Practice5 - Outliers and Transformations.ipynb

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    "## Handling Outliers and Transforming Attributes\n",
    "\n",
    "This notebook contains examples and code from various sources:\n",
    "\n",
    "<a href=\"https://www.kaggle.com/getting-started/52652\">Kaggle Data Cleaning Challenge </a>\n",
    "<br><a href=\"https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/\"> Outlier Detection PYOD Module Tutorial</a>\n",
    "\n",
    "The goal of this notebook is to illustrate how noise and outliers can be detected and handled, and how to perform different transformations on the data.\n",
    "\n",
    "Detailed explanations for important code snippets are provided by Mervat Abuelkheir as part of the CSEN1095 Data Engineering Course.\n",
    "\n",
    "Pay attention to the <span style=\"color:red\"> <b> paragraphs in bold red</b></span>; they ask you to do something and provide input!\n"
   ]
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    "## Part 1 - Scale, Standardize, and Normalize\n",
    "\n",
    "Scaling, standardization, and normalization all involve the transformation of the values of numeric variables so that the transformed data points have specific helpful properties. \n",
    "\n",
    "<b>Scaling</b> involves changing the range of your data, so that it fits within a specific scale, like 0-100 or 0-1. Some ML algorithms, such as support vector machines (SVM), k-nearest neighbors (KNN), or k-means, are based on measures of how far apart data points are (i.e. distances between data points), so a change of \"1\" in any numeric feature is given the same importance. Therefore, variables/attributes with larger value ranges will outweigh variables with smaller ranges and will distort the distance computations for these methods.\n",
    "\n",
    "For example, you might be looking at the prices of some products in both EGP and US Dollars. One US Dollar is worth about 17 EGP, but if you don't scale your prices, methods like SVM or KNN will consider a difference in price of 1 EGP as important as a difference of 1 US Dollar! With currency, you can convert between currencies. But what about if you're looking at something like height and weight? Or age and salary? It's not entirely clear how many pounds should equal one inch (or much money equals one year of age). By scaling your variables, you can help compare different variables on equal footing.\n",
    "\n",
    "<b>Standardization</b>, or z-score normalization, involves changing the range of your data based on the mean and standard deviation. The attribute will have a mean of zero and variance of 1. \n",
    "\n",
    "<b>Normalization</b> involves changing the shape of the distribution of your data. You change your observations so that they can be described as a normal distribution. Normalize your data if you're going to use an ML technique that assumes your data is normally distributed, such as linear regression, linear discriminant analysis (LDA), or Gaussian naive Bayes. \n"
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    "### Important Notes (taken from Kaggle)\n",
    "\n",
    "Scaling your data is a very crucial procedure in most ML algorithms.\n",
    "\n",
    "    Scaling is used to make all features contribute the same amount in prediction. Refer to the weight and height, salary and age examples above.\n",
    "    Scaling makes the algorithms converge faster since it transform the variable space in to a much smaller range.\n",
    "\n",
    "When it is time to work on training and test datasets, you must apply scaling separately on train and test sets in order to correctly represent the distribution of the variables in each set. "
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    "### The Kickstarter Campaign Dataset\n",
    "\n",
    "Let's experiment with these preprocessing tasks on a dataset. The dataset we will use is the <a href=\"https://data.world/rdowns26/kickstarter-campaigns\"><b>Kickstarter Campaign dataset</b></a>. Kickstarter is an online crowdfunding platform aimed at helping people get their ideas funded while building a community of fans to support their ideas. While Kickstarter publishes many advice and best-practices articles on their blog, over half of campaigns still fail.\n",
    "\n",
    "This dataset contains data on 20,632 Kickstarter campaigns on the site as of February 1st 2017. Download the dataset from <a href=\"https://www.kaggle.com/kemical/kickstarter-projects#ks-projects-201801.csv\">this link on Kaggle</a>. Important attributes are described below:\n",
    "\n",
    "<b>project</b>: a finite work with a clear goal that you’d like to bring to life (aka campaign)\n",
    "<br><b>funding goal</b>: amount of money that a creator needs to complete their project\n",
    "<br><b>name</b>: name of project on Kickstarter\n",
    "<br><b>blurb</b>: the short description displayed under the name of your project and on the browse page\n",
    "<br><b>pledged</b>: amount of money that a project has raised\n",
    "<br><b>backers</b>: number of people that have supported the project at the point of the API pull\n",
    "<br><b>state</b>: successful, failed, cancelled, live or suspended\n",
    "<br><b>deadline</b>: deadline given for successful funding\n",
    "<br><b>state changed</b>: state changed when campaign went to success or failure\n",
    "<br><b>created at</b>: time the project was created at\n",
    "<br><b>launched at</b>: time the project was launched at\n",
    "<br><b>usd_pledged</b>: conversion in US dollars of the pledged column (conversion done by kickstarter).\n",
    "<br><b>usd_pledge_real</b>: conversion in US dollars of the pledged column (conversion from Fixer.io API).\n",
    "<br><b>usd_goal_real</b>: conversion in US dollars of the goal column (conversion from Fixer.io API).\n",
    "<br>Other attributes in this dataset: <b>country</b>, <b>currency</b>, <b>category</b>\n"
   ]
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   "source": [
    "Let's begin by some routine imports and data loading:"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>main_category</th>\n",
       "      <th>currency</th>\n",
       "      <th>deadline</th>\n",
       "      <th>goal</th>\n",
       "      <th>launched</th>\n",
       "      <th>pledged</th>\n",
       "      <th>state</th>\n",
       "      <th>backers</th>\n",
       "      <th>country</th>\n",
       "      <th>usd pledged</th>\n",
       "      <th>usd_pledged_real</th>\n",
       "      <th>usd_goal_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000002330</td>\n",
       "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
       "      <td>Poetry</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>GBP</td>\n",
       "      <td>2015-10-09</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2015-08-11 12:12:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1533.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000003930</td>\n",
       "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2017-09-02 04:43:57</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>15</td>\n",
       "      <td>US</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000004038</td>\n",
       "      <td>Where is Hank?</td>\n",
       "      <td>Narrative Film</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2013-02-26</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2013-01-12 00:20:50</td>\n",
       "      <td>220.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>220.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>45000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000007540</td>\n",
       "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>USD</td>\n",
       "      <td>2012-04-16</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2012-03-17 03:24:11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>failed</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011046</td>\n",
       "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>Film &amp; Video</td>\n",
       "      <td>USD</td>\n",
       "      <td>2015-08-29</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>2015-07-04 08:35:03</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>canceled</td>\n",
       "      <td>14</td>\n",
       "      <td>US</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>19500.00</td>\n",
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       "           ID                                               name  \\\n",
       "0  1000002330                    The Songs of Adelaide & Abullah   \n",
       "1  1000003930      Greeting From Earth: ZGAC Arts Capsule For ET   \n",
       "2  1000004038                                     Where is Hank?   \n",
       "3  1000007540  ToshiCapital Rekordz Needs Help to Complete Album   \n",
       "4  1000011046  Community Film Project: The Art of Neighborhoo...   \n",
       "\n",
       "         category main_category currency    deadline     goal  \\\n",
       "0          Poetry    Publishing      GBP  2015-10-09   1000.0   \n",
       "1  Narrative Film  Film & Video      USD  2017-11-01  30000.0   \n",
       "2  Narrative Film  Film & Video      USD  2013-02-26  45000.0   \n",
       "3           Music         Music      USD  2012-04-16   5000.0   \n",
       "4    Film & Video  Film & Video      USD  2015-08-29  19500.0   \n",
       "\n",
       "              launched  pledged     state  backers country  usd pledged  \\\n",
       "0  2015-08-11 12:12:28      0.0    failed        0      GB          0.0   \n",
       "1  2017-09-02 04:43:57   2421.0    failed       15      US        100.0   \n",
       "2  2013-01-12 00:20:50    220.0    failed        3      US        220.0   \n",
       "3  2012-03-17 03:24:11      1.0    failed        1      US          1.0   \n",
       "4  2015-07-04 08:35:03   1283.0  canceled       14      US       1283.0   \n",
       "\n",
       "   usd_pledged_real  usd_goal_real  \n",
       "0               0.0        1533.95  \n",
       "1            2421.0       30000.00  \n",
       "2             220.0       45000.00  \n",
       "3               1.0        5000.00  \n",
       "4            1283.0       19500.00  "
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   "source": [
    "# Modules we'll use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# For min_max scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# For z-score scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# For Box-Cox Normalization\n",
    "from scipy import stats\n",
    "\n",
    "# Plotting modules\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "\n",
    "# Read in all our data\n",
    "kickstarters_2017 = pd.read_csv(\"./data/ks-projects-201801.csv\")\n",
    "\n",
    "# Set seed for reproducibility\n",
    "np.random.seed(0)\n",
    "\n",
    "# Inspect the first couple of rows\n",
    "kickstarters_2017.head()"
   ]
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    "#### Scaling one attribute\n",
    "\n",
    "Now we proceed to scale one attribute, the <b>goal</b> attribute. We will use the `MinMaxScaler()` method to perform min-max scaling. We will illustrate the difference in scale by distribution plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled data')"
      ]
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     "execution_count": 3,
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the usd_goal_real column\n",
    "usd_goal_original = kickstarters_2017.usd_goal_real \n",
    "\n",
    "# Scale the goals from 0 to 1 using MinMaxScaler()\n",
    "# fit computes the minimum and maximum to be used for later scaling, and transform performs the scaling\n",
    "usd_goal_scaled = MinMaxScaler().fit_transform(kickstarters_2017[[\"usd_goal_real\"]]) \n",
    "# MinMaxScaler does not accept Series and needs a DataFrame object. \n",
    "# A DataFrame is a 2-d array, with potential multiple columns\n",
    "# In this case, we have to use kickstarters_2017[[\"goal\"]] and NOT kickstarters_2017[\"goal\"] or kickstarters_2017.goal\n",
    "# Because kickstarters_2017[\"goal\"] results in a Series - a 1-d array that is practically one row with all the values\n",
    "# while kickstarters_2017[[\"goal\"]] results in a DataFrame - a 2-d array with multiple row and one column, which is what MinMaxScaler() needs\n",
    "\n",
    "# Plot the original & scaled data together to compare\n",
    "fig, ax=plt.subplots(1,2)\n",
    "sns.distplot(usd_goal_original, ax=ax[0])\n",
    "ax[0].set_title(\"Original Data\")\n",
    "sns.distplot(usd_goal_scaled, ax=ax[1])\n",
    "ax[1].set_title(\"Scaled data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a different scaling method; the `StandardScaler()`. This is the z-score scaling, which will scale the attribute values with zero mean and unit standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scaled data - z-score')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale the usd_goals_real using mean and SD\n",
    "usd_goal_z_scaled = StandardScaler().fit_transform(kickstarters_2017[[\"usd_goal_real\"]]) \n",
    "\n",
    "# Plot the original & scaled data with both methods to compare\n",
    "fig, ax=plt.subplots(1,3, constrained_layout=True) # layout spaces out the figures for more visibility\n",
    "sns.distplot(usd_goal_original, ax=ax[0])\n",
    "ax[0].set_title(\"Original Data\")\n",
    "sns.distplot(usd_goal_scaled, ax=ax[1])\n",
    "ax[1].set_title(\"Scaled data - min-max\")\n",
    "sns.distplot(usd_goal_z_scaled, ax=ax[2])\n",
    "ax[2].set_title(\"Scaled data - z-score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><span style=\"color:red\">Try the same code on the goal attribute. What's your observation?</span></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling two attributes at once\n",
    "\n",
    "Let's scale the <b>usd_goal_real</b> and the <b>goal</b> attributes in one take. We can do that because the MinMaxScaler can handle multiple DataFrame columns at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'z-score Scaled Attributes')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "usd_original = kickstarters_2017[['usd_pledged_real','usd_goal_real']] # Select only two attributes from the dataset \n",
    "\n",
    "# Scale\n",
    "usd_minmax_scaled = MinMaxScaler().fit_transform(usd_original) \n",
    "usd_z_scaled = StandardScaler().fit_transform(usd_original) \n",
    "\n",
    "# Since we are working with two attributes, we can use a scatter plot to visualize the effect of scaling\n",
    "fig, ax=plt.subplots(3,1, constrained_layout=True)\n",
    "ax[0].scatter(usd_original['usd_pledged_real'],usd_original['usd_goal_real'])\n",
    "ax[0].set(xlabel='usd_pledged_real', ylabel='usd_goal_real')\n",
    "ax[0].set_title(\"Unscaled Attributes\")\n",
    "ax[1].scatter(usd_minmax_scaled[0],usd_minmax_scaled[1])\n",
    "ax[1].set(xlabel='usd_pledged_real', ylabel='usd_goal_real')\n",
    "ax[1].set_title(\"min-max Scaled Attributes\")\n",
    "ax[2].scatter(usd_z_scaled[0],usd_z_scaled[1])\n",
    "ax[2].set(xlabel='usd_pledged_real', ylabel='usd_goal_real')\n",
    "ax[2].set_title(\"z-score Scaled Attributes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalization\n",
    "\n",
    "Now let's try normalizing the <b>pledged</b> attribute using the Box-Cox normalization method.\n",
    "\n",
    "The Box-Cox normalization transforms a non-normal attribute into an attribute with normal distribution. The Box-Cox transformation is a formula that has an exponent, lambda `λ`, which varies from -5 to 5. All values of `λ` are considered by the transformation function and the optimal value for your data is selected. The “optimal value” is the one which results in the best approximation of a normal distribution curve. \n",
    "\n",
    "This transformation only works when the attributes holds only positive values. If you have an attribute with negative values and you want to fit it to the normal distribution, you may want to consider shifting the range to start from zero by adding a constant to all the attribute values.\n",
    "\n",
    "Let's now investigate how the Box-Cox transformation works. We are still using the Kickstarter Campaign dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Normalized data')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the index of all positive pledges (Box-Cox only takes postive values)\n",
    "index_of_positive_pledges = kickstarters_2017.usd_pledged_real > 0\n",
    "\n",
    "# get only positive pledges (using their indexes)\n",
    "positive_pledges = kickstarters_2017.usd_pledged_real.loc[index_of_positive_pledges]\n",
    "\n",
    "# normalize the pledges (w/ Box-Cox)\n",
    "normalized_pledges = stats.boxcox(positive_pledges)[0]\n",
    "\n",
    "# plot both together to compare\n",
    "fig, ax=plt.subplots(1,2)\n",
    "sns.distplot(positive_pledges, ax=ax[0])\n",
    "ax[0].set_title(\"Original Data\")\n",
    "sns.distplot(normalized_pledges, ax=ax[1])\n",
    "ax[1].set_title(\"Normalized data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><span style=\"color:red\">Try the same code on the pleadged attribute. What's your observation?</span></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Detecting and Handling Outliers\n",
    "\n",
    "\n",
    "\n",
    "Python has a basic module, <a href=\"https://github.com/vc1492a/PyNomaly\">PyNomaly</a>, that provides basic outlier detection functionality with limited algorithmic implementations. A more sophisticated module we will use for outlier detection is PyOD. <a href=\"https://github.com/yzhao062/pyod\">PyOD</a> is a scalable Python toolkit for detecting outliers in multivariate data. It provides access to around 20 outlier detection algorithms under a single <a ref-\"https://pyod.readthedocs.io/en/latest/\">well-documented API</a>.\n",
    "\n",
    "You will need to install PyOD before you can proceed with this part of the notebook. To install PyOD, run these commands on the command prompt:\n",
    "\n",
    "    pip install pyod\n",
    "    pip install --upgrade pyod  # to make sure that the latest version is installed!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The BigMart Sales Dataset\n",
    "\n",
    "The data scientists at BigMart collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. BigMart wanted to have a predictive model that can predict the amount of sales for each store, and thus attempt to understand the properties of products and stores which play a key role in increasing sales. The data may have missing values as some stores might not report all the data due to technical glitches. \n",
    "\n",
    "The data is divided into a training set (8523 records) and a test set (5681 records). The training dataset has both variables plus the amount of sales. The variables/attributes in the dataset are described as follows:\n",
    "\n",
    "<b>Item_Identifier</b>: Unique product ID\n",
    "<br><b>Item_Weight</b>: Weight of product\n",
    "<br><b>Item_Fat_Content</b>: Whether the product is low fat or not\n",
    "<br><b>Item_Visibility</b>: The % of total display area of all products in a store allocated to the particular product\n",
    "<br><b>Item_Type</b>: The category to which the product belongs\n",
    "<br><b>Item_MRP</b>: Maximum Retail Price (list price) of the product\n",
    "<br><b>Outlet_Identifier</b>: Unique store ID\n",
    "<br><b>Outlet_Establishment_Year</b>: The year in which store was established\n",
    "<br><b>Outlet_Size</b>: The size of the store in terms of ground area covered\n",
    "<br><b>Outlet_Location_Type</b>: The type of city in which the store is located\n",
    "<br><b>Outlet_Type</b>: Whether the outlet is just a grocery store or some sort of supermarket\n",
    "<br><b>Item_Outlet_Sales</b>: Sales of the product in the particulat store. This is the outcome variable to be predicted.\n",
    "\n",
    "<b><span style=\"color:blue\">Important Note: </b>Item_Fat_Content has catagories: Low_Fat, reg, regular, LF, low_fat.</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the <a href=\"https://www.kaggle.com/brijbhushannanda1979/bigmart-sales-data#Train.csv\">BigMart Sales dataset</a> and save it to your local machine.\n",
    "\n",
    "Import the modules we will need and then import the data. Each of the outlier detection modules assigns an <b>outlier score</b>, which means a module scores each data point rather than uses threshold value to determine whether the point is an outlier or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import font_manager\n",
    "\n",
    "# Import models\n",
    "from pyod.models.hbos import HBOS # histogram-based outlier detection module\n",
    "from pyod.models.cblof import CBLOF # cluster-based local outlier factor detection module\n",
    "from pyod.models.knn import KNN # k nearest neighbors module\n",
    "from pyod.models.lof import LOF # local outlier factor module\n",
    "\n",
    "# More outlier detection methods can be used, such as \n",
    "#from pyod.models.feature_bagging import FeatureBagging \n",
    "#from pyod.models.abod import ABOD\n",
    "#from pyod.models.iforest import IForest\n",
    "\n",
    "# Reading the big mart sales training data\n",
    "bigmart = pd.read_csv(\"data/bigmart_train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s plot Item MRP vs Item Outlet Sales to understand the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc853014320>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WsvhpzKhkwjSEecvqCHusdABuWryOUDT9NXfl0pP7sfwP0Ben4n4+cDpOb5aCEZHhseOMV0q1i8gi4BLgHOCXSqlHROQ3OAbj/tjXnUqpMSJyCXAHcLGIjI/tNwEYBvxTRMYppfJzhmo0mqwkGhOXlvZImoQIQNhSWeMVPaVepRADl6nQM2Ip7nuhnkunjcx6Lq/9XdeZzxTaXA21LEbFPZ/fdFxpLq4L78V3Pk7KQEt8rcdLuiilXot92yoi1wO7VOeqK31AhYhEcAzWBzjG6tLY6w/jCF/eD5wf+x5gMXCvOKJl5wOPKKVCwGYRqccRynylE/PSaDQxEl0siX5/y7bxGZLWIyTTDa0npRpD/rUz7grnhsXr41L1LvncvCv7B7no2CoWrNkaH7t02kiuO2Mcz2/8iLlL62gLJz8HV/hNoradFGO5ZcZ45qd0kIzYNv0CJvc9X5923rBldaukS85GXyJyq4gcFfs+KCLPA+8B20XkzGJOqpRqxJHg34pjUFqA13GMlasD3QC477zhwLbYvtHY9pWJ4x77pF7H1SJSLSLVH3/8cTHT1mgOKFIbeUUsRdR2/P5RGwxRaTeQ9kg07YZWroZgmeZc6iZhM6cM58lrTyJgJucq5ds9ctHrDUlji6qdn0876hAsj2fzqG1z23kTWPu9M/nzVdNYPed0Lpt2BHdeMImgz6BvwCToc5qMtYUtAmb6bXz2aWO7dVWYTwfJi4G3Y9+7rq+Dgc8BtxdzUhEZgrPaOBLHhdUP+KLHpu5v3Sv7TGUZTx9U6gGl1FSl1NSDDz648ElrNN1EuTsqeh2/uTXE8xs/wmdkTvz0m6aTKZWAeHQ0LEUHxXxYUtPI9DtW8NUH1zL9jhUsrWks2bHHHDqAn104uaTdI93VUNCXYrAsxfwnnNXJ5BGD4+dQ7v9q309erragz8jpois3+bjCwgkury/guJ4s4F8iUkiMJpEzgc1KqY8BROT/gM8Cg0XEF1uVVAFNse0bgBFAQ+ycg4BPEsZdEvfRaHo95XYheR1fAXMeW48pkuamSSQUtQj6DKLhhApzn5nmHsqWiVWquEspCzAzzakc3SNnThnO4L5+/uvPb7A34XdtINQ17eaUcQcnXZ8TS7Hi17d6zuk9ItsulXwMQ0hEJgLbcepXbkh4rW+R590KnCAifYF24AygGngemIWTGXYFsCS2/dLYz6/EXl+hlFIishT4i4j8AmflMxZ4tcg5aTQ9inJXq3sd/8bF60mVIwHoFzQJRx2/vylCyFKYhkFbOHfqbqZMrFK2IS5VAWYuQ56azJDr2PlkoU0YNgg7xSW2N2LxzQXV3DXLOX+268ukyVbX1AIIE4YN7JFZYdfhBMwPxsnY2gwgIucAbxZzUqXUWhFZjJNSHI0d5wHgCeAREflRbOz3sV1+D/wpFpz/BCcTDKVUXSyjbEPsONfojDDN/kK5q9W9jm8aAkpwn4rBqTqfd94ETjvqEHa2hTnnnlU4xsdO2saVw08Um3RvdKk3P9inAFwKo1mKAsx8DHliV8ywpZh73ngum3ZE1uPmWum4xufGWMqwSyhqx8+f6/rcY9Y17eaV93bwu5c24f55fAb84qIpXZoskY9W2FrgKI/xJ4En3Z9F5Aql1MP5nlgpNZf04spNOFldqdt2ABdmOM6PgR/ne16NprdQ7mp1r+NbtiI1TGkpxWlHHUJl/yANO9tJDbsEfQbzZk6Ib7NwzRbmLavDbxpYSiXJ5VcNqYj3bPcyasUazVIUYOYy5F7Fkt//ey0ouOyE7MYlVxZaJpeYe/7JIwbHr880HNHLW2aMTzJ431lUg1fWctSGGxev61JdtmJjJF5ch5MirNFoSkBl/yAXTa1iwSv7UlUvmlpVspuD1834lhnj+aQ1zL3Pv5vWxbG5NcS2T9rSFH5DUacbpGtUvv94LUC85uL7f6+Na22JCH18ZlzaJJG2kEVtYwuTRwwu6no6W4CZy5A37Gz3TGaYt6yOsyce1um/i5dLLDUes6cjGjPawm1L62jYuZcLjq7ipsXrPI2KiyldW9dSSsOidcM0mhLS3BqKp6a6LKpu4LozxpXsBpF4M65tbGH+8g2xLKbkyvK4XpZH1lfQdIL8za0h5qXUWrjsSwJQRCynosD0uGPMf2JDp27SXiuDfBMEsq166rfv4c2tOwlF0j3tfrNzN+3E+WVbdTW3hpj/xAbCliIc0yy7/4VNPLByE6bH3yURS3WtLlspDcuB14pSoykjXaUIXNk/yM62MPOWbyCcoFXlVpZ7uYASEUPiLi6/IYTzPG/Qb6JsRXu0+OvL1dGxtrElo76WF16rnlsffyupwDEVS6mib9peyQKr55zueU2ZVkyWDVaW26/PgLtmTe7SAL5esWg0PZSuUgReUtPIjY+uiz8FuyTWmmTTu3J9/U/Xfpg1PTkVr3hOvtdXv30PD63ezGNvNCapCs+cMjwpwN7qSqbkmSCQaqiqNzd7GhWfAUFfcsJCoXiKeT66jif/+2RPd2DVkIq0v5GL3xRsW8XFl/2mcNVJR3Lip4b22KwwAETkSDcjLMPY6pLOTKPpJnqKplWhAeli5u3e3LxuWIk3eS+9LIAKv8GIWD3KDzO4wYI+R1K+PRKNx1gyxXNuOXd8zlTe1BWEm51202OOwnK21VWmFVFza4iFa7dyX8JcLppaxV/Weq9Uvn3mOA4d2IcpIwanKTo7qb67AcWEYYMA774qnmKeluKce1bxs1npK6vK/kHmnjfeSRhIwRDhqf85maaWDkAxbFAFbWGr297DhaxYHgOOSRlbjKM4jFJqdqkmpdF0Fz1N0yrfgHSx827Y2Y5KjaIDAV9yZbmTDrs+TYG3PWLzzQXVzD5trJOqnEJfv8lvvnYsgyr8SUHw1HjOeZMP54jKfjndVvXb92R0S/kNg5ptu7KurrxWREtqGrkp4dpCUScGlJg0kcqvV9SnrZTcYyVmZ5mGIKT3VQEnRbsjGk07djiWZjz+8IFpxuGyaUeAgluX1CatTu6aNYkxhw5gSL8AC9du5ZsLqpOSL7r6PZzTsMR0wiYAg0Tk3xNeGkhCJ0mNprfTk9rnJpIrVbUz8+4XMAl5rFb+cuXxTD2yMv6za+D+snYr96x4N2mFE4oq7lnxDk5XjWQsZXu6Yi5+4JWk+f7l1X2Sf9muoWbbrozX0hG1mDJisOfqKrXOxqW5NcQNj65L6lmfC9MQQlE7aaWUKF+faHutmNFO7KviNvVyzpv5POf8+iWCvnTjcNkJR3D2xMOoa9rN7vYIAyv8TBg2MKOB7I73cD4rlk8DM4DBwHkJ43uAb5ZjUhpNd9BT2ufmi+v68qoJyXfebWGLPn4jyXXUx2/g95lp21b2D3LtGWOZPGIw//Wn19mbcFcMWzClagA1DS3xMcE7aJxPj5JM1zAlSyqyHbuJe6VQu3U0qXOpa2rJ26gI8JMvT+THT26MG4rEeYKT1ptYXOpFXdNublq8Put53b9H2PI2DpX9g+zcG/bsTJmKslWXv4fzKZBcAiwRkROVUlqOXrPf0lPa5+aDV7vbRPKdd6Zt3HGvuM2EYQOxVLpRSDQq4LjT3Cf51GNnitnkuoYxhw7g8hNHerqpLEU8PpEpsyqd/HOOfKbQv4+f9pRlRkfUcVftbAvnvK6OiM3u9oin2zB+HkMwhaSVZKqRzaczpUvIUvQLpD8olJN81I1dmkXkORGpBRCRSSLygzLNS6PpctxgeaEKtl1Nqgx9KKpQShH0FT7vbNecSS24sn+Q2aeNzXnsgOmtYLxP1Tfz7cdvSsZr+OH5/8bi/zwBr93d+MTOtvySnicMG+hZT+NFxFJ8e1FNWg8apRRP137IjHtX5byhBk1hYIU/7iLzwhAFKXUpYcumpT1C/fY9rNu2i7qmljTV5Gz8YfX7ZVPH9kLy7dUlIi8CNwK/VUodHRurVUpNLOP8ysLUqVNVdXV1d09D00PpKVlhmVi3bRdffXBtkjtmQNDHfZcdEw+SFzrv+u17qNm2K57l1Nwaimt5ufTxG6yec3q8Cv+zP12RtZ1u4vaQ/nttbg3FYjbvkJqlHPQJL3/3jKzXsbSmkRs80qSDpqBECKYE173+rktixygkxpJKhd+Jg0STVhgw//yJzF1Wl6T/FfQZ/O7yqWzbuZfbltZ5nrev3+SKzx7BQ6vfj8m32Ni2whCI2M7vVSmV5voyxelM6dXd0z33XR7ZZvkiIq8rpabms20hWWF9lVKvpvRbSE9p0Gh6Od3RI7wQMrnsiq1XSM0ou/7McbSGomnV3InumMr+Qe6aNcnzxg77ssrAMYSZChWdmM2gNI2sgJkuv5/KzCnDGX/4QL7wq5UkTsFxIal4x8ebHlvPno5o2vmnjxnKnMeyxzryIdU1BtDH72PYkApmnzaWe593Msg6ohaWbXPNwjeIxJp5Darw8+1FNUlz2BuxeGDlJpQCTIm/5m7iGnunlsZAxBnzGQKimD66ktWbmtPmlChqWe73dyGusB0i8iliFU0iMgun+6NGo+lCSumy8+ruePtTG/n1ivq0Yse9keR2tzOnDOfJ/z6ZQIpPKmAKT157EgpHwfjS363h+4/XZuwgmUsjK9vcm1o60pqNpWIawrxldWnnr2tqydrIrDN0RC2uevg17n/hPZSy+eqJIzGEeAfOjojNvGW17Gjt4P+d+in8Kf44S4ENnhl7LhV+Hz+/cFI8aSFkOe0Oqrd+ktbt0sXt81JuClmxXIMjbX+UiDQCm4GvlmVWGo0mK50VXIR9XSJz6Uy5WLZiZ1s46VxjDh3Az2alF3EO6RfIu1CxGGXiuHYZknPFEbEUftOIi2K653/5veZ4Zb5LwBQMgY5o9mMGTMFWKpZ67FFcGpuTe84HXtxEH79JiORMutuW/QsoTrYkYtsMrAgQ9Jnx7DFnbibHH3kQz238KG2f1D4v5SJvw6KU2gScKSL9AEMptadss9JoNEl4xQc647JLlD0pRIalZtuupErz5tYQR1T2Y/nsk5KK+dblKFQMW1ZS5pnXMTKRS7vMHzMO8Wr+GeOZn6IKELZs/rB6c9q+N5z1aX7xz3fIJn0YMIUHrziOYYP6MOPeVVm3dbEUdGQpWinUGRcwnbTqCcMGprlF94ajnkbFpStcYvkUSF6fYRwApdQvSjwnjUaTQCnUAFzD1C9g0tTSkVRI51LhE9pzPKn38ZtxF9bCtVu5d8U7+AwTS9ncNWtyXOMqV0rx7NPGJqkmJ15bLtn8THUwfQMmlq2YfdoYvjjxsCQjNSDo48bF6zDFwFI2s08bwwMrN8WLCJ39DaaNrtzX90SEjqiFKEj8tQR9JoMq/Iw5dEBaW4NsiFASqd6AafDgFccyYdgg6ppa+PpnR/HQ6s0ETKcdgWUDOZKyyl2flc+KZUCW17SisUaThc5mmBVSVZ/pXO7NG5wgr98QIinprv2CTpfIV9//JE2qP5Hv/p9jkBIFD113z/WLauLzytQVEZyAc6pqciGKAV5GK+gTLjq2ioVrt/CbF9/jvhfqk4yUMwNxfE5KOKh/wONJ36a2sYXLTjgi7mbsFzCZce8qogmrIzf+49XWIBvZ+qUUggi8/F4zVz5cHXe5mQKzTxvN5BGD+H8L30hz8aVS7vqsfAok5wGIyHSlVJLQpIhML9fENJreTilWGvmqAWQ6l5fbKNWogBM/Oe2oQzjtqEM4ZezB1GzbxZ/WbElb1WS7YUVt+Efdh4yPVbknysC4mVGJ8ZN123ZlzTzLhFdM5nPjDuaPr2xxri/m2kuUWpnzWPIKbf7yDVz/+XHc/uTGpGOn9oNp2Nked6Wlxn9WvvMxhkd0JOAzuOS4Kv72mmN0sqVk54MpYBhCwGcQjtpELZvfvLgpaRtLwb3Pv8sT156cVmeTiJtFVu76rEKC9/eQLkLpNabRHPCUSncsHzWAbOfKJZ/SN2BiK8Ut545PUvcNW3ZRN8Sb/14br7P4xvQjOfFTlVw6bSSXThuZtpqqbWxJi+/k+yTtGq26pt387qX3eKZue9o2pki8QDNVaFPZioP6BugXMJPm4Bq2VfU7kgz1LeeOZ+LwfbIwqbpcidxw1jguOKaKMz9zGLvbI3zn0RrPAH++WAr6mEI4ZaWYfr23ZkbLAAAgAElEQVQGbWGLOy+YxPUZ2hQrpXji2pPTFJlLTT4xlhOBzwIHp8RbBgJdqxOg0fQSSqE75rq2bjl3fFoNRi75dfdc2WIdQZ9w5wX/RvX7O7lt2b5ivZCH4m4huKuj+1/cxP0vbsJvCj+/cHLSas3thpjKzMnDAKf2JZf7cFX9jow3d9iXILCzLZyWthuyFBHLJmKlG7Z+ATPNUM9/YgPLZ59Ew852Nn/cyo2L18frZFK546mN3PXM2wRMA0spLj5uBIuqG/AbBiHLBtsmXKDNbstjB7dLZNWQCkzDIOrxd/ebRkHJGsWSz4olAPSPbZto5nYDs8oxKY2mt9NZ3bFU11Y2IcVs50p0G4Fz0w+aghhOTOK6R2oyPgGXioiluHHxPreUI5wZ8Uxzfuz1BpbUNHlK0ifirtKyraquOmk0lf2DNOxsJ+gz0rb9weO1add+0dQq2sJW2gonErU5555ViFJZa0vAWWFYloobrUXVDfGMNzdmQx5aaQZOssTebBLICVx83Ii4izFTeU7E6hrtu3xiLC8CL4rIH5VSW0p1YhEZDDwITMSJrX0DeBv4GzAKeB+4SCm1U5wUtLuBc4C9wH8opd6IHecKwNUs+5FS6uFSzVGjKZZiajNcvFxb85bW8crN+yROUgP1Xoq+rhsosebFdf30C5ice89LWY1KYtpuKBolapMmdpkvoajNLUveYsXGj2PCmU6sIBVLgeUhSV+oQrIpcNXJowHH5eZlgLyu/ZFXtzFz0rA04+HOqxhMkXiGWuoKdG84mvFvYAhpRsUU73kD/PXVbRw/6iDe3d5Ke4ZU7LnnTegSVYlCYix/FJG0S1JKnV7kue8GnlZKzRKRANAX+B7wnFLqpyLyXeC7wBzgi8DY2L9pwP3ANBE5CJgLTMUxTq+LyFKl1M4i56TRlIxsRYzZerV7FS2GLcWtj9dy31ePzRiod8+V2EQr8fXE8ziB88wS70GfcNesyUwfM5SFsR4sqdXxqfiM7JlPT77lxEFcY5BPUWAm92E2F58h8MuLpwCw8p2P+OHyujzO5BC2FH9/symtlUBnCEUtnq79gIdWvx9fibkr0Jb2MN/68+ueri6vsIylHB0yr6lFLMXsv9Z4zkGAH315otMorAsoxLDckPB9H+ACitQKE5GBwCnAfwAopcJAWETOB06NbfYw8AKOYTkfWKAcxcw1IjJYRA6PbfusUuqT2HGfBc4G/lrMvDSaUuNVxJjJMOQqWnyi9kO+vrk5vU/6Yqfb4JhDB7CzLcy8ZXWELZUUyE/tRlg1pMJT+h7gW58bHX/ar2vazT0r3s1LT0sp+NVFk7h+0focQu6x7T3G/GZyJX0m92FiOrMpTjzhvMnD+MxhA/CZBh+2dDD9jhUY4l0Zn41HX9+WKi6cNz5D0rKyorYTb4J9GWLzlm3gyWtPomrIoIINWDH2zjTg7AmHFb5jkRRSef96ytDqmOJxMYwGPgb+ICKTgdeB64BDlVIfxM73gYgcEtt+OLAtYf+G2FimcY2mR5IpgytXr3aXle/uSO+THrU559cvcclxI3ikusFTFNKrG+FdsyYnZQ+ZhnD1yUdy1cmjebr2Q+Yt34CByluk0VJw42NvYRp5hRA8mXD4QDZu35OX+zCxNkVE+Gh3B4+90VjciRMwBE4cXcnq95oLDnZfc+qn+M3KTTkz6ty/2ZeOHlb2GBc4K5a6phZOGXdIzm1LQd6GJeZ2cjFwet0XawJ9OGnK1yql1orI3Thur4yn9xhTWcbTDyByNXA1wMiRIwubrUZTIjJlcOXq1e5yytihPPDSprTxsKUy9oLP1I0wMWX3lfd28PtVm3n4lS389sVNea04vOisUnBNQwu/umgSUZu4hL8X9dv3pGVmvVSfruibSF+/iY3ioqlOjYlpCFHLJmKppJtGR1Tx/Ds7ADjuiMG8ue0T9kZyX5fPEH770ia+cnwVf3w5dzV+2FIsqu6cIfTFjHiuv1fEhiseeo27L5lSVo2w+LwK2DZxxRLFEaG8ssjzNgANSqm1sZ8X4xiW7SJyeGy1cjjwUcL2IxL2rwKaYuOnpoy/4HVCpdQDOCKaTJ06VSsGaLqFqiEVtEeSPcjtkahnr3bTkKSGUJefOJKpRzqSIzc8uj5j341EfKZgkqyS69Z3uCuBxp17+e3KTdiKJKHGYskWYM6H/1m0Pp655pUVtqSmkRszyPVn44rPHsFVJzuZYpefMIqabbtobgtz5zNvZ2y8tXpTs2dDMS+itiJqKxau2ZZ74xLhMw0umTYiXhyaDQXc8GhNl8jmF+IKO7JUJ1VKfSgi20Tk00qpt4EzgA2xf1cAP419XRLbZSkwW0QewQnet8SMzzPA7SIyJLbdWcDNpZqnRlMOJEU0SkQY0i/gmUU2/vCBSQ24iO+Z3011dsw1k3inbwtb1Da18H5zG//zSE3JdZlK4doJWQoslZYV5roSCzUqAH94+X0uOKaKB1/axIOrNhMwJa/6kJPHDI2vYLwImJI0Hy9lg3LREbH505r8k3UFKatGmEtehiUW67gGmIDzjt4A3KeUyiyhmZtrgYWxjLBNwNdxXGyLRORKYCtwYWzbJ3FSjetx0o2/DqCU+kRE5gOvxbb7oRvI12hKTSk6SzbsbKePzySSJHPuZD5lyiJLVRMu5Mb6m5WbuP7Mcdz+VLJ0ybylG7BtuyRGxTQEQVHh9xGKWohIp2VMXFKzwjKlGXsFzVNRCs761cp4ynS+bru1mz8hk36kmedxUpMSSkkhh7WU6hl1LDE9sL8AfwQW4MQ1jgFeFZHLUvXD8kUpVYOTJpzKGR7bKhzD5nWch4CHipmDRpMvpdD9Au802bawk47qGpNsRqthZ3tBzakER7uqf9BM0vnKx42Wib4BM1a1HutsaCuCPoP7LjsmQUq+NKRmhXn9/vwmzPnCp/lRiu5XKsUau71ZEirycRz2DRh5G5VSpjl7MW/mxC6pY8nHe/hz4EtKqblKqaVKqSVKqbnAlwAtma/p9TS3hli3bVdcDt7r9dQui4kdEAuhsn+QW84dnzZ+/4ub+OxPn2NpTfZgbm1jS07l2kTaIzbzl2+gvYDMJlPgP048ImMXwqhlp8uyK5UkJV8Kgr70zpip3TNNAaWEu/7xTknOWQ7aC9BvUUqldZP0Gc7fpLPMnHw4l53QNXUs+RiWgUqpN1MHYyuO8iqZaTQF4GUgchmNJTWNTL9jBV99cC3T71gRv7En7ue6XxIxjX0Ch4XOMegz6BdIl9kLRVVWg5VJXysXsXBF3vTxm/zl1a0Z3W1HDu2bVksRshQ79nSw8p2P+NtrpQlee+mLrXznYwb39fPnbxzP58cfgqWcoHmpXG/lQJG/2+3i40bw8wsnE/QZ9A2YBH0G150xjr6BQvKsvHm69sOiHoaKIZ/ZiogMSa1mj6Uf55kvodGUFy9XlYKs7qtMNSV7OqJJoo+3nDs+3X0VsqhtbMnZlMprjq7EhxfZhCpzyZiUimx1Gybw9vY2z9euXPB6zur7QhhYse/2tKSmke9kUOztKWSqiC+ERdUNXHfGOF7+7unxWNvOtjD3rHi30/Mzxel3f8q4gzt9rFzkYxh+CfxDRD4nIgNi/04Fnoq9ptF0G85T7EfctHhdkqvqxsXruGlxdveV50pEhHnLNyTtN/+JDVx/5ri0c89/YkPeT4CJRizbjdur0rx++x4WV28jErWydmV05p97LgL0C5hpLpd8OGXc0Kyvl+rG7zeFCcMGAc7v7qbF6wo6dq4w1OjKvp2YnTeWDRX55iZnIDEVfPKIwayq38GMe1cVrQSQSHvU5psLqnO6W0tBPiKUD4hIEzCf5KywHymllpV5fhpNRtwVgJdshylGWvls6mrAUxXYsmMNlZL3O6hf5t4d+QRD811tXDS1Kul4tz7+VlLh48ljKnlty078hkF7JIqIOPpTls3c8yYwoI+Pby9al7EuA5wPcChq843pR/DAS+/nnLuLAZw14bCsqbel4raZE5IywbLpmnlhSHbBzE3Nezs5Q2/aO2lZE9WHvZq0dZau6HcPeaYbK6WWA8uzbSMiNyulflKSWWkOWPJN6c31obOUDSrZsqSuBjxVgWPKs6n7TRkxGCslYF2IDH6uHvAuriuksn+Q+u170qrpX6pv5veXH8vQAX3i5078fTW3hnL2OwcnLvHAS+9j4F217TeFrxw/gr+9tg1TDEJRC8MQbn9yY8bU21JR4TOYGFutAFl1zTLhN737kZSTUpwtUX24XK7Pcve7h9LGSC7MvYlGk5lMgXQvvNxY4GQSBX0Gd82azF2z9mUP9fGnZxiBo0C8es7p/PmqaSyffRIThw/ilnPHp+035tABSdlImY6XCdeI5XI/uR96gFX1H3tu859/fp0tzW3x1OTJIwbH5/HKe80FBeoz3bIMIebrP4PffO1YfKaTMrsnFC2rUXHmpNIeAO6aNTnvCnggZ01LOSjGtZiIKTCgz75n/aohFSVRQkjFbYBWTjqfarCPEngBNQcqidpP+bTyzbQCcJ7AnZtKNtn6RCr7B9Nb0Xo01sr3eC6pq6+ZU4YzbFAfLvztmow3Z3cVtKSmkfnL/+W5TdRO7ufunmNV/Q5ufHRd1jml4jcES6k0t1HANGnY2c7kEYOp2barS7OubvXoGZKoawaKSNTmPxe+QTSDFT1n4mEsWfdBF8x2H4UUQBoCFQGTtoTUcUvBjYvXM7hvgAnDBrKqfkdWl2ax2ApW1+8oq2ZYKQ2L1t/SFEUm7adsS/ZEN5ZpSPwD2p6hQZS7CvA6lld22PzlG1g95/S4eynRQOSzSlm4Zgvzlm8gYDoV4YlZasEsRXC3zHBqXG5avD57Ey7DYOHarfzvC/WxxlkWtipcBDKT/Eh7JEq/gMnydU3c/c+uqxHpY0qSGyyRyv5BThl38L7sOhzRQi9tsmXru9aokGEeiUw7YjDHHnkQ/350FUP6BXh+40fMXVqXFLcLRW3+60+vYykbW5UuGSKRiIdUTqnRKxZNt5JNoiRXDMN9ivX6gLpGKXUl4lUx7+XLNkSoa2ph595I8krm3PFMHO7dIthl4ZotfP/xWoB4EsCNi9cDKmtvkH4Bk4nDBlHXtBuVI07SHoly74p3CFuUJf1YKeELv1rZJZLuiXRYitqmzGncXrE1rzl2gycs6++qj9/gmjOczMKmlnaG9Atw2lGH8IMltWnb5tuKuDOUO85SSsPyaAmPpTlAyBSgDHhUXXtR2T/o+QGN2Db9AqZnnYr7pOauRPoFzDS32t6wxVUPv4bC0Xhy9//+47X0D5rxVUiqkWpuDTFvWXrHQkMgnKPhlKUUtY0t3LokvRd7Kvk8yeajn5VtLt3F3CV1fPqQ/kw9sjLtta6q5SmEoCkowDAk40o0ErX5+h9ejf9d/abw8wsnx1fdhgh7C1BH6CyFJJ4UQyH9WI7EEY4clbifUmpm7OvtpZ6cZv/HK1YSMIUnrz0pYy+OVFKzu8KWxTWnjqGppcOz94nXSmbm5GE8/mZj0srJ+Zyn32BdSRUvd0LDznb8ppEWdI1YdkZj0S9gYinFLTPG88NldZ1eJbgumVyrnnIypMJgZ3txN/+orZj12zVcfuJIfnj+vyW9lm92XVfwky9P5LhRB9EWtugXMDn77pWe25kCIskPAxFLcePi9Txx7Uk88LVj2d0e4fpFNXSFbQn6pKDEk2IoZMXyOPB7YBmlyazTaDxTft0srEJw3WIL127lvufreWDlJsKWjZVap5JhJbOouoFggVk9Xu4EJzU2/YZ+9oRDWR7r+Z5IwDSYN3MCpx11SMwFVtAUPHENU1e7sRIp1qgksuCVrVx+wqik94Krtea6GktFMSnU1Vs+4awJhzGkH1y94LW0VaQA93zlaAZW+PivP71B1E62Gpat+MLdKwmYJuGo5VifMoeq3bbTPUI2P0aHUurXZZuJ5oCl0GyrbPzvC/WEonY8i8lvCkGfk+XkGq22sOXpTglluRMHfelFmF7uhKSkApF44eKgCp+nYVEopowYHDeI2eTwTZGSuKiCPqegsjviEIVSs21XWtsAV2stMabWx2cQihbfBqCY/R57o4nH3mjKaJQUsKW5jahle8ZNXDdlu2twumCFOfKgvl2iblyIYblbROYC/wDiOhZKqTdKPivNAUe+2VapJGZsefnf+/hM7rvsaAZVBJKKCAt1pygF3/viUfzin+/k7MeemBq7uz2C20Xbqxo8ainOvntlntk/pbnxXHhsFRV+k9+t2lyS45WTT/aG499n01rr6GT6VGfWCtn262mqy5t2eOu8lZpCDMu/AV8DTmefK0zFftZoupxE4cmwZfON6aPS+oxEbJsJwwalGYBrTh3DPSvqPfuSGDgrncQVTNBnMm10JavnnJ7XympV/Q5ueHRdUvqvkJ6Sqsg/pbRUaZd/Xpu7H3tP4RfPvsMFxzgy/KWWN0mkFyzeSsIXxh/aJecpxLB8GRitlArn3FKjKTNetSf3v7gJU2L9KwwDpVTaqiLxqTc1/uJik+4WC1sWLe1hqoZU5FQ0dkQT16fVlCg6F/fIkVS2X5KoRNDTssHKQdBnIALHjxrCynebS378tz9q9cy2KzWFSLqsA/LXCNdoykgmSRcrVlQWitqELUX1ln2dqlMVhvO9yZsxF9Y1C9/M2LMldW5mAV0eczEg6CNgCsFOKuf2Rtw4Vk/KBsvFqMqKjE3ScmErm+WzT+Kqk0eXeFYO85blr8jdGQp5px4KbBSRZ0RkqfuvXBPTaLKR741mwStbqd++B3BVcgv7wPfxGZiGxHWyXPn9hWu2ZNQ1qxpSUVIpjv8+Ywz3X3ZMt9aWdBe3zBgfj7/decEkgr6eXYcdNA1uOOsoTh6bvb1AJgRh44d72N0ezb1xEfjN4hrUFUohrrC5ZZuF5oAgX+XifKjsH+SWGeO5dUldzpu4m1lUNaSCSIG93juidmylsO8cpiHMW1ZHOKFwMrGmxRFNnMR3UmIsxfLwy5tp2NU1nf96En1SVI5nThnO4L5+/uvPb3RpMWEhhCyb2X9Na7ibN2FLdWr/XFi2KrsAJRRgWJRSL4rIEcBYpdQ/RaQvTkM5jSYnC9dsYd6yOvymgaW8q9bzwTVOtY0t/HB5bqMCsHNvmObWEJX9g9xw1qe5/amNGbf10ntKFWCMWCqtCDK1psXNDFu2rpGfPv12p4LOB6JRAScVO/UmOGHYoLIIMx4ozJw8rEvSjfN2hYnIN4HFwG9jQ8NxiiaLRkRMEXlTRJbHfj5SRNaKyLsi8jcRCcTGg7Gf62Ovj0o4xs2x8bdF5AudmY/Gm1x943PhameFLUVb2KIjYnP9ohqqNxcWnFxS08hnf/ocF//2Fb7/eG1W3a1EfvzkRk74yXMsrWlk2ujKrLEKn2nQJ8XdEjSFgG+fXP7c88bn1Zulsn+Qk8Yc3CvqRXoapsBdsyan3QRX1e8g3JP7E/dwHq9p6pIYSyGusGuA44G1AEqpd0XkkE6e/zrgX8DA2M93AL9USj0iIr8BrgTuj33dqZQaIyKXxLa7WETGA5fgdLYcBvxTRMYppXrmOrkX4tVLvpCVRnNriHnLN6SNR20yynZkOs6+nueF36ldCY3vfH5cVgl4r9cU8Jcrj8fvM+NuvAFBX5paQOpN0P3d2UUGnQMGhA/Ae+jxo4bwuXEHM/7wgUnjbotibaeLxxCoa2rhlHGdvXXnOE8B24YSU41FxEcn0r9FpAo4F3gw9rPg1MQsjm3yMPCl2Pfnx34m9voZse3PBx5RSoWUUpuBehzjpykBiVlUmfrG56JhZ3vWDJnE4Ho26pp2d1pCXCnFHc+8nff2+xYuiq8+9Gq8uVZza4gjKvuxfPZJ/Pmqaayec7qnGOVNi9fREbGLnveBaFQAXn1/J3f94x3O/OVKbl3yVnx8X4tiTbF0RGy+8cfXyt73vpC/0osi8j2gQkQ+j6Nm3Jme978CbmJfsWUlsEsp5aZDNOC424h93QYQe70ltn183GMfTSfxSulNrCvwItVtlk/A/A+r3/fcN5nszzD5JHuFLVWQf971tIUtPLPBZty7Km5sUlm4dmverjpNZhIfPIppUVwMnWwEWTRVg8sf+wDHW3Dj4sIeEAulEFfYd3FcUm8B/wk8qZT6XTEnFZEZwEdKqddF5FR32GNTleO1bPuknvNq4GqAkSNHFjTfAxWvlN5scttebjNF7t4Yi6q3Mn7YQOY/sSGjy23CsEH4TcmYZdUVmbiiFPOWb8jZ5bK5NcR9