juliaL2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 2: Exploratory Data Analysis\n",
    "\n",
    "CPSC 340: Machine Learning and Data Mining\n",
    "\n",
    "The University of British Columbia\n",
    "\n",
    "2018 Winter Term 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject <module 'seaborn' from 'C:\\\\Users\\\\jasonsyc.STUDENTS.000\\\\.julia\\\\conda\\\\3\\\\lib\\\\site-packages\\\\seaborn\\\\__init__.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Printf\n",
    "#= run once\n",
    "    using Pkg\n",
    "    Pkg.add(\"PyCall\")\n",
    "    Pkg.add(\"Conda\")\n",
    "    using Conda\n",
    "    Conda.add(\"seaborn\")\n",
    "    Pkg.add(\"FileIO\")\n",
    "    Pkg.add(\"Pandas\")\n",
    "    Pkg.add(\"ImageMagick\")\n",
    "    Pkg.add(\"ImageView\")\n",
    "    Pkg.add(\"Images\")\n",
    "    Pkg.build(\"ImageMagick\")\n",
    "=#\n",
    "using PyCall\n",
    "SkText = pyimport(\"sklearn.feature_extraction.text\")\n",
    "using Pandas\n",
    "using FileIO, Images, ImageView\n",
    "sns = pyimport(\"seaborn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Admin\n",
    "\n",
    "- Get a CS ugrad account: https://www.cs.ubc.ca/getacct/\n",
    "- Course website: https://github.ugrad.cs.ubc.ca/CPSC340-2018W-T1/home\n",
    "- Course Piazza sign-up: https://piazza.com/class/jhjgkk9un036np\n",
    "- Tutorials start next week\n",
    "- The lectures will be a mix of PowerPoint and jupyter notebook (this)\n",
    "  - both will be available online\n",
    "  - you can view the \"static\" notebook directly on GitHub\n",
    "  - you can run the notebook locally and play around with it\n",
    "  - the notebook covers very similar material to Mark's slides, which are also posted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Typical steps of ML\n",
    "\n",
    "1. Identify question / task\n",
    "2. Collect data\n",
    "3. Clean and preprocess data\n",
    "4. Exploratory data anlysis\n",
    "5. Feature and model selection\n",
    "6. Train model\n",
    "7. Evaluate and communicate results\n",
    "8. Deploy working system\n",
    "\n",
    "(but not necessarily in this order...)\n",
    "\n",
    "Today we'll discuss steps (3) and (4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What does data look like?\n",
    "\n",
    "Often, it is tabular (but certainly not always!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject    survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
       "0         0       3    male  22.0  ...   NaN  Southampton     no  False\n",
       "1         1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
       "2         1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
       "3         1       1  female  35.0  ...     C  Southampton    yes  False\n",
       "4         0       3    male  35.0  ...   NaN  Southampton     no   True\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each row is an **example**.\n",
    "- Each column is a **feature**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of features\n",
    "\n",
    "- Categorical (e.g. `survived`, `embark_town`)\n",
    "- Numerical (e.g. age, fare)\n",
    "- Some are more ambiguous, like `pclass`: is this categorical or numerical?\n",
    "\n",
    "Many of our methods are meant to work with numerical features. We can convert categorical to numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject    survived  pclass     sex   age  ...  alone  embarked_C  embarked_Q embarked_S\n",
       "0         0       3    male  22.0  ...  False           0           0          1\n",
       "1         1       1  female  38.0  ...  False           1           0          0\n",
       "2         1       3  female  26.0  ...   True           0           0          1\n",
       "3         1       1  female  35.0  ...  False           0           0          1\n",
       "4         0       3    male  35.0  ...   True           0           0          1\n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PyObject(get_dummies(titanic, columns=[\"embarked\"])).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do this for all our features, we can now interpret objects as points in space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 280)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_num = PyObject(get_dummies(titanic, columns=[\"sex\",\"embarked\",\"fare\",\"class\",\"who\",\"adult_male\",\"deck\",\"embark_town\",\"alive\",\"alone\"]))\n",
    "titanic_num.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- So we now have 891 objects and 280 features. \n",
    "- In other words, each object is a point in 280-dimensional space.\n",
    "- This is why multivariable calculus is a prerequisite for CPSC 340."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other feature types: text data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"The University of British Columbia (UBC) is a public research university with campuses and facilities in British Columbia, Canada.\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"The University of British Columbia (UBC) is a public research university with campuses and facilities in British Columbia, Canada.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One approach: **bag of words** features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject <1x15 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 15 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = SkText.CountVectorizer()\n",
    "feat = cv.fit_transform([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "public        9\n",
      "is            7\n",
      "and           0\n",
      "university    13\n",
      "in            6\n",
      "facilities    5\n",
      "research      10\n",
      "british       1\n",
      "the           11\n",
      "campuses      2\n",
      "with          14\n",
      "canada        3\n",
      "of            8\n",
      "ubc           12\n",
      "columbia      4\n"
     ]
    }
   ],
   "source": [
    "for (k,v) in cv.vocabulary_\n",
    "   Printf.@printf(\"%-14s%d\\n\",k,convert(Int64,v))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Any,Any} with 15 entries:\n",
       "  \"public\"     => PyObject 9\n",
       "  \"is\"         => PyObject 7\n",
       "  \"and\"        => PyObject 0\n",
       "  \"university\" => PyObject 13\n",
       "  \"in\"         => PyObject 6\n",
       "  \"facilities\" => PyObject 5\n",
       "  \"research\"   => PyObject 10\n",
       "  \"british\"    => PyObject 1\n",
       "  \"the\"        => PyObject 11\n",
       "  \"campuses\"   => PyObject 2\n",
       "  \"with\"       => PyObject 14\n",
       "  \"canada\"     => PyObject 3\n",
       "  \"of\"         => PyObject 8\n",
       "  \"ubc\"        => PyObject 12\n",
       "  \"columbia\"   => PyObject 4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Bag of words ignores the order of words but still can work well.\n",
    "- You can interpret each document as a point in space, compute distances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other feature types: images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m registry at `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\registries\\General`\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n",
      "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m WinRPM ─────────── v0.4.2\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m RoundingIntegers ─ v0.2.0\n",
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      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ImageView ──────── v0.9.0\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Cairo ──────────── v0.6.0\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m HTTPClient ─────── v0.2.1\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LibCURL ────────── v0.5.2\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Libz ───────────── v1.0.0\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GtkReactive ────── v0.6.0\n",
      "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Gtk ────────────── v0.17.0\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\environments\\v0.7\\Project.toml`\n",
      " \u001b[90m [86fae568]\u001b[39m\u001b[92m + ImageView v0.9.0\u001b[39m\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\environments\\v0.7\\Manifest.toml`\n",
      " \u001b[90m [e1450e63]\u001b[39m\u001b[92m + BufferedStreams v1.0.0\u001b[39m\n",
      " \u001b[90m [159f3aea]\u001b[39m\u001b[92m + Cairo v0.6.0\u001b[39m\n",
      " \u001b[90m [4c0ca9eb]\u001b[39m\u001b[92m + Gtk v0.17.0\u001b[39m\n",
      " \u001b[90m [27996c0f]\u001b[39m\u001b[92m + GtkReactive v0.6.0\u001b[39m\n",
      " \u001b[90m [0862f596]\u001b[39m\u001b[92m + HTTPClient v0.2.1\u001b[39m\n",
      " \u001b[90m [d9be37ee]\u001b[39m\u001b[92m + Homebrew v0.7.1\u001b[39m\n",
      " \u001b[90m [86fae568]\u001b[39m\u001b[92m + ImageView v0.9.0\u001b[39m\n",
      " \u001b[90m [b27032c2]\u001b[39m\u001b[92m + LibCURL v0.5.2\u001b[39m\n",
      " \u001b[90m [522f3ed2]\u001b[39m\u001b[92m + LibExpat v0.5.0\u001b[39m\n",
      " \u001b[90m [2ec943e9]\u001b[39m\u001b[92m + Libz v1.0.0\u001b[39m\n",
      " \u001b[90m [a223df75]\u001b[39m\u001b[92m + Reactive v0.8.3\u001b[39m\n",
      " \u001b[90m [d5f540fe]\u001b[39m\u001b[92m + RoundingIntegers v0.2.0\u001b[39m\n",
      " \u001b[90m [c17dfb99]\u001b[39m\u001b[92m + WinRPM v0.4.2\u001b[39m\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m Homebrew → `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\Homebrew\\s09IX\\deps\\build.log`\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m LibCURL ─→ `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\LibCURL\\lWJxD\\deps\\build.log`\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m WinRPM ──→ `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\WinRPM\\Y9QdZ\\deps\\build.log`\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m Cairo ───→ `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\Cairo\\p68X8\\deps\\build.log`\n",
      "\u001b[32m\u001b[1m  Building\u001b[22m\u001b[39m Gtk ─────→ `C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\Gtk\\aP55V\\deps\\build.log`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling ImageView [86fae568-95e7-573e-a6b2-d8a6b900c9ef]\n",
      "└ @ Base loading.jl:1187\n",
      "┌ Info: Precompiling ImageMagick [6218d12a-5da1-5696-b52f-db25d2ecc6d1]\n",
      "└ @ Base loading.jl:1187\n",
      "ERROR: LoadError: LoadError: ImageMagick not properly installed. Please run Pkg.build(\"ImageMagick\") then restart Julia.\n",
      "Stacktrace:\n",
      " [1] error(::String) at .\\error.jl:33\n",
      " [2] top-level scope at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\ImageMagick\\d5KBL\\src\\libmagickwand.jl:25\n",
      " [3] include at .\\boot.jl:317 [inlined]\n",
      " [4] include_relative(::Module, ::String) at .\\loading.jl:1038\n",
      " [5] include at .\\sysimg.jl:29 [inlined]\n",
      " [6] include(::String) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\ImageMagick\\d5KBL\\src\\ImageMagick.jl:1\n",
      " [7] top-level scope at none:0\n",
      " [8] include at .\\boot.jl:317 [inlined]\n",
      " [9] include_relative(::Module, ::String) at .\\loading.jl:1038\n",
      " [10] include(::Module, ::String) at .\\sysimg.jl:29\n",
      " [11] top-level scope at none:2\n",
      " [12] eval at .\\boot.jl:319 [inlined]\n",
      " [13] eval(::Expr) at .\\client.jl:399\n",
      " [14] top-level scope at .\\none:3\n",
      "in expression starting at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\ImageMagick\\d5KBL\\src\\libmagickwand.jl:22\n",
      "in expression starting at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\ImageMagick\\d5KBL\\src\\ImageMagick.jl:18\n",
      "Error encountered while loading \"Z:\\\\Documents\\\\mikelectures\\\\img\\\\elephant.jpg\".\n",
      "Fatal error:\n"
     ]
    },
    {
     "ename": "ErrorException",
     "evalue": "Failed to precompile ImageMagick [6218d12a-5da1-5696-b52f-db25d2ecc6d1] to C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\compiled\\v0.7\\ImageMagick\\0LbNX.ji.",
     "output_type": "error",
     "traceback": [
      "Failed to precompile ImageMagick [6218d12a-5da1-5696-b52f-db25d2ecc6d1] to C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\compiled\\v0.7\\ImageMagick\\0LbNX.ji.",
      "",
      "Stacktrace:",
      " [1] handle_error(::ErrorException, ::FileIO.File{FileIO.DataFormat{:JPEG}}) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\error_handling.jl:80",
      " [2] handle_exceptions(::Array{Any,1}, ::String) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\error_handling.jl:75",
      " [3] #load#27(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::FileIO.File{FileIO.DataFormat{:JPEG}}) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\loadsave.jl:193",
      " [4] load(::FileIO.File{FileIO.DataFormat{:JPEG}}) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\loadsave.jl:172",
      " [5] #load#13(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::String) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\loadsave.jl:118",
      " [6] load(::String) at C:\\Users\\jasonsyc.STUDENTS.000\\.julia\\packages\\FileIO\\JAtC1\\src\\loadsave.jl:118",
      " [7] top-level scope at In[1]:5"
     ]
    }
   ],
   "source": [
    "img = load(\"./img/Irving_K._Barber_Library.jpg\")\n",
    "imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img[1:3,1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "size(img'[:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now, again, the image is a point in space. \n",
    "- But now the space is 8,257,536-dimensional!\n",
    "- We'll talk about this towards the end of the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "- ML+DM typically assume \"clean\" data.\n",
    "- Ways that data might not be \"clean\":\n",
    "  - Noise (e.g., distortion on phone)\n",
    "  - Outliers (e.g., data entry or instrument error)\n",
    "  - Missing values (no value available or not applicable)\n",
    "  - Duplicated data (repetitions, or different storage formats)\n",
    "  - Encodings (e.g., date-times in different formats)\n",
    "  - Identity resolution (e.g., no reliable primary key across tables)\n",
    "- Any of these can lead to problems in analyses.\n",
    "  - Want to fix these issues, if possible.\n",
    "  - Some ML methods are robust to these.\n",
    "  - Often, ML is the best way to detect/fix these.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much data do we need?\n",
    "\n",
    "- A difficult if not impossible question to answer.\n",
    "- Usual answer: \"more is better\".\n",
    "  - With the warning: \"as long as the quality doesn’t suffer\".\n",
    "- Another popular answer: \"ten times the number of features\".\n",
    "  - I don't like this view. Features are not the enemy!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature aggregation\n",
    "\n",
    "- Combine features to form new ones\n",
    "- Useful if there are few examples of a particular case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyObject C    59\n",
       "B    47\n",
       "D    33\n",
       "E    32\n",
       "A    15\n",
       "F    13\n",
       "G     4\n",
       "Name: deck, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.deck.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <th>886</th>\n",
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       "      <td>False</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>True</td>\n",
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       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
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       "      <td>Southampton</td>\n",
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       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject      survived  pclass     sex   age  ...  embark_town  alive  alone  upper\n",
       "886         0       2    male  27.0  ...  Southampton     no   True  False\n",
       "887         1       1  female  19.0  ...  Southampton    yes   True   True\n",
       "888         0       3  female   NaN  ...  Southampton     no  False  False\n",
       "889         1       1    male  26.0  ...    Cherbourg    yes   True  False\n",
       "890         0       3    male  32.0  ...   Queenstown     no   True  False\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_agg = titanic.copy()\n",
    "\n",
    "titanic_agg_df = DataFrame(titanic_agg)\n",
    "titanic_agg_upper = DataFrame(Dict(:upper=>titanic_agg.deck.isin((\"A\",\"B\")))) # not shown: we should still fix up the NaNs here! \n",
    "titanic_agg = PyObject(merge(titanic_agg_df,titanic_agg_upper,left_index=true,right_index=true))\n",
    "\n",
    "titanic_agg.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "PyObject    survived  pclass     sex   age  sibsp  ...  deck  embark_town alive  alone upper\n",
       "0         0       3    male  22.0      1  ...   NaN  Southampton    no  False     0\n",
       "1         1       1  female  38.0      1  ...     C    Cherbourg   yes  False     1\n",
       "2         1       3  female  26.0      0  ...   NaN  Southampton   yes   True     2\n",
       "3         1       1  female  35.0      1  ...     C  Southampton   yes  False     3\n",
       "4         0       3    male  35.0      0  ...   NaN  Southampton    no   True     4\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_id = titanic.copy()\n",
    "\n",
    "titanic_id_df = DataFrame(titanic_id)\n",
    "titanic_id_id = DataFrame(Dict(:upper=>titanic_id.index)) # not shown: we should still fix up the NaNs here! \n",
    "titanic_id = PyObject(merge(titanic_id_df,titanic_id_id,left_index=true,right_index=true))\n",
    "\n",
    "titanic_id.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Remove features that are not relevant to the task.\n",
    "- `id` probably not relevant for prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformation\n",
    "\n",
    "#### Discretization (binning): turn numerical data into categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: age, dtype: float64\n"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(titanic.age.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = cut(titanic.age, bins=(0,20,30,100))\n",
    "ages_cat = get_dummies(ages);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>age</th>\n",
       "      <th>(0, 20]</th>\n",
       "      <th>(20, 30]</th>\n",
       "      <th>(30, 100]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject     age  (0, 20]  (20, 30]  (30, 100]\n",
       "0  22.0        0         1          0\n",
       "1  38.0        0         0          1\n",
       "2  26.0        0         1          0\n",
       "3  35.0        0         0          1\n",
       "4  35.0        0         0          1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PyObject(concat([titanic.age, ages_cat],axis=1)).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mathematical transformsations\n",
    "\n",
    "- e.g. log, exp, square, sqrt, etc.\n",
    "- also, scaling/normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare_x</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>fare_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>2.692582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>8.442944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>2.815138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>7.286975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>2.837252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject    survived  pclass     sex   age  ...  embark_town  alive  alone    fare_y\n",
       "0         0       3    male  22.0  ...  Southampton     no  False  2.692582\n",
       "1         1       1  female  38.0  ...    Cherbourg    yes  False  8.442944\n",
       "2         1       3  female  26.0  ...  Southampton    yes   True  2.815138\n",
       "3         1       1  female  35.0  ...  Southampton    yes  False  7.286975\n",
       "4         0       3    male  35.0  ...  Southampton     no   True  2.837252\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_mod = titanic.copy()\n",
    "\n",
    "titanic_mod_df = DataFrame(titanic_mod)\n",
    "titanic_mod_fare = DataFrame(Dict(:fare=>broadcast(sqrt,convert(Array,titanic_mod.fare))))\n",
    "titanic_mod = PyObject(merge(titanic_mod_df,titanic_mod_fare,left_index=true,right_index=true))\n",
    "\n",
    "titanic_mod.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis (EDA)\n",
    "\n",
    "- You should \"look\" at the data before further analysis.\n",
    "- But how do you \"look\" at features and high-dimensional objects?\n",
    "  - Summary statistics\n",
    "  - Visualization\n",
    "  - ML + DM (later in course)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical summary statistics\n",
    "\n",
    "- Summary statistics for a categorical variable:\n",
    "  - **Frequencies** of different classes.\n",
    "  - **Mode**: category that occurs most often.\n",
    "  - **Quantiles**: categories that occur more than $t$ times:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    0.290640\n",
       "B    0.231527\n",
       "D    0.162562\n",
       "E    0.157635\n",
       "A    0.073892\n",
       "F    0.064039\n",
       "G    0.019704\n",
       "Name: deck, dtype: float64\n"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(titanic.deck.value_counts(normalize=true)) # frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"C\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get(titanic.deck.mode(),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous summary statistics\n",
    "\n",
    "- Measures of location:\n",
    "  - Mean: average value.\n",
    "  - Median: value such that half points are larger/smaller.\n",
    "  - Quantiles: value such that $t$ fraction of points are larger.\n",
    "- Measures of spread:\n",
    "  - Range: minimum and maximum values.\n",
    "  - Variance: measures how far values are from mean.\n",
    "    - Square root of variance is \"standard deviation\".\n",
    "  - Intequantile ranges: difference between quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.204207968574636"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25     7.9104\n",
       "0.50    14.4542\n",
       "0.75    31.0000\n",
       "Name: fare, dtype: float64\n"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(titanic.fare.quantile((0.25,0.5,0.75)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2469.436845743116"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.6934285971809"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.fare.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the mean and std are sensitive to extreme values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean with outlier   : 21.000000\n",
      "Mean without outlier: 7.230769"
     ]
    }
   ],
   "source": [
    "data = [0,1,2,3,3,5,7,8,9,10,14,15,17,200] # the \"200\" is an outlier\n",
    "Printf.@printf(\"Mean with outlier   : %f\\n\", mean(data))\n",
    "Printf.@printf(\"Mean without outlier: %f\", mean(data[1:end-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std with outlier   : 51.796941\n",
      "Std without outlier: 5.570067"
     ]
    }
   ],
   "source": [
    "Printf.@printf(\"Std with outlier   : %f\\n\", std(data))\n",
    "Printf.@printf(\"Std without outlier: %f\", std(data[1:end-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas the median is not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median with outlier   : 7.500000\n",
      "Median without outlier: 7.000000"
     ]
    }
   ],
   "source": [
    "Printf.@printf(\"Median with outlier   : %f\\n\", median(data))\n",
    "Printf.@printf(\"Median without outlier: %f\", median(data[1:end-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distances and similarities\n",
    "\n",
    "- There are also summary statistics between features.\n",
    "  - Hamming distance: \n",
    "     - Number of elements in the vectors that aren’t equal.\n",
    "  - Euclidean distance:\n",
    "     - How far apart are the vectors?\n",
    "  - Correlation:\n",
    "     - Does one increase/decrease linearly as the other increases?\n",
    "     - Between -1 and 1.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations of summary statistics\n",
    "\n",
    "- Summary statistics can be misleading\n",
    "- A famous example is [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet), four datasets with:\n",
    "  - Almost same means.\n",
    "  - Almost same variances.\n",
    "  - Almost same correlations.\n",
    "  - Almost same linear fits.\n",
    "  - Look completely different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code below from seaborn documentation: https://seaborn.pydata.org/examples/anscombes_quartet.html\n",
    "\n",
    "# Load the example dataset for Anscombe's quartet\n",
    "anscombe = DataFrame(sns.load_dataset(\"anscombe\"))\n",
    "\n",
    "ds1 = anscombe[anscombe[\"dataset\"]==\"I\"]\n",
    "ds2 = anscombe[anscombe[\"dataset\"]==\"II\"]\n",
    "ds3 = anscombe[anscombe[\"dataset\"]==\"III\"]\n",
    "ds4 = anscombe[anscombe[\"dataset\"]==\"IV\"];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64\n"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(ds1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64\n"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(ds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.0\n",
       "y    7.5\n",
       "dtype: float64\n"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(ds3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    9.000000\n",
       "y    7.500909\n",
       "dtype: float64\n"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(ds4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Figure(PyObject <Figure size 800x800 with 4 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the results of a linear regression within each dataset\n",
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=anscombe,\n",
    "           col_wrap=2, ci=nothing, palette=\"muted\", height=4, scatter=false,\n",
    "           scatter_kws=Dict(:s=>50,:alpha=>1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "</svg>\n"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1:0.01:10\n",
    "Plots.plot(x, broadcast(cos,x),\n",
    "    seriestype=:line,\n",
    "    thickness_scaling=5,\n",
    "    size=(1800,1500),\n",
    "    legend=false\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PyObject    sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\") # iris flowers, a classic dataset\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "  \"/>\n",
       "<polygon clip-path=\"url(#clip8802)\" points=\"\n",
       "1302.58,1001.23 1302.58,1255.28 1462.54,1255.28 1462.54,1001.23 1302.58,1001.23 1302.58,1001.23 \n",
       "  \" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
       "<polyline clip-path=\"url(#clip8802)\" style=\"stroke:#000000; stroke-width:5; stroke-opacity:1; fill:none\" points=\"\n",
       "  1302.58,1001.23 1302.58,1255.28 1462.54,1255.28 1462.54,1001.23 1302.58,1001.23 \n",
       "  \"/>\n",
       "<polygon clip-path=\"url(#clip8802)\" points=\"\n",
       "1462.54,1037.52 1462.54,1255.28 1622.49,1255.28 1622.49,1037.52 1462.54,1037.52 1462.54,1037.52 \n",
       "  \" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
       "<polyline clip-path=\"url(#clip8802)\" style=\"stroke:#000000; stroke-width:5; stroke-opacity:1; fill:none\" points=\"\n",
       "  1462.54,1037.52 1462.54,1255.28 1622.49,1255.28 1622.49,1037.52 1462.54,1037.52 \n",
       "  \"/>\n",
       "</svg>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Plots.histogram(convert(Array,iris.sepal_length),\n",
    "    xlabel=\"sepal length\",\n",
    "    ylabel=\"frequency\",\n",
    "    size=(1800,1500),\n",
    "    legend=false,\n",
    "    thickness_scaling=5\n",
    "))\n",
    "# sns.distplot(iris[\"sepal_length\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"survived\", y=\"fare\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"850.273\" cy=\"448.218\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"850.273\" cy=\"448.218\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1235.83\" cy=\"290.742\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1235.83\" cy=\"290.742\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1197.27\" cy=\"330.111\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1197.27\" cy=\"330.111\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1197.27\" cy=\"428.533\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1197.27\" cy=\"428.533\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1043.05\" cy=\"467.902\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1043.05\" cy=\"467.902\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1120.16\" cy=\"428.533\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1120.16\" cy=\"428.533\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"1004.49\" cy=\"389.164\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"1004.49\" cy=\"389.164\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#000000; stroke:none; fill-opacity:1\" cx=\"888.828\" cy=\"448.218\" r=\"18\"/>\n",
       "<circle clip-path=\"url(#clip0002)\" style=\"fill:#009af9; stroke:none; fill-opacity:1\" cx=\"888.828\" cy=\"448.218\" r=\"18\"/>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plots.scatter(convert(Array,iris.sepal_length), convert(Array,iris.petal_length),\n",
    "    size=(1800,1500),\n",
    "    markersize=20,\n",
    "    thickness_scaling=5,\n",
    "    xlabel=\"sepal length\",\n",
    "    ylabel=\"petal length\",\n",
    "    legend=false\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U4BYJIUTXNTmbZBcSIbqp/dggx5jD6OzR5BhzAKkjIWIhU/gpwJ+fkmHQkmHo/D9ZtmydLIRox5/Y3/EDSEFWAQvOX0CmPpPj9uOYDcqSXrVvoewuO7o0HTanLeJxsYjHOYUQqSlcf+B/3J/jYHfbyTRk8uz3n+XJz54MWaUiu5CI/i7aa6vZYKYov4i5RXOZOGQix1uOk5uey4eHP2RZ9bKkqyMZM4hkJRMqKcCfn9LZDj9+/q2TZUJFCAFtu2T48wVAmUypnFFJ1ddV/PL9X0YMnVMLretuOF08zimESE1h+4Oicl7b9VpQmHZxYTGlZ5Zy0HaQyhmVzFo3K2hSxb8LiV6r78XfSIjeEcu11eP1sHTaUiprKlm4dWFQjS2dthSP15M0dSRjBpHMZEIlBRwO5Kd0frsPQFbr1smSoSKEAPVdMhacv4Cqr6tYsn1J4Di1UMdwoXXdCX+MxzmFEKkpUn/gw8eZeWcGVtdZndZAnzU2byyrvl7Fvefdy7x358kHLNHvxXpt9eFjRc2KkHHAku1L0KDh5vE3J67xEciYQSQ7yVBJAf5A2s52+PGTFSpCiI78u2RsumET7934HhcOuZBVtatUj20f6hgptK6r4Y/xOKcQIjVF6g+qaquYNGRSINfBb1XtKiYNmcRbe99i8tDJvHfje2y6YROzx8+WyRTRb8V6be2s9pLlWixjBpHsZEIlBcR8y48/Q6VJJlSEEG38u2TkpOd0GlTrD6OL9rhYxOOcQojU1Fl/0OBoIN+Ur/q4UWvE5rKRk54ju5CIfi/Wa2uqXItTpZ2i/5IJlRTgD6WN9pYf/wqVOlmhIoQIwx9Uq6Z9qGO0x8XjvYUQfV9n/UGOMYc6e53q4w6PQ/oLIVrFem3t6Wux3WXH5XFx3H4cl8eF3WWP6fXhyJhBJDuZUEkBbbf8RBtK27Ztss/ni1u7hBCpy+11U1JYovpcSWEJbq87cFxpYanqcf7wx668d0+fUwiRmiL1ByWFJXx4+EMaHA1BjxcXFvPh4Q+5uuBq6S+EaBXrtbUnr8X+0Nipq6dy6epLmbp6Kst3LMfh6f6XuzJmEMlObjpLcj6fj0ONyi0/0WaoZLXe8uP0eGm0uwITLEII4adBQ9m4Mnz4WFW7Kijdv2xcGRo0gHqgbXfDH+NxTiFEagrXHxQXFlNeVM6rO18NbPvefpefdXvWSX8hRDuxXlt76loc79BYGTOIZKfx9cMlDFarlaysLBobG7FY1JeQJYu6JgfnP/g2GuDF8gvQpUW3qGjOi59w0uFhw92XMHqwLIUTfUcq1W8yc3lclK0rY07RHCYNmUSDo4EcYw7bDm/jheoXWDFjRdB2iXaXHV2aDpvLhllvxu11dzuvIB7nFMlN6leE4+8PjtmPkW3MZtvhbby681WuH3M9Fw69kJOuk2QZsrC77Zh0JukveoHUb2qI9dra3Wuxy+Ni6uqpqjknFoOFTTds6pHtl2XMIJKVrFBJcv7bfbIz9FFPpgBkmwycdNg5ZnPIhIoQIoTNaaO6rpr5G+eTY8wh35RPnb0usLTe5rKRq80NHO8ftPgf64nBUTzOKYRITSa9iQZ7A/PemRfUF71/8P1AH1VxZQXZ6dmA9BdChBPrtbW71+JoQmPbjye6SsYMIlmlXIbKiBEj0Gg0IX/uuOOO3m5aXMSan+LnD6Y9KsG0QggV7UPeGhwN7DqxK/ABRjXkzdkMHiecPKb8dDYnuslCiETqhZrPNGRypPlISGZKg6OBI81HGKAfEPc2CJFUUuDaK6Gxor9LuQmVTz75hMOHDwf+bNiwAYCf/vSnvdyy+DgU2DI5thyUwNbJMqEihFARU8ibuwW2PgOPj4bHz1B+bn1GeVwI0ff0Us1L+KQQ7aTItVfqVvR3KXfLz8CBA4P+/b//+7+MGjWKSy+9tJdaFF/+FSp5A2JboZLVGkR71JZcna4QIjlEHfLmbFYGcJsfbXtxy4m2f0+5CwwZCW69ECJuerHmJXxSiFYpdO2VuhX9XcpNqLTndDr585//zD333INGowl7nMPhwOFoW6lhtarf55eMDjX6b/mJbYVKToasUBF9QyrXb7Izao3MHj+buRPmBoW8BQ1+tDr4aIn6CT5aApcsSExjRUqS+k1BvVzzUfVLIiGkfntRil17pW5Ff5Zyt/y09/rrr3PixAlmzZoV8bhHHnmErKyswJ/hw4cnpoE9oO2WnxhXqJgkQ0X0Dalcv6nApDeh1+rJTc9Fr9WHJua3NCnfigFk5MGgscpPUB5vkQG2CE/qNwW1NLbVfMhzJ6DFFvcmdNoviYSQ+u1FndZh8l17pW5Ff5XS2yZfeeWVGAwG/vrXv0Y8Tm2Gffjw4Smx7dsFD73NUZuDh64dz8iBmVG/bsfBRh5662tGD8pkwz1983Yo0T+kcv2mqiZnE0atMbBs1+E6if7EftLyzsBmr8dsysO9ZyOm956Cm9eCNrYVdKL/kPpNQR6nktXQ8cNc/hiY/iCMukz5sJeeBR43GDLatjN12jAbgrczjfRcNM+L3iP124vC1SHAsPNh5hugM4TUYm+KtZZDxhoeB5mG6D/rCJEsUvaWn2+++Ya3336bNWvWdHqs0WjEaEy9JWdOt5djTcqFLNZdfmSFiugrUrV+U5XD7aCyppJVtasCg5ySwhLKi8r55daFrN+3Xrk3+ns/o3zm6xg9HtD2dqtFspL6TUEeN0y8NTi7IX8MzF6r3Gqw5hblQ156Nlz2KxznzaJiRwVVtVUh2QkaNGGfM2qNODyOiM+L3iX124vU6hCUWrzpNfjgD0o9+mtx4q1w8T2gS++V5sZay+HGGnOK5mDUyf/nRGpJ2QmV5cuXM2jQIK6++urebkrcHLG24POBXqvBnB7bf6qc1lDaRrsLh9uDUSefeIQQkTU5m6isqWTJ9rb7tq1OK4u3Lwbg7nPvZv2+9VidVhZVLwVNGrPH34x8lyxEH2LIUD6YQdsHtukPKn9/77G241pOYM8eTkX18yzu0Gcs2r4IHz6mnT4t0H+0fw5g5riZrKhZEfb52eNny0oV0X+p1WF6Nvy0ErY9m1RhtXaXnYodFVHXcmdjjbJxZbJSRaSUhGeoOJ1ODhw4wP79+4P+xMLr9bJ8+XLKysrQ6VJ2TqhTBxradviJFLqrZoBRiy5NeY0E0wohomHUGllVu0r1uaraKgZlDGKEZUTgsZW1VejS+m4fLES/pUtXPpjdtwvu26Pc5vPx0uBjMvLQjZxKVYQ+Y7hlODnGnJDnVtauxKg18tbet1Rfu7J2pfQtQgTV4W7lZ/7oyGG12sTXjS5NR1VtlepzarXc2VhDVqeJVJOwCZVdu3Zx8cUXYzKZOP300ykoKKCgoIARI0ZQUFAQ07nefvtt9u/fz8033xyn1iaHg61bJg80x96xaDQasmWnHyFEDKxOK1anetCd1WnF5rRx0dCLAh+QrE4rNlf8AyqFEL3AkKHkIw3IU3Ia0rTBodSZg7HZ6yP2GcdbjpNvyld9zua0hf3gZHVaaXI19divIkTKCtRhvvIzCcNqbU5b5LFDh3FC+7FGjjGH0dmjg8cVThlXiNSSsGnMWbNmodPp+Nvf/saQIUNiXnHR3vTp00nhLN2oHWhoBmLPT/HLMumpa3JKjooQ/Y2zWfmWKoqwOn+I3EnnSSwGCxaDJWRgVJBVwILzF2A2mLlp3E3cee6dfHj4Q5ZVL8OsNyfiNxJC9CadCe6qhpN1yge7PRthy+8xm/JU+wwAi8FCbnoudfY61efMBjMOT+j4xN/fZOozOW4/LkG1on/reD3XmZRQ2oOfhh6bng3piQ8LNhvMEfuBjuMEi8FCUX4Rc4vmMnHIRI63HCc3PbdtXGGQcYVILQmbUPniiy/47LPPKCwsTNRbpryDDV1foQL+HJWTskJFiP7E3QJbn4kqrK5jiNz6n6ynpLAk6D7ogqwCKmdUUvV1Fb98/5eB8LjiwmKWTluKx+tBr9Un+rcUQiSKuyU0APOCuVC8CveBjyktLGbR9tBbEEoKS/jW+i0NjoaQ54oLizlmP8YPCn4QVX8jQbWiXwp3Pb/pNVh2OdTtDD5+4q3KFygJ3nnP7XWHjB38SgpLcHvdQeMEp8fJ0mlLqaypZOHWhSHjCqfHKeMKkVISNqEyduxY6upCv6UQ4fkzVPIzu9Yxyk4/QvQzzmZl8BVFWJ1aiNyDHz7IU1OfAghMsiw4fwFVX1eFhMct2b4EDRpuHt+3b70Uol8L16e89zgApqIbKR9+IZDGytqVqrv8/HzCz4OeKy4spuTMEhZuWciDFz0IdN7fSFCt6Hc6u55fvxwqr06KXX40aCgbV4YPX9CuPcWFxZSNK0ND8F0JaZo0KnZUyLhC9BkaXxzvnbFa25Z+ffrppyxcuJCHH36YoqIi9PrgmcdE7mdvtVrJysqisbExoe8bq0se28j+48385odjKTwl9na+8tkBXv3nAYovOI1HriuKQwuFSLxUqd9e4XHC46PV769Oz1YC7Vq/uXJ5XExdPTVkie7Fwy5m4aSFDDQNpMnZRKYhU/U4UJbtbrphk3yTJKIm9ZtiouxT/LcO2lw2zPrgW3T8zx2zHyPbmM22w9v4/T9/z97GvRRkFXDvefcyeehkmlxNZOqlv0lmUr8JFE3tgZKZkm4BjwsMAxLbxlYuj4uydWXMKZrDpCGTaHA0kGPMYdvhbbxQ/QIrZqwIqttw4w+QOhepKa4rVLKzs4OyUnw+H5dffnnQMT6fD41Gg8fjiWdTUo7H6+OQP5S2ixkqbaG0LT3WLiFEEosmrG6AEhDZPkQux5hDvimfOnsd7x98nytfvZItN24hx5TDcfvxTsPmcrW5cfl1hBC9rMUKlqFKIG1zfYfnTkCLDQbkBSZP/H1B+w9DJr2JBnsD896ZR529LnALUI4xB51Gx6+3/pq/XvtXctJzaLA3SH8jBER/PW+9pif6Np/2bE4b1XXVzN84nxGWEYzKGsXuxt3ss+5Tnu9Qt+HGHw2OBqlzkZLiOqGycePGeJ6+TztibcHt9aHVaFqzUGInu/wIkboC3/g6bdGHMqZnKd9chftGq11YndlgVg2F++LYF+jT9GToM2hoDYQMFzZXlF9EujYdl8cVWOLr8DjINGR299cXQiRCpABrZ7PSZxS/1BZE+/ZvldyG/DEw/UFIN8PJY62v9WDXoNpvZRoyOdJ8BKvTSkFWAXefezcTh0ykydnEAMMADGkG6u31WAwWnv3+szz52ZPsbdwb1FS1cEsh+qxI1/Nh5yu39nicUYXPB4Sp9y6NN9oJN54IF14fzfEujyukPd1tpxDxEtcJlUsvvTTw9/379zN8+PCQ3X18Ph/ffvttPJuRkvxbJudlGkhL69qOSNkmZSJGMlSESC0dw2KjDmX0uJX7qNvfc+3XIazO4/WohsKVFJYwc9xMytaVUV1XzXOXP6caNleQVcDSaUtZvmN5UDtLCkuYUzQHo07CI4VIamEDrO8FfKHPXTAXZq+F138O1y5Snltzi/L8sPNxzHydipoXVfstn9dHaWEp675ZFwidfb76eZ69/FmW71gelLtQUlhC5YxKZq2bFTSpUlpYGhJuKUSfFe56nj9GCaXtGBTdWYaKWr1f9isc583q2nijfVPDjCfChdd3dvxB20FK15YGHp931jyuG3Ndt9spRLwkLJS2oKCAw4cPM2jQoKDHjx8/TkFBgdzy00F3t0wGyGldoVLX5MDr9XV5YkYIkThqYbFRhzIaMpQBFXQ60PL53KyoWRESCrd4+2J8+JhTNIf5G+fzxKdPUDmjEiBoIPPEpU+womZFSDv9/y4bVyYrVYRIVpECL8deA1+9ETaIlqufUvqX9x4LPG2/5F4qaipZXL008FjHfqt8QjnTRkwLhM7+4bI/qAbQ+vuQe8+7l3nvzpMPTqKf0sCF88DnhY+fb7ueX78ctj0bVfh8QJh6t2cPp6L6eRZ3MwTah091PBEuZLaz488ZfE5gVazVaeWUzFN4vvp5CasWSSstUW/kz0rpqKmpifT0xCdSJ7vubpkMbbv8uDw+TthdPdIuIUR86dJ0VNVWqT63snYlurRO5sF16cqA6r5dcN9u5eeU+SHfWum0xrDvs6p2FR93WHsAACAASURBVJOGTCLHmMPexr3MWjeL8fnj2XTDJjbfuJlNN2yiwFIQ9vVVtVXywUeIZKbVKZMiHWXkQc4I9edA+WBnHgzVrwS9RjdyKlX/ekn1Jf5+y6g1MsIyglW1q8gx5jBxyERW1a5SfU1VbRWTh05mw082sOmGTcweP1v6FNG/aLXwpx/D0LPhnq/gru3Kz/zR4evzoyVKbYecS6Xe/XUbpgajGm+0ijRuqaqtCjlPZ8efPfBscow5AJ32FbG0U4h4ifv/A++5R/m2VKPR8Otf/5qMjLZZU4/Hw0cffcTZZ58d72aknLYtk7s+gNBp08g06mhyuDlmc5A7oPcCq4QQ0Wkf1tZR1GFt/m+nIoTV2ZzWiO/T4Ggg35RPg6OBvY17mffuPN67YTO5JuW96+31kdvptAWOFUIkmfaBlxl5kDkYmo4oP0/WdRKG2Qj6dhO0mYOxddYftPZb/v5tdPZojrdEDry2Oq2sqFnBLWfdQk56Tnd+WyFST0sjHPwUXiqFvNEwsBC8LrjqsajD54PO1fE1MdRtZ2Idt3R2fPvxR74pv9O+QkJsRW+L+4TK559/DigrVKqrqzEY2gb2BoOBs846iwULFsS7GSnnQA+sUAElmLbJ4eaorYXvnSJhbkIku0ghsN0OZQwE0tkwp1sivk+OMYc6e13QY5kGc9C/I7bTIP2NEEkrPUsJtrz4Hhg5VZlEGZAP32yFzIGdhFtngavd7oFNRzCb8qLqt/z9W529jtz03IivsRgsfHHsCzL1cuug6Ac6BsaGq9E0XdTh822PqwTcxlC3nYl13NLZ8e3HH9H0FRJWLXpb3G/52bhxIxs3bqSsrIy1a9cG/r1x40bWr1/PkiVLGD16dLybkXIOBrZM7t6qkuzW236OWiWYVohU4Pa6KS0sVX3OH8rYtRO3BtI9PhoeH4m7uY7SwmLVQ4sLi/nw8IeB7U2V9y7G7Wn7EOXwOCgpLFF9fUlhCQ6P9DlCJC2PRwm2PPQ5PDUWfj9B+fntx21hmGom3gpuB0z4adtjzfW492yi9Hs/U31J+37L3781OBr46PBHFIfpg0oKSzjafJSLh13c9T5PiFQRdH0+Q/nZ8I16jdbtilyfHpV6Uatpf92GqcFYxhuxjlsiHV9SWBI0/vD3FeHGG90aFwnRQxJ209ny5csT9VYpz+v19UiGCkBO620+31lbOjlSCJEMTHoT5RPKAeXe4B5Js1cJpDOdOEB50ZzW9wneYaNsXBm3bLgFoPW9iykvmhu0c0+mIZM5ra+XXX6ESDU+JdjSHzQLbcGzAwYquUsdwzDbh1t3CL82vfck5TNfB00aKyPswtG+f/Pv8gOE7PJTXlTOO/vekSBa0feFC4huPADVfwmt0Vdmw5x3lH9Hu8tPmMB604lvKT9vLpDWrfFGrOOWiMcXlbNm55rAihSLwcJ3Td8xp2gOGjQ9Ny4SogdpfD6fLxFvdN1116k3QKMhPT2dM844g5KSEr73ve/FvS1Wq5WsrCwaGxuxWFSWxvWyo9YWLnj4HTQaePHmC9CldX0h0cuffMvrXxzk/006jQevLerBVgrRO5K9fnuK3WVHl6bD5rJh1ptxe91dT7H3OJVvvNpnJtxVDW/Mwz79t+gyh2BzWjEbLLib69Fo0tCactoe87RgCnMLT5OzCaPWiM1pw2ww4/A4ZHcfEVZ/qd+k17FPaC89G+75Gva9DyMuguY6yMgHnw+M7Wo7cIuCVbnNwOPGrtFE1W/5+7eT7pMY0gzo0/SBPqTF00K6Nr17fZ6IC6nfOFCrRf81+qmx6jU67HyY+QboDO3qzwWGAZHfK6Rmldf01Hgj1vOEOz7Wx4XobQlboWKxWHj99dfJzs7mvPPOw+fz8fnnn3PixAmmT5/Oyy+/zKOPPso777zDlClTEtWspPRt6+qUvAGGbk2mAOS13jJ0+ISsUBEilfgHCf6gNb1W3/WTdQyk8wdP1qzBVLMGxl5H7qjLYPdG9F+tUY65by+5A/I6fW//5Ik/gLZb7RRCJIZaSGXguRPQfAze/u+2oNqmI3DHx8ETKirh1/6PNp31W/7+LVubHXisYx8ifYnoF8IExkYMhz74qXKbkDEzYvh8iDCB9T013oj1POGOj/VxIXpbwiZUTjnlFEpKSnj22WdJa50k8Hq9zJ8/H7PZzEsvvcRtt93G/fffz5YtWxLVrKR0oKEZ6N4OP355rbf8HGqUCRUh+g21cLv2gXRNR5QB1aQ7aLr0PoyGTGUJ7dnFOH74FJmbH4f04FUmgW+GWr9Flm+GhEhhaiGVgeeylRUpTUeguV75Ey7sspW/f7C77Zh0psCS/I79RKR+RPoY0S+FCYxlQH7n4bMeZ9t13uNumzCJUay151+Z6q/zzlamSm2Lvi5hEyovvPACW7duDUymAKSlpXHnnXcyefJkHn74YebNm8fFF1+cqCYlrX11yoTKKRaV+yBjlNc6KfNdo73b5xJCpAB/uF37e6tnv6XcX+2/R7u5Hur34LjiN1RWPx+SXzDnit9g9PhAqxzu8Dio2FERlJUi9y4LkcL8IZXtcxv8LpgLezYq/YSfP+xS5Vtwh8fBa7te46qRV7Hy65VB/Un7fiJSP6JBI32M6J/UarG5HvZsgom3wObHQl8z8VY4thMqr44uQyWCWK/vDreDyprK0HFDmOw0GT+I/iBhEyput5va2lrGjBkT9HhtbS0ejweA9PR0NBpNopqUtL6pPwnA4KwemFBpXaHS0OzC7vRgMmi7fU4hRJIKF273l1khIXZNOcOprH6eJduXBA61Oq0s3r4YgLJxZWRixO6yU7GjIvC4/7hF2xcBMHv8bPmmSYhUEyakkom3woXz4E8/Vp5Lz1YmWC6cB4SOz/z9w9i8saz8emVIf+LvJ2aOm8mKmhWq/YgPH9NOnyZ9jOifwtVi4wG46F5Ao16jyy5vW73ScqLtuj/lrqhXqsR6fW9yNlFZUxl53NBupYqMH0R/Efdtk/1uuukmysvLefrpp9myZQtbt27l6aefpry8nJkzZwKwefNmxo0bl6gmJa1vjisrVAabuz+hkmHQYtQp/5kPyyoVIfo2rU4ZeHVUt1P5gDT5v+C+XXDfPoz6AayqXaV6mqraqsA3R7o0HVW1VarHraxdiS4tYfPyQoiepEtXPnzdtwvu2638PP0iOPRPKHsT7toO93wFQ85S+g9t6BcyujQdb+19i4lDJobtT1bWrsSoNfLW3rdUn6+qrWK4ZTg5xhzV10ofI/o8tVo85/+BXuXxyf+l1GPdztDzfLREGQdE+7YxXt+NWmNU44aunl+IVJWw/yc//fTTDB48mMcee4wjR44AMHjwYO6++27uv/9+AKZPn86MGTMS1aSk5V+hckoPrFDRaDTkZRo4dKKFw40tjBwou28I0We1WNu+scobDQML4Vgt1O/qEGJnwGqvx+q0qp7G6rRic9rINeXS5GwKHJdjzCHflE+dvY4GR4NynMsWCIgTQqSY9iGVJ+vgxR8q/87Iawuj9d/602JtC7NsZXPaMGqNHG853ml/4v+w5e9HTDoTA00D2d24m+Mtx8k35dPgaAh9rfQxoj8IExgb8nhLo3I9V9NyQrVOw7E5bTFd361Oa1Tjhq6eX4hUlbAJFa1WywMPPMADDzyA1aoUV8ct10477bRENSdpNTnc1DU5ARhs6Zl7C/MGGDl0ooVDJ2SFihB9TVDYW3oW3rkbcVlOwZgxEKuzEYshC0fTd2RuW6R8C+ZxQksTlnQLFoNFdXBkMVgwG8w02BvINGTywvQX8Pl8FA0s4njLcXLTc/nw8Icsq16GWa++nbIQIkl1DK32uAGNEnLpD8H0h9H6hQml9W+Vnpue22l/MjBjIHeecycXDr2Qk66TWAwWmt3NZOgycHgcjM4eza4Tu0JfK32M6A/U6lLt1p3OAqUjhEd3ZDaYKcovYm7RXCYOmah6fXd5XIEwWYuh83FD++PTdelcOeJKri64OuT8b+19i3RtetDxElYrUlXCbvlpz2KxyP71YfhXp1jSdWQYema+y5+jclh2+hGiT/GHvU1dPZVLV1/KL7f8Et/gsVT+ew1TV09l6urLmLp6Kiv2vInjit9A7d/h8dHweAEOVxMlhSWq5y0pLOGDQx9wyepLmLp6Kp989wlj88cy5x9zuGrNVUx7ZRpf1X/F0mlL8Xg9Cf6thRBd5g+tfnw0PH6G8nPrM+B1wbcfK3kpavyhtB1P53VzdcHVfHT4I4oLi1VfWlxYTH1LPU9e+iQ19TVc/pfLW/unqfzpqz/R5GrizX+/yW+n/JaLhwVvTFBaWIrbG/q+QvQp4erSrTJu94fYqglTp+F4vB6WTltKTX0N016ZFnJ9P2g7GBhfTF09lb3WvRHHDXsb9wYd//qu13lwyoOq539wyoO8vuv1oOOX71iOw+OIuv1CJIuErVA5cuQICxYs4J133uHo0aP4fL6g5/3BtP3dN/Wt+Sk9sMOPX16mTKgI0deohb3dc+49LKteFj4wbuSPyGz9Vitzw2+Zc5USYtc+fb+ksITiM4uZtW5W0Ot9+JhTNIf5G+djdVpZsn0JGjTcPK4sQb+xEKJbwoVWb34UfF44fQpMvE15/OPno9o9xKQ3UT6hnDX/WkPpmaUAQbt/FBcWU3JmCfut+9l6cGtI3+T/99i8sbxQ/QILJy3kylevlJ1ARP8RqS5BJWRWo4TS+rzBdRohPDocHz5W1KxQrUsNGs4ZfE5gNYrVaWXB5gVU/UDJROk4bigbV0bJWyVBxw/JHKI6JvGf/8y8M4OOl7Bakao0vo4zG3Fy1VVXsX//fubNm8eQIUNCdvO55pprEtEMAKxWK1lZWTQ2NibdSplFm3bz6LpaLjojnzsuO6NHzrmx9ihL39/D1O8NpHL2BT1yTiF6SzLXbyK5PC6mrp4aGIyMsIxgzY/WBD3WnsVgYdMNm9D/34VKpgrAebNpuuI3GA2ZgSW3Hxz6gCc/e5K9jXtDXr/h+g3MeHVGIOcgcE6tPr6/rOgzpH57kcepfPMd7laBe76CF6+Fi+bDyMuUW37Mp4DHBYYBEU/tv/XQ7rZj0pk4Zj9GtjGbbYe3UbmjkiXTljDtlWlh+6YN12/gp3/9Ka9f8zonXScZoB8gy/+TkNRvHHRWl/ftCt6u3OOEiqvg4rtb67QOMvJh97uw5Rm4ea3q9uZqOo4j2lO75gMU5RexbPoy9Gn6wLjB6XUy9x9zqa6rDhyXY8xh3U/WdVr3Hc8v4wqRihK2QmXLli28//77nH322Yl6y5QU2DK5B1eo5JuVb3cONHQxQ8XrhS9WwrZnoX435IyAc0ph4s+VBHIhRMK1D3sDGJU1ikZnYyeBcVZyBxa2Tah8tpzMz5bDffvIHZBLg72Bee/OC/v6BkdDUHBk4JymvJ795YQQPa+lUf1DG7TmptSBswleKm0LpS37GwzovL79Ex96rZ7j9uPMe2deIHxydPboTkNrGxwNGLXGoFBL+UAl+oXO6rJjyKw/lLZ9nXYSHh1Ox3FEe2rXfIDqumpaPC1k6DMCtWpz2oImUwDyTflR1X3H80tYrUhFCctQGT58eMhtPiLUnrqe2+HHb1DrhMr+4814vTH+N/B64W/z4c15ym4hXpfyYezt/4YXpoH1cI+1UwgRPX9AHCjfBHl8HrIMWUGPjc4eHdiKVAmMs4DtOxg0VhmIQWuInfLtc6YhM/D6jiwGCznGHOrsdUGPmcMcL4RIMv4wy44y8mD4ROVb7iZlF0aa68F6CNJjD4Q1G8wcaT4S+JBUZ68LhNaq8fctDo8Ds0ECaEU/074uM/JUrs+W8MebciF3lPIz3PERtB9HdOSvS5fXFTqW6BAUrXaeaOu+/Zgi3PmFSHYJW6HyzDPP8Itf/IIlS5YwYsSIRL1tytlzrAmAoT04oZKfaUSr0eB0ezlia2FIVgxLaLc8Cf98ETRpcO5MOG0KHNkB/6yE77bD8qugfANkDuyx9grRZ0Wb4g/YnU3otEZsTitmgwW3x4HJ0LbtudvrZt5Z8zgl85RAer7D4+COs+5gSOaQkET975q+w+FsgllvYrPXYzbl4d6zEdOJA0o7tAbcXjelhaWB+5jbKy4s5sPDHwZ9k1RaWILDo2Qz+dvp8TjxadLadh6S5H4hkoM/zNKfzZA/Bq74bxg5VemT0nTwoz/A279Vnv9ppfLz5LHW/sqLHTc6rZGTrpMYtIagZf8urwunx8kA/QCWX7mcBe8tYG/jXhocDYHQ2vZZCn4lhSU0tDRQWliKw+MIWZkStJOZ9Ceir/G44bJfQdapSi2erFNWmOzZCI0HA9fnoOOvegz7iMnoMk/B5mzEbMjC3XQY075t4PGAJ7qxRqRr/h1n3UFDSwMv/+fLQWOJI01HcHvdQXWqdh5/3ZcUlgRlvfmVFJaEjCkA5p01LxBM27HmpS8QySphEyo33ngjzc3NjBo1ioyMDPT64Avm8ePHE9WUpNXY7ApsmTw0u+c6CG2ahnyzgSNWB9/UN0c/oXL0a9jUOvCadDuMmaH83TIETimCfzwADXvhpWKYvRZkea4Q4flT/D9a0mnYo8PtoKKmkqp24Y6lhcWUF83FqFNWnJn0Jq4bcx3PVz/Pwq0LsTqt3DT2JuafMz/oMX9g3JyiOWw7+AEPfLAw+JznBZ+zfEI5ACtrV4YEzt2y4RaA1teWUF40h1d3vsJzX/4fVqeVovwilk5bwoodlUGBdRIuKUQSMGQo/Q3Av9+Bkpfho8Xw+u3BwZY3r1c+wH3wh7b+atx1OH68iIrqSrYe+oBnL3+Wih0VqgG0ZevKmDJ0ClU/qOKWDbdQXVfN89XPs3TaUjRogvoG/2vW7lnLT8b8JNAX+fl3MpP+RPRZhgw4twzefzK4FifeAhfdG3prvSEDx7hrqah+Xn2MgAbefyKqsYZJb6K8qBwfvqAau+OsO/jJmJ+wrHpZSPjsnKI5IXUabuzwXdN3zCmagwZN0OOlhaWUF5WzZueawDbMFoOFeWfN47ox14W8r//413a9xnNfPid9gUg6CQulXbFiRcTny8oSt1NEsoZq/XN/A9f93wfkDjDwXMm5PXruR976mu0HG3nsJxO44T+GR/eiP18P/94Ap14A3/81dAgSpvEgvHUPOE/CJffB9xd2qW1ur5uX//Uyr+x8BZfXxaQhk7jj7DvISc/p0vlE35as9RuRWoq/36X3B6X4251NVNRUsljlm9yfT7iV2eNmYTJkqu7y84fL/kBNfY3qt8C3TbiNM/POZP7G+R3O+fOQRP3At0AuG2a98i2QxudFqzUEVqI4PC28+e83eeST/43q/dXeR/Q/KVm/fY2zWdkh5IM/hO+TzvwRLJ4SeMh+9w4q9rzB4u1LItb5rRNuZWzeWOZvnM9tE25j9vjZtLhblOBKjxMfPvRpeupa6sgx5rDt8DZ+/8/fs7dxL7dNuI2ycWVktq7EU+vj/KQ/6R1Sv3EQw/gAohgjjLkR0+Mqm1qonKvJ2cQb/36DIZlDmDRkEg2OBnKMOTS0NPDav1+L+VquNnYIWlnSyeMOj4MVNStUaz6WMYwQiZawCZVkkqwXhL98+i33vbKd8cOyeOAHZ/bouV/Yspe3vz7CHZeN4r4rCzt/wcHP4PnvK7f6XLtEWZWiZt/7ykVAkwaz/g6nT46pXS6vizvfuZOth7YGPZ6bnkvljEoKsgpiOp/o+5K1fiOKIcW/s9R9f/p9x+MSnajflfeX5H6RkvXbF0Wz488zRUqWSt5oXLdvY+rqqWg12qj7GY/PE1LzLo+La9+4FqPWGAitbf/a9sdH2xeKxJH6jYMYd/mJqi7a7+YX5blyjDnkm/JxeV28/J8v98q1vCu7DklfIJJBwkJpAXbv3s3ChQspLi7m6NGjAKxbt46amppENiNp7T6mBNL2ZH6K32CLshzum/rm6F6w9ffKz5GXhZ9MARhxMYy6XPm2a82tykx7DJ745Am2HtqKUWuk9MxS7jznToYOGMrxluP8/O2fU2+vj+l8QiSlaFL8W9mc1k536lGOC07njyVRP+ScLlssv41qOzu+f8dQ3K6+jxAiDlqsYBnaFn4Z9Fzrjj+Zg5V/DyzE5mxEq9Fy7qBzaXA0RNXPqNW8zWljv20/u07sCslOUPo3W9CxEftC6U9EX9B+fNAxlLbD+ACiHCMMVPniVOVcVpVz5aXndTqWiFftRbvrUKLaI0S0EpahsnnzZq666iqmTJnCe++9x0MPPcSgQYPYvn07y5Yt45VXXklUU5LW7tZA2mE9mJ/i59+Gef/xKCY8mo5C7d+Vv4+9tvPjJ96qBNQ27octT8P3H4iqTR8f/piq2ioA5hbN5dzBym1Oo7JG8dBHD3Gw6SCPfvwoj136WFTnEyKpBAJorW2p/OG+gWqXym82WAL3FHfUflcdf6q+/7j2ifrhXtuTifod2+l//6L8IuYWzQ0JxV1WvUyS+4VIBs5mpc8pfqkt/PLt30LdTuX59GzIPAX8IdjuFtJ1Gay/fj0NLQ0MNA2Mqp/x9y0dgySf/f6zPPnZk+xt3Bvy2va7/HTs40KOlf5E9AXpWTDsfCXjZOTU4FDa958O2bUnqjHCsVqV9wndAchisKhes7Vp2oTUXse+IV2XTlF+UcgWzP73lV2BRLJK2AqVX/ziFzz44INs2LABg6Ftudlll13Gtm3bEtWMpOafUBkSxwmVqFaofFEFXreyA0BuFLfc6DPgP+Yof9/6ezi+N/LxgNfn5cnPngTgsuGXBSZTACxGC7effTsaNKzdt5atB7eGO40QyckfQPv4aHh8FOzeqATMqZl4q5LA73+px0FpYbHqoaWFxbhb0+/9qfp+DY4Gvjz2JSWFJaqvLSks4YtjX4R8K1xaWILb61Z9TSQd29ngaKD6WDVLpi2hpr6Gaa9M46o1VzHtlWl8Vf8VS6ctxeP1xPw+Qoge1L5v+v0EeGosHPpCCZbPH6Mcc8FcqNsFxatg9HQc1y1h+Y7lXPGXK7jy1SvZdnhb2H6m/W5g886ah9fnpWJHBVNXT+XS1ZcydfVUdtTtUL2lt6SwJLC7B4T2ce2VFpZ2qd8SIul4PHDTa3Doc6Ue29flTa8pz7fT6RjB0Rh6uw+EjDUAnB4nS6ctDblmf2P9JmyN91Tt+QOn2/cNy3csZ+m0paq3+4fbFUj6ApEMErZCpbq6mqqqqpDHBw4cSH293Nbh8njZ3zrZEY9bfgaZlVt+Gu0uGptdZGVEuNdw+2rl5+jp0b/BaZNhyNlw+AtY90soeSni4f/45h98Vf8V6dp0rjnjmpDnT7eczhWnX8GGbzbw+CePc+HQC0nTJPQONSG6Ri1g7h8LlQ8sAB8tjZi8bzJkUl40F4CVnezy0zFVX5emY+a4mfjwhey+UTaujLrmY0GJ+qXf+xnlY2di7EKSllo70zRpvFjzYlCQndVpZcn2JWjQcPP4m2N/IyFEz1Drm1pOwHuPK3+f9js4+ClMvA2WXwXjrsX+n09TUVvF4uqlgZc88ekTVM6oBFDdsWfeO/P4+YSf86PRP2JZ9bKggEmr0xr4973n3cu8d+eF3T0k3M4hsrOH6Ft8sO3ZtjqEtrrUpClBsu2Y0FFepHyJGTpGmIPR61UCaKPY5SdNk0bFjoqQa/aCzQuo+oHymS0eO2ypBU637xueuOQJZq+f3emuQNIXiGSRsFDaU089ldWrVzN58mTMZjNffvklI0eO5LXXXmPBggXs3r07Ec0AkjNUa+cRG9Offg+TXssLZeej6bijTg+4feVnNDS7WHP7ZM49LcwOOnW74NnzQaOFG/8MxhiW0Z34Ft6cBz4PlP0NCi4Oe2jpW6VsP7adH436EdeeoX5bUbOrmfveuw+7286Tlz7J9BExTPCIPisZ6zdIuIC5/DHKB5Yzvt96G5AFPC4wDFA9jd3ZhE5rDOyq4/Y4MPmX4Lc/rnXJbJOriUx9JmXryphTNCcosX/b4W28UP0CK2YsB68Hm/04ZlMu7t3vYnr/abh5bVBQXSz87Wxy2sg0mCVEUkSU9PXbl0UTfrl7ozIBXLcTckbimvexak0XZBVw73n3Mnno5MAOHU6vE5fXxQDdANxeN7o0Xaf9QZOziUxDJg6PI7C7T0fhdggRiSf1GwcxhtLiccKaW7FP/y26zCFtYwTbYUwbfgPXLVFWogRuOQ4/1ogUAluUX8Sy6cvQp+l7vPaiCdYFot4tSIjelrAVKiUlJdx///385S9/QaPR4PV62bp1KwsWLGDmzJmJakbSqv1OCVQ6NccUl8kUgGE5GTQ0N/Lvo03hJ1RqXld+Dj07tskUgOzhMGYG/Ovv8M5voXxD6FbLwI66HWw/th2dRsdlwy8Le7oMfQbTTp/Gm7vfZPH2xUw7fVrc/rcRoseEC6Ct2wmrboT79ij3R0PESQz/5EmuSQmnCzcR4R9M5GhzOG4/TnVdNfM3zg8k9rffScN28hi5r8wlt+UENB1B39y6OrDFBgNUwimj4G9njimX4/bOg+xytbldeh8hRDd1Fo5t+w7e/m+lr8rIg8HjVAMwc4w56DQ6fr3117xx7Rvkpis13b6P0mv10fUHptDXduTv4/x9h0zKij4lmtD6AfnBx9eswVSzBvJGkzuwEI7Vovff5vODx9uO72Ss0T4EtuOYobqumhZPCxn6jB6vvWgCp3PTc0PeV/oCkawSNqHy0EMPMWvWLIYNG4bP52Ps2LF4PB5KSkpYuHBhopqRtHa2TqiclpvRyZFdd2q2iR0HlQmVsGr/pvw8fUrX3mTCjbD7bTjwCfxrLRT+IOSQVbWrAPiPU/6DLGNWxNNNP306/9j3D3Y17OLDwx9y4dALu9YuIRKl0wDaroendQxw6/jtTPsQxwZHQ8jWgmZTLtT9S9kKFSB/DPYrH0aXbsFmr4+4EiYaEiIpRBIIBGI3Kv2Rxw2GjM77pow8WXUZ3AAAIABJREFUJYj2Z1XKDn9OW1AAZkFWAXefe3cgvDLPlEekRc7R9AcN9gYyDZnyTbPoPzrWZ6S6HHY+doMJncfVthJFb8I07jqoWaNkpbTPS1EJno3EbDD3SpC8jBVEX5OwUAq9Xs/KlSvZuXMnq1ev5s9//jO1tbX86U9/QqvVJqoZSatthUr8JlSG5SiDlZ1HwmwvdrIODn/ZevD5XXuTjFw480fK39/5H+gQQtnsambDNxsAIq5OCZxOn8GUYcrkTtXXoRk8QiQdj1u5X1mNSihctMIFuIWGOIYLkivGvWdj0GSK4+Z1VDT9q/WcU5Vz1lTicDtUz9EZCZEUopcFBWKfofzc+ozyeKS+6YK58O3HShDtoc/hqTPhiTG492yitLCYgqwCKmdUBoVXXvGXK0L6oKCmROgPSgpL+ODQB1yy+hLVvkyIPkmtPut2qddl/hgcM1+nYkdl6DX6x4vUcw5jHGN4vB7VUNp4B8nLWEH0NQlboeI3atQoRo0alei3TXr+SY7TcuP3Dc2prbsH7ToSZoXKnk2AD3JGKBMjXTX+J/Cvt+DY1/DVGzD+usBT7377Lna3nUGmQYzKju7/B5efdjnv7H+HzQc2863tW4abh3e9bULEmyFDCX+DqELhohEuwG3R9kUAzB4/G5PehMnnpXzsTPB5Wfmvl9qFz7aG1X22IvBNmP3Kh6n41yoWdwijW9T679njZsW8UkVCJIXoReFCZ/3/nnJ3+L7pwnnw3Xb4aHFQOKZp/a8ov3kd00ZMp+rrqpDwysXbF6NBE+iD2gvXH5QUllB8ZjGz1s0KnKdjXyZEnxOuPv8yC+a8o/y7XV3ab1hBxVcvqlz3W6/R1/4fpj+e360xhg8fK2pWJDxIXsYKoq+JayjtPffcE/WxTz31VLyaESLZQrVOOtyM+816AJbcdB6W9PjcE2hrcXHLnz4DoOa3VzLA2GE+7fU74Is/w7jr4PxudqJfVMGXVTC4CG57P5Cl8vO3f86Wg1sihtGqefLTJ6mpr+GWCbdw5zl3dq9tIqUlW/2GFVjW23kAbWeiCXDTa/VKWF3FVdgvuQfdyMuCw2erX4Uf/VHZNcB+HFfm4LgFyEpwnAgnZeo3FUUbbqnWN5EGWq3660+9ANfsvzF19fe71F907A8+OPQBT372JHsb98Z0HtH7pH67IVJ9DjsfZr6ubAjRfAwyBuLS6pi6+rLINYevW2OMqMcWcSJjBdFXxHWFyueffx7Vcf09aHRXa6ZJtkkft8kUAHO6HotJj9Xu4t9HmzhreHbbkz4f7H5X+fvQc7r/Zmf+EGpegyPVsHM9fG8GDS0NbDu0DYBJQybFdLqLhl1ETX0Nf939V+44+w7ZQlkkP0Pr7XtRBNB2JqoAN20u2Bvh4KeYVpVARh65mYODw2evegw2PwaWodjOvylsGJ3VacXmtAYCcWMlwXFC9IJowy3D9U0nj6m/3tmE7eRRtBoto7NHBwVdQ+eB0+37gwZ7A/Penad6nARXiz4tUn0e/BTsx2HNbcoxQ87GNuN3ka/7/mt0N8YYkUJpE1GPMlYQfUVcJ1Q2btwY82sOHDjA0KFDSUsL/4H54MGD3H///axduxa73c6YMWN44YUXOO+887rT3F7zr++UzuzUOAbS+p2abeIru4tdHSdUjv0LbIeUDnnwuO6/kdEMhVfDjleU5cNjrmTTt5vw+DycZj6NUwacEtPpzhl0DiadicMnD/Ppd59ywZALut9GIeIo2m2PoxFVgNvJOjBlQfHLsOHXyk4d/okUUL4B05tg2m/B3oDZkBU5jM7QjW8fw4ViCiHip9NA7Ag17WgK/3pDJunpOay/fj319vpAP/HMP59hb+PeQB9kd53E5HKAMTNszWcaMiWMUvQt0V7vOg2Fzm8Ljfe4MBuyIteKwYKrfVht+zGGwwY6I9hPgCkb3A7VnTt7K5RWiL4m6b7mHzt2LPv27Qv7fENDA1OmTEGv17N27Vq++uornnzySbKzs8O+JtltP9AIwIi8BEyohAum9a9OGTy+W9+kBxl7jXKug5/C3s28u195j3MHnRvzqQxaAxecokyivLH7jZ5pnxBx4nA7qKhRCZKLS9hra9js46OU5cSHPoPZayF/TNtB+WPgptfggz8oYZNPj8XTXM/SaUvChNEtweNxdqmtEUMxhRDx09VAbFcLbP097N6ohNO2lz8GR8lLLK9ZwRV/uSKon6icUUlBVgHFhcV8cOgDlldX4MAHFVeFrXkJoxR9SizXu4j1eQu0D42v34W76TtKC4tVDy8pLGFv4171MYbLAVv/oLTlidHKzw/+oNR5xyb1UiitEH1NwkNpO9NZpMujjz7K8OHDWb58eeCxESNGxLlV8fXlAWW2etTArn17HQv/tsxfH+4w4727NRCrJ2738TPlwOgrofavnHzvMT7QHgHg3MGxT6gATBk2hc0HNrPhmw08MPEBMvTyjbdIPnZnExU1lQkKey2m/HvFGCtmKAe2nFBu6QGY9jtYdaPyzddPK2Hbs0FheL4T37Ci7tOeDaPrNBTzLlmpIkTcaJRwWZ8XPn6+LazygrnK46jcXu2wKR++3ntMmXidvVZ5vPX14cKr/f3G45c8zsCMgcxaNyuQiTL7knuU2w4hpOYljFL0GbFe7yIF1l90D/yzLTSe9GxM+z6gvEiZ4FxZuyoo1LlsXBklb5UEVq+0jTE0zB42FdN7j3VoU+u/J/9X0EqV3gqlFaKviWsobVeYzWa+/PJLRo4cqfr82LFjufLKKzlw4ACbN29m2LBh3H777cydO1f1eACHw4HD0fbNsNVqZfjw4UkRqtXi8jD+N+txe3384WfnMNAc38HEv4/a+PUbNeRnGvh04TTlQa8H/vc0cDbBD/8IuQU994Ynj8GauWxI13PP4IEMyhjEIxc90qXcHJ/Px6+2/IojzUd4cMqDXHPGNT3XTpG0krl+1cQz5C0kwG3Pu5jWL1Ru72nPH0DpD6uD4DC8jDxcd+9g6ivTerad0YZiin4j1eo3pbWGUnPx3TDyMmiuU24j2P0ubHkGbl4bWn8dazZ/DFzxG+X19oZOw6vfveFdyteX8+WxLwOPbbp+A/qnxytjizA1L2GUqUHqN4KuXu/CBdaHedzutKHTpgdu7XF6ncz9x1yq66pDTh1Uf+1v+Q3Tpt4OpRWir0i6W346s2fPHhYtWsTo0aNZv349t912G//1X//Fiy++GPY1jzzyCFlZWYE/w4cnz7a7Xx+24vb6sKTryM+M/weNU3My0AB1TU6O2lqX/x39WplM0Zsg+7SefcMBA2HkZbyfoQyUzh54dpdDiDUaDZOHTgbgzd1v9lgTRXJL5vpVY3NaOw2S6yqT3oReqyc3PRd9SyOmlTeETqZAcACl1hAahpc5GJu9PiiMbnT2aHKMOW3tdNlCz9uZaEIxRb+SavWb0lqUUGpeKoVniqDqZ8rPl/+f8rha/dlPBNds3c6216/9Raf9WZ29jmZXM6D0I4MzBtPUcgIyB0es+aC+TKuXyZQkJfUbQVevd4YM5brsvz77d+YJ87jJYFZqxZSHXqvH4W5RnUyB1mu3/bhSf6ptagx6qGMobY+MA4Toh1JuQsXr9XLuuefy8MMPc84553Drrbcyd+5cFi1aFPY1v/zlL2lsbAz8+fbbbxPY4si+/Lbtdp9E7HaUrtcyJEvZo/7rw60d5YGPlZ/5YyBN2+Pv6TvzGraalPc8J2NYt87ln1D55LtPONp8tNttE8kvmetXjdlgwRIm0NUfJNcj0v8/e2ceH1V1N+5n1sxkmeyEVZIQJMqmyBpFgohF39rX4vIqtAIFQbEuxWprfX+t9i1t1brhBrggWq1FtPq+VVFEdgRFkR1MSNi37JNJZp/5/XFzJzOZJZNktsB5Pp98hpy7nXDv99w733vOcwzSG6eAy9oIKGUZnozpDGn6bIbmDGXRxEWsunEVi66SPp+b+BxDc4b6y+hszdIbuaYq6dPWHOC46eHXSXBe0N3it1vjHX/NNXB2X+tbajn+mqp941efEThmm2vgzN5227PMpExSNCmeduT5Sc+TmpyLefIfJRG2iPlujYjfEMTpftfuM4Y+C0xngtQpvc2+0jr2HCAQCAKScAmV9pIKvXr14uKLL/Ypu+iiizh69GjQbZKSkjAYDD4/iYIspC2MgT9F5oIW+e2+ky3Z82PfSJ+5xVE53g9aNWfVavQuF1ce3dmlfWXrsynKKMKNm9VHVkeohoJEJpHjNxAOpzWoSG568W04nJ0T0/rREQFl23Wba3Ae/yaElLaNjC5c8V5npZiCc5buFr/dmvakl+Vftsqr5fh1WKVlgRh2Ew6nhenF0wIuvq34NnZV7eK5q57ztCNT3p8iCTIbD2C9/UNwCqlld0bEbwjidL8L/YwxDUddpf9wH5DivI0YX0hpBYLIkHAJlfaULpdffjkHDx70Kfvhhx/o379/NKsVNb5vEdIW9UiJ2TH7Z0nH8ohpj0c3obKpbh8AoyxW+hz8DJXV1KX9jeo5CoBVlau6XDeBINLotanMHnoHdw2b53mLZNAauGvYPGYPvaPTUyf7IQvuJvym9S2ZLkP6ffyCwDI8r3XdChXL977Jkl1LfMR2S3YtYfne5bjdLQ+DtmbY+LQk2pO7N8vivY1P+/ZU6UidBAJBZAkafw/B6HnSdOrgG78KDVzxgLRO222ueAA9WmZffDt3DZ3r057dOexOZg6eiUqh4p397/i1Iy/vXspr+97EHP2OtwJBfIjT/U6PmtlD5wR5xpiDPrMoaDy3nTrZ7XZ4pLQhnwMEAkFIEk5Ke+zYMXr37o1KFXjoyTfffENJSQmPPfYYt9xyC19//TV33HEHS5cuZfr0wFPxtcVoNJKenh53qZbRYmfYo58DsOTnl2HQxUb8tONoHU98dpCBPVJZfedQeKJFQnvrPwLOU99VZu18hu0NZfza5GBG1UmOjZ3L6Utu6fT+6ix1/Hr9r6VeKjetpmdKzwjWVpDoJEr8tofZZkKtSvKI5BxOa+SSKd4EE9yFXLcRu84QnoyuM+K9jtRJcF7RXeK3W9M2/sq/lJIpweTVKq002486SXIs6NKlN9lJaR7RrfnKBagLJ9JoriVNn4Xj2DYU6mRUfUYIqeV5hIjfAMT6fue0wQfzMF/zGOrUXq3PGI2n0K/+A0xdIsVvoHhug5DSCgSRIarTJk+dOjXsdT/44AOAdoVXo0aN4l//+hcPP/wwf/zjHykoKODZZ58NO5mSSOxuGe6Tm5oUs2QKQP9sqaE/VGXCduRrtACGvlFJppgcZr43HgLgwl6joOojeuz5F2eGTsWt6tzll6nLZGDmQH6o+4HPDn/GjMEzIlllgSAiyMmTLH02QPQeSuS3YCk50meoWXQ862b7SGnbIstzs/TZ4Yn35GN3pk4CgSCyeMdfUw18NF+SVCZn+w4F8I5f+f6fkit9yjHbIrrV/2MaJGeTlZoHpjNommugx8XUzvp36HbE3kiWKitKf6hAkADE+n5naYC9H6Df+wFkDyQrtxiqDqCpKZOWX/dka13axnMbwpHoy88wAoEgOFFNqKSnp7e/Uif48Y9/zI9//OOo7DuW7GwZ7jMghsN9ADKTNaTp1DRaHNQd3EQeQO6gqBxra/0BHG4XedpMNBdcjv3glySZqsis3Eht0cRO73dUz1H8UPcDnx/+XCRUBOcPnjdhLW+dnC5MOEhSJWG0GTFoDdicVlQKNSqlikZbI2nawFOSymK7YG+mPPJcWbwXrIdKgkknzTYHKqWSRoudNJ0Gh8tFsjaqtzpBHBDnOQBy+2Brlr7kmRtAnw737ZQSJ/oMqFgLXzwm9VYJJ36947+5xjchY7e0344IqaWgHbp9LPvdlx2hh/vIvcHM9VJMBuk9EhTvmKwpk348yzp2Tw7nOcDutHueL6xOK6nR6GnbBs+06iGeYQSCRCKqLdayZcuiuftuj/cMP7FEoVDQPyuZPSeNuI+1zPATLX9KreRPGZrWH7dKzZmCy+l7YBU9d75H7YBS6OTMRiPzRvLO/nfYVb2LE6YT9Ent2uxBAkHCI4thty2RHqQGT8X605d5Y/cb/OPAPzwPPNOKpzFj8Ax+seoX7K7ejUFrYHrxdGYPm02SKql1dy1iu5d3LfE7lCzPlYb8tIj31j/uXydZvJcgPVCsdieL11ewbEslRrMDg17NrJIC5pcOIEkT+RnMBPFBnOcAOCyw4y0YchNsexm+fkVqJ3QZMHquFKvLfwIDJsKsT2HZtTD4p+3Hb6j4H3YzDqeNacXTWLxrsd/iacXTcLgcYsiAICjdPpbb3pd1GVK8jF8Aap3/+nYLbF4EXy/1Wn+u5DfRBFg/EBG8J4d6DphWPI2Khgp+8dkvfJ4v5gydQ5I6KcDeIoPVaeX1Pa/zzoF3PMcN9AwjECQS3SgFfO4hz/AT64QKwAXZKew7WU9m3S6pIAoJFbfbzabavQAMNeQDUJVfQu8f1pBS9QOpp3Zj6j2sU/tOT0pnUNYgDtQe4PPDnzNryKxIVVsgSDxszdJDm9cDlOmaP/LG7ldZ4vUgZLQZWbxrMW7czBk6h/vW3icJIndJ08rPGjLL85ZHlucCvO2VkJlefBuzh97R+sAki/cg/IfGOGC2OVi8voLn1rS+rTOaHZ7f500o7F5vPQUBEec5AHL70Gu4lEzZ8GTrMks9bHgCcMP4X8G7LcOjb1oGOUXtx2878e9yObl98O24cfskdm8rvo0Zg2fgcrui8icLuj/dPpYD3Jc9wmeAy+/37alibZSSKRueaLN+y+8l94bXUyWC9+RgzwHyi5lpn0zzkdXKidMZg2dEpaeK2W7m9T2v+yRogz3DCASJREyltCtXrmTFihUcPXoUm83ms+y7776LVTUSQqp11mhh9J/XoFTAazNGoYtxJn5jWRVr1q/ls6TfgkYPt74LysjWoazpJFO//RMahYrnB9+FVindGPt/v4IeR7ZSl3855VMe6/T+1x1bx5v73mRw9mDe/fG7kaq2IMFJhPiNOW3FsNkDsc//KqRMbvVNq5ny/hTqrHWeskCCubDluQkumrU5XIxcuBqj2X9WAoNezfZHJqNVJ9zEducdXY1fcZ4D4LTBC2Pgrk3w9MXBh+ct2AfPDgWXM7BMOhRB4t/utDNj1QzmDJ3D2F5jqbPWkZmUyVenvuK13a+xfMpy0UPlHCKS999uH8sdFbZ3RvAeigjek9s+B9hcNu74/A52V+/2WzeaslohyRV0V2LWUi1atIhZs2bRo0cPduzYwejRo8nOzqaiooJrr702VtVIGHa29E7pk6GPeTIF4IKsZEYopbcA7uwLI55MATy9U4pT+3mSKQBnBkwAIOPwFpLqj3d6/yPyRqBUKNlbs5djxmNdq6xAkMhYjL4PYbnFGG0NIWVyddY6cvQ5PmWN9ka/dfXaVDQqDVn6bDQqTfCZiLTJ0sNeSo70mUDJFIBGiz3ggzlIbz0bLfYY10gQDcR5DoClQRou0FQd+MtacjYYeoPVCENvkUSVlsBtR1CCxL/RZmR39W7uW3sfU96fwj1r7mHK+1O4f+397K7eTaPNv80RCOAciGVvYXtyNvS4WPqEVuGzN+b6dtZv6NjxI3hPbvscYHFYAiZTQJbVRieuG22N7UquBYJEJGYJlZdeeomlS5fywgsvoNVqeeihh1i9ejX33nsvDQ0dbETOAXa1CGkL4zDcB6REzmXKcgCM6dER0m6uk/wpQ9L6+5Rb0vKoz7sYBW7ydn/Q6f0btAYuyroIgM+OfNb5igoECYjZbsbutFNrrsWuS8c8/T3IuVBaWHUAgzYdgzbwG0KD1kBmUibV5mqfsjRNyzSoTVXSp60ZbGb/sgTEbHNgc7ioMVmxOVw023wfxNN0Ggz6wN3DDXo1aV2YSa29YwtiRzjnOdj56rbn0dYcOkZ16ZKbISVHetMtk3Mh3PoO/Gov3P6R9OWt9Lcwf0vgqc472A6Y7WYMLVJLgDprHWX1ZT694tK0QkorCEyoWB7eLx2dRpXYcaxLhz4jpRi7fzfc9q70eevbUnlbOaw+I/j6Y+8GlU6KPdNZ6bOjSc8I4h3XgZaladNan0+cdsx2c0SOm6ZNC31cIbkWJCgxS6gcPXqUkpISAPR6PY2NUpbx5z//Of/4xz9iVY2E4fs4CWll1Colo9RSQqVcPTDi+29yWPi2Qdr/sLR8v+WnW3qp5Bz8DFUXbhqjeo4C4LPDIqEiOHeQpWylK0qZsGICpStKWWb6AesvVklfkmrKsJpOM634toDb31Z8G1tPbfV8sQGYXjwNR91hqcvxk0XS5+ZnwWWH16/1LXNYYvSXhocsLhy5cDWX/ekLRi5czZL1FVjtTs86TpeLWSUFAbefVVKAw9U5l0M4xxbEjlDn+YHJg3C53X7n673tx7F01/MoSy/bxq13jDodMOxmqFgHoyUfAjkXSvLZk9/DU8Xwt4HStl+9AJZG2PmOlIQJ9xhtkNuoKnMV04qnBVxnWvE0rE5rhP4jBOcawWJ5QG4qf589hqUb/OPVYneyYvvxxIhjpxN+/i84uUMaavfcMOnz5PdSubNNnZz2wOtbGmHS72HLIin2PLH6fGuMxhir0xoyrisbKn2fT/Ysi0isO1wOphdPD7hsevF0HK5ukgQXnHfEzPbUs2dPampq6N+/P/3792fr1q0MHz6cyspKYqhxSQjcbreXkDY+3ebV1nr6u08A8LWjiMsivP9t9QdxuJ300KaTl5Tpt7wxp4im9D6kNJygx75/c2pE4Ia7PUb0GMFb+97iQO0BDjccJj89v4s1FwjiS3ApmySfnfWjhejfvpnUz3/PnJ++DCh8bPiyTG7u6rmA9FZn+qBbmX3x7SSd+La1y7Esz3O7WmWVoYR6cSJccaFeq2Z+6QCAiM0Y0e2liecgoc7zjZf14eV1h/zOV+8MHS+tK2fRmnKf8oQ/j+FKL2VJ5bfLYcxd0rK+o2Db4gCC2pbfe18Cm56CcfdISZZwxZr4tlF7a/bydOnTAH7tULRnAxF0b4LF8ovTLuXVjZUB2103bob0TvcMFYpvHLul2AkUYwqlFDs+KAKv3/9y2PhUcFntuHs6NBVyJEjVpjJn6BzAP64DyWojJY3Va/TMHjYbgLcPvC1m+RF0G2ImpZ0zZw79+vXjD3/4A4sXL2bBggVcfvnlbN++nalTp/Laa6/FohpA/KWWh6ubKP3bOjQqBa/PGIVaFXvpVsaJtVz05WwOuXrxeO/nWPqjyH5x+mPZO7x3ahOTsoczvc/EgOtkH9tO4XfvYEvOYtf0v+Pu5NSrT3/7NHuq93DPpfcwd9jcrlRb0A2Id/xGm7CkbKbToM+CqjJM2fkkaVJotDWSpk3D2lyFuv44qp5DaDTXkqbPwnHoS/Qbn4EZ/ytJKZtrWnfqLauUyzsjyIsSHRUXNtscqJVKGi120nQaHC5Xpx+yu700MQGJVPwGOs9qpdLvfGWlaNn0m4mM/cua7nceOyqxlCWVNjNo9aG3XbAPXr4CfrkNXpsCJ78N7xj4t1Hj+4znkTGP0CO5h+cLkNVpjcosIIL4Eo37b9tYBkK2u1sfnsQVj6+ltsnmUx7zOI6ElDZ7oDQEL5Ky2ghisplIUiV5ni9iJas1282olWoa7Y2kadJwuBxidh9BQhOzVO7SpUtxtXS5vvPOO8nKymLTpk1cf/313HnnnbGqRkKws8Wf0j87JS7JFIC0qu8B2OEeyP6ayHaVdLvdbK6V/Sn5Qder7XMJfff9G21zLVnl66gZdE2njjeq5yj2VO9h1eFVIqEi6Pa0K2VrPkvWpw/D0S3QXEMqwINHyErJAlM1mqckrxDZA8nKLYaqA2hqWt70NVdDap5vQsVS718uC/VScog34YgLs1Nb31rJyRO5TNuFka0dPbYgdgQ6zzUmq9/5yk1NosZk657n0Vt66bcsQIzKPUn0WsmFEmrb5mpJZGtpkIS1J7+VHCupeWA6I7UFQdoBk81EXnIeKoWKOmsdG09sZMoHU8g35DMgfQCPljxKhrfLRSAIQdtYDhTHMkazg9omG7mpST4JlbjEcUfjM9D6ucW+stqA+2mQRNJxQE6KZumzAOn5RE6mZCZlkqPPodpcTZ21ziONzVJldfm4cvJE3peY2UeQ6MQsoaJUKlEqWx9sb7nlFm655ZZYHT6h2HlMHu4Tv7c3qVXSNNXfuQZyrNGN0erGkKSIyL4rzac5aa1FrVBRnNo36HpupZozhePpt+9jeu58j5oLJ4Oi43UY0WMEbyrepKyujEP1hxiQMaAr1RcI4oosZQvWQyVNl+lJppBzIVzzJ9AlSzN86A3QZyTmKx9AXVhKo7mGNH02joq16Dc8Dck50pclb3QZ/uW6jJh3MQ6GLC4M9rayK7LZRD62oOMEOl9VJivZqdrueR516VIsBntz3TZGfaZRbWfb5BzJz6BLB6VaEmUWlkrtSEoOVKyFjc/4HcNsN5OqTeX5Sc+TmZTJ1lNbefa7Z6lsqOSw8TC1llpSNIk1A5ige9Feu5uVoqXKZPUrj3kchxOfTpuUENGlB16/6oAkqw25n/TAx/fEe8v+nY6oD9NN06YxNGcodwy9gzG9xlBrqSVLl8XWU1t5dferQhorOG+JafeIuro6/va3vzF79mzmzJnDU089RW1tbSyrkBDIM/zEy5+Cy0lqzU4ADmuKADhQG7leKhtbeqcMSulDkjL0Da6q/zicKi3JtZUYTnzXqeOlaFIYkjMEgFWHV3VqHwJBoiBJ2QI7haYPuhVHxdrWZMqsT+H49haZ5AA49jXW2z/k9aZDlK6czIR/XUfpysksa6rAevuHcPwb394pIEks5X3KjJknPZwlANGSzSb6sQUdJ9D5qm2ysbm8mpkl+QG3Sejz6HRIsRiI0XdA1Q+t4lgfsewAOLot9LYVa2HYTVJS5SeLwhKufPcTAAAgAElEQVRresuyp7w/hckrJ7OvZh9vTHmDgnTp/12IIwVdJVS7O7Mkn01l1T69UyBOcRwqPsfMk+LTW/RcXea/fk0ZGE/DmCC9q8fMDSym7YRIOhI4XU6WTl7K3pq9TF45mWs/uNbTDiydvBSnK8El3wJBlIhZQmX9+vUUFBSwaNEi6urqqK2tZdGiRRQUFLB+/fpYVSPuOJwu9pyMbw+V5IYy1HYTTpUOp6EfAPtrIncj2ly7F4ChIYb7yDi1yVRfMBqAvJ0rO33MMT3HAPBp5afnneRYcG6hd8Psi2/nrqFzPdMHGrQG7hp2J7MHz5R6moDUM2XbEklk1/Jmy+x28Oq+N1m8e6mvMG73Ul7b9yZmQ+/WaVV1GTDhN1Byr/QmWi678kEY90sgMeJIFhfeN2mgZ4pNg17NfZMGMr90QFQlhPE8tqDjBDtfJ+stzC8t6n7nUZbNTviNb9xe+SCMuRNWzoKNT4O1Ufpc/3jrW263S4rjgNveBQ3H4YoFUu8UWZTpLaze8KRU3tIOmO1mXt31Kot3LfZpW5bsWsI7+9/hgcse4K5hdzF72GzhOhB0idDtbhGnGiyJEcfB4nPCb6TYWznLN6bemxk4Jo9+BVc8ABMearOfh1pitI1Q3dbsH++ySHrj02FNed5Z3LhZvnc5S3Yt8WsHlu9djjtBnhsEglgTMyntkCFDKCkp4eWXX0alkhoHp9PJ/Pnz2bx5M3v27IlFNYD4Si33nTRy3aKN6DUqXp0xEmUnhrh0lR4//IMB2x7BlDWY/0l5hH+WwX8Va3h8QtcfgpqdVq7Y8iB2t4OFF95OL137YymTmmoY+sWfUeBm9y2vYsnK7/BxLQ4L96+9H5vLxj9//E8uzr64E7UXdAfOdSktThu8fi3mKxegLpzYKpY9tg29Qg0XjJGmWdSl+YrskrOx/2oPpSsnhxDafonG6YDmWkjLgyNbAZc0I0hztTQM4NCXsOlZ+MWnCSGllYmkbLY7HftcIxbxG+x8ddvzaDVJw2G9Y3TNH6H6B8gslMSybdoC7t8N7/4c/mu5lDSxmkCfLn3Z0iaD8SR88RhMXQIvjIG6Cv/jegkxw5FlC3HkuU8s77/dJo59htoZwGGDN/8TTmz3X7fPSLj9I1Brfddf+2ep94qhF5gbpFj1jtH25LYyUZbYhiXNF74TwXlIzFqgQ4cO8f7773uSKQAqlYoFCxbw5ptvxqoaccd7uE88kikAadXS0Bpz+kAKW4ZbRkpM+039D9jdDnI0BnoGmC45ENaUbOp6DSHr1G567lrJ4dJfd/i4OrWOYbnD2H5mO6sqV4mEiqD7YmmAE9vR/2NaYLHsg5WQku0vnUzNo9FcE1po21RF1so7oNdwGP8AvHm9tLCtjFKuR5xEeIGIpGy2Ox1b0HGCna9uex4dZnhxjH+MJmdD3mAwGyWxrFIlLUvNk5Km2mTp85WJUpkmBdJ6St4GuT257gmpTKPz3Tf4iDXblWXbG8kK4wWKQBAu3SaOZW+JLKBtuYcD/vfWE9ulYTlJqa3rm+th64vST/ZASVTbNka978UdleFGkLDagQhIaQWC7kbMEiojRoxg//79DBo0yKd8//79XHLJJbGqRtyRZ/gpjKuQVprhpzl9IAUtL5MO1rpwuNyolV1L8mxsGe4zJK0/ig4kjE4XTSTr1G6yf/iCk5f9HFtaXoePPabXGLaf2c6nhz/l/svuR6noJg/LgvMHP4mcC9x2UCdJD1X6DFAlwdi7If9yX0nk8W9AoQJdqlTWVnBnOkOaPju00FafBdUHpWTMNX9s3b65xn8q5WAivE5itjlQJdJbxQStk6BjtD2HdqcLu8NFcpL63DmfunRwOeGs5Ccj50K4+lEonAhWoySjnvlvUOvh+NegUEs90Kb8Rfr03tZnvy1x/rOVvjLaLx6Ter94iW/blWULGaWgk5gsdrRqFUazHYNeg83hJDVRRdGBaHtfV+th8FQYepO/6Hn3+6DW+cpqvaW0NWWtiRQIfC/uqKw6goh2QCAITMyeNO69917uu+8+ysvLGTt2LABbt27lxRdf5K9//Su7du3yrDts2LBYVSvmxHuGH7W1nmTjIQDM6QPopQGdCixOqGxwMTBT1c4eguN2u9lUF74/xZumrHyMOUUYqsvp+f0Kjo6/p8PHH5YzDJ1Kx+mm0+ys2smlPS7t8D4EgqghS+S2LZEehAZPhRtegk2L4OulUpkuAyY+DJN+Dxufgg/nS+V9RkqCyK9egH/+TCqbtkIS1q1/Qtp/cw2OinVMH3QrL+9e6nf46cW34ahYi0ZOnsgiPHl7b8bMBYc1Yt2GrXYni9dXsGxLJUazA4NezaySAuaXDiBJ0/k251yrk6BjBDqHM8blM7Mkn5uXbKH0wh7nxvmU5ZfrH2+VUW9b3No+6DIk0ezYX0Kfy2DL8/DP6YHbCW9kceYb/+G7n1mfwrJrYfBPpWOrtC2y7Om8vOtlv93IIlrR1V/QUSx2J0s3VvDGlsOeGJ5Zks/80iJ03SFu297XdRlSXN3wEnz7hiR4lstLf9tyz3/Gd/1Zn7TGd1vGzPO/F3u3B4HWb4nZqPy5oh0QCAISs4TKbbfdBsBDDz0UcJlCocDtdqNQKHA6z01LtNnm5OCZRiB+M/ykVku9U6zJPXFqDSiBfAMcqIN9NV1LqBwxn+WEpQaVQslFqf06vP3JCydjqC4n98AnnBoxDXtKdoe216g0jMgbwZaTW/ik4hORUBEkDrZm6aHL+wHo6sckgdwGry86lnpI7yclU7zLxy+ALYskUaTM5/8tffEB2CYlZPQbnmL27R+CQsnbB97BaDNi0BqYXjyd2UNnk/TtG61vtr74g/Rw57W99DA4VxLkaXQR+dPNNgeL11fw3JrWt25Gs8Pz+7wJhTHvRZCIdRJ0jGDn8PkvywG4u7SIuW99C5wD51OWX4KUMNm22LctkCWyF98A+z/yTZ4EaCc8X/rG/RJeneQvowW4aRnkFElv0wG9Rs/sYbMBePvA275ty7DZJKmSovk/IDgHMVnsLN1YwaI15Z4yo9nh+X3u+MLE7qkS6L4uy2FxS0NrvWMr44LA9/z3ZsKcNdLv3omW0XdIMdq2t7V3e9A2kTN+gSdmo4FoBwSCwMRMSnvkyJGw1+3fv38UaxI/qeW3R2q58eWvyNBreGn6iA4NiYkU/b5/hr67n6e+13hODLkLgBd3wSdHYN5wLQ+P7XxD/PcTX/L4oZVclNqPBwtv7PgO3G6KN71AWm0lp4fdyLGSuzq8i91Vu3nmu2fISMrgy5u/FJnyc5BuKaVtK5HLHgjzt/iL5WSZpPxWK1iZTM6FMPl/oOiqVsGd04FZoUCtVNNobyRNk9Yqi2wrz3M6we2QhhzJ3Y8dVkiKXLddm8PFyIWrMZr9p1I16NVsf2QyWnVsh+clYp3OFyIVv+2dw60PT+KKx9ficLnOnfMpx28gIWXY7YQc5yHEmSHElma7OXDbIjgviOT9t9u3w+3JYRfsg2eHSj1CQ8UntMhqP5SG9TZXhSeI97uf20Ebm5e1oh0QCHyJ2SubaCdJugPfy8N9eqTGJZkCkFolCWmb0wd6ygpb7oldFdNuqpXGaHd0uI8HhYKTgyYz6Kul5O77N6cuvRWHPjyxrczgnMGkJ6VTb61nw/ENTOo/qXN1EQgiSVuJXG6x5Exp+2CVmieNt24jm/Urk6n+Af7xX/BgRauETqVFfqyR5XCexGJbeZ53hzRZehfhrsKNFnvAB2aQ3kY2WuweuWCsSMQ6CTpGe+ewtslGbmoSB880njvnU5vsL6OWCaudONQa597izLaEEFvKX5r82haBoIMYzaFj2Gixk5PIcdueHLa5ukUQXRM6PkGKRXMtfHCntE44gni/+3nsZuUT7YBA4EtMU79vvfUWl19+Ob179/b0WHn22Wf56KOPYlmNuCHP8FOYE5/hPricpNbsBKA5ozWhUtDiu9pf6+r0ri1OG9sbpK7XnU6oAMbcQZgy+qFyWOm56/0Ob69UKCnpVQLAh4c+7HQ9BIKIIkvkZKoOtIrovDGdkR6OvMsDlfnsO0OaQrmz2JqlN21NVdKnrbnz+2rBZLFjc7ioMVlJ02kw6APn7g16NWltunSbbQ7PtjaHi2Zb4Adu7+NUN0rrmiz2sOrX0ToJEo/2zmFWipYqk9XnfHb2ekko2rYlMmG1E149CoLtJ9C6AkEUMOhDx7AhSDvc2CaOG+MVx+3FUHKOFJcQXnwm50jS+LP7WpMpoQTxUbh3CwSCzhGzhMrLL7/MggULuO6666ivr/d4UjIyMnj22WdjVY24svOYlFAp6hEfIW1yQxlquwmnKglrSl9PeX4aKICqZjdVzZ1LqmyrP4jVZSdLk0bvpC5MmaZQcHLQNQD02PMRanNDh3dR0kdKqGw6vokac007awsEMUCWyMnUlLVKYb1proGKdb7lctnoOwLvW5bQdQZZqPfkQHiySPrc/KxU3klkyeDIhau57E9fsKm8ipkl+QHXnVVSgMPV2ubIklF525ELV7NkfQVWu3/vOe/jjFworbt0YwWWAOu2xelyMaukIKw6CRKTUOdwxrh8NpVVU9tkY8a4fA5VmbDYnaz87ninrpeEom1bItNcA7WVgZdBq2i6vf2A1NZU/dCldkAgaA+bwxn03jCzJB+bI3C7/0qbdv+VeMVxqBgaM1ea1UdOjAS6t4da37vcO25lonDvFggEnSdmCZXnn3+eV155hUceeQSVqrWf+ciRI9m9e3esqhE3GprtHK6RsseFOfFJqKRVSYI+c3oRKFvPgU4NvVs6zeyv6dwXibU10ixNlxgKuzycqSHvYprS+6Kym+m5c0WHt++T2ocCQwEOt4NPKj/pUl0EgoggS+Qm/Kb1DdUXf4ArFsCEh1rLdBnQcEySwnqvu/FpKLnXt0yXIf0+fkFr19+OYGuW9rv+cV9x3vrHpfJOvO0yWey8tK6cRWvKPV25F358gBnj8rl3UpHnbaRBr+a+SQOZXzrAIws12xy8tO4Qz60p82wri2JfWnfIp6dKoOPIMsOX1pW32/NAr1Uzv3QA900aGLJOgsRFAcwZX8A9V/leV/dcVcTMknxeXFfu+fcv39nBS+vK6Z2u79T1klAEakt0GXDlg2DoCyX3BGgnHpLaFG83Uqj9jLkTVs7qdDsgEISDUgnzS4v87g33TipifmkRyjbfUBrbafdj3lMlWAxN+I0Ubw0n2tzbj/vf233WP95+3EJU7t0CgaBrxExKq9frOXDgAP379yctLY2dO3dSWFhIWVkZw4YNw2w2x6IaQHyklhvLqvj5a1/T06Djmf+6JCbHbEvRpgXkVn7I2cKpVA24yWfZX7bDplPw8Jgk5l3SsTGrLreLSdt+R7XNyIKCnzIkreu+nPTTe7lw22s41Tp2TX+rwy6VL49+yd/3/51BmYNY+ZOVXa6PIHHollJamY5IYf3WbUkoREpC155QL4iUMuSfF0QyOCA3ld9dV8z4gbk0Wuyk6TQ4XC6fxEVHBIWRkhk22xyolcqgdRJEnkhKaW9esoW7S4u4YmAO9c2Sb8HmcGKyOjDoNWwsq+aJVQc5VGXyEdXWNtk8++kW8stAtG0fbM3SFzynA9zO8EXTVhMoFJLvQRZhrvmj5F3pZDsgOHeJtJT2Vyt28NspF5Fn0GG02DHoNJwxWvjrqv08c8ulPnGZsBLbYHLYjpZbG8OL2yjcuwUCQdeI2ZNjQUEB33//vZ+c9tNPP+Xiiy+OVTXixq7j0tCVgjhNlwyQdlYS0DVnDPJbVmCQEir7OiGm3dN4hGqbEZ1SS7HXUKKu0JB3MaaMfqTWH6Pn9ys4Pi5It8ogjO45mncPvMvBuoMcqD1AcVZxROolEHSJjkhhQwnnIiGha0+oF0RKGYpgksFDVSZmL9/Ot/99tUcOqm3TQbIjothIyQzl5EmwOgkSl0aLnZ3HGpj71rdkpWgZ1T+T319/Mde/sJnc1CSqTFafxIm3qLZtecLLLwPRtn3Qt7QFPu1EGKJphxleHCNJM71FmNDpdkAgCAej2c7Hu07z8a7TFOamMDA3lbIqExVVTQA89hPfuExYiW2we3VHy+XkSXtxG4V7t0Ag6Boxe3p88MEHufvuu/nnP/+J2+3m66+/ZuHChfzud7/jwQcfjFU14saeE1JCJV5CWm3zaXRNx3GjkIb8tKFQFtN2YsjPupbhPkPT+qNWqtpZO0wUCk4WTwGgx57/Rd1c26HNU7WpXJp3KQD/PPjPyNRJIDiXiIKUsj3JYCjZq7dkNCtFy6C8NLJStAG37azMUHDu4H291DbZ+OZIHZkpWhwuFwfPNAL4XUOyqNab8/560aWDy+krwvQsE3JaQfTwbsfrW4bF1zfbW5b5x6Vo91sQQmmBIOGIWQ+VWbNm4XA4eOihh2hubmbatGn07duX5557jltvvTVW1Ygbu1sSKgVxSqiknZX8KZa0C3Cp/eeKl6dOPlTvwuJwo1OH70Fp9acM6HpFvWjoUYwpsz+pdUfoteNdjl0+v0PbX9XvKr45/Q0fV3zM/SPuJz0piCldIDgfkYV66x/3XyaLbjs85EeSDC5aU+63TJYMBuuS7XS5eGDyIHpn6Li8KIcak43sVC2byqo51WDB4XJ5epB05TiCcwNZSvvcGml2udomG5vLq/nV1RfSN1Pvdw2dbDB7RLXenPfXSxTaAYEgHGwOJwsmX0ifjMDx2jYure20+9bzJY5FzAoECUfMEipms5np06dzxx13UF1dTUVFBZs3b6Zv38gMEUlk6ppsHK+THDFxS6i0CGkDDfcByNaBQQNGO5TVuRiaG15Pk2PmKsqbT6FE0aXpkgOiUHCieAqDvlpCj33/5vQlt2DvQDfGCzMvpG9qX46bjvNR+UfcPvj2yNZPIOjOyEI9gG1LpK7CugzpgWz8AlDrOrzLVJ2G+aVSD7g3thzGaHZg0KuZWZLP/NIidJrg7Ypeq+a/RvXjpXXlPPDezpDbduU4gnMDWSwMsGxLJUazgw+/P8FTN18S9Br6dM8pDHq1uF68iUI7IBCEQ6pOw62jLgirzQepV5po9xExKxAkIDGT0l5zzTVMnTqVO++8k/r6eoqLi9FoNFRXV/P0009z1113xaIaQOyllokgpB368U9Ird3DsaG/xNizJOA6v/sKdlbDExN03FIcXnb7reNf8kTFSopT+vJQG9FtRHC7Kd70Amm1lZwZcgNHr/hlhzZfd2wdb+57k35p/fj3T/+NUnEevL04x+nWUtpEJJggrwuYLHa0apVHMmhzOEltpzu22eZg8foKT48Db+6bNJB5Ewr9hLGdOY4gvkQ6fr3FwjqNiqUbgl9Dc8YXkCSul8BEoR0QnHtEMn470+aD5E/yjmOrwxlyOOk5i4hZgSBhiNm3y++++47x48cDsHLlSvLy8jhy5AhvvvkmixYtilU14kK8h/so7U2k1O0HgvdQAUlMC7CvAx6VL2t2AtJ0yVGhpZcKQO6+j9GYqjq0+bhe49Cr9RxrPMbmE5ujUUOBoHujTZa6B6fkSJ8ReCBL1WnQqpXkpCahVSvD+tKqUipZtqUy4LJlWypRt51Ds5PHEZxbJGvVaNVKslOT0KhCX0NJapW4XoIRhXZAIAhFZ9p8kHqqeMfxeZlMARGzAkECEbOESnNzM2lpksH6888/Z+rUqSiVSsaOHcuRI0diVY24sCfe/pTqHSjcTmy6bBy67KDrFXoSKuHN9FNtM/JdgzSWdUR6ZP0p3jTmFGHMHoDSZaf3d+90aNskdRLj+0iJvHcOdGxbgUAQO8KZ5UcgCIW4hgSC7oOIV4FAcK4Qs4RKUVERH374IceOHeOzzz7jmmuuAeDs2bPnfLf9ePdQST+1BYDmzNDTU8s9VPbXOAlnJNiX1Ttx4SZfn0eONorCV4WCk8U/AiDnwKdoG890aPOJ/SaiQMGmE5s4UHsgGjUUCOKHrRmcNmiqkj5tzfGuEWabA5vDRY3Jis3hotnmwBKgzBvvWVva0t4MQZ2pj+DcI5rXULcgAdsCgUCmbTt83sdruIi4FggSnpglVH7/+9/z61//mvz8fMaMGcO4ceMAqbfKpZdeGqtqxJz6ZhvHauMrpE0/LSVUTFmDQ67XLw3UCmi0wQlT+wmVL6p3AHBZgGmYI01jThHGnIEoXQ56dbCXSl5KHqN7jgZg8c7F0aieQBAfHBbY/Cw8ORCeLJI+Nz8rlccJq93J4vUVjFy4msv+9AUjF65myfoK7C43Ny/Z4lNmtbf2hpNnbQnErJICHK6OT+keqj7exxZ0f6x2J8dqm6NyDXULErAtEAhkArXDFVWm8zdew0XEtUDQLYhZQuWmm27i6NGjbN++nVWrVnnKJ02axDPPPBOrasScPSeMAOQZkkhJitmkSh5UNiMptXsAaGonoaJRSkkVaH/YT73dxNf1PwAwMgYJFYATnl4qq9AaT3Vo2+sHXI8CBWuOruFg7cFoVE8giC22Ztj4tDR1oqVeKrPUS79vfDoub7HMNgcvrTvEc2vKPF25jWYHz60pY+mGCu5umaFBLntp3SFPbxF51pb7Jg30vLU06NXcN2kg80sHBJQTdqU+3scWdG/k83zGaGbO+ALuuarI5xq656oi5owvCKvnZbckAdsCgUAmWDt89zs7mDO+gHsnnWfxGi4irgWCbkNMpzzp2bMnl156KUov0dTo0aMpLi6OZTViyr5T0nCf/Oz49E4xnNmGwu3CmtwrpD9FptAz7Cf0m4G1Nbtx4qKvLoe8pMxIVLVdTNmFNOReiNLtpPd3b3do296pvRnVcxQgeqkIzhFUamnKxEBsWyItjzGhJIPLvzrMFQNzyEppnUGsrXgwSaNi3oRCtj8ymW//+2q2PzKZeRMKSerkdJidlR4KuhcqpZIPvz/OJRdk8rPXtjG0TzpbH57Ept9MZOvDkxjSJ52fvbYNjeocnVY1AdsCgUAmWDt8qMrEz17bxtzxhedXvIaLiGuBoNsgojHKHDjVCED/OCVUZH9Ke71TZDwz/VSH7qGyuvo7AEamD+x85TrBieIppFf9QM7Bzzl16W1Y0/uEve31A67nm9Pf8MXRLzhYe5BBWcFnPBIIEh5LQ+tbK79l9dJUiik5Ma2St2QwK0VLbmoSVSYrtU02jGYHtU02clOTqG2yAa3iwezUJM8+5J4ocpm2C3n/cKSH3scWdE8aLXZ0ajU1Jhs7jzUw961vKcxNYWBuKmVVJuqb7eSmJp275zsB2wKBQCZUO7zzWAP1ZjtPrjpIVoqW74/Xs+OodC0bLXZyzsV4DRcR1wJBt0EkVKLM/tNSQuWCrOS4HL/VnzIkrPULW9yye0IkVIyOZr6qk+SusU6oNGXlU9+jmIyzB+j97dtUXvVQ2Nv2Se3DqJ6j+Pr01zzz3TMsvlr0VBEkMLZm6Q2UpQF06eB0SNMkyujSQZcR+IFLlwG62Mi+zTYHKqUSk8VOmk7D8H7p/HJiEZcX5VBjspGdqmVTWTUvrisnK0VLlcnq2TaQeFDeX2PL/hwuF8laNY0WO0lqFUazHYNeg9XhbFdaKEsPAz3MC+nhuUOaToPF4SA7Vetz/TU020nTqdGolVQ32kjTaWiyOrA7XCQnqXG2OBoCXW9Ro7247gwJ0hYIBIEI1Q7/x7CepOs1PHnzcBrMdtL1Gs4YLfx11X4MOg02h8vT5tscTlJ1GkwWO1qve4FcHuzeETe6GusirgWCboNIqEQRu9NF+dn4JVQ0zWdJbijDjYLmrNAz/MgMTAcFkpT2bLOLHsn+b4fX1+zG4XbSKymL3rqsCNe6fU4W/4iMswfILvuCkyOmYc3oG/a2Py36Kd+e+ZbNJzaz4fgGrux7ZRRrKhB0EllEt22J9DCly4Ax82D8AlDrpHWcDqls/eP+24+ZJy1Xaf2XRRBZNLhsSyVGs4N3547h77PHsHRDBQ+8txOj2YFBr2bGuHz+PnsMO47UeXqnQKt4UO6F0nZ/Br2aP/5kCFOG9OSVjRW8seWwp3xmST7zS4vQhRgOJItun1tT5res7bEF3Reny8UNl/Tl+6N1nuvvhbXlvD5jFEs2VLD8q8M+1+LMknx+tWIHf506jFc3Vvpcb7NKCphfOqDTw8xCEk5cd4YEaAsEgmAEa4cnDurBUzdfwkvryv3a9qduvoRmm4Mrn1zrKX/sJ4O5dkgvlga5F6zYfpynVh+MTSy3RyRiXcS1QNBtEAmVKFJR1YTd6UavUZGTGvtGL/30ZgAsaf1xalLD2iZZAxekwZFG+P6sk2vy/b9srG6Z3SfWvVNkmjL7U593MRln9tFz10qOXHl/2NvmpeQxuf9kVh1exRPfPMHonqPRdeVBViCINLZm6UHM+yFKFtEBXH6/9JZLmyw9nEHkv6CFgdnmYPH6Cp+HZIfTzasbK3n+y3JPmdHs4Pkvy1EqFIwplBKw8hdbb/FgoP0ZzQ5G5mfy0rpyFq3x3af8+x3jC4P2NJFFt0DsvjQLYo7L7WbO+ALOGq2e6++V2y/jjS2HA16LAL+dchFLN1T4LZevv3kTCiP7djvcuO4McW4LBIJQBGuHn7xpWMi2fdroC3wktqPys0KuP6R3up98HKIQy+0RqVgXcS0QdBvEq7kocuC0NMPPBVnJKBSKmB8/8/gaAEw5l3Rou0Etjtnvz/gP+2lyWNhcuw+IzXTJwThdVApAzsHPUZsbOrTtjwt/TEZSBkeMR1i6a2kUaicQdIGOiOjUOunh7MEyePCQ9Hn5fTF50GorGsxK0TKif2ZICezI/lls+W1g8WAgcWFhbgo90nS8seVwwH2+seUwSerQSZFIi24FiYdGpeJ//r2XflnJLNtSSVaKlsuLclj+1eGA6y//6jB5Bh3/u/NEwOVRERZHWzAZx7ZAIGiPQO2wQa8J2bZnpSRRmCv5B8O5F7SVnkOc5OORjHUR1wJBt1SjUaIAACAASURBVEAkVKLI/hYh7QXZsR/uo3BayTi5AYDG3BEd2nZQhvS546x/QmVj7R5sbgc9tBn008VPhtWYPYCm9L4onTZy9/1fh7ZN1iTzs4t+BsDre15nd9XuaFRRIOgc4YjovNEmS91+U3KkT21sBNhtBbSj+mdS1yKfDYQkpbXw0Y6TTH91G/Pe+padxxpotNj99iczMDeVBnNosayxZftQJGvVaNVKslOT0KqV8R1XL4g4JoudguxUGi121EplWNeisUVkG2x5YxjXVYfoaFx3hji1BQJBOLRth41m33vIoLw0T0JEjtFL+2UyKC+NS/tltnsvkKXnbcsjHsvtEelYF3EtECQ83e6p8tFHH+Wxxx7zKcvLy+P06dNxqlFwvHuoxBrDmW2o7Sbs2gzMhsIObSv3UNlV5cTpcqNStvau+dwz3KcoLr1uPCgUnC6awIBv3yZvz0ecHn4LbnX4w6pG5I1gdM/RfH36a3678besuH4FKRpxkxK0Q1clc+FsH2MRXbgiv7br6TQq/mNYT264pI9HQJubpuO1GSP58ycHOFRl8mw7cVAP/nTDYLJSdNw0sh+zxxd6xINpOg11JmtAcWFZlYl0fWixrEGIZc9L5OvRbJeElLeM7odOreKrh6+iutFGblpSwGsRWq8biyPwl7OoCIvDietg7UM0RLYCQZwx6ENLzA06Df9zw2BPuVqpDHkvaCs9BxjeLx2dRoXN4YqerNbaCOokMNeDPkPIZAWC85Bul1ABGDx4MF988YXnd1WCzlUvT5kcj4RK1jHp/6cxdwQoOtYR6YI00KugyQ4/1Lm4KFv6/zU7bWyq3QvAZXHyp3hT1/sSbHv/jdZcR1b5WmqKf9Sh7X9+8c8pry/naONRfr/59/xtwt/imyQSJDZdlcyFu30MRXSBJLCB/CKB1ntg8iCeuvkSXlxb7iOgnVmSz4p5Y7llyVYOVZmYOKgHL/9sRFDx4L6TDdzw0hZenzmSmSX5PuPjK6qaONto8SuXmVmSj9XhRKsWnS3PJ6x2Jyu2H+f64b1ZtrmSDWVVvD5jFIvXH/IR0La9FmVmlRRQ32zjJ8P7+DhUvJdHXFjcXlw7rLDlef/24YoFsOMtWPtn4VAQnFPYnc6QEvNjdc389KXNnvIVc8eFlIx/20Z6PiA31bP/qDm07BbYvAi+Xtoan7M+ETJZgeA8o1smVNRqNT179ox3NUJS12TjtNECQL/MGCdU3G6PP6Ux97IOb65SwEVZ8F0VbD3p8CRUNtftxeyyka1JI1/fI6JV7gxupYozBVfQb//H9Nj3fx1OqKRoUrhz2J08/s3jfH7kc5buWsq84fOiVFtBt6arkrmObB8jEV0wCWxbkV+w9Xpn6HhpXbmf1FNOfPzuumJmL9/On24YHFIkeOuoCwBY+PEBVswbC+CTeNl+uI75pUV+5eHM8iM495CvxyF9DCzbHFpA2/Za9JYh43Yz98pClApFbITFoeL6igXw3fIg7YMbeg1vfdsdKZGtQBBn3G5CSsxH5mf6SGbvfmcHH95dghu3z71AjumqRqunB4tBr+bFaZfy6sbKdu9xncbaKCVTNjzRWmaph/dmwhzpGVzIZAWC8wOFW55ioZvw6KOP8uSTT5Kenk5SUhJjxozhz3/+M4WFwYe1WK1WrNbWboBGo5F+/frR0NCAwRCdrndfHarhtle20iMtieduvTQqxwhGSs0ehn3yE1zKJA6ULsHdiUz4ijJYfgCuyVez9EfSA9tD+1/n06rtXJMzglt7J8Z0w2prI8M/+yNKt5O9N75Mc27He86sO7aON/e9CcBjJY8xdeDUSFdT0AXiEb9+OG3w5MDgXXgfLAv9xqkz23u6+RulLsJOe0THTtscLkYuXB20+/T2RyajVSsDrpeVomXTbyYy9i9rQm5f12whM1nX7nGmPLeBiqomBuSm8rvrihk/MNeve3ajxU6SWoXRYseg02B1OCM/LEMQcSIdvzaHi8nPrOPT+65k7F/WoFYqw7oWq00WMpK1bCyr5qV15bw3rwSny4UbUIcx5C1iBIprlSZ0+7BgHzw7FJprfMvba3cEgi4Szftve/egrQ9P4orH1/r0OhneL523Z49BqVRQ22QjK8U3pgFPLANh3eM6Taj7ep+RcPtHoNZG7R4uEAgSh27XT3rMmDG8+eabfPbZZ7zyyiucPn2akpISampqgm7zl7/8hfT0dM9Pv379ol7P/afi50/JOroKAFP20E4lUwCGtfhmt51y4HK7sbrsbKjdA0j+lETBkZRGXe9hAB2W08qU9ivluoLrAHh0y6O8/8P7EaufoOvEI3796KpkrjPbR1lEF0gCK2M0OzCFkMXmpiZRYwot/TzbaOGDb0+GJZWdMDCXrBQth6pMzF6+HZPF7ieQTdNp0KqV5LSUi2RK9yDS8dvYIpOVr79wr8XH/ncfVzy+1iNDNlrs6LXq2AuLA8V1e+1DczWk5vmXR0JkKxCEIJr3X2MnJLM7jzVQb7Yz8/VvmP3Gdp+YbrTYfWK5vXtcl2W15vrgcXtiOzjMQiYrEJwndLuEyrXXXsuNN97I0KFDufrqq/n4448BWL58edBtHn74YRoaGjw/x44di3o9PULaWM/w43aRU/kRAA09x3V6N0XpkkelwQr7a1xsqt1Lk9NCpiaVwuRekaptRDibL72VyC77EpXV1M7agblx4I1M7DcRN24e/epRXvz+RVxuVySrKegk8YhfP2TJXMBlYUjmwtm+qVp642VrBsBsN2N32qk112J32jHbzV34A/yRJbBtGZCbyuszR5Kq01DjJYv1pspkJTtVG3B7aBUEfr7/jEcqG2w9g07D3CsL2PSbiSz9+WUM75dOqkiWnDNEOn7TWmSy8vUX7rX4jZdfIeFkxu21D8k5YDrjXx5BuWW02xtB9ySS8Wuy2LE5XFQ3WrE5XBjauTcEkszK5eVVJg6eafSJ6bZJ9mD3uGDrdxh9Rjv39fSu7T8KiDgXCKJDt0uotCUlJYWhQ4dSVuYvqZJJSkrCYDD4/ESbA6fjI6RNO7sdXdMJnGp9p/wpMmolDM6W/v3VSQefVX0HwKj0C1EmmLjVlF2IOS0PlcNCdtmaTu1DoVDws4t+xrX51wKweOdi7lx9J6ebEm/2qPONeMSvH7JQMhCyZK7T28+F8i/hyQFS9+Edb2F1WHl9z+uUrihlwooJlK4oZdmeZVid1sD76AROl4tZJQU+ZQNyU1kxbyzfH6tn5MLVXPanL9hUXsXMknyf9WqbbGwur/Yrl5kxLp9NZdXsOFrvkcoGYmZJPmcbLYz761rG/mUNu0808PfZY3C6RDLzXCHS8et0ubjhkr5sLq9mxrh8z7U4Y1x+wPXla9F72IAsM04Y2msfKtb6DveB8NqdMLE6o9/eCLonkYpfi93J0o0VjFy4mpELv2DkwtVUVJn87kEyM0v84zZUuSyS9ibQPS7U+h3GYZXiMxBj5krLEwgR5wJB9Oj2CRWr1cr+/fvp1Stxek04XW4OximhklvxLwCMPcZ0eriPzPCWYT9rj5lZV7MbgFEZ8Z/dxw+FwtNLJXff/0mms07tRsHNg27mF0N+gUap4atTX3H9h9fz4vcvUmMOPqRMcB4gCyUn/Kb1jZQuQ/p9/IL2xZBBt38IRs+D1f9PKrPUY87ox6u7X2HxrsUYbVJPN6PNyMu7Xua1Xa9F7I2SApgzvoB7riryvMV75D+KWf7VYRatKfd0lV748QFmjMvn3kmt6xn0ak7WW5hfWsR9kwb6lN83aSBzryzkxXWSaPAvn+5nfmmR3/b3TipifmkR/+9DaeYwWUb46sZKupXYSxBT9Fo180sHcLLewqzLpev3hbXlzCzJ97mWA12L3tddYg0ZU8C4X8KVD/q2D1c+KAlrG050rt0JA7PdzKu7Xo16eyM4fzFZ7B4xeVvJ7JzxBX73EDlGTzVY/OI5ePkAv+F6clsR6B4VaP0Oo9BI8TnhIf/7+hULpOUJgohzgSC6dDsp7a9//Wuuv/56LrjgAs6ePcuf/vQn1q9fz+7du+nfv39Y+zAajaSnp0dNanmoysSkp9ajVSlZNnMUSmVsenQonFZGvjcatb2RysseoTlrcJf2d9wE89aCzrAbTZ+3ydak8UTxLxJyamGV3czwzx5D5bSx/z+fwdRraJf2d6rpFMv2LKO8XnoQVyvVXNP/GqYOnMrIvJGolGJmkXgR7fgNSVdFsW23L/9SSqZU/yAtT87G/qs9lK6c7Hno8cagNbDulnVoVF1/ULM5XNy8ZAt3lxZxxcAc6ptt5KQGFsiGksU22xx+Uk+3241G1SqQdbicuFyg9ZLKnjVa+H8f7WXtwbO+f2MkZIGChCVS8Stfd2abE71WRW2TldQkNRq1kupGG5kpGrYfrkOjUnBZ/yyqGq3kpiUlpszYaYPXr4Xxv4LCiZIzJTkHDn0Jez6AnzwfNbml3WmndEVp1NsbwblBZ+I3lHx2eL903p4zBq3X/eKM0cKyLZU8MHkQGpW/MDrQPSdUcqSj64eN0wYfzIOrHwVDLzA3gD4djCfhi8dg6pKEkUaLOBcIoku3mzb5+PHj3HbbbVRXV5Obm8vYsWPZunVr2MmUWCD3TumXpY9ZMgUg8/ga1PZGbLpsmjMv6vL++qRA7xSoTWvpnZJ+YUImUwCcGj21fS4l9+g2cvd93OWESq+UXjw8+mG+OfMNnx3+jMqGSj6p/IRPKj8hW5fN5P6TmVIwhUt7XIpSIb74nTfIb4RTWrpvdfRhyXv7pmr4x3/5Lk/No9FcE/ChB6Q3So32RrJUWR07bgAaLXZ2Hmtg7lvfcukFGdw6sh9XX5wX8KFXlsV++99Xk90iCdS2dHCUH0zblgPkBCmrM1m54om1gf/GFllgdhsZoUDgjXzdadVKakxWZrz+DVUmKxnJGgbmplJWZaKiqgmALb+dyB//bx9/mTqUrES8riwNksTy3emQnC0JaE1nWof5XPckJKV2vt0JQaOtMSbtjeD8JZR8duexBuqb7fzhf79HrVD4xO380iJSktQduucEoqPrh42lAfZ+IP1kD4TcYqg6ADUtCoLrnmyN2Tgj4lwgiC7dLqHy7rvvxrsK7SL7U/plxna4T16Z9H/T0PNyiMCXfIUCRvSwsU67H4BRGRd2eZ/RpCp/LLlHt5FVsYGjV/wSZ1Jql/anUCgY3XM0o3uO5nDDYdYfX8/2M9upsdTw7sF3effguwxIH8Cdw+/kR/k/SthkkyBB0Rmk7sHeswSYzpCmz8agNQR9k5SmSYvI4dN0Gv5jWE8evvYieqTpaDBLb+9emzGSP39ygENVvoLnjkj8TBa71BvFbMeg12BzOH1EsyktssBg01kmXA8CQUKTptOg0yp5/MahXF6UQ43JRnaqlk1l1by4rpyMZC0HzhhJ1WkwWew+16LZ5kAVy2mTAyFLaS31UhKl7fTIEZTPtiVNmxaT9kZw/iLLZ4O191kpWnYcrfebHlmvUWFzuOIbm6HwjtuastZECkQ9bjuKiHOBILqIV+tR4EDLlMn9YuhP0TUcIuPUJtwoqOt7VcT2m5l1AIXSDvZMLtD1iNh+o0FTxgU0G3qhdNrI6qScNhj56fnMGDyDZ0qf4f4R91PSuwSdSsehhkM8uOFB5q6eS1VzVUSPKTjHCSSibK7BUbGO6cW3BdxkevF0HK7IiCjdLhdP3XwJK7YfY+TC1YxqEQXuPF7PinljGZDrm5CcVVKALQyRZyD54NKNFVjsrdtGXRYoOK+wO538ffYYdh1vYOxf1jD+CV/J8Y4jdfxkeB82llX5XItWu5PF6ys8AuaRC1ezZH0FVnuMhbVdlV53AYfLwfTi6QGXRbK9EZy/2BzODslnB+Sm8vfZY1i6IQFiMxRxjNuOIuJcIIguCZTqPXc4eCb2QtqeB98CoDF3BHZ95BIfJxTScB9rwzAq6rQMzLZHbN8RR6Gg6oIx9N/zIbkHPqVqyH9G/BBqpZphucMYljuM5uJmvjjyBR9XfszWU1u59eNbeXHSixRnFUf8uIJzEFlUC7BtifSWS5eBvv4Ysy+7A1Dy9oG3MdqMGLQGphdPZ/aw2SSpIjNkwe5ys3TjIRatKfeUGc0Oz++/u66Y2cu3Y9CrmTEunznjC1C1M4TRZLGzdGNF0H3OHV9Iqk7jkQUCLNtSidHswKBXM6ukgPmlA0jSCEeRIHzcbnh1YyXPf+l73T3/ZTlKhYLrh/diZkk+tyzZ6ul5dcf4Ql7dWMlza8p8tpF/nzehMHZvw4O0BYyZJ5WrdVE7tF6jZ/aw2QBRbW8E5y9qpYI54wtwud0s/+qwT3t/V+kA/vH1UU8PFoNezQvTLk2c2AxFHOO2o4g4FwiiS7eT0kaCaEotm20OBv/hM9xuWPKzyzDoo991XWVr5LL3S1A5mjg84mGasrvmD5GxuKzMrfwTNredpop7uGFAOrcNNbW/YRxR2Zq45LPHULoc7L3xZZpzoz8r0emm0zy/43lONZ0iIymDZT9aRlFmUdSPe74SVyltNAgiujXbzaiVahrtjaRp0nC4HOg1+sgdNoQoUBbDVpssZCRr2VhWzUvrynlvXklIWWw4+/TePmqyQEHCEo34be+6++aRq7lt6Ta+O1rnKdv+yGQmP7OOIzX+s1vETYzcVel1F4h2eyM4N+islNZbgF7bZCM7JQmn281v3t/Jfw7v4ynPStGiUioYtfCLsO8jcSeOcdtRRJwLBNFBPLlGmB/OmHC7IV2viUkyBaSpklWOJqwpvWnKGhKx/X7XdACb204yGTRae7P9hCPhEypObQp1vYaSfWIHufs/4UjufVE/Zs+Unjwy5hGe2v4UlcZK7vziTlZcv4IsnRB8CcIgiOhWfsiRRXGRNvCHEgUazQ7ONlr4n//bxzdH6jzdsduTxba3T6PF7hHVQhRlgYLzivauu6pGK01Wh0+Z0WJHpw78CBQ3MXJXpdddINrtjeD8xVuAnpWiJTc1iSG9Dfz2uov4eNdpPt512lOenqzhqZuHh4znhJOWxzFuO4qIc4EgOoin1wgTa3+KwmWn175XAajtd41kko0QX5l2ATA0uQClAo42aDhtSvyu+FX9xwKQVb4Gpd0Sk2Mma5L51WW/omdKT840n+GhDQ/hdCXQWF+BoA2yKDDwMkkU6J1MkWWxNSYrNocLk8V/+F97+zQI2awgwphtjrCu5SqT1afMoNNgcQT+0ibEyAJB5EjTtcZnbZONg2ca2XG8vuXFo295+VkT2anakPEsYlMgECQaIqESYeQZfmLlT8mp/F90TcdxaA3U9Z4Qsf2aXVa+bz4IwPCUAgozpG7R35xIoLcCQWjMGYAlORu1rZnMig0xO26qNpW7L7mbJFUS205t4+39b8fs2AJBR7E5nMwsyQ+4bMY4f1HgzJJ8NpZVeQSBbUWz7e1zZkl+WFJbgSBcZKlsVaOlQ9fyrJICrA4nN1zSN+A2QowsEESOQBLyiqomakxWv/LaJhuby6uDxrOITYFAkIiIhEqEOehJqMRgTKLLSZ89L/H/2bvz+LbKM+//H+2SFzmJ7WxOYsd2NhLCEmiIIcQhBAq0UFq60pZkmMIQWph52ufp0Jl22ulQOjNtf4UWyIQOCb+hDN2etsxMKVDaLJBAk0A2yObYWewsduzEkm3t0vOHIsWLbMu2bEvy9/16+eX4nFtHt5xz6T6+dJ/rBjhbehuRFBaV2tm+n0AkSKG5gInmCcwvbgdge0P6FNnqlcHI2dLFABQfeHlEn7okr4RPzfkUAD9690eccJ0Y0ecXSVae3cKa6koeWlEZ/zTQ6TDz8IpZ3Hd9OU9urIlve2hFJfcsKeM7vzsAXCw0+9TGmi4zVXo75kMrKllTXdlluVqRofD4gzy18QiPv36Yv//Ne0mfy1+6oZK/XDoTs9HAmuoKHl4xq8dj1lRXqJaPSIrEipB3j7VAKMxfLp3Jl27oGrf15zysqa5UbIpIxlBR2hQWtYxEIlz57dc41xHg0Y8soLzbsqOpVnj0v5m95SGC5lwOL32CsDl1SZx/PfUcO9v3szTvCqqdV3Hea+I7b5ZhMER45vYmCuzp/QmBxdPKZa/+IwYi7P3Uerzjpo/Yc0ciEb6343vsb9nP9dOu58kVT47Yc48FWVeUdpS1eQNYzSZc3gBOuwV/MITJaMDUqVjslsNNfOd3B+IrpMT0ViAw0TGVTBFIXfx2L0S7fM5E/vGO+Uxy2uPn3fajLRCJcEXp+HjBy+4FllUYWSR5Q4nf7rEG9ChWG4vRl3Y38C8fuwyLSbEpIulP70wp1OT2ca4jgMEA08YP8y0/kTDT9v4YgJYZt6Q0mdIR8rK7/RAAlzjKARhnD1GS76XBbWfnSRs3lPdcGSGdBBwFtE6ax7gz71O0/2Xql9w3Ys9tMBj43CWf4+tvfp3N9Zt5s+FNri25dsSeX2QgYomOWLHYzsmRwjwbzW0+7n1uB0C8cGBTm4+Wdn/CQrP9HVMkFdzeroVo/3SwkaX/0siy2UXcumAKKy+ZxN0/eRvoed7GHl+YZ1NhZJER0j3Wzrp98WK15cW5zCrO43BTG7VN0RnR37p9Abk2s2JTRNKeEiopFKufMsVpH/Y/IMbXv07O+UOETA6aZ9yU0mO/3b6XICGKzeOZaLm4Us2C4nYa3Hb+3JD+CRWIFqcdd+Z9ig69SsMH/oKIaeRO98m5k1kxYwWvHnuV7+34HkumLsFo0MWAZAaPP4jJaKTtwieDl00v4IvLK7m2sojmNj+FeVbeOHyWJzfWJCw0G3t8f58sJttOxrZE50ms0GXnGSrf/sh8JubbcXkC5Nkt/Ps9V8VnVnWuoaLCliKjz+nof2zxB8MaH0Qk7emdKYVi9VOGfYWfSIRpe34EQMv0lYQtqb21aIv7XQAudVR22T6/uINXagvZc9qGJ2DAYUnvu8VaJ83Db8vH6jlPwfG3OD/zuhF9/g9XfJgtDVuoOV/Dq8de5YNlHxzR5xcZjFihz/Vb63B5grx432Kev3cx6zbX8uVf7MblCeJ0mLlnSRnP37uYQCjUJYHc/fFOh5nVVTNZU12BzWIacDsZ23o7Tx6oruDLK+fwDy+9x/I5E3n6s1fy1MYaNmw9Gm+3qqqMn99/DZ/4t7e63K4WK2ypT7xFRk8gFOpzbDnd6uFDP35D44OIpD1dTaTQ/tMjs2TyhOO/J69lHyGTnebSW1N67LOB87zvqQVgQbeEyqRcP4WOAIGwgT1nrCl93uEQMZponnE1AMX7fzfiz59ryWVl6UoA1u5aSziS3nVnRDoX+ox98h8MRfjJljp+9Mea+DaXJ8iP/ljDT7bUYei0VHuix7s8QR5//TBPbTxChz84oHYytvV1njy98QgfW1TCwytm8U8fmc9TG2t44vWu5+gTr9fw3LajfO3WuYAKW4qkk3CEPscWs8mo8UFEMoISKil0cCSWTA6HmL7rBwA0l95KyJraopxvtu0CoNQ6hQJz15kvBgPx1X7+XJ8Bq/0ATTOiq/0UnNiBta1xxJ//ptKbcJgdHGk9wqtHXx3x5xcZCJPRyPqtdfGfJ+RaubJ0fJdtna3fWofZeHEY6f743tom207Gtv7OE6vJxP3LyinOt7Nh69GE7TZsPcrSWcVs+9sb2PF3K7l/Wbk+4RZJAzazqc/4LsqzUV6c22O7xgcRSTd6V0oRfzDM4TPRKcXDmVAprvs1Oa4jBC15NM9I7eyUSCTS6+0+MbGEys5TNoIZMOHCl1eMq6gCQyRM4cGRT2jkWHK4ufRmANbuXksoHBrxPogkq3uhz+I8G81t/i7bOnN5grg7LZvc/fG9tU22nYxtyZwnOVYzLk/f7VzeAOs219J2ob2IjL5k4vaK6eOZMymfCbnW+HaNDyKSbpRQSZFDZ9z4Q2FyrSYm5tv6f8AgGEI+pu1+HICzZbcTtqQ2cXPMf4p6/xlMmJjnmJmwTWmBl1xLiHa/kf1N6X/bD0DTjGsAKN7/MozCbTc3lt5IjjmHI61HeO3YayP+/JI+PP4g/mCY5jYf/mA47aYuxwp9xjS1+SjMs3bZ1ln34p7dH99b22TbydiWzHni8QdxOvpu57Rb2Hm8RUt3i6SRZOL22x+Zz0/uuYo3vrqcdZ9bxGXTCzQ+iEjaUUIlRd472QpAWVFul5oCqTTp8M+wtzcQsI2nZXpqV/YB2OJ+B4DZ9hnYjYmTQkYDXBK/7Wd4Ekepdm7qpQQtDmxtZ3DWvzviz59jyYnXUnlm7zNEIuldzFeGR6y45lWPvsaif/oDVz36Gv+2qRZfIH1mLYXCYVZXXUymtrT7ebPmLPcsKUvYPlbcs7fH99Y22XYytvV1nnx55RzCkQhrN9XyRk1Tr+foqqoyzri8LJ8zSeeVSBrxBUOsqipLuG911UyONLVxzWOvs/Rf/sQ1j73O3oZWnr93MSHFsYikGSVUUmRvQzShMrMot5+Wg2MMdFCy98cANM28k4gptbNDApHgxdt9cmb12XZ+UTShsuOknUzIDURMVpqnLQKg+MDIF6cFWDFjBTaTjUPnDrG5fvOo9EFGT6YUYXVYzaypruDhFbPinxz++E813Hd9eZdtvRX3TPT4RG2TbSdjW1/nyccWlfD0hZh69H8OsKqqjC/dUNml3UMrKllTXcnOY+d0XomkmXy7hTXVlTy0orJHfP/l0pl88YV3ExarzYDLThEZYwyRMfhxucvloqCggNbWVpzO1BR1vePJN9l94jwP3VDJkoqilByzs6l7n6J01/fwOyZyuOp7YEztheFW926eOPOf5BtzeWjSpzAaes+1BUIGvrm5jEDYyL/cdJaZ49Pjj8G+OFobWLDx+4SNZnZ/7mcEHQUj3odfHPwFLx99mYXFC3n+lueHbSZTthuO+B1u/mCYqx59LeH94k6HmR1/t7LL0sOjrcMfxGw04vYGyLdbCIXDRKDLtmA43OsfaqhoegAAIABJREFUqN0f31vbZNtJ9hhM/CY6T8xGY5eYqijO46sfnMN1s4poafczMd+ONxDCbjHpvBJJkeEYf93eADazCZc3gNNuwR8Kc/dP3mL3idYebdNxvBQR0TtSCgRCYfafii6ZXDYMM1RMvlZK3l8HQGPFXSlPpgC81voWAFfkzukzmQJgMUWYPcEDwI6TmXHbj6eghPZx0zGGgxQeGp06JjeV3YTZaGZP0x52nNkxKn2Q0ZFpRVhzrGasZiOFeTasZiMOq7nHtr7+QE227UCOKWNXovOkc0xNyLViNhr42/+7l+v++U/cu2EHbd4ATodF55VImsu3R+O06EJ8+wKhhMkUSM/xUkRECZUUqGlswx8M47CYmORM/XLCU99fh9nvwps3ndbJVSk/fr3/DPu9dRgwcEXO3KQeE6ujsqMhM5ZPBmgqjS6hXHzgd4zGvUoFtgKWliwF4Jk9z4z488voSZcirN2L4nr8waQL5aZ7QV0ZW/LtFi6bXsAzn1/EG19dHi9c+d2PXordaiTPbtE5KpIB3N4A/mCYs+7o2GK3mLhseuJZxCpaLiLpSB/bpMC7x88D0fopxhTfxmHpOMOU/RsAaKz4OPQze2Qw/tD6NhAtRus0JTfDZl5RBwYi1J6z0NxhpDAn/YuENZdcyfR9L+E4dxxn/U5c068a8T7cMvMWNtVvYtupbew7u48FRQtGvA8y8mLFNR9//XCPfbEirNZhzm/HiuKu31qHyxPksukFPH/vYn6ypS6+zekws7pqJmuqK7BZTL0+trd2IiMlHA7z/L2LWbe5li//Ynf8vLxnSRnP37uY9062svFgk85RkTTmDYR4ZkstG7Ye7TK2PH/vYj7y5FaONLV1aT9S46WIyEDoHSkFdhxrAWDO5PyUH3vGrh9gCnnoKJiFu3hRyo/vC/vZfGF1n0U585J+XJ41RGmBF8ic237CFnt8lsrkXT8flT4UOYq4Zkp0GWfNUhk7RrsIa6KiuF9cXsm6zbX9FsrNlIK6MrYEwxF+sqWOH/2xJmHhyklOu85RkTTm9gZ4amMNT7xe02Ns+cmWOp78zBUqWi4iGUEJlRTYcfQcAHMmpTahktPyPsVHfgnA6dmfhWEoYrrF/S4dYS/jTPmU26YN6LGXFHcAmXXbz5mKZUQMRgoa3iHnbM/ZAiPh1pm3YsDAH0/8kZpzNaPSBxl5NouJ+5eVs+PvVrLz729kx9+t5P5l5SPy6bnJaGT91rr4zxNyrVxbWcRz244mbL9+ax1mozHhY3trJzKSrGZTn+dlUZ6N8uJcnaMiacpmNrFh69GE+9ZvraO8OG9UxksRkYHSVcYQNbq8HG/pwADMmpSXugNHIpTtfBQDEVonXYNnXN9LGQ9GOBLmv85Hl/D9QO78Aa86c8mF5ZP3NVrxBDJjxRp/zgRapl4GwORdvxiVPkzNm8qVk64E4N/3/fuo9EFGx2gVYe1eFLc4z0Zzmz+pQrmZVlBXxgaXp+/z0uUNMKs4T+eoSJpKJoZVtFxEMoESKkO041h0dsr0CTkpfbMf1/BHCk5vI2y0cGbWp1N23M7ebtvHmUAzDoMt6WK0nU3MDVCU4ycYNrDrtHUYejg8TlcuB2DCkY3YWhtGpQ+3zbwNgJfrXuaE+8So9EHGju5FcZvafBTmWZMqlJsuBXVFOnM6+j4vnXYLh5vadI6KpKlkYlhEJBMooTJEsdt9Zqfwdh9DOEDpzu8C0DLjgwQcxSk7dkw4EuZX5/4AwNW587EaBzdwXVIUve1newbd9tMxbhrnJ87FEAlTsv25UelDWUEZCwoXEIqEVEtFhl2sKG5MS7ufN2vOcs+SsoTtY4X/Ej22t3Yiw63zSlO+YIhVVWUJ262qKuOMy0ttU7vOUZE00X2luP5i2BcMjWwHRUQGSfPnhuiNmiYA5k1JXUJlyv715LiOELTk01R2R8qO29kW97vU+xuxG2wszhv8SjPzi9vZfHwc75yyEQqDKUNSdA3zbmVc4wEm1PyJU1d8Ck9h+Yj34fbK29nXvI/f1PyGu+fdzZwJc0a8DzI2xIriAvGVen6zq4Hvf/xyDAa6rLCwqqqMB6orsF+4Vz3RY7XKj4y07itN3bZwMt//+OVAz/N3TXUlX/7FrngRS52jIqMr0Upx/3j7AtZUVwKJY9iuuBWRDGGIRCKR0e7ESHO5XBQUFNDa2orT6Rz0cRrOe7j2u3/EYIB1n72KPPvQ81O2tnoue+lmTCEPDZfcx/mS6iEfsztv2M+Xj3+f5mArK5wfoCrvskEfKxyBb28poz1g4u+XtXDZZH8Kezq8Krb//0w4uYtzpUuoueXbo9KHp3Y9xY4zO1gyZQn/tvLfBlzHZixKVfyORR3+IGajEbc3gN1i4pc765lSYOe6WUW0tPuZkGtly+GznG718vGrpnW5jbHzY/PtFoLhsO5plwEbTPx6/EHWbqrtsez48jkT+de7FuJ0WHB5AzjtFnzBEJFwBLvVrHNUJMVSGb8AP/zk5ayYNxGb2dQlhnWbnohkEl1pDMHGg40AzJ6Yn5JkCpEIM//8D5hCHtrHzeX81GVDP2YCv2r5A83BVgpMeVydO39IxzIaYOHENrY1FLD5qCOjEir18z7I+FN7GH9sG/n17+CeduWI9+Gu2Xexq3EX205t442GN1g6bemI90HGjtgfl4V5NvzBMN9/7SAuT5AJuVaK82w0tfloaffjdJj59Adm9PpYAKvuGJUR0ttKU3862MgNP9jIjr9bSVHsvDRfPC91joqMvr5WivvGS/u49dKVWM3GhDEsIpIJ9K41BH86EL3d5/Lp41JyvKK63zK+4U+EDSZOzrt3WJZJPuKt53/OvwHALQXXYjEMPRF05RQ3AG832DJmtR8AX95EGsuWAFC2+XEMQd+I92FizkRWlK4A4Hs7vkcwnLjivUiqdV69p6Xdz8EzblraowlRrYwi6UQrTYlkLsWviGQ7JVQGyeMPsfXIWQAunzH0hIrNfYKZf/4GAGdn3ok/r2TIx+yuI+Tl8dMvECbMPPtMZtln9P+gJMxw+ih0+PEFjfy5wZaSY46U+nm34bcXYHc1MPWdn45KHz5c/mHyLHnUttay4b0No9IHGXu0eo9kCp2rIplL8Ssi2U4JlUH6/Xun6PCHmJhvo3RCztAOFg4y682/wRxoo33cbJpmpr4QbSAS5Aenn6cx2MI4Ux4fGpe6W0sMBlg0pQ2AV2uG+LsYYWGLnWOX3gnA5Hd/Rk7ToRHvQ44lh0/M+QQQraly5PyREe+DjD1avUcyhc5Vkcyl+BWRbKeEyiD9fHs9AMtmFw+5kGjZzu+Q3/QOIbODhgUPgjG1lc07wl5+cOo/2OepwWqwcNf4G7EbUzuTZPFUFyZDhEPNVg6ezaxPG85PXUjLlEsxRkJUvvJNzJ7WEe/DtVOv5dKiSwmEA/zvzf8bT9Az4n2QsSW2es/DK2bFPz10OszxlVFUzFPShc5Vkcyl+BWRbKdVfgaxSsjx5g6u/9c/YQCe+PQV8UJagzF5/7PM3PFP0eMu/Gvckz4w6GMl8l7HEf696TecDDRhwsSnC29mpi31txMB/OL9YrafcnLNNC9fvvb8sDzHcDEFPFyy6f/D3n6W1pIrOHTbd1Oe2OrPed95vrn1m7j8Lj5c/mEeve5RrfqTgFb5SS2t3iMjaSjxq3NVZHQpfkVEetI72SBs2HoUgAUlBUNKphTV/oayHY8CcHrWpxMmU84H3RzyHuOo7yTnQi7OB92EImEsRjNWg4Vco4N8Uw55xhzyTDnkmRz4wgEa/I2823GAWl8DAHnGHD4xYSUl1omD7m9/ls5oZfspJ2/X2zjSYqZiQuYUWA1ZHNR8YDXzNj9OQcO7lP/xn6m94asjmlQZZxvH/Qvv5/s7v89/1f4XU/Km8KUrvjRizy9jk1bvkUyhc1Ukcyl+RSRbKaEyQCfPe/jp28cA+NDCKYM+zpT3f0LZzu8A0DLtRppLP3TxOfxNvOnexba2PZwMNA2pv0aMXJkzl2XOReQY7UM6Vn8m5/m5crKbd07n8+w7Tv5pRctwLFQ0bDzOKdQu+iwV2zdQWPNHDOEAtSu+RsQ0crcwzSucx+fmfY7n3n+OdXvWAfDFy7+omSoiIiIiIiJpRgmVAYhEInzzpffwBcPMnZzPpSUFAz6Gye+ibMe3mXjkVwA0z7iF07PvpiXkZpt7N2+27YrPKomZaJ5AibUYpymPfGMOJoOJUCREIBLEE/bhiXjpCPvwhL14w34sBjMFpjymWScyz15Ojml4Eymd3VLZzL6mXA41W/mvgzncPrdjxJ47Fc5PWUDNB1ZTuX0DE2q3YHM/TO2KR/COmz5ifVg2fRkdwQ5+cegXrNuzjhPuE3xzyTfJsWRWwV8REREREZFspoTKAKzbXMur75/BZDSwqqpsYLMGwkEKj/2O0ne+i63jNBEMHKj8KL8fP4W3Tv6E/Z46IkTL2RgwUGGbxgJHJZX2aTiGeWZJKhXYQtxa2cxvDhbz/J58puSHuLrEN9rdGpDWyfM5vPgvqdjxH+Q2HeKSX/4Vp674NI0LPkLIljcifbhl5i3YzXZ+uv+nvFz3Mrsbd/N/PvB/uGH6DZqtIiIiIiIikgZUlDaJoloef4jHXz/M2k3R5WzvWVLKBxckd7uP3VXLhOOvMunwi5jajvOezcq2/CK2jJ/GvuCZeBIFYLp1EgsclcyzzyTX5Bjci0sDkQj86kAxfz7pxGCI8Mn5bXx4bjvWka3xOmQWz3nK3/lPnGcPAxC05nF2zk2cK7+etsmXgGH47/89dO4Qz+x5hmZvMwBzxs/hrtl3sWzaMqbkDf6Ws0ymorQimUvxK5K5FL8iIj0poZJgQDjS1Eb9OQ+NLi+768/zP3tOca4jAMCdV5Tw8UXTMBgMGANthFsP0eZtIhx0Ew62EfY2E/Y20tF+nLaOelpD7Zwym6ixWjhqsRDsNrtgqqWYSxzlzLPPZJw5f0Re/0gIhuHXB4vZfjL6+x1nj85UqZwQYIIjhMMSYU5RYJR7mYRImAkNu5h68DUcbWfim0OWHNqLZ+MZX4o/rxh/bjFBh5OIyUrYZCNstuJzTiVsGfrsIl/Qx3/X/Td/OPYHfKGLs31K8kpYWLyQGfkzKMkrYbx9PDnmHHIsOeSYc3DanBQ5iob8/OlGF3QimUvxK5K5FL8iIj2NyYRKa2sr48aN48SJEwkHhAde3Mebtee6bCvKtfDxK6dw1YyLdVMKT21m776/5V8Kxyf93A6DjemWycywTqLSOp0C08jcQjIaIhF490wBrx8rptXXtbBrjiXE2ltPjFLPBiESZkLTIYpP76GwaT/mYP+3Mb1z83dxTZyXsi60B9rZ3ridnWd3ctx9nDDhPtsvKl7EE9c90Web/Pz8jLuFqL/4FRlLMi2GFb8iFyl+RTJXpsWvDJ8xmVCpr69n+vSRKzIqkq4y8VMmxa/IRZkWw4pfkYsUvyKZK9PiV4bPmEyohMNhTp48OaDMosvlYvr06crK90O/p+Sky+8pE7Prg4nfVEqX/7tUyrbXlG2vB3p/TZkWw6Mdv33JpvMmm14LZNfr6fxaSkpK0i4O+pJM/GbT/1Wy9Jqz/zUner3pOI7J6BiTq/wYjUamTZs2qMc6nc4x8cYxVPo9JUe/p4EbSvymUjb+32Xba8q21wOZ/5rSJX77kum/486y6bVAdr0ep9OZcX+MDSR+s+n/Kll6zdlvrL1eSc7wL1MiIiIiIiIiIpJllFARERERERERERkg0ze/+c1vjnYnMoXJZKK6uhqzeUzeKZU0/Z6So99T5srG/7tse03Z9nogO19Tusmm33E2vRbIrteTTa8lkWx/fYnoNWe/sfZ6JXljsiitiIiIiIiIiMhQ6JYfEREREREREZEBUkJFRERERERERGSAlFARERERERERERkgJVRERERERERERAZICRURERERERERkQFSQkVEREREREREZICUUBERERERERERGSAlVEREREREREREBkgJFRERERERERGRAVJCRURERERERERkgJRQEREREREREREZICVUREREREREREQGSAkVEREREREREZEBUkJFRERERERERGSAlFARERERERERERkgJVRERERERERERAZICRURERERERERkQFSQkVEREREREREZICUUBERERERERERGSAlVEREREREREREBkgJFRERERERERGRARqTCZVIJILL5SISiYx2V0RkgBS/IplL8SuSuRS/IiI9jcmEitvtpqCgALfbPdpdEZEBUvyKZC7Fr0jmUvyKiPSUVgmVxx57DIPBwF//9V/32mbDhg0YDIYeX16vdwR7KiIiIiIiIiJjmXm0OxCzfft21q1bx8KFC/tt63Q6OXjwYJdtdrt9uLomIiIiIiIiItJFWsxQaWtr4+677+aZZ55h/Pjx/bY3GAxMnjy5y5eIiIiIiIiIyEhJi4TKgw8+yG233caNN96YVPu2tjZKS0uZNm0aH/rQh3j33Xf7bO/z+XC5XF2+RCQzKH5FMpfiVyRzKX5FRPo36gmVF198kZ07d/LYY48l1X7u3Lls2LCBl156if/8z//Ebrdz7bXXcvjw4V4f89hjj1FQUBD/mj59eqq6LyLDTPErkrkUvyKZS/ErItI/Q2QU1z47ceIEV111Fa+++iqXXXYZANXV1Vx++eX88Ic/TOoY4XCYK6+8kuuvv54nnngiYRufz4fP54v/7HK5mD59Oq2trTidzqG/EBEZNopfkcyl+BXJXIpfEZH+jWpR2p07d9LY2MiiRYvi20KhEJs3b+bHP/4xPp8Pk8nU5zGMRiNXX311nzNUbDYbNpstZf0WkZGj+BXJXIpfkcyl+BUR6d+oJlRWrFjB3r17u2xbvXo1c+fO5atf/Wq/yRSASCTCrl27uPTSS4erm2Oaxx/EZDTi9gbIt1sIhsPkWNNmcSgRGaJIJII35MVhdox2V0RSQuOWSPZSfItIuhnVd6D8/HwWLFjQZVtubi6FhYXx7Z///OcpKSmJ11j51re+xTXXXMOsWbNwuVw88cQT7Nq1iyeffHLE+5/tfIEQazfVsn5rHS5PEKfDzOqqmayprsBm6T/ZJSLp7eW6l/neju/R4m3hoSseYtX8VRgMhtHulsigadwSyV6KbxFJR2mf0j1+/DhG48XauefPn+e+++7j9OnTFBQUcMUVV7B582Y+8IEPjGIvs4/HH2Ttploef/3irVQuTzD+8/3LyvWJgEgG29W4i69t+RrBSBCAH+z8AYWOQm6vuH2UeyYyOBq3RLKX4ltE0tWoFqUdLS6Xi4KCAhXV6oM/GOaqR1/D5Qn22Od0mNnxdyuxmkd9kSgZgxS/QxeJRLjrv+7i0LlDXDHxCibmTOSVo68wOXcy/33nf2Mz6Z55GR7DGb8at0SG12iOv4pvEUlXeueRhNzeQMJBC6KfCLi9gRHukYikypaGLRw6dwibycZfLPgL7qy8k/H28ZxuP83v634/2t0TGRSNWyLZS/EtIulKCRVJKN9uwelIPHXS6TCTb7eMcI9EJFWef/95AJZPX06uJRerycqykmUAvHL0ldHsmsigadwSyV6KbxFJV0qoSEKhcJjVVTMT7ltdNZNgODzCPRKRVGjqaOLt028DUD29Or796slXA7Dt5DZafa2j0TWRIdG4JZK9FN8ikq6UUJGEHFYza6oreHjFrPgnAk6HmYdXzGJNdYUKf4lkqFeOvkI4EqZiXAUTcybGt0/Jm8K0vGkEI0HeaHhjFHsoMjgat0Syl+JbRNKV3n2kVzaLifuXlfPg8krc3gD5dgvBcFhL04lksFePvQrA4smLe+ybXzSf+rZ6tp/ezm3lt41010SGTOOWSPZSfItIOtIMFelTjtWM1WykMM+G1WzUJwAiGey89zy7m3YDcMXEK3rsnzN+DgA7z+wc0X6JpJLGLZHspfgWkXSjhIqIyBix9eRWwpEwJXklFDoKe+yfPX42BgwcdR2lqaNpFHooIiIiIpI5lFARERkjtjRsAWBh8cKE+3MsOUzPnw7AzkbNUhERERER6YsSKiIiY0AkEuHPp/8MwILCBb22Ky8oB+D95vdHpF8iIiIiIplKCRURkTGgvq2exo5GTAYT5ePKe21X6iwFlFAREREREemPEioiImPAjoO/BuCSYIRpB1/rtV1ZQRkA+5v3E4lERqJrIiIiIiIZSQkVEZFs13yEHdufBGCx+xxlm39I0f7fJWw6NW8qJoMJl99FfVv9SPZSRERERCSjKKEiePxB/MEwzW0+/MEwHf7gaHdJRFIlEoHfPMBOS/TtflbeDABK3/gx5o5zPZpbjBam5U8D4EDLgZHrp8gQaSwTyTyKWxHJdFq8fYzzBUKs3VTL+q11uDxBnA4zq6tmsqa6ApvFNNrdE5Ghqv0Tp07uoGFGCUYM5C38NG3uVvLOH2fie7/l5NWrejxkWt40jrmOUXO+hpWlK0e+zyIDpLFMJPMobkUkG2iGyhjm8Qd5auMRHn/9MC5P9BMBlyfI468f5qmNR/QpgUg2eOOH7LDbACh1TMRhtnN61nIAJu37LYZQoMdDSvJKAKg5VzNy/RQZJI1lIplHcSsi2UIJlTHMZDSyfmtdwn3rt9ZhNur0EMlo549D3SZ22u0AzMmN3spzbsqlBGz5mH1u8k/u6vGwWELlyPkjI9dXkUHSWCaSeRS3IpIt9G41hrm9gfinAt25PEHc3p6fXItIBtnzcwB25zkBmJU7NbrdYOTclAUAjK97s8fDYgmVY65jBBLMYBFJJxrLRDKP4lZEsoUSKmNYvt2C05G4jI7TYSbfbhnhHolISu37v7QbDBwxhgGYmTM5vuvclIXAhYRKt+WRx9vH4zA7CEaCHHUdHbHuigyGxjKRzKO4FZFsoYTKGBYKh1ldNTPhvtVVMwmGwyPcIxFJmfPHofE93rfbiQATLPmMs+TGd7uLKgiZLFg857CfO97loQaDgal50dksR1p124+kN41lIplHcSsi2UIJlSyVzDJ0DquZNdUVPLxiVvxTAqfDzMMrZrGmuoIcqxaBEslYB38PwN4J0bopM3MmddkdMZppG18GkLCOyqQL7Y+7jvfYJ5JO+hrLHqiuwDDK/RORntelgK5BRSQr6N0qCw1kGTqbxcT9y8p5cHklbm+AfLuFYDis5epEMt3hVwDYl1sAgUbKHZN7NHEXVVJw9jDOk7tpWnBHl32xhMox17Hh76vIEHUey1ra/TgdZrYcPssn122jevZELcMqMop6uy59sLpC16AikvGUUMkyHn+QtZtqefz1w/FtsWXoAO5fVt4j6x/7uTAvurSqVROXRDJbKADHtgGwJ+IBoDwnUUKlAoD8U3ujdVQMFz/Ln5SrhIpkFgPw7Bt1/PrdBprafLS0+wHYfaIVSDz+icjwSua61Go26hpURDKW3rWyjJahExFO7oJAO40OJ2eCbgwYKHVM7NGsfdw0wgYjFs85rO1NXfbplh/JNCajkac21XDwjDueTInR+CcyOnRdKiLZTu9iWUbL0IkIR7cAsK+4HIASeyF2k7VHs4jJisc5BYDcxoNd9sUSKud852j1tQ5nb0VSQuOfSPpRXIpItlNCJctoGToRiSVU9uaNA2CmY1KvTTvGRYvW5jZ1TajYzXYKbAWAZqlIZtD4J5J+FJciku2UUMkyWoZOZIwLBeD4WwC8Z4rGe/cVfjprHzcDgJzGQz32xQvTulVHRdKfxj+R9KO4FJFsp+psWSa2fCSQ1Co/IpJlTr4LgQ4iNicHvGcBEtZPiWkfNx2A3LOHexSmnZwzmUPnDmmGimQEjX8i6UdxKSLZLq0SKo899hhf+9rXePjhh/nhD3/Ya7tf/epXfP3rX+fIkSNUVFTw6KOPcuedd45gT9PbcC6F7PEHMRmNXY6rVRNE0kjdZgDOTJ7LueBpjBiYZi/qtbknfyIRDJh9biwdzQRyL7bVSj+Srnobi4Zz/BORwekrLnVdKSKZLm3esbZv3866detYuHBhn+22bdvGJz/5Sb797W9z55138utf/5pPfOITvPHGGyxevHiEepv+hmMpZF8gxNpNtfqEQSSdnXgbgAPjpoLrNFPsE7AYe3+rj5isePOKcbQ14mip65JQmZgTndmihIqkk/7GouEY/0RkaBLFpa4rRSQbpMVVRltbG3fffTfPPPMM48eP77PtD3/4Q1auXMkjjzzC3LlzeeSRR1ixYkWfM1pk6Dz+IE9tPMLjrx+OV2t3eYI8/vphntp4hA5/4gruIjKCIhGo3wHAfmt0VZ8Z9t5v94mJrfST01zbZfvk3MlAtChtJBJJZU9FBkVjkUh2UCyLSLZIi4TKgw8+yG233caNN97Yb9tt27Zx0003ddl28803s3Xr1l4f4/P5cLlcXb5kYExGI+u31iXct35rHWZjWpxKkoUUvwNwrg48LWA0cyDcDkCpo7jfh3VcSKg4WrrG+ETHRAwYcAfcnPOdS31/JeulOn41FomMnOEcfxXLIpItRv3d6sUXX2Tnzp089thjSbU/ffo0kyZ1XbFi0qRJnD59utfHPPbYYxQUFMS/pk+fPqQ+j0VubyD+CUJ3Lk8Qtzcwwj2SsULxOwD1O6PfJ1RwoL0BgBlJJFRiM1QczV0vbi0mCxPsEwDd9iODk+r41VgkMnKGc/xVLItIthjVhMqJEyd4+OGH+elPf4rdbk/6cYZOq1AARCKRHts6e+SRR2htbY1/nThxYtB9Hqvy7RacjsR1GJwOM/l2ywj3SMYKxe8ANERv92ktKuekrwVILqHizYsmqe2tDRDpuoRlrI5Kvbs+lT2VMSLV8auxSGTkDOf4q1gWkWwxqgmVnTt30tjYyKJFizCbzZjNZjZt2sQTTzyB2WwmFAr1eMzkyZN7zEZpbGzsMWulM5vNhtPp7PIlAxMKh1ldNTPhvtVVMwmGwwn3iQyV4ncicIs/AAAgAElEQVQALtRPOZAfLSxbZHWSY+o/We3LLSRsMGIKerG2n+2yr8gRPVZ9mxIqMnCpjl+NRSIjZzjHX8WyiGSLUV3lZ8WKFezdu7fLttWrVzN37ly++tWvYjL1rPC9ZMkSXnvtNf7mb/4mvu3VV1+lqqpq2Ps7ljmsZtZUVwCoGrtIOgr64PQeAA5Yo5/slSZRkBYgYjThyynE0d6E7Xw9/ryLj4slVBrcDSnusMjAJRqLSgsd3HtdOZ+8arrGIpEMoetKEckWo5pQyc/PZ8GCBV225ebmUlhYGN/++c9/npKSkniNlYcffpjrr7+ef/7nf+aOO+7gt7/9LX/4wx944403Rrz/marNG8BqNuHyBHA6LPiDIfKSmFpps5i4f1k5Dy6vxO0NkG+3EAyHNeiJpIPT+yDkB5uTA8FWILnbfWJ8ecU42puwnz+Be9qV8e3FF47R0JZ8QiUYCnO0uR1fMMwlU5x93pIpMlCdxyJPIITDYsLlDWAwGOjwB+PLs3bn8QcxGY1dxq/e2orI8BvMdeVgr2FFRIZL2l9JHD9+HGOnSt9VVVW8+OKL/P3f/z1f//rXqaio4Gc/+xmLFy8exV5mDm8gxLottWzYejT+acCqqjLWVFdiTyIxErv4LMyzAWAd/brGIgLx+ikUzeZQ+0kAptmLkn64N28inHkfx/mut/bEZ6gkmVB55/g5Hn7xXU60eAC4bPo4/vljlzJ3sm7VktTJsZrxBUI8+0ZdUp9u+wIh1m6q1SfhImlmINeVQ72GFREZDmmXUNm4cWOfPwPcdddd3HXXXSPToSzS5g2wbkstT7xeE9/m8gTjP9+3tFxZfpFMdaF+SqBoFrXutwCY5kg+oeLJi85EsbV2S6jkRI9xpuMMgXAAi7H394itNWf5/LN/JhiOYDMbCUci7D5xns//+5/5ry9dxyRn8sXHRfri8QdZu6mWx18/HN/m8gTjP9+/rDz+h9pA2opIetI1rIikK00vGEOsZhMbth5NuG/D1qNYzcrui2SsCzNUjhVMIhgJYTNaKLQkPyvEe6Fuiv1811UcCqwFWIwWwpEwp9t6X56+/lwHD77wDsFwhEWl43nq7it54lNXMH28g0a3jzU/fYdwODKIFybSk8loZP3WuoT71m+tw9xpZutA2opIetI1rIikK11FjCEuTwCXJ9jLviAub2CEeyQiKdHRAi21ABy2RWeBTLMXYhxA7RJvbIaK+wyGoD++3WAw9LvSTyQS4ZH/u5dzHQFmFuXy0A2zyLGaGZdj5cs3zcFuMbLz2Dl+/17vCRmRgXB7+x7P3J3Gs4G0FZH0pGtYEUlXSqiMIU6HBacj8bRmp8OMU1MlRTJTw87od2cJh33NAJQMoH4KQNCWT9Bsx0AEm+tkl3391VF55b0zbDl8FrPRwEM3zMJqvji0THLaue3SKQB8/9WDBENaClOGLt/e93iW32k8G0hbEUlPuoYVkXSlhMoY4g+GWFVVlnDfqqoy/MHQyHZIRFKj/mJB2sODKEgLgMEQn6Xi6HbbT18JlUAozHd+tx+AD182lckFPeuk3HrpFPJsZo40tfOH/Y0D65dIAqFwmNVVMxPuW101k2A43KXtV26aw4RcKxNyrcyZlM+EXGvCtiKSnjpfw3aPY13DishoUhW2LOH2BrB1WkbOFwz1+NQtz25hTXUlQNIV0rXMZHI8AQ9moxm3302+NZ9gOIjD4kh6v8iQxFb4KZ7D4da3gegtPwPlzZtI3vkTPeqo9LV08q/faeB4SwdOh4XbL5ua8Lg5VjM3zJ3IS7tP8rPtx/nggskD7ptIZw6rmTXVFQBdVu758so5fGxRCWajkfPtfixmIxaTkVsvncKnrp6BPxjC7Q1SkGPhdKuXSU4bZqOR5jafxrgM19c4qzE4ffV2ndl9eWSAL1ZXcuuCKcwozKG5zU9hnpVjzR2UF+Vqta4RNNB4UvxJttNVQxbwBkI8k+QycnaLifuWlvPF5bNweQM47Rb8wVDCZIqWmUyOL+Tj2X3P8sKBF3D5XTitTu6eezf3LrwXm8nW736RIYlE4rf8tI+fSUPj74BBzFChUx2V1q6Jk95mqARCYX70p+hKKbcvnNrnspXVc4p5afdJNh1q4uR5D1PH6WJKhsZmMXH/snIeXF4Z/2MsHInw9MYjbDzUyLP3XM2/bzrCc9sujo33LCljVVUZ//uXu/nuRxeybnNyyy5LeutrnDVg0Bicpnq7znyguoJfvlPPD147FN/+v1bO5lNXz+DlfacTxqyMjIFe0+oaWMYCJVQynNsb4Jk+lpH7wtLyhDNVAIryom9knesdxGiZyeR4Ah6e3fcsa/esjW9z+V08vedpAD4///M8995zve5fvWC1svQyNC214DkHRguHrdHYLjDnkmce+Hnlu5BQsXdbOrk458IMFXfXhMrv9p7iRIsHp8PCjZdM7PPYUwocXDLFyfunXPxqZz1fWjFrwP0T6S42DhXm2bqMW898fhEbth7lR3/sOjbGfv7bD85j3ebaHvs1xmWevsbhCBFWlq7UGJyG+rrOjBBhwdSCeBFalydIyTgHT22s6XG9q5gdOf1d83aPp4G2F8lUqqGS4Wz9LCNnG+QyclpmMjlmo5kXDryQcN//1P0PNpOt1/0/PfBTzEYN/jJEsfophRXUeKP1SQZzuw+ALyf6OJur62o8sRkqzd5mPEEPEF3Z55kt0ZWFbr5kUlLvNdfNih7n5X1a7UdSLzZuTci1cm1lEc9tO5qw3XPbjjLJaeel3YmLLGuMyyx9jcMvHHiB6c7pjLeN77FPY/Do6us6c8PWo1w3qyheIyUW071d7ypmR0ZfsZYongbaXiRT6d0nww3XMnJaZjI5br8bl9+VcJ/NZMPld/W63+V34Q64h7N7MhbE6qcUzRl8QdoLvLnRhIq1oxlD0BffnmvJxXFhxsvJtuhzvF3Xwr4GF1aTkRsvmZTU8ReVjsdogPdPuTje3DGoPor0JjZuFefZaG7z9zs22s2JL+Y1xmWWvsZhl99Fi7clnhTuvk9j8Ojp7zqzpd1P8YWZ1MnEtGJ2+PUXa93jaaDtRTKVEioZbriWkdMyk8nJt+bjtDoT7vOFfDitzl73O61O8i35w9k9GQvqt0e/F8/hSMcpAKYOcoZKyJJD0Bxdpcfm7jqLpHth2v/YdgyApbOKkn6fcdotzJsSjYffv3dqUH0U6U1s3Gpq81GYZ+13bPQGE/9xpjEus/Q1DjutTibYJ3DWczbhPo3Bo6e/68wJuVaa2qKJ/WRiWjE7/PqLte7xNND2IplKCZUM5+tnKWTfIJeRG8iSlGNZMBzk7rl3J9x328zb8IV8ve6/e+7dBMOJL+hFkhLwwOm90X8Xz6G2I5oEmWqfMLjjGQz4cmO3/XRNeMQ+4a1319Po9vLKe9HnWpnk7JSYD5RF+/bKe2cG10eRXsTGrZZ2P2/WnOWeJWUJ292zpIwzLi+3X1aScL/GuMzS1zj8mbmf4YTrBOd853rs0xg8uvq6zlxVVcYbh8/S0u4HiMd0b9e7itmR0VesJYqngbYXyVS6eS3D5Q9iKeRk9LYkpVZA6MphcXDvwnuB6P2giSqY97dfZNBO7YFwEOzjcNvyafK3AjDFNsiECuDPmUBua0OPGSqdV/r5xY56guEIsybmUVqYO6DjLyodz/qtR3n3+DlaOwIU5OhTRUmNzuPWb96t5zsfvQyHxcTazUd6rPLzjZf28d2PLsRoMGiMy3D9jcMGDDyw8AGNwWmmS7zuqsduNuMNBvnI5dN4oLqCF7cfx+kwx2Oz4byHNdWVGFDMjpZkrnmH0l4kUxkikUhktDsx0lwuFwUFBbS2tuJ0Jp6Klmnc3gA2sym+FLIvGIpPf/T4g5iMxviykv5gCKvZFP85GA73Whm9wx/E3OmxfbUdyzwBD2ajGXfATb4ln2A42KPSeV/7JXnZGL+Dtu1JeOVrMH0xu6/6LJ/d9a+MM+fyg0u+MOhDTt/3EpOPbOT0wo9xouqB+PY/HPsDLxx4gRumr2DHn2+n4byHv1pWwbLZxQN+jq/8YjcN5z08+ZkruW3hlEH3VTLPSMRv9/EwEAoTCIbJsZnxh8IEL/w7FA4TAY1xWaKvcVZjcGoMR/zG49UTwOm4eP3a5g1g7RTH/mCIvD62y8gZaDwp/iTb6aohC/gCIX6ypY4/HTzDFKeDUy4Py+dM4sHqCiLA2k21XbL5q6rKWFU1k0/821s0tXn7zO53XpISwKq7xBKKDQwTTNGZARaTZUD7RQalU/2U2O0+UwZ7u88F3n5u+TnYfIyG8x5yrSaWlA+uVstl0wpoOO9h06FGJVQkpbyBEM9sqU04Y9NqNmI1GyH+oejF8UxjXObra5zVGJye+orXWJKkKBabZiO+QIhnttT1mNGiGSoja6DxpPiTbKeESobz+IOs3VTL468fBmAP0Wrae+pd3LJgMi/vOx3fB9FK6E+8XkMkAl/94Bzu+4+d8f33LyvXJ3MimaR+Z/R70RxqO44AMHUIt/tA9JYf6JlQKc6JzkQ53REtSnv97OLoH6eDcNn0cfxu32k2HWoiEolgMBiG0GORKLc3wDNbanni9Zr4ttiYB/CFpeUqXCmSJtq8Adb1Ea/3LS3vMvOk+/VujK5hRWS06aOYDGcyGlm/ta7H9gm5VmYU5iTcB/DctqNcN6uICblWIFonxWzU6SCSMdxnoPU4YICiWUMvSHuBL+fCDBX3aeh0R2jRhaWYQ3jA2MGKeQMrRtvZ3MlOLCYDZ1w+ahrbhtRfkRib2cSGrUcT7tuw9Sg2sz7BFkkX1n7i1dotXnu73gVdw4rI6Bp0KjccDlNTU0NjYyPhbpW1r7/++iF3TJLj9gZweXpWyS7Os9Hc5k+4D6KfArS0+ynOs9HSHm3n9gbi055FJM017Ih+H18KlpyLt/wMcYaKL2c8EQyYAh7M3laCjnEA2Mw2bIZ8fBE3ZZN8lIwb/P3PVrORWRPzef+Ui7fqWpg1SUsnytC5PInHw+i+IC5vIH77gIiMroHGa2/Xu7H2uoYVkdEyqITKW2+9xWc+8xmOHTtG95q2BoOBUGhwS/XKwOXbLfEq6J01tfkozLMm3AfgdJiZkGulqc0X/1lToUUySKx+StFsvCE/Dd5mYOgJlYjJQsBegNV7HpvrVDyhAhD0jweLm9nTAkN6DoBLpjp5/5SLt2ub+dw1pUM+nojTkXg8jO4z49QYJ5I2BhqvvV3vxtrrGlZERsug5sf91V/9FVdddRX79u2jpaWFc+fOxb9aWlpS3UfpQygcZnXVzB7bW9r9HG/uSLgP4J4lZbxx+Cwt7X4AVlfNJNhtppGIpLH6CzNUiudyzNNIhAi5JhtOc86QD+3L7VlH5VhTEG/H+OhTjusY8nPMmxJdIeLtupYeiXmRwfAFQ6yqKku4b1VVGb6gPuwRSRf+fuLV3y1ee7veBV3DisjoGtQMlcOHD/PLX/6SysrKVPdHBshhNbOmugKgy0o+q6tmUl6Um3BfbJWfj6/dFm+rCukiGSQcgoZ3ov8ums2RjmjiY4ptQkoKvPpyCslvro3WUblg0wEv4UA0oeIOnRnyc1QW52ExGWhy+6g72055cd6QjyljW77dwprq6HVJolVD7BrjRNJG3gDjta/rXV3DishoGlRCZfHixdTU1CihMkQefxCT0YjbGyDfbiEYDvdZobx7+0AoTCAYJsdm5gtLZ/Lg8soux7JeGFzuX1beZZ8/GMJqNvHz+6+Jtx2rA5En4MFsNOP2u8m35hMMB+PLu4mkrcb9EGgHiwMKplN7fC8w9Nt9YrrPUAmFI7xxwEvEEk2oNHtP9/rYZFnNRion5rH/lJu3aluUUJFeJTtWtnkDWM0m7r22nC8unxVv7wuGlEzJAhqvM1tvcfyFpdF4dXkDOPuJV5vF1OOadixfw441eg+QdJV0QmXPnj3xf3/pS1/iy1/+MqdPn+bSSy/FYul63+LChQtT18Ms5QuEWLupNukse6L29ywpY1VVGR//t61Uz57ImuqKeEEua6e7uWIXnvF9F5Y6TdR2LPGFfDy771leOPACLr8Lp9XJ3XPv5t6F92IzqbCZpLETb0W/F80Go6nTCj+FKTl8bKUf64UZKvtO+DnXHianIJpQOesbekIForf97D/