004-CNN-Fashion.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution Neural Network - Fashion MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CNN allow us to extract the features of the image while maintaining the spatial arrangement of the image. \n",
    "\n",
    "Lets apply this to the Fashion MNIST Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "import vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Activation, Flatten, Dense, Dropout\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 3us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 4s 0us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 2s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = vis.fashion_mnist_label()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Prepare the images and labels**\n",
    "\n",
    "Reshape data for convolution network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'channels_last'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.image_data_format()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_conv = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
    "x_test_conv = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
    "input_shape = (28, 28, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert from 'uint8' to 'float32' and normalise the data to (0,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_conv =  x_train_conv.astype(\"float32\") / 255\n",
    "x_test_conv = x_test_conv.astype(\"float32\") / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert class vectors to binary class matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "y_train_class = keras.utils.to_categorical(y_train, 10)\n",
    "y_test_class = keras.utils.to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: Simple Convolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2: Craft the feature transfomation and classifier model **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_simple_conv = Sequential()\n",
    "model_simple_conv.add(Conv2D(2, (3, 3), activation =\"relu\", input_shape=(28, 28, 1)))\n",
    "model_simple_conv.add(Flatten())\n",
    "model_simple_conv.add(Dense(100, activation='relu'))\n",
    "model_simple_conv.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 2)         20        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1352)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               135300    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 136,330\n",
      "Trainable params: 136,330\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_simple_conv.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3: Compile and fit the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_simple_conv.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      " - 20s - loss: 0.8811 - acc: 0.7063 - val_loss: 0.6455 - val_acc: 0.7710\n",
      "Epoch 2/5\n",
      " - 25s - loss: 0.7154 - acc: 0.7725 - val_loss: 2.1814 - val_acc: 0.7042\n",
      "Epoch 3/5\n",
      " - 25s - loss: 8.1733 - acc: 0.4493 - val_loss: 9.7974 - val_acc: 0.3832\n",
      "Epoch 4/5\n",
      " - 22s - loss: 9.0385 - acc: 0.4202 - val_loss: 8.4062 - val_acc: 0.4716\n",
      "Epoch 5/5\n",
      " - 22s - loss: 9.7800 - acc: 0.3887 - val_loss: 9.8707 - val_acc: 0.3854\n",
      "CPU times: user 1min 56s, sys: 5.1 s, total: 2min 1s\n",
      "Wall time: 1min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "output_simple_conv = model_simple_conv.fit(x_train_conv, y_train_class, batch_size=512, epochs=5, verbose=2,\n",
    "                    validation_data=(x_test_conv, y_test_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Check the performance of the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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oY1Y02iclr03Cqq+39oi5M0vElqJztTtW0Lk6PZzlizoTyb5YgHkfc+wEP61e9QQR9RBzs4Nf1PVZPISAG/159dyJW4dM/zNjqXdDZsaT/Xt2vZa3u7+zuzd8aWxef895TU0X3e1gz5KlXHIVxjUAbrGcCrm/7yfWHmy5SmNHAPtbK4YiDsc51naHqVb0WhyY+dY+bIlcyx1TTi45c1Dl6FWpY+IgYjcGS7TmekluWfU9ky6YseP7P91pO9f7rpc4Sz9TfR2cHGJUlSnO7minjslQApmytcpNT2+POCYE+Hnw8mcwuM8g+idMvkwdk/RwT3d/Ds0tOQ1sXPc1j8NTfbub7T17cRcK6YdFHUb36tXBb8zijk95YtPSxiOcWsoloCL7rWWPiTgmEqX5m7XPWrY03GNdkyEn0spWBomkSFRanI8mABHHRJbSypUash1C/if/Ldsicia5/nLnBhmvfIULzQ0cCEbJ17tW3zz+b40Hun2EXbonh6F9pWxWs+haQaDi7TefMPWND1c2k4lb2+ZXPuVGn9IXdbcoq0PkHunMS/LKXB0h7eacqQ5R+k/IXLM5mnkzEMl+qjWJmZ8o6u+bRnVffZPukab9yhnCXLfeOl35+Quf69v14Of6dsHrA+IPDCbCQGirDfKLPvoi9NRqLuxi4lva51U+7IzkcC0RR6Td2icie/bk4CfZMyJJIiRyGbk4JrKE6/8AvAqg1VoyuyeAfwBYCkCu0BBHZY4VMXH1MIZUmahjkirBIeXdnBxcPMJuREJu6bvfefeXFOT1yheEwdG5+YSpb364KryOt7e/4KQnLj001r4kxyzslr7DG6xyHTNhzIq8xtkdqrGlqGPijhW81rfd1LezITCXgGMA2rh/3a2R99U7bwH8fRDlgflTA+YRhbUr5KTRtCU39R2qxGiSe3ntmcf3UcHVr/TtUNyFQktNXkFM38DAR8zojLx7MCHU11fwe4ffPWQp1wPWEqxaa5/IVtb+Edk7IgeM9A9xTCKmkPef31h7U34a3goDyB4/SUcDcHK5Wdr68NCK1TFxELNbg9TlI+wy7phMqV5UacCoZGCcAWwxrjB/i2+6+8KOSRq+WmRcX3VMvLXXw615w8GpzrGq1DFxDKU63UMIuD2mOhsC3xBo3Dc/Pg/jXr5k33zq/7TfzF8Ewk6y+YSBq4v6gjOpDr3psLjb+kZ0yHW5+9U0bzp2AMcVoeusb1E0UfTy8QAXGaGnvjbHXelf5nusc0tzV4P4JCIqCa/W+O/KW02mWx6dX5GufRtjLAdkYMiJtXI/VOTjrERMZLmXXDYueeQodnFYIklOsZWDomQTfCgd/S3ddapj4iBhtwbpdzbcGYCZN26q0fvNe2D8l4DbRtu61rJZi66GaRxJhPUi5mLw8wRaacJsdnidpzomFgG3+rM6YplxxByc+pKuSh2TpNElVFDHckK4Esrc2VDyQ4LxOgPvBCv+snXkkmWuQ15XfkCW1FQD5GPGv0EDRxTXrJDlNo4mtW/8OPc7786ivLz8acR0jEQhiMJHnGMr47/YMe/t178aM+GAc2qu/DxajS5zlnVkctGiRESGnqwlp21dD2Bz64j2+JXPkZzqmDhoKLc67VDHhIDxDPxijRpsvsrMNcVzVsjFZmlN6dT3gJn3bWJQ72kEnAQiKyzJJjN1EaEYzMtB9BJMvsqtfSbp1DeWoVRuWrvxmsq9xtkdqrGlqGPijhW81rfd1LerIXAOgy6Xl8WOir+cFnFMIpYN1U/8Mci3GKBJAPeBUeffOthE08Nfux1Jbuo7tMG5I5epfGbLr2DgWGYcSkTh43/XpZXmXnl/N/Yp+Ft3CVb8rmhOUO7RGzG5rK9coSFLueR+ki+GNEqOC5aIiGxoZ0c6UJZVoo6JgwZxq9MOOzsdvaW7Th3T8bf3TKbNiMKnMEjw+F0DuK6wv++2dG2+c15fpvLqxb9mxukElIPCF2uGdQHwFRF9Jf8mwgESG2fmB/oG+o5+4tJjXAlXOq9vfJ1P5cbHKdVcXuOcKi8nyqtj4gRF+zq81rfd1DdUX9oOwhRi8/DPK1+8d7hjEn6G1W3k78ofuAIguTxa0t8Nk2cUzgl+YG89+xxu6ptLjsn+1a3fy+OB44lkrwVtar1PhH6Q9/5/p+U/vt32eXJXoflWnjFw6NjZX8hdIDFTpjjbtWu0/a6OiYMWdbPThhoCJwF0oDS/Z+M9p4YvdTLMpWyii5jOAJGcfS3+dBfA9+SZuGbMBUG5RdSx5JS+B5/9wIT+MT0nAPxbEMkXAvkM0E/AQzD5eomISPi1wFdwkkkc2KKkeNZHy0NgxuPtTRWy6cuV5JS+iTZW5SZKLLn8XuOcHCVnS6lj4izPkWrzWt92S19uha/r3cDXckG0v69nvWVHv7IymmMSsUtnQ8n+YLpT9iuAuROM/yuaE7w51V7glr7D25mNcvetah0/lgYqwXQsCLsNOiNgIjy7IX3+yDz/lUcVGj07DepiLvCPW34unRXej2GbMqWvbcNGWQZ1TBw0qNudViInBXko+eLXN724zqOnTB56y2xnwwY7EhtngagCiBwxwX8hxoLCDYL30qnoS1X1VPWdcn7rjuQb+B0xVYKsNjJ/BsLN5oDvhqWXTO+I1sbX33qba+78R7eUMQeMnZZeMt3xNbvR5Kaqb7K8VW6y5BIr5zXOidFJT251TNLDNRdeINOpuVtjuWtuyR7MxvMAXimq6fhxPHK5bp31uvIK7wCRHOcqX+Ee8xs4hmZ3JH07dzxy08E7W+ROm9bqC33PPMAw+VgGTSWCbCCX9B6YFvbmmQsfKDx7ZyYsJJAsf19JJo4qmtMhR+vGnTKlb9wNHCUZ1TFx0JCZ6rSx5HLdhAnd+QUnMtNpIJo8OA9ykEA3Ia//hqKqLz5LFkEy+h7wu6VjjOJvK2Cap1MkqjPYqKdMouu7xpY89EzdL4eeMLFW80TuWTf//TICzgXjkbaminDkKN0pGX2daJPKdYKifR1e42xPJP051DFJP2OR4LW+7Za+ofqSC0DGRcTmfH/t8qpE5HbWlx4L8NVEtA4DXxLxcUWzg23J9IhE5CZT/0hlMi23vKrlR0x8LDGOjOxFZcbXICwG88L2psoXuA5ju/ICV4NITraS8Mlfud88pLhuRdQPn7H4uKHv/ue0rmcUmD/1GdjGNE0wzH+bvatfevyKk8JL2b2Q1DFx0MpudNpozY1HLjOoa+4GvwEbpwOYAiIDzP0APWQQFhTWdPwpURTxyI3UOaXmvs2Ngb4zAD4BoPUHJwisArAQ5sCCtkuOlH0kcSWRe8otr08sNHs/djNqkoi+cSkSZyaVGyeoFLN5jXOKuBwpro6JIxhtK/Fa33ZL386G0j8T8HM2zf2L5yx/IlG5oaaJG3FfXjNR5AAbvs3fZ/6e6lZ02hp1SIZE5SZSd6Zf1IfLn3ruookH/XzzFQ+++PHrIAxeasnhgwSeAPHCgdD4Bx+9Zkp4adbquRO37jfzHibC9wEeAKjev1XHxckePJBuzuUzm/dhA+cxaEvD2tZuDr6lL2OYlz7aeMTTTtluSD3fs26QH+kjtdyNIqtS4lru5kT71DFxgqJVR7o77UhNTVRud/2GmzMGTjdhnECE8NndDPyLgOv8ef13UtUX38aDxV4u05RZi8sME6cz8JvIsXwMvCJ7R77q8y168YrpcgZ3Qikit6yq5QoinM2Mh9qbKg5OqJIkMtvrm0SlcRRRuXFAciCL1zg7gCzlKtQxSRlhXBV4rW+7oS/XoaArLxA+eMXfHyySO0qSkSsfDbvnlvwfw2gAMAaMD8kwK/2zl78Yl3E9EBGbVtda0LXaPAjMx4JofwLyrPeWNyUy0jcw5s7hlx2GGgKnAHTl4FJ2/pyIp/lnL5eLCpNOydg3XmFTZrWUk8kXQZahydUPRGZYR2YDRFuCeTWAC9qbKuVm+FhpCwBXAzgozlO7fgVAjkf+9wiVnmjdLh/Xe2G8+sbKp46JExStOtLZaWM1M1m5fDXGhL4tmTF0szyDvyWmu3zcf/XYOV/8Jxm58kXDzKcTAfotATJIZPmYDKrFZPqua5s//W+pYI/oO+X81lLDZ37IjHwwtmufXzHSwEpF3JqyyXJOVbjKTZVgfOW9xjk+KunNpY5JevlGavda33ZD386LN/gNGb5HGXi2uKZDbuVOaclcz9z1f9Bn5rUS0Q5gNolwSeHE4Jx49oO6oW+0nppuuQfMbNnNZ8i+EcwgkByTKx9RV+y1XckGz/1z+S6PXFLx6vB2cd1663Tl5y8EyPpYye3+vp6jqG6VrNBIKaVL3/Ka1o15wLwD4I0JJEeFrZUY5laA8Qn5jOPaGqbLEcLRktweL6tf9gBwLADZt7Qj5NoFQCIfxwG4xLob5VAA4qjtCyAIQByPKgDrADgCwP7hKBQgq2zuArAngL0AyN4oySN9/n0AvwTQAkCWIUpURY7OjqpDvPDVMYmXVBz50tVp7UQ7ITfaZnkG/8lgXlDYv/whqvvOzaLhJg2XWz6rdXc2B04HaNrQzWcMuiF/dcGtD155SMoTw3C5ZVUtVxPhd8y4v72p4jA7Vqn87gTnZOSr3GSoJV7Ga5wTJ+R8CXVMnGeaiRfIkbQYzWOqs6H00sF9jjynqDZYH+2ZmKh1+Ubkd38RuJgZ58tya2Z+I9/onz5m9pdvx6prNHE+cGbzRgOE40B0LAFbW3r3MqONgTu7CgPttx69WV+0089CDRN3Buc9CIIcDdxDjPP8tR3XJmoHt/tz+czmw9nAXHmtIlDUg4mYzQIQTWLwrKWNR9wXQ6efW7fEiyPyawCiv5xeKvVeB0AiJDsDkNPg5HSyowHIUnpxYuTI5B8CkHteFgLYznJKzgCwD4BGALsDkH29m1kXPcoHbJEld6tIlG9XAC+nwlwdk1ToDSs7GiaHaJvlwfwpiG/099KNdFFQbiFd45gcde0r/nXHmEcS4wyisGceXu/JQBsRrmtrnPFHgBy9BGgoZzejJqPBvol0d9U3EVrJ580U5+Rb7FxJdUycY+mVF9d4iLkxpkINgdcA2pHI/Flk2ZVTcrvmluzOptEMCt/u3UNszi6sWS5Ll6M+S52SGw/boXmckrv7Oa2F6xeY00AsX/n3Bsiw5PxdlmoxmYvaG49cOfTdY6hjIsvhQnNLZxJzPYjywPx+nkEHjpndITenO5ac0nd4g6ZUt5xPwCk08nKqcBGTeVsycEP7vMpLYygle0ZusyIacru9REMetfKL0zHJin6I0ydRpYhjInynAfgDAIm8vAmEj1uWCMtMK1LyXwDPAtjSipBIXpEnyxAlSdTkIrmnJxXo6pikQm9Y2XR1WrsmpkNu1M3y4D4G7vOxuWDa11cG9/rhRu88+2ZwFQgTwv4Io4PAtxiM6x+eX5n0aV+J6lte1XwtiM4A871tTZUysNKS0sE5noaq3HgopZ7Ha5xTJ5Z6DeqYpM4wnhq81rfTra98wOvKH7sS4JB/q+D4yGZqJ+Vy3QbFXXnGlSBZFh1+vv6Z8vsro52k6aTcePpTJE9qcpnKZrbuDcM8lkCy2qE4rGf4pnO+m4luWzqvIuqlh0Pl8oWBklABWggkS4qkhtv8fb7fUd1nXYnoEk/e1PQdWcKgY8InEyhmZIzBPwDhJhvHRO6Cu3dINEQiGn+1lmzdYy3X+gjAWQCE+1FWxEQcE4mENAEYC+D1KI6JRFZesG6kFxmyh0eiK5GolDomI5lZH3bxDLH480Q2y/cj74RXBrab+Hjvz/Fm/1bhK9itieBZk43rgitXPfDKTaemfD+KXcuGTw5uRU3SNSklqq9dfqd+V32dIhm7nkxxdke72FJ0rnbHCpnqY6NVbld96TQmtIK5rag2OHgfSYp7TEbqCaG5gXI2catcysjgr33gMwtrlt89NH8ucZ56XuuW7Bs4gQlHE0giQuKNdAP8AIgWto19+0nU1YU3fo+UIvp2X1y69yrQqscAACAASURBVABhSfgQH+ZOAp3gr+1Ykq5RlS7O5bOaD2dGAxjvRja9D9eBYRqAsRUIs9rnVdwfQ8dtrD0fslwr4nSIMyH7SyTiJku0ZF/JBVZUZWjEJFHH5AQAdwLh09E2AfCBLuUawTL6sHN2WO533v0l+Xk9pxLjVBBtLLWPRTd+kf8y9st//ttNjI5b83y4Zuys4DJnJUevLdrkUF7dfL1stgd4cVtjpVwq6XhK16Rk11CVa0fImd+9xtkZaqnVonN1avziLe21vp1ufUMNgRsBOoWYz/HXBuWrcTilS+5alzIC9/t9PSdQ9Uq5dT5tcu36V7z6HjjzoXH91H0EgY8hop+FfREGg/h5mFgY8vsWP1M3Pe4jkpe99w6Xtuw5n4nOt76QvmIwHVZY+/mHdm1O5fd49U1Uxt5n3z7BP7ZwEQGb0uDLfbS0pQl83MtG5ZNN08N2HyHJxnTZ6yH7SYRpJGIi+0kihwXIHhWJlsh+ENlHInmkTnGy51sRk1esJV9Dl3JF6pKozE3WvpPThkRMpEnft/aqJIphTX5dypU0urULpqvT2jUxXXKnzmrdywxvZschRCRrDmUy+QcI18/zN72x0fd2eqHwg6Vy3K8cxyfTzB9hYIG/N9hGdYj5xcNOp1i/R9N3v5rmTfMHaBkYPtPEVo9eUiGnRTia0sXZrpEq146QM797jbMz1FKrRR2T1PjFW9prfTvd+obqA+/JhcV5A/zDMRcE34jYIe1yGwInMHAlgcaBucNgHFk4J/h0uuWO1M9iyZXb2Lu/x/uzaR4LwoEEkuVBckLnMgLuMn0Fty9tOCxhR6Krab1Nesft8HH+SnlHZibmSwr7l9dEO6An3vERb750cj6gumU/g3neWscFS6SESU437QEZVe2NM56Mo70+K4/c7zI0+TF41PI31j4SiaDEvNDaRtaGQPgOHolSyb6g58L35AFr9gPF0da1sqhjkgy1Ecqks9Mm+qKerFryZWPA6D6amE8HkXjS4nL0ALyEDN91bfOmrzlbXfSddNcu6w6/WV7OYWfwDUX93TdR3TeO31Y6Eufyquab5HZXBje3N1bKcXeOptFg30SAqL6J0Eo+b6Y4J99i50qqY+IcS7eeEYm0OFN9O51yu+rX25Sp4CMGVhbXdKw3lEc65UbkdDeuu5nZX9AMK/IAxnXB6U+fPmnrbV15n9vvvDuLCnxjfgXwpEN/ttmV97/w8YG9Az1PP3HpMeE7XcpmtmwDAyeCcSQRSi1n5BswLYHBC9vnVcrLa1IpVB84iAkLCTSewUGfSRWFczqeSaqyJAql275l1Yv3Bcxz5YJFklOD2CSQYRLMDwDfZXE6JUlolnQR2V8iy8Qkycb6MwEsSLo2q6ArHTnVRiZaXh92iRIDDpjVuq1hmmeSrEkkjLMmk2XExo19vXTL41dMX8vBGDpIo2+WD59pvZgYC/y1HSndXRLP5B+JmhDYGBigrZ2OmqR7UhrJaio38f6cTAmvcU6GUZxlCoDwcZ2yjEBOhBkx6VwdJ9EUs3mtb6dT31BD4ASAbmXm5uLa4Hc+gKVT7tAuwHUwQnml5xFwMQgF/cWbwNf54U7FNSvkhu60pvLq5qsAklOZsP3mE6a++eGqR5jxGRhvk8HHACRLhuSTpgnQk3KqVmfh6vufqTte7jJLKnEdxnblBa6WD49SQU/Jzlh3xXMTafa3XyZVYZKF3LDvPrMWrl9gjvmJQea2DDKZB942e1e/9PgVJzn+kTdJDNGKlViRF0faqI6Jg5Zxo9NGa26ycnc55cb8Dddb51BmOp0oHI6LTCZLBxjXPdo047FYR/2OJDfazfJgvMyEBUXjOprprLDDknSKpW9ZdfOtsgGOme9ub6qUTV2OpWQ5p9oAlZsqwfjKe41zfFQSziUbIOU4yaVWSH+2fJwYqRZ1TBLmm1QBr/XtdOrbWR9YRESVMPmkojnBW4caJJ1yoxm+pyGwfR9TKxG2AXM/iOr9fR0N6VrWNHjhIWRMQz4AbrJBcdnHyzuDDCohQvh9khn/BvFCHvAtXHrJ9I6kOuyQQqvnTty638x7mEj2LsjJoFQTrHh+/uTJ33P9/dVt+6bKLlfLu25YN0Dpwy42ZbnAyCScxqCTIqFWMC8H4dZeH65/oqHy43jsZDdIo98sjy8N4DZiWpDsRrVYctMZNbHTNx5myeRRuclQS7yM1zgnTiiuEnLSi3y1fdjaQCnHS0qIP+qaY52r42Kaciav9e106tvZEOggUIC4dzN/7VffeVamU+5InYDrULB8+3N6it9eZMqljGC85vMNTB87a0VKt29HkzelelGlj+l4E9gCRJsTINFRMHMfEZ4zCbOXzqt8KeUOa1UQagicApAcLlAI4L9A/2FFNV+8mgnO0qRMyXWKZ67Uo46Jg5bKVKeNV66sXyRmucFzKgiDm6OYnzfJuK5oLN23pG56byI44pUrda51szxLmBdLmc0FRbUrHh/p4qho7bGTW1bdcjvJ0XiMO9uaKuTCJkeSnVxHhESpROWmi+x36/Ua5zRR/b11ZKQ4JrL+WM7lV8fEgu21Pjba9JUIRT/oDYCXFdUEJw8fQ5nUd8PFu/9szaWMDLnD4/yi2g45mcmRdMCsxXv42LwEILn5O5yKx+bh257+l4nxuQmzeWnjEc1OCOO69dbpys9fCJBcACgb5luLCnACnR8M72PJJOdoN847obPW8T8C6pg42BuycbAcUHf3OkZX3gkwcBoBctOnjPJvGbjbNIxrH503PembUZPRN+rN8uD3iPn6wv7e26hu1So7k9jJPeD8lsk+H4cvZur1YYt4I0CpyrUrn+zvdvomW69dOZVrR8iZ3zPF2ZnWr1VL5EjK/wNwEoCJAH4gEZOmpqYLmblueIlp09J2J2qaVNRqvUqg6D+LMe61q9E1+UB8s2tV1mEw+rsx7pXLUfiBrKQEegK74OvdLoRZuH5SbR0wGa8u+wrPvtGBj78YvK/QIGBCUQEmjh+LgrzIBe3AIbttionrhA/eSinlffU21n1uFnzdy2H6xuDbXc5F96SylOp0snC6HZPw0dD5/p8Sm9vI11si/nd/X89L49NwkJCTXJysSx0TB2m6/YJRVn3PJCZjg3MP3Pavlz/05uT2xiPX3CMy5fzWHckwzyRCJQA5Ik7Wfsqxhtf7uPDuh+cf9G2qqqei7wib5bvBvCjPxFVDj2Ac3s545JZXN98J0NFg3N7WVCGXAKWc4pGbspAoFajcdFBdu06vcU4jVTnjfkcAssxFjpHUiIkF22t9bLTpG6oPPAKicgJm+Gs6WpN5NqVj3A3nHL6UkekOAtZn5lUG+AR/7fIH4pUdvrvM13sGEZ8KUMB6f3gfjGvJMPoBcz/5W2TzuxPREnknCM0tnUnM9SDKY+a38nw4MNr9aKOtX0Xs0tkQ2IeYzmOYW0ZcvsF7F4xlzHzpuDnBp+O1YZz55BLGt6zDSsTz/GxIOTlWWO6ckVvj7VbTrGvV8c845cbMpo6JExQz8NCZWt1yEgNyS+eQyYGXghAyTDodhN2sZvUy+D6Dfdc90jT9eQfVdSycGnWzPPhFAq4t7AveS3WDg0IGLRglX//sokXr/eXCfeT89pH0iURN5FSLPh9PciJqMlonw5EYqr5OjpaR68oU5zRpJ7cLF1sXbu0BQO5BktuH5bz8tZLuMUmTFYZVm6k+Nprkcit8Xe8GvgaoyN+3et1o0f1s0pfnlm7QZeJOEH5jORZ3FfX3nkl1X8kdFlHTlFnNPzVMnMXAtMjdZWA8BdBVbU3T2yKH4YjjkufrCsw6/Ef/vHjxu4EnLj10eSo9mS8MlIQK0EKgXw7Ww9f4xwXPH+mgnGzinIreQ8uGLg6Uw6CLwDwmvJ+GaNAnYTZA4VPQ5FSzC4pqOtodkinv/1KXHBIkN7d/DuDfQ+qWu+s+ASA3yUc7bUt+fxmALO2TU7nkA5Qs4U05ZZNjsgEAubFSLtwZfuGLfPEPX/A3JMlaw6gXw4z2h51ESgi+NbfNbr3J+Kn/+eTr9wi8GUCDm9HAHxKMG6lv4OZHLjvii5R7SpQKnJ4com6WZ15OhJtBWMe6ZAg9G+85dcynzz8C5jeKaoM1I+lWXtV8D4iOYMat7U0VsqwkpeS0vvE2RuXGSyq1fF7jnBqtEUtHTuWaBEBeVuSBN+LJPKN9rh5OyWt9bDTp2zW3ZHdm4wUwv15UG5SI4FopG/UNXRw4GYTLQVQM5k8Nw6wsnL1izV0ig6dzjp/OzGcR0U8GX4bRDfDd/ey77LH50+Umw6jJCX27Ly7de4CwhAgT5W4YMnFU0ZyOwbVoaZSbzPznhL7R5HbVr7+xibw7iLAxQFEPLWDwVmB8YqD/OH/tl5+O0H4JtMhJcXMByMEjco+MHEhyNoCzAMgt7jIvH2rdP3IzgJkAJHIiR7u/aX1MmgdAboeXyxJlKa7Ucy+AbQE0ArgIgCzXbQBwG4B6ALtYeeSD1P3WgQVynHYbgAMA7GXdLC/v+XvLVqGRbJAtjokofzuARRiMAoiCQ/canANg/yGOiCw4lLXMUc/sHu0POzkZw4AhS7TkM+R4Grx505pP+HViY05b04xHkhl4iZRJ1yCVNqy1WX7wZvkOMui/qzfac/ewYyLrL5nn+muDEmpcK02Z1bK1wQhPqqYvf4tkbpkdWmk69fXSJGzXx5SzHaGs/10O1pBF7fJlNubdBaN9rh5uKe3b7vTddHAONZTMAYw/MOPy4tqOc6Npkg658RCzk9t9cWBL0yA5tnvX8BlaTFf84ZtTL/uXse3JzPzbNRchMn/CoAUFPWNuePDKQ1Le7xmr7VyHvO68kgYmOl92UoDxPPcPTCuuW2F7xLCdvvEwSyZPuuR+e3Hp4T6D5zLx+2CjL2rb2CwgoklgnuWvXS5Ow0jpfGvevQYIL+WXj0QPAZCDEH5lOSHikMg79E2WYzLVcmTGA5CP3j8HIMv1LrMcEznUQJyRvwN4HEC1FU2R96ufWre+lwO4Ewi/c4mjI0vDXrPqkg9U4tBIdEXe8TezHKaoOmSDYyK7pWSNmzRYPDnx6iQ6IkCiJfHAZM3jFSNZZbQ/7IY6JsJgbIFvanfvwHtE9F+TB25z6mQMu4GbrkE6VK5sll+dX3CNyXQoiMJ7Zcz8Yhh9nRHHpNlfGxzxJJDy6uYWgGYwcFN7Y8Wpdjqpg/A/Am7YN5ce7qn0nWzsV+nSJ5F6R/tcrY7J+5zuzcJuzSGdDaXPkHz1NVE20hf9bJ4zw0vR3glUfWBuelF77155L/TvzAPwWXeP8AsM46qiZXTfkiXTB+Idw8nq29W03ibcX/Dg4EdoHgDzH/z9y+upDoPLl2xSsnLt6rX7PV1yOxtKzzfApzBo6FKqtZvD5rZkGDf4Z3dcGqOtssdP2MrSqiesCIg4C/JvcVL2xOBBSHLi2XDHRBwUebeS29zlPVwiKLItQPabbAdATqKTe2xmWL+9PujsSqQn7HB8BGCjIe/vsrQ3soz3v9YdV7IsTaIo0p6ofS0bHBMJ/YsHFmmkhIHEU7swCnjJ+wrkDG1IqDF6Gu0Pu6GXHAmByAY0+e8BE3MfnV8RNYJgN+gS/T1dg3R4O7rqA5UsF1qBS5ixDRFJKPBVAJ8Sc0zHpGxmyzYgvAXCQKp7TdzSV19mRs/LTDxjKlP9Kp62pTvPaJ+rdSyPjrEsd4V05QXCR9X6+33jqe6zwSOqhqVMjWU7uXvX/SmvqDt4OBGfFTnuNw8D2M332sCP895csA89dk68TsFQle3kRmMUqg8cxISFBBoP8OdEOMQ/O5jQ3SfJyHViLkuXXHFMwHwyEcnx6iMmBv/AILrJxjGR5Vx/sSIh11orkGTp1D0A5Cg5cR4kAHAYgBuHRUzE6YhERiRoII6HvJOLoyMb22WJluxZkmVdsmJJfhfHRZZ6RRwTiZaLXEnimMj+FNkcL0vLxOERx0nKS8QmqiOaDY6JbKwROAJOGin/Ps0CN9xAVwF4EUBLLON54WFXXt18FRDeEDXEMeEP2hor5R4BV1K6Bunwxq+qC0zKzw9fsiRR6A1AJANBHhJP9/Xx2RPqgmtOI4umeHlV8xIQHQ7wDW2NldK3kkpu6ZsrD7ukIMZRSDnHAWmUZPHCXJ3qi5wTptYxlRrFzotL9iPDeFyWGxXVdsgyl6gp2zhPPXfRRM7z/RYwTwORfMmWZ+hyHw/cfMm4pokbG8vlAkMC8wtGXm9lYfVKeWmNOyWiL9dhbFde4GoQnTwogNv9fT1HxXNFwGh/JnbNLT3cZLOBmN5ds+l9mNLMbBjEW4Ewyz97uTgIsdKRAO4GcAyAuywHQSIXCwHsay2jknfu4RETeaeUPSVzrHfxZ6xlWX+29pdIhEOcC7kPT66aiNxTFXFMpF0SDZHoivgXsjle7pKTpVw55ZjIJVx/so4lk83ssiZuaCgoAl/WvsndFOJtrTlVwstn47/x4Up0dg/u/y8uzMMOm4tTOjpT0TutKAhKsAzIX/46jL5vsXqL32DVbjJ+YqeOld2Yd++b8BmECyt/iPH+8PkAmpRA1hDIxHKXbFBeHRN3rJDIC6STLRotcrsaSuczIGv3Lyqq6VjrLp4Is2zRd0pV6w5EA+eCqYIIcsqTLKiRVQZXFRYaLZHLlLvnbvBz0zSaQbQxg78l4OyimqBsZo4rxavv6rkTt+438x4mwvfB4X1n56Zy+WO8cuNSIoFM6ZIrS9ZDeWMXEfGmAH0QrUkM3hJMHxfl9VRS9cqvbZotq4vEcRAHQY5tj9wvJcVkf4pES2QJ1m+HRExkuZdcKSHv4/KbJDmVSza8z7fyynYLOQ1Vlm6JYyMb7SVCIntOZI+JbK6XiEvkxUz++w/WyV9Sv6zmkXd4cYj2yeaIiYSL5I3zRAASzpOoiHhnEuoRL0vW3MlxsbZHT0YMpQ+7BEZaClnTNUhHapJETvLysWXnj057ctzrN8hM+55/dnDreG6NL6tquY8Ih4J5QVtT5ZnJqO22vtn2sEuGWTJllHMy1HKzjM7V7thNx1RqnEMNpfKOsrNBA78YeqLV8FozyfnEuz7K93cvP1SWa9Hg+5I4IwMAP0DwXTXSdQFyy3ooP38BgeRYb4moPOLv7z6O4rjQLx59Qw0BicrIiodCZvwn38ChY2Z3JH2xszQxHrmpWTx66XTKtaJy84YfFyyREtm+QEQ9PpOrxs4JPpmkbrJHV/aKyId92T8iEZSoJ9sCWC/KEcHy5Vvuv5My4uz2WO2QOofXI3llBZSdAxVVlWxYyiUNkx38g5/DBz2wUwDIZ205OjhyhvLxAOS0l1vsjKIPOztCzvyezkEaq4Uit7R5j9dA2InAlf6aYMylfVJXZK8JEfrMAWPzpZdMtz35I5seOpn4op5J+6q+zozRbK9F52p3LKRjOXnO8jW7K3/MVwD1+rfqKKLp0TfsZuqFef9zWtc7ZM+Nvnzkbx9/QkTypVyuC1jJTDfnMV/18PzKoZfmjQiiq77kEBN0GxFNYOblID6muGa57P8dMcXqV+LwdOXnLwRINllLq27x9wV/R3WxT+qLx1KjtT+vvjiw7wDRuXLBIsgggGWZnQk2PsgDX5aCUxIP1qzJky2OiQARb04u5krpoh6pSB927vSvTE4OpS27TQF8SxOMmjxIhIOYcU17U4Vs/kooZVJffVFPyFRJZfaafZOC5HAhnasdBjpCdV7r207q21VfchiTcS8YjxXVdsh9DEm9qDtt6QNmtW5r8IAcW3wEgWTliXwC/xczru4q7F74TN3xMY/qjtaezoaSALFxR+RSRjDf7O/3nZ3oZv9Qw8SdwXkPgrApmDvlktWi2qAcWetIctK+iTTIDbnfzB23vo+LfsLhJVRsEsy3+/t6XhofRwQrEV2yOW82OSaOcdKHnWMoY1bkxiCN1oCI3FB96auJRE2mnN+6o+Ez5Vzt3mSiJpnW1x2r/k+K6usO8Uxxdke72FJ0rnbHCpnqY6NBbqi+9HoQfkswz/PXLB/pGoOwIdOt77Rprb7QZD6YwpchDt5fJjd8bbfZeHrzw5X7LZ1/xB+d6FGhuSWnwTQuBckHY14GHphRVPuFbGT+ThquLzMoNLd0JjHXg0iW+LxiMB1WWPu5rH5xLKWb80gNzZRcx8DlSEXqmDhoqEx1Wq/KDV1cOgUG2hOJmpRXt8gZ3eXMuLK9qUIu7ow7eZVz3IAcyqicHQKZA9WoY+KOkXRMJc+5sz7wDhFtxRjYqbhmRdRLnSO1p4vzwWc/MKFv7OpTiel0UPhyOgmPfMvAHaCBK68+ebf3nY6qD7uUcQCMJn9/8EKq+99+gqH68oWBklABWgj0S3GXiNFU2B+cMzR/8laI7RA5Va9dPemyr51cr/2ujomDFs9Up/Wy3KSjJozubqNg06fmHfplvF3Ay5zjZeREPuXsBMXcqEMdE3fspGMqOc7yst1VQEEGVhbXdMiG4JjJac6yXMvH5jlgHAlCYdgfYbwPxrU+FN768PyDZDNy2iI14UsZ3y2ZDaYLwhEQxmtM5nHEtDmAHb794akN4/5544E+n9nXP+C7iwgTGRz0mVRROKdDjppNS3Kac7yNzJTceNs3WvKpY+KgJTPVab0sN9RQWjZ4bja/V1QTlGPobFN5VctSEA4A4/K2pgpZoxtX8jLnuAA5lEk5OwQyB6pRx8QdI+mYSo5z58Wlx5OB28BYUlTbMd2uFkc419UZ5au/fxBAsg9SjmQdTIynALqqrWl6G0CR27TDPzkiN4ZynQ0b7Ag2WsORI+YBMuhtEJb1bLjH1LGfPv8eM74n16GA+Qm/0XUEzf427g9+dkyj/Z5ufUdqU6bkJsMol8uoY+Kg9TLVab0ud03UhLnCXxtcbGfSNXtNEoyaeJ2zHVenflfOTpHM/nrUMXHHRjqmkuPcWV96JxGOhsmnFM0J3mxXSyqcZblW/9iekxl8BiEckRBnpBvgu/vZd9lj86fLPRBRUypy7XRa4xfVYWzIF1hIBoUdNAa+5Pzi9Y0+2dsurhJf7a8NunLBsxv6ZpNDFK+NRks+dUwctKTXBku26BuaGygH0yNyIklxTYdcKGSbyqpaHiPC/sx8aXtTpVycZZuyRV/bhjqUQfV1CKRNNZni7I52saWoY+KOFTLVx3Jdbqgh8BlAG/oMnjx2VnCZnbWS0fc3M1u/7yPznLADNHg6KZj5EwYtKOgZc8ODVx6yKh1y7eqM9ntXfaASBv2OTewEQvgkMDB3MdErBvP1/tpgczL1JlomGc6JylDHxAliydWhjkly3KKW8tpgySZ9E46azGr+qcH0V/kiFe9ek2zS18FuO2JVqq8blNO/DMMdLZKToo5JctwSLaVjOVFiQM/c0m37GW8B+KiopmMwguHYRwamslktU8mks0DhG7Ctd3x+gWFcVbSM7luyZPqAnbzI727ZVxwTJqoEI5/BO5j+iRv7ur98VC7YI+ZmdUzitZjmi0VAHRMH+4dbk8PwJqtcIFQfmAqihxOKmlS3/JGAfRnc1N5YWW3XFZSzHSFnflfOznDMhVrUMXHHSjqmEufcVR84i4muAvi2oprgifHUYMf5gLq71/F1550E4EwQtrTq7GXmVjZ9ly29ZHrMU79GaoOd3HjaHk+eVXWBSfn54Vvcw6ln4z2njvn0eTnpEn19fPaEOvuoUjxy7PK4pW+2vGvZ8Rhtv6tj4qBFvTZYsk3f/53QhRn+mo5WO9NOiURNgM7VbGzyZNP0r2OVyTZ97fRL9XfVN1WC8ZXPFOf4WpfeXOqYpJdvpPZM9bFclhtqCDwE0IEEPtJfE1wUj6VG0rf8/Hu2YsN3DsDHEFFRuC7m5Qy6oW+gYMETlx6a0sXSbnJeEzUZ4pi4GS0RdG7qO9TumZIbT98bTXnUMXHQmpnqtCp30Iih+sBBIHowoahJVcvTRJBz1+e1NVbOVsfkfwS0Xzk4OcSoKlOc3dEuthR1TNyxQqb6WC7LDTUEOgEq8vdygC4Kjug47HfenUUFvjG/Ypg7lO+6SUP73z87sHeg5+knLj26a8qsxWVkymWI9Os1lma8ygZd7R9DzUvqpvc60QPc5hy+s8SHwJdld/1zs4ePjsnHCf2G1+G2vpl28NPBMJvrVMfEQet4bbBko76JRk2mzmrdi9mU89ZtoybZqK+D3XetqlTfdNLNvAPojnbqmGTDF1cdy4n19q65gZ8y01+Z+a3i2uD2sUqXVTXfQkQlkmf7zSdMffPDVY8yQ6IiGxNhcrgsYwDgBwi+qx5pmv58Yq2xz632tWfkRI5McXai7blUhzomDlorU51W5f7PiKG5JQeDjQcSiZqUV7W8CMJuYK5va6qcM1KXUM4ODpYYVSlndzhngxSNmLhjBR1TiXEONQRqAKpn8FXFNcGzRypdPrN5Hxg0eEQusX+94rH7fPltTz8BeYP+CK9kppvzmK96eH7lZ4m1Iv7cat/4WaWSM1OcU2lzLpZVx8RBq2Wq06rc7xqxs6H0LQK2JcS31+SA6kW/8sF4yi5qopwdHCzqmKwhkKl+5Y41Y0tRx8QdK2Sqj+Wq3M6GwNME+iWYDyyqDYY3d0dLU6oXVRowKgd/4zKAjPB/MTqJzZZOf8/vnqk7fnW6rZyrnJPl4jV9k+WUq+XUMXHQcl4bLNmqb1d9ySFMxv3JRE2Y8Yf2pooLo3WLbNXXwS78napU33SR/W69meLsjnbqmAwlkClbq9z4ezvXoaArLxACwefv8xVT3Wdddo4Jg9cn0M8K8gz09g38FUQrTJjNSxuP0Hs94kcfd06v9ee4wYySjOqYOGhIrw2WbNZ3TdSEMd1f27HEzsxTZi76tWEYT0jUpLe/t/SJS48JDS+Tzfra6ZfM76pvMtQSL5Mpzom3NO4SsoxlEoBvAHTEKqURk7iZppQxU30sF+V2NpT8mmA8Aea/FtUGd48Fvqz6nkkE35UMr6uY0gAAIABJREFUfJ+ArdcbNwZffdsTjrAwBs5ubzzS9lLGlAxrFc5Fzqno7TV9U2GVi2XVMXHQal4bLNmsb9fckkOZjfuSjJpc2N5U8Qd1TN7nyZMnuz5HZHO/cnC6WFNVpvRNhy4ANgAgh0nI0ki5OO4uAI0jyVLHJE1WGFZtpvpYLsrtagg0MqgK4IaimmCtnYXKZjUfCMYdBFp3sw2K8NGK0CNMfEv7vMqH7co69Xsuck5Fd6/pmwqrXCzr+kuHG5D0YecG5ew/SzzhqEn1ot8YMB4FY1XvQO8mw6MmXpsMVd/RPY7SpN0RQPjiuAYA6wJ4GcA2AKIei6pzdZqsoI5J0h9VQvWlfwfhxwbwq8Kajj/ZWWhq3SN+szvUSYSBcw7aJq/pvvcDqd5LYidTP5p566NZov0h1/OrY+KgBfVFzkGYMaqKl3NXfclhTMa9CUVNqptfBWgnMM1pa5pRP7QZ8cp1moLKdZpo9Pq8xjlNVCViInc+XARgLwB/BDBXIyaDBLzWx3JNX66bMKErf8xXAPX6t+oooulyzG/sVF7dfAhA9zPjz1efsusvNMpsRyz133OtX6WusbdqUMfEQXt7bbDkgr5roiY0MM0/e8W9duYun7V4Cpjbo0VNckFfO/0S+V31TYRW8nkzxTn5FscsuROAZwGcDmAHANsBOBzA6qampguZuW546WnTpqWpKVqtEkiMwNhPnsGE52vQU7orVu59ZVyFW5//EH/593JM+fHG2H+njeIqo5lyl0AmHM/cpZVcy9UxSY5b1FKZesFQuSMbsWvuBocz+5YkFTUBzW5rnDEvUrtydnCwxKhKObvDOU1SbgTwIIBHAcjRqX8BUAbgq2jydClXmqwwrFodU/FxDtWXLgDhdAaqi2s6muIpVV7V/A6Ithowsfu1p+76YiZeXNW+8Vgq9TyZ4px6y3OrBnVMHLRXpjqtyo1txISjJlWLp4L4YYmadBZ2bxg5h145OzhY1DFZQyBT/SpN1rwAQL+1fEv2mPwTwA8BrFTHRJdypanPrVVtsmOqs770bSJ8H9y/a1HtF7I/KmY6YOZ9m/iMvo+ZOdTeVFmcrFw7OXa/q1w7Qs78ninOzrQ+d2pRx8RBW2Wq06rc2EbsaiidzsDiZKImDK5ub6wMfzlTzg4OFnVMRqtjEjmVa1tLwTMBLBjJ3Box0TGVDgLJzNV8YaCkq4CCDKwsrulYL552TaluOckAbgbjwbamikOSkRuPHLs8KteOkDO/Z4qzM63PnVrUMXHQVpnqtCrX3oiJRk2mzlx8MBv8AJi/6CxcvalETZSzPWcncihnJyhmvI7xQHjjcGeslqhj4o6ddEzZc+6sLz2WCHcAuL+opuMw+xJAeXVzC0AzGDijvbHiOuUcD7XU83iNc+rEcqsGdUwctJfXBksu6dtVH5jBRC2JRE3KqlveImBbZj6/vany0lzS14lurfo6QdG+jkxxtm9Z+nOoY5J+xiIhU30sl+R21pfeSYSjwTijqLbjungsU17VshKECRgY2LrtkiPfzSV949HPLo/qa0dIf0+GgDomyVAboYwOUgdhxqgqWc6hhsC7AH2P2DzcX7v8PrvWTqlefJgBvjcSNbn16F9068ZGO2qp/56sfVOV7DW5qfJyorw6Jk5QtK/Da307GX07GwIdBAr4zP4fjJ3zxX/sqB5Q3byTD/QqwB+3NVZupg6gHTHnfk/Gvk5Iz5RcJ9qeS3WoY+KgtTLVaVVufEbsaghUMKg5magJGOdedcqul6ljEh/rVHJpf06FXm6VVcfEHXvpmIrNuefi9bfpN/L/xeBgcU2wNB6rlFctngniJmbc2t5UcZI6JvFQcyaP1/qzM9RypxZ1TBy0ldcGS67pywzqmht4J5GoSVl1yzQCWiVqcskJP564zfe3cn3M5BrnVIeU6psqwdwpr46JO7bSMRWbc1d96ZlMuAbAnUU1HcfGY5Wy6pY/ErAvwBVtjZWL1TGJh5ozebzWn52hlju1uP6S5QYafdi5QTk31y131QcqmWhRMlGTQ3ffDCcc9DPXx4zXJmHV153xmw1SdK52xwo6pmJzDjUEHgDoYIZ5bHHN8jvtrDKtrrWgq9v8hghjVrMx4cmm6V+rY2JHzbnfvdafnSOXGzW5/pLlBhZ92LlBOTcdk+9ETcg8zD97+f12tMqqWiqI0LxOYT7+8+kXBa/cdGqfXRknf/faJKz6Otl7srsunavdsY+OKVvHpBOgIn8vB+ii4HI7q5RVL96XwH8E+LW2xsqdI/mVsx05Z373GmdnqOVOLdnkmMj59+sA+NC6oCsaxa0B9AL4byzE+rBzpwPm6uTQ1RA4gkH3xB81YSqvXvwOgO8x46z2pgoJ+buWcpVzsoBU32TJ5V45navdsZmOqZE5d9WX/oQJLzHwdnFNxzbxWGRKVUuTQZhpMuYvbaqoUsckHmrO5fFaf3aOXG7UlC2OyRQAtwNYBOBAALsAWDUEobRzofXvYgB5AA6xzslfi7Q+7NzpfLk6OSQTNSmvbjkSwN0M/rzjq282dzNqkquck+2Fqm+y5HKvnM7V7thMx9TInDsbArMINBcwFxTVLJcLQW1TeVXLKyDsbJrmfkvnH/FHdUxskTmawWv92VF4OVBZNjgmYwG8BWB3ABJCPQtAPoDLhvCbBmAHABcAkDbvD+DPALqiMdaHnTs9L5cnh+6GkqNMGHclEjU5bu6D5hff9CBymZY7lHNzyVwqbHK5XyWjd6b0TaatTpfRudppotHry1QfywW5nQ2BJwm0D7F5qL92+QN2Ftm3qnX8WDJXMaPHX2iss6RuuqziCKdc0NdOv0R+V30ToaV54yWQDY7JJgAeB/BDKwKyB4D9AFw4RInDAdwGYJz1t18B+NNISurDLl7zp5YvlyelZKImzY++xPc8+wHcjprkMudkepjqmwy13Cyjc7U7dtMxFZ0z16GgK7/0G4AL/H2+Yqr7LOrHzqGly6ubZwDhy3qfbG+s+PXQ35Sz9md3CIxuKdngmMiazhsB7A3ABCD/Ps2KnEToS8Tk9wAOAlAC4CUAkwGsaGpqupCZ64abado0KaJJCYxMYOwHj2LCS/XoX2cLfHHA3QDFHg4mMy5q/idWhXoxfc/Nscc20hU1KYHUCWTifpzUW516DeqYpM4wnhr0hTk6pe6GwD4m6EkwXi6q7dg1Hpbl1S03AziJwdXtjZVN6phMdv090mv9OZ5+OZryuN6hosArsqIfP7M2vVcC2GjYUq7jAMjekmut8s0ALgfw92jG0IedO1001yeHYfea2IbxRd/f3fy34w3Q7WB8VLjMmLRkyfSBdNPOdc6J8lF9EyWWu/l1rnbHdjqmonMONQTmAjSLmBv9tcFZ8VijvLr5I4A2pQHs8sglFa+qY6KOSTz9RvPETyAbHBPZY/IKgBOtSMhV1v6RewH8CMC/AewF4AQARw05uWtzACvVMdF1rfF397VzdjaUHEMwFsaz10Qe7tXVr+R1TzKXgbAZmE9pa6qUr2dpTfpSkVa8ayr3Gmd3qMaWoo6JO1bwWt+OV99QfeAlEP3EZ/Kvx84JPmlnjf2rW7+XD/NdMFa1NVWsOzx/vHLt5CT6u8pNlFhy+TPFObnW5m6pbHBMhJ6cAy7OiaRbAZwCoMA6OliWdokDch2A31p5jgFw10jY9WHnTofM1CB1Um4iUZOI3LKZzSeSQbe4FTVxUt9EeobKTYRW8nkzxTn5FjtXUudq51jGqilTfSyb5XLdRv6u/AG5v6TX39exDtWFryKImcqqWk4jwnXM3NreVDlDHZP3ORPLULO5X9n1If3dnkC2OCbSUr+1XCvW5UZyz4ksnQnFUk0fdvaGdyLHaJkcOutLjyXCHXZRk4i+06a1+iJREzb5pPb5leJMpy2NFs7xAlJ94yWV+/l0rnbHhjqm1uYcmltyMNh4gMF/Kq4JyoE6tqmsuvl+Ah1iAicvbay4RR0TdUxsO41mSJhANjkmCTd+pAL6sHMMZcyKRsvDbjBqUiqXdm4GMg8pmr38wWiKD9W3rLrlFJJDG1zYazJaOMfbK1XfeEnlfj6dq92xoY6pKI5JfeAaEJ0JcG1RTbAhHkuUVTV3ElHRgJm/6aPzD/tEHRN1TOLpN5onMQLqmCTGyxMv6vEiGU0Pu86G0uMIuD1W1GSovhI16Zo88DGBNjTBxy9trLwjXm6J5htNnOPRXfWNh9LoyKOOiTt21DG1NufO+tJ/EWEbIt7NPzsoJ33GTFNmNf/UYPorgPfaGiu2svt4ZVefk7+rfZ2kOXJdmeLsjnbZI0UdEwdtkalOq3JTNyK3wtf1bumyWFGT4Zwj640latLWNGMLgDj1lqxdg9o3HVSVsztUY0tRx8QdK+gc8l3OfGGgpKuAggCHimqCcuKnbSqvWlwL4ouZcX17U8Xp6pjowTu2nUYzJEVAHZOksEUvpJO/gzBjVJUuzqGGwAkA3TpS1GS43F1OuTG/dL11PpSoCRjHtjVV3JkOAunS166tKteOkDO/Z4qzM61PrRZ1TFLjF2/pTPWxbJXbWV9yNJFxJ8APF9UE5X4021Re3fwMQHsBfGhbY2XUG+KzVV9b5ZLMoPomCU6LxSSgjomDHUQHqYMwM+CY2EVNotm3vKr5DBDJ/TrvtTXO2DodURPtV7ndr+xanyn72rXLjd/VMXGDsn7ZHk45VF96BwjHEvPv/bXBq+2sMK2utaBrtRkCw2cUFhU/Ujc16g3xmRrLKtfOgs78ninOzrQ+d2pRx8RBW2Wq06pc54wYujhwIgy6JVrUJBrn70RNgKPaGivuca41gzWpfZ0mGr0+r3F2h2psKeqYuGMFr/VtO307GwIdBArkDWD7MRd0vGVnhfJZi6eAuZ3BL7U3Vu42Un47uXZykv1d5SZLLrFymeKcWCtzP7c6Jg7aMFOdVuU6Z8TvRE2YDy6qDT4UqX0kzlNmNZ9lMMnFoGmJmqh9nbNvrJq8xjlNVOXY9/xhdcvx7v3R5KljkiYrDKvWa307lr6rL574/QEj720GB4trgqXxWKCsuvlyAp0D5vq2pso56pjoR7N4+o3mSY6AOibJcYtaSid/B2HGqCrdnEMNgZMAuhmM14pqO+Tyz3AaSa5ETTZcd53PQDTRBB2xtHFGs5Mk0q2vPmS9+ZB1so8OqescAPsPcUTKAOwE4B/qmGj0M019bq1qY82ZofrS00FYwMz3FNcGj4qnTWXVLW8QsD2Rsfcj86Y/q3OmN+fMTD2L4+mjoymPOiYOWjNTnVblOmhEACNFTWJxnlLdfI4BujzWUZLJtlLtmyy5xMp5jXNidJLKfQSAAIArRiqtEZOkuCZcyGt9O6Zj0lB6H4BD2cQJxXM6breDecCs1g18bC5nRo9/mVG0ZMl0ueQ5alLOdjSd+d1rnJ2hlju1qGPioK28NlhGs76hiwMnw6CbhkZNYum7d93tY4u7x34cjpowzVjaNKPVqa41mjlHY6T6OtVzMlrPJgBeAbAFgG51TPQLs5u9MdYc0tlQ+hUB6xL3buav/epju3aVVS0+iojvArC0rbFCIoAjJp277Gg687vXODtDLXdqUcfEQVt5bbCMZn2/e0IXH1Q0O/iwnb5lVc3nEdElsnG+vbFiO6e6lp1cp+QMr0flpovsd+vNFOc0ayd7rl4E0BKR09TUdCEz1w2XO23atDQ3RatXAoME8r/6N9Z/4iT0F2+ML8rj+3Z0zzMf4G/vfoGDd9sUv9whri0pinsUE5g8efKofG/OJpONSsC6PMCdLpapFyq35IYaAqcAdGMkamInd2jUBCZPa5tfea8TlrCT64SMaHWo3HSRHfWOyXgA7wCQ27G/iUVR5+rR3ceybQ7pbCitIqAR4BuLaoK/jYd+eVVzEEQlbGCH9rkVb8Yqk236xqNfKnlU31ToadmRCKhj4mDf0EHqIMwYVbnFeXjUpGPGCw/ZfS0pq26uIlCjk1ETt/Qdjlzljq7+7I42YSl7AJCbsWVjMatj8j8COqbc6YUjce5sCDxBoF8TY7q/tmOJXWsOmNW6rY/Nt8C8vK2pUvZLxUxqXztCzvzuNc7OUMudWtQxcdBWXhssXtA3NLfkVLBxg0RNOir/spOdYzIYNSn8HIQJsW4ITqTbeYHzUB6qbyK9IyvzHg/AB+AWu9ZpxMSOkDO/65hac6iJHF09xt+3el2qW7XKjm55VcvvQbiSme9ub6o82i6/crYj5MzvXuPsDLXcqUUdEwdt5bXB4gV9+Ubkd30R+BCgDVf+vBGb/OI42zFTXr14FsBznYqaeIGzOibeXLesjomDD6AYVekcAnQ3lP7SBJ4efgx8LAuUV7c8AqAcjGPbmirutLOWcrYj5MzvXuPsDLXcqcX2JSt3VPlfS/Vh547VvDI5hOaWnAY2ruubsBUmnPGc7ZjZ77w7iwp8BZ9I1MRkHLy0qWLNJY3JWMYrnCNsVN9keklultG52h276ZgCQg2BeoBqmHFpcW3H+Xbkp01r9XVNGviaiIoGyCh5dN70FXZllLMdIWd+9xpnZ6jlTi22L1m5o4o6Jm7byiuTw9CoCZgPLKoNyle0mKm8anEtiC8G+LW2xso1lzTalYv2u1c4q2OSTO/I7TLqmLhjP51DgFB94EUQ7cbmwAHFc1Y8Zke+bFbzz4npz4lEvpWzHVVnfvcaZ2eo5U4t6pg4aCuvDRYv6Ru5LTjeZQDfiZoQpi6dV9GWbFfzEmdhpPom21Nyr5w6Ju7YzOtjius28nflD3SCMeDvDxZRHXrtyJdVtVxEhAvAuKqtqeJsu/w6d8VDyJk8XuvPzlDLnVrUMXHQVl4bLF7SV6Im33Zu1+tb/SXijppUL74QkHsbUouaeImzPtwdnJByoCp1TNwxktfnkNDcwIFgeoiB54prOn4RD/Xy6uYXANqdmcvbmyrb4ynjdc7xMHIij9c4O8Esl+pQx8RBa3ltsHhN388fm8/rvHK5HID6WlFth+3yrH2rWsePJfMTAMUgKmubN2NpMt3Na5xV32R6SW6WUcfEHbt5fUx11geuIqKzwOaFRbXL/2BHfWrdI36zO9QJwoAxtmj8I3VTu+zK6EeVeAg5k8dr/dkZarlTizomDtrKa4PFc/q+8zaXLtnrczmhC8RTi2YHbZdnlVU3/4FAc1KJmniO8/vvs92xzA4O2zVVeY1zOhgmWqc6JokSSy6/1/r2cH076wNvEtF2ROae/tnL/2JHcUpVy0EG4UEGP9feWBlXhEUdEzuqzv3utf7sHLncqEkdEwft5LXB4kV9N2ze43dMuCaZqAkz/aa9acbjiXY5L3JWxyTRXpKb+dUxccduXp5D+MJASVcBBQEO+bcKjqfpGLCjXl7VfC2IzmDwBe2NlRfb5Y/87mXO8TJyIp/XODvBLJfqUMfEQWt5bbB4Ud9JT04uiNxrEn/UpKWegBow/trWVLF7ol3Oi5zVMUm0l+RmfnVM3LGbl+eQ7vqSI00y7gZjaVFtR1k8xMurWt4G4fsg42dt8/6fvSuBj6uq+v/zJknbmUBbpJkIFoTKKpZN9EPwcwGRNimo0DQBV1YRBZElaZPSIEmbKaAUREFAUbAJKaLSpKCiuCCKyFIWkfWTRcyk2AUy07TJvPP9bjqpaToz772Z+96byTv39/Nnyjv3nHv+59z73n/uVvdnO3VkxsQuSoXLBS2fC0estDQIMdEYr6B1lqD6m2yPfo1B1+Uza2Ka5glrlp/2aydpF1ScnWCkQzZoOOvArFAdQkwKRdBe/aDl9lh/E23RH4DoS8TmxeGW/m9ZITZnYfeMEJv9zJzojTVUWsmPfR5knJ3gVKhs0HAuFK9Sqy/ERGPEgtZZgurvDvea2NxrUtvYtQyEpnxmTYKKs8auaUtV0HC2BYrLQkJMXAY4rT5ouT3W34H2aB+Bogzz0Mrm/ietEK9p6jyDQLeC8fOeWP2nreSFmMzy/DsyaPnsJAcngqznCeUFaPKy8wLlYN83kWyPXsiga+3Omhy38O53TDG3vgbClBTM4+7tOO23dqMUtEFY/LWbGaUvJ2O1NzEMap8avHLGfikj9DwDGyqb+3azg3ZNU+dKAjWA+as9sYYb7NQZlQkqzk4w0iEbNJx1YFZKOoSYaIxW0DpLkP1VsyaJddVvEGF3ALWR5j7Lc+5rm7qWA7jU6axJkHHW2D0tVQUNZ0tAPBAQYuIByAG+tDTRVvVlkPE9Zr6zsiVebwft2sauDSBMMwkHrFlW/7ydOkJMnKBUuKyM1YVjWMwahJhojE7QOkvQ/U22Rb/ORN/OZ9YE4I/1dDT8zk76BR1nOxjpkAkazjowK1SHEJNCEbRXP2i5Pepvor16FYBTYfI5kcXxm63Qmntp92FGyHwc4Nd6Ohr2spIf/zyoODvFqVD5oOFcKF6lVr+YiMkMALsCeAXAcAYg1bPR9jKAt7KBLS87b9IwaIPDeH+5FZMTZdWvOZo1aey6BoRvAPhdT0f9x+xEKug428FIh0zQcNaBWaE6ZKwuFEF79YOW26P+DrRXrydgesjgWZMXxl+2QqumsfMSIrqKwT/o7Wg400peiIncOeU0R0TeGoFiISZzAfwQwEoAJwE4EsDGMc2fDOAZAI9C3bsN/B+ARQDMTC7Ky8468DokgvqyG4tdsj16EYO+lc+siUn8P2uWNTxsFQvB2QohPc+DhrMe1ArTImN1YfjZrR203Fb+Vnd98EigTH0zvBpp7tvbDla1TV3qnqkTmNHQG6vvslNnrEwQcZaj3Z1michbIVAMxGSUdKj7HfoBXACgHMA1Yxr/rjQROT9NTHL6JS87q7DreS6DMJDPrElNY9e3ifB1MH7TE6s/3ioagrMVQnqeBw1nPagVpkXG6sLws1s7aLmt/I12HXMZAcsBviXSHD/bCqv5rd0Vyc3mW0SYNMjGtPtjdZus6ox/HkSchZg4zRKRt0KgGIiJIh3qV4rZwMiNrMeoXywALBnT+P0AjN2EVgsg62ZjedlZhV3PcxmEt+GYbKv6BpNxjd1Zk7mXdlcbIVMtWaywM2siOOvJVystQcPZCg8vnstY7QXKwTxBsbrrGPVd8UkCN4Sb45azH3OaVn48BOM3zHiiN1Z/eD6RCdoYIv7mkyVSxwqBYiAmBwG4CcBH00uz1L/PS8+cjLZfkZX3A/gegAMBqAvqFFnJuM9EXnZWYdfzXAalbTjuMGtioiayuG+NFcI1jV3XEeFrzPzr3liDIuJZi+Bshaae50HDWQ9qhWmRsbow/OzWDlpuv/TCc1zd/ZEtACaFhwanU+vGsUvDM8K2/a4p4KqejvrL7GI7Vi5wOL8ke0zyyROpkxuBYiAmEQAPAPhQetN7A4A9xi3lqgCwNe2KarO6A0JtTHs5FostYebW8W7Onz9fYi8IeIZA5B8rscsTN2Bo2n74z4m3WdrdlNyKKzqfRMpkXHTSQXh31NEFw5b6RaC0EPBjOUQxICTExJsoBO2D+bU/38W7/farYOanKlviajWGZalp7HyEiN5vmuYJa5afpn78dFyChrP46zhFpIINBIqBmKg9JmqDmiIaaiPwCgB/AHAXgEMBPAugEcAggKsAqKVfSl5mTNIBlsHBRqZrEMmFc16zJk1dNxDwFQbf19vRMCdbEyW+GoJnQ0XQcLYBiesiQkxch3jEQNByO/7zJq585jZFTK6tbIlfZIXy8Y3dUyfB3KB+AA1PMXZd1Vo3+kOoVdUdngcNZ/HXUXqIsE0EioGYqKYekSYb6u9bAZyj1t+njw5WS7vCAH4PYN+0X58B8LNsPsrLzmb0CxSTQWlHAAfaqy8d2WzJeDzS0qdyOmfZYa9Jyjh8zVV1T2SqIDhbIannedBw1oNaYVpkrC4MP7u1g5bbG248niv+8zRAPC+yKN5jhVNNU9d8ArrtHkiSTV/QcBZ/rTJLnueDQLEQE9V2RT7UehZ1Mle2Mh3A5vTsSVYhednlkwrO68igtCNmO57QlZobaV53rxWqNY1dNxLhXABrejrqa4SYBO/XXb/6kVVuFvh8//Ty23/m0iNjdYEo26zuV475YZdb9wgnKihB5vBweDg0lVrfSFrBVNPUdRMB5zC4qbejIWYlL8RkGwJ+xDeIdvPNx1KtV0zERBuG8rLTBmVORTIo7QzPQFv1ZUSI2Z01OaG5c2Z5il4moMzMMmsiOEs+e4OAFivqnfKjtCb1Q1MZgE+nT1zcyYCM1Vowt1QSpDEksTRaC6bVYH4o0hJXB+dYltqmzlcBmskw3t/bUaeWiudVgoRzEAmCX/HNKxlLuJIQE43B8ytpxa7GIOZQZQfnfGZNapu6bgZwFhire2L16oLRHYodu24gIHbdQHVnnX7h7JJ36tSR9wG4HIB6v3wyvWcw46/WQkxcikKAx5CBtui3iejrAK6MNPepPMxZ5lzaNSsUwotgbOyJ1atVGXkXv/qy2M07ZI4q+oWzo0ZOAGEhJhqD6FfSil2NQSyQmKjqA+3VjQR06Jo1CUp8eUm0aqACVevn3PHUXr/4bJSuiOda1qk96EHBWTtwOyo8FcAPAOyS/s8fT5+6mNGsEBOXo5FWH6TcHmiPPkWgQwzCR6cs6lN7U3OWuY1d5xqEG5m5uzfWsMBKPtfzIOGscBB/C8kWqZsNASEmGnNDOqlGMDUQBN2tsRvfbbMm0X8T0TTA5l6Tps5bCXQGM37RG6v/1Ni227Xrl7867Cbao2cBNDJbtGXPY+dN+teDq4m5M9wS79Sh346OIOBsB4cCZdSMyYUATgZQlT5pcRaAdXK0e4HISnVLBGjrW4jePQdslCN+6v2AoVYS5i4/uP9FrP2/DVhw7N740EEqZaUIAtkRCOrR7l7mhBATjWgH7cNG/M2ePANt0SYiWuZ01gSMEBjv7V1er47JHikTHeeNrdF9y8vp2lF/R4mJ+vfQEH99Wmv8ZY3dNKuqiY6zFxgC+GL6EJPvpO18RixGAAAgAElEQVQpYvktAI9ksi8zJt5EJSi5nWyLNjDRyq3v/CCmn/ELW983tY1dG0CYljLLZ967/JTXC4lIUHAOyrtpfC74Fd9CcrIU69rquKXmmLzsvImYX520FOzyVdFIYgteV7MmbKbmVC5ed59VVGqaOm8j0BeYcXdvrP6UoAz+6Y+JBoOwS4rxPnPStHcYWzb+gYBNXs6alEJeWeVQETz/BIAzAHwWwK7pI9/3BqDuiNipyFjtTcSCktuJtugtIDrzrUO/gnfWXm75fTOvqesoBv4K4MWejnp1N1pBJSg4B+XdJMSkoO6Qd2XLjpu3Zh8rysvOG/BlEM6N85hZk79FWvqOsorKtk2Y/Dwz0dhZk4mOc6Kt6stEtJBBe+2IEf+LGMvDLfHrrLDT8Xyi46wDIxs61DvluwC+nJb9PIDbs9WTsdoGohpEgpLbifbqVwDs9eYJt2Lvo2osv29qGjsXEVE7wDf2dDScVyjUQcFZiEmhmSL1cyFg2XFLET552XkTNRmEc+PMrTMqE2WhV4iwm+1Zk8bO24nos2C+qyfWoNbrT9ilXGpWKbkVC8F0KWjkQlVV/jkcqX53aKDPJIIx8l+YvxM26Ju0qG+dm5kdtHx2E8v0bEkKQCKXHRmrXY5CWn0QcnvwyqpZKcN4kYEN8QUPTp/1nvdYft/UNHb9lggfYxOn9C6vv7vQaAQB57EYib+FZozUz4SAZcctRdjkZedN1GRQssY50RZdBPWLnM3b4DPNmkw0nLkboeSLVWcx0xUEiirmwUzPE/g1EG