z9eXf0IHCG5Wn9ui+Pq4S7RzeKkOQ/eJd+5oi/DVaSO7RBnBNKSs/VgKWbFcq5T6nVLqQqXULKXU70TkuiLPOx2YKSLvA4/guMB+BQyOudgAqoCm2PcNwAiIu+AGAZ8kjnvsk4RS6gGl1FSl1NSDDz64yGkfWLi1A/n0efdym924eJ1nBXoqERtuXVKb1eVW2T/Izy+cXHThWSkIWyrt/F4rOMf9p102pWJKgtLBzCnDWfu9M7n3K0dz41nj8Hfi12waglFgXZOLr8gCWEMcuf17v3I0fQPJSbUGQr9A1yTaljvtuJAVyxXA3Slj/+ExlhOl1M3AzQCxFcsNSqnLRORRYBaOsbkCWBLbZWns51dir69QSqlYgeZfROQXOMH7scCrhc5Hkxkv5eHEehRwbqQt7ZE0yQ0pQN0q1fb4DYO6pt0MqvDHlWz3dES7NXD7jZOOZMErW5LGwlb6Cq5qSIWnBpmmcC6aOjythYLbSwech5JiCVuKcycexjMbPix4BVRsAzWfAcOH9GXYoD5EUnr27I1Y/Pal8guD+k3hrlnd3I9FRL4CXAocmVJpPwAotZjNHOAREfkR8CZO/xdiX/8kIvU4K5VLAJRSdSKyCNiA0/76Gp0RVnoSlYddd5fPENrDFoYh9PGZnr1POqM42x6J8s0F1Yg48Y2AKVkl5cuNAfx+1eY0t17EslldvyPJbbeqfgdRbVhKwoxJ6UkRpRSjfPZf23n6ulN47f1P+MHjuTt3dpawBVc89CqGSJe73Nz+MCd+qrJH9GN5GfgAGAr8PGF8D7C+sxNQSr0AvBD7fhMeWV1KqQ7gwgz7/xj4cWfnocmNZ79xSxGxnHwLt/eJKcLeAj/4ftORlQ+YJmHLwlbJqb/5GhVTwDQcv3kp60cyZTnbygmEJrY7nvPY+m5tsrV/kfyLrGtqKdh9lU0S32ca/HnNFv60dmvJ/mZe7RESccRIu/4NooCBFb6e0Y9FKbUF2AKcWPbZaHo0Trpn5tcDPoOzxx/KknUf5DyWKc6HOmDuC9a7Lre/v9HAH1NcToVw1mcOw+cTHq/JPY9SkBgI7Yk92XszwwbtczMuqWnkpsXrCs626+MzaM+wem4PW516r3kxqrIvm3bsLekxS0fXxCjzcYXtwdvgC6CUUgM9XtPsh9Q2ttCWpbiiLWTx2JueuRNp/PD8iZw98bC0WM2aTc2d+qBbCpbXflj0/kWdMyEQ2ptUeHs6AXNfQy93JVhMCncmowKl78NyzMhBvLG1pcRHLR0ThnXN7TqfFUthzcc1+yXNrSHmP5FeQV8oAdNg7szxXDbtCMCJ32TrDJhK0Of0WTll7FBefHdHSUQeO0NqINTNpLtx8fqsFf6a3NiKpIeOUqwE3RV3Od42PzjnKAb3DfDG1k5HCMrGzrZwz3CFaQ5s3Awwr6wvF7/peLE92non0ddvcuesSYw4qC/12/fQFrboFzALCsa6N+uX6puRbhb3EOA3lx3DGeMPSxp3VHgD/OeC6qxPy5rsJGZelWolaCvwG+UxLKGozbZPeqoLzMGtCSo32rBoPGluDbFw7Vbue/7dWEA9PevLxRDJy0URtiy+8+i6eKaX33AMkllEzYdSCp9hJikMdzUKuHLB6556ZxOGDTwg+6eUmlfea2ZGTJH3zgsmxfXZ9kasolSOFeWTyulp/e29GFXZt0vOI+oAfPNPnTpVVVdXd/c08qaUfUzywQmSprty3MwtlNO6N+gznJvr9FE8tGpzWjvf1H2BbnddlYt/fvuUtCfBGxbVsPiN8moy7e9ccMwwfn7R0fGfm1tD1DW1sLs9wp6OKO9s38OW5r289M7HRIp8a2XLGtvf8Bnwi4umFNWyQkReV0pNzes8BR9d06UUqy5crDHaFyRNf6zr4zP5yb//Gx0Ri22f7OX+F9/Dbxo8tPr9rAVjpsD8mRP40ZP/SisK219IdTHc+vhb2qiUgOXrP+R754Ti7+FV9Tu4oUQN1FwOFKMCrk7YujQZolKjDUsPxlXIDUW9OxVmojNS99na93ZELb7zaA1+06A15BgI1xWV0mQxCdOAHyypLYm+U08lUXakfvseFqwpv97TgYDP3JfK7XwecksEabJjilFWnTAoTCtM08V4KeTmoy7cGal7J6U4fVURMAWlFKGoihuVRKI2HH/E4LRxcKqNcxmVaUcOyWt+PZGLplYlrVZqtu3qxtnsX0QslZQZZhap0aXZh6UyC8mWCm1YeiiZFHLDlpX1TVGM1H3iOb1Sir/1udE8eMVxVPizL3DXNe4uWiRy7eadvfKmUeGTeOq0yydt4Qxbawpl7nnj40/WVUMqdFviEnDxcSPKHqvVhqWHkkkh9xsnHZn1TVGo1H3qOVONUt+AydkTD2fCsIE50z0DpsG1p48l6BP6FCE72xtvGu1RRW3TvoK45tYQP382/2Zimsx863Ojk4y2I5s/KZ4I4oUhTqJItm2KYfxh/bqoZr38/O21bWVvT6wNSw8lU97+71/alLX7WyFS9/mcc2/YYu2m5qTj9guaBEwjTd4lYtsc1C8AOHLkflPwGVDRGW3zXsDcJXXxD6ruclgaDIGrTh6dNj5zynDW3HwG3/rcaLyy1P2mcP7kYXx1Wul6LgVMOHfScCeOuB/gxljKyX7yq9r/cG/kwZR3c9iC6xfVZH3imDllOKvnnC6HHbcAAB47SURBVJ6xbW42vv7ZUWljtz+1kYVrtzBzynBuOXc8kaiN3xQk9nRY4Xc0v67//DjmP7GBUNRmb9iKBVllv1f6jdqKB1/aBHRdl8P9HgWr63d4vlTZP8icL36Gm75wVNproahi8RuN/OHl0ul/hS2nRmV/yRnQMZYDnJlThvPzCyenjUdtpwe8i1dL38r+QSaPGJy3L3VJTSPT71jBwxk+kPOWbaB++x7mP7GBsKVoC1tEbSe42h6xCVs2dzy1Mc2dFbVVp3pm9BZ+++ImmltD8S6H+8vTbXdhQ9akk+bWEHc8vbFr59QFhqXULjwvjh91kI6xHOgMrPAOmC95s4Hm1lDcIHz1wbVMv2NF3E2WvX/8PppbQ6x85yNuWryOjojN3gy6LD5DqNm2Ky0Gk4il9t8CyFzY7DP2M6cM57YZ47t3QvsBylYZXTavvLejS270XY1SinI3kXypvpn67XvKeg5dx9KDcYyCE6dITdd97M0mlr/1IZZtE7VJqnPZ0xHN2j/exa13yUeSJWorpowY3K0SKj2ffb/DF971duNo8idkqYyterc092xNrkxcc+po7nthU8bXfSJccOwIFr+xjailyuZ+K7dmmF6x9FDclcg1C98gUzJWKGqnGRxThHnLN+SsY0msd9mbQ1EY4IbPj2PMoQOYfdrYYi9pv8ZvChOGDYr/POGw/t04m/0D04CmlvQVy5KaRu5+7t2ijhkwC2mYXTimQaf61ndYisVvNECZJVYTC3rLgTYsPZDUIsdCQhQRy06rJTFFeH7jR0nGxSu1OBsft3ZQv31PLOtLk8qXjx4e91svXLOFe1/M/FSqyQ/Lhiseei0pC9L9bBTbpjpsqbLesIOmQSSLj+63Kzfjy1GvFYrahKJ22Vx9l584suwKx9qw9EAadrbnfPO5pG52wuiD0nS+2sIWc5fWcuJPnmPhGic4X6gM+e9eep8zf7mSHzxem/c+BxKP1zQ5itBrtvD9x2vZzxPhugwF3PDouqR07kIeiLqasGUz97zxadmcLlFbZdXVKxemwG3nfYZ/fvuUNCXuctBz/0IHCF5B9trGljTZlHyTRVa+2xwPoCe+t9vCNmFL8f3Ha1m4dktavUu+FfP7Yby0JPgMoa6phXnLO98MTZOMiMSD+D29Q6eIcPaEw7j5i+mp0N1J34CPo0ce1CW9WEAH77sVL7HI6WOGMndp+qpgzhePol/Ax7xldZji5Osj2VMgM+lzzVu2gbMnHMbMKcPjfebXbmrm9qe6Nn1zf8Kp1RHkAGxDUW6sBOWIyv5Brj9zXJe8V/v4DSJRu6AAus8Qlq1romVvfrI+ppSn6Vgq+apvlIpuWbGIyAgReV5E/iUidSJyXWz8IBF5VkTejX0dEhsXEfm1iNSLyHoROSbhWFfEtn9XRK7ojusphvrte7jx0XVpQfZX3tvhaRB+9sw7DOjj44azPk3YVtgUn1fvN/c9AVb2D9IvYPITbVQ6hWUrNn6wO2tPGk1xpNrqrorz2bbi7kuOLkg5oj1ic9uyDfzyuXSdv1QEuPuSoym3MIUAlxw3orwnSaG7XGFR4DtKqc8AJwDXiMh44LvAc0qpscBzsZ8BvgiMjf27GrgfHEMEzAWmAccDc11j1JNZUtPIOb9elRaANEVY19DiuU/Ysvn2onXc/tTGTvvvLXufYuySmkbOvnuldnF1EkvBz/7xdkbfuqZ4gj4zqZ6lq0Q+/abBwAp/2YLoCtjRGsIsc8xIAX98eQvH3/5cVjmoUtItnwKl1AdKqTdi3+8B/gUMB84HHo5t9jDwpdj35wMLlMMaYLCIHA58AXhWKfWJUmon8CxwdhdeSsE0t4a44dF1hD2sQ1vY4i9rM0tRdFaksW/ATNIOczNs9uc+KV2J3zS0gS4DiRIkXSny2Ra2WL6+qSSSRJlWJX18RpcVelq24sbF+bfQ6Azd/nglIqOAo4G1wKFKqQ/AMT7AIbHNhgPbEnZriI1lGu+x1DW1ZK1ObytTQ25T4Fuf+1SSdlhPz7DpbVhK8aUpw7p7Gvsdt86YEE/l7mqRz0XVDSWLgXg10Pve47WeD5nguKz/48Qj8s4QTT5X5tfKLUAJ3WxYRKQ/8BjwP0qp3dk29RhTWca9znW1iFSLSPXHH39c+GRLRveIb1sK7nsh2e9bNaQi45taUzgzJw9jSRe5Gg4U+gVNJg7fV3jaW0U+v3xMFV63Jq/Vis8Q7v3KFNbcfAZfPqaqqBYU2VZBXRHE7zbDIiJ+HKOyUCn1f7Hh7TEXF7GvH8XGG4DE6FMV0JRlPA2l1ANKqalKqakHH3xw6S6kQCYMG5h36nCpSW34Vdk/yDemj+qeyeyHPF7TRHnrug88EuOBQK8V+Xz8zSbP/kpeBHwGIw7qR2X/IFVDKoqqe8m0x8XHVZVdgBK6LytMgN8D/1JK/SLhpaWAm9l1BbAkYfzyWHbYCUBLzFX2DHCWiAyJBe3Pio11GfmKPbqsqt+BUeDS1qA06xyvlMMTP1VZgiNrwHFdWD24xqI3UeE3MvYSmj5mKL+6+GgumzaSYIp6St9yKzgWiQh5ewcSjalbb5bPw2g+HVgXVTd0SYylu+pYpgNfA94SkZrY2PeAnwKLRORKYCtwYey1J4FzgHpgL/B1AKXUJyIyH3gttt0PlVKfdM0leNehZOt94gbLM8VYTEMwIEkSwhSn6Kqz1bqm4PkhnTBsEEaOehhNfkQtxW0zJ3LLklr9++wkc84+ipPGDE0r6FtS08gNj67L+BmyleILEw7hmbqPPF/vLlLVMDLhN4W7ZiV/TqePGYppGFhZDFPQZyDkTvBxvRblXrV0i2FRSq0i80P4GR7bK+CaDMd6CHiodLPLj0Q9r0Rl4eljhsb/aM2tIRp2ttMvYNIWtmhpD+M3jPj2qVi2IlUO0lKkJ/IXgaVgT0c0bXxnW1jfBEvE7NPGcPbEw5i3rK5oLSuNw51Pv81P2Zj0sNbcGuKmxd4PZv0CJpZS3HnBJMJRu8cZlnwI+oTfXX4cp4xLdtXXNbU4q5EsWrFKKWdZlIOuKpTUlfdF4mZUJRqJxBjGgy9t4sFVm2OS9I4wpOSxMgiY4hiYMtyX5i2r4+yJhyU9rdRs21X6Ex2AmAKXThvp6LyZhm4v0EncvkCJD2sNO9s93T19/Ab/79TRgNAWSn94Khd+g7Qmdn5T+OHMCfzoiX/RlodqeCK2gmGD+sR/bm4NsXDtVu5d8Q65DhWxFF87cQR/emWrZ3wlaApiSN5tyjuLNixF4qVZFLFtahtbmPWblxOeqpyv7hNsLjdo2FJlC//6zfRl8Mp3uzNDbv/B/XNXDanY71sxdyWJrpuqIRWerp5QxOauf+SW0Q/6cvcdypeAKZx+1CE8t/GjpBVUH5/J8CEVWEV4GQSYce8q7rxgEgq4afH6vF1oCvjrWm+j8vvLj2XogD5UDanoEqMCPaCOpbeSKuLYx29w/ZnjuG1ZXdY6lXzcTuVyokRTlsH12/ewdN0HZTrbgUdd024q+wc5c/whuTfW5EUkRSfsrlmTktr3GuT/efnv08fmLbaai7CleLpue9pnfW/EYsKwQfF7Q2r2mnt29xr8CbkGYUvREbG5cfF6blq8Lm+jkn70ZLbtbGdyrP9KIYlGnUGvWIqkuTXEEZX9WD77JNrCFrWNLczLYVS6m9mnjU16YllVr7sclhZFc2uIf9Ru7+6J9Hp8BvjM9KwwVzi1rqkFEDZ93Mpty/JXlC63RqhlK3a2hZk5ZTjjDx/IOfesIrFTn99n8Jcrj8fvM+kXMKnZtou5S+uS3GamIaCErEEVT7wv7vYn/8WmHa0sqm7IO9Gos2jDUgRLahq5afE6TDGwlM13Pv9p7nzm7W7ps5AvQZ/BpdNGJo0N7a+bdpUKQ5wMu4ad7YhI+e9g+zlRGx755vFMPTI9Hb6yf5BTxjmrwsSYRC7ufu7dnK7oTPgE8vWiuW1/28IWQdMgnLDy8JvC3ojNIJ/JkH4Bp913ysokatn5xOGT52fAzy+awqPV23ipvjnptbClWPDKVoCMiUalRhuWAmluDfGdRTUxfS3niaI3yM3fOmN82pvoxE8N7abZ7H8oBavrdzB9zFDsHvyA0Zt4v3mvp2FJZMyhA7j8xJHxG2c2smXqBX2Cbau0YLzLZw4fwDvbW/NSr54yYjDNrSFa2sNpSRxtIYsrH36NPj6Tjqjl6eGI2opZxw5nUXV+Kg4/+fJEzppwGOAYjHwod9qxjrEUSF3T7l4p2jh3aZ2nsulp47RxKQWKfR/qS44fmX1jTV7k25f9ujPGdTp2ErUyGxWAt5r25GVUTENY8Mr7TL9jBdcsfNMzphqxFHtC0Sy1OPD3Nxrpl2exp9804llz+Wr/lTvtWBuWAtndHunuKRRF1FZ8J6HF65KaRqbfsYKXN3VZPel+j/sU+OWjtRBluXEVL+q37+H5jR8R6ITGiykUrIYRiAlEXnPq6KSqeMtWLFizNd5nqdiYq4jQkWe6csSyWfnOR+zY08HeSHq6ddBncPKY5JXfRVPLK+2iXWEFsKSmkRsfXdfd0yiaiKWoa2phwrBB8eJOTelwnwIbdrZj8P/bu/fguKr7gOPf3z60Wj0ty6CHn+BHbMtgBQy2J0B4DTa0BdoCTUggnSnQtIQCaUtgyHRoprS0TCDJJM2kBJoQoIBJAjShA4HSYh52kFP5IcBY8VOW5YcsvHqsV/s4/ePe3aykXWll3X3q95nZkfb4Snvu8dX93XPvOb9DmmmwKlPxZxWjxTNemJghFDVTHkbscgk+j4tw0m2rMjdEoqn/D30eF4/dfC7NtX5e3tqNz+NOzLtxymQm2N7387ErzoIVMO+6fAlXrmjk97/79oh/e76tizsvW6LPWPItvo5KIY/6ykQgGE45uVOdukqfm2jMjBjB5HVwzsR0VVfhHVOWnPEiLrmd/V43wUme5F0w5iJLxIVLYmmnB/xiazfPb8nsGYjXLbjs1EzJnzPZVEpu4KtXLOZbr+8a97ZdYnuXJCbtppvMrc9Y8uyHG3cXfVABK62Lpst3zp+unc8zt6wZsc5NPANvvrJY51q2drNvaOxt5/GeI1SUufjimnlMNg1lKGrGjOgMRWJpR4Gtb2nIKKhU2gvrffP6lfzyjgvHDOqY7BgPl0toqPFT7s2sPyBiLUGebjK3PmPJs96BEI+9tTvf1XBEKBLVdPkOWrVgJivnzhhz5de293hW0vIUogyzwU9aqof3c+r8BFM8RwAYGo7xzOb9jLcOmBPB/hfbJp5UXOlz8/dXtyQuOAaHo/g8U8u8HI4Zdh3uT/kcJZVQJMaGtgPsOTowZjJ3tlO76K2wDMTzP0WKcTjYKBcsshLctTTXTrClykSNf+yfUOfhfp7cNPHw11JhrYLhbBSdXetL+Xxlos8bLz+X3+vm1gvP4PG390w6j1eyTHoaw5EYH3SfYM+xAZY11bK0sXpMr+FU/GDjnklt/9Tm/Ty1eT8XLqrnna9dmujBlGR242Izp86PKZHVzOsqdVKkU9ySOkAXcmLPeXXlXLK0gac27XOsR5WNW8QHT4ToPNw/Jrh0dAes2eNJD9p9bsElQnCCC79wJMqShqopT2Qe76e9biEcNYSjhife3Zcod7uEL6yey3Pvd2WcquW+dUt46NWPHTnzbOzsZc/RgQnnBTlFb4VlIH7PvNgby+vCToWR+kpbTY6I8E6KtDiZzr/Ih/19Jx0NKtk0OkC/1H6QW59sGzMCKxQ1GT0zjBi467l2orFYyvXnp8pN+oQL0Zjhufe7+KtLF2X8+wbDsSkNox7trV25S+FU7OfKnLm6dTa/d1ZDvqsxJeEY3PpkGy+3H6SluRbPqea3UIA1N+hvX9g2JqlffDZ4oSqGoAIjM2/HR4Olu9rPdB5KJGa9TiX78ERiWAuNpROOxnh3Evn5qss91jorDmmqLc9JAkrQwJKxl9oP8ssSSC4YipjEDPE7L1uc59oUv1DEemA82p2XLcGjcXtKXt56iM7D/cD4o8HAueW7p8Iw/vOXmIF3JjEh+cFXPnL0NuM/vvIRn/nn/06ZgcNpGlgyEL9aKpUUUPEx7DeunjcmrbeavO++uWvMleDTm/dnnLRQpffv7+wFUq9/lCwULc6noGVuwTvO36CT+9QfinAyHOOen47tZTtNTysZ6OoLZuWebL7kannS6aLM7U6sHArWhch3Xv84jzUqHRu2HKB3IDRm/SNviUwSEhFcGeb3ckrySrfZok9wM1AqEworvC5ikBjDvvXAJ7iyMFR0uglFIiMC9ddf3KG9FYd4XJKYIR5fi6WrL8iB40P8zYZ2ThZ5Q4fzMIVhOJr9C0vtsWSgvsrHH5zdlO9qTNlFi08bMUN88+7eSeUkUqkNR+Hbb1g9lM7D/fzXjp4816h0jD4J1lf52Ns7yN3PF3ZQid9i9k3Qs4qR+5xy0Vgs5WhGJ2lgydBfXpz5MMFC9cZHvxt80DsQ4uFXC38dmWLx5Hv76TzcX9BzWIrRPeuWjpjM1zsQ4p4Xtjk+d2b9cmdHfMY7IjGspJWZysUNvkiMrD9nKYnAIiLrRWSniHSKyL3Z+IySmFhoSNxb7eoLWkugKse0H/ikoOewFJsbVs3hts8uHFGWjeO2sszNksbUs/ynyutyTaq+ufqTzPZzlqIPLCLiBr4HXAksBz4vIsud/pyO7oDTvzLnwobE4kFz6vxFM5+hWLTOnVHwc1iKxRdXz+Nfrls5pnxOnZ+ow8Mzo8Zw0eLsLHgXNbFJ1TdXl3qahHJi5wOdxpjdxphh4FngGuc/pvjPwl63JHIk1Vf5eODqljzXqHTcvHZeIv3IN645i8dvPjfPNSpu17amXizNyoJx9ohRYR6X1f6pysq9rrS9gHKvK5GQcdUZ9RldEHjdgtcteFwkRqfFvy/3uhKfGX//8HUrefi6kQkgR9c17ua183jkT1rT1hUyS6I53og5n1tykoRSnJzZmQ8ich2w3hhzi/3+JmC1MeYr6X5m1apVpq2tbVKf0zsQ4rx/eL2oVzDxeYR3771sxAH16K928u03OvNYq+LlEvjyZ8/kjz49J2XCxL97aXtGa7FPJ26XTHgFf+Gien5yy5pxt+kdCNnpiYSW5hrqq3xpy7r6ghzrP8mmPcdpqPaxpLGG5tpyBoejYxIyxp+Ttc6dQV1lGR3dAQ72DRGKxFjRXIPX405c6ccTOiZ/n/yZyb97dFm8rgf7goQiMS5YNCtxDPUOhHito4fdxwZZt7yBM06roqsvSGWZm8HhKJVlbj7q6efYwEnm1lVwoC9IucdFc11FYr8qy9x0nwhysC/I8cEQ8+urWNpYnXKfMyUiW4wxqzLatgQCy/XAulGB5XxjzB2jtrsNuA1g3rx55+7bt2/M75rI05v3cX+a1doKnccFj9zQmhgRluyeF7byfFvXiLK1Z9axeXffuIFUgIWnVdJ5dNDZyhYBt0t49IaVKdszWefhft7uPEa5x0V/KMLOngDRmGEwFKXG7+HKFU3Mqi4nHImyt3cIjws2dh5jOBxjRkUZRwdCzJ9ZgcctBIcjlJd5WN5Uy9qF9fQNDvOz/+siEAxzyadOJxiOcWwgxAWLZvHJ0DBv7TrGyjm1HOkP0dEdYFZVGV63UF/po6M7wI5DJ/jj1tmsWTiLtzuP0v3JSTqP9rNgZiVXndVET+Ak+3qH8HlchCIxAsEw+44PcVqVj2A4QkNNOefMq7NPciEqytwsa6phaWMNbXuP07avD69b8Hvd1Ff5WNfSSF1lGV19QfYc7efNnUdpqinH7RaODw7jdrm4trU5Z4kS1eRMt8CyFnjAGLPOfn8fgDHmn9L9zKn0WOKe3rSP+18sjuDiFuGmtfO4dGlD4gounfgJcFZVGWsXzkq6qgoAhuZaP90nggSC1loQNX7viKvC1zp62LLvOF63C7/Xjb/MzbKmWhprfOzoDhCOxvC6hRXNtQyFY3zcE6AnEGJ9i3VF9lpHT2LZ5CtaGmnf38eGLV0015Zz4+r5gPVwvK7CS99QmAX1FfQEQnx4yEqqGYkadh0ZoNbvpa7Ci7/MzZy6CgQ40DdERZmH1WfMpPPIAG93HkOAGRVl7Dk2wEAoTDhqOHvODC5f1sCBviA+j4vZdX6aa/181BPgg0MBgsNRKspcLGuawdqF9VlPPa5UIZlugcUDfAxcBhwE3gduNMZ0pPuZqQQWsLqq7/22lw8PnWBoOGpfqdXi97rYtOc4p1f7qPJ52LznOJ09/VT7vSyYVUFdZRlz6yoYCEXY2dNPTbmHxQ3VCLDzcD/9JyN8qrGaptpyBk5G6B0cZn59JUsbq+k+cZJAcJgav9c+2fUnrk7jV4HhSJQd3YHESbGluVZPfkopR0yrwAIgIlcB38LKXP2EMebB8bafamBRSqnpZjKBpSRSuhhjXgFeyXc9lFJKlcZwY6WUUgVEA4tSSilHaWBRSinlKA0sSimlHFUSo8ImS0SOApOfIZkbs4Ds5rQufNO9Dab7/oO2QSHu/3xjzGmZbDgtA0shE5G2TIf0larp3gbTff9B26DY919vhSmllHKUBhallFKO0sBSeP4t3xUoANO9Dab7/oO2QVHvvz5jUUop5SjtsSillHKUBpY8E5G9IrJdRNpFpM0umykivxKRXfbXunzX0yki8oSIHBGRHUllKfdXLN8RkU4R2SYi5+Sv5s5J0wYPiMhB+zhotxOrxv/tPrsNdorIuvzU2jkiMldE3hSRD0WkQ0TutMunzXEwThuUxHGggaUwXGKMaU0aXngv8IYxZjHwhv2+VPwIWD+qLN3+Xgkstl+3Ad/PUR2z7UeMbQOAR+3joNVOrIqILAc+B7TYP/OvIuLOWU2zIwL8tTFmGbAGuN3ez+l0HKRrAyiB40ADS2G6Bvix/f2PgWvzWBdHGWPeAo6PKk63v9cATxrLJmCGiDTlpqbZk6YN0rkGeNYYEzLG7AE6gfOzVrkcMMYcMsb8xv6+H/gQmM00Og7GaYN0iuo40MCSfwZ4TUS22MsnAzQYYw6BdQACp+etdrmRbn9nAweStuti/D++YvcV+1bPE0m3P0u6DURkAfBpYDPT9DgY1QZQAseBBpb8+4wx5hys7v7tInJRvitUQCRFWakOY/w+sBBoBQ4B37TLS7YNRKQK+ClwlzEmMN6mKcpKtQ1K4jjQwJJnxphu++sR4OdY3dvD8a6+/fVI/mqYE+n2twuYm7TdHKA7x3XLCWPMYWNM1BgTAx7jd7c5SrINRMSLdUJ92hjzM7t4Wh0HqdqgVI4DDSx5JCKVIlId/x64AtgBvAx8yd7sS8BL+alhzqTb35eBm+1RQWuAE/FbJaVm1DODP8Q6DsBqg8+JiE9EzsB6gP3rXNfPSSIiwOPAh8aYR5L+adocB+naoGSOA2OMvvL0As4EttqvDuB+u7wea1TMLvvrzHzX1cF9/g+sLn4Y6yrsz9LtL1b3/3vAb4HtwKp81z+LbfATex+3YZ1EmpK2v99ug53AlfmuvwP7fwHWbZxtQLv9umo6HQfjtEFJHAc6814ppZSj9FaYUkopR2lgUUop5SgNLEoppRylgUUppZSjNLAopZRylAYWpZRSjtLAotQkiciA/XWBiNyY5c96QESMiCxKKrvbLltlv48vvbBNRP5XROYnbRu106/vEJENIlKRzfoqBRpYlJqKBUBWA4ttO1bK9LjrgA9GbXOJMeZs4H+AryeVB42Vfn0FMAx8OZsVVQo0sCg1FQ8BF9o9grtFxC0iD4vI+3bv4c8BRORiuyfxvIh8LCIPicgXROTXdk9j4QSf8yJW2nRE5EzgBHA0zbbvkT7r7UZgUZp/U8oxGliUOnX3AhvtHsGjWKlZThhjzgPOA2618zoBrATuBM4CbgKWGGPOB34I3DHB5wSAAyKyAvg88Nw4267HCkQjiIgHK4P29kx3TqlTpYFFKedcgZUssR1rbY16rGSBAO8ba3GnEFa+p9fs8u1Yt9Qm8izW7bBrsbJgj/amiBwBLgeeSSr32/VpA/ZjJT5UKqs8+a6AUiVEgDuMMa+OKBS5GAglFcWS3sfI7O/wP4GHgTZjTMBKjjvCJcAg1rLH3wC+apcHjTGtme+CUlOnPRalTl0/UJ30/lXgL+x1NhCRJfZyCFNmjAkCXwMenGCbu7B6TTOd+FylToUGFqVO3TYgIiJbReRurOclHwC/EZEdwA9w8K6AMeZZY6+TPs42h7DS8t/u1OcqNVmaNl8ppZSjtMeilFLKUfrwXqkCICL3A9ePKt5gjEn7TEWpQqW3wpRSSjlKb4UppZRylAYWpZRSjtLAopRSylEaWJRSSjlKA4tSSilH/T+wHXbZyRwd9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the simple scatter method associated with pandas \n",
    "bigmart.plot.scatter('Item_MRP','Item_Outlet_Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The range of Item Outlet Sales is from 0 to 12000 and Item MRP is from 0 to 250. We will scale down both these features to a range between 0 and 1 (min-max scaling). This is required to create a explainable visualization (it will become way too stretched otherwise)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Item_MRP</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.927507</td>\n",
       "      <td>0.283587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.072068</td>\n",
       "      <td>0.031419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.468288</td>\n",
       "      <td>0.158115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.640093</td>\n",
       "      <td>0.053555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.095805</td>\n",
       "      <td>0.073651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_MRP  Item_Outlet_Sales\n",
       "0  0.927507           0.283587\n",
       "1  0.072068           0.031419\n",
       "2  0.468288           0.158115\n",
       "3  0.640093           0.053555\n",
       "4  0.095805           0.073651"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "bigmart[['Item_MRP','Item_Outlet_Sales']] = scaler.fit_transform(bigmart[['Item_MRP','Item_Outlet_Sales']])\n",
    "bigmart[['Item_MRP','Item_Outlet_Sales']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store these values in the NumPy array for using in our models later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = bigmart['Item_MRP'].values.reshape(-1,1) \n",
    "X2 = bigmart['Item_Outlet_Sales'].values.reshape(-1,1)\n",
    "# values method takes a column from pandas dataframe and makes it into an array of values --\n",
    "# reshape arranges the extracted values in a numpy array with a shape we define --\n",
    "# an array shape is the number of rows and columns in the array\n",
    "# when we use parameter -1, it means we don't know the shape of the resulting array (e.g. X1)  -- \n",
    "# and numpy will figure it out based on length of array and whatever dimensions left\n",
    "# in the case of reshape(-1, 1) we tell numpy to figure out the number of rows (parameter -1) but we have one column (parameter 1)\n",
    "\n",
    "X = np.concatenate((X1,X2),axis=1) # define a NumPy array from the two arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a dictionary data structure to store the model names and function calls, and will add models to it and see how each model predicts outliers. This way it's easier to write the model application code once and run it in a loop.\n",
    "\n",
    "You can set the value of the outlier fraction according to your problem and your understanding of the data. In our example, the goal is to detect 5% observations that are not similar to the rest of the data. The value of outlier fraction as therefore defined as 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = np.random.RandomState(42)\n",
    "outliers_fraction = 0.05\n",
    "# Define four outlier detection tools to be compared\n",
    "# KNN uses the data point's distance to the farthest KNN for the outlier score\n",
    "# Average KNN uses the average score for the data point's k nearest neighbors as the outlier score\n",
    "\n",
    "# Create dictionary structure with model names and function calls\n",
    "classifiers = {\n",
    "    'Histogram-base Outlier Detection (HBOS)': HBOS(contamination=outliers_fraction),    \n",
    "    'Local Outlier Factor (LOF)':LOF(contamination=outliers_fraction),\n",
    "    #'Cluster-based Local Outlier Factor (CBLOF)':CBLOF(contamination=outliers_fraction,check_estimator=False, random_state=random_state),\n",
    "    'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction),\n",
    "    #'Average KNN': KNN(method='mean',contamination=outliers_fraction)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will fit the data to each outlier detection model one by one and see how differently each model predicts the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OUTLIERS :  501 INLIERS :  8022 Histogram-base Outlier Detection (HBOS)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OUTLIERS :  344 INLIERS :  8179 Local Outlier Factor (LOF)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OUTLIERS :  311 INLIERS :  8212 K Nearest Neighbors (KNN)\n"
     ]
    },
    {
     "data": {
      "image/png": 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d8155t99bfX099uzZg1NOOSXs7SUiIupIbNdsiQS33TXXaD+GHlpw5513YvLkyR6vL1u2DIsXL8aKFSuQmprqCmoAbeZx7zqtlJQUAFog1tLS4tr3P//5TwwcONDnuMFMO+BvG3/7FZGA+83Ozsa6detQUVGBF154AW+++SZmzZrl8flA9M4xkKSkJLS1tQGAz3xWzn2576+j9ndWZ9tLRETUFcxs+XHGGWdg1qxZ2Lt3LwDghx9+gM1mQ319PbKzs5GamoqNGzfiiy++CGnfzz33nCuIWbt2LQDg5JNPxpw5cwAA69evR3V1tc9nBw0ahK1bt+Lbb78FAJSXl3e439NPPx1lZWWuwGLXrl0e+9yxYwfa2tpw4YUX4sEHH8SXX37pc8za2lps3rwZAPD666+7skahyM/Pd3WROmvCAjn++OPx8ccfY+vWrR7tz8jIQENDg8/2mZmZyM7OdtVjdbW9REREXcFgy4/TTz8df/jDH3D88cdj6NChuOiii9DQ0IAzzzwTLS0tKCwsxD333IMxY8Z0et/33HMPmpubUVhYiIKCAtxzzz0AgGuvvRZ79+5FYWEhHnvsMYwePdrns2azGTNmzMDZZ5+Nk046CXl5eR3ud9KkSTjyyCNRWFiIY489FnPnzvXY5w8//ICxY8eiqKgIEydOxMMPP+xzzFdffRUXX3wxhg4dioSEhC6N/Lvlllvw0ksv4YQTTsCOHTs63D43NxczZszABRdcgGOPPRaXXHIJAOC3v/0tFixY4CqQd/ePf/wDt956KwoLC1FVVYV777035PYSERF1heqoiyhcRo4cKd5zSG3YsAGDBw+OSnuI3PFeJCIid0qpNSIyMpTPMrNFREREFEYMtoiIiIjCiMEWERERURgx2CIiIiIKIwZbRERERGHEYIuIiIgojBhseTnhhBM63MZ9IeUJEyZgz5494W4WERERxanYXq4nCpYvX96p7T/44INObd/a2uqzpA8RERF1X3Gf2bLb7Vi1ahXsdrsh+0tPTwegrYE4duxYXHTRRRg0aBAuu+wy3TUC8/PzXbOgv/HGGxg9ejSKioowefJktLa2uvZ577334rjjjsOKFStwxx13YMiQISgsLMQtt9xiSLuJiIgoNsV1sDV//nwMGjQIU6ZMwaBBgzB//nxD97927Vo8/fTTqKmpwZYtW/D555/73XbDhg2YP38+Pv/8c1RVVSExMdG1zuG+fftQUFCAlStXYsiQIViwYAG+/vprVFdX4+677za0zURERBRb4jbYstvtuO6667B06VKsWbMGS5cuxXXXXWdYhgsARo8ejT59+iAhIQFFRUWora31u+2SJUuwZs0ajBo1CkVFRViyZAm2bNkCAEhMTMSFF14IALBarTCbzZg0aRLeeecdpKamGtZeIiIiij1xW7NVW1uL/Px8FBYWAgAKCwuRl5eH2tpa5ObmGnKMlJQU158TExPR0tLid1sRwRVXXOGziDOgLeTsrNNKSkpCZWUllixZgnnz5uH555/HRx99ZEh7iYiIKPbEbWYrPz8ftbW1qK6uBgBUV1ejrq4O+fn5UWnPuHHj8Pbbb8NmswEAdu3ahbq6Op/t9u7di/r6ekyYMAFPP/00qqqqIt1UIiIiiqC4zWzl5ubixRdfxKmnnoq8vDzU1dXhxRdfNCyr1VlDhgzBQw89hNNPPx1tbW1ITk7GCy+8gLy8PI/tGhoacO655+LAgQMQETz11FNRaS8RERFFhtIbYRcJI0eOFOdcVU4bNmzA4MGDO7Ufu93u6lKMVqBF3U8o9yIREXVfSqk1IjIylM/GbWbLKTc3l0EWERERxay4rdkiIiIiigcMtoiIiIjCiMEWERERURgx2CIiIqKQGb1sXnfEYIuIiIhCUl5ejry8PIwfPx55eXkoLy+PdpNiEoOtLpo9ezZ+/PFH1+9jx46Fc0qLCRMmYM+ePdFqGhERUdjY7XaUlpaisbER9fX1aGxsRGlpKTNcOhhsdZF3sOXugw8+QFZWVtD7am1tNapZREREYVVbWwuTyeTxWnJycsB1hHuquA+2wtFX/Le//Q0FBQUoKCjA008/jdraWhQUFLjef+KJJzBt2jS8/fbbWL16NS677DIUFRWhsbHRYz/5+fnYsWMHAOCNN97A6NGjUVRUhMmTJ7sCq/T0dNx777047rjjsGLFCtxxxx0YMmQICgsLccsttxh2TkREREbKz89HU1OTx2vNzc1RWzYvlsV1sBWOvuI1a9bg1VdfxcqVK/HFF1/g73//O3bv3q277UUXXYSRI0dizpw5qKqqgsVi0d1uw4YNmD9/Pj7//HNUVVUhMTERc+bMAQDs27cPBQUFWLlyJYYMGYIFCxbg66+/RnV1Ne6+++4unw8REVE45ObmYubMmbBYLLBarbBYLJg5cyYnGtcRtzPIu/cVOzNKpaWlKC4u7tIX/dlnn+H8889HWloaAOCCCy7Ap59+2qW2LlmyBGvWrMGoUaMAAI2NjTj00EMBAImJibjwwgsBAFarFWazGZMmTcLZZ5+Nc845p0vHJSIiCqeSkhIUFxdz2bwOxG2w5ewrdu+6c/YVd+XL1lsrcs+ePWhra3P9fuDAgU7v84orrsDDDz/s857ZbEZiYiIAICkpCZWVlViyZAnmzZuH559/Hh999FEnz4CIiChyuGxex+K2GzFcfcUnn3wyFi5ciP3792Pfvn1YsGABzjrrLNhsNuzcuRMHDx7Ev//9b9f2GRkZaGhoCLjPcePG4e2334bNZgMA7Nq1C3V1dT7b7d27F/X19ZgwYQKefvppVFVVdelciIiIKPriNrPl7CsuLS1FcnIympubDekrHj58OCZOnIjRo0cDACZNmoRRo0a5itj79u2LQYMGubafOHEipkyZAovFghUrVujuc8iQIXjooYdw+umno62tDcnJyXjhhReQl5fnsV1DQwPOPfdcHDhwACKCp556qkvnQkRERNGn9LrNImHkyJHinI/KacOGDRg8eHCn9mO329lXTIYL5V4kIqLuSym1RkRGhvLZuM1sObGvmIiIiGJZ3NZsEREREcWDmAu2otWtSeTEe5CIiIwUU8GW2WzGzp07+bCjqBER7Ny5E2azOdpNISKibiKmarb69OmDbdu2cRFLiiqz2Yw+ffpEuxlERNRNxFSwlZycjL59+0a7GURERESGialuRCIiIqLuhsEWERERURgx2CIiIiIKIwZbRERERGHEYIuIiIgojBhsEREREYURgy0iIiKiMGKwRURERBRGDLaIiIiIwojBFhEREVEYMdgiIiIiCiMGW0RERBSz7HY7Vq1aBbvdHu2mhIzBFhEREcWk8vJy5OXlYfz48cjLy0N5eXm0mxQSJSJROfDIkSNl9erVUTk2ERERxTa73Y68vDw0Nja6XrNYLKirq0Nubm7E26OUWiMiI0P5LDNbREREFHNqa2thMpk8XktOTkZtbW10GtQFDLaIiIgo5uTn56OpqcnjtebmZuTn50enQV3AYIuIiIhiTm5uLmbOnAmLxQKr1QqLxYKZM2dGpQuxq5Ki3QAiIiIiPSUlJSguLkZtbS3y8/PjMtACGGwRERFRDMvNzY3bIMuJ3YhEREREYcRgi4iIiCiMGGwRERERhRGDLSIiIqIwYrBFREREFEYMtoiIiIjCiMEWERERURgx2CIiIiIKIwZbRERERGHEYIuIiIgojBhsERERUVyw2+1YtWoV7HZ7tJvSKQy2iIiIKOaVl5cjLy8P48ePR15eHsrLy6PdpKApEYnKgUeOHCmrV6+OyrGJiIgoftjtduTl5aGxsdH1msViQV1dXcQWqVZKrRGRkaF8lpktIiIiimm1tbUwmUweryUnJ6O2tjY6DeokBltEREQU0/Lz89HU1OTxWnNzM/Lz86PToE5isEVEREQxLTc3FzNnzoTFYoHVaoXFYsHMmTMj1oXYVUnRbgARERFRR0pKSlBcXIza2lrk5+fHTaAFMNgiIiKiOJGbmxtXQZYTuxGJiIiIwojBFhEREVEYMdgiIiIiCiMGW0RERERhxGCLiIiIKIwYbBERERGFEYMtIiIiojBisEVEREQURgy2iIiIiMKIwRYRERFRGDHYIiIiIgojBltEREREYcRgi4iIiCiMGGwRERERhVFQwZZS6kyl1DdKqc1KqTt03j9SKbVUKbVWKVWtlJpgfFOJiIiI4k+HwZZSKhHACwDOAjAEQIlSaojXZncDeFNEhgG4FMCLRjeUiIiIKB4Fk9kaDWCziGwRkSYA8wCc67WNALA6/pwJ4EfjmkhEREQUv4IJtnoD+N7t922O19xNA/BHpdQ2AB8AmKq3I6XUNUqp1Uqp1Xa7PYTmEhEREcWXYIItpfOaeP1eAmC2iPQBMAHA60opn32LyAwRGSkiI3NzczvfWiIiIqI4E0ywtQ3AEW6/94FvN2EpgDcBQERWADADOMSIBhIRERHFs2CCrVUA+iul+iqlTNAK4N/z2uY7AOMAQCk1GFqwxX5CIiIi6vE6DLZEpAXA9QAqAGyANurwa6XUA0qp3zk2uxnA1UqpdQDKAUwUEe+uRiIiIjKA3W7HqlWrwPrn+JAUzEYi8gG0wnf31+51+3MNgBONbRoRERF5Ky8vR2lpKUwmE5qamjBz5kyUlJREu1kUAGeQJyIiihN2ux2lpaVobGxEfX09GhsbUVpaygxXJ0QjK8hgi4iIKE7U1tbCZDJ5vJacnIza2troNChE0eoGLS8vR15eHsaPH4+8vDyUl5dH5LgMtoiIiOJEfn4+mpqaPF5rbm5Gfn5+dBoUgmgFPNHMCjLYIiIiihO5ubmYOXMmLBYLrFYrLBYLZs6ciXiZuzKaAU80s4JBFcgTERFRbCgpKUFxcTFqa2uRn58fN4EW0B7wNDY2ul5zBjzhPo9oZgWZ2SIiIoozubm5GDVqVFwFWkB0A55oZgUZbBEREVFERLsbtKSkBHV1dVi8eDHq6uoiNmWGitbcoyNHjpTVq1dH5dhEREQUPXa7Pe66QZVSa0RkZCifZc0WERERGS5QQJWbmxs3QZYR2I1IREREhorW9A6xit2IREREZBi73Y68vDyPEYcWiwV1dXVxnc3qSjciM1tERERkmO4yy72RGGwRERGRYbrDLPdGY7BFREREhon29A6xiKMRiYiIyFDxPMt9ODDYIiIiIsP1tOkdAmE3IhEREVEYMdgiIiIiCiMGW0RERERhxGCLiIiIKIwYbBEREVGPZLfbsWrVKtjt9rAeh8EWERER9TiRXL+RayMSERFRjxLK+o1cG5GIiIgoSJFev5HBFhER9WiRqtuh2BHp9RsZbBERUY8VybodMlZXguRIr9/Imi0iIuqRQqnbodhQXl6O0tJSmEwmNDU1YebMmSgpKen0fux2e9DrN7Jmi4iIqJMiXbdDxrDb7SgtLUVjYyPq6+vR2NiI0tLSkDNco0aNCntwzWCLiIh6pEjX7ZAx4jFIZrBFRESGi4ei80jX7fgTD9cqlsRjkMxgi4iIDBVPReclJSWoq6vD4sWLUVdXF1LdT1fE07WKFbESJHcGC+SJiMgwLDoPHq9V13SmuN0ILJAnIqKYEI/1NNHCa9U1kSpuNwKDLSIiMkw81tNEC69VbAln7RyDLSIiMkw81tNEC69V7Ah37RxrtoiIyHCRrqeJZ7xW0RVs7VxXaraSut5MIiIiT7m5uQwcghTKtWKAZhxn7Zx7sOWsnTPq2rIbkYiIKI5wuojQ+KvJikTtHIMtIiKiOGHkUjU9SaAANRK1c6zZIiIiihOrVq3C+PHjUV9f73rNarVi8eLFGDVqVBRbFruCrcnqqGuW82wRERH1AJwuIkibNgGPPgrs2RP0fGbhnLeLwRYREVGcCKbLq8evtfjf/wKjRgF33IEDV1+N3bt3Rz1A5WhEIiKiOFJSUoLi4mLdLq/y8nKUlpbCZDKhqakJM2fOjPh6j5Hk0/VXVgZcfz3Q2goAML/9Nl776iskJCTAZDLBbDajubk54vOZMbNFREQUZ/S6vHpa8fzLL7+MI444AuPGjUNeXh7eff554NprgdZW7L/xRky3WPDjzTfjjepqLF++HKmpqXjrrbeisuA4gy0iIqJuoCettfjyyy9jypQpOHjSLhjQAAAgAElEQVTwIBoaGtDY2IjLb70VbZmZAIDNJ5+MdwYPxuFPPAGYTCgsLETfvn2RnZ0dlXnJGGwRERF1Az2leN5ut+PGG2/0eb0tKQlb/vpXoLoavz7pJNTW1qK6uhoAUF1djbq6uqhdCwZbRERE3UBPWWtRL4MHaIFl5pVXAkOHIvfQQzHnjjsw7/jjMbl/f5x66ql45JFHUFtbG5VuVRbIExERdROBiueNFq0lg/Lz89HS0uLx2mEA1h5xBHJ/+glwtOVMqxVn7t8PW0EBhp95Jm688caoDRxgZouIiKgbCed8UU7RXDLIPYOXkZGBlJQUrBoyBL/evBm47bb2DR2TmGZkZ+Omm26K6sABBltERKSrx8/XRLpiYdRjSUkJ6urqsGTJEnz//fc4wvnGmDHaf7//HnjySQCArVevqA8cYLBFREQ+uNgx+RMrox49MnjPPqu9eP/9QE4OkJcHbNsGjBmD1JtuivrAAQZbRETkIRYyFxS7YnLU47hxwNVXa3/evRswmYDzzwfeegu5hx8e9YEDDLaIiMhDrGQuKDbF7KjHF18Edu4E5s0D7HbgnXeAPn0AtHc7Ll68OCqTmioRiegBnUaOHCmrV6+OyrGJiMg/u92OvLw8NDoKjAHAYrGgrq4u+g9UihnRGo3YkXC1Sym1RkRGhvJZZraIiMhDzGYuwKL9WBKJUY+dFau1hsxsERGRrljLXPS0RZapc8KdkWVmi4iIDGdk5qKrGSkW7VNHYrnWkMEWERGFlRFdO7H8IKXYEJOjJB0YbBERUdiEkpHSy4LF8oOUYkMs1xoy2CIiorDpbEbKXxYslh+kFDsCTfEQzcEVLJAnIqKw6UzRcjDbxlrRPsUHIwZXsECeiIhihnsGoTMZqWCyYLE43QDFtlgYXJEUsSMREVG35y+DUFxc3GFGinVZ1BX+sp7OIN49Y+oM4iMVtDOzRUREhgiUQQgmI8W6LApVoBGvsRDEM9giIiJDGDE9Q7TXsKP401E3YSwE8exGJCIiQxiVQcjNzWU2i4IWTDdhsF3Z4cLMFhERGSIWMgjU8wQb5EdzcAWDLSIiMgy7ASnS4iHI5zxbREREFPfCPQdbV+bZYs0WERER+Yi3CWRjudaP3YhERETkwYjFw6kduxGJiIjIpTNLLPUkXK6HiIiIDGHEfGnkicEWERFRD+O+fqW3WJhxvbthsEVERNRNBAqinDqqx4qHqRTCLZjr2Bms2SIiIuoG/C0C7q4z9VjxNhrRKP6uY1dqthhsERERxblgg6hVq1Zh/PjxqK+vd71mtVqxePFijBo1KqJtjkWBruOhhx7KAnkiIvJkdFcIxS69ovakpCR88MEHHt8/67ECC9fgAAZbRETdUKzOk8QAMDz0gqiGhgZMnTrV4/tnPVZg4QpG2Y1IRNTNxOo8ScHUFFHonNc3KSkJDQ0NHu95f/89tR4rGM7rmJycjObmZtZsERGRr1isy4nVALC7sdvt+OCDDzB16lSPgCva33+80QtGOakpERG5xGJdTk+eKDOSXae5ubmYMGECWlpaPF6P9vcfb3JzczFq1CjD/iHAYIuIqJuJxbqcWAwAIyEatXOx+P33dOxGJCLqpmKtLsdfLUx3Fe2u02h9/7F23xmlK92ISUY3hoiIYkNubm5MPexKSkpQXFzcLR/Eepxdp+7BlrPr1Ihz7yioCdf3H+i4HAShj92IREQEIDK1RUbXwsSycHadRmtqj0DHtdvtKC0tRWNjI+rr69HY2IjS0tKA95P3PdddpwZhsEVERDE7L1c8C1ftVChBjRE6Om5nB0F433POOcG64z3Imi0ioh4u2rVF3Z3RNUzRmtqjo+N2dt1F7229xdo9yKkfiIgoZD15WoZIMLrrNFojOzs6bmcyeXr3nLdA92C8dTcy2CIi6uH8PUTT09Pj6oHWU0RraodgjltSUoK6ujosXrwYdXV1fovj9e45b/4CyFC7vKMaoIlIVH5GjBghREQUG+bOnSsWi0WsVqtYLBa5/vrrxWKxSGZmplgsFpk7d260m0hebDabVFZWis1mi8vj+rvnnL/r3XM2m00sFosAcP1YLJYO2+I8VlfuZwCrJcSYhzVbREQEoL22KD09HSNGjGANV5yJ1fmtArXL+72OziGUejWjahJZs0VERF3mrC3au3cva7jiTKyOJu2oXd71bB3Vt4VSrxYLNYkMtoiIyENPXVonXkVrKohotCuUerX09HQcOHDA47VI388MtoiIyAPX1osvsZC50ROudgVbhA9ombURI0YgIUELd8xmc1TuZ9ZsERH1QMHU98RqDRB5itV50qLdLr3jp6SkYO3atRg8eHCn98eaLSIiClqw9T1GzQ8Vb3MixZtYzURGu116mbWUlBTs3bs3Isd3x8wWEVE3pjfaK5LZBi5MHDmRyESGcoxoZUiNvteZ2SIiIh96GaxI1vfEauF2dxXuRb5DHfEYrcXHo51Zc8fMFhFRN+TvX/Vr1qwJeg6trmYkorWGHxnPyCxRpDNdRh0v7JktpdSZSqlvlFKblVJ3+Nnm90qpGqXU10qpuaE0hoiIjOEvg7V3796g/rVvxLxNnEKi+zAqIxqN+cCilVlz12FmSymVCOB/AMYD2AZgFYASEalx26Y/gDcBnCYiu5VSh4qILdB+mdkiIgqfjjIRHc3qbVQWw1mzlZycjObm5riq2eJozHZG3BNG3VfR+l7CndkaDWCziGwRkSYA8wCc67XN1QBeEJHdANBRoEVEROHVUb1KoH/tG1nX1Zk5kWJJrM7IHi1G1D8ZcV/F6/cSTGbrIgBnisgkx+9/AnCciFzvts1CaNmvEwEkApgmIh/q7OsaANcAwJFHHjmirq7OqPMgIiIdoY4ei8V5myKlp59/IF3JKnX1ukb7ewl3ZkvpvOYdoSUB6A9gLIASAK8opbJ8PiQyQ0RGisjInn7DEhF1RrBzVXlvF0q9SiyN4oqGWJ2RPRZ0pf6pq/dVPH8vwQRb2wAc4fZ7HwA/6mzzrog0i8hWAN9AC76IiGJSPE20GWzXiZFdLPHa/WcEFvaHT1fuq7j+XkQk4A+0rNUWAH0BmACsA3CM1zZnAviH48+HAPgeQK9A+x0xYoQQEUXD3LlzxWKxSGZmplgsFpk7d260m+SXzWYTi8Ui0HoUBIBYLBax2WwhbefvGJWVlUFt21M47xGr1Rrz90i868z9F83vBcBq6SBm8vfTYWZLRFoAXA+gAsAGAG+KyNdKqQeUUr9zbFYBYKdSqgbAUgC3ishOA2JBIiJDxdtEm8F2nYTaxRJrBcexknGMhcxerFyLcOrs/RcL30tIQo3SuvrDzBYRRUNlZaVkZmZ6ZICsVqtUVlZGu2m6wpnZ6ko2LBziKeMYbj3hWsTa/dcRhDOzRUTUncRb3UewRcWhFB/HUsFxvGUcw6mnXItYuP/cs4fhzCQmGb5HIqIY5gxKvCfajOWRdiUlJSguLu5wyH2w2znFUuDpfPC6D+t3Pnhj+bsJh55yLaJ9/7kvkt7Y2AgRQWpqalgWTOfaiETUI3F2cI0RM7wbcS2jPYdSLDFiPqp4ubejtcLAhg0bMGzYMBw8eFD3fb3r3ZV5tlizRUTUw3VlNKKRtUUcAdgu1Gvh/FxGRoakpKRIWVlZmFvadYaPht23T+T++0UOHNB9e+7cuZKSkuJRK+b9o1fHiS7UbDGzRUREMTPTfDxlZcKts9dC7/sAgLKyMkyePDlczYwtbW3AxRcD77wD/OlPwGuvAU1NwE8/wZ6airVr1+K8887zuUbejM5ssWaLiKiHc69dCbZexW6344MPPkBSkudjpKu1Rbm5uT0+yHLq7LWora31+T4A4MYbb8QFF1zQM67rihVaoGW1AnfeqQVfEydix5o1GLRjB3Jzc3UDraSkJCilYLFYwlLHyWCLiHokZlA07iPfnA+h0tJSFBcX+70u8+fPx3XXXYfevXujoaHB4z33AudQrzG/m9DoFZwD3bO43q+0NO2/ubnA4MHAL79A3n0Xh+zfj+ULFiDnxBNx5JFH4sCBA66PpKSkYO3atTjkkEPCdt9x6gci6nFibSLPaOrs8Hu73Y7rrrsOS5cuRXV1NR555BH0AtAvLc1jugm9axzM0Hp+N6HLzc3FM8884/N6a2trzE5tYgSP+6qgAEhNBb79FrDbAasVO4qLAQADH3gAuVlZmDVrFpRSSHPcs6+++ioGDx7cpXUfOxRqsVdXf1ggT0TREG8TKQZiRGFxZ69HZWWlDB8+XPtl+3aRqVOlSSlpU0r2/+EPfveZnJzcYSF9d/puoqmsrExSUlIkPT292w80cA4ISEtLaz/XceNEAJGHHhIREfvmzWJXSnutqkrWrVsnWVlZUlFR0al7C10okGewRUQ9SrzNIO9PV0YBegdpAUe+ffmliN3u8dmcnBypfeIJkfR0EUBaAWkzmbQRYKJ/jb1/9IKo7vLdxIKesN6lzWYTk8nkcb8kJSVJ7auvauGNxSLy448iInIgLU0EkOKCAsnJyZF58+Z1+ngMtoiIgtQdsiddOQd/QZrfh/OQISJKibz8suulefPmydaEBBFAqhIT5T+PPSayY4fIrl3avrZvl9+bTHJEJ4fWd4fvJhp6QmClp6KiQvfeSklJkQ1nnSXy5psibW0iP/+s/aMgLU0qV64M+Tp1JdhizRYR9SihLGsTa0Jd5sTfMjCLFi0CgPZ6lQ0bgK1bgb17gV27ABHgn/907eeSSy5Bn4EDAQCDxo/HmbfeCvTqBWRnAwByX3sNc5RCkckEq9UKs9ns0169mcK7w3cTafFS4xbJRbUPHjyI4cuWwT52LKAUUFkJAEg4/niMGj06OvdTqFFaV3+Y2SKiaIrnbECoGSB/3Xse9S4iIvn5WsfHpk0i77yj/fnccz13VlsrctJJ2jbu2tpEevUSAWRHVZXrGndmks54/m4iqaamxmdyzljMBIZrUW2bzSbJyckdZ0737RNZvlxkw4YuHQ/sRiQi6llCmWFcL0jTfVBnZWmPh2HDREwm7c+XXuqxn8rKSrFt36690NYm8pvfiFx7rcjf/65tn5Wlve51/FgLoowaZBDp8/I3C3qs1biFu2vY33UIR9DJYIuIqIdwf7CH8pB3H73l90F92WXa48H5k5YmsnChx+edWYoHH3xQdi5b5rk9IPL//l+4LoFhfDIuc+aIbNvmEyR2ah8RGPkXKGiOtcxWJAY92Gw2efDBB8O+1BODLSKiHsCoB7vNZpOKigoxm836D+r9+7Wg48EHRT79VOTgQdfn9B7yZrNZPnj4YZGbbxbp21fkyitFWlqMPHXDeZ9LKiDvOor+5dJL/a6rF2gfkQp2/HUHp6SkxNw0D5G8RuHOMDLYIiLq5sLx0OpsV2SgKR1iLaPijzPQfPbZZyUjI8PV/ju8M3PPPKP7WfeHebSmqtC7F1JSUqSmpiasxw1Vd1lgvCvBFkcjEhHFgVBHIAZSUlKCuro6LF68GHV1dR2uh+hvORgj2hIJ5eXl6NOnD8444wzccMMNHksNDXb8t+nUU7U/VFT4fNZ71J/e9dAbZdlpu3Z5/v7f//q89te//tVj1KZzFvRYpHefRXJ0YkwINUrr6g8zW0REwYuVOaicWQrEWWYrUJ1TRkaGWCwWeefFF0U2btQyWxkZIs3Nfj/rPF+jszYNd98tB3v1kp0ffaS98PXX2uScffqIfPyxzJs3T3JycmT48OGSlZUl06dPj+nrricadW5GALsRiYi6v0h0x/jUvezerXWpbdzosc2DDz4oZrM5brqGKisrdQcFpKamyuzZsz0DluHDRRITXbOPd9RdaEitUFubfHXRRa5uzNK0NG2W861bRcaMEQGkTSm5z2KRdevWiYjIunXrJCcnR/e4XW1TuOqfYuUfDaFgsEVEQYvF4fcUHGe9UWfXdAuWbsbhiiu0R4XJJHLffSKNjR7tiZdpEwIV9+seu6nJNSox3AGC/X//k23jx2sBVUKCyKxZnoFUU5PIXXdJm3N9v8cfd312WFGRrPrsM4/9dTVz5Fxb0Znx63CVgU6I5yWZGGwRUVDiNX0fyyIVLIT7u/MXUNi//tqVbRFAZMQI1+jErursOXX1Ws+dO9djLb3k5GSZO3eu2L/5Rvb37StNw4Z12FajM3kVDz8sPzmCqEZAPnObMmPYsGEeQUj9s8+2fw9vvy1fff65/OwMwF5/XUS6HhiWlZXpdhGXlZUZNhI2ljJbnbmnGGwRUYdi7S+57iBSwWskvruAGYetW0WWLhXJzNQeG//5T5eP19lzMnraC2d2cOGLL8qi5GQRQL5MTJSP7rpLyyT5+ayRgfWOtWul3hksHX+8bFy4ULKzs8Vms/ntIqz+/e9FAKnIypKjnJPPAto0HWVl8sPEiXKo1RpS5shms+lOEJqenh7STPW616utTd6cNcsVuPY2m7X5zSLEvU2dvacYbBFRh+I5fR+LIhm8RmpiSL3z2blsmbb8TlGRyIAB2mPjppu6fDx/51RRUeHzgDb8Wre2ijz3nDQNH94erOTkyPd33SUCSPPgwa7i+HCqrKyUW/r2FSktdWUL+/XrJ71795bMzEytZsvbN9/I/quukk2PPKKd/7ZtIt9/LzJunOtc3k9IEBXCtaqsrPSYDsP5YzKZfF7v6P7Tm/zWZrOJbN4skpgoB8aPl1XLl0vTsceKnH9+RFYb8G6T91I/HV0nBltE1CFmtowVyeA1Ut+dblfZxx+L/OpX7UEJIHLccV0+lt45mUwmMZvNPpkGw6/1/Pme5+P185Pb0kThZLPZJCcnx6fg/aWXXpKsrKyOv9+lS0WeeELk0EO1th9yiEivXrLqiitC6vL0V9f2+OOPd+r+C1Qft2LKFK2t558vsn69SFKS9vvata7PhyNj3NFSVcHcUwy2iCgo3WVywVgQ6eA1Ut+dbhH+/v0iL70kMmSI7L/ySvnuhhsMOU/vc/KXaTD8Wm/eLHLxxT5BVlOvXvL3lBSx1dZ2+dwC2r5d5H//ExGRefPmidVqlaOPPlpycnJc2Szvei1dQ4a0t3/sWJGffhLZuVNEQs8MbTr1VFmUkCDnmc2SYjJJWVmZiHTu/gs0+e31ji5bmTRJ27iwUPt9zRpXu434roOZgNb7Jzk5mZktIjIGRyMaJ9LBayS+u0BZhblz54pFJ/PUFc5zqqioCJi9Mvxat7aKbN8uHz76qFyQkSHnDBkiOdnZ+l13RrvtNhGlRJ58Umw2m1itVsnIyJClS5eKiJbhys7O1h116nEP3HuvyDXXiLz8clDLCwWlpkZa09JEAGk+5hgR53xfIlJTUyOzZ8/ucKb6QFmka8xmLfS47DKRX34RcS6R9NNPImJMFlPvHvbXpqMBGQHIqYA8nJAgB48/XuTww0XefNNnvwy2iIiipDsFr4GyCuHO5AWz/3DO/RSx77C1VevuA0RWrpTKykoZPny4a7LSYcOGSWpqqlgsFhk+fLhHtst9QlP31z38+KPIKad4dMt1Sn29yKOPihx2mNbGww4TaWnpdNeev8lvLzKZ2rNxjqBOMjJca2l29T7raALaVMfccCkpKWKxWORjf93JRx3ls28GW0RE1GWBsgqRqFGLdjd3RIKu9eu1R+/hh4u0tXnUbdlsNpkzZ46YzWaPLFdOTo7U1NTo1nd5j/STs87S9v/b34Z2Pps3i1x1ldat16ePCCC73nsvpABIb/LbV555Rvb37dse1JxyilYX6KYr94Hf+3TlSpH33pPmo46Sqvfek5qaGrFYLPJ3QFYD8gUgzycmyr7rr9dm7C8t9dk3gy0iojDqTtmrQKKZ2XJvQzSudcTmoHvmGe3Re/bZrpfcs1pZWVly5JFHenykoKBAZs+eLcOHD/d43aeu69VXtX3n5MjCF1/sOAvmrblZpH9/bR/OKSkA+emyy7oUaDu/U9dcXVarjDeZ5N+PPRbwM6uWLw+pVsv7Pk0zm+XAhAntAZ5jUli/QV1Tk8iOHT77ZrBFRBQmPW0i2EBZhWhnnsIlooMdpk3THr0JCR71UM6A5LPPPhOLxXNJHovFIp999lnAzJbNZpPt550nAoh98mSxWq0e2bFDsrPF/tVXHbfvp59E/vxnEWchOyD7L7+8y9cnqGtss4ns26f9+dtvRfr2Ffn3v7Xf29q0UaStrR0ey/s+Xfjcc9q5JCdrwa7btB7O627fvNnVlekPgy0iojDoqdNl6GWXnK/V1NRENPMUiUxXROeg27u3vasvM1Nbe9KrLX379nVlunJyciQ/P18qKys9MmB6tVx35OeLAPKpUpKQkCAWi0UWvPCCyHffybrUVNk7cKDuhK2613jLFpHly0VmzhTZt6/LgXbAa7x8ucgRR7QHoQUF7b+ffbYWaDmnjLjjjqCO53FOO3dqn83K0t9461aRkSNF/vAHvxPaijDYIiIKi7icCPaXX0RmzxaZO1ds27cbEqhEK7vXnWbo99DSInL77boj3pw1XEuXLpXKykpZunSpTwbL/Tt1r/myb9kijYC0AnIoIP0cmaldRUVS6xz19/zzHsfrzDXuSuDrfo2PBmSc+zX+7jtxFcwnJroyajJ0qFawLyLy4Yftry9c2Lm2NDWJq2t0/XrttbY2kS++EPnjH11ZvJYjj5Qv//Mfv/tksEVEFAZxldn69lut+ycjQwSQH489tvM1OzqidQ266zxm7jwCBrfuMX8ZLJ/PiLhGMzr/vNVRa9Uf2nQGAsiniYmy4eyztUf+XXd57Cti1/iHH+Tr3/1Oahzt+xGQ8jfe0N5raxOpqdGuwf79IitWaPVn7pnVn35yBVuf3HRT54Pw00/XPm+1ilx3nciRR7YHbwkJsvXEE+Vot2lNysrKfII5BltERGESF3VK773nCrIEkKZjj5XfZWS46nuqV6yQfsHMSK4jWtm9aBy3o2yJkV2azvsq22qVm5KTZVdeXnu9kp9jzZgxQ6xWqwwdOtQVhHmPZhxjMslvAUlxC7b2HnecNuoP0O4Vh4hd448+ap/lHpDmjAxpvPBC1wSsHZk3b55clp4uAsiWhAQxd3KZHRHRru1ll4m8/75IaqrWll//WuTmm2XH6tW601RkZGR4/D/PYIuIKIxiejTi8uXtE0Oed55IdbVHtkPsdpHRo+WjzMyQHqI9JbPVESO7NN3PLRGQKkcQ0uCWdfI2Y8YMsVgscqwjY/noo4+6uhfdM2FpaWliMpnEarXK6c45rXr31qYzAFzZIudKAd7XuJfZbNwM+nv2iFx+eXsG6bTTRJYsCVgX5Wyb8/83ZzD582mnufbT2yso6nSA+NprWvbMkU3saHZ5533HYIuIqCdqbW1fsmXqVNdivu7ZDtm2TVods3bv+s9/QjpMtLJ70TquXl1UsIFfMIG598O92JmRzMkRaWz03Hj3btn83ntSlJ4uNYsWibS1uUYiFhQUuIIM7wCl6l//Evu332rdZs5gZ8IEkV27PCZHdQVnGRlSmpwsB1NTRXJyRF5/3Wdx6GCvl0tjo8jo0SImkzbbfQej/UR8J26dPn26HHXUUXJJerrUJybKdqUk3U8wFFIbpeN1E53BHIMtIqIewuehUVkpctFFPsu1uGc7nnIukVJcbNxx4+CzodDLYAXb3RZs9kvv4V7lnNfqr39t37CpSfbl5LQHS4DIqaeK2GxSWFgoVqvV97q0tYn85jfatl9+KXLzzZ6fB+Rxs9ljCok8q1W2jxnjuV1SkjbBaQd0Z7V3Dxh/+EFk0ya/18Ffsb+zbc5Ae926dSJ2u2x69VVJTk4Ws9ksR6WnyycJCbL09tsDtrHD78Vul+XXXiv/LzlZbjaZZAogwwBRzGwREfU8/h4azi4h77X0XHMIffONiKPmRerqYqLNschfBss523igbEpnuz29s3aL7767fSLRm28WaWkRm80mU9PSpPGww+TbhARpc3QXH/zVr+TElBSZMWOG505XrGifVgIQ+b//E9m1S/txfv+A7ExKav9MS4uscS6bY7WKzJqlFac/+mhQ18s7OLo+LU0Ojh3bYVZs3rx5kpWVJQMHDpSsrCyZN2+eVFRUyMCBAz2u2YABA+SYY47x+GxBQYFUVFTI1ttu09qdkiLip+sz4PfS3Czy299qgaVXQCqAPGsysWaLiKgn8ffQmDFjhmRkZEhqaqocffTRkpmZ6Rq95rFwcHGx9lf+u+9Gvc0xWfsmgQvGO+rSDKXY3CdrN3t2+4P/oYe02rthw0REC06GZGXJl44sZUtSkpY1EtFqo669tj1QsFi0kam//NJ+sGXLRPr2lYa775Y/pae7AqTv77lH29+vfqXNNxVMO93O+dTCQpE5c7TgasECaXG2Yc2agOedkZEh2dnZMnz4cMnOzpbU1FTJysqSo48+WrIdC4KvW7dOsrKy/E/muny5yIknase7/HLdY3X4vYwdq003ccYZ2jX8859FLr9cWnr3lv899hhHIxIR9STeD41HAVmSmCjFFotkZ2f7PIyuueYasVgsMmDAALFYLLJk2DDtr/xp08LaTvcHcyzPU+Zv4tZAwWFn635CCiyXLBE56SSRPXt8MkdLly6VQzIyZNcll4i4L91z8sni6vq7/XZtUEQA7l3Mv8rOlq/PPVfEvZ7PZhNZvFhk3z6fbsIFM2ZoGTQRsW3fLu87Z5rv188V7O295ZaA16uiokJSU1M97tnU1FR56aWXPH53Zrx0p8Joa9OugTO4O+QQ3XPt8Hv5+muR7dt9P9jW1j4dR2uryNy5DLaIiLo774fGu46HzJ8PO8xnzbzBgwf7LPnygDNj8pe/hK2N3l2GzrXwYi2zFahrsytF+YYV9Lt1wfmdc8t95vlrrhEZM0YkmOV4HALWwjm65xp/9zuPYG/jwoWyNSFBWnr31mbCb2uTL6ZMkb1uXW+biotF2tr0a7kc5s+fL/369fM4ZL9+/aSiosL1e2FhocfvPu3dvVvknOv0gbQAACAASURBVHPag63DD/d7rn6/l127RF56yWPKDR+7domMHy+SlcVgi4ioJ3B/aLzumGn7Op3MVnp6ugwYMMDjswX9+sl706aJ/O9/YWmbvwzC448/LikpKZKenh4TNVvBZKBiraC/o4zamv/+V2w//2zM/rdvd81D9fXs2TJ06FCprKyU3QsXasvdANIwZIiI2/F2fv65/FJUJAdPO03kwAHdWi73WfD1MlsWi8VjLUf37d3bue2aa2TXBx+0v7hxo8jLL4usXRv8OYpoXax5eVoYdNhhIk8+qXXHevvySxFA9vXrJwCqhMEWEVH353xo7Js8WQSQqpISSU9Pl9TUVOnXr59kZmbKk08+qbuYcU1NTdjapddlaDabJSUlRTIyMiQlJUXKysrCdvyutDPSXZtGBWRdHnyweLHUjRkj/2exaBmo7GzZNH68Fhqcc46UlZUJAOmfmiqNjgzS+8nJHc7D5Zznzf08i4qKZPbs2a6pKdLT0yUrK0sKCwslKytLzGaz31nzRbQM342OIPAXQN555ZXOXi5fixZpayI6zq0lJUX2/+lPIp9/rq3I8NVXsunUU0UA+U9SkgBoFQZbRETdR4cP5Nmztb/CTzlFdzTiDTfcIBaLRfr37y+pZrPccMMN2ufa2kQeekjkd78Leh6lYNsbaK6iWOlCjHbRvlGjM404j50ffyzNzm646dOlwRF4tCklu5Ysce3fCojNsd3GE07we98479mamhqfAniTyeQx871zNOKAAQNctVn+7nmbzSaT0tJcozG/u+8+3cxXSNra5P0//1mWOUeC6vy0AHKB4xoLgy0iou4hqAdyfb2Ic/4sP9M5OEcj2idNEjn+eG0x35IS7TNvvtn+0Gxp0Ua2rVypXyzcyXZbrVZJSUnxCQZipTg+mpOlGhXoGZGhq6yslBd//WuPwGJ3YqJsuftun/2PBtprs3SmhXCv0crKypIMt+WinPNl2Ww2jy7CYDN8mx55pD0ofPBBEREZNmyYIfeSM3sHQAYC8jwgbwJSm5AgtkMPlcVJSTLS7RoLgy0iovjXqQfy73+v/TV+551+91VZWSnNxxwjrtoUQFtH8d//bt/QuUgxoE0b8OqrXWq/M7sRi8XxTpGeLFXE2C7MrgRu7t9Rbna2/PCXv4hcdZVsLy2VfMcamnr7P89kkqaiIpEff/TZn3uN1pw5c+Too4/22MY9OPIOlAJ+FzabtDn+UfHzNdeIiP+ars6y2WySkpLi+X0AcpnZLMsnT5apFotcajJJIoMtIqKuicZDN5BOPZA3bhS55BLd0VTumYbr0tKk1bl+YmqqVvTr9Mkn2uu5uSKDBrUHXSUl2rD4Lgg1gxS176StTSucNrB71Z3RXZihXF/vrOnUqVP91krp7r+1tf37+e47kU8+8VyL03Ge3gXw2dnZPpktET8z0Lt75BERQH4oKpKc7Gy/NV2hqKyslIyMjPbvAhC7TjfieqXknNRU1mwREYUiFmc379IDef16kXXrdEeDXZCRIQdPOEHkX//y/Zz7wsCzZrm6J3cWF7cf127XMmkdzOGkdz6dCZyi8p20tWkj2vr3F0lL61JXakeM7sLszPUNNEN+oNGO7u+5fz9liYnSppT88re/+dxv6enpriAuMzNT0tPTfQKljkYtiojIK69oyw8tXGh4EG6z2STNbPa4HrMA+fHoo2XXOefI6yaTtDhHLGrvczQiEVFnRLtQOtCDI+QH8vnniwDyS2GhPNW7t0h5ucjHH4usXSvjHEucBPNQ/c9jj8mSpCRZ0KtX+8PRucbeSSf5LpZskKh8J7t3i5x3XnsmY+rU9vd27dJGpXVCMAFBtDJ3Xe3G9P5+7ndcs7akJKmYPt0nQ+Z+nt7nbLPZXItMuwd77qMWfdx2m8i554p4r7+o086gru/atbIrL0/OM5kkIyNDkpOTxWo2ewaFjY1aN712zpxni4ioM6I5BUAw2ZtOP5Db2kRuuklb306nK+RvJpPf7hrvrpz09HRZunSpVFZWytKlSyUnJ0d2fPmlSO/e2v4GD9aK6Q0W8e+kurp95vPMTJE33tDWy3N66intvTFjRJ5/3nMiUR2xmCl119VgVu/7edFk0q7RwIFiq60N6p6dN2+epKamuqYrsVgs0rdvX91Rix6cC2w7v69bbhE5eNBjk6C+A7tdW5rHudbk8cf7DQpFRKShQeShhxhsERF1VrQyW6F05XRKQ4PIzJnyzZlnysLkZFmbliZfJSTIbWazbneNXldOSkqKR/CVn5+vBTzV1SIDB2qPjoQELRgxUES/k7feck3eKUVFIlu2+G7zwAPS5twG0LpXn3zSsLZHI8PVlW5MvXPMNpul2XlPXHyxyM6dAT9fUVEh6enpuktMLV26VHfUosuGDSLPPScyYkT7d3Lqqa6Aq6amxqfg3ec72LJFJDtb+2xiosiNN2r/zwSBwRYRxa1oFqgbUT/T2fbrZQcsFoukpKQYnhFxtq2iokIrYH7rLW0ix/37XSPCgilu9pgQdf9+kVtvbX/YdWKJmGBEZFqGgwdFTjtNa/+f/qSdk4558+ZJn+xsuTM/X5Y6lzsCtPUHvXQ2KxfNLFhX/p/T/X6+/LJ9GpJDDhH57jufzzmzp/369ZPevXv7LDHlvB91Ry22tfkOWli+XMQ5bcXUqTJ37lyfQEv3O6isbP8eL7mkff1DPU1NIqtXu35lsEVEcSkWul2MePB0pv3RmPzTmb066Hg4ffPmm34zW3PmzPFZt66goMA3aLj8cu0R8thjhrXTvb0RCcDXrfM78rCmpkasVqvHEjKT0tKkzbnw8qJFPm0ONrMV7XrBrtL9ftavFxk7VuSss3yuqfs9VlFRIRadJaacmS3vUYv1L7ygdY27L0c0e7bInDlawJWcLA0nnSRpJpMAkAsBGQOICnRd33hDJDlZCxD9jLi1r18vu088UdpMJpF33hERBltEFIe6wwMn1PZHavJP94fiu88/LwLInsRE6ZWdrVuz5VzuJzMz0+dB6Jyd3rnPnZ9/rmU0wjRNQjS5z27uXjs0bNgw+XbaNJGHH9Y972CzcrGwZFBYOKbO8A7G3LOnNpvNtU6me83WEUcc4TNq8d0XXhBx1oR98ol2jH37tGlKAJHp02XR9OmSmZEhCQkJAkA+dWStvgPk2cRE+eLqq0WWLPEM1kREPvpIZMGC9t/r67VjvP++VF94oexx7Gc3IB8+8ICIMNgiojgU7w8cI0Z2dTj554EDgUf+bd+u/StdpxvMO+tWMW2aCCANBQW6AWFFRYUMHDhQbDabK/jq37+/WK1WSU9Pl+HDh0tGRoZkZmb6nxMpHjQ2inzzTcBZ97OysnSzLsFMpOmT9Wls1EaFem0TC//QCEcG0XnfnZmWJq8nJspbr7zikz199NFHxWw2S15enlgsFrn//vv1C9Qff1wEkANnnNH+2sGDrrrBhocecn03zut5JyB1zm5C95/kZK3m0I+9d9zh85n3Aenj9t0w2CKiuBMrD5xQGdl+74zI/FmztMLd1FRt3qfLLxfZvNnzQ3v3igwZIgLI3sGDZYdb7ZRe285zZgjOOMPv+bg/EJ3FylarVdY55u5y7/r56vPPjVufzq0NYe8+XLlSuw6jRvm85Rwl592N6gw6fYLL3bs9Ry96W71aG7kJiCxcqL128KDIvHkh1abpXp8vvtBG1nVQmO79uQ4nEw2B+323xBGw/L//z963h0dR3e+fhCR7351d5KYQkmxIuOSKl1prrbXqV2u11ssPa9ViFa0WiS1aIQpfxUaLlG8xtYJBbbykbkRMvbtWjZcUdFXkYtcbIF4QGC4KCAK5vL8/zpyzZ2bO7M6GAKLzPk+eB3Z3Zs6cmd3zzvv5fN5Pfr6OwDPVqqmpKfO1HjsWIAQXaWQ/EongWc3kFH4/3nrxRa6YsfnMzc2Fu6CAPlxMn05z8kaOpAqZRfPqf7a0oC03F4v79cMzOTm4nxD8SPIQ5ZAtBw4cHJQ4UD3q+gp9OX7dgvjXvwJDh+qftE87zXhw3ftNLhdfMGWq2y2MbJ1/vuUYjAtiQ0MDX8x0ifSbNgEeD55VFLzxn/+Yx98L7Lf8vSVL6DwUF+tCgaqqQlEUaaWcoiipAgGG3/wGcLmApUt1L7N5+PKhh2jrI0KAsjJg8WL6gd/+lr42ZQrUDRtsz5nl/DAyd/zx5o16evDKNdfghbw8vBIM4q9uN56cM0dagdoXxFm8736p3Zft/fpxtTere6Szk9/b72pGvMuWLMErrFChrs7yAcF0rdavN4cRNdjJoXS73Y6y5cCBg4MbB7IasS+wT8a/YQMNDXZ0pCqu7rxTf9xPPkn1tSME6q9+pUt6FxeQakKwmxGzeNz2+YiLmU7Z6u7Gbq3PYmf//uj40Y9QuRfhRdmCxxa4PseePcDgwXQu/vMfXcVmNBpFaWmpjnR6vV5MNfae3LgRyMmhaolgG8C2u374cOxh8z1+vD7M29JCLQcIocSrqyvjkNOqqCx/iRDg1VfpBl98AcRi2FNVpSfshODkYDBVndrWRtvhoG8aO4vj/KF2vFdzc3ttf7HluOPouH/2M0qMr7wSIAS7+/fnLv/GB4Rs7z3Zg4nx72at+bVDthw4cODg24gZM+jP9FFHAV1duoWJK00a2cKNN+oWTFF1K3O5sGHkSBpuyhLiYub3+xEKhVBbW4vTAgGs7d+fL+I9/foBZ5+Nj7U8mmyIktWCxxa5Psd11wGE4KMf/lAXSnO73dz2QlVVtLS00Hw3I0G9/3563iefzF9ixPSTm27ic/JXtxuqrPXP44+nrBJOOAGor6ftghjWraNO6Vu3AsiQH8iajF97bcoigZFJQrAxL48qpQsWANOn45iqKsTjcYxWFHRraue6K64wXbPePkSw++40zZ9s/ahRlp/NFMrctHw5vszJwdfFxcDGjVg/YQL2EIIvFizQfS7tWL/6irao2rSJhnB7eoAPPwQaG4EzzsAXra2cIAYJgctwD4qpAQ7ZcuDAgYMMOCgVtLVrgWHDsOWFF3DzzTfrwkjz5s1DYTiMrkAAIARrZs1Kv2B2d/e6zY5R7WKJ/ZFwGM9Nn45nwuGUWvOTn+hVkm3bbO3fbehRt09z+D74AD05OQAh+ExTrZYtW4ZgMIj8/HyEtYbH4XAYfr9fP4Z164BDD6Xn+ve/85c5+WVq0pQpJrVIdz1eegnQrh0Iwe4f/CB1jFWr6OvTp/PtTMqW242vrr6afi4/HxvffTe174suwu6jj0byyitxqMRigeVQXe7zoUs7/lIhvLy3IV1VVfHOfffRsR12mE69Y3PQ0dFhstaQkfR4QwMGa9ejMhTCE3PmSI9n/G6z1za99ppe3WMN2dnfSy/x8/1zQQH2EIIlhODPBQU4wuXCP1ta+D4dsuXAgQMHafBN8PPqLR6+5x5LIjJ/7lxc7PPhjiFDTHYOdtFbEsrIBVN0ks8+C9xxBz6aPTu1aL72GqAoVAkSYWixAgA333yz6Rxl1Z19RZpX19enFtxFiwDQUFp9fT1VA8vKoCiKfk7vvReorqbb/PCHugbebB4+fOAB4NlnsWLxYh15EFUcRVHQ0NCAh2+8EXNdLtw1eDAu9PtTx3r5ZXoMv583/v7nP/8Jt9sNn88Ht9tN7+H6eqBfPyyaOFGnENVNmsT/L6qRRvVIVVWsuvFGTjzx6KOWIcuNyWT6YgAjenqA0lK6X822IRaL4fhgEAv8frybk4OP8vPxUW4u3j/lFD7/slBmumvO5vWY6mqM8/vx6vHH4/nLLsNh4TCfj+X/7/9Rry6W76UotKn6/Pn8YUBVVWz4xS/QYyRjFRU8L88hWw4cOHBggYO56jFd8q7b7daFFKXn090NXHYZsHChySlbVWkjYEVRUFlZiWAwiKampqzGxvK5YrEYwuEwotGofkEXneb/+EfaX7CmBigs1O/sySehrl6d8Tr1JWlWVRXXezzYMH48sHUrPvvjH1GqKGb7ARFnnZVSa9atM+3TKn9INlelpaXweDyYOXMmAImy8z//Q491ww26c/f5fKlz7+nBZs2SQkwS93g8pgR/5pMmxV/+Qo8VCuGllhb4fD5+DY4mBB1MtQwGaZ7Z2rX2JnnWLOCII4CeHqiqilksdCr87R40CN8PhWxba4hg87rynntSxQjaX2coBHzwgXleOzvTu8Zv307zGidMoG74hFAF8pNPHLLlwIEDB1Y4mP28MiXvmqqujGDhqMGDdZV3jBSUlpZy5aO6uhoejycrwiWSC6bW6BbLnh6qHhjVAp8v9Znly6niMHQoXp482bK6c1+QZjb+GcOGAYSgKy+P9ve7/XagvR3405+AN95IbRCPA/fcw9UmGWREzagCWlUBcmVn0yZKSLVQonjuEwjBIOHcje2WEokEysrKdGPKmPze00OrXQnBw/n5yMnJQQkhWGAgRixkCbtz3tnJ2zklEgn8Qyuq4H+/+hWwZg1GlJbKrTUygJ/700/zfSZdLrwYCAAzZ3J3+F4n/3/9dSo8nkw6ZMuBAwcOrPBtVbY8Hk/mBeT//o/+zAu2EZYVhkipINmqCyK5kKpCt94K5ORgN6suI4S6egN0MT7iCP7612edhSXPPWcaw74izaqq4t0778Sun/zETAoJobYNQrgw3X7efuop7Dz/fGDePNN7iqJgxowZqK2t1b1XVVWFRCLBideW559PhSqPOgrYtYuf+3htTCsJwYBAgM9zJmUrEokgmUwiHo9bKlyb3nwTS/r1w8qmJrTeey82a8faQQhWnHkmTdZfsQL4xz96Pc+RcBhvzJ+P2W43VRTvv9/aWsPuPtm5a/5bewjBWEkHhL36vr/zDgA4ZMuBAwcO0oGFYPx+P1wuF+YZFsNvMliuTtbJ48kkMHAg/ZlnhprQ+2UZVREAqKmp6TWBSVdd1vrgg4hEIpzEJCZMSG3Y1UXVJK2CDbW1pvyg/UKaP/kE+NvfgEsvBb73PeDwwzNaZQCp8758xIgUSfvwQ937oVAIxcXFJiLk8XhQUVGRmq8PP6S2EoceCqxZw899iNuNXdq+LzOcuzF8OUnL2WKK47hx4+Dz+eD1elFaWopQKGRSkRKJBMYKRPCrKVPwSDCIl4QEcROEc7TEa69R5fKQQ/g4i4qKzOdtgKwoI13OVjQaxUy3G4kJEzBz5kx4PB7U1NTYsoOwmwfokC0HDhw4yIB58+bB5XIhEAgcdEnyqqryasSMBqpdXbTvG/NfOvFEXg2mqiri8ThvR2OlbMXjcSSTyawS0WWNm6XNrp99Fqtvvx3FMgVt5Upg+HA67upq0zG+iSa4unPbvBm7tJBkT0EBcMMNUNesMbWqEYmA1En9ySd1/l0A8LSm3Lybmys9d5nC2NDQYC8/zHgeAJYtXWqtCHV3A7/7HTV1ff1168lZt46axxJCCawwznT3l0ja2fe1srIyLTFj+YfZONSrqoqpU6fqtuP7X7sWePdd3ecdsuXAgQMHadBbVWRvK9/62m4i7f6YErR+fUpdOflkbqgpLmBiw1+fz4dAIIDq6mre+7CyshIejwfFxcW2lIGmpiaEQiFp4+ZEIiFV0KxsEb6IxWg4T0yi7+7mCtM3zcLDdG4bNuAxQcHbNWQIfm3IoaqoqEBzc7OOGL3x6qvpz0nrGLCtqkr6OeO8yHo8iuE6Fr4UkY1B6M7x4wFC0DV4MFfgdHjjDVpIQAhVCCX9O2WwKiYIh8OYOHFi2jB3pntDfD8Wi/FiA+kcseKOhga+vUO2HDhw4CANemOaubeVb/vVbmLJElpmv2gRtVUYOpS2k9F8tawSs+vr63k1ot/v530Qxc9kqhJramqS5giJ22VKDDeGHxfefbd+Ab/2WmoC2kufsH0Jq3Pb8sQTPPdKzcnBCs3d3erct+Tl4cPcXCy8917zQW64IUWgzzzT9LZx/urq6rh9hQjW43HmzJmW19RODl4sFsOgcBhv+P0AIVhryEPDe+8B4TAd7w9+YNkqRwZGXmVKqdfrhcfjQYNAgOyiqalJp2B5vV54vV6MGTNG9zk2R5uiUTr+G2/k7zlky4EDB98JZKNqGHM+ZInmVi1h9jY/aL8n5TNzy4EDpSaiiUTClJhdU1OjI1ctLS0oLS3VfYapT+n8j4LBIKoNIT9Z42aWtxSNRuH1euHXfKUyETEAqcR1Q2jtgGD3bprbJcBSEdqzB/j5z5G47DL014w5I5EIYhrxZue+YtEigBB0FxQgEg6b75MHH6RVgOedB3zwge4tqwT5doMlhEiC7VadGklcU1MT4vF4ar/r16NLs1zY8tRTdKMtW4CSEnq9fvYzqacaQzKZRHNzMzo6OnhoMR6Pc4VVppS2tLRknfBufCBgodwRI0ZIw6vt7e34tUYkkZcHPPQQAIdsOXDg4DuAbJQi2WftmmYCe1/5tt/tJvbsSTUkfv5509sy9YkpWkAql4u1qmGfyaRsJRIJnksjbhcKhUzVZawir6WlBaqq8v3zPn0CTOSO+TP1omKtz7BqFTB5MlVsjj469foXXwB/+YutENYnv/sdOqNRaicQDuPT3/6Wnvt//8uVq8ciEWw+4QRg2rTUxrt2SX29ACAej+vIbiKRwIgRIwBQsqQoCkpLS3WkxU4RhJHEMYJSVlamJ+WaOezWww+n4d5Nm4Bf/IKGDr/6ynL/dXV1OsIzYMAAuN1uFBYWZlRKs7FyMD4QyMi9LGReW1uLtRdfnFIU58xxyJYDBw6+3chGKbL6bDKZNFX1ZbuPb6yy1dOTSiw3VM+xxYWFjlg+1k033aR7vbq6GgUFBfD7/aioqIDH40FRUVHa3B3jvquqqkyqCSMhVqRKp5TAQtliIR23m1a37Q/cfTdQVET/iotphSCzQygthbp2Lc1DOvJI6vRuBzfemFq8CUHniBHUluHpp1Pu5uyvXz9g9eq0u2NkyuPxcBJrtH5oa2uDy+XK2BbHCDEXLZ1dyDuvvIItbG5Y+6KeHt7X0QhVVdHa2gq326275l6vlz8cVVVV6bYpLS1FIBBALBazTO63IrrGB4JEImFSYktLS5Gfn2+eow0bUvYp9HvskC0HDhx8e5GNUpTus9lUs/Wm8k380d+vlXOvv05/zgcPNlkmiItmU1MTgsEgSkpKoCgKLrjgApOK4Ha70draqqsWs1N6X1FRYXKhN4ah/H6/ZZ++tOaon31G1RJCKPn58su+nkGKXbtS/166VEd+egoKsPq44/CTYJCfz6Pz59P3w2F7+//0U0oWWeL4HXfwcz999Ghc7PPh9d/+ltpPZLCcEIluKBRCaWkpvF4vfD4fBg4cqAtrDhgwQF5xx7BiBW2p9M473F1dVVWENL8qI0GJxWLwer2oqqpCJBLBizfcAIwfr58/CcR7xev16sbBQoQej8eU2B8MBhEMBqXjz9TMms3TtGnT4PF4MHr0aKnKO1trMyWdo9tvB+jDk0O2HDhw8O1FXyhb6RJ+0x03o2GnBlnoMt3n+7Sq7ve/pz/nV19t2jdbbGQ5PKFQCCUlJbrPjxgxAs3NzXzXmRYzq3ORhWtCoZBleT4r3xePpSNdu3YBY8fS87z88r2fM2GcS+JxfH3uucAJJ+ic9p+aPRtVgQBOLC7GUC2PSDyfCkWh4xk0yP4BFyyg25SWcrPU3twL6UK4iqKgvb0diUSCX3dLq4W33kq5pBMCnHMOH5Pf70c4HOZKZ6YWQJnud+NYw1p+mki8o9EoLrnkEpNPlt17TKbaxWIx/pCRSCTQ1NSESCSCESNG6Ppfpr0OH3/skC0HDhx8+7GvValMiMViKFUUHFtdbSId2YYNs6pU7OwE5s4FJk6kfQ6vugpfXX01Vjz0UGr/Ggn5oq3NRFhisRhXIqLRqG7X1dXVyM/PR1hr2hsOh1FQUMDzrYyL2futrZjjdmPTkiUZ58vK7iEej/MFT1EUlJeXQ1EUvgAaQ0u6ZtDvv0+TlWVWA71ALBbDscEgvtDIRld+Pk9AV1UVgUCAz00oFMLgwYN123+vuponiNsy+KQnBhx7LO1HuBewKk6oqanh94AdCwckEjoFDxdcoL2cajHErpeoZhn3mYmUNzQ0mAowotEoDjvsMIS1JuriNZf5ZBkfIpqbm02FH1b5XEYrjPb2dgSDwayc6x2y5cCBg+8E9kaV2tvjnhAMosvrBYJBfF5Xh0FC1Vg2YU5bxOzzz1P/7ukBBN8m9tdFCB4oKMBjd94JrF2LVydPxqGhkDTJnS00RgPTcDgMn8+ney0QCOjOS0eYtObIe0IhaZhLpqjJVAcjkQmHwzxJWURvq8/sQFVVFIbD2KX1INx+5JE4PBTix5EVDLAqP/F8vj7nHHpNZsywf/CeHlO41zi2TMafgLXtRqbQr/E4W++4A7jkEmD2bF0Y0Xj9rBpaZ1KYWHGEzEB3/PjxCIVCqKqqQjgcNoeQte3r6+sRDAZRWVmJQCCAYDAoVdzS3SuZfMQyzbtDthw4cOBgHyKRSOBtn09Hdv7r8WDZo48CyE7ZSkvMenqAKVNowrWYl3TLLcCsWdh2222Y4vVi0znn8ITq8X4/kskkIpEIWlpapGpSc3Mzxo4dq1tsvF4vrrjiirSVgMZF9J0XX0zNgddLK/E0yJQNq8UtHo/rkrqZomEkfr2pPrOLRCKBv7LcqcpKYOdO3XHi8bhUiRFzh5qamvDe7bfTfRj8mnTYsYP2S7RIGhfB5owVMhQWFppy4UQwhdBuaxrjcdKFh+2anBorIgH9fcRIu/H+Y35ZmXICmflotaYqi/dJX7XmYWMrKSnhrYSYwobOTuD99x2y5cCBAwf7Eqqq4sxAAO+0twOPP47dhx5K1aXCQkD70bYburQkZhs2ANdcA+7toykoInRK0wcfANdfj9qaGk6mrBQGRsZYNVlLSwt3ys6U8/LMbbchYvSIqqyk4/zPf/g5pVOxjIvb1KlT4fV6dQt9NBpFfX09IhHaFZfCpgAAIABJREFU504MLenGdMkldJ62bNmr6xmPxzHL5eKqlEyNMeZoMUsLMe/nqJoabCcEe9xuSj5nzgROPRW46ipKsK69NqVM/vKXGccls1uQVXkat8tGxTUeZ3kigTILZ/ZM+2YVkTJFVZxL2f2XabxMETNeB6/Xq9vW6MifLZiBaltbm9QW4uOKCjjViA4cOPjOoE+TyrOE+FQ+XFFSDtPHHafrPWhnfFJidtNNKaL1+OPS7eyQKdbiJBqN6p70rVSKtOrFa68BLhd2/fSneGPRotR5/fKXdKz/+AfPnWGeXQw1Ggk0zoWsjUw4HIbb7UZ7eztUVcXEiRPhdrvN/QM//pganPbrZ7v9i9Xch0Ih/K+WqzV/0CCpKsJUo8rKSl2Iy3gd3lu4MBVWvuoqnQLK/448EmhrsxyXMQfJKoxnJyyYKYyoI+3btgEVFdiZm8uVWrtY/dBDuNblQvuLL1redwx2VTJx3IlEAuXl5aa8rGg0ihatQbadeUkHRhbLysoQDAYxatQo3fujR4/GnQUFDtly4MDBdwP7tf2NBb68+2689cIL9Id97VqgthZYuLBX+9Ithiw8l5sLPPxw2u0mTZqkM4OcNGkSAP1iJrVPMB4z3eudnUBTU8pM1Fj9p1U/Lv3lL/kxjU7cHo8H0WhUl+DOqsKMbWSi0SjOPPNMXTiMESymII0dOxbHBIMAIdQYtBcwqoqXEILb8vLw4oIF0rCSoijc8d7n8+mqJC3DryyJf+ZM4KKLaFFDmmbNqppqosxCh1Z+UOnMSNl4y8vL4fP5EAqFMtohLFu2jPdc5PO6ciX/TCZF60d+P74XjWKMdo1VVUVZWRniFrYVdvYphjYZ2TXmenk8HgQCgfSK3549VB1O42BvJLRGjzJ2rFlaU3eHbDlw4OBbjWyr/fYJ7r8fIARdAwfizVdeoccWbAJ0/84WRx9Nf46FPmwixMRdZuMglvWL4Zq9Uv7eegu4/npAC5OCEGDCBLN/0u9/jx6vF5cYcqxYrovH4+HkJBwOwy/klcksKAKBgK5HY319vVRB+qClBSAES/r1s52bJMJuIYMsgd/n8/H8MkVRbFkOZAIjF6zRciwW46HD8vJy28nf4nhlxNfKDiESiaBh6NDUtc7JAdra9KQnHMZLU6bQKsU//hFYuBCbVqxAJKK1GVq1yla3AUt8+CGwfLm0FyIjXH6/n1fT+v1++Hw+qa+bDhddRM/p+9/XF5wIkFXMDh48WGdPUlhYiHn5+Q7ZcuDAwbcf+739zbZtwFNPYetdd2F1fT12Xngh9yKqN+Qacfz5z9h1yin48NZb8eV991F1I4OikUgksPmFF+hPcSgkbW9iVFiMYQ67yeNSImZ86i8tTS28I0ZQgmmBxOuv40hJv8XJkyebSILX60VjYyNf2NhiztQ5l8ulC4Gy9jImBenVVwFCoGrO69mSG7ukXVaJ6PV6uWJTW1trz17ho49ok3DIPdusCBvLQZo9e7at5HerVkvsWOl6W7532WUAIdh1+unAI49AVVX0Z0pSdze2HXNM6p7QCFlnMIjJhspRWT/MjHj3XUCzzng5Lw9nFxbqzpONm+XYsUrIjA8Vixfrxtw1eDDeeu45W1WUHo8HbW1t/GHG4/HgkzFjHLLlwIGDbz/2u7J1333oEo0etb+ZhhYjfEHbuRNfayEu09+UKbycHqtWAVdfjRenTOEh0UK3Gy9dey3w739Lz5uVuTOH8IKCgoyqhRHSyrOHHwYKC/UtYcaPp4rASy9lVOqsyEJra6u0iq+1tdUUsnG5XLjtttssk/uNCtL7miFo17Bhva5QNOXLPfgg9Sk791xOPq0qEePxuH17hc5Ofg/cOmmSaf6tfMiMVhd21ErZeKuqqpBIJNLeI01NTbjA6+XjXHH22Vj6+ONY6XYD991HFU0thAZCaPj4Zz/DF//6F+oN5I4VXIiQEUzdubBKTuFv47hxGBAO904l046xbMoUgBB0h8N8v2eNGmUrl2ySdq1qampS1Yha9wKHbDlw4OBbj/3Z/kZVVfwiEMAXJ54IXHIJ1l1xBX4QCJiSwMUn75GKgnUTJwInn4ytxx2HJ/Pz0cMI2z/+QTe48kqAEHQSgrNtEEeZYsGSySsqKmwlGrPw3TuvvALccQc2nX023hGJ5A03pMhgNujqQttdd5nUHVWlZpvGKj5VNbflKS4u5iTLyraCKUjRaBQRRcEuzYKjWvDEyha6RX/VKjoPgmGprBKRhUjt2iu8fN11fI7PkxBkWSWo1+tFMBiU5ttJxy68Zpxz5lumM4UVwD26li4F/vY39OTmAoTwe/ar2lqguxvvPfooVubm4uuzz9Ztnynhnb3P7Brq6urMhH/mTIAQLOzfH7juOsDlAk48EaM1i41sQ8WxWAyhUAijS0qwnhDsJgQnejxYM2uWbu4z5Sya5vjZZx2y5cCBg+8O9lc1okx1qKmpMS1o7IfbSqV47447aK6LVq2IjRv5AtxJCE4mBOVpQqIyxaK2tpa31LGTaBwMBlE7Zgwg5uYQgi6XCxgyBOjfn6oXgmt8xjletIjaGQwejI3vvKP7fCwW0+XYGBdNIwlk4cOQhSErU5BYEvmj/fujraAAT82ebT2+bHDPPXROTjpJOofMMV3maG4FVVXxgFbBBkKw4ZJLpGE9I2FhRQLs/0bSlc4bi5EnRginTZtm6ZDOyJkuAf+VV7BUU7m68vNxVCjEx7Hw7rulIW4r4tfa2gqv18vzr2SJ55FIBFsbG7GnuhrXsev+xhtYsWhRSiUz5kWmgbHKdWVTE0ZrxTTi+HqliPb0AE8/7ZAtBw4cfDewX8jWzp34IhbDkQZlQ6yOKisrM/VUs5swrW7YgL9qhqQgBJsIweEulzQsJVMswuFw2lJ3aYWV242tP/oRQAi2H3EETgkEqI2CpiyAEKCsDAvuvttexedXXwGjR9PtDj+c5rgZjq2qmf2UjErXuHHj0qolfX79u7qAkSPpedx9t+nt3h4vHo/jjojg+j9njmVYj+Uj1dXVSXPdFKHSL9M9xhqNyxS4ZDKJ5uZm7hMm66uohEL44pFHgDff7NW5M9LMQt5+v5+HTEeMGKH7rEh6pCpZVxfNGzzmGFrx+/LLNNT+5psmAsaqXGXHOOyww3Q2Eb0JTWLXLuAvf3HIlgMHDvYN9peSZAf7zfph8mSAEPz35z83LQDG0Ii4mNn1EQKAhx54AK9qYZv1hODpP//ZUrVgCwlLkGcLmBVkKltRUREURcGR1dUYpFW9AQDWrwf++1+gqgogBE/n5sJHCPLs5MWtXw8wr7GTTwZ277ZU+ETfpKxDOPsKW7cCc+bQ8Q8fzptC7y3YdTyiuBiv5uXh5euu42G9oqIiy/tm6NChJhVzzJgxPIcrHo+ndftnkM0fI3JlZWXweDyYMGECIpEIZs6cydW7dKapdiAjg+zBoK2tLWNlpWncyaS0TRUIwbaaGmzRchzZcWVVruFwmFe62vleWt57TU1wwogOHDjYJ/gm+FoxWCXIZ+od1yvE4/SnccAAbF60yFavP3GcdonDxmQSn155JTZp76fbt7EaKx3SJa5bbr96NTqFBP8ThIrPzy65BLjwQpow/c47+p5+K1cCAwfS7f72N8tji15ZdnOejOeUSCSwbdYsen36ghhdfnlqAZ83r1fjsVPd5vV6uRWGMSwofj6ZTEo9npLJJG/g3Ru7Cav9zp49G5FIJLOFgs25sGrZM2zYMAQCAbjdbvPDyCOPSMOTHNu304bdxx4L/PCHWFdRgS05OWjr3x9P5eebCg2YsmZ8MLFD4NO2L+rqAu66yyFbDhw46Fvs9+q/DJBZPzDLgD4ng11dwPHHA4Sg0+/Hlw89BCBz/zcj0qlgsvPLpFpko/iIKlsoFILf789IdLY88QRUQtBNCK7S5tjndqNbrEYjBAgEqN8SG0drK329vBwwJMGLRCsTSbA6PxYaO3LMGHzFxrBmTcY5MGHnTh7uBEA9xY48kjaQTtMUGgCwfDnN7fr73/H2r36FXwQCqfn85z95H0vZdaysrERjY6P0ujU0NHA1K5FIoLi4WDd3RUVFuurEbNRThubmZpOJrJjzZ3VP9Yag+P1+k7LEilpMpIfZM5SWpjUeFccTiUSwoqMDWL3astCgvb0dfr8fra2tludlfHDp6OgwFUQMCYXwgaFC2CFbDhw46FPsd1+rDJCRP+NfX5LBh++9F49qYb4uQvD0kUciGgym9TISxxqPxy0r8qzOLx0hsdM02IhkMonGxkZTa5x0ashDDzyAQW43b/z7z3/+k6pXt94KnHUWDbcxwuPzAWecATQ00Hlyu7F50SJ+PmLoMBORFJsp83J7pJK+q6urMd7vBwjBG/36ZX+du7qAM8+kjv/r1tnfrrMz1ZpI+Pvyxz8GACxbsgQPFhSgc8wYYMuW7HL3tHy8QCDA2xSFNcsD5vEk5myJ22UTdk2nmFnhiTlzENEMXSORCDU13b6dvnnzzQAheG3OHNO5hkIhBAIBnbIkraz88kugpobO59SpNi6ERmRra3UN2q0KDay+H4y4izllp556Klwulz7fq6cHL3q9+DwnB09rlYyAQ7YcOHDQx/imKVuA3vrB5XKZxtdXZJCdew4hmC4ssMu0vCo7/d/KysoQNbSUYT5NVouj1YIh5qTYdY0XxyGrZrTT8sXKMgCJBHDaaXRetET/2W436izmJBMB4RYEkjCXSBTX/e53ACF4OBzO/jrfcAMdr6LQHDULsLlk4enNHR0pkjV0KDacfTYeHDgwZeWxaRM+Ys2sf/hD4OuvbS/8DQ0N8Hq9vEVPUVERXC4XAoEANzFNZwHB5i4YDPJkd6tjWbV4MmHHDuz85S/RTQhWaYT302nTsKxfP3zx2GPA11+jS7vmMS0RXgQLd1qFvFVVxZJnnkFneTmds8MOS5G4dNi9G1vvvBNv9euH7WPHAp2d0lB7OtIpu8+YjYqxAfWKjg50ate90e3m+3TIlgMHDjj6KsF4f/paZcR77wELFuCLxx/H0rY2JLUFeV+QQaOq92NCcB8hSCxeDIDOb/2gQei46y7ddsZKPGM/N6/Xi/r6+rQKlezaJRIJlJSU6LYrKirSPdGL+8s0DiulRdYgOm1O0IoV2HbbbZjrcuEDLdSaqTWMjEiaLAhAQ28+nw81NTWpF19/HSAE63JyoGajTrW10aUuN9fSOFbswcjIT3FxMSLhMD486SS6vdZCyEgcK0IhdA0ZQj/zox9xhSvdd1C2n1AoxK0a7Hx/RdVPTHZPp3CxakQpPv8cezS1aXdODjUc/eQToF8/7MzN5S2qjgsG8dnUqVA/+sj2vQXQe6BcUbBK67f55dCh9HudCc88Qz3QNPKzMScHZ40caUvhFckzI6UiamtrMXToUP5dUhSFdy9449JL+TE3nXIKsGWLQ7YcOHBA0ddJ7d+IasRLLkmpC+xv4EAkLr4YPre7z8mgVciSeQa999hj2EM088c77+TbGcNlsVgMXq8XVVVVvLdeVgnOH38MFBdj99ChmNuvH1bNnQv09FDlx+3G6okTU21VhP3F43HUCm102Dis1DiALtw+nw8lJSW61zN5EtkJEYrzKiOSozRnb6Mb+ciRI/Xkb+lSrMzJodf/+ectx6TDl19SHzFCACEcxMBIoNgAmo2BqYmHhMPYOX489UiDBXFcsYJ6lhGCr8rKoFr04ks3b9FoFA0NDbZOS1VVKTGuqKjonbq7cye2a4UOnxYUoNbloias06YBhOCx/HxpSFi8x9ORH1VVMTgcxtclJQAh+DoaRVkaSxCOVasA5nBfUQE0NUFds8bWb5KxzdWAAQNMc8aULfbdbmtrg8vl4v//uKEhlSdYWOiQLQcOHOzb0N8BJV2s2u2kk2hTWfaUO3Ys1PXr98m4jKreVVddxRfYUkXBhyeeSMfQrx9XS2RqhdvtRklJCRRFQX19PcrLy3VjTUtmtHJz/hcK8ZDLHdqcXFRertuEhXGMi4qiKCgpKeG9/fSHoQoJK/83Op2nq4DMJkdJBpZPZLQgmDx5si65nuVyrRo7ls7Fgw/a2j9rHo7vf9/kzSSOPR6PW3YHMF2jF1/EjgkT8EZHh+48l2q5XZvz8nCIaK8hQbbzZlSl2LhEVFVVIRgM9uq7sHH1amxlRPa88/DeI4/gX1rOIgjBS9ddZzluRVEyVslyknbjjZQ0bdhgz1z0jjvoGE47zdLcVPbbpKoqfD4fvF4viouLeZurvLw8/uDh8Xh4zpYYYj311FN1ZPrJv/4V0NQ4h2w5cOBgnyW1H0gLCFVVse5Xv6LKwubN9MWeHloy/p//pD5o02U622OnTUSur6c/oeEwTSKHWfVgruOMNJSWliKsLcQZicmkSQAh2HXaabjN7aa5Ki++iGXLlmGulif0N0OvxpODQajr13MCNWfwYLQVFOCfl19umcxvJGYshBYIBODxeDLmA/WmQo5BrMBjFgQDBw7Uuc7r5v3662kFod17+swzAUKw5tprTYtxc3MzKisruQIiK36Q9uerrqbX/ckndft7mDnGP/ywLdJpd96MHlmTJk2Skh6ZT5YxB80qRJlIJPCbsjJa9CAQ/M6CAmy75ZZejVsbAPDyyzyM1/7CC8D27fZJ+YQJdCy33552Do1h9PPOO88UYg2HwwiFQkgmk2hpaYHf7+fk1UhmdfdcVxftCXnSSQ7ZcuDgYIKdfI7eqDXZKFt2j3EgE+Vtk7zt22mo8f339/mYdOjuBk4/HaLtASAnaTLvJbfbbZ2oDAA//Snd9xVXmBa417Qei1sPPZS/flYgQD+/cCEAqlgt1vrc7SIEjzc2mg4hU0gqKyvhdrvhcrlM+UBWizWrwLRSOIyLvnFuxOR/WUPj3uI/552H1/v1w8ljxvDFWJxL5n+1bNkyXfGDlQEpAGoVQQj16RLmcWxtLaCFnwB7bWGs5oUhXSUhOw9jBSfbL+spWVJSAo/Hg5EjR8Lj8aCwsNB0Xuw6rLznHuD007F70CDc63Jh04oVaced8XfgN7/B18EgSt1u/jsyfPhw+6T86aepyfDbb0vHoCgKWlpaOIEKBAK8klYWYi0rK+PXpDdtexyy5cDBQYJMBGJvVSQ7Se3ZHONAWUDISN4QtxvbGxr0nkg9PbScnxCuLu1XbN3K3dfxm99IP2LlvXTdddelbWWDOXNovopWkRZ/+mm8PW0adlx+OcAMSC+/nC98W+fPB6uaY9j86qvYevjh9PX//V/TIawUEqPKoygKhg8fbunXlc6agr3HFn2jUmalBu4toZdVnymKIiW+IvkrKytDa2ur9RiamwFC8PW552ZleGuFdHOXziOLjVdWicqUura2NqlyOW3aNNP4xOswJEMYlB1bRrDFMe362c8AQjBR+B67XK69JtOqquKKK66Ax+PR3VderxfnnnsuqqqqdJ+vqqpCIBDg9iu9bdvjkC0HDg4CZFKJ+kpFSvfUme0xDpSyJSN5zfn59CertpY6mQPAZ5+Bl/Tvg1CiLbz/PnDiicALL0jftiI0bHGwTIru6QGeew6tmqnl2NparGd5NYQA554LfPEF0NICvPIK8PDD9PXvfU+/n+efp68XFVE1zgCjQjJ16lTLBHtmByEuVixE1NbWZrKmYOrD3LlzLSsdxUXb6DSvI17Ll9OEaRtQVXmVY1lZmem1aDSq650XDAbT5yBpeUTNLheOqa7GIwUFeGbWLNvhNfH7mYmkZeuRxfbX0tKCkpIS+P1+FBYW6s6F5XbJkuntKlasoMLr9aK0tBShUEinGrLr96pmDnxzHz6ssWOw4xrNSBVFMVVJejwe5Ofno6CgQNo30hLd3dj+pz9h2YIFUFXVIVsOHBwMyKQS7Q8VqTfHOBAWEDKSd0Z+PnYOGpQiG1OmAFddBZ48/w0GWyBYiMqYhG43AX395ZdjnsuFLx5+mBKnzk7qVSQm0huS5tHdDRQW0veYP5TkODICoKpy64h4PI6Kigo0NDRAURQceuih8Hg8GKOF65g1BfOSqq6uhtfr1S1wtbW1PNQ1duxYKIpiWjhFJWzNMcfQc7j//ozznUjQRsuF4TBW//3vwJNPciJlJDfBYBCKoqCqqgoulwvBYDCteey2W27RzzchWNKvH9QNGzKSFaOPWUNDgykx30iC0nlkGY/HVNSOjg7k5+fzptDGPMGRI0f2Opm+qamJ+4ExM9aWlhbp3F6mVRLG9uZhbfFiYPx4fNnSYmpZ1NLSYvL6qqqqQkNDA+8nyjpN1NfXZ93e64nGRoAQbM/NRSQcBiFkFfYl2SKEnEIIeZ8QspIQMiXN587RJvSITPt0yJaDbztkuTv7Q9nKNKbeHKO3eWR7A5Hk5efnIz8/H4O8XlzDFC7x7+mns97/Pjunri7p/pPJJM4++2wUFxfrPp4ud4SHIB99NNX0mRAsW7Ag9aF584CjjqKJ+sOGyefivvtSCmAGWwIgvSlqNBrFiBEj4Ha7+YIrU+46OjpMr4fDYR7GMYb0rBbORCKBZcuW4UGWgD5/fsbxM8I4/5prAEKwpqCAJ5DLWhlVVFTA7/ebXP9lRPit55/Hwv79AY+HJpQXFuKskSNt5WcFAgGENWd2ZgdiR7mSeWSJthWstyE7RiAQMIWCmRv9zJkz0zedfvFFQOsEIDsHr9cLr9eLsrIyHpobO3YsvF4vhg8frvv89VrHgYf69ev9w1plJUAITgoGUV5errtH2HiM81dUVASPxwO32416zR/N6nyMoVAxj25IOEwtXnJzsfy115CTk9ONfUW2CCH9CCGrCCElhJACQsgyQshoyecChJBXCCGvOWTLwXcdVnlRmVSi/aEifaPMSjOA/Rjm5+dzcphPCE7Ny8OOK68EjjgCuOaarPfL5iAQCMDlcmFelo2IpVi1ira0+elP8fCDD+rCKZMmTUrr58QXA6M9wbp1aGUkgxD05OXh49xcbHnuOfkYrEKpPT004X7CBF27k3Rgc29FmFpaWrgrviw019jYKPWSYgatDQ0NuvdlC6c4N/cxCxBNFcyEWCyGYkUBCMFXhKBJMKE1KnmJRAL19fW23PYZkXvn1VeBNWuw/I03dCFRKwIfj8elBGjQoEG68CNTBdOBjYEVLlRXV3PX/VAohJaWFuncDxkyxLLptKqq+GDWLH6vbVyxwjRHra2t/BxkqqfH4+EeVcuWLcMfNGVr5wUX9O7BZscO9OTloZsQLF+8WHpM1k2ChQebmppsNW1nTatZ6x6Px4MJEybw7y03Qa2ooHPy+usoKCjowj4kW98nhMSF/08lhEyVfG4OIeRnhJCXHLLl4LsMOwrWvqhGzHaMB9ys1Cbi8bhuLtlfutY36aCuX48itxtuw/4sn/Tt4oMPAM2S4R8ul+UixJQF1pKFlau/sWgRdp1yCjB3Lt1fTw9f+LYTgj8PHZrRvykt9uzp1WZMQamoqNCFAlVV5Y3AZSSitbVVStTYdTOGSNvb21FQUIBwOIyamhpTuHUKM7c89VTbY1c3bMCecJhuJzE1ZefBelnadURnbugL+/fHhX6/NF/JeJ3i8bhUJfT7/ZatmKwQj8d5yFYcr9/vR21trTQXLBAIWDZnZmN/SGg6/mMtnCo2Mmd9BQF54Qez72DEcfVxx9H93Xhj2vORorOT9t4kBEmPRzdW0Ui1oaFBGh7MlLtqVSHM7rn29nZ4PB5s1uxDNowfv8+VrXMIIXcL/7+QEHKH4TO1hJCF2r8tyRYh5DJCyJuEkDcLCwuzn3wHDg4CHKgKvm8lNm3CR2efjcsMxCiHEEyZMiX7ys2ODnRrvd12EYKXCO1/ONLr7XUOiw4vvYRu1itv7lz+g19cXKy7/hUVFWhuboaqqojFYugfDuMpjRR87fMBmzal2szk5OCLRx7ZO3K8YAHw6qv8v3ZJqqqqmDp1KoLBIEpKSuAWfL3YYmS0TVAUhXsYsZwsWZse0X+straWKwnie+Ki+mhTEw3dEZK2v6EJjzzC5xGPPaZ7i5EMllPGFMja2lqUuN146Ne/lhYVYOtW7NHCWyAE2//0J2miu0gCVFU15aSFQiHdHGRK3FZVaung9/u5NYeIiooKXoggXpNwGpKuqioGhcN455VXgM2b8d9p0/B9lwvLNLsFUc0Ur7lVPp+O+OzcSZXIDRtsXSqO7m7g178GCEG3ouAYQ3g3k5GqSHxZbpz42UQiYZq72tpatGjFKOyzRUVF+LHWAH1rTs6+zdkihJwrIVt/E/6fqxGsImQgW+Kfo2w5+LbiQFXwfeuwdi1vtbI+JwdBLXn5l/n5aOzXz94ct7fTRHqGDz4ACMEmQtAt5Hx9SQjGlZT0CSHe+ve/A4SgKzcXR4dCPMQzbdo0AOZKvEgkgs9Zoj8hONbloirb119T9/wxY6THsa3qffgh4PdTsvH225YKjDF/JRaLmXKKRFUuGAxixIgRuvEMGzYMfr8fdXV1psWOLcJWFYfJZNJEWEyL6tFH03n63e+yuyh/+hPdzucDli7l45Upb8lkEgvuuotfD0j8yfCb36Te1/6uN+QrFRcXQ1EU3TyzuTe2t5HlDhkhVuF5PB4ccsgh0pC0SN5kRMOIRCKB/wSDwMiRwNKlSCQSJrsJMZxaVFTECwr8fj9CoVCvDG3TYvz41PVavDgrI1XxujLCyfodGpu7y1RMMU+QkcdtWs/IfVqNmCmMSAgJEUI2EULWaH+7CCGfZyJcDtly8G3GwZQX9Y3FokX0J2rgQMRvuQWKoqC6tBRrc3Kwy+VCOBjMrB4ecQQwYABvc4PubmDHDsybNw9hQnCe243ntLYka3Jz8dyTT+49KV65Et3aPt/TDEZZKNFYds5NRa+/ni/aPxk1CoMDAezWyuZx4ompfX/6KfDTn+KJ229PG7LiePPNVM7J//t/UDdskC4yTU1NuvwVn8/HE62NCsDo0aPR3NyMtrY202Lv9Xr5AmxUcBRFyZhSooNNAAAgAElEQVSzxhbHiooKuRrz7LPUY6y+Prtr0tMDXHABnYennuJzL8trKisrQ4mioNPlolWuQv9LjokTAUKwp7ISH9fVoScvDxdr5qiAXvUznqeMJBurFGUNymXXbfLkydL7SnYMK3Kuqipu8Xiw+vbbgeXLpWMXCxtkip1uv7t20etjULOyCvk/+igQieh6YGYKC7L34vE4ysvLpeTdeK+xikWxWlM2n/j3v4H+/fc52cojhKwmhBQLCfJj0nzeUbYcOMDBlRf1jcSqVfQnatgwAKn57NLsH8pdrvTK1kcfpZ6Od+407b6pqQnBYBA1Y8ZgQX4+TvB4MpMXO9B65D0Wieherqmp4aFDBlWlPlTL33gD+OEPOelbw5LiBwwAHn/ctO9thGDl3XcDkOQWdXfTbX7845TyMmIEsHWrlGAwlcpIjoYMGcJtF4z5Z2wxOuSQQxAKhXQLlqyqkHlZWYVvGEmuq6uD2+1GYWFhymF/xw69ke377+v/bxe7dgGXXsrDgqqq4qxAACsWL9bNI1eXdu2ShxABoKcH7VOn4oxAAGPHjkVUUXQhSJ5cbXGeImRVin6/3xT2smr4XVRUhMbGxrQ5oMyiw+r+NipH4rmwnC1b6tV77wGaCoTDD+cFG70ya9661fIt8bdVVGoDgQDPKwsEAhmvgaqqqK+vp78DWg6lpanul1/uW7IFSqB+Sgj5QKtKvF57bQYh5AzJZx2y5eCggkOKvqHYsYP+ROXl6XN0NMf4f0+bll49vPdeuv3//I/lIdiTsJXhZq+wbh12XnQRRtvcJ3M6P270aKwWmv92DR1qbkG0eTO2aKQMHg9w223A7t36RYQ17yUECARoteb69fx8ZaG6MYZQZU1NDVwuF8LhMK94Y6rUtGnTdMncra2tugbbyWRSmjBvTIpPJpOYMWMGz5WzMvDcdPHFwOzZ1sSnt3jkEXTn5qLB48k6DGalNIk9CNOpKmLYUKzwE+dLbBpudbz29nbuii4bIyNZdnpyGn8Hxf/b+o1cvpzajxAClJRQVRVZpFUsX07NitOYE7NzCmo2ECw/UJZDlk5dlO3Xzhqwz8nWvvhzyJaDbwL2d5Nlh9hlifPPpz9TxcXAmjWUODAisXp1+vl8+236uYKCtH0TuWogKCjZ9k2zCg3ZzTNhKttpI0Ygnp+PjquvBj75RH6szz7DvwSvsd0DB2JRXl7q2Nu3U1Vh1iypzUNTUxMUReFP8lOnTjUlOjMPKL/fD6/Xi6KiIvh8Plx66aWmnCNZjsygQYPg8XgwatQoRCIRviACNO8rPz/fZNRp1ZpmXXk5JZYW3k+9xuTJACHYccUVWX8n0ylNDLLrL5IFFrJ1u91SJVAkW+L+KioqLHscGj/LSNaC++/HsiVLONnoi/ubY+tW4KabKLEnBDjtNJ0qZbtg6Jxz6PaaYis7p3A4jCFDhnAD3VAohPz8fE5yjdeksLAQiqL0WU6ZQ7YcOOgF9nci+/4mdt8K7NhBicNxxwHMPT4nB19NnmxvgbzoIrrNmDHAjh2WuSyRSAQbLr4YyMnBuokTs1K20l3XbMh1Np+NxWI4x+/Haq3ycY/Lpf+AhTogGmH6fD74fD5uSOnxeBCNRrlpJSMHxqR5WW6R2I9PVBZcLhc6Ojp0xIMpkUYF64knnpC+vnvYMHoN33sv47xkBa2ZN+bMkb6dKUconXJl3AerzDTOEQvZGs87nVrF9mdrbGvXYvMZZ+ArQrDluedoxd2DD+Jynw8bbVZ0Ggsqnrv55tSb3d00sZ49AJ11Fi3sMIwn4+/s7t0psvbRR9JzCoVCXMkzqoCBQABNTU2ZqyN7i9WrgYsucsiWAwe9wf60aDiQFYoHrZr29ddUadqyhf6oH3ssUFqKZ2fMsE9av/ySLwQv3nCDrkpL9NWKxWKYrxGXlbm5WKA1+s0E43W9lxDMzsvDxmSSLuKXXcar39jn++paqCr15tr8wgvAkiW2Pm9lUMoW+NbWVt7H0Bg+YvllLS0tumRpdi6NjY266kSAVuRNnz5dt6/GxkbL5srS1jSHHEKv4bvv7vWc6cAqCi++2PRWJs8s8TOZVBORiLa0tEhz1oYMGcIr/HqjwIjXSafwXH01v/8XT5gAr9eLG91u+lptLe2tmWafuhD7rl3YeN55ACHYKlZozpoF/OAHwEsvWe4rY8EQU6EN9w87n9bWVpSWlqK1tRVDhw7VfX+YZQMr7Oir6kjdd7WuDlo1YhIO2XLgIDvsTwJ0oLy3Dko17dVXgUsuAUKhlC/S448DhKCzrAx+tzu7a5ZMYuf552OgxO1aJFyblizBLqaenXQSTZDOAPG6RgUbifVnn5162vd6ga1b6eIcDuPY6mpbC4FVDk0ymbTlkG1EQ0NDWod047+ZR1a1Nt5x48bB6/XqCIi4jTHnSmbeKvscuxasFY2pNQ2rIrzhBtvnagtLlwKMeAjkOplM2s7hy5TXxAguc3S3IryKovRagTESQ2b9sGzZMl4kcWFBAbeB2LhiBSU1hABjx9Jw6u9/D0yahC3PP8/tOX4RCKC1f3/Mz8vDyhNO4Inve3Jy8JFop9LVZasJvNX8JBIJbH7ppZQCLTmvYDDI+1YaqwdFy4beGB3LruHs2bN5kUMkEkGivJyRrX3nIL+v/hyy5eCbgP1l0XAglK0DqabZGZvpB/Gzz4ALL0yRFEKwY+JE+rlPP6VJt4Tgibw8nJyOtL72GvDb3+qOxy0WBFRVVZmNTN9/n3pbEUIr/zKMXZzja7Qxv5eTA54rduGFwHXXQVVVjFEUrP3DHwBCsH3sWBRr6pEMxgWU+VaxJHWXy4XS0lKEQiFbT+9MlTKGWZiCJfu3sULR4/FgrmbUKpIE8RyYMlVcXJw2OTldc2UT2tvpfA4dagpR9Qa6e++ee8CLDd5/n9sBpPOZku3L6B0mWnsYSZZoOcCsMsRraAzdZjoXKyuPSCSCzZqB79yrr9bv65NPAK1vofh3UX4+AoEACCF48ac/Nb2/+9BDcWJfmP9C/yA4mhkBa31DjedlZTPC7v/eFLWw/DlWpcn6PLKcuOHDhyMSiWDatGlYoJ0/IQRwyJYDB/ZgpRjsaxLSG2K3N2P7pjrZG9W2hx54gKoWTGVwuYA//hFPzZ6tW8BeufZa9AjVeufLCOTatcDgwfQz//43PyYjG8ZFqaKiwjwfy5bxtjtYty71elcXFk2cyMv92aLKzucRbWHbxshaWRlV6f73f7HTuLDl5+Pno0ZZLuBsoVFVFXPnztU5t8t8j6xyhdjrbNEXQ18sT6uiogIulwtut5uHX2R9AqPRKMrLy/l5R6NRNDQ0mMafTCYxffp01NTU6F43EhZZc2Xp/S7mBf3hD6bjZQOp0tvUBJx7Lr68/35e4WcnH4sVNbDFWWwtJHpqGYsHmMt+fX29iVDFYjHeRLq0tBTBYBBXXHEFOjo6pL8D6RL1VVXFbnYvjhlDzW1FfP458Ne/Arfdhu033YRr8vNRLvxWHO1yYduttwJ33on6AQNw7eDBKJJ4gPUGxgfBYu170aXZvBjPq7m52RSijkajKCgoMHti2YCxgMAq36u9vR3BYBCtDtly4CA7HOiwWjbkaW/H2ltla1+ST9mYrtdICggBzj0X+OADyyf2zR0dSJ52Gn3KJgSnFBSk5mX3buD736f7Of54Uy9AZrFQVVUFRVEwceJEkzLDceKJdD8sjLl4sa49C265BcuWLtUtql8eeSR97777UmEa4W8bIdj6gx8A06fjvYULTVYAxpwbYysZcTERiQtzvGaQtSoRrQhUlTaRDoVCvHFxaWkpFEXB+PHj0draKrUjEAmeTNViSCaTaGxszNpOI22e1Ntv05DYZ59Zbp8Jmb4P4gLPxjJixAgEg0GTCzu7l1iI1XgviddHJLjpHN1VNeW3ZVRw3G43Bg4caJoXq+8J3//77wOjRtF7UGbOqkH2YDbM58PGU0/lVYzZhq3TwXi8sdp35CtNUbSjbLGiimwfnGVhYtbPUQS7hqNHj8atDtly4MA+vslhNSP6aqzZqmn7mozefPPNunMihGBQIEC9o158kX8uU2n9jssvBwhBZzRKSRaQcmEfOtSyFxvrt8eUA8sw3NKlwP/9X+r/mpnoxrw82vaGEKChITWmt95KhR/fegtQVeC662gF3fjxwHPPobWlBYqioKyszFTJZ8y5kYX8jGRHpmzJlBTWqsRoVOnz+XSEiuVYMUXF7XYjHA6jqqrKRPasVK26ujp4PB6UlZWhoKCAVzhazTNTtzo6OkzJ96ZWPWJe0O7dtvKERGRSeo0LfHt7O7xeLzcbFe0bjIs1UwkbGhqkxNIOCYjH4xg6dKj0vm9paYHH40FbW5tp3xkT9bduBTJ8j/dUV2NlTg5uJwQ/IQS/JQQfEYKPCgr0zc937Eg/yTZh/H3LIwSlLhc2vf665XmdeuqpaUPPmYoaWNjQ7/ebwsQVFRWmKkembHk8Htx37LGMbDk5Ww4cZMI3NawmQ1+O1a5Sta/JqKqqcBuS262OkfGJvbubhgmZerV4capq7eWX044h7X537gS2bMHGZBJvvvyyLjz51e9/j6HhMNbcdhtACLq8XkSZmvH739NjV1amyJ8BolIl86gSx3PKKacgGo3qto9Goxg+fDjP2YpGowgEArpFRZYjJO5XTK5vbW3llXGyzweDQWkDaiuVSpb47na7cdttt5lUMFVVMW7cOLjdbpSVlcHlcumS7+vq6uD1enVzxdHTQwns6afrw7wZILu/b8vLw/bp0/k1M6pQsgU4Ho9LVRBWESdrt2MH8XgcHo9HGs5SVZVXbMryxzJ+x0U39m3b6IOASJy0BHDj344RI7Bp+XK6TVUVNS3t6rJ3zAzgD4KBgOWDnfEYxtAze99Oax4WNgyFQlL7CFbJyHpPsh6QTU1NwAcfoOuww+BUIzpwYAPfRWUrG+xrMioLHfgIwc2ib48AVjJfUlICv9+vqxw0geX0VFbqFA+r3CURfPG6/35AyAnblZOD7xsUGfaj/Z9AAPH8fPybjX3HDuDGG4GvvpIOz4r8xONxafscn88nVU98Ph/a2toQj8d1Fg3G44jVb+z8a2pqeIUhCzGyRUfWRodVd4kJ4OlK6q1MSRlBYCSvrq4OgUCAV5UZ82UyOn9//DGtVCWELv4PPCCdcxlEpVdhOYJ5eTp3ejZn8Xjc1O6loqIC8XjccmFnc2YFWZ6aeFy/388VR1nPPpmylRGLF9P5uv124F//SoUVL7ss9ZnNm4HFi7Fj0iTsKC3FnqOOAlpb9W2RiorodkuWmBTwefPmyYmXRsyk6OnBttmzsa26GhuzsPVgBQRTp07lRSN+vx+DBg3SHV/MXTNeL7/fz8PEHo8HEydOhKqqPEdLmiPX2QnHZ8uBA5s4mBpEZzPWbJ4yrT5rRfD6xBRQsv8PCcEXhGDTa69JP9/U1MQXnoyVdxMmUOfq9nb+UqbcJcCwkBvyrNrz8vDohRdKQ0Lv3H8/DWGmW0wEWJE82cKtKAoqKipMyex5eXko1qq1xH0YyTAjqazdDuu3V1BQAJ/QLJktOuFwGEVFRRlbm2S6x6wsHdra2nRhSyOJMObLJBIJUzK06Tw//pi2YWLXa8aMjNfAeB6bli6l20Yi0pBkOosKdm2sbAhkEEOsVhWY4gNGfn4+3G43JwQDBgyQ5mxltAM580yzajV6dPYmsSedBBDqsWX8nSCE4PQRIzDB50Pissuo99aPfkRtO0R0dtK/N94ATj01NZ5bbrE1BFZA4PF44PF4cNVVVyEcDvMqXRbuFq+F1Xdv7ty5CAaDmK0V4tjx5nLIlgMHWWBfJoD3NeyMNZs8q0x5DUaCN3HixD7N4WL7DwcC6CIEPTk50rBbU1MTzxnKJsmawSp3STE0DBbnoLO0FCAE7z/yiO540opFANi1C+qGDbZDtMYEdaZKGXNTRJ8kVVUxceJEuN1uRKNRy6o3I5LJJAKBgCksaFRHGeFraGjQkSG7lhJGGC0dGEGQKXVs7MZ8Gds97Xp6gHnzUmqkUH1qC11dqTw7wXiWIZFIoLi4WHdtioqKdDlezDrA2JLH6NvU0dGR1ltMhLi9mNNmvM9YNSQLLbP2P0YrCTz6KFXvCAGOOQb4859pS6ds8MordPuCAix79FGdQp1LCGYUFKCnXz8zqevfPxXqX7iQes55van3FcW2MplMJuH3+xEKhXDVVVdxciXOKSNiYihXpmwZw7121wSHbDlw8B2FZbjx88+BP/4RYD+A1dXY/MILtkraxaflfRHKVFUVbz/xBB3X4MHS91mukPGJ1Fh5Z4V0uUuBQECq1n35ve/RMR19NA25wMKLSwNLZLfrVm0s6xcJjfHH3tgHz7hIV1RUwOv1SpPU2fnLnMoPO+wwtLS08H0Zk+uZOiLOT7aqaWtrKxobGzlBiMfjKC8vl46H5Tnla/5ObC6NhLipqcl6DDNm0Os2ZAiwaVPGMepw2WV02+nTpefCkqQTiVTjbavvi0icx44dy9W88vJyeL1eyxBrb8CqIWW2E9JK0Q0bep/cvnEjcOihdJ6uv970m3OxSK7OOANtkQjWn3su8I9/pHLFenqoyzz7XGkpcPnl1H4iA0RSy0h8MBjE3LlzpfYkxcXFln0l2f1kVRGaCQ7ZcuDgOwqrPKulbW2mp8wNv/hFxua5dvbdJzlc77xDx1VSYnrrzfZ2jBo5UqpssSq8TDDmLomIRqNobW01bbPj0ktT8zVyJCc2slwxttjZJa5MpcjGDkFVVZ7vJKKmpgYzZsyQttQRtzWakobDYQQCgYwE0UgY/H5/2rY1su3Ez6oqrd6T5aC53W6MHz8eyWTSpAgx4sdIbTQalefudXVRq47TTrPVtkjEtltvBQjBrtNPT3tOdgi1jNh7PB5UVlYiFAqhoKDAlrIl26/R3sB4bcX7qLa2FmVlZX1XTCP+llx4IXDjjVg6bhw8Hg8CgQByCUGyqgp47rn09/TOnbSZuA0VWCSvxn6SLS0tKC0tRTweNylbTNmTHb8vUi0csuXAwXcUVspWU1MTbvF4sD4/HyAEX/Xvj22zZuGQLMJy+zRJf/fulIkps3zYvBn47W/Rk5uLZbm5uOKQQxAUQlvplBwZRCXJ+IPcKPZ2Y+jqwovXX4+/uN1oGDrU1D+RgS12RqWmpqbG0u+KhWGNuUiWIUrhWOlCIFYEp6mpCQUFBTqn8oKCAjQ1NaVddGTHy2SgyraTWTcwMhGLxeD3+3XXMh2RFVulsHCRy+XizvRpiyVsIhaLYbEWXpvo81kSKbuLtCw3iCmxjFyJOVjpXPPZMVlIubKyEsFgEHV1ddK2S+JxwuGwpRorAwtV3nTTTbp55/OxezfN12JGv9rfJq2wwk7xRLpzFMcpNjoPBoMIhUKmByZVVeH1etHe3s5zDmtra3le4t7cG6qqor6+Hn6/HyNHjuT5eAwO2XLg4DsMY57VvHnzUgvmjh1YO2kSzY/Ky8Pjd9yR1Q/jPi0ouOEG+hM0aBDQ0ZHKnxH+to0Ygecfe0xaeWcHHR0dyM/P52oO+0FOpyhkWlwTiQRfkIyJ7SxB2Sp8aTdvR4RVCER2DGbvwMigqBBlInbiuYmw6p8oztXUqVNNfROZIaiocDHLCSuDzNmzZ8PlcmHMmDGIRCKYOXMm9+tiNhAzZ87s1b0AAGhpAc49F+qaNYiEw1h7zTXAUUdhxeLF2Vf5GeYgk/1ANBrF/PnzLasRGcTr7fF4cP7553PLEI/Hg7y8PNMDBLMqEEm1HbCkfUYAhw0bxudYNx+7dwOJBHDTTfR7e9NNtFuDYQ7sKkqyhwRVTZm6sgeUwYMHS+9zn8+HQCCAQYMGwe1247DDDstcsZwBsVgMPp/PdK8FAgE+dodsOXBwEKIvE/X5vj76COoZZ2C1y0XzLJh6RAjWnX8+sGoVvj7nHLz5yiu2j9uX49ShsxM44QTaSkRVaXXUlVcCr7/Ox7yVEBzfi3YcIiZNmgS3241hw4bB7Xan78NnA+zHny1IVVVV8Hg8unBbQ0ODSeUoKyvj/dZkCdeZjmm8BlZVVs3NzZZkMNM1lFXgyZQtlkcTDAYxbNgw6TaKoljmOcnOSebKzki+kcCMHDnSPG9vvQW8+651hejChfy+euv557MKqVuN2UgaWK5ZTU2NKZcqFArZmn8x3CorFggGg8jPz9cpOvn5+Rg+fLhtwqGqKubPny/1T2PXzA45z3QM0WpEJFUyUirrWuDxeNDe3q5rdSTuJxN5txoXI8di2NrKV62oqIinHThky4GDgwz7zKn9q6+wp6ZGpw7tGDUKF/r9UNevB446ir5+6qnArl0ADnB15rZtwPr19N+C1xHmzAFOOw2bXnutT8ZmZYaYbr/pPiMmsDNvJDGEFolETDlKoVDIVsK1XaRTtkQyOHr0aLhcLsyePTvjeRkr8NjCPnr0aL7IiQaRTGmR9a1jIV8rewpxEWZ5WcZwKTNvFVFVVSXPy/H56L29bZt5st5/HwgEwPKO1E8/zapYxJj8bqwaNc6/GF6rqqqyXUBhbIKdSCRMifU1NTUYMmQIr0Bk4W5Z3p7sOsdiMYRCIR7OlbWBSlcYkg6MANXX1yMYDJrIUyQi95arra1FY2OjNOGdWYOka3VkNRYrRY1ZRbB7vaGhAeXl5dJm9S6Xi6cdOGTLgYODCPs0FwoA1q/Hk3/4A0YHg/iBMf/i3XdTTuvjxx/wXpEcjz4KzJ6dUiWybMXCYIdExWIxRMJhjLWZJG71mXQhtNraWv5Uz9rj+P1+7meVTfNcq3NiyoEs4Z2Nv7CwUHc8pryka2nCCGFDQwN31GZ+RDKCx0ikTEUzEpBkMonW1lb4/X60tbVx0hkMBqWNq+fOnSsNvTLiyPHFF/Sezs2V3ztauxWccw5/P1Pyu9Gnzah6KIqSNuSa7toZIc67OJcyZSscDiMUCiEYDEoVHVVVMXXqVCiKgpqaGl3uoZ2cPHbMbENyYo4k626QjSlsMpk0zXEoFMrK58+Y6yYqzVZhXjbnwWDQVJDj8XiQn5/PH9IcsuXAwUGEPq/y+/xzmkehKUPi01swGDT/aC5eDBCCHrcbAwztcw6Yo/7//R91uZ47t9e7sEuQDg2HsWvYMODnP5cqGlaKkd2EckVR4Pf7eYWd0U4hm8XDyl7CeK6yJ35Z9SMLy6Q7L1kF2LJly3jloKxVDSOWpaWlCAQC8Pv9fMx1dXU6NcHlcmHw4MG8Ui8SiWDYsGGWlZosTMmUtAkTJpgnaupUupz9+Mfm9z77jL7n9erb1kAeUmKvi9eVVcCJqKmpSVsVmA3EkLDYBFtRFPTv35877jO1z+SlpUHMOzJed6Z+VVVV6baJRqMYOnQoPB4PhqYpDEkHVjSSyS/NaAprdV/bVQNlyqMshMuqFK3IMbuHxSIORrTEtAOHbDlwcBChT5Wtjz4ColH6Vb75ZvtEQXvSv9QwjgPWK/LOO+k5XH551psyQmPnvBOJBC4Q+8CtX29SI4y5UKqqoqysTNqGRZY3FY1GUVJSAkVRMGHChIwE0Aosh8kYdsyUiM3mY/z48aYQXGlpqe5cxZYmItmIx+O6RTkWi8Hr9fJqQJky0t7eDpfLhSeeeEJHMGVKmIwEMidvmSt7R0eHdWL5Z58BHg+9nrJuBHfdRd/7+c+l8yzrWSm7B2QhsWwr8dKplEZFKxgM8vY8zCHeKj+JXfNgMIhAIIAxY8bo3h89ejSCwaCUgIfDYTQ2NqK4uBjNzc0ZQ+viOBhJnTp1KlwulynkWVRUxEOWxhzFdHNh52Ekk/IofiesVFJR2WK5W/F4HPPnz0djY6PpfnPIlgMHBxn6pMpv3TrqU0UIcPjhwMaNlknTJgLV2AgQgli/ft8MZYtVJor92gxIl4NRXl5uUh6sGvae6/dzsrXy7rulyhb74Rbd59OF3WShD0Yipk2bxt+zq3wwpaCoqEhH1oqKitDc3Gx5jcVQjqwFj0zZMoZcYrEYd+tub2+Hqqq68MrMmTPh8XgwZswYeDweFBQUcNXpmGOOgaIoaQsFotGoiQSyZGxZaNSYj2S6D371K/AQoQwXX4yeggJ8NHWq6TozgiK7fsbryvrpGYmV7L5Md69aEW+jkS279rJ7TpakX11dDbfbDUVRTInvHo+HFxUwEm+31ZCYhxUIBDBw4EAUFBRg8ODBcLvdPFRtDIEyRSkQCKChoaFXOYoy5TGZTKKxsVF3T8qUR9EOgx2XNZfONMdWcMiWAwcHC/77X0p0zjkHe6qqsLq+vvfk5sIL6Vf4yCN5eETM/UibhP3yywAhULU+bQeqV6SqqljyzDPoYS08XnhB+jmrUnH2424kBOkWj7amJvze68XC/v0xzOCjwxYWn8+HUCgkVTNkYTeWiC4m3cZiMVOisVXFm3EBTSQSGDVqlJQsdXR0WOa2KIoiJUY1NTWIRCI45JBD4PV6eQ6XLMk7FAohEAhwAsXCgyIqKiq40tTa2opf//rXPL+LLeDMB8mopMjIgIzwynymjErhK9dcQ+8btxtYtcr2vcNeKysrMxG/aDSKeDwuDXXZzglMc6+mu5eswppW+xavH7NxYNdNDOUWFhbq7kPWDouRLqv2TCyZnrUCYtuw//t8Pvh8Pq6kicUTgUAA48aNy6rLguxcS0pKeMiZ9UQsLCzUXTeZ8si+i+zcZPmDtotvPv0UmDnTIVsOHBwU6OkBqqu5qsL/pk7Nfl/bt6d6jK1cqXtLbHjrdrsxbtw48w/Ktm00d2vtWtuyfV+DJao/2r8/QAjWGHJJGKwWqng8rsvBYKGuysrKjHkn6ZSH6upqeL1eTJ06VdpiRkaWmLeVqBqxJObRo0ebnrCN88CMHJlZKVO2jOGgMWPGIB6Pm8wc/X4/b4tjVJJGjhyJ5uZmtLe38wbVra2tfA6MeSzRaFTX0icYDKYlR1bqnqIo/B70er26BfwZGMsAACAASURBVJdV08naDomEIxIxW2yIuTiF4TB2/exnwN/+ZnmdZfcOI4Ayku71ennIONvvRrp7NVurCeOxVdVsHOv3+1FWVibtvch836wS3sV8Qjv5iFbJ+ozYGUOg2VT+Wh1bVMtEWxLZdfP7/fB4PDzfa9q0afD5fLyaMVuyB4D+Zt9zDxAMAlT9d8iWAwffNEh/ZJ5/HrjgAmD+fGDWLPoVDAaz3/myZXTb4mLTMdmPU6YQ2IEEIyfvaspET79+OMLlkhIkq9Boa2ur6cff7XbD5/OZXbAzwCrRXZa0LRqXMlIga6tTWloKn8+HgoICy+pDVdUbOTLipKqqtCWQoihobW3lfR/Z/cUaShuVLfaEz0KKBQUFuoRfma+W1+vV3bO1tbUYNGiQZWjPKm+NEbb29na43W4dSQiFQnjiiScwY8YMvjAbq8gURUFhYWFaRai2thaJ11+3rF7lY+vupj5uoOGl8vJy/hlG0quqqnTzn+l+kREIq3vVbk4hg9FFPxaLoaGhAV6vl+eW1dXV8T6bPp9Par9x2GGH9SrhnZ2L+LCRSCRMx2D9Ni+55BKdgmr7t6a7mza5fvNN07ErKyu5ZxwANDc363LC2HUTf9+amprgcrl0ViXZkD3+2Q0bgGeeAY4/PvVQ/POfO2TLgYNvGtJaKrCFob2dfgWPOir7A2zfDh4+6ezkL7MfeyN5+O8LL2C6x4Ntc+Zgy7PPYsWDDx6Y3CzQuXG5XMjNzUXQ7cba6mrgX/+y9PZJpxaInlCRSASDBg2ybLScDlaEQVRgmAN8ZWUlAoEAQqEQD22wvBVZ8m0gEOBKkhFW/d0YoWMl/Cw/6tBDD0UwGLSsgjO2xQkGgxg/fjxmzJiByZMno6Ojw3T8wYMH6+bQ5XKZ1AvWeieRSGD27Nkm4iXL1RHPl+XKMBsM1oLHWL1oVK5cLpepek6Wi2MFVVUxQlHwdUkJ0L8/d4o3kmi3282LGowLtHGxTteAPF240G6fRUayx4wZg2AwiGnTpvEKVyuVyaryr66urtffc7vKFssTS9ssHAKRWbuWGtBOnw4UFtLfMZ+PtusyzIGoZsoeDNjDlagoi50c7JyjzEftCa3NGQhBp9+P/8/et8fHVZVr76aZmb1n9tzSS5qmSXNP2qZpUg8cOYpXQAuiXC1K0Z7TFqmWFKkeTLT1FB01xYBGJPzST8wRgwmgUXvwOPBpPIdwlIBIEQMIyqUCsil3KLc2z/fHnndl7bXftWfSFuz5nPf3yw86s2fvtddee613Pe/zPu9zl18OTE0Vna2iFe1IMsfxZxvapok7tm/Hq8ceC5x8sluU9Z57gM98xhXwPBi76y73PMq1y8qUAsyPPw7kQnXy3zbLetPRLq5vLNPUqlar3B2VP6Py0yzL8mRJFaqCrVskR0ZGMDIyggsuuEDUtdM5O1u3bhWlT+TFVC1bI1s2m2WFHLu7uz0ITyQSQTKZFOhXKBTS6nXJhOZUKuXhvKjHkrMn92EkEvFIDcRiMWzcuBGTk5PaAtzy80mn04jFYr5jZGdNdRTi8bhIKpAdqJaWFtaJoPu++9RT3c3K3r3aZzv8gx9gYvZswDBwiWl6OFscUicvuuRUE0dqw4YN7P1z4WidM5YvdJdKpUTolEr0lJeXezhKHMpE8g1UeoeVyMDMwnqqAGppaSls2/bU2+xSEg905ykrK8M/NzX55iGk067j9dpron3qu0jobCQS8d1joU6srk0rVqwQiCZdc88XvoBHZ81Cd2kpqiQ+a9HZKlrRjiCTdbRihoEvGQYelyaXlxMJzy7uYE03afp0kh5+GAekndor1dVAIoEHrrpqxtlBh2rUN1HDwELDQMIwXIQrkfDVY+PIwBS2owVLXuQJeaKQGaFBhYZQ1EmbBECXL1+OSCTiq9kmG/FviJw8OjoKgE8vl81xHB/ZPZFI+Eq2mKaJ/v5+wcGJRqPYsWOHCMGpxGriO3EZYirfiuN/jY+Po6+vD6tWrYJpmoJ7ZZqmtgA3hxJwmXuEaMl93tTUJFAlFbkiJI1CVMIxevzxadX4xx8PfLbPXH89pmbNco/duVO0hcsipP6iZ0PhT3JE8xUg151bHWOJRALdSoIMbRA4ZyMfklVWNi2RMT4+rr3+nHQaH2tuxjdME3uOOkqEV3UmO4iE6tXV1RVcGsjDZ7zxRr+zdfLJQE0NcNllog9UlHnFihXYuHEjUqkUWlpaEIvFPMK2hTqQ8nuivncyGuv85S+YI20IKVPbMIw7UXS2ila0I8MIvTnZMPCwNKm8XleHxzdtQsvBFtGVLF8aueM4HgXzk+NxPFpZidE5c9wDfv5z4MQTcTyD/LyRhHnqm3/O9clVuYmspaXFtyhzTsLAwIAnO4omfOLEcM5FMpn0hc+C2ic7K7t372ZDfZyEAiFblFZeUVHhuScdIVoVclTlEjKZDCKRiHBSkskkysvLkc1mPYKicgkS4vsUIgVCzjk5PGeddRbKyvzikP39/SJVXuWR6VAaFcXJZrOwLAvpdJp9VhSWUhE7dkz+/vfuu1VdXdCzxeWXg1Tmn73qqrycK+JIyTIWdXV1Bd+/3A/qmCI+JYV65fukIuKyLV26FKWlpR6nWP633Fe6ucFxHKyxbbxaXu5xdg7Mnev2ZYE2k/nh+sFBfM40ccncuWjIOdP4y1/w1UWL8Nxb3gKQA2wYwLHHAlNTgaHYQq+tludS+8W2bRZRJgrCNd/7Hh40DPzcMHC8pEFoGMYkis5W0Yp25NiN//ZvYhK53TBw07ZtgqtFi93knXfiZ93deOTyy2d0bpqM9p52GvD+9+PWq67yZP+ox8rcEzGJnXoqYBj4vKKr9WaU77nmmmvwyRzSNjh7Nq688krfBCovenQPLS0tniyyoaEhkfVG2V7c72pra33E8Hwmn4cL9dXV1QkJBcuy2GLMHMLFLRiqUyKjXbT4cqT3wcFBkXqvOn6jo6Na7SMdwkaFpXVyF+R0qtmB5557Lis4ypG8qS+Hh/11AAF3wfv4xz/ucdBU1E60ncRKP/zhgp8rtm8HDAMvGQaOlURM5X7QOe0kWaHefxC6owpv1tTUiFChWktT5oRxqNX69et9BdW5saSG1uncu6+7DgdkROmTn8Stto19ixe7WdKH2SYnJzEYDovrvbpwIcokJ9txHODRR10n+Hvf83BPDzY0CHizsS3Lwpo1a0TyCPVpf38/y5WkzMVjbRsvSX11ThHZKlrRjlB79lk88+Mf4+7Nm1HGKBufe+65qDBNwDDwgmGg8/zzCz61cAT+5V8Aw8BPQyFPGCbIaBK7Nlcf8Y6PfUx8x/KpAkRODwUBey6HMrx82mns+WjRULkr1dXVngUsGo2ioqLCh2ypvyN+CatArrk3Wmw5Yq5lWdi1a1dgNmJra6sv7VwlV5977rk+srXjOKLkSkNDAxYtWsSS90tLSwURXtZJImeeSOmF1GNkuX45o7De2NgYwuEw0um0SBiIRCJCOdyyLOHQygTnVCqFrVu3Ct4WOZJcKEflltGzVgsHDw8PA6ed5i5hV1xR0DMFAOeJJ/BISQlIMoVzPglJ5BzB1atXC24hWwoLPJIl39/69et9tTSpj8lonKjZfRxiI7832WwWCxcu9AjLkmr7k3/6E27PcddQWYndu3ejIp3G3okJ4MorC+7DQoyc6c0VFcCyZe41u7sD+Yu6fgyaX+T+cByHzVAmx0suuu04jqAGyOFz2aG/fmAAXy8tFXP0rq9/vcjZKlrRjmRTd2kegnBZGWAYqDHNGTsC/zMyghdo9/WTnxScfec4Dp5vb3d/95//KT6XuWb5yvccMgL24x9D8DXAhz64HT5dU/1Mrrc2b948rR5QX19fwU2U+URqxl5VVZWHp6QroSNP3ur9UNiRy7Jsbm7G8uXLtXIOJDDJZT/K/y1UwFGXxUr9S2V6SCG+rq4OpmmirKzMIxpJoqtcOR7qMzlsats2kskkW88ulUqxfTo2NobydBoHqBLAQw8V/EwnJiawvrHR5Q699BIAvni0WuePxpCMTHJ9KjuHsVgMra2tnus3NzezY5MLRRbibMgk73g8jkQi4VOGp/cDAEauuQbHJRK4aAbK6UHtCOK96dDBQue5fCYjWJTU0djY6BOplTmActFtEtwN2qg6TzyBvSec4I6z9vais1W0oh3pJk9KHr2Yf/onwDBw8bx5+N7AAPDwwwWdj3aOPTmECh/4AIA8QokHDgDDw8DHPgYRSnjkEU8bC0G2ZoqAsZZTsMdRR2k5Gt3d3b7w3ZIlS7Bo0SLPZ3LGYTabRXV1tU8MlPSARkZGCm8jvHUX1dBMb2+vJ0xGqAeX4aZycXTcnPb2dp+DNTzsFheW5RwymYzvtw0NDYhEIr4SJIUs2vIzkPlEdJ8bN25EIpHAypUrhcipnB1JqEBVVRW2bNmC9vZ2z/nb2toQiUQ8xbnl+nqynhJZXV2dz1mh8X1xVZU7fpTvC3mehYRVVQkNVWGd2xyQgj+hqlwFAE6otr6+3iPqOpN7kcPNugoKugzfQt5XnRq+qokmjzc5BE+/p6LaM81+1rVVJ3Aa1A+A29dNTU2+ewnsi+eeE2XRis5W0Yp2JNnUFHD++cCWLcAzz/i+9oSlfvQjwDDwWk7YE8qCg//4D1GKhzvP+pyK/N7jj89fe2xqCli8eNrR2rbNd0ghNRtngoBp7bnngJISYPZs3PvNb/pCV+3t7SJLTkVIOFkBmbPCiZGSHAHnOOpS8eWJWA3rnHvuubAsSwhM9vT0CML+xo0bRVFlmVxNTtnu3bsxMTGBZcuWacnWhIJZlltOxTRNvPvd78a8efMwPj6uRaAotEn3ki+RQjYZgU2lUshkMnAcx5NN5jgOLrroIp8TEYlEBKpCyvnqM2ppaWHJ/OSYyvfT09MjlOd1yNa+j37UFQmeib32WsF8IN3Y4Po+kUggFouhrq7Og4ip5ZI4TTL1neU4fJxDIHMJdYkQQ0NDBaHduvvn7jORSLBIpMxJlH+nKsrrrqVDCVcw3Dp5w6reuyx2yrWxUA0uj/3qV0AkUnS2ila0I8ruvtt9tcrKgP372Ymks7NT6MUMEY+ipARoa3MRKADIZoHZs13y6iuvsJf6z6OPBgwDV6VSHs4M4E5ge847D09OTk4LqX7jG0BPD5761a9mFBpQvz9kZAtwnVHDwAsXX8w6HcuXL/dwf8ipIVK8brFUUQnSiuKOo6LNlOXHZXQlk0nYti3q85EUAedMxONxEXJTv5dRD6qjyJGtVadn7dq1IjRK+kKdnZ1iISe0i1LX84U28yFcqoCnbduoq6sTmlNtbW0+/ovqFKmlU3p6enySFrLzlEwmcdZZZyGRSIikAxVlI1mPmRKmPfYP/wCccw6evPfeGUkFyM58X18fWlpaPMfV19ejv7+fzXKrqalBX1+fzwHmxq86JqncDOdwyFmyHKITjUYLRpO4d55DltVSTvJ4klH1mRDcdegZVyCcrlVo6Z6FCxfm5SsWbE88UXS2ila0I8YefxxobAQMA0+edJIWaqcwYHV1NdLxOG7o7fUJlKKzcxqF+vOffZdyHAdDlO3z3e96Jj+awPaEw/jD7Nn45ec/L353ODIOC0HA8trUFHDTTQD8kzP129DQkJAwoIm2vb0dfX19gWgUaXFRqIo7juNCpVIpjI+P+5wUmetBO3vZKExGoUaupiKhOkSupqLKMtlahyZw3DXTNNHd3S3uT75GR0cHbNtGS0uLp+/y1eOTjRzdFStWIJVK+RY/6pOhoSHfotzR0YHu7m7hPMXjccRiMR/pnNpTX18vMjqPP/54T7KB4zhoamrCyMgIbvvv/4bz178W1H6f7d3rviumCbz6KnuI7HSoTkBnZ6fQcuOkQCYnJ9Hd3a2tCCCbjuguj0nOiZAdDsfxaqRRUgWhaIRM5jP1PjOZDHp7e1lkUa0MEKTmr+NzyaF1XRmjzZs3awuEk8kb1nA4jHg8zhYNL6TgtNou3fFFZ6toRftb26OPAv/zPy6HxDCwe/Zs/FMOveBI03kRhx/8YBrtyokwAgBeflmETiYmJnBURwcwMADs3w+AqcOWyQCGgVtKS+E88cThQ6Vw+PW41PP5xFnBZ6yRzSRkNjExwRZtJjRhyZIlWieltbWVJYBX5/SegnhB6gIgC7TS4tPW1ubpi+rqap9aOIVIVEdRvi4R8OVQZ9CCKIeviH9E5+Icqvr6ejQ0NCCVSvnI5PL9Bmmg0eeyM2tZFouAOY4D9PQAqZQrFzBT+9nP3Hfq7W9nv1ZlGtR7orI0HK+NykQ5jptJqma5FRIeU8ckFxpUHQ5VI63gkjmSw6Y+E0q+6Onp8dwnIcTquM+HGnEcL0KLm5ubWacqHA7P2Gmd6XxEY3Pz5s2Ce0kIcXt7uyeUTlZ0tor2d22He9E/KGtrEyjUvSUluDsA4RgcHAwWmnQcgDKt5EXlv/7LTaO2LOCpp7SLOmlOAXC5Uek0YBh44OKLDw/f6nDb5CQwMuLWS1PMcabFWWfCE8kXMtMhW2VlZdiyZYvPSZGdARl5a29vFxIIsvNDIT6OGMyFKOn/LcvyqNWn02nfuWmRI14Y7eYzmYxAhHScLkLPuMWPQle0qNbU1Hj6i+NPkZMYVP6GI0yTs7hgwQJPSBJwyxtt3LiRD0N94APue3H11TMZYa7lNLbw6U/7vvKonMPVYVq0aJFn/FRVVXneZ0Lc+vv7RYi0ra0N4XAYtm2zWW6Ow1cMIGchH7LFORyFzn9qeJqeUT6ul+M4aGhoQDgcFhIeFPbu7e0NvLZ8TfXdTafT2L59uzZzWHVaOc7lTExFLUkcmHvXBwYGkE6nPUWu0d1ddLaK9vdrb4YIZ0F2zjnAW96CJ1etwgm5DCmdA5AX2SJZhLe9zQ21vfgi8PGPC2cODQ3AffcB4LkR6nUf/exngRwB/4WLL0bMNA8LsnVY7H/+Z/q+br9de5jjOJ6MNZo4KdOqEKV0+VzkbKgZZxyBmSZ+rvRMJpMR/LF4PI5wOCx4Slu3bmXL6QSFKDlyOQkzqpwt+V5oUSTkhXP029vbPaFMEj3NZrPo7+9HPB4X5YBIyV0WSw2Hwz7dMLkNhNapoXO1T4kwPTg4iFAo5JOwIPkINnGBMmkvuijPwGKsvx8wDLzW3o6JW2/1IaiUpbZ582aUlZX5JBRkZIvaSigQCdzK4eB8pHay+vp62LYtdMXkMSn3OYeSFWo655scdm4ukkOEpA+mhr2DrqcLEcp0ANu2Wd5ifX09Nm3ahFQqhYaGBpimWXDZLa4tmUwG6XRahMRN08SOHTtYBJXuT/38+aLOVtH+Xu1whsQOZ5vkCaanpwdfLS3FjoULPTt+rrCysG9/G1MlJXj8rLPw5B/+ABx1lPuqRiLAF7/IFp/WZfJ0dHSgLJ3G3SQAaRh4oqUFMdNEIpGAbZr4wfe//2Z0jd+efno6O/LCC/MeTn2rCpbquE4csqWGGgcGBjAyMgLbtrV8qxUrVrAcMR2/yrZtT2hFviaFK2STncKmpibf9RsbG0WoRA6ZcM5IV1eXR9le7Q/ZGaupqRFk7IqKCliWJTIka2pqUFdXJ/hphIRwod4gx0pFAqldRHTv7OyEaZriMxJJJbTP927ceqs7XlIp4IUX8o4Zz/h56CG8kEOMP6vJguRqDhLRvLOz0+MIJRIJUaA7iB8k9xlX+ikajaK/v98XepVrEQZqQeXhJtFGheMZktPEOZcUIqRnr96LzmhMNDc3+xxL2YFLJpNC2kPePA0NDQmksBDHTmfkZHFVEYg8z7UvFotp39Gis1W0vyujFzObzfpCYvPicfz++9+fzuh7o+2qq4BLLgFuvll8JDs6LamUcHIGrrzStzCp/AlyxCrSaXxg2TL8gTIVa2uBe+6ZUdN8E+OPfwwsXAhs2ya+e/7rXwfKy4E9ew5blxRsF1/s3ts//IOWsKwaJ3RK5Xp06A9ZkENGz4xzUmSOGNVhpD8OTctmsz60SS5mzIVNZGSLQyDk2o5BWZSO4yAej2NoaEiEQohXlMlkBBI4Pj4O0zS1NQop+UFewAvh+xAKo/aJKgArn2dyclLodqkhI9ZpPuYYd9z84AcFjRlgGgHfmHsX99bVibEjL6wTExM+flxbW5sI3emcXHouKmeQc0a5wt+O4+RFYtV5Qh63rMK+1Cb6nkNx6FwySsvNT0HGIbcy0krXJKeapDI4xzMejxeugaUx4poRB5NDE0dGRlhpmWg0qi1ZVXS2ivZ3YyqJNRoKYalhYI1h4NpcWYVX3/GOghfvQ7Xnv/pVwDBwoKzMJxA6MTEB59FHgd278fQNNwQiL+yk+Ktf4UAkgntLSvDk3XfnbUtBk9Ozz3plJDZvBtVJe7Ptpdy1X/zc5wDo2y9/PjEx4VnMSVOHBCQXLVqERCIhuCXyufKFGlUuE8cRkwtCkxRCEJqmFjMeHh4WZXQ6OjoEZ4v+PxqNCh4JyR1UV1d7QsQ6rpk6lqhUDpF81ezC+fPn++pJkqlCm5wjwf2OSx7Ix52jPtJxk3xOCCl6/+Qn7Dm5axACbhkG3mEYaItE4DgO2tvbPSEjzhkOar98De795vqC0KrKykqP+n6+61BfyfMfN/7k6gEqyi5vHPIlTKifqckcgOsoq4K3svNKz1Z24JqamsQ5ZD5mKpVCV1fXQUcm5HbK/aKrg5jNZsX8UV9fj1gshlAohNraWoRCIfFeys5r0dkq2t/MDmX3cTDXKisrw+477wSGh/FiezteIb5P7u9ASQme/tnP3pS2fOlLX4IVieDGXP0sp6kJeO019vighZ7ua2xsDIODg2hra5s+aHwc71+2LC+BfSaZeB67+25g1iwgHPY4i2+0DQ8P4/OWBRgG+iMRdHZ2su1XM7fksI/j8Fo8tNhwytlBDq/Kf1I5Ytls1rcQ27YtxDxJ1FRGmTh+Vjwe96A7k5OT6Ovrw8jIiFjQLr30UhHadBxXXoEQPC6LUq2tVwgKRRykfMWqdf2m4x5yIfKgUHddXZ02zZ91Qqqr3fc9x1vMZ7qkECKBq+2lsVhoEWQaKyqi19bWhubmZs9n8juvIkn5tKgoM5fC3f39/azkBm1IuESclpYWUUaokHuTnxFlCBKaumrVKh+SrDqrY2NjME1TW3Sb+u9Q1xB1E75gwQLP+dU6iDJyapomFi9ejFQqhd7e3sDsxqKzVbS/ib3Z5HSPw/L+9wsH66UFC3BfSwv+1bJwfHOzN8afq392OI3umybuOYaBPbm2vPDlL7O/CVroJyYmUFdXFwj3cxMRB91zv8k7mX34w25ffvazh9o1BRn1xZ4vfhEwDDz1wQ8Ksrbcfq5QcTweR1dXl3aRJgHJpUuX+sJggF5sUSesKCMAy5cv92XOLV++HLZts4uJisIBEGiV3J5kMimyAMlZo7EutysajaKrq8uXsUaOGBfqI+O4aA0NDSgtLUUkEsGcOXNgWRaWLl3q4ehMTk5iy5YtqKmp8ZyTFnVdf3KhLlnqQH4fKPTJhZtYh2DvXmB8HHj99fyDDTy3c9asWWiQCiLrQnX5NJryhenyoXzkxOv04OiYsjJXc47mCRqnkUikYGRLfq/yIcjydUdHR2GapieRgUMASWtMRm5n6rwGhUx1x+oKfsvzSSQSgWmaqKmpgWmaiEQinqSYQp29orNVtDfduAnsjSanexyW3/4We77wBSxOp8XLJhOn3xsO47m5c4HTTjvsbVDv2zAMnJRztl6LxVzNLcZ0C5OnfA+m4X5ONVo9F4WEamtrPROTuhgGIl6/+Y07FcyZ4+p4vcEmnOZ77gG+9S3g5z9HY2Ojh+vU0dGBvr4+z86dIH8Kj61du9YTHqB+I97Ve9/7XvbeC0F9aFHkOGIyxyqVSmk1uTjOisqT4ZAvKtarCxcS70cNaa5evRqpVIp91uq1xsbGEAqFhPyCZVmituKiRYtQVlYmkAtC7SzLYkNeQY6J4/ilDpLJpI/vNjw87LtWoaKchZgswrvANHFvSwv2L1oUSLLXvTu6RV4tzSNztoKqHej4d2T0vqjzBDkVRCTnHNRCldx1YeK6ujqkUinxXGRNMJXb1tjYiItz1SDUsVAIejUwMOCpNUpZodx4zpd0Ul9fL0ROU7nqGuPj49pwaKFWdLaK9qbbm67XdMstwOrVuPbqq32Tx8TEhKfuHABM3nADYBg4kEgUvAMuxLj7pr8bcg7X04sXC5FR1bhJh9ovW0tLCwYHB7U7OnUBi0QiYqEl0u34+LhHO0iLkk1NAR0dgGHg4QsvfMNDwpyTwbV/ZGQksBxJKpUSZGOV2J6PeyNn9mUyGW04ZmJiwpfFVV9fL/g2XI1E+TpcuSEZ+eJ02JqamjAxMRHYrt7eXrakDWWscc+aULQFCxaIrL/R0VHW0RwcHNTqH3FZcTrHZGRkREtOVq9L/LKRkZFAjpd4fx591NWkK9DEbx97DFi50l3+zj+fPbfOAZdlLWiTI1tra6vvvdU5GuSMB/Hv6DhCttR5orW1VWxSCkWsuH7h7nV8fFy70dAhWwkJLZyJ0XuybNkyJBIJXHjhhdr3l9s4qMeShEpQFufBWNHZKtqbbm86svX1r7vD9YwzWGSC6ofJ9jCVsvnMZ7TOz0xNh2wZhoG5hoEbDQNXz57tSjbM4JxBYUDVVK0ebsdLXCK5wj3gJxtTGOPak08GDAP/Pn9+QVyOQ7KHHsKf3vUuVOc4FKlUyhdGSiaTmJycFM5UU1MTGhoafOgd8V4WLlzoCSlOTExoS8Ns3rwZlmWhqanJIyLK9b/s1NB3sVgMO3fu1IYv1DT13t5exGIxtLS0+BASHbKlLipyuJAEOFesWBE4drgsQHJgk8kkmpqaWFSgvb0d27Zt8yEXHR0dqKurY0U1Kdw0ODgoHDhZx0xHTtahLpyDIKO550ejeK20FM9/7WvBY01nv/41BNfzwgtx3fe+53EW6Ch4oQAAIABJREFUM5kMWxyd00DLu5nR2MTEBI6tq8PvYjFg1Spg/Xqgtxcrc+WpZJM5W4XOE0Em96+OTypzFsmoagAhxzJna/Xq1QfdFrluKEm6VFRU+NpEGyC1vWrostBsRq5sktq2iVtvxSsnnogXL7gAhmFMouhsFe3NtsNSH69Q++UvIWQCGPOEex55BLvvvBOdOQI2DAM49ljg4YcPS1Pk+45EIgiHw4eE8DnOzMiyslYPhSHUhZErGEtcDpYvZFn413AYu/r6pifxBx88bE6qsO98B4hGAcPAn888M1BCgcKgsVgM4XDYp6xOKfPkbBaCbHG7dVWNPRqNIpPJYGBgQDxfmVxLQpO6BVkOh1JopKWlBbZtY/v27T4OWTKZZAn29L0cZqLrEpI7NDQUqABOSAxxitQFm+unVCrFIluEJO7atUsQ+mnsEupFDuy8efMEqqqTOgDyO1XywimjuY/k+H67QqGD3xh85StuoXfDwH0lJXjg//wfz71yyBuHLHFCr4GWywZ2HAepZBKTJSXT85RhYJNGKZ36ulD+E/f7kZERrF27VmQPymOE43apn9MYlTl9Wmdlasr778suA97zHuDEE4FTTgHOOw+49Vahr5WPd6VDtvJx0XQmb7pM0xTOIo1J6pcPLFsGGAb2uvP7fhSdraL9LayQ3cNhseeec7PmQiH3/xkbGBjAedEoXiopwYZcCu9p8Tj2kdbV/Pn+Ys8HaTJaMFOET+4zNfyi46moIQ7btoVUAJdNphaMbWxs9ED8+VCVTzQ04JWKCmBwUNuOGdvNN4sF5UfhME7ITbC6yV6eWMfGxoR+FPGlqCgvOZ+qEvUpp5ziWZgymQwymYwP8WpoaMDFF1/sQY/Gx8dh2zaqqqrQ1NTkuW/ipnALcjKZFOFQ27ZFaMSyLFFoWeXV0D3oCmaraAoJPvb09LBoB4lwksSA3GfhcNgX1iNB06W5Wp5VVVVIp9OC00Wk/nA4jEWLFnm4VVRKiHNgaQNAqExVVVXeMJOO4+ULRz78MGAYeM00sVwqzjxjm5jAvpqaaWcnt0FRi3rnc0p0WmQ+u/9+N5vyT38SfdMRjeL0cBjfTyQAw8BjCkLP9RF3vaBr06aFZA4oe1ANj+oSR+R3qKC+fuYZ4L3vBf7jP0S7XpaEleW/53buZCMTLS0tWke2UC6art/kaIAchidRYrm80HPf/jZgGPhxbn5H0dkq2v/39q53uUNWCR3IL9GDV1+No2Mx/O7SSwG4E2JjKoUD6TQQj89YGDSv7dmD3511Fi4tLcVoaSl+VFKCa7/zHe3hqnNl23besIBOLNG2bc+CVlZWJmrxqedNJBIeLkwQX2hoaAgX5NAnnHSS+P6Qs0/Hx8UEu/uOOzz3rJvsKWTA6VUR+iWHVWks1NTUiAyv7u5upFIp1NXVaesM2raNuro62LaN9evXI5FICAV1XdYVtyBTv3McMxldlDPGghZnXXmX7u5ulJVNi7BSODCZTAqtou7ubkQiEU/R7traWjYURuKP0WjUw3OZnJzE6tWrRekezqkvLS1lC2XLRYupRA+RlHXOge5+OS2uX+QkV/4cieC3N95Y8DBUzXn4YXzFsuCcfTbwi1/4nP1Cslnl72TEaPfoqCtF88orLhk/h5LgM5+Zvn7O2b7lyivd75Yty99mCQ1fuXKl0HzTJUdw3DB5HMshxZlyvnx2113iPp+pqoKVE6xdGongl11dwK5dQGvrtMP1X//FJqLkez9m0i45azSRSGDt2rViE8VtOmlzUlZWhju7ugDDwE+KzlbR/m4sm3V3s8kk9uaKFnPIEBdWuf1Xv/LB2oeE0rzyCvCFL7hFodXd2mWXsT/hXmwVgVIJtmqBXHkhoIw1eUEjDSc5M4omYuJHdXV1YXx83JcpJxOgf3zFFe69WBbw8suHh6N34ABerqyEICbnnkfQZE8LBddWutdsNutzIOLxuAjLWpYlRE53796Nzs5O4WBYloVVq1YhkUgI+QW54G5PT48oJ0PHy4iAvCDL4dAgXTX6b2trK2zbRnNzs3Bu1D7QlXfJZrMihNPb24toNIpQKITq6mofcT+VSmHTpk1IJpMwTVPcGzlqcjFh9fqO4yAWi2FZTuuNu6cdO3YEhmaJ67Rq1SrYti2cQtu2PfppExMTnqQI+X6ppBJlxqXTafzwO9/BvoYGd07o6ACeeqrwsajYTJAS3TiV3+2enh6kTRMv5kKE+0Oh6dJULS08On///e73ipyJrq0NDQ2eosm6TRtlDqrPjhDdg+V9+WxqCvjyl93og2Hg9aYmNEciMAwDtmGgUp4zHn0UuP56T/YzIbFyNudMLejZyJww2nTpwvBEA2lra8PN3/kOkJP3KTpbRfv/wvI5P8M/+AEenTULMAz8IBzWwvqFKFgfMkrT2zvtXJ16qou2XX458LOfaZ26bDbLEk6JW0UOkqxPIxfI5UjuGzZs8JBUVbVmrhisZVkwTRPV1dWekJBt294wQS5DEYODhy379JnhYbxK/XbjjQWRfLmsvPr6ekQiEYFAkeCiHAoI4ttMTk6iuroaO3fu1IZTaXzV1tYiGo3ine98ZyA/R15wC0G2aPwRB82yLB+CSSFjlfMkc7HC4bAnI3Lr1q2+EAk5krFYTIQyuZpz6jt44oknikLLQeKn9B7JDingLqAUbpTlDXp6egSCoI57KiOkcrwoHFlRUQHTNNHa2oplqRRemDfPHU+f+tSMxiIAN7R9003Aq6/mnX+I89TX1+fjKMmOqMfxUjdi8bi7aUwmgVzlBGFPPeVWdPjKV7ROQzab9c1vQSWS6Hc6ZCsorMs53oHaY7nNMAwDeMtb8IerrsK/miZuMAy8ahh4yjCwyTQD54yD2QBTvxCCTegVjW0uW31sbAylpaVibOuQLcuysHzZMjyXu68iZ6to/+stn/NDE9jdY2PA97+PB3t7WcKqL7STTuPek05ys4+kcx0ySvP5z7vk2q1bAw8bHh5GWY7UTXov3IRHKAOpQ9NirCJaMsldTZfeunWrUBmXd7WcsCY5eBTeYdPtBwfdKaK8HE/+/veHLfv01vPOQ1c0ihOXLi0oc4gWC3JiiFdVW1uLWCwmVOOz2Sz6+/vZDDuagFVHIZvNsuHUhoYG8RxIHDWIW0VGqAOV/0gmk4ILRXwnQp9UQUodIZicDArv9ebG/u7du1nkS9Zd40je4+PjomSKbETmp2LUb3/7231Om1xKSC5aXFZWhp07d3ocEXpuyWSSdTxJtoDjvenS9tkFf/du4LvfBR58MHDcsUbiyN/9ru8r+VqZTEZw07jam3JfixD9yy8D6TRgGPj84sW477LLgHvvBS64YNopefFF7RjiaiwuW7bMJ6zb2tqat2wUadTJnK2g7EFV84qESWtqalwHRApNC3v+eZenxfCyDhgGbjYMnBAOz3jOCOKoZTIZDxeNNlpqkXqVE0ZoH5Ubog1JPB73lOkhxHXfunXkbBWzEYv2v9fyOj8PPog9GzcWlIqthnZevPBCd5jPmycmtsOmETY1FZit5zgOqtJpvFJVBRx9NHbfeScikYgnXFFTU4NsNovBwUFRRJYm2YqKCk+bZJK77IDI986VqFGPU0OXugK4OHBguujv3LmeLMzGSATX5By2gzF50swnvOo4Drq6ujzoCE2qO3bswJw5czxinoTayM6ZaZqYO3euWCwp+4jLuEqn0zBNE7W1tQLhkvkxhYSZ6PiWlhZYloUNGzagu7sbiUQCzc3NsG3bh9aRg6c+F9nJo0WQSP4cx2n58uWIRCJsiKStrQ39/f2+xVium0ghl1Ao5EkmcBwHVVVVyGQyWLduHUzTFMfLZU7ISH6jqamJraEYj8e1zq6c0fmG2QsvuGWqZs0CnnjC8xWNybq6OqE4ruPuqb+hjdNdExNAZydgGHh81iw8ee+97oFvfeu0IyK/Q88+i+e++U1UM3pyQbwiQiyJKqAbn0HInGzcWLAsS6vJ5nlGU1PAlVcCn/40sGABHnzb27AhFEJ9LllkphEEObwbi8VE2J2cotraWt9zUUWH5Y2pjGyp/47FYtoyPXAc4OSTUdTZKtr/agt0fvbvdzkOhoEPKlpMQcRqAMD+/ZjKaW09K73khwXZKvC+zmppARYsABYtAp5/HvX19ejv7xeoicy/4ibz0dFR8W/SWKJzc7XYEomE7z5UYU2ZuJw3jDc5CRx1lPsHt+9uu/lmHEgmgbY2lwx7CMahL3J7gsqhkF6UirIQX0iVTIjFYhgbG0NXV5fgaNGuXa1vuHXrVuHc0bEqkpOPtDswMCB23RRe6+npgeM46O/v95Q/0SFbVJqI+HaxWAyjo6OCx0e7dvU869ev12YqhsNhrF+/XnrEk6IOo3ztyspKdkySXhjt+s855xyP7MOaNWswODiI8fFxRKNRERZUzxONRn1itDL6xoWs2IXwwAG/zEAhNjEBjpBOY5Lez/PPPx+RSIRVTB/UZOvSvPSW9nbckiPy48IL3YOIt2gYwBe/6NZT/da33AoOhoEL6uo852xqavI5pPX19WhqakI0GsXGjRtZvt3BmG4TV1tby9Za1G7UAPFMZtouGVGUw/IUTqeweywW0zrr1Kb29nbx/yonjNA6mhcL4YgVna2i/a+2QOfn2mtBRO2fXXIJ61jpXuaf5FJ2nywt9b1Mh6QR9thjwLPP5g19ZbNZpJJJ/OGmm4DnnxchEk5Pi3gFsgWlPnNOCsHmnNHCX1NTI3bphRaj9dmdd7oyGoYBxGLAddfN7Pe33w5ceilw++2BRHI5OWBiYgJLly713L9O4HHJkiVC+0l1NDiRUgqBrV69WvRRMpkUHBDuGtwiIyN0JNSqtoFkG6jWYSwWE5O9XEMumUwKR019xvF4XDifFRUViMViIuQj1zUkhC0ejwtHMhwOY/HixWKsyCFKeSy0tbUhEong9NNP94TOzjzzTE8Yh4j4ahvr6uqEXERPT48QN5VlJEhYlcrzkEQGhSqXLFki7jGVSmHDhg2e0JYYtzt3Av/4j8AvflHwEHQcB7uvuw4wDOyvqfG8xzQmZa4Px1dTkS3uGhMTE3jqF79w35VIBNizx0VJVq50ieSXXw40Nwvn67WjjsIHlE0lh2yVlZWhv79fIDczNd3cpdvEBVUbOJTrq6Vz1DI8ra2tAPSJIlwlAjWbUOW95XXeA6zobBXtf72xzs9rr7noiWEAV1wBoPCXw/nrX/FZki846qiCXryCbd06TM2ahc2KFAGZPGFQ1lU+ZWOd8yRniqkmhy1s20Zvb2/gfclhBDkF/6Bs3z5gzZrpHfrVVxf+2y99yf3NZz+rRbZoF0pOwJo1azyL+tDQEFvKZmhoCJZlIRQKsaT6aDQqdsLUT+SAEmejrKwMZWVlaG5u1l6DG0vyfQwNDWHRokU+R1IVm02n0x4O2OTkJPr6+pBIJNgwoPp7y3JrvunEJQcGBmCapq9QNnHRuL6nEEtpaangmM2ePRvHHHOMCLOQrERpaSkbxpyYmPA4JLT5qKiowIc+9CGhQVZZWYloNCocXW5BJ66RGtoSz4CclQLD2vTenJiTH3jaMNgC5NRHNF7kLECVsxVkjuNg7/HHu23cuNH9cGgIqK2dfn8aG4HRUWBqis2MVEOU2iLdBZgciuY4cdw8RILCxNmqr68/6OuraPWCBQvExkOVC6F3XicJks1mRZizvr4etm0jFosdUkZjkBWdraIdcRa04Besm/K5z7lDtKrKWyD5wIG853zqPe+Znshy8H0g5F3oPdxyC6ZKSnDAMHBfDtGRQ3w656GQoqdBdfSCfiMjZYUWbz0sE9Fvf+tyXgzDTfku1Oi55oREVfV8VYxzbGwMkUgEixcvRiqVElpiKimYQlbEtQqHwz5Uqbq62tO3RKTdsmWLLzHBcfwZhSQYSosy7cop01RGfSzLYn8vP09Z6kNeUKPRKJtty/2e+o9I+d3d3di1axe2bNkiyMyy0XvAhaeoGHUsFhM6WyeccIInTChnfpqm6QtjymgLyQvQdzKnMpvN4pRTTvGce/ny5Z6yS3SPHAG8tbUVE7feKqQGdGLHsnnez7POAgwDt8+e7UNC6FmQEK3sAFDd0UKMHJuP1NcDhoFnKyvdL/r73TZXVADf/jbw6qu+dnIbssBsQM3v5M97e3u1lRjI6N7b29sFUiojoFTF4GDCglxCBDm2lJErn5f6n5JMVCeQ0Pnu7m4xxx7MJlrtW10fF52toh1RpsssnNFif8kl7vAsKQH++7+9323c6E6U99yjPefzl1yCv5SU4K/nnQe88sqMIW/2Hp57DsipTX+3vNxzPJHXdTpfhTh5FLaQX3LutxynAQguvsw5gTIHjDt/UF85jz8+rbx96qkuClmo9fUBhoH7jzvO8+xoAlcFTDOZjEgekPlKsvIz6UzJvCNCQ8h5HRgYEDwubuKmkJGcmEBZXHQOaqNaRse2bYTDYQ/yYtu2J3ymSlLIkgdcfUhKX6d7pIVRJfUScVgN0zU0NAiNMG5x05WkkTNTdeWNZC6hbdsCiYlGo1i3bp3nWF0oXMdTVD/jpA08/ERCtnK/0dojj+AOuTTUFVcAixfjxJyGGOCXTCAHpRD9Jzr+yQceAEZGMLhjh6sxZdv4x3AYN37gA1hBavePPgr8/OeidM/hMN1cKHOVOKkD0m3j7uVQIwHkbDY2NiISiaC8vFxbXL2jowOVlZW+MmPbt29HJBIR2byUdBKE+s/EqI0UXidJHA/N4u67gV/+suhsFe3IMR3/isv68jg/Bw642UFkFGrKLW7ipXr2WcC2AcPAVCiEy0wTe774RaC7G0+sXTt9zldfxcg11+jJ8zO8h6Rp4sm3vAUwDLzW1oZyjYYSCXCqYZtCJgQdKsbtOonTIKMWhFbIFlS8VS3ho55f12d0zBlLluDqcBjXfv/7BfTq9D3e/7WvAYaBn4VCvnvlxkkymfQIjVZWVgrCciqVQnd3N2KxGBtmIBKt4ziCAzY0NMQu3nLoS01MkJFJx3FJxCqio+rzUNo5x0lRieFDQ0M+RKe+vl5kw23atAmO4wji/sKFC8XixSFocltisRjKysoEOkDonhye4sqxUIkXTh1eJoa3tbUhm81iaGhIoItycWIdSjA4OMiO102bNnnkJTjRTsFPnJoSGyD85Cf6gXfPPUAigZfOO296fL3wAn5/882BHB+yfI4G9eP7W1vxWE4L8NzSUhiGgcWSBEJXZeXMs56Z63P/5uYOlaOoG2eqs3UwbVJNlqaxLEs4M1yCDiFbJEZM77bsMFKNUU4f7mDbLEcTLMsrfiy376UNG5CTfig6W0U7MkyXWSgyWe66C9i2DVi+HM/Mno191dVuKvScOW6aNNmjjwI59EJe/McvuMCVIzjlFDGBib+SEpy4dKlP+mCmux/1HmYZBn6Qu4YzaxZuuPRSAas3NjZ6nBIqh0Iikmph4XwWpGStTqhc+vJMkS3iyOiOoe+IS8SJgBbqTNK99SxaBBgGRmzb831HRweb8VRTUyOELAlpJKSIJl2u3IecMDA8PCx4Kvmc2iVLliCRSGizlLLZLCorK7U7dMCbBaWa4zgYHBz0OHyO4yf8R6NRbNmyRSixL1y4EJY1LRy6ePFixGIxzJ8/XxsqpP8nKYWGhgb09fX5nEdV2oJCmKOjo3mRLeIQJRIJdHV1YefOnSI8uGTJEq3TziFb9ByWLl2KjRs3iizRcDiMeDzuCW0BcEnxJBb6l7+w/Y2XXpouk3PSSZ53jONUcs8raA6h8XTXbbe5RH3DwIOzZuEjpolPGwZekuaodyth4EKM5hqqNECJFPKGiNtMEZdTDhVz44zLYC6kTcS7Up0f2mBwsjzEg5NDg+Xl5YKzNTw87NG2U/v5UJAsrpySmgDDaTe2t7cLzl3R2Sra396mpoD+fry8ejXGSkowaRj4T8PAdsPAMZGIQCyepfqG3N8JJ3hOqS6Kv7/lFjyZ2zXuravDZaWl+L5hYHD2bNx3/PF4IOcIHCqsrCJb1YaBvxoGDtg27rv2Wo8DIpfSCXJ2Znp9XaYQ54jIfCc5o43jbKVSKbFYU2iuJpeNpcsMPOusszwZaQsWLHC/nJoCbrgBJykOru6exLP8l38BDAOfVsJ+HLJFobalS5eydQrl/qXJkzg+VCOQntPg4KCYXOUQJEky0DlJVFMuoyP3IYUag5Sndc+diOzj4+M+hy8cDotnmU6nEQ6HRWjzzDPP9N07lWHi7kFuC/0/hR25EkFqW+TQnVreKBQKiZBlPB7H5s2bsXnzZjGmKIxJPKyPfvSj2v6gc3PyHTU1NTBNE8cdd5xe/+jss925Y/t2/eD75CfdY5qbXeFN6IUy1e9UTiTHVRLvzSc+AdL0+5eaGtycm6tgGBgyDJQbRkGIjMpxisViHo5VOBzO+97QM6RQmDrO5NB2IaWJdu3ahW3btmF8fFyMF5X32NvbKwSlV6xY4RNfpQ3A8uXLYdu2KDklq7/PNApRqHFlzxKJBNrb2z3HceLHqVQKr7zvfUVnq2hHiP3ud1on6ucnnihCKB+zbfxwzhycbtsYvfJKV8fp5puBBx7waeVwi/+HW1qwLyc70CehZ7NmzfItjIdixNmKxWKYNWsWfnLZZUDuRZVRA3mXzCmXz5SUL1uhoYKCtIhyn+/cuRORSMQz6VBGmw794pCNsbExt0SRYWCoAFVoz7P8+MeBaBTn5FBAdYKlDLra2lqPFhUXAqHwn3yPMkpDEgrkKM6fP184DsSxIxSntbVV6HHlQwcdxxGLYEdHB+LxuIezJS/K9DxIJoGckFWrVnkcYyrPRKKrW7duheM42LFjB0KhEJbl9KAIOeBkIShbjDhb9P9yFhk5RcQBk2s6krW2tnp2/aOjo4jFYmKcyKHR3t5ehMNh1NXVIZVKeUrxULtaWlq07wGpeKuJHpz4pm9s79sHXHUV8Ne/8gNvagqgrORc8fMgU8P0apKFnCAht6k2lWLnvscMA2fksvmuvPLKwGs7jiOEb8lpJfkL2ZFWOVY0x6ioOI1rcoooVNbb25u3EgL1BXEJ5c3W0UcfLaoMyH2Tr/QNIVsy4qze/6EgV7rf6+RN1DFOY5X05Qh5GxgYcIGAorNVtCPCXn55Wnupvx9P/fKX+OFHPoJvRiI4XQolzOSF0jkXv7n0UsAwcI/kbBE357DY888D//7veOX978eNN9yQt9aivBPVhadmOpHouFP5woy6SZR+x8kZNDY2IhaLoaurS0zQbW1tSKVSOP3001nOTiQSwYnNzdhvGDgwezbwyCOB9+N7lnfcgbnpNMvnIYI8ZeXJemrqZJ5MJj0CmFQ7Ti57pE74o6OjiEQinowy4oSQg8LtyFXnf3h4WIRBTNPEkiVLBGGdnhtlh3LineTkqhwS27ZRV1cnQqbRaFQsdIQSNTY2styb888/H93d3YhGo5g3bx5s28YZZ5yhJcnrtIrkxZoI/JlMxve8BgYGWGV/uZIBja98Y18nX0HPnlCTGWfT5sLWuP/+wMO4+YartsBJfwwPD+Oz0Sj+K5HAX0pK8PBb3wp86lN48t5784Ygs9msENqtr69HKpXy1faUr0fCyNx36jwjcwRnwnWizQQlXqjjljYWstXX17OfkeYaZRPPhG8VtHHU6XPJY0O3hpBuGo1xCk+vWbOG54ade27R2SraEWTj48DTTwMojOxdiHHOhfPoo3g+t3OsMA6TAvzLL7tZcm97G6Zmz57enf7oR4EOTiHtnankQr6+k507mojU7LhkMslOOI6jL5BsWW6B6ng8LnZ3VPBZRcIIEXn51FPdfrrggrxdXEg/cu0jKYHdu3cLQVZCaBYsWCAcHtqNEldOLt+jZpuRM0CTNgmQyurUakhQF27TOXdyWHliYsK3EKkK5PnC0vK/uX4iR6eyshLhcFikzUciEW3iBJGjdc9GdnLULK3e3l6B7qkOSiQS8SBbpAGXb2yQIKycyk/IhE/ENp3Gk3/6k5s0E2RUr+973/ONNU7MVDZV14zGQlCWsOM4BSnaDw8PI5lMsiVnVCdPTuAgEnmhIbeDQYwoLMxpvTU2NmLt2rW+NuvEVylceLCZjCQ3IfMzVV6szjktJDEoKIws7JvfLDpbRTsybeLWWw9aAgEI5lQAwGM5sdNzcqniM6255bFnnwXe8Q7hYL1uGLgjFsP5sRiuzy2GM0Xk5LbP1OnUcafkvlMdOG7R0004JGdA5WpU1fDR0VGkUils2rQJqVRKZBORY2Oa5vQE/+tfu/2mLObavnn8cUzceqv2/jOZDJtVWFFRIYo3U7iCSNocV04NY42Pj4sC1rSrpZT+uro6lpxeV1en5b2RIKQsS6E6d3J2KDcOKEuXzksp8jLpWXaSJiYmPCgjPUfis8ViMfGs5s2bJ/qJUz8nBztJUgTgkdHJyUmkUils3brVJ44aiURQVVXFOijhcFg4ERs2bNCPh9w1aewODw8LNEVGyrhF/xfJJPaecIJLfv/jH7XXeHHLFrw6fz6evO8+33Mk7trAwACrAUXONFd4+1A2d/J44BxxTryW9ONmGiGQr1nIbwit1CWT0LgdGBjwlFzq6enxcCEPxRGkNqhctbPPPpt919XNtlxXlKNGBCn/s/bDHxadraIdgfb669i3Zg2+aZq467bb4DiOWOiC4HQZpcmLBOWEMR/JpcQftE1NiZ3v/vJyrI/F8PtceEk7qU5NAb//PfCd7wCf+hRw8sna3XUhjhO3swpy0DgEJBQK+cielJLPLSK2bfsQq7Iytwba2rVrxWKnkpYvvfRSb388/bQ7jSiZhVrr63M5NF/4gu/eHceVVOCQrXA4DMtyS7cQf0TnlMh97DgO5syZIwjBtBPesGGDmLR16tR9fX3acCxxs7jJnxYj1QmUla5JFV1HUKdwZ75s01Qqhc2bN7N1Fvv7+8XYo3ZTOFKWfyBTyeBUM5L+oqTyAAAgAElEQVScUVn7bHh4WCAOHMLW1NSE8vJyjIyMaIcCOTxz585FTU2N9vlHo1E2RN+RTOL1XN1UVFez7+A111yDtGniaKkQsrNnDxol0jlxszo6OmDbtijoTJmK5EzE4/FDInDLY112sLjnL5f1Iu0yLkOvUFOfbZBWmLwhIQdYTiZ53/veJ9pJsihcyJmbl+U+oDapWb+O47Dhy2g0itLSUlHCh0ynz6WGUg+JfP/668BjjxWdraIdYfaLXwC5TJzbS0owJ0dIlkNbsqkhBE4t2odwOY4IWc7YfvUr4Prrp/99yy14fdkyXHfJJYUR3D/4QYGCYfZst8yGxnQLqZqFowp76iYIlexJnCAuFCHX4bNtW3BCSLtIFf3s6enRimA6jsMW3sXUFECFdgtQ8EZ3Nyh7THWqSRBWvvdoNIqurq5AhIaTJ6B7TCaTLGlXTol3HMc35rh0eO5ZRiIRT81GwHXUFi5cKJw6uhfKpuzv7/eEKLliuiQCSXyo+vp6JJNJX7YphVS436tONinxb9++3ReGJj0mORxKEg/cwpdOp2GaJnp7e33CrUSQp80Vh15MTk56inxbloVNmzahublZG8pjUZPnnwdy+nc4/3zf85KziolucO9JJ8ExDEzeeKPnfuiZyLpq+RD2Qo2THZCfjeyIE0pEm1TLsgKTf/K1i5NF0SFzJC4qt5kSNj74wQ8iFotpnbZ87ZD7gEj3qoNJ56ivr2c3QOXl5T4OrazPpXOoDuXZyVZ0top2RJnjOJgcGMCrCxcChoG9Z5wBQE8ulycdruYdlSQ5KHKsbK+/7pbuMQxA0jgaHh7GnFxWmZr5w05K5FycdhrQ0+NmUgaY6jh1dnaKIsNqeEItBxOEeAUhJ7Swy8gVcWEIeYjFYoLTU1NTI3aOlPVGJtcc9O2sX3kFeM97gETCW1JJZ6tXA4aB5/r6WHhf5pYRGtrf34+GhgYWJSSyPpHJ29rahIMVjUbZ8NPSpUvFgq1DnWzb9hHCuetTaEdFlaqrq0XZm4GBAY98hjx+SQOLQzao9M6ll16KTCYjlN0p9DYyMoJMJiPq5KltUFX2E4mEcLS2b9+OZDKJ9vZ2JBIJhEIhcR56x0hJv7u7myXjl5eXC8estLRUZD3K+nIcSk0bBk7uwTRNbfFuLWpyyy3u+7hkiaeNnOZfOh7H44sXu8d/7nPiWFWXrBC6Q0FcH+g3XLQZoDFHz7u7u9szXwShRN3d3bBt21OcWw4HEwquOuM6HThujiERXfn8M3FcqD3ye8LN88QfJHSNq6hAWbpyv9FYO1wOlSdR45lnXE3HnGNedLaKdsSYXObmqFAIrxAC9LOfAfBPZOoCpivh4csIzO1EC7Ynn3SdAsNwnaXPfAaAnkvjKdUg26uvTp9Dtqkp4KGHxD91E/Hk5KRwcridpi7biesrlcMDuIv/7NmzEYvFfKnOHR0dHh0c4i2RWGlfX58ohaHC96ZpYvXq1fo+f/HFfE/AtVzY525GvFRuH4VuKioqRH9xyFYikcC2bdtg2za2b98utIVs28ayZcu0z5ckCsrKplPiyTkjhEOutcihEXQuUp2mwrqUMUj6Q0Elc+g5E0Illx4qLS0VxH9CfyzLwvr160UyBB1PDielq8+bNw+WZaG5uVncB8kIcHpldIx6b6Ojo3Acx7fwpVIpmKYp3hNyTMkBHBkZwfj4OCsaqQuRt7e348wzz/ToQBEaHriQvvii+06GQsD+/eJjHbL14NAQYBh4PZ0WpbyCNNJUdIsQadmJXL16tXYzGEQl4Hhy6jVlo8/V8WLbNnp6epBIJMRGgzhvFDJWn0M+BIoy9Aopi6Nrrxx2z5dZLEtayNw9+R4J2aV3vLu7e8bOleO4EjgbN27Ezp07PXM0bYqamppgmyb+VFEBwUndv7/obBXtyDBucvscOVvLluH3OSHHfMjWggULPDu7iooK385swrbxQmsrIJFetXbbbUCuECzKy/H0T38aiFa0t7eL4sC+SeSFF0AhxH3r1gH/9/8CP/qR+Ax79gSmIff19SESifjuR3WydNlO6q5TJS2XlZWxYViuFp5cm08OSS1evNizcwyFQjBN89ALWP/xj254ORSCs2eP716Ik0J9Pj4+Dtu2PY4RZcRRmE1eVGQkSub4qOGnqqoqrF27FrFYDAsXLkQoFEJJSQmrCE/PRE0VJ04PtaGmpgahUEggXSqRnJPPUNEfEnssLS2FZU2XOZHvgeodqs4PhfVs2xaLIyUANDQ0CERocHCQHX+Uoq9+RjpolPGlojByphkhNYQ2cvfNFb+mfiZUl8jqhRRvB+Ai1vT+KSrytPkjB/Oaa64BpqbwTHU1YBjYlFO+16nIy0T6WCyGeDwuHF8ZAVdRZHmey8fBDDJ5/pHHCRc2T6VS4v3QbQhoY5FPfmEmKNFMZBe4MlLUJlUeREZw1YoHhxLOjcViIlxbXl6OWCyGzs5OH4/1oR07XKd8zhzgT38CUES2inaEGAfbl5sm7i8pAQwDX1H0i8jkQqnE2ZK5I9xueC+pMz/2WHCjvvENl1eVc/h+csUVgdwJeSLUhUCuD4fxsqQOLf7e+144f/mLNmRQVlaGpqYmNuwTjUbFLj5oMpZDkqSDI+/8urq60Nzc7PktVyRbtwD09vaKsjiywyDLL4i2TU25Nedefz3/4DhwADj2WLef1q713AvxzeRQCJmcnUiTbHV1NUpLS33p3qrgJJ2fEJJMJiNqC0ajUZFdSROvTsxUloug8IIcFqGQZzweF+rq8pghQVF1AVSFZB3HQX9/v0CYiERdSKi9o6PDQ+rnni8hHoTUqe1RkS3S4KIFj/5kVEcmkUciESHEmk6nWSSSe5dp7KtCsLLD5TgORkZGWKFTAK7g8PHHu+NMMXZxPuYYwDBwz+WXa/lYhOilC6AYNDY2ejZHuuzhmcg1EMpL4VNCXrPZLMstbWhoQCwWQ1tbm+c7yladqdZWUNsKybbWSWlUVVWJUH9NTY14Hw96E1dAO+nfnGYihUrj8bg3zH/HHYBh4FXTBL773SJBvmhHjulg+/tvugmPfexjcB5/3PcbebdGEwHpz3DwcUdHBxaTWrNp5tez+fnPgZIS4NOfhvPQQ4GOkFx3jZtExELx298Ct9yCvaefjl+XluL12lpXTX3/fi1SRiUgaCEipEbOdLJtW0gKBE08xNXgFnDbtn1ZhJTqLN8Tl25O/CHTNH2LuczxEAvJI4+4z6GyMvgZAK6g5Pz5QHk58NRT4mOSFuAma47rQd/39/f7eEStra2+ECypk5NQYTgcFs48TbTk1KsZYJlMRlxv69atHhQwk8n4+o8TLqW0dMp+lAs0A9MhJjkzS81SVHlnXAiGkC0K633oQx/CokWLPJyzjo4O7NixQzgNMqowZ84clJaWCh4XJREMDAx49Nso0UK+tm3bnrBpV1cXmwVJfao6Hioiee6556K0tBShUAjV1dWCTE3zQTgcRqdcR3WmduCAW0vRMNxEG8YIhfZpe0ljSy1erkO25HMWgshwumKEQpPqOVcEnRIWuDnuYHSuuLbTXEmbiZ+efjpuSqU873R9fb1wnDlky7ZtjI2Nic2FbdsHnWWpto/uj1B6eQM3MTHhS2ahOY97zmNjY7gxBxTQX9HZKtoRYyxsrzHdrogWYDU8RhPGXioNZFl5lcsBCOXoIO6E+nLKKBCZLgSiyjhwTpq8Ex0eHhbinIsWLYJt2wiFQkilUoJLlc/Z4kivqjYPZXvJiAEtdJxjUFZWJpw4Tqndh2xdd537HN71rjwPIGd797q6XJLpnFMq31JXV4dQKIRIJCIQGXJEOGXryclJT/kSOQRE56BFn3S0aOGorq5GNpvF0NAQTNMU/LUFCxb4ZDK4rFkKManPhPp5cnISF198sSfT0XEclsNH4RbKEFXvVeY1kQNCqIua2SaX5qGxQ59HIhGcdtppSCaTmD17NizLEmV3yAEklGpiYsLn5HLPQe2bsbExxONx330TckULOCmNU7KDXFtRrfVomub0Av3oo/oi1Jw9+CCIUsCZjIjqykPReKPapJ2dnTPSl1KNnAUKfw8NDYmsQG5OoTEjP/+tW7cC8D5/XQZ4Ie1Rnw+H7t1bUoLtpolHcxxYOaQpRwc4keeDlWLgHCs5E5E2NjSX0sZCt7HjNm4UCv/seee5cjUnngiUlxedraK9uZZvh8Z+//TTLrdJ0sLROT99fX2+UJjHqZmacrWtDMMVI33lFc85nvzzn3FXLnNLbRfHE+KKAnPhjiByr2zqZKKGKqlcjKrKnm9nLPcbOYVqOEb+TUVFBRKJBMul4Ei2sVhM7JhVbR3S8ZKdnaeOO859BpdcwrazEOMWEnIEqC6jXDybFpTdu10ulNzPNTU1AtmanJwUISDiuBAXsKenh009lxd4yto755xz2CxCQqji8bin/1QHjAjGshCqTPimhAkdh4oyCKmcC51jYGAAIyMjiMViuOiii4Qzr9vA0Fin/9IzraqqEgR7+f43bNiAbDaLxsZGn2hrJBIJDGk2NDQgFAr5sjtVLpSMrBGniJPxsCxvbcWOjg5UVVW5MiQ7d7o0gY0bCx90+/e7/MGbbw4cj+SYqm2hjMtYLIbVq1ezmZIHy3miDZ8c8tKJnhJnTnWIaNNUMOeNaY+MZgaFTz+0ZAm+cfLJKJdQ3+HhYc98rQvRHgzSpm6KZb08ah83l1LFCI6zRRI46sZJLucFoEiQL9qba3K2YT7kymPDw+5wO+448RG3MNACqYbCfM7HX//q7kwNAzjjDMHVGB4expcsC/sNA9sZjpjKE2ptbUUymUR1dbXnuI6ODnR1dQkumborU4X4VJMXUxl+p+vS4j0wMICJiQnU1tYGcj7Uc5PCu7xgyQgDJ4Ap9yGFIonETOeTF07HcVBZWYnu7m7BV6LdamU6jX05iP2n3/qW/rkD7rMJCPfShCk7VNlsVoS81ImUaiaGQiGBuJADS2U4gkIDZWVu0gWHQlHoivhrQVmExCckLpO86BFyWFtbK3hNtNOmkDgtrktytUPl6yQSCYyMjAh+WFdXF9LptAf55DhduvAwZa5S/xBysXPnTm1ImlLtue8sy5XZ4JzWVCqFnTt3inNTmFAeV5yu2fLlyzE4OOgTqKUQJIts3X23OwdEo8ATTwSPQwD43e/cdH6NqRvA4eFhoZJO76tK3lcdBxlRLYQSoG4AqT9JH4syTPNt8mbiwKjzk8pr0lWjALzoXlAk4mBDlmo7KcOVCrnTO0D8S05jTp4729rafKjqueeei3A4jEWLFglkspB5vehsFe1NMR0nq6CX6rvfBZHUsW+f+FhGgTgugkp69tiddwLJJF7q7MTEb34jIPhn3/UuwDDwcI5gqraPg5NV1CQej/u4ZNTeQidSdRdGCIfKw5B393R91THi+AjknJSWlgqEQs7UW6LoDhE/oaurS2SNqdyuIAeFduzJZBL3Xn89YBh4pbraU/KFtR/+0E3L/8d/ZPuIMu9s28acOXM8GW0qvN/e3i5Cce95z3tgWRbmz5+PSCTiKa+jU4XPZDKCF8WhFoODgx7nLSiLEHDJx2vXrvUI0/b29goUyXFc8rwchpV33rS4qhyqyspKkQFIGWYyv4wWOVqoCYnkyPiyYyg7PVTbcO7cuayO1tDQEDKZjO+7pUuXitqYjjOtBL5ixQqkUils2LDBJ01BumXk5HHPxrZtLbIVDof1nK2TTnLnllxVAq3t3u1SD667TnuIjgZQKEpEYWFSoac6m7rfc+g+hSYpCSYWi4k6l1pJGk1bguQYKGyr8po4YVk1fKq242BDg0FOXyaT8SQAyaFyctB1yR5q1IBLCuAI9IHO6r59RWeraG+s0SDMZrO+bMNEIlGQACD27QPq6twh19oK/OEPnvPTi6XuaqnkjM5+fOWVAiVIpVKora0F1q93r3PllSxCJKsky9eJRCKCgEo7b1osaaEqdCImJ0LmDVAaPd0zN0noJjFayDnYPJ1Oizbs2rUL69atw6ZNm3zHkVCmjvRLC72MzFDmJLVHLJSTk24ft7QIMULWXnwRqKlxj1UQMDlUAqCg8jSkB0VE4C1btmj5GRyJOJlMwjRNxGIxgTIuW7YMyWQS8+bNEw62ZVmYO3cuu/CTir4qIktOKz1n+dkRTwzw77xrcvIDdXV1gntDISwaNyQqqdZflJNJCHHasGGD57Py8nJfFhqhBBQ25PqZnGtunG7fvl1kZjqOIwoXk9o4V3KJQsLd3d2sxtL8+fM9qDltJo499liBcLDZiOPj7vhKp11Fec6eeQZoaHCP+/zn+WNydiicIhUllDdU3LmCeKtyxh/nkNDvZW0zFV1TizjT9XTVGGgjqj4/eW4iNP5QkDW5n1Wnr7Ozk934yVwwAALxUzcqVLVBTng6ZPv1r4F584rOVtHeOFPDhqFQ6OCQLcCtJ9jU5A67WAzYswdA/gkgiBsmH3/Prl0YKynBs8uWAYaBu049lRXvUxd5Oa4/ODiIbDbrKRtDi+XGjRuxfPlyj+5NNBr1kdp15ye+TRAPI5PJCMeJq4FIBWJlo0xBWkCJT0TimqpoJ0dIJ4V4ysTk9G0AiPDcPT/9qYts5RA1rbN1ySXu825v90lETExMiDT14eHpQsyyET+GdreWZQk+CqWyq+EL4mdQmR950ayvr/dIHFAogjStVG7K6aef7unTUCiEZDKJiooKn1QCOd4tLS1szUAa4+rOm5CQpUuXikzEbDYrdu06tIecDpnMrBLQN2/eLDgqhMQGCQdTJibnMBBydeyxx3ocNSLrk95ZRUUFi4w0NTWJjYtt20JSgcj72WxWhG5J1X779u38uFLt6KPdcXbFFfz3a9a4369YAbz0Ut7TFeo4yMeRgxxU87AQjmehqFUmkxHPVubddXd3CzqGXMSZnKSVK1cGJgupgqLJZFLrYOXrE48au2Tk1HFzvmV563rK7aNKEnQcOVaqrMXBcsK0dtRRKGYjFu0NMy5sGA6HYZpmQdmG6rkmJibw5AMPuCElwxBlbuQXX04Vzxem80wYU1PA298OStF93TCwoaJCm41DyBPtODdt2uSp5cbt7ojDQs6VTpdJ1oaiyaaqqgqhUMizsHJoAhHaKXtLrYFIi77sFKRSKWzfvp0NH9FuuKmpCYsWLWIXALouiWGq6d1y/zmOWyR2QSqFD7e0YGmOj6Sd1LZtc58JQ2B2HK8eEzfxptNpwV+S60jqNK2In0GhLcuyfHphREDmyuTIiyEtPlVVVQId2rx5swhzytpA5HgTKnbqqaf6QmXEmZJlTHSZiCMjI4KPQgkRspFDKfelurhQ/UEi8VNIqq+vj+VF9fX1CWdIJVzTYsuNM8tyleblf3OJAvR+UaUANWlAbndfX58HqVGdGs+97t4NRCLuOONChPfeK8R0KTOZM90Crdt8qKhzJpPxJK4E8ecKvbb83fj4OAYHBwWqq9IACLkl51d9x0lioZCNLefABxm1UR43tm0jHA4Lp7yzs1PMjzS3cU4fV9dT5upRmPVwOVZcOFF+3pOTk3gtJzdUdLaK9oYYJ1KaSCSQzWZnNLhldKzONLG3rg6QEAzVARgbG4Nt257JljP1d3f95jf4ZCyGoVAIf7z6agDB6JhcjFktlC07TGSctIJ8XuJE0cLy0Y9+1IMAhEIhjI6OIpFIYOfOnYjFYmJ3n0qlEA6HRehSdvS4cJpMnN20aRNKS0t9qJDKs9BlGsphuULqQ8ohinwOMW67zZ1mFi5kBSczmYxA62giljP8wuEwTjnlFIyMjHjQQq5PaDKmsj1qbUDiU5GIZnNzs+8Zq+RfWpRo0lWdiHQ6jcnJSRbF4krVqGiUTsKDJC8IBePeD8qW4sR3BwYGYNu2CM2RcxgOh7UZXA0NDR7Hisj5lACwc+dOLY9NLlDe0tKC0tJSgYxYlisYqya7cOExmY9I7+aaNWtE2Rl6dp6NwL59rlDuhg38GDz7bHcMar6ncSfz7mQnmuOf6cSQ5WoHhRLbg4yeLZVaojJM3Dk7OjrQ1NSEhQsXsnNXXV2dQK4oFD1THlhQG3XK9jJXkRCzoA0WjXn53ZWRO12tyEJMHnMjIyNYvXq1p3oAbaYqKioERcCyLPwxBxAYhvEnFJ2toh1u45CtGYUNg86h6GPJUHo8HvdPqBrjBBJ18DjXNt3ujiPRq86V6nzRorxy5UrBYeEQJJJkIG5CU1OTKCYLFFYDkUoK1dXViULS3AJK2ZYLFizw8XtM08TAwIDgqeVDeNS+K8jhnppyHS3DcDPHlHNks1mB7lDYI5VKIRKJYGRkRDhVpI9FY4HQOtnq6+txyimniD6lseM4DpqamoTyOSECAwMDLDeFnGMtX0255qJFi3wk8hUrVmDt2rWIx+NYunSpOI8cVtFJeFiWJcoIkbo2LbKVlZWesOb69et9v6cMQbncD31HSKEqsnrGGWdgcHAQ27dvF+HEeDyOeDzuWXjIYcuHbPX29gpHjRBJXTkcchQTiYTv3BQCsyxLcHvYcTo1xVcy2L3bHXuRCPDAAz7Ehq7PZT/THKC+GxT2VHmfam1P6ueD5X/pEnmam5t91x0aGhLIViKR8PEVqZi3ihQWKnbKoT+yLlghSF5DQwM+8YlP+KIYNL7I6ZOR31Qqhc2bN/s4aQdjMkeMNjI0/js7Oz0hfvWdurm0FDAMzJo16wCKztaRY4c9Xvw3tJmIlHKmQ8c45+fJP/4Rf+jsxFlKGZY5eYpOqyGGQnlfOt4CTZiLFy/2TAIqNB+Px32LCF3/oosu8jlINCnS4kQIF6Em1O5CkK1UKiW4PMT/IW4NTSDvfOc7xbmp7Y7jqjbHYjFMTk6KiZhLGJARHrb/v/Y14P3vd0v2BNn73gekUiJsDEwLERIRX11k5QxHuS20YMh94jjT5XLURYZ2zxT6Vr8jdLOtrU0kEDQ3N/vEYIFpvpp6jpGREVZg1TRND3H96KOP9jhztBCrBF8in4+Pj2NkZAQnnngi4vG4+J08LkKhEFpbW0UbHWdaZFS3+FH4Znx8HNu2bcP27ds9SMymTZsEWsctPLZtwzRNMc6oDBAlCnCEZDU0SP9WkUJ1QyOL784kLCfsjjuA008HLrxQbDYINZN15eTnSSjR4OAgmpub2WuS8Cv3W51jQmHA8fFxH3KohuuIw6huKJYvX+7ROaM+sywLkUhEIIhy8ganczYTUzOwidLgSUpC/lqIlmX5NA1lrcMZZQYWYLJDSBGHsbExNnRvWRZGRka0oc1v5QpSh8Ph/Sg6W0eGHbQOVYH2t3DkZnpN1fnhkK2nb7oJaGsDPvpRN0vtk590tXIMA/+ZTk+f7I9/xCPhMO7/6lcLageFBArZTXKTA5F3iedDLycVRVZJvdzOkCZK3S68tbUVmUxGkIopfCmjdLZti/TxsrIykeFF4QTaDablvoK7OB133HEwTVPw0Xp6esQE097eLnSfaOdIiA+HislZdD774AfdKeTaa/OOCdmILxe0yOqcPQqHRCIRlJaWCuSHnJqKigrPtYgnpWqIAdPOL+lCURkR9XmREV8tlUqhvr7eg4RQ/1Kfq/en06ui8K1K8KUxRPfV09PjcTZkbqMc9pVFRnWLnyy0SuNJfRaEtOo2JNXV1di2bRtGR0eRSqUQi8W0yIMaGiRUQeW90fOV0WIKKevu54R4HPvOPhvo7/cPNMm438rnVsdEPmSL47UFOTOEdJNqP9XlpGLmpMcmb9hIrkOdQwjdJO4oIT+bN2/2bP6CCnoH8dPkrE/HcTxke1rXuLAf4M3QJc4WjVFyEtXQ/kycwHzcNhVNXbx4MSKRCJqbmxGNRtHd3Y358+eznMWLLrpIi2w1pVJ4oa6uiGwdKXY4wm5B9kY7cofDuDb60LGhIWDVKvgKORsGXn3HO3CuRDz/a07G4UBJCTqY0KL88skhCVnNOcjUMOSpp57qI11TluDKlSs916Ndtepc0m5q9erVYuJRQxTqjp7bFdP/U2hSzU5UJzr6NzcZUriQhEllp3TlypWi5h1lH/X29nokL1hycne3+9zWrCl4fDhOYaWGKIxHjgShVzKSQtINuomfnGdaNDj0if7kZAQaWxxqMjAwILh24XBYtOGYY44RwrQjIyM+x66ystJHcm9sbBTPQ93Vc4hLELmZnGO1VI6KcIRCIZSUlCASiWB0dBSDg4O+wsX19fVYt26dduGRicr5UBMqbC5vXoJ4Pclk0sPZU8eznBlZVlaGmy+80B2Db3sbe30yDhXjSlZxxczlzDz1fgvZAFL2JyGYVEPypJNO8twLfU/vO+BP5KH7P/vssw9afoHj+NF8IKOUlmWJ+ZD6aGhoiK1JSuh4WVkZ1q9fL55bVVWV4ISqmooz5V5x7SYjhFouY0XOnty/8Xhcu/GJRCJCNqW8vNzD2SJKQZGzdYTYTEJmM7U32pE7HBbURpUrgakpV6vpm98EzjwT6Ox0pSHgdYDmptN4qK0NMAw8tnkzAG8ISH75bCX8WCgZVXaQuF0sLcbc+Qkh6Ojo8ISh5DR7qvNHk/ucOXO0xGxd+8rK/AWJAXdhnD17ttg9vvWtb/WFBEnagRZs2vnFYjGfBAHpT9GiIocMCBETRWj7+twMr1mzPLppQabjKSUSCQ+fLBwOY926dYhEImICramp8Wg4cQvo0qVLBYlbdtT6+/sRCoU8yGQ0GsX4+LhWokEdP0SWpjYuWLAAiUQCW7duhWmaYuxw4UbLslgHiSvAy3HDaHz09PSwJHXi8NGmQy7DVFpaine/+91IJBIiNE4aViSYyiE38+bNg23bnoWHFjTiZAWhJtu3bxf1FuWMPY5rRyFS+XlR5jOdQ+apCYfiuecA23aXstw9eOy114Dbb8dTjNOYTCYxf/58z2aroqLCJ2MiIybq/arODYVmx8fHxXd9fX1CcDdoY0ChXDUTj+RJli5dKg/lm8sAACAASURBVDZChZguq5N792gjzHEYKUxIv+dC6ZwoKTc+ZhIlkY9V52Y5iYrkQrjSQrIjT0kBxIWUf0PSGNzzlu+tmI14hNjk5CQikcgb4hC9kY6czmYaPgxq40xROfna93/1q+5Qffvbxfft7e2s2rzK96AFqND2q44KoVo0OXMFT9VdtxxmVDkIu3btQiwWCySkq/cPTPMmOKE/0zSxdu1a4fhxad+JREIgbfLO1bIsUU5FLffDcVrUyWvf2rUQJZM4u+8+IJEAjjlG3BfHUyLF9LVr12J8fByrVq3y7Cx7enqQzWZRVVUVWAcwlXKLV8diMfT29npqvEWjUY9eEPFyuNRzUjyniZYyEVVNL3LKamtrRUZbbW0twuGwL9NUvec1a9aw75fOWSMUbdOmTWy5m+7ubkxOTmJgYACRSARVVVWwbTsQpSorK8O6det8emRtbW0iTL5u3TqEQiHMnz9fZOLJ45QjnJMTp6K6XBiK0BxCJ2gBXLVqlQhn6fSaAACbNrljcP16/3dXXOF+95GPeBJECMFJJpM+Zf5C5wu1ADJl8sqabET8n2lZGRVxL1TniuYNVXNP1dmSrb6+HhdffDEaGxt93zU0NPgkbqLR6EGT/vO1Xa31KIcuyUnnhHx1c6mMTlP/yiFG0zSxefPmgp950dk6AoycCUJ2CKo8XKG+NxvZOpiQpa6NBKMfbNtf+sQnAMPAC7kxQ4uqGpapr69Hf467ITs++XhbQeR6OV2fpB1o8c0nYKiKfdLkXFNT44G7VS0wHVzuOA42btzILow0SdOCFYlERNr/O9/5TuEEcM4hiVEuWLBAXF9elOS2yZNXR0cHfnfDDdPo1m23+Tv4xhvdaeaf/sl3fxR+oNR+cog4mJ8cXXX3SvdA5G5CbjZs2ADHcXw7ddlZJNkG1YkiLTR6TuQ4RCIRT2kT6ovW1lZRWJwKYG/ZskWgS6ZpetC5aDSKM844Q1v2yXEcHweM0I2zzz7b04eyJEAkEhEhQrkA95w5c1BfX6/lX2WzWViWV4/MsqYJ/uSYdHd3e1BqEs+k50blhXRcI8dxUF9fj/e9732wLEu0XdWMk/lCWm0t2f75n90xduqp/u+o0kE8Djz/vKft9C5SCHYmjoPKO9TJs4yPj2P37t0CSQlCtijcL4+DmWx21THBhcmII6jOU0HK8fQbWQV/pm2TjQubk0CrZbmZuJxgMCeCyhVCl/meKkH/ULW5is7W39g4JyMSifC7sEOwQ80MLNQOxbHj2nhIqNz3vgcYBg7Mno0TJF4Ap3NDCzXVEdu921tuR20/TU4U09+wYYMoZ0O7psWLF3uQGDoHhcNSKbcAazabxbJlyzwvMJWxcRy36LNpmmIXPTo6ymqJcU6bfF3i83Cq6dlsVmTkqToxW7du9fBzuOtYlpslGZRRpCJbjuMAn/mMO5V88pP+5/flL7vf5XaPHLdNvR6VTZItiNN17LHHCmfn/7H37eF1VWX6u8m57HM/OW2T5tokJ0nbJE0T5I6AFipWRwRqLQMFy7QBiiVB6hSbsXWSMUI7hsEIhGlmMM6QmigYsaJzcLCKUeSIDghGQURF5LKBAuV+ad7fH/u8K2uvvfZJegH8+XQ9T5+257LP3muvvda73u/93o/vM2tKZ9XQ0NAgQqIEoLKmieNXFXTL2g+G2rjI0oJBTm5YuHAhAoGAEPKXlpYiEAigvLxcFM2mw7dqDDsyMoJIJCJsPaLRqCN8NDExga6uLlHeJ5FICEZNXWi9FiqZneP3CGYJVru6uoRYPpVK4cILL3T0DcG4HHLPZDIuHRiF5/IzGo1GBehsbW1FUVER1q9fL9g53h/Z7NcFiO66yx5fgYBtXqprJ55of0YJv8ljcH9tEFTdoc6epa6uDlu3bhXXTw+nlpYWJJNJrF69WsgLDrasjPw8exXz7unpEb+TTCZRX1/vkF+MjIy4mG/WoDwYcCU32ZNLtsZgLVT2q26TRzPUesWjUccC85mJRCKi8sFBl+159dXDYOvdbu9kiO9QDfp87WCvR7dzOWBm64c/BObMAa67znVcZjlVV1e7QhXNzc2etel4TmoohjtNAjS1eLC66ySw4UShLsgscyFPJrJ/WElJCUZHRx19Jotj2VShtk40m0wmcfbZZ3uGi2QmIZ/eKRKJuBaNdDqNyspKoVtzsYUvvggMD9s6PLWtWAEYBn6eAyNq8kI2m0VjY6PjvtbU1EzrcbZgwQKYpomamhpEo1E0NTU5fra+vh6BQMAVBpWZk127dgmAyfdV920dY0HtBxcq3W6ZoTw5lLNq1SrB+uiuT9UKEVCtX79eGJhaliVCrARGrKGYStnZiboMO4qfmQVHjVRlZSVisRg+8YlP4KabbsLWrVuxa9cuXH311VixYoUIIVmWJYTP6riKRCIiLFteXo6BgQHPjdCZZ54pdIMyg7Vq1SqYpona2lr4fD6YpukwYvUMubMI9ebN7rHHtmuX/Zm5c4GHHtJ+JJ/4Wm063eF0zJYuAUYdM15tJp+RWUuvckwTExOitJecbCNnB3KToq1BeZDNS0JAnd50LLRlWdrSVWTECKo6OzvFJveQrZePPQZUVx8GW+92e6dDfG93ezuu56BYuWeecS3knBxbWlqEFoettbVVhIK8JmmdCJl1t9hkkMNJio2hnuHhYa2DOOl3Ml/qZ3bn6hwGAgEcd9xxIizKrMB8ei5gCmg2NzcL0FdeXu4ZLmJ5F4bdvExQV6xYoXU+p35nRpPXW29N/bu7GzAMPFRQgF/dfbeY2OlWTt2bDFLJxKmCefVch4aGBEuoA0QU0ZJxorfSEUccIUL+sqmhZVkIBoOOcZHNus1TW1paEIlEMDg4qNXZcSFTz4mZbqWlpdpsTBlskdmSWYalS5fC7/cjEAgIFm94eFjUUCQo8hL8E2jKPk+9vb0oKioSBqglJSUuQXplZaUwmlUZRxZwHxgYQFFRkfCe4/giY2iaJjZt2oShoSGcd955DsC3cuVKsehyYyJvnnSVGsSz+Morti7QqwA1YM8dp54KUcngwQcdb0/HJqvNCzQwAYYg2O/3H7SuaaYgUL0GVq/guXR0dLiu62CzA1W9nu59ea7wSo6hZk+nJZNZaPZlR0eHQ+bADc/BgKrpQPBzuVqwhmHci8Ng691t71SI751qb8f1HKpdhm5ylHdBsVhMy9DIk7SXCFkORcnHTCaTDuM9FjlevHgxIpEIFi1a5Pit1tZWXHLJJcJGQjb/Uz2SSkpKEIvFEI1GsWTJEsHIyBoJXWO2ERdXy7LyhovY78zqO/74410MGUGiOpnJOo284Zaf/tQu9vvtb9v/f/VVvFJTAxgGXtq0adqMLAKrUMh2yi4sLIRpmiIsygWaIStO3nPmzBEAQe6z5uZmjI6OYvv27fD7/RgYGMD4+LgrzGyaJrZv347q6moH2PViLKhfYpNNWgnwVJDGUJppmq6NQCQSEckBBGsyaGIWoqrzI0CUwRvrNC5cuFCML3UcWZbtpxSLxRwCcV0YMhQKYePGjSJsrmP5gsGga5yMj4+LbDgKtr3sOubMmeM5ZnWVGvZ7DnnpJeDkk+Hyhbv1VrxcX4/7w2HgAx+wy/589rN4f85jzKupukNu9nTZiAcy3+359rfxfEMDHi4owCt1dcApp+CFk05CtrAQL23cqC19pYrq+/r6sGbNGkSjUbS2tmrnkgM9R9UgVrX/8LKX0Nm+tLS0IJPJaGvRejFU0wG9fI1jn+wdQ9Y0qpUzsAnuTorHcdhB/q+ovRMhvney/VVczx13AIODwBNPiJe8MmqYhUZ6XCd2p0bKsiyXSWkwGHTsVKPRKJqbm4XdgCykjUQirp3thg0bHOBMx3bpNFFkHmRwR3GyV+1BTmZqfT8utKpPjJoObZomurq6RL003mPZ+0mezMikUSzuKSi+5hp7WpGyOp+7+WY8PWsWni8r02b+yYsad+J9fX2iFh/BGNnAoaEhLWhjSHFsbEyAB4ahCFTq6upgmiYaGhpcYWYyOtu2bXNYJ9B9nsWc/X4/KisrEYlE0NnZKQwrVR8lr/AXz0G+P7xfPp8PBQUFKC4udmRd6sYTx40uocA0TcyePVsI21nfkLYf8XgcZWVlmDdvnmNh0W1S6urqcO655wqhvgrQY7GYS3uoluHJF75uaWmB3+/31HmxAPyBmGA62ksvAWedZYe9Abvygc7rr7gYXzLNGWm3DnZ+1IGGkZERPDtrlvbcxJ9c6Hsm53Qo53EVpANOuxDOrV7AWWdozPkkGo0iFos5klAO5jx1TBV98rhpCQaDjrkhFotpWfS7broJMAw0NjYCh8HW4fa30FwTw8c+Zg/T665zfEbVSwSDQZimiaqqKqFZkeuehUIhESKSHdtpFcAQk7xTpR1CPB7X2inodDcEaboajXy4vRg32SuJuz2dsajsOaMLGyWTScHkrFy5UhQFlsMF6XQag4ODME0TAwMDyGQyGBsbQzAYxK5duxxC9s2bN4sFVgVuLpbh1VeBUMi+Z48/Ll4e+drXtLYUOmaLodFoNOpiqlj0WO3bxsZGxONxV8r70qVLHR5YADA2Nubw65Lv3/LlywWTxpAdBcwst8NjM1PX7/cLDZd6bQQJcliMY5HjQdbNcKxSW0XneK+0/GAwiI0bNwqbC5o5sgQRx66aMLFt2zaMjo5qFz41EywUCqG/v1+cIzMf58+fj0gkgu7ubjFexsfHRSYmGTO5dJXXQqwLa/N5Ilh2hbm+/W07y/X663FA7XOfs4X1hoEXysqwKhrF5ysrsTUUwm0z9LJSmwqe8gEd+rbJlSSo8Xx4cBCYPx8wDPyvz4fnvv51/OFLX0JvKIRXV60CnnvuwK55Buc/k/NVn0u5DJRX1is3VWSBybbJIcBDAQzV8Ojq1asRjUZRWVkJv98vWGXLsnW7unlA/v36+nr88rvfBQwDOdxyGGz9tbW/CmboYNrnP2+L0y+5BHj99YM/3vbttqDVQ3ipUs+jN91kezQZBvCHP2g/K2eT6YoL7969G7FYzJWtJu+0ZK8ceUJnqNHLTNSr9mEqldJm2aVSKVH2R8dQMCxGFk1mXSKRCNra2oTxqByiUv2JSktLhTaI6fiJRAKXXnqpg3nhrk5ehGfPni3OgyatOhND9pGqZQMAfOhD9j0bGnK8bFnuUkosOtva2ppXLCv3kY4dJBume724uNilz4tEIi4heTqdRklJiQBbXOQ7OzsxODioBSZjY2NIJBLCqVpuLS0tuOyyy4T9g1q2JJPJIJ1OewIQXi9duHVM6THHHCMKT5eVlQnBuhwipWmlmsGqc7pPp9NCo8nxxOO2traK8k4f+tCHHMBzzpw5roQAGuAmk0lH3zFcTQaDySTd3d1IJBKuMjSeBYj/6Z/scdbV5X5vJu0f/9H+/tlnA/v27fd8rX5eDa3JYV+dvYeOrfSytFGrG+xvIwiWdYa68/fSh03Hrs6U2fLqu+naTD5PoCv/tmxvw3sSDAYxMjKCbDaLBQsWaOd2NfP5h//7v8Cvf32Y2fprbP8/lNbJ2954AygoAGnr+8bGDh40trXZE2MuHRqALXD98pfxwuAg5ioP8odiMfv3FT0Um87xnXXfuLPnzqmystIhQCZzlG9SyLcYshiv+t2JiQk0NDQgk8m4NBScvJgZRkEtd/VbtmxxCL5lLU0ikdAWB+YEX1paCp/P50h1jkajGBgYQF1dnSd9r2MxZFAai8XQ2trq6HddYWhH+9zn7Pt2xRW2OPnqq4E9e8TbOop/aGhILDK6nTFZnGOOOcbRZwz1RSIRdHV1ifvFlk6nhcGoqgdRxw6BkGxtEAgE4Pf7hcZObgy5trS0CE8zXf/W19fDNE2ceuqpGBsbEzvnfGCeTEA6ncaaNWsEiJIF85s2bRL2D6xdx3C4bApJECQvohz/ajIGiwKvXr1alCOS2QeGk2cyluSwOLVgfCbPOussBINBkSFLW4v9WoSXLrXH2Te+Mf1ndW1szP6+IvqfSVOLM+usaBja5viU7T28FnpaIqjzyoFok9jU7NVAIIBVq1YhmUyKMaE7f3ku9DpfPpdemq2DTRBQN2iqhYMcHlRlFV7a3kQiIeZTHbMVCoUcG3BuCNva2g5rtv7ammX9jWQn/vd/4/4zzsDlOer1oEHjvn12ZuHevfYi/PWvA1VVAtD9IJFwfPzf582z38t5vahN5/heX1+PWCwmxNCy2Dsej6O3t1fsxPr7+13fl+luWUgtp0dz186CsGTVOjo6HJl2LNejOl9zl8gsxZ6eHiFiJhNQVFTk2GWWlJTg1FNPFZMdJ/tgMIhkMqkN8cXjcREK1RVi1rFzqoaKImt1MqI5ZyQScfvXrF1r37frrwf+9V/tf7e2OrMUlSZPjDpmKxQKoaKiAqGQ7WlFpumKK67AxMSEowagGrKrrq52TP60Abn88stFGRRZj6ROzky48GK24vE4IpEIfD6fYGySyST8fr9DPxYK2QknJSUlgmXiIqxjD8nKjo+Pi/tN09aVK1eKgtjqfefvePWlXNuQQIx9II/Z8fFxZDIZDA4OiizGVCqFnp4el/i/rq4OFRUVns9SOp3GOeecg2g0KpIciouLEQwGsW7dOgfIuummm3D66afj+uuv9w4vvfSSHQKcNcueU/a3TU7aFSkMA/jCF/brq5blLM5MJlrVnKnmmqFQSGz48jFF+1Pgeromz2H8DQJjyik4V+SbC73ON5FIeDKPBxLZkb/jVTUjFLJteGTPLl6jPN6Z/aheU0NDA8rLy1FeXu7Qc8reYl6ar8PZiH9lLZvNIhaLvaOldd6OdjCg0fNB+8lP7FqIdXUCZGHxYrxZU4MLpLIQv9+xY+p9ZrZpfkOXHUVhNHVcOrBkmqbjIZW/z5R4huKoI0omk0ilUlofJp2ui+yJjpaXXdq9CqPKDJNpmvD7/Y7SGdu3b3d9j95gALBo0SL09PQIkbdOm6Bjd1QN1ZYtWxyJAIFAwOU27gBcH/ygfd8+8xl7MTQM4Oabpx0znDwpRKdfmhxepDGmymqp2X28NtnglsA2GAwKQXp9fb3YLWezWRcgbWtrQ319PRoaGkS5IzIEiUTCEYaNRqMoKytDb28vYrGYGDsMl5WWlqKnp0eMF1qURCIRkU25YMECR+iOx+jo6EBfX58D5HuxELQAAbyTSVimRLWCoN9VQ0ODo8oBWb7i4mJPH6d8zBafBWoi29vb0dvbi8HBQXR2dopC4Kq5aiAQQCwWc/jTjYyMADkNDY4+etpxpW0//7n9/UTC0zaC7CMTC8gu6TKZqe30uif0IFPtPeQi13IliemAik5YL7/H727cuFEbLi8rKxOSB8uyxPyWL/SX73wPRZPDmCyoPjw87LIboT6T56smXajZuDpmKxaLiXJcFP3n9Rb72c+AD3/4sM/WX1u74YYbBDhpNAysNAyc7/djz/e//26f2n61/7vtNmwPBvEZw0BR7npisdi09Qb50Bzf2oqucBg/kZmp226bAlFz5gA33GAzHm+9hdGdO8WObjAYtD9TWTmVPZRr8mTC8Jgs2OTDxkVCR5FzISAjQrFmR0eHaze1e/duxONxjI2NaTUVLS0t6O/v12ot8qWss4BqaWmpa0KUPb94PVyAZKChm0gzmYwjjEHhv8ruEMTIQIqaLU5oLOHDPq+trdV6LbFcEADg0UeBdBooL7fv4ZYtnmNFPvbExISjlEpVVRV8Ph/Ky8sdu32VyWHJGPWcBgYGkEgkBOPE/tLVfEylUlq/Ltk7iuOgq6tLZCGqxzFNU2Rq6cJK4+PjaGpqEmWfZNaGSRCbNm1yaQwJ6nif5s+f77pesigUMXsxW4lEwgFwmFjAzUhRURHOPfdcbV+YpomxsTGcccYZDn1WR0eHALAyUJIXZZnl3bFjhxD0M3Sru7e8b+q9en5oyB5bJ52Ud2x5NpaRWrhQC2xkMTiTFShi7+zs1D53dIj3ygCOxWJaYLS/FgY6YT1Bj6q7am9v1wJD2YiWoGsmjNqBnO9MmhryYxkeL2Df0NCABQsWaL/LZ4CieHm+1IU8Z9T6+pDz2ToMtv5aGtkg0zDQZxjYJ6fryqDj97+33bWff/7dO9l87Qc/wL45c8S5E2wZhoFjpJCD2izLQnFRER7/1Kdsx2bDwC2BwNSD+cwzwNAQ9txxB36e86JRv5/NZrHnu9+1LR+eesrxvk7E2dfXJxYnHduVSCSwePFix6QqU+QsWE1Ru05DU19fL7IUdYuobheVz4yR59zU1OQSEMsMk1dIrbi4GGeffbZ2Iq2trXVM9jKIIDuUTCZF2Ky6uhqRSASbNm1CT08PotGoqHavXqvO/TyVSqE5V6pItC98wR471dXAa695DjOGCsjWLFq0CEVFRejr6xMFu9V6iDJwtSxLXK/aDwyVmKbp6CddPTXe346ODheb4/f7UVJSgmAwiKVLlyIajYr3S0pKHMepq6vzNJZdtGiRg03QNYYW5TCdZelLkqgM5MKFC4XgXGalZCYtFos5fMsY8pTT3enppUsAoeUF60H29PRgYmJCsFe7du0SLvSDg4PYuHGjIwxKcEUWzCsZgv2TTqcxe/Zs13n84o47gMJCwOcDXnjBa3h5t1dfBcJhvBEKoTqZRFNTk3i+5ZCZDjR7ZSfLzBftNVSvupk0dfMhAxudB5vMyOueV7LatLhhUWqZAZ4pozbT81fP27Is/PjGG/Gbiy/G3iuvtDfZ11wDXH45cMYZeGzdOhwhhTE55oeHh0WmL0FgVVWVS3OpbppVX0AylAcMEnNZ8YfB1l9R+8Udd2BVOIwHciDlTcPAbYWFeGbZMuArX5n6IA32Lrzw3TpVbXv2rrvw/DHHCJD1ZGMjrvL5RFh0W28v0NCAR/r7tYtGNpvFtaWlUwDzqKPwydziw0aR7f76qeTLciFLVFdXp9UmMeV3wYIFIsuOIQ4KUOlirPsduSA1F46GhgZHCKqzs1NYLKRSKVfWIUN8ZONk13RVQEwHaDkkxNbU1CTCT5FIxDGRhsNh9Pf3i/I1BKdcXClI5gS2bt06RCIRIfD22inLu11XuaArr8R/BYN46dOftllKy7IXQcOwS6V4tPHxcfj9fqGZkPsqGo3mDZW1tLQI9/O6ujpH/1HAzUzVDRs2OICPDrxQG0V2av78+YhGo+js7BR9JoctZU2YWpMxHA5rF2l1YdON76GhIcEWyeBQHQNkHsl0LV++HLFYDI2NjSJjsLOzE9FoVIQmed8pENZtDDj2dedPZothR248dOa79B6jPouMggxkmRGmglKZpctbrmfLFuDGG2391gG0PXfcgblSqSNm6W7evFmIwXXnR/kBtZJMRjkUrBXnRmrzZNPQjo4O4XWn6zOdJQpLNSUSCeFDyMzmVCq1367x07WdO3ciEAjAsA1AEYlE0NHRgYp4HC/m8Q7be+WV2iQk+XmStY+yZkuul3tIs/8nJ4E77wTOPRfw+98ZsGUYxgcNw3jQMIyHDcP4jOb9yw3DmDAM41eGYdxhGMb86Y75twq2nrvlFjGAfmMYONJL53TrrfbnPvCBd+dENe3n//APeCV37i8aBh446yzgrbcwMTGBrVu32uGj73/fPu94HNeWltr+I1KzLAu/LCy0P/PlL+O+e+91LJI6oOGVFqw6lefzb6EY0suJOBgMOgStgUAAjY2NCIXsLC9mFclFWRlW0emtODnL72UyGYRCIaFnklPcWWxYl0GZStk+Q2vXrkU8HkdjY6MIG6m1+rjAM0QjA79QyDbFrK2tFUWv5QmMYTBZo5NMJjEwMJBXr6Hb7QrX5ZoaXMeQbygE3H+/zRoUFwPRKPC732nHGhdj2bNKbul0GuXl5Y7X5EVYZavYL6Ojo9oMVXUiJzgjkJo3b56r9IdsgKiCDmZhkqmRTT5plltVVSXq/dHfTMdWyGOKobQTTjhB9I9pmi7gzoVYdi+3LNsTjR5GoVBI1DdUS/zoNiTDw8MO/YvsU0fQ0yGx85ZlYXBwEOvXr3dYCuhCP6rXGfvRq9RVc3OzEJX7/X4RZjxYwTgAO9P6mWeQyWQcpY74+4lEQrBHXtUYaBnDDdf+slY6QCBvYnRhSIZwvYT1XnYzaoLBfrFXL7wgwKz2e/v2icQXncbXNE2YpmlbJ3zgA3qwdeONwL592o2dZU1lI9J6RJeNeKgAluN4K1eKc5ycNQuvrljx9oItwzAKDcP4vWEYtYZhBAzDuM8wjEblM+83DCOc+/d6wzBGpzvu3wzYeustQC4g+/jjeLKxEdt8PszL+d9oM/hYHPXDH37nzjVPsywLm8Nh+5zOPx8P3HmnI44vxOT/939TRqOGgUmfD7jggild1Z49mJw1C68bBo5TdrxegnbW7QOmJniGhmT2S951ZzIZbN++3VGzjxl+3B3KcXod7d/b2ytAWFtbG+LxOAKBgAiT6OqF5WPXLMvtSh+JRMTC2tbWhqGhIVfWD+vLRSIRNDU1CW8sTqgy8KODOVPK77tvSvhNANHY2CgyMtWsMTlkumbNGoTDYSxZskQwDWxq8Wu18T59IafLesvncz4HP/4x8Oyz2u966TDkgtAMA6qfqampERO4TjfT09Oj3fWTBSN4rqqqwuDgoDDf1N3LoqIiDA8Pu/yzmNFEsT41Yawh2N7ejng8jrlz54r7kUgkcMYZZwidkloGSRearqurQyQSQV9fnwPgJxIJUWdw165dInOwq6sLpmm6khc4VuTf0zGJpmmKhAQZoAeDQZxxxhkYHBx0uNAzsYOA2TRNjIyMYGhoyDXuKioqtPUgKysrRT1Qbn4CgQDKy8thmqbIdj1ki+pjj9lZiCeeiNu/+11XqSPAZq56e3sFEydXY9gfEbvavDZpPFa+DQ9DuNlsNq9Q/VBZLuD55+15PbdxfrmoCN8qKBBZud+87jrgIx+ZAkzr1+OeH/4QkUjElRjG88bksRKT0gAAIABJREFUJPD44/hgUxMa6+tx//Cwc87I06cHe/+n+z5BnSOh6X3vw+OGgc8bBqoMg4zd7/E2gq3jDMPISP/fbBjG5jyfbzMM4yfTHfdvBmz98Id2xtU//7PjZS92RjQyRKWlwJ///A6esL5ls1m8p60NyC08AFwZKmRq2pYswZmxGP581FFTXlzHHmsvrtu2AYaB148/3nXdOqsGWVydL4TBiaezs9ORASZT7IlEAjU1NaLAMyftSCSi3cl/9KMf1S5OBFwuSwNMFVOVr00GJrI2xTRNl85I3X1yMSssLERtbS2CwSCOPPJIVFRUuEJf5eXlKC0tFeatsm2ATtfEAtXq/WMJIp0AWg6t5puYxDUUFwOGgS069tajMVQmt/r6evj9fsFoEDhGIhHMmzcPgUAA55xzjmBuvGrseTFeDJ/R+iOVSqGrq8s1LmjSms1m0dzcrAVCZFzi8bio3UhtVHl5Ofx+vwD5cqkh9XwZJtEV4WU1Ac4fExMTWjZQFqRTNK36acn2AxT6654hmdHjBovgkcbA4XBYWEb4/X5XeIfM6UyYLbJl1CYODw8jGo1ixYoV2LBhg9B75Z1Lf/9722pEmX+17fHHgYoKe74qK8Mzd9+ttfSQAbcuG9Gr5VvQLctyhW7lzGGvuVG+d2SrOZ6i0ajWduFQANMXP/tZezNdUIDJnMv+9yUQtUPyYBShwC98wZPZ8irt8060fCBXfr8uncbaSASPHn00dt9xBwKGAZ90Lbk/b5/PlmEYHzMM4z+k/59nGMa1eT5/rWEYn/V470LDMO4xDOOeqqqqt6Nf3/n2zDNTgEMpoZDPkRevvw6ccAJofYAXXjjklOiM2uQksHevdlHRea+QGRHn+Otf22UlCgvtXcrLLwOrVwPj4wCcD77XDp6ghmFCr3BhJpMRmWE6LQktCpqamly7c93kT+dtdXGKxWIim0UuPm1ZltjRe4VBZbPDSCSCWCzmCjWQeVN9ukZGRkStO53ZHicoLgjUqenCCdlsFtXV1TjttNMEwFJ1QDoAWltbO+2O2HF/Lr0UMAz8KhzGPXfemXeosXkxW4FAQDi4n3LKKTjxxBMdHjiqfofgsampSWipGEJlv4ZCIRQWFmLx4sVC2C6Dslgsph2PsnB7ZGTEwZQSjNP0U/b1Mk0TwWAQiUTC4fqdzWZdbE86ncb8+fM9zXUJeuQqCcwivfjii6cd4+qi1tbWhoqKClEDVGasmPbOHT61QdS/JRIJJJNJh76JWYR8fkpLS1FVVYVsNoszzzzToV/s6Ohw3UvTNLF8+XIR9kwmk1i1ahWOOuooASqp9dIx3QBsHaxh2HNOvvbaa8Bxx01tDHOJNyMjIw6z2JnaGahzdd65HrbEQPe8yb5burmRfnbcUO4va6X6/M1ERzYyMoKTWbnDMPDrhgbAMPABCXTUGAZepEdiKAQsXw7s2eOp2dIVtn8nGkHun666Cjj2WLxWVYVnZ83CG0ccAfzyl85+37sXb8yeDRgGHrjsMsyaNUsFWm872FqpAVtf9vjsasMwfmYYRnC64/7NMFsA8L732V2ZS/MH9A8Pd7FikD/zDLBgAeDz4YcbNzp2q++Y4/xttwGpFHDDDVrBoRfD5GiPPQbccovr0OoEtGPHDkHVyvF3eefqxWwlk0lcffXVKC4uFuJvNlLsZHFU3xUADoF4MBgUdgHyQsnFaeHChaKOommaKCsrE3YBNTU1iMViIptHBotyKFPWbMkCYvYJtUIq85XJZFBfX+8KFYRCIVeYr6enx1M4zbCv3+9HX1+fCF/Kfa3zBcvrNZNrjrH93HN4I5d1+vK6dVMfevNNoKXF0/2/vb3dwcr4fD4sW7YMQ0NDInsyEAho2RB5/FVXV4sxpbIsfr9fFM4+8cQTHVl4NEhlWj8ZNWZAWpYlUuzZ/z6fD4FAQABYHeCWw7vyfdXZSoTDYXE9Xs+el9Cemj02LzC3YcMG8T1e39VXXy3us1pLkmJ21ZG+pKTEoW+SwSiPHwqFhFCcIXWZNWbBapUlmpiYwLJlyxwAWS7blI/pFn5bp56ad8xi/XoIKxklw1kHQHRsGl/r6+tzJPjMZJ708uZSfbdkbVJfX5/nec1kQ64mKZxyyimOckIO0POJTwDz5uGFf//3qWv5+McF4HrTMLBGAh2zZs2yz/35512l3Lz680AtI6a75omJCfT392tZvkwmg6Orq4FIxMXEwTDw++5u58Z+505QFrHKMFD4DoOtGYURDcM41TCM3xiGUTyTH/6bAls5Dw6sWSNeUt1r6ZXkojIffBB7br/dRb9qRfV4G+otrl5tn/uVV2qPr2beHUzmoLwzpRaKQmvWD+PujeVYCEpk3YSXeJQgzOu3u7u70d/f71nrj4tTKBRyFAcms6BmLe3YscNRG5BleAguVVFzMpnMa3y4ZMkSrF27Vvw+dWmBQAA+n88VeqTOS71OmbKXU9Lpet3W1iYMOWUXctaym8muWQYHxyYS+Etrq1Oj9cQT9rgKh232VPN9AphoNIqqqirHYkYNlJeQm9cXj8eRyWQcHkFkTWhYStDLmopeiQMTExNCj0UfrImJCQwODmLZsmUihKZjE5ubm0UYzMtWghmmrGZAICw/Mwxb9ff3i7GlY3kHBgamZbao/ZMZsU2bNolFr7u7W/sdHSj0+/2IxWJiE+Nlltrb2+saI/JmQ2W6qY3S9WkmkxEhe8/Cxg89BCbs5M1KXLDA/twM6iiqzuTcYFGmQDaVQHs693UAWj2nVwbj/szvan/y3o6PjzskEnmZzz//WYCPva2tU3398MPAd76DH8XjOCYQgGmaiMfjQkv3TkRg1Nq1sl4uk8ngjDPOcDCmsVjMMXdlMhmcwgQew8DT55yDOtPEnz7yEeBjH8NrH/6wc6249178jxQi/YNhoCMHut4JzZbPMIxHDMOokQTyTcpn2nIi+vqZ/vDfCtiyLAu/vvFGuyulXbwcKtHtAuVFNpvNIpFIuISFqkD5UNdbtCwLr1ZW2uf+i19oPzNdvNur6SbIxsZGDA0NicVOtmtgGCgSiYid765duxCJRDA0NJSXYudunEAjm80KDykyJ+3t7QCgNSSksR8X6ZKSEgdgy2azYnJVNRdy8dXpqsc3NDRozTepy2DI6/3vf79jR3rOOecIfySGgRhikf2UYrGYMCFlS6fT6OrqwujoqAvo8fcYUvXSrXgJVvPuVL/4RXtcabJtZeC3ePFiRCIRwaZ4ld7geVHITcf2aDQq6sl1d3fjggsucDEz0WgUNTU1IjXey25gYGBAsD8EbgwFkxFLp9Pae0jvqC1btri0OQSOBC1+vx/V1dXaenLUXXFRz8fqlJSUOFz2OS/Q6qG6uhrJZBK1tbUi5BkKhUQ6fWFhoTbJQC27k06nUVxc7Kiv6TWnjY+Pe4at5PA5F24mAajn0dLSgtHR0Wn7AABw/PH2WNNoLEX79rftzyxd6ll0mueq+61EIuF5HgsXLpzWfZ3390A2rl5NttCh9EHOHJU3fr29vVrftIaGBjujPAcuXn/f+zxD2ofy3OXmNcfo6rnyXMjQcRypIFJm1KKRCD4XCuGBcBhfyoWlLcuyKwe88IKLVb6quxt7r7oKqK8X/fKbiy+GZVlvbzYibDD1IcMwHsoBqn/KvdZjGMbpuX//r2EYTxmGcW/uz7enO+bfAtjiTTqmtRWvk5rMGexls1lBu+sWWbXuVNg00ZqH2bKsQ1tvcefOnSg1TcAw8Iph4Gv/9V+uz+gYonzC6XzfFeL63ICWw348Nne4LS0twguLDteqOzsftM7OTjHRqSGRFStWiLASwZ1OQE1KffPmzRgcHBShQC7IlmUhHo9rs6m4k9eFLnUsjE68zSysFStWaM+PzAnT+Ql8yWpZli0upjBbvbbS0lIEg0HtghYMBjE4OOjpz8MwsJyZmVef8vvf24WoQyH7ebj1VtfYUEMq8q47m806aszJ1QAYGvT5fEKXpbKNhYWF2j4IBAJ5mS0yhYFAQIAlXRjLK3wmawYZuly0aJFYHMhYqWOe/m0MQcvH5jMjAz217qNpmiIJhNYS2WxWgJ6bbrpJmwQyNjamtbXgs6BeH7VyXOjkkkp8plnwmJuE1atX45JLLhH6NSY9qOy0jnUJhWyX/Hnz5jmYbtUHDgBw0032WFPYJVf7zneAN9/Ujl8ZTKhzdWNjIxYsWOAJ0mWz4+n0VNMxVzNltlQLHZnh1vUngbiO2Xr2Jz8RoAIrVnhmNB6qqIp8HK+5xGvOVZO2dNpTObsdmAK5x6TTKPbQ5Gmvbd8+Wx5z1lkiVPq2g62348//72DLASb++Efs8/mw1zBgPfqo432Gg/LufN58E8/lGKa/z+kmBGv1yisAZs5+zfTcQ6EQinIP2CuGgZhpauPdqj5KHcj5Gh8kVZx93312+RGWfeGOUjcZeFHgXKQ4AU4HDJubm8UiKlsphMNhnHzyyWKhAqZ2jPJC5FWkmMyO7vfJRtHVmBmEsVhMOM4XFxcjHo+jr68PqZTevX7x4sWiRlw+5qy+vh6FhYWIxWJobW0VLI1XH1J83Z8zqFX7l6+xv6hxUxdj0c/j41OTtmEAf//3Lj0Hx5U8QWazWbHr1vUj60JywWAYrqqqSst41tTUOH4vnU7jfbkdO72vUqkUTNMUmi25eLRaV1BdZAkAOfHv2LFDON7T+0rW23Cc6rIfGWLs7e0VRprq+yxo3dXV5dCQMSxOrQrF0BxLRxxxhIvhGBkZQX19PYaGhtDc3IxAIOByF5ctK+gu393d7dBaEniPj49j48aNouajDqSpjKXu+SRLx/OQzW7JVo+NjSEYDGI8l3wj2muv2eFqwwCefNI13uSmG19yRqIXmKZFiG68yZrNmYARr8/lAx7y5wnMveYCL/0eNyQuofqLLwLNzXayy/XX79e1TNfUMGdXV5dgo1n/UDcmKMNR+zsajTo2Y5bltjGRma3p+nx/22Gw9S401y7n0UfRUVvrckonU1MRieCaQADnl5bqdz79/YBh4M1IBG+m07YlRChkF0rFFEBqlkR7B8psycDtd7mF8YRIxLUbYCaSOrlMJ6Dm+VLzwoldbqTet2zZgng8jkWLFuX1epJ39zqPIh1IkUN0LFfCc6OhobrbZnYUrRu4uDHUxc8yhCVPjCMjI46sm1AohL6+PvGQy/o02Z+Ji8mSJUs89WYLFy50LNSWZVtBDAwMAJhiQXhsv9+PQCDg6E+KwmUDzlDIrtfHAsS0zvjoRz/qCWI9yxA9+CAwb54tjM+ljuOf/kk7NmT2SNYfTUxMYOXKlY574vP5cPHFF7tYIZqJyo33St7lm6aJQCAgQrAEVzS7Va+Nlh06Zoufi8ViAmirQuT29naXX9yiRYu0xcBlps2rDItcgJqMXnt7u8h6jcfjOP74412hZ7mfeTzZHJOgJhSyLSto/7Bq1SoEg0FUVlbCNE10dHS4wsaWZeGMM85AIBBAaWmpS7DPe+H3+wVjqbIUqlN8MBgU4VU+h2q9RdlU1dG2bAH+9V/tpCNde/FFoKMD9+7a5bJUUb22CKb5nHDuSSQSgmFjmS2dPUy+5gWonn7gAXzRNPHikUcCTz8t7v1DxxyDPxQU4MfxOI7OsTI6mwiZRddtrFi9gCyrK/y/b99+Xcf+Xis3SrSpSSQS2LBhg2fEh/OOWruVGwn52lQpxSHNdPzTn4DOTqFHPQy23oU2kxAb0fk9d96JN5YsAQwDr82ahbv6+90HfPNN4D3vcTIDhgGYpu14DOBrN92EJw0DvykowFmBAHZK2Y/7e+4MSe7M/c5Ffr9j50QAw1AoKeXq6uppmS1dFqKur8477zwHFb5t2zaH15P873g8jlgs5giHkN0iTawL0TU3NzvOgeCO9hDqYstdLENnPp8PqVTKcZ4bNmxwecfw3tMslDswOUyho/ap9WlsbBThnh07djgMOAmK+H2COi6+ZCJ0i6p8jgzDBYNBod9ZunSpyLJkxiUnNmaT6YTQngW2OWnnUqjtKUY/RuRiugz18dxM08Sxxx6LiYkJUc5IXcx1YnVO6sFgEMXFxULnZpomCgoKtIuQChyHh4fF2GFGKAvaMqzNLNqrr77aVTSafSeDNIINOURDY13+rmykyYWjt7dXjAeys2TRYrGYYOq83PR1947hQ4IrXeaY7AzPZ4fPc2dnp9h88P7FYjFXWR36kXkxv9SVFRUVOUKg8nnIIdGZbPI8W05D+PpxxznOQ57jdGB69+7djlBvXu9ETZNBaj6H97259QGGIWqJNjc348esxmEYeD23UdcdJxQKCe0bNwxyiPdQlOXx0ml66d/UUL1ujsrHdsqREblSgrqpjUQiWLNmjTYb8aDa5CTwwQ/a/b92LYDDYOtda/LEWVVUhFuvvdbxvhhw994L/OM/iofmrXnzbIM9tT37rG2S+pvf2HYKL77ozOR67DG8RW8TwwDe/34gxw7sL0VKsf3ycBiX+v34zvbt4j2ydiznIqfUT6fZ8gKhqqaBGgn1c9XV1SgvLxeLb1NTk1hAGxoaHNfKEIxa1sGrVhY1XTK4UxdbGUhs27ZNa/7J8JsMOpubm3HJJZc4wm3U1sTjcUGdyy2dTqOqqkq4vfv9fiGkDgQCjgmyra1NgDxdan51dbXj2G1tbSgvL3eUOUmlbCPPiYkJVFRUuPpDzWyMx+NanRkTA9ra2rQu+wCAE0+0x6jPJ16SPX/kMHs2m8WaNWtc94agJZFIYGBgQKsvYpYcs1YJPGnfQeBMFkd3D3TAcdGiRdi6davDhiIYDAqA6/f7HQ7qMktRV1eHkpISB/stPxfU2NHUkhsHJoVs3LgRF154IVKplDb8xiQBbhi8Cqf39/e7mLJEIuGwXuACSq+tvr4+AYJlXzFZX0XGVxduk3WZIyMjrudJZl9DoVBeEKCGoOTzlP3vpm2Tk0DOKwq33uqYtxnKUhkUuX7ggZbikT28mAigjj8yOS9u3To1r3/pS+L5+/jChcB//If9enOz+LxqE0Hwr7KPhypzfceOHcKqRNbNeTF1auRHF9qkvCSf1s3rGryA38E0129de63d76mUvR7jMNh6V5tlWXjwmmuwLxq1hXRsr78O/Md/YGTnTjGY6pNJPLVwod3tRx3lTlVeuhSToRDu/da3vAfQa68B//ZvQFERYBjYN2sWrgsGccQBlGfIN5A5mcqGijM5fj5DUvnh8HJMDgaDGBgYEDoj2hvs3r1bnJOcZca6frLTttc1eYE7ebEl+JLZPa/zJJuxbds2bUkgLtzpdFoUftZpC1asWOEKATG8qB6ruLjYUwAuv5ZMJkW4iIVo6+rqBLtHEOPF8liWhbq6OvT29uLCCy8UYlWOgYmJCQEutUL5l192hCbUUNuqVauEgSqL7uoA7UUXXYS6ujpYljt1nqzg3LlzEQgE8N73vleEGnXam3A47ApFEjhSfL5jxw5s2LDBEUY75ZRTHKxiUVGRpwWE7KSuhh8JNlpbW5FIJAT7yZB0dXU1TNMURcG9wm/pdBoDAwMzCj3zPusWM4IBZqTKXlvyBks2R81ms6isrERDQ4Mna6aK8U3TxIIFC0RIVPVEkhkjzhH0smpqakIsFnMYj3K8U7M3MjJib1RvvtkWwavtzjvtObe01I4gwDn36RgUFehNB1p0bL7qTu+lm+Rxf7FmjQBcG8NhsUF94pJLAMPA02ef7fj8oQRTok1OAps321EWnw9Ip/HCnDn4w6xZ+POsWXi1thZ49FHc94tfaGuO8vxmwmxR7/a2XEeeJgP33t5ejI6Outjb266+eirJ5+abxXcPg613uz3+uD0wDQM45RSgvX0qbfSPf3QOpqeeAnL15LB5s/M4uQftn+VJxKvt2YOX29vxFndD//M/2lDmgTRV67F7925EIhGHMNWLUtdN/LFYzLX78VogKisrHefS3NyMiooKrf6E2hvVyVpX/T0fuCOYJGs0Ew3V6tWrkUqlRNFoOsXLTbaUiEQi2LBhg0OzRdCim4RYukfNQKPZqtz4GbnuXiAQQEFBgWApdOEbLw8rGVCwLAvPZ926dZ5hW6+x53WNDHPqsk15b8bHx8Xv0OiVzvvRaFQAXYYOKbT38oFau3YtUqmUcAvfsWMHdu/eLTIc5cxHusPTikEeCzoQVFdXh2AwiI6ODrH4Uu9DjQ81fEzCSKVSLqaSujpA/zzJ3ml8T076oKaLIbje3l4RpqMBZE9Pj8NpX81ylGvEcawQfHlVOAiFQigrKxMh2KKiIqxfvx7j4+OuZAGG3NevX49kMikYvHnz5on7uWTJElEcXlc1gqx7KpXCsz/4gT0PzpsnZBeiffrT9nubNrnGJpvXgj8T0bruHkWjUW3dxaqqKq1vFNveK68EDLvwMW65BSMjI/hmrlzOJyORg95MT8sIPfSQbQBaWjrFtOn+PPww6urqtHVDyfjLDGIikXA8X3JY8FC26UK9fO74rMraUNm78fMEWmec4fj+YbD119Da290DcsEC4JFH3J/96lft7JlLLhEvWZaF4xIJPLRzJ/DaazMCTtlsFjvnzrXL5eR2ijRknFH74x9tcHj77a7j6sISDNnpTP/kiUNH0+vCirI+hRlcuknVK1TS0tKCTCajnfTVsh5eoGnOnDkIBAJoaWlBOBwW1yTroNSFrLi4WOy2GfL0cvom8KHZpWmaOOeccwTDoivaW19fj0gkgpKSElRUVKCgoEDYCVDQr14HS8Sojt3BYBCNjY0z7jcyCDoD1/vuu0+IvHX3w6t4te4a6+rqUJtLKNFlHYVCIaxbt070YygUcujaeI2pVEqc03QWDbQqKC4uFqFX3h8COB1Txc+odiC6cw6Hw4JNXLdunQDZpmmKhXbHjh2i7FRpaamWqdTVtGSGKesJyu8RPK5fv15om8gmyokGdNH3shghEyuzvRxf1D/OnTvXodmivoxAUl3oCM7Zx4sXLxb3VAV6qVTKMV8MDw9ri1jTI4qbqOzdd9s+h4bhqOQBAPjYx+zXNb6E+ViV6SQRsoeV+izU1tZqtWAyC+85t191FdDYCOQy21+++GLAMPBinvqPuhCmyrT19vaiNBrFRYEAPlxZiURuPne0yUlbyvLlL3sDrfJy/Pab38zLbOnO6+0I/cltunVpJj5xPK8rqquh8wk8DLbepeYY4G+8Yae+33ILMDAAfO1r7h0W25tvAm+95XhJB3BcdQg1vz87N2B27twJ0zRFluKMDE8vu8weAuee6zquSgFHo1EMDQ15PmCqnot9oyuyK3ulUL/CMAUfCFn8yCK2Ou2Q7vgEE/JxqelKJpMis2nLli3i+nS6GJ/Ph2QyiZaWFkQiEfh8PkdoL5lMYnR0VDBEfNh5fPpeqQsKGY5UasqTSV2029raHP1A/6d4PC6AEDUPpml6GlRygVPLAjF8Eg6H0dTUhGg0iuLiYmH3oVskLMtCZWWlZ4Hm/WW2ZGZiy5YtSCaT4pp8Pp9g1kzTxKJFizA0NCQySuWxVFxcLMYA7wEBq2yVUFlZiWg06tKmJRIJ9Pf3e+pp5KLl8hiUgQ4BupzdyvsWCATEmCB7yuN41cxjJQFuRGgrYZom2tvbHQJhCu3lvmef7969W4SReSyCch1YJhMrH4vnI+s2GfLTld6Rxex0MpcZ1nwLXUtLCxYsWCB+27IsMVbyMVuWZQH//u/2fFZRISxzANjZsYYB3HWX43rJcixatAjRaBRdXV0uNlxXIFxXBUI9P1aLULVgM2JzJiedEhPLclZnUJpsbiqDQfV5iweDmJDc0V+rqEDTNAWhn37wQSxIJnH/XXdhdOdOselUN977q2+brqkMlRxi1oFVr/lIHq8yg+7lmcbs2IXJpN1PLS0OOcRhsPUutLfDzd1L35EvxZi7RrVoJrOA8rajj7aHwPe+53lchkGo5yD4kBsXpdbWVhc41D0E+QpcT0xMuLIOE4kEdu3aJdgbanYikQgmJia0ImCGQcLhMJYtWyb+Ty8lejFZloWhoSGHz1M2axdxbm5uFrURA4GAa3e9ePFil1ElQ66RSMR1Xqw9yAK/W7ZsEWG6UGgqvV1XvoSMhnqeLBOjY7xkn7JgMCicxWXgZZomqqurRSYiw3rqtba2tmJ4eFiEkFTGb7qJtqOjwzGO5FAbnyHaTlAnSL8wej15+Rypfc3rraioQCQSQWdnJ0ZHR9HV1YVwOOxy0k6n0wIw6SbreDwuwl5r1qxBIpEQ3mnt7e3YuHEjdu3aJZgk9R4wZMLkCbKjvI+68Lhpmli3bp3YIDAzsrOzUyw4+ViCoaEhlOay1+SEDZpAeoUnCQbVcSuHvlhrU80OPO+88xAMBoXdBZ+fcDjsAHdeus7h4WEUFRW5+oOAVdVsVVZWOsfdG28ALM3ywANTBz/9dDur7LnnxEvc1DU1NTkYurjE9uj6iCFP9dzV5BwZiLBe6X6HzR57DOjtBX7726nX7r8fOOkk4F/+Bbj7buy44QZXYonuHJubm3F0WRnemDXLwVKtTqdnnF1OoKmC0kOtudLZppDVJytNw2AvQT4wtS61tbVhaGjIwaDrAD/HrDjupZe6rEQOg613uHG3dUBu7i+9BHzmM3YBT6XlMwHV7Yo4yEdHR1EcDu+/4ekJJ9hD4M47tW/v2rXLdR5e2Xlkh+TdlSpA5UTU1dWlBZYMO8qmlJZloaysTGii5Ow3lk4h0OBCrjIX4XBYZM9Qc1JUVCR2nXRwV20hZI8gHdtDLcns2bMdYGnVqlUug0pVIE5RvN/vx+7du4U2aWhoCFu3btX6zxD4yqAiHo+L7+p20TLTEwqFxLnyGs855xwRLgwEAojH48LmQLcQy6VkyFDONK18fHwcW7dudWj/1BpuXmOMoTUK0wmOSktLhRBZXox9Pp8DuJFB1AE2glIa13I8sB/43JG1oQbpsssuQyKRQEtLixDs60qicBFXn2ddKDAFFhovAAAgAElEQVQWizlCgZZlYXR0VNiJEJAGg8G8i/f4+Lj2OZULkMtgORaLYc2aNRgbG0M0GnUBB841zGCVXeIzmYxIIlCzSQcGBlx+bV7JC+r9lNltWlTkzUZ86y3A77fntBdf9Owby7IczHq+cJI6d+UrOu2lkToQIGJZFv64caN9LXLilSJXebigAI9fdhnw1FPiXCj0V88xHo/j9GgUj3z5y8CnP42Hctc2k3Ob0XX89Keea4nuOHJYUWawZEZKvgZZy8cxJvf9dMwWv0OWV9ZsxWIxrdZXtMlJYN++w2DrnW7ZbNYFtkzTnAI3zz8PXHONc3f11FP2DaNYc/ly7bHJtKjhEmYtcScrx+R/m6OGZ+8v+Fu61D4XSbMlPwyq1saybCNNioy5KNF4kIyJWppHtmYgw8QsKS9guTtXCFrOFJN1HXyIMpkMqqursX37dgwNDWHFihWetQ/5+YaGBpfvFcGPzhaCD7IKZnw+H8bGxrT2GHJqvFcYrbKyEmVlZWJMEZzpPk/fLP627HzOUJl8/6qrqzEwMKCdgLig0rJAZTEymQxWr17tALAnn3yyI1RCoDdT7yMvoTELPMuvV1ZWakOiDFnR54nAi6AqEAhg7dq1ouamuttnNQQ53Ksa6ZaVlSEajYpMPu7iVbBMvzLVX0t333S2H2Q7CNS6urpcIRLZi4zAQ61/5wW4stksFkm1WoGp54CLDMOSxx13nEu0LQNLFsbWbb74HOj0XzU1NY5ag6r2MRSaMtilrq22ttblwD9joMIKBuXleT8mJ8vkCyexeemhZhI6U3VKcnjMKyzG4/80FgMMA3dJ2l4895wtVbn4YrxeXDwFvOJx4C9/cZUPYomvbdu2OawoqPE7JGG/yUlbZ8Zz+djHRNan3OQ5gIbCBNicy7jZVhOaLMvtFE9gqQryuaYwA1zVEpMlbm9vF9mIecfY/fcDF10EXHPNYbB1qNpMdyETExMOoMU/E7/6la3XoqHjRRfZX7jtNvu1jg47a3HWLCA3QLx2Q6oGgJMuFxBRMmXvXkwWFOANw8DsXFHOGYc1mWr8b/8GwL0gctHShYy4C1i4cCEqKioci71ugZeN+KjTYlhNF1KkeF63g6SJIpkqv9+PoqIi1NbWaoW/FKW2tbVhYGAApmmip6fH4Sg/nS0EAeTChQvFhEWDS9Udv66uTqTTh8NhzJ0718V4sAwL+1fdYdP+QQ6b8LV58+Y5Svd4pVV7MS3MpItEIiJcyCy1+fPnY3R0VADaTCaDgYEBbcaglyDe0Z58EtYTT2jHhI59IUOk3sNoNCoSKLzKc/Be64pqL1q0yBXujUajWgZgYmJCbAwaGhpchp1kwFR/rXQ6LYqIy/dNV7OSvyPrUmThNa+DY0LHwDBEOVNxNxneNWvWoKenB2vWrBG+a/I8xHlg/vz5jkSCwsJC13hqaWmBaZp57VFUBm3VqlVIJBKYPXu2CG+rbLjKgKj6HW3G2auv2nNaZ+fUiTzyCPD97zt0N9OxbDNhe2ayVqjGvbFYDNFoVITHZNBMMbeDOfv4xwHDwFWaUmoAYD3xBC4P58oUVVXhgR/9yMHE8hxlj8NkMimA/SEJ++3bZ/e3LKBvb9f2l248xmIxbRidm1i+rquBSGCpYxLzWQDtF9P4xBNT13XjjYfB1qFonhqs+++3MzMuucQ2Jr31Vtzzgx84mK2QYeA8vx8v19VN3Zj3vhfYtcs+xs03OwfjhRcC8N7tA85SPxRbqgPVsiyblTIM/CqXDr5fA2lwUOxEvEDSkUce6Zm5RA8eCrcZctNlDg0NDeGII45wZIwEg0GcffbZWs3T4OCgqy4jd+Y026yqqhIhRJl2lid3aq7o1k7nbIaXdu/eLeL5cmN2It3dGYqKRqPTAh3+RiQSwcDAAAYHB7VgqL29HV1dXWJRZ8iEuoGzzz4bs2fPdtRtZP1D9XzJLpI1mDdvHiKRiLaO4e6cKzbB9DnnnOPQrZA1kscnwzzTLkoTE3bZHgD45S+BsjI8cfbZWj3F1q1btTXcTjvtNEQiEQd7SibvtNNOy1vUvaamRjspx2IxAZJkQTvNSWUtmWVN+czJxrFc+HXAnFmLZMVM08TZZ5+NiYkJjI+P49RTT0U0GnV4lQHuDCp5camoqJhW55TOo7lRGRiOo87OTkfyBq8bmFoU2VcyO6gr35VKpUTpLXV8H3300QKUmqaJo446ymWGq7rDkwHnuONizP5hOJh/qxlnmJx0sioEAp/5jLZvmpqaHHqw/WV7vBZvy7JEMoK68csn9nfoWXN2FpbPh5//5CfAX/4CbN8OnHyy7WOXu47TYzEcn2OFvJjOAwlnTvudyUngH/4BMAy8bhg4PxjEgkAAX7/xRtdHvcZvT0+PtpC0LGfwWgN10ppD2p56Cjj2WHv8LFt2OIx4KJpOg3Wx3483WludICn3540jjxSfP9MwsFd+f/58G1zJzu8AsH49YBjYl0zil7ffri25QAaGjWU55s+f7+mJhJwvyw25xWC/2oMP2tYP11wjHgb5ASNYCYfDrpCE7FMSiUSEvw4XLd0uPplMCq0UTTapr2HJGO74KIrW7cxHRkYEUGAauZp+PT4+Dr/fL8KWFJibpulij7jT1IVHeJ2XX345stksBgYGXPeCJpRquFPO6qJAnLoun88nQoCyTqy6ulpMml7hRx3zw4WeNhR8nWJ7GTCGQiFs3rwZlmWhu7tbW7OPGw81xJjPIwiAXdC2pcVeDO68U2ho3qOx/9AxWwSckUgElZWVrlAdM1O9mC0unOq1FBcXC7a2v78f559/vgjJJRIJ9Pb2YmxsDPF4HO3t7dr+pZGomiBSX18vWAtdSIildlR9m7zBUTcpnJOmY7aYJSo3hq5GR0cd2YK0W9EZsoZCIXHuDOfoQjbUU8n1NWkGK4cITzzxRAHEIpEI+vr6XCE1dbPZ3t7uSA7xYp24sZL/nltUhD3f/757PC5YAK8kIF1IL98cqrJtsixCfR56e3sRDAZdm4mWlhYt20wbCzlTG5OTeCW3gX/54ouBOXOm1plvfEN7XgfT5P7YvHmzo/C4FtTcc484n5XS2jnb58OLPT3A3r2OY3tFPPKxvjS35diLxWLCf+1tA1ovv2zXdo1G7eurrDzsIH+oWiaTQSQScYCtL+aM5BCNAuefD/T12UV1TzgB6O4WTNgRkQhgGHiyuhp//PSnYf3pT/ofefVVPHDmmfhoLCZi1io7ofOyamtrE0yOPCCZcXdVRQVgGPjdsmUH1Qe63TwnVjmcyN+Px+Nid8zv0Ndpy5YtLqE2YE9AOu+ZRCIhgJS6AESjUcH0zJ49W9RzUxcDlsRh2IKhPnXBZFkb9eFnZmBjY6Mon8NdPUEZQQaztSzLElqTRYsWuUTLanHYQCCA888/X7BN0y0mlmVh+fLlDpAmh3VVPZuOam9ra0NXV5fDyC8YDIpEhvXr16OystK162xoaEC5on1hJQCvnfz/ffe79jPj909NtLnF4rac1YUK1FQQ6vf7sWjRIiQSCcRiMdTX1zt+q76+Xixy9fX1womdVhkFBQUIBAIoKioSTu2hkF0nkBM3NwZyv9P3qra2Vlg1yC2dTotwjw6ohMNhV322iYkJF7MYi8XE+/JuX7cY0VmerItsQkrLB/Yjw8AELHLh9lgsJjR4XuxzXV2dyCSmlkxuZAdN08SmTZsQjUZFdivNd6PRqEvcL4cm5cLZapIM+1EuSD9dhhn/vudHP8KjgQD2BYM2GyG3HPOimlNO1/IBK26Q1LJcqlhbJ/KejtmSQ4ltbW3YGgo5N/vvfS/w9a8LZutQNTkD0KvWpgvc7N2LF6XycXcZBn5iGHiT57pwoYNl5G/ojJpDISUTMM+9eDvd5q0nnsBzTBwzDDx33HF45t57xfuHwdZBNIKmpGHgKAlsNQeDeP6//1s/qHOsFW/8ziuv9AwHsqmTqS78JId4dBOvulhZloWHtm2zb+Pf/d3MLpgi/euuc5xbJpNxaV1Y5X54eBibN292TPTRaBQlJSWOh3J4eNgl1JYFjJzIZZBJr6fKykrHAsfXudMjgKPWRmba+Fku2Cz5o1swCZSYYcjvUzDN7+e7T/yMjs1Si2eTmSQbtWTJkhktJul0WixIY2NjGBoawvbt2x3XlMlkUFVVBcuyxGIrM3fsq/HxcWzYsAGFhYXCgZ4eQKyzp7PPkK9bznxUJzv236dqa+2Nhxz+zW1G8Nxz2omSizCLRqsAV+duvnv3bmERsnv3bliWhUsvvVSANjKYLEBNnRcZmWAw6ApRExhzXKjAnya269evx3nnnYdEIoGmpibE43EhQpZBUEdHh6gWoP7O6OioeB7k51y2e5GfcZl14X0eHBwUmXkrV650/L5OqC+b2+qAHZktAK7kEb7v9/sd4UaVFfIqGM2M00QiIVjkiooKbbiXPkdFRUVaWxcvZuu7zELcutVxTDz++NQYvOUWx1vyeJT9wWTGjWCewIoMnnpOlmWJZ1d+ruW5RS7LpOr6VDE3z+3Zu+4CckJ5XHKJVnier6nPnDqe5PHA/tR5sC1evFirD7z9ttvwPcPAa3LkxzCwp6nJk00kcCVrtnnz5v0q7n0gTXfdahsZGUGqqAhXl5biScPAyYWFiMViDlnRYbB1gE0OH37dMPCsYWBBLrNwpr5ZXvSoekNJz8sDqrq6GolEwiW0bWho0NZD6+rqcg9IUrmKS7hn27t3apf0l7+IyWDBggXayS8SiQifH9M0MTAwgEwmIxZy+TsTExOusI/cF5ZlZ3X5fD5cccUVGB8f14ZJdIxPKGQ7igNwGIHqPhsOhzExMaFdMLu6upBIJIQeYPfu3VpGjQvH6OioVlekehHxOmkkyoeUbJpOCE6jPd1iwnIfCxcuFL9rWc6MHNpYnHbaaQiFnJlysh0FhfgM8S5fvlyAwdraWmH2KWe90cST2UFkHVSdjGP85zKSrmNI+8c/tsdZMukOq8M5yauZjrrwYjKZFFmjvC6CbHXc0fCWx57J8WVWQjWOZUkmMkb0+yFAV++taZrYvn27ll3t7e0VfaCzFvASiTM0eN5552HWrFnw+/1a53tqq+R5oqamxjHOVUNWgig2ssdLliwRonpd5ilBCgEfxyCPJ7vYs/ROPlaX58znSM1a5nPLvwlSHjjzTHusLV3qOkd8/vNTc95FF8F64gmXLiwQCKChoUEYsKr3kxsoL58tZjvLY0jdqMiJCNOZdLraww/bYvTRUX1VEqnxN5hQwrHV2dnpkG9Eo1HHpk+WkugYOXnjLP9WIBBAxDCw3DDwIcNAyu+fFjS9nQyVemzWqaypqXFct/odPh833HAD4kryG5+nw2DrAFs2m0UikcDHcw/iXsNAUyg083I3yG+mJjfuFlWPkPHxcWEmCTi1MTqq2TU4X3kFuOEG4Gc/m/mF50R/L1x/fd6sHFLHHLwyvc2FQWdwKU+CshBYTmMPhULCFZ2to6NDFOJVGQG6PI+NjSEcDot6cg0NDVoGa2BgwBFqo7aM5x6Px8VkrmMf5MlXt8tfsGCB44GWJ9yBgQFRooeWEDrwXFlZCb/fL1iSUMi20VBrdp1zzjnit+X3CJB0lD8ZLi/d1/j4uBDDE/BkMhls375dMEPcjXsBwlQq5XTw37IFMAwMzJtnl0857jg925BrajhIfl0HcFm4uq+vTxTBJhBUNyzyM6jbvLB0DkPd9FQDpoAIs2KZPacyKl4huba2NlRWViKTybiKZwcCAcFsselAFRMnlixZItLk5SQGjhXTNLXjt7S01FVyZ8uWLY76mX6/HyeffLKnfcd0CyKBFAGnbJYbCoW0VhGyjpGMNMXv6vdpuyEDFN3fz/70p1Nmprq5+803bQ2O349HTjopb0arzM7L95NjqbW11QXGuBGQF/C3xVn9uuvsa6ytFREXtW84R6vF3XUbDK4pOm8rdT5nZEE3FhgdouzkYA2+Z9JkJpJ9MD4+jjVr1ohnJJlMCl84ed3VraWs5GBZFoLBoMtpIBaLIZvNHgZbB9osy0LUNPFIDmy1G/thTiodQ8ds6WhaLyAju0STOaDmSH5YZ5RqP5N2/fVgPFoGivLk5yVAVCdgAhqVcYlGoxgcHBSTJcXx6m7JNE2MjY3Z9yJnEkrgoU5opmkiEAigtrYW6XRaLExq3/KzrAHX1NTkCK+x/+PxuCf7MDAwIP4vl2AJhUKCtVINHKknYz82NDQ4NBiyF5eseSNF393djUAgoM3sKikpQTweR29vL7q7u4W1RE9Pjyvtvr6+HlVVVQC86xJedNFFCIfD6OzsFLt8ObwxnQ6MAMnR/zkPuX8OhWA9+ijwiU8Ac+c6hLLqWJK1KrzmsbExTw8rCq29NDF8XWUjdH1aVlYm7kFHR4ejBI5pmjjzzDORSqVE6JdJGNNprchsUT+YSCRQXl4O0zRhmqandQE/S+Yhkis8fN99+sSAVCrlOX4JANra2hAOhwX7SaGxjqlSn23dgia/p5NB8P36+nqsXbtWC5plEJhMJpHJZBy+UPstfj71VHspW7Mm78ee/fGPUZdM5q3rqbLHKrPF+8JKFqlUytPUd3/ZG13IT/x/z56pcKhhAJ/7nCuTVTbuVatA6DYwsjCfx1JNg30+n9upfwbnftBtYgK44gq7TqPyG+3t7YJFJeNM1l1mu1XLFo7R5uZmcd1smUwG4XAYN19/PbYGAviGYeBiw8B5hoGlhgEzEDjMbB1s+8727YBh4LFZsxDxCB/mG0gES9zFMMYv67d07Fdra6uDymbBZTXzamBgAOPj4+jp6XHHzF97zc5GfO21/bvo3bsBw8AbuYwiFQB5xbW9+iGTyYgHm+GH+vp6Qdn29va6HNUBO0xZUVGBUMjOCJQnObX4Llkiht/kkj5ySETV9/j9fpHBIjfuxmWWjOyDnOafzWZRU1ODdevWIRgMYtmyZVqmiBYRuhTlVCqFs846yxHKKyws1Iq0y8vLXf1EFozhma6uLsyePdvBCuiAZD5mi9cdj8dFwobOJsIrW0gW8XOi/lJpKWAYeOSkk+wT/+EPgW9+M+9QJOPLhWLevHnC2ykUsu0ZOKGyKHZXV5cL9LBx3LFwMlnWYDCISCQiwBSPyY1Fd3c3otEoNmzY4An4CdpUkCePP1mzxYndNE3Mnj0b8XgcnZ2dgm01TRM1NTXC+yhftqFXMeZMJuMav4FAAFu2bBFMjbwR8JrHeA/JpNGahWFB0zQdc1p/f7/Wc4uaKy9mi8fSiaEPeMFOJOylzCs5Kdey2SyWLl6MF/r7RU1Zua8JqJhgIc/nKrA6GHChA1VqZiPHD/9/67XXTrnjGwZeueAC19zNxAZKVqbTCKsMzwGHOPenPfmkHYX5+tftWpb/+Z/AV78KfPKT9t9suTUKhgGcfDLuyDFrskmrGo3JJw9gY6KHymxxs98Qi+F5jQPB0xUVwLe/fRhsHUz7ca4kwg9z2iR1QuIkdFxrK4pzCzmbLKRMJpPo+dSnsD4UwvPvfz/w4Q/jAaVUgDpx6xgZeQA0NzeLsJHsAwTA3umcfDK8TOTyNmoYPvnJGdPd+TzBuLunN4/6QDMs6sVsRSIR/N3f/Z3Wb4UZUjKb0tvbi3g87hDrr127FhUVFa4Ha+XKlSKkKf92IpHA0NAQgsEgVq5cKRgFsmcM8xLMnHnmmaiqqkIwGNSGBAn8enp6UFlZ6RhDFG3LJWlCoZBLpM0ahbpFV/6e6nml6m9OO+00RxjA5/M5wj2yg/h0DBYLE8sFnUtKShCLxVw2Bvd+61t44ctfdtSg0zWZ9h8aGkJ3d7fYRcuM4VlnnSXc4WUQHY/HPZktCo6DwSB6enowPj4uakeyFEx7e7uW6Zo9e3be8GA6nRZ+ZtwksIwTLSXUbESK2TOZDHbt2gXTNIWjPDdYyWRSKxgn88CalF7+Xgz7lpeXi1A5GfJgMIgPfehDeTdPu3btQiQSwe7du0VoUJ5v2D8TExPid72842gx0p6bk9SamKtWrXIt4PkYNF2JJxfQWbsWuOAC4Omnpx13g7lw44Mf/KB4PqLRqKg7ymtWQ7uHCnCMjIxgflERNldX4+PRKC7fsEEI6HWJIOzbVCqF57/yFeDqq4FPfhL33Hmna6PBZCI+F2rFC7WAuU67dLDNq68sy8K9t96KfUVFLiAj/pxwwtQXnnjCrk2YTIr3/3TVVY7+cEgY4C0/UMPpZIzVxijN8XPm4CrDwGBhIYYLCvCidM6HwdYBNsuy8NlQLrX2wgsdZWbKiorwza4ubAmF8OJRRwE+H16rqsLs3MKnhg9+fccdeFwu8rloEXD66QIk6AwGdQyGPDB0JoKc9LBsmf07ZWW2eeT+tI98xP7utdeKftBlqMiTjRoGU+P3O3bs0AKR1tZWNDQ0oLe31yHCpmZLnsx1QI0gg7+fSCS0CyxZGvX7ZHbIWHAxqK6uFloYguWuri4UFxcLQaR8rEgkgsLCQtTW1roE9eFwWIRBqCHiIrxt2zahtZGBKm0+5ONEo1GccsopwiuKmVtySKW+vh5z5851LczV1dU48cQTRYijuroagUAAhYWFuPzyy0UmolfBZR2DRWAWiURQVVWFcDiM7u5ukfm3P3oUVfdH2r+0tFSAI/n3daaaHHMyK1xdXS2ABlmT5cuXO8raEMSRpfFyQpezFHXhQbJ/rBzQ19fnWljk65TdwyORiAtMcr5hRqUOZDNs7fP5HOabBL28R52dnejv70d3d7fQI4ZCdlJDUVGRK9TF+YiMYn19PZLJpOd8U19fj6GhIQBTjBr1jATxc+fOFU7/cpPBFPuI841aazEQCIhC7QSNcmJHvk3ftO3pp7EvV9psRc6Ilf2inuP+NlU7lW8efWnxYrFOfKOwUBuKra+vd4S5VAmJl3yFzw2Z4YULFzqKYOvm+kPR5AgP51P2rXzPTjBNZGfNwi6fD18pLMTvTzoJOPdc23T2F79wH/j55/HE3/+93V+SHUlLSwsymcy0zBalKXLmY77r1t7HV1+1q6wce+xhsHWgLZvN4pPpNFBYiJc/+UmkUik8tmkT0NiIydxD6fizaZM2vReA7cVlGHigoABPXnQRYFmC2cr34Klsi5w9sn79ehdSr6+vx+g114B+Rs/cc8/+77q2bAE2bQKefRaAm7Xq7Ox0/L+3t1eIk/ladXW14+GnzYN6TTJla1kWurq6EAwGMWfOHMdOtrm5GRs2bBC6EppEyqEgAh65UDUA4TBM1kfeuZ1wwgmOEA8LP/P8COhkdoigRG58nUwcfZyovSoqKtIyLQQAOk0PRaeNjY0IBoNIpVJi8e3s7NSyOrxGnU4nEAg4BPbNzc3CL42fVxkby7LQ0NCAgYEBIYZXF3O5WLWZKwnlpdXRNS+XdH6XgIJjnVoundZCzfo688wzHQvyypUrHeFQL+sO3TUw3KgyhjQ+lZNF+PusGyhbBixevNhlp+Fl9cI6cOwnefxGIhFRg7GyslL4HxEgLV++HIODg+js7EQ0GhVsG5kMWXAui7jlzZM8XnXi8Pr6evT09LiYLS6irORAuYAuJKUKuMkiyxssy5ryrKPjvO6+iY3OY4/hmY99DJ9SogF521NPAYaByWAQ2Z/97IBDgHKITU5mKCsrg2maIoSnm0ePOOIIO2SWW1OumjPHcXyyyTpmSz1fr1qAjM683XYKmJwELr8cL5SV4Va/H5/3+fBQziOyqKhIsHVyBqppmtosv3zt+RtvBAwDL+fKonEssB6qTrPF4u8c74ekHyYnD4OtA22WZSFVVISJ731vahHKObKjoAC/CwYx7Pfjj9u2AXv24L577hGD/vmhIazmpP7YY5j0+QDDwO41a1A9nct2rulCePIOy0tv8/jnPgcYBv78nveICSgUOrAsEBX0ecX2VcBgmqYjZMLjqNS13+93iV3PO+88sRhwESeDI4cOObGNjo7isssuQygU0mYusWQQy9MwXDddDJ9Ag7UAp4v9b9++HX6/H+l0Gjt27BDaFgITnYaoqakJZWVljoc9nU6joKAAoVBIuM9T7zV//nwt2GDosKioCMuXL3foSoqKioSou6CgQAvEampqRH/y2GrG0fLly0VIi079al/kE8wDsHeBb72lHV+60By1QWqGn85UkywbfXk4jtTng8a1gL0J0NWl/MQnPiGACcdtIpFwGIcmEgkRyiRDwALJmzdvFqCX/cdnhL85XYhDrh/I86efV21trWAHZMAiL/RyOF3HDMsLtFqMXad5syy3OJzMGesf5tNZcXzRYoAL4Ny5cwXLQtNjjqV0Ou3wopLD2irwq6urQ21trR1imjcPMAw84fcj+9OfYsaNJpx33z3z7+SanMAgh+Kp/5PP3wtci+f6zTfxo+99TzvPJJNJodmabh3xYtT2p+UL/XG8Pf3rXwNf/CLw299OfWDTJhchMen3o1XD/FtPPYVf3n47ToxGHWArHo9Pn/T1n/+JfQUF+JzEpvf19WmzEeWw/SEFmQ8/DACHwdbBNHV3MHH77cDPfoZf3X03iouKMDgw4B70ueryryQSmFtUhO7KSseAe2FwcMYDXx7oOopc1Tx0dHQAq1cDhoGvFhbu9y5BbWpm1dDQkD2hSW3x4sWOiY87cIZDduzYgUwmg1WrViEQCDio66amJmzdulXQ8wSQXvouWbiYjxHhokoGi0yQvKDpFtrq6mr09PSIHXZFRQXi8Tjmz5/v+FxJSYnIOJJT9ufMmeNZ3mZsbEzL7MlmnWRLKioqXHUWeZyBgQFtcW6GkVhzzTRNwXh0dHSgpaUFV1xxhZaVI/vW1tYmwlK6PlV305lMxtGnugVZLOp/+YutsTjhBOCll7TjS6ddJFPJ++3l0j5nzhwEg0ERUisvL/dkIKdjtiYmJkTVAH6nsrJS2C34fD6RQXjppZcilbLr6HGRJfDVHZdgeTpmi5sR3hc+8zJjRhNZJp4sWMlhWmUAACAASURBVLBAlC3xMhRmkwXrvEdyBqmOiWXmLeebZcuWYXx83BF60i3OBCIEftwM8bp1GyvLsrT9mC+snUgk8HhHh5hrj0kk9m/Ou/RS+7vz59s+cFKzLAs//+lP8fT997t84eSxK4d8deFf3XMDQOhN5fVEBVU6dvBQgAYvDZo8x8rhRtmqp66uDrfmyATMnWsfcM8ecQ9enTULr551Fr7r9+Nn69Zp5TEjX/mKiBYddSBr1t134+lHHnl7mTqv1t8PzJoFrFp1GGwdbFP1JG1tbbgxGMS+WbOAaBQvX3QR7v3Wt6Zu8uQkkE4DhoHnbrkFv/j+9/HaBz4ArFhh14ZTfHSm+12vsCInHTlUAQD40Y+wr7AQjxQUoGh/dwls//VfwNq1eO5b39IyUqrnDUGBbnLh4iNnY+3YsUMACwp1161bJ6wIRkdHUVdX59iRVVdXi3Ifo6OjopBzJpNxMR2LFy+Gz+fD9u3bRd1FdfFQFzg5LCRn4hFMqaaY4XAYqVQKfr8f8XgcW7duRTAYxLx581zsDsv8MHuOgEFNPQ6FQli9ejWy2SwymYwLVDU2NgpQJWvkGD5kfxIY8X2ye/IiZVl2NhoBgnoexcXFjt/W6UQGBga0Ogh1A2JZFv588cVTm44LLhBjXL4nvAcLFy4Ui6fqeh8MBgXzx8V7xYoVjsWawEZnaMswrwyUZQBhmqb4/dNPP10YVSaTSVHWhh5bcq1Fjv3h4WEXcwXYzEtPT4+4TlljRc2WyoSRKZMzv1QdE8eUrGPasGGDKxysAyfV1dXakjJcZJnlyrDeqlWrMD4+jt7eXnR1dTlCYclkEqtWrRJsWiQSQVdXlzYBiL/DbF4dyOa91vlaMalBPjdqto5KJLA3t3D/37nnYr/aE08ARx5pj9GVK8XLd190EX5TWChKzTxXVWUvsnv2AICDlZU3EDo22+u5kcPfnhYPh6Cp6woZUAJ1jqNUKoVoNOoCwjRDlZ+t3331q6hLJvH817429UObN2MyV9burWgUIzt3inlTvm7KY26ZPRswDLxkGPh47hl+Jzy5vJoXABXvP/UU/nLBBVNz2rZth8HWIWkvvABMTopOf3n9ehvNyjTppz8NwL5Jf1mzxn7t4ounjrFv34x/TmWxdIJ5poXrxKDP3Xwz5h5A/Fu0c8/9f+y9e3xdVZk+vpuc+z2nbZLm1rTnJGmatE2QmVEEFSpKRZgiQmsp2u+0qVOmNEAVbMYyNhqd1kkd4yWYKgalmIBjxqmXOcoQYYLAYUQqEBWUAS9oT0FBUC6FPr8/9nlW3r322ienUPx94ev6fPJpc3LOPnuvvfZaz3re531e+/z37fMsz0HmKBqNIhQKIZ1Oe5qISmE/hb2hUMj1IF9++eXK0ZyLzqJFi5T2ibXpJHij6Fs/v3e84x1Ip9Oora1VoT2CEh6bmiDJlpgAI0X2/BwXY0701C9VV1cbfZvo6cJQZyqVwsaNG4191dfXp8KjJo1Xd3c31q5d6wAIwWAQu3fvRiQSMQq8M5kMtmzZgoMHD7oMMKPRKDqLegc2UwhLMltcCEOhkFro9TIyjt1xVRV+Ucz0+l1zM/DQQ+q7TC7pAwMDiEajSsRORjSZTOK0005DOGwbx1ZWVqKpqcnVT5FIBLlcTrGsDAeynxKJBBoaGhAMBuH3+9HT04OBgQHs3bvXMe5mY/eSyaRyDOfCamKu+Fm5kI2MjDgYqvHxcWzevFl5X9XX18Pv96tC1tIpXQ/Z6+PfEZKCGwRzPNNrTlot6KEnWWanUChg9erVjg3D7t27XSHn2tpa9VxEo1FVCkiOL24ETIkz3PBw8dfvLdlar2zE+z/+cVDuIb2YymrPPmtnZBc1q4VCAZ8OhdQ8/4L4P1IpoNgv5TJbxxoKfClNv5ckDGQNR1Y9YFjXdK46OMpkMkarHtdm/te/xvRZZ+GzwSC6u7sVIDdtxu689VY8ff75gGXhaEWFnblcTnvuOSCfB5588iX3F5uUgfDZ4/nTK/GjYTt57ohl4fYtWwDgL2DrJbdnnwX++q/tnc7jj9uvPfmkfZN/+MOZB6+uDmNjY5hXVYWbit4uj2iTSDnNi8WaraSILpKkc28iHkf6GEoMAbDL+1gWcNttylJBtkwmg/7+fkxPTyvBJoW4Jk2QPK/u7m40NzejtrbWdZ10Gdavi2FF02Ija9ux4ntPTw/S6TSWLFmiROvy71u3bkWyGGJgX3IHbdqNcvLfunUrLr/8cuM58jg7d+50ZFZSyGwKr5lYIbqzU7Avw4HnnnuuMc2fIdba2lqceeaZDuBIa4C6ujpV5FcuYF5jacmSJYjFYuq7V65ciXA4rCbntrY2xXp6+a9xLP8on1ebk0WGSgf6LlJfYFnKZWpqyjFmaHBqsmHI5XJqcRgfH8eePXtUeFR6dQUCAeVrJcOnJg1ZNpt1LChMgtAX1rGxMRewX7VqlWJl+/r6sGrVKkfBZprhtrS0qA0H2StZs1M/p8bGRiNY1jVb9B7av3+/cgbXtV5cjBnW1q1syBrKsLfJpkaGIScmJoyglQka3ITo95uaTwnGq6qqHMWsS7Z02p7DPvCB2d9bouXzeZzX3m5nwz31FPDMM7i8uXlm3i/6P8lQqazkwP8vXbrUlfl5rKyVF9si9at8DnWZBce79KDStZCm50jfuK1YsUJVPii1/nidd8nrPnpUJZPh1FPL6hN0dNjvz2Zt0GX43unpaeTvuKOsfpfVXEy+iOGwbQ9xpGg78dDHP66u/S9g66W0u++eoZWbmtRuR7X777fN5Coq8PhXvmJ3epEVej4ex+uOVTMA82Lf2dmJLVu2qGxEvWAzYC4DVPj1r1E46yw8+9rXlm9u+thj9vUGg8AzzxjBn7QzOOGEExTDdPDgQaV3YvaWyXGZtf06Ojocg76hoUFlYPE1+sOUmhDS6bRihKR2ZHR0FJ2dncbsMlnLS5qmeuksODFt377dNQGx7ynyZSZMZWUlAoGAUWe1fPlyJbimC7neV8FgEMlkUj34sVgMdXV1Lqagu7sb9fX1aGhoUCBOL99CLQxLT8jGcJEuCE8kEso+IRgMYmJiwjMT0BSidozlN7wBsCw8GAza5Xo8mun8uru7kclkMDo66rr/ZEpNzCt1T/rCp5fnYAFhsqWuuo7iuHq2qvysDA2S7fX5fEilUvD7/epcTPUweWyvxJdpjUHh30zMFsEMLUtYtNyUbDM4OOipfeSGpFAoeG6EFi1aZDRU5SYtn8+rDMnly5er0DDtJ6LRKNavX69S8E0sz4sKpf3xj7bL+COPlP8ZQ/Pa/BYeeQT48pftLEbxXj0b8XiYfxYKdqY2PdjItpChogCfrKKXCTHnMFMShOk6qSnVjzMyMuLYUDJh5iW3o0eBE06w15/Pf768z9x7ry3PsSygshJ/+ru/wyMXXoj7TjkFK4rPu2VZ2Or346dz5iDf1oZTixIRU0KHnN9MCT9k/Y+yBNQzz6hx/hew9WLbt78NVFba3dDYaBd1NrXvfhe45hrk83m8tbMTqKqyAdjk5DGX0OHDKm84hbqk+pmB5KWDcLRHHgHq6+1reP/7yzuJb3zDfv/JJ6uX9FBPOOwWrkoAxsWb4TaG4Bi+iMViauHRxeH6axJYmiYEZkNy0ZAPEf29TNlr8XgchcJMyZZIJKK0PFxYstms44Fcvny50bBRMltbtmxRnlPxeFx5FJkWRGYCRqNRBAIBB4gy6WzIdunfT00Wr8eUGk/2a3x83Hhcn8/nYHwYcpNh3h07dhjDB67qBeIa1P0aHlZswBNXXeU5/Fgaw3SPZZFxAMp49tRTT3WEdVls27IsVRonl8sZQfeSoqeS/n2FQsGh44vFYgo4ccMTCASUR8/AwIDKeBofH8e1116LUCiEv/mbv3Gc2+7du0uyZl4llKTeSx6P/lV0CE+nZ7KxKKDX2Y/Vq1cjHA4rr7ZSGxkChmAw6NrgLV++3KFbk/3KhZ3/Dg4OIpFIoL29Xbnle3n36WNofHwcAwMDjgxnz888+6zn2Hqx7WWpZai1UiJ1KUZn+I8bQy8zW1NEgZm89KDyqnZA4D0yMqKSL3QgLIHlcROlP/EE8La3AdXVduZyue3pp4FLL51hG4s/m4SMZp94/flAALj3Xte6qUdxTHNwOp3GooULlSfbwbvv/guz9VIb/TvUz3/8R8n3c2GZ/ta3gK9/fVZqlZ8hzSlN31gGgouLybyRE8Cs9cJuvdU+/2SyvAH80Y/a77/0UuO5UlRpMs6Mx+PGLKZ4PA6/34/h4WFH6E4HH9xF6eEv+V5OCAQxY2NjaG1tNS6Y0WgU69atM36X3+9XJqb19fUOp22CPpNuqbq6Wgm0ueAtWLDAARgJ/PQQYTKZVAucLo7XAZIpgyyTyWDOnDnqHCWAlQtAU1OTUVjMxZNmfmQaKisrVVbo8PAwKioqjM76piLonJRNjWMlnU7j5OXL0ZVM4o73vrekfrFQKDg8vajZoiEn/cdoaULd2imnnKIyXrkhaGlpUexgW1ubcZcaiUSMYXICbW52otEotm3bhlQqpcabrCih232QRTNN1qbN0mzMliyUTjB14MABV4q7NIYNBoOor69HIpHApk2bFFNhAlheDDYXVJOQneOYOhyG+alhZNauXKD1cJJpwZbXwmdRJtiMFf2aHJurr3wF+NCHgJNOUoWYj2dzAbtnngF27wZ+9avy3g8trCX+Jq9FlnRLpVKIx+OOja1kW0wAmaBK18rKDSETPBiBoCwgHA6jsbHRkXnodS3HqxUKBfzPzTc7j025zjEe582BAD5iWbjCsvBey8IyAbb8loUtcj2/9VYAzoiQ6RlgmFtn/VsTCfRrdSH/ArZeROPgv7CtDVNMa2Xs/7bbbJbo6FH7Z2pKubSrHVAZdLhucqhPyl519GRW2ODgoDL3LLnj6u62r+Hf/m32i6e4vwT7YFoQyF7l83nXDliP/Q8MDLjAwOLFi9HQ0OCYeCKRCDo7O1XJFg74nTt3Ih6PKyPFYDDoWEgLhYISGDc0NKj+ra2tRSAQUP5VNIUkqCoUCiqr8MCBA6r0jDQ9lcaSHR0dCAQCaGtrU8Avk8ng3e9+t6sPKEaur69HPB53ma9mMhm88Y1vVAuLl3EjF/HLLrsMwWAQe/fuNfqcmZgtMlC9vb0OLZjf71eLVl9fn9IQmZgO9h37gAyFnvovw8zSMbqcxtIYTU1NCIVCWLt2LaanpxWg0fVuFFqTfdG1KV7h4XA4rLIAdWAcCASUCHzp0qXKn0culAQLLGOzYsUKVFVVKcsGU1FjCokJzAn4pWaL2jECyZ6eHteCp5cDk4w3N2Ws4chNWzQaRX9/v8Mg1sRsyJqjvM5kMqmOS1C1efNmFzPF8Bn1Q/rv8vxpqEtjWAmiuru7S45jfVH8JIXrFRXAt75V1jh7Se3ss2e+78wz7c140RKCbJBkwnQNFcErnxMTs7p//340NDR4+p2VYra4MfIyNZXPqlfx85e73fChD2EoEMAfKipw4ksMRebzeSSTSUdSGH9SloV7BdD63QknAEePGgkRE4nBeceUSCI/+6oFWy8X2nZMQDt24PFTT8U9lZV49uSTgTe9Sd2we669Fn8YHLR/X7ECKBSABx/EE5/7HB7u7cVTl1xiv1Zs+k5MxtRNseHW1lbPLLGpqSljlqAnk8bzfNvbZu+Af/1X4IwzbFDp0XK5HOrq6hwTyvz58xGJRDwFsZL6TiaTSCaTrnqA+i6Ou+tw2C6jEolElI2C3+9XZXWkCzozo5gR1dHRod4fiUSUwJsL0bp169DW1qZAiFzk+JmLL75YMSQmNoITYzKZhN/vd4l+vZi8iYkJ5PN5JfT2+/1obGxU4I+gg7orWZCZIaT+/n41cfNe1NTUqCLbEigODg56hmJ5H2KxmCN7U7eY4ELBUKtcMPmaKbNpNpaXjfoUZjvSz4r6t5aWFkfGJZ+rlpYWtWDz+/T6aMxOJLvo9/vh8/lcLFljY6MyD6UGjwwia6dJE0uCO15rsJh95aX7ouXCyMgI9u/fr0K43CC8853vLFkiRh5XOnIToJokBgwtTU1NGZniFStWKPNfMnemWq9Lly5VzIdX6IvnyKxS6gez2awyfy0V6i7F7nZ3d7sLb99//wxjcf31s46x49Luu8+285Fsyde+5jkvlwJFOrNKtqVQMHuNBQIB9Tn68cnKDnwOvJi0l60dOYJHf/jDkmOCv3/tiivwrOi7G888s+w5wtQKjzyC1wSDWGdZ2GNZuKEItOhKf9ecOfitZeHG7m5kZjEWL8VKqteOHnUx9K9KsMVMu5fiju5qhw8DN96I/B13zEzQJ53kfJiKP3f4fHjDihVYlErhyepq93tCIeC00+yMRXhT9HIQmpgtXeTIMBYnMb1Y8bJly9Tu0TEwCgUgXKzzeKy1EjFD94+Pj2PHjh1qck8kEtiwYQMmJiZULcF4PK7CanLx3yqKqtbW1qqFiosY9WheBY8J2Fj2goucFOZz4Z8s1kk0gRvT4rdo0SLFLJjCJFwoGMJgy2az2LFjhwKdfL9cuLxK/LS1tSlGjpMkGQq5kIyPj7vCmTIkQKGqBEVMj29sbERlZSVCoRAymYwqEG3y/iFb2tzcjFgshsnJSaXtIQjx+Xyu/mE/64u+bgRbjn6RJV6YXFFVVeVw4adJ7ZYtWxQQ9NIumuqjkfWjRq6np0eF8KnZCwaDCthJ1oVierKkpbKxmpubXf0ibRFYi5Lh4M2bN+PAgQMuGwOvJm0m9Os3aRT10BLNkDn2Nm3aVNpTqOAsDk6WkVoeWjSwNBazgRctWuRp8svnRLb29na0tbU5vtek39OZrXtuuw2/ZAm1D3941v4ztVLAUbUXXgAeftj5u5j3f3/99UgkEsZKCPK65D2RCUDyGuXmR4rRvYCuiT08bu2nP7VNidluvRX44Afxx61b8Zu1a/GnjRuBdevwTCyG/5kzB8lkEoFAANFo1FjmLZVK4dpidvIPLQsnFK2JvBJtSrZbbrHdAihYFz/XfuQjCmzePTGBQvEaXjJR8+lPA83NwNe/7nj5VQe2iPaPyUPquefcmYSADTzWr7cF8OKBUQ/yjTfif/fuxfmxGH4/NobfX389ViYSOHj33QDsB6MrmcSRtjZbTN/YCLzudbbhnShLYsowlDF1LpzUqZDuZdiovb0dqVRK6UcSiYRKC9drnDEzkDW4FDi49FLA5wM++1nvfjI0KdA0ZVFxYe/p6UEkElHvNTEi/LxXeKzU4pnJZLBx40aXqzqF+dzVNzc3GzPampubsX37dld4b9myZcjn8+jv73dlGRKEeU2EXrtz/j2dTmPx4sVYuHChMYV4eHhY2WdI41U5+Q4PDxt39/l8Hh0dHejr61Mgk0CJ44LCfx3El5rcE4kEVq1aZQxt66LbbDarWEEvDZJ+H72aPIZJJE+ASfaDY8aUGUpwzqxQGfrVC0/39PSoMSmLUev3neGzbDYLv99vZJ3z+by6dhlyZjbnrl27kMvlcMkll6i6geHwTCJJU1NTaf2l1lemMCWPZeo7qX+KRqNl2SiQ0eKz29HRoZIDSoVn+fybDF6XLl2qNmTyc6yVqrM4pTRb3NDdtHOnPYcHAjbrdAxNbt4DgQD8fr9zI//ww3aptkzG/pEtnbZ/zjgDd37/+2rene2Zk8kD0jCbmi3dh+rlKBDt1QqFAu6cmsJTl10GLFli9+uXvzzzhk98wkhCwLIwbVmosixUFlklk0b3+pER/Kn4/ubiGk6d5jFd34MP2glp/P5Fi/DMqlX41ebNePxLXwL+8Ifj3DPFNjBgf9/69Y6XX3VgyxSb9XRHv/tum+qNx4GenpnXjxwBduywswZ5o6JRm8nav9/xIEu9iZcjcP6OOxzgSm+mxSiRSCCRSKjMPbIHa9asURQ8NUsMbXgBFeo+WA/OJKhnmLNk++537QepqD2gJqCU9YKsqzYyMuLSNkn9zP79+40mh5lMBjU1NUgkEpg3b56aXKW7NdOZZW07YMa2gJmE4XAYGzdudCzWDJUwdCgBKrP4vHRoXV1drnMlI+Hz+Yz9kcvlkM/nVciT4TW5aLAwdDhsZ/9EIhEMDAygu7tbFd2mT5jX4snj7tmzB2vXrkUkElHHYh1IXe+xePFibNiwQekS4vG4oywNQZZpIddFt1ygTO9lAW2vdH69ybHlZf/AbCH6npHh0gFhMBjE3LlzlY5r3rx5KkPR9PzomjcTE8kC6KlUCo2NjUbmtLm5WYGlqakpbNmyBb29vdi7dy+Ghoawa9cuBYa9NhZcmGYDXAwxm0ApXb45L0i9jrQjYJbfvn37HKyIDD9JUGAC3qY5kbpSzh86GKNdh16ZYteuXUaT27KzETdtsufyjRtL9p1sps27/LnQ71e1bWFZdna3aeMO7xqwNMKUejfeE1PywPGUyBxrKPG6665DWzCI25iJb1k2oDlwQL3nd9/+Nvp9PlxhWbjMsnCxZeEinw+viUSw1LLwI8vCDrEu5/PO2p9Pve99gGXhO1pfzzbmXe2mm4DeXmDr1hclqje2Rx+1PdV+/euZMOGRIzZ+WL8euPBCYPlyu1+0rOFXHdgqi9k6fNh2bye1bFnAW94y8/d9+2Zef+97ba8ODSwR8EidlUnMWE6cmceqqqpSqfKxWMxT22LKVuKiqC9C2WwWPp8Pvb29xt1Tc3MzBgYGSp4fAHsXQIavqHvI5/Noa2srmbHE6+/s7HQIhWkeKu+NNHs0hRUIOqlfkvUHGdIhA5bP57Fz506jwJnHq6qqMhqhEsgwY5G7L70ESGVlpWcIJBQKob293dUfdOrmRFtRUaH0U+FwuGQhbDI2qVQK1dXVKtOLoIrglbou+Zqs+cfFUI4jGc6ShqkM9cyZMwehUEgBJ9O9ZjYbQ3s9PT0KPJjugZ7eP9szwu8rFNzGppLZCgQCqKiogM/nQyKRcIBYn8+HeDyuWAIyWbwfCxYscD0/jY2Njte83OOj0ajKRhocHFRJF7x+MkUU1EoHfAKMtrY2Bay9wrml7DT0PtPr6emp+ePj444fmpfG43EVruXmiNYCzIZLJBLKC88LeJditgB3aSKGZLn5YLmu0047zXFdXjYQJQHDPffgTxs24Kd795YNVEoJq8+wLDzHdeKcc2xbnBKbasBZT9crq+94aqi8jsmxwXJT4XAYf93YiHPicXxz715jZvrh++/HR3w+xTo9ZVnY7Per8FupPovH43iL34/fFz97r2UhHgy657qnnsKzNTWAZeHNgYBa164qkZA1a9NqVb7o9thjM1ZJluUE1e3tThavsRH4znccH3/VgS1AuKMXJ1qHZuvOO22fDsuyQ3vbtjnj7IAdUmtqAm64wfM7TItNKpVSaf3leq7oWYc1NTVqJ+3FFI2OjhpdzE2eNtyZt7S0GFPbuRtXD/bzz5vF7729dp+deKKN5OFmtoAZlkhnnhhO1PtrwYIFyvJBOmzL1H4uSHroTE6uNI2srKx0MIF+v99Y9uY973kPEokE5s+f72IpVqxYgdHRUXVcZthls1mEQiHMnTsXgUAAu3fvVnoJArBAIIArrrgCU1NTqKqqcuxivTII58+fj2Qy6chA1Hd7PG+fz6cyDeV1sjD21NSUStsuFeLjYkgtm4kBSiaTCqTzXsgNhQmgMTV8165dGB0dxeWXX640TOGwLY4nsHSFsmdp0oWbwJdidmq29Pvv8/kUSDTVQ9TDmabf9XGrhwH5PdlsFlVVVbjwwgsRi8UUUAgEAsoN3tTXDEkdS2hp6dKlZetXTGEmvXC3Xkpn3bp1joxFU1iZi6SXnQvtQ/T0eNqt0GE/FAph3rx5quwSx4Sp3A6vxwQcYrGYyuodHBw0jh+5OS5n3HkxW6+xLPyxuLD+8aKLjmlBP57MFI+nZ/zmcjllVstnhM8bSwFxvI6NjeGhPXucYEH+FMOu933hC47XL7YsNMXjbrNsQ5+dGQjg+SIb9u8+H1J+v8OuRJYnmltVhW8WAfFx6acSVjLH1FjrcN48oKEB0/fdN5OkcuONds3ga66x6xs/9ZTr469KsAV4DOiHHpoRyp10ks1YGT5Tzs7Cix7nbqWcQeIF2Nra2hw0vT7RjY+Pe7qYc0fIxZ+MhtcOc2RkZEac/OyzwIIFtnZLb/Pn2/12552Ol8fGxhwCTS66erZYKBRyhAeZ+SWzZAiKCOJkNqIECybNDkNjJjBjeo3C0/nz53tqlCYnJ9VOXv+ucDisnLb7+vpQWVmJuro6h7CfBqNk4yorK13AjiyFXLgAc4YiASt9vPRrisfj2L9/v4NtZDOF+HS9i1x8vAri6q7ooVAIGzduRCKRUCaZLIbM6yZjRKAaCoXQ0dHhDmWX0biQbN68WWUExmIxbNiwAeedd56xXxha1k0JAXcBbSYmSFsFXTDu8/lQWVmJk08+GePj4+jt7XUkO8gQGA1fKQZOJpOora11nENra6uxXiUzY2W2abl9Jpkr6vZqamoQi8UUYJ4tVEmG2MurCZgJ01NfxtAknzHW/ZSmuMlksqTFgBdjxWQFsjGJRAIXXnihIyOXoJsJDvIYLybqADg379RsXRwK4WnLws/f+Mbjx5zM0kxrmsx65bwkHePpO1dqg5FOp/H02rUKRP0yEMBRGSYsurUXDh3CN2Q0qMjsPfuGNwBf/KKjH3TC476zzgIsC8/X1yP//e8bw6HHG4QCAL7yFXutL0OfVSgUcNunPoXvHDhgHouLFtnX/d//ja1btzrA5NatW2c9/qsWbHm2Sy4BLrrIVZ7Gy+fEa/czPT3tqkeYSCRURls5sXUTYKPQlLoq7gKllofnGIlE0NLS4jjPTCaDTZs2OcIU/BsBTmdnp/JAckw6hw7NIHfZDh+2X4/FjBNLoVBQEzrT4GXIjaL8UqJZ1v0jo6dnTOkJAwy9yDBiLBZzLAyFQgHZbFb1IXfp8NgUTwAAIABJREFU27dvV+J8hn/JmBCgcoe/ZMkSRx8WCgW0trZieHgYsVhMpa7Tk0q3q2hqalKWAGSg9AmfWTZcnHgefr9fLSSSJWQBYtlaWlpw7rnnKpCrC7hZEFz3BDOBTJ5XLpczsmtSk0btEy0PpLaN95n97LW4H2uWkaxPRvCRSqUQjUY9ixbzfLzsOeTvtK6gFUYul8O+ffvQ19eHrq4uB3N2yimnKNaGHlqm8LHsW/07vTJdGQ6urq42ghU9w4zzDcczwRrHsgSLXV1d3hrTonCdc1epa0mn01i/fj2CwaAa66eddprD3JUeXzq7Vo4OSZ+Xa2trFXNLYCc1ljKcHAqFlMBfXetddwHXXgs88MAxVfAwneujd9zxshikqu/8zW9w59SUy9RaCuPlvTFt0EzWEfoGo7u7G9ePjGAgHMaP3/9+ez145BG7tu+BA445/4Z9+7DT58NnAwFMVlQop3RYFvCudznCj457eujQTETpE5+YOZmpKeB//9d98b///YtipBzf+cwzdk1Ey8ITZ5yB0S9+0ZHwId87NjaG84pEzLctC3Msy6WbQxEw/mrPHmNYebZkkv83wFYx7AXAeAO9dj1cHLwmAFLwnNRI01IHNRtlrU/+Xn5LgUAAe/bscYE7Winoi4WuZ9FThaPRKJqbmxVAUed22232bdUWcnzve/brf/VXrvM3MXCm6+DkR7bLFNqj/QMXc1M4lMBj/fr1Dk3Mjh07kMlkVB/pACqZTCIYDKo6ay0tLQiFQjjvvPMwNTWlaohNFl269UWZOpxUKqXsC6Teh9YW8n7LDDSKqgkUpOMwxxpDppWVlTj//PPR0tKCXC6nMvrY9LI07B+GCwhsJRgNBAIIBoMKDHo50Dc0NCgfHi92ze/3K1G51M3FYjEMDQ0pgMYFTv9XtnL1R3LMmcJrixcvViEo0/NDsEG2UQ9pZTIZxGIxDA4OOhghWQpFDwN79Y8O+gEnG8QUferECKoJ7ilHoJ3C0NAQ9u7dq0TgHNtS88W5gKCkFMANh8PKvmM2ZgtwSgPI9OoyCd33a7aNJnVr7e3tiEQiSBjq0XnNy6YNC79HJko0NjZidHTUcazHVq+22ZsrrzwmRrXcJhlFr7qHprCu3sbGxvBtvx8PBwJYaKghy82QHGcm6cHSpUuNVR3kmkHG3AUuSlyjuqZHHwU+9zl7I15fX7rW5Ne/bq8j73iHDeCeeca2SPD5bPZp0yZbS02Jz49+VPI89Ga0fPrpT/F0MdFtS7HayLZt21ymv/F4HOMCOL67qPV2YIAPfxiwLEyvXGkEWxxrXu3VD7a++U1bvKaFDIGZQT80NORC/9z56bsffQIYHR01MhomAa+p7AQrpHd3dytWQ/69vr4e9fX1SKVSLldxAiaTdkS2TCaD1tZWtVAaJ6qf/xxYvNi+rTIzE7DZQMuy9W3FJrOCkskkmpqa1N+4M5aNxZCvvPJKYxiMYUICHT1jJ5FIKENQGZ6TixW9vXTTUN6TK664AgAwNTWlBOB1dXUIBAJIpVIKCJhAgWRIALsmpZeWhf+XWjjuwgkEKUAOh8NoaGhQdDuZwcHBQU89DK9ZshXr1q0DYC+MDIPJSbGzsxP19fXqeqanp13+XASkfr9fAV6axRKMkKUYGhoyhnJlbchymC2GssttplDg8uXL1XOkj5vKykpEo1FccMEFiEajqKurU2FuaTVSV1eHRCKBbdu2KSATCoUcz7FuLuylq9u4cWPJscH7RtaK3mAsr8NwLEEVtV/V1dUqe9UUKq2qqsLAwICyK/ECuNlsFm9+85sd4T+WY6HpZU9Pj2KP9FqFxxLu0cFHoVBwMZP69XBO8gLnuieV3NR4MVuAPWfdXQyPrZWbzGNt999vsz6yzuKRI7j97/8eX/X5sLS4KSTQl9pEPewXM5wH15ifXX018Lvf4avDw8bs21wuN+smNxy2NaEM6+r6qHR6pp7uSwrh3X233SeztcnJGabssceACy6YqTEsfxIJ4Je/dH3ca+yZdGLhcBhTU1PYz+N/6lOqT3Rz3IaGBnxVuA+cb1mIRqNODHDbbUA6jUOXXvoXZsvVpM/Gxz7m+JM+6PVadDLeLW+sPgHQgkHuysgQyJbJZNDW1ubYOfBY3BVOTU25nM5Zg427S53Fam9vRywWQ19fH/bt26cWR33yGh4eVtlDsnV3d+Pea66ZoXhPOAF48klnP37728Df/q0qtm3accpz82K2otEootEo6uvrXUJmGUKUhaVpkcA0/dHRUeOEIvvFZNxIUML6dfQjk/ef/VYouLPdyBzxvEzmhDp7wTj+wYMHlT6MDuzJZBIDAwPK8NXERtLjiewdJ3D2HRdIaoB4XyYnJ43sD/uNY4vZZlKLooeczj33XORyOQwPDzvAo8l+IZPJYHx8XGmcWlpaFPCljQU1PgxnmhabUs1r7DF9nvXcKEzv6+tTAGF8fBxDQ0OYmppSZXRojnrCCScoBpBjSQdXhYLTRNNrcQuFQmqB4z2TzDdDgPv27cPQ0JAjQ5Hgh1myFKpT+7Vz505XsoMEG7RSmA3gsspEIpHAkiVLyir+bPq9FEPD+0Hmn5shHVzp1yMd0sthtmQ/S83WNrE5BKCc5I9EoygYFvKyW1OTPVc+9JD9+1e/iuf5mmXhsbPPVufGZ0beG/05dyQowb3G6ONOAlKZNEKGSlaGiEajRjD1suijZmme3/n443Ym51VXAZ/5DPCpTwE/+5nr86XMyr0sn6688krcHwjY9+b73wdgrwWSDCBQe30ggH7LLkq9wLKwxe/H97773RkMcPSoCpN6ara+8Q3g/POB2293nf+rE2wdPWpnBFDU/fa3O8KHpoeYaesyvGPadcvPmkIadDDWM5h0nQMXAJag4WTv8/kc5WX4eYYng8Ggp1aBbAgXNlpH1NTUqEneNHkdaWmx++nUU+3K6rM0nV0oFApoampCIBBQjADZK6mDYmYgRcRtbW2qeDAXJJMxKh3Ha2trsX37dhebkM1mHezjokWLjNQ5gdqePXtcxqqJRAI+nw+BQACNjY2uMFw0GnX4iVHg7XWPOZ6kRooMSn19vWPCWbJkCerr6x1hh+bmZkcpGplsYNoY6GJmU7iSlhSRSMRVH4/iadNCxh2bXpTVlPEYi8Wwa9cuB7CR/z/nnHNUdmwqlTpmgTyvjXXdZMiNrEk8HseSJUuUtkka79LxfWRkRCV26NeQy+XUNep/Z5FujmuCIYKjXbt2KY1Nb28v1qxZg0QioTIRd+3apSo88HxYa1HP/POy/2hubnYVEZZhNFqqyILPEkCvWrXK8ezqi7C8X4ODg1i3bh0uv/xylbyTSqWwZs0aV59K0CyBv/6ceGXGmq7H5D9Fg0v2l8yAHRgYUODC1Xbvtuc5zWzymBvT/L/0JVtiwdqy/BEJEATCgLnEWnd3N1pbW0tGT/Rxp4f6dNBbTpjyxbSyANpPfwp89KPA737nePmlVnXxYq7kuDX9/aGit9pRnw/405+MzBb7NxqNYs6cObAsC3stC7+srMSlWuIQABtLfO97mL73XnfJrC9/2R4Dc+e6ruHVB7YefdRmYTjw3/Qm1403ZdgsX74cdXV1jsHEUKLeuCNcvHixcXc/MDCgDAyXL19uzPTi95gWN7/f72KgWPeQoQayLGx6WQdquoLBoNrdA87wn3po770XyOeN3iqmJkGmTP9PJpNYs2aNCuklk0mceOKJCnRVVVVhcHDQqLlob2/H+Pg4zj//fIeuhmwY9XE0Ji3FqgWDQWzfvt0RTkqn00gkEqiursbQ0JByqCYjWVtbi2Aw6ACqcnLp7u5GX1+fg33QQ1aSsSFTpFsOMLTG82fYjw7vwWAQdUVtwcaNG12JGKbFaunSpaowsgT5rCjAvquoqEAgEDAKuMl+6uO5paUFo6OjaqGXgFBmJZKpI0Cor69HOu0sw0HmSP/ecgXyhcJMPb1AIIB58+ap3TuBu54YEA6HlfWEDlqampqMz+/w8LB6n/SA4mKfSqXwnve8Bz09PQ4dYjgcdjB1fL51YEsrER2ol6PBWbZsmSPZQd90RSIRZZwqw3fllPsZGxtTujRZJorjubKyUt1Hhu5NbAvPfcWKFSU9uOQ9IuPLRBD9vuvZiqbXZm0sDi3dzl9Mo0N6IGAXtf6f/8ERZqpZFgpFMPdimS3ADDKPtWD7sTYTmCL43rBhAxKJBJYtW6YiKa7zuPVWuw+qqlQpOh6jFFDybM8/b7NRn/oUCmefjZzPh7dozJWcN765ezd6/H6sKmZlXnfddcA11+CFOXOww+dTURSp2fJy4v/Vxz+OH9x0Ew6bQPt//dcMttDbgw/af6upcf3p1QW2jh4FTj8dKub7uc+5GC0vSwWGlqT2St9xc7LnTTLVJ0ylUpiYmEBVVRWmp6cxPDzsYrl4XGZ76bs8FnHWF4358+erBS0YDKK/vx/T09M4eNCddcLMQ9Yjkw/pMVHI119v/2iuyFxoTL5epcKJMhzHRg8q6Xg9MjKiJn6ZdcYwjVwQqHfSQ2Dz589X2XkULYfDYWzYsMF4XswkNRXL1s1IGcprbW1VjuS6dk+6aFNHQ+o/EokoBk0uyDzHc8891yiC5RiRmZk+n0+FAIPBIHw+n2IId+7cqdg7Ai+vEKjJUoPMAbVCDI91dHSgqqoKa9aswdy5cx3AlSyZqR9NIbBYLDbrWKQjOllRPcuVi4DJWsPv97t0Xny/KVzM4/D4vb29GBgYcDi9S3CtVzLg4pnL5VRWs/wOiuNN/V+OBmfnzp2IRqOIxWIKuJFp5XMun/Fy/0/rAH2ukhqoAwcO4OBBc6hesjilmC2CYoLF+fPnIxQKqeoY5bbZwpyAEO7fd5+9AFoW8MADZX+HsR09CvzDP8wAi5/+FADwb1/4Al4TiSAiGP1AIKC0UlKzVU4Y/UUByhfZTB5kNJmmJIARCk+9WTH5AH19jmN7hfg8N1gPP2z3L++X+HmrOMa8UAiF3/zGJgm2bp0Bu2edNdNXzz4LPPCAsXD7iw6lnnee/V0f+pD9+z/9k+1F9sILdgjUsoCTT3Z97NUFtgDbVGzhQlc6qT6YKITloGc2D8WoTGWXnzeVv0gmk0ilUipkR31CKBRyGbZ1dXWpXac07tTNPwOBAM4880zHID/33HOVMSTBhtQ7MVNOZ0+k9su0a3Q0sRtR7U1vsm/1xIR6iRPpnj17jI71fIi8duamBS4ajTrKuphCtMwAJdDx+/2YKJ6XScuVTCYVGI3H45iamlILhX7enZ2dCvzmcjl1H2n8agrn0A9tfHwcTU1NrgWdLInJqkAvYG0qKs5z0fuBIUqp5ZKhSrmIycbMJJO4nwuj7jzv8/mUdswLXJtMPxcuXIjR0VGjgFxnNUwGlLJxvA0PDxvtKsLhMHK5nBGcpNNpY/kchtX1BJOenh4HI6S7vTOxwktDk8lksHjxYuTzeeRyOQSDQRewLVVcWJaOSqfT6OnpccwvjY2NDt2XibXiXMXQZTKZVB5fdH+Px+Pq9XQ6jR07dsDv97vul8zua2howMZiqZvm5mZjEpBcuAgsyBaTAZUsgtSt6fdchjNN/lL6fK6DBVasaG1tRTwUwnOVlXbyz/HwxXr++Rmm7KyzHOd9PLIRX0qTIILJLHopI/39Jqbby7eQc6qLlUun7f4o1gaWxy+b2XrsMbt8HgFWJmObiX7yk/j+RRchVUxaCYfDeOi1r7VBDcO4Ph+wZg1w9dXHrzMBOzJ2ww22nowgu7IS+MUv7IxKk8D/ootch3n1gS3A5aHlNZgGBweRSCSUBkoXo9IUj5831Xjr6urC+Pi4MqaUx5cPEyftZcuWIZVKuSYq6U3FiV+yEuGwrR+jcaJpN042Y9GiRchms8brNqbZ33efzQiuXevuy/e/377Vl1+uXmJowIsJKcVsSe2YLHsjz3dychKjo6OuotAMpTY3NyMejzv0D6Ojo64MyOXLlyv9DbVi27ZtM+rC6HhNWwdqtoLBIBYsWOBiTFhwd3JyEuPj4yp8YwoJ6/crFArNKhJuaWmB3+9HU1OTApfSJ4ljxKvvTb+z72k0KccaF8RAIOAo09La2qrAvVc9RJnlyNe8mC0yUQxt9uiZr4Ymx5up6Db1bgDUdUitGkXZBFWJRAI7duxQJWroobV27VqVlcq+ks8px7OpH1gDlMCZbJHJMT6dTivAK+0UuBmjBo3eVHpJnR07djjAlN/vV7YfPT09SgfKkLsMM5JxN4VVTZYrXswW9VqyzI6XvY2ejShf52sySYhu8DIkX11d7Qj3lErQ4fXo15INBmfNGDumduiQHUoMBu3asse7Pf20Q0NbDhMjQWhTJILTAwG8vaEBkWI43XSPvLI+a2pqXOCbsgLAoDd75zvttaK/3/UdJau66G3HDltk/oMfuIAx++DwPffY2jiCm8WL7USul8P37CMfcYOpYtWTfD6Ppy65RNVDfLa6Gn/asAH4zW9c52xZ1t14xYOt3/4WuOcez77y0g1wwSsUCka3bKJ4eh2Zwo/hsF3jTT9+Z2enKvkyPT2NRCLh8DjSmZWOjg6X3kTuFLu6urB//36l+ZAtm82iv7/flf3jtTj2aTQvfvELG51XVs5k2LB94xv2rX7DG9RLEhQx5Z6GnMFgUAk5ucBJ5oATPz87NTWlPnvw4EG1G/WqP7dz5071efl3r5CVFGHzM1ygCGCou5EhQApSOzo61HuloD4cDuP0009Xovrm5maXh9Pu3bsV86IDKV0oa2J8GA4k28QQMvU9OhjVzQp5Dzo7OxEMBrFz506VRCHHvd/vx5VXXunJttCTy7TIMdSm3ydmWJ500kmKhSV73NXVpXzLymlyvJmyNgluJAiQBqBcoKk9kWOS7/UKCZqsHrz6IZlMIhAIOJJqxsbGHEAvHLbtFqqqqtDX1+cpajZlS7e3tyMUCrkyKPnM7d69uyTrBniX++rq6kI0GlW+Y16aLVm8OpVKqWsoR0/Fa6T2kd8hrVxMJa0I9Bjmz+VynsWt2fSMM75HeSH99rdljb1Z27/9m12U+Di2QqGAX2zbNrOwv+lN+OrVV7uYO/T22mBPJHSk02k89M//7AIHL/h8OF0vzSa+77gwW9/5jv19Cxd6ml8bweJvf+tcd4T0p2R79FG7dnFPj22OGgzaxZ8NpXKOqT35pG3zQH/O558HNm+2v2vnTiCXcwr+QyFc+t734q+CQSQ1MCnfZ1nWC3hFg60nngC6u3E0FMJPi47PetNFyQwxLVq0SO0E2traXLvmlpYWXHTRRSp8FY/HlWM0J52FCxequDaPz1AAPagogOaDwrCMzjjpPloypVsCBxODMTU15Shxs27dOuPEK3fejnbBBfYtveAC5+v33mu/rnnbsN9qamocImJmdTQ0NLgWdU6sjY2NiEaj6Ovrw8TEBGKxGILBoCM7jKwAS3PE43HMnTvXMYnTRoBGjq9//euNiyZZQWAGgBBk1dTUqGLVun0Hwc/+/fvVTplhZpltyT7dunUrFi1a5JhQyLwMDw8roJZKpYyJEaFQSC1AFRUVxpIvhULBqO+Ru3sCJIJIudCbQkXZbNa4AMtEDt2ShAayHNvhsLtOIc02JycnjeHQY8lC5HgjKylZDwlwdaGrFPNLNlWCfhObUygUsHTpUk+rB9kP4bCtF/SqySdBRiwWQ1NTE6LRKNasWYPx8XFV8ikWi3ky3wS+plBqJBLB8PAw0uk06urqjDVQOY94MVuScW5tbVWZk8xGPHDggBFESR2rDFXyPjEcKv3L2P8ElPJ6TBtRaVA629jn7yZmi0ABd9wBJJO2yeafoenauNnsMtLpNCaTSQWUjgaD6DSEwV9IJOz39PYCEKTCpZc6wdaCBcDixbiymBBi0kqZBOOmhIlAIOCtN3vgAfv7IpFjC9Xu2mVrrP/zP4+tY2UbGJi53lmaHmYdHR3FT2+91baHWrIEmDPHPs6Pf+z5eT0sqv9wrOnvwysWbD3zDLByJWBZ+FlFBVZ6FLbVd5dM/Q6Hw55ZYgz3SGaEi978+fNdYUOiV2aPzBZaM2WymWoX0uRSCtwXL16sdEdcpC+++GIHYKBD9MDAgMN3hdYGrgfuwQftXdKcOU4B6dSUfatf+1rXoGNYx8SISGG3TL2XVhByoSLDQMCbz+dRX1+v6t9xQWdGF0FuZWWlCksBwPj4uDL0BOwHo7m5GUNDQ6rv5X1LJBKeOiCGHPjQ6OOALBs/43Usgh5ZNFrXV9XU1CCRSGDLli2KpeOCQvAUDtu2FfF4HNPT0w5nb+qJCH5Yt1Afi6YCx+HwTHKAaYE/ePCgw4Wdz0UgEEA0GsXOnTtVORnZt5LVM7HLxnIpd95pjz9NCsB7OTAwoLSWdHz3YlLkAqKH0wioTLpChgTTadtwUxZFl/eN9S7r6uoQiUTMGVri3KPRKOLxuNJfsSwU7xkzeLdu3WoEHNSBmZz/c7mcKvps0kR2dnYqINTd3a1sPnQneJOAfmpqSoX4xsfHHU72Jh2r1xjTQ9rcHM7mKaUzW2T+JDjQjTrHxsYcfm/hcHjGd+tf/sWe05LJly6WZ3v+eeB97wNuv93Rh2NjY1iUSuGtRfaedQtNYMXBMD3+OH71gQ/g0kgE3//MZ3BVbS0wNOQYDz8aG3OEL9Xnf/hDPH7dddgeDuORSy4Bjh7FvTff7Jn5KD/vlY1IXV1JsPjII8AHPmD3Q7nthRdsF3nLspmxMpqRIduwwT7GP/6jeunwgw/iBzfe6HiflPOEw2HEQiF8OZnE0xKc+v12WNDglQWYBf/6TyKRwOjoqOt9eMWCrWIGwm/nzMG/f+ITamGQO2Y5gLlopVIpFT6RExezxCSFbsomCofDLtPSbDaLXC6H0dFRdHV1Of4mKe58fsZdnQO3oaEBCxYscGWq+P1+vO51r3OJp1n0t7+/H8FgEJdddpkxhMYJUy/qLIGEo7GqeXGnBGAmjHjGGa63c2Is5W5MJkcCSYYQSUfL94VCIUxOTmLVqlUIh21ROWsf6ju7yclJ5dsiWTSZHCCLZIfDtr+KbK2trcb0/3Q6rdix/fv3q1IW+qIiGSyWg5HgliByeHjYIeJOJpOq9p2+CBUKduWA9vZ2B2vAMSEz5U477TTlrE8gNhtLwGPKYsu6kJksGMO5J510EqamprBhwwZHmR46+5uSDsjqceyZ2BTHhPmnP81MePPm2ank2ngzlVGSi6/UW5lCI/w+sj0mZossMnf3iUTCAajYDzIV3u/3q9CaqRD9jh071HhmiJkMlkk/ZQIchULBeL7JZFK9vmnTJtUXZPu8ij7rJq979+7F9u3bMTU1pUT2BIOsz6izilu3bi3L5V0P83V2dqoyVPp9isVirg1iMpl0baT1Rde0CJuy0HD0KHDOOfBM338x7dOfBiwLTyeT+CuReNAciagxfbImE9HBj+eGZM+emefiP/+zJCssQSg3qHIuetGu+S9Xu/lmwLLwfEMD8kWgqjeK/KmtlLpKdT2vf73dPzfdBAC45X3vw/0VFfhaMVN6bGzMkahE7dgv/umfVN9+u6ICD191lbMygKH9v8ds3XMP4PPhhTlzcFIo5Oh8uWM2DWBqOajVkECMr01MTCCXyyEWi7k8r9rb2x2ZcHLnxhs6G7NF1omibDIlJmA0ODiIdDqtCkjLB6azsxPRaNRVnoVhMJOn2NKlS81ptz/8IZRtBl3kb7/dRvrvepfr7YXCjCmrPolMT09j69atqkAt7wEnA5payhqCS5cuRWVlpeO+8JheLtOdnZ1oaGhw6KVisZjSG5jYQj3coGvlpDcOwVIkEnH1I+0c5O7b5/NhcHBQmY+S3ZG7WQrmTfeGrIop621ychLRaBTj4+MKsFJrRgF5KcdzfdEOBoP4+7//e+TzeUxMTMDv9ytzVxnSJJDiNZqSIryYMWa9kYkysSmqPfaYXcGAZTPe9jbHn0u51psSQUxlfXi/qDuSmi1e7xvf+Ea1OHO8EiT19PQYtSxc2Hgs3b/HFEJdunSp49lgIzsuGTVqtsish8MzFQQWLlzo0sDpDIScG0ZGRhyhfjlOZQksycCSSdbvbyKRMG5CZmO25AZJD8nSaiQYDKK+vl6ViSo35FxW+8d/hJ5J+JLac8/h2WLm9h87O4EjR3Dw4EHUhsM40t6OPy1ahFXt7Y6P6AJzL+3Uo7ffjscbGvCbOXNwfrH+59jYGPD5z9sGq4ZNybFkI76k9sADNnC94YayP+IAxUWQOqrVlWWjhldGAKRERIHO006z7+c3v4nDDz6IRxkK3LHDOCb7+/tt0mN83H7fzTfPaPquuQYYHrazDy+7zB4jp5wCvOc9wH/8BwC34J9GwnoCgHzfK1OzdcIJwF//NWBZuFrL9JK7BQ402clSRLp//36lxZJp+Ol02sEmmMATjS0bGxvVIsQwEkETqUqW0KHYman7pkVL123RJJX0fSwWU0aZZOmmpqYQjUYdolmyOLImn/ye9evXm8WKJ58MhMNqh4Dnn/c0OyWQ1XdTMpRFLZYsIaIviqlUCpOTk6rURH9/v6dlgGQnJyYmEAqF4Pf7MXfuXEfNO4YfTQkCnMRDoZAy2pSCafmwE2Twodf7kQs2w2nhcNhxvSYgyl2PScckQ1UyxCJBKkFbTU0NGhsb1SRt0vNw8afujZqLcDismAqK1xli0kNuMpxoYrDIWkhmlgt2Om2byZqE1ADw2M03Y/pzn3OOw5/9zJ5eqqsd2g9T9mskEsHQ0JARsJgWfHoeMRsxl8uppAECfc4JfX19RlabxZN1o2JTkg0F3V71HE3MVjhsawx17ZnJUoCv630rmT5p+RCNRo0Ce9PY3r59u3rdlO3b3d2NxYsXY2BgQIEluuFzfuT8s379egdrH41Gcc455yCVSqkszC1btjhsHjwF1VozaXBkyEvPhMTvfjdjL1As4XI82g9uugm/4UYaA4hjAAAgAElEQVRhzx4AznnLJLfQw3pG02n9Og8dmgGLF144KxMza3viCRtMnHmmHdX4xS9m/cjh++/Hb971Lhzl9WYy9loBjzBfselO8ne99a2AZeE3//APql8IoMgMmSIafI8iVrZsAUPDR6hla2lR2Ymtra0OXzh17B/8ANi71xnxYVjT9PORj6hj/C6XwyPr1+NwsbSQ13Xz9VdmNuLy5UB1NZ6trsYpBsNHlsoIh8NK3yQLz3IR4wOgO6/LG7x//35V7oYPQFNTk2OHnEgkMDIy4nAmp59UY2OjEivLyceUKdjZ2ekKGRKcyawk+j5Rf7Zr1y4XO0OPKWqfyPqk02nFODHs4WAYfvxjl4GpVzOFaFlrT54LDRhZIFm/X/Pnz1fnlc1m1YMgs/+SyaQrq0uKNQOBAHbu3Im+vj5VF49gSp4L0+zZd3pGqu4ZpftO8bNkQOrq6hwTeUtLC2pqalQRYBNzNXfuXOVxxLqEvA6CoBUrVqj+NYFUAnyyZAQ6XEhZZigQCCAQCKCmpkbZOQwPD5d0tdfDFJIppu2GF2uhu9azlAoXe7mAjI2N4bt+P56ZMwdnxeMz4/Do0RnPHuGXVyg4C7czE9HLpJiMsCmcxuNdcsklCIdn/PE6OjrUuGhoaJhVbC6lC/Rp0++3l6Cb90WC/Xg8jh07djh0Uvl8XvnoyeLBElQxOYH1F/V5hGwzw0mzbfKy2SwuuOAC9byaFj6OQerBuFht2rRJMdrBYBCbNm1S/a3rfWZboCSY5Hv0sFJVVRVWrFihgF5ra6tiGeUmSmmkxsbssVVujd0yW6FQwP+JRu1jV1Xh4N13Kx9G6uRkeNTL0HRWkHn77VBeT8x8fe45YHRUAZ5Zz/XQIdWvj951lxNQhELAFVcAt9ziFq0/+STuPv98PMYsR8vCz9/wBuBXvwJQuiyPKfz25YoK+zs//3n1Ps43XgCfvm/UVRYKhRkz0eLP7T4ffvK1rwGYmQ/0Z1BuNh2avg9+0M4+7Omxhfc33AB897t2X0sPMWZ9dnS4s/gN7ZXrs3XLLfjdN75hpF2npqbU7lwKmql3kSBhYGDAld6tF6mMRJx19ORClMlkcOqpp6qMRtMOcd++fWqC4wLsRRnrWVNeE9yZZ54JAFi3bp0RxNBjav/+/YhGo2hublZhS07M7e3tjtR5Vzt0yGa1SpTxYdh06dKlSCQSOPvss9Hc3OyYLOjFNGfOHGP4ZWhoSO1kI5GIKmEjH4RNmzZ5WhMQXNJKYvXq1QiFQliwYIGLtdIXoUgk4pjQOUlQCK4Di1AohA0bNmB6etq4AIXDYWUDYAJJ1KXpnxku+rbwd36OyQWmSae+vh5+v98hiJelXBKJhJHF4GROsO/ls0NQkUwmld5Mht9l6j6ZW6+dqA5MCJCeXrwYsCzc/5WvOHUoNIzUDAqpJWptbVX18EZHRxWw4maGTvmRSAStra2uTYVM2vACs15Ze/q4ZlijVGYjQRFZnM2bN2NgYAD79u1T/lm0RKBZLcc+QxP6uGUFC9P5m8Luo6OjqK+vNwrsTd59Q0NDju+lLYTcGESjUWP/GrVSszSOS86BnKdkCJ5aTrnJMrGwpntBJumx226zNYEf+1jZ51Zu+8mZZwKWhZuExkyCp5IC83Lbr35lPxvxuLIneOKznwUsC8+eckpJW4vCoUO47sIL8T9z5qCRYm6/Hzd+8IO2cfX55zuB18KFjs8/X12t/jZpWeiyZgxKTWCKfwPMwvLPFpmxX192GYDymS2pq7Q74AlbX7x2LfBf/4Wx4nyiZ1fKZ3BkZORFjVPVDh4Eli61+6O21q6VWeKevnLBVrGZaNehoSFVm02CJIbvuIhVVVW5FgiTuzaNCRkWlA83Fxt+j67vymQyaGxsVBOcBFm6VmHr1q1qoojH4xgfHzcWLqWwmnFi02RLoCZDOl6hvEgkgvHxcffoeOc77TTeInovHDrk2nHl83k0NTUp3xwuxHqJHeqBGO6k1xL7judCrZK+yMlMRTZpWkpWgNcsF8BAIIDBwUGVWq/fHxlCDgQC6l6aagUy86tQKKCvr8/l10UWlQsm9TVygTJZjOj+WFu3blU6PVY0MIFuafUQi8WQy+XQ0dGhnO29bAIkm2VitkKhEObNm4eKigr4fD7U1dUhFArhvPPOcz1XqVQKe/bsQSgUMjJ53InyGh1eT/X19vh6+GFnduKnPw2ceKLNQmiNGUXUHMlSIqaMSAnyuCik0zMmxaUyJZk04/Xsn3jiiY6C48wG5Hw0MjLi6YSui/rJCJiYJ31ekSaqJt8p3beN4NFUDYGMrQTQXV1dSKfTmDdvnuP1M844Q8kaZNjO5PKut1ILm545unPnTtc8lUwmXRsVPRTH+6Zvmvk3pZEyZLu+5Hb0KPD2twOWhXu//OXjrpFyMF6ZjP3c5PO47rrrcHYggENFndIfUymj7+TX9u3D93w+BZY+4wGKcMcddkjxxBPtot0M5f/ylzZrVFmJ07TMO/Z5qbI8JjD2V8EgvrVtGzoEaytBOzNK9aztcjR8Joaw3NB02e33vwdOPXUGnMZiNmNqsI14xYMtwNmBBDD19fWuYsMLFy5Uu3tqOvr6+lBdXe3QN+m2DOecc47yTxoZGXGV+QkEAli9erWRrTLtkGWohaJqggkeU2ZPmCYXpttz4eZkJUXNckHk8bmwyD7LZrNIJBJOSvvpp22xcnEQPVFX5+hLvlfuPkyLdjgcRjweR2Njo9J3UTci9U7s+2g0ivr6emNSgwk40JqDjJSJVchms1izZg3OOOMM12IsQ2CS6WIIw/SdGzZscDhcV1ZWOhimcHgmw6+/v1+VAqKuxrSYynOgy3o4HFbaMl6fXNT9fr/jnulicS9BeSAQUKFl1ukjOF+0aJHKbpTXU1tbq0KeumO8TFYwCcG9mK2FVVU46vMBFRX40W23eWZY6c+6V2g1mUy6wLQEcCwsr7PLXswWz5cFuMm46E7vzE6MRqNKPrBlyxbs2rVLhe3YjwxVmOYKMpz62OfzaRrbmUxGeWzpfcHwlW75oDOT69evd2UjAjPg6MCBA0aQxEzN+vp6NZ8ZEx8AR+kcR8jGoy9MCQ6dnZ1obGx03V96R5XLbL0sQnHZShhs6yyXybLE9B6OPc6/D7z5zYBl4ZnTT0c4FIJlWWi1LDxXnK+fKjJF8ntViNOy8EfLwscsC28ugp5oNGpOmpLtoYfw9Dnn4MM+H86yLNRqQG02ZgvwdpIvBYJk2PjlKnH0otp3vwtcfDHwjnfYYcy3vMUG242NwEc/6nr7qwJsscmH1ivEQ9CRTCaxfv16VQ+RAk1py5DP59He3q50IgQU0g2eD3EwGMQZZ5yBc889V+kYIpGI0omwDpteLJl6Hd2Ec3R0FIlEQu3wuCAy/ZzFXHVLhWAwiN7eXjQ1NTkeTukNxs8xtCRDEY6BfPToDGK3LONkL9k8E0PAxZ1slRcL5/P5sHbtWsVQmYTl9PUx9Vdvb69xJ0t/Kh0M1dTUKGNb2Wpra5WeqqqqyuGrRBAk60xS2G8CmZ2dncaFhEypFKonk0m0trY6MsD0/o7FYg4LiaqqKmV9QCCYy+UUm8jsNXkcirJZ85Hglg7zMjtW96UifU/2T/+bDIPTD42Zv1zs5WJ8a28vYFn4QTTquUircVgMl3CMmcZaV1eXpxkoQZNktiS7rNteyNqmAwMDmJqackz4+uLH8DSPx42UaVxwcTWBKi99F+cBfewTVJGhl0WPTYs2/y+9kwC4GCpeH7Ode3p6XAwBAToBFJlWfR7xmov53aa+YB3PcpgtzrfcDEim06jZYjt4EPjiF23B/J+hMQTORCkm8TBqkk6n0dvb60psIKsqr7szlcILySSeS6fRGY8jYFnIF+fpn1VU4C5Na5XP5/Ga7m48uWsXCmJO3ydA0eGf/MS2U1q1Cshm7Qx0ZqX/+td2SRzx2RcsC3t8PnzlS19S31NOWR5PYHU8alb+udp//7ejL1RZpT/9ydZ4aS72hUIBlmVN49UCtvr6+tROPp/PG0NGekmUtrY2Fe4ysSNcMKQQVQ8DUQwsmSkaWRLUpFIp5TpumnTkDlpqNqSwt7q6GolEAitWrFAaGd2bSwro9e+hyFZnVrhgG00miwvit6qqHC9T+EudEzMkTSApHo/jtNNOU47iJn1ZMBjE1NSUQ8BvyhDM5XIqA4+to6NDied1ForMoX5ekUhEfR8ZQFPJn1gshn379qkwmQ6AcrmcsQh1NptVoT9WDJC1DamtIsNHbWE6nVYJFnofeem2CNIqKysxf/58BW5ZO0+vM8dNAzcR1DSaijWzn6nhymQyqKurQywWUxYFPH+OU4ZsmRXa19dnLMjLqgUP9/Z671YPH7Z3juecA6A0s0VAztA8kyG4UZFldGh/QJASDodx+eWXq4LODBPHYjEVCpaLuN5XZER5TuwrE6BiGS8TqJqcnHTJC7hwLVmyRDHFurWEiSFh08XmenYeWae6ujoHSNdrxUomdXx83DjHjI+Pu+YRU0FyWTrHqy9YIUKCJZNmi7UhyTbOmo3IRm+mohShnKYDBcfvBCYen9PrSXLzLDfLJjZOGkSzdXd3Y/qqq3D4vvvszaNl4Yhl4RHLwpKiLlP/fvbx9VdfjfdUVODDloWzLQuBQMAGRddf7wQQlmVnpz/zDHDXXXbSygknAO97H5495RQcpbj91FMdwvxjDtX94Ae2zcv731/e+493+/3vbZBUbKbnSWrtpqen8fvXvc6+9rVrgW9+s6SumQDUsqzn8UoEW/oNHRwcVKEPr5CWLhrl4rdx40a14zjnnHMQDtuZe2Q2JKulp3d7PSCmArQm3REnHWpDONHprIIJCITDtr+R7s1l+h6GG4aGhow6MEdmh2zPP48nrroKWcMuU4rtdYdtucPu6urC6Ogorr32Wrz97W936Y+SySSWLFmiNDIstXHxxRejvr7eWAx4tkWKwHliYgLBYNAFhtgfa9eudSykNTU1rr5h6EkuGIVCAa2trdizZ48K+ennRHd7nh9Dugxn7NmzRy0o+mdN4TgTaxMKhTB//nylV+JEzrAwmbf9+/c7Jm3dRsFkgCpF8hyD3ExQ2L1v3z4MDQ1h165dSKVSWLJkifJo4o68trbWzFy98Y32NFLKOfrGG+33pNPqJW582Hd8jiORCLq7u5FKpbBmzRqkUimV4Svd3aU+KJVKYceOHa5xzxC+KenFZH+RyWTQ39+vwFWhUHCZ+eqMjpcL+vLly5FMJh1WCKbag3rGokmAze+Q94L3kA78HJ9e4E/+zmSaoaEhF/jPZrO44oorjpnZMvUFgfts2YgmO5Gy2+bN9tj69KfLerueaUfNLH8/nM3a4/TOO12fzeVyCIfdvn9yTdLnGMA7m1X28XXXXYc3BQK4q6ICfx0MehZ51se9rM0JwLbB2L0b+NrXgO99D2hosPvnoovsv997r12PkO2WW2ybhJdqlMoqJXV1NmN0jAyXF2s7q47w6FHgE5/AUZ8PT7zmNSgcOmTM7tWd/wOBgLL4+MYnPjHrucnQKl5pYGvx4sVqx1VVVYXVq1cjHA6r9F+aAVKbQD2QafJgORxqr1jgN5fLYcOGDY76id3d3crXSfpI6cCmu7sb8+fPd03ITJHWJ52pqSnFDuliXgDKmdw0wfv9ftekZ/oeGmCaCjy7MjsMTZ8MvbKgmDbf39+vGLtwOKzEzNls1iEiJjCVWorp6Wls2bJFZZPt3r3bkTJNZoi/S4BEEDQ+Pq7+pd5InidNE2fTT1Fzt2fPHqX3kqCOlh66zo9AIBgMGnVEra2tGBoaQjAYdImfm5ubcfbZZyORSKixS+sGnUFlQkEymTRmzkk7Arm7LccAlawVwZ8Ms8manwQ769atM4Z5JRPlmPh+/WvbZuSFEoVn77vPnmo0R3IdcBw4cMCl6ygUnHX7ZJjTa+HisQl2TawM5wx93Ovlmnbv3u3SvkmtkryOQsHbgFK+TmCxefNmRwivurpaieu5KESjUSNbKUPQ8XgcixcvNobyTMWda2pqFAgyASiX9rPYPEvnePRFuc2LaZqensa+ffuwfft2XHvttWZh/kc/ao+tMsrLmPRI+s+/k+m5/nrX51kppFQig9fGvVAoKGkAExf0Pi63746pj/P5mRJuXjq0l+rxBdgSgZaWGTbtta+1AZg4Zx1wT01N4corr8SuXbvU882IjnzeqqurzRu9J58E1qxR3/lAKISFmqTGi7SpqqrCkWIW4hsSiZJ9qScN4JUGtiorK0sukGRY6PPCRYo6Ehrubd++3cE2hEIhTE1NIV6sji61K4WCnfHl8/mUxmt4eBjj4+OuCY3H0hmNdDrtCilVVlZiwYIFiESchZBlORGZ3aZfN71C9AmV3yNDHxw80meLC3WuDCqdD+r4+DhWrFhhnKCp8ZA0/zve8Q7XOaZSKaUZyWQy6OvrM/oJrVmzBolEQrEzBKOhUAh79uxRQFUHUvL+SS8j7liCwaCn11kwGER7ezvi8bhKXpAPsEyT379/vyPTNJ/PK4d+CWT1hTkWi2FoaAgVFRWO8CSTJ5gQQDDFa2d25Z49e9SYbGhoQFtbm9G+QWdiOWnzWZDnRVaSY+bEE09EMBjEm9/8ZuPYk/1eyvbAlJFYdiuW88BJJ3m+xRQG8wJWNBuWTT8vnivgzcqEw2HH5oPhPII7hlhZdF3ahZgAlS4gX7NmDQqFgstBW2qSTCy31KhxN266VgJ+GtoeK7MFuAEUz9mrvaQ0e7iBgqxzJ5nBxYsXq7FM/zTOBQ6Qt3+/Pbbe+c5Zv9uUaaf/fI4Gn8WohH7uUvsLzPQpNWfyGvTEBsn2/VkF4l/84uwFoh9+2GakXmQrFAq4K5ezRf3por9eKgU884zDooW1Q7u6uhRRwuegUCgYN5x8LlwbvXe/WwGt32zZot6vZ/d6ZSo/8ZrXAJaFK4uemwBs64n77lP6Ul7bK5rZCgaDjounsDSXy6nJqaOjA4lEAuvWrTPu2Kuqqhw73ubmZjQ2NmL79u2ehVwjkYhyACeTsWzZMgQCAZcuhsyDnIx6enqUSJxASl+0JFiTmoVwOIyVK1e6Snhs3brVJRynbkV+D8X66XRahSo5EVVUVJQ9ATLMR38iE1OnA1/qe2TLZDJoa2tzuN2bSoJQoKyzC9lsFhdddBHS6bR66BYvXmx05tfF3Xv27MHy5cuNWhF6UzU0NCAUCqGystLo8i530qbv498zmQzOO+88dd+oW5OCbJ4XAbW0zaBeR04UknHgw2yaaAj0Vq5ciVAopK6JJVCof2EWIRMopKUDE0R0zUg2m3XUv/NKtWd42sVsHT1qGzHO1n78Y3uqaWoy/lka4JYDrEwiehOzJY9FfylZ+F0XoOvnZMrik8CJ2p2xsTFPQGeSIlRVVWFoaMhTAyWBI+esUsxWKpXCpk2bHPMBwSoBo/RT0xmCFwugdD2V7oiv66x0mwxuJnWLmYmJCfXMeAFIda433GCPrXe8o6zznY3ZupBgq6vLGAobGxtzMS9r1qwpKxvxzwqwtMZ7dfOXvoTDmzbhlxddhMc//3k73Lh+PY6Gw3jB58Pj1157zMfWQ7PjV1+t7IYevftuz1A85ynOS/l83rjhpEbStdG76iq8UFlp369QSJkn69m9XszWH/buxeMrV6K2uLnBs8/aLKBl2f5nn/mMGgOvaM1WRUWF4+KlyzrDGQwzeumXTCZ+fr8ftbW1rt1gc3MzVq5cqcTGtJQgi9PU1OSYIJqbm9Hb22sEeXrqcn19vVHIbxK508RQMjxcUGnqGIvF0NDQ4LpmZoWZJt5w2DZena3JRcjkWM5ixvq1VFdXe9LjcvdB8T8wM4kvWbLEKMYlIyeZFWqJZFkGYGY3n8lksGDBAoelxsjICOLxuGKSKCDn93iNH5looRe71r2YGIpjgoSJVTJlWnETsGDBAldiBwEtNVm005CarYGBAQwODipBdzabRSqVQm9vL6qqqtTzMjAwYPRq6urqUrYcpkVrNmaLCQpNTU2u2n24916gqsquPVaqPf88wJT1w4ddf2aYr9zsRMl4edZohNk8VYbyvIwpdY0U7RCojdKfATK8JvB0zjnnGJkpL3G6zmwxRC+BU01NjUOzJS1cTH5ZU1NT2L59O/bt2/eSFnwJGiRbIRk7RhwYBuU5mhhKfVPHTdfo6Ciampo8x4QU5uNLX7LH1fr1ZV3DbPXwxkZHgblzQf8rr37QM0H/HE0XeHvp4fQm71XHvHl4UhfQF39uraxEfSjkqRfzOicdwIbDYTy9ejXwlrfghwcOeAIojiUJio6J2QLw6O23YyIQwBOnnKLeb8ruNRVG7+zsxNyiVKJ4McDq1c5SP5s3qw1lofAKzUasqKhwLPB6uE5fjEwLW3Nzs+sGssSEDNtwx8RJgQvU5OQkcrkcLrjgAmXmyB0W9RL6Is3vkIuxqaitDBfIJs1R2TKZDN761reqc0smk8bQYjQa9QwpZLNZbN++HbM13aNr3759aGxsdAh1w+GwAwzy+nRNk8wM4+6D4QAyCQyp9PT0qJCF9BHzSgLwApVc/KkrI0jiMV/3ute5julVWzIUCilPpY0bNyrDXF2jw3stx6EpU3b58uUuIM4sTYZvyJCSDSQTxkkgFAqht7fXoVniNZYCRaXc0gkS9PvHOotkRGQxY7LK4XAYc+fORTAYVOFbFcb513+1p5D3vGfWcYfXvtZ+b/GZlq0UsyWBFYEX+0eyCdR/MAtRhrNZ6keWGNLDGvybiSnl2OP9k40eUV7g6cCBA56bFD4jHBPz589Xmi3d6kDqmHTWyMSc6BoZnWkpmeFnaDorRWNjfU42MQgylCabrifjs3JMzNYXvmCPq/e+d9ZrkH3jmY0I2JsHy7JLuBw6VPZxj/V7+Vo55Y+4MZYCbwJbJkyYSgdRu8goSSoUwj9bFv7FsjBhWbh5zhxc6fMho4GlR/N5O8PzmmvsEKRHP+ih2QrLbYJaitkCZmQRrDWqM4csBeelRx4bG0NNEUuUyu41gVXPsf/lLwPBoD0OVq9WgOsV6bPF1PbGxkb4fD6XeemCBQscDyJ3czL2rbNOLGzMOm5S/Kzf6J07dzp2Zrrn08jIiKeGha+zxlwwGFT0vdzlmSYKkw5MOonzfYlEApdddpk6ZlVVlaLdvc5nSggSvVqhUFAeWNy5Mxy2aNEihws1H+CBgQEsXbpUfT6fz6O5uRnDw8MOcMzdB8/bNEHu3bsX6XQa1157rVErRxBGEMD+bG5uRlWVXSewt7cXDQ0NCtjpY4B9KXV2jY2NymqD+jmfz+fy7iIzVSgU0N/f7/DwkmE206LsVcbH7/erxSaVSqnyNNxVlwqHkc3Ty1GZiny3traq5JJly5YhmUwqYELgQYuNCy+8UGUgyiydaDSKwcFBVWDbK4N2enoa2LXLnkI++MFZxx2+8AXgyiuBBx4w/plAnM/RkiVLFIPH89dT7jkWVq5c6QAt3M3qDGU6bRuxmiZ/9ruXviOfz6tjmpitQqHg2kxs27bN8czL86a+hzYHpbIRTSEpbo7IhkutqL4w8zmm7xOfMRPYNDXTWDdlB7e2thqzpFtbW5WtQylmKxy2S3qRDeaGU25IHGCf7cknjysowuHDdgmXtjbgkUdKvrXcEOHY2BjaUyn8n9ZWvDsWw51/93f4xt69LtBPvZe8n6b+57jj/MZ1RDd8laG5fD6P4UAAz1kWhi0LSywL0UgEp4VCGLUsLLJmHOMPr1rlZr8aGmwtm5AOSGbr7ywLD1oWFoZCLqAoIwdSs8Vn5bTTTntx2YjHeB+Oqd1+u607syzgQx8C8AoFW695zWtUWR2veoQTExOO330+HzZu3OgIXREkkNFKp9OoqanB8PAwBgYGEI1G0dLS4rgZ1Ifpdgv0yOrs7FRAj0Akm806BI7Nzc244oorlIFlIpFQCzm1V8BMKKOxsRGhUAi7d+/G6aef7mB9tm3b5trltbS0qCLY5513npocdXaC4b1Vq1aVNX5I1ZoAWzqdNv7NVCSYfc8Fzu/3I5VKoaamxphd2dLSos5ZZjNJ1lFq5NatW4dQKKRYFbqwb9u2Te2W4vG4S4e0bNkyVFZWOvp33bp1DnNThtSo39PDZl5aromJCQQCAVUqRz/3SCSC9evXK8809ose7mafypArGx3S5f2iFqsUs5VMJlXIMRqNOoxcudiOjIxgYGBAlWUyhVw5YTMkWdJf6QMfsKeQgYGyxp4+DnVA0d/fjzPOOAMbN25ULB/veygUcvQB+95Uy9FU3od9Ozo66pmIoIc15P3Sk2N0zRabzMKVO21TNuKxMErd3d2qVBYZDVNWsglISuNchqxL1YvUmwmAmiQNpZgt1rGToV8CC1nnjmOj7GzEF9HKWpgPHcKjP/pRSc8z3RTXC7D+4WMfcwMXy8KnAgGcEY/jwU9+Evj5z/Gj2293AdB0Ou3p0cUwq54wYZo/qqqqMHnTTfgkNU7Fn8fF/98kmK0n+/uBlSttU9STT7ZL2PC9Z53lOJfrrrsO51PrZFm464ILjH3ulY1YDknw/1u75Rbg9NNVke5XJNhiyO7gQbuQtJcmS2aPsXYdGQRmeZGClCJLLigMN0m39UAggE2bNjmyB6nPIttDZovmpvX19SoNmwaBmzdvRnV1tYth4zkePHhQgS1OzhS+6xOSZLYmJiYQjUYxOjqqGKvm5mZkMhnHoF24cKES9pfamcpWaue+fPlyh2AagAKeLOvB+2ECx9y9bN++3fV3k+idx5Eh3/7+frWYSHARDAZxbVG8WSjYGWMEZqbwoIld0o/p9/vR1dXlGnd6GjeF/3pGI81ng8EgKisrlZ5penoa0WgU0WjUWLqFdQVNzBzracod3qpVq+Dz+Vz1++LxuNKOMZRAfyx9jBGwzeZbxwmbvkAlma0rr7SnkEsuKWvssZmYF6W+s9EAACAASURBVD2Lkt54ZGOoT+P9Z79Jga28Bi440mcslUrNymzx/KRGioavUhtlykbkueVyOeVdpru7H2tRZ9Mmh4XVTddeil2i11xTU5Mn2Cz3PDiv6s+DbmKq68pmDeEdSzty5JhtC/RwaKnQVHMqhVsSCbxOMH+6nq8cwPrHbNYIttRPV5ddLPqee1ybbi+PrnKZLZ4z5+bq6mq0Wxb2VVbij8XvfzqRwB6fD4uLUQ2jZuuFF4Bvf9t2oP/Wt2Zen5oCbr0Vf9y0aeZ6joeVxP9NTSRKvCLB1pIlS1RtM6+YLo0euYPSXZkJNPRFZcGCBRgeHgZgZgH00KLpPZJqZ1jJdI4mXVZLSwsGBgaMTu/RaNS4wFN4Tc0MFx0K6smQSNBIILhz5061c52tzbZzN4X1uEAUCnYaeyAQcE3ymUwGq1evVqFehgFJE2/dutUokgyHw+pYBBc+n8+YuRiNRlUpDO7qWR+Qkzt3/LJ1dHRgaGhIfQ8nTBMzQL0AAV97e7vqZ5NGRQqZE0W/Fkndm/o7kUhgcHAQiUQCu3fvRiqVwqJFi5RWjNcq7TcYWuUzw3Gs14kMBoNobW01LqZNTU3GjFK92DEnbPaT7r6uwji33GJPIdr4N7arrwa+9z0UHnnE1R9emkc9xMT3SJDhFWKR7Dc1e2ROTGENL8+jUllmeiOIlFYFyWRSZYyaagqWatK+gq2zsxMNDQ2e1/5yMFu8Nj0hQdd+zZaNWE6TIn+CVlk029EOHLDDPP39ZR3ba+4zAebqqio8eeKJgGXhqRUrkC5qIssF+bI9etdd+A+/H38o6haPJJN4oKICP6yowO2VlXji9a8HvvpV47jn+ZmqjBDYUmJTatNt0isd/slPgFtvBZ59tnzQqwOpv/1bew7YsWOmFJDQ8v7f0rxAvtQqmzZJentFgi0Cnv3796O7u9tVMZ6TEwsac0eqswByYWVaLh3LmaGlL9z19fUOgGRKeaebNEXQJkbIVECWLMzExIQyV5XNtMBTvGiqpxcO2zUWTdqzZDKp0vubm5vL8tkC4NKWcHGora1V4JYZHKlUChdccIGieulBZdpx+3w+BzCdnJzE6OgoOjo6PHfpLFPD8OiKFSs8dXZk+nQt3GSxVuT09LSj//lZGQqlpk8XZzJ7NBqNuu4RfbBKsQGSPdBFqQMDA2qi1NkxXTNGPzivBZM+coDNYpjMVE11KclscTdMPRv9z7xqz5UsZHzkyEyW4c9+5j3gnnzSfo/fjzu//33XItXQ0GB0MpcLVyaTUSWbdO0U7yHHczgcdnhkxWIx7Nq1yzXZzqaL0v9fihXh+PYSdOvAsRyGi+NIBwema2ekQGq2ZKiTmkGOITkOZ9NsyfN5MSyUrsGRi768B7TVIKjX64/SrkO1jRvtcfXhD5d1HqVYff19wwsW2MdesAB46CHFRsvqAuUAN7axsTGkq6pwojA0NYF+6dFlGmMvJhvxZW2XXz7DaJ15JpS26/HHj+vXeI09E2Osv1d/brlhp0RIH28uP7ebbwa2bAGuu+6VCbaWLFmivKUkezQ8POwQKTOTJR6PG0XB0uiThnOyrEUymXTpUpLJpGux1hf3SCSiMvO8mC2GYHgzdfM9k11COp1WWjXWTeSDZ9I6ZbNZbNmyBdXV1UaXewqofT4fxsfHMVvjJDExMYHR0VGMjo4iFoth7969qjj2zp07HSFDDspVq1apfuA1yPAt+0NOatQmTRrqxbG2IBkp2U86M7Zt2zYlDPeaNGlGKcv2EMAAcGR/yYmsubkZfX19amHTd60UgZoATKFQUCVwIpH/r71vD6+rKvNeO8m5X5O2SZtb09zbpkmDBUHET4o4oIyXqVAsLUZ70fLERilDpxlbp9E8fu1M4kxmpNCq0xlb6PHzmSrVGeOoHTWKxgvWD48yyEVFLgccQJBLS/v7/tjnt7L22mufc1JIIX77fZ4+Tc7Z2WefvdZe631/7+/9vVEZFdHx11Nj6ubpVYBhknCgM1dfX4+dO3d6ohjJZFKioHpV28DAgHRkVaHcXbt2GaUBSkq5/O53Nq+jEEH5mWeA8nKgvBy5++47Y2Rr7dq1SCaTWLhwoYOTFo/HXT0RWY2YTCalJl1jYyOSyaSjilb/nolEQjYx59rDvqiF+k5yzpjmpu44OqQLihglTtTnjOlzPhsbNmyY8WpEr81ORwD1zf/w4cOudCMLilSSfqlzgOgxANnzFW97W0n3siQH6emn8cThw7iZlWgXXIDjd95p5K4WU4Qv5R6auEwvKbX6StiePfa9+rM/s/suCgFs2OA4xBTAlOooqml9tqfK5XKyTZuKGOtrli45ohZzlZLdymazNiovBHDttbPT2WIj6lwuh02bNsnoOhgMOqJ1LjamiDEajaKsrEwKTJrE/+i8hcNhyRGhc8ONOZ1Oy424paXFoYVFjZ3e3l4ZhXR3d0sCLz/Pi9uydetWpNNp1wa/aNEiBAIBHDt2TDowXudgGqmQuGE4HC7J2ZqcnARbJXFDYbuZSCSCuXPnGh0AteJx+/btCAaDWLRokWNRYEqK34doldquhhFqNBpFMpnE6tWrUVdXJ9MlfBA7OzuxdetWBAIBiVKxRyKrHgs50Lx3TM+a5B/4vaLRKLZt24Zly5bJRYDvEQGKx+OOTYNRENO+OkrIqip9vqpIp6mqsLu7G+Pj48aUJaUpampqZPWsiaTP5szhcBibNm2SkbBJYJNOoJ7imm7kXtTOPddebr72NSPywqIG3keKtPL/ffv2OTamiYkJvPe970UikcDixYtdTpR+/Wp5uZpSVI/zQhRzuZyx76SKiswEskWjyjorGIeHh2X640wI49lsFsPDwxgeHpZ/X0iLy8vp1nvQ0aklv5ZNxE1zWecPql0caLqT2traikZV7fuhh6aI28UU0rXv4kKOXnwRuOUWoKYGKC/HNz76UTxiWYAQWKugvV79H2eNY1SCTdsxf+ABewwqK4G778bTF12Ez3/yk67+oWow09TU5NDmCgQCRq4Ynys188E5ZuIA60gwC1VoXHP1wh9TdksGRbffbn+/t7xldjtbAwMDrn5xKg9LfQhVB4kaQYyWysvLjST7jo4ORKNRrF+/HuPj40gkEo62KaFQCG1tbYhGo+jr60N7e7uMyKhEfuTIEbnpssVPOBxGRUWF5Cix3F7//GAwKIns+oK7Y8cOVFVVobW1VV67qXyc6cp9+/Y5Kt0WLFgg+yG2tLSUlEY0KV2rHJdkMulKTalaXjt37pS8LdNkVzdRU9qTvRaj0SgaGhowPDwsyd68H+oDRWI4nRhWrpJHRPHPcDhsfFjmzZuHjo4O7Ny50zU+bW1tiMfjCAaDssyc6Rh+jooApVIpDA4OYu/evUin00YNJW64oVDIyM0juRnwjqZYYUfOluqoMrqPxWKOBZ8LYjqddi1MfN+r+XIsFnNwnIhSFE25nD4NPP980TkHYEq76KMfBeBOiYyPj2N0dBRbt27F0aNHXXwpr4jV9F0BZ8rIi9uko7CFvnMuV7jLADDF2VI7C6icrUI9BYuZaQMshAh4cc643qpIE6uo1UCimO6YGpB4cea80u9dXV0ukr7p/hZFtgCAlX5/9mdndC8B2FIGq1ZBpsMuuQQA7Io8IfDc299e+O9fReZV4VeoqEM19Tnj/ldUJuT0absBtRDYc+WVDifqfe97nyuY4bqnq/eHNckIAFJyxVRIpK/nra2txh7DpmzCtJCt737Xnhevfe3sdbay2ayDAM8vSW5Jd3e3kczOCWCKlryciKoqW9NGb8ljWZZMu7BVzPHjzipCcj9UB4dpM/bnMwlKEkno6uqSqAQ/myrtXDjVVOqePXskN4eOCSshly1bhlAohMsuu0zylPj9S3n4JyfdYpyseCGiZOKJcDLeeOON0ilRG4bTIWlubkZ9fT3Gx8dd5F5146qrq0MgEJD8KFMJP1OxyWTSNbbpdBr79++XzrGa8lX/Xu+paXpf7Z+pvqcv9AeVVha9vb3YuXOnC5nid1y0aJGxNJ7oGKsbKRuyfPlyVFZWyl6GnZ2d0olUr4M8PaYb1THn2Joci0wmI/t36mgbmxgDU9FcUWTr7//ebpGxY0fROQfAJs0KAbz3vY6XdSeqVJ0hFekxOVHq35jIzJTYKBXZOn7cTnd68WloXkgANzu1ybaXFUrZDQ8Pewqy6vfUpICvk+O9CPMmZ5Sm8peKOaim9LsJ2eIzpjqlDNg8OVuATY4XAjDIDZRsN91knyOVsvW1zj3X5iN+8YtARYXtiE3HnnrK3qCnaV7pNb1owGv+6E79ypUrAXi3mDJ9vp5yK7mY4sMfls7qt/I6XnSiFi9eDGAqmJmcnEQsFnM5W7FYzMWfIz1If357enqMLflMe5eX5AgDdq7/nG8uztbXv25/tze+cfY6W9ddd53coPV0VHNzM5LJJKqrq2UqiLyFK6+80shfImeHk0qdUEyT6AtKKBSS0hC9vb1IJBKStKwfe+DAASSTSQmPdnd3Y/369bLpMHkHheQR1qxZYxTzZL89VQ2d6SKTXASRD10du5iZNi5uIkQHVq1a5fgejNRXrlzpihLi8Tiqq6tRWVkpNxi1JFnfuPhdiEjxPpg2xO7ubiSTSezdu9eYwhkcHJRO0eTkpLxuFY1Sx1HvJKBz+dRFxJTCeOc73+k41uTg0TENh8NYt24dEokEuru7pUDnW97yFpdg69y5cx2CnUR316xZY0Rrx8fHXXpcHFsv7S6iuupcZysYIo6HDh1CKBSSWlCsqDWmTKjcXWKbFBWKV6+3WMrNi8NG/kYhnTI6HZ2dncZx0gm0pAqkUiljE2G1Aq8Qx0Y/htpbdLRbW1tlAc90UnbpdNrovKjfxctJPpbvgVpbW+u4l5OTk2hoaHDdXzZifynIFtcWbmRqUKY2lldJ+tSJI49r48aN3tWIALBli+0QlZhGdNmLL05V0S1dav9fVgbccYf9/qlTwB//WPQ0dIh++YMfyPPcky920c3kVNEhYDCtViWrjcsZHOpOtlewaRJO9pKI0B3nacmEPPIIfrFypXS4fqA5UWeKbAHwbJOl7iV0jrzSxKZntORqxNtus7+XjdrNPmerp6fHRbROp9PYsWOHFFUkR4eqtqFQCIsWLTJWq/EBP3bsmNxA1PdDoZCr315PT49sj6Efq+tNtba2IhAIYEc+klcROKY1N2/eLDWjxsfHXZ/HdhRU91Z/9qpENKV0crkcmpubMTg4eEZVKOqENPVvjEQi2LVrF4aHh7Fx40Zs3rxZKvkSreI56MRWV1fLDXn79u2IRCKor6+XY0vlavVeqzwYr0W9q6vLgfyp7yUSCVfLE97LQCCAQ4cOGTfpoaEho3yFqslkQraYuksmk9i8eTOy2awUuaSDzIpOveKQyB31o/T7rUuYVFZWSgdMR6KYxjSNu9ozUr1XTMUxeqNYqElFn841UbLh4WGXgON3tm7FtNI3P/whYFkOMrMJFSlVZ4hITSEnCphaVKmgr5OZTRvfxMSEpwApi3r0KFlF5lTkiUGXqedmNBqVNABeSyHH5tChQwUFWb3uKZGmnp4e2ZKpVGQL8OY58XXObZWzpTpQuVxp1Yj6mJW8rj32mI1EFTDTOXO5HI6zkXU0ajttrG588EHb0YJ3SpZG5Ki9vR3XsJG1EPhKZaUrENZ5buocKQVd9RLs3blzpyuFxv3Gq8WUKVg7Y2QLtsP5ViHwBmGr09OJYpcDNZhZuHBhSZwtGtdZFfVkRw6uxYXG+iUZUfn3vGd2OludnZ3GlEckYvOv1Mo+9qtTCcvBYNClO9Lc3CwRLQ4O0ZG5c+ca20XMmTOn5F56XFQIP5o4Bo2NjQC8y7YZMfJ4psmGh4eN2lUq4fz4cafWmA4JOybZ6dPA6KjdXiGvfqsaj81kMi6ncMmSJbL5MJG/wcFBZDIZz2hWTTUuW7ZMVhqqJPqxsTGHmn82m3WkT/v7+2W/QpWPo8picHF6xzveIdMjpuuIRqNG5XsuFI2NjYjH4445FYlMccJ04vvy5cuRy7krYKLRqERZbrzxRoyNjbkcfXWB1EnygO3I6z0Ve3t70dTUJDdHIlHU/GJLHVOPvF27diEej8s0G+cHNwUWZ0Qi5sIB3dFMp9Ou+/h34bC9fPzlX7rmltFOnLDTK9ocLAXZUjlb+oZP56enp8dBpNc3RxMiRbSoo6NDkrnVTVCXwjA5siannONv2jjVjaq7uxuHDh2SrxVL2XFNOVNkq6qqChs2bHD1/uQ8M3G21LE6k2rE6ZrumBVCDEs1Np9mwHfrrbfK1+Ykk/itZeHX551n91cUwm5EHQgAX/iCyzni2qQK3Kpz4md33IHD5eW4e2AA+Pa3HePDsdHXTzXoLMaVNAn2AmZkSw2GS0G2ADj2F9IYpiMT0t/f73Ci+vv75didaTUiTZV5eNkdqkL2X/9lo5U7d85OZ4s5VxP6E4vFHERiblLqA93c3IxMJoO6ujrMmTMHsVgMK1asQCAQwJ49exCPx7F06VKJikQiESxcuNCxwcRiMU8l4Pr6eqTTafm+zudpampypSr1JtUmeQR1YWxubkYikXBxTPg5KneNvSRVB+9YXl+KlVhqhD2utoj43veMc4ibjclpJEeNXKdgMOhoIO3FVVLRIXK6eG4ilaowa0VFhSt9qiJBNTU1SKVSkqtEx4hIpymXf+DAATQ0NGBoaMhRVKBX8DEtw2iJqVxunnTAyNUqVlzAlJdJwZuLok6SV1Ezcv/27t2LWCyGUCiEiy66yNG2JhQKYdOmTQ6u3Pz5813OglrFp1+76gSY0rc6utTe3u465qfU1/rSl4xzq1TTnagNGzY4+FhqIKEiT+qiS8mSZcuWyeeBOluLFi1yyDmo6UDqkXEuxmIxV4DEjcmrvZJ+r1pbWyUqbto41eeDa4HKcSqEbDHYUqtiC3G2CingT6cacTrGcaIYKTlqXuKw6oY7MjIiOZZqc24TYjgnmcSxwUHgueeKXo+esopEbP6X2jw5Eokg9+CDwHnn2fPasvDM9dcXDC5zuZyxnZWOGnF8Vc6SLi1TSDy7FGQLAFauXGncb4q1mDLds9WrV8tgORgM4rLLLjsjp+hPzWals/Wa17zGM+VRW1tr5KrEYjG5gFRXVyOdTstKsgsvvNCxCDEq3bt3r8tBITrA17j5dnZ2ykkaiUTQ3Nxs1L5SF0x98hN6ZY/AiooKxGIx6bToTlsgEMDu3btdiygfCC5Kra2t6Ovrk9eipvFMlX+3UCdGCJuXQMs3EVUXdl3/ilWBXg+82o6I77NKU1UfJxGcD7op/WvS1yH5PRqNora2FplMxpVG5SJgQhrYK5OtlJgu4+JBNEInWCaTSXvRVRYUtcrTtLCqjlRXV5cL2aJDzEIGjrm6+PF3teEur5s8lng8jiVLlkhZEhNykUwmS9oUilXqlYJsPZUviy/WqFfaV74C/Ou/ul7m5pzJZLB9+3Yp95BKpTAyMoLJSWcPOhadMGIv1uWhqsrc7zOTyRhTtHr3AeqbebVXmi6yRecnmUzi8OHDrk2zWMqOqZNiFIJiqS/TsaoTOzExgc2bN2NwcBATExPIZDLSeWLVKCveiEatXr0a0WjUkTKnFqKKCjH1qvKTWlpaPKkUrBxTx/Fnk5M4IQROh8PAnXd6frfJyUmkUikXETsWiyEiBK4XAuV5msrk5CTwyCPA//7fwM9/7nCOvNAmUwDmhc56IVvHj08VUy1dutSFMuqBbiGEadeuXXK/0R3sUqoRVftTdprO1Gats5XL5eQkU73xUgXuiBwlEgljNRtL5E36LQsWLHAsQuwyz2q8iooKGTEXgu1bWlpQX18vJzbFLLl4MG2UTCaxYcMG6cSxopGiq5FIBHv37kUmk5HViPpGQGfA9LBSUBQAkM3ipBA4XVYG5I8BAHz600BnJ/D440YeWGNjo0xzFoKyW1pa0N/fL9OiqnQHUTb1nhHR2rNnj4vo3t7ebnRmee9V3bNjeTV2tdck9ZmIQK5ZswZVVVNkf86RY8eOIZvN4v3vf79sRG6SQViwYIGjfU0kEpGEyUwm44ls0UFftmyZlJPQnSemvolSqlWsXkTjdDrtcGxNekSqNIdXCxEvZAtwBhtqmpzoEhtYq07AH+bMAYTA4z/8IYraHXcAkYi93HznO/JltWrOq2sAgysvZX0eA3gjSTr/sre3F2NjY8bxN3HniGypaAH1ujZu3Oi4L1u2bHGogkcibmHb6upqhMNhF3pHK5ayM71OZ2h0dNSBKE1MTGBwcBCbN2+WkhpqKodpWM7VtrY2R7m/6iyphSX8PsuXL5ffUQ1yOadGRkaMY3ZM0RfknIxGo55Nz1XEkPa5uXPBKjG1f51+b0zIVl0ohGw+GN1GZMtwX4shW8CUXA/vyeWXX250mAE3z029ZyxAOdNqxGLzxDenqc/NyMgIBgcHHWivetzk5CQeP3589jpbLFVXRfsY6dNRYSTX1NTkuAHkYjU1NUlCeltbmyOSa2lpkb3z1AeFiwc1lXbv3i3PNXfuXCnRUFVVhTVr1rg4Dio6xcbWXDy4MZo4W6oUBFNabW1tDsVvVpqQX6Cmv+hUmUiPao+7Jy67DBACz77nPVMHnDgB5Ht94YorkHv0UdcCwpYuxSqMWLrd1NSEWCzmQohUhIBcrYaGBiOaQAdS/3vd2SQaafr7hoYGHDhwAIsXLzZ+J/LGeD0kmZuQjUAgIBFUOv2dnZ0IhUJobGyUKKKaejQ5+qbvkEqlkMlkkMlkXA6Cl4OrbzJqyoHnLQXZApybAp+vzs5OWfWVyWRkNQ75MiqqlE6nJVn+3fE4Blpa0JCfm572wgtAQ4M979avl5uijqjt2bPHs/KS38NUlUittOkiW9ls1tgHVG+krHK21PScKqKqb246t0hFCAq2P9LOpWommc45NjaGgYEBmRrS+6qyilt1mDo6OmSVJZEltUgol8s5GpkXS2t5FSul02ksWbJEFpXoY6am1fgckGBfCrJ1/PhxNKfTOFVZac+tf/onzylIfhbXmlsPHcKDvb2AEPhlWRleFwp5krN158jLQdZRoEIOz5lylv6UzMT1K4bEFuLseXEHvcRYDx8+jEQi4SoK4v+UfeD4X93ZCdjO+r2Yjc6WHm2z9JyRTzqdltG/vqGrqAo3BEKtixYtQjqdlhunuugQ7dIfaCIgpoe9oqICe/fuxfj4OK644grHgkZnLJlMor29XW4YauUeJ4PJUUgmk7IdkY7chcNh1NbWOhbk3t5e7N+/33U/UqmUrJL6GitibrvNOVt//WtbS0YI4PbbjWrIvAcsUCAHiCXHXGjGx8fR2NjoWZbPJsvkHjU1NSGdTiMcDkvEimgPNzhy6cLhsENYlffOq7qQPCsTp4YbinpvSd4nf4zk80AggHg8LsnRwWDQJZrJatnBwUGEQiEkk0kMDQ25NpTu7m5jFZCXtlOpyNbx48elCCwXiqqqKpkaIWrClOPAwIBL7+m6665z9BicP3++dDRVh19vdcHnzlT44blZfPGL9nzr7JQpbMCNQo2NjRmdX6ZwvZT1VfSEaZdUKiXnEiVZKOdgSs0RVaf4q9r8uFgjahNpV99EVRSpENlalX1QuUtqIMZgS0dMt27d6umoeDlMDH7U52pyctJR8VgM5a6vrzfKslDeorOz0+jUmpCtyspKV3XxnDlzJIJobB7+z/8MSZf43OfMc1Afty98ARACp1Ip3Hn0aFFHZzop2dlmpaBgpmPI+xscHMTo6KhE/4udL5fLyewEnwOu/3S2WfikI4KpVMpYuKLrytFh0hvCq/xPU1DGNZhc3YmJian1749/BISAZVmnMNucrZ6eHoyNjbmqrcrLy9HQ0CB5P1VVds9B5vPVdCMdIa8bRxSCyNPg4CCi0ahRaTwUCmHFihVoampyTBYSmjkJuFCQqB0Oh/H6178e2WwW27ZtQzQalZ+lIhts/aOf+41vfKOEz9UJ1tHRIR0cFdnixmbidnCiP7N9O5BO22J9uo2MQFVJVqMFLr78fkz/DQwMYHBwUKbygCnyuMlJiETsPnZMj6oIIjcLOgX8OzpLDQ0N2LNnj2PjYLWOiV8Uj8elnAIfWv1a2IJITYM0NTVJZ5zoiAmhM3HTurq6sHPnTjQ2NqKnp8d4XSanpLKyUpKFt2/fLh0mbp6UjWDxRSwWkxC3KluwcuVK+dwQhTXJTbA3p7rYeF2rqVWU3uoC0NJyJ04Ap04Zm/lKe9e77PmWR4Np+nUcOXLEtRboyvZEvtXNOBwOSzmON73pTXKDj8Vi2LVrl5GszdRaX18fRkdHZfRLjadFixbJeZpIJFxIFk0t+eexal9WXisdPTrrxUjwhXqy8rnT5xWLI1RrbW3FgQMHPB0mplJ15//lQLbC4TB27NghnUEVFSJnq6ury5E14HENDQ3ymY5Gow4E0Yhs/O3f2nOsvBzIt/bytKefthslC2FeH19BMzkqJkeP92F0dBSbN2/G/v37PR1BE++Kr+3atUv2DjXNb8Cs+8YuBHq6ORKxq7O9eqnyXGr2plBlu851MwWiesV5oX2J55ucnPQsYiIfm91S5HPz9NOAEAgGgy9itjlb5eXlEplSibAcODoY6iawb98+Bz9rZGQE/f39nhyW3t5eHDp0yMFnmpiYcCEdRJeIeLBfItOLyWRSQvpqFEcSuFrloZKfE4kEQqEQent7ZQpRdzxMiJ3aA5Cvc+NnTrlgpPXss1IjxmVPPAGQPP/YY672DHqUyk1D7XWmawzRyVEd4WQyaUx3LVmyBMPDw2hsbHRJIFAXJpPJSA4J+TxEI3Xpi2AwiDVr1iCdTkstNNWxoi4aH0I9tceekiaErqWlxeWYk3939OhRR6EAU+Gcu/v27ZOCgqqT19jYKB0gIob8p6MVrLCLRKaaKK9bt07ea6a0TRuiqVekVyqO8gI6kF8XjgAAIABJREFUX4bomMmJ/PXwMDA0hGfb2vDhaBS5Bx7wnmuWBfz2t6632RJr/vz5KC8vl6ni2tpaufGqhHCO08GDB7Fq1SoMDAxIx4mdIZjWXr58uXzm1M2gtrZW3nfV0WVUvXDhQkcwp7eQKlbdqd4nkwPLCmfT/ee4qJpJuqM0Oenu4dbb2yuLQqaDbKkZA/W5CgaDRidInceqJAodLnXu1tXVIZlMOnpzFqtGJC9ycHBQjnfJKNJHPgK88512b0MP/haAqWBzxQpn4VAR0xFML30wBq3qv1J0xExODV9jcRUra1VUm+hNRUWFCylVgwGmxvganwO+x0KmQv1FOX/D4bB0lrz02XTEWz8XA+hCyKlaxWniorJwRX8+2tvbC1YCnxGy9T//M3uRrbq6OsciZiJSMrpX0aZczhb0rK2tlZPYi8PCwSafSa+6o+gm00G6LEEwGJQ8psOHDzuiStOAeZF89+zZY0R/Kioq5MLJB1BFI9QHh+iLVwqiZDt1CggGASGQu/9+Y1RQLIpWFxEuQl1dXY4FpLm52eUEL126VLYhYtpTvyd0YDhGRJ7ofHBx7+/vdzzUXMybmpowPDyMsbGxknpnFWpPwxZLx44dw/j4uOyJuXbtWiSTSdTV1TmU/9ljjj0zqRGnpsZNi1coFDLeC72iybR5ezlQJrkGpuJKRbaYWtMR1MOHD+PqeBwPKwKOaGwEfvYz51z79Kft9y6+2DUNc7kpzTJ14+DGzmqqq6++2pEiULmManCjpn5NCI36nY7lm7ebWmypa0djY6MR2czlvKs7aV4OLJ31lxvZCofDEoGnI2TibLW3tzuU8YnoUniYApFnUo3olXo9K3b6NPDkk8B//3fh41avtuek0nrLKzVMQvo73/lOh2OiPt9EjMkB0pGempoaR9rL5FR5oc26RIyJUqA6B6x4rqoyd7fg+loITVL1t0zzmpqAHR0dRQV2VcTbVJDFLMrZRraA6XO2Vs1mzlZ5eblrIuhl18uWLcPrXvc6ByLEnD4J7Lt373ZFXjU1NRIF4s03dQlnM+ErrrjC2Fg5Go0ik8nIn3XkSlU/n5ycNEpWMPVhIrSTsKlXOKkEfPVhGhsbc00stX2QtKee8o7uHnzQHvbqauPDRG4VuVC6w0LSKze3WCwmtax4Xbt37zaS2SORKX0yNn7WU1+FyvjT6bR0otQFUpXiIM+HUbvOg9NRTUYwk5O2GKjK92G3Ar1JL9XnuWgWc1DZ09Ir2qqpqSmq38TN21S5aeIymZAtpn90HSY6fOXl5fJ7JhIJ2U7GFMnncjn88Hvfw1O33AJ0d9tzKpkE8vdWzsMDB1ytVLz03aqqnGR2071UnVDT+0Sxde6Reh8ZNetRsfp+d3c3AoGASweQ7YDOFNlimkxNq5nkHVQnUkWZ9DWIqcqBgYEzqkbUfz4TU/s+MgibmJiQWl4TExPYv38/Nm/eLNO2/MeCDLVgQEeITIUGjmv+/e9Lv9jTp4Fs1kZd4UaU2F+wtrZWOqi6dJDJ2WXrMS/nnUF7qWjzsmXLUF9f73jNFEBxvjJo5Gumvq10lAqhSWq7q5lEto4fnypUU4tSuEd4cbaKFa5EIlPV1HpDeFPVb6nViHePjtLZmn3ViJFIxPGFeKN0pKjYRI9EIrKv0qFDhzAxMYHzzz9fVuwxElm/fr1ronJTjUQixv57qsYSpRwqKyuxePFi6eCpBOpwOOwqHefEMDVSTqfTuP76642IAicp+2PNnz/fkUPmBGtra5PIm7TVq23O1tGjcNmpUzZR/sc/9oyoCO17NYvVnRV+R5V0ffy4u5m33qqEn7N//37U1dVhcHBQjpGXYzI+Pi4/S0UgWeHJEmqSKfnQEXUkJM903iWXXCJRDDpYoVAIf/EXf+GJjqgLSigUcuT/CWOrtnz5cs8qz5eKbHGx0tOn5eXlklzMBYocMG6w5DAySk8mk9i+fTu2b9/u2IBMzaEd9txzwJVXQqpvF9De4pw7dOiQEXlTKy9N91IVjDS9r3ZdeCnIViQSwTXXXFPw2VSrO1m0wPlPRIMbk95f0gv9UZ2IUqoRmWorlMJSK7MKpbbUdB6vkZvRtm3bsGrVKoyOjjr+jmkpprPUVGIxHTmmK9UqSnas0M+nInNEaM455xxcmkzi+Xgc+PKXPedcsbnI8eXzReeosrLSVZRjSuN2dXWhsbGxINLT3d0tOZ6qebWjouDy2Ua21PkNmHXftmzZIhF9E2fLq1G7fq7Vq1fLwjJ2UTlb1YjTtn/7t9nrbFmW5RhsltmrG0Z/f79j8nppI5WVlaG9vd1BwFNJ5Yw8TI7D4sWLJQlTr5qJx+PIZDJyMuuTl+iM+hqjUKpZV1RU4NixYw4lc05EL7SipaUFw8PDEqFgOuvAgQOOh8JrE8ANN9hDOzxcdA55iSgCkA8VH6Z4PC7bEdFUBIDpFZVUPT4+jpqaGhdCpqd2OWaFyvjVKEh/ndplpgo6U/9Hcry8mmZHIhFkMpmiUDkrX4shWxs3bpQOOhXyuRmrqUZ1I1JTQmydwQBERQPVZ4a9DpnCZBBC55Dcvw0bNhj7nhGeL+b4uRavU6eASy+FTCs2NxvL8elEezn6LxXZ4n0iCklnUue4kLPF+0zOVlNTk3ze1Pd1kUiaictjcnYKlfqrf6c7R6Zj9XY25Auy6EOdD3RwdIdHT4GplV7BYFDej2Aw6Kjq4muHDx+W6J7p+VFJ9JGIzR0zHaNqKvKZ93oe9SAvm81KmZs/5tvCTMf0gI5rsYqK6vN0JpAtFW1WnXI9gK2pqZGV6/r4VlRUuJBSXf9ry5Yt8jWOKZ1XE5qkzrmXsxqxUHXvq9aefx74zGdmp7O1YMECRzscqg2PjIwgEAhg6dKljonOBUFPPSQSCalT5SU+2tvbiwULFjgU2om0ZLNZDA0NIZFIuJoK8/OCwSDKy8tlipATZM6cOUYOUFlZmawqU9Nj/H3fvn0F0QpGenQe6urqsGLFCqTTacydO9fYLsfRif3mm+2h7etzTphs1ibPa2aa8Lz3Kl/JhNBVVdmK/7FYDMe0cm7Au5kpCw9M48kiBW50asf7bdu2GR3usbEx+T306NFLOJUaQCYUra2tDcPDw3LBO6ZUoJIrQKmSNWvWyAWQUTgJ25w/erPnYDAo+XkbN27E+Pg4BgYGHPIN6meqKG82m5UtrUwVj6lUCrlcDnv37jUWj/C6W1tbjVIaunikKaVprD783e/sfpx0uEZGXIfoz3QymZQ8xPnz50uESBcN5YbD1DvvLTs0qOkFigdzE4lGo+jr68NNN92E9evX46abbnJUIw4NDeGaa67Bjh07cM0112B0dBQ33XQTLrzwQlxyySXYtGkThoeHcfDgQezcuRMHDx7E2NiYTNllMhlMTEzguuuuk+21OI/ppA0MDBhL11VHR908WZjCuaTygtQ5RKeIRSKm1KyK2JgQlELVhqZ0dCRiC0mPjY1JMrJXeovzxytlW19f7yoG8Hoe1TnX1taGz3/yk0B5OU4KgZ+aUHyTvf/9QEcHcNddJSFbnKcq1/fCCy+Uc5BZhcOHDyMej5fM2TIFt/o6zMyAivyoKOZLrUaks/6nKGkxUzYrnS0qyLMMc+HChUgkErj44oull82Jzp5Y+sRPJBJSzZiRqmrcWEiqZfR06NAhuZCr3j1RKG5wlZWV2LZtm9QwisVijrQh0Qh9ITJVBalRGRGN6upqhEIhia6pD+DSpUsxODgoUR9WTC5dulQKEXoiW9/9rj20mkOG174WmDfPRWQ28TfGx8cdaBQrL/XKQ5VDx8VFr/CaN2+eAyFjRD0+Pm5svr137145BqlUCuPj4ziWl/LwarOiLkT65qCOvzomN954o2f5MSF3tl1SnT5ymxYsWCC/q0qO52tElEzRMFMCfI+VL+Thmcj9qhAqCapeVTocQ9Pmm8vZ7Y5MaO8ZI1uqPf008MtfAo8+6nqL6ct4PC4lGnp7e/Ha174Wwq72kRvUFVdcgYMHDzoco/3792NwcBCbNm1CX18f+vv7sWrVKtkEXCVzDw0Nyc2IfeM4hhdddBGGh4dl6kNFCkxl7RxT3SkiL0TlG6rzMpFISA0/ExpSrCqKzjP7pJqcIs6hQ4cOuThmKnruRXgu1MvTxBNqa2uTbbReSWTrN3v3AkLgWxUVpTsKJMjnG6jrzg85WyzWULmagUAADQ0NEi0dHBz0RCinW43oZbMG+fn/xGals7VkyRIAzhQiSYmpVMpBGq+oqHA4UrlcDu3t7Q5+lBdZlk6UCQ0xOUVqBNDS0iI1mih9wGonfmZ5eblEYhjx6U6fHpV1dXW5xPv0dA9fMy1gLArgQuDibJ08CVBZ+Z577Nfuucf+PRazBdryphJEWaWkcqFMKb0dO3Y4OHG6Y5NMJnHkyBEpmWEq8eZr+qJKLhcXGHXhb2pqwgc/+EHJgyEXiqXoapSpkimDwaBDa0vlkRBO11MlnBtevc+OHDli5FBUVlZKvSWaiVvU3d0t+UdtbW2Sjzc5OWnswadG2uQjmSo6VWTr2LFjsqJW70QQiUSwdetWB9rL+8cWLtT1IrdiyZIlSKVSGBgYcJGaSzFTuorOjWlsOBbkr6gpMNXBUVOE6rn0NjMqZ5AcPpOzw8bVhbSmdKcoHo9j+fLlju/LYC+ZTBrlGhobGz0dHfX/9vZ22UrLdCznVy7n7loxk8hWLpeTaSmVj0XUTUUfVcdU52xxfFkMEIvFXMfqDarD4TD+sqkJEAK/Oe+8kuYfAOBHP7LXwWgU8EhlebXHKcQZ+pOyn/wEyHOVAdhFVTfcYMyKFLUTJ4Cf/vTM/nYa5pXq1HmNhQpE9PS9Sa5ECJHFbHO2mJYj3H/8+HFcd911iEanmhezrQg3BH2RUFXGAYCpSW4cq1evxp49e4ycnUjEXf3Y2tqKoaEhx2foixernXSC+ubNm3H99dd7VuHpUZnuSPE+EDHq7+93VTtSXgGAbC1EQqkr13711fbw/uM/2r9/4hP272vXOiZooYiU/J729naXs9rZ2Yn169dj+fLljonLzWF8fFz+r0Z8g4ODSKfTDvK1mnJjEQIdvkgkIh1MdTGfN28eQqEQAoGAKz3JByiTyUghVjooaj9C1WkOhUI4ePAgDhw4gEwmI6N5E6eOaZFCyJKabp0uslVVVYUdO3YgFouhrq4O4XBYNiunllcoFJIkVfW+BAIBudFxToXDYcybNw/hcBidnZ1IJBJobGxEOBxGY2MjKioqcNFFF2FsbMwhdEg0t6OjA5GIreYdDoddpOZIZKpU2stMc408Fi+FcfV3EzKiH0dHs5CDxIWztbUVzc3NRj6eTnYuRQuIjqjp89hzcqaRrePHj8suEHpJu8nh0TlbqhOkcrZ0J5ecLdorVY1479/8jb2mrVlTcO657K1vtf/u2msLa3L9iZoRLTt9GvjWt4C3vQ0QAs/V1yP38MP2e3leHBYtArZtAz7/eTz+4x+XxM16Mn+vH+jsLBqUcc1mat4LDfRKt1IfUxVeVZFJAgkMplSAQaUzMWsRibibp4dCodknatrT04Px8XH09fVJFWN9gyJnh+lDtbLN1PMwErEriNTIOJVKybSkujkUSgE2NzfL1JRJ6NKkm8XUIK9VbUlDXRamPgOBgJFDVFtb65hQ0WjU1S6GfC/VKWLqxzHxP/5xe3i3b7d/37QJqmoyyey6PlZXVxfGxsYwPj6OpqYm2Rw7Ho/LlkVsDUJ5CNU54gZNAjo5NurEJ4Ki3jc6rXo1p7rImzYlOr/q+LCCVN0kvX6mqaXTqmPghWzt3LkTFKXVU8/JZBIjIyNIJBLSWSKPr6GhQTqJ3MQuvfRSZLNZ6cBXV1dLR4o8P6bJw+EwysvLpaZPfX29fE5UjiA/lxpMc+bMQSgUkg4TxyEej8u5xXSrKryrzjPec6/gpdBiauL10LHR0R0TP8fE+TEhxuSbmcaYc52Onheh+UyQrXQ6jZGREenI6Cgitfz00nXV0VGdIaZSS+Fsqc3sWTUdDoexcOFCxONxjIyMzFg14itq//Ef9pr2hjdM7+9++lPZGP3ha65BzpDuBkpr06M7AaoemS6VYjzHiRM2kjQxYcumfPe7yN1/f8GqO3Wc9DEzHf/jr38dz65bB7z5zfjhe94jn/WloRB+c+65wGteY1cR57mWzwmBm4JBzAmH7Z6RP/4xsGSJfJ//Ph0MYi6PUYzr2IV1dY7jF1dUeAZlhw8flq3d1L1S1yZTsy8sJIjFYq4A3cThTqVSxgrkUlLeDOxe85rXALPN2cpftNzYTNIL7e3tjnQM04d79uyRG5qqP0MisqmKjRsceVJbtmzB6tWrHQ7Yxo0bUV9fL1ODOuxOBMSrEbSqi5LL5bBgwQLMnTvXke5Ip9Oora01Ctbp7WtMIql0TgYHB+XDqPJ0pN19ty39cPfd9u9XXWUP9623yslLuQNdw0xN8+j8FZK6uQnphHkVqeRrqtI6X6PEhIoCUlVYtba2Nqxfv97IRZmcnJQcL/Xzh4eHMT4+jqNHjyIWi2HPnj3Yv38/otEodu7cieHhYcRiMVfallpFExMTsqS9stJuIVUsRcI5RGRJrQrT02LcUNmxQFV45j3RnZ5IxBbH1ZEMkyPgtXgU4taY0DcS03WRwsbGRldaVHVWTfZqQLaIZjOlePjwYUQibs4W01vqwq9Xh+oiiJwTgUDAVRFI5NkrDaUiOWdSjahu9KVWhZVirDjTtYd0B0NtiaQXDRRrtp3NZqUO2MTEhKNRN6vdPJXkH30U+OhHgX//92l9r1wuh1vXrcOJvBPwoUDA02Eg4uHVr49OQCKRkIUaqrMcj8cxMDDglk55/HFg3bopuofy74QQ2FZdbdSTIn9ZpT0QODD12pxXWYk7Egl57pvLyyFsCQOcq33uqblzMVxRger8+0IImVnAiRPAl76EZ66/Hl8tK8ML+b8ZUo+Bs7Dq4xUVjvN/qKIC4XDYOB+8gh8vqgbXN1PWKxqNYt68ecZgq5C2HlA8sJv1ztbw8LARaUqn08YFOpvNore3Vyp6q5sm2+Oo1tzcjFQqhTVr1mBsbAx9fX0IBAKSezQ0NORoZssUjbqYqm1WvAjXoVDIcS2pVAqZTMa4mdx4442OCrZYLCa5MfzMOXPmuPhfixcvdjQMZmNtF7KlW74s/8nbbjNuvF4lzVSmJnFVbfZsQg8WL17s6tHW0NCAJUuWOBb/ZcuW4aqrrkJdXZ1cdMfGxhz3kM7tBz/4wYL6MoFAwEHMp7No0veJRKb4Waw2pSOuO02q5EUsFkNDQ4PLITY5KGqFqQkZU69ddyroFOrHs68m73sx9XLT4lHoWC9eGbk5LxXZAtzChMFgEDU1NQ5URnVg1apgPQWmjxHHVT2X+nMikcCVV16JSGSqVUlXVxfS6TQ2btyIrVu34qabbsLWrVuxf/9+2RJo3bp1csM/evSoqxqRQohMg5Hnk8lkMDIy4qoWm5iYkCk3kwYWg4S+vj709fVJlXbAyUHJZrPYv38/tm7dKtPfqpNFR0e9HvX8/J7qeY4ePSqdwU2bNjnGJBgM4tJLL8XIyIjDwVCRNmrGRaNRiTCogQTnAP9eRzKY7lQdCFW/adrdMgx26623IhKxtRmvEAL/KQQieYfhsWwW+OQn8YdPfAKD0Sge2rIF+MQn8MDu3bg4mcS3vvpVmRpWg4dCyGcqlXJRRqqqqpB76KEpR6ulBXjd6/DC616H4+XlOF1WBnz7246sRTablfw6de32+mzyYR/YswcQAicrK/HuSATnRaPSkaoSAtdGIvj5Zz4DPPQQJr//faRSKfm+EALJZNIRxE9OTiKVSqFLCHxBCES1Y2RF6Q9+gHvLygAh8AEhsDh/XENDgysoI2/VJLOzZMkSV3W0ur6ZhMRVTUwf2co7W3zw1PJldZHdsmWLi6uzaNEiVFZWSodIbxzd1NRkdG748OoLSFlZmeRZHcvLHKRSKdkY9w1veINEGtRz6rIERD8oillRUYFUKoWenh7XQkGHrrm5GcFgEOeee64jiqLKuYm8ys1Hf83URNRhbW2AEPjZbbcZN16TWB9TFPPnz5cTXCVvZ7NZ47Xoi4upIbU6FvyZ9zKVSrmqv9RULJ1fNdXFBV6/FtODozo7XkR3E2qiV2iaHBS9TYupZQvvpSrQaQ9RG7Zu3epC99gzktdztpAtojLcMOm06qRmdTMtZiq6ozaEHhoawrp163DxxRdjx44d6Ovrw+rVq7Ft2za84x3vwIoVK/DGN74R27ZtkxyfsbEx3Hjjjbjmmmuwe/du7Ny5E0ePHpXcwGuvvRaXXnoprr32WukQDQwMYNWqVTh48KB0QtRG1V4iiCriQoeHmm5MX7DVDQAXZyQScTdE5zqmH8cKa877yy+/XCKtejCgc+fIwzOhcEytmNYtkwCwKfAKhUKe/Tj57HqJ76q95oiKm84xMTHhiXC8FKQul8tJR0v/l0wmcdeBAy6USf2X+/M/dwQnpXD6KHhqeg9Hj9pVu3mTqfY//MFGkuDsAUhnpJTPlujMBz5gX/8nPiFpH+r31lEp/f6o75dyDJ3Q79x+O+62LDwuBMryx1H37+VEtkhT0efQ2rVrXdWkaosqCg0zLUlpGT5XfGb05umzshE1N97jx4/jTW96k4RpVf4LnRw+5OrmR1K6aRPXK/UaGhqwd+9exwZFSYhwOIydO3eioaFBcqOi0Sje/OY3y0XQy3smh4l6Rzq/ik2QTRugunGGw2F5LjahzmQyOOeccxxaK5FIRFa6qXwClack7b77bIj95pvt33/xC+DoUeR++9uSkC3dyaUsBu8z4WxOSrVCjKXu3d3dMlWkPwyqZpTpYdH/horfQ0NDsuUEH/De3l7MmzfPlWospO9D55LOUCntcnSl55cD2Tpy5Iic75FIBAcPHpSaZer9oPNZV1eHWCyGc889F7FYTJKvY7GY1KtiI2AWTwSDQWMLC44X/47PQ2NjIxKJBPr7+7F582YMDAxg165dEv1guspVjXj6NHDLLbIVSiFTKxN1RFJtdFxeXu5yEBjI6C1W+Iz09va6HBiiY6pUDNFhFWXjvVbTN3R01EpVU4UjU/xr1641asvpDjyf50IdCnjNpmfkwIEDJT07XiiL1/w1adlRE2toaAhVVeYWM62trejo6DAGGGrFLTAV3JnOoR5HU6t3pb3//cDHPmY3CS7BiMqYnK1IJILHf/QjYMsWPNvXh5tCITz6vvcBW7fiyZUr8XfhMI5985svC7I1J78X6OZItT/2GB74u7/D1fmsxZkiW8+2tgJC4J68IPbNN98s50gkEnGlT4n8eb2vHxMLh/EfH/+4o00XQZSFCxeiKn9/LctCIBDAhzzEZyktVIizpTpHuqK9KfDzqxHz/zo7O3HOOedgYmICwWAQFRUVaG5udkzC1tZW1NbWIh6Pux5+lv3rC01dXR3S6TTWr18vdakikQj6+vrQ1tYm1ZaXLVsmI8O+vj65EKobH1EbL4eAg9HR0eFZrs/v09LSIkvT1Woeav3omw4FP+nAEfXiphmLxeRr8+fPd/EJ8A//YA/vW99qnNhVVVVyc+E9WrNmjVSi9lrEWcWmprqYXjxy5Aii0ahEgKj1ZFpQiaKZFmZTw2g6SCwcMEXdrO5Tx/DlRLay2Sz6+/slwVxNVdJh4fgwFdbS0uL4ORKJoKqqCuFwGJZlFUQZVCRp06ZNLnK0riLPxtiRiLl9yqJFiyR5mkT5pUuXIp1O48orr3SJ/qrq4Wr1mqft32/POXUjueUWmwCsmL5RFYpcvZq7HzlyBABcKMqRI0c8HZ0jR47IcfTSF+MGSVkNU9sTvWhAdfqp4VdbW1uU0N/a2orGxsaCHQra2tqk1p5+LlP/u7a2Nk8kRUdZTGh2b2+v1M8yPWPZbNazH+dZRbYefhgoKwMCAeCxx1CKeSFb4QIkbyIeXv366ASQamDibBEV6e3txeJ0Gk/W1wNf+YrxGhmEvCtPLs9ZFr7wz/8s34vH444gV5U40RXkv5wXGP5jWRnm5ws2eB9KqSQshCLymCfybWyQTtsSEQBw6pTDgclkMsh8+MN4rrMT6OmRqJ3pnGdSjQiYxVtfVrvrLuDii2enzlZPT4+sqFIJpWqlWiQSwdGjR7Ft2zYXH4qVieoN7urqQiwWwwc+8AEXEqZujDoS1tjYKB0WRsvz5893VDYReaNHXVNT4+jBlkgkjAKdbLsTiUSkR86qPTUNZ4pQTBuNWhKuc2nkwv/ii0A+osHnP2+c3OPj4+jo6JCTdPHixZicnEQmk0F9fb2L99ba2orh4WEcOHAAc+bMcTjGLFwYGxtDU1OTLGA4cuQIxsfHjY4MHeXpIFv8vjrHh+gN0QvdeWHKS29xUV5e7uINeaUtdX5QeXm5JMNThqK1tVWqSEcidpooEAggHo87HJzOzk5ZQVYIZUin0zhy5IiRI2hK/3EOeaULqU9met9UxODVi824CP/mNwCJuIxyv/lNwLJsbTdF4VutTDRVKaqcjPr6epej0draKnkfujTHgQMH0NDQYHTw2UrKC+1kkUlXVxfa29vR3t5udGjU61OboPPesPvBbEW2yJXUnfUtW7bIz+jq6nK1juGcjkajrmdR52z19vbK8+qcLZ1vF4kYOFv/+I/2XPvzP8d0TEduPvaxjxV1OhwIRzYL/Mu/uI5RixQ8qxHvuAMvvP719nW/6U2eshO5XA7jX/0qnli82D42nyGR7xWrRlSQvv/5yldw344dLyn9WtBOn56S0rjsMjuDsmyZrWem2rPP2tIRQtji2j/5iX3stdfarXAAu8fq4OD0GoufLXvySWDlytnrbEUiEbnBqaXShON7e3tllKg/+CTpMnVAvpea1qKXn8vlsHDhQllhCEwtai0tLZJfcSyvx3To0CFJDNc3MbauIS+LSI8Xz4EPtioQyuiXZGGTKCNYAAAOSklEQVTAnHsnsVw1OnDqgq9r/uBzn4PURTlxwv7/3HOBBx6Q51HRBWrz0An14j+l02mJenHMBgYGZGGBSZBSJS2rqSG9VF19f968eYjFYohE7AqbVCrliBqpQcXUWSqVQiAQMDpo/ByKrKppOzq91LGyLEs6bepY8VpMUbzJcVERtUKCkqpshRfKQF6Y13smsrwXj6O+vh4dHR2ec61YY2i+5kjlAPZiuXKlPefe/vapTeT554FrrrFfLysD9uwBTp/+k0K2iHroUg86ZyQSiTgag6vOiYmzpTr9l19+uStVwmCATg1fD4fDssuDLidBwWj9WVR/VxGZkZERDA8P4/zzz3eIGG/cuPGVrUY8edKWK8hXV0/XSkFujPbgg0B5ORAKTYlFl2onTwKbN9vXXFNjI3PFbGICUoCVVeWF7LvfBS6+2H7Wnn56etf3Ukwl+/PfFVe4j/ve94DaWudxQgC7dtnv79pl/75woSMt6foun/uc7ZT93/9rAwu//S1wxx3AI484j3vySeA//xP41a/sNemZZ4B9++y98K//euq4Rx+1u6rcf789Lr//vV0t+uMfA1//+tRxL7ww886WEOIyIcTdQohfCSH+yvB+SAiRyb//AyFEU7FztrW1ebYS6ezslIuAaZNis1k9MidxV31drZjRS2kpmsoITNXwiEajsi0LIy19UaQzxbQf+7BRefvw4cPo7u7G2NgY2traHJU4FAzVUYliyBbL26kSriNbv//GN+yHUwg7hXPiBNDYaP9+3nnACy/IuUM+mEmE1dTLrxjPyxS9q1E1RQ1ZQalWkl511VWorq52lLMvWrQIBw4ckIt6a2srWlpapLOgLpoNDQ1GNe7q6mrPViW9vb1obW2VCEZNTY0xrWNKa5KfUkiz65xzzpFpUtNxLS12M26ve6neu1ctsvXHP9roghB2K6iHHnJ8R5w+bXMHubC+/e3AE084KhP1yjOds2VyEEhwVTlbdAaWL1/ucnRUzhaVynXZGC/OFh0dnoucrUQigX379mH16tWOLhImzshsq0b0KhCYsTTNdOyDH7Tn0ty5Z9epAIB3v9v+7Pnzgc9+1m7AXsyefnoK/amocG7gxYzthSIRGwXSESMAmJy0nRs+Y6GQK3U/45bJTH3+295mOzYme+op4EMfsp1WIYBVq6aOvesuYMWKqfOsXWufKxQC7r136hznnTd1DM/Dn//qr6aOO//8qfdSKfsff8+nZgEAf//3bgeQ/6qrHZc/o86WEKJcCHGvEKJZCBEUQhwXQizRjrlOCHFz/uerhRCZYudta2szkjCJ+syfP9+xsaq2dOlSNDQ0yN9JriPZVc1Nm/g9jGCLVToQUaEKfbG0ABEuNd1JdMMUbVdVVUkyudem09/fj2jU2TeRPCBWcZJPcOTmm4GGBntY162bQhgefHDq9Q99yHEv6Qiq1tPTg7GxMdTX12Pr1q3IZDLGhs0mNMarabGKxHR0dKCxsdHxtyYEQb+/quq6yVnQKyNZ7eXltFOAlAhGKBTybJo9E8hWPB6XjYvp4JpQhmg0ii1btjjEL1XOlpryJJKnyl3QmVCdEFO1Dfkg6meo53Fxth55ZGrhq6y0xSK97EtfmlrsmpuB3/3OMyVCiQVu/vv378fAwAD6+vqwf/9+I29DdwaoETU4OIjR0VFJ6FdRFB2VmW414lnljPhmG9OHwSDw7W+f/c9/6ingwgunNuPeXuAb3/A+/sEHgeXL7WPnzJn+NT/zjB2g8PM+9amp9yYmnNcSiwEf+YiN6LwS9rWvAV/+cmmq/L/+NXDnne5jX3gBGBqyx5ffy7JsNIv2zW8CGzdOoWk1NfY9rqiwkSvapz5lj09NzdS5zj/fPpfqDH72s8DSpfYeWV1tnzeVArq6bGfv5El56Ew7WxcIIcaV37cLIbZrx4wLIS7I/1whhHhcCGEVOm9PT48r7cP0oVpa7JV+IbJVqPT90KFDripCplPYRb2QRpHK4yilYq21tRWXXnqpa8OMRCK44IILjM4l+WCrVq1CS0sLMpkMOjo6MDw87Oh3pvPVjB3bf/pTYMEC4IIL7Py3anfcYZNJ1651RGOFquX0ypuXgmyp50qn00atJj2lQSeSJNVQKOQSk/RyFuhghMNhSVBV0yu6OjdgIxgVFRUu1IMkV1UjikUCuuNCXoz6OlEVOi5tbW1SBHHJkiVIJBKyqW2hTV11DgYHBzE4OCgdhNHRUYlOjI6O4oorrsDGjRsxMjIihS453jraop9fR0BGR0fN/eDuugtIJoGmJpvLUszuvddeFOvqzBG6b74VsxtusLctdfM923bqlP359fX2tdxwg/exP/mJ7QS1tgL//d9n/pm/+pXtSKmVvv39U6jNDTcYG7/PWstmgQ9/GPinf/JOub74onOfe+QRpxOlOnIPPzz91K/BXoqzZdl/722WZb1LCHEZgA3539cJIV4LoF855q78MQ/mf783f8zj2rk2CSE25X/tEkI8bllWXTAYFCdPnhRz5swRzzzzjFiyZIkQQognnnhCPPDAA6KsrEycOnVKBINB8eKLL4qqqirxxBNPiFOnTomKigpRVlYmurq65OfcddddAoA4efKkEEKIxYsXi0gkIp577jlx9913i9OnT4vm5mYRj8fFXXfdJTo6Ohzvd3V1iZMnT4q7775bCCFES0uLuPfee4UQwnHsL37xC9HW1iYSiYT8vbm5Wdx///2iurpalJWViYcffrjgZ7W0tIh77rlHAPilZVntbW1tZffee69oamoS9913n1i8eLF4/vnnxa9//WtRUVEhTthk9/uFEE8UHLjpWYNlWdXBYFCcOHFClJeX45TtkD2gfE6lZVlN5eXl4tSpU2XKMY9bljUvGAxaJ0+eFKdPn4ZlWSIYDOLEiRNl5eXlp0+dOiUM51okhLCEEIKfm5/MjwrbYX9CCPGH/M9BIcTp/P/l+XOcEkKcEEKU5f9/MX9sVAgRyP97QQjxtPJeMP+Z0fz5Tgohns2/TwsLIdL5876gHcPzi/xrQvldvRa+zus4mb/ugHI9PC6oXP/ZsrnCDoh8m53mj9/sNX/sZrd1AEicyR9WlHCMZXhN99BKOUYA2CeE2CeEEJZl/QjAihI+37dXofnjN3vNH7vZbf74zV7zx252m2VZPzrTvy0r4ZgHhRANyu/1QoiHvI6xLKtCCJESQvzPmV6Ub7755ptvvvnm25+KleJs/VAI0WZZ1iLLsoLCJsDfrh1zuxDiPfmf3yWE+CaK5Sd9880333zzzTff/j+womlEAC9altUvbBJ8uRDiswB+blnWkLDJYrcLIT4jhPicZVm/EjaidXUJn73vJVy3b6+8+eM3e80fu9lt/vjNXvPHbnbbGY9fUYK8b7755ptvvvnmm29nbqWkEX3zzTfffPPNN998O0PznS3ffPPNN9988823GbQZd7Ysy7rMsqy7Lcv6lWVZf2V4P2RZVib//g8sy2qa6WvyrTQrYeyutywra1nWzyzL+oZlWQtfiev0zWzFxk857l2WZcGyLL8k/VVkpYyfZVlX5Z/Bn1uWdevZvkbfzFbC2tloWdYxy7LuzK+fb3klrtM3t1mW9VnLsnJ5/VDT+5ZlWWP5sf2ZZVnnlHLeGXW2LMsqF0J8SghxuRBiiRDi3ZZlLdEOWy+EeAJAqxDik0KI3TN5Tb6VZiWO3Z1CiBUAuoUQXxBC7Dm7V+mbl5U4fsKyrIQQYouwe5r69iqxUsbPsqw2YXf0uBDAUiHEh876hfrmshKfvY8IIT4PoFfYBWU3nd2r9K2AHRB2P2gvu1wI0Zb/t0kIsbeUk840snWeEOJXAO4DcEIIcVgI8XbtmLcLIf4l//MXhBCXWJZlEkn17exa0bEDcAwAldS/L2wNNt9eHVbKsyeEEB8TtpP8/Nm8ON+KWinjt1EI8SkATwghBIDcWb5G38xWythBCJHM/5wSbu1K314hA/BtUVgn9O1CiH/Nd/D5vhAibVnWgmLnnWlnq04I8Vvl9wfzrxmPAfCiEOIpIcScGb4u34pbKWOn2nohxH/M6BX5Nh0rOn6WZfUKIRoAfPlsXphvJVkpz1+7EKLdsqzvWpb1fcuyCkXjvp09K2Xs/kYIsdayrAeFEP8uhPjg2bk0314Gm+7eKIQorV3PS7GXrdWPb2fdSh4Xy7LWCiFWCCH+14xekW/TsYLjZ1lWmbDT9n1n64J8m5aV8vxVCDuV8UZho8rfsSyrC8CTM3xtvhW2Usbu3UKIAwBGLMu6QNg6lV0ATs/85fn2Eu2MfJaZRrb8Vj+z10oZO2FZ1puEEH8thHgbgBfO0rX5VtyKjV9C2M3g/8uyrAeEEOcLIW73SfKvGit17fwSgJP55vR3C9v58u2VtVLGbr0Q4vNCCAHgDiFEWNhNqn179VtJe6NuM+1s+a1+Zq8VHbt8GuoWYTtaPl/k1WUFxw/AUwDmAmgC0CRszt3bAJxxo1XfXlYrZe38ohDiYiGEsCxrrrDTived1av0zWSljN1vhBCXCCGEZVmLhe1sPXZWr9K3M7XbhRDX5qsSzxdCPAXg4WJ/NKNpxBls9ePbDFuJY/e3Qoi4EOL/5GsafgPgba/YRfsmrcTx8+1VaiWO37gQ4s2WZWWFEKeEEH8J4Pev3FX7JkTJY7dVCLHfsqwPCzsF1eeDDK8OsyzrNmGn5ufmOXUfFUIEhBACwM3C5ti9RQjxKyHEs0KI95Z0Xn98ffPNN998880332bOfAV533zzzTfffPPNtxk039nyzTfffPPNN998m0HznS3ffPPNN9988823GTTf2fLNN998880333ybQfOdLd98880333zzzbcZNN/Z8s0333zzzTfffJtB850t33zzzTfffPPNtxm0/wdlUHDF/K/iXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a grid with equally spaced cells using NumPy for visualization\n",
    "xx , yy = np.meshgrid(np.linspace(0,1 , 200), np.linspace(0, 1, 200))\n",
    "\n",
    "# for iterator, call each model name and model function from the dictionary\n",
    "for i, (clf_name, clf) in enumerate(classifiers.items()):\n",
    "    \n",
    "    # Fit the model in current iteration to data X which contains the two scaled attributes\n",
    "    clf.fit(X) \n",
    "    \n",
    "    # Predict raw anomaly score for entire dataset X using decision function\n",
    "    # Decision function is a conceptual separator between two classes in the dataset, in this case \"normal\" versus \"outlier\"\n",
    "    scores_pred = clf.decision_function(X) * -1 \n",
    "        \n",
    "    # Label each data point in X as outlier or inlier: label = 1 is for outlier and label = 0 is for inlier\n",
    "    y_pred = clf.predict(X)\n",
    "    \n",
    "    # Count normal data points as all data points minus the data points for which the prediction is 1 (outlier)\n",
    "    n_inliers = len(y_pred) - np.count_nonzero(y_pred)\n",
    "    \n",
    "    # Count outliers as those data points with label 1\n",
    "    n_outliers = np.count_nonzero(y_pred == 1)\n",
    "    \n",
    "    # At this point, you can simply print the outliers [y_pred=1], but if the dataset is large, it is easier to visualize\n",
    "    \n",
    "    # Let's visualize\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    \n",
    "    # Define a copy of dataframe and add to it another attribute/variable and name it \"outlier\"\n",
    "    bigmartx = bigmart\n",
    "    # then append the labels array y_pred to the copy\n",
    "    bigmartx['outlier'] = y_pred.tolist()\n",
    "    \n",
    "    # IX1 - inlier feature 1,  IX2 - inlier feature 2\n",
    "    # This is to plot the scatter plot again but with outlier flags this time\n",
    "    IX1 =  np.array(bigmartx['Item_MRP'][bigmartx['outlier'] == 0]).reshape(-1,1)\n",
    "    IX2 =  np.array(bigmartx['Item_Outlet_Sales'][bigmartx['outlier'] == 0]).reshape(-1,1)\n",
    "    \n",
    "    # OX1 - outlier feature 1, OX2 - outlier feature 2\n",
    "    # This is to plot the scatter plot again but with outlier flags this time\n",
    "    # Defining a NumPy array is the same as taking the values of a dataframe column\n",
    "    OX1 =  bigmartx['Item_MRP'][bigmartx['outlier'] == 1].values.reshape(-1,1)\n",
    "    OX2 =  bigmartx['Item_Outlet_Sales'][bigmartx['outlier'] == 1].values.reshape(-1,1)\n",
    "         \n",
    "    print('OUTLIERS : ',n_outliers,'INLIERS : ',n_inliers, clf_name)\n",
    "        \n",
    "    # Threshold value over entire dataset to consider a datapoint inlier or outlier\n",
    "    threshold = stats.scoreatpercentile(scores_pred,100 * outliers_fraction)\n",
    "        \n",
    "    # Decision function calculates the raw anomaly score for every point in grid for visualization\n",
    "    # c_ method (in next code lines) stacks 1-D arrays as columns in one 2-D array, so xx will be first column and yy will be second column\n",
    "    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1 \n",
    "    Z = Z.reshape(xx.shape) # make Z array have the same shape as xx array; 1-D array\n",
    "          \n",
    "    # Fill blue map colormap from minimum anomaly score to threshold value\n",
    "    #plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),cmap=plt.cm.Blues_r)\n",
    "        \n",
    "    # Draw red contour line where anomaly score is equal to thresold\n",
    "    a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')\n",
    "        \n",
    "    # Fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score\n",
    "    #plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')\n",
    "        \n",
    "    b = plt.scatter(IX1,IX2, c='white',s=20, edgecolor='k')\n",
    "    \n",
    "    c = plt.scatter(OX1,OX2, c='black',s=20, edgecolor='k')\n",
    "       \n",
    "    plt.axis('tight')  \n",
    "    \n",
    "    # loc=2 is used for the top left corner \n",
    "    plt.legend(\n",
    "        [a.collections[0], b,c],\n",
    "        ['learned decision function', 'inliers','outliers'], loc=2)\n",
    "      \n",
    "    plt.xlim((0, 1))\n",
    "    plt.ylim((0, 1))\n",
    "    plt.title(clf_name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style = \"color:red\"><b>Now try to write code that will remove the outliers from the dataset.</b></spn>\n",
    "<br><span style = \"color:red\"><b>You can start by one column to make it simple. You can dig up code from the Web that will do the trick and apply it to the dataset here.</b></spn>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Encoding, Discretization, and Aggregation\n",
    "\n",
    "Encoding categorical attributes involves either replacing the categories/levels in a categorical attributes with numeric values/codes (label encoding), or expanding a categorical attribute to multiple attributes whose number is equal to the number of categories in the attribute (one-hot encoding). One-hot encoding is more frequently used, where each object will have the values of the expanded attributes encoded with 1 or 0 according to the presence of a category value for that object. One-hot encoding is a way of transforming a categorical attribute into numerical values that are either 0 or 1, while label encoding involves the use of multiple numerical values corresponding to the number of categories.\n",
    "\n",
    "Discretization, on the other hand, involves taking a numerical attribute and dividing its range of continuous values into intervals, and then assigning nominal labels to these intervals. This helps control the number of values that can be present in an attribute.\n",
    "\n",
    "Aggregation is a way of reducing the size of the dataset and investigating interesting summaries according to different granularities in the dataset.\n",
    "\n",
    "Let's see how these processes can be applied to a famous dataset; the US census dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Adult Income Dataset\n",
    "\n",
    "The <a href=\"https://www.kaggle.com/wenruliu/adult-income-dataset\">Adult Income Dataset</a> includes data about an individual’s annual income. Intuitively, income is influenced by the individual’s education level, age, gender, occupation, and etc. The dataset contains 14 columns detailing attributes related to the demographics and other features that describe a person. The target attribute, Income, is divide into two classes: <=50K and >50K. A description of the attributes follows:\n",
    "\n",
    "<b>age</b>: continuous.\n",
    "<br><b>workclass</b>: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "<br><b>fnlwgt</b>: continuous.\n",
    "<br><b>education</b>: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "<br><b>education-num</b>: continuous.\n",
    "<br><b>marital-status</b>: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "<br><b>occupation</b>: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "<br><b>relationship</b>: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "<br><b>race</b>: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "<br><b>gender</b>: Female, Male.\n",
    "<br><b>capital-gain</b>: continuous.\n",
    "<br><b>capital-loss</b>: continuous.\n",
    "<br><b>hours-per-week</b>: continuous.\n",
    "<br><b>native-country</b>: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n",
    "<br><b>income</b>: >50K, <=50K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import some important modules and then import the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'workclass', 'fnlwgt', 'education', 'educational-num', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n",
      "(48842, 15)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.preprocessing as preprocessing\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "income_df = pd.read_csv(\"data/income_data.csv\")\n",
    "\n",
    "# List attribute names\n",
    "print (list(income_df))\n",
    "\n",
    "# Display the shape of the dataset (#rows, #columns)\n",
    "print (income_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may want to inspect some records from the dataset to get a feel of the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\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>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30326</th>\n",
       "      <td>51</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>170579</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3693</th>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>82488</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32277</th>\n",
       "      <td>58</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>141165</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15558</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>163167</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42165</th>\n",
       "      <td>52</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>192065</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25635</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>180532</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33612</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>184801</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48679</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>222005</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8170</th>\n",
       "      <td>56</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>140729</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15761</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>101108</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  educational-num  \\\n",
       "30326   51         Local-gov  170579       HS-grad                9   \n",
       "3693    36           Private   82488     Bachelors               13   \n",
       "32277   58  Self-emp-not-inc  141165       HS-grad                9   \n",
       "15558   29           Private  163167     Bachelors               13   \n",
       "42165   52       Federal-gov  192065  Some-college               10   \n",
       "25635   49           Private  180532       Masters               14   \n",
       "33612   39           Private  184801     Bachelors               13   \n",
       "48679   24           Private  222005       HS-grad                9   \n",
       "8170    56  Self-emp-not-inc  140729       HS-grad                9   \n",
       "15761   29           Private  101108       HS-grad                9   \n",
       "\n",
       "              marital-status         occupation    relationship  \\\n",
       "30326     Married-civ-spouse   Transport-moving         Husband   \n",
       "3693      Married-civ-spouse       Adm-clerical            Wife   \n",
       "32277     Married-civ-spouse    Farming-fishing         Husband   \n",
       "15558     Married-civ-spouse              Sales         Husband   \n",
       "42165              Separated    Exec-managerial   Not-in-family   \n",
       "25635  Married-spouse-absent     Prof-specialty   Not-in-family   \n",
       "33612     Married-civ-spouse     Prof-specialty         Husband   \n",
       "48679          Never-married      Other-service  Other-relative   \n",
       "8170      Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "15761               Divorced      Other-service   Not-in-family   \n",
       "\n",
       "                     race  gender  capital-gain  capital-loss  hours-per-week  \\\n",
       "30326               White    Male             0             0              40   \n",
       "3693   Asian-Pac-Islander  Female             0             0              40   \n",
       "32277               White    Male             0             0              50   \n",
       "15558               White    Male             0             0              55   \n",
       "42165               White  Female             0             0              40   \n",
       "25635               White    Male             0             0              40   \n",
       "33612               White    Male             0             0              40   \n",
       "48679               White    Male             0             0              30   \n",
       "8170                White    Male             0             0              45   \n",
       "15761               White  Female             0             0              15   \n",
       "\n",
       "      native-country income  \n",
       "30326  United-States   >50K  \n",
       "3693     Philippines  <=50K  \n",
       "32277  United-States  <=50K  \n",
       "15558  United-States   >50K  \n",
       "42165  United-States  <=50K  \n",
       "25635  United-States  <=50K  \n",
       "33612  United-States   >50K  \n",
       "48679  United-States  <=50K  \n",
       "8170   United-States  <=50K  \n",
       "15761  United-States  <=50K  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#income_df.head() # first 5 rows\n",
    "income_df.sample(10) # random 10 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A description of each attribute helps us understand the distribution of its values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>4.884200e+04</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>1.896641e+05</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>1.056040e+05</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.175505e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.376420e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  educational-num  capital-gain  \\\n",
       "count  48842.000000  4.884200e+04     48842.000000  48842.000000   \n",
       "mean      38.643585  1.896641e+05        10.078089   1079.067626   \n",
       "std       13.710510  1.056040e+05         2.570973   7452.019058   \n",
       "min       17.000000  1.228500e+04         1.000000      0.000000   \n",
       "25%       28.000000  1.175505e+05         9.000000      0.000000   \n",
       "50%       37.000000  1.781445e+05        10.000000      0.000000   \n",
       "75%       48.000000  2.376420e+05        12.000000      0.000000   \n",
       "max       90.000000  1.490400e+06        16.000000  99999.000000   \n",
       "\n",
       "       capital-loss  hours-per-week  \n",
       "count  48842.000000    48842.000000  \n",
       "mean      87.502314       40.422382  \n",
       "std      403.004552       12.391444  \n",
       "min        0.000000        1.000000  \n",
       "25%        0.000000       40.000000  \n",
       "50%        0.000000       40.000000  \n",
       "75%        0.000000       45.000000  \n",
       "max     4356.000000       99.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style = \"color:red\"><b>What do you notice in the results of the</b> `describe()` <b>method call?</b></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate descriptions of individual attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n",
      "count    48842.000000\n",
      "mean        38.643585\n",
      "std         13.710510\n",
      "min         17.000000\n",
      "25%         28.000000\n",
      "50%         37.000000\n",
      "75%         48.000000\n",
      "max         90.000000\n",
      "Name: age, dtype: float64\n",
      "\n",
      "\n",
      "workclass\n",
      "Private             33906\n",
      "Self-emp-not-inc     3862\n",
      "Local-gov            3136\n",
      "?                    2799\n",
      "State-gov            1981\n",
      "Self-emp-inc         1695\n",
      "Federal-gov          1432\n",
      "Without-pay            21\n",
      "Never-worked           10\n",
      "Name: workclass, dtype: int64\n",
      "\n",
      "\n",
      "fnlwgt\n",
      "count    4.884200e+04\n",
      "mean     1.896641e+05\n",
      "std      1.056040e+05\n",
      "min      1.228500e+04\n",
      "25%      1.175505e+05\n",
      "50%      1.781445e+05\n",
      "75%      2.376420e+05\n",
      "max      1.490400e+06\n",
      "Name: fnlwgt, dtype: float64\n",
      "\n",
      "\n",
      "education\n",
      "HS-grad         15784\n",
      "Some-college    10878\n",
      "Bachelors        8025\n",
      "Masters          2657\n",
      "Assoc-voc        2061\n",
      "11th             1812\n",
      "Assoc-acdm       1601\n",
      "10th             1389\n",
      "7th-8th           955\n",
      "Prof-school       834\n",
      "9th               756\n",
      "12th              657\n",
      "Doctorate         594\n",
      "5th-6th           509\n",
      "1st-4th           247\n",
      "Preschool          83\n",
      "Name: education, dtype: int64\n",
      "\n",
      "\n",
      "educational-num\n",
      "count    48842.000000\n",
      "mean        10.078089\n",
      "std          2.570973\n",
      "min          1.000000\n",
      "25%          9.000000\n",
      "50%         10.000000\n",
      "75%         12.000000\n",
      "max         16.000000\n",
      "Name: educational-num, dtype: float64\n",
      "\n",
      "\n",
      "marital-status\n",
      "Married-civ-spouse       22379\n",
      "Never-married            16117\n",
      "Divorced                  6633\n",
      "Separated                 1530\n",
      "Widowed                   1518\n",
      "Married-spouse-absent      628\n",
      "Married-AF-spouse           37\n",
      "Name: marital-status, dtype: int64\n",
      "\n",
      "\n",
      "occupation\n",
      "Prof-specialty       6172\n",
      "Craft-repair         6112\n",
      "Exec-managerial      6086\n",
      "Adm-clerical         5611\n",
      "Sales                5504\n",
      "Other-service        4923\n",
      "Machine-op-inspct    3022\n",
      "?                    2809\n",
      "Transport-moving     2355\n",
      "Handlers-cleaners    2072\n",
      "Farming-fishing      1490\n",
      "Tech-support         1446\n",
      "Protective-serv       983\n",
      "Priv-house-serv       242\n",
      "Armed-Forces           15\n",
      "Name: occupation, dtype: int64\n",
      "\n",
      "\n",
      "relationship\n",
      "Husband           19716\n",
      "Not-in-family     12583\n",
      "Own-child          7581\n",
      "Unmarried          5125\n",
      "Wife               2331\n",
      "Other-relative     1506\n",
      "Name: relationship, dtype: int64\n",
      "\n",
      "\n",
      "race\n",
      "White                 41762\n",
      "Black                  4685\n",
      "Asian-Pac-Islander     1519\n",
      "Amer-Indian-Eskimo      470\n",
      "Other                   406\n",
      "Name: race, dtype: int64\n",
      "\n",
      "\n",
      "gender\n",
      "Male      32650\n",
      "Female    16192\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "\n",
      "capital-gain\n",
      "count    48842.000000\n",
      "mean      1079.067626\n",
      "std       7452.019058\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max      99999.000000\n",
      "Name: capital-gain, dtype: float64\n",
      "\n",
      "\n",
      "capital-loss\n",
      "count    48842.000000\n",
      "mean        87.502314\n",
      "std        403.004552\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max       4356.000000\n",
      "Name: capital-loss, dtype: float64\n",
      "\n",
      "\n",
      "hours-per-week\n",
      "count    48842.000000\n",
      "mean        40.422382\n",
      "std         12.391444\n",
      "min          1.000000\n",
      "25%         40.000000\n",
      "50%         40.000000\n",
      "75%         45.000000\n",
      "max         99.000000\n",
      "Name: hours-per-week, dtype: float64\n",
      "\n",
      "\n",
      "native-country\n",
      "United-States                 43832\n",
      "Mexico                          951\n",
      "?                               857\n",
      "Philippines                     295\n",
      "Germany                         206\n",
      "Puerto-Rico                     184\n",
      "Canada                          182\n",
      "El-Salvador                     155\n",
      "India                           151\n",
      "Cuba                            138\n",
      "England                         127\n",
      "China                           122\n",
      "South                           115\n",
      "Jamaica                         106\n",
      "Italy                           105\n",
      "Dominican-Republic              103\n",
      "Japan                            92\n",
      "Guatemala                        88\n",
      "Poland                           87\n",
      "Vietnam                          86\n",
      "Columbia                         85\n",
      "Haiti                            75\n",
      "Portugal                         67\n",
      "Taiwan                           65\n",
      "Iran                             59\n",
      "Greece                           49\n",
      "Nicaragua                        49\n",
      "Peru                             46\n",
      "Ecuador                          45\n",
      "France                           38\n",
      "Ireland                          37\n",
      "Hong                             30\n",
      "Thailand                         30\n",
      "Cambodia                         28\n",
      "Trinadad&Tobago                  27\n",
      "Yugoslavia                       23\n",
      "Laos                             23\n",
      "Outlying-US(Guam-USVI-etc)       23\n",
      "Scotland                         21\n",
      "Honduras                         20\n",
      "Hungary                          19\n",
      "Holand-Netherlands                1\n",
      "Name: native-country, dtype: int64\n",
      "\n",
      "\n",
      "income\n",
      "<=50K    37155\n",
      ">50K     11687\n",
      "Name: income, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def summerize_data(df):\n",
    "    for column in df.columns:\n",
    "        print (column)\n",
    "        if df.dtypes[column] == np.object: # Categorical data\n",
    "            print (df[column].value_counts())\n",
    "        else:\n",
    "            print (df[column].describe()) \n",
    "            \n",
    "        print ('\\n')\n",
    "    \n",
    "summerize_data(income_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label Encoding\n",
    "\n",
    "We can encode categorical attributes as numbers without expanding. This involves assigning a numeric code for each category in a categorical attribute, or <b>Label Encoding</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48837</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>257302</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48838</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>154374</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48839</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>151910</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>201490</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48841</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>287927</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt  education  educational-num  marital-status  \\\n",
       "48837   27          4  257302          7               12               2   \n",
       "48838   40          4  154374         11                9               2   \n",
       "48839   58          4  151910         11                9               6   \n",
       "48840   22          4  201490         11                9               4   \n",
       "48841   52          5  287927         11                9               2   \n",
       "\n",
       "       occupation  relationship  race  gender  capital-gain  capital-loss  \\\n",
       "48837          13             5     4       0             0             0   \n",
       "48838           7             0     4       1             0             0   \n",
       "48839           1             4     4       0             0             0   \n",
       "48840           1             3     4       1             0             0   \n",
       "48841           4             5     4       0         15024             0   \n",
       "\n",
       "       hours-per-week  native-country  income  \n",
       "48837              38              39       0  \n",
       "48838              40              39       1  \n",
       "48839              40              39       0  \n",
       "48840              20              39       0  \n",
       "48841              40              39       1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to encode categorical attributes in a given dataset as numbers\n",
    "def number_encode_features(df):\n",
    "    result = df.copy() # take a copy of the dataframe\n",
    "    for column in result.columns:\n",
    "        if result.dtypes[column] == np.object: # if attribute is categorical\n",
    "            # Apply LabelEncoder method to attribute\n",
    "            # fit will infer the number of numerical values needed by counting the number of categories\n",
    "            # then transform will replace each category with its numerical counterpart\n",
    "            result[column] = preprocessing.LabelEncoder().fit_transform(result[column])\n",
    "    return result\n",
    "\n",
    "# Apply function defined above to income dataset\n",
    "encoded_data = number_encode_features(income_df)\n",
    "# Now that the attributes are numerical, it is possible to calculate correlation and plot it\n",
    "sns.heatmap(encoded_data.corr(), square=True, cmap='PuRd')\n",
    "plt.show()\n",
    "\n",
    "# Display last 5 records in transformed dataset to verify numerical transformation\n",
    "encoded_data.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>Can you tell if there are redundant attributes from the correlation matrix shown above?</b></span>\n",
    "\n",
    "If there is an attribute that you are sure is rdundant and adds no new information to future analysis, you can delete it using\n",
    "\n",
    "    del dataframe[\"attribute-name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>Do you think that attributes in the dataset need scaling?</b></span>\n",
    "\n",
    "If so, do apply scaling using any of the methods illustrated in Part 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>What about missing values?</b></span>\n",
    "\n",
    "You may want to make a decision on how to handle missing values before you proceed, or apply encoding and see if it gets affected by missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-hot Encoding\n",
    "\n",
    "Now let's try one-hot encoding.\n",
    "\n",
    "The reason one-hot encoding may be needed and useful is when specific values in a categorical attribute may be correlated with specific objects with certain traits. For example, if you investigate the correlation between the <b>gender</b> and <b>relationship</b> attributes, you may find that \"female\" and \"wife\" are correlated, and \"male\" and \"husband\", but not gender and relationshiop as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  fnlwgt  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
      "0   25  226802                7             0             0              40   \n",
      "1   38   89814                9             0             0              50   \n",
      "2   28  336951               12             0             0              40   \n",
      "3   44  160323               10          7688             0              40   \n",
      "4   18  103497               10             0             0              30   \n",
      "\n",
      "   workclass_?  workclass_Federal-gov  workclass_Local-gov  \\\n",
      "0            0                      0                    0   \n",
      "1            0                      0                    0   \n",
      "2            0                      0                    1   \n",
      "3            0                      0                    0   \n",
      "4            1                      0                    0   \n",
      "\n",
      "   workclass_Never-worked  ...  native-country_Portugal  \\\n",
      "0                       0  ...                        0   \n",
      "1                       0  ...                        0   \n",
      "2                       0  ...                        0   \n",
      "3                       0  ...                        0   \n",
      "4                       0  ...                        0   \n",
      "\n",
      "   native-country_Puerto-Rico  native-country_Scotland  native-country_South  \\\n",
      "0                           0                        0                     0   \n",
      "1                           0                        0                     0   \n",
      "2                           0                        0                     0   \n",
      "3                           0                        0                     0   \n",
      "4                           0                        0                     0   \n",
      "\n",
      "   native-country_Taiwan  native-country_Thailand  \\\n",
      "0                      0                        0   \n",
      "1                      0                        0   \n",
      "2                      0                        0   \n",
      "3                      0                        0   \n",
      "4                      0                        0   \n",
      "\n",
      "   native-country_Trinadad&Tobago  native-country_United-States  \\\n",
      "0                               0                             1   \n",
      "1                               0                             1   \n",
      "2                               0                             1   \n",
      "3                               0                             1   \n",
      "4                               0                             1   \n",
      "\n",
      "   native-country_Vietnam  native-country_Yugoslavia  \n",
      "0                       0                          0  \n",
      "1                       0                          0  \n",
      "2                       0                          0  \n",
      "3                       0                          0  \n",
      "4                       0                          0  \n",
      "\n",
      "[5 rows x 108 columns]\n"
     ]
    }
   ],
   "source": [
    "# Try one hot encoding\n",
    "\n",
    "# Copy the original data\n",
    "encoded_income_df = income_df.copy()\n",
    "\n",
    "# Select the numeric columns\n",
    "numeric_subset = income_df.select_dtypes('number')\n",
    "# Select the categorical columns\n",
    "categorical_subset = income_df.select_dtypes('object')\n",
    "\n",
    "# One hot encode\n",
    "# pandas has a handy method to apply one-hot encoding --\n",
    "# takes a categorical attributes and defines indicator \"dummy\" attributes\n",
    "# We will note one-hot encode the income attribute, since it is the response attribute that is the target of analysis\n",
    "new_categorical_subset = pd.get_dummies(categorical_subset[categorical_subset.columns.drop(\"income\")])\n",
    "\n",
    "# Join the two dataframes using concat\n",
    "# Make sure to use axis = 1 to perform a column bind\n",
    "encoded_income_df = pd.concat([numeric_subset, new_categorical_subset], axis = 1)\n",
    "print (encoded_income_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>What do you notice with the workclass attribute?</b></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the sklearn to do the one-hot encoding, much the same way we did label encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <th>48841</th>\n",
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       "      <td>287927</td>\n",
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       "<p>5 rows × 109 columns</p>\n",
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       "       age  fnlwgt  educational-num  capital-gain  capital-loss  \\\n",
       "48837   27  257302               12             0             0   \n",
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       "48840   22  201490                9             0             0   \n",
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       "\n",
       "       hours-per-week    0    1    2    3  ...   93   94   95   96   97   98  \\\n",
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       "\n",
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       "48840  1.0  0.0  0.0   <=50K  \n",
       "48841  1.0  0.0  0.0    >50K  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply one-hot encoding function defined above to categorical attributes in income dataset, except income attribute\n",
    "onehot_categorical_subset = preprocessing.OneHotEncoder().fit_transform(categorical_subset[categorical_subset.columns.drop(\"income\")])\n",
    "\n",
    "# OneHotEncoder() returns a matrix object, and we need that to be converted to a dataframe to construct new encoded dataset\n",
    "# So, convert result to array object, then make the result as list and convert to dataframe object\n",
    "# The reason we use the list method is to keep the sparseness of the resulting one-hot-encoded attributes\n",
    "# Experiment with removing the list function call to see how the resulting attributes will be different\n",
    "new_onehot_categorical_subset = pd.DataFrame(list(onehot_categorical_subset.toarray()))\n",
    "\n",
    "# Join the dataframes using concat\n",
    "# Make sure to use axis = 1 to perform a column bind\n",
    "# Remember, we applied one hot encoding on categorical attributes except income, so we need it back\n",
    "onehot_encoded_data = pd.concat([numeric_subset, new_onehot_categorical_subset, income_df[\"income\"]], axis = 1)\n",
    "\n",
    "# Display last 5 records in transformed dataset to verify numerical transformation\n",
    "\n",
    "onehot_encoded_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can notice that the `OneHotEncoder()` method does not keep meaningful column names. It works fine if you have one categorical column or multiple columns representing the same semantic meaning (e.g. words in a document). \n",
    "\n",
    "A better package we can use that will keep meaningful column names is the `OneHotCategoricalEncoder()`, but you need to install the feature-engine package using:\n",
    "\n",
    "    pip install feature-engine\n",
    "\n",
    "at the command prompt. Let's try this method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>48840</th>\n",
       "      <td>22</td>\n",
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       "      <th>48841</th>\n",
       "      <td>52</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt;50K</td>\n",
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       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
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      ],
      "text/plain": [
       "       age  fnlwgt  educational-num  capital-gain  capital-loss  \\\n",
       "48837   27  257302               12             0             0   \n",
       "48838   40  154374                9             0             0   \n",
       "48839   58  151910                9             0             0   \n",
       "48840   22  201490                9             0             0   \n",
       "48841   52  287927                9         15024             0   \n",
       "\n",
       "       hours-per-week  workclass_Private  workclass_Local-gov  workclass_?  \\\n",
       "48837              38                  1                    0            0   \n",
       "48838              40                  1                    0            0   \n",
       "48839              40                  1                    0            0   \n",
       "48840              20                  1                    0            0   \n",
       "48841              40                  0                    0            0   \n",
       "\n",
       "       workclass_Self-emp-not-inc  ...  native-country_Ecuador  \\\n",
       "48837                           0  ...                       0   \n",
       "48838                           0  ...                       0   \n",
       "48839                           0  ...                       0   \n",
       "48840                           0  ...                       0   \n",
       "48841                           0  ...                       0   \n",
       "\n",
       "       native-country_Yugoslavia  native-country_Hungary  native-country_Hong  \\\n",
       "48837                          0                       0                    0   \n",
       "48838                          0                       0                    0   \n",
       "48839                          0                       0                    0   \n",
       "48840                          0                       0                    0   \n",
       "48841                          0                       0                    0   \n",
       "\n",
       "       native-country_Greece  native-country_Trinadad&Tobago  \\\n",
       "48837                      0                               0   \n",
       "48838                      0                               0   \n",
       "48839                      0                               0   \n",
       "48840                      0                               0   \n",
       "48841                      0                               0   \n",
       "\n",
       "       native-country_Outlying-US(Guam-USVI-etc)  native-country_France  \\\n",
       "48837                                          0                      0   \n",
       "48838                                          0                      0   \n",
       "48839                                          0                      0   \n",
       "48840                                          0                      0   \n",
       "48841                                          0                      0   \n",
       "\n",
       "       native-country_Holand-Netherlands  income  \n",
       "48837                                  0   <=50K  \n",
       "48838                                  0    >50K  \n",
       "48839                                  0   <=50K  \n",
       "48840                                  0   <=50K  \n",
       "48841                                  0    >50K  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import feature-engine\n",
    "from feature_engine.categorical_encoders import OneHotCategoricalEncoder\n",
    "\n",
    "# Define encoder method\n",
    "encoder = OneHotCategoricalEncoder()\n",
    "\n",
    "# Apply encoder to categorical subset, except income\n",
    "onehot_categorical_subset2 = encoder.fit_transform(categorical_subset[categorical_subset.columns.drop(\"income\")])\n",
    "\n",
    "# Beauty of feature-engine encoder method is it already returns a dataframe, so we need not worry about conversions\n",
    "# Concatenate and reconstruct new dataset\n",
    "onehot_encoded_data2 = pd.concat([numeric_subset, onehot_categorical_subset2, income_df[\"income\"]], axis = 1)\n",
    "\n",
    "# display last 5 records\n",
    "onehot_encoded_data2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discretization \n",
    "\n",
    "Now let's discretize some of the numerical attributes. We will work with the <b>age</b> and the <b>hours-per-week</b> as examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fnlwgt</th>\n",
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       "      <td>160323</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>10 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fnlwgt  educational-num  capital-gain  capital-loss  workclass_?  \\\n",
       "0  226802                7             0             0            0   \n",
       "1   89814                9             0             0            0   \n",
       "2  336951               12             0             0            0   \n",
       "3  160323               10          7688             0            0   \n",
       "4  103497               10             0             0            1   \n",
       "5  198693                6             0             0            0   \n",
       "6  227026                9             0             0            1   \n",
       "7  104626               15          3103             0            0   \n",
       "8  369667               10             0             0            0   \n",
       "9  104996                4             0             0            0   \n",
       "\n",
       "   workclass_Federal-gov  workclass_Local-gov  workclass_Never-worked  \\\n",
       "0                      0                    0                       0   \n",
       "1                      0                    0                       0   \n",
       "2                      0                    1                       0   \n",
       "3                      0                    0                       0   \n",
       "4                      0                    0                       0   \n",
       "5                      0                    0                       0   \n",
       "6                      0                    0                       0   \n",
       "7                      0                    0                       0   \n",
       "8                      0                    0                       0   \n",
       "9                      0                    0                       0   \n",
       "\n",
       "   workclass_Private  workclass_Self-emp-inc  ...  native-country_Scotland  \\\n",
       "0                  1                       0  ...                        0   \n",
       "1                  1                       0  ...                        0   \n",
       "2                  0                       0  ...                        0   \n",
       "3                  1                       0  ...                        0   \n",
       "4                  0                       0  ...                        0   \n",
       "5                  1                       0  ...                        0   \n",
       "6                  0                       0  ...                        0   \n",
       "7                  0                       0  ...                        0   \n",
       "8                  1                       0  ...                        0   \n",
       "9                  1                       0  ...                        0   \n",
       "\n",
       "   native-country_South  native-country_Taiwan  native-country_Thailand  \\\n",
       "0                     0                      0                        0   \n",
       "1                     0                      0                        0   \n",
       "2                     0                      0                        0   \n",
       "3                     0                      0                        0   \n",
       "4                     0                      0                        0   \n",
       "5                     0                      0                        0   \n",
       "6                     0                      0                        0   \n",
       "7                     0                      0                        0   \n",
       "8                     0                      0                        0   \n",
       "9                     0                      0                        0   \n",
       "\n",
       "   native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                               0                             1   \n",
       "1                               0                             1   \n",
       "2                               0                             1   \n",
       "3                               0                             1   \n",
       "4                               0                             1   \n",
       "5                               0                             1   \n",
       "6                               0                             1   \n",
       "7                               0                             1   \n",
       "8                               0                             1   \n",
       "9                               0                             1   \n",
       "\n",
       "   native-country_Vietnam  native-country_Yugoslavia  age_group  \\\n",
       "0                       0                          0      25-34   \n",
       "1                       0                          0      35-44   \n",
       "2                       0                          0      25-34   \n",
       "3                       0                          0      35-44   \n",
       "4                       0                          0        <25   \n",
       "5                       0                          0      25-34   \n",
       "6                       0                          0      25-34   \n",
       "7                       0                          0      55-64   \n",
       "8                       0                          0        <25   \n",
       "9                       0                          0      55-64   \n",
       "\n",
       "   work_hours_per_week  \n",
       "0                32-48  \n",
       "1                48-60  \n",
       "2                32-48  \n",
       "3                32-48  \n",
       "4                16-32  \n",
       "5                16-32  \n",
       "6                32-48  \n",
       "7                16-32  \n",
       "8                32-48  \n",
       "9                  <16  \n",
       "\n",
       "[10 rows x 108 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the \"age\" column\n",
    "age_group = [] # define array structure\n",
    "for age in encoded_income_df[\"age\"]:\n",
    "    if age < 25:\n",
    "        age_group.append(\"<25\")\n",
    "    elif 25 <= age <= 34:\n",
    "        age_group.append(\"25-34\")\n",
    "    elif 34 < age <= 44:\n",
    "        age_group.append(\"35-44\")\n",
    "    elif 44 < age <= 54:\n",
    "        age_group.append(\"45-54\")\n",
    "    elif 54 < age <= 65:\n",
    "        age_group.append(\"55-64\")\n",
    "    else:\n",
    "        age_group.append(\"65 and over\")\n",
    "        \n",
    "# Copy dataframe to keep original \n",
    "new_income_df = encoded_income_df.copy()\n",
    "new_income_df[\"age_group\"] = age_group\n",
    "del new_income_df[\"age\"]\n",
    "\n",
    "# Same thing for \"hours-per-week\"\n",
    "work_hours_per_week = []\n",
    "for hours in encoded_income_df[\"hours-per-week\"]:\n",
    "    if hours < 16:\n",
    "        work_hours_per_week.append(\"<16\")\n",
    "    elif 16 <= hours <= 32:\n",
    "        work_hours_per_week.append(\"16-32\")\n",
    "    elif 32 < hours <= 48:\n",
    "        work_hours_per_week.append(\"32-48\")\n",
    "    elif 48 < hours <= 60:\n",
    "        work_hours_per_week.append(\"48-60\")\n",
    "    else:\n",
    "        work_hours_per_week.append(\"60 and over\")\n",
    "        \n",
    "new_income_df[\"work_hours_per_week\"] = work_hours_per_week\n",
    "del new_income_df[\"hours-per-week\"]\n",
    "\n",
    "new_income_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an easier way in python than using if else statements. Pandas `cut` function can be used to group continuous or countable data in to bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
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       "      <th>native-country_Yugoslavia</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>226802</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89814</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>336951</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160323</td>\n",
       "      <td>10</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(40.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103497</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>198693</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>227026</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104626</td>\n",
       "      <td>15</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(60.0, 80.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>369667</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(20.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>104996</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(40.0, 60.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fnlwgt  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0  226802                7             0             0              40   \n",
       "1   89814                9             0             0              50   \n",
       "2  336951               12             0             0              40   \n",
       "3  160323               10          7688             0              40   \n",
       "4  103497               10             0             0              30   \n",
       "5  198693                6             0             0              30   \n",
       "6  227026                9             0             0              40   \n",
       "7  104626               15          3103             0              32   \n",
       "8  369667               10             0             0              40   \n",
       "9  104996                4             0             0              10   \n",
       "\n",
       "   workclass_?  workclass_Federal-gov  workclass_Local-gov  \\\n",
       "0            0                      0                    0   \n",
       "1            0                      0                    0   \n",
       "2            0                      0                    1   \n",
       "3            0                      0                    0   \n",
       "4            1                      0                    0   \n",
       "5            0                      0                    0   \n",
       "6            1                      0                    0   \n",
       "7            0                      0                    0   \n",
       "8            0                      0                    0   \n",
       "9            0                      0                    0   \n",
       "\n",
       "   workclass_Never-worked  workclass_Private  ...  native-country_Puerto-Rico  \\\n",
       "0                       0                  1  ...                           0   \n",
       "1                       0                  1  ...                           0   \n",
       "2                       0                  0  ...                           0   \n",
       "3                       0                  1  ...                           0   \n",
       "4                       0                  0  ...                           0   \n",
       "5                       0                  1  ...                           0   \n",
       "6                       0                  0  ...                           0   \n",
       "7                       0                  0  ...                           0   \n",
       "8                       0                  1  ...                           0   \n",
       "9                       0                  1  ...                           0   \n",
       "\n",
       "   native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
       "0                        0                     0                      0   \n",
       "1                        0                     0                      0   \n",
       "2                        0                     0                      0   \n",
       "3                        0                     0                      0   \n",
       "4                        0                     0                      0   \n",
       "5                        0                     0                      0   \n",
       "6                        0                     0                      0   \n",
       "7                        0                     0                      0   \n",
       "8                        0                     0                      0   \n",
       "9                        0                     0                      0   \n",
       "\n",
       "   native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
       "0                        0                               0   \n",
       "1                        0                               0   \n",
       "2                        0                               0   \n",
       "3                        0                               0   \n",
       "4                        0                               0   \n",
       "5                        0                               0   \n",
       "6                        0                               0   \n",
       "7                        0                               0   \n",
       "8                        0                               0   \n",
       "9                        0                               0   \n",
       "\n",
       "   native-country_United-States  native-country_Vietnam  \\\n",
       "0                             1                       0   \n",
       "1                             1                       0   \n",
       "2                             1                       0   \n",
       "3                             1                       0   \n",
       "4                             1                       0   \n",
       "5                             1                       0   \n",
       "6                             1                       0   \n",
       "7                             1                       0   \n",
       "8                             1                       0   \n",
       "9                             1                       0   \n",
       "\n",
       "   native-country_Yugoslavia     age_group  \n",
       "0                          0  (20.0, 40.0]  \n",
       "1                          0  (20.0, 40.0]  \n",
       "2                          0  (20.0, 40.0]  \n",
       "3                          0  (40.0, 60.0]  \n",
       "4                          0           NaN  \n",
       "5                          0  (20.0, 40.0]  \n",
       "6                          0  (20.0, 40.0]  \n",
       "7                          0  (60.0, 80.0]  \n",
       "8                          0  (20.0, 40.0]  \n",
       "9                          0  (40.0, 60.0]  \n",
       "\n",
       "[10 rows x 108 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take another copy of the original dataset\n",
    "new_income_df2 = encoded_income_df.copy()\n",
    "\n",
    "# Cut the age attribute into intervals\n",
    "age_group2 = pd.cut(new_income_df2.age, [20,40,60,80])\n",
    "\n",
    "# You can label the intervals for more meaningful representation\n",
    "#age_group2 = pd.cut(new_income_df2.age, [20,40,60,80],labels=['young','middle-aged','old'])\n",
    "\n",
    "new_income_df2[\"age_group\"] = age_group2\n",
    "del new_income_df2[\"age\"]\n",
    "\n",
    "new_income_df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>It is possible to use</b></span> `cut` <span style=\"color:red\"><b>with quantiles instead of intervals (depth-based binning, # of ovservations in bin instead of interval of values). Read relevant documentation of the function on the Web and apply quantile cutting to the age attribute and construct new dataset.</b></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red\"><b>Apply one-hot encoding again to the discretized attributes!</b></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregation\n",
    "\n",
    "Now let's try to aggregate numerical values according to a categorical attribute. If there was a time attribute then aggregation could have been performed on different time intervals. For the dataset we have it is sufficient to apply aggregation over the workclass attribute for now. We will use the `groupby` function. Then, it is possible to compute aggregate values (e.g. mean) per workclass group for the numerical attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>?</th>\n",
       "      <td>40.141122</td>\n",
       "      <td>187254.773491</td>\n",
       "      <td>9.251161</td>\n",
       "      <td>502.946052</td>\n",
       "      <td>67.634870</td>\n",
       "      <td>31.812433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Federal-gov</th>\n",
       "      <td>42.577514</td>\n",
       "      <td>183590.028631</td>\n",
       "      <td>10.937151</td>\n",
       "      <td>923.287709</td>\n",
       "      <td>108.884078</td>\n",
       "      <td>41.513268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local-gov</th>\n",
       "      <td>41.676020</td>\n",
       "      <td>190161.134885</td>\n",
       "      <td>11.032207</td>\n",
       "      <td>798.228635</td>\n",
       "      <td>102.124043</td>\n",
       "      <td>40.847258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never-worked</th>\n",
       "      <td>19.900000</td>\n",
       "      <td>215033.300000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>36.903144</td>\n",
       "      <td>192669.212499</td>\n",
       "      <td>9.875715</td>\n",
       "      <td>896.135374</td>\n",
       "      <td>80.768478</td>\n",
       "      <td>40.273137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age         fnlwgt  educational-num  capital-gain  \\\n",
       "workclass                                                               \n",
       "?             40.141122  187254.773491         9.251161    502.946052   \n",
       "Federal-gov   42.577514  183590.028631        10.937151    923.287709   \n",
       "Local-gov     41.676020  190161.134885        11.032207    798.228635   \n",
       "Never-worked  19.900000  215033.300000         7.500000      0.000000   \n",
       "Private       36.903144  192669.212499         9.875715    896.135374   \n",
       "\n",
       "              capital-loss  hours-per-week  \n",
       "workclass                                   \n",
       "?                67.634870       31.812433  \n",
       "Federal-gov     108.884078       41.513268  \n",
       "Local-gov       102.124043       40.847258  \n",
       "Never-worked      0.000000       28.900000  \n",
       "Private          80.768478       40.273137  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will work with the original dataset income_df\n",
    "# Group workclass attribute by its categorical values\n",
    "grouped_income = income_df.groupby([\"workclass\"])\n",
    "\n",
    "# Compute mean per group using agg function\n",
    "grouped_income.agg(np.mean).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `agg` function intelligently ignores categorical attributes.\n",
    "\n",
    "<span style=\"color:red\"><b>Why did we not apply the aggregation function on the encoded dataset?</b></span>"
   ]
  }
 ],
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