73 327.883 463.68 328.258 462.621\n 328.617 c 461.938 328.863 461.254 329.086 460.566 329.312 c 459.828 329.539\n 459.047 330.156 458.738 329.312 c 456.945 324.5 454.719 319.406 450.973\n 315.77 c 446.84 311.754 441.32 311.348 436.695 314.863 c 438.238 310.562\n 437.426 305.957 433.738 303.039 c 430.691 300.625 426.695 299.555 422.918\n 298.945 c 420.816 298.621 418.688 298.434 416.57 298.324 c 416 298.285 \n415.441 298.262 414.863 298.25 c 413.969 298.227 414.266 297.891 414.246\n 297.121 c 414.238 295.887 414.227 294.652 414.246 293.438 c 414.305 289.074\n 414.691 284.656 415.742 280.414 c 416.656 276.699 418.168 272.801 421.098\n 270.207 c 424.336 267.363 428.766 267.098 432.82 267.863 c 440.809 269.398\n 448.305 274.004 454.941 278.496 c 461.559 282.973 468.055 287.824 473.711\n 293.488 c 478.508 298.293 484.816 305.344 483.332 312.75 c 483.266 313.238\n 483.805 310.398 483.332 312.75 c f\n456.723 345.637 m 455.559 341.438 453.199 337.715 452.082 333.484 c 450.992\n 329.406 450.945 324.535 448.055 321.184 c 446.773 319.688 444.969 318.715\n 442.996 318.566 c 441.992 318.496 440.98 318.633 440.059 319.027 c 439.383\n 319.367 440.598 319.574 440.703 319.555 c 445.047 319.086 447.32 322.641\n 448.238 326.324 c 449.094 329.781 449.336 333.297 450.5 336.699 c 451.734\n 340.328 454.172 343.523 455.066 347.305 c 455.512 349.152 455.309 351.137\n 454.25 352.754 c 453.758 353.469 453.141 354.098 452.418 354.578 c 452.273\n 354.672 451.039 355.219 451.703 355.566 c 452.84 356.156 455.191 353.688\n 455.711 352.941 c 457.004 351.156 457.371 348.875 456.977 346.719 c 456.906\n 346.363 456.82 345.992 456.723 345.637 c 456.426 344.578 457.043 346.77\n 456.723 345.637 c h\n423.746 307.324 m 425.434 307.273 427.156 307.645 428.555 308.672 c 429.305\n 309.215 429.934 309.949 430.383 310.77 c 430.637 311.199 430.973 313.105\n 431.414 313.145 c 432.504 313.238 430.992 309.387 430.723 308.973 c 429.625\n 307.211 427.859 306.031 425.875 305.562 c 421.676 304.523 417.281 305.883\n 413.07 305.973 c 409.102 306.047 405.25 305.082 401.281 305.191 c 397.176\n 305.398 392.859 308.289 393.254 312.863 c 393.305 313.152 393.844 313.176\n 393.891 312.883 c 394.008 311.934 394.363 311.027 394.883 310.258 c 395.992\n 308.578 397.82 307.562 399.797 307.324 c 403.371 306.914 406.945 308.125\n 410.473 308.352 c 413.785 308.578 416.918 308.031 420.184 307.59 c 421.355\n 307.418 422.551 307.367 423.746 307.324 c 425.598 307.262 422.746 307.355\n 423.746 307.324 c h\n393.359 384.055 m 391.156 381.66 388.555 379.676 385.73 378.098 c 382.484\n 376.281 377.832 374.805 376.336 371.047 c 375.953 370.105 375.797 369.074\n 375.883 368.059 c 375.934 367.551 376.047 367.043 376.203 366.547 c 376.289\n 366.27 376.684 365.727 376.637 365.438 c 376.512 364.691 375.664 365.82\n 375.566 365.969 c 374.98 366.891 374.652 367.965 374.574 369.047 c 374.227\n 373.742 378.34 376.773 382.004 378.828 c 385.355 380.691 388.477 382.59\n 391.262 385.297 c 394.246 388.203 398.035 391.727 402.582 390.918 c 404.297\n 390.617 405.922 389.695 406.953 388.246 c 407.078 388.059 407.387 387.363\n 406.82 387.438 c 406.539 387.488 406.184 387.977 405.973 388.137 c 405.473\n 388.523 404.922 388.828 404.332 389.035 c 399.902 390.703 396.164 386.938\n 393.359 384.055 c 392.648 383.293 393.91 384.617 393.359 384.055 c h\n417.484 387.922 m 419.566 391.367 424.227 391.781 427.523 390.016 c 431.195\n 388.059 433.852 384.715 437.156 382.27 c 440.48 379.844 444.508 378.441\n 447.516 375.578 c 449.008 374.141 450.039 372.27 450.184 370.18 c 450.25\n 369.234 450.125 368.266 449.809 367.363 c 449.711 367.086 448.832 364.973\n 448.352 365.355 c 447.824 365.5 448.488 366.469 448.562 366.703 c 448.891\n 367.68 448.969 368.742 448.805 369.77 c 448.488 371.816 447.148 373.492\n 445.461 374.617 c 443.797 375.746 442.07 376.688 440.27 377.551 c 438.766\n 378.266 436.867 379.129 435.684 380.316 c 432.418 382.57 430.086 385.84\n 426.82 388.09 c 425.191 389.207 423.199 389.797 421.242 389.355 c 420.328\n 389.141 419.461 388.738 418.699 388.152 c 418.523 388.02 417.996 387.309\n 417.707 387.504 c 417.562 387.57 417.406 387.746 417.484 387.922 c 418.488\n 389.582 417.445 387.82 417.484 387.922 c h\n384.941 318.934 m 381.309 317.082 377.156 319.855 375.559 323.09 c 373.621\n 326.98 373.57 331.492 372.137 335.555 c 370.738 339.539 368.09 343.242 \n367.742 347.547 c 367.59 349.539 368.102 351.57 369.363 353.113 c 369.699\n 353.527 372.898 356.309 373.234 355.199 c 373.379 354.691 372.301 354.352\n 371.965 354.082 c 371.223 353.488 370.613 352.734 370.199 351.891 c 369.188\n 349.859 369.496 347.621 370.277 345.59 c 371.625 342.113 373.555 338.949\n 374.777 335.422 c 376.027 331.801 376.008 327.922 377.234 324.293 c 377.812\n 322.555 378.949 321.012 380.594 320.148 c 381.434 319.707 382.367 319.449\n 383.32 319.406 c 383.359 319.406 385.738 319.48 384.941 318.934 c 384.941\n 318.934 385.027 319 384.941 318.934 c h\n459.211 352.34 m 457.805 358.996 450.637 358.734 445.309 357.566 c 442.031\n 356.852 438.852 355.688 435.762 354.391 c 435.191 354.164 432.504 352.586\n 432.176 353.148 c 431.609 354.129 434.23 355.086 434.988 355.5 c 434.625\n 355.449 430.375 354.711 431.359 356.027 c 431.676 356.465 433.824 356.578\n 434.375 356.703 c 435.742 357.023 437.098 357.379 438.43 357.773 c 442.273\n 358.883 446.281 360.312 449.508 362.711 c 453.266 365.488 454.441 369.328\n 452.082 373.539 c 449.98 377.277 446.559 380.332 443.238 382.973 c 439.691\n 385.785 435.926 388.473 431.938 390.633 c 428.543 392.484 424.285 394.582\n 420.297 393.801 c 416.645 393.086 414.98 389.508 414.199 386.199 c 413.227\n 382.035 413.293 377.664 413.562 373.414 c 413.602 372.891 414.199 368.527\n 413.852 368.473 c 412.766 367.664 412.465 371.297 412.359 371.855 c 412.273\n 371.383 411.848 367.457 410.809 368.508 c 410.441 368.727 410.98 371.234\n 411.02 371.668 c 411.145 373.059 411.23 374.449 411.27 375.82 c 411.387\n 379.977 411.309 384.43 409.824 388.371 c 408.398 392.141 405.488 394.395\n 401.336 393.812 c 397.359 393.266 393.574 391.199 390.18 389.102 c 386.461\n 386.801 382.859 384.148 379.555 381.273 c 376.512 378.621 373.301 375.52\n 371.855 371.668 c 368.504 362.738 381.895 358.809 388.102 357.18 c 388.648\n 357.039 393.543 356.027 393.477 355.781 c 393.871 354.504 390.363 355.348\n 389.797 355.414 c 390.133 355.227 393.969 353.375 392.445 352.855 c 391.934\n 352.68 390.094 353.852 389.574 354.082 c 388.305 354.617 387.02 355.125\n 385.73 355.594 c 381.789 356.996 377.504 358.27 373.281 358.086 c 369.219\n 357.906 366.25 355.855 365.488 351.723 c 364.758 347.797 365.594 343.598\n 366.52 339.781 c 367.59 335.496 368.957 331.23 370.711 327.176 c 372.301\n 323.465 374.238 319.406 377.473 316.859 c 385.258 310.719 393.477 323.258\n 396.906 328.824 c 397.242 329.379 398.793 332.695 399.371 332.453 c 400.434\n 332.02 398.844 329.668 398.484 328.918 c 398.746 329.148 401.789 332.234\n 401.734 330.598 c 401.703 330.066 400.211 328.879 399.844 328.449 c 399.008\n 327.488 398.188 326.5 397.387 325.504 c 394.785 322.207 392.293 318.566\n 390.961 314.543 c 389.73 310.551 390.547 306.723 394.441 304.668 c 397.926\n 302.824 402.051 302.262 405.922 301.961 c 410.414 301.613 415.02 301.621\n 419.488 302.027 c 423.344 302.363 427.523 302.965 430.914 304.949 c 439.305\n 309.977 431.309 320.891 427.137 326.051 c 426.512 326.832 425.875 327.582\n 425.211 328.336 c 424.805 328.793 423.199 330.066 423.148 330.648 c 423.055\n 332.281 426.156 329.199 426.387 328.973 c 426.145 329.5 424.633 332.113\n 425.309 332.414 c 426.242 332.828 427.254 330.102 427.695 329.387 c 429.363\n 326.668 431.23 324.199 433.18 321.699 c 435.203 319.355 437.562 317.008\n 440.492 315.863 c 443.508 314.699 446.359 315.719 448.562 317.957 c 452.68\n 322.137 455.125 328.656 456.688 334.207 c 458.285 339.914 460.586 346.344\n 459.211 352.34 c 458.785 354.379 459.605 350.648 459.211 352.34 c f\n445.762 407.902 m 445.684 407.402 444.73 407.387 444.652 407.891 c 444.527\n 408.812 444.285 409.723 443.922 410.598 c 443.121 412.488 441.812 414.172\n 440.133 415.367 c 436.629 417.914 432.32 417.719 428.234 417.387 c 423.707\n 417.039 419.324 415.863 414.777 415.715 c 410.23 415.543 405.887 416.012\n 401.414 416.785 c 393.949 418.062 383.969 418.852 380.738 410.156 c 380.527\n 409.594 380.461 408.004 380.035 407.637 c 379.449 407.133 379.074 407.902\n 379.055 408.363 c 378.957 410.422 379.527 412.473 380.527 414.254 c 382.629\n 418.008 386.48 420.336 390.672 420.977 c 396.184 421.824 401.59 420.648\n 407.043 420.148 c 412.195 419.691 417.254 420.035 422.359 420.734 c 427.426\n 421.426 433.363 421.984 438.141 419.82 c 442.695 417.762 446.223 413.051\n 445.762 407.902 c 445.73 407.684 446.059 411.277 445.762 407.902 c h\n450.27 414.754 m 447.891 422.957 436.938 425.113 429.68 426.203 c 420.875\n 427.52 411.754 427.574 402.879 427 c 395.152 426.516 386.445 425.562 379.777\n 421.297 c 376.375 419.117 374.207 415.715 374.082 411.633 c 373.938 407.188\n 375.719 402.797 377.723 398.906 c 379.566 395.504 381.617 392.25 383.93\n 389.133 c 387.059 391.332 390.324 393.418 393.844 394.957 c 397.195 396.438\n 401.117 397.664 404.805 396.855 c 408.699 396.012 411.203 392.949 412.332\n 389.254 c 413.785 394.469 418.148 397.562 423.543 397.062 c 428.863 396.574\n 433.805 393.793 438.199 390.945 c 438.641 390.652 439.094 390.363 439.527\n 390.07 c 440.211 389.602 440.664 388.828 441.242 389.621 c 442.09 390.777\n 442.93 391.949 443.719 393.152 c 445.098 395.297 446.406 397.496 447.551\n 399.789 c 449.73 404.246 451.793 409.793 450.27 414.754 c 450.152 415.129\n 451.078 412.113 450.27 414.754 c f\n403.449 277.914 m 401.531 274.68 397.879 272.766 394.188 272.465 c 389.41\n 272.074 385.316 274.469 381.645 277.258 c 378.082 279.973 375.105 283.309\n 371.605 286.109 c 367.945 289.02 363.977 291.227 359.891 293.488 c 352.828\n 297.418 344.688 303.781 347.32 312.957 c 347.512 313.598 349.082 317.781\n 350.055 317.117 c 350.816 316.785 350.191 316.02 349.988 315.531 c 349.527\n 314.469 349.246 313.312 349.16 312.168 c 348.812 308.145 350.75 304.441\n 353.832 301.961 c 357.887 298.699 362.742 296.859 367.203 294.281 c 371.77\n 291.676 375.883 288.34 379.496 284.543 c 382.898 280.949 387.137 276.555\n 392.129 275.441 c 394.238 274.965 396.48 275.121 398.516 275.891 c 399.738\n 276.348 400.895 277.02 401.867 277.891 c 402.148 278.121 402.668 278.93\n 403.102 278.617 c 403.359 278.504 403.621 278.199 403.449 277.914 c 401.715\n 274.996 403.555 278.09 403.449 277.914 c h\n394.805 300.887 m 392.379 301.941 390.027 303.5 388.707 305.84 c 387.137\n 308.633 387.301 311.828 388.266 314.789 c 385.191 311.703 379.863 311.527\n 376.336 313.746 c 372.82 315.977 370.449 319.73 368.629 323.379 c 367.676\n 325.309 366.848 327.301 366.086 329.301 c 365.77 330.141 365.52 329.727\n 364.777 329.5 c 364.246 329.332 363.715 329.16 363.188 328.98 c 362.02 \n328.598 360.855 328.184 359.719 327.75 c 355.934 326.285 352.203 324.594\n 348.84 322.281 c 345.789 320.164 342.703 317.336 341.656 313.633 c 340.375\n 309.102 342.379 304.648 344.824 300.914 c 349.391 294.016 356.164 288.523\n 362.723 283.594 c 368.004 279.645 373.457 275.879 379.312 272.777 c 384.594\n 269.98 390.73 267.098 396.879 267.508 c 410.684 268.465 410.684 288.191\n 410.559 298.227 c 408.844 298.285 399.73 298.773 394.805 300.887 c f\n364.738 396.633 m 364.547 396.066 362.773 396.539 362.309 396.539 c 361.309\n 396.527 360.305 396.387 359.344 396.125 c 357.359 395.559 355.566 394.461\n 354.16 392.949 c 351.48 390.023 350.574 385.934 349.797 382.156 c 348.93\n 377.832 348.551 373.492 347.262 369.242 c 346 365.031 344.129 361.242 342.105\n 357.359 c 340.152 353.594 337.965 349.051 338.109 344.727 c 338.254 340.836\n 340.48 337.133 343.957 335.289 c 344.352 335.078 344.93 334.562 344.344\n 334.125 c 343.879 333.805 342.773 334.426 342.348 334.602 c 340.5 335.395\n 338.891 336.699 337.664 338.273 c 334.871 341.832 334.418 346.469 335.48\n 350.762 c 336.742 355.938 339.371 360.461 341.367 365.352 c 343.57 370.695\n 344.43 376.16 345.672 381.754 c 346.781 386.629 348.387 391.914 352.34 \n395.234 c 354.227 396.82 356.586 397.836 359.055 398.137 c 359.844 398.23\n 365.605 398.191 364.738 396.633 c 364.672 396.445 364.738 396.633 364.738\n 396.633 c h\n378.293 390.586 m 373.535 396.273 366.742 404.445 358.457 403.777 c 350.035\n 403.277 344.883 393.359 341.703 386.707 c 337.898 378.746 335.066 370.145\n 332.945 361.57 c 331.113 354.133 329.266 345.305 331.809 337.828 c 336.086\n 325.27 353.168 329.566 362.543 332.602 c 363.266 332.828 363.984 333.074\n 364.691 333.316 c 364.418 334.219 364.141 335.121 363.891 336.043 c 363.445\n 337.582 363.062 339.145 362.742 340.715 c 362.184 343.441 361.836 346.223\n 361.934 349.012 c 362.051 352.07 362.754 355.254 364.852 357.586 c 366.953\n 359.938 370.125 360.801 373.207 360.762 c 370.738 362.465 368.715 364.973\n 368.301 368 c 367.879 371.102 369.168 374.203 370.848 376.742 c 372.504\n 379.262 374.621 381.461 376.848 383.492 c 378.043 384.582 379.293 385.613\n 380.566 386.613 c 381.125 386.816 381.164 387.094 380.664 387.449 c 380.441\n 387.758 380.191 388.078 379.969 388.398 c 379.422 389.133 378.871 389.863\n 378.293 390.586 c 377.688 391.312 379.18 389.469 378.293 390.586 c f\n0.0313726 0.580392 0.278431 rg\n511.109 316.453 m 511.406 316.066 511.746 315.684 511.996 315.277 c 511.562\n 315.105 511.078 314.957 510.59 314.824 c 506.406 313.691 502.023 313.105\n 497.668 312.969 c 495.48 312.895 493.316 312.977 491.168 313.203 c 490.262\n 313.312 489.375 313.465 488.496 313.645 c 488.422 313.973 488.324 314.305\n 488.238 314.629 c 488.188 314.809 488.133 314.996 488.074 315.156 c 487.977\n 315.449 487.871 315.707 487.766 315.984 c 487.34 317.066 486.793 318.109\n 486.09 319.121 c 485.926 319.367 485.723 319.648 485.52 319.895 c 485.117\n 320.438 484.664 320.984 484.164 321.523 c 483.883 321.82 483.594 322.105\n 483.285 322.395 c 482.68 322.965 482.023 323.523 481.32 324.055 c 480.992\n 324.332 480.645 324.582 480.289 324.828 c 480.289 324.84 480.27 324.84 \n480.27 324.84 c 480.414 324.828 480.57 324.828 480.742 324.828 c 481.543\n 324.828 482.332 324.855 483.113 324.914 c 483.883 324.988 484.645 325.102\n 485.367 325.234 c 487.109 325.551 488.699 326.078 490.156 326.793 c 490.625\n 327.039 491.098 327.281 491.551 327.562 c 491.793 327.715 492.035 327.883\n 492.254 328.035 c 492.727 328.363 493.172 328.711 493.594 329.074 c 493.816\n 329.285 494.047 329.5 494.277 329.727 c 494.875 329.465 495.473 329.16 \n496.059 328.844 c 497.969 327.836 499.797 326.645 501.531 325.355 c 504.152\n 323.41 506.648 321.246 508.922 318.887 c 509.297 318.496 509.672 318.09\n 510.031 317.676 c 510.395 317.293 510.762 316.875 511.109 316.453 c f\n472.176 431.973 m 472.207 431.484 472.195 430.984 472.176 430.469 c 471.977\n 426.148 471.164 421.805 469.961 417.629 c 469.355 415.543 468.594 413.496\n 467.699 411.527 c 467.312 410.668 466.859 409.82 466.418 408.992 c 466.406\n 408.992 l 466.289 408.992 466.203 408.992 466.098 408.984 c 465.762 408.984\n 465.422 408.984 465.086 408.945 c 462.969 408.793 460.809 408.211 458.719\n 407.207 c 457.977 406.859 457.215 406.465 456.492 406.012 c 455.762 405.582\n 455.059 405.09 454.375 404.547 c 454.934 406.219 455.375 407.902 455.559\n 409.516 c 455.75 411.105 455.75 412.629 455.586 414.098 c 455.512 414.773\n 455.406 415.457 455.23 416.109 c 455.191 416.297 455.133 416.484 455.078\n 416.684 c 455.027 416.906 454.953 417.129 454.875 417.348 c 454.777 417.629\n 454.672 417.922 454.547 418.207 c 454.258 418.891 453.922 419.566 453.527\n 420.207 c 453.547 420.219 l 453.797 420.508 454.055 420.738 454.336 421.016\n c 455.906 422.52 457.582 423.871 459.355 425.145 c 460.695 426.09 462.051\n 426.984 463.469 427.82 c 464.402 428.371 465.355 428.922 466.328 429.418\n c 467.773 430.168 469.258 430.863 470.77 431.465 c 471.242 431.652 471.707\n 431.852 472.176 431.973 c f\n420.797 264.105 m 420.797 264.098 l 420.703 262.863 420.559 261.652 420.348\n 260.434 c 419.969 258.301 419.402 256.223 418.707 254.145 c 417.332 250.016\n 415.488 246.004 413.207 242.301 c 412.945 241.883 412.656 241.465 412.359\n 241.086 c 412.059 241.457 411.762 241.871 411.48 242.289 c 409.113 245.926\n 407.203 249.914 405.723 253.992 c 405 256.051 404.391 258.129 403.949 260.242\n c 403.707 261.43 403.543 262.621 403.43 263.809 c 403.949 264.066 404.469\n 264.379 404.961 264.699 c 405.414 265.012 405.867 265.359 406.301 265.715\n c 406.5 265.883 406.703 266.059 406.887 266.242 c 407.078 266.43 407.281\n 266.609 407.457 266.816 c 408.602 267.953 409.594 269.305 410.441 270.859\n c 410.633 271.215 410.828 271.582 411.012 271.949 c 411.32 272.578 411.609\n 273.254 411.859 273.949 c 412.059 274.469 412.223 274.996 412.387 275.535\n c 412.91 273.844 413.543 272.281 414.344 270.859 c 414.758 270.066 415.238\n 269.344 415.742 268.66 c 416.066 268.207 416.414 267.789 416.762 267.395\n c 417.137 266.973 417.512 266.598 417.91 266.215 c 418.109 266.047 418.305\n 265.863 418.504 265.695 c 418.863 265.395 419.219 265.113 419.613 264.855\n c 419.805 264.719 420 264.605 420.203 264.461 c 420.395 264.332 420.598\n 264.23 420.797 264.105 c f\n370.066 405.805 m 370.18 405.391 370.316 404.984 370.469 404.582 c 370.461\n 404.582 370.461 404.59 370.441 404.59 c 370.27 404.734 370.086 404.848 \n369.91 404.973 c 369.746 405.09 369.555 405.203 369.391 405.336 c 368.301\n 406.07 367.203 406.688 366.098 407.227 c 363.969 408.23 361.82 408.801 \n359.727 408.965 c 359.555 408.984 359.383 408.992 359.207 408.984 c 358.445\n 409.02 357.688 408.992 356.914 408.898 c 356.684 408.871 356.414 408.832\n 356.172 408.793 c 355.828 408.727 355.5 408.664 355.172 408.594 c 354.699\n 409.379 354.254 410.188 353.859 410.996 c 352.926 412.949 352.145 414.98\n 351.492 417.047 c 350.191 421.203 349.293 425.527 348.996 429.848 c 348.949\n 430.34 348.918 430.852 348.949 431.336 c 349.398 431.211 349.883 431.051\n 350.375 430.863 c 354.43 429.344 358.301 427.23 361.914 424.793 c 363.707\n 423.57 365.43 422.227 367.012 420.77 c 368.09 419.773 369.121 418.703 370.066\n 417.613 c 369.969 417.367 369.891 417.141 369.836 416.926 c 369.758 416.652\n 369.691 416.371 369.613 416.109 c 369.094 414.047 368.969 411.832 369.273\n 409.516 c 369.43 408.27 369.699 407.047 370.066 405.805 c f\n344.523 324.855 m 344.523 324.855 344.516 324.855 344.516 324.84 c 343.793\n 324.336 343.109 323.812 342.453 323.258 c 342.145 322.965 341.828 322.676\n 341.527 322.395 c 341.211 322.125 340.914 321.82 340.633 321.523 c 340.219\n 321.078 339.844 320.652 339.508 320.203 c 338.426 318.879 337.598 317.469\n 337.039 315.992 c 336.914 315.73 336.809 315.449 336.723 315.156 c 336.637\n 314.887 336.539 314.586 336.461 314.305 c 336.309 313.715 336.184 313.125\n 336.086 312.523 c 334.793 312.203 333.477 311.934 332.125 311.754 c 329.969\n 311.453 327.801 311.348 325.625 311.371 c 321.277 311.406 316.875 311.887\n 312.664 312.957 c 312.184 313.07 311.703 313.191 311.238 313.371 c 311.5\n 313.766 311.809 314.18 312.117 314.566 c 314.832 317.957 318.051 321.004\n 321.48 323.652 c 323.195 324.988 325.008 326.211 326.887 327.27 c 328.223\n 328.035 329.613 328.691 331.047 329.273 c 331.059 329.258 l 331.402 328.938\n 331.75 328.656 332.105 328.371 c 332.484 328.09 332.867 327.836 333.262\n 327.59 c 333.496 327.453 333.715 327.32 333.945 327.188 c 334.652 326.793\n 335.383 326.453 336.152 326.152 c 337.184 325.758 338.281 325.457 339.438\n 325.25 c 339.543 325.215 339.652 325.207 339.758 325.195 c 339.812 325.195\n l 340.41 325.062 341.02 324.988 341.656 324.93 c 342.031 324.902 342.426\n 324.895 342.809 324.855 c 343.012 324.855 343.207 324.855 343.406 324.84\n c 344.016 324.84 l 344.188 324.84 344.352 324.84 344.523 324.855 c f\n0.137255 0.121569 0.12549 rg\n426.82 339.527 m 426.32 340.273 425.605 341.176 424.699 342.266 c 425.906\n 343.586 427.004 344.898 427.977 346.117 c 428.812 344.934 429.488 343.902\n 429.941 343.008 c 428.969 341.852 427.898 340.703 426.82 339.527 c h\n421.992 334.633 m 421.309 335.141 420.441 335.891 419.383 336.832 c 420.691\n 338.062 422.012 339.375 423.246 340.703 c 424.211 339.633 424.93 338.762\n 425.441 338.09 c 424.324 336.926 423.16 335.758 421.992 334.633 c h\n417.07 330.145 m 416.195 330.609 415.133 331.27 413.949 332.133 c 415.191\n 333.09 416.473 334.18 417.832 335.383 c 418.91 334.465 419.824 333.766 \n420.547 333.277 c 419.383 332.168 418.215 331.137 417.07 330.145 c h\n408.469 336.645 m 409.652 337.684 410.992 338.91 412.406 340.254 c 413.832\n 338.91 415.164 337.684 416.348 336.645 c 415 335.383 413.66 334.277 412.406\n 333.285 c 411.164 334.277 409.824 335.383 408.469 336.645 c h\n407.734 330.145 m 406.609 331.137 405.434 332.188 404.277 333.277 c 405.008\n 333.766 405.914 334.48 407.012 335.383 c 408.332 334.18 409.641 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409.691 364.055 410.867 363.207 c 409.621\n 362.238 408.332 361.141 407.012 359.938 c 405.914 360.848 405.008 361.551\n 404.277 362.059 c 405.434 363.152 406.609 364.203 407.734 365.18 c h\n416.348 358.688 m 415.164 357.625 413.832 356.402 412.406 355.078 c 410.992\n 356.422 409.672 357.625 408.469 358.688 c 409.824 359.93 411.164 361.062\n 412.406 362.043 c 413.648 361.062 414.98 359.938 416.348 358.688 c h\n417.07 365.18 m 418.215 364.203 419.383 363.152 420.547 362.059 c 419.824\n 361.551 418.891 360.848 417.832 359.938 c 416.473 361.148 415.191 362.238\n 413.949 363.207 c 415.133 364.055 416.195 364.719 417.07 365.18 c h\n421.992 360.688 m 423.16 359.559 424.324 358.406 425.441 357.238 c 424.941\n 356.57 424.211 355.695 423.246 354.617 c 422.012 355.949 420.703 357.246\n 419.383 358.488 c 420.453 359.445 421.328 360.18 421.992 360.688 c h\n419.824 347.66 m 421.176 349.078 422.387 350.406 423.449 351.613 c 424.68\n 350.234 425.816 348.918 426.793 347.66 c 425.816 346.418 424.68 345.086\n 423.449 343.711 c 422.387 344.914 421.176 346.25 419.824 347.66 c h\n418.273 346.043 m 419.652 344.613 420.887 343.305 421.965 342.133 c 420.672\n 340.773 419.324 339.387 417.957 338.109 c 416.773 339.199 415.461 340.441\n 414.027 341.812 c 415.453 343.184 416.887 344.613 418.273 