{ "cells": [ { "cell_type": "markdown", "source": [ "## Mount your google drive" ], "metadata": { "id": "8ksHq50GtNJ1" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o0DFJ4NsGNNF" }, "outputs": [], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "markdown", "metadata": { "id": "9aRxtZp2uGe_" }, "source": [ "## Model importing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "qi1Uc1PQr03u" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers, optimizers, datasets, Sequential\n", "from tensorflow.keras.layers import Conv2D,Activation,MaxPooling2D,Dropout,Flatten,Dense\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": { "id": "24WGnDLUuvco" }, "source": [ "##Data Reading (already preprocessed)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ScY6SJJKr_CI", "outputId": "70b0e223-871c-4989-a1ea-d70e977f80be" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", "170500096/170498071 [==============================] - 2s 0us/step\n", "170508288/170498071 [==============================] - 2s 0us/step\n", "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n", "[6]\n", "[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "array([6], dtype=uint8)" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "#download Cifar-10 dataset\n", "(x_train,y_train), (x_test, y_test) = datasets.cifar10.load_data()\n", "#print the size of the dataset\n", "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n", "print(y_train[0])\n", "\n", "#Convert the category label into onehot encoding \n", "num_classes = 10\n", "y_train_onehot = keras.utils.to_categorical(y_train, num_classes)\n", "y_test_onehot = keras.utils.to_categorical(y_test, num_classes)\n", "print(y_train_onehot[0])\n", "y_train[0]\n" ] }, { "cell_type": "markdown", "metadata": { "id": "av9GWwCAvVgz" }, "source": [ "Snapshot of the dataset images\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 268 }, "id": "z58mVfIps5ck", "outputId": "5c11ac07-8b86-48fc-e041-ba2e0ab656b8" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAD7CAYAAAAmeCzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9WaxlaZbf9VvfsIcz3CGmzKyszBq6q6urR3erDcgI4QcsWUjdfgHLBotHw4MFlpAA8cADL5YQSCAsHlrC9gsICZAAS5ZAvCAsLKvoNt3qqYasISunyIi4N+49w977mxYP3z4nbmZVdlVWZWR2ZMVKXd08J+45+5z97b2+tf7rv/5LVJXn9tye23N7bk/fzCf9AZ7bc3tuz+2nxZ473Of23J7bc/uY7LnDfW7P7bk9t4/Jnjvc5/bcnttz+5jsucN9bs/tuT23j8meO9zn9tye23P7mOwTcbgi8pdF5Gsi8k0R+Y8+ic/w3D56e76un157vrYfjcnHzcMVEQt8HfhLwBvAV4G/rqp/9LF+kOf2kdrzdf302vO1/ejMfQLH/OeAb6rqtwBE5H8A/grwgYvnvde27SgloygCCGAFRMBZgxGwxiAC1hoEQYT6lyIIAgKqkEsGhcNWY42pbzRvPqql/vv8WIwcP4uiaFEQMMYgN58//P3x2foZjNT3N+bwOWQ+jvJku7txjMPnmB9/7+2HD1X17oc4x5+Efeh1XZ+c6q2790hhpJRCjgHmcyJisM5jjMX5BjEGax0igoigWghhpJRMihNaSl2X+moEwRgzL7/BGEPX9xhr8b6py10KpRRCqK8/nPGiipZCCIGihZIz3Fhf1fk6yvV/ih7+TUGkXn8i83UFUI6vY36tAlePr5+FdYUPubZ37tzRz3/+8x/fp7tpCkULqJJzBsBYi1DvK+FwP9fr6HDb6Y37T77vTT+c/c7v/M4Hrusn4XBfBr534/EbwD///j8Skb8J/E2Apmn48i98he12g5ZMYxRv4MRD5wz3TlraxnF20tN4y3rZ4oydHbDBugbEoGLIpbDd7ck5k7IixrBc9BgxGE2gSoyRUpSUIiA0jQcRsmaKZkIYESMsV8vZidabPcWEAhY3O/x607dtg7WWruswxmKtB4SYEkUBqchOEYMqpBhRhTJvLX/7P/sH3326S/KR2Ide11t37/Ef/J3/gsdvf4dpv+Hq/hvkGCnqsK7l/N5nafslt+6+RNv1rM7v4pzDN4YYRt787h+z313x4O3vEsMIISIKXhqsdaxO1ljv8F1P3y/44s/9PKvVmnsvvoSzhrDfEcaBt976HiFMlBJRLaQUGcaBb732TYb9juvrC3JKpJwoquRoyFnZbgMpFcYpUEoh5YAxwunpGu8dy1WLMaCMFC2kWCgFQlC0wD/8X//PZ2Fd4UdY25vr+uqrr/LVr36Vjy171uNnoJTCNAzEGLm+eoxqoV/0GIEUAsYYVus11jls0yBiUKn3WZldreEnc7rW2g9c10/C4f5Ipqq/Dfw2gG8a/e7rr7O7voaSWXlonSArR2osJ6XHdB7aEyiOoIYoMgcWAsZRFELMlFJvkFy0RigC13OE7E0NQXKuEYlQIxXvPQBTnChayCXW6DassNYipu6eOUYAGlMdakrVgW+0OvaubRFjEVMdss6RLyKoQkg14sopo4Ax7hgNf1rs5rq+8oWf0TAMLLqe1gDjKSllMD3Wd5zffRHvW8RAKQkpE1ISGgplGoi7DXG3pUwTthTWyxWtb7l16x5d13P7xTu0XcfZ+S26ruPW7Ts0vqFpe0QgL2vWdOfuLbRkck6UUogxsNlcs9sOXF5ecHW9IZeCsf54SxoLy5WnFKUJEzln9oMiAr6xOGdQCorUdSyZEAI5F1JUPm0d9TfX9Td+4zcUqgP8WOw9h1FSGAjjwPbiPjEEHoSRnDPDMNG0LT/7lV9gsVxi2wYxghxLWU8c7tOyT8Lhvgm8cuPxZ+fnPtBUlRQCKUyIFtRUJ2VUsAgGRdD5d2FGBNBc5n8pFIUYE6UoOc2/y5zqFTDVLyMCpdSczxoHCFqqA9SSn/xQyCkBilGhppyHPVIR1flvqxMVEaJQHa6kmho7T42PaxobQt0QypwKIe4nTm8+RvvQ6wpQSsYiiBhELNaAaTuc7yqEYAw5Z8QIqhlUoGREC84YvHMsF0sE5Wy1pms6bt26Q9f3nJ3fou1azs7PaZuWRd/jnKupPsC80UrbVnhHc73WUosq9P2S/X5AxNYsRA6JZ01FnXOUohTNFTqa31ZEEambt85wQylaN9OcKYVnzeH+WGv7SZhqIcVAnEaG3YYwjkxDzWiHKdL1C3IM5Nx93yJ8HPfaJ+Fwvwp8SUS+QF20vwb8G3/aCwRoLeDBItxdOfrGcu9sQd96zk5XdK2n7XqcNfXmLE+ggVhSveihYmze110sz9ibJoyFftVgRaj+TnC2qZGoCqqKcR7VQkwBgKIGyYJzfsaOG0SExnlQyDJVZ6vVsRepyYqZ8d+2aUBkTkm1RlgzzqSqhGnPvCc8C/ah1xVVNGVSSuSYmaa6Ed1en+F8S0yJKSZyiXjvWZ8sMcZjrOCc4d4L90DvsOy/ROM9t09v0zQtq9U5xlloBGMNfdtijcGbGdsvhaKFOI7kkok5I9RMxhjLou8RHC/c+yzWtHzve2/Wa0LqhphSAIS+WwLgIoQo7PYF1YKSZky/bt7DUCPgaazQwzO0pgf78Gv7MdqxdiJKyZGri4dsry55/et/zLjfk6YREKTpWa5PGHdbvHPoagXGcKi86KHmw9Nzvh+7w1XVJCJ/C/jfAQv8PVX9wz/tNYcCmbeCE+gbx6K1dN7SOYu3Bmtq0UtV0Tyn+LnMkUWNUOVQRDEVK5UbV76I4J2dC2GHVLBiO5q1FutmrKdGvgoqx01SEMSYuahj6t/PRTIt8uSLPDkTHCt3qlT8o8bjs8cl5RodPwv246zr7JHQMhciZ6soS11PEcE5W3+8w3uHdwaKRZYrjMBq2eKdp12u6ubnGzCGpBmykvYDRoTe1ui2834udh0gnHQsUBpjgYrRet/ifYdzDdZ6cimoHkq2NwsuClqOUa0xh4JtDWVrZFvIx+/5DOUt/Jhr+zHZk7JzvZZKzoRpYBr2xGkihYky12LEOHIMpBAq/HeoXlITp+NbPcXl+UQwXFX9R8A/+lH/XlBaU+g7Q2uFl24tWDSO0646W09CcibsEkiNPEtRwlRTRCwYK/SLDmME4yqmW/KIUjAGvBOWXYM1hlEnSi7kOB1T/VrEqo6gRqZQckQKmGwx1mCPFfR63NlfU7RCBBW7VYxmJBfStKvfLhekKM7MPkiVpIVhikc8+VmwD7uuFfcOSK7ny1mPKozbHc4nlqfn+Lbj1t1btH3L+b07eO8q1ABojvMmm8i5cH8zkPKOFK8oQJJEjBP333oDC3z+xZc4Xa34uS98nsb7OdUvDONAiolhqMVK7xpyLqg6mmbJen0LxHJxcZ+USy3CAlMMlJIZhutaYJWCc4a+bzDGkNKB5aCUDCUXVME58/Hhmx+Rfdi1/bis1kJKhe5SIEwDjy8esru6orEG33f4ZY+qshkCGid2jy8xAmd372KtOxatdV6STxuG+6FNBJwBZ4XWGTrv6BpL6y3OCNbMaXjJxwKUlif4Wd25ZoqWEawRpMxRpc50oDnSPERbWnSOSgppdgiImSOg+n5PMKCZbiKVllZpQjMdSQ40sZkPhMxpp0Cq+3ONZCEVnX8XUi6EWH9/eq3Sr2aSTr34Ae89TdOw6HvavmOxXNC0taJckOOaaq6vjykSU+JysyWlQoyComRJjNPAG2++g0E5aReICjkr6p9Qg4xUNksuB+dYi1vTFIkxY6x9cmMeMxjQuchWSoUSrDVYa44Rbs75mGXdrNhba585h/th7Wl/v+9nQFRILsXIOIyMYy2UUQrM96zmRE6Rcdjj25YUE9ZlcJZ6P/N90e1H/T2eCYdrRFh5S+M9nTcsu4ZF6zjpLdbUohgc2I71BtKZblXQmloaqTeOMZjqkdEYappoBLRwfb3HiJBjJh/ZDIWpJEBomw4LkErleVKj2FokAeNrkS1PEVRobAumYKmRVIqZPPMDq8+oHM7dGEm5sA+VqjaFQirKFKC8/7r6FJmWWgxtvcN5Q7dc03jP51/9HIvlitsvvIT1nihCzJl3L64IKTLGSE6Zab8npch2vyWEwMXlllKg69f1RjGJ3faa3/0n/xQHuGJ45TMv8ctf+QWM8TibMNZy6jwpZRRHDIlhPzIME6+//ga7/Z5cwLqGtu+xqWZHOmO5qhkRxVlD3y+x1gB1k97uNpUJUyrLxRiLMcJi0VeO8HP7CGwui2tht9txfX3FgwcP2V5dETZXSCn0jQNVxmGPsY63v/sdNtfXnL/wEsuTxOL0HLFHfAjlAAl99PZMOFwBGmdoxdI4g5sjCWvNnIYfgO85ggRUBGNrJCSlvokcA9IaGVU6Q0HVzMWQfOTylVyjy6yZlOvzuegcnVbWgUGxBnya8cBYDzSFjKhgnan8XjxIxfhEy3zDKllrBB1jIubCOCViVvZDImUlFvOsVbN/PJtTOnEN4jzGN7VAKUJWGENkipGrqy1TjIwxkFMiDAMpRTb7LTFGNpsdigHTYoyQdWS/37Pb7nAiDMPIOE6EmIj5gNsKxjosQtPUomdOhRAThTLDQTPs5B0iM73wcMXN0bZQG2jM4U49ZkvlmNkYUxswrLU3MqXn9lGYaiHEwDRN7IeR/TAw7QbQQkkeQ6VtmlIY9nusbxh2u7qRLk+wYo6R8NO0Z8LhWiucrlp6a6rjbS3eV4dqjODmRoJc6m1wSDtrV5mSVGtlek4xCpmSMyXHesMYhyKMeQKglIoBj6XM9LH55pox4TFPqApWCtbAYhSMJLIpFfPdTVix3F4taBrPetXjXEHcQM6RuN+SS2IKmZgyuyESU+Z6H5hC5sGjoTp7sUdc6dNocqBWmR4VIUhPdpYHU8amHd+62JCSst2NTFPk4cUlKWXMoThFTeVDqhDAOEWstTSnghgYr66Juw2tFRyGMIxsN1vefvddhmni9HSBdbbCANZwen4C1M1wt9/z4PIBV48tb7/5AGVifboipszDiy05FTRpZbqkUrOVWMAaGi+IsXhrQSHGgkhtfLHWYm3zPML9SOzQWVjIKfL44pJHDx/y7oOHPL685OKd+2jOnPQdjbOcLT3OObI+ZBxHXn/tNU5u3cb3S7rFkqbv39N99jTsmXC4IkLrDZ1zeFcpQXbGyUSesAmKzpXtGbsVSo1CD621UilZOmNuxszNEQIFJcTKZsjZUlSJqcxtvgeqVo1Op1A7xKzoMaIRMaiZ20rVAo6SDSVZUIdQsMYCZcZ65QbOzA3MV7HWUFTIIk9z7T95k9oUkDGUIkwpYTRzvRsQoUb8KbPbToQQub7akHPGzf0ihroBhhzJJTOFhLWWYdhjDEzTRAqBxnmcmJmjGdlut1gj+MbgvaNt25lqZudWbCFmT9t6msZiyEDGiJ1bgusmXHLNhHSuB+i8kAfM1hiDNZCNHFuSD5jgp3+W4A/7fu+7sg9sgR/yuvffEfU06syfjsRYs6FpCuz2IyUnKErnLL2v9RWxAWNHdtdXWOuYhgFrHb7rDsnWEc59wkJ6/7fS9zz/o9oz4XCdFW6fdix8xhlh0flKkCffxAlqU0RRUqzVYVGwYlh2PdZYuq5BKYzDhKL0XUdRGGMhpcTj7Z6YClMyaBFMUZzAWVOhC2sDscB+mwgZ8lzPdKamk13XsWg7vvji52hMQ9gYTAQmEBW61ezIva2NhGIQFDenmE27oCicnp2RinI9JLIq37h46xM790/TjDicP2EYlDEk3r28JqaAsAcUTC2AphApuTDuBrQUjCkzH3okl8R+GEg5MUwjIob7D7+Hcw43QwV3b92ldQ4nQhj2fPPrf0K/6Ln3wj36fsGLL75A27WsVn2Ndr3FOqHvHdNgMTpC2hOiIcTCNAXiFAn7gZICOQeMUZrGUaQwDSMItE1L0wg+mZkBVxsfpmn61BfNqv0kPCu94e0Or3//+xwajmrLfS4V/hunyG6ceHS1JYXAY9nSeEsuJyy6hnMVci68/e1vc/3ogtNbtzg5v0WzaPGmmyEuOUJF7/ka30ft/HD2TDhcI0LjLd5WzNRawcqTotUNJh4wF7AQdK4o2xk7k5kxYERQI0gx1UGnQkiFcSqElBljfbOWKoSzdA2tM/jGEopyMep8w5cjI6KYQlMqpOCswYqFY4caUGq0jYA1s5DNHHVbI3XfsAYwuNZWKETCEc74VJpI5bfmqkkw7EdinNCyAzJq5NgyrTkTxoPITEE1M8WBlDPDVBsYQqot1y405OKxfYNYR996GmMoORHCxG63JedEv1iQUma1XpFyxntbaWczbavxjrZxLFoPxZFDIUu99vLMbqjtZQZjmHnYHEVTrKtsBGdtpSHO4jrPCrf6o7ObceEPjmyPTCFuRLl64KQ/aUdQ+eD7wVqLdbYylYCYMiEmEpDVsp8CiLCKCSOGab/DWcv++grnHClMGGsxrpkd7YFddCQ6Hej39dPoh3e5z4bDNcKq87gcq+aBrYWH1rUA5JgpuRahEEPfL2Y+bE0MslYstJTaiuvbBpOV7SYxhsL9hwNjSFzta9FqytXJ3/GGk4Xnz736CrfWHeenHWPK/NPvvMuj7cRr714zpUTRNDtPRTQz7rdkGZmGiAj0va0iJgVECt4blCrCglEacdWxlIK1wsnZCjD024H0Kb45rbV0iyXTgw273Y7HDx4Q4khjAqqJIezJOTEO12hJMNP0hnEi5cwuTBURMoJ1lsV6ReNbfNfTdkvO77xK1zTc9haJE5dvfQchYyTRdR25FHzbcnW9oe87XnnlZZbLnhdeuAsI56creqf06fPsdzu+984F2yESoye0ieiUUhKqY63MSmWg7PdbSik477HW0S16KrtTZ62GODvfT7v9oAj3Axo/bjrbA0wwN44caHjy/SHm7AMFZx237tyhKPTLFe5qwxQTw1idrI2Z/OiaRdvQWM+iidiQkGnkza//Mde3b3Ny54zF6RnL87sY6zFzi7/cKKToMcD7FEMKtQvMYXQ+5Qe5xfnrZq28y+pfb3R8qVZGwA0ubuVR2pm/WZhCZpoKUyzEpKQCqdSONlFwIqxbx2nrOes8YzKctB0hCp0bqkZDLpiZl6ulME4jCcsUI1YgZo8UQykWjM6fscr4HahhRRUpWrHFWXeyby25fHqLKxV3C0xhYBz3jOOOGEaKiaCZEMe6SZaIUcV7V5tCUgJmcR/ANw3Oe1arU9quY70+o+mWdIs1rfM0tlBKTeU1R3a7HTlnfNvTlELXVo2FEMLxGEKVV3S2ZlJOlN47tAjrhWFyiV0aKFlJuTqEMje8lFzIJVfC0o2s+AAj5AOv+6fRvu9r3wgT9Qbz4+Bw57+ROWr9IAcnxtA0DW3XsVgu6RcLjHVgTOVCa2EMCaG20ltVkjFEa9hvNljv2F9vMNbTrW9V/YxjV+ET9tP3f/r3P/unu+BnwuFaYzlZroi7AFpquo5hnDvAQpw7u2Y3POXKzaugtxJyQYywcD1ihFwgauTB5Y7dELnapdp0IG7uSjNYgb4pLJvMqR05EaXdRSiGF9s1jS64XAvbKXC9vyaXTA6ZfRx5ff9O3Rmj4pyQup6ueFieYK1FMYiFvm8oqvRF5yLd3JFWJowx3F75TzXWF+PI229/nTfefovNdsfDi7eI44ROEwK0XS1avXD3FotFz2de+gzGOC6vp8rmuJ5QEdan1dHeffEeTduxPD1HxbKbCpoisntISoWLRxeEac/V7pqu63k5wsnpKZ99+VWWywUpZsIUKRmMKJoScT/w1muvkcLIrduf4faq5ex2z34KfOu1LfshMFyPVQ8iVwxxCpUnHNOAdY6mW2GdnZXmMlOYfkpghR907X4ApKA3Ilstc8ZXN1bnHGDm1un3vociIAYxjsViTSnCz335K6zXZ3znW29gHl3w6OGjqlOSM9El3rGPWbUNzV1DVCjv3Ge33bE6/RNO7tylXZ7SL1eIbyunX2r09X4GQ36fs/1R7tRnwuEamTmSoakdPa6p2Kjmujjz7qdzEUtnnKXiuFScTQTEogghR6aYmWKqfMuZKXDAVRFDY2oDijVCzokYDENUohrQDiuG1jliUbx1VMJDXYKUqlgORdFSK94uV36vMYdFq8IqooqRWcSaA6ezdl85I59qh5tzYru9JMSRlCv8YmzVLTZG6LqOvms4OztjuVhwfnqOiKNoYAiZoQwUDMvlCV3XsV6d03Qd3eKUgjCm/az8JjNWnwghkvdCKrAfRtpuUSvUvsE5P3e7zbTCXPHj/fU1aRo5ObmDM57loplbxStWXNe0djrllG8AfvXnIE5fSj7+PEst2z+WHc/BbPKeX++L8A9JeuUuxxSrsPzhmjAzRi4GnbPc93q3ep6NsTjXcHp2xjiMnJ6eEULk8uIxKWVyVhKVPmgQxpgq3DAFjBnZPr7G2oZpt8dah3O+1hGO8MFcSJthhcNAhANT4kfhcD4bDtda1qdnaN+AKsZUWbz9sCPmRJpqxGBm1X6dUfYyt1C21te2UNMQU+btBxeVHD0NxFxqRRvBu8rH9E1bBc5txjvD6w82PLAGzZaMY9+dETEseodtBKEn5UTOkaKFYCO5KGFSVJirpwZmOUdmn2uhAvy2pqTO2npjplSLaEVvVGg/fTYOe779jT8hL+7Q9B13P/NZpICPmcZ5Xn75BZarnpc/c4em8VhtSAnUBLopMXFNKor3HcY1GLNCpDrBXJRpzJQQsTGh8w0XUmbY7HBjpOkvULE0vmW9OuHeC3frcZwnh4kwBPaPd7z12uuE/RYNyvLsjJd+8YzFsmV85S7bXc+U9uy2cH29IaWE9w1NY1hae6SGqSaGoXbGhZ+aCPcDTL/vf4DaPBRT4P679wkhEOOEc44XX3qJpmnx3iJS4JjLVitz41PB4tuen/nZL3Hn9l0e3H/Em2+8xcXDS0reUGIipsKj6z3bJuBbz7JteCEbSoB3vvZttmePOb3zIid3bvPSl76Isy1hDqSsCgbBUgvl27Cf7+1UC+el/NAq2jPhcEUE6xyUhornWKQUrHMUwNh05NnCjLYIiDUgBus9YMjFzrScGukcOsUOCu+Nq8W4vvd4I/SS8QaiCmRIMaMipJLIxuJsFRHv24ZSHJi2ipmEXW0VLXGOmCsnuHa2yY3vNaNDM99W50KfivCnFGM/NVbmrp92ZbGuw1uPKLRZ6bzn5PScxbKjXyxxxhLHg1ZxHZlijMdKmTH7OYuZcdMyY6lV52DOHIwBsaSZOxtzOU79qKN3PM77480891dAKtVhh4kcA1YKxjlWqw4xhfV6CapY97jKcWIqpuhbzNxUcchuaqdZZc98mu09PlV4n299EtHejHhTikxh4urqinEaCdM4y3KezALwFmtmLvR7SldP2A8iQt/3pJi5c/cuYYqsVytSSOzjjqKZmBVSZj8FBIhtwmEZdwPONmwuH2OcYxpHioFgauBkSv0qjhrdDtOOlDNhpqGWuQHmT7NnwuECIA6cRY7al4LzFgy0tO/BcI/FCalpiGsWaIHxemIclWmzo4SJ271FEGIEweC7hrZpePHeOY2zuBSwgFhX1buGPUrB2AHE0rmGVhwnq3s477l77xZI4sGjb7Mf9rz9ziUpZxqfcFaBEVVLKVUqXYyrxTNjjy3FergMpX6mH4/D+GxYTpnd1cDtL9yhXZ7i3BorlpVA6wy376xwzjBOkRQjF/d3xFgIyZFUQdOccirGFAqJrALF1kJJmigxVDU2hLZfEVXYjBPqHOI65AhPHYSODrCOoXU9y3bFnfPbTI2rUqBSMDLSeMNnX75NzOcsl0uurraEkNhsdlxf7REs6/UZ1lvUJFQLztmZxdD+VES4x4LhkeWlx8IyaJ2UQr1fU05cPr7g8dVj/tnv/TOuNxuGYU/bNFxeX3F2dsbPf/kr9H2PdzegGmZhU507QlVo2w575vjVX/tzfOblz/DWm2/yzltv80e//4cMY2LIhUkzbz2+YuEbVtISfaYJQpkSr/3+H7C6c4456ehP13CyQAWm/QCl4LSQS+Jy/4iQAvtxqLBjjD+0GPpMONzDSZWZcVCLYXMX2A3qySFq0Hkxcs5gqpauHhxaKRgKzhSWTcVew4wPdYuOpm1YLxq8NRAqlmpslfKTFADFNbZObqD+bvuetmk5O1uBZEJa4pxyebkhhNqCeoy6ayh7g2Vx+P8n9v6A4FNrClqqSpizHms81licq11fxnnE1llUKVVti5Qr1/lQuTamOlwRPV4Tx2vj8AMVq3MN1iXEliejjoybiyI8CWlVEZ2xwznyLd5jbT0eWjvPrNSLa7XoKblwenKCYAgTgOCaOsuuUNkydh5m2Pjmp8LhvodApRVa01JFwmvGMXMRihJj4PH1FZdXj3l0eclmc81+t6NpW+5cPEKBEANN01R+M4e6DcdpGilXFkLjHNZZ1icrYpy498JdUox0i45YIjFN5KKMMWEQhhBxKgSTEBPYXl1RLFxdXBBKwrtaJxp3e0rJmJzJJbIZq5jSMNaJEp8eh6sQQpodXhUTqTSfoc6ISrVglqU6xinUVs8hjBhjOL9tK4abBywjt5eKRXjh3GKtMAWpQwtffAXfNLS2QMns9wlFaBar+kFag0G4dXaGMYb9FDHOcfelF2un0kmPklmsPsN2s2V7NbDbDezHPRTB2gbrPELVfc1aU5XDsNmSn8hFyrxpPM3mXhH5gqp++33P/XlV/epTO+jNYxlD2y1xGKQU9uEaI7bql1rPVApWhRChZMH1HaZVfJHqdGOpIjJWEKOkPKGmQNVnAxIiuc6Os552dUayLR6PGItp15hmAc6hVlAJqMywj9ZOQGMttnW47Gg6h28NJU/EsTCNsfK+3ZLmZMGv/8ovstsHvvXdB4whknKlisVUhdDrniv0Xf90z+snvK5P7LCpCEUL+/2uDujcVZ5yZQxlrjYbdvs93/jWN7m8eszXvv0N9rs9++0O7xwxRe7du8fnPv85CpkTs55nCa3G7vsAACAASURBVNaaxzAGUkrsd3uMMTS3znHecna+pusc/9Jf/Au8/dbbPHz0Nu/cv8+33vgeIUTiZIgx8w6P2fqW9tTQhMh1nvCXD5ka6M9OuPtzn8c0jt3c0RjDQC6JwIasmXiAFIr+0EjpmXC4zFxVOUYsN7A5FDGVM1fFamAKsdJzYsRYQ4xxll0MlBxoPTTGcLb2OGfYjxWTW697fNPSSHXolXUgtMuOA+3aYFiulhUCMHusc/QLS9MarKt8P2Mtxlrc/GONnfHlA0Tw3vHZT77m4aZ8z1d/mvY/i8hvquqbACLyLwN/F/jlp3rU2UQMzje1Y2uWrSxSiKXBFEsqUEzlRRc1qLXHKPTAuZ7fqK4Hc6AqIFZwFkqpnOeK886ZidQfY+rjOvOI2UnrHBnre/UPZGa86OHfS+UIi2AlY8Sw7DuMcZydnTCMkd0wkVIk5fo5qwD5QdvjqZ7aT3Rdqz1ZJ5GakaYUCCGw2dZOv6yZlDKX11fshh0PLy+43lwzThNTDBUbLVV2cbvdst1tadoG5x3O2Zo9ZGW/H4kpsR/2WGMIYUGxtq6RZharnvXJguXpgsWuQ2ztWCtlFvoPEavCEBMFsGOiSOH64oJYEsurc2zXMMZAKpkx7CklkWSq32sWwSrlhyG4z4jDrZBCHd7IzNE7zKACoTMNucDVNjKGxIPLDTElxBScNTh3iVElPL7Gkbi7hNNVyy9+5QV8Y3n3ckehYXH3BN8sWa9OUIWr68egymJZx5uTa5WycZ6SM1ebC5SC70Yw09yFlHl0uWfYBxCH9y2LWc4xJakDBO0MJ8ypbNV9OIzw4agk9TFMY/m3gf9FRH4T+HXg7wD/6lM94g1zznN66w7WOxCqBgZwNViarLglOGtI2VWIwFgUnRsiCkllluF0qHUU4zGuwfeLWqRct2gAxEKpAuM5FygyF7WamqLOQkg6yzGW+RrDSi28WqEITNMIDrRkRB1eaqQ9bq9RNTi/ZNU5vvzFzzKlzDvvPmS33/PGm5fElJnGOknE2afOr/5E17XesXGGTWqelktkGLdsNhu+/s2vs93ueHR1SYiBq92WECOPt9eVdWQV03l87BBgs98hl4avfeNrnJ6ecPfeHZq5wJlz4eLRNSlWDNU7R5hqy+40VghAS0J95MXP3YY28trb3yGVQBqVVAqX+5HBJlrn6b3jvHhcmojf/i7Nqsd4qVnX6YJiYJ8mygwTCqCldorGFH+onOoz4XBrRdqgxswKW3VJzREDBRRizISQCKHqnTpbo5EwBgyFkiIqhdY7+rZhuVrgG0M7RjIe3zh829CvTwAhzljgondVi0FNxfYQShG61FA0Y+e2NC1ae72NRayj6boaSdlZvUyEJy0Z8w13rNhWvvHhu1QR5B/c3fJRmap+VUT+XeD/AEbgX1HVB0/xkO81Eayrm6Zq7cAqcGwgSLlmGGnG3sn1d8r5WGAUqUVHaw3WWZw1NLOGAa2liEWDpXiDd4eMo+prPOGxP8FuVfUQm9X3mIXrjTFHPu1cZau8baiiSRREq25y29TXLBcNkFl07eykK50ozSOXnpZ90uuqWghhnCO+er5iikxhZAoj+/2O7W7L46vHTCGwCyOpZEJJFBTfNdhc0FBgHgY7DCMXl5eVm2sU33i8qw738nJTxf1TxDmLd1UDJYxVBMl7wzgO+M7QLhu6viGGSAkJUo1yA5l9DCiFVQIo6ACIMlxdV2dqC8UKu1IhBHvwrnOnaAzhJ8dwRWTD9ye2V8D/C/z7qvqtD70iH9KMMSyXHdlWHdtpivPOVW/AMAxMIfHw4TXDFNjt687WNFXuMO/3OIG1MbSd4/zslFvnPevbL2Cd4AetuWvjsYue81e+gHUNzeNHaAq0ukXIFFI9XohIUU7bFYdpwFkVWsUXoDOkWFiszolTYNw+JsXIZrMnFyWLpagQc6oOV6oIj3MeUSXFXDcZN4/n/ohNRP4h713TBXVN/9tZg+K3PvKDfoApUh1thjAOFBV801CyYRx3iBhSjHU44DBCUcwsrdk2VRFsvfT4pqFfdTSN53TV1HW3C0p0RJ8ZO2Fz75zltiPOGLDViOaJkuv71zZwochM77GC9Y5+uUQ0gAassWiujqBta6TqRMlZGaaxvkceMcbyygsrUl6y6j1XV1u2Vxu2ccd2syU/haLZn5V1DWHi9Te+UzO1eSNNKbHZbtjvBzb7LZthy8PLmrK3qwXeOFbLFjGG5WoNRbm+f8m0H3nnrTe53m7gDwpt17BazrrCrqqwTVM+Ft5AcVLmzbTgrOX81hrjDOojy/OWVz/3EleXW17/+juEnAhaJ6y8s72md4a2dHTOssw9GiLvfvM7aOvYn7YkK2wkUxBcmqdzpxoAhBB+aDH0R4lw/0vgDeC/pwYEfw34GeB3gb8H/MWfZHF+dKuV6Jvx4RNIrQq/hBiIMYIWjFCHOgKaa5XReoP3nnaxpul7snR1Z8qGVOpECWMcxrXYpqVdrNEcaQqgiaJTTTkPkXYqx2PPMwvIGNRbUgIpLSlEvDeEaWScEsRELjd4CPIEWjioHM3Et6d5Mv/zp/nmH8qOYsAVVqm6BeCN4m0VUPMYigGbqwaG0apr3HpwDnoveC+sO0vTOtZ9U5sN7IKSHJPmKkB9usZZx2ZIFC0s+45F1zyZQ8aBbsQNMLgK41hn0VwneBzMzJodVS6ysiVQqpgRipsF8peLjpwy69USLcp+Pz2tUel/JtY158zV9WMMh9lvtQWfWSP4IOpTs5hCb2wNLkwNMvpFD1mZmoEcaqaTU+J6c40fHXHyx4wGhFxMpYXlSB0/cDj/VYNlDBavDr90tOo4PV1Bqa3jJSshVAhpmumDUzQYdXTJg8C0GyjRMkggWmGwNVCwuYUCMcRZAH98z/TpH2Q/isP9LVX91RuPf1tE/j9V/Q9F5D/+sVflQ1gpmXG/wcQ6MsNQ6xzMwyONMRggTgMpJPrWY41h2dWIMe4GnBFOz1acnKy4/erP0y973tlCDCNvvl1HqJzeWuBlwTAUGoTVnc9WKUVGVCMpbUAzq3l6xHi9paRMSRmMoVkuKWLZFYeqRbRHc2HaXjJsr8m/97tsr7eMl1tSLoi1x8KPAmH2tolaRJKcKxb5EZuq/l9Qq9nA26o6zo974IWP/IAf/EmgJExJGGPpfXVQtxewWBg+c29B23ga5yvHWmuzRBj3lJzIcUQE2kWhbZWXXlzSL3ru3L1dxcTNPbRkhu2OaRy5e36L3W7g5ftX5FJYrhuWy45b5ycslz3OeYytBdgCqKnFN982lNiSY+XSGqoGUZ3SDIGCGFit2ipQHwNFE2G4BgxnyyXL1mN/7Ze4utrwu7/3R+z3w0d/Nv+MrOswjvzRH/8hcQwANK5huVjyS7/8q1jryOp5eHHBN7/zBqVM9O0C6x2BiHOO85MTKEJ6HCghM4bIOA5M0xZrYNVU6KjtK+unWazquK2mYC30izreatFVUaLlqv5u+hYt0Nue66sdu+sdjy82vPnmg9riX4RchIf7wML5Ck/kjM2ZbIRhW0hWyJ1HjUM85KxcXlwRYmTYjz9UBe5Hcbh7EfmrwP80P/7XqLgQfGx00dqHLjnxnm6tY7GpYkWpFNIcAYFUzBXFGYt3hsVqyWK9pluf4duWzeaaaSzEWEXODxhtDBGxiZVrcd5hTIOSkFhHoMsshGEnBZsQM3e6uRYxjoYOFYd3J1AU70CM4FqP8aaOdZ4FyJGDopTOAyPLEUcU/T710I/a/kfgL9x4nOfn/vzTPWw1I4ZF17JadLUgVqr85smyZdG3nK06urZl0c7j7TFVjW1vK9F8qqwF3zS0bcvJoqHvG9adr+vmbJVJpOCs0PdVi+PW2QpVWJ529H1D2zazg64skmOWcSCUGHPUuj22Bx4yrdnpqlQdCEEhHgj+dcCkEcU7w3pVx3WvVguMfaor+4muay6Z682GaT8gKiz6BdY4vHM432KtwxhLSrXQVXLB2AMzSNCYK1e9HDKG2vZ7qCFbqT91tqFgK9Q+T4MRus7inKFta+eoP84/rOvIqjZCnZ6uybngH1zUY6dCViFksAghZaAWZtVQoSYrGO8RAWdmdk1KpBCJU/hIHO6/CfxXwH8zP/4nwN+Yd82/9eMvy4czo0qJE5TEsY1Pa6pSEDKRfVb2CWxWnFE0JhoLJ23Der3gi1/5Cqe3bvHCq18hhsTX/ugfM+6uWBpH7xtcjug48Pjdd2lWJ5y/9CquXeOXS8SClj1aImF3TY6RpqzIKdWWz1LYTgmMo1vdwTU9q9O7NQIaFvhFi1s5ZFLUJwoZMXN0W9KRbiQHWhJQsn3aO5pT1XB4oKpBRJqne8gn1nctv/KVL/HCK19AjOHi4gJBuX37hEXf8fLLL9J3HWfr04rZUalVw7CrDjeNKPMoG2tYLpc451ks55vaeUpRrCakJHLcQom88tI5bddy98W7uMZh3GFcUz3fRWdKoIAaObbn5nqOKDnX7jUjddqrnwEgK2iBVCqmaKRuCJoHRAzrZYNvlnz5y19giulpntpPdF1TjLx9/232V1ussbx450Va3+FsgzGOq6stjx495t37Dxmnkb5r8d4Rc1XJk33AYBiuA9N+QFVx3nPv7imLtuGFk74Wvlc9irDdToCyXAlNK5zdrkNEwxQBwVMzkkYNxjgWq4ZF0/NLv/ILXFw8ZrPZc3214fpyQ06ZTSqEorTDQOscJ/S1/ZtK6Vu3C2zX0Z6eE0Lk0f37TDmQwngUn/8g+6EOdy6K/eYH/PM//rCL8ZOYljoE8tAWcFDVUq0aBEUrlcrIE/aCkRrZLJY96/MzVmdn2KYj5pEUAnmKNEtL49zsxmeu79wmWIElXyMbk0ENSlMf2xbUkLRO3d0OE2ISvs9YBetqJ1tyBrEGsYJx9e0OzDCAcgSjtX6/Mm8kmp/80dOxByLyW6r6vwGIyF8BHj7NA940ay23zk64c36KWFt1EVDOz9f0Xct6tawR7qKrY4gQtBScOwgCNZX9YWq03LZN7VSbsdXKdy3zKPs6KcR7Q997uq6h6zzWO/LMGjhUmMsNtgL1xBzx9UNr6k2sTswB963YfMlatTQ0Y0RoW3NsQa6RdlupcE/PPtF1LaqEaWKaJpx1c3Ra/ytamKaJaZzmYmkmhYioklNEjTBud4gK4y4xjSNaCtYYzk5PWfUtZ6sWZw3iTO08jFU1sERDkRohq5G52UTJZDBCkoS1imk8VgynJ2u0wNnZKSjsrvfkXKp8a1GmVCPt2FQYMxdFci0Eis6TW+y8tjIP//ghimE/Ckvhs8B/DfyL81P/N/DvqeobP+G6fCgT1Qq+5zSnfjOOqwqh1DSk1FRguWhoneWsKSx7xxe+8AJnt2/x6s//HO1iTRgLUxiwaU8nE3fPz+gXHcYKxgon6xXNsgLracq1F15qV0wphTRWMfGiLSHDu4/37HZb3njjOxgr/OzPJk5Ozjg5X4M1DOM1Y9ggPuN7w/puWylPxlZKSSpogZwSJWWGzZ6clDQmtDxVh/vvAP+diPxdqmf/HvBvPc0D3rSu9XzpC6/wyhc/j/MN4/gSItCvFrXDy1atCWOrrKbMDSTLrgZrZW7Sfy+PoxLQRQVylUKs1evC3Xu3yDnT+QXWWnIc66DPo/CNBamUMJ3HJRXqhlj79WsbaYoR6wy5tBhrq8OncrRzgXEbKo3p4SXOWX72S6/SOEPII1IKjdenLQL3ia5rhX32TGFCPVhfW20PLI6HFw+4vLqgbXy9h2OuXZYxkkvh6mJLTpnHl3VqQ84DJ2drfuPXf43z0zUm7BnHga+/9nU2mx0PXr9AU2a9dHgvXL5laBrH6ck5xrpaTBNBnMFax3J9StN0vHTnJe6c3SMFy4MHj9hc/z9srzbEMZILXO4nGpvA1rFLMSnEzLjZ4GOE5YJUSi3Gd561LD6SotnfpzIU/vX58d+Yn/tLP8mifFg7XOyqNT04Vvd1Zi6o4I1BndA6U6f8dqZiemcnrM/PWJye4pqe3fYxMU5Yqzhv6BY97aLDtR226fBNW7Uwy6xhGmMtaoU6gSCOA5ozOdWpsCEcpoWOmAzTtGWaHCmNOCwlhdo/PjMtjNHafCqzXKPmuSW1IFKwfsarTHkqtLAn51RfA/4FEVnNj7dP7WA/wIwxdF3Lou/wTUPjABHaRYcYc6MxtCrByWEOnDFPdCmYHa5yFKAp5MMXfCJKI+Abjy0Oa20VltaZOz2zC45Dqm4Oq5o50VVzeY56D7zgOSuRm59TqaPTYyHsA8UZckjozOU9FHztUxQL+6TXtTIGUhXlL4mUEyEGNptrclGur6/ZbrfHGkye7680jjWoCpEUE+N2TygJXKVJWlNZIoepH/thYL/fM+xGNGVssTgvqBpC42jsAmMyMc48d191O7RYmjbRdpWG6KzH+3rPG+dIpk5jjqX6likXLBC1bpQSE8UmQoxHSMs5CzQfiZbCXVX9+zce/wMR+ds/7mL8WKYVuwtJaxTbNhhTW2ZVQCTROMu90yWpFNrW0jSOe3dOOD1d8/mf/wVO79zjzqtfJKXCt/7kG+wvHnF60tDYlluvvEK7XOFOX8T4DtevMa6tQhdjIe4HYkpcXz4ihond1SNyipRcsSPjBC2R1dKgJB5fv05Il5zduk3jW9K4p0wTOoU6zWDcISWixCq0QnUIjTPgKhhfsrD3ctReeRomIv/J+x7X0636nz69o773eG3X0TWepmno2hphykz3ORSvBJnTtgOjY25+mfVH8+xQZS5AltlVi5jaiCJVma1brMilPEFvMDUFzIf3nV9npEJKhcpYMJZi7BFSICfIFtFaoHWlFttcEUwwtMGQJ0GuAllge/+avOo4e/GcxgjXBuKneF1V6+TsKU6knLj/4D6b3bZqRIfIH/z+77Pd7okhgirX+5GSEpv/n703i9Uly/K7fmsPEfF9Z7pD3sysyqquobtRe2jUjQeELPFgy8aAhf1k0YCFGdSSERJ+c4N4QH7ARgIsLDGoJSwZxCwjYWQjy7Lgwd2i23YLd7ur6HLNWVU53uGc8w0RsYfFw9rxnXOzMm9mlvPmHbjr6twzfUOc2BFrr+G//v9371JzxlfbCOdipQF3NlDGiW987R+yGjrS/oJx3PPt119nv5vY30tormy8lWyOTg1hcH7fSnL7nSmIrNcdzglVPIjHD9+wIaUwME6JfrVmXYVNuaSmwpwrBUHHbGWjCFIc/W4iFsVdnOO8p193dKvuIcjgB9lHcbh3ReRfAf6H9v3PAXd/5NX4UU2V0iaNtFgTzUkLMjCmsL4L+FIOGNzV+pj1yQ3Wpy+xPr1F7I+ozOQ5kdNMDOaYu6NjuqNT/OoEfCQrkDNpv0MR0ztLiYsHd0nTxO7iHrUk66o7GI4GkMrQG/OUYTobH6urh5qPVLVpqcbpUHU+RE0iUDWYY0HBCbFzj7uksL329QD8EeCrj/MNHzIRizabqnI9sHVxmHUHGjvYVTV7mWCqxR5/FVVYFJpLbpwFBinc7a2ZkZqW2ILkbqy6zSFdYaMPHP/tZZ1zVw3OVodc1EZQd5gOlPYRRAjiCM3h55QpKTcGMttQ1T3WdugTXVeBlo3Y+ZrTjNs77t+/xzwlLs/P2e1G20itS0nN2br8KTeMsjGASfAEZx85JWaBeT8xL4iAdv9XHCmbekuXG0vJaNfBfmcDEUtNPSsoDt3N4AKhX5NKbXz/xpl82ItpkW3rZUtVQgWvC58HjWKAA8rlUfZRHO6/jtVw/wJ2Cf4y8K/9KAvxo5rBb4ydK6dMna2u13WdwXKoaFBOjiIpO+6f73G+4/bnfwd3XnmVO1/6XRydnuLDLdifU6ZEnRJ9NzAcrTl99Qt0x2fM8ZRpLrzxve8wT0Y8Ukphv9+R5onzu+9QUoI8IRjUJ3YBd+cm/arntZdvE2Kk+o4QVvTxlOADIWSq3xNyxo0j87v3yfN0kOgoDW5UnTXXuqMOHyOnpyeIf3zNFVX9Tx46zyL/MfA3Htsbvsec8wzrY8RHqjjmvET8RgZisiil3Vz14EhT07ArjQxmEWUsJVNrZZpmai3M00hVGwVeShU+BPrViuCjDT24QPAWWddaMM9ZEKkoBS+VYQiQA7nxeeR5bDCmq9l5QZqjsI1XtOOll85aYygxz4LWbLLtQ0d8jKq9T3xdvWN9siaLbY7TPJHmmfFyxzRO3HvjDcZxxoB+Qu+9bUZVcBIMiaRWF/fOc/PsBkdnJ5yuT4jBs51mRD0vv/QZxilzt26Zp8Ruu6EKlHhkzbfZpIymyVAlm8ud5U1NpyyJpyiMuVLUxGiNasNTnaDBYH4TtgnXqrgKQ4hIHOjXR3b/t8GljyIM+si7WUQ88B9+mqOeH2gtD7SaXKVWo3wThEVgIwTrGK6OVqyPjzm5eZvjmy/RH50S+iNqVdOcqoZEUDjIHltQm5nnmYsH9xn3e0qeqcVYiEqaGbcP0FLwWiwizVBrYJ4GfKBhDTukPyWEAec6OzoD8aHFSiM5ZXLKNgEHtEouxSniFULFo/RNIeBTtDXwuU/rzWqtbLZ77p9f4rxnmvdN82txuNlumNbRXtijFoe7tMtquzZKXUa/J0q2G9DQDBXxjqPTY0KMDKs1IUTGVSaEwKo3GJE4q9H50DbxRpXpvTtgPrW0+Peh+vBVwVccxM6jGlmtB9skoik0L3XnEDzy6dLhfqrrKmLEUl1vXAdk0KLM48i8nyAXG+oREFy7jxuuSDB+4gWJJNaORI0rBW2ZTbUcxbW0QZ01PytWrnEKc2mIkcYkqLmxwYllOZMWclV2s0FNXeytXm9/hL3u4R9UFi7uxWdYhuTahv0wP/f72yMdrqoWEfmCiHTXcX0f8aR/HvhvsAkXBX5RVf8zEbkF/E/AF4FvA39cVe9/yHGQUyb6NloZDaQenC0Utand9kLs1/z4T/84p7fu8Dt+z89wcuM2py/dQRQu773F9v5dtM5AZj/v0F3h4t03CJfn3J/g8nLLb/6dX2XcbemDA5Rp3uOccnzUEYNn6I1A+u6DC1QcSQrr/Qknt14B3/HKq18gxhVOVmjOjLvCbpcYZ2Wche1cSLOlo1ohJaUoTChVKrJPdIPgjyA+RvSkiPwGV1eIB+4Aj6zzfZLrut3u+Nu//Cu47oiiynZ7brLp+01jhsOkcFIipczFxXkjrrFGxfr4lBAj6+Mjc4rSBiM2W6Zx4s23fsA8TeynEec9t19+idBYppwPrFcn9F3PnTu36YeemzdPGYaOl+7coAueI68ImSE4XBepqxW15oZMcAcVXmmSOSEKLjhOXzqmlkq3NodTqEgQ4mDPOwoR5fFtpE96XcU7VqfHuNVALYWymSljYnP/Hrqfudn1FBdBgnEhJBu1Tq6iKkgXERWCCBI8m82OcU5oqXQxcBI7qlY22y3jnNmOE3Mq7Iu5xnkzHhwjNMfsHBIUaeRHuVQudnuKKpnGHucN7inibIrVL2Wm5cNqytNsqIf9bqZWWB/HQ3/hQ/ztRyopfBP4JRH5q1yrDanqf/ohz8sYuc2vicgJ8PdE5G8CfxL4W6r650XkF4BfAP7Mhx5FGwpwbRc0ZMKCwzWyCu+Mnf/GzRuc3b7J0ckxw2pFrZWai0FVxl3DuVaLaJMw7bctklLG7YZxc8G43yG9YXNrmcAJXiJeFMEwljknVIR5ngnzzDQnutmIWIqDXCZqtm5mSoWqnoonVUeqgqMB7Q9RtsXqpQquCkX1ipHo8dgfufZ1Bt5S1Q9D5H9i65py5u137kI3UReHm0Yuz++hJRO8b6l6JaXM/fv3yLlQVXHec5YLXdeTqVYLrm06LxVKMohdKYWSDdec5tzITmYQx7hLjeIvMQw9KY2s1wOrVaQOHatVbKrKQnCOruvQ4tDgr9R9Wap2TRzJqWEzvRCHiK+Vomplwca763GP1eHyhNdVsI3H03igfW642IrUSu+DEcdLMC6TXKi6AFmhkRM3Ln5p9w5sNzuSD4TBSknjdmIuxoIsbiEkN1UQWl+nIempKCo2Sl/USgi5VEpVqrjGVN0ia2c35FLbl9ZbEK2Gx24KEylnXPKttOVYmNEeZR/ocEXkv1XVPwH8C1j91gEnj365K1PVN4A32teXIvJV4DXgj3JFePOXgf+LD1lAa1QsY70VKfaH10Ngb2mFD5FhvebzX/wiZ7dfZjUMaC288frrzOPI5d03mLbnjPs98zyx326YJ8fdN79P6AZ2NTLuRySPxJoYGo0fnY1+nh2vEZSLzSVzyuSUUHHM+xHBce+du+y3CTjB+cg0TWgteDLzfktyR+RwyqYeMddA10WcOOI6EsXRu0hFGecRHxpl4WOi8mvlor+hqj/1cZ73Sa7rbj/y9//B11jf+gziPTXbhvidf/gVak7cvnmTrus4OjqilsK9u+eklChaiV1kODmG6Mmi5JrZP7ig85Gf+sKX6LvIl7/0BaoWxtmSs9B35FK4e/8e+/3I2+/cZZ5nvv3tbyACJ8crTk9P+D2/93dz++YNTl97hQgEFZwLrM5ugSq5ceT6xoWhSsO7W0NNXEU8hHVseCTMK3ubXvMf54R/THsa1lUF43w2UcHDIEqoilRYD0eGLhFPKk3M0QkudqhzuAbJTBd7tCh5N4PA7nKPBy4wx7qZJyQGbnzuNfAO5zaN08B4Krre0sM8WxW2uABiPYCklbkY8ZT5VUfwjUtDrPl2wHm3SFdKKymg5FK4vNyxb5hd7x0ufFjL7NER7u8Skc8C38WaZj+yicgXgZ8FfgV4pS0uwJt8AKmGiPw88PMAN497GtCyOd3WU9bWpRaLDZdUr+RCmmd2mw1IYL/Z2uTLfsc8ja0xY6bVJlysXiNQC8GJzUy3/qfxp4oNX2BNmlLKVde8WES1327RKlzev4vzkXHco1qJ3khycq3GJuY9GjwarfYk0SSAnPc4VaLYLHjXB0J8woWy3gAAIABJREFUPJFQKxf9loj8mKp+90d5jX/UdV2tjwmxJ3QrnPdUb3X0ac6UlCilsehXK7vXao2yosXwtNETu0joTe138p4YAzfOTlmvVjaOrcqUZotEvSMVKwHs93tTBZlG3IVlSc4ZHG15n5wKoqVxpyaopb1egSBIFpw4fGhIhwVu4hpyJtisvTVVlNqW0gpVjydzeRrW9ezmCUXNmRm7m+UB3nnEV3xuPZNGCF9qMWifd4j3iI0SUpHW9LRzVUrBKbhqopxTnvFtIuWgtIwcSgnOWYSbaXAmb9jruvBSi61Ea8tf/2MOWctDeG9n2bVrKtFVl2vSZNRDa34/yh7lcP8r4G8BX8K4bw+H047yy4985cOxyzHwV4A/raoX15nuVVVF3l8QXFV/EfhFgM+/fKKLpIk2ogtpRCaqViMLYt1nEcdXf+M38HHF6a3vEruB9fEZAGncUObRdLNCB/0K7yC4gHcevIcauXF6zDQGyrynViW4AEXZXm6oqKWmVeljNJc8z4wp89b4LbyL3Hv9+4gIKRvsi2Cp55wTc56JNyxVlaHgXMVFkwCKWnA4jjXSd5HPff4OsYsf5TT/qHYT+E0R+VUeLhd9aJP0E1nXH/uy/r5/+vfT3fgs6hzz+IAH997h+9/+FtNuy40bt+mbEGNKmS6uAE9WEwR89bVXOT474fjWGQKMp8ecDCv+iZ/9ac5OTugGIzkYUzIol3HCMjdQ/n4eSSlxfn7JNCXu3z0HhNNTq+FuzvfUacs7r79JGTe4eSblzDsXl/g+8KXf+UWOTtbcun2DEExRAhGkkZvQhiOkNVOcWNvFSeW983GfsD3RdX31cy/rdrMjTYbP6hM4dRyfnlGnzO7uJdM489a9+4wp8SBNqPesT07wXYfEDs2FGcPuFsQa5VNGquHxRZVMxdXMxfkG8Z79frTyUbKyTucNxZTcjKoYl6cq1SeqQuj7JiyriHPkUhBVlkHkXK0KI23i0fuIc8LQNbRLb4FCKXZdOXEfOkH4gQ5XVf8i8BdF5L9U1T/1YQv1fiYiEVu8/05V/9f247dE5DOq+oaIfAZ4+0Nf6FD8thPh2obkMKj6lfaU8dRuLy8RP6Ia6PoVXozcpMwjNadWQI+45nCdX2TKtUU67fFLNxr7eSlXSE2RpRYnB+REmWeqZKZ2vLkkWzxvndfqhKIJ37W0a7B2ag2GtjD5aEeUnq73DKuevn+sDnfBaC4mwH/0YU/6pNbVe8/xyTH0g43Q5tguasPleme6YyYxZh1rJw7X8Lsh2odvY1vuwNe+IFlKQ5/YRNBSIwzB43H4KKTckXMhhESaClohhg4HbDcXlP0lm/NL8riB1ry7d/8c3wduPtiQqzKsBkK0eqQ4oapcu/EUr9U647JETD+s1PwJ2xNdV1WjLSzFSgi1OsPc+gjeIs5ZYUyZMeUDK1dpZCiuWm3VoFrGkWKkQJbqaGnkTmJ42HlOiC+Gd66mK0gjmbdKT4tXFz6MxhrmQjhMDTpxVzmH2H9yjadaGr5+UXJesNkPSyV9+Jp+FPKaH9XZCvBfA199T4PtrwL/KvDn2+f/7aO8nqpYag9Er4jUFr4L6jwuBKRUSp253NxHFfbn9+m7ga7siLE7ENusVyuCW7G6dYYTa3SkUticXxikaEE9iAcs7akqrWEn9KGj4bUNL1ivQNBCRfMOWOjl1F5TCjkkNBS6s5EQMnO/o0phUvPkYRIikdPVHVYr4fTGEX03/Cin/6NaWDhUF2sscB9on+S6GtdL4a23vs+cMrvdfTbn9w9y0zlnBGty5WwYXHGO6ALBC3UeSXvHzlkT4/ydd9n4wN//9b9PHyP7eSKXwnbcATAMFpmsjo7w3tMNnlIL7969j1ZlvTolhp7gAmWa+fpXvsb+/F0uvv9NyrQzcH7K/OD+AwiOd8c96+M1n/ncZ+h7o4YMwbM6Mq6GrjNp9VXncQ58XPDjoV1bj82e6LpaOhfA+MCpBMPHOkgO7otnj3CvKlNR9llBC2z2uDDjQ6BWZWwNUi2m+5eKiceiBaENzaCkzQZEyA2XbXtrwW23iHMso93aWN9djEgIrIKpfNeF4as5z4U68zAa4xsZUgiHYM0Y7u01pZHXm4f4Rx98+FHt9wF/AvgNEfl/2s/+PWzh/mcR+TeA7wB//MNfyhyrYVIX9BvWTRTbOcX7AzaSWmxGPo1UUeq0o9RinWHvkH6Fc55uaGxeNRn5iI84b/wFyzTSoUzeIEpGfC7XxkxpqWLrUys2kqhKKdZhH8tEkYrKbDWsqEgECQpSKc2ZgMOLwwWHa/ys4j751FNE/hTwbwFfFpFfv/arE+CXPuTpn+C6Qskzlw8u2U8T2+19tpfn5JQoJTNPEyVn27BKMV2pWqFFM7vtjqqVuZ2/NCech904klLmcrcjpcTl9hJVJfZWnlitdjjvCJ2QS+bdd98FhNs3E6vhiOP1GSUnLs4fsL3/gO3FljKPxt+aM5v9ZCOn9y7ZjBniir63QYoQPOujER88XW9qEuu+1eSHSIgdp6enh6j8k7SnZl1VkGr80qjRp6oqc1WSKrMqBswUMjbNJQo5FzwWxFSF0sQ8tZVgdUltuTYC7hxW4m1adC0axcmV0rcsGUdDODmHqJogqC5ag8vEojRWLPtk/bSrDNogY9JQwo23cPE7H8Eem8NV1b/NB8fYf+DjvZrgXCSGzqZ1WnTbhQ7nA/1wAs41cb7KcW/qrkY8nSjjBaSISkC8Z/IKfY+/cYoPnpozEeXV05cYx5GLiy1zgcyE0mStVcnTjIiwEo9zDcyOOd+qypQScy68e75jngu7XTXpH0akq5y8Al0UbqwjEj1ZAlpsEqdmJcxieM3+mK4/Iida/e8Tt/8e+D8wNddfuPbzS1W996gnfpLrWkvm/N03+eqv/ToPLi64++AuKU2k7YXV6PY73IIC0KuRXaTig2M/mkx9jJGu63nttc/RH3dkvwLv2eeJcUq8fffSxAu3O0s5i1By5vzyLuO05623vof3ji9/4cvcufMyf/AP/GGkVL759a9zce8d8uV9Sk6MKZFV2ddCQXjzW/fBneO/fg8nwhCqSQJFj3iHj9GmrvpA7AK3XnqJ07Mb/Ozvus1q/VhKRU/FugrgJZDVItMpZUoubPZ7GyxKM1POJCdUb9JFKmIIlFJaVGq8whaGGCqkDwNIIzISOZQCtXnGxekt0ke+/V5aGWeJXWQhvWrR7yLyWtu9pnLNMR/KlUuJoQViYgFYFRvgWCYeHy4x/LA9E6q9YGN+pQXstg8ZWHmp93FgDjPeU2kJgXeNhb/VfkXU5FmKbyfW42OD93hPUKFbra3DmpNJLDdC6lJqq/XWh+pwtoC1Mdhn9vuJKVX2k2Ewiy/4Auqc1Y/ENLmkOKR4JHmkKEEDkY4udAQXSXOl5k+eqFpVzzFxwZ/7xF/8Y5qglDSR55Fp3JPz3G4om0TTw0WuV5GKWLF2ThVfCxAJwTEMx6zWJ8SGeghhxLlsEuVT4uJyax3lanP39+6fM4477t1/gHfCyfE7OO/Z7Xd4hSkX5qKk6imqzE5MlcN3dh06b/jeydAsiQmhCRgK0FJQI1OKZO2ZS2Q3gXsM6JOnaV1rbVwiDa9aciFlW4uiekCNiDpcaeWV5qsWBIcpJdsI+HUHetCSWya8ludpY/MSd3icvU5D2L73lNd6iE6XUoQux3HtfQ7OFg6ve2Cvay91/fePsmfC4dqUnUUlWgsSOxBPdRHEMZWEcU4YnDy6DhGH9x3eB2J/TAiBYdWjqsZclGem+SUkdBzffAkVx4PLLRocn/nCT1DSxP7iPvO45923fsA07ZknG5jwzhNCaFIhgmixaO3BA7b7mXfe3pKKoq7HBcewinQrz+nxim4l+JLRUmDb41JkvfF4hJsnx6z7FXfWL+Fdx903N5T8WAcfnqgFH7h98zafefllhm6wskCeGxG5ERDRmhVgEQUihGjqrhI7QojcuHGT09NTfsfP/AzHx8ecHB1BrRzFYy4uHvDd73yLcTfy5ttvU0rl9MZtS3FVSDik6yg184O332JKmde//z2GboW7cYcY1mzCcavxrk39IVjXOvYdqoXt+T3SPLJ78BZpnthsLyilmtIOllL7ELm38by0cfy287IQlD2XVmtlv9uz34/kXJnGuXGS2GdUcV5YrVeUUohdPKT2SxQpInQutNppvObormB4zjcY3xKZfkBav8DDlhHsWh/m4Fiem4txm1S4Bv9qeOtmy9iyc47oFzhgfejnj7JnxuEGD04yhQwagcYmJQo1I03JVaSVrsXA6iL2J1qt1XANWq1uOo57VBzDSUbFMU0jOSV7T+cIIVBDsJv7AOORFumWRpYi1GrTTLUYT0NYdtbOdLWGPtD1rdkjQLKurEweyUIskeg8R3HNUbdmiAMQmMedzY8/x+ad4/jo2Ii780TOGd84DZwskUk796ogjhA7RDzqbdM7O7vB8fERfT8Y3O96R7NabXeaJuPHUGVdbRJJxaHiEOetFpwy+2ni/OKSeaUQOtyg+JWVMuLamm0hGDwo9oFaMuN2Qy0Z5wPOZ8BRsYmnqoKKZVPjXNmNmc12wofxg0/KM26qhgzJOZNzbZwYpRH51JauC6FlAEv9dOFSMKfrELfAsczxLrVU3/D2/sBh4Br29sp5Lt9Zy2WpJTSMkTxcd12+9tpQTwv+1j0cKdtzaWgZuebIrx73XDjc4IWzU6VcbklzIqlSS8AGSBw202k1Vec9qyE0whFTYJ2nEZ89ffCWNiRHKplvf/0bhBh55fIBiPDu3QetM243fHRCLRnB430kdgO1ZOY5H25igFqNpSp6RzgaOD25gbhAXK0RL2hQcBUnGZ0LaQ+1CuwiQWEIK1ZdzxdffY2joyNund5mngrj5ZvsdtOTPfmP0UoppCnzxS982UovrTIUgrOu/tL9XSLcpUYntq6l2I1SNCMC8+acvDlnk21D3Zxfcn7xgDd/8APuPbjPu/fvgXMMZ7fxIaA+GFlF6AEh18x2Svzm177O0dEpZzdepRuOuXVyG0HofMCLowvRSiF5Ypr2jNNMmma6rid4T9WWPm8nBM9wfBvnA1kdl7vKV3/r26xWjwQNPNNmIpIbcjKFlHk2EqKcjV1tGfsNMVK14mffECjLerdB2wPdoRUFa5sKEy94J3R9OJQPzLTtsQ/3PQ7p/rUStR2XkSEtEa/31vvRdr0tG/17ywTLyG+L4ojLBvw8RbhDLwy9gcdrstlrw1nWQ6rZHt0WppgmWIOCoEbzt8zU55LZzXt89GwvVzjnSOPWItVWC6reUUu1nbnaghiuzxLF2qIo40eFGALiPLHrcS7YBJRzFG8qvUWLOdokUASXLRrvh8gq9hwNK9Z937S7lDSbQ3reretiEwKt4IS+8zjviMEfauXLhB9gqsxK40jQpu9QGzF4pYwGI5un8UDtqCghBJvsW/ht29chWMZUksnqbHd7kMjJjSZQKf4QKYkYkgQMkE/rqCuC8wFT9PCILKrMntivcD5SckUkME4zj5lL4YmapevZ6rh1iSSv0n1TN27II3WGIFK9puTRHG2LTFXloefb2i09nCvOhIXLQFpyc3j8+zjch5tqiybiw02zpbRx/e+CKzSSql57r4eDgg+yZ8LhhiC88nLgSCLTWHnrXmKcMxeXSimOrLbTDYNNn01uj/NC1dFC/7a4l+eX1Aq7vaU4U97hg+B0S4yBGAekKheXO3IuTHNqo3uKc8LJ0drqiTUbsfVuh2oluojzwno1tNqO1ZZKxTaENjY8ZUepSh49Uh2hCDEGXrl5xvHJwO1bZ4TgeXC+4fJy5OLBnt3uY5G0PVPmROiiZx4z0zTzvTffoNbC6emaroucnh7jnDukotvdzso2KSFVcZrx3nPjpTO6LnBj1aOl8uD8nDom5v1MzjPHxye4GOhPjlERuvUppSrTlECFsxu3KWnm/MF9FGGztaGZeRrbpBigSskmJoiPCNZFz2kmhIhQiQI1G4C/VCNL8aHj9OZLhNgb410IbYT4+bWlPmqZ+eLYHKFJKMXGY03r9C9YsMXhatvAavumHnDulgK50Jrl3tL40O43Wmmi1vd3ftqKs4sTXa/XAAeMd2qfq1wP4K7+piUSrqWptNSr/kIp8vygFJyDrhf63v7w2ClFBR8aQ2W2UveSctRqhNNNgQXFajwlmSLnNC96S1avTdMIGvHibT46J0oqxkCvStWlcG7gZtdqSt57apWmGiuH2o602mM5oHmNgd7RppDUmWKFsyhutepZrYbDhbPdjlxe7tntE/sxPanT/tjNmqFgzGszFxcPyGmm5D1dF6h5OjjckgvbraEMJFnZp3PQ95GbLx0TfGDVB7QUNlIRzaScyCXjgydqx3G0Wqq6gBO1EkAIxDCQ88x+u2uKvMZbrKWaxlbLZDQZb6pxJxl5kqoSY4dziiuZ2tJg1XY9hMBqtSJ2A2lOhGCMdjE8v05XrjWctEEInFsqqhBCaBm5Td95b3Vcdzh3LZ5Vi26Fxp8i7lBikgNIdnlPc8oLBGxBLMBVtHu9zvte5IFe+9ze+Iec7pU9HOnWatOpC3rpUfaMOFxhdeSYzxO1ThydOkLyMBiX7OVFomSrwRV1uLDC40mpncRS27hhopTKbt6iqnTRGiYpJWqpTHNBVZiT1ZxM0kdRtZM57ieC96xWEecCw7Bq1IGWtm42O8TB0FsXfcHsLWvgXUQkkDtbz+Ad63XHnVfusFr31OLZ7Se+9rU3uHtvyzdfv8c4Pb9NMxEIrlDSjt32nG98/Te53FzimhTK0WowSZRs6rvTNNnopwoxeG7fvsmNWzf4wk/9GKc3T3jlpZvUlNi+8y7TPHK537AZJ8R7hjBw82iFqnC5makKJ6+e0vUdn33tFaZp5P/+5b/Nbrsjug6nnjIlUrHrRttIqUOgK4fRUAme2y+9RCkzm/tv2pSiGAlL10eGozWf/eyrDMOaeZrwXrhz62YTHXw+zTtvaiUEK7nUBQXQ9OScfZ8ax4XJzGsrJVjUa1wGNtbr2gDFMmiwTH4Zi2AxDouHCgbvtSXybRCwVmV0bfhkQSvkYuXEhaHvvTXch3C+GLXA4tyt7PjhmPlnwuEihq11XnGtz6Fe6TCFhDhZwT2nZcdqTxPf6n9YhEJpxBRWU5WFbGJpaLcLYqk9oS2VaR9aqjENVW1NHTvxxTlqUVLJUATvi0U8B9hIA2Yu7ETiLSIHwxZ74/Kcc2U/Zu7f33Lv/pbL7cw0P78O1+pgRkCk1AOuWnO2mm1KVDC4TtW2cdqNqVzhmlUMiJ5bt7k0aE9RJddKKkZw7SeTW6+lWAmqHxhWA0frI4J3dKEj+bnV6e16qFLadaE25SZCqa200LKUbhjQ4tk31ETseyPjmQ05vuByndQmKlNNxv05tQUiJbqwr7V6pzPdsLJko05wKuhh6s7OSWVxoBXXauRLeWJ5fVkWieuR6w+fU6sG6Q9/2BvZ+7XG2VIyWKbb3LWSxPXyxNLEFa7Xhz8cgwvPiMMVEXzniasAIXA0CH11+LmQcsH3Sk6w33pq8Tb2K54uniDimwOt1LzDuUSoHlWDmwQXcL5vN7HaTVqyUQMu+jtgzjcnG4II2HivM7ltfCADm006jJo671mvB5z3xqmLPb6izHhShWk3scpwuS8kzUzzxN17G37jKz/g3Xtb7u4K+dOVYvlUTbUypxkVYVit+Z2/8x+37/MEWvG1GupvUdo94HINhTKsV3RDzy4L5XzPbjOiOfNgN7NLlRoCWYQ333mX/W7HbrvFOc/tWy+zPjrmlc++xmq9QkulpEz0nuiNGpBqxPEHYUH0QBQ/q7FV+VroguP05k1ryo4XdH3Paj0wTiPf+s7rpGnPg3ffpOs65mk0aOK0aeWj59Occ6xW6ysB1CZxk7JFo/t5RIRWSjA8rXIV5NDuRS82WCJar23OrS+iC8zTTNrvr9siPnolNrkgBs2pLrXbJcKtzdG2cvDij685aaPa9M7QCOEw8mtbgf8Ia/pMOFywOqw4614HL1AhYkz6wwDZQy1KLVCzkZIv8LuDLtEBVuRAHN5ZA+N6tCGH3dT6yCqtctsCVGcyoyyspmAXmFOLlrVeLeDCkyneI4eoC6ZcmVNhs53IRdntJrsgS2KaE/txZj/N5GKcm8+zLUMtIXhOz04trUujOdxSD5haRYwwxDnDZTpnXLjRNs9SKlPFVJFdgKjEriNGgyKVWpnmGed864rXlsFUSrEmqveOECz7cK0XUKrpBYCNIoPBAJ0Ivi6M01avtfl/u3Frre3xlXG/oeaOnEzFIs8z+hyXFGiYVAs8hYcv4SVzbFjXBXurgkq5qqFefzwcnOBDQeQBDfY+7MLLz/QqU9WrJ7T/9SFn2g6l1XOv3v96dHtw8FxF2g9Fuh8S5D4TDrdWYZoCKgPiK31MdCihN5TCEAMlw/pIKRmmvZFXz+M5pQjjZBCu3NLz3h1ZKugXCZ0GN2ok40GU6gQfl3TB4CfDYJ9dsB1tEb/z0SHBszrqSSmz240UhT0GJVs1EHeumSkV3npnw2Y38cbrd+m7wI3jNWdnAyc3IuO0Ryl4B8erDnPsuydx2h+7OScMq8haodfI+uzESggpgSpRbdzEYw5Wu85gXH2wQlGeD1GyVPBdRIJw+vIRQy7M/QXDxSXff+sNfGznUoQ49OCFBxf36KaOk3xEzon1eoV30hQCoJaRUibm2Wr/eR7RWoywHvBdx2p9xM3bZ4jAO/fP2W8vubj3Nmka2W0uEHG8/b3v0nUd67UNZ9R83Hionl87CEJyVd9MeT7gca9oVVsA1Eo2lbZZqZIaDePhVC2DMK03cphALDzkdBeneUhOl3rvUiaUNlrsrOGN94cHX6/06KFMYXzGV7SbFnRdjffXVoL68PLfM+FwVWGehZSaAm6wxfSCqdxGKM520lJg4TUoxdJV8U2GRxTUyC+uowlqKxKJNjYyMdVPPUQvbXHbx2Ex2yKLFhbNtaVbWg8pi+FFcTBrYUqF3X5it53YbGdyKoz7mdXgDSnRkAtdDHgJPM94TTuLCeesWRlj61BX0wUtJbeLXK7hn+2Gcw0eVMuiBMcVTykCrhCCJwTjMej7jtV61R5nN+A47ik1GyKilgb9lAOQM2XLPJbR1DSZqnBJ5pB97gHY7/c4J6Zd1/g0SlW62DUkikEFh76n73u6NpL8vNsSPV6vkdZaDrh1lWXQwbKNQ1NLl8mzJdo8eFz7//3CSJErOML1x4jQ6kJWmmhQs2V2YfEHh2PmKvK1YPzKdS+TrFfB79XvHj7OD7ZnwuHmBO++LaRtRKRjOKm4UAix4KUp24pwcmJp3TjO5AzbzUTKwvbSk5KwvQStgleH0KZKVBrHph6GJBZOe3eoGRolW6rWLaXtqF4ropUyWeRTp9Fwu04oVZinhEgmt8x4s0/sp8zbb1yw3Wc2D3bUVUcaC5qE0+GUwWVeu3Obk35knwxG9rxarTPz+AO0NAgVA6Uqu4s9427mnTfeJadK5zuc8/j1EaELHJ8e0cXAjaMB742cKPjA8SratJDCOCkl76llz8lJT98Jt2+fUEvhcrMhpZG33r7AOU/OCRGYpok8z+z3O5Nln63UsN/tKTmTptEcRjaNLBd7htURR6enhBiY9iNalZOTU4ITzk6P6GPH2dkNuq7n9OwGIXQMw/pq3PR5tFZ3Tym3Wmmi1kpeItxk+Pbc8KyHlH2pHrSJsxDaSG9rqtVikeb1kdz2BPskV2Q1FgFfneMDKqLWJvKtNrHYIGzLY5babm1q0Xptk4/xGqcDrcSkHIK3EMPzAQtThFwCuQyGCigWwajajeJ8Z3SJ3SIaVSjZICUhCaVE/CzkDLU4pETA4bRFGdcufkVpBJsH3J9rYoHZLSOGFjEvCYcc/l+4OJszF5sbrw1b2AjrAdPC6vuOYeiJ0T6Gfo13mdPjI7R63Fj5CEiTZ9dU0TK3VMLq4FWBMlPzyPmD+8xTwmMoDr9eE7uOWs4Yho51sEERVKneM+07nLfhknGa2W4v2O22zNOeOSV8QxjY95lxtJrubrdFEMZxJKeZcRyNj7eVEqYW2dZiJQwRi5CCN627pR7c9z2ikSg9IThunp7QdR2nJ6fE2HF8fELwgRD6jxQNPcu2qKAsUWu9Frm2Mu7D9VM43D+uDTgsXAZLYXRR073+HnC9frrAMN9DqQhGFL7Ulhf00XuW4L2MX+/Hc7sExkvdGVkYyh5+vw+yZ8ThOmq4RQ6KkEjM1FqQcomXStcJITrWJxHn4ZhCrXA8VXIWtpcdaXZsLxw5C2kKaBVqjYcOKmoNL21kJ4pJsZi4ow1OjNkmiOYxGUkNGU9lkJVJr8sOqlKKDVCEwSghu0aooiERU+FlOaIUCF+KHB2t+MJPfJmbN4555bXPUnLmJ35y4uJyz737U5N8/q0nvAKPyVTRPLcbzeGKI1RY+cpURl7/1m/x4MEFFxdb2+RiRz8M/PiPf5Gzs1PiT36JGKOR0tSrEewpJaZ55vW33ma73/PGm29auj/PRlxUrBPu+iOcC+x2e7RWdpsLSpoZdxsrMSw3NDZpOPTGyXzj5JjYRY6Ob9L1A7fu3Kbres5OjoghcLzq8M7Rh9Aagi0y8rGlpAtHwPNpS5R4HQWwQK7A4JI4lqkXgIMzhKuIcynflRZ1+Pbz5bXsW8HJMqLrHnKa1yNhEYuYD4TjehXBLhNiC8pgccrLMMOCry2loM61jdbRxXBwwB/F2cIz4nARQfEgPab/7lAyisGHDOJq8sreC4gBmKMaPjd21jCJnc3Qa20TYiWy4EBUQYvVm1zDDR5o4ZrDFW0Dh6Iglt7YggfUgThDI1D9oeNpLEcGAHdecdVm9503yZf1eqDre2LXt4klTz/09KkSu4orzzlM4aq1YedXm7yiVuZpYtzv2G4vzUmGSM4z437Hqo/tdSQZAAAgAElEQVTklHACKTXuhNkIrMfmcPf7Hfv9nnE04ut5mlq9HiMXCoP1B1TQWpjniZIS8zwfIEaCgSME1xAVjhgDXbS68GFaLUZWw4quCxytB6vFX2ObspvxiozleXa4cC16vR4lHqJFaf2Oa9Er74kSl/b/I+xhXO4PR5iHiTMerrFed8bXj/eh136P83z491ev464d//s974eO+cPIFp4GE5F3MPXRd5/0sTzCXuLxHd8XVPXOY3rtJ2Yv1vXFuj5BeyLr+kw4XAAR+buq+ruf9HF8kD3tx/e02tN+3p7243ta7Wk/b0/q+J7jVukLe2Ev7IU9XfbC4b6wF/bCXtinZM+Sw/3FJ30AH2JP+/E9rfa0n7en/fieVnvaz9sTOb5npob7wl7YC3thz7o9SxHuC3thL+yFPdP2wuG+sBf2wl7Yp2RPvcMVkT8sIr8lIl8XkV94Co7n8yLyf4rIV0TkN0Xk32k/vyUif1NE/mH7fPNJH+vTbC/W9fm1F2v7iGN5mmu4IuKBrwF/EPge8HeAn1PVrzzBY/oM8BlV/TUROQH+HvDHgD8J3FPVP98uspuq+mee1HE+zfZiXZ9fe7G2j7anPcL9vcDXVfWbqjoD/yPwR5/kAanqG6r6a+3rS+CrwGvtuP5ye9hfxhb0hb2/vVjX59derO0j7Ik43I+RcrwGvH7t+++1nz0VJiJfBH4W+BXgFVV9o/3qTeCVJ3RYT8xerOvzay/W9pOxT93htpTjPwf+WeC3Az8nIr/90z6Of1QTkWPgrwB/WlUvrv9Or7MW///EXqzr82sv1vYTPIZPu4YrIv8U8B+o6j/Tvv93AVT1z33QY0X4QyG4h5jZuc68/j5mvLXGU2pyH3LYXRZxuYWRSNpruSZtvtDD1VIOvJ1XL6y8lzjomgKIPb99+EacnJvGmTYSzuV9bIn1INUTgjtwa7a/H4D75/t3n3aSkx9lXU9Oz/7QnZdfvf5zFj7TA+koV5pYCy/TQhDfWEuvv/JBIuW6CpXo1e/tmN77GK4W8UPuhw/6rT783/s+/r2P+c43vvLUryt8/LU97vtfvn1ygtZrC2UEwiBCbWrZXZO0SvNEKYXtfqLUeqXjd/12txc/0CgaUxfGBhhMqHW9Ghpnrt2jC62jOE9VZU6NUH5vQgHL6y+X23uZvxS9TmZ3/W8EMAVwNUEJU36x359vtx+4rk+CnvH9Uo5/8r0PEpGfB34e+GnnhFfvrOi67uAcEWky5VeUbFWrnRcxFYf9fkRV6V3EidBhEue7eTIZ5MEkUPoumgpsPxBCYL0+QlW5vLwwXs/JhAgXmjc9kCqbbLb3pnXWxUDwjlUf6ELg5tkxoNx7cM6cM7spgwjr1RHivEnJ1Mo47vAebt9aE4M7XFDGfA//y1/79e88zgX5hOxjr2vfD/y5v/CXMIlIoQumXeZDNA0zb0TxhUYEv3yudnGHtmEtUkcq9lHb54NvW7SxNACC1kYkL5Vr3vhgP6woYJ9q1UXNiuWlrz/uOm/qQ6/XPupynI368d/8Yz/9LKwrfIS1vbaudCHw7/+Rf540GR+u9wIxUG/fosTIRYzE4PjHTnp8Trzz3W/y4OKSv/u177DZT2xTfkjzbNmIXQh471mfnBCCYxWFvvO8+tIpZyfH/MxP/zZWw0DJRtN5vt2jCPHohJQLr7/xNg/OL/n1f/Bb7McJnN1jfXQE5zjpe7wIjoQ2ma5alZyXIEtxCMGZeGh2lQJss6MUqFNGq/K//9KvfOC6PrV8uKr6i8Avisg/5538Ne+Ms3RxeAASgjnXJr0h3l3thMBqiDhxrPvelCLGmZIzOpvWWPQdwTvWXUcInr7vCN6zioFSKhepUFNCawKBEE0eeRhWoMp2t0NViV0kxsCNG6cE7wli0eqNsxMjPu46xjnxzr0H1FoJweG9o+s6FGUYwDvh9GhN8O7A12m8rM9XBnt9XU/Pbv41dXLQIcsolMKYshGS93JQakaaF8UUQACqmESS5iZSmJq4Y7QbomKb4jRPjXg64sTTdyvjRWZR7jXVgWXjXhz1QYmgecyD7PZ7IuXF0V7X8IL3REJcl+1+/mQ8lnUF+LEbZ7q99xautMzNK9UHHmhidIE3U2GIkS999g5eKnWekVo5PT4ixJ5uzi3KbdpjTV8l5WISWtstiDC6iveONM9c3pj58k9UiihlTuScubi0x511HVormvdQRmKolGhr4p1wOnQMXc/n77yMd8Lm8j45J+Z5NH7l0a4fxCMC0Vecd/RnK/CB7E+Yk/L2D+6SU37keXoSDvf7wOevff+59rP3NVX9633ncYeEkia9cZ3sd7kxtN2YdvPEtiMOgykuTCmhepXqxxCMPDp4c5TOmNyDc4gqXoQqsjBQW+oSPEMfUWBOthB9bw637yLBO1C7EJy3MkjXd6gIXYyUWgnBmyR3bFLr1Te9e3t/J56qFSfyLMmkf+x1/fJP/lRzPi3izKbYMM8ZFUfIiviADz0izqR2lAPxNNqUXueM1so8zYASOiOOr1SqVvbz2DbpgPeBimURPl5XYX1v9vhe6e3r31+Vla4+f0Ck/Egi62fGPtbaVlWmlJFScKgR+4fCNM1MUphTxZVKGWeyq0xzYsqJUvWwOVnWYxuqHJbbNrTStlx1Jt0zzon9lNiOCfEBcqXkQs7FMqWDtE9FqIsWLIriHQxdx3roOVqv8U4oaSQlD5opAtmbNJZrmWfwJmDaxYCLAbqeaa4E79Hy6M30STjcvwP8pIh8CVu0fxH4lx71BCv/NBkW51iv14jAnBJVK6G6Q2SxpP7eWYTZdZEbpydorbyjiTQLeHDOc+PWDYIPOLUIa3F4qy5Sq+dkPZBLoErBeWG9XhN8oB/65hALoJycnRBCYOgioIz7PUqlUhDnGVYrYteTi1JqxXuTZw6hSX24aJtF867d0LTWslLdMxMNfex1rbWyHfeUUiilsLncME+Jew8uKEXx3QrnA/3qCO88QeIhg1FVci1W3smFWgvTfgRVuqbUgShVC/u8p1SlFJO7efnOq6yGFXdevk0Xw0EcUGttKf8P12EXqe2HNbWunMD72VVAYFuEXnMcz5h9rLVNKrwxBeY0gxaiJHCBUiaKi1Q8miqbuxfsNPGtu+9yPo1898Elcy7U2XonFlSZwgYi5FxaydA1JZeAVtjOCtvEN7/3FkfrFWdHAw6YMvjQJHZQgofoIQQIBajQx8Crd+5wcnTEqy+/jBPH8apnnicePHiHeRqblh306xuID2joEKf0/UwMnhsnA9OsvBt6yI/GIXzqDldVs4j828DfADzwl1T1Nx/1HMEK5c45k6DpuxbB6JVWkio5ZUsTxBFCk0Hp7KPWQtfZDSve472n7yPBBaS6VjQ3h+ubgF3XRXwR1HmcF4a+w3tPjCbRE2MAlBgDIfiDRJNztitXrUgVoGkwiWk5BW+1I+9bLFUcVL2K4q/3Gp4R1d4fZV1rrWy326Z/Vdhstsxz4nKzpRRFYsY5z5AqznmitBqsJTHkajXCWszxpmkChd5Hu1mdbXtjHqlaSQm8jwyrDakUjnYrSt8BXJPBaX+PLBX765+WLx7OrD76SXrofH285z5B+7hrqwgTMFa7L2cqogVXFRVtqreOMmeKZvZzZj8nxpyZU4FSmniVbVrqrKu11NARRVRwxTIjVypTLlxstuRaCd50xxCHF9MlFLHactd1DMOAItRcCS3L9SEQopUUhVPmeWKaNhbsbSNVFecj+EiRHhFFtYAKEaiA6SM+ZQ4XLJ0E/vpHfbxzjqOjNTFa6n52dgbA+WZDrZUYgjXDdjsEOD0+IYZA1wdCcPR9APXcvHWGaqXr+lYnNYcWsEWJ3m7oVDJS4dbZCSoKvoITui4CQqkFrcLJ8QpQogehkHMChH6wGz6XRMqJcZ+ppTJNBXFC1w2EIHS9LY6nQgVfAqiQ50zVahvIM1RT+LjrOo4jX/l/v8o4jU3AU6gVxqlQqpJMKI4YL62k0C7m2j6SFtt0W/21cx4njk48XoQY7UYtWCY0TgV05HK/pwuRB+d3Wa0GXr5zh77rOT4+xgdPbM1Z6tLsqg1NYhHuoc+mj3acV003OdSJr9d4nyX7OGurwOSErVNKLbiacCjHTXewH47oHOQ0UvLMbj+znRK7OTMXW11twZRgbVMRwS2d0WpN1ilbfX9QZS6Fb3z3dVbDwPbVV1ivVrx65yah7/BxwDm4/dIrHJ1MjOrZ7ycuzi+t1DgXQsho6OiOjvjsF79EyQn/Tc/m8oKpFOa5MKllvNt0DFTSPLPyym03QarkvZLGR5+bp7Zpdt1ELJq0SDIc1DVjjKgqXfucU0JEWA2D1WSD4BYZa5QQPWCRLQglWTfZYzdqCB5VSPmaKqgI2hpZS71Pa0W14tzSPKmtyVLb4+zxi4psyZlStD3ugHVoTRRtzRus+adXqqS11I8bQ33c8yrAvwx8WVX/rIj8GPCqqv7qY3zbg5Va2e32jK3GKgSLXItVV0pVkAqSEYSqJsJY7IwxYw63NLRI9REvgorVv1U8OFCp1OYwtSopz5Rc2LToehgG+n5Gxa6poSm2Onk4WtH3fqNXnve9fncpbbXf/hBi7HFGuE96XW1HKohUnFMc1gaJXnBeUG8QLGkR8FKWabcp6i1KVm2bq1bLcltpZrmDlq/FO8Q7FMiqjKngYiYrqDh87AherIbrPLdu3ma3msipklNhTgU/J3u8C6xPzqg106/WTGlGxVGozEXI6hiL3d9HfgCpqBriSCReKQ5/gD0TDtd7x+nJSes0CyVnnHOcnh5biaHrLNJ1lpTfPDvFO0etiVozu+0GULreVHF9cLaw2SKWYIEUzkEplTTvKaUeVlQ6S0lSluaQrRseggdVpv1sKUvfIeLQWsmqbLdbagUv5uABUCgNbrLbT7QwCQrkvTmEnAsCBxjcY7T/Arumfz/wZ4FLDBj+ex7nmy5Wq7KdZmy/ktY6E1zwDQBmt5hzloEEtcs1/X/cvVmsbVmWnvWN2ay1dnPOuV3caDKyq8wsm5KN5USlMggQzQMvyFjC+AGEBEIUyH6wZITwAxK9jB+wsP0AFELIphMvWFgC2SDLNiUbXKJclKtwlZsi05UZGRG3O+3ee601m8HDmGufm1WZEZkZ9+TNzCldnXvatfeee405xj/+/x9UCkrBbqLDbK/nnO3G7MRoeoNGnBOctyaL8x3qlDJm5lJ48uIc54Qn5+d479muN3Rdx8MHD+j6jpPtttEE18cAbDDPguV+c9C0slIbi8EOhZeDcT2yGO68anmt+ypUercjdBlHZY1Vj9tth/rAM5lwQB8rASWKEoG+YbOli6hYdlyrknIGKhVrLsfO2EchemKMnD24xzAM3H/4EOcDY67kOXOxnyF0nNx/g76L7C4vWIvw7hd+gnGa+cX/55c4P7/gK7/2FULc8/izmXgWuPepz+JE+eD5B+wVnu8T1zcT+7Qiq7IvlaHv+PTjz7DtoOiBoonNSUfof/CaZt/9amKEpcSoxZpVXqJxVYWj0MDRhAdwxHZrKSh65EfW6m4zFG0ZaoUi5ZhdLpQhA48UFSPcK1Cr/Z0Fr9GXMp0lc7V59moBN0gLHBxJ/apQcrFytVqgrRm0mlDCWccA+ZgT8xOun1LVL4vIL7TncS4i3V1e8OWltCwW+CaW+/KVIzHdmh7uyK211/n4K2LfqG2/E4pTwRdnHxtXO7jb6yiQcgGBVCveWXXTzYkYO/rZXoYudojzhODpYmcHgX7zo32ZsdAeGbCM4pZjhnuM08fv3dl6rfsKindK5z0exyme6Du23lO847omPIp3FXUVnOE0ItZ/iUMPvlHqauUwjgbDZOvnOG+B13o6YnvTRbYnW5wP6JgMckiZcU7kCrFCVTHIqV+h4nE+IuKZcyVr5pAKU6lI7Ky69RF1nilVxlQZS6uyJOJcR9dvCJ1QElQf2Gwgdh+9rz8UAdeyikrNyTKEbIGoiOK8I83+trxAGfc7nABaqVqopVBqYT8eTHgwDHjxdIYZHJttthTnrUwptaBaKVnRl2hotfEL1VUD1UODHJqwIpV8BNm9F2KIxrDADoYYA7UW9sm4o4f9wbqgfoWIcU7Fe7rV6gif3NFKTbZpvQiRN2iJ2vdjqSpzVrxfGlbSDsB2oFGwV7XicMdolkuyDNdDFWHoesN+x9lu0JysIqkGDYSmRBqCadnFBVM4FaUquOpwqqSbEcfEze5gfOxVT4yR+/cfMAwDbzx6gy5GBh/s73p739VaqJi+wo6GdgAsOe4xINvXy93D8q91X8UJwzryRr9hcI7H6uglsO4GEsB0iUpltQqMkpnDxBQnaiiE0PPpz7zLarXm5GSL1sqTJ08YDyPnLy4opeBcsANPM8FVOlfZ9J4vfv4zdKs1V5NyOEz8+t/9O+xubnjvjTdY9z1lGvE+wLBlSpmbw8xuyhwsgebDixtktWOfIUbHWIVDFq72mct9IYUeHza88eAx2/WKk/sPiEG4ubnG9ZUff2SQFv/Dt39tfigC7vI2Xj5aglOptbRGRMW1AtSURJUq4Fq5vhDXF6lfyQWcUqrx6r6pwhOOCjaTuxy/fKwgXZMYLp1t91K6ow2DrcoRy7Wfc3g4BmawBlEtUIplQhJvcUNx3p7Lt8j8XuH6E8CfAR6LyH8E/F7g377LC768rFpQy2C/xdM8MjJ16VQ1VodaU6W0ysO5cJRnSvs91VaSqoKTlk1XlFb9YNu7/FWDiw0zn2eluIyKEnMmdDekktmsN+SuQ0PEO0/fL81Xbdnrsl9NYqzSqGP2fKouj879Jsz3Fa/Xuq8AiKMLgUE8fRU6dcScAWXrBBUhOjsUvSgeJTSc92zo2axXPDg5QbUiuz0H5/H7AzkXjrtcCsHBlsqGytYLvXdUb0yhXhVfCnm3Z84ZTTPVB3ZX10wpcxhHxmkmV+utjNPMYZqZs9FAYxwYhjWnp/dQmbhIxlYKXvAOcjYa6mFKeFGGk57+YxKkH5KAu3BxXyrhRawcL5WULOBuYte4tNKAekdVYc4OL0oIJlgouVAp5DyCCKE3KWnwwSAKbzdELVYDxhgRxAK1wCoOOO9auWkZWNXKNE0UrZSWOYn3iHhC7CxIlwKq5DmTS6EmQYsjuAFxjtVmi3PO8GNgTBk+RrnySZaq/nci8vPAP4m9i3+Pqv7KnV3wN1+fKc240g6ZdpDFxqO19Iwl8UXlVsJbqr3eCsTOGpXBWS5cSqs0UgInVgk5a6o4EUI71HSJvAt27BalovXJx3lkmmF32BFC4Orqihgiq25NFzoe3L9P7DrW6zXeeXwI1gxoq1bjky5a+7I0gvRuk83Xva9VhZQDgwysEHRvAW9/eIYPnh/71GN89NQ640rhgQoeK8eHHr606TnbDrx7dkJA2FdI08hljOR5Zh731JLRbM+uqxPDtOOd50/x3Q3dvpBS4WTdoVoJH3yd0prZM/DBr3+NXUp87ckTduPEYRoRHzh/cU6MHRdXF9w/O+Gz736Wdx6+yePNGzw/v+Qv/Nwvc3OY0XzJYbfjq1+5otbMxcWHbDrhi/3brFfxI1+bH5qAC7clvTR8c9HLG1+v/VDD15wIwXuqOsPnMKgItNFL9NhJFmeqMMODG3YqimunlXOmcKoLztR4vMGHBT60ZnrLXhf4YMl2lswLONKCFpEGCE4ax9gHnHdUNX5pypm7SIVE5MFLnz7hpSJIRB6o6otXftFvtVrw0dafpBHdvdZjwBU1vrWgJuXFAq8B6otQwQ4yEYcTTCHopJkXWUZclcZpuDU9MjmvXVikVRnLd47NLaugci2Ew57gA3muxNDhQ6TvOhQx3ncvjWvdAnptJiot+7XOQ9O3/Ajvq1G5HB2OToTQMOucZlSb9N0JVTx9CJz5gPORzgu999wvlbNcOJsTHmGolawweEcOjuQcVR3aEjAHxFpZ7XbInFnvi+HyNVOp6Dg3aMqRFJgLmjIcDkhKRK2teZLQNJPniZJXbIaBPgQev/EI7yMn645aCl4qQmaeKzknDocdPkOe9hT3IxFwm8NWi65d14EISQxbDcv35