0e2WPyxh9/xYw9iHEgiMrAPACia8IVfBVdGs/5sWsdlcwSEw3nfHHwsp6M1d6gHYTcTrRHzwHoJjB+2tfwp1OsNinPb+2uSA6aCbWvbwuM6ffH6jYVGo0g4CzEZNaE/G4uNPd11p+QAMvLTmeKZNclg7A1zjvsNYF5YmVz/y+tatU0da4kUAPAd/Z0NNRPJJwT7dU1DCwn4OBtOPDDnOJzKy/vX5tYGj2JGPu8dcTXr93l8WtPjiyK3zO4dPf9U2bZUhBG9twwc79BaJuye/xGOhdDVlg6eT6RcHbit5+yMlZ7g34QcnugPXongepA5lf6Fvz5u1bEZG7TyhMNGPcy8197Yw0f1BGJIOAsxESIiY6+kkuHEBONCMugpBHMHKpKDedEW7QFRFeC+S+RlvjRViipWRPDwAtEzKkU7f+dLx/1otVL1kpnPs914rzlm9XvHQ7xTQCN3MTMwPNEZmNkUf/Px7ctk93Est2PhBlaMVof4JcJWDxlUbxzZEeOhqLTXyfN8cuukza6JSvExC1kd9TrV455aXegvXo9AdNDZmr/f532l+etxsyapq6rCbiYgfbejvoWHZHw0l8hCN4TBL/iqyM3S0mHEBON0fIracWuxiC6QIjymzVZeS/BOJGBf71v72l7PvXPDc2M1Oo1sc8+5Y23eva2JGK778FDZR1E/FmAiIF1BFwRHuq7iVoxnMmXXPmcaJ8xh2F0EGj2SF3G40zmQjszUVa4Ba0fWeHhxXMhJl6grKcv59NSr/rUQPuMwwihxxkcr2yOV9uxW9vUuRag2UTGR1cvq7O8iNGO/3bs2tHjVEbsOkUsP3m/cM6vtaVbS4iJxtj5lbRiV2MQXSAmSmWivWoxYHzTzqzJCZf8OFIeKr8ToLmq7v577kIvvPH2agYPDA0Pnf2rqz/vyh6L8a4Xklcc232X5HBIbWy/CITJYAwS+Nop5aml1Pjm27kiZmWXGTS4tOp0E8aVAN69jZ/g92QMXxxZ+Oaj+WaDld189VrV88uuVbu8eC7ExAuUJz4xSbZXXcwwrgZwW6S570tWfWrOwu4ZITb7mbEl/LIRWbWqTh3UUHCxsluwgSwKxK5byO6o1y+cvfGueKwIMdEYC7+SVuxqDKJLxMTJrEnNws6TiOksAEcC2GNapBwbE0OrR5pm8oqe5Q2/8cLjfPKKW1GWLK8+l4ElBMwY2RUC/ITKUo2RxjffsNNuu3a5FRWby6PnmUwtRNhd2QLTXSE2F05e3P+SHVtjZezadarXSt4vu1bt8uK5EBMvUJ74xCTRXr0GwBwD5uemNPffYdWnahs7TwPRT8C4tydWP/IDkI5iZVeHjUw6xK5byAox8QbZHa0IMdGIugwOGsF0iSAU0sJC45toq7ocZFxhNWsyt2llgwGjAYRdwPjoDm1m3sCEhwF6FIynwVg79pb4QvwbX9epv4m26MkMihHhgG26+E+c4vPVxnYn7XJqd2R2Zih0GQgXARQB8zBAtzCZrZXN/XG7tp3atavXSs4vu1bt8uK5EBMvUJ7YxGTklL8XqtUs8qTwVo7SFfF+qz5V09T1QwK+aIK/saaj4du6omBlV5edQsdqXe0Qf3UhKXrGIiDERGM+SCfVCOYEJCY73gZvfrJycf+vMrk5ZsZEfdzvWzm5/L1vDw4NEmhyJnkGD46QFJAiAGsZtLaMJz9+z/KTcy6ZsoqW3Xwe+GbVoRQitbF95GQbZjxLBl8WWRTvsbKR6bldu+Pr8tLqGUmGIn5njxwxDFZHgX47XJ5abrV8TOnK124+Po6t45fdQtuto74QEx0oWuvwK8e8sLt56YwPmxz6gxp3Klv6Rk77s7Jb29gZB1GVycbsNbE6bfv2rOxaRyo/CbGbH25Oa/mFs9N2lrq8EBONEfQracWuxiC6TIgS7dVLALTmmjU54ZK7q8rLt64gRkQ155C9p817+pWNq5kxlDJStxpsHKA2fxP4UGY6mAiTMhMW/FPNqIyQFaInU6C1v+yY/xKg9qBbF6u8SrbtNtNEuZohqd+2sZ3jxNwa3r//ZqpD3mu2rexatVxdtDZs0FIC5o+0i/GmAW6fMhz/LrVia7b6hdq1alex2c23vTrrCTHRiWZ2XRM5txPt1a0AlgB8faQ5foEVMZm3cOWBzMazYO7viTVEdUZgIuOcCSfxV2f2iK5RBISYaMwF6aQawXSZIOTTUh3x5Y7pU5PDFa+DqJLN7LMmcxvveJ9BobMA2kcRkydf2bAMnOocfyrX/PndocF9hw9MUWg2wTwUwKHEmA2iPbL4OACw+oVwhLCAQmtp0pS1q1vnJcfLZ/OXW3fbdXN5RQsD6iNAkaLNYL4mPAkdOi4/1IGz8kXN5CBkrCDgIyO+MV5hmIsjzf13ZDpiWJddp7nll12n7XRDXoiJG6jurNOvHPPCbqI9+uDIMeLMn4q0xH9hRUxqGru+RoTrmPmO3ljD53RGwAt/hSBYz4jpjOlYXX7F1y1/ilWvEBONkfEracWuxiB6QIi2/8Jn816TfOL7qa//bNqWyZuPINChVrMrI1vGCWqz+FoGrwVhLRsVa68/47B/jr0LgG9C+eY3o18xQYsJeAeYTRD9mGE2OdnLYRWtfPzNpXPgyqoTiIwOEA7fxk/4STJpYWRxn9owu73otmvl5+hzv+zabZ+bckJM3ER34uc2t+4RTpalNoEQCg+FKqn1jZEfWHL1qZqmzl8Q6CQm+mLvsgU/0hkBv/qy2NUZxey6/MLZG++Kx0q+xGQ/AGo9+cvqh0kAzwHYUixuycvOm0j41UlL3e4OsyYwT6hs7v91rojp8nd0doVBhzJB3QOybYYF9M5M9ieXh7B5aPiPau/K0aHHyuZW/LF239Br7yqn4ZHjectTOH/S5X3P6M42Xf6ObZc6Ynhze7SeCW0A7bvtGf8JRurC0SOG3bBrBxuX7cpYnSEILmOeNexi106PsC+TuLJ6Lgz0gvmvkZb49tvbs+GsxsDkvqlNRBRJkVF177K6dfatWUtKfK0x0iERNJx1YFZKOvIhJvMBdKedPAfAh6B+PQU+DeS/rlwnaEJMdKJZfL8eTIRBKdFe/U0Ai61O6LL69U9HpGuafjKdTOMI08BsGiEqmJ1t7wox82Rjy+ubedJDgDEyuwKD1va01/1LR1vc9ndk1mdd9bkmeDERVaXbfHeIhhf+a8HDz1ndFq3Lx7F6XMxnGauzBMxFzHOmiNjV24MG2qqvIcI3AF4WaY4vGtWeDec5C+88JsT8IAN/7+2of6/e1gRviZHks+4MEn0KAafERJ0KpC4wuw/AWwDUvQSvAPglANXJ/14MsAox8SYKMijlj7OTWRM/cI5f+Z5Z/44e92L/G6/j/1J74GVz5pYXUu8e3IqKqZm95v9sWwo28r8n1f/3r3/r6Ue/f+6QU5S88FedkJbcSpeA+RK13wfgVHLfk0JTXv5Ftc5laXZ8d8lfGatzgO8S5pbhFruWEDkSGGiPrlVLVQ2Tj5uyOP5bK2JS23Sn2iTfCvB1PR0NFzoyZkNY4msDJA0iQcNZA2QlpcIpMVGnBD0JqFN41IVmmAlgJQB1LOlh6Q21vgMgxMSbEARtcNDtb6IteiWIWqxmTXTbzZUdvGzq9IQ5eTExnQ9CxciRu6Crw1P6YvQNbB6dXQGp5WA8G0yHEkEd0VkxXi8DwwD+ocYFSm+2Nwxz7T1LT9vpbpE5l3X9T8jAmQCi6VPIbkmZuPXe5fV/cTObR44YNvlyEM4FqFxt5CfmFVOGh5ZR63r144vrxaX4ylgtxGQ7Ai7lmGXfcNMut06bliyfvEEtIw8P9e069sS9bHZrmroeJOAYkzBvzbL6vI4zz+W0m/6K3f8iEDScLTvaBBNwSkwMAOrWaXUjtVpbrgiJepmrDxO1ltmTF7lVDISYWCGk53nQBgfd/tqdNdFtN1P0t21sr7rAhNFMwPRtMwjzQlNevsdyBmFk3fZ+5kGU4tnmyGZ7HAri2dn2rgAcH71zRZEWk+k1Ap9HNO54ZPDA0PCkr//q6s/068nY7Fo2L6t+d+Jdx//flFd/zekjhtcbZC6dMtR/fa4jhnW0y6X4ylgtxGRCE5NkW3QBE3Ux+DeVzfHjx4Y7U5+a17o6bA4mNik5Y3JkaqaTCAvtzy71ZctmiV1LiLQI+IWzlsaXkBKnxES59i4AvcDI5tnR8kkAGS+L8wMLISbeoO5XJ51IdhPtUbUZuznXrInb/o684IFlINpnJHMY95Xx0DdeP+2vfy9kz4WaXQHKjiTm2SA+DEyzQSMb7jMWBjYR8NY7p4dn/ntD8iEAm5j4xt5lDfd4kdEK52jX/xxObMRAdEIai9eYcHlkqO/H1ArTjXa4GF8Zq7MEzEXMc6aI2NXXgxJt0e+D6GwGL6psji+zIia1jXfOA/E9YH6wJ9bwYX0t+a8mia8bqO6sM2g4e4Nq8VjJh5iMtl5tHFWzJWpt+WDxuAQIMfEmGkEbHNzwd+ysScjkT0xeHL9/fPTcsKtsJNuqP8DADSC8fxsf4adDbJ4/pWXdH9S/3bC7fXbFxKEmb9tsr074I8K08X6zOkoL6GPQ74jwGEx+jA3zsd6O09XyDe1lrL+b22b8r0nGCoDUElUw8zMELIy0xFfrNuwGzuPaKGP1OEA8wDxjmohdfb0n0RZ9Wf2YQowPhlv6/mpFTGoau64jwteYsaQ3Vq8OH9FeJL7aIZV+5A2kRWUlH2JyA4CvjPNCLbU4EIArHwxOERNi4hSx/ORlEM4Pt/G1Em3RdhAtyjZrohtndTN6iugqEKmT9NRX97+YqCWyqO9HYy8e1G03G1q1l3UexwYuIcY0Jtp1Wrj84I2JoQEiVGaqw2B14MZjAD1KRI+nQI/oOPZzvL8jRwwvrZ7P4HaA3rOtLfwwMV0w/kOokExwEWcZq7MExkXMc6aC2C2kp/y3brJtt5lMFa+qPXCR5vhO40QmnGsau/5OhINAxod6ltX9WU9LdtQi8XUD1Z11Bg1nb1AtHiv5EJMmAEeM/MC6rdQBUMREvbjVnhPfixATb0IQtMHBLX+tZk102eWlu7wjyeElAL48stGbWd1BFAuH49eoje3js0aXXatsPOGSH0fKyytuId5xj4kJDDLMG0NMBzHhCDAdAfB7iUY2qe9QGPgXMR4D8WNMeMxMVTx27/JTXreyPfZ5Nn+5FWXJiqqzYdLlIKrexk/4F2U8vHDS4v8868RGJlkXcZaxWojJCAIu5ljO9HfLbqI9ehZANwP880hzfNsPLGPKeLtzFnbPCLHZz8yJ3lhDxh88Cu3HExFnK0zciq/YtUJgYj/Ph5iMR0QdE6yODz4ofdmi74gJMfEmBDIo6cM50R5dCtDCTLMmheLMrahIlFdfBPBCAk1VG9sB3BImWkyL+rJeMFaoXSfo1DT9ZF+AGsC0z9EHzjjzz8/1twDc2dtxurrEdXuZ39pdkRzE+wDzSDCOIMIRDH4fgdTxuDsW5n5FUsB4jMl4LDRMj62+uu7/srXLyt+RI4a34CIGLiWiXdM4/ghlqcWRxjfV0el5FSu7eSnNXEnG6jQuHmKe84NZY2xzqppo/g60RbuIaAExvhZu6fvOeOfH+zu3qfOLBuiHzPhFb6z+U27hPtFwtsJJ/LVCSJ7ng0A+xERtqBz9CFB3FBwFYBWA/QG8kE8j0nVmANg1fS+KOmY0W1G3VG9Ux3pmExBiUkAUHFSVQckBWBaiY2dNDPDxU5rj6vS7kZIvzmop0mB71WkmGUsB7DWijLknZKQunrzozeetWp+vXSu9Vs+d2lX7Vt7eb/i9hklH0LZZFXVq4KHqdufxthi8gYDHWc2ugB5lA4+tWbbgBYDYrl0185TgsDpS+byRI5UZg0S4foqxeRkt3OR4Oatdu1a4ZXju1lht1RQ1jo++W9TMetbTGmWstoJSz3MXcyxnA92yO9BevV6dHlhmDh2cadZyvN2apq47CDgdzF/tiTWoJY6uFLf8tWqs2LVCSM9zv3DW0/rS0eKUmCj5ewGoU7jGFvWr5iG5yIIFJHMB/DB9J8pJ6eOIFfkYX9Qyin8DOBpA1vsN5GXnTQL61Uknqt1ke7SDQY3jZ03y8TfZVvUhJrph++Zt8NMG87nhln512pWtko9dW4othLTYbW01apIHHkCGWgKGI6BmVhiHE2HnCyIZbzPxEx99b/WHf/90/AscwmPhF4xnV62qUzNLWYta525S+VJinAYig5k3gqgjMtS3glrtHwiixd+dW+nWWG0VYvWjlTpKXl3Eq0iJmqFSN3JnPNFMxmorOPU8dynHLBvnht2B9qrZBGMtg+OVzfFtSyvHlfF2axs74yCqGjaNA+9bXvecZcPzFHDDXztNEbt2UCpcxi+cC295aWlwSkyUd+oYzd3GuLkVwIPpfSb5eD/6IlNkQ+1VuSB92tc145SNnsv/0fQszd+yGZOXXT5hcF7Hr046Ue1u2wMSeQ3AlLGzJk78HVy6+/4pM3Q1iOaNRJTxGsNsjjT33zF2Y7udaDuxa0efXRn37DJ9smnVrBDzEUTmCGFRS8EAesdObeORGdm1I0vBTH7MNPBYtpvst7RHDxkGLQcwZxvm/C8QWsP7xX9IdchJbpS4e/5qH6vthFDN0igicv6YfYhZ68lYbQfSwmVczLGcjXPDbrI9ehGDvsXgOyqb45/L1ICxdk9ctHJ2mWmsBXN/T6whWjia2TW44a+d9opdOygVLuMXzoW3vLQ0OCEmaqmWIgfZyovYdtOz06JeZL9M34uiXuLHpMmP2qQ7tlwO4JH0y//29N8ZbcnLzmkI8pP3q5NOZLvJtqoYk3HZ2FkTO/6mSc2VYD4bRGXM/BYIHZGh+Led/Ho/NhPs2M0vc3LX8truvMbuvcB8xCeOqP7Zr5749xreRlgy/RK7FYynocgK+FGYocdSmyvX3nv93C3Ko5FZKhgrth+/zHiWDHNRZFH/z3N57IK/bo3VdsKtLtodu0ywNn3vlYzV7pJQzwmCnWRwIbeRaKvuBWEuA1+qbO67zYqY1DZ2fQOEaxj8g96OhjPttDtfGTf8tdMWsWsHpcJl/MK58JaXlga7xESt1VYn+OQq6lfH9Xm4rzbN3wRAzYSo6X717/PSMyej6j4CtT4UOAdAJ4BvjRKTWCy2hJlbx9udP39+Hk2RKoKAvwjQlo2ouufToNRWbPjYCmyJjlwxkrVQagsiz92J8N9/DGN4MxgGku85GcnZZyNVsfOqJX+9Kx3rm5JDeP3NBF7/TxKvrUvg9TeT2JBQk8M7FiKgetoUvGv3MGbOiGDmOyKYNfgI3vHM91H29qsjwlt3OxhvH/41DM0YeyctsGUohfUD23R+s+uxyl9d/fmEBoTcHKvtNE/9sKSS9nvpI+R/DUCRlbdkrLYDn8jkRMAcRtVPPwkjNYj+T62GOXns4o3MNW+873k8+9omfP7js3DkLGt5iYAgkAuBQi4dFmTtIWCXmJQB+F8A6v+zld8DGPnl0GFRL9IHAHwoPePSAGAPAKNLuVQb1aVmNemlXuqyMFXUBtfHMtmSGROHEchT3K9fDya63WR79XJ18tPorEkmf9XG9kR79eeJ0AZAzTqOHGEbYr548uL+l/IM6Q7VJjrO4zGy8veTF3XvVj7JfL85ugRM7V0B9iXavtE7rZLVDyzPzzTeeOuY8icO3N/45677lr2GKdjcW2YOX6o269Ys7DyJmM5SFQ7Ze9q8p17Z0AlCl4Zb7t0cq+2kVYXiYmlBNXb/FoD6lXqH09VGFclYbQfSwmWscrtwC5k16LabXFp1DLPxIAPPVzb3HZCt3aN2Ry503ddMEGHSIBvT7o/VbXLLV6VXt7922yp27SJVmJxfOBfW6tKrbZeYjPdMnfAzWlctv9onTRLy+cVP7TFRGyXVy+thACsAqJun71In6wBQ9wQo8qLuLlC2ugFcC6An2xpmedl5k4h+ddKJbnfsXhMeNj+77uS77tjnsP/d3lfVreQpMm4gkDpwQnWDJ4j5fCcb2+1kyETH2SkxyYTZnNY7dg0Nho5gGEcSmyOb7LedUEg7LXutMt7EvvQ6Ryjx92f4AKOCt4Sn0JbJ4SmTo5uSwy/38+6vrh+atuBXV39G7bXTVXSO1XbatBgY2fh/VZowq7F9ZMYkU2UZq+1AWrjMROnLibaqy0HGFWpGLtLcN/6i5+1Ajfpb29T5UYAeYMYTvbH6wwtHMreGiYKzXZzEX7tIiZwTBPIhJmqZ1XfHGSn05nf1MlcvMFVuTS/ZUr+8qRue1dKusUvE1LIvtUzgiWyOysvOSQrkLyuDUv7YWdVMtEXvVjezM7Bh657HTp/8xh87KWX+2jRCao3itk3WwD+JedGU5niX043tVvbVc4mvHZR2llEXRk4KTT6cYR7JxIcDdCRtO7Vwp1LOQ+YBleuNocFE3AQNbzYnL7xx2UVqD52O4sZYbdUuNXunZs/3TQt+BsDPZKzehoD0Kav0yf18oL36DwR8mNg8NdzS/9Ns0qM41zR2thPRIma+ujfWcGlh1q1rS3ytMdIhETScdWBWSjqcEhM1u6FmNdSMhlrapY4eVbe9n5JeT6zuNcm3hAGoG1kL/rVQiEm+IXBWL2iDg1f+8pJo1WCF8WMTfDyA0PBuB6Bs/XOKpO+tIjRyLC24vbKl/2pnEXMm7ZW/41s1Ee3O+dqaSaEpA4e+s+zfx1XTuks38tTpr5l7wIDJn9hlLb2Y3C2ucJhhvHl705Xf0vEB5eZYbSeRpqePj1ezJ1mLjNV2oCxcZiL0KXVRbLIsmgAhFB7ashu1bsx0pcAOBLCmsfNhIvqAuuKgp6P+V4UjmVvDRMDZCUbirxO0RNYuAk6JiVpS9SSAkwGo9Z3qskM1e6IuVlQfUVlvVbbbIB1y8rLTgaK1DhmUrDHKRyLZFm1gIrXX6mAAs8bqYOaeiJH8Ii16+z/56HZSR+LrBC37sre1fKFtHe3+5RPK/6h+iCn/uXGq8dbm4RFi8gF68oYFbV1X2teWVVLG6hwgSm5ryDAbKnTiPHDljBPJCKl71B6NNPflPBVE2T33+49OmwRTXXi6NTzF2HVVa93Op1fY8MGJiE5/xW52BIKGs5NcmAiyTomJ2ufxJwDqBK4YAPWLrVp69XUAh6lz/4sBFCEm3kQhaIODV/5uJyZMkxh8PBEMEN4gE/8A+JZwS1ydTOd68crf8Y5MdLuJpdGTlifPvH6AKydFKGnw5N1mJDcPxitpYMulU249o7I5/hsNwZWxWojJdgQmQp8aaK++moCLic3l4Zb+xlx9RPn7tZv/dqoBvgvg3/Z0NBynoU9ZqpgIOFs6OUZA/HWClsjaRcApMVF635femK46uloLrY75VWRFzZjknLa326hC5YSYFIqgvfoyKNnDyanUQHv0OAJdmK63z9CM2YeUr3tSnUwHBq/Q9OFq2SyJryVEeQnwVdHIui3hHz48PHvv51L7TK/cddp+eyYe/+sHy558Zcak5Jfo0ng+h4hkaouM1VkiJLmdV+o6rqQT50R79HGADmPT/GTl4v6cy7KU3Qtv/uuNAH3ZZCxcE6vvcNz4PCro9NeJebHrBK38Zf3COf8Wl2bNfIhJPYCnATyTvnBRXZaQz/0lriEmxMQ1aHdQ7Fcnneh21YdrcgtWgGjkaOwtex47b9K/Hlytbi4OT8KFGj9ccybKRMd5vPNe+jvwzej7KISTAPrgtvj+sYVTuKfy8vhTGnuvjNVCTEYQ8DK3x0Kuyy63TpuWLJ+8AczD4eF4hFq3H0mdMcLK7gXff+QlIsxiGO/v7agbPVxHY/faWZUuf502Uuw6RSw/eb9wzq+1pVsrH2KijukdvVNE3ca+BsBrxQSBEBNvouFXJw2CXUVONm/FWSYQTRz8+YW7/P3HX59SgVu8IiUT4WPGaS+YgHklY7UQkwlBTJJt1fOZ0M3A7yub+9QqjZzlb2v/zq2dT6op5o09sXp1EIMnZQKOIfLj1RgE/IqvJ8lbREbyISYz0ydyfRLA59K+/AXAJ2zcDu+J60JMPIG55H+Fc4qSX4OS2HUaqfzkJyDOMlYLMZkQxCTRHr0JoHMA8/JIc7/l4RC39/yZ73zwFTCwqrejvi6/EcF5rQk4hggxEWLivCMUWCMfYjJqUp3KdTGAs9NHBr+7WO3ppfYAACAASURBVJZ0CTEpMCtsVpdB2CZQBYoJzgUCaLP6BMZZxupxOTCBY50x20vd30Rb9EUQzSIyPxRe1P9nqy7d8v1f8RMvr1dnq5/TE2u42Upe1/NSx9kpDuKvU8RE3g4C+RCTbwC4ZozyHwBQpwT9FoBpx6jbMkJM3EZ4m34ZlARnNxCQvNKGqozVWaCUHNOWY67/op5s220mU8WrACfC+8WnUh1SVq2ff/kq3rw1hZRZPvPe5ae8biWv67nklS4kc+sJGs7eoFo8VpwSEyXfBeA9ANQpFw8AeLN43NnWEiEm3kQkaIOD+Ct55QYCLuWVjNU5guUS5pbpIXYtIdpJINEePQOgW6HucGqJz7PSUNPUfSTB/BszXuqN1atvFc+KxNcbqIOGszeoFo8Vp8REtXwGgHXF48LOLRFi4k10gjY4iL+SV24g4GJeyVidJWAuYp4zRcSu8x400BZdSUQNxHxRuCV+rZWGuY1dTQZhGcA39nQ0nGclr/O5xFcnmtl1BQ1nb1AtHiv5EJPiaX2Wlggx8SZEQRscxF/JKzcQ8Cuv3PDFqU4Zq50ilp+8Xzmmw+5Ae7SPQNGyFM+eZOM47dqmzt8A9HETdOqajgU/zQ+x/Grp8Dcfy2I3H9Sc1/ELZ+ctLe0aQkw0xs+vpBW7GoOYQ5XgLDi7gYBfeeWGL051CjFxilh+8n7lWKF2t7RHDxkGPcXAhsrmvt2svJ/f2l2R3Gy+RYRJg2xMuz9Wt8mqjs7nhfqbb1vEbr7IOavnF87OWln60kJMNMbQr6QVuxqDKMRkOwKSVxM7r7zxLrcVISbeRKFU+3KyPXohg65l5s7KlvhpVmjVNnWdAOCXe82I4LsXz/P8+6ZUcbbCNdtz8Tdf5KReLgQ877hehENedl6gLKdyeYOy4Cw4e4WA93ZkrPYG81L9gEy0RVeDqBYmnxVZHL/VCq3apq7lAC79xGHvxIX1H/H8+6ZUcbbCVYjJNgT8im++8SnVep53XC+AkpedFyj710n9GhzEruSVGwj4lVdu+OJUp4zVThHLT96vHCvELncjlHwhugmgCPHWvcIt61+z8r6msetxIhz21ZoDcOKHD/f8+6YQf618y/Vc7BaCnv26fuFsv4UTQ9LzjusFbPKy8wJlISbeoCw4C85eIeC9HRmrvcHcrw+qQuwml1YdzWw8BPDLkeb4LCukjm/snjqZzI3M2PLts94/af/93uP5900h/lr5J8TkvwgEDedCcqMU63recb0ASV52XqAsH8zeoCw4C85eIeC9HRmrvcG8FD/kEu1ViwHjm2C+OdISP8cKqZrGrnoidDL4vuvO/sCJs2bN8vz7phRxtsJVCJH/hKiQGJViXc87rhcgycvOC5Tlg9kblAVnwdkrBLy3I2O1N5iX4gfzQHv17whQG0UWhJv7uq2QqmnqvJVAZ4Bx8YpzjrpGiIkVYoU/L8W8KsRrv/wtpM2lWFeIicao+ZW0YldjEHOoEpwFZzcQ8Cuv3PDFqU4hJk4Ry0/erxzL1y63oiJZFk2AqCw8NDidWjdutPK8tqnzVYBmmmzMvv6cI58UYmKFWOHP841voZaDZrdQvEqtvhATjRELWmcRfzUmjxCi7QhIXnmTV8VgRYiJN1EotT41cGXVCWQYvwTz2khL/DArlOYu7NrfYDwH5v6eWEO01Py18s/qufhrhZCe537hrKf1paNFiInGWPmVtGJXYxCFIAhB8CadfMfZYzczmhNi4k0USu0dkWyvXs7Apcz4VmVL38VWKNU2dp4Pou8w8JPejvrPlpq/Vv5ZPRd/rRDS89wvnPW0vnS0CDHRGCu/klbsagyiEBPfP5gln73JZw+svBOAWoKzOZstISYeRMHH+xfy7cuJ9upHARwBEzWRxX1rrFCqaez6ORFOZqIv9i5b8KN87VrZsXoudq0Q0vM8aDjrQa10tAgx0RiroHUW8Vdj8gghEkLkTTp5ZaUawL8BHA3gL0JMtiEgY6Z1+nHrtGnJ8knrwUiFh0NTqfWNpFWtmsbOASKKpMioundZ3TrB2QoxPc8FZz04ipYdERBiojEjpJNqBFM+1OVD3Zt0CizOLsJrAPgNgI8COArA34SYCDGxm2/JtqpTmIy7wHgw0tL3Yat6tQu7jwabDzHj2d5Y/cFCAK0Q0/dcvnn0YSma/ouAEBON2SCdVCOYQkwC+8Es/cibfuSilcsBPAJgDoDb039nNCdLuVyMwhjVpdSnEm3V3wPhywCuiDT3tVohVNPUuZhA3wT4up6OhguFmFghpu95KeWVDq/98ldH20tJhxATjdHyK2nFrsYgCiESQuRNOvmOs0tufgTA6QDUhXidAL41SkxisdgSZt7pQ3P+/PkuNUXUliICu/csQNnA61h/3A3YOsPyQC6sWP0sXu4bwNkn7IdD9p5Wii5Lm0sIAT+OoS4heLQ0VYiJFhi3KRGCoBFMIQi+f7hKPk/sfHbBO/U+WQ2gBkA/gKq0jSMBPJbJnsyYuBCFDCpLpS/zkmhVsoLiALaE9+uLUB1SuRCa17o6bA4mNikZY3Jk6urWeSP7UUrFX13RF391IZlbj184e+Nd8VgRYqIxFn4lrdjVGEQhREKIvEkn33F2yc3pAMqBkQ9KdVv3tQB6ALAQE/lgtsq5gSurv0QGfgDGfZGWPrUUMGepaeysIaIeMD/YE2vYvh9F3olWyOl5LjjrwVG07IhAMRGTGQB2BfAKgOEMgZoCYE8AbwNQv6hkLfIrnDdpLoOS4OwGApJXbqDqi86bAHwPwBPZrMtY7U1cSqVPDbRF7yCi05nNSytb+q+2Qqe2setaEC4EqLWnY8EVo/Kl4q+Vf3afi792kSpMzi+cC2t16dUuFmIyF8APAawEcBIANfWvzr8fLfukj5xUGylHN1R2yMtuGwJ+dRax602HF5wFZ28Q8N6KEBNvMC+VMWSgPdpHoCgjdXhl87qshHYUtZqmrmcIOJiZj+mNNTwkxMSbfBKcvcU5aNaKgZhMBvBM+rx7tS75gvRSgGvGBENtpOwDcA8AtVTgQQCz08sFdoqZvOy8SeNSednpQkP81YVkbj2Cszc4F4MVGau9iUIp9KktS6sPHmY8w8CGyua+3ayQmbOwe0aIzX5mToRfDk1dtapu+36UUvDXyj8nz8VfJ2jlL+sXzvm3uDRrFgMxeReAX44hGscAOAHAkjGQhtIk5FgApwEYAHCZzJjIjImX3c6vQUnsehPloOHsDaq5rQgx8SYKpZDbybboBUy0AoxVkZa+Oitkahu7Pg/Cjxh8T29Hw8lj5UvBXyv/nDwXf52glb+sXzjn3+LSrFkMxOQgAGotsrqMywSg/n1eeuZkPKqfBvAZAGpp1/EABjPBLi87b5LRr04qdiW+biAQtLxyA0OnOmWsdopYfvKlkNuJ9ugvADoJZH45sqhffRPkLDWNnbcT0WcBfK2no/47Qkxmef49Vwp5ZZVHTp775a+TNk4EWc8TOQNoEQAPAPhQetN7A4A9AIxdyqWOn3wcwBvqVMD0fpMTAayXs/EnQhqKD4JAsBEI6tn4Qky8yXu/Pqic2E20RwcAioSM1H6TF6570QqZ2sbOOIiqhk3jwPuW1z0nxESIiVXOFPrcST4XaivI9YuBmKg9Jo8COBPAwwBWAPgDgLsAHArgWQBXpfehfB/AO9OnvBwIYIPMmMjmd686sF+Dktj1JsJBw9kbVHNbEWLiTRSKPbeTS6MfZKa/AHg10ty3txUqNYu6DiETT4G5vyfWEB0vX+z+Wvnn9Ln46xSx/OT9wjm/1pZurWIgJgq9I9LkRP19a/rW4Ir00cFqaZfaCPenMRd2nZWWy4i8vOy8SUi/OqnYlfi6gUDQ8soNDJ3qlLHaKWL5yRd7bifao80AtQH8g0hzXP1ImbPUXNb1dTLwbTB+2BOrP0OIyUvsx6xrseeVVR45fe6Xv07bWeryxUJMFI5hAJXpG4Mz4ao2wE9VN8ICSOQCXl523qSlX51U7Ep83UAgaHnlBoZOdcpY7RSx/OSLPbcH2qO/JdDHCHx6uDmurg3IWWqbunoBzDVBp63pWNApxESIiVXO6HjuVz/S0fZS0lFMxEQbbvKy0wZlTkV+dVKxK/F1A4Gg5ZUbGDrVKWO1U8Tyky/m3OZWVCTLogkQlYW3cpSuiKtrA7KW+fO7Q8l9U5uIKDLIxrT7Y3WbhJgIMcmvZzir5Vc/ctbK0pcWYqIxhn4lrdjVGMQcqgRnwdkNBPzKKzd8capTiIlTxPKT9yvH7NgdvDJ6fMqgXzPzM5Ut8UOsPJzbeOf/GsS/B2NtT6z+sEzyduxa2cnnudjNBzXndYKGs3OESruGEBON8QtaZxF/NSaPEKLtCEheeZNXxWBFiIk3USjmPpVsj3YwqJGZr6tsiV9ohUhtY+eVIGph5qt7Yw2XCjGRA3CsckbXc7/6ka72l4oeISYaI+VX0opdjUEUgiAEwZt08h1nj93MaE6IiTdRKOZ3RKKt+hEQ3g/mkyIt8dVWiNQ0df6FQOoUrxN7YwvU5cw7lWL218q/fJ6Lv/mg5ryOXzg7b2lp1xBiojF+fiWt2NUYRCEmvn8wSz57k8/FYEWIiTdRKNY+xa3TpiXLJ61XKISHQpXU+kYyFyLzWleHzc2JARBS4clGZFVr3VYhJjJj4k0v8g9nr/wrFjtCTDRGolgHf40u7qBK/HUL2R31Cs6CszcIeG9FiIk3mBfrGJJcWvUZZuOnYP5LpCV+tBUatU2dnwboboB/29PRcFw2+WL118q/fJ+Lv/ki56yeXzg7a2XpSwsx0RhDv5JW7GoMosyYyIyJN+nkO84eu5nRnBATb6JQrO+IRFv1DSB8BeD2SHO8xQqNmsau7xLhPIAW9XQsWCbEZBsCxRpfq3jm+zxo/uaLU6nWE2KiMXJB6yzir8bkEULk+4d60PLZm+zNbUWIiTdRKNbcHmir/gcRDjCAj09p7nvACo3axs7nQbSfafJRa5Y3/E2IiRATq5zR+dyvfqTTh1LQJcREY5T8SlqxqzGIQhCEIHiTTr7j7LGbMmMiv2zvkAO8JFqVrKC4ujQ5vF9fhOqQypWTcy776btCxtBrYGzsidVPzyUr70Rverfg7A3OQbMixERjxKWTagRTCILvH66SzxM7n73xTmZMxiIgfeq/aAy0VX+BCLcx+NeVzfETrPKx5rLOM8mgW8B8V0+sYb4Qk/8iIHlllT16nvuFs57Wl44WISYaY+VX0opdjUEUQiSEyJt08h1nj92UGROZMdkhBwbaqn9MhM8x0FTZ3Bezysfaps4ugBYwcG5vR/33hZgIMbHKGd3P/frW0u1HsesTYqIxQn4lrdjVGEQhJr5/MEs+e5PPxWBF9ph4E4Vi7FMD7dE+AkXBw0dFWt7Mul9kFKHaxq4NIExLmeUz711+yutCTISYeNN7/MfZaz/9tifERGMEinHw1+jeTqrEXzfR9X8wlPhO7Ph6411uK0JMvIlCsfXlLUvfceAwlz/LwIbK5r7drFCY09R5eAj0GINf7u1omGUlX2z+WrW30Ofib6EI2qvvF872WjdxpISYaIylX0krdjUGUWZMZMbEm3TyHWeP3cxoToiJN1EotndEsq36q0y4Hsw/i7TEP2OFQm3jnZeBOMaMm3pj9V+2ki82f63aW+hz8bdQBO3V9wtne62bOFJCTDTG0q+kFbsagyjExPcPZslnb/LZRStTAOwJ4G0A6tSlrEWIiYtRGKO62PpUoj36M4A+BeCrkea+G6xQqGnq+jUBx5ugU9d0LPiplXyx+WvV3kKfi7+FImivvl8422vdxJESYqIxln4lrdjVGEQhJkJMvEkn33F2yc19APwFwO0A5qT/vyObLSEmLkVhnNpie0ck2qMDAEVC5vCBkxe/+VwuFOa3dlckN5tvAajYAmP6/bG6TVaoFZu/Vu0t9Ln4WyiC9ur7hbO91k0cKSEmGmPpV9KKXY1BFGLi+wez5LM3+eySlXMA9AG4B4C6a+JBALOBzHdUCDFxKQpFTEySbdUfYMLDDI5XNserrRCoabrzeAL/GsAjPR31H7CSV89lDLGDUuEygnPhGIqGnREQYqIxK6STagRTCIIQBG/SKbA4uwRvKE1CjgVwGoABAJdlsyXExKUoFDExGWiPLiTQUgA/jjT3fcEKgbmNXTGDcBkzL+2NNTRbyQsxsYOQHhn55tGDo2jZEQEhJhozQjqpRjCFmAT2g1n6kTf9yGUrnwagNjWrpV3HAxiMxWJLmLl1vN3583PeledyM0W91whMf+ACTIo/io0fbMbgPnMtzV/9s7/jtTcTOH/uAdh/z10t5UVAEHATgVmzZk3I72Y3MXOqe0ICLL/COU2D/OTlAzI/3JzWEpydIpaffNBwzg8ly1o1AB4H8AYAI73f5EQA6zPVlLHaEk8tAsWS29yKimR5tdovMim8laN0Rbw/l4PHN3ZPnQRzA4Ct4ZeNyKpVdSk7gBSLv3baqkNG/NWBorUOv3C2btnEkhBiojGefiWt2NUYxByqBGfB2Q0E/MorN3wBsALAMwDUzdzvBPAEgAMBqI/LnYoQE5eiME6tXzk23u7m9uhxJuh+Bv5R2dx3kJX3cxvvrDOI72Twfb0dDeowBVulWPy11VgNQuKvBhBtqPALZxtNm1AiQkw0htOvpBW7GoMoxGQ7ApJXEzuvXPLuPQD+BKAqrf8sALdmsyXExKUoFCkxSbRHlwK0EDBviDT3f9XK+9qmrpsBnMWgS3o7FlxjJT/6XMYuu0gVJic4F4af1M6MgBATjZkhnVQjmEIQhCB4k06BxdlFeNUG+KkAtgBI5LIjxMTFKIxRXSzvpkRb9GEQfYDY/Ey4pf9nVt7XNnW+CtDMYcM89L6lpz1pJS/ExC5CeuSKJa/0eGOtxS9/rVs2sSSEmGiMp19JK3Y1BlEIUWA/1IPWj7zpNbmtCDHxJgrFkNvcukc4WZ5Sp7QhPLRlN2rduDGX959s6n5POcwXwNzfE2uIOkGqGPx10t5CZcXfQhG0V98vnO21buJICTHRGEu/klbsagyiEBMhJt6kk+84e+xmRnNCTLyJQjG8IxJLqz4FNn4Gxt8iLX1HWXle09h1HhG+C+aVPbGG063kxz4vBn+dtLdQWfG3UATt1fcLZ3utmzhSQkw0xtKvpBW7GoMoxMT3D2bJZ2/yuRisCDHxJgrF0KcSbdHrQfRVAsfCzfEmK89rmjrvJtCnTfCX1nQ03GYlL8TE+2NsiyGvnORFobJ++Vtou0utvhATjRHzK2nFrsYgCjERYuJNOvmOs8duyoxJwG8kH2ir/jsRDgqZ/InJi+P3W+VfTWPnABFFUmRU3busbp2VvBATISZOciQfWb++tfJpaynXEWKiMXp+Ja3Y1RhEISa+fzBLPnuTz8VgRWZMvImC332Kl0SrkhUUB/NweDgeoVZszeX53IWdHzSY/gLwP3o6GiyPFR6vy29/vYnqf62Iv94g7hfO3nhXPFaEmGiMhV9JK3Y1BlGIiRATb9LJd5w9dlNmTAI8YzLQVvU5IuPHDH6gsjn+cavcq228swXEVzLj+t5Y/QVW8kJMXmI/biSXbw+nmSnydhAoJmIyA8CuAF4BMJyh8WUAZgEYTMtk9U9+hbMT+sJlZFAqHEM7GgRnOygVLhM0nAtHrHANMlYXjqEdDX7ndqKt+jYQvgBwS6Q53m7V5tqmzt8B9BEwndQTW7DaSl6IiRATpzmSj7xf/SiftpZynWIhJnMB/BDASgAnATgSwNijBKcD+B2ABwHsCeA/AM4BkMoEvrzsvElJvzqp2JX4uoFA0PLKDQyd6pSx2ili+cn7ndsD7dE+AkWJ+ehwS/wvubyY39pdkRw0R+6/MSZHpq5unZd06rXf/jptb6Hy4m+hCNqr7xfO9lo3caSKgZhMBvAMgKMB9ANQ07blAMbe8no2gJkALldjFYDHAcwH8LwQE8CvziJ2vRkIBGfB2RsEvLcixMQbzP0cQ/Zc+cEDU0bZPwBORJrjlVYez2nsmhMirGHgT70d9cdayWd67qe/sqQqn4g5qxO0+DpDp/Sli4GYvAvALwHMTs+AHAPgBABLxsCrlngpQqJmUfYD8CiAvQFsEGIixMSrbhi0wVD89Saz/MLZG+9yWxFi4k0U/MoxZbe685jzQbgB4HsizfGTrTyuaer8FoEuAqi1p2PBFVbyQkzkGyCfHMmnjl/9KJ+2lnKdYiAm6sSNmwB8FIAJQP37vPTMyVhsVVvPBHAzgBPTZAaxWGwJM7eOD8L8+WpCRYogIAgIAsWPgB+/shYDKkJMvImCXx9UI8Sk65i7AXyGmC8Mt8Svs/K4pqnrKQIOSREde++yBX+ykhdiIsQknxzJp45f/SiftpZynWIgJhEADwD4UHrTewOAPcYt5ZoE4DcA3kiTFrXHJGuRl503KelXJxW7El83EAhaXrmBoVOdMlY7RSw/ed9y+8UXOXrnsRsImF6WwiGTLu9Ty7azljkLu2eE2Oxn5kT45dDUVavqMu4jtULBN39fkk3oVrHR8Txo8dWBWSnpKAZiovaYqKVZajbkYQArAPwBwF0ADgXwLIB5gDrRA/VpcNUelE3ZgJaXnTcpGLTBQfyVvHIDAb/yyg1fnOqUsdopYvnJ+5Vjrz7Sw+/41VlgcLyyOV5t1fqaxjs/S8S3M/ie3o4Gy2Vf2fT55a/YtYqwnudBw1kPaqWjpRiIiULriDQ5UX/fmj5xqyJ9LLBa2qVO4Fo2Bla1Sf5A2WOyDZGgdVLx15sBRnCe2Dh7411uK0JMvImCX325r+cK3mXt9xQxWVnZHD/dytvaxq4fgfB5ZlzQG6u/3kpeiIl8A+SbI/nU86sf5dPWUq5TLMREYRgGoE7sUKSjoCIvu4Lgs13Zr04qdm2HqCBBwbkg+GxX9gtn2w10UVDGahfBHaParxxbf+s8ntT3CNjEGZWL+9SVADlLbWNnHERVROZBq5ed9g8reSEmQkzyzZF86vnVj/JpaynXKSZiog1HedlpgzKnIr86qdiV+LqBQNDyyg0MneqUsdopYvnJ+5Hb3I1Q4qWZw2QOgXjrXuGW9a/lav2chd0Hh9h8Bsz9PbGGaH6eyod6Ibg5retHXqk2Bs2u07iUurwQE40RDFpnEX81Jk8OVYKz4OwNAt5bEWLiDeZejiEbW6P7lpeh3iB83AQdx+D/DA/hA9Na4y/n8ra2setCEK5l4LbejvovFYKMl/6ObafYLSRq9usGDWf7yEwMSSEmGuMYtM4i/mpMHiEm2xGQvPImr4rBihATb6LgVZ/iq6KRzUO4mZkqGXwggfYD8ytk4KEp5TibLo2P3OieqdQ2da0GUAvm03tiDSsLQcYrf8e3UewWEjX7dYOGs31kJoakEBONcQxaZxF/NSaPEBMhJt6kU1FZEWLiTTi8GqsTS6Mngeks5RUDx6pjggF+FKA3GLyisjmujv3fqcyf3x1K7pvaRESRFBlV9y6rW1cIMl75K8REjkcuJE+lbmYEhJhozAwZDDWCKR/q8qHuTToFFmeP4c1oToiJN1Hw6t2UbIs2MFEDwIcAtI/yziDcZzKGiLkz3BLvzOTxvMbuY5nMPwL8ZE9Hg7omoKDilb9CTISYFJSoUjkjAkJMNCaGDIYawRRiEtgPZulH3vSjYrAixMSbKHjVpwaWzvgSzNA1RGqmBBjeZSbK3n5NLdECiG+JLIrfk8njmsauK4hwOQPX9HbUX1IoKl75K8REiEmhuSr1d0ZAiInGrJDBUCOYQkyEmHiTToHF2UV4ywDMAjCYvosqqykhJi5GYYxqL95Nm9ujx5mgVVDLt5iHifC3wT0//D+T/vXgagYSka18IV0Rz3gdQG1T50MAHc1MJ/bGFvyyUFS88DdTG8VuoZGzVz9oONtDZeJICTHRGMugdRbxV2PyCBELLEHwqx+5lL3ql/LfAXgQwJ4A/pO+IDeVyZ4QE5eiME6tmznGDEourV4C5sUgMsD8EgF/Y6Lwlj2PnTfp9T/eyiZWVF4efyqTt/NaV4fNzYkBEFLhyUZkVWvd1kJRcdPfXG0Tu4VGzl79oOFsD5WJIyXERGMsg9ZZxF+NySPERIiJN+nktpWzAcwEcLnaXgDgcQDzATwvxGTi3b/Ay6ZOT6QmdxPR8SPxZb41vGv8fLoAW9Q/7bwj5jZ2nWwQfs6MB3pj9R/XkaB27OqwM16H2HUD1Z11Bg1nb1AtHitCTDTGImidRfzVmDxCTISYeJNOblvZNU1INgLYD8CjAPYGsEGIib0PdTcC5MZYnVhWfRRS/DMQqZmxLcT8pfGb2+3YrW3s/A6IzgdoUU/HgmU6/LdjV4cdISayx8SNPAq6TiEmGjNABkONYMqHunyoe5NOgcXZRXjVe+VMADcDOBHAyJ6BWCy2hJlbx9udP19NqEgpJQQiz3ej8vHvgDiF4cg7sfHDMQxPU9uKnJf27qfQv2kQF3/qYOw1I+JcgdQQBDxEYNasWRPyu9lDCC1NTUiAZd2yZdy1CAgR0wKjpRLB2RIiLQJBw1kLaDsrmQRA3VXxBoDz0ntMspqSsdqlKIxTqyu3uXWPcLI81QnQSdtM8D3hIfN0al03kMkTK7tzFnbPCLHZD8bGnlj9yEleOoqVXR028vFX7OpBIGjx1YNa6WgRYqIxVkHrLOKvxuTJoUpwFpy9QUCLlVMAfAFAfVpbOYBN2TQLMdGCuaUSHWPIlqXvOHDILLuHaOQ292ECN4Zb+r+Vy7iV3ZqmzjMIdCuY7+qJNWibOrOyawlYngJiN0/gHFYLGs4O4Sl5cSEmGkMYtM4i/mpMHiEm2xGQvPImr1yy0gRg7D4BdTzsgbLHZBvapZrb6YsTbwUwBcx9CNFJkYV9j1jlkJW/NU2dKwnUYDK+vCZWf5OVPrvPreza1eNUTuw6RSw/+aDhnB9KpVtLiInG2AWts4i/GpNHNzycrAAAIABJREFUiIkQE2/SqaisyIyJN+HId6zm6zAp+Xb0OoDOUS1l4PeRocSp1Pr2m3ZabmW3trFrAwjTGKlZvR2nv2xHpx0ZK7t2dOQjI3bzQc15naDh7Byh0q4hxERj/ILWWcRfjckjxESIiTfpVFRWhJh4E458xupk224zTZT3EtH71DnAYLSHh+NLqBWm3Vbnslvb2HUoCE8A/FpPR8NednXakcvHXzt6rWTErhVCep4HDWc9qJWOFiEmGmMVtM4i/mpMHiEmQky8SaeisiLExJtwOB2rB66ccSIMo4tAUxm8KQScMqU5rg41cFRy2a1p7LyEiK5i4Pu9HfXnOlJsIezUX122xa4uJHPrCRrO3qBaPFaEmGiMRdA6i/irMXmEmAgx8SadisqKEBNvwmF3rOZuhDa/EG1n4DKACIzHCVtPDresfy2fluacMWnqUsdInwCT5/csb7grH/3Z6tj1V6dNpUvs6kY0s76g4ewNqsVjRYiJxlgErbOIvxqTR4iJEBNv0qmorAgx8SYcdsbqgfaqKDHdDaIPjbSK8d3wcN9F1Iqt+bYym935rd0Vyc3mWwAqtsCYfn+sLuvJbfnYtuNvPnqt6ohdK4T0PA8aznpQKx0tQkw0xiponUX81Zg8QkyEmHiTTkVlRYiJN+GwGquTS6uONtn4BQEzAE4Q8xfCLf0/LbR12ezOaVr58RCM3zDz33pjDUcVamd8fSt/ddsb1Sd23UJ2R71Bw9kbVIvHihATjbEIWmcRfzUmjxATISbepFNRWRFi4k04co3VA+3VjQRuByjEzC+Uhcy5kxeue1FHy7LZrW3sWgZCE8DLejoaFumwNVaHvJt0I5pZn+DsDc5BsyLERGPEpZNqBFM+1OVD3Zt0CizOHsOb0ZwQE2+ikOndxK277Zosr+gCMCfdirvCQ8YXqPWNpK5WZXsn1jR2PkJE72eTPt67fMEDuuzJzIVuJHPrk28eb/EOijUhJhojLZ1UI5hCTAL7wSz9yJt+VAxWhJh4E4XxfWpLe/SQYVAvgL3A2ArCNyLNfTfobk2mvnx8Y/fUSTA3ANgaftmIrFpVl/LCrm4bmfTJ2OUFysE7ZMAbVIvHihATjbGQQUkjmEJMhJh4k06BxdljeGXGpEhObUq0R88B6DoAk8D8LyaztrJ53RNu5EOmd2JNU9d8ArqZ8cveWP2JXtl1w854nfIN4AXKQky8Qdk/K0JMNGIvg5JGMIWYBPaDWfqRN/2oGKzIjIk3UVB9at/bZ01JlEdvJdBpyioz3x8JDdbRwk1q9sKVkoWY3ETAOQy6pLdjwTVuGJYxxA1Ud9YpOHuDc9CsCDHRGHHppBrBFGIixMSbdAoszh7DKzMmPs6Y/POJ3/HuPfXPEuEgMJsgbg0v6m8buanExZLpnVjb1PkqQDPBOKwnVr/WDfPyLnYDVSEmfuWVN9EsHitCTDTGwq+kFbsagyiEKLAf6kHrR970mtxWZMbE/Sgk2qLzzLIp9xipQcVC1oU4deqUlnV/cN/yzktuapp+si8h9BIYG3ti9dPdakPQ+rL461Ym7ajXL5y98a54rAgx0RgLv5JW7GoMohATISbepJPvOHvspsyYeDxjwq0oS5RFryGiC0bAZ/4rE59U2dwf9yr2499Ncxu7zjUIN4J5ZU+s4XS32iHvRLeQLY4P9aDF15toFo+VYiImMwDsCuAVAMNZICpLy6zPBaH8CudNggVtcBB/Ja/cQMCvvHLDF6c6Zax2ipg9+URs9z0wFPoZiD6gaiQOWIAZT68op9as71Z7ih1Kjc/t2qauuwCcAhNn9Cyv/6FDdbbF/epTYtd2iAoSDBrOBYFVgpWLhZjMBaAGqZUATgJwJICN4/CcCmA+gCiAdiEm/0UgaJ1U/PVmpBGcJzbO3niX24oQE/1R2NwePS7F1E2E3Rj8NjFO72t46J5Zs2Z5/r7fiZg0dm0AYVqKjKp7l9Wt0+/9No0ydrmF7I56BWdvcA6aFc8HqgwATwbwDICjAfQDUNPO5QDGntYxCcD1AM4G8FUAOc9bl5edN2ksg5Lg7AYCklduoFqcOmWs1hcXZlByafUSMC8GkcGMZ0MhzJ2ysO+fxdCn5jV1HcXAXwH+R09Hw0H6PN9ZUzH466Z/43WLv96g7RfO3nhXPFaKgZi8C8AvAcwGoC5aOgbACQCWZIDpFADvHkdadhKTl503CeZXJxW7El83EAhaXrmBoVOdMlY7RSyzPC+bOj2RmtxNRMcrCQavjAzFz6RWDBbLDEJNY+ciImpn4Du9HfVf0+N5Zi1B68vir5vZ9F/dfuHsjXfFY6UYiIn65eQmAB8FYAJQ/z4vPXMyHim1Wa5aiMmOsPjVWcSuNx1ZcBacvUHAeytCTArHPLGs+iikWO0n2ROMQRB/LdIcv2Ws5mIYQ2oau35LhI8x8cm9yxruKdzz7BqKwV83/ZMZk5e4GJYmehnjINkqBmISAfAAgA+lN703ANgjC/nYiZjEYrElzNw6Pmjz56vtKFIEAUFAECh+BPx4yRYDKkJMCotCsj16IQNXAaSWP79aBq6Z1Bx/utg+XOe3dlckB82EapcxOTJ1deu8ZGGe564txMRNdP+rW3D2BuegWSkGYqL2mDwK4EwADwNYAUCdsa5O7zgUwLMAtqYDIzMmGTJUBgdvuq3gLDi7gYBfeeWGL051CjFxitg2eW7dI5wsT3UCpA6LUeXe8NDWempd/1YmjX7l2KjduU0rTzRg3MvMD/XGGtRybVeL3/666px8AwTucAOv88lve8VATBQGR6TJifr7VgDnAKhIHx2slnaNHg98KoC9ZSnXjmkjg7A33UhwFpzdQMCvvHLDF6c6hZg4RQzYsvQdBw6ZZfcQ0X4Ap5hpUWVL3/JcmvzKsVG7NU1dVxNwMcBX9HQ07LTCwTkKuWv47a9uf6z0ib9WCOl57hfOelpfOlqKhZgoxMIAKtMncxWEoLzsCoLPdmW/OqnYtR2iggQF54Lgs13ZL5xtNzA/QblzyoVftpNt0QYmUj/eTQFzHxn8mfCi/j9bhcivHBu1W9vUuRag2SmiY+9dtuBPVu0t9Lnf/hbafqf1xV+niOUn7xfO+bW2dGsVEzHRhqIQE21Q5lTkVycVuxJfNxAIWl65gWFap9w5lQXcfHOMr8Ok5NvR6wBSqwnULe4PhYeTJ1Pr22/aiWO+du3oziWj7H71lkerQmz2M3Mi/HJo6qpVder0TVeLn/76sV9M/HU1nbYr9wtnb7wrHitCTDTGwq+kFbsag5hDleAsOLuBgF955YYvAOTOKc1jSLJtt5kmynuJ6H2KkRCwfMp+8WaqGzle31bxK8eU3Qu//9fTQfQTMFb3xOpH98TYane+Qn76K8Qk36jZrxe0+NpHZmJICjHRGMegdRbxV2PyaP6Y0dEyia8OFK11+IWzdcsKkpA7pzLA5zTWA1fOOBGG0UWgqQzeBNOsr1y87j6nkXFq16n+bPLK7gU3P3IbAV9kxgW9sXp1UbLrxU9/hZi4Hl7Z/O4+xL5aEGKiEX4ZDDWCKR/q2xGQvJK88gYBrVbkaPc0nG++NYjHXlqPf2/YPPJf3jl9Co6YtRt231UdSJmlcAq7PHkTIs/+ZERgaOq+2PC/V8GMqGu8Sqs03/44BgaHsfDUQ1A9fUppNV5aKwiMQ8AP4hm0IAgx0Rhx+YDUCKYQEyEm3qRTYHF2GV452h3ACZf8OFJeVn4zgdTBLjhk72nznn5l42oGDwwND539q6s/P3K3x9gy0F4VJaa7QaTu9lIbSr4f3iV+AV2ALfnGzK9300OPPs1LVz2tVqD198Qaovm232k9v/wVu04jlZ980HDOD6XSrSXERGPsgtZZxF+NySNELLAEwa9+5HL2CjEBUHtZ53Ew6MJRrKunT5nXtyG5FmRsQYpvZyO1JmRWrrtn+clvK5nk0qqjTTZ+QcAMAJuJ+cxwS7yz0Fj5lWO3rf4z3/WnV8DAbb0d9V8q1A+79f3yV+zajVBhckHDuTC0Sq+2EBONMQtaZxF/NSaPEBMhJt6kk1dW5M4pAHObVjYYMBrGgD4vUwAYPBjmwcFo6D9Tp9LbNAVb3nrB3Ounb/L058ikfjOEdQCvCw2F+t+uxLrftdYNOAmkX2N10/fu46df2aia+tmejvpt69I8KH75K3Y9CC4ge0y8gdk3K0JMNEIvg5JGMOVDXT7UvUmnwOLsMbwZzU30o91rFnaeRExnKecZbOy+y+SaN98efJ2ACma8TaByqNkRQh6bL/g1MK1jYB0R96u/zfTfxMa6FPM6w0j1qxmZb597yFt+rI0/paWbtwyboCFzxuprTrN1tLGOvJR3sQ4UrXUIztYYiYRzBISYOMcsaw3ppBrBFGIS2A9m6Ufe9KNisDLRickJl9xdVV6+dQUxIgrv7XtMCImhoYoLV08/r2oY1LsFFXu9ZVYO/z01a8X1m0//TQiYwUQzAFMt6ZpBrP5W/42rRv4m7OIkfmUhwvAwv8q0beaFmPq3/61hRmZsW+Y23vE+QmgeyJhD4GOZuY+ROmFN7LNPOWlzIbIyhhSCnv26grN9rETSPgJCTOxjZSkpndQSIi0CgrMWGC2VCM6WEGkR8AtnLY0vUMlEJyYKnjmXdf2PQXwWEVUpYvLUPzfcajLdctfUC2cDdN3I3S/8/+2de7AlVXWHv3ORAWdAA8wjAVMEEMRIIEKs4mFFJiQmpUIJJJpBrZASqMIYKZIqGTBxIECYATFB8yLySKUSRUwqKXlEiAYwaoGApfhKrESKRKIzREEegwxwO7Vm+sDh5t7bfc7pvc+53V//A3Pv7r16fWvt1f3rvXff4kF26p244tzv3V0H6RFnXLnz6h9buYaZbat2ohQtszHzUqzasT+lt6robd+nsgriZ7096vTbbxNLy14gXmoKmTmb/Q8CXlEU/Ce94osLbfYf5rrqtp3UmNJu3QiN165rnMejtfTOVpg0GLOuDRb9bTB5FulKznLOQyC/lS4Ik2LDmtVbd+a0osfqrQe95awV//7xDfRmDgNOCuIF3LFi5skTe+f+8OFUEYga8q6rv7x3j6dW9WZZOVPMrN4hWljZ6xWrtwsZitXlpvsQNnsNcy3bhQw8VBS9bb1esXsB27b3TW/XAu7qwRZmiytuvHTdZ4bpd9S21sxRyQ13npyH42XregQUJvU41WrlIK2FaexGch4bYa0O5FwL09iNJsV57AtvoIO2C5PisjUrnnyajxTFjs8Fb1tzxPG7bL7nsYLe7hTFLPQuWv7M9y7onc9sAzgX7GKUHItlaLvs9KNVz7LTXr1eEZ/6XUURYqNYXZSzMvRY1aMIIbPwp4B73EzBs7PMfuzmjaeM/YWxOpxG8bdOv1VttFtFqJnfd41zM9SWTi8KkwZj1bXBor8NJs8iXclZznkI5LfSdmHy+MVrjuux43PBPVg525s5qlfMxkb4p2co3rf8fVsuy0E9Rw355bOv33Nm2exbZ2Z4G7PFLr0Zlu2z54pDH/z+1hvCx6JXXHXTJes+2RZ/5/MjB2ft+lWuHGNokjYUJg3Styg1CNMH9ecImFfmVR4C+a20XZhsvWjNuqLX2/654B7F8llmjutRPFxQ3DNTcG0Tf6OkTtRy1ZCqzf63fuCkLXWud9w2ufyde53aHTdy9c7vGud6VNrTSmHSYCy7Nlj0t8HkUYgpxPKk01RZabsweeIP15xA+bngAP/0qkOP3/mh+7bPINArrlpx3ubWzSDEV7lmejudBr39dnyF7OEPzRbPXuVXudINPe/F6dgO9jwpznm8mx4rCpMGYzGppNVug0FUICgQ8qTTxDlndnNec20XJts3vi/rXdV3/ql9Xnv8Lg9+brswWb6tOK13wWZnEBIkovfEBFDn6VLOeTh3zYrCpMGIO0gbhKlAmPiDq/nc7nzO493iVtouTML7rRetOXK21zurBytCmCx78HPXzRTFFct/b/OduWLgWM5DWs5yzkOg3VYUJg3G16LUIEyFicIkTzp1lnNmvJ2cMRl0Or7Qdf8Jn3l8/1cckv2+670pT7bLWc55CLTbSvYCmQNnF97CDXK0GObIqu59CcS8ande5fHOGRNr9QHZnzOsXXlGt5zzcO6alewFIwdghUkOyj6o56EsZznnIpDfjrU6D3MfIOWcgoB5lYKqfSpMGswBB2mDMBfpSs5yTkGga3mVguGwfSpMhiU2Wvuu5bb+jpYnw54l52GJ2b4OAYVJHUo12zhIa4Ias5mcxwRY83Q51wQ1ZrNJcR7zshs5XWHSCMbKTiaVY9qtDE0jDeTcCMbKTibFufLCWtZAYdJgQCeVtNptMIjO1DxHwLxqd17l8W5xKwqTPFFwLMs5BQHzKgVV+1SYNJgDDtIGYSoQFAh50qmznDPjndecwiRPFLw3yTkFAfMqBVX7VJg0mAMO0gZhKkw6+8DsOMozjqbBisIkTxQcU3JOQcC8SkHVPhUmDeaAg7RBmAoThUmedOos58x4nTHBL+zlyjnvxXlIyzkP565ZUZg0GHEHaYMwFSadfWB2HOUZR9NgxRmTPFFwTMk5BQHzKgVV+1SYNJgDDtIGYSpMFCZ50qmznDPjdcbEGZNsKee9OA9qOefh3DUrCpMGI+4gbRCmwqSzD8yOozzjaBqsOGOSJwqOKTmnIGBepaBqn9MkTFYBLwEeAJ6ZJzQvAvYFHgc2LxY6b3Z5EtuiJOcUBMyrFFSz9mmtXgC3uZ0nD+Us5xQEJpVXKXyZ5j6nRZi8AbgW+ChwAnAE8MgAuOXArcBXgGOBC4HrFgKrMMmTcpMapNo1vikIdC2vUjAErNXO9jrbm2hwLdRt12pX1/zNnE4TNzcNwmRX4OvAUcAW4D3AzsDlA3ROAQ4G3g+sAL4NHAg8Oh9BhUmevOpacdBf8yoFgUnlVQpfAGu1wkRhkmhwKUx2EJhUzZyU3czpNHFz0yBMXgbcAhwKPAscA7we2DBA5xLgBuALQFzzp4G3A99VmHRvkE6qOGg3T72Scx7OiaxYqxUmCpNEg0thojDJnFoTMTcNwuSVwJXlEq1ZIP59Zjlz0odyI3ABcHcpTGIZ17kxc7Jp06YNRVGcP0hv5cqVz6xduzbWOXtIQAISmHYCjxxwwAF7TPtF1rw+a3VNUDaTgASWHIE21eqphT8NwiSWZt0GHF1uel8H7D1nKdfpwP3lTEks/Yq9JkcCD08tWS9MAhKQQPcIWKu7F3M9loAEJNAYgWkQJiE07gXeCdwFXAF8Fvg74DDgm8DxwFnAWmB/4FPAIcCTjZGwIwlIQAISGJfAydbqcRF6vgQkIIHuEpgGYRL0Dy/FSfz/1cAZwLLy08GxtCtmRq4BTi1DFYLlvu6GTc8lIAEJTCWBuKdYq6cyNF6UBCQggeknMC3CJEjFZyZ3K7/MtRC51cDW8m+ZTD9dr1ACEpBANwlYq7sZd72WgAQkMBaBaRImYzniyRKQgAQkIAEJSEACEpDA0iWgMFm6sfPKJSABCUhAAhKQgAQk0BoCCpPWhFJHJCABCUhAAhKQgAQksHQJKEyWbuy8cglIQAISkIAEJCABCbSGgMKkNaHUEQlIQAISkIAEJCABCSxdAgqTpRs7r1wCEpCABCQgAQlIQAKtIaAwaU0odUQCEpCABCQgAQlIQAJLl4DCZOnGziuXgAQkIAEJSEACEpBAawgoTFoTSh2RgAQkIAEJSEACEpDA0iWgMFm6sfPKJSABCUhAAhKQgAQk0BoCCpPWhFJHJCABCUhAAhKQgAQksHQJKEyWbuy8cglIQAISkIAEJCABCbSGQCuFycaNG28Djm1NlHREAhJoM4Hb169fv7bNDi7km7W6i1HXZwksWQKdrdU5I9ZWYVKsX78+u28bN27UbobslXMGyICc2805j3eLWzHH8kRBznJOQcC8SkHVPrM/vOdA7mDJQdkH1zyU5SznXATy27FW52EuZzmnIGBepaBqnwqTBnPAQdogzEW6krOcUxDoWl6lYDhsn11jrr/DZsho7eU8Grdhz5LzsMRsX4eAwqQOpZptHKQ1QY3ZTM5jAqx5upxrghqz2aQ4j3nZjZw+Kd+120j4KjuRcyWiRhrIuRGMlZ1MinPlhbWsQSuFyaZNmzacc845F+SOlXbzEJeznFMQ6FpepWA4bJ9dY66/w2bIaO3lPBq3Yc+S87DEbF+HQCuFSR3HbSMBCUhAAhKQgAQkIAEJTA8Bhcn0xMIrkYAEJCABCUhAAhKQQGcJtF2YrAJeAjwAPDNPlHcH9gb+B3hsyCyo6jvs9vkWwKND9r9Y87D9fWB2nkYvAvYFHgc2N2gzulrMbip/fwJ4KfDgAjFK5W+V3VT+vgzYZZGcTeVvld1U/kZehU9ryhjPTdlU/lbZHdXfZcCLB5x4Yp7ak9Knhod8tu6q6qm1erhQWKuf5zXqWK4iXlUzU43zKrup/K2qman8rbI7qr/W6qoMn9Dv2yxM3gBcC3wUOAE4AnhkgPN+wLeBS4H3AocB99WMQ1XfuwJfB+4FQpTcD5y3gJCoaXJ7sxhIPwncDBwF/GDOycuBW4GvlH9g8kLgumEMLNC2ym4qf98CfBi4BlgP/FzJtH+ZqfytspvC3xiL5wJvBj4PnFLm7HcGYpLC3zp2U/g7mGrh99uBQ4FnE/tbx+44/q4D/hj4dPmC4Ezgqxl9amC4Z++iqp5aq+uHxFq9477bP8YZywtRr1MzrdXNPHv0Y7DQPWKc+Fqr69eVrC3bKkz6yRoP71uA9wA7A5cP0P0Y8BfAHcCrgb8q/zvfLMRgUOr0HW80Qoj8VilMmgrqLwIhNvYHDgYentNxPMzGz98PrCiF14ENzNZU2U3hb1x/zPrsVQqwuIYQDGcM+JzC3zp2U/n7xVIgx+zexaXIjPzsH6n8rbKbwt++T68DbgduKEXZ4PhL4W8du+P4uwH4BPCNBQZ9Sp+aqjM5+6lTT63V9SNirX7hPWKcsbwQ9bhHVNXMFOO8jt0U/tapmSn8rWN3HH+t1fXrStaWbRUmkay3DLyBPQZ4PRCJGEeIlHgr/UbgIWCP8uEoZlXmW/I1GJSqvqNtiIFvDZz0JuCmhiIb1/414Mh5hMkl5QPeF8plZPHWNt5Ef7cB24vZTeXv6lJYRp5eUS5vGhSXqfytspvK3whT5OKJwNWlAI3Ztv6Ryt8qu6n8jeVyIUp+DfgTYO2cGZNU/lbZHcffj5cCOpiGQDkd+GGmGDYwzLN3UVVPrdXDh8Ra/TyzccZyFXlrdfp7k7W6Kgtb+Pu2CpNXAleWy5niDWz8O5ZUxMxJHPHm4d9K4RKzDvHvz5YP+09XxLmq7zg9hFAsO/rzcgbjn0ux0sQ+k3jDGEu15hMmNwLxmeS7S2ESy7hiCjSWrI17LGY3pb+xjOMfgf8G3jbnIS+lv4vZTelvFOJ3lTND7yhnTfqxS+nvYnZT+BvrkUOU/Eb5MuCT88xYpvC3jt1R/Y2+/6BcPvof5Qxt1JlYjpgjhuOO8UmcX1VPrdXDR8Va/TyzUcdyHerW6rR1zVpdJwtb2KatwiRuZrcBR5cPPbGWMDa599+2h9/xZvOscjbh5cDf11zKVdV3pEms891W5kvY+hfgnRkEQrydjTfsMVOy2M1plFRerL9U/sYb9GC30IxTKn+r7KbwNzarxk00RFgcv1LmY8wa9I8U/taxm8LfeCCN5U6xryv2lsRxPRBjtb+cK4W/deyO4+/gua8BQlz2X4iEjyl8GmU8T8s5VfXUWj18pKzVzzMbZywvRL5OzUwxzuvYTeFvnZqZwt86dsfx11o9fG3JckZbhUkU5tgAF2LgrnIZUMyIxKbxmNqNGYeYUYnlViFWYsNxbMAc3L+wUADq9P37wI+Ay4BYqhDX0sRej7imuTed2GTX9+nkUmzFg3XsQ/kUcAjwZAPZtJjdFP72157H2tXgF36G2JtJ7G8duyn87edJf+/Q2aWvMeuWMr517KbwN24KsRQiZihfBVxVftAhxk1Kf+vYHdXf8Ce+ABhj73+BEJXxNblYphYf1/gmcHzCMdrAMM/eRZ16aq0eLizW6udryKhjeTHidWpmintxHbsp/K1TM1P4W8fuqP5aq4erKVlbt1WYBMTDB77gFOv1Q3QcUL6Rjje0e5ZLSX66nMmY+9WuxQJR1XdM8cam5XhAieMk4B8aimzcdEJsHVvuMYmHuHjLHj7Fm+b4gtWppa1hvjRWdXmL2U3h7z7Al4DY79E/3l0ubUrpbx27Kfztf+klNr3HEcvvYgYlPpOa0t86dlP4O5hvMWP5EeC4OWM0ZT6H/YXsjuNvfPAihEgcd5b72LaWgiXeAMbS0VRjtGoMT+vvq+qptXq4yFmrn6+Z44zlhajXqZkpalcduyn8tVbvINDk89RwI7pjrdssTCKU8ZZ9t3ID9Xyh3akUKPGwULXpfe75VX1H+1DlMVsRb4FzHvEwHw9D8VWrnIf+jk87vskea2vnfgp6sOcU8a1j1/jWi2/Uhng4zB3Delc3na2q6qm1utm4OZbH51mnZlqrx+dcp4dR89laXYdu5jZtFyaZcWpOAhKQgAQkIAEJSEACEhiFgMJkFGqeIwEJSEACEpCABCQgAQk0SkBh0ihOO5OABCQgAQlIQAISkIAERiGgMBmFmudIQAISkIAEJCABCUhAAo0SUJg0itPOJCABCUhAAhKQgAQkIIFRCChMRqHmORKQgAQkIAEJSEACEpBAowQUJo3itDMJSEACEpCABCQgAQlIYBQCCpNRqHnOUifwq8AflX+UMv6GTZ0jvpP+OuDzwEOLnHApsC+wrvyDl3X6to0EJCABCfx/AtZqs0ICHSOgMOlYwHV3O4G42f0psF/5hyjrYPkp4H7g1cCXFzkh+o2/VH0KUNTp2DYSkIAEJDAvAWu1iSGBjhFQmHQs4Lr7nDD5BLAJ+E3gduC9wAPAgeXPTwT+qfyeM+5pAAACsklEQVT/bwF/A/wCcHcpOqKjjcDJwHXAxcDXgA8BbwX+Fjgb+DBwLvCE7CUgAQlIYCgCIUys1UMhs7EEljYBhcnSjp9XPxqB/s3uFuDGUjyE+DgMuKucRQkxEWLlCOA1pRi5BLgM+Esgzt0N+F3gcuBx4JBSyPw2cGG5lGsDcDpw1WiX6lkSkIAEOkvAWt3Z0Ot4VwkoTLoa+W773V8eEEIi9oucCZwPHAwcBBwNbAN+pxQf8fOXlku5fhZ4Fvgq8PPAvwIvB1YD9wAfAKLNWmBn4F7gz8qlY92mrvcSkIAEhiNgrR6Ol60lsOQJKEyWfAh1YAQCc9ctn1rOdBwO3Az8OPBu4LXAr5eCZSUQsyohOmaALw3sN4mN8SFcvgN8EIj9KG8GlilMRoiOp0hAAhLYQcBabSZIoGMEFCYdC7juPnezi3XL5wF3DiyzehPwDSC+rBVLtv4a+BngVcCaUpi8A7gD+K9y7XPsLYl2seE9Zlti34rCxESTgAQkMD6B/lIua/X4LO1BAkuCgMJkSYTJi2yYQP9m1+/2MeBY4D7gGiDERxw3AW8shcmj5exHLNk6BtgHuL5stwX4pfL82PweIiY+FxwzJrFnJTbAu8ek4SDanQQk0HoC1urWh1gHJfBCAgoTM6LrBF5SbnZ/ZgBELM0KsTL4s/6vdwGeKv+xO/Bi4AcLtO06W/2XgAQk0BQBa3VTJO1HAlNMQGEyxcHx0iQgAQlIQAISkIAEJNAVAgqTrkRaPyUgAQlIQAISkIAEJDDFBBQmUxwcL00CEpCABCQgAQlIQAJdIaAw6Uqk9VMCEpCABCQgAQlIQAJTTEBhMsXB8dIkIAEJSEACEpCABCTQFQIKk65EWj8lIAEJSEACEpCABCQwxQQUJlMcHC9NAhKQgAQkIAEJSEACXSHwfx+f1lGgKrL6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.metrics(output_simple_conv.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 2s 200us/step\n"
     ]
    }
   ],
   "source": [
    "score = model_simple_conv.evaluate(x_test_conv, y_test_class, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 9.870681315612792\n",
      "Test accuracy: 0.3854\n"
     ]
    }
   ],
   "source": [
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5: Make & Visualise the Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_classes_conv = model_simple_conv.predict_classes(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252</td>\n",
       "      <td>716</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>965</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>984</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>806</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>919</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>731</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112</td>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>9</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>936</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    1    2    7    8\n",
       "row_0                    \n",
       "0      252  716    0   32\n",
       "1      965   33    0    2\n",
       "2       12  984    0    4\n",
       "3      806  185    0    9\n",
       "4       78  919    0    3\n",
       "5        2   37  731  230\n",
       "6      112  862    0   26\n",
       "7        0    0  999    1\n",
       "8        5   80    9  906\n",
       "9        1   45  936   18"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test, predict_classes_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba_conv = model_simple_conv.predict_proba(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "hconcat": [
        {
         "data": {
          "values": [
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.imshow(x_test[i], labels[y_test[i]]) | vis.predict(proba_conv[i], y_test[i], labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2: Convulation + Max Pooling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2: Craft the feature transfomation and classifier model **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pooling_conv = Sequential()\n",
    "model_pooling_conv.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))\n",
    "model_pooling_conv.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_pooling_conv.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model_pooling_conv.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_pooling_conv.add(Flatten())\n",
    "model_pooling_conv.add(Dense(128, activation='relu'))\n",
    "model_pooling_conv.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_2 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               204928    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 225,034\n",
      "Trainable params: 225,034\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_pooling_conv.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3: Compile and fit the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pooling_conv.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      " - 89s - loss: 1.4153 - acc: 0.5399 - val_loss: 1.0333 - val_acc: 0.6821\n",
      "Epoch 2/5\n",
      " - 68s - loss: 0.7455 - acc: 0.7268 - val_loss: 0.6891 - val_acc: 0.7457\n",
      "Epoch 3/5\n",
      " - 90s - loss: 0.6430 - acc: 0.7596 - val_loss: 0.7239 - val_acc: 0.7280\n",
      "Epoch 4/5\n",
      " - 103s - loss: 0.5869 - acc: 0.7807 - val_loss: 0.5805 - val_acc: 0.7880\n",
      "Epoch 5/5\n",
      " - 99s - loss: 0.5475 - acc: 0.7957 - val_loss: 0.5970 - val_acc: 0.7657\n",
      "CPU times: user 9min 24s, sys: 1min 43s, total: 11min 7s\n",
      "Wall time: 7min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "output_pooling_conv = model_pooling_conv.fit(x_train_conv, y_train_class, batch_size=512, epochs=5, verbose=2,\n",
    "                    validation_data=(x_test_conv, y_test_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Check the performance of the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "values": [
         {
          "batch": 1,
          "metrics": "loss",
          "type": "validation",
          "value": 1.0332670288085937
         },
         {
          "batch": 2,
          "metrics": "loss",
          "type": "validation",
          "value": 0.689058997964859
         },
         {
          "batch": 3,
          "metrics": "loss",
          "type": "validation",
          "value": 0.7239361187458039
         },
         {
          "batch": 4,
          "metrics": "loss",
          "type": "validation",
          "value": 0.5805359273910522
         },
         {
          "batch": 5,
          "metrics": "loss",
          "type": "validation",
          "value": 0.5969944979667664
         },
         {
          "batch": 1,
          "metrics": "acc",
          "type": "validation",
          "value": 0.6821
         },
         {
          "batch": 2,
          "metrics": "acc",
          "type": "validation",
          "value": 0.7457
         },
         {
          "batch": 3,
          "metrics": "acc",
          "type": "validation",
          "value": 0.728
         },
         {
          "batch": 4,
          "metrics": "acc",
          "type": "validation",
          "value": 0.788
         },
         {
          "batch": 5,
          "metrics": "acc",
          "type": "validation",
          "value": 0.7657
         },
         {
          "batch": 1,
          "metrics": "loss",
          "type": "training",
          "value": 1.4153327997525533
         },
         {
          "batch": 2,
          "metrics": "loss",
          "type": "training",
          "value": 0.7454997641563416
         },
         {
          "batch": 3,
          "metrics": "loss",
          "type": "training",
          "value": 0.6429860845247904
         },
         {
          "batch": 4,
          "metrics": "loss",
          "type": "training",
          "value": 0.586879178237915
         },
         {
          "batch": 5,
          "metrics": "loss",
          "type": "training",
          "value": 0.5475010043144226
         },
         {
          "batch": 1,
          "metrics": "acc",
          "type": "training",
          "value": 0.5398666666666667
         },
         {
          "batch": 2,
          "metrics": "acc",
          "type": "training",
          "value": 0.7268333333015442
         },
         {
          "batch": 3,
          "metrics": "acc",
          "type": "training",
          "value": 0.7596499999682108
         },
         {
          "batch": 4,
          "metrics": "acc",
          "type": "training",
          "value": 0.7806500000317892
         },
         {
          "batch": 5,
          "metrics": "acc",
          "type": "training",
          "value": 0.7956666666666666
         }
        ]
       },
       "facet": {
        "column": {
         "field": "metrics",
         "type": "nominal"
        }
       },
       "resolve": {
        "scale": {
         "color": "shared",
         "x": "shared",
         "y": "independent"
        }
       },
       "spec": {
        "layer": [
         {
          "encoding": {
           "color": {
            "field": "type",
            "type": "nominal"
           },
           "tooltip": [
            {
             "field": "batch",
             "type": "quantitative"
            },
            {
             "field": "value",
             "type": "quantitative"
            }
           ],
           "x": {
            "field": "batch",
            "type": "quantitative"
           },
           "y": {
            "field": "value",
            "type": "quantitative"
           }
          },
          "height": 300,
          "mark": {
           "size": 50,
           "type": "circle"
          },
          "width": 300
         },
         {
          "encoding": {
           "color": {
            "field": "type",
            "type": "nominal"
           },
           "x": {
            "field": "batch",
            "type": "quantitative"
           },
           "y": {
            "field": "value",
            "type": "quantitative"
           }
          },
          "height": 300,
          "mark": "line",
          "width": 300
         }
        ]
       }
      },
      "image/png": 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ptvMWyG8YiiEfdsFEWB52DkOCY30zE1cbLxl30Wod++0dnHVjC9ETq09aodBlAixTyCuOuMvcbiwrdQtelro3vd2eVCaf+8exTlHHeKcbZYhIr9iAyHioev/2iQ2B7giITQmNd1k/M0BsOGtcuKsBXeOos9qJuGvuLHrpLdTVDXaeR0wbx5/fdKgTwdmiKDVh8szKdU1uD668+9KqR+IVapj83c6ksi2ChxztYl8djDfZd5FzJggwrnyjasLE1uf9DMBTntD4IQA7SsN2pLWP8jYd1qZ62ayh2wHYs8L+tR1sX/KO2bBlDpu8HWy/5m0Wtb+t8fOtpAFklEvCxLfq8mHnG0q+qPcjwE7Yv7jaWL/9bm534akQx6Zw7hbN+W13++52GVUAt7tzG9kQKRA3sk63xfvuNliHbXr/fS/6s26L99xtu9/TbbVNSwpUJIH+TF0o3gZktdpBrYLVoljjOrr69GP2uvKG+1/+XLdGVm8qxpoH6mYM+QHEDxq2Q9fPZh26Yd+9P5pA2f2wmFt5sK8Oxh/su8g5EwQYV75StalcdhTGzwHYiLpNi7Xz+w4BYGuw7SiNT3hbsNuMoajg+L633GGqN5JtAuZSb8TFRl7svKlh9cErLx+GfNj52lgGzYydEjknQ0CvwMi2jeOmA3KqQD7fiQKsdcdglbvTxie794683LO7u87dVlyncESXK24PChIZ1egtQiG6MNpZj+3lfWwn6zFaPsBoWd85UjatLZX2laWycdloZ/1ze2P5EwosK/5Y60qZseWhf2GL52R8l6m07KszRXbLfMMW26wv4yoTBDIcV9H11rbGxISJbQz1mLfO2pY03Oodk2E70powsZEUmzFg4qMBQFSY2HbtdqSGLYew/+z/bartsLkoTHx0VYaDlgLBI0DOPgbtEFn5wXn63Jt3OdZ59MslzvqTurXgkFZ3x6K33B2wxh2r72ObRPqfDVB9TUVWQ3WNQt50gFabSqXqrD6s8MmeaUUPlk4sWFEmquUK2LZ45QrZUwQ7DF497VLFCoEsg7jL7N/3PnfpFWP+XrN/kbtmmdShIxjK6e8GFlQ5M2GHwiQTVLfO04+2nEpJaTcVasnfQ87JM0vljgxzjgqRFm+diK3Xs42fbM2IXTZCYoeRmzCxKVw/APAkgMUAbHT/MwD+A2AJADtCw4TKj7wRk1dTqW+27knkxSBbZUvZLh92KaNL6sYMN1IKsWEgxA77/uKRY4q6J7qOlKNHJkIwEdCJEfR8DCIfcREZ+jwh1ZcV8r4jGKOKjSpo33P8NpOWrVl/H4CNPS4WpLruQut22LZ9hLOXuAXlLtQEy54K6RUugO4CDDb1SxXAaoX0rmuB9q1vAWTZSKdtmcxZ/05SDSVO4my1Iz/rkGpe7KtTJZfcfdmKMdpNzk+ppibnVMkld1+GOdtUrj96U7DmeetE9vLWj9jaEdtspLufMIkW3tZlf9Fbm/JpALaro50hZdfXAfwuuVpmPzWFiY8+yHDQ8kV9GLyo+xhOm7PKblxNdKad37STSmSiwp0I0YkAJiqkV4AIxHYBiXkJXOwo72NH573OCNyX33G3+/tqjHks4kaWawGWN8+f/iYgJgJQUdN4nYj0dqbRXapUdW1LQ/WsTDDVqzCi8/0dJ3ZLwZ59Iywo37TzYd8rWv3ISwLdAxB7SMS8FPq+jbAo9JVeweJimWM/S+eykXPfXSWC3jrFu/p2I8O49z9Ve+12j9VPK53X2nuWSZguCpNgvJ3dPoRne2Xay/Rvpgn35R8Q5xGeALHNQ6I71p7hTe+yYtiIiU33OsDbYGSjlz4KwXaxtY2ibBF8IFvt+02fwsRHogEF7VYlpl0fnThEVvnKOTrqoYj0io0+8SETy0YXV7a+17ERgpGDv6TjrVFof2f3yJvFe0VW7FzmvFM0znkXY/BO145Y11xU0HVDyZ6tdw9czzEwP1sEXlBQMA2QAyZ/YufZ9/7njZnd3d133nv5KYF1rFH/ah2cjhHjd4Or5a5NDbMpYuLsCVU7wbwc0nfeyiDXJgWWC3SZqgkWLFORZQXStayo851XpQ6ddt+GS8quFBGbI7z53BaFvjpqbqttFxmai8IkGFfna981GD3Wl3GVCQIBx5WtK7GDFm1EpP/OWrbb1q8BDHo+VCbqHmSeFCY+0g44aDeXnHZ9dGIOCZOK2lsnqjhjfzht30eu+POz5S31X0txnqhK/1EPGx3oFSCwaVcyUezcjsGVx3qILleV5QCWq6PLHddZfsyIf3TPLLr7qFHSVtV/Vy2oPgbB9SWRztuk9r33U/FMrsfzhvnjyhxFuSvYU9QpV5smJr2jSDb6MjZOnVdBsRYOxoiiXRXtm3b9zEF2oKTdJ6oLSua1DqsdVFLxcfQeCpN06CV+b663qcRrklhK1jcxTummIud0CQ55vx2hYVO57HySt/ultO2CbUTEFrQnNDqf0VJmIHMKEx+hspH6CDOHBEK0KEH6d2pt0ywFppnt6NQmF+6dS+pnbj6Yrz8iG/XYYaRbDtWJ/dd6mPCA6B4CsR0/Yl2dtrgcIstV7Uu/vKomRFxdXn/qpKUH7vvhNrZaN3ZUe6FTBZVvQHoX2nmXrhbI7xzpvq54ztu2n3paV5Cc+xfUD7t6WVlpW6e7l6NS7sIpF9FyVewpEBODEyBbn5+yaefDMeLNf0WFSWPJvNbGtAAOo5spTIJxlh+xnUpJaTcVasnfQ87JM0vljmxxTqWsw/keChMfvZetoKVdH52YA4LIRkoEETtoqffqEybv36WCYlH3ehWnGK6NeCQy6qF28N8bvSMeossFznKo9o58uD1Fy+++9MQ3ous8BlY9GlcdF5cd2+3gNIHM7J9GVW8TcW8unfvW3X56IJ/juePiMR/rFuebAqfCFYwUoKhzl8/s2m/EhMLEz2AaJK98jrFYVWZ9Awiq4NYgbFUZ+je//RtM7XLHCoWJj75g5+AjzBwQCIO9qGe6llNqF80SOLMEup1CRo8ocMZ3dg91sLi+Y6MdNuohimUusALqvuqormi+7Gsvp1LejReX7fHBfl9/ddQLv1sFYNcP89BHoXpTSaF7q9S8vT6VvOPdk+/taF1d2cTCQtksPDft8pmpUWHS1aXnjK5rTXHKXjyyufd3jpgE45N8b1PZ6qtpd5mWl3Nzg2BacXisUJj46Gt2/j7CDIkwOb7ud9uiwzlY1DlERA8B5JMCfKR/9a2Ruor1Am1XwVMK3Ouo80p3pOc1t7N7mV8LxLVhzDbtPc7M3qlaEDsttu9SfUOAW5yIe33x7LdeybSXw9CO2uaXzQKkd6reh8JE7yyd2xpzql6mmWcrfwqTYMiHoU31J8n6Mq4yQSCIuDru+4t3cIrcT0cc7OO6LhTuC25nx6P3/HTWu5moUy7mSWHio1eCCNpYxQ2TXZvDv3za/Rsmfmz/wGM3Xc5H1S0etc0mTHJVD4bqwSI4WBW2cPrDuthZHnBfETh22NI6iK7bf/ftj372tXW9axDSOdcjVuzYDlRtkbGTIY6dxn4CBNG1KBs37nbsyOIV9x5X2rP2PqnDUEM2PraiwLZk3KrM6fo3WQg2clJUgHFvf+Gaf09YcmaZXNRqu6+E6qIwCcbdQcd2tFa0S/9mgkC+xlXl+Y3HqINzFbKH4y1rd/veDl5VuJffXT/zbxnguad3gvybg+RtZ6PYwY22/XAgV+Avd0HUig+7ICgH+wLZfknZoXBwlqqMsi/MxW/+oxEurgpyB6NkOkNbjD66WA+KuHqwok+EAPgo8OHiZwW6Re0kV30MwFLXjSwtXYFnbr99Rk9lbeOVQN92stHF74Aub66vPtsP73YsHLun2yNnqMjJgOy8OU/Vf0FxU0mP2/jq1x9Zz2F6P2gPnUcycZX50gRrgX11MLyzFWO0S/9mgkA+xtWU2U2V4upFEIyAYoWI9H4MVFUHtrW8ageAC1oaqu1k+KEum3FxFYATEty162g7UBjAC4Nkerp3unxGpm7Hsklh4mOrycfGMhSeoOqrF5aNay+SzVNc+s/JL+nUWUF9aR6svsf/vyUjZNT7n3BcHKwiJkAOFsU+ENhBR72XKlSgNg1qqaoshehjbSM7nnyg7jTrbGJeFbMbpzkq40767Eeubfzn8hNaFlbbia4pX1q//Xbt3SO+Bug3IHJIv4xWAnqL4+L6kT9qtW2Be6+g/DuwQrSbsouH3Y0UJsG4jG2KnDNBgHHlD9XKuYt30R73JkB3EUjM6dIKdy/AWSUR59Tm+TNsC+FYlx0M/HcARwD4BoC3AHwCgB1UbCMfpwK4zDsb5UQAdqL8sQBaAZjwqAGwLQDb6OY4APcC+D8AtwC9O3EeCWCql8ZOol8G4PMAmgA0e6MqVwBIa8o3hYk/ccUXOR85Dsyq74RsmSXAaAV27ynecbdIx9uvqsomx0EL4P5J3MiqkfNW277eGbusE6695YkRbe09BzqOTcPqFSGTILB5ZQVbGFZ9UyFLbTREVZdqifvo3XUnf5BK4dLt/NsWlFXClVMgmL7ZvqIDgqaIq7cW/6j1vljlStduKnWlIEqV2vC8j8IkGL+xLZNzJggwrvyhWnl+41fVwQJ70RdIV6xcVd0iiExU6Owl9TP/MITlz3qnxJsQ+QKAXwD4IgDL91cAbITETpS/FsAnAXwdgG2SYyLGtvs/EIBNJb8ZwH6eKPkugGMA1AOw9ae2PnI376DHFz3RY2er/BvApwA8ng4ZCpN06A24l43UR5heVr0LsrsLrlLVE0XElHycS9+EYiUgbyj0dUd0FeC8DnFfh9u9qmTeu6/HyyH69+nTF0faPoIDEHFtBOTgCWNLvrXq7XY7ubtoSxGCdQpdaqMhjjpLBe5jd15aPdh8zUTNb06XSlx1LBjz0W4tOFNUvwaRDw9QVPwTojeWFOE2Oa91yFPVU7GbdOVi3EC7flAcHnlQmATjJ7Ypcs4EAcaVP1Sn1DadJ8CZMvh0ql5Druq+4uA3LQurLx/Csq0ZuQGAjWic4o2GRLf0N9Fhhyvb6MdHAXypnzB5D+j9ePljADby8iyAQ70RlvO9kZIVAB4EYNPMbYTE0pq9+V557HcX2btQOmQoTNKhR2GSsa0CbU2JCs6AyszogmyF9ojKqu5td9u9cP3KFwGNuIpVdhYEIBMgmJCgO1cB+hpU3hTBCkBXdzqy6s8dXyj8R/enxq3pHrun9q0J+fhWBxP2Lk7Xp8QbDYHrLk11S94Ey5rwlCpduN32G93iUxRiX0Am9cvftvy9MeLoTcWzE9+Klg+dRD2UXrpscU6v1P7cTWHiD8d4uWQrxmg3nmf8+Ts5+8MxXi6Z4twnTPQMgfxvqDIodG8IrokjTOy0+N/3Gw2xEY1HvClbt3rTtVYCOAvAVwCc7I2YmDCxkZAGoHcTnKdjCBMbWfmXdyK92bBt7210xUZl7KIwGcyBfNjFa17+/N3vRuqNjpyi0DMFYsq+71I8BUE3FG9B0BNdY6JAW2mRzop++VeFtF86ZifpiUyAq7uaWHF7BYvuKjDRIhMA3UnViazWsVjWsxuW9UzAMnc3vNazCzYNGAgRuDpG3nu/zHnntfHO2y8ceshBVXs+Pe/TxdL5WumctWtF4O2b4Q/PwXIZirPWoaC9qGwKXJs7KhUQrxKqGwD5g6O4qXjemgdTKavf/k2UEu0mSmr4p2NfHYwP2abIORMEGFf+UK2c3fhVVcyH4uXooveBOStcB3D2gmB2y8KqO4awvI+35sOma0VFh4kJW19i7yw2RcvWlVzgjapEp3KlIky+CeC39hHXO+/M1qhyKlcs5/Bh509jiZeLX51S+4Jxh6k6Z0IxA4KSPi2i6wVYVNCDX464oPWZvgXwOEMV49o/Ov2sUS/ffs7ITjQmsvC98rxb93IjBQc7tk2v6CQBJilk1Bb1U8UYZ11HubNC946sKC4vWCl7RF5HEbpjY1B0AvqGQl6H6CoHeL33/1VXQXpeLwH1Dr8AACAASURBVJGC12XOGpuzmfJlo0YusMf7h1+0aLt/XnjgqAtan4lmtuniHffpkoIzbZGaiIzzFJyqig2z3lRasmax/AAbUzbOxe/poEvqXr/aUVJGcyQx++pgHJGtGKNd+jcTBPItro4658bRJcUjF9kHVAE2b0AzgN0eLvB6pzrV9zXMeH8Irjbl3dZ62HqSDQCiIya2nuRJ7z5bo2KjJSYibB2JpbE8bWH7pd6IyRPelC9bLB+dyhXNy0ZlrvHWnXyn34iJZf8xb61Kyq7nVK6U0W19Y741lnho0qmv7RC1safoFLd3XqXs38/WE3D16pKeyK1S92Z79PfHn990aMSBbVtX5m2fe12Pi+vvvrTKhig3X1PPXbyHW+Da+SAHO2LnhchBEIzeqi7RxemCpeq6j/VfnK6LEel4dftd1C2cAJUJrvaNvmza9cizit94aClUbdpYGSDx2s8mEyq9ggV4XWy9i+jrgLyu6q4qlY7XZc76d2JxbptfNhcQW7y2+QA+R3ueVjjvqNhuG2I7bXiXvgrVmwXdNyazhiaT/o2X91B/TyeuaDcdAsHfS2ESDHO2KXLOBAHGlX9Uj69tmuyoLtxqu2AbKVGxLYA3QZyalvqTYm5WM6Ak0R1Bewb83j782kY9thGPrSOxEZRBvr4mVLedAHwOwO0AHAD/ADAFgI2+pHzFe7FKOeNs3siHXTD0U+mUNl4y9jMuIrPQ+3K9+cXaFmH/TuH+ZtTct+wgny2uyefeMa6wYNPPxBvliJ7r4cIO/dM/iWIv6d2mVycBsuNWtVesU8HjUH3MFqdroSwdYru9QeENrO/Gi8v2QMTdFa7s1iPOrgLXmy4mOynwEQHGxvVE7+5YukoVK0VkFVRXCrRIRY4USIeKbureYZ/JBe+80ApI2QBmtznq3jxy3lsPxbWTQoJU/JuCma1uoV0/KA6PPNhXB+MntilyzgQBxpW/VCtqbzsWcH9oBywKIKquQBxX4C4HIj9JUJT4W6ihc7P1JTZNzC6bfv89AL9MtwAUJukS7Hc/G2lsmN7oyKmu4gwRsWHD6BVzdGRgLnaeh6jYPtzWVHcfNaLwwPUd3Z19i963vBTaIcBTaou91HlSHX1sycIq2wIv7StZ/2odijZFxk3QiLMr1J3QA5kgKrsCvSMuE1RlVxGMSbxgal83nnOgDcVda38vdRj0DJTE8xw8ZbL19cOm5UG7fpHM/XwoTILxEdsUOWeCAOPKf6rHzL55xyJ3xCGOuPsqxFXt+Z/b2fHoPT+d9a7/1nzL0aaT28iLL2WkMPHNL3yhGohy4yVjP9cD50wR+dqHf9MhR0diuWNK7aJqB041FLtBehdZ9bt0par8FYLHXOjSu+urn/LRpVtklalO2Lb2VS3YpUexm/Qt1P+8QvZX1ZEiGKmR4kJ0b3pRoK8LcEPJvNbGTNWxf76Zqm+8stNuPEL583cKk2B8yTZFzpkgwLjKBFXmSWHiYwywkQJaN3r0xsIRp7kqZ4jAdoeIXgmNjsRyR+X5jcfAkUu8reswbrsReOv9zodduOvExU+bL62+30c3DppVUP7d8OOyAyQi0X3BN68xsYJ1dek5o+sS3/I3HS5B1XdgGWk3Ha8Nr3spTILxF9sUOWeCAOMqE1SZJ4WJjzEQ5ka6ccH4I3u0d5tf2+lhhGEduLNWqqi/eO6iAyMReVxEClX1fwd8ZPu9n31t3V0qaOvq6px17+WnDHlQYKp2s/nCHGvxO6CPls5t3SxY/KrXYPmEOZ4zzbZ//tniHGQdB7NFYRKMF7IVY7RL/2aCQNjiKhMMczlPChMfvRO2xrL8+Se17I9fPNdVx0ZHbIu46JXy6MhAdxwz+44di7XzcQE+onbgj+CFgybuUPXksrfnAdrYUv+1V3104ZBZBe3fdXVlE4sK8Ol1h1+0qOShC8uDGimJQgi6vrQbVCTnjh0Kk2B8wbZMzpkgwLjyn6rWbbtDe2HJp0XdfVxbViv6QnfXpke3q/vAl/Ub/pfY/xwpTHxkGpZGunH++M/3QM+EFFaJ9u0059foSH93TK9bXNS+sedhb8etBzcUjz/2gbrPd4eFM1/UfWycCWQVtrhKAEnGk4RBmPTuKhjZNMvOGzpy/7KzH3hmzdyunhHX3Xv5iWszDtgzELbYZn2DiSxy9pfzhvllx4jKuQp3D9t71y4TJ4Dzqqpevs2PWv/mr0XYIYzPofcAatjxDG/2y9+2FT7cOzW+M47d7b08/utH+ShM/KAYgs7fVHxbYcnpULWdtexwnejl2+jIQFdU1jb9vvcQINWXHS2ZdOelJ6y3NOwMfQzaIbIi5/zmHEzthraS78Jk8rm/LS0sKLx24FbnCt3Q1d11Rj5OQ+3vcfYhwbQych7+nNsuLquEIxdB1abCr4BInyZRdSCyB9C7C+cFpXPXtPhUW3v/t7zs5HfbVGg1gBf65W3nnKwCetcKxxqtsb8/DuAwALYrl20XfKcfZcslYWJnPtiJla/FOPDFDoUxCP0vW1cQ82CYfH/YDXR8pjolVUjHgrKje+wQRJUvQfq2542OjrzzxVu+tdukyRmJoYqaxvkiMkeh72mk6JNL5n/F4qL3ylR94zUo2o1HyJ+/k7M/HDOci/XXdjho38Mz9mWHb60DsHGwBPneV3sbd5wdrX/0DKben129Mt827gjq2RQvttmHxCPkz9/J2R+O7ZfsuIuLgptEsAsgr8TKVaF7QbHKQfepJfPeeWMQyzbQcj2ABQBeBjDexAyAcwCcBcBOcbeR2hO980eu9U51t5GTVgDPAvg/AAsB2Onwdlji3l4+9qF4XwD1AC4C8AMAtub1BgC2OdEkAJbmCAB3ABgJYCaAZgDHAzjSO1ne3vOPsle5wehl5KUyBVdZ5W8EsAjANK+C9kCLXt8HcFw/IVIB4JMAtjqMz27I94ddpjt/rdtmTFth6ekCnQXInv3sbTE6kqlOaUrtbdUOdJEqNrmKowae7p4pu/HilnbjEfLn7+TsD8cM5WIfJ2zYf4n3pWywec/2QLQvcPY17ZGwCpPNW533HbG8zY6jio56Z8Omvwiky4XbuKR+Jrf+zkCgsg/JANQYWZKzP5zXXzz+qxFHF6joMqjTFTNXdYtEZCJUZ5fMW2uiYbDrPG905ecAqgFMBPBnAL8CcDTQO33LBIm9Q1/jCZOpnpDZDsDPAHwWwGQAP/GEifVTJkaWArgHQK03mvIigE97p75XAvgtAPud2bCpYXZ8g+VlIzImaOx5YO/4u3mCKWYdckGYFHtz3KzApuRM1dnoiAGJdZkCsxOwfxrWh10mhImNjrQtGHeswDkT0BMA6R2hGmrtSCY6pePPbzrUEX2odwcuF19pubTKlPcWVybsJtK90G4ilNJPQ87pM8xgDscCuNh72NmXtPdi2LKvdraFt30V+5Q33B+zSPn+EWmLw2FVJ0FkZ1fxoiN4SUWva1lY7cvUh3j+ZpuKR8ifv5OzPxzj5ZJvnDfMH3+eAz1TIf2nUm2NQd19xXF+UzJnzeVDMLKp9n/yplbd642AmFiwqVYmUj4D4KMAvhRDmJhAucsbTbF3QBtBORSArTexw7HLAcwBcJL3t6e9Pn4XT3CsBLBzv/f3U/u+yfReKwA8CMCmpdkoipWnJ1Y9ckGY7OopsGghbRjIlNqFMQpsaZ8A8JEwTw/w80VdLywb11aE0wWw0REL2ugVd+2I353DcbWL9yxAz2MC2R6Quub6k0yhb3X5bTdeJxj9O+0mSiq9dOScHr8A7u7/wIolTGzqgH1Zs+H7W7z/D6UwsYXvRQWd11nlFbqNQI5S1S4I7u3qHvHNoBbAs00F0Co4zTgYyHnI2YSJt373f0NBVOjejsg1cYSJfRh62BsJ+YU3A8k+Et0KoAaAiQcbALCjHa4eMGJioiM6MmKDBiY87J3chI4tbLcPxV/0pmzZjCX7uwkXGyG3kRDLe0cAZtcuEyY2qm6L421q2b8AmHCyKV82YhNzKnAuCBNbWGNwDJwV0n7+jgduoI+uBPBvAE1DOS/fv8KlK0y80ZHJvaMjqtMgYmo46Z21/HzYfemcP47uKt70lG0LDNVFzQ3V/U6L37LGftpNpiel3WRopZ6WnFNnF9Cd0QeWPZAGChObR2xt90wANvx/RZiFifmjdxQ4grNFUbrNyIKp6zd2w4VctaT+pM1rTzLtN7apTBPuy5+cyTkVAu0Lxn/VVXe+qLy8edH7gIxU1XFE94JgdsmctVvNJBmQ3Prg3wE4xfs4FB25uBmAjXrbxyN75x44lctGM2wk/Efeu/gD3rSsh7z1JTbCYeLiuwCeB2BCygYVosLEymWjITa6YvrCFsd/w5vKNayESSmAv3vbktlidpsT138oKMrb5r695KmtD6K/bGhouFBV6wYGw/Tp01OJj7y+J9LxHka+eidGLrsLkTab/t13dW3/MWzc80vYuPtkaIG9cwR7dfe4+Hnz/7BibRs+Mq4UZ03dGxEnullesGWhNRLIBoHy8vJc+EiUaNUHEyZWB5sGYGsAbVqu7dRily2KfDLsffWmrh6sWLsBv1ryEkpHFODHX/s4CiLs5xINOqYjgVwgkIm+WutGj24rKF4kohMAWR6rngrdAyqvlxZsqpba996Pw8JmF5lwMIHwujc68aR3j61PsdESm2b77X4jJjbd6xnvfdz+ZpftymUL3i/10lq/blsW29QtEza20N5GSGzNia0xscX1NuJiwsYu+/8fezt/Wf623tBGTEwQHZPLIyb2kLPpWacDeBSAjYqYOrOhHlswY3PubA9lG06y3QJO7jdnLaZvOGLyIRYbHWlfMPaL6B0dQWWqoyOxQPv1dSi6LbACKwo7RnzyTz/7cv+ND7Yy7ZfdZDs52k2WWGrpyTk1bgHeFUuYRPtq+9BkU73sy9pibyGlfUGLzjPeophh7KvPvvaxBwE5EtDvNNdX/yYIv7FNBUGZIybBUM5PzhsuHjdZHGfhwO2CbaTEli+IyKaIqzXFP2q9L0XOtrutzY6xD/vWR1ufHHNnWwA7xNgi2KZj2ZENdo9tabzJK4flOTAfS2szoOIJqJhVyZWvdLaC38SJXabAbBqA7f5iW8RG91A+DUAEQO983aGusDzsbH3IhiKMe/f43z2z259PLpOLWjcf2LWhbux4KZQzoM7pEOzej1fctSPx+Eb/7sfDrrK2sQ6QC21b4G5EDrmnfkbMrfL6l8kPu4nWkXaD/5JP/6YSnYHeY8LEPiLZFzObymU/9++ro4WxKbq/Hmz3REsUlr66f5959rVLbYfJexT6WsmySPntt8+IuQDUT4+yTflJc/C8yJmc0yHQcXHZsT0iP7QDFiGOACpQdaHO8gLoT9IQJekUK/B7c0WYWMVNzY3ypgCkBSIMD7u2BWXToDLLQG3a5TNTR7zxz7sQ0evRI92wU9l7p1OICbmk144kCj/dTvjDbYG1y1X53MBtgQcrR7p2E63fwHS0myq55O4j5+R4DefUYeirY33cqKhtelyASa7oKUsWVtsGARm92KYyindz5uRMzukS+GDBNjtGtPQQ7Z1Cpa7A/V9316ZHt6v7YLCt2dM1mXP355Iw8Q1Ovj/s1tWVTSwsFNtruvfqHH/I1MI1S18S2PzE3kNtopdvoyOxnJNOJ9x/W2AXMnNJ/UkJ7+efjt10gox206GX+L3knDir4Z4y3/vqwT5uVJzfdKI4+ANUX25uqLatOzN6sU1lFC+FSTB4Q8s5YLxZN0dh4qMLgur82y8pq1YR2yTAtj3YToHPfVgN3QSV61V6rh01962YB1D6VeVU6ztl7h92l57Op2xbYFVd0NJQPTeZMqVqNxkbfguxdGyzvunQS/zesHFOnEzmUoZVmBjRyprGlyCy12DnNflJPWyxzfr6GT2D50XOwXAOmxUKEx89HlQj7S9MIBCVwkpxO9dBsdJx5acjL1hzk4/VGjSrVOpr2wJ3j+h4zB7IAP7QXF/11WTLmordZG1QmOTnAsOh4iBsceVHm0g3jzALkymzG7/uqPwWiiebG6ps57KMXWGLbdY3Y6G0RcbkHAznsFmhMPHR40E10g3zy44RyOY98Dt3OmRq0erHbJtOiOqCknmttiVbxq9k63tU3d8LRnWseQiQw1T18ZKRkSNur5thO64ldSVrN6nMh0hMu36RHDofcg6Gcy5YCbMwmT59caS9vGeZQGxzkuOa66vslOaMXGxTGcG6VabkTM7BEMhvKxQmPvo3yE6p7ZKy6yDSe05AdPG7qi4fNa81Zw/tqqxpvBUiM21b4A4pOvj+hSe+kwr+IDn3Lx/tpuKt5O8h5+SZDdc7wixMzGcVNU3fEcGvVPFQS0OVHU6ZkYttKiNYKUyWLdNMnOsRz1thi+d4PPLt7xQmPno0yMail5WVtnfhGCgOWH/AmZds8+w1J5QU4n45r7XNxyoNmVUy9a2obZorwCUK/aAbkUmJbAs8mPFk7PrJgnb9pDl4XuQcDOdcsBJ2YTLpzKsLx++w3RsCjIU4hzcvnPHvTPiFbSoTVLfOk5zJORgC+W2FwsRH/7JTig3zwx1o0ONCjl7ScJIdoJnyRc4po0vqRnJOClfKibPFOeUC+3hj2IWJoayoaTxXRC6D4u7mhqopPuLdnFW2Yox2M+FNCqKwxVUwUZQ7VihMfPRF2BpLIvWtqF08Ceo+LIIRLvS0JfXVaS/MT8Suj27lwz0TMIfIk/4NGHgWzVGYAEfV3Vg8auPI1RCM7hFnv7sXznjeb5ewTflNNHZ+5EzOwRDIbysUJj76l53SljBtW2Cnu/NxiIwBcFlzfdX5fuAmZz8oxs+DnOMz8iNFtjj7UfZ086Aw6SNYWdtYB8iFCtzeUl81I12uA+/PVozRrt+epCAyAmGLq2CiKHesUJj46IuwNZah6jvt/D9v40r7E962wM3N9VVT/UJNzn6RHDofcs5vzsHUbmgrFCZ9fI6tWbxdMdzVEB3RhcjH0lmDF4s423Iw0U7O5BwMgfy2QmHio3/ZKXkw6+qcyo6P/Q2QI21b4LaRHZ99oO60Dr9Qk7NfJClM+hMIW1wFE0UUJonGWEVN42Uicq4CN7XUV53mp3/CFtusr5/RM3he5BwM57BZoTDx0eNspH0wK2uaboDgNFVd1eGM+ESq2wIP5hpy9jFoh8iKnPObczC1ozBJVJhM/eGiMVrovKGAoz3OhCWXzVjjl4/Ylv0iyY85icZzJomHLZ4zyTIX86Yw8dErYWsssepbWXPb+RBtUNU2x9GD71o4838+Iu7Nipz9Jho7P3LOb87B1I7CJJkXucraxl8D8m2o/rK5ofp7fvmIbdkvkhQmycRzpqiHLZ4zxTFX86Uw8dEzYWssA+tbUdNYAchdArgq8sWW+pPu8xHv5qzCzjkTTGPlSc7BkM4W52BqR2GSzIvc1JrFu7niLrM+dKMU7ezXaHO2Yox2g2ll5JzfnIOpXe5YoTDx0Rdh7hxsW2BR9x8QjPRrW+DBXBNmzj6Ga9ysyDkuIl8SZIuzL4VPMxMuft8aYGVt428B+bqruHRJQ1VNmoh7b89WjNGuH96Lnwc5x2fkR4pscfaj7MMpDwoTH72VraDNtt3KuYt3QXfPf2xbYFX8rKWh6vs+Yt0qq2zXN5N148gFX6KCjq9s2qMw2Zr+cbWL9yxQ9yURtHWos+t9DTPeT9dH7DPTJZjY/eScGKd0U4WNc7q8htv9FCY+eixsjcXqe84tz5e6G9seF8E+qvrXloaq4wBRH7FSmCxbpuXl5YG31TDGc5g4Z7KNJpo3hUlsUhW1jXcI5MsKvaClvvriRHkOlo5tOV2Cid1PzolxSjdV2Diny2u43R/4y04QgPiwC4Iy8PLLr+g51y39q4h8AdD/SvGow+6qm9qeaeth65RY30xHVF/+YeMcDNWhrbCvjs1n6nlNB2kET0CxbsPIjTulu9162GKb9Q2mdZNzMJzDZoXCxEePh62R/viG+/Sxl96GKtZEIu4n7lwws9VHnINmFTbOrG8QUUVhEgzlLa1QmAxOvbK26R4Ak6H4YXND1RXp+Id9SDr0Er+XnBNnlU7KsHFOh9VwvJfCxEevhamxTKlt/L4DuQKKjS6cTy9pmPGMjyiHzCpMnMP4JZ/+DaolZd8OhcngPpg6e/GRqu4DCl295t0Pdn/imm91peoxtqlUySV3HzknxyvV1GHjnCqn4XofhYmPngtLY7FtgUVwp0AcV7WypaG6xUeMcbMKC+coCNY3bkj4kiBsnH2BlmYmFCZDA6yoaXxYRA53Fd9e0lB1daq4wxbbrG+qkZLcfeScHC+mTowAhUlinBJKFYZGOqVm8QEO3EdtW+AvH7obTv/S4YHHUBg49w841jeh5pd2orBxThuYDxlQmAwNsXL2bVOg2qLQ11rqq/ZIdWORsMU26+tD40wgC3JOABKTJE0g8JfKpEuYwg182KUALYFbps1ZVNbT4/xHBOOhuPHKMz91Wph2MWInnECQ+JCEnH2AOEyyYF8d31EVtU3PCLC/qny9peGk38W/Y+sUbFOpUEv+HnJOnlkqd4SNcyqMhvM9FCY+ei+fG8vUurtKtGPDvwE5sHdb4JEvfnHZ17/eQ2HiYwANklU+x1WsKrO+mY+pXLFAYRLfE1NqbpvhiN4G1ZebG6o/Gv8OChP2IalESfL3kHPyzHhHfAIUJvEZJZwifxupSkVN0z22LbAqXnBGlh5s2wLnb31ju5z1TbgppJWQnNPCN6xupjBJxF0qlTVNL0JkL3Hly3ddetKfErmrfxq2qWSJpZaenFPjluxdYeOcLJ/hnp7CxEcP5mtjqaxtvBKQs6D6Ngoin2ieP+MNw5av9R0sJFhfHxvLEFmRczCcc8EKhUliXqg8v+k0OLgBiiebG6omJXbXh6nYppIlllp6ck6NW7J3hY1zsnyGe3oKEx89mI+NpbK28duA/Nq2BVZxPttSP+OJKLJ8rO9Q4cD6+thYKEw2E8hWXAXjzaGtUJgk5oXp0xdHNk7sWQmRnV3Xnbzk0pl/TezOvlTZijHaTcZLqacl59TZJXNntjgnU8Z8SEth4qMXsxW0mbJbUXvbsQL3HlURQKcO3BY4U3bjuYR24xHy5+/k7A/HeLlki3O8cgXxdwqTxClX1DT9PxFcBeiDzfXVRyV+J4VJMqzSSZuttky76Xgt8XuzxTnxEuZHSgoTH/2YraDNhN2psxft7bryuIiUAji/ub7qsoGoMmE3EXfQbiKU0k9DzukzTCSHbHFOpGyZTkNhkjjho+puLC7tGLlSgLE90IPurq9+KtG7sxVjtJuoh9JLR87p8Uv07mxxTrR8+ZIul4TJWADbAngNQPcggG1Hkk4AK4ZyAB926YVn37bAvaJkV9sWuLmh6puxcsxWI6Xd9Pyb6N3knCip9NJli3N6pYb11+8AcGPkUwCgHECH158Paop9dXJemFLTVOsIFqpqS0tDdWWid2crxmg3UQ+ll46c0+OX6N3Z4pxo+fIlXa4IkykAbgSwCMA0ALa4b10/yFbOm72fRwGwB9+XAfTEcgQfdqmHp32VG9VR/KhtC9w7ZaD4xaNRVxfr5YPzllPHnNSd2eoMaTcpN6WcOFucUyxwEYAJAJYAOAzAuwPy2R7AAwD+CWAXT7ycyb66j1K6vp587m9LiwqK1gAY1SPOfncvnPF8In5M124iNvjxKn3/knNiBMIWz4lRyZ9UuSBMigE85z3k1gI4C0AhgJ/0wzwdwAEALgBgZT4OwEMA2ilM/O0MK2ub7gJQaXvmO1oy6c5LT1g/WLiHrXNgfYPp+Mg5GM4pWjkWwMUAJgLYG8B7A/I5wxMu1lc7AGy6kfXfL7Gv9qevrqhtukSAuaq6uKWh+qRE/Mg2lQil9NOQc/oME8khbJwTYZJPaXJBmOwK4B4AB3pf1Y4AMBnAhf1AfxXADQC28X53NIC/D+YIjpikFqIVtU2XC/BD2xbYLSg6eMn8r9i0ukGvsHUOrG9qcZXsXeScLLHA09uHo2cBHBpDmNh0XBMkNuK9FwDbxW/3GOl6C82+OnnfHVuzeLtiuKshOqILkY/dUz/jlXi5sE3FI+TP38nZH47xcgkb53g88u3vuSBM9gFwNQDbZcSmDNnP3/FGTqK87Yvb2QBOADAOwKPeHOa3GhoaLlTVuoGOmT7dbuGVKIFHX3wLix5agYgjOGfaPthtrK1550UCJBAEgfLy8lzoixOtqo1yPz2IMLE8rC6nA7gWwBe9D09gX50o3vjp7vj3Sjz4bCs+/dExmHnkHvFvYAoSIAFfCAyzvtqXOgedSS48DO0N2EY/DvcWvVcD2HnAVK5TbU4tgF94gBoBXAFgaSxg/AqXXBhNqbntcw70bxBE1MVXWi6tuiORHML21YL1TSQq0k9DzukzzHAOQwmTEQDuB/Cm94HJFsgPerGvTs1Tx835w04FbtdKu1t7nAlLLpth604GvdimUuOc7F3knCyx1NKHjXNqlIbvXbkgTOwhZ8P99oXNRkKu9NaP/B7AxwG8AOBIALYz1Mn9du7i9AAv7tJppL3bAqs8KpBtFZjXUl81P9FwTsduojZipaPddOglfi85J84qnZTZ4pxGmWMJk2hfPRXANwBUefnbtK/3B7NFYZK6Fyprm2xEapYCv2ipr/p/FCYfEshWm6Ld1OM5mTvDxjkZNvmQNheEiXE8yBMn9v/XA7BdXGz3F1vjYFO7bIHlrwB824N+CoBb+LDrI5BqIz1m9h07Frub/tO3LbAuam6o/loyQZ2q3WRsUJik7l9yTo5A2OI5OTpbpDZhYh+RbPqt9c32c7Svtr57Yb/UtqFJrEXyvUkoTFL3wtSaxbup9CwHpHujFO18/8ITBx2dCltss76px1Uyd5JzMrSYNlECuSJMrLwl3nQte5ANdtnCStsiuG2oCvJhF9/90+sWF7Vv7HlYRA4G9N8bisd/nmuGGwAAIABJREFU7oG6zw92fkzMDNkpxefsRwpy9oNi/DzCxjk+kcynYF+dHuOK2sZFAqmGor65oWr2YLmFLbZZ3/TiKtG7yTlRUkyXDIFcEibJlHvItHzYxUdZWdtkU+W+YtsCF2wqPuRPP/ty/3Nj4meQxkhNQpkPkYidYboEE7ufnBPjlG6qbHFOt9x+3M++Oj2KU2sW7efCeUYEbZ3dnePvvfyUmB/tshVjtJuefxO9m5wTJZVeumxxTq/Uw+9uChMffZatoE3WbmVN00IIahX6nkaKPhlvW2B+hesjkCxnv0KLdv0iOXQ+YeMcDNWhrVCYpO+FypqmOyGYCpUfNTecdEmsHMMW26xv+nGVSA7knAglpkmWAIVJssSG+Zf8KbW3VTvQRara5ap87u5Lqx5JFQE7pVTJJXcfOSfHK9XUYeOcKic/76MwSZ/m1POaDtIInoBi3YaRG3d6oO60joG5hi22Wd/04yqRHMg5EUpMkywBCpNkiQ1jYWLbAgvc+0Sk0IXMXFJ/km27nPLFTilldEndSM5J4Uo5cdg4pwzKxxspTPyBWVnbeD8gR7vQHyypr/4phckyzcZ5E2HrQ1hff9ovc9mSAIWJjxGRy430uNrFexag5wnbFhjQi5rrq7c6lDJZFLlc32Trkkh61jcRSumnIef0GQ6XHChM/PFU5fmNx8CR+xS6es27H+z+xDXf6uqfM9uUP5zj5ULO8Qj58/ewcfaH2vDJhcLER1/lamPp3RZYOx8X4CMA/tBcX/VVP6qdq/X1o26x8mB9M0V2y3zJORjOuWCFwsQ/L1TUNlkfP0mBb7XUV11DYVIe+PsN+y7/4nmonMLGORiquWMl8IYbRNX5sPuQ8sBtgUcWR466vW5Gpx9+CFvnwPr6ETXx8yDn+IzyJQX7av88WTG7cZqo/Fmhr7XUV+0BiEZzZ5vyjzNfmD8kwLgKJq7CZoXCxEeP52IjjW4LrMCKwo4Rn0xlW+DBEOVifX1051ZZsb6ZpMuHXTB0c8sKhYm//qiobXpGgP0BnNxcX3UrhYm/fOPlxmdEPEL+/D1snP2hNnxyoTDx0Ve51lgqaht/LJAfKfSDbkQm3VM/4xUfq8vtc/2EOUReuRZXma4265tpwrmTP4WJv76orGmcCZFb7Xyq5obqj1KY+Ms3Xm7su+IR8ufvYePsD7XhkwuFiY++yqXG0n9bYIVz7JKGkx7ysaq9WeVSff2uW6z8WN8gKDOugqGcG1YoTPz2g0pFbdNygezuKr60pKHqz+yr/WY8eH58RgTDOmycg6GaO1YoTHz0Ra40luPPbzrUEX3Ir22BB0OUK/X10YVDZsX6BkOanIPhnAtWKEz890JlTeMZELkGiiebG6omUZj4z5jPxD4C7KuDi60wWaIw8dHbudBIvW2BHxPI9oAubK6vnuNjFbfIKhfqm6m6ccSED52gYitb7Sio+g1lh8LEfy9MOvPqwp2233YFRHZWyBda6k+6L1sxRrv++5fPpvA9m4KJotyxQmHioy+y3Ql/6Zw/ju4q3vSU39sC8+sQvw752EziZpXtdhS3gD4nyFZ9fa5GStlRmKSELe5NU2obv+9ArgDwQHN91eezFWO0G9dVviQgZ18wxs0kW5zjFizPElCY+OjQbAWt2T39lpWFozrWPATIYar6eMnIyBF+bQtMYUJh4mMziZtVNttRmE6LjuuIABJQmGQG8lF1NxaP2jhyNQSje6AH/eKMQ54MU2yzD8lMXA3MlZyD4Rw2KxQmPno8yEY6peZ3BwgiUyHYf1L5jtVPvPLu0yL4uKqu6nBGfOL+hSe+42PVYmYVZH37F4B2M+1ZCrFgCGeXc5B1HMwWhUnmvFBZc9s8iF4MoPnKMz5VSWGSOdbRnPlsyjxjsxA2zsFQzR0rFCY++iKoxjL53N+WFhYUXiuQUVb8caOLp65d1wFV7ep2uw+957KvP+ljtQbNKqj68ivNMuVLReYjOmzxnHmi8S1QmMRnlGoKe04UFRStATCq9qv74zMH7x/48z5sbYr1TTVak7svbJyTozP8UwfeUQWBLN8fdt4Jv7M8luMBfEoVCsG/xNULmy+tvj8IzmHrHFjfIKIqfF/DshVXwXhzaCv53ldn+6NKRW1jvUBqPjlxe1x85nGBP++zFdu0G0zrJudgOIfNSuAdVRCA8/1hN6V2UbUDp9pYKnSHAsc5oqdHn4ZgpQu3cUn9zMYgOLNTCoIyX9SDoRw+zkFxHcpOvvfV2RYmU3+4aIwWOm8IUNQJZy+/D9mNF0N8RsQj5M/fydkfjvFyyRbneOXKt79TmPjo0aCCdsCICfadMHrq86+vu6tXqIhe17Kw+k4fqzVoVkHVN9sP96h91jeIqAqfQMhWXAXjTY6Y9CeQDV9X1Db9XIDvKfSGlvrq04P0ezbqa/Wj3WC8TM7BcA6bFQoTHz0eVCOdfO4d4woLO68URakVf//dR0999rV1d6mgraur6Ox7Lz9xrY/VojDxCATlXwoxrqkJov3mgg2OmGTeC8fN+cNOI7TrzR5Ft/Y4E5ZcNsPWnQRysc8MBDOFWDCYs8Y5oOrljBkKEx9dEWQn7O3KdbaIjDNh8syK965X9Fy5pOHkZ3ys0pBZBVnfbH915Fe4oKKKXzuDI519SxQmwfjgxzfcp4+99DZU8fOWhqqzgrHKtkzOmSEQtnePzFDM3VwpTHz0TdgaC+vrY/AMkRU5k3MwBIK3QmESDPNHnnxOL1n8XxcqmzY6RROC2E6eH3OC8S055z/n4GqYG5YoTHz0A18gfYTJF/XNBBhXjKtgCARvJUzCZMOPyw54p/J3/939oC8E/ty1PuSsa5feLsB0QBc211fPCcLb7LuCoMyRqWAoZ49zUPXLFTuBd5BBVDxMDzt+LQkiovps8CEbDGtyDoZzLlgJQ1/dtqBsmgBVqjJq0y6fmVr85j8aFWgqndMayCYl0b7rnGse3V/FeRbAhs7uzvH3Xn5KW6ZjgG0504T5bAqGcHY5B1nHXLBFYeKjF9gJ+wiTIyYcMQkmnELLOWC8Mc3luzDRC8vGtRfJddHKmzAZ8cY/e3dQLOnUWXJRa6AblVTWNC2B4HgF5rXUV83PdAzwmZhpwtl9YaZ/g/Fv2KxQmPjocTZSH2FSmIT2hZntKJh2lAtW8l2YtF9SVq0ivWdO2dU19sCphW/9t1eYQPS6oEZNom2qcvbiw6Duv6BYt2Hkxp0eqDutI5NxwLacSbof5k3O+c05mNrljhUKEx99wc7BR5gUJhQmwYRTaDkHjDemuTAJE4Hu6MI5XOCuBuRxUW0smdca+GG4FTVND4rgc+ri+y2XVv0sk3HAZ2Im6VKYBEM3+5yDrme27VGY+OgBdsI+wqQwCe0LM9tRMO0oF6zkuzDZML/sGIGc3ctadayKHOo9dF8X1Rkl81ofCcIP/dtURc1tx4noXxS6es27H+z+xDXf6spUGdiWM0V2y3zJOb85B1O73LGSS8JkLIBtAbwGoDsGIvtbtLwK4IPBMOb7w25gvdkpBdOgyJmcM0EgW3GVZl2sv34HgBsjnwIAu9siawCtQ9nJ975aLysrbd+EKyEyzjh07bD31MJ3X1BABKoLSue1zk3TDwndPjDGKmqbnhFgf6ie2dxQfW1CmaSQKFuxTbspOCuFW8g5BWi8JS6BXBEmUwDcCGARgGkAJgFY16/0xQCeA/CEfXcCsByAbXcY66GIfH/YUZjwZPC4LduHBHzo+AAxgSyyxTmBosVKUgRgAoAlAA4D8O6ARCUA7gXwNICjAFwMoCnMH5F6xUknqhWY2LbvKbNHPXfLbyD4tjER1bNL5rVelaIvEr5tYIxNqb3tKw709wp9raW+ag8rScKZJZEwW7FNu0k4KY2k5JwGPN46KIFcECZR0WEPOduhxE6lLQTwk36l3tUTIt/1hMmQLqUwCSbi2SmRcyYIMK4yQdW3PI/1xMZEAHsDeG9AzjO9318AoBTAqwD2GmyEO6x99YZLympFZKGxU8Wpo+atudk3D8XIKFabqqxpfAkie7mQmUvqT8rIWhe25Ux69cO8yTm/OQdTu9yxkgvCxETHPQAOBNAD4AgAkwFc2A+TPdhe6vdzJYCWMH+F6193dkrBNChyJudMEMhWXKVRF/twZOdhHBpDmNjLtu069S9v6u19AE4GsDqWvbAKE2OxYX7Zz3rXn6i6EJlWOnfNoM+0NHzVe2tsYdJ0CgQ3Q/W55obq/dO1Eev+bMU27WbCm1vnSc7BcA6blVwQJvsAuNob9repWfbzd7yRk6g/TKwcDODX3te4v/IrHL+WBN1Y2QkHQ5ycg+GchhUb5bapWrGESTOAiwAs9YSJTeOa7Y2cbGUyzMLEYLTNH29TmE+FotNxMHnknDUPpuGXQW+N1aamT18caS/vWSaQ3VX0hJaF1b4f+Mi2nAlvUiCELa6CiaLcsZILwsSG+/8O4HBv0bvt+b7zgKlcNq+508NmZf4bgNPtYdfQ0HChqtYNRDp9+vTcocySkAAJkMAQBMrLy3OhL07UR0MJkzO8NYA2UrJFOvbVMfCqi9EPz0PxqgehkRF45+hfontH+zYXzPWP59fi9w+/hl13LMF5J+4XjFFaIYFhTGCY9dXDknQuPAzt4WWL2k1oPArgSgAPAfg9gI8DeAFADQA7COoyADb1y9Jz3rIXcmH7esD6BtPXkHN+c06jdrGESbSvngrAtsf9PABbh/IX2O5PwMZY9sI+YmJMdDEi7S+VLYHIZFVdV+jIESPmrHk+Df9sdetgbXnSmVcXjt9huzcEGAtXj22+tPr+IOz6aSNWXuy7Mk24L39yDoZz2KzkgjAx5gd5YsP+/3oAZwKwURLbOtg+H9lOLzbEbQ86u04E8MfBnMWHXTBhzE6JnDNBgHGVCaq+5mnCxD4i2a5btvjdfo721fbzDb3Tk/ouEyz/ZV899IucXoUR7evH/wPAp1R1rVPYNamk5t1VfnltqDZVWdt0HoBLAf1bc331MX7Z5IurnySHzot9ZjCss8U5mNrljpVcESZGxMTHKG9nrsEIbe99ebPRk0EvCpNgAixbjZR26d9MEAhbXGWCYb887dyOdu8sE/bVHoGhYkzrt9+urbvoYRHZD9BXS+wwxjlr3vLDT0PZnXzub0uLIkWrIBjdAz3o7vrqp/ywSWHiF8X4+YSt7wpbfeNHQH6lyCVh4htZChPfUA6ZUdg6B9aXcZUJAtmKq0zUJdk82VdvSUwXjB/brvoIIBNV9bnSgs4jpPa995PlOjB9vBirqGm6SAQXQHFXc0OVnSXmyxXPri9GYmRCu5kiu2W+5BwM57BZoTDx0eNspD7CHCIrcibnTBAIW1xlgmGyeVKYbE2svWGHXbW78FFAbBOYpSUj1xwpP4i9RidR3vFi+9iaxdsVw12tQLHrOPvfvXCGL2tc4tlNtPzJpqPdZImllp6cU+PGu4YmQGHiY4SwkfoIk8JkMwHGFeMqGALBW6Ewic284+Kxe3U7kX8LsKMqHij96JpjZUbvOV8pXYn0IRW1TZcL8ENVNLU0VNnumGlfidhN20iMDGg3E1S3zpOcg+EcNisUJj56nI3UR5gUJhQmwYRTaDkHjDemOQqTwb2wYf64AwH5p0C2geIPJXPXTBeBpuK3RJ5NU3+4aIwWOm8AWiAa2eOuhhkrU7HV/55E7KZrI9b9tJsJqhQm2YqrYLyZO1YoTHz0RbaClnZ9dCIFUWhf1MPWjoJpNUNboTAZmk/7gnGHqes8AOndpfKm0rlrTkvFb4nGdkVN029E8C1VXN/SUDUrFVsUJsGfSZSof9P158D7addvoszPCFCY+BgHbKQ+wqRAoEAIJpxCyzlgvDHNUZjE90Lb/PEVgP4ZkIhCrxw1t/Wc+HdtmSLRZ9PUmsW7qfQsV4irPc6EJZfNWJOsLQoTCpN0YiaRexON50TySiZNtuwmU8Z8SEth4qMXsxW0tOujEymIQvuiHrZ2FEyrGdoKhUliXmifX1alwCJARKFzRs1tXZjYnX2pkontiprGW0TkZECvaq6vtsMyU76SsZuykRg30q6fNAfPi5yD4Rw2KxQmPnqcjdRHmBQIFAjBhFNoOQeMN6Y5CpPEvdC2YNy3oM5v7A5VnDpq3pqbE707mWfTcbWL9yxQ9yUBOjY6RRPuX3jiO4naGZguGbup2oh1H+36SZPCJEogW3EVjDdzxwqFiY++yFbQ0q6PTqQgCu2LetjaUTCtZmgrFCbJeaHtknEXQJyLoOqKo9NL5qy9I5Ecko3typqmP0LwJVVd0NJQPTcRGxQIyY1MpcqUnMPH2c9YGQ55UZj46KVkO3+/TNOuXySHzoecyTkTBLIVV5moS7J5UpgkSwxou2T8ryH4NlS7VXTKqLlr/xovl2RjbOp5TQdpBE8A2NDZ3Tn+3stPaYtngy/M4XthTjauUokhxpVf1IZPPhQmPvqKjdRHmBy