346.043 c h\n414.027 353.527 m 415.453 354.879 416.762 356.121 417.957 357.23 c 419.324\n 355.934 420.672 354.578 421.965 353.207 c 420.875 352.004 419.641 350.695\n 418.273 349.277 c 416.887 350.707 415.461 352.133 414.027 353.527 c h\n408.129 347.66 m 409.535 349.09 410.992 350.547 412.406 351.945 c 413.844\n 350.547 415.289 349.09 416.676 347.66 c 415.297 346.23 413.844 344.785 \n412.406 343.383 c 410.98 344.785 409.535 346.23 408.129 347.66 c h\n406.559 346.043 m 407.938 344.613 409.371 343.203 410.789 341.812 c 409.391\n 340.453 408.062 339.207 406.859 338.109 c 405.488 339.406 404.16 340.762\n 402.852 342.133 c 403.938 343.316 405.191 344.633 406.559 346.043 c h\n402.852 353.207 m 404.141 354.559 405.508 355.934 406.879 357.23 c 408.051\n 356.137 409.371 354.898 410.789 353.527 c 410.074 352.828 409.371 352.133\n 408.66 351.418 c 407.938 350.707 407.254 349.992 406.559 349.277 c 405.184\n 350.695 403.938 352.004 402.852 353.207 c h\n405 347.66 m 403.648 346.25 402.445 344.914 401.367 343.711 c 400.133 345.086\n 399.008 346.418 398.035 347.66 c 399.008 348.918 400.133 350.234 401.367\n 351.613 c 402.426 350.406 403.648 349.078 405 347.66 c h\n426.82 355.781 m 427.898 354.633 428.957 353.469 429.941 352.324 c 429.488\n 351.438 428.812 350.379 427.977 349.207 c 427.004 350.434 425.906 351.738\n 424.699 353.066 c 425.605 354.145 426.32 355.066 426.82 355.781 c h\n429.133 347.66 m 429.941 348.789 430.625 349.82 431.125 350.762 c 431.184\n 350.844 l 431.156 350.969 l 431.031 351.418 430.875 351.891 430.684 352.359\n c 430.605 352.559 l 430.52 352.773 430.402 352.992 430.297 353.207 c 429.98\n 353.852 429.594 354.504 429.133 355.18 c 428.594 355.762 428.055 356.328\n 427.484 356.91 c 427.512 356.934 427.523 356.984 427.543 357.023 c 427.621\n 357.191 l 427.496 357.324 l 427.215 357.66 426.926 357.988 426.629 358.332\n c 426.348 358.629 l 426.156 358.262 l 425.125 359.316 424.074 360.367 423.023\n 361.391 c 423.391 361.59 l 423.082 361.863 l 422.746 362.16 422.418 362.453\n 422.09 362.738 c 421.953 362.859 l 421.789 362.785 l 421.742 362.773 421.695\n 362.754 421.656 362.738 c 421.078 363.281 420.52 363.828 419.895 364.398\n c 419.047 364.973 418.168 365.465 417.312 365.84 c 417.098 365.914 l 416.656\n 366.09 416.195 366.254 415.723 366.395 c 415.598 366.422 l 415.508 366.363\n l 414.586 365.867 413.543 365.18 412.406 364.371 c 411.289 365.18 410.238\n 365.867 409.324 366.363 c 409.219 366.422 l 409.094 366.395 l 408.629 366.254\n 408.16 366.09 407.727 365.914 c 407.523 365.84 l 406.664 365.465 405.789\n 364.992 404.883 364.371 c 404.316 363.828 403.738 363.281 403.168 362.738\n c 403.121 362.754 403.09 362.773 403.043 362.785 c 402.879 362.859 l 402.746\n 362.738 l 402.398 362.453 402.07 362.16 401.742 361.863 c 401.434 361.59\n l 401.789 361.391 l 401.801 361.391 l 400.75 360.367 399.699 359.328 398.668\n 358.262 c 398.484 358.629 l 398.188 358.332 l 397.906 357.988 397.602 357.66\n 397.312 357.324 c 397.195 357.191 l 397.273 357.023 l 397.281 356.984 397.312\n 356.934 397.328 356.91 c 396.77 356.328 396.223 355.762 395.652 355.152\n c 395.219 354.504 394.836 353.844 394.516 353.188 c 394.41 352.98 394.312\n 352.773 394.227 352.559 c 394.141 352.359 l 393.957 351.91 393.805 351.445\n 393.68 350.969 c 393.648 350.855 l 393.688 350.762 l 394.188 349.84 394.863\n 348.789 395.703 347.66 c 394.863 346.543 394.188 345.5 393.688 344.578 \nc 393.648 344.465 l 393.68 344.371 l 393.785 343.902 393.957 343.43 394.141\n 342.98 c 394.227 342.773 l 394.602 341.918 395.066 341.043 395.684 340.141\n c 396.223 339.574 396.77 338.992 397.328 338.418 c 397.312 338.391 397.301\n 338.355 397.273 338.297 c 397.195 338.137 l 397.312 337.996 l 397.602 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    "path": "Figs/CollegeShields/Licenses.md",
    "content": "This file contains licences accompanying the images in this folder.\n\n# King's College's shield\n\nFiles:\n        Kings.svg\n\nDerivative files:\n        Kings.eps\n        Kings.pdf\n\nSource:\n        Provided by the college / Vectorised by Krishna Kumar\n\nLicence:\n        Public domain\n\n# Gonville and Caius College's shield\n\nFiles:\n        Gonville_and_Caius.jpg\nSource:\n\n        Provided by the college\n\nLicence:\n\n        Not specified\n\n# Fitzwilliam College's shield\n\nFiles:\n\n        Fitzwilliam.pdf\n        Fitzwilliam.eps\n        FitzwilliamRed.pdf\n        FitzwilliamRed.eps\n\nSource:\n\n        Provided by the college\n\nLicence:\n\n        Not specified\n\n\n\n# Queens' College's shield\n\nFile:\n\n        Queens.svg\n\nDerivative files:\n\n        Queens.pdf\n        Queens.eps\n\nSource:\n\n        http://en.wikipedia.org/wiki/File:Queens%27_College_%28Cambridge%29_shield.svg\n\nLicence:\n\n        Creative Commons Attribution-Share Alike 3.0 Unported\n\n\n\n# St. John's College's shield\n\nFile:\n\n        StJohns.png\n\nDerivative files:\n\n        StJohns.eps\n\nSource:\n\n        http://en.wikipedia.org/wiki/File:Johns_shield.png\n\nLicence:\n\n        GNU Free Documentation License, Version 1.2 or any later version\n\n\n\n# Peterhouse's shield\n\nFile:\n\n       Peterhouse.svg\n\nDerivative files:\n\n       Peterhouse.pdf  (rsvg-convert -f pdf -o Peterhouse.pdf Peterhouse.svg)\n\nSource:\n\n       https://en.wikipedia.org/wiki/File:Peterhouse_shield.svg\n\nLicence:\n\n        GNU Free Documentation License, Version 1.2 or any later version or\n        Creative Commons Attribution-Share Alike 3.0 Unported\n\n\n\n# Trinity College's shield\n\nFile:\n\n        Trinity.svg\n\nDerivative files:\n\n        Trinity.eps\n        Trinity.pdf\n\nSource:\n\n        http://en.wikipedia.org/wiki/File:Trinity_College_(Cambridge)_shield.svg\n\nLicence:\n\n        GNU Free Documentation License, Version 1.2 or any later version\n"
  },
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1.391 31.838 l 1.391 30.541\n l 1.391 18.584 5.379 12.776 17.66 6.838 c 21.543 4.963 l 22.898 4.33 l \n23.012 4.198 l 22.992 4.385 l 22.992 31.823 l 23.031 31.991 l 22.938 31.932\n l f\n35.141 31.932 m 34.992 31.991 l 35.031 31.858 l 35.031 4.459 l 34.992 4.256\n l 35.141 4.424 l 36.367 4.979 l 53.047 12.83 56.801 17.506 56.801 30.428\n c 56.801 31.803 l 56.859 31.991 l 56.672 31.936 l 35.141 31.932 l f\n0 g\n4.883 42.764 m 4.941 42.698 4.984 42.651 5.012 42.616 c 5.082 42.534 5.133\n 42.479 5.16 42.448 c 5.184 42.424 5.238 42.369 5.328 42.28 c 5.359 42.248\n 5.41 42.198 5.477 42.135 c 5.422 42.092 5.395 42.069 5.383 42.057 c 5.199\n 41.893 l 5.051 41.725 l 4.992 41.67 l 4.992 41.494 l 4.992 39.991 5.383\n 38.967 6.219 38.287 c 6.367 38.166 l 6.219 38.065 l 6.051 38.084 l 5.699\n 38.119 l 5.383 38.178 l 5.199 38.213 l 5.012 36.877 l 4.75 36.877 l 4.566\n 38.213 l 4.379 38.178 l 4.219 38.143 4.121 38.123 4.082 38.119 c 3.711 \n38.084 l 3.543 38.065 l 3.465 38.069 3.402 38.092 3.402 38.123 c 3.402 38.155\n 3.434 38.202 3.488 38.241 c 3.766 38.455 l 3.996 38.631 4.148 38.854 4.418\n 39.405 c 4.539 39.651 4.602 39.862 4.602 40.014 c 4.605 40.405 4.664 40.721\n 4.734 40.721 c 4.77 41.409 l 4.789 41.67 l 4.715 41.725 l 4.566 41.893 \nl 4.379 42.057 l 4.305 42.135 l 4.371 42.198 4.422 42.248 4.453 42.28 c \n4.711 42.537 4.738 42.565 4.883 42.764 c f\n11.922 42.764 m 12.066 42.565 12.094 42.537 12.348 42.28 c 12.383 42.248\n 12.434 42.198 12.496 42.135 c 12.422 42.057 l 12.238 41.893 l 12.09 41.725\n l 11.996 41.67 l 12.035 41.409 l 12.07 40.721 l 12.125 39.737 12.672 38.6\n 13.242 38.287 c 13.34 38.237 13.398 38.19 13.398 38.166 c 13.398 38.116\n 13.363 38.073 13.316 38.073 c 13.074 38.084 l 12.719 38.119 l 12.219 38.213\n l 12.035 36.877 l 11.793 36.877 l 11.605 38.213 l 11.422 38.178 l 11.105\n 38.119 l 10.754 38.084 l 10.586 38.065 l 10.559 38.065 l 10.484 38.065 \n10.434 38.088 10.434 38.123 c 10.434 38.162 10.461 38.205 10.512 38.241 \nc 10.789 38.455 l 11.031 38.639 11.098 38.737 11.438 39.405 c 11.574 39.662\n 11.645 39.873 11.645 40.014 c 11.645 40.405 11.695 40.721 11.754 40.721\n c 11.793 41.409 l 11.809 41.67 l 11.785 41.694 11.766 41.713 11.754 41.725\n c 11.66 41.823 11.602 41.877 11.586 41.893 c 11.402 42.057 l 11.328 42.135\n l 11.395 42.198 11.441 42.248 11.477 42.28 c 11.566 42.369 11.621 42.424\n 11.645 42.448 c 11.672 42.479 11.723 42.534 11.793 42.616 c 11.82 42.651\n 11.863 42.698 11.922 42.764 c f\n8.41 38.643 m 8.543 38.494 l 8.672 38.327 l 8.703 38.284 8.758 38.229 8.84\n 38.159 c 9.008 38.01 l 8.914 37.936 l 8.746 37.768 l 8.578 37.619 l 8.523\n 37.545 l 8.523 37.369 l 8.523 35.827 8.891 34.862 9.75 34.162 c 9.898 34.041\n l 9.75 33.94 l 9.582 33.959 l 9.23 33.998 l 8.914 34.053 l 8.727 34.088\n l 8.543 32.752 l 8.281 32.752 l 8.094 34.088 l 7.91 34.053 l 7.594 33.998\n l 7.242 33.959 l 7.074 33.94 l 7.055 33.94 l 6.98 33.94 6.926 33.967 6.926\n 34.002 c 6.926 34.03 6.957 34.073 7.02 34.116 c 7.297 34.33 l 7.891 34.784\n 8.301 36.03 8.301 37.369 c 8.301 37.545 l 8.242 37.619 l 8.078 37.768 l\n 7.91 37.936 l 7.816 38.01 l 8.102 38.256 8.238 38.401 8.41 38.643 c f\n15.469 38.643 m 15.645 38.401 15.781 38.256 16.066 38.01 c 15.973 37.936\n l 15.805 37.768 l 15.637 37.619 l 15.582 37.545 l 15.582 37.369 l 15.582\n 36.362 15.875 35.147 16.215 34.76 c 16.586 34.33 l 16.613 34.295 16.684\n 34.241 16.789 34.162 c 16.957 34.041 l 16.809 33.94 l 16.641 33.959 l 16.289\n 33.998 l 15.973 34.053 l 15.785 34.088 l 15.602 32.752 l 15.34 32.752 l\n 15.156 34.088 l 14.969 34.053 l 14.652 33.998 l 14.301 33.959 l 14.133 \n33.94 l 14.113 33.94 l 14.039 33.94 13.984 33.967 13.984 34.002 c 13.984\n 34.034 14.016 34.077 14.066 34.116 c 14.336 34.33 l 14.922 34.795 15.359\n 36.041 15.359 37.369 c 15.359 37.545 l 15.301 37.619 l 15.137 37.768 l \n14.969 37.936 l 14.875 38.01 l 14.949 38.077 15.008 38.123 15.043 38.159\n c 15.121 38.229 15.18 38.284 15.211 38.327 c 15.34 38.494 l 15.469 38.643\n l f\n19 42.764 m 19.145 42.565 19.168 42.537 19.426 42.28 c 19.461 42.248 19.512\n 42.198 19.574 42.135 c 19.5 42.057 l 19.316 41.893 l 19.168 41.725 l 19.094\n 41.67 l 19.109 41.409 l 19.148 40.721 l 19.199 39.764 19.766 38.584 20.32\n 38.287 c 20.414 38.237 20.477 38.19 20.477 38.166 c 20.477 38.116 20.441\n 38.073 20.402 38.073 c 20.168 38.084 l 19.797 38.119 l 19.719 38.127 19.613\n 38.147 19.48 38.178 c 19.316 38.213 l 19.129 36.877 l 18.871 36.877 l 18.684\n 38.213 l 18.488 38.166 l 18.184 38.119 l 17.828 38.084 l 17.66 38.065 l\n 17.637 38.065 l 17.566 38.065 17.512 38.088 17.512 38.123 c 17.512 38.155\n 17.543 38.198 17.598 38.241 c 17.867 38.455 l 18.113 38.651 18.191 38.764\n 18.516 39.405 c 18.652 39.666 18.719 39.873 18.723 40.014 c 18.723 40.405\n 18.773 40.721 18.832 40.721 c 18.871 41.409 l 18.887 41.67 l 18.664 41.893\n l 18.582 41.979 18.527 42.034 18.496 42.057 c 18.48 42.073 18.453 42.096\n 18.406 42.135 c 18.473 42.198 18.52 42.248 18.555 42.28 c 18.641 42.369\n 18.699 42.424 18.723 42.448 c 18.75 42.479 18.801 42.534 18.871 42.616 \nc 18.898 42.651 18.941 42.698 19 42.764 c f\n39.043 42.803 m 39.172 42.635 l 39.305 42.487 l 39.391 42.385 39.445 42.323\n 39.469 42.299 c 39.508 42.264 39.562 42.217 39.637 42.151 c 39.547 42.077\n l 39.379 41.909 l 39.211 41.76 l 39.156 41.686 l 39.156 41.526 l 39.156\n 40.03 39.656 38.721 40.383 38.327 c 40.492 38.264 40.543 38.213 40.543 \n38.162 c 40.531 38.139 l 40.383 38.1 l 40.215 38.1 l 39.996 38.1 39.609 \n38.17 39.359 38.252 c 39.172 36.912 l 38.914 36.912 l 38.727 38.252 l 38.465\n 38.166 38.086 38.1 37.871 38.1 c 37.707 38.1 l 37.598 38.1 37.551 38.123\n 37.551 38.166 c 37.551 38.221 37.598 38.268 37.707 38.327 c 38.422 38.698\n 38.934 40.034 38.934 41.526 c 38.934 41.686 l 38.875 41.76 l 38.711 41.909\n l 38.543 42.077 l 38.449 42.151 l 38.527 42.217 38.582 42.264 38.617 42.299\n c 38.641 42.323 38.695 42.385 38.785 42.487 c 38.914 42.635 l 39.043 42.803\n l f\n46.121 42.803 m 46.25 42.635 l 46.398 42.487 l 46.566 42.299 l 46.715 42.151\n l 46.621 42.077 l 46.457 41.909 l 46.289 41.76 l 46.23 41.686 l 46.23 41.526\n l 46.23 40.045 46.742 38.717 47.457 38.327 c 47.57 38.264 47.617 38.213\n 47.617 38.162 c 47.605 38.139 l 47.457 38.1 l 47.293 38.1 l 47.074 38.1\n 46.688 38.17 46.438 38.252 c 46.25 36.912 l 45.992 36.912 l 45.805 38.252\n l 45.504 38.155 45.176 38.1 44.879 38.1 c 44.676 38.1 44.633 38.112 44.633\n 38.174 c 44.633 38.233 44.68 38.272 44.801 38.327 c 44.953 38.389 45.219\n 38.666 45.379 38.92 c 45.672 39.389 45.844 39.795 45.844 40.034 c 45.844\n 40.436 45.902 40.76 45.973 40.76 c 46.012 41.448 l 46.027 41.686 l 45.996\n 41.721 45.969 41.744 45.953 41.76 c 45.719 41.998 45.703 42.014 45.527 \n42.151 c 45.695 42.299 l 45.859 42.487 l 46.012 42.635 l 46.121 42.803 l\n f\n42.59 38.678 m 42.648 38.612 42.691 38.561 42.723 38.53 c 42.793 38.448\n 42.844 38.393 42.871 38.362 c 42.895 38.338 42.941 38.272 43.027 38.186\n c 43.062 38.155 43.121 38.112 43.188 38.045 c 43.094 37.971 l 42.926 37.787\n l 42.758 37.639 l 42.703 37.565 l 42.723 37.323 l 42.758 36.635 l 42.812\n 35.619 43.344 34.522 43.93 34.202 c 44.039 34.139 44.09 34.092 44.09 34.037\n c 44.078 34.014 l 43.93 33.979 l 43.762 33.979 l 43.543 33.979 43.156 34.045\n 42.906 34.127 c 42.723 32.787 l 42.461 32.787 l 42.277 34.127 l 42.027 \n34.037 41.656 33.979 41.344 33.979 c 41.156 33.979 41.109 33.994 41.109 \n34.061 c 41.109 34.112 41.152 34.151 41.273 34.202 c 41.418 34.264 41.699\n 34.553 41.848 34.795 c 42.152 35.291 42.332 35.705 42.332 35.909 c 42.332\n 36.311 42.383 36.635 42.445 36.635 c 42.48 37.323 l 42.5 37.565 l 42.277\n 37.787 l 42.09 37.971 l 42.016 38.045 l 42.082 38.112 42.133 38.162 42.164\n 38.194 c 42.422 38.452 42.449 38.479 42.59 38.678 c f\n49.723 38.678 m 49.781 38.612 49.828 38.561 49.855 38.53 c 49.926 38.448\n 49.977 38.393 50.004 38.362 c 50.027 38.338 50.082 38.284 50.172 38.194\n c 50.203 38.162 50.254 38.112 50.32 38.045 c 50.285 38.014 50.262 37.991\n 50.246 37.971 c 50.141 37.866 50.078 37.803 50.059 37.787 c 49.961 37.698\n 49.902 37.651 49.891 37.639 c 49.883 37.627 49.863 37.604 49.836 37.565\n c 49.855 37.323 l 49.891 36.635 l 49.945 35.619 50.477 34.522 51.062 34.202\n c 51.176 34.139 51.223 34.092 51.223 34.037 c 51.211 34.014 l 51.062 33.979\n l 50.895 33.979 l 50.676 33.979 50.289 34.045 50.039 34.127 c 49.855 32.787\n l 49.613 32.787 l 49.41 34.127 l 49.16 34.037 48.789 33.979 48.477 33.979\n c 48.289 33.979 48.242 33.994 48.242 34.061 c 48.242 34.112 48.285 34.151\n 48.406 34.202 c 48.555 34.264 48.836 34.553 48.98 34.795 c 49.285 35.291\n 49.465 35.705 49.465 35.909 c 49.465 36.311 49.516 36.635 49.578 36.635\n c 49.613 37.323 l 49.633 37.565 l 49.41 37.787 l 49.223 37.971 l 49.148\n 38.045 l 49.215 38.112 49.266 38.162 49.297 38.194 c 49.555 38.452 49.582\n 38.479 49.723 38.678 c f\n53.254 42.803 m 53.387 42.635 l 53.531 42.487 l 53.699 42.299 l 53.848 \n42.151 l 53.758 42.077 l 53.59 41.909 l 53.422 41.76 l 53.367 41.686 l 53.363\n 41.526 l 53.363 40.045 53.875 38.717 54.594 38.327 c 54.703 38.264 54.754\n 38.213 54.754 38.162 c 54.742 38.139 l 54.594 38.1 l 54.426 38.1 l 54.207\n 38.1 53.82 38.17 53.57 38.252 c 53.387 36.912 l 53.125 36.912 l 52.938 \n38.252 l 52.637 38.155 52.309 38.1 52.012 38.1 c 51.809 38.1 51.766 38.112\n 51.766 38.174 c 51.766 38.233 51.812 38.272 51.938 38.327 c 52.086 38.389\n 52.355 38.666 52.512 38.92 c 52.805 39.389 52.977 39.795 52.977 40.034 \nc 52.977 40.436 53.035 40.76 53.105 40.76 c 53.145 41.448 l 53.16 41.686\n l 53.129 41.721 53.102 41.744 53.086 41.76 c 53.004 41.846 52.957 41.893\n 52.938 41.909 c 52.754 42.077 l 52.68 42.151 l 52.828 42.299 l 52.992 42.487\n l 53.145 42.635 l 53.254 42.803 l f\n25.762 23.612 m 25.906 23.412 25.93 23.385 26.188 23.127 c 26.223 23.096\n 26.273 23.049 26.336 22.979 c 26.262 22.905 l 26.078 22.741 l 25.93 22.573\n l 25.855 22.498 l 25.875 22.256 l 25.91 21.569 l 25.941 20.963 26.195 20.182\n 26.504 19.729 c 26.711 19.432 26.934 19.198 27.082 19.135 c 27.203 19.08\n 27.25 19.041 27.25 18.983 c 27.25 18.924 27.203 18.912 26.988 18.912 c \n26.695 18.912 26.32 18.975 26.078 19.061 c 25.891 17.725 l 25.633 17.725\n l 25.445 19.061 l 25.195 18.979 24.809 18.912 24.59 18.912 c 24.426 18.912\n l 24.316 18.912 24.27 18.932 24.27 18.975 c 24.27 19.03 24.316 19.08 24.426\n 19.135 c 24.602 19.229 24.855 19.491 25 19.729 c 25.328 20.276 25.484 20.635\n 25.484 20.846 c 25.484 21.244 25.531 21.569 25.594 21.569 c 25.633 22.256\n l 25.648 22.498 l 25.594 22.573 l 25.262 22.905 l 25.168 22.979 l 25.234\n 23.049 25.281 23.096 25.316 23.127 c 25.402 23.217 25.461 23.272 25.484\n 23.295 c 25.512 23.327 25.562 23.381 25.633 23.463 c 25.66 23.498 25.703\n 23.545 25.762 23.612 c f\n32.395 23.612 m 32.539 23.412 32.562 23.385 32.82 23.127 c 32.855 23.096\n 32.902 23.049 32.969 22.979 c 32.895 22.905 l 32.711 22.741 l 32.559 22.573\n l 32.484 22.498 l 32.504 22.256 l 32.543 21.569 l 32.574 20.963 32.828 \n20.182 33.137 19.729 c 33.34 19.432 33.566 19.198 33.711 19.135 c 33.836\n 19.08 33.883 19.041 33.883 18.983 c 33.883 18.924 33.836 18.912 33.617 \n18.912 c 33.328 18.912 32.949 18.975 32.711 19.061 c 32.523 17.725 l 32.266\n 17.725 l 32.078 19.061 l 31.828 18.979 31.441 18.912 31.223 18.912 c 31.055\n 18.912 l 30.945 18.912 30.898 18.932 30.898 18.975 c 30.898 19.03 30.949\n 19.08 31.055 19.135 c 31.234 19.229 31.488 19.491 31.633 19.729 c 31.957\n 20.276 32.113 20.635 32.113 20.846 c 32.113 21.244 32.164 21.569 32.227\n 21.569 c 32.266 22.256 l 32.281 22.498 l 32.227 22.573 l 32.059 22.741 \nl 31.891 22.905 l 31.797 22.979 l 31.863 23.049 31.914 23.096 31.949 23.127\n c 32.035 23.217 32.094 23.272 32.113 23.295 c 32.145 23.327 32.191 23.381\n 32.266 23.463 c 32.293 23.498 32.336 23.545 32.395 23.612 c f\n29.012 17.592 m 29.098 17.397 l 29.273 17.28 l 29.441 17.092 l 29.59 16.944\n l 29.516 16.869 l 29.328 16.702 l 29.312 16.686 29.262 16.635 29.18 16.553\n c 29.164 16.537 29.137 16.51 29.105 16.479 c 29.125 16.237 l 29.16 15.549\n l 29.215 14.541 29.781 13.369 30.332 13.116 c 30.457 13.061 30.5 13.026\n 30.5 12.971 c 30.48 12.932 l 30.352 12.877 l 30.184 12.893 l 29.812 12.932\n l 29.73 12.94 29.629 12.959 29.496 12.987 c 29.328 13.026 l 29.125 11.686\n l 28.883 11.686 l 28.695 13.026 l 28.512 12.987 l 28.195 12.932 l 27.844\n 12.893 l 27.676 12.877 l 27.656 12.873 l 27.574 12.873 27.52 12.909 27.52\n 12.959 c 27.52 13.014 27.562 13.057 27.676 13.116 c 27.855 13.213 28.105\n 13.471 28.25 13.713 c 28.578 14.26 28.734 14.616 28.734 14.827 c 28.734\n 15.225 28.785 15.549 28.844 15.549 c 28.883 16.237 l 28.902 16.479 l 28.844\n 16.553 l 28.68 16.702 l 28.512 16.869 l 28.418 16.944 l 28.566 17.092 l\n 28.734 17.28 l 28.883 17.428 l 29.012 17.592 l f\n25.723 11.463 m 25.855 11.295 l 26.004 11.147 l 26.168 10.963 l 26.289 \n10.784 l 26.227 10.741 l 26.059 10.573 l 25.891 10.424 l 25.836 10.35 l \n25.855 10.108 l 25.891 9.42 l 25.945 8.405 26.477 7.307 27.062 6.987 c 27.176\n 6.924 27.223 6.877 27.223 6.823 c 27.211 6.799 l 27.062 6.764 l 26.895 \n6.764 l 26.746 6.764 26.477 6.803 26.039 6.893 c 25.855 5.577 l 25.594 5.577\n l 25.41 6.893 l 25.016 6.803 24.723 6.764 24.477 6.764 c 24.285 6.764 24.242\n 6.78 24.242 6.846 c 24.242 6.897 24.285 6.936 24.406 6.987 c 24.551 7.049\n 24.832 7.338 24.98 7.58 c 25.285 8.077 25.465 8.491 25.465 8.698 c 25.465\n 9.096 25.516 9.42 25.574 9.42 c 25.613 10.108 l 25.633 10.35 l 25.598 10.381\n 25.574 10.409 25.559 10.424 c 25.477 10.506 25.426 10.557 25.41 10.573 \nc 25.223 10.741 l 25.148 10.815 l 25.297 10.963 l 25.465 11.147 l 25.613\n 11.295 l 25.723 11.463 l f\n32.395 11.463 m 32.504 11.295 l 32.652 11.147 l 32.793 10.936 l 32.969 \n10.815 l 32.895 10.741 l 32.711 10.573 l 32.691 10.557 32.641 10.506 32.559\n 10.424 c 32.543 10.409 32.52 10.381 32.484 10.35 c 32.504 10.108 l 32.543\n 9.42 l 32.574 8.811 32.828 8.03 33.137 7.58 c 33.34 7.284 33.566 7.049 \n33.711 6.987 c 33.836 6.932 33.883 6.893 33.883 6.834 c 33.883 6.776 33.836\n 6.764 33.637 6.764 c 33.395 6.764 33.102 6.803 32.711 6.893 c 32.523 5.577\n l 32.266 5.577 l 32.078 6.893 l 31.641 6.803 31.371 6.764 31.223 6.764 \nc 31.055 6.764 l 30.945 6.764 30.898 6.784 30.898 6.827 c 30.898 6.881 30.949\n 6.932 31.055 6.987 c 31.234 7.08 31.488 7.342 31.633 7.58 c 31.957 8.127\n 32.113 8.487 32.113 8.698 c 32.113 9.096 32.164 9.42 32.227 9.42 c 32.266\n 10.108 l 32.281 10.35 l 32.227 10.424 l 32.059 10.573 l 31.891 10.741 l\n 31.797 10.815 l 31.949 10.963 l 32.113 11.147 l 32.266 11.295 l 32.395 \n11.463 l f\n29.012 29.666 m 29.125 29.502 l 29.273 29.354 l 29.441 29.166 l 29.59 29.018\n l 29.516 28.944 l 29.328 28.776 l 29.312 28.76 29.262 28.713 29.18 28.627\n c 29.164 28.612 29.137 28.588 29.105 28.553 c 29.125 28.311 l 29.16 27.623\n l 29.215 26.619 29.793 25.42 30.332 25.19 c 30.453 25.139 30.496 25.104\n 30.496 25.049 c 30.496 24.987 30.445 24.967 30.262 24.967 c 30.016 24.967\n 29.723 25.01 29.328 25.1 c 29.125 23.76 l 28.883 23.76 l 28.695 25.1 l \n28.258 25.01 27.992 24.967 27.844 24.967 c 27.676 24.967 l 27.566 24.967\n 27.52 24.987 27.52 25.034 c 27.52 25.088 27.566 25.135 27.676 25.19 c 27.852\n 25.284 28.105 25.545 28.25 25.787 c 28.578 26.334 28.734 26.69 28.734 26.901\n c 28.734 27.299 28.785 27.623 28.844 27.623 c 28.883 28.311 l 28.902 28.553\n l 28.844 28.627 l 28.68 28.776 l 28.512 28.944 l 28.418 29.018 l 28.566\n 29.166 l 28.734 29.354 l 28.883 29.502 l 29.012 29.666 l f\n25.668 63.569 m 25.727 63.506 25.77 63.455 25.797 63.42 c 25.867 63.342\n 25.918 63.284 25.949 63.252 c 25.969 63.229 26.027 63.174 26.113 63.088\n c 26.148 63.053 26.195 63.006 26.262 62.94 c 26.188 62.866 l 26.004 62.698\n l 25.988 62.682 25.93 62.627 25.836 62.53 c 25.824 62.518 25.805 62.498\n 25.781 62.475 c 25.797 62.213 l 25.836 61.545 l 25.871 60.901 26.16 59.998\n 26.43 59.69 c 26.801 59.26 l 26.82 59.241 26.891 59.182 27.008 59.092 c\n 27.164 58.971 l 27.008 58.869 l 26.84 58.889 l 26.484 58.928 l 26.168 58.983\n l 25.984 59.018 l 25.781 57.737 l 25.559 57.682 l 25.371 59.018 l 25.188\n 58.983 l 24.871 58.928 l 24.516 58.889 l 24.32 58.869 l 24.254 58.869 24.199\n 58.897 24.199 58.928 c 24.199 58.963 24.227 59.006 24.277 59.045 c 24.293\n 59.061 24.387 59.131 24.555 59.26 c 24.793 59.444 24.863 59.541 25.203 \n60.209 c 25.34 60.467 25.406 60.678 25.41 60.823 c 25.41 61.221 25.461 61.545\n 25.52 61.545 c 25.559 62.213 l 25.574 62.475 l 25.5 62.53 l 25.352 62.698\n l 25.168 62.866 l 25.094 62.94 l 25.16 63.006 25.207 63.053 25.242 63.088\n c 25.5 63.342 25.523 63.369 25.668 63.569 c f\n32.395 63.569 m 32.539 63.369 32.562 63.342 32.82 63.088 c 32.855 63.053\n 32.902 63.006 32.969 62.94 c 32.895 62.866 l 32.711 62.698 l 32.559 62.53\n l 32.484 62.475 l 32.504 62.213 l 32.543 61.545 l 32.598 60.561 33.152 \n59.397 33.711 59.092 c 33.809 59.041 33.871 58.994 33.871 58.971 c 33.871\n 58.92 33.836 58.881 33.797 58.881 c 33.562 58.889 l 33.191 58.928 l 33.113\n 58.936 33.008 58.952 32.875 58.983 c 32.711 59.018 l 32.523 57.682 l 32.266\n 57.682 l 32.078 59.018 l 31.871 59.037 l 31.578 58.928 l 31.223 58.889 \nl 31.055 58.869 l 31.031 58.869 l 30.961 58.869 30.906 58.897 30.906 58.928\n c 30.906 58.959 30.938 59.006 30.992 59.045 c 31.262 59.26 l 31.508 59.455\n 31.59 59.573 31.91 60.209 c 32.043 60.471 32.113 60.682 32.113 60.823 c\n 32.117 61.221 32.168 61.545 32.227 61.545 c 32.266 62.213 l 32.281 62.475\n l 32.059 62.698 l 31.977 62.784 31.918 62.838 31.891 62.866 c 31.875 62.877\n 31.844 62.905 31.797 62.94 c 31.863 63.006 31.914 63.053 31.949 63.088 \nc 32.035 63.174 32.094 63.229 32.113 63.252 c 32.145 63.284 32.191 63.342\n 32.266 63.42 c 32.293 63.455 32.336 63.506 32.395 63.569 c f\n28.977 57.756 m 29.105 57.608 l 29.254 57.44 l 29.344 57.338 29.398 57.276\n 29.422 57.256 c 29.453 57.221 29.504 57.17 29.57 57.108 c 29.477 57.03 \nl 29.309 56.866 l 29.145 56.717 l 29.086 56.643 l 29.105 56.401 l 29.125\n 55.768 l 29.18 54.752 29.727 53.6 30.312 53.28 c 30.426 53.217 30.473 53.17\n 30.473 53.116 c 30.461 53.092 l 30.312 53.057 l 30.145 53.057 l 29.926 \n53.057 29.539 53.123 29.289 53.205 c 29.105 51.866 l 28.844 51.866 l 28.66\n 53.205 l 28.41 53.116 28.043 53.057 27.734 53.057 c 27.531 53.057 27.488\n 53.069 27.488 53.127 c 27.488 53.186 27.531 53.225 27.656 53.28 c 27.805\n 53.346 28.074 53.623 28.23 53.873 c 28.527 54.346 28.695 54.748 28.695 \n54.987 c 28.699 55.389 28.758 55.713 28.828 55.713 c 28.863 56.401 l 28.883\n 56.643 l 28.848 56.674 28.824 56.698 28.809 56.717 c 28.727 56.799 28.676\n 56.85 28.66 56.866 c 28.473 57.03 l 28.398 57.108 l 28.547 57.256 l 28.715\n 57.44 l 28.801 57.537 28.852 57.592 28.863 57.608 c 28.891 57.639 28.926\n 57.69 28.977 57.756 c f\n25.648 51.959 m 25.781 51.811 l 25.91 51.643 l 25.941 51.604 25.996 51.549\n 26.078 51.479 c 26.246 51.33 l 26.191 51.287 26.16 51.264 26.152 51.256\n c 25.762 50.866 l 25.762 50.69 l 25.762 49.713 26.051 48.487 26.375 48.131\n c 26.785 47.651 l 26.785 47.647 26.855 47.592 26.988 47.483 c 27.137 47.362\n l 26.988 47.26 l 26.82 47.28 l 26.469 47.315 l 26.152 47.373 l 25.965 47.409\n l 25.781 46.073 l 25.52 46.073 l 25.336 47.409 l 25.148 47.373 l 24.832\n 47.315 l 24.48 47.28 l 24.312 47.26 l 24.289 47.26 l 24.219 47.26 24.16\n 47.284 24.16 47.319 c 24.16 47.35 24.195 47.393 24.246 47.436 c 24.516 \n47.651 l 25.137 48.143 25.539 49.342 25.539 50.69 c 25.539 50.866 l 25.316\n 51.088 l 25.234 51.174 25.176 51.229 25.148 51.256 c 25.133 51.268 25.102\n 51.291 25.055 51.33 c 25.129 51.393 25.184 51.444 25.223 51.479 c 25.301\n 51.549 25.359 51.604 25.391 51.643 c 25.52 51.811 l 25.648 51.959 l f\n32.355 51.959 m 32.414 51.893 32.457 51.846 32.484 51.811 c 32.555 51.733\n 32.605 51.674 32.637 51.643 c 32.656 51.619 32.715 51.565 32.801 51.479\n c 32.836 51.444 32.887 51.393 32.949 51.33 c 32.898 51.287 32.867 51.264\n 32.859 51.256 c 32.469 50.866 l 32.484 50.604 l 32.523 49.916 l 32.578 \n48.893 32.977 48.069 33.695 47.483 c 33.844 47.362 l 33.695 47.26 l 33.527\n 47.28 l 33.172 47.315 l 32.672 47.409 l 32.484 46.073 l 32.227 46.073 l\n 32.023 47.463 l 31.684 47.389 31.672 47.327 31.559 47.315 c 31.188 47.28\n l 31.02 47.26 l 31 47.26 l 30.934 47.26 30.887 47.284 30.887 47.319 c 30.887\n 47.358 30.914 47.401 30.961 47.436 c 31.242 47.651 l 31.484 47.834 31.551\n 47.936 31.891 48.596 c 32.027 48.858 32.094 49.069 32.098 49.209 c 32.098\n 49.6 32.148 49.916 32.207 49.916 c 32.246 50.604 l 32.266 50.866 l 32.188\n 50.92 l 32.039 51.088 l 31.855 51.256 l 31.781 51.33 l 31.848 51.393 31.895\n 51.444 31.93 51.479 c 32.188 51.733 32.211 51.76 32.355 51.959 c f\n18.441 54.952 m 18.516 54.67 l 18.582 54.42 18.633 54.221 18.664 54.077\n c 18.898 53.018 19.098 52.737 19.613 52.737 c 19.906 52.737 20.152 52.901\n 20.227 53.151 c 20.336 53.522 l 20.363 53.608 20.426 53.655 20.516 53.655\n c 20.715 53.655 20.809 53.448 20.82 52.983 c 20.824 52.846 20.867 52.545\n 20.949 52.073 c 21 51.791 21.023 51.522 21.023 51.26 c 21.023 51.022 21.004\n 50.924 20.914 50.678 c 20.977 50.315 21.027 49.823 21.027 49.541 c 21.027\n 49.08 20.941 48.655 20.801 48.432 c 20.879 48.123 20.895 47.998 20.895 \n47.733 c 20.895 47.534 20.875 47.319 20.84 47.112 c 20.758 46.666 20.742\n 46.553 20.742 46.467 c 20.742 46.385 20.754 46.299 20.781 46.221 c 20.82\n 46.116 20.84 46.037 20.84 45.998 c 20.84 45.944 20.812 45.905 20.781 45.905\n c 20.41 46.053 l 20.004 46.276 l 19.871 46.346 19.781 46.42 19.574 46.612\n c 19.215 46.284 19.109 46.237 18.164 45.979 c 17.977 45.928 17.836 45.869\n 17.754 45.811 c 17.637 45.725 17.551 45.682 17.508 45.682 c 17.348 45.682\n 17.273 45.94 17.273 46.467 c 17.273 46.862 17.312 47.229 17.383 47.502 \nc 17.105 47.608 16.871 47.67 16.719 47.67 c 16.691 47.67 16.598 47.659 16.438\n 47.631 c 16.367 47.627 l 16.312 47.627 16.266 47.705 16.266 47.803 c 16.266\n 47.877 16.316 48.057 16.383 48.209 c 16.566 48.655 l 16.633 48.815 16.785\n 48.998 17.012 49.194 c 17.195 49.35 17.363 49.444 17.496 49.471 c 18.164\n 49.6 l 18.562 49.678 18.797 49.889 18.797 50.162 c 18.797 50.432 18.617\n 50.612 18.219 50.752 c 17.422 51.03 l 17.215 51.104 16.973 51.221 16.695\n 51.385 c 16.254 51.647 16.18 51.682 16.074 51.682 c 15.914 51.682 15.82\n 51.561 15.82 51.354 c 15.82 51.022 16.035 50.791 16.34 50.791 c 16.406 \n50.791 16.469 50.803 16.527 50.827 c 16.617 50.866 16.684 50.881 16.719 \n50.881 c 16.805 50.881 16.852 50.834 16.852 50.744 c 16.844 50.659 l 16.809\n 50.342 l 16.777 50.073 16.602 49.748 16.215 49.229 c 16.047 49.006 l 15.895\n 48.409 15.523 47.866 15.043 47.537 c 14.922 47.459 14.855 47.409 14.84 \n47.389 c 14.824 47.373 14.773 47.284 14.691 47.131 c 14.652 47.061 14.527\n 46.909 14.32 46.686 c 14.16 46.514 14.008 46.373 13.855 46.256 c 13.648\n 46.1 13.52 45.987 13.484 45.924 c 13.297 45.608 l 13.262 45.549 13.207 \n45.514 13.148 45.514 c 12.984 45.514 12.832 45.686 12.738 45.979 c 12.672\n 46.198 12.609 46.256 12.469 46.256 c 12.398 46.256 12.289 46.237 12.145\n 46.202 c 11.887 46.166 l 11.215 46.166 l 10.883 46.166 10.41 46.08 10.195\n 45.979 c 10.109 45.94 10.043 45.916 10.004 45.916 c 9.957 45.916 9.93 45.944\n 9.879 46.034 c 9.844 46.096 9.836 46.123 9.836 46.182 c 9.836 46.522 10.078\n 47.162 10.398 47.67 c 10.602 47.991 10.664 48.065 10.902 48.264 c 10.824\n 48.58 10.621 48.823 10.379 48.897 c 10.066 48.987 l 10.02 49.002 9.988 \n49.037 9.988 49.077 c 9.988 49.174 10.141 49.307 10.418 49.452 c 10.781 \n49.643 11.145 49.748 11.438 49.748 c 11.746 49.748 12.047 49.627 12.387 \n49.358 c 12.891 48.963 13.027 48.893 13.273 48.893 c 13.668 48.893 14.047\n 49.256 14.047 49.639 c 14.047 49.893 13.91 50.166 13.559 50.623 c 13.039\n 51.291 12.883 51.639 12.883 52.131 c 12.883 52.44 12.977 52.827 13.094 \n52.998 c 13.277 53.28 l 13.32 53.338 13.34 53.401 13.34 53.459 c 13.34 53.58\n 13.199 53.67 13.004 53.67 c 12.711 53.67 12.148 53.463 11.699 53.186 c \n11.477 53.049 11.324 52.889 11.324 52.791 c 11.324 52.733 11.352 52.678 \n11.402 52.647 c 11.605 52.518 l 11.633 52.502 11.645 52.471 11.645 52.44\n c 11.645 52.412 11.613 52.354 11.57 52.295 c 11.199 51.811 l 11.004 51.561\n 10.711 51.252 10.418 50.994 c 10.32 50.909 10.258 50.846 10.23 50.807 c\n 10.23 50.803 10.18 50.713 10.082 50.53 c 9.949 50.28 9.598 49.92 9.266 \n49.694 c 9.129 49.6 9.043 49.53 9.008 49.491 c 8.98 49.459 8.93 49.369 8.855\n 49.229 c 8.809 49.139 8.73 49.006 8.617 48.838 c 8.434 48.573 8.285 48.377\n 8.168 48.264 c 7.648 47.744 l 7.488 47.584 7.348 47.272 7.129 46.592 c \n6.996 46.182 6.879 45.94 6.809 45.94 c 6.781 45.94 6.727 45.983 6.664 46.053\n c 6.297 46.475 5.98 46.721 5.812 46.721 c 5.773 46.721 5.703 46.69 5.605\n 46.627 c 5.469 46.541 5.301 46.479 5.105 46.444 c 4.418 46.315 l 3.898 \n46.213 3.695 46.104 3.395 45.756 c 3.324 45.674 3.266 45.623 3.234 45.623\n c 3.176 45.623 3.133 45.717 3.133 45.842 c 3.156 46.014 l 3.246 46.518 \nl 3.348 47.053 3.469 47.452 3.617 47.725 c 3.934 48.299 l 3.996 48.412 4.027\n 48.514 4.027 48.604 c 4.027 49.03 3.586 49.42 3.043 49.471 c 2.906 49.483\n 2.855 49.518 2.855 49.592 c 2.855 49.686 3.004 49.873 3.211 50.03 c 3.582\n 50.311 4.07 50.51 4.383 50.51 c 4.57 50.51 4.859 50.436 5.215 50.287 c \n5.625 50.119 l 5.777 50.061 5.918 50.026 6.039 50.026 c 6.473 50.026 6.891\n 50.42 7.406 51.311 c 7.641 51.709 7.742 52.002 7.742 52.264 c 7.742 52.733\n 7.52 53.018 7.152 53.018 c 6.875 53.018 6.57 52.885 6.352 52.666 c 6.16\n 52.479 6.062 52.284 6.062 52.092 c 6.07 51.885 l 6.074 51.838 l 6.074 51.666\n 6.016 51.569 5.91 51.569 c 5.738 51.569 5.48 51.819 4.75 52.702 c 4.207\n 53.362 4.086 53.643 4.062 54.299 c 3.434 55.104 3.277 55.444 3.277 56.018\n c 3.301 56.401 l 2.867 56.924 2.742 57.209 2.742 57.694 c 2.742 57.866 \n2.773 57.971 2.875 58.182 c 2.656 58.506 2.582 58.655 2.523 58.909 c 2.43\n 59.299 l 2.367 59.565 2.266 59.795 2.133 59.987 c 1.965 60.225 1.945 60.256\n 1.945 60.342 c 1.945 60.557 2.094 60.643 2.746 60.803 c 2.719 60.983 2.707\n 61.119 2.707 61.213 c 2.707 61.323 2.719 61.428 2.766 61.659 c 2.895 62.346\n l 2.945 62.623 2.973 62.823 2.973 62.94 c 2.973 63.069 2.953 63.159 2.84\n 63.534 c 2.773 63.744 2.719 63.889 2.672 63.959 c 2.609 64.057 2.559 64.174\n 2.559 64.217 c 2.559 64.28 2.605 64.319 2.688 64.319 c 2.871 64.319 3.359\n 64.022 3.859 63.608 c 4.926 62.725 5.145 62.373 5.289 61.303 c 5.301 61.221\n 5.32 61.096 5.348 60.932 c 5.859 60.557 6.125 60.104 6.125 59.616 c 6.125\n 59.233 5.988 58.784 5.754 58.424 c 5.383 57.85 l 5.273 57.678 5.215 57.467\n 5.215 57.233 c 5.215 56.881 5.316 56.565 5.57 56.119 c 5.855 55.619 6.094\n 55.416 6.398 55.416 c 6.887 55.416 7.316 56.268 7.316 57.237 c 7.316 57.366\n l 7.312 57.702 l 7.312 57.889 7.324 58.077 7.352 58.315 c 7.387 58.604 \n7.406 58.78 7.406 58.85 c 7.426 59.299 l 7.434 59.592 l 7.434 59.698 7.375\n 59.799 7.242 59.948 c 6.832 60.393 l 6.242 61.037 5.918 61.643 5.918 62.096\n c 5.918 62.662 6.324 63.084 6.867 63.084 c 6.973 63.084 7.07 63.069 7.258\n 63.014 c 7.258 63.139 l 7.258 63.959 7.426 64.565 7.652 64.565 c 7.742 \n64.537 l 8.078 64.241 l 8.484 63.979 l 8.598 63.869 l 8.598 64.096 8.609\n 64.119 8.84 64.389 c 8.895 64.452 8.98 64.569 9.098 64.741 c 9.191 64.877\n 9.254 64.971 9.285 65.018 c 9.312 65.065 9.344 65.123 9.379 65.186 c 9.426\n 65.284 9.477 65.327 9.531 65.327 c 9.582 65.327 9.625 65.256 9.691 65.057\n c 9.738 64.928 9.766 64.897 10.082 64.612 c 10.195 64.51 10.27 64.428 10.305\n 64.369 c 10.336 64.319 10.387 64.194 10.453 63.998 c 10.465 63.963 10.484\n 63.916 10.512 63.85 c 10.648 64.022 10.715 64.084 10.883 64.182 c 11.16\n 64.35 l 11.266 64.412 11.336 64.448 11.367 64.448 c 11.391 64.448 11.426\n 64.401 11.457 64.33 c 11.488 64.268 11.535 64.174 11.605 64.053 c 11.805\n 63.709 11.871 63.541 11.871 63.381 c 11.867 63.198 l 11.867 62.975 l 12.477\n 62.784 12.703 62.467 12.703 61.811 c 12.703 61.338 12.457 60.795 11.977\n 60.225 c 11.66 59.846 11.621 59.772 11.621 59.506 c 11.621 58.131 12.578\n 57.334 14.262 57.311 c 15.508 57.291 l 16.309 57.28 16.785 57.202 17.531\n 56.955 c 18.414 56.67 18.672 56.604 18.926 56.604 c 19.016 56.604 19.098\n 56.631 19.168 56.678 c 19.238 56.729 19.277 56.787 19.277 56.842 c 19.277\n 57.104 18.82 57.315 17.328 57.737 c 14.266 58.608 12.961 59.838 12.961 \n61.869 c 12.961 63.436 14.266 64.651 15.953 64.651 c 16.496 64.651 17.332\n 64.506 17.809 64.33 c 18.574 64.053 l 18.711 64.002 18.848 63.975 18.973\n 63.975 c 19.23 63.975 19.363 64.194 19.371 64.627 c 19.375 64.866 19.43\n 64.944 19.582 64.944 c 19.797 64.944 20.129 64.678 20.43 64.256 c 20.734\n 63.834 20.934 63.358 20.934 63.037 c 20.934 62.616 20.734 62.323 20.281\n 62.065 c 19.863 61.432 19.352 61.135 18.66 61.135 c 18.125 61.135 17.363\n 61.44 16.102 62.159 c 15.66 62.409 15.438 62.494 15.207 62.494 c 14.879\n 62.494 14.664 62.303 14.664 62.01 c 14.664 61.741 14.871 61.569 15.414 \n61.377 c 16.215 61.096 16.414 60.991 16.734 60.655 c 17.68 60.491 18.094\n 60.327 18.684 59.873 c 20.316 59.358 21.324 58.244 21.324 56.967 c 21.324\n 55.729 20.488 54.838 19.32 54.838 c 19.043 54.838 18.871 54.862 18.441 \n54.952 c f\n53.105 54.952 m 53.18 54.67 l 53.246 54.42 53.297 54.221 53.328 54.077 \nc 53.562 53.018 53.762 52.737 54.277 52.737 c 54.57 52.737 54.816 52.901\n 54.891 53.151 c 55 53.522 l 55.027 53.608 55.09 53.655 55.176 53.655 c \n55.379 53.655 55.488 53.432 55.504 52.983 c 55.508 52.811 55.547 52.506 \n55.613 52.073 c 55.664 51.76 55.688 51.471 55.688 51.213 c 55.688 51.022\n 55.664 50.912 55.578 50.678 c 55.613 50.455 l 55.676 50.088 55.691 49.948\n 55.691 49.776 c 55.691 48.979 55.652 48.741 55.465 48.432 c 55.543 48.127\n 55.562 47.983 55.562 47.764 c 55.562 47.623 55.543 47.424 55.504 47.112\n c 55.43 46.553 l 55.445 46.221 l 55.496 46.096 55.52 46.022 55.52 45.998\n c 55.52 45.944 55.488 45.905 55.445 45.905 c 55.074 46.053 l 54.664 46.276\n l 54.535 46.346 54.441 46.42 54.238 46.612 c 53.883 46.291 53.828 46.26\n 53.402 46.147 c 52.785 45.975 52.559 45.897 52.438 45.811 c 52.316 45.725\n 52.227 45.682 52.18 45.682 c 52.035 45.682 51.934 45.959 51.934 46.369 \nc 51.934 46.963 51.969 47.272 52.066 47.502 c 51.703 47.623 51.523 47.67\n 51.391 47.67 c 51.34 47.67 51.312 47.666 51.117 47.631 c 51.035 47.623 \nl 50.98 47.623 50.949 47.69 50.949 47.803 c 50.949 47.983 51.078 48.346 \n51.25 48.655 c 51.48 49.069 51.863 49.412 52.16 49.471 c 52.828 49.6 l 53.227\n 49.678 53.461 49.889 53.461 50.166 c 53.461 50.432 53.285 50.608 52.883\n 50.752 c 52.102 51.03 l 51.84 51.123 51.594 51.244 51.359 51.385 c 51.164\n 51.502 51.027 51.584 50.949 51.627 c 50.883 51.662 50.812 51.682 50.75 \n51.682 c 50.586 51.682 50.484 51.561 50.484 51.366 c 50.484 51.049 50.727\n 50.787 51.023 50.787 c 51.09 50.787 51.152 50.803 51.211 50.827 c 51.297\n 50.866 51.352 50.881 51.383 50.881 c 51.469 50.881 51.527 50.799 51.527\n 50.686 c 51.527 50.354 51.32 49.784 51.098 49.506 c 50.879 49.229 l 50.711\n 49.006 l 50.648 48.53 50.18 47.842 49.707 47.537 c 49.594 47.467 49.523\n 47.416 49.504 47.389 c 49.488 47.373 49.438 47.284 49.352 47.131 c 49.316\n 47.061 49.191 46.909 48.98 46.686 c 48.824 46.514 48.668 46.373 48.52 46.256\n c 48.309 46.1 48.184 45.987 48.145 45.924 c 47.961 45.608 l 47.922 45.541\n 47.875 45.51 47.816 45.51 c 47.648 45.51 47.496 45.686 47.402 45.979 c \n47.336 46.198 47.273 46.256 47.133 46.256 c 47.062 46.256 46.953 46.237 \n46.809 46.202 c 46.547 46.166 l 45.898 46.166 l 45.547 46.166 45.074 46.08\n 44.859 45.979 c 44.773 45.94 44.707 45.92 44.664 45.92 c 44.57 45.92 44.523\n 46.018 44.523 46.202 c 44.523 46.791 44.973 47.686 45.562 48.264 c 45.477\n 48.608 45.297 48.834 45.062 48.897 c 44.852 48.952 44.738 48.983 44.727\n 48.987 c 44.695 49.006 44.672 49.037 44.672 49.077 c 44.672 49.198 44.805\n 49.311 45.098 49.452 c 45.625 49.702 45.781 49.748 46.078 49.748 c 46.41\n 49.748 46.699 49.635 47.051 49.358 c 47.551 48.967 47.691 48.893 47.934\n 48.893 c 48.332 48.893 48.711 49.256 48.711 49.635 c 48.711 49.893 48.57\n 50.178 48.219 50.623 c 47.727 51.256 47.551 51.666 47.551 52.202 c 47.551\n 52.561 47.621 52.756 47.941 53.28 c 47.992 53.362 48.02 53.432 48.02 53.475\n c 48.02 53.58 47.852 53.67 47.664 53.67 c 47.465 53.67 47.219 53.6 46.883\n 53.448 c 46.324 53.19 45.992 52.952 45.992 52.803 c 45.992 52.748 46.027\n 52.69 46.082 52.647 c 46.27 52.518 l 46.293 52.498 46.309 52.471 46.309\n 52.44 c 46.309 52.409 46.273 52.342 46.23 52.295 c 46.195 52.252 46.07 \n52.092 45.859 51.811 c 45.602 51.467 45.352 51.205 45.082 50.994 c 44.984\n 50.916 44.918 50.854 44.895 50.807 c 44.746 50.53 l 44.621 50.295 44.258\n 49.912 43.949 49.694 c 43.809 49.592 43.719 49.526 43.688 49.491 c 43.648\n 49.448 43.594 49.362 43.52 49.229 c 43.332 48.889 43.031 48.467 42.832 \n48.264 c 42.332 47.744 l 42.188 47.596 41.988 47.162 41.793 46.592 c 41.641\n 46.143 41.543 45.94 41.473 45.94 c 41.445 45.94 41.391 45.983 41.328 46.053\n c 40.969 46.467 40.645 46.721 40.484 46.721 c 40.445 46.721 40.371 46.69\n 40.27 46.627 c 40.129 46.541 39.957 46.479 39.77 46.444 c 39.098 46.315\n l 38.539 46.205 38.316 46.084 38.059 45.756 c 37.992 45.674 37.941 45.627\n 37.91 45.627 c 37.844 45.627 37.797 45.721 37.797 45.854 c 37.816 46.014\n l 37.82 46.034 37.852 46.202 37.91 46.518 c 38.008 47.045 38.141 47.483\n 38.281 47.725 c 38.617 48.299 l 38.676 48.405 38.711 48.51 38.711 48.6 \nc 38.711 49.03 38.309 49.377 37.707 49.471 c 37.582 49.491 37.52 49.537 \n37.52 49.608 c 37.52 49.694 37.664 49.858 37.891 50.03 c 38.285 50.327 38.723\n 50.51 39.031 50.51 c 39.234 50.51 39.512 50.44 39.879 50.287 c 40.289 50.119\n l 40.441 50.061 40.582 50.026 40.703 50.026 c 41.137 50.026 41.555 50.42\n 42.07 51.311 c 42.305 51.713 42.406 51.998 42.406 52.268 c 42.406 52.737\n 42.188 53.018 41.824 53.018 c 41.535 53.018 41.258 52.897 41.031 52.666\n c 40.828 52.459 40.723 52.256 40.723 52.077 c 40.734 51.885 l 40.738 51.787\n l 40.738 51.635 40.691 51.569 40.578 51.569 c 40.438 51.569 40.277 51.705\n 39.953 52.088 c 39.82 52.248 39.648 52.452 39.434 52.702 c 38.867 53.362\n 38.75 53.627 38.727 54.299 c 38.098 55.104 37.941 55.549 37.941 56.018 \nc 37.965 56.401 l 37.516 56.944 37.426 57.151 37.426 57.686 c 37.426 57.928\n 37.441 57.991 37.539 58.182 c 37.316 58.506 37.246 58.655 37.188 58.909\n c 37.094 59.299 l 37.031 59.565 36.93 59.791 36.797 59.987 c 36.629 60.225\n 36.609 60.26 36.609 60.33 c 36.609 60.526 36.793 60.651 37.262 60.764 c\n 37.41 60.803 l 37.383 60.983 37.371 61.119 37.371 61.213 c 37.371 61.323\n 37.383 61.428 37.426 61.659 c 37.559 62.346 l 37.609 62.612 37.633 62.819\n 37.633 62.955 c 37.633 63.116 37.609 63.241 37.52 63.534 c 37.465 63.721\n 37.398 63.866 37.336 63.959 c 37.262 64.065 37.223 64.151 37.223 64.205\n c 37.223 64.268 37.285 64.319 37.371 64.319 c 37.414 64.319 37.492 64.291\n 37.559 64.256 c 38.184 63.916 38.867 63.373 39.359 62.827 c 39.781 62.362\n 39.914 61.971 40.008 60.932 c 40.578 60.506 40.793 60.123 40.793 59.522\n c 40.793 59.162 40.676 58.819 40.418 58.424 c 40.191 58.077 40.066 57.885\n 40.047 57.85 c 39.938 57.662 39.879 57.444 39.879 57.221 c 39.879 56.881\n 39.98 56.569 40.23 56.119 c 40.512 55.627 40.758 55.416 41.055 55.416 c\n 41.539 55.416 41.984 56.256 41.984 57.182 c 41.98 57.366 l 41.98 57.866\n l 42.035 58.315 l 42.07 58.85 l 42.109 59.299 l 42.121 59.545 l 42.121 \n59.729 42.086 59.784 41.516 60.393 c 40.883 61.069 40.586 61.639 40.586 \n62.166 c 40.586 62.666 41.016 63.084 41.531 63.084 c 41.637 63.084 41.734\n 63.069 41.922 63.014 c 41.922 63.237 l 41.941 63.627 l 42.051 64.108 l \n42.141 64.494 42.184 64.565 42.316 64.565 c 42.406 64.537 l 42.738 64.241\n l 43.148 63.979 l 43.262 63.869 l 43.285 64.1 43.309 64.147 43.5 64.389\n c 43.598 64.506 43.684 64.627 43.762 64.741 c 43.949 65.018 l 44.059 65.186\n l 44.133 65.299 44.164 65.327 44.215 65.327 c 44.266 65.327 44.297 65.26\n 44.355 65.057 c 44.379 64.971 44.438 64.885 44.523 64.815 c 44.766 64.612\n l 44.961 64.444 45.094 64.202 45.172 63.85 c 45.32 64.034 45.391 64.096\n 45.543 64.182 c 45.844 64.35 l 45.953 64.412 46.02 64.444 46.039 64.444\n c 46.059 64.444 46.09 64.401 46.121 64.33 c 46.164 64.237 46.215 64.143\n 46.27 64.053 c 46.512 63.67 46.531 63.616 46.531 63.354 c 46.531 62.975\n l 47.133 62.811 47.367 62.487 47.367 61.819 c 47.367 61.319 47.137 60.803\n 46.66 60.225 c 46.344 59.846 46.289 59.725 46.289 59.44 c 46.289 58.127\n 47.277 57.334 48.945 57.311 c 50.172 57.291 l 50.973 57.28 51.461 57.198\n 52.195 56.955 c 53.055 56.674 53.336 56.604 53.598 56.604 c 53.785 56.604\n 53.941 56.709 53.941 56.834 c 53.941 57.104 53.492 57.315 51.992 57.737\n c 48.934 58.604 47.625 59.838 47.625 61.869 c 47.625 63.436 48.934 64.651\n 50.633 64.651 c 51.164 64.651 52 64.506 52.473 64.33 c 53.234 64.053 l \n53.375 64.002 53.512 63.975 53.637 63.975 c 53.898 63.975 53.996 64.139 \n54.035 64.627 c 54.051 64.85 54.121 64.944 54.262 64.944 c 54.457 64.944\n 54.836 64.639 55.113 64.256 c 55.395 63.866 55.594 63.35 55.594 63.01 c\n 55.594 62.616 55.387 62.315 54.945 62.065 c 54.527 61.432 54.016 61.135\n 53.324 61.135 c 52.797 61.135 51.898 61.494 50.766 62.159 c 50.32 62.42\n 50.125 62.494 49.879 62.494 c 49.543 62.494 49.328 62.303 49.328 62.01 \nc 49.328 61.741 49.527 61.577 50.078 61.377 c 50.957 61.065 51.145 60.963\n 51.398 60.655 c 52.344 60.491 52.754 60.327 53.348 59.873 c 55.043 59.33\n 55.988 58.284 55.988 56.944 c 55.988 55.721 55.156 54.838 54.008 54.838\n c 53.703 54.838 53.543 54.858 53.105 54.952 c f\n18.797 19.877 m 18.957 19.467 19.012 19.268 19.035 19.006 c 19.074 18.225\n l 19.102 17.444 19.363 17.057 19.852 17.057 c 20.031 17.057 20.18 17.1 \n20.262 17.186 c 20.469 17.389 l 20.547 17.412 l 20.68 17.412 20.75 17.327\n 20.75 17.17 c 20.727 16.869 l 20.711 16.733 20.738 16.448 20.801 16.014\n c 20.84 15.764 20.859 15.51 20.859 15.264 c 20.859 14.869 20.828 14.717\n 20.691 14.362 c 20.82 13.994 20.895 13.584 20.895 13.241 c 20.895 12.834\n 20.828 12.561 20.672 12.338 c 20.746 12.018 20.766 11.885 20.766 11.705\n c 20.766 11.522 20.738 11.284 20.691 10.998 c 20.625 10.612 20.598 10.311\n 20.617 10.108 c 20.617 10.061 l 20.617 9.944 20.582 9.885 20.512 9.885 \nc 20.441 9.885 20.355 9.94 20.281 10.034 c 20.168 10.178 20.094 10.233 20.008\n 10.233 c 19.879 10.233 19.84 10.162 19.648 9.608 c 19.59 9.436 19.441 9.202\n 19.203 8.92 c 19.02 8.698 18.906 8.514 18.871 8.381 c 18.809 8.159 18.773\n 8.026 18.758 7.991 c 18.738 7.94 18.695 7.905 18.648 7.897 c 18.621 7.897\n l 18.316 7.897 17.965 8.549 17.605 9.791 c 17.504 10.139 17.375 10.299 \n17.188 10.299 c 17.051 10.299 16.914 10.209 16.863 10.088 c 16.75 9.83 l\n 16.73 9.784 16.695 9.756 16.656 9.756 c 16.508 9.756 16.281 10.022 16.141\n 10.369 c 16.023 10.651 15.957 10.959 15.957 11.221 c 15.957 11.803 16.387\n 12.237 17.68 12.967 c 18.242 13.284 18.461 13.69 18.461 14.416 c 18.461\n 14.994 18.289 15.323 17.793 15.698 c 17.402 15.994 17.172 16.108 16.965\n 16.108 c 16.789 16.108 16.652 15.991 16.652 15.842 c 16.652 15.67 16.75\n 15.561 16.992 15.475 c 17.281 15.373 17.406 15.276 17.406 15.147 c 17.406\n 15.03 17.285 14.776 17.086 14.471 c 16.875 14.151 16.785 14.057 16.492 \n13.862 c 16.406 13.35 16.164 13.022 15.602 12.651 c 15.488 12.334 15.426\n 12.217 15.23 11.967 c 14.84 11.463 l 14.715 11.307 14.578 11.022 14.559\n 10.889 c 14.52 10.604 14.492 10.553 14.383 10.553 c 14.238 10.553 13.934\n 10.815 13.633 11.205 c 13.453 11.436 13.41 11.541 13.391 11.815 c 13.195\n 11.666 13.137 11.643 12.758 11.592 c 12.531 11.565 12.363 11.518 12.258\n 11.463 c 11.973 11.315 11.879 11.272 11.824 11.272 c 11.75 11.272 11.723\n 11.315 11.719 11.444 c 11.699 11.854 l 11.699 12.416 11.918 13.069 12.258\n 13.506 c 11.93 13.787 11.605 13.959 11.348 13.991 c 11.234 14.002 11.207\n 14.014 11.207 14.057 c 11.207 14.127 11.281 14.268 11.383 14.397 c 11.523\n 14.573 11.633 14.698 11.719 14.772 c 11.98 14.994 12.438 15.159 12.793 \n15.159 c 13.035 15.159 13.391 15.073 13.855 14.901 c 14.02 14.838 14.168\n 14.807 14.297 14.807 c 14.504 14.807 14.617 14.94 14.617 15.174 c 14.617\n 15.51 14.449 16.041 14.078 16.889 c 13.824 17.463 13.707 17.846 13.707 \n18.116 c 13.707 18.76 14.012 19.428 14.395 19.619 c 14.215 19.709 14.133\n 19.729 13.977 19.729 c 13.793 19.729 13.645 19.682 13.406 19.545 c 13.133\n 19.385 12.98 19.244 12.98 19.147 c 12.98 19.112 13.035 19.053 13.129 18.987\n c 13.273 18.885 13.297 18.846 13.297 18.713 c 13.297 18.252 12.918 17.639\n 12.387 17.241 c 12.27 16.592 11.969 16.198 11.383 15.92 c 11.305 15.569\n 11.258 15.467 10.977 15.069 c 10.488 14.385 10.375 14.17 10.289 13.784 \nc 10.242 13.588 10.195 13.502 10.137 13.502 c 10.055 13.502 9.891 13.627\n 9.711 13.823 c 9.555 13.991 9.457 14.112 9.414 14.174 c 9.336 14.299 9.293\n 14.416 9.211 14.772 c 9.078 14.733 9.039 14.725 8.945 14.725 c 8.672 14.733\n l 8.488 14.737 l 8.078 14.737 7.648 14.647 7.426 14.51 c 7.348 14.459 7.293\n 14.436 7.266 14.436 c 7.145 14.436 7.047 14.69 7.047 14.998 c 7.047 15.67\n 7.234 16.225 7.668 16.834 c 7.508 16.967 7.457 17.014 7.316 17.166 c 7.254\n 17.233 7.203 17.276 7.168 17.295 c 6.961 17.409 l 6.895 17.448 6.852 17.491\n 6.852 17.53 c 6.852 17.627 7.121 17.885 7.445 18.096 c 7.801 18.327 8.086\n 18.432 8.344 18.432 c 8.648 18.432 8.812 18.354 9.191 18.022 c 9.453 17.791\n 9.637 17.686 9.762 17.686 c 9.852 17.686 9.949 17.748 10.027 17.854 c 10.09\n 17.94 10.121 18.037 10.121 18.131 c 10.121 18.487 9.699 19.084 9.23 19.397\n c 8.879 19.627 8.496 19.748 8.121 19.748 c 7.605 19.748 7.16 19.491 6.961\n 19.08 c 6.758 18.655 l 6.711 18.553 6.648 18.502 6.582 18.502 c 6.461 18.502\n 6.398 18.557 5.977 19.041 c 5.496 19.6 l 5.348 19.772 5.246 19.936 5.199\n 20.084 c 5.152 20.221 5.121 20.299 5.105 20.323 c 5.086 20.346 5.023 20.397\n 4.918 20.471 c 4.547 20.737 4.277 21.26 4.121 22.014 c 4.109 22.069 4.09\n 22.151 4.062 22.256 c 3.609 22.815 3.336 23.502 3.336 24.104 c 3.336 24.182\n 3.344 24.217 3.375 24.354 c 3.113 24.623 3.082 24.686 2.875 25.397 c 2.727\n 25.905 2.633 26.119 2.449 26.362 c 2.398 26.424 2.375 26.479 2.375 26.506\n c 2.375 26.608 2.531 26.69 2.801 26.733 c 3.055 26.776 3.07 26.776 3.176\n 26.776 c 3.543 26.752 l 3.523 27.26 l 3.523 27.561 3.543 27.756 3.617 28.108\n c 3.656 28.284 3.676 28.416 3.676 28.494 c 3.676 28.53 3.664 28.635 3.637\n 28.815 c 3.617 28.952 3.598 29.045 3.582 29.092 c 3.555 29.162 3.543 29.217\n 3.543 29.252 c 3.543 29.311 3.582 29.342 3.656 29.342 c 3.707 29.342 3.781\n 29.315 3.898 29.26 c 4.223 29.1 4.488 28.94 4.695 28.776 c 5.336 28.276\n 5.84 27.506 5.84 27.026 c 5.828 26.881 l 5.805 26.717 5.793 26.623 5.793\n 26.608 c 5.793 26.526 5.836 26.42 5.922 26.307 c 6.125 26.026 l 6.309 25.78\n 6.426 25.491 6.426 25.299 c 6.426 25.123 6.387 25.049 6.09 24.616 c 5.863\n 24.291 5.773 24.049 5.773 23.784 c 5.773 23.452 5.906 23.03 6.145 22.612\n c 6.363 22.225 6.617 22.01 6.859 22.01 c 7.273 22.01 7.453 22.381 7.52 \n23.369 c 7.555 23.889 7.602 24.354 7.668 24.784 c 7.688 25.037 l 7.688 25.151\n 7.652 25.221 7.5 25.397 c 6.945 26.045 l 6.41 26.666 6.25 26.987 6.25 27.432\n c 6.25 27.944 6.59 28.295 7.074 28.295 c 7.297 28.276 l 7.293 28.42 l 7.293\n 28.537 7.324 28.709 7.391 28.924 c 7.504 29.327 7.652 29.631 7.73 29.631\n c 7.754 29.631 7.816 29.6 7.91 29.537 c 7.938 29.518 8.051 29.475 8.242\n 29.409 c 8.348 29.373 8.422 29.334 8.617 29.221 c 8.703 29.545 8.77 29.69\n 8.914 29.873 c 9.152 30.182 9.426 30.432 9.52 30.432 c 9.578 30.432 9.688\n 30.35 9.824 30.205 c 10.176 29.834 l 10.234 29.772 10.305 29.655 10.379\n 29.483 c 10.453 29.315 l 10.555 29.373 10.629 29.416 10.676 29.444 c 10.824\n 29.534 10.922 29.588 10.977 29.612 c 11.203 29.717 11.32 29.776 11.328 \n29.78 c 11.422 29.842 11.492 29.873 11.52 29.873 c 11.582 29.873 11.617 \n29.764 11.754 29.147 c 11.848 28.729 11.863 28.639 11.887 28.33 c 12.477\n 28.248 12.758 27.909 12.758 27.272 c 12.758 26.67 12.465 26.077 11.828 \n25.397 c 11.656 25.209 11.613 25.123 11.613 24.952 c 11.613 23.307 12.188\n 22.791 14.246 22.573 c 15.508 22.44 15.883 22.358 17.402 21.905 c 18.203\n 21.662 18.66 21.565 18.957 21.565 c 19.305 21.565 19.504 21.69 19.504 21.92\n c 19.504 22.26 19.254 22.373 17.773 22.682 c 14.332 23.409 12.98 24.584\n 12.98 26.854 c 12.98 28.471 14.191 29.686 15.809 29.686 c 16.52 29.686 \n17.156 29.471 18.164 28.889 c 19.098 28.346 19.289 28.256 19.508 28.256 \nc 19.758 28.256 19.855 28.416 19.855 28.819 c 19.855 29.08 19.934 29.205\n 20.098 29.205 c 20.242 29.205 20.469 29.053 20.672 28.815 c 20.98 28.452\n 21.098 28.264 21.098 27.76 c 21.098 27.088 20.742 26.647 20.039 26.455 \nc 19.965 26.373 19.91 26.311 19.871 26.268 c 19.492 25.838 19.156 25.694\n 18.547 25.694 c 18.02 25.694 17.355 25.909 16.902 26.233 c 16.008 26.862\n l 15.625 27.135 15.344 27.252 15.074 27.252 c 14.695 27.252 14.465 27.026\n 14.465 26.643 c 14.465 26.272 14.711 25.967 15.117 25.842 c 15.953 25.58\n l 16.25 25.491 16.391 25.432 16.75 25.248 c 16.957 25.28 17.055 25.287 \n17.168 25.287 c 17.688 25.287 18.246 25.147 18.738 24.893 c 18.895 24.815\n 19.008 24.764 19.074 24.744 c 19.539 24.616 l 20.594 24.319 21.453 23.139\n 21.453 21.991 c 21.453 20.674 20.602 19.858 19.227 19.858 c 18.797 19.877\n l f\n51.117 20.159 m 51.441 19.729 51.898 19.448 52.27 19.448 c 52.387 19.448\n 52.586 19.479 52.773 19.526 c 52.871 19.549 52.953 19.565 53.02 19.565 \nc 53.109 19.565 53.145 19.53 53.145 19.444 c 53.145 19.381 53.102 19.221\n 53.051 19.096 c 52.992 18.959 52.953 18.846 52.938 18.764 c 52.863 18.373\n l 52.68 18.041 l 52.418 17.799 l 52.344 17.725 l 52.344 17.471 l 52.344\n 17.018 52.211 16.76 51.844 16.518 c 51.812 16.498 51.77 16.467 51.711 16.424\n c 51.676 16.276 l 51.594 15.932 51.531 15.713 51.488 15.623 c 51.441 15.526\n 51.324 15.35 51.137 15.104 c 50.914 14.811 50.766 14.569 50.672 14.342 \nc 50.598 14.159 50.539 14.104 50.418 14.104 c 50.234 14.104 50.055 14.33\n 50.023 14.604 c 49.98 14.545 49.949 14.51 49.93 14.491 c 49.707 14.342 \nl 49.352 14.1 l 49.199 13.994 49.098 13.92 49.055 13.877 c 48.852 13.674\n l 48.816 13.639 48.777 13.619 48.746 13.619 c 48.652 13.619 48.555 13.776\n 48.461 14.084 c 48.277 14.698 l 48.227 14.862 48.203 15.061 48.203 15.295\n c 48.203 15.604 48.223 15.713 48.332 15.959 c 47.961 15.92 47.855 15.901\n 47.664 15.83 c 47.605 15.807 47.535 15.791 47.504 15.791 c 47.438 15.791\n 47.402 15.834 47.402 15.905 c 47.402 15.948 47.438 16.077 47.477 16.182\n c 47.719 16.815 l 47.906 17.303 48.512 17.748 48.992 17.748 c 49.105 17.748\n 49.266 17.717 49.465 17.651 c 49.586 17.612 49.684 17.588 49.746 17.588\n c 49.961 17.588 50.133 17.748 50.133 17.948 c 50.133 18.041 50.066 18.151\n 49.984 18.19 c 49.953 18.202 49.73 18.272 49.316 18.393 c 49.004 18.483\n 48.852 18.6 48.5 19.006 c 48.242 19.299 48.145 19.381 48.051 19.381 c 47.98\n 19.381 47.941 19.338 47.941 19.252 c 47.961 19.006 l 47.969 18.893 l 47.969\n 18.377 47.812 17.881 47.441 17.205 c 46.918 16.256 l 46.672 15.807 46.566\n 15.467 46.566 15.123 c 46.566 14.748 46.727 14.416 46.938 14.362 c 47.219\n 14.287 l 47.254 14.28 47.285 14.26 47.309 14.233 c 47.348 14.194 47.367\n 14.155 47.367 14.123 c 47.367 14.084 47.332 14.022 47.273 13.952 c 47.238\n 13.916 47.129 13.772 46.938 13.526 c 46.617 13.108 46.418 12.955 46.027\n 12.838 c 45.805 12.291 45.438 11.975 44.785 11.76 c 44.711 11.631 l 44.488\n 11.241 43.996 10.885 43.684 10.885 c 43.613 10.889 l 43.57 10.545 43.328\n 10.194 42.965 9.94 c 42.711 9.764 42.566 9.659 42.535 9.623 c 42.516 9.6\n 42.48 9.545 42.445 9.455 c 42.359 9.272 42.281 9.198 42.18 9.198 c 41.98\n 9.198 41.816 9.381 41.699 9.737 c 41.625 9.686 41.57 9.651 41.531 9.623\n c 41.324 9.475 41.203 9.397 41.18 9.381 c 41.094 9.342 40.879 9.28 40.531\n 9.198 c 40.074 9.088 39.68 8.897 39.582 8.733 c 39.414 8.455 l 39.34 8.424\n l 39.246 8.424 39.145 8.549 39.098 8.713 c 38.996 9.1 38.93 9.502 38.93\n 9.756 c 38.93 10.416 39.148 10.846 39.824 11.518 c 39.824 11.682 l 39.824\n 12.705 40.605 13.401 41.758 13.401 c 41.941 13.397 l 42.574 13.358 l 42.633\n 13.358 l 42.973 13.358 43.289 13.487 43.633 13.768 c 44.086 14.139 44.219\n 14.369 44.219 14.784 c 44.219 14.912 44.18 15.065 44.039 15.534 c 43.949\n 15.838 43.902 16.127 43.902 16.405 c 43.902 17.17 44.465 18.123 45.23 18.655\n c 45.426 18.787 45.574 18.967 45.574 19.073 c 45.574 19.116 45.551 19.151\n 45.523 19.151 c 45.473 19.151 45.391 19.1 45.285 19.006 c 45.195 18.924\n 45.047 18.83 44.84 18.729 c 44.543 18.577 l 44.207 18.448 l 44.105 18.409\n 44.055 18.354 44.055 18.28 c 44.055 18.241 44.07 18.182 44.098 18.116 c\n 44.121 18.053 44.133 18.006 44.133 17.979 c 44.133 17.936 44.102 17.862\n 44.039 17.76 c 43.781 17.334 l 43.527 16.916 43.344 16.737 43.02 16.592\n c 42.809 15.979 42.512 15.67 42.07 15.608 c 41.723 14.901 41.414 14.655\n 40.789 14.584 c 40.703 14.315 40.555 14.084 40.23 13.729 c 40.102 13.584\n 40.02 13.487 39.992 13.432 c 39.848 13.17 39.836 13.155 39.766 13.155 c\n 39.68 13.155 39.488 13.28 39.34 13.432 c 39.273 13.502 39.219 13.577 39.172\n 13.655 c 39.008 13.952 l 38.953 14.049 38.898 14.092 38.828 14.092 c 38.711\n 14.084 l 38.301 14.026 l 38.043 13.991 37.68 13.784 37.559 13.6 c 37.336\n 13.264 l 37.312 13.229 37.277 13.209 37.246 13.209 c 37.078 13.209 36.977\n 13.577 36.977 14.186 c 36.977 14.717 37.059 15.073 37.242 15.327 c 37.762\n 16.053 l 37.836 16.155 37.875 16.252 37.875 16.334 c 37.875 16.416 37.84\n 16.498 37.781 16.573 c 37.672 16.705 37.566 16.784 37.484 16.795 c 37.094\n 16.85 l 37.023 16.862 36.996 16.877 36.996 16.909 c 36.996 16.94 37.016\n 16.987 37.055 17.037 c 37.184 17.198 37.344 17.334 37.52 17.444 c 37.98\n 17.725 38.281 17.838 38.594 17.838 c 38.906 17.838 39.25 17.725 39.562 \n17.518 c 40.102 17.166 l 40.18 17.116 40.27 17.092 40.359 17.092 c 40.609\n 17.092 40.797 17.241 40.977 17.577 c 41.145 17.901 41.219 18.162 41.219\n 18.436 c 41.219 19.135 40.723 19.635 40.027 19.635 c 39.707 19.635 39.379\n 19.51 39.062 19.264 c 38.969 19.19 38.902 19.119 38.875 19.061 c 38.727\n 18.729 l 38.691 18.647 38.641 18.596 38.594 18.596 c 38.5 18.596 38.289\n 18.866 38.113 19.209 c 38.023 19.393 37.934 19.616 37.855 19.877 c 37.742\n 20.244 37.695 20.518 37.695 20.807 c 37.727 21.307 l 37.473 21.702 37.387\n 21.991 37.387 22.459 c 37.387 22.627 37.414 22.76 37.52 23.112 c 37.363\n 23.369 37.316 23.561 37.316 23.94 c 37.336 24.17 l 37.391 24.432 l 37.426\n 24.655 l 37.246 24.889 37.195 25.041 37.02 25.862 c 36.918 26.323 36.855\n 26.522 36.777 26.639 c 36.742 26.694 36.723 26.733 36.723 26.752 c 36.723\n 26.862 36.98 26.955 37.277 26.955 c 37.539 26.936 l 37.473 27.112 37.461\n 27.178 37.461 27.315 c 37.461 27.541 37.508 27.795 37.594 28.053 c 37.691\n 28.334 37.711 28.416 37.711 28.506 c 37.711 28.725 37.68 28.952 37.633 \n29.073 c 37.484 29.444 l 37.449 29.53 37.438 29.584 37.438 29.635 c 37.438\n 29.674 37.461 29.686 37.527 29.686 c 37.895 29.686 38.59 29.295 39.137 \n28.776 c 39.68 28.264 39.855 27.85 39.855 27.084 c 39.824 26.678 l 40.332\n 26.237 40.551 25.869 40.551 25.467 c 40.551 25.178 40.457 24.987 40.176\n 24.709 c 39.676 24.209 39.562 23.952 39.562 23.311 c 39.562 22.327 39.855\n 21.791 40.402 21.791 c 40.883 21.791 41.25 22.311 41.309 23.073 c 41.363\n 23.78 l 41.379 23.963 41.41 24.186 41.457 24.448 c 41.488 24.616 41.516\n 24.838 41.516 24.932 c 41.516 25.186 41.352 25.498 40.863 26.065 c 40.344\n 26.674 40.164 27.002 40.164 27.483 c 40.164 28.049 40.562 28.471 41.098\n 28.471 c 41.23 28.471 41.297 28.452 41.457 28.369 c 41.465 28.69 41.48 \n28.752 41.664 29.205 c 41.809 29.565 41.961 29.819 42.035 29.819 c 42.059\n 29.819 42.098 29.791 42.145 29.744 c 42.227 29.662 42.305 29.596 42.387\n 29.537 c 42.504 29.459 42.562 29.416 42.574 29.409 c 42.613 29.373 42.668\n 29.319 42.738 29.241 c 42.855 29.659 42.977 29.854 43.316 30.19 c 43.574\n 30.444 43.672 30.522 43.723 30.522 c 43.762 30.522 43.812 30.491 43.855\n 30.428 c 44.078 30.131 l 44.395 29.78 l 44.516 29.647 44.559 29.53 44.617\n 29.205 c 44.801 29.315 l 45.098 29.483 l 45.152 29.514 45.227 29.561 45.324\n 29.631 c 45.375 29.67 45.414 29.69 45.441 29.69 c 45.473 29.69 45.508 29.635\n 45.527 29.557 c 45.637 29.131 l 45.879 28.647 l 45.953 28.502 45.973 28.389\n 45.973 28.166 c 45.973 28.053 l 46.098 28.057 l 46.461 28.057 46.777 27.623\n 46.777 27.123 c 46.777 26.463 46.25 25.608 45.562 25.155 c 45.5 24.932 \n45.484 24.842 45.484 24.662 c 45.484 23.186 45.875 22.873 48.145 22.534 \nc 48.715 22.452 49.223 22.346 49.668 22.221 c 49.742 22.198 50.23 22.037\n 51.137 21.737 c 51.641 21.569 51.977 21.494 52.238 21.494 c 52.516 21.494\n 52.668 21.573 52.668 21.721 c 52.668 21.971 52.293 22.202 51.156 22.647\n c 49.539 23.28 l 47.898 23.92 47.121 24.924 47.121 26.401 c 47.121 28.119\n 48.41 29.409 50.133 29.409 c 50.781 29.409 51.414 29.276 52.324 28.944 \nc 53.5 28.518 53.559 28.498 53.688 28.498 c 53.914 28.498 54.035 28.651 \n54.07 28.983 c 54.102 29.256 54.176 29.373 54.332 29.373 c 54.559 29.373\n 54.805 29.159 54.98 28.815 c 55.137 28.514 55.223 28.159 55.223 27.85 c\n 55.223 27.237 54.902 26.834 54.312 26.713 c 53.922 26.202 53.426 25.948\n 52.816 25.948 c 52.078 25.948 51.711 26.092 50.449 26.881 c 49.996 27.166\n 49.805 27.237 49.473 27.237 c 49.031 27.237 48.703 26.959 48.703 26.588\n c 48.703 26.248 48.977 26.022 49.594 25.862 c 49.965 25.764 50.016 25.737\n 50.207 25.545 c 50.355 25.58 50.422 25.588 50.516 25.588 c 51.121 25.588\n 51.863 25.264 52.492 24.729 c 53.641 24.373 54.656 23.139 54.656 22.104\n c 54.656 20.955 53.664 19.928 52.555 19.928 c 52.254 19.928 51.656 20.026\n 51.117 20.159 c f\n0.992157 0.878431 0.0705882 rg\n20.281 46.815 m 20.23 47.037 20.211 47.155 20.211 47.264 c 20.227 47.409\n l 20.281 47.705 l 20.301 47.948 l 20.301 48.202 20.258 48.389 20.168 48.506\n c 19.949 48.803 l 19.871 48.901 19.809 49.022 19.762 49.155 c 19.738 49.225\n 19.723 49.276 19.723 49.315 c 19.723 49.397 19.773 49.448 19.859 49.448\n c 19.91 49.448 19.93 49.44 20.098 49.342 c 20.168 49.299 20.23 49.276 20.277\n 49.276 c 20.387 49.276 20.449 49.459 20.449 49.772 c 20.449 50.131 20.414\n 50.33 20.336 50.416 c 20.059 50.733 l 19.816 50.94 l 19.758 50.987 19.719\n 51.053 19.719 51.1 c 19.719 51.182 19.781 51.237 19.871 51.237 c 19.953\n 51.237 20.133 51.194 20.281 51.143 c 20.367 51.123 l 20.441 51.123 20.473\n 51.166 20.473 51.28 c 20.469 51.346 l 20.41 51.737 l 20.301 52.147 l 20.266\n 52.276 20.246 52.299 20.16 52.299 c 20.113 52.295 l 19.648 52.202 l 19.578\n 52.194 l 18.969 52.194 18.324 52.709 18.125 53.354 c 17.754 54.561 l 17.641\n 54.936 17.363 55.287 16.863 55.694 c 16.711 55.819 16.582 55.889 16.492\n 55.897 c 16.156 55.936 l 16.094 55.944 16.055 55.979 16.055 56.03 c 16.055\n 56.104 16.129 56.139 16.289 56.139 c 16.902 56.084 l 17.121 56.065 17.461\n 55.975 17.902 55.823 c 18.461 55.635 18.832 55.549 19.148 55.549 c 20.078\n 55.549 20.633 56.053 20.633 56.893 c 20.633 57.471 20.336 58.119 19.871\n 58.537 c 19.613 58.772 19.352 58.948 19.262 58.948 c 19.207 58.948 19.164\n 58.905 19.164 58.85 c 19.184 58.612 l 19.188 58.588 l 19.188 58.494 19.133\n 58.424 19.059 58.424 c 18.965 58.424 18.859 58.545 18.738 58.795 c 18.387\n 59.526 17.832 59.963 17.254 59.963 c 17.156 59.963 17.086 59.92 17.086 \n59.862 c 17.086 59.827 17.117 59.733 17.18 59.596 c 17.328 59.26 l 17.367\n 59.17 17.387 59.116 17.387 59.092 c 17.387 59.069 17.352 59.049 17.309 \n59.049 c 17.223 59.049 17.086 59.123 16.992 59.221 c 16.715 59.522 l 16.473\n 59.893 l 16.281 60.19 16.121 60.323 15.711 60.541 c 15.48 60.666 15.332\n 60.744 15.266 60.784 c 15.18 60.834 15.102 60.858 15.035 60.858 c 14.969\n 60.858 14.938 60.827 14.938 60.76 c 14.938 60.702 14.961 60.631 15.008 \n60.561 c 15.145 60.35 15.199 60.241 15.199 60.19 c 15.199 60.127 15.152 \n60.077 15.102 60.077 c 14.957 60.077 14.656 60.369 14.395 60.764 c 14.176\n 61.092 14.059 61.455 14.059 61.811 c 14.059 62.608 14.641 63.198 15.43 \n63.198 c 15.836 63.198 16.352 63.026 16.828 62.733 c 17.699 62.198 l 18.004\n 62.01 18.449 61.862 18.715 61.862 c 18.832 61.862 19.078 61.905 19.262 \n61.955 c 19.34 61.979 19.414 62.01 19.48 62.045 c 19.535 62.077 19.57 62.119\n 19.57 62.151 c 19.57 62.19 19.551 62.221 19.52 62.233 c 19.41 62.268 l \n19 62.346 l 18.895 62.362 18.727 62.424 18.496 62.53 c 18.008 62.756 17.699\n 62.909 17.57 62.994 c 17.266 63.194 17.059 63.315 16.957 63.346 c 16.805\n 63.397 16.711 63.467 16.711 63.526 c 16.711 63.577 16.742 63.608 16.793\n 63.608 c 16.848 63.608 16.918 63.58 16.992 63.534 c 17.266 63.362 17.504\n 63.272 17.68 63.272 c 18.48 62.994 l 18.875 62.858 19.457 62.733 19.715\n 62.733 c 20.051 62.733 20.324 63.01 20.324 63.35 c 20.324 63.569 20.195\n 63.893 20.004 64.166 c 19.961 64.225 19.914 64.26 19.879 64.26 c 19.801\n 64.26 19.762 64.205 19.762 64.108 c 19.781 63.959 l 19.809 63.737 l 19.809\n 63.498 19.648 63.307 19.441 63.307 c 19.363 63.307 19.266 63.327 19.148\n 63.366 c 17.941 63.756 l 16.93 64.084 16.461 64.182 15.953 64.182 c 14.508\n 64.182 13.406 63.19 13.406 61.885 c 13.406 60.405 14.445 59.452 17.012 \n58.573 c 19.539 57.709 19.891 57.494 19.891 56.846 c 19.891 56.471 19.516\n 56.139 19.09 56.139 c 18.918 56.139 18.773 56.151 18.648 56.178 c 18.559\n 56.194 18.141 56.291 17.383 56.475 c 16.621 56.659 15.926 56.737 15.074\n 56.737 c 13.25 56.737 12.238 56.416 11.363 55.565 c 11.148 55.35 11.074\n 55.319 10.82 55.319 c 10.695 55.323 l 10.438 55.342 l 10.402 55.342 l 10.184\n 55.342 10.023 55.241 9.879 55.006 c 9.684 54.69 9.578 54.526 9.562 54.506\n c 9.512 54.436 9.43 54.393 9.352 54.393 c 9.184 54.393 9.082 54.522 8.895\n 54.971 c 8.801 55.186 8.668 55.377 8.504 55.526 c 8.133 55.862 l 8.074 \n55.916 8.039 55.967 8.039 56.002 c 8.039 56.045 8.066 56.104 8.113 56.178\n c 8.289 56.436 8.352 56.549 8.375 56.659 c 8.422 56.909 8.43 56.92 8.531\n 56.92 c 8.648 56.92 8.68 56.807 8.691 56.381 c 8.691 56.26 8.742 56.088\n 8.84 55.881 c 9.043 55.436 l 9.07 55.377 9.137 55.338 9.211 55.338 c 9.402\n 55.338 9.645 55.791 9.711 56.268 c 9.766 56.67 9.809 56.791 9.891 56.791\n c 9.926 56.791 9.957 56.776 9.973 56.752 c 9.992 56.725 10.047 56.616 10.141\n 56.42 c 10.188 56.315 10.27 56.221 10.379 56.139 c 10.488 56.061 10.562\n 56.018 10.602 56.01 c 10.625 56.006 l 10.777 56.006 10.828 56.104 10.863\n 56.455 c 10.883 56.643 l 10.844 56.901 l 10.82 57.08 l 10.82 57.202 10.859\n 57.272 10.93 57.272 c 10.969 57.272 11.02 57.248 11.066 57.198 c 11.254\n 57.014 l 11.375 56.889 11.5 56.827 11.621 56.827 c 11.777 56.827 11.871\n 56.889 11.871 56.991 c 11.871 57.08 11.836 57.17 11.699 57.42 c 11.57 57.662\n l 11.418 57.94 11.301 58.053 11.16 58.053 c 10.977 58.034 l 10.754 57.94\n l 10.684 57.912 10.637 57.901 10.605 57.901 c 10.574 57.901 10.547 57.932\n 10.547 57.975 c 10.547 58.139 10.656 58.401 10.863 58.733 c 11.012 58.967\n 11.113 59.467 11.16 60.19 c 11.223 61.159 11.352 61.787 11.488 61.787 c\n 11.582 61.787 11.617 61.709 11.617 61.502 c 11.605 61.248 l 11.598 61.069\n l 11.598 60.897 11.637 60.756 11.684 60.756 c 11.844 60.756 12.031 60.994\n 12.184 61.377 c 12.246 61.537 12.277 61.666 12.277 61.756 c 12.258 61.842\n l 11.887 62.53 l 11.512 62.643 l 11.445 62.662 11.398 62.694 11.383 62.725\n c 11.383 63.182 l 11.348 63.405 l 11.273 63.588 l 11.18 63.795 l 10.773\n 63.573 10.578 63.428 10.438 63.237 c 10.289 63.041 10.207 62.967 10.137\n 62.967 c 10.09 62.967 10.047 63.014 10.047 63.065 c 10.047 63.112 10.066\n 63.213 10.102 63.366 c 10.113 63.471 l 10.113 63.869 9.871 64.264 9.469\n 64.518 c 9.359 64.369 l 9.23 64.182 l 9.008 63.924 l 8.934 63.842 8.871\n 63.713 8.82 63.553 c 8.793 63.471 8.781 63.401 8.781 63.338 c 8.781 63.311\n l 8.785 63.241 l 8.785 63.088 8.77 63.049 8.699 63.049 c 8.652 63.069 l\n 8.523 63.252 l 8.316 63.479 l 8.004 63.682 l 7.836 63.811 l 7.801 63.737\n 7.777 63.682 7.762 63.643 c 7.703 63.518 7.672 63.416 7.668 63.346 c 7.648\n 63.049 l 7.633 62.733 l 7.633 62.682 l 7.633 62.518 7.594 62.455 7.496 \n62.455 c 7.461 62.455 7.367 62.494 7.277 62.549 c 7.195 62.596 7.102 62.623\n 7.008 62.623 c 6.719 62.623 6.535 62.424 6.535 62.112 c 6.535 61.741 6.645\n 61.385 6.832 61.135 c 7.074 60.823 l 7.141 60.737 7.262 60.666 7.27 60.709\n c 7.316 61.1 l 7.258 61.416 l 7.246 61.557 l 7.246 61.678 7.289 61.748 \n7.359 61.748 c 7.461 61.748 7.527 61.635 7.594 61.323 c 7.742 60.635 l 7.891\n 59.76 l 7.93 59.526 8.004 59.276 8.113 59.018 c 8.262 58.67 8.367 58.452\n 8.43 58.369 c 8.637 58.104 8.656 58.069 8.656 58.01 c 8.656 57.963 8.621\n 57.924 8.578 57.924 c 8.539 57.924 8.48 57.944 8.41 57.979 c 8.348 58.014\n 8.273 58.037 8.188 58.053 c 8.043 58.073 l 7.746 58.073 7.684 57.819 7.684\n 56.577 c 7.684 55.823 7.844 54.795 8.039 54.299 c 8.43 53.315 l 8.492 53.159\n 8.523 53.002 8.523 52.854 c 8.523 51.498 6.727 49.373 4.75 48.393 c 4.02\n 48.03 3.785 47.682 3.73 46.869 c 