l5u26Zj6zeEZKjinZJdmx0hsIsW5LbfyT7tJCB/deN5NPXDVDtRWygMbrzNZbHD9QXcGv3qln/Zt12M1mvMEgd1xe0ueMsh7XtGP0Gnas0XuApLOkEyqXX345BoOhy73uf/EXfxH/d2yfitL2z+MPsnZTLY+/fji+zeUJxn++f1l5l0/femv/oz/WAPBgdSX3/cfOhI+VxDwBD8/ue5a1e9bGt7n8Lp7e8zQAqxesVtZb0texbdHvE+cDUBdf4Wd8Sg7vy7kwQ8UdvbVny4Fo8epZRYXUAs3eU/H3+6GYN9kJNPB2bUtKjifZJdmxss0bYN2WWp54vSbe7lizh2/89n3Otvm5b2k5eUqqZCyN15mtrziOEGHB1AKONXvi+2JxrLiVGL0HSLpLOq1bV1dHbW0tdXV1Cb9i+2pra4ezv1nBZDSyfmtdwn3rt9ZhNhqTbv/ctqNcN6uICbnWhI+VxMxGMy8ceCHhvp8e+Clmo5JSkqYiETi2NfrvSQsIRkIc9TQCMNWemlt+/DnRxIy1rZFQMMjOumhC5dKS6MwVX9hLW7B1yM8za1IeJqOB0y4vx1uGXuhWskuyY6XVbGLD1qMJ223YehSrWbcDZDKN15mtrzjesPXiNWz37YpbidF7gKS7pP/6Li0tjX8dO3aMkpKSLttKS0spKSnh2LFjw9nfrOD2BhIu+wbRrL3bGxhQ+5Z2P8V5toSPlcTcfjcuvyvhPpffhTvgHuEeiSTp/DFwnwSjGYpnU+85SzASwmowM8HiTMlT+O0FhA1GjOEgR2sbafNGcFhhZrGVXHP0OVJRR8VmNlF5oXbK27VaIU66SnasdHn6bufSuJjRNF5ntmSvYbtvV9xKjN4DJN0NajrD8uXLEy6P3NrayvLly4fcqWyXb7fgdCTOpjod5h73e/fXfkKulaY2X8LHSmL51nyc1sR/fDqtTvIt+SPcI5EkxW73KawAsz2+ws9k23iMqbplxmDE74jOUqk/fAKAOVONmIwGCqzRWTDNKayjAvBWbXNKjifZI9mx0unou51T42JG03id2ZK9hu2+XXErMXoPkHQ3qIRKb/e6Nzc3k5ubO+ROZbtQOMzqqpkJ962umkkwHE6qfXlxLt++Yz7vHGuhpd3PmmWVhCLhHu2kp2A4yN1z70647+65dxMMJ/40RWTUHb9wu0+3+imput0nxn+hjkprQwMA80qiw4XTEt1+NgUzVADmTYleCG0/phkq0lWyY6U/GOIrN83ucdsAwKqqMvxB1XXLZBqvM1tfcbyqqow3Dp9lXI6Fmy+ZRHlxbny74lZi9B4g6W5AN5199KMfBaIFaFetWoXNdnGKXigUYs+ePVRVVSV9vKeffpqnn36ao0ePAjB//ny+8Y1vcMsttyRsv2HDBlavXt1ju8fjwW63D+CVjC6H1cya6gqApFb56d5+0YwJ/NNH5lOcb8flCeB0WNj9jZtwWE24vAFMBiO+YAib2ZTUkswDXb45GzgsDu5deC8Qvf9SFcMlY3SqnwLEV/iZYkvNCj8xvgt1VMb5GrGYoHxSNKGS6hkqsybmYzDAiRYPp1u9TC7InPdyGV7JjJWxpZJvvXQqn7hqBm/WnOU7vztAU5uXL6+cw8cWlWA1mWhu842Z8S3baLzObH3F8QPVFXj8IX7/8PW0egIUOCzR73Yz/lAYfzAcv871B0ODKlI7Fq9xs43eAyTdDegdpaCgAIjOUMnPz8fhuFhR2Wq1cs011/CFL3wh6eNNmzaN7373u1RWVgLw3HPPcccdd/Duu+8yf/78hI9xOp0cPHiwy7ZMSqbE2Cwm7l9WzoPLK7u8ySdaMrl7+3AkwlMba+LLQzodZlZVlbGmupIfvHqIr9w8hw1b67rs7y1ZM9Dlm7OJzWRj9YLVfGHhF3AH3ORbomva641Z0lZbIzTXAAaYeAlApyWTUztDJbbSzzTDWWZNNmIxRWclpnqGisNqoqwwl7qz7ew41sKHFk5NyXElO/Q1VnZfKjk2fv3PQ9dhiEAwEmHtpiM8t63/sVDSm8brzNZbHBuIJlk6x/D/WjmbT109o0dsx65ze1tOOZGxfI2bbfQeIOlsQAmV9evXA1BWVsZXvvKVId/e8+EPf7jLz48++ihPP/00b731Vq8JFYPBwOTJk4f0vOkiliEvvFCMy9rPHVi9LQ/p8gTjP3/l5jmsf7MuvqRybH+iJZkHunxzNootszbBFP0j0WLSPbuSxuo2R7+PLwVbHpFIhDpPdGnjKbbhueVnmqGJuSUX35viM1S8p1L2XHMm5VN3tp3tdUqoSE+JxsrexsLYUqyrq2bybJJjoWQGjdeZrXsc+7wh1m450iOGS8Y5eGpjTa/Xuckup6xr3Oyj9wBJV4OqofIP//APKa+VEgqFePHFF2lvb2fJkiW9tmtra6O0tJRp06bxoQ99iHfffbffY/t8PlwuV5evTNXf8pB5NjObDjUm3N99WeWBLt8sMhqyKX6HrHZj9PuUKwA44ztHR8iHCSMTbQUpfaoGigCYZmxi9pSL7wXxGSq+00QikZQ815zJF+qoHD2XkuNJ+hiu+O1rLPztrgZybWae25Z4v8Y3keQM5/hrSxDDE3KtXFtZlJJl0HWNKyIjJenU7BVXXJGwEG0i77zzTtId2Lt3L0uWLMHr9ZKXl8evf/1rLrnkkoRt586dy4YNG7j00ktxuVw8/vjjXHvttezevZtZs2b1+hyPPfYY3/rWt5LuUzpLZnnIKU4He+g56MWWmYx9OpDMkpSFeZpKJ6Mrm+J3SCKRiwmVqZcDcOTC7T4TbQWYDamdvrzTPZmlQImhGYc5TITo8fMt4wEDgbAPd+A8Tuv4IT9XLKFy4LQLlzeg1R2yyHDFb19jod1s7nes1Pgm0r/hHH8TxWhxno3mNn+/17lFScSurnFFZKQknZ79yEc+wh133MEdd9zBzTffzJEjR7DZbFRXV1NdXY3dbufIkSPcfPPNA+rAnDlz2LVrF2+99RYPPPAA99xzD++//37Cttdccw2f/exnueyyy1i6dCk///nPmT17Nj/60Y/6fI5HHnmE1tbW+NeJEycG1Md0kszykKdcnl73d15WeaDLN4uMhmyK3yFpqYXWE2A0x1f4uViQNrW3+wC8eXYi/ogJMyEc3oszR8xGM3nm6GyY/8femYdHVZ79/zP7TDIzIQu7SBJAwm4tlk0EVNpq29e6oYRaoAFUtG7tr7ZqF/u2r7Z1xSqbRKgl2Fq19n3r0koFgQCCimxGgSQICCELZLLMnJkzM78/zpzJLGe2ZAJZzue6uJI5z3OecwLc53vnOc9zf9NVmDY7w0h/uwmfHz7+4mxaxlTpGnRW/MbTQpcoJtRKVd9UVBLTmfqrFKO1zQK5VmNabNDVHFdFReVckfSEyi9/+cvgn9raWu6++262b9/Ok08+yZNPPkl5eTn33nsvNTU1Kd2A0Whk+PDhTJw4kUcffZQJEybwzDPPJHfzWi2XXnophw4ditvPZDJht9vD/nRX3KKXBVPzFdsWTM2nWRCZcVE/xfZIS+ZU7ZtVVM4HPSl+O4S8OqVvERikQtzBCZU0F6T1eGFfrZkv/dK2H2tr+DbCrMCqlLo011EB2FWl2if3JDorfuNp4bUXD6ZFEJk/Rbld1TcVleToTP0VFGK4ocXNtsN1cfPcZO2U1RxXRUXlXNGuakyvvPIKu3fvjjr+ve99j4kTJ1JaWtruG/L7/QiCkHTfPXv2MG7cuHZfr7thNRtYOlNyRVKqfr5qcyU/mFaARiPtIzfr9bhEke9efEFUVfNU7ZtVVFTOI8H6KRe3HZIdftJsmfx5vQGXqOWkKY98arC2nuZ0bttWTLshhxNUpW2FCsDIAXbeP1THrmp1QkUlMYm0sGxnNYumFwKoLj8qKl0QW0gMh+arJ84648Z2si4/ao6roqJyrmjXhIrFYmHr1q1RdUu2bt2akoXxgw8+yNVXX82QIUNoamri5ZdfZtOmTbz99tsAfP/732fw4ME8+uijADzyyCNMnjyZESNG4HA4WLZsGXv27OG5555rz49x3nG6RXRabZiFXGjF8WaXB6Neh8PpwW4x4Ba9WM0GzAYdi6cXctesEcF6A4LoRavRUDzpQjJN+rb2kHNl8Ygcd9H0gqTtm+PibgWdHlyNYM4CrwjGjLaf1+NEr9XT5G7CZrTh8Xlwe91kGjIRfWKwereKikoEPm+bw8+gtgmVKqe85afjdUxC2Vcj7StvNvYBD9ickStUpAmc+jRZJ0NbHZU9x87iFn0Y9WrBQJX4mA06lihoodfn43uTC2gRRG6fMYwfXjEiqG+C6EXweNFoNHgDb6jj6XCvIYF+d4RI7Rd9Iho06LS6sGNqDtD7CMtnAzmpIHpj5rlmgy5mbqxEPNv1Hk0nxnM6UXo2WAyWmMdVVLoq7coa7r33Xu644w4+/PBDJk+eDMCOHTsoLS3lF7/4RdLj1NTUcOutt3Ly5EmysrIYP348b7/9NrNnzwbgiy++QBtShfvs2bMsWbKEU6dOkZWVxVe+8hXef/99vva1r7XnxzivCB4vKzZXxpw1d3m8rNpSGTU7f+fM4fiB1Vsq2fTZab4yJIf7rhpBaXlVVN8FUwu4edUOaptdLJxawJ0zh+EDxXGXzhyetH2zIqILtj0NO1eC6yyY+8Ck22D6/aA3I3gFSveXUlZRhsPtwG60M7doLsWjipn/9nwuG3QZJeNLVD95FRUlTnwoxZUhE3KliewznmbOeJqB9G/5OVhrBEDMzIKzYG2tDWu3B6yT07nlZ2DTefYAACAASURBVFCWGZtZT5NLZP+XjVxyYXoniVR6JvIvUnaTns2f1TD9on48v+lwmMbdMWMYP5hWwM/f2Ec/m5kFU/P52V8/5rHrx/PClir17XUC/e4IStpfXFTM/DHz+cHbP2Bf3T7sRjvziuapOUAvxOXxsjpGTnqsvpWfvr6XgXYLJx1OfjCtkG+OHRAzh421ckXJdr1H04nxnE6Ung3ziuZRMq6E1w+9znOfPBd+XH0+qHRh2jWh8tOf/pTCwkKeeeYZysrKABg1ahRr165lzpw5SY+zZs2auO2bNm0K+/zUU0/x1FNPpXy/XQ2nW2TF5kqe2dhW+8XhFIOfF00vYPWWSpZtPBzWvmzjYa4ZO5C39p8Ktv3wihGs2VbFs/+J7uv3wwPfHMmSlz7kmY2HuHrsAN7cf1JxXIAl0wtjzvLHxd0qPbw3/67tmOts8LNz2t2UHljHir0r2q7rdrBy70rp5x23iHveuweAhWMXqrPQKiqRfC6t2mPwJaCVksaqwHafXIMNkzZ9xfXcXjhcL42nz7IFJlQiVqiEWCenC41Gw8j+NnYfPcPu6gZ1QkUlaZpdHlZtqWTOxCE8v+lwlMb97u3PcHq83DVrBNN//x4AP/3mKFa9XxmlnbIO3zajsHesVEmg30y7t91vtp0eJ6X7S6O0f8XeFfjxB7Xf4XawfO9yQM0BehNNLk/MXBdg0WWF7D3uCLpWTszPVozvDuewPYlOjOd0EuvZsHzvcvz4GZU7CofbEXYc1OeDStel3dO0c+bMYdu2bTQ0NNDQ0MC2bdtSmkzpzei0Wl4sr1Jse++zGkx6HWvLq6PacjKNXJibETw3J9PItOF5rNse3RekfeOXjcgjJ9MYPFdpXJD2qRr17Xwjp9NLM+FK7H0Fvc5EWUWZYvOGig1MHjiZbFM26yvWo9f2ggRWRSVVPn9H+npB22q8znL4OVRvwOPTYDOKaG3SNhxrxJYf2Sq5QajB7/en7drytp8Pqs4k6Kmi0oZRr2PTZ6fpZzPH1bj+djOFfTNZt136/h+fnFDs+2J5FXptD3+LLRNPv3eulNrbiV6rT0r7ZdQcoHcRK9cFKV7NBh23XHoBAIV9MxPGd7tz2J5EJ8ZzOon3bCirKIt6NoD6fFDp2vSSjKFr0eTy4HCKim0D7RYcTuX2vlYT9c3uYFvk50gcTpGGFjd9raak+jpcnvb9QK5GaQZcCYOZJrcjONMcdV23gzPCGfIseTjcDpo8Te27BxWVnsrZY1CzHzRaGPzV4OHOcvg5eFra7lPYx0mruQ8Amc56NL42ZwWbIRsNGjw+N42e9BWRLQpMqHx4tAGfL30TNSo9G4fTw0C7hcYY2in1kTRuRF9r8HuzXjk5dzhFmtqrh92NePrtOgsuZe1OhiZ3U1LaH3pMzQF6D7FyXalNitGZAdfKEX2tScV3r6cT4zmdpPpskI+rzweVrkrSEyo5OTnU1dUBkJ2dTU5OTsw/KvGxmQ3YLcqJ3EmHE7tFub22WSDXagy2RX6OxG7Rk5NppLZZSKqvvb1LJc1Z0h5NJTwubEY7dqOy1Z7daCfblE2dsw670Y7NYGvfPaio9FQOBVan9C0Cc1scddYKlU/rAhMq2S5cRhtejQ6t30uGq23iRKfRYTVIMZ/OwrT5uZkYdVrOtHqorGtO27gqPRu7xcBJh5OsGNop9ZE07lBtc/B7l6j8y5ndosfWW7YOxNNvc5+wZ06q2Iy2pLQ/9JiaA/QeYuW6UpsUo5s+l1ZHHqptTiq+ez2dGM/pJNVng3xcfT6odFWSnlB56qmnsAWWf8u1TGL9UYmP1+dj4dQCxbZZI/sjiF4WTM2PamtocfNFfWvw3IYWN9sO1zF/SnRfgPlT8tl6qI6GFnfUuZEsmJqPW/QqtiXEK0oFr5QYfxOiV2Be0TzF5rlFc9lxcgdnhDPMK5qH6FNOcFVUei0K230AqltrgPQ6/Hh90pYfgPwsF36NllZTFhC97Ueuo5JO62S9TsvwflYAdlWr235UksMtepk5sh+nm1yK2gmSxtU4XFTWtjB/ivT9f00YrNh34dQCxIADUI8nnn5Puk1qbyeiT0xK+2XUHKB3ESvXBSleXR4vL+86DkBlbUvC+G53DtuT6MR4Tifxng3FRcVRzwZQnw8qXZukN6PNnz8/+P2CBQs64156DRajnqUzhwHEdBdYOnM4QFQl84K8TO6YOQw/ft7Yc4K/7DrOH24cj0YT3XfB1AJuWrE9OHZhyLmpVEhPiDFDqh4OilXFLXozJeNLAGkPZKTLz10b7+KO8XeoFbxVVCIRmtvski+YGDzs9Lr5UpBWjKRzy89xhx6XqMWk89Hf6gag1dQHm6sBa2stNbltfbOMORxvPZLWFSogbfs5eNLBrqoG5n7twrSOrdIzsZoNLJ05nLf2n2TpzOHkWY2s2VrF0XpnmMb96JU9/PCK4fzgsgJ+9+ZBHvr2GLQaTe92+Umg3x1xBbEYLIraL7v8LPn3EkB681wytoTvjf4eRp2xwz+SSvfAFohbgDf2nMCs1+MSRa69eDBLZw6n8rS0mkyOzd3VZ2Lmxh3KYXsSnRjP6STWs0F2+Xnt89ewG+2qy49Kt0Hjb0dFwXnz5jFz5kxmzJjBRRdd1Bn31ak4HA6ysrJobGzEbj9/y99a3SJ6rZYmlweb2YDo84W5CjS7PBj1OhwuD3azAbfoDVYwb3J5MOl1OJwe7BYDguiVPgf6yp+Vxo43bocI+t47pGWFXg8YM4PNQV95TxM2gw23z43H6yHTkIngFbAarUldptndjElnCj5oUzlXpfvTVeL3nLDvb/BqCdgGwnWrQKMB4NPmY8z56FGsOjPLxtyetsv9+7CFVR9mMTy7lSWXSJbIEw//ncKaD/l45M18clFb4fHymrfZXvsO0/t/m1tH/Dht97D3+FkefauCITkWtvzkirSNq9I16Mz4DepiQNucbi8Wow5B9OL3+TEb9bi9Pjyij0yTHo/Xi0ajiavDvQYl/UYLOp1Ul8GcJb3dbodDSKT2iz4RDRp0Wh0tYgtGrRGD1hBT04Pnu5uwGaXzVaeP80NnxK9SPmszG2LmyLFyWKdbRKfGsoTQBHpTW+yKApg6f7tMqrGq9GywGCwxj6v5v0pXpV1PGqvVyhNPPMFtt93GgAEDmDFjBjNmzGDmzJkUFRWl+x57LPKDPtcqzbgaI3ZgyZMceXK7XmoXPF5e2FIV9lZtwdR8Fk4t4Kev7uXQ6Sa+e/EFLJ05THHsWON2GDnRygwUkop40yQ/VK1+K386uI7XDr2OSWdC8Ap8q+BqSsYtxqSPP/ssiAJrD6xlQ8WGsLddi8YtSniuikq348Dr0tf86cHJFOi8+imfB7b7XJglBI+1mKT92NbW2rC+dmP6rZMBhvezotHAsQYnpxpdDMjqGm/UVLo2SroorzaxmQ24PV5Kt1bx/ObDUe1GvTamDvcaIvXb74MtT6blLbes/Tk66Zlh0LW9wLH4LazetzqmpgtegdL9pZRVlKlvq3sg8eI2Vo6slMMKHi8rNlf27tVmMqILyp+Fva+AwQweF4y/qdNXqLQnVmM9G5SOq/m/SlemXRMqK1dKllynTp1i06ZNbNq0iWeeeYY777yTfv36cfLkybTepEobTrfIis2VPLPxUPCYwymybONh/H6YM3EIS176MNh+24zCLjVD73Q3U3pgLSv2htu6LQ98XjhmAZYYs83N7mbWHljLypBzHW5H0Md+/pj56ky1Ss9BaILD70rf518W1tRZDj+f10uToEOzXMFjLYEaLdZAzRaZYA2VNG/5yTDqGZqTQXV9K7uPNvDt8YPSOr5KzyOWLsqfF08vYPWWqpjtXU0nzzvuVtj2NGz+Xdsx19m2z9PubddKlUiS0fR1B9YFP8vty/cuB2Dh2IXqSpVuTKK4TTYu0zVOj0ApdiHtsRuJ0+OkdH9pp8Wqmv+rdHU69CrGZrORnZ1NdnY2ffr0Qa/XM2DAgHTdm4oCOq2WF8urFNvWba/mshF55GRKvxS9WF6FXtu13rbpdSbKKjYotq2v2IA+zhsnk87EhhjnllWUqW+rVHoWn78jvWmyD4Ls8GLSVZ2wQqXZreHLJinpvNAeMqFiVl6hkhVYodIg1ODzp7eAZ9EAaSn5brUwrUoSxNPFF8urMOp1/H3P8ZjtXU0nzzs6vbQyRYmdK6X2NJCMpm89sVWxfX3FevTaXvJLcg8lUdwmG5fpGqdHcI5iNxK9Vk9ZRZliWzpiVc3/Vbo67XrKPPDAA0yePJm8vDwefvhh3G43P/vZz6ipqeHjjz9O9z2qhNDk8uBwKle5djhFGlrc9A0sg3Q4RZpcnnN5ewlpcjvies83xWiT2+Ofq/rTq/Qg5O0+Qy8L2+4DoRMq6XP4qTojLbXNMXvINLZNkLQGrpHpqkPja3NRsBqy0KBF9HtodNen7T4ARg6Q9np/UNWQoKeKSmJddLg8mPXKCX1X1MnzjqtRWpGi2HZWqrOSBpLR9P4Z/WO3e1TN784kittk4zJd4/QIzlHsRtLkboofyx2MVTX/V+nqtGvK8A9/+AN9+/bll7/8Jddeey2jRo1K932pxMBmNgSrnkdit+jJyTRS2ywEP9vSUWw2jdiM9mDl7kjsRju2GL70cnv8c1V/epUeQpztPqLfS3XAwnhQGrf8VDZIz4rBdiHsuNNoxavRofN7yXDV05LRDwCtRofN0AeHp4F64RTZpr5pu5eL+kuxXHHKESw8qKISi0S6aDcbcInKv3B1RZ0875izpJopSr+YmftIRWvTQDKaXhOx1TCs3aBqfncmUdwmG5fpGqdHcI5iNxKb0RY/ljsYq2r+r9LVadcKlY8//piHHnqIDz74gMsvv5wBAwZw8803s3z5cj799NN036NKCF6fj4VTCxTb5k/JZ+uhOhpaJLvThVMLEH3pXYrfUUSvwLyiuYptJWMX4vXFfpMgeAWKi4oV24qLinF73Wm5RxWV806c7T7HnXWIfi9GjZ4cQ/qSoyNnpPn1C2zhEypotLSasoDY237qXOmtm5WTaaSfzYTPDx9/EeNtm4pKgHi6uHBqAW7RS8llhcHtsJHtXU0nzzteUSpAG0lGLlzxsNSeBhJpuuAVuHbYtWSbssk2ZTOizwiyAyvm5hXNQ/Sl5z5Uzg+J4jbZuEzXOD2CWLEL0vE0xW4kok9kXtE8xbZ0xGoyzwoVlfNJu1aoTJgwgQkTJnD33XcD8Mknn/D0009z99134/P58Hq9CUboPcSzcYtsC1ofB6zjlOyMLUY9S2cOA4iqZr5wWj43LN9+bqubB60Wk7NVtBitlIxbDEg1UxxuB+PyxvHI1F+Rby+gye1ApzUgeAVMOlOY9ZrVaGXRuEUAYVXE5SrfTtGJx+tRONeNxSOAyRp1f6odo0qXJM52H7kg7QBTNtqIto4gr1C5wB6dmLSYsrG5GrA6T1PDmOBxeycVpgUoGmDjdJPA7uoGZlyUvtUvKj0DWT+dHi8Wg447YujiHTOHodVouGbcQOZMHMK2w3X8z5sV1Da7eq8LiBKRWn7Z/dLxnSvB2g++/hsomCGtntPppf5xtNTr8+LHH1dfE2m6VqPl6/lf5/qLrsftc9PsbibLlMUxxzHys/Ix6qInyFS6D/Hy2VTiMl3jdFli5dmxjk8Pid0kHbqc7mb0OhNNbgc2ox3RK8Q0iIDoeNegoWR8CSDVTEnW5SdZG+REzwrV5UflfNPuKkEff/xx0OFny5YtOBwOLr74YmbNmpXO++vWxLJxu3PmMPwQ1bZgaj4LphZw86od1Da7WDA1n6Uzh2OOEAMNcPXYAdw2o5CGFjc5mUaO1rdiMeh49fYpZJj0iD5f54uI6JKqiadoq2jSm1g4ZgGLx99Gi6cFi97CC/teiHpIFo8qZsE7C6h31rc9lPUm5o+Zz5LxS4IP8srGSha+s5B9dfuUzx15CyVFxZhKr4bhVwbvT7VjVOmSCE1w6N/S9xHbfQCqnNIS+HRu92l2a6hpkeRgcOQKFaDV3AcaY69QqU+zdTLAyAF23j9Up9ZRUYlC8Hj56+7jfGfCIF7cVsX7h2pZt+BrfFNBF70+Hze/sJNPjjUGNfifd1+GVqM5NzrZHVDS8lkPwpS74LL7pD5bn4LXlihqfaSWjssbx6rZq1h3YF1CfdVoNNxcdDNLxi8J+6Xq1c9f5blPngsem1s0l+JRxZS8U8K0QdNYNH7RefrLUkk3SnGbKiaDjttmFHLnrOFhLzC7fXzHis1L5sfPv6fdC5f/WKqZYraD1xMzLxdEgdIDaykLsSOeVzSXknGLFScqYuXOi8YvYuHYhSwev5gmTxM2gzSJGiufTtUGWSn/F7yCOpmi0iVo14RKdnY2zc3NTJgwgZkzZ7J48WIuv/xy7PbO2ZvXHYln43b12AG8tf9UTOvjB745kiUvfciyjYcBWDK9MLhSJXTcnEwjfa0mapsFGlrc3HPlCG6bUYhRr8XYMQOnxHTQVlGe+TZ5Dbywb3WYjbJshebHz72X3Ms9790TZr0mz15n6DP408E/8fRHT8c/d98q6dzL78eyQVoy6Jx2N6WqHaNKV+Tzd8ArKG73gdAVKumbUAkWpLV4yDBEL49uMQWcfgK1W2TkFSp1nbBCRS5Mu+fYWdyiD6O+Fzk1qMRE1sCxg+28uK2KZ/9zmNXf/yprAt9H6uIPrxjOnTOHs+SlD3uvlWo8Ymn5Wz+BllrpF7eP1sH7fwhvD/RX0tLF4xYrWpxG6muo1Wq+PZ9hWcO4pegWdtfsjjpX/rxo3CLuee8eNGhUne7mNLk8rN5SybKN0XF795XDWTy9MKX6J3I85waMGTo9D+5sYsVm1gWw5Ql4//fhx5Xy78w86WuM1VxOdzOlB9ZG5eDLA58XjlkQtlIlGXtkg85Ajk7KDQw65X+/9togy8dyLPHHV1E517TrafPSSy9RX1/P7t27efzxx/n2t7+tOJly/PhxfL1p72IIsWzccjKNXJibkbT18dryaoz6thn20HEbWtx8VtMUrJlyTu3h0mTNptcZY9oob6jYwOSBk4N7piOt13RaHaX7S5M797OX0RfOkvaA730lYN/ceRZvKirtJrjdZ3rUdh9oc/gZlMYJleqzgdUpVuV9yC2BOLK2hk+oBGuodMIKlUFZZqwmPYLoY/+XjWkfX6V7otNq+fue40wbnse67dXkZBqD30O0LkZqKvRCK9V4xNPyfa+CrT98sFq5XUFLs03ZTBo4KabFaai+hlqtVjuq+ej0R4zvOz7muaG6rup098ek17G2vBqIjtu15dWY9N18dUlHUYrNjFwonAkfrFI+J0VrZCl+Y8XqBvQRq0vSZY+s2iCr9DTalVHEmkCJZPTo0VRXV7fnEt2eWDZufa0m6pvdKVkfO0Is37qMPVyarNkS2SifEc6QZ8kLfg61Xktk0xZ1rrMBrP3BYE5s36zaMaqcD8K2+0yLavb7/cEVKgPTuOXni8CEygCrcmHnFrM8oRK+5UdeodIg1ODzp7d2lkajoSiwSmV3tbrtR0WiKWCBLOtoqpoqH+tVVqrxiKflBjM4z8Ztj9TSPEseDa6GpPQ1UsOTOVfWdVWnuz8OZ2K7816NUmxa+0NLXdqskRPmwhFt6bJHVm2QVXoanfqKxu/3d+bwXRrZxi2S2maBXKtRsQ2UrY9DLUNjjSv3PWf2cLI1m2Jb8tZsso2yEnajnWxTNnXOuuDnUOs12aYt6XMtOdBcAx5Xwuuqdowq54UE231Ouxtp8brQoqG/MUb8tYOjjdJzY2CsCZXAlp9MVx0aX9vEidWQhVajw+f3ctZdn7b7kZG3/eyqPpP2sVW6J7aABbKso6lqqnysV1mpxiOelntcYOkTtz1SS+ucdeSYc5LS10gNT+ZcWddVne7+2C3x81l7b49RpdhsrpG28aQh/4bEObgtoi1R3p1sTNoTXleNbZXuhbrmtZOIZePW0OLmi/rWpK2PF0zNxy22/QLTZezh0mTNFs9GeW7RXHac3MEZQfplKtJ6LZ5NW9S5I29BrHwPWuth/E2B63aexZuKSrtIcrtPX2Mf9Nr0LIf2+uB4o7xCRXnLj8toxavRofX7yHS1TZxoNVrsBmn1Sq3ry7TcTyihK1R8vt47Qa/Shtfn47sXX8C2w3XMn5JPQ4s7+L0SkZoKvdBKNR7xtHzcDeA4CV9brNyuoKVnhDPsPLmTuTF0PVRfIzU80bmhuq7qdPdHEL0smJqv2LZgaj6C2MsdQ5Vis7UeKjfBpCXK56RojRwvB59XNBcxwo44XfbIqg2ySk9D3YDaScSzcSvMy1Rsk11+blqxPfg50uWny9jDtdOaLZJQG+V/Vr2FSWdC8ApcU3ANxaOKmf/2/JjuABaDRdGmrbiomHmj57H84+WMzR3L9EFTJZef9XNgxgMw/X4senO7LN5UVDoNoRkOvyt9r7DdBwjZ7pOdtsueatbh8WkwaH3kWGIkQxotraY+2Fz1WFtP05zRL9iUZcjhrLuOOtdJRmZdnLb7AsjPzcSo03Km1UNlXTPD+6lvrXo7sga+vOsYC6dJLxf++N5hSudfCkg1U0J1cdH0Au5c/yEj+9twiSLfvfiCnmGlmi5kLc/sC9ufhzOVbVo+5S5452GY/QgYMmDbM1FaH6ql/6z6JyadiZc/e5knZjyBBk3QCeRC24XcOupWrrvouqC+WgwWFo1fhFlvZs3+NTjcDlbvW82q2avCzg11+blr413cMf4OVad7ADazgaUzhwNSzZTQXFjJ4bLXEZpn731F2oLncUHjcbjsR4AGDr0rrWh1fAkjrkop/4bYOfi3Cq5WdPmJlXenmjurNsgqPQ2NvxP35dhsNj755BMKCws76xLtwuFwkJWVRWNjY6c7E7W6RfRabZiNm1yJvNnlwajX4XB5sJsNCKIXk8Jn+Vz5c4sgYtRrMeiUxz2nuFulAlih1mzGzJSHUfKiN+lMYdZrcjV/p7sJvU7au20z2hG8Lkw6c7CvfG6Yr71fA3pD1P05PU70Wr3idVS6Jucyfs8pB16HVxaAdQBcv1pxhcpvDr3MX06+z9V9J3LTwGhL5fZQ/oWZp7b3YYjdxQ8vPRGz3+UH1jLg7BG2Xnwnh4dcETz+7xN/Ze+Z7XxryK1cO7QkLfcUyn//30EOnnTw2+vGMm/S0LSPr3JuSVf8ytrqdHuxGHW0CiKGCF10ukUsRj0erw+36CPTpMcteoOueSq0abizESxZ0mdjhvSW2+8FvUmqo2LpA6Jb0lBTZng/Y0aUhru9bnQaHTqtDqfoxKK3BNtEn4joEzHrzcFjHp8Ht89Npj4Tr8+LH3+YNrt9bjw+D5n6TFWnzyMdiV+nW0SnkA83uTxRua+6JS8EoSkiDgUw2aS8W65zZOkjTbaksN0nFKUcXMllRyZW7hw8HrA1ThSr8nVDbZCtRmvK46ionG869TdwjcIvBL2NWDZugsfL6i1V/H3Pccx6fdibszyrCcHj5YUtVbxYXkVfq5lXbp/C2vKqsFn8O2YM4wfTCs6NTXIskrRmi4fgFVh3YF2Up33J+BJyzOHWaIIoUHpgHWUhvvXziuZSMm4xOeac+L72OmPU/ckP6EQWbyoqnc7Bf0hfh05VnEyBznH4+SKw3SdW/RSZNqef8MK0fYxS7HfGlh+AUQNtHDzpYGdlgzqhohJE1lbZTtuol57tLo+Xf+77kjVbqzha78Ru0TN/Sj4LpuZz08pyZl7UT12hIiO6JFvWqFWmPwKtFrYotE39Iay/BSo3SsdmPYjw1QUxNdyPn5cOvhRsG5c3jpWzV/Kng39S7G/QGcJ0WEmbVZ3ufggeLys2V4atrP71f43lm2MHsHpLJR8dPcOIfjYOnW7ikqHZ6goVGdEF5c+Gx+GsByU78+3Pws5VIfG5RFq5Ykh+hQrEz8FjrThRyp0Fr0Dp/tKkx5Gv+2bVmyErY75FybgSXj/0Os998py6elyl29CpEyq9uShtPJxukRWbK3lm46Gw4/LnxdMLWL2lKvj5iZtG8uK2Kp79z+FgX4dT5Hdvf4bT42XJ9MJu+8YtGU/70JUppQfWsSLCt3554PP3x8xn7YF1Kfvaq6icdzwuOPQv6fuhytt9ACqd6Xf4OZrA4UemNVCYNtI6ubMnVEYPtPMqJ9hRWY/f71cn6lVi0uTysHpLJcs2hmulrJ13zhzOkpc+BOC2GYXnZ1VnV8HdKk2mbP5d2zHXWenz6Gvh4BvKbX4ffO0H0oSK6yzOPkMo3bdaQZeX48fP7KGzw/R98bjFrFPQaSXNV+kZKOW8DqfIxPxsnt90OBivWw/Xh31dPL2wd69UiRWjWRfAlifg/d+HH98c+Dz1bmkFSxKkkoOncxyl/kDwuTEqd1TQBUh9Pqh0Bzp1WcPBgwcZOlR9oxiJTqvlxfIqxba/7zmOUa8LtudkGpk2PI9126sV+68tr8ao776z+Kl42ut1Zspi+NZLez/Nqq+9SvfkyH/A3QwZeZA3QrGLQ2ylLpBgDDSlr4ZK2wqV+EXgWgKuAlZn+AqVLGMuALXOzplQGd7PhkGn4XSTQGVdS6dcQ6VnYNLrWFterdi2bns1l43IIyfTyIvlVei1vbwmv04vvfWOJCMXsvOV2wA+WA2Fs6R+GbnoC2fG1OWyijKG2IeQHXheZZuymTRwUkydjtR8lZ6BUs5b2DeTfjZzzHhdW16NqRvntmlBKUYzcqFwJnywSvmcnauk7UFJkkoOns5x4vUvqyhj8sDJwedGe+5HReVck/T/zOuvvz7pQV977TUAhgwZkvod9QKaXB4cTuXij2a9Hoezrb2v1UR9sztmf4dTxOHykGftnpMFyXjay0sKm+L41st7PxP52udY0vdmX0UlbXwqb/eZAhrlX/SqWmsAyDZYsaRpctDp0XC6JbkVKm1bfsJXqMgTKs1iI06xBYs+9RpK8TDqKQ3BfgAAIABJREFUtYzoJ2372VFZz7C+6iozFWVCtTO6TaShxU1fq4nPappocnmCW3F7Ja5G6a12JNb+0FKn3AbS8dY6qR/Q5KyPq7sNrgbyLHmcEc6QZ8mjwdWQtOar9AyUct4Rfa00JojX7pzbpgWlGE0mPl2NUpHpJEglB0/nOIn6y88L2akz1ftRUTnXJP2KJisrK+k/KvGxmQ3YLcpzWS5RxG5pa69tFsi1GmP2t1v02LvxkshUPO1tcXzrBa+g+tqrdE9EN3z2pvT9hXG2+wTqpwzohNUpdpNIpiG+jWxLYMtPhqseja/NztKkM2PRSZModZ1WR0WK6+1H6hP0VOnNhGpndJuenEwjtc0Cdou+d28lADBnSXUXImmukWqiKbWBdDwjT+rXXIPNkhtXd3PMOdQ56wCoc9aRY85JWvNVegZKOe+h2mayEsRrd85t04JSjCYTn+bkfw9LJQdP5ziJ+mebsoPPjfbcj4rKuSbpCZUXX3wx6T8q8fH6fCycWqDY9t2LL8AteoPtDS1uth2uY/6UfMX+C6bm4xa9im3dgVQ87UWvi3lFcxX7fqvgagSvS/W1V+l+VL8vvVEy94F+o2J2q2o9CaS5IK1cPyUz/uoUAJfRilejR+v3kekKn9iQ66ic7qQJlTGDpMRrR2WDWptLJSaC6GXB1HzFtvlT8tl6qI6GFjcLpxYg+uJPIPZ4vKJUZDaS1no4U63cBvC1xVD5ntSvtR6xclNMXS4uKuaY41jwLfMZ4Qw7T+5kboz+kZqv0jNQynkra1s43eSKGa8LpuYjdOPcNi0oxWhrPVRukgrQKjFpieQClCSp5ODpHCde/+KiYnac3BG2OiXV+1FROdeom9HOAxajnqUzhwGEVTxfOLUg6D4gt/99z3H+sus4f7hxPBoNYS4/C6bmd/tK6Kl42luMNkrGLQbkmilyVfCrKRm3GJPepPraq3Q/Pv1f6euFk0EbO5aPyA4/6SxIm2T9FAA0WlpMWdhd9VhbT9Oc0S/YlGXM5aTzKHWuk2m7t1CG97Ni0GmoaxY4UtvM8H7qWyqVaGxmA0tnDifPalR0+fnBul3cc+UI1eUHJIe+6fdL3+99RXIGsQ6Ar8yD3OFSW2Zf2P48nKmUJnwvuw8uXQRv3AX9RoPHhaW5lpIJiwEt/6z6JyadiTxLHtcNv44rhl6BBg13jL8jqO+r961m5eyVaNCk5Cqi0n0JzXnf+6yGgXYLJx1OdlefYenM4QC8sedE0PHy2osHd/vcNi2Exuihd8E+CBxfQuNxyc0HOuzyE5qDbzmxhf4Z/alprWH64OkpxWMquXxkf/m5Eery89rnr2E32tXng0q3QeNv5+u+v/3tb/z1r3/liy++wO0Of7v50UcfJTXG8uXLWb58OdXV1QCMGTOGX/ziF1x99dUxz3n11Vf5+c9/zpEjRxg2bBi//e1vue6661K6d4fDQVZWFo2Njdjt7fNsj4XTLaLTamlyebCZDbhFL0a9LvhZ9PmCzgKtbhF9SN/QNpD2nZr0OhxOD3aLAUH0YgoZy+31IYo+Mkz6qHPj4m6Vil25GqWlgV6xzf4Y2jzvXYGvemPMvk53M3qdiSa3A5vRjsfnwe11k2nIRPAKAX95qU30ClhiuOxEetrL58oPU8ErYNUZQXTTrPFj0plD2lyYdOYwH3slX/uw68Tom4rXvTxWi6cFo86IQWto1zgqqdGZ8XvO8fvhyVHQdBKuegQGfzVm12/u/DknhHoeKLyRkdYL0nL5X/wnh09rjdw8uoavDmxO2P/yA+sYcPYwWyfcyeELrwge31bzJjtq/83lA77D94b/KC33Fslv/nmQA186+O/vjuXWyWqx8+5KR+I3Ul+9gVUmOq2WVkHEoNdi0GlxuDzYzQacbi8Wow6314dH9JFp0uMWvd3WFa9dyHrvbpW029kIlizpLbbJ1qb3crurWdJ8vbGtr8eFU6dDrzUGddcpOrHoLYg+Nxavl2atNkyz5XbBK2DQGNBqtUF99/g8kkbqLcFjok/E5/dh1BnDdN/n92HRW/D6vPjxh+l3MjobqfmqNneMjsSvUk5rMxtiHo+M95Ty3K5MrBw81nGXQ5okcZ4FSx/JFdBsbzsu95ePE+f/fYxrNLubo3PuOK6YscaXx4nMvxP1j7xu5O8EatyqdHXa9WRatmwZDz30EPPnz+eNN95g4cKFHDlyhF27dnHnnXcmPc4FF1zAY489xvDh0gz1unXruPbaa/n4448ZM2ZMVP/t27dz880389///d9cd911vP7668yZM4etW7cyadKk9vwoaUXweFmxuZIXy6voazXzyu1TWFteFbaqJHQViiwMcmE8Y8gOLJfHy+otlcorUvRaXthSyYr3jyiOGxfRJdmwhXraT7pNmgXXm6UH8rZlkutI8V+kvh+sVuwriAKlB9ZSVrEh+DCcWzSX4lHFzH97PtMGTaN4VDEL3llIvbOeeUVzgytJIgn1tBdEgbUH1rIhZFx5lYlGZ2DdvtVh1ywuKg5cZ0HgOtJMtlyA1qCTkmfBK1C6v5SyijJyLbms++Y61n+6Puw6yc6Cy2Nt+3Ibf7zyj5TuL23XOCq9nFN7pckUvQkGjIvZrdXr4oQgbbMZbM5Ny6X9fjjWmFxBWhm5jorVGVmYVrZO7pwVKiDZJx/40sGOI/XqhEovJFRfHU6RCUOy+HPJJF7YUsWmz09TOv9S1mw+wrrt4Xq7aHoBd67/kBqHG5co8t2LL+g9K1REF3z8Eoy9EXYuj9DywNvsijdh+FVS+5H3onV/8ESE7/+d0v1rw1aUzC2ay7xR8yg/voWr8r/Oun0vROUC80bN463Kt7jhohsw6AxY/VbWHVzH64dex6QzYdKZ+K9h/8UNI25Aq9GyZv+aKN0vGVfCUx89xdIJS1l3YF1Kq1pCNV/V5vOLUk77yH+N4eqxA6OO3z/7Im659MKweE8pz+3KxMrBL7tfitX3/qft+KwH4ZL5sP1Z5ZUon5TBjpXSpIrHBeNvhMt+jKBF+f/9uBJMkdeY8yeECyfHzLmV8vWYcTWuhDcOvxF01RS8AtcUXMOicYvYeHQjj+56NNj/wUsf5Kr8q2JeN/R3AmjL41VUuiq6X/3qV79K9aT58+fzxBNP8MADD/D73/+eP//5z9xzzz20trZy9uxZrrnmmqTGGTlyJCNGjCA3N5fc3FyuvPJKnnrqKUaPHs0ll1wS1f/HP/4xF1xwAStWrCAvL4/p06dTXl7OJ598wo033pj0/QuCwGOPPcbPfvYzTKY0uWW4RZZvruSZjYcQRB+/v3Ec7xw4xbP/OYwgSm/RBNHHzqoGdFoNE4ZkYdApl7BpdnlY8f4Rlm1UPrev3czS9R+lPC7uVtj6lORpL7qkY6ILjm4DjQ4GTpAe9O//Hr71OFT8H7z/B8W+zsEXs2Z/KSv2rgzWJhG8Ah/WfIhJZ+Ib+d/gNzt/g1Fn5DvDvsMbR95gd82H6DQaxuaOxqAzKv/s7mZK95eyMmLc3TW70Wq05Fpy+cmWn0S1hV9nNzp0jM0bG3wIOz1O1uxbw4q9KxC8Ar+e+ms2frFR8TqR50b9W4eM9fCkh/nPF/9p1zgq7aMz4ve88dFLUg2VwRNh2BUxu33WcpxXT23Drs/gv/pPTsulGwUtrx60osHPd0bUE+uxEUpWaw39GytptvTji4Ftk9hun4sDZz8A4MpBN6Tl/iLRaGDz57U0tLhZcnkhGo2mU66j0rm0J34j9RXgN98dy5v7TrHsP4f5zXfH8s6BGkW91Ws1zB49gLXl1TQ6xeS0sicg633eRVDxv8partXCyGtg9xqpXUH3nd9+gjUn3mPFvmitN+qMzLrwCtZXlEVpoNxe0KeAf1X/i9G5o1mzfw3P7XmORncjDa4GTjtPs+XEFr5R8A3+9vnflHVUo2PhmIX86eCfUtLZSM1P5hyVxLQnfmPltD//9mjWlldHHZ836UL+ue9kzBy428ZuvBxcq4W8EbCnrO34xIVw4HUpJ1fsf5EUuy214DoDR7fhHHc9aw6/pvz/XqNhrG0ohj3rg2M1X/N7Sg+8GDPnHp07GmNIvh4vrrQaLQV9CvjLZ3+hwdVAo7sxeHzWhbNYs39NsP//u/T/sb5ifdLXVVHp6rTrifTFF18wdepUACwWC01NTQDceuutbNiwoV034vV6efnll2lpaWHKlCmKfbZv387Xv/71sGPf+MY3KC8vjzu2IAg4HI6wP+lGp9XyYnkVADmZRqYNz2Pd9mrFvi+WV6HXxv6rN+p1rC2Pfe7Q3AxyMqMfNInGVfS0l5H3UH+wKsTnfnXMvnqdmbIK5X/rDRUbgh7yod8DrK/YgD7OmyGTzsSGGOOWVZQxxD4kyps+8prSdcL96kM977NN2UwaOCnmdRJ53ctjdXQcleQ4F/F73vj8benrBZfG7Xa4Jf0FaU84pP+b2WYRgy65nZ+JrJMbXKc6rWjcsL5WjDot9S1uDp1OvD1JpWuQjvgN1VcI19hEertuezWXjcgL08yEWtkT0OklXS+cGVvLd64C+0DY96qy7mfkoi+cSdlnLyue/lbVW/TL6BdTA2VdfrPqTUw6E9tObIvqk23KZohtSFCfI3mz6k0yDZkp62yo5id7jko06YhfpZy2sG8m/WzmqONyPMfLgbtt7MbLwXeugsJZUhxCSDyuSq5/4Bx9dn6c//cb0IeeM+wqTHpL3Jw7ciVXvLgqqygLy8NDj/fL6Ee+PR+AfHs+fTP6pnRdFZWuTrueSgMGDKC+Xlp+PnToUHbs2AFAVVVVyg4M+/btw2q1YjKZuP3223n99dcZPXq0Yt9Tp07Rv3//sGP9+/fn1KlTca/x6KOPhtk6DxkyJKV7TIYmlweHU/pFoq/VRH2zO/g5EodTpMnliTmWw+mJe25Di5u+1uiHTaJxFT3tZeT9ma6ziX3uDWaa3I6kPORDv5fbmmKcJ7fHG7fB1RAcK9Y1g9fxNAXbQz3v8yx5NLga4l4n9NxI5LE6Oo5KcpyL+D0vtNTBiQ+l7wdPjNv1SMDhJ13bfQCOO6Rl0/2ScPiRaY2x5ceqt6PT6PHh44z7tNKpHcag03JRf2lP945K1T65u5CO+A3VVwjX2GT0NlIzE2plT8DVKOl6PC13nZXqpBjMyrpv7U+Tsz6mxpl0JhrdjQlzAbmmQv+M/lF98ix51Ce4RqK8QElnQzU/2XNUoklH/CrltCP6WmlUON7R/LlLEy8Hd52F1jopDiFxHh7ZP3BOvHh1uB00ORvazsm/LHFsucPjJFFchebhoccdbgfDsqTCxMOyhiV8bkReV0Wlq9OuCZUrrriC//1fyZmipKSE++67j9mzZ3PzzTenXCB25MiR7Nmzhx07dnDHHXcwf/58Dh48GLN/5DJvv9+fcOn3z372MxobG4N/jh07ltI9JoPNbMBukd541DYL5FqNwc+R2C16bHGK4tkthrjn5mQaqW2OduVINK6ip72MxyUVuzL3Sexz73FhM9qT8pCP9JO3G+3YYpwnt8cbN8ecE+VNH3nN4HVC/OpDPe/rnHXkmHPiXiee1708VkfHUUmOcxG/54VD/wb8kDNMirc4yBMqg9I4oSKvUOmXmXxy2mKW3jxlOBvQhKxE0Wi0wVUqtc7OsU4GGD0oC4DtR9QJle5COuI3VF8hXGOT0dtIzUyolT0BuUhlPC039wkWnVXU/eYabJbcmBoneAWyjFkJcwHBK2Az2qhprYnqU+esIzfBNRLlBUo6G6r5yZ6jEk064lcppz1U20yWwvGO5s9dmng5uLkPZORJcQiJ8/DI/oFz4sWr3WjHZslpO6d6a+LYMobHSaK4Cs3DQ4/bjXaONB4B4EjjkYTPjcjrqqh0ddo1obJq1SoeeughAG6//XbWrl3LqFGjeOSRR1i+fHlKYxmNRoYPH87EiRN59NFHmTBhAs8884xi3wEDBkStRjl9+nTUqpVITCYTdrs97E+68fp8LJxaAEBDi5tth+uYPyVfse/CqQWIAXcCJdyilwVTY597tL6Vhpbot8qJxlX0tJcZf5OUVE1a0uZz/zXJopiMXMkiUV4mOP4mRK+LeUVzFYeaWzQ36CEf+j3AvKK5iN7YFq2CV6C4qFixrbiomGOOY1He9JHXlK4T7lcf6nl/RjjDzpM7mRvj/hN53ctjdXQcleQ4F/F7Xjj8b+lrgtUpAEdaOmOFipSw9k9hhYrLkIlXo0eLj0xX+KRGH3lCxdWJEyoDpX/7nVUN+HztMqhTOcekI35D9RXCNTaR3s6fks/WQ3VhmplQK3sCXlHS9VhanpELVzwETTUw7oZo3QdorUes3MS8kbcoXuLqgqs53Xqa4gS5wDUF1yB4BS4bfFlUnzPCGY41HYup+9cUXEOLpyVlnQ3V/GTPUYkmHfGrlNNW1rZwuskVdVyO53g5cLeN3Xg5+KQlUPkeWHKg6DvS18pN0vF4/VtDdLi1HvFMNfNixNK8ormIoecceRdBdMbNuYWIfD1eXBUXFYfl4aHHT7eeptpRDUC1o5ra1tqUrqui0tVp1yZSrVaLNmQP45w5c5gzZ05absjv9yMIyoE0ZcoU/v3vf3PfffcFj/3rX/8K1nM5n1iMepbOlJazvVhexWNvfcYrt09BoyGmy08srGYDS2dKzkeRLj93zByG4PHywyuGR7kZJKx+HuppH8vlR/a23/IkzPsbjP4u5BRISw8z86ChCvJGYNGbKBknJV7rFVx+7tp4F7ePvz3o+CNVAS+mZNwiTB4nTk8Leq2xzULNK2DRGbH6fCwatwggrIJ40OUHuGP8bWHXlF1+2q4TXcU/1PN+fcV6nvroKdZ9cx0aNFHXKRlXglOULNuUrNpCx1q9bzV/vPKPAKrLj0ry+HxSsgQw+Ctxu7Z6XXwpNAAwyNQZK1SSn1BBo6XFlIXdVY+19TTNIUv4s87BhMqwvpmY9FoaAnVURg5Q32L1BiL11eEU+eN7h/lzySS0Gg1/fO8wpfOlOkRKLj/fW7MToPs7hcSyVVVC1vsP18Hku9q03HlWslbVGSXbVbMNpvwQMvtKul/8F+n8gMuP5f0nKPn+30GjZX0Mlx9ZmyNzAdnlR3YLiaXt+bb8mG0l40pY9vEylk5YGqXXsrOIVqOlwdkQZscaqfmqNp8/YuW0u6obFI+fOOtk6czhaND0LJefeDn4ZT8GdyMsLW+zR3Y1wrDZgf4KLj8frZM+h4xjyR5GSe4wQBP9/35cCaYP14adYz3xcdycO9LlJ25cjSvh1c9fxW60R43zbvW7Ycc/rkntuioqXR2NP9WiJwHOnDnDmjVr+PTTT9FoNIwaNYqFCxeSk5N80cQHH3yQq6++miFDhtDU1MTLL7/MY489xttvv83s2bP5/ve/z+DBg3n00UcBKC8v5/LLL+e3v/0t1157LW+88QYPP/xwyrbJDoeDrKwsGhsb0/62u9UtotdqaXJ5sJkNNDo9WE166lsEcjKNHK1vZVheJsYkBKHZ5cGo1+FwebCbDQiiF5NeR6sgYtBrMejariP6fEEb5oQEkzKHlFh5PWDMlNpEt/SgtfQBn1eqSB5r8gVwupvR60wBr3grbq8bj7uZTHMWgteNSZ9Bk9uBzWhHrP8cy+bHEb71OC9UlFH22cshD+O5lIwsxlT6DRg+m+ZZDwTOlb3sXVgxgLsRpzEDvc4cHFfwCtIe7ST86iO97SPPrWys5JHtj7Cvbl/C5Eseq0Vswag1YtAakroHlY7RmfF7zjj5Cay8HPQWmLsB4hRI3OeopnjP78nSZ/DU6Bhvq1Kk1aNh/mvSZMgjl1dhMST/xm/6gT8x8Owhto2/nUNDZwePf1T/Pu+dfJ1Lci/n9lG/Tst9KvE/b37KvhON/Oo7o1kwrSDxCSpdio7Eb6S+en0+/IBeqw3TRVkzm1weLEYteq2ufVrZlRBd0oRHHD1WxO2ULLK2PgmHN0oTJjtXRFgo3wbT7pX6u86AKTDhIjik3OCLnTh1BvQXXEqT2ILNYMMpOrHoLYg+NxZRxKnXSy9JAhootwteAavRGrydZndzsC6K3WinprWGpz96mlmDZzHjwhnBeiuS7gv48GHRWfD6vPjxh+m32+fmjUNv8Nwnz8WcMInUfFWbO0ZH4jcyp3WLXow6LQ6XiN1iCB5vdHrIMusR/f6weO+2sRtJVA7ulWJ0yxNSEdrIiRNRAKNFqndkyZK+mrKg/nNpkrS1Ttr+E3jhid4U+/+9wrXdWh2CT8CsMwdjz+V1YdKaMOqVnXZijS/Hd2gMW43WlPurqHQ32vVk2rx5M9deey12u52JE6Xl6suWLePXv/41//jHP5gxY0ZS49TU1HDrrbdy8uRJsrKyGD9+fHAyBSQ3odCVMFOnTuXll1/m4Ycf5uc//znDhg3jL3/5S0qTKZ2N/LDPNOko3VrFY29XkJNppK/VRG2zQEOLm3uuHMFtMwoTCoM1sE80L1BMz6jXBr62PeBy5bZUdm/Jb7Tkug2yNZm7VbJN3vw7uPXvcLRcsmuTcZ2V2kBKvowZWIxWcLeSs/dV2PQoBpAKXjXXYGith28+Rs6Y62H3sxg2PoJz7gZKK8pYsa+tcrnD7WD53pXg97Nw9q+wbCjGuuM5+PYycibMgY/LMPzf3dISZWt/LM01YMkhZ+ZPYeQ1GAIP32T86mN522f4MvjTwT/x9EdPR9yXtIVt4diFiitVAPro2va4JnMPKioceU/6OmBs3MkUgMOdWD/FZhRTmkwBaLbkwtlD2FvCt19mGTp/hQrA6EF29p1oZEdlgzqh0suQNTM3WGBW0j2nW+Qfe7/k6XcP0SfDwIi+Vg7VNlNZ2xLU23ZpZVchVJtlFPRYGX+bVestZdJkyvt/iB7H75Pcxt79VVuNhRkPSL+wrb8JC0BGLjkzfwYXz8Ngkn6ZNugMYABZHSM1MFILdRodf/3sr+w6tYsjjUeC2wDeqX6HO5ruYOHYheRYonU09PscXQ5Oj5O1+9eyYu+K4HElzY6l+Srnnsic1uvz8dymIzyz8RCFfTMV49ao13bv2FUiMgf3OKD8WYV8O/B58l3w/FToWwS1FTD7Efhyj9Q/kBfTXCNt5ZnxAEy7F0vgGlH/7yOu3extZu2+Nazcu5Jpg6Zxaf9L2VWzi21fbuP28bczf8x8xQmOWHEl942M4VT7q6h0N9r1dLrzzjuZM2cOVVVVvPbaa7z22mtUVlZyyy23cOeddyY9zpo1a6iurkYQBE6fPs27774bnEwB2LRpE2vXrg0758Ybb6SiogK3282nn37K9ddf354fodPRarQ8v/kwIO0J/aymKbiHu8vavsmWbhm5MORrcezaVkp9Q8/7z2+kh3lrPZw+2LZHc+cqyMiGbc8ktGBc/9nL4ZZu7/4CtDopkYTwsesPwT9/FH4fHfnRtTpK95cq35dqsaiSbioDEyqD4m/3ATjSKk1QDE7rdh/Z4Sd1t4Rms5T42AJ1XWT6GKUErdb1Zcpub6kg11HZUVWv1lFRASRb5cf/9RkNLW4qa1t452ANlbUtQBfW21SIa7e6Mr4Ohup64czYFsofrIb8y9p+MWuth/f+B4ZOa9Pk1npJ6zugu3qtnuc/eZ6NxzYGJ1NkUtFa1Ra5+xNqh94j4zZZDOb49shGC/QZChX/C86GcDvlqJw7wfMgApPOFLQv3vblNp7++Gm2fSnZm6v2xSoqydOup9WRI0f40Y9+hE7Xtm1Fp9Nx//33c+TIkbTdXHcm0uYxlC5r+yZbuiVj1+ZyRJ+nRIQdc0qWbq6zbdaPydxHB1AtFlXOGR4nHN0ufT8w8YTK4RZ5hUry2ykTIRek7ZeRQv2UAE2BlTL21ogVKkbp/lzeVprFxg7eYWwKA3VUzrZ6+KxGjUuVbqq3qZDIbjWeDqai65E2rLGOdUB306W1qmZ3f3p83CaLnCMrIefB+YGCzqnm5wlI1TZZRUVFmXZNqFxyySV8+umnUcc//fRTLr744g7fVE8g0uYxlC5r+yZbuiVj12a2R5+nRIQdc0qWbuY+bdaPydxHB1AtFlXOGV9sB68gvfXNuiBh98rAxEU6t/wcb09B2gDNFnmFyikIWYmi1xqw6iVb41rniTTcpTJ6rTZYjFa1T1aBbqq3qZDIbjWeDqai65E2rLGOdUB306W1qmZ3f3p83CaLnCMrIefB1Vulz6nm5wlI1TZZRUVFmXZNqNx9993cc889PP7442zdupWtW7fy+OOPc99993Hvvfeyd+/e4J/eSqTNYyhd1vZNtnRTsk8MZdJtUt/I85QYdwM4TkpjJbBgnDfylnBLt0m3ScW4xt+U3H10ANViUeWcUfW+9HXgxVIhuji0iC5OBhx+0rvlR55QSf0NYKupDz60GLwClgh7xGxTXwBqXJ03oQIwJrDtp1ydUFGhm+ptKsS1W02gg8nq+tcWR9uwKh3roO6mS2tVze7+9Pi4TRaPK749stsJR96VPstxHLN/avEpeAXVvlhFJQ20a5Pp3LlzAfjJT36i2KbRaPD7/Wg0Grxeb8fusJuiZPPY5W3fQi3ddq2BG9aAIQM+eknaduNxSZMbka4CMa3glsCkO2BXKUxeChoNlg9eYNGNpZj1Zl49/HdMOhOCV+BbBVdTMvJ7mDb9FgZ9FUZc1XadRFbPaSCWFVzJ2BK+N/p7GHXKlc5VVFJG3u7Tf2zCrkcCBWmz9Jlkpun/utsLNS3S86d/O1ao+LR6Ws1ZWF1nsLecxBmyFSnb2JdjLYc57TyWlnuNxZjBWbDrGDsr6xG9PvS6XrLXXkWRbqm3qRDPbjWRDsrnZvaVdP36QO2FSJefKXfBS9dJbaHH/roA+o0O/NJ3O0xcCDGcP5IhVGu3nNhC/4z+1LTWMH3w9JTsjFVb5O5Pj4/bZDHbJTcfgL1/C8m3b5SOH9sJ2YVtxxuPB/prYO8r8fPzBFiN1qB98ZtVbwZz8mvBR7bcAAAgAElEQVQKrlHti1VUUqBdtslHjx5Nuu/QoUNTHb7TOZe2q5E2j93B9i1ohex2YDPaEH0eLG4XmG04RQG9vs1aLcyCMNKOzeNqq6Fi6RM8t8XTglEn2QzLSZBsX9z22YVVZ5L2jpqzpJUqepOy1XPwuoG+XjGO40GCn13JClnpZ41xXjJ9VTpGt7ZN9jjh0SHg88B1q8E+MG7310+V84vP/8xo64X8uDA9BbiPntXz43fyMOu9PHJ5daJFMopcfmAdA84eZuuEpRy+8Mrg8d1177H51D+YmDeLJUW/TMv9KuHz+Vny0m5a3F5eWzqVSy7M7rRrqaSXzozf7qi3KRFleRqig0r9hGbJxU9vbLNcdTulNp0hXF812ohjbsAv6a7QKtkih2h0m21yuP1pqKbHsz9NtT8o66wGDTqtLqEtsqrR6aEz4rfHx20ksXJWlyMsZ8bjkuJcaJLiUD4uCmCy4XQ3odeZA7m6HdHrwtLOLTqx4jFW3KTaP9X4U+NVpbvRridWV5wk6apE2jx2dds3wStQemAdZRVlbW98Rt5CyajvoXG3UlrxZ8oqNii/DQq1Y/O4JBu4D1aBtR/CD96h9LMytn1Zzh+v/COl+0vZEDJOcVExxaOKWfjOQuqd9RQXFbNoXAmmvy+FYx+0vYmLtHoWXZILUJpWr8gPbAsWXtj7QvjfQ4w3X4JXoHR/aVJ9VXo5Jz6UJlMsOWAbkLB7ZxSkDW73yfC0azIFpMK0AziMPcLpJ9sY2PLjPN6he0yEVqth9CA7u6rPUH64Tp1QUQG6n96mTKTdqtLKSVkTD2+E4r9I30euRpl+v3RuphSv+H2w5cm2t922gXDTWtj+R6g7hHDdckr3vRCm/XOL5jJv1DzKj5dzVf5VrD2wNkrTY73hFkQhpf4QX2cNOkNcW2RVo7s2PT5uQ1HKWWc9CJfMh+3PSq4+oau8L/sR7CmTHLdC+gtfXRDI1UPz8bmUjFuc8qoSwSuwLiLvv2vCXVx/0fXKcTOuhDcOv8HznzwfPH7nhDu54aIbovrHHSdG/KnxqtIdafcU8EsvvcSKFSuoqqpi+/btDB06lKeffpqCggKuvfbadN6jyjnC6XFSur+UFXtXBI853A6W71uFX6Nl9tCrWLF3ZXjb3uUALBy7sG32WGiCbcvg/d9L4163nNKK9azYt4pls5ZR9mkZKyPGWbF3BX783HvJvdzz3j2Be/Az/9tPYn1qLGz+ndR52r1tSaW7VRImuQ0kwVHqm46/B4WfNZW+KiocLZe+9h+TsH4KtG356RzL5NS3+8jIhWntLeFOP30CNVROO48Ft312FmMHZbGr+gzbDtdz1xUjOu06KirdhlBNvKUMdq6A9//Q1q6kj0o6esXDUL4M3v8Dzvv2U7rvhSjtlzX8uuHXsXrfakVNB5g/Zn7YypNmdzNrD6xNuj90TGdVjVbpMsTKWbMugC1PBHPm4PHNgc8DJ7S5+rjO4uwzhNJ9qxXycenzwjELsCRY7SUTKz4GWAcoxvXyvcvx42dU7qigO5DD7WCgdaBi/3jjQHT8qfGq0l1p1zTw8uXLuf/++7nmmms4e/ZssE5Knz59ePrpp9N6gyrnDr1WT1lFmWJbWUUZQ+wXkm2KfhO8vmI9em3I3JzeJK1MAcjIRV84k7LPXibblM2kgZOCnveRbKjYwOSBk4PXKKvYgMk6AHIDvyztXCktk5TR6aVjSkT2TYF4fw+RP2sqfVVUwiZUkuBwy5cADO4Uh5/2W1I2B+7HFjmhYshFgwbB56LR09D+m0yCsYMlR6EPj57B6e6dtbpUVMKQNTEjFwpnSitTlAjVx0gdDT03dwR66wDKYmj2W1Vv0S+jX0xNL6soi3qjbNKZUuoPHdNZVaNVugxKOWsw3lYpn7NzFRTOkvoF+usLZ8aMyfUVG9CnsIpDKT4S5eplFWVhuXqs/onGUYo/NV5VuivtmlB59tlnWb16NQ899BA6XVvRqIkTJ7Jv37603ZzKuaXJ3RTXj77B1UCeJU+xrckT4lXvPNs2m27tT5OzHofbQZ4ljwZXQ9xrnBHOBK/hcDtocjugb5HUwXVW2mMq42psu04kkX1TINHfQ+jPmkpflV6OV5S2r0FSBWmbRSc1bun/d1q3/DTJW346sELFLK9QORlmnazT6skySolfTScXph2YZSYn04jb62P30c6dvFFR6RbImmjtDy11yeljpI6Gntu3iCZ3Y0yNM+lMNMZplzS8KepYKv2hYzqrarRKl0EpZ00mVlvrpH6B/nJOrUQwb04SpfhINVeP1T+ZcSLjT41Xle5KuyZUqqqq+MpXvhJ13GQy0dLS0uGbUjk/2Iy2uH70OeYc6px1im02Q0ghLEsfaZ8nQHMNNksudqOdOmcdOeacuNfINmUHr2E32rEZ7VBbIXUw95EKdMmYs9quE0lk3xRI9PcQ+rOm0lell3PqE/C08P/Zu/P4KKuz4eO/WTKTyTKTjQQIS9jDEgGVffWhYvVptVqtBVxf0D4K2paqfbFWu75UW62V+gjFAl2E1l1bK4qKooDgArIJAoEQSCAQkplJMktmef+4MyHLTGYmuTOZSa7v55NPkns597mTXHPunDnnXBjSIGNA2MOP1imjPzL0qaTo1Mnw4/VBWaBDpQNTfmqTM/GhIcnrxORq/iAYWEelopPXUdFoNIzuq8Te1iOSPlmIxjax5oyyzkok7WPLdrTpuWcPkm6whGzjXF4Xljb2K214eqtt0RwPHWtnpY0WcSPYM2sksZqSoxzXcHzgmTqYxufmCAWLj2if1UMdH0k5LeNP4lUkqnZ1qAwaNIjdu3e32v7mm28yatSoDldKdA2Pz8OCwgVB980vnE+p7QRVrqpW+xYULsDja5L33uNSFtMCqKvEU/w+C0Z8lypXFTvKdzCvcF7Qa8wrnMfH5R83XmN+4TxcNaeh8rBywKTvKe/yB3g9yrZgWh4bhbZ+Di3vNZpjRQ8XmO6TO0rJqBHG0U6Y7nOuTke9V4NO4yfL1P6/TZ9WT51ReQA0N9QzIMOovGvV2SNUQFlHBWDb0dYdvUL0OIE2sa4Sit+HiXcEP65p+9iyHW16buVhPDWnWRCizb5y0JVU1FUwv3B+0P3zC+fj8rqabXN5XVEdDx1rZ6WNFnEj2DNrIN4Cz8wtTboTijcrxzUc7yl+P2RMLiichydIDIUSLD4Cz+ptxWnTZ/VQx4crJ1j8SbyKRNWuyWj3338/ixcvxul04vf72blzJxs2bGD58uU8++yzatdRxIgpycTCixYCylzFZll+CuejQcNdF32PN4692Zir/r8H/XfrlbeN6crK5AA7/oRp0yMs/D9vgUbD6r2r+eOcPwIEzfJz68Zbm6z4vxDjP28OnbnHkKJsA9Wy/LT5cwiyyng0x4oermS78jnS9VPqAhl+1F+QtleKG20H14u1p+SQ5qrCXFPGmewL95RpyAU6P9MPXFhHZe8pK9a6eiwprTN8CNFjBNrE1F6w88/w7YZ1GYJl+Qm0j8Ha0Q+fgJtfAY0W09sPs/DaZwANzzXJujGvcB43jbyJrSe3sqhoEQD/OfafxmeDqwZdFTRrT5ohrfH4plk82sry05F2VtpoETeaxlogo1a9E6wnmz0zt8ry8/lflO8btpuqS1l4yR20jMn2ZPkJFR+na06zqGgRGjS8ceyN5s/8RQt56auXMBvMjceX15Q3Hh+qnEjiT+JVJCqN399kAnwUVq9eza9+9StKS5V3Ifv168cjjzzCwoULVa1gZ7DZbFgsFqxWK2Zz+6aFxL1Qee4j4HDXoNcZm+R/d2Oqd4ExjRqvC6MuuUnueSdpgSG6La9Z71QajIbvHV4Xel0ytZ5aDFoDSdqkxmu4vC6MTa7p8rpI0xmUed7JZvDWgyE1zL1GcGwUHPUO9Fo99no76UnpeHyeNjMJRHqs6JiEjF+fD347GBxVcNXj0GtE2FO+t3cF26q+5Lb8rzEzO/yaK5H418EU/vqFmYtya7ip6EyHyhp77E1GlG1j/+Bv8Mno2xu3H685xEvHV9LHNJCfX/KXjlY5rB+9sJuyaicrb7qEr48Jn4padK2EjN9E4bIri8I7rGBqaIM1WtAbLrTL7jrlWaDlM0GwdtTrAYMJ3HU4dHrluaChjXN4HJj0JqWtNqRR467BqDM2eTZwYdAY0Gg1TZ4lLrSLgeObtflhMpN0pJ2VNlodEr9BRPu83Rin1coUeY9LeSPSaWv2zEy9U4nFwPGB7Q3HX3hWt5FuMOPxui5k94myTqHiMVhcN93e8vhQcRZt/Em8ikTTrhEqDoeDBQsWcMcdd3Du3DmKi4vZunUr/fr1U7t+oj2C5bmPdNSGx4lp61Ow5wWyGnrPky65FSbfjcvvZ93+v7QaWbKoaBFGNKGvmaqsqWDSGQBIIYW/HfgbLx9+udm7WTeNvImVe1ay9+xeZuTPUHqjUxsWwW04N6hAIxHJsVEIvHhn6ZQFOJN0od/9juZY0QOdO6R0puiNkD0kolOO1AZGqKi3IO2FDD/tXz8lwN6wIJ2l5lSz7YE1VM46y/D5vWg1ulbnqmlMXwtl1U62HjknHSqi56p3wtanlGwhTdvgqfeCqxb2vQwfr4Sq4tAjVSB4O2oyYEKZrvPWsbf4+5d/54T9BGaDmSVjl3Dd8OtYt39dq2eDW0ffyp0b72Tvub2t3mUOdJ5kmSJvMzvSzkobLTpFtM/bHidsW9H8+MsehItvhe1/bD5y5aIbYPpS2L1eid2m22csbew8yTIpo1gb/6ajrJPL6+Iv+//SbITZTYU3hYzrRUWLQsZvqDiLNv4kXkWiaVeHyjXXXMN1113H//zP/6DX67n66qtJSkri3LlzPPHEE9x1111q11NEKlSe+8D3034Qupc62LkA7zxCzcU3se7QP1rlkg/kir91xI2kRXDNYDnmAVbuWYkfP5P7TGbDwQ3sq9wHSM550U2UbFU+9yqECNL+2Tx1VDRm+FFzyk8gw0/7UyYH2ExKx0nLDpX0pAx0Gj0efz2VrjP0Su7b4Wu1ZUxfC28fOMNWWUdF9FQuu9KZsuWxC9sCbfDIq+HL1+CDIPug7WeCJoK13Ta3jd5pvVm9d3XQZwM/fhYVLeL7m7+PzW3jmT3PANKui24i2uftUMdb+sGHjzePX2g4zg99xiodoc22Bym/HXUK9UzeVlwD3Dr61rAjyoToSdq1KO3nn3/OjBkzAHjxxRfJy8ujpKSEv/71rzz11FOqVlBEKVie+4Adq5T90Z6bPQyj0dJmTnqj0QLZw8Jes60c8xsObmiW215yzotuI7B+Sm5k66cUN2T4yUxKI0WlOcN+P5xUIcNPgD1F6VBJqzuLrskieFqNtnGUyum6Ex2+Tjgj+5rRaKD4bC2nrc5Ov54QcUdvVEamtJSSDVmDlHUZggn3TND0EkHa7kxjJpP6TAr5bNCyTQdp10U3Eu3zdrDjU7Jh8Ozg8QtK7A6+TDkuXPntqFN74nr9wfWylokQLbSrQ6Wuro70dGXdjLfffpvrrrsOrVbL5MmTKSkpUbWCIkrB8tw37qtW5mhGe26vQmxua/i8970Kw14zXI75prntJee86Bb8/gsZfvIiWwvlaGC6j1G96T42l5ZatxYNfnqpMELFpU/BpTehwY+5przZvixjHgDldcc7fJ1w0ox6BucoayZ9dERGqYgeyFEdvO1Oy4Pac+1/JmgiWNudY8rhvPN8xG16YJu066JbiPZ5O9jxkcRo3TnluHDlt6NO7Y1ru1tiWIim2tWhMnToUF599VVKS0t56623mDt3LgAVFRWySFVXC5bnvnFfhrLAVbTnnj2I2WAJn/f+7MGw1wyXY75pbnvJOS+6heoSsJcpU316DY/olCN16qdMPtmQ4Scz2UOSrl1rkTen0WAPMe0nJ1lZy6SsLjYd7EUN2X4+PHw2JtcTIq6YMoK33TVnlDVR2vtM0ESwtvuc4xxZyVkRt+mBbdKui24h2uftYMdHEqMpOcpx4cpvR53aG9fpBolhIZpqV4fKww8/zH333UdBQQGTJk1iypQpgDJaZfz48apWUEQpWJ77gEnfU/ZHe27lYVwua5s56V0uK1Qebr1z+g/B5238tq0c8/MK5zXLbS8550W3EBidkj004lTeh2sDHSo5YY6MXFnD+im9VJjuE2ALsTBtdmCEiuO4atdqy9h+ygPklq/O4vWp0FkkRCLxuJQUq02lZCvvalefaP8zQdNLBGm7q1xV7CjfEfLZoGWbDtKui26k6TNzSjbkjrowNSdYbDU9PnsYFH4TTFlQ/H7r+A2YdCcUb1a+Dld+y2u0Kqv1Oe2J6/mF83E1meYrhGjnorTXX38906dPp7y8nLFjxzZunzNnDtdee61qlRPt0DTPfaSrjgfSq3mcyoriQc5NS0pnUdEiQJk/GTTLz6wfXzgv/1Ic16xAnz1MSaum1TemPQuWY35+4Xzmj5zPrRtvTcic840p3oKkhxQ9XON0n8jWT/H7/XxVq3RQ9FexQ+VChp+OT/cJCKyjklFzstn2bKMyQqW8rgS/349Go1HtmsEMzUvDlKSjqq6efaesjO0f4h06IRJRuBSoxnSY/iPl6yPvKW394NnK8Sk5MO374PfBztWtnwl8fvC6w6ZXDdV2n645HfLZ4NbRt3LnJuUfxZbtery0mfFSD5GADCkw40cw6hrILFCm7qTmwPljkDNMWduo1fH3wYRFyqiyQNpkpx2GzAE0rZ/bp/8Ih+0E+h/uw+6oJN2UjafqOKasIa3Lb7xG5P8DtCeuFxUtwhjs2u0g8Se6C43f7+9xb+fZbDYsFgtWq7X7TlFqfACzKUP8vPVgSG19nMcJHz7RrCOEb/5BmZrgtIEhTek9//TP8O0/U+NzYzRaGvPeu5zVpBktoDdcuKarFpchhWf3PtvshTjow1RDjnmX16XktE/AnPMur4tn94S+V6GuhIvfpy6G80dhziPQb0LYw8+5rVz28TI0aHhmzGIMKi3g+KsPMvnitJFvF1YwKV+d+c99zh9ixpd/57y5gNdnPd643evz8NSBH+PDx6MTnifTmKvK9dry+01fsfP4eZZePpx75wRZIFvEhYSL367Wso1u6w0Sdy1odPDRE63Tso6br/wD1thxUq9MQ/zw8cjffIFWbXegra5x1yhteMM/Ri6vC71Gj06ra3VsvLSZ8VKPRCLx20I08QlK2uMPH2+R3vxOmH4f+L0tnts9uHS6IH+j81l40aK2/0Yj/R+gQTRxrVZ2H4k/0Z3IUuvdVeAdptSGd7h1htbHBEuvdupTWDkNvvZzKPxveHoi1FXCd9fDtqdI2/JbyB5GVq9COHuQpMrDysiUpqmRk7ys2ftsq/SKwVImtswxn2g550OlkpT0kAIA+xmlMwUN5I6M6JSvGqbP5BkzVOtMgQspk/PUHKHSMOXHXFOmvAOuUWaR6rR6Moy9OO86Q1ldSUw6VC7qZ2Hn8fNs+eqsdKiI7iHatKxolM6Ulse/+YDSjk/7AaQqo8rwepR/7CIuWxGq7Q78k5Vlat2GNz02XtrMeKmHSGDRxmfI9OYN30+9F3Qpjc/tDp+XNXuCPUuvBDRt/41G8j9AE+2J646Q+BPdTbvWUBHdRFvp1T76PZj7Kl83pnVbrXxfeRgO/uvCmilRpEbubikTe9K9inY40TDdJ7NAGe0VgcMNC9L2U3G6j6New7k6ZVFaNddQqU3OwKvRo/e5SauraLYvO4aZfgAualhH5fMTVVjr1Os0EqLLqJGWNdTx0ZatknhpM+OlHiKBRRtDodKbg5IeucU0mu78N9qd7030TNKh0pOFS68WSNUWZerFcKmRu1PKxJ50r6IdSrYrnyNcPwUujFBRs0OlzK50pqQmeUlN8qlWrl+jw5ai1DPTfqLZvsA6KmUx6lDplW4kP8OEzw9bj0r6ZNENqJGWNdTx0ZatknhpM+OlHiKBRRtDodKbNx5vbbapO/+Ndud7Ez2TdKj0ZOHSqwVStUWZejFcauTulDKxJ92raIfGBWnHRHxKYISKmgvSnmpckFa90SkB1hSl4yTT1jxF8oURKrFJnQwwtp+SPvmDQ5I+WXQDaqRlDXV8tGWrJF7azHiph0hg0cZQqPTmjcdbmm3qzn+j3fneRM8kHSo9Wbj0auePKfOu6yqVhWkn3hH62Cap2NpKjdzdUib2pHsVUXJUw5l9ytcRjlDx+L0crS0H1E2ZfMre0KGSon6HSnWq0qGS1bJDJTmQ6ec4sVr7PJDd54OvzsbsmkJ0mihToEZ1fLRlqyRe2sx4qYdIYNHGULD05o3H36nsb3p4N/4b7c73JnommaTWk4VLrxZIhbznBdj1N7j6f0GjUeZ6trGaeag0bN1x9e6edK8iSqU7AL+yFpEpM6JTTjgqcPs9GLVJ5IR496Y9TnVCyuQAa4oyEqXlCJVMQy80aKjz1mB1V5JhVK+DKJTC3maMei2nbU72l9kYk28Jf5IQ8SrKFKhRHR/s2MzBMOVuuPjmkFl+Oipe2sx4qYdIYNHGZ9P05jtaZvn5ESRFltK4O/yNdud7Ez1Tl6ZNXr58OS+//DIHDx7EZDIxdepUHn30UUaMGBHynHXr1nH77be32u5wOEhOjuwBIFHTvrWZr70xRVogJaIn6Ar9QbnszdMp1juVF3Z3HQ6dHr3O2Jgm2eN1YtIZI0rFFioNG4DDXdOiXBemCBftjMe89W3dq1BXwsTvpkeUDABDL4dp34/olI0Vn3L/wTUMSenNT4Z+V7Wq/ODNHE7Z9CwcV8aIbIdq5QIku+1c/clj+NDy3FV/x9vkQWjNV8upcldw7+hHGZM5SdXrhvL424f4tKSK788Zxg8vHx6Ta4rIJUz8dpb2tNWRpkANHFfvgCTThWt4XMo/c22U7fA40etNF9rkUG1Y4BruOmp0eow6Y+M/Q9GkVA3WZmrQKGmWY9i2S9sdnR4fv8GEis/As7WjWpnuE4jDls/cge0hXhtC/o2247Uk1POzWtujJfEnuosuHaHywQcfsHjxYiZMmIDH4+EnP/kJc+fO5cCBA6Smhs6XbjabOXToULNtkXamJCqX18WafWuC52v3o/zjFkkPeUseJ2xboYxCSUpWOlMuuh4mLcal8bNm31rWH/pH62tGkIotVBo2l8fFmv3rWH9wQ5Ny57Gw6A6M+rZ7pdv8OXRhj3aoexU9WDvWT/mqVv0FaT0+OF2jLEqbm6L+CBVnUhrOpFSS62vJsJ+kMmNI475cUz5V7gpKa47ErEPl0oJMPi2pYtOBM9KhIuKLx9m+tjqSFKgepzKSdMz1sOMZ2PtSkzb9htDXMKQo7eqBv4VvVwP1P3cY17XPsG7vs2xo0o7PL5zPoqJFYdtxaN1m+vDx7J5nY962S9stOixYfNY7lRTJO4OMRAl0bgZSmOsMbb42BP0bbcdrSbDn5yVjl3Dd8Osi3r6gcAELixbyyuFXePqLpzscqxJ/orvo0g6VjRs3Nvt+7dq15Obm8tlnnzFz5syQ52k0Gnr37t3Z1Ysbbedr93N7/mWYAnnvoSGvfcP3034QusfaXae8IDc9F+CDx3CMvpY1pzazcu+FFG9q5Ih3uGtYs38dK/dcSDWnlKt8f/vo20KOVJG89SJhuOugbJfydRQZfg7Xqp8y+UyNDq9PQ5LWhyW5E+YlazRYU/JIthaTaStp1qGSl9yPQ9ZdlNR8pf51Qxg/IBONBg6U2zhZVUe/zAhH6gnRmYK1t5G21ZGW3Wes0pmy5bfN97dxjYjb1Sb1r/nhPtbtfZZVLdrxQBm3jr414pEqUdVBiETgsiudKVseu7DNWQ0fNHw/9d7mI8aifW1ox2tJqBjrndab1XtXt4rlUNuf2fMMfvyMzB7ZmKVHYlWIOFuU1mpVUoZlZWW1eVxNTQ0DBw6kX79+fOMb32DXrl1tHu9yubDZbM0+Eknb+drXo88sgJTs1jt3rFKGA4ai0yvHtJSSjT6zoFNyxOt1RtYf3BCi3A3o2+jdlrz1PVNCxu/JT8BXr8RlWl7Ep3XGCJWTTdZP0WpUK7aZxnVU7M3XUck15QNQWnu4cy4chDk5iRF5ysPqOwfOxOy6IriEjN/OEKq9hfBtdSRl73kBBs+GnaujukbE7Wqg/tnDMKb1ZkOIdnz9wfVRv0stbXv8kvhtB71RGZkSzI4/Kfubiva1oR2vJcFiLNOYyaQ+k1rFcqjtAesPrmdyn8lkGpuvDSexKnqyuOlQ8fv9LF26lOnTpzNmTOgh8oWFhaxbt47XX3+dDRs2kJyczLRp0zh8OPQD+/Lly7FYLI0f/fv374xb6DRh87U7zgf/p81ZrczpDMVpVY5pKS0Pu6OyU3LE2922tssNsU85V/LW90QJGb/HP1I+9y5SFnKOgN3joMx1HlC3Q+WEVXnA6d0JKZMDrKnBF6bNTe4HwFlnGXWemk67fkuXDFQe9DZ9KR0qXS0h47czhGpvIXxbHUnZSclQey7qa0Tcrgbq36sQm9saph2Pri2Wtj1+Sfy2g6M6TBxaW2yL8rWhHa8lwWIsx5TDeef5iLcH2Nw2qlxV5JhyWm2XWBU9Vdx0qCxZsoQ9e/awYUPwHtGAyZMnc9NNNzF27FhmzJjB888/z/Dhw1mxYkXIc5YtW4bVam38KC0tVbv6nSpsvnZTFtQE+cchOUNZICuUZItyTEs1Z0g3ZXdKjvh0g7ntctvIbCJ563umhIzfQIdKXlHEpxxpmO6TmZRGqooZNk5UK3OSe6e5whzZftUpyhTMlh0qJn0q6UlK50Zp7ZFOu35Llw5URjnuKD6P1aH+ujEicgkZv50hVHsL4dvqSMqudyprOER5jYjb1UD9zx7EbLCEaceja4ulbY9fEr/tYMoIE4ctss9F+9rQjteSYDF2znGOrOSsiLcHmA1mMo2ZnHOca7VdYlX0VHHRoXLPPffw+uuvs3nzZnS24zYAACAASURBVPr16xfVuVqtlgkTJrQ5QsVoNGI2m5t9JJK287XPx1N1HOoqW++c9D1l1e9QvB7lmJbqKvFUHWdB4fwQ12x/jniP18WCwnkhyp2Hxxv6nz7JW98zJVz81jvg1KfK170jX5D2cCdM9wEoaRih0iet80ao2FJy8aHF5LZhcp5vti+vYZRKaU3spv30tiSTn2HC4/Pz/qGKmF1XtJZw8dtZQrW3EL6tjqTsKXdDyVaYeEdU14i4XQ3Uv/IwrprTzA/Rjs8vnI+rjXY8GGnb45fEbzt4XMoCtMFMulPZ31S0rw3teC0JFmNVrip2lO9gfotn/VDbA+YXzufj8o+pclU12y6xKnqyLp3s5vf7ueeee3jllVd4//33GTRoULvK2L17N0VFkb8TnGgu5Gv381yz1bbns/CiRUqWn1k/bl/mgBlLla9bnGsy92Nh+nzw+3guWJafdq66bzKksbBIeeB7LsosP5K3XiSEk5+A1w2mLEjvG/FpX9acBNTtUHF54LRdyfDTmR0qXl0StpReZNSdIaf6CKW9JzbuyzXlc8S+lxMx7FABmFCQyandDt7YU8414/Jjem0hWmmjvW1sq6NJgxo41mEFkwWKbgB9CgycquzfuTqi54GI29Um9U97+2EWXassQrm+nVl+2lUHIeJRy/TIGq2SzQdN61ifvlSZntdUJK8NHTme0DF2uuY0i4oWoUET0fZAlp+Xv3oZs8EssSpEA43f7/d31cXvvvtu1q9fz2uvvcaIESMat1ssFkwmZZXoW265hfz8fJYvXw7Az3/+cyZPnsywYcOw2Ww89dRT/O1vf2Pr1q1MnDgx6HVastlsWCwWrFZr4vS2e1w4zh9Fn1mA3XGedFMWnqpjmLKGKi/kjQ9iNmW4n7ceDKFTTzeqd0HlYcgaBHXnICUHzh+D7GHgdePQadFrDarniHe4a9DrjNjdNtINZjxeV8jsPq3Olbz1PVrcx+/m/6estj9oFsy8P+LTbvz8NxyoOcFdA65iQoY66X6PVOpZ9k4OaUkeHp5ZEv6EDrj08CsMrvicL4Zdz64m714fte3n1RPP0jdlED+7eG2n1qGpE+fr+PFLezDotHzy0NewmCQdYzyI+/jtbKHaao8TPnwisn+Qgh078Q6YdBcceQcKr1KeCxo7ZsI/D0Tcrgbq73ZQo9Nh1Bmxu+2kG9JxeV1RZfdpdx1El+nx8dtSvRM+fDxIeuT7oLoELPnNn61zhrVelDYg2uf4djz3h4oxtbYL0VN16QiVZ55R3uGYPXt2s+1r167ltttuA+DEiRNotRdmJlVXV3PnnXdy+vRpLBYL48ePZ8uWLRF3piSkhhRppg8ehZRsstLyoOYMSXWVysiUpinSUhve3dYZwpfbNLVbIBtJzRll+tCsB2DqvZiSlBdntXPEBzpPskzZUZcreetFXGu6IG2E6n2expTJBSmRZwUKp8QaWD+l80anBFSl5UPF52RXH222PZDp53RdCW6vC0OM3sEakJVC/0wTpVUONu4r58YJA2JyXSHaFKytjiYNaqhjA6mS+46DbSuUc1J7XbhGGBG3q4F6mAwEuk6yTOq0xdK2i4QSLj1yn7Gw9ustnq1/HDpFerTP8e147g8VY2ptF6Kn6tI1VPx+f9CPQGcKwPvvv8+6desav//9739PSUkJLpeLiooK3nrrLaZMmRL7ysdS0xRpdZVQceDCmikdSbfYNLVbq3KDpHYTQrSt3gknG9ZPiWJB2sO1ZdT7PaTqjOQkqfeu34nqzl8/JeB8mjK9Kcd6FJoMfEzTW0jRpeHDF9P0yQBThyoPmq/tLovpdYWISjRpUNs6dudqGHyZkkK5I2mYhRDhhUuPPPgy5Wu1ntmFEHErLhalFWF0VrrFaFO7CSHadvIT8LqU9VPMka+fsr/mBAADTXloIkyzHIlYLEgbYE3tjVejI9ltI81xtnG7RqOhT0oBoEz/iaVpQ5QRcNuLKzltdcb02kJELJo2PtyxdeeUNRo6koZZCBFeuGfounPK6JSW2yU2heh2pEMlEXRWusVoU7sJIdrWdLpPFB0jB+yBDpVc1ari98cmZXKAT6vH2jBdqeW0n76BDhV7bDtUeqUnMyIvHb8f/r1HRqmIOBVNGx/u2JQcZaRcR9IwCyHCC/cMnZKjTPVpuV1iU4huRzpUEkFnpVuMNrWbEKJtgQ6VvMjTJQMcaBihUqBih0qVU4vdrUWDn7zUetXKbfOaacp6KTnWI82256coGdyO2vYR63XQpw5VRqnItB8Rt6Jp49s6duIdULwZLrqhY2mYhRDhhXuGLt58YapP4/YOpkgXQsQlmciXCEKlSJv+Q5h8V/vXOjGmN6R2Q5nv2WyF8h+1Tu0mhAjNXadM+YGoFqR1++r5qvYUoO6CtIH1U3ql1JOki00nxvm0vgw503qESp6pP1qNDlv9ec46yxoXqo2FyYOz+eu2EvaesvLVGTvD89Jjdm0hItK0jd/zgtL21juVjpGWWX5CPQ8EsvzsezFk6lQhhIqaPkPvebFJ3F6vbP/8L0psRpDWWAiR2KRDJcYcbg86rRa7s5705CQ8Ph8phgh+DfpkZWXwmfeBqxZ0SaA3KPOpNRalxzvYquHhJCXDlHuU9K6BFIv1zs7vTGlM9xZI69jO+gsRL0q2KeunpPYCc+QdBodry/H4vaovSBtYPyUWGX4CGkeoVB9F4/fi1+gA0GuT6GMawKm6Yxyy7opph4o5OYmLB2bwyfEq/ra9hF9+K7rRQyK+tbtNjTf6ZJh6j9LGO6qV6QQeV/B/vpo+DwRSprrrlDZ0/E3g84PXHVn7Km2xiFMJEdtNn6EDcRt4hh5/M1x6e/O0xm11prjsyhukTePfKG8ACJEI4uyVqXtz1XtZ+UExa7cdw+bwYDbpuX3qIO6ePQRjki58AYGHHIMPPnyi+btT7e359jhh+x/h8DvKIpq2Mhj2tc7tRfc4lbSPatRfiHhx5B3lc9/x0a2f0lkL0jasn9InBuunBFhTc6nXGjB46siwlVJlKWjc1z91GKfqjnGw+nNm9P5GzOoEMHdUbz45XsXLn5/kga+PID1ZUjx2Bx1uU+OJx6mkO460XWyZMtXUkDLVH8XzgbTFIk4lTGwHnqGDxVA0aY3rnUoK5p0yWlyIRCQdKjHicHtY+UExf3j3QtpQm8PT+P33Zg2OrOfdXac8AH3w6IVtzuoL34fKbx9JWWWfNf8cTVmRUrP+QsSTo+8qn/teHNVpX9iKARhkUm+6D8Q2ZXKAX6Oj0jyA3tVHyDv/ZbMOlQFpw/j47NsctO7C7/er2nkUzui+ZvpmJFNW7eSVXae4ZUpB2HNEfFOtTY0HarWL0ZQjbbGIUwkT22rFkMuudKZseaxFOQ3fT71XRqoIEedkUdoY0Wm1rN12LOi+tduOoddG+KvQ6ZWe8GCizW+vZlnxfE0hOlt1KZz7CjRa6DMuqlM/tyrrjQxLjTzNcjguD5TaGjpU0mPXoQJwzjwAgLzzXzbb3sdUgF6ThL2+ipO1R4Od2mk0Gg2Xj+wNwF+3l8R8YVyhPtXa1HigVrsYTTnSFos4lTCxrVYM6Y3KyJSg5fyp/eskCiFiJk5elbo/u7MemyP4yt42hwe7M8IsHE5r23nvo8lvr2ZZ8XxNITpbYHRKznAwpkV8WoWrmlLnWTTAUBU7VEqqk/D5NaQlecgwxjajwFlzAQC5579Ucjc30Gv1DEgbBsCequ0xrRPAzOE5GPVajlTUsL24MvwJIq6p1qbGA7XaxWjKkbZYxKmEiW21YshRHaYca/vqJ4SIGelQiZH05CTMpuC91WaTPvI5/cmWtvPeR5PfXs2y4vmaQnS2I+2b7vN5Q3rh/sm9SNGp9y7U0fPK60k/syua5VxUcT4tH69GR6rzPGmOimb7hqQrC8J+UbkttpUCUgx6ZgxT5rKv3Xo85tcX6lKtTY0HarWL0ZQjbbGIUwkT22rFkCkjTDmW9tVPCBEz0qESI16fj9unDgq67/apg/D4fBEW5FEWvAom2vz2apYVz9cUojN53FD8vvJ1fnQdKp/ZlKkvw1PVzXpztOpCh0qseXUGqtKU0Ta5lc2n/QxOHwXA8ZovqXadi3ndvj66Dxpg04Ez7C+Td/0SmWptajxQq12Mphxpi0WcSpjYViuGPC5lAdqg5dyp7BdCxDXpUIkRk0HP3bOH8P05wxp73s0mPd+fM4y7Zw+JfIEtQ4qyevisH1/o0U7OUL5vuqp4rMuK52sK0ZmObwGXTfk7zhke1amBESpqd6gcaRih0r8LOlQAzqUHX0clLclC35QCAHaefTfW1SI/08TkIdkAPNVkwUOReFRrU+OBWu1i03IyB0PuKOVzsHKkLRZxKmFiW60YMqYr2XxmPdCinAeU7bIgrRBxT+Pvgavz2Ww2LBYLVqsVszm2w1rr3B70Wi12Zz3pyUl4fL72NQ7uOmXBq6b57Q2p7auUSmU53DXodUbsbhvpBjMerwuTocl6EoHruGqU9HF6gzr1Fz1KV8ZvUP/6AXy2FoZ/HaYsifg0a30dM7bfjx8/vx95B5Ykdf7+HfUabn05Fz8afjr9OOlGryrlRqNv5ZdMP7gea2pfXvmvFc32fXF+K++UvUi/1CE8PP7PMa/byao6HnhxD37gjXunM7qvDKeOJbXjV7U2NR6o1a677MpClo5qZTqBxxX6n7KW13TXKf8Iej1h/yF01DvQa/XY3XbSDel4fB5MSabo6ysSRizb34SJ7UC8Oa3K9Jy24k3FcsI+cwshYiYOX5m6t0BjkJ2mrJdgaO8goWjy28egLJfHxZr961h/cAM2tw2zwcyCwnksLLoDo94IHqeSXm7HKmWRreQMmP5DmHyXcr2O1F+IruLzwaH/KF8PmBLVqbttR/HjJ8+QoVpnCiijU/xoyDDWd0lnCsBZyyB8aLHUlpFaV0FtSm7jvuHmcWwuf4WTtUc5bj9IQXphTOvWLzOFKUOy2Xa0kj+8c5g/3XJpTK8v1KVamxoP1GjXPU7YtqJ5Wzvpe8o75vrk4Nf0OGH/K7D9f6GqOPw5gMvrYs2+Naw/uL5Jm7+AhRctxKjielCi50qI2A7E254XICkZ6p1w0Q1txk5Igc6T1F7K5zbiP+wztxAipuLw1UkkGoe7hmf3rmblnlXY3Mqq5ja3jWf2rOLPe1fjcNvhwyfgg0cvrGTurIZ3HoEPH1feERMiEZ36FGrOQJIJel8U1ak7q78C1J/uc/CcMt2nIMOparnRqNcnU5neD4D8s7ub7TPpUxluVlJLbzr1fMzrBnDd+H5ogLcPnOGL0hDZFYRINO664G3tB48q24O1tYFz/nOf0pkSwTmOegfP7nmWlXtWtmjzn+HPe/6Mo97RWXcoRPxoGm9VxVBxQPncVrypIPwzd02nXFcIEZp0qIgO0+uMrD+4Iei+5w5uQK9LVnrvg9mxShluLEQiOvhv5XP+BNBFl3ngo6r9AIxJH6hqlQ6dU97VKrB0XYcKwJnMoQD0rdjdat+lOZcB8Nm59znjKI1pvUBZS2X6UGUUwMOv7cPr63EzX0V3pNMrbWowodradpyj1+pZf3B90FOeO/gceq206aIHaE+8qSD8M7eMUBEi1qRDRXSY3W1r7CVvyea2YXfblKGQwTirlbnbQiQavx/2v6p8PWByVKeeclZSXHcaLRpGNyzgqgavD76KgxEqAKczhgHQ99xeNL7mU49yTfkMTh+FDx/PFz9NVyzlNW/SAExJOr44aeUfn5yI+fWFUJ3TemFkSqt9Idradpxjd9vbbvPr7ZHWWIjE1Z54U0FEz9xCiJiSDhXRYekGM2ZD8MXJzAYz6QazMq80mOQMZSE8IRLNiY+hukSZJ91vYlSnfnh+HwBDU/uSootynnVbVbLqcXi0JOu89E5zq1Zue1Sl9cWlN2Hw1NGrunVGnVm9r0GLlr1VH/PRmTdiXr/MFAM3XKpMS3ps4yEqayQ1pUhwyZYLWUJa7QvR1rbjnHRDetttfpJkJRE9QHviTQURPXMLIWJKOlREh3m8LhYUzgu6b0HhPDzehkW6gpn0PSWbgBCJZs8/lM8Dp4UegRXCO+eUaTBj0wepWqWDZ5XpPgMsLrQaVYuOml+j5YxlCAB9K3a12p9lzGVq3pUArD/6JNvObIz5SJW5o3ozMCsFq6Oe5W8ejOm1hVCd16O0qcGEamvbcY7H52FB4YKgpywoXIDHJ2266AHaE28qCP/MLW8OCBFrMtFVdJjJkMbCojsAZf5m0BXHZyxVDo4084AQ8ay+ISsGwJDLojq10m3nk4YFaS9tmBajlr0VSofK4Mz4WBTydOYwBlTuo/+Zz9gd5AFwYs4cKp2n+dL6GesO/4b3y19jmLkIo86EVqOl3ldPktZA75QBjMy4hFS9uu9867Qa/s/0Qfzs9f28+NlJvjYyl6+P6aPqNYSIGUNK9G1tO84xJZlYeNFCQFkzRbL8iB6pPfGmgoieuYUQMaXxd8Xk9S5ms9mwWCxYrVbMZhkapxaHuwa9zojdbSPdYMbjdWEypF04wF2nLNLltClDIb31YFAvXazoGeIifg+8Bs/fAinZ8O01oNVFfOo/y7bwqyP/oMCUx8PDgr/L1B5eH9z+ai6Oei33TDhJf3PXv0tlqK/l6p2PocXHS//1NPbU3q2O8ft9bD/7NjvPvovXH/odPYM2mcv6XMvVA28jSavuA+P6HSX8a085FlMSG38wgz4Wk6rliwviIn67u/a0te04x1HvQK/VY6+3k56UjsfnwZQksdOdSfwG0UXPtmGfuYUQMSMjVIRqAi/kWaZsAJJaZj0xpCifU5XsGugMsaqaEOra9ZzyefBlUXWmALx8ehsAkzJGqFqlI+eTcNRrSdF7yU/v+s4UAHdSKmctBeRZixlYvp19Q69tdYxGo2Vq7te5KHMKR+37qHZXUu9z4/f70Gn01PvclDmOcd5VwVunNnCg+hO+P/oxzIYs1er5nUv7s7/MRvG5Wn74z908t2gyuq6eMyVEe7WnrW3HOYHOkyydEout2nwheoIuerYN+8wthIgZ6VARQohoVB6Fw28rXw+9PKpTv6wp5UDNCXQaLVMzR6parT2nlYe4oVmOLl8/panSnDHkWYspKAveoRKQlmRhbNa0oPv8fj9H7fvZdOqflNYe4Xd7f8CPxz6t2hQgvU7Lkv8ayrKX9/Jx8Xl+v+kr7rtC3Q4vIYQQQgjR/ciitEIIEY1PngX8kH8JWPKjOnX9qfcBGG8eQrpe3aHxu04r02CGZtWpWm5HncoaiQ8NOdajpNVVtKsMjUbDUPMYvjv4XtL0GZx2nGDVl4/g8/tUq2cfi4mF05VFgv+4+Qiv7T6lWtlCCCGEEKJ7kg4VIYSIlMsOu/6ufD3y6qhOPe2q4t8VOwG4IudiVat13qHlcKUyQmVkdnx1qLgMaZy1FAAwsHx7h8rKNPbiuoI7SNIaOGj9nHfLXlKhhhfMGNaLb1ykLEp7/4t72F1arWr5QgghhBCie5EOFSGEiNTuDeCygTkf+o6P6tTVJzbi8XsZnprPkFR1M8l8ckoZnTLA7MSS7FW1bDWczB4DwNDSzdDBddB7Jfdldu9rAHjl+J8oryvpcP2amjdhABcPyMDt8XHHXz/lRGV8dVAJIYQQQoj4IR0qQggRCY8Ltj6pfD3ym6CJ/OXzaG05L5VvBeDavCmqV23HSSU945jcWtXLVsOJXkV4tElk2kvpVXWow+UVZU6hIK0Qj7+etV/9Bp9fvU4krVbDksuGMSArhbN2Fzev2UGF3ala+UIIIYQQovvo0g6V5cuXM2HCBNLT08nNzeVb3/oWhw6Ff9h+6aWXGDVqFEajkVGjRvHKK6/EoLZxxl0HXjfUnlU+u+VdVCE61ed/BdspJVXysLkRn+b1+3jk8N/x4mO8eQgj0vqpWi2rU8v+CmW6z5heNaqWrZZ6vYnSnCIARpRs6nB5Go2Gufk3YtAmc7zmS7ac/neHy2zKZNDxf68sJDfdSEllHbeu+QSro17VawgRMWnvhYhfEp9C9Hhd2qHywQcfsHjxYj7++GM2bdqEx+Nh7ty51NaGfpd1+/bt3Hjjjdx888188cUX3HzzzXznO99hx44dMax5F/M4lXfKfzsMfjtU+bz1SWW7EEJ99Q7Y8jvl66Ibo0qL+KcTb/KF7RjJWgML+s5WvWofHE/G59cwwOwkJ8WjevlqOdr7UgAKyrZhcHe84yc9KYPpeVcBytQfm/t8h8tsKjPFwINXjcRiSuLLchu3rtmJzSmdKiLGpL0XIn5JfAoh6OIOlY0bN3LbbbcxevRoxo4dy9q1azlx4gSfffZZyHOefPJJLr/8cpYtW0ZhYSHLli1jzpw5PPnkkzGseRdy18GHT8AHj4KzYcFEZ7Xy/YdPSM+4EJ1h55+g5jSk9oJhkadK3nR2F/9b8gYAC/rOJsugTprfAL8f3i1OAWBCX5uqZavtfFo/qlPy0PvcyloqKhibNY3c5Hwc3lpeOr5KlTKbyjMns+zKQtKMenaXVnPLn6VTRcSQtPdCxC+JTyFEg7haQ8VqtQKQlZUV8pjt27czd27z4fZXXHEF27ZtC3mOy+XCZrM1+0hYOj3sCPGPw45Vyn4hupEuj1/rKXj/UeXrcQtAlxTRafvtJTx46C8AfC1nHNOyRqletQNnDZTZ9Rh0Psbmxed0n0YaDUf6TAJgdPHr6LzuDhep1Wj5Wt8bAA3bK97ikHV3h8tsaWB2Kj/575GNnSo3/3mnTP+JQpfHbyKT9l50MYnfNkh8CiEaxE2Hit/vZ+nSpUyfPp0xY8aEPO706dPk5eU125aXl8fp06dDnrN8+XIsFkvjR//+/VWrd8w5rRd6wlvtqwanNHaie+ny+H1rGdTXQq+RMOS/Ijplv72EO/Y+hdPnZnTaAG7sM7NTqvbygVQAxve2k6zvWPacWDieO55ag4VU53mGnXhHlTL7pAzkoszJAKw/+iQen/rTngqadKp8UVrNLX/eIZ0qEery+E1k0t6LLibx2waJTyFEg7jpUFmyZAl79uxhw4YNYY/VaDTNvvf7/a22NbVs2TKsVmvjR2lpaYfr22WSLZCcEWJfBiSbY1sfITpZl8bvoY1w4DUlo8/kuyPK7LPfXsIde57C7nEwLKUvdw/8BrooMgJFXLVzSew5Y0Sr8XPZwBAPdXHGp9VzsJ/SuVR05BVVRqkATM/7b0y6VMrrjvNu2YuqlNlSQXYqD/33SNKNer44aeXGVds5bZV58uF0q/Y31qS9F11M4rcNEp9CiAZx0aFyzz338Prrr7N582b69Ws7A0bv3r1bjUapqKhoNWqlKaPRiNlsbvaRsLwemPS94PsmfU/ZL0Q30mXxayuH1+5Wvh55DWQNCnvKF7ZjSmeKV+lM+cGgb2GKYgHbSHl98Nfdynosl/Sxk2VKnLg/lncxdQYzqc7zFB77jyplmvSpzOx9NQD/OrGO864KVcptaWB2Kg99YxQZpiQOnrbz7We2caQizqdadbFu1f7GmrT3ootJ/LZB4lMI0aBLO1T8fj9Llizh5Zdf5r333mPQoPD/sEyZMoVNm5qn3Xz77beZOnVqZ1UzvhhSYMZSmPXjCz3jyRnK9zOWKvuFEB3j88Ird0JdJWQNhotvCXvKtqovWbTnD42dKT/spM4UgLeOpPBVpQGjzsflg9TNbtPZfFo9+wbMAWD8oX+SVht6umY0RmdcSn7KINw+J/8s/qMqZQYzICuFX1wzmj6WZE5VO7hh5TY+P1HVadcTPZi090LEL4lPIUQDjd/v77KJ93fffTfr16/ntddeY8SIEY3bLRYLJpMJgFtuuYX8/HyWL18OwLZt25g5cya//vWvueaaa3jttdd46KGH+Oijj5g0aVJE17XZbFgsFqxWa+L2trvrlAWvnDZlWKG3HgypXV0rITpdp8ev3w9v/hh2rgJ9MnzjSbC0PXLu7bOf8+ODa/H4vYxOG8CSgm9i1CqL1/r8cPR8EvsrDJyw6jnv0OLxaUhJ8tE33cuQrHqGZ9eTl+aNqHp7zxj4f1sy8fg0XDfiLJP7JeA8bb+fWfvXkmc9xqmcsWya/FNoY9pmpM46y/jbkcfx4+OeUb+hKGuyCpUNzuao57G3DnL0bC3JSVp+d8NYvnFR3067XnfRLdrfWJP2XsQJid8gJD6F6PG6tEMl1Lona9eu5bbbbgNg9uzZFBQUsG7dusb9L774Ig899BDFxcUMGTKEX//611x33XURX1caBCESV6fH70e/h3d+pnw968dQMKPNw18s/4hfHN6AHz8TLMO4o//X0Wt1VNTq2HzMxAfHTJyt04W9bH66h0vznUzIdzE0qx5dkPGDO04aeXqHBYdHS1FuDQvGnEHb8X6ILpHmqGTu7j+i93nYOeo2Dgz5pirlvl/+Gp9Vvk+WMY+fjFtFelKIOe4qcNZ7efKdr/jipJKh7s6Zg3ngihHog/3yBCDtrxCJTOJXCCFa69IOla4iDYIQiavT4tfvh4+egHd/oXx/6SIY/a2Qh/v8Pp4ueYM/nXgTgFlZY7g5/784UW3g1YOpbCtNxu9XejuSdV6GZjnoZ3aRmexBr/VTV6+lvMbISZuRk3YjPv+FnhGL0cvFfV0MyvSQbvBhdWr5pMzI/gojAEMz67h97GmSdIn98j2sbDvjj/0HPxrenfBjTvae0OEy3V4nfzv6ONXucww3j+UHY36HXhtZquv28Pr8/POTE/xrTzkAEwdl8bvrxzIgW4Z7ByPtrxCJS+JXCCFakw4VaRCESCidEr8eF7z1IHzyrPJ90XfaXDfF5qnjp4f+xnuVXwBwVa8JjNTO4rVDaewqT248bmhmHRP62hnTq7bNzg+HR8uhShMHzqZysDIFpyf4iBatxs/MAdVcMfh80BEsCcfv55KjrzPkzKfU65LZfOn9lOWO63Cxlc7TrC9+ErfPTK9XRQAAIABJREFUxbS8q7h56H1oOyHTUlMfF1ey8oOjuDw+TEk6/u+Vhdw0eSC6RB1C1Emk/RUicUn8CiFEa9KhIg2CEAlF9fg9vRde+R84sw/QwIRFMOqakIdvrzrITw/9lTPuavQaHTNTv86XxZP4qlJZgFaDn4tya7isoJq+6dGnBfb6oLjaxJHzJipqDTi9Wkx6L/npLi7pYycjObK1VhKFxudl+pd/p0/1EXwaLTtH387Bgis7vKZKsf0Ar5Q8C/iZ3GsuNw/7EUlaozqVDuGMzcmqLUf5stwOwPC8NJZePoIrRueFnOLa00j7K0TikvgVQojWpENFGgQhEopq8Xv2EHz4OOx9Afw+MJph6vdhQPDFrQ/VnOTpkn+zuXIPACYyqS+7kSprAQB6rY9L+tiZNaCanBRJlxgNrc/DJUdfZ1DFLgDOZI1k5+jbqMwY2qFyD1R/ysaTG/Djo1/qEBYMWcoQ82g1qhySz+/nnQNneP7TUmrdSudXYe90bpzQn2vG5ZOV2jmZnxKFtL9CJC6JXyGEaE06VKRBECKhtCt+/X5wVEHFASjZDofegLJdF/YPnAaT/gdMmY2b3L56DtWcYvv5w7xR8TnFzpKGsjS4qybjqrgS/AZMei+T821M728l3di9Ro/ElN/P8LJtjDnxLnpfPQDnLEM4lj+NM1mjqDIPxNuONNTHaw7xRunfcHprARiSPoZpeVcyImM8OcY+nTZypNbl4d97ynlzXzkujw+AJJ2G8f0zmTw4i4sHZjKkVxp9M0w9alqQtL9CJC6JXyGEaE06VKRBECKhhI3fA68po07qHUo6Q6cVqk+A2978OI0W+k2Esd/lVyVeNtr+hdfvxYsHt8aOV2MHzYWXR79fg8dWhPvc19B6chia6eDSPnZG9apF3x3WM4kTJpeVopJN9D+3D52/eQeVw2Ch1pSDKykNr87A6exRHBhyddgy6zx2Pjz9Bgesn+JrUqZRZyLT0Is8U38Wj/q16vcCYHfWs+1oJVu+OkvxudpW+w16LTmpBsymJCwNHwa9lvTkJJZfV9QpdepK0v4KkbgkfoUQkbrtttuorq7m1Vdf7eqqdDp9V1egKwT6kGw2WxfXRIiulZ6ennBrO4SLX0PZAZK//FfQfb7UXLy5Y/D2HoenYDb+hhEpu3d9hNVQ3PpanhS8zv4kOYfTT3sRgyzpjBoMI3LAqE8FUtW5KdHMid4XU+a2kXtqG5nn9mE+f4ik+lpMbismt7XxOIMxHXtqdgQlZjPCshiru5rtFe9zyLqXE7XFuLwOTjtOAF7M+s6ZpmVO03DD2BxuGJtDmdXJnlN29pbZOXqujnKrC7fHR5nVSZnV2ew8i0nPsq8NDFt+osWwtL9CXCDxK0TiSrT4jbU//OEP9JRxGz1yhMrJkyfp379/V1dDiC6XiO8ySfwKcUGixbDErxAXSPwKkbgSLX5F5+mRHSo+n4+ysrKoehZtNhv9+/entLRUgqcN8nOKTLz8nBKxd7098aumePndqam73VN3ux8IfU+JFsNdHb9t6U5/N93pXqB73U/Te8nPz4+7OGhLJPHbnX5XkZJ77v73HOx+47Eda+nFF1/k5z//OUeOHCElJYXx48fz2muvsXjxYqqrqxk/fjxPP/00TqeTefPmsWLFCgwGZb06v9/Pb3/7W1auXEl5eTnDhw/npz/9Kddff31j+fv37+eBBx7gww8/xO/3M27cONatW8eQIUNaTfkJV15VVRVLlizh7bffpqamhn79+vHggw9y++23x/4HF6UeOeVHq9XSr1+/dp1rNpt7xAtHR8nPKTLyc4peR+JXTd3xd9fd7qm73Q8k/j3FS/y2JdF/xk11p3uB7nU/ZrM57v8Zayma+O1Ov6tIyT13f4l0v+Xl5cybN4/HHnuMa6+9Frvd3tjxAfDuu++SnJzM5s2bOX78OLfffjs5OTn8+tfKmnIPPfQQL7/8Ms888wzDhg1jy5Yt3HTTTfTq1YtZs2Zx6tQpZs6cyezZs3nvvfcwm81s3boVjyf4FOpw5f30pz/lwIEDvPnmm+Tk5HDkyBEcDkfMfl4d0SM7VIQQQgghhBBCiO6ovLwcj8fDddddx8CByppsRUUXFrs3GAysWbOGlJQURo8ezS9+8Qvuv/9+fvnLX+JwOHjiiSd47733mDJlCgCDBw/mo48+YtWqVcyaNYunn34ai8XCP/7xD5KSkgAYPnx40LrU1taGLe/EiROMHz+eSy+9FICCgoLO+tGoTjpUhBBCCCGEEEKIbmLs2LHMmTOHoqIirrjiCubOncv1119PZmZm4/6UlJTG46dMmUJNTQ2lpaVUVFTgdDq5/PLLm5XpdrsZP348ALt372bGjBmNnSltOXDgQNjy7rrrLr797W/z+eefM3fuXL71rW8xderUDv0MYkU6VCJkNBp55JFHMBqNXV2VuCY/p8jIzylxdcffXXe7p+52P9A97ynedKefcXe6F+he99Od7iWY7n5/wcg9d3+JeL86nY5Nmzaxbds23n77bVasWMFPfvITduzY0eZ5Go0Gn88HwBtvvEF+fn6z/YGfgclkirgukZR35ZVXUlJSwhtvvME777zDnDlzWLx4Mb/73e8ivk5X6ZGL0gohhBBCCCGEED2B1+tl4MCBLF26lD179vCvf/2LkydPNnaMrFq1ivvuuw+r1UptbS29evVi9erV3HzzzUHL+/nPf85f/vIXDh06FHSUStNFae12e9jyWlq1ahX3339/QqRplxEqQgghhBBCCCFEN7Fjxw7effdd5s6dS25uLjt27ODs2bOMHDmSPXv24Ha7WbhwIQ899BAlJSU88sgjLFmyBK1WS3p6Ovfddx8//OEP8fl8TJ8+HZvNxrZt20hLS+PWW29lyZIlrFixgu9+97ssW7YMi8XCxx9/zMSJExkxYkSzukRS3sMPP8wll1zC6NGjcblc/Pvf/2bkyJFd9NOLjnSoCCGEEEIIIYQQ3YTZbGbLli08+eST2Gw2Bg4cyOOPP86VV17JP//5T+bMmcOwYcOYOXMmLpeL7373u/zsZz9rPP+Xv/wlubm5LF++nOLiYjIyMrj44ot58MEHAcjOzua9997j/vvvZ9asWeh0OsaNG8e0adOC1idceQaDgWXLlnH8+HFMJhMzZszgH//4R6f/nNQgU36EEEIIIYQQQogeoOl0HNFx2q6ugBBCCCGEEEIIIUSikQ4VIYQQQgghhBBCiCjJlB8hhBBCCCGEEEKIKMkIFSGEEEIIIYQQQogoSYeKEEIIIYQQQgghRJSkQ0UIIYQQQgghhBAiStKhIoQQQgghhBBCCBEl6VARQgghhBBCCCGEiJJ0qAghhBBCCCGEEEJESTpUhBBCCCGEEEIIIaIkHSpCCCGEEEIIIYSI2vHjx9FoNOzevburq9IlpENFCCGEEEIIIYQQIkrSoSKEEEIIIYQQQsQJh9uD2+OjssaF2+Ojzu3p9Gu++OKLFBUVYTKZyM7O5mtf+xq1tbUArF27lpEjR5KcnExhYSH/+7//23jeoEGDABg/fjwajYbZs2cD4PP5+MUvfkG/fv0wGo2MGzeOjRs3Np7ndrtZsmQJffr0ITk5mYKCApYvX964/4knnqCoqIjU1FT69+/P3XffTU1NTaf/HKKl7+oKCCGEEEIIIYQQAlz1XlZ+UMzabcewOTyYTXpunzqIu2cPwZik65RrlpeXM2/ePB577DGuvfZa7HY7H374IX6/n9WrV/PII4/wxz/+kfHjx7Nr1y7uuOMOUlNTufXWW9m5cycTJ07knXfeYfTo0RgMBgD+8Ic/8Pjjj7Nq1SrGjx/PmjVruPrqq9m/fz/Dhg3jqaee4vXXX+f5559nwIABlJaWUlpa2lgnrVbLU089RUFBAceOHePuu+/mgQceaNaZEw80fr/f39WVEEIIIYQQQgghejKH28PKD4r5w7uHW+37/pxhfG/WYFIM6o+J+Pzzz7nkkks4fvw4AwcObLZvwIABPProo8ybN69x269+9Sv+85//sG3bNo4fP86gQYPYtWsX48aNazwmPz+fxYsX8+CDDzZumzhxIhMmTODpp5/m3nvvZf/+/bzzzjtoNJqwdXzhhRe46667OHfunAp3rB6Z8iOEEEIIIYQQQnQxnVbL2m3Hgu5bu+0Yem3n/Ps+duxY5syZQ1FRETfccAOrV6+mqqqKs2fPUlpaysKFC0lLS2v8+NWvfsXRo0dDlmez2SgrK2PatGnNtk+bNo0vv/wSgNtuu43du3czYsQI7r33Xt5+++1mx27evJnLL7+c/Px80tPTueWWW6isrGychhQvpENFCCGEEEIIIYToYnZnPTZH8PVSbA4Pdmd9p1xXp9OxadMm3nzzTUaNGsWKFSsYMWIExcXFAKxevZrdu3c3fuzbt4+PP/44bLktR574/f7GbRdffDHHjh3jl7/8JQ6Hg+985ztcf/31AJSUlHDVVVcxZswYXnrpJT777DOefvppAOrrO+dn0F6yhooQQgghhBBCCNHF0pOTMJv0QTtVzCY96clJnXZtjUbDtGnTmDZtGg8//DADBw5k69at5OfnU1xczIIFC4KeF1gzxev1Xqir2Uzfvn356KOPmDlzZuP2bdu2MXHixGbH3Xjjjdx4441cf/31fP3rX+f8+fN8+umneDweHn/8cbQNo3Kef/75zrjtDuuRHSp+vx+73U56enpE87WEEPFD4leIxCXxK0TikvgVovN5fT5unzoo6Boqt08dhMfnw9AJk0x27NjBu+++y9y5c8nNzWXHjh2cPXuWkSNH8rOf/Yx7770Xs9nMlVdeicvl4tNPP6WqqoqlS5eSm5uLyWRi48aN9OvXj+TkZCwWC/fffz+PPPIIQ4YMYdy4caxdu5bdu3fz3HPPAfD73/+ePn36MG7cOLRaLS+88AK9e/cmIyODIUOG4PF4WLFiBd/85jfZunUrK1euVP2+1dAjO1TsdjsWiwWr1YrZbO7q6gghoiDxK0TikvgVInFJ/ArR+UwGPXfPHgIQ0yw/ZrOZLVu28OSTT2Kz2Rg4cCCPP/44V155JQApKSn89re/5YEHHiA1NZWioiJ+8IMfAKDX63nqqaf4xS9+wcMPP8yMGTN4//33uffee7HZbPzoRz+ioqKCUaNG8frrrzNs2DAA0tLSePTRRzl8+DA6nY4JEybwn//8B61Wy7hx43jiiSd49NFHWbZsGTNnzmT58uXccsstnXL/HdEjs/zYbDZpEIRIUBK/QiQuiV8hEpfErxCxU+f2oNdqsTvrSU9OwuPzdUp2H9Fx8lsRQgghhBBCCCHiRKDzJDvNCNAp03yEOuQ3I4QQQgghhBBCCBEl6VARQgghhBBCCCGEiFLCdagUFBSg0WhafSxevLirqyaEEEIIIYQQQogeIuHWUPnkk0+a5bjet28fl19+OTfccEMX1koIIYQQQgghhBA9ScJ1qPTq1avZ97/5zW8YMmQIs2bN6qIaCSGEEEIIIYQQoqdJuA6VptxuN3//+99ZunQpGo0m5HEulwuXy9X4vc1mi0X1RA/iqHeg1+qxu+2kG9Lx+DyYkkxdXa1uQeL3Avk7E4mmK+L39S/KOFlVx/dmDkGnDf1sIIRom7S/ojPJM43oLhJuDZWmXn31Vaqrq7ntttvaPG758uVYLJbGj/79+8emgqJHcHldrNm3htnPz2bW87OY/fxs1u5bi8vrCn+yCEviVyF/ZyIRxTp+y6od3LthF49tPMS/95R16rWE6O6k/RWdRZ5pRHei8fv9/q6uRHtdccUVGAwG/j97Zx4eRZX14bfTWzpkJyiLKDCAAUnADVRAQUThc0SHEYckjCxhVRQEdcZtBAfXcRQZVED2keCGuwOoCI6yiQqCSAAV3GCAhOzp9JZ8fxTV6aV6SUg6C+d9njxJV926VQ11+56+dX7n99577wVtp7XC3r59e4qKioiPj6/vyxSaMVaHlaXfLmXB7gV++6akT2Fsj7Gy2n6ayPiV+0xoukR6/L73zRHuWL0TgMw+5/LYH9Lq/ByCcKYg869QH0hMIzQ3mmyGyk8//cTHH3/M+PHjQ7Y1m83Ex8d7/QhCXWCIMpCTm6O5b1XuKgxRTVpV1yiQ8Sv3mdB0ifT4/a3Q6v77xxOl9XouQWjuyPwr1AcS0wgAhw8fRqfTsWvXrkbZX01osgsqy5Yt46yzzuL6669v6EsRzmBK7CUU27U1xcX2YkocJRG+IqE5IveZIITHbwXVCyrHiiV1XBAEobEhMY0A0L59e44ePUqPHj0a+lJOmya5oFJZWcmyZcsYPXo0BoOsYgoNR5wpjniT8sQmyZxEl8QuJJmTAIg3xRNnjGvIyxOaCZ73mS9ynwlCNZ4ZKnmlsqAiCILQ2JCY5szA4XAE3a/X62ndunWj+i5vt9trdVyTXFD5+OOP+fnnnxk3blxDX4pwhuOsdDK151TmDZzHuj+uY97Vyu/nBj7H1J5TcVY6G/oShWaAs9JJVmqW5r6s1Cy/+8zqsOJwOThpPYnD5cDqsGoe25Roju9JqHuOFlW4/y6pcGJzuhrwagRBEJofNZ2PfdvbXLYaxTRnLPZycNmh7ITy215eb6dauHAh7dq1o7Ky0mv7sGHDGD16NADvvfceF198MdHR0XTq1InZs2fjdFb/X+l0OhYsWMCNN95IixYtmDNnDgUFBWRlZdGqVSssFgtdunRh2bJlgLZEZ+/evVx//fXEx8cTFxdH//79+eGHHwAloeKRRx7hnHPOwWw206tXL9atWxf0fX366af07t0bs9lMmzZt+Otf/+p1zQMGDGDq1KnMmDGDlJQUBg8eXKt/v8azJFQDrr32WppwLV2hGWExWhjedTgv7XmJBzc/SLG9mHhTPJmpmYxPG4/ZYG7oSxSaARajhez0bEDRF6v3WVZqFtnp2Zj11feZWjk/JzcnaLumRHN8T0L9UFTu/XTpZJmdNglS3FAQBKEuqOl8rNV+as+pZKeFF9OcsTgrYPNc2L4QKgohOhH6TIL+M8AQXeenGzFiBHfeeScbN25k0KBBABQUFLB+/Xree+891q9fz6hRo5g3b557kWPixIkAPPzww+5+Hn74YR5//HGeffZZ9Ho9Dz30EN999x1r164lJSWF77//HqtVewHut99+48orr2TAgAF88sknxMfHs3nzZvcCyHPPPcc///lPFi5cyIUXXsjSpUsZNmwYe/fupUuXLpr9/d///R9jxoxh5cqV5ObmMmHCBKKjo5k1a5a73YoVK5gyZQqbN2+u9fpCk3b5qS3FxcUkJCRIlXLhtCm1l7J873IW7l7ot29y+mRGXzCaWFNsA1xZ8+VMHr9WhxVDlIESRwlxxjiclU6vSvjNsXJ+c3xPZzL1PX4v+Ns6yuzVWSnv39GPHu0S6vw8gnAmcibPv0LN5+Ng7e+/9H5u6HwDZr05YExzxmIvVxZTPn3Sf99Vf4G+08EUU+envfHGG0lJSWHJkiUALFq0iIcffphff/2VgQMHMnToUO677z53+5dffpl7772XI0eOAEqGyvTp03n22WfdbYYNG0ZKSgpLly71O9/hw4fp2LEjO3fupFevXtx///288sor7N+/H6PR6Ne+Xbt23H777dx///3ubb179+bSSy/l+eef9+vvgQceYM2aNezbtw+dTgfACy+8wF/+8heKioqIiopiwIABFBUVsXPnztP6t2uSkh9BaAxYHVbMejOrc1dr7s/JzZFVdqFOsRgtGPVGkqOTMeqNfoFHbSrnN3YpjbgBCOHidFW6F1OSW5gAqaMiCIJQV4QzH3vGE8Haz/9mPma9OWhMc8aiNyiZKVpsX6jsrweysrJYs2aN2yp91apVjBw5Er1ez1dffcUjjzxCbGys+2fChAkcPXqU8vJqKdIll1zi1eeUKVN45ZVX6NWrF/feey9btmwJeP5du3bRv39/zcWU4uJijhw5Qt++fb229+3bl3379mn2t2/fPi6//HL3YoravrS0lF9//TXgNdcGWVARhFpgc9nY+MtGiuxFwSuV26VSuRA5alo5X03FHfDaAK567SoGvDaAZd8uw+ZqPF9CxQ1ACJfiimpd9FlxZr9tgiAIQu0JNR8X2AoYt34cV712FePWj+NkxUmZv2tDRZEi89HcVwgV2v+mp8sNN9xAZWUlH3zwAb/88gufffYZo0aNApT6JbNnz2bXrl3unz179nDw4EGio6slSC1atPDqc+jQofz0009Mnz6dI0eOMGjQIO6++27N81ssoRfUPBdHAKqqqvy2BdunCnM8t/tec22QBRVBqCFWh5XFuxfz4jcvkmBKCF6p3CSVyoXIUZPK+ep9vGD3AnfAU2wv5sXdL7Jk95JGk6kibgBCuBRbFUeBaGMULczKE7xSWVARBEGoE0LNx7HGWH4q/gmAn4p/kvm7tkQnKDVTNPclQnT9yO0sFgvDhw9n1apVrF69mq5du3LxxRcDcNFFF7F//346d+7s9xMVFXw5oVWrVowZM4aXX36ZuXPnsmjRIs126enpfPbZZ5ruQPHx8bRt25bPP//ca/uWLVvo1q2bZn/du3dny5YtXnVRtmzZQlxcHO3atQt6zTVFFlQEoYaoKYyHiw9zovwEmamZmu0yUzMb1ZN+oflTEzegpiKlqanDkXDmUlyhBGEtTAYsRj0AZTa5PwRBEOqCYPNxRmoG245uo8BWAECBrYDtR7eTkZqh2V7m7yC4nEoBWi36TFL21xNZWVl88MEHLF261J2dAvC3v/2NlStXMmvWLPbu3cu+fft49dVXefDBB4P297e//Y133nmH77//nr179/L+++8HXACZOnUqxcXFjBw5ki+//JKDBw/y73//m/379wNwzz338OSTT/Lqq6+yf/9+/vrXv7Jr1y6mTZum2d9tt93GL7/8wh133EFubi7vvPMODz/8MDNmzAi5CFRTGkfELAhNCM+Uxznb5/DMgGcAvCqYi8uP0BDUxA0oHClNsj45ItcdjJq8J+HMpuhUhkqM2UD0qQWVEllQEQRBqBMCzceZqZlkdMtgzLoxXu2f/fpZlg9Zjg6duPTVBFOM4uYDEXP5Ubn66qtJTk5m//79ZGZWPzC+7rrreP/993nkkUd46qmnMBqNpKamMn78+OBvxWTivvvu4/Dhw1gsFvr3788rr7yi2bZly5Z88skn3HPPPVx11VXo9Xp69erlrpty5513UlxczMyZMzl+/Djdu3fn3Xff1XT4AaWI7X/+8x/uueceevbsSXJyMtnZ2SEXgWqDuPxIlXKhhjhcDga8NsD9ZbR/u/480OcBzoo5yz1Z2Fw2cfepJ2T8hiaUGxD438eexJvi2XTLJox6/8JgDUU470lo/NTn+P1g91Fuz/ma88+O4/zWcbz7zRGy+3Xkod93r9PzCMKZisy/AvjPx/ZKOxM+nMCevD1+bdNS0lh87WKMUUaZv2uKvVwpQFtRrMh8XA4wnX69D6HuEcmPINQQ35THz377jCFvDmH4u8P54ugXOCudspgiNCih3ICg6UlpwnlPwpmN1aE4/JiNUe4MFZH8CIIg1C2+83EUUfRr20+zbb+2/dChk/m7NphiQG+CFinKb1lMabSI5EcQakiglMehHYZy9XlXa6Ywulfz7SXEmWR1Xmh4mpqURsaQEAqbU1lQMemj3DVURPIjCIJQt/jOxwDj0xXpR1OIJwShrpEFFUGoBWa9mbE9xjIhfYJXCqPWpKFa04p+VGhs1OQ+bkhkDAnhUOGoBMBoiMJiUhJwJUNFEASh7gg2H4/rMa7RxxOCUB/Igoog1BL16bhauFOr3oTVYWXpt0tZsHuBe5tqTQswtsdYecouNCjh3McNiYwhIVy8M1TENlkQBKEuCWc+NuqNjTaeEIT6QmqoCIIHVocVh8vBSetJHC4HVof1tPprKta0wplBXd/fkUDGkBAu7gwVfRTRRiW8KZUMFUEQhKCEGxvIfCwI2sidLwinqA9ZQVOxphWaP01VNiNjSAgXd4aKoboorVqoVhAEQfCnJrGBzMeCoI0sqAgC9ScriDPFEW+KD2hNG2eMq/1FC0KYNGXZjIwhIVxspzJUTHodJoOSoWK1y4KKIAiCFjWNDWQ+FgRtRPIjCNRfGmNTs6YVmidNOU1XxpAQLmqGilEfhVmvhDcVkqEiCIKgSU1jA5mPBUGbxhtFC0IEqa80xqZmTSs0T5pymq6MISFc1BoqJkOUO0NF3SYIgiB4U9PYQOZjQdBGFlQEASWN8dy4c/m55Gev7UnmJM6LP++00hibijWt0Hxp6mm6MoaEcPB0+VEXVOyuSpyuSgx6ScgVBEHwpDaxgczHZyazZs3i7bffZteuXafdl06n46233uKmm24Kq/3y5cuZPn06hYWFp33u+kIiDOGMRq1sXmwv5s1hb/LcwOfomNCRjgkdmTdwHuv+uI6nr3ra3TZUP4EqpFuMFsVKLjoZo97YaOtVCM0D3/vR5rIxtedUv3YdEzqyfMhygAZx/qmJ65CMISEUni4/ZoO+ertTslQEQRB8CSbhmdpzKjaXTXOOrqv5uKbOg03RqbC5cPfdd7Nhw4Y66evo0aMMHTo07PZ/+tOfOHDgQJ2cu76QDBXhjEWrsnlmaib/HvpvqqqqeHnfyzy4+cGQKY1N1T1FaJ4EvB/TlDTd+d/Mp9heTFpKGosGL2LF3hUNcu/KuBHqGk+XH6Ne595e4XARa5ZwRxAEwZNAEp6pPacyvOtwFu9ZXG9zdE1jAIkZGpbY2FhiY2MD7rfb7ZhMprD6at26dY3ObbFYsFga90M0yVARzkisDiuLdy9mwe4F6HV6uiR2Qa/Ts2D3Ao6XH2fVvlUs3L3QnQapVj1fsnuJ14q4Zz+h2gpCfRP0ftyzhBs638CmWzbx6Z8+ZfG1i1mxd0WD3LsyboT6wDNDRafTYRanH0EQhKCoEp5Nt2zi85Gfs+mWTdwkKVuXAAAgAElEQVTQ+QYW76m/ObqmMcCZGjNEMiNn4cKFtGvXjspK74zOYcOGMXr0aGbNmkWvXr3c28eMGcNNN93E448/Ttu2benatSugZJ9cf/31WCwWOnbsSE5ODh06dGDu3LnuY3U6HW+//TYAhw8fRqfT8eabbzJw4EBiYmLo2bMnW7dudbdfvnw5iYmJXtf17rvvcskllxAdHU1KSgrDhw9373v55Ze55JJLiIuLo3Xr1mRmZnL8+PG6+8fSoEkuqPz222+MGjWKli1bEhMTQ69evfjqq68a+rKEGtDQaXuGKAObj2x2y3rmXa38fmHQC5wTd07YVc+bsnuK0PwIdT+a9ebqNN0oY43u3UBjtjZjWcaNUB9UZ6go2SlGcfoRBEEIG1el8llp1pvrdY6uaQxwJsYMakbOgNcGcNVrVzHgtQEs+3YZNpetXs43YsQI8vLy2Lhxo3tbQUEB69evJytLWxa2YcMG9u3bx0cffcT7778PwK233sqRI0fYtGkTa9asYdGiRWEtZjzwwAPcfffd7Nq1i65du5KRkYHTqe0a9cEHHzB8+HCuv/56du7cyYYNG7jkkkvc++12O3//+9/55ptvePvttzl06BBjxoypwb9GzWlyd2BBQQF9+/Zl4MCBrF27lrPOOosffvjBb+VKaLw0hrS9MkcZ8wfNJ2dfjpesZ2qvqXRv2T3squdN2T1FaH7U5H6sSdtgMqK3Dr7F8988X6OxLONGqA88M1QAzIYoSm1glQUVQRAETXzn916tevH0VU/X6xxd0xjgTIsZrA4rS79dyoLdC9zb1IwcgLE9xtZ5Hbnk5GSGDBlCTk4OgwYNAuD1118nOTmZQYMGsWXLFr9jWrRoweLFi91Sn9zcXD7++GN27NjhXuBYvHgxXbp0CXn+u+++m+uvvx6A2bNnc8EFF/D999+Tmprq1/bRRx9l5MiRzJ49272tZ8+e7r/HjRvn/rtTp07MmzeP3r17U1paGlS2dDo0uQyVJ598kvbt27Ns2TJ69+5Nhw4dGDRoEL/73e8a+tKEMGgsaXsmvYmcfTn+sp5vXiTWGEu8KV7zON+q52qF9HDaCkJ9U5P7Mdy2geRxL+5+kcV7FtM6tnWNx7KMG6E+8HT5AdySH7FOFgRB8EcrJv+p+Kd6n6M9+08yJ9ElsQtJ5qSA/Z9pMUNDZeRkZWWxZs0abDYlC2bVqlWMHDkSvV6v2T4tLc2rbsr+/fsxGAxcdNFF7m2dO3cmKSkp5LnT09Pdf7dp0wYgYGbLrl273Is+WuzcuZMbb7yR8847j7i4OAYMGADAzz//HPCY06XJLaiomqkRI0Zw1llnceGFF/LSSy8FPcZms1FcXOz1IzQM9fEhURPJgdrWEGVgde5qv/0FtgK2/297wKrnWalZOCurU9CCVUj3bSvUDhm/4VOT+zHctoHkcc8NfI7NRzZzWZvL3IGQSqixLOPmzCGS49edoXJqIUW1TpYMFUGoHTL/Nm+0YvICWwHbj24nIzVD85hgc3S48biz0snUnlM144qpPaf69e+odAS8nozUDByVjlBvtUkRTkZOfXDDDTdQWVnJBx98wC+//MJnn33GqFGjArZv0aKF1+uqqirNdoG2e2I0Gt1/63SKbNe3notKsAK1ZWVlXHvttcTGxvLyyy+zY8cO3nrrLUCRAtUXTW5B5ccff+TFF1+kS5curF+/nsmTJ3PnnXeycuXKgMc8/vjjJCQkuH/at28fwSsWPKnrD4maaAzVtuPWjyPPmhfwOp7+8mnGpY1jSvoU94p4vCmeKelTyE7P9kqzUyukh9NWqB0yfsOnJvejxWghOy2byemTvdpOTp9Mdlp1W1Uetzd/L4PfGMzQN4cy+I3BfJf/HfMHzafEXkKKJcXrOkKNZRk3Zw6RHL82h3eGikmK0grCaSHzb/MmUEz+7NfPktkt0y8+CDZH1yQetxgtDO863C+u2Je/j+Fdh/v1b3fZyeyWyaT0SV7XMyl9EpndMrFX1t8X5YagoTJyLBYLw4cPZ9WqVaxevZquXbty8cUXh318amoqTqeTnTt3urd9//33FBYW1ul1pqenB7Rwzs3NJS8vjyeeeIL+/fuTmppa7wVpoQnWUKmsrOSSSy7hscceA+DCCy9k7969vPjii9x6662ax9x3333MmDHD/bq4uFgmhQZC/ZDQ+gCv6YdETTSGnm2TzEkkmhPd15FkTiLFkkKeNY8CWwH51nz0Oj1je4xlQvoEShwlxBnjcFY6NetCqBXSw2kr1BwZvzUj3PvR6rDy1sG36NayGx+P+JgiWxEJ5gS2HNnCmwfe5KYuN2ExWjDpTSz9dikLdy90H1tsL3a/HnPBGPKseV59hzOWZdycGURy/FY4ladZJneGipKmLEVpBaF2yPzbvAkUkx8qOsTUDVNZfO1iJqZPDDlH17Tmh9reN65YsHsBOnR+7VsYWzB63WjGp43no5s/osBWQJI5ia1HtzJ1w1RWDFlRZ/8mjQE1i1f99/NEzRAy6o0aR54+WVlZ3HDDDezduzdodooWqampXHPNNUycOJEXX3wRo9HIzJkzsVgs7qyTuuDhhx92l/sYOXIkTqeTtWvXcu+993LuuediMpn417/+xeTJk/n222/5+9//XmfnDkSTy1Bp06YN3bt399rWrVu3oLoos9lMfHy814/QMISb6h8sbVDdp4/S18qNR01nvK3nbUHTDS1GS7Ujit4Y9Kl5TdoKNeNMHb+n44QVzv1oiDLw9YmvSU1KxaAzYNabMegMpCal8tWJr9zjxxhl1JTHAazOXa05qYcr25Fx0/yJ1PitqqrC7vQvSguyoCIIteVMnX+bOjWR3gSKyfu17YcOXVhzdDA5/+dHPqeKKq/rqan831nppF/bfkzbOI0ha4Zwx4Y7GLJmCNM3Tqdf237NTibckFm8V199NcnJyezfv5/MzMwaH79y5UrOPvtsrrzySv7whz8wYcIE4uLiiI6OrrNrHDBgAK+//jrvvvsuvXr14uqrr2b79u0AtGrViuXLl/P666/TvXt3nnjiCZ5++uk6O3cgmlyGSt++fdm/f7/XtgMHDnDeeec10BUJNUH9kADlQ1PLGSSQo8j49PFUUcXSb5ey7eg2Hu//eK0dTV4/8DrPDHiGxXsWe7n8ZKZmMj5tPGaDPCUXGo5IOGFZnVbm9J3D4j2LWZ272msMzOk7B6vLilFvDCnTK7IVcV78eRScKGgQxy5BALA5q7XWbsmPXmqoCIJwZlGT+EGV/lZR5dU+MzWT7LTssGPhQHFCx4SOzB80n2XfLjstFyHf7w4HCw96LTA0x3ijobJ49Xo9R44c8ds+a9YsZs2a5X69fPlyzePbtGnDf/7zH/frX3/9lePHj9O5c2f3Ns+aKh06dPCrsZKYmOi1bcyYMX62x8OHD2f48OGa15CRkUFGhnfNnXDquJwOTW5B5a677uKKK67gscce45ZbbuGLL75g0aJFLFq0qKEvTQiTYB8SwdIGB3cYzIeHP2TB7gWkp6STYknh3Lhz+bnEPzspkKOJ+gE+ousIFu9ZHHa6oSBEikjZ5UXpogKOAYDRF4wGQsv0ksxJLL1uKRWuCqL10SLbERoEm4eTj9GgpBZLUVpBEM4kaiO9UaW/vlIaT+lvKDzjBE8Z/V0X3eV21FTxdBGqifzf87tDubOcGENMs4831H97dXGpvmQ+dcknn3xCaWkpaWlpHD16lHvvvZcOHTpw5ZVXNvSl1StNbkHl0ksv5a233uK+++7jkUceoWPHjsydO5esLO2UNaFxEuhDIlAaYJI5iXPizuFQ8SHWD19Pq5hWFNuLeXPYm2w+spm5X8/lUNEhd/us1CysTiXF0VnpRIfOrUdMMifRp00fHtz8oOa1rcpdxYT0CXX6fgUhXEKlwtbVvWnWmwNKeXJyc5iYPhEIruWd2nMqNpcNs96M3WUnWh+Nq0q+vAqRp+KUZXKUDgxRPpIfKUorCMIZQE3jB0OUgee/eV6znmC8KZ6bz785rPOqrj2tY1vTp00fTlacpKWlJa5KFy/t8XZi9XQR8lxoUQmnRoirUj7TGysOh4P777+fH3/8kbi4OK644gpWrVrl5eLTHGlyCyoAv//97/n973/f0Jch1AOB0gZTLCmU2ksDShRWDFnB6HWjybfmk5maSUa3DEatHUW+Nd+d6jg+fTwAW49u5WTFyRqlGwpCpAjHCasu7s1ie3Hw89hLSLYkB0wJvr3n7QzvOpyX9rzkNx5FNidEGjVDRa2fApKhIgjCmUVN4wfP9gW2AgpsBUHbB0J17Xlpz0t+Mvr5g+YzZt0Yr4eequxehy5sqVEkpNDC6XPddddx3XXXNfRlRJwmuaAiNF/iTHH0aNmDb/O/9dqeZ83DYrAElSi8PPRlzHoznx/5nNHrRrs/vD1THdV0wSqq6sxtSBDqkrp0wgpGvCk++HlMynkCpQTbXXZe2vNSUMlQrCm2Tq5VEEKhZqioiyief1d4yIEEQRCaKzWNH+oq3vB07UkyJ9ElsQt51jwW7F5AFVVMv2g6s7bMcmfAjOg6gjUH1oQtNYqUFFoQakuTc/kRmg7BqoyX2ktxuBzkW/NxuBzu18X2YlYOXcm64evo366/u32COSGkRMFisHDHJ3cwfeN0r5VwqK4arrqKuCpdYbkNCUKkCVR1v2NCR5YPWQ4QsnK/1vgC7zFpc9nITPWv4N4xoSPLhixzn8cQZaBdXDvmfj3XXV1/wocTaGFsEXQ8mvVmCqwFNXYoEoTaoJWhYpaitIIgNHFq4vgXrpNmbdsHwhBlYPORzZrOmYeLD9O7dW/W37yeeVfPY/3N67m09aV8cOgDTdeer058havK5RXD1NQVSBAijdyBQr0Q1Kmnqorle5f7yQQyu2Uydv1Yt2znmQHPMGPTDL458Q239bwtLIlCjCEm8H6P1MVw3IYEoSHQujfTUtJYNHgRK/auCJnuanPaNMfX+LTxbPhpA4/vUNyxrutwHXP6zgFw9xnoPBmpGSwfspwx68ZwsPAgg9oPosheFHQ8nrCeYOqGqRwrPybjSqh33BkqGpIfsU0WBKEpUlOZS01j27py+SlzlDF/0Hxy9uV4SX5u63kbc/rOYcmeJX79e0qBVKlR/3b9/aT9tXEFEoRIIwsqQp0TLDXvmg