{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "xXvzVBhbupcT" }, "source": [ "# Optimizing Risks for a Portfolio of Cryptocurrencies\n", "> \"Intro into Optimizing Risks for a portfolio of cryptocurrencies using stochastic discount factor\"\n", "\n", "- author: Vladimir Eremin, Dmytro Karabash, Maxim Korotkov \n", "- categories: [sdf, treasury, crypto]\n", "- image: images/pixabay-game-of-thrones-4180794_640.jpg\n", "- permalink: /cryptosdf/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P6fjUwxHLMe4" }, "outputs": [], "source": [ "#install requirements if needed\n", "!pip install cvxpy==1.0.31 pandas_datareader pandas matplotlib seaborn -q" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MrPfwTmGLJUc", "outputId": "5338cdf9-e28e-4e87-8237-1625e42157b6" }, "outputs": [], "source": [ "#import modules\n", "import cvxpy as cp\n", "import pandas_datareader as pdr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import pandas as pd\n", "import math\n", "import os.path\n", "import time\n", "\n", "#select plotting style\n", "import matplotlib\n", "import seaborn as sb\n", "matplotlib.style.use('fivethirtyeight')\n", "\n", "#seaborn-white fivethirtyeight\n", "import sys\n", "#Python versiond\n", "print(sys.version)\n", "\n", "print(cp.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LLJQtt2JR9Yi" }, "outputs": [], "source": [ "#read input dataset that was downladed from the binance \n", "fuldf = pd.read_pickle('data.pickle')\n", "y_label = fuldf.columns[0]\n", "factors = (fuldf.columns[1:]\n", " .tolist()\n", " )\n", "\n", "\n", "total_size = fuldf.shape[0]\n", "# print('Combined DF size:', total_size,'\\nFull range: ', fuldf.iloc[0].name,fuldf.iloc[-1].name)\n", "train_set = .8\n", "\n", "train_id = int( total_size * train_set)\n", "df_train = fuldf.iloc[:train_id]\n", "df_test = fuldf.iloc[train_id:]\n", "split_index = fuldf.iloc[train_id].name\n", "# print('Training period: ', df_train.iloc[0].name,'-',df_train.iloc[-1].name,'shape:',df_train.shape[0],'\\nTesing period:',df_test.iloc[0].name,df_test.iloc[-1].name,'shape:',df_test.shape[0],)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "GNz7_FGEQj3R" }, "source": [ "## Data Acquisition\n", "\n", "---\n", "\n", "The data for this exercise was obtained from the Binance API using the Python API client `python-binance`.\n", "\n", "An example of the data acquisition code:\n", ">Note: Install the module if needed using pip:\n", ">#pip install python-binance\n", "\n", "```python\n", "from binance.client import Client\n", "binance_api_key = 'YOUR-API-KEY'\n", "binance_api_secret = 'YOUR-API-SECRET'\n", "binance_client = Client(api_key=binance_api_key\n", "\t\t\t\t,api_secret=binance_api_secret)\n", "klines = binance_client.get_historical_klines(symbol\n", "\t\t\t\t ,kline_size, date_from, date_to)\n", "data = pd.DataFrame(klines, columns = [COLUMNS])\n", "```\n", "\n", "Following daily pairs was downloaded and formatted to pd DataFrame:\n", "1. BTCUSDT\n", "2. ETHUSDT\n", "3. BNBUSDT\n", "4. LTCUSDT\n", "\n", "For each timepoint, Binance provides conventional OHLC (Open, High, Low, Close) and Volume data. In this exercise, we used only the `Close` column. It's possible, though to consider the combination of all five values and came up with a more reliable metric, for example: the weighted average price.\n", "\n", "The joint time-series of four crypto assets looks the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "id": "GItfRqgqkC1b", "outputId": "2e185e13-b114-4bbe-cc16-2f3fb6686b35" }, "outputs": [], "source": [ "df_train.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "oOOWsE-4MS2g" }, "source": [ "Each trading pair has a different amount of data available: The oldest tradable crypto instruments on Binance are BTCUSDT and ETHUSDT - data points are available from _2017-08-17_. For BNBUSDT and LTCUSDT first trading days are _2017-11-06_ and _2017-12-13_, respectively.\n", "\n", "We joined these time series together on the earliest common trading date: _2017-12-13_ up to _2021-06-14_. The full joint dataset size is equal to 1280 samples.\n", "\n", "In order to properly infer the SDF, We split the dataset into training and testing sets at _2020-10-02_.\n", "\n", "Training interval: _2017-12-13_ to _2020-10-02_ (1024 data points).\n", "\n", " \n", "\n", "Below is the joint plot of the full dataset in the logarithmic scale. The dotted line represents the train-test split at _2020-10-02_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 516 }, "id": "KrT57ZAljK45", "outputId": "153addb5-9a71-4f89-c7a5-08670d2cb1a7" }, "outputs": [], "source": [ "plot_data =np.log(fuldf)\n", "fig , ax = plt.subplots(figsize=(12,7))\n", "# ax1 = ax.twinx()\n", "# plot_data.drop(y_label,axis=1).plot(ax=ax)\n", "plot_data.plot(ax=ax,alpha=0.8)\n", "ax.legend(loc=0)\n", "ax.set(title='Assets',xlabel='Date',ylabel='price, log scale');\n", "\n", "ax.axvline(split_index,color='grey', linestyle='--', lw=2);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oldD9O-nW1ds" }, "outputs": [], "source": [ "#Transform raw data to log-return format:\n", "lret_data = np.log1p(df_train.pct_change()).dropna(axis=0,how='any')" ] }, { "cell_type": "markdown", "metadata": { "id": "tCjpd5h6W51t" }, "source": [ "## II. Data Transformation\n", "---\n", "In order to be properly trained, input time series has to be transfored into the log-return format:\n", "\n", "$r_t = \\ln{\\frac{P_t}{P_{t-1}}}$\n", "\n", "Where $P_t$ is a price of the asset at time $t$.