3.711 46.627 l 4.012 46.784 4.133 46.834\n 4.344 46.889 c 4.895 47.034 5.004 47.104 5.086 47.373 c 5.16 47.612 l 5.188\n 47.705 5.234 47.737 5.664 47.967 c 5.809 48.045 5.91 48.127 5.957 48.209\n c 6.055 48.362 6.133 48.448 6.18 48.448 c 6.227 48.448 6.25 48.424 6.25\n 48.389 c 6.238 48.319 l 6.051 47.791 l 6.031 47.659 l 6.031 47.268 6.246\n 46.948 6.645 46.741 c 6.711 47.17 6.758 47.319 6.887 47.522 c 7.223 48.041\n l 7.348 48.233 7.398 48.467 7.398 48.834 c 7.398 49.041 7.383 49.26 7.352\n 49.416 c 7.34 49.565 l 7.34 49.655 7.387 49.713 7.469 49.713 c 7.535 49.713\n 7.586 49.69 7.594 49.655 c 7.668 49.303 l 7.684 49.229 7.762 49.174 7.852\n 49.174 c 8.027 49.174 8.262 49.405 8.504 49.823 c 8.594 49.979 8.645 50.151\n 8.645 50.315 c 8.617 50.569 l 8.559 50.846 l 8.555 50.901 l 8.555 51.022\n 8.625 51.108 8.719 51.108 c 8.762 51.108 8.797 51.092 8.801 51.069 c 8.914\n 50.643 l 8.949 50.498 9.051 50.401 9.152 50.401 c 9.277 50.401 9.492 50.612\n 9.617 50.866 c 9.73 51.084 9.785 51.338 9.785 51.623 c 9.785 52.022 9.828\n 52.135 9.973 52.135 c 10.02 52.135 10.055 52.116 10.066 52.088 c 10.141\n 51.85 l 10.164 51.76 10.219 51.702 10.273 51.702 c 10.348 51.702 10.555\n 51.924 10.695 52.166 c 10.734 52.229 10.754 52.287 10.754 52.338 c 10.754\n 52.416 10.699 52.494 10.586 52.592 c 10.391 52.752 10.379 52.776 10.379\n 53.022 c 10.379 53.252 10.418 53.373 10.586 53.67 c 10.672 53.827 10.754\n 53.975 10.828 54.116 c 11.062 54.573 11.145 54.694 11.23 54.694 c 11.301\n 54.694 11.367 54.616 11.367 54.53 c 11.367 54.479 11.352 54.428 11.328 \n54.373 c 11.277 54.272 11.25 54.198 11.25 54.155 c 11.25 54.08 11.312 54.022\n 11.391 54.022 c 11.477 54.022 11.59 54.061 11.719 54.135 c 11.77 54.162\n 11.941 54.229 12.238 54.338 c 12.852 54.561 l 12.953 54.596 13.078 54.616\n 13.223 54.616 c 13.336 54.616 13.449 54.604 13.559 54.58 c 13.777 54.53\n 13.918 54.502 13.984 54.502 c 14.152 54.502 14.301 54.651 14.301 54.815\n c 14.301 54.94 14.172 55.182 14.004 55.377 c 13.941 55.452 13.906 55.51\n 13.906 55.545 c 13.93 55.619 l 13.953 55.666 13.992 55.694 14.027 55.694\n c 14.152 55.694 14.445 55.381 14.707 54.971 c 16.309 52.452 16.883 51.842\n 17.961 51.514 c 19.055 51.182 19.34 50.928 19.34 50.276 c 19.34 49.924 \n19.156 49.51 18.758 48.952 c 17.953 47.819 17.754 47.362 17.754 46.616 c\n 17.754 46.479 l 17.848 46.518 17.914 46.549 17.961 46.573 c 18.109 46.651\n 18.211 46.694 18.258 46.702 c 18.723 46.795 l 18.859 46.823 18.984 47.002\n 18.984 47.174 c 18.98 47.221 l 18.926 47.631 l 18.922 47.686 l 18.922 47.83\n 18.992 47.998 19.094 48.077 c 19.121 48.104 19.156 48.116 19.191 48.116\n c 19.242 48.116 19.305 48.069 19.336 48.002 c 19.633 47.373 l 19.754 47.112\n 19.895 46.991 20.281 46.815 c f\n5.305 60.284 m 5.168 60.284 5.102 60.166 5.086 59.909 c 5.051 59.557 l \n5.008 59.229 5.004 59.194 5.004 59.108 c 5.004 58.842 5.082 58.647 5.188\n 58.647 c 5.309 58.647 5.496 58.873 5.586 59.131 c 5.645 59.287 5.672 59.424\n 5.672 59.565 c 5.672 59.69 5.641 59.838 5.586 59.987 c 5.52 60.174 5.418\n 60.284 5.305 60.284 c f\n3.602 63.182 m 3.602 63.018 l 3.602 62.787 3.547 62.514 3.359 61.807 c \n3.324 61.674 3.301 61.545 3.301 61.467 c 3.301 61.342 3.352 61.209 3.449\n 61.045 c 3.512 60.948 3.57 60.881 3.617 60.858 c 3.844 60.748 l 3.879 60.487\n l 3.867 60.385 3.887 60.244 3.934 60.077 c 3.984 59.893 4.012 59.705 4.012\n 59.522 c 4.012 59.366 3.996 59.307 3.961 59.307 c 3.895 59.307 3.77 59.498\n 3.656 59.78 c 3.477 60.229 3.316 60.397 3.062 60.397 c 2.859 60.397 2.727\n 60.362 2.484 60.244 c 2.543 60.174 2.582 60.123 2.598 60.096 c 2.609 60.073\n 2.668 59.967 2.766 59.78 c 3.023 59.28 l 3.203 58.932 3.309 58.733 3.34\n 58.686 c 3.406 58.577 3.496 58.487 3.602 58.424 c 3.633 58.405 3.73 58.35\n 3.898 58.256 c 3.988 58.209 4.051 58.139 4.051 58.084 c 4.051 58.002 3.98\n 57.94 3.891 57.94 c 3.879 57.94 l 3.637 57.979 l 3.5 57.994 l 3.43 57.994\n 3.395 57.928 3.395 57.787 c 3.395 57.284 3.793 56.705 4.492 56.198 c 4.574\n 56.135 4.641 56.045 4.641 55.998 c 4.641 55.936 4.578 55.877 4.504 55.877\n c 4.461 55.877 4.352 55.912 4.27 55.955 c 4.215 55.979 4.172 55.994 4.141\n 55.994 c 4.047 55.994 3.988 55.916 3.988 55.803 c 3.988 55.67 4.094 55.409\n 4.324 54.971 c 4.477 54.682 4.699 54.463 4.957 54.358 c 5.793 54.002 l \n5.836 53.983 5.867 53.94 5.867 53.893 c 5.867 53.827 5.816 53.768 5.754 \n53.76 c 5.586 53.744 l 5.438 53.725 l 5.199 53.744 l 5.16 53.744 l 5.047\n 53.744 5 53.721 5 53.662 c 5 53.569 5.102 53.338 5.27 53.057 c 5.469 52.725\n 5.645 52.534 5.75 52.534 c 5.793 52.534 5.84 52.573 5.867 52.627 c 6.07\n 53.057 l 6.105 53.131 6.273 53.26 6.555 53.428 c 6.73 53.534 6.906 53.604\n 7.074 53.631 c 7.406 53.686 l 7.496 53.702 7.543 53.764 7.543 53.866 c \n7.539 53.909 l 7.48 54.264 l 7.445 54.655 l 7.406 55.006 l 7.371 55.416 \nl 7.367 55.522 7.336 55.569 7.277 55.569 c 7.234 55.569 7.16 55.506 7.109\n 55.436 c 6.82 54.987 6.617 54.838 6.305 54.838 c 5.316 54.838 4.566 56.58\n 4.566 58.885 c 4.566 59.088 4.578 59.342 4.602 59.651 c 4.652 60.241 4.66\n 60.389 4.66 60.608 c 4.66 61.608 4.449 62.311 4.027 62.733 c 3.766 62.994\n l 3.602 63.182 l f\n5.996 49.6 m 5.738 49.639 5.613 49.67 5.457 49.729 c 5.414 49.748 5.297\n 49.799 5.105 49.881 c 4.805 50.006 4.582 50.065 4.395 50.065 c 4.113 50.065\n 3.883 49.971 3.617 49.748 c 3.785 49.619 l 4.156 49.358 l 4.512 49.08 l\n 4.602 49.01 4.711 48.971 4.816 48.971 c 5.18 48.971 5.496 49.139 5.996 \n49.6 c f\n13.148 46.221 m 13.25 46.452 13.301 46.514 13.52 46.702 c 14.039 47.147\n 14.281 47.479 14.281 47.744 c 14.281 48.151 l 14.281 48.389 14.32 48.467\n 14.445 48.467 c 14.5 48.467 14.547 48.448 14.559 48.412 c 14.652 48.19 \nl 14.676 48.139 14.734 48.104 14.809 48.104 c 14.879 48.104 14.988 48.19\n 15.082 48.319 c 15.266 48.58 l 15.359 48.709 15.402 48.819 15.402 48.94\n c 15.402 49.014 15.387 49.088 15.359 49.155 c 15.344 49.194 15.301 49.291\n 15.23 49.452 c 15.188 49.549 15.152 49.674 15.152 49.744 c 15.152 49.866\n 15.215 49.936 15.32 49.936 c 15.375 49.936 15.422 49.916 15.453 49.881 \nc 15.602 49.694 l 15.633 49.655 15.684 49.631 15.742 49.631 c 15.809 49.631\n 15.887 49.694 15.934 49.787 c 16.082 50.084 l 16.121 50.159 16.141 50.217\n 16.141 50.252 c 16.141 50.307 16.082 50.369 15.973 50.416 c 15.73 50.53\n l 15.41 50.678 15.172 51.19 15.172 51.721 c 15.172 51.791 15.184 51.862\n 15.211 51.924 c 15.34 52.241 l 15.379 52.33 15.398 52.428 15.398 52.537\n c 15.398 52.729 15.332 52.924 15.211 53.112 c 15.062 53.342 14.98 53.471\n 14.969 53.502 c 14.859 53.795 14.801 53.877 14.711 53.877 c 14.617 53.854\n l 14.484 53.78 l 14.281 53.67 l 14.039 53.373 l 13.781 53.241 l 13.625 \n53.162 13.477 52.834 13.477 52.557 c 13.477 52.077 13.531 51.944 14.168 \n50.846 c 14.418 50.42 14.547 50.053 14.547 49.784 c 14.547 49.022 13.867\n 48.479 12.496 48.151 c 11.645 47.948 l 11.016 47.795 10.645 47.366 10.512\n 46.627 c 10.77 46.764 10.871 46.784 11.332 46.784 c 11.605 46.776 l 11.648\n 46.776 l 11.898 46.776 11.93 46.795 12.09 47.018 c 12.273 47.276 12.453\n 47.428 12.703 47.537 c 13 47.67 l 13.242 47.799 l 13.316 47.819 l 13.383\n 47.819 13.418 47.791 13.418 47.744 c 13.418 47.67 13.348 47.549 13.242 \n47.428 c 13.027 47.19 12.945 46.987 12.945 46.709 c 12.945 46.53 12.988 \n46.424 13.148 46.221 c f\n10.809 49.1 m 11.023 48.967 11.238 48.772 11.363 48.596 c 11.543 48.354\n 11.566 48.338 11.742 48.338 c 11.82 48.338 11.91 48.35 12.016 48.373 c \n12.184 48.412 l 12.488 48.471 12.594 48.518 12.594 48.592 c 12.594 48.635\n 12.559 48.694 12.496 48.748 c 12.293 48.932 l 12.09 49.119 l 11.957 49.237\n 11.699 49.327 11.477 49.327 c 11.254 49.327 11.125 49.28 10.809 49.1 c f\n16.828 48.096 m 16.938 48.116 l 17.105 48.135 l 17.273 48.116 l 17.355 \n48.112 l 17.77 48.112 18.164 48.459 18.164 48.827 c 18.164 48.955 18.047\n 49.026 17.836 49.026 c 17.336 49.026 17.023 48.737 16.828 48.096 c f\n54.945 46.815 m 54.902 46.983 54.887 47.065 54.887 47.155 c 54.906 47.409\n l 54.953 47.756 54.969 47.924 54.969 48.131 c 54.969 48.268 54.93 48.377\n 54.836 48.506 c 54.609 48.803 l 54.465 48.994 54.406 49.143 54.406 49.307\n c 54.406 49.416 54.434 49.452 54.508 49.452 c 54.594 49.432 l 54.602 49.428\n 54.656 49.397 54.758 49.342 c 54.836 49.299 54.898 49.276 54.945 49.276\n c 55.055 49.276 55.113 49.452 55.113 49.799 c 55.113 50.284 55.02 50.514\n 54.742 50.733 c 54.48 50.94 l 54.434 50.975 54.406 51.034 54.406 51.092\n c 54.406 51.178 54.457 51.237 54.543 51.237 c 54.594 51.237 54.668 51.221\n 54.758 51.198 c 54.965 51.143 l 55.043 51.123 l 55.105 51.123 55.137 51.174\n 55.137 51.28 c 55.129 51.346 l 55.074 51.737 l 55.016 52.143 54.949 52.299\n 54.828 52.299 c 54.797 52.295 l 54.312 52.202 l 54.23 52.194 l 53.652 52.194\n 52.969 52.744 52.789 53.354 c 52.438 54.561 l 52.332 54.92 52.02 55.319\n 51.547 55.694 c 51.391 55.815 51.254 55.885 51.156 55.897 c 50.82 55.936\n l 50.762 55.944 50.719 55.979 50.719 56.026 c 50.719 56.104 50.785 56.139\n 50.922 56.139 c 51.395 56.139 51.895 56.045 52.566 55.823 c 53.18 55.623\n 53.512 55.549 53.809 55.549 c 54.738 55.549 55.297 56.034 55.297 56.842\n c 55.297 57.241 55.152 57.748 54.945 58.073 c 54.766 58.35 54.461 58.639\n 54.07 58.889 c 54.016 58.928 53.965 58.948 53.926 58.948 c 53.871 58.948\n 53.828 58.905 53.828 58.854 c 53.848 58.612 l 53.852 58.588 l 53.852 58.498\n 53.797 58.424 53.73 58.424 c 53.629 58.424 53.52 58.545 53.402 58.795 c\n 53.062 59.522 52.512 59.959 51.941 59.959 c 51.816 59.959 51.746 59.916\n 51.746 59.842 c 51.746 59.784 51.781 59.698 51.844 59.596 c 51.902 59.502\n 51.957 59.389 52.012 59.26 c 52.039 59.19 52.055 59.135 52.055 59.1 c 52.055\n 59.065 52.023 59.049 51.969 59.049 c 51.887 59.049 51.75 59.123 51.656 \n59.221 c 51.473 59.42 51.379 59.522 51.379 59.522 c 51.137 59.893 l 50.949\n 60.182 50.742 60.362 50.395 60.541 c 49.93 60.784 l 49.836 60.834 49.754\n 60.858 49.691 60.858 c 49.633 60.858 49.602 60.83 49.602 60.776 c 49.602\n 60.717 49.633 60.643 49.688 60.561 c 49.785 60.416 49.859 60.26 49.859 \n60.19 c 49.859 60.127 49.82 60.077 49.766 60.077 c 49.637 60.077 49.402 \n60.311 49.074 60.764 c 48.84 61.088 48.719 61.444 48.719 61.807 c 48.719\n 62.592 49.324 63.198 50.102 63.198 c 50.496 63.198 50.996 63.034 51.488\n 62.733 c 52.383 62.198 l 52.699 62.002 53.133 61.862 53.402 61.862 c 53.547\n 61.862 53.594 61.869 53.922 61.955 c 54.02 61.979 54.102 62.01 54.164 62.045\n c 54.207 62.073 54.234 62.116 54.234 62.155 c 54.234 62.229 54.188 62.26\n 54.07 62.268 c 53.656 62.299 52.797 62.639 52.234 62.994 c 51.902 63.202\n 51.699 63.319 51.621 63.346 c 51.469 63.401 51.363 63.483 51.363 63.545\n c 51.363 63.573 51.395 63.588 51.445 63.588 c 51.555 63.569 l 52.363 63.272\n l 53.145 62.994 l 53.527 62.858 54.117 62.733 54.391 62.733 c 54.742 62.733\n 54.988 62.979 54.988 63.33 c 54.988 63.51 54.941 63.709 54.871 63.85 c \n54.758 64.069 54.609 64.26 54.547 64.26 c 54.477 64.26 54.434 64.186 54.434\n 64.061 c 54.445 63.959 l 54.469 63.721 l 54.469 63.471 54.328 63.307 54.117\n 63.307 c 54.031 63.307 53.93 63.327 53.812 63.366 c 52.605 63.756 l 51.555\n 64.096 51.148 64.182 50.633 64.182 c 49.172 64.182 48.07 63.198 48.07 61.885\n c 48.07 60.405 49.086 59.479 51.695 58.573 c 54.227 57.698 54.555 57.494\n 54.555 56.827 c 54.555 56.448 54.195 56.139 53.746 56.139 c 53.582 56.139\n 53.434 56.151 53.309 56.178 c 53.199 56.198 52.781 56.299 52.047 56.475\n c 51.273 56.659 50.613 56.737 49.738 56.737 c 47.895 56.737 47.008 56.455\n 46.047 55.565 c 45.824 55.358 45.734 55.319 45.484 55.319 c 45.359 55.323\n l 45.066 55.342 l 44.844 55.342 44.676 55.233 44.543 55.006 c 44.246 54.506\n l 44.203 54.436 44.121 54.393 44.031 54.393 c 43.84 54.393 43.734 54.522\n 43.559 54.971 c 43.465 55.209 43.336 55.397 43.188 55.526 c 42.797 55.862\n l 42.738 55.912 42.703 55.963 42.703 55.998 c 42.703 56.034 42.734 56.096\n 42.797 56.178 c 42.852 56.252 42.91 56.338 42.965 56.436 c 43.02 56.541\n 43.051 56.616 43.055 56.659 c 43.074 56.827 l 43.09 56.881 43.145 56.92\n 43.207 56.92 c 43.332 56.92 43.352 56.838 43.352 56.381 c 43.352 56.264\n 43.402 56.092 43.5 55.881 c 43.707 55.436 l 43.73 55.377 43.797 55.338 \n43.875 55.338 c 44.066 55.338 44.309 55.791 44.375 56.268 c 44.43 56.666\n 44.473 56.791 44.555 56.791 c 44.629 56.791 44.664 56.744 44.82 56.42 c\n 44.871 56.319 44.945 56.221 45.043 56.139 c 45.137 56.065 45.211 56.018\n 45.266 56.01 c 45.285 56.01 l 45.441 56.01 45.492 56.104 45.527 56.455 \nc 45.543 56.643 l 45.508 56.901 l 45.48 57.08 l 45.48 57.202 45.523 57.272\n 45.594 57.272 c 45.633 57.272 45.684 57.248 45.73 57.198 c 45.918 57.014\n l 46.039 56.889 46.168 56.827 46.289 56.827 c 46.449 56.827 46.547 56.897\n 46.547 57.01 c 46.547 57.104 46.527 57.147 46.363 57.42 c 46.348 57.448\n 46.305 57.526 46.23 57.662 c 46.066 57.979 45.992 58.053 45.848 58.053 \nc 45.656 58.034 l 45.414 57.94 l 45.344 57.912 45.293 57.901 45.27 57.901\n c 45.234 57.901 45.211 57.932 45.211 57.975 c 45.211 58.143 45.32 58.405\n 45.527 58.733 c 45.652 58.932 45.762 59.432 45.844 60.19 c 45.934 61.061\n l 45.992 61.596 46.051 61.787 46.16 61.787 c 46.199 61.787 46.227 61.772\n 46.23 61.748 c 46.289 61.471 l 46.289 61.248 l 46.289 60.979 46.316 60.756\n 46.352 60.756 c 46.43 60.756 46.547 60.85 46.66 61.006 c 46.738 61.119 \n46.805 61.244 46.848 61.377 c 46.906 61.577 46.938 61.741 46.938 61.866 \nc 46.938 62.295 46.758 62.479 46.176 62.643 c 46.117 62.659 46.078 62.678\n 46.066 62.698 c 46.047 62.866 l 46.047 63.182 l 46.012 63.405 l 45.934 \n63.588 l 45.844 63.795 l 45.695 63.702 l 45.316 63.463 45.223 63.393 45.098\n 63.237 c 44.93 63.022 44.871 62.971 44.801 62.971 c 44.75 62.971 44.711\n 63.006 44.711 63.049 c 44.785 63.366 l 44.789 63.42 l 44.789 63.827 44.559\n 64.221 44.152 64.518 c 44.023 64.369 l 43.891 64.182 l 43.668 63.924 l \n43.539 63.772 43.461 63.565 43.461 63.373 c 43.469 63.248 l 43.469 63.104\n 43.445 63.053 43.375 63.053 c 43.336 63.069 l 43.188 63.252 l 43 63.479\n l 42.668 63.682 l 42.5 63.811 l 42.32 63.42 42.312 63.381 42.312 62.881\n c 42.312 62.725 l 42.312 62.557 42.258 62.455 42.168 62.455 c 42.125 62.455\n 42.047 62.487 41.941 62.549 c 41.859 62.596 41.758 62.623 41.664 62.623\n c 41.395 62.623 41.195 62.393 41.195 62.08 c 41.195 61.741 41.27 61.506\n 41.496 61.135 c 41.641 60.897 41.836 60.698 41.922 60.698 c 41.934 60.698\n 41.957 60.873 41.98 61.1 c 41.922 61.416 l 41.91 61.557 l 41.91 61.678 \n41.953 61.748 42.023 61.748 c 42.129 61.748 42.191 61.635 42.258 61.323 \nc 42.406 60.635 l 42.555 59.76 l 42.594 59.526 42.668 59.276 42.777 59.018\n c 42.922 58.67 43.031 58.452 43.094 58.369 c 43.297 58.104 43.324 58.069\n 43.324 58.01 c 43.324 57.963 43.285 57.924 43.242 57.924 c 43.203 57.924\n 43.145 57.944 43.074 57.979 c 43.012 58.014 42.934 58.037 42.852 58.053\n c 42.719 58.073 l 42.41 58.073 42.348 57.827 42.348 56.592 c 42.348 55.823\n 42.508 54.799 42.703 54.299 c 43.094 53.315 l 43.156 53.159 43.188 53.002\n 43.188 52.854 c 43.188 51.494 41.352 49.323 39.414 48.393 c 38.66 48.03\n 38.441 47.655 38.375 46.627 c 38.695 46.78 38.828 46.834 39.023 46.889 \nc 39.574 47.041 39.691 47.119 39.77 47.373 c 39.844 47.612 l 39.875 47.717\n 39.945 47.768 40.324 47.967 c 40.469 48.041 40.57 48.123 40.621 48.209 \nc 40.719 48.366 40.793 48.444 40.844 48.444 c 40.895 48.444 40.926 48.409\n 40.926 48.35 c 40.918 48.319 l 40.715 47.791 l 40.691 47.659 l 40.691 47.272\n 40.91 46.944 41.309 46.741 c 41.336 46.838 41.355 46.912 41.363 46.963 \nc 41.43 47.248 41.492 47.436 41.551 47.522 c 41.906 48.041 l 42.031 48.229\n 42.055 48.354 42.055 48.83 c 42.035 49.416 l 42.023 49.565 l 42.023 49.662\n 42.07 49.713 42.152 49.713 c 42.219 49.713 42.27 49.69 42.277 49.655 c \n42.332 49.303 l 42.344 49.229 42.422 49.174 42.516 49.174 c 42.691 49.174\n 42.922 49.401 43.168 49.823 c 43.262 49.987 43.301 50.116 43.301 50.268\n c 43.301 50.385 43.273 50.647 43.242 50.846 c 43.234 50.955 l 43.234 51.045\n 43.289 51.104 43.375 51.104 c 43.422 51.104 43.457 51.092 43.465 51.069\n c 43.594 50.643 l 43.641 50.498 43.738 50.401 43.84 50.401 c 44.125 50.401\n 44.418 50.983 44.449 51.608 c 44.469 51.998 44.516 52.135 44.637 52.135\n c 44.684 52.135 44.719 52.116 44.727 52.088 c 44.801 51.85 l 44.828 51.76\n 44.883 51.702 44.934 51.702 c 44.98 51.702 45.078 51.784 45.191 51.924 \nc 45.258 52.002 45.312 52.084 45.359 52.166 c 45.398 52.229 45.414 52.295\n 45.414 52.362 c 45.414 52.452 45.398 52.479 45.246 52.592 c 45.113 52.694\n 45.043 52.827 45.043 52.983 c 45.043 53.252 45.16 53.541 45.488 54.116 \nc 45.793 54.639 45.832 54.698 45.906 54.698 c 45.977 54.698 46.031 54.627\n 46.031 54.537 c 46.031 54.483 46.016 54.428 45.992 54.373 c 45.941 54.268\n 45.914 54.194 45.914 54.155 c 45.914 54.08 45.98 54.022 46.062 54.022 c\n 46.152 54.022 46.27 54.061 46.398 54.135 c 46.48 54.178 46.648 54.244 46.902\n 54.338 c 47.246 54.463 47.457 54.537 47.531 54.561 c 47.648 54.596 47.781\n 54.619 47.914 54.619 c 48.023 54.619 48.133 54.604 48.238 54.58 c 48.453\n 54.53 48.594 54.502 48.668 54.502 c 48.836 54.502 48.969 54.635 48.969 \n54.799 c 48.969 54.869 48.914 55.014 48.852 55.119 c 48.758 55.276 48.699\n 55.362 48.684 55.377 c 48.602 55.463 48.57 55.51 48.57 55.545 c 48.594 \n55.619 l 48.613 55.666 48.652 55.694 48.695 55.694 c 48.844 55.694 49.129\n 55.397 49.391 54.971 c 50.848 52.592 51.551 51.838 52.621 51.514 c 53.738\n 51.178 54 50.94 54 50.276 c 54 49.92 53.812 49.494 53.422 48.952 c 52.68\n 47.92 52.418 47.315 52.418 46.631 c 52.418 46.479 l 52.512 46.518 52.578\n 46.549 52.621 46.573 c 52.773 46.651 52.875 46.694 52.922 46.702 c 53.387\n 46.795 l 53.531 46.827 53.648 46.998 53.648 47.186 c 53.645 47.221 l 53.605\n 47.631 l 53.605 47.698 l 53.605 47.928 53.719 48.116 53.855 48.116 c 53.906\n 48.116 53.969 48.069 53.996 48.002 c 54.293 47.373 l 54.418 47.112 54.559\n 46.991 54.945 46.815 c f\n39.711 59.557 m 39.695 59.26 l 39.688 59.08 l 39.688 58.776 39.734 58.647\n 39.848 58.647 c 39.973 58.647 40.164 58.877 40.25 59.131 c 40.305 59.287\n 40.348 59.506 40.348 59.635 c 40.348 59.725 40.324 59.807 40.25 59.987 \nc 40.168 60.19 40.082 60.284 39.977 60.284 c 39.82 60.284 39.746 60.08 39.711\n 59.557 c f\n38.266 63.182 m 38.266 63.018 l 38.266 62.85 38.215 62.534 38.133 62.233\n c 38.062 61.967 38.027 61.827 38.023 61.807 c 37.996 61.698 37.984 61.584\n 37.984 61.467 c 37.984 61.205 38.109 60.959 38.301 60.858 c 38.504 60.748\n l 38.543 60.487 l 38.531 60.385 38.551 60.248 38.598 60.077 c 38.648 59.893\n 38.676 59.705 38.676 59.518 c 38.676 59.366 38.66 59.307 38.625 59.307 \nc 38.555 59.307 38.434 59.498 38.32 59.78 c 38.141 60.229 37.98 60.397 37.727\n 60.397 c 37.523 60.397 37.391 60.362 37.148 60.244 c 37.344 59.994 37.461\n 59.784 37.688 59.28 c 37.984 58.619 38.098 58.487 38.559 58.256 c 38.664\n 58.205 38.73 58.139 38.73 58.084 c 38.73 57.998 38.664 57.94 38.562 57.94\n c 38.52 57.94 38.402 57.959 38.301 57.979 c 38.164 57.994 l 38.09 57.994\n 38.059 57.928 38.059 57.78 c 38.059 57.287 38.48 56.69 39.172 56.198 c \n39.266 56.131 39.324 56.057 39.324 56.006 c 39.324 55.975 39.301 55.944 \n39.266 55.916 c 39.234 55.893 39.199 55.877 39.168 55.877 c 39.129 55.877\n 39.043 55.909 38.949 55.955 c 38.895 55.983 38.848 55.994 38.812 55.994\n c 38.727 55.994 38.668 55.909 38.668 55.776 c 38.668 55.608 38.793 55.311\n 39.008 54.971 c 39.203 54.655 39.418 54.444 39.621 54.358 c 40.457 54.002\n l 40.5 53.983 40.531 53.944 40.531 53.897 c 40.531 53.83 40.484 53.776 \n40.418 53.76 c 40.27 53.744 l 40.102 53.725 l 39.879 53.744 l 39.844 53.744\n l 39.727 53.744 39.68 53.721 39.68 53.662 c 39.68 53.573 39.766 53.385 \n39.953 53.057 c 40.16 52.698 40.305 52.534 40.414 52.534 c 40.457 52.534\n 40.504 52.573 40.531 52.627 c 40.734 53.057 l 40.762 53.112 40.93 53.244\n 41.219 53.428 c 41.391 53.541 41.57 53.612 41.738 53.631 c 42.184 53.69\n 42.219 53.713 42.219 53.897 c 42.219 53.963 42.203 54.084 42.164 54.264\n c 42.145 54.358 42.125 54.487 42.109 54.655 c 42.07 55.006 l 42.035 55.416\n l 42.031 55.518 42 55.569 41.941 55.569 c 41.902 55.569 41.84 55.51 41.793\n 55.436 c 41.574 55.077 41.254 54.838 40.992 54.838 c 39.957 54.838 39.246\n 56.494 39.246 58.92 c 39.266 59.651 l 39.32 60.526 l 39.332 60.834 l 39.332\n 61.58 39.07 62.354 38.691 62.733 c 38.43 62.994 l 38.266 63.182 l f\n40.66 49.6 m 40.27 49.666 40.207 49.686 39.77 49.881 c 39.465 50.014 39.266\n 50.065 39.07 50.065 c 38.777 50.065 38.551 49.975 38.281 49.748 c 38.449\n 49.619 l 38.82 49.358 l 39.172 49.08 l 39.266 49.01 39.375 48.971 39.488\n 48.971 c 39.852 48.971 40.156 49.135 40.66 49.6 c f\n47.812 46.221 m 47.914 46.452 47.961 46.514 48.184 46.702 c 48.703 47.147\n 48.945 47.479 48.945 47.744 c 48.945 48.151 l 48.945 48.389 48.984 48.467\n 49.109 48.467 c 49.164 48.467 49.211 48.448 49.223 48.412 c 49.316 48.19\n l 49.336 48.139 49.398 48.108 49.473 48.108 c 49.605 48.108 49.699 48.205\n 49.93 48.58 c 50.027 48.737 50.066 48.842 50.066 48.948 c 50.066 49.014\n 50.051 49.084 50.023 49.155 c 50.008 49.19 49.965 49.291 49.891 49.452 \nc 49.848 49.549 49.816 49.678 49.816 49.744 c 49.816 49.866 49.879 49.936\n 49.98 49.936 c 50.039 49.936 50.086 49.916 50.113 49.881 c 50.262 49.694\n l 50.297 49.655 50.305 49.631 50.363 49.631 c 50.43 49.631 50.551 49.694\n 50.598 49.787 c 50.746 50.084 l 50.785 50.159 50.805 50.217 50.805 50.252\n c 50.805 50.315 50.746 50.381 50.656 50.416 c 50.395 50.53 l 50.09 50.659\n 49.836 51.182 49.836 51.682 c 49.836 51.737 49.855 51.819 49.891 51.924\n c 50.004 52.241 l 50.039 52.342 50.059 52.448 50.059 52.549 c 50.059 52.729\n 50.004 52.905 49.875 53.112 c 49.746 53.319 49.672 53.452 49.652 53.502\n c 49.543 53.78 49.477 53.869 49.387 53.869 c 49.32 53.869 49.262 53.846\n 49.148 53.78 c 48.965 53.67 l 48.703 53.373 l 48.441 53.241 l 48.285 53.162\n 48.141 52.827 48.141 52.545 c 48.141 52.147 48.227 51.928 48.832 50.846\n c 49.09 50.389 49.211 50.049 49.211 49.787 c 49.211 49.03 48.441 48.409\n 47.18 48.151 c 45.676 47.842 45.348 47.596 45.172 46.627 c 45.434 46.764\n 45.535 46.784 45.996 46.784 c 46.27 46.776 l 46.312 46.776 l 46.559 46.776\n 46.594 46.795 46.754 47.018 c 46.934 47.276 47.117 47.428 47.367 47.537\n c 47.664 47.67 l 47.906 47.799 l 47.977 47.819 l 48.047 47.819 48.082 47.791\n 48.082 47.741 c 48.082 47.678 48.023 47.569 47.906 47.428 c 47.723 47.209\n 47.621 46.983 47.621 46.815 c 47.621 46.565 47.66 46.44 47.812 46.221 c\n f\n45.473 49.1 m 45.684 48.967 45.902 48.772 46.027 48.596 c 46.207 48.354\n 46.23 48.338 46.406 48.338 c 46.48 48.338 46.574 48.35 46.68 48.373 c 46.848\n 48.412 l 47.16 48.471 47.258 48.518 47.258 48.604 c 47.258 48.655 47.23\n 48.705 47.18 48.748 c 46.957 48.932 l 46.754 49.119 l 46.629 49.233 46.367\n 49.323 46.168 49.323 c 45.938 49.323 45.746 49.264 45.473 49.1 c f\n51.488 48.096 m 51.602 48.116 l 51.785 48.135 l 51.938 48.116 l 52.035 \n48.108 l 52.223 48.108 52.32 48.131 52.398 48.19 c 52.516 48.276 52.633 \n48.416 52.754 48.616 c 52.801 48.698 52.828 48.768 52.828 48.827 c 52.828\n 48.955 52.711 49.026 52.508 49.026 c 52 49.026 51.688 48.741 51.488 48.096\n c f\n18.48 8.846 m 18.59 9.069 l 18.852 9.346 l 19.074 9.584 19.191 9.916 19.191\n 10.307 c 19.191 10.498 19.156 10.705 19.094 10.905 c 19.047 11.057 19.016\n 11.182 19.016 11.237 c 19.016 11.35 19.102 11.463 19.195 11.463 c 19.23\n 11.463 19.281 11.432 19.336 11.369 c 19.613 11.053 l 19.785 10.862 19.844\n 10.823 20.059 10.776 c 20.078 11.037 l 20.098 11.342 l 20.098 11.784 19.934\n 12.284 19.688 12.596 c 19.539 12.787 19.473 12.901 19.473 12.967 c 19.473\n 13.037 19.52 13.096 19.574 13.096 c 19.59 13.096 19.617 13.084 19.648 13.061\n c 19.828 12.928 19.922 12.873 19.969 12.873 c 20.059 12.873 20.141 13.096\n 20.141 13.342 c 20.141 13.694 19.992 14.1 19.762 14.381 c 19.645 14.526\n 19.57 14.616 19.539 14.659 c 19.516 14.694 19.5 14.729 19.5 14.764 c 19.5\n 14.846 19.566 14.901 19.66 14.901 c 19.734 14.901 19.777 14.885 19.891 \n14.807 c 19.949 14.772 19.996 14.752 20.035 14.752 c 20.18 14.752 20.23 \n14.955 20.23 15.51 c 20.227 15.737 l 20.207 16.073 l 20.184 16.463 20.152\n 16.534 19.984 16.534 c 19.836 16.534 l 18.93 16.534 18.508 17.108 18.277\n 18.655 c 18.105 19.772 17.691 20.561 17.18 20.733 c 16.871 20.834 16.762\n 20.901 16.762 20.983 c 16.762 21.037 16.848 21.088 16.941 21.088 c 17.008\n 21.088 17.164 21.045 17.289 20.994 c 18.031 20.694 18.656 20.549 19.199\n 20.549 c 20.09 20.549 20.793 21.202 20.793 22.034 c 20.793 22.67 20.488\n 23.315 19.949 23.834 c 19.797 23.979 19.637 24.073 19.48 24.116 c 19.449\n 24.119 l 19.352 24.084 l 19.391 23.819 l 19.465 23.631 l 19.484 23.553 \nl 19.484 23.51 19.445 23.483 19.398 23.483 c 19.344 23.483 19.289 23.51 \n19.262 23.557 c 18.926 24.057 l 18.66 24.455 18.082 24.787 17.648 24.787\n c 17.496 24.787 17.402 24.721 17.402 24.612 c 17.402 24.557 17.449 24.463\n 17.516 24.393 c 17.609 24.287 17.684 24.159 17.684 24.096 c 17.66 24.022\n l 17.641 23.979 17.613 23.952 17.59 23.948 c 17.57 23.944 l 17.48 23.944\n 17.434 23.987 17.031 24.448 c 16.699 24.827 16.023 25.194 15.66 25.194 \nc 15.609 25.194 15.562 25.174 15.527 25.135 c 15.504 25.112 15.488 25.084\n 15.488 25.065 c 15.488 25.01 15.535 24.932 15.617 24.858 c 15.734 24.752\n 15.824 24.627 15.824 24.573 c 15.824 24.537 15.777 24.502 15.719 24.502\n c 15.613 24.502 15.375 24.655 14.934 25.006 c 14.238 25.553 13.93 26.065\n 13.93 26.659 c 13.93 27.381 14.441 27.944 15.098 27.944 c 15.484 27.944\n 15.992 27.76 16.344 27.494 c 17.254 26.807 l 17.668 26.494 18.18 26.327\n 18.715 26.327 c 18.832 26.327 18.941 26.35 19.035 26.401 c 19.117 26.44\n 19.168 26.487 19.168 26.526 c 19.168 26.545 19.098 26.58 19 26.604 c 18.672\n 26.682 18.406 26.795 18.219 26.936 c 17.289 27.643 l 16.945 27.905 16.703\n 28.073 16.566 28.147 c 16.074 28.401 15.98 28.463 15.98 28.522 c 15.98 \n28.549 16.027 28.573 16.086 28.573 c 16.137 28.573 16.191 28.561 16.25 28.534\n c 16.734 28.33 l 16.891 28.264 17.148 28.112 17.516 27.866 c 18.508 27.202\n 19.016 26.987 19.598 26.987 c 20.152 26.987 20.562 27.385 20.562 27.916\n c 20.562 28.022 20.535 28.139 20.484 28.237 c 20.395 28.424 l 20.359 28.491\n 20.301 28.541 20.281 28.518 c 20.227 28.444 l 20.168 28.053 l 20.168 28.037\n 20.148 27.994 20.113 27.924 c 20.078 27.846 20.035 27.784 19.984 27.737\n c 19.883 27.639 19.797 27.604 19.645 27.604 c 19.355 27.604 19.031 27.76\n 18.164 28.311 c 17.281 28.873 16.488 29.166 15.859 29.166 c 14.523 29.166\n 13.445 28.088 13.445 26.756 c 13.445 24.893 14.582 24.057 18.145 23.28 \nc 19.5 22.983 20.098 22.588 20.098 21.983 c 20.098 21.432 19.652 21.084 \n18.945 21.084 c 18.73 21.084 18.562 21.1 18.441 21.123 c 18.324 21.147 17.684\n 21.323 16.527 21.643 c 15.73 21.866 14.84 21.991 14.047 21.991 c 12.652\n 21.991 11.707 21.577 11.066 20.678 c 10.992 20.573 10.918 20.51 10.867 \n20.51 c 10.832 20.51 10.777 20.534 10.715 20.584 c 10.453 20.787 l 10.273\n 20.932 9.98 21.041 9.789 21.041 c 9.648 21.041 9.512 20.987 9.395 20.881\n c 9.301 20.799 9.234 20.752 9.207 20.752 c 9.102 20.752 8.91 20.987 8.766\n 21.291 c 8.555 21.725 8.449 22.159 8.449 22.553 c 8.449 22.741 8.504 22.869\n 8.59 22.869 c 8.652 22.869 8.699 22.819 8.727 22.721 c 8.875 22.182 l 8.957\n 21.881 9.125 21.643 9.254 21.643 c 9.312 21.643 9.41 21.725 9.527 21.866\n c 9.594 21.952 9.645 22.041 9.676 22.127 c 9.789 22.467 9.82 22.518 9.91\n 22.518 c 10.02 22.518 10.074 22.452 10.148 22.108 c 10.16 22.049 10.184\n 21.952 10.27 21.791 c 10.344 21.651 10.418 21.537 10.492 21.459 c 10.547\n 21.397 10.617 21.362 10.684 21.362 c 10.848 21.362 10.996 21.635 10.996\n 21.932 c 10.992 22.014 l 10.973 22.358 l 10.973 22.494 10.992 22.534 11.066\n 22.534 c 11.113 22.534 11.145 22.51 11.199 22.444 c 11.277 22.338 11.387\n 22.252 11.512 22.182 c 11.629 22.119 11.719 22.088 11.777 22.088 c 11.832\n 22.088 11.898 22.123 11.961 22.182 c 12.008 22.229 12.035 22.284 12.035\n 22.33 c 12.035 22.385 11.969 22.467 11.773 22.666 c 11.668 22.772 11.539\n 23.026 11.402 23.409 c 11.348 23.553 11.281 23.651 11.227 23.651 c 11.211\n 23.651 11.172 23.639 11.105 23.612 c 10.863 23.522 l 10.695 23.444 l 10.633\n 23.432 l 10.555 23.432 10.488 23.491 10.488 23.561 c 10.488 23.631 10.66\n 23.834 10.863 24.002 c 11.012 24.123 11.098 24.307 11.105 24.502 c 11.117\n 24.815 11.172 25.061 11.234 25.061 c 11.289 25.713 l 11.32 26.037 11.453\n 26.655 11.57 26.994 c 11.594 27.061 11.621 27.104 11.645 27.104 c 11.695\n 27.104 11.742 26.967 11.742 26.811 c 11.734 26.713 l 11.727 26.526 l 11.727\n 26.291 11.766 26.17 11.836 26.17 c 11.918 26.17 12.008 26.287 12.09 26.491\n c 12.258 26.92 l 12.305 27.045 12.332 27.162 12.332 27.264 c 12.332 27.627\n 12.113 27.889 11.816 27.889 c 11.68 27.866 l 11.609 27.842 11.566 27.83\n 11.547 27.83 c 11.449 27.83 11.367 27.936 11.367 28.061 c 11.383 28.33 \nl 11.363 28.702 l 11.289 29.092 l 11.273 29.28 l 11.105 29.186 l 10.902 \n29.018 l 10.602 28.776 l 10.363 28.573 l 10.32 28.537 10.27 28.518 10.223\n 28.518 c 10.082 28.518 10.062 28.553 10.062 28.842 c 10.066 28.916 l 10.066\n 29.151 9.961 29.334 9.582 29.78 c 9.562 29.803 9.531 29.842 9.488 29.893\n c 9.414 29.76 l 9.211 29.428 l 9.008 28.909 l 8.969 28.573 l 8.957 28.475\n 8.922 28.405 8.883 28.405 c 8.844 28.405 8.781 28.452 8.707 28.534 c 8.426\n 28.862 8.336 28.92 7.91 29.073 c 7.871 28.944 l 7.805 28.725 7.77 28.592\n 7.762 28.553 c 7.742 28.202 l 7.742 27.932 l 7.742 27.83 7.707 27.772 7.645\n 27.772 c 7.633 27.772 l 7.625 27.776 7.586 27.807 7.52 27.866 c 7.477 27.905\n 7.398 27.924 7.293 27.924 c 6.949 27.924 6.727 27.737 6.727 27.44 c 6.727\n 27.217 6.805 27.049 7.148 26.53 c 7.297 26.299 7.473 26.119 7.543 26.119\n c 7.59 26.119 7.629 26.241 7.629 26.381 c 7.594 26.604 l 7.559 26.709 7.543\n 26.807 7.543 26.889 c 7.543 26.994 7.57 27.049 7.629 27.049 c 7.793 27.049\n 7.969 26.565 8.039 25.897 c 8.113 25.209 l 8.141 24.967 8.215 24.717 8.336\n 24.467 c 8.508 24.119 8.672 23.885 8.82 23.78 c 8.996 23.655 9.105 23.534\n 9.105 23.463 c 9.105 23.405 9.059 23.354 9.012 23.354 c 9.004 23.354 8.875\n 23.397 8.633 23.483 c 8.477 23.506 l 8.105 23.506 7.984 23.295 7.984 22.655\n c 7.984 21.881 8.504 20.772 9.078 20.307 c 9.934 19.619 l 10.367 19.272\n 10.695 18.694 10.695 18.28 c 10.695 17.737 10.102 17.202 9.078 16.834 c\n 8.469 16.608 l 7.992 16.436 7.801 16.131 7.613 15.237 c 7.855 15.311 7.961\n 15.33 8.125 15.33 c 8.207 15.327 l 8.484 15.319 l 8.734 15.319 8.914 15.471\n 8.914 15.682 c 8.914 15.846 l 8.914 15.897 8.98 15.967 9.078 16.014 c 9.246\n 16.096 9.332 16.139 9.34 16.147 c 9.543 16.311 l 9.578 16.338 9.613 16.354\n 9.648 16.354 c 9.703 16.354 9.75 16.303 9.75 16.241 c 9.75 16.194 9.738\n 16.135 9.711 16.073 c 9.648 15.905 9.613 15.682 9.613 15.432 c 9.613 14.975\n 9.684 14.733 9.914 14.397 c 9.938 14.522 9.957 14.604 9.973 14.639 c 10.012\n 14.733 10.117 14.901 10.289 15.143 c 10.57 15.534 10.777 16.014 10.777 \n16.272 c 10.77 16.35 l 10.715 16.776 l 10.715 16.854 10.75 16.928 10.809\n 16.983 c 10.848 17.018 10.895 17.037 10.934 17.037 c 11.016 17.037 11.062\n 16.991 11.125 16.85 c 11.168 16.748 11.219 16.682 11.258 16.682 c 11.324\n 16.682 11.449 16.854 11.531 17.057 c 11.594 17.202 11.625 17.334 11.625\n 17.448 c 11.625 17.514 11.605 17.596 11.57 17.686 c 11.531 17.78 11.512\n 17.854 11.512 17.897 c 11.512 17.948 11.527 17.971 11.586 18.022 c 11.617\n 18.045 11.648 18.057 11.684 18.057 c 11.719 18.057 11.758 18.045 11.793\n 18.022 c 11.961 17.909 l 12.016 17.873 12.066 17.854 12.105 17.854 c 12.184\n 17.854 12.289 17.936 12.367 18.057 c 12.531 18.307 12.633 18.51 12.633 \n18.588 c 12.633 18.635 12.602 18.674 12.496 18.744 c 12.129 19.006 11.883\n 19.401 11.883 19.741 c 11.883 19.815 11.902 19.909 11.941 20.01 c 12.125\n 20.51 l 12.219 20.764 12.27 20.893 12.273 20.901 c 12.328 20.924 l 12.395\n 20.924 12.43 20.869 12.43 20.78 c 12.43 20.717 12.414 20.647 12.387 20.565\n c 12.34 20.444 12.309 20.299 12.309 20.213 c 12.309 20.08 12.375 20.01 \n12.504 20.01 c 12.594 20.01 12.699 20.034 12.812 20.084 c 13.375 20.327 \n13.82 20.455 14.09 20.455 c 14.34 20.455 14.5 20.401 14.766 20.213 c 14.836\n 20.162 14.895 20.139 14.938 20.139 c 14.969 20.139 15.008 20.166 15.043\n 20.213 c 15.066 20.244 15.082 20.276 15.082 20.307 c 15.082 20.373 15.039\n 20.459 14.969 20.549 c 14.871 20.666 14.836 20.737 14.836 20.807 c 14.836\n 20.869 14.875 20.905 14.938 20.905 c 15.227 20.905 15.684 19.983 16.121\n 18.522 c 16.465 17.369 16.559 17.252 17.66 16.627 c 18.594 16.1 18.949 \n15.58 18.949 14.729 c 18.949 13.893 18.719 12.827 18.238 11.428 c 18.086\n 10.983 18.02 10.682 18.02 10.436 c 18.02 10.237 18.059 10.049 18.203 9.549\n c 18.27 9.311 18.312 9.166 18.332 9.123 c 18.352 9.069 18.402 8.975 18.48\n 8.846 c f\n14.168 11.444 m 14.281 11.702 14.316 11.764 14.523 12.077 c 14.824 12.53\n 14.934 12.772 14.934 12.987 c 14.934 13.284 l 14.934 13.565 14.98 13.694\n 15.094 13.694 c 15.133 13.694 15.168 13.67 15.191 13.635 c 15.285 13.487\n l 15.312 13.44 15.363 13.412 15.414 13.412 c 15.473 13.412 15.57 13.487\n 15.625 13.569 c 15.758 13.772 15.844 14.03 15.844 14.233 c 15.824 14.436\n l 15.805 14.647 l 15.805 14.76 15.852 14.807 15.957 14.807 c 16.031 14.807\n 16.059 14.78 16.121 14.639 c 16.16 14.549 16.215 14.491 16.262 14.491 c\n 16.312 14.491 16.371 14.534 16.418 14.604 c 16.566 14.827 l 16.676 14.994\n l 16.707 15.084 l 16.707 15.131 16.664 15.182 16.566 15.252 c 16.223 15.502\n 16.07 15.881 16.047 16.573 c 16.027 17.096 15.996 17.44 15.953 17.592 c\n 15.945 17.627 15.832 17.897 15.617 18.393 c 15.535 18.588 15.461 18.811\n 15.395 19.061 c 15.352 19.233 15.297 19.284 15.164 19.284 c 14.66 19.284\n 14.277 18.854 14.277 18.284 c 14.277 17.948 14.414 17.42 14.672 16.776 \nc 15.031 15.873 15.125 15.553 15.125 15.233 c 15.125 14.549 14.852 14.252\n 13.91 13.916 c 12.848 13.534 12.348 12.987 12.164 12.002 c 12.246 12.034\n 12.309 12.061 12.348 12.077 c 12.469 12.123 12.555 12.155 12.609 12.17 \nc 12.906 12.244 l 13.16 12.307 13.367 12.452 13.406 12.596 c 13.484 12.858\n l 13.5 12.92 13.535 12.959 13.594 12.987 c 13.93 13.135 l 13.965 13.061\n l 13.891 12.819 l 13.867 12.741 13.855 12.592 13.855 12.385 c 13.855 11.979\n 13.934 11.741 14.168 11.444 c f\n4.195 28.627 m 4.219 28.526 4.23 28.455 4.23 28.42 c 4.23 28.362 4.207 \n28.202 4.156 27.94 c 4.125 27.561 l 4.125 27.229 4.184 26.877 4.289 26.584\n c 4.383 26.319 4.406 26.233 4.406 26.159 c 4.398 26.084 l 4.383 26.006 \n4.355 25.952 4.328 25.952 c 4.293 25.952 4.223 25.991 4.137 26.065 c 3.926\n 26.248 3.691 26.346 3.453 26.346 c 3.375 26.346 3.266 26.33 3.137 26.307\n c 3.227 26.104 l 3.508 25.416 l 3.898 24.819 l 4.305 24.541 l 4.348 24.514\n 4.383 24.475 4.418 24.432 c 4.453 24.377 4.473 24.338 4.473 24.307 c 4.473\n 24.276 4.441 24.244 4.398 24.244 c 4.027 24.225 l 3.969 24.221 3.926 24.186\n 3.926 24.147 c 3.926 24.084 3.977 23.897 4.082 23.592 c 4.27 23.057 l 4.586\n 22.756 l 4.918 22.518 l 5.016 22.444 5.078 22.366 5.078 22.303 c 5.078 \n22.256 5.047 22.205 5.012 22.202 c 4.824 22.182 l 4.766 22.174 4.73 22.123\n 4.73 22.034 c 4.73 21.893 4.863 21.643 5.105 21.307 c 5.391 20.916 5.594\n 20.787 5.922 20.787 c 6.199 20.787 l 6.332 20.787 6.414 20.725 6.414 20.619\n c 6.414 20.534 6.359 20.479 6.238 20.455 c 5.98 20.401 5.902 20.346 5.902\n 20.229 c 5.902 20.182 5.934 20.112 5.996 20.026 c 6.145 19.823 l 6.27 19.655\n 6.406 19.541 6.496 19.541 c 6.547 19.541 6.664 19.592 6.77 19.713 c 6.934\n 19.905 7.125 20.057 7.371 20.119 c 8.016 20.284 8.152 20.35 8.152 20.498\n c 8.152 20.553 8.125 20.627 8.078 20.713 c 7.871 21.08 7.797 21.323 7.797\n 21.623 c 7.797 21.854 7.738 22.014 7.652 22.014 c 7.617 22.014 7.562 21.959\n 7.5 21.866 c 7.348 21.631 7.09 21.475 6.852 21.475 c 6.207 21.475 5.434\n 22.483 5.051 23.819 c 4.961 24.123 4.914 24.479 4.914 24.819 c 4.914 25.17\n 4.961 25.561 5.051 25.952 c 5.172 26.498 5.242 26.803 5.254 26.862 c 5.277\n 27.116 l 5.277 27.577 4.852 28.17 4.195 28.627 c f\n5.531 26.045 m 5.395 25.787 5.27 25.205 5.27 24.834 c 5.27 24.619 5.34 \n24.448 5.434 24.448 c 5.461 24.448 5.5 24.483 5.531 24.541 c 5.738 24.912\n l 5.77 24.975 5.805 25.041 5.867 25.139 c 5.941 25.252 5.996 25.358 5.996\n 25.416 c 5.996 25.635 5.793 25.909 5.531 26.045 c f\n7.574 17.666 m 7.617 17.627 7.648 17.596 7.668 17.577 c 7.75 17.494 7.797\n 17.444 7.816 17.428 c 8.18 17.053 8.23 17.014 8.406 17.014 c 8.57 17.014\n 8.906 17.108 9.098 17.205 c 9.18 17.244 9.23 17.295 9.23 17.33 c 9.23 17.354\n 9.16 17.432 9.043 17.537 c 8.84 17.725 l 8.684 17.866 8.453 17.994 8.344\n 17.994 c 8.133 17.994 7.914 17.901 7.574 17.666 c f\n11.887 14.307 m 12.082 14.221 12.156 14.186 12.258 14.119 c 12.422 14.01\n 12.523 13.948 12.555 13.936 c 12.609 13.909 12.711 13.897 12.848 13.897\n c 12.934 13.897 13.008 13.909 13.074 13.936 c 13.465 14.084 l 13.836 14.213\n l 13.938 14.248 14.004 14.307 14.004 14.358 c 14.004 14.385 13.918 14.424\n 13.797 14.455 c 13.715 14.475 13.621 14.506 13.52 14.549 c 13.062 14.729\n 12.996 14.756 12.832 14.756 c 12.469 14.756 12.102 14.58 11.887 14.307 \nc f\n16.586 10.573 m 16.711 10.869 16.793 10.92 17.215 10.944 c 17.695 10.967\n 18.148 11.823 18.148 12.401 c 18.148 12.459 18.129 12.506 18.102 12.506\n c 18.082 12.506 18.035 12.487 17.977 12.448 c 17.844 12.362 17.75 12.303\n 17.699 12.28 c 17.16 12.037 l 16.801 11.877 16.484 11.401 16.484 11.022\n c 16.484 10.893 16.508 10.787 16.586 10.573 c f\n50.449 15.01 m 50.523 15.217 l 50.598 15.475 l 50.613 15.526 50.68 15.631\n 50.801 15.791 c 50.992 16.041 51.086 16.35 51.086 16.717 c 51.086 16.83\n 51.059 16.936 50.988 17.073 c 50.949 17.147 50.934 17.205 50.934 17.248\n c 50.934 17.272 50.945 17.303 50.969 17.334 c 50.992 17.366 51.02 17.385\n 51.043 17.389 c 51.055 17.389 l 51.105 17.389 51.184 17.334 51.25 17.26\n c 51.328 17.166 51.398 17.112 51.438 17.112 c 51.52 17.112 51.609 17.252\n 51.656 17.463 c 51.684 17.577 51.695 17.635 51.695 17.647 c 51.695 17.741\n 51.621 17.971 51.527 18.17 c 51.492 18.244 51.473 18.307 51.473 18.358 \nc 51.473 18.424 51.52 18.475 51.578 18.475 c 51.629 18.475 51.719 18.42 \n51.824 18.319 c 51.875 18.272 51.922 18.244 51.965 18.244 c 52.082 18.244\n 52.164 18.362 52.25 18.655 c 52.305 18.834 52.328 18.916 52.328 18.944 \nc 52.328 18.998 52.301 19.03 52.262 19.03 c 52.195 19.026 l 51.977 19.002\n l 51.637 19.002 51.156 19.233 50.934 19.506 c 50.41 20.139 l 50.211 20.385\n 49.934 20.752 49.949 20.752 c 49.52 21.03 l 49.449 21.077 49.41 21.135 \n49.41 21.182 c 49.41 21.252 49.48 21.299 49.586 21.299 c 49.684 21.299 49.988\n 21.19 50.543 20.955 c 51.078 20.729 51.918 20.545 52.426 20.545 c 53.312\n 20.545 54.047 21.264 54.047 22.135 c 54.047 22.612 53.832 23.053 53.309\n 23.666 c 53.211 23.784 53.043 23.893 52.969 23.893 c 52.906 23.893 52.863\n 23.85 52.863 23.787 c 52.863 23.737 52.883 23.662 52.922 23.577 c 52.945\n 23.518 52.957 23.459 52.957 23.405 c 52.957 23.319 52.926 23.272 52.867\n 23.272 c 52.75 23.272 52.629 23.463 52.531 23.799 c 52.406 24.213 52.164\n 24.459 51.602 24.744 c 51.387 24.854 51.102 24.955 51.012 24.955 c 50.977\n 24.955 50.949 24.92 50.949 24.873 c 50.949 24.803 50.977 24.729 51.027 \n24.655 c 51.105 24.526 51.16 24.405 51.172 24.299 c 51.176 24.284 l 51.176\n 24.209 51.137 24.17 51.074 24.17 c 50.953 24.17 50.926 24.198 50.656 24.616\n c 50.516 24.83 50.141 25.112 49.816 25.248 c 49.715 25.291 49.609 25.315\n 49.504 25.323 c 49.492 25.323 l 49.422 25.323 49.371 25.291 49.371 25.248\n c 49.371 25.237 49.402 25.178 49.457 25.08 c 49.484 24.975 l 49.484 24.92\n 49.445 24.873 49.395 24.873 c 49.285 24.873 48.938 25.159 48.668 25.471\n c 48.371 25.811 48.184 26.244 48.184 26.584 c 48.184 27.291 48.859 27.924\n 49.621 27.924 c 49.812 27.924 49.973 27.893 50.098 27.83 c 50.746 27.494\n l 51.508 26.994 l 51.949 26.705 52.547 26.487 52.91 26.487 c 53.047 26.487\n 53.148 26.51 53.496 26.604 c 53.676 26.655 53.789 26.725 53.789 26.791 \nc 53.785 26.807 l 53.719 26.846 l 53.309 26.881 l 53.082 26.905 52.566 27.135\n 52.16 27.401 c 51.305 27.959 l 50.543 28.276 l 50.484 28.299 50.449 28.338\n 50.449 28.373 c 50.449 28.412 50.488 28.444 50.535 28.444 c 50.535 28.444\n 50.605 28.432 50.746 28.405 c 51.453 28.202 l 51.629 28.151 51.914 28.018\n 52.309 27.811 c 53.082 27.397 53.586 27.217 53.961 27.217 c 54.387 27.217\n 54.703 27.514 54.703 27.912 c 54.703 28.045 54.672 28.268 54.629 28.444\n c 54.605 28.53 54.57 28.612 54.52 28.686 c 54.484 28.729 54.449 28.756 \n54.426 28.756 c 54.371 28.756 54.344 28.702 54.344 28.604 c 54.352 28.479\n l 54.359 28.369 l 54.359 28.03 54.227 27.909 53.844 27.909 c 53.539 27.909\n 53.125 28.03 52.176 28.405 c 51.293 28.756 50.738 28.889 50.188 28.889 \nc 48.789 28.889 47.566 27.748 47.566 26.444 c 47.566 25.323 48.312 24.463\n 49.836 23.834 c 51.414 23.186 l 52.719 22.647 53.199 22.264 53.199 21.756\n c 53.199 21.33 52.809 21.061 52.191 21.061 c 51.926 21.061 51.598 21.135\n 50.988 21.327 c 49.508 21.799 48.551 21.994 47.703 21.994 c 46.324 21.994\n 45.344 21.619 44.559 20.787 c 44.457 20.678 44.398 20.647 44.305 20.647\n c 44.242 20.647 44.18 20.67 44.039 20.752 c 43.898 20.834 43.742 20.885\n 43.633 20.885 c 43.496 20.885 43.285 20.768 43.188 20.639 c 43 20.397 l\n 42.945 20.327 42.887 20.287 42.836 20.287 c 42.703 20.287 42.461 20.518\n 42.312 20.787 c 42.223 20.955 42.16 21.119 42.129 21.272 c 42.102 21.389\n 42.09 21.58 42.09 21.78 c 42.109 22.127 l 42.164 22.498 l 42.191 22.674\n 42.211 22.78 42.219 22.815 c 42.234 22.858 42.27 22.889 42.309 22.889 c\n 42.406 22.889 42.449 22.799 42.449 22.596 c 42.445 22.479 l 42.441 22.397\n l 42.441 21.866 42.648 21.381 42.879 21.381 c 43.062 21.381 43.219 21.705\n 43.352 22.35 c 43.379 22.479 43.434 22.553 43.496 22.553 c 43.57 22.553\n 43.613 22.459 43.668 22.182 c 43.703 22.002 43.824 21.752 43.949 21.588\n c 44.02 21.494 44.098 21.44 44.16 21.44 c 44.312 21.44 44.41 21.666 44.488\n 22.202 c 44.516 22.409 44.578 22.537 44.648 22.537 c 44.68 22.537 44.707\n 22.522 44.727 22.498 c 44.742 22.483 44.785 22.412 44.859 22.295 c 44.906\n 22.217 44.977 22.155 45.062 22.108 c 45.156 22.061 45.23 22.034 45.277 \n22.034 c 45.336 22.034 45.398 22.061 45.453 22.108 c 45.535 22.182 45.582\n 22.237 45.582 22.26 c 45.582 22.287 45.543 22.346 45.473 22.424 c 45.348\n 22.557 45.254 22.729 45.137 23.037 c 45.059 23.241 45.004 23.381 44.969\n 23.463 c 44.941 23.53 44.91 23.577 44.875 23.592 c 44.816 23.608 l 44.711\n 23.592 l 44.688 23.588 44.559 23.553 44.32 23.483 c 44.199 23.467 l 44.145\n 23.467 44.109 23.498 44.109 23.553 c 44.109 23.604 44.176 23.694 44.266\n 23.76 c 44.734 24.135 45.012 24.76 45.117 25.674 c 45.184 26.229 45.254\n 26.44 45.488 26.772 c 45.539 26.842 45.582 26.881 45.605 26.881 c 45.676\n 26.881 45.715 26.823 45.715 26.717 c 45.715 26.647 45.695 26.557 45.656\n 26.455 c 45.629 26.385 45.617 26.307 45.617 26.229 c 45.621 26.159 l 45.656\n 26.045 l 45.859 26.119 l 46.082 26.381 l 46.215 26.534 46.328 26.885 46.328\n 27.139 c 46.328 27.319 46.285 27.44 46.176 27.569 c 46.062 27.709 45.98\n 27.764 45.895 27.764 c 45.77 27.756 l 45.637 27.744 l 45.516 27.744 45.457\n 27.78 45.457 27.854 c 45.488 27.998 l 45.508 28.202 l 45.398 28.666 l 45.305\n 28.889 l 45.246 29.073 l 45.117 28.983 l 44.84 28.815 l 44.77 28.772 44.691\n 28.702 44.617 28.608 c 44.352 28.287 44.336 28.276 44.23 28.276 c 44.164\n 28.276 44.125 28.299 44.125 28.346 c 44.133 28.424 l 44.172 28.596 44.188\n 28.721 44.188 28.811 c 44.188 29.135 43.953 29.623 43.668 29.893 c 43.281\n 29.522 43.074 29.112 43.074 28.721 c 43.074 28.498 l 43.074 28.412 43.047\n 28.369 42.988 28.369 c 42.98 28.369 l 42.938 28.377 42.891 28.409 42.852\n 28.459 c 42.629 28.756 l 42.613 28.78 42.516 28.862 42.332 28.998 c 42.297\n 29.026 42.246 29.069 42.184 29.131 c 42.109 28.983 l 41.926 28.612 41.859\n 28.393 41.848 28.088 c 41.84 27.842 41.82 27.768 41.762 27.768 c 41.734\n 27.768 41.691 27.784 41.645 27.811 c 41.363 27.971 41.336 27.979 41.215\n 27.979 c 40.879 27.979 40.652 27.741 40.652 27.389 c 40.652 27.252 40.73\n 27.045 40.883 26.772 c 41.051 26.467 41.309 26.19 41.418 26.19 c 41.453\n 26.19 41.461 26.213 41.461 26.307 c 41.457 26.342 l 41.441 26.659 l 41.402\n 26.901 l 41.398 26.932 l 41.398 27.002 41.426 27.037 41.484 27.037 c 41.652\n 27.037 41.797 26.545 41.922 25.565 c 42.031 24.698 42.27 24.104 42.648 \n23.76 c 42.742 23.674 42.797 23.6 42.797 23.561 c 42.797 23.526 42.766 23.502\n 42.723 23.502 c 42.668 23.502 42.586 23.534 42.48 23.592 c 42.414 23.631\n 42.344 