taEEsUFz9n2pJmZKHPJUC1b7E1+QvYjIsL63hackObUsmdw4tmuBgQhT0asd2JuRyFEI+JH60PkNKIqBN+ZCjgKpSqHppjJxcomKYYJp1QopaJVEA3EaFLTEKNlVzmRS+b65rr5RLzy9fPcnlXLWj5X4Mfu4qK/cSlKVoUFs20slJfx8gXTLuJM7SVQpRj1ywOqlJwsULuIE+g7T60OKYaiVqntWvYEU7XQGsS1BusiadCWjVpoLHrriVBUudpdtfT6GkF4/8lTQgicnp7S9x337j0kxkjfDzjvjdctgrbHXhrEUOud0f1+IPbVI5y5yANxbMVcvJIXrm5mZhV2eccQVjw4PWVbBn7y4k1yNyL7iSCOh0+viOd7Vh+8QIDSejfUgtSKKwoqiLP7RSWgqcJXv4YibHJCHZRNhy4Hb1EYlazCWnpuSiUdRvalcCKKOmWlM5syMl0+Y4rKw7ceE/yaVd8xnAx88e1HXKyvuTkoc6lcTTMpT8zzDftcefqhY999dEj9oQi4qkou1ZpVYthn60C17PZYG2KqNEcIjhCDIWbe4dThcUdOr6jia28uX51BDWmeMWsSo4G527Y4y/v2FttdwLkFU7asevmWNCqCqmU6VSBIe3zOteza7oPgPS74I9fXOWlWcY0t8epfz8+/8j/6Pa6qeowGqhVRIdfGZ8YYCkWt0igto61UVOoRt71FTy1/Nds+8HVxluOIAS+BlaUroC3A60tmNgvxnpdMaHSx+xQ0W+Y854x3HvHClDpciMQQGVLC+cZqcALOo631Z9e8xaNf5fpB2VcRYeUcXa1EgdDKzuA91Sk1Z2pJuNZ7OXWe6iPRF7zCNiV8zkfox6kdgE2tjW/vmUWZWVUsQW0Qk9MMzvojVLEeSVVILeA6j6pyhtKJCavUebrg2TiQeUSnA1ISzkGI5ptwshmoKaNkQqlMIiCVYdUzSEV8bL2Xb79+KAJuKZXnlzsW2WyfFB8c/apHnEn0HNBLpAue+/c2dNHTD868C5xZcYYYcE7YdJHgPf39M+P9BSGlxPX5C2otrFYDDqVMO+MBTqYs886UaCUXqghzE1os7lHmVlatJCmVMtmNnavhz/3KbCD9OpBSYd4fqCibdY8PHhfs76z6juI9tazuJMMVkd+qqr8qIl/+Vt9fxpHc9TJc89jCb5mfMo9mY+mdeWfUdlN4ybd4LsYmcZipDdp4vO2AVFEkGJTUIGLUvXTN4/XkqDrUBrZWimXSbul2HR8iFr2toVmLQoH9xTVOHE9efIhzjhh7vHP0fd8qISPE+xAso27shle9flD2tXPC2zGw2e2scSUVQXnr3hkZJe321HGCht3e04ITpQ+CqxWfdrjW7FaF3A7K7BYzqdAqIPsZ3wyQgtoel15sr5O5AtbUoDyVFuAzaxVWsSOrMItHuw69f0bYrtmenxNyYg5Q+w76Hsi8/eYj7p2ccDVl5qI8yJmUC/u3H9AJfPreit7/CAgfFFN/ldr4qc4T1OOj0XOkSTDxzjZAmhuQNoiB5URc/llXtI8exHGoybJnVct8nWVJWRf+p4AXvDT/3SN1UG/ZC2JZqaqgpZhKrLZA/Bv+mTOYLFAfMThcsMLW4oW2JqG3N9irX38I8y79T77Ny/1P3MVFv9U6ZriLEgzjJC+gfEVxxUrJ2lzibtPW2yrB9qFljg0flfbRNYFfLQZBLTaftCqjtoBLUz5ZBv3SYdDWsYZqXy7t7y8sk5zNVjKmGec8KU8tAMfWU7AKa/E7voP1A7GvVj1mJM/GMXfV2CTBmDp5tv3Uw4gCIWdcLTipODHfBRo/fdmjYw0jjura/dzeBwtdXtp/JERwzWvBGgG2d5031z4f8AoqnqqQ1VNDpIjDV0Wud2ip5FWHDj3uZEtNBa8VL9B580TpGvRV+54o4GN3dCL8duuHI+CqMuXMbmdZ7jAlQvCknI2VgFkrdj0EVebpQC3COFWsmmglSa1IVXJNSAiE9YpSM8+fPGNuJYzrAqum8Ep5BoSwHoziVQNUqHNGMA8FFMasrXESyaWQ5omUC1oi3gf6xp7IOVGyWoZci5nYBMcwBEQc+2ki10pKMwD9HSnNVPWn28d//JX/8e/ucZj/RHuOWheBgDXEasPc5zR9U6BaaHmheSfcwj1NQbhAOe2wrWpJ6tz4shmDJlw7QL1YeeobxqvSAq5UY0a0zNYYKXJ8vMvW6PH5OINEkn1jP7WA0QKIlKkdph89reQTvJ4/EPtacubmxYfMBVBldkr1wsSAAnHK+Krkqyd20JIRgdI1w3GXcFUJqs28KTaec8Pwl0TH+dtNWJqsLuBOH+FcpNsLUoXUKdV78sM1zgu+TqhW+jmjRSmHSlFh2k3U/Ux+cU3xDj3d4Iee4d23SOK4fnbOWJQUN2QcZbZ7ebreUUU5+EQJPwIB17LHW19L1Uqt5lWA3opCbdn3tQpZswXcyjFrdCJE74jeZLlaKyllcs50XdeykFshw2IPJ+KheNP8lyUTXjTgBRVpWW29zWZl+dh8GFTbG8bUbCGGFkiaR0A1H89FVhwXDPDOXlYZgN8P/MNY3PhZ4D9X1Y+x4Hg1y7i09Xio3Cao9h8LuIqpkATnOsPXEXOLauo/cQbqSwuk2gQrtRiNzzkzE7pVHzaMwN1e96gGOz6ylunqbcB1OMN7l3ebvFThcMvHXegzWlumVia0Zuq4t8D7UoZ+F+u176tWUpqYs71W1SlaHKlrEu12X5SUQBXnavM8aZhPbUye0jglznwxJEYD54O0A9H9pldRj5CPt4C8HJDRI30PHnyy9wchmM+tU3yFWoWqZmAOxhKqfSQMPdl7dLdvB3k0+uFoMvx0OIAoeSv4j3H3+6EIuN45zrYDJR2otX5R6C4AACAASURBVNokB+csc6i12b0JXfTEzvT3tSpzMtepXOxFj8HKu7ffegPvHDc3O8YpkeZKUcew2pqBDbVZwVnmGfseJ54yW7nrG4HdSaWUwpxm49VOdtP2XUeMMM52m6oWy368tzdgCzL3Hty35+G8YUHjxJyyTYXwnu1Jb8qYu1t/GtPZ/8n2+T8P/DfAP3eXF12W8XCTqXOQBrIKaEVrYZ6ugELwSgieYXhglYzFYFy14OW8iSdUjG53mG8oOTOPB7z3PHz0FuIjY/GkYoY5VSH6HriVigtLRtzC78vYrShG+1QC1WiDrhjmW1qA1ZeAI1U0T1ASdf+COh+4OX9CLdme592u17qvRSuXhxueT4lYq7E9vMfLGSF29OstWpWbaY/XypkLeBG65ExReihQbMQN4mHbIf2K7s3HEDxJJ2opTDcj1EqoNhvN5QwK+WpEQgW/RryHdQ/BtSZ4pY4J8Q739iPoIrgeLUJ/PVPnzHR+ThlHDh8+I9WKPr2gBE83dBAiZaOoOm4uJsZ55vzyOdHDm/EepfsRaJqJ2LSFoTNXsKE38wpa19+pJSvHjNI5RPTWIUpvSzjLKG2DSyltbpHdbk5MqFAWt65vaiZry0zbtZDjvVXVhA+lsSh8F3BAqADuqPn2oZmftIC7GF+nYvh0qYuD2G3f/Y7Xb1PVl6ev/kUR+Zt3fdFlWbJZqbKwJNvrIw4Vm7ixvN5OwKkajlaN5xyWSmQZyilQAN+4DEHM0d8w8mBuYhVKahVG6EAgO+M9C8u+55atLljvgiUuDczGqG0/J9VZsG1uZku2S8loTtRpT5kP5HFnFKcqd0FSeHm95n01efuYkzWPa0Z8oCsFCWou4AjaR7RUk1qDVY5FkGzVSckVvGt+1IG4WiFdQIpHSoaEWcslE0At/rTGJqmEIUKI0AdLemsGNb/d2xLUgTdowvXmmxE3a5wTJu/QWiiHkeqdvZfUOL9ZFdJMnWfKPJvReSnH4unbrR+KgOsEtkNk0z9AxLq/0jrYpVSuzq8QtRJUFbp+gwA3h7kZ3oSlEEUrx8kAV5dXHKbEnMy2TfGoOqbJMNaqGe+FaTIMcdybKcYQ+0YBKxQyKgWkHA01VoMpx8zv1ihq3jmGVXOvwh0rypwz51dXTHNCxZmhRntu05yZ73biw18Xkd+lqv8XgIj8FPB/3+UFv3ndGgo5HEPo8c6z6jpAmXuPUkCykdvnQqDQFRvkd3q6woVA6jqqYFJehD6aK5X0PS4E1psNEge23T0qwv3T1PBiq0jmNtplViWXwuX1ZcPb96CK12LN02LddqfmMlTLwR676zBXY3OFU/EGJcwjdT5wuHhKGffM1+eUlJimfNeOYa91X6sq1/PMxTQScqEcJlwMnNw/ozqo90/wIRK2K0KuxLESU8Ff3iBpIo8zpRQmtcO49gG3GQiPHuGG3vxxS6HfTOiYqB8+R8tMzRl1kKTgBsf6S28jfcc87tBpgidPISVICiXAxTUaAll34AN+e4Zb9Zy+/QhSInaOdLNj/+QZlJn1ZHzq9eA5IDx1M1kmeleJ3jw7QviEwodmgvGnVfVfeFUb8t0uYdEv25Tdro9NmRVtY2JA26lW27RNMDpZaQMmdVGRYRQwrZV5TqS5MRRwR12/uXw1tZFAKVZMHj/6YO5hzWfVOUG5HbFu3Evj/YIcO+vSuprLqrVSaiWXcsQypf3+IkW9I0XSL2E5VgT+qoj8evvWZ4BffeUX/HbLOmSW3TpjbgQn1sFuyi/jlJhTnCmVoFMPobFGxKh/FcgNQ9e6YOx2zKaUEU2Ir9AmAKhUSkqG2TWmQvXB9jH2BgW14ZOuZb+egmhF6ozWQs2jZb5eAWcVkLhlrgtOi3FIG+9US0ZrtkGmP8r7Kjb8s2K1QKkKtXlfAISAxICmQNHKnpmg5uonTYVZa6FglV8tBVcrqRR8ae8NFTRENGL+Fgq1zJbJDgOsBnQ1QBep0x5FkZwgJcOKa4FpoqZsUnAXIQ7QXkCcs+BeM27doblYtu2FEO1+H3pPqh7vpdmABtzHQIAfG3BVtYjIZ0WkU9X5E27F97ScwCoG+q7DeX80IV+vN1RVeoE0J8b9nnka+cb7OxYnKhBCGIghcDKscQiXF5bBXF1eMqfClAMuKIf9iPe+BbqmCiqV/X4PQE7WFMkp4wS65pO73gxYYG0mKa1hsnTdc6lNAmwuVrUFhdRKrpyT3SVdzyI9FREbF3439KF/+i7+6He/FFcLq24ges+2t6bk5dULcq1M3m7arAXNGXf5jKjwxuaE9WrFmTe+5QeXl8ytnJOqxHEiAKs+UEk8ffY1qgv0Z5PJcb2j5MSzD9+3SbD9GukGVo/fxXUd8exNXEmMJaPziM97qIlBR0QLadyRS2Ier1CtBL9CcRySR8XTrU/w3rENirrCQbLNtxNtwUg+tvT8HtcPxL6GEHjw8AGhgkwJPxmPmmJVfcDj1LGfKtM08yvPX6DzzP1pz5Azb+pIoNh8s5qZz89hnMlhDV1HQlHv4fQe6h3p8SlFC3PpicHzqbc+RRhWzP0JlMp0OcN+xN/MSEmUIaBayC+eo0XJVzPg8PcucH3PdO8MCd7gi+GM9aMNtWT2F1fgHOszz8oHPr96yOV+5Hq+QUUI6xOzlvyo1+Y7fA3/P+CviMifBXbLF1X1j33Pu/JdLBGhC96m5jpPiBZwuxgM0+0608/nmZxgtzsYBqSNcUDBIYaxKtRjlmFE+8XfICWbR99cG4/sAjEAj+A9YN4MztmIHCfSMlnLqC3RbuY1LEqzRqQvhvtawK02CcJ+0NDLduElt2sv8it/PVX177302v4O4B9pn/6sqv7iK7/gRyyj1zmis2nHi29B1koRT0Uo6tDG4hBAokeiN78EgVkrsy5OYOabKwq5KkWVwzhRJKPDiC8VggXceRpJpZDUuNykTPQ25w4RfDcY72FuTa6aoSTKbKNh8ri395CvqDrm2fBGnLexT65lUrW0KSG3AyfviO73A7GvNqm6x/mAuGr+te3ekSPPBHIuHObEB/sDZZ6ZysxWC/ca22MZc19zQqeJ+WaHhpnJCTUGyrCiBk9ylSpK6Txd11HWa2o3mG9CLtQpwZhwudwynSo2fDRVShNhSNih00wVB11ANj0SHXHj0SC4wdzhFh7/KnpSHxiGiOLohp7Yv5qA+2vtnwNOPslmfC8rBM+De6dAm2+2XpuBjLOA60821Fq5f3bC4TDyK7/6gsM4otUYAJtByMFzoTNdcJyuPV6U9aonpMouJUqaubm6xAfPej2Ykq2z6RDrTbShk92G4CPD2nxqh1VjHaRMmhPPnjwn5YJrbmNTzkitbdqokmUZwWMsB2M6qB0cOJtmijXzVM0j4C6bKyLyB4F/Ffif2pf+WxH5GVX9kx/xa69uqRK0ctYbJn5zsOGOua8UcbjVgHM2/UFzopQbonc8/Ny7DH3PFD1zVdKmI1czrxEFSR1aKodpJKXCIc0UHDrt8XkiuYpope8dvsB+nmw0y/UNMVf8ZkOMA2dvfRqdDxy+McPhmvn6CTrvubl8Sk4T+/HasP5qQ0V3MyCBbntG30VWD09wminjnjIdmKeZ0uZx3WU/9HXvqw+ee/dOGZ9fQlG6TfNCcZEggSDWVLy8ueHJ1Q1/+e99nf0886lNxyPvuB8iZ+IxNFTQaaKmym58jxICN9sVUxd5miaSc2RRXPScPT5lEwI3mxXJR8rVDewP9M/OceMe8myNzxHAEaSjVtjPswXfw2hj1V+cU4Nnv4rQR/q37xOGnrMHD3EIebejKmxjRxDhc++8ASHy5luPXk3AVdV/D0BE1qq6/2Tb8b2t4D3KraH04uAlmIdmbSTo3My/S6moNmOSxsstRanO471JbMU7Yq7cJE+uSoweHzxD3xGCp+893jujk4XA6XZLjB3rNmyw7410n8eReZq4iKFNHZCGBxtu65sf69ErQbASxDtjQtTGSGhKtdKCsdZ6pwEX+Few6QA77PH9UeD/5JZOdKfLXhtH5y2oWnlQEA/Om00mzmHC7Urse1bBs1qv6fuOfU7m4OYd4tRYDgpBguHA1dgiJrm2tFelkqvp+H304BySixHvS0ZyJtdquG3XIUDoN2jJVDWudUmJnGZqTobDl0KtUJKgZGTqcJrJU8RT0JIaJ1ftnyyeyHe2Xuu+IoL5PNkN6j0Go8hiJ6+NyVAZU+F6ntnNie0Q6MUx4lghODGJd2ne17mad+2cA5MoN+PBnMOwAaxDzoSUuD4cmEOG6YCfJ0I11z3vmzCmGItFKGi2arOWQi7mKqa5UL0jlYimiNz0aCnkYY1HKPuDwYV9wYljHTx0geAgyEffsN9RwBWRfxD4r4At8JlWrvxrqvr7P+HWfBdLCd4dx6WLQG1NDS82pA7vCMmCVlWHNJpX4ycgWvHO8fDhGauVTWktVXh0PpKLjTu3jHYwBZsXai3c7C6JMfKlH/sx1pst9x7cx3uHSqaWzOHmmt3NjuurG/a7g3lmYsII74RhWNnJoMs916aRDgNVbbZZqdqcjZTcGnm1lLsOuIL1NZa1uDt/X5ZzjpP1itNhZaySm9Ymix7aEFDEeLLOO07eeJN13/HOwzcQ53jvxVOkVlbB07Wmi3fC6cmKIAJ5RUqZ0YtNf1gbrXB/c4NTeLi9BzimkJnVsSOh5cBud8Wce6R7SNdF7r3zY+jukstnXyPVPfOcKHk+muTYbFDDoyuQ91fI5LjmgKeSDocmwpDjz9/xeq37WrVwqDtE9zhma156yF4prrKvM0WF67myS0qqnkzl4DtuQuBF6FGBdWOHZFdRMlUTRSp79exL4sX1SEIYJBJiYNfBeHXFi+cv6ELkzdNTVggyGLOpDxEpmen5BTUVdB4NBsyZUhLj7rpBP6DimLsAIZB3V/gYSMM38OLoGq0sdQHtOu6//Q7iBC6ek1+RAfl/CvxTwJ8FUNVfFJF/9JNty3e+TAKaWR5uKTZ6RRfCumBqFJWmKHPGxW2NLN/m1DtX8N6xGnrWq4HNZk1VYZ8cpbaA6x2rdW8OY84GVs7TDTEE+i4ydB2rvjeBhDNQnzxTS2boO3IqhGT92Sp2Z8VoPMMFjjWivXmo1lqZXDK81znzDhBrBi1S1Ttc/zXw10Tkz7TPfw92sH5flhNh1Xecbjc4YL/fMJdE7i2z9dFGE5Vc8SLc63pWfc+6s7Kt94FalZUYuT6rEpyw6jpjO4TK7B19H3FFkWhMgiaFx3tjQXTRo1VwmtEilDTaDZUSLgTE90hc4bq1/fOBWkNzRWkKs9qyJhVQEzfUPLP49tJGrS9J7h1v7GvdVwDFGBqOgm+4p7bJDuZPbKZOpUozbwffdUgIzD4wokQDkyiuOaCoUJ35Gi8ew4hrzJFCmSdKceT9gTkETrwd3IdqEF1fFSnWq6lzps6KFsOIa87kks0HpZr3QyGaJeTo0eSZptwekcWcuanXunsHi0Fjh4ZXJHxQ1a/9BqC/fLuffdUr58zTZ88Z+h7vbaa8d46Vd02d2ZpSPjDPidB3dAplrngfOD3Z0AVHdDPbdc+bj++z2azous5mWoWOUi0wOi92g7ZsJOeMzmuceOo0k9xIGWd837E5szHJUmacKI8ePmQ9HIjdgZQzh2nXJLxt6F0LoEfuqXeWySZrnrnYU1TRmtAa6Pu7ndqrqn9MRP4SJgEF+JdV9Rfu7IK/YQXv+dTjB/zk7/gJ+hB58sGbTGlizHOj9xmbo6RC8IHH9x4Qgzm95VrR7RlTyeyKwQDTNOK949HJKd57m8KRknGtc0G7jqqVQTdQbVKsAqerjqlUxusb0gRTmZDQUVKl61b0p/foGDh7+0uUs0do3jHtL7m+fELOM1MbcmgkfhiiI/hKkNlaoM6aqGWqx7lnd7le974Khs17zQStnLQKNLuKSCGWTK1iCs8qnG23rJzw8PFDtt5zNWeKWnUXpOJjsOSpBjObkYKKcrbZUsUTqomxp92Vyet3M8U5Lq5HdiGyj54OeFQyIWe4fG583LkZ5GRrdE/5QNXC1JrcKh2Cp5vtWe32E1IrfQu6U61I17FOhbjdsK1v47tXY0D+NRH5hwAVkQj8QeD7OoolpdRGn9fjAL9BouGezd2pYsyDGCO5KKWZwPhGSu5CpOsj/dAxDIbTVoUhG5Yaog0b7DqzcRRnlnBD7ABpp2Emp4T3gmqHYLDBwppIXUcXDXuak930XpYRMGYj6Bq+IM5RsMAjCxapSnAOdUoMiznLna6vAEv5ICLy5e+XjZ8TWHWeB/e2rPsBV2bmNHOYR+MnJxMIlGRTdR+cnRKcN9ilFs7KmrkUupzJtTB6E4xshxXeO3IpeHFs+57JFzQEqlZCP6ClQnHUarSzoErfjDanNBmuN42G22bzygirU7zA5t4jQteRSyZNIyVfG+tFZkSU4BufuOkFj4LF5T/fn+L+te0rCK4arKcqTSUIQatlvSUjRazSq5Vh6Anesd6sGbwn1z1zhaIBJ2bFaga3i9eCCWE656jOEUSoWpinhOaMTMlUo3MBdUzOGA+Hw0xIM36cIFvAlQpS20SKau6CefFGqQUpINn49ynnZVyHNcVLgQpud6A6x2qa+bjK5TsNuP868MeBTwHvAf8b8Ae+x934rldVZZwSFfNIzaXShcBJbDZ3WhoIb32X+/fvMxxmvrH7gFKMMxuj597ZwMnJintnJ6zXPV4s8wjeXqYQDR/uF2GFw+SFcyGn2qgrlZsYmKcOlYQPYlp/b0bmpSirleLnmf14DY0O5FCi6yyA+sCisnIoq9hZZusdRSur3tgXsY2Av6slIv8B8C9hDJTlnaJ8n2z8HMppX/niZx5w/+yU9JmHlFI5TDNVlVxtwGcp1mSR1uBIKVNq5c30kFJthHUplXGaTBjRJOC78cCcMpFIypm0lIvbM3LOXFxeMk4z4/kLXFHeXm3JCi9u9uRZEPX4bgXdCvqB/v6n6XzlrbffRNOB62fvM+5ueP+rX2U83HB9+QFaZnq3x1FweWqHg5CqmS+JcuxB3NUg3de+ryr4NHBZ1vgyc6aZQTPreY8nES7PbW/317gCb7/1AFYDb7z7Dl6V6dc/oM4zD8QRRNFgZ5RXB1XpJmuj+kbX7LtAznDY7WBObA4wxJ434xrpOy5iZU6Jq/Mb/DTR7a+RkpGiUDFpNjBLpgokwQRPUxNZ1NEkv6EHhENJ1KqMSSEr1xdX9Cnj37hPzB89jf47ZSk8A16b0syaTcY8QKH4evQ7+KbzpIGkvs0oq2Xh2Zoks+87hr4jxoXTq63BZiIH3wwuYpu+4L1tdN9FHIVpvuXrVhRuKj44VqvQPBkWlVqbWdYmCAi37AQvi0Di2EHDNw6q2eeayY5WbX65d4r1/T7gC69L0AKKaMG7QueVfm2Kob43qKeIR7WNH6/GBimlMLWA3Ne+vfHt/TCOvQW11nxElc7bvuRcmFNqHOhCypk8jriqNnfLw3a9sqkMFVIVsnc2vRUlgLFbgjAM96Gu0Qpx2LHbZfrDDRIcNY/EfAF1phyuyblQMX53M2S4tXm8u/Va91UVShF2xTLdUexwXdeCq4KkGVewiQo4NpsVbrvmZLuBWhmdIwNFm51QbT7GrUxok5WouZj/tbMEJpqgjaAW2KL3uBDpV44awnHyRi4Vybd+u8YSMrq1AsWZb24Rb/67mDpRokF8OZulwJRbwZIKaU7sxon4Mbz575Sl8OPAfwa8qaq/TUT+fuB3q+p/+D3uyXe3REC8vXmlEn1A3TJkUQjR5L/SxAa1zJR5YtzvQJXpsGfdCQ/vn3DvbMPQe/rO0XcREc+a2ALvQowuzQ3KfBC224GcKvVqJhe4vN6Ta+Hqqxc4L7z5+AGI8OL8hmlKvDi/IuVEqobtdv1AcMYrdJgtI0DxnlIKaZ6PJtnqHP1mjVZlnKe71tz/MnAPGzj4fV+1VPaXl7x48nV82XH/7BEhdqzXp4gPuG4AcVTx1FI4XF+TU2K/M+3NerNBxDFOpU3xNeXePM9Gqh/XZgiEBeBpsq50SjNpnjnrArv9gZAyOM/nv/AlfNdzdUhMufL8aiKrUMMKXEByxquHbouPJ9x79y0UOPv876TmTLp+QZl2HN7/NabdJc/e+wqH3Y6r8QMKMxImRNssNlGYprt6aV/vvlbl4pD5lX1BU6FslXsCK824XOmuLggFujSy7tc8/Oxjunv3ePDGO8yHiQ//1lcZU2afkjXIfMELhCYwCk26PZ9fos7hNlucE95YbaixUOrB4ME+0J2uefdL76Klsk+QLy7Y31wYZFXNx1q9+e3OjZrZdVvEB+h61DnmYCNj6nYL3pHUkUrh8mpHyonDuMfvRm7e/5Dwippm/yXwbwL/BYCq/g0R+e+B70vAFRGCD0DFieG3wbUZYGbdxdH8B23jzrTZ/rWviWWqQx+bL8OSyXqctPHpxUavl9ymRDjDW2OzXHMNL55zZkqJy+sdIjAMAyIGqk9T4nCYKDXjojEOQhvdbhMi7PEhLcCKNC9Qc7sCTIcuS7l5pwH3jwC/ICK/DBzvflX93Xd50ZeX1kqaDszjQN5MNl2jt/0KwduMKBeoxVM6Gzef0wxio4jEObyvxjIJ0QLv6ExsUo2T7UIHqsx9pJbCPFvAzeOBIUbG3R5ZMOLY0Q+FKRWQAynDrJGCUZdEQbNNjZVgXXLXRSTafK7Sr6n7HRLWrG9GJF6zOiTcNMLh0gyWaD6/d7de774qNgQ1ZUoqnGeTth/U41XpS4FiXhPRKev1in6zZr0ajtNbzGOkEqhttpz5nNjfd1Y5pmLTJFJGvE0HrmLqQ5FbFz9pEI7rAhIjxXmycySx8Vh1CbiKsR5ih/hADZHqhLE5lmXvUeeYqpAEdj6QqrKriqeynSZCfjUG5GtV/bnf0DHP3+0+fK8rOM+Ds/vHQNnH2Ohd0l5Yy0y9Kuorq1iRIfD4/imicLIa2K4GHpytuXe6Zt0H+s6x7iO4QHKdCQ7UML7D3lgEsesQJ4Q+MM+Z88uRPBde7GZ2h4lvfHBJLYXzCyNM73YHo5GlAyEIb799ytAFhi5YGTTnJY090olAid6bNHnJZp2j1MXc5k7JIH8K+KPAL3FXgOJHLCfCygfm/TX7UPFaid2KkyLEbmDlPSF0dEM0M+n1QMkBJzZxYz2YCflmGGyiLwZB5JyYp5lvfMMy2pOTLSEEYjRsdxpHc+p/8yE5Za6+9AWTWPtIRRiLTVp+541CypXLm8ycChdXB+Y0cf3sGakW9tg04bkbwAficIZ3pwzv/HZWmvn0u38fZd7z8IOvsr+54Ot/928w7q+5vnpqvrh3t17rvmrNjLsXPLm+YD8n5hK433k2fssDHJ+pxSAhV4id8PjBffoHD9msT/HZqHupZNKc7T3hzX84qTWvgpjfiU/GJkjcWOLVpnerKFkLV7tLvCZu3rMmeJBK6Txj15PUceg6C77DigJM0wRO6E9OUCfsSiGjHGo1mluZyRkuryZyAfWRoo59KXSlMBz29O7VBNxnIvIF2rEsIr8XeP8T7Ml3t8Q4q13wDWMN5tCzlGal3Ip4MN5tFwKb9QpRMwTvYiCEQGjBbRnTIm1+WRVoItLjlAXnmwzXOXx1zYCmchgn9uPEYTKBgnczAhwO89HdSMThpHnhyjJ5C0BveX6lNrNrAGk4H42vuSjk7vR+2avqn7jLC3zUWuZ8acnUnJmnkVoVd3ONCxP7ueBDZLXZmsRXlteoOcCVhDrXMFHzxlURggMN5r9RndB3gRgiXW+ZbhCl5sIgRtHru45cKlOxJmpXIBVFSUypMI8Fp8ImChEh+YrTypiy6ZVqQV0ga4fzkdp1eAkM3RqcYzh9AN6xOb2Pc8I4XpPzncKrr3lflbWvDL6SnDLlwk6Ey9l4uIegx/vMOTH2kI94HE6tv1G1HnshVZrBfCPUfdMdsXD0m1Ofqk0FFlXSOJpb2eWV9UnGiZISRTzZKwfnyc4zimHFc5OGJjHMeC/G7R6bTwc5USrcpEypgvgOFWHm1r364wa0fKcB9w8APwP8VhF5D6OcfGQTTUQ+jTnPv4nFmp9R1T8uIg+A/xH4HPBV4Pep6vnHPgLnCV1nFn5NlGA2ac11qy5NNWHVD6x6Ybs6xQFD59iuO7rY4UPA+4hzdkpqrai36bveOdTBMBiEEOMit3WQndG1cuH9Dz7gaj+x308NdJ8QYJ6bhFAcaEAkIhIadU2pznil1rxRDnOyJo8YTSJnawSWlCjVgPg7znB/VkT+CCZoebn0/Lb0oVe5r9477q1X+KrUeWaXrin1mouvvM9hnPn6B08pCmf3H7LZbvnil77EZj1wthkIDhLJ3uS+A0yrLy/dePfPBgTjVXvvicHoeTX2xh5ZR2vGFYMkDnOhqDIVYUqZ+uEzXEnclGucKg8ebVBWHM48U0o8ubxkP828/+IZu1T5+vw+E5452qSBkyEyeOHNzQn9euBzvjDuLpG/A/N44PzZNz7Z7n379Vr3dR0d/8CbA5fXK57tZr72YiYdMn+z7DntPN0bHesguC4Qh8jK90TpqFOmTMlgPYxDLVrpmmVpdUJzlQIw+ECVXet1aK5IqbgpGfR0k5EQ4P2niAh9i9SzBA5d4L1a2JXE05uJokqoDQKssyVbwa6nuVFTb4oJpUqPumDxQgIlCCKeB5s120+C4YrIH3rp0/8V+IuYSGcH/LPAR7mFZeDfUNW/LiInwM+LyP+O0VX+gqr+xyLyh4E/DPxbH/kogdvZUrCAtlWX+WZtOkMznrUx1CY0MFNwbUq0Rgxof6NWRZwphMBEFErzOlgUbKJH7t1yeo3TxOFwYM6mLJolNzey9nO+bVS7XqlGC1uulJsP7pySCS/aHMTcTGtqmzZcb5/wXa3f2T7+rpe+9nH0oVe3r6qkNHHY76FkcIlUKs+f33CzP/De179BqcqUMtv9nrN7Z4zbSLLHUAAAIABJREFUDVHO6IIj6Gx4vbdpuUvAXf45Hywz1oJUbRp6mmcDCMvMM/tyCGI0oVKoZeb68pyb3Z7L83NrzopZD65XHV3nmdJIdMqlU6oUOp0p1XHAISWQvFVNpUa8CP16g1BZrTeWcd3deq376gXurzxvn0aiU65uMuOszLmwB67mSlZTfwZxbWpDohTI83xkdBQtrbHlGoXZ7qvank1pD3qizeBozbRYzd86NIERhyY1qRhcNwQywl4rO63cZDtoB8Sa3GrsikA4+qEY8yJTVFAN1mSv5o/cIfQCPY7uY3jzH5fhLs5gvwX4SeB/xvpT/yLwcx/1i6r6Pg12UNVrEfkVjMf7zwD/WPuxPwX8JT5mA1Uh5cI4TWY6M/Q4lGlvm7NMfu2CTVpwEtCqHA4jaCV58yXdjRM+OnLZ4jx2IjpdJmzgpYVFO9KOtUueMzWZq7t3yvXVJRfXe+YaEBzJm/3jYtvoxFGKMM8KFEou1gRq/