activity_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_df =pd.read_csv('./label2id.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = label_df.sort_values('id').label.values\n",
    "labels = [x[:2] if x is not np.nan else 'nan'  for x in labels ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sleeping-', nan, 'Bed_to_Toilet-', 'Meal_Preparation-', 'Relax-',\n",
       "       'Housekeeping-', 'Eating-', 'Wash_Dishes-', 'Leave_Home-',\n",
       "       'Enter_Home-', 'Work-', 'Resperate-', 'Housekeeping-Leave_Home-',\n",
       "       'Enter_Home-Housekeeping-', 'Eating-Relax-',\n",
       "       'Eating-Meal_Preparation-', 'Meal_Preparation-Relax-',\n",
       "       'Leave_Home-Meal_Preparation-', 'Enter_Home-Meal_Preparation-',\n",
       "       'Eating-Wash_Dishes-'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df.sort_values('id').label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 今回認識対象とするラベルを指定．（簡単のため「Others」ラベルは除く）\n",
    "target_label_list = ['Sleeping-', 'Bed_to_Toilet-', 'Meal_Preparation-', 'Relax-',\n",
    "       'Housekeeping-', 'Eating-', 'Wash_Dishes-', 'Leave_Home-',\n",
    "       'Enter_Home-', 'Work-', 'Resperate-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268687, 84)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_fet_df = pd.read_csv('./train_fet1_df.csv', index_col=0)\n",
    "train_fet_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(268687, 84)\n",
      "(102079, 84)\n"
     ]
    }
   ],
   "source": [
    "target_labels = label_df[label_df.label.isin(target_label_list)].id.values\n",
    "\n",
    "print (train_fet_df.shape)\n",
    "train_fet_df = train_fet_df[train_fet_df.label.isin(target_labels)]\n",
    "print (train_fet_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]CPU times: user 4min 19s, sys: 1.96 s, total: 4min 21s\n",
      "Wall time: 4min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "n_train = -1\n",
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='linear', random_state=42, verbose=True)\n",
    "clf.fit(train_fet_df.iloc[:n_train,:-1].as_matrix(), train_fet_df.label.values[:n_train]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_fet_df = pd.read_csv('./test_fet1_df.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54446, 84)\n",
      "(23896, 84)\n"
     ]
    }
   ],
   "source": [
    "print (test_fet_df.shape)\n",
    "test_fet_df = test_fet_df[test_fet_df.label.isin(target_labels)]\n",
    "print (test_fet_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 予測ラベルの作成\n",
    "pred = clf.predict(test_fet_df.iloc[:,:-1].as_matrix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sleeping-</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bed_to_Toilet-</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Meal_Preparation-</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Relax-</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Housekeeping-</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eating-</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Wash_Dishes-</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Leave_Home-</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Enter_Home-</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Work-</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Resperate-</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                label  id\n",
       "17          Sleeping-   0\n",
       "1      Bed_to_Toilet-   2\n",
       "13  Meal_Preparation-   3\n",
       "15             Relax-   4\n",
       "9       Housekeeping-   5\n",
       "2             Eating-   6\n",
       "18       Wash_Dishes-   7\n",
       "11        Leave_Home-   8\n",
       "6         Enter_Home-   9\n",
       "19              Work-  10\n",
       "16         Resperate-  11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df[label_df.label.isin(target_label_list)].sort_values(\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n"
     ]
    }
   ],
   "source": [
    "used_label_ids = sorted(list(set(train_fet_df.label)))\n",
    "\n",
    "print (used_label_ids)\n",
    "\n",
    "classes = label_df[label_df.id.isin(used_label_ids)].sort_values('id').label.values\n",
    "classes =  [x.replace(\"-\", \"\") for x in classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAALLCAYAAACPcm2kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd4FNUax/HvBggElJIEAqgUKYcioPSmKGJBwC5iQRFQ\npEiRIiioKChFpDfpXUC6SLFgQy8iIKLAUUR6TUKkS0Jy/5hNSGICCyTZ7PL73IfHZObMzHlnN7l5\n9z3njCsuLg4RERERERGRSwnwdgdERERERETENyiBFBEREREREY8ogRQRERERERGPKIEUERERERER\njyiBFBEREREREY8ogRQRERERERGPKIEUERERERERjyiBFBEREREREY8ogRQRERERERGPZPV2B0Ti\nHf9rW5y3+5CecuQv4O0uiIiIiFyVwNwhLm/3ITUVi9bLsL8lf939Taa9D+lNFUgRERERERHxiCqQ\nIiIiIiLi81yua7YomKFUgRQRERERERGPqAIpIiIiIiI+z+VSbSwj6C6LiIiIiIiIR5RAioiIiIiI\niEeUQIqIiIiIiIhHlECKiIiIiIiIR7SIjoiIiIiI+LwA9BiPjKAKpIiIiIiIiHhEFUgREREREfF5\nLpcqkBlBFUgRERERERHxiCqQIiIiIiLi8wJcqo1lBN1lERERERER8YgqkCIiIiIi4vM0BzJjqAIp\nIiIiIiIiHlECKSIiIiIiIh5RApmJGWNijTEPZvA16xljzhtjcmfkdUVEREREJPPTHEgvMsaEAu8C\nDwBhwDFgM9DXWvujl7q1FihkrT2ekRc1xgS0bPYEK7/+lohjx8gfHEzjBvVp9VTTix43b9lnfPLp\nZxw4coRCBQrwQtPHeODuu9K1rxt+3cKwiVPYuXsvBfPn54Vmj9O4Qf2E/YtXfs5nX67hr917AChT\nsgTtWjxLlfwF0rwv8xYsYu6CRRw4cBCAEjcX5+XWLalbuyYAvfv2Y+nyFUmOqVOrJmOHD0nzvmSk\nOfMWMG3WbMIjIjClStGrWxduKV/O291KU/4eo+Lzff4eo+Lzff4eo7/HdyVcaA5kRlAF0rsWApWA\n5kApoAmwBgjxVoestTHW2iNeuHTPRStW81q7NnwyfjSvtHyeGZ8sYt6y5ake8MnyFYydPpM2zZ9m\n3riRvPh0MwaN+Yjvf/r5ijtx8PARqjd6JNX9Bw4fpsvb/alWqSKzRg/jyYca02/4aNZt/CWhzcYt\nv3HfnXcwbmA/Jn84kLD8obzS+22Ohodfcb9SUzCsAF06tGPezCnMnTGF6tWq0LHba+z8e1dCm7q1\na/L1qk9Zs3IZa1YuY1D/vmnej4y0cvUXfDB8JO1eas28mVMpXaokbTp24VhUlLe7lmb8PUbF5/v8\nPUbF5/v8PUZ/j08yN1UgvcQYkweoC9Sz1n7n3rwXSDX7McbcCAwB7gVige+ATtba3YnatAZeBYoD\nfwMjrbVj3fuKurc9BXQEKgM7gPbW2m/dberhJLF5rbXHjTHPA8OAJ93/vQn4HmhhrT3sPiYLMBQn\nEY4GJgA3AHmstalnY0nVuqNmdWpXrQxAwQL5WfX1t/xu/3TS6hSs+OobHm14H3fXrQ1A4bAwtv75\nJ9PnL6Ru9aoJ7Rav/JzZi5Zw4PARCocVoOmDjXi8UcNUO3KxFbwWLF/JDQXD6NiqBQDFbryBzVu3\nMXvxUmpUvhWAd7p3SXJM707t+Wrtj/zvp59p8sD9l7oPl+WOunWSfN+xbRvmLVjE5i2/cXPxYgAE\nZgskOF++NL2uN82YM5cnHnmIB92v4Zu9evDt2h9YtPRTWj73rJd7lzb8PUbF5/v8PUbF5/v8PUZ/\nj+9K6TmQGUN32XtOuv89bIwJvFRjY0xWYBXwD1AHqA2cAFa692GMeQZ4G+gFlAFeB94xxjRPdrpB\nwGDgVuBHYJkxJnGGEZesfU6gK/AMcDtQBPgg0f6eOEnp8zhJcT7g4RTOczE/rN/8K3v2HwDgj51/\ns3nrdupUq5LqAdHR0QQGJr112bMF8vsff3L+/HkAVqz5hgmzPqZ9i+bMHz+Kds8/y/gZc/jsyzWX\n0bULtmz/g+q3VkqyrWblW9my3aZ6zJmz/xJzPoY8edJ3WmlsbCwrVn/O2bNnubVihYTt6zdupN59\njWjyeDP6DRjMP/9k6OjkNBUdE8PWbdupUe3CBwQul4ua1aqxectvXuxZ2vH3GBWf7/P3GBWf7/P3\nGP09Pn9kjLndGLPUGLM/tTVOjDHvGGMOGGNOG2M+N8aUTLY/uzFmtDEm3BhzwhjziTGmQLI2+Ywx\ns4wx/xhjjhljJhpjciVrc5MxZrkx5pQx5pAxZpAx5rJyQlUgvcRae95d3ZsAtDXGbAS+AT621m5J\n4ZBmgMta+1L8BmNMK5x5k3cCX+Akj12ttUvcTXYbY8oDLwMzEp1rpLV2sfscbYH7gVYkTQoTywq0\nsdbuch8zCuiTaH8H4D1r7VL3/g448zovx4B7bq/73hNtOhAQEEBcbCxtn3+We+vdnuoBNavcxpJV\nn1OvZnXKlCzB1j92sGT1F8ScP0/U8ROE5MvLhFkf06n1C9SrVQOAQmEF2LlnLws+W5XiXMm4S+S8\nEceOEZwvb5JtwXnzcur0Gc5FRxOYLdt/jhk5ZRoFQkKoVb2aJ/fhsv254y+ebdWGc//+S85cORk2\naADFixUFoG7tWtxT/y5uKFyIvfv2M3z0ONp17srMyR/55LOSoqKiOB8bS0hIcJLtISH52LVndypH\n+RZ/j1Hx+T5/j1Hx+T5/j9Hf4/NTuYBfgEk4U9iSMMa8hvP39HPALqAfsMoYU9Zae87dbBjQEHgM\nOA6MBhbgFHfizcZZV+VuIBCYCowHnnVfJwD4DDgA1AQK4+QI54DengajBNKLrLWLjDHLcV74mjhv\nih7GmFbW2unJmlcEShljTiTbnh0oYYz5ASgBTDLGTEy0PwuQfED8/xL14bwx5meg7EW6ejo+eXQ7\nCBQAcK/WGgasT3TOWGPMBrismcxPrvr6W/q/1pXiRW7ij51/M2T8RPIH50t1UZxWTzUl8lgULV99\njdi4OELy5aVxg/rMWLCIgAAXZ8/+y76Dh+g3fBT9R4xKOO58bCzX57rwYcyTbTty6Igz7TPOnT/W\ne6xZwv7bbinPsL6J82XPTZ23gC++W8v4gf3JlkJymRaKFyvKJ7OmcfLUST7/cg1vvP0OUz4aw83F\ninH/PXcntCtZ4mZKlSzBA488wfoNG6leNfXqroiIiIivyawfjltrVwIrAYwxKXWyE/CutfZTd5vn\ngMM4I/rmuf/ebgk0s9Z+427zArDNGFPdWvuTMaYscB9QxVq7yd3mFWC5MaabtfaQe38Z4C5rbTiw\nxRjTBxhgjHnbWhvjSTxKIL3M/anCl+5//Y0xE4C+QPIE8jqc+ZFP89/E7Kh7P0Br4Kdk+89fZTej\nk30fl0Ifrtag55s+SoPbnTl9JYoW4eDhI0ydtyDVBDJ7YCC9O3eg1yttiYz6h9DgfCz8bBU5g3KQ\nL08eIt0TyXt3ak+50qWSHJsl4EKlfvg7fYiJcW7RkfAI2vbqw+zRw4lzZ5M5sl8YJhuSLx+Rx5Lm\n45FRUeTKGfSf6uOMBYuZ8ckixrz/DiWKFrmSe+KRrFmzctONNwBQ1hi2/L6NWR/Pp0/P7v9pe+MN\nhcmXNw979u7zyQQyb968ZAkIICIiMsn2iIhjhIZ4be2pNOXvMSo+3+fvMSo+3+fvMfp7fNcaY0xx\noCBOLgCAex2SdUAtYB5QFSdvS9zGGmP2uNv8hFOMOhafPLp9gfN3ew1gibvNFnfyGG8VMBYoj/M0\niEvSHMjMZxtOmTu5jTgrtR611u5M9u+Ee+XUA0CJFPYnH89QM/4L9wI4VYCtV9JZ9+M+DgMJ4zPd\n5fHKl3mqnAEBSd+OLpeL2LhLT6PMkiUL+UOCcblcrP72O253DxUNzpuX/MHB7Dt4iBsLFUzyr1DY\nhSHjBfPnv7C9QH4AbigYlrAtNPjCEJEKZQ3rN/+a5PrrNv5ChTImybbp8xcyZe58RvR7C1Pi5su7\nE1cpLjaWc+fOpbjv0OEjRP1znPyhoRnap7SSLWtWypUtw7r1F9aaiouLY936n5PM+/Rl/h6j4vN9\n/h6j4vN9/h6jv8d3NQJcrgz7l4YK4iR5h5NtP+zeB85ov3MpPGYvcZuCQJInKVhrzwORydqkdB0S\ntbkkVSC9xBgTDMwHJgO/4iyIUw3oDixO4ZBZQDdgiTHmLWAfUAx4BBhorT0AvAUMN8YcxymTZ8f5\nxCKvtXZYonO1N8bswElWXwXyAlMS7b/cn4qRwOvGmL+A7cAr7nNeziI6yyZ/PL9FWGgINxcpwva/\ndjJn8TIeuq9BQoPRU2dwNCKSt7t2AmDP/gP8/sef3GJKc/zECWYtWsrO3Xvp27VzwjEvPduMIeMn\nkStnELWqVOZcdDTb/tzBiVOnePrh/8xfBkioPKbksQfu45NPP2Pk5Gk0ubcB63/ZzJdrf0wyxHXa\n/IV8NHMO/V7rSsH8+YlwVyzzXXc9OYOCLuOWXNrw0eOoW7smhQqGcerUaZavXM3Pm35hfMuhnD5z\nhnETJtOg/p2EhgSzZ+9+ho4aQ9EiN1HbPSfUFzV/uhl9+vajXNky3FK+LDNmz+Xsv2d5qPHlTrvN\nvPw9RsXn+/w9RsXn+/w9Rn+PTzI3JZDecxJnLmJnnLmL2XAe4zEeeN/dJiGTsdaeMcbcAQzEmTB7\nPbAfp5R93N1mkjHmFNADZ6XVU8AWnEm3ifV0/6uE8xiPJtbaxOMgLifxw92nMGAaznDZCcBqwKNx\n1G4d7q5Tu8WgMR8RGfUP+UOCeazR/bR6qmlCg/DIYxw+eqHiHhsby6yFS9iz/wBZs2ahSsUKTBoy\ngILuKiLAQ/fdQ1COHEz/ZBEjJ08nR47slCxWlKceSuXZIFx8/HzhsDCGvt2HoRMmMXfpcgqEhtCn\nU3tq3HZhZdaFn60k5vx5er43KMmxL7duSdsXW17GLbm0yGPHeOPtfoRHhHNdrusoXaoE40cOpUa1\nqvz777/88ecOli5fwYmTJ8kfGkqdWjVo36Y12bL67o/+/ffcTVRUFKPHTyAiMhJTuhTjRwz1q0eV\n+HuMis/3+XuMis/3+XuM/h7fNeYQTvEmjKTVwTBgU6I2gcaY3MmqkGHuffFtkq/KmgUITtYm+aqO\nYYn2ecR1sWqL+Bf3cyB3ArdZa3+9VPuruI4Lp7o511r7lqfHHf9rm1+/GXPkL3DpRiIiIiKZWGDu\nkMy5Ug1Q1zTOsL8lv7efXtF9MMbEAg/HP73Ave0AMNhaO9T9fW6cZPI5a+189/dHcRbRWeRuY3D+\n3q7pXkSnDPA7UDXRIjr34qy6eqO19pAx5n5gGVAofh6kMeYlnGJQAWtt8nVPUuS7ZQi5Umn+Q2+M\nKQLci/MYkhw4yxAXw1lKWEREREQk3bky6fIu7mcxluTC3+E3G2MqAZHW2r04owV7u6eY7QLexZmu\ntgQSFtWZBHxojDmGM/VtBLDWWvuTu812Y8wqYIL7MX2BONPM5rhXYAVnhOBWYIb70SGF3Nca5Wny\nCFpE51qUHp/MxAItcFaA+g5nFae7rbU2Ha4lIiIiIuJLquIMR92A87f4EJwFMvsCWGsH4SR744F1\nQBDQMNEzIAG6AJ8CnwBf4yye+Viy6zyNsx7JF+623wJt4ndaa2OBxjhTzn7AeerDVJx1VDymIayS\naWgIq4iIiEjmlpmHsN5R5qEM+1vy2+1LMu19SG+qQIqIiIiIiIhHNAdSRERERER8Xho/n1FSoQqk\niIiIiIiIeEQVSBERERER8XmutH/YgKRAFUgRERERERHxiBJIERERERER8YgSSBEREREREfGI5kCK\niIiIiIjPC3CpNpYRdJdFRERERETEI0ogRURERERExCMawioiIiIiIj7P5dJjPDKCKpAiIiIiIiLi\nEVUgRURERETE5wWoApkhVIEUERERERERj6gCKSIiIiIiPs+FKpAZQRVIERERERER8YgSSBERERER\nEfGIhrBKppEjfwFvdyF9xcV5uwfpSxPXRURERPyeEkgREREREfF5AS4NrswIussiIiIiIiLiEVUg\nRURERETE57k0nSZDqAIpIiIiIiIiHlEFUkREREREfF6AKpAZQhVIERERERER8YgqkCIiIiIi4vNc\nqAKZEVSBFBEREREREY8ogRQRERERERGPKIEUERERERERjyiBFBEREREREY9oER0REREREfF5Lj3G\nI0OoAikiIiIiIiIeUQVSRERERER8XoAqkBlCFUgRERERERHxiCqQIiIiIiLi81yoApkRVIFMQ8aY\nKcaYhd7ux9UwxvxojHkv0fcHjTEvebNPIiIiIiKSOVyTFUhjzBTg+USbIoH1QA9r7ZYM6sMaYJO1\n9tU0Ol8sEAcpfvQSB/S11r7jwakaAueuoh/vA3daa2td6TnSysQp0/ny62/4e/dusmfPzq0VK9Cl\nQzuKFS2SpN2ocRNYuGQZJ06c4NZKFenTsztFbrrRS72+chOnzWDEmPE826wpPbp0BCAi8hgfjhzN\n/35az4kTJ6lS+VZ6de3ik/ElNmfeAqbNmk14RASmVCl6devCLeXLebtbacrfY1R8vs/fY1R8vs/f\nY/T3+K5EgEu1sYxwLd/lFUAYUBCoD8QAy7zao6tTECjk/m9n4B8uxFcI+MCTk1hro6y1p6+yL3FX\neXya2PDLZp5+8nFmT5nIhNEjiImJoc0rnTl79t+ENpOmzWDO/E946/XXmD11IkFBOWjzSmeio6O9\n2PPL99vWbXyyaCmmVMkk2zt2e40DBw8xcsgg5s+cSqGwMF7s0CnJPfA1K1d/wQfDR9LupdbMmzmV\n0qVK0qZjF45FRXm7a2nG32NUfL7P32NUfL7P32P09/gkc3PFxWWKv/UzlLsCmcda+2iibXWAb4EC\n1toIY8yNwBDgXiAW+A7oZK3d7W4fgJOUvYCTfE7GSdhyJz7vRa7/PBcqhnFAcWvtHmNMPWAQUAmn\nMjoNeMNaG3sZ8T0PDLXWBqewrwEwALgFiAAmAW9Za+Pc+38E1lhrX3d/f9C9/yP398HAh0AjIBvw\nE9DZWrvVGNMGGJssrqestfM86fe54xHp+mY8FhVFvXsbMfWjMVS+tRIA9Rs+SIvmT/Pc080AOHny\nFHfe34h+b/Xh/nvuTtsOpNPP2unTp3nyuZb0fq074ydPoUzp0vTo0pHde/bS5ImnWDx3FjcXK+ru\nQhx3NmxC53Yv88iDjdO2Ixm08tkzL7xIhfLl6NmtC+DE1KDxwzzz5BO0fO7ZDOlDevP3GBWf7/P3\nGBWf7/P3GL0ZX2DukEw70fCxyi0yLLFZsHFqpr0P6e1arkAmMMZcBzQH/nQnj1mBVThVvDpAbeAE\nsNK9D6Ab8BzQAqgLBAOPeHjJTsCPwAScpLMQsNcYUxhYDqwDKgIvA62A3lcZIgDGmKI4Vdav3efv\nALQHul/GaRYBOYEGQFVgG/CF+x5OA0YBG7kQ1+K06HtaOHHiJC6Xizy5cwOwb/8BwiMiqFGtakKb\n667LRYXy5dm85TdvdfOy9R80hHq316VGtSpJtp+LjsblchGYLVvCNuf7QDZu/jWju5kmomNi2Lpt\ne5LXzOVyUbNaNZ96zS7G32NUfL7P32NUfL7P32P09/gk87sm50C6NTHGnHB/nQs4AMSXZJoBLmtt\nwuIxxphWwDHgTuALnCTwPWvtEvf+l4H7PLmwtfa4MeYccNpaezTRNdoDe6y1Hd2b/jDGvIVTMfRk\n/uKlvAJst9Z2S3T+YkAvnKrnRbmrl2WB+tba8+5tnXES54ettTONMaeA6MRxZQZxcXEM/HAYt1Wq\nSImbiwMQHhGBy+UiJDhpoTYkOJiIiAhvdPOyrVj9Bdv/2MHH0yf9Z1/xokUoGFaA4WPG0adnd4Jy\n5GD67LkcPnKEo+G+EV9yUVFRnI+NJSQk2WsWko9de3Z7qVdpy99jVHy+z99jVHy+z99j9Pf4roZL\nz4HMENdyBfIrnCpcJaAaTsVxpTHmJvf2UsaYE/H/cIZ7ZgdKGGNy41TXfoo/mTuh+vkq+1QGpzKZ\n2FrgOveQ2qtVFvghhfOHGGNCPTi+Ik6lNSrRfTmOcy9KpEH/0k2/gR/w185dDH4vLfLwzOHQ4SMM\n/HA4A959i2xZ//tZUNasWRk26H1279lL3QYNqV6vAT9v2sTttWsREKBfsCIiIiJy+a7lCuQpa+3f\n8d8YY17EGbL6InAdTjL4NP9d1fRoCtuuFdcBf+PMC01+D45lfHc803/QEL5b+wPTJowlf+iFPDk0\nJIS4uDgiIiMJTfQpXkRkJGVMaW909bJs3W45FhVF0+YvJMyvPB8by4ZNm5kzfwEb135NWVOaeTOm\ncOrUKaKjY8ibNw/PtHyR8mXLern3VyZv3rxkCQggIiIyyfaIiGOEhoR4qVdpy99jVHy+z99jVHy+\nz99j9Pf4rkaAKpAZ4lquQKYkDgjCmcNXCjhqrd2Z7N8Ja+1x4CBQI/5AY0wWoEqKZ03ZOSBLsm3b\ngOSPv6gLnLDW7rvMWFKyDWc+Z/LzR1hrwz04fiNwI3A2hfsSn0CmFJfX9B80hDXffMeksaMoVLBg\nkn033lCY0JAQ1q2/UDg+efIUW37/nVsrVsjorl62mtWrsnD2dD6ZOZVPZk3jk1nTKF+2DI3vv5cF\ns6YlGcaRK1cu8ubNw+49e/l9m6V+vTu82PMrly1rVsqVLZPkNYuLi2Pd+p994jXzhL/HqPh8n7/H\nqPh8n7/H6O/xSeZ3LVcgsxtjwtxf58OZH5gTWIpTfewOLHHPQdwHFMOZ6zfQWnsAGA70NMbsALYD\nrwJ5L+P6u4Aa7oVtTlprI4AxQCdjzEicxWjKAG/jrAabFkYC7YwxQ4DxQAXgDZw5lp74DPgF5770\nAv7CSSgbAzOttb+54ypljLkFJ8k+bq31yjMx+g0YzGerv2DkkIEEBQUR7v6k7vrrcpE9e3YAnn2q\nKR9NnkqRG2+kcOFCjBr3EWEFClC/3u3e6PJlyRkUlDCfM15QUA7y5MnDzcWLAbD6yzXky5eXQmFh\n/LHjLwZ9OJy776xHzepV/3tCH9H86Wb06duPcmXLcEv5ssyYPZez/57locYPeLtracbfY1R8vs/f\nY1R8vs/fY/T3+K6U65odJJixruUE8n6chXPAWWF1O/C4tfY7AGPM7cBAYAFwPbAf+BJnzh84SV1B\nYCrOYz4mAwuBPB5e/wP3sVuBHMaY+Md4PAAMxknUInFWau1/pUEmZq3dbYxpjBNXO5x5nSNJ+ozI\n5MsfJ3xvrY01xtwLvA9Mx5kPeQhnVdf4RXPmAk1wHnuSG3gK8OgxHmlt3sLFuFwuWr7cIcn2d998\ngwcbNQSg5XPPcvbsWd55fxAnTpyg8m2VGDv8Q7IlWrnUlyT/xXk0PJzBw0YSeewY+UNCeLBRQ15q\n+byXepc27r/nbqKiohg9fgIRkZGY0qUYP2IowfnyebtracbfY1R8vs/fY1R8vs/fY/T3+CRzuyaf\nAymZU3o/B9Lr/P1nTfMORERE/F5mfg7kk1VbZdgfW3N/npRp70N6u5YrkCIiIiIi4ie0iE7GUAKZ\nDtyPAtmKM/wz+Ts5Dih3uYvipMc5RURERERELocSyPRxAOf5khfbnxnOKSIiIiIi4jElkOnAWnse\n2JnZzykiIiIiInI5lECKiIiIiIjPc2kOZIYI8HYHRERERERExDeoAikiIiIiIj5Pq7BmDFUgRURE\nRERExCOqQIqIiIiIiM9z/edJd5IeVIEUERERERERj6gCKSIiIiIiPk9zIDOGKpAiIiIiIiLiESWQ\nIiIiIiIi4hElkCIiIiIiIuIRzYEUERERERGf59IcyAyhCqSIiIiIiIh4RAmkiIiIiIiIeERDWEVE\nRERExOfpMR4ZQxVIERERERER8YgqkCIiIiIi4vNcqAKZEZRAimQUPx9WUbXCo97uQrr6aeNcb3ch\n3bkC/Ps96u8/g66ALN7ugoiIXAOUQIqIiIiIiM/THMiMoTmQIiIiIiIi4hElkCIiIiIiIuIRJZAi\nIiIiIiLiEc2BFBERERERn+fSHMgMoQqkiIiIiIiIeEQVSBERERER8XlahTVjqAIpIiIiIiIiHlEF\nUkREREREfJ7mQGYMVSBFRERERETEI6pAioiIiIiIz3OhCmRGUAVSREREREREPKIEUkRERERERDyi\nBFJEREREREQ8ogRSREREREREPKJFdERERERExOcFaA2dDHFNViCNMVOMMQu93Y/MyBizxhjzobf7\nISIiIiIimU+mqEAaY6YCzwHjrLXtku0bDbQFplprW3qhb1OA54E4IAbYA0wH+ltrYzO6P2nFGFMP\nWAPktdYeT7TrESDaO73KWBOnTmfEmPE8+1RTenTp5O3upKk58xYwbdZswiMiMKVK0atbF24pXy5d\nr1m5WkVatGlG2QqlyV8ghM4vvsHXX/yQavuQ/MF0692O8hUMNxW7gVlTFvDBu6PTtY8AVWveSrfe\n7ShRqhgHDxxh4qgZLF2wKsW29zepz4ARffhq9fdMmj6TL7/5jl2795A9e3YqVShP53YvU6zITRe9\n3vqNmxgycgx//b2LgmEFePH55jz4wP3pEZrH11y49FOWrVjFjp1/A1CuTGleafMi52KimTpzDtvs\nHxwNj2DYwP7cdUfdi14rOjqasROn8tmqzwmPjCR/aAgvt2zBQ40bpl98GzbxwYjR/PX3LgqFFaB1\ni+Y81OjC9RYs+ZRlK1ZeiM8YOrZ98aI/Axs2bWbqrDls226d2Ae9d8nYr9bH8xcyddbHRERGULpk\nSXp168wt5coCEBMTw8hxE/j+x/+xb/9Brr8uFzWqVaVz+zbkDw1N1355yhu/Z9LDhk2/MHXGbLZu\n387R8AiGDx7AXfVuT9jfu28/li5fkeSYOrVqMnb4kIzuapryl9fvYvw9Rn+P70q4XCpBZoTMUoGM\nw0nMmhljssdvdH/9FLDbWx1zWwEUBEoCg4G3gG4pNTTGBBhjvPbuNcZk87CpC+e+J+mrtTbKWnsq\nzTuWyfynAREYAAAgAElEQVT2+1Y+WbQUU6qkt7uS5lau/oIPho+k3UutmTdzKqVLlaRNxy4ci4pK\n1+sG5czB9q076N97KHFxcZdsHxiYjcjwY4wfOR27dUea9KHQDWH88veaVPcXvrEgoya/z7q1G3mi\nYStmTfmEtwb2oGbdqim2ffX1tmxYtxmAjZt/5anHH2XmxLF8NGIIMTHnadu5G2f//TfV6+0/eJBX\nuveiRtXKzJ82iWeaPk7f9wfx40/rrzjGAwcPcWudO6/qmj9v+oWG9zZg0ujhzJgwlrACBXi5SzcO\nHT5MmdKleKN7F4//T7jr62+yfuNG3unTk2XzZjHwnbcoVvTiSfWl4qtUq17q8R04SIduPalRtQrz\nZ0zmmScfp+97g/hx3YX4Nmz6hQfuvYfJo0cwc+I4CoYVoE2nrhwND0/1vGfOnqVMqZK80f3VNPkD\nZMnyFbRq2zHV/Ss//5IPRoym3UstmTd9MqZUSV7u1DXh5/Ts2X+xf/zJy61eYN70SQwd2J9du/fQ\nqfvrV923tOCt3zPp4cyZs5jSpXjjtW6pvvZ1a9fk61WfsmblMtasXMag/n0zuJdpy59ev9T4e4z+\nHp9kbpmiAum2CbgZeBSY4972KE7y+Hd8I3dy1hN4ESeps0A/a+0C9/4A4COgvnv/HmCMtXbEVfTt\nX2vtUffXHxljHgUeAgYZY1oAQ3EqqAOAUjiJ5h5jTGvgVaC4O4aR1tqx7n4WdW97CugIVAZ2AO2t\ntd96Gou7QpoXWA+0B84CJYwxzwKdAAOcAr4COltrj7qv/RVOAnnMGBMHTLPWtjTGrAE2WWtfdZ8/\nLzACaAxkB74BOlprd7j3Pw8MA550//cm4HughbX28FXc83Rz+vRper35Dn1792T8pKne7k6amzFn\nLk888hAPuisyb/bqwbdrf2DR0k9p+dyz6Xbdtd/8xNpvfgI8+wTw4P7DDHZXHB95slGq7R5t1ojm\nrZtyw02F2L/3IHOmLmTezCWptr9Y8tr02YfYt+cgQ98fB8CunXupXLUizVs/wf++/zmhncvl4r1h\nbzD6w8lUqV6J63LnYvSQQUnO9W7vntzV6GG2brdUrlQxxevNW7iEGwsXokv7tgAUK1qETZt/Zebc\n+dSqXi2h3cKlnzLj43nsP3CQGwoX4qnHH6Xpow+nGsfF7q8n13zvrd5Jjnm7Vw++/Ppb4mKh/Uut\ngIvfx3jf/7iOjZt/5bMFH5P7+usBKFQw7D/tFiz5lBlz5rL/4EFuKFSIp554jCcfu9L4FnNj4UK8\n+ooTX/GiRdi4eQszPp5HrRru+N5OFt/rPfhizTesW7+Bxg3vS/G8dWvVoG6tGkDKsUdHRzN8zEes\n/PxLTpw8SakSN9O5fRuqVr4t1b5ykThmzJnH4w8/mFAZ7tOzG9+u/ZFFy5bTsvkzXHddLsaNSDqb\n4PXuXXimZRsOHT5CwbACqV83A3jr90x6qFu7JnVr1wRSf98HZgskOF++jOxWuvKn1y81/h6jv8d3\npQJUgcwQmaUCCU4yMxlIPEy1JTCFpFWy14FngZeAcjjJ2wxjTPx4kwBgL/AYUBboC/Q3xjyehn09\nCwQm6ndOoAfQCigPHDHGPAO8DfQCyrj7/Y4xpnmycw3CqWreCvwILDXGxP+/lKex3A2UBhrgJHrg\nfDjQG6iIk+wWxbmXJDonOAlvIZxkMyXTcJLbxkBNnNfiM2NMlkRtcgJdgWeA24EiwAepnM/r+g8a\nQr3b61Cj2n+rTr4uOiaGrdu2J4nN5XJRs1o1Nm/5zYs9uzIPPNyAtp1bMHzgRzxUvzkjBk2g3ast\nafzIvVd0voq3leN/azck2bb225+oVLl8km1tO7cgMvwYS+YnHbaW2ImTJ3G5XOTJnTvVNlt+30qN\nqlWSbKtdozqbf/s94fvlqz5n7KSpdHz5JRbPmcErbV5kzMTJLFuR8rDaS/HkmsmdOXOWmJgY8uS5\n/rKu9c33aylfxjBlxmwaNHmMJk2fYcjIMfybqCq7fOVqxk6cQqd2L7Hk45l0bPsSYyZMuuL4fv19\nKzWT/ezW8TS+i7xWl9J/8Ids+X0rH7zXlwWzp3Lv3XfSrnN39u7bf9nnio6JYdt2S41qF14nl8tF\nzepV+HVL6nGcOOG853Jff90VxZBW/O33jCfWb9xIvfsa0eTxZvQbMJh//jl+6YMyqWvh9fP3GP09\nPsn8MlMFEmAWMMAYcxNO8lQbp7J1F4AxJhAnIbvbWrvOfcwud/LYBvjOWhuDk2jF222MqQ00BT65\n2g4aYxoA9wHDE23OCrS11v6WqN3bQFdrbXypZLcxpjzwMjAj0bEjrbWL3ce0Be7HSUQ/uIxYTgKt\n3e0BsNZOTbR/lzGmM7DOGJPTWnvaGBPp3nc02RzIxLGWApoAteLvtzsx3gs8DCxIFH8ba+0ud5tR\nQJ+UzultK1Z/zvY//uTj6ZO93ZV0ERUVxfnYWEJCgpNsDwnJx6493h4Jfvnadn6BD/qN4evP1wJO\n1bJk6WI88eyDfLpo9X/aX6ryGZo/mIijkUm2RYYfI9d1OckWmI3oc9HcVrUCDz3RkMfvT33KdVxc\nHIOGjeK2ihUoUbxYqu3CIyMJCU72WgTn49Sp05w7d47AwEDGTZpC11faJcy3K1yoIH/9vYtPFi+l\nSQrVsjguXhn05JrJDRszjgL5Q/+TmF3Kvv0H2bj5VwIDAxk+qD/Hov6h36AhHD9+nL5v9ARg7MQp\ndOvYjrvuuD0hvh07/2b+wiUpx3eJymd4RCQhwUkrQcGXiG/oaHd81a/sQ6NDhw+z9NMVrF62gNCQ\nEACee7oZ3/+4jsWffsYrL7/434MuEkfCz2myOEKCg9m1e2+Kx5w7d45ho8fR8N4G5MyZ84riSCv+\n9nvmUurWrsU99e/ihsKF2LtvP8NHj6Nd567MnPyRT863uhZeP3+P0d/juxq++DPpizJVAmmtDTfG\nfAq8gFPpWm6tjTTGxDcpiVPt+jzZPMNsOENgATDGtHefowgQhFMt3MSVa2KMOeG+jgsn0U2c2J1L\nljzmBEoAk4wxExO1ywIkH5z+v/gvrLXnjTE/41QbLyeWLYmTR/dxVXDmalYC8nGh2lwE2O5BzOBU\nTqOBnxL1MdIYYxP3ETgdnzy6HQS8O74qBYcOH2HgkOFMGD2cbFkz1VtfUpAjR3ZuKlqYvoN68PbA\nHgnbA7IEcOL4yYTvF66eQqEbCgIXRgz++PuFyuGGnzbT4YWeHl0zKGcQ/Ye+Tt+eg5NcI7n+H3zI\nzl27mDbu6hb9OXP2LHv3H+Dt9wfRd8CFIbKx52O5PlGV6dFnWnDw8CHgQl5Sq8GFRXGqVKrEqCED\nr6gPk6bPYvVXXzNp9HCyZfN0CjXuvsTicgUw8J03E5Ka7p060O2Nt3ij+6ucj41l7/4DvPXeIN5+\nP/X4Hnn6eQ4eShpfzfoXkssqt1Zi9IdJhxB7Ht9MVn+5hsljR1x2fPH+3LGT87GxNHn86SQJfHR0\nDHnz5AGcJPPhZs1x4SKOOM6fP09MzHlq3uVUy124aN2iOa2ev/yhZTExMXR9/U1cLhe9e7x6RTHI\nlbv/nrsTvi5Z4mZKlSzBA488wfoNG6merNovInItyIx/RU8BRuEMDW2XbF/8XxwPAAeS7fsXwBjT\nDGdIaBec5OwEzvDS6lfRp69wKofRwIEUVl89k0o/W5Mo+XI77+lFLyOWU8mOywmsxFn852ngKM4Q\n1pVcGHqblpKv2vqfxXkyg63bt3MsKoqmzV9I+Cv1fGwsGzb9wpx5C9j4wzc+/8lV3rx5yRIQQERE\n0ipbRMSxhMqJr8iZKwiAt18bzJZftiXZFxt74Uew7fM9yOr+QKBgofxM/HgYj98f/xkUSYZThh+N\nJCR/0k9sg0PzcerkaaLPRVO8RBEK3RDGyEnvJ2SjAQHx/w3gXFQ4/QcP4fsf1jFl7Ejyh178noYG\nBxMRmey1iDxGrlw5CQwMJCLyGABv9exOhXJlk7QLCLgww2DMh4OIiXE+Izp85CitX+nM/OlTEt7H\n2bMnrD12yWsmNm32x0ydNYePRnxIyZuLXzSWFOMLCSEsf2iSitjNxYoSFxfH4SNHE7a//XqPi8Y3\nduggYmKcX42HjhyhdfvOfDJzCvG5WvbsF/odGhKccN/iRaYS39RZc5gycw4TRg69ovjinT5zhixZ\nsjB3+qQk/QYICnLep/lDQ/lk5tSE7Z9/9TVffv0tA959MyGO3LmdIcIJP6fJ4oiIjCQ0WUUhJiaG\nbq+/yeHDR5g4erjXq4/gX79nrsSNNxQmX9487Nm7zycTyGvh9fP3GP09Psn8MmMCGZ/knAeSj1Hb\nipMoFrXWfp/K8bWBtdba8fEbjDElrrJPp6y1f1+6mcNae8QYcwAoYa39+BLNa+IsOoN7XmEVnEVr\n4MpjKQMEA72stfvdxyVPOs+5/5uF1G3DeY/UwF0pNcaE4CzMk/pEnUyqZvVqLJwzI8m23n37cXPx\nYrR6vrnPJ48A2bJmpVzZMqxb/3PCMvRxcXGsW/8zzzR7wsu9uzyREVEcPRzOTUULs3LZV6m2O3zw\naMLX8Ynl/r2HUmy7eePv1L2zRpJtte+oxuaNztv577/28Nh9LyTZ/0q3F8mZK4jKFYrSf/AQvv7u\neyaPHpHiYjHJVbylPGv/ty7Jth9/Wk+lW5w5lyHB+cgfGsq+/QdomKjKkVziBVPiE5gbCxe6omvG\nmzJzNpNmzGLc0A8oa0pfMpaU3FqxAp+v+YYzZ88SlCMHALv27CHA5SKsQH4CAwPJHxrK3n37LxHf\nhXt5Ib7CKbatdEt5vv8xaXw/pBDf5BmzmTR9JuOHD7ni+OKVKV2K2NhYIiIjuS2VBZOyZMnCjTdc\n6HNwcD6yZ8+eYhzZsmalbBnDuvUbEoYuOz+nG3m66WMJ7eKTx337DzBpzIiEBNTb/On3zJU4dPgI\nUf8czzSPU7lc18Lr5+8x+nt8kvllugTSWhtrjCnj/jou2b6TxpgPgKHuZOt7IA9QB/jHWjsD+BNo\nboy5F2eV0+ZANWBnBoYBzvDR4caY4zhJcXagKs5zF4clatfeGLMDJ1l7FWdF1fjFbq40lj04CWJH\nY8w4oALOgjqJ7cb5XLyJMeYz4Ezyx3dYa3cYY5YCE4wxL+PMtRyAMwdyqWe3IfPIGRREiWRViKCg\nIPLkyc3NxYt5pU/pofnTzejTtx/lypbhlvJlmTF7Lmf/PctDjR9I1+sGBeXgpmI3JCTiNxYpTOmy\nJfgn6jiHDx6lY48XKRAWSu+u7yccU7psCVwuFzlzBpEvOC+ly5YgOjqGv3c4czjGDJ3Ca2+9wsmT\np1n79ToCAwMpV9GQO/d1zJyc8pTmi30QMH/WUpo99wide7Zh8bzPqFGnCvc0rEe7Fq8BEH0ump1/\nJp0/cuL4SeKIo9+Awaz44ktGDHyPoKAcCVW+63LlSqgAjhj3EUeOhtOvj/OohSceeZC5CxcxdPQ4\nHmn8AOt+3sDna75hdKLhpm1bv8CgYSPJlSsndWrW4Ny5c2zdbjlx8iTPPpnyHwIXmyfoyTUnz5jN\n2ElTGNC3D4UKhiXE4goIIDwiIuH8+w4cwP65gzy5r6dgWBjDx4znSHg4/d98A4AH7mvAhKnTefPd\n92nb+gUio6L4cNQ4HnmwUUI1sN2LLzBw6Eiuy5WLOjWrcy46mq3bLMdPnqB5s6ZXEN9DfLxgEUNH\njeXhJo1Yt34Dn3/1NWMSDXGdPH0WYyZOYeA7b1IoLCzhU/qgXDnJ6a4YJnf6zBn27t2fMER13/4D\n2D92kCfP9RQtchMP3NuAN/r2p2vH9pQpXYrIY8f46eeNlC5VktvdK3hejueefpI+77xHubKGCuXK\nMmPOPM6ePZvw/MyYmBhe7dkH++efjBwykJiYmIQ4cufJ7fVh+N76PZMenNd+34X3/f4D2D/+JE/u\n3OTOk5txEybToP6dhIYEs2fvfoaOGkPRIjdRu1aNi584E/On1y81/h6jv8d3pQIy3wC4+Kcq9MVZ\nbLIgzijKqdbafsnavYMzejEvsBZnfZUdifZnBz7EWR8mO7AKaGetPZKoTT6ckZyNgVic9Uo6pfUj\n+jJdAglOoniRfX2MMUdwHuVxM86cwo3Ae+4m43FWNP0YJ0GaA4wG0u+p1in3c5Ix5hTOkNNBOMNM\nt+A86iKxnu5/lXAe49HEWhs/JuGKYnHPJW2Bc09ewbk/XUmU9FlrDxhj3sJJCCcD00m6Am68FjgL\nBi3DqQx/AzSy1no8FDcz84Oi43/cf8/dREVFMXr8BCIiIzGlSzF+xNB0X4K+XEXDpI+HERcXR1xc\nHF17OyPQly5YxVvdBxKaP4SChZNOjZ332cSEP9rK3lKKBx66m4P7D/PA7U8BsGjuZ5w5fZYXXn6K\nLr1e5szpM/xp/2bmpPmp9uNiyceBfYfo8MJrdH+zA0+/8BiHDx7lrR6DWJdsZdaUzF+8FJfLRasO\nnZNsf+eN12jS0JmPeDQ8gkOHE36Pc0OhQowaPJDBI0YxZ/4Cwgrk5+1ePZIsVvNok0YE5cjB1Flz\nGDZ6HEFBQZQsUZxnm6b+KfLFkmRPrjl/8RKnuvXGW0mObfLAfSxdvhKXy4XL5WLIiDEAPPjA/bzT\nuyfhEZFJ4ssZFMT4ER8yYMgwnmrZhrx5cnPf3fXp0KbVhfgebExQUBBTZsxm6OixBOUIolSJm3m2\nWeoLY180vsKFGDVkIIOHjWL2/AWE5c9P3zdeS7JAzrxFSxLmDSb2cqsWvNz6BVKyddt2WrXrlCj2\n0Rdi79OLd998nY+mTGfIiNEcORpO3jx5qHhLOerdXifVvl7MfQ3qcyzqH8Z8NMn5OS1VinEjhiT8\nnB45Gs63a38AoGlz51dzXFwcLpeLiWOGU/W2W6/oumnFW79n0sPvW7fTqm2HhNf+g+EjAXiwUUN6\nv9aNP/7cwdLlKzhx8iT5Q0OpU6sG7du09noSfzX86fVLjb/H6O/x+ZmeOIt9PoczmrIqMNUYE2Wt\nHQVgjHkN6OBuswvoB6wyxpS11saPGhyGkwM8BhzHyQkW4DwBId5sIAznCQ2BwFScfCJNn+3i8uRZ\nX5L23M9i3AncZq391dv9yQzOHY/Qm9GHVa3wqLe7kK5+2jjX211Id64AP/xEJTF//MQoEVfAxWYk\niIikjcDcIZn2l2nXu7tn2N+SQ74c7NF9MMYsAw5Za19MtO0TnEUon3N/fwAYbK0d6v4+N3AYeN5a\nO8/9/VGgmbV2kbuNwRnBWNNa+5MxpizOFLMq1tpN7jb3AcuBG621Kc/vuQKZ6TmQ16JM+wMoIiIi\nIiJX7Qfgbvfj8TDGVMKZfveZ+/viOENbv4w/wP2IvXVALfemqjgjRxO3sTjT1uLb1ASOxSePbl/g\njGJM0zH3vjv+Ig24nze5lZRXDY0Dyllr96VjF1RxExERERFJAwGZc6TJACA3sN0Ycx6ngPdGooU2\nC+LkBIeTHXfYvQ+cYannUnh2e+I2BYEjiXe6HxEYmahNmrimE0icSayVLrE/XVhrd3PxFVBFRERE\nRMS3PYnzWL1mOIWrW3EW2jzgXgDU51zTCaR7IZiMXp1VRERERESuDYOA96218SsA/m6MKQb0AmYA\nh3BGQoaRtAoZBsQPRz0EBBpjcierQoa598W3SbJaofupFcGJ2qQJzYEUERERERGf53Jl3L/LkBPn\n+faJxeLOw9zPmj+Es3IqkLCITg2c+ZMAG4CYZG0MUAT40b3pRyCvMea2RNe5Gyc5TfoA5at0TVcg\nRURERERE0tEyoLcxZh/OKqmVgS7AxERthrnb7MB5jMe7wD5gCTiL6hhjJgEfGmOOASeAEcBaa+1P\n7jbbjTGrcJ7f3hbnMR4jgTlpuQIrKIEUERERERFJLx1wEsLROENMDwBj3dsAsNYOMsbkxHlmY17g\nO6BhomdAgpN0ngc+AbIDK4H2ya71NDAKZ/XVWHfbTmkdkJ4DKZmGngPp2/QcSN+n50D6Nj0HUkQy\nQmZ+DmSPe3pk2N+Sgz4flGnvQ3pTBVJERERERHxeJn2Mh9/RIjoiIiIiIiLiEVUgRURERETE57lQ\nBTIjqAIpIiIiIiIiHlEFUkREREREfJ5LcyAzhCqQIiIiIiIi4hFVIEVERERExOdpFdaMoQqkiIiI\niIiIeEQVSBERERER8XkqQGYMVSBFRERERETEI0ogRURERERExCNKIEVERERERMQjmgMpImli/S/z\nvN2FdNWobntvdyHdLf9+tLe7kK5cAVm83QURERGfpwRSRERERER8nh7jkTE0hFVEREREREQ8ogqk\niIiIiIj4PBeqQGYEVSBFRERERETEI6pAioiIiIiIz9McyIyhCqSIiIiIiIh4RBVIERERERHxeSpA\nZgxVIEVERERERMQjSiBFRERERETEI0ogRURERERExCOaAykiIiIiIj7PpUmQGUIVSBEREREREfGI\nKpAiIiIiIuLz9BzIjKEKpIiIiIiIiHhEFUgREREREfF5KkBmDFUgRURERERExCOqQIqIiIiIiM/T\nHMiMoQqk/IcxZooxZqG3+yEiIiIiIpmLKpB+xhgzBXgeiAPOA/uA+cCb1tp/vdm3zGDegkXMXbCI\nAwcOAlDi5uK83LoldWvX9HLP0taceQuYNms24RERmFKl6NWtC7eUL5eu19zwy2amzpzDNvsHR8Mj\nGDawP3fdUdejYzdt3kKrdh0pWeJm5k2flK79XL9hEx+MGM1ff++iUFgBWrdozkONGibsX7DkU5at\nWMmOnX8DUM4YOrZ9EYAcOXPwQsdm1KlfnbzBedixbSdjBkzlj9//SvFa3fq1496H7iQuLi7Js6l2\n79jLi490TbcYK1UrT5vuz1Gs5E0cORjOrI8W8PmSbxL217m7Ok+/9CiFbypI1mxZ2L/7EPOnLvX7\n17BChQpp3pcNm35h6ozZbN2+naPhEQwfPIC76t2esL93334sXb4iyTF1atVk7PAhad6XjOaN3zMZ\nyZ/ju9T71l/482sI/h+fZF6qQPqnFUBBoDjQGWgDvO3NDmUWBcMK0KVDO+bNnMLcGVOoXq0KHbu9\nxs6/d3m7a2lm5eov+GD4SNq91Jp5M6dSulRJ2nTswrGoqHS97pkzZylTuhRvdO9yWQ/yPXHyJL3f\nfY8a1apedR8OHDxEpVr1Ut2//8BBOnTrSY2qVZg/YzLPPPk4fd8bxI/r1ie02bDpFx649x4mjx7B\nzInjKBhWgDadunI+Lppu77Slco0KvP/acF58uAsbfviVQRPfJDg0b4rXG/3eZJrWa82Td75I03qt\neap+G078c5KvV/14xTEWKBTK6i3zUt0fVjg//cb0ZNO6Lbz0aDcWzvyMrn3bUrlWxYQ2x6NOMGvc\nAl55+nVefLgrKxetoXu/9pw9F+PXr+HR8PCr7l9yZ86cxZQuxRuvdUv1ntWtXZOvV33KmpXLWLNy\nGYP6903zfmQ0b/2eySj+Hp8n71tf5++vob/HJ5mbKpD+6V9r7VH31/uNMZ8D9wC9AIwxNwJDgHuB\nWOA7oJO1dndKJzPG3Af0Bm7BqWr+6G6/072/OTAGuNVa+5d72xjgTqCytfZsegR5Je6oWyfJ9x3b\ntmHegkVs3vIbNxcv5pU+pbUZc+byxCMP8aC7IvNmrx58u/YHFi39lJbPPZtu161bqwZ1a9UAIC4u\nzuPj3h04hAfuu4cAl4s1337/n/0LlnzKjDlz2X/wIDcUKsRTTzzGk489nOr5LvbH0LyFi7mxcCFe\nfaUtAMWLFmHj5i3M+HgetWpUA+C9t3snOebt13vwxZpviM32L3Ub1KBPhwH8vskCMGPsfGreWZUm\nze5j2qi5/7nemdNnOXP6wtu/dv1qXHd9LlYvXpOkXcPH7ubx5xtT8IYCHNp/hMWzVrBs7upU47jY\n/W3S7D4O7j3ChCEzAdi36wC3VC7DY881ZuOPvwKwZcO2JMcsnvUZ9z5UjzvuuIM61atc8hrJ+cpr\n+L+ffqbJA/d7HJcn6taumTCCIbV7FpgtkOB8+dL0ut7mrd8zGcXf4/Pkfevr/P019Pf4JHNTBdLP\nGWNuAeoA59zfZwVWAf+4t9cGTgAr3ftSkgsn4awM1MdJIhfF77TWzgCWA7ONMQHGmEZAS+DpzJQ8\nJhcbG8uK1Z9z9uxZKlW8xdvdSRPRMTFs3bY9SSXI5XJRs1o1Nm/5zYs9S9niTz9j/4GDtG3VIsX9\ny1euZuzEKXRq9xJLPp5Jx7YvMWbCJJatWHVF1/v1963UTFYlq1OjOpt/+z3VY86cOUtMTAzZsgYS\nkCWAc+eik+w/9+85bqlcxqPr3/9ofTb+71eOHopI2Fa/0e08164pk4bO5oXGnZg0bDbPd3iSBk3u\nuIzILihbsRQb//drkm0/r/2FcpVKp3rMbTUqcGOxwsTGnLvs6/nSa5gnT+4ruubVWr9xI/Xua0ST\nx5vRb8Bg/vnnuFf6kVZ87ffM5fL3+K4F/v4a+nt8V8OVgf+7lqkC6Z+aGGNO4Ly+2XESvnbufc0A\nl7X2pfjGxphWwDGciuEXyU9mrU2yoI4xpjVwxBhTzlq71b35ZWAzMBJ4FHjLWvtLWgaVVv7c8RfP\ntmrDuX//JWeunAwbNICbixXzdrfSRFRUFOdjYwkJCU6yPSQkH7v2pFhg9prde/YyYuxHTBs/moCA\nlD/LGjtxCt06tuOuO5y5OYULFWTHzr+Zv3AJTRre95/2l/okPTwikpDgpJWg4OB8nDp1mnPnzhEY\nGPifY4aOHkeB/KHEHszK1s1/8OzLj7N3536ORfxD/UZ1KVepNPt2H7xkvMGheale9zb6dx+aZPtz\n7ZsyfvA0fljjDME8cjCcYiVvovGT9/LFsm//c55LDTcLDs3LsYh/kmw7Fv4POa8LIlu2rERHxwCQ\nM1cQH6/5iGyBWTkfE8uIfhPo1O2JS8aRmK+9hrWqV/M0tDRTt3Yt7ql/FzcULsTeffsZPnoc7Tp3\nZUi/uX8AACAASURBVObkj3x26KAv/Z65Ev4e37XA319Df49PMj8lkP7pK5yE7jqgCxBjrV3s3lcR\nKOVOMBPLDpQghQTSGFMSeAeoAYTiVK7jgCLAVgBrbZQ7sVwFrLXWDkzroNJK8WJF/8/efYdHVaxx\nHP8ugdBbAiT0JgxF5ao0Fa8iohQFsZdrQ1BBBVEEFVERRXpvUYp0pNlAEBRsWCgiosBgQXpNQEgg\nQMr942zCJgRIlk12s/w+z5OH7Jw5Z993T9jk3Tkzh3kzphAbF8uyL1fQ6403mfzu2KApInODpKQk\nXn6jL507tqdihfLAmYXD8fh4duzazev9BvLGOwNP75uYRNGiRVIft3vgEfbs3es+htPW+MbThclV\n/6nHmKGn98+KiVOns/TLFUwaN5KuDw+mf8+RdH+rM7NXvEtiYiJ/btzK8kXfUaNutfMe65bbm3L0\nSBwrl5+eq5e/QCjlKkbwQt9OPP9mp9T2PHnyEHs0LvXxex8NJaJcaeD0TZI/WTUtdfuGNRvp1fmd\nLOV2LO44T97xAgUKFeTKxpfRqcejuPKcJDnh1Pl3Jneew3z58nl1jAvRonmz1O8vqV6NGpdUp1W7\nu1m99mca1r8qx+MREQlmufWDudxGBWRwirPWboXU0cX1xpjHrLWTcYrKNcADcMb4+wEythDYCnQA\nduMUkL8D6T/qvx5IAMoaYwpba+MIQHnz5k39g7e2MWz4fRMzZs+l90sv+jmyC1eiRAlC8uQhOjom\nTXt09CFKhYf7KaozxR07xu+bLJu3/Em/Qc6IXFJyMsnJyVzZ5EaiRgxJnZP6xis9uKxO7TT7e452\njRs2kISERAD27t9Ph6efY970yc5HHED+/Kd/TEuFhxEdcyjNsWJiDlG4cKEzRq7enzGLydNn8d6o\nYVxSrapz/F376f7YG4TmD6VQkYIcjv6XXoO6sWfnvvPmfEu7piz75GuSEpNS2woWKgDAkNfGYzf8\nkaZ/oke/V556m7x5Q5wcIsIZPPkNnmj3QmoxeSL+9KWnMQcPUzK8eJpjlSxVnGOxx1NHH1Ps2bkf\ngK1btlG5egVatL2OU7GZW4Aht55Df6tQvhwlSxRn+46dubaAzC3vM94K9vwuBsF+DoM9Pwl8KiCD\nnLU22RjTDxhqjJkJ/AzcAxyw1saeb39jTBhQE3jcWrvS3XbGuv7GmGuAF4HbgAHAGOBRX+WRnZKT\nkjh5MutzvwJRvrx5qVO7Fj+tXpO6JHtycjI/rV7Dg/dl7fLE7FSkcGEWzJySpm32vAWsXruOoe/0\npVzZshQokJ/SpUqxY+cuWnqM4qQXGRGR+n1KUVKhXLkM+9a7tC7f/fBTmrbvV62m3qV107RNmjaT\niVOnEzViCLXNmXMHT544yckTJylSrDD1r61H1OCp58y3XoO6lK0YwZIFX6ZpPxxzhOj9hyhXMYKv\nFq886/6ecyZTCsu9u/Zn2HfT+i00uO6KNG31r6nHxvVbzhmj86lt5j+5ze3n0F/27tvP4X+PULpU\nKX+H4rXc8j7jrWDP72IQ7Ocw2PO7EHk0AJkjVEBeHOYCg4CngXE4hd7HxpjXce4TWQVoBwyw1u5O\nt+8hIBp4whizF6gMvEPq2AAYY4oCU4ER1trPjTG7gFXGmE+ttfOzNbMsGjFmPE2uaUzZyAji4o6x\naMlS1qz7haj2w86/cy7x0AP30bvPW9SpXYtL69Zm2swPiD8RT9tbW2Xr8x47fpwdO3elXsa4c/du\n7B9/UrxYUSIjIhgxNor9Bw/y9mu9cLlcVK9aJc3+YSVLEhoammY13M4dH2PAsFEUKVyYaxs35OSp\nU2zcZDkSe5SH7rsnwzjONYfu7nZtmT3/Q4aNHsftt7Xmp9VrWbb8K8Z6XB45aeoMxk6YzIA3X6Ns\nRETqJ7xJyUk0uPYKXC7YsXU35SuX5YkXHmLb3ztZ+tFXALTv+gClIsIY+MroNM/b4o4b2fzrn2z/\ne9cZMU0Z8wFPv/wYx2KPs/q7deQLzUfNS6tTpGhhFkxblGEe57pE59MPltLm/hZ0eP5/LFmwnCsb\nX8Z1Nzem11P9Uvvc1+F2tvz2F7t37CNfaF4a/fcqmt32X47+G822v/8K2nNYqGhRChUseNZje+PY\n8ePs2LHz9Gu2azd2yx8UL1aMYsWLMf69Sdx04w2UCg9j+45dDBs9lsqVKnKNe8Xi3Mpf7zM5Jdjz\nO9fPbWRkxHn2zh2C/RwGe34S2FRAXgSstYnGmNFAD5wC8jqcUcL5QFFgF/AlcMbSgO4RzHuBkcAG\nwAJdgK84XUQOx1nJtZd7n9+MMb2A8caY7621519hJIfEHDpErzfe4mD0QYoULkLNGtWJGjXMJ/ev\nCxQtmjfj8OHDjIl6j+iYGEzNGkSNHJbttxHYuMny+NNdcblcuFwuhowcC0CbVi1489WXOBgdw959\nGY+anc0dbW6lYMGCTJ42k2FjxlGwQEFqVK/G/+6766z7nKu4Kl+uLKOHDGDQ8NHMnDufiNKl6dOr\nJ40bnj7/cz78mISEBF545bU0+xahFIWLFOLxbg9QqkwYR/+N5ZulPzJ55CySkpxRwbDSJSgdmfby\noUKFC9LkpoaM6Tc5w5iWLFhO/PET3Nu+LR1f+B/xx0+w9Y/tLJiacfEI5y6w9u0+QK9O/ejU81Ha\nPdiSA/tiGNJ7HD//uCG1T4GCBXj21Q6Uigjn5ImT7Ni6i3d6juCmWy8L6nP4VIf2dOrYPkvxn8/v\nGzfzeKdnUl+zwSNGAdCmdUte7dmdLX/8ySeLFnM0NpbSpUpx7dWNePrJDuTLm7t//frrfSanBHt+\n5/q57ftaLz9H5xvBfg6DPT9vaQ5kznAF6/1/JPc5eSRaP4y5WHJiwvk75WKtmzzt7xCy3aLvxvg7\nhGzlCsndRZuISCAILRYesFXa0Dv75tjfks/P7x2wr0N2030gRUREREREJFNUQIqIiIiIiEim6Hoe\nERERERHJ9TQHMmdoBFJEREREREQyRSOQIiIiIiKS6+k+kDlDI5AiIiIiIiKSKSogRUREREREJFN0\nCauIiIiIiOR6WkQnZ2gEUkRERERERDJFI5AiIiIiIpLraQAyZ2gEUkRERERERDJFBaSIiIiIiIhk\nigpIERERERERyRTNgRQRERERkVwvjyZB5giNQIqIiIiIiEimaARSRERERERyPRcagcwJGoEUERER\nERGRTNEIpIiIiIiI5HqaApkzNAIpIiIiIiIimaIRSBHxiVNHj/o7hGz12Q9R/g4h2yXExfo7hGyV\nt3ARf4cgIiLZSKuw5gyNQIqIiIiIiEimqIAUERERERGRTFEBKSIiIiIiIpmiAlJEREREREQyRYvo\niIiIiIhIrufSIjo5QiOQIiIiIiIikikagRQRERERkVxPA5A5QyOQIiIiIiIikikagRQRERERkVxP\ncyBzhkYgRUREREREJFM0AikiIiIiIrleHg1A5giNQIqIiIiIiEimqIAUERERERGRTFEBKSIiIiIi\nIpmiOZAiIiIiIpLraRXWnKERSBEREREREckUjUCKiIiIiEiupwHInKERyABhjHndGLMuQOL42d9x\niIiIiIhI4An6EUhjzGSguLX2jnTt1wMrgBLW2iN+Ce5Myf4OABgEjPR3ENlp1pz5TJkxk4PR0Zga\nNXi5ezcurVvH32H5lL9yPHAwmpETJrNy9VriT5ygUvlyvN79OWrXuCTD/m8MGsbCZV/icrlITj79\n41+tciXmvDc22+Jcs/5XhkVN4O9/thNZpjTtH7iX226+KXX7h599zqIvvuSvf7YBULvGJTzX5Zkc\n+zk5duwYo8a9y/KvvyEm5hC1axl6Pt+VunVq58jzA0yaMZvREybzwF3t6P70Uxn2eb3/YD79fNkZ\n5696lcrMnfxutsW2Zt16ho6L4q+t24iMKMPj/7ufNi1uTt2+YOFiFi5dxl9b3eev5iU827E99a66\nyuexrF33C+9Pm8nGzZs5cDCaEYP60/T661K3R8fEMHTkWH5ctYqjR2O56soreLl7NypVrODzWHJa\nsL+XKr/cL9hzDPb8vJFHQ5A54mIfgQyEgi2gWGuPWWsP+TuO7LJk6RcMHjGKzk90YM7096lZ4xKe\n7NKNQ4cP+zs0n/FXjkdjY2nf7UXy5cvHmHfeZP7E8XR78nGKFSly1n1efPpJln4wnc9nT2PpB9NZ\nPHMKxYoWpbnHH+BZtWfffurffOtZt+/eu4/nXu1Dwyv+w6yo0dzfri19h47kx7WnLwD4+dcNtGh6\nA+8O7s/7I4cSUbo0TzzbjQMHD3odV1a81vcdfly9hv5932DB7Olc3agBHZ/ummPP//tmy4KFn1Gz\nerVz9uvRpTNfLJjNsvmz+GLBbJbMnUnxokVp3vS/Xj/37r37uLLpLWffvmcvXV7uTcMrr+CDieN5\n4M529B00jB/XrE3t8/P6X2nZ7EbeGz6IqWNHEFmmDJ26v5wtr9/x4/GYmjXo1bN7hos3dOnek917\n9jBq6CDmzphC2cgIOj7dhfj4Ez6PJScF+3up8sv9gj3HYM9PAlvQj0BmljHmTqAPcAmwBxhlrR3q\nsT0JuN1a+4lH2yGgq7V2qjEmHzAMuAMoCewFxltrB7j7FgeGAG2A/MBq4Hlr7a9niac6sBRYZK3t\n4m5rAvQD6gMHgI+Al621x9zbQ93b7wNKABuAl6y1X7u3PwIMBx7FGWmsCHwNdLDW7nT3ed2d5xXu\nx5Pdx/oOeAEIBWa7805094kEJgJNgd3AK8AAYJi1NqBGM6fN+oC727WlTeuWALz2cg++Wfk9H36y\nkPYP/8/P0fmGv3KcPHsukaVL89oLXVPbykaUOec+hQsVonChQqmPV6z8gdjY2DSjgeCMCM6Y/yG7\n9+6jXGQE995+G3ff1vqsxz3XKmzzPl1E+bKRdO3YHoAqFSvwy2+/M3P+RzS+6goA+r7UPc0+vZ/v\nwvLvvufHVWu4rVWLc+Z0oU6cOMGXK75i1NBBXFHvcgA6dXycr75dyQfzPuSZpzpm6/MfO3acXm8P\n4LUXu/He1Jnn7HvG+ft2JUdjY9OMBoIzIjh97nx279lLubKR3HdHW+5pe9tZj3uu8zf3k4VUKFeW\n59yvQ5VKFVm34Temz11A4/rOCONbvXqm2ee1F7vx5TffZsv5a3JNY5pc0xggzSgswLbtO9jw20Y+\nmjODalWqAND7pRe5ocWtLF66jHZtzv5BR6AL9vdS5Zf7BXuOwZ6ft7QKa864mEcgU3/CjDFXAR8A\nM4FLgdeBvsaYh7NwvK7ArcBdQE3gQeAfj+3zgHDgFuBK4GfgC2NMifQHMsZcDnwLTPcoHqsDi4G5\n7hjvBa4FRnnsOgZoBNwDXObuu9i9b4pCOAXe/4BrcIrDWelCSD8y2xSoBtwAPIxTgD7qsX0aEAn8\n