radarchart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "# Set Seaborn style\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.read_csv(f\"results/best_results_{top_n}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>_all_pre</th>\n",
       "      <th>_leisure_pre</th>\n",
       "      <th>_working_pre</th>\n",
       "      <th>_all_rec</th>\n",
       "      <th>leisure_rec</th>\n",
       "      <th>_working_rec</th>\n",
       "      <th>all_ndcg</th>\n",
       "      <th>leisure_ndcg</th>\n",
       "      <th>working_ndcg</th>\n",
       "      <th>Delta</th>\n",
       "      <th>Imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>mul</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0679</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>sum</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.0589</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.147903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>global</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0407</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.0579</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.207506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>LORE</td>\n",
       "      <td>mul</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>0.0247</td>\n",
       "      <td>0.0404</td>\n",
       "      <td>0.0319</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.0686</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>LORE</td>\n",
       "      <td>sum</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.0607</td>\n",
       "      <td>0.0222</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0624</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.419825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gowalla</td>\n",
       "      <td>LORE</td>\n",
       "      <td>global</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.425656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>mul</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0463</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>sum</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0222</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.239741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>GeoSoCa</td>\n",
       "      <td>global</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0222</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>0.360691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>LORE</td>\n",
       "      <td>mul</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.0491</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0231</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>LORE</td>\n",
       "      <td>sum</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>0.281938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Yelp</td>\n",
       "      <td>LORE</td>\n",
       "      <td>global</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>0.314978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset    Model  Fusion  _all_pre  _leisure_pre  _working_pre  _all_rec  \\\n",
       "0   Gowalla  GeoSoCa     mul    0.0344        0.0655        0.0205    0.0329   \n",
       "1   Gowalla  GeoSoCa     sum    0.0335        0.0589        0.0210    0.0339   \n",
       "2   Gowalla  GeoSoCa  global    0.0322        0.0564        0.0211    0.0326   \n",
       "3   Gowalla     LORE     mul    0.0450        0.0913        0.0247    0.0404   \n",
       "4   Gowalla     LORE     sum    0.0345        0.0607        0.0222    0.0346   \n",
       "5   Gowalla     LORE  global    0.0352        0.0617        0.0228    0.0355   \n",
       "6      Yelp  GeoSoCa     mul    0.0184        0.0481        0.0056    0.0215   \n",
       "7      Yelp  GeoSoCa     sum    0.0165        0.0395        0.0067    0.0216   \n",
       "8      Yelp  GeoSoCa  global    0.0150        0.0345        0.0067    0.0204   \n",
       "9      Yelp     LORE     mul    0.0183        0.0491        0.0044    0.0203   \n",
       "10     Yelp     LORE     sum    0.0162        0.0374        0.0069    0.0215   \n",
       "11     Yelp     LORE  global    0.0155        0.0370        0.0064    0.0205   \n",
       "\n",
       "    leisure_rec  _working_rec  all_ndcg  leisure_ndcg  working_ndcg   Delta  \\\n",
       "0        0.0257        0.0397    0.0368        0.0679        0.0226  0.0453   \n",
       "1        0.0225        0.0403    0.0354        0.0610        0.0224  0.0386   \n",
       "2        0.0214        0.0407    0.0339        0.0579        0.0220  0.0359   \n",
       "3        0.0319        0.0467    0.0498        0.0973        0.0287  0.0686   \n",
       "4        0.0228        0.0436    0.0363        0.0624        0.0226  0.0398   \n",
       "5        0.0236        0.0450    0.0373        0.0632        0.0238  0.0394   \n",
       "6        0.0227        0.0185    0.0196        0.0519        0.0056  0.0463   \n",
       "7        0.0179        0.0222    0.0173        0.0422        0.0070  0.0352   \n",
       "8        0.0158        0.0222    0.0157        0.0367        0.0071  0.0296   \n",
       "9        0.0231        0.0146    0.0196        0.0505        0.0051  0.0454   \n",
       "10       0.0180        0.0227    0.0169        0.0395        0.0069  0.0326   \n",
       "11       0.0173        0.0213    0.0159        0.0377        0.0066  0.0311   \n",
       "\n",
       "         Imp  \n",
       "0   0.000000  \n",
       "1   0.147903  \n",
       "2   0.207506  \n",
       "3   0.000000  \n",
       "4   0.419825  \n",
       "5   0.425656  \n",
       "6   0.000000  \n",
       "7   0.239741  \n",
       "8   0.360691  \n",
       "9   0.000000  \n",
       "10  0.281938  \n",
       "11  0.314978  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_results_group = results.groupby(\"Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_radar_chart(dataset='Gowalla', model=\"GeoSoCa\"):\n",
    "    categories = ['Leisure', 'Working', r'ΔnDCG']\n",
    "    label_loc = np.linspace(start=0, stop=2 * np.pi, num=4)\n",
    "\n",
    "    performance_list = []\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.subplot(polar=True)\n",
    "    plt.tick_params(pad=23)\n",
    "\n",
    "    ml_results_group = ds_results_group.get_group(dataset).groupby(\"Model\")\n",
    "\n",
    "    for eachline in ml_results_group.get_group(model).sort_values(\"Model\").itertuples():\n",
    "        performance_list = [eachline.leisure_ndcg, eachline.working_ndcg, eachline.Delta, eachline.leisure_ndcg]\n",
    "        if eachline.Fusion == 'mul':\n",
    "            lbl = r'$\\odot$'\n",
    "        elif eachline.Fusion == 'sum':\n",
    "            lbl = r'$\\oplus$'\n",
    "        elif eachline.Fusion == 'global':\n",
    "            lbl = r'$\\boxplus$'\n",
    "        plt.plot(label_loc, performance_list, label=lbl, linewidth=2)\n",
    "        plt.scatter(label_loc, performance_list, s=30, zorder=10)\n",
    "\n",
    "    lines, labels = plt.thetagrids(np.degrees(label_loc), labels=categories, fontsize=23)\n",
    "    plt.legend(fontsize=24)\n",
    "    plt.savefig(f\"plots/results/{dataset}_{model}_{top_n}_radarChart.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tZts+DEOFZWkd+xQEgkFBCARBajx0nACO43O5ERDCBgKpqgxFkUEISTc4zwvRbntcvq9uEMcxgiDaJYWURFs0TUGppAMAwjBKH8ubuEoaduk6ez9CKAgEe0YIhAFmvP2xD8cJer2cOUEIgaaNC4I4jtPNy7YDbtIDRSZJuUwsYU0+73JZhSyTNBoTBBE3xrFkBoimySiV9NRwKhAIJiMEwgCSVCS02x5X/QtUVYamKVBVOb3qdd0AYej1emkDQyIGHIf9nfkZZJRKGmRZQhhS+H7IxajuxNDI1s8qHwQCwThCIAwQvEUMJIlA15XUic42J5YuEBSDJMrguux4SlIS1ao69p2xq/UiGx+DIMLICBMKACs/FUb5znFdFz/72f/BmjWrsXXrFgRBiEWLFuHEE0/CO97xZ1i4cK9eL1EwA6LV8gCQmA8dJ4Bt+71ezh5RFAm6rkBVFVAaj12NhtzluwUMTVPSVFAQRGl0oahs27Ye++13ICRJEiKhAx577Je44Ybr8Prrr017v2maeP/7/wYrVlw6r7LFLVvW4+ijj+p0mYIZEBGEPmZiVUK77Rc25Kuq8pgokBEELETdbtu9XpYgAxKBB4yLP8vSQWkMzwvg+2HhzKNJxcdEE6MQC7PnvvvuxU03XQ9CCM4//90477x34fDD3wBFUbFx4wb8/Oc/wz333IVvfetmrFu3Dtdcc63obVBQhEDoUyhl5rFWq5h9DBRFhmEo6ZWl54VotUTqoJ9h5ZI+AB+yTKDrKmo1a2xS37iQKApJfwkmEogQCbNgzZrVuOmm61GpVHHddTfg+ONPnHT/4Ye/AYcf/gZccMGf45Of/DgefvjfsGTJEqxceUWPVizYEyLF0GdQypr8NJtu4VzlSZ29prFIgeeJyY8CdlzougJdVxCGdCyy0JvjYtu29dh33wOnvW981HSXF8UJYRjgoov+Atu2bcOtt96G4447fo+P37lzJy699GLs3LkDd9/9z1iyZOmsX0ukGLqDqO3pE5I+/InhqijigBDAMFTU6xZKJQ1BEGF42Ear5QpxIADAwvm27WN42Ibj+NA0BUNDFixLK1SfgmS+BZut0evVFI/HH38MmzdvwrvffcGM4gAAFixYgCuu+AjCMMQ///OPurBCwVwRKYY+IAnRttseajUTsiz1XCCoqgzDUCHLEjwvQKPhFNYDISgOYUjTVJOuK6hUDACsyVERSnL/n/ufx+pXd/Z6GR1x+iELcPP/9SeZP+/TTz8JADj33PNn/W/OPvscXH/9l/HUU09kvh5B54gIAsckEYPhYTst/Wu3PZTLek/WQwhgmhqGhizougLH8TEyYsNxROMiwdzxvBCNhoNm04UsSxgaslAu65BlcdoqItu2bQMAHHLIYZNu37JlC0455QSccsoJ+Ld/+9dJ92mahqVLD0j/raBYiAgCh1DKqhNaLXeXXG0YskmDmiZ3LY8ryxJMk/Xwd90AIyO2CMEKMoPSyeOcEwHsOH7XvQrfuPCYaW9PRMsg+xMoZVFLWZbn9O8URUEU9T46JNgVIRA4Y2I6YXe02x6qVRO+n2+poK4rMAw17bEgqhAEecO6HzqQZQLD0FAq6emMhV6KUlYWiYHun7DXXosAAJs2bcRhhx2e3k7IuGiY2tKaUopNmzal/1ZQLIRA4ITEhDg66iCK9nwmZDXmIUxTzaVjomGoME0Vvh+h2XQLWUYp6G+iKEa77cG2kZZLshbQvZtCmvRPGNRqhxNOOAE/+cmP8cgjP50kEPbbb3+sXv30tP/m6aefxOhoA2eddXa3limYAyKZxwGUUti2h+Fhe0ZxkOA4PnRdzawBCSGAZTF/ASHAyAjzPQhxIOglcYw0rRUEEapVE5WK0VOfQiLmBy3NduaZb8XQ0BDuvvtObNq0ccbHe56HW275JgghuOCCC7uwQsFcEQKhwFDKJhQmRr+5Yts+SiWtozVIEkGppI81tInHStF6G84VCKbD90OMjNhw3QDlso5q1YSi9OYUl0QTmF+oJ0voOqVSCR/+8Edh2zY+8Ym/xZYtW3b7WNd18NnPXo21a1/Geee9C0ceKXoaFBEhEArKfKIGU/H9ELIszeskKUkkPckmvRWSgTwCQZEJggiNhgPb9mCaGmq13gmFQYsmnH/+Bfirv1qB9ev/gEsvvRg//OEPMDw8nN7vug5++tOHccklF+PRR3+BY489Dp/85NU9XLFgT4hOigVjLl6D2SDLEsplHY2GM6vHSxKBaWpQVRm27Reu/a1AMFdkWRqLpBHYtrfHyZJ76qTYKYPkTbjzzu/jtttuhe/7kCQJCxYsgKIo2L59O8KQnVPOPfdd+Lu/+zR0fe5l2aKTYncQAqFAsKhB9qOYy2U9nXewOwghsCwhDAT9i6JIsCwdQIx225+2mVieAgHAQFU6bN26Bffddy+efHINtmzZgigKsddei3DCCSfiggv+HMccM/9mTUIgdAchEApAHMegNEaj4eTSAZEQoF63MDw8fdmjZWnQNNbYqAjd6gSCPFEUCaUSmyg51Wibt0BIkGUxAKoThEDoDqLMscdQStNywbyIY8BxAliWBtv209sNQ4FpanAc5gIXCAaBMKRoNByoqjzmsQlh235XfQJRFI9FE4RIEBQXYVLsIZSyvvN5ioME1w2gaQokiUBVZdTrFmRZEuZDwcCSmG/DkKJet2Caaldfn42THhwDo4A/RAShBzAjYozR0flXKMwHx/FRr7OGMqOjjuhhIBCAzXzwvBCWpaUmwu5GEwa3uZKg2AiB0GVYl8PutyUulTSoqoIoonCcQIgDgWAKLM0Qpxt1N38jlMYgJB4YA6OAD0SKoYtQStFsul0VB5qmYGjIQhTFGBmx0Wq5PZv2KBDwQDIMTZYlSFL3duukuZJIOQiKgoggdAGWa4zRaNhduypJ+h9EEcXIiJOOW46iGEEQwTAUuK6oWBAIpiPZrAkhkGWpq82ORMpBUBSEQMiZpF3ybBsVZUFStthqudM2hbFtD/W6Bc8LxdWKQLAHEnGfVBt0S+CzlIOochD0FpFiyBFKKRzH75o4UBQJ9bqFOI5Td/Z0sLJHf6xpjEAgmImJaYdubdisP4pIOQh6hxAIOcH8Bt6kvgN5UirpKJV0NJvOrDoxum4IVZXHGrYIBIKZmJx26M7vRvgSBL1ECISMSWqbR0acrrQrTnoaRBEd68Q4+zNJq+WhVBJRBIFgLlDK0g4smtAdoZCIBCEUBN1EeBAyhJ042EbdjQ7WpZIOWZbm3dMgDCPEsQpVlREEUQ4rFAj6l8RMKEmkK/1MktcTLZoF3UJEEDKCtUxm8+jzFgeyLKVRg04bHrXbIoogEMyXydGE7rye8CUIuoWIIGQApTEcx4dt59+y2DQ16LqCZtPNZLATpTF8P4RhqKLlskAwT8ZLE/OvdEh8Cd00TAoGExFB6BBKY7Rabu7iQJIIajUThAAjI3amUx9t24dpquJkIxB0QLcrHYR5UZA3QiB0AKUsxJ/3iGRNU1Ctmmi3/dyqImzbF6kGgaBDkqv7xJuQN0IkCPJEpBjmCaXMjLi7XgNZwYyIBI2GneuJwPNYmkGWpUyjE4MMIRjrhkfS/09uByb+f+JOjyf8//gVKXOvi12AJ5LmSrKcv4FRpBsEeSEEwhxhzUtY2+Q8f/iSRFCtGvC8EO12d7wB7baHclnvatdH3mDd7STIsgRZJmN5Z2naK0a2uY9v8IkISPb6cVEw2ZWeCAhCCCRpXGRMfe5kKmhiXIsimv5d0HuSbojdaNXMRIKocBBkixAIcyA5KY+M5DtTQdPksaZH07dKzoswZJuMpild6eFQZBQlEQESFEWCJLFsXHIMRBH7rMKQgtIwvb1bJEIlESaSJEFV5fQ2AJPWmaxVRCK6y7ihkCCO8zUwsqgFIESCICuEQJglcRwjDGnuoX7L0qAo8qQBS92k3fZRq5kDIxAIIVAUtrkmoiA5qYchRRhG8LygK3XucyFZY7SH9hUsxM3ek64rsCwmHpL5IGFIEQSRiDh0gW6lHCgtjkhwXRc/+9n/wZo1q7F16xYEQYhFixbhxBNPwjve8WdYuHCv3i5QMCMkFpcUM9KNgUuEAJWKgTCkXWvPvDtMUwVA4Di9XUceJGKAtZmWJm2WYRgVTgjkASEEqipBUeQ0OhLHbMpnEERjDbR6vcresG3beuy774G5PX8S+cnb59PraZCPPfZL3HDDdXj99demvd80Tbz//X+DFSsunVc3yi1b1uPoo4/qdJmCGRARhBnohjiQZYJKxYRt+4W4cnecYGzaY8D91WUiBlRVBiEEYcg2wXbbGwgxMB1xHMP3I/j+ePiBiQYZmqagVNIAsM/K9yMEgZj6mRUTexjk6Uvo5TTI++67FzfddD0IITj//HfjvPPehf322x+KomB4eBg///nPcM89d+Fb37oZ69atwzXXXNu1ltWCuSEiCHugG+JA02RYlp5Z46Os0DQZuq6i2XR7vZQ5IcsSNE2GqiqQpImbXCTy73MkEVaaJgMg8P0wjTL0K3lHECYy0SuSF8zo2j2RsGbNanziE3+LSqWK6667AccffyIA4Nxzz8HOnTvw7//+M9TrQ3jttW345Cc/jpdffgl//deXYeXKK+b0OiKC0B1EH4Td0A1xYJoqTFMbG7JUHHEAAL4fQZJYfr7oqCozdQ4NWSiVNMQx0Gq5GBmx0Wp58P1QiIN5EAQRbNvHyIiDRoOND9d1BUNDFioVA7qu9DzPzTOJMMizX0Jinu3G4R+GAb72ta+CEAlf/eqNqTiYjn322Rff+MYt2HvvffC97/0TNm3amP8CBXOm+Gf/HtANcVAus0FL3RrsNB+KPO1R0xRUKgaGhizoOqu6GB62MTrqwnX5T40UjTgGfD9Eq+VheNiGbfuQZQm1moVazYRhqCJMPA8mdl/Mi26JhMcffwybN2/Cu999AY477vgZH79gwQJcccVHEIYh/vmff5Tv4gTzQngQppC3OGBmRBNBEBXeBJg4+XVdyb1b5GxI0h6KIsHzQjiO39UyUME4UcTMtLbtQ5IIdF1BtWoAYE23PC/oS99C9V/eB339I71eRkcEB52N1gXfz/x5n376SQDAueeeP+t/c/bZ5+D667+Mp556IvP1CDpHCIQJ5C0OWPMjE47jF2LDnQ227Y0ZFnuzXkWRYBhsJLXvR0IUFBA2rCyA4wSpWKjVLFAaw3WDQhhvBePkpdu2bdsGAFi4cC+cdtpJk+6Lxupxzzvv7ZNuv/nmb2Pp0gOwefPmnFYl6AQhEMaI4xhRlJ84kGUJ1aqBZtNDGPJj8opjVtVgWVrXyi8JITAMBbquIoooXDdAq+V15bUFnTFRLMiyBMNQUCpZ8P0IrhsUzmszV0bP/17ur5F3hYMkEZAYmftHKGXfrSzLqSCYytTb4ziGoiiIIiEii4gQCEjEAcXISD7iIDHRjY46XJbWuS4re8w7t6+qMkxThSRJcN0g96ZUgnyJIop220e77Y91B9UgSRJXEbRekAx7AvKZwZFXM6W99loEAGg2m3jiiecm3Te1imF8LRSbNm1K/62gWAy8SXFi++Q8SNom8yoOEtrt/AyLhqGiXmdmw3bbx8iIDdftzxz2oOL7EUZHXYyOOpBlKa046cbEQx6Z2Mcgr+cfnw+SDSeccAIA4JFHfjrrf/P0009idLSBN77xpJkfLOg6Ay8QEnGQx2ak68pYGWO+sxu6QRBEIIRd5WeBJJG0NJEQoNFgJYm8h6AFe4bSGLbtY3iYlU1WKgaqVSOz46qfyLsMkj1/duelM898K4aGhnD33XfOqmzR8zzccss3QQjBBRdcmNk6BNkx0AIhGdmcx+ZtGCoMQx0rY8z86XtCFmWPsiyhUjFQqRgIggjDwzYcR0QLBhHPC9FoOLBtH4aholYzx5oyCRLyFglRlF0UoVQq4cMf/ihs28YnPvG32LJly24f67oOPvvZq7F27cs477x34cgjRdOjIjKwnRQTcZCHI54NXJIwOspXF8LZUCppiCLmTp8LzF+gAQAcx+/rbnyC+SFJJB1W1iufQjc7Kc6FZK5CXpFIWZYy8yP8wz98HT/84fdRrdZw6aUfwDvfeR5ee20bwjDAwQcfgjVrfoX//Q4pZWEAACAASURBVL+/jfXr/4Bjjz0O//AP/whdn9uFh+ik2B0GUiBQGqPZdCb1os8Ky9IgyxJ3LYpnCyFAvW7NOi2jqjIsS0tDyyKFIJgJQghMU4WmKV0XCkUVCABST0JeXqYsRcKdd34ft912K3zfhyRJWLBgARRFwfbt2xGG7Ps899x34e/+7tNzFgeAEAjdYuAEAqUxWi03l5NOv4uDBF1XoCgy2u3dlx4qioRSSRfCQDBvCAFMU+uqUCiyQAD4Eglbt27BfffdiyefXIMtW7YgikLstdcinHDCibjggj/HMcf8ybyfWwiE7jBQAoHVaPu51PMPijhIqNctNJu7VmbIsjTmU4jRbgthIOgcQggsS4WqKrlPPC26QAD4Egl5IQRCdxiYPgiUxgiCUIiDjGi1XJRKBkZHWe8Ilj/WIcsE7bYnuh0KMiOOmdgkJECppME01YE+xuKYnc9kOR+RQCmFJBVfJAjyZyAEAut1QHMxDQ6iOACAMKSI4xiaJkNRZGiaAtv2cvF1CAQA+x23Wl4apWLCweO+hHg+5CkS4jhp0NS9MdGCYjIQZY6s10H2XRJNczDFQUIQRKhWzbSXhBAHgm4QRRSjow5cN0C1aqJU0nq9pJ6QiIQ8SiCz7pEg4JO+FwgscpD9SGU2QGgwxQEb82tCliXYti+uMgQ9IQgijIzYiKIYQ0MWNG0gAqKTYFf7+fRJyLJHgoBP+voXxSoWss9V6roCXVdyG+xUVAgBLEuHokiTuh4ODVlw3TCXvvECwUy4bgDPC1N/wqB15IzjGIQQSBLJPN0SRZQL06IgH/o2gpCMms26NErT2EChxJw3KGiagnrdSsdhTzwBt9v+wIZ5BcUg8Se02x4qFQOWNVjHY54dF/OcLCkoNn0pEOI4RhhGe6zTnw+s6Y/eV+2TZ4IQgmrVgK4rGBmxpxVcvh9CliXIcl8eTgKOCEM61sQrRr1uQVEG55hMRALJ+HI/8ToMyjlPME5f/noojTO/wk+c08zPkOlTFxZdV1Cvm3DdAM2mu8f33Wp5KJfzmfYoEMwVxwnQbDoolfTcppAWkWQKZNYpATb5UYiEQaPvBAKbsZDtdEZCCCoVA82mOxAlVSxqYEJV5VlXJ0QRRRTRgTSKCYpJFMVpOmyQoglJZUPWImEQzn2CyfTVL4ZSOmZQyvZArtVMtNuDYXxiXgMTjuOj1fLmJLSEF0FQRFw3wOgoiyYMijchimJIUvan9ygSfoRBom8EQhzHCIIoc1NitWoMxPRBQoByWU+9BvN5v3HMjKGDchIW8AOlceodqtdNyHL/2/KTCoSsEabFwaFvBALzHWTbk6Bc1hGGtCdjZ7uJokio1SwEQTSj12AmHCeApim5za8XCDohiYxVKiYMQ+31cnKH0uxFwninRUG/0xcCgfkOsjUlmqYKQkgusxuKhGGoKJfZTIWshJBtewNlDBPwRVLpoCgyKhWj18vJFVaBQDMX7KKqYTDgXiBQmv3UQFVlswX6uUsiISx9oigSRkbsTA1Ivh+BEDIwpjABn7RaLnw/RL1u9XWJbrKRZy0ShB+h/+H6V5H4Dlw3yOw5JYmgXNZzGexUFJKUgueFaLWy7RWR0G6LskdB8fG8EM2mi0qF9froV8Z7JGT9vEIk9DNcCwRKYzSb2aYWqlVzLA/fn0e9risolw00m9mlFKYjiiiCgPb1SVfQH0QRSzlompJ5k6EiwcofhR9BMHu4FQjjQ5iye86kYqFf58yXSvrYDAk7lznyU7FtT1Q0CLghSSn2c4VDHpUNwo/Qv3ApENichTDTjdyyNERR3JcVC8xvYCKOWaVHt37MccyqGoRIEPBCHMegNO5rTwKrbBB+BMHMcBn/ZcbE7HLnqipDVeW+nM4oywSVignb9uH73Rc/rhugXrfguoHoxCbggjgev9Luxxz7xBHRWf4mr7nms/jv/35h3j6He+65P7O1CLKBO4GQpBayIjEljoz0nzhgZVzMcNnLLpCJYbGfjZ+C/oOJBAJK+y/PPrEdc1Zvbdu2rdiw4Q/ZPJmgEHAlECiN4ThBpptdpWKMtRTurxOApimwLA2NhtPzK/cgiGCaKlRV7vuOlLuDDdAhY38m3k7GTtDsO0pMX8L8VQxYy2L2vfX6d5Q1SSol64uHNWue2yWKsH37H0EpxV57LdrFKHnFFZfh179+NtM1CLKBO4GQZeMiy9IQBFHfbVqmqULTlMyHVnVCq+WhWjUxMmL3eimZIssEksRGXUsSSf871Q0/cRrexI0/jrGLYJgoJqY+B6XJH4ooihFFtC/D4EUiudrOOiRfBJIoSZamZXZ8TxbC73vfX2Hnzh3493//Ger1ocxeS5Av3AiErFML/eo7KJV0EILCvS9KY/h+CMNQM+1b0S1kWYKiSFAUORUBANINOopihCGbBcJc3dlvJIQAkiSlm5WiSNB1ZdLmFUUUYcj+RFEkhENGJGOUs95MiwCl2foRWGSifytBBgkuBAKlNNPUAiH96TuoVAxQGufW/KhTbNvH0JAFzwsKvXFJEkkFpKLIADC28Ubw/XBMFHT/DSTmuWg3AS+2gTERYxgKFEUDQNK1B0HUtyW83YC1Lc4nLN9L4jhOI1dZ/S4Tk2cft5UYCDgRCNmmFvrRd1CtGggCCscp9uwI2/ZhWXqmVSidIkkEmqZA0+T05M86dIYIw+KscybiGGn0YCKyLEFVZZimBlmW0g6kvp9tqfAgcNVTn8CTf1zT62V0xMmLTsVXTrpx0m15CJ/pUg0Cvii8QMh6SqNhqOkG0C/UaiY8L+QidO95IUxT7flVmKaxeRuqKiOKKHw/ynymR1FgUQeaHh+EEGiaDMNQJ71/zwv7SjQL5kYy1KnT6FijMYJ3v/sdk26LxsJe55339km333zztzt6LUG+FFogxHEMz8sutSDLBIah9pVRrlYz4boBVw2eWi027TFLT8lMEMLaTLNR1BKCIITr5jeLosiw31WYHjOyzCIo1aoBQgDPi+D7Qd/l2rNg6pV3r4VuliSGWVZZM//vnqXCpr8Am3q7EKTFptACIeuGSJWKiVarf2rxeRQHAAuDx3EMTZPh+/lFcghh5Z66rkKSCDwvRLvtiY1vClHEyocdJ0ijC6WSMfaZBanxUrArSa69X0TCeKph/t93vV7H448/k/5dlgnOO+/tu61iuP322+b9WoJ8KaxASMRBVgLTsjR4Xv/kXHkVBwntNit79P3sozmqysLnsizB90O0264QBbNkYnSBRV1UVCoGAHB9vOVJv4mErEsfxW+PXwosEGhmJyNFkfqqpJF3cQCMlz2apgrH6dw7QQhJ+z+EYdTXQ7e6RRwzUeC6ASSJpeeGhiwEQZR5wzLe6TeRwNINnaUaBPxTSIGQtTExGW/cD/SDOEhIyh5dd/7mOEWRUne+6wZ95S8pEkklkW37UFUZpZIGQkjfHItZ0E8iIYtUg4B/CicQsjYmJqmFfjjQq1VjkrmsH2i3fZRK2pzNgqyVtJrmz8Owf6pSik7SfVSSWNTGsqw00jDoF5z9JBKyTDXccMM3EYYBSqVKBisTdIvCCYQsjYmyLEHTlL64qiyXdYQh5aKUcS4kaQZFkWaVEjAMFYahIggijI66wjzXQ9hv1Ue77cM0VdTrFnw/hG0HAx2aZuOU+0MkzCfV8Fd/9Rd7vH9qC/Ft27bNa22C/CmUQMjamFip6Gg2+a9aKJV0xDEybRZVJFotNu1xTx4Rw1Bhmio8LyzUjAkBI6mC0HUFtZqJIIhg2/5ACgXWcTH7GQe9YD6phg0b1ue4IkE3KZhAyM6YaJoqfD/iXsWbJivR6wehszuSRj66ruzy/U8UBiMjQhgUnSQFNi4UwjGh0OuVdZekLXM/DHiabQOlW275zqyeL5kdIig+0swP6Q5ZzhCQJAJdV7m/4mbtf5W+FgcJ7bYPy9LSv2uajHrdgiQRjIzYA7nJ8Ewi6MKQol63YJpqr5fUdZLjlffNMHkfWbVMZsPMsnkuQb4UJoKQZfvjUqlYvf7ng6JIsCwNjQb//onZEMcxXDdAuayn+dvRUYf7q69BJ4komKaGoSELtu33lcl2JpIoAu8lg1nPahj/XDJ5OkFOFCKCQCnN7CpZVWUQAq5nLUgSQaViYHTUGRilTQiBLEtp5KfV8oQ46CMcx8fIiANVlVGrmZDlQpx6ugLbDLO7Au8VyaaeBTyLpUGi5xGEZLJcVsq02z3+s4YQoFo10WwOjkM/8Rm02+zqMqlSEPQXcczSiLIspVU5tp2dKbnIRBH/Y6LjODuBACTeBjESusj0XMYnJ40sME2N+77xlYoxMF0AZVlCvW5ClgmGh234foggiEAIgaLIvV6eICeiiKLRcBCGEep1C5rW8+uUrpD0SOAZJnSyiiJk8jSCHOnp0Zr0fc9iQ2fGRAWOw68x0bI0RFF2lRxFxrI0VCoGmk0P7fbk76zddlEu6z1amaBbJEZGXU8mSfb/pWRS/sgzydTHLKCUCqFQYHouEKZuDvOFd2OipilQFDmzz6OosKiBhTgGRkbsaUOuUcTSTro+GFeWg0wcA82mC9cNUK+bff+ds/JHvisbmBchm61DiINi0zOBEMesRW4WZhVFkUAI4TZvLcusYqFf5kXsDtNMogbujJEe2/ZgWZrITw4Ivh9hZMSGpimoVIy+/t6Tcx7PEZMsDYtRJKIIRaVnAoHSOLN0AO/Rg2TT7NcfiSQR1GomCNl91GAqccy6803sjSDob5JogueFqNctqGpvfCjdcNhnucH2gjiOMxY4s//MRQVE9+iJQEhq3rP4njVNSTvx8UhiSuR1/TOhaaysrd3259y4ynUDqKrC9YlUMHd8P0Sj4cCytK4LREIkxHF3fou8mxaTDotZEEWzb55EacT158YTPRMIWUUPLEvjtmOiYbB8a7+aEkslHYahjXXUm1/6p932hGFxAKE0TmdzsOhTd0Siqmrw/e5FI7PcZLtNlmbFueC6DkqlUvdfeADpukBg0YMwk+iBYajwfT7LGmVZgmFofdlGOUkpUBp33OwpCCLEMXoWbhb0Fttmkad63ezKMaBpJhyn1bUw9ngbYz5FQhRllyqZTUUDpRHa7SZqtVomrynYMz0RCFld8Zsmv/MWEt9Bv6EoEmo1E7btZxYlarc9lEoiijCoBEGERsNBqaTBMPKd6VAqVRBFEUZHdyIMuzONknc/ApBNJGF3H3Ucx4iiEO12E9u3v4ZarYJKpdL5CwpmhMRddHywvgcBms3OQ3imqQFglRC8USrpiCIK1+Vv7XtC1xWYppbLDAXL0kBp3HefmWBuVCpGps3VpiO5SnWcFijtXmUUz/Maslw7E0uTFYcsSyiVSqjVaqhUKtxGXHijqwKBUorhYbvjzYMQoF63MDzM3yAjVZXTTbSfKJU0SJKUa1RkaMgSI58FME0Vmqb03awSNtpd4rIiq1Ix4LpBJqXmCxaUhAmxIHT1WwiCKJMrS9PUuIwcEAKUyzparf5KLVQqBgDknjKxbV+kGgRwnACO46NWs7gPzU/EcQIoisSl3ybpW5IF7bYY1FYUuiYQKKWZdAkkhIWyeQw1l8sG2m2/bw5+QpjDPAiirnSA9LwQsiyJqwsBfD9Cq+WiVjOhKP1zPDSbLkolnbtGUVEUg9I4E3HjeSG3qZZ+o2u/rDDMplcBr9GDZCCN7/dHSSOrVLDgOH5XxZooexQkhCEb+lQuG1xedU9H0kCOx0gZMxNnE0WwbR+U9mdvGJ7oikBg0YPO82q8Rg8IYTn6PI1V3USSCKpVE+22B9/vbnvrMKSgNIam9ceGIOiMpF9CqaT3zVRIzwshSfylGiiNEYY0k98mb+f4fqVLAiHOZHwxr9GDUkmHbXenZCpvZJmVMbZabs9mX7TbHiyLvyssQT7EcYxGw4Zpqn0z7KnVcrmMIjiOP1Zh1jmsXw7/50yeyV0gUJpd3wMeoweqKkOSSF90S5RlCdWqgdFRNxPBN18oZWPCszoRCfgnjoFGw4Fh9IdISEp6eRMJiRchC1+I4/THRRXP5F7mSCnFjh3tjp/HMFRIEuGuMVK9buXSF6DbjIsDB1FUjPdSr1toNBxxEhFMolYz4bpBX4hyNsfE66kgnyuKwqbTjo6OVzVRSnHjjddh3bq1UFUVV131WSxZsjS9f9WqB/Dgg/dDlmVccskHcNppZ2B0tIFXXnkRv/rVarzyyit429vehksuuaQXb2lgyVVqJ22Vs8AwVDQafPU9sCwNnhcIcZATrOyxf7wdgmxoNBzUaiYA/uectFrMlJvMpeCBMKQghECWpdSY/thjv4Dv+7jttn/C88//F2655eu47rqbAAA7dmzHfffdjdtv/z5838eHPvQBnHTSyfjd717Cxo1/wOc+9zlIUv9UqvBErp96VkOZdF1Je/LzgiQRaJrCpWdiIsyQyNIKRRIHAKsIkWWpr8rcBNmQpBt4Ny5GEUUYUu7SJrY92Yvw29/+J04++VQAwDHHLMdLL72Y3vfiiy9g+fJjoWkayuUyFi9eildeWYvf/e5FPPzww/j973+PT3/603j99de7/j4GnVzPrInjvFOYOZGv1ALrecD3lW1SrdBsuoUdR91qiTkNgukZHWUjo3mrBphK0oSIp94IQRBBUaS0kVW73UapVE7vlyQJYRhOe59lWWi1WjjwwIPwgQ98EPvsswQXX3wxrr322u6+CUF+KYaszImqKiOKshEa3ULTZMRx3DOXfxYQgrSUscj5zyhi/TV0XeEmnMxO9NOf7YWfIjsS42JSdVPk43hPxPF4F1Ge0mmO48Mw1LG1l2Db4yniOI6hKGz7mXqfbduoVCo4+uhjoOsGPC/AsmVH4MUXX9zlNQT5kptAyGqD5HFio2XxlTOcjmrVhG17XIicdttDvW4VQiAknR5lmaT/P3WwzJ5EwHSPpTROhVDyR+iI2RHHbOR4rWai0eDXLOx5IQxDnZTXLzqeF2JoyIJt+1i+/FisXv0Yzj77HDz//H/hkEMOSx935JFH4zvf+RY8z0MQBFi//lUcfPCh+F//6wt485vPwtlnn4NNmzbjPe95Tw/fzWCSSxVDMtK50409yX+PjPCz2ZqmCkL4q7aYSKViIAgirkpKe1Hloqpy2jtfliXEMXbZyKMo7igqQAgLx04VHZJEQCkT4WEYIQioiD7sAUWRUC4baDT4HfbFqgN0rga9WZaGKKJwHB833ngdXnllHeI4xqc//XmsWfM4lixZitNPfzNWrXoAq1Y9AEop3ve+9+MtbzkbW7Zsxle+8kXEcYwjjliGq676O5im2eu3NFDkIhAopdi50+74hJUcXEW4MpwNPE+ZTGC5TsKlfyLvklJVlaFpSprTZhsz26B7YeAkhEBVJSiKnPbbCEMK3w/h+yG3G2FeaBqbpMpzdC/LqYndIPExjYx0fk4cGioJQ3KXyUUgBEGU0QHB12ZbKukIw4gbQTMVXVeg68qk+mWeUFUZhqFmNlUyqUTRNAWSRBAEEXw/LPTJWVGkVMQQwoYa+X7Ibf49awxDhaLI3E5UzXLD7RaVigHH8Ts+Bk1THRtkxZFbk3My9yBQSjMrbeRpo5UkAlWVubzyBtjGwq6u+DnxTCUIIpimClWV572JJ/M+dF0FwPKorZbLTe46DCnCkP3+CGFDwkxTgyxL8P0Qrst/X45OcN0A5bIEw1C5SqElsLRSCMNQMusxkzdJ++VOhbvrhmmEU9AdcjEpZrGxm6bK1ZVsqaRzKw4IISiXWSMk3sPSrZY3ryusJPogyxI8L0SzyY8o2B1xzH6LnhemYqFSMQCw23ncILOg1fJQq5mIIlroaNDusG0f9brFjUAIQ5qadTsJWMdxjCiKIXomdY/MP+og6DyUKcsSKAU3J+jENMbjyQYAqlXWs4GXz3tPjF9hqbN6vGGoqNct6LoCx/ExMmLDcfy++CwmkoiFRsNBs+mCEIKhIQulkpbWqg8So6MuymWdy/eefJezPcaLgOsGMIzOr0fZb1Oky7pFpgKBDRjpPL3AW/jPsjRuqxYsS0MQRNyKm+lgXdzU3TaWIYTAsjQMDVkghKDRsNFqFbvfQ5ZQyjqcDg/bCEOKatVEpWIMlAEsjmO0Wl4aUeENFrbnRyB4XpCm7TrB9/mImvQLGZ8RYvh+5xuNpsncHAg8Rw9UlbnfeRU3uyOO2Ql06khoQghKJT0NLw8P22MT43q00ALgeSFGRmy4bgDL0lCtmpDlwRAKzHQaoVTibypoHLOcPC8iISkB7lSExjEGRsgXgUzPBFls6pqmZCIyukWppKHd5m+DZb4DnSufx1xw3XCsPwEBISxSUquZCENWYcOTAbYbBEGE0VEXts2GA1UqBpfh97niOD5kWYam8deOOelUyAsszdD5evsxBVhUMhMIrHqh87QAT+mFxHgThvwImoRKxUCr5fV1cx0WQjZRr1ugNBbCYBaEIUWj4cB1A1SrJsplnasZAPOh2XS5LZ9z3WCXKAKlFDfc8GV88IPvx0c+shKbNm2cdP+qVQ/gAx9YgZUrL8Xq1Y9Nuu/Xv34WF154Xi5rDYIok7kY7AKyf89bRSIzgZBF6EeSCCQJ3LQS5dV7YBgqtw7u2cI657HNrdXyuBGdRSHpZRIEUWri7FfiOEa77aFS4W/o13RX5RNHK19++ZW45Zavp/clo5X/8R/vwE033YLbbrsFvs/OYa+9tg333HNnOkQpDzwvzORY4inKzDOZCIQ4jjO5MtN1fmp7JYm1veVtk5VlAsNQuS3JnAlCWMlpqaSj2XTRaDhi2mMHJB4FVZVRq5mQZf6usmeD70egNOYqZA+MVzRM3HTnM1rZ8zx87WtfwSc+cVWu63XdbMyKjhOIaoYukKFA6PwKjafmSKbJZ/SApRb603egaTLqdQthGKHRcNIpoL7Pj5mriMQxi8Kwq2wTlsWfqW82tFpeOtODJxwngGmOfyfzGa389a9fj7/8yxVYtGjvXNdKaQxJIh2nc3hM6/JIRgIhm/RCHPMx7pYQVgHAS6VFgmmq8P2oL13A5bIOw1AxMuLsIjJtm5m5eMwxF4kwpGkDqlrN5G4jnQ3ttodyma/SxziOEYZRarSc62hlVVXxm9/8Gt/97nfwkY+sxOhoA5///NW5rZeVPHaeZuAtessjmQiELL4oXefHnGgYGjdrTZAkAl3nb3T2TMiyNBY1oBgddXcrMNlM+v688u02tu2j3WbdCDWtv7wJQRCBUsqd54L1/mDH9/Llx+KJJ1YDwLSjlX/721/D8zy0Wi2sX/8qjjzyaNx11/245Zbv4JZbvoNqtYZrrvlKbmvNyofguqGoZsiZjr8lSrNLL/AyB8AwFK6GSAHjVQv9hGGo6XCmmYytSec5WZa4McEWmSSaUC4b0DS5r46tdttDvW7B9yMuIpoAOw/HMRPMZ575Vjz99JO4/PK/Tkcr3333D9LRyv/jf7wXH/7wZaCUYuXKD0HXu+vRSTb1ZGT5fGER3BhA/0WyikLH0xwppdixo93RImRZQqmkcVGTn0zK48nkp+tszf10Eq9UjLQb3mxRFAmlks71uN8ikgi1PEdtdxs2xZOv34yqytB1hYs1s5QfOi6Nr9etTEonBdPTcYohi3w2X+ZENZNpld0iaRLEk6DZE4QQ1GomgiCa84kwDCmiiPZdWLzXuG6AVstFrWb2Tbtm3w8hyxJX7ycIIiiKzEXfiuzSDAE3UR4e6ejoZ7MXOk8vsO6JxRcIsiwhjvkZIgUAlqXDcYK+aCcsyxJqNRO27c/7uGu3/b514feSpMFSuWxwl7/fHa2Wy51hMatuhXkTxywl0qlx2PdDIRBypEN53PnsBVa9EHOxgZmmmskwqm4hywSqKnNnqJwOVZVRqRhoNt2OTLFJSe7EsjBBNlAao9GwoetqX4iwKJrbZNAikNVQpG7g+51HESiNIdoh5EfHEYRO1Zum8ZNeYKWN/JTWlEp6X6QWNE1OvQNZGAwdh5VZ9WOZXq+JY2B01IEkkb5oUDXTZNCiwUrOs2lpnDe+H2YyA8PzRJohL+YtELLsnshDeoEnnwQAKAr74fFeK6xpCixLQ6NhZ3oSsG2vLzawopL4Q8plvj9jNhmUr4iT6/IR9YiiGJLUucdDpBnyoyOB0OnGTgjLQfGQ0+dpiBQAlMv8GxN1XYFpqmg0nMxTUL4fQZIIVyY03mi32TAw3kWC6wbQNH4iTmEYjQ2S6/VKZiYIOo8i9GPjt6LQ0dmx0y9GVfmIHiQnBh6EDMCuupljn4/1ToeuKzCMfMRBQqvlcb95FZ12m43mrVT4MvtNxbb5MreyKoHiRxE8L8qkqkiIhHyY9zeTxeaj6woXJYO8RQ8sS8PoKL+1/pomp+IgT9hES8pd+og3ki6WPHtifD+EZWm7NPehlOLGG6/DunVroaoqrrrqs1iyZGl6/6pVD+DBB++HLMu45JIP4LTTzsD27dvxpS99FkEQoFqt4nOf+xIsq5Tpej0vQLVqFP68xfwSnYt03w+hqrJop54x84ogsPRC5weeokhcKD+ejJQsehBxE+2YiqJIsCy9awLHtj2urgx5pd32IUmE66FZ0x0r8xmtfOed/x/+7M/Ow7e+dTsOP3wZfvKTH2e+1qSzIg9pkTCkkOXOUn08db3kiXlFEJhA6Mz8xkvLW17WmcBz9ECWCSoVI9e0wlQSE5pl8TmdkyeaTRfVqjnWnp0PwT0R3492iSLMdrSypmnpaOWPfvTjiOMYlFK8/vpr2Hff/XJZb1LyWPQobRCwQVOOM//zLE/naJ6Yt2zr9Mpf0/goGTQMnqIHMrfRA0IIKhUTo6Nu19fPmwmNZ0ZHHZimykUZ3nRM9SLMZ7QyM2ZTvO99F+G5557FiSeelMtas+pWmDcsPSB8CEVkXgIhC/8BLwZFXro8AoBp8nsVXK0aaLe9nl0JsDG/wrDYDRoNF+WyzqUg8/2knTFb+1xHK1cqvf0xxwAAIABJREFUFQCAoij4wQ9+hE996tO49trP57JW1vW18/B93lAaQ5Y7PxZEuWP2zPnIycp/IMvFL2/kxSMBsCZOrKtYsT/T6SiVdPh+1NOeDclr83plyxNxHKfpBh5xHD/1Usx1tPLBBx+Kr33tOjz33DMAAMsq5Wqs4yWKEASdN3cSPoTsmfM0R0pZz/VONk5FkWEYxZ86VirpCIKQi1RItWr29Ap8vui6Al1XCjHJU5IIqlUTIyN8jfLmFcNQoShS4c8D0zE0ZGF42E6rGF55ZV06WnnNmsfT0cqrVj2AVaseGEspvB9vecvZWL/+D7jhhi+DEAJJkvCxj30KBx10cC7rJASo1azCH9OapkBRpI4joAsXljJpviRgzEsgdDre2bI0RBEtfG6/Xi/+DwtgG1ti7uMJWZbG1m0XZhZHqaQhirIZQiaYmUrFQBBE3H3epqkhjvk4Tmo1E81m9709c4EQgmq183NYrWaKaa0ZMmeplcVBpihy4VsAsxQIH1fjpql1PFe9FyTDl4oiDgD+eu/zTrPpwjDUwufJp8LL1ESApRmKvmnGcZxJqiUIRJohS+b8q8xiY+fBf8CTOZFVhPCx1oRSSYPnBYVLicRx4lQXhsVu0Wy63HVajOMYUUS58KxkMTWxG4Rh1HHrcyYQMlqQYG4CgdK4Y4Egy3wY/1hzpGJHOQC+yjATFEWCosiFjXp4HuvKxttVLa9EEU07FfKE4/hcRBEoZVfnRY+KZWFUDMMIgFAIWTHHM2DnAkFVi59eSIZI8RCq0nW+2kADQLnMUgtFptVyxbTHLmLbPjRN4UqUJR0AeWjvm1WvgTzJYkx1HENEEDKk6x4EVZXHVF5x4aVHgyxLY7XO/PwiSiUdrhsUfs1hSBHHcSbz6gWzg8dUg+cFMIxib7wAKwEs+rEcRXEmArFoaUuemXOKoVN4SDFoWvGjHAB/Q6RkWYKiSNysud32hBehiySpBp7mNbhuwMXUxCyuzrtBFNGOG2gJo2J2zEkgdLppEgIuvjge0iAAf+bEcpmvaX6UxtxtWLxj2yyvz0PYHuCnWyHArtCL3r0yDDs3fgqjYnbMOjaWhUFRUeTCRw8kiWTSSjpveBExCZqmIIpo4b//qdi2j6EhC647vzausiylhkdZJrNq4pI45JPPi6fvOQuS8dC8NFBy3RCGoaDdLnab8yBg5Y5FjuAlkQ6vg68+DJMIQrHFEA/MIXkWd+wdYK2Li32y0zQFQVD8q3Jd56t6wbI07ho5JbTbs9+wZFmCpsnQNAWEkLFNPoLvh4giOqs0HSEkFRS6rqRmyaSrZ78LBs8L094IE/PJSdfCdevWQlVVXHXVZ7FkydL0/lWrHsCDD94PWZZxySUfwGmnnYFt27bhK1/5IqKI/VY+9alP44ADDsp0vb4folSyCi8QfD9CqaQVWiAEAYVpdlbNIqIH2TEnd02nV9aKIhd+mJCqyoUfjwqwdfJyhWWaGjyP30EqSZphd6O/JYmMtYxWQSmF70cdda6LYybGwxCpCCSEmWd1XUG5zGZXeF7AXURmtiTDsyaKysce+wV838dtt/0Tnn/+v3DLLV/HddfdBADYsWM77rvvbtx++/fh+z4+9KEP4KSTTsbtt/8j/uIv/ifOPPMtePLJNfj2t2/Fl798Q+brDUNa+NktUVT8VEgcZ5MG4eG98sCcUgydsrsTbJHgwUSpqnyMyk4wDAXDw8VvWb0nWq1dNyxFkWCaGmSZGS/zbBkdx0yoJJ4TTZNhWRoIIXCcgCsvymwIQxZtmZhK++1v/xMnn3wqAOCYY5bjpZdeTB//4osvYPnyY6FpGjRNw+LFS/HKK2vxkY98DOUyG7kcRRE0LZ9eC57HzIphWGzRnpgAi1xFlHglOlljEESF7x7JA7OWWEXf2LOAn94HfJRhAoBp8lVpsTsST0AyVKZWM2FZGhzHx8iIDdcNuhra9P0Io6Mumk0XqiqjXre46JY3F1hHy/ENvd1uo1Qqp3+XJAlhGE57n2VZaLVaqNfrUBQFGzb8Abfe+g389V9flstafZ+PKoEsmhHlDeuo2GnDpNml8wR7ZtYCYRD8B7wY/3hZJ8BKMYvaMXGuOE6ASkWHZbFqjNFRt+fRJkpjtNseGg0HisKEQqcn16IQRawXRdJ+t1QqwbbHI1HsPmXa+2zbRqVSAQA899wzuPrq/xef/ewXM/cfTISHagY+BALtuOUyu6AVAqFTZvUtsJxo5/6DXp9MZ4KHjbfoec6JsD4NfEQ6ZsIwVFQqBjwvHPMHFOs7iON4TLQ4KJU0lMt64VvrzoaJczGWLz8WTzyxGgDw/PP/hUMOOSx93JFHHo3f/vbX8DwPrVYL69e/ioMPPhTPPfcMvvnNr+HGG2/GEUccletaeRiKxDbfYguEKOp8JsMgRLy7wazGPUcRRaPhdPShl0o6fD8s9AZcq5kYHXUK7YLlZVQ2AAwNsXHZRf48ZyIZpR2GUepSr9ctjI46hQ5h6roCy2KVF0X+zc2GatWEbXvw/RA33ngdXnllHeI4xqc//XmsWfM4lixZitNPfzNWrXoAq1Y9AEop3ve+9+Mtbzkbl1zylwgCHwsWLAQAHHDAgfjUp/4+l3VmNbI4b+p1E41Gsc9z9To7d3TCggWlwkd0is6sBAKlFDt2tDt6oWrVRKtV7JnkWRyUecNEjFt4r4SmKVBVqfClX3tCVWWUy/oum6ymydB1tfDzJBJxEwRR4auH9oSqytB1hYuqnXrdytWsmgXlsg7XDQud8s3iXDw01D/ptl4xyxRDBi9UcOesLBMuwlK8GClNk2/vgWmqsCwNIyPOLlfgvh+BENJxGDRvKI3RaDgghHA342AiQcBMazykTIIg5CDHH0FVi37sdt5yuWhpQB6Z1VFS5I09K3jwSPAyI0KW2Q+b1+OmXNYhy9JYGHb698Dq9PnYdNttFp6v100uNtnpSMoIiw6rZhA+hE7Jwqg43lFRMF9m9Q10emXNQ/8DJhCKvfnyMmXSMDRuoweVigFK4xnD2VHEWiDzUlroeSHabR+1msXNnIOJuG4Awyi+QOClSqDouXnW6Kizz5FVwWS0oAFlxqMkjrOYwcCDQCh+dQAPo7IB/oZIJVQqBqKIzjpfb9veWLOinBeWEUEQod32UKuZ3ImEOGYn/KKndQB2zuTs4y0cWXRCZOdzoRA6YcbLn+SH2QmSVHyBIEl85PaLvkRNU7jq8phQKumgdPbiAGDfxR92bMQP//sf8cKmx3CM52NF7URUTvw4aGX/HFc7f8ZFgoGRkWK77aeSRBGKblZMBg4V+XeQbMBFPS+z9XWmsnhNcRaJWUUQKO00xVBsAyAhxT+YeIkesCFSfKUXTFOFJJE5V1y87ryGFT99Lx5Y/6/4b9rE/YqLi5uPw/vR2yE1t+S02s4JggiOE6BaNXu9lDnBQ/geGDdVFpmizyqIY2QS5Sr6BVXRmfEIyWLzlCSp0BtwkZV0gqLwYVDkIVUzEVVlkxfnU7J49ys/gBvaCMdOZCEhsCWC75oE6x56L37yws14busv8Efn9cJFp5KGTxNbGfMAD5tvENDCC5ksTIA8ULCfHXfMymHV6Ydc/BJHHgSCBNsu9pU58x4UX8QkEEJ2GcA0F15q/DfCKTnOkBC8oGu4YMcmfH39XcD6uwAAZgwcQAwcoC7AUmt/LK0ejsULlmPxXsfD1Gsdv5f5YNs+ajVz2g6i8xmtnHDvvT/Ejh07cMUVV2a+Zt8PoetKoaNpWU0kzJMoooU32FLa+dAmFv3ufyGUF7PyIPQ7iiIVfmNjIqbYX4amKVx0eEyoVFgTpPmegI6oHYW1jRcx8chR4hjHeD6IXsN7fBnrYxfrFQk7ZRm/g4vfBVuAxhag8QywkYmHfSLgQKKPi4fKYVi8cDn22utYEGMB8nS8jY66qNfNXTpezme0chxTXHfdtXjxxRfw5jeflct6fT9CqaSj3Vnfttwp+tTEoqcYgKQXggRK539uLurnzwuzEAidfcA8NPZhm29xr84J6fx76AaqKhfeQJag6woo7axC572H/t94aMMDIHGEkBAocQyTxrhoNMLdB34b//NNJ2Lfig7ibEd754vYvOO32Di6Fhvtzdjg78D62MVGGXhNJngNHp6KtgLNrUDzWWDLPdApxQFRjIOg4wB1CEut/bGkciiWLDgG5tAbEFWWAHJnKQI2w8FHuWxMSrPMZ7Ty4sVL8c53no+TTjoZ69f/oaN17YkkPF7kVFbSa4DHap6ikBgVww4+wqJHhovOjAKhUwVWdIMiUHyPhKLIhf8MZbnYJ+yJEIKxLomdtXKta3UoMZs4eAhVcWArwiHDh2CFcyH++GKEe9Y9hx++7wTsW10Ey1qEw5ecicOnPEcU2Pjjjt9g0/bfjImHTdjg78AG6mC7JGGtBKxFCOCPgP1HwP4N8Nr92CuMcFAY4sBYxQFqHUvN/XFA5VDsPXQkUDsIUe1AxLOMPiRh+4mpht2NVlYUZbejlavVKv70T0/BQw/9pKPPdSYSs2KRj7dkZLFf4A7XSTlmUa89WIqhsygHpXTsfRY75VNUchcIRQ6z8QIPmy8PkzATLEuHbfsdnxiffvXHsAlwREBxcOk23L/uj0i/Jgo0vRCX3f0bnHRAHQtKGhZYKhZYY/8d+3vNMLHvvqdi331PxRunPH/Lb2Lz8AvYvOM32Dj6Mja1N2G9vwMbqY3tioztioxnAACjQDAK7HwJ6o5/wQFBgIOCEAdGBAeqNSw198OSyqEo1w8DrR6AqHogospiQBnvBNlue6hWzVQ0zXe0cjfw/RDlsl7oZlxhSAvf2ClJMxT13EJpDEXpvNSRVURktKgBY0aB0OmVKw8phqI305BlqfChSlWVuRgIJMsSFEVCu915KuSRV+8BALzdPBQPvO5iuvPstqaHn7zw2m6fQyJA3dxVOIz//Q3Yb+HROHop+7sqS6AxxevOa9jYfAWbdv4XNjXWYmN7Izb4O/A6XLyiaXhFS1IPPoD1QHM9/n/23js8rvJc9/6tPk1dlmR1uWNsY2xj00PdkAAJPVSTmBQS2OGk7FzZZ7ewry/7nLMPJHAom5BgsAkYMJgNoUMKGIOxjTE27kXNKrZVR5q62vfH0shNfUbymlj3dcmypJl3vWvNzHrv93nu535yOt6jUtep0g0qdIMKKUCZdyLFgSqErErM/Cp8mRVEPMXMnjWHNR9/xMUXX9pna+UnnniMWCyGruu9rZXHCpZlp0H+PPnd72jj8BzdShCS78fg6LZsYJwhjAQDEgTbtlNikpSsj8JoIh3Ef04Fg3uvIaSHWyY4qYVUEJnuWAdr4k0IwPkzlrBrT4AdB7o4MlgmCXBmZQ5fmZJPe1inLRynNeR8T/zcGTVoC+u0DbFCJUOTjyASueT6LiXX9zXOnKDyNa+C32MSEw7QZTZyMLSH/cFd1IfqqY+30i5BuyTx+VEtJA4hxw5S2rCGyhon8lCp61RZErOzZar/16uEYl7+v6uuYe3yf8JbPJPTLriW66+/ibvv/i6WZfG97/0QTdOSvqbDQcIuOB3ec25FKsyIRhOJKoZkxxjHyDFgu2fLsgkGj+9mNxz4/RrxuOHa8LOqSsiyu3e/OTk+2tvd24ZakkT8fpVgMD3aH4+0rPFIvPf5/+F/Nb3KAl3gP7/+Ec1dMb7x+3W9BEEWBXyqxLO3z6Mos/+mTrpp0RHRaQvptB5BHI4kEq3hOG1hnY5wnOFwWY8s9kYkcnwKfm83kqcFSzpIlEa69Rpa4420mB39xtAyTfMwaej5XqkblCi5yFnlmJmJrwrMzHKsrHIsXwEIo7t79ngUBAFXpxkyMjyEw3HXkpiEB0gqommjhVS0fc7L87s+muNWDJJisP/mNQhunx+4V0SUgCyL6Lo7b4JHwutViURSQwT/1PgeCHBp3kIQBOraI1g2aLLApDw/s4szuefiqXgHefEUSWRCQGNCYPAduGXbBCPG0UQirNMejvcSjLbEz2GdqGHR2BmlsfNI4pbZ8zUFON/5laAje1rJDLTh8bUhe1owhUaiwgGCUozNksRmz9HzE22bEmM/lR01VB5ySEOVrlOp6+Sh9GgdyntIQwVmRhlmVjlmRjmo/mFd676g6wmTJ/cSBMuyekTaJ3omfSMVIfx0gNvvn27GqJc5un0BFgR3p0Cc6+fe+UGCILj0LngEVFVKyW6prW0H6wmh2HDWLMcM6NnP9gOwZFEFS84sB8DrUQA7ZbtcURDI9ilk+wYXv9m2TShuDolItIYkQpEi2iJFx46CIHchqocQ1Rbnu3YIUT0ESjv1ikK9orCaoy2bA5ZFhR6kMryJys71VO3tEU0aBh7bxvLmH448ZPVEHnqiEJa/EMTBXQjToY7fNBM5fnd+NlIRwh/H3zZOeh8EURQwDPfOLx00EpIkuTrUC4keEakRen649VFsQeAcAgSyJ7GnJcTH1e14ZJFrT5vY+7hIJE52to9o1Bjzz4AgCAQ0mYAmU5YzeM+FmGHR3kMkgjGDrrhFQ1uoh0hMpjWs0xaK096i0xHRsQUDUWntIQwtiOpB57t2iG4pwlZNY+sxugTBtikyzJ5IQzWVzbuorHdSF4WmiQDYooqZWXq42iKz3Ik8ZFZgZZZhq2NXLZEsTNNCVd1ruZyqfgejiUSJopvXkL9lnPROim6PcKRDBEGS3H0NwclZj6TfwnGwbd5r/wxkuLjkqwA8t8GJHnx9VhHZ3qN39+FwHL9fdb2BlCaLFGV6evUSA+V+DcumI9J3RKIlFOdQuI2WeAOdZgNhDoDSQx7UVpoUgSZF5uNjj29BmW4yVY9SqbdS2dpMZfMaKnWDwBE3oZiagx4ow8oqh8IpeP2lGIEyJwoRmAiie+yD06GSwe2wrOS9GsbJxcjhnk/TCYLbF2A31ylDenTCFARnp5SKeTZWv852GfyWzYKZ36OlO8Zb2w8iCnDz/JLjHh+PG3i9iuud/46Frhs9rbuPj7rIokC+XyXfr8KEgcexbZuumEFbSOdgKMy+znrqumtpjNTTEmug02wkQjMxsZs9msQe7Xh9QpYhUh43ma6HmWLEqIzsojK4lUC1SeCIx5lIdKhFdHlKiPpLMTPLEXMqUfOq8BdMQvLlJHlVhoeTJcc/mkj0tUjmsztOEEaOQcocx2oaJw5O+OpEz6J/uD28lg6lZv0tdCPBX3cvB+ACTxma4uf3a6sxLJuLp+VTmt13KL+7O5ZUU6gTgVjMwOtVk75ugiCQ6VHI9ChU5vlYSD5w+nGPC8aD7A/VsSdYzZ6OGuq6a2mK1NMab6JTNtgiC2zBDxwmEJIlkKfLlOkm0/UIs4wuquKHqOxqJBBcB01HH6MTP81iES3yRIKeEsK+UuIZZZBVgZpTSk7AT65fIc+n4klBN8bxEH7ySMU1dOmppQVO+giC2+H2CIfbbarBKeeKRlNAEGJdvButBUXmoim3EY6bvPyFswrdtqC036eZptXbPS9dmlmNdTvgTDWTmeosZubMgorDvzdtk+ZwE/WhOupDdezvdr7XhWppi7VyUNM5qMFnaMBhzYPf0igwFErjJpNjYWbHO5hhRJhs7GV6fK/jHxUEmnvO1xZptPOoswvYZhfQJBbRoRYT8pYQ9ZehZeSS69cOm1j5D5tZZWiy64lAf0hFCH80kbCDTnaMcYwMo0oQ0qXJkJvhdo1EOvTakGUJw0heA7Bv2++pU2TybZE5FVew8vNmumIGc0symTUxc8DnhkJxsrO9aUMQIKHCP7HvP0mQKPGXUuIv5UzOPupvYTNEq3WAHQd3OQSihzzsD9URIka1GqNahdUBAcgBcpCRKBAzKbZUSuIWldEQ08PtzIq1UG4dopxDwFbnAAbQ5XwFm7zU2wXU9XxtPeL/B8UCAl7vccQh16dSOiGAB7v352yvguSitEMqQvijiWT6KLz55h/5j/+4jwceeIArr7wyxTNzcNFFF9HQ0MBLL73E7NmzR+UYJxKDOikmAzeHrtIFbk+BOFUg7iUIqSSpf6r7IyhwUdZp2LbIip7Sxlvn9x89SMC2baJRPWVOjmMBw3CaIrmV1PgkP8V5syhTJh31e8u2OBg9QH137VHEoT5Ux6HoQRqtdhqBDSqgApkaUEKWnEG5mkuF4KFMtykNdzOpq5VJXQ1kmmFOFWo5ldrj5mHaAk16HvXtBdS1OaSh3i5gcw+BaCODhNWvQI+19jFEok+bbZ+KOspRHLenQVIxv0jE3QZubsaoahDcvriNI3k4KQZ3LiCQuk6Ydut23hVCgMQFM+7kL7tbaAzGKM/xct7kvCGNEYnoPWWPumt3bEci0ZEw3SAKIkXeiRR5J3LGhDOP+lvEiLA/dJgw1PWQiP2hejqNLrYYXWxJPFgGckDKLaTYU0SZmkuF4KXShMpYmEndreQHG5C6Gym1WyiVWjiLbcfNJyJ4aRQKqbUmsNeYQF28gPpYAXWtBWy284nRf8vugCYNiUjk+hV8ijTsxTQVIfzRRCLCkQwikdHT/jz99NPouk5p6eCbhHTEKEcQ3J1icPv80gFu10ikqgrkyy3/RassUSZ4mJY7l//1zhcA3DK/ZFgh41Aoht+vpabkcpSRDh0Jhwuv7GVq1nSmZk0/6ve2bdMSPdRDHI6OPByINFMfaaA+0nB0eaYKgaIsyv2zKFPzqBB9VJpQFQtT0d2KJ7gfOViLN97FZLuGydRw0TF3XBuBbnUCrXIRzWIRdRRQbUxgl57Hl5E8DsQy6Y6Z1LUPvshpsjgkIpHrU8n0yIg9EV63RxCSbbQUjUZH7TzLy8tTPqabcFKLFMcjHMnD7dcwJZ0wzTjvt34CXoVLii5iU2OQbc1dZHsVrphZOKyhdN3E61V7dBHudNhLIB3q+FOlwhcEgQneAiZ4C5iXf3Tj7ZgZY3+o/jB5OELr0G10sa1z23FxAxGRwsIiKqdeQZlWSBleKg2ojEcp6DqI3FWPFKxD7NpPRvwgGfGDVLKZo+IdKliyl6i/lG5PCW3qRA5IE2kUCqg2C9gbz+VATKIt5BhcxQyLpmCMpuDgehtJFMjxKuQHVPL8KlkeuU8iketTyPEqyCfQtTLZdT0eH3pKz7ZtXn/9dV588UW2b99OPB6npKSEyy+/nDvvvJNAIHDU4/vSINi2zcqVK3n11Veprq6mq6uLCRMmsGjRIr7zne8wefLhzqeffvopixcvZurUqbz++uvHzecXv/gFr7zyCj//+c+58847AVi1ahX/+I//yD333IPf7+f3v/89oVCIyZMns2LFit7GaR9++CHPPPMMmzdvJhQKUVRUxIUXXsj3v/998vPzh3Q9RjWCkB4tNl28uo0jaaRCgGXve4s/aU6o/cKpi/nN+w0A3DB34ojK4UKhKBkZ3qSb0IzjcIh8NEmqJmlMzpzC5MwpR/3etm3a4209hKG2N11RH6qjOdxEU7iRpnAjnxwznk/2UZZfTlnFpZT5yiiXAlSaAhWxMP7uJsRgHVKwDqmzFjHWga9zN77O3RQAM44Zy/QVYuWVY1SWEwuU0qmVcFAuokkoosnMpDViOg6YEb2XSLSF43THTFpCjrHVUJDlkfuOSPREKvJ8Cjk9P6eiRPQwkn9hh7qOWZbFz372M9544w08Hg+zZ88mJyeHzz//nMcee4y33nqLZcuWUVg48Kbg3//933nuuefwer3Mnz8fv9/Pzp07WbVqFW+//TbPPfccp5xyStLn9cYbb1BdXc2iRYuQJAmfz9dLDv7zP/+TJ598ElmWmTVrFoWFhWzdupXly5fz1ltvsXTpUqZNmzboMcagimE0j5A83D6/cSSHVKRA1u18mrAocoqSjxHP58O9NWiyyPVzi0c0nmna6LqJxyOnpvzyJIYjYjsxxxYEgVwtj1wtj9PyjvZ2iJtxGsMNHDQaqe6soaazujcC0aV3sbNzBzs7dxw3ZqG3iLKccspKL6bMX0G5lkeFKVIUDSIHnaiDFKxDDNYide1HCh9ACh9ge+sX/DI/l/2KTKlu8MuWNq4yhZ6eF47zpFl22Lo66iulVVcIGTat4TjNHZFeItEaive0II9T3L2V/2k9Trl9kLquAn7R+l3eY8px8z4SflUiZwhEIs+v4lcH1k0M9PpuaezkV+/tpqEjSkm2h3+6dCqzi7OOe5xl2UN6nzzxxBO88cYbzJw5k4cffrhXVxCPx/nlL3/Jyy+/zM9//nOWLVvW7xjNzc2sWLGC7OxsXnvttV4yYdt2L3F4/PHHeeihhwaezBBQXV3NP//zP3P77bf3nKdzn/vjH//Ik08+SUlJCY899hgzZszo/fvDDz/MY489xo9+9CNef/11ZHlgCnDSpxjcjHGNRPJINgUidjfxbrQGfF4uqria53oqF66YWUiur39x2WAIh2NkZ/uIxQxXk1S3G+k4EHBbJFCVVCozqpjpm865hVZvmsu2bTrjHb2RBkfr4EQeGsMNHIg0cyDSzIaWdUeN55E8lPrLKMsqp6z4QsoC5ZR5SylHpe7geu7a+3DvY3erKrcWF/Js4wHmtO9Gbt/d5xyLvPlY2RXY2RXE/T121aWHm2bJBzeT/fI/gehc4RlCPf/t+VfenP80e5QZR7Ukd1qUO8QiFDcJxU32dwyus1EloZcw9KWdKMjUKMr24RFssjyHS0S3NHayZMUXvePsbQmzZMUXLL35tONIgm0PvkHQdZ2nnnoKgAceeOAo0aGqqvzyl7/k448/Zu3atWzZsqXfksaDBw9i2zYZGRnk5Bx27hQEgXvuuYdJkyYxZcrABGuo8Hq93HTTTb0/J9KBTzzxBAD33XdfLzlI/P3ee+/lo48+YvPmzbz//vtcfvnlAx5j1AmCu28s4xjHwIhte5bVXg8iMHfCV/n1m3sQ6NtWeTiwbaeqwedTCQ0xzHsiMBYh/GSQDir8I+cnCALZWg7ZWg6zc0876rGGZdAUbqSuRySBzKLmAAAgAElEQVS5v7c8s5aOeAd7grvZEzx+sZc4JqQvOP/887QzefrUf+6NOkjBWsTOxP/rESMtiJEWaPqMY6WotqiSIF3CEcPawN9V/28W3vx+v+fbHTN7+nM45OF4InH457BucqArxoGuwXUTotBTIupT2d/Rt2jzV+/t5vk7jtaQDCWCuHXrVjo6Opg4cSKTJk067u+qqnLWWWexatUq1q5d2y9BmDZtGjk5OdTX13PddddxxRVXcO655zJz5kzy8vJ6d/upwNSpU1GUo1+5Q4cOsWvXLmRZZtGiRX0+7ytf+QqbN29m7dq1J5YguPmDm4Bbb3wO0uAC/i3DtlhdswojIHCGfzLvb4sRN22+MjmPylxf0sNHozq5uX5UVXatGFAUBRTFvYFGQQBNc2+lReIeeIy2rV8Uks1cZh73+2AsSE2wmppgTe9XdWcN+zr3YvbTTro5eoDs6QuBhcf9zbYt7K4mhI4aaK+BthroqHX+316DEDrY9/kAclcdeXn9n1A+UDnIeSYQiZu0dMdoDcVp6Y45/+9O/D9++G9dMToiek/qo//OsY2d0d65XXXV5cyfP4eysrJB16LGxkYAmpqamD59+oCPbWpq6vdvHo+HRx55hJ/97Gfs2rWLXbt28Zvf/Ibs7GzOP/98rr32Ws4666yBJzNEZGdn9zs3wzAGNW4a6DwSGNVPvrsXXwfuJjFpcAH/hqE0ruVtKQp4OLf8eh5607mJDGSrPBx4PArRqO5q46TMTA/d3THX+jYEAhrRqO5asy6vV8Gy7BSYTYmUiJMpyZ7MOdkQ0kOsqn2RA6FmQkaoz2cUeSfS2to9wJhZaLnzEfMXHG7XbsbxbH8B/9r/jRjrPO4ZNmBklNMx4LjDgw/w+WTKfDIUHN2sS1EkVFUmFIoRMyye39jAI6ur+x2rOMvTe85vvvk2//Ef93HXXT/g3nvvHfBen4h0FxQU9LvzTmAwArFgwQLeffdd1qxZwwcffMCnn37Kvn37eO2113jttde46aabuO+++wYcIwHT7L/Sqa9NRSJaEggEuPDCCwcceyipjlHfGrg9zz+OcfSHjm3L2eD1oCHS1X4qndFGTi3K4LSSgW2VhwJBcBaP9nZ3VzK4XX/g9jLbVF+/br2bV2pW8lLN83TpXQBMypjCvq49xz32p7P/cegDmzqenS/h2/AQUpejszEyK5CCjnPkkSqP7gv/bzKnMGzYts3qva089lENe1ocMlScqdHYRznnP1069bjfDWUNKigoAKCoqIj7778/yRk7KYkLL7ywd5E+ePAgq1at4qGHHuL555/ntttuY+rUqb2LvGH0TSCDweCwjps4D03TUnIe7oxrjhHcfOMD99ugpgNGmqMWYkH+fMixxTkz7wxe+rwNcKIHqXhNfD7N1ZGDBNy+ADtw/QSTRrfexfLdS7nlL9fx1O7f0aV3cVru6Tyw6GF+d+4yHjnrd1QFJuERPVQFJvHIWb9jZs6pg44r2CbSly+S+9wFZPzlH5C69mPkTKPzssdpv201Hdf9ESN3Bpbsw8id4fxcdHw3ztHCpv2d3P70Bn7y31vZ0xKiMEPjXy+bxqo7F7L05tOYnO/DI4tMzvf1KVAEEITBl7nZs2fj8/nYvn17v6H3733ve1x//fV88MEH/Y7z3nvvcdlll/Fv//ZvR/2+oKCAu+66q7e0MJHS8PmcVGVbW9txWgld19myZQvDQXFxMaWlpbS2trJp06Y+H/Mv//IvXHPNNbz88suDjjfKKQZ3C4jA7SmGcSSLhNnPcO2Wtd2v8qbXqVIoUC+mviNKcZaHC6YOzWBkIEiSiKKIhELJN5A62eFmAeVhjHyC3XoXL9e8yEvVLxAynND53Nx5LJ66hLl583ofNzPnVJ48/w9DH9gy0fb8Ed+GB5HaneiDkT2J8Bk/ITblKhAd4aNRdDod/QgSRxN7W0I89lENH+5tBRwfhiVnlnPdacVoPf0pZhdnHSdI7AuiKAx6n/d4PNxyyy38/ve/5+///u/59a9/3euSaFkWjz76KB988AFer5c5c+b0O86MGTOora2lubmZq666igULDs9v8+bN7Nu3D0VRmDnT0ZlMmjQJTdPo7OzkpZde4sYbbwSciMKvfvUrWltbBz2/Y3HnnXdy33338fOf/5wHH3yw91gAK1euZOXKlQiCwGmnnTbAKA4GJAgnx+71ZDjHkxcOQRAYIJXXJxp2PMtOn0qGqLF+RyEQ4ZZ5Jcgp6MQXCGh0d4+Tg1TA7RGOkc6vW+/ipeoXeLnmxcPEIG8ed0y58zjPhWHBtlD3vol//W+Q23YCYGVV0L3gx8SmXQ3iiRWkNgWj/PbjWt7cegAb8Cgidyyq4Ka5EwloI5tbQ8N+Hn74YV544YU+/x4IBHjnnXe499572blzJ6tXr+aKK65g1qxZ5OXlsW3bNhoaGlAUhYceeuio8sVjUVZWxr333suDDz7Ibbfdxpw5cygqKqK1tZWNGzdiWRa/+MUvmDBhAuCUKn7rW9/it7/9Lf/yL//CqlWryM/P54svvqCzs5Mrr7yyT4fFgXDzzTezdetWXnrpJa6//npmzpzJxIkT2bt3L3v37kUQBO677z53aBDcjHSIcLgdbi+DM00LSRLR9aEzBKllG+/G68GXxays83l3a4RMj8xVs4qSno+qSliW7VpR3ZFwe58NSAeNxPBSmV16kJeqX2BVzYu94sPT8+azeOoSTstNhhjYqNXv4l/3AHKrYwxtZpSin/0T4jNuIGac2BthR1jnqXV1rNzUiG7aSKLAtXMmctd5VRRmeYlERp6OUxSF1tZWWlpa+vx7LOaQdVVVeeKJJ3jllVd45ZVX2LFjB1u3bmXixIlce+21x9kk94cf/OAHlJaWsnLlyt4xMjMz+cpXvsLixYs5++yj25b/+Mc/ZuLEibz44ots27YNj8fDwoUL+dGPfsRnn302bIIgCAK/+tWvuOCCC3j++ef58ssv2bFjB4WFhVx++eUsWbJkSNEDAMEe4N2r62ZSdrCSJOLzqa5uTJOd7XO15a3b55eR4SEcjqekY+JoQJYlPB55WDt234f/ynXtb9OgyEw3f8aGXfl8e1EZPzy3Kun5ZGf7CAYjrq0KOBKqKiHLkqu1Eunw+QiFBq8CCcaDvFTzPK/UrOwlBvPyFrB46hLm5M4d+QRsG7X2z/jWPYByaDMApr+I8IJ7iZ7yTXwZAQzDJB4/MX1BwnGTFRv388z6/YR65nDZjAncdU4lpdleNE1GFIXDVRYjgM+n4Pd7UjXlkwqDpBiSG3x8h/63D8uyRhTCHyuYpokkDcPx0Iyxo+ZVGvL95MpZfLY9F0USuHGEtspHwutViMWMtCAHQE9DKXcSv3SBKA6cYgjGg6ysXsErtSsJGw7RmZ93BounLjnOSGlYsG2U/avxf3o/yoGNAJi+AsLz7yE68xaQnQXzRKVodNPilc3NPLm2ttfX4KzKHO4+r4rpBYc9FlIxv5MjVT46GFUNgttbiaYD3B/Ct5EkAX3kBH9UMdxKEG3fu7ylOotipnU2NiJfPaWA/ICW1DwEQUDTFFfvdo+FLEtEoy59YUlNI67RRn8pkM54Jy9Vr+CV2pcOE4P8M7hjyp3Myu1fBDcUKA0f4/v0AdSmTwGwvHmE591NZNbtIHuPmd/YVnNZts27Ow7x+JoaGjqdyPKpRRn8/flVzC873vhHEEj6NR5fg0aOUTdKGn9xksNIVfhjBcuykWV3V8sahomiSEPSIUjbV/Cu3yk92l3tGKLcMj95YyS/X3V1qL4vuH0BTgeNxLHojHewsvp5Xql5iYjpEIMz8hexeOoSTs0Z2PluMMhN6/F/ej9qwxoALC2b8LwfEJn1LVD9fT5HEMbmNbZtm09q2nl0dTW7DjkplIocLz88r4oLp+T1u044BCu511hMgbD4ZMVJLVJMB4xUhT9WcFIMqWzvmnrE40MjCGJXA5+1rKe9aALZYhH14ULOqcplcn7fN9ehQpZFJEkkHk+fygVZFjEMl77peiCKoqsJzJHojHfw4j4nYhA1nT4CCyecyeIpS5iZMyupseXmjfjXPYBa79TnW1oWkbnfIzJnCbaaMeBzB0uBpAJfNgV5ZHU1n9U7zowFAZXvnV3BFacWDVoVNJ5iOLEYVQ1COsDt3eosy91pmkSVgJuh6wY+n2/QHbxnx4u8EXCiB8HW0wEhJbbKfn/6lTVqmtLbgdCtcEiMeyMIggBtkVZ+v2Mp/1276ghicBZ3TF3CKdmDGxkNBPnQFnyf3o9W+ycALCVA5LTvEJn7XWzteMOgvuc4eve+mtYwj35UzV/3OLX8mR6Zby0s44a5xXiUoW0qUpECcfHt0/U46SMIh3fobiUIFpLk3ne4bbs/hGfbzussSQO8zraFtf1F/pLr5GiDrbOZXhBgftnQbrT9QdNkTNNybYqoP6iq5HojJ0kSicXcqZFoj7WxsmYF/13zMlHTybUvmnAWi6feySnZxzdjGg6klm341z2AVv0OALbsIzJnCeHTv4/t6b9Gf6xwoCvG7z6u5Y9bm7Fs0GSRm+eVsPiMMjI8w1tyUpECcfMGy+0Y9NVys0AuFXB/CN9Glt0dwjdN2/X56lhMR9OUfqMIyv41fGi1EhHzkeOV2HpuSmyVfT6Vjn5a07oV6VK9IEmi64h9e6yNF/Y9x2t1q3qJwZkTzmbx1CXMSJYYtO3Ct+7XePY6dfG27CEy6w7C836I7c1Leu7JojOis2xdPS9uaiRmWEgCXDtnIt85q5wJIxT5piIFMk4QRo4hEITkQlDpEMJ38w7YCeG7d37glBLKsnjCaqmHgljMICen/zSDZ/vzvBFwtAbdbadRmKFxybTkbJV9PpVoVHfte78/eL1KUnXnJyPaYm28sO9Z/lj3Si8xOKfoXJac8l2qvMc3EBoOpI59+Nb9Gm33qwjY2JJG5NTbCM+7G9tfMOJxU1XBENVNVmxsYPn6erpjzj3gkmkTuOucCiqSbIvu5rXjZMCQCEIyXuLpEMJ3M8NMVDG4GYZhIUkS9NOX3i2Ix01UVT4uty5EOwjVvMPHJRPAFjGCs7n53BLkJLQVoiigqnJalTWC83mXJPcLFGXZHZU9bbFWXtj3LK/VvkLMclIyZxecy+KpSzi9eA7xuDksF88jIXbW4t/wINrOlxFsC1tUiMy8hfD8u7ECyftyJBv1M0yLV79s5vef1NEScoj3wvJs7j6viplFA4sjx5EeGIQgCEmH390ewrdt95fpuR2GYeL3y0RcHkmPROJkZnqPIwjarld4wyNjCgJG91R8UhbfmJ2crbLfr7k+h98XnOiB+8sxZXloZaujhbZYK8/v/QOv1b1C3HKu1zmF53H7lCVMy3LKY50qi+FHYsTgfnyfPYRnx0oEy8AWZSKn3ER4/o+wMpMXzfYeZ4RVIJZt86ddLTy+poa6dudDf0phgLvPq2JRxYnXQBwLF+//XI9BqxiSDb8nnPbcCtO00TR3EwS3p2kcsyR3X0OgpwfC8SWPR6YX9M653Dxn5I1hwFm8BEE4oQvYSCAIjqiyvd39UQ9FOTEW0K3RFlbs+wOv1/33EcTgfBZP+TZTe4hBAsPVSIjdTfg+exjPthUIlo4tiERn3Ehowb1YWRUpPQ8YmY/Ep7WOl8H2A04DqfIcLz84p5KLpuUjpnglTk0Fg3vXnnTAoE6Kyd74x3P8ycMwLGR5eA2HxhpOmsEdYd+BEA47UYRE6F8+tIUDHTvYVFaCbSnYoVP55rySpI4RCGh0dbk8nNIHfD41bbQHY51iaIke4vl9f+D1uld7icG5hV9h8dRvMyVzWlJjC6GD+DY+gnfrswhmDBuB6LRrCJ/xY8zsSamYfp9wUklDu4bbmrt4dHU16+o6AMj3q3z3rHK+PqsoqVTcQEiFz4Wb1550wKDbpGQJgm27P4fu9pbPI+lIONbQdWdn7naCYFk2um6iaTKxmIFn+/O85HeiB0bXTC6bXkphxshtlT0eGV03Xau56Q+iKKAoMqGQ+6MHTrnq2LzPDkUP8fzeZ3i9/jX0HmJwXuEFLJ76bSZn9i8+HMruXIi04tv4GN4vlyEYjrAxOuUqhxjkJkc6hgJnAR7Y66K2Lczja2p4f5fTCTGgSdxxRhk3zSsZspfByOeXfGWUs/lz9/3dzRiUICSfYrARBLcTBHfDMCw8w6wfHmvouoHfr7nauz+BcDhGdraPeLgLddd/80a+o7TWg6dz68Ujz/EKAni9atoJE8GJeqSLZkJR5FEny4eih1ix9xneOIIYnF90AbdPWcLkzCmDPn8gEych2o7v88fxbn4KoacPQ2zS5YTO+AlmfnKlkMPBQETrUHeM331Sy2tbmjF7vAxunFvMHQvLyPIqYzK/VBAEURTHNQhJYNQJQjqE8BM6CbfW8TtlhMPoSHgCkC46BHB8PcLhOJkNf2KPHWavmoVl+JifdwbTjugkN1z4fBqRiJ52viGaJmNZuDpCdSRGU39wKHKQ5/Y9w5v1r6H3CAzPL7qQxVOWMClz8pDH6ctLQoh14t30BN4vnkTUnRx+rPISwgt/ijEhuT4MI0FfNsZdUYNl6+t5fmMDMcNCFOAbs4v47lkVSUXWRoJU9NoQRWFch5AEhljmOHKkQ8Mm07RcXcfvfIjdfQ3hcJohHRaaWMwgsPlZ3uhNL8zhtnNGLgSTJAFFcb/74LEQRaHHzCl9oh6joT84GDnAir3P8Ob+P6JbOgICF0y8mNunfIuqjKETgyPnmIimCfEuvF88iXfTE4jxIADx8q8QOuOnGEXzUnoeI0VUN1m5qZGn19UTjDpphwun5vPDcyqpzEvOy2CkcNKqyUUk02XT4lYMyUnxbx0JgZ2b6/gTOgQ35/hjMQNNG/3wbyogBuugdjVvljmCxAIWcWYSJVrpWtaYkeGhuzuWNlEPVZVSSuQPRg7w3N7lvLX/9V5icOHEi7ltyrepyhi5QFCSRKxoN94tT+H7/HHEmCPui5ecQ2jhTzGKF6bqFEY8P9O0MCyb179s5nef1HKw24nKzC/L4p7zqpg1MfOEznFcpHjiMaTEdrJ2y4lKBveG8C0UZWzyaiOFYZiuMYfpD7puEhihpepYw7P9BT7zaByQJax4Nt8+7bwRR7oURcK20ydEn4DfryVl5HMioGmp8Wk4EGnmub3P8Fb9HzFso4cYXMLtU75NZUZVUmMLRhQ+WUrexw8hRpxGRfrEhYQW/Qy95Oyk554KiKLA21828+Cfd1PT5lTcTJvg5+7zqjirMscVUd9UaRDGMXIMShCcZjzJLUzuz/GnQ0dCR6gYi7m7w16iJNPVXv6W6XRu7EkvqNEFXH9GJbERLjx+v0YwmF5ljR6PgigKaRf1SPa91Rxp4rk9y3l7/xu9xOCiiZdy25RvJU0MMKJ4tz6Lb+OjiOGDAOiFpxNa9A/opee5Jhy7oa6Dx9bUsKXRSXeUZHn4wTmVXDpjQsq9DE40/sZOZ8wxJA1Csk6ICQGbWxeNdOhI6EQQ3L87TzRFMgz3LjxK/YeY3U28m1sGwNfKL0MSRpYm8HictshuJb99QVVlNE2mszO9SI2ijDy90Bxu4tm9y3hn/5sYtoGIyMXFf8dtU75FRaAyuYmZcTzbn8e34f8hhZoBMArmEDrjp8QrLnLNKrXzQDePfFTN2pp2APL8CneeWcHVs4tQXLZBSlWfCJdc+rTFkDQIydbgJ0SAboZTjulet0IHtuu7a8bjZs9Ce6Jn0j8821/gI5+XLknAjk3kznmLCIfjBAIafr9KKDS0SIJT1qikhfNgAqoq4fOpdHamz5wT8HiGn15oCjf2EgPTNhERuaSHGJQnTQx0PDtX4lv/EFJ3AwBG3ilw4f+ko/ACbJcIi/d3RPivj2p4d+chAPyqxHfPm8Q1Mwvwqe7sFJsKvZUkJd8J8mTHkCIIiiIlVd9umhZaEta1Y4FEjt/N+Vhdt5LaRY0V4nGj14jIbRAibWjV7/BGXhYAp/jP7+1R390d6yEJQ4sk+P3aCbH7HSlUVe4lB+l24zzcRGpoi0ZjuIHn9iznnYYjicFlPcQgSdtiy0Db9Qr+9Q8iBWsBMHKmEVr4E+KTv0Z2TgDbBVUhLaE4T35SyytbmjEtG0USuGFuMd9eWE5lcZarK1dSQxBEV2gp0hlDWrWTzc+nQ47fMCzXl+jF40ZPN0L3zhEgEtHJyPC4kiB4dq0iZBv8xeeUbt019+qj/t7dHcPvV8nI8NDVFe13HEkSkSSR7m73plKOhMej9KQV0o8cgDP/oWxSGsMNPLtnGe80vIXVQwz+ruSr3Dr5DsoC5clNwjLR9ryGb/1vkDv2AWBkTyJ8xk+ITbkKRMkV95DumMEzG/bz3Ib9RHu8DK48tZDvn11BUabH/RohEjbQyV1HhyCkaEInKYZEENyen08FDMN0fZRD153wvduRyMc7Tm0uWo1sG8+2Fbzh96GLkGFPY07B8bvJUCiOx6OQne0lGIz2qS9IJ+fBQEBDEIS00xwcCY9n4NbZDaH9PLt3Ge82vO0QA0HispKvceuUOyj1lyV3cNtC3fsm/nW/Rm7fBYCZWUHojB8Tm3Y1iIfvG6oqo+snhhjHDIuXNjXy1Kd1dPZ4GZw/OY8fnlvJ5Hx/7+McEyd3bzKO9JEY+RjSeAQhSQy5zDFZuL/UMR16Rri/s2MCkYiOx6O6ahGVD36B3LaTVwsnAnBF+eX9PjYa1TFNi6wsL6FQ7KiojarKTg25y3dhoiiQmelEctKlCVNfSOzK+3rLN4T284c9T/Ne4ztHEYPbpnyLEn+SrZFtG7X6HfzrHkBu3Q6AmVFKeMG9RKdfD9LxpdEnwizLtGze3HaAJz6upbnLOfbpJZncfV4Vp5VkHff4VCy+o41UeCC4XfeWDhgSQbDt5HNCiTSDZbmXuTqLr7tFgLpuoqqSK8P3RyIeN/D7VcJh91xPz/bnaZFENngVsCVumv61AR+v6yYdHREyMjyoqkUo5BgKOXl8d+/GPR4Fj0ehuzvqeiIzGHw+9bhUzv5QPX/Y8zTvN77bSwwuL72CWyffkRpiUPtnfOvuRzm0BQDTX+QQg1O+CVLftudjTdxt2+bDva08+lEN1a1OdGVKvp+7z6vknKrcfnfPTgTBPcR9tDAePUgeQ44gJE8Q3C8CdGr4T3wOcSDEYgY+n+p6ggBOFMHrVd0h5NMjaLtf5WW/H1uA6YEFZKqDO8XZtk0wGEHTZLKzfei6RSxmuDaCI0kigYCGYVhpqzc4ErIsYtt2772nvruOP+x9mj81vIuFhShIfLX0Sm6dcgfFvuTadGPbKPUf4l93P8qBzwEwfQWE599DdOYtIHsGfLqmScTjY/O5/Hx/J4+srmZzj5dBcabG98+p5LIZBUhpnhJOVbfOcX6QPIaoQRB7ehWM/EBOR0J3uxWmQy+BdCgZTSAa1cnJ8RGJxE/4QqXtfQMx3sXKvArA5vopA0cPjkUsZmAYJtnZPgxDQNfdJfRK9FSQJJFQKOaquSUDn88hmHXdtfxhz9P8ufE9LCwkQeJrJVdxy5TFyRMDQNm/Bv+6B1Ca1gFgefMIz7ubyKzbQfYOaQxVlUc9vbD7UDePfVTDR/vaAMjxKiw5s5xr50xEHcJ9IR30B5KUfNt4t4vi0wVDVuUluyglduduhq6beMeolWkyiMfdT2QSiEYdLUIq7HGTgWf7CmplmX0eG0XwcE7hecMew+fT6OqKYpo2fr+KIAiEw/ET+jpIkoDXqyLLEuFwzPUVLsOBJInUBGt4fNPj/Lnx/V5icEXp17ll8mIm+oqTPobcuA7/uvtRGz4GwNKyCc/7AZHZ3wZleE2KHMfZ0WHCDZ0Rfrumlre3H8QGfIrErQtKuHVBKX516OLqdLhvOFUWyc3RWa/GQwjJYsjvrJOBkSUEgG5HwmfA7R90cNIMiSjCiYLUsQ+18VNezcoF4PyiC/BIA4eLj4Usi4ii0LsAB4NRJEnE61Xw+zViMZ1odOxSD5omo2kKggCRSDxtyi2HitruGl6ofYZ3at/GxkYSJK4s/Qa3TF5MkW9i0uPLzRvxr3sAtf4DACwti8jc7xGZswRbzRj2eKo6OgtvWzjO0rV1vPxFE4ZlI4sC1502kSVnlpPrG34LeEWR6O52t0BRlpPz3QFnvToZqu9GG0MmCKm42OnQkTAdegmkU1MkcKIIiVDxiYBn+4vYwKpANmDwd6V/N+wx/H7tuEXYNC26u2MIgtNEKCvLg207BC4eN1K6mxQEAVWVUFUZWRaJxQxCoZirP0sjQU1XNc/seYq/Nv0JGxtZkPlq6ZXcPOV2irwpIAYHN+Nb9wBa7Z8AsJQAkdO+Q2Tud7G14xX/Q0WqmkglEIobPLthP89uaCCsmwjA12YW8L2zKyjJGlrKoy9IknsryRJIxRwVxd3R6nTBkAlCKioZ0qMjoYGiSK4mCJAeeokEIhGd7Gwf0ag+9jcny0De9gLbVJVW1SBLyWFe3oJhDaFpTlljf+9b23ZIUDSqI4oCqirj92uHW+oaztdAYxwJURR6jZgURUKSHKGerptEInHXvzdHguquffxhz1P8tenPDjEQZb5WdhU3T7qdQm9R0uNLLdvwr3sArfodAGzZR2TOEsKnfx/bM/I2373jp6jXTNywWLW5iaVr62jvKU09d1IuPzy3kqkTAq6Y42giVaXwJ0PEeywwZIIgCELSi7thWKiqOy14E9B1k4wMj+vrxmOx9EkzAITDMfx+bUB3wtGAWvdX1OghnstxhGwXFV+MJA7PEMvnU4dsS2tZdi9ZAOeGpygSsiyiaTKSJDBYbtSyEmTC/pslBAlUd+3lmT1P80EPMVBEhSsrvs63Zn6bDDs36fGl1p341v8Gz97XAbBlD5FZdxCe90Nsb17S4wM97qbJ3dNMy+adHQf57ZoaGoNOpGr2xEz+/vwqTi8deWTjSIxWGiSVSIX+QFtnGdMAACAASURBVBCE8QqGFGFYKYZk6+8Nw8Q3grzZWCLRtMntSLc0Qzxu4vGoY56+EbY8hwm8F/AAOpeUXDas5/t8KpGIPuIqDMuyXU2ITxSqu/ayfPdTfND8ZwAUUeFrpVdx8+TbmV5cRUdHmGT2kVL7Xnzrf4O2+1UEbGxJI3LqbYTn3Y3tL0jNSfTA45GH3ODrWNi2zZrqNh5dXcOeFqfDWVWej7vPreL8yf17GYwEiiLT3T22BH24cMrMk7s/jAsUU4dhbaWSrUKw7fQwrzCM9Ajfu7kpUl8IhWJkZHjGrEmMED6Ev+5PrNW8RGSdYl8JM7JmDvn5iXSBm5vapBv2BfeyfM9SPmz+C9BDDMq+zi2TbmeCtwC/PzlCJnbW4N/wENrOlxFsC1tUiMy8hfD8e7ACyWsYjoUgCD3VC8Nf1L5o6OTR1dV83uB4GRRmaHz/7Aq+NrNwVLwM3Oxkm0AqBIqKIo0LFFOEYRGEVAkV3S4CTJcyQjc3ReoLpmmh6wZerzImKRxp20okTJYGSoEYFxf/3bAI6lC7Oo5jcOwN7uGZPUv5sPmvACiiyhVlX+fmSbcxwevs6GVZ7LEqHr5LpRjcj++zh/BsfxHBNrFFmcgpNxFecC9WRvI+Cf3B45GHvaDtbQnx2Ec1fLi3FYAsj8y3F5Vz/dxitFHyOHF0Ve6+n8G4QNFtGHZ3omQb8CTEdW4mCLpu4PP53OEAOAASH6R02BkkEArFyc72pVzlfxxsG3vzc8QE+DzgvNcuLr50yE9XFAlBwPUk0e3YE9zF8t1P8dEBp5xQEVWuKv8GN026jXzPhKMeGwgM3EGzL4jdjfg+ewTPthUIlo4tiERn3Ehowb1YWUm2dR4CNE2hs3NoEabmYJTfflzLm9sOYNngkUVuWVDK7QtKCYxyo7h0aBOfKhHluEAxdRjmu1JAliVMc+Q7Vl1P6BDcKwK0bSc3mA4Lr2NEpLiezByJ7u5oT6ph9PoZCE2fkRup4WVfHrqoMzVzOuWByiE/3+/XCAbd3W/BzdgT3MWy3UtZc+BDAFRR5cryq7lp0q3HEQOgxz5cH3KoXgwdwLvxUbxbn0UwY9gIRKddQ/iMH2NmT0rpufSHhGh7sHRIR1jnqXV1rNzUiG7aSKLADT1eBvn+sdFkqapMJOLuVFkqohxpkMFOKww7xaAoyQkV08UqOB43UNXhhw/HGrGYQU6O+6MdR8IwLOJxE6939BwWOz99mnzguYxioJNLiofufeDxKOi64Xpy6Ebs7tzJ8j1LWXNgNeAQg6vKr+GmSbeS58nv8zmJ1MJQGmAJ4RZ8n/8X3i+XIRhOtCE65SqHGOROS92JDAHO+7f/+0M4brJi436eWb+fUM/u/bIZE7jrnEpKs0fuZTBciKLTROpE250PBkWRkr4fjLd4Ti2GHddKRX7Haa3s7t15LGYQCHhcTxAg/cSKAOFwvKf5kZH6dFO8m4mN79ApCuz1dSMgcGHxJUN6qiCA16uMCxOHiV2dO1m++0k+PvgRAJqocVX51dw0+TZytYHLCZ3UwsDkQIi24/v8cbybn0IwnNcmNulyQgt/ipl3SmpOYhgQBKEnJH78jlc3LV7Z3MyTa2tpCzv3jzMrc7jn3CqmFybnZTASuL20PIFUpBjGBYqpxbAJQioufkKH4OY3rWXZafNGSzexYgJdXREyMrwp7zp4YP1KZhHhQd9kTHROz5vfZ1i7L/h8GuHwiW8ulS7Y1bmDZbuX8skRxODrFdfwzUm3DkoMgB7PkXi/ehQh1ol30xN4v3gSUe8GIFZ5CeGFP8WYMDt1JzJMeL0K0ejRu13LtnlvxyH+a00NDZ1OdOPUogzuOa+KBeXZJ2KagGP0Ndb+I8NFqlplq+q4QDGVGJEyJtkqBF038HgU1y9oum6kxARltGFZNrZtu7465FiYpk04HCcjw5vSfL+6bQUAf8nLBQ5wSfHQvA8kSehR0Y9XLgyGnR3bWbZnKWsPrgESxODaHmIwNIMjr1fBtvv2iRDiXXi/eBLvpicQ404ZYLz8K4QW/gyj8PTUncgIoWky7e1OJMO2bdbWtvPo6hp2HnRITEWOlx+eV8WFU/JOaMhbEJzF183RWkididO4QDG1GDZBcDzhZQxj5Lkiw7DSohQlFjPwelXXEwRwQvY+n0ow6O6dwrGIxw1kWUxZr4bmmi3Mjm9jn+ijWjyIIqqcV3TBkJ7r93tcbyRzorGjYxvLdy9l7SGn+6FH8vCNiuu4sepmcoZIDMAJBauqfLzuIB7Cu+UpfJ8/jhjrcH5Vcg6hRT/DmHhGys4jGRy5ufmyKcgjq6v5rL4TgIKAynfPquDKWUXILohApsMGB5z3Q7Lp3HTQtqUbRkwQkr2ZO42bkiuZHG0kGjelAwzD6smLuvua9oVwOE5WlhdVTb4U69DHTwPwh/xZ2DRz5oSzCSiD531VVcK27bSKwIwltndsZdnupaw79AngEIOrK67jhmESA3DSlIGAdjQ50CN4v1yO7/PHECOOP0B84iLCi36KXnJ2ys4jFfB4FL7Y18KjH9Xwl90tAGR6ZL61sIwb5hbjcdHmR9MUQiH3k16ny2RykTunNPnEk7K/JYwoxeD4yScHx4xIxjTdLQKMxdIjzQBO21+vV03L1r/BYJSsLC+WFR3xIt3WHWJO29sgwIYcEeJwccnQqhd8vvGyxr6wrf1Llu9ZyrpDawHwSF6u7okYZGvDb3IkCJCZ6aW7O+aEvY0o3q3P4t34KFL4IAB64TxCi/4BvfRc19WttUcN/s+qLaza1IBlgyaL3DSvhDvOKCPDM7peBsOFIDhkzO0bBmeOyRNzTZPHCUKKMeJ3dCp0CH6/5voqgVjMwO9PjzRDPG7i82murxDpC7ZtEwxGyMry0tkZGdH8N3/4MjcInaz1lFEdb8QvBzhzwlmDPs/rVYjHx8saj8TW9i0s372U9S2fAuCVfA4xmHQzWerIBXeZmV7C4Th6NIxn+wv4Nvw/pFAzAHrBaYQX/pR4+YWuIwadEZ1l6+p5cVMjMcNCEuDaORP5zlnlTHBpTxRNU4jF3H1/hUQaZFx/4EaMiCCkQodgmnZavKCmaSGKIoJAWijbw+EYPl96RhEsy6arK0pmpkMShqNqjuomE6pfBuDtsplgbOf8ogtQpYFv3oIg4PEovYKzkx1ftm9h+e4n2dCyDnCIwTWV13ND1U1JEQNwKhZi4QjiF8+Ru/4hpO4GAIy8mYQW/Yx45aWuIwZR3WTFxgaWr6+nO+YsYpdMy+eucyqpyPWd4NkNjHSoXgAnvTfSZlcJpEsqON2QFEFIVoeg62ZaKO8Tpklur7qA9I4igKOlCIViPZGEoZc//mXTVm6xP0cXJD6W28GAi4dgjuT3p0Ycme74sm0zy/Y8yWct6wGHGFxbeT3XV91Mlpp8u2G/V0L44jl8ax5ACtYCYOROJ7TwJ8QnfRUEd93gDdPitS+b+d0ndbT0LF5nT8rjB+dUMKNg7L0MhotEiXY63AMkaWTNro7EuP5gdDDiFEOqdAjJRiLGAtFoevkMOFEELW0V+bpuEg7Hycx0yh8HIwmmZRPZ+ByyYPFe4dk0RfeTp+VzWt7A5XCSJCJJYlpGW1KFLW1fsHz3Uj5rdYiBT/ZxTcUNXF91U0qIAZZJZv1bKB//X8S2vQAY2ZMJL/wJsclXgugeQR84qa4/7Wrhv9bUUNfuaFJOKQzw44umcO7UCWlTAuvxKK5P3wIpa4o3rj8YHSSlqkl29x+Pp1dTpHSpEEjYGKeCmZ8oJDQfCU3CQCRh9Z4WLo2/DyKsLi6Gjv1cVHwJkjDw4hMInLzdGje3bWLZ7if5vPUzAPyyn2srb+S6ym+SqWYmfwDbQt37JhkbfoPYuhMAM7OC0MIfE5t6NYjuEvQBfFrbzqOrq9l+wPEyKM/xctc5lVw8LZ/cHP+QrKDdgnRpU54qAXg6pKvTESP+lKZChwCHHQvdHgqLRJymSMnmysYKoVAs7RsOOTcOm6ws34CahI1r3+EGsZkOJZ+/hHcBg6cXVFXGNC3Xp7dSjS/aPmfZ7ifZ1LoRcIjBdZXf5LqqG8lQUkEMbNTqd/CvewC5dTsAZkYp4QX/g+j060BSkj9GirH9QBePfFjNujrHdyHfr/Lds8r5+qwiZEnsFbGmwulvLJAurZ2BlBiTjesPRg9JEQRNS16HkOgjMFDTEzcgHjfw+31pQxAMw8K27ZSF8E4U4nET23Y0CcHg8dUNmxuDzO94EyT4dNqFdIQ/pcxfztTM6QOO6/ero9pN0m3Y1LqR5buXsqktQQwCXFd5Y2qJQe2f8a27H+XQFgCsjGJC835E9JQbQRqbroXDQW1bmMfX1PD+LsfLIKBJLD6jjJvnlfR6GQgCaSdi9XiUUWuClkpIkohlpaK8URlPL4wSkorziaKQtId2PG70+LG7myBAQjPh/r7qCYRCMTIzvWkRahwIum7S3R3rqZ8/2ifh5XU7uV90SvHe8csQhkuKLxvwhuHzqUSjetrsCJPBptaNLNv9JF+0fQ44xOD6qm9yXeWNBJSM5A9g2yj1H+Jfdz/KAecYdqCQ+MJ7CU69EWRP8sdIMQ51x/j9J3W8uqUJ0wZVEvjm6SXcsbCMLO/REY5Eb450gSCkpunRWCBVwu9x/cHoIclEoBNFSEYMY1k2giCkRRlhNKoTCGjE4+mx87Qsm3jcSBvB0kAwDJNgMNJbRx+PG+zviJBd8zo+JUZr4Rms7tkdX1R8ab/jiKKQNvnZkcK2bTa1OcRgc9smAAJyBtdXfZNrK29IDTEAlP1r8K97AKXJKYm0vPnY5/wPuk+5hbjlvohBV9Rg2fp6nt/YQMywEAX4xqwivnt2BYUZx5fDSpKIoohppVNxbKDT47OuqhLBYHJzdTapKZrQOI5D0hEEjyc5ggDpU0aYEPyli1gRHBvjnBwfsVj65FD7g2XZdHaGycz0IkkiK/60h5ulDwD4oHI+0YNvckr2qZT4S/sdw+/XCIfT54Y/HNi2zeetn7Fs95Nsaf8CgAwlg+srb+KayhuGZDk9FMiN6/Cv+7+oDY7tsuXJIbbgbpSzv08wKrlOGBvVTVZuauTpdfUEo8495oIpefzw3Cqq8vr3MggEtLSrcNE0hc5O95PfxI4/2XuSqo5HD0YTSUuJU6EejcV0/P70KCOMROJ4PGpa7SpCoTiBgJYWpimDwbahszOCLgps37qe06U9GEqAt612YGBxoiyLCIKQNimiocK2bTa2bmD57qVHEYMbqm7mmoob8Cv+lBxHbv4M/7oHUOs/BMDSsojM/T72ou+jBrLpCEaxbfeQA8OyeWNrM098XMvBbidNML8si3vOq2LWxIF1F5qWfiJWVXXEiemwD9C01GwIPZ5x/cFoIgW1RkLSeXnTtHtDRW5/c8fjZs8u1P1zTSCRZkh3weKReObjWr5h/xmA1mlXsq71U0RB4oKJF/f7HIckpQ+xGwy2bfNZy3qW71nKl+2bAchUMrmh6maurrg+dcTg4GZ86+5Hq3Wut6VmEDntO0TnfpdAfiGmabmqBNC2bf6yp5X/+qiamjZnXtMm+Ln7vCrOqswZdEERBEenkm5pqHTqw6JpctKdZx29xTg5GE0kTRBEUUDTlKR3ZbGYjqalR648Gk2fuSbQ3R39mxAsAsQMi5c31vCW9BEAfy6ZjLn7Y87IX0RuP50FNU1G1y3Xhb9HAtu22dCyjmW7n2Rbx5eAQwxurLqFqyuvwyenhhhILdvwr3sArfod57iyj/BpdxKZ+z3kjHyyenwk3BSR2VDXwSOrq9na3AVASZaHH5xTyaUzJiAOcacZCHgIh+NpswGAwwtlOry/Ey6PyaYXFGU8vTDaSIlbiaom74YWiznVDOmw6EajOtnZvrSYawKWZRON6vj96W8O9Pb2A8yPfUqe2oWRO50/tji75ysmX9Gvp0Y67giPhUMMPu0hBlsByFSyuHHSzVxdkUJi0LoT//pfo+19wzmu7CEy+1uET/8BtjcPv19FlqURN9UaDew80M0jH1WztsZJNeX6FO48s4Jr5hShDCMN6lj2khbpziPh9aaPZXjq0gvjBGG0kTI7s2RdFRM3mmTLJscCtu2kGlL1Rh8rRKM6WVnetOh/0R8s2+bZDQ38q/RXAKqnXcWWAy+iiRpn5Z1LZqaXaFQ/irz5fCqRiJ5WO8IjYds263uIwfYeYpClZnNjlUMMvHJqmgZJ7Xvxrf812u7XELCxJY3IrNsJn/5DbH8BiiLh92vEYrprUgr7OyI8vqaGd3YcAsCvStx+Rik3zyvFN8yNiyCQluZioiggy1IapRdSI6RUFHfZdP8tIiUEwTFNUjCM5N6gsZiBx+N+0yRwxIqZmd60IghAb7fEdN1Nf1zdRqStnvM9W7BFhbf9zuJ4duG5KHjo6Ajj96tkZ3vp6oph23baljXats2nhz5h+e6l7OjcBjjE4JtVt/CNimtTRgzEzhr86x9E27UKwbawRYXIqbcQnncPVmAigiCQEdAQBKFPs6oTgZZQnKVr61i1uQnTslEkgRvmFvPtheVk+0bm1uj3e4hE4q44v+HA600PYyRwUiGWZSdN1sfJwdgghQRBTjp0HYsZZGV504IgWJaNYZhpJ/xzUg3xtCzhAvjDhv1cL32IhEW06mu8f2g1ABcdUb0QCsWRJJFAwOlqmW4pFYcYfMyy3UvZ2enYFWer2dw46Va+UX5N6ohBcD++DQ/i2bESwTaxRZnIKTcTXvAjrIwSwFGJe72OxXgqPPOTRXfM4JkN+1nx2X4iuuNlcOWphXzv7AomZo7clMkpl0u/1IIgOHMPhdKDAGtaanwaPB4FURy3WB5tpCzFIAjJpxls28ayrLRpMhSJxAkEPK4Jtw4V0ahBZqacVq6Q4Hjmb6xv50HN8T7YWvUV9u19mAwlg4UTzjzqsaZpEQ7H8ftV/H4NSTJcv8uybZu1Bz9m+Z4n2dm5A4AcNYdvTrqVq8qvwSt7U3IcsbsR34aH8Wx/HsHSsQWJyIxvOsQg6/9n772jJCvrfe/PzqFC9+ScmEAaZwhDEGYIIqIgKgiKghxEFEHe61U5Z13f+66z1rnL+573nOPRc7yiiIoSRY9gQFGQIJJhhgwDzAyTejIz06Hiju8fT+/qnhx6V3VV9fNZq9b0dFXX3tVdtZ/v8wvf3wxALDqua+L7Ad3dBz92u15Ug4h7X9nIrc+uo6ffy+CM2WO4fvFMZo8dWu2Fqir91tutscgOxrbNlthQJViWnoptdRp1b5IDk5pAUFUVxzGH3GtfqSRDkZp/1xeGMVHUmvMO+vqqdHY6BEFzhIwPhruWdnGqupxpylbC7GQejHYAcObED2Coe4aVk3xyFMU4jsGoUS7lst90xaVxHPPM1qe4fcWtvNM7IAwuO+IKLpxxEbaWjl2xWtyCs+z7OG/chRJ5xChU5l1M6aT/Tth5BEB/nYFJEERNkU4Io5gH3tzCLU+vZXN/i+pxU/LcsGQWC6ekMI4ayOVsCoXqsIugQ0XMiUhnwW0EaV0nTVMHZHFiI0h15moaqi7xGSgWUzihBlAqVVsyihDHMYVClVyuNc59U2+Fh9/exr/rfwWgdOQlPLLpYQDOmbKnOZJtiwl8yQKXCAPHMWsdKMMtFOI45umtT3L7iltZ0StGIo8yR3PZ7Cu4cPonUhMGSuk93Bd/gPP6bSihWGQrcy6kdNLXCUfPBcRn13FMoiimr68y7E6hcRzzt1U7+MGTq3l3u1gA54zN8JUlMzl91ujUqtdFlCRsOYEPonOhlaIHaQ2Rchyj1iopqS8pD2UfumkSDFgvN0PO80C0chTB98N+QWY2/ZTKe17cQCYucr7+AsSwdMpCtr72W8bbE3jfqIW7PFZRxEVk951VHAvr6cQNM7GgrlT8hu6UhTB4gttW3MrKXjGeerQ1hsuOuIKPTv94esKgshP3pR/ivPozlECIwOoRH6F48tcJxxwNiIu2bRsEQUihMPzCAOClrh6+/8RqXt3YC8CkvMWXT5/JeUeNR0txYTAMDcPQWkIg746ipBeubwSKoqQyRCpJZUsaQ6oCQVUVHMcc8jAj0a9vt4RAgGRqot2S44OTboxmrkfoqwT89tXNXKw9jRl7eFNO5y89wvvgA5M/iKrsesE40AS+OBavu1z2sCydfN4hDCMqFb+uIi+OY57a8jduX3krK3tXADDGGstlR1zOR6d/Akvbc2DQ4aBUunFe+THOKz9B9UUorjrzXEonf4Ng3Hw0TcG1jFqbbk9PuSlai1duK3LTk6t58l2ROup0DK4+dTqfXDAJM+VFQVUVslmrJT+z0IrRg6HP7AGZXmg0KUcQEr/7odkQh2GMorBP05tmQ3Q0RE29yO6Pvr4yHR0uYVhpyuLQ37y6iZIf8vnsExBA39GX8vi6nwBwzuTzdnnsoU7gq1YDqtUAw9CwLJ1s1qp9L63fRRRHPLnlb9yx4mes6hsQBp+ZfQUXTPt4esLA68N55Sc4L/8Y1RO7b2/6WRRP/gbhxBPE67N1okg4lzbL7nNjT4UfPb2GP725lRhwDY3LF03h8kVTyZipX6IAyOcH2mBbjVaLHkB63geua8r0QgOpw6dPSWUyo8gXG00f+k4olcRO3PNa50ObEMfCH0HUIwx/xfpg/DDinpc2cLSyltnBSiKrg6fyo+n1e5mVPYLZ+Tm7PD6TObz2zcF5aMvSyWREi2S1GuB5hycWojjiyc2Pc/vKn/Fu30pACIPPzr6SC6ZdiJmSMMAr4rz2M9yXbkatdotvTV1M+ZQbUWe+H9fUUFW13+Co0jSL4s6Sx0+fXce9r2wiiGJ0VeGTCydx9anTGe3Wb1x04tgaBK0n5uHAEbJmwzDEhM+hvu1UVZHioMGkLhCSNMNQBUK1GuC6bssIhCiK8X0xFGm4i98Oh6QtMJdzmspJ7qG3trGt4PHN3JPgQ3XeJ3h481+BPSc3mqZGHMdDznMmEQRFETU1rmui62qtZsP3g/1e7KI44onNf+WOlT/j3b5VAIy1x/HZI67k/GkfTU8Y+GWc12/DffEHqBURlg+mnEKw5Jtos8/AiWM8L6BY9JoqMlT0Au5euoE7l3ZR8kMU4CNHj+fa02cwpSOdVs594TgGcRy35GcUxPXVMLSW6PJKcBwjFUEjrZUbT13id5qmpJIeqFaDlrIzLpW8lpvRMBjPC2oGQ81gohTHMXcu7cLC44JYGCLtmHcRT7/89wB8YPK5uzzeddO1yY3juCYWAHRd6xcMwqgoCCKCIOy/RURxxN82/5U7VtzK6sK7gBAGl8++ko9MTVEYBBWcN+7EffEm1JKwGI6mLCI68//Gn7oYz4/wmywSBOAFEfe9uolbn13Hzv78+emzRvOVJTOZOy5b9+Obpo5p6i1ZlJiQzbbWLJVk15+Gtbttm1IgNJi6CARFUbDtoavGSsVvKTtjUfzm47qtMzhld4T5k9Vv3zq8Que5tTtZ+V6RzzovYwW9+GOP5fHwPapRlfmjFjDRnVR7rOPs2tZYDxIxkPxtdV1F1zUsW+fpDY/w49dvYVWPiBiMd8Zzxdyr+PCUCzBU47AX6+QCq2kqWuxjvvEL9Ke/i1LYBEA44TjKp95IZeqZxCjQhBG3KI758/Kt/OipNWzsFYvb+yblueGMmZwwtbMh56DrKq5rppIHHy50XbSRt1K3lGhtHPp1JKltkzSWphYIURQThlFLDRcaPOmxFQos90ahUCWfd4iieFjF2Z1LuwD4Uu4p6IXK0ZfxyMaHgF3TC8n7rdFFW1Xf5y/rHuL2lT9jbWE1AOPtCXxu3lVcOOtjOKaNpikoirLHzmdvdQB7e0wcx0S+h/LqnZjPfBe1bwMAwZhjKJ5yI97Mc2nWK2ccxzy1egc/eHINK7aJbopZY1y+sngmZ8we07DdoKoqNb+PZouqHArZrElfX+tEDyC9YkpRnCjbGxtNfUqEoZa/HWpVf7nspeLQ2EiKxSqZjNVS57w7SWeDqK1o/I7lna0FnlvbzWxjOzN6lxJrFptnns3Sp25FUzTOmvSB2mMb7eMQxiF/3fQId678OWsLawAhDC6f83ecN+V8TM0k8qHoD/FiHgVYb99HZul/oPWuAyAYfSTFk7+Od8RHQGneC+arG3v5/t/e5aUNoptiQs7iS6fN4IJjJqTqZXAgVFUhn3fo7a20rGAHkX/3/aipakkORFrpYUWRw5mGi7oJBFVVcF1ryFX9QRClVtPQKHw/xHGMljRPSohj6Okp09HhUChUGh7BuXuZiB58c8IylPdiKkd8mMd2LCWKQ04ddxodpghN67qKpql4Xv13VmEc8teNj3DHyp+xrrgWgAnORC6ffSXnTb1gr3bPh0UUYq34He4L30XvEZGJoHM2pZO/TnXOhU0tDN7dXuQHT6zh8VXbAeiwdT5/ynQuOW4yVoMNbhRFtDMWCtWWWlh3Rxh/td6sCMcxU6kJsm0D6X0wPNRNIEB6xYqlkrDIbaXinCRM32of6sHEcUxvb7m/Z7xxHglb+qr8+a1t6ErEGWWRUqgcfRmPrL8d2NVa+XDbGg+FMA55bOPD3LHyZ6wvip38RGcSl8/5Oz405SPpCYM4wlr5R9wXvoO+U/glhPkZFE/+GtW5nwC1rh/XIbG5t8KPnl7LA29uIYrB1lU+u2gqn1s0lazV+PNWFOjocCiVqi3bzpiQyYi2xlZKj5imRhCEqWzqHEd6HwwXdf3kKoqC65pDvoB7XoDrmkM2YGokUSRazJqh2G8oCG/+ci1M2wiR8KuXNhBGMf9tehfm1o2EuWmsHT2TN197A1tzOG38EkBUpYdh/cKuYRTw6KaHuWPlz+kaJAyumHMVH5ryEfS0Fuw4xlz9ZzLP/zv6djGsKcxNo7Toq1SO/CRoKQmQOtBd8vnZ8+v49csb8cIYTVW4ZMFEvvD+GYzN1M/LYH8kkYNy2W9J47LBJBGyD1ftIgAAIABJREFUZugqOhQcx6RQGHqK1TC0Zi2xGRHUXSBYlp7KmzsZtNNK3QFJ22O1Wt/q+noThgORhHqLhEI14N5XRIX+FaYY61w5+lO1wUyLJyypjT0WI3rTb1kLo4BHNv2FO1f8nK7SegAmOZO5Ys5VnDvlw+kKg7WP4D73bYz3XhfHzk6idOJXqRz9KdCGZ4E9GMp+yN3LurjjhS6K/Yvwh44cx5dPn8m0UfX1MtgfiTioVPyW6X7aH9ms1XKFibquEsdxKnM9ZHHi8NKQ2F8axSqVis+oUW5LCQQQBYutMjFxfzRKJPz+9c0UvZAzJiuM2/gIMQrlIy/lkZeE90Firey6JpWKn6orYBgFPLLxL9yx8mdsKIkaiEnuZK6Ynb4wMNY/Tua5b2NsfVkc251A6cQbqBz7WUjLL6EOBGHEb17bzE+eWcuOkoiMnTpzFDcsnsWRE+rvZbA/2k0ciNbdsOXqJ9Jq81ZVRQ5mGmbqLhBUVcV1rVQ+sJVK6zkV+r74gLeS4dO+SERCLlefwsUgjPjFMtHG9/XxL6Hs8PCmnck7cZF1xbV0mJ2cOPYkVFXYeadV3xFGAX/Z+CB3rbytJgwmu1O4Ys5VfHDyeekJA8DoeorM89/G2PQCAJEzltIJX6E8/wrQh2/nfSCiOOYvb23j5qfX0NUtQsfHTsxxw5JZLJreGC+D/aEoSn/NgdcyQ972h6oqWJbRcjVMmqaiKOkYIzmONEYabhoSQVBVYfIx1GKhSqU1nQqLxSqdnS6eFzaND/7hMjiSUCxWU+3SeOSd99jcV2VGp82x2+4HRHHiw/3eB2dNOgddFXMS0ihYDaKAv2z4M3etuo2NJSFMprhT+4XBh9DSFAYbn8N9/tuYG54BILJHUTr+OsrvuwoMN7XjpE0cxzy7dic3PbGGt7cWAJgxyuH6JbM4e07jvAz2R9LKmPb7cTjJ5exUcviNJk2TOGmtPPw0SCCo/aNVh6aG41jYL7daFCGOk1SDRW9v633odyeK4loLZFo7tsRWGeCrRxUwXl4uFtFZ5/Do45cB8MHJH+p3VFOGtBAkwuDOVT9nU2kjAFPdaVwx5yrOmXxuqsJA37yMzPP/jrn+bwBEVgfl466lvOBqYnN4Q/IH4vVNvdz0xGqWru8BYHzW5Ivvn8FH509Eb5Kqck1T6hbRGi5s2+i38W6t15N0raUh0hxHtjY2Aw3rP9I0YRc71HxaudyaUQTPC7Esoy1SDSAW9J6eEvm8g6YpQ+7UWLa+h7e2FhjtGnzI+xMAlXkX82rPcrZX32OSM5ljOueTzdr09R1ePUcQBTy04U/ctfI2NpX7hUFmOp+bcxUfmPTBdIXB1ldwn/93rLWPAhCZOcoLr6G88IvEVj6149SDNdtL/OCpNTy24j0A8rbOVSdP49LjJmM3kWGNYWhks1bDumsagaoKV9BWSy1AulMmZWtjc9AwgZBEEYZarBfHohahFdsHC4UKHR0uvp9Of/Bwk5gpZbM22ezQWrGS6MFnFozBffN3AFSOuYxHNvwWEIOZbNvor+k4tN+dH/k1YbC5LDokpmamc+Wcz3P25A+iKektetq2N8i88B2s1Q8CEOsupYVfoHzcl4jtUakdpx5s6avy42fWcv/rm4lisHSVy06YwpUnTSVvN1erpWXpOI5JT0+5LT5LCa2aWtA0NbXogWXJ1EKz0FAHE11XUzFOKpc9Ro1yW04gDKQaWr+rYTCFQgXXNfsNlQ7d7/7d7UWeWr0DS1f5XP4VVK8Xf/xCyp2zeXzZYwB8cMqHcN1Dc5PzI58Hux7grlW3saW8GYBpmelcOedqzpp8TrrCYPvbZF74d6xVDwAQ6zbl911F6fjriJ0xqR2nHvSUfW57fj2/enkj1SBCU+CiBRP54vtnMC7bfB0VYvy2Rk8TTqwcCo5j4vthy6UWIF2780zGktGDJqGhAkFRlNRmFAhfhNaLIogLQNiS574/SiUP09Tp6HDp6ysf0i7/7qWiQPDCYycwetX3AFGc+Ny2ZygGBebk53LMhCMpl/2DWhD8yOfPXX/krpW3sbWyBYAZ2ZlcMecqzpqUsjDYuQr3he9grfg9CjGxZlGe/zlKx19PnBmf2nHqQcUPuefFDdz+Qhd9/WmvD84by7Wnz2Tm6OYsnMzn7VqhbDuh6yqWlV5nTiMZ6FwYevTANGX0oJlouEAwTS2lKILwRRC98CmdYIMoFr1aV0O75E5BOF6GYUQuJyxuD8bF7r2ixwPLt6AAV80LMe9/mli3qc79OI+88S8AfHDKeRiGTrG4/4unF3r8ueuP3L3q9l2EwefmfJ4zJ30gVWGgdq8ms/Q/sd65DyWOiFWT8rGfoXTCDUTZSQd+gmEkCCN+/8YWfvLMWrYVxK7vpOmdfGXJLI6dmBvms9s7SadCuey1RQ3P7gyltma4EdGDdMycMhlZe9BMNNwkPS37ZRAiodXcFRP6+ir9qYb2CpOGYURPT5l83kbXwwP+bf7rpQ34YcxZc8Ywa+PvAajOvoA+VeWZrU+hoHDh3Av2ewHyQo8/df2BX6y6Y5AwmMWVcz7PGZPOTlcY9K7HXfqf2G/9F0ocEqs65aM/Q2nRfyPKTUntOPUgjmMeeec9fvjUGtbtFIvR0ROyfGXJLE6Z0bz1Eaap1SKPrRh+PxC5nE2l4qfiPNhokimLafxdDEOT4qDJGBaBYFk6xWJ1yAtjpeLXOhparVApDCPKZa9/59B6RUn7Q3Q4lHFdk44O4by4N/+Hsh/y68RW+cRJ2A//CoDK0Z/mic1/xY88jh97AuOdCXv9HQlhcD93r7qDbZWtAMzMzuLKuV/gjIlnoaY49VDt24i77P9gL78HJfKJFY3yUZ+mdNJXifLTUztOvXh+7U6+/8Rqlm8RXgbTOm2uWzyLc+aNRW3ikG4mY6FpKt3d7SWkE6z+QVat1pWVkMlYqaV7RO2BdE5sJoZlPJyiKKnt/Eulamp1DY2mWg0wDK3lfB0OllLJwzA0OjvFyN3dK5zvf30zvZWA903Kc1L4MlpxM2F+Bv7k9/Po8/8dgAuO2DN64IVVHlj/B+5+93beq2wDYFb2CK6cezVL0hYGxS1CGLxxN0rkEaNQmXcxpZP+O2HnEakdp14s39LH9/+2mufXdQMwJmPyxfdP5+PzJ6JrzXsxVlWFXM7G80KKxdYMvR8ITVNbcoxzgmXpqXVkiaFUzStURyrDKBAMyuWhjzD1vBDHMdF1tSXDj4WCcFkMgtasXj4Qvh/WWiFNM6ot9mEUc1e/rfIVJ03FXv4DQBQnbq9u56XtyzBUgzMmnk0UiDeJF1b54/r7uXvV7Wyvih79I3KzuXLO1SyeeGaqwkApvYf74k04r9+OEopzrsz5GKWTvkY4em5qx6kX63aW+eGTa3j4HSGgspbGlSdN47ITpuA0kZfB3khaGNvJ/Ghv5HKtHT081K6i/ZHN2jJ60IQM24B5UYuQjmVuoVBNxWNhuOjrE/MN2q0eISGKRNW5bRt0drr09VV45J0tbOypMLXT5qzJYD78F2JFpXLUJTy66WEiIs6cfCZ6YOOFVf6w/vf8YtUdg4TBHK6cezWLJ5yRrjAo78B9+WacV3+GEoj3U/WIj1A8+euEY45O7Tj14r1ClR8/s47fvbaJMAZTU/jU8VP4u5On0ek0l5fB7iiKWCgSE652/Cwk5HI25bLXskXKjmOmViBuGBpaE0ezRjLDKhBsW6dc9oYcogrDiCiKMU2tJee/h2FMqVQln3daVuQcDJWKj++HZLNWLXrw2ROnkllxH0rks3bGEn6y5m7+3PVHAOaPWsi9q3/JPe/eVRMGs3NzuXLu1Zw+YUm6wqDSjfPKj3Fe+QmqXwSgOvNcSid/g2Dc/NSOUy/6KgG3v7CeX7y4gWoQoSrw8fkTueb905mYt4f79A6Iaeq1Xvp2GLa0PxzHJIrilu3GSK7dO3emFT2QvgfNihIP4/SgOBYfkjTCbEkbVKvm80CE7BRFSa1lqFl5uauHL/7yFUa5Bn+89hTG3/NB3utdxcUzZ1OKfEL2FHlz8nO5cs7VnJa2MKj24rz6U5yXf4zq9QLgTT+L4sk3Ekw4LrXj1IuKH/JfL2/k58+vp7ciFpyz5ozh+sWzmDWmOb0MBqOqCtmsRRwLw612jhqA6MhIHCBblWzWwvPCVIScaerkcrI4sVkZtggCpOuLIBS5n+o0sUZTKnnkcnbbzGvYF4mt8icXTmJcaTn6znf46fhJlOI9xUGH0cGNC77JaeOXpGug4hVxXvsZ7ks/RK2KYUTe1MVCGExalN5x6kQQxfzxjc3c8vRatvZ7GZw4rYMblsxi/qTmnvWQYNsGjmPstYC1HdE0Fde16Olp3U2MKCZU8bx0NjEieiDFQbMyrAIBhEhIy3q4XG7dtseEvr4KnZ0uYdh609wOhrU7Svxt1XZMTeGShZMJnv9/MIDXO8YRBt17PH6SO5nTJ5yR3gn4ZZzXfi6EQWUHAN6kUyidciP+lPend5w6Eccxj63czg+fXM2aHeIzM3dchhuWzOL9M0e1hAudrqtksza+H6QWpm52FGWgKLGVoyRpGjrJmQvNT1MIBF1XU+tCKBartQlvrUpvrxil3G6DaAB+8eIGYuD8YyYwxgywVghzpHljTuStrY8TxAORE13ROarjmHQOHFRw3rgTd9lNqGVR2e9POIHiKX+PP3WxuII3OcvWd/P9J1bz+qY+AKZ02Hz59Jl86KhxTe1lkKCqwiRN09RDtuNudfJ54S7aqkWJICI+wi01nb+bnLnQ/Ay7QIBk0qOdSv2A74fYttGyBYuQVP1XakWLw1gmkio7Sx5/eEM4HV5+4lSsVX9E9Qv4E07gU8fcwF92PE85KBPEAbqiY+sOl82+YmgHDavYb96Du+x7aEVxbH/8Qoon34g//ayWEAZvby1w0xOreWbNTgBGuwZfOHUGFy2YiNEi1d+ua2Ka+kFbcLcTuZxNtRq09OtOWtPTivi4riGjBy1AUwgEAE1TMAwtlVxksSg6AjyvdcOXYRj1dza0z+THX7+8iWoQsfiI0cwc42I/fg8gxjpPcCfyy/P/i1te+jFv9bzJMaOP5ZoF1zDGHEep5B26SAp97Ld+hbv0e2gF0THhjz2W0sk34s38YEsIg67uMjc/tYYH3xIRj4ypccWiqXz2xKm4ZnN7GSQkdQblst/SBcSHi+uKjoVWN0LLZq3UpjUqiujkkNGD5qdpBEISRdi5szjk50o+kJlMOj4Lw4Xnhahq0PKGKiCq7X/18kYArlg0Fa37XcxNzxHrLtU5HyOTMTG80fy3+d8Y+CEfAjWko8PB9wNKJf/AQiEKsN6+j8zS/0DrXQdAMPpIiid/A++ID0OKHRD1YnvR46fPruO+VzcRRjGGpnDpcZP5/MnT6XSb28sgIREG1WrQtjbJB8K2DXRdbel0JwifAkUhtfbTTMaS0YMWoWkEAogcZVq2w5WKT0eH07IOiwmVil9rBUtjwNVw8cCbW+gu+xw9IcsJUzuwn+13TpxzIZqb32dldLUaUK0GWJZeEwrl8l6KUKMQa8VvcV/4LnrPGgCCUXMonfR1qnM+2hLCoFANuHNpF3cv66LsRyjABcdO4NrTZjCpBbwMQCyKSa56pAoDEO17lqW3RfQvzXkLmqbI4sQWoukEQiZjUq2m49BVKFTI5VrbGwFE+2MmY9WMZFqNKB5kq7xoKkocYr31a0AMZspkDix+BguFfN4hCELhRBeEWCv/iPvCd9B3rgAgzM+gePLXqM69CNTmD8VXg4h7X9nIrc+uo6ffy+CM2WO4bvFM5ozNDPPZHZgkZJy057a7C+KBMAwN1zVbup0xIZOxUu0Ky+Uc2dbYQjSVQABRDJPWhMMwFEZMreyNkFAsVsnlbBzHpFxurdfyxKrtrNtZZlLe4gPzxmGufRittJWgczbazPcThtFBV3cnQsHQVXIbHkb727+gbnsTgDA3jdKir1I56hJQm+6tvQdhFPPAm1u45em1bO4TAum4KXluWDKLhVM6hvnsDowYNmSg6xqVij9iWhb3h66rZDJWf3HxcJ/N0Ei6y9JK05qmLgcytRhNdxVNzJPSSg2Uyx6dnS7VatDSLUYgPBLyeZs4bq3pj4kx0mdOnIquKtjL+4sTj/40bsY6tAhPHGOufQT3uW9jvPc6AFFuCtHib1A56lKqvgJNfmGO45i/rdrBD55czbvbxWufPdblK4tnsfiI0U0ffjVNHccxiGOoVLyWTn2liaaJOqre3vboPEpeS3rPJ02RWo2mEwggChZzOTu1HUlfX4VczqK7u/Xzgb29FTo6HKA1Zsi/trGXlzf0krN0PjZ/AkpxK+aah4kVDfW4z1AuH2Q6KY4x1j9O5rlvY2x9GYDQnUBp0f9F5ZjPoOgWtmbQ4RqEYVSb+9BsvNzVw/efWM0rG4Wt86S8xZdPn8l5R41Ha+Kqbk1Td2kfLhQqI8rH4EBomlrrOGoH75Ik1ZvWa5GFia1JUwoEECIhrYLFMIzwvLAtUg0APT3llhEJdy0T0YOLF04iY+rYr/8aJQ7xZp2HMXoKxQNFD+IYo+spMs9/G2PzUgAiZyylE2+gfOzloDvJwyiXfcplH10X7x3hGR9QqQx/9GjltiI3PbmaJ98V7o2djsHVp07nkwsmYerNuatSVVFQZllGrTOolbuC6kW7iQNd19B1LbUCS00Tw51kW2Pr0cQCId2CxVLJa4uuhoRWEAld3WUeW/Eeuqrw6eMnQxxjL/+luPOEKw642Bgbn8N97t8wNz4LQGSPonT8dZTfdxUY+x5EFARRLextmnrNvc/zAjwvaOjff2NPhR89vYY/vbmVGHANjcsXTeHyRVPJmM338VNVpVaBD/QXHbZHyLwetJs4ECO30+taAFmY2Mo03xVqEGkWLEKSw2/9roaEZhcJ97y4gSiG848Zz7ishb7pBfTuVUTueOI55+IX9t5XrW9eRua5b2N2PQFAZHVQPu5ayguuJjazh3QOiSgA4f3uOCa6ruL7IdVqULc0xM6Sx63PrefeVzbihzG6qvDJhZO4+tTpjHbNuhzzcNF1FdPUMU2dOI7xPDFhtR0WvHrSbuIARN1Bueyl9npkYWJr0/QCIc2CRREm9VreU2AwPT1l8nkbRREh9mahp+zzu9c2A8JWGcB+UxQnxgs/Q7G858Ksb30F97lvY617DIDIzFFe+EXKC68htoY+oTDpgADRimaaOpmM1b8ohvj+0KMLRS/g7qUbuHNpFyU/RAE+cvR4vnTaDKZ2OkN+DWmQRAkMY+Cz5XmyPfFQ0HWNbNZqK3FgmgNRo7SQhYmtTVMLBBC1CPm8w44dQ3dYBKhUAvJ5vaVnNexOb2+FXM7GdZWmqbG479VNVIKIU2eOYs64DIpXwF55PwDesZ/ZpcBN2/YGmef/HWvNQwBERobygi9QPu6LxPaoupyf74e16IGqCptvxxGpiDiOa/cHQXhQi6YXRPzm1U389Nl17OwXaqfPGs31i2cyb/yhRT3SRtdVDEPDMDQ0TSUMI3w/pFTyhr02oxUxTQ3XFWH4dhEHSUo3zehqLieHMbU6TS8QQEQS0rRN7uur0tnpEATt8wHv66uQzVpNER3xgoh7XhwwRgKwVt6PEpSIp51K0Z4KMWjb3yLzwnewVj0AQKzblN/3eUrHX0fsjG7Y+UZRvEt0QVGUWoTBdYVnfDJ+OwiiXXwbojjmz8u38qOn1rCxV/ze3zcpxw1nzOKEqZ0New0gLvKapvb3rwsxABAEIUEQUSxWZefBEBFpKqPtoi25nE2hUE3tNSWfH9m50Nq0hEBILJirVT+VVEMcxxQKwnioHaxQEwqFKq5rDvvshj8v38qOks/ccRlOni4WyZr3wTGXoe5YifvCd7FW/B6FmFizKM//HKUTvkLsjhu2805I8vCDvedVVemv7laxLFGR/fiK9/j2Q2/z9pYCALPHZvjKGbM444jR/c+T7nmpqjLopqJpKpqm1EK4URT3ixfhMtkOxbjNhG0bNfvkdhIHrmvuElFLg1zOlqmFNqAlBAKIi2OaqYbkA9Gq9sX7olTysG2Djg6n37ClsceP4rhmjHTFoqlohU24z/y/GJuXAaAu/y2j1n8dJY6IVZPysZ+hdOL/RZSZ2NgTPUSiKBEN8OrGXr7/xGpe6uoBYGLO4vozZvGJ46Zg6CqqqqAoyh67pziO+2+7fg+U2nBJ8a+CuLbu+vNRFBFFce3meaJ9s12iYM1MJiPC5e20oQBqqac0X5dMLbQPLSMQIP1UQ6nkkc87qY2ZbhaEd3pER4fb8DzpM6t3snpHifFZkw9P8Rn1y/NQqr21+611fyVWNMrHXiGEQW5Kw85tqLy7vcgPnljD46u2A9Bh63z+lOlcctxkLF2lWvHZ3ztTiIYBESC+NzjSIMSDuMlFv1nI522CIKKvrz0KmxNEl5iVqjiQqYX2oqUEQtqpBhC5+85Op62qkUGMio4i4brY11dpWLj5zqXrAbjshCnkX7kZxSuiDPI+jlGozP0EhbP+v4acTxps7q3wo6fX8sCbW4hisHWVz544hc+dNI2sdfAfoV2jB+3zXmtXFEUhn7epVoOmbCMeKvm8qDtI87onUwvtRUsJBEg/1RDHMX197VePAMIwSLRBOpTLXqrtS3tj+ZY+lq7vIWNqXLRgEvrvX0aJdz2mQozevaqu55EW3WWfnz23jl+/vBEvjNFUhU8umMg1p05nbNYa7tOT1BFNE3bvxWK1raKLCfWrO5CRg3ai5QQCDITG0qrWD4IQzwuaogMgbaIopqenRC5no+taXa1y7+qvPfjE+yaRtXSCCcehv/c6SjQgEmLVIJhwXN3OIQ3Kfsgvlm3g9hfWU+xvhf3QkeP48ukzmTaqObwMJPUj6V7p66u0ZRuoaeroupaqW6JILWgytdBmtKRASDziK5X0Ug3lsk8uZ6c2/6GZiGPhleC6Zt2KFzf3Vnj47W1oqsJlJ0wGoHzC9dgrfktcLaDEAbFqEBsupeOvT/fgKRGEEb95bTM/eWYtO0riPXDqzFF8ZfFMjpqQG+azkzQC1zX75xC0VxtjgqapuK5JT096fgeKIlML7UpLCgQYMFDaubOY2gdZ1CO4tb7xdqNU8jBNnY4ON/Xd0S9e3EAYw3lHjmVi3gbAmTiLwuceQX/2/6BveZlgwnGUjr+eKDc5teOmQRTHPPz2Nn741Bq6ukV76LETc9ywZBaLpjfWy0AyPCSLXBhGqe6sm4nkNfb1VVIVP/m8I1MLbUrLCgQYqEdIs3agt1fMN2i3osWEpDUul7NTq0voqwT89lVhq5wYI4kefZWCOQHO+NaQj1EP4jjm2bU7uemJNby9VXgZzBjlcP3imZw9d6wMl44QdF0lm7UplbxdvC/ajXzeoVSqproxcBwDXZephXalpQWCogjzGscxUptDEEWiaLGdhjrtThhGdHeLugTT1IdsqvTb1zZR8kMWTe+sheKz2fTaUevBG5uEl8HS9cLLYHzW5Ivvn8FH509El7uhEYNtG9i2QV9fua1dJjMZC98PU7WXT9IVMnrQvrS0QAARRUgqctNKCwRBSKXiD7sjYb3p66tg2zqdnYefcvDDPW2VTVOvWRM3G2u2l/jhU2t4dMV7AOQsnatOnsanjp+MbWjDfHaSRiHGGtvEcdy2G4EEyxITFXt70xXsHR1yjHO70/ICAepTj1Cp+Gia2nZOi7tTqQT4vkg5VCr+IRdo/uXtbWwteMwa43LaTDFYSRRBNVced2tflVueWcsfXt9MGIOlq3z6+Cn83clTydvGcJ+epIEkkxgb0fo73BiGhm0bqX8e83nZ0jgSaAuBACKSkMs5qRYYFYvVtu1sGEyScshkLPJ5p7+I6cBKK97NVllRFBzHpFr1m8YJsLfic9vz6/nlSxupBhGaAhctmMg1p85gfE56GYw0XNfEMLS2msS4L8QGJ12nRBARCcOQdQcjgbYRCMkEvrQX874+4UaYjMhtZ4rFKoah0dnpUCweuGDr+bXdrNhWZEzG5MNHja+1nzZDyLbih9zz4gZuf6GLvv5d4gfnjeXa02cyc7Q7zGcnaTSJ8VG1GjRddKseiA2TTV9fOVWxrmkq2awlUwsjhLYRCDAw09z3w1QrdXt7ByyL29E4ZTC+H9LdXSKbtbEsnUJh3y1RSfTg08dPxtTFbqVUGt7CxCCK+f3rwstgW0Gkhk6a3slXlszi2InSy2Ak4jgGlmVQKDTOcnw4URTRsVCP8d6y7mBk0VYCAUQ9QkdHuvUIcRy3ffvjYOJYRE5MUxQw7i2asGJbgWfX7sQxVC5eMAm9f4phmlXSh3bOMY+ueI8fPLmGdTvFDvGo8VluWDKLU/prIyQjCxE1sPC8sCmiWo0ilxPW6mlHPKXfwcij7QQCiEhCR4eb6kVBtD9War4LzZJjryeeF+D7AdmsjW3ruwx2SWyVPzZ/Ih2OQSYzfDbVz6/dyfefWM3yLcLLYFqnzXWLZ3HOvLGoMk86InFds9bC2+5Rv8Hkcja+H6RefOm6hqw7GIG0pUBQFAVNS3deA4jhR8VitT+S0J5WrLuTRBMMQ6Ojw6Fc9lm3rcCDb21DVeAzJ07BsnSCIGr4hXj5lj5uemI1z63tBmBMxuSL75/Ox+dPRNdkGHQkYhgamYxFteqPqKgBCO+RMIxS84RJME0Nx5F+ByORthQIIFINlqXj+2Gqatr3Q0olL3UHx2bH90N27iyRyZj8bvlWgijmg/PGMqXDwXXNhl6M1+0s88Mn1/DwO9sAyFoaV540jctOmIIjvQxGJKL+yEJRGBEdCruTyZjEsbBTT5OkuFPWHYxM2lYggBAJiapOszjJ84JaIVC7+rbvi607S9z9/DoArllyBK5rUi77DYmmvFeo8pNn1/Hb1zYTRjGmpvCp46fwdydPo9ORXgYjlaQIsVSqDlsNzHAidvdaSVkOAAAdzUlEQVRq6qZuiiKLEkc6bS0QYMBEqbu7lOquoloN+mdB2PT2tq/b4u787rXNFKohx0/Jc+zELK5r1t1Sua8ScMfS9fxi2QYqQYSqwMfmT+CL759RGwwlGXmYpobrWnheMOLSCQm2bWAYal2uQR0drkwrjHDaXiBAUrTosHNnuhcRketT2t6SOSGIYn6xTNgqX75oGoah09NTRtc1Ojvd1J3pKn7If728kdueX09PRTzvWXPGcP3iWcwaI70MRip6f0ttFMUjMp2QYNsGpqnXJYqZzVpomiKLEkc4I0IgiKJFtS4pgXLZw3HMESESHn1nG5v7qkwf5fCBo8ahKKI2wffF7IpMxsRxDIrFobVYBVHMH9/YzC1Pr2Vrv5fBCVM7uGHJLN43OZ/Wy5G0GEmdgaoqFArpTiVsNeopDixLx7J0mVqQjAyBAANOi5lM+lMGR4JIiOOYO14QrY2XL5pKLmvvcnGK45hCoYqmiYt4kno4lNqPOI7568rt/PDJNazeIaI9c8dl+MqSWZw2c5TczYxQkoFsuq6N2DqDwdRTHBiGJp0SJTVGjEAAcaGxbYMoSr8VqN1FwotdPby1tcAox+Di46fg+8FeQ7thGNPbW6kNugKFUunAQmHZ+m5uemI1r23qA2Byh811p8/kQ0eNk14GIxRFUWqzE0olb9h8NpqJeooDEWWVHQuSAUaUQICB3UgYxgecNXCotLNISGyVP3X8ZEbl7QMWhYVhRG9vBV1XcV0LiCmVvD2EwttbC9z0xGqeWbMTgNGuwRdOnc5FCyZhSC+DEYmqiqFfhqFRLnt1L4JtFeopDpI6LSkOJIMZcQIBRGdDLmfR2xunbkcqRILRVi2Qq7eXePLdHVi6ypWnzaRU8g66rTEIInp7y/1CwURRFMplj3e3Frj5qTU8+JbwMsiYGlcsmspnT5yKa0ovg5GIpon3iKaplEpSGAxGpFjUulxTFEWhs9NFk4JcshsjUiBA0v5o091dTr3YqVz2iaKYjg4hElrdcfGuZSJ6cOH8CYzvcA6rpUwIhQrdFZ+fP9/Fr17swg9jDE3hkoWT+fwp0xjlmmmfuqQF0HUN1zVQFIVSKf0ZAq1OUphZj1ZGRYHOTjljQbJ3RqxAgIHBTml7JIDwSYhj0UvcyrMb3it6PPDmFhTgmiWzKRQO7yJVqAbcubSLu5d1UfYjFODjCybx9XPnMdYVI7pHarvaSMW2DWzbIAyjvaafJKLdMLE7rwcdHSJyIAuAJXtjRAsEEGHNzk6XnTtLqS/inhcQxwORhFZcAP/r5Y34YczZ88Yyc4x7yBcqL4j49SsbufXZdTUvgyVHjOb6JbOYMzYDxIRhRD7v1Hzkg0DuINsVUV8gcunVatDS4rne5PM2QRClbp888PyOFAeS/TLiBQKIi1Znp4gkpH2t8v2QQqHSLxJaa7Jc2Q+59+WNAFx7xuxDygmHUcyflm/hR0+tZXOf+LmFk/PcsGQWx03t2OWx1aqYPqfrKo5jomli2E6lEsjFo02wLB3bFnbYlYpPsTgynQ8PhsTGvVoNqFTS7bZKyGYtDEOTqQXJfpECgQEjpbRHRCckhXq5nEOxWG2ZHOv9r2+hpxKwcEqe+ROzB9UaGscxT7y7gx88uZpV74nf5eyxLl9ZPIvFR4ze724lCCL6+iooCliWQUeHQxRFVCr+iO99b0V0Xe23AtaoVgP6+iotGUVrJMK+3aFU8lLvskrIZCwsy5DiQHJApEDoR1EUdF2kG+ohEsIwpqenTEeHTaUidsfNTBjF3N1fnPjFM444KHHwclcP339iNa9s7AVgUt7iy6fP5LyjxqMdwsUojun/HflomlhkMhmrNpmzVQTWSETTxBRV09QJQyHupH/BwaHraq1Ful71GK5rYtu6FAeSg0IKhEEMiASH7u7024niOKa7u1wzI6lXbjENHl/5Hht6Kkwb5XDa9FGE+6kLWLmtyE1PrubJd3cA0OkYXH3qdD65YBKmPrTWqTCMKBarFIvC5c2ydLJZC88TYkHWKww/mqZgmgaWpRNFEdXqyB2edLgkg6d6eupXq+S6wgpdeh1IDhYpEHZDpBs0Ojocenrq42PQ21shk7Ga1lApjmPu6DdG+vxpM/cpDjb2VLjl6TU88OZWYsAxVC4/cSqXL5pK1kr/rZXMfQBxQbVtHcOwCAKxKNUrJCvZE13XsCwNwxgQBT096dfwjARsW4irev7+XNeQ4kByyEiBsBdUVUHX6ysSisUqti3y7M2Wm311Yy+vb+qj0zE4b+7YPe7fWfK49bn13PuK6HDQVYVPLpzE50+ZzphMY7wMPC+s1SXoughru65LFMX4foDnhS1VENrsqKqYZWKaOrqu4vshnhdQLDZvFKwVyGYtFEWp23UGEnFgybSC5JCRAmEfJCKhXukGEHn2MIxqIqFZ+sATW+XLFk3F1AYuKkUv4O6lG7hzaRclP0QBPnz0eK49bQZTO51hOltR3BgEHuChqgqmqdec54IgwvNE3UIzibBmR1FESscwdAxDI45jPC+kXJZ+BWmgKAr5vI3nBanPhRnMQFpBigPJoSMFwn5QVQVF0epWuAgibN7TUyafdyiXParV4Q2Tr9tZ5vGV2zE0hYvmTxTnGEbc98omfvrsOnb2X8xOnzWa6xfPZN747HCe7h5EUVwrcAQRXTAMnWzWQNMUgiDC90OCIJQL3SCSCIFhaOi6BsT4ftQfJZBFhmmiaaIYsd4dTbLmQDJUpEA4APXubgCxqHV3l8jlbHRdG9YL8t3LuoiBC983iU5H50/Lt3DzU2vZ2CNqJd43KccNZ8zihKmdw3aOh0ISXSj3B4E0TcUwtH6/BRWI+x8T4vvRiEhLJNExw1DRddELH4ZiLkmlEhAEUhDUC8vScRyTvr4yYVi/iFYmY/V3K0hxIDl8lFg60RwUcSwc/7q76+v85jgmpqk1vC5hc2+FHz+zlvtf30IMXH3KdJ54dzsrthUBmDXa5frFMzlzzpi2cl5TFFFwp+tisUwG1oRhtMutFaMNmib8PTRNvL7ktUVRVBNFQRDJ1EuDyGQsNE2hr69S12LOXM7CNGVaQTJ0pEA4BOI4ru3263lRNQyNbNaiUGiMqdLm3gqfvf1FCtWA3V/VhJzFl06bwQXHTDgkL4NWZ1+LK4gFNgyFYIwi8Z6I46j/38acn6oqg24qqirOV6TFlNp5BkFri5x2QFUVcrn61xsAdHQ4GIbWViJeMnzIFMMhIFogxWjUnp70p0AmJHUJuZyN74d190u4/YX1lP09xcGCyXl+cOkCrCF6GbQiQgCEwJ4CLVmMNU1F14WXfbJY735hjuO4/0bt3/57dhET4seUQV+L95t47oH7EqJoQJxEUUwQCF+IKIpkq2ETYZoamYxFX1+1rp4diiIHL0nSRwqEw0AMeBKzFeq1w48i4bzoumZtjkO9gj1vbC6wt81lGMUjUhwcCLEohwf1t1eUgYU+EQEDAmDgcYP/tMnfWUSskPMoWpRMxkTTtLqnJRVFbFo0bU+BKpEMBSkQDhNVVcnnbfr6qnU16CmVPAxDtFvWK+Vw7MQs72wtEAxKm+iqwjETm6tDoRVJIgfsEZ+RtCsDKYWQYrF+/gbJsTo73b1GrySSoSK3h0NAVVVyOas2pa5eJCkHxzHJZKzUn//Kk6bhmhpJsEBXFRxD48qTpqV+LImknbEsnXxeDGUrl+ubGky6q2RaQVIvZJFiCkRRTLnsNWS2gm0b2LZBX196o6Nd12Rjd5lbnniXNzcXOGZilitPmsbEvJ3K80sk7Y6iQC5nE0UxxWK17nUghqHVZrpIJPVCCoSUSCx+e3vrP1shMVqpVoMh71KS8bJyuI5EcngkXUfFYv1GNA/GcQxcV1onS+qPFAgpIorXorpOZBuM65oYxtA8E/J5m3LZlyOUJZLDIJu1UFW139ug/p/5XM7GNDUZOZA0BCkQUibxSujtLTek71zXVbJZm2rVP+Qea+EoaDQk6iGRtBNJ1KBcHrD1rieKotDR4dS8LiSSRiAFQp2IoohCodqw2Qqua2Ka+iHVJnR2uvT2NibaIZG0A4oC2ayNokChUG3IZ0fTVDo6HNmpIGk4UiDUkSiKqVZ9CoXGeNsntQmeFxywYNK2hRVrIworJZJ2wDR1MhmzYbUGyTFzOUumFCTDghQIdSaOxRCc3t5ywxzuHMfAsox9TotTFBE92LlTFiZKJAdCVRWyWYs4hkKhvnMUBiOnMUqGGykQGkBSl9DTU6rrBLfBDL6oFYu7hkIzGatmzSuRSPZNkrqr92jmwSgK5PNObdKmRDJcSIHQQBpdlwDCC951rVoRo0hDWHR319fhTSJpZQxDzFA4nOLfoSDrDSTNhBQIDSaKov6UQ2M7B5KdkIgoVORkP4lkL+wv8lZvhL+BKVMKkqZBCoRhYCDlUL+JkHvDsvRaeqFYrDYs3SGRNDuKkviKNDadkBxbphQkzYgUCMNIFEUUi15D+qhhoK1RVVWyWZMgEMeXbwHJSMa2DRzHaJinwWB0XSWflykFSXMiBcIwIyyaQ/r66tvl4DgGoOxizWxZOo5jpmLZLJG0Gqap47rmQbUF1wPXNXAcmVKQNC9SIDQB9XZfTFzY9jVvwXHEAKjh2EFJJI3GMDRc1yQMI0olr+FGYYqikM/bMqUgaXqkQGgioiiiXPZT381ksxaeF+7X3EVRwHFMLEunVPJkC6Sk7UiEQRTFlErDU4Njmnr//AaZUpA0P1IgNBlRFBFF0NubTgGjrqtkMhY9PQfX1pgIBdPUKZelUJC0PslnQAgDr6GFwQliHLSDYagypSBpGaRAaFLSiiZ0dDgUCtVDvigqilKbFimFgqQV0XURMYCYYnF4hAEIL5Js1pZRA0nLIQVCEyOiCTG9vQc/gGkwlqVjGNqQZkEoitJv3axTqTTWNEYiORxMU8NxzGGNGICMGkhaHykQWoDDjSaMGuXS3V1OpY1RUUQ7mG0b/V0PvmyPlDQVSVdOEITDUnw4GBk1kLQDUiC0CIcaTXBdkziO67Ljt20d2xYX4nLZH7YdmkQihKuJbetNIVxF1MDGMHTZoSBpeaRAaDGiKKJaDSgWq/v0TVBVhXx+322NaZGEcgHKZQ/Pa5z7nGRko2kqjmOg6xqVSnO05yZOpTJqIGkXpEBoQRLfhGJx74OfcjmbSsVvmF2spinYtolpDlys5btKUg8sS8e2jVp0rJGWyPtC11VyORtVVWXUQNJWSIHQwkRRTBhG9PUNpB2Syu3e3sZPa1QUsCxRpxCGUUNFiqR9EQLUwDR1PE+kEYazviBBUYTHiGnqsghR0pZIgdAGDE47dHS49PWVh30Qk66r2LaBYWhUqwGVSnNc1CWtgRCbOpZlEMdQqfj7NfpqNLat47oynSBpb6RAaBPiOCaOY3w/Gpbowf5IwsIgLvTSU0GyLwxDw7IMdF3F85pPWMp0gmQkIQVCmxFFMVEk0g71mOswFDRNwbJEqDgMRdSjmXaFkuFB19X+94WG74dUq0HTpaZUVSGTsTBNTaYTJCMGKRDalCiKCIKIQqEy7OmGvaFpKratY5o6QRBRrfqyC2IEIUTBwN+/WetVFAVc18K2dRRFphMkIwspENqcKIrwvJBisdpUodrBDN5BBkGE54nIgnxnthcifSDcPYUobO4IUjKOWQoDyUhFCoQRQFKfcCD/hGZA0wZ2lnEc43kB1WrQtOJGsm8URUwvNE29v6ZATBRtxkjBYGzbwHWFMJB1BpKRjBQII4hEKNRjpHQ9UFWltsCoqkIQhHheiO/L6EKzYhgapqlhGHp/0ayoKWgFt81kFLMUBhKJQAqEEUgUJULBa6nhS7qeLD4aiqLg+wGeFxIEoRQMw4Suq/2iQOTohYgTUYJW+ZuYptbvgCg7EySSwUiBMIJJhIKY0ui1zAUdRPjaMMRO1TA0AIIgxPfFTaYk0kdRhEgTv3etXxBE+H7Qkr/zxBpZUZCdCRLJXpACQbJbjYLXslMak4VL1zU0TSEMo/4FLCQIopZ9XcOFrqvoulb7F4TPRiLCWvX3KWsMJJKDQwoESY1EKDR718PBomnKLgucqiqEYUwQCMEQhlFL5Mbrjaoq6LqKpqn94krsppPfU/JvK6Mo4DhGbbiYjBhIJAdGCgTJHgx2ZSwWq221iIrFUKstiMlimMy1CIKw9nWrC6TBKIqCpim117zra49q0ZZEOLULiqLgumIcNMiIgURyKEiBINkvYvGIKZW8pu5ZHyqqqvTvoFVUVUXTlNouM5memYiGpHYjiqL+r4f/3Aduau1rTVNr/ftxHPdHTOJa5KSdhMDu6LqK65q1WgnpYyCRHDpSIEgOilYuaBwqSRFbIhqSAT3JQrz74pNEYOJ44Pc2cB/A3kXFwNMota/F4kbteOL7g48X10TLrjchBkbax9uydFzXrIkliURy+EiBIDkkBtIPIaWS1/K56XqQLOjJTaxTSu2+gccN/GdPEQGJkEh+51HEiFvwDwZVVXAcmUaQSNJGCgTJYRNFEXEMpZJHteqPqKiCZPgxTREt0DRFphEkkjogBYJkyIhivpggiCiXPTl0SVI3dF3Ftg0sS0YLJJJ6IwWCJFUSseB5IeWyTEFIho6qKti2gW0b0tRIImkgUiBI6kaSgqhWAyoVv62r5iXpoihgWQaOY9QKQWUKQSJpLFIgSOrOQJGd6IKQ0xkleyOZ/ug4Rr9Hg0whSCTDiRQIkoaSCAUQkYVq1ZdpiBGMqipYlo5tGzUxIFMIEklzIAVCP2EY8oEPfIDNmzczY8YMHnzwwdRCml1dXZxzzjl7fF9RFEzTJJ/PM2fOHM4991wuvfRSTNPc7/OVSiUefPBB/vjHP7J69Wq2bt2KaZrMmjWLs88+m8svv5zOzs79Pkdvby8PPvggDz74IOvWrWPr1q0oisK4ceM4/vjjOf/88znzzDOH9LoPhigS4sD3QyqVoK3NmCSCpNBQTIBEpg8kkiZFCoR+HnvsMb785S9jmiae53HLLbektkAOFggXXnhh7fti7oHH5s2bWb58Ob7vM3PmTH76058yderUvT7X888/zz/8wz+wadMmHMdh3rx5TJw4ke3bt/Pmm29SKpUYNWoU//Ef/8Gpp5661+f49a9/zT//8z9TKBQwDIP58+czfvx4giBg9erVvPvuuwCcfPLJfO9732PUqFGp/B4ORFLgGIYiFeF5MhXRDojJmzq2PTB5U0YJJJLmRwqEfq677joeffRRrr32Wn70ox9x5plncsstt6Ty3IMFwttvv73Xx6xfv55vfvObvPDCC8yYMYN7772XXC63y2OeeuoprrnmGuI45tprr+Xqq6+mo6Ojdn93dzf/+q//yr333ovjONxzzz0cddRRuzzHz3/+c/75n/8ZTdP40pe+xBe+8IU9jvPWW2/xj//4j7zyyiucdtpp3HrrrQ3f4Q12IwyCEM8L8LzWGyk8EkkEgWWJcdyJ+6OsJ5BIWgspEIAtW7Zw9tlnk8vleOyxxzjrrLPo6+vjwQcfZPr06UN+/oMRCACFQoFLLrmE1atX8+Uvf5mvfe1rtft27NjBRz/6UbZv387//J//kyuvvHKvzxHHMddddx2PPfYYJ510EnfeeWftvnfeeYeLLrqIIAj4zne+wwUXXLDfc7ngggvYvHkzP/3pT1m8ePGhvuxUkYKheZGCQCJpT2ScD7j33nsJw5Bzzz0X13U5//zziaKIX/ziF3s8tquriyOPPJLPfe5zdHd3861vfYuzzz6b+fPnc9ZZZ/Gtb32LHTt2HNZ5ZLNZbrzxRgDuvvtuwnDAcOg3v/kN27dvrx17XyiKwle/+lWOPvpo5syZQ7Vard3385//nCAI+PCHP7xfcZCcy9e+9jWuuOKKXaIUw4WwLBYTCC3LIJu1GTXKZfToDB0dDq5r9A/mGe4zbX+SGoJczmb06AyjR2fI5Wxs20TT1Nq8ColE0tqM+AhCHMecc845bNiwgbvuuotFixbx6quvcumll9LR0cHf/vY3bNuuPT6JBsybNw/P89iyZQsLFy7EcRyeffZZyuUyc+fO5b777qsVGx5sBAHA8zxOPPFEPM/jnnvu4fjjjwfg4x//OG+99Rbf+MY3+NKXvnTIr7NarXLaaadRKBT48Y9/zBlnnHHIz9HMJNGFZH5BGEb4fojvhwRBKG2gDxNdV9F1DdPU0PUBASYLCyWS9kcf7hMYbp566ik2bNjA9OnTWbRoEQALFizgyCOP5O233+b+++/n0ksv3ePn3nnnHY455hgeeOABJk+eDIg6gk9+8pOsWLGChx56iI9+9KOHfD6maTJ9+nRWrlzJqlWrOP7444njuFY4eNxxxx3W61y5ciWFQgFFUfZZvNjKJFMPk6FImqZiGNoeoiEIIoIgrI07lsJBoGlqbdx1IgqkGJBIRjYjXiD86le/AuCiiy7a5fuXXnop3/rWt7jrrrv2KhAA/sf/+B81cQAwbdo0zjnnHO677z7efvvtwxIIAPl8HqCWqti5cyee5wEwduzYw3rOTZs2AdDZ2bnXNsq+vj7+6Z/+aa8/O2nSJL7xjW8c1nGHk72JBtPctZ4BxP8HxIMQDokLZDuhqgq6nggBrT8doOySlpHdBRKJJGFEC4Tt27fz6KOPoqrqHgLhYx/7GP/2b//G8uXLWbZsGSeeeOIeP5+E/wczfvx4AMrl8mGfVyIGkl2brg/8mYLg8HwCknqGfWWUKpUK999//17vmzt3bksKhH2xtx2xrmtY1oAvQ0IybjmKxC2JPCT/bwYhoapKLe+vaUpt4R9cCzDwcpWa94BEIpHsjxEtEO677z583+e0005j0qRJu9zX0dHBhz70Ie6//37uuuuuPQSC4zh73Ykni/nuC82h0NPTA1AzO8rn89i2TaVSYfv27Yf1nMnr6+npwfO8Pc593Lhxe9RHPPfcc/vslmhXDmYHnUQf4jjeZfc9WCgMfsyuX+/6mITBkY7k/4NnEOy+qO99fZedAxKJJD1GdDzx17/+NQAXX3zxXu9PUgsPPfQQ27Zt2+W+eu3Aent76erqAuDoo4+ufX/+/PkAvPTSSwf1PN/73ve4//772blzJwBHHHEElmURxzHLli1L+axHFqKjQhlUsa/WOiwGcvkahqFhmjqWJSYROo6J6w7cMhmrdnNda5f7HMfEsoTboGFotZRAcht83IGbFAcSiSQ9RqxAeO6551izZg25XI5zzz13r4855ZRTmDFjBr7v88tf/rIh5/XII48QxzFjx47dxeQoOccHH3zwgNGJt956i5tuuokbb7yRdevWAaJtMXmORBhJJBKJRLIvRqxASIoTzz///F3aGHfnkksuAeCXv/zlYef/D5ZSqcTNN98MwKc//eldag8uvvhixo4dy1tvvcXdd9+9z+cIgoB/+Zd/AWDhwoUsXLiwdt+1116Lbdv84Q9/2Ge9wWBef/31w30pEolEImlxRqRA2LlzJw899BCwZ/fC7lx00UXous7WrVtrP1MPurq6uO6661izZg2zZs3immuu2eX+fD7PP/7jP6IoCt/61rf47ne/S29v7y6P2b59O1//+td5+umnsW2b//W//tcu98+bN49/+qd/QlEU/v7v/57//b//N1u2bNnjXJYvX84NN9zAv/7rvwIwZsyYlF+tRCKRSJqdEVmk+Lvf/a7WKXDDDTcc8PFJMdldd93FggULhnTsxCkxed5KpUJXVxcrVqwgDEPmzZvHzTffjOu6e/zseeedx3/+53/yD//wD9x8883cdtttHHvssYz7/9u7XxUFojCA4qdoUCz+QfANFHwAYZqoSTQafQ3fwmfQYPAFJk0ciyBGEQT7RJMwYDPs3V2WNYnnl6Z8XKYdhss3jQZZlnE8Hrnf79TrdZbLZfAfBoDpdEqz2WSxWLBarViv17TbbVqtFnmec7lcuF6vAFQqFebz+b8WM0mS3ttHBsJ2u30+Z1n257n9fs/tdnvp7K+f9ovFItVqlSiKGA6HTCYTCoXCj/Oj0Yhut8tmsyFNU06nE4fDgVKpRKfTod/vM5vNnrsUvtPr9YjjmCRJiOOY8/nMbrcjz3NqtRqDwYAoihiPx5TL5ZfeV5L0nj5+1bIkSQp95B0ESZL0OwNBkiQFDARJkhQwECRJUsBAkCRJAQNBkiQFDARJkhQwECRJUsBAkCRJAQNBkiQFHnnHsnuZSzaKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_radar_chart(dataset=\"Yelp\", model=\"GeoSoCa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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