23.651 42.277 23.651 c 42.094 23.651 41.887 23.436 41.848 23.205\n c 41.68 22.147 l 41.551 20.659 l 41.527 20.377 41.566 20.002 41.664 19.545\n c 41.75 19.143 41.793 18.85 41.793 18.662 c 41.793 17.553 41.098 16.67 \n39.953 16.33 c 38.766 15.979 l 38.023 15.756 37.66 15.272 37.559 14.362 \nc 37.746 14.514 37.805 14.537 38.098 14.584 c 38.449 14.639 l 38.598 14.662\n 38.703 14.694 38.766 14.733 c 38.797 14.752 38.867 14.823 38.969 14.936\n c 39.285 15.291 l 39.406 15.424 39.512 15.498 39.578 15.498 c 39.621 15.498\n 39.656 15.452 39.656 15.393 c 39.656 15.323 39.613 15.217 39.527 15.084\n c 39.438 14.948 39.391 14.784 39.391 14.608 c 39.391 14.373 39.453 14.221\n 39.695 13.877 c 39.723 13.991 39.75 14.065 39.77 14.1 c 39.801 14.162 39.891\n 14.291 40.047 14.491 c 40.305 14.823 40.488 15.174 40.492 15.327 c 40.496\n 15.616 40.523 15.709 40.594 15.709 c 40.656 15.709 40.668 15.694 40.695\n 15.534 c 40.703 15.491 40.723 15.452 40.754 15.42 c 40.797 15.373 40.836\n 15.346 40.867 15.346 c 40.891 15.346 40.941 15.373 41.012 15.42 c 41.141\n 15.506 41.242 15.596 41.309 15.682 c 41.387 15.776 41.449 15.889 41.496\n 16.014 c 41.559 16.178 41.59 16.295 41.59 16.366 c 41.586 16.608 l 41.586\n 16.67 41.613 16.721 41.648 16.721 c 41.695 16.721 41.789 16.623 41.848 \n16.518 c 41.891 16.444 41.914 16.424 41.969 16.424 c 41.98 16.424 l 42.043\n 16.432 42.105 16.455 42.145 16.498 c 42.371 16.717 42.52 16.991 42.52 17.194\n c 42.5 17.537 l 42.426 17.815 l 42.422 17.842 l 42.422 17.897 42.465 17.948\n 42.512 17.948 c 42.598 17.948 42.688 17.83 42.812 17.557 c 42.875 17.428\n 42.93 17.369 42.984 17.369 c 43.074 17.369 43.141 17.436 43.316 17.686 \nc 43.484 17.928 l 43.52 17.983 43.539 18.045 43.539 18.116 c 43.539 18.182\n 43.52 18.244 43.484 18.299 c 43.336 18.522 l 43.309 18.561 43.297 18.623\n 43.297 18.709 c 43.297 18.959 43.387 19.248 43.578 19.58 c 43.711 19.823\n 43.793 19.979 43.816 20.045 c 43.848 20.123 43.879 20.159 43.922 20.159\n c 43.953 20.159 43.984 20.112 43.984 20.065 c 43.965 19.862 l 43.965 19.565\n l 43.992 19.494 44.039 19.467 44.125 19.467 c 44.184 19.467 44.25 19.483\n 44.32 19.506 c 44.785 19.674 l 44.895 19.713 45.039 19.784 45.211 19.877\n c 45.938 20.28 46.164 20.366 46.523 20.366 c 46.734 20.366 47.012 20.284\n 47.402 20.1 c 47.484 20.065 47.547 20.045 47.594 20.045 c 47.648 20.045\n 47.707 20.08 47.758 20.139 c 47.793 20.182 47.812 20.233 47.812 20.28 c\n 47.812 20.346 47.785 20.428 47.738 20.51 c 47.676 20.612 47.625 20.741 \n47.625 20.787 c 47.625 20.838 47.656 20.869 47.711 20.869 c 47.75 20.869\n 47.816 20.823 47.848 20.772 c 47.906 20.682 48.047 20.436 48.277 20.026\n c 48.621 19.412 49.074 18.983 49.465 18.893 c 50.281 18.709 l 50.535 18.651\n 50.598 18.553 50.598 18.237 c 50.598 17.659 50.328 17.151 49.855 16.85 \nc 49.188 16.424 l 48.941 16.268 48.742 15.842 48.742 15.483 c 48.742 15.256\n 48.797 14.85 48.871 14.53 c 49 14.659 l 49.316 14.846 l 49.594 15.123 l\n 49.742 15.534 l 49.891 15.905 l 49.965 16.084 50.051 16.202 50.105 16.202\n c 50.172 16.202 50.211 16.1 50.211 15.916 c 50.207 15.866 l 50.207 15.799\n l 50.207 15.53 50.242 15.412 50.449 15.01 c f\n39.469 9.381 m 39.695 9.592 39.734 9.608 40.363 9.737 c 40.871 9.838 41.109\n 10.073 41.219 10.573 c 41.297 10.94 41.348 11.01 41.68 11.205 c 41.789 \n11.264 41.871 11.327 41.922 11.389 c 41.965 11.436 41.996 11.463 42.016 \n11.463 c 42.051 11.463 42.082 11.428 42.082 11.381 c 42.082 11.319 42.039\n 11.209 41.961 11.073 c 41.91 10.983 41.887 10.873 41.887 10.717 c 41.887\n 10.42 41.945 10.217 42.109 9.979 c 42.223 10.225 42.285 10.295 42.574 10.498\n c 42.941 10.756 43.082 11.057 43.148 11.725 c 43.176 12.006 43.23 12.155\n 43.309 12.155 c 43.344 12.155 43.375 12.131 43.391 12.096 c 43.52 11.815\n l 43.539 11.78 43.582 11.756 43.637 11.756 c 43.691 11.756 43.789 11.811\n 43.871 11.893 c 44.133 12.131 l 44.242 12.233 44.316 12.491 44.355 12.912\n c 44.371 13.065 44.422 13.174 44.484 13.174 c 44.523 13.174 44.562 13.135\n 44.578 13.08 c 44.691 12.709 l 44.719 12.619 44.77 12.561 44.824 12.561\n c 44.848 12.561 44.898 12.592 44.969 12.651 c 45.07 12.737 45.156 12.83\n 45.23 12.932 c 45.316 13.049 45.359 13.178 45.359 13.303 c 45.359 13.619\n l 45.359 13.686 45.387 13.76 45.434 13.823 c 45.469 13.869 45.5 13.897 \n45.516 13.897 c 45.559 13.897 45.598 13.862 45.621 13.803 c 45.695 13.6 \nl 45.727 13.51 45.797 13.452 45.867 13.452 c 45.965 13.452 46.066 13.534\n 46.215 13.748 c 46.258 13.807 46.293 13.858 46.324 13.897 c 46.41 13.998\n 46.457 14.084 46.457 14.131 c 46.457 14.159 46.43 14.205 46.383 14.268 \nc 46.109 14.619 45.973 15.045 45.973 15.53 c 45.973 16.147 46.207 16.666\n 46.918 17.631 c 47.348 18.205 47.59 18.748 47.59 19.119 c 47.59 19.229 \n47.539 19.28 47.441 19.28 c 47.375 19.28 47.309 19.26 47.219 19.209 c 47.148\n 19.174 47.102 19.155 47.078 19.155 c 47.043 19.155 47 19.182 46.957 19.229\n c 46.828 19.377 l 46.734 19.526 l 46.699 19.584 46.641 19.619 46.582 19.619\n c 46.57 19.619 46.523 19.608 46.438 19.58 c 46.27 19.565 l 46.078 19.557\n 46.004 19.487 46.004 19.303 c 46.012 19.19 l 46.012 19.108 l 46.012 18.948\n 45.871 18.698 45.676 18.522 c 45.324 18.205 l 44.887 17.819 44.551 17.198\n 44.551 16.784 c 44.551 16.608 44.605 16.323 44.711 15.979 c 44.875 15.42\n 44.934 15.159 44.934 14.979 c 44.934 14.084 43.574 12.912 41.051 11.631\n c 39.77 10.979 39.449 10.596 39.449 9.713 c 39.469 9.381 l f\n38.246 28.869 m 38.246 28.409 l 38.246 28.311 38.238 28.241 38.207 28.108\n c 38.148 27.85 38.133 27.76 38.133 27.69 c 38.133 27.33 38.184 27.159 38.395\n 26.791 c 38.508 26.588 38.582 26.342 38.582 26.166 c 38.582 26.092 38.555\n 26.045 38.504 26.045 c 38.473 26.045 38.438 26.065 38.41 26.104 c 38.266\n 26.307 l 38.145 26.467 38.031 26.514 37.73 26.514 c 37.41 26.491 l 37.539\n 26.174 37.578 26.053 37.633 25.842 c 37.781 25.237 37.805 25.194 38.133\n 24.893 c 38.465 24.596 38.496 24.557 38.496 24.452 c 38.496 24.366 38.441\n 24.299 38.367 24.299 c 38.246 24.319 l 38.172 24.327 l 38.07 24.327 37.98\n 24.139 37.98 23.932 c 37.98 23.815 38.027 23.623 38.098 23.463 c 38.145\n 23.354 38.188 23.28 38.227 23.241 c 38.41 23.057 l 38.461 23.006 38.488\n 22.94 38.488 22.866 c 38.488 22.776 38.434 22.702 38.375 22.702 c 38.336\n 22.702 38.281 22.721 38.207 22.756 c 38.16 22.784 38.117 22.795 38.078 \n22.795 c 37.992 22.795 37.953 22.744 37.953 22.643 c 37.984 22.369 l 38.098\n 21.885 l 38.121 21.78 38.191 21.659 38.301 21.534 c 38.395 21.424 38.488\n 21.346 38.578 21.307 c 38.812 21.205 38.938 21.151 38.949 21.143 c 38.984\n 21.116 39.008 21.061 39.008 20.994 c 39.008 20.92 38.949 20.862 38.883 \n20.862 c 38.863 20.862 38.797 20.877 38.691 20.901 c 38.5 20.92 l 38.312\n 20.92 38.301 20.909 38.301 20.76 c 38.301 20.67 38.312 20.573 38.34 20.471\n c 38.41 20.174 l 38.488 19.862 38.594 19.694 38.703 19.694 c 38.73 19.694\n 38.777 19.717 38.84 19.768 c 39.109 19.971 39.648 20.159 39.984 20.159 \nc 40.336 20.159 40.734 20.116 40.883 20.065 c 40.969 20.034 41.027 20.018\n 41.062 20.018 c 41.105 20.018 41.121 20.065 41.125 20.233 c 41.16 20.565\n l 41.234 21.143 l 41.293 21.569 l 41.293 21.811 l 41.223 21.752 41.176 \n21.709 41.145 21.682 c 40.824 21.381 40.625 21.291 40.301 21.291 c 39.445\n 21.291 38.957 22.248 38.957 23.928 c 38.957 24.619 39.059 25.592 39.246\n 26.713 c 39.289 26.952 39.309 27.116 39.309 27.233 c 39.309 27.842 38.918\n 28.44 38.246 28.869 c f\n39.508 24.709 m 39.676 24.877 l 39.844 25.026 l 39.98 25.147 40.094 25.405\n 40.094 25.596 c 40.094 25.682 40.074 25.764 40.027 25.862 c 39.965 25.998\n 39.84 26.123 39.766 26.123 c 39.703 26.123 39.645 25.971 39.621 25.729 \nc 39.582 25.452 l 39.527 25.174 l 39.508 24.893 l 39.508 24.709 l f\n37.871 17.112 m 38.062 17.041 38.102 17.006 38.301 16.776 c 38.555 16.475\n 38.699 16.405 39.043 16.405 c 39.133 16.405 39.223 16.416 39.305 16.444\n c 39.547 16.518 l 39.73 16.573 l 39.82 16.596 39.879 16.635 39.879 16.666\n c 39.879 16.705 39.734 16.83 39.508 16.983 c 39.359 17.08 39.211 17.166\n 39.062 17.241 c 38.902 17.319 38.77 17.358 38.637 17.358 c 38.375 17.358\n 38.121 17.276 37.871 17.112 c f\n40.398 11.666 m 40.727 11.811 40.844 11.869 41.219 12.077 c 41.543 12.256\n 41.723 12.358 41.758 12.373 c 42.098 12.557 42.258 12.686 42.258 12.78 \nc 42.258 12.901 42.047 12.975 41.688 12.975 c 40.973 12.975 40.379 12.483\n 40.379 11.881 c 40.398 11.666 l f\n47.941 16.385 m 48.203 16.44 48.293 16.51 48.426 16.514 c 48.758 16.498\n l 48.801 16.514 48.875 16.561 48.98 16.647 c 49.039 16.694 49.129 16.752\n 49.242 16.834 c 49.613 17.084 49.633 17.1 49.633 17.182 c 49.633 17.287\n 49.48 17.334 49.168 17.334 c 48.586 17.334 48.227 17.061 47.941 16.385 \nc f\n0 g\n9.859 61.155 m 9.766 61.416 l 9.742 61.565 l 9.742 61.717 9.816 61.799 \n10.027 61.881 c 10.395 62.018 10.512 62.08 10.66 62.213 c 10.863 62.401 \nl 10.883 62.409 l 10.934 62.409 10.977 62.346 10.977 62.268 c 10.977 62.03\n 10.723 61.596 10.461 61.151 c 10.266 60.823 10.18 60.616 10.098 60.616 \nc 10.008 60.616 9.918 60.76 9.898 60.932 c 9.859 61.155 l f\n8.766 61.897 m 9.078 61.733 l 9.168 61.686 9.211 61.623 9.211 61.553 c \n9.191 61.436 l 9.145 61.28 9.18 61.104 9.18 61.037 c 9.164 60.705 8.91 60.709\n 8.809 60.709 c 8.75 60.709 8.781 60.748 8.633 60.912 c 8.227 61.377 l 8.098\n 61.522 7.992 61.846 7.945 62.213 c 7.945 62.26 l 7.945 62.362 7.977 62.401\n 8.055 62.401 c 8.105 62.401 8.199 62.362 8.262 62.307 c 8.766 61.897 l f\n9.879 59.967 m 10.176 60.151 l 10.348 60.112 l 10.555 60.112 10.605 60.053\n 10.605 59.827 c 10.605 59.494 10.398 59.358 10.027 58.424 c 9.863 58.014\n 9.832 57.772 9.586 57.772 c 9.312 57.772 9.105 57.994 8.895 58.518 c 8.652\n 59.112 l 8.48 59.534 8.406 59.784 8.406 59.936 c 8.406 60.08 8.516 60.174\n 8.684 60.174 c 8.773 60.174 8.867 60.147 8.949 60.096 c 9.094 60.01 9.168\n 59.967 9.172 59.967 c 9.387 59.944 l 9.488 59.948 l 9.879 59.967 l f\n44.586 61.092 m 44.43 61.416 l 44.406 61.565 l 44.406 61.721 44.473 61.795\n 44.711 61.881 c 44.945 61.967 45.203 62.104 45.324 62.213 c 45.527 62.401\n l 45.547 62.409 l 45.598 62.409 45.641 62.346 45.641 62.268 c 45.641 62.03\n 45.422 61.659 45.156 61.209 c 44.965 60.885 44.844 60.616 44.762 60.616\n c 44.672 60.616 44.582 60.76 44.559 60.932 c 44.586 61.092 l f\n43.426 61.897 m 43.742 61.733 l 43.832 61.686 43.883 61.604 43.883 61.514\n c 43.871 61.436 l 43.781 61.1 l 43.723 60.729 l 43.719 60.686 43.676 60.651\n 43.625 60.651 c 43.613 60.655 l 43.535 60.678 43.34 60.76 43.211 60.909\n c 42.93 61.377 l 42.887 61.428 42.777 61.604 42.684 61.897 c 42.652 62.006\n 42.625 62.112 42.609 62.213 c 42.605 62.268 l 42.605 62.362 42.641 62.401\n 42.719 62.401 c 42.77 62.401 42.863 62.362 42.926 62.307 c 43.426 61.897\n l f\n44.559 59.967 m 44.84 60.151 l 44.945 60.174 l 45.152 60.174 45.387 59.971\n 45.387 59.756 c 45.387 59.573 45.145 59.463 45.043 59.205 c 44.84 58.369\n l 44.688 57.971 44.492 57.776 44.254 57.776 c 43.973 57.776 43.762 57.998\n 43.559 58.518 c 43.461 58.76 43.383 58.955 43.316 59.112 c 43.16 59.479\n 43.078 59.748 43.078 59.885 c 43.078 60.065 43.18 60.174 43.352 60.174 \nc 43.441 60.174 43.531 60.147 43.613 60.096 c 43.754 60.014 43.828 59.967\n 43.836 59.967 c 44.051 59.944 l 44.152 59.948 l 44.559 59.967 l f\n8.301 27.178 m 8.207 27.459 l 8.188 27.541 l 8.188 27.569 8.223 27.588 \n8.266 27.588 c 8.309 27.588 8.406 27.537 8.484 27.475 c 8.566 27.412 8.617\n 27.416 8.746 27.385 c 9.25 27.252 9.359 27.159 9.359 26.916 c 9.34 26.604\n l 9.266 26.327 l 9.234 26.205 9.203 26.162 9.145 26.162 c 9.105 26.162 \n8.785 26.397 8.746 26.455 c 8.559 26.713 l 8.301 27.178 l f\n10.547 26.717 m 10.305 26.287 l 10.246 26.221 10.215 26.186 10.215 26.186\n c 10.195 26.182 l 10.098 26.182 9.996 26.409 9.988 26.639 c 9.953 27.014\n l 9.98 27.166 10.074 27.233 10.305 27.272 c 10.492 27.303 10.621 27.342\n 10.676 27.385 c 10.855 27.51 10.953 27.58 10.977 27.588 c 11.004 27.596\n l 11.055 27.596 11.09 27.557 11.09 27.502 c 11.086 27.475 l 10.957 27.159\n l 10.547 26.717 l f\n10.066 25.452 m 10.363 25.6 l 10.414 25.627 10.461 25.639 10.504 25.639\n c 10.648 25.639 10.754 25.534 10.754 25.334 c 10.754 25.123 10.59 24.838\n 10.363 24.428 c 10.117 23.987 9.93 23.686 9.738 23.686 c 9.477 23.686 9.094\n 24.112 8.727 24.819 c 8.641 24.987 8.586 25.186 8.586 25.334 c 8.586 25.573\n 8.688 25.698 8.887 25.698 c 8.969 25.698 9.043 25.674 9.098 25.639 c 9.266\n 25.526 l 9.41 25.428 9.578 25.377 9.738 25.377 c 9.855 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    "path": "LICENSE.txt",
    "content": "The MIT License (MIT)\n\nCopyright (c) 2013 Krishna Kumar\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n"
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    "content": "% ******************************************************************************\n% ****************************** Custom Margin *********************************\n\n% Add `custommargin' in the document class options to use this section\n% Set {innerside margin / outerside margin / topmargin / bottom margin}  and\n% other page dimensions\n\\ifsetCustomMargin\n  \\RequirePackage[left=37mm,right=30mm,top=35mm,bottom=30mm]{geometry}\n  \\setFancyHdr % To apply fancy header after geometry package is loaded\n\\fi\n\n% Add spaces between paragraphs\n%\\setlength{\\parskip}{0.5em}\n% Ragged bottom avoids extra whitespaces between paragraphs\n\\raggedbottom\n% To remove the excess top spacing for enumeration, list and description\n%\\usepackage{enumitem}\n%\\setlist[enumerate,itemize,description]{topsep=0em}\n\n% *****************************************************************************\n% ******************* Fonts (like different typewriter fonts etc.)*************\n\n% Add `customfont' in the document class option to use this section\n\n\\ifsetCustomFont\n  % Set your custom font here and use `customfont' in options. 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    "path": "README.md",
    "content": "Master Thesis: Bayesian Convolutional Neural Networks \n======================================================\n\n> Thesis work submitted at Computer Science department at University of Kaiserslautern.\n\n[![License MIT](http://img.shields.io/badge/license-MIT-brightgreen.svg)](license.md)\n\n## Author\n*   [Kumar Shridhar](https://kumar-shridhar.github.io/)\n\n## Supervisors\n*   Prof. Marcus Liwicki (Professor at Luleå University, Sweden)\n*   Felix Laumann (PhD candidate at Imperial College, London)\n\n\n---------------------------------------------------------------------------------------------------------\n\n## Abstract\n\nArtificial Neural Networks are connectionist systems that perform a given task by learning on examples without having a prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node.\nGenerally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions.\n\nIn this thesis, **Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference** is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks.\n\nBayesCNN is based on **Bayes by Backprop** which derives a variational approximation to the true posterior. \nOur proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective. \n\nIn the first part of the thesis, the Bayesian Neural Network is explained and it is applied to an Image Classification task. The results are compared to point-estimates based architectures on MNIST, CIFAR-10, CIFAR-100 and STL-10 datasets. Moreover, uncertainties are calculated and the architecture is pruned and a comparison between the results is drawn.\n\nIn the second part of the thesis, the concept is further applied to other computer vision tasks namely, Image Super-Resolution and Generative Adversarial Networks. The concept of BayesCNN is tested and compared against other concepts in a similar domain.\n\n---------------------------------------------------------------------------------------------------------\n\n## Code base\n\nThe proposed work has been implemented in PyTorch and is available here : [BayesianCNN](https://github.com/kumar-shridhar/PyTorch-BayesianCNN)\n\n---------------------------------------------------------------------------------------------------------\n\n## Chapter Overview\n\n\n### Chapter 1 : Introduction\n\n*   Why there is a need for Bayesian Networks?\n\n*   Problem Statement \n\n*   Current Situation\n\n*   Our Hypothesis\n\n*   Our Contribution\n\n\n### Chapter 2: Background\n\n*   Neural Networks and Convolutional Neural Networks\n\n*   Concepts overview of Variational Inference, and local reparameterization trick in Bayesian Neural Network.\n\n*   Backpropagation in Bayesian Networks using Bayes by Backprop.\n\n*   Estimation of Uncertainties in a network.\n\n*   Pruning a network to reduce the number of overall parameters without affecting it's performance.\n\n\n### Chapter 3: Related Work\n\n*   How Bayesian Methods were applied to Neural Networks for the intractable true posterior distribution.\n\n*   Various ways of training Neural Networks posterior probability distributions: Laplace approximations, Monte Carlo and    Variational Inference.\n\n*   Proposals on Dropout and Gaussian Dropout as Variational Inference schemes.\n\n*   Work done in the past for uncertainty estimation in Neural Network.\n\n*   Ways to reduce the number of parameters in a model.\n\n### Chapter 4: Concept\n\n*   Bayesian CNN with Variational Inference based on Bayes by Backprop.\n\n*   Bayesian convolutional operations with mean and variance.\n\n*   Local reparameterization trick for Bayesian CNN.\n\n*   Uncertainty estimation in a Bayesian network.\n\n*   Using L1 norm for reducing the number of parameters in a Bayesian network.\n\n### Chapter 5: Empirical Analysis\n\n*   Applying Bayesian CNN for the task of Image Recognition on MNIST, CIFAR-10, CIFAR-100 and STL-10 datasets.\n\n*   Comparison of results of Bayesian CNN with Normal CNN architectures on similar datasets.\n\n*   Regularization effect of Bayesian Network with dropouts.\n\n*   Distribution of mean and variance in Bayesian CNN over time.\n\n*   Parameters comparison before and after model pruning. \n\n### Chapter 6: Applications\n\n*   Empirical analysis of BayesCNN with normal architecture for Image Super Resolution.\n\n*   Empirical analysis of BayesCNN with normal architecture for Generative Adversarial Networks.\n\n### Chapter 7: Conclusion and Outlook\n\n*   Conclusion\n\n### Appendix A\n\n*   Experiment Specification\n\n### Appendix B\n\n*   How to replicate results\n\n----------------------------------------------------------------------------------------------------------\n\n\n### Paper\n\n*  Journal paper of this work is also available on Arxiv: [\nA Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference](https://arxiv.org/abs/1901.02731)\n\n*   Feel free to cite, if the work is of any help to you:\n\n```\n@article{shridhar2019comprehensive,\n  title={A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference},\n  author={Shridhar, Kumar and Laumann, Felix and Liwicki, Marcus},\n  journal={arXiv preprint arXiv:1901.02731},\n  year={2019}\n}\n```\n----------------------------------------------------------------------------------------------------------\n\n## Thesis Template\n\n*   Cambridge Computer Laboratory PhD Thesis Template [https://github.com/cambridge/thesis](https://github.com/cambridge/thesis)\n\n----------------------------------------------------------------------------------------------------------\n\n### Contact\n\n*   shridhar.stark@gmail.com\n\n---------------------------------------------------------------------------------------------------------\n\n"
  },
  {
    "path": "References/references.bib",
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Oseledets and\n               Victor S. Lempitsky},\n  title     = {Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition},\n  journal   = {CoRR},\n  volume    = {abs/1412.6553},\n  year      = {2014}\n}\n@article{DBLP:journals/corr/ChangpinyoSZ17,\n  author    = {Soravit Changpinyo and\n               Mark Sandler and\n               Andrey Zhmoginov},\n  title     = {The Power of Sparsity in Convolutional Neural Networks},\n  journal   = {CoRR},\n  volume    = {abs/1702.06257},\n  year      = {2017}\n}\n@article{DBLP:journals/corr/GongLYB14,\n  author    = {Yunchao Gong and\n               Liu Liu and\n               Ming Yang and\n               Lubomir D. Bourdev},\n  title     = {Compressing Deep Convolutional Networks using Vector Quantization},\n  journal   = {CoRR},\n  volume    = {abs/1412.6115},\n  year      = {2014}\n}\n@inproceedings{denton2014exploiting,\n  title={Exploiting linear structure within convolutional networks for efficient evaluation},\n  author={Denton, Emily L and Zaremba, Wojciech and Bruna, Joan and LeCun, Yann and Fergus, Rob},\n  booktitle={Advances in neural information processing systems},\n  pages={1269--1277},\n  year={2014}\n}\n@inproceedings{DBLP:conf/nips/AlvarezS16,\n  author    = {Jose M. Alvarez and\n               Mathieu Salzmann},\n  title     = {Learning the Number of Neurons in Deep Networks},\n  booktitle = {{NIPS}},\n  pages     = {2262--2270},\n  year      = {2016}\n}\n@article{Kiureghian,\nauthor = {Der Kiureghian, Armen and Ditlevsen, Ove},\nyear = {2009},\nmonth = {03},\npages = {105-112},\ntitle = {Aleatory or Epistemic? Does It Matter?},\nvolume = {31},\njournal = {Structural Safety},\ndoi = {10.1016/j.strusafe.2008.06.020}\n}\n@article{lafferty2001conditional,\n  title={Conditional random fields: Probabilistic models for segmenting and labeling sequence data},\n  author={Lafferty, John and McCallum, Andrew and Pereira, Fernando CN},\n  year={2001}\n}\n@InProceedings{MartinFTM01,\n  author = {D. Martin and C. Fowlkes and D. Tal and J. Malik},\n  title = {A Database of Human Segmented Natural Images and its\n           Application to Evaluating Segmentation Algorithms and\n           Measuring Ecological Statistics},\n  booktitle = {Proc. 8th Int'l Conf. Computer Vision},\n  year = {2001},\n  month = {July},\n  volume = {2},\n  pages = {416--423}\n}\n@article{DBLP:journals/corr/RadfordMC15,\n  author    = {Alec Radford and\n               Luke Metz and\n               Soumith Chintala},\n  title     = {Unsupervised Representation Learning with Deep Convolutional Generative\n               Adversarial Networks},\n  journal   = {CoRR},\n  volume    = {abs/1511.06434},\n  year      = {2015}\n}\n@article{shridhar2018bayesian,\n  title={Bayesian Convolutional Neural Networks with Variational Inference},\n  author={Shridhar, Kumar and Laumann, Felix and Llopart Maurin, Adrian and Olsen, Martin and Liwicki, Marcus},\n  journal={arXiv preprint arXiv:1806.05978},\n  year={2018}\n}\n@article{hafner2018reliable,\n  title={Reliable uncertainty estimates in deep neural networks using noise contrastive priors},\n  author={Hafner, Danijar and Tran, Dustin and Irpan, Alex and Lillicrap, Timothy and Davidson, James},\n  journal={arXiv preprint arXiv:1807.09289},\n  year={2018}\n}\n@article{tibshirani1996regression,\n  title={Regression shrinkage and selection via the lasso},\n  author={Tibshirani, Robert},\n  journal={Journal of the Royal Statistical Society. Series B (Methodological)},\n  pages={267--288},\n  year={1996},\n  publisher={JSTOR}\n}\n    "
  },
  {
    "path": "compile-thesis.sh",
    "content": "#!/bin/bash\n# A script to compile the PhD Thesis - Krishna Kumar \n# Distributed under GPLv2.0 License\n\ncompile=\"compile\";\nclean=\"clean\";\n\nif test -z \"$2\"\nthen\nif [ $1 = $clean ]; then\n\techo \"Cleaning please wait ...\"\n\trm -f *~\n\trm -rf *.aux\n\trm -rf *.bbl\n\trm -rf *.blg\n\trm -rf *.d\n\trm -rf *.fls\n\trm -rf *.ilg\n\trm -rf *.ind\n\trm -rf *.toc*\n\trm -rf *.lot*\n\trm -rf *.lof*\n\trm -rf *.log\n\trm -rf *.idx\n\trm -rf *.out*\n\trm -rf *.nlo\n\trm -rf *.nls\n\trm -rf $filename.pdf\n\trm -rf $filename.ps\n\trm -rf $filename.dvi\n\trm -rf *#* \n\techo \"Cleaning complete!\"\n\texit\nelse\n\techo \"Shell script for compiling the PhD Thesis\"\n\techo \"Usage: sh ./compile-thesis.sh [OPTIONS] [filename]\"\n\techo \"[option]  compile: Compiles the PhD Thesis\"\n\techo \"[option]  clean: removes temporary files no filename required\"\n\texit\nfi\nfi\n\nfilename=$2;\n\nif [ $1 = $clean ]; then\n\techo \"Cleaning please wait ...\"\n\trm -f *~\n\trm -rf *.aux\n\trm -rf *.bbl\n\trm -rf *.blg\n\trm -rf *.d\n\trm -rf *.fls\n\trm -rf *.ilg\n\trm -rf *.ind\n\trm -rf *.toc*\n\trm -rf *.lot*\n\trm -rf *.lof*\n\trm -rf *.log\n\trm -rf *.idx\n\trm -rf *.out*\n\trm -rf *.nlo\n\trm -rf *.nls\n\trm -rf $filename.pdf\n\trm -rf $filename.ps\n\trm -rf $filename.dvi\n\trm -rf *#* \n\techo \"Cleaning complete!\"\n\texit\nelif [ $1 = $compile ]; then\n\techo \"Compiling your PhD Thesis...please wait...!\"\n\tpdflatex -interaction=nonstopmode $filename.tex\n\tbibtex $filename.aux \t\n\tmakeindex $filename.aux\n\tmakeindex $filename.idx\n\tmakeindex $filename.nlo -s nomencl.ist -o $filename.nls\n\tpdflatex -interaction=nonstopmode $filename.tex\n\tmakeindex $filename.nlo -s nomencl.ist -o $filename.nls\n\tpdflatex -interaction=nonstopmode $filename.tex\n\techo \"Success!\"\n\texit\nfi\n\n\nif test -z \"$3\"\nthen\n\texit\nfi\n"
  },
  {
    "path": "sty/breakurl.sty",
    "content": "%%\n%% This is file `breakurl.sty',\n%% generated with the docstrip utility.\n%%\n%% The original source files were:\n%%\n%% breakurl.dtx  (with options: `package')\n%% \n%% This is a generated file.\n%% \n%% Copyright (C) 2005 by Vilar Camara Neto.\n%% \n%% This file may be distributed and/or modified under the\n%% conditions of the LaTeX Project Public License, either\n%% version 1.2 of this license or (at your option) any later\n%% version.  The latest version of this license is in:\n%% \n%%     http://www.latex-project.org/lppl.txt\n%% \n%% and version 1.2 or later is part of all distributions of\n%% LaTeX version 1999/12/01 or later.\n%% \n%% Currently this work has the LPPL maintenance status \"maintained\".\n%% \n%% The Current Maintainer of this work is Vilar Camara Neto.\n%% \n%% This work consists of the files breakurl.dtx and\n%% breakurl.ins and the derived file breakurl.sty.\n%% \n\\NeedsTeXFormat{LaTeX2e}[1999/12/01]\n\\ProvidesPackage{breakurl}\n    [2013/04/10 v1.40 Breakable hyperref URLs]\n\n\n\\RequirePackage{xkeyval}\n\\RequirePackage{ifpdf}\n\n\\ifpdf\n  % Dummy package options\n  \\DeclareOptionX{preserveurlmacro}{}\n  \\DeclareOptionX{hyphenbreaks}{}\n  \\DeclareOptionX{anythingbreaks}{}\n  \\DeclareOptionX{vertfit}{}\n  \\ProcessOptionsX\\relax\n\n  \\PackageWarning{breakurl}{%\n  You are using breakurl while processing via pdflatex.\\MessageBreak\n  \\string\\burl\\space will be just a synonym of \\string\\url.\\MessageBreak}\n  \\DeclareRobustCommand{\\burl}{\\url}\n  \\DeclareRobustCommand*{\\burlalt}{\\hyper@normalise\\burl@alt}\n  \\def\\burl@alt#1#2{\\hyper@linkurl{\\Hurl{#1}}{#2}}\n  \\expandafter\\endinput\n\\fi\n\n\\@ifpackageloaded{hyperref}{}{%\n  \\PackageError{breakurl}{The breakurl depends on hyperref package}%\n  {I can't do anything. Please type X <return>, edit the source file%\n  \\MessageBreak\n  and add \\string\\usepackage\\string{hyperref\\string} before\n  \\string\\usepackage\\string{breakurl\\string}.}\n  \\endinput\n}\n\n\\newif\\if@preserveurlmacro\\@preserveurlmacrofalse\n\\newif\\if@burl@fitstrut\\@burl@fitstrutfalse\n\\newif\\if@burl@fitglobal\\@burl@fitglobalfalse\n\\newif\\if@burl@anythingbreaks\\@burl@anythingbreaksfalse\n\n\\newtoks\\burl@toks\n\n\\let\\burl@charlistbefore\\empty\n\\let\\burl@charlistafter\\empty\n\n\\def\\burl@addtocharlistbefore{\\g@addto@macro\\burl@charlistbefore}\n\\def\\burl@addtocharlistafter{\\g@addto@macro\\burl@charlistafter}\n\n\\bgroup\n  \\catcode`\\&=12\\relax\n  \\hyper@normalise\\burl@addtocharlistbefore{%}\n  \\hyper@normalise\\burl@addtocharlistafter{:/.?#&_,;!}\n\\egroup\n\n\\def\\burl@growmif#1#2{%\n  \\g@addto@macro\\burl@mif{\\def\\burl@ttt{#1}\\ifx\\burl@ttt\\@nextchar#2\\else}%\n}\n\\def\\burl@growmfi{%\n  \\g@addto@macro\\burl@mfi{\\fi}%\n}\n\\def\\burl@defifstructure{%\n  \\let\\burl@mif\\empty\n  \\let\\burl@mfi\\empty\n  \\expandafter\\@tfor\\expandafter\\@nextchar\\expandafter:\\expandafter=%\n    \\burl@charlistbefore\\do{%\n    \\expandafter\\burl@growmif\\@nextchar\\@burl@breakbeforetrue\n    \\burl@growmfi\n  }%\n  \\expandafter\\@tfor\\expandafter\\@nextchar\\expandafter:\\expandafter=%\n    \\burl@charlistafter\\do{%\n    \\expandafter\\burl@growmif\\@nextchar\\@burl@breakaftertrue\n    \\burl@growmfi\n  }%\n}\n\n\\AtEndOfPackage{\\burl@defifstructure}\n\n\\def\\burl@setvertfit#1{%\n  \\lowercase{\\def\\burl@temp{#1}}%\n  \\def\\burl@opt{local}\\ifx\\burl@temp\\burl@opt\n    \\@burl@fitstrutfalse\\@burl@fitglobalfalse\n  \\else\\def\\burl@opt{strut}\\ifx\\burl@temp\\burl@opt\n    \\@burl@fitstruttrue\\@burl@fitglobalfalse\n  \\else\\def\\burl@opt{global}\\ifx\\burl@temp\\burl@opt\n    \\@burl@fitstrutfalse\\@burl@fitglobaltrue\n  \\else\n    \\PackageWarning{breakurl}{Unrecognized vertfit option `\\burl@temp'.%\n    \\MessageBreak\n    Adopting default `local'}\n    \\@burl@fitstrutfalse\\@burl@fitglobalfalse\n  \\fi\\fi\\fi\n}\n\n\\DeclareOptionX{preserveurlmacro}{\\@preserveurlmacrotrue}\n\\DeclareOptionX{hyphenbreaks}{%\n  \\bgroup\n    \\catcode`\\&=12\\relax\n    \\hyper@normalise\\burl@addtocharlistafter{-}%\n  \\egroup\n}\n\\DeclareOptionX{anythingbreaks}{%\n  \\@burl@anythingbreakstrue\n}\n\\DeclareOptionX{vertfit}[local]{\\burl@setvertfit{#1}}\n\n\\ProcessOptionsX\\relax\n\n\\def\\burl@hyper@linkurl#1#2{%\n  \\begingroup\n    \\hyper@chars\n    \\burl@condpdflink{#1}%\n  \\endgroup\n}\n\n\\def\\burl@condpdflink#1{%\n  \\literalps@out{\n    /burl@bordercolor {\\@urlbordercolor} def\n    /burl@border {\\@pdfborder} def\n  }%\n  \\if@burl@fitstrut\n    \\sbox\\pdf@box{#1\\strut}%\n  \\else\\if@burl@fitglobal\n    \\sbox\\pdf@box{\\burl@url}%\n  \\else\n    \\sbox\\pdf@box{#1}%\n  \\fi\\fi\n  \\dimen@\\ht\\pdf@box\\dimen@ii\\dp\\pdf@box\n  \\sbox\\pdf@box{#1}%\n  \\ifdim\\dimen@ii=\\z@\n    \\literalps@out{BU.SS}%\n  \\else\n    \\lower\\dimen@ii\\hbox{\\literalps@out{BU.SS}}%\n  \\fi\n  \\ifHy@breaklinks\\unhbox\\else\\box\\fi\\pdf@box\n  \\ifdim\\dimen@=\\z@\n    \\literalps@out{BU.SE}%\n  \\else\n    \\raise\\dimen@\\hbox{\\literalps@out{BU.SE}}%\n  \\fi\n  \\pdf@addtoksx{H.B}%\n}\n\n\\DeclareRobustCommand*{\\burl}{%\n  \\leavevmode\n  \\begingroup\n  \\let\\hyper@linkurl=\\burl@hyper@linkurl\n  \\catcode`\\&=12\\relax\n  \\hyper@normalise\\burl@\n}\n\n\\DeclareRobustCommand*{\\burlalt}{%\n  \\begingroup\n  \\let\\hyper@linkurl=\\burl@hyper@linkurl\n  \\catcode`\\&=12\\relax\n  \\hyper@normalise\\burl@alt\n}\n\n\\newif\\if@burl@breakbefore\n\\newif\\if@burl@breakafter\n\\newif\\if@burl@prevbreakafter\n\n\\bgroup\n\\catcode`\\&=12\\relax\n\\gdef\\burl@#1{%\n  \\def\\burl@url{#1}%\n  \\def\\burl@urltext{#1}%\n  \\burl@doit\n}\n\n\\gdef\\burl@alt#1{%\n  \\def\\burl@url{#1}%\n  \\hyper@normalise\\burl@@alt\n}\n\\gdef\\burl@@alt#1{%\n  \\def\\burl@urltext{#1}%\n  \\burl@doit\n}\n\n\\gdef\\burl@doit{%\n  \\burl@toks{}%\n  \\let\\burl@UrlRight\\UrlRight\n  \\let\\UrlRight\\empty\n  \\@burl@prevbreakafterfalse\n  \\@ifundefined{@urlcolor}{\\Hy@colorlink\\@linkcolor}{\\Hy@colorlink\\@urlcolor}%\n  \\expandafter\\@tfor\\expandafter\\@nextchar\\expandafter:\\expandafter=%\n    \\burl@urltext\\do{%\n    \\if@burl@breakafter\\@burl@prevbreakaftertrue\n      \\else\\@burl@prevbreakafterfalse\\fi\n    \\if@burl@anythingbreaks\\@burl@breakbeforetrue\\else\\@burl@breakbeforefalse\\fi\n    \\@burl@breakafterfalse\n    \\expandafter\\burl@mif\\burl@mfi\n    \\if@burl@breakbefore\n      % Breakable if the current char is in the `can break before' list\n      \\burl@flush\\linebreak[0]%\n    \\else\n      \\if@burl@prevbreakafter\n        \\if@burl@breakafter\\else\n          % Breakable if the current char is not in any of the `can break'\n          % lists, but the previous is in the `can break after' list.\n          % This mechanism accounts for sequences of `break after' characters,\n          % where a break is allowed only after the last one\n          \\burl@flush\\linebreak[0]%\n        \\fi\n      \\fi\n    \\fi\n    \\expandafter\\expandafter\\expandafter\\burl@toks\n      \\expandafter\\expandafter\\expandafter{%\n      \\expandafter\\the\\expandafter\\burl@toks\\@nextchar}%\n  }%\n  \\let\\UrlRight\\burl@UrlRight\n  \\burl@flush\n  \\literalps@out{BU.E}%\n  \\Hy@endcolorlink\n  \\endgroup\n}\n\\egroup\n\n\\def\\the@burl@toks{\\the\\burl@toks}\n\n\\def\\burl@flush{%\n  \\expandafter\\def\\expandafter\\burl@toks@def\\expandafter{\\the\\burl@toks}%\n  \\literalps@out{/BU.L (\\burl@url) def}%\n  \\hyper@linkurl{\\expandafter\\Hurl\\expandafter{\\burl@toks@def}}{\\burl@url}%\n  \\global\\burl@toks{}%\n  \\let\\UrlLeft\\empty\n}%\n\n\\if@preserveurlmacro\\else\\let\\url\\burl\\let\\urlalt\\burlalt\\fi\n\n\\AtBeginDvi{%\n  \\headerps@out{%\n    /burl@stx null def\n    /BU.S {\n      /burl@stx null def\n    } def\n    /BU.SS {\n      currentpoint\n      /burl@lly exch def\n      /burl@llx exch def\n      burl@stx null ne {burl@endx burl@llx ne {BU.FL BU.S} if} if\n      burl@stx null eq {\n        burl@llx dup /burl@stx exch def /burl@endx exch def\n        burl@lly dup /burl@boty exch def /burl@topy exch def\n      } if\n      burl@lly burl@boty gt {/burl@boty burl@lly def} if\n    } def\n    /BU.SE {\n      currentpoint\n      /burl@ury exch def\n      dup /burl@urx exch def /burl@endx exch def\n      burl@ury burl@topy lt {/burl@topy burl@ury def} if\n    } def\n    /BU.E {\n      BU.FL\n    } def\n    /BU.FL {\n      burl@stx null ne {BU.DF} if\n    } def\n    /BU.DF {\n      BU.BB\n      [ /H /I /Border [burl@border] /Color [burl@bordercolor]\n      /Action << /Subtype /URI /URI BU.L >> /Subtype /Link BU.B /ANN pdfmark\n      /burl@stx null def\n    } def\n    /BU.BB {\n      burl@stx HyperBorder sub /burl@stx exch def\n      burl@endx HyperBorder add /burl@endx exch def\n      burl@boty HyperBorder add /burl@boty exch def\n      burl@topy HyperBorder sub /burl@topy exch def\n    } def\n    /BU.B {\n      /Rect[burl@stx burl@boty burl@endx burl@topy]\n    } def\n    /eop where {\n      begin\n      /@ldeopburl /eop load def\n      /eop { SDict begin BU.FL end @ldeopburl } def\n      end\n    } {\n      /eop { SDict begin BU.FL end } def\n    } ifelse\n  }%\n}\n\\endinput\n%%\n%% End of file `breakurl.sty'."
  },
  {
    "path": "thesis-info.tex",
    "content": "% ************************ Thesis Information & Meta-data **********************\n%% The title of the thesis\n\\title{Bayesian Convolutional Neural Network}\n%\\texorpdfstring is used for PDF metadata. Usage:\n%\\texorpdfstring{LaTeX_Version}{PDF Version (non-latex)} eg.,\n%\\texorpdfstring{$sigma$}{sigma}\n\n%% Subtitle (Optional)\n%\\subtitle{Using the CUED template}\n\n%% The full name of the author\n\\author{Kumar Shridhar}\n\n%% Department (eg. Department of Engineering, Maths, Physics)\n\\dept{Department of Computer Science}\n\n%% University and Crest\n\\university{University of Kaiserslautern}\n% Crest minimum should be 30mm.\n\\crest{\\includegraphics[width=0.6\\textwidth]{Figs/CollegeShields/TU_KL_Logo.png}}\n%% Use this crest, if you are using the college crest\n%% Crest long miminum should be 65mm\n%\\crest{\\includegraphics[width=0.45\\textwidth]{University_Crest_Long}}\n\n%% College shield [optional] \n% Crest minimum should be 30mm.\n%\\collegeshield{\\includegraphics[width=0.2\\textwidth]{CollegeShields/Kings}}\n\n\n%% Supervisor (optional)\n%% for multiple supervisors, append each supervisor with the \\newline command\n\\supervisor{Prof. Dr. Marcus Liwicki, Felix Laumann}\n\n%% Supervisor Role (optional) - Supervisor (default) or advisor\n% \\supervisorrole{\\textbf{Supervisors: }}\n%% if no title is desired:\n% \\supervisorrole{}\n\n%% Supervisor line width: required to align supervisors\n\\supervisorlinewidth{0.68\\textwidth}\n\n%% Advisor (optional)\n%% for multiple advisors, append each advisor with the \\newline command\n%\\advisor{Dr. A. Advisor\\newline\n%Dr. B. Advisor}\n     \n%% Advisor Role (optional) - Advisor (default) or leave empty\n% \\advisorrole{Advisors: }\n%% if no title is required\n% \\advisorrole{}\n\n%% Advisor line width: required to align supervisors\n%\\advisorlinewidth{0.25\\textwidth}\n\n\n%% You can redefine the submission text:\n% Default as per the University guidelines:\n% ``This thesis is submitted in fulfillment for the degree of''\n\\renewcommand{\\submissiontext}{This thesis is submitted in fulfillment for the degree of}\n\n%% Full title of the Degree\n\\degreetitle{Master of Science}\n\n%% College affiliation (optional)\n%\\college{King's College}\n\n%% Submission date\n% Default is set as {\\monthname[\\the\\month]\\space\\the\\year}\n%\\degreedate{September 2014} \n\n%% Meta information\n\\subject{Thesis} \\keywords{{BayesCNN} {Master Thesis} {Engineering} {University of\nKaiserslautern}}\n"
  },
  {
    "path": "thesis.tex",
    "content": "% ******************************* PhD Thesis Template **************************\n% Please have a look at the README.md file for info on how to use the template\n\n\\documentclass[a4paper,12pt,fourier,numbered,print,index]{Classes/PhDThesisPSnPDF}\n\n% ******************************************************************************\n% ******************************* Class Options ********************************\n% *********************** See README for more details **************************\n% ******************************************************************************\n\n% `a4paper'(The University of Cambridge PhD thesis guidelines recommends a page\n% size a4 - default option) or `a5paper': A5 Paper size is also allowed as per\n% the Cambridge University Engineering Deparment guidelines for PhD thesis\n%\n% `11pt' or `12pt'(default): Font Size 10pt is NOT recommended by the University\n% guidelines\n%\n% `oneside' or `twoside'(default): Printing double side (twoside) or single\n% side.\n%\n% `print': Use `print' for print version with appropriate margins and page\n% layout. Leaving the options field blank will activate Online version.\n%\n% `index': For index at the end of the thesis\n%\n% `draftclassic': For draft mode without loading any images (same as draft in book)\n%\n% `draft': Special draft mode with line numbers, images, and water mark with\n% timestamp and custom text. Position of the text can also be modified.\n%\n% `abstract': To generate only the title page and abstract page with\n% dissertation title and name, to submit to the Student Registry\n%\n% `chapter`: This option enables only the specified chapter and it's references\n%  Useful for review and corrections.\n%\n% ************************* Custom Page Margins ********************************\n%\n% `custommargin`: Use `custommargin' in options to activate custom page margins,\n% which can be defined in the preamble.tex. Custom margin will override\n% print/online margin setup.\n%\n% *********************** Choosing the Fonts in Class Options ******************\n%\n% `times' : Times font with math support. (The Cambridge University guidelines\n% recommend using times)\n%\n% `fourier': Utopia Font with Fourier Math font (Font has to be installed)\n%            It's a free font.\n%\n% `customfont': Use `customfont' option in the document class and load the\n% package in the preamble.tex\n%\n% default or leave empty: `Latin Modern' font will be loaded.\n%\n% ********************** Choosing the Bibliography style ***********************\n%\n% `authoryear': For author-year citation eg., Krishna (2013)\n%\n% `numbered': (Default Option) For numbered and sorted citation e.g., [1,5,2]\n%\n% `custombib': Define your own bibliography style in the `preamble.tex' file.\n%              `\\RequirePackage[square, sort, numbers, authoryear]{natbib}'.\n%              This can be also used to load biblatex instead of natbib\n%              (See Preamble)\n%\n% **************************** Choosing the Page Style *************************\n%\n% `default (leave empty)': For Page Numbers in Header (Left Even, Right Odd) and\n% Chapter Name in Header (Right Even) and Section Name (Left Odd). Blank Footer.\n%\n% `PageStyleI': Chapter Name next & Page Number on Even Side (Left Even).\n% Section Name & Page Number in Header on Odd Side (Right Odd). Footer is empty.\n%\n% `PageStyleII': Chapter Name on Even Side (Left Even) in Header. Section Number\n% and Section Name in Header on Odd Side (Right Odd). Page numbering in footer\n\n% Uncomment to change page style\n%\\pagestyle{PageStyleII}\n\n% ********************************** Preamble **********************************\n% Preamble: Contains packages and user-defined commands and settings\n\\input{Preamble/preamble}\n\n% ************************ Thesis Information & Meta-data **********************\n% Thesis title and author information, refernce file for biblatex\n\\input{thesis-info}\n\n% ***************************** Abstract Separate ******************************\n% To printout only the titlepage and the abstract with the PhD title and the\n% author name for submission to the Student Registry, use the `abstract' option in\n% the document class.\n\n\\ifdefineAbstract\n \\pagestyle{empty}\n \\includeonly{Declaration/declaration, Abstract/abstract}\n\\fi\n\n% ***************************** Chapter Mode ***********************************\n% The chapter mode allows user to only print particular chapters with references\n% Title, Contents, Frontmatter are disabled by default\n% Useful option to review a particular chapter or to send it to supervisior.\n% To use choose `chapter' option in the document class\n\n\\ifdefineChapter\n \\includeonly{Chapter3/chapter3}\n\\fi\n\n% ******************************** Front Matter ********************************\n\\begin{document}\n\n\\frontmatter\n\n\\maketitle\n\n%\\include{Dedication/dedication}\n\\include{Declaration/declaration}\n\\include{Acknowledgement/acknowledgement}\n\\include{Abstract/abstract}\n\n% *********************** Adding TOC and List of Figures ***********************\n\n\\tableofcontents\n\n\\listoffigures\n\n\\listoftables\n\n% \\printnomenclature[space] space can be set as 2em between symbol and description\n%\\printnomenclature[3em]\n\n\\printnomenclature\n\n% ******************************** Main Matter *********************************\n\\mainmatter\n\n\\include{Chapter1/chapter1}\n\\include{Chapter2/chapter2}\n\\include{Chapter3/chapter3}\n\\include{Chapter4/chapter4}\n\\include{Chapter5/chapter5}\n\\include{Chapter6/chapter6}\n\\include{Chapter7/chapter7}\n\n\n\n% ********************************** Back Matter *******************************\n% Backmatter should be commented out, if you are using appendices after References\n%\\backmatter\n\n% ********************************** Bibliography ******************************\n\\begin{spacing}{0.9}\n\n% To use the conventional natbib style referencing\n% Bibliography style previews: http://nodonn.tipido.net/bibstyle.php\n% Reference styles: http://sites.stat.psu.edu/~surajit/present/bib.htm\n\n\\bibliographystyle{apalike}\n%\\bibliographystyle{unsrt} % Use for unsorted references  \n\\bibliographystyle{plainnat} % use this to have URLs listed in References\n\\cleardoublepage\n\\bibliography{References/references} % Path to your References.bib file\n\n\n% If you would like to use BibLaTeX for your references, pass `custombib' as\n% an option in the document class. The location of 'reference.bib' should be\n% specified in the preamble.tex file in the custombib section.\n% Comment out the lines related to natbib above and uncomment the following line.\n\n%\\printbibliography[heading=bibintoc, title={References}]\n\n\n\\end{spacing}\n\n% ********************************** Appendices ********************************\n\n\\begin{appendices} % Using appendices environment for more functunality\n\n\\include{Appendix1/appendix1}\n\\include{Appendix2/appendix2}\n\n\\end{appendices}\n\n% *************************************** Index ********************************\n\\printthesisindex % If index is present\n\n\\end{document}\n"
  }
]