54MhFMOEUWs4B441pjsIkNS9suKRskYhUQ9Ehjnt0yZy1/x4qp1SeTRU1jfeKyBdUdW5LQ/WCVEqait1U7Ay8h3b9oBg/D3KOz4gpkidAYZI8s0HvYCP1ESaFSWhfmNmOgmlHuWCFwiQ1L2gdnPaCsj9DpFKh6wtFDh0xZ82gp7Wn0qYqaxuPAuTvUH17w8iOCQ/UndaRbGlTsZusDX5R50iNHzGTSB7ZiudEypZPaShMfPRmtoKWdn10IgURBVEw4ZR1zgFXM6Y5CpPUvaBXo7DtrfH3iuAoVV0bURw68kety/18Ua+obXpcgElQnNPcUHVlsqXlsylZYqmlJ+fUuCV7V7Y4J1vO4Z6ewsRHD2YraGnXRydSmGT9hZnxHEw854IVCpP0vKBXYGT7xvEPAvgUFK9Duj9dOvft1QNzTbVNVdQ0VohIs0JXlyyLTLj99hk9yZQ4VbvJ2PBTiNFucgTo3+R4MXViBChMEuOUUCo20oQwpZ2InNNGmFAG5JwQprQTZYtz2gX3IQMKk/Qhav3227V1Fz0sIvsp8FJpV/thUvfBu/1zTifGKmqbnhFgfxc4Y0l91XXJlDgdu8nY8UuIpWPT7mV90yWY2P1h45wYlfxJRWHioy/D1lhYXx+DZ4isyJmcgyEQvBUKE3+Y64LxY9tdPAHBBKg+XVLSepj8ABujuafTh1TWNp4ESJNCX2upr9oDEE201OnYTdRGrHS0mw69xO8l58RZMWXiBChMEmcVNyUbaVxEviQgZ18wxs2EnOMi8iVBtjj7Uvg0M6EwSRNgv9s3Xly2R4/gEREZp4oHSseumSzfQpclSS/GVCprml6EyF6qqG5pqGpKtNTp2U3UytbpaDd1dsncSc7J0GLaRAlQmCRKKoF0bKQJQPIhCTn7ADGBLMg5AUg+JMkWZx+KnnYWFCZpI9wig00Lxu/b5apN6xoN1eaS7tYTpA5uujFWUdv4TYFcD9Xnmhuq90+01OnaTdTOwHS0myq55O4j5+R4MXViBChMEuOUUCo20oQwpZ2InNNGmFAG5JwQprQTZYtz2gX3IQMKEx8gDsii7ZIxBwMFD0JQoqqNo+a1zkw3xqZPXxzZOLFnJUR2hsq05oaT7kqk5OnaTcRGrDS0myq55O4j5+R4MXViBFIVJnsB+DSAV+1kWAAvAtiUmMnMp+LDLvOMzQI7JXLOBAHGla9U2VfHwJnvMbZxwfgjXRf3QlAExW/WVD/87fLy8lSf970Ep8xuPMtRuRKKJ5sbqiYlEqX5znkgA9Y3kahIP03YOKdPbHjlkEpHNR3AYq+aZwI4HMCOAL4MIKmtBDOFisIkU2S3zDdsnQPry7jKBIEMxhX76kEclkHmQ4ZIkHbb5o+vgOqdEHE27H86yk6Yn8rzfnN9jqq7sbi0Y+RKAcb2wD3m7vqZf4vXHoKsb/+y0G48z/jzd3L2hyNz2ZJAsh1VMYAnAPwFwAcA3gTwGoB7AOwHYNCTZ4MET2ESDG12SuScCQKMK1+osq8eAmNYYmzDJeO/IYKbelGI++3SOWuvTie6Kmtvmw3oAijub26oOjZeXmHhHOXA+saLCH/+HjbO/lAbPrkkK0xKAfwXQBWAMQAmAFgEYD2ATwB4OheqTmESjBfC1jmwvoyrTBDIUFyxr6Yw6SXQfknZWSpyJaAqwMz/3965gNlVlXf/t/aZyW2ScDHJBAhECCpCAIXyKWALCiIQAgomH0Grtqit1Q+oFwiQwlgNJNwErW1pQSu2JIKgNVwKykWqCFW8kICtFTQYIAkhgVwml5k563vWzJ4wDHPm7L3P2uucOft/nocnCWet9e71ey97/89ae+9xF69J/FStwQhP+OxNbaNaRq0GxvdgD7t70bxfDJcTOcV21TSU3aqIvDQQZy8YNcggAmmFSQTcB7j9pU/EgqQVOBBwe5ndKkrdPxImYVygoiTOeRBQXHmhqlotYbKTwJrvXGDHP/kNwPaAOa3t4tV3Zo2yWRcsWWiMuchiv3fnonmnSZi8QkC1K2tUpetXNM7p6Iz81mmFiZvxNMAVtUMGTP89wL2NgkPCJIwnilYcNF/FVR4Ecowr1eoKDsuR+bAhUk+7U5cc/Y8Y/hLLDhOVjx130dqfZInn2Z+5eZJtiZ6xMKYcRTPvvnxuxS3c9ZxvrTf7Z2Gj+Wahlr5P0TinJzSye2QRJv0zngK41ZIXgW2NhEHCJIw3ilYcNF/FVR4EAsSVavUgxwVgPmSo1Nvu5i+232yMmWexm8C+Y/zFa93W7NSfUy5Yei2Gcy12yZ2L5p1VaYB6zzf1xGrsoPnWCDBh96JxToilaZplESZfBf5qEIG1wAHAhkYgI2ESxgtFKw6ar+IqDwI5xpVqtVZMegn0x5jtIOpsab8bY06w8GJLuefIMX/zwv+mjev3XHTbHi3lrmcMNjK2tO+yxXOfGWqMHGN72EOW3bQezdZenLNxU6/hCWQRJvOBw9xm1XjouYATJvvH95zUnbmESRgXqCiJcx4EFFfeqKpWS5i8Spi4f9gvM7pzY/v9GHMU2OdMS9fbxl2wflXaqJt1wdIbjOFs4IY7Fp35MQkTvdsrbQxlbV+0c0RWTiO1XxZhMniu7jHB7vHBb45ftlh3FhImYVxQtOKg+Squ8iAQMK5Uq2MHBmT+qpBpFLu2Y/L4zpboRxhzKNinx3VtPcJ0bFyfJr5nX3DLPtb0/M5iyrYn2vuuK+e6p3U15HzTzKuWto3i31rmkKav5puGltomJZBFmLgbKt0z8t2nCzgCuBV4I5B6SXjAgU4GJsbvRekeZgJ7AC8BWyu1kTBJ6v7a2qko1cYvaW9xTkqqtnZNyLletboF2C9+SuNrLlYHekm1uraYTdp7qNi2HRN339I67ifGnbut/dW47vI7TMcLm5OO6drNumDpEmM401quvXPxmX8tYfKU1U33aSIoW9smrNXZQDRpr7TCxLW/G3BP4Rr4eRqYOZxYqMLvZODr8TtRTo0fR+zEx+DPVOB54EjgEQmTPgJFS1LNN0w1EucRzbletdr9wPRg/Fj544BvAotUqxuzVm9ZOGkPbMujGPbG2ofHTVzzLnMO25NG/uwLbj7ImmgFlq3biPb4weK5Lw/sqxqSlGRt7cS5Nn5Je9eLc9Lja5Z2aYWJm/cJwO4DAOwAfhTfZ5KFi1t9ce9EcWLD3atyTvy0r6sHDdb/XP5j41Wan+lk15gnuyxBkKZPvYqD7KbxUva24pyd3aCe9ajV7glN+wILgd0AV6fdNl93nnjNRysm3nw97EDD5dTWL7Tv22N4xBgzBWvvHffGNSebufQkPbJT5i9dBpxiYeGdi85cIGEyI8t1VVLcQ7ZTzawJX+LO9eKc+ACbpGGaBHJbtZw4qPT5LTDcFqxK/dx2g3vi96K4Ynh0LH4uHdThEuCnwEnxr3Du70N+dLILE531SlLZlX/zINBEcVXPWu1WTNwPTJ8HjgG+D1ymWt1HoFFjbPtlUw/ssvYRg5mA5bZxF6+eY8zOB9wMm26zP7f0MFviMXeP6Y7uHVPvvepDW/o7NOp886gfjexfzdcPgXrFs5+jHzmjJBUmbQlubH8dkOrmuRiT+zXtesCthJTjX9c+Ea+c9JN0J7gPAB8HlgDXxCKFxYsXX2qt7RiMfM6cOSPHCzpSERCBQhPwuC+93rX6rcAP40fKHwy4G+7f7951pVrd2CHeum45u99/Dqa8g637nsTLb3vV4sewB/+VO/6b3z6/iVOOmMa73+JuA9VHBJqTgMda3ZyAPMwqqTBxNzP+CeD+rPRxJ6PEe1MHDOJOpA8AR8UrLvOAPYH+rVzuGN1S8az4lzj3sjD3ORz4+VAHoxUTD5GRYIh6/Xoguwmc46GJOHuAmGAIz5zrWavdbN2PTN+N70V0K+w/jmv3kD9aqVYnCBAPTZLG2OaFU95trLkLY1qw9rK2BWsuTmJ+1vxvHW+w38fadZvHbtv7wY4/633pclK7SWykaSO7aWhlbyvO2dmpZ2UCSYXJ4BH2Afr7uu1Xbk+xEwk7l3BTQHf3mLhlYPc89EeB64CHgG8DhwK/Bpx4cW+Zd7ZuAa4F7hjwLpVXmdPJLgX9GpqqKNUAL0VXcU4Bq4amTco5dK12705xW3rd9i13j4l7s/ghlV6+q1pdQ8Cm6Jomtjsvm3K6LZtbMSYy1p47bsGaLycxNWv+0p8ZOLxs7Ll3XT6vt08au0lsJG0ju0lJ1dZOnGvjp95DE8giTNw2q78fNFytb353L2x04sR9boy3bI2KHx3stnoN/LXN/SL3D8AvKzlVJ7sw4a6iJM55EFBceaNaj1pdip/KdWA8i08B7g30Q35Uq735etiB0ubU5i9O/bAx/Isb1Fo+Mn7B6m9UO9KTL1h6WmT4rsU+P+6p0t633jq3J63dajaSfi+7SUnV1k6ca+On3n6EiVvdcKsabkXDbe16OH7b+xnAAfF7TbKyHgeMr+HpXjvt6mSX1QUsskIYAAAgAElEQVTp+qkopeOVtbU4ZyWXrl+Tca53rd4lXuEe9r0YqtXpYjRr6yyxvXnh1AuMe9SztWWMObXt4tV3VrN/ygVLfoMxb7DYs+9cNO9rWexWs5Hke9lNQqn2NuJcO0ON8FoCaVdM3JYqtzR/GvAmwN3l5lZP3IsVjwd+1wiQdbIL4wUVJXHOg4DiygtV1ephMCrGksXY5oXt1xrMuVh2RBEnjL1otbuXtOLnlPlL3UNq/tViV9656Mx9n3rq6XI9bhaWf5P5t9ZW4lwrQfUfikBaYeLu83A3M7oncC0Groq3Xp0HvAX4VSNgljAJ4wUVJXHOg4DiygtV1WoJk50EasmpLQunupcffwRLJ3Qf07ZgXcV3iIE1s+Yv/Z3BTLfWfu7COQdf+bdLf9N+71Wnu+3ewT61zLeWg5TdWugl71s0zsnJNEfLtMLEzdo9AtLdmN7/Vl/3mF8nVtyKSe+TOOr9kTAJ44GiFQfNV3GVB4Ec40q1uoLDcmQ+bIiMRLvWYjoXTr0VwxnW2pdaI3P06ItWP1lpoidfuOTvIms+iWXTzNfvOmHFypeWWWNvuPPyed/LI3+GGnMkcq6FjeZbC73kfevFOfkRNkfLLMLkTGBF/LZ29zhIt5c4y/tLciMoYZIb2lcNXK8klV35Nw8CTRhXqtUSJr0Eao1tewulzt+0u8cIn2CtXRu1dh0+7oL1qwbjnTX/3/YzlK6z2OMNZsw+U9p4Zu0W97h/LD3n3bnoA0/nkbuDx6x1vlmPUXazkkvXr2ic09EZ+a2zCBP3mN7+d4q4t7HfBfyhkVBImITxRtGKg+aruMqDQI5xpVotYeJFmPQKiy8zunNj+/0YcxTYp8cZ83Zz0eoXBiI+ef7N8yKieRZmGDhwzKgS23Z0/8TCyxZ7012LznIvSM79k2NODXvsspu7a73Fc5YjrZd/sxzrSO6TRZjsHT+R6z3An8aTfwR4d4K3wwdhJWESBHPNv8JlPcp6FQfZzeqxdP3EOR2vYVqrVkuYeL2Qsx2Tx29piR4xxhxkrX2irWXH0Wb+hpf7MfcLEwwlbO/2bvfY/96PtSx3j/rv6hl1W973nNSjhmz+2/aDXzzlXx+ffti7s1xX1ZT09ZivO2DZrclt6lyBQC0J5J7K9RngY/Ejg1/fKFu6JEzCxLuKkjjnQUBx5Z2qavUgpIqx7DFmL5s6udPaR8DsB/x03NjVx5hPs9WNeMr5S44jMuf2KRFG7Tlp3Huee3HLWjCTALf1231RxvJw2XB7yZZuW7Z47jPZj2boniH9u+Wy9lMNnGmtGb99r3fMHvPcfy6xsLTtojW6p8a3Y+PxQvp34BTqZTcnjA07bBZh8mng6gEz+hrglmfvB8qNMFMJkzBeqFeSyq78mweBJowr1eoKgdKEvh42JXzPt3Px7tNsd+ujYPa0lgfb3rj6eDOXHncQsy5YcoMxZor7+8zpu852N7+XKW8wtvR9TPl0Y82JGMb2H7B1L1e29nZTLt96x5UfcK8eqPnje76VDshe2j6lc5S5of97J0xGP/uj3ntqxu2wHzWfXxPkaWSh5juYg+zWHKoaYAgCaYWJa78U2B/34iV4AFjXaGQlTMJ4REVJnPMgoLjyQlW1ehiMirHaY2zbFya9qdu0PGwMu2O5rW3B6ve7UU/47E1tra2tx1nL2094y54Xfv9Xz53W1dV1371XfWiL+352x7JxPds6TzKU3YuZZxnMxJ0ixfJrDLdHPdy+7Mozf571KPP277bLJ+9vu0vTbFR+vyV6F9a6F5qO6Rk/dY+WzjX3W8sWjL0h1KpJ3vOt5AfZzRqh6jccgbTCxI01GXjVDW+NhljCJIxHVJTEOQ8CiitvVFWrK6BUjPmJsS1/O/mtRNFDGDMea29sW7DmowNHrsZ5Tsctozq39rzbYM7A2FPBuHek9X4s/N5Ybu+JzO13Xz73YTA26VFXs1tpHNux6647WkdP68LuZcpmGpHdE2v2xphpFrsX1kzrFWLVPtaujuCGsQvW/E21pj6+zzrfWm3Lbq0E1X8oAlmEScOTlDAJ4yIVJXHOg4DiKg+qjTmmanUYv+SZU1svm/zHZVt6qFdMWK4Zv2C1u/e095PG7pw5t5S27ttzrI3M6WDfZzB77BQpltXG2O+Wy/b2tt+13H/rrXN7t41V+gxld3PH5KlmtN2LntI0JzTA7mGt2Qdj9wSzt7FMwzAugUe2gn3WWrMKbE9k2AdM7zb27rGT3lTaOmATieUXlvKXxi9Y+80E42ZukoZzZiNDdJRdnzQ1Vj8BCROPsaAk9Qgz5UknhGX5NwTldBczPo+oaP71yS7rWBImWcml65d3bG9eOMWtetwNpmSxF42/eM3laYXJq2dkzawLlh7pVlKs4XQD7uE6vR+L3WCsWWYje1t58y733PW6k3u2jdltT9vdOg0TOcGx16Y3nnlN2/8s/ZYxTHOrHLH4aK1GzVrWY+wqg3kWa1dhzLOU7SpL+dnWKFo1Ktr2rLnw5Q07j+XK9rbO7VxHfE+Nu8dk1HP/+QMsU4HpBjMhbrvKwpfbunZcbzrWb6x2HGm/z9u/aQRg2mPP0r5o883CaCT3kTDx6L2iJYvm6zF4JMR2ElBchYmrRrAiYRLGCyFyqnNh+5kWbgZjjLXXWnhh0yF/sXDC49efOm4095vPrem9xyTNx17D2O3bJu1929ZZx/6yZ/9Zq+weR26y490Wxd7PKLvDHtbyJG9rfdwc1voEY9heYXjbg2U18Cymd6Wjd8WjRPlZomiVMT3Pjm574Q/mnIoDVDxs68TJDty7W/bbcuCHLpzwxE1njR3N99jSVdra2vIxS3QOuFWV3iWlzdZwY8lGXxq74PmVaVgM1zaEf4eyL7u+PKhxBhKQMPEYD0pSjzB1oa4L9TDhVFjOgfEOaU7CJIwXQp2btlw25S+w0T/2burC/Gz7Xu84wj2lymKfHn/xmvMGztZ2TNx9RzR2r+5SeRrlaBqR2Qtrp4HZyxo7Dcs0Y8yugwmtKk/lv7oP4ZGuQ1lZ3mvn1yW6y6+PVj1/YOl/lx9/xJtO3GPFVWfgVj7K3avGvvHF1f1PDcuT+FCc7S2Utv6mfa7FfBrDH/XZd0LJ3A7dV7QtWPezWo8plH8HH6fs1uo59R+KgISJx7hQknqEKWFS2Atm5VGYPGoEKxImYbwQKqc2L2w/LsL8nYUD3Mx6dnk9pZd/v8JgR5eteRoYbbB7O/GBwT3JatiPxb6MZZWJVzlw93UY+yyU/2B7zHN/v+2s0g973vau3i1f8DZj6L2miYyhbMv3Y7m9XC7ddteVc91qSe6fapy3fnHyn5RN9Ndg3rvzYKx92GCvGbdg7W1ZD7Ca3azjVusnu9UI6fssBCRMslCr0EdJ6hGmhImESZhwKiznwHiHNCdhEsYLoc5NnV9sn2eNmQf2oPgFjJUnaK0TC+7lis+7lQ1rbO8WqxLRH0y5+7nRbeue6X9xYxJKJ3/ulqlRqfx+azm9FJl3lt2iTd8N+dbAo+4xxN3YW/9j0bzfJxkvS5uknLd9YfIbekzJvWfoI68INPu0seZLY7ujr5mO5zrT2E9qN82YSdrKbhJKapOWgIRJWmK6YC7shZyKsMdkUR7VPY/CeHN4KxImYbwQqna9Ikx653Vgz5hJM1q2vfBbY9huLT/A2G+ZqOvZsfM3eH/b+0CSjz/xP/bCb/z8o8a4xxBznLsd5ZXv7ePWmtsocfudl525wqcH0nK2l014XWd57CetMX9lMO19QspujLD/aE352raL1z2f5PjS2k0yZpI2spuEktqkJSBhkpaYLqjqfkGlYugxaBXPhY3nMFEkYTKQQLPXrpc62vdrbTXX9s954JvQu7rsebt2rHHbuXL/DOR8Use/Tixta5ltsWcYzHvcS9l3HoC1/2sNt5ty6fY7rpj7X7UeWFb/2g5Gdba2f9BaPm2MOajvOGwXmCUtPfaq0ZesWT7csWW1W6/5ym6tBJq7v4SJR/+qOHiEqQvmwl4wK4/C5FEjWNGKSRgvhMypLZe1n4o1vS9a3ClMAr4F3dmtNN/+t85Htny6NZzy6rfOu61k5jtRFN22bPST/0lHR++7SdJ8fHDevHDKe8B8xmDe3W/bYu8zlK9uu/iFu4c6Hh9208yzv63sZqGmPtUISJhUI5TieyVpClg1NBXnGuCl6CrOKWDV0LRenGs4ZG9dJUy8oRx2oNAxZi9tn7KlRPuLs775+D7f+9N28/k1a8PMtM9Kkvm6t85v3W6Pt7Z8hoHTBr11/gUD/95juX3thpd/8Ng//UVXkuNPYjfJOL2i7rKpB3aXOR+Yh+nbimbhSYP90riuNTeZDnZIICSl6aedT//6OaLmHEXCxKNf6xW0suvRicMMJc7inAeBesVVHnNJO6aESVpi2drXK8ZGil331vnNM7qPiYjOMNa+F2P27CdtLS8b7J1lE91eGjPu7mUdsyvemJ7HfHvfWN8S/T+L+Utj2L1XoFi71mC/Oi7a+lVz0aYX87CbJNJkNwkltUlLQMIkLTFduO4koKLkMXgUV4qrMOHUUFYkTMK4Q7U6Dee+t85jzOnGcjqGfXf2tmy1xv4HNrp9O2bZDxbPffmk85e+vRRxNtA+c/qus1esfOmGnjI33n3FmY+ksVqtrXvZZOfWqX8O9jww+/e2t2zD2JvWnXLLx6cfekzw6znFVTWv6fssBIIHcpaDTNtHJ7u0xLK1V1HKxi1tL3FOSyxb+6JxzkbJby/Var88K41WtNj2Od+T5i95a4Q5A8vpxvDmfsbW9t6c/iNrGB9Z1mPYEQsT90LJzV3do8+796rTvW9hsxbTefmU0yhHn8HwjldEk70jsubqsX+z+sEwUZVsy1wex+LTv2mOr1520xxjM7SVMPHoxXoFrex6dOIwQ4mzOOdBoF5xlcdc0o4pYZKWWLb29YqxZrN70oW3HBjZ8vsM1r1V/q0DvWGtXT998vjdV67b/LDBbLbGfvXOy+d9L5vHkvXa8sVJf2RN6XPGlOZid96r//PIlq8Z0732W6aD7mQjZWvVbP6tRqFe8612XM32vYSJR4/WK2hl16MTJUx2ElBcNXdchZnd8FYkTMJ4Qbnsn/OJ85e8vsWwkLI5CcNugy1Y7BascY8g/pXBPtljzfKWcW2PD3ePStaj/P2Kn9hJ//7eLwEfNZgJ8TirLHy5rWvH9aZj/casYw/XT3GVB1WNKWHiMQaUpB5hSiBIIIQJp8JyDox3SHMSJmG8oHNTPpxPOX/JcUTmXGA0hj13Hdc6c8OWHS8bzC6VLFr4vbEst4bHsayIKC9ftvisJ2o5wn7/2o7dJ25pafm4IToHw969Y1q72RpuLNnoS2MXPL+yFjuD+yqufNLUWP0EJEw8xoKS1CNMCZPCXjArj8LkUSNYkTAJ4wXlVD6cT/jsTW2traNuMJY2Z6H/HpMytpOe0qLI9OxPxExrzcEGezCGGWCiIY5mB9j/tpjlYJcbEy03ZbNi2eK5zyQ58sH+tR20bG1pn2ON+QxweN8YtgdrbofuK9oWrPtZknGrtVFcVSOk77MQkDDJQq1CHyWpR5gSJhImYcKpsJwD4x3SnIRJGC/o3JQf51nz/20/MPMw0czD9tv9zJ8/tW4B2CV3LvrAa95yf2zH18e0bWs7yNJzcGTNwbZPrMw0mD2GOkL3qGIMKwx2uRMt1poVOzC/ck8DG9h+OP9uvWzqMeWydTfKnwKm75rP2odNZK8eu2Ptd00HqV8k2W9bcZVfXBV55EYSJpOBiYBbahzqhq2xwF7AJmDNcE7TyS5MSKsoiXMeBBRXeVD1Oma1Wu2MvRF6XwD3e9XqVwgotr3GYcXBRhrn9573nV27x2x7C5iZFvpWV6yZiaH/fpFXzdXCs70rK26FxbLic2cc9I1F3/zDmLu/cvL2SlC2XT55/56ekhMoHwbc9ZRTKE8buHZsV+lG0/FcxfezVBpzpHGuNfrqNd9aj3uk9W8UYXIy8HXgZuDUeOnxpQEw3XPE3TPBvwmcFP+5qBJsCZMwYVivJJVd+TcPAkWLq4wMq9Vqd075Rjz2eKAFeB/QM5Q91eqMXkjZrWix3SzzPfni26ZT7opXV8p9YgV7gDGm9TUhYOnB2P/duR0MVnRRWn7PojlPgbH97e3lu+zWWR79KYv5pMG098oT2BBZe325u3zd+I4XVicNr2bhXG2+L3W079fawkEb/uSK742///wZu3asec1qWLUx9H1yAo0gTMYA7savIwH3zO9zAJd0Vw+YxscBlyzu0Xvu6Rc/Ag7Rya6PUFGKQ388aL7JE7yWluJcC73kfevFOfkR7myZpFbPAQ4GLgHc+eU9wEPAkL/GSphk8EKGLvWKMdnN4KwqXY7teKClbceaA2zZHGxs2W0DO3j3iaNnv7hxuzWmN+cGf1zuPWF7t4O5bWHR8lLUs/zfR521obO1/YPW8mljzEF9nWyXtWZpa9leOfqSNcurHX0R/Nu5sP1ciznOsdi+1ztmj372R+49NfeNv3jNddX46PtsBBpBmEwD7hkgNI4GTgAuHTClUixC3MuEzgI2A+dXmrJOdtmCIW2vIhSlgUw037QRkq29OGfjFqBXklr9fuBrsHMLyruAB1Sr+wgotgNEaUE5f+Iffjy+FI06uGTswRbjfhyY2XfDvZk0FHULL7h7V4AVh7U8YU9sfeioA1p+f8To3h2YbhXF3mcoX9128Qt3D+7vnkZWjuyUj7xr/5u/fv9vj7t70Vn3h/Fs2DzqXSlpNdf2z61fmLh/d3XZ87Ryko/XG0GYuDepXg8cC703Ybl/fyJeORk8a7cl4HTAbe06Htg2FBYJk3yCZfCoOsmKcx4EFFd5UPUyZpJa7VZM3ONTTwOmAI+CexIRL6hWS5h4icIEg6iGvALp5M/dMhXTfTCRmdl/w70x5kBg3GCU1mJ3iTZt3j9a2Ta99Hw0vfQcU6MXnppmV1+xS3nVv5gOdsyaf/PFo033H7fSPXq/PXY59n+e33LP9nLrQ3cunndZAtd4aZLGv9Zi+PzuE7aOZWJph5nQU2qZWLZ2YmSZWLbRxCiyEy12oiGa4P60lonGMNFiJkbW7mNhssW0GGOj7Xv9cYtbMXGTMNYuGbdgzRIvE9IgryLQCMLEPWbP/aJ2VHzT+zxgz0FbuWYBvwCeA9yj9tz9JicC6xcvXnyptbZjsF/nzHHnR31EQAREoPEJzJgxoxFqcTVQSWr1RwB3b8nfxYO5E/c1wE9Vq6vh1fciEIaAtbBu03aeX9/Jc+u38tz6TlZv2MoLL2+n7L4c9Gmhhz1KLzBpwihe7BpDa3kbY6IuRpm+W8fKYyfzziMPYd92l/qePraH0o5NmO5OTNcWzI7NmO6tRO7v3VswXZ19/69rC1F/G/dn7//r3Nku6t7q6YBg+55HM/q5H0uYeCM69ECNcDJ0+5YfA86Of11z+/bcnuRvA4cCvwaujO9D+SfAPVbvl8ABwIahpqUVk5yjJh4+za8WPo9Idn3SrDyWODc35wyzS1KrjwH+HPjggKcsTlet7qOtnMoQdRm6iHMGaHGXWfNvOdzY8kFlOKiFnsNaTdcR2xkz5Asjx0bu9SvlHcZa22Ojx7Yyetlupc1r92hZ/eLbohUvz2p5sDOydmK5ZPtWJ6ydaA0TjPuTaKI1dqKxfasTxlj3VFb39wmm7wmto7PPYmBP66TWZmPZaDEbjbEbrWWTMWYjsNFau9G4PzGbDOWN7v9bw8aoXN5I2bzeRtGHjTFdZWz39r3eMWvAisll4xascT+S6+OZQCMIEzelw2Jx4v5+I+Budh8VPzrYbR/YHfhxvDXAtflo3G5IHBImnqOkwnAq/uKcBwHFVR5UvY1ZrVa7H4v+HvjL2OKH4qcoqlZLmHgLwmoDqYZUI5Tu++MvuGWXD45e9qkIc/4Gu8vEZ3r24A/lPejsf+pwheFKdPO66CUmmQ29/012/5XW9/45KXL/bz0t8arLUENYyzqDda+I2GiNeclgN1vLxq37nzZv7G+/d5Ux5Y0Gs7FcNhujUs8mY6MN1rKlFJmNPaUdG8duZaPpWO8ESObP5oXtlxnc09BedfP7ivEXr7ko86DqOCyBRhEm7iDdfke3DuiezDXUx90A71S7e073luFmJWESJupV/MU5DwKKqzyoeh2zWq12xtwvnm6fh2r1APSKba9xWHEwcc6H8w8vOfbcX3DIJUe3/GzcgaWnx2yybbxgd2ddeVfWlV/H2vJurLG7d6/reZ1dZ3eNOhnnrtsqftw9LaOi7g2tdsfqVtP9h8iWV2LMU+vLE5+gZFZGo9qeXtYx+zVP9Avt380L24+LLFNeOurzN0/8yaXHj794zX35ENaojkAjCRNvHpEw8YZy2IFCF4f+g5Fd+TcPAkWLqzwYph1TtTotsWztixbbmm+2OKnWq/OL7W+/eccp//zrnhmTdzMvl143tmfSpq09L06LVq9739j7/2rshWte9XQu96b7sdvb9qNcnl4ydjrWTLe9f+K2d+5jjLuf2Lj7hiuLF+wGrFmJsc/0/WlW/vlx+131L/c99X+2d49aee9Vp1f6MbvadFJ/X6+4Sn2gI7yDhIlHB9YraGXXoxOHGUqcxTkPAvWKqzzmknZMCZO0xLK1r1eMyW42f6XtFZLz5i+2X/dMec+DnirvM2HspH3+z57rf3TfPtFzT4xfsMY9jS/VZ86cW0rbprOPbemZbk00nbKdjrHTjTXTMUy3lr2NqXKvicXd3f4MhpVYu9Ias7JXwETllVG5tHLZ2CdX0dHhnvha0+eEz94+5YqPHLTmLTPf1JTXzTXB8dy5KQHrZOc5SioMF7IYDjwE2ZV/8yBQtLjKg2HaMVWr0xLL1r5osa35ZouTpL22XNZ+qrHsu/Gw866d8ItrT2u7aI17+XUOH2vec9HtU6PurumlyK20RNOhvM9B03f75IqVLy3vFTEYt2208sf2bil91hq7EkvviouxtvfP7nK0csI4fndrx9y+l7cM8XHvbLERZxvM+JnTd529fOWGJabMjXdcMU/buXLwuBtSwsQjWBVDjzCHGUqcxTkPAkWLqzwYph1TwiQtsWztixbbmm+2OEnbqxE4v/e87+zaPXr7dOuES7lvpcVYt/JiptP3p3uf0vAfa9c6ofLKigsriexKeqJNROXzDabLDeCEyYqVL/U+LnhH96iPhtxGVm0KzfS9hIlHbzZCknqcTtWhNN+qiLw0EGcvGKsOUjTOVYEEaCBhEgCyngYWBrI4NyznE8+/5U2lyO5tbHlfa9gby759AqZXyOxT7cAtdBvL1gP32XXCk3/oEybW2BvuvHxeTitF1Y6oub+XMPHo36Jd2Gi+HoNnmKHEWZzDEAhvRcIkDHPVEHHOg0BTxFVHRzR764HTylHPdMpRfI9LvOKCnYk17RhKWHpmvn7XUv+KSZnykrsWnaU3v+cQWBImHqE2RZKm4KH5poBVQ1NxrgFeiq5F45wCTW5NJUxyQ/uqgYsW25qv4soHAXd/CZE5t+xe9mjs6EP22e2YfmFC2V6n+0x8UH7tGBImHrmqGHqEqRWEnQQUV4qrMATCW5EwCcNcNUSc8yDQ7HF1wmdvamttHXWDsbQ5fv33mFhr13b1dJ1771UfGvY9TXkwL8KYEiYevdzsSToYlebrMXgkxCTEwoRTQ1mRMAnjDtVqcc6DQBHiyj0meFRpx7yyse0nvGXPC3/wi+fP29EzaolufM8jovrGlDDxyLYISToQl+brMXgkTCRMwoRTQ1mRMAnjDtVqcc6DgOIqD6oaU8LEYwwoST3C1IW6LtTDhFNhOQfGO6Q5CZMwXtC5SZzzIKC4yoOqxpQw8RgDSlKPMCVMCnvBrDwKk0eNYEXCJIwXlFPinAcBxVUeVDWmhInHGFCSeoQpYSJhEiacCss5MF6tmOg9F8FCTufiMKjFOQznolmRMPHocSWpR5gSJoW9YFYehcmjRrCiFZMwXlBOiXMeBBRXeVDVmBImHmNASeoRpoSJhEmYcCos58B4tWKiFZNgIadzcRjU4hyGc9GsSJh49LiS1CNMCZPCXjArj8LkUSNY0YpJGC8op8Q5DwKKqzyoakwJE48xoCT1CFPCRMIkTDgVlnNgvFox0YpJsJDTuTgManEOw7loViRMPHpcSeoRpoRJYS+YlUdh8qgRrGjFJIwXlFPinAcBxVUeVDWmhInHGFCSeoQpYSJhEiacCss5MF6tmGjFJFjI6VwcBrU4h+FcNCsSJh49riT1CFPCpLAXzMqjMHnUCFa0YhLGC8opcc6DgOIqD6oaU8LEYwwoST3ClDCRMAkTToXlHBivVky0YhIs5HQuDoNanMNwLpoVCROPHleSeoQpYVLYC2blUZg8agQrWjEJ4wXllDjnQUBxlQdVjSlh4jEGlKQeYUqYSJiECafCcg6MVysmWjEJFnI6F4dBLc5hOBfNioSJR48rST3ClDAp7AWz8ihMHjWCFa2YhPGCckqc8yCguMqDqsaUMPEYA0pSjzAlTCRMwoRTYTkHxqsVE62YBAs5nYvDoBbnMJyLZkXCxKPHlaQeYUqYFPaCWXkUJo8awYpWTMJ4QTklznkQUFzlQVVjSph4jAElqUeYEiYSJmHCqbCcA+PViolWTIKFnM7FYVCLcxjORbMiYeLR40pSjzAlTAp7waw8CpNHjWBFKyZhvKCcEuc8CCiu8qCqMSVMPMaAktQjTAkTCZMw4VRYzoHxasVEKybBQk7n4jCoxTkM56JZaSRhMhmYCKwEuodwRAswA9gWt6noK/0KFyaMVZTEOQ8Ciqs8qHods1qt7je2B/ASsLWSddVqr36pOJhySpzzIKC4yoOqxmwUYXIy8HXgZuBU4PD4hNbvod2AB4EfAXsBLwIfB3qGcqFOdmECW0VJnPMgoLjKg6q3MavV6n5DU4HngSOBRyRM+jTjvjwAAB4HSURBVAgotr3F4bADibM450GgXnGVx1waecxGECZjgCfiE9ha4BygFbh6ALiPAXsDlwAR8AtgDvAbCROd7EIlWL2KkuyG8XDROGegmqRWu2Fdjb4POBY4AviZhImESYZ4y9ylaLms+WYOlVQd68U51UE2QeNGECbTgHuAQ+IVkKOBE4BLB/B1W7zcyc5tC3gD8BgwHdggYSJhEioP61WUZDeMh4vGOQPVJLXaDet+QPopcBLwzfjvQ5rT6nYGL2ToUrTY1nwzBEmGLuKcAZq6VCXQCMLkzcD18a9rZcD9+xPxysnACbhjPRv4Z+DEWMywePHiS621HYNnOmeOW1DRRwREQAQan8CMGTMaoRZXA5WkVh8DfCDearsEuKZfmKhWV8Or70VABBqdwAip1Y2Ocdjja4STYRvwAHBUfNP7PGDPQVu5RsdbA56LRYu7x6TiR7/ChYlJ/VoiznkQUFzlQdXLmNVqtTufLANmAW5b7pTYqrtn8OdDHYFqtRe/VB1EOVUVkZcG4uwFY9VBisa5KpAma9AIwsTtW3Zbs9xqyKPAdcBDwLeBQ4FfA7OBDwNnxvzdPSgvV/KFTnZhorRoxUHzVVzlQaBecZVhLklqtRMvrj67B5PcAlwL3AFYCRNtu80Qc5m61CunZDeTu1J3Khrn1IBGeIdGECYO4WGxOHF/vzHeBjAqfiyw2z7gnsB1+QDW7te4A3SPSR+RoiWp5hum6ohzc3POOLtqtXr9gHHdFt1/AH6pH5FUqzPGW6Zuql2ZsKXuJM6pkalDAgKNIkzcoY4DxsdbABIceuUmWjGpCV/izipKiVHV1FCca8KXuHPROCcG89qGqtUZ4RUtxjTfjIGSsps4pwSWsXm9OGc83BHbrZGEiTeIEibeUA47UL2SVHbl3zwIFC2u8mCYdkzV6rTEsrUvWmxrvtniJG0vcU5LTO2TEJAwSUIpYRslaUJQNTYT5xoBJuwuzglB1disXpxrPGwv3SVMvGCsOki9Ykx2q7rGSwNx9oKx6iD14lz1wJqsgYSJR4fWK2hl16MThxlKnMU5DwL1iqs85pJ2TAmTtMSyta9XjMluNn+l7SXOaYlla18vztmOduT2kjDx6Lt6Ba3senSihMlOAoqr5o6rMLMb3oqESRgvKJfFOQ8Ciqs8qGpMCROPMaAk9QhTAkECIUw4FZZzYLxDmpMwCeMFnZvEOQ8Ciqs8qGpMCROPMaAk9QhTwqSwF8zKozB51AhWJEzCeEE5Jc55EFBc5UFVY0qYeIwBJalHmBImEiZhwqmwnAPj1YqJ3jkVLOR0Lg6DWpzDcC6aFQkTjx5XknqEKWFS2Atm5VGYPGoEK1oxCeMF5ZQ450FAcZUHVY0pYeIxBpSkHmFKmEiYhAmnwnIOjFcrJloxCRZyOheHQS3OYTgXzYqEiUePK0k9wpQwKewFs/IoTB41ghWtmITxgnJKnPMgoLjKg6rGlDDxGANKUo8wJUwkTMKEU2E5B8arFROtmAQLOZ2Lw6AW5zCci2ZFwsSjx5WkHmFKmBT2gll5FCaPGsGKVkzCeEE5Jc55EFBc5UFVY0qYeIwBJalHmBImEiZhwqmwnAPj1YqJVkyChZzOxWFQi3MYzkWzImHi0eNKUo8wJUwKe8GsPAqTR41gRSsmYbygnBLnPAgorvKgqjElTDzGgJLUI0wJEwmTMOFUWM6B8WrFRCsmwUJO5+IwqMU5DOeiWZEw8ehxJalHmBImhb1gVh6FyaNGsKIVkzBeUE6Jcx4EFFd5UNWYEiYeY0BJ6hGmhImESZhwKiznwHi1YqIVk2Ahp3NxGNTiHIZz0axImHj0uJLUI0wJk8JeMCuPwuRRI1jRikkYLyinxDkPAoqrPKhqTAkTjzGgJPUIU8JEwiRMOBWWc2C8WjHRikmwkNO5OAxqcQ7DuWhWJEw8elxJ6hGmhElhL5iVR2HyqBGsaMUkjBeUU+KcBwHFVR5UNaaEiccYUJJ6hClhImESJpwKyzkwXq2YaMUkWMjpXBwGtTiH4Vw0KxImHj2uJPUIU8KksBfMyqMwedQIVrRiEsYLyilxzoOA4ioPqhpTwsRjDChJPcKUMJEwCRNOheUcGK9WTLRiEizkdC4Og1qcw3AumhUJE48eV5J6hClhUtgLZuVRmDxqBCtaMQnjBeWUOOdBQHGVB1WNKWHiMQaUpB5hSphImIQJp8JyDoxXKyZaMQkWcjoXh0EtzmE4F82KhIlHjytJPcKUMCnsBbPyKEweNYIVrZiE8YJySpzzIKC4yoOqxpQw8RgDSlKPMCVMJEzChFNhOQfGqxUTrZgECzmdi8OgFucwnItmRcLEo8eVpB5hSpgU9oJZeRQmjxrBilZMwnhBOSXOeRBQXOVBVWM2kjCZDEwEVgLdFVzTErdZP5zrdLILE9gqSuKcBwHFVR5UvY5ZrVa7Oj0D2BbX84rGVau9+qXiYMopcc6DgOIqD6oas1GEycnA14GbgVOBw4GXBrlnF2AO0A4slDB5hYCKQ5hEFmdxzoNAveIq41yq1erdgAeBHwF7AS8CHwd6hrInYZLRCym71SvGZDelozI2F+eM4FJ2qxfnlIc54ps3gjAZAzwBHAmsBc4BWoGrB9AdDXwF+BjwKeCrEiYSJqGzr15FSXbDeLponDNQTVKrXY3eG7gEiIBfxD8o/UbCBIoWY5pvhizL0EWcM0DL0KVenDMc6oju0gjCZBpwD3BI/Kva0cAJwKVDkD0DeP0g0fKaZvoVLkxM1itJZVf+zYNA0eIqA8Mktdptx3WCxK14vwF4DJgObJAwkTDJEHOZuhQtlzXfTGGSulO9OKc+0BHeoRGEyZuB64FjgTLg/v2JeOVkMN4PAFMlTF6NpV7JIrthsl+cxTkMgapWktZqd145G/hn4MT4h6chB9ePSFWZe2mgGuIFY9VBxLkqIi8NisbZC7QRNEgjCJM24AHgqPim93nAnhXEx2uEyeLFiy+11nYMZj5njrsdRR8REAERaHwCM2bMaIRaXA1Uklrttt3eBzwX/8Dk7jHp/ahWV8Or70VABBqdwAip1Y2Ocdjja4STodu37Jb73S9sjwLXAQ8B3wYOBX4N7IhnoRWTIdxZtF8PNN8wNUecm5tzhtklqdWzgQ8DZ8bju/sFX65kSysmGbyQoYtyOQO0DF3EOQO0DF2KxjkDohHdpRGEiQN4WCxO3N9vjJ/iMip+1KTbPtD/eOD3x/uVB94Y/xoH6GQXJiaLVhw0X8VVHgTqFVcZ51KtVrsncF0+YGz3QJMDdI9JH5F6+Vp2M0Z7ym7inBJYxuZF45wR04jt1ijCxAEcB4yPn8xVE1AJk5rwJe5ctOKg+SYOjZoainNN+EJ0Vq3OSFmxnRFcym7inBJYxubinBGcug1LoJGEiTdXSZh4QznsQCpK4pwHAcVVHlQbc0zV6jB+UU6Jcx4EFFd5UNWYEiYeY0BJ6hHmMEOJszjnQaBocZUHw7RjSpikJZatfdFiW/PNFidpe4lzWmJqn4SAhEkSSgnbKEkTgqqxmTjXCDBhd3FOCKrGZvXiXONhe+kuYeIFY9VB6hVjslvVNV4aiLMXjFUHqRfnqgfWZA0kTDw6tF5BK7senaiVmp0EFFfNHVdhZje8FQmTMF5QLotzHgQUV3lQ1ZgSJh5jQEnqEaYEggRCmHAqLOfAeIc0J2ESxgs6N4lzHgQUV3lQ1ZgSJh5jQEnqEaaESWEvmJVHYfKoEaxImITxgnJKnPMgoLjKg6rGlDDxGANKUo8wJUwkTMKEU2E5B8arFRO9xyRYyOlcHAa1OIfhXDQrEiYePa4k9QhTwqSwF8zKozB51AhWtGISxgvKKXHOg4DiKg+qGlPCxGMMKEk9wpQwkTAJE06F5RwYr1ZMtGISLOR0Lg6DWpzDcC6aFQkTjx5XknqEKWFS2Atm5VGYPGoEK1oxCeMF5ZQ450FAcZUHVY0pYeIxBpSkHmFKmEiYhAmnwnIOjFcrJloxCRZyOheHQS3OYTgXzYqEiUePK0k9wpQwKewFs/IoTB41ghWtmITxgnJKnPMgoLjKg6rGlDDxGANKUo8wJUwkTMKEU2E5B8arFROtmAQLOZ2Lw6AW5zCci2ZFwsSjx5WkHmFKmBT2gll5FCaPGsGKVkzCeEE5Jc55EFBc5UFVY0qYeIwBJalHmBImEiZhwqmwnAPj1YqJVkyChZzOxWFQi3MYzkWzImHi0eNKUo8wJUwKe8GsPAqTR41gRSsmYbygnBLnPAgorvKgqjElTDzGgJLUI0wJEwmTMOFUWM6B8WrFRCsmwUJO5+IwqMU5DOeiWZEw8ehxJalHmBImhb1gVh6FyaNGsKIVkzBeUE6Jcx4EFFd5UNWYEiYeY0BJ6hGmhImESZhwKiznwHi1YqIVk2Ahp3NxGNTiHIZz0axImHj0uJLUI0wJk8JeMCuPwuRRI1jRikkYLyinxDkPAoqrPKhqTAkTjzGgJPUIU8JEwiRMOBWWc2C8WjHRikmwkNO5OAxqcQ7DuWhWJEw8elxJ6hGmhElhL5iVR2HyqBGsaMUkjBeUU+KcBwHFVR5UNaaEiccYUJJ6hClhImESJpwKyzkwXq2YaMUkWMjpXBwGtTiH4Vw0KxImHj2uJPUIU8KksBfMyqMwedQIVrRiEsYLyilxzoOA4ioPqhpTwsRjDChJPcKUMJEwCRNOheUcGK9WTLRiEizkdC4Og1qcw3AumhUJE48eV5J6hClhUtgLZuVRmDxqBCtaMQnjBeWUOOdBQHGVB1WNKWHiMQaUpB5hSphImIQJp8JyDoxXKyZaMQkWcjoXh0EtzmE4F82KhIlHjytJPcKUMCnsBbPyKEweNYIVrZiE8YJySpzzIKC4yoOqxpQw8RgDSlKPMCVMJEzChFNhOQfGqxUTrZgECzmdi8OgFucwnItmpZGEyWRgIrAS6B7CES3AdGAzsGY4R+lXuDBhrKIkznkQUFzlQdXrmKrVGXEqtjOCS9lNnFMCy9hcnDOCU7dhCTSKMDkZ+DpwM3AqcDjw0oAjHwfcC/wKOBb4ArC00swkTMJEvYqSOOdBQHGVB1VvY6pW14BSsV0DvBRdxTkFrBqainMN8NS1IoFGECZjgCeAI4G1wDlAK3D1gKM+CzgAuARoA54G3gBsHGpmEiZhIl5FSZzzIKC4yoOqlzFVq2vEqNiuEWDC7uKcEFSNzcS5RoDqPiSBRhAm04B7gEOAHuBo4ATg0gFHfDmwDHgYcMf8A+CDwPMSJqDiECa7xVmc8yBQr7jKMBfV6gzQBnapl69lt0bHJewuzglB1disaJxrxDXiujeCMHkzcH28RasMuH9/Il456Qd6B/B54KexMHHbuC50KyeLFy++1FrbMZD8pEmTut/5zne6e1L0EQEREIFGJ/DSjBkzdmv0g4xrs2r1CHCUDlEERCAXAiOlVucy+VCDNoIwcVuzHgCOim96nwfsOWgr18eA38UrJW47gbvX5O3AhlCgZEcEREAECk5AtbrgAaDpi4AIiEDeBBpBmDih8RhwNvAocB3wEPBt4FDg18Bs4FzgncB+wH8AM4GteQPS+CIgAiIgAr0EVKsVCCIgAiIgArkSaARh4iZ4WCxO3N9vBD4OjIofHey2drmVka8BH4lpOMHyeK5kNLgIiIAIiMBgAqrVigkREAEREIHcCDSKMHETdI8EHh8/mavShKcAnfG7THKDooFFQAREQAQqElCtVnCIgAiIgAjkQqCRhEkuE9SgIiACIiACIiACIiACIiACjU9AwqTxfaQjFAEREAEREAEREAEREIGmJyBh0vQu1gRFQAREQAREQAREQAREoPEJSJg0vo90hCIgAiIgAiIgAiIgAiLQ9AQkTJrexZqgCIiACIiACIiACIiACDQ+AQmTxveRjlAEREAEREAEREAEREAEmp6AhEnTu1gTFAEREAEREAEREAEREIHGJyBh0vg+0hGKgAiIgAiIgAiIgAiIQNMTkDBpehdrgiIgAiIgAiIgAiIgAiLQ+AQkTBrfRzpCERABERABERABERABEWh6AhImTe9iTVAEREAEREAEREAEREAEGp+AhEnj+0hHKAIiIAIiIAIiIAIiIAJNT6AphcmiRYseAI5teu9pgiIgAs1A4MH58+e/sxkmknYOqtVpiam9CIhAHQkUtlaHZN6swsTOnz8/+NwWLVokuwGiV5wDQAbEubk5h5nd8FYUY2G8IM7inAcBxVUeVDVm8Iv3EMiVLCEo68I1DGVxFudQBMLbUa0Ow1ycxTkPAoqrPKhqTAkTjzGgJPUIc5ihxFmc8yBQtLjKg2HaMYvGXPNNGyHZ2otzNm5pe4lzWmJqn4SAhEkSSgnbKEkTgqqxmTjXCDBhd3FOCKrGZvXiXONhe+ler7nLrhf3VR1EnKsi8tJAnL1grDpIvThXPbAma9CUwmTx4sWXXnDBBZ8P7SvZDUNcnMU5DwJFi6s8GKYds2jMNd+0EZKtvThn45a2lzinJab2SQg0pTBJMnG1EQEREAEREAEREAEREAERaBwCEiaN4wsdiQiIgAiIgAiIgAiIgAgUlkCzC5PJwERgJdA9hJcnAHsCzwGbUkZBtbGd3X6+FtiYcvzhmjvbLwLlIRq1ANOBzcAajzbdUMPZzWu+ewC7AM9W8FFe861mN6/5TgNGDxOzec23mt285uviys2pPfbx4JDNa77V7Gad7yhg7IBJbBmi9uQ5J88pH2y4avVUtTqdK1SrX+GVNZerEa9WM/PK82p285pvtZqZ13yr2c06X9XqahFep++bWZicDHwduBk4FTgceGkA532Bp4ErgPOBQ4HHE/qh2thjgCeAxwAnSn4HXFRBSCQ02dvMJdLewF3AkcD6QZ3HAfcCv4pfMPkFYGkaAxXaVrOb13znAl8BvgbMB/4oZtp/mHnNt5rdPObrcvFC4L3Aj4Gz4phdNcAnecw3id085jsw1Ny8PwgcAvTkPN8kdmuZ7zzgWuAH8Q8EnwCWB5yTh3QPPkS1eqpandwlqtV9593+Ty25XIl6kpqpWu3n2qPfB5XOEbX4V7U6eV0J2rJZhUl/sLqL97XAOUArcPUAukuAfwR+CLwV+Jf4z6FWIQY6JcnY7hcNJ0Q+GQsTX049HnBiYz/gAGDDoIHdxaz7/5cAbbHweoOH1ZpqdvOYrzt+t+rzuliAuWNwguHjA+acx3yT2M1rvv8VC2S3urcwFpkuPvs/ec23mt085ts/p2OAB4FlsSgbmH95zDeJ3VrmeylwK/BkhaTPc06+6kzIcZLUU9Xq5B5RrX71OaKWXK5E3Z0jqtXMPPI8id085pukZuYx3yR2a5mvanXyuhK0ZbMKExes9wz4BfZo4ATABaL7OJHifpWeBbwA7BZfHLlVlaG2fA10SrWxXVsnBn4zoNMpwJ2ePOuOfQXw9iGEyeXxBd7D8TYy96ut+yX6eQ+2h7Ob13ynxMLSxel18famgeIyr/lWs5vXfJ2bXCy+D7gxFqButa3/k9d8q9nNa75uu5wTJXOAvwPeOWjFJK/5VrNby3y/FQtox9QJlI8BLwfyoYc0Dz5EtXqqWp3eJarVrzCrJZerkVetzv/cpFpdLQqb8PtmFSZvBq6PtzO5X2Ddv92WCrdy4j7ul4f/joWLW3Vw/34ovtjvquLnamO77k4IuW1H/xCvYHw/Fis+7jNxvzC6rVpDCZM7APeY5J/GwsRt43JLoG7LWq2f4ezmOV+3jeO7wB+ADwy6yMtzvsPZzXO+rhD/Vbwy9Kfxqkm/7/Kc73B285iv24/sRMmH4x8DvjfEimUe801iN+t83dh/G28f/W28QuvqjNuOGMKHteZ4PfpXq6eq1em9olr9CrOsuZyEump1vnVNtTpJFDZhm2YVJu5k9gBwVHzR4/YSupvc+39td/N2v2yeG68m7A/clnArV7WxXZi4fb474nhxtu4Hzg4gENyvs+4XdrdSMtzJKUsoDzdeXvN1v6A7dpVWnPKabzW7eczX3azqTqJOhLnPiXE8ulWD/k8e801iN4/5ugtSt93J3dfl7i1xn1sAl6v927nymG8Su7XMd2DfIwAnLvt/EHFzzGNOWfK5UfpUq6eq1ek9pVr9CrNacrkS+SQ1M488T2I3j/kmqZl5zDeJ3Vrmq1qdvrYE6dGswsQVZncDnBMDj8bbgNyKiLtp3C3tuhUHt6Litls5seJuOHY3YA68f6GSA5KM/TfANuBKwG1VcMfi414Pd0yDTzruJrv+OZ0Riy13Ye3uQ/kPYCaw1UM0DWc3j/n27z13e1cdPzdPJ/ainOebxG4e8+2Pk/57h/46nqtbdcvTv0ns5jFfd1JwWyHcCuVBwA3xAx1c3uQ53yR2s87Xzcc9AdDl3jrAiUr3NDm3Tc09XOPXwOwcc9RDmgcfIkk9Va1O5xbV6ldqSNZcHo54kpqZx7k4id085pukZuYx3yR2s85XtTpdTQnaulmFiYN42IAnOLn9+k50zIh/kXa/0O4ebyU5MF7JGPzUruEcUW1st8Trblp2FyjuczrwHU+edScdJ7aOje8xcRdx7ld2Nyf3S7N7gtVHYltpnjRW7fCGs5vHfPcCfg64+z36P5+KtzblOd8kdvOYb/+TXtxN7+7jtt+5FRT3mNQ855vEbh7zHRhvbsXyn4HjBuVonvHs7FeyW8t83QMvnBBxn0fi+9g6Y8HifgF0W0fzytFqOdyo31erp6rV6TynWv1KzawllytRT1Iz86hdSezmMV/V6j4CPq+n0mV0wVo3szBxrnS/so+Pb6AeyrWlWKC4i4VqN70P7l9tbNfeqXK3WuF+BQ75cRfz7mLIPdUq5EfzrZ22eya721s7+FHQA0fOw79J7Mq/yfzraoO7OAztw2RH15itqtVT1Wq/flMu184zSc1Ura6dc5IRssazanUSuoHbNLswCYxT5kRABERABERABERABERABLIQkDDJQk19REAEREAEREAEREAEREAEvBKQMPGKU4OJgAiIgAiIgAiIgAiIgAhkISBhkoWa+oiACIiACIiACIiACIiACHglIGHiFacGEwEREAEREAEREAEREAERyEJAwiQLNfURAREQAREQAREQAREQARHwSkDCxCtODSYCIiACIiACIiACIiACIpCFgIRJFmrqM9IJvB/4UvxSSvcOmyQf95z0Y4AfAy8M0+EKYDowL37hZZKx1UYEREAEROC1BFSrFRUiUDACEiYFc7im20vAney+Cuwbv4gyCZbXA78D3gr8cpgOblz3puqzAJtkYLURAREQAREYkoBqtQJDBApGQMKkYA7XdHcKk1uBxcCfAQ8C5wMrgTfE//99wN3x338D/CvwLuCnsehwAy0CzgCWAguBFcCXgf8L/Bvw18BXgAuBLWIvAiIgAiKQioATJqrVqZCpsQiMbAISJiPbfzr6bAT6T3b3AHfE4sGJj0OBR+NVFCcmnFg5HDgiFiOXA1cC/wS4vuOBzwBXA5uBmbGQ+X/AF+KtXJcCHwNuyHao6iUCIiAChSWgWl1Y12viRSUgYVJUzxd73v3bA5yQcPeLfALoAA4A3ggcBewAPh2LD/f/d4m3cr0F6AGWA38C/CewPzAF+BlwFeDavBNoBR4D/j7eOlZs6pq9CIiACKQjoFqdjpdai8CIJyBhMuJdqAlkIDB43/JH4pWOw4C7gKnAp4B3AGfGgmUS4FZVnOiIgJ8PuN/E3RjvhMsq4BrA3Y/yXmCUhEkG76iLCIiACPQRUK1WJIhAwQhImBTM4ZruzpOd27d8EfDIgG1WpwBPAu7JWm7L1k3AwcBBQHssTP4U+CHwTLz32d1b4tq5G97daou7b0XCRIEmAiIgArUT6N/KpVpdO0uNIAIjgoCEyYhwkw7SM4H+k13/sJuAY4HHga8BTny4z53ArFiYbIxXP9yWraOBvYBb4nZrgXfH/d3N707EuMcFuxUTd8+KuwFe95h4dqKGEwERaHoCqtVN72JNUAReTUDCRBFRdAIT45vduweAcFuznFgZ+P/6vx4NbI//MQEYC6yv0LbobDV/ERABEfBFQLXaF0mNIwINTEDCpIGdo0MTAREQAREQAREQAREQgaIQkDApiqc1TxEQAREQAREQAREQARFoYAISJg3sHB2aCIiACIiACIiACIiACBSFgIRJUTyteYqACIiACIiACIiACIhAAxOQMGlg5+jQREAEREAEREAEREAERKAoBCRMiuJpzVMEREAEREAEREAEREAEGpiAhEkDO0eHJgIiIAIiIAIiIAIiIAJFIfD/AV1cGp0PgPq5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.metrics(output_pooling_conv.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 4s 405us/step\n"
     ]
    }
   ],
   "source": [
    "score = model_pooling_conv.evaluate(x_test_conv, y_test_class, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5969944985389709\n",
      "Test accuracy: 0.7657\n"
     ]
    }
   ],
   "source": [
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5: Make & Visualise the Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_classes_pooling = model_pooling_conv.predict_classes(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>214</td>\n",
       "      <td>6</td>\n",
       "      <td>262</td>\n",
       "      <td>2</td>\n",
       "      <td>489</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>806</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>814</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>240</td>\n",
       "      <td>2</td>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>950</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    0    1    2    3    4    5    6    7    8    9\n",
       "row_0                                                  \n",
       "0      556    6    6   60   14    5  332    0   21    0\n",
       "1        0  954    0   25   12    1    6    0    2    0\n",
       "2        3    6  214    6  262    2  489    0   18    0\n",
       "3        5   17    2  806   66    0   97    0    7    0\n",
       "4        1    2    6   23  814    1  143    0   10    0\n",
       "5        0    1    0    0    0  923    0   45    6   25\n",
       "6       67    2   13   34  240    2  613    0   29    0\n",
       "7        0    0    0    0    0   45    0  876    1   78\n",
       "8        0    3    1    4    3    2   32    4  950    1\n",
       "9        0    1    0    0    1   12    0   34    1  951"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test, predict_classes_pooling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba_pooling = model_pooling_conv.predict_classes(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.imshow(x_test[i], labels[y_test[i]]) | vis.predict(proba_conv[i], y_test[i], labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3: Convulation + Max Pooling + Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2: Craft the feature transfomation and classifier model **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dropout_conv = Sequential()\n",
    "model_dropout_conv.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model_dropout_conv.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_dropout_conv.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model_dropout_conv.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_dropout_conv.add(Dropout(0.25))\n",
    "model_dropout_conv.add(Flatten())\n",
    "model_dropout_conv.add(Dense(128, activation='relu'))\n",
    "model_dropout_conv.add(Dropout(0.5))\n",
    "model_dropout_conv.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 128)               204928    \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 225,034\n",
      "Trainable params: 225,034\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_dropout_conv.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3: Compile and fit the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dropout_conv.compile(loss='categorical_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 112s 2ms/step - loss: 0.6454 - acc: 0.7582 - val_loss: 0.5631 - val_acc: 0.7825\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 137s 2ms/step - loss: 0.6352 - acc: 0.7639 - val_loss: 0.5547 - val_acc: 0.7872\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 137s 2ms/step - loss: 0.6313 - acc: 0.7649 - val_loss: 0.5493 - val_acc: 0.7889\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 84s 1ms/step - loss: 0.6239 - acc: 0.7688 - val_loss: 0.5470 - val_acc: 0.7933\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 97s 2ms/step - loss: 0.6194 - acc: 0.7717 - val_loss: 0.5416 - val_acc: 0.7945\n",
      "CPU times: user 10min 15s, sys: 1min 4s, total: 11min 19s\n",
      "Wall time: 9min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "output_dropout_conv = model_dropout_conv.fit(x_train_conv, y_train_class, batch_size=512, epochs=5, verbose=1,\n",
    "                    validation_data=(x_test_conv, y_test_class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Check the performance of the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "resolve": {
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            "field": "batch",
            "type": "quantitative"
           },
           "y": {
            "field": "value",
            "type": "quantitative"
           }
          },
          "height": 300,
          "mark": "line",
          "width": 300
         }
        ]
       }
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.metrics(output_dropout_conv.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 8s 837us/step\n"
     ]
    }
   ],
   "source": [
    "score = model_dropout_conv.evaluate(x_test_conv, y_test_class, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5416207976818085\n",
      "Test accuracy: 0.7945\n"
     ]
    }
   ],
   "source": [
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5: Make & Visualise the Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_classes_dropout = model_dropout_conv.predict_classes(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>944</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    0    1    2    3    4    5    6    7    8    9\n",
       "row_0                                                  \n",
       "0      790    3   45   81    9    4   49    0   19    0\n",
       "1        1  946    3   37    8    1    2    0    2    0\n",
       "2       15    2  686   13  190    1   80    0   13    0\n",
       "3       38    8   34  847   39    0   28    0    6    0\n",
       "4        0    1  147   43  718    1   80    0   10    0\n",
       "5        0    0    0    2    0  919    0   50    6   23\n",
       "6      227    1  255   53  188    2  250    0   24    0\n",
       "7        0    0    0    0    0   36    0  905    0   59\n",
       "8        2    2   17    7    6    8   10    3  944    1\n",
       "9        0    0    0    0    1    8    0   50    1  940"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test, predict_classes_dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba_dropout = model_dropout_conv.predict_proba(x_test_conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis.imshow(x_test[i], labels[y_test[i]]) | vis.predict(proba_dropout[i], y_test[i], labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}