q6HZMqni5tdGxjpWVhigKnXMG6vcx99Yn6S9b1Md32V+zpNI7/2t/82evWEVd+z2p4w58JXvv4Nrm/2/Pp7H+JD4MeBq/UlT549YbNZ8eWf+HG26551qATviHGFOI8PfXuvzDhxnGxPCD6icbADutnzLUuaqVBWgyJwkZQLHz4/5+mz5/y5P/fnef78BZcX18QQ+cIXfwv3Hjzkt3/5pzhbrTm7f8Z02NOXmf048SAL+wLvHYSkCr4gNbN7ccXs4c37G2IXefzOu+R54v+9IxnC697XLgiffxyJ/UMu94lH8QXnN4m/9eFMSplffZ4ZYuDTD+9xkj2Hp89Ju4k9jqvdDs0JamGfjf53pg6HNSoRm+xRFG60MgNX3UBWJfmCFGXAE0To1ytz/yo2gkf2o8GTcWAvwouUuK6Vq2qQQfCezjm6ridEz9D3BjLOiVIyNU1AxbkZJeF3E1Hh7QIneLZjYUifgBb2kkvY/wF8WVWv2+f/LvC/fORffmmJyOewU/evYdSyBf/9ACthvtXv/DTw0wCbVUcubay42MwiVTXsjHp0IVrUPBXjw5bmQypi2eM4z/RTYEoJRRsO7OijYboL33f5+wuXs5aClmqKGUxjP00zYymWRwcIzhG9mAKtuYCVouSsaM0Lnk9VmEthzpm5Teg9sokbTUGkthltdy58OAP+HWDxxfjLwL+vqpff4e9/jk+wr6u+Y7/fc3kdmKaZucKcC5eXV+wOI6rmpXpysqUbVlztbkAL5+cvSFNP7Q1eit2Mc54QLeDOaTZKn2AjT7qMiFBawF2av+JsCu8x4Hpjn7x48ZSnz57y5Mn7vHhxwe56R4gdp0/vUVSZ00w3DHQxIKVjFQNaClsHrsA6VWY1YRTapg+/lHUPw4ryMaXnJ1mve18/fdrRBThbeRzKo01EqrIKBqEdcqEgHHIl5kqeExImsjhLnlo1W2olNw8K80hpSUm70QuW2dam2EzV8l/fRdQ54npl8FJuCtGdjdYaaqW2OXhWzjbflTY8zxqwrUIV6zXQ1K20qR5L065T2KBs/n/23u13ti277/qMOedaq6p+l3055/Tuy2m7bezYiQmJBQqKwgMIESACEQcJESQEAmQhgeQ8cRMP5gHIP5AXS1gkEhchLGEgBhIlIWBh44DlxFfsdtttd/e5771/l6paa805x+BhzFW/2rtPn9OnvffZl+xxtM/vVlVrVc21xhyX7/h+zYUQ+idUw70HHFf552/1wX/IQpwCPwX8BTO7PEY6mJnJAevxqJnZT+B1Y26fbex6t6NLp4gI+8lHKKvOhCBsTjaICNNYUL0ZQJhz9g8IYFTeeud9Lq8GRJSh70hB6LrE3ddv0/cdsurAhDy14YXikW4tM7UYqTnc/W7P5cUVbz/cgwlvnN1m6HpOVmtv6EkgBGWcapPP2QPqo8TAfprJtXI1Ta0c4qmSEH2iRpqG1vTU4UM/iWM2/6X287+KE5/8uY974pNY17OTjT24HonJhz3ixRWlFN754AMkRL73D30f9+59lj/7536EECN/42/+71xePORXf+WX6cT4/J0T+s4VQEIMDCu/DqpWZyI7OadPHeent4gxEcQbbDHGQ1NTgbFJ9NQYeHh1xd/4m/8nb7/zHl/+7d9kvx/beLXwwcP73L7zGq+/+SXeeOMzfOGNzyBAlzqGpGxMCRh3Vi6bpVFQdRhREJBaiAKv3b4L9lRr8890XX/4s2vTOrNJkFbC9332lNfPCpej8WCX+erDPdtc+cbllmsSn59G1l0ip0i1gqlgKoy5uJRRn8Gi02myyNF7qU+C0DUdsb3hnNLnJ3TDwNmbn3ehVjXybuTd8TfQKXO3FCwmbq9P6UVcAsmMIbpTH82leMZ5JEngvHMypNunJ043KRFRI2739KVyL2dOQuCNtRMhfZR9uw73rwC/8Bj70H/5cU9qvAs/BfxXZraQIb8jIp8zs7dE5HN8G+BsM2OaM7nVZFR9MMHrr62xhfgsdF24cN1pglHVI9zdfg9Utrux6VypIxf6jr7vm4SOcXFxTdXqVJACfeIA51ouP2/WFcykOXboY+e1parO5VC8JlFqwVCXXDZjKoVSHXjt2mmHd+qDG42Wzh3vU7V/wMz+xaOf/xMR+aWPe9KTWlcvoEayOWuTWKaUQlali5HN6Qlnt865c/cOIi7Oub2K3H94QbDCRnJDnzht57CevCmmTsOpudClHi1KiulA7ZlSag7XG6eTGVWMWeDi4pL33n+P+w/ug/iY8Hq9RtV48PCK3W7L5cUDZ4a7c4cIh267WVObzTPVFrgiiBWfLNMKoXFnPN2VfcbrCuC0qSnAyRApFc7XiaJKn4S5ejYzlUI18/LqogwhTeGjZZlaHf4lQT1T8Jd3tW6DsGSfDf2z9EoOOPdlcKEphJgqBG/GmQgriagYXXSfom1PEW2lPRE6cSx9EJokrXMmd6YMpqyAdS8M/ROAhZnZfyoi/ws3kh0fyz4kvjX+F8Cv26NSPP8j8K8Bf7F9/emPO34phQ8e3CclD+u1zsQYuHvnHAmRafZU0WFZ6s0rayk5MGkhz8r07iVD5xFoDIGLBxdgcOv8jK7r2JycUEvl7bffJucCYmw2A3/sH/xeNus1sa4QjJP1wMnJmtWukLOy3Y9MMmPF6LuOlAJVK7txIEaYig9CzNW1y4iOF51yZaGYO+zcWinTHsE4W6UDRO0p2V5E/jEz+1kAEflTwP6jnvAk1zV1Hbdfv4fEiWqKaaZaJA4D/WrF7bt3uXXnNhI9Gr1z+w7jbscvfe0bTPst+wen9F300esY6IfBm2d4vtAnRyesVxtScPmV0ORvggS61DvBeUpUjKs88uDyit/96u8yTZnv//7v4/T0jC9+1xeZppm//X/8LDlXfue3f5XLB+/w2TunrFcrQmdINa4fXHO53/N777zDVCth6EkxctqvGFJCe8Wil6CeMiXRM11XlmvaPKK/te4YYuQPfWbDg21iVzJXU+X+PrMvmVGg7xKnd+8g+4k0rJE0klUIRcnjSIiBWFxdO8WOiLAGuqrU6QoxY63VG2m7EVLHtqpPLp5ssFxYt+CmKzOiyp1hRY6RBNQQCEN0EcrotYswVhLC7TjQSaDzXj2hOCIijyOpZE5k4qxL3LsbWa+fDFvYAin5JGX+P4WnMr98tLv+R/jC/Xci8m8CX+Um7fnWx27RpHf11XG3GGrmTPvVgdTV7AAPM2skITiSoVKp6in6fsoEhIcPt40bwehSYnXt8szvv3/hY8NinI5rtjtPK3sStZamAhFJXeeIgrZL18b+n3Nxp1vVi/xVKaouWIgdpmlKUwaWo461/9531nqoVz01+7eBv9JqfgAP8Jvqo+yJrauIEJILPSLm8LtgB/XTEJ3X2HHL7kQx2O1Hxu2Oq8FTuLnvXFx0LDf1WRG6mAghMA4z8eBwA11ycqI+9UiIWJcoGA/HLRdX14zjiBG4e/cud+7c5c0332QcR27fvsX19Y7d9oo+RXa7K4RKzpOPC+eReR6Z9lvGUuhswGJsHK6JPEd3uBo+Fq/5B7Rnuq5u7rQEj3L7CGd9oJbI2eAK2RejX+Bmvr5d39MrxJh8XYx2j6vfN1I8AjWPTBtLLrFUzJROndQGFSem2e6xrvgtVJVeHcsbTQkog1WiQjZpiCcv2gqhAaGct6FTiOKDVeAkSpTqSr5W6ToXlF2v05NzuJ/U2u76rS6rf/ITvRaQVbne7+lSZNUlxLwWGpuGGCY+nWXCNDusKojg3s15SVedMxldbWdKrvzu779Pngvr1QVCOJQiSsmAkbrAyenE1956j7OTDbf6ke2Y6ZKwWXfcvn3KNFW2+aLVk7xhd3V9RS49r712Ri/RSThMyNWd8lxHP44u0LEZCcKqKX4Wc3jLVOxp35iXZvbHROQcoNXsvucj1+KJrquhKLUR/MTUE6IyrKHve8a5sN3teXhxiYhwcXXFxeU1Hzy8Ynd1idVKlyJ9IxWpjYzEEYLiE3xBWHXueGMjJRpapNt3HuFaFEqtvHd1wX6cmabM+a3b/PEf/mG+8IUv8IM/+IPsdnvee+99vvGNt/h7v/KrXFy8z1d+9zOcbk4ok3ffL+9v2Y0j8+X7XpLSNZoSs46UEBiv3ycEYUjppV5Xf0Ec3qULvhU+fxK4lSIX+xUPhsqYC5IClguUyunJKTEVhvUJadizdewnpTiXQs0O/8thbnP8XuJbtwZahzvoMu/cIc+zU572HQG4vaCFRBEUzXtMIrdDRzVht5uwGEirDULAqh+faYepsi+z37fVy5o679kkOHvtlNdurbn3xi026ycjIvlMTWjwqIaz9dlo5ztVM6L521B14gxPTw0L7rjAp18OKIFcKbkyzj4+GEJCxNC67LgBpNV+i7Hbu0zIygby7GOlCxeDqrOMoRxUIrRWj3SL78jS+HA93XXFUDX8Kx6RB1tUKmhQMGvsrE/1zvwpHH1yefS7/x74h5/mQRczw+vurdZOI2j3dTLGaWI3juz3IyLCbr9nN7owZK7KXBzOow2/l+dyE6mAM8KJMC0E8NHXoO8dxdB3gzOLBXe4D6+umHPB1IghcnZ6xvnZOScnp4gEzs7O2Jw8ZM4T0zzy8OF95mmPzoWcC1eXO3bTzLzfktUhhxojsWbPttr1YU008ynaM11XuEnMvMvi9+EQQVPgfEgUFTadolGQWrDiWPYQguPoQ3SKRBa8vb+awzYdlSSt1rvMQgRAzLUADaBkRF0PD/GWtADeKhVCyRAUUSHS6BlDQIIrjdhCgK/Os10bN0dWXInbFBWhX3X0q56+755Y0+yZWoiB07MzNps1KQb65A5pN86tseEE4bX4EEQu3lyLDVaVkhfii7lo3OV2osyFKYNaJA4b+r7n9PSEEEITBVTGaU+Mxv2LLeM4050n5qJOgCzKqvcmDOcniMFm2CAmTLs9VZUPPviAYdXxxmdu08WBYoIEF8Nrc8igxqQjSYz1ZuXvr3fpnZorT4MsTER+EPgh4JaIHHeuz4HVkz/ih1vVyuX11eHziK0EpKX62PTvf42Lqy1f+p6vIwhf+d2v8u477zKWSiFQYuPCqM669vDhzptuJQPQRR/3Ta3Z0d47qUs+UZScaNVhhMp2GkECq80pKfWcnp6y2WyYp4k8z5yfn3N6dsY47thut/zKL/9dUorkcaLkyuXlnrkq2+o3Yho6JARSG8Y5OT+j63vOb90ifoz21Xdiz8u6AgdaQ7PqGFg11sF5qb/77im3R+Vqv2UiwvUlJSXKfk9ViH1HHDpKCJgEihkFJ7sBQxtNqzRa1SoO78tR8IDJ73+pFbOANtl7GoGULuPyOSMmdJZ8o6+FKsJ1v6WGQI3RdQajZ6+hdzjYNBWQSmzc1Sd3b3F+e8PQ9XTxo13qC+FwD0V42o7ZRrMMGh7Xa0W+Cy6PtZttr93ItJno0lAEy+4YUiJ2jlYIISDVywtBMxKM0sY/c8PNphTpq9FpcPRBF5dN1GtWXULw0kTIfk43UW7TtVrghX7G7ghaU7AWh79o4BGg/hO0HwD+OZzC758/+v0VLq/96Zg5y36tHuEuuGerFRHlersjxMR7770PCA8ePuTy6opcHfg+F89mHLqnTKVSitfQDSjVa6Ux3GACRIRYlBCEGMthYnGR45EQ6Fc3yBFrs/qllmXAsdXpM1dXV8QQ3OGWynY7UdTIErAQMPHsRhFiStRSCNFpRp/SEOHzsa5tfE9Cg3EVH50P4jpymyFSUc76SDRByozmiZInijl5jQGHwUBa1tOmg9Saw21q19UaN0lwryAHXLuBKqLaSsp+HciCdmqlLGuTqrlUssAk4g5Xkp9/9Ig6NQSEiZPppCR0XWjQxHg43kfZC+Fw1ZTdOKJW6FOib2KPse99xrm4Imrf+ew80d+0p/OQOk88pUoDxhulmmslSWB1tqLvOzQaJtUXMChd7F0NOEWqRMYpg8Abr93lpCj1YmI/Feo4U3JhP+1IMfHGG28gGBcP32XO6lIvwZMZVJnG3Iip3RH3XU8/JNZD18hNnDxD+tVTgQ+Z2U8DPy0if9LMfu6JH+DbPQ9ocB+Xvpnn7PSJow89mF3y/v2HXF3vsap87fe+zrgfudpNLr754Mo9oOGcCKWgC8+pGaE1LWP7DJfPMkhtu5w+cjam5vCyVWaaJq6vr7m6uqLrOsZxz26785HuXBjHmffev+9ySbViauRqPt/f9Yf3J+IE2V3qGLoVKXbk2Sjy0Tfmd/R5PifrKkHohjWxGzxTY4SqaHUHdyv1DFn54nbmOsPb0xX5Ci7uv8+ewHa8Zpf37FVJaszRU/7UZnp9kxXEWsQpgYowmQMrI07faNmFKy3nQ9kBWtkBIQcPwKw6R8JoShVhsuK/F6d29Oaro4jEjF4zvcBnzgbubHrOe2EtStnumJ4QW9gzN8MJq2vw3W9pjCx/E2g10BvGIQkc8LpOUO7F06UmJI0UI6ZASB6RGDi4z5amju+0CwpBxIclhmB0yfG0KUWv8eSCiPkEkjg5i4jTLEptdeWjSTYJXrd1FjNnrfcJm0qpBpKeMkiBHxWRb4p8zOzfeLqHdZNWV7UgiCo11MPnYhXMvBZ+cenigNM0U0pBomu92SHr0TYpFhoU8KZ2T7sJ/X35dVLaBWTSus43b9yj7uoyPtvtlu12y+nJCfOcGceRcZzQ6tF4ybX5bW0d9Yak9tSrAfS9W28LuZIoYuWQsT0le6bretxnCaJoTL5G5oFRTIEEHumKEiZHEJUyU4g+uqvVp8wwikERDtumej3B7y1zPoWC89m2ToBHru0+P/DxLQiWpiQxHd3fasaohrbGN0AwQ0x981dx6SBgEwKrKNwaOs6Hjl6MaBXNuSkMf2t7IRxukEA/dJh69JmLw8JCc2jOheBsX9IwrQhIdIcb002asSy8Gp72pEC/SaQuMI0TYHRdhypM4w4MYlxRY2Q0T/m7YcCCsepdWUnP1+S549qMECLDymuE5+enmCnzPEGeyXWReHcZoGG9IabIetMTozhPq1YuLy+95CErnjJi838++n4F/Ajw1CisHrcQA2fnZ4DDhOb9iGolD6tWSvGbZtyPVDVOTk9RNTZ1cXBLWak1NWpx8UetBwdnhpPWGIeNLjcY0cJHvHwV8ZHs3X7Pw8sLfuvLv8XV9RVd3zFNM1/72tf5xltveySuDU1CU2MGl+fByYpA0VIJoiDmjb7iE3ShWz9th/tM1xUAaeTxYoQerOpBbTd2Pihy93ZPPynv3FemkNEyUS2imqlW2Gkh1MIlgSKwPpQG7cj5CjuD2YyL6lDKpSEZ7NHPePkptrbZhVUyxr459dE81rol0AlsVIkKYS50AuehY5MSX7pzzsmQ+NzrA5teONVMPxVvrn1MM/SFcLheEvJGmd8Uiqq0STMO3LaLLcMCjhxxbbJ2x7USS735+5JqCA0L2r5vxXkzI+dCMKMkb4IswpARIwUYUiAQyUNqY6AVQei6eKgbHbq14o0DEPo+EVNsE1Ae+dZaD5HWUrl6WmZmP3X8s4j8N8DPPrUDPmZOpeifhW+MqSmoAu3GUVVK8VHdaPFQs7+JKA011wzT4rP62ljXypHDtaY1Z2qE3B7favAsNcFWAxARaq08ePCAlBIffPAB85x5eHHB9dWVH7/d7MuNv6Ar7CiyCqrOlyugYqjNTrJj8ak63Ge9rjfn0bKK1tSyoIewB3FV6l6FlAolgFYXB9VSqKW2Dc3YtczPOashtM99iXAz/m82z14WTxCOvkf8ed7dEedeMFdumDGK+Dh2wzS0PkuLjLV6ZitGj7EOwiYGVkHoRZyJDqNKfTkcrohzmgreVV7kjJM5/WKfUiMS92K2N8l8dUIIrNdrAAo7qhRKLIgaoVMkQq4ZDc7aJUAtQi2QZ49I5/maPkXOYkenUKdLKkZUZYXRr5yy7dbmzHlux4dknJwlhK7VKI1RC51Ebt297Y67iSTmrK2G6TC3zWrjgxyLLPSnZ98PfObTOtiCw9XFwaZIsEjXavELheHp6a3WTPMGmStjNLIRM2qdfeZeS/v7EfkQy41v1LKgT1yMspTSJJVKm/LTxo9bIAi//Cu/wnq95qu/93uUWvm1X/8Nrq+35FwRiYf2zEI/pC1aXjjSa/UUum3v3pANgc6qn/unZ5/quoJgulAt+qDPUtB2iSIfXR+GHg3G+TqSiOy2V8yzsX94wf5yy86MIsJvWaUDTtUd1loiERhaJpuBgkvdFPPM41BmxB1+wJunEaFfSkxNcCC2erpFxwsvNd2qHir1KDEI61RZJxDdozkzbRW6yE4DJQp09YkRkD9Tc2xqu2DhphgHh51u+Qj9T9ai1ZvYcvmb2VL/WRjnW7dy4TTQ5tBLGwe76W+7/hFQS6XSXkNaCiMuBbTIdXspQpqCcEIxcvEmTtc7AN9xwzh64sDR6ooVwVrk/RQdrohcHR3BgHeAf+/pHfGb7fD2WjYi5im6SDhApw50Kc3hLuXZBa3iflYxjQdHe+Nwbx6v1RUEYhRqU182s1aKuFFbLsXlb3L2Uez7Dx744+cZVSV1HSEuk0ntPbQ01w4VQ266NO37hTwnBHskI3vS9jysqzcy9fhWbe/5kAoQpGkLxkBSyOPIPCl5dBheBbIIO4TUSgkJ51hIiN+D7euBOQyostz1fuQYml5g41xZcosjnj73MdIUG9qmuVQk3HEbQQwRRbVQK575YpTeaVpL0JfE4YpLHveD702q2j4gbaivpm3W1BEOZYEgVA3I6JCNeb/DaoVGftN1fgNUrViBOjv5zbwvmIKQ6FPH6iTRdZHQu4x3rZNPsiVrTQBnn7IUqCqsa48p9CmSYmJ9cubX2Hjtzwse2+WcqcWYJk+1UtcdUlp/H083vDWzM3FG/+/nBqf5KcbUbRMVj+RlYYBX/wyc1av97ihqCeGmTg9gTTdbaLSdC5H7AmJun+MyQm122taxPOKYdWl+tWGMPLus+vXWR8Dv3LnLuRq7sbg6cHPYS1tmqSkvsKbj4940deVAUH/98Ol8qs9+XRvHewtwPJuDJMmDi1wPTjcB50PCJuXLX/8G7+8yF+8/4HoqZIVC4Ap3eDt17OugtAi3yTQ1JQiNLm/uBDfSyoVethLxRno1Y69etittQGIZnGkgMKQCQhshpjXdwXB16N24o8ZItMrUJTpZ0XcB0cjH9MxeDIcLvhPG5owOCYEs7Yolen30OXJwXks9tP2lRVNd56UIcTiDoxbMDjd0iMl34MZIdYzv9ek3T2tjiE5m3S6sLiXMIAavzzotoz/OvYYeTkfENwzDHczhGPC09cwQkX8L+DHgTeCXcIWAn+OjlQGe9FkAC+9vW9vHoj9ZUhg7clyyPNcwa/X9BgeiIQVEWgzTfhcOEi1AW2e/bvxxuowGL2qv4h1zV+UwwuDrKtFrjWmeW53vRs1j4fhYUAoczvdmI326Q2bPybq2iHJZT4ED9+ySkkqrmfZR6AJQMjrPWM5Yro4SAJSFKN6XMhgtw/TvtWWDdvy5yrI9+++XDbC1WVs56ygbod2LS33Y5FADlptXApq4K84CGMSZCIMYNQkfFyO9EA43iDBEZ/mCZS1dTBKMaZocd9ku7Jh8dr4bekKMdJ0TSvQiWC3k/Q7BWK/OkeiAaRDSKiEWCLfizSoJqJQ2sOAA6W4YHB0xRJ9eaYDoKg4bCd0KMSERCRKJqUPMSF2PaAOxiDH03lTrh9Y4ihHDWhfclUufcpT7Y7iSx8+b2T/RJpX+s6d5wMfNrEWr3KSA7hPl8N6XkVxCPPxtiV58Z3OHu3SnY3Oc5RDBPrZxmd9yIgkxa7pxdohOpG2ow9rPJYa4+Ggfrc9eay+N0NzUy1SOQ4SFr/Vw/ofI2loT1ifhfvfLv/mUPtVnv64hJWKLcw4lhaXxrYrVipWJANxa9YgY50nZxUrKM12urFskm9v9tYixanRHObamWrCGXmgZbliCGfBSTl3CzsXBLkVlt9gESGMIXuMVh62tgqMVVuL0i0lhqdybGWWekFqYRrAUWYX+Y9n9XgiHC8uHbY/cmkEWlYdHL3BPIx5VzAXHxRrB56WFQ4S7RK2RiEikCx03EyjW8IB66FoukW2IjrddljGEiAkNeA1B237ZasFBPAp2J+M3uOM0vVapLKnYUTr8dG00s7FFX4OZ/YaI/MCncWCg1fk4LpPTyu8HxyUHGAk3kqji8exNlLs44cby1J4bDjX7Rx2uA9g5bKImcqjHwk1EvUSlKfkUvpoRzKgSmh6el6oW7bmlEbZch8v5Lw53KXUsIpdP0Z7tui5pfZM5EbMllWv9l+AcBu1+7sQYgnAyRE6nyGkfyaqssxFbFHyIRY/L4oszX6pOLKn/kbWMBVky4OUCu4lcg/i9mQhE8YmyJO5sk1j76gM0C1/DAk/DtJWgOIjYfpS9EA5XgGhGUL87U3OYIuLpRAiLksQeAAAgAElEQVQNfO4XeGpE0weZZW0sQTj+Na1Wh6aZR8qdL1XxBev7xj5VXPI85wVSVJtoYWwDE/7xzfOMidD1nctvFBeAnIuLzEUv+fvNJ/FQ5lBxB1uzL9ZuGqm1QcOgNY2eav75NRG5DfwPwF8XkQc4Bd+nYmZQsq+ptIaFiGvLLYMLquoOq8mhw80NVeEm4sWVU0XElQEMCI7X7m66qu3AN47RuKn5HtAKrbFTG6mOFjD0gH6Y66KNJsf55iGStjYgsxCeh6PC3uJwn7I903UFCDEBbWNZNpcW4aIRqY07w5RBKqEXfuDzt/jM7cJuKtzfTvzOB9fsS+WqKNUgqw8qOMlqK+EBi8iktlp/WBxqu89Cy2CXdVqCsGBeQuhDIEqgD66Ftg7ucNdB6RDOIvQBViLufFGXbxIjijlMtELJc1MW/tb2wjjcIEvkY4drfEktjy/gQ61McDwmeKTaUgZrrxXalnfQLmKJTJyfsz2i7Z5LxHJUpzucxc3kmrVU1XlxlTpnQOhImAhZCybmKsJ4dKfqTqfUyjy5LLpZm4x7DF/8pM3MfqR9++Mi8reAW8D/+tQO+KHncPR9c3TWQpfjScJDTVQ+bAs6dsQtbmo1RHBPLsePbQu7pIZwNJV0cLhLtY+WFi/POyLNWF71qB5/iMrhm74u3z/NNYXnYV3b7qkt1V/qCi3CtWVKUJY6rvPOngyJavD66UAQ42Ib2c0gVLLaQZ13tgWO52u91Hj1sevjZvlvfilLlMoNprdvEW6Pw8aWiLZb/gX/XRQhttLVEhxIqzU5m5i+HDVcEaGP8dCMWPLQRSE3rNffVDNTVfbjzqeOGjeudh0pBIYYWhrpwPc5a+tl+c1ipRJDZOh9Jv6QPbQfplJcRrlRsc1z9q63uGbZ1W6klMJ4tUcQNpszEOFq2mIoXS/t5ozUamyvZ0qp7Fotuus6Ygz0dXjqN+diZva3P5UDfdNxl8+CmzKKlJbKJ5YhBARU/caJLcW35TogNjV7n+qK7f5eUv3FEd/0zJYbsE0qBq+dh7hkIsv56KFksKAfFEXaprj8W9ANoR3MFnlueOS6XOzTWtN2/E9/XUUg9UA9vnG81m6GLrV1FrVqvz9vdbCOkT/+Xbe53s/cicbVfubtyz1jVi6nymzGtopHvBZQhNICodoQz4uo7NJcDU0wUnAHH1szLqo741WIzfE62VEn5ux9QekEToM73XXw5n0fOcgHBbE2rCTMoh+7ti+Ew4UWGbR622N/cPiyfPOFrS3SpM1wa5MpJx6nlJ4uLr48mKEfQyyix7UiaxE07bnWZJwbsH4hNkdcKsjQhozwc3Bl30Ip9YD/XOBQh3T6JbcWmLQeZcNSH0e4dvjLzXNu8o5HfofdrOsjry/L6y9fl5CnRaHwSCS8XGbW6rbeMF14i23xyIcHH1UWQOSbrsXH7dN0up+6HWcX7WdPCFrP5RAa3nRlAGKADmHTR6wmNl2klsA6CqgwBkCFWZb8RJzcX5Y6MTevJzfX0PEkqqs4+ABGFN8kI9Ki1+Xnm6+L4k5kyYyXLOXofXEz+fhxd6s8baznkzAReQ/YAu8/63P5CHudp3d+321mbzyl135m9mpdX63rM7Rnsq4vhMMFEJH/x8z+kWd9Ht/Knvfze17tef/cnvfze17tef/cntX5vfz56it7Za/slT0n9srhvrJX9spe2adkL5LD/YlnfQIfY8/7+T2v9rx/bs/7+T2v9rx/bs/k/F6YGu4re2Wv7JW96PYiRbiv7JW9slf2Qtsrh/vKXtkre2Wfkj33DldE/hkR+f9E5Msi8h88B+fzRRH5WyLyayLyqyLyY+33d0Xkr4vIb7Wvd571uT7P9mpdX157tbYfcS7Pcw1XRCLwm8A/BXwN+DvAnzezX3uG5/Q54HNm9osicgb8v8CfBf514L6Z/cV2kd0xs3//WZ3n82yv1vXltVdr+9H2vEe4fwL4spl9xcxm4L8F/oVneUJm9paZ/WL7/gr4deAL7bz+cnvYX8YX9JV9uL1a15fXXq3tR9gzcbifIOX4AvD7Rz9/rf3uuTAR+RLww8D/Ddwzs7fan94G7j2j03pm9mpdX157tbZPxj51h9tSjr8E/LPAHwH+vIj8kU/7PP6gJiKnwE8Bf8HMLo//ZgvP499H9mpdX157tbZP8Bw+7RquiPxJ4MfN7J9uP/+HAGb2n3+rx57e6v/0a5/dsN8XTIUgfWPsCQfWJxFX4HV57YRIIITk/JeNcavUjJmS6+S8tFGRYMTkCrxOuGtYk+oQWVh3jwikF5kCacdBjiSvXatMmwJwrU4/aBrABLVHX2ehqVu4WFUXlSVfk1qdjer+N7bvP+8kJ9/JukoIfzqlDljYl5rCQr+QtDtbV8nlwPJ3WE/hQCAfGitUTAmBxktqjUCcg/qvqVM66ULw3iSZUkpO/7iwPtVFOkcfYRY7XicwJIbGPtZYyo6/PvJ+wekhj9nPYL/dP/frCp98bTfnd/6v2/dugtrFxRwzsR1xtB2ueD3yRQdFBRpheFtvuNENe1wU5cl6shsO7E9mwltf/uVvua7Pgp7xw1KOf/TxB4nIjwI/CvzRYZP4j3/yH+fXf/k+0y4QuUeUFev1GTEkuuDM+ushklLibH2XPq04Xb1GlwZOT04A5XJ3n7Fseff6d1DZM9y5Jg2Fze2RGCsxZcwqZdoDRt/HptCwQQhNYkcxG4khcLK+S4wdKa4b1+aM1spuNzPPxsV9o+RAmU+ompjLyvXua6XWwn5/SS2FcZyptTp/rymGK01cX++p1fivf/znvvoprMsf1D7xuoYQuPfmlxCL7mz7QDckXvv8Hbo+EqJQSuW9t++Ts4JFJETW6xUxRVbrRIqBdR8Y+o7XX38NCYHtfs9cMheXlxhwenbmeniTYqWSr3cE4GS1pu87br1+B0mBsWZKLeyvrym5sN+PmBqBpvo8FWqpXF1fYxj96eCaeOYOutTSCLbbXWoudNilRAhC3zm/ci0Fw/j5v/YLL8K6wrextkfrSrda8+/8pf/pEEAsqsgLT3EKHsRIcGL+alDNGMvcCNxBTCG75tl66HydV6nRlvq/fQ0YR1SnTRZSm6KHfIzczeN2E3za4d8NKT0cEXDePOzRDwEBfvzPfM+3XNfnlg/XzH4C+AkR+TPnd4a/GntlOJ3Imvn6711hdeC1299Nl1ash54YhVwiMUTmPXRxYNpA3w9UJlIvrG7BECLx9UgJUFZXmIzMYYeZYvuImBBrRxTBOufXVJ0RAiEMhBBZDR0pCEPnRMc6r9CayHNPKcbuas80znzwjfvkokhUEMVCpVTlwcMLVJW+E8yEcbTGi5saqbZR1VD9eF7VF82O1zWE+FetOFu+4XL1uVbiw2v6IXF2OoAqXRAXkqRHQiARkSqU3QxRiHGgF7hzfkpKiVUfGeeJaXdFrRUpE6nreOOztxi6jrPVym/goSelQL8ZkCDMmlFT5nmmlspuu8fUiES0Vq4eXjGNM+++r1Q10tkaiYHQBUwMpSABulUixECXIiFE1t3gAoXRxSgXovWf/2u/8EzX4knasq4AX/iBf8gsNO5pA9+vjLk4ofusHrGmRgS+kMsGic4XbU3V+pic3owpF490SVgTfqRlPAvXMUBoT/92OYeXO2zJPw9qDvj5lup82dUei3gff/lv43jPwuF+Hfji0c9vtt99qJnZz3zPD91BTekGSIMgsQLVL/YYDkzCuWSqVGzekWIhhQ1VK8M60EkgUCFMhDgSw0jtRkwmVGfU3OmJBkRdXNIFBhfC4UAMHSEEYrhJZ8UAFUwjZR4oGcpeyXvYXSm5VOKqujrF4PLbpVa0qkv+mKFV0WpefjBB1ah10WN7ulLpT9A+8br2w6oRSYOXcgytRi1GjUaQAEE8OpWmfYYQrGlSqbTIMTH0PevVQNcl1CohwGroKaXQdZG+j5ycDqxXA3fPTulSpO888kwrLykMFluk2lFrZbWKLcINaKkkUcYpUfSMako8WRNSJHQBglHFHW6/6ZrDTcQQWC0OtykP2KcjEPok7ROtLXgpDTsmjZdDlLgQzqs+Konjf3WmeJfTCq3UF1zRQRdR0eYk5fC/JnXT6OWFRSfyE9tCWr4UEw/n9CEFi28KcD/0UY/as3C4fwf4fhH5HnzR/mXgX/moJ5RaudrvObu7ZnXqEY3VNeebzxJDj4hSS+big/fJU2b/8D6BxN27E6vVwMSGfmWcxB2hHymr38e6HYQLTCrZAtWEcVeRmiAMpBhIyUgpsFoPpNiz6u8iEqla3XHqiKiAGjUL+8sz5n3g+p3I7jrw1m+PzHViuKt06447nxEIQjdESjHGeUSLMk+l1aMCtRrX25FSMznPL1KE+4nX1eunAdTFQNXU00ANRBLnm1MEI1+NZArz5EpyXfD6a98lVuuOz3/2HmfnJ3zhC/dIXWI77pmmidgHSslIEPoh8eZ33ePkZMW9N+6QUqCWGTDXWgFXchBXXwajzhVTo04Vq8q8v0MtyjRmj6BS73XcIUAATQWCEQZBohCS1207ab2ExVksUiEvjn2itTVrdfT66HsMBEzccbrETr1RyjiWzGhBRooe+GgrPUy1IgJ9bNFtcEUzO8SmelD88Jc8VI3biT12orJo2dnRzyCL6FmTgVnURR7V33vsF4B9G8HRp+5wzayIyL8L/G+4csVPmtmvfuRz1BinQuoiqYtsTtdYXRGj4FLXFZOKoahVxmlCrLDdbSma2WSwBIPtCDZSGTGb0DKhopSaqCVQNRLUjjbKiEggxZ6UeoJ0QCTP+O4d241UvaZULVItUmpPzjPjDuYK4UwJnetthQAxup5atiWd9maLtoZbydWjYP1m2aDn1b6TdRX8s3CZJNDqOlQuRx2IEhExYohUUUTVG5ZFXVleQdQ8wRGaXLW0pqcx9B0pBUIKDEPH5mRgczKwPh2IMVCK62kR/EYOcWm+tiZbX7Fq1FTQqvRR0KqsB6/FSuhdlmcInjb36k5gAAJIak1ciU115tGm2Yti39E9uzQ5j99ru69uKqWPeTA5FvvksQc3EfRD8/SRAPfI99nRs+0mOv2weu7jDrPVj5fjPH56H/Xcb9eeSQ3XzH4G+Jlv9/HzrLz99sTrb5zR9QOv3XsNLZEP3vuAnCvTPIIaqY9EM6ayJ8/K9X7Pat2zfu2z2CpyPmRkmKnRa4UP37umaMWsJ9CxllW72RQkktKGoes53dwihI553zNN8NZbFS3CZjgnxcBqFUESeRMoMTH1t9iHxMOrM3KJDK9HTCL9EIhdoOpAzoUy7ykmxBSoRRl3EyVnxmlGq0dLL5J90nWVIKxOI1E9QqkaEAkMfU+fOkJ1h9WHDgmQy94zgjG78xoE5o6L+x3ztKXqhMTAnGcQYbXZMKxW3Lpzi/W657XXbrFa9wynAzEKyUDESMkIQehiaKkpLbLNWDVsSFhVcgpYVSyr1xe1oSk6QZLQnfdIJ9TesGAUcZl2dwpt8zTQFom9SPZJ19YO0eHjqbYhsqjbtr8/JiMvImCetYoYiYIEIYXOsyJuguHWxj5qc3Go+37C99e+tk346BhqSw3jY1702zjwc9s0OzZV2O+VafaLd9UbJkq1kaKFUkcwoU8bkgZSF9AKuVSKFqoWihpzyYRQmKuRDXIOVG27n4B0/iGH6LVBr9n2CB1YpBShZGOeQEtgCB1igVIjFjomembpmWNijlBZ+80lIOIKwd4AqF6fPSjCem23lkIpHk2pmfvbT9ZofaFMAgzrRKxe79MqIIEIiCp5yn6zFnVH1/7VWgkCNQglKNNuB1aQYEgI5JoJIRIkIGpYLlgXPYWwJnMdAjF2iBhdcgXvPjnMS6unw0UqJuoKz7iop1ZFS/XXqtVv8gqhC0jxDSNET1Vj9EvLGuRt6XiHl3lRj+zxtPvRfpNvQI/HtI9GwYbYIoTu9VwRwbEJHMHwPryeenPU40Lx40iDD6nNPv47+5DXf6xBdlyW+Ch7IRxuKcZ771aMzDDA7uw+IjDZlhoqNRaiJNabM6Qm7PM986hcXu4JSchW2I6F/TeusZC9uREDw+YzxGBgEzFA7CpdFE5OEn0aWK/OiLFnmtyBX14U5smoc0As0MmKIB1j2TCx4mvlHlNZMa1Pmc726O13CfMl/bAlxcJ+v0OpPHwwUnIlzxmrSsmZUirjtKfkQm343Nhkwl9W6/rEvS/epssGFepolGxcXOyZR+Odq0t3UhW0GvN2cgnyOhPEkGzYKLz3lS0xRWLvza9qSoyR89u36VcD16/d4eRszWDfxfmtU25vNvSbjpPTE1IKDJ01eWxPgcs4U7Uy1UrNUPeFMmX2712Rp5nxckfNlTpm1IxMRfrI2edeI6465KRDukA6G5AuIJvQ5F4bpvQld7gGlOYs4Qh/y1EES9vYMG98y82jNCgoB1n7PgoxwtC7o9Z59mPIgIpgBxf8+FlweM3v9H0sUfqTKgO9EA7XDHKGaXTJ866bCQGqFsyUEL02GjqPTlebjhCU3TRCgGoVqYZNXp8t5k2Nrh9aOmlEafg/wW+O0IDVag01ADkbpSyQETncRMUSU+14eJXY50QZI3lOaFgRUyZIQRBy9sZQzoWS9VCjrXqDSNA2VHEY7HiJHW5MgVt3T4ljwbKyrzOiBdGKlcpcFDUQc7SA1YqpEk0RjGDqTcu5YFV9wZZgJhq6n33dhh0zxv7BNVGN3YNrLBfWqwSWSDESA/QAZkgVYhVqASmGTRUZK7ab0XGiXO2puZD3M6rKaBnpImHoiKuOMA9In+gxwhDp+x5JoXmQv3/swxI0aWn3h7kvkSVu9WEi0ZZ5tjUNGGKKlhkQQup5HEMgi0T941L1jx3wW63EhznW43LDh7/LDz/Gh9kL4nCFMkV2l5XcKWXydC32SkyB9fkJXeqQ3ve6s88k8lzZ2UythblmyhzpWaNmXG9nQjQ23Zq0CpzfOSWmiskVKrC3zFyFcXuF0NGFDaaB/QRaYRg6gijWbcn0XM63eXAd+Lu/WLnazahmos7cLSd0MRIIUEauH+6oVpnHttMHwQRyrdRaUUBCZFi1ZtxLHgltTtf88J/4w8zvX7K/3PHbf+8rlLonlBGZlXlfqQqqATFIqiSBdR9JwRiSkELgLPZ0MbHpN4QYsOCbVdCEZCE+mChXmd/f/hZpSLz7ld/j9PyEP/xHv5/zWydsPvc6qUsMKSCq1F2lzhUulTJVyvtbbD+Sv/aAeT8yPriklsw8z5RaeTBuqSLI199H+kQ82xCGxPDGKcPZis/90Jv0Jz2xT15iePFgYZ/YrDUyxW7wrUvfKoTgzuuoNyWHSLdNj5oRg0fJ1bzEM48j1ML44CESIqf31pASxQGf6FFp3JZi7vGBP8TkkSbeY18/wmF/8xv+dh70gjhc7zkIWpRKwHpQBLLvh1qNEipzmUhSWfWRFJT1qVA10PWBECNd6qlViZLAKlpAC2ABzBo0y5hKJggkZiKGpKFFT62LLl7zI3hEOpXCfixc3J+5vCqoFjoyJ3EipZlpymgtlKQovoFYQ6AeOtc4tldbh96zqeedzO0PZilGbt85ZTtmyBXMa6QBb4U7RMxHcQMOt+4CrFMgBRiikIKwCpEuBNbJB19IPrlGw71GE4fxjgWrlfHhNbFWdh9c0JVKOdlQhx7tIsEM3c1YrjAWmAsyV2SqMBeYCjpO1JzRkqm1UHYTBc9UpEvEqoShQ5NgptS5oH0idDdoo++0y/0i2DKOHVSRVjMXpNWyb+qfN6O8Pvgi1h7X3LCJ/7Tcl2rmTczmpW8ac8szbu6l46/HDbYFDfytfKMtx/+Quu3xax0/nqUx+rJEuIIQLVBzJpLYDCeICA+vLlEyu3lEgiIh0/eJL37hs6w3A99775QUI0O3Qkho3jBPleGtmTlP5GlLLcrQJ0JnaKqoVKbdFgmB01WljyvupI6UOobTjmCQ3FUyK0xz4d0HH/Duux1v/8aWywdKGd+jCxNy94r1UJgutgyDcf5aIHWQ1t5YIWSnZQg+mdT3ycsXuYJBlOGlrvf1Q+TNL93lnTljuTCNhWmf2aSBQaCWylwq13VLEOP2OrLuAvc2A30QoigBoQuRGBOn656UErEfEAm+KUug730QYRgAMabrHbKfeSf/OtcnK04eXHKyWZNPVkQR6lQwVeoMVpUwKnGGrhqlVubdNWWeW5lD0d1Eqcruco+FwHAyEvpE3e7RyxPy936WKEJYRyQE9MO6PC+RlVz44J13vQQgjbMiCBKjw+iCO9YYk++Lqv64hq/V4GiRrlXt0IoYqEWESDx/HSSwq4ZqZmxNzRAcv7CMVusBbRBaqck9vhzQE4+WNm6WZfnLkRc9xqAdsGiH2N3/k8rHLewL4XANT8NMG261japY6/LrXCBUnxqzwjSPxGichIBEI8TaUnR/7rAOEALzrqBamXMlmGI2Y0Gpkh2kTQaJmMze8IjeIY1EzPwVTSCXQs6Qx5EyVihXSJoQ2SJSUNu3ALn3i63hDX2nlqP3dtNyFVse9/I63BCE1ZAa9tEwBdTLBMGEFJQq4PmMkkKgC7DpIkMMRNRJayQQ2t+CQFziHREkQGpTXkMUHPIXwBTGCRXI1zvmoszVJwLrXDzCKYIp1KxYA90fRj7FWrRkbdrQ0Oyohhpmd8T7DlsXqIZUaAt7iNBeVlNVrvejNza5caSSYlsT/7mLDf9s2h4nzeFGH3iInk0uZYmAD4+E5PCw0hA+LCgSc1IibevyiMPFkSl+OVlzvb4MS4VHl6jWFodtj2Qi0i6AA6nVIVg+xOR8eHX6xl4Ih6tVGXcj61Mfi73ePyTGQOw9ZZlyg1XVSo7KW/IefR+5fxLpUmC9Hkip4/zsNUJInN6FoQSm92dyHrm/vUSk0q0rMQmbsxVd13G6nukSxHAfCclhYpJIssEs0ZVTUk0+2lsVdEdKmTfubdmsJ968t2PVFzarTNfB2W1FQiSX3gclanLymmtHJ2jxXbULHUEajOkld7j9IOx2W7bXO4SOFJSV9ChGZ5M3PC0DSjRhEOG1Vc+6Sw7jEqjirq9qwWphLKPDy7oVMSYsCiKRHidBuT2cEgJ0CToJ1AdX7NMOvfCBB9XsG6p0ePfUIWEEc06O0540Q9kX76J3naeWY6aqMuVK6hIn64F+VlaWGCRiQTExCi+1v2Uqhd9+74FvatyMwccWQETazzESRLxsJ5CcVI8sDss8X69ZpcTrJyuGGBg6Lx2tekfvzKVQzZAaqAa77J//XDOqhmoFhBAdIhhT9whqa+FgcDY6v9cagg8zHKJp3qz1gCv55GDXO+SwjaQta1nFPqpcDLwgDhc4jAkaRq0Zk0BMERCkeP1Hq0ck06jUYkAlJaHUStclUt+TUkeKPSbFo18zKM7wJA2f2ceOPnV0MXm0JYbgi+dnMPtOJhUkoFXQGpDgIPqzW8LJRjg5qwy9crKC1MFqA4hRt34tagUtXkKopaKl4Q37rtWtD/97KU1EiMGnt2qtziehvhaO0byJIpcui1ggSiBJpGsRkQSPRqo2WsWFLUpo2M3lNXx0OEogipCaE9BcqKpk85Swam4fewUCpi2bqo6KSTFAitQ2oRZD8IEZa+tqFUWoc/b6bfHa9CMR0cu7rKjBdGB6McQCgh0crbspIVZfo9gi4dQy9FmsZSTJm2a1QwV3mmK+7sE7bg5MMaQatRRyVcbmeJdR2xC9lBOrHrgYpP0eg5wzpnZwxgvuIWd3uAudZ4iFEAJq5k1ZaROJy7/w8Xfri+FwW01agn9SY96RiJxvzggSiURyrozXBVVjbx0hCrvrgoRKGvakzriaLnw0eHVCCM7yNPQrNjE6CL8PpJi4fXqHrku+kwaHo4CT21QqpV4DEQ1CqSv2u1tM+46hh/WQ+MEfOuPsNLJKD+lT5fatnq4LdH2kZHh7gjEr025mnmbm3UwpGaoSQiT2K4JIu2Be3gjX08roI7ulMm53jNcjErLDsyx7/7mCEdE6UGtPLR01+NhviBCDN9dqyahC7Lxmu151xOjj4ALM8+QlCo2kKKQ4YAi5FKpKG+FXana4V56dWGgcZ8yg7wMSYN131BAou4KK0XU91RbnbJSilJqpH1wyYly895ASjbN7d30oQusj3K8vnYUAq5NH0nEDCrC4M8DZxDDEnFNhaCl8MSWKspoyooauMhKMTRdZ93B7LaQYEFlRVbnez2wn5a2Lmaup8M71RKl2gOGJTO2rX3MeRPnGjRkl39BCHtytGbk4zrqWdubteX3fN8hmODwWvFf7cfZiOFxuSL19Aksx/f/Je/NY27bsvOs3ZrPW2nuf7javq/fKVa/KveOKG8BBtkhCFBEROiWKJZAQRIFYCRKCQEgUQCGBP0yUgOhEsAPIkfgjRgQFKzSCKLESDCi2SeLgqnK5KtW+5r7bnGY3a63ZDP4Yc+977vOrd29R97ykinl1dO7ZZ6+19llzrbHG/MY3vm9f5QcnitvzO/ZZBnI4ITkboD3NiaKuKX4Fhm5p2F8MOAfBeaLzBFngCXjxtl9Mp8Hu/CYYbugPVZV5cqTZ4YPQBTg9i5wcd0QiwcNyFfDBGZYFli1rpZZsYte14YANo9pLnld5b17gN9M4SMibkARa60FExjtH8C0rUdOfqOrYU24Nz+eQWpj+qhivNvg2r77hrhY8EaVUTApwP5ftq7ZOtFKs2y/PpYnVjPbQF1P/ip3Rl4C9voldDZXH3YMF6pxw48y4m4jjjJY2z89Y0f7GHXLtS6+xB/YlKXu9Xnu/oOy1bux8tvNYK7lUSrHvqdiKVbEss1Q94O9zrqRcmXIlFUWdo82MHeWQSQecKF6MR5ZzE5S//ieoknITsi9txeUsziRyu0/d4e8SwLunp0ffEAG31sput8ONQqggwZb5eZ4tu9EJR6ULM1UFJ9UaIFZLxJsOLS6R6hV5VqYpE3xPd+uU6NXqkQIAACAASURBVAcW8cik8wr44ii7JS54wtDjHeBzu7knA+K1WBXcBXL1vHPP8/BBpF8GTo4rH339mNtnHR0FIaFiT9A8C5oLte7IOZHnHSUVgnjwJr7tvCOE5oDgvrkzXFShFHyBoOARgvMsFwPOOfqaGVPGb0drYKieVB1jUpwUgp/wHlwIlLbkF+c5PlrS9QOr4yNUhM1m00Tfk6lVFVu1lGwyOdUHC9g5W/Y5T9RSmbeJlAu79c6Ccu2IIbCISwQTks85M06FcS5MyW76UixY5JSYRLj35gMmVzmZ7xB7Z5jmN3MTROtUOnS7cp0BcO1ZI49zXQWKVBxK1IoHXC3W4bmbmVKh1kIXHPc2Uyua2cN3PWbmXLnYZaZSGYsyV2Vs3HaHHFApc4TZB9drmgl6TSh0H/hrAJRaTT1OW9OTPRlaIL8G+z1Lj9I3RMAFjKZTBSmQk1Uu85RRD85loFqbbgVteKtzinggCOz1bdWoPNbLLaDOHAfUN4V4ByWC82jxBoK7ZpnTlOa1tCewF1IWdqMwzYILEHtlWMCwEDoNtmQphVqhYMsQ34JqDNbOVoPd8F3X9HZbVdddA+S/KUfDx7SY/KEXZ4wC700rWKoJ24iVqivmDJBrIVehtNRyj3fvJRBjDHSdfRneZv34pZaWiTqqe/LcGkvCMuxaLRsuTTshJ3NoSLO0z1w52O0oLfMq5KqHr6pKMkEPdruZvnWlaeuG+yaf2PaA2kfcJzPc9iLXQ/A+F9aG2xsrwQJbalZT2xnmYip7iJCqM6eIqZ37lgnvlx3VOoR5rP61p3PB3k5r/0mAQyuyPv5YgBxYDAf+tLzrfe/az/uNb4yAKyBBSMkcE3abjHeQhokYheMza+3toqdU2KWd9Vi7hI/C4gRUHOt1pBZF6oCWnpo8BWH2ieAKgR6cR9wSzY7NOuF8NeFwKlc70zmYxxFVD/6IR+ee+w8867VwfLqjP8l0iy1xSPhkWgl17ijFoWmJr4Vbp4nVIrHqe9JcOL8/oQWGobcHhMlkPdbl/CYdtVR2l2vGyzV5u2MROkLn6WMHAillo1G1BpNZZkYq6+xQcSweAxKNYhaJMbBa9vRDx3KIFK1sN0rNmWncQVU8A2jEyeLgd6eH4ompgdViug4pVcYxUWphHEdi3EMVjoKSUdbjyGbKrOfZlrfZKElzSXTe8ejhFSwj05Twi4C6p1ezv6GH0gpN+4jUSp+NtbB/+XGW3/LNFgS9VKJYk4tDWU8zgjBn30SlrFNtMoQP33ZTLd+ik4qIYcFFodZoxdLr6LFAdS3Mt+Je3QfSPdah+9/v93+oeraPfS0y67M9Q78hAu7h+diemtIeYmU2RS3NoOFxM6y2VsBSM65a1mqIkmsYTiT4SHAB7/zjLwJeQvMxc2hNFLGlatHCNE+2hNztqNVTSayvCvMs5AzeZ3yYqYzUmqi6r3b3OIJ5n8XKcrWg6wLBdaSpovNALUrXG21FXbJswN98GiQiHwG+TVX/NxFZAEFVr278wFhGY+wMq+QH503nVvcrCssyXTT2iOsApyRNlt3UjtIyVSdCDOZpF4MneM9eaXPv0LE3hzRGxJOSgKqWHWk1JbDSXDdyeYwhghW75jnjvGvZE2St5FpItZKqklVbJg5SLUNL5bFC3GOPrP+fjMYKeHdGuL+z5T1f5ZDl7jPMuSiugnMGQcy5vd8ZBLfn0XqD/A+NE6ldT3v4fP9h5N1B8ho8cBDFfleMfWKLa6mwvutXX218QwTcfSVTU8U7YTUcGa0nKR6oOw9BqMEbdjaPqFSuribCZMHYeyFKxHeR4+UtujhwdnJKF3tOVyd48VAN04uusyWsN1bCOK+Z88jDi3uM08TFwwvmKbC5gs36NrvNR1EV+sUVcdhytfkyQmZJwNMT3BneL+mXryAIt+8sQAuaAzU7NueBWqDojFKoMqFamfPNOj6IyL+IGf/dBj6OWaf8aeC33NhBrw816cSym9E5seo6plKYdhOpZC7nLUkqy1sL8MJRHwhVubq4ZJ6FZTpj0GB6FcGxWi7p+shi6IhdIHgrPA6LCFQe1kzOpeH+1orrqzCOZh46T5NJQGaz+VnvEmnObHaZkjOVTPAO310RYsD1keyFUSs7LWxLZa6VpGaMmBT6KszVtQeEuR1UzXyzh9w9I+DwA1xbTewxzwMP65AQWTHNHqJBTKdYNFAQdsWIB71vLr7BoIcYrGg6p2JsEid0XlhERypwvxSywtzWQ072PWc8sYKUls3uG3H2yxB5/IbHMVgfF+O+lvENEnCxJ1LzNArOE8ThpJhmbHVolWsnyDQJ8myXdRoVjY4hRIJ09N2CPg7EYJnuvqFB1CMYSR7Za9dWpmliSiO7ccc0jUzjjnmKjOvMtDUlMi/Qh0rnM7VYJlxEQAqhZarOGtwsuIvD6wItnqALKwrlHUqm0KFa6HK5aZbCvwT8A8D/ZadYPyMiL97kAX/NaHibVFOXEGjFqOZ64WF5tMR3npNVxJdC3m2gKKlWfN3r+guhZbgHfYpaUa0HU8CqpshWSiGXbN8FtGZUGxe6Vmo2i/rUqt6lQml+c1lgSpmCrUiqCFWMFZNRkiq5YbtFhYKgYhDIk/nQN3HAPQRbfc9K0tMU8A4NXtp0hwmHQGctvGIdhc4KrYvOWnpD42XtpTQEYS6VucyMRdnlx9myPo71j4t6LU1V5dqr12aqvS7XZ/Dd0/gUqOgbI+CqQkkE7wkIQ4hEcfh99bA0mlY00ZkuBnKtbNaGAdZZ6HvPyd3bLLsFt1cv0XUdMXQ4CaQ54vB47cx3yglKYcx2c715/yG7ac3D9T1yTtQ5U5NStw7ZOlY1EyK8soKThSA5U6ZE8h0qBZfXVGfeWs47fBwJPrAcTvAskOUdahF204ZSM6kkBCG6lSmN3dyYVHXe3wAiYmXZD2hY15HD9NScSR5Oid1uZC6ZSRNx6PnYd32E41srXnv1jDpN/OrP/U2miy3by0IpcHu1xIXIYhisUFYyiUIqtUEFiVozOWXSnKHWZkO/bp5qCbQ2ix5lHpWcTVWu5EpWj4pDnKeKcrkZ8dFzdrSkeA9dhxYYdcdYTdxe1TrgxHnoelzXo84dcMIbP7ciHfCd2Hx+WlXnD+TA7x4tZXzfxKHqoaalagwDqYUyWku9yhIk4ENPdI6ThSd6R9d43HePjxhi4PZxR+eFvjPw0CnsUuZTb19wvk186t6a7VzZloOSp40WnPfU033gvd5W/+THl/d47fpvvvp434ArIv8J73MDquq//JT9P7fhRQ7Op06MVmO207bsf5zmc+ht12STWGdMeagGnHYEGfDSIXSgHs0dKh4wIryd9ExOgTQH0uhIkyPPgVIUVz2u9vga8dXhy0jwkYVTlh4i0WgteFBHJUMVUtngVKgyowSUCZzHhYI4jy9AdZRq6FX0ve3j5sbPisgfARYi8luB3w/8zE0e8N3DNc8476NlhbWSaiFrwQVH7COnt484u3PC3ZfOKNPMOy+csvWe7bS17sJDdduq0TkXpECVgmolZ8teS2MQSK44V0g5ozhEG7+6MRRSruRsGG6pihmU2R2qVFK2wk5WKFjvf3VWgN1/Gbbb+DLe25fbaxzf7DkVkd+OQUOfxWLA6yLyY6r6P93skdvQPWzwZOj4qkFX9+iqbXPQJNCMAL0UcI5+iHQxcGvREYMQW8A9XfYsYuCF444uOPpgGbBUGFPgwWbEO+Ho3KMKu3qNS6881tD9NdmqHD7fvt338ee9VjB74m95/8l9Wob78+37DwPfDfy59vPvAn75Kds+t+GcsFz1xKGzIBsc4hx9tBbYqokqbbmoBd0lyBXZ1QZBRAIdMgfrsZ2PQJeIP0YIaF1QCYg32+6ixtncbWBMA3Wzg7SjS8dULXTqgY7oXmQk8mj3Zboi3JUVt3zgbvwQoatUsWJMTomkE5v8EGui2BJDQOqWPq5YdjtEot20BcaxgAZif2RW4Tc3/jDwe4BfAn4M86z6Mzd5wOtDEHq35PT4RcpmIPOIUQsbJjTCyStHnL14yrd/z2vcfvGUD3/4DpTCSe+5uH/B3/4/fpn5amScZiRXxqknJ0++3FJrJRfDStVZ3/12UlKGUQqdCkdToque0BpaaiuObcaJkpW5VKiC8xFousVa2CRrCQ8JqoNJembnmGVgpphUI5W5TkY1GgJ+EemGjn6Irdp9o5nunwJ+s6r+KoCIfBz4i8AHEnAVo+7tr1zZX8PvDrh72GG/lG9gbvXGzPWMDA4+tIwsFpEPf/QlFsOCWytvOHyDkvoQ6Lzj7mkg+r1QuXnkpWJ00fPdzHaq3N/MXNzbMlc90BEkN8hvn7nuseZDwG3vq1at33vSvbvY9yzjfQOuqv6UnRf5fcCPqGpuP/9p4K9+Dcf5uoat8l3Tib2Wzj9xPtqfbSasuCKEah1BvgYTo9aIaAdlQPwALIEIddFw26Hto0CJSD3Ca6DzdxAdqUS0FkL1qHqqLCkCQdeECh09vQSGsMCHylRny761NDw4o2RqnYFKLhPeOQqXCBGViIqQq0KNzGmHl3xj51WNp/OTwE+KyG3gNf0gW9uaJXrwHc53BzxUOo/rhOPbK07uHHFyuuD4ZMFi1SG1cvbiKc4LR3dPGIND729IpTCmhKeQZ8Nic24B14sxBbKSionXFzVebwWKiRS35og9K+FxZbvV2BrzwDBaqTDmQhFlzJUpK7lhtkXNeHB/DBccLvpDt6HWdwsDPvdxtQ+2bXwO+ECYJ4ehe4HDJ0nH73l56X71AAcugTMIxzvogrAIwlHnWfaeZRcs4Iprgc942jlb1yY5tT11di+pBdPOCbHhv9fAgn2obw+A6xmqPvn/lhXvs/c9miuHtz6dYP2sGO4t4AR42H4+aq99MEPBJWvPrU7JZFQcE0a7mcYJpeKDPYkG7ahqSlCII9YlXR0Y5AV6OcHnlxFWKGdAR/BLHB4ptnyXWvAop8OLKIVbq5lKZipbSinMm0QaM++cnyNuzcp9gd4XVvQcuSV3T+4iHbxxeY9SKsISR8VLRtUZ9Sh70qwIE3HxOZOl8x2pOnYpUpJnt36I8aRuZojIXwH+Cew6+AXgnoj8nKr+qzd20GtDqzJPFfyAdDO5E+rKc/rSbZYnPZ/4B7/VMtvX77A6GhgW1jjy6rd9iDuv3qF4uLh3zud+9m+yvtiyfWe0hv1kN2JphHkNRpC/GE10aLWKEDyuC4h3pDFRc2bcma7tOO+r1d74wMVETLYpkbUyNbxv/eiKpMK9y8SYKutUDk0PqkpCKM4xHC1ZHi8PGHOappvWUvh5EfkfgZ/GIsDvAv66iPwOAFX98zd5cJTW4PE4AL1XoWwvLiMNxrPOSnCuFbKHiARBeo/GjjxPTKKsfW86152tFs7XEykVPrPbUfLMtD4HcXQndxEXzA8vV8ZtQudCj3FuE1jzU8vAH3MOrHW47oWLW0XNiquPGzrcEzx5o58+DcV91oD748D/LSJ/ue3xHwL+nWfc9usfCppMRQqvVDHB4mqqxGixCrLiWnXS4RRcEywOsiC6BZ4VjiU19xQ1DNcRUe1QMZsOlCZiooa7iZq6GBVxA9UXXEq4MhPCRPAzwSteCqLZcCcpiAPx2rQ8QyN9+4YV9QYVqImdpLJt3MKeigdnRYLKDDdbNDtV1UsR+ReAP6uqf1RE/tZNHvD6qKrMKTPOM1OekWjUvaMXVhzdWnD35duc3TliWHbE3psrL0LoPZ1GTu+cQKnEVc+8m9nmDXVWJBkfzEwKgSJUIDe9273Wwl4MqaphtSm3fv1q19P+zNf2WbNWMpXqhdLYCnNVdnM2daxWHdfmulxptQNnjiNAS5QM/rrBMQBvA7+x/fwOsAD+cfsE3GzA3Y89x4onM9vHHmG/FuPdE7ZMn8+RgV0VyHA1zswVknOEUOhbC/WjzcQ8Z7aXl5R5Zr56hDjHaXeM94rmatAC2hxD7Bg1G8a+H449u+E6O0FaZistztBs2R+7tVzXgHjaeGrAFQNgPg38UPsC+EOq+tbTd/+cRgG9UEqn4CrJJbzzhGi6sd51IIJrIhTUYpJ61eNCz8nwLXTDMZHXIC1Yny8R17WGB0fpTOBam/5laozq0Jn+pXhw3jEsTvFOCIPQSWJ1oqgG4mKBE2XMOzZz4nyerHX3uBLFE0sEhZQWqFakDPZQcB7VkcvNfXAzwS0QHVguj6DvcHmAmy2aBRF5BfhR4N+8yQO918il8Pajd/iVL/0KV5cj/a3A2dExf99v+gFu3T3htY/fph883cLW8KXMTczElp2vfMvLnJwcc++zb/Jg8YAvv/FJpnHG5x6qmHC4qj0sRekDDH3k1umSxdDZ8r5ZJKWcWadKLrBJnqqPuZre28N+6xPqwC0jtSoPzzeMqfBom6jOsbx1TEUPrsxFhIpDJEB1jNsJJTEV4/3e1FDV331jO3/GcUgTrjV5vDvQ/ho+blXDTVWpVXg4e1yCL20KwWW+ePWILniWx4vGzxVKrVytZ8o8kh58BeYtsr7HarHg+198mSF6rjYXUCt3+47TXnBx4CoVfvXBlilXS85EzLpJhCE6s9gK7iA6DpAabXCeZkqtjFlJFa6yoz5j6+BTA66qVhH5z1T1+4G/8Ex7fc5DcHQyoC4YxUQEJ47oFwTncbEFXFrHUJ6oolQc3g14f4R3K9AFWntSap0pXqmuWnYKlJTthM7WIRZKtcqys4DrnLV0ilgLcW18XxWjDaWamUthytWq58H4vFUmwyvFqq7BO5rssompVMN5nZifVxcjzvc4f+MB948B/wvw11T1r4vIx4DP3OQBnxgC1Sljnph1oj/qWJ0tuPPiGWd3j1keDYROEDc35oi1+O71SPshUpY9w/GK7mhLcjBpITQ1udw6y5RKcLD0VuVeDD3D0Fm3WDWmQVYhVZgLjI02ZBmPMRJUKsWDBKE/WuCqwtWOkq1IJg6OThcgwm4cEQcpa5P32zMlKqXQ3D1u4HSK/Buq+ie+Grvog2IVCbQuLstw3wvZ3OuaPFH932/fqFm5LQK22TRzO8l0oaLRmy5t07zY7ibKPFJ2W3Te4nZbggMpCWokzztqrURXcM5z3C9w3nHcezovBMwb7ygGgoNFNMnPGJo10F6o/BBwHblUrqbMlJVpo8x6zTHifcazQgp/SUR+J/DnP9CiShtd6Pnw3W+l9hGco3qP955bqxNCiMTloi3RoZbM1dUDy1imCq4n9B9CwpKcj9HiSaWAVLrOssycTX91nmZKKUzjaJw89tJ9FSfCcrnAe08cOkop3D+/ZLfZMRFwEjif15SxsriCqILTDgXq7m1EISIEifjYgncQhImaHaoRJ0d0/pjbq1cIcoTXO8gNBVwR8cCHVfUT+9dU9XPA77yRA77HCDFw8uIZrKyQ9dpHXuLW3TNe/fhtVicLxCdUCqXOVC2khqHRRMd97+iOeu689hIZRx4Cu43gGy97N2e0Kr5p2N65c5fT4yUvvvQysQuM88Q8J2ZmdnXmfMpMSTmfrTXXOUWk4N2IBKU7geXxwEc/8XGKCuf6WerFFsmPGFYLvu83fDchej75y5/h4tGGz376IXMuPHp0RX8UmadC6I3yd0Pjk+37z7/vuz6A4TBIRVQJDewsTRUs13oty6UJxAO6X8xbpM1qzSPrZBY8wcGgsJgnghNqAa2FPl2haWTWLao7ilYohbK9IKUtFw/eICVjMnXDko9+x/dAGDjtI6XCyeDpg+PO0UD0jj6Y8WUXgzVZ+L0TSG2eg6Yid+98y6PNzM9+5gHnY+Ii6VOx+WcNuD8G/AEgi8hIK8ep6smzT8H/9+Gd53h1Cx06I4/HQPCB4+UxMQTicmk+SSrknEg54ebEriaUSK7BeueTomJ973bTzk2p3x6p0zRZwJ3n1qVkAbeUjIjJ8Xnv6VJHrZVpnJhTIit4VeacmHIhF49kB7Pp6M67DVSlIxBcxNVVo6o5qszkbNYrtUIVU7zyzhPEIzfUm6KqRUT+aeA/vJEDPMMQJ/TLgePbR/Rjz9ndY05uHdEtPL4zx9sDJ5P90lMOnUy28rB99MuB2Hf4LkJyVIxRYEr+1unlY8QH6w4rFaamnbo7fGmT9zNczmlBxFZAQWC5GuhPFpy8cEqpQrccCFMyW3YvLE8W9H3k9p0TQAjxAoDtbmS72ZmDQLY25APl6DkOVf2Z9v2nnvvOv8Zhym2GyXq3p4WZW8KvzXaf2PCAo1rJSg9NCiLGMOicZaS5VpRK1UzVjNNy6CbMJbPbrCldZ/dyYxk4lCF6Qhe4s7IAfzZ4+uC5c2wBt/PWUnwIuK4FXLWAm1NgnjPTNJNythZkGs3sKenoM93Nqnr8LO+7qdH1C17/2CeQ5QKCR4Ye7zxH/WA4bFNgd3jmNCFhYLPbcnn/ginD5VVGGQneur1ib+2BU47GEazBOoW2poOaW1NO8B7VyjyNxk6YJkAZOjOxSxlymtilGecTl+MaRiWnu7jZs50qc8o8fOctSpoJxeElcDScE7vI2d0F4pVNuTCGS+4ZomMXLqlBkbAw/O/mxv8uIv8pxq/e7F9U1V+8yYPuh/OeF159gd/4j/wIpdRDccwNlSIj0lxQHQ5VR2yNcPulaNUCrnJ655iSCq+89gpDt+TRl9bMNYMmqIrzHSId2XXsVPjSOw+pqlxsNkwp8+h8yzQXzteJucJGnVkuuRnnKkOXWS16Xv6OD/PCS7f5th/8DtJc+NTn3mJCyW/cJ6nSLTynt5b8+h/8dh49WPOVL1yy2Ux85YtvMs8bLh6+ig9wcmeJDzdXDBWRbwf+deCjXLvHVfUfvrGDPnF8iA1Oc05YdgFBmKpJWU4578lf7zmC7IO0wRFeleDgzmAwwGtnS4LAZrMlJ2W3nUkyk9U0c7e7kXGc+NSnP81iueKl1z7CqusI0TH0AyfLFf3Q88KtjuAcpwvj8R4NEe/sQeEc9DE+oV2cs9EGt+PEZpz48v1EzhO1zGjOBht+PY0PIvKdqvopEfmB9/r9B3Vjehc4Or6FLBfm/Nkb/raMph9rLp7mMeQmjw8duJlShZSVcUpULcRg4s8VU58qtZoZnJUerap9WO6oSTweOpUS4zha1pXnRmVx5DKT84wnkWomNZ3Wos2vLFfm2bihuzHhSdQc6PpIPKq4oEw6IU6Y0ggEdtMVtYBwjJfuJk/t97Xvf/zaawp8IDcmAj56js+ObenfCc5jjg/oHgjksUfZHg1sdK+WAccuMix7zm6dUmdl/fZkSnLOMFoaPbBYsZupVopWdiUz5cxYK7NWkkB2RhlSUXwQXHD0xwOL0wUnd085vnPK4niBmzL9siMOETB7ntKcCE5Ol2hVlsueNGd22x2bdWTcTaRpME1kd6PY/H+LdZr9GeDmqnNfZUi7v8TtXW5atqqPVyvGSHhyK/vF/qfH/Ng+mBjNUR84HjyL4PECs7cJFap1C9bSstl2lTT4abFcMSwWxOjpu44YIzEE+hiJ3rHorFW4j8GU5ay7ny6YFrM21bppHEk5c7GdWI8T59uRi93MXGrTZn66x/bT0qc/gKlJ/SmeTJb3V/4HcmPGfuCVj34bhA51ggazSgl+Ngk3taKZRI+MJiAyzpmHD9dsd5mrdIniWAy98V2rdaD1cSD4wMnSsOBVb40P4zxRa6GUjJZqPf4pMW631FJI2vSMaqHozG56B99NjCURq2OnI1V9M6YsBDlmLhNv3nuHWjLLkx1971j7pRWFuhnvhV3eEV3H1XpHH4544fSK4PobO6+q+ptvbOfPdHzrxuqWdhmqlBZs7aZxYku+etCUvk5MV3JJUIXFcY9zZ3zf3/+9PHzrgt3DX+TCrZk2lZwqIh71no0rlOA4PllQUcYiTB7mIuTscYPHK3SuIh5OjpcsVz0f+9ZXOLt1xHf9wLdzfLqku9VTNsrZiys244i4QErKvXv3EZf5lk+8zmoZefW1W8QgfOnL98hl5v7bD4md8OKH7hD9jT5Is6r+5zd5gPcb4oRh6HHO6h+7cUcppk1S1YTCrXFpbzm+n9XWpNDMPrVUohdePFtyPAS+55UjFtERqon6b7w9ILVmSk5M4448z/T9wDAMfPijr3N26zbf+p3fzWKxZOjjQSkMzMLJnuFy6LcQkYMHXqmVkmcenZ+z3m75lb/zJS52I3/n0cjVnPn8RWKblAfbQFYxaOkp5+ZpnWa/t/33H8X67H/Ezgp/FfjAJlScox+WqI/WnOQzSMG5jGAamSIgEVxWqhZKzsy7kWk3k4plONoZUTJNs/0VSakhksKAVJDQs3cVVaCkRCmZPJvJY06peZBZt7xootaJWiZEZ0ymHOY8I9ksoKESQkcMWPsu1tmUijKlTBUhRitxpmImiK6syQGOhiuivznNERE5Bf4oxqsG+Fngj6vqxY0d9NpQNaFuf2CQ7/mPajfcvpZCC7iP25EOGRMoIYDvPce3TkhTpVt0+M6bCNE+aDsj0Ptlx+rOMSowukycEtp3pKLEZJT32RW8d5ydDaxWAy+8fIeTW0esTlf0qx71inqlGyLdEHHOU1TZ7SbG0SiBw+A5PhrYLHvDFJtwTppz+xueP4bbugUBfkZEfj/w3wPTtfP98D03vJFh0E/dOzFU+/++l2CPydrnPmxy4MAK5s7bBcfdo57jReR40dF7ocxmeeUbpqvGASUGj5eO1dHAcrnk7NYtTk9PWS2XDMOi2atDzaU90B9rJTsx0fRcMYEqrUy7HfM88fbbb3O12fLFN97icky8sYNNhodbZSpCqsY4kvcFSmw8K0D4U8Al8B+3n/8Z4M9i/M33HCLy4fael+xU8hOq+h+1i+LPYfjS54EfVdVH73dw5xz9ckkx4hf1QJA0UWhV4/CEweHKSJ0vmbePuHj7K2y2E7XviV3P0e0FgnDv/jk5V2Yx0RQZE1034FTx3opoNScuHt5nnmfGaUvJ88bA5AAAIABJREFUmXHcorXiqhHC0BFlRuMaJKPBkaXy8PIRfRKWix7vAme3zjg6UlLqmOdMrhPiC7XMlKIsmlh2ziZenfOO6JTIPYK/UQz3vwL+No/n8Z8F/mvgd3y1DZ7nvEKzvN/DmWoNLT4L9dB+L6h6uw3VxEfKvqApBjtUn9FBuPXabaSLDHeOCOPEjgfMNYELSBc4e/0uL7xyi+//oV9HN0QePnrENM9cXGxIqbCbTGA8SyF4z+3bxyyGjpdfvm1+cwsHXtnlLVMZOb29YLc5IoYFeZy5//YF0UPwmWEV+OhHXqQPkc9+9g1QIc2ZeTJ6WM03Qvb5BXjirv+DPLky/dhX2/B5zmutlc1mi7bGhNQKZRVzWXy3u+3+AzehPqOAeQu0Z6ueH/7OV1k1ZlApha1sEQer6HFJeDDtkJJ49ZWXWS6XvP6t38lydcStW2d0XcdyWBKcI1JwIoRFbIL1Di8QXUW1sN2smaaRN99+m6v1ml/57Oe4OL/g05/6NJtx4lGJ1G5F9/r3ot2CbfYUxZygscz8aeNZ7+Zfp6rffe3nvywiTxOvycC/pqq/KCLHwC+IyP8K/PPAX1LVHxeRP4wJqPyh992TgHiH1GukPYV9N3yVxudDQGe0zGieKNOOMk9IFEQdrhqwXdJEnq0jrLrEFKySOe0GvA+oKCln5mlknifSNFJKJqURrRWvVpEUZnAz4io+KD44jPRgGqq1VgPgPXh1LBYLvC+k0oEUglvjpeBdQWSPc7kWWIRS8pO3y/MfH1fV6zSwPyYif+Mp2zy/eQVo3On9/43+YpnsY7k8bYGXRopvb7cGeOs4dIIfAmER8UPAdZ6slbkWvHNUB91Rx3C64OTuMf2ih16Z50S3Gki5MI65dZQVvBfOTo/o+8jqZIEPjuwLde/gLJhvWm9W7E5c49laoSh2nuOTBeur0XRaXSti1z3j4lmm52sbqvq67Vt+FPifWxfhvw38APDvPmXz5zevam3VtUEHKk3LzcG7M0ALtnrg7gqmDNh5x9kycnvVc/uoZ9FFrrYTSdUkNdXTx4B2kUXfodFz69YZR6sj7t69y2KxZLlamsuyb/q5xnxvHaHmGJOp7NJIzolHjx6y3e340pe/wuXVFZ///Bc4v7jkS1/5Cru5sBtuI8vIcbZ4VNQ1fd16TU/h/cezBtxfFJHfoKr/J4CI/BBP4fqp6pvAm+3/VyLySeBV4J8EflN7208Bf4VnuDFFLMiZvmlGSZSaUMloyEit6FiZd2sYL2F3RRnP0TmxOO3pQqGsL6m5Mp3fZ54TRQXnAnnc4n3H1cW5tWEGR9XKOK7JJTNPW0pN7MY1VVuhTRRkJnSVs1NYnAROby9YHAlDDz5Y8aAqTOUSwXPnzi2oHa6eolpJ9cuo2+HDI5BKrhGRSB9u44hQPc/w0Px6xk5EfkRV/5qdY/lhYPd+GzzveUWt8cMKLXbrqYIWJaXSHkKex/5SSq0ZaKsRZxmGeCEuHe7IM9xaEi4GrvLMdhpZeGHhleMXlpy+vGL10oLFaqC7E1oTgpk75ilbcSSlAxNCBEIAxB7uDhAXoMLxyRGbk8Jy1VNViWEghoFuWLFaDXzLx14m9j2nt5ak0qrYFbrg6eKNFs3+LVX9aRH5EazO8icxCPCHvtoGz3NeTS2sHh6kzgmIHqr92jD5umchSLPGqpXghJPBc2fV84Mfe5E7RwOvnPbGctha7SQsBnSoHEVHTktOBmM1vPahVxkWC05Ob+O9p1ZraIrOTEQdjcVwtWaeZx4+eIftZsPnv/AFLq/WfO7zX+BqveGNN95knEYury5JOTPNGboli9dfJyzvMDPgaoRScKqHFvFn4Vc/jaXwS1jcjsDPicgX288fAT711L0/3s9Hge/HnAVeapML8Ba2hHmvbX4vVrDj5VdeaXw849hZj3ylajFBGxIiFUempJmaE1oS1L2bry1Vas7kOZPmkTTNFMUUiXyH942q4hpPkkpKM7Vkcp4oJZHLzm5O5+0C8sluyE7oBsew6OgHh5NWVBNzjFUxrNl7o0JFVqAVXxZWWAsBJVOS+bIF1+Ml2oV4A5nQtfH7gJ9qWC7AI+Cfe9aNv955fenV23sKNHZZubZSsWHQAQfcb89aqEbhMHF2I5jYdgJ48H3Ad8FMHlsfPE4IfTBoINpDNRBQ9hxKpQTTX5Bk2XWtzaXQ7T2r9nPqTMc3RGKMhOiI0RFiJMQO5wMuRIZVz+Kop19EJDWSkz62ebnBsWcm/HbgJ1X1L4rIv/esG3+98xqO78KBK9B+v/8nVlRTNRdrsHtTGq4QxCx0FtFxtuw4XfbE9n5Ry1Bjw/zD0FOjx3FK8I6TkxP6vqeP5voxFzMFnZPx6kvLZM8fPWIcd9x7+y3WmzVf+OKXuLy64ktf/jKbzZZ77+yhxB1aleojPgCxh9ijtMz2mmrYs65En5bh/mPPtpuvPkTkCPjvgH+lLXEOv1NVlUObyZNDVX8C+AmA7/ru79E5zeTZ6BkpT6aBy4zqTKkbhEwhM663TOst824Cb7JsbvDghd1mZNpNXD18wDiPZM047yl1IsSBQc5MXWhuWHGaqDUxTZcUnZnLBUoxbqxYj73vPEe3Tzi9M/Dyh16g6x2Xu0uohRDA+YLICJrJ05ogcLRcNBvw2+B6WGyZ88TuaotWYTg+Jvolfexu+sb8JPAnMD+zM+AC+KeApwrYPI95/Y5PfETn5lMlTgjeaDgVw/7mxnucm035XhvY/i+o6xFxVGkZlCSqFBYnS5ZnKzREK7D6gI8dq9WK1WrJXq81kQ0aqhnRCloaLmzfs+aWirXsuhXznPPgIsPiiMWqcHQcEV+5deeMk7MzCAsIA6vbyklOvPDyMbvdZJ5etYnpfz2z9vTxFRH5L4DfCvz7ItLzjO1tz2NeFy9/rJU4mxqYmqxqEDMQWEQrXue2Atw/2FwuLALcHhwvLD2v3lpxsrCiY8oVasJVpe96vA8cHZ1a9lzvNNfmhV0XNVNyYbw6Z5pG3r53j+12yxtvvMF6veYLX/wC682Gt956i3Ecubq6IufUGp8qKSWjxlTFOU9cnBBWZyxO7uBXZ/b3tuL5k5H26VH3aSyFLzx1D+8zRCRik/ffXJOEe1tEXlHVN5twyr2n7WfPqdvL3h0Ixu0hqimjmsmaKLNpYTovLJYDrlZCt6d57GGIgjhT9xKnKAlVR62pZUrBlq7F3l/rhGoiROsIk1jt8vUFFyH0jth7y3ZCUwFTUxO61hyFeKuKxs4RfQDpUJeoLcP1rSPKibkHU/2NdCRdG38BOAd+EfjKs270vObVslUOhgp1X6Vu7IW6/94En4uWx733ziQSRZqrgpjDQ9VKiB4fg+nniYnVi/MEH3EuopXH7rlqHE6pFdHHHOw9tmyJ9R5fN6yuFtfw5ZZ5OcPpQwyEEPf3KjRLpdiHRoUyvu4em77B8aPAbwP+pKqet/n4g0/b6LnNK42B0K77GEwkqu86gncc9SYKZfoDSs7GdfW+MnhYeKV3SmyW6KUYv1bE4Akv9nCOIeC9w4tvvwOthd12yzzPPHr4gO1ux1tvvc16s+HLX7GA+5U33mK73XLvvmWy07ijFrNYMvlFe1D4ZpHkQo+LPS5EnA8HCtt+Cr8WtYMbK4GLPRr/S+CTqvofXPvV/4AtW3+8fX8mQZx6oAk5QhhAAzmN1AppaxX/XdkyTRNhMXB8x/Edi4G5FC7rzDQlHq7PyXHi6EWz2K57/UpNIEpxI06UbrGk1spuM5LzyJwu8bHywocG64XvoFLYjBu6obA6jSyPO5z3KI55FLvR6XBSqUQTNzlZ0IcFp6ee6D0iR1Qc2zwgAscrQUtPHwaokfVFppYbvTNfU9Xf9rVs8DznVTGxGFEzkSwYNltK0x1tGVIIgb0O6aEpRSHlDAhztqJL9lgRbOgYlj0hGjwUuo4QB0I4JrgVOQniKkVMtUvzBFXx6iyhLXuYQxpmbFDWnGZQJbpESXB1NbLZbBnTRC6ZrouE6NnszDU6J/NVi/2CnB0pKeNUmaZC7G5UWH7LNQnG6/jsVxvPc17Nj8/w9egdL58ds+g7Xjw7YYieF446nMBmnMilsJsmtBR8TQQtnLDjxCU0VXIopGSC8H3fAcaf997hVHAqdF0ErWx3V2w3Gz71qc9wfn7BZz7zq1xeXvGlL7/Fbrfj/PySlJPxgmuxVmstaNnjWq1457zRQ32HxIFwfBe/OkW6JRKiNVmgBxfifW3BwK33v19vknP0wxjN6JeuVb7/CDZxPy0ivwf4Au9DLTuMdj5aDRtpjvWCt+VKy3YVS3xdFwhaGbTH10IuxiLYHTl853B9T62BSqFWGLdiKvyiqFMTqxBrHS01g9j2q+OOODiyt4LbjBJ76IZWrQ7W+RbjAJTmBpyp1SAI5zzOO1QSKorz0j6/aSZErygdgkNVyKkeRLRvaPyciHyvqv7S17DNc5tXo3ipBdw9Bqt6cGrYu+16bzJ5phK2F4bmwGCwc6Q4tW4voxeJZc6uLVtVKRlyhpIU8ZXqmhtHqQepJ1UoxTLrUpo6Wfues1W3RRwlmarcNM/m0FxLW9EIOZtfWq0mgrOvw89zYZ4yuXmm/T02ntu8Oieshg4nhS54bh0tWHaR28uORQzcXgYcMEghFWHrCloEXwquKkNWOtmvMhpLBQ62OtZ+K2gtxn3PQi2ZzdUVl1dXvPX2PR4+fMQbb77Ner3m/v0HjNPMerNtjs2pccDL/iJqn7xl5WJC984HJASk63FxwNogzbh2j+cDh2D9LBXuGwu4rfL91dbDv+Vr3JddoOoadcSW7FSjh/gYkNBRY0WzYzEuYCds39ngfOXOy0eoV66+xTXHqTOrSidh3BU+/yuP2G0r63UEiXSLgZwyc54peWJYOlangQ9/7C5xgPvrt5hzhgX0g+f2C6ecnJ5wfHybEALDcBvVQuGKojOlpOZM2pFL5dH6DYLzrJZLoDLPC0oODEFQCdQslFRIKd1IwL1WDA3A7xaRz2EEeQt51xTE3mMunuO8wpjMrlxQK1ZqZdxtAGXoIyF4jldHOOdQvw+0zVGhYbxpnltxo5JmC36VigRBnbKbd1xtt9x/Z43veo5uL+kXDhdHw2trtnulQQW7lCi1Mud0OJa2B4EAxStpqtx/cM79B+esNxuL105RB+M0mXuBmqvHPCu7XeGttx8xpczVej74pP29Mp7nvC66yK9//VUWnTJExyunK3rvWApEgaNgReXsnYkIDcbGqZNaZ+ck9L21B++rxiJKF4zNEJyCFqb1FVozYxnZjSO//NnPc+/BI372536e84tLHj54QGoNJ7U93FWFKg6oSPM128vkiA+Ic4TQte8B1y3wJ7dxy1MkDOBCC7CPWRiPe3ae3kX9jWGTDsZfbH+f7nklWGbkvOGmrmnXVjJFE2keESpBIuJhiEYh0n11O0e8z4To8AG8DyDBqsxNaaqq8Wtj9MTOEzvdm6bR9ULfO0LTaEhzphQOTrJVbFJUbYJLrqgzbNg7Zy2EAqV4tErrrzdls71qmLobwXC/7mLo8xiKdSHV2niMDTIojZawxzotc7RzuGc0qO6ZBPv3GbWrXnNklUbuzCUzp5nLyw3D0cC4PQXxdHsWSTtOLdq80Mphyam6V42j0YyEIuZ/Nk0z0zSTSwHkgNkXteKuVNPr2LsRj+PMbjeR5mJFoG/SEbzjxdMlyx46LxwvIgFFduPh3hVRXCmIKlUzpWZKGtGSEWqj5LXroCRqra1IBUUTtVR2m0tqTpR5w3acePDwgofnl6y3I5vdxDTbA3lvTinexI9c3ceQAsrBGdscXppDhzgLwCHgYoc0zW0ADopne9+2/ctPMjPe89w897N9A0MVairUZCfcO6sk42acSwzBvI1q2jClkfOHb3J5ccmbX3zTlNy7DxEXwrw6J7tMKgnBs4p3iE5xmggIx6tTRBZ0/YIJIVfDjhdHC4ajaFSxkhnHNVUTp2crFoseL5l5t+HTb3+OWhXfKT4oR7czPmgTyIGL0TDDUjc4L1yNW6LvWIYTvEQW/YDgKT4Qo1KaHNzzP59fXzH0eY1aK+M04Z23ZgFn5vIhRKDixCySbBlnxRMTiJ+sZTQXC7B7iAG7BQqFQkWiQIDtbku9rPyNv/X/8OLbt1mdBM5urbjzQkcIzkTkqYf9T3MybHEcG35r153zJnwtWpmmzPnFmvPLNXPKeB/MTTr4ZtdTqEWZ5kyuxrS4Wm9IJXFxuUX8DfMU/i6O4yHyw9/2Ml1UihYurjZs11s++6ufgTTx6kmk8w68dRAmlJRm7r35JUB58YUXcSEYa6TMbM/vkaaJeWsSl5cXV0zjxFtvv804jTy8uiJV2MmSKVfi0S2O4xJcJOdMzZV9h2rFViq1Fuo8Ilqb0y8I9nDNybzvNAxot8IdneKGY0ANxqgGYe1rDHtWmLEt3v/cfEMEXBv76qGaeQIF5LELbikT03bDtN1Sppk6J/KccKpM6y21CMmNFJeZc0YksBQTPo0eShCkaS6ICAeDFYHQOWIngD1lrYNGCd4TvFBSIqfK+cORUivdUgk9hKNKVCG4DlRsyaQVlRkpsNttSb6AXxCDYxGC4bzRUZwSZqHcbNHs7/5obI7rMnjeOwRnHVzONXzesNpSijVEaG2iNo/Pz57ZYE4DxlTx0dFrR+g8m+2Gi8vA/XceUXKi746JvcNHw+MUeYINsy/UHR56zYogY59hHCemcaZq4/K2jNrwQQ7Ftj1TpdRCLqVhuN+8Ga4TYdVHXKjkIpRquiEXV1fUaceRRrrg8CEaDVCUlGfSNCIih5hVigWwaZyYxpH1xf/b3rn8WJYkaf1n7n4e9xGvfFRmdVX1VDXTdLfEBgmxYouE2MAKwWr4D2AHYo/ECrFGAmkWSAgJJGaLEEKs0Dw20NM9Pa1hWjVdlVXVGRkZcR/nHH8YC/N7IwqarBqJzHjofqlQRN5IRXgev8eOudln33fJNE6cn1+wHQa+fHnOMI6crzcUHM3RHPENJyenpJzogjftk3q/TtUxZJwm24thAM1ISSZENQ3XQVMEgvFuxTeI8/tT1O50fc1TqLTB8kAyXBHFOdMgoB7xkUQsV+S85uryU4bNFZ//6aeM44SLwiwG+tSQxokXP/0U1yj+aaF45SpO+NDQv7dAxPPoODDOPK9eW5NMswVV560+fPJYWZxkxA/W1RwdKoGWgMvw8osvGYbMz35+TiqZsw8cs6Wgc6FtPL4soXjK1iMIiyMBp6w3X1GyIw9XzLoFf+mHC5aLhqPjllIyY46Q3rm63juDiHWYd0LPTbBZ+zbMTVPVm47qztpks96Qc2IcTY+l62YgrrqpFlKcmCZruIkT2lnLsR7x9OkznBNer1/x4sWG//qfL1ku5vzoR9/j+GTO8w/PaPvA7LipI7jGVnDSgK/1OszOJZfCEDesr7a8+OIrzl9ekUsmSEB3AtiV7jalTCyZ0ELbC2FrrAkzzoy3e/HfIhRhKI44ZoYp8dmrgfPzK3766S+Jmyt+1dtww7JvCU7ovQ0nNcHTtD3dbI5rOi4uN5SifP7VJaurFX9a+bOfvzxnnBKrIZKLDVD0s44fvvc+R0dHvPfsMU3j8Wo8pMYHcim8urpknCLnl1fElNgOI+Mw8uXnn7FdXfHiFz8nTpOVjnyDO3kPNz9GQ08Rh04bLKBabdma6/Y0NTbLg6nhGle2qFnh5CwgkZi3xLRmM1wwDFesry6IY6bxS1yCoA4tkKeRHAtsHMUreUoQYNqMhCbQ9YIEwW8ypR430Gx13QCzBcwXStOY0pd3DUpGizl/Fqwwn1OyWXsVigo5Q3Lept6yEgcbZmxbY0GMcSJnYVx7ShSmOJFysFoSmayjsSQeKGzi6usfIoCz5oiv2W2qmWLR62zTsuIqUl0pDrus1Opw9uGD5+h4ifPCVNakGLm6XDMNka++uGAcIvOjnnlqCTPBBbnme9ezouzG32rHPMbEOEXGMRJjtAdz8HV95vwrzoRWihZ8kDqN5gnBlMXeMvvkVlFQNqkw5cwwZdZjYjMlxjgxTRMrzUwOcooEJ+RQaJvAyekpXdfT9TO6rjd6FopvOnwbIbRIk/BdT3CZeTsHBPGB2WzOk8dnHB0d8fy9x7RNoK1shjY05FJYXi4YponZcskUE+vtyDAMjMNgmgvdzJqfCWhaJDTgA0ULkjNa7CEptfSle9Wl65PWN3Fy70XAzZpYpa+IcYKsDCPkPHK5+QVjes351Y8ZNysuXl3C5DnxLZIdyyJEYBs3kArhoqc4IYwZ9YWL8oJ20fLeD84oznFVtrBNbF57ikaWJ4l+XvjuXxCOThzdvGGaGl5fPmYaI+v1gHOZtusQaXn+/iNwmcXTSOgyTiJaHCV7yiS8fjWhGcaVBZQYJ1JWNts1s/kVL8+PyWVOPzuhaOJi9QvG8e3JM94FBM1IwUxBxdWywjXdxnSCFFSM6O5swMSyY9Mv1sG87HJxVTimoWkDKsZUePL8EfN5zwcfn7FZb/ifv/czXr1e8fu/+mMWyxnDMHH2ZMEn/RPaWaBYgYCc8r5pZ+whK/FcXq64er1msx2JSTl7dEbXd8QUWW82XLwONG3A4YhppJ8LRRucP6ab9aQU2Q7DrV3zt40hFX76ckWMkThN/OrVhs3VgPdKaJRNHNjkzItXCQ+czTqOl0s++v4HnJ6d8dFvfEI3m9H2C1SFo0dPGKfIyYcfMsbIMEZEhOV8RvCOeetpgud0aZZb/WyG946+aWwE29nDeDNNxJy53EbzmltHVpsNx8fHphynymq15mJ1RbajFoVMXl+AmG0TztG2nelpyHUJEr7dAMS9CLhKIZaBmAc0FyQrOY8M44oxrZjihhi3lDwhOVA0ItnhnaLe5OAU8Ji1coPJ/EkxhmTbOYp3+FZxMVNK9Uiyuj4hCNbjKIDQNi1aHDGPJmaTrH4XvCDB0TSW0ex4mTulzJSUEpVt5WCW6upKVZGKaWSKjpi72lwbyfqQA26th5U6UquhZpTAro4uO5aC8ZhFFFW3z3BVbdwWduLPcn3Mq3+cE0IT6OcLQnB0XU8cle0qggqrqy1N76xU4RWc6UrdiPt7VkTOhZisFuu8t+DedHR9W0tdpjSHKE5MQKXpPF1pUKDtWuMEf4vj531FUWUzReKUbPijqqjt5Bf3vOiSURGKBAgt/eKY+fKYfj6n7XpCa4MOLgSaLvMoJ2JOpJRxznE0t97HvHEE55i1rbF/Kh++bUyG0Yu9F8Q7UlEIiZgKxUd8CJyenVGA00dPcE3PRjEXB7AyUZrQOkIu6iilMSGcqu2whyrfFHLvRcAtJXG1fcm4uaCkhMaRnEZWw2ektCaNG8gTs9bOA2n4EorQziFowfU9KkporeY3TwLe4U5bmmXg6LQjiWexsDLAOG4Yh1S5zI71pSMnyLpCCSwWJ7S98urVSEqR9RRRCsWZoVwIStNYCcI5CwUlCzkVhm3h/DyiGVoXaNuGpx+eMj8KpDix3RauVoBkxCVC83CPntSOcE42tWONa0/b1hvFe0Bw4muTsjpD1CN/rjzckrKZRRaPZHuo5ZRJKRFjZr1e0XSODz9+jpYTLn80cHG+5k/+6DOyFl6evyLqwNlXDbNFR+htVn82W7AzD805M2xHpmliux1IufD0+RNUHfPZiTkQn7b4AOM4Mk5UJ2nl9OkJJSvTYHW/IokxPtx9zTmzulrbAElKRvUqhYBHpSG7BkE4XkDbNDx+9l0ePXrMs0++z+nJMYvjJSF4e8Ai2ECXcnbcg+5kFiE42Zel7HN90FaellQtjFQzT8GCb+c9Xhwnc08fAr/5ycdcPXtGM1vy6uI15Sc/5nK14vzyFXkcTLMFIfumcnU9zgc8ESqFDNhnum/CvQi4Nk00kHWgaER1IDOhTKhUzVgVy2QbtVFNdbjgTbGjqzY1LqBFbHjCOZqup+nDjSMqtC14b1oLRtYVchRSNJ1UESF0YkZ3wdcJpHyDd1u5wXI9KeU8+CD4EPAuE6eIRuMPexEcRn9KMTFNyjAOZjUj7Dv3DxFGpTGusnNG81K1UV6A68lJvVFL3aXAu8BrnWPNSrW1IkUT+Xa7Om/Nkn1Vvl4cz0mp0M2bWnc3J+c4ZUJjYjemrWHNtx0f2IbRbL7eJg/nCJ5+NiOEQNsJ4mxEWdVofWAaCxoAjOOJYBn9A4bb9fDFAqN6R+t8PT34eiLEjAGOj1geHzObzen63ia8boqQYNNfTb2fG1dn97SO4lY27O6EU/ZHk+p9t1/Lboii6uOKEIJn1s8oCKcnJxRgPpszxmhnJC2oJhSHumCDEqpVYlKrzsa338v7EXBLZhougNeIjzg/4TQx62xaLG5b624fVXrH8dpujLDE+ZbF/AOKwsXFFdMQGb4YCKHhyckj+pOO+VxIojx+nGmbzNWTNauryLRRSlKmrT1lpUlIKIR2wuM5PpsTp8Trc7NlLppvDGUAmBj1rAt0bct7z5asZ5nXn50zpULJDRnPdpWsuTJLtAPEsqJpHbN5R2jeqvfVrUKLsh2M9C4ipCIEH0jJstuutYdXLqVWHypZz+0GVwolFfKUySkzDplhiLw+X7G+3LCcz1EcR4sls74nacZ54dF3lvQnntV0wjRN9J2n6TzTCDhIrhBKJnQF52wgQhVcaGlc4PjMMtfgAiDVqJJ9JpaKI2fz8gKl68xoUtoa+Ht/Yw7/4SE4x9mit6GP7EmyIIVCM1syFDiPNhzSzDzz4wV/8Qff4/HZGWfHS7quQxVSVpS0L+eI2Eivd0bL3iWTAviqlbx7uO4V9nZGnaXOTFU9lpvkLYczZ5YQeP9ZYTbref7kEcHBy5dfktRGtK1EVAdw9EYhoVIW64jmN1+b/z+X+O1CKZS0te6IuU6hrlQVd0doG4qoTWqpvftVrjvZrg+QQU0dj5yy1WKSkpONDWevOK//CnyJAAALw0lEQVQ0rbI4AhDWC493QgiO4BXfmY5q12M1Rt8R28A0JJN3yxHfVK81MZ1Mk/NziHe13udqnUGropIpJZVs3W+8MoxCVk/Xd29bD/dWoZhtds4WWFM0nrP32V6r/67kfCOHsWCsyt5hYfdRstbgaFnwfDEDHF3f0nbG+USg6QOdNixPeuLkCd7TtoEQWrxr6lSbu3Fn1szIObNoCQ2gNDUTT8lm8mU36y8BJOOdtxryLmOr5qXO+wd9cnEizBpPLmaVFNtAmgJdCGjT0DUtRaBbzlgcLVkeLVksFtU5Q77W8Vd2esimQyzFJvodcj34xc2Z5H094XpBlWVyHSR3Uddecc4RPHRtS9+2zPueed/R+ED0iayK1mBr7w3Z59M3fuO3wv0IuHliWv8SF0bTKcXkEX3vcW3DyQenqMY6UTKRgz2FyjCHGBjXkTQVtpuRaRWZXk2Mmvg0vKA5CSznHjdzFFqaueOTHzTkyfPsGUDm9HGm6Qqz4wbfOrojI0PDCTk5Xr9MTNPE5foLCltcf474VHVUGygNgolihw5cA65V+k5pmgwy1NpxJpVClkLbetqmNXuWB4pSlM1m2lO5SvE4VxgnG/V1dfInVpUua5o5KwE5G4zQomzHiZwLKSsJ6OY9x87xmz/8GOcCz99/Sts3hM4ylX7Z0XSe737vA7QoXWgJPrA8WiDOkV2099fNoy3QSNXwqEMNOxEU01gQGm/r6tseBZrGGmW7UkZKRvGz9T/cgNt4x/PjuY3s5szGK6NkxsXCvMWWS0Lb8t4nH7E8WvLs2XeY9119ACej9HHjebevEJTqf7cTLr92jIBfX37T/zOlZSf1Wm3UsYxcgmlA5HnPs8ePCc7x+ekLLlcbztdbsgrqG3ABJwGxFvy1BCs3DrZvwL0IuGDjt7KTP5Nax6s0It9UcZHoUQ0UlyjFjnpalDJl0lQgCZIFiqAFpjFShkIbm9phti545xy+FY6OGsDTtdE8y3xVKnJaNVBN/2C5bImxRd2KXBy5XaM402nF6oFStR6cd7S9NQ3aTghBcL4grhKnayc8F7dvDj1UmG7CdeZabMaAmAqiCmo6qHGyoOycHcUVYyxQRYx2tVVTB/OErqETYZHt33WzxjSRnTlFeBxCYDazJkwXutqs6xAHUxU0ucF7sHu2mIqYFpt63Gs4KHVSrQreexPcVrWAK07qmku99+VrAeChQQT6xtv4tUAMntwEfD8jiCN4R9t3nJ6esljO6brOTg1i12ZXbNk96nTXC5HrxpTc/GWwD372tbJjqUjNmKX+oL0Kwo37yolQ9hq7nsV8xjhNHC2PKCqspsxUlFyz2yr0cF3X2L9THkhJIYhw2nqmRigCnsZEXYJDPIS5dRHTekaJgWkFacisf7lGR8FvesiObpgTUiE7R5ZCopiffPHk6NiuE5qhyYkmeB6dLvBBKJopObM5X4HLuO0V0jiaTmjCnEePPsQ5x9nUksrAOgVSnrhaDZQikDtUPeId3Vz46HuPKSkTdINgmhAqhchEKRakd5SoP9d55d5BUMI+kIqY5c00mjXKbghlHEZrrHmHd56FuuqmYc0UgolCN+IJRTltPCkl+s0ACP3Sfn6pNDSn5r7rF3adu6bDO191dwupepq5Ui3Ws1GYxmE094H6fRvAF1yoqla1Pd4Ey2CdN3bFOKV6gzegyjjEb5UN3Vd45zieteTkzZyzyfgZtB9+TFsKZ8sZs77hg+88omuDjbRLbXKL4OtbvxqA4GsE3ln0hFqO27ETruOfXf9dfXyvseFq9lnbK0lNtF5rgBcc3gld0yACH334Hc4enRJRXr2+Iv7sf7HeDlxuMwWxcsbu840TkNV333xt7kXAFaAV6/QWwQYJVMjRso2d3oBmjyalDJ6yVfsYFZnUOLcl4FBc06IUfAeu9Uj9wWU0+2odExoUXWTA72+mEk29S6PUElGyOWy1gnrbdniF7JfENLL1WP0n78TTjRPazxtKcrhs2poqrrIdrAtv7q5cE0AfMFSvjSNLHXRIqdjxMdtkWd71IQVA944Kuxqf1sEEBNRZAPTiCZ29vcXDzlJCazFvX5rdXV6tR9ZKN9tnRbBfS6qiNjHZxJFvgjnR1p+xmx4rWiyow77WvPu/FS1VC+Lh7uuOorXzmnO1/NPNFwjK4njGvGuY9S1t8HY4l+vj+S6I7lk+N3OP/de7N0TNZlWrupjsyxD2Wfe3Udl/aFVw2zEYZP9eQoSmbelKYXl0RMxK13VMsWBWWTeCPOy92Ki/85twLwKuV88Jx0zzngRcvtoyxchmc0nWTOkn24iph1iQ84IbIs3rjPkNNqAB8gwRR/e0gwbCc8X1jiAL8gRcbsjbyNVXl4goeTXSzjxHj3tcEKQI4gJtnKEZNuvE4Df48pK2a5kfLfGh4WThSTlSpnOGceLVxRWlKKHr8N4xPwqmvRpbwOG8mEj5ldUG81aJWUnTN6sP3WdYcNWqhwtMVkIYhhEUgveAR6TDOcW5KsUpxsSMtXscc6Gg5L1+rVkhhZlFQ3WRotag02zME81KHG3wIfmEE8ueixYuV68BZTmfI840emNKXF1cmb5qiohzLE9Pan3WbvwY1+axhTkKi3OkVHh1sd4HXdREbB7wthp1Kk/ElEi5EBzMu4b5e48I3vF42dIGYVZdkFwNtl5ulHEURNUOEXojwFFDam16KdZEE6pdE6DFHnalPkBTsQA7pkIqyrBXcFOKCkUdhTrsUApZHNK0nJw9Bt9xfPQFRT2XmxFKwUm1W6+7+Odh+N2LgGsNFF81DOxIKEXQaKrt2RWrzcUAEZgEiVavdTctXdWhOKQJSKuW4TYOEW+bWhwkIY+KSCaNVq8tVdvUyseCqDeqSY5IbZo4b1bedjxpa80u4CSbuHJVEhJR/M4OvNi6nLf/oRN7vXwty73FC/8OsMtCLD291rdF1dyRga9zMm98vtEPuc6Q6ytSWQ7y9brdXgGsOkCLiulf1Js9F6OYWSZt4ie7+q0NWtj3nef6l+9+f9n5lpXrCUNVcip7jrHVnB/u0MMe9cFH5co6MX518I628TRecFJ2VZlfw8a5ZqncfG2Hr32vslJ27BS44RSh11/v/OTKjfdZUaWwM7OsSnHs2DDBuPPePNn+78GG3QPi+vM31QDlPhxtROQrYA386rbX8gY84e2t7zdU9elb+tm3hsO+Hvb1FnEr+3ovAi6AiPyeqv6V217H/wt3fX13FXf9ut319d1V3PXrdlvre7jjLgcccMABdwyHgHvAAQcc8I5wnwLuv7ztBXwD7vr67iru+nW76+u7q7jr1+1W1ndvargHHHDAAfcd9ynDPeCAAw641zgE3AMOOOCAd4Q7H3BF5G+IyB+JyM9F5B/fgfV8JCL/RUT+UER+LCL/oL7+SET+k4j8cf18dttrvcs47OvDxWFv37CWu1zDFREP/Az468CfAb8L/D1V/cNbXNP7wPuq+gcicgT8PvC3gb8PnKvqP6tvsjNV/Ue3tc67jMO+Plwc9vbNuOsZ7l8Ffq6qf6KqE/Bvgb91mwtS1c9V9Q/q11fAT4AP6rp+u/6z38Y29IBfj8O+Plwc9vYNuOsB9wPg0xt//7P62p2AiHwM/GXgvwPPVPXz+q0XwLNbWtZ9wGFfHy4Oe/sG3PWAe2chIkvg3wP/UFUvb35P9woqB9w3HPb14eIu7O1dD7i/BD668fcP62u3ChFpsI37N6r6H+rLX9Ra0a5m9OVtre8e4LCvDxeHvX0D7nrA/V3g+yLyiYi0wN8Ffuc2FySm0favgJ+o6j+/8a3fAX6rfv1bwH9812u7Rzjs68PFYW/ftJa7zFIAEJG/CfwLwAP/WlX/6S2v568B/w34H1T3ZeCfYDWhfwd8F/gF8HdU9fxWFnkPcNjXh4vD3r5hLXc94B5wwAEHPBTc9ZLCAQcccMCDwSHgHnDAAQe8IxwC7gEHHHDAO8Ih4B5wwAEHvCMcAu4BBxxwwDvCIeAecMABB7wjHALuAQcccMA7wv8GF3UWbXslUQUAAAAASUVORK5CYII=\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "#Create a image tag list\n", "category_dict = {0:'airplane',1:'automobile',2:'bird',3:'cat',4:'deer',5:'dog',\n", " 6:'frog',7:'horse',8:'ship',9:'truck'}\n", "#Show the first 9 images and their labels\n", "plt.figure()\n", "for i in range(9):\n", " #create a figure with 9 subplots\n", " plt.subplot(3,3,i+1)\n", " #show an image\n", " plt.imshow(x_train[i])\n", " #show the label\n", " plt.ylabel(category_dict[y_train[i][0]])\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "RJeCLgLtuDvN" }, "source": [ "### Data Normalization" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nFmN5PKmsqmB", "outputId": "8a0cdc3d-a91c-4e99-c768-e77fee3c4d30" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[[0.23137255 0.24313726 0.24705882]\n", " [0.16862746 0.18039216 0.1764706 ]\n", " [0.19607843 0.1882353 0.16862746]\n", " ...\n", " [0.61960787 0.5176471 0.42352942]\n", " [0.59607846 0.49019608 0.4 ]\n", " [0.5803922 0.4862745 0.40392157]]\n", "\n", " [[0.0627451 0.07843138 0.07843138]\n", " [0. 0. 0. ]\n", " [0.07058824 0.03137255 0. ]\n", " ...\n", " [0.48235294 0.34509805 0.21568628]\n", " [0.46666667 0.3254902 0.19607843]\n", " [0.47843137 0.34117648 0.22352941]]\n", "\n", " [[0.09803922 0.09411765 0.08235294]\n", " [0.0627451 0.02745098 0. ]\n", " [0.19215687 0.10588235 0.03137255]\n", " ...\n", " [0.4627451 0.32941177 0.19607843]\n", " [0.47058824 0.32941177 0.19607843]\n", " [0.42745098 0.28627452 0.16470589]]\n", "\n", " ...\n", "\n", " [[0.8156863 0.6666667 0.3764706 ]\n", " [0.7882353 0.6 0.13333334]\n", " [0.7764706 0.6313726 0.10196079]\n", " ...\n", " [0.627451 0.52156866 0.27450982]\n", " [0.21960784 0.12156863 0.02745098]\n", " [0.20784314 0.13333334 0.07843138]]\n", "\n", " [[0.7058824 0.54509807 0.3764706 ]\n", " [0.6784314 0.48235294 0.16470589]\n", " [0.7294118 0.5647059 0.11764706]\n", " ...\n", " [0.72156864 0.5803922 0.36862746]\n", " [0.38039216 0.24313726 0.13333334]\n", " [0.3254902 0.20784314 0.13333334]]\n", "\n", " [[0.69411767 0.5647059 0.45490196]\n", " [0.65882355 0.5058824 0.36862746]\n", " [0.7019608 0.5568628 0.34117648]\n", " ...\n", " [0.84705883 0.72156864 0.54901963]\n", " [0.5921569 0.4627451 0.32941177]\n", " [0.48235294 0.36078432 0.28235295]]]\n" ] } ], "source": [ "#Pixel normalization\n", "x_train = x_train.astype('float32')/255\n", "x_test = x_test.astype('float32')/255\n", "# first picture in the training set\n", "print(x_train[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "QLS-bUABwQDJ" }, "source": [ "## CNN Construction" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "58oKwDbHtO9U" }, "outputs": [], "source": [ "def CNN_classification_model(input_size = x_train.shape[1:]):\n", " model = Sequential() \n", " #the first block with 2 convolutional layers and 1 maxpooling layer\n", " '''Conv1 with 32 3*3 kernels \n", " padding=\"same\": it applies zero padding to the input image so that the input image gets fully covered by the filter and specified stride.\n", " It is called SAME because, for stride 1 , the output will be the same as the input.\n", " output: 32*32*32'''\n", " model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_size)) \n", " #relu activation function\n", " model.add(Activation('relu')) \n", " #Conv2\n", " model.add(Conv2D(32, (3, 3))) \n", " model.add(Activation('relu')) \n", " #maxpooling \n", " model.add(MaxPooling2D(pool_size=(2, 2),strides =1)) \n", "\n", " #the second block\n", " model.add(Conv2D(64, (3, 3), padding='same')) \n", " model.add(Activation('relu')) \n", " model.add(Conv2D(64, (3, 3))) \n", " model.add(Activation('relu')) \n", " #maxpooling.the default strides =1\n", " model.add(MaxPooling2D(pool_size=(2, 2))) \n", " \n", "\n", " #Before sending a feature map into a fully connected network, it should be flattened into a column vector. \n", " model.add(Flatten()) \n", " #fully connected layer\n", " model.add(Dense(128)) \n", " model.add(Activation('relu')) \n", " #dropout layer every neuronis set to 0 with a probability of 0.25\n", " model.add(Dropout(0.25))\n", " model.add(Dense(num_classes))\n", " #map the score of each class into probability\n", " model.add(Activation('softmax')) \n", " \n", " opt = keras.optimizers.Adam(learning_rate=0.0001)\n", " \n", " model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy']) \n", " return model\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "F3MXqNdoztpt" }, "outputs": [], "source": [ "model=CNN_classification_model()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RM0o9MAIzx4X", "outputId": "143c950b-4e03-423c-8437-87cf5b7af1bb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " conv2d (Conv2D) (None, 32, 32, 32) 896 \n", " \n", " activation (Activation) (None, 32, 32, 32) 0 \n", " \n", " conv2d_1 (Conv2D) (None, 30, 30, 32) 9248 \n", " \n", " activation_1 (Activation) (None, 30, 30, 32) 0 \n", " \n", " max_pooling2d (MaxPooling2D (None, 29, 29, 32) 0 \n", " ) \n", " \n", " conv2d_2 (Conv2D) (None, 29, 29, 64) 18496 \n", " \n", " activation_2 (Activation) (None, 29, 29, 64) 0 \n", " \n", " conv2d_3 (Conv2D) (None, 27, 27, 64) 36928 \n", " \n", " activation_3 (Activation) (None, 27, 27, 64) 0 \n", " \n", " max_pooling2d_1 (MaxPooling (None, 13, 13, 64) 0 \n", " 2D) \n", " \n", " flatten (Flatten) (None, 10816) 0 \n", " \n", " dense (Dense) (None, 128) 1384576 \n", " \n", " activation_4 (Activation) (None, 128) 0 \n", " \n", " dropout (Dropout) (None, 128) 0 \n", " \n", " dense_1 (Dense) (None, 10) 1290 \n", " \n", " activation_5 (Activation) (None, 10) 0 \n", " \n", "=================================================================\n", "Total params: 1,451,434\n", "Trainable params: 1,451,434\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "CCbchTdx0Pa3" }, "source": [ "## Training the model \n", "Choose which models to saves, here we choose to save only the best so far" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ftuGLxKH3s0n", "outputId": "3e3542da-4237-4243-e091-d8da582ea3d3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/3\n", "391/391 [==============================] - ETA: 0s - loss: 1.7661 - accuracy: 0.3608\n", "Epoch 1: loss improved from inf to 1.76612, saving model to /content/drive/MyDrive/Colab Notebooks/2022/final_cifar10.h5\n", "391/391 [==============================] - 25s 40ms/step - loss: 1.7661 - accuracy: 0.3608\n", "Epoch 2/3\n", "391/391 [==============================] - ETA: 0s - loss: 1.4329 - accuracy: 0.4860\n", "Epoch 2: loss improved from 1.76612 to 1.43290, saving model to /content/drive/MyDrive/Colab Notebooks/2022/final_cifar10.h5\n", "391/391 [==============================] - 14s 35ms/step - loss: 1.4329 - accuracy: 0.4860\n", "Epoch 3/3\n", "391/391 [==============================] - ETA: 0s - loss: 1.3145 - accuracy: 0.5329\n", "Epoch 3: loss improved from 1.43290 to 1.31452, saving model to /content/drive/MyDrive/Colab Notebooks/2022/final_cifar10.h5\n", "391/391 [==============================] - 14s 35ms/step - loss: 1.3145 - accuracy: 0.5329\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 8 } ], "source": [ "from tensorflow.keras.callbacks import ModelCheckpoint\n", "\n", "path = \"/content/drive/MyDrive/Colab Notebooks/2022/\"\n", "model_name = path + \"final_cifar10.h5\"\n", "model_checkpoint = ModelCheckpoint(model_name, monitor='loss', verbose=1, save_best_only=True)\n", "\n", "\n", "e = 3 #10\n", "b = 128 #32\n", "#train\n", "model.fit(x_train,y_train, batch_size = b, epochs = e,callbacks = [model_checkpoint],verbose=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "AZKPbYp41HlZ" }, "source": [ "## Testing the model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xNcu-V9uR8z8", "outputId": "b99a392b-abef-499e-d3ed-97f837e3f662" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "313/313 [==============================] - 3s 8ms/step - loss: 1.2204 - accuracy: 0.5674\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[1.2203800678253174, 0.5673999786376953]" ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "new_model = CNN_classification_model()\n", "new_model.load_weights(path + 'final_cifar10.h5')\n", "\n", "model.evaluate(x_test, y_test, verbose=1)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Orwzhe5VSV2H" }, "source": [ "Predict on a single image." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9tlWZRguSQUt", "outputId": "9e5d0402-f6da-411b-dd5f-89692e9cd6c9" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0.01906835, 0.00901849, 0.0862297 , 0.5253493 , 0.02819557,\n", " 0.12737074, 0.16151509, 0.00292499, 0.03649128, 0.00383641]],\n", " dtype=float32)" ] }, "metadata": {}, "execution_count": 10 } ], "source": [ "#output the possibility of each class\n", "new_model.predict(x_test[0:1])\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r6UPuc00H2KB", "outputId": "d664f0be-63d4-49c4-ef1c-125cef5d62a8" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([3])" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "np.argmax(new_model.predict(x_test[0:1]), axis=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "xjGIsbzaSlxz" }, "source": [ "Plot the first 4 images in the test set and their corresponding predicted labels." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 264 }, "id": "E4THm_tDSkvC", "outputId": "e68ff114-5a07-4c6a-cd77-e8c30aa6edad" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "#label list\n", "pred_list = []\n", "#predict\n", "pred = np.argmax(new_model.predict(x_test[0:4]), axis=1)\n", "plt.figure()\n", "for i in range(0,4):\n", " plt.subplot(2,2,i+1)\n", " #plot\n", " plt.imshow(x_test[i])\n", " \n", " #Display actual and predicted labels of images\n", " plt.title(\"pred:\"+category_dict[pred[i]]+\" actual:\"+ category_dict[y_test[i][0]])\n", " plt.axis('off')\n", "plt.show()\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "RJeCLgLtuDvN", "QLS-bUABwQDJ" ], "name": "2022 Lab2 Train.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }