From 5529168f48e0a5ef509a56e32861178d952f0e0c Mon Sep 17 00:00:00 2001 From: Siddharth Dikshit Date: Mon, 9 Nov 2020 10:23:23 +0530 Subject: [PATCH] Adding example notebook on Loan Defaults. An example notebook using US Small Business Loan Dataset and demonstrating how DiCE is able to generate counterfactual explanations. --- docs/notebooks/DiCE_SBA_Loan_Default.ipynb | 1818 ++++++++++++++++++++ 1 file changed, 1818 insertions(+) create mode 100644 docs/notebooks/DiCE_SBA_Loan_Default.ipynb diff --git a/docs/notebooks/DiCE_SBA_Loan_Default.ipynb b/docs/notebooks/DiCE_SBA_Loan_Default.ipynb new file mode 100644 index 00000000..3001fa93 --- /dev/null +++ b/docs/notebooks/DiCE_SBA_Loan_Default.ipynb @@ -0,0 +1,1818 @@ +{ + "cells": [ + { + "attachments": { + "Screenshot%20from%202020-11-01%2012-50-48.png": { + "image/png": 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qCBERERERkRNISkpi7NixKgiRs5DWkBIREREREREREa/SDCkRERERERE5pY4ePUpKSgoAISEhmKbmToic6RRInQY8B+bx/bfLOWAbRf6/YRiYzkCqVK9PVMcedGsehk++/7eMWBZ99iOLDxmAQVDTodx6caMCn2Fn7mDx75OYuWQd2/YmcijDwsc/lJoNWxDdbRAXXNiBev52gfcc/7lVWl7B6MH1ClWa9O1/8O1vWzmS83cHNWl/1Uh61zWK/80Jc/nu25UkAoYdRPMhN3NBUxMMN9v//JDfN2RhAw6rBtHXXMN5tQt/VlrMj3w6Mw434Azpxt0398zZ7h3M+2Aiq9KNogoTw+FLYFANGkR1pEuPKMJ97ONe42H/X1/w/dLD2IDT2YZBdw6kmXnc67BI3TmXSX/MY0nMdvYnHyXd9iWoWh0aN+tA9wEX0q9DOH72CSqA4WbXjHFMjMkg+6UGVduOYFT/2nllfVy5lPoAD+vFlaO7UcvI967MWFZOn8yMRTFs2ZVA0lEXmP5UqV6fxi060nPwBfRpVbVw42B4SFjwJd8vScEq+odgGCY+AVUIr9uM6J7daF7dUeQrbTONPQt/Z/KspcRsjeVASiYenwBCwyNo3q4nA4YMoFM9v7IfVKUp0+Nev2fmOMavyShV+ZpWNdpedR19G3jK9r76NsmLvubrf5KLKcvCfOoN5uarWnLcYYrNEXbO/43Jc5YRsy2BpNQM8AmiWq1IWrTvSb8hfWlfM6/F8JibmfXOZNa5TqZATWp2GsXVvdOLbWus7X/w8e9bSbcBw6R6h5GM7F2jUHnbGSv47cO57LANzJzju3cRx7ftSWTz7MlMXbCC9dv3kZSaBX6BhFZvSFSLzpx70UB6Rgaf1DRfz96pfP7zBo4AphVKq2E3MCDCOKmqZZvp7F8xjSnTF7N6y272J6WRiQP/kHDqRbSiY8/BDBrQnOqmXSn1tLztY0nbUnxdNjBw4BsYQnj95kR370Kz6oW30DISWPb1tyw8UMrD1HbSqO9tDI32KfsxWL/gb/cY25j34e+sTiv5dXnbnP98YeAfeSk3X9q4wDkWwJO6hAmfLGCvbYDtoG6vG7mqW/BJ16sCx0kpOTy16Th6BOfUMMqw70pfdqfDuaEk6Xv+YvIfc1i8dhuxCYdJ9/gQUKUWjZpF033QRQzsUpsA+8TlXeR5EfBs/Y2PJm0nM/9ra/XjumvaEVpMwVq7/+Sz8Rvz+j+BnbnslvNoWMzxX9Y2qCLaruL7d0V2PDFNf0Kq1ycqugfd2lQv1JfJv00G/jS9cAwXNTcL9ZMOr/yeL+cm4AYcVn263jKc7lWM8u3bEvppBc9xBs6Q7gy7sQd1j9snluMI63/6lBm7bcCkevRIrutTI/v/Di3gxy+WsM82SnmMRnLuPZfRwdeusH5Omer7abDd5bVjxw7cbjcAnTp1Ijg4WANFEQVSUtnslNXMmjCRbZz4BGIQTOR5d/PEMxfSNOcEYhuJbJg6gYnxJmBQ65zuBQKp9M3f839Pfsy8/Z7jPi2RfbHbWPPPFCZ805trn36EER3yBni2kcjaPycwcZ8JmDRIOZ9rL6iHM98J0BU3gf8+8BZzknM6unY4Xce8xk31DErsGR9czcxfJ2QPSj3VGRB9Ixc0NbENN/GLxjNhtid3IPxPQgRt/nsO1YyCnRzXtjn8OnE9WUBwWJW8QIo4Vk0az8TDjhOWZkD1rgy972Fu6l0zdwBiY3Fo7RR+n3gANxBkZ9Hy9oE0y9fPsq04/vngWV75eSPJVsH9lnggjl2bljPv92/5qccdPPTMZbQJLr4wPKxhyoffMiEh7wuCF1SjZ9/RNDeP7WM3+xaPZ8Isz0nVreAa1eh/XTdqGdklc2TdN7z8whfMj3cXem3igVi2b1zM7N++5ZcBD/Doo/2IzBfW2VgcjvmT3ybux12axsU/kr63Pc9DwxoVCFGsrLX89szTvL8giczjqnzivr1sW7uAaT9/T6/RT/DQqNZULUOfuzRlWjDU8ZCw/HcmTjpaqgGd090Qu8+19GlQtvf1bWCTumE6v03cU6qyBAhp2ZLrrmqJf75/y0qcyyfPvMLPa1OP+5xEEuJ3sXHVHP749lvOHfMUD41oRhUbbHsPyyf+zJ+uk2j+bQctHZcy4vyUYtsaO/ZvJo1fyOGc/RU4KY3a0Y8zMPS4fZO1hYW/TGCxaeLjaoJj0MjjAimL1C0TeGfsx0zdUXiAnRC3my0xC5j20ze0veReHrjvfBr7lG50b+9bytQJ00kwDBzuOmT0HM2AiNIPjD1pKxn/wljGLTxQqO6SmEDsjvUsmT2Rn76+hNvG3s3gCGfF1tMKaB+LLZuTOAYcjrp0vvw+7rujB/Uc+doII4VNs35h4rbSlalh+9Cj/s0MjfYp8zHYt37B77LXTeTT735jz7EBme1gve/5nHt3o0IhU+HzhUH1rp254bLGHF+l7LT1zP9lAqtNE8N2El11RG4gdTL1yor7h0njF+QeJ6Xhm9Ecn0uvKjaQKn/ZnR7nhuJD4DQ2//wMz76/mLis48ogaR97d6zm76k/8XO3m3nwmeF0yBd2HN8uAQS4Uoi4sjtDAu2C4fDc7/llQlyB3xAc2ZDLr25PqFHU8ehi468f8d3E5Nz3+Gasx7dfL26LMiu0DSpv21V42/P37050nPpSN3o0979wLV3DjCK3ybRC6NvqlsKBlGGTtmEGEyduxw34HY2mysgr6F6lfPu2pH6abe9m2cSfmZpzjnN4FpDc6DOeHlCl4AUMK50d835i4ion2A6a2hfmBVJH1jJv/ETWG6U7UANSelL7jqG5wU55+jnlqe+ncrtFRMpK8yD/beEVqWyb9wr/eXERh0rRkfMcnsFbj3xwXBhl4HCYBT81cS6fPfgsP263Sr0t7gNTeeO+twuEUV3GvMqzoyIJsivqF1vsn/8xny/PqJTSTE9azI9P3sd/pyeVeqaKZcQxf+ydPPXjpuPCKKNApGgb6Wxf9CbP3TeBze7iz9rWyunMSyj4/5l75zFzs6dCf23quo947IFxhQccxnHbbR9mw7T/8PRDf7LTU/behjtjO7Nfe4K35qbmlq1lHODvlx7jnYUFOzuGw1GgMfJk7WHBx4/y/Le7ySpLrfFSmZ5KnqSpvHb7M3x/fBh1XEfU497J/Hcf5LlvduHy4valZU7n+/diSD3JKnR0wzieuPtt/jw+jDLNAvXUMhJZ/ftzPPnwNHZbld8rtjNW8M29D/Pe38eHUUbBIjc8JO+eyBt3vsCvu61KrqeV2T6WUPc8cSz56Qkef3peqa/Ee+X8aGawbuo8YvNvk+Fhz+zZbLY0cjqdzw0l1fFD8/6Pp985bnBuOMjfjbGNdHYveZcX7/+VLSfY1y5zIxtjXMd9SzLrY/aW+gIBgGWtZNacxALvcfltZfmknWU6b522/U7DRezqT3j56akFj60K6N9V9L4tsr1yJLDona9Zlu6dNqB8/RzvlEnFb7eISNlphtRpxvSEEX3d7VyQfyq2x0VKwirmjJ/BupTsDnbctK+YcH1XbmxcwonIcLN3/OfMyAmMHI5Izh9zN9dd2J7GoQZpCRtZ/MfHfPDVCvZZBi73Mn796G8ueuUcqpwgUPKkzOf9+19myj47L4y69VWeG9WkAsOonM6rz07mvvozF3x3Ha0cJ/nhtoO6vcYwuk9YzgnVwpW2n52r5jFz3jYOWuAx9/LXf99lcsdnGRJ+gs8zPCRNeZXXZybldkL9q/Zk+D03MbRXE6o7jxC7bhbjP/iYiRvSsLE4sOlj3v++O6+NqlP49iUzjdXT5rPvuNlxWb7bWTZpC64WzfEleyZB02FP8FR3K2+QbnjY9uvL/LA2+1/8As/hqvvOp0G+K7lmYFPqGjaWawGfPPsdMTkdMsMOpkn/0Vw/oj+do8LwyYhlw4Lf+WrczyxJsMDwsHf5W7zxZWtev7FBkTMLfH27MeyhAUTm+z5PVgr7Y6bz69TNHLTB7dzDwo8msf28q4kybKxd4/lqRgpuw8CwAmjcZwy33ziADo1DcGTsY/Pi3/j87e9YlGhjmYfZ8Om3zB76JIOCSh+UlrZMS+Ln34Nh9/ejcTG3XRh2EA0am+V4X8HAwdfZmaGPDCbKKL5+m2HtcqfnW0YcM19+nan7j32uD7U7XcutYy6hR7MwnGl7WDf3B8Z9MIm1qQaWkULMR2/x83lvcHXD1lzy2LN09OTvsG9g6thfWGVlz4as0flGbh5UK18n1CCkcTiQUvpjz/Cwe/J7fDfsPW5tXror+e60+Xz89DesSsurp5H9ruP6EQPo0rQ6vpnxbF0xi4mffc3UrenYhoe4Zf/j/V878n+Xh1faFRbLcYQV77zAl5tcubfWVWk4mKtvuYKBXSOp7nOU+A3zmPL5p/ywLIksIOPIXL548hdaf3kVTYua7VQB9bTc7WNJx0BYX667vSc1jWNXzlM5sHMlf01bwKYUC9tws3vem7w3qS3PDwkrouwNqrYexZjLGpTQ0TAJb+VbYceglbWMWXMPYh23NRn75jJ77XW0buc49ef45ldy/9O9cef/WVt/4bUfNpFpgGnVoMuNt9I/31PPTSuURrVLP+g8mbI7Xc4NxfY1zK1M+nQO+6zsfRdcZxCjHxjF4I4NCXWkkbBlIZPHvcvXSw7ixiZxw9d8P3MwzwwMKPxhgaGEulJIsfezc/l+3N3yliCwrLWs25B961Zw1SDSDh05YVjmXjaN+UlGoXYvbuYs1t7dhA4Om3+FQv0ksD0uUpO2sXLqZBbsycTGJmXJ70zeOZhbG1dMAFKh+/YE0pJ/5fPPLiT6zsalalcLHLNWFdpdcxcXRRolvKYmzXPuaSxPP6ciy8Sb2y0iokDqDGLY/tRpN4BBPY7vOA/hwqaZjH5sHgcMgyyfTWxemg6NA4sfRHGQmFU7cZM9Hbl+vwd57Oq22esA2BBYozXn3/wSoUlX8cAfKVjYHF66hFVZ53Kes4QOYtpSvnjoecbv9uSGUZ1vfZXnrqv4MOqYw3Hf8+mPA3l5ZK2TrrSh9XrSf1DDAh1n+/JrGD7pEW5/eRnJGGRmzWPSdzsZfE9EiZ/vYQ0TvljKoZxum5/ZlRveepGrI4/tr1AaRF/OXW+G47n2CX5LNLGNo2z54Q9iRo4p1EG1XIuYNT8FCweGFUD9qFDit+/DbXhImDmL1fe3oIvDBttBjTYDGNgm/4A2gwUL/ssPxwYBZiM6DhhER0fhNbHiJn7O5P0GGGBY/rQc/iov3986b12MoAa0G3QnL0XXY+ytrzPnoIFtprHl6++Yd9Xj9C+iw+FDPdr2H8Q5x9UV69KBNM8aweMz07AB14ZVrEgbSVSQh6MrV7MtZzpJUNAQbnt2GN2Ovd+/Ni17j+E/wQcYff804jDIdK1g5cosBp1T+oFkqcu0BD7UJ3rgILqf4HV2Gd9XaABn16PVgEH0KeX7rPU/8M3iDGyy172p3vwuxr52OU2PlWVIIzoMeZiXa2Zyy8MzssvSXsXMHzZzxcPNad2/Nq3zD6wMg5Vjc95rG4TU7kq/QS0KddxPdn5ZlmMDU1+bzOCPLi12TZXcsjSy2PXDB0zKmTFkWAG0vvI1/ntvq7yAPLAOLc65lke7NCXkugf5Kd7EMg+z9peZ7L7saiLMymmA7NiJfD05EXfOcR9a9zpe+OBmOoQc+74Q6rW9mJteb0fDJ2/npQXZg9lDO37kxzlDeKpfQKXU04poH4s9BpyRdBs46Li184Zx9RUTeP7GN5h/xMRyJLHii9/ZdNH1tCxU9gaB4dH0H9z5xOvoleMYLFDfFk3jr4PZ7ZzpbESDWnvYFWvhdu5m6aRNuNq1OunBaIUHUjWi6TfwuOPKbzZvAZmAaQVSv1N/BrUve3hW6rIzPOw7Lc4NxW+ndXgVMVsNMMHhqU+/hx7lqi7HankgNZsP4LqXg0i54hEmJJpYzmQ2LNhA5qCOheqdD1E0a7qcpRs87Fm3liNGPcJyXmNsjmF9ponDqkWTFg7WLSo5kLLNVFZMXUhCTqDs06gxNXdvJ9Y2SE+ey5zVN9Kh46kPQMvTTwK47Hx/7hj5LRsNA7dPHAe2eqBxxbQy5dm3J3sc22YmW394nwmXvcKIeicXqBm2P3VbD2DQeSfanzZglaufU5H13ZvbLSJSrr6RiuDfw69lcxrYdm7c5HZlneAKXgYZrtwzE3aWu9B0dMMKpO3Q2xl13ShGjRrFVVe2JbyEc7Unay2/PPIUX23KXlTXtMPpfMsrPF9JYZRPlRACDLCNVNZ+No6ZyRUV/PlQc8j1XFwvb42mPX//ze4TTEe3185kXnze2iQNh9zKsMjCJ2NnUE+GX9mRBvXqUa9ePWr4HWD3kcIF5Fo4g4VHst/v5+7IkHvOy53hlHbkL+asKP+EaI+xm79mbsidgh0Ucik33NGmyEVafWoP4a7r2+TOxEnPWsDChekn16hYITRrXp9jpWLbWWS5LDBsMtNduSGOTRbuIhIOnw7DueXa6xg1ahQjb7iA6PCT+73eKNNTyTZcbJo+L3eNHB9XJH3uvSQvjMrXvAf2uJrhnetSr1496tWvg5G0h8PeuMXKJ4SQAAOwSd7wJZ9OP3jC2QYWa5g2Oe8WgKDqVzDmjtZFztY0/Tsz4sG7GD0qu90a1q9OwRknFcnwsG/WbGKs7NOl092APg+OyhdG5dsuoyH9HhxFF2f2r7XMRFb+ubzI2xYrop5WVvtYEmftIYwakrfoeuauRSzYc+qvkFuOIyyd/g/JOQOqKi2u4q7e4dnbaXhInDeTFW7dtndanhtKOvwyMsjMbfwssrIKnzRMZycuvn00140axahR1zKga1WKmnTlzKxBsxbVMAH3+rWsyq0PFofWrGOvbeBjNaVVKWYAWZl/M3tB9ppdhhVIqytuo2+d7C91O+JYOWl94XXm/o3nm6BggnL6nYYdgG9IBTatFbhvi+/r+RJcxR8DyDSW8uub84t9gFAFdS7L1c/xRplUxnaLiJSrb6ki+PfI2rydWDP7ROrw1KFuq+wFyIubtWAY1aldyw9jnQcbi72zx/KI3zCGDOpF93aNqJpzKcwn6kJuiirQBSny89Kcu5j2xDg+WpOeG0Z1uuUVnh8dVUkzowz8O1/D8N0f8flWm/TMWXz33sWc80w0wRXwfabdkg4dQvg6NrtTae/Yzka3QaRv8QPTxNXrcm+xcbob0HZAk6Kv1NlOGo34H9+OKClYOMg/0xfn3gTl3/4czutUl4M1fmBHgoHHEc+qSTFkdO1QqoVfi09o1rNha16IVuP8vgWeqFJwoxxUHdiPjm/GsBATy0hl58qdMLB56QeGZhrbdsTlBhA+tZvQrEr2k6mq1q1FIFs4DBxN+50373azc+gAevZoQ2TV7ObINFvQ79YW9CtTWOOlMq3owbSRRUZSIonF3WJj+BEcHoKfDbYRz7p1B7Bymm+/8O6c08pZ5HHrsJoy/I0fGc6Jj++KFOjuxZWjtvDNp9vINA+w5H9fsaj3vfQMKP677d1LWZVggpFdDxv0608rZ/H1NLzrCG7qWvm/yzLT2LRuR26YHxB6Lr07+hX7fWZ4f/pEv8viZdnblL5xHVvc5xaYHVkx9bRy28fixy0+NO3UnmrfZi9m7PbfTdw6NzQqfPOW5TlK8oHEIm/rAgPTN4RqVSpmzpLn6Hxm/52BjYlhBdDivHPo2G4jdb7PXuA8PXUBc5fcQfee6vacdueGko7d6nWo5Q+4wOOMY8ZLD8EVlzDwnG60iaySc0u7H1EDbiHqRHXX46BRm+YETPgH19GNbNjuoU9TE9tMY92arWQBfpGtaePcys8l/xIy5k7nn/ScmdJZ7el1bmfa7Qzn+1+ScecEoEtc7TjX599x254nK4VDiYl5NxAbHo4eWM/Cr7IX8gfw9+9Ex2ifimtry7FvS7sFhhVCh1GDSfrwa9Z7IOHvj/licTce7u5/Ev0KG1d6EomJxVzDN0z8gqsR4kf5+zkVWN+9ut0iIgqkzhy2mcae5ZP5I7HgGlKHD6zlr99nsz+nu1AlYggXRztLPC0bViAdLzifWrNnsg8DyzhAzNQPiZn6IaZfDSKat6Z127a069CZLtGRVPO1S+yAHZz7Du+4XPkGZv0YcWXT3KtnlTKwdUVy+f0XMfuuP9iFh9ip7/H1sA+4vVX5J/cZOKhTJxwHR3EDnqxEkpItqF3MvsFDfGxCXmfa1YAGzX2Ass0O8ByZx5xFmdg4MCx/mp/bk1oEcU7PMH78NQULi4PzZvJ3Rkf6+pW9jO19cex359ySYfvQoGlkidPdzZCmNK4NC/dld0oPxscDhQcdLnaxYvIfHDTzrxNymP0bZjNlRvZitYYVQMSwi4nOGYw7uw+mT9h8fjtoguHhwMY/+PilPxhnBFC9UQtatWlLu7Yd6NitHVHVfE5ZmR51z+fjB7fyfREvcXgacP7jD3JJEdP+y/q+NPcUXho+pfjjILM/dy54jiE+FhZxxMXn1X9nw8js2VGn0bjHsJzUHHkXl/15Lz/Gm6Qd+Z0vxl1Ip3ujKG5yv7FzF3E57ZtpB9OgZX18Toc22dpP/D435Gy52aQJzUsob4NQmjSpiXNZ9mLHVsI+9lhGgUCqouppZbaPJZZJ/brUxCYBA9tI51BCChBe6Jyxb8HTXLWghPFop//w3Vu9KSqnPLljySJt9gwWZ2QHmn5Z7YgeUBUjvDfdq/3KniQDjyOBlZOXc7RXt0q7tfx0UdqyO53ODcV+p7M7/fuHMn1K9i10WSmrmfLJaqZ8YhJQrTHNWrehXdt2RHfuQrumVUu8PdSwDWjbiib2QmJ8d7Jj2RGspqFYbGbt+kywfQlv1Zoa/FpyHGUksWDmcg7n/N2vXS/6VPWlep9u1PxlCnEYpGcu4K+Fd3Lu+b6nf4UxPGz+9U6GlfCzff3bctEzN9OnAq/kVOS+LYl/7au589Lp3DvhAG7nHua/9gODv7+B1qXcNZZ5gJkvDmdmcb/DE0zvJ37j+Yt8yt3Pqcgy8eZ2i4gokDqDWGYKa356jTXFdqh8qdlyGHc8e+Vxa3sULaDbvTx9436e+yKGA/mexGFlHmD7mrlsXzOXP74Fn6AoelxyIzffcC4RxXQ4LJerQPSSljKB99/syVtPdChyin9F8Wl/M2P6zuWpWalkOTYz49U/GPzZZYSVe1Rl4BsYkHtF0DZdZLpK+iE26WnpuWVg+lWhSpkXLbU4PHsGS1wOMMA3qw0dBoThsKHFed2o8es09mOQ7vqHv+dn0HeAX9n7mmlppJkG2GDYgQSEnuh+/xCCQ2zYl/23rIyib8vIci3nl9eXF/+9jhp0uOQBHrqxET45o3fT9xxuffE6Yp/6lmXJ+WaM2Okk7lzJ/J0rmT/pKwwzlCZdL2fUnSPp08jP62Vqu/ezdcX+ojvmGWlEpdmAUWHvOzlppKdbZN9xbeATGnxaNuQ+Zkeuvas3s5+azwFc7Bj/Pr8MfZ2rqhb9etfhVDJyisa0qhBQrWA5uY1lfHz5vfxwoHD9DTjcndELX2NkZSyuaqSRcdTIjZv8QkoubwOT4JAgIDG7TmSmk+6CvHStYo/9SmsfSxzhBeCf2yraZGVU/G2wJ3MsWWYCC2aszL010r/VOfSuZuAwDqY2wQAAIABJREFU2tKzZxXG/5GKhUXKwjksSO/BIP8zexHe0pbd6XRuKDa08ITQ6Z4XuCnuOT5flZJv2QGL9ORtrP5rG6v/+o1vbCehjXox5IbbuLZ/PYqbjGnVbEOLGjZrklzsWLOezJE98Nm3hg37DQwjgEbtmuCML3mbrENzmb00CzAxLD+ievekhmFjtT2P7qGTmZBiYJmJrJq6lNQ+vSp1xqJ3mATWakCtKhUbQlT0vi3+ewJpecttDJj9An8eMji8/yc++2EQr4yunLWPytPP8VaZVPR2i4iU7ywj/y5GAGF1I6hbrXQnUoOqtL3+Xb78ciz3XNmb6IhQ/IoYD2cd3cr8757m0Qcnsb2E++sN2xc/v2N5ThY7J73M/2YcpDK796Zdje533USPAAuwSd7yNZ9OOVT+7zRsXOl5j5Y3LD8CgkoOCxzOfI+et924yxguWEY882es4dhTiP1anMN5NXNmh3Q8l24hOWvQOJNYOXUxKeXJMBxOjt35ZJsu3JknekMW7qz8g4eyxR1Ov2rUblKXsAK3XZmEtLmV1777jJfuGUa/6IaEFXGV0rZS2Lroc1665XnG77JPvzKt6MPaqELtRhFERBT9p0FUTYJzA2gHTjNv4+0sz2naWJmEnHcHN3X2zV6/w17BH6/PyV0EuPCrC/67edrsHweOfLfduLNOFL7YZLnd+dpgJ05n5dXTSmsfS5JvnRMDA9+gop965QiuS6OI4up1Y+rUC6yQToiVOJvZKzy556jI3j2pY9gYlj9tzutK9ZxZvBnuhfwzN1X9iNPy3FDCZgZ24tr/fcdnL93BVX3b0biqT6FWxDbcHNo9j2+fu5vnfoottF5m3tHcnNY5tzinr1vHJo+Be3UMWw0T37QoIjudYIBteDg4cyYrsrJrrq+rNR36Zz/h02F2oGfP4Jxtszn81xz+OvLv6Gb7hjUoeHw2akjd8ICc8N3i0K4pfHTPU3y3rWJblorctyXWueC+3HRLB6qQvebe+i8/Zkpp19yzHYTUaVTs+blRZENqBhsV1s+psDLx8naLiJSVZkidbnmTHUyT3oOIrpk7bwd3RhJ7Vy9hxZ40LFLYNOtVXg5pzAcPtSjlFGaT4Ea9GX5Xb4YDmYe2s3F1DDHrY1i9bCmrNh/MXnzT8LB/9ad8N7s/T/XzK2LbgmgxdCyPdf2Nx56cQ7xt4HHE8dfLr/Nbq/8wtG7ljSAd4Zdw2zV/sHzcdjLMRJa/+zlLri/f1S0bN3GxB3LX4HL416ZWFaPEcqxaNRQH2bfieFxJJB+2oFoZBlD7ZzFnTc4VfttB3Y5REB9LHGCb9Wnexh/jnyxsbFL/mcW8w+dzSUjZOoJ2eDXCbBsMA5t0kuKTsahZ7EDQtuLZl5Cv7oQVPdfC6ducHkPaUSt3NU036Qd3EbN4FbtTbbLSNjH91ecJivyEu9sXbGrMwCb0Gn4vvYYDWUns3LCGmDXriFmznKUrtpLkMnLGvX/z04eLGPTfHie8ylyRZRoQMIjb3hpOyyJWCjXwp1pDB0Xds1XW9wWZfbj9i4dO8JS9nIVlCSO0qpEzS8HGlZjMYcvA3zj9OoamXY8B913N5NFfEOOG/UvH8cHyC4uuT9XCCAJcgGUe4kiCBflu8DMMH4LCqlM953HY2JkcSU7FVdltMtUJC7Nhd05579vPAdugQTEzO2zDzf74vHbFDKtKtXwD78o49iujfSzRnr25t48bVgjV6wYX2V7W6vQQn4w9wVP2ivm/Uh9LhofE6bNYbWe3aD6ZkTSJzmJfXBwAnnptaBkwkwMZBpZ5mDVTFnHwgr65T1fDyH5SZb4TXalrxumqtGV3Op4bii/tECJ6jeDOXiO4w8jk0I71rFkbw7qY1axYvIrNyVk5gW8ia979hrlDHqd/ETPhTCuQlq0icf61DVfcWtYkZmKs2US6AUE1WtKm1gnOM0Ys82asI9PILiVH47a08sQTF5f9W2u2bkXIn8s4DGQY/7BgzhEuGBJ0enc8bQdN+r/Eu3cf9zRiw0XCov/x9BO/sTHLwGWtYvLnKxj+Ymf8s6sNtpnvGLbKduiUZd/2Czj53xg+5G5G/n4zH26ySc+cy3fv9mSoceLj2GFV59w7v+Cx806uTS1PP6ci6vup2G4REQVSZwDTE0Lzi+7inh4FTyCWZyXjrr2bb2NNbMPNvvkL2XR/S9qVdNue4cGV7sJjZ3e6nT7++DjAr2ok7XtH0r73pVxjuEhc8h5PPzae9VkmlnGQbct34OrX4ri1XgxqtL+fF+7vTE2jCQ9fvJZH/sgOZjIy/+KrJ8fTetwVNHNWzlnJsH1oMPIehv15D9/GmqSlTuKTiVXLtWyOxXqWrziCndP9djSNoqXjBNsQ1RAfOxG3AS7/7WxdnglF3VJjeIif9CIvTd6HG/Bxt+fS/91GX//sAdT+abOIyRlAYXjYOP4uRo4v+nszHEtZNP0QFw+rUrbZBFWjiKhhsyQx+7v2LFtO4o0XUrO4gdf65azJWazVsJ3UbRpZ5Mv8aM0Fd95T6NHeGRve4LYxE9mOgduxi1WzdpLVPgofwOPKwOXJuV3E6YO/jwN8qhPRrg8R7fowBMhMXsDnDz/Fd1sswOJIzBo2u3vS0VFyXa/IMnXYVajTrAUtiv3Oov+9rO87qWPBaERkhJl724x7+1pWZl7GoCJutbXM3Uz/z3/5PTb77/5NR/PCA9282nl0NhrJnZdP466f9uN2xDLv/cnUdBqFBi9W8yga27M4aJhYRhpbV2/BNbhV7po2Dqs9oz/5ndE5f/dsfIPrx0xkVyU/NdCwqxEZGY65OnvWUdbOFSxKvoIGxQTRlr2elasyctsVM6IJLc3KqaeV2T4WO64zXGxesir3aXY+WY2p37riuxKlPZY8xi7mzNyAK6c74/LfyM/3jDhuUWoz9z1Hl81i9sG+DKt6rOz88PPLe7aUOz2z6NkGGRlkmHm3bvr6+XO6KnU7dBqdG4rlcZHh8mQ/zQ4nzgAfnLYfYREd6B3Rgd4Xg+3Zx7IPH+XJH3aQYUCmax3rNmXRv72jiCzEQXj7ltRiK/H+W1i/ZjuOdQexcOLXqjUtnfaxprXokts7gznr8kKVtPgveeDqL4tuC8xU1k9dQOLFgwk3/n0jdsP2pUb36xjSegIbVzkBi5TNm9hrdSHKsLH9/XPDZpssMjM8RQwrbDIzMwt8pk+AmZ1elWPf9osuQ/+apgy9/1Km3zaR7YaH/dPGMaFW2dcBLb7KlqOfQ2aF1nevbbdDiZSIlCP/UBH8SzoGzlZ0ig7JW+8o8QDxVskDMXfWFP4zsD+DBg1i8ICLeOSrxEKnXcP2pVq3i+jV4NhlmeLWAzHwC69JVdPGoCod73mcK+vndTiSt4zjzQ825N6GUimV1RnNyLv7URsb23CTEJdYpqnbxwaCB6d+w5R4M3dAF9GrB7VP0Gn069qdtsce6e44yOqfprG7iP3gYTMzf57GqrVrWbt2LVuOhNAwJ7fyGNuYPWtrqbfdNlJZN2MBiWUceDuMNnTvHpp7sKfE/MJPa4q+N8My4pnzzTT22McGm81pc/7JrUbjbNWVdqE5Nc2wSElIwGNkP5Z9ztP9GDRoEIMGDWTYzb8SW8Rv8qvenQvOqZvXrc3MPOEsGG+X6akNrasQ3aNV7tPX0u0FTJ8QX+Rvt7b9yYSpMaxdu5Z1MRvJDKjn9acLGlYgzW66nQuq5dSJfXEkFHHMmOG96NY8X3Az42emJRa3/5JY9NOs3Hpa2YOy5ud0oUbOIN5lLGX6FxuLbusMDwenfsef+83c90b07Jo7GK3Melqh7WNJx1ryVL6fkpD72X5R3Tm31qk7juyd05mzqfQDsQxzOUum593SaBg1qBGeb/HtPbvY7in8e7J27CQ+d1ZYMKE1A/i3O13ODSXur5mPMGTgIAYNGsSQcx/m2yKWxjIctekwpAcROceobbhwZZaw9EDztrTwsbCMNDYsn0zMdgPDdtKwbauS1+Ix3OydNosNRmm7zjZHV8xh9sF/72DdNo5yNK3IOBNq1iTcOlbmWezbsYesQqFcOrt37M+biR4QTnhQ5e3bE/FvfQNjBmXXecvYx76Eig2jytvPORVlUhHbLSKiQOos2VVBQQF5s5Zcbk60konh05jIBuR2FtZP/Jy5Ccd3jCzS1k5j4a5jHW0fakXWO+HUOdOvC6OfHUFLn+yTuW2ms+nHl/jgn6OVWwa9xnBTd/9y3SxhexLZMOVlHntjKQePnWTNngwcVvfEv7vaQC45Lyh3jYikjR/y0ptLSMjXq7aNZNaPe4UftjtyBqXBtLpkAJE5g1J78zRmbzdzf1NI3Za0bFn4T5OaeTOv0lbPZmZi2Tq1hhVA9LAhNM2ZTec2t/D702P5ccPRAgGl7Ylj6XtP8vaCjLybw7oO5aKGJ9lMGEEEBeaVhztnPR3D8iciKq9upW+ZwOdzCg+aPWlrmLEgb00ER4NGRJxgE7xdpqe6Lag6aCi9c27jso1UVn36HB8sTC7QJnhSl/H1Sz+zKWcmiy+dOO/S+qdkWqwzsDfXj+lC1ZIGx1YEg6/qkbsYd7prNuPufZMp2zKOGwwlEvPl07w66zDeWpra0fVyhkSQ2/nfOvE5xn63hRQ7/8DNxf7F7/DMW4vz2hVHLwZenlfnK7eeVkz7WPzANIvkTb/x9gPvMC81Z/aXFUq7EYOJOEWzP2wji51TZ7E1p44bdhXqFlGeLVpFUjMnibXNNDZOm0+Cfew9tWnZMjy3M5SeOJVfpiUUaJesrHVM/GFJ7lPVfFyRNIr+908wP13ODSW2HU2iaJBTvzIDVjP7nYXsOy7QtjnMpj//ZkdOzXe661G3cfHbZvi1plVU9mzlg7Oms9I2cXjq0rhjWImdYg8bmTVjd27dMIMb0KKI+tYyKjw32Mp0rGTJn8n8G5fRt62DbJ34ARO3OHLbmOCmTah/bGZ+zdY0r3nsYqaHvdN+YOa+gm1B5uYf+flvV26I5WzagvbHnrpbCfv2hFXQDqPL7TdzTpBVScdU+fo5p6JMKrt/JiJywnO9iuDfw+HMF3qY1gmXunDQkoGXtuandzZy1ID0Q38wdtQqxndpS2TNIHytNJL3rmfliu0ke7LPKP5+59L3wmqYwImWSvZvcROPjl7KneO2cdQAt7mD6S+8S9uvH2VA9Uoaitt16XfvNUxe8QmrXKU4Cxoetky5lxFzzdwALvPwIQ5n5v060wqh1ZhbuaAU22zY1eh1x630WvomC1LN7BkMvz7C9Uui6dK5EdWcR4hdvYhl24/knLANwhpew/XDa2JiYxtZbJ06h105HQnfzNYMe/ldbmxUeAiZseAprnpiPgcBl2M1S6YkMGJ0zTKlyGaza7nnqvk88F0smQZkHJrL+7evZFJ0Z9o0DsWZsZ8dK5YTE5+3yLuvbw9GPNQv3zogpezY4MjXsNgYnuwrqobtQ+OLhxD940csc5m4nXuY8fwo1o3vTNsm4YT4ekhL3s2mZavYeijnqXxWONHDzi/+FpKcQWl5y/RUz5NKtafzxpV/824Jr/FxteSyT8YyohY4g/pz4x1TWPLKSg4CWVnr+OWJa/m7fTc6RFbFcXQ3axctZ1uKlVNvA2l27W1cVhc4FdmB7aDahXdx7W838+56T7GBSujAe7lz9hj+b+ERLGwO7ZnIyzfO44eOHWnTqCq+WQnsWLWU1XvSsTAxTbCssv0gjyOBWS9dyRKf4o51J82H/Y/nr6mF027NsEeGMf+e8Wx2G3iMOBa8P4Y1kzvQsV0DwpzpHNi+guVr9pOWU5tMTzXa3z2GC8O9V09Pun0sqU4m/8CjV/yW295YmYdJOZz/djaD6s1u4IaLwzCLrFQW+xe/wMjLS34yV0C963n57UsoyxKElrGOWbPicedsZUiDa3nxw6tpclx7YZup/PPcUB6fmYkNpK2bzYz4SxhVN3sWW4uLBxM18Xs22wYex37mvXQjN8zoRvvI7LZx29JFrI535/7u0G4XMCjcKPJgOql6dTrE26fBuaHE9zS7iIvajeftNR4wPOyY9xQ3Xt2Wju0bUzPEiSc9kb0blrNq22GyjOx2pFqvixlYzP45FkK2alUDc8NBrIx00oEguwVRLXyAElZ23zCdObHHgkwf2lz2Cm/dWvgCnsc9i1cvfoYpaY7si3XT5xJ7zbBC686dTF2p1DpWqJ+UffxmHTnEoQwPx+5P9PE0pc9VnXPDNgftGXhRQyZ8GUsWkJ4+nVdv3MrM7u2IqGaSfmAzK/6JIc6VF2C3vTTvfF6+fVuOgU/1ixkz6g+Wf7CFo6X4KI+ZxOzXrmTp2yX1DZ20GP4eL4wML1c/x67A+u7N7RYRUSB1FjAwCQ4KzOsquI9wOB1KXHzBdlDniqd4cMMDvDwzgUwD3Ol7iJm/h5jjPh3A6YhiwDN3c0EpF+k2bD8irnuKW5aN4e1V2VfA0lL/5NPn2tP87QtoaFbOScrRYAR3XPEnd34bT2keNu4+msSBo8WVayhthj/Pk9c2POHjp3O/v9ZQHvtvAk89/j2rjmR35lLjlzPnj+WFPr1KzUu487WRtMl5Spdlr2LWnP24c+a6+TfvQ/8GZpEdZ5/u59M9cA5/pjmwzUy2zpjLnlFX0agM5Wp6Qmh9+2s8lfIAL06OJ8PIflLKrhWz2LWi8Ot9/Tpw1ctPM7RhWTp+IQQGWxybgJl15AjpQABg1h3BQ49u4pH/zmN3loFtpxIbM5fYmKLqVwBRFzzC3RdWLWbAS4WVaUPHKT7ArXRSDqSX+BK/o3U46slbDLv2kOd4Yf8jPP3VJg7ZZJflqlnEripcjk36PcHjt0ThZ5+6jqPDbsKl9w9l6q0/s7WYW9BMuz4Dnn2No889xjt/H8QN2HYyO5fPZOfyguFVeLMbGd1hMm//GF/GRtVDxqEDZJTQftY6nBe/BLW9ixefOcwjY2eww2VgG25Sdi9lzu6lRfyOUNpfO5anhufNuqyQempWfPtY/CgslaQDqcWWTXiTa3j41aG5s2uK/IiMQxzIKPlrgv3Tstc5LMths2o68+KPBQS+RPXvQ6OiFvK2gunYpytVZ/6VHfL5rGXplH2Murl2dpk1u56HbljBQ59t5jBgk8KuZdPZtayIAC20L6MfH1D8IOwk69UpD6ROk3NDsfXZaswlTz3IxvteZ1qcJ+d8u4r58auKPqPXupw7nuxDjRIGyYbtS1SbZgT9spgjOT/D0awV0b4lXfjIZP3Uebm3LPpmtqHLRUXPqjZ9u3Fuj0D+nJUdgKZvnMPMvUO5ob5RMXWlEupYSf2k7PChFl3ufIIb2jtzmyzD9qHJdY9x/bKH+GRd9m91p25n6cztLD2uf2nYThp0u4fbLs0LsMuzb8t1JrOd1BtxD8Mn38WXe4zynytyyrxuqgcLcJSnn1OR9d2b2y0iUp6+iIrgX8J2UCMqkio5DX9m4EpmfLr2hPdum0ZD+j03jncfu4weEUFFdp4MI5iG0Vdy//vv8EDvsJOqFCZNuPSp2+kVfGz6s0X8qv/xxle7Ku2+csPyp9n1d3FReBmnXDt8CAqLoG2vK7nnzS94476OJXZei/rVVdrdzqufvcit/ZtRs4jH4jr8G9Dt0id47YuHGFAnr8NjLZ/GvANm7u9oPvD8vOnvx/9Ov26c2z0gd1ZExuY5TN9lleNgr8d5j43j3ceHFlsXTGcNWp5/K89+/hq3dAwq22wsahPZJDh3u9M2/cL3yz25Hdi6A17gvQ8e4sruDajiKLquh9TuxrAH3+P1J7qf8OrbqSzTU8mww2h387t89PL1DGhVDT+j8J4IqdWNYQ+9z1vPn1dsmXiTT6vruf3CktsYw78ll730CW/fdwEda/kVeq3prE3HIU/x6jujGNS0Hj7e+lm2k1rnP8W7HzzMVcXUXQM/akQN5PoXx/HKHW0INbxfT8vdPpZwDvIJrE6jVudzxd1v88G4W+he/RSuHWVmEDNtHnHHZpxltafrRcXPCPHpfj7dAo7d5upi28zZ+coskOY3/o93nruCcxoV0zY6atCmzx3859OnGFLP4ExyOpwbSmw3al/M45+8y2PDO9M4pOhvdgY0pOulj/PKZ3fTO7wU3YD2bYmyrbz+VZtWJfYDLHs5s+Yk5d56F9CmL/3rmsUcg8F07NOF0Jy/Z/muY+mk2H/bGQaHTyBhtZrSofe1PPj+J4wd2bjQGoSmT3uuffsDnr26azH7xsAvtA2DbnydV18ZWOhCZWXs21LVRbM9I+4dSN1KCFPK2885VWVSGf0zEZHStT+2rdakgnz55Zds376dRx999DTd225S4zezeUs8CYfTceNLSLV6RLRoTqMwTZYrK09GPFvXb2FvQiqZRgChNSJo0TqS6n6n76FlG1kc2bOJjdviSUp1YTv8Ca3RiCYtmlA7yHuDLffRWLZu2kZcwhHS3Aa+weHUiWhO84hQfFW1TuLY9pC+fzMbNu0l4UgWpn8oNRu2okXTMK8vYl7RoUPS5nVsik3i8FEICGtA0zbNqRdy6q+lZB3ZzZb124lLPkqm5cQ/tBYNmjQnqk6ArvT8ixVoG4+6sM0AQms09HrbeLafG4o/4R5h35ZNbI1NJCXdjeETQvU6kTRr2ZAwH3VnT+2+SWHvpg1sjz1IqsvG4R9KzfpNada0JkHm2btvy9XPOYVl4u3+WVJSEmPHjuXcc8+lQYPsBXA7depEcHCwji2RM30Yo0Cq4pz2gZSIiIiIiMhpRIGUyNlLF3JFRERERERERMSrFEiJiIiIiIiIiIhXKZASERERERERERGvUiAlIiIiIiIiIiJepUBKRERERERERES8yqkiqFj79u3jrbfeUkGIiIiIiIicwKFDh1QIImcpBVIVKCIigszMTABSUlJwuVwqFBERERERkWKEhoYSGhpKYGCgCkPkLGPYtm2rGCremjVrOHjwoApCRERERETkJHTq1Ing4GAVhMgZTmtIiYiIiIiIiIiIVymQEhERERERERERr9IaUpXEMAwATFOZn4iIiIiIyIlYloVhGLljKRE5s2kNKRERERERERER8SpN3xEREREREREREa9SICUiIiIiIiIiIl6lQEpERERERERERLxKgZSIiIiIiIiIiHiVAikREREREREREfEqBVIiIiIiIiIiIuJVCqRERERERERERMSrFEiJiIiIiIiIiIhXKZASERERERERERGvUiAlIiIiIiIiIiJepUBKRERERERERES8SoGUiIiIiIiIiIh4lQIpERERERERERHxKgVSIiIiIiIiIiLiVQqkRERERERERETEqxRIiYiIiIiIiIiIVymQEhERERERERERr1IgJSIiIiIiIiIiXqVASkREREREREREvEqBlIiIiIiIiIiIeJUCKRERERERERER8SoFUiIiIiIiIiIi4lUKpERERERERERExKsUSImIiIiIiIiIiFcpkBIREREREREREa9SICUiIiIiIiIiIl6lQEpERERERERERLxKgZSIiIiIiIiIiHiVAikREREREREREfEqBVIiIiIiIiIiIuJVCqRERERERERERMSrFEiJiIiIiIiIiIhXKZASERERERERERGvUiAlIiIiIiIiIiJepUBKRERERERERES8SoGUiIiIiIiIiIh4lQIpERERERERERHxKgVSIiIiIiIiIiLiVQqkRERERERERETEqxRIiYiIiIiIiIiIVymQEhERERERERERr1IgJSIiIiIiIiIiXqVASkREREREREREvEqBlIiIiIiIiIiIeJUCKRERERERERER8SoFUiIiIiIiIiIi4lUKpERERERERERExKsUSImIiIiIiIiIiFcpkBIREREREREREa9SICUiIiIiIiIiIl7lVBGIiIicfdIz3SQdyiDLY4Gl8pAKYILTYVIt1I8gfx+Vh4iIiJRIgZSIiMhZwLZtdsQeYfGaeLbsTCH5YIYKRSpNaIgfTRuH0qVtbZo1CsUwDBWKiIiIFGDYtm2rGERERM5c2/akMGHmdmLjjhDg70PD+mHUqhFC1VB/fEyHbuCXimGB22ORkprB/gOp7NqbTFqai9o1gri0fyQtI8NURiIiIpJLgZSIiMgZKstt8cfc7cxfHEdYaCBdousTGRGOw1QCJZXPsmx27E1m+eo9HEhMpWt0LYYNjMLPx6HCEREREQVSIiIiZyJXlodxv6xny/aDdI5uSOd29XE4FESJ91mWzer1cSxavpN6dYK5fUQbArXGlIiIyFlPgZSIiMgZxuOx+OjndWzdcYjBfVrQuGF1FYqccnv3HWLKzPXUrRnMnde0xVczpURERM5qulQqIiJyhpm6YDebtx1k0PnNFUbJaaN+7apc2LcVu+MOM3HmNhWIiIjIWU6BlIiIyBlk7/5UZi7cTfs29YhsFK4CkdNK/bpV6doxgn9W7GPLrkMqEBERkbOYAikREZEzyKS5OwkO9KNbh4YqDDktdWhTl7CwAMZP34xWjhARETl7KZASERE5Qxw4mMbGrclEt6mPj1Pr88jpyWGadG7XgH0JGaxYu1MFIiIicpZSICUiInKGWLb2AA7ToHmTGioMOa1FNgrHz8fBgmW7OXRIt+6JiIicjRRIiYiInCG27D5E7Vqh+Pk5VRhyWnM6TOrXDyMpxWTjxo1YlqVCEREROcsokBIRETkD2LZN3L6j1AgPVmHIv0LN6iEcOWqTkZHJ7t27VSAiIiJnGQVSIiIiZwCX2yIjw01oiL8KQ/4VQkJ8sYF0l01sbKxmSYmIiJxlFEiJiIicAdye7MG8w6FTu/w7OHPqqscCt9tNUlKSCkVERORs6guoCORsl3gwnfU7DrI77jAHEjPIyHSjh1BLRXE4DEKCfalXI4jIhqG0aFxVTz+TSmWoCORfU1fzaqthGCQnJ1OjhhbkFxEROVsokJKz1obtB5n59x627cp+uk/VqgFUDQ28wIAqAAAgAElEQVSkaoA/pqkhnZSfbYNt2aSmZfL3yn3MWbQXPz8HPTrWoW/XelQJ9lMhiYiQvQba4cOH/5+9+w6rsv7/OP68zzlMAQERcAIq4kZFzb2tXC2tNFvasqWllZWlzW/1td3Xflm2zIapOSpXuffeGxQFQQQUBBln3b8/MEepWYoDXo/r4roQbg7nfvs597nvF5/P+1YhREREShEFUlLqHM21M2HmbrbuyiSorA9tW1QnOiIEbx8PFUeK9WLrUGYu23alsWRVCsvXpHLDtdVoGRuOYSgAFRHJz89XEUREREoRBVJSqiSl5vLJD5txON20bxVNnehQhQFySRiGQViIP2Eh/jSNrcKiVXuY+OtuEhKz6dezpvr+iEipZ5ompmnqfVlERKSUUCAlpUZSai7/+2Yjvr5e3NKtDgG6E5VcJn5lvOjavhZbd6WxcFk8dqeb/rfUwmpRKCUiIiIiIqWDrn6kVMg5ZueTHzYXhVFd6yuMksvOMAzqxYTTuW0MW3ZmMH3+XhVFRERERERKDQVSUir8MHM3DqebHl3qqFeUXFFiqpenScOqLFxxgN3HG+yLiIiIiIiUdAqkpMTbvucIW3dm0rJpNc2MkitS09gqBAf7MmHGLtymqYKIiIiIiEiJp0BKSry5K5IIKutDnehQFUOuzAOxxaBFXCQZhwtYuWG/CiIiIiIiIiX/OkglkJIsIyuf+L1Z1KtTUXftkStaRKUgAvy9WLhqHzk5OSqIiIiIiIiUaAqkpETbvucIADUiy6kYckUzDIPoaqGkH4EtW7bidrtVFBERERERKbEUSEmJti/lKGUDfPD19lQx5IoXHuqPywXpWXYSExNVEBERERERKbEUSEmJlp5RQFCQrwohV4XgskVjNSfP5MCBAzidThVFRERERERKJJtKICVZod1FgI/urCdXBy/PokOywwVut5uDBw9SuXJlFUauSq7D85n+0way3AaeHg1pe08Hqlgu3l0kXVkL+XnSOrJcfkR3uo9WNS7f39jcyb8yYeYuHK5Q6tx0B03CL0bPQjeZa8bxy7osrJZ6tBrQhSiL7sIpIiIiJYcCKSnRTNPEombmcpUwTrmeNgyD9PR0BVJylQ5mFwe+f5wX/7MdO+BHHAcbrWBow38eGpmGnS3vtOTxbw8RYnmY4Sufp5HFxJU6lc9Gfsgewrg98B5a1bh8S7PN7V/w3ktTKaQBdzfpe1ECKdNwkjbned4anUYF+hNwdxeitPpcREREShAFUiIiVyDTNMnJycE0Td0hUq46Dusmfp1UFEYB5LKRrV9swPFhYzz++asBx5F4Dh7MwYdsCt2ABSy+UdRp0pyyrhDCQ0teBwIDCz5VWtMoLgUfZ3XKqcmCiIiIlDAKpERErlCmaZKfn4+vr/qgydXF2PodM3aBQQDBYXYy0wpInfwja9+No7ntz8vO3OQkLWTpks2kZlsICKtPw/ZtqB5kwbTkkb5zE/sOF/1MARkkbVpPxaqxVA29hYdeaUmB6UlgpBXTdZDEbcnkugw8ytagZlTZE40yj6VsYu8hOxaCCI+tTrBRNPMqc8cclq2M53ChPxVqtqdF6+oEWP9+/wrTV7Lk91UkH/WnSvMetPrza9fIImVLPIcdBt4htYmuXPQaNl1pJG5L4pjLin+lWCLKWyA/kV27MrATQHi96lgSpjN/bh4Rfe6gzrUjeK5+ARZbCJUsJqblGGnbt5Oeb+AbVo+qPttYMmsxe7M9CYvuSOv2NSn7p+Aq7+ASFs9bS0quP1Wa9aRNrWPs25mBw+1HaO3alPfWMkARERG5PBRIiYhcwRwOh4ogVxXTsLN9wjckAOFcx4CnjvLh07NJYirzFr5G804nTz1c1mSWvdKLYWPWkOE6+RhliOPmdyby9F0HmdC/FR/tLfr6Ab7khZ7f0Wv4YV7q8A6Dux1fsvduIs/fsZ2Jd3VmbDrU5HVGJT9HbcPEZUtk+mONGLkSohnJf5JGEujcwJQne/GfKXvJOfFbrVSr8BiDJ79N14izTEcynCT/MoCHB3/LzvyiL1kIJ7ZDBU69BYHdNoePru3LT1hp0WMRn45pjhfgzB7Hy9c/y3I8uWHoQUYNKYtr/UgeuHU8hXSg+9vlWfTMjxxxN+fOjr3xnXwttx1fsjd47+dc77mW8f068GmqjWo3PkTlJaNZlHnylK5BxDuMXPA49T1NTMPO/il38dBTk9hz4rlWoOHNMSRNWUAhzbl77hIG1dIMTBEREbk8NAFcROQK5na7VQS5qjgsi/l1cjpODEKjb+baG3vSBLCTwPYvl1Dwx4aGi0Pf3ctTH68h0+VJRNx93Df4QdpWsXGMtfw2dCg/plWh+cMj6dPUFwMIoB23DhlJuxZ/babk4W5F91vDsAEZzGVRctHMH2fObBavBANPqt7ThzoeOax/rRsjpuwlj0iuuX0YDz54I3X9XexJ/ZAxLd9jq+vMIY0z6V1eeLQojAoOvo5bHx9Gn+v8iJ+/ntwLrNsRVjL5pYnsc4OBFcs5cyIne6eNZV/kAAY8MZjudX0xcLJ132tMmHSsaIvk9xk5qCiMCi7bhV6PPsedPQKIn7KAdA1TERERuQJohpSIiIhcNObS8czKAAsBVOtzLaFlj9KpOcxf4SZ1/g/Mz+9AVx8Tp2U7v3w2nyNANM/w6qRXaOxp4ujkpvsNY9nPXHZNDuTuB4dh3fEuP66G8jSjx+BnaW4zsW8//fcapicxt91F9Mdvs5PVxE/PhEfL4Zr/K6uBsrSgwf3RUDie8WMP4sBG214TGf1uY7yAh9r2pcedPxLPp0ydP5i6nU8/RTINO7vGf8QqBwTQmrt/+IVH6lpwWR/jmv4RPDHzwsJjkzzK2x5n8OwRdKkXjKdhZ/s5tq/BMP4z+SViPUwcfQNIueZV1nOEw1tTcBqR7Pn2Y1a7oCxtuGvSDB6tY8FteYymD1Rl0C8uDVQRERG57DRDSkRERC4KtzWX1T+O5yAQQjvq3RKE1VWZtt2b4AUc4FeW/1w0g8dlrmXbdgArIdddR13PohlNHo0/4KfNB1m+IZ5n7/T+Z0+gdj+61wI3ueyfvogs6zHWzP6Vo0Alj1vpHmWBbcvZRtEspD27X2bQvTfy0L03MvTzbdgBB/tIX5nx130zMtm2JgUnUJEedK5d1GzK6qpIh1u6EXDBJ2SBNBn6Kj3qBeN1Plu3ak0tj6KauatGEH78Oy6HE7ftMNvWJuEAwuhC++PP1eIOp33PzvhpqIqIiMgVQDOkRERE5KJw2Wfw61Q3biDHK4XlbwxgKyZmTj7eQDap7PxyNtm334x3bhZHATDwLhd08oTE8MYv+GQQ9U9abttcdbm+d30+fG0zh7fMYZ7Dj22/g4E/1YbcQmXDxJ2VTS5g4iB111wydp/yAD4+eJueuB15f3ls03qYI9lFn1uDylHOap54crbQ8gQAGRd0QlYe/yif89zawFrmlLDuT3fiNN055Bw7/lxtAaeFZTa/ADwBdacTERGRy02BlIiIiFwUhbO/ZX5h0ef5hWv4/cc1f9rC5OCmCcw+3IubAv05fu85Cg5n4QSsgOlMZOvy3WSbVoKqtaN2lX/wBEwr4bf2I+61Z1nHAlZ+78mmLChPZ+LuCMMCuIKC8QeyqEKf/+3kxa4e5/XQhrssAf5Fn9uPHCDFZVDOUpRI2VNTyDrteVhOTEE3HKcs5cvN4djZHh8LVuvF+X8wjHKUCy76vMCZQIITqngAuMnYk0AO4K3hKiIiIpeZluyJiIjIBXNb0lgw8ReOAB7UpHnfAfTu2//ER682VfAA0pjDmh/TMT3iqBsD4OLQrJ/ZdKxolk/OvKEM7HM9D/S9j0mbT+/L5CKfgr+ZMmUNv40eLaCQeBaMmsBeDCrU7cv15Y9vULcVDazgIIkdY2dxyARwc2TRMB7vcx333daPr5c5//q47nBimxftQzKTmbH4+NJDdjDt69mnNTU3CMQvqOi7OVt2k2UamIadlJkzib8UJ3cufxq1jcMb2Mf3/DhqLVluOLb/a94Zsw67hquIiIhcATRDSkRERC6YM2cKM34rWsUWU+Fp3hp1HxWMk+mRI+U1EpuOZC3ZJHw+ncMP3c0ND7bny6ELSGAUgzquJjbWZP+i+RzCoK5lEL2u9QQK8S7jj4UcEvmEJxr8zC3PbePZpmd+HlZnFTr1ac+byxdwJD0dKxWodd/1BP+xvM52E/cOqcPcUdtYs+J2bu3SgdpVMtm5eDUH8qEmL1L7Kw/+sljQtFF9wHN0+PwR5uRuZtw9cWzsVBevfQtZucuKlZONwm3u+sQ2AeM32HLgCe7sPZ2aXrtYu3QH+Zfg/8IwrYTdOZI+Y2/gq6QMfh99DYvGlcHIzcHl7YPtkjwLERERkXPTDCkRERG5QG6yp4xnuQkWylLjvhsIM04PdIyqvehSr+jzlAMTmXnASli/b3j/qY5E+LjJTJ7HvF/nk5BThjoNR/Lk6ieoazUxTE9q9nuF3o3C8LM5yT96kILcc5/alOlxJ22Ot1WqRA9a3HCyjbfh9iZmyBzGDL+RmgF2UrfPYt6c1aTkl6FOoxEMWTWCZr5nnoblEfIAr377Au0qWHE4drFm1hQ2ba/JLcPvIvLUZ+AOo8tLo+lexYrJUfatmMr8hQVUfe4p2l+i/xGrd3eGTv2JQd1jqeBvwSywEVVvEK882wkfipq646GRKyIiIpePYZqmqTJISfXGmDUEBfnTqXW0iiFXPLvDyWfjV9Aw2iQqrOhrDRo0ICgoSMWRv3WswMHwd5bTqU1NatUIvaqeu6tgH7s37iKjwJtyEY2IifS7JH8xMx2pxG/YysECb0IiGhFTtcx5/V7TlcbetZtIs4dQtWFDKvkZZ97OzCBxw0YOHPUirFYzosM8L914OLCB3WkFWNz+hNWrR5i3CYaTve/Wpcfb8QTRkwe2TueeQPdl+39PTDrMr79vo3OcG3+fohq2bdsWwzD0ghYRESkFtGRPRERELiurdwS1rom45L/X8KhAdNMK/NM/WRjWMKo160K1v9vOCCGqUSeiLvmeucmb/xj9hy0nDx9i2jxBz2vDMdMXMH1sPA6gvFdnWgfob5IiIiJy+SiQEhERESlRLAT3eY+n597Em3MOsmPxG+xYfPK7FTxv47apD1HdokBKRERELh8FUiIiIiIljNXWlL5fJ9Bx/a8sWr6N1CN2DJ/yVIluS8sujQjzVBglIiIil5cCKREREZESyHB7Ex7bi9tie/3pOwqjRERE5PLTXfZEREREREREROSSUiAlIiIiIiIiIiKXlJbsiYiIyKVnuEiaP4a5u+x/WkBmYBge+AZWJeaaTsRW9SkhO+wmc804flmXhdVSj1YDuhClpuIiIiJSiimQEhERkUvONArZPeFx/jP9XCcp1eg6dBIvD43Fz7za99dJ2pzneWt0GhXoT8DdXYjy1DgQERGR0kuBlIiIiFxWFiOcyMbVKHt8rpTTnsL+7fvIdu5h7jsDqNRiJUNaXN2nLAYWfKq0plFcCj7O6pRT0wQREREp5RRIiYiIyGVVyezGY5PG0sPzj2lQbjJn9ubm+6dxkC1s+X4TjhaN8QBMw07mjjksWxnP4UJ/KtRsT4vW1Qmwnv6YppFN8orZrNicTK4ZSIWarWnRuiZl/7Sd/cgGls9fTmK6izLlG9CkU2siy1r++ljLZ7F8azK5rgBCq7ekebu6hPzpLMqVt5t1cxew/UAO+FakRqPONK0fggeAaSPs2hE8V78Aiy2ESn9armc/vJ4VC1eReCgfa5kq1GjemaY1yp5s9lmwn/idhyh0+xFSuwae+35h3uIEciwVqdmyO81rBagxqIiIiFxVFEiJiIjIFcaCf4uWVGMaBzFxHSvEDbgdG5nyZC/+M2UvOSe2tVKtwmMMnvw2XSOKIhnnkWl8eM89fL42B8cp20WWf5DBkz+ie3UD03KM+B/688TwyezOO/mb/Yxr6P3+jwzrXRkr4Miby2d39mb0yqOnPJZBJa++DJz2JbfXtwFusta8yKD+b7HisHnKVn40bvk+I74bQC3PQhI/v5bbji/ZG7z3c27wNHFbs9k69i6Gvv4rifmnVqA8LW4YzYj/9SbKauLYPYrHu31MCg1o9XAIm8fMI839x55VovOTM3j7qXp4afCIiIjIVXPGJyL/iGkUkvDbh3z56fuMH/s7e93GVfG8XZZU1k18ny8//YAf5yTiugzPwclGJg3tweN932RJ7sm6FaYtZOr/hjD8kd48em9vhg55nnGTN5Lhuvx1cxrLGf9odwbf/QGrjxl6AYgUz5EVt9OO3V70cSxrGys++Z7NgI0KRHaLxcOay/rXujFiyl7yiOSa24fx4IM3UtffxZ7UDxnT8j22ugxc1j1Me+wWxqzNwU0EzW57hgf6dybCy0Vi+id8fdf3pJkG+aueZvCQycTneRHZ+H7uH/wo19fxI89cyZTBd/LpJjemJY+Nr/fmw5VHsdGY6x94gYcf7kU9f5MDhd8xoe/n7DcNHMzl4/vfZMVhCIu6jXueeIH+N9ejLLmsX/YkYz5Iwn3G/XaT/dvDDH7hVxLzbVSp1Y97Bg+jb5dqlCGdZdP788YLGyk85Sfy2cT8/9tDRO+nGfhwL+r5gYsDbHlvFDPydIwSERGRq4dmSIn808smSz5bv3qSNxZAFQYQeAU2pnVseZ577v2GQ3kx3DRuDo81seA29rJo1FA+PmDQ6Joa3HBtJNZLWTfDzv6PB/CfHzZTv8swGvubgIuDvw3k4Ue+YOux07f/ecI7/PDGEIZO+w+dKhmYhp0t77Tk8W8PEWJ5mOErn6fRP7hD1Zlqcj6sxNE0ajdvvTef3BdaUv+9pnjrZSByUSXxJU9Ff8lTf/q6hTBa9hrNA7eUwVU4jvFjD+LARtteExn9bmO8gIfa9qXHnT8Sz6dMnT+Yp2O+ZsICMLHRqvdkRr/XCE9LAZ2MitzxRTZp+6Yz29mNsI/HEG9CDZ7i1cmvEudp4nyiHS/H3saEo8tY/u4y7h/vy+olR3FjUDPySUa8fAchuOhd/VYGf74X36x97HRAhQPzWZsOFgJoeefHDB8YhNN2B9HO2xkXbyEvZSd2I/Qv++207WL62xNIBqJ5nldnvERjLxOX9VGa3R3BkDnH2D3uXWa88DU9Tjl1a3PrVD56rz5ehoveZRvQ9c0d5JBAcpIbYhRKiYiIyNVBgZRICWQUZpKemkIaAeQWuAELBgFUaNKCRuEWKjQI5lJfsjgzv+Ld/27Agw60HNEaXxPsOV/xxgNfsNUOZXya0PLmtkQEHOPg5pnMX7qf+NRRfNK7FjWX3Utlw8RxJJ6DB3PwIZvCot26oJqc18+ZnkQNfJp2Hw5k1Y/P8c3A33hAF3wil+gkpSzB1asSbDFh83K2AQZW9ux+mUH3Fm1jOhOxAw72kb4yA/JWsxvwJIpqN9UvWsLm9qbhC3tZMdiOaXhi9drAJxuKfj61/DzGPLgRAzAtBaTZAFwcnbsJi3kTYZWAeJONif3p3uRjGjZvRmzs3Yz4qisNK3sBJvbQKoQCm8lm6us1WD+tDY0bNyG222eM6dKMCj4mpmn/y/65CxazamPRnlbu05v6XkUhu9VViU63dsVvzgzSWcH2FQ56hP5RkwpU7lyraL9MKxWqVMFgB24cOB0aMyIiInI1neuJSLEznansXDKPDfFpFBhBVIpuS9PW1Qn8SybiJidpIUuXbCY120JAWH0atm9D9aBTNjScZO2ey5IVO0jLNvEOiiSmeUcaVytqaJt/aCu7Eg9jB9zYObx/HdsToqkVHUWHwe9TJ9/As2x0UZPdP56fUUjm9t9ZviaBjGM2AsLrE9e2NZFBJ4OX/NQtJKQVYHhXIaqWhf2/T2dVfA6ewQ1oel0Hqpc9e0hjGoXs+uQV5tuhVqV+3BRlAUzc8yax2A5+xNHvp6UMbVB0SHIbGSwdUoMHfsghaf93/HLoVnod3cS+471ZCsggadN6KlaNpWqI8a9rUrt60PHnd+4mybayt3JTj4H8Nm0hC0fOo++ETlf9LehFriRh9KL/7y/RxjABJ7lpC/ju+SFMS9zF8v++wMS+0+mXlU0uYOIgdddcMnaf8gA+PnibnricueRlHcEBeFIW33In54FavMoSeLzBUqEli9zM48e2w+tZseTU45cP3j6ARyFWV0W6vz6WjYOeYfK6wxxJWc78n5Yz/6cP+GhkBJ0HfsvLI1oQ5N+fp0av4sjIb1mXkUXipp9J3PQzP301knIeXbnnm295qK3PX3c8+zC5gIGBT1C502atepQNwg/II5vCDDccD6QseGHzOfl8DeXjIiIicpVSICVSzHK2vs0L97/AzP2O0156URUGMnjCu3SrXnQJ4rIms+yVXgwbs+a03klliOPmdyYyvE8EWJNZ/Px1PP3FDrJPeTSDslzT9Qv+M7YHuV91544Pko43393D1KdbkFBpPD+sjeC7u9ocX7I3ja9/6o434C5czneP9OGdWcmcumrO39qMW0b9wDO3R2Az7Oz+tDN9Pk0nMPQOuteaxbeLDuM8vm05uvLgr1Po39DjjDVw8juTx6fixIsqN19PecME3BTm5mIHbDix59pPHJIsZgjNB3/P63EHcDuDqOqzhQm9WvHR3qLHO8CXvNDzO3oNP8yrj2f865pMXtX3vJokW1yBtOjSBb9pv5G8+EtmZ3WmV1klUiIXiydlCa1el+g/7rJXuy6Bt37MjFHxHGU3B7Y6MUOC8QeyqEKf/+3kxa5nPt4UTAkoakbOUfIPu04cV+wpq1ibcBTDGUJYx2ACQoCDFpq0/pXPv2t/1mbgXlH9eemXO3h85zyWLl7KulWLWbl4OXuy97Hkk0F82WMlQxt5U/2mL/m+xxvsWjaHZauWsm7lfFasSiDTMZOf+7zPNcnP//V3BIUTBJi4yU5NxkH4iW3yU1I4CnhQDp9KavkpIiIiJY8CKZFi5Dg2ibf6DmNmJvj5tqB9vw5UcK5j7oRZ7En9H5+0DaNi4nAaejo59N29PPXxGrLwJCLuLjq3trL7py9YlLSW34YOJbrjJG5OeIGXvtjBUfyo2f4+OjX2IHP5OKYsP8Tmma/xw/oe3NtuGI8eHsfX36wim1Aa3fEInZo2gNPimiJuSxqLhl3Pa7NyMahI7A230zzqGLunj2P+3lVMHtIXv+hFDIo7+TMZh75jamFHbnikOWVTpvPT1C1kMotZz8+g98wb8T9TTrN2OouzwYvqVGxT/vhiOQs+jZsSyTJ2sZGv+8awqtW1tGzRhqbXdKBxXFd63VH04y5rCpaHR5I+cRQTVufhTzuuH3IdbVp44ljy72vituayfmQ3Rkw5iJtIrrn9dmLL7mDp99PYmvohY1pWour+p6hrNfFs2pwa/MYm5rNpdj69biu+TlI/T5/OkZT1WCwWgoKC8PHxwWKx4OXlhb+/P35+fic+zvXvwMBALBZdyMpVyLTgH1i0tNiNHVeBCXVb0cD6MXtcSewYO4tD1/ck1HBzZNFzjPh4A3nuENo+8TV3NG5KNL+ziT3ET1xHbodm+FozWPxqSx6ZbhLBwwxN+g8Nm4Lxs5sDC8cz91A7uoUaOHKn83+Pj2ZdvoWKTV5j5CMreSJ6MItt3rTutYwP3n2NG+8Dx6r7uP7mr0ghlaMJdrJ2dqPjc8uw2Rty+y9LGPrU3dxrKWDNsPLcNS6PApJJckKNP+2m1bM9bVrBjKUu9k/9jAUvxXFdeQOXuZGJ38znGBBBB5o284CdGhYiIiKiQEpEzofh4uA3rzMts2iW053fzmNIM0/ATf+mPbnhkVns5n9MnjiYenft5ZfP5nMEiOYZXp30Co09TRyd3HS/YSz7mcumn3No4ZlNcO0GVDFv49Gvn6e5zcSxJZ+1140mkYPkJrgJ6f0wD9o2MOWbVeQRTOyNz3F/axsOlv3lKboOjOWzibmYeNKmz3Q+eqcR3oDj4Wt4usl9zMhdxbIRv3LvjK4nfqYMjbn925k81ciG06MPIbsaMGqbSf7GXaS6DPz/3GjccJGxdjUpQCBRlK9bVAMAW91neO6hOQwes52jzhS2LvyKrQu/4jPAyzuauA530e/pIXSJqUiTO4dh3fEuP66G8jSjx+Bnae7hJHHcBdSk8JvzapJct7MNs0otqgLrySBl2W64rX6xDZ2jOTmkpqZimibp6elYLBZcLheFhYXk5uae+HC73ed8HA8PDypUqEDFihXP+REYGIihdT9yRbHg7Vs0V8jEgVkINttN3DukDnNHbWPNitu5tUsHalfJZOfi1RzIh5q8SO2vPLD496dvuzfYvNDJkqlduSWtPdGe21i5yMSgLNUffpSOlgDcg58jdvYbbLB/yfAOm5jYPIy8zb+z/oAdX+Jo8HhdrP4BtG/uZt6KPBZM6MLN+1tRPdhBxqa5JANhtKJuW2/KcAONzIUsZjVf3x7Hhta1CSCJHYvycGNQrmYnmnmYHP5zIOWM4Prhgxl/wwdsdY7luXYbmNiyIvad81i1B6yEUnf4UNp7mrg1KERERESBlIicD7c1i/VLN1EIVKUnXZoUNb8FCwE33kHzR2bxCxns/20Drjvj2bYdwErIdddR9/iyFY/GH/DT5tdwuCzY/Mrg7zOFib12sHrmr6x67X5+TEpg1+olJABW3OD6Z8/RvnQBW4Ay1Kf23bEn7h5nDexFtzb3MWOmSfb6RWxwdCPo+PdCaEpsPQ/AxOqKpFJFYFvRRaPzDJmGiYu0pL24AC+CKVvmZGBlcYfTcuQ6Zt40iWlTfmHpimVs3pbEUScUFuxm2cwRJMzcwOElP3JbtTPsgGkl8q4LqMm282yS3DkcrOUIDASyXOTsSwKKL5Dq168fUWH9AGjQoAFBQUF/3XXTJD8//7SA6tSPo0ePkpaWRkpKyomPdevWkZKSgt1+enNlb29voqOjqVOnzmkf0dHReHh46Hijq80AACAASURBVMUsl4V3ZDVCWEwKB0mYvISjN3cgZsgcxng+yssfTWfn9lmkbgeDMtRpNJRBY16kma8Jrurc+PEvHHz0IT5bcIDE5VNJBDws1bl2wCc8P7wOHphQ+xU+/tGb14b9lzk717J0FoBB+bLd6PPfz3iklTdWVzS9PplMyuOP8fXiVOKXTyX+eGAWGtCV3u9/RO9QsBiP8/pXBxj+9P9YkrKFlbO3UPRoftSJHcJjX9xOmOH4SyAFUKbhf/nfV1ZGPPc/liStYdHM41+3NeS6Jz/h2UcjsZookBIREREFUiJyfkwzm+ysos89CCb4lLDG9ChL2TLAMTcFmUcgN4ujxy9fvMsFnXxhGt74BZ9cGmbf+wFP9BrC72lF4ZVvYHWi6tel3JJNZP3Dlkam4SIn6whOwIcAfEJPLu0y3J74B1qw4MbOEQ67ORFIWfHB68S+GOdxtz4TR+Gx45dwXnj/6QcM05PyDe7g/gZ3cD/gzN/HjuUz+W38KL6anUga05k3eju3vVP9jI9+ITUxzqNJstuRd/wLXnj5AFngLrBf9vFlGAa+vr74+voSGhr6D8alyeHDh0lJSeHAgQOkpKSQnJzMrl272LZtG9OmTaOgoKDoDcJmo2bNmqeFVI0bN6ZGjRqaUSUXPobdvnT8Pxe7/u/kseK0E5RmX7DgwBenHkrArECTR35i+gOpxG/YysECb0IiGhFTtcxp9820BXbl0W8T6Z+6iW3xaditYVStH0tlf+OU32MhpOkLvLfgKTK3r2F3SiGe5WKIqVcZv1MezFb+Jp784QYePLSFXbsOkOPwJiC0NtG1wynzx3amlfD2oxi7eiSHdm5kb8pRnLZAwmrEUr2Cb9FzMz2p83wK257/0/6aNip1GMXYFSNJ27GBvan52MpUpVqDWpTzNk9s5lH/I2Ye+OivJ3I3zmLLjRpPIiIicvVRICVSXBdblKN8yPHQhBQOOKHS8ckmlmMpHDpWdEHkWzEMw+cQvscvUAoOZ+EErBTN1Nm6fDfZppWAmLoceGUIc9OgIv0ZPOu/dKsfjLHjKXp12kTWP31+ppXA0FC8gXwOkbPXCRVsx6+tcjh00I0b8CKMcMuFVaJMUBgWUnBTSMEf12CGnU3vXc/Ls3Iok3cd98x+nc4+JjafCOp1HEjNTmU5UPFOpuPmWPJB4K+BlNuWwbwLqIkZdH5NkosUUphftD+eQYFX77g0DMqVK0e5cuWoX/+vs7xcLhf79u1j27Ztp33MnDmTY8eKgsWwsDBat25NmzZtaNOmDbGxsVitVr3o5dKNY48KRDetQPQ5t7LgW6EhTSr8zWO5vQmJaU1IzLkfq0xoAxqFNvibo50fYTGtCIv5N0dKP8JrtSa81okjlP6jRUREpERTICVyAexkkbZ9I9s9Tr9wsLj9KFc7irgurfGfs4T9TOTnGUNocmM5DEsB+z8fywrAg6pUuzEWw8ODujEwe6eLQ7N+ZtOxljQtY5IzbygD+0/lMFW59rsfidkDJgahdbrRuUEwnsYxds2aw76zPD83hTjzzn5RY23TjThjNgvMHWx+9zfSW3SlvAH2+E+YvLjoIiyk/bU09DDZ/W8vHE1PImrUwoMUCkjjcJYBoSYGVkIDjrF78zocJOAxohU1R3ajqp+J20xj57hvWQ0YeBJc//TLThf5FJjgYj97L6Qm59kk+Z6WNkx3GplZRcv7ytWJLrFj2mq1Uq1aNapVq0aPHj1O1s3tZv/+/axatYrFixezePFifvrpJ0zTxN/fn5YtW54IqJo1a4a3t7cOECIiIiIiclYKpEQuQBo/8Va3n/7y9UDa0n/JfB7o8wp3jenIx/EJTHy8EZsmNaOifTOrl8aTg0GDiOe54zovPFz1ueHB9nw5dAEJjGJQx9XExprsXzSfQxjUtQyif4dIkqOAeJNN2wbRr9+3hGatZPXmdFwULTtzO46HB75+eAEO9vLdIxXZEvcp30wK+2v4EHIvjzz6ISv+l8Cq5b3p3aUjdSOPsWfpIvY4IYTuXP9Ke7xNxwXVybNlW+owj80kkLbJDp1tYFopd8dI+o3tyRf7slnyXQ+6/OhPYKA3zmOZ5OQXdUyJ5EFufqQqUIh3GX8s5JDIJzzR4Ge6vbyIVhdQk+8nnl+TZDCx7tzCHsCbGKp0rlDqxrrFYiEyMpLIyEhuu+02ALKysli2bNmJgOqVV17Bbrfj7e3NtddeS+/evenZsyeBgYE6WIiIiIiIyOnXGCqBSPGxWdrx6I8TebhtJbxdB9g+bwpzl8STQzBN237AszPuo7rFBNNKWL9veP+pjkT4uMlMnse8X+eTkFOGOg1H8uTqJ4h1l6fzyA/pVtmKm1R2zJ/Kpi0xdBv7Pj0McHOU/XNXFL2w693PQ3c0IryMFWf+YRx7C858AHD5Uf/52Xw4qB1VfApI3T6D32cuZM9RK5Uq9WXgxHH0q37hvYKMqjfRqTbY2Ufy73tO9Bn39O7GU7/M4dlb46jgDaYzhyMZ6eTkuzGMcjRs9TLPLX2bDkEmhulJzX6v0LtRGH42J/lHD0JGyAXVxHB7FzVJHn4jNQPspG6fxbw5q0nJL0OdRiMYsmpEUZNk3GStWMQeoCLdaN1Ey9MAAgMD6datG2+88QZLliwhOzubhQsX8vTTT5OQkMDdd99NaGgoXbt2ZezYsaSnp6toIiIiIiJSdJ1omqaaFEiJ9caYNQQF+dOp9eVeYuUm7+BmdsSnUkAQ4TUbUS3U84xbugr2sXvjLjIKvCkX0YiYSL/TkmPTmUbCuo2kOytRvVE9Qn0uzkvYVbCf+C27SD9mwz+8HrViQvC6aEcaF2nftKfbsGVU4k3+m/wMtYzTn7c7P4W923eSll2I4R1O5Zj6VAk+v+DnYtTEdJy9SbLLup8pt0QxfJk3He5dy+jXa1EckZTd4eSz8StoGG0SdXxC29nusnc12LlzJ5MnT2by5MmsW7cOi8VCu3bt6NWrF7169SI8PFwHqYvoWIGD4e8sp1ObmtSqEaqCyBUvMekwv/6+jc5xbvx9iv740bZtW900QUREpJRQICUl2pUTSInDPYO3GvdkQnot+n2/gWfbelw9zz3xRe5o9R8yuJMnNo3jxnLFc9gsaYHUqfbu3ctPP/3E5MmTWb58OTabjVtvvZVBgwZxzTXX6AL0IlAgJVcbBVIiIiKlm5bsicgl4WG5nkfeuplgdrB22Hckm1fHBYfbcojF/x3FNkJpNPxVepRThv9vREVFMXToUJYtW0ZycjIjRoxgwYIFtGjRgmbNmvHNN99QWFioQomIiIiIlBIKpETkkh1uAq//hHffepbW1xeSnOW+Kp61ad9LTuSTDBw2nqcfjkDdoy5cpUqVePHFF0lMTOS7777DZrNx9913U7VqVUaMGEFKSoqKJCIiIiJS4q8QRUQu1QHHDCHuztcZPPJBmgddHTOkrLZruPGZN3h8UCcqGJoddTF5enrSt29fli9fzqpVq7juuut48803iYiIYMCAASQnJ6tIIiIiIiIl9fpQJRARkcutadOmjBs3jqSkJIYPH86kSZOoWbMmL774Ijk5OSqQiIiIiEgJo0BKRESuGGFhYbz00kvEx8fTv39/3njjDWrUqMGYMWNwOp0qkIiIiIhICaFASkRErjihoaGMHj2aLVu20Lx5cwYOHEhsbCwzZsxAN4cVEREREbn6KZASEZErVq1atZg2bRrz58/Hx8eH7t270717d1JTU1Wcs1BcJ1fPWNVoFRERKc0USImIyBWvffv2rFq1iq+//prly5fToEEDfvnlFxXm1Dd0o+hGAW63LvLl6mAeH6sWQ7UQEREpleevKoGIiFwVb1gWC3fffTcbN26kTp069OzZk0cffZS8vDwVB/DysGK1GOTl21UMuSrkFRT1hfP0UC1ERERK5fm9SiAiIleTqlWrMm/ePF5//XU+/fRTmjRpwsaNG/WGbjEILe9LxpFjGiRyVcg4fAxvLwMPq6ZIiYiIlMrzV5VARESuNlarleeff56lS5ficDho1qwZ77//fqlveB5VOYDUg9latidXPNM0SU45QrC/xqqIiEhppUBKRESuWs2aNWP9+vX069ePJ598ksceewyXy1Vq6xFbqzz5+Q6SDhzR4JAr2qH0XLKPFlCxvAIpERGR0sqmEoiIyNXMz8+PL774gtq1a/PMM8+QmZnJuHHj8PT0LHW1iI4oS0iwD+u3HqBq5SAMQ0uh5Mq0fusBPD2gYpAJFI1TwzA0ZkVEREoRzZASEZES4emnn+aLL75g0qRJ9OzZk9zc3NL3pm4YXNu6KgdSs4nfl6FBIVek5JQsEhIziK5sYj3lFns+Pj4qjoiISGk6d1UJRESkpOjfvz+TJ09m4cKFdO7cmczMzFJXg6b1QqkRFciCZfHk5BZoUMgVJa/AztzFuwjwhxoVTy7XMwyDgIAAFUhERKQUUSAlIiIlyo033sjs2bPZvn07bdq0ITk5uVTtv2EY3NkzBqsBU2dtIfdYoQaFXBEKChz8PGcrhXY7zWq6sZyyPM80TYKDg1UkERGRUkSBlIiIlDjt2rVjwYIFZGZmcv3113P06NFStf+B/l48eHsd8gsKmDxzI4cyczUo5LI6nJXH5JmbyDpyjOZ1TPx9Tu8VZbPZCAkJUaFERERKEQVSIiJSIjVq1IgZM2awZ88e+vTpg9PpLFX7H1U5iJvaBYLLwaSfN7BsbSIFdocGhlxSdoeT1RuT+HHaegoL8mkdaxJyhpV5VapUUUNzERGRUkZ32RMRkRIrLi6Ob775ht69ezN06FA++OCDUrX/TRrGYM9fyZZEk/Wbktm8NYXo6uWJqhpMWHl/fL09NUjkoisocHAoI5e9SYfZtecQdruLiAom9SJMPG2nh06GYeDl5UXlypVVOBERkVJGgZSIiJRovXr14vXXX2f48OHUrl2bgQMHlpp99/Lyok7tGCzGDqpXcJOQapKwN43tu9IA8PCwYLNZsKCZKefipqj5tur093VyuUzsdlfR+LJBpfIm1SuaBPgYcJb61a5dG4tFk/ZFRERKGwVSIiJS4j333HPs2LGDxx57jBo1atC5c+dSs+9hYWHk5eWxf/9+GkRB/Ug32XkG2bmQU+jC7XJpgPyNpUuXUlhYSMeOHVWMv2Gxgq8XBPpCoJ95vHH52YO8mJgY3V1PRESklFIgJSWe2zRVBLkqmG7VoLgYhsFnn31GQkICvXv3Zu3atVSvXr3U7H9UVBQWi4XExEQsFguBZUwCy2hcnK8fP59JdnY2T9ynQOofvvLO+noEqFWrFqGhoSqTiIhIKaX50VKieXlacTj113+5OtgdRWPVpiNz8RwPvLyYMmUKPj4+PPjgg5ilLKyOiIigfv36eHh4aDDIZfFHEOXj40Pjxo0VRomIiJRyuuyREi2knA/ZWfkqhFwVjuTkAVDGW7UoLqGhoXz44YfMmzePr776qtTtf3BwMM2aNSMqKupEMGUYhu5uJsXm1PHl7e1NzZo1adKkCX5+fiqOiIhIKacle1KiVangx7rNhygsdOLlpeEuV7a0tFwsBpQtY4KaJxeb3r1707NnT4YOHUq3bt0ICwsrVftvtVqpWrUqVatW5ciRI2RlZZGbm0t+fj5ut9aNnq1mFosFLy8vFeM8GYaBxWLB19cXPz8/goKC1CtKRERETqMrdCnRakcFMQ3Yk3SY2jW0NECuXKZpEr83nXJBYLUojCruC+XRo0dTp04dnnjiCb7//vtSW4ugoCCCgoI0KP5GcHAwbreb5s2bqxgiIiIiF4mW7EmJFh5ShiqV/Nm8PaXU9YuRq8vBtKMczsojovzp41S3Qi8eVapU4Y033uCHH35gxowZKoiIiIiIyCWmKx0p8To2r0x6Ri7x+zJUDLkimabJsrWJ+HhDpZDTAyktESo+Dz/8MM2bN2fgwIHk5uaqICIiIiIil5ACKSnxGsaEUC2yLIuWJ5BXYFdB5IqzaUcqBw/lUL+aieWU5tJWqxVvb3U4Ly5Wq5VPP/2U5ORkPvnkExVEREREROQSUiAlJZ5hGNzRvSZup5sZc7fjcLpUFLli7DtwhGUr91A5zKRS8OnjNjAwUAUqZvXr1+eWW27hvffeo7CwUAUREREREblEFEhJqRAS6EP/W2uTnp7D9NlbKChwqChy2SXsz2TG79sIDIBG1U9fqmeaZqm7+9vlMmzYMFJSUhg/fryKISIiIiJyiSiQklKjdrVydGsbTEZGDj9MXUdi8mEVRS4Lu8PJohV7mDV3O8EBJq3quLH96c56np6ehISEqFiXQNOmTenYsSOjRo3C5dIMShERERGRS0GBlJQqbZpG0zEOPDwc/PrbNqbO2kzC/kxcbreKI8Uu51ghqzck8c2kNWzZkUJMhEmrum5sVuMv20ZFRWEYhop2iTz77LPs3LmTadOmqRgiIiIiIpeAYZqmqTJIaZKSksKuXbvYf8hg1wGD3DywWS2EhfoTFOSLl4dNQYBcNC63m9xjdtIzc8jKyscAKpaHWlXcBPj+dZz90TuqQYMGKt4lZJomTZo0wWazsWLFCh0D5DS33HILGRkZLFq0SMUQERERuUhsKoGUNhUrViQ7OxvDOETVUDeZOQYHs1wcOZpNwuGjOF3KaP+O2+Vi7+6VVK/VUsX4GxbDwNMTAnxNqtYwqRRs4u1pAGcOo7y8vKhVq5YKd4kZhsGwYcO4/fbbWbRoEe3atVNRRERERESKkQIpKZVq1aqFaZqkp6cTEgAhAQDm8Q/5O3Pm/M6in8cwsO93eHl5qSDn9OcxdeaZN3+EUbGxsXh6eqpsl0GvXr0IDw/n22+/VSAlIiIiIlLM1ENKSiXDMKhTpw6RkZEYhqHlOf+A0+nkzTffJDMzkxkzZqggF2EsAgQGBtK4cWO8vb1VlMvEarVy0003MXXqVDU3FxEREREpZgqkpFSLiIigcePGBAQEnBYOyNnNmzcP9/Em8OPGjaOwsFBF+Rf+GGseHh7ExMTQoEEDPDw8VJjLrFevXqSnp7N48WIVQ0RERESkGCmQklLPz8+Phg0b0rhxYypUqKAZKufgdDr58ssvT4QpR44c0Sypf8FmsxEcHEydOnVo3rw54eHhKsoVol27dgQHBzN58mQVQ0RERESkGOkueyJn4HQ6yc/Px+12o5fISRMmTGDgwIGnfa18+fJs2rRJQd55sFgseHl5qe/WFW7AgAHMmTOH/fv3Y7Ho7zaiu+yJiIiIFAc1NRc50wvDZsPf31+FOIXT6eStt97CMIzTQrr09HQmTZrEY489piJJiXDLLbfw5ZdfsmrVKpo3b66CiIiIiIgUA/3pV0TOy/fff8/evXvPOGPs1VdfpaCgQEWSEqFLly74+/tr2Z6IiIiISDFSICUif8vpdDJy5MizNn0/dOgQY8eOVaGkRPDy8uK6667jt99+UzFERERERIqJAikR+Vt/nh31RzB1an8dzZKSkqRZs2Zs3bqV/Px8FUNEREREpBgokBKRc3I6nTz++ONFBwyLBZvNRsuWLQGoX7/+ie0OHTrEZ599poJJiRAXF4fT6WTTpk0qhoiIiIhIMVAgJSLn9O2335KdnQ3AwIED2bt3L7179wZg/fr1/Pzzz8TGxgIwaNAgzZKSEqFx48YArF27VsUQERERESkGCqRE5KycTidvv/02/fv3JykpidGjR1O5cuUT3zcMgx49erB+/XomTJhAXFyceklJiRAYGEiNGjUUSImIiIiIFBObSiAiZ5OZmcnMmTNPC6HOxDAMbrvtNm699VbWr1+vwkmJEBcXp0BKRERERKSYaIaUiJxVWFjY34ZRpzIM48RSJ5GrXVxcnBqbi4iIiIgUEwVSIiIiZ6DG5iIiIiIixUeBlIiIyBnUrVsXgF27dqkYIiIiIiIXmQIpERGRMwgJCcFqtZKWlqZiiIiIiIhcZAqkREREzsBqtRIaGsrBgwdVDBERERGRi0yBlIiIyFmEh4crkBIRERERKQYKpERERM5CgZSIiIiISPFQICUiInIWCqRERERERIqHAikREZGzUCAlIiIiIlI8FEiJiIicRVhYGJmZmTgcDhVDREREROQiUiAlIiJyFqGhoQBkZGSoGCIiIiIiF5ECKRERkbPw9vYGwG63qxgiIiIiIheRAikREZGzsFqtADidThVDREREROQiUiAlIiJyFjabDVAgJSIiIiJysSmQEhEROYs/ZkipqbmIiIiIyMWlQEpEROQs/gik3G63iiEiIiIichEpkBIRETnbm6Sl6G3SNE0VQ0RERETkYp5rqwQiIiJnZhgGoEBKRERERORiUyAlIiJytjfJ4zOktGRPREREROQin2urBCIiImemGVIiIiIiIsVDgZSIiMjZ3iQ1Q0pEREREpHjOtVUCERGRs7xJqqm5iIiIiEjxnGurBCIiImf2x5I9zZASEREREbm4FEiJiIic7U1SS/ZERERERIrnXFslEBEROTMvLy8ACgsLVQwRERERkYtIgZSIiMhZ+Pr6ApCXl6diiIiIiIhcRAqkREREzkKBlIiIiIhI8VAgJSIichYKpEREREREiocCKRERkbNQICUiIiIiUjwUSImIiJyFl5cXhmEokBIRERERucgUSImIiJyFYRj4+voqkColduzYQW5u7nlvX1BQwJYtW1Q4ERERkX9BgZSIiMg5KJAqPby9vYmJiWHkyJHnDKYKCgp48803qVGjBi6XS4UTERER+RcUSImIiJyDAqnSIzIykmbNmvHKK69QtmxZ3nrrrdOCqfz8fD788ENCQ0N57rnnCAsLIzY2VoUTERER+RcUSImIiJyDAqnS5b333gPA7Xbz7LPPUrlyZXbs2EFSUhIREREMHjyYnJwcAL744gsVTERERORfUiAlIiJyDgqkSpfIyEjuv/9+LJaiU6Ts7Gy2b99OYmIi6enpAFitVm666SbNjhIRERG5AAqkREREzkGBVOkzfPjwc37f5XLx0ksvqVAiIiIiF0CBlIiIyDkokCp9IiMjGTBgwIlZUqfS7CgRERGRi0OBlIiIyDkokCqdzjZLSrOjRERERC4OBVIiIiLnoECqdDrTLCnNjhIRERG5eBRIiYiInEOZMmXIzc1VIUqhP8+S0uwoERERkYtHgZSIiMg5hISEnLi7mpQuf8yS+oNmR4mIiIhcPAqkREREziE8PJwjR45QWFioYpRCp86S0uwoERERkYvHphKIiIicXVhYGACHDh2iSpUqKkgpExkZyf33309mZqZmR4mIiIhcRIZpmqbKIKWVaZokJB1l+57DJCbnkJ6ZR6HDBW7V5lwcbicuuxNvb28V429YbAb+ZTypGFqGalXL0jCmHAF+XirMVWThwoW0b9+e1atX06RJk6t6Xwpyc5n3/+zdd3QUVfvA8e/sbnrvBUgCpEACoVcpAkpvAlKkSBdEBRFEQERpvq8ICIqvIiIq8KOIIF2kSZFeQ00CISGk9152d35/JJQgShEwgedzTs5JsrNTnp25e+8z995Z9A0Jh49glpyGoxF0iiIf8j3E5eaQY9BTydpWgnEPBhXSFJUcexsc69ai5YgRWDs4SGDuUReJTsjmbGgyV69nEJ+UQ26eXuoi4tE1+LQKVhYmeLhaUbG8DTWqOONsbyGBEUL8++WTJKTEs8hoVDl0Jo5dB6NJSsnFRKfF1cUGR3tLTE21aDTSQBOPopEBRqORrJwCkpKySEnLQVEgONCFts954+FiKUEqAy5dukSVKlXYtGkTHTp0KJPHkBITy6b3p+B+9TpWigbF2hIHHy8snJ3QmJqgSFJKPJIyT8VYUEheShppVyMxZGSRh8o1d2fazfgIt4oVJUh3xOt0aDI7DkQRHZuFVqMU1UUcrDA300ldRDyyuoiqqmTnFJCcmk1SchaqCv6VHWjbxJtK5SXRLoT490hCSjxz4pNz+HHDJaJjMinvaU9wVQ+8yjug1ciUauLxysrO50J4AiEXYsjPK+SFJl60aeIl514pl5aWhoODA4sXL2bIkCFlbv93/O8rCtasx0ZrgnudGrg3qId1OU9JQonHLjsujrijx4k7dJxsfSEF7VrRcfw4CQyQnpnPii1hXApPwdXZmhpBnlTyckKn00pwxGOVm19I6JVEzpyLISMzj0a13en6QmXMTOTcE0I8eZKQEs+UixGpfLvmHKamOlo29sOrvAwjEE9eQYGBQ6ciCTkXQ2Vve4b1DMTcVKb0K61UVcXc3JypU6cyadKkMrXvK0a9gffFCKx9KuDXvQuWLi7ygYon3wBOTeXy+o2kX7pMeDln+i/9Ds0znIi/Fp/FVytC0BtUmjaoiH8lV0kQiyfOYDBy8ux1jp6MwtXZkpF9qmFnI1MKCCGeLElIiWfGxYhUvll5FlcXG9q/UBVzUxMJivhXRUQl8+vui1QoZ8PrfapjKncnSy0vLy9eeukl5s+fX2b2efnI1/EJjcSjaUN82rV5phMA4t+nqirXdu/h2vY9hLs50n/Z98/kOXk9IZsFP5zC0tyUTq2DsLGWuRjFvysuMYPNv53H2sqE0QNqYGNlKkERQjwxUjsVz4Sk1Fy+W3MeF2cbOrUOkmSUKBUqejnR4YVAoq5nsmprmASkFHN3dyc+Pr7M7O+WufPwCY3Es1kjKrZvK8ko8a9TFAWvli3watsS3/gU1n0w9ZmLQU5eIYtWnsXczJSu7atLMkqUju83F1s6t61GZmYBS36+gNEofRWEEE+O1FDFU09VVZZvCkWj09CuVRVMZH4GUYpUKOdA4/oVOR6SwOnQJAlIKeXm5kZcXFyZ2Nf4iAh0m7Zj618Rn3ZtZCiQKFXKN2+GY80gHP84zpUTJ5+pY1/72xWycwpp36oqlubSC0WUHi6O1rRq5k9EVDp7jl6XgAghnhhJSImn3qlLSUREpdOsUWWpAIpSKbiKBx5utqzdFobBIM/5Lo3c3NzKTA+prRMnY6Uzxa9bdnissAAAIABJREFUF0lGiVJHURQqd+qAqYU5ez746Jk57quxmRw/E0/92t442ssTVkXpU9HLCb/Krmz5PYLsvEIJiBDiiZCElHjq7T4YjauzNZW9nCQYotQ20BrW9SYjs5Dfj1yRgJRCZSUhlXQtGu/EdDyb1sfcXh7aIEonUysrvFs0wS+ngPOHDj0bdZHD0VhamhJc1UNOAFFqNazthaFQZfOuCxIMIcQTIQkp8VSLTcomKiaTalXlEeeidPN0tcPR0ZKDp2JISUmRgJQy7u7upKamkp+fX7obvQu/xBTwqF9XPjRRqrnWqQ1aLbvnzS/119U/lZNXyNkLSQT6u6PVStVblF621uZ4eTly+mI6MTExEhAhxGMn34riqXYxIhWAShUcJRii1PP1diE5Fc6dv4Ber5eAlCJeXl4AXL16tXQ3fM+cxdzTDXNH6REqSjdTa2scKvtgnZDMxYsXn+pjDY/MwGBU8fWR61KUfpV9nMnOVTlzLpzc3FwJiBDisZKElHiqXYvNwtHeEjMznQRDlHpubtYYVUhON3DligzdK00CAwMBOH/+fKneT5e8QuwressHJsoEW58KVFB0JCcnk5CQ8PTWReIyMTHRyNxRokzwcLEBICUTQkNDJSBCiMdKElLiqZaUnIudvYUEQpQJDjZFjZWsXJW4uDgKCgokKKVExYoVMTMzK9UJqbysLOwUDRbO0gtDlA0Wzs6YKQqZ12NKfe/DfyIxJQ97O0uZOkCUCbbW5mgUyMxVSUtLIzMzU4IihHhsJCElnmr5BQZMdFoJhCgTTE2KzlV98YP24uLiJCilhE6nIyAgoFQnpLJS0wDQmpnJBybKBK1p0bmal51Nbm4u6enpT+VxFuilLiLKUONQo6Az0WI0Fj10ReaSEkI81jJHQiCe+pNc7kiKMkK5rURWVZXExEQJSikSGBjIhQul98lDKkWZTOmFIcpMmactPldVI4qikJSU9FQep6qCXJaibF2cYCyuizyt16UQopS01SUEQghROmVnZ2M0GiUQpcSNhJTBYJBgCPGIqar61PaQEqIs0+v1Mrm5EOKxkYSUEEKU4gaaVAJLj8DAQPLy8oiMjJRgCPEY5OTkSBCEkGtTCPEMkYSUEEKUYnq9XoJQSpSVJ+0JUVZJ70MhpC4ihHi2SEJKCCFKMRmyV3r4+vqi0+kkISXEY6SqqgRBCLkuhRDPCElICSGEEPfBxMQEf39/SUgJIYQQQgjxCEhCSgghhLhPgYGBkpASQgghhBDiEZCElBBCCHGfgoODCQkJIS8vT4IhhBBCCCHEPyAJKSGEEOI+tWrViry8PPbv3y/BEEIIIYQQ4h+QhJQQQghxn+rXr4+trS3bt2+XYAghhBBCCPEPSEJKCCGEuE86nY5WrVpJQkoIIYQQQoh/SBJSQgghxANo3bo1p0+fJj4+XoIhhBBCCCHEQ5KElBBCCPEAWrduDcCOHTskGEIIIYQQQjwknYRACCGEuH+VKlWicuXKbN++nb59+0pAhBBPFWP0JlZtDSNPVW79U1HQmdjhVrEx9Zr446B9/Pth0MRyetUqTqcqWPl0oXtrH7SlNGb58Yc5sP8UMakFOFYbQNuGdnLXXwgh7oMkpIQQQogH9OKLL7J+/XpUVUVRFAmIEOKpYTi/hLkf/kL6XV/VUN7xVUat+h/dA00eyfZUpYCzcxrz5vIEnDUjmXx4ErU0KkYlgr2z3+HL6wq1GvjSuZQmpAoj/8PwlpM5mFfUtGr1alvaNLSTE0kIIe6DJO+FEEKIB9S6dWvi4uI4e/asBEMI8ZTS4VSxEbXqNKJW3XpUKW+NDiPRKd/x/YtzOWt8VMl4lcLUcOLirpMdk06+sei/CrZ41G1ErTqN8Qh2pFSm/hUD8Zv+j2N5oMObhgMm0qK1M3KbQggh7vebRgghhBAPpGXLlpibm7NixQo+/vhjCYgQ4qmjwZoXhu1i+qumABg0kfw6pApjtxQQwwZ2hb5LtSpFqRdVySf5wg4OHrtMUrYOW/fq1GnWBB+HW6mZ7OtniEgsQGvpQ6UAlavb13EkKYCGdc2ITFEByCOJa2dO4ulVgwquFWkx+jMCcxVM7fy4vT+Wqo/l0v5dnAqPJ09xoJxfM+o1qYz9bbfac2PPcjk+D8W8AhWraIjasYEj4ZmYOgZTr00LKtvdO230d9tRNTkkXgrhbGQSKuBEbZq83JnaHuZyx18IIe6TJKSEEEKIB2RnZ0ffvn1ZtGgRU6ZMwdLSUoIihHiqaY3eNGlRD92WA+SRSNY1A1TRYcw/yIrXezNnWzTZty1vo61Pt9krebeXNzoln5DP6jBwhZFyvpMZXH0xs9bF41xuFBEWC1kWXvSe63zH+51W0H1yCh+9eZoV/ZsWD9n7he9/7oA5kHnuU94f+j5bowpLNGkqeoxg9Kq5tK+sRVUKCFv0Ar0XJWLv+godqmxj+d4U9MVLO9GO4ZvXMajmXw87vNd2XqwSwqpBjfk8ouiVeNbxaaeNHOl+hP8tqIGJnDJCCHFPksAXQgghHsLo0aNJSUlh+fLlEgwhxFNP1eRx9XI4RkCHNWYOGoyaePZOaMuMbdHk4UmNzm/z2ujhtKxoTpbhCGvH9uHLE/oS67kWM5/Z6+IpBMyz7ak27EN617NEAWxpzstjp9K8keld96Ew+yf+22cCW6MKsbZsRMdhkxg2qC2VLPVExH7BV83+y6nCkj2fkhJWsP50TTq/PolBXathBySzjW2TtpD5F52k7mc7p/O9aDhyKq82dUIDWFKXTmOn0vol91I7+boQQpQ20kNKCCGEeAjVq1enZcuWzJ8/n6FDh8rk5kKIp4pKHud/n8n8BAUUAzkxu9m2Nh494Ek9qgZpMVxfzDdrslAxpWnvDXw+pxbmQOHIBoyvO4QtWUf444PNDNzS9uZ6lRyo3nUV70/rQoCTCaqSj8+lOaw+Ci7Up+Po92ioUym8c38UA3E/zuSXZLCiDv2W72JsfVPAyKB6nej8+jbC+IK1a0ZTo++t/klW1KbX8q2Mq6VDb9Ib59BgZp9XyT0dSqxBwUajltzQfW7nl5Wjmd5vEg6xS1mxLxkngmnx5mQ6mqpy8gghxH2SHlJCCCHEQxo9ejTnzp1j586dEgwhxFNFJY+QX2ew8LPpLJw3i+9WHSRWDzp8qDn1PdpYqBQc2MNZwIrqVB1QA/Pi92rtu9O+adFa0k/u5eRtvZbcaUen//QkwOnBBrUZtemcPHCGfMCTTrxY1+xmc8a2yys0BAwkEfXbqRLvc6YeNaoVbUtr8KGc543jK0Sv3G07aQ+1HSGEEA9OekgJIYQQD6lDhw74+vry0Ucf0apVK+klJYR4qpoJ7oGt8HdTUBQNOp0dLj51afzSq7QOdkCjGMhMS0UPWGCLheut+9yK0RQbew0ajBSQSqoKHsWvWVAeD4sH70WkqmmkpxX9boIjjrcVt6qJHXZWQLaRvOTUEu/TYoHZzWWVez4BT1XTH2o7QgghHuabRgghhBAPRavV8sknn9CtWzeWLVtG//79JShCiKeCBmua91t/8yl7f6JqsXd1xRzIJYHMCD146IpfyiQhzogRMMMN99uSOgrah5pjSaM44eJc9HsBMVzXQ7niTlaa7BgSsov22tLT7R8dt8KT2Y4QQghJSAnxeCgG4g5+x69nstDofagz9CUCH8GcAsbozazaGkqhwZXArq9Q173s9sbIDVvD6t3XMd5jOVO3NvToUhUzOatEKdW1a1fatGnD+PHj6dSpE/b29rfaa6rK6tWr6dy5MxYWFg+03gMHDuDq6oqfn1+Zi4lRDWPTisNcRoNFxRYMbexRJiocqpLPxd0/sS3mzgaqFlMrF3yr1qZ5gANmD1mc5yWc4bejoVzN0ONcrRM9q1s90smPjWnHWbHlAkmqNVWf70Kbck9fj73IyEguX75My5YtpfApBbRN21NH+ZU96kVC5v5GYqN2uChQEP4Va/cBaHB+vjU1TVRO38f6DOSS9xfXl8ZgQ50Xm2CzfT9RrGHjlrHU7eKEoskj6tvFHAJM8KJSlxr/6Jie1HaEEEJIQkqIx9OoQU/0hrf4+Pt8nGjP4H7dHklCSr2whHkfriefYAbU7fNYElKFZyfx6sAfScgJoOsP23mj7uOYas5I3qHPmP3RIQrusWQ1T0c6da360A1AIR43RVFYsGABNWrUYMCAAaxfvx6NRnPztevXr1O+fHmGDBnCRx99dN+JqcqVK+Pr60utWrVYsmRJmUpMqYaTLJ4/n40oBDxXkQHPeaArA9ewqsnnyPLPGPsXLWcFcwJqDGb+J4No7fBg69ZHLKZ9n6/ZbSyqfjXr/xwvV7d6tPsfv4vZn63mLK4M9OxEm3JaDJoTzOw5jm8yrQlsM4dNb/mVycfRR0ZGMnToUPbv309ISIgUPKWE1nkgr49awKEvLnPkYA96vNiSIJ9srhzYyxU9ONOBttOex1z9u297BXMrGzRkcpWvGBO8kW4TzzNx0J8uUBx6T6P/1y35Mvwya96sxZmf6uNZEMLRA+FkohDsPYlX2pgB+f/gqB5kO4VyEgghxD8qcYUQ4vZqYX4yibExxKfHkpVnfExb0aB1r0GtOo2KfxpSyUkpfsUVnzqNb77m2sRVMuei1PP392fJkiVs3LiRKVOmlHhtxIgRpKSkMHv2bLy9vVmwYAG5ubn3XKe7uzudOnVi//79+Pv7M2DAAMLCwiTYT6osNHGjenAwjasH0yCwEj5WWlTyuHj6f7w+cBWn1Pu/IaAqeqJ2rGW/ETR40LLnIPo0tOOJ9F9SC0mLyCQ6MYGktEKMZazTVGRkJK+99ho+Pj7s2LGDmjVr4uvrKydoaWlIGKypPulXFrzVnAoWecRe2MKOrb9zJUNLuXJ9GLHmB/pW/vuTTlFN8e87jR613LDW6cnNiCMv6+7L6jTNGbV6DSOblcPccJ0Lu9axc384mThSr9l83tsyhMqaf579flLbEUKIZ52084R4QlRNNvEXLpCYq2DpVg0vi/Ps37aPiHRT3Pxa0uR5f+zuSBHnJx5m/44jRGfYUKFhR57701qNZF47Q2SyHo2JJ17VPLFWQVUKSA0/y/VMIzrrSvj7OhYPCzGSGb2H/XtDiE1XsHKpSq2mLfF3K3o1N+EcoVdTKACMFJASdYILl/0I8NcRd+4SKXlarD2CqWAWwu+b95JZuScNnaNJyAKtzo0K1StgU1w/M2ZGEHolGT0m2HkFU8GhZIXU9sUv+fHFG7HJ4beBNrzxK7jTiuGrl9PDXEUtjOHK+RgunzyOuWMV/Lysbh139Bkik/RoNE4EBnvfjIPWuhK+vvmE/rqFk5F52JZ/nufaBOF0x7iYgtRTHNx9kKuJBqxcgqnbqgk+dpKjFw+vT58+hISEMGvWLBRFYdq0aWg0GiwtLZkzZw7vvPMOiYmJjB49munTp/P+++8zfPjwv+0xNW/ePFauXAnAihUrWLZsGf369WPKlCllcijf38lPucDuw+cITc5HY+1Btdr1aepl/achbfqMMHYfPM35hByMpo74+Nfi+ZrluFnE5Mdw7nIauaolHn7lMY3cy8bjMaRrXahWtwktK97fMDmPwrZ8/s2bNFfU4rxOOD9MeIWh+1UiYpcwe2tHlre3Kk44FZAQdojfTl0jsdCacr51eLFOeRw0oGpyib0cztHodFTAliq0a9+cps5maAFVKSTlylG2n7jK9WwVc/vy1Khdl8bli/ZTxUh6TBiX0wxg6oK/nws2xeV8YsRlInNUtFblCPa+S4IrL4ELV6NJ1AJ6yM2O4tR5OxpULVcmElGzZs3i22+/RVVvNfx//PFHKWweM5PWP3P0+v0vr1Ur8vyEXTQdHUX42VASs3XYuFejSoDzzeH2impGw9mFXJx9s1ZUskFSYRDTNg1i2u3/NDRmzBEDY+7cP7duvP1/XXktLoSL4bHk4YC7fy0quZreXK+imhI8NY7zU0tuTzFa0u57A+24+3486HZQdfiOu8K5cfdenxBCCElICfGvKdQcZ1nfFiyK1VGpy2uU37+Qvcm3LsVg7zlM3fMm1U1VUPREbxrMyNHLuVTckUKDOzVaeKAvkeQq4NjHdRjxC1RiHFMiP6GxTsWgvc62t+vx4XEIcviGb88Nxp4kTsztzJh5h0kw3FqHqVKFTu+vZepIXyKXduCV+deKO6BfYf34Rlwut4z/O2HL4s6dWZ5tQs1Rn+Lz82jWxepoNrQ5jjnPMWyFHgdaM+TYVoZ6AIqBmGVd6D7jHFY0Z9C+nYx0ePCY6U2O8WPPl1iRBUHWX/LFxRGUU1T0ulDWDqnDrLNQlbmsj33tZhzsAwbTyWIZP54qKK4WavFzG8+4LTNo4a6garIJXzmIMZPXEpZza1vWSgN6fLaaCT3KP9I5XcSzZcaMGaiqysyZM7l06RILFy7E1dWVESNGMHPmTFJSUgBISkpizJgxzJgx428TU+7u7owdO5bPPvsMg6Howl2+fDnLli2jb9++TJkyBX9//zIdM6Mmk+M/vke/L48QetvoF0VjT6N2k1g6pSV+GhUVI7F7p9N58iaOlxiNo6GcU3fm/PAuvVygMHQJ3Yf8QgR+dOlnx4Hlx4hRbyzpSptRn/PzwEqYP2C7UTHxpe/4ISzev5j9pHLs15PkdGiCRcEllkx5i3d2pZB+q4lOhQo9mbPwbbp5hvHVa0OYXvzUrjR2M37gPtZ0/4F9E23ZPmMoA9bHcfvzuhSsqdvmQ9ZMfx4vpYC9c/vRdS94MIBlh9+ihaKiKgmsnjSAN8PBx/sDjq7txJ3FrCHye/oOWM2Zor+4sHsK7Xa3IOXIJ6U6EfXxxx/fTEQZjUU9dTUaDf369ZPeUaWY1tyLgLpeBDyRrWmwdK9BbfcaT8l2hBDi2STdAYR44vRE/LKYSJ/BDB4zmg5BlijoORc5g1U/ZRctcW0u748qSkY5Orbh5Tcn0LuNNeG7T5L1kFstODqRiZ8eJslgT2C7sYwYO5qOQVYUqhfZM30sKyI1ODefwKj+9XEobrjVeeVD2o0LvpUAQ8/FpRNZHwsKGsyMVtR+uR/lgFQOcunnogybQRPLwZ3nKAQ8TbrTvuLDFTU6zQt06GaJBkjM2saupKL7/8bU7Rw4CwoWeI/qVuI9qZeWsDGiKV1fn8irXapjj4Gw+P/yVb81xKsKuUfGM3rsWsJzzPCpPZSho0fRNtCaHPUw60b3Y9EZo5yi4uG/VDUaPv74Y7777js2bdpEpUqVmDx5MuHh4UycOPFPy99ITHl5eTF//vy7DuUbP348Ot2t+0dGoxFVVVmxYgVVqlShf//+hIaGlsl4qRhJ2TWNnvOPEFqoo3yV9owZMpCRjT2wMaZxcPNUBi8II18Bo3E/M8Zt4ni+gqN7C94YMpTxXatTXmPkevI65n93ucRsLgWEsXZ5DP4dX2VSv5bUMgMjCRxd+D0/FzxcmaR41qSeUrTnuWciuKLksG92f0buSiFT8eT5LoN4r09TaloYuHZtJe8OWcFp1ZNWw4fyVu2issyUQPoNH8aglk4U/vEZb6yPIw1LqjTpw/tD+zOspi06sjjz62K+DP1n5ZHi0ozXR3aiiVlRlc+z6su8PaxtqTwX4uLimDNnDr6+vixevBiDwXAzGXXDncNhhRBCCFG2SQ8pIf4Fvkxg1toPqWGiUtjHlpgG0zlJKinnYtArXoQu+5wjhWBLEwas3MTrQRoM2jdoMMibMVsfooGi6Ineu4cYwJUW9PxkDq84GtH3qYzm1W8IN2STcUaPc8eRDNedYt2PR8jBkRpdJjK0iY4Crt5sPirZVek3bymjugTiaAYF2n60cVnK4sQsItfuJu2NHljl7eDAQVCwxOeNrlRQHq4Lu2K0pGbPfnj9sIgo9nF+UxoMsqVg96+cBBxpSs3+FYC8m++xpTEDVm9jVDUNRs2b1DOW582NRqIufMfa5Beo/OXXhKvgyzimr51OHVMV/ZjmfFSjJ6sy/uDg3D8YubTJv3p+LF++nJjwAwBYWlqWSEaIsiMwMJDr168za9YsZs2ahVb7133vbiSmxowZg7e3N25ubjcnRgewt7cnMTGxxNClG431ZcuWsWzZMnp0e4keudmUpYF8quYqPyzcw1WgnPlgVi8ZTiOdilF5mSajOtDvSC4XVyxj7chp9IqPIsW3MjVVV7pO+A9Ta2owaOqibBvBJ3kGUmMTKFQq3zZht46G3eeybUJlTNEz0LQ9QUtSySaa0FgjeD1MoWSHgxOQBMbsLJLzdrNwvUohOhp0+4St7wVgrsJ7tcdTffweohPX8u3hPnzx8mAcY5fyvxNgjx9dBg+lu7aAsP/LxtnXDw/TF/nvnME0U1QKG2eyd/B6wkkm4ZqBf9LdROPYgKGD4dJXG9mPgnvljrw7PLBUjSqKz8tl8fIV7Diw/6/DrijY29vTv3//MlkW+NcbSLWaDaVQFEIIISQhJcS/TYP9c02oYlLUIjB6eeNe/IqhUI9Rk8z5YzHoAU868kLVollEtAZPWnRrj+3WTfd8Mt3dtulUrgLmXCGOdXxc34/1jRpTs0YD2szYwowGnpjfX2sM/4B3eatXIHbFDRoT43N0eNmVpV8mkHRpC79nv0ybw1s5AjjxPDX6lftnXTHr9qOd5yL+F5PGlfW/kzG0BSd3bCMTCHLrQbvyJWdN8aADLYOKYqYxutG8S3tsN24ig1BCz58m61TRcrEuu/h6+GkUQNXkEa8DMJCx8wzw7yakbG1s0Lu63kxEmJqaymVTRvn6+lJQUEBSUhJxcXGEh4f//RWmKJQvXx5PT08U5da5HRsbe89teZUrj21SVpmKj5p7nL2RRdURn84vULf4UXwa1ZWuHWtje+QEaYTwxxk9r9Trx/+t6ELYkV1sOjyXYT/FcjnsJAeK89FGtWSyXoszgY18ip/QqcPLszwaUlHRozc87B7nkpNd/FlpLbAJO81pinqMxl36mpfHFi9WeJ08QE8sl8+kQQObP3/Wqg7/3gs40ukKu/fsZ+e8aXwVG03IyVNcAjQYwPD0XyNWOhOc7GzueV2YmZnh5eVVJo/R3NICIYQQQvyZJKSEeOIUtFbmt9e0S7xq1KSQWjwRidbBCSetevNuts7VBVsg6YFbfRpsX17Ah8cG8p+fTpKYG86pXeGc2vUD388bS2DVD3lvzQQaONzrtrkGCx/PEnOvKKopAS/3x/fLOYSzi+O70il3cDMpQFC5HrR3U/gnt+NNjA1o/3IFFs+/RvKxLewotODiLlCwpfLgzrjd0ftK5+CKm+ZWzLTOztgAWeSTm5dOVvG8XbkpJzm0//bYW2BuAZjk/+tnSKfOnano1hmA4OBgHBwc5LJ5CkydOpXp06eX6OWk0WgwGo0EBQUxY8YMOnfuXKJnFEBERAS+vr4l3nejkW5hYcHYsWN5++23MWRnETpwZJmKiZqZcXPeJWs7+xLJa1M7B2yBTDLJTDFiVMP4etSrjDlmoADQmjrg4+9PoM1xTmXeraQ1wcxcue3vR7C/aRcJKR5VaR7khXfaeTIoGs58PewoCbdvxNwcS1WHqs8D7p5wyb/yIz1fW8CGdAAtlrblCQqohOvR8Acv58soa52Ol9q1ZcCI11i3bh2rV68mIyOj5Pei0UhsbCwTJ06kZs2aZe4Yv159jowsvRSCQgghxJ9al0KIUkUx2mFb3HYpSL1OjOFWC6cgNoa0O5cvHgWkYrjtZnoWWdkll9NqqtFp9jF2nzjBks9nM2rASzSobIuOfM5dmMK3H525vx00+XOxoQa+QvsA0BPDH7t/ZO/uXMCOykM6/ilh9OAtQB2VevWjKhDHLg783zb+SAVXWlG7l9ufCjF9aiIJtz3XPD/qGqmACQ7YOzlg61xU9NVtspmj4VmcueNn/YW35SQUj1xqaipz5sy5mVS6kXSqWrUq69at48yZM3Tt2vVPySgomii9xDWvKFhaWjJ58mSuXbvG9OnTcXR0LJvlnZ0zzsUlWHJCHPrbEjrZcbGkAlrscXTXkLHlUyYfM2DEhwFTVhG7ZzuXvhtJxydUk1GVfC4s/4FdFM1fF9C8Fta2ttgCWtwY/N895OzdV+Ine99ufh3hedf1GbWprJu7gI3p4GTeme+WbSdl508ceLM2bndW1jQ3ynnjrXJezSEj++m5RqysrOjXrx9RUVHMnDkTW1vbkt9hWi0ffvihFCZCCCHEU0QSUkKUMlqjOzUaVsAEiGYtW/YVtTgMXOSX738tOam5qmBja44CZBLO1ZSixq7+6kb2h91azKCNY+0rWoJ8zOlW7RdsOr3D6I9/4tsDK+njWLT2zGvRJRtL5KPPub9kkomhOu16BGKKgaidM/kpCtxoTZ2eLo+m0Vr5FdpXgwKusH/2MsJRcA/oSWvXP8+ndY01bDhYHDPlHD8v20k24ERt6lavRc16oGDk+u/L2JlQdHyFWRtYMKgNA3u3Y9Knx+UkFI/cvHnzyM7OvjkMLzAw8J6JKIArV66wZMkSjEZjmU1E5efGERIWyqk7fk5HpaGY1aN1taIyKPKXdWwpzrgbjZf45qfzZAGO1KVVdYiOuEQWYEcVWrX1xUUHeaG/syP98ex3AelcLd7XYyEH+P7rt+m6NIlcwJnWDOzuhK5KbeoBBuI5uOYPYilKGiUdmEu3N0bRZtT7zPmLByWoahxhV0BFwbFqE7oF2GKq5HB+1x5KDuxUsLUu6uGVQxSXMorLrWt7+C32QY5IpTAvv9Q/lN7W1pZJkyYRHR3NrFmzbiamDAYDv/zyC6dOnXrmyo/Cyz/z46LPWPr1NxwIL4sP3jCSFr6eFXNG8d7r3Xl9cG/emzCV5b+EkGxAPAJ6bRgHln7Gd4sWsHF/IvJ4FiFEWSFD9oQoZRRVR+XBE2nx7etszwrhh1frcLpVEGaRv3M4VIv2tn5QimqCb/3m2Cz9lUQ28UX7FzhQW8P1g3sIu605BGXFAAAgAElEQVTVoTW60LBtIyx+P0goMxnRch81g+wxxB/laErRXCsV2tctqjZaWmMGFBLBitc9OVtnET+svcccRqoWj579qD1zEocS40lCoVZgT9o4Gh/J5Lk6QwDtXq7FvLMnSU1MRIsLfgPb4nSXdedwkh961+R4y2Aso/dw5AIomFGxx+t0MrVFGT2RGr9+zKmC75jc4gxrGrqRE7KDk9cLsKQOwW8GyUkoHqnU1FSmT58OQFBQENOnT6dLly4l5oj6K6NGjbr5+/vvv8+YMWPKWG8olasnZtHwlT+/UsHlHaI29+aVMS/zv2FrOKmuo3+PSzSt7UJ+2GF+jwYNDtR7pz9tNRoyfAKw5hTJ7GDCoEx+dknixNFwYk2AQlALH23LNon1DOy3/i4Vp0q89PEoXrI0olFaMnbgQjYuTeDsgfeo268udd3SOX3kHJH5UMF8OJOraLnbZFCK4kblikCSyuWTs2n+1hY80kP4PTSdosFdBvR6QDUhqHYt7DadJJV9TOn/OtsDIeLYcc5p4N4tT3MsrYFMIyF73qJKvdZcPVL6n1ZnY2PDxIkTeeONN/jiiy+YNm0aeXl59O3bl3Pnzj1byYbjX/Dfj37HgDu9nF7lOd+yM6+gUb3Crg978sF3J0m64zL4ednH/DBjFKNXfUr7Stoy/zmpSgFn5zTmzeUJOGtGMvnwJGppnkwK2KA5ycbJ7/AzOpp1b07bJi7S60AIUSZIWSVEKWTiPIzpy9+nuYeWwsJQjm1bx5kL/nSb3B+fOy5hq66fMLGjJxaopMbuZvfmA+gqTmd4i5IJI/eBy5n7ZjPKW+pJvrKLnRt/Zs+RaxRqK/PiwOWMHVw0ibam2lBee6UW7lZa9LkpFEbk3dc+a9170b74IUJa3PAb0gb7R1UPU7U4d+tH/eI/3WlL/e52d13UpdxABryQw/nf1nHgQioGHGnY5lve+6Qh5oBZ1Wl8ufoj2gdYkZ92nAPbtnDyeiEudu0Z8vUGRj9nLiegeKQ+++wzgoODWbJkyc0eUfeTjLpy5QpHjhxh1KhRJCcnM23atDI7NO/vWNZ4m3Wf9qaNq5ac9PNs2/07u6PzMDULoPcb81naxwMdYN9uPP99zhor9MSFHmDjH6n4dv2Yz5uBgkrm0WPsz3pc1RoNZhZu1K7fi88WLeLLVg5oAUU1p+aopWwY8RzVLAuJCf2DDfvOEZVvQdWaw1i4dijNTO9eEGoMjnQbN5aXXRSMJHLq4B72R1Rk4Ox36AUYyeb8HyEoaHDoOJ5Pm1hhhUpq3FE27jpDod8oJtS7nz0PZMDbrWnoZIaJPo8MMsvU+XEjMZWQkMC4ceOIj49/JntJlUVGTRz7323KmMVFySgrm7q80PtthowYRvtGFbDCQETMAr5o+gEHs5Wn4IhVClPDiYu7TnZMOvnSTUkIIe5JUe+cJVWIp8jHXx/DwcGGVk38yuT+q4Z4Io6fIb7AGa+aNSln/RcVNkVP2pWjXLyajYlLMNWquWL2VxXEvGjCz10iPk2PqU15KgYG4mr9zyuCqlLA6Q/K8criFDwZzviwr2hj+eiKlwLdHj4LbsXieA31mmzm69WtsS5evarJY88IK0b8Av58zCfRo3EOO0RYjIJ9pboEeln+eX81eSRfOEZYTD6mTgEEVCuP9b+coi8o1PPNskPU9FOpWDyJjExqXsabJ6rKnj17eP755+8rCXW7kydP4u3tfd9JqMRrUYQOHIlfr5dwrVWzDMYqm+jwUC4l5qO19iCwijdupneWM/kkXj7HqUQNnpUDqeZcenqKqIWJnD93hWv5Zrh7BVDdw4L76fOh6pO5EBLKddWNoKBKeJr9VRlbSMrV85y+noeJqx/1/BxLPGCiLEoNDeX8kuVE9+iIe2AgAM2aNfvbayUzM5OLFy9Sr169MnOcNyY179q2+kO9P3d1S+q9XdxDan4EH/Ywvcf3cT7JF3Zw8NhlkrJ12LpXp06zJvg4KH+uO4TtZP+hi8Snq5g7+BDQsCW1K9miAVRNNvEXLpCYq2DpVg0vi/Ps37aPiHRT3Pxa0uR5f+z+5nsz99hwunT5lqso+FpM5P3D02jsVLQPRm0mlz5rSb//nCAbOzpOusSno1xQlTRizoaTUqhg7lwVv/KWN+tDV89fI9ugxaZcDbxdNMXlRhJXDmznyKUYsvSWOJWrSZ0mjfC2v7WduHOXSMnTYu0RTAWzEH7fvJfsigPo0NgezT1iAJAbe5bL8Xko5hWoWEVD1I4NHAnPxNQxmHptWlDZTkHV5JB46QwHPmvDxPVZeDCI1ze+QQOvGng5F+1LQeopDu4+yNVEA1YuwdRt1QSfOwKoKgUkX9zOH4fDScm3wcP/eRo1qYyt9s7yJpqzu7dxKiIXG8/m1OlyiYUevYt7SB3hfwtqYPIPztlvVhzE201PteIHWwYEBODu7i5f6kKIR06G7AlRiilaNyrVf5FK92zR6LCv2IiGFe+9To15efzrlMf/ke2lkZQzK9nw+w62rkpBjw6vbkN5/hElo4yaeM6uX8m+A8tYGw8WBFFrTMubyai7xg1zXPyb4/I3B6kYzXEOaIJzgJxn4jFew4pCixYtHuq9tWrVesZiZUUFv1pU+Jv7B4pqhmul2rSuVAr338SFoJouPOigX0XnRGCtRgTeaznVBCfvGrT0vlHuP5vXlI2NTZlKRj1pxvyDrHi9N3O2RXP7nPc22vp0m72Sd3t5YwIYtNHsm9SG8Usukl7i+9OOBu2WMOubl3DVHGdZ3xYsitVRqctrlN+/kL3Jt5oQwd5zmLrnTarfpRegUZvF4R++JRKwoDqtlk29mYwC0BhsCBi5lHlOB4lVNdhVKlpHgW47n7fuw89oadRxL4u+bogZoE//gY/avsdBTOn8Thyzx9qhT13LJy/34/sLBSUuB0tq0mPeJib19ECv7GVx584szzah5qhP8fl5NOtidbR6tR2tm2Zx8B4xKKfJJ2zRC/RelIi96yt0qLKN5XtTuPHMRCfaMXzzOvrVC2HVoOf4PKLo/9f5jvc7raD75BRmvmEgfOUgxkxeS1jOre1YKw3o8dlqJvQojxYwFp5m3dvdmbUu4rZ+jFoqebzB6LWf0s67KHlVGLeM6b2GsCpcX3zcOqy+a0ElU6BArgEhRNkiQ/aEEP+IquiJ3zqRT/7zPSczwZ2XaD+pzl/20HpQBiWCPf8Zy/xlJ0hFQ5XAifRtaCKBF0IIIW5j1MSzd0JbZmyLJg9PanR+m9dGD6dlRXOyDEdYO7YPX54oSqUU7n+fD5dcJANr/J8fzcix4+jZyBUd6YRsncGqU7dP+KQn4pfFRPoMZvCY0XQIskRBz7nIGaz66e6PejRwltNHi/KmnrxIo7p//t7W6IJo9spQevUdTNtGrg9W99Bkc2xWP364UIApgbQc9D6vvzmIhm4KOZzi4NuLOHPbE3dV9FxcOpH1saCgQaNV0D9QDCApYQXrT9ek8+uTGNS1GnZAMtvYNmkL2aoXDUdOpXc9SxTAlua8PHYqzRuZkntkPKPHriU8xwyf2kMZOnoUbQOtyVEPs250PxadMWLUZnFyRns+WBdBDj406DWB4cO7EGRj4ErsAr5uPI9zBgW97jyrRr7KynA9Wnxp3GcCwwa3xuPsb4RIMkoIUQZJDykhxD+ioMWx0VsMNaZhZl2JGu170cTj0a1fo3pSe9AERiSZ4+DxHC17vICHUvJurKLq8Gr/PsPK52Nm3xAnZCSyEEKIZ4vh+mK+WZOFiilNe2/g8zm1MAcKRzZgfN0hbMk6wh8fbGbglo6khqfjWDWYCmpPRn0/iYY6lcKzuRxvs5CrxJF5WQ+3dUTzZQKz1n5IDROVwj62xDSYzklSSTkXgwHfPw1PNSqxJMcU/W6qq0CF4gVUpYBz/+vOzC2pt83Hr+Be/WPmz2xy/8eqiSQyPYCAqjp8Gv+PT6bVw1Qp4GzBJnp9nUge0UTrIfDm6EYVJbsq/eYtZVSXQBzNDVz94T5iUPvWNq2oTa/lWxlXS4fepDfOocHMPq+SezqUhIKu1O03Ae3Fuaw+Ci7Up+Po96hvnsJvA74mXAVfxjF97XTqmKroxzTnoxo9WZXxBwfn/sHQxREsWxxHITqadV/Dwrm1MQNea9aHjv1WE84i1u8ezfhy37LyCChY8PyQDcyfFoAJRgbX70rnEZtJlMtACFHGSEJKCPHPqFrcmr3D2GaPZ/VaoxdNhs3iVjX1LskmVUfljh8xrqN8HEIIIZ5NBQf2cBawojpVB9TgxiM6tPbdad90CFu2qqSf3Mvpgq4067+ONd0vcnTrZo7MGMrqa5cJPbqfy4AW4x0PhtRg/1wTqpgUff8avby5MZuQoVB/131R0KLcGIehV29LPqlkXd7GyePGEgmpWgXJD9aA0QfSa9EJ2l/dyc7fNjFn/DyuRZ3h5OHE4uF06h0PoFTwD3iXt3oFYqcW1V18HigG4Ew9alQzAVS0Bh/KeQLnQaUQ/V9Me6bnDKeL5+CPddnF18NPo1A0j2W8DsBAxs4zcP4s54vjdiXsI94aWBwt/VUKgEIiSTiSQI7XYa4CNtSkal//4nmiNNh06k2jEZvZIJeBEKKMkYSUEEIIIYQQZZliIDMtFT1ggS0Wrrdm5VCMptjYa9BgpIBUUo1QEDGfMd3HsiMeQIulfWUqVg/Caf8Z0v5030dBa3XbE2jv4wENChVw9wFCIZfLXNGDtwko6PDu9iXv+WVj0KZwetFMtkU/+OGqSj7hP3Zm2OQdxOgBjQXO5WrgG+DIsbMpd3mHBgsfzxIPAniwGIAWC8yUW0d4P4+pUEkjqzjXlptykkP7b3+XBeYWgEk+Slo6WRQlt2JDd5IUdvtiFpirphj12WSkpqICZthj6aqFG2k3nQsOjkCKXApCiLJF5pASQgghhBCiLFO12Lu6Yg7kkkBmhP62lzJJiCvqkWSGG06myeyaNpad8eDJIP67LY7D5y6w6sNWOD+iEe86QyANmtihAaLZwM5fUopSJ6oWj0bDGDh8DAM62ZERc+dxaG42TpTC2/o4ZWWWmKRdn7+She/tIEavo3aLH1l/KpUDh3czuaXlXzduTG69YtQlPfYYACg4YOtc1OSq22QzR8OzOHPHz/oLb6M6OGIDmFCBV75I/dMypy+n8PkHlbGytUEp/ozTo27rnZYfQ6Iko4QQZZAkpIQQQgghhCjlVPSkJYRw4fzpkj/nznAt3oC2aXvqKJDLRULm/kZicWKlIPwr1u4rqvY7P9+aGiaRRFwBFQXXwPa8EOyIqSabiG3biXxE+6qoZlQf9g51taAnik1j2vLR4oPEZaug6Em+sISZfSdz0HjH+7DH2gHAQObZMNJUBVUpIGbrVsJvX/D6Ja6poMEW3xe7EuhkgrHwNL/vikZ/H/tnIOqxxsBALnkq6NQa1KwHCkau/76MnQlFH0ph1gYWDGrDwN7tmPTpcQh6jmAtFHKNi4u3UbSYkdS9E3izdxuG9OzL938YsWvYlEpAFqcJ+foQmUrRvFxxyxZzQC4RIUQZJEP2hBBCiFLIYDCg1WolEEIIAIwksWVmfbbMLPl/BRNavxbK/I8G8vqoBRz64jJHDvagx4stCfLJ5sqBvVzRgzMdaDvteSzVRLwqAuEqZ86/Rd++y3FNO8zRkEQMFA0bMxY+gkaG93imzz7IoHFbiTEe4/+mNuH/pllhY2MkNy0XA9ZUDPYm+szlW+8xVqdGXVB+g7PXx9Cvxwb8zUI5fuAiubcfs7sf5RUIUVPYPOt5YvY5k3FqD+cTdYAelUL+7hA0lH8MMVAwt7JBQyZX+YoxwRvpNvE8E0ZPpMavH3Oq4DsmtzjDmoZu5ITs4OT1AiypQ/CbQeh0QQwcG8jO2ec5dqgXL7/YgqoVkrm07yjXc8GfKVRdaoJi8zqDW81jwk49f/zSgW6xLfG3DOPEvotkFR26EEKUKdJDSgghhCiFNm/ezLhx44iPj5dgCCHuXak3WFN90q8seKs5FSzyiL2whR1bf+dKhpZy5fowYs0P9K2soNW78sLUBbQvr8VILBd3r+fM2QDaL/6MjgoYySBq56F/vD+KaopP759Z9uNE2laxKZqA25BNZlouGq0/7V/bwLf/bU352x5WojG68eKHC+lQQYtKBpGH1rP79zy8Jo7j+dvWrbXqw5ufdsTbHLKzjnNg625yrUfz6SddcAASOcb5vRl/uW+PIwaKaop/32n0qOWGtU5PbkYceVlgVnUaX67+iPYBVuSnHefAti2cvF6Ii117hny9gdHPmaMYzQkYu52vJ3fB37aA2Avb2LX9KDG5VgTW+oCxRz6gvqWK1lCRjgt/ZWxzV0zJIPLIenbuicNlwEe8XE6uASFE2aOoqirPRxdPrY+/PoaDgw2tmvhJMESpV1Co55tlh6jpp1LRreh/wcHBODg4SHCeQQaDAT8/PyIiIhg7dizvvvsubm5uf7l84rUoQgeOxK/XS7jWqikBFKVeamgo55csJ7pHR9wDAwFo1qwZiqI8Vcf59epzZGTp6dq2+pMrP/KiCD8bSmK2Dhv3alQJcMbsjmVUfTyXT5wmUV+OyrWq4Wrx+JoEqlJA+pVThEanoNd5UrF6dTys//pzVtUkrp46zfUMM9yq1MfPzfTu35spZwkJiQPn6gQFuWH+oPv1JGOgySP5wjHCYvIxdQogoFp5rO/SNUAtjCX81Dni8sxx9q5FgJfVn3oQqEoBqWGHuRhtxL5iXap4Wz3SXgbfrDiIt5ueal5FfwcEBODu7i6FlhDikZMhe0IIIUQppNVqeeedd3jjjTeYO3cuX3zxBW+++Sbjx4//28SUEEJozb0IqOtFwN8so+jc8K3fGl8AHu/9aUU1xb5ifepXvM/lFWcq1mrFvRY3daxGnebVHn6/nmQMjOY4BzTBOeAey5l44FfPA797xNPRtymNfeVcF0KUbTJkTwghhCilRowYgZ+fH4qiUFBQwNy5c/Hy8pKhfEIIIYQQosyThJQQQghRSmm1WqZPn86N0fWqqlJQUMC8efMkMSWEEEIIIco0SUgJIYQQpYherycxMZGwsDDOnTtHQEAA3t7eaDS3vrKNRmOJxNSYMWNISEyU4AkhhBBCiDJD5pASQgghngBVVYmJiSE0NJSwsDBCQ0OJiooiJSXl5k9qaioZGRn3vc4bian58+ezZMkS5gQEI49wEEIIIYQQZYEkpIQQQohHrLCwkBMnTrBv3z6OHDlCaGgo4eHhZGdn31zGzc0NHx8fnJycCAoKwtHREQcHBxwdHXF0dMTe3h4TExMUReG1114jKioKo9FYYjs3ek317duX0SNeI2/KLAm+EEIIIYQoEyQhJYQQQvxDOTk5/PHHH+zbt4/9+/dz6NAhcnJy0Gq1BAcHExQUxEsvvYS/vz9+fn74+flhZ2d3X+tet24dV69eLfG/G4moIUOGMGnSJHx8fEi8FkWofBRCCCGEEKKMkISUEEII8RDy8/PZtm0bK1euZMOGDeTk5GBhYUHDhg0ZN24cTZs2pWHDhlhbWz/0NoxGI1OmTEFRFFRVLZGImjx5Mt7e3vJBCCGEEEKIMkkSUkIIIcR90uv17Ny5k5UrV7Ju3TrS09Px8fHhrbfeonPnztSpUwdTU9NHtr3vv/+ec+fOAUVP3LvRI0oSUUIIIYQQoqyThJQQQghxDzk5OXz77bd8+umnREVF4enpyeDBg+nduzf16tVDUZRHvk2j0cisWUVzQg0bNkx6RAkhhBBCiKeKJKSEEEKIv5CSksLChQtZsGABSUlJdOrUiaVLl9K8efObw+cel61bt9KqVSt27NghiSghhBBCCPHUkYSUEEIIcYf09HRmzpzJl19+SV5eHq+88grvvvsu1apVe2L78OKLL9KhQwf5MIQQQgghxFNJElJCCCFEMVVVWbNmDWPGjCE5OZnhw4czbty4f6WH0qOci0oIIYQQQojSRiMhEE8zRVEwqqoEQpQJcq7+uy5fvkz79u3p1asXVatWJSQkhM8//7zMDJfTaIruMalyHokyQjUai7+sn+7qqKKAXJaiTDGo0kgUQjyZ+quEQDzNTM20FBboJRCiTCjINwBgopVYPElGo5H//Oc/VKtWjRMnTrBs2TJ27NiBv79/mToOKwd7VFXFkJsrH6ooE/R5+QCY29g81cdpbqYlP1/qIqJsMBiNFOqNaKUuIoR4AiQhJZ5qbk6WpKRJ40yUDalpOQBYW0gsnpT09HS6dOnCxIkT6d+/PxcvXqRv376P5al5j73Ra2lJimIkJyFJPlhRJuTEJ5CLiq2nx1N9nC6OFqRn5GI0SjcpUfqlZeSiSl1ECPGESEJKPNW8PK1Jz8glJ69AgiFKvdjETLRasLWURsuTcPHiRRo0aMDOnTtZ8f/s3XlYlPX+xvH3zLCDgMoqKoIKgiuuaLZoWZqWZZapqbmc0kxL0ywrjy16zklbTru5pJmW5ZplVj/33DX3BUREFEEEEdm3eX5/mBxNLUVAlvt1XVyOwyzMPc88y2c+3+8zfz6ff/45VatWLdevKdnJgZRjMXpzpVxIjYnlhJFf4mesvNX8a1Qhv8DKmbPpetOlzEtIvLCcVquifRERKXkqSEmFFhJ44eAyKiZZYUiZZhgGR6IT8XQH8yXdOeWxU6c8+P7772ndujU5OTls2rSJ3r17V4jXVbVlC3ITk8k4fVpvspRp2amppMXEkl7Du8K/1ro1XbG1NXPkmLoXpeyLik7E1RkcbbX/ISIlTwUpqdA83B2pF+DOvkOnNNGvlGnH41I4n5ZDHZ/Ll9OK3jlwK3zyySd0796d1q1bs337dpo1a1ZhXtvdI54hy2oQv3mb3mgp005v3U6+Af7dul12fUUswtvb2RDW0JPDR06Tm1ugN1/KrORzGZyMT8Vf+yIiUkq0dpEK7+7wWpw7l8XBI+oYkLKpwGpl8/YYqjiBj/vlO4H29vYKqBjNmDGD4cOHM2DAAFauXImHh0eFen1VqlXnVB1fErbuUJeUlFnZ51I4uW4zB+zNuPn6VIr1Xcc2NcnNyWfn/pNaAKRMMgyDTdtisLWFOl7aFxGR0qGClFR4IYFVaRTswcZt0aSmZSsQKXO27znB2XOZNK1rXNYdYGNjo53AYjR37lyeeuopevfuzcyZM7GxsamQr/OhKf8hxVrA4QWLsObrzF5Sxg56rVYiv1tKRn4utfo/UXi9yWSiSgU+256PhzO3t6nBrr0nSDhzXguClDmHohKJjUuhcYCBjeXyTkUXFxcFJCIlQgUpqRR6damHrY2ZH345oAnOpUyJOHqanbtPUNfPwNONyw7OyvsE22XJggULePLJJ+nRowdffvkllgp8PmtXDw/cB/cjKy6BI98twbBatQBImWAYBtHLV5B2NIaYsNDLuqMMw6jw67xudwXgUdWBH//vIOfO6wzAUnacPHWOdZui8PWA2p7GZfsiLi4uFXqbKSK3lgpSUilUcbZjaJ/GZGRmseTHfeqUkjJxYLbvcDz/t/4Ivh7QqI5xxe+9vb0VVDFYtWoVffv2pWvXrsyfP7/CdkZd6va+fYkLDyNpz34Of/0d+bkqxMutZc3PJ2rJ9yRs3s7vvtUIfvDBy35vMpnw9PSs0BnY2VgY1qcJJqOAxSv2kpiss+7JrRd9PIkffj2Au4tBy/rWyzq1DcPAx8dHIYlIiTEZmulZKpEtv0ewZNVprIaZ21oHElrfW2cxk1KXnpHDuq3RxBxPpqa3QYt6xhVn1rO3t6dNmzYK6ybFx8fTrFkzAgICWLduXaUbAjnjueepv/8IjtXdCer5EO4BAVoopNSlxZ0kcuEysk6dZndNLxoPHnj5zqjJhLe3N8HBwZUij0ORx5n343Eys0y0CqtNWCM/LBZ9RyylKycnn007YzgYkYCHO4SHWLH901A9i8VC27Zt1SElIiVGBSmpVPLz81m3YQs7Iq3EJ4GbqwNNQvyoF1gdJwc7BSQlxjAMEs+kczDqNBFHTmM2GzQMMKjjZVy1KNqwYcMKN+H2rfi833PPPezdu5ddu3bh7+9f6TJITU1l+fTp2P34C35mW1zr1cE3vA1Vg+tjsbXVQiIlpiA/n9SjR4nfup1zB49w2prH+Y63E3DHHVfc1mw207p160pTMDYMg01btrH9UDYx8SacnGxpHOJHcKAnVVw0b6CU7LJ39lwmh6ISOXQ4nvwCK8H+BsF+V98XqVu3LjVr1lRwIlJiVJCSSufMmTMcPHiQ0+cg8qSJpHMXrnetYk+1ai7Y2Vou61YRuamDMqtBeno2ycnp5OZbsbWB2r4GQb4GDnZXLmcmkwkPDw9CQ0MV3k169dVXmTRpEsuXL6fbn04tX5lERUUREx1NxLff4R0Vi6/FBqvZjJOPJ87enlhs7UDrPCmeo12s+XlknE4iKz4BrAZnCvI5WduHoMd7Ye/sfNW71a9fnxo1alSqqM6fP8/u3btJPm8QGWciIQkMwNnZjurVnHGwt9W+iBTfR9NqkJGZS9LZdLJz8rFYoKaXQZCfgYvD1fdFqlSpQlhYmMITkRKlgpRUStHR0Zw4cQKAjGyDhHMmUtIhI9NEfoEJfSj+2tnkZDIyM6lVsxZof/mvV7JmA3tbcHU28HAFbzcDi/nqoZlMJpydnWnWrJna42/Szz//TJcuXRgzZgxvv/12Ja8RGOzZs4fz589TUFDAiW3bOL97DzZJZ3HLs2JrNmHSrsBfis1IJzM/nwZu7td1+18TTtLJp/J1FRgmE/mGQarZTF51d6o0aUTt227DbL72cDQfH59KM1Tvz06dOsWRI0cAyM41OJ1iIjkd0jJN5OdrX+R61m27ti6nefiDCuNvmE1ga2Pg6mRQ3RW8qxpXDM+7dF/Ezs6O5s2bY2en0QMiUsLHSipISWV19OhRTp48qSCK4NVXX+W3335j9erVf3mgITewMv6jGNW0adNKMceoragAACAASURBVOl2ScrIyKBBgwbUqlWLdevWYauhaRQUFLBv3z5SU1O1gBTBxIkTSU1N5b333vvb28bGxjJgwADeffdddRf8jcpcjLooLi6OqKgoTCYT2iW/MVu2bGH8+PHMnz9fE28X476InZ0dzZo1w8HBQYGISInTkaRUWnXr1iU4OBiz2ayJzYvIqtPJF8vOH4C3tzdhYWEqRhWD//znP5w6dYpp06apGPUHi8VC06ZNC+cC0Trv+h07doy1a9eya9cu9u7d+7e3nzNnDoZh8Mknnyi8a6zzTCZT4Ta4svPz86NRo0ZYLBZ9Lm+AYRh8+OGHWK1W5s6dq0CKaV+kWrVqtGjRQsUoESk1KkhJpebj40Pr1q3x9PQs3EnWDuH177jo29ybz9DZ2ZkmTZoUFkfl5sTExDBlyhSGDRtG48aNFciflrm6desSFhZGlSpVLlsO5drmzJlTuG344osv/vK2sbGxrFq1CoAjR46wa9cuBfindV716tVp1aqVJkq+RPXq1WnTpg2+vr6F2wF9Nv/a1q1biYuLA+Cnn34iPj5eodzE59LBwYHQ0FAaNWqkL3JEpFTpq3ip9Ozt7QkJCSEwMJDTp09z9uxZ0tLS1P3zFy7uMCujou38OTo6UrVqVby8vHB1dVUoxWjMmDE4OTnxxhtvKIxrcHV1JSwsjPT0dE6fPk1KSgqZmZkqMF/Fxe6oiy52STVp0uSqt58zZw5msxmr1YrZbGbWrFl8+OGHlX574eLiQrVq1fD29lbnxbV2yG1sqF+/PnXq1CExMZHk5OTCed/kcoZhMHPmzMLPGsBXX33F2LFjFc4NcHBwwN3dHS8vL6pWrapAROTWHBtpDimRq8vNzcVqteog7Sr69u3LkiVLSEpKwsnJSYHcwIGZnZ2dvvkuIWvWrKFjx458/PHHPPPMMwrkBg/wLq7z5H+eeOIJli5dWpiLxWKhffv2/PTTT1fcNiIigubNm1+xzVi5ciV33HFH5dvBNJkK13lSdHl5eRQUFGhf5E+fqR49elyxfT1w4AD+/v4K6Dr2RWxtbdWVLSJlY39BBSkRuVG9e/fmm2++ISUlBXd3dwUiZUK7du1IS0tj165dmotLbtqBAwdo1KjRVX+3YcMG2rdvf9l1ffv2ZcGCBZd1tFgsFsLDw9mwYYMK0SLFwDAMmjdvzt69ey8roJvNZgYNGsT06dMVkohIOaLSuIjcsIvfeGdkZCgMKRO2bt3K5s2bGTdunIpRUiwmTpyIxWK54nqLxcJrr7122XWHDx/m66+/vmJ4VUFBARs3brxs2J+IFN2KFSvYvXv3Fd2cVquVWbNmERMTo5BERMoRFaRE5IapICVlzXvvvYevry+PPfaYwpCbtn//fhYuXHjV+XsKCgpYu3YtGzZsKLzuzTffvObwl4sFLDWki9wcwzB49dVX/3Ko2aRJkxSUiEg5ooKUiNwwFaSkLImNjWXhwoU8++yzmq9GisXrr79e2B116cHvxcsWi4UJEyYAV3ZH/fksaeqSEikef+6OuvgZu/REK+qSEhEpX1SQEpEbpoKUlCUfffQRdnZ2PP300wpDbtqfu6MCAwNp3rw5oaGhhIaGApd3SY0aNaqw+8lsNvPwww8D8NBDD112EoN//OMf6pISKSLDMBg0aBBwoRDl4ODAAw88AECPHj0uK0q9+OKLCkxEpJxQQUpEbpgKUlJWZGRk8Pnnn9O/f3+qV6+uQOSmjRw5EgA/Pz/mz5/P4cOH8fPzw8vLi71797J06VJCQkKAC2fhW7lyJQCDBw8mKiqKyZMnF/7/+PHjjBo1CoCjR4+qS0qkiFasWEFiYiIAL774IrGxsTzyyCMAfPrpp0RERNC/f38AvvvuO44dO6bQRETKARWkROSG2dvbFxYDRG6ln376idTUVHVHSbHYv38/p0+fZu7cuRw/fpzevXtjsViwWq2YzWZMJhPdu3fnwIEDLFq0iLS0NLp160ZUVBQzZswgICCgcKif1WrFx8eHd955h7i4OEaNGsXbb7+tLimRG2QYBlOnTmXs2LGcPn2af//733h6ehIXF4ednR3VqlWjXr16zJkzh8OHDzNgwADefvttBSciUg7oVEQicsMudkilp6crDLmlFi1aRGBgIM2aNVMYctPc3NzYu3fvFWfXMwyjcOgdXBgy9NBDD9GiRQsA/P39C393cejQpROi16hRg3fffZfTp0+TnZ2No6Ojwha5TpmZmXz77bd4enpedv2xY8fw9/e/bJ634OBgZs+ezfHjx6/43IqISNmjgpSI3DAN2ZOyIDs7mx9++IFhw4bpoEOKRa1ata56/cUOqUuZzebLClGXXn/xPn/m7e2tkEVukLOzM87OzldcHxMTQ506da56n6t9NkVEpOzRkD0RuWG2traAClJya/3666+kp6fTo0cPhSElymq1XnfR868KUiJSfI4dO0ZAQICCEBEpx1SQEpEbdvHATAUpuZUWL16Mn58frVu3VhhSonJycgrnzvvbHSsVpERKXEZGBjExMdSrV09hiIiUYypIiUiRnT9/XiHILZGfn8+yZcsuO923SEnJzMzEycnpum576aTmIlIyNmzYQH5+PnfeeafCEBEpx7QXLyJFdvr0aYUgt8Tu3btJSUnh/vvvVxhS4rKysq57IvKrTWouIsVr1apVuLm5FZ5YQEREyicVpESkyFSQkltl8+bNmEwmwsPDFYaUuBspSF2cfFlDmkVKzqpVq7jrrruuOCOmiIiULypIiUiRJSQkKAS5JTZv3kxoaCju7u4KQ0rcjRSknJycsFgsGtIsUkLOnDnD7t276dixo8IQESnnVJASkSJTQUpulU2bNtG2bVsFIaUiKyvruueQMplMuLq6kpqaquBESsCXX36JyWSie/fuCkNEpJxTQUpEiiwpKYm8vDwFIaUqPj6e48ePqyAlpSYzM/O6O6QAXF1d1SElUgKsVivTpk3j/vvvx9/fX4GIiJRzKkiJyE1JTExUCFKqNm/eDEC7du0UhpTKAXBOTo4KUiJlwJo1azhy5AhPP/20whARqQBUkBKRm6KJzaW0bdu2DXd3d4KCghSGlLjs7GyAGypIubm5acieSAmYNm0atWrVokuXLgpDRKQCUEFKRG6K5pGS0hYZGUlISAhmszZhUvKysrIArnsOKVCHlEhJ2L9/P4sWLWLYsGE6u56ISAWhvXkRuSkqSElpi4qKom7dugpCSsXFgpSG7IncWmPGjMHHx4eRI0cqDBGRCkIFKREpMnt7e+Lj4xWElBrDMDh69KgKUlJqMjMzAQ3ZE7mVVq5cyc8//8ykSZNwdnZWICIiFYQKUiJSZP7+/hw7dkxBSKlJSEggMzOTevXqKQwpFenp6QC4uLhc933UISVSfPLz8xkzZgzNmjWjf//+CkREpAKxUQQiUlRBQUFERkYqCCk1R48eBVCHlJSa5ORkAKpXr37d91FBSqT4TJkyhQMHDrBq1SrNHSgiUsForS4iRRYcHExERISCkFJzsSClDikpLUUtSGVmZpKXl6cARW7Cli1beO211xg2bBgdO3ZUICIiFYwKUiJSZEFBQSQmJnLu3DmFIaUiOjoaFxcXPDw8FIaUiqIUpNzc3ABIS0tTgCJFlJqaSp8+fWjQoAHvvPOOAhERqYBUkBKRIgsODgbQsD0pNWfOnMHb2xuTyaQwpFQkJyfj7OyMvb39dd/H1dUVQMP2RIrIMAyGDRtGfHw833zzzQ2dVEBERMoPFaREpMiCgoIANGxPSs25c+dwd3dXEFJqkpOTb6g7Cv5XkNKZ9kSK5t133+Xrr7/mnXfeoVGjRgpERKSCUkFKRIrMx8eHKlWqqENKSk1KSgpVq1ZVEFJqilKQujhkTx1SIjfuq6++YsyYMQwdOpRhw4YpEBGRCkwFKREpMpPJRFBQkDqkpNSkpKSoQ0pK1c0UpFJSUhSgyA1YuXIlAwcOpEePHnz00Ucani0iUsGpICUiNyU4OFgdUlJqzp07pw4pKVVJSUk3XJCqWbMmALGxsQpQ5Dpt27aNnj170q5dO+bNm4fFYlEoIiIVnApSInJTgoKCiIyMpKCgQGFIiVOHlJS2onRIOTo64uvrS3R0tAIUuQ7r16/n3nvvJTAwkGXLluHg4KBQREQqARWkROSmNG/enKysLA4ePKgwpEQZhqEOKSl1RSlIAQQEBHDs2DEFKPI3li1bxr333ktQUBCrV6/Wlw4iIpWIClIiclPatGkDwNatWxWGlKi8vDxyc3OpUqWKwpBSW+bOnz+vgpRICZk5cyY9evTgzjvvZPXq1Xh4eCgUEZFKRAUpEbkpXl5eBAQEsGXLFoUhJerisFDNKyKlJSkpCQBPT88bvm9gYCDR0dEYhqEgRf7EMAwmTZrEkCFDeOyxx1i+fDkuLi4KRkSkklFBSkRuWnh4uDqkpMRZrVZABSkpPSdOnACgVq1aN3zfgIAA0tLSOHv2rIIUucS5c+fo0aMHr776KiNHjmTevHnY2dkpGBGRSshGEYjIzQoPD+ebb77h/PnzuLq6KpDrZDUMEpMzOZWYSVpmLgUF6qT4K9nZ2YTd9hhns6uzeutJBfIXLCYT9g4WvKs54efjjJ2NinhFcfEseUUpSAUGBgIQHR1dpCF/IhXR7t276dmzJ6dOnWLOnDn0799foYiIVGIqSInITWvTpg2GYbB9+3buvvtuBfI3TiZm8NvOOHYfTCI7O1+B3IA7ugwnIQ2+/z+dvex6mc0m6gW40y7Mlyb1q2M2mxTKdTpx4gQmkwk/P78bvm9AQAAAx44do1WrVgpTKjXDMJg1axbDhw/H39+frVu30rhxYwUjIlLJqSAlIjetWbNm2NnZsXXrVhWk/kLK+WwW/RLN/ogkHOxtqBfgSa0abnhUd8HZyQ6LWaOopXhYrQY5efkkp2QQfzqNqOgzzF54EI9qjjzapT7BdXQWq+sRGxuLr68vtra2N3xfPz8/bG1tiY5W8VQqt6SkJEaOHMnXX3/NY489xvTp09VNLSIigApSIlIM7O3tCQsL08Tmf2FPZBLzv48ATNzeti6h9byw0TAqKSFmswlHe1tq+rhT08edlk1qEhefysYdx/h03l7uaFOD7h0DVQT9GydOnKB27dpFuq/FYsHf319n2pNKyzAMvvvuO5599lkyMzP5+OOPGTZsGCaTujRFROSPfVZFICLFITw8nC1bthROPC3/s2VvAl98dxDP6lXo/VBzmjTwVTFKSpXJZKJmDXce7daMls1qs37rKWYvOUxBgT6vfyU2NrZI80dddPFMeyKVTXx8PD169KBXr140bdqU/fv388wzz6gYJSIil1FBSkSKxd13382ZM2fYtWuXwrjEnsgkvlkeSd0AD7p1CsXZSWcSklu40TebaBNWm453BLHvcBJf/xSJYWgy/Wu5mQ4puDCPlDqkpDKxWq3MnDmT0NBQ1qxZw8yZM/nll1+oU6eOwhERkSv3TRWBiBSHu+++GwcHB3744QeF8YeU89nM/z6CmjXc6XRHkIZHSZkRUteL9uGB7NiTyLb9iQrkKnJyckhISLjpDqnjx49TUFCgQKXCW7t2La1atWLIkCHceeedHDx4kEGDBqkrSkRErklHRyJSLJycnOjYsaMKUpdY+MtRwMQ9t6sYJWVPkwa+1K5VlaU/R5GelatA/iQuLg7gpjuk8vPzOXnypAKVCisyMpKHHnqIDh06kJOTw08//cTSpUupUaOGwhERkb+kIyQRKTbdunVjx44dxMfHV/osTp5O50BEMm2a+2uYnpRJJpOJO8PrkpNbwC8bNM/Rn8XGxgLcVIdUSEgIAHv27FGgUuGcPXuW559/noYNG7Jp0yY+++wzdu/eTefOnRWOiIhcFxWkRKTYdO3aFYAVK1ZU+iw27IzDwd6GkPreWjCkzHJ1caB+oBdb95wh+WyKArnEiRMngJvrkAoNDcXV1ZXNmzcrUKkwkpKSeOWVVwgICOCzzz5j7NixREVF8fTTT2NjoxN4i4jI9VNBSkSKTe3atWnSpEmlH7ZnNQz2HEqmXl1PbCxazUrZFhzkRU6uwbrNB8jN1dC9i6KionB2dsbT07PoO1lmM+Hh4WzatEmBSrmXkJDA2LFjqVOnDlOmTOHxxx8nIiKCyZMn4+rqqoBEROTG95UUgYgUp65du/Lrr7+SnZ1daTM4nZRFdnY+NX3dtUBImVfD0xWL2UTiOStHjhxRIH84dOgQDRo0uOkJmdu2bcv27dvJy8tTqFIunTx5kpEjRxIQEMBHH33EoEGDiI6OZtq0afj7+ysgEREpMhWkRKRYdevWjYyMDNauXVtpMziVlA6AVzVnLRBS5lksZqpWdeJ8+oWhOKmpqQqFCwWpi3NA3Yx27dqRlZWleaSkXDEMgy1bttC/f3/q1q3LzJkzefbZZzl27BgffPABNWvWVEgiInLTVJASkWLVpk0batSowVdffVVpM0jLuNAJ4exkrwVCygVnR3ty8y5MdH7x7HKVWX5+PpGRkcVSkGrTpg0mk0nD9qRcyMzMZObMmbRo0YK2bdvyyy+/8NJLL3H8+HGmTJmCj4+PQhIRkWKjgpSIFCuLxcLAgQNZuHAhKSmVc5LkAqtxYQVrNmmBkHLyuTVhNS50RSQlJZGfn1+p84iJiSE3N5cGDRrc9GO5ubnRsGFDTWwuZdqRI0cYPXo0fn5+DBkyhCpVqvDNN98QGxvL66+/joeHh0ISEZFip4KUiBS7QYMGkZOTw7x58xSGSDlgXFI7NQyj0g/bO3ToEECxdEjBhXmk1CElZU1BQQHff/899913H0FBQXz++ef07t2bvXv3sm7dOnr16oWdnZ2CEhGREqOClIgUu8DAQO6++26mT5+OYRgKRKQcMZlMZGRkVOoMDh06hI2NDfXq1SuWx2vXrh2xsbEaDillwtGjR5k0aRKBgYF0796dEydO8NFHH3Hq1Ck++eQTGjdurJBERKRUqCAlIiViyJAh7N27l507dyoMkXKmMp8lEy4UpOrVq4etrW2xPN7tt98OwOrVq7VwyS0RGRnJ5MmTad68OfXq1WPixIm0bt2aNWvWcODAAYYPH46rq6uCEhGRUqWClIiUiIceeohq1aoxY8YMhSFSzlit1kr9+g8dOlQs80ddVLduXRo0aMDy5cu1cEmpOXjwIG+88QZNmjQhODiYiRMn4uvry6xZs0hISOC7777jrrvuwmTSfIciInJrqCAlIiXCwcGBfv36MX/+/Eo//EekvKnMQ20Nw+DQoUPFNn/URQ8++CArV64kJydHC5iU2LK7d+9eJkyYQGhoKA0bNmTy5MkEBATw5ZdfkpiYyI8//sjAgQOpXr26AhMRkVtOBSkRKTGDBw8mLS2NuXPnKgwRKRcSEhI4f/58iRSk0tLSWLdunUKWYmMYBjt37uTll18mKCiIpk2bMmXKFEJCQpg/fz6JiYksW7aMfv364e7ursBERKRMUUFKREpM48aNuf/++5k8ebK6AkSkXNi3bx8AoaGhxfq44eHheHh48P333ytkuSmGYbB161bGjh1LYGAgLVu25IMPPqB58+Z8++23nDlzhkWLFtG7d2/NCyUiImWaClIiUqImTpzIiRMnmDlzpsIQkTJv+/bt2NnZFfuZxiwWC926dWPp0qUUFBQoaLluhmEQGRnJtGnT6NWrFz4+PoSHhzNt2jTatWvH4sWLOXPmDAsWLODRRx/FxcVFoYmISLlgowhEpCS1atWKBx54gEmTJjFo0CAcHBwUioiUWTt27KBp06bY2dkV+2P37duX2bNn8+uvv9K5c2eFLdcUExPD6tWrWbNmDWvWrCEuLg640Ln32GOPce+999KpUydtU0VEpFxTQUpEStzrr79O8+bNmT59OiNGjFAgIlJmbd++ne7du5fIY3fs2JE6deowY8YMFaTkMnFxcaxZs6awCBUTEwNA/fr16dq1Kx07duSuu+7C29tbYYmISIWhgpSIlLiwsDAefvhhJk+ezJAhQ3B0dFQoIlLmxMfHExcXR6tWrUrk8c1mM4MHD+b1118nMTERLy8vhV5JnT59mrVr1xYWoY4cOQKAv78/HTt2pEOHDnTo0IGaNWsqLBERqbA0h5SIlIqJEyeSkJDAp59+qjBEpEzavn07QIkVpAAGDhyI1Wplzpw5CrwSSUhIYPHixYwYMYJGjRrh4+PD448/zvLly2ndujUzZ84kOjqamJgYZs2aRb9+/VSMEhGRCk8dUiJSKpo0acKjjz7KW2+9RZ8+ffDx8VEoIlKmbN++HWdnZxo0aFBiz+Hn58f999/Pp59+yvPPP4+tra2Cr2BOnTrFzp07+f3339m5cyc7d+7k1KlTAHh6etKhQweeffZZOnbsSP369TGZTApNREQqJRWkRKTUvPfee4SGhjJ8+HAWLVqkQCqwgrS9rF+6kA2/R5KYkoeNaw0CQ++hU88HCPFQc66UTdu3b6d58+ZYLJYSfZ6XXnqJ9u3bM3fuXAYNGqTgyynDMIiLi7us8LRz504SEhIAcHR0pGnTpjz88MO0aNGC1q1bExoaqgKUiIjIH1SQEpFS4+fnx9SpU3nqqadYuHAhPXv2VCgV7QDNlMupn5/nhRc+5/ezxuW/XPQJs99qQ8/3vmHso7VRX4iUteLCjh07GDBgQIk/12233UanTp1488036devn7qkysnycfLkycsKTzt37iQxMREAJycnmjVrxqOPPkqLFi1o0aIFDRo0wMZGu9oiIiLXoq2kiJSqIUOG8PXXXzN8+HA6dOhA9erVFUqFYSVl7RAGD55HtBXMphqE3n0/LYNcyD61nY2/bORE5lYWPP8wtt6/MfYOTW4vZUdMTAzJycklOn/UpV5//XXatWvH7Nmz+cc//qE3oAwxDIPY2NjLCk+///47Z86cAcDZ2ZmwsDB69+5dWHwKDg4u8c46ERGRikYFKREpVSaTienTp9O4cWNGjx6tiX0rkDxjNdOev1CMqkI4feb+wKgOVQvPnpEW8U+e6fwWW3N3s/WF72B7fwAy4vZy7EwuFqc6BAYbxPyyhB2JTbm/dxuqmsEw5ZB86P/YvOMoSRk2uPo0psUd7alT9fJhL4YplZObV7L5wEnSC1zxqtuO8Dsb4vGnLV1B5hF+X7WWQ3Fp4FSDemH30Kqxhzq2KrnSmND8Um3btuW+++7jzTffpHfv3ri4uOhNKGXZ2dlERUURERFx2c/hw4dJTU0FoEqVKoSFhfHEE08UFp/q16+v4pOIiEgxUEFKREpd3bp1mTRpEqNHj+bxxx+nS5cuCqUCKFgzneWJFzYtzR75kGcvKUYBuISM4/WZtdkRb2BrFwxcKDbte78FT8634lfvFQY1nsHkJacJ5F+06BOOW84m5j/zOO+sPEnGJY9VxdKaHlO+4cVe/tgCeZmrmP5ETz7eep68wluZ8LPvzdBlX9CrsQ1g5dyO1xg58D9suWQ4oQkXmrd7nwnzBxFia+iNrKQ2bdqEh4cHgYGBpfacU6ZMoUWLFowfP54PPvhAb0IJMAyDhISEwkLTpYWnmJgYrFYrAHZ2dtSvX5/g4GA6duxIo0aNCotPZrPmvRMRESkJKkiJyC0xcuRIvvvuOwYMGMC2bduoU6eOQinPB32mXI5t28Q5wJ5A6vVsgv2fbmOyOhHYcTDXOtw/ceq/TIlKJw8wYcZqPs36cZ15a2U6JmrQ9MFehAdkcOT7L1lzbBuLRvfGpf56RrbMZc+knnyw9Tz2NOfuf9xPgM0hNny1iP1p81nQuz1t9w3F17SKT4b8my1nTXgHPEbn7kGYjy9lyZL97No0imn/7cS7Y2qiQ8/KafXq1dx1112lOuF048aNGT9+PG+88Qa9evXitttu0xtRRFlZWRw5cuSyLqeLl9PS0gpv5+PjQ4MGDejUqRPBwcGFP3Xq1FHXk4iISClTQUpEbgmLxcK3335Lq1ateOCBB9i4cSOurq4KprwyWUk6fYoCwBU/3AP+t3nJOzCJ0eN/IvGS5iOXnPuY+fNrlz9EJjR+aAGvvtGd4Oq25J2axKTv0jGw4/bHv+fDd8JwAPKGtWFsy8GsSN/Gpgk/0vfn2mz/7TxWTATVGcWE1/vgQQE96z7KczOP4XTuOBF54Bu3hp1nwIwr7Z74hFeGViXfpg/183vxZZSZzFMR5FITB72blU5iYiL79u1j2LBhpf7c48ePZ9GiRQwePJhdu3bh6Ki51a7l/PnznDhxgtjYWI4dO3ZZt9Px48cxjAsrGQcHh8Jup86dOxcWnYKCgnBzc1OQIiIiZYQKUiJyy9SsWZPvv/+eO+64gz59+rBs2TJ9Q12OXRzVYmCFgkuuT41k747NxF9yW7+r9En50IUH/v0YwVUuDKHJ3biW/YAzjQnp37SwUGRxf4T7bx/Mip8MUnetZ2/uGLz9gCiDPTED6dryE5qFt6Zp0/5MmN2FZjXtAYNcr1p4AftIZemkeuxadjvNm7ek6f3TmdapNb6OGq5XWa1duxaAjh07lvpz29nZMWvWLNq2bcuQIUP46quvSrVLq6zIy8sjLi6O2NjYwqLTxZ+L/784r1PhesTPj+DgYLp06UJwcDANGjQgODiY2rVra5idiIhIOaCClIjcUq1atWLOnDn06tWLcePGMXXqVIVSDpkMG3z86mLDUdKJ5ezhPKjzxzThgU8wYmIz0oD0be/x6Yq4qz6GIzX/VxQyFZB2LoV8wBFXHL3+d3BpstpRxd2MGSu5pJCWV4Ouk2awZ+SLLPr9LCmnNrNm8WbWLP4vH/7Tn3uGzuP1CW2pWmUgYz7eRso/5/F70jli9i4nZu9yFs/+J9VtuzBg7jyG3q7uicpo9erV1KhRg6CgoFu2Hvzss8/4xz/+QWBgIG+++WaFytcwDJKSkq4oNl16OT4+vrDDCS4U6mrVqkXt2rUJCwuje/fu1K5du/C62rVrayJ4ERGRck4FKRG55R57n0POegAAIABJREFU7DEOHTrExIkTCQkJYfDgwQql3B1x2uB/Zwf83jlKDMc5MG0lyfc9QHUTWHw60fMfnci3HOHbFWPIv8ZDmLBQ2B9nWHD38sIByCKRtGP54Gvzx6/SSEywYgXs8cbbDPYBA5n4Qx9GRKxm44aN/L5tA1s3bCY69Ti/fTaSL7pt5YUwB+o+9AVfd/sXkZt+YdO2jfy+dQ1bth0lOe8nlj/+PuEnJtLMrE6pymbNmjV07NjxlnYmDRkyhKNHj/LWW28RGBjIwIEDy0V2WVlZJCYmFv4kJCRcteiUnZ192f18fHwKi0tt27a9otjk6empLicREZEKTgUpESkTJkyYwKFDhxg6dCguLi706tVLoZQ3rUfQp9kMJu+28vu2QYwc+m9G/bMPzWs4UnBuOyv+NYh3tl3/w1luv58Wpp9Zaxxm37u/cqZtFzxNkBv1GYs2AJjxuOteQvI+Zpj/c2ywcaD9I5v477tv0X0w5G0bTOeHZ3OKeM4fzeVcxP10fHkTNrnN6PXDb7wwpj9PmrPZMc6Tfl9mks1JTuZDMzu9lZXJyZMniYyMZNy4cbf8b5k0aRLHjh1j8ODBnD17ltGjR5d6kSwvL48zZ85w5syZywpNiYmJV70uIyPjisdwcXEpLCzdddddlxWaatWqRc2aNbG3t9fCJyIiUsmpICUiZYLJZOKLL74gIyOD3r17c/bs2VsywbDcxAaloBG9P/2Q37uPYGXiWbb/8BR9fngGRzcXSD9HVoEJmwZhBEbuIsf6949n8XiSZ4Z/wJaPjrJtc096dupIwzoZRG9cT3Q+eNCVzm/chbNLTe4Kt7J6SyZrF3Ti4djbqFstj6S9qzgJeHMbDe9wwJkHCTPWsYHtzOnVgt3tQ3DlBIfXZ2LFRPWgu2llq/exslmzZg1wa+aP+jOz2czcuXNxd3dnzJgxREZG8tFHH2FrW/QF02q1cvbs2b8tLF28LiUl5arr5+rVq+Pl5YWXlxf+/v60bNmy8P9eXl54enri5eWFt7c3bm5ulXIeLBEREbnB4wdFICJlhaOjI4sXL2bw4ME888wzJCcn88orr+jAphyxr/0MU37yI+i1l/nylwjO5eeTlXoOE1Vo1HIcz3xxG7s6dWBFwnUcnBe40Hj8z3xgHsxb09cRe2gF8YcubLr8/B5l4Puf0LeuCQrq88hnizg14lnmbIgnavNSoi48Al6uXej5/of09AKzaQSTZsfxytiP+O3Ufrb+vP/CwTYuhDYdzbOzeuFt0nC9ymb16tUEBARQp06dMvH32Nra8umnnxIcHMzo0aPZvXs3b775JrVr1yY1NfWaP+fOnbvq9SkpKVitV1aA3dzcLiskhYaGXlFcuvhTvXp1nXBCREREip3JuHQGSRGR6/D+++8zatQoSmr1YbVaeeGFF3j//fd57rnnePfdd8vVXCKrtp1k+a/RDB/YvlIvJ7mpEUQcjOVcriOe9ZoT5OdEUd/FguxYovZHcibDhio+jWgQ7MGVA36sZCTuJzIyjrQ8B1y9Qqgf4oPzn57UIJ3EiD0cO3WefBt3vOs1pa5v0f+2imDFmkOknk3mziYGJpMJT09PQkJCKvzrNgyDOnXq0KlTJ2bMmFGs67Ds7GwyMzPJysoiIyPjhopIf1dMushsNuPq6oqbm9s1f6pVq4a3t/dlhSZPT08NmRMREZFbTh1SIlLmmM1m3n33XTw9PXnllVdISkri888/x8nJSeGUI3ZuwTRuG1wsj2VxqE1wy9r89aOZcfZqQphXk7+8lQkXvINvwztY71FlkpeXR1ZWFllZWYWFooiICGJjY6levTrLli0rvP7iv0W9nJOTc13ruasVk/z8/C77v6OjI+vXr+f7778HoEOHDjz++ON0796dqlWrqoNUREREyi0VpESkTDKZTIwfPx4PDw+GDx/O9u3b+eqrr2jVqpXCESkB6RkZbN26D8MwcHNzIzo6urAL0jCMKy7/1e8KCgrIz88nLy+P/Pz8G75c1Ptd7XJubi5ZWVnk5+df87W//fbbV1xnNptxcnLC0dGx8N9LL7u6uuLj43PF9Ve77OTkdFmRyd3dHRcXl+suJo0cOZLY2FhmzpzJ3LlzGTRoEG5ubjRt2pRGjRrRqFEjfH19sbOzw8HBAUdHR+rXr4+Hh4cWbBERESm7x3wasiciN6qkh+z92c6dO3niiSeIiopiwoQJvPzyy9jYlN16uobsSXmzYs0htm7cyNefPl3iz2VjY4OtrS02NjbXvHyzv7/0sp2d3TULRi+++CI2NjZ8+eWXV9zG1ta2THYfGYbBxo0b+f7779m/fz8HDhwgNjb2qretWbMmYWFhtGvXjgEDBuDr66uFXURERMoMdUiJSJnXokULdu7cyUsvvcSECRNYsWIFc+fOpV69egpHpJgE1A1kzpw5mM1mqlWrRr169QoLMiaT6ZqXr3bdxaLQn4tEFoulzBR5EhMT2b9/P1OnTqVhw4bl5n0ymUy0b9+e9u3/V/A+f/48Z8+eJScnh5ycHNLT0zl48CC7du1i165dvPbaa7z22mt0796dYcOG0bFjRw31ExERkVtOBSkRKRecnJz44IMP6Nq1KwMHDqRZs2a88sorjBgxAhcXFwUkcpPs7ezw9/cvnNQ8KCioQr/eH374AavVSvfu3cv9a3F1dcXV1fWy69q1a1d4OSEhgVmzZvH5559zzz338OCDD/Lpp59So0YNLfgiIiJyy5gVgYiUJ/fddx/79u3jkUce4ZVXXiEwMJB33nmHzMzMEnk+wzD+8ixXIlI+LV26lCZNmhAYGFjhX6uPjw/jx4/n6NGjzJo1i/Xr19OwYUNmz56NZm4QERGRW0UdUiJS7lSvXp05c+bw0ksvMXHiRMaMGcPUqVMZP348Tz31VLGeztxkMjFq1CjatWtHz549MZvLZh3fMOUQ/cs01h+z/unvt2Dv5EvtJnfSsrEnlyZTcG4dyxf+zrkCF+rfPZjb6l3fa8u3iWDz9J84mmfBq0l/Ooe76dsNKVcyMjL49ddfefHFFyvV67ZYLAwcOJD77ruPoUOHMnDgQHbs2MEHH3xQZtdtIiIiUnGpICUi5VZISAgLFizglVde4Z///CcjR47k7bffZsiQIfTp04f69esXy/P07NmT22+/HYAFCxaUycKUYc7iwOxR/Gvt1X9vwpHQpuMZ88XL3OZ9Ye6YgvilTP/nB0TjTS/3AdxWz+66nqvA2MX3r77AMmy4e0Bn7gt308JYRHk2vzH93l58c9qeJp2X8N6kptgqlhL3yy+/kJ2dzUMPPVQpX3+NGjVYtmwZU6ZMYdy4cWRkZDB9+vQyfbIIERERqXj0dZiIlHtNmjRhyZIl7NixgzZt2jB58mSCgoJo3bo1//3vf0lISLipx2/fvj22thfKBL169SoshBUUFJTNFbtNTYJbtCWsRVuaNmuIn6sFgywO7JnAO+EfcqDgQkHK7BRAaMtwwsJa4eOlzcGtYBg5nI9LICHhJBlJOWhwaOlYunQptWvXplmzZpU2A5PJxIsvvsjHH3/M7Nmz6dOnD/n5+Vo4REREpNToqzARqTBatGjBwoULOXfuHIsWLWLevHmMGjWK0aNHc88999C9e3fatm1L48aNb7gTYPXq1YVdUlFRUTz++OMEBQXxxhtv0LNnTywWS5nJISC/N68s/g/hNhfmhrHmHmD5iDt5+YcUDuX+m3nfDGByXzdsvHrw9BvtyDbscK/zv7/fMKVycvNKNh84SXqBK1512xF+Z0M8rhFZ1qk1rP2/3ZzJrkrtVl1oH+ZdODTQmh5N5NGz5BuOeNRthE+VC39TbvJhjp5Ix2pyx6dRParapZFwIIKz2RZcfJtQy34f635cT0ZAf7q2c8eMlbSTa/lt/T7iU004e4YQdntHgrwvz90w5ZJ8+Bc2bY3ibE4VfIPuom37urha/vfaEg4eITnHgpNPQ3yNzaz+dSens6oS0LY7dzSphjn7CNt/XkXEGQd8G99Nu/BaOBk38BzmDE4fOsSZLBNO3o2o7XiQ31Zu4FiqHd71O9L+riDczEBOHMeiokn5owaQnnGEw3uq07RpXX2YS1B+fj7Lly+nX79+OtMc8Mwzz+Dk5MTAgQOpV68ekydP1kIiIiIipUIFKRGpcNzd3Rk8eDCDBw/m5MmTLFiwgHnz5jF8+HDgwhn7WrZsSdu2bQkPDyc8PBwfH5+/fMz27dvToUMH1q9fX9gZVdYLUxeZ7Rpy/+ujWPDDBHaQyNGFG8ju2w1zzDs8d/8fQ/bejWFiLzvyMlcx/YmefLz1PHmFj2DCz743Q5d9Qa/Gl242DA5Fjebx21cQmX3xlu607f4lkz7uhp/JwNg8lv5PLiWLYJ74ei/j7rABrKQs68ejr/2OG10YdHAF/aqvZ8aDDzIvw5Zmw6dSZ/FzLIm34e4BXejcvoDf33mA59/bSuIlTWl2pgY88OoiJg5tgD1gzdvDklGPMHnJMdIKb2Uh0PdZnls0lS7+ZvIs6/js/of5OteGut364Lz6S/b+MR++HW9zzzsjsH9vJEtOGn+8HmfadPuWqZ91xst0nc9h3slXfTvwebwNgd2fpuZvH7M++X+b3Sb+7/DPtSMIiXmfsfe+y2EACtizpj8j1jzE+rhF+hCXoPXr15OSklJph+tdzZNPPklERAT/+te/CA8P58EHH1QoIiIiUvLHKYpARCqymjVr8sILL/D777+TnJzMTz/9xNixY3FwcOCzzz7j4YcfxtfXF3d3dxo2bEinTp0YMGAA48eP56OPPmLx4sVs2LCBLVu20KdPn8uG6V08+97FwlRoaCjz588vk0P5DL+2NPEAMMjcdpBY65WdIYY5kz2TevLB1vPY0JzO/3iVYcMeoVEVg7ic+SzoPZNY49L7FRC/cQ15TQYz6Lln6RLqDJxj67Kn+fDLxCINPzPI5/Dsl1kaDybMmC0mcre/xMtTt5JU4E5ol9EMHf0c3Ro6k2ccZu2bo5l3HKyWdHa9dT8Tlhwjkzq06TWOp57qTsMqBUTHf8C0du8VDlW8IJ+YH9bh+OA4hj51H/62kEskK154jq2OfXjy+WfoWMeCQQYHf3iLbw8bRXqOY8tmcLzOIAY9/xxdGzphIp8Dx99iwcIMTF6d6f3yQFpWubA5rtloGH2e66MPbQmbO3cuPj4+hR2PcsGbb75Jhw4d6N+/P9HR0QpERERESpw6pESk0qhWrRqdO3emc+fOwIWCUkREBFu2bCE6OppTp04RFxfHrl27+PHHH0lOTr6ux71YmIqMjKRv3740b9We8C6vlanXbjJVxc0dSIICznPuKrfJNx9m+2/nsWIiqM4oJrzeBw8K6Fn3UZ6beQync8eJyAPvS7YcIZZJvL3oeYLMBrnD22AX1o9lGfFEvrmE+P5P43PDf6mBKSOEJ96bzfDuoVRzyCfqnbWcArzowGNvv0Ofalbye9fFPGA6UQUZnN+XS4HfEr6akUAeNtzxyHd8/G5z7IGn7+hNtye+JYrPWbrmOcZ2LkyEJqFT+fjdHriYU2kUV40RP0J1OvPkgi950htyWiRyV7+FnOMYSQfyKKh/o88B9RjH5EUTaWprkNfblVNt3mQXKZw9cAr63M0jz8Hx6V+wI82Ef53+DHyxtT6oJSgjI4OFCxfy9NNPawLvP+8Q2tjw9ddfExYWRt++fdm4caPOvCciIiIlu/+hCESksjKbzYSEhBASEnLV32dnZxMfH09ycjL5+fkcPHiQwYMHX/PxPD09GTNmDI3b9uDn9afK1os1Msn+Y2iaBWdcrjJ1jtnwwdsPiDLYEzOQri0/oVl4a5o27c+E2V1oVtMeMMi5ZBPi26cLdc0XhrfZuHblntth2UpIy9rDwXyKUJAyERT8IiN7heJmAIaZ6n61cCCaBJbwr9b1Wdq2Hc2atuG+t1bwVpsaOAB5ezdzEDBhIfrI64x88o+XnR9DLpDHcc5sTYLCYpEZhzo1sDfAZHWgupczJjJwIRD/aibAwFytGlWAFHKxZgMHb/w53G9rTwPbP+byqu1fmEdBniaPvhUWL15Meno6AwYMUBhX4e3tzWeffUb37t356quv6N+/v0IRERGRkjseUwQiIlfn4OBAQEAALVu2JDw8nKVLl151jigvLy8+/PBDYmNjefHFF7Gzdyhzr8V6fg+H/6iROQYFUctsXHEbS0ENuk6aQe/m1bAjn5RTm1mz+L+8/89H6N0mhBFvbubsJYUsEyYcq1XjYiImqwOuVc2YgQKyyDSKtllyrFMDB+N//3d99AMm9gnD0w5ysqLYvfpLZr83nGd71KP3PW+zNcWE6Vwq6YBBHvGRq9j824WfLVuOkuHoiIODE9a8zGtuAU0mc+FrMl1tU2mmCM9hwuLscOmT6EN1i82ePZvmzZvTuHFjhXENDzzwAPfddx/jxo0jLS1NgYiIiEiJUYeUiMh12LVrF8uXL7/sOi8vL1577TWGDBmCg4NDmf3bDVMOsTM+YTMXJun2e7A9zgZcrUfHPmAgE3/ow4iI1WzcsJHft21g64bNRKce57fPRvJFt6082/KPx8UgLe4UeXhgCxiWdJISrVgBe7zwNgNm8x8FHoPCqbVMVrLS0rhmvcr28u9KLOZGPDBlB/eN38O2NavYuX0TOzau4vej5zlw6DVmvt6F5oMvdDOdoxaPfxTBa11sr/rQuey/4fxMmDBZwKh6vc9xUh+YMig2NpY1a9bw/vvvK4y/Wt5NJt5//30aN27MW2+9xX/+8x+FIiIiIiVCHVIiItfh4YcfLrx8sSPq+PHjPPvss2WuGJXDWeIO7eHQwT3s2/0TS959iKHvHCIbqEkvOg3yuerKPyfzE4b52dK4tgevflaTzoPf4vVp61g+ux+1gGziSTt6aRkrn5iFM/gt5ULJKT92DkvWA5hwa3kbDW0NDDd3XIA8Ejh7IAUrUEAE61YdueQsftdWYI5nUR8LDes40KPRMqo88ALP/WshMzd+Q+9qAAWknTgJDW+jiQXyOMHhGStJNACspKwfx4jH72PwY32Zs6mow+T+6Joq0ecwyM/M1getBM2dOxeLxULv3r0Vxt9o0KABI0eO5L333iM2NlaBiIiISIlQh5SIyN/YtWsXx48fB+DDDz8s8x1RJ5nJy51nXnG9HaHc8dEkOrtdvTfJUqUTd4VbWb0lk7ULOvFw7G3UrZZH0t5VnAS8uY3QO+wuu89xPuHFDrto1qoKSdt+5UAeONGMNuM6U8WA3NB2NDLN4oRxnp+m3kH8robYHV/L9ggzXMd5+CyGF+Gd2+K4bjORTGJoxw00a+hOwentbD8LFjyodX9LbGyq8OToUFZNOciOLb14tFMHQmolE7FhO3FZEMRrhMy2LWKiZjCBjc1Dxf4cJpxwdAKwsnVdVx7y68VPcTP0oStmhmEwZ84cunbtiqenpwK5Di+//DKffvopH3zwAVOnTlUgIiIiUuzUISUi8jdmzJjBhx9+SFZWVpnsiPq71bydUy0a3zacV75bw2sPe11zxW9TUJ9HPlvE0Nt9ceAMUZuX8vOPP7LzRC6erl3oOetDenpddg8a9h5LGJtZt+JnDiRZcTA3ptvr83im3YXCjK1zX55770HqOEBeXiTbVy7hyJE7eOa5u3G8rkqCBZ8n5/HuiDuo6ZRPcvRqVi1fzNptJ8iz1KXTk/MYPcgLk9WB4NG/MO2V7gS55hJ/aCWrf9nOqSxnQsMmMHrbBFo7GUXM0ITJTIk8h621Fd3H9SPMxwnbgkwySNEHrgRs2bKFI0eOaDLzG+Dh4cGTTz7J9OnTOX/+vAIRERGRYmcyDMNQDCJyI95//31GjRpFZVh9GIZBbm4u9vb2132fVdtOsvzXaIYPbF9OX7WVjMT9REbGkZbngKtXCPVDfHC+ViUrJ47IXQdIzK1G7SYtqO1+5eTd+amHObAvhkxzLeo1b4Snw40vO9bsk0QdiOD0uXzsqtQkIDQUr6ucLtDIiydq9wESsh3w8A8juLZzsX/7UhrPUZpWrDlE6tlk7mxiYDKZ8PT0vObZJ8ujoUOHsnDhQk6dOoWdnZ1W4tfpyJEjBAcHM3XqVEaPHq1AREREpFhpyJ6IyF8wmUw3VIyqGMw4ezUhzKvJ9d3c3o+gcD+C/mpj49aApu0b/PG/ohUyzQ41CWpR8y+fB8Bk60v9Vr7UL8nlohSeQ4pHeno633zzDf369VMx6gbVr1+fBx98kPfff58RI0Zga2urUERERKQYjzpEREREKqg5c+aQmprKM888ozCKYPTo0Zw4cYIff/xRYYiIiEixUkFKREREKiSr1cp///tfOnfuXKGGIJam22+/nYCAAL7++muFISIiIsVKBSkRERGpkFauXMmRI0d47rnnFEYRmUwmevfuzfLly0lLS1MgIiIiUmxUkBIR+X/27ju8qbJx4/j3JOnepVDaQlkthULLLCJDlCkIKuoLys+F+AoOBPcCFRVFcKAo4ngdiAMVQQFF2XvPFihlllFaRindI8n5/VFAUNQCBTruz3VxtTRpc3KfJOfkznOeIyIV0tixY2nQoAFdu3ZVGBegX79+5OXl8dNPPykMERERKTUqpERERKTC2bx5M7Nnz+bhhx/GYtHuzoVo1KgRMTExOmxPRERESpX20ERERKTCeeedd/D39+fOO+9UGKWgX79+/Pbbbxw9elRhiIiISKlQISUiIiIVytGjR/nyyy+577778PLyUiCl4MYbb8ThcDB37lyFISIiIqVChZSIiIhUKB999BFFRUU8+OCDCqOUREVFUbNmTX7//XeFISIiIqVChZSIiIhUGAUFBbz33nvcdNNNhIeHK5BSYhgGXbp0Yfbs2ZimqUBERETkgqmQEhERkQrjk08+ISUlhSeffFJhlLKuXbuyd+9ekpKSFIaIiIhcMBVSIiIiUiHk5+fz6quvcv3119OyZUsFUso6deqEYRg6bE9ERERKhQopERGRSs6oIEdgffTRR6SkpPDiiy9qpV4EQUFBNG/eXIWUiIiIlAoVUiIipcxmMQBwOJ0KQ8oFu9PJiYdtuZWbm8trr71G7969adasmVbqRdK+fXtWrlypeaRERETkgqmQEhEpZX4+bgBkZRcoDCkXsrLzcXMt3/dhwoQJpKamanTURdaiRQsOHz7M/v37FYaIiIhcEBVSIiKlLLSqJwCHj2YrDCnziuwOMjLy8PcqvyNecnJyeP311/nPf/5DbGysVupFdHJurjVr1igMERERuSAqpERESlnVAA+8PF3YeyBDYUiZd+DgcUwTqviW3/swfvx4Dh8+zAsvvKAVepHVr18fb29v1q5dqzBERETkgqiQEhEpZYZh0KJxNXbuPkJBkV2BSJm2eXsanu4Q6PPHCCmLpfzsHmRnZzN69Gj69u1Lo0aNtEIv9o6jxULz5s01QkpEREQufL9CEYiIlL52LUIosjuI33JQYUiZdfRYDnuSj1InxMQw/pjV3Gq1lpv78Pbbb5Oenq7RUZdQixYtWLt2rSY2FxERkQuiQkpE5CKoFuhJq6bBrN24j4zMPAUiZY7TaTJ/2Q7c3aFu9T+KBdM08fLyKhf3Yd++fYwaNYoBAwbQoEEDrdRLpEWLFhw5coR9+/YpDBERETlvKqRERC6SG66pi7u7ld8WJFJY6FAgUqasXJ9M2qEsmtYzsVmNMy7z9S0fE0o9+eSTuLi4MHLkSK3QS0gTm4uIiEhpUCElInKReHm6cPdN0aSn5zBz7haVUlJmrNm0n3Wb9lO/pklIwJmXubm5lYsRUosXL+bbb79lxIgRVK1aVSv1EoqMjMTHx0cTm4uIiMgFUSElInIRRYT7cVPXMFLTjjNl5kaOZuQoFLlsCovszF2ynZVr91A31CQ6/K9zAIWFhZX5++FwOBg8eDDR0dE88MADWrGXeudRE5uLiIhIKbApAhGRi6tti7qkH0lj+eZcvpu2ntjGNWgaHYqXp6vCkUvC7nCyffcRVqzdTX5eEY3qmESGnjmROYDNZiMkJKTM359PPvmEjRs3Mnv2bFxcXLSCL4OYmBimTZumIEREROS8qZASEbnIDMOgbVwUnq7xbEk22JSwn40J+wkPC6RmDX+CAr3w8nDFarGAobzkwplOk/xCO8cyckhJy2LHnsMUFjgI8ocrGzjx9TQ424MtIiICm61s7xocO3aM5557jt69e9O5c2et7MukVq1apKSkYLfby/xjRkRERMom7UGIiFwCVapUoVbNMFysKUTVcLL7kEHK4XSS96crHLmo3FwNQgKd1A6GQB84WxFlGAZBQUEEBweX+fvzwgsvkJ2dzZtvvqmVexmFh4fjdDpJSUkhPDxcgYiIiMg5UyElInKJREZGUlRUxOHDh2lYAxrWMClymGTmGBTYwelURlI6DAvYLODjYeLhyl8OzTvjuoaBn58fDRs2LPP3Kz4+nvHjx/PMM89Qp04drejL6GQJtW/fPhVSIiIicl5USImIXELR0dEkJSVx8OBBDMPAxQpVfJWLXCz/fgxoUFAQDRs2/MfSqiyw2+0MGDCAsLAwnn76aa3ay+xkCbV3717atm2rQEREROScqZASEbnE6tevT2BgIElJSRQVFSkQuTw7ADYbERER5eIwPYAxY8awevVqZs+ejZeXl1bgZVa9enVcXFzYu3evwhAREZHz2x9VBCIil15QUBABAQGkpqZy4MAB8vLyAMr8KBUpv0zTBMDd3Z3Q0FBCQkLKzWTUCQkJvPjiiwwcOFATmZcRFouFGjVqqJASERGR86ZCSkTkMrFarYSFhREWFkZubi6ZmZnk5ORgt9tPlQciF8owDGw2G56envj6+pa70UV2u53+/fsTEhLCmDFjtELLkPDwcBVSIiIict5USImIlAGenp54enoqCJE/GT16NGvWrGHOnDn4+PgokDKkZs2abNq0SUGIiIjIebEoAhERESmL4uPjefHFFxkZ4BykAAAgAElEQVQ0aBCdOnVSIGWMRkiJiIjIhVAhJSIiImVOUVERd999N6GhoYwePVqBlEHh4eFkZGSQlZWlMEREROScqZASERGRMuf1119n3bp1fPrppzpUr4wKDw8HYN++fQpDREREzpkKKRERESlTFi9ezIsvvsiDDz5Ix44dFUgZdbKQ0mF7IiIicj5USImIiEiZkZaWRt++fYmNjeWNN95QIGVYzZo1ARVSIiIicn5USImIiEiZ4HA46NevH7m5ufzwww+4u7srlDLM19cXf39/FVIiIiJyXmyKQERERMqCESNGMG/ePKZOnUrdunUVSDmgM+2JiIjI+dIIKREREbnsZs2axcsvv8xjjz3GjTfeqEDKidDQUFJSUhSEiIiInDMVUiIiInJZ7du3j9tvv522bdvy2muvKZByxNfXl6ysLAUhIiIi50yFlIiIiFw2hYWF9OnTB4vFwuTJk3FxcVEo5YiPj48KKRERETkvmkNKRERELptHH32UlStX8vvvvxMWFqZAyhkfHx+ys7MVhIiIiJwzFVIiIiJyWbzzzju8//77vPrqq3Tu3FmBlEMaISUiIiLnS4fsiYiIyCU3bdo0HnnkEQYMGMDTTz+tQMqpk4WUaZoKQ0RERM6JCikRERG5pFatWkW/fv3o3LkzH3zwAYZhKJRyytvbG4fDQX5+vsIQERGRc6JCSkRERC6Z3bt306tXLyIiIvj+++81iXk55+PjA6DD9kREROScqZASERGRS+LYsWNcd911WK1WZs6ciZ+fn0Ip51RIiYiIyPnSpOYiIiJy0RUWFnLTTTexd+9eFi9eTM2aNRVKBXCykNKZ9kRERORcqZASERGRi8rpdDJgwAAWLVrE9OnTadasmUKpILy9vQGNkBIREZFzp0JKRERELhqn08n999/PpEmTmDBhAj169FAoFYgO2RMREZHzpTmkRERE5KIwTZPBgwfz0Ucf8dZbbzFw4ECFUsGokBIREZHzpUJKRERESp1pmgwdOpTx48czevRoHnnkEYVSAamQEhERkfOlQkpERERKlWmaPP7447z77ru8+uqrPPHEEwqlgtIcUiIiInK+VEiJiIhIqTFNk6effpq33nqLl156iWeeeUahVGBWqxUPDw8VUiIiInLOVEiJyN9KS0tj37595/RGdO3atQpOpJIyTZNhw4YxevRohg8fzvDhwxVKJeDj40N2draCEBERkXOiQkpE/laVKlW47rrruPvuu/+xmDJNk2+//ZaWLVuyYsUKBSdSCZmmyfDhw3n11Vd55plnGDFihEKpJHx8fDRCSkRERM6ZCikR+Vs2m43HH3+cL774gvDwcB544IEziinTNJk+fTpNmzbltttuY926dQwYMEDBiVQydrudgQMHMnLkSJ5++mlGjhyJYRgKppJQISUiIiLnwzBN01QMIvJPbzSrVq1KRkYGFosFi8VCq1atWLZsGbGxsWzatOnUdceNG8dDDz2k0EQqkdzcXG699VamT5/O22+/zdChQxVKJdO2bVuqV6/OlClTFIaIiIiUmEZIicg/stlsjBs3DgCn04ndbmf58uUAJCQknLpecHAw9957rwITqUSOHj1K586dmTVrFt98843KqMq6M2mxoM83RURE5Jz3IRSBiPybW2+9lbp16546BOfkGw+n03nqOsOHD8fd3V1hiVQSycnJtG3bloSEBGbNmsWtt96qUCrrzqTFcsb2QERERKRE+xCKQET+jc1mY8SIEX/7CXhwcLDmjhKpRDZt2sSVV17J8ePHWbRoER07dlQolXlnUoWUiIiInM8+hCIQkZL48yip02l0lEjlsWDBAtq3b4+Pjw/Lly+nadOmCqWy70yqkBIREZHz2YdQBCJSEn83Skqjo0QqB9M0GT9+PF26dKFhw4YsXbqU2rVrKxjBMAwVUiIiInLOVEiJSImdbZSURkeJVHx5eXncc889PPjgg9x6663MmzePoKAgBSPFO5Oa1FxERETOZx9CEYhISf15lJRGR4lUfMnJybRv355Jkybx7rvvMnHiRDw9PRWM/LEzqUP2RERE5Hz2IRSBiJyLW2+9FavVCmh0lEhFN3fuXFq0aMH+/fuZN28egwcPPus8clLJdyZVSImIiMj57EMoAhE5Fzabjc8//5zQ0FCNjhKpoEzTZMyYMXTt2pXIyEjWrl1L+/btFYycfWdShZSIiIicB8PUQf9Syd907dqfyZad6SSnZHH4SB75hXbQfvU/cjrt7E5aRb0GbRTGv71Rsxn4erkSUt2LujX9aFo/CF9vVwUjZVZ2djYDBgzgu+++Y9CgQYwdOxY3N7dKcd+L7A427zzG9t3H2JuaTfqxfOwOp7YJ/6KwsBAAV1e9tv3zBgFsVgsB/u7UqO5FZK0AYiIDcXWxKhsREamUVEhJpeR0mqyIT2Pesn0cSc/DxWalWlUfAv09cXW1YrHokBS5cKYJTqeT7NxCjhzJJj0jF8OAJg2r0q19OCFBXgpJypSVK1dyxx13kJyczAcffMA999xTKe53XoGdeSv2s3hNCvn5djw9XahW1Rd/H3dsNis6SlFKa5tgdzjIzMwn7UgWOTmFuLlZadMshM5X1sTL00UhiYhIpaJCSiqdtKO5fPnzNvanZFEj1J/YhiGE1wjAatERrHJxZecUsHXHIeK3plCQX0TnduF0axeux55cdkVFRYwcOZJXXnmFqKgoJk2aRLNmzSrFfd+8M51vpieRm1tEZEQ1YhpUp1oVb82VJRfd4fRsEhJT2bY9DVc3K316RNKsQVUFIyIilYYKKalUEncf43/fb8bV1cbVbSKoXSNQocglV1joYMWGZOI3p1Cvlj//7RONu6tNwchlkZSUxB133MGqVasYMmQIr732Gh4eHhX+fpumyexl+/hlwR6qV/OlY7sIAvx09kC59I5n5bNg2Q72p2TQsU0Nel1dR4WoiIhUCiqkpNJI3H2Mj79NoFpVH3p0bIi7u4bGy+W1e+9RfpufSM0wHx64LUbziMglZZomH374IY899hj+/v58/vnndOnSpdLc/1+XJPPbwmRiGoXSrmUdHaotl/35uHL9XtZu3EeH1mHc2LGuSikREanwdJyIVApHMvL47PstVA3yoVfXRiqjpEyoE16F6zpHs/dAFpN/3a5A5JJJTU2lZ8+e3H///fTq1Yv4+PhKVUZt2HaE3xYmExsdRvs4lVFy+RmGQevmtYhrFs7CFQdYGZ+mUEREpMJTISUVnmmafDU9CYvNQvdODXCxaRSKlB01wwJo06oOa+MPsTHpiAKRi/56+P333xMTE8PSpUv56quv+OabbwgMrDyHL2flFPLtjCRqhPrTrlVtjUKRMiWuSU1q16rCj7/u4FhmvgIREZEKTYWUVHgbth1h997jXHVlPTzddUpqKXtiG4QQEuzDlFnbcTh0fnm5OHbu3EmPHj3o06cPsbGxbNq0iX79+lW6QubXxcnYi5x0bBuhMkrKHMMwuKZNPQzDYOqcHQpEREQqNBVSUuHNX76fakHe1AuvojCkzL4Bad2yNplZRSxctUuBSKkqKCjglVdeoXHjxqxdu5YvvviCOXPmEB4eXumyyMkvYtXGVBpHh+Dj7a4Hh5RJnu6uNImpQfzWdA4cTFcgIiJSYamQkgrt4JEc9qZk0bhhqD4JlzItpKovgYGeLN+QQnq63oBI6Zg3bx5NmjTh+eef56677iIxMZE777yz0r4ert1yGLvdpHFUiB4cUqY1igoGA35dsIXCwkIFIiIiFZIKKanQEncfA6BuzUCFIWWaYRhE1KrK0WOwectW7Ha7QpHzlpqayv/93//RqVMnPDw8WL58ORMmTKhUc0Wdzbadx6ga5I2fj0ZHSdnm6e5KSLAfB9NNEhMTFYiIiFRIKqSkQtt3MJtAf0/c3GwKQ8q84GBvnCYcPe5g1y4duifnzm638/7779OgQQOmT5/O2LFjWb16NVdccYXCAfamZBFczUdBSLlQPdiX45km6enpHDp0SIGIiEiFo0JKKrQjR/Pw8/dQEFIuBPh4ApCdZ5KamqrDNKTETNNk2rRpxMTE8NBDD9GtWzcSExMZMmQINpsKeYBCu4Os7EIC/DwVhpSPbYKvB3YnFNoNkpOTFYiIiFQ4KqSkQisodOBisyoIKRdcXYofq/YTJ9pLTU1VKPKvFi9eTNu2benduzd+fn4sWLCAyZMnExoaqnBOU1RY/MRy1TZBys02oXg33e4wyc3NJTMzU6GIiEiFokJKKv6DXJOZSzlhnPaKbJomhw8fVijytxISEujVqxdXXXUVR48eZcqUKSxfvpwOHToonLMwDbP4eXbiq0iZ3yac2H8xT3x/5MgRhSIiIhXrvboiEBEpm3JycnA6nQpCzrB371769+9PbGwsa9eu5cMPP2Tz5s3cdNNNOpuoSAVlmibHjx9XECIiUqFoYgkRkTL8BiQvLw8vLy+FIaSlpTFmzBjee+893NzcGDlyJEOGDMHTU3MiiVQGOTk5CkFERCoUFVIiImWY3W5XCJXc7t27eeONN/j0009xOp089NBDPPvss1SpUkXhiFQiDodDIYiISIWiQkpEpAzTIXuVV3x8PKNGjWLy5Ml4enry8MMPM3ToUEJCQhSOiIiIiJR7KqRERETKkCVLljBq1ChmzpxJ1apVeemll3jggQfw9/dXOCIiIiJSYaiQEhERucxM0+SXX35h1KhRLFmyhFq1avHee+9xzz334OHhoYBEREREpMJRISUiInKZZGRkMHHiRCZMmMDWrVtp3LgxkyZNok+fPri4uCggEREREamwVEiJiIhcQqZpsmbNGj744AO+/fZb8vPz6datG6NHj6ZHjx5YLBaFJCIiIiIVngopERGRSyAnJ4evv/6aCRMmsG7dOqpWrcqQIUP473//S926dRWQiIiIiFQqKqREREQuooSEBCZMmMCXX35JZmYmV111Fd988w29e/fGzc1NAYmIiIhIpaRCSkREpJSlpqYyefJkvv76a1atWoWfnx933XUXgwYNIjo6WgGJiIiISKWnQkpERKQUZGRkMHXqVL7++mvmzZuHaZpcc801/O9//6Nv3754eXkpJBERERGRE1RIiYiInKe8vDxmzpzJ119/zcyZMyksLKRly5a88cYb9O3bl9DQUIUkJWa3bmfF/2ayvfBPFxgGVqsXgdWb0LxDK0K9zItw607S101i+pp0rNYY2vXvTG2LeZZl3MGqT2ewreDv/5LFGUiD6+/gilBDK1VERET+lgopERGRc1BQUMD8+fP59ttv+fHHH8nKyqJ+/fo8++yz3HbbbdSvX18hyXlxmOuY8dxj/PgP1wk2ejPgpy+5u4VH6d644eTQnOcZ/c4+QriXwDs7U/ssJ3x0GBuZ+exjfP8Pf8qNKG6P7scVoSXbzTSNQhLebMPgrw4RZLmf51Y+SzNLyUu3ooRnuevuLzmUG8WNE3/noZY6U6WIiEh5oEJKRETkX6SlpTFz5kxmzJjB77//Tk5ODqGhodx3333069ePZs2aYRgaDSKlx92vEQ0ifDEADDsFh5PYnnycNHMq318/ikZ7XiLOxbysy+jmG03DSD/+/Mh3KapFdd9zKYVMio7tIDU1Cw+OU+AEzuHXjYKjHD6YQhq+ZOef4y+LiIjIZaNCSkRE5M9vj02TjRs3MmPGDKZPn86qVasAiImJ4eGHH6ZXr160atUKq9WqsOQiMIhu+DqfT+mO+4mfOKwpzB1Yj4d/KmQ/P7Fo7QvEtS4uXkxHKkkL57Bu52Hs7jWJurIrLSJ8OePRadjJ2D6XJSsSSTtu4h5Qm6jWHWle1/c86xuD6IhX+eLnXqeW8W+fT8Zx9i+fxfLN+8l2+FKtXhtad2hEkA1MSy6Ht20iOb24XMvnCPs2rSc0vAnhQca/Lnfeoc0k7UmnEHBSSPredWzdGUnDegElz0ZEREQuCxVSIiIiFM8HNX/+fKZPn86MGTPYv38/rq6uXHPNNbz33ntcd9111K5dW0HJZWF1VKdZXAy2n9bipIDCEyOBcnZNYMTdj/LTzgJOjpeyUIU2N37Ei+N6E24xcVj3s/jZbjzxaSLHT/ubBn5c0f1TXv24NzUu0gC/oty5fHz7Lby/MpOi0245zO02Bv30GTc1j2dy/7aM2118yQE+Y1ivr7n5uXReHnzkn5f7k55kf34d/d7Zd+Jv72LaE1eyM2wSU1bdVqJsRERE5PJRISUiIpVSXl4eK1asYMGCBSxYsIAVK1ZQWFhItWrV6NmzJz179qRLly54e3srLLnkTBwUFRZiAUyjiOwDc5j0/TqKgBq0J7aVC0XmbN6/7UGm7Qd/v050ujMOr10z+PmXBJZOu5vXqkXx7gsNcSwZxoufJpKJN/WvHkCn5i4cXT6RqcsPEf/rK0zecD2PNT/3Jdxz8FNee+63P42wMvCpO4DBA5pis+SyceQtvLsyEzea0+m/Pahj28riSVNIyPqaybe1I27L9bS+/wUOfz+Gyatz8aED1z7ajfZXulL0L8v97fqe3N3hKR5Mn8gXX67iONVo1u8BOsXFUmTOKVE2LnqoiYiIXDYqpEREpFLIz8//SwFVUFCAh4cH7dq144UXXqBTp07ExcVhsWgOGrmcTNavuIEWdf56iY/1Srq+M4LuniZ5M8by/X7wpAl9PpvJ41e44LQ9TvuHQhj4fRa7PvqYpcPeoPaO4wQ2jKWm2YcHv3iW1jaTooQ81nZ7nz2kkrXTDs3P/TF/7ODPfPP5X3/epP7VDLq3KVgSWb0kEycG9Ws/wvMj+hGEg1vq/Ych/9uNZ0YyO/ND6Xz7U1gT3+K71VCVVvQc8jStXezsmfjPy52900nQLfdzn20DU79cRS6BNLnhGe5tZyNnxlP/ms2y596mg02jpERERC4XFVIiIlIhZWVlsWbNGhYtWsSCBQtYvnz5qQKqTZs2DB8+nKuvvpq4uDhcXV0VmJQDBl6uYYTUCsAwCtmxeiU5gMlxln3wHwZ+UFyuFB7wwiSD48STeMjC1XdM5fubE1n960xWvXIv3+3bSdLqJewErDjBcX5L41u1I1e1r/6XEVK+ETWxmmAxqxMcBuww2binP9e1HE/T1q1o0uROnv+8O01ruAEmZ62ETCu1z3O5zRJms/WwSYcQPapEREQuFxVSIiJS7uXn57Nx40ZWr1596l9iYiKmaeLu7k6bNm0YNmzYqQLKzc1NoUkZZhAdO4GRY6/E1QSnmUXKmrcY/cwUdub9wE/3XkvX9beRnZWOAzAt+9m+JI0dp/0FNw8PbHkmzmyTwt3vMvTmR5mTBmDF078edWIaUWXJJjLM81/GejUf5tVx/zCpuSOU60Z+wsaHn2TKunSOpSxn/o/Lmf/jO4x7oRadB33FiOevJOBvfr1w9zvnt9yGs8TZgM6OKSIicrmokBIRkXLFbrezZcuWM8qn+Ph4ioqKpzWOiooiLi6OQYMGERcXR/PmzVVASbnj7hlGvahGp8qeejHDSPpsCm9ug5y0zSTZrfj7VcHKUWo47+Opre/RyeWvLY3TdoTf73iUuWkQSn+GzBpNj5hAjMTHubnTJjIu8v1wq9OfF2f0Y/C2eSxdvJR1qxazcvFydh1PZsmEh/ms50oePcv8VU7bEea9dJ7LbVrwKUE2IiIicnmpkJJKLW/793w3/wAmVqrG3E73KwNOHXpgWnLZ/uvHLE02cQnqzE03N8bzEu/POg/8wne/bCPP/PtPcF3dW9LxjvaEGBd/4ey2bSz/+Fd2FlmpFnsn17b2+5vThTtJXzeJ6WvSsVpjaNe/M7UtDo6umciMdRlYLY1pe08X6pTwDEeO9AX8/ON6Mh2BNOh5J1eE6RPtysA0Tfbt28fmzZvZsmXLqa/x8fHk5uYCEB4eTlxcHH369CEuLo4WLVrg5+en8CqBJUuWUL16dSIiIirF/TVMf3z9T77CFlDkdCXyijb4fjSdA/zKvJ8Pc83NQWBJY+lr9/Hp+ny8zOvoO/kqdu8CE4Nq0T3oHBuIq5FD0qzfSb7QbWj+PrZt2chfDng1DdyDogj1+R9DI4ew2OZOu5uX8c5br3DDAChaNYBre39OCgdPzF912us9eeSb4GDvOS+3kwLsuSaGWbJs7po0hDgVVSIiIpeNCimpxJzkrxjLmBErKATqsAffpLdo71W8c2qSSfzHj/Lacmjk+QFdezfG8xL3IOa2zxn74hTS/+E69XicOv3aE1LCZ3ORbQkfd+3Lt2luxF47lbdHNinxWYYc5np+HvYYP2Gj013X0q3137zxN5wcmvM8o9/ZRwj3EnhnZ2pZ7aT9/iyvv59GCP3xvbMLdUo4bY/zwBQ+emE8+4igX8T/cUVY+XjpupCsK9Uz0ek8a/G0ZcsWsrOzAbBarURERBAdHc21115LXFwcLVu2JDg4WAFWUvXq1SMyMpLmzZvz6aefVvhiyjA88Tgx0M+kiELA5dqnubvRdN7avJspQ5uTMCWO6jmrWbHmAPl40u62l2niEkZmHWCHyaYtD/N///cV1TJWsjr+cPEhbRThLDqvLRSJWwbzny5n373scMtqPnivC1e3djJvRS4LJneh99621Ass4simuewHgmlL9FWuQCHuXj5YyGIPExgaO50eIxbRtoTL7fT0xg0oYjdfPxBKQouPmDT537Np5GqC+igREZHLRoWUyAl7+JBJr9xNq9dicCuDT9Uq9eII9//rJX5utfA9h5MjmWYBmQdSST1ipd6RApyX6s0UFjxqtqNZixQ87PWoUglOYna5si6L8vLy2LNnD7t37/7Lv6SkJHJycoDi4ikyMpLo6Gi6detGdHQ0jRo1on79+jrsTs4QEhJC9+7d+eGHH4iMjOTOO+9k+PDhFbaYMhw+hIW7YKGIVBayan4m3bu25r/f/47tqYf48Jckti48wFbAxRJB17vf4ZkRV+BbZNL5hXfpsfURftl/kMT500h1uZoenzyHs/9DTDUz2Tt3BdzRpvS3XI5Ibp4whZTBD/HF4oPsWD7txFxOFqr5dueWseO4pRoYpiv1/+8lblnyHDPi08jJTIUjQSVb7tuvwtL4Xgb2W8iYnzaRmpNO0e58bPx7Np6m2igREZHL+y5XRIrLA/LYNPFpvvnvDO6u+89DoQqPbWD5/OXsOezAq2osLTu1o7ZfccOSm5bAroP5YPhSvVF9gmxAXjJJSYcpNN3wrxtLDV8T08ggJWEH6UUG7oENiAz3+tvbsxJA1/vnMeI2179d+pMcudtZN3cBWw9kgWcoEc06ExcTVDwyp+AAu3fs4pi9+LrZOdtJ3FiFJk3qnShQjrBr6e+s2pZCtt2TKmFNadHuSmr5nz2PvJT5LJizgcP5AYTHdadds+C/L/NMG8Fdn+eZmHwstiDCLCVc5j9xWuwc2/ErCxbuIMulJtFX9aBlbc+Tl5J9IJ49h4uweoRTp04Om3/9lU0pDvxqXk2H7jEEuGZxYMkMliWk41q9Ja06tibMyyzx+gXIO5jAzrR8DPea1GlgYe+cn1m1IwvXwFjiul1DPT/jX7OuUM8d0yQjI4PU1FRSUlLOWjqlpaWd8TvVqlWjTp06REVF0bNnTxo1akR0dLSKJzkn7777Lj/88AMAkyZNYtKkSdx+++3ltphyc/Zl1IG+jDpbIWW6Efd6Pomv/2n74NeJeyds5Y6j8WzeeoACawi1YmIJ9TZObRvc6jzI20tv4aF1GzlsD6Nes8ZU8zBh//28ftpmpMGTe9j85F+3K2cso/1mRh5wMLKkO5pVb+SRb6/nvkMJJCUdIKvIHd9qDYlsWB2v0z6YsNXsz0sz+vPSGb9dguUGbI6G9Bqzhl5jzj0bERERUSElcpmfCGFUCTtI2oHf+e2BqXT/9Waqnu2NtyWHHd/2Z+hzU9ie+8fPvY0ruGXsdzz5n1Dsi4Zy59D5FBHF7d9s4qmrbGTPvJe+Q+aRizvXPbaHNx6tiuPoxzzX9WmW40H3R3Yw9nGvC7wXTjLWDOfh/q+zIv2PHW0Db5q3GcvzX99D5J6xPNH1LRKLayA2zr+TwfNvZNGBKdiPTWH0f27ni62FZ+yme9KUW96ewbN9Qs4owLbueJRb2/9CUv7J2/HnyhsmMvL9npxtiifTKGTP/7rS58Qhe0N2/4/rXR3/uswNT5vfw0k+CVOvo9e0BRw6cbpvF6K4cdQsXrojHIulkI1vNeeeb8Ev/D909v+ZKZsKKD6PkisNeo3jNpeXePXHA5xYbOrwMI+ve4suwca/rt+nbqmBxShk+0edufWjw/hX68d1DWbx1aJ0TvROVKE7982cyh0ef591eSmZsrOzSUtLIzU19dTXk/9O/39aWhqFhYVn/L6/vz916tShdu3atGnThjp16pz6V6tWLby8vPTCIxcsJCSEIUOGMG7cOJxOZ4Upps6rzKoSQ/N2MX97uWELJqJVVyL+oWy6eCx4VYulWbXYc/7N0ljuf8tGRERELtf7cBEhgFhuGnYlU+//gfj45/j492482/2v18tb9QRDHp3CTtON2s3voHN7N/bP/oLft6xk6pDbCag/j3s7XUsT5rOMXRxYlIrj6qokbVxNHmBSSOrSdRQ92g1WL2ML4EdLIm4K/peqKYvV0x9hRMKZTY9hehJz4yh6t7JQxFzG3zuKFekGwXX6cO0N9bEkT2Pq1ATWL3uED9/pwuh7r+W2Z44x/b3PWJNloUbjgfTpdA2mJYc1r97OxK2FuBJN2/430cD7AOu++5wVaRtY/shHbLrlRRpaT96yg4NL51O71QDuudKDg7M/Y9aWDFb+NJBxV6xn5N0BJcq9JMv81uM1Tl3fzn42TrHSvM+T3BS0g0UTf2RL9jZWPf0qs/t+SLfTzj1+fO9PrAm6iwGPuLBzygTm7y1k6/SBjHJtRadBd1Bl52S+m72bPXzAj+8+QJeRkSVav4Oa/HEbRw59zbSCjlz/QGv8Un7mx2kJHGUWsxwmX6UAABoFSURBVJ79hd5fnz3ri8XpdFJQUEB+fv4ZX09+n5WVRUZGBsePH+f48eNnfP/n/5/83m63/+V2/P39qV69OsHBwdStW5c2bdoQHBxM9erVqV69OiEhIdSuXRt/f3+9sMgl8dRTTzF+/PhThdTJr1999dUZxVT1sHCFJSIiIqJCSqSsMXDt/BqD2/7AsKVJrLjnTdbsG3jGNRy2Yywe/yE7TIjgcV6e8jItXE3sQzswokkfJmcuY/lby/jvl9fSNuoplm6zk7p4NdmWpmzZlIXpWY0g4xCZK9aw3eyIsWYl2UBdOnN1bQv/9MmvST47Fk5gx8Izf27Bh5sjXqJ3K3fYPZ+1h8GCL21uH89zgwKw2/oRae/LxB0WclO24fTvxM1DIPnjz1iTZVCr9p30f7IVDssWko9HEdXQRu02HzD6pThcjUISCmfQ98PD5LOf/XZoeNoxdA2tIxk9ZSj1LSaFD16Ba7M7+CnnIEkvTyX1rntKFnsJlrmQGlhPe8lqe8sU3n+7GW6GnevdGnDD27vJZDt7Uk2o/cefDuFmBn77ITd7meRF7+Wq/84gE286Df2JMUOqYTeakRfel+/tdo4l7sBpCyrR+h30RatTt+FFc/p+9SuPN7Nhd7mVoKRYxmwxyduYxCHfJ8+adUm88/ZYEjfMKl7HljMn2zJNE6fTidPpPPW9aZqY5zgXisViwWazYbVasVqtp7632Wy4ubkREhKCzWbDxcUFV1dXXFxccHFxObU8BQUFJCcnk5ycrJcPuewCAgI4fPjwGc8Dh6N4GOXEiROZOHEid951LwGRdygsERERERVSImWLuz2C60cNY8rVr7DBMZYP3+tG19Mut7OJjRuKvz9YdR4f3rcRAzAt+aTZABxkzt2E1XkvbToHY9uWRlbCKtY4TLash5A6d3G93ximrl3LstxUfNamYcdK1Ru6EWn55zLBwI3wVjfRpMZfR0g1iix+GpvValINiOc400ZGsP6n9jRv3pImPT7mwy6tCPEovo3Cs70Q2KPp+9E6euyZy9zZM3jzibfZt3cT61cePnEomvmnCblthPTrTr0Ty23zvY7O7eGnWZCVt5HNdoOSjEUo6TIX/XGr1OzauHieKtNGWO1aGOzGQQFFeWdm6EEoISemInKpFowfkEtVAqMCsABYg/ALAA6DM6+wxOsX/iiVgoijSWMXwMTqqE1YKLCl+OxP9gs4I6O7uzs+Pj4AuLm5nSqBDMPAYrFgsViwWq1nfD3bz07/erJcOvnPMAw96aXCSElJwTCMfyxma4aHk62oRERERMoMFVIip3GJfIKn+n/C7Z+kkvD6C9DstPKE42QfLf4+L309K5YYZ9Qf7h6AS0Hx2YK6dKf6+59zjDVsXGUj0WEQHt2T9tU+5rO1G9m4dgV+a8CFcCKub/qvZ/Wz4EubPp/+w6Tm4OLTn8ffX8WxF75i3ZEM9myazp5N0/nx8xeo4tKdu778ikHt/c76u6ZRwI4vr+e/z80hxQ5YPAgKa0JEVCBrEtL/cn0DA4/AwFMjlwynO74BFiw4cZBHbgkH65zrMltww8X9j9z/KFVM/vo+9LT1YxgYJ5abs5zdzzAMTDJKtH5PZ8UDN+P0VErHwPsHUSd4EACxsbEEBAToySnyN3bu3En9+vVPHap3+vPaw8ODxx57jKFDh+Lq4c2wt1YoMBEREZEywqIIRE57Qji8afTUWG7wh2PMZvH6058sAfgGFX/Xst1MVu/IZtOf/k3b+ggAzlbdae0CGWwi8ctV7MGPmlc0o1HLK3BlP7t//JlNDqjONTTt4Foqy2443al342d8s3Yv07/5jGceuZduberhZ4OjRb8y/daxbHCevTKxF3zL+0/PIcVuo/k1XzJtwzGWrpzPcx09z/oiYWKSdSDl1Mgl05rNkUNOnIAb1Qg2Lv4ylyqrBeMc1q+IlB2vvPLKGSP+DMPA09OTYcOGsW/fPl566SUCAwMVlIiIiEgZoxFSIn/i6nUTD7/YkXlD53H62CCbGUvTODCmOzmwcBJzD3WgRzWDouyf+WDw+6zLsxDa8hVefbwFNuvVtL0afph9hKUzFuFFa0I7eOHi1pr6/MbGmT/ixCAmuhvtPM1/PXGQSRHHDsWzdctfn7KG6UGVupG4/NSJjs8sw1bYlL4zlvDY43dytyWfNU9V5Y6JuafmgYp2+eOv2nNPnGvuwDb2mcUjsSK63Eh0FRccRatZOG8/9rMukZ09P3zCkhfHcU2AiX3vF0xdBGDg17ItjV1NDvxr0k4yvi3ZMje62CvdYmAzm5Ro/Y584nzO1HRa1iJSanbu3Mnnn39e/Fr4pxFRlaWEstu2sfyjX9nx5xdrw4KLWyAhEW1p2boOfhfjI0jDwcFln/JbfA4237b06BtH4Fk+Q3BkLGT6D+vIcHgT2WkAbSP0eaiIiIiokBI5S3dgJei2Nxn4WTNe2/jHjy0OP9oPeYYmv73GhsLPeO6aTXzfOpjc+DmsP1CIJy2IHVxcnVjsgbTu3Br32SvILygkkBbE1jQw3VvTyBfWZuZj4E3t66/BpwSHtznJYNboVswa/dfLPGnC7T+vYUjH62lmLmQxq/mibws2tGuIL/tIXJSLE4Mq9TsR5wIGnnh4Fv/VlQuv48awvkzf0ZYaBsSb6cx89WpSFgeRuWEBWw7bADsmRadGQ52UzHievGY9TeN8OLJqNpuLwJOmXPHUtSW6T2DBq4TLfLEZhnFO67fEf/csWf964BM9x0RKyf3333/q+2HDhlWqIuokh7mOn4Y/xs//sKtXL3gwQ38eQ7capTvi1MRO8o8PMOprJ5G8TPO+rQg8yycsjoPT+PiFd9l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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Beyond Predictive Models- *What if* my rejected loan application got accepted?\n", + "\n", + "![Screenshot%20from%202020-11-01%2012-50-48.png](attachment:Screenshot%20from%202020-11-01%2012-50-48.png)\n", + "\n", + "\n", + "In the recent years we have seen an acute rise in the adoption of Machine Learning by businesses to make *Data-Driven* decisions. One of the several industries making immense use of it is the Finance Industry wherein machine learning is increasingly being used to solve problems in *Portfolio Management*, *Algorithmic Trading*, *Fraud Detection* etc. Here we focus on one particular application, i.e. to gain a better understanding of **Loan Defaults** so as to minimize them. Using past data, a straightforward application of machine learning would be to design a predictive model which can accurately predict the probability of default. This model would then be used in tandem with the loan officers instincts to decide as to whether the loan application should be approved or not. \n", + "\n", + "But is that score generated by a black box machine learning classifier enough for the loan officer to make his decision? In addition, what if the applicant who has just been denied the loan asks for an explanation. Will the loan officer be able to explain the decision made by the black box? or simply what the applicant should do differently in order to get an approval? In extreme cases, a highly accurate model might also be biased towards a particular gender, race which would ultimately hurt the banks reputation for being unethical.\n", + "\n", + "In this case study we consider a large and rich dataset from the U.S. Small Business Administration (SBA). Our task is to create a ML Classifier which can accurately predict the probability of default and can assist the loan officer in answering:-\n", + "***As a representative of the bank, should I grant this loan to a particular small business (Company 'X')? Why or why not? If not, then what should 'X' do differently in order to secure an approval?***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is divided into 2 parts, in ***Part-I***, we focus on feature engineering based on domain knowledge and design accurate predictive models.\n", + "\n", + "In ***Part-II***, we shift our focus towards explaining individual predictions made by the black box classifier designed in *Part-I* through *diverse* counterfactual explantions which lie in the *vicinity* of the original instance being evaluated. An vital aspect of our explanations would also involve probing the model to dig out the latent biases(if any) embedded deep inside the model.\n", + "\n", + "## Part-I: Designing Predictive Models\n", + "\n", + "### a) Data Cleaning\n", + "\n", + "A raw version of the dataset ('SBANational.csv') associated with this notebook can be downloaded [here](https://www.tandfonline.com/doi/suppl/10.1080/10691898.2018.1434342?scroll=top)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/siddharth/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (9) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shape of the dataset is: (899164, 27)\n" + ] + }, + { + "data": { + "text/html": [ + "
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LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFY...RevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppvSBA_Appv
7332137626714000DATABASES BY DESIGNBONITACA91902BBCN BANKCA5415112004-07-212004...0N14-Feb-0631-Jul-04$5,000.00$0.00CHGOFF$4,681.00$5,000.00$4,250.00
6267386224144009GAMELENDER, LLCSAN JOSECA95120BANK OF AMERICA NATL ASSOCNC5121992003-04-042003...YNNaN31-May-03$10,400.00$0.00P I F$0.00$10,000.00$5,000.00
6292486249453008POLSON READY MIX CONCRETE, INCMISSOULAMT59807U.S. BANK NATIONAL ASSOCIATIONMT3273201994-02-221994...NNNaN31-Jul-94$913,500.00$0.00P I F$0.00$913,500.00$685,125.00
5060264898024005AUNTIE ANNE'SOLATHEKS66061BANK OF AMERICA NATL ASSOCNC7222132001-09-102001...0NNaN30-Sep-01$144,453.00$0.00P I F$0.00$150,000.00$127,500.00
644821487374007ALAMITOS BAY MARKETLONG BEACHCA90803HANMI BANKCA01997-08-161997...NNNaN31-Oct-97$330,000.00$0.00P I F$0.00$330,000.00$247,500.00
7088117371624005RUSTY NAIL LLCSTOWEVT5672VERMONT 504 CORPORATIONVT7224102004-04-212004...NNNaN14-Jul-04$470,000.00$0.00P I F$0.00$470,000.00$470,000.00
5326325195223008NMT ASSOCIATESWINTER PARKFL32789TRANSAMERICA SMALL BUS. CAPITASC01992-10-201993...NNNaN31-Jan-93$242,000.00$0.00P I F$0.00$242,000.00$205,700.00
5416125308354005FONTANA CHEVRONFONTANACA92335FIRST-CITIZENS BK & TR CONC4471102002-03-292002...0NNaN31-May-02$718,800.00$0.00P I F$0.00$718,800.00$539,100.00
2127532502096002SASSAFRAS RESTAURANTFAYETTEVILLEAR72701IBERIABANKAR7221102006-10-312007...0NNaN31-Dec-06$380,000.00$0.00P I F$0.00$380,000.00$285,000.00
4370884241134009ANN N. HEBDA, D.D.S., P.C.ASHBURNVA20147BUSINESS FINANCE GROUP, INC.VA6212102001-01-242001...NNNaN12-Jun-02$178,000.00$0.00P I F$0.00$178,000.00$178,000.00
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\n", + "
" + ], + "text/plain": [ + " LoanNr_ChkDgt Name City State \\\n", + "733213 7626714000 DATABASES BY DESIGN BONITA CA \n", + "626738 6224144009 GAMELENDER, LLC SAN JOSE CA \n", + "629248 6249453008 POLSON READY MIX CONCRETE, INC MISSOULA MT \n", + "506026 4898024005 AUNTIE ANNE'S OLATHE KS \n", + "64482 1487374007 ALAMITOS BAY MARKET LONG BEACH CA \n", + "708811 7371624005 RUSTY NAIL LLC STOWE VT \n", + "532632 5195223008 NMT ASSOCIATES WINTER PARK FL \n", + "541612 5308354005 FONTANA CHEVRON FONTANA CA \n", + "212753 2502096002 SASSAFRAS RESTAURANT FAYETTEVILLE AR \n", + "437088 4241134009 ANN N. HEBDA, D.D.S., P.C. ASHBURN VA \n", + "\n", + " Zip Bank BankState NAICS ApprovalDate \\\n", + "733213 91902 BBCN BANK CA 541511 2004-07-21 \n", + "626738 95120 BANK OF AMERICA NATL ASSOC NC 512199 2003-04-04 \n", + "629248 59807 U.S. BANK NATIONAL ASSOCIATION MT 327320 1994-02-22 \n", + "506026 66061 BANK OF AMERICA NATL ASSOC NC 722213 2001-09-10 \n", + "64482 90803 HANMI BANK CA 0 1997-08-16 \n", + "708811 5672 VERMONT 504 CORPORATION VT 722410 2004-04-21 \n", + "532632 32789 TRANSAMERICA SMALL BUS. CAPITA SC 0 1992-10-20 \n", + "541612 92335 FIRST-CITIZENS BK & TR CO NC 447110 2002-03-29 \n", + "212753 72701 IBERIABANK AR 722110 2006-10-31 \n", + "437088 20147 BUSINESS FINANCE GROUP, INC. VA 621210 2001-01-24 \n", + "\n", + " ApprovalFY ... RevLineCr LowDoc ChgOffDate DisbursementDate \\\n", + "733213 2004 ... 0 N 14-Feb-06 31-Jul-04 \n", + "626738 2003 ... Y N NaN 31-May-03 \n", + "629248 1994 ... N N NaN 31-Jul-94 \n", + "506026 2001 ... 0 N NaN 30-Sep-01 \n", + "64482 1997 ... N N NaN 31-Oct-97 \n", + "708811 2004 ... N N NaN 14-Jul-04 \n", + "532632 1993 ... N N NaN 31-Jan-93 \n", + "541612 2002 ... 0 N NaN 31-May-02 \n", + "212753 2007 ... 0 N NaN 31-Dec-06 \n", + "437088 2001 ... N N NaN 12-Jun-02 \n", + "\n", + " DisbursementGross BalanceGross MIS_Status ChgOffPrinGr \\\n", + "733213 $5,000.00 $0.00 CHGOFF $4,681.00 \n", + "626738 $10,400.00 $0.00 P I F $0.00 \n", + "629248 $913,500.00 $0.00 P I F $0.00 \n", + "506026 $144,453.00 $0.00 P I F $0.00 \n", + "64482 $330,000.00 $0.00 P I F $0.00 \n", + "708811 $470,000.00 $0.00 P I F $0.00 \n", + "532632 $242,000.00 $0.00 P I F $0.00 \n", + "541612 $718,800.00 $0.00 P I F $0.00 \n", + "212753 $380,000.00 $0.00 P I F $0.00 \n", + "437088 $178,000.00 $0.00 P I F $0.00 \n", + "\n", + " GrAppv SBA_Appv \n", + "733213 $5,000.00 $4,250.00 \n", + "626738 $10,000.00 $5,000.00 \n", + "629248 $913,500.00 $685,125.00 \n", + "506026 $150,000.00 $127,500.00 \n", + "64482 $330,000.00 $247,500.00 \n", + "708811 $470,000.00 $470,000.00 \n", + "532632 $242,000.00 $205,700.00 \n", + "541612 $718,800.00 $539,100.00 \n", + "212753 $380,000.00 $285,000.00 \n", + "437088 $178,000.00 $178,000.00 \n", + "\n", + "[10 rows x 27 columns]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read dataset and store date in the correct format\n", + "dataset = pd.read_csv('SBAnational.csv')\n", + "print('The shape of the dataset is: ',dataset.shape)\n", + "from datetime import datetime\n", + "date_list = list(dataset['ApprovalDate'])\n", + "date_list_updated = list()\n", + "for date_str in date_list:\n", + " d = datetime.strptime(date_str,'%d-%b-%y')\n", + " date_list_updated.append(d)\n", + "dataset['ApprovalDate']=date_list_updated\n", + "dataset.sample(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### b) Feature Engineering\n", + "\n", + "There are a number of variables that consistently emerge as indicators of risk that could explain the variation of loan default rates. Seven variables that are discussed below include *Location (State), Industry, Gross Disbursement, New versus Established Business, Loans Backed by Real Estate, Economic Recession, and SBA’s Guaranteed Portion of Approved Loan*.\n", + "\n", + "Based on domain knowledge and the work done by [M.Li et al. 2017](https://amstat.tandfonline.com/doi/full/10.1080/10691898.2018.1434342), we create the following new features:-\n", + "\n", + "#### 1) Default (*Response Variable*) \n", + "Response variable *'Default'* attains the value 1 if *MIS_Status='CHGOFF'*, and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "dataset['Default']=0\n", + "dataset.loc[dataset['MIS_Status']=='CHGOFF','Default']=1 #Default if MIS_Status = CHGOFF\n", + "#dataset.sample(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2) Was the Loan active during the Great Recession?\n", + "Creating a new feature ***Recession*** which denotes whether the loan was active during the recession period (b/w 1/12/2007 to 31/06/2009). A dummy variable ***“Recession”*** where ***“Recession”*** is 1 if the loans were active in between December 2007 to June 2009, and equals 0 for all other times." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "dataset['Recession']=0\n", + "dataset.loc[dataset['ApprovalDate']>='2007-12-01','Recession']=1\n", + "dataset.loc[dataset['ApprovalDate']>='2009-06-30','Recession']=0\n", + "#dataset.sample(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3) Is the loan backed by real estate?\n", + "Creating a new feature ***Backed_by_real_estate*** which denotes whether the loan was backed by real estate or not. Loans that are backed by real estate generally have terms greater than 20 years, so our newly created feature takes the value as 1, if the term is greater than 20 years and 0 otherwise. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "dataset['Backed_by_Real_Estate']=0\n", + "dataset.loc[dataset['Term']>=240,'Backed_by_Real_Estate']=1 \n", + "#dataset.sample(10)" + ] + }, + { + "attachments": { + "Screenshot%20from%202020-10-25%2012-15-29.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4) Which industry category did the small business seeking loan belong to?\n", + "\n", + "Creating a new feature ***Industry_code*** which tells us which category the given company belongs to. By default, the industry code is present in the first two characters of NAICS.\n", + "The industry codes are as follows:-\n", + "\n", + "![Screenshot%20from%202020-10-25%2012-15-29.](attachment:Screenshot%20from%202020-10-25%2012-15-29.png)\n", + "\n", + "Table taken from: [M.Li et al. 2017](https://amstat.tandfonline.com/doi/full/10.1080/10691898.2018.1434342)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract Industry Code from NAICS to create a new Industry Feature\n", + "dataset['Industry']=dataset['NAICS'].astype('str').str[0:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5) How much *portion* of the loan amount is guaranteed by SBA?\n", + "Another risk indicator ***Portion*** was engineered which represents the percentage of the loan that is guaranteed by SBA. This is one of the variables generated by calculating the ratio of the\n", + "amount of the loan SBA has been guaranteed and the gross amount approved by the bank. A *guaranteed loan* is a type of loan in which a third party agrees to pay if the borrower should default and is used by borrowers with poor credit or little in the way of financial resources; it enables financially unattractive candidates to qualify for a loan and assures that the lender won't lose money.\n", + "\n", + "$Portion = \\frac{SBA_{Approved}}{Gross_{Approved}} $" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "dataset['SBA_Appv'] = dataset['SBA_Appv'].str.replace(',', '').str.replace('$', '').str.replace('.', '').astype(int)\n", + "dataset['GrAppv'] = dataset['GrAppv'].str.replace(',', '').str.replace('$', '').str.replace('.', '').astype(int)\n", + "dataset['Portion']=dataset['SBA_Appv']/dataset['GrAppv']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a plot to check the distribution of Default v/s Non-Default Cases over the years. There seems to be a high default rate from 2006 till 2008." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "plt.figure(figsize=(15,8))\n", + "dataset_sorted = dataset.sort_values(by='ApprovalDate')\n", + "ax = sns.countplot(x='ApprovalFY',hue='Default',data=dataset_sorted)\n", + "plt.xticks(rotation=60)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6) What is the total amount disbursed?\n", + "Gross disbursement (represented as ***“DisbursementGross”*** in the dataset) is another risk indicator identified as a key variable to consider. The rationale behind selecting ***“DisbursementGross”*** is that the larger the loan size, the more likely the underlying business will be established and expanding (i.e., purchasing assets that have some resale value), thereby increasing the likelihood of paying off the loan.\n", + "\n", + "\n", + "#### 7 & 8. \n", + "Finally we claim that the Location of the company represented by ***State*** and whether its a newly established company or not might also serve as potential risk indicator. It may be argued that established businesses already\n", + "have a proven track record of success and are requesting a loan to expand on what they already do successfully. Whereas, new businesses sometimes do not anticipate the obstacles they may face and may be unable to successfully. So a feature ***NewExist*** was engineered, which attains the value = 1 if the business is less than 2 years old and 0 if the business is more than 2 years old." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_shortlisted = dataset_sorted[['State','Industry','DisbursementGross','NewExist','Backed_by_Real_Estate',\n", + " 'Recession','Portion', 'Default']]\n", + "dataset_shortlisted = dataset_shortlisted.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how our final dataset looks like. Using these risk indicators we will now focus on designing Classifiers." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Industry DisbursementGross NewExist Backed_by_Real_Estate \\\n", + "408738 MN 44 15000000 1 0 \n", + "276740 NY 54 2500000 1 0 \n", + "232605 CA 0 50000000 1 1 \n", + "178854 FL 54 949800 2 0 \n", + "34024 MS 54 16700000 1 1 \n", + "28858 NY 48 1266700 1 0 \n", + "322578 CA 0 32850000 1 1 \n", + "312876 NM 54 5400000 1 0 \n", + "122868 MI 44 2530000 1 0 \n", + "298624 PA 33 5000000 1 0 \n", + "\n", + " Recession Portion Default \n", + "408738 0 0.75 1 \n", + "276740 1 0.50 0 \n", + "232605 0 1.00 0 \n", + "178854 0 0.50 0 \n", + "34024 0 1.00 0 \n", + "28858 0 0.50 1 \n", + "322578 0 0.75 0 \n", + "312876 0 0.80 0 \n", + "122868 0 0.50 0 \n", + "298624 1 0.50 0 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_shortlisted['DisbursementGross'] = dataset_shortlisted['DisbursementGross'].str.replace(',', '').str.replace('$', '').str.replace('.', '').astype(int)\n", + "dataset_shortlisted['NewExist']=dataset_shortlisted['NewExist'].astype(int)\n", + "dataset_shortlisted.sample(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 644860\n", + "2 253121\n", + "0 1033\n", + "Name: NewExist, dtype: int64" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check counts of New v/s Established Companies\n", + "dataset_shortlisted['NewExist'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# NewExist contains some values as 0 which does not carry any significance, so we remove them\n", + "dataset_shortlisted=dataset_shortlisted.loc[(dataset_shortlisted.NewExist == 1) | (dataset_shortlisted.NewExist == 2),:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One Hot Encode all the categorical columns present in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(897981, 82)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_encoded= pd.get_dummies(dataset_shortlisted,columns=['Industry','State'])\n", + "dataset_encoded.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected.There is a clear class imbalance present in the dataset!" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 740501\n", + "1 157480\n", + "Name: Default, dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_encoded['Default'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Treating Data for Imbalanced Classification\n", + "As seen above, the distribution of *Default* v/s *Not-Default* classes in our dataset is highly skewed. If we go on to train models on this dataset, then even the best performing models would give us misleading results. So it might happen that even though the overall accuracy would be high(falsely) but the model would not be working well in idenifying default cases(Low Specificity and Low Kappa Scores).\n", + "\n", + "There are several methods which can be used to treat imbalanced class problems but since we have access to a good amount of instances for each class, therefore we simply downsample instances from the majority class (**Non-Default**) to match the minority class (**Default**)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_bal_0 = dataset_encoded[dataset_encoded['Default'].isin([\"0\"])].sample(157480,random_state=26)\n", + "dataset_bal_1 = dataset_encoded[dataset_encoded['Default'].isin([\"1\"])]\n", + "dataset_balanced = pd.concat([dataset_bal_0,dataset_bal_1])#.sample(26000,random_state=26)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Artificial Neural Network\n", + "\n", + "We tried out several linear and non-linear Machine Learning Classifiers and the best performing linear classifier (Logistic Reg.) gave us a balanced accuracy of 50% whereas the non-linear classifier(XGBoost) gave us a balanced accuracy of 70%.\n", + "\n", + "Keeping the [Universal approximation theorem](http://cognitivemedium.com/magic_paper/assets/Hornik.pdf) in mind we construct a single layer ANN Classifier which will be used as an input to *DiCE* for generating counterfactuals. After experimenting with different choices of hyperparameters, the architechture described below gave us the best validation score on several metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/siddharth/.local/lib/python3.7/site-packages/tensorflow/python/client/session.py:1751: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6165 - accuracy: 0.6597 - precision: 0.6379 - recall: 0.7422 - auc: 0.7179 - val_loss: 0.6055 - val_accuracy: 0.6675 - val_precision: 0.6493 - val_recall: 0.7320 - val_auc: 0.7277\n", + "Epoch 2/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6080 - accuracy: 0.6634 - precision: 0.6417 - recall: 0.7434 - auc: 0.7230 - val_loss: 0.6064 - val_accuracy: 0.6653 - val_precision: 0.6473 - val_recall: 0.7296 - val_auc: 0.7262\n", + "Epoch 3/26\n", + "6300/6300 [==============================] - 10s 2ms/step - loss: 0.6065 - accuracy: 0.6645 - precision: 0.6430 - recall: 0.7428 - auc: 0.7247 - val_loss: 0.6024 - val_accuracy: 0.6677 - val_precision: 0.6365 - val_recall: 0.7860 - val_auc: 0.7305\n", + "Epoch 4/26\n", + "6300/6300 [==============================] - 10s 2ms/step - loss: 0.6051 - accuracy: 0.6654 - precision: 0.6439 - recall: 0.7436 - auc: 0.7259 - val_loss: 0.6010 - val_accuracy: 0.6699 - val_precision: 0.6463 - val_recall: 0.7540 - val_auc: 0.7308\n", + "Epoch 5/26\n", + "6300/6300 [==============================] - 11s 2ms/step - loss: 0.6044 - accuracy: 0.6661 - precision: 0.6451 - recall: 0.7418 - auc: 0.7271 - val_loss: 0.6001 - val_accuracy: 0.6707 - val_precision: 0.6458 - val_recall: 0.7593 - val_auc: 0.7327\n", + "Epoch 6/26\n", + "6300/6300 [==============================] - 10s 2ms/step - loss: 0.6038 - accuracy: 0.6660 - precision: 0.6453 - recall: 0.7402 - auc: 0.7277 - val_loss: 0.6005 - val_accuracy: 0.6697 - val_precision: 0.6419 - val_recall: 0.7712 - val_auc: 0.7328\n", + "Epoch 7/26\n", + "6300/6300 [==============================] - 11s 2ms/step - loss: 0.6037 - accuracy: 0.6663 - precision: 0.6454 - recall: 0.7414 - auc: 0.7278 - val_loss: 0.6005 - val_accuracy: 0.6686 - val_precision: 0.6470 - val_recall: 0.7459 - val_auc: 0.7311\n", + "Epoch 8/26\n", + "6300/6300 [==============================] - 11s 2ms/step - loss: 0.6030 - accuracy: 0.6673 - precision: 0.6474 - recall: 0.7383 - auc: 0.7288 - val_loss: 0.5997 - val_accuracy: 0.6711 - val_precision: 0.6469 - val_recall: 0.7569 - val_auc: 0.7332\n", + "Epoch 9/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6026 - accuracy: 0.6670 - precision: 0.6466 - recall: 0.7397 - auc: 0.7292 - val_loss: 0.6014 - val_accuracy: 0.6686 - val_precision: 0.6428 - val_recall: 0.7624 - val_auc: 0.7313\n", + "Epoch 10/26\n", + "6300/6300 [==============================] - 11s 2ms/step - loss: 0.6022 - accuracy: 0.6673 - precision: 0.6473 - recall: 0.7385 - auc: 0.7296 - val_loss: 0.5992 - val_accuracy: 0.6694 - val_precision: 0.6499 - val_recall: 0.7380 - val_auc: 0.7342\n", + "Epoch 11/26\n", + "6300/6300 [==============================] - 11s 2ms/step - loss: 0.6019 - accuracy: 0.6682 - precision: 0.6481 - recall: 0.7393 - auc: 0.7298 - val_loss: 0.5994 - val_accuracy: 0.6699 - val_precision: 0.6557 - val_recall: 0.7189 - val_auc: 0.7331\n", + "Epoch 12/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6016 - accuracy: 0.6685 - precision: 0.6488 - recall: 0.7377 - auc: 0.7302 - val_loss: 0.5991 - val_accuracy: 0.6705 - val_precision: 0.6490 - val_recall: 0.7462 - val_auc: 0.7334\n", + "Epoch 13/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6017 - accuracy: 0.6684 - precision: 0.6487 - recall: 0.7377 - auc: 0.7302 - val_loss: 0.6000 - val_accuracy: 0.6695 - val_precision: 0.6566 - val_recall: 0.7137 - val_auc: 0.7338\n", + "Epoch 14/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.6011 - accuracy: 0.6679 - precision: 0.6485 - recall: 0.7363 - auc: 0.7307 - val_loss: 0.5992 - val_accuracy: 0.6714 - val_precision: 0.6488 - val_recall: 0.7506 - val_auc: 0.7343\n", + "Epoch 15/26\n", + "6300/6300 [==============================] - 14s 2ms/step - loss: 0.6009 - accuracy: 0.6692 - precision: 0.6500 - recall: 0.7366 - auc: 0.7312 - val_loss: 0.5990 - val_accuracy: 0.6694 - val_precision: 0.6583 - val_recall: 0.7079 - val_auc: 0.7348\n", + "Epoch 16/26\n", + "6300/6300 [==============================] - 13s 2ms/step - loss: 0.6006 - accuracy: 0.6686 - precision: 0.6491 - recall: 0.7370 - auc: 0.7312 - val_loss: 0.5983 - val_accuracy: 0.6711 - val_precision: 0.6558 - val_recall: 0.7234 - val_auc: 0.7347\n", + "Epoch 17/26\n", + "6300/6300 [==============================] - 14s 2ms/step - loss: 0.6004 - accuracy: 0.6690 - precision: 0.6492 - recall: 0.7387 - auc: 0.7313 - val_loss: 0.5983 - val_accuracy: 0.6702 - val_precision: 0.6516 - val_recall: 0.7349 - val_auc: 0.7340\n", + "Epoch 18/26\n", + "6300/6300 [==============================] - 14s 2ms/step - loss: 0.6002 - accuracy: 0.6691 - precision: 0.6493 - recall: 0.7385 - auc: 0.7317 - val_loss: 0.5981 - val_accuracy: 0.6694 - val_precision: 0.6547 - val_recall: 0.7203 - val_auc: 0.7345\n", + "Epoch 19/26\n", + "6300/6300 [==============================] - 13s 2ms/step - loss: 0.5998 - accuracy: 0.6698 - precision: 0.6501 - recall: 0.7383 - auc: 0.7322 - val_loss: 0.5981 - val_accuracy: 0.6710 - val_precision: 0.6442 - val_recall: 0.7674 - val_auc: 0.7345\n", + "Epoch 20/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.5998 - accuracy: 0.6692 - precision: 0.6494 - recall: 0.7385 - auc: 0.7318 - val_loss: 0.5971 - val_accuracy: 0.6715 - val_precision: 0.6608 - val_recall: 0.7082 - val_auc: 0.7365\n", + "Epoch 21/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.5992 - accuracy: 0.6702 - precision: 0.6506 - recall: 0.7384 - auc: 0.7326 - val_loss: 0.5997 - val_accuracy: 0.6685 - val_precision: 0.6680 - val_recall: 0.6729 - val_auc: 0.7345\n", + "Epoch 22/26\n", + "6300/6300 [==============================] - 13s 2ms/step - loss: 0.5997 - accuracy: 0.6697 - precision: 0.6503 - recall: 0.7373 - auc: 0.7319 - val_loss: 0.5973 - val_accuracy: 0.6720 - val_precision: 0.6499 - val_recall: 0.7493 - val_auc: 0.7353\n", + "Epoch 23/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.5992 - accuracy: 0.6702 - precision: 0.6516 - recall: 0.7344 - auc: 0.7325 - val_loss: 0.5969 - val_accuracy: 0.6721 - val_precision: 0.6476 - val_recall: 0.7585 - val_auc: 0.7363\n", + "Epoch 24/26\n", + "6300/6300 [==============================] - 13s 2ms/step - loss: 0.5993 - accuracy: 0.6701 - precision: 0.6512 - recall: 0.7357 - auc: 0.7326 - val_loss: 0.5980 - val_accuracy: 0.6685 - val_precision: 0.6651 - val_recall: 0.6818 - val_auc: 0.7365\n", + "Epoch 25/26\n", + "6300/6300 [==============================] - 13s 2ms/step - loss: 0.5990 - accuracy: 0.6700 - precision: 0.6515 - recall: 0.7342 - auc: 0.7329 - val_loss: 0.5982 - val_accuracy: 0.6700 - val_precision: 0.6405 - val_recall: 0.7786 - val_auc: 0.7353\n", + "Epoch 26/26\n", + "6300/6300 [==============================] - 12s 2ms/step - loss: 0.5991 - accuracy: 0.6701 - precision: 0.6512 - recall: 0.7355 - auc: 0.7328 - val_loss: 0.5964 - val_accuracy: 0.6713 - val_precision: 0.6429 - val_recall: 0.7738 - val_auc: 0.7370\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# supress deprecation warnings from TF\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n", + "tf.random.set_seed(26)\n", + "# Adjusting for class imbalance by downsampling\n", + "dataset_bal_0 = dataset_shortlisted[dataset_shortlisted['Default'].isin([\"0\"])].sample(157480,random_state=26)\n", + "dataset_bal_1 = dataset_shortlisted[dataset_shortlisted['Default'].isin([\"1\"])]\n", + "dataset_balanced_dice = pd.concat([dataset_bal_0,dataset_bal_1])\n", + "import dice_ml\n", + "sess = tf.compat.v1.InteractiveSession()\n", + "d = dice_ml.Data(dataframe=dataset_balanced_dice, continuous_features=['DisbursementGross', 'NewExist',\n", + " 'Backed_by_Real_Estate', 'Recession', 'Portion'], outcome_name='Default')\n", + "train, _ = d.split_data(d.normalize_data(d.one_hot_encoded_data))\n", + "X_train = train.loc[:, train.columns != 'Default']\n", + "y_train = train.loc[:, train.columns == 'Default']\n", + "X_train.head()\n", + "\n", + "from sklearn.metrics import roc_auc_score\n", + "METRICS = [\n", + " #keras.metrics.TruePositives(name='tp'),\n", + " #keras.metrics.FalsePositives(name='fp'),\n", + " #keras.metrics.TrueNegatives(name='tn'),\n", + " #keras.metrics.FalseNegatives(name='fn'), \n", + " keras.metrics.BinaryAccuracy(name='accuracy'),\n", + " keras.metrics.Precision(name='precision'),\n", + " keras.metrics.Recall(name='recall'),\n", + " keras.metrics.AUC(name='auc'),\n", + "]\n", + "\n", + "ann_model = keras.Sequential()\n", + "ann_model.add(keras.layers.Dense(200, input_shape=(X_train.shape[1],), \n", + " kernel_regularizer=keras.regularizers.l2(0.001), \n", + " activation=tf.nn.relu))\n", + "ann_model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n", + "\n", + "ann_model.compile(loss='binary_crossentropy', \n", + " optimizer=tf.keras.optimizers.Adam(0.001), \n", + " metrics=METRICS)#=['accuracy',auroc])\n", + "ann_model.fit(X_train, y_train, validation_split=0.20, epochs=26, verbose=1)\n", + "# the training will take some time for 26 epochs.\n", + "# you can wait or set verbose=1 or 0 to see(not see) the progress of training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enter *DiCE*\n", + "## Part- II: Counterfactual Explanations for individual instances using the trained model\n", + "Now that we have trained a black box classifier to identify default cases, we aim to generate explanations which can assist the loan officer in making an informed decision and at the same time provide actionable insights so that even if the applicant is denied a loan at present, they can secure it in the future after encorporating the suggested changes.\n", + "\n", + "Given a trained classifier and the instance needing explanation, [*DiCE*](https://arxiv.org/abs/1905.07697) focuses on generating a set of counterfactual explanations by adressing a ***diversity-proximity*** tradeoff. \n", + "In addition to facilitate actionability, *DiCE* is flexible enough to support user-provided inputs based on domain knowledge, such as custom weights for individual features (A higher feature weight means that the feature is harder to change than others) and restrictions on the perturbation of certain features that are difficult to modify in the real world." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# provide the trained ML model to DiCE's model object\n", + "backend = 'TF'+tf.__version__[0]\n", + "m = dice_ml.Model(model=ann_model, backend=backend)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate diverse counterfactuals\n", + "Based on the data object *d* and the model object *m*, we can now instantiate the DiCE class for generating explanations." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# initiate DiCE\n", + "exp = dice_ml.Dice(d, m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Would a loan be granted to a Real Estate business based in New York during COVID'19?\n", + "\n", + "Let us consider a newly established small business based in *New York* that advertizes itself to be the best in helping fellow new yorkers and newcomers in finding the right apartment to rent (despite the abnormally high costs of real estate in New York!). It goes to JP Morgan & Chase and puts forward their case before a loan officer requesting for a loan of USD 125,000. To make their application strong, they have a guranteed payback of USD 112,500 (*Portion=90%*) which will be paid by a third party in case of default. However, at this stage they don't have any backing in the form of real estate which can be used by the bank in case of default. Another important point to note is that the US Job market is going through recession right now due to COVID'19.\n", + "\n", + "We now provide this instance (in the form of a 7-d vector) whose decision will first be evaluated by our ANN Classifier and then explained by *DICE*." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counterfactual sample: {'State': 'NY', 'Industry': '53', 'DisbursementGross': 125000, 'NewExist': 1, 'Backed_by_Real_Estate': 0, 'Recession': 1, 'Portion': 0.9}\n" + ] + } + ], + "source": [ + "# query instance in the form of a dictionary; keys: feature name, values: feature value\n", + "query_instance = {'State': 'NY', 'Industry': '53', 'DisbursementGross': 125000, 'NewExist': 1, 'Backed_by_Real_Estate': 0, 'Recession': 1, 'Portion': 0.90}\n", + "print(\"Counterfactual sample: {}\".format(query_instance))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try-1. Naive use of DiCE\n", + "We start by generating counterfactuals using default parameter values so as to know what all needs to be tweaked.\n", + "\n", + "The results obtained suggest that we might need to restrict change in some features that are difficult to change in the real world. Also continous features might need a scaling factor to prevent them from attaining abnormally high values in the generated counterfactuals." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root: MAD for feature NewExist is 0, so replacing it with 1.0 to avoid error.\n", + "WARNING:root: MAD for feature Backed_by_Real_Estate is 0, so replacing it with 1.0 to avoid error.\n", + "WARNING:root: MAD for feature Recession is 0, so replacing it with 1.0 to avoid error.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diverse Counterfactuals found! total time taken: 00 min 21 sec\n", + "Query instance (original outcome : 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Industry DisbursementGross NewExist Backed_by_Real_Estate Recession \\\n", + "0 NH 21 0.0 - 1.0 0.0 \n", + "1 VT - 0.0 - 1.0 - \n", + "2 ND 42 16777763.0 - 1.0 - \n", + "3 - - 59944280.0 - 1.0 - \n", + "\n", + " Portion Default \n", + "0 0.3 0.082 \n", + "1 1.0 0.025 \n", + "2 0.8 0.066 \n", + "3 - 0.09 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate counterfactuals\n", + "dice_exp = exp.generate_counterfactuals(query_instance, total_CFs=4, desired_class=\"opposite\")\n", + "# visualize the resutls by highlighting only the changes\n", + "dice_exp.visualize_as_dataframe(show_only_changes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try-2. Slightly better use of DiCE\n", + "\n", + "We notice 2 major problems in the counterfactuals being generated in Try-1:-\n", + "\n", + "1) **Model biased towards certain State's and Industry type's**- Given that *New York* witnesses comparably high default percentages (with *California* being the highest) and that the *Real-Estate* Industry also has the highest default percentage, there seems to be some bias towards these two categories. This is confirmed when the generated counterfactuals suggest to change *State* and *Industry-Type* respectively. \n", + "Removing *State* & *Industry-type* from the *features_to_change* list can solve this problem.\n", + "\n", + "\n", + "\n", + "2) **Abnormally high values for *GrossDisbursement***- It may be the case that some features are harder to change than others(In our case *DisbursementGross* seem to attain erratic values). DiCE allows input of relative difficulty in changing a feature through specifying feature weights. A higher feature weight means that the feature is harder to change than others. For instance, one way is to use the mean absolute deviation from the median as a measure of relative difficulty of changing a continuous feature. Let's see what their values are by computing them below:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'DisbursementGross': 130.1, 'NewExist': inf, 'Backed_by_Real_Estate': inf, 'Recession': inf, 'Portion': 6.13}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/siddharth/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in double_scalars\n", + " import sys\n" + ] + } + ], + "source": [ + "# get MAD\n", + "mads = d.get_mads(normalized=True)\n", + "\n", + "# create feature weights\n", + "feature_weights = {}\n", + "for feature in mads:\n", + " feature_weights[feature] = round(1/mads[feature], 2)\n", + "print(feature_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diverse Counterfactuals found! total time taken: 03 min 20 sec\n", + "Query instance (original outcome : 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Industry DisbursementGross NewExist Backed_by_Real_Estate Recession \\\n", + "0 - - 0.0 2.0 1.0 0.0 \n", + "1 - - 124396103.0 - - 0.0 \n", + "2 - - 813923.0 - 1.0 - \n", + "3 - - 69567365.0 - 1.0 0.0 \n", + "\n", + " Portion Default \n", + "0 1.0 0.037 \n", + "1 1.0 0.35 \n", + "2 - 0.105 \n", + "3 1.0 0.023 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Assigning custom weight for Gross Disbursement because counterfactuals being generated are having higher values of this feature.\n", + "feature_weights = {'DisbursementGross': 13000, 'Portion': 1} # Setting weight of DisbursementGross to 130.1 still gave bad results, so we went for an even higher value \n", + "dice_exp = exp.generate_counterfactuals(query_instance, total_CFs=4, desired_class=\"opposite\",feature_weights=feature_weights ,\n", + " features_to_vary=['DisbursementGross','NewExist','Backed_by_Real_Estate','Recession','Portion'])\n", + "# visualize the results by highlighting only the changes\n", + "dice_exp.visualize_as_dataframe(show_only_changes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try 3. \n", + "\n", + "Most of the counterfactuals being generated in Try-2 (despite assigning custom weights) still seem to suggest scenarios wherein the *DisbursementGross* is too high. More importantly they recommend that the customer should wait for the COVID'19 recession to get over and get some security in terms of Real Estate Backing to support their application.\n", + "If waiting for the recession to get over is not an option, then the only realistic option for them to secure the loan is if they increase the gross disbursement to USD 813,923 and get some real estate backing to support their application.\n", + "\n", + "Now what if the business is certain that it cannot increase the gross disbursement to USD 813,923? because that would imply that they have to a secure a bigger amount as guaranteed payback as well.\n", + "\n", + "In our final try, we explore if its possible to get the loan without any changes in Gross Disbursement. To do so, we remove *DisbursementGross* from the *features_to_vary* list and run DiCE once again." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diverse Counterfactuals found! total time taken: 03 min 24 sec\n", + "Query instance (original outcome : 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Industry DisbursementGross NewExist Backed_by_Real_Estate Recession \\\n", + "0 - - - - 1.0 - \n", + "1 - - - - 1.0 - \n", + "2 - - - - 1.0 - \n", + "3 - - - - 1.0 - \n", + "\n", + " Portion Default \n", + "0 0.8 0.213 \n", + "1 0.8 0.213 \n", + "2 0.8 0.213 \n", + "3 0.8 0.213 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# assigning custom weight for Gross Disbursement because counterfactuals being generated are having higher values of this feature.\n", + "#feature_weights = {'DisbursementGross': 13000, 'Portion': 1}\n", + "dice_exp = exp.generate_counterfactuals(query_instance, total_CFs=4, desired_class=\"opposite\",feature_weights=feature_weights ,\n", + " features_to_vary=['NewExist','Backed_by_Real_Estate','Recession','Portion'])\n", + "# visualize the results by highlighting only the changes\n", + "dice_exp.visualize_as_dataframe(show_only_changes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Voila! As it turns out, quite luckily there is a feasible counterfactual scenario wherein the business would just have to get a real estate backing and they can even get away with a guaranteed loan portion of 80% (given that they were already willing to guarantee 90% of the loan portion)**\n", + "\n", + "So now the loan officer can safely approve their loan application once they have made these suggested changes!" + ] + }, + { + "attachments": { + "Screenshot%20from%202020-11-01%2012-50-48.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A caveat to note is that currently the counterfactual explanations being generated by *DiCE* suffer from lack of causal knowledge about the features being modified. As it turns out features do not exist in a vaccum, they come from a data generating process which often constrains their independent modification.\n", + "\n", + "In our case, the causal process might take the form of the graphical model mentioned below. This would constrain the way in which risk indicators affecting loan defaults are perturbed. Future versions of DiCE would incorporate a post hoc filtering procedure based on the constraints specified by the causal graphical model given as input by the user.\n", + "\n", + "![Screenshot%20from%202020-11-01%2012-50-48.png](attachment:Screenshot%20from%202020-11-01%2012-50-48.png)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}