151/J6B0+rz87VRCAhs3baZRg/qpbS6Xi8YNGrB+w29+jMx3/Jnjtz+uonbNGvTs+w7N736QBzp1\n4cPPPs/SMT5ZspSGV/6HyDKnf3w++3IF706bwTOPP8L8SVE83f4Rxk+ZzqJly72Kc8MmS6Mr/5Om\nrXH9K/l106az7nM8Pp6EhASKFy/m1XNmRWJiIolJSYSG5kvTXiB/ftatX5/tz//OiFH89+rGNLzy\niizv+9Hiz2l01ZVEljn9wcFny74k6v1pPNuxPQumTuTZDo8xbtJUFi79wqv4ft24iYZXpY3tmgb1\n+fX3852/xBw5f55OnjyJy+UiNF9oalvK459/yf5zmV2C/b1U+eV+wZ5jsOcnge9iGYG8zRhzNF1b\niMf33YAvrLX93I//NMbUBV4EpmbyOSoCf1hrv3c/3pGywRhzLc6oYRlr7Sl3cw9jTDucgmuCR9+r\ngYVAX2vtcI/jv4RTUKYUjH8bY54DvjLGdMIp4B4FKlpr97r7DDXGtAQeA151t+UFnrbWrnE/3yPA\nJmNM/ZS2DMQAz1hrk4EtxphFQDNgojGmlvv7q6y169zH7AD8kYnXLEcdPnyYxKQkwsPD0rSHh5fk\nn+3b/BSVb/kzx1179jJv4Wf87652PP7AvfxmtzBobBSh+fLRuvmN593/QHQMK1ev5Z10o0fvTp1J\ntyc7cMM1VwPOqObf/2xj/sLPMjxu+lGg9KIPHSKsRNrPbcJLliTu2HFOnjx1RuEGMHLCZMqUKc3V\nDRucN48LVahQIepddilREydTtUplwsPCWLRkKes3/Jbt8+aWfLkC++ffzIganeV9D0RH8/1Pq3nn\ntVfStI9/fxrPd36Cpk2uAaBcZAR//bONeZ8s5NZ0I80AnO/8xRwivGTJNG1hYSWIO3aMkydPEhoa\nesY+I6ImULpUeI6cP09Vq1QmMqIMI8aMo/fLPShYoABTZ85m3/79HDgYnaOx+FKwv5cqv9wv2HMM\n9vwk8HldQBpjwoAKQIH026y1qy4kqGywHHgKj1FHoDHOyBlAbZzLQT2tBLoaY1zuwul83geWGWMs\nsARYaK1d5t5WDygKxBhjPPcpAHiODlYGlgGvZHDpZz3gMmOM53UJKflUdR8nBKfA88wzFPCc+JPg\nWShaa60x5jDOa3C2AvL3dK/BHpxRUHBGW0+lFI/uY/7lvrxXLiJJyUnUrWno/OhDANSsXo2/tp69\n0Evv06VfUKxoEa53Xw4IzsjRzj17eHPICPoOPf1fIjEpiaKFC6c+vqdjZ/bs2w+cLiCva3NX6vYr\nLqvLyLf7eJXX5NlzWPb1d7z/7ljy5TuzuMwO77z5Oq/17UezVm0JCQmhTi1Dq1uas3GTzbbn3Ltv\nP4NHj2f8kP7ky5v1Xw2fLFlK0aJFuOHaa1LbjsfHs3P3HvoMHMqbg4alticlJVGkyOnzd9ejHc84\nf9e2bJu6/cp6lzGq/1tZjgmcxYCWrfiGCSMG59j5S5E3b16GD3qH1/u+Q5NmLQgJCaFxwwZcd+3V\n5/2gQ0REJFBl+a8EY0wlnGLp+gw2u3AufwzJYJs/xVlrt3o2GGMqZvEYyaQtQAFS/xqx1q4zxlQB\nWgI3AXOMMcustfcARXDmBl6fwTE8Zzvvd/e73xgz2VrrOWpaBIgCRmRwjO04BWYCzuWxSem2x2Ym\nwXM4le5xMrnw8ucSJUoQkicP0dExadqjow9RKjzcT1H5lj9zLBUWRtVKaUfIqlaqyIqV359lj7Q+\n/XwZrW+6kbwhp98+jh+PB5w5iHVr1UzTPyTP6X4j3+5DQmICAPsPRPPkiy8zO2o0ye6rsfOH5k/t\nG16yJDHpFhmIPnSIwoUKnjH6OHXufKbOmc+4gf245DwLyvhShfLlmDR+NPHxJ4iNi6NUeBgvvtKb\nCuXLZdtzbty8mUP//sv9TzydOgqYmJTEz79u4IMPP2HVskXnnFvyyeKl3Hpzc/Lm9Tx/xwFnDuKl\ntWul6Z8nz+m3kNED3yYhIRGAfQcO8ES3HsyZND51MDJ//tOjiuFhJYk+lPbzqZiYwxQuVOiM0cep\ns+cyZfZcooYOoHrVKpl7IXystjHMmf4+cXFxnDqVQIkSxXnwsY45uqKurwX7e6nyy/2CPcdgz08C\nnzcjkBMAA3QGNgInfRqRf2zCmU/oqQmwxWPk7QBQNmWjMaYGznzCVNbaWJx5h3ONMfNx5h+WwJnv\nGAkkWmu3nyOO4zjzKBcDnxtjbnYfE/cx6qQvhD3iWYdTuEdYa1ee4znyel6uapwh0RI459Ib1n3M\nKzwuYb0EZyGhgJIvb17q1K7FT6vXpC6zn5yczE+r1/DgfXf7OTrf8GeO9erWYduOXWnatu3cmWY+\n3NmsWf8rO/fs5fZ0i6+ElSxB6fAwdu7ewy1NM/rMyuE5ZzKlsCxfNjLDvpfVqcX3q9emaftx7Tou\nr532D/opH8xj8uw5jOn/FrUuqY4/FCiQnwIF8vPvkSOs/HEV3bs+k23P1bhhA+ZMikrT9nr/wVSt\nXJHHHrjvnMXjmnXr2bl7D7e3TrtATVjJkpQOD2fn7j20aNb0rPt7/oykFJbly5bNsO/ldeqwctXq\nNG0/rFnL5XXTnr/3Z81h0ozZjBv8DrXOchuZnFTYPWK+bfsOft+0mWc7PenniLwX7O+lyi/3C/Yc\ngz2/C6E1dHKGNwVkY6C9tXaer4PxoyHAKmPMqziL6VwDPI1z2WuK5cAzxpgfcV63/ngUz8aYbjiX\ndq7DGaG7B9hrrT2Ms1jOD8BHxpiewBagPNAKWGCt/TnlONba48aY1jhF5GJjTAtrbRzOqqY/GGNG\n4RTxcUBd4CZr7bPW2j+MMTOBqcaY7u44ygA3AuuttYvdT5EAjDLGdAUScRbh+d5am/Yv6kxyXwL7\nJfCeey5mAjAYOEYA3ibloQfuo3eft6hTuxaX1q3NtJkfEH8inra3tvJ3aD7jrxwfvON22nfrzqRZ\nc2h+/XX8ttny0eKlvNrt2dQ+oydOYX90NG/2eD7Nvh8vXsqltWpStXKlM4775MMPMnjsuxQuXIhr\n6l/FyVOn2LjlD2Lj4njgjtszjOVclwfedWsr5n6yiJHvTaJNi5tZte4Xvvx2JSPffiO1z/uz5xI1\ndQZvv9KDyDKliT50iHyJyRQqVJBCBQtm8ZXJuu9//Ink5GSqVK7Mth07GDZyDNWrVsnWc1ioYEGq\nV6mcpq1ggQIUL1aMau7zMuq9Sew/eJC+L/dI0++jz5Zwae1aqf08PfXYQwwaNc45fw0bcOrkSTba\nPzgaG8uDd99xRn849/m7u01r5nz0CSOiJtC25S2s+nkdX3z9bZpLXCfP/IDxk6fyzmsvE1mmDNEx\nzohl0TwhPj9/x44fZ8eOnakx79y1G7vlD4oXK0ZkZARLv1xOyRIlKRsZwZY//2Tg0BE0a3o9jRvW\nP8+RA1uwv5cqv9wv2HMM9vwksHlTQO4lOEYdU7kvP70HeBNnsZk9wKvW2mke3V4AJgHf4Fxm2hXn\nctEUR4EeOLcBScS5TYfn/+JWwNvuY5TGeR2/AfZlEE+ce/GbJcBCY0wra+0GY8z17mN8g3MZ6184\nBW+KR93xD8YpUA8CPwKfevRJKUZnAuXcx+pwvtfoPB7CuY3H1+68XsEpbuMv8Lg+16J5Mw4fPsyY\nqPeIjonB1KxB1MhhhJUMuAFTr/krxzqmBoNff5VRE99nwozZlI+MoHvnJ9KMHB6MiWHfgQNp9ouN\nO8aKlT/w4tMZj8jc3vIWChYowJQ58xn53mQKFCjAJVUr88AdbTPsD+dexrtcZAQj3nqDIePfZfZH\nn1KmVClee74rjTxWHZ2/aDEJiYn07PtOmn2f6tCeTu7bf2Sno7FxjBgzjv0HDlC8WDGa39iUZzs9\nQUhIzs4OSP8yHoyOYd/+9OcvjuXfrqRHl84ZHqNd65bO+Zs9lxHjJ1CwQAEuqVaVB+5qd47nPcf5\nKxvJyP59GTJmPLPmf0SZ0qV4vcfzNK5/+u143icLSUhM5MXX086bzI7z9/vGzTze6RlcLhcul4vB\nI5x1ztq0bknf13px4GA0g4aNIubQIUqXCqdN65Y80f5Rn8bgD8H+Xqr8cr9gzzHY8/OWbuORM1xZ\nnchvjLkfZ3SulbX2SLZEJdnCveLqMGtt2Hk7X9jzVMCZl9nMWrsis/udPBIdcCOWknknDwf3ukmh\nJYL/l3JC3IVOlw5seQsX8XcIIiK5Xmix8ICt0mZ0GJpjf0s+OOH5gH0dslumRiCNMXPSNVUCthlj\nVpN2ERiAZGvtvb4ITnIHY0xTnEV+NuCMag4E/sYZ3RQRERERyXYagMwZmb2ENf2KpSkrZRR1f8nF\nLR/QD+d2IkdxboFyv7U20a9RiYiIiIj4mTGmHM4UspY4i3D+ATzmuQ6KMeZNnGllJXD+lu5krf3T\nY3t+YChwL5Af+BzobK3d79GnJDAaZ1HOJGA+0NW9norPZKqAtNZe7csnFf+w1k4BpmTDcZcCl/n6\nuCIiIiIimZUnAIcg3XdkWAl8CdyCs0ZJDeCQR5+ewDPAw8A/wFs4d2Soba1NWXtmOE4BeidwBBiD\nUyBe5/F0M4EIoBnOveDfx7kNoOd95C+YN/eB7AFMtdbuzWBbBPCItXagL4ITERERERHJxV4Ctltr\nPRet3JauT1egr7V2IYAx5mGchTZvx7m3fDGgPXCftfZrd5/HgE3GmIbW2lXGmNo4BepVHrfWexZY\nZIzpnlHt5i1vbgb/Ds4cyIxUcG8XERERERG52N0GrDHGzDHG7DPG/GyMSS0mjTFVce4X/2VKm3uh\n0p+AlKtA6+MM/Hn2sTiLVqb0aQwcSike3b7Aua1eI18m5E0B6eLs9/eLAP71PhwREREREZGgUQ3o\nBFjgZmAcMNIY85B7eyRObZX+1n773NvAqbFOZnAHDM8+kcB+z43u9UhiPPr4RGZXYb0buNuj6R1j\nTEy6bgVwKt8ffRSbiIiIiIhIpgTgFEhwBuxWWWt7ux+vN8ZcCjwFTDv7boErsyOQxXBWYk1ZjTXC\n43HKV3FgAc6LISIiIiIicrHbA2xK17aJ01MC9+Jc4RmRrk+Ee1tKn1D3XMhz9SnjudEYEwKEefTx\nicyuwjoRmOgO5AfgcWvtRl8GIiIiIiIi4i1XYA5BrgRMujaDeyEda+1WY8xenJVTfwVwF4qNcFZa\nBVgLJLj7fOjuY3CK0B/cfX4AShhjrvCYB9kMpzj9yZcJZXkVVt3SQ0REREREJFOGASuNMS8Dc3AK\nww5AR48+w4FXjTF/4tzGoy+wE/gYnEV1jDETgaHGmEM4910fCay01q5y99lsjPkceM8Y0wnnNh6j\ngFm+XIEVvL+Nx7kkW2sHeRmPiIiIiIhIULDWrjHGtAP6A72BrUBXa+1sjz4DjTGFcO7ZWAL4Fmjp\ncQ9IgG5AIjAPyA8sAZ5O93QPAKNxVl9Ncvft6uucXMnJZ1tQNWPGmOMZNOd3/3sKSLTWFrrQwOTi\nc/JIdNZ+GCWgnDx86PydcrHQEiX9HUK2S4iL9XcI2Spv4SL+DkFEJNcLLRYekNeJAsztNDzHAa6o\nnAAAIABJREFU/pa8e9xzAfs6ZDdvLmEtmL7NGFMQZ1naPsD9PohLREREREREAkyWC8iMWGuPAx8b\nY8rgDL3+1xfHFRERERERyYwAXUQn6GT2Nh6Z9Q9wlY+PKSIiIiIiIgHAZwWkMaYs8ALOxFARERER\nEREJMt6swnoASD9BNRQoCpwE7vRBXCIiIiIiIhJgvJkDOZEzC8h4nHuVLLTW7rvgqERERERERLJA\nUyBzRpYKSGNMXmAqsNdaG5M9IYmIiIiIiEggyuoIZDLwC3ArsNT34YiIiIiIiGRdHg1B5ogsFZDW\n2kRjzDac+Y4iIqnyFSvm7xDkAuUtXMTfIWSr5MQEf4eQrVwhPrkzl4iIyDl5swrrAKCXMaaUr4MR\nERERERHxhsuVc18XM28+rrwFiAS2GWPWAvtIu6hOsrX2Xl8EJyIiIiIiIoHDmwKyArDN/X0+92MR\nERERERG/cV3sQ4M5JMsFpLX26uwIRERERERERAJbpuZAGmP+NsbUy+5gREREREREJHBldhGdKkD+\nbIxDREREREREApw3q7CKiIiIiIjIRSgrcyCTz99FREREREQk52kNnZyRlQJyhTEmKRP9kq21xb0N\nSERERERERAJTVgrIWcDO7ApERERERETEW7qNR87ISgH5nrV2VbZFIiIiIiIiIgEty/eBFBERERER\nCTQagMwZWoVVREREREREMiWzI5B90PxHEREREREJUJoDmTMyVUBaa/tkdyAiIiIiIiIS2HQJq4iI\niIiIiGSKCkhJwxiz1RjTxd9xiIiIiIhI4NEqrLmUMWYy8AiQDKRc8J0MfG6tbZWJ/R8BhltrS6bb\nVB+I82WsgWbWnPlMmTGTg9HRmBo1eLl7Ny6tW8ffYflUMOV47NgxRo2fwIpvviUm5hC1TU16PN+F\nurVrAdD7zX588tmSNPtc27gRY4cP8ke4WTJn/od8MP9Ddu/eA0D1alV5qkN7mlzTmISEBEaOi+K7\n739k567dFC1SmMYNG/DcM50oXaqUnyP3XlJSEmOiJrBoyVKio6MpXboUbW9txZOPP5btz732l/W8\nP30Wm+wWDhyMZviAt2n63yZn7b9u/QaGjxnP1m3biY+Pp2xkBHe1a8ND992TrXGuXruOwSPH8NfW\nfygbUYYOjz5E29YtU7fP/3ghny5ewp9/bwWgjjF06dSRyy67LFvjSi+Y3mcyovxyv2DPMdjz84am\nQOYMjUDmbouBSI+vssD9mdzXhVNwpmGtjbbWxvsswgCzZOkXDB4xis5PdGDO9PepWeMSnuzSjUOH\nD/s7NJ8Jthxff3sAP61Zyzt9erNg1lQaN2rAE89048DBg6l9mlzdiBWLP2b5Z87XgLde92PEmRcZ\nUYZuz3RmzvTJfDBtMg0bXEWX7j35e+s/xMefwG75g04d2zN3+vsMH9Sff7Ztp8sLPf0d9gWZOGUa\n8z78iFd7dueTebN4/tmnmTxtBjPnzMv25z5+PJ5aNWvQ68VumVpooWDBAtx/9x28P34UH38wnSfa\nP8LoqInM/3ih1zHs3rOXeldff9btu3bv4ZnuL9Go/lXMnTaJB++9iz79BvLDT6tT+6xd9wutbm7O\npDEjmT5hPJERZXiy6wtp/k9kt2B7n0lP+eV+wZ5jsOcngc2VnHxGDXEGY8x/s3JQa+03XkckmeIe\ngSxurb3jLNu7AY8B1YAY4FPgRWvtMWPM9cAKTo9eJgN9rLVvGmO2AsOstSPdx0kCOgKtgVuAXcAL\n1tpPPZ6rDTAYqAB8B0wDpgAlrLVHMpvTySPR5/9hvEAPPtaRy+rW4aXu3QBITk7mpltv58F776b9\nw//L7qfPEf7KMTkp0efHPHHiBFc3vYWRQwbQ5OpGqe33PdKB665pzNNPdqD3m/2IjYtj2IC3ff78\nnlx5QrL1+Cma3NSCF7o8Q7s2t56x7feNm3jgsY58/skCIiPK5Eg8vvZMtxcpFR7GG6++nNr2fM9X\nKFCgAP36vJatz52cmJD6fb2rrz/vCGRGur30KoUKFeTt13qlts3/eCHTZn3Arj17KF+2LPfffSf3\n3nl7hvvv3rOXVnfexy/ff5Xh9mGjx/HdDz8xf8b7qW09evchNjaWscMyHlVPSkqiSfPWvNLjBW5r\n1SJL+Xgr2N9LlV/uF+w5+jO/0GLhATvOt7j72Gz/WzJFy8GdA/Z1yG6ZHYH8Cqfg+Mrj+xUebSvS\nfYn/JQLPAnWAh4GmwED3tu+B54AjQATOyOXgcxzrNWA2cBnwGTDDGFMCwBhTFZgLLADqAROAfmQw\nuulvpxIS2LhpM40a1E9tc7lcNG7QgPUbfvNjZL4TbDkmJiaSmJREaL58adrz58/Pz+s3pD5e/fM6\nbmjRhjb3PMhbA4fw77+Z/twiYCQlJbF46TLi4+Opd/mlGfY5cjQWl8tFsaJFcjg63/nP5Zfx4+q1\nbNu+AwC75Q/Wrd/Addde7efIzm+T3cKvv/1OgyuvSG1btGQp4yZMpmvnJ/h49nS6dHqCse9N5NPF\nn3v1HL/+vpHGHv9/Aa5t1JD1v/1+1n2OH48nISGB4sWLefWcWRVs7zPpKb/cL9hzDPb8JPBldg7k\nFR7flwEm4hSK84B9OEXI3cANwOM+jE/O7TZjzFGPx8lAP2tt/5QRRLftxpjewDjgGWvtKWPMv0Cy\ntfZAJp5nsrV2DoAx5hWgC9AQWAo8CWy21r7k7vuHMeYy4JULS833Dh8+TGJSEuHhYWnaw8NL8s/2\nbX6KyreCLcdChQpR77K6RE2aQtUqlQgPC+Ozz5fx62+/U6liBQCaXNOYm268nvLlyrFj5y5Gjo2i\nc7cXmT5xfK64H9Qff/7F/x5/kpMnTlCocCGGD+xPtSpVzuh38uRJho8ZR6tbmlOoUKGcD9RHHn/0\nIWLj4mhz9/3kyZOH5ORknu30BC1vbu7v0M6qeZs7OXT4X5ISE3mqw2PcfuvpaebjJkyme5fONP3v\ndQCUKxvJn39vZe6Cj7mt5S1nHOt8V/0cjI4hPCzt1PSwsJLExR3j5MmThIaGnrHPsDHjKVO6FFc3\nbOBNelkWbO8z6Sm/3C/Ycwz2/C5ELvi1HxQyex/I9SnfG2PmAbOttT3SdfvUGDMI6AQs812Icg7L\ngac4vYgOOJerYoy5CXgJqAUUwznX+Y0xBbyY45g61OO+BPYIzgcJADWB1en6r8ri8UXOql+f3rze\ntz833XoHISEh1DY1aXnzTWzabAG45aYbU/teUq0qNS6pRus77mP12nU0rH+lv8LOtKpVKjNvxhRi\n42JZ9uUKer3xJpPfHZumiExISOCFl17FBbzas7vfYvWFJcu+4LPPlzLw7TepXrUKm7f8wYAhwylT\nuhS3tWp53v39YUrUGI4dP86vv/3O8DFRVKpQnhbNm3E8Pp4du3bzer+BvPHOwNT+SYlJFPUYJW73\nwCPs2bsXgJT6sfGNp4vLq/5TjzFDT++fFROnTmfplyuYNG4k+dKN1IuIiGQHb1ZhvQXIcN4dzojU\nAu/DkSyKs9ZuTd9ojKmMM+dxDM5IYAxwHc7lpaFAVgvIU+keJ5MLF2AqUaIEIXnyEB0dk6Y9OvoQ\npcLD/RSVbwVjjhXKlWPiuJHEx58gLi6O8PAwevR6nfLly521f4kSxdm+c2euKCDz5s1LxQrlAaht\nDBt+38SM2XPp/dKLgLt4fPlV9u7bx4Rxo3L16CPA0JFj6fDoQ6mF/yXVq7F7zx4mvD8tYAvIcmUj\nAecDioPRMYybMJkWzZtx7NhxAN54pQeX1amdZp88eU6/RY4bNpCEBGeO8N79++nw9HPMmz459UL/\n/PlPjyqWCg8jOuZQmmPFxByicOFCZ4w+vj9jFpOnz+K9UcO4pFpV3ySbCcH4PuNJ+eV+wZ5jsOd3\nIfJoCDJHeFMExALNzrKtuXu7+NdVgMta291au8pa+ydQPl2fk4AvVgWxOLf+8NTQB8f1uXx581Kn\ndi1+Wr0mtS05OZmfVq/hP5fn7PL32SWYcyxQID/h4WEcOXKUlT+t4sbrr8uw3959+/n33yOULpU7\nf4kmJyVx8uRJ4HTxuHPXbiaMHUXxYjkzxy07xcfHpymuAPK48pCUlOSniLImKSmJk6ecz9TCw0pS\nulQpduzcRYXy5dJ8pRSdAJEREafbI532CuVO9/W8LUu9S+vy05q1aZ7z+1WrqXdp3TRtk6bN5L33\npzF++GBqm5rZlW6Ggvl9BpRfMAj2HIM9Pwl83oxAjgHeNMZEAB8B+3EuZ2wHPATkjvXzg0N+93nw\nlAD8CeQzxnTBGYlsgjNX0dM/QBFjzI3AeuCYtfa4FzFEAd2MMf1x5sZegXN/SgjAhXQeeuA+evd5\nizq1a3Fp3dpMm/kB8SfiaXvreW+dmWsEW47f/7iKZJKpUqkS23fsZNjocVSvWoW2rVty7Phxxk+Y\nzE1Nr6dUeDjbd+5k+OjxVK5UkWsaNzrvsf1txJjxNLmmMWUjI4iLO8aiJUtZs+4XotoPIyEhgW49\nX8Fu+YPRwwZzKiGBg+5Pm4sXL0a+vLnzNr7XX3ct706aQkREGS6pVpVNm7cwbdZs7mjbJtuf+9jx\n4+zYuSt1HuLO3buxf/xJ8WJFiYyIYMTYKPYfPJi6wurseR9SNjKCqpUrAbBm3S9MnfkBD953d+ox\nO3d8jAHDRlGkcGGubdyQk6dOsXGT5Ujs0bPeL/Jc8yDvbteW2fM/ZNjocdx+W2t+Wr2WZcu/YqzH\nJa6Tps5g7ITJDHjzNcpGRKSOQhQqWpRCBQte2IuUScH2PpOe8sv9gj3HYM9PAluW/wKx1r5ljDmM\nM7/O80b2e4DnrLWjfBuinEMLYHe6NmutrWOMeQHogbMi6jc452uqR6cfjDHjgQ+AMKAP8CZnFn0Z\n/aWT2mat/ccYcxcwBGdxnR+At4GxwAnvU8seLZo34/Dhw4yJeo/omBhMzRpEjRxGWMmS5985lwi2\nHGPj4pw/7A8coHixYtzU9AaefaojISEhhOTJw5Y//+LTzz7naGwspUuFc03jhjz9xOO5osCKOXSI\nXm+8xcHogxQpXISaNaoTNWoYjRrUZ/eePXzz3fcA3P2g85lMcnIyLpeLieNGUf/KK8516ID1So8X\nGD3+XfoNHEJMzCFKly7FPXfewZOPP5rtz71xk+Xxp7vicrlwuVwMGTkWgDatWvDmqy9xMDqGvfv2\np/ZPTk5ixLgodu/ZS0hICBXLl+f5Zztx1+2ni9072txKwYIFmTxtJsPGjKNggYLUqF6N/91311nj\nONfiTuXLlWX0kAEMGj6amXPnE1G6NH169aRxw9MXesz58GNndPqVtLc9eapDezp1bJ/l18UbwfY+\nk57yy/2CPcdgz89buoI1Z2TqPpAZMcbkwbnvX1mc4nGntTZ3XIMk2coY0wt4wlpbOSv75cR9ICX7\nZMd9IANJTt0HUrKP530gg5ErJPA/NBGR3C+Q7wO5rOe4HPtbsvmATgH7OmQ3r3/buIvF7e4vuYgZ\nYzrhrMQajXO5bHdg5Dl3EhERERGRXMerAtIYY4A7cUYgC6TbnGyt1b0gLy41gFeBkjgfKAwC+vs1\nIhERERER8bksF5DGmIeAyTi3gtiGs5qnJ12GeJGx1j4PPO/vOERERETk4nWuOebiO96MQPYG5gHt\nrbXHfByPiIiIiIiIBChvCshyQCcVjyIiIiIiEig0AJkz8py/yxm+AS71dSAiIiIiIiIS2LwZgXwF\nmG6MiQeWAYfTd7DWxlxoYCIiIiIiIpnlyqMhyJzgTQH5s/vfcZx9wRzdME1ERERERCTIeFNAtkcr\nrYqIiIiISADRHMickeUC0lr7fjbEISIiIiIiIgHOm0V0RERERERE5CKU5RFIY8xWznMJq7W2mtcR\niYiIiIiISEDyZg7kx5xZQJYErgdcwIILDUpERERERCQrXJoEmSO8mQP5XEbtxphQ4CNg64UGJSIi\nIiIiIoHHZ3MgrbUngdHAi746poiIiIiIiAQOby5hPZdSQFEfH1NEREREROScdAVrzvBmEZ07MmgO\nBWoDzwDLLzQoERERERERCTzejEDOO0v7KZwFdJ71PhwREREREZGs0yI6OcObArJqBm3xwH5r7Tlv\n7yEiIiIiIiK5lzersG7LjkBERERERES8pQHInOHVIjrGmMLAo0ATIAyIAb4Fplhr43wWnYiIiI+4\nQny9bpyIiMjFJ8u38TDGVAR+BUYCBkhy/zsSWO/eLiIiIiIiIkHGm49jh7r/rWOttSmNxhgDLASG\nAPf4IDYREREREREJIFkegQSaA694Fo8A7se9gZt9EZiIiIiIiEimuVw593UR86aAzAscP8u240CI\n9+GIiIiIiIhIoPKmgFwJvGqMKe7Z6H7cy71dREREREQkx7hcrhz7uph5MweyO/A1sMMYsxzYB5QB\nmgGngPa+C09EREREREQCRZZHIK21G4DLgQlAOeBG97/vAfWstb/5NEIREREREZHz0BTInJGlEUhj\nTF6c4nGHtfb57AlJREREREREAlFWL2FNAn4EWgFf+D4cERERERGRrHPluciHBnNIli5htdYmAX8D\nJbMnHBEREREREQlU3qzC2g/obYwp5+tgREREREREJHB5swrr3UBp4G9jzK84q7Ame2xPtta29UVw\nIiIiIiIiEji8KSCLAJvTPRYREREREZEgl+UC0lrbNDsCERERERER8dbFfnuNnOLNHEgRERERERG5\nCGVqBNIY4wJuAvZba9d7tI1I1/WotbaXb0MUERERERE5N5eGIHNEZi9hvReYBlzt0ZYHeAbYA5x0\nt0UYY3611n7guxBFREREREQkEGT2Etb/AbOttWsy2HabtbaqtbYqMAp4yGfRBRljzPXGmCRjTLEc\neK4Vxpihvu4rIiIiIhKIXK6c+7qYZXYEsj7wdCb6fQM87H04OccY8yQwCChhrU1ytxUGDgPfWmtv\n9Oh7A7AcqG6t3XqBT518/i4ZM8Y8Akx2HyMZOAJsARYBI6y1Rzy6twNOXUCcQWvWnPlMmTGTg9HR\nmBo1eLl7Ny6tW8ffYflUMOV47NgxRo2fwIpvviUm5hC1TU16PN+FurVrARAdc4hho8fy46o1HDka\nS/0r/sNLL3SlUsUKfo78wgTLOZwweSpffvU1W7dtI3/+/Pzn8svo9kxnqlSulNrn1T5v8cmixWn2\nu/bqxowbMSSnw/WZYDl/5xLsOSq/3C/Ycwz2/CRwZXYEMgznfo+prLWJOPeE/Muj+V9339xgBVAY\npzhOcR3OJbmNjDGhHu03ANt8UDz6wr9AJFAe55LiKJyi/RdjTGRKJ2vtYWttnH9CDFxLln7B4BGj\n6PxEB+ZMf5+aNS7hyS7dOHT4sL9D85lgy/H1twfw05q1vNOnNwtmTaVxowY88Uw3Dhw8CEDXF19m\n9569jBzcn7nTJlE2MoInnu1GfPwJP0fuvWA6h2t/Wc8D997FzMkTeG/MSBISEnjy2efOOD9NrmnM\nV58vZMWST1mx5FMGvt3HTxFfuGA6f2cT7Dkqv9wv2HMM9vy85XK5cuzrYpbZAvIwUDZ9o7V2vrX2\nX4+msjgFTsCz1m4B9uIUhyluAD4CtgKN07WvADDGdDPG/GqMiTXGbDfGjHGPXOLeXskY84kxJsbd\nZ4MxpkW6p69vjFltjIkzxqw0xtTIQujJ1toD1tp91jEZuAbnfpwDPeJIc1mqMaazMWaLMea4MWav\nMWZOuuPmMcYMMMZEG2P2GGNe99xojClujJlgjNlvjPnXGPOFMeZyj+2XG2OWG2OOuLevNsZcmYW8\ncsS0WR9wd7u2tGndkmpVqvDayz0oUKAAH36y0N+h+Uww5XjixAm+XPE1zz/bmSvqXU6F8uXo1OEx\nKlYoz5z5H7Ft+w42/L6R3j27U6eWoXKlirza8wXi40+weOkX/g7fa8F0DseNGMJtrVpSrWoVal5S\nnbdef5U9e/excfPmNP1C84USVrIk4WFhhIeFUbRI7r3FcDCdv7MJ9hyVX+4X7DkGe34S2DJbQK7G\nWUjnfO4FVnkfTo5bAXje17Ip8BXwdUq7MaYA0AjnElaAROBZoA7OyF9TYIDHMcYCoUAT4FKgJxDr\nsd0FvAV0A64CEoBJF5KEtfYAMANo414dNw1jTH2cFXNfBWoCt+BcbuzpEXecDYEewGvGmGYe2+cB\n4e59rwR+Br40xpRwb58B7HDndCXQnwC7hPZUQgIbN22mUYPTg84ul4vGDRqwfsNvfozMd4Itx8TE\nRBKTkgjNly9Ne/78+fl5/QZOnnJ+xPKFnt7ucrkIDc3HuvW/5misvhJs5zC9o0djcblcFC+Wdir4\n6p9/5vpbWnPbXffxVv9B/PvvkbMcIbAF+/mD4M9R+eV+wZ5jsOcngS+zcyBHA4uMMb2Bt1PmDKZw\nFy29cObd3erbELPVCmCYMSYPzuWs/8EpHkOBJ4E+OKN7oTiFJdbakR77b3e/JuNwVqQFqAjMs9Zu\ndD/+J91zJgOvWGu/AzDG9AcWGmNCrbUn8d5moChOkXcw3baKOMXhIvdlrTuA9en6/Gqt7ev+/i9j\nzDNAM5wisQnOpb5lrLUpRWEPY0w74C5gAlAJGGit/SPlGBeQS7Y4fPgwiUlJhIenvco6PLwk/2zf\n5qeofCvYcixUqBD1LqtL1KQpVK1SifCwMD77fBm//vY7lSpWoFqVypSNjGDkmChefak7BQsUYNqs\nOezbf4AD0dH+Dt8rwXYOPSUnJzNg6HCuqHc51atVTW1vcs3VNL+xKeXLlWXHzl2MGDOezs+9wPRJ\n7+a6y4SC+fylCPYclV/uF+w5Bnt+EvgyVUBaaxe7C50+wJPGmOU4RUgyUAGn0CgHDLDWLj77kQLO\nVziFYwOcuZtbrLXRxpivgUnueZA3AH9ba3cCGGNuAl4CagHFcF7D/MaYAtbaeGAkMM4YcwvwBTDf\nWrsh3fN6Pt7j/rcMsPMCckn5KyujRXqWAduArcaYJcAS4ENr7XGPPumHa/a4YwK4HKc4jTHGePYp\nAFR3fz8UmGiMeRgn77nW2r+9zEUkVb8+vXm9b39uuvUOQkJCqG1q0vLmm9i02RISEsLQ/m/xRr8B\nXNe8NSEhITRuUJ/rrmlMcrLX61VJNnlrwGD++vsfpk0cn6a9RfPTFztcUr0aNS6pTqt2d7N67c80\nrH9VTocpIiK5VC77zDHXyuwlrFhrXwFuAyzO4jkvA6/gXLZqcW7n8Up2BJldrLV/AbtwLkNtijP6\niLV2D06BfC1OAbkcwBhTGfgU+AW4A+dSzZTVaUPd+04EqgJTcS5hXWOMSb+CreelnSl/5Wb6XJxF\nHeCItfaMYRdrbaw71vuA3TgfBKxPdzuR9JebJnvEVMS93+VAPY8vg7OSLdbaPu4YFgI3Ar8bY9pe\nYE4+VaJECULy5CE6OiZNe3T0IUqFh/spKt8KxhwrlCvHxHEj+emrZSz7ZD4zJkWRcOoU5cuXA6BO\nLcOcqZP4/sslLP/sI8YOH8Shw/9Swb09twnGcwjw9sAhfLvyeyZHjaZ0qVLn7FuhfDlKlijO9h0X\n8pmafwTr+fMU7Dkqv9wv2HMM9vwk8GWpaLHWLrLWNsMpKCLdX0WstTdZaz/LjgBzQMo8yBtwX6bq\n9g3QEmdO4Ap321WAy1rb3Vq7ylr7J85qqGlYa3dZa9+11t4FDAE6Zl/4YIwpA9wPfHi2PtbaJGvt\ncmvtSzjFXxWcQi8zfsY514nW2r/TfaW+e1lr/7TWjrDW3uKO5TEvU8oW+fLmpU7tWvy0+vTtTJOT\nk/lp9Rr+c/llfozMd4I5xwIF8hMeHsaRI0dZ+dMqbrz+ujTbCxcuRInixdm2fQcbN1uaptueWwTj\nOXx74BBWfP0tE8eNpmxk5Hn77923n8P/HjlvoRmIgvH8pRfsOSq/3C/Ycwz2/C6EVmHNGZmdA5mG\n+xYe+30ci7+sAMbgvBZfe7R/gzP3Mx+nC8g/gXzGmC44I5FNcOZKpjLGDAMW49yfMQynON3o0SWj\nn7is/BS6jDER7n1K4MzRfBk45P73DMaY1kA1d06HgNbu/Tdn1D89a+0XxpgfgI+MMT1xcisPtAIW\n4OQ3CGehna04cy4bAHOzkFeOeOiB++jd5y3q1K7FpXVrM23mB8SfiKftra38HZrPBFuO3/+4imSS\nqVKpEtt37GTY6HFUr1qFtq1bArD0yxWElSxBZGQEf/zxFwOGjaLZDf+lscfiArlNMJ3Dt/oP4rOl\nXzBqyAAKFizIQfcn5kWLFCZ//vwcO36c8e9N4qYbb6BUeBjbd+xi2OixVK5UkWuubuTf4L0UTOfv\nbII9R+WX+wV7jsGenwQ2rwrIILMCZy7fJvdqpim+xhlp3Wyt3Qdgrf3VGPM8ziql/XAKspdwLldN\nEYJTeFYAjuAUk897bM9oYlZWJmsVw7mcNNl9fAtMBka6L1XN6JiHcS65fd2d6x/AfdbazRn0PZtW\nwNs4K8aWxrkFyjc49wdNxFm8ZwoQgbOIz3zgjSzklSNaNG/G4cOHGRP1HtExMZiaNYgaOYywkiX9\nHZrPBFuOsXFxjBgbxf4DByherBg3Nb2BZ5/qSEhICAAHo6MZPGI0MYcOUyo8nDatW/DEYw/7OeoL\nE0zncM6Cj3C5XLR/6pk07X1f60Wb1i0JyZOHLX/8ySeLFnM0NpbSpUpx7dWNePrJDuTLmzt/RQXT\n+TubYM9R+eV+wZ5jsOfntQudECaZ4tJCExIoTh6J1g9jLpaclOjvELKVK0+Iv0MQERHxu9Bi4QF7\n/eaaIVNy7G/J+i88ErCvQ3ZTnS4iIiIiIiKZkjuvDwpSxpjfgMoZbEoGnrTWzsrhkEREREREcoWL\nfXGbnKICMrC0xFm0JyP7cjIQERERERGR9FRABhBr7Q5/xyAiIiIiInI2mgMpIiIiIiIimaIRSBER\nERERyfVywxRIY8xLOLcDHG6tfd6j/U2gA8593lcCnay1f3pszw8MBe4F8gOfA52ttfuL7NDyAAAg\nAElEQVQ9+pTEuZ3grUASzm31ulpr43yZg0YgRUREREREspkxpgHwBLA+XXtP4Bn3toZAHPC5MSbU\no9twoDVwJ/BfoBxOgehpJlAbaObu+18gytd5qIAUEREREZFcz+Vy5dhXVhljigDTcUYZD6fb3BXo\na61daK39DXgYp0C83b1vMaA90M1a+7W1dh3wGHCtMaahu09t4BbgcWvtGmvt98CzwH3GmEgvXs6z\nUgEpIiIiIiKSvcYAn1prl3s2mv+zd9/xUdT5H8dfSxVFWlApFor4oShWEAUPFRvYzrNjO8vZzsbp\nqViw4Z2giIgcIqIIByiKBRsiiv1+KrZTkY+doic9gCAiZH9/zASWGHQhm53M5P18PPaR7Mxk9/PZ\nySb7mW8zaw40Al4q3ubuS4G3gX3CTXsRDD3MPMaBWRnHdAIWh8VlsSkEywHunctENAZSRERERERi\nr6KOgTSzk4DdCArBkhoRFHkll+ybG+4D2AZYFRaWGzqmETAvc6e7rzGzRRnH5IQKSBERERERkXJg\nZtsSjF88yN1/iTqeXFAXVhERERERib9UKn+37O0JbAW8b2a/mNkvQFfgUjNbRdCKmCJoZcy0DfBD\n+P0PQI1wLORvHbN15k4zqwo0yDgmJ1RAioiIiIiIlI8pwC4EXVh3DW/TCCbU2dXdvyYo8LoV/0BY\nKO4NvBVueg9YXeIYA7YH/hNu+g9Qz8x2z3jubgTF6du5TEhdWEVERERERMpBuAbj9MxtZrYcWOju\nn4Wb7gKuM7MvgW+BW4A5wFPhYyw1sxHAnWa2GFgG3A286e7vhMfMMLMXgOFmdgFQAxgMjHP3nLZA\nqoAUERERERHJn3TmHXfvb2abE6zZWA94Heju7qsyDusFrAEeA2oCk4C/lnjcnsA9BK2eReGxl+Y6\n+FQ6nf79o0TyYNXShfpljLF00ZqoQyhXqSpVow5BREQkcjXqFFTQuU7ho3vG5O2z5K4XnVJhX4fy\npjGQIiIiIiIikhUVkCIiIiIiIpIVjYEUEREREZHY27jVNWRTqYAUkZzQGEERKXdJn7dBn35FJAZU\nQIqIiIiISOyldBEmLzQGUkRERERERLKiFkgREREREYk9NUDmh1ogRUREREREJCsqIEVERERERCQr\nKiBFREREREQkKxoDKSIiIiIi8adBkHmhFkgRERERERHJilogRUREREQk9lJV1AKZD2qBFBERERER\nkayoBVJERERERGJPQyDzQy2QIiIiIiIikhW1QIqIiIiISPypCTIv1AIpIiIiIiIiWVEBKSIiIiIi\nIllRASkiIiIiIiJZUQEpIiIiIiIiWdEkOiIiIiIiEnuaQyc/VEACZvYgUNfd/xR1LJvCzIqAP7r7\nxBLbY51XeRk3fgIPjRnLgoULsVat6H1FL3Zu1zbqsHIqKTne/+AoXnrlVb6ZOZOaNWuyW/td6HXR\nhTTbYfu1x7Tv2JlUKkU6nV7vZy+/5K+ccWrPfIecM0k5h7/n/pGjuPtfwzj15BO4stelUYeTM0k+\nf+998CEjR49l+owZzF+wkEG338YBXfeLOqxNNnT4Awy9/4H1tjVvtgNPPTJm7f7nX5zCD3PnUb16\nNdq2Ni654Dx2ifH5HD/hCR6Z8ATff/8/AFq2aM7555xFl307RRxZbiX5fQjJz08qLnVhlUpl0uQp\n3DFoMBeeew7j/z2SnVrtyHmX9GJxYWHUoeVMknJ878OP6HnicYx98H6GD7mb1atXc97Fl7Fy5c9r\nj5k66Wlefn4iUyc9zdRJT3Pz9ddQpUoVDu52QISRl02SzuFv+eTT6Tz2xESs1Y5Rh5JTST9/P/20\nEtupFddedQWphFzu37FlC16Z9DRTnw9uo+4bunZfsx2259q/X84T40Yzevi9NG3cmPMu7kVh4ZII\nIy6bRttsTa+LLmT8vx/kkdEP0rHDnlxyxVV8/c23UYeWM0l/HyY9v02VqpLK260yUwvk7zCzusAA\n4CigJvAu8Dd3/2+4vwVwJ9AJ2AL4DOjt7i+F+28Furl7pxKP+xHwqLv3De+fA/wNaA58Awx296Hk\nkJnVAO4ATgTqANOAXu4+LdzfFZgKHAbcBrQG3gJOBvYKX4emwDPA2e6+Mvy5FHA18BegEeBAX3ef\nkMv4c2H0uEc4/pijOerw7gD06X0lr735Fk9MfIazTj814uhyI0k5Dh00YL37fW+4jq6HHM70GTPY\nY7ddASho0GC9Y15+9TU67LkHTRo3zlucuZakc7ghK1asoHefm7npuqsZNmJk1OHkVNLPX5d9O61t\nqSrZ8h9X1apWpUH9+qXu637IQevd//tlF/P4xGf4/Msv6bjXnvkIL+f+0KXzevcvueA8xk94go8+\n/oQWzZtFElOuJf19mPT8pGJTC+TvewwoAA4F9gDeB6aYWb1wf23gWeAAYDfgeWCimW0b7h8DdDCz\n5sUPaGbtgJ3DfZjZKcCNQG+Cou0a4GYzOy3HudwOHAOcBuwOfAm8kJFLsRuAC4F9gO2B8cAlwElA\nD+AQ4OKM468BTgXOBdoCA4HRZlah+jT9sno10z+bwd4d9lq7LZVK0alDBz76+JMII8udpOe4bNmP\npFIp6tapU+r+hYsW8fqb/+HYo4/Mc2S5k/RzWOzW/gPoul/n9fJMgspy/pJm5uzZdDv8aLofczxX\n97mJH+bOLfW4X1av5tEnnmLLLWtjrVrlOcryUVRUxPOTX2TlypXs2n7nqMPJiaS/D5OeX1mkUqm8\n3SoztUD+BjPrTNDytrW7/xJuvtLMjgGOA+4PWyL/m/FjN5jZnwhaLP/l7tPN7L9AT+DW8JhTgLfd\n/Zvw/o3A5e7+VHh/Zlhkng+MzjLcceFYyEw1CIpbzGzz8PFOd/fJ4ba/AAcDZxO0LgKkgWvd/f/C\nY0YA/wBauPvMcNtjBAXz7WGrZm+CVta3w8f4NiwezwNezzL+cldYWMiaoiIKCtZvsSooqM+3s2ZG\nFFVuJTnHdDpNvzvvYvdd29OyRfNSj3nqmeeovcUWdDuga56jy50kn8Niz09+kRmff8HDox74/YNj\npjKcv6Rpv3M7+va5lmY77MD8BQsYOvwB/nzuX3n84dFsXqsWAK+98RZ/v64PK1f+zFYNG3Lf4Luo\nW7f0C1lx8cWXX3Hq2eex6uef2XyLzbmr/220aNYs6rByIunvw6TnJxWfCsjftiuwJbDIzDK3bwa0\nBDCzLYCbCFrmGhO8ppsRtNwVGwOcyboC8iSCrqTFhV1LYISZ3Z/xM1WBjenIfhnwUolt/VnXytwy\njO2t4p3uvtrM3gHalPi5jzO+nwusKC4eM7Z1CL/fEdgceDHsylqsOvDBRsQv8pv69ruDr77+ltEj\n7t3gMU8+/SxHdD+U6tWr5zEy2Rg/zJ1HvwGDGD5kENWr6V+QRK/zPnuv/b5Vyxbs0q4thx51LC9M\neZljjjwcgI577cljYx6isHAJE56cyOW9r2fcyOHUr1eyA098NG+2A4+NeYgfl//Iiy9N5dobb+bB\n+/6VmCJSKqnK3TCYN/rv/dtqA98DXfn1r2RxcTcA6AZcDnwF/ARMIGj9KzYOuM3MdiMYJ7ktQbfQ\n4ucAOAd4p8RzrNmIWOe6+9eZG8xsGVB3Ix6j2C8Z36dL3C/eVlyYFsffg+C1yvQzFUi9evWoWqUK\nCxcuWm/7woWLaVhQEFFUuZXUHG/tP4DX33yLh4YPZauGDUs95r0PPmTmrNkM+GffPEeXW0k9h8Wm\nz5jB4sJCTjjtTAjHz60pKuK9Dz5k3PgJvP/Wq7HuGpT081cZbFm7Njtsvx2zZ89Zu22zzWqyXdOm\nbNe0Kbu0a8sRx57E4089w9lnxHesWbVq1dhu26YAtDHj408/Y8zDj3L91X+POLKyS/r7MOn5ScWn\nAvK3vU8wKcwad5+1gWP2BUYWL6FhZrWBZpkHuPt3ZvYqwTjBWsCL7r4g3DfPzL4HWrr7w+WTBhAU\nt78AnYGHw1irEbQk3lmGx51OUCju4O5vlDXI8lS9WjXatmnN2+9OWzvlfDqd5u13p3HKScdHHF1u\nJDHHW/sPYOqrr/PgsCE0btRog8c9/tQztG1ttNqxZR6jy70knsNMnTp24PFx6/fMv+6mvrRo3oyz\nzzgt1sUjJP/8VQYrVqxg1pw5HNnjsA0eU5QuYtUvq/IYVflLFxWxalUyckr6+zDp+UnFpwJynXpm\ntmuJbTOA/wBPmtlVwOcEs5D2AB539/eBL4A/mdkz4c/cTOkN6GMJurrWIOhumukGYJCZLQUmEcz2\nuhdQz93vKnNmgLuvMLOhBOMWFwOzgSsJCtrMgUgb9enN3X80szuAgWZWFXiDoNWzM7DE3bMdw5kX\np/U8ietv6kvbNq3ZuV0bRo99hJU/r+ToI3pEHVrOJCnHvrfdznOTpzB4QD9q1arFgvBq65a1t6Bm\nzZprj/vxx+W8+PJUrux1SVSh5lSSzmFJm9eq9asxrLVq1aJu3TqJmf0xyecPYMVPPzF79py1M7DO\n+e57/PMvqFunDo0abRNxdBtvwN330LVLF5o0bsS8+fMZct/9VKtWjR6HHMxPK1cy/IGH2P8PXdiq\nYQGLC5cw7tEJzJ+/gEO7HRh16Jts0JB76bJvJxo32obly1fw7KTJTPvgQ4adNTDq0HIm6e/DpOcn\nFZsKyHW6ErQ4ZhpBUCzeSlBkbQX8ALxGMA4QgqU3RgBvAguAfgTjJkt6DLiHoBXwycwd7j7CzJYT\nFHT9geUE4xCzLR6znUf9aoICcVQY4zTgEHfPXMxqo+dkd/frzWxe+PgtCLr3vk8w+U6FctjB3Sgs\nLGTIsOEsXLQI26kVw+4euMHp2+MoSTmOf/xJUqkUZ51/0Xrbb+lz7dqpywEmvTgF+PV0+3GVpHOY\njZg3Ov5K0s/fp9NncPYFF62difCOQYMBOOrw7tzS59qIo9t4c+fN56o+N7JkyRLq16vP7ru2Z8yI\n+6hXry6rVq3im5kzmXj1JAqXFFK3bl12btOGh4YPjfUFj0WLF3PtjX1ZsHABtbeozU6tWjJs8MBE\nzYqc9Pdh0vPbVHHvxRIXqaSs4STxt2rpQv0yiojIhiX9M4s+/EoM1KhTUGF/UT8f9Vje/kjsdPpx\nFfZ1KG9qgRQRERERkdhTC2R+qICs4MysN3DNBna/5u6H5zMeERERERGpvFRAVnxDgUc2sO+nfAYi\nIiIiIlJhVfn9Q6TsVEBWcO5eyLo1J0VERERERCKjOl1ERERERESyohZIERERERGJPU2ikx9qgRQR\nEREREZGsqIAUERERERGRrKiAFBERERERkaxoDKSIiIiIiMSexkDmh1ogRUREREREJCtqgRQRERER\nkfhTA2ReqAVSREREREREsqIWSBERERERib1UFTVB5oNaIEVERERERCQraoEUEREREZH40yyseaEW\nSBEREREREcmKCkgRERERERHJigpIERERERERyYrGQIqIiIiISOxpCGR+qIAUERGReEj6p8N0OuoI\nyl/Sz6FIJaACUkREREREYi+lCxR5oTGQIiIiIiIikhUVkCIiIiIiIpIVdWEVEREREZH4q6IurPmg\nFkgRERERERHJilogRUREREQk9jSJTn6oBVJERERERESyogJSREREREREsqICUkRERERERLKiMZAi\nIiIiIhJ/GgKZF2qBFBERERERkayoBVJERERERGJPs7Dmh1ogRUREREREJCtqgRQRERERkdhLVVEL\nZD6oBVJERERERESyohZIERERERGJP42BzAu1QIqIiIiIiEhW1AJZCjN7EDgDSLNuRZk08IK798ji\n57sCU4F67r603AINnusM4C53r1/KviLgj+4+sTxjiJP3PviQkaPHMn3GDOYvWMig22/jgK77RR1W\nzo0bP4GHxoxlwcKFWKtW9L6iFzu3axt1WGV2/4OjeOmVV/lm5kxq1qzJbu13oddFF9Jsh+2jDi3n\nknoOiym/+Et6jknKb978+Qy8ZyhvvPV/rFy5ku23245b+lxD29YGwHU338rEZ59f72c677M3Q+8a\nEEW4OZOkc1iapOe3KTQLa36oBXLDngcaZdwaAydn+bMp1i8+N5mZVc3isHRZn6ey+OmnldhOrbj2\nqisS+0dm0uQp3DFoMBeeew7j/z2SnVrtyHmX9GJxYWHUoZXZex9+RM8Tj2Psg/czfMjdrF69mvMu\nvoyVK3+OOrScSvI5BOWXBEnPMUn5LV22jNPPuYAa1atz79138tT4sVxx2UXU2XLL9Y7rsm8nXpn0\nNFOfD279+94UUcS5kaRzWJqk5ycVWyqdVu1RUtgCWdfd/7SB/UXAX4DDgUOB74DL3f1pM9sB+IZ1\nBWQaeMjdzzKzFHB1+LONAAf6uvuE8HGLWy57AH2BnYFD3P2134j1DGCguzfYQJxrWyDNbGdgELAP\nsAKYAPzN3Zdn5F0PeAe4FKgJDAD+Gd7ODn/uencfmfE824bHHQIUAa8Dl7r7zA3FXZpVSxfm9Zex\nfcfOiWyBPOXMv7BLu7ZcfUUvANLpNAcd8UdOOfF4zjr91Iijy63FhYV0PeRwRt73L/bYbdeow8mZ\npJ9D5Rd/Sc8xsvzK4TPZwHuG8tHHnzBy2JANHnPdzbfy448/clf/f+b8+X8lTxdv9TtafmrUKaiw\nV+DnPDcpb58lt+1xWIV9HcqbWiA3XR/gYWAX4DlgjJnVA2YDx4bHtCJoubw0vH8NcCpwLtAWGAiM\nNrOSFcw/gauANsB/cxGsmW0OvAAsBPYEjgMOAgaXOPTAMOb9gF7AzcAzwCKgI3AvMMzMmoSPWy18\n3CVAZ2BfYBkwKdwnefTL6tVM/2wGe3fYa+22VCpFpw4d+OjjTyKMrHwsW/YjqVSKunXqRB1KziT9\nHCq/+Et6jknL79XX36Bdm9Zc3vt6uh52BCecdiYTnvz1yJZ33/+ArocdwZHHn0zffnewZEm5jsAp\nV0k7hyUlPT+p+PQBf8OONLNlGffTwD/c/bbw/oPuPh7AzK4BLgE6uvtkM1sUHjO/eAykmdUAegPd\n3P3tcP+3YfF4HkGrXbHr3f2ljYi1npkt5dddZjOvwpxC0KJ4uruvBD4zs4uAp83sKnefHx630N0v\nCb//wsyuAmoV521m/yRoRe0CjAdOAlLufm7xE5nZ2cBiYH9gykbkIWVUWFjImqIiCgrWb5AuKKjP\nt7M2qkG4wkun0/S78y5237U9LVs0jzqcnEn6OVR+8Zf0HJOW35zvv2f8hCc4/ZSTOPfM0/l4+mfc\nNuAuatSowZE9DgOgyz6dOPiA/WnapDGzv/uOQUPu5cJel/PvEffFcrhH0s5hSUnPTyo+FZAb9jJw\nPusXZYsyvv+4+Bt3XxEWcFv/xuPtCGwOvBh2ZS1WHXg/434aeG8jY10K7M6vC8gvM75vDXwUFo/F\n3iRohTaguID8tMRjzGX9XIvMbCHrcm0PtCpRbENQrLZEBaSUk7797uCrr79l9Ih7ow5FRKTCKipK\ns0vb1lx8fnCd13ZqxZdffc34x59cW0AednC3tcfv2LIFrVq2pMefTuDd996n4157RhK3yCaJ3/WO\nWFIBuWHL3f2b39j/S4n7aX67S3Dt8GsP4PsS+0rOALL898NbT1FpsZrZRj4MUHpev5VrbWAa0JNf\nv23nI3lVr149qlapwsKFi9bbvnDhYhoWFEQUVe7d2n8Ar7/5Fg8NH8pWDRtGHU5OJf0cKr/4S3qO\nSctvq4YFNG/ebL1tLZrtwJSpr27wZ7Zt2oT69eoya853sSwgk3YOS0p6flLxaQxk+VgVfs2cQXU6\nQaG4g7t/XeL2XR5i+gzY1cxqZWzrAqwhmMxnU71PMNZzfil5lWyVlHJWvVo12rZpzdvvTlu7LZ1O\n8/a709it/S4RRpY7t/YfwNRXX2fE0Hto3KhR1OHkXNLPofKLv6TnmLT8dmu/C9/OnLXetm9mzqJJ\n4w3//fxh7jwKlyxlq5gWI0k7hyUlPb+ySFVJ5e1WmakFcsNqmtk2JbatdveFWfzsTIJWuiPN7Dng\nJ3f/0czuAAaGS3O8AdQlmHhmibuPDn+2vH4jxwA3Ag+Z2U0EXVDvBkZljH/c1Me9AnjKzG4A5gDN\ngGOAfu5esrU1Uit++onZs+dQPPvwnO++xz//grp16tCoUcnTHU+n9TyJ62/qS9s2rdm5XRtGj32E\nlT+v5OgjfncJ0wqv722389zkKQwe0I9atWqxILz6umXtLahZs2bE0eVOks8hKL8kSHqOScrv9JNP\n5LS/XMD9I0dx6EEH8t9PpvP4xKe58ZqrgeD/4r3DH+CgA/enYUEBs2bPYeA9/2KH7bdj3332jjb4\nMkjSOSxN0vOTik0F5IYdxq+7mjrB7KmlTRG8dpu7fx8WU7cBDwCjgLPc/Xozm0cwCU0LoJCgBe8f\npT1ODmTG9JOZHUqwjMc7BMtxPAZcnu1j/Mbj/gHoR7AsyJYEy5q8RDA2s0L5dPoMzr7gIlKpFKlU\nijsGBZPQHnV4d27pc23E0eXGYQd3o7CwkCHDhrNw0SJsp1YMu3sgDerXjzq0Mhv/+JOkUinOOv+i\n9bbf0udajjq8e0RR5V6SzyEovyRIeo5Jyq9d2zbc1f8f3DXkXoaNGEnTJo25+m+X0f2QgwCoWqUK\nn3/5FROfm8SyH39kq4YN6dypI3897xyqV4vvx8QkncPSJD2/TRbDSZ/iSOtASoWR73UgRUREKpTK\n8JlMH/BjryKvA/nd5Ml5exM1PeSQCvs6lLf4XloSEREREREJxXHZmThSAVnBmdknwA6l7EoD57n7\nuDyHJCIiIiIilZQKyIqvO8FakaWZm89ARERERESkclMBWcG5++yoYxAREREREQEVkCIiIiIikgSV\nfH3GfKkSdQAiIiIiIiISD2qBFBERERGR2NMsrPmhFkgRERERERHJigpIERERERERyYq6sIqIiIiI\nSPypB2teqIAUEREREREpB2bWGzgGaA38BLwFXOXun5c47mbgHKAe8CZwgbt/mbG/JnAncCJQE3gB\nuNDd52UcUx+4BzgCKAImAJe6+/Jc5qQurCIiIiIiEnupVCpvt42wHzAY2Bs4CKgOTDazWsUHmNlV\nwEXAuUBHYDnwgpnVyHicu4DDgWOBPwBNCArETGOBNkC38Ng/AMM2JthspNLpdK4fU2STrFq6UL+M\nIiJSeVWGz2SaJTP2atQpqLAn8YdXXs7bm6jR/gdu0utgZg2BecAf3P2NcNv3wO3uPjC8XweYC5zh\n7uPD+/OBk9z9ifAYAz4DOrn7O2bWBvgU2NPdPwiPORR4FtjW3X8oQ7rrUQukiIiIiIhIftQD0sAi\nADNrDjQCXio+wN2XAm8D+4Sb9iIYeph5jAOzMo7pBCwuLh5DU8Ln2juXCaiAFBERERERKWdmliLo\nivqGu08PNzciKPLmljh8brgPYBtgVVhYbuiYRgQtm2u5+xqCQrUROaRJdEREREREJP6qVNjetcX+\nBbQFOkcdSFmoBVJERERERKQcmdk9QA9gf3f/X8auHwgWINmmxI9sE+4rPqZGOBbyt47ZusRzVgUa\nZByTEyogRUREREQk9iroLKzFxePRwAHuPitzn7t/Q1Dgdcs4vg7BuMW3wk3vAatLHGPA9sB/wk3/\nAeqZ2e4ZD9+NoDh9e6MC/h2ahVUqDM3CKiIilVpl+EymWVhjryLPwjr39Vfy9ibaZr/9s3odzOxf\nwMnAUUDm2o9L3H1leMyVwFXAn4FvgVuAdkA7d1+V8TjdgTOBZcDdQJG775fxXM8RtEJeANQAHgDe\ncffTNjXP0mgMpIiIiIiIxF/FvEBxPsEkOa+U2H4mMArA3fub2eYEazbWA14HuhcXj6FewBrgMaAm\nMAn4a4nH7AncQzD7alF47KU5zAVQC6RUIGqBlAqtMvytrJj/eEUkQdJFa6IOoVylqlSNOoRyV6Fb\nIN94NX8tkF26VtjXobypBVJERERERGJvY8cmyqbRJDoiIiIiIiKSFRWQIiIiIiIikhUVkCIiIiIi\nIpIVjYEUEREREZH4q6IxkPmgFkgRERERERHJigpIERERERERyYq6sIqIiIiISOxpGY/8UAukiIiI\niIiIZEUtkCIiIiIiEn9qgcwLtUCKiIiIiIhIVtQCKSIiIiIisZfSMh55oRZIERERERERyYoKSBER\nEREREcmKCkgRERERERHJisZAioiIiIhI/GkW1rxQC6SIiIiIiIhkRS2QUmZmNhX4wN3/FnUsIiIi\nIlI5pdQCmRdqgUwgMzvPzJaaWZWMbVuY2S9m9nKJY/c3syIza57/SKMxb/58eve5if0O6k6HLgdw\nbM/TmT7Dow4rp8aNn8BhRx/LXl3255Qz/8Inn06POqScGDp8BO07dl7vdvQJPaMOa5MNHf4A7ffu\nst7t6BNPKfXYm//Zn/Z7d2HMI4/mOcrcuv/BUZx8xtl02v8guh56OJf+/Wq+nTkr6rByLqnvwUxJ\nz1H5xceKFSvod+fdHPbH4+n4h4M44y8X8ulnM0o99pbb7mDXTn9gzCOP5TnK8nP/yFG079iZ/gMH\nRR2KVBIqIJNpKrAFsFfGtv2A/wF7m1mNjO37AzPd/ZuNfRIzq16WIKOwdNkyTj/nfGrUqMG9gwfy\n1KNjueLSi6mz5ZZRh5YzkyZP4Y5Bg7nw3HMY/++R7NRqR867pBeLCwujDi0ndmzZgldeeIapk55m\n6qSnGTX83qhDKpMdW7bglUlPM/X54DbqvqG/Oualqa/y8afT2XqrrSKIMLfe+/Ajep54HGMfvJ/h\nQ+5m9erVnHfxZaxc+XPUoeVM0t+DkPwclV+83HBrP96e9h7/vOl6Hh83ik57d+Dci3oxf8GC9Y57\n6ZXXEvO3tNgnn07nsScmYq12jDqUiiGVyt+tElMBmUDu/jnwA0FxWGx/4EngG6BTie1TAcxsOzN7\nysyWmdkSM3vEzLYuPtDMbjCzD8zsbDP7GviptOc3s8PNrNDMTs5lXrkwYuRoGhvsvhkAACAASURB\nVG2zDTdd15t2bVrTpHFj9tm7A9s2bRJ1aDkzetwjHH/M0Rx1eHdaNGtGn95Xstlmm/HExGeiDi0n\nqlWtSoP69Slo0ICCBg2oW7dO1CGVybp8glvJfObOm89tdw6i3y03Uq1q1YiizJ2hgwZwZI/utGje\njJ12bEnfG67jfz/MZfqM0lsL4ijp70FIfo7KLz5+/vlnXpr6Kn+7+EJ237U92zZtwgXnnMl22zZl\n/IQn1x43d958+t05iNtu7kPVBPwthaDltXefm7npuqvZMkEXwqXiUwGZXFOBAzLuHwC8ArxavN3M\nNgP2Bl42sxQwEahH0Fp5ENACeLjE4+4I/Ak4Btit5JOaWU9gDHCyu4/LXTq58errb9KuTWsu730d\nXQ89nBNO/TMTnpwYdVg588vq1Uz/bAZ7d1jX+JxKpejUoQMfffxJhJHlzszZc+jW4yi6//E4rr7+\nRn74YW7UIZXJzNmz6Xb40XQ/5niu7nMTP8xdl086nebaG2/hzNN60qJ5s8hiLE/Llv1IKpWibp14\nXwgoVhneg0nPUfnFy5o1a1hTVESN6ut3iqpZsybvf/QxEP4tvalv4v6W3tp/AF3367zeuazsUlVS\nebtVZiogk2sq0NnMqpjZlgTF3qvA66xrmdwXqEFQWB4EtCMo/D5093eB04H9zWzPjMetDpzm7h+5\n+3r/aczsQuAe4Ah3f77cMiuDOd9/x/gJT9Bs++25b/BdnHDsMdx2x0Cefq5ChrvRCgsLWVNUREFB\ng/W2FxTUZ8HChRFFlTvtd25H3xuu5d67B3J97yv57vv/8efzLmTFT6U2hld47XduR98+YT5X/53v\nvv8fZ5y7Lp8RD42mWvXq9DzhuIgjLR/pdJp+d97F7ru2p2WLZAzDTvp7EJKfo/KLl80335xdd2nH\nsAceYv6CBRQVFfHM8y/w308+XZvPiIf+TfXq1Tn5+GMjjjZ3np/8IjM+/4JLL7og6lCkEtIsrMn1\nCsE4yA5AA+Bzd19oZq8CD4TjIPcHvnb3OWZ2DDDb3b8vfgB3/8zMCoE2wHvh5pnuvqiU5zse2Aro\n7O7vlbK/QigqSrNLuzZcfMG5ANhOrfjyq68ZP+FJjuzRPeLo5Pd03mdd7+tWO7Zkl3ZtOfTIP/HC\niy9xzFFHRBjZpum8z95rv2/VskWQz1HH8sKUl7FWOzLmkcd4dPSDEUZYvvr2u4Ovvv6W0SPiPY5V\nRKL1j5uu54ZbbuOgI/5E1apVaWM70f2Qg/hshjN9hjN2/GOMH/1A1GHmzA9z59FvwCCGDxlE9Wr6\nKC/5p9+6hHL3r8zsO4Luqg0IWh9x9/+Z2WygM0EB+fIGH6R0yzew/X1gD+Bs1hWbFc5WDQto3myH\n9ba1aN6MKVNfjSagHKtXrx5Vq1Rh4cL1a/yFCxfTsKAgoqjKz5a1a7PD9tsxe86cqEPJibX5zJ7D\n8uXLWVxYyMFHHrN2/5qiIm6/azCjx41n0pPxno311v4DeP3Nt3ho+FC2atgw6nBypjK8B5Oeo/KL\nn22bNGHE0LtZufJnli9fTkFBA6689gaaNm3C+x/+l8WFSzjkyHWtj2uKirhj0D38++HxPP/E+Agj\n3zTTZ8xgcWEhJ5x2JqTTQJDTex98yLjxE3j/rVe1nIWUK3VhTbbicZD7E7RIFnsN6A50DI8B+AzY\nzsyaFh9kZm0JxkR+msVzfRU+19FmNrisgZeX3dq3/9WSAd/MnEWTxo0iiii3qlerRts2rXn73Wlr\nt6XTad5+dxq7td8lwsjKx4oVK5g1Zw4NE1KArMungCN7HMaEMQ/xWMZtq60acuZpPRl2951Rh1om\nt/YfwNRXX2fE0Hto3CgZ771ileE9mPQclV98bbZZTQoKGrB06TLefPsdDuy6H0f1OIzH/v0gj2bc\ntmrYkD+fejL3DhoQdcibpFPHDjw+bnTwv2HsKB4bO4p2bVpzRPdDmTB2lIpHKXdqgUy2qcAQgvOc\n2cT2GsFYxerhMbj7FDP7BBhjZr3CfUOAqe7+QTZP5u5fmtkBwFQzW+3uvXKXSm6c3vNETjvnfO5/\ncBSHHnwg//1kOo8/9TQ3XntV1KHlzGk9T+L6m/rStk1rdm7XhtFjH2Hlzys5+ogeUYdWZgMG3UPX\n/TrTpHEj5s1bwJD77qdatWr0OOTgqEPbJAPuvoeuXboE+cyfv14+devU+dXEMtWrVqNhQQE7bL9d\nRBGXXd/bbue5yVMYPKAftWrVYkHYCrJl7S2oWbNmxNHlRpLfg8WSnqPyi5e3/u8d0qRptv32zJo9\nh4H3DKVl82YcfXh3qlatSp06689QWq1avP+Wbl6r1q/GjdeqVYu6deskapKgTaLiOS9UQCbbVGAz\n4DN3n5+x/VWgNjDD3TOnsDwKGBzuLwKeBy7J4nnSxd+4++dm1o11ReTfy5hDTrVr24a7bv8nd90z\nlGEjHqRpkyZcfflldI9pAVKaww7uRmFhIUOGDWfhokXYTq0YdvdAGtSvH3VoZTZ33jyuuv5GlixZ\nQv169dl9t/aMeWA49erVjTq0TTJ33nyu6pORz67tGTPivg3nk4B/jOMff5JUKsVZ51+03vZb+lzL\nUYcnYxxykt+DxZKeo/KLlx+XL2fQv4Yxb/586tapw0EH7M/F5/9lg8t1JOBP6a8kMSepuFLpdPr3\njxLJg1VLF+qXUSquyvC3Up9ARKScpYvWRB1CuUpVScYak7+lRp2CCvvPYtF/p+Xtn3WD9ntV2Neh\nvGkMpIiIiIiIiGRFXVhFRERERCT2NIFQfqgFUkRERERERLKiFkgREREREYm/KmqBzAe1QIqIiIiI\niEhWVECKiIiIiIhIVlRAioiIiIiISFY0BlJERERERGIvlVLbWD7oVRYREREREZGsqAVSRERERETi\nT+tA5oVaIEVERERERCQraoEUEREREZHYS6kFMi/UAikiIiIiIiJZUQEpIiIiIiIiWVEXVhERERER\nib8q6sKaD2qBFBERERERkayogBQREREREZGsqIAUERERERGRrGgMpIiIiIiIxJ6W8cgPFZAiItnQ\nPyURkTJLVakadQgiUkYqIEVEREREJP50sTcvNAZSREREREREsqIWSBERERERib+U2sbyQa+yiIiI\niIiIZEUtkCIiIiIiEnupKhoDmQ9qgRQREREREZGsqIAUERERERGRrKiAFBERERERkaxoDKSIiIiI\niMSf1oHMC7VAioiIiIiISFZUQIqIiIiIiEhW1IVVRERERERiL6UurHmhFkgRERERERHJilogRURE\nREQk/lJqG8sHvcoiIiIiIiKSFbVAioiIiIhI7KWqaAxkPqgFUkRERERERLKiAlJERERERESyogIy\nh8zsQTMrMrM1ZrbKzL42s35mVjPq2HLBzKaa2Z1Rx1FW48ZP4LCjj2WvLvtzypl/4ZNPp0cdUs4l\nPcek5wfJz1H5xV/Sc1R+8Zf0HJOen1RcKiBz73mgEdAcuAw4D7gxyoB+j5lVmrGwkyZP4Y5Bg7nw\n3HMY/++R7NRqR867pBeLCwujDi1nkp5j0vOD5Oeo/OIv6Tkqv/hLeo5Jz2+TpVL5u1ViqXQ6HXUM\niWFmDwJ13f1PGdseBZq7+17h/W2BAcAhQBHwOnCpu88M9+8P9APaAb8AnwA93X22md0A/BEYClwH\nFADPAOe4+7KM5zwH+BtBEfsNMNjdh4b7dgi3nQRcCHQEzg8f5x7gD0B94CvgH+7+cEZuZwBpIBV+\nbe7us8xsZ6A/sB+wHJgM9HL3hRvz+q1aurDcfxlPOfMv7NKuLVdf0QuAdDrNQUf8kVNOPJ6zTj+1\nvJ8+L5KeY9Lzg+TnqPziL+k5Kr/4S3qOUeZXo05Bha2eln/3dd4Kmy2atqiwr0N5UwtkOQoLq87A\nqvB+NeAFYEm4fV9gGTDJzKqZWVXgCWAqsDPQCbiPoFgrtiNwPHA4cCiwO/CvjOc8haDFszfQGrgG\nuNnMTisR3j+BgUCbMKbNgGlAd4LidRgwysz2Co+/FPgPMBzYBmgMzDazusBLwHvAHmFMWwOPbPwr\nVr5+Wb2a6Z/NYO8Oe63dlkql6NShAx99/EmEkeVO0nNMen6Q/ByVX/wlPUflF39JzzHp+ZVFKpXK\n260yqzRdF/PoSDNbRvDa1gTWELT0QdDql3L3c4sPNrOzgcXA/gRFWB3gWXf/NjzESzx+TeA0d/8h\n/PmLgWfN7HJ3n0dQPF7u7k+Fx880s3YErYyjMx5nYMYxxTLHNw4xs8OAE4Bp7r7UzFYBK9x9fkb8\nFwHvu/v1GdvOAWaZ2Y7u/uVvvFZ5VVhYyJqiIgoKGqy3vaCgPt/OmhlRVLmV9ByTnh8kP0flF39J\nz1H5xV/Sc0x6flLxqYDMvZcJirXaQC9gtbs/Ge5rD7QKC8xMNYGW7j7FzB4CJpvZi8AUYHxxsRia\nVeL+fwhaks3MfgRaAiPM7P6MY6oCJTvFv5d5x8yqANcStG42BWqEt+W/k++uwIGl5JQOY6kwBaSI\niIiIJFhKnSvzQa9y7i1392/c/WPgbKCTmZ0Z7qtN0E20PUHhVXzbCRgL4O5nEXRdfRM4EfjczDpm\n+dy1w6/nlHj8nYF9SsZZ4v6VwMUEXVv3D39uMkER+XvPObGUnFoBr2UZd17Uq1ePqlWqsHDhovW2\nL1y4mIYFBRFFlVtJzzHp+UHyc1R+8Zf0HJVf/CU9x6TnJxWfCshy5O5p4B/AreFSHu8TFFbz3f3r\nErdlGT/3kbv3c/fOhJPoZDzs9mbWKOP+PgTdZGeEXVi/J2jNLPn4mX0aShtgvC/wlLuPC4vfbwgK\n20yrCFozM71PMGZyZinP+VNWL1SeVK9WjbZtWvP2u9PWbkun07z97jR2a79LhJHlTtJzTHp+kPwc\nlV/8JT1H5Rd/Sc8x6fmVSZVU/m6VmLqwlr9HgduBvxLMnvp34KlwRtU5QDPgGIKZV2sA5xK06H1P\nMAlOK2BkxuP9DDxkZn8H6gKDgEcyxiXeAAwys6XAJILusXsB9dz9rvCY0n7rvwCONbN9CLq79iKY\nLOfTjGO+BfYOZ3L9MZxldQhBi+fDZtYfWBTGfCJwdlhEVxin9TyJ62/qS9s2rdm5XRtGj32ElT+v\n5OgjekQdWs4kPcek5wfJz1H5xV/Sc1R+8Zf0HJOen1RsKiDLmbuvMbN7CLqIDiVY6qIfMAHYEviO\nYBbTpcDmBEXj6QRLdPyPYAmO+zIe8gvgceA5guU2niYoToufb4SZLQ+frz9BV9WPgbsyHqO0oq4v\nwbIfk4AVBLO/PkFQpBa7g6CYnQ5sZmbFy3h0DnN6gaBgnQlMqmjFI8BhB3ejsLCQIcOGs3DRImyn\nVgy7eyAN6tePOrScSXqOSc8Pkp+j8ou/pOeo/OIv6TkmPT+p2LQOZIyErZZHu/seUcdSHvKxDqSI\niIiIbLqKvA7kirmz8vZZcvNttq+wr0N50xhIERERERERyYq6sIqIiIiISOylUpW2UTCv1IVVKgx1\nYRURERGp2CpyF9af5s3O22fJWltvV2Ffh/KmFkgREREREYm/lEbn5YNeZREREREREcmKWiBFRERE\nRCT2NAYyP9QCKSIiIiIiIllRC6SIiIiIiMSfxkDmhV5lERERERERyYoKSBEREREREcmKCkgRERER\nERHJisZAioiIiIhI7KWqaBbWfFALpIiIiIiIiGRFLZAiIiIiIhJ/FXgdSDP7K3AF0Aj4CLjY3d+N\nNqpNoxZIERERERGRcmJmJwIDgBuA3QkKyBfMrGGkgW0iFZAiIiIiIhJ7qVSVvN02Ui9gmLuPcvcZ\nwPnACuCsXL8G+aACUkREREREpByYWXVgT+Cl4m3ungamAPtEFVdZqIAUEREREREpHw2BqsDcEtvn\nEoyHjB1NoiMiIiIiIvFXgSfRSRIVkFJh1KhToHe9iIiIiGySCvpZcgGwBtimxPZtgB/yH07ZqQur\niIiIiIhIOXD3X4D3gG7F28wsFd5/K6q4ykItkCIiIiIiIuXnTmCkmb0HvEMwK+vmwMgog9pUqXQ6\nHXUMIiIiIiIiiWVmFwJXEnRd/RC42N2nRRvVplEBKSIiIiIiIlnRGEgRERERERHJigpIERERERER\nyYoKSBEREREREcmKCkgRERERERHJigpIERERERERyYoKSBEREREREcmKCkgRERERERHJigpIERER\nEUkEMzv5N/bdns9YRJKqWtQBiOSLmT0BpEvZlQZWAl8CY93d8xpYDpnZCcCT7r6qxPbqwDHuPj6a\nyCQbZraZu6/cwL7G7v6/fMckksnM9gB+cfePw/tHA2cC04EbS/7tkYrJzKoB+wMtCf7vLTOzJsBS\nd/8x0uDKbqiZFbr785kbzWwgcBLw92jCyj0z25HgHL7m7j+ZWcrdS/ucI5JTKiClMlkC/BEoBN4L\nt+0B1AMmAycCV5lZN3d/M5oQy2wc0BiYV2J7nXBfrAtIM9sGuAPoBmwNpDL3u3vVKOLKoffNrKe7\nf5i50cyOBe4FtoomrNwxs0s2sCvzQs5r7r4mf1HljpnV2cCuNPBzAgqsYcBtwMdm1gJ4GHgCOB7Y\nHLgswthyxsz2A84j+HB+nLt/Z2anAd+4+xvRRlc2ZrYDMAnYHqgJvAgsA64K758fXXQ5cQowzsyO\nKD5XZjYY+BNwQKSR5YiZFQCPAAcS/G1pBXwNjDCzxe5+eZTxSfKpgJTK5DtgLHCRuxcBmFkVYBDw\nI8GVyXuBfkCXqIIsoxSlt7I2BpbmOZbyMJLgQ88twP8oPdc4ewX4PzO7wd37mdkWwBDgBODaSCPL\nnV4EhfDmwOJwW31gBcH7cGvgazM7wN1nRxNimRTyG7+XZjaH4Pf4puK/QzGzE1B8geN4gmK/p5l1\nJigmY19AhhdsRgNjgN0JiiqAusA1QI+IQsuVQcA0YFdgYcb2J4DhkUSUQ+7+rJldCEw0s4OBs4Gj\ngQPc/fNoo8uZgcBqgv+Hn2VsfwS4E1ABKeVKBaRUJn8BumR+aHP3ovDK5Fvu3tvM7gFejyzCTWRm\n/yH40JoGnjOzXzJ2VyW4Ojk1ithyrAuwX8kWuqRw9wvN7FngfjM7gqDw/xHo6O6fRBtdzlwFXACc\n4+5fwdpuWMMIPry+QVCIDASOiyrIMjgD+AdBkfhOuK1juP1WoCFwBfBzeFzcpFg3f8JBwDPh97MJ\nckuC64Dz3X2UmZ2Usf3NcF/c7Qfs6+6rzCxz+7dA00giyjF3H2tm9QjO2Xygq7t/GXFYuXQIcKi7\nzylxDr8AdogmJKlMVEBKZVIdaA2UvALZmqDIgqALXRxbtV4Jv3YC/gMsz9i3iqBl9ZE8x1QeZlOi\n22oCPQ88TlBkrQaOTFDxCEHRdFxx8Qjg7l+a2RXABHdvYWZXAhMii7BsTgMuLzHe+Gkz+xg4z927\nmdksghblOBaQ04DrzGwK0JXg9xSgOTA3sqhyy4DXStm+hGDIQ9xVYd3/vEzbEnRljR0zu3MDu+YD\n7wMXFhda7v63fMVVjrYg6LVRUgOCi1Mi5UoFpFQmownGB/wDeDfc1oGgS9Ko8H5X4NMIYisTd+8N\nYGbfAg9taCKWBLgMuM3MznP3b6MOJtfMrCVBN+tGwKEEv48TzWwQcK27//JbPx8TTSj9f081grwB\nvge2zFtEudUF+Gsp2z8A9gm/f4Og61kcXUbQtfOPwK0ZrTrHAW9FFlVu/QDsSNAil6kLwTizuJtM\ncB7PDe+nzaw2cBPwXGRRlc3uG9j+JcEcAMX743iBuDSvA6cD14f30+GQnCtJRm8jqeBUQEpl0ovg\nCvmVwDbhtrkEXeX6hfcnE0wuEEvuPszMapvZqQSTP9zt7ovNbGdgvrvHvYXgEYKxc1+Z2QpgvYLK\n3RtEElXufAg8S9A1qRB40cyeI7jAcTAb/pAUJ1OBYWZ2jrt/AGBmuwNDgZfDY3YBvokovrKaQzDm\n6uoS288maEEHKGDd+M9Ycff/Epyfkv4OxHLio1IMBwaZ2VkEBUcTM9uHYAKvWyKNLDcuB14ws+nA\nZgQXrVoBC4ANLoFRkbl7IibH2QhXAi+Z2V5ADaA/0I6gBbJzlIFJ5aACUiqNcFbHW4Fbi2dKdPel\nJY6ZFUVsuWJmbYEpBF0fGxO0ui4GTiUoms+MLrqciP0EHb/jQncfnbnB3d8KC6y7Ioop184m+L18\nL2OsbjXgpXAfBOM+4zoJxBXAo2bWnXU9HfYi6CpfPKazAzHuUh6OLTuO4CLV7e6+CGhLcEHuuyhj\ny5HbCLp5vkRwweo1gm6Bd7j74CgDy4Vw3NyuBBPHtQdqAyOAMe7+U6TBlVG4ZNVPwG4J6/q/Hnf/\nxMx2Ai4i6HZcm2DowxAt9yT5kEqnk9KaLyJmNplgRrbLCGZd3dXdvzazLsBod28eaYAiITNrTTCj\nJ4DHef3VksysOcESEGvzA4Ylodu1mbUnKKwKgWaAhX9j+gLbu/vpUcaXS2ZWg6Ara21gegLWR6wU\nzOxrgnWPP4o6lvJiZtsDs0tb89HMto/7xXCp+NQCKZVGJVhDEILZHi9093SJmdnmsG58WayZWVWC\n8Vdtwk2fAhPjum5gacKW5O0JuiYVS7v70xGFlHPuPgOYEXUc5cHdv+HXXViT4k7gQXe/0swyJ1x5\njqArZGKEa3ZOjzqO8mBmTQjGdG7Null1AXD3uyMJKnduBf5hZqeFreNJ9A2lrPkcrg/5DaVPkiSS\nMyogpTIZSbLXEIRgTOAWpWxvCcT+H2m43MNzBFPNF7dY9QZmm9nhmTN7xlG4MPsTBGPM0qy7yFH8\nuxr7DwXhBYA/s+5CTskPrwdGEFZOhV08O1J6fqNK/aH46EDQulrSdyTnItVmwMUEi86Xdg73iCKu\nXDGzPxMsm7OKYB3IzP+FaSDuBeRFBC3H35vZTNaflTz25y+0oTWfaxPMJi9SrlRASmWS6DUEQ88C\n15pZ8UQIaTNrDPyToDCJu7uBr4BOxVeWwyuu/w73HR5hbLkwiODqcbfwa0eCCVcGEIytS4JBBAXk\ns8AnJOxCjpkdSTBLaW2CbuQlP5zHvYD8mWBWy5J2IlgyIQlGEKyz9xjBWp6J+h0luIh6M/DPzHWR\nE+TJqAMoLxnLlaSBW8LJ5IpVBfYmmIxNpFypgJTKpDKsIfg3gkLxf0AtgllltyX4h9I7wrhypSsZ\nxSOAuy80s6sJFoyOu32AA919gZkVAUXu/oaZ9SYokJMwC+tJwAnuHtflAn7PAOAB4Bp3L22dtrib\nCPQxsxPC++lwPFY/4rt2Z0lHAD3cPQl/U0qzOfBwQotH3P2mqGMoR8X/A1IEPVVWZexbBXxEMFRH\npFypgJTKJNFrCAKEhVVXMzuIdbPrvQ88l5APCz9T+vqAtVn/H2lcVWXdQt4LCNZMdGAmweLmSbCK\nYG22pGpKsHxOEotHCGbHfYxg7FUt4FWCrqv/Aa6NMK5c+o5178MkGgEcTzDbbGKZ2Z5kjJUvXjYo\nzoqXKzGzB4FLS84kL5IvmoVVKg0zW0xw5bUakMQ1BBPPzEYBexAs9/BOuHlvgnXb3nP3P0cUWk6Y\n2evAAHd/0szGAvWBvgQLfu/p7jtHGmAOmNnlQAvgotJmEIw7M3ucoHVnfNSxlKdwZue1F6ncfUrE\nIeVMuATLJcD57j4z6nhyLRyH/AzBBYCP+fX/wr9FEVeumNnWwMPA/gSzBQPUI1iD9iR3T0pXa5HI\nqAVSKpNEriFoZudme6y731eeseTBJcBDBK0dmWsITgQujSqoHOrLukmQ+hB8yHudYKKLE6MKKse6\nEExO0t3MPuXXH17/FElUufMscHs4k25pH84nRhJVjrn7G8AbUcdRTqYBmwFfh2PMknaxsTdwKOsm\nIis5TjfuBhP0VGnn7p/B2pmtHyIYCnDyb/xsbJjZXsAJ/HrG7iT8HZUKTgWkVBru/lDUMZSTbMd7\npIFYF5DuXggcbWatCBZmB/jM3RPRJdLdX8j4/kugtZk1ABYnqLWukGRM6LQhw8OvfUrZlyYZM+l2\nY8Oz6J4VSVC5NY6gK/I1wFySUVRluhw4y91HRh1IOTkMOKi4eARw9+lm9leCeQFiz8xOIpiQ6wWC\nCZ8mE0xktQ3J/vsqFYQKSEk0M6tTPEbAzEqbOXCtuI4lcPfGUceQb+7+BfBF1HHkQ9LWMXP3M6OO\noTy5e5XfPyq+zOwGguJ4GsldDmlfYJ8EL0T/M8mYdGxDqlCi1Tj0CyUueMTYNUAvdx8Srsd6KcHM\n3cMI3pci5UoFpCTdYjNr7O7zCFo+SvuwU7yeUuxbBpIonLb8endfnjGFeaniOHYnHDOXFXVLkgrg\nfODP7j466kDK0QyC8YFJNYhgnctLog6knLwMDDKzk939ewAzawoMBF6KNLLcaUnQXR6Cicm2cPe0\nmQ0kyP+GyCKTSkEFpCTdgUBxC84BUQZSXszsH0Bfd18Rfr9B7n5NnsLKpd2B6hnfJ82SqAMob2b2\nPtDN3Reb2Qf8RqtVHBf5NrNLgPvcfWX4/Qa5e9wXaa8BvBV1EOXsamCAmV1L6eNYY9lbJUNH4EAz\nOwJI4jjkiwjGxX9rZrPDbdsRrDt7amRR5dZi1s1I/h2wM8Hvaj2CyQJFypUKSEk0d3+1tO8T5gCg\nP8HMsr9VJMeyq1nxtOUlv0+KpHfpDD1F0G0OkrnIdy9gDLAy/H5D0gSTeMTZ/UBPgsXok2pS+LVk\na1VSeqsUAln3fIgLM2vu7t+4+2wz2wM4iPXHyidmpmDgNeBggqLxUYIW1wPDbUlpZZUKTMt4SKVi\nZvUJloAoXhtqOvBg0saZJZWZPUCw9tWyEtu3AAYnYQIPM6tGMP18S2Csuy8zsybAUnf/MdLgpFIq\n0XX8/9u77zC7ymqP498JJSABEdCLCFEE/AkqAVSkFztKUQQEfQRBqvQiICId6aFdEKQEqYqKCoLS\ni3QRrqCRBZIQQpcSwEhn7h9rH+bkMElOkjOzZ+/z+zzPPHP2u/dM1hBy0lwu1wAAIABJREFUZq/9\nvu9aw4AtgHuLj1q1gACQtOa0ztf4YWSlSXqL7Jl7PbmM84aIeLTcqAZGUVxtroh4XNIwYG9y7+6D\n5Iqk50sN0GrPM5DWNSStAVxGLhm8qxjeBThA0noRcVNpwQ0ASQsBRMQzZcfSQVuQy8tam3zPDWwO\nVDqBlPRBcvZjJDAcuJr8WfcpjrcvL7rOKkrQv/0gJyL+WmY8nSLpAODYiPhvy/jcwA8i4pByIpsl\nrUvH/6/43NqXtBZPpLslQZT0XkDFYdSgP+JnyYdva5GtOuaUNI5MJq8Hro+Ip0qLrkOKh4zrkhVY\niYi3gCNLDcq6jmcgrWtIuo/sH7hDRLxZjM0GnAqsEhGfKDO+TpDUA/wA2AtYsBh+FjiGvKmt5D/4\nooJuD7nvYymg+UZnNmA94MiIWKSE8DpG0u/IhPF75N/bqIgYJ2kt4IyIWKrM+DpB0qJkm4RVmbLJ\n961kk+9KzxhIehNoFO5qHl8QeDoiqr78sStImp8pV6v8Azg7Iiq/Z7mxYoN86NaoSvom2RZi59aH\nH1UkaS5yRm6t4mNFci/9/RHxsfIi64yiP+nSETGh7FisO9WlnLFZO5YEjmskjwDF69HFuTo4mJyt\nOgJYufg4kpy1q3JVtklkMaRe4AEykWx8PAOcDZxSWnSdszq5/Oi1lvGHyb50dXAmeSO3dEQsUDRl\nX5r8fXRmqZF1RmOfXKtR9BX0qg1J80n6mqSPTv/qaihmxx8i97MuUHzsATxU7K2rutHAmuSDt/mL\njw2KseNKjKtjIuKViLgOOIz83XcS8B/69kRW3Z3AcmUHYd3LS1itm9xN3qhGy/jSQF36fX0P2Doi\nmhsJ3ylpAlm6/aBSopp1a5M35tcB32DKG/HXgAmNcu0VN4z+C3QsyjuX7VbVmuSM/9v/DiMiJO0M\n/Lm8sGaNpOfJxLEXeEBScxI5GzACOK2M2DpJ0sXATRHxv8Wy3LuADwE9kjaNiN+UGmBnHE9W8dwm\nIt6At5cNngmcAKxRYmyd8A1go4i4oWnsCkkvAxcDO5QSVQdImhNYifydsRbwGWAiWXRmJ6Auy5NP\nBUZLWgz4KzC5+WRE3FtKVNY1nEBaNzmJrFS2JHB7MbYSsCOwr6RlGxdW+M13QXKpVav76FvSWjmN\nPUmSFgcmFns+6ugqYDdg2+K4V9IIcmb5itKi6qyJ9LVlaTYbUOWHALuRDznOJmc8mpc6vgY8HBG3\nlRFYh60BHF68/jr5M89P7k/eH6hDAvkpmpJHgIh4Q9LR9O2fr7J3Af3tBXyaCreAkHQdmTCOJxPF\n04FvRcQTpQY2MH5RfG6u6txLfSoF2xDnBNK6yUXF56Oncq4Ob75/J5OPvVrGtyvOVVpjv4ekd5GF\nZuZsOV/VxL9hT+BKSWOBuYALyT2fz5JFIergB8DJknaMiLvg7SWDJ/LO/28rIyJ+DiBpPHBrRLw+\nnS+pqnfTtwLgy8Bvih60l5N7revgRfL95f6W8cWox0qA24CDJW0eEa/A20WeDizOVdXqwBMUFViB\nGyPi2VIjGjiLlx2AdTcnkNZNuuENd1/gMkmfo6/Z9ypkpb11S4uqQ4qqgWOAdaZySVUTfwAi4lFJ\no4BNgWXJZY9nkT0G6+IccpbjDkmNGZ7ZgTeAs4tWLQAU+yMrpbmCZ1HIo/UhR9Wb0E8EVpb0HJlA\nblqMv4fsg1kHvwTOkrQXfe+jq5IJ8kVT/arq2JWs4PmopMb2jVHk39+XSotq1s1PJpFrkbUALpL0\nADkbeQOZUFa90izQ9zDVrCxOIK1rdMMbbkRcUxSz2Jm+6oHXAl+ryc9/AnmT8BnyhuDrwP+QS+f2\nLC+szimWzZ3fOJY0nFxmvTewcFlxddBuZQcwkIrZ8aOBTeh/2XilH3KQ/wYvIAuSTCD/HUIubb2v\npJg6bS9yJcq59N0nvQ78lHxIV2kR8XdJSwHfpq+ozEXABRHxcnmRzZqImEy2QfoTgKR5gdXI/ZB7\nAxdIejAiWtvPVJKk75CtnRYHVo6ICZJ2A8ZHxO/Ljc7qzgmkdZW6vuE2954rEsXKLgWcjs8CG0TE\nXY2m0RFxtaQXgR8Cl5cb3swpksSDgC+Q++WOjojfSdqS3G/2JlnYo/IaSz1r7BjyhnUH4Dwy+f8A\nuYy8DsnHqZLuJJdzXt20H3kc+SCn8ooqyLtK+iGwRDH8UB3aWzQUP8sZZccxwCaTy62fIyt2v0Hf\ng9VKk7QDcAj5QOdH9D2YmkQ+pKvs/YxVgxNI6xo1f8M9kKzwWJsbnKmYhyz0AHlD8F6yrcd9QJXL\n6x9CJhhXk0vlfiVpDFnkaQ/gV83tZ6pO0hLAluTN+a4R8bSkdYBHIqK/IlBVsh6weUTcUPwd/jki\n/lVUQv42NViOXOxdvatlrJIPb6alSLLqMquKpPXbuS4iLh3oWAaCpGFkAaS1yIc4q5K/Mx4Dricf\n5lxfVnwdtjNZ6Ol3kpofTN0FHFtSTNZFnEBaN6nzG25P2QEMkiD3cz5Mtl7ZTtLD5KxylSvtbUwm\nHZdK+jhwL/n+PCoi+uspWFmS1gT+CNxCLnv8EflQYBTZhmaj8qLriAXI2TjIYiyNfZw3k0sgK0fS\naODHETG5eD1VEbHHIIXVcZIuaee6iNhwoGMZIL9rOW4Ujmsdq+oy60lkwvgkmSjuDtwQEQ+VGtXA\nWBy4p5/xV8n/BmYDygmkdZO6v+HWKtGYihOB9xevDyb3unybXPb53ZJi6oRFyV5ejf1JrwLH1y15\nLBwJ7B8RoyU1V7S8juzTVnXjyPeaR8gqnpuQTb/XI29wq2h5+lqvLD+N66r+/+sLLcffAi6jHpVX\niYhhzcfFv79RETFuKl9SNT8Aro+IB8oOZBCMB5Yj9yE3+zLwz8EPx7qNE0jrJnV/w21tXv4OVaxq\n2Swizm96/VdJHySLQDwSEc+UF9ksm41MghveIIuU1NEnyBvzVk8DCw1yLANhDDmbeiOZLF8maScy\nAatkoaeIWLu/13UTEVs2H0vaCNi7RglWrUXE6WXHMIhGA6cUlZ57gBUlbUbWAti61MisKziBtG5S\n9zfc1ubltSJpDnJGZ92I+Ce8vUfp7lID64we4Jxi5hGyB+RpkiY3X1ThpXPNJpGzyONbxpcn9ypV\nWkQc3/S6URX5k8C/atCnFABJPWSF2d4a99kzG7Ii4kxJLwOHkW2RLgQeJ/eU/6LU4KwrOIG0rtEF\nb7i/iIinp39ZNUXE60XyX0etlUnP7/eqevgFcJSkjcklj8MkrUruQz631MhmgaQrgM0i4oXieF/g\ntKIq8gRJC0oaGxHLlBroLJC0MNmiZH1g3mLsReC3wA8j4qkSwzPrKhFxAdma5F3AiDr//rehxwmk\ndZUav+FWfe9Ru04B9pG0ddEvsRZal87V3H7k3+NEcunu2OLzheTDnar6EjC86Xg/4GL69j3OThaA\nqiRJ8wG3AiPIZbr3kzPnywCbAatJWiEi6rr0uo566Z7fHbUk6X0U7yuSeiPi3yWHZF3CCaR1FUmz\nkyW+lyBvWJG0CPBixW98ZqgKq6RFgceberhVxaeBzwFflHQf2efrbTVZ4llrRY+9bSQdCnycTEju\niYgHy41slrX+G6xbZeRdyX6kH2u9SZV0GFlVdxfgJyXE1hH9tLkYBnyuqIz8tgq3uXieKRPGEcA9\nRU/dt1V9r3w3kDQvcCr58KZRHOlNSb8EdmyshDAbKE4grWsUBVf+BIwkZwquJqvr7VMcb19edLOm\ntbpeG8aSBYWqVhxiEvCbsoOwjngSmJts0F6b2eQa+yrwk/5mOIo+nkcA21DhBJJ3trkAaC3MUuU2\nF7uVHYB1zJnkvvGvArcVYyuTlcpPBzYtKS7rEk4grZucSPZ8HAU0F374LXBGKRGVp5KzI1221LOW\niuXjJwNbFEMfAcZJOhl4LCKOLC24WdPfcsA6LQ/8CLmEdWpupeL9dGfiQVylRETrXutpKorMXRoR\nk6d7sQ22dYEvRcTNTWNXStqGfFBuNqCcQFo3WR1YJSJek6bYivQw8IFSIjLrPkeQD3HWYsobnWuA\ng8jWF1U0vUq6w/v/ssqYj2n3sZxUXNM1JF0ObB0RT5QdywA5HbiD6q1U6QbP0n/V9ReA5wc5FutC\nTiCtmwyj/6VHi1KTRtHdoOjNtgm5FHnO5nMRsUIpQdmM+BrwzYi4vaVv6T/IvclV1U4l3cpWmSUT\n5Gntme6loisbZsEa5DLsuuq2v88qOQwYLek7EfEkvF0l+Rjg0FIjs67gBNK6yVXkHpBti+NeSSOA\ng4ErSovK2iZpF+Bw4BxgA7Ia5BJkcZ1TyovMZsB7gf6qH89DhZd8dsHy6h7ggZakv/W8mQ2OHYAl\ngUckPVKMjQReBd4rabvGhX6wagPBCaR1kz3JPQJjyeVlFwJLAc+Qlcy6SVVv1L8PbBsRF0n6LnB0\nRIyTdAjgyoHVcBdZ+OHk4rjx/+LW9BWDsKGn7gmyWZX0V/DJbNA4gbSuERGPShpFVidblixhfhZw\nQUS8XGpwg6+qswUj6Svk8TJFM3PgPOB2YKcygrIZsh/wR0nLkL+Ddi1erwKsWWpkNlUuwGI2dETE\nwWXHYN3NCaR1laJdQH97k2qn6PVIRDzaz+llgMcHN6KOeJKcaZwAPAKsBPwNWJzqJsVdJSJulrQc\nsC9wH/BF4G5g5Yi4r9TgrJNcgMVsgEhaDOht/H6XtCLwLWBsRPys1OCsKziBtFrrpzH0VFW1OXQz\nScOA/cnluiOKsZeA44DDI+ItgIiYWFqQs+Y6YH3gHnL/4/FFUZ1PAZeUGZi1LyIeInsGTkHS3F24\nGqCu/EBnCJI0G7AqcG9ETKuqLuSDutcHPiqbCRcCPwPOK4rnXAP8Hfi2pIUj4pBSo7PacwJpddfu\nPoEqN4dudjjwPXJ255ZibDWyPcJcwI/KCatjtiWr6RIRp0h6llz6eCnvbPhtQ5CkkyJil37G5wH+\nAKw9+FGZzZSfAM+VHcSMiIg3JV0FLM2027IQER8fnKhsJnwcuLN4vQlwX0SsKumLwGmAE0gbUE4g\nrdbq3hi6H1uQfcmaZ1PvlfQYcCoVTyCLGdS3mo5/AfyivIhsJnxV0vMRcWBjoKiG/McSYzKbgqTv\nANuTy+NXjogJknYDxkfE7wEi4ogyY5wFfwc+DIwvOxCbaXOQFVcBPk8+RAW4H3h/KRFZV3ECabUn\naWVgwYj4Q9PY5mT7jnnIWcqdI+LVqXyLKlmA/AXS6n5qUqVU0nvIWdali6GxwJiIqNRMQBf7IvDn\nIok8QdK8wJXAG8A65YZmBpJ2IGdwTiAfujVWp0wiW0H9vqTQOmV/4FhJPwb+CkxR6CgiXiwlKpsR\n/wC2l3Q58AXgx8X4IsCzpUVlXaPbZmesOx0IfKxxIOkTZPXVa4AjgfWAH5YTWsf9jf4rke5UnKs0\nSWuQT813Ad5TfOwCjC/O2RBX7H/8MvDjoq/nVcBrwDqu2GlDxM7ANhFxOPBm0/hdwCfKCamjrgBG\nkbNWjwLPFx+Tis829O0DbAfcAFwUEY3f7+vTt7TVbMB4BtK6wSjyiWvDpsAdEbENgKSJ5GzkQYMf\nWsftDVwu6fP09dRbGVgM+EppUXXOKcDFwA4R8Sa8XRTi1OJcHW7uai8i7pW0LnA1WalzXRfPqZ0q\nF2BZnCzU1epVctVK1XmfccVFxA2SFgLmi4jmpP9nwH9LCsu6iBNI6wbvAZ5qOl6TKfdb/YVMsCov\nIm6U9BFgR+CjxfAlwKkRUcW2Ha2WBDZqJI/wdlGI0cDm5YVl0yLpHrJQVatXySVXt0gCICJWGMTQ\nbCZJmhN4Hy0rmSLikeJzlQuwjAeWI5PgZl8G/jn44XRWRNxYdgzWET3AJyUtAVwYES+RqzmcQNqA\ncwJp3eAp8onyxOKmZwVyWWvDvFT3SfkUJI0EJkbEO4rlSBrZuLmrsLvJvY/RMr40NViiW2PtVkO2\nIU7SUsDZZPXjZj3Up5r1aOAUSXORP9eKkjYjtzpsXWpkHSJpdXIJ5IeBjSPisaJw0PiIuLnc6Gx6\nJH0Q+BMwEhhOruZ4iVzaOpwsAGU2YJxAWje4AjhS0j7A18inc39uOr8s8FAZgQ2A8WQFtqebByUt\nWJyr+s3dScCJkpYEbi/GViJnXPeVtGzjwoi4t4T4rB8RcXDZMVjHnEMWPFoXeIL+Z5YrLSLOlPQy\ncBjwLrLn3uPArkXl50qT9A3gPOAC8oHq8OLUu4H9qMd2h7o7kdyTO4opi+b8FjijlIisqziBtG7w\nY3IZ543Af4AtIuK1pvNbkYU86qAxC9BqBPDKIMcyEC4qPh89lXO91GsmpJYkzQ9sBCwBHBMRz0la\nAXgqIh4rNzqbjuWAT0ZEf9WeayMiLgAukPQuYEREPD29r6mQ/YHtI+JcSZs2jd/ClPUCbOhaHVgl\nIl5rLP8vPAx8oJSIrKs4gbTai4hngDUkvRv4T/P+ucLGZGJZWcUeQMjE6VBJzXsgZgM+A/zfoAfW\neYuXHYDNmmKW+BrgBeBD5NPy54ANyeVY3ss6tI0FFio7iIEkaX/ggogYHxH/pX57ygTc1M/4C8D8\ngxyLzZxh9P+QdFFyKavZgHICaV0jIl6Yyngd+gcuX3zuISuRNs+wvkbuDzx2sIPqtIhoLWph1TMa\nOCci9pbUfKNzBblU0IYYSfM1He4DHC1pP+A+WvaP16SH4MbAwZLuAM4HLi4eRNbFk2RBsodbxlcD\nxg16NDYzriJ7km5bHPdKGkFWlL+itKisaziBNKuBiFgbQNIYcp/ONG/iJC0KPB4Rbw1GfJ1UFHrY\nnpyNXDkiJkjajSz+UPUG393g02TxjlaPAQsPcizWnklMuTS+B7i25ZraLB2PiFGSPgZ8G9gLOEHS\n1eSewd8Vs5JVdga5l3wr8u9sEUkrkw8ZDy01MmvXnsCVksYCc5EP35Yi90NuVmZg1h2cQJrVSERs\n2ealY8m9TJV62ixpB+AQ4ATgR/TdrE4in8Y6gRz6XgXm62f8I8C/BzkWa0/X9Q2MiH+QBWX2k7Qq\n8C3yfec0+v//t0qOJJdAXksWCbqJ/Hd5bEScXGZg1p6IeFTSKLKv9bJknYOzyIccZgPOCaRZd+op\nO4CZtDOwTUT8TtK+TeN3UYMlul3iUuAASZsUx71F+5mjgN+UF5ZNjfsGMhl4mdwOMG/JscyyiOgF\nDpd0DLmUdQQwNiIqXQug20TEG+QSawAkDScrku+NV3PYAHMCaWZVsjhwTz/jrwLzDHIsNnP2BH5N\ntpqZm6yOvDBwGzmrbEOYpC+TxchuLo53BLYhVzXsGBHPlxlfp0hanJx1/BZZdOZGsn/wr8uMqxMk\nnU1udXiJ/HtrjM8DnBwRW5UWnE1TkSQeBHyBfKBxdPFAdUvgcOBN4PjyIrRu4QTSzKpkPLn0trWY\nzpeBfw5+ODajimJWXyiWBY4iZz/ujohryo3M2nQMWUgHSZ8giyIdRy5zHQ20u4x+yJJ0O7lX915g\nDHBRzdrLbAHsyzurdc5NVkF2Ajl0HULuIb8aWBX4VVH7YCVgD+BX/VSaN+s4J5BmViWjgVMkzUUu\nw11R0mbAD4GtS43MZkhE3EL2nWv0hbRqWJy+WatvAJdFxH5FH8+6VH+8FtgqIsZO98oKKarp9hQf\n80pq7g08G/AVcmWADV0bA5tHxKWSPk4+5JgdGFUsTTYbFE4gzbpTJX/RRMSZkl4GDiOLP1wIPE4u\nx/pFqcFZWyTtAzwcEb8sji8GviHpSeArEfG3UgO06XmN/LcH8Hng3OL1c1S/uAwAEVHXpdSNarq9\nwAP9nO8ll+na0LUo8FeAiPi7pFeB45082mBzAmnWnSpXREdSD7AY8JuIuEDSu4AREeEn5tWyPdke\nAUlfIPfyrANsQi6P/GJ5oVkbbgZGS7oFWBH4ZjH+EeDR0qLqsKLV0frASGDO5nMRsUcpQc26tcn3\n/uvI2ePmHsivARMi4vEyArO2zcaUfZ7fAFz8yAadE0iz7rQMOXNXJT3Av4CPAQ8Wvdiq3o+tGy0M\nTCxer0s2ab9K0sPAHWUFZW3bCTgV2AjYoWlv4DrAn0qLqoMkfY6sFjwO+Cjwd+BD5HvQ3eVFNmsa\n1XSLAkETq9gH2OgBzilmHiF7QJ4maXLzRRGx4aBHZl3FCaRZxUm6pN1rG79UImLi9K4daiLiLUkP\nAgsCD5Ydj82058mZ5Ilk8aP9i/EeatCEvu4i4hEy8W8d372EcAbKEWRPxAMlvUTO1j1N9tirfJIc\nERMkzS9pReB9ZE/I5vPn9v+VNgT8vOX4/H6vMhtgTiDNqu+Fptc9wNeLsbuKsU8C8wNtJ5pD2L7A\nMZJ2iIi/lx2MzZRLgAubHgb8sRhfnpxhtoooilm1Lu98saRwOmlpYLPi9RvA3BHxH0kHAL8Hflpa\nZB0gaT0yGR4BvMiUe+J76dvXakNMRFS+yrHVgxNIs4pr/oUi6SjgYmD7RilvSbORS87qcGN3LlnA\n42+SXiObe78tIhYoJSqbEbsDD5OzkHs3NS9/P/n/qQ1hRa/Ao8g9qwv2c0kdZpEn05cYPwEsAfyj\nOF6olIg66zjgbGC/YiuAmdkMcQJpVi9bAas194GKiDcljQZuBX5QWmSdsTsVrSBrKSJeB47tZ9zN\nr6vhaLIYyw7AecCOwAfI3nT7lhhXJ90OrEb2lr0COK7oeblhca7qPgCc5OTRzGaWE0izepmdLPoQ\nLeMfpWWfSxVFxDllx2CzRtLm0zrv/VdD3npkH7obigbmf46If0maQFbXvaDc8DpiD3J5J2RbixFk\ntdkHi3NVdyXwKbJIkJnZDHMCaVYvY4CzJC0B3FmMfYacGRhTWlSzSNIwYC9gA3Jp2bXAwRHx8jS/\n0IaiE1uO5yCXJb9GVtV1Ajm0LUBf4vFicQzZ3qPSewMbImJc0+vJZOuZOrmc3Eu+DHAf8HrzyYi4\ntJSozKwynECa1ctewJPAnuSeMsg9PMeQ+16q6kfkTMA1wCvArmT1wK3KDMpmXES8p3VM0lJk8nHM\n4EdkM2gcsDjwCHA/uRfyTnJmclKJcXWUpPnJViVLAMdExHOSVgCeampdUlVnFJ8P6OdcL/XYx2pm\nA6int9fbiczqSNJ8UI+qiEXFzmMi4mfF8efJp+hzu5dZPUj6FHB+RHy07Fhs6iTtDrwZEScV/w4v\nI6s/zwHsERGtM8yVI2lZ8mHVC2T/R0XEOEmHASMjYprLsM3M6q7ye6LMbEqSZi9u7DajKDgjaRFJ\nI6b9lUPaSPraPRAR15A/2yKlRWSd9gb++xyyiubzRMTxEXFS8foacn/1t4Dl65A8FkYD50TEUuSK\nh4YrgDXKCWnWSbpC0rubjvctZlobxwtKGltOdGZWJV7CalYjkj5INroeCQwHrgZeAvYpjqu6l2d2\npryRg9y3M0cJsdgskLR+y1APudx6J+CWwY/I2vRQUSjneuA64IaIeDQiJgATyg2t4z5NVpVt9Riw\n8CDH0klfIn8PNOxHtn1qLD2eHdBgB2Vm1eME0qxeTgTuAkYBzzaN/5a+fS9V1AOcI+nVprG5gNMk\nTW4MRMSGgx6ZzajftRz3Av8mk5I9Bz8ca9NngbWKj82AOSWNI//ergeuj4inSouus14F5utn/CPk\n/6tV1TOdYzOztjiBNKuX1YFVIuI1aYoHyQ+Tvb+q6uf9jJ0/6FHYLIsIb52ooIi4AbgBQNJcwCr0\nJZRbAHNIuj8iPlZOhB11KXCApE2K415JI4GjgN+UF5aZ2dDgBNKsXobRfwW9RcmlrJUUEVvOyPWS\nFgUed4GdoU1SD0BEuJpbhUTEK8B1km4mZx/XIZd81qUA0p7Ar4GngbmBG8mlq7eRFaGrqrf4aB0z\nM5shTiDN6uUqYDdg2+K4tyieczBZAKJbjAWWw42yhyRJmwM/AJYqjh8gq+yeV2pgNk2S5gRWAtYm\nZx4/A0wEbiL3sN5YWnAdFBEvAF+QtCq5HWAEcDfZtuR4+t5fq6Z1K0DrNoDh/X+ZmdmUnECa1cue\nwJVFJb25gAvJm/RnyX1L3cJ7e4YoSXsAhwL/S1/RnNXIG9mFIuL40oKzqZJ0HZkwjicTxdOBb0XE\nE6UGNoAi4haaCjtJGgV8j+omkK1bAfrbBnDuYARiZtXmBNKsRiLi0eImZ1NgWfLJ+VnABRHxcqnB\nmaWdgR0iovlG9VJJ/wAOImd4bOhZHXiCogIrcGNEPDvNr7AhZUa3ApiZTY0TSLMakbRgcVN3vqTF\ngG3IsuyfAv5canBm6f3Arf2M31qcs6FpfjKJXItsC3RRsfT4RvoSyipXKDUzszY5gTSrAUmfAC4D\nFpP0IDkD+SdgHrJIwu6SNoqI1hYKZoPtX8AmwE9axr8JPDj44Vg7ImIy+Z7yJwBJ85JLj9cG9gYu\nkPRgRHy8vCjNzGwwOIE0q4ejgfuAbwPfAf4AXE7OQAKcDOzLO3vw1ZUrCw5dBwK/lLQGffvLVgU+\nRyaWVg2TgeeKj+eBN4ClS41oFkm6ZDqXzD8ogZiZDXFOIM3q4dPAZyPiXkl/I4s8nNpoYyHpZOD2\nMgMcZC6iM0RFxG8kfQbYHfhaMfxPYMWIuKe8yGxaJA0jl8KvRc46rkqucHiMbOWxY/G5yl5o47yL\nzJhZ13MCaVYPCwBPAkTEf4qy7M83nX8emLeMwEqyDPB42UFYH0nzNR0+CHy/v2si4sXBi8pmwCQy\nYXySTBR3B26IiIdKjaqDXGTGzKw9TiDN6qOWDaLbWFb2tojYsPg8ceAispk0ifb+n5xtoAOxmfID\n4PqIeKDsQMzMrFxOIM3qo64Noqe3rMyqYe2m1z3AFcDW5BJIG+Ii4vSyYzAzs6Ghp7e3FpMUZl1N\n0ph2rvMSLRsqJL0EjIqIcWXHYmZmZu3zDKRZDTgxNDMzM7PB4ATSzCpF0kZku4eRwJzN5yJihVKC\nMjMzM+sSw8oOwMysXZJ2AcYATwHLA3cCzwIfBv5YYmg2c7yHwszTE3cvAAAHTElEQVTMrGI8A2lm\nVfJ9YNuIuEjSd4GjI2KcpEPIViY2RPVTTbe10BPQV0nXzMzMhiYnkGZWJSOBW4vXL9PX2/I84HZg\npzKCsra0VtM9v5QozMzMbJY4gTSzKnmSnGmcADwCrAT8DVicbA1hQ5QLPZmZmdWD90CaWZVcB6xf\nvB4DHC/pauCXwG9Li8rMzMysS3gG0syqZFuKB18RcYqkZ4FVgEsBNzo3MzMzG2A9vb0ugmdmZmZm\nZmbT5xlIM6sUSasD2wFLABtFxGOSvgOMj4iby43OzMzMrN68B9LMKkPSN4AryQqsywPDi1PvBvYr\nKy4zMzOzbuEE0syqZH9g+4jYBni9afwWYIVyQjIzMzPrHk4gzaxKBNzUz/gLwPyDHIuZmZlZ13EC\naWZV8iSwZD/jqwHjBjkWMzMzs67jBNLMquQM4ERJnwF6gUUkfRs4FvhpqZGZmZmZdQFXYTWzKjmS\nfPB1LfAucjnrq8CxEXFymYGZmZmZdQP3gTSzypE0J7mUdQQwNiL+U3JIZmZmZl3BCaSZDXmSzm7n\nuojYaqBjMTMzM+tmXsJqZlXwXWACcA/QU24oZmZmZt3LCaSZVcFPgc2AxYExwPkR8Vy5IZmZmZl1\nHy9hNbNKkDQc2BDYClgFuBw4C7gqIvxGZmZmZjYInECaWeVI+iC5rHVzciXFx1xIx8zMzGzguQ+k\nmVXRW2QfyB5gtpJjMTMzM+sanoE0s0poWcK6GvAHcj/knyLirTJjMzMzM+sWLqJjZkOepFOBTYGJ\nwNnAZhHxTLlRmZmZmXUfz0Ca2ZAn6S3gEbKNx1TftCJiw0ELyszMzKwLeQbSzKrgXKaROJqZmZnZ\n4PAMpJmZmZmZmbXFVVjNzMzMzMysLU4gzczMzMzMrC1OIM3MzMzMzKwtTiDNzMzMzMysLU4gzczM\nzMzMrC1u42FmZrUg6UDgwKahZ4D7gAMj4uYB/HOfB46PiEOK4+uBlyJi/Rn4HgcCV0bE7S3jbwF7\nRcToTsZsZmY2s5xAmplZnfwXWBvoARYFfgxcI2mFiBg7SDHsALw5g19zIPAScHvL+ErAhE4EZWZm\n1glOIM3MrE7eioi/FK/vlPQX4GFgO2DX1oslzRURr3QygIi4v4Pf685OfS8zM7NOcAJpZma1FRET\nJT0DfFjSFsAYYBXgkOLz2cAuAJL2ArYBPgg8BpwcESc0fz9JGwBHAh8C/gbs1PpnSroBeLF5Cauk\njwI/AdYE5gIeBI6IiF8Wy1R7gWMlHVu8XjsibupvCauk7YDdixieAM4EfhIRvcX57xY/1wrFn7kG\n8DhwaEScN8P/Ec3MzJq4iI6ZmdWWpPmABcgEquEC4Frgq8B5xXUnAQeRCeZXis9HSdq26XstB/wa\nCODrwM+Bi4E5W/7Y3pYYlgRuA5YgE871yARvZHHJyuSS25PIJasrA3dP5efZGfgp8Edg3SLOg4Cj\n+vnzzweuBDYovt8YServ+5qZmbXLM5BmZlYrkmYrXi4GHEc+LP01sEgx/tOIOLbp+g8DOwLbRsRZ\nxfB1kuYh9yb+rBjbl9yP+PWm2b5XgMbXTM3BwKvAKhExufH9Gycj4o4ir3tkWktWJQ0j93ReGBG7\nF8PXSBoO7CHpiIh4vulLTo6I04uvvY1MOL9BzkqamZnNFM9AmplZnYwAXi8+xpFLRneMiKuL873A\nFS1f8/li/BJJszU+yFnK90tarLhuReCyRvJY+HUbMX0W+HVT8jizPgos1M+f+UtgeBFfQy/Q+JmJ\niP+Sye+isxiDmZl1Oc9AmplZnfwXWL14/UxETOznmqdajhciH6g+28+1veRM5kTg/cDTzScj4qVi\nFnJaFmTKJbQz6z1FPK3xN44XaBmf1HL8Grn/0szMbKY5gTQzszp5KyLumc41vS3HzwFvAauSM5et\novj8BPC+5hOS5mX6Sdmz9C2fnRXPkXsl39cy/j9N583MzAaUE0gzM+t21xafF4qIy6dx3Z3AepL2\naFrGunEb3/8aYCNJ+0xjGevrTD8RDeDfxZ/5+6bxb5J7LN3yw8zMBpwTSDMz62oR8aCkU4HzJR0D\n3AHMAQhYKyK+Xlx6JPAX4PfF9UsAewIvT+ePOJis+HqLpKPJmcxlgLmbivn8E9hA0s3AZOD+1mQz\nIt6SdChwoqR/k3s5Vwb2Bka3FNAxMzMbEC6iY2ZmddK6PLUtEbEL8CNyNu8PZHuPjYEbmq75P2Aj\nYCngEmAL+mb/phpHRPyL7Dk5HjgFuBTYiixq0/B98nfyFeRM4iebvk/z9/pfYAdgHeAyYEvggIjY\np40fc4rvZWZmNjN6env9u8TMzMzMzMymzzOQZmZmZmZm1hYnkGZmZmZmZtYWJ5BmZmZmZmbWFieQ\nZmZmZmZm1hYnkGZmZmZmZtYWJ5BmZmZmZmbWFieQZmZmZmZm1hYnkGZmZmZmZtYWJ5BmZmZmZmbW\nFieQZmZmZmZm1hYnkGZmZmZmZtYWJ5BmZmZmZmbWlv8Hv8eoPoRAbCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6e7b7c4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "def print_cmx(y_true, y_pred, classes):\n",
    "    cmx_data = confusion_matrix(y_true, y_pred )\n",
    "    df_cmx = pd.DataFrame(cmx_data, index=classes, columns=classes)\n",
    "    df_cmx.columns.name = \"Prediction\"\n",
    "    df_cmx.index.name = \"Ground Truth\"\n",
    "    \n",
    "    plt.figure(figsize = (10,7))\n",
    "    sns.heatmap(df_cmx, annot=True)\n",
    "    plt.show()\n",
    "    \n",
    "print_cmx(test_fet_df.label, pred, classes=classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9201540006695681"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(test_fet_df.label, pred)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}