\n", "\n", "The SDF model needs two vectors to perform the optimization: \n", "\n", "1. Feature vector $I$\n", "2. Target vector $R$\n", "\n", "The $R$ vector contains values shifted to $t+1$ compared to the $I$ vector:\n", "\n", "$R = \\begin{pmatrix}\n", "r_{1}\n", "\\\\ \\vdots\n", "\\\\r_{t}\n", "\\end{pmatrix}\n", "I = \\begin{pmatrix}\n", "r_{0,0} & \\dots & r_{0,l} \\\\ \n", "\\vdots & & \\vdots \\\\ \n", "r_{t-1,0} & \\dots & r_{t-1,l} \n", "\\end{pmatrix}\n", "$\n", "\n", "Where $l$ is the number of features in the model.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hW48zI-ILJUf", "outputId": "b501e795-14ec-41b5-9fdf-e8ebb106b90b" }, "outputs": [], "source": [ "R = lret_data[y_label].shift(1).iloc[1:].values.reshape((-1,1))\n", "I = lret_data[factors].iloc[1:].values\n", "# assert R.shape[0] == I.shape[0] and I.shape[1]==3\n", "\n", "print('R:',R.shape,'\\n',R[:10]\n", ",'\\n I:',I.shape,'\\n',I[:10,:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "mov7bsEVb5Fb", "outputId": "a534fa17-1817-4635-e79a-dad9d07f521d" }, "outputs": [], "source": [ "#correlation matrix\n", "F= pd.concat([pd.DataFrame(R),pd.DataFrame(I)],axis=1)\n", "F.columns = [y_label]+factors\n", "F.cov()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 650 }, "id": "pYqE9uELlNzj", "outputId": "676f58b4-81e1-4acf-da45-28cac12ffa5c" }, "outputs": [], "source": [ "f = plt.figure(figsize=(10, 10))\n", "cov_data = F.cov()\n", "mask = np.triu(np.ones_like(cov_data))\n", "dataplot = sb.heatmap(cov_data.corr(), cmap=\"YlGnBu\", annot=True, mask=mask)\n", "plt.title('Covariance Matrix', fontsize=16);" ] }, { "cell_type": "markdown", "metadata": { "id": "lfEcVEmJapAz" }, "source": [ "## Model \n", "\n", "---\n", "\n", "Our goal is to explain the differences in the cross-section of returns $R$ for individual stocks.\n", "\n", "Let $R_{t+1}$; denote the return of BTCUSDT at time $t + 1$. The fundamental no-arbitrage assumption is\n", "equivalent to the existence of a stochastic discount factor (SDF) $M_{t+1}$ such that for any return in excess of the risk-free rate \n", "\n", "$R^e_{t+1} = R_{t+1}- R^f_{t+1}$ it holds:\n", "\n", "$E_t[M_{t+1} R^e_{t+1}]= 0$\n", "\n", "\n", "For demonstration purpose and in seak of simplicity, we assume that\n", "$R^e_{t} = R_{t}$: excesses return of the BTCUSDT is the same as \n", "the market return.\n", "\n", "We consider the SDF formulation as:\n", "\n", "$M_{t+1} = 1 - \\sum_{i=1}^N w_{t,i} R_{t+1} = 1 -w_t^T R_{t+1}$\n", "\n", "\n", "The Generalized Method of Moments (GMM) was used to approximate the SDF $M$ vector. \n", "The first moment condition equation can be expressed as following:\n", "\n", "$ \\underset{M}{\\text{min}}\\underset{t}{\\sum} || M(I_{t}) R^e_{t+1}||^2 $\n", "\n", "Since the above problem is convex, we can estimate the SDF for BTC using the `cvxpy` framework." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HFAjd_WHLJUf", "outputId": "a6b57e33-0fa4-479d-85f1-072d2b2030e2" }, "outputs": [], "source": [ "#4 - Solve GMM problem\n", "n = R.shape[0]\n", "#define weights for the SDF\n", "w = cp.Variable(shape=(I.shape[1]\n", " ,1),nonneg=True)\n", "\n", "#define SDF \n", "M = I @ w\n", "\n", "#Define expression\n", "Sigma = M @ R.T\n", "\n", "#Construct the problem\n", "prob = cp.Problem(cp.Minimize( cp.norm(Sigma) )\n", " # , [cp.geo_mean(M) >= 1 , ]\n", " , [cp.sum(w) == 1 \n", " ,cp.sum(M) == 1 ]\n", " \n", " )\n", "\n", "prob.solve(verbose=True\n", " #, max_iters = 300\n", " )\n", "\n", "print('SDF weights from BTC:\\n',dict(zip(factors,w.value)))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "sWfKMQpnWXgh" }, "outputs": [], "source": [ "def sharpe(x):\n", " return (x.mean() / x.std() * np.sqrt(365))\n", "\n", "\n", "\n", "def plot_return(data,title):\n", " datac = data.copy()\n", " fig , ax = plt.subplots(figsize=(13,5))\n", "\n", " y_return = (datac[y_label].pct_change().fillna(0)+1)\n", " \n", " p_return = ((datac[factors].pct_change().fillna(0) + 1)\n", " .apply(lambda x: (x @ w.value)[0] ,axis =1)\n", " .rename('SDF Portfolio')\n", " )\n", " \n", " \n", " \n", " p_return_c = p_return.cumprod()\n", " y_return_c = y_return.cumprod()\n", " portfolio_benchmark = pd.concat([y_return_c,p_return_c],axis=1)\n", "\n", " portfolio_benchmark.plot(ax=ax)\n", " ax.legend(loc=0)\n", " ax.set(title=title,ylabel='growth factor',xlabel='date')\n", " \n", " return portfolio_benchmark\n", "\n", "def calc_metrics(data):\n", " #y_return = data[y_label].pct_change().fillna(0)\n", " \n", " p_return = ((data[factors].pct_change().fillna(0))\n", " .apply(lambda x: (x @ w.value)[0] ,axis =1)\n", " .rename('SDF Portfolio')\n", " )\n", " y_return = data.pct_change().fillna(0)\n", " y_return['SDF Portfolio'] = p_return\n", " \n", " #portfolio_ret = pd.concat([y_return,p_return],axis=1)\n", " \n", " return '\\nSharpe ration: '+str(portfolio_ret.apply(sharpe).round(3).to_dict()) \n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "ym4-09mgHS-i" }, "source": [ "## Results\n", "Below is the result of computing the SDF portfolio on the training dataset and plotting it against the index portfolio (BTCUSDT):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 450 }, "id": "qSUOksp2bafc", "outputId": "a4247541-1e12-4567-ed2a-6245c9a0c24b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Portfolio