7X8NHhjzRlAmpa4LSN09zHPnnlk27nEEOUIaRE4Vj5Mc1r0kpdbChLMkEIhe+9Ga2PZtm3y0Kmu5baS1m+d3lAGc5NnW9yj5/1h9dT7ijnkb6PMDF9IiX2EqIN2udR+1PH5w9FP5BgSgg6HhPNieRZ8yQtV4gI7gwVD8mPURZUBEFootRW5lKT2LauXH5MepNb8uN5rW1j2waU6nvG/CoP9nnQr31tXYEEIZKI5Eeoc4I59bSPax8wbW/tobVc3uZyOsR3AJSn5tH6aIx6IxajBUelg4zUDM1jM1IzcFQ6GHzeYM39gVIXVQlQcnSy+zyC0BjwujejjGGlu4aSxZ0Vc5Z7fAF89ttn3Pj2jQAkW4KfZ3Xuai5rcxlJ5iQOFx/mRPkJTckQKONx29FtXgXuJC1XqE/URROTXufepmarSA0VQRCaGqcjcwk3tlVdfqZtnMaEDyfw8t6XmfDhBKZvnM78b+aHPWcbo4x+sYfqqBkoJvGMKQA6xHegVUwrv/aerkBaiExfaAxIdCvUOcGcevKt+X77OiZ05K6L7qJPmz6UOct47YbX2HpkK3O/nkuJvYQ4UxyuShd2l53MbsoXOE85Q0ZqBpndMrG77IzqNgqr0ypVwIVmRbiV+0PJ4ortxfwu4XccLj4MKOm1D/ZR7MMLKwqprKoMenyhrZAUSwquKhc7j+1kfJrinOUrD8rslsmYdWMCXqcg1DWq5EfL5UcyVARBaGpEwvGvxF5Cz1Y9ebDPg7SKaUWRvYgEUwLHy4/z6PZHwz6H1rWmWFJCOmqWOko5L/48Ck4U0COlR8AY5tmvn2X5kOV+rkAS3wuNBVlQEeqcQKl5edY8Wlpaeu1TC2366i4zUjNYMWQFscZYbl17K4PPG8yfu/+Z0etGMz5tvF9q4tQNU1l87WLu2HgHMy+eqUgYRMYjNBPCTXcN5dwTb4rnh6IfAGUx5ZkBz7i1ynqdnvU3rw96fCtLK5Zct4RYY6wyrgxmRl8w2i0ZijPFseXIFj+LRN/rFIS6RpX8eLr8mNUFFacsqAiC0LSIhMwlzhTnFQd41jh5ZsAz6HX6Wl9rnjWP5OjkoO8hyZzE0uuWuuN1dbtv+0NFh9xxvsT3QmNEJD9CnROoaniBrYAjpUeY2muqO8XvrovuImdfDgt3L3R/gKo1G3L25VDmKOPb/G959utnOVx8mL5t+2pWBb+i7RVsO7qNLUe28Mf3/sjr+1+nhb6FyHiEZkG4lfgDOfcAZKZmcrz8uDs7xVOrXGwvpsBWQIG1IOjxNpeNpOgkr3EVa4pVUostyTgrnezN28uhokNu60R1rEtarlCfaBalFcmPIAhNlLpy4AmGzWXzigOgusbJkj1LsLlsYfejxg7q3A+w/ej2gDGF+h48pUnBYpi+bftSWVUpMn2hUSIZKkKdE8ilZPYVszk37lwSzYkM7zKcr459xYVnXciDmx/U7CcnN4fstGySzEkU2Ap4eMvDLLtuGTp0rMpdxcHCg8Sb4pmUPsktMVDlQ5e1vYwyRxlRUVE4K53yoSs0acKt3B9ritWU4aguPx8f/ph4UzzJ0cl+WuUkcxJJ0UmM6j5K8/hR3UeFfBJkMVoYnz6ewR0Gc07cOeRb82lpackvxb/QIaEDJr2pPv55BEGzKK2arWK1S4aKIAhNi0i4UYaquzYxfWLY/YzqPorBHQbTPq69e+4/WnqUK9pe4Y7bfV0/XZWKPbKa4arX6ZmQNsF9ft8YJlzXIUGINLKgItQLnlXGy5xlWPQWFu9Z7Kd9vOjsi2hpaamZDlhsLya/Ip8USwoFtgL25O3B6rJWVy8/5Uqy9ehWd70GLfmQaCyF5kC4lfu1ZDg2lw2zwczV513NtR2vpcJV4adVTrGkUGQvItoQzTXnXcO4tHGcrDhJcnQyPxf/TBVVlDnLSNQnBr3OKqr48PCH/sFQ+vh6+XcRBNDOUFHrqdhE8iMIQhOkvt0oQ9VdK7GXkGwJXUOlzFEGwEeHP9J8GHPrBbd6vQdXpYsqqgJaQo+5YIxmDCMIjRVZUBHqDTUrxFxpZvGexQGt32ZePJOpn0z1O17VV+ZZ89yvWxgUGQ8oms2b3rmJn0t+BmDewHlu+ZDWecSyVWjqqPevWiROHQu+xJpilXYW73bq8Ua9EYfL4aVVzrPmEWuMddsuJ5mTSLGkkGfNo8BWwKT0SYzrMS7o9QWyeVywewE6dDIGhXrDnaGiWZRWJD+CIDRNwp33a0OoumtxpvDqtASyTVbtkcf1GKdIdU69B2elkyW7lwS1hFalxFC371kQ6gOpoSLUO6Gs3zxt0zzxtV711Yw6K51c3/F6ILQ9m1i2Cs0Vq8OKw+XgpPUkDpcDq8Ma1nFaWmWjvtr6sMBWwMHCg+7xtzp3NcYoIwXWgoDnOR2bR0E4HaozVKptk80GpaCiuPwIgiD4E6rums1lCyu+0LJNVlFjB08kVhCaG3LHCvVOKOu3MmcZU3pOYf6u+V5pgpndMhm9bnRA2Y6nvnTr0a0h7dnEslVobthctoAps6FSgn3rrQSyNVcptheTV5HH7R/fzrHyY5rniYTNoyBooeXyo8p/nJVVOFyVXtkrgiAIZzqh6q59dPgjHt/xeMj4oqZzv8QKQnNDFlSEeieU9VsLYwvOiTvHbYWcHJ3MSetJWhha8O+h/ybGEBNQM+qpL62iqt4t5gShsRBIXlMTiZtnvZUKZwXRhuigYyjBlECeNS/geSJh8ygIWqh1UrQkP6BkqciCiiAIgjeB6q59dPgj7tt8n7tdsPiipnO/xApCc0OiC6HeCWX9tu3INm7bcJvbCnnihxM5UHAAFy5clf6p2r4SB1CkCq5KV71bzAlCY6GuU2btlXYA3rnpHfq36++331eCp3WeSNg8CoIWWhkqRg/5j9RREQRB0CbWFOuuWWLUGzHrzTy+43HNtp8f+ZwqqrzicJvLxtSe/rUQQXvud1Q6yEjN0GyfkZqBo9Jxem9IECKMZKgI9U4w67dxaeO45f1bAKVmQ2J0Is9d/VxApx4duoASh0hYzAlCY6EuUmZtThvL9y5nde5qr1TfZwY8w4ObH2T94fXEm+LJSM1wW5MHO4+MQaGhqNDIUNHpdBj1OhyuKqmjIgiCECaB4ouOCR2ZP2g+y75d5h+Hpylz//xv5oec+52VTsanjUeHzk9qlJ2W7X7AIwhNBVlQESJCIOs3vU7vlYVy10V3BXTqqaKKwecNDilxUM9T7iwPKhcShKaMZ8qsryNPOCmzpfZSL0efLoldyLPmucfXrMtn8dBlDxFjiGHLkS2MWTeGQ0WHvPrQOk992zwKghbuDBUfWY/ZoMfhcop1siAIQpgEii+CxegAt15wKzeff3PIud8QZWDNgTV0a9nNLfdPMiex9ehW1hxYwx+6/CFi71UQ6gJZUBFqRKm9FLPe7F5NtrlsmPVmt+7SWWnH4rCBORZcTjDFuI/1tH6zOqwYogwU24t5c9ibbD6ymaXfLqVPmz48uPlBzXPn5OYwLm0cSeYkL9kBKE/DJ6RP8NqmJRcShOaCs9LJ1J5TaR3bmj5t+nCy4iTJ0cnknsylU0InAPKt+e5xqlopq5j1ZrYc2cK8gfO8jt92dBuL9yxmYvpEyuxlALRp0UbzGtRUXl9Lw/q0eRQELdwZKgbvBRWTIQpsIvkRBKF5ocbR1fG3s7quib0c9AaoKILoBL94PFRfNpeN+y+9nxhTjFd8oI/SB4zR1Tjc0x450NxvjDLywjcvBHwgdMv5t+BwObTfmyA0QiK+oHLgwAE2bdrE8ePHqaz0DnD+9re/RfpyhBrgKQ9oaWnJiiErWLVvlZdcIOv8kWSnZmJeOhQ6D4L+M8AQ7d2PhjNJZmomzw96nkJbYVAZw8mKk6RYUvwWVFTpQSyxtXY9EYSmhMVoYXjX4by05yUveVxmaiZdkrowet1o9uTt8arYbzZUj4EyRxnzB833k9dlpGYwf9B8yhxlzNo6ix3HdpCZmknO/+Uw8aOJXn1mp2V79SkIDUWgDBX1tVUkP4IgNBOCOvxVAZvnwvaFUFEI0YnQZ5JmPB60r7Rs3jjwBoPfGEyxvZherXrx9FVP14k7j6ekqMBW4BXTF9uLKbQVMmPTDHad2CVxvNAkiOiCyksvvcSUKVNISUmhdevW6HTVBeN0Op0sqDRiPOUBAHP6zmHVvlX+aX97FgE6xg74K5ZVNys7+k53r4x7OpP4ygwsBgujuo8KWvk7OTqZPGue5r44Yxwrv1t5Wq4ngtBU8B2ToNzvC3YvoIoqxqeNZ9rGae5tAKMvGO3OVDHpTSz9dqnf8errcT3G8UPRD17HLxq8iCJ7kTs1980Db3JTl5tkXAkNTnUNFZ3XdjVjRWqoCILQHAju8FfF2HYDsXz6ZPUBFYWgvvaIx0P1VUUV3Vp2Q6/T0yWxC4W2wjpz5wnVT7w5np+Kf/J5bxLHC42XiLr8zJkzh0cffZT//e9/7Nq1i507d7p/vv7667D6mDVrFjqdzuundevW9XzlZy6qo45Zb2Z17moAksxJ9GnTx/3al1X7V2PoNADrqDfg+w1K2uEpDFEGNh/ZzLyB81j3x3XMu1r5/dzA5/j4549xVjoDVv7OTM3kl+Jf/LJTQJEeOCodLP12qfY11cL1RBBOB183KqvDWqPjS+2lOFwO8q35OFwOSu2lXvs9x6Qvq3NXc1mby0gyJ7m35eTmeD3dMUYZgx5viDJQZCvyOl4fpeeBzx5gyJohTN84nfnfzJdxJTQKtFx+AEynFlhE8iMIQnMguMNfDoakDhDT0n/n9oVKPO6yQ9kJcNmD9pWTm0Pfdn3dsfqrv3+VgoqCOnHnCeny4/LvR+J4oTET0TuzoKCAESNGnHY/F1xwAR9//LH7tV6vP+0+BX/UNMBfS39l5iUz3SvJKZYUTlacDJr2d8J6gv/aT/CHrNcw28ogxgSElhk4K52M6j4KwEtKlJGaQVa3LCwGC1PSJ7PKKzUxg+y0bKxOa52kIgrC6RI0HTeMlNVA7juesp1ie3HQ+73AVuAljyu2F1NiLyHZooyBUC5BvvI6dZvat7tPGVdCI0DNUNEqSguSoSIIQvMgpMNfRQHJsWdDeb73zopCKMuH10fDL9uhfR9KRq4M2leRrYiZm2a6pTe39bytTtx57C47md0yAf9YP7NbJmWOMj95v8QbQmMmogsqI0aM4MMPP2Ty5Mmn1Y/BYJCslHrG6rDy9vdv8+r+V0kwJ5BgSnCn5+VZ80iOTg6arpdoTuSF3Qsod5aR2S0LVbUZjsxgxqYZ3HL+LX6Vv2/bcBsrrlvG2NiuTLj5I0qsJ4mzJOP84RPM37xCVK/MOklFFITTIXg6buiU1WBSHqiW7cSb4oPe70nmJC95XLwpnjhT9RgImXJrivc7XrNPGVdCI0DNUNEsSossqAiC0DwIKbuJSYHSY/4HRidCdDzkf6+8zv+euBBxRKwx1kt688SOJwAlDhmXNo7CikISoxPZcmRLjdx5WhhbMHrdaManjfeL9adumMriaxf7yfsl3hAaM/W+oDJv3jz33507d+ahhx5i27ZtpKWlYTR6V3++8847w+rz4MGDtG3bFrPZTJ8+fXjsscfo1KlTwPY2mw2bzeZ+XVysvRorKKjVvq9sfyU3dr6RbUe3kV+RT2ZqJgt2L6DAVsD2o9vJSM3w+tKnkpGawa4Tu5h9xWwua3sZ5Q4r+iij4gYSQmYwKX0SvVv3ZtrGaX6Vv6ekT8Z56FMsq0ZATEtlBb70GMbyfIhOxJl+M1mpWbfhmp8AACAASURBVO4vrp4EciMRGj9NbfwGT8f1d6PyJZiUJyc3h4npEwElC0Ydk75kpGaw7eg2r6c7mamZ2Fw29xhwVtrJOj+DF/doj2Hf47W2ybgSQhGp8asumAQqSisLKoJQc5ra/HsmoMplAsXfjkonmjNy7wnw48bqzJXyfJw/biIrNYMXA/TlO+cDvPDNC4w4fwTOSic6nY6qqip06Pjg0Afccv4tYb0HZ6WTfm37acb6k9Mna55X4g2hMVPvCyrPPvus1+vY2Fg+/fRTPv30U6/tOp0urAWVPn36sHLlSrp27cqxY8eYM2cOV1xxBXv37qVlSw3NIPD4448ze/bs2r+JMwgtqUJGagaXnH0J2WnZgPKl7tmvn2XFkBV+aX8ZqRmM6jYKdPDydy97yXru6HUH13a4NqQsZ1T3UVQ4K1iVu4qDhQeJN8UzJX0K2WnjMC/orzQuz/dOZ6woxOKwn7rGSlZ5Og+dkgSJG0nTpKmN35DpuCFSVkNJeVTZTqwplvFp4wH8Um9HXzCaiR8pCy+BXH4sTgfZ6eOp0kGOj7RI63jfbVJ1XwiHSI1fm/NUhorPgkp1UVqpoSIINaWpzb9nAqHkMvZKBzED7oNP5ni7/PSeCMuGevVlOfAh2UOfpEpDwpPRLYMx68b4nV914fGUArndAZ1lJOoTQ74Hi9FCdrryncI/1s/mzQNvujNnJN4QmgK6qqqqqoa+iNOhrKyM3/3ud9x7773MmDFDs43WCnv79u0pKioiPj4+Upfa6NGSKqhMSp9E33Z9OctyFmfFnOXlVW/WmzlhPUGiOZEd/9tBUnQSX/zvC577+jmvPpLMSXx080dc/frVAdMLN92yCaPe6M6SKXGUEGc85UEfpYd/dFEmCF+iE+Geg7DnLayWWAydBnpJgixFv8GFf/aqbi40DZra+HW4HAx4bUDIe/x0j7c6rLx18C1ax7bmsjaXuVNmd53YRZuYNrSLa0e+Nd+djnu89LiXI4/VUXbq+LZc3vZyd+ruzuM7aRvThnZx51BSfpy46CScv2xHl9AefeJ53mNSqu0LIYjE+K2qqqLjff8BYMGoi0mwVI+vRf/9kY37jzNzcFfuGNSlTs4nCGcKTW3+PRNwuOyMXjeG8Wnjveb+rUe3smTPElYMWYHR5VAK0FYUKzIfpx1W3gi/fenVV+ld3/LOb/+lTWwbrzjAVeli4kcT2ZO3x+/88aZ4Prr5I4asGeKVRTIpfRLjeowjxhh+nK0Z6xstAbcLQmMloi4/jzzyCOXl5X7brVYrjzzySK36bNGiBWlpaRw8eDBgG7PZTHx8vNeP4E8wqcLq3NWcn3Q+GR9k8Pr+12lhaIFRbySWKJyVdnYd28nXx76md5vetIttR1a3LJ4b+BwdEzq6+yiwFbD9f9vJTM3UPIdnhXCL0YJRbyQ5Ohmj3qh8kLqcyiq7Fn0mgb0cuv8eS1UVxhU3kvzyCIzP9sDyShZsfMzLbUhoOjS18eusdJKVmqW5T01ZDYYi5QnkdJWBzaUEt4YoA89/8zzTNk5jyJoh3LHhDoasGcITXzzBL6W/oENHVFSUOx33vUPveVXIN0SZeP/Qf9BxKmX3VOpuhbOC+zc/AFSS/Pp4ZQz9516i83/ASBXJlWCkCkvTXosXIkQkxq+anQL+tslmNUPFKZIfQagpTW3+bZbYy72ceZwuO/3aXOE390/fOJ1+bS7HWWlXHh7qTdAiRfmti4LOg7z7bdkFc2xrPjj0gV8cUGQr4oq2V2heTiAp0Orc1RijaibH0Yz1g2wXhMZKRBdUZs+eTWlpqd/28vLyWqcU2mw29u3bR5s2bU738s54QkkVCm2F3NbzNv54/h8xG83grIDNc7G8PZVB5w1i14ldXPP6NQx4bQDXvH4N3+V/x/Ihy70WVZ7+8mmy07KZlD6JeJMyMceb4pmUPklJVXQFqRBuioH+M+CqvygZKaD8vupe6D0JlgyGZ7rDkV2QsRoqndWyoIpCZaVeEOoZNZV1SvoUr3t8SvoUstOzQwYGsS4n49PGM9lnjExOn8T4tPHEupQvhp7jtcBWwMHCgyRGJ7J8yHJ2n9jtHoeD3xjMd/nfudNxVVTHrb35exn8xmD3j7ttRaEybmJawti18OsOJUPsqY7K781zlc8AQWhgbB5yHj/bZJH8CILQVDkVZ/OPLvCPzvCPLlh2riI7fTxT0ibhqnJxsPAgrioXU9Imkp2aicVh8+9HK35ud1HAOGDTL5tOxSGTfeKQyWR2y2Tu13P9TqFKmgXhTCSij+zV1U9fvvnmG5KTw7PBuvvuu7nhhhs499xzOX78OHPmzKG4uJjRo0fX9eWecYSqHN7K0oobO9+oaBjt5cqH/KdPYs1YzdLdi1m4R9u5Z/pF05m2cRoA+dZ8qqqqSG+Vzrge4yh1lJJgTuDz3z5n6oaprBiyIvhFGqKh73S48m6wlYHRAt9/BMuGQN4Bpc1//6H8vuZheOVUpoBa3VwQIoBZb2Zsj7FMSJ/glbIalv7XFIP5zUmMvvYRJqZPosReTJwpHlvJUcxvTYHhyrjSGq93XXQXOftygrpouU8TynHrgjHKuOk/A7YvqB5XoCy0fPqk8nff6SKlExoU26nsE50O9D4xhlpTxSpFaQVBaEp4xNluKgph7b2YqyoZ220YE9LGUVJRSFx0ouJ4ueoWGLdWuz/P+PmUFMhU6dSMAzzdfCamT3THMfZKOxM+nMChokN+3YsLj3AmE5EMlaSkJJKTk9HpdHTt2pXk5GT3T0JCAoMHD+aWW8KrDP3rr7+SkZHB+eefz/DhwzGZTGzbto3zzjuvnt9F8yccqYL76breANsXQkxLDJ0GkLM/sHPPZW0uI8mcBCipgluPbuX2DbczZM0QZm6aiavSxewts+nX9gqcLo2V9UBUOaGqUomiffniJeg0UHm6DookyGlXJihBiAC1Tll1OSGlC7HP9sD4wuUkvzMN4wuXEzs3DVK6KPvxH69J5iT6tOkT1EXLGGU8lTqcF9Jxy6g3w/BF0OkqaHcJpHT1b7h9ofJZ4JGOLGNMiDRq9olJH+X30EZskwVBaFT4SHgCzplqnK3FnjewRCdhrEKR4VZVYQFIHwFOW+Bz+EiBgsUBL3zzAma92SuOiSKKfm37abYPR9IsCM2ViGSozJ07l6qqKsaNG8fs2bNJSEhw7zOZTHTo0IHLL788rL5eeeWV+rrMMx7fqttBq2tXFCkr5Wd1p8SaH1QqVGAr4Lz487ilzS1kdst0Vw0vsBVQcKLglJRoCn/oMjy0E4+a/rh9YXX18t4TFEnCsqHVWSoVhVCeBy07wyXjlOrmK29UNKT9Zygr9YLQGFFTc0G5z/MPnpK2/cXr3vUdrymWFApsBSEcgopJXnsf/G8PJX9+I3jb8hMk59wCxUe0xxgo46wsH14fDb9sr3YTkDEmRBA1Q8XX4QeqbZNtIvkRBKGh0YphA82ZapztS0pXyHwVtvzLp5+J0G+mUi/ls6fDOke4roIqNfqeIAhnEBFZUFHlOB07duSKK67AaBQP8cZK2FKF6ATlQ7r0GHGWliGlQouuXcSWI1sYs26MV6qgW0rUtj/mvW9DtxsCywcCpT8GkvjEtoE/vwU/fFL9RVCtcC4yBaExo5Gai8vhFwx5jtdSRymxxtigYzHOFA+/fQ3Wk8RFJwVve2p8BxxjUC2ly/9eeS1SIKEBcGeoGPwXVMySoSIIQmMgUAwbaM5U42zfRZVrZgWQ4T4F6KDbsLDPEW+KDxEz+Et4TkvSLAjNlIgWpb3wwguxWq0UFxd7/ZSUlGC3BylGKkSUsKQKquNOeT7OHzeRdf5Izb6yzs/AYS1g9XcvM33jdD/dZVZqBs6fNmN59gJYe29wJ55g6Y9aEp9ftsPcNHh1lPdTdVWmIAiNGd8q/aYWms3U8ZoUnYTTZSMrgENQVmoGTlcFWE+GMW5H4vxxY3VRZ/AfY6Bkrvi2AxljQkRRM1RMWhkq4vIjCEJjIFgMqzVnOu3KHAvKvHtWd2jZBToNUObjQP0kd/Sep4OcQ3EV1HbezEzNdLsK+iIuPILgTUQj3sTERM2itCrnnHMOY8aM4eGHHyYqKqJrPUJN8ZAlWP77T7KzXgNg1f5XPFIAM8lOG4/ZWkhW9z9T4bKzKjenev/5I8k+PwPz0iFKn6oTT4sUZSVfb1BSHqMTlKfzjnLt9Ef1WFXic+l46HcXLLzS/4ue73kEoRlhcTnJThsPwKrc1R5jMUMZixVFEHs2lOdj+ehhsrM/BF2U97hMzSQ7NRPzkmu9O68oVMZT7NlQ6VIWLXtPVLK/fJExJkQQNUPFqJGhoi6yWO2yoCIIQgMSSMID1XNmdEK1TMdph8tuh+43KYskZXkQ2wocNog9S7svNRY+Nc9rnsNjXo41xTL+VMyQ4xEHZKZmMj5tfGgZviAIQIQXVJYvX84DDzzAmDFj6N27N1VVVezYsYMVK1bw4IMPcuLECZ5++mnMZjP3339/JC9NqA0esgSzrYyxaeOY0HPyqRTAWJx5BzEv+z/47UvM7S5h7I3zTqUIlhKnt+D8YYOymKJmj6jyAU+NaexZMHYdfL1S0YdqpT+qx8a2gTHvK4svUXooPa593eL4IzRXTjkEjb12NhM8HIKcqkPQjfMVGc8pzFUwtt0AJvQYS4n1JHGWZJwnD2Gu0ug7OhHiWkPWa2BpCVUuWHmTd/aXZ1sZY0KEqM5Q8X9gYxTbZEEQGgOBJDygbDfFwvzeUPAjJHWC27aBowz2vQPbF3nXQxm7ztvd0rOfmBSved5rn8a8bDaYq9187CXEmeKwuWyymCIINSCiCyorVqzgn//8p5ejz7Bhw0hLS2PhwoVs2LCBc889l0cffVQWVJoKqhYzxoSa8JfsMsPW5zF+PKu63W9fYnnhCrhmFsmXZMOC/hjzD3r31WeSUp18y7+q9Z43vQDbX1S0oq26KumPnrpRz2MrndUyCXu5ss1TR+rZ1uVU2glCc8Jpg5TOWJ5Ng5ZdSG6VCidylbF21b2KPAeU1OHBs2Hb81j++w+IaUly7NlQegxjeT5ceY9/vZTeE+Dw57BjKZzIVY7vfHV1XSJPZIwJEcSdoSKSH0EQGiuqVF4zLp0IP3ysLKaA8rv0GOz8N/z3qep27nooVf5zNCj9nzykZKfEtFQyVUqPKa+DzMuxplgAdwFao15qXQpCTYjogsrWrVtZsGCB3/YLL7yQrVu3AtCvXz9+/vnnSF5Wk8HqsGKIMrhXkL1sjGuCn5zG6V0Iy3e/067Yrplj/dt6YisBgxmijPD5XO02n89VUhgnfw4/boKPHlIySfpMgn4zlOrkqsY0pqWiFX37NuX1x7MUpxFQ9KPBqpf7OqWEqqYuCHVNqHEWglqNd4MZ+kwBdP4OQf1mQOHPMH0PlJ+EuLNhzSl9dnm+f72UGd8pY1CV9/SdruxL/p2SMlx0BH43qPpc6hgbeD/0ylTee9mJWr13QagJasFZrRoqalFacfkRBKFBCRaX9p6kZJyoxLRU5tkvFmn3tX0RzNyvZLIU/FjdzxV3QJQZpmyBpA6KTKhFirLIktIVa5ULg8tx+t8jBEHwIqILKueccw5LlizhiSee8Nq+ZMkS2rdvD0B+fj5JSUmRvKwmgc1lY+m3S700jrWyKQtl2RbIlrjPZFg6NLDtsKMCNs+DQ5/C8JeC60RLjypf5DoNgEn/VeQ5Jw7AO7fDoL9VHxt7tjIZqK/zDij1Gq55WPmyV56nyHwq/d1PgLCdUgShzqmJNaIGtR7vtlLcVf77TlPGSItWyqKo0wrfvq4EYvFtIfO14OPUYYWpX4G5hZL5svPfsPExn/cz03+MVVbC58/IQqYQMWzOwC4/6iKLuPwIgtDgaMWl9nJYMthbvhN7tvJAItgcbSuG2zYr7WJOLZpEmYFK+O4dvwcdtpQudfM9QhAEPyK6oPL0008zYsQI1q5dy6WXXopOp2PHjh3k5ubyxhtvALBjxw7+9Kc/RfKyGj1Wh5Wl3y5lwe7q7J5iezEv7n4RgLE9xoa3whzMss0YoyycfPFSYFvi/ndVpxd6Wq/ZSpTFlP8+Vb2qHkwnGpOi2Kzmf6982et4FSzoqxw7bF71saXHvPuKaQlRBnj3TrAkQ7uLlJoQAdxPgOprVItwiQRBqG9qao3ow2mNd71JOfcpGQ+xZyuLiBM3wubnqsdylF6pTxRsnEbHV8vnPGV4Wu9HdSM6zfcuCLVBXSwJJvmxyoKKIAiNAd+41ER1zT/PeTtULG1qAQuuBL2xWtYzZYuymOIzB1sT27N0z0ss2F3tMlSr7xGCIGgSUSudYcOGsX//foYOHcrJkyfJy8tj6NCh5Obm8vvf/x6AKVOm8Mwzz0Tysho9higDObk5mvtW5a7CEBXmupiWZVtKVxiZoyymOMoV146Rq5TtnnhapvparxnM1WmJ5fmKlEe1evOl9wT45QsY9i9FetDlWkjuoDwtj2npfaza14C/Ktc4fQ9kvgoz9ilfEK+do7Szl4f3/gUhEgSyRkzpCu0urpbCuOya925Y470sT/t4g6naTrE8H45/p9gk603+Nosue/Bx6rRDWb5yvW0v8v9MAP/PgpraQgpCHRAsQ8XokaFSVaVVbVkQBCGC2MuV+VeNAxwVMOSJ6jg34xWY9Kkyz/eZpN1H7wlKTbT8g8o8r9ZMSergPwfHtMTQaQA5uas1u6rR9whBEDSJ+Ajq0KGDn+RHCE6JvYRie7HmvmJ7MSWOEpL1yaE78rVsS+mq1CTZvkCpU+Ip8Rm7VpHXqCmInlZsx7/ztl6zFnr3G6jWSe8JcNltgA62Pa99zrenwE0vVh/71XK4ZSV89ox/+1AyJEFoCLSsET3H2psTg0phQo738uMk//tmKD7if7zWudWidJ7bY89WxnCfycpr33HaZ7KyoPLeNDj8mfZnAvjbMIZjCylWykIdU52h4u/yoy6yVFaBw1WFyeDfRhAEISIEkgP3m6HEu89095Dp3Kdsp8rf5af3RGU+9sRXJu+xvcSaXzffIwRB0CTiCyqFhYV88cUXHD9+nMpK7yJxt956a6Qvp0kQZ4oj3hSv+WEYb4onzhgXXke+lm3XzFK+4Hm65nhKfDwriHtasflar1kSvfv1rXXisCopjt9vgOO5cGwPfLm0ur3nOS+/HVZnQL9pyrEuB3z+rH+V81AyJEFoKLSsEa+ZpT3WNKQwIcd7dFL1Aonv8VrnLj2mPLnylM5FJyrjdsUwZQypNYliUuCHTyDnTzBureLmE+gzAfw/C0LZQoqVslAPuDNUghSlBcXpRyuLRRAEod4JKomtgjY9FTnuWd2VeXvtX5Q2vUYpc3x5HsS0gqpKWHmjv2Wyr0zeY3ucpWXdfI8QBEGTiEYW7733Hueeey5Dhw5l6tSpTJs2zf0zffr0SF5Kk8JZ6SQrNUtzX1ZqFs5KZ3gdqZZtUO2g4ysDUPGU+EB1eqGn9Zr7Am2K5ZsneQeUL15fLFKenu9+XZEGtb8Eug5R0hp9pUVfvATnXg63vq283vee8iUxkIQgmAxJEBoKz3EGoceaz70bdLyfPxKnOg61jvc9N1RL5wbeV51S/IcFSj2iG56Dj2fD3DTIGan8fnUU/G6gkqFiPVndj+9nAvh/FmidP1BbQagj3BkqGoslhigdOp92giAIESeYJPb7T5R6gtO/VSQ/aoy8+3VlkWThVafm6B6KS1/nq/37KM+HgsOaMYDzx01kpWZonrpG3yMEQdAkot9AZ86cybhx43jssceIiZFMgnCxGC1kp2cDitax1tW5PS3bftyknRqookp8WnaGS8YpEoCcPyn2q77yGnMc9Jup/O2VljhRsXA1RkP6n+DzfyruPoGkRRWFUPq/Uw5AV0G/u0NLCALJkAShofC1RgyUhqviI4UJON7PH0l2aibmpUMCHx/IlhEdXDQGPvunt3SuzyQYvwH+/Qf47Utl25X3KOPdVqJcu7p4U1Go/B17drWVstiVC42AYBkqOp0OkyEKm7OSCrtYJwuC0EAEimdTuir1Abf8y99hM/NV5bXeqMS5AP+ZqUjhwT/mTu6sOQdbCn8h++IJQNTpfY8QBEETXVUEq7S1aNGCPXv20KlTp0idUpPi4mISEhIoKioiPr7ppKBbHVYMUQZKHCXEGU/DP95erqyUVwFPd9H+gE/qBLd/Aboq5Ul1VRUYzYoEJ5Crjq1EyUKpKFJS/502ZbFFK81R5cp7oG0vJZslOlGRHsxNU764XTMbLr8N/hHgGj3bV7rgnoPi4nMG0GTGrzrOKkogOi74faxx7/qN9x8/wbL+Qf80X63j3ec+ZcvoqICt/4JPn8KPq/4CV9yhjN/oBEXy8/lcGP1u9Vj0PI/6foJ9FvieP1hb4YyiPsbvjFd38ebO38jsfS439Gzrt3/Cyi8ptTn58K4r6Xq2pLYLQm1pMvNvY8Rlr44DVDef0mOKScORnd6SYJUr74G+0+C5nt6ZqRcMVzJMjdHVMbejolpWG2AOrrPvEYIgeBFRyc91113Hl19+GclTNissRgtGvZHk6GSMemPtPwRNMcqXr0qN9HzV9ee2rWAvA4dNcQ5xWpX9VT5P+DyrlRvMSkp/i1ZK/+ZTgWuwNEdPGYGnrAiU2ilOe+gq51oyJEFoaNRx1qJlraQwXuPd5cDy61f+iykpXWHsf5S/PZ2D3OdOUX4bo5UnWVpsX6iM3apKQAc6HaTd7D0WPa+zRUulz1B25Z7nl8UUoR4J5vLjuV0kP4IgNBguJwy839vNZ/oe6HxNcPm91oPClM7VfxtOZfx7OvUEmIPr7HuEIAheRFTyc/3113PPPffw3XffkZaWhtFo9No/bNiwSF6O4CdNOAvGrlOKZ+55A256QXHX+cInpbDfTOULWqBq5b6p/SFlO/nKU/Ief/SuWl5RqKyqa0kIVCeSQDIkQWhMnK4URuv4dpfAn9+CrfND9+nrxOVJRSGUHodVI045B01UnAXevk3ZL5IdoZFT7fKjvaBidi+oiORHEIQGwhQDF432lt627wMjVgSfn62Fivy+PN87Dv/uHVh7r0hrBaERENEFlQkTJgDwyCOP+O3T6XS4XPL0KOIYopXq4VferTzZ3vaC4qgzbY+ymPLfp5TsEbXquCoZuPwOxalHs1o53o47oZw/4lpDUgd/S9boRDC3UFbX1WtU0xeddmWxZdxa5bdMIEJjx3Oseabhhnvv+h5viIYt88Ibg75OXJ5EJypZZW7noKcAnZKG/H//qPl1CkKECZmhopcMFUEQGhhV/u7pWpn/PcQke7vwqVIgdQHFkgRj3veW9nz3DrzlYQYRaO4XBCEiRHRBxdcmWWgkqB+8JpQn3S27QHxrpZbCyBzFoaQsT0kd/HEjfPasouvsPUFJO/x4tvdCyPaFypc+FVXuoFVDpc8kyP8Bcm7R3udynkpXPHWNatFZzxRIqZsiNBWC3cc1Pd5l15bSpXSFdhcrUruyE9UB2IC/wrq/+rfvPUHpyxN1DJtja3edghBB1IUSraK0UL3QYpUFFUEQ6hp3vZJTCx4up/aCRiD5u8uuzM+J5/rH24W/KPvNscqDD5W192pfi2/8LQhCRIhoDRVPKioqGurUQiBUaU6rVKXwZOarSqGsZ7rDc+nK7yO7TlUdL4X371Jej13rbX+suo6oqHKFq/5yynEE5bcq1UnuFHifrLILgjZaUrqUrsp4/HWHUvzuH52V31vnw8VjYMgT3uPM7ehTrDwVc/ddqPQvCE0ANUPFqNdp7jdKhoogCPWBKn33nG83z1W2+6I1Z8eerSzIXDxGiac94+2j3yjbfS2NQ8noPeNvQRAiQkQzVFwuF4899hgLFizg2LFjHDhwgE6dOvHQQw/RoUMHsrOzI3k5gi+qNOdErlKkcvNc76rjFYXVr/tOh/wfYferyutrHlacemJaKlrPaJ/q76HkDqcjhRCEMxEtKd01s5QaSL7j9tMnFaeuC0fBRbcqduMxKUoWWs6fFEef0mMefScq/QtCE8CdoRKiKK1NaqgIglBXaDlYBpPeaM3ZpceUNp/9U2Pefkpx4+w7zfu8oWT0vvG3IAj1TkQzVB599FGWL1/OU089hclUnUKelpbG4sWLa9Xn448/jk6nY/r06XV1mWcetpJqd5A+k8B6UknxD1V1fPQ7MHIV/LAROl0NWW8oFctHLPfuV3UfgcDOH+IKIjQ3PB2w1PFlt/pvC/t4q/c2XwesmJZKunDAcbtIGV8Lr4KckYol8quj4HcD/R19ek9Q+heEJkB1hkqIorROyVARBKGOCOZguX2hst+TQG5/QePtRf6S21q4BgqCUL9ENENl5cqVLFq0iEGDBjF58mT39vT0dHJzc2vc344dO1i0aBHp6en/z959h0dZpf0D/06ftElIoQdICEWQJjXUADYsq/vuurqLgthAwYauyhbb6qK+v13rLrYVrCgour4WFOlIR3pvgRBaCmQyvf7+ePJMZiZTkymZme/nunIlmXnaDJxznpy573NH8jJTi9UE/Pya0GmLVX4y8gH9+cAhhfrzwOd3AEVjhRQgmwmoPQZ8cZdntSCPCkFcgZxShL8KWKWzgPevAyq3Bm4T3vv7qugjPgY0VOlqJ+ReBwsFTs8FKjY1ptaVzgI+/LWwjXsFLbs1eu8PUQQFjVBhyg8RRVooqTfiemmA8MHh6NkAnMCmhnvjvBLAUBv4OOY6QF7geZyWVA0kooiL6YRKZWUlSkpKmjzucDhgtYZ3867T6TB58mS88847eO655yJ1ianFXC9MpogrjpsuAvOvBq56XpgUCRRSmNZGWJ28YpPw2KgHhTBH00Wh3PKmeb7TDgCuQE7JLVAYsNMBjHlYSI/z1yZ87T9mtlDRx71NVW4F3p0I/HY+MOYRYTIlsyBINZ+8hmoBDal1JzYCp38RUn6804Du+D7y7w1R1CcQbAAAIABJREFUFASLUHEtSmthyg8RRUi4qTcWA7D9Q6DDAGD2vsYxVyoPchwf6bctrRpIRBEV05Sfvn37Yu3atU0eX7x4MQYNGhTWsWbOnIlrr70Wl19+edBtzWYztFqtx1fS85Vu4E2uEiJI3FUfAj6+CTi+zn9I4bC7PVMExBQgIHjaga8wSKIAEq79BgoD3vwOUDxeaCci7zbhvX+gNlV9CFhwLSCRABJZ0zQgd2Iaj5haZ7cBJ9YBH9wgpP+4pwGVTGTYMEVELNpvsCo/Cqb8EDVLwo2/sWSzCOOqL2VPAFaz5324TA6s/LvwgYr7mHvkJ//HEcdtX/fzTJUnajVi+pftU089hdtuuw2VlZVwOBxYsmQJDh48iA8++ADffPNNyMf59NNPsW3bNmzdujWk7efOnYtnnnmmuZedePylG3iHAhov+g8zXPo4MH2NsJCle9qOmA4wf1LjtqaLQkcvVgkJJe3APQySKICEa7/BwoAN1UJbESckvduE9/6hpPLUnxWiShTpnmlA3u3WPY3HO2z4/D7PClv8pIsiINrt1+l0Bq3yw5QfouZJuPE3luwWYVwFhA88xPG27AmhOs+6lxvH4cLhwE3vN47jhprGe4Cfnhaq80mkfsZtC/DtbKEIBFN7iFqlmEaoXH/99fjss8/w3XffQSKR4Mknn8T+/fvxf//3f7jiiitCOkZFRQUefPBBfPzxx1CrQ+tM5syZg7q6OtdXRUVFS15G62YxAGv/2Zh+AzSmFqz9p2ekSlpOYwlVb7rzwifeox4AHj0E/PGoEKLYYYAwmVJ9qHFbdQ6QUSCsVq47J/xh6O+4XIGcwpRw7VcMA/b5XI4Q4tukoo5bm/DeP5Q2lZ4nbCemAV3yK6HdPrSrsd1+cjOg8voESwwb/uNhoY3/8bCQvscbNYqQaLdfcTIF8L+GimtRWlb5IQpLwo2/saTKFMbVjgOFcVYcb3tdI1TtWf0iIJUBbfsI66Sk5/oex6sPCccZeT8we7+PcTsTqPxF2Nbf/TwRxVXMcy+uuuoqXHXVVc3ef9u2bTh//jwGDx7sesxut2PNmjV44403YDabIZPJPPZRqVRQqVTNPmdCCbbq+NhHG3+3mYHh9wil2bwNuwc4uV5YeDYtF5i+Wth/uY9PKobd3VjFx1ADHFslPOa+3oNIXIHce9VyIj8Srv2KK/C7r4Ei8k6Xy+/ZWBVLXyVMpljNQvsT1zYK1qa8jymmAc3eByyZAVQfFJ4b97jvtieu3SJGyLBtUgRFu/16TKgEWUPFzAgVorAk3PgbS3abkB776WThQ43MdkIU6PTVQvXLWz4R0nX11Q1ptlb/43jJBOD4auDrB4Tj6M41jNuPAdozQM1hz+297+eJKK4SbjGLiRMnYvfu3R6PTZs2Db1798bjjz/eZDIl5YSz6rgqCxj9iPDzJvdqPPcIK5EvmiJ06JnthPSgy6YAVoNnaKMrJNEGlM0BVjzXEL64VFjXYROr/FCKCbQCv3tFHV+Ve9Q5wPg/NezvbGxra/8pbOsvJNg9BQ9oSC2qEb477EzjoaQlTpJIJIBMGjjlx8gJFSKKFF9ps4XDAYteqH656U3gq/saU35u/sh3itCwu4V77l8+EMZrV/rtY4334t6YPk/UqkR9QqVNmzaQSHzf5Hirra0Nuk1WVhYuvfRSj8cyMjKQl5fX5PGUFM6q4/5WHD+6Ati2ABg8FTj8ozBTnpYDvP8roUKJ97ZiRZCBk4V9TFpAnQWMfAAY+0euQE6px98K/JAKbcWkFbZZ/1rTakDfPyZUAxp0GzD64ca2VrEZ6Dba85hHVjRNwQOEtp7VHpj6jdAW2fYoSYkRKkqZ1O+9hkohfNBisHBChYgiyNdYb7MI6xi6R6LUHBEWjfV3H/3VfcCvXgeGTBM+GFVnA1aT8PjhH5uel+nzRK1K1CdUXnnllWifgtwFSjfwTrcRVxw3XQTyegAFvYGqA0JooTpH6PDT84RPui+UNw1tdIUkNqQS+EsdYCoBpaJAqTQZ+UKanJie592mVr0gRIS9NQ6QKTzbWqchwv5mHXB6W9PJFECIMrOZhTLJ3ucmSiLiQrP+SiYDgLoh5cdgYeUqIoow77EeaFqVT0zd7T7e/320RNpQtadA2MduA/J7+D4n0+eJWpWoT6hMnTo17H1eeOEFzJgxAzk5fhZh9LJq1aqwz5G0AqUbeIf8m+qAzLbAjf/2zPM8thL46Rlh9jyvBBh6l/CdFUGIIidY+zNphUXsKjb5bmuqzAApe48ACrZJSn6uCBU/C9ICjFAhoiiyGIQPKMXIEotBGNu9I8W9q/kEu48O536eiOJK4nQ6nfG+CG8ajQY7duxAcXFxVI6v1WqRnZ2Nuro6aDRJGjLn6uDd0g28a9TbLYCpHtg0z8e6KPcKa6xI4LlvKMcliqKkab/B2p86S9guWFsz1wNyVePNnM0stF2iVijS7XdreS1+++YGtNeo8fLNA31uc+CsFs/83z4U5Wdg5aNlLT4nUapKmvE3UmwmYY0z7wmPYdOB+Vc3jSDtNASY8l9Argz9Ppr33UStXqtclLYVzvEknlAqd9jMwqJZ7nmepovC7xKJsAaKKstzX1YEIYqMUNtfsLYmTp6IYcJsk5RCxAgVhcz/Wm0quRChojcz5YeIIsRiENZK8V4HbfWLAJzA5U8J6T3uSia6pfaEeB/N+26iVs9/jCwlP7kK2Py27+c2vQ3IVMKn6Kx1TxR5wdqfPAqlKi0GoU3rq9i2KSmIa6gESvlRKxqq/DDlh4giRSZvXAfN26a3ge6XA20aIu3dU3vECRJvHJ+JElarjFChGAlWYll3BvjibmF9B+ZrEkVWOCXOI8FmEj5NYy42JRGTVYxQCbCGihihYrHB6XSGXHmQiMivYGO4RQfM2tyQjhuk0iXHZ6KExgmVVBasxHJ6vlDqreaIkE4wcLL/mXUiCk84Jc5bKmBoMoSyj2zblIDMtoYIlUBVfhoiVBxOIUVI3bBILRFRs4UyhssUwVN1OD4TJTym/CSqSIQGiiWWfRl2N1CxGfjV68BDu4EeVwrhjQxBpFQWyZDcQO1PLIkYqfMHDE1+S3ieKAG5IlQCpfzIGydQWOmHiCKiOWO4LxyfiRJeq2ylY8aMQVpaWrwvo/WKVGigv5Jsw+4GRtwHQAJs/Bfw1X0MQSSKdEhuuCURW3L+WKcXEcVIKBEqUqkECpkEVrsTBosNuRlc1JGIWkiZDoyeDcAprJniGpfvER5XhHhfwPGZKOFFfUJFq9WGvK1Ygu27776L1uUkvkiHBsrVwj5jHxU6bUUacHQFcHY3UL62aQUShiBSKopWSK53+/OXZ93S88cyvYgohhrXUAm8LopKLoPVbmOEChFFhrke2P4x0GEAMHsfYKgWUuWPrgB+WSCkyYtV+ALh+EyU8KI+oZKTkxN0AThxkTi7nTc6QQULDRz7aPjHFP8QU2YIx1j/mpDms2hKZM9DlKii0e5EoZREbOn5xdBk9wkZkRiazFKMlIBcESoBUn4AYR0VnZkpP0QUIXIVsGquMBGSngdktgN05wBDjTARMuSO0I7D8Zko4UV9QmXlypXRPkVqiWZooCINGDFDmFjRVzMEkUgU75Dclp4/3PQiogQRSpUfoLHSj8Ec4roGRESBGC82jsuGGuFLZLoojNsZBcGPw/GZKOFFfUJl3Lhx0T5FanEPDfQ1I97S0EC5WghTlMkZgkgkindIbiTOH2p6EVECCSdCBWCEChFFSFpOkPvx7NCPxfGZKKHFZVFag8GAkydPwmKxeDzev3//eFxO4rAYAKcDGP8nILszUFwmRJJk5APHVgJ1lZEJDVSmC+diCCKRIFIhuRaDMFlpqhNutuw2ABJAJvN8zHs9lEidP5T0IqIEIkaoBFqUFoCrVLLewggVIooAmxkYPwfILvRxP35KeD6cMZbjM1HCiumESlVVFaZNm4bvv//e5/NcQyUAscJH9WHgxn8Da//pVX3nHmD0I6GvKh4MQxCJGkWiPfir0lM6C3j/OqByq/9jsj0S+SRGqISc8sMIFSKKBFUWcNntwNp/RPd+nIhavZhOqDz00EO4cOECNm7ciPHjx+PLL7/EuXPn8Nxzz+Ef//hHLC8lsbhX+LjlE6HzblJ95yUAkshW32EIIlGjlrSHQFV6nA5gzMPAp5MDV+5heyRqwixGqDDlh4hiyVwvFHFY81LjY677cQAjHwityg8RJbzAdyARtmLFCrz88ssYOnQopFIpunbtiltvvRUvvfQS5s6dG8tLSSxihY/0PCGscPM7vrfb9JawbSQp04Www4x84bsyI7LHJ0okzW0Pgar0bH4HKB4vtG+Rv7bM9kjkwWQNM0KFi9ISUSTIVcCmt30/t+lt4XkiSgkxnVDR6/Vo27YtACA3NxdVVVUAgH79+uGXX36J5aUkFrHCR2a70KrvEFHrEqxKj6FaaN/uj7EtEwVlakj5UYUaoWJlhAoRRYB7lR9vYpUfIkoJMZ1Q6dWrFw4ePAgAGDhwIN566y1UVlbizTffRIcOHWJ5KYlFrPChOyd8Mq3O8bMdq+8QtUpiG/b5XA6Qni+0b/fH2JaJggp1UVpGqBBRRIlVfnwJt8oPESW0mE6oPPTQQzhz5gwA4KmnnsLSpUvRpUsXvPbaa/j73/8ey0tJLGKFD0MNcGwVMOxu39uJ1T6IqHUR27Avw+4WqgIYahofY1smComxYU0UBddQIaJYspmFBWh9GX6P8DwRpYSYLko7efJk18+DBg1CeXk5Dhw4gC5duiA/Pz+Wl5JY3Ct8rP0n8IfPhJ83v8NqH0SJIFCVntJZwIe/Fp5jWyYKS6gpP6zyQ0QRpcoSqvkAwpoprPJDlLJiOqHy7LPP4tFHH0V6ulC5Ij09HZdddhmMRiOeffZZPPnkk7G8nMTiXuHDrBd+HvcYq30QJQp/VXogBe74nm2ZqBnCr/LDyC8iihCFWqjmM/aPwpop6mwhMoWTKUQpJaYpP8888wx0Ol2Txw0GA5555pmQjjFv3jz0798fGo0GGo0GpaWl+P777yN9qa2TWOEjvQ2gyvSs9gEJYLcA+irhu8UQ76slIm++qvQo03xX7rEY2KaJgjA2LDIb6hoqekaoEFEkqbIaxvAC4btYKpljOFHKiGmEitPphEQiafL4zp07kZubG9IxOnfujBdeeAElJSUAgPfffx833HADtm/fjr59+0b0ehOGzQT8/ErTVAKmDRAlJrZpopCIZZNDjVAxckKFiKKNYzhRSonJhEqbNm0gkUggkUjQs2dPj0kVu90OnU6HGTNmhHSs66+/3uP3559/HvPmzcPGjRtTc0LFYhA67dUvNj5mutj4+6iHhE/FiSgxsE0ThcTpdDZGqARbQ0UhRqgw5YeIoohjOFHKicmEyiuvvAKn04k77rgDzzzzDLKzG0uJKZVKdOvWDaWlpWEf1263Y/HixdDr9QH3N5vNMJsbV9vWarVhn6vVksmFGXBfNr0lrNdAlMCSuv36wjZNSSSa7ddid8DpFH4OtiituuF5g5kRKkShSrnxNxI4hhOlnJhMqEydOhUAUFRUhFGjRkEub9lpd+/ejdLSUphMJmRmZuLLL79Enz59/G4/d+7ckNdoSTimOmHm2+dzF4WFLjNYQYkSV1K3X1/YpimJRLP9mhoWpAWCr6GiVohVfhihQhSqlBt/I4FjOFHKiemitOPGjcOJEyfwl7/8Bb///e9x/vx5AMDSpUuxd+/ekI/Tq1cv7NixAxs3bsS9996LqVOnYt++fX63nzNnDurq6lxfFRUVLX4trYY6W8jN9PlcjlA1hCiBJXX79YVtmpJINNuvuH6KRALIpE3XZ3MnRrCwbDJR6FJu/I0EjuFEKSemEyqrV69Gv379sGnTJixZssRV8WfXrl146qmnQj6OUqlESUkJhgwZgrlz52LAgAF49dVX/W6vUqlcVYHEr6RhtwkLXfkyfLrwPFECS+r26wvbNCWRaLZfcUJFJZf6XPDenRihYnM4YbE5Am5LRIKUG38jgWM4UcqJ6YTKE088geeeew7Lli2DUql0PT5+/Hhs2LCh2cd1Op0eOZ4pRZkurBo+7vHGGXF1jvD7mNlc+Ioo0bBNE4VETPkJlu4DACpF4zZM+yGiqOEYTpRyYlo2effu3fjkk0+aPF5QUICampqQjvGnP/0JkyZNQmFhIerr6/Hpp59i1apVWLp0aaQvN3HI1cKq4WMfFXIz1RrAbmVpNqJExTZNFFSoFX4AQC6VQi6VwOZwwmCxI4d/0xBRtHAMJ0opMZ1QycnJwZkzZ1BUVOTx+Pbt29GpU6eQjnHu3DncdtttOHPmDLKzs9G/f38sXboUV1xxRTQuOXGIM97iQlcypf9tiaj1Y5smCsgUxoQKIESp2Mx2RqgQUfRxDCdKGTGdUPnDH/6Axx9/HIsXL4ZEIoHD4cDPP/+MRx99FFOmTAnpGP/5z3+ifJVERETU2rkiVEJI+QEAtVwGvdkOPUsnExERUYTEdA2V559/Hl26dEGnTp2g0+nQp08fjBkzBiNHjsRf/vKXWF4KERERJTCza1FaWUjbiwvT6hmhQkRERBES0wgVhUKBjz/+GH/729+wdetWSCQSDBo0CCUlJbG8DCIiIkpw4qK0ihBTftQNC9MyQoWIiIgiJaYTKoCQsvPyyy/j8OHDAIAePXrgoYcewl133RXrSyEiIqIEFW7KT5oYoWJmhAoRERFFRkwnVP7617/i5Zdfxv3334/S0lIAwIYNG/Dwww+jvLwczz33XCwvh4iIiBKUyZXyE2qEijChouOESlNWI6CvBjQdAWloKVREREQU4wmVefPm4Z133sHvf/9712O/+tWv0L9/f9x///2cUCEiIqKQiCk/oVb5YYSKDxYDsOxJ4Jf3AbsF0HQCrvlfoPe18b4yIiKihBDTRWntdjuGDBnS5PHBgwfDZuMNDhEREYVGTPlRhJjyo3ItSss1VAAA2jPA/KuBLe8IkymQANpK4NPJwP5v4n11RERECSGmEyq33nor5s2b1+Txt99+G5MnT47lpRAREVECM4eZ8pPmWpSWH+DAUAt8cANwZieg0gBX/A24dQlQcgUAJ/DVvYCuKt5XSURE1OrFZVHaH3/8ESNGjAAAbNy4ERUVFZgyZQpmz57t2u6f//xnrC+NiIiIEoRrUdow11BJ+QkVmxn45Gag+iCQngdc/SKQ1V54rnQmUHsMqD0KrHweuP6V+F4rERFRKxfTCZU9e/bgsssuAwAcPXoUAFBQUICCggLs2bPHtZ1EIonlZREREVGCMYVb5UfJRWnhdALfzgZObQaUGUJkijiZAgBSOTDsbmDpE8COj4FxjwkL1RIREZFPMZ1QWblyZSxPR0REREkq3EVpGaECYMu7wPaPAIkUGPsYkNOl6TbtLgXa9gHO7wN++QAoeyL210lERJQgYrqGChEREVEkhJ3yIxcnVFJ0UdoT64XIEwC4bArQabD/bXtOEr7vXChEtRAREZFPnFAhIiKihGMKd1FaZcOitJYUjFAxXgC+uAtw2IBuY4C+vwm8fZdSQJ4GXCgHTm6IySUSERElIk6oEBERUcIRU3fEVJ5gUjrl59tHhZLIWR2AkQ8AwdaqU6iBbqOEn3cujP71ERERJShOqBAREVHCEVN3Qp1QSVOIi9KmWMrP7s+BPZ8L66aMeRRQpIW2X/F44fuBbwFHir1nREREIeKEChERESUcMXUnTcFFaf0y1TWum9L/FqCgV+j7tusLKDIAQw1QuS0610dERJTgOKFCRERECUecGFHJw0v5MVrtsDtSZKHV1S8B+ipA0xnod1N4+0rljQvXHvw+8tdGRESUBGJaNpmIiIgoEvQWIQ0lTRleyg8AGCw2ZKkVUbmuVuPiSWDTm8LPw+4GZM14vYVDgfI1wKGlwOVPhb17rakWlfWVqDJWQWvRwul0QqPSoDCrECU5JZBK+LkeERElNk6oEBERUUKx2h2w2BwAQl9DRSGTQCoBHE5h/ZWkn1BZ94pQ1afDgMAlkgPpNERYe+X8PuBiBZBTGHSXSl0lPtn/CVZWrERFfYXf7dqlt8P13a/H7X1vR7Yqu3nXR0REFGecUCEiIqKE4r4OijrENVQkEgnSFDLoLXbokn0dFe0ZYPuHws/9b2n+cVRZQH4PoOogcHwNMGiy302tDive2P4GFuxdAIdTmOySQIIcdQ5yVDnIUGRACinqLfU4rT+Nc4ZzeHf3u/jqyFd4aexLGNp+aPOvk4iIKE44oUJEREQJRUz3UcgkkEtDTxtRN0yoJP3CtNsWAHYL0LYP0L5fy47VfkDDhMpqvxMqFrsFD618CGsr1wIA+ub1xYQuE9CrTS+kK9KbbG+1W7Gzaie+PPIlzujPYPqy6Xhl/CsY23lsy66ViIgoxhIueXXu3LkYOnQosrKy0LZtW9x44404ePBgvC+LiIiIYiTcBWlFKVHpx+kEdn0m/NzrmpYfr8MA4fvxNcKxm5zOiafWP4W1lWuhlCpx34D78MiQRzCo7SCfkykAoJApMKT9EDxZ+iSGtBsCq8OKR1c/igO1B1p+vURERDGUcBMqq1evxsyZM7Fx40YsW7YMNpsNV155JfR6fbwvjYiIiGJAnBBJC3H9FJGYHiRGuCSlU1uAC8cBuRooHNHy4xX0BqQKoP4MUHOkydNfHfkK3xz7BjKJDLMGzcKQ9kNCPrRKpsI9/e9Bn7w+MNqMmLV8FurMdS2/ZiIiohhJuAmVpUuX4vbbb0ffvn0xYMAAzJ8/HydPnsS2bdvifWlEREQUA3qzMCGiDrHCjygtFSJUdn4qfO86ElCoW348uQpoe4nw8/HVHk9VG6vxv1v+FwBwY8mNuDT/0vAPL5XjvgH3oV16O5wznMNLW15q8SUTERHFSsJNqHirqxM+ycjNzfW7jdlshlar9fgiosTA9kuUuKLVfvUWYUJELQ/vNkZM+UnaRWltFmDvEuHn4vGRO66Y9nPMc0LlzZ1vot5ajy5ZXTCpaFKzD5+uSMed/e6EBBJ8ffRrrKtc15KrpQjh+EtEFFxCT6g4nU7Mnj0bo0ePxqWX+v9UZO7cucjOznZ9FRYGL/vXGhgtNlhsDtTozLDYHDBY/N8AhrMtUSJJ1PabyqLRH7GPS0zRar/NTflJ+giVI8sA4wUgLRdo3z9yxxWPVb4WcAgVfM7pz2HJYWHy5uZeN0MqadktZUlOCS7vejkAYO6mubDarS06HrVcLMbfcPt2jgVE1NokdJWfWbNmYdeuXVi3LvAnGXPmzMHs2bNdv2u12lb/R5nZasebq49h/vrj0Bpt0KTJMW1kEe4r6w6V1w1kONsSJZpEbL+pLBr9Efu4xBWt9iuugaIO899flewTKuJitEXjAGkE20Z+T0CRJkzWnNsDdOiP+Xvnw+qwomebnuid2zsip/l1ya+x6cwmnKw/iUWHFmHyJf7LNFP0RXv8Dbdv51hARK1Rwk6o3H///fj666+xZs0adO7cOeC2KpUKKpUqRlfWckaLDW+uPoZXlx92PaY12ly/Tx9XjHSlPOxtiRJRorXfVBaN/oh9XGKLVvsVJ0TERWZDldawvc6chIvSGi8CB5cKP3ePYLoPIEzOtO0LVG4FytfhQptCfH7ocwDA9cXXQyKRROQ0arkaN5bciA/2fYC3dr6FX3X/FbKUWRE5NoUvmuNvuH07xwIiaq0SLuXH6XRi1qxZWLJkCVasWIGioqJ4X1LEyaRSzF9/3Odz89cfh1wqbda2RETRFI3+iH0c+VJvEtJB0sL8A0qMaEnKNIF9/wXsZiCnK9AmCvdG7fsJ38vX4uujX8NsN6Orpiv65PWJ6GnGdBqDDhkdcMF8AR/v/ziix6bWI9y+nWMBEbVWCdf7zJw5Ex999BE++eQTZGVl4ezZszh79iyMRmO8Ly1i6k1WaI2+b/a0RpvrRjLcbYmIoika/RH7OPKlzij8u2eqwlxDRZnEi9KK6T7dxwMRihjx0DCh4jzxMz4/tBgAUNa5LGLRKSKZVIYbut8AAPho/0fQW/URPT61DuH27RwLiKi1SrgJlXnz5qGurg5lZWXo0KGD6+uzzz6L96VFTJZaAU2a70/dNGlyZKkVzdqWiCiaotEfsY8jXy4ahD+eMlTNi1BJujVULp4ETvwMQCKsnxINud0BRTq2woRy7QmoZCoM6zAsKqca0n4I2qW3Q525DosOLorKOSi+wu3bORYQUWuVcBMqTqfT59ftt98e70uLGLvDgWkjfYfrThtZBFvDCvvhbivSmayw2ByorhdWSNcFmNXnaupEFIh7H2G1h98fBROsj7Pa2T+lIjFCJSPclB+5OKGSZGuo7GqYdGjfD8goiM45pDKgXV98kZUJABjRYQTS5GnROZVEiuuKrwMALNi7ACabKSrnofgJ1rdbbHaP+0+LzR7W9hwPiChWuHpTK5SmlOO+su4AEHQl8zSlHPeWdYcTTixYX+7a9vaR3XBvWfcmFRBMVjveXnusybb3lZU02ZarqRNRIN59xIDCbHx053AAnn2Xv/4oFMH6uEcW78C3u86yf0oxWmPzIlTSlOKitEn0x5bT2ZjuUxzhxWi9GNv2wYqaMwCA0Z1GR/VcwzsMx3+P/hfVxmp8cfgLVvxJMsH69qV7zuLJr/e4Hn/kil4Bt/9sSwX+sewg71eJKOY4odJKqRQyTB9XjJnjS2Cw2JCulMPmcDQZGCxWG5bvO4dLO2Zj45yJ0Bqt0KQpsPZwNT7bUoGbhnR2rXquM1nx9tpjeG35Edf+WqPN9fs9Y4qR2RAyydXUiSgQX33Ezoo63Piv9XjjD4Nw3/juuKC3+O2PwjnPoq2nfPZxCzefxA0DOuHbXWfZP6UYV4RKmGuoJOWitGd2ANWHAJkS6Doqqqdak5EB4wUpOtrsKM7qGtVzyaVyXFN0DT7Y9wHm75mP3/X8HRTWQiveAAAgAElEQVQypnUkC+++3WpzQCGXYu3hany65SQ6Zqe51kzRGm146uu9cMKJmwYXYsa47rhgsKJNugI1Ogs+3XIST3+9z3VsjgdEFEsJl/KTzLzTa+wOJwDA1vDd0fAdEBbnstgc0JrsuKJvB4wozoNUInFtKwGwZPspj1XPlXIZFqwv93nuBevLoZQ33phyNXWi1ODd7xgtNp+pfiavxxxOYNWh8z6PeabOCAkksDdk+GQqZejZLhNyqTTscGyZVIol20+51th07+O+2lGJ0T3ykZuhdG3P/ik1NDflJ00hLkqbRCk/YrpP4XBAmR7VU/1gPAUAuEqnR0btsaieCwBGdRyFbGU2zhnO4Ztj30T9fBQ7MqkUeosVI4rzIJdKYbU7IZdKMaI4DwaLrUnfDgBfbq9Em3RhUk3WMCa0SVfgy+2VPs+x6tB5OJ1gKhARRRWnbFsJ99D5gkw1Fs8oxYL1x5uENc4sK4ETwDsNaTv+tp1a2g3vTR0Kg9kGpVwYkLTGwCuka01W5GeqAIS2mnpew7ZElJj8pey8u/Z4k1S/u8YU4dZ31mNnRZ3rsfemDsXv3tqIo1U6AED3gkwsmj4CC9aX46HPdjQ55vSPtoUdjm0w2/De1KFYsL4cjyze2aSP0xqtKMhUoVZvAcD+KRU4HE5oTcL4lPKL0tptwO7PhZ+jnO5jsJuw9sJeAMDVej0yT++EoaBnVM+pkClwZbcrsfjQYry35z3cUHIDpBJOmCYDq82GO0YV49+rjvhMQzdZ7R59e/eCTLw3dSje8TE+eY9F7tu/vYap60QUXRyVWgGjxYZ/rzqKV5cfhtZowxOTemH+z8fx2vIjHuGOry0/gpO1Bvx71RHXc/62fX2FMEAp5I3/xNlpCnTNExaQy81Qole7LNfsvyZNDg2rBxGlDO9+BwBmjS/B22uENB65VIpe7bIgl0rx6vLDeHvNMcwsKwHQGE69YH05Hr+6l+uYT0zqhQXry/H6iiPIz1Thqj7t8OiVvVzHdO+jXl1+GP9edTTop4UKudR1TF99XKZKjiqd2bU9+6fkd9FodUVw+hun/BEnVIxWu+sYCe3YKkB/HlBpgE6XRfVUq2p2w+SwoqNEiUssVmgqd0T1fKKywjKky9NRri3HipMrYnJOij4HJK77Wffx5rXlR/DvVUcgk0pgdThc96ri+OJrfPIeiwB4bN+csYeIKFSMUIkjo8UGmVTqkV6Tm6HEqJJ8PLJ4p8e23Qsy8dfreqNrXoYrbcfftqL3N5Tj/gk9oDfboJBJoTVZ8eND43C+3oz8TCWqdRbkZSqx7nA1TtcZYbHZoWyYgBFXX3dfH0EkVutQcj6OKGF5p/WJ/ckbK4/gnSmDMaokHzVufcS/Vh3BvWXdkZuhdH1i+P6GcmycM9E1MTuqJB9f7ajEusfHo22WGlqTFRq1AjM/+cXnNcxffxwzx5d4PFZvskIll7nWSgGANYerfO7//oZyzJrgub+v/knsa+tNVmSpFbA5HGHl1Ld0f4qs6oYJtEyVPOz0rjS3T6X1FpvHBwkJaecnwveisYA0uv8nf6gS2vHwrGJIcASZZ3cDDrtQ/SeK0uRpmNBlAr459g3e3f0uJnaZCImYA0gJQ2eyQunWt6vkMqw+VOV3vJk1vge+uX+063GZVBLy+BTs/tjX2ENE1Fy8I4wTMdR+3ZEqvHLzINfseUGmCjU6i0e6jRhG/82u0+heYAq4rbuCTDXsDifeXH0U72/wDKe8fWQRbp+/BVU6k88qP+FWDyKixOKd1leQqUKdwRp2eo3WaENRfjp0Jjv0Jhv+cdNAVwh3B00a3p06JOT0QZPV7kpnDBbOLe5fq7egIFMFW8MksHcod0urlbHaWetTXS9MqGSnhT8ZopBJIJUADqeQ9pPQEyqGWmB/w7oiJZdH9VQ6mxHraoV0n4FtL4NNvgZyiwHp1UdgaNsryN4td3nXy/Fj+Y/YW7MXm85uwogOI6J+Tooc7wqTg7vm4K3bhgQcbwxmG5ZsP4Vnv9mPwV1z8OatgbfXm+0oyk9Hrd6Covz0oGnuTA0lokjhhEocuFfHGNQlBwVZKnTNS8OJGiOqdGbkZSqhSZNDa7QhN0OJ527si8+2VODddcdx05BC13NWhwP5WUrXvt7+fG1vvLn6KF5f0bSqj9MJPH51L9zz4Ta8tvwI0hQyTC3tinRVY5Uf99XXxRn/llTrIKLWQ0zrE/uZ7HQFstRyvLXmWJM+Q/x9+thiZKcrXJ8CatLkyM1QYuHdpQ1RHI0h3AAgl3r2Z97c03PqTVa846MK2avLD8PhdLr6K+/98zJUWHj3cGQ2RI64T3K0tFoZq521TmKKV3MmVCQSCdIUMugtdugTfWHaPV8AdjPQpgjI7R7VU62s2QWL04YOqjbonFaA+vzuaHN2LzSnd8RkQkWj1GBM5zFYfnI53t39LidUEoh7hUkx3fyCwYo0hcx1jyo+XqUzu8abGeO645eTFxq3V0rx1mr/49OMcd2x8O5SVyQhgJDGHiKiluKdYJT5ChWXS6U4UlXvERb/w0PjsOZQFV5cehA/H6nGw5f3ROc2aRhVkg+DxY6BhW1w/YCOcDqdeOSKXuiYo8aoknzUG20e+4qf4OZmKDGyez4e+sx3jrMYqn9Zlza4t6zYdR65zOG6xn8sO+j6Y6sgU4Uqndn1R9Tvh3WJ4btIRJHg3h+pFTKPvqRGZ4FSLsPAwhx0L8hssrjfoC45UMpl+MdNA1xh1gaLvSFVUAabw4k0pRwDOjfuX6u34Ocj1Zha2s3jJlg0bWQRrHaHq3/0V4XMPbVIjI4BgKml3WC1O5Db8CmjdxpioGplqw6dxz1ji2GxOfym8gSrdsaQ8fio1gn/B5ozoQII66gIEyoJvIaC0wn88oHwc48rgCinwCytEiYzh2b3hEQiQX1+Cdqc3Yus0ztxduDNUT236KpuV2FVxSpsOrMJe6r34NL8S2NyXmoZpZ/UHrlUijWH/af83D+hB/73twNRrTMjP1MFh9MZMP3z/gk9oJRLXVEnRouNqetEFBOcUImiQKHi7mHx7uk0i2eU4v/9cBBPXt8H/151xCOs8faR3XDX6CLcPLTQ53OLZ5Tipjc3oEpnwoMTS6Az2QKGO17QW/He7UPwn3XHPY41bWQR7i3rjoJMNbRG4Y8i9z9iGCpJlHi8+6M7RnfDY1f1btKXTC3thkXTR7hSbHxV7tGkyTH7ip64ZWgXzFt11KOP897/he8PYtF04dNk99RDsXLQH97dCJPFETQ1SEwtEid1p5Z2w+0ju8FqcwB+uiJ/1cpCrf7Aamet0/l6E4CWTagAgC6RJ1RObgTO7gJkSqCoLKqnqrMasP7CfgDA0Byhqk99nhARk3VmT0zWUQGA/LR8DO8wHOtPr8fbu97GaxNei/o5qeX0Piq1/XpQRzx5Xd+AKTz1Jivu+XArtp24GFL6p3d/nKaU474y4f8pUzaJKJo4oRIl7qHixQUZKC3Kw+EqHV5dfhjX9e+A/9t1uklo+2vLj0ACCZ6Y1BvzVh31+fykSzvg+z1n/O77+YxSqBRSbDxWgwyVPGC4Y36WEv9Ze7xJuKU4m/+na3rjzve3+tyXoZJEicNX6sodo4o80nMAz/BpMcXGvXKP+3adctJC2v9olQ6/e2sj/nRNb9w/4QrU6s1ok6HE0So9bvzXehyt0iE3Qxk0NSg3Q4kP7xzukX54x/tbsHj6SL+v2z2tyZ2/1+SdyuNvf/Ga2A/GR+UFIcU1v5mTWZkNpZbrjNaIXVPMbfyX8L14PKDWRPVUK2p2wua0o5M6D53UeQAAQ3ZH2BRpkFsNyKg6BH27S6J6DaJriq7BhtMbsLJiJaNUEoRSLm2Sfr7zVJ3Pxz1STMcV43i1wfV4sPRPX/2xSiHD9HHFmDm+xCMSkZMpRBRJjHWLEplbWs/SB8fiuV/3w9IHx2LHX6/wqNTjbf7641ArZPhqx6kmz+VmKNE1Lz3ovte9vg53LNiK8ho9bh/Zzee2U0uFT3Z/2HcW70wZjHWPj8e7U4dg3ePj8fZtg7Hq0HmMKsl3Ve9wJ4ZKElF4jBYbLDYHanRmWGyOmJVt9E5dKS7IQNssdcAUm9E98lFckIFRJfl4f4PndmIFhWD7u/cfnXKEku1KuQwSSHDmYuO6T+6pQb7cPrIbzmlNGP3iSty5YCtGv7gS0z/chrKebWG1+38/xWplvq7d+zWJ5q8/7qoc42t/EfvB+DnVMKFSkNW8CZUstTCh4h55mVBqjzUuRtvnhqif7oeGdJ9h2T0bH5RIoc0XUt6yK7ZE/RpEHTM7orRjKQDg9e2vx+y81HwKmdRnf+vvcUAYQ5QyGXLSFU0e9x5bAGGMMNt8r4mUrpS7UoGUcinXvSKiiGOvEiUmi81nWs+jV/bENf06Bg5tN1mhljf9pwlW1UeseNEmXYEHJpagQ7Ya00YWwelEkyo/d4wqgtFiDxhuabLY8dDlPfD/fjzIUEmiFopntRjv1JUeBZlBKyBcNFjRoyATF/RNtwu1LyrIVKEwNw0f3Tkc76493qzUILEK2dI9Z2FzOHDwXD00aXI8OLGHK2VoZ0Wdz/fTV8h3ONUfGDLeOlVeFCNUmk74h0KMULloSNAJlVUvAnACnQYDOdFdz6zGUo+NFw4AAIbl9PR4rq7dJcg9sxvZJzfj9JApUb0Odzd0vwGbzmzC+tPrsfnMZgzrMCxm56bwaX2kToYyBmlNwhh0rErv8bjO5Jn+6atSJRFRLHFCJUokXtUuAGEgeOWnI/jdkC4Bw8g1agVMtqbPeVcA8rVvQZYKH905AtU6M1RyKeqMVkwfW4xZE0pQo7MgP1OF8/UmqORSKGSBwy1njOuO3w7ujFuGdmGoJFELxLtajHfqyuEqHTRpgdNZ8jNVmPs//ZDpI+0llL6obZYan9w9HGqFDG+taZrCGCw1SGuyQqNWwGyzQ62Q4cq+7XBNvw6uvuholc6VMiQe09f76SvkW7zGUFJ5GDLeuhgsNlQ1lE1ueYRKAqb8nNsL7PpM+HngrVE/3Y/Vv8AOB4rS2qGdqo3Hc3VthTSfjPMHITdegC2tja9DRFxBegHGdR6HFRUr8MKWF7DoukWQS3k721ppfIwhoYxBGrUCh73WStGkyZGTLlSWE8eIc1oTlu45iyv7tmP0CRHFBVN+okQll/kMh6/VW7D+aLXfMPLbR3aDyWrHDQM7+dz3ZI3B775CxQs7Fm4+gTEvrcSoF1di6ntbMPZ/V6F07gr8tP8cNh6rxugXV+La19dBLpMEDLdUyKRNQiUlQFxSFogSWbBqMWKKSbR4p65cNFhhsdv9pti4V8+x2h1NthNTdAKlFG46XoNXlx+GQiYNOTXoaJUOO09dhMVmR35DnyNObrj3RQBw89sbmixMCDS+n7VufZR3PxZuKg9DxluPw+eEf3ONuvlr2Ij7JVyEitMJ/PAnAE6g62ggv0fUT/ndeSGdZ3hO09LI1rRs6LM7QQInsiuarrcWTTeW3IgMRQYOXziMRQcXxfTcFB6Lze5zrDD7eRxoTPN0j04BGsemuz/Ygj8v2Y2rX12DMS+txJNf74n6OEpE5A/vCqMkUCjj898ewDf3j4bD6fQZ2j5v1VHcVybkJntXAeqWl4HbR3Zrsq9Y8UJvtuM3lxXiotGKBevLXeHx00YW4XdDCnHta+sAAAqpFFX15rAqWMQzZYEokcW7Wox36kpBpgp6U+PNrK++RKyeY7E5fG536oIR95WVQAJJkz7hrjFFuPU/m9A+S426IGHdYmqQzeEIOXQ72Pt5vt6EOxdsxRmt0WcfxVSexHXwXD0AoDA3vdnHyBQjVAwWOBxOHDpfjx5tsyCTRrf0cIvt+Bg4tkqo7HNZ9FNsThmrsUN7DBIAw3xMqABC2k9GXSWyT25GTc8ron5NokxlJv6nx//gw30f4o3tb2BClwlon9E+Zuen0GWqFU3uaQd0zobD4fR7ryumeYoRLO5jk8XmgFouww/7zrnOwaprRBRPnFCJkkChjFU6EwAnBnVpg3vLuqNWb0HbLDXO15tw70e/YOXB89h1qg7P3tAXs8b38Ah9l0oluH3BZswsK8HGORP9Vry4e0wxZo3v4RGiLpVIGs4thOznZgQO2Xf/9C/eKQtEiaw1VItxT13RmazIVCtw01vrg/YlGSq53+0eWbwDL/1mQJN0GKfTicXTR7rOEyw1aOHdw5GpVsDSkN4TTLD3MzdDiSqdOWAfxVSexLTvtBZAyyZUNA3trVpnxrPf7MOC9eW4tl8H/GvyZRG5xqioPQ4snSP8PHAyoOkY9VMurRKiTnpnFiJHkeFzm7p2l6DjoZ+QfXJLzMoni8Z1Hod1letwvO44/rLuL3j7yrchlTBKoTVSK2S4x+u+FAAeXrQdT1x9ice97jmtCS/9cACzL++JK/v6Hpt8pQKx6hoRxQv/Ao4wo8UGmVQKk1X49Nd93QDR1NJuWHu4Gvd8uA25GUo8dHkPjCrOx8SXV7u2WXnwPMa8dB5PX98HtwwrhFIuhVIuhd5sw9geBa59CzJVqNKZUau34P4JJbDaHchQCeHpAFyz9UpIYbTYMG1kEV5dftijqob7GioiMexd2ZAVFixlYeb4kha/d0TJSkwxcZ+QFHm3tWgSJxRyM1UwWmwo69nWZ1/y4MQermuy2h0B+xwAHqk47q8jN1MFncnqty8UPm20I1fcVx7aexDo/Zxa2g3rDld7VHDx10eJ74eva6fWaeOxGgBAr3ZZzT6GuPbKwbP12FMpTNB8u/sMnqo3oW2WuuUXGWlWI7BoCmDWAgW9gD43xuS0354XJlRG5PT2u42uTVfYFOmQW3TIPLcPug79YnJtACCVSHF3v7vx9IansensJszfMx939rszZuen8IgRYOJ3s82O7gWZGPPSShQXZKBHQSYOV+lwrEqPByaWYP3RGjyxZLfHmPPAxBKfqUCxHEeJiLyx54kgMSVmyPPL8OCnO3BfWQkemFgCTZpw065Jk+OBiSWYNqoILy49CE2aHLeN6IqbhxSiMDcND07s4bHtgxN74PfDukCtaJz3sjaE398/ocRV8cLmcOD+CSWNYfp+iGHu4nle+P4gpo0qanKND07sgfvKunt8mhtKygIR+ebd9gD/bS2W13RvWXc8MNGzL3lgYgnudbumlvQ5QGO4t6++8L6yEmQ241NFf++neE0vLj3osT37qORwssaAA2eFlJ9LOmiafZy2DRMqVrvT4/FdFXXNv7hosduAL+4Czu4CVBpg3BMxiQI5oKvAEcNpyCUyDM7u7n9DiRQX2/UBALQ5tjbq1+WtfUZ73NLrFgDAq7+8iuUnl8f8Gig49/vjgc8uw+Nf7AScTtfYUK0z44d951CtM7vGhtN1xiZj031lJdh24kKrGUeJiABA4nQ6ncE3Sy5arRbZ2dmoq6uDRtP8mzJ3vlJixvdqi2dv6It2GrVH2o5KLvMIMRcHAYPFBrlU6vM5kcXmcIXfj+6R7xEK+e9VR7B4+sign/J6n8dis0Pp55rczzvk+WV+Q+y3/vmKkD9dJmqJaLTfWAmljceK0WLDoq2n0CFb3aQvOVtnwk1DOiNdKY9InwMAOpMVSrnM1RdabPZmTaa4834/1x6uwt+/O9BksVr2Ua1HS9rvU//dg/c3nMCAztl4YtIlLbqOGR9tQ53Rc5LtgYk9MPuKnn72iAOnE/j6fmD7h4BMAVz+LNA+NhEgzx1eiM/OrMXQ7B64t+u1AbfNPrsXPTf9B5b0POy8bSEQ47Qbp9OJj/Z/hJUVK6GWqfHahNdQ2rE0pteQKprTfn3dHxcXZGDpg2PxwtL9mDW+BzJVctfYoDPb8MPe07iufyeofIwZrWkcJSICmPITMb5SYsS0nf6dNfhixigoGtJ2AN8h5qGEn9sdjpDC9ANpcp4A1+R+3taQskCUyFpTiolMKsU/lh2E1mhr0pdo0uT4/bAuACLT5wBwTZ7kh5neE4j7+2m02LDrVJ3Pyj/soxLfBb0Fi7aeAgBc17/l64d0yFa7JlSGdG2DrScuYP8ZbYuPG1E/PS1MpkikwNjHYjaZYrCb8E1DdZ+yvP5Bt9cW9IJNrobSUIPMs3tjmvYDABKJBH/o/QfUGGuwq3oXZi6fiedHP49JRZNieh3km6/742NVepyvNyFTJcdlf1uGQV1yMLBzDnacuojtJy/igYklkEAYJ7zHjNY0jhIRAQma8rNmzRpcf/316NixIyQSCb766qt4X1LAlJhdp7TQ+gg3N1psYZcgdg91dw+FjHbIY2tMWSCi5nPvs2r1Fhw8V+9ad8Q9RSaafU5z+kB/2Ecltw83noDRake3vHT07djyyLTRPfIBAO01aky8pC0A4JiPybi4+fk14OdXhJ9LZwFdYhdx8d35rdDbTWinzEHvjM5Bt3fK5LjYMImSe2RltC/PJ5lUhpmDZmJIuyGwOqx4bM1jeGbDMzBYDXG5Hmrk7/74r1/txX1lJXhwYg8crdJh/vpyHK3SNfTZzUsHJSKKh4S8w9Tr9RgwYACmTZuG3/zmN/G+HADhV/FoSQnieFWnYFUMouQRTp8VjbYfjTLs7KOSk93hxEcbTwAQolMkkpaXN57Qqy0K26SjY3YazDY7AOBEjQFWuwMKWZw/a9r8DrDsr8LPl90O9LgyZqd2Op1YeFpYIH9cXr+Q3+uaTgORX7EFeUdWoqJ0BpxyZTQv0yeFVIEZA2ZgyeEl+P749/j80OdYXbEaDw9+GNcWX8sKQHHib6w5WWuAxWbHpEvbY/q4Ylc66YkaQ2J+2ktEKSshJ1QmTZqESZNaVyhnOCkxkShBHK+QR4ZaEiWHcNP4Itn2o1mGnX1U8ll/tBrn683IVMkxvCg3IseUSCTo2VApKN0pg0ouhdnmwMlaA7oXZEbkHM3yy4fAd48KP/e7Cbg0th8arb2wF4f0lVBJFRjdpm/I+2nb9oJFnQOl6SLalP+M2pLxUbxK/6QSKX7b87fok9cHH+z9AOeN5/GndX/CwgML8djQxzCw7cC4XFcq8zfWPDGpF95ddxyvLT/iM520JeMAEVEspcSdptlshlar9fiKtHDCzYOVIJZLU+KfhSgksWi/qSieKTLsA1NHJNrvNzvPAABGFOdBHoXoEalEgo45aQCAo+fjmPaza5GwCC0AXHIDMGgKEIFonHD85+QPAICy3H7IlIdRQloiRVWXoQCA/P3fR+PSwtInrw/+Nvpv+G2P30IlU2F39W7c9v1t+OPqP+K07nS8Ly9hRKL9+hpruualYUyPAixYXw6gadopxwEiSiQpMfU7d+5cPPPMM1E/T6jh5qGUIBY/XSVKdbFqv6koXiky7ANTRyTa76bjNQCAwV1zInFJPnXIVuN4tR7HqvVRO0dA+/4LfDkDgBPodQ0w9K6YT6ZsuXgIv2iPQi6R4cqCy8Lev7rrcHQ89BOyK3+B+sJJmNp0icJVhk4hVeCa4mswqtMoLDm8BOsq12Fp+VKsqliFhwc/jFt638I0oCAiNf56jzUatQJ1HAeIKEmkxEgyZ84c1NXVub4qKiqidq50pRxKuRR5mSoo5VKfn/KK+aS++FpvhSiVxbL9pqJQ+qxIYx+YOlrafs9pTSivMUACuFJ0oiGuESoHvgU+vwNw2oGSy4HhM2I+mWJ3OvD/ji0BAIxu0wdtFOGnPVnSc3GxvZAm1H7nooheX0tkq7Ix7dJpeKr0KfRs0xMmuwlzN8/FPcvuwTn9uXhfXqsWyfHXfaxRyKXQcBwgoiSREhMqKpUKGo3G4yuexHxSX8S1C4hI0NraL7Uc+8DU0dL2u/l4LQCga156VCf7OmY3TKjEutLPzs+Az24DHDagaBxQer9QJjnGvjq7Aft0J5EmVeLG9s2vKHSmxwQAQN6hn6DQVUfq8iKii6YLHhv6GCb3ngylVIlNZzbh5m9uxrZz2+J9aa1WNMdfjgNElCxSYkKltWF5TyJKZewDKVTihErvDtGdSO2YI6wXcrRKD6fTGdVzuWx+B/jyHiEypfsEYPRsQBr7ilTVljq8Wv5fAMAN7UZAI09v9rH0ud1Qn1cMqcOGjr98FKlLjBipRIqJXSfi6ZFPo3NmZ9SYanDXD3dh4YGFsft3JwAcB4goeSRkb6XT6XDkyBHX78ePH8eOHTuQm5uLLl3im7MbKpb3JKJUxj6QQrGlvGFCpX300n0AoEN2GiQA6oxW1Oot0V27wWEHlj8D/Pyq8Hvv64Fhd8clMsXudOCJAwtwwapDZ3U+JuQPaPExT11yDS5Z9wYK9n+H831/BWNecQSuNLLaZ7THn4f/GfP3zsfms5vx901/x97qvfhr6V+hknHdjljhOEBEySAhI1S2bt2KQYMGYdCgQQCA2bNnY9CgQXjyySfjfGXhicfaBURErQX7QArkgt6CA2frAQC920c3QkUplyK/YRLlaFUUF6Y1XgAW3tI4mTLg98Cwe+IymQIA/yr/BpsuHoRSIse9Xa6BXNLyP2R1ecWo7TgAEqcDXde+JkwgtUIquQrT+0/H73r9DhJI8N+j/8WU76fgjO5MvC8tpXAcIKJEl5ATKmVlZXA6nU2+FixYEO9LIyIiogjY1JDu0yknDdlp0V+gsjHtJ0rrqJSvA+aNAg7/CMhUwNg/AgMnx3wBWtH8imV4p2IpAOC2ThPQQZ0bsWNX9L0edrkKWWf3oMP2hRE7bqRJJBJc3e1qPDLkEWQqMrGvZh9u/uZmbDi9Id6XRkRECSIhJ1SIiIgouYnlkvt0jM1C1B0aKv0ci/SEiqkO+O6PwILrAG0loOkITHpJWIQ2DqwOO146+jn+efxLAMBv2o/EqNw+ET2HJT0XJ/r/BgDQaesHaHNsTUSPH2l98vrgydIn0VXTFRfMF3DPsnvw/MbnobfGqYw2ERElDE6oEBERUavidDrx036hpO2lHbNjcs5ODRMqe1OdDB4AACAASURBVCq1kTmgzQxs+Q/wxlBg89sAnEDJFcB1rwJ53SNzjjDtqS/H1J3/wIeVKwAAN7YrxbVth0XlXDWFQ3C+6whInA4U//R35B5aHpXzREp+Wj7mDJuDss5lAIBPD36K//nv/+DbY9/C3krTloiIKP6YqEhEREStypbyC6ioNUIll2JAYWwmVMSJmy3ltbhosCAnXdm8A108KZRD3rYA0J4SHtN0AkbcC3QYGJmLDYPJbsGGiwew+MxarK3dCwBQS5W4s/BKDM4uieq5Twz4LWQ2M/Iqt6P7irnIrtiCymHTYMlqF9XzNpdSpsSUvlMwuP1gvL/3fZzWn8YTa5/AmzvfxE09b8KV3a5E+4z28b5MIiJqRSTOFKwTp9VqkZ2djbq6Omg0sQklJqLIYPslSlzB2q/ebMOBs1o89fVe7KnUYkLvtrh7TOyqxDzxxS6cqDXg98O64Knr+0DtXW3EbgWsBsBqbPgyAHWVwMUTQNUB4MR64Py+xu3T84BLfwv0vBqQRW4dGIfTAYvDBqvTDqvDBovTBqvDhgtWHaotWtRY63HMcAYHdKewt/4EjA6La9/SnEtwU4fRyFFkROx6AnI60PHAD+h46CdI4IRTIoW240DUd+wPY14xLBn5sKmyYFdmwClTwCFXx21dGXcmmwnLTizD0vKlMNqMrse7abrhsnaXoUhThC6aLmijbgONUgONUgOlTAm5VA65VA6lVAlJK3gdkcTxl4ioqZScUKmrq0NOTg4qKio4IFBKy8rKSrgbPrZfokaJ1oaDtd/lB6vx8Bf7AQAZShmeuqYEeRnNjBRphh2ntHh99QkAwDV9C/DCDb09ns/48CrIqvb52tWDvaAvbN3GwVY4EpBF/vqXnF+Pl04uCXn7HHkGBmUWY2xOX7RV5kT8ekKRdbEC3Q7/iDa1RwNut/4378GSnhejqwrOZDNha9VWbD6/GeX15XAitNvmhZcvRJesLgG3Sbb2S5RKEq39UvSk5ITKqVOnUFhYGO/LIIq7RPyUie2XqFGitWG2X6JGbL9EiSvR2i9FT0pOqDgcDpw+fTqsmUWtVovCwkLOygfB9yk0reV9SsTZ9ea030hqLf92kZRsrynZXg/g/zUlWhuOd/sNJJn+3yTTawGS6/W4v5ZOnTq1unYQSCjtN5n+rULF15z8r9nX622N4xjFR0ouSiuVStG5c+dm7avRaFKi42gpvk+h4fsUvpa030hKxn+7ZHtNyfZ6gMR/Ta2l/QaS6O+xu2R6LUByvR6NRpNwf4yF036T6d8qVHzNyS/VXi+FhmWTiYiIiIiIiIjCxAkVIiIiIiIiIqIwyZ5++umn430RiUImk6GsrAxyeUpmSoWM71No+D4lrmT8t0u215RsrwdIztfU2iTTe5xMrwVIrteTTK/Fl2R/fb7wNSe/VHu9FLqUXJSWiIiIiIiIiKglmPJDRERERERERBQmTqgQEREREREREYWJEypERERERERERGHihAoRERERERERUZg4oUJEREREREREFCZOqBARERERERERhYkTKkREREREREREYeKEChERERERERFRmDihQkREREREREQUJk6oEBERERERERGFiRMqRERERERERERh4oQKEREREREREVGYOKFCRERERERERBQmTqgQEREREREREYWJEypERERERERERGHihAoRERERERERUZg4oUJEREREREREFCZOqBARERERERERhYkTKkREREREREREYeKEChERERERERFRmDihQkREREREREQUppScUHE6ndBqtXA6nfG+FCIKE9svUeJi+yVKXGy/RERNpeSESn19PbKzs1FfXx/vSyGiMLH9EiUutl+ixMX2S0TUVEpOqBARERERERERtQQnVIiIiIiIiIiIwsQJFSIiIiIiIiKiMHFChYiIiIiIiIgoTJxQISIiIiIiIiIKEydUiIiIiIiIiIjCxAkVIiIiIiIiIqIwcUKFiIiIiIjiwuFwxvsSiIiaLa4TKnPnzsXQoUORlZWFtm3b4sYbb8TBgwcD7rNgwQJIJJImXyaTKUZXTZFgtBphtVtRa6yF1W6F0WqMyb7RPBZRKtJZdLDaragx1sBqt0Jn0YW8L9sfEQXrB8TntWatR1/D/iI57Ky4iH5P/4B31x6L96UQETWLPJ4nX716NWbOnImhQ4fCZrPhz3/+M6688krs27cPGRkZfvfTaDRNJl7UanW0L5cixGw347097+GTA59Aa9FCo9Rgcu/JuLP/nVDJVFHbN5rHIkpFZpsZC/YuwMIDC11t6A+9/4C7+t0FlTx2bZmIElOwfsBsN+PLw19iUvEkfLz/Y4++hv1Fcnj8i13QW+x47tv9uGtMcbwvh4gobHGdUFm6dKnH7/Pnz0fbtm2xbds2jB071u9+EokE7du3j/blURQYrUa8t+c9vLnrTddjWosW83bNAwBMu3Qa0hRpEd83msciSkU6iw4L9i7AW7vecj2mtWhdbWpq36nIVGb63Jftj4iC9QNT+k7B+3vfR5+8Pvh4/8dN+hr2F8nhrJYR5kSU2FrVGip1dXUAgNzc3IDb6XQ6dO3aFZ07d8Z1112H7du3B9zebDZDq9V6fFF8yKVyfHLgE5/PfXzgY8il/uf4WrJvNI9F0cX22zqpZCosPLDQ53OfHPgk4KfGbH+pg+2X/AnWD6hkKqyrXIfhHYb77WvYX0RXLNpvukLm+tlmd0T8+ERE0dZqJlScTidmz56N0aNH49JLL/W7Xe/evbFgwQJ8/fXXWLhwIdRqNUaNGoXDhw/73Wfu3LnIzs52fRUWFkbjJVAI6i310Fp8D8haixb11vqo7BvNY1F0sf22TlqLNnAbssSmLVPrxvZL/gTtByz1aJfeDrWmWvYXcRKL9iuVSlw/6y32iB+fiCjaWs2EyqxZs7Br1y4sXOj7UwjRiBEjcOutt2LAgAEYM2YMFi1ahJ49e+L111/3u8+cOXNQV1fn+qqoqIj05VOIspRZ0Cg1Pp/TKDXIUmRFZd9oHouii+23ddIoNYHbkDI2bZlaN7Zf8idoP6DMwjnDOeSqc9lfxEks2q/Z1hiVojfb/j975x4fRXX3/8/u7CUb2A25ACKCBEXCJUEsckegiq3aqtSqJREREhAsFEVra9VarYiXeqM+EkBj0BLs0wrFPoL1BkERsXgLoPHnBR54KIgJIbtJdmd2dvf3x8nsZXZ2d/aW3Um+79crLzLnnDlzsszne2bPnHM+Ka+fIAgi3WTFgMqyZcvw6quvYseOHTjrrLPiOlev1+PCCy+MOkPFbDbDZrOF/BCZQfSKqCipAADkm/MxrM8w5JvzAQAVJRUQvZE70+Bz5cQ6N511EemF9Jud8B4e5SXlinnlJeXgPXzEc0l/PQfSLxGJWHGA9/CYNnAa9h7fizklcyKWo3iRPrpCv22uwP9fh0D/lwRBaI+MLjz1+XxYtmwZtmzZgp07d6K4uDihOj799FOUlpamoYWEHKfbCYPeAIfggNVkhegV49oMzmK0oKqsCrOGzMJZ1rPQ7GxGoaUQR+1HMSRvCEycKeq5lWWV8MEX4ghQXlIe907/Ul0AW4NNrgFEdyJZnao5v7epN6pKqwAgTI9VpVXQ6/Q45TyleD7pjyC6F/HGnDahDWbOjAWlCwBEjgOVZZXY/OVmVIxgAy/k8tO9ED1eON2BZT5tPC35IQhCe+h8Pp8vUxe/5ZZbUFdXh61bt2L48OH+9Ly8PFgsrCO+8cYbMXDgQKxatQoAcP/992PixIkYNmwY7HY7Vq9ejZdeegm7d+/G+PHjVV3XbrcjLy8Pra2t9LYsDngPj+cankva5lSpnvKSclSVVcWsR/AIONx6GINsg3DKdQoFOQWqBmMi4X8IdDtgNcb/xZPoeki/0UlWp/GeL30xkr5ICR4BW7/eiv/67L9ink/663mQfrsf8cYMXuSxfv96bGrchEJLIW7/we2YOGAi2sV2xTggxQmn6ITFYKF4kUFSrd/WDjfGPPCG/3hj1QRMObco6XoJgiC6kozOUFmzhlnezZgxIyT9hRdewE033QQAOHLkCPT6wMqk06dPY9GiRThx4gTy8vIwduxY7Nq1S/VgCpEYqbI5jVRPdUM1dNCptk3ON+ejyFKEJmcTWvgWLClbkpB1olS+gGPOUkbOGNf5BJFNJKvTRM6XrJELLAVwup2oPVir+nzSH0Fom3hjhtxu3S7YsfSdpcg352PJmCX46Tk/DbNbl86X4gPFi+5Dhzt0iQ/toUIQhBbJ6AyVTEFvyOLH7XFjxn/PUNxp32ayYed1O1U93CRTT6raQGgb0m9kktVIps8nuj+k3+5FvJqnGKFtUq3fQ03tmPmnnf7jJ68fg9lj49tLkSAIItNkxaa0RPaTKpvTbLFNJojuSLIayfT5BEFoi3g1n4zdOtH9cLlD90wRghx/CIIgtAINqBCqSJXNabbYJhNEdyRZjWT6fIIgtEW8mk/Gbp3ofsgHVHgaUCEIQoPQgAqhilTZnCZTD1mtEkR0ktVIps8nCEJbxKv5ZOzWie6Hyx06gEIzVAiC0CIZ3ZSW0A6psjmVbJNzDDl4/sDzcdUT3IbXDr0GM2cG7+FxRfEVZJ1IEEhep8lqjDRKED0LJc2bOTOuOucqzD5vdpjmY9mtmw0UI3oSvEgzVAiC0D60KS1tihcXydic+s/ttFd1e90QvAJ6GXqB9/Ah1qvR6pVsWqUHMfm50nFwvtw1INUotSnd1+yp9GT9yjUUSSfx6FR+7woeAQBg4kwhaT74VN/jpAciEj1Zv90ZSfOStbGkfdErQvSKyDHkhKR5fV6YOFNIv+31eWExWODxeuCDLyzW6aADp+dUPSeoRW1MJRip1u/rB05g8V8+8h//6uJhWDHrvKTrJQiC6EpohgoRF4nanPIeHjUHakLeSElvzX3wYcPBDYp58rdbvIcPK1teUo7yEeW46V83odnZ7D+e/6/5/uN0vvniRR61B2uxqXETvW0j0kY0Dcl1olanke7deaPmYd7r87C/aT9Ki0qxbtY6Rd0p3eNKGo13JhtBENqB9/B49etXcdnQy7Dxi43+eFJaVIq1s9bixc9fVIwHRs4Iq8mK//7yv/HsZ8/6z4kUb+aNmocFry/A/qb9KYkr8cRUIj3IZ6jQkh+CILQIDagQacfpdqLmQA2qG6r9aXbBjjUNa+CDD7POnqWYBwDzR8/3fzmMVE91QzV88OHWC27F8h3LFY8BYN6oeSl/S94mtKH2YC3WNqwNa1O6rkn0PKJpCAjViVqi3bs++FBVWoXlO5ZjYelC1fd4OtpJEET2Iml+ZOFIbPxiY0icWFi6EBsObgiLHVI8uHHUjdhwcENIvIgWb4LjUrJxhWJVdkAuPwRBdAdoU1oi7Rj0BtQ11inm1TXWYZBtEPLN+WF5Gxs3wqAPjPlFq2dT4yZMHDDRX4/8uK6xLi1vnMycGZsaNynmpeuaRM8j2r0v14laot27kn6G2IZgwoAJqu/xdLSTIIjsxaA3YNuhbWFxIt+cHzV2bGzcCDNnxrZD21SfI+/XpXoSiSsUq7ID+Z4p8hkrBEEQWoAGVIi04xAcsAt2xTy7YMcp1ykUWYoU8xxuh+p6WvgWfz1Kxw7BoXhuMtgFe9Q2peOaRM8j1r0frBO1xLp3W/gWnJN3Dk65Tqm+x9PRToIgsheH4ICZM4fFiSJLkarYETwgq+ac4H7dX08CcYViVXYgn5FCM1QIgtAiNKBCpB2ryQqbSXnzMpvJhoKcAjQ5mxTzrEar6nryzfn+epSOrSar4rnJYDPZorYpHdckeh6x7v1gnagl1r2bb87HN63foCCnQPU9no52EgSRvUgbysrjRJOzSVXsCLZJVnNOcL/uryeBuEKxKjsQPN6oxwRBEFqABlSItCN6RVSUVCjmlZeU46j9KFr4FuSb8zGszzD/dN6KkgqIXlFVPXNK5uCD4x+ghW9RPC4vKQ95cEsVvIdHeUm5Yl66rkn0PKLd+3KdqCXavSvp57D9MPYe34s5JXMUy8nv8XS0kyCI7EX0irii+IqwONHCt0SNHRUlFeA9POaOmOvv82OdI+/XpXoSiSsUq7IDtxhqNMq7aUCFIAjtQYtEibRjMVpQWVYJgK1Nlu+mr4MOm6/cjLOsZ6HZ2YxCSyGO2o9iiG0IOsQOGPQGv5WhUj2Sq8+81+dFPE6X405vU29UlVYBgCoHFIJIhFgaku/Vo8YKNNq9O2/UPCx6cxEAYP3+9Vg3ax100MW8x+NtZzyQvSlBZB+S5jd/uRkVI9gAheTys37/eqydtTYsdlSUVKCytBJ6nR6zhszC7GGz8cHxD/D4R49HjTfBcSneuCKPHzro0harCPW4O2ekmA168KKXZqgQBKFJdD6fzxe7WPfCbrcjLy8Pra2tsNmUp3wSqcf/QON2wGoMfCHiPTyea3gu7OHphpE3YO72uWh2Noc85Mjr4T08zJw5/LjzwYn38Gl12hE8AuyCHXmmPH/7W4VW2Ew2mDhT2q7bU+nJ+o2koWCU9BTpS4LSvesQHMgx5MCoN/qv4/F6IPpE1bpS0854iOdvIrKbnqzf7oykeafohMVg8Wvf7XVD9ImwcIE0wStg61db8V+f/VeInheULgAAfHLyE3A6DmP6jkG72O6PITrowOm5uONKpPhRVVYFj9eT0ljV3Um1fldt/wJr679FH4sRp51uTD23CH+pmpCClhIEQXQdNEOF6DKkh5QCrgAAYOSMqq2QlawMg+tRPLaEHqeD4PYPsQ3BOXnn4JvWb3DYfhhLypaQ9SKRUiLd+xLxWIGquXcLcsKvo0ZXsdoZD2RvShDZj6TBSP2xlOZ0O1F7oFZRz154Uda3DL98+5cAmOvPLWNuwVXnXhWi8Xjiipr4YeSMKYlVRPxIS35yjBzgdJPLD0EQmoT2UCEySjxWyNloZRjc/sP2w3j76Ns4bD8MIDvbS3Rv4rEC1cq9S/amBNF9iNXnj+s/LmRPlT9/+uekNE7xI7uRlvxYTBwAcvkhCEKb0IAKkVHitULONitDsl4ksol47ket3LtaaSdBELGJp8+X0pLROMWP7MY/oGJkAyo8DagQBKFBaECFyCjxWiFnm5UhWS8S2UQ896NW7l2ttJMgiNjE0+dLaclonOJHdiNtQptj1IccEwRBaAkaUCEySjxWyNloZUjWi0Q2Ec/9qJV7VyvtJAgiNvH0+UDyGqf4kd24PWwPFf8MFbJNJghCg9DiUSKjqLVCzlZHj3TaxBJEvMRzPwaXfffYu+if2x/fdXyHaQOnZdW9SxojiO5DtD7/ptE34fadtwOI3xY53utR/MgORP8Mlc49VGiGCkEQGoRsk8m2MStoE9rCLFmDrZCDrQz9lqzyshGOo52bjEWiVFe7ux0mzhRiM0vWi+mD9BsbJT1FsjiWykpfNASPAL1Or1onqdRUNFJtxUxkBtJvz0MeI9xeN3w+H0ycKSRGeX1eWAwWv/Wy4BXg9rjRy9grYb1HsnOm+JEYqdZv1YZ/460vTuInZQPwPw3HkWcx4rP7Lk1BSwmCILoOmqFCZBzew2PDwQ3YdmgbzJwZvIfHFcVXoLKsMsy2lffwqDlQg7rGurDZLPfsvgcrp67Exi82YlPjprA3UTrows5N9C2VUjsqR1fihpE3wMgZyXqRyBjR9CS/z3mRR+3BWr9eSotKsW7WOtU6UdJBut78ptKKmSCIriFSn33jqBux7J1lONlxEryHx2XFl/lnpU45cwrmjZqHRW8uwv6m/QnHFfm1B1sHY+6IuZh93mwaTMkSBPmSH7JNJghCg9CACpFRnG4nag7UoLqhOiR9TcMaAMD80fNDZpfIy9oFO6obquGDD3dPuBsbv9iItQ1rQ/LXNKyBDz7MOntW2LlK10mkzXbBjic/fhIu0RVXXQSRSuLRU5vQhtqDtSF6WVi6MCwtkk4i6SARTREE0f2I1WdfN/w6LN+xHAD8MaeqtArLdyyHDz7/74nEFaVrH3EcwcoPV+KU6xTFpyzBLcqW/JDLD0EQGoQ2pSUyikFvQF1jnWLexsaNMOgNqspuP7Qd/XL7YVPjJsX8usY6DLINQr45P+Z1UtlmguhK4rk3zZw5RC/55nxMGDAhoobi0SPpgCCIaDFiU+MmTBwwMaRPDk5Tyo8nrlB80gZu2R4qXl9gXxWCIAitQAMqREZxCA7YBbtinl2ww+F2qCpr5sxoFVqj1nXKdQpFlqKY10llmwmiK4nn3rQL9pCyRZYinHKdSokeSQcEQcSKES18S0ifHJwWKV9tXKH4pA3c3tAlPwDA0ywVgiA0Bg2oEBnFarLCZlLe2MxmssFqtKoqy3t45JnyotZVkFOAJmdTzOukss0E0ZXEc2/aTLaQsk3OJhTkFKREj6QDgiBixYh8c35InxycFilfbVyh+KQNAkt+Al9HRE+P88ogCELj0IAKkVFEr4iKkgrFvIqSCoheUVXZy4ovw8mOk5hTMkcxv7ykHEftR9HCt8S8TirbTBBdSTz3Ju/hUV5S7j9u4Vuw9/jeiBqKR4+kA4IgosWIOSVz8MHxD0L65OA0pfx44grFJ20gLfkxGfTQdaaRdTJBEFqDFpESGcVitKCyrBIAW9cczSkkUlnJ5efu9+7GyqkroYNO0XVEBx2WlC2JeZ1E2yy5/Jg4Uwo/IYJQTzx66m3qjarSKgDwOwK9/OXLeHz64xE1pEaP6XL5IQhCW0TrsyUXH4DNGJlTMgflI8qx9O2lWFy2OCw/3riidO1glx+KT9mBNKBi0Oth4HRwe3z+NIIgCK2g8/l8GZtbt2rVKmzevBmNjY2wWCyYPHkyHnnkEQwfPjzqea+88gruvfdefPPNNzjnnHOwcuVKzJ49W/V17XY78vLy0NraCptNeUpoT8bpdsKgN8AhOGA1WeH2uiF4BPQy9oLoFdOyM77/mm4HrEYreA8PM2f2P4DxHh69Tb2jlo10HNxm+bnJ/D1SXe1iO0x6E4x6o/8zk9cr/0zT9Tn2BHqKfpO5Z9qENqaBznOD9ROpbLDWDDoDOD0XohMddCxN1p5UaioapKHuQU/Rb3cl1vOB6BWRY8jxx5OQ2NEZIwSvAJ/PF9JPC14Bbq8bvQy9FM9Ro3elGCHV4xSdsBgsIe2i+BE/qdbvlIffwbHTTjx49Wg8+NrncLm9qP/1DJxd2CsFrSUIgugaMjpDpb6+Hr/85S9x4YUXQhRF3H333bj00kvx+eefo1cv5WC6Z88eXH/99fjjH/+I2bNnY8uWLbjuuuvw3nvvYcKECV38F3Q/eA+PmgM1IW+npTdH816fh6lnTk3L22fpwaaAKwAv8qg9WItNjZtC3mhVlVbBbDCHlAUAI2dUdSy/jjwv0TZbYMFzDc9FfKOv9JnSW3wiGsncM5J+th/a7r//Li++3K8f+XU2HNygeB0jZ/TrxAtvxHs8lZpKx+dBEERqiPZ8cNd7d+G+Sffhxc9fjBlPgmOEUlqsfLVtk67tgw8vff4SxY8sRPDPUNHBoNcD8NIMFYIgNEdGZ6jI+f7779GvXz/U19fjoosuUixz/fXXw263Y/v27f60H//4x8jPz8emTcp2n3LoDZkyTrcTNQdqUN1QHZZ3c9nNGFk4Est3LMeSsiWYP3p+Wt7utAltqD1Yi7UNa8PypGnAkd60Z4Jon9mSsiW4cdSN2HBwQ8T8dH2O3Znurt9Y91S0eyYe/ai9TjLtSQWZvj6RWrq7frsrsZ4PZp87G1u+3qIYe9Kt02htW1y2GLPOnoVr/nlNl7erO5Jq/Y594A20dLjxp5+PwYOvfY7TTje2/WoaRp5JsYEgCO2QVZvStra2AgAKCgoiltmzZw8uvfTSkLQf/ehHeP/99yOew/M87HZ7yA8RjkFvQF1jnWLepsZNmDhgIvLN+djYuBEGfXomN5k5MzY1Kg+M1TXWZd3bpGif2WuHXoOZM0fMT+fn2J3oafqNdk/Fumfi0Y/a6yTTnlSQ6esTydHT9NtdiabD7Ye2o19uv4ixJ906jda2usY6DLINQr45v8vb1R1It37dnY4+Bk4HA6frTKMZKgRBaIusGVDx+XxYsWIFpk6ditGjR0csd+LECfTv3z8krX///jhx4kTEc1atWoW8vDz/z6BBg1LW7u6EQ3DALih3lnbBjha+BUWWItgFOxxuR1raYBfsUdvgENJz3USJ9plJ+1JE/XvS9Dl2J3qafmPpMNo9E49+1F4nmfakgkxfn0iOnqbf7kqsvq5VaM2YTmPFiFOuUyiyFHV5u7oD6dZv+JIfQPTSgApBENoiawZUli5dioaGBlXLdnQ6Xcixz+cLSwvmrrvuQmtrq//n6NGjSbe3O2I1WWEzKU+ztJlsyDfno8nZBJvJBqvRmpY22Ey2qG2wmtJz3USJ9pnxHj7235Omz7E70dP0G0uH0e6ZePSj9jrJtCcVZPr6RHL0NP12V2L1dXmmvIzpNFaMKMgpQJOzqcvb1R1Ip359voCjD6fXgdOz53hBzJqdCAiCIFSRFQMqy5Ytw6uvvoodO3bgrLPOilr2jDPOCJuNcvLkybBZK8GYzWbYbLaQHyIc0SuioqRCMW9OyRx8cPwDtPAtqBxdCY/Xk5Y2CB4B5SXlinnlJeXgPXxarpso0T6zK4qvAO/hI+ZXlFRA9IrpbF63oKfpN/ieyjfnY1ifYf7p6rHuGd7D+/UjP1eun2j3bvB11JZLF5m+PpEcPU2/3ZVIOsw35+OGETegydmEOSVzFM9Nt06jxYjyknIctR9FC9/S5e3qDqRTvx6vD9IujpJtMkBLfgiC0B4ZXTzq8/mwbNkybNmyBTt37kRxcXHMcyZNmoQ333wTt912mz/tjTfewOTJk9PZ1B6BxWhBZVklALa2WL6L/0N7H8IrV76CYlsx7IIdnJ6LascaDbm9oWR17Pa6UVVaBQAhO/IHu/x0FWpsWiN95d10oAAAIABJREFUZsEOArHyCSIYi9GCqrIqzBoyC2dZz0KzsxmFlkIctR/FkLwhMHGmkPJy2+ObRt+EK4Zegf69+oeeaxsCk8EUch0196bacun8PEhDBJFZ5DostBTijnF3YMIZE9DmboPVZEVVaRV00IW56SwoXQBOx8Hpdia1AWykPjlWjNBBhyVlSyh+ZBnS/ilA5x4qehpQIQhCm2TU5eeWW25BXV0dtm7diuHDh/vT8/LyYLGwTvfGG2/EwIEDsWrVKgDA+++/j4suuggrV67EVVddha1bt+Kee+6JyzaZXAai439ocTtgNVoheAV4fB6Y9WY8t/+5pAc6eA8fZsFaXlLut2bubeyNP0z6A4rzikMGXLrS3UepjdEewOSfmXzwJVY+oZ6eoN9IGqkqqwq5/3iRx/r968MsxueNmodFby7C/qb9Ec+VUHtvZvoezvT1idTQE/TbnZF06PF58Pz+50Ni1C1jbsFPz/kpjJwRDsGBfHM+Pj75MVbuXYlmZ3NSgxhq+uRoMYLiR2pIpX7tLjfK/vAGAOClBePx4Gtf4MvvHKi+4QL8ePSAVDSXIAiiS8joDJU1a9YAAGbMmBGS/sILL+Cmm24CABw5cgR6fWBl0uTJk/Hyyy/jnnvuwb333otzzjkHf/3rX1UPphCxkR4yCjjmtmTkjGgX2rF+//oQS0S7YPfbFKq1M1ayN5Tq8cGHWy+4Fct3LMc1/7wGt11wG8pLymHkjDByxlT+iQm1cU0Du1+VbBaVPrN48glCIppGdND57z8li+RgLVWVVmH5juWK5waj9t7M9D2c6esTBIGIVup2wY6H//0wWvgWjOk7Bk9+9CQuH3o5huYNxaHWQwAQtQ+Nhto+OVqMoPiRfbjFwEwUTh9w+RE8tIcKQRDaIqN7qPh8PsUfaTAFAHbu3Ina2tqQ837+85+jsbERgiDgiy++wM9+9rOubXgPxMSZUmJnrNaaGQCeP/A8OD2XWIOTgGxaiUyi9v6LZpEs15L8XIIgiESJ1Y//oP8P0ORswgsHXkhJHKI+uXsiLfnh9DrodIFNaYMHWgiCILRAVmxKS2Q/qbIzVmvN7K83A5aGZNNKZBK1918sTQZrSX4uQRBEoqjtx1MVh6hP7p64gyyT2b/6kHSCIAitQAMqhCpSZWes1prZX28GLA3JppXIJGrvv1iaDNaS/FyCIIhEUduPpyoOUZ/cPRHkAyrk8kMQhEahARVCFcF2rHLisTNWa80MZM7SkGxaiUyi9v6Lpkm5luTnEgRBJIrafjxVcYj65O6JNHDCceyriDSwQnuoEAShNWjhKaGK3qbeKbEzjmRvWF5SjooRFbh3970YbB2MK4qvyJilIdm0EplE7f0XTZOSyw8AuncJgkgpkWLU/NHzce151+LOXXdicdnilMUh6pO7J2LnwElgyY+uM51mqBAEoS0yapucKci2MXHahDaYOXOInTE7tsNqssHtdUPwCOhl7BWUF1q23d0OE2eCUW8My5MelOTnKtofKuTFi7wuxTaSzWJW0VP0q6Q1JSctpXI66GDiTGHnSmUlnXk8HogQw7Rn1Bmh1+tDNKbzecFxJr/WRQ8PSxdamRPdg56i3+6GPHbwHh56nR5GvRHtYjtMetZfKsaRzj7U6/XC7XOH1OPyuJDD5cDj9cAHn2LfLvXTTtEJi8ES0ifroAOn51LyPEDEJpX6/fhIC3727PvoZzXj6V+Mxfp3v8U7jSdxx6XnYekPh6WoxQRBEOmHZqgQcSF9oSuwFIAXeWw4WIu6xk3+h6M5JXNQPqIc816fhylnTkH5iHLc9K+b0OxsRnlJuT+v2dmM+aPn47rh18EpOvHi5y9iU1A9UlnpXOlNlA461ByoCXkjn+hbKt7Dh9Ulb2Pl6ErcMPKGLrduJno2vIfHhoMbsO3QNpg5M3gPrzhrS16ub25fPD79cdQerA0/t7QSW7/eimc/exZ2wY7lFyzH3BFzUbu/Nkx7laWVuOu9u/Cvw/8KmvVyIxa8Pg/7m/Z36m4OKksXqp6dRhCENuFFHrUHQ+PEnJI5qBhRgff+7z1MHzQdz+1/TrFfNnJGv1Ux7+NRe0A53nh9Xrxw8IWQOpaOWYqfnfezkH56sHUw5o6Yi9nnzQan57C+YX1KngeIrkdy85H2TqElPwRBaBUaUCESwim0oeZgLaob1vrT7IIdazuPq0qrsHzHcvjgw60X3IrlO5ajuqE65Pjpj5/GRWddhDcPv+k/T6pHXnZNwxr44MOss2ehuqE6pOyahjUAgPmj56t+M+V0O1FzoCasLvl1n/z4SbhEV1x1E0QyKN2bAMLuc6Vyy8YuQ+3B2hA9Sef64MOIwhF+t4zrz7se6/evV9QeANx2wW341+F/hehC0jXTHTtv/qibaKYKQXRT2oS2sJgS3NfPPne2YhyRx6tI9Ujx5vqS68P64zN6nxFW9xHHEaz8cCWaXc0pex4gMkPANjl0DxXalJYgCK1Bm9ISCWHgzKhr3KSYt6lxEyYOmIh8c37I7/K8fHM+BlkHoa6xLmY9ANsnYpBtkP84mI2NG2HQqx8fNOgNqq8bb90EkQzR7s3ge1FeLt+cjwkDJmBTBF3WNdb57+spZ05BjiEnatl+uf0wxDbEnybXBWvPJhjoTTBBdFvMnDlinNh+aDv65faLmB8cr6LVU9dYhzxTXki8URPPUvU8QGSGMNvkzs1ppZkrBEEQWoEGVIiEcAh2/5tuOXbBjha+BUWWopDf5XlFliI0O5tV1SMdn3Kd8h/Lyzrcjjja74jruvHUTRDJEOvelO5FebkiSxFOuU6puq8v7H8h7DE0bBfsOCfvHMXzQ9oToQ6CILRPtDhh5sxoFVpVxat4442aeJaq5wEiM4TZJtMMFYIgNAoNqBAJYTXZYDMpb0hmM9mQb85Hk7Mp5Hd5XpOzCYWWQlX1SMcFOQX+Y3lZq9EaR/utcV03nroJIhli3ZvSvSgv1+RsQkFOgar7+t/f/Ru2GBq2mWz4pvUbxfND2hOhDoIgtE+0OMF7eOSZ8lTFq3jjjZp4lqrnASIz+F1+OvdQ4WgPFYIgNErCAypvv/02fve736GqqgoLFiwI+SG6P6KHR0XJHMW8OSVz8MHxD9DCt4T8Ls9r4Vtw1HEU5SXlMesBgPKSchy1H/UfB1NRUgHRK6pvv1dERUmFquvGWzdBJEO0ezP4XpSXa+FbsPf4XsyJoMvyknL/fb37P7vhEl0RtVdeUo6THSdx2H7YnybXBWvPHIgePt4/kSAIjcB7+Ihx4rLiy3Cy42TE/OB4Fa2e8pJytAqtIfFGimfRzknV8wCRGQJLfmgPFYIgtE1Ci0zvv/9+PPDAAxg3bhwGDBgAnU6X6nYRWY7F1BuVpQsBsH0U5C4/S99eisVli/2OOXIHHalsv9x+uGHkDQAQ0W0neOd+HXRYUrYEGxs3JrWrv8VoQWVZZWf7N8a8LjkGEF1FpHtTfi8qlVu/fz3WzVoHHXThzhellXjl/70Cm8kGu2DHX//fX1FVWgUgXHuVpZW4Z/c9ABDk8jMPi95c5E8jlx+C6P70NvVWjBOSy8/u/9uNqtIq6KCLGq8i1eN3+fF6sbhscUjeibYTUetO1fMAkRnClvxIe6jQgApBEBpD5/P54p5bN2DAADz66KOYO3duOtqUdux2O/Ly8tDa2gqbTTvT1Z1uJwx6AxyCA1aTFaJXzPgu9k6hDQbO7G+T4BXg9rrRy9ALvIeHmTPD4XbAarSGHQteAW6PG72MscsG/62Ba9phNdkgeviEXUb8n6mK6xLZgVb1Gy9tQhu7Fzu1xXt4v215MPJ72OP1wAdfSJp0H8vr9Hg8ECGGXceoM0Kv14ecr/N5wXGmlOiO6Ln0FP1qjVjPF/LY4RSdsBgsLC55vXByHAx6U8y+U16Py+NCDpcTNW7JY1zI80CUPCL1pFK/G/f+L+7ecgDjzs7H7ZcOxxsHT+CF9w/j8tIz8GzFD1LUYoIgiPST0AwVQRAwefLkVLeFiALv4VFzoCb8rXOG38RIX6gKLAUAACNn9OdJvxdwBZGPjXGUBQDRBcvu1UDD31BgzAHcLhjLrgWmrQAMOfG3v/PBK+Z1CaIL4T08NhzcgG2HtsHMmcF7eFxRfIWi3iPdw0ppveV69XmAd1cDJ79EwZnnA//5FMZ+w5meOKNynZbCsDSCILSLmueL8NjhA/5dA+POhwDXaVhy+gBTb0PBxCUAZ4wYH8Lq6SwXLW5Fi3HR8ojsRnLz8e+hwklLfmgPFYIgtEVCAypVVVWoq6vDvffem+r2EAo43U7UHKhBdUO1P80u2LGmYQ0AYP7o+T3jjYzQAex+Cqh/JDRdOp5yK2DK7fp2EUQKUdI7gNTrXa6nL/7R+W9nPumJILo9CT1fKPXFrtPAW/cB7g6KHYQqRC8bOOH8e6jQkh+CILSJ6gGVFStW+H/3er1Yt24d3nrrLZSVlcFoDH0j8MQTT6SuhQQMegPqGusU8zY2bsTCsoVd3KIMwRmAvWuV8/auBS66o2vbQxBpoMv0TnoiiB5PQvGGYgeRAqQ9VIxkm0wQhMZRPaDyySefhByff/75AIADBw6ktkVEGA7BAbtgV8yzC3Y43A7/dNdujauVvQVTzDsNuOxAr6KubRNBpJgu0zvpiSB6PAnFG4odRApwi9IMFWlTWl1IOkEQhFZQPaCyY8eOdLaDiILVZPU7c8ixmWywGq0ZaFUGyMkDcvooP8jl9AFyaINDQvt0md5JTwTR40ko3lDsIFKA6JX2UAld8iPQDBWCIDSGPpGTFixYAIfDEZbe3t6OBQsWJN0oIhTRK6KipEIxr6KkAqJX7OIWZQiPCEy4WTlvws0snyA0TpfpPVhPuYVAv5HsX4D0RBA9hITiTaS+OLcQ+OE9FDsIVUgDJ9IMFY6W/BAEoVES2pR2w4YNePjhh2G1hr65cDqdePHFF1FTU5OSxhEMi9GCyrJKAGxNcza5/ITAOwCDGXCeBix9AJEHzBHepgsdbB22q5W97fKIsTexM+Uy9xGArdN2nWZvwybcnLDLTzZaURM9m7j1noiWgE493Q7nqKthyB8Ch7MZVkshxJbDsBScw7SshrDrewD4EmsTQRBdSiDe+LAxxOWnHJWllTDrdED796E6lvfFvfsBlz4IFE9nzwGcgcWFODVP/XHPQlraY6A9VAiC0DhxDajY7Xb4fD74fD44HA7k5AS+wHo8Hmzbtg39+vVLeSMJwMyZMX/0fCwsWwiH2wGrkT1sZM1gitsF7F4NfLguaKBjETD1dsAoG+gQXcwhIJFBEUMOcxC46A62TjvHBnjcCQ2mZKsVNUGo1nsyWgLA64CaYztQ97bsi1ThOVClAPn1B44D5m4B9jyTskFPgiDSi9kHzB84EwtHz4fDeQpWSwHElkMwe9zAi1cBx/aF6zi4L/Z6gPeeBDYvSljz1B/3PAJLfmR7qJBtMkEQGiOuAZU+ffpAp9NBp9PhvPPOC8vX6XS4//77U9Y4IhTpTY20QZyRM0Yr3nXwDjaYsuvRQJrrNFDfeTz5V4GZKpHsFuOxPpbypU3vOFPcTSYraiLbian3JLUUWQPVAHSxNaB0/WkrgPdXA7seS6hNBEF0MZ06ttQ/AuQWoqB3f6DtOxg7moGLfg1Muw14uUJZx6bclPTp1B/3TKSZKJxOmqHSuYeKSDNUCILQFnHtobJjxw68/fbb8Pl8+Pvf/4533nnH//Pee+/hyJEjuPvuu9PVViJbMZjZzBQl9q4LXToQy26RS2gVWtzEsoo06LumHQSRMElqKWkNyK+fWwgMnQF8uD7hNhEE0cUE67ijGTj5OfsXYFoeOjOwtxIQruMU9OnUH/dMpJko/k1pOVryQxCENomrl5o+fToA4NChQxg8eDB0naPKRA/HeTqGhWIr0Ktv53F22C2SFTWheZLUUtIakF+/d3+gvSkr9E0QhEpixZGOJqZtaZBFruMU9OnUH/dMpIET+R4qopeW/BAEoS1UD6g0NDSEHO/fvz9i2bKyssRbRGgPS58YFop5QcfZYbdIVtSE5klSS0lrQH79tu/YF6cs0DdBECqJFUdyi5i2g9OCdZyCPp36455J+IAKm6nipiU/BEFoDNVLfs4//3yMHTvW/2+0H6KHIfJsA1olJixi+RJZYn1MVtSE5klSS0lrQH79jmbg253A+IUJt4kgiC4mWhwZvxD4dkdgdgoQruMU9OnUH/dMpCU/nGxTWoGW/BAEoTFUz1A5dOiQ//dPPvkEd9xxB379619j0qRJAIA9e/bg8ccfx6OPPhqpCqK7YrYyNx8AaPg7c/Vxu4Cyn4e7/HRatcKYC3z+KmA7E7D/Bxh2SZe6gGjGipogIpGkjXjSGlC6/rtPMJcfnZ5cfghCC0SLI5OWAn+7Ceg3srNPvzZcx8F9+kcbgvp/hbIRoP64ZxJpyQ/toUIQhNbQ+Xy+uBcrjh8/Hn/4wx9w+eWXh6Rv27YN9957Lz766CNV9ezatQuPPfYYPvroIxw/fhxbtmzB1VdfHbH8zp07MXPmzLD0L774AiUlJarbb7fbkZeXh9bWVthsPXAKutDBNopztbLpuh4xdBf+WPlKdYkCnPDBYMiBQ7DDarJBFF2wmK3hZYUOODkDDJw5tKzBHLimyLPNbJXakML2O91OGPSGEGtachPIbrSiX6fQFnqPe3hYTL3VV6D2PvaXk2zERTjhVb52WJ1eOOGGgcsJLQsdwBnZ/kiWPkyPOiPA6cPbo3B9ALI0N2DqlZoPltA0WtFvtyNWPHHZ2WCIvw8WAPhYPxwSB/QsNrgccJpyYNAb0e7ugIkzwag3Jh7vQP2xFkilfiue+wC7v27G0pnnYsq5RWhziVj40j4AwDcPXQ5OT/s0EgShDRLaOn3//v0oLi4OSy8uLsbnn3+uup729naMGTMG8+fPxzXXXKP6vC+//DIkkPft21f1uT0e0cUsDiO9PY6Vr1RX01fgZ69Bzf7nUNe4Kejt0hxUli6E2WBWWbYK5ndWAj+8F9hbzZyDQtpwOwBf6tqPLLaiJjQNL/KoOVgbWQ+xiOc+ltmI8z4favbXyK5dzvT1yUvAjodYnQPHAXO3wLLnGaDhbygw5gDWATBeWwvseUZ27UXA1BXA5puBg5tD2xPNxjwJa3OCIFJErHjidgHv/xnY3znDtPcA4LpahTjQOWtl61LwV/wJNftrsPv4Hjxz8TOoOVCDTSExJ/7ZJdQf9yz8S346B06CB1DcHi84PZeRdhEEQcRLQgMqI0aMwIMPPojnn38eOTns4Z7neTz44IMYMWKE6nouu+wyXHbZZXFfv1+/fujTp0/c5/V4hA72UFX/SCDNdTpwPHkZe6iKlD/l1tBZIp11OW87gJr9z6G6IWCdaBfsWNN5PH/UPFh2/1ld2Ut+D8ueZ4Fdj4W3YeRVwOdbU9N+gkgTTqENNQdro+jhpuhvbmPpNMp9HPna1QB8mN+nGBZp88hpK4D3V4dq7Yf3hKe5TgP1nUs5L/kDG1AhXRGENogVTyYtZf3mrqDl2hHjwCOAzwvnpQ+gprEO1fvXYfXM1aj7og5rw2LOGgDA/NHzaZYJoUjYkh8uMKAieLzIMdKACkEQ2kD1prTBVFdX46233sKgQYNwySWX4JJLLsFZZ52FN998E9XV1aluYxhjx47FgAEDcPHFF2PHjh0xy/M8D7vdHvLTI+EM7G2TEnvXsqm9DX+LnM8Fjb9JdRUOg6H3Gahr3KR42sbGTTBwOcBXb6kra7ICX78TnplbCOQPSV37Cc2gNf0aOHMMPcR4YxtLp1Hu45jXHjqTaSm3EBg6A/hwfaCAUlrItdcBtgFA4TDV7SEIrem32xEtnpz8ks1I+XBdIC1WHNj/CuvHv3wZ+eZ8TBgwAZsixpyNMOgpPmiZdOpX7JyhIg2khMxQIacfgiA0REIDKuPHj8ehQ4ewcuVKlJWVobS0FA899BAOHTqE8ePHp7qNfgYMGIB169bhlVdewebNmzF8+HBcfPHF2LVrV9TzVq1ahby8PP/PoEGD0tbGrMbVqmxtCLB0V2voBrJh+UEdqVRX3xI4hFZFu0OAvalyCHa2+Ww8ZeX07g+0N6Wu/YRm0Jp+HYI99j0ejZg6jXx+zGs7TzEtKelJjcacrUDfktA00hURBa3pt9sRLZ6ceT7bHyWeOGDM8ffjRZYinHKdih5z3I4k/wAik6RTv9IMFa7TLlmv04HTSRvTxr29I0EQRMZI+NVBbm4uFi2KYJWbJoYPH47hw4f7jydNmoSjR4/iT3/6Ey666KKI5911111YsWKF/9hut/fMh7qcPLYOWulBKacPy3e7Ipzbh20uKa/r+0ZYTXmwmWyKD1U2kw1Wk405+fB29WXltH3H9mNIVfsJzaA1/VpNttj3eDRi6jTy+TGvbSlgWgLC9aRGY5Y84PtG1e0hCK3pt9sRLZ7851Ng8i/jiwNul78fb3I2oSCnIHrMMVrD6yA0Qzr1K8iW/ABstopH9JHTD0EQmkL1DJVXX30Vbrfb/3u0n65k4sSJ+Oqrr6KWMZvNsNlsIT89Eo/INpVTYsLNbAf/smsj50vuHcF1NX8Fse0EKkrmKJ5WUTIHosfFbJHVlBUcwLk/DM/saAZaDqeu/YRm0Jp+RQ8fQw989Api6TTKfRzz2t/uYFrqaAa+3QmMXxgooJQWcu1FgP040BwUb0lXRAy0pt9uR7R40m84ewkxIejlWKw4UHoN68eH/wItfAv2Ht+LORFjTgVEL8UHLZNO/YqyTWkBsk4mCEKbqJ6hcvXVV+PEiRPo169fVGtjnU4Hj8eTksap4ZNPPsGAAQO67HqaxpTLNqIEIu/2HytfoS7LG79H5Wy2Ad3GSK4mqspWwbz9t8zlR6djezYEt6Hw3NS1nyDShMXUG5Wl7MtIRD1EQ41O4752p8vPR7WBN8/vPgHM3cJsUBv+xpbL/ft54Npalqbk8vOPW9iFSFcEoQ3UxJOpt3fmrwuKDf9gy3/2PAu0fBs4Z/IyWF7/HSpn/QEAsH7/ejxz8TMAkLTLD9GzkG9KCwAcpwfgoSU/BEFoCp3P58tY1Gpra8PXX38NgG00+8QTT2DmzJkoKCjA4MGDcdddd+HYsWN48cUXAQBPPfUUhgwZglGjRkEQBPzlL3/Bww8/jFdeeQU/+9nPVF/XbrcjLy8Pra2t3fdtmdDBNqNztbIpvx4x1KGHM7C9D3JsgMcNmHopnBshX+k6ghNOjoOBM8Mh2GE12cB7XDBzOf5jUXTBYjArlhU9Llg4c+CaIs82mZUf823MgtVgCvxtbhf7Migdh50rAB4BMPcO/RwITaIV/TqFNtk9zkd395ETpkMRTp0OBr0BDsEBq8kK0SvC4gPAcUFa98IJNwzB2vO4YDFZAd7RqY2guOAVmX6cpwFLH6YnPQdwxlCN6Y2h1xF5wEzT+Yn40Ip+NUki/b4UE9xOwGgJ7WOdrWyZn9DB6hEF9pPTG3C7WD+uN6Jd7IBJb4JRb4TD7YDV2Bmborj7ON3O8FhGbkBZTyr1O+7Bt9DUxuORa8owuIDdp7+s+xin2gX8z7KpGD0wLxVNJgiCSDsJ7aHS0dGB3Nzkv5Tu27cPM2fO9B9L6zTnzZuH2tpaHD9+HEeOHPHnC4KAO+64A8eOHYPFYsGoUaPw2muv4fLLL0+6Ld0K0cVsEiO9jZIesHoVsX85U+j5sfKVylpMkB6FCiyF4EUeGw5uQJ3SG3pLXkhZADByRuVr9ipif8/7fw79e6YsB8ZVAnw78MGzzKVA/reabezt2ntP0mwVosuRBk/C7nG1yHTIw4eahudQ11gXOvNk5I0w1/wEOLYPGDgOmLsFlj3PAA1/Q4ExB3C7YCy7ls0w+bQO+KCaDaAMHAdc/ijTVoh+Omej/M/trE63iy2lm7QUeLkCaDseSCMtEUR2kEi/73YBu1cH9D9yNnDVM8wyWV7PpKXAS7NZTOhMs0xbAXAm9Al6RijgCgBEj3e8h0fNgRpZLKMZLT2NwKa04Ut+BFryQxCEhkhohorJZMK4ceMwY8YMTJ8+HVOnTkWvXhFmMGQh3foNmdDBHqrqHwnPm/4bYMqtaZ+h4RQcqDm4AdUN4VaNS8puxvxR89jbcjVE+3sW7wa+2ArUPxqeN/03wIgrgeopynld8DkQ6aFb6zcCTrcTNQdqUN0Qbku/pHQR5vcaCsumcuAXdcB/PgF2PRZeyfQ7gQFj2KAIACzfD3zyErBLST93AuffADxdFki76NfMFUQ6HyAtEXHTE/WbdhLp93kHG0wJ1n+0+JEi/UeNZWVLMH/0fJqpksWkUr8jf/86OgQPnrr+fPS3sYH52//2Kf5z2oW/LpqICUMLU9FkgiCItJOQbXJ9fT2uvPJKfPzxx7j22muRn5+PiRMn4re//S22b9+e6jYS8cAZ2JslJfauZflpxsDloK5xk2LexsZNMHBxvNGO9PfkFgIFxWzNtxJ717L8XIUOuYs+B4JIFQa9AXWNdYp5G798GYahM4HCYcDQGcCH65Ur2bsOGDqTaaJwGGA7g72ZjlTWNoCVk/hwfeB8fznSEkFknET6fYM5VP+5hdHjR4r0HzWWNW6EQU/xpKcgbUobsodKp4Uy7aFCEISWSGhAZdKkSfjtb3+L119/HS0tLdi1axdKSkrw+OOP4yc/+Umq20jEg6tV2eoQYOmucGvDVOMQ7IoWigBgF+xwRMhTJNLf07s/0N4U/W/taGLllPK64HMgiFThEBzRNeU8BfQtUa+JviVsz5RoZZ2trJzS+cFppCWCyCyJ9Pty/SfSpyag/5ixzO2Iqz5Cm/h8Pv+yHnL5IQhC6yT8KqCxsRE7d+5EfX09du7cCbfbjZ/+9KeYPn16KttHxEtOXsDFIyyvD9uMLs1YTTbYTDbFhyabyQarKY42RPp72r5ja8Gj/a25RaycUl4XfA4EkSrQMKWcAAAgAElEQVSsJmt0TVkKgO8b1WvC42Yb0EYra8ljdSqdH5xGWiKIzJJIvy/XfyJ9agL6jxnLjLTRdU/A4w3MQDFwgXe7tIcKQRBaJKEZKmeccQamTJmCt99+G1OnTsUbb7yBpqYmbN68GcuXL091G4l48IhsAzklJtzM8tOM6HGhomSOYl5FyRyIHl59ZZH+no5m4NSh6H/rqUOsnFJeF3wOBJEqRK+IipIKxbyK4b+AeHg3c+X53/eB8cw2GbmFQL+RgSn6ExYB3+4ABowFzq8AOk6xNCUmLALsx4HmrwJp4xey84M1RVoiiMyj1E8WDgNKfgrM+J2yRkUB+OHdLD7kFrKZJ/+7OxA/5KRI/1FjWUkFRC/Fk55A8JKe4CU/Bo5mqBAEoT0SmqFyxhln4IsvvsCRI0dw5MgR/N///R+Ki4vRu3cclqBEejDlsl39gci7/cuRbBODbVONOQDfwexTjbK8GG+kLCYrKkvZQ9nGEJefclSWVsEstAOcEGpn7Lc6ltk9Rvp7xi8EbAOBCYsB+NieD/K/FTq2aZ7az4Eg0o1ca9Gsh4MsUC05eagsrQTA9hkI0dToBTD7fMCcl4HefYHBk4DS64C8gWwKf68iNrhYeB7gcQHDLg1cf9IyoOBcYPudoRqZugL4xxLWDrnLR3AaaYkguo5ItsjB/WTTV8Al97M9klyd8YYzAG3fd1ogO9kxZ2KuPhfMY/2wyw5Y8oGzpwI6fWSXHyAp/VuMFlSWKcUycvnpSQTPQAkZUOncQ0WkPVQIgtAQCbn8AMDp06exa9cu1NfXo76+HgcPHkRZWRlmzpyJhx9+ONXtTCk9wmXA/+BlZwMgHjdgUnBicruAdx9nm9P17gfMfx3YWw00fw1c/Szw7hMKlqq3swGXaIgCnHwrDDl94BDssJpsEJv/HyxbfxWwXRy/kA2I1F0PnPtDYPxi4IUfA20nQx/WRJ49JBYUs7fqvfuxB0CPCJh7BQ3GKPytaj8HQjNoVr/BWoulJ9HFtBf8pWbm7+C84AYY9CY43A5YjVaIHh6WTzYCOx5i5Tptk7HnmfAvRFNXAB/VAjtXhV/f5wM6TrIp/acOsbfbel2obqAHOI60RCSFZvWbaZRignxQQ3ABOrA48807QPlfWfkP1ykPjij1xWXXAWPmsBlvLjuQm9/ZjxoDaSnQv9PthEFvCMQyr0juPhogVfptbuPxgwffAgDUVU2ATscGVR59vRGfHD2NR39ehuvGDUpJmwmCINJNwnuo9OnTB1deeSWmTp2KKVOmYOvWrairq8O+ffuyfkClRyDZGPYqYv9ypvAyctvEq58F9q5hlonL97OHt2BLRdfpgEXxpGWRZ6p0Wjha6h8BCoeh4Oo1wOF3YXz7/tC6JGvGabcxK0afD7jkPva7ZP84eRnw/p/ZsTQtWVrDPeMutnRBesOv9Leq+RwIIt0oWZQG62nyrwL3sZIFqus0sP1OWDqagSm3oiCnABA6YNy9OrTctBXA+6tDbU9dpzvL+JhtsrQ/QvD1x94I1P2CaaujOWCHqqQb0hJBdC2RYoJ0LFkXewXWX+56lFkg761WjgU+b6DfVeqL278HBo0H3ryPDZxwRqDsemD8opTpXxo8KeAKAABGzphUfYS2kJb8cDqdfzAFoCU/BEFok4T2UNmyZQuWL1+OMWPGoF+/fliyZAna29vx5JNPoqGhIdVtJNJFsG1isGWiGkvVaDNUgi0cnaeA/iOB3U8rlw22YpTbMu5dy9rY8Dd23NEMnPyc/dvRDLzzIFm2EtpAblEazN51LF9CrQWqvFws29Ng22R5urU/++Ik7Y9AdsgEkT2ojQnGHBZnErFAlvfFZ09hA6zNX7F+970nKSYQKUMaMJEGUCSkJT9ukQZUCILQDgkNqNx88804duwYFi5ciE8//RTfffcd/v73v2Pp0qUYNWpUqttIpItg28Rgy0Q1lqqu1sj1Bls4xmPFKLdllK4TafCGLFsJrRCPntRaoMrLJWolHskimbRFENmB2pggxZlELZAj9cXy6xBEkrgVLJOBYNtk2kOFIAjtkNDrhpMnT6oq9/DDD2Px4sXo06dPIpch0k2wbWKwZeL3jbEtVXPyItcbbOEYjxWj3JZRuo7bFeE6ZNlKaIR49KTWAlVeLlEr8UgWyaQtgsgO1MYEKc4kaoEcqS+WX4cgkkQaMDHIBlQ4sk0mCEKDJDRDRS0PPfQQTp06lc5LEMkg8gHb1I5m4NudbHO65q8A+4nolqqRBjmAUAvH4HqVCLZilNsyTriZtbHs2gjtIMtWQiMEa03OhEUsX0Kt9bm8XCytSbbJcitxJYtk0hZBZA9qY4LbxfQcT7+rlEYW6USaCSz5Cf0aQnuoEAShRdK6IDZBAyGiqzBbmcMHADT8HfjkJeCqZwGdDnjrPrZJLSCzJFbh8hNs4djwt9B6g+sKdhaYfmfA5UfuXhCvDTRBZBvBWoulJ7XW50rl3n2Cufwo2Z5OXQF8XBt4a+2//gpg251Av5HsC1nZtaQtgsgm1MaEHFsgznz4PPDz5wDoIrv8AIG+eOItwN+r2IbUwfn5Q4FJtwAXzKWYQKQM/5IfXYQ9VGhAhSAIDZGwbbIarFYrPvvsMwwdOjRdl0gIsm2U4bKzL3TO02zKsNvFjqVZKMYctoZbWn4TXFbkA+4kcngH22xTXq9kuygKbCPMWNbHANkfE340rV9JE5Keoukn7J73AD4xVFMeN3PsCK5TKc3tYnVIWg9O1xvYddRomiCSRNP6zTSx+kEp3+0EjBbA1RlvDKaA5gUnK6M3ArwdMNsAj8B0n2Nl5+qNrIzQwQZznK1sWaBHDDjnET2SVOn334dP4drqPRiQl4Mnrjvfn75x7//ifxqOY9FFQ/G7y0ekoskEQRBph7Zs7+mILmDPMwpvvW5nD2HvPgF89RZgOxOw/wcYdjGbSVJ7BdB2MvKMFdHF7BuV3qYp2S5Kv0eyZCT7Y6I74Lf47sv+jXYfy+95rwt4b7XsbXPnDJP/uQM49u/ADJNJS5kNsuN46KwTaQ8E6fo+L9M4zf4iiOwnWj8ouths0NE/B/auYU49rtPAwHHA3H8AB/8BfLoJEJ0sJvxgHjBhCfDZJuDN34fr3+cDPniWYgORFiQXn0ib0grk8kMQhIagAZWejNAB7H4KqH8kkOY6zY5HXgV8vjWQ95+PAv/6fMAl9wEvVwD1j7L0yb8KfFmMVi8ATLmV3nIRRDzwDmD3amDXo4E01+mA/i66HXi6jP1e/wgbKLlwAdOolAaEao90ShDdA0nLA8awwZRdjwXypq0A3n86NA1gy3rd7ewcaePaYP2PuJJiA5E2pE1nwzelZUt+RC8NqBAEoR3SuiktkeVwBvb2SU5uIZA/RDkPYG++hs5k5QC2J4TBHLtegKVzNI5HEHFhMLOZKUrsXQfYBgCFwwJpco0C4dojnRJE94AzsP3Khs5g2pfILQxPC2bvuvA4ATD9FxSHp0t5FBuIJBEll59Im9KKtAcjQRDaIa0DKtOmTYPFYknnJYhkcLUqWyr27g+0NynnASy9o4mVk45drbHr9Ze1J9duguhpOE9H15SzFehbEpoWrFEpLVh7pFOC6B64WtmyW3m/HW9fHitdyqPYQCRJ5E1pyeWHIAjtofo1g92uvgOVNqratm1b/C0iuo6cvIDjRzBt37E12kp5AEvPLWLlpOOcvNj1+svSRoQEEReWPtE1ZckDvm8MTQvWqJQWrD3SKUF0D6QNpuX9drx9eax0KY9iA5Ekbq80Q0XZ5UegARWCIDSE6hkqffr0QX5+ftQfqQyhETwi22ROTkcz0HJYOQ9gFovf7mDlALYxpsjHrhdg6R4xqWYTRI9D5JnOlJiwCLAfB5q/CqTJNQqEa490ShDdA4/INp7+difTvkRHc3haMBMWhccJgOn/1KHwdCmPYgORJBE3peVohgpBENpD9QyVHTt2pLMdRCYw5bIN64DwnfwLz42Qt4i5/Lzw4yCXEZnLT7R6ySGAIOLHbGU6A9i+B3KXn3/cwvIknU1aCrw0OzRNrj3SKUF0DyQtf7SBOfcAAZefd58A5m4BdPpwnU9dAXy8ITCDJVj/0AHTf0OxgUgLbv+mtKHvdTn/kh/aQ4UgCO2g8/l8PS5q2e125OXlobW11b88SfMIHWyjOFcrm/7rEdXvws872KaX0rluFxsg4duYLaPBxNZM59gCeVJZkQ+4+0RsU+e5Hjdg6pW6v5nokXQ7/UbSrqRL52m25Efk2ZcizhiqP50R4PShOoMe4Dh12iOdEl1It9NvvCTTV0erL7i/FjpYncHXAJR1Hk3/FBsIGanS70sf/C/u/ccBjC8uwG2XnOdPf/er7/Hszm8wbVgRXqqckIomEwRBpJ2ktmrv6OjAkSNHIAhCSHpZWVlSjSLiRHQxy8RE3iSJLuD9PzOHAGMOGzAp+3lgFkrbSWDqbcDEJexhjTOx83r1Zf9Kx0pID4m9imKXJYieiJJ2Z/4OuGAe8P5qhdkotzMdKelPSWdqtEc6JYiuIZm+Wm19U5YDP5gPvLIIGDIFuGBu6ICNXOfR9E+xgUgTEZf8dM5Y4UVa8kMQhHZIaEDl+++/x/z587F9+3bFfI/Hk1SjiDgQOtgDVf0jgTTX6cDxlFsjv/1SOhcA6h8FfD7gkvuAlyuAt+4D3B3R6yIIIj4iaTfvLODdx4Fdj4am13ceT/5V5FlhBEFkJ8n01fHU9/b9rL8u+znrv9u/p76byDpEr7TkJ3RAxWhgxzSgQhCElkjINvnWW29FS0sLPvjgA1gsFrz++uvYsGEDhg0bhldffTXVbSSiwRnY2ykl9q5l+Ymc++F6YOhMILdQXV0EQcSHkv5yC4GhM4AP1ymfs3cdWwZEEIS2SKavjre+4P6b+m4iC5H2SJEPqJi4zhkqbnoxSxCEdkiol33nnXewdetWXHjhhdDr9Tj77LMxa9Ys2Gw2rFq1CldccUWq20lEwtWqbIcIsHSXPTBdN95zO5qA3v3ZTv+x6iIIIj6U9Ne7P9DeFEPTrYElPwRBaINk+upE6pP675OfU99NZB3SDBQDF/pe12zgAAAuGlAhCEJDJDRDpb29Hf369QMAFBQU4PvvvwcAlJaW4uOPP05d64jY5OSxddOKeX3YRnKJnptbBLR9p64ugiDiQ0l/bd+xLz5RNZ2X/rYRBJFakumrE6lP6r+p7yayEF5kAyZG2YCKycCOXW5a8kMQhHZIaEBl+PDh+PLLLwEA559/PtauXYtjx46huroaAwYMSGkDiRh4RLapnRITbg7s7h/r3NxCoN/IwBKf8QuBb3ew2Sm5hcAP74leF0EQ8aGkPwD4difbgFaJCYuYsw9BENoimb46Un1TbwvttyWC++9E6iaINMN3DpiYOOUlPy6RZqgQBKEdElryc+utt+L48eMAgPvuuw8/+tGPsHHjRphMJtTW1qquZ9euXXjsscfw0Ucf4fjx49iyZQuuvvrqqOfU19djxYoVOHjwIM4880zceeedWLx4cSJ/hvaQLAyFDkDHAUYz4HYCU1ew/HidA0y5wLTbgZFXAflD2FKDXkXAqUOA7Sxgy0Kg/L+B4unMwpUzhFssB1s+ptoOkiCyjXju8bCyHgC+0LTJvwJKrwVsZwb01/of4JyLWR1KLj96Pdto0m+Hquu0SCbdEUTWYsplfTIQua9Wsko3W8PTPW4AOmDSLUDZdUz33+4A3n0SOGcmMGExUHc9MP037PlAHjMoPhAZRvAoL/kxdW5KS0t+CILQEgkNqFRUVPh/Hzt2LA4fPozGxkYMHjwYRUXq1+m2t7djzJgxmD9/Pq655pqY5Q8dOoTLL78cCxcuxF/+8hfs3r0bt9xyC/r27avqfE0j2SM2fQVc/Szw7hNs40rXaWDgOOCnTwMX3cHWSufY2AOXKhtGH/D51vAHvKkrgOteAt57Ati8KPRL3fjFwAuXM0tl6WEQutTaQRJEthGP5am87MBxwNwtwJ5nws+ftBTYcCVwbF+Q/m5ngy0X/TowUCLywMcbgB0PKZz/k9DzSXcEkX0YcpjjjlJf7XYBu1cH+vXgQdRPNgI7V8WOJTe9Bvg8bDD3pn8CogB89AKw82Hql4msQpqhIl/yIx273F74fD7odLqwcwmCILKNhAZUHnjgAdxxxx3IzWVvOXJzc3HBBRfA6XTigQcewO9//3tV9Vx22WW47LLLVF+3uroagwcPxlNPPQUAGDFiBPbt24c//elP3XtAJdgecfl+NpgSbKl6bB9QPQW45A/A+EUAZ2I/8dQrEWzjOOJKhTyZpXLUsgnaQRJEthGP5alS2WkrgPdXA7seCz/f5wWm3cb05K/TxwZUOBPbgFboAN7/s/L1Fc8H6Y4gshFJk9ImsZyJzUDZvTqyVfqAMYENaKPFEgAYNRv4+wJg1gPA0b2Ry1F8IDKItIdK2JIfgz6ojBc5Rq5L20UQBJEICe2hcv/996OtrS0svaOjA/fff3/SjYrEnj17cOmll4ak/ehHP8K+ffvgdrsjnsfzPOx2e8iPppDsEQuHAbYzIluqvvcUwBnjr1eJvWuBguLwtdmAsqVypLJk2UgkSVboNx7LU3lZvxXyeuXz5XoCwu2R47E4V2oTQWSIrNBvtmMwR7dKl/QdK5bsXQv0GcxmvZw9OXo5ig+ECtKlXyGCy0/wgAot+yEIQiskNKASaRreZ599hoKCgqQbFYkTJ06gf//+IWn9+/eHKIpoamqKeN6qVauQl5fn/xk0aFDa2pgWJHvEviVsDXUsS9V4641Ul2S7GCsvVlkXPUATiZMV+lVjeRqprBorZLl+5FqOV6ukOyJLyAr9Zjux+nVJ32pjSd8SFfbrFB+I2KRLv5JtsnzJj0Gvh77z6wU5/RAEoRXiGlDJz89HQUEBdDodzjvvPBQUFPh/8vLyMGvWLFx33XXpaisAhA3k+Hw+xfRg7rrrLrS2tvp/jh49mtY2phzJHvH7RrYhXaosVeOxTY6WF6ssWTYSSZAV+o3H8lReVo0Vslw/ci3Hq1XSHZElZIV+s51Y/bqkb7Wx5PtGFfbrFB+I2KRLv5GW/ADB1sk0Q4UgCG0Q15zPp556Cj6fDwsWLMD999+PvLzAA7/JZMKQIUMwadKklDdS4owzzsCJEydC0k6ePAmDwYDCQoXlJp2YzWaYzeaI+VmPZLdY/whgP8E2qqt/NLycZKmqZv8Ueb1hdd3M3H46msPzgi0ZY5WVLBvVtokgZGSFfmNpJfgel5ftaGZWyOMXhu5nICHXExCu5WjXVzyfdEdkB1mh32xH5KP368H6jhZLpL64+avY5Sg+ECpIl36FCDNUAMBk4OBye8k6mSAIzRDXgMq8efMAAMXFxZgyZQoMhq5dgztp0iT885//DEl74403MG7cOBiNcewdojWC7Rbfuo+5/ADKlqrGOHbuj2XjCB2zXQzJk1x+fqyiLLkJEN0ENZan0cq++wRz5tDplV1+XprNykfScrTrh51PuiMITWG2Ms0Dyv36x7XsOFYsCe6L330CKP8rq/PD9dQvE1kFH2EPFSAwa4WW/BAEoRV0PmnNTJx88803eOGFF/DNN9/g6aefRr9+/fD6669j0KBBGDVqlKo62tra8PXXXwNg9stPPPEEZs6ciYKCAgwePBh33XUXjh07hhdffBEAs00ePXo0br75ZixcuBB79uzB4sWLsWnTprhcfux2O/Ly8tDa2gqbTUPTXoUOtpGc4AR0OvaFK9hS1WxNrt5gG0dTL+U8kWcb6KkpG5xHECkio/qN5x4PKyuydPn5ANtMWo2Wla4PPcBxpDtCE2i2/+0KeEdn/yqLBfJ0jxuALna/zbez2GIwUXwgUkKq9HvJE/X4+mQb7r1iBEaeGbpU/fa/fYr/nHbh5UUTMXFo5NnnBEEQ2UJCm9LW19ejtLQUe/fuxebNm/2OPw0NDbjvvvtU17Nv3z6MHTsWY8eOBQCsWLECY8eO9dsuHz9+HEeOHPGXLy4uxrZt27Bz506cf/75+OMf/4jVq1d3b8vkYEy5bIquJY89GEmWqpwp8cGU4Hp7FbF/gx+25Hlmq/qy9NBGdDfiucfDyuYqn2/qpV7LiudbSHcE0R3w96+yWCBPN/VS12/n5gPm3hQfiKwj6pKfzjQn7aFCEIRGSGjNzm9/+1s8+OCDWLFiBazWwMP/zJkz8fTTT6uuZ8aMGYg2Qaa2tjYsbfr06fj444/jai9BEARBEARBEJlH2pRWcclP56a0PA2oEAShERKaobJ//37Mnj07LL1v375oblbYmJQgCIIgCIIgiB6PtIeKKcoMFdpDhSAIrZDQgEqfPn1w/PjxsPRPPvkEAwcOTLpRBEEQBEEQBEF0P3i3tOSHbJMJgtA+CQ2olJeX4ze/+Q1OnDgBnU4Hr9eL3bt344477sCNN96Y6jYSBEEQBEEQBNENEDyRXX6MtIcKQRAaI6EBlZUrV2Lw4MEYOHAg2traMHLkSEybNg2TJ0/GPffck+o2EgRBEARBEAShcUSPFx4v2z9RaclProkDALTzYpe2iyAIIlES2pTWaDRi48aN+OMf/4h9+/ZBp9Nh7NixOPfcc1PdPoIgCIIgCIIgugHS/ikAYDSEL/nJNbGvJnYXDagQBKENEhpQAYDnn38eTz75JL766isAwLBhw3Drrbeiqur/s3fncVGV+x/AP7MywzIgiyD7ouKC4oIpKIFppm2WZVbXcqnMLJe83byt1yyz8lZqqYV11epXWZmVpaWZkIq5kEsqIrIIEqigMCyzz/P7Y5iRgQFmYIYzM3zfrxcvmbN+j+c8zzk885zn+6jdgiOEEEIIIYS4B3XTBhV+yx4qXh6GP01qGjRdFhMhhHRGhxpUXnrpJbz77ruYP38+kpOTAQAHDx7E008/jeLiYrz22mt2DZIQQgghhBDi2ow9VAQ8Hvj8lj1UvBpf+ZErqUGFEOIaOtSgsn79emzYsAEPPPCAadqdd96JwYMHY/78+dSgQgghhBBCCDGj0hoGm7X0ug8AeHoYX/mhBhVCiGvo0KC0Op0OSUlJLaYPHz4cWi2980gIIYQQQggxZ+yhIrTwug9wvYdKjYIaVAghrqFDDSrTp0/H+vXrW0zPyMjAP/7xj04HRQghhBBCCHEvDWpDDxWJqJUGFWMPFQV9QUsIcQ2dGpR2165dGDVqFADgjz/+QGlpKR5++GEsXrzYtNw777zT+SgJIYQQQgghLs2YDlkiElic70ljqBBCXEyHGlROnTqFYcOGAQAKCgoAAEFBQQgKCsKpU6dMy/F4lt+PJIQQQgghhHQvxgYVaasNKsYeKhro9cziwLWEEOJMOtSgsnfvXnvHQQghhBBCCHFjxld+PFppUPFufOVHz4B6tRY+ElGXxUYIIR3RoTFUCCGEEEIIIcQW9erGV36Elv8EEQl4EAkMvVKqG+i1H0KI86MGFUIIIYQQQojDtffKD4/HQw9PMQCgvEbZZXERQkhHUYMKIYQQQgghxOHqVW2/8gMAgd4eAIC/qxVdEhMhhHQGNagQQgghhBBCHK7B+MpPK2mTASDIx9CgUlxV3yUxEUJIZ3Q4bTIhhBBCCCGEWKu+cVDa1tImA0BUgCcA4K+LNV0Sk1OprQD2vg4U7gWEUqD/7cCYpwEPH64jI4S0ghpUCCGEEEIIIQ7X3hgqABAX5A0AOHGxBowx8HjdJHVyVQGw8VagruL6tH15wIktwLRPgbBh3MVGCGkVvfJDCCGEEEIIcbjrY6i0/idIdIAX+Dygsk6Fsu4yjoq6Afh8mqExxTcSGL8MSFsCeAcD8ovA5juBkj+4jpIQYgE1qLgohVoLtVaPqjoV1Fq96Z1UQghxRlRnES7QdUeIczGNoSJsvYeKWMhHbGMvlYMFVV0SF+d+XwlU5QNSf+CW5YbeKNGpwJ3vASGDAHWtocGlqoDrSAkhzdArPy5IpdHhg6xCbMwuglyhhUwqxKyUGMxLj2tz1HRCCOEC1VmEC3TdEeJ8jGOotPXKDwAkhPri/OU6HDhfialJEV0RGndqLwF/rDf8PmoeIO1xfZ7IExi3FPjleaAyD/jifmBOJiD24iBQQogl1EPFxSjUWqzLLMDqPfmQKwyt/HKFFqv35GNdZgF9+0YIcSpUZxEu0HVHiHMyjqHS1is/ADAwVAYAOHrhmsNj4ty+twGtAgiMByJGtpwv9ABuetHQe6XyHLDrpa6PkRDSKmpQcTECPh8bs4ssztuYXQQhn04pIcR5UJ1FuEDXHSHOqbpBAwDw9mi7k3x0gKEHxsVrCsiVGofHxRlVLXDsU8PvQx8CWhuAV9oDGLPY8PvRj4GCvV0THyGkXfRE4WJqlRrTt23NyRVa1LrzTYcQ4nKoziJcoOuOEOfDGEN1gxpA+w0q3hIh/L3EAIC8ilqzeTUKNyq/p7cBmgZAFg70Smx72dAhQPxtht93LgF0bvT/QIgLowYVF+MjEUEmtXwTkkmF8JGIujgiQghpHdVZhAt03RHifGpVWmj1DACsKoNR/p4AgNxyuWnap39cwNBlu/Dqj2ccE2RXO/654d/e41rvndLUsIcAD5lhPJXDGxwbGyHEKtSg4mJ0ej1mpcRYnDcrJQZavb6LIyKEkNZRnUW4QNcdIc7nWr2hd4qHkA+xsP0/QSIaG1TOX64zTVuzJx96Bny8vwh1KhcfC6mqACg5CPD4QOxY69YRewPDHjb8vv8dQ7plQginqEHFxUjFQsxLj8PCcX1M377JpEIsHNcH89Lj4CmmxE2EEOdBdRbhAl13hDifyjpDg4qPxLryFyKTAACKKusBAAq1DlV1KtP84yXVdo6wixl7p4QOBbwCrV+v93jAOxiovwLkbHJIaIQQ6znFE8W6deuwcuVKlJeXY+DAgVi1ahVSU1MtLrtp0ybMmjWrxXSFQgGJROLoUJ2Ch0iAx9Ni8eTY3qhVasWK0wYAACAASURBVOAjEUGt1YHH46GqTgUfiQhavZ4eGAkhDqdQayHg8011kaW6x1KdpdXrKXUtcSiL90qdHgq14X5J90lCulZFjRIAEODlYdXyvXzNG1TyLtWi8Y0hw/SqeozpY0NDhDPR64ATXxh+7z3etnX5QmDQVODg+8CB1cCIRwGh2P4xEkKswvmTxJYtW7Bo0SKsW7cOo0ePxocffohJkybhzJkziIyMtLiOTCZDXl6e2bTu0phiZHwIDPD2gEqjw4Z9RdiYXQS5QguZVIhZKTGYlx5Hf7AQQhxGpdHhg6xCq+qepnUWAIipgyTpAsbrzsdDiP/tL8K6rPN0nySEIxVyQ4NKDy/rxjAKaWxQKatWQKXVmY2lAgAXr7nw6y6FmYC8DBB7WU6V3J64cYYeLnUVwNntQMI9dg+REGIdzp9o33nnHTzyyCN49NFH0b9/f6xatQoRERFYv359q+vweDyEhISY/XRXCrUW6zILsHpPvimjgVyhxeo9+ViXWYAGtYu/X0oIcUpU9xBXoVBrsTazAG/8fJauVUI4VHZNAQDwt7KHiq9UBKlIAMaAkqoGnG1sUOE3jt16sXF7Lsn4uk9MOiDoQO8SgQjoe4vh98Mf2S0sQojtOG1QUavVyMnJwYQJE8ymT5gwAdnZ2a2uV1dXh6ioKISHh+P222/HsWPH2tyPSqWCXC43+3EXAj4fG7OLLM7bmF0EIZ/zNjNCOsWdy68ro7qHWMMZyi9dq4R0jL3Lb8EVw+Cyob7W9Srn8XimXipFlfXILTekT06K8gfgwg0qimrg7I+G32193aepPrcYBrQtyQYunbZPbIQQm3H6FFFZWQmdTofg4GCz6cHBwaioqLC4Tr9+/bBp0yb88MMP+OKLLyCRSDB69Gjk5+e3up8VK1bA19fX9BMREWHX4+BSrVJj+satOblCi1ol5agnrs2dy68ro7qHWMMZyi9dq4R0jL3LrzFbT5if1Op1mg5Mm1thaNAZFtUDAFDmqq/8nNoKaJWAXyQQ0Lvj2/EKvP660Ikv7RMbIcRmTvG1DK9Z3nXGWItpRqNGjcL06dORmJiI1NRUfPXVV+jbty/ee++9Vrf/3HPPoaamxvRTWlpq1/i55CMRmTIYNCeTCuEjse49VUKclTuXX1dGdQ+xhjOUX7pWCekYe5ZfhVqHsmpDj5LQHtY3qBgHpj1QUIVapRYCPg+J4b4ADFmDlBpdh2PijPF1n97jgVb+3rGaMd3yqa0ApYMnhBOcNqgEBgZCIBC06I1y+fLlFr1WWsPn8zFixIg2e6h4eHhAJpOZ/bgLPdNjXpqhddvfS4z4YB/4exnexZyVEgMtVa7Exblz+XVlOr0es1JiLM6juocYOUP51en1eGZCvOne2BRdq4S0zp7l1/i6j7eHEDIbGjGNr/z8fu4KACDcTwpfqQiCxoFUqurVHY6JE1fygLKjhld1jI0hnRGeBIi8DAPclrQ+XAIhxHE4zfIjFosxfPhw7N69G3fffbdp+u7duzF58mSrtsEYw/HjxzFo0CBHhemUjKlK61U6zB4Tg3uHh8PLQ4jKOhUCvMUoqWpAbKAXxBayF9QqNfAQCiBXaCCTiqDS6lr9hs6alKiEkO5HKhZiXnocAJhl+Vl2ZwLG9e8JIZ+PyloVZFJDWndvKx+gW6tzLE1njEEkuF6X2bIfW1A96Lyanxu9Xg+NnhnucUoNZBIR9Ay4Y3Ao7kuKwIHzlXh9x1lcqVPinzfH457hYRDy+aiqU9G5JcSB8i8bxj8Jt6F3CnC9h4pRpL8neDwe/KQiVNWrcaVWZdMrRJz78xPDv2FJgLRH57cnEAPRo4H8XcDJr4DoMZ3fZqM6dR3K6soAABE+EfAUedpt24S4E86fGhYvXoyHHnoISUlJSE5ORkZGBkpKSjB37lwAwMMPP4ywsDCsWLECAPDKK69g1KhR6NOnD+RyOdasWYPjx49j7dq1XB5Gl2qaqjTIW4Kv5ybjkz+KsSm72Cwd5BONf+w0pdTosGFfodmyM1OiMS+9NyTNGl9sSYlKCOl+PEQCPJ4WiyfH9r7+By1jWJd53qo6pjlLdc4/b47HtBERFuuiR1NjMPXDbJworbFpP7agetB5NT83tw0OwdtTh2DDvgKz629GcjRmpkRjWsZBpPftiR/njwEPDAygc0tIFzl3ydBDJbyHbX+UR/p7QcDjQccYAKB3sDcAwM/zeoOKy9AogeP/Z/i970T7bTcm3dCgcuZ74NaVgNC6LEqt2V+2HxtPbcTRS0ehZ9d78EXJonBTxE24LfY2xPvHdzJoQtwH5w0q06ZNQ1VVFZYtW4by8nIkJCRgx44diIqKAgCUlJSA32QE/urqasyZMwcVFRXw9fXF0KFD8fvvv+OGG27g6hC6lEKtxQdZhVi9x/CK09tT47HxQBHe++28aRljOkgGhjmpsaZvbGuVGmzYV4g1e8yXNX5+LDXW1FOl+X6abhcAHk+LpW/xCCGmeiDA2wN1Sg0y2qhjmtZHzbVW54T6SbAu83yLba7ekw89Y3gyvTfmfJpj9X5sQfWg87J0bp6b1N/itWK8PxqvFT1juH9EJL48UmLx3gnQuSXE3s5VGHqoRNjYQ0Us5KNPsDfONq6fEGoYP8VXKgZQ71oNKme+BxTXAK8gIGy4/bYbPBDwDAAaqoDze4B+t3ZoMwqtAkuzl2JH0Q7TNG+RoQGrTlOHC/IL2Hh6Izae3ogRISPw8ICHcWP4jeDznGJITkI44xQlYN68eSguLoZKpUJOTg5uvPFG07zMzExs2rTJ9Pndd9/FhQsXoFKpcPnyZfzyyy9ITk7mIGpuNE3/6O8lxujegdh8sNjispuyiyEWXv+WzUMowKbs1pf1aLIspZkkhNhK3E4d07Q+as5SnWOs41rb5uaDxRjTJ9BsbIz29mMLqgedV/NzExvkhZ4+EquulR9OlCHIx6PVeyedW0Ls71wHX/kBgKlJEejp44FbBoYgtPH1Hj9PQ6O5yzSoMAYc/tDwe59bAL4de8HxBUBU46s+uT90aBMNmgY8tusx7CjaAT6PjwlRE/DWjW9hzU1rsOamNVg9djXmJc7D8ODhEPAEOFJxBPN/m487v7sTX5z9AvWaevsdDyEuhr5+cTFN0z8GeXugqk7dZjpIuVKDQG+Pxs9tp45suqw1aSYDvDvXpZAQ4l5sqWOas1TnWFPHXa1XI8jbA1cbByZsbz+2oHrQeTU/N32CvFHTzvVnvFYAtLssnVtC7KdBrUXpVUOGH1tf+QGAAb1kWH3/ULNpftLGBpU6ZecD7AoFvwFlOYYxT/reYv/tR6UAud8DZ3cAWjUgbDkId2t0eh2W/L4EJ66cgJfQC/OHzUffHn3NlvER+yApJAlJIUm4qriKPSV7kHkxExfkF/D6odex5s81uKv3XXig3wOIlEXa++gIcWr0FYyLaZr+8UrjALRtpYNsOpK6TNp26simy1KaSUKIrWypY5qzVOdYU8f5e4lxpU5lNs2WDBJtoXrQeTU/N/lX6uDbzvVnvFaUWm27y9K5JcR+zl82jJ8ikwghk9qnbPm6Ug8VxoDfVxp+7zvRPoPRNtezPyD1B1Q1QFGWTau+k/MOMi9mQsgXYuGwhS0aU5rzl/pjavxUvJ32Nh7s9yCCPYNRp6nDZ7mf4bZtt2H6jun49MynKK8r78wREeIyqEHFxTRNVXq1Xo0D5ysxIzna4rIzU6Kh1upMn1VaHWamtL6sqsmylBKVEGIrdTt1TNP6qDlLdY6xjmttmzOSo7E/v9LUO8Wa/diC6kHn1fzcFF6px+VapVXXyp2JYbgsV7Z676RzS4h9lV0z9E4JlknaWdJ6flJDDwyXaFA5/S1QchDgi4CBUxyzDx4fiGwcAuHMd1av9kf5H/jkjCHz0JxBc9C7R2+r15UIJRgfNR7LxyzH08OexqDAQeCBhxNXTuCtI29hwtYJuO3b2/DKwVfwc9HPuKq8atMhEeIq6JUfF9M0Vel3xy9iy5FSrLw3ETwe2s2q4SMRYV66oaJsb9nm+5EIhVBqtbhrSDhlQCCEWORtQx3TXGtpmP+uVmJeem/wwLOY5Wf6x4cAwCFZflqLiTLBcK/pudmbdwm9ZFL8b38hnp3YH4HeYny8vwgXqhRmWX5mbz6C+Tf1xuwxMXhzxxm8cPtA8Hktrys6t4TY1981htdymo531VmmMVTqnLxBpeEqsHOJ4fdBUwGvQMftK3o0kPcTcPYn4PZVgKDt3kB16jq8fOBlAMDYiLFICknq0G75PD4GBQ3CoKBBuKa8hpxLOThScQTnq8+jpLYEJbUl+ObcNwCA/v79MTpsNG6NuRV9evTp0P4IcTY8xhrzkHUjcrkcvr6+qKmpgUwm69J9K9RaCPh8U4pRjU4PjVYPTw8htHq9WVaB5ss2nV+n1EAsFECu0EAmFUGl1cFDKIBcqYFMYvjcWpflWqXG6mWb70et1dklewYhHcVl+e2stsq0O8XUvI7R6HRgDFbVJQ1qLYQW4rFUbwn5gIB/fZqj6qfWYiK2s3f5NV4X9SotxEI+RAK+6XpQqHWQigVQ6/TQNt5n1Y33XC8PITQ6HXg8Hp1bQqzU0fK7/Kcz2LCvCLcO6oWHRkXZJZZLciUWbTkOiYiP3GUTwePx7LJdu9KqgE+nABf2A74RwB1r2m3k6BS9Dvj6YUBZAzy0DYi7qc3FXz/0Or44+wUCpYFYlrIMEqH9ehABhoFuz107h7NXzyL3ai5Ka0vN5icGJeLJIU8iObT7JBch7omeGrqQSqPDB1mFZt+GGb85m/phNtL79jR9M2ZpWeM3ZzwAG/YVmc2bmRKN2SkxeG9PPo6VXkN6fM9Wv6k1Np4YB20UCy2/+aXS6Frsh769I6Rj2irTXJUnR8Sk0ujw0b4iU6+BsB4SPDvRkM7Wml4rTdMwA4AYfCg1OmzYV4jvj5eZestNHhKGeem9IRby263LOstSTIR7xusi69wV/G/GCHyQVYDNB5tcY8nRmDUmBr+cqsDdQ8Pw7Z8X8frO3BbXuljIp3NLiAMZe6gE2LGHim/jWCxKjR51Kq3zjXvUcBXY+qihMUUkBW78l2MbUwBDtp/IZODcz4YUzW00qBTVFOGrvK8AADMHzrR7YwoAeIo8MaTnEAzpOQQAUKOqwZmqMzh66ShOXjmJE1dOYM7uOUgNS8UrKa8gyDPI7jEQ0hWoQaWLKNRafJBViNV78k3T5Aot3vvtPADgyfTemPNpDgDgsdQYbNhX1GJZ4+dJCSEt5q3Zcx6MoTGN8gWcvCgHAMxJje3QN7atxWv8/HhaLH2LR4iVnLE8OSKm5ts8CTn2LxmLdZnnsWbPebP9GD+3V0fVKTXI2Fdotj4Aq9cn7qnpdbHh4eHYlF1sup8CjdfYb+fBAAwK88XazPNICPU1ZfbhuvwR0p1UOOCVH4lIAKlIAIVGh8u1KudqUCk9Anw9E5BfNGT1SX8e8I/tmn1HjTE0qOT+CNz2Tqvpmd/NeRc6psOQoCEYEDCgS0Lz9fBFcmgykkOTUaOqwU+FP2Fv6V7sK9uHe7bfgzdS30BKaEqXxEKIPdFXMV1EwOdjY3aRxXmbDxZjTJ9A+HuJ8d3xixALBa0uuzG7CJEBnhZvSk23AxjGMBALO/Ytc1vxbswugpBPlw4h1nLG8uSImJpvMzbICz19JNiUXWxxeWvqKLFQ0Kn1iXsyXhf+XuLGLxKKLS5nvC9+f7zM7P5oRPczQhyvwgE9VIDrvVScZmBaxoA/PgA2TjQ0pshCgVv/C4QObX9dewlJADx8gIZK4EK2xUWOVBzB3tK94PP4uLfvvV0XWxO+Hr54sP+DeCXlFYR7h+Oa8hqe/PVJ7CjcwUk8hHQGPUV0kVqlxvTNWHNyhRZX69UI8vaARCiEXGHdsu3Nkyu0kCs1Dom3toPbJaQ7csby5IiYmm+zT5A3atqpz9qro9qrDztaxxHXZrwugrw9UFWnbveeKREKLd476X5GiGPp9AwVcvv3UAGaDEzrDA0qej2w81ng5yWAXmvoKXLbqq7rmWLEFwIRowy/n/m+ZZhMj7ePvg0ASAtPQ6h3aFdG10KodyheGvUSRvUaBS3T4t/7/o1t+ds4jYkQW1GDShfxkYggk1ruUiyTCuHvJcaVOhWUWi1kUuuWbW+eTCqErINdINuL16m6VhLi5JyxPDkipubbzL9SB9926rP26qj26sOO1nHEtRmviyt1KgR4i9u9Zyq1Wov3TrqfEeJYlXUq6PQMfB7Qw9ONG1R+fRk4nAGABwyfDaQtAcSe3MQSNdrwb+4PhoaeJnYU7cDpqtOQCCSYHDeZg+BaEglEeHTQoxgbMRYMDEsPLkVWaRbXYRFiNWpQ6SJ6pse8tJa53f29xPjnzX2Rc+Eartar8cCIKKh1OsxKibG4nVkpMSipasDVejX8vcSID/YxtfjPvTEOZ8trTTeYp8f3gU6v61C8Or2+zRi0zSpoQkjrnLE8OSKmptu8f0Q4np0Qj2qFGjNToi0uPzMlGmpt23WUWqszrd+8zrNmfeKejNfF1Xo1DhVexYzkaACG18xuGRCMoZF+iA/2wdwb43C6rAYr7x2MM39XA4DZNUT3M0Ic6+9qBQDAz1MMPt++mXj8pIZyzHnq5ONfANnvGX4f8zSQMAXgMutQr0RA5AXUXQJKD5kmK7VKrPlzDQDgttjbIPNwnkyJfB4f0/tPx+jQ0dAzPZ75/RmcrjrNdViEWIVGYXMwY0rHOpUOs8fEID0+CEu+PYk6pQ4v3NYPKXGBqFNpIZOIcOLlCZCKDekfn0iPA4AW2TeeSI8DH8DPC1MRGeCJqjo1Ar09UK8y9GyRKzT4eeGNUGp0kIgM29IzLUSC62khTSmWm6dcbpbOdF4bMSjVOgj5ekoxSYgVpGJhq+WpK7P8NE+R3GY9w+OhslYFmVQEnV4HrR4t6g0fiajFNufcGIM5qTEQNVl2Xnpv8MBrdz+W0isDwFPpvXFrQi9TnRfgLcaFqgbEBnq1+L+zJQ20M6axJi01P086vR58vmEw91sTeiEqwBMpsQGYkRxtuA82S5us0emhbkyVnPWvdOh0DGIRHxU1SgTLPCDk81FVp6JrgBAHcNT4KQDg6ww9VOTlhld9ACDxgXZTFXcJgQiIHAkU/GZ47SfKkJb4/3L/D+X15egh6YGbo27mOMiWeDweZgycgWpVNU5XncbivYvx1R1fwdfDl+vQCGkTPTU4kDGlY9NUobNSYvDVnGQwAOszC7Boy3GzNKIzU2Lwz69O4N1piZiYEILH02JxrV6DIB8PFFypw1u/nMWzt/TDjlPlLVKQzkyJwbSMP3ClTomZKdF4Mr03eDy0TCXZuOw7u8/h5TsGtIixaTrTx9Ni8eTY3qYH2YIrdZiWcRAnSmucIu0rIa7Cw0J50ur1XVZ2mqdIvm1wCN6ZOsRUzxh7vV2oaoBOr8e0jw7hRGkNZo+JxrO39MOGfeZpjxff3Bf3j4i0mHb50dQYTP0w21RP/PPmeDyaGoO5aXGoqleZ9sMD8PRXx/DTyQokRvjis0dG4iMLKeHnpffGoaKruC/jYIvGqLaOsa06yhnTWJOWmp8n43VyqPAqxvQJxM5TFTh/pRZvTx2CT/4oNrtGZyRHY9boGHx/vAx3JoZi6ofZSOsbhHnpvbHtWCluHxyGjN+L6BogxIHKHZDhx8jPGQal3fksoJIDAX2AwfdzF0dzUaMNDSq5PwC3vI6r6mp89NdHAIB7+twDscD+58MehHwhnkh8Aq8cfAV/1/+Nlw68hNVjV4PHZY8fQtpBDSoOUqvUYEOzVJ/GNI2TEkKw81RFq6mPl00eiI/3F+G9387D30uMtQ8OxffHy/DWL3ltpiBlDFgyMR5zPs3Bmj3ncWtCL+w8VdEylWTjsv+6Jd7qdKZeHgL8b38R3vj5bIvjASjtJCHWMJaRgMaBMcVd9NalpRTJz03qj7WN5d/fS4wgbw9cqVPhar0a82/qbUrlPn9sH4v1RJif1OL01XvyoWfMtL5cocV/fjiNyjoVhkX64Y2deab9LBjXG/+e2B8/nazAU2N7I+P3Qov1FYB2U97akgbaGdNYk5YsnSfjdTJ7dIzp+mvtvmi8lgaF+WJTdrHpmgSAR8fEWrze6BogxL7Kawyv/DiihwrnY6iUHjY0WPD4QMqCVlMUcyJ0KCCSAvIyoCwHH/y9G3WaOkT6RGJUr1FcR9cmT5En5g2Zh+V/LMfe0r345MwnmDFwBtdhEdIqGkPFQTxaSfXp7yVGZIBnmymUg2US/HCizDQtMcIPH/xe0G4K0qZpk63Zj5eHEN8fL7M4v3k6Uj6Pj3VZ5y0uS2knCXFu7aUzvlqvRt6lWlytVwO4Xpek9Q2El4ewRZ1jTFVrTV3UdNrI2ABTYwpgqGeCZRIMjfRrM/XtpuyW2wPM6x5b0kA7Yxpr0lLz82S87o6VXDNdl9beF384cT1t8qbsYkhEApy/XGtxHboGCLEfYw+VAAvZKTvLl+sxVPYsM/zbezzgb3lMMs4IxED4DQCAgpOf4uu8rwEA0+Kngc9z/votShaFB/o9AABYlbMKJ6+c5DgiQlrn/CXKRbWW6tOaFI9ypQYSobDF8takIDWmhbR1P63NN3LGtK+EEOt0JJ3x1Xo1RsUEWKzLrE1V2zRFbWvT5EoNhoT72bw943Rj3WNLHUX1mWtofp6M112fnj6m69Laa7lp2mTjdTc4zK/VdegaIMQ+HPrKT2MPlarGTEJdqvgAULzPkKbYmV71aSpqNBiAFeV7oWVaDO05FP0D+nMdldXSI9JxQ8gN0DItntv3HBo0DVyHRIhF1KDiIK2l+rQmxaNMIoJSq22xvDUpSI1pIW3dT2vzjZwx7SshxDodSWfs7yXGH0VVFusya1PVNv3WsLVpMokIxy9W27w943Rj3WNLHUX1mWtofp6M113+5VrTdWnttdw0bbLxujtZVt3qOnQNEGIfjhyUViYRgQdAz2Dq+dhlDq41/Nt7PODds2v3ba2wYfjVR4ZDIkDEE2Ba/DSuI7IJj8fDwwMehr/EHyW1JXjzyJtch0SIRdSg4iCqJqk+m7par0ZJVUOr6UpnJEfjklyJOxPDTMsfOF+JGcnRKLxSj8u1ylZTkM5Ijsb+/EpcrVdbtZ96lRaTh4RZnN88Hakzpn0lhFinefm1ti7JOleJepW2xXLGesmauqitaTNTDPXdsZJqUz1nycyUlusC5nWPLXUU1Weuofl5Ml53QyN7mK5La6/lOxPDTNfQzJRoKDU69O7pY3EdugYIsQ+dnuGS3HE9VAR8Hny4GJi2qgDI22H4fcDkrtuvjWrB8GZgIADgAZ4feno6acNPGzxFnnhs0GPggYdv87/Frxd+5TokQlqgEdccxEdiSBUKoEUGnZhALzyRHgcGZjFTzz+/OoG370sEj2dY942defh6bjJ4POC9PefwyuRBFrc7MyUGUz84aLafR8bEQM+YxSw/K3/Jw8t3DLC4LWOWHyNnSftKCLGdpfK7Ymcu3p46BABaZCJ7NDUG0z8+BAB4b28+nr2lX4vlyqoVraZDbrp+a9OM9cx/fjiF+GAffHqwGGv/MRx8Hs9ilp8tR0ohkwpbrXtsqaOoPnMNls7T+3vPm7L8GO+xrV3LTbP8zEyJxuzNR7BgXG9Tlp85N8a2uN7oGiDEfirrVNDqGXg8wM/TMVll/KQiyBWarh1H5dAHABgQlgT4RnTdfm30ZsHXuAQtIjUazKs4hTxlDXQS10tBHO8fj0kxk7CjaAeWHlyKQYGDEOwVzHVYhJjwGGNd/NIh9+RyOXx9fVFTUwOZTGbXbSvUWgj4fFNaVJVWBw+hAHKlBrLGz8auxLVKTYt51nyuV2khFvIhErS9H1uXNX5Wa3Wm7D7NNai1EDY5Pq1eT5kQSJdyZPl1d83LL/R6qPSsRfnn8QCR4Po0nV4HrR4W6zJLdQJjzGx9lVYHfrNtanQ6MAaIhQLIFRrIpM2mNauPrK17mi+na+xpILCwLtVnXa8j5bf5eTL2nhQLBVBqdJCIBFCoteDzefAQCkzLKdRaSMVCqHV6aLV6eHoIDdciGERCIXR6PRhA1wAhVrK1/B4vrcZdaw80Zqwc5pCYVuzIxcmyGrx172Dcl9QFjRuKauCdAYCmHrj5NSB0iOP32QGZVScx//QH4AH4QK5DSlUZSkc+ioqhTjreSzu0ei2WH1qOC/ILGNlrJDJuznCJwXVJ90BPDXak0ujwQVahxW+7Ao1pUoXXC7+xYaX5vEBvD6g0Ony0rwh78y5haEQPLLq5LzYeKDL79u2JtDjMHh0DsZBvcd2mccxL643Zo6NbLNt8v81jbI6rtK+EkM5rXn6VGoYN+wqQmXcZvWRSlMsVSI/viXnpvfHGzjMou6ZEuVyBsfHBmJceB7GQ36KeaKtOsFSnGKcxxrAus8BifWnrfto6RpWm9f1QfeYaPMVCqDQ6/PRXOT7eX4gLVQrIpEIsHt8X/7ghEiIhH2AC/N/hC9h2rAy9ZFLw+cDEhBCk9+mJnafLcffQcLP7n8H13+kaIMT+/q52XMpko54yCVBWg8Ir9Q7bh5k/NxsaU3pEA70Su2afNrqguIzn8zYDAG4JHI4QPw+g6kuEnNyKS4PuBhPaP+OSown5QswZPAevHHwFh8oP4dMzn1IqZeI0qEHFThRqLT7IKsTqPfmmaXKF1vT58bRYq7/1ar6t+Tf1wf/2F+G9366nLZYrtHjz5zwoNDrMSY019ShpLY43fj4LhUZnUxyEEPdVp9QgY18h1uwx1CsnITf8e9Hw76zRsUh9a6/ZNHvVH/asL51hP8SxWjuPS7efwbUGDR5NjcEGC9fyzlOXMP+m3hgU5ou1mefN7pWEEMcru2ZoUAnycdwf8JH+UgDAuUuW06DblU4DHMow/N7/ToDHc/w+bVSrVWD+qfWo1SoQFwTRLAAAIABJREFU59kLd4ck4yp4CDv7CzwU1xB0dicuJ9zFdZgd0surF+6Pvx+fnPkEq/9cjVG9RiHeP57rsAihr2LsRcDnY2N2kcV5G7OLIORb/1/ddFv+XmKM7h2IzQeLLS67KbsYYqHA4rqdjYMQ4r7EQgE2ZRdbnLcpuxjBMglig7xM0+xZf3RVPUX1oXto6zx+d/wiPNq4ljcfLMaYPoH4/niZ2b2SEOJ4F68Z0twGejuuQSWihycAIK+iCxpUznwPyC8CEj8gNt3x+7NRrVaBJ069jyLFJfQQeeOpqNsh4gvB+AKU9xkHAOj15+fgq7uoN48DpIWnYUjQEGj0Giz5fQmUWiXXIRFCDSr2UqvUQK6wnIJYrtCiVqnp0LaCvD1QVaduc9vyJtu2ZxyEEPclV7RdV8iVGvQJ8jabZq/6o6vqKaoP3UNb51EiFLZ7LV+tVxuWo/NNSJe62AU9VML9DQ0qZdUKXHNk6mTGgAOrDL/H3woIHPcaU0dcVdfi8b/ewwl5ETwFHlgQfSd8Rde/FKmMugFKryCIG64iNOczDiPtHB6Ph5kJM+Er9kVBTQHezXmX65AIoQYVe/GRiCCTWu46LpMKTeOl2LqtK3UqBHiL29y2rMm27RkHIcR9yaRt1xUyiQj5V+rMptmr/uiqeorqQ/fQ1nlUarXtXsv+XmLDcnS+CelSpgYVB/ZQ8fYQIqKxUWVv3mWH7Qfn9wAVfwFCCdDvdsftpwNO1RZj2rE38FdtMbwFEvwr9h5ESc1TJDO+ECWDDCmeg09+C88r+ZY25RJkYhlmJ8wGAHx+9nNklmZyGxDp9qhBxU50ej1mpcRYnDcrJQbaxkwTtm7rar0aB85XYkZytMVlZ6ZEmzIe2DsOQoj7Umt1mJkSbXHezJRoXJIrzQb5s2f90VX1FNWH7qGt83jXkHCo2riWZyRHY39+JSYPCTO7VxJCHIsxdv2VHwf2UAGApKgeAIAfT5Y7bif73zH82/cWQOIcGQYVOjVWFX2Hh47/FxWqawgW98CSuKktGlOMaoIH4GqvweAzHeJ+fQ18dUMXR2w/g4IGYXzkeADAc/ueQ2F1IccRke6MGlTsRCoWYl56HBaO62P6pkwmFWLhuD5m2SQ6sq03duZh1ugYLBjX22zbC8b1xrz03maD7NkzDkKI+/KWiDAvvXer9cobP+eaptm7/uiqeorqQ/fQ3nn0aeVann9Tb8waHYO/axQt7pWEEMeqkCtRr9ZBwOMh2MENKqPjAsHjAb+dvYxMR/RSKTkEXDgA8IXAgLvtv30bMcbwa+Ux3H30VXxcugtapkeSbx+81Od+hEkC2ly3eMhUqKR+kNSUIW73a+DpLL8u6Qrui78PfXv0RZ2mDk/99hSuKa9xHRLppniMMcZ1EOvWrcPKlStRXl6OgQMHYtWqVUhNTW11+a1bt+Kll15CQUEB4uLisHz5ctx9t/UVnFwuh6+vL2pqaiCT2beVuUGthZDPR61SAx+JCFq9vsMP7c23pdLq4CEUQK7UQCYRQa3VtfqAaM84CHEmjiy/3VGdUgNxs3pFyOeB3wX1R1fVU1QfOo/OlN/2zqPxWjbOV6i1kIqFbd4rCSHWs6X8Zp27ghn/O4wwPyn+O9Xx6YU3HijCrjOX4CMRYusTKegb7APA0NN779nL6Bvsg0HhvrZvmDFg461ASTbQZwKQssDOkdsSCsO+q6fw/oUfkVtXCgDwF/ngH6HpGOobZ/V2vK5eQHz2Ogh0GlTFpaPopiVgAtesI+VqOV49+CqqlFVICEjAR7d8BK8mY8cQ0hU476GyZcsWLFq0CC+88AKOHTuG1NRUTJo0CSUlJRaXP3jwIKZNm4aHHnoIJ06cwEMPPYT77rsPhw4d6uLILfMUCyEW8hHg7QGxkN+ph/bm2/KRiCAW8hHY+LmtB0R7xkEIcV/eFuoVSRfVH11VT1F96B7aO4/Ga9k439dT3O69khDiGPmNaYzDeki7ZH//GBmF+GAf1Cq1mLXxCCpqlPijsAo3v5OFf359Ane8vx9fHrb8t0WbcrcbGlMEHkDiA/YP3AqMMWRfPYPpx1fiydPrkVtXCg++CLf1HIHX+j5kU2MKANT7R6Eg6WHoeXwEFGSiz44XIFTUOCh6x5KJZVg0fBG8Rd44VXUK83+bjwaN677KRFwT5z1URo4ciWHDhmH9+vWmaf3798ddd92FFStWtFh+2rRpkMvl2Llzp2naxIkT0aNHD3zxxRdW7ZO+4SbEdVH5JcR1UfklxHXZUn7/9fUJfJ1zEVOGhWHq8Iguia9WqcHL359GhVwJT7EASo0OegZ4igVoUOsgEvCw6+k0xARa2YNBcQ1YP8aQKnnw/cDQ6Y49gGYYYzhUnYd1F37CMXkBAEDME+KmwERMDBoOmdCzU9v3vZSLuCObIdCpofYKROFN/0Zt2BB7hN7limqKsPLISih1SiQEJGDt+LXwl/hzHRbpJjjtoaJWq5GTk4MJEyaYTZ8wYQKys7MtrnPw4MEWy99yyy2tLg8AKpUKcrnc7IcQ4hqo/BLiuqj8EuK6OlN+j14wjGcRG+jtqPBa8JGI8Pyt/RAik6BBbWhMGRXrj3X/GIbEcF9odAzLtp+2bmOMAT/909CY4tMLSLjXscE3oWN67L5yDA8efwuP/bUGx+QFEPEEmBA4FG/2m4X7eqV2ujEFAGqC+yM3dQEU3j0hrq9Ev+3PIHrvSggVrjcWSYxvDJ5JesbUU+XBnx7EySsnuQ6LdBOcNqhUVlZCp9MhODjYbHpwcDAqKiosrlNRUWHT8gCwYsUK+Pr6mn4iIrqmpZwQ0nlUfglxXVR+CXFdHS2/FTVKFFXWg8cD+vfycXCU5oJ8JHjznsF4ZkI8lt4xEAtu6gMPoQAzkqMh4POwN+8K9p61YuDazBXAqa0Ajw+kPgOIJA6PXa5twBd/Z+Guo8uwOHcDTtVegIgnwLiARLzRbxbuD02Dr53HB1H4huJM2tO4HDUKABCU9wsGfTETIce+BF9d387aziXWLxbPj3weQdIglNWVYcbOGVh3fB0UWgXXoRE3x/kYKgDA4/HMPjPGWkzrzPLPPfccampqTD+lpaWdC5gQ0mWo/BLiuqj8EuK6Olp+d58xfMkZE+DFyZhVYiEfw6N6ID7Ex/T3QS8/KSYlhAAAXv3xDNRaveWVdRrg5+eBrDcNn2+YAwTFOyxWhU6NzKqTeP7sJtz0x3N4/fwWFCsuw0vggdt73oCV/R/BP8LGoofIcT199EIPXBhyH86kLkC9bziE6npEHPoIif83HaFHNkNc54DMSQ4S4hWC/yT/B0nBSdAyLdafWI87v7sTn+d+jnqNazUQEdfB6ch8gYGBEAgELXqXXL58uUUvFKOQkBCblgcADw8PeHg4NmUbIcQxqPwS4rqo/BLiujpSfpUaHTYeKAYApPYJckBUHXf30DD8nl+Jwsp6vLL9NF6dnAA+v/ELWaUcOPsTsP9doDLPMG3YDKDf7Xbbv57pcVldg/z6v3Gq9gJOyAtxtCYfKr3GtEyYJABp/oMwpscASARiu+3bGvX+0TiTtggBF/9Er3O/Qlp3GWE5nyI05zPUhg1BdeQNkIcNhbJHlFNnBfIUeeKJxCdw5NIRfJ33NSrqK7Di8Aqs+nMVxoSNQWpYKgYEDECsbyxETnwcxHU4xaC0w4cPx7p160zTBgwYgMmTJ7c6KG1tbS127NhhmjZp0iT4+fnRoLSEdANUfglxXVR+CXFd7ZXf8hoFnvjsTxwvrYa3hxBr7h8KqVjAQaStO1p8FW/vPgcAuNsvH/fqfsYQj3J41ZcArLHXiocPkLIQiBxl1TY/Lt2Fak0ddEwPPdNDBwYd06Feq0KtrgFybQOuqetQrroGDdO2WD9A5INEWSySe/RDrDSkzV73XYbp0ePvk+hZdACyqgKzWXq+ACpZKDTSHtBK/aAXSsAaYxZoFChOWwydR9eNndMWtU6NfWX78FvJbyivLzebJ+QLEeoVikBpIAKkAfAUekIilEAqlGJizEQMDBjIUdTE1XCeO3Lx4sV46KGHkJSUhOTkZGRkZKCkpARz584FADz88MMICwszNa4sXLgQN954I958801MnjwZ33//PX799Vfs37/f6n0a25BocDzS3fn4+DjHjdsGVH4Juc7VyjCVX0Kuc7fyWyNXoriyDt4eAjw3IRZBUgagZQMCl9LjZGC6GKzfdwEaeSVGiw8CjR1E9L6R0MRNgKb/PYDY+rFKvio/gL+VV6xalg8+giUB6O0VgTivCAyUxSFS2ssprwNNXCTK4m7H5bpL8Ck7Aq+KE5BW5UOgaYC0uhTSasuvgFWOXAiNqPOD5tqDJ88Td4TfgdvDbkeBvAA5V3KQV52HC7UXUK+tR0ltCUpqW6bTjpJGIULU9phBrlZ+ieNw3kMFANatW4e33noL5eXlSEhIwLvvvosbb7wRAJCeno7o6Ghs2rTJtPw333yDF198EYWFhYiLi8Py5csxZcoUq/d38eJFGhiPEMAlvyWm8kvIda5Whqn8EnIdlV9CXJerlV/iOE7RoNLV9Ho9/v77b5taFuVyOSIiIlBaWkqFpw30/2QdZ/l/csXW9Y6UX3tylnNnT+52TO52PEDrx+RqZZjr8tsWd7pu3OlYAPc6nqbHEhYW5nTloC3WlF93OlfWomN2/2O2dLzOeB8j3OD8lR8u8Pl8hIeHd2hdmUzWLSqOzqL/J+vQ/5PtOlN+7ckdz527HZO7HQ/g+sfkLOW3La7+f9yUOx0L4F7HI5PJXO6PMVvKrzudK2vRMbu/7na8xDpOkTaZEEIIIYQQQgghxJVQgwohhBBCCCGEEEKIjQRLly5dynUQrkIgECA9PR1CYbd8U8pq9P9kHfp/cl3ueO7c7Zjc7XgA9zwmZ+NO/8fudCyAex2POx2LJe5+fJbQMbu/7na8xHrdclBaQgghhBBCCCGEkM6gV34IIYQQQgghhBBCbEQNKoQQQgghhBBCCCE2ogYVQgghhBBCCCGEEBtRgwohhBBCCCGEEEKIjahBxUYrVqwAj8fDokWLuA7F6ZSVlWH69OkICAiAp6cnhgwZgpycHK7DciparRYvvvgiYmJiIJVKERsbi2XLlkGv13MdGmnHihUrMGLECPj4+KBnz5646667kJeXx3VYHbZ+/XoMHjwYMpkMMpkMycnJ2LlzJ9dh2Y071NVLly4Fj8cz+wkJCeE6LLfjbmW7KVcvB+7yXNGd7v3r1q1DTEwMJBIJhg8fjn379nEdksP8/vvvuOOOOxAaGgoej4fvvvuO65Acyp3ryta4+7MSsQ9qULHBkSNHkJGRgcGDB3MditO5du0aRo8eDZFIhJ07d+LMmTN4++234efnx3VoTuXNN9/EBx98gPfffx+5ubl46623sHLlSrz33ntch0bakZWVhSeffBJ//PEHdu/eDa1WiwkTJqC+vp7r0DokPDwcb7zxBo4ePYqjR4/ipptuwuTJk3H69GmuQ+s0d6qrBw4ciPLyctPPX3/9xXVIbsfdyraRq5cDd3qu6C73/i1btmDRokV44YUXcOzYMaSmpmLSpEkoKSnhOjSHqK+vR2JiIt5//32uQ+kS7lpXtsWdn5WIHTFildraWtanTx+2e/dulpaWxhYuXMh1SE5lyZIlbMyYMVyH4fRuu+02Nnv2bLNpU6ZMYdOnT+coItJRly9fZgBYVlYW16HYTY8ePdhHH33EdRid4k519X/+8x+WmJjIdRjdjjuUbXcoB+70XNFd7v033HADmzt3rtm0fv36sX//+98cRdR1ALBt27ZxHUaXcoe6siPc4VmJ2Bf1ULHSk08+idtuuw3jx4/nOhSn9MMPPyApKQlTp05Fz549MXToUGzYsIHrsJzOmDFjsGfPHpw7dw4AcOLECezfvx+33norx5ERW9XU1AAA/P39OY6k83Q6Hb788kvU19cjOTmZ63A6xd3q6vz8fISGhiImJgb3338/CgsLuQ7J7blD2XaHcuBOzxXd4d6vVquRk5ODCRMmmE2fMGECsrOzOYqKOJI71JW2cKdnJWJfQq4DcAVffvklcnJycPToUa5DcVqFhYVYv349Fi9ejOeffx6HDx/GggUL4OHhgYcffpjr8JzGkiVLUFNTg379+kEgEECn02H58uV44IEHuA6N2IAxhsWLF2PMmDFISEjgOpwO++uvv5CcnAylUglvb29s27YNAwYM4DqsDnO3unrkyJH45JNP0LdvX1y6dAmvvfYaUlJScPr0aQQEBHAdnltyh7LtLuXAnZ4rusO9v7KyEjqdDsHBwWbTg4ODUVFRwVFUxFHcoa60lrs9KxH7owaVdpSWlmLhwoXYtWsXJBIJ1+E4Lb1ej6SkJLz++usAgKFDh+L06dNYv369yz34ONKWLVvw2Wef4fPPP8fAgQNx/PhxLFq0CKGhoZgxYwbX4RErPfXUUzh58iT279/PdSidEh8fj+PHj6O6uhpbt27FjBkzkJWV5ZIPCu5YV0+aNMn0+6BBg5CcnIy4uDhs3rwZixcv5jAy9+XqZdudyoE7PVd0p3s/j8cz+8wYazGNuD5Xrytt4U7PSsRBuH3jyPlt27aNAWACgcD0A4DxeDwmEAiYVqvlOkSnEBkZyR555BGzaevWrWOhoaEcReScwsPD2fvvv2827dVXX2Xx8fEcRURs9dRTT7Hw8HBWWFjIdSh2N27cODZnzhyuw+iQ7lJXjx8/vsUYBcQ+3KFsu1M5cKfniu5w71epVEwgELBvv/3WbPqCBQvYjTfeyFFUXQfdaAwVd6grO8OVn5WIY1APlXaMGzeuRVaFWbNmoV+/fliyZAkEAgFHkTmX0aNHt0iddu7cOURFRXEUkXNqaGgAn28+dJFAIHDL1InuhjGG+fPnY9u2bcjMzERMTAzXIdkdYwwqlYrrMDqkO9TVKpUKubm5SE1N5ToUt+JOZdudyoE7PVd0h3u/WCzG8OHDsXv3btx9992m6bt378bkyZM5jIzYizvVlZ3hys9KxDGoQaUdPj4+Ld4N9PLyQkBAgNu/M2iLp59+GikpKXj99ddx33334fDhw8jIyEBGRgbXoTmVO+64A8uXL0dkZCQGDhyIY8eO4Z133sHs2bO5Do2048knn8Tnn3+O77//Hj4+PqZ3wn19fSGVSjmOznbPP/88Jk2ahIiICNTW1uLLL79EZmYmfv75Z65D6xB3rKufeeYZ3HHHHYiMjMTly5fx2muvQS6Xu90rAlxzp7LtTuXAnZ4rusu9f/HixXjooYeQlJSE5ORkZGRkoKSkBHPnzuU6NIeoq6vD+fPnTZ+Liopw/Phx+Pv7IzIyksPIHMOd6kpruduzEnEQTvvHuChXTUHoaNu3b2cJCQnMw8OD9evXj2VkZHAdktORy+Vs4cKFLDIykkkkEhYbG8teeOEFplKpuA6NtAOAxZ+NGzdyHVqHzJ49m0VFRTGxWMyCgoLYuHHj2K5du7gOy65cva6eNm0a69WrFxOJRCw0NJRNmTKFnT59muuw3I67le3mXLkcuMtzRXe6969du9Z0bxk2bJhbp9Tdu3evxbpjxowZXIfmEO5eV1rSHZ6VSOfxGGOsC9tvCCGEEEIIIYQQQlwev/1FCCGEEEIIIYQQQkhT1KBCCCGEEEIIIYQQYiNqUCGEEEIIIYQQQgixETWoEEIIIYQQQgghhNiIGlQIIYQQQgghhBBCbEQNKoQQQgghhBBCCCE2ogYVQgghhBBCCCGEEBtRgwqxq5kzZ+Kuu+6yatn09HQsWrTIwRFZJzMzEzweD9XV1VyHQohTsKUs22LTpk3w8/Nrc5mlS5diyJAhbS5TXFwMHo+H48eP2zM8QlySLfcwa8pgV4qOjsaqVau4DoMQp+DI51Eej4fvvvuu1fnW3led6fmdEGdADSqk26EbASHcmTZtGs6dO2fTOo5q3CHE2ThbY4c9ufOxEdKcM17v5eXlmDRpktXL05eNhFhHyHUAhBBCug+pVAqpVMp1GIQQQki3EhISwnUIhLgl6qHiZr755hsMGjQIUqkUAQEBGD9+POrr6wEAGzduRP/+/SGRSNCvXz+sW7fOtJ6xm9+XX36JlJQUSCQSDBw4EJmZmaZldDodHnnkEcTExEAqlSI+Ph6rV6+2W+xqtRrPPvsswsLC4OXlhZEjR5rt39ja/8svv6B///7w9vbGxIkTUV5eblpGq9ViwYIF8PPzQ0BAAJYsWYIZM2aYvt2eOXMmsrKysHr1avB4PPB4PBQXF5vWz8nJQVJSEjw9PZGSkoK8vDy7HR8htnCVsrx9+3b4+flBr9cDAI4fPw4ej4d//etfpmUef/xxPPDAAwAsf2v3xhtvIDg4GD4+PnjkkUegVCpN85YuXYrNmzfj+++/N5XZpsdSWFiIsWPHwtPTE4mJiTh48GCHjoMQe0hPT8dTTz2Fp556ynQfevHFF8EYA9D2fS4zMxOzZs1CTU2N6VpfunQpAOCzzz5DUlISfHx8EBISggcffBCXL1+2W9zbt2/H8OHDIZFIEBsbi1deeQVardY0n8fj4aOPPsLdd98NT09P9OnTBz/88IPZNn744Qf06dMHUqkUY8eOxebNm03fbrd1bADQ0NCA2bNnw8fHB5GRkcjIyLDbsRHSEc5elhljCAoKwtatW03ThgwZgp49e5o+Hzx4ECKRCHV1dQBavvJz+PBhDB06FBKJBElJSTh27JhpXnFxMcaOHQsA6NGjB3g8HmbOnGmar9fr8eyzz8Lf3x8hISFm5ZmQbocRt/H3338zoVDI3nnnHVZUVMROnjzJ1q5dy2pra1lGRgbr1asX27p1KyssLGRbt25l/v7+bNOmTYwxxoqKihgAFh4ezr755ht25swZ9uijjzIfHx9WWVnJGGNMrVazl19+mR0+fJgVFhayzz77jHl6erItW7aYYpgxYwabPHmyVfGmpaWxhQsXmj4/+OCDLCUlhf3+++/s/PnzbOXKlczDw4OdO3eOMcbYxo0bmUgkYuPHj2dHjhxhOTk5rH///uzBBx80beO1115j/v7+7Ntvv2W5ubls7ty5TCaTmWKqrq5mycnJ7LHHHmPl5eWsvLycabVatnfvXgaAjRw5kmVmZrLTp0+z1NRUlpKS0rmTQkgHuFJZrq6uZnw+nx09epQxxtiqVatYYGAgGzFihGmZvn37svXr1zPGDOXY19fXNG/Lli1MLBazDRs2sLNnz7IXXniB+fj4sMTERMYYY7W1tey+++5jEydONJVZlUplOs5+/fqxH3/8keXl5bF7772XRUVFMY1G08kzQEjHpKWlMW9vb7Zw4UJ29uxZU9nKyMhgjLV9n1OpVGzVqlVMJpOZrvXa2lrGGGMff/wx27FjBysoKGAHDx5ko0aNYpMmTTLt13gPu3btWrsxNi+DP//8M5PJZGzTpk2soKCA7dq1i0VHR7OlS5ealjHWKZ9//jnLz89nCxYsYN7e3qyqqooxZqh3RCIRe+aZZ9jZs2fZF198wcLCwkwxtXVsUVFRzN/fn61du5bl5+ezFStWMD6fz3Jzczt/QgjpIFcoy1OmTGFPPfUUY4yxq1evMpFIxPz8/Njp06cZY4y9/vrrbOTIkablAbBt27Yxxhirq6tjQUFBbNq0aezUqVNs+/btLDY2lgFgx44dY1qtlm3dupUBYHl5eay8vJxVV1eb/m9kMhlbunQpO3fuHNu8eTPj8Xhs165ddvifJ8T1UIOKG8nJyWEAWHFxcYt5ERER7PPPPzeb9uqrr7Lk5GTG2PU/wt544w3TfI1Gw8LDw9mbb77Z6j7nzZvH7rnnHtPnjjaonD9/nvF4PFZWVma2zLhx49hzzz3HGDM8BAJg58+fN81fu3YtCw4ONn0ODg5mK1euNH3WarUsMjLSLKbmDTmMXb+B/frrr6ZpP/30EwPAFAqFVcdDiL24WlkeNmwY++9//8sYY+yuu+5iy5cvZ2KxmMnlclZeXs4AmP44av7HXHJyMps7d67Z9kaOHGlqUGktFuNxfvTRR6Zpp0+fNtsXIV0tLS2N9e/fn+n1etO0JUuWsP79+1t9n2taPlpz+PBhBsD0R1pnGlRSU1PZ66+/brbMp59+ynr16mX6DIC9+OKLps91dXWMx+OxnTt3mo4xISHBbBsvvPCCWUytHVtUVBSbPn266bNer2c9e/Y0NcISwgVXKMtr1qwxlbvvvvuOJSUlsSlTprC1a9cyxhibMGECW7JkiWn5pg0qH374IfP392f19fWm+evXrzc1qLQVS1paGhszZozZtBEjRpjti5DuhF75cSOJiYkYN24cBg0ahKlTp2LDhg24du0arly5gtLSUjzyyCPw9vY2/bz22msoKCgw20ZycrLpd6FQiKSkJOTm5pqmffDBB0hKSkJQUBC8vb2xYcMGlJSUdDr2P//8E4wx9O3b1yzGrKwssxg9PT0RFxdn+tyrVy9TV8mamhpcunQJN9xwg2m+QCDA8OHDrY5j8ODBZtsGYNdu1YRYw9XKcnp6OjIzM8EYw759+zB58mQkJCRg//792Lt3L4KDg9GvXz+L6+bm5prF2jz29lCZJc5m1KhR4PF4ps/JycnIz8/H0aNHrbrPWXLs2DFMnjwZUVFR8PHxQXp6OgDY5f6bk5ODZcuWmcX02GOPoby8HA0NDablmpY1Ly8v+Pj4mMpaXl4eRowYYbbdpvfi9jTdNo/HQ0hICJVjwjlnL8vp6ek4ffo0KisrkZWVhfT0dKSnpyMrKwtarRbZ2dlIS0uzuG5ubi4SExPh6elpdnzWalpmAfPncUK6GxqU1o0IBALs3r0b2dnZ2LVrF9577z288MIL2L59OwBgw4YNGDlyZIt12mO8mXz11Vd4+umn8fbbbyM5ORk+Pj5YuXIlDh061OnY9Xo9BAIBcnJyWsTk7e1t+l0kErWIjTW+z9o8XqPm89vSdPvG7RjHhiCkq7hvajYuAAAG30lEQVRaWU5PT8fHH3+MEydOgM/nY8CAAUhLS0NWVhauXbvW6gOdPVCZJa7Emvtcc/X19ZgwYQImTJiAzz77DEFBQSgpKcEtt9wCtVrd6Zj0ej1eeeUVTJkypcU8iURi+t3S/ddY1hhjdrv3Nt82Ic7IGcpyQkICAgICkJWVhaysLCxbtgwRERFYvnw5jhw5AoVCgTFjxlhc15byaQmVWUKuowYVN8Pj8TB69GiMHj0aL7/8MqKionDgwAGEhYXh/9u7l5Co/zWO42/tj228kCUuohzBtBnI1LCL4YytxBYFLZIUZtHVMhnIwEAzQ/LSRQy7uAgdutIFwxKh2yYRNK0g05wJvNImJ1wUSdh0zuLAnDOl5lgeHfm8YDYz3993noF5+M48v+/z+/X19ZGdnT3l8W1tbZjNZuA/F3h9+fIlhw8fBqClpYWUlBQOHTrkGf+7Svx0JSYm4na7+fjxI6mpqTOaIywsjMjISF68eOGZw+128/r1axISEjzjgoKCcLvdfyVukdniT7lsNpv5/Pkz1dXVWCwWAgICsFgslJeXMzo6is1mm/RYo9FIW1sbVqvVK/b/pZwVf/Lz97etrY1Vq1ZNa52b6Lve29uLy+WioqKCFStWANDZ2fnX4k1KSsLhcBATEzPjOVavXk1zc7PXcz/HqDwWfzPfczkgIACz2UxjYyNv374lNTWVkJAQxsfHqa2tJSkpiZCQkAmPNZlMXLt2jbGxMc+d9yZaewHlrchvqOVnAWlvb6esrIzOzk6GhoZoaGhgZGQEo9FISUkJ5eXlnD9/HqfTSVdXF/X19VRVVXnNcfHiRe7fv09vby+5ubmMjo6ye/duAGJiYujs7OTRo0c4nU6OHz9OR0fHX4k9NjaW7OxsrFYrDQ0N9Pf309HRQWVl5S8/0qaSl5dHeXk5jY2NOBwObDYbo6OjXmfODAYD7e3tDAwM4HK5VFGXecffcjksLIyEhASuX7/u2b5sNpt59eoVTqfT89xEbDYbdXV11NXV4XQ6OXHiBN3d3V5jDAYDb968weFw4HK5GB8fn3GsIrNteHiYI0eO4HA4uHXrFjU1NdhstmmtcwaDgS9fvvDs2TNcLhdfv35l5cqVBAUFUVNTQ19fHw8ePKC0tPSvxVtcXMzVq1cpKSmhu7ubd+/ecfv2bYqKiqY9x4EDB+jt7aWgoACn08mdO3ew2+3Af3eOTfTZROYzf8jltLQ0bt68SXx8PKGhoZ4iy40bN6Zce7OysggMDGTPnj309PTQ3NzM2bNnvcZERUUREBBAU1MTIyMjnrsFiYg3FVQWkNDQUJ4/f87WrVuJjY2lqKiIc+fOkZGRwd69e7ly5Qp2u501a9ZgsViw2+1ER0d7zVFRUUFlZSVr166lpaWFxsZGli1bBkBOTg47duwgMzOTDRs28OnTJ68z3H+qvr4eq9VKfn4+cXFxbNu2jfb2dk8VfzoKCgrYtWsXVquVTZs2ERwcTHp6ute25aNHj7Jo0SJMJpNnu6XIfOKPubxlyxbcbrfnB9ySJUs8OWY0Gic9LjMzk+LiYgoKCli3bh2Dg4McPHjQa8y+ffuIi4vzXPOltbX1j2IVmU1Wq5WxsTHWr19Pbm4ueXl57N+/H/j9OpeSkkJOTg6ZmZlERERw+vRpIiIisNvt3L17F5PJREVFxS9/fP5Eeno6TU1NPHnyhOTkZDZu3EhVVRVRUVHTniM6Opp79+7R0NBAfHw8ly9fprCwEIDFixdP+tlE5jN/yOWf114Ai8WC2+2est02ODiYhw8f0tPTQ2JiIoWFhVRWVnqNWb58OSdPnuTYsWNERkZ6drmKiLeAf/1pE50sCAMDA0RHR//SHuPvfvz4gdFoZOfOnX/1jJ7IfLVQc1nEH6SlpZGQkEB1dfVchzLnTp06RW1tLcPDw3MdiojPlMsiMl26hoosKIODgzx+/BiLxcK3b9+4cOEC/f39ZGVlzXVoIiIiC9alS5dITk5m6dKltLa2cubMGZ3RFhGRBU8tPzIrhoaGvG4l9/NjttpsAgMDsdvtJCcns3nzZrq6unj69OmULQciMrm5ymURmZmMjIxJ87WsrGzW3vf9+/ds374dk8lEaWkp+fn5lJSUzNr7iSx0c5XLIuIbtfzIrPj+/TsDAwOTvm4wGPjnH22QEpnvlMsi/uXDhw+MjY1N+Fp4eDjh4eH/54hEZCaUyyL+QQUVEREREREREREfqeVHRERERERERMRHKqiIiIiIiIiIiPhIBRURERERERERER+poCIiIiIiIiIi4iMVVEREREREREREfKSCioiIiIiIiIiIj1RQERERERERERHxkQoqIiIiIiIiIiI++jeKW4W+oVQUjQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Figure(PyObject <Figure size 1117.75x1000 with 20 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue=\"species\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CPSC 340 meta-discussion\n",
    "\n",
    "- This is the only CPSC 340 lecture on data cleaning and EDA.\n",
    "- That is not representative of the time typically devoted to these tasks.\n",
    "  - In fact, data cleaning is often the most time intensive step.\n",
    "- This is a weakness of the course.\n",
    "  - But not as bad if you're aware of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- Typical data mining / ML steps:\n",
    "  - Involves data collection, preprocessing, analysis, and evaluation.\n",
    "- Object-feature representation and categorical/numerical features.\n",
    "  - Transforming non-vector objects to vector representations.\n",
    "- Feature transformations:\n",
    "  - To address coupon collecting or simplify relationships between variables.\n",
    "- Exploring data: \n",
    "  - Summary statistics and data visualization.\n",
    "- Bonus slides: see Mark's slides on GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.7.0",
   "language": "julia",
   "name": "julia-0.7"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}