growth (x times):\n", "{'BTCUSDT': -0.34, 'SDF Portfolio': 2.92}\n", "Sharpe ration: {'BTCUSDT': 0.222, 'SDF Portfolio': 0.997}\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "portfolio_benchmark = plot_return(df_train,'In-sample: Performance of the SDF Portfolio')\n", "\n", "\n", "print('Portfolio growth (x times):\\n'+\\\n", " str((portfolio_benchmark.iloc[-1] - 1).round(2).to_dict())+\\\n", " calc_metrics(df_train)) " ] }, { "cell_type": "markdown", "metadata": { "id": "-DsCHbW6jz-k" }, "source": [ "Resulted plot and metrics for the hold-out dataset:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 441 }, "id": "mUaH71YAbhVz", "outputId": "013b070a-a85c-4e4c-a256-c0b80d82f283" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Portfolio growth factor:\n", "{'BTCUSDT': 2.84, 'SDF Portfolio': 11.08}\n", "Sharpe ration: {'BTCUSDT': 2.704, 'SDF Portfolio': 3.394}\n" ] }, { "data": { "image/png": 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u3TomTpzIjRs3aNasGVWqVMmVMVq6yUNOnz5NUlKSUcC0detWbt68aXRcv379sj2uLrXExER966BuEqSNGzeydu1aKlSowPbt2ylatCiQMiatU6dO7N27l/nz5xv8DtS5desW4eHhBi8iAM6ePcu5c+fo0aOH0UQpo0ePJiwsjNWrV5vs+j1+/HguXLhAz549mTNnjv7na8SIEXz44YcEBQXRrFkzqlWrlu3Pf+LECaZMmUKpUqXYs2ePfrKYcePGERgYyJo1a5gwYQJTpkzJdtlCmIt0vxQin9F1qTPV8pCWLs/du3fztE4Z2bp1Kw8fPqRdu3YGAR2kvL2tUqUKFy5c4MSJE698LisrK5NvidN+kXB3dzcK6AA+/vhjihYtyt69e02W//777+sDOkh5g60br6hSqYxmiOzSpQtAui1iXbt2NQjoIOULt729PevWrTPZnSm14OBgtFotP/74o1F3xG7dulG5cmW2bdvGs2fP9OnVq1fn+PHjhISEZFi2KWfPnmXSpElMmjSJf//73/j5+TFt2jQsLCyYOHEikBJMPnr0iJEjRxoEdAC+vr707t2bu3fvmvyy3KpVK5OTSWRkzZo1JCQk8OmnnxoEdJAS6Li4uLBz50792JzUPvnkkwxbYgYMGGAwg6yDgwMtW7ZEo9HQvHlzgwlbrK2t6dChA4BRt8P0ZuzTPS/pPW/+/v4GAZ2uzmDYJe7+/fts3LiRkiVL8v333xsEdAB2dnY4OjoC8PjxY3237dQBHYCNjQ1jxowBMBkEp3Xs2DEuXrxItWrVDAI6gEaNGtGmTRsePHjAtm3bMi0rt0yePFkf0AE4OTnRqlUrYmJiuHz5sj79559/Jj4+nu+//94o+Kpfvz4tW7bk9OnTXLx4MdNztmjRgqCgIGxtbVm8eDHdu3enfPnylC1blk8++YRdu3bl+PPogonk5GSTXcO3bdvG5MmTjf5lt+v11q1b9T/bw4cPp2bNmhw6dIgSJUowbNgwIOWlF6QEcbqADlKe/e+//x7AqKu7zr///W+j38OvIjExkTVr1mBnZ8eECRMMfr5cXV0ZOnSovqtoTuhehA0dOtRg9k+FQsGECRMoVKgQq1atyvR3tBD5ibTUCSEydObMGaPxCw4ODgwcOBBAP0FA2oBOp1GjRpw+fZrTp09Tq1atHJ+nc+fOLFy4kFq1atGhQwfq1q1L7dq19V9mU0tMTGTJkiVs3LiRixcvEhMTg0aj0e83FQAAJgMA3QD7ChUqGH2Z1n0Z+Pvvv02W9/777xulOTo6Ur58eU6cOMHly5cpX768yWMh5Uu1paUloaGhhIaGGu1PSEggOTmZq1ev8t577wEpX/DfeeeddMvMyLlz5/QBi5WVFU5OTnTs2JFBgwZRo0YNfZ0Azp8/b3IduStXrgAprXZ+fn4G+3KyLEJGz5etrS116tRh06ZNnDlzxmhq9szOl9H9NrUvvfsdGxur7wp69epVnj17ZtBtNb3nTXfPUitdujSQ0t1S5/fff0ej0VCnTh2T3XxTi4iIICkpCaVSafL+JCUlAXDp0qUMy4Gs/WyHhoZy+vRpOnXqlGl5r6po0aIml3Exdc10z+nhw4dNTmJy//59IOU5TftywpSAgAB69+7Nvn37OH78OGfOnOH48eNs3ryZzZs306tXL2bPnp3t6fhNdW9OLbeWmdm2bZs++LaxscHNzY3PPvvMYFZI3XUytfRAxYoVcXJy4sqVKzx//tzoOcztJU8uXbrEixcvqFGjhslgsVGjRgZ1zq6Mnu2SJUtSvnx5IiIiuHLlitHLJCHyKwnqhMhnSpYsSVRUFH/99VemeXV58nJmr7Nnz+onUtFxc3PTB1sxMTFA+rOX6boIPX369JXOM2nSJMqUKcOqVauYNWsWM2fO1C/3MGHCBIPxN3379mXLli14enrSqlUr1Gq1foKD+fPnG3RVSy3122kdXYtfRvvSe5ub3jXRTV+uu3bpefToEUlJSUbXJa3cGtTfvXv3TMda6SYKyazLZ2xsrFFaTma4e5XnK7Pz5cb9TkxMpF27dkRERFC+fHk6duxIiRIl9C3KkydPTvd5MzVmVXdccnKyPk332bKynpju/pw6dcrkpDU6WXlmcutnO7ekN8ZXd19SXzPddchsVkdTz2l67OzsaN26tX7sV0JCAkuXLmXUqFGsWLGCli1bZjgpjCm6XhYWFhYmX1DllqwEhzExMRQtWtSgJTQ1tVrN/fv3iYmJMQrqXqUraHp1gbx79vLbsy1EbpCgToh8pk6dOoSFhbFv3z6DyT9M2b9/v/4YHV1rUuovOKk9ffo0W4t89+zZM8MvA7ovv+nN6KjrJmTqS3J2zmNhYUFAQAABAQE8evSIo0ePEhISwtq1a+nYsSPHjx+nWLFiREZGsmXLFho1asSGDRsMumtqNBpmzZqVYT1yU3rXRNdKkNk1KVq0KImJiUYzG5qTrs779+832dKUkZwsKvwqz9frWMR427ZtRERE0KNHD+bNm2ew7+7du5kG5Fmh+3lNr8UvNd116N+/Pz/88MMrnTe3frbNQVenP//8M8+CJWtraz777DNOnDjBunXrOHDgQLaDuoMHDwJQtWpVk13GX6eiRYvy+PFjXr58aTKwe50/a3n97KUuP23X9twoXwhzkDF1QuQzPXv21M/qdv78+XTz/fbbb/z+++8UK1bMYByY7g+Ubhr+1K5evWqydcjUm+6s0o0Z001Jn5buS0t2A4CMFCtWjFatWhEcHIy/vz8PHjzgyJEjAPqpylu2bGk0/i4iIsLkFPN5JTw83CjtyZMnXLhwATs7O3x8fDI8vmbNmjx79ixXZrHMLbpxZrrrndcyer7i4+P13ezSjl18XXTPW9u2bY32mbr/OVG9enWUSiVHjx7NtIWtRo0aKJXKXLk/mf1s68ZN5sbPtlKpNOgi/ap0z+nhw4dzrcz06GZIzO5SEbGxscyePRv4Z3yuOenu96FDh4z2Xbhwgfv371O2bNlMuwCnpvvbkt17+84772BnZ8eFCxeMJsKCV3/2Mnq279+/zx9//IG9vX2mv6OFyE8kqBMin/H09GT48OEkJibSrVs3o0kZIOWPbv/+/QEICgoy+CNbtWpVlEol69atM/gCGBsby4gRI0yeUze1vqlAMDOtW7emWLFibN682ehL7MqVK4mMjOTdd981mHQiu+Lj401+SdVqtfpWLzs7O+Cf6dPTfjG5f/8+w4cPz3EdcmLt2rVGYz4mTpxIbGwsnTt3znRxZt1EG1988YXJ7rhxcXFG1+XFixdcunSJP//88xVrb1qvXr1QqVRMmTKF48ePG+3XarUcOXLEaPmAnOrSpQvW1tb8/PPPRuPApk+fzt9//42fn1+WuibmhfSet+vXrzN27NhcOUeJEiXw9/fn3r17/Pvf/zb6gvzy5Uv9JBslSpSga9eu+klvdGPoUvvrr7+yNKaudu3a+Pr6EhERYTSxyoEDBwgNDaV48eK5sih9sWLFePDgQa69dOnfvz/W1tZ88803Jj9rUlKS/oVTZhYvXpzuRE+XLl3i119/BUyPoU3PtWvX6NSpE1euXKFq1ar06dMny8fmlY8//hiACRMmGPztSExM5N///jeQMvlVduj+tmS3t4GVlRVdu3blxYsXjB8/3miM6o8//ohCoaBXr17ZKldHd9z06dMNJp3RarWMHTuWFy9e0L1790x/RwuRn0j3SyHyoZEjRxIXF8eMGTNo2LAhjRo1okKFCmg0GiIjIwkPD8fS0pIpU6YYveF1dnamW7durFq1ivr16+vXzdqzZw/u7u4mv/w2aNAApVJJcHAwjx8/1o8z6N+/f6ZdNe3t7Zk3bx69e/emQ4cOtGvXDk9PT86dO8euXbtwcHBg/vz5r9Q95+XLl7Rs2RJPT0+qVq2Km5sbiYmJHDp0iLNnz1KzZk394P5q1apRp04dQkND8fPzo06dOty7d4/du3fj4+PzWr/8N2nShBYtWtCxY0fUajWHDx/m2LFjeHp66mchzEiDBg349ttvGTt2LNWrV6dZs2Z4enoSFxfHrVu3OHz4MO7u7gYBRURERK6uU5eWo6Mjy5Yto1evXvj5+dGgQQPKlSuHlZUVf/31FydPnuT27dtcv37d5ELN2eXu7s7kyZMZOnQojRs3pkOHDqjVao4dO0Z4eDiurq5MmzYtFz5ZzrRo0YIyZcowd+5c/Xpat2/fZufOnfj5+eXoRYkpU6ZM4Y8//mDp0qWEh4fTpEkTbG1tuXnzJnv37mXu3Lm0adMGgB9++IFr164xefJk1q5dS7169VCr1URHR3PlyhVOnDjBxIkTM51QR6FQMH/+fDp06EBAQACbNm3Sr1MXEhKCtbU1wcHB+hcqr6Jx48asX78ef39/6tWrh42NDRUrVsx02YX0+Pj4MG/ePAYNGkTdunVp2rQp3t7eJCcn89dff3Hs2DHi4+NNLheQ1u7duxk6dCju7u7Url2b0qVLEx8fz9WrV9m7dy+JiYm0bdvWoMeEzs2bN/UT1iQlJfH48WPOnj3LyZMn0Wq1NG/enLlz5+bKz8qr8vf3Z8eOHaxfv546derQunVr/Tp1V65coWHDhvoxzlnVuHFjZs2axYQJE/jjjz/0PUnSe8GY2tixYzly5AjLli3jzJkzNGrUSL9O3ePHj/nqq6/0EzhlV61atRg6dCjTp0+nbt26dOjQgaJFi7Jv3z5Onz5N+fLls/Q7Woj8RII6IfIhhULBuHHj6NChA4sWLSI8PJzDhw+jUChwdXXls88+IyAgAG9vb5PHz5gxA7Vazfr161m8eDFqtZrOnTvz1VdfmZyB8p133mHhwoXMnj2bFStW6N+Wd+nSJUvj71q0aMGuXbuYPn06Bw4cYPPmzTg5OdG9e3e++uorPD09X+l62NvbM2HCBMLCwjhx4gTbt2+nUKFCeHh48N1339G3b199V0sLCwtWr17Nd999x65du1iwYAEuLi707t2b4cOHU7t27VeqS3YMGjSI1q1bExwczNWrVylcuDC9evVizJgxWZ7+e/DgwdSpU4fg4GCOHDnCjh07KFy4MC4uLnTp0oWOHTvm8acw1qBBA8LDw5kzZw579uzh+PHjWFpaolarqVWrFuPGjcvVsSh9+/alTJkyzJ49m61btxIbG4uLiwv9+/dn+PDhOZqAJbfY29sTEhLC+PHjOXToEEeOHMHT05MRI0YwaNAgNm7cmCvnUalU7Nq1i+DgYDZu3MiyZctQKpWUKlWKzp07G3RDK1KkCFu2bGH58uWsX7+eLVu2EBcXh5OTEx4eHowdOzbLz021atXYv38/U6ZMYf/+/ezZswcHBwdat27NsGHDsrR4d1ZMmjQJpVLJ/v37OXr0KBqNhu7du+c4qAPo1KkTFStWZO7cuRw4cIB9+/Zha2uLs7MzzZo1o127dlkqZ8KECdSrV4+DBw8SERHBtm3bSEhIoESJEjRu3JguXbrg7+9v8sXVrVu39OMqbW1tKVKkCF5eXvTv3x9/f/8MZwQ2hwULFlCvXj2WL1/O8uXL0Wg0eHt7M2HCBAICAkwuKZORxo0bExQUxH//+19++ukn/aRBWQnqVCoVO3fuZObMmYSEhDBv3jxsbGyoXLkyAwYMyPL9S8+YMWOoXLkyCxcuZP369cTHx+Ph4cHw4cMZMmSILDwuChzFkydPstcJXAghRIYCAwNZvXo1oaGhJqcHF0IIIYTITTKmTgghhBBCCCEKMAnqhBBCCCGEEKIAk6BOCCGEEEIIIQowGVMnhBBCCCGEEAWYtNQJIYQQQgghRAEmQZ0QQgghhBBCFGAS1AkhhBBCCCFEASZBnTCry5cvm7sKbzW5/vmH3Iv8Q+5F3pNrnH/IvTAvuf75R0G/FxLUCSGEEEIIIUQBJkGdEEIIIYQQQhRgEtQJIYQQQgghRAFmae4K5HdJSUnExsaauxpvLFtbW54+fWruarwyS0tL7O3tzV0NIYQQQgjxFpKgLgNJSUk8e/YMlUqFQqEwd3XeSDY2Ntja2pq7Gq8sNjaW+Ph4bGxszF0VIYQQQgjxlpHulxmIjY2VgE5kiZ2dHXFxceauhhBCCCGEeAtJUJcJCehEVshzIoQQQgghzEWCOiGEEPPgbZ8AACAASURBVEIIId4mWi2Kv65D7DNz10TkEhlTJ4QQQgghxNtCo8F26ggsz0egtS/KyxFT0Hj5mrtW4hVJS50QQgghhBBvCYvzJ7E8HwGAIjYGq12/mLlGIjdIUCeEEEIIIcRbQnnlguH2nZtmqonITRLUvYECAwNRqVT6f2XKlKFr165cunSJlStXGuwz9S8sLAytVsuyZcto1qwZpUuXxs3NjQYNGjBz5kxiYmL05+natavR+cPCwlCpVDx8+BCA5ORkZsyYQa1atXBxccHDw4NGjRoRHBysP2bSpEn68xcvXhxPT0/8/PyYPn06z58/1+fLrO6BgYF5fHWFEEIIIQouixuXDbYVj++bqSYiN8mYujdUo0aNWLBgAQB37txhzJgx9OrViwMHDtC0aVN9vgEDBuDo6EhQUJA+zdHRkQEDBhASEsLQoUMJCgqiRIkSXLx4kUWLFlGiRAl69uyZ5boEBQXx888/M2XKFKpXr05sbCxnzpzh1q1bBvl8fHzYsmULWq2Wx48fc/ToUaZPn86KFSvYvn07arWaqKgoff6dO3fy+eefG6S9CWveCSGEEELkFeX1KINtxdNHkJQIllZmqpHIDRLU5YBqyV+v9XxP+rpm+xgbGxvUajUAarWagQMH0q1bN/126ny2trYGaZs2bWLdunUsW7aMdu3a6dM9PDxo3rw5T548yVZdtm/fTr9+/fD399enVahQAcBgbTdLS0t9PZydnXn33Xdp2bIldevWZezYsQQHBxvU08HBwejzCCGEEEII0xRPH6F8/MAwTatF8fgBWicXM9VK5AbpfvkWePbsGRs3bqR8+fIUKlQo0/zr1q2jbNmyBgFdaiqVKlvnV6vVHDp0iHv37mXrOEgJ7jp37sy2bdvQaDTZPl4IIYQQQqRQpul6qaN4JF0wCzoJ6t5Qu3fvxtXVFVdXV9zc3Dh8+DA//fRTlo69du0aPj4+uVaXiRMn8vjxY3x9falTpw6DBw8mJCQErVabpePLlStHTEyMfoyeEEIIIYTIPuX1S6bTH2X/xbvIXySoe0PVq1ePsLAwwsLC2Lt3Lw0aNOCjjz7i9u3bmR6b1WArq8qVK8eRI0fYs2cPH3/8MY8ePaJv37506dIlS61vuvooFIpcrZcQQgghxNsk7SQpOtJSV/CZdUzd9OnTCQ0N5cqVK1hbW1OjRg3Gjh1L+fLl9Xm0Wi1BQUEsXbqUJ0+eUL16daZOncq7776bYdmbN2/m+++/588//8TLy4tvvvmGtm3b5kq9czLG7XWzs7OjTJky+u3Zs2fj7u7Of//7X7755psMj/X29ubSJdNvclIrUqQI169fN0p/+vQpSqWSwoUL69OUSiXVqlWjWrVqDBo0iLVr1zJgwACOHDlCkyZNMjzPxYsXKVq0KMWKFcu0TkIIIYQQAkhOwmrnBpR3bpL4QQs0vpXTbalTSEtdgWfWlrpDhw7x6aefsnPnTkJCQrC0tKRDhw48fvxYn2fmzJnMnTuXyZMns3fvXpycnOjYsSPPnj1Lt9zjx4/Tr18/OnfuTFhYGJ07d6ZPnz6cPHnydXysfEmhUKBUKnn58mWmeTt37szVq1cJCQkxuV83UYqPjw8XL140KvP06dO4ublhY2OT7jl8fX0BiI2NzbAud+/eZcOGDbRp0walUhqWhRBCCCEypdVi8/MUbNYGY3VwG4WmjkB56SzKB3dNZpfulwWfWVvqNm7caLC9YMEC3N3dOXr0KC1btkSr1TJ//ny++OIL2rdvD8D8+fPx8fFhw4YN9O3b12S58+fPp379+gwfPhxICSDCwsKYP38+P//8c95+qHwiPj6e6OhoICUIW7RoEc+fP6dFixaZHtuxY0e2bNlC//79uXjxIk2bNqVEiRJcunSJhQsX0r59e3r27Ennzp354YcfCAgI4Msvv6Ro0aIcPnyY4OBgxo0bpy+vd+/e1K5dm9q1a1OyZElu3LjBhAkTKFmyJDVr1tTnS0pKIjo6Wr+kwbFjx5g2bRqOjo6MHTs216+REEIIIcSbyHL/FqzCd+q3FQnx2C6alG5+xUPpflnQ5aslDZ4/f45Go9HPrnjjxg2io6P58MMP9XkKFSpEvXr1OHbsWLpB3YkTJ+jfv79BWpMmTVi4cGG657582biPsa2tbYatTflVcnIy+/fv17eGFS5cmLJly7Jo0SJq1qxpsIxAcnIyycnJBmkAc+fOZcWKFaxatYoZM2agVCrx9PSkQ4cONG/enLi4OGxtbfn111+ZOHEi3bp1IyYmBi8vL8aNG0ePHj30ZTZo0IDNmzfz448/EhMTQ4kSJahZsyZTpkzB0dGRuLg4kpKSuHz5Mr6+viiVSooUKULZsmXp1asXn376KYULFzaqY0JCAoBRurnExMTkaIZPczP17AvzkHuRf8i9yHtyjfMPuRfmldvXv9Df13ln+UyjdOW9v9M9RvPgjjwH5O+fhcwmMVQ8efIkd2fFeAV9+vTh6tWr7N+/HwsLC44dO0bz5s05e/Ysbm5u+nyDBg3izp07Ri19Ok5OTsyaNYvu3bvr01avXs2QIUOy9aX76dOn+rXQRN7QBYdvgoL4vFy+fDlXZzoVOSf3Iv+Qe5H35BrnH3IvzCvXr//zGOzGfobyQXT2D/1pF1hZ515dCpiC/rOQb1rqvv76a44ePcqOHTuwsLAwd3WEEEIIIYQoUGzWLchRQAcpM2Bq1fl/MkBhWr6YeWL06NH88ssvhISE4OnpqU9Xq9UA3L9v2M/3/v37lCxZMt3y1Gp1to8RQgghhBCiwNJqsTyx3yAp8cP2aEo4G2dVKEl29zZIk8lSCjazB3UjR47UB3TvvPOOwT4PDw/UajX79u3Tp8XFxXHkyBFq166dbpk1a9Y0OAZg3759GR4jhBBCCCFEQaV4dA/Fi39mFdfaFiK+5/+R0Kq7UV6NqwcatZtBmqxVV7CZNagbPnw4q1atYtGiRahUKqKjo4mOjub58+dAyjT8gYGBzJw5k5CQEC5cuMDAgQOxt7enU6dO+nLatWvH+PHj9dsBAQEcPHiQH3/8kUuXLjF9+nTCwsIIDAx87Z9RCCGEEEKIvKa8ddVgW1PaGyytSKrfAo2Do+E+j3fQFnMySJOgrmAz65i6n376CUC/XIHOyJEjGT16NABDhgzh5cuXjBgxQr/4+MaNGylSpIg+/59//omr6z99gGvXrs3ixYv57rvv+P777/Hy8mLx4sXUqFHjNXwqIYQQQgghXi/lrWsG2xpd90prGxJbdsNmzXz9vuSKNVDEPDE8XrpfFmhmDep0i1hnRKFQMHr0aH2QZ8rZs2eN0tq3b28ULAohhBBCCPEmUt40bKlLdiuj/39i804o7t/B8vQRkqrUJanOh1hEhBnkV6QN6rRaLE4fQfn3TZLqNkXrWCLP6i5eXb6Z/VIIIYQQQoi3XkI8JCdBIftsHWaRtvulW6qJUJQWJPT+ggS+0CdpHTPufml5YCu2S6YCYLVjHS+mrgbrgrd+89vC7BOlCCGEEEIIIcDifAT2X3TCPrAtVpuXZf3AhHgUd28bJGlKe2V4iLaY4azwabtfWh3a8c++p4+wuBCR9fqI106COiGEEEIIIfIB641LUMQ+Q6HVYP3rf1E8fZSl45R/XUeh1ei3NU4umbb0aVXF0Cr/CQUUz2MgPu5/BWiMJl5R/n0zi59CmIMEdaLA02g0fPHFF3h5eaFSqQgLC8v0mBs3bqBSqYiMjDS5LYQQQgjxWmm1KG//E0gpNBrDyU+ex2Dx+yHjsW+YmCTFzdsoj/FBFmhVhuPkFI9TumAq7t9BEffSMHualkCRv0hQ9wZ68OABw4YNo1KlSpQsWRIfHx/atWtnsHZf69atUalUqFQqnJyc8PX1xd/fn7Vr16LVag3Kq1Spkj6v7p+7u3u651+5cqVBXl9fX/r06cP169df6XOFhYWhUql4+PChQfquXbtYuXIla9asISoqKkfrEZYuXZqoqCgqVar0SnUUQgghhMiRF8+NA6k7Ka1jiicPsf+qJ4VmfoPdN5+ivH7JMN+tKwbbWQrqMNUF8/7/yrtqlFd591aWyhTmIROlvIE+/vhjXr58yZw5c/Dy8uLBgweEh4fz6JFhE37Pnj0ZM2YMSUlJREdHs2vXLr788ks2b97M8uXLsbCw0Of96quv+PTTT/XbSmXG7wPs7OyIjIxEq9Vy6dIlvvzyS3r06EFYWJhBuVmVkJCQ7r5r166hVqtfaXF5CwsL1Gp1jo8XQgghhHgVyofGLXCK/wVSlod2oIh9lpIW+wybRUG8HL8ALK1Sjk3TUpd65suMaIo7YZEqHtS1AlrcNA7qFHel+2V+JkFdDhT+pNFrPd/zpfuznPfJkyccOXKEX3/9lYYNGwLg7u5OtWrVjPLa2dnpAxlXV1eqVatGzZo18ff3Z/Xq1fTq1Uuft0iRItkKehQKhT6/s7MzI0eOpH///ly7dg0fHx+WLFnCrFmzuH37NqVLl+aLL77gk08+0R+vUqmYMmUKBw4cYO/evTRt2pSQkBAAvL1T3j51794dgNWrV+uPcXNz4+zZs8THxzN27Fh++eUXYmJiqFSpEt9++y1169Y1Wd8bN25QpUoV9u3bR9WqVQEIDw9nzJgxnDt3jqJFi9KpUyfGjx+PtbV1lq+DEEIIIURWKB5FG6XpWuqUNwxb4ixuX8Nq62oS2/dOWXogo5kvM5C2pU7xv8AybcsfgPLpY3jxHOwKZ6ls8XpJ98s3TOHChSlcuDDbtm0jLi4u28c3adKE8uXLExoamqv1srW1BSAxMZHQ0FBGjBhBYGAg+/fvJyAggGHDhrF9+3aDYyZPnoyfnx+HDx9m/PjxLFuWMgvU0aNHiYqKIigoiKCgIL766itcXV2JiorSdzEdM2YMmzZtYs6cORw8eJDy5cvTqVMn7t69m6X6/v3333Tu3JnKlStz8OBBZs+ezS+//ML48eNz8aoIIYQQQqRQmGipU95JaamzuH3NaJ91yHIUf99A8eRhyiQn/6O1tkVbslSWzqktZrisgb77pYmWOpBxdfmZBHVvGEtLS+bOncu6devw8PCgWbNmfPPNN5w8eTLLZZQrV85o/Nu3336Lq6ur/t+0adOyXN5ff/3F7NmzcXV1pWzZssyZM4euXbvSv39/vL29GTBgAJ07d2bmzJkGx3Xs2JHevXvj6emJl5cXjo6OADg5OaFWq3FwcMDBwYEiRYqgVCpRq9WUKFGC2NhYFi9ezLhx42jevDm+vr78+OOPODk58dNPP2Wpzj///DPOzs5MmzYNX19fWrRowdixY1m0aBEvXrzI8mcXQgghhMgK5UMTLXWP7sHzGH03zNQUSYnYLp6C8mba8XRekMkwGX3etC11j+/Di+coH5h+Ca5rORT5jwR1b6D27dtz8eJF1qxZQ9OmTTl+/DhNmzbNciCm1WpRKBQGaYMGDSIsLEz/r1+/fhmWERsbi6urK6VKlaJChQokJCSwfPlyrK2tTU5mUrduXS5evGiQpusGmV1//vkniYmJ1KlTR59mYWFBrVq1jM6RnqioKGrUqGEwdrBu3bokJCRw7Zrx2zIhhBBCiFdhqqUOwPLUYRQajcl9FpfPYbN8lkGapnTWul6Cie6X9+8ajc9LTSZLyb9kTF0OZGeMm7nY2trSuHFjGjduzMiRIxk8eDBBQUEMHjw40zFhUVFReHh4GKQVK1aMMmWyNugWUsbrhYWFoVQqcXJywt4+47VSAKNAMivHZFfac5irDCGEEEKI1NIu/q1jeeJAxsfd/9tgW5PFSVIANE7OBtsWf1/H8uTBdPOnXeBc5B/SUveW8PX1JSkpKdNxdnv27OHChQu0b9/+lc6nUCgoU6YMnp6eRsGZr68vx44dM0g7cuQI5cqVy7BMXTCanJycYT4vLy+sra05evSoPi05OZnjx4/j6+ubpfr7+vpy8uRJNKnejB05cgRra2u8vLyyVIYQQgghRFal11Jncc5wCE1ivWZo7YukW06ye9msn7SIimSfigZJVnt+TTe7UmbAzLekpe4N8+jRIz755BN69epFhQoVKFy4MKdOnWLWrFk0bNiQokWL6vO+ePGC6OhogyUNZs2aRatWrejatWue1XHw4MH06dOH9957j/fff5+wsDDWr1/P8uXLMzzOzc0NhULBzp07admyJba2thQubDwDk729Pf369WPcuHEUL14cDw8P5s2bx/379/nXv/6VpTp++umnzJ8/n2HDhhEQEMD169cZP348n332GXZ2djn63EIIIYQQJiUn6Rf+TkuRlGiYtXJtElt0wXbWNygfGI/D05TO3svnxPebY3H53D/nS05KN6/y7m3QaLI8Zk+8PhLUvWHs7e2pWbMmwcHBXLt2jYSEBFxcXOjUqRMjRowwyLty5UpWrlyJlZUVjo6OVKpUienTp9O1a9c87WLYpk0bfvjhB2bPns3o0aNxc3Nj2rRptGzZMsPjSpUqxejRo/nuu+/4/PPP6datG/PnzzeZVzdL5aBBg3j69CmVK1dmw4YNODs7m8xv6lzr169nzJgx1K9fHwcHBzp16sSYMWOy92GFEEIIITKhePIo3XFzaWlKl0HjVoYX4xZgO/9bLM9H6Pcll/aCDFrxTEmq1QjtylkoEhNN7tdaWukDS0VCPIrH99EWl7V98xvFkydPtOauRH719OlTHBwczF2NN1pcXJx+uYOCriA+L5cvX8bHx8fc1RDIvchP5F7kPbnG+YfcC/PSXX/lpbPYTRycaX6thSWxC7frFx0nOQnrX5ditXsTWhtb4j8bTXKF6tmuh83c8Vgd32eUrimiQlvSBYurf+jTXn41leQKNbJ9jvyuoP8sSEudEEIIIYQQZqRMZzxdWhoX938COgALSxL8PyWhfe+ULpFKixydP+l9P9NBnbs3WlUJg6BOeefWGxnUFXQS1AkhhBBCCGFGikfGY+NMSXdmy9SBXg4kV6qJpqgjypjHac7nbTQpi6k184T5yShHIYQQQgghzCi9mS/Tyu4kKFlmYUlS3abG53Mvi8bFzSBNeUeCuvxIgjohhBBCCCHMSPnQsKUu2c30AuKa0llfgy67kj5obnw+N2+0zu4GabIAef4kQZ0QQgghhBBmlLalLr3JTrKzsHh2adzLkly2wj/bTqXQlPJAo3ZFm2pWdMXDaEiIz7N6iJyRoC4TWq1MDioyJ8+JEEIIIXLKqKWuovFEJFo7e7TFSuZpPeIGjiGxZiOSKtUkLvAbsLQEaxu0xf85r0KrRRn9V57WQ2SfTJSSAXt7e548eYJKpcrTddtEwffixYs3ZmkGIYQQQrxGL2NRvHiu39RaWpH8TiW0CgWKVC+NNaXLQB5/H9UWVxP/f+OM0jXO7gYLnVuvDSbBvx8ar3J5Wh+RdWYN6sLDw5k9ezanT5/mzp07zJ07l549e+r3q1Qqk8f961//YurUqSb33bhxgypVqhilb9iwgaZNjQeAZsTS0pIiRYoQExOTreNE1sXExFC0aFFzV+OVWVpaYmNjY+5qCCGEEKKASbucgdbRCWwKoS1eEkWqQCovx9NlRuPiBudO6Lctzx7H8uxxkqq9T9y/RmV7wXOR+8wa1MXGxlK+fHm6d+9OQECA0f6oqCiD7cjISLp160aHDh0yLfuXX36hYsWK+m1HR8cc1dHS0rLALShdkNy7dw83N7fMMwohhBBCvIHSjqfT/K+rY9rWsWQzBnVJNRthtXuTQcshgOXv4VjvWEeC/6dmqpnQMWtQ5+fnh5+fHwADBw402q9Wqw22t23bRtmyZfnggw8yLbtYsWJGxwshhBBCCJGfKB6laakrnvL9Nfm9ulj+r3VMa2FBcuVar71uOhrfysQN+wHr9QuxuHHZYJ/y0lkz1UqkVmDG1D1//pyNGzcycuTILOX/+OOPiYuLw9vbm4EDB9K+ffs8rqEQQgghhBDZk3aSFN2kJImN20FCHMobV0iq3wKtk4s5qqeXXKkmLyvWwPL4PmznTdCnyxIH+UOBCeo2bNhAQkIC3bt3zzBf4cKF+fbbb6lTpw6WlpZs27aNvn37Mn/+fLp27ZrucZcvX053n8hbcu3NS65//iH3Iv+Qe5H35BrnH3IvzOv59SsUS7V9Nwke6u7JOzVT/gHkl/vk4MJ7SgsUmmQAlE8ecvXcGTQ2hcxcsVeXn38WfHx8MtxfYIK6pUuX0qpVK0qUKJFhvuLFizN48GD9dtWqVXn06BEzZ87MMKjL7EKJvHH58mW59mYk1z//kHuRf8i9yHtyjfMPuRfmdfnyZRwSXxqkOZWvRLF8fk+0JUuhSNVC52NvjcYrf9c5MwX9Z6FArFN35swZIiMj+eSTT3J0fPXq1bl27Vou10oIIYQQQogc0CRjcT6Ckkd3obz1p+Gu4vl/TgiNs+Ekd8o70gXT3ApES93SpUvx8PCgUaNGOTr+7NmzMmmKEEIIIYQwv6RECv0wHIuo07ia2K0t5vTaq5RdGhc3OPXPtoyrMz+zBnXPnz/Xt6BpNBpu377NmTNncHR01E9z/+LFC9avX8/nn39ucgHw8ePHExERQUhICACrVq3CysqKypUro1Qq2bFjBz/99BPjxo17bZ9LCCGEEEIIU6x2rsci6rTJfRonF7C1e801yr60LXUKaakzO7MGdZGRkbRt21a/PWnSJCZNmkT37t2ZP38+ABs3biQ2NtZgUfLU7t69y59/GjZbT506lVu3bmFhYYG3tzdz5szJcDydEEIIIYQQeU3x4C7Wvy4zuU9rW4j4LgNec41yRuPibrCtvHvTTDUROmYN6urXr8+TJ08yzNOrVy969eqV7n5d8KfTo0cPevTokSv1E0IIIYQQIrfYrJiNIiFOv51UyJ7kDn3QuHqSXKYc2BcxY+2yTuuSZkzd3dug0YCyQEzX8UYqEGPqhBBCCCGEKMgsfj+EZWS4QdrfH/pTrEVnM9Uo57RFVGjtCqN48RwARUI8isf39Quni9dPwmkhhBBCCCHyUvxLbFbMNkhKLluBh++9b6YKvSKFImWylFRkBkzzkqBOCCGEEEKIPGQduhLlw2j9tlapJP6ToaAouF/FNc5pxtXdkXF15lRwnyQhhBBCCCHyOUX0bay2rzVIS/TrhMbd20w1yh1pW+oUsqyBWUlQJ4QQQgghRB6xWTUXRVKiflujKk5Chz7mq1AukQXI8xcJ6oQQQgghhMgDFqePYnnqiEFaQpcBUCj/r0WXGeMZMCWoMycJ6oQQQgghhMhtiQnYrJxjkJRctiJJ9ZqZqUK5S1PSFa1Cod9WPoyG+LgMjhB5SYI6IYQQQgghcpnVvlCU0bf121qFgviPP4dUgVCBZm2DtoSzQVLqzyteL1mnTgghhBBCiNyk1WK1L8QgKalRGzSe75ipQnlD4+yG8v4d/bbyr+vw8gUKTTLJ5d57cwLYAkCCOiGEEEIIIXKR8tofKP++od/WWli8EZOjpKVxcYOzx/XbNouCUCQnAZD4YXviP/nSXFV760j3SyGEEEIIIXKR1cHtBtvJVeqgVRU3U23yTtq16nQBHYDlwW2QEP+6q/TWkqBOCCGEEEKI3BIfh+WxvQZJifVbmakyeSvtDJipKZISUbyMfY21ebtJUCeEEEIIIUQusTx50CCY0Tg4kly5thlrlHeS3b3RWlmlnyHu5eurzFtOgjohhBBCCCFyieWhHQbbSfX8wPINncaisAMJXQPRWtuicShmtFsRL0Hd6yJBnRBCCCGEELlAcf8Olhd+N0hLrN/STLV5PRKbfUTsgq28mLWR5LIVDHdKS91r84a+NhBCCCGEEOI1SU7CIvIw1ltWGSZ7l0fr6mmeOr1OSgsAtDaFDJKlpe71kaBOCCGEEEKIHFJGncF24USUD6KN9r3prXRGbA2DOmmpe30kqBNCCCGEECInNMnYBn+H8tE9o13awkVJqt3YDJUyH2mpMx8J6oQQQgghhMgB5Z+XTAZ0yeWqEN9tINgVNkOtzChNS51CWupeGwnqhBBCCCGEyAGLcycMtpO9yhHfbwQad28z1ci8tGm7X0pL3Wsjs18KIYQQQgiRA5ZpgrrERm3e2oAOTHS/lJa610aCOiGEEEIIIbLrxXOUV84bJCVXqmWmyuQTMlGK2Zg1qAsPD6dbt268++67qFQqVq5cabA/MDAQlUpl8K9p06aZlnvo0CEaNmyIWq2mSpUqLF68OK8+ghBCCCGEeAtZXIhEodHotzWlPNAWL2nGGpmfTJRiPmYN6mJjYylfvjxBQUEUKlTIZJ5GjRoRFRWl/7d+/foMy7x+/TpdunShVq1aHDx4kKFDh/LVV1+xefPmvPgIQgghhBDiLZS262VSxRpmqkk+Ii11ZmPWiVL8/Pzw8/MDYODAgSbz2NjYoFars1zmkiVLcHZ2ZsqUKQD4+vpy8uRJ5syZQ/v27V+90kIIIYQQosBRXjmP5ZHdaMq8S9L7fq9WmFZrPElKxbe86yXSUmdO+X72yyNHjlC2bFkcHBx4//33+c9//oOTk1O6+Y8fP86HH35okNakSRNWr15NYmIiVlZWeV1lIYQQQgiRjyge3adQ0BcoEhOBTcRpkkl6hYXBFdF/obx/R7+ttbIiuVyVXKhpASctdWaTpaAuLi6OTZs28c4771C9evW8rpNe06ZNadu2LR4eHty8eZPvvvuOdu3asX//fmxsbEwec+/ePRo1amSQ5uTkRFJSEg8fPsTZ2dnkcZcvX87t6osskmtvXnL98w+5F/mH3Iu8J9c4/3gb7kWJiP3YJybqtxW/LOayugwocjYSqcSJvdin2n5WuixXb97KUVlv0vW3u/cA31Tb8U8fF6jPl5/r6uPjk+H+LAV1tra2DBkyhB9++OG1BnX+/v76/1eoUIH33nuPSpUqsXPnTtq1a5er58rsQom8cfnyZbn2ZiTXP/+Qe5F/yL3Ie3KN848CdS/iXqD8+yYaJ2coWti7JwAAIABJREFUosrWodYnfzPYtnl8n3Ivn5BcpXaOqmK7xXASPptaDXN0HQvU9c8ChZ1hj7hCaAvM5yvo9yLL3S/Lli1LdHR0XtYlUy4uLpQqVYpr166lm6dkyZLcv3/fIO3+/ftYWlpSvHjxvK6iEEIIIYTILS9fYHliP5YRYVicP4kiMRGtbSFejp6JxvOdLBejjP7LKM1qz6acBXVJiVj8EWmQlFyxZvbLeRPJ4uNmk+U25xEjRrBo0SLOnz+feeY88vDhQ+7cuZPhxCm1atVi3759Bmn79u2jatWqMp5OCCGEEKKAUNy5id1/PsX25x+wPHXkf+PhUha0tt703+yVZSKoszhzDMW9v7NdL+W1P1DEx+m3NariaNzKZLucN5EsPm4+WW6pO3ToECVKlKBBgwbUqlULLy8vo2UIFAoFU6dOzfLJnz9/rm9102g03L59mzNnzuDo6IijoyNBQUG0a9cOtVrNzZs3mTBhAk5OTrRp00ZfxoABAwBYsGABAH379mXRokWMGjWKvn37cuzYMVatWsVPP/2U5XoJIYQQQojXJCEey4gweBFLUu3GULgoxMdhO2eswWQkqVmcPwFxL8DWLvPyNRqU942DOoVWi9XezSR0C8xWdS2izhpsJ79bFRSKbJXxxjLVUqfVyvV5DbIc1KVewPvo0aMcPXrUKE92g7rIyEjatm2r3540aRKTJk2ie/fuTJ8+nQsXLrBmzRqePn2KWq2mfv36LFmyhCJFiuiPuX37tkGZnp6erFu3jq+//prFixfj7OzM5MmTZTkDIYQQQoh8yPqXn7HesQ4ATchy4j6fgNWBrVjc/jPdYxSJiVicO0lyjQaZlq948kDfypeW1cHtJHzUD6xNT8BnisWlMwbbyb6Vs3zsG8/SCq2FJYrkJAAUycmQlAhW1mau2Jsvy0Hd48ePc/3k9evX58mTJ+nu37hxY6ZlbN261Sjtgw8+4ODBg69UNyGEEEIIkceSErHa86t+U/nkAYUmDk4JBlJne68eWhtbrI7t1adZRoRlKagzNZ5ORxEbg+XRvSQ1yOLyBppkLC6fM0zyqZS1Y98WtoUg9tk/2/EvJah7DXI2j6sQQgghhBCvSHnjMorEBIO0tAGdxtmNuIBvSPzQcOZzy1NHICkp03OYGk+XmtWuDSldBLNS31vXULyM1W9r7YuiKeWRpWPfFjKuzjyyvfj4xYsX2bVrFzdv3gTA3d0dPz8/ypUrl+uVE0IIIYQQb660rV5paa2siBs0FgrZofGpiLaIA4pnTwFQvHiORdQpkivUyLAM5T3DoC6xblMsj+5B8b9AzuLWVSxOHSG5ar3M63spzXi6dyqBUtpIDNgaB3VZC5nFq8hyUKfVahk+fDhLlixBq9Wi/N8DrNFoGDduHP369WPKlCkoZCCkEEIIIYTIgsyCuvieg9G4l/1fZkuS3quHVdj2f46POJR5UJempS65Yk1ITsbq+D+zpVuHLOfle3VR3ryCbfB3KB5Gk9ChD4mtuhmWZSqoEwbSttTJsgavR5ZfLcycOZPFixfTvXt3Dh8+THR0NNHR0Rw+fJgePXqwePFiZs2alZd1FUIIIYQQbwqtFuVlwyAp7tOvSC6b0iIX37EvSY3aGuxPql7fYNvy90Og0WR4GkWaljqN2pXEtr0M0iyu/YHl/lBsp4xA+fcNFPFxWK9bgOJuqgn5tFqZJCULtCZa6kTey3JL3fLly2nXrh1z5841SH/33XeZM2cOMTExLFu2jCFDhuR6JYUQQgghxJtFce8vlE//mYhPa2NL0vt+JDVole4xyRWqo7Wx1a8Tp3z8AOX1S/w/e/cdXkWV/gH8O+WW9J5AKIFASKH3IhBAQKSKoMiKYMH6swsqFiyssroIdlwVO64IwkoVUZEeikBAIBACoQVCCum3zcz5/XHhJnNLMgnpeT/P4/NwzpyZ++Ze2L1vzjnvUaI9bANiDLzTWXQsogWYfxCkbgMgHtzp6Dd+tVAdH2MQk3fB1uy2q/FmgM/LKX2O3gAlKkbbD9uU0ExdndA8U3f+/HkkJiZ6vJ6YmOhyvAAhhBBCCCHuOC+9lNslAEIF8w16A+TOfVRdut9W2cvmu8EVXFHNFDGjN5hfIADAOn6a23tUMR7eU/pn51m6dgmAqKvwGU0NzdTVDc1JXVhYGJKTkz1eT05ORlhYWLUERQghhBBCGjfhhPPRAJ003Sf1GKhq63ZshNfL94NPcf2e6lz5UgmPdByErbRLgNSxZ/kxpiQDVsvVeNVLRZUOtPTSLZqpqxOak7qJEyfi22+/xb///W8UFBQ4+gsLC7FgwQIsXboUt956a40ESQghhBBCGhfeeaZO43lvUrf+YAajqk/ISIf3/CegW/df9Ws47adjES1Ubev4u8p9Lc5mhZBy0P4azkVSYqlIijs0U1c3NCd1L7zwAhITE/Hmm28iOjoaCQkJSEhIQNu2bfHGG28gMTERc+bMqclYCSGEEEJIY1BUACEj3dFkHA+5fYK2e338YH7oZTAff5dL+p+WqA6+dq58qYSrkzoltiuk7jc42nLLtrD1HaYaIxzeAy4/F/ylc6Xx8rx9+SVx5ZTUoaEmdYoM4fAeiNt/AUwldR1NhTQXSvHy8sKqVauwfv16bNq0CefO2f9i33TTTbjpppswatSoGguSEEIIIYQ0HsJJp6WXrdoCXj6a75d73IDit76BYdl/VEcccLIE8cAOSAPt30s5pyIpitNMHTgO5kfmQvfnWkBRYBs6FsLf+6Db/YdjiHh4D5RmrdTPiYoBjN6a421KXA4fb6DLL3UblsHw46cAAGnbLzA/v8ixdLc+qvTh46NHj8bo0Z6rEhFCCCGEEFIe5/10WpdeqvgFwjLzOTD/IOjXfe/oFvducSR1LjN1zkkdAOgNsI2cVBpLQg8wQQAny/ZnXDzn+HLvGBPfo/LxNhGNYvmlLEFfZimvmHIQ/NmT9braqebll8HBwVi+fLnH6ytXrkRwcHC1BEUIIYQQQhov58qXWoukuCP1c1ou+fc+oKQIgJs9deFukjpnXj4u8XDm0uV3TG+EbfjEKkbbBDSCQin8yaPgyizjBVz3VNY3mpM6xli51xVFAVePpyQJIYQQQkg9INnAn05RdcnXkdQprdqpZuA4yQbx4C6gqED1xZzp9GCBIdpCdDo2oSzrLdPBQsKrHG9j1xhm6sSDu1z6+JNH6iAS7TQndQDKTdr27duHwMDA6w6IEEIIIYQ0XvzJo+BsVkdbCQoFC4mo+gM5DlIv9VnK4t4tboqkRAK8tq++zmfhOZ4RGQXbTbdVLc6mohHM1AnJrkmd8+xyfVPunrrFixfjk08+cbTnzJmDefPmuYzLz89HQUEB7rjjjuqPkBBCCCGENBri0b9UbTm+x3UXoJB6J6r21QmHd4Pv0lc1xvk4g/IordpBCQgCn39F1W+Z/iQdOF6Bhj5Tx2VdhHAh3aWfz8kEl3sZLLh+ztKWm9SFhYUhLi4OAHD27Fk0b94czZs3V43hOA4+Pj7o1q0bZs6cWXOREkIIIYSQBk84sl/Vlis4AFwLpU0HKKHNwGdfAgBwNhsMP/5HPUbLfrpreB5y5z7gt290dNn63Qg5vvt1x9roNfCZOndLL68RUo9A6tsAk7rJkydj8uTJAICxY8di9uzZSExMLO8WQgghhBBC3DMVgz91VNUlJ1RDJUmOs8/WbVhW2nW1WMo1Ssu2lXqkdcw/IO7dAs5ihhLREtapj1x/nE1AQ5+pc7f08hr+5N9A36G1GI12mo80WLt2bU3GQQghhBBCGjkhJRmcojjaSvPWYMFh1fJs56SuLDmqg8u+u4qwyCgUL1wG4Wwa5LaxlTpHr0lzTuoa0kyduQTCsYMeL9fnfXWaC6V8++23uOuuuzxenz59Or7//nuP1wkhhBBCSNMmOO2nk6ph6eU1SnQ8FDf7nWyDbobpxfcBryocFu4bYJ9JpIROM+fDx9GAZuqEo/vBSTZHW/EPUl3nz6TW2+WkmpO6JUuWICLCc2WiZs2a4fPPP6+WoAghhBBCSOMjHHEqklKNSR04DrabpziaTG+A+b7nYJn5HGAwVt/rkPLpDWBlCt9wNisgS3UYkHbO++mkvsPUx2UoCoRTKc631Qual1+mpaVhxowZHq/Hx8fjhx9+qJagCCGEEEJI48Ll5aiqCjKOhxzbtVpfwzbiViiBIeAzz0PqOwwsPLJan0804Dh7sZQyB7bDYgZEHbgr2WChEYCgOQVxjzFwOZlgQaHX/6wyzxSSk1Rdcrf+4ExFquMx+NS/62XBHM3vAsdxyM3N9Xg9NzcXSpk10oQQQgghhFzjPEunRMcCPn7V+yIcB7nPEMjV+1RSSczoBa5MUsdfPAfjJ/PAX86A3KYDTC+851olUyvJBq+3noZw4jCU0AiYnn8XLKx5xfdVwJh1AXxejqPNDEbIsV3AZV2ErkwVVOHkETgWaDIG3e//g/j7z2CRrWGe8TTgXzfndmteftm1a1f89NNPsFgsLtfMZjNWrFiBLl26VGtwhBBCCCGkcRCOOh1lkFCNSy9J/eKUsOl++RH85QwAgJB+AuLeLVV+tJj0O4QThwEAfHYmDN++V/U4y/A7fUzVluO7Azo9lJhOqn4h9W9AUQDGoF/+GQzfvgchIx3ivq3Q/++raomlKjQndU8//TRSUlIwevRorFmzBidPnsTJkyexevVqjB49GidOnMDTTz9dqRffsWMH7rjjDsTHxyMwMBBLly51XLPZbHjllVcwYMAAREZGIjY2FjNnzsS5c+fKfea2bdsQGBjo8t+JEycqFRshhBBCCKkmjLkUSanW/XSkXnE+1kA4qa4aybs53FsrISVZ1RaTk8CfOFTl513jktRd/fupREaBefs6+rmSIhg+fwv6pR+oDrwHAPGv7QBj1x1LVWhefjl06FB8/PHHePbZZ1V76xhj8PPzwwcffIDhw4dX6sWLi4uRkJCAqVOn4qGHHlJdKykpQXJyMmbNmoXOnTujoKAAL730EiZPnowdO3ZAFMsPPSkpCUFBpRVrQkNDKxUbIYQQQgipHvz50+BzsxxtpjdAbpdQhxGRGuU0U1f2swcALutSlR/t7lgBw/LPYHrhfft+vqqQbPA9o54Acswk8zzk9h0hHtrtuKbbsRHu8HnZ4C6eBYuMqloc16FSOwvvuOMOjBkzBn/88QfS09MBAG3atMGwYcPg51f5NdEjR47EyJEjAQCPPKI+0DEgIAD/+9//VH2LFi1Cv379cPz4cXTs2LHcZ4eFhSEkJKTSMRFCCCGEkOrlvCxNjukM6A11Ewypcc4zdc747ComdYV54C+5rtoTThyGcGg35K79qvRYPu0YBFvpFjMlIBhKizaOtm3IWFVSVx7h2AFI9T2pAwA/Pz9MmDChJmKpUGFhIQAgMLDiDYhDhgyB1WpFbGwsZs2ahcGDB9d0eIQQQgghxAmf+jfEfVtVfVLimDqKhtQGl7PqnHBVTOqE1CMer+mXfwZT5z4Ar3l3mYPo7qiNMrN+cs9BMD3zFgz//Rh8xpnyn3V0P6Qbb6l0DNerSjVACwsLUVBQ4LbaZatWra47KHesViteeukljBo1Ci1atPA4rlmzZli4cCF69OgBq9WKZcuWYcKECVi3bh0GDBjg8b7U1NSaCJtoQO993aL3v/6gz6L+oM+i5tF7XH/U6GfBGGK+XqTqKo5sgxNBkQD9HQDQOP8ttLbaUN56Ob4wD2lH/oZSydnayL3b4CldFM6lIfvn73GlU99KPRMAYv7aDn2ZdkZoS+Q6fy5ewcDdcxByYDuab10NXXEBLIFhuDRwDKLWflU67u99SD1+vErJZbkxxsSUe71SSd1XX32FDz74AKdPn/Y4prxjD6pKkiQ88MADyM/Px3//+99yx8bExKh+6D59+uDs2bN4//33y03qKnqjSM1ITU2l974O0ftff9BnUX/QZ1Hz6D2uP2r6sxD2bYXX+TRVHzfjScR0iK2x12xIGuu/BX14RIVjYgJ8oLRsW6nnev2YoWorAcHg80tzj5Yp+xA6cVqlnglTMXwy1LlN6NDRCAkOcz8+Ng6W2+6GNesiWFhzhHAc2OafwBXbVxSK5hLE6gGlbZnPVVGg++Nn8OdPwzZoFJQa2E+qOYX85ptv8NRTTyEqKgovvfQSGGN4+OGH8dRTTyE8PBydO3fGBx98UO0BSpKE++67D0eOHMHPP/+M4ODgSj+jZ8+eOHXqVLXHRgghhBBCPJAkGH78VN3VbQCUuG51FBCpNRrOoKv0EkzJBv50iqrLMvM5VVs4e7LS1SeFlGRwZVYfKpFRYJ4SOsdNIlizVvaDz3nB5TBy5+M7dBuXw/Dte9BtXg2vBbPB5V6uVIxaaE7qPvnkEwwZMgQrV67E3XffDcBe6OTll19GUlIS8vLyUFBQUK3B2Ww23HPPPThy5AjWrFmDiIiKs353Dh8+XOV7CSGEEEJI5YnbfwGfed7RZhwPy+0P1GFEpLZUVCgFqHyxFP5MKjib1dFWgsMgd+oNZjA6+riSInBlDhDXQnDaTydV4agNl6TuWJmkTpGh+2V5mRiLIe7cVLkXUOQKh2hO6k6dOoXRo0fbb7q6RtRms5+nHhgYiOnTp+Pzzz+vVHxFRUU4dOgQDh06BEVRcP78eRw6dAjnzp2DJEmYMWMG9u3bh88//xwcxyEzMxOZmZkwmUyOZzz44IN48MEHHe2PP/4Ya9euRVpaGo4dO4bXXnsN69atw/3331+p2AghhBBCSBUxBt3GFaouKXEMWJmKgqQRq4GZOuciKXL7TgDPQ4lso+rXfAZecSGEI/sgHtypfm5C5ZM6KaGHqi0cPwxI9jxJOLIffF626rq4pxKHr1stML77YoXDNO+p8/HxAbs6nenr6wtBEHDx4kXH9eDgYGRkZHi63a0DBw5g3Lhxjvb8+fMxf/58TJ06Fc8//zzWr18PwF7JsqyPPvoId955JwDg/Pnzqms2mw1z585FRkYGjEYj4uPj8eOPPzqOTiCEEEIIITVLOPIXhIx0R5vxPKzjK7nXiTRYNTFTJ6QeVrWVGPvxZkqLNhDKLMvkL5yG3KmX5wcpCgxL3oa4YyM4p6WajOchx3WtVFwAwJq3hhIY6kjeOKsZ/MmjUOK6QnRzpp1w5gS4yxlg4ZHlP9higvHdFyE6Led0R3NS16FDBxw/ftx+kyiic+fOWLZsGe644w7Isoxly5YhKqpyZzIMGjQIeXl5Hq+Xd+2adevWqdpPPPEEnnjiiUrFQQghhBBCqo/uV6dZul6JYCG0FabJ0DJTl3WxwjEOjIF3OnRcjukEAKrz5ICKZ+qEfVuh2/6L22tKdALg7as9rms4DnJCd/BlllWKR/fDGtUe4l/b3N4i7t0C25ipnp9ZUgSvhc+7PWzdHc3LL0ePHo1ffvkFZrMZADBr1izs3LkTbdq0Qfv27bF792489dRTWh9HCCGEEEIaIe7iWYjJSao+202T6ygaUheqe6aOy76kqnLJ9AYordoDAJQW6gqaFSV14tG/3PYrvADrLdM1x+RMdlqCKe75A7ptG8BZLW7Hi3v/LPd5hm/e1ZzQAZWYqXvsscfw2GOPOdpjxozBunXrsHr1agiCgFGjRmHgwIGaX5gQQgghhDQ++l9/UrXl6PgaKeFO6q+KDh8HAK6oADCVAF7eHscIxw5A3PMnhGMHVP1KdBwg2tMYpWUb1TX+Qrq9AmaZw8NV108dV7WlnoMgdeqFk/4RaNO5T4Vxe+K8F4+/eA6GpR96HC+cPg7u6rEIzriCKxCT/qjU63tM6qZNm4ZHHnnEcbbbjh07EBsbi9DQUMeY/v37o3///pV6QUIIIYQQ0kgVFUDcrt5DZBs52eMXbNJIaZipAwA+5xKUltFur4l/roXxywVur8kxnR1/ZsHhYEZvcOYSAABnKgaXmwUWEu56o9UC3uncRPM9zwB+gbBd5yHwLCQctr7DoNvtORlTwiPBXy6tQSLu3QLb6Dtcxol7/gTHyhyzENGywtf3uPxy/fr1qiIk48aNw+bNmyt8ICGEEEIIaZp0W9eDs5odbSUoFFLvxDqMiNQFLcsvgfIrYOq2rvd4TepRZnUgx7nZV6c+TNzRf/YkOLn0eAAlrDngF6gpVi0s986C3DbW7TU5phNsI25V9Yl73VfBFJ0SQ9vgmyt8bY9JXWRkJP76q3TNKWMMHP2WhRBCCCGEAIAsuZyfJe7frmrbhk90LJMjTYje6LZbCVUXy+GzPCd17g7oliPbwDzzOfvyy7LP1VgsRTilPrxcbhvndlyVGb1hfvpfUNxUtbQNHAWp12CneI65JLZcTiaEE+pKn1LfYRW+tMd/ZbfffjsWLVqE5cuXw8/PDwAwZ84czJs3z+PDOI7DwYMHK3xRQgghhBDScInbNsDw/YdgeiMsD7wAuWNP+9I2py/NUv/hdRQhqUueZurkhJ7gy8zAeZypYwxcgboKfvG7K8CCQt0O11oshT+t/vvpnBxWB+YfBNOst+E171Hwhfafgfn42Wesffwgt+8E4WRpART96m9huXe2oy3uVq+MlNsluN1358xjUvfyyy8jNjYWO3bsQFZWFs6dO4fmzZujefOKH0oIIYQQQhonruAKDF8tBCfZwJUUw/DlApT8+3sIaUfByZJjnBLajI4xaKoMrjN1TBAhx3ZRLav0WAGzpEj1d4npjR4TOqAezdRdxSJawjTnXRh+WAzOYoZ14t2Aj32STOo3TJXU6basg9TjBsjd7HVMnJdeSv0qnqUDyknqOI7DlClTMGXKFABAUFAQHnvsMdx2222V+qEIIYQQQkjjIe7cBE6yOdp81kXwZ0+CP35INa4qhziTRkIQwXR6cDaro4uFNoMS3kI1jPOw/JIruKJqs4Cgcl/OJanLSAcUBeDL7DQrLgR/6VzpMzkeSpuYcp97PViLNjA/85ZLv23waOh+XwX+Ymkshi/+jZJ/fgmupAhC+okyMXKQeg/R9Hqaz6lLTk7GmDFjtA4nhBBCCCGNDWMQt7gWsBAO7oJwPFnVJ8dSUtekOS3BVCJauCwj9DRTx+U7JXX+5Sd1LCgUzNun9H6zCVxOpmqMkK4+ykBpEQUYPR+nUGMMRpgfeBGsTMLJ51+B8T9vQP+/r1RD5bhu5c5QlqU5qWvdujW8vevgByeEEEIIIfUCf+oYhIx0l35x31YIJ4+o+uTYLrUUFamPnM+qU8IjwQKCwUSdo48rLgBMxS73uszUVZDU2Stglr+vzvl8OiU6vvxn1iAlOg7W8eqDzsW/90K36zdVn5YCKddoTuoIIYQQQkjTpnMzSwcAwtmT4KwWR1sJDAVzWmpHmhbnYiksogXA8y77LN1VwOQrm9Sh4mIpwqljqranowdqi23ctHL39DFBgNR7sMfrziipI4QQQgghFbOYXIo4eCLHdqEDx5s6p6WN1/bTKaHNVP3uKmBWdk8dUHGxFJfKlzVUJEUzUYT5wRfAPCwBlW64CfAN0P646oqLEEIIIYQ0XuLereDMJZrG0n46Isd1cyzJZV4+kDt0tv/ZKanjsy9Bdrq30ssv4S6pKz2AnLuSDf5KdunzdDooraIrfGZNY81bw/TyRxB3/gquqBCQbIBkgxLV3n7GYyVQUkcIIYQQQipUthQ9AEidekP8e6/bsQrtp2vyrBOmAxwH7nIGbCMnAd6+AAAlzGmmLivD5d7KFkoBPCR1FhNg8HKdpWvdHiizt68uKS3bwnr7g9f9HFp+SQghhBBCysWfSXWpbmm581EobirzMb8Aly/YpAnSG2CdPBOWR+ZCad/R0a1EtFQNE44ecLnVdaYusMKXYwHBUEJL9+txNhvEg0n21zh5VDVWrsMiKTWlUjN1x48fx9KlS5Geno68vDwwxlTXOY7D6tWrqzVAQgghhBBShxiD4bv3VV1yTCewyCjI3fqD37xGfa0D7acjnskJ3cF4HpyiAACE86fAXc4AC490jHFO6pSA4IofzHGQeiVC/8uPji5x75+QeidC3P27+nl1vZ+uBmieqfvhhx8wYMAAfPrppzh16hQURQFjTPWfcvXDIYQQQgghjYO46zcIJw6r+qxj/wEAkLoNcBlPRxmQcvkG2BP/MsT9O1TtquypAwCpzxBVW0hOgrB/O/js0jPrmE4PqVv/SgTcMGieqfvXv/6FLl26YMWKFQgJCanJmAghhBBCSH1gKob+h8WqLqlrP8hXkzk5oQeY3qA6zkCO61arIZKGR+5xA8SUg462eGA7bKNuszcsZnBmk+MaEwTHfryKKNHxUEIiwF89eJyzWmD4aqFqjNR7CODjd30/QD2keabu0qVLmDZtGiV0hBBCCCFNhP5/X4PPz3W0maiD5c5HywwwwHbjLY6mHNMJSqt2tRkiaYCkHgNVbf74YaAwD4CbWTq/IIDXmLJwHKTeiepnX33uNbahYysZbcOgeaauY8eOuHjxYk3GQgghhBBC6gCfchBcYR7kLv0AgxEAwF06B92mn1TjbDdPAXMqdGGddB+UVu3AFebDNmCE9i/gpMliYc0ht2oH4VwaAIBjCsTkJEgDR1XpjLqypD5DVPvqylIio6DEdK5a0PWc5n91b7zxBr777jskJSXVZDyEEEIIIaQW6dYshff8J+H14aswLnweUOynhul+WwVOLj1BTAkOh3XcnW4eoId0w0j78jkNVQoJAQDZabZO/Gs7AIArUM+saal8Wda1JZju2BLHNNoiPh5n6m677TaXPj8/P4wePRrt27dHy5YtIQiC6jrHcfjxR/eZMSGEEEIIqV8M2RehX/WFoy2mHIRwMAlyp17Q7dykGmudPBMweNV2iKSRknrcAP3PXzvawt977fvpyiz3BQDmr6HyZVlXl2A6z9YxUQfbDSOrHG9953GmLiUlBcePH1f9Z7FY0LJlS5jNZpw8edLlekpKiqfHubVjxw7ccccdiI8ORB38AAAgAElEQVSPR2BgIJYuXaq6zhjD/PnzERcXh2bNmmHMmDE4duxYhc/9+eef0bdvX4SHh6Nv375Ys2ZNhfcQQgghhDQpjKHlr8tUs3EAoPt1BcT928EVF5YO9fV3qSxIyPVQomKgBIc72pzVAuHIX1U6o86Zu7+rUs9BgF/jnUn2OFN3+PBhT5eqTXFxMRISEjB16lQ89NBDLtffe+89fPTRR/joo48QExODt99+GxMnTsTevXvh5+e+as2ePXtw7733Ys6cORg3bhzWrFmDu+++Gxs3bkSvXr1q+kcihBBCCGkQhIO74HvqiEu/eOwA+CvZqj7bgBGATl9boZGmgOPss3W/rXJ0ifu3gxm9VcO0HmdQlhIdDyU0QnWUgTSkcRZIuUbznrodO3YgOzvb4/WcnBzs2LHD43V3Ro4ciblz52LChAngnTbVMsawePFiPPnkk5gwYQISEhKwePFiFBUVYcWKFR6fuXjxYgwaNAizZs1CbGwsZs2ahYEDB2Lx4sUe7yGEEEIIaVKsFhiWfujxMn/pnKotDR5T0xGRJsh5X51wbD+4Aqfll1oOHnfGcbDeXjphJPUcBDm+e5VibCg0J3Xjxo3D5s2bPV7fsmULxo0bVy1BAcCZM2eQmZmJYcOGOfq8vLwwYMAA7N692+N9e/fuVd0DADfeeGO59xBCCCGENCW6jcvBZ2VoGiu3jYPSKrqGIyJNkRzTyX4O3VV8dib4C+mqMVVZfgkAUt+hKH7nB5S8/BHMj77aaAukXKP5SAPGWLnXrVary2zb9cjMtE+XhoWFqfrDwsLKPVohMzPT7T2XL18u9/VSU1OrGCm5XvTe1y16/+sP+izqD/osah69x3WHs1nQeY26jkFWzyHwT/sbhjzXVVkX4nohhz6vGtPU/y3EhreE98UzjrZw/rTq+pm8Qpiu6z3SA2mnNI2sz59FTExMudfLTeoKCgqQn5/vaOfm5uLcuXMu4/Ly8rBixQo0b968imHWvYreKFIzUlNT6b2vQ/T+1x/0WdQf9FnUPHqP65a4ezMEi8nRZj7+8Lr3aWDHRuD7j1Rjmd6A4PF3INjbt7bDbBLo3wKgi+8GlEnqnLXq3A0sMKTG42jon0W5Sd3HH3+Mt99+G4D9uII5c+Zgzpw5bscyxvDqq69WW2AREfbzJbKystCqVStHf1ZWFsLDwz3dhoiICGRlZan6KrqHEEIIaSq4rIuAooBFtKjrUEgdEZN+V7VtA28CfP1hGzwa+pVfgjOXOK5JvYcAlNCRGiS3jYMOP3u8znwDajGahqvcpG7YsGHw8fEBAMydOxeTJ09Gly5dVGM4joOPjw+6d++Obt26VVtgUVFRiIiIwObNm9GjRw8AgNlsxq5du/D66697vK93797YvHkzHn/8cUff5s2b0bdv32qLjRBCCGmIdL/+BP33H4JjDJZJ98E2/q66DonUtuJCCIfUdQak/jfa/+DlA9uw8dCv/wEAwDgOtmHjaztC0sQo0XEerzFff0DUvFusSSv3XerTpw/69OkDwH78wLhx49CxY8dqe/GioiKcOmVf46ooCs6fP49Dhw4hKCgIrVq1wsMPP4yFCxciJiYG7du3x4IFC+Dj44PJkyc7njF+/Hj07NkTr7zyCgDgoYcewujRo7Fo0SKMGTMGa9euxbZt2/DLL79UW9yEEEJIg8MYdKu/BXd1j7x+zXewjbod0BvqODBSm8R9W8FJNkfbHBQOpU2so22ddB9gMUNIPwHbkLFQ2lff9z5C3FEiW4MZvcCZTS7XqnKcQVOlOfX9xz/+gdatW1frix84cEBVMXP+/PmYP38+pk6disWLF+OJJ56AyWTC7NmzkZeXh549e2LlypWqM+pOnz6NFi1Kl5D07dsXX3zxBf75z3/izTffRNu2bfHFF1/QGXWEEEKaNosJfGGeo8lZLeAzL1BVwybGeenllU594Fe2KqCog3X6k7UcFWnSeAFym1iIKQddLimU1GmmOanr2rUrWrRogQEDBjj+69Chw3W9+KBBg5CXl+fxekX7+AD3h6RPmDABEyZMuK7YCCGEkMaEK8x36eMz0impa0K4K9kQjh1Q9V3p2Ad+HsYTUluU6DjATVLHAiip00pzUvfee+8hKSkJu3btwvLly8FxHEJCQtCvXz8MGDAA/fv3R9euXcE18jMgCCGEkIaIK7ji0ud8HhRp3MQ9mx3LbwFAjuoAS2jDrVxOGg/Zw746Wn6pneakbvr06Zg+fToA4OLFi9i1a5fjvw0bNkBRFPj5+eHMGc8lSQkhhBBSN7hC15UxlNQ1fuKu3yBu+wVcUb7LYeNSv2F1FBUhakp0vNt+Suq0q1I5mebNm2Po0KEwGAzQ6XQwmUw4deoUiouLqzs+QgghhFQDrsBNUpdBv4htzPgTh2D85J9urzGOsyd1Oa7LcgmpbSw4HIp/EHinFQWU1GmnOam7dOkSdu7c6fjv+PHj0Ol06N69O2655RYMGDDAUSmTEEIIIfWLu5k6LvM8INkAUWfvsJgAg1ctR0Zqim7zGo/X5NiuYMHhlNSR+oHjoETHgT+4S9VNSZ12mpO6+Ph4CIKAwYMHY9KkSejfvz969uwJg4FKIRNCCCH1nbuZOk6WwWVeAAsKhdfbs8CnH4fccxDM//cKwAt1ECWpNlYLxP073F5SgsNgveuJWg6IkPLJ0fEQnZM6KpSimeakLiIiApmZmTh48CCMRiMMBgO8vb3RpUsX8DxfkzESQggh5Dq5S+oAewVMYftGCKdTANjPMRN3/gZp4E21GV71YQy6tUshHtwFqUtf+wHrTbCIm3BoDzhziaOtBATD/OwCML0RLLQZQN/dSD3j7hBymqnTTnNSl5KSglOnTjmWXy5ZsgQvv/wy/Pz80Lt3b8cxB/3796/JeAkhhBBSBVyha/VLAOAvnIF4YLuqT/xrW4NN6oT922FY8bn9zyePAAYv2EbdVsdR1T5xz2ZVW+qdCKUlHV9B6i+5baxLHyV12lWqUEp0dDSio6Mxbdo0APYqmJs2bcKHH36IN954AxzHIScnp0YCJYQQQkjVcQXu906JyUngL55T9QlH9gFWC6BveFssnJcc6n/+CrYBIwD/QAiH90K/6gswbz9Y7nkGLCSidKAiA6YSwKcRnNpmMUM8uFPVJfUdWkfBEKKRbwCkjr0gHtkHwL7vEwZjHQfVcFS6+mVBQQF27drlmLFLTk6GzWaDTqdDt27daiJGQgghhFwnTzN1wqljrmMtZgjHkyF3bngF0IS0I6o2V1IMw8olkHoMhHHRHHCKYu//8FWY5n4McBy4jDPwevsZ8FeyYRt4Eyz3Pdug9xQKyUngLGZHWwkKhdK+Ux1GRIg2lodehLL+B4AxWMf8o67DaVA0J3XPPfccdu7ciWPHjkGWZfj4+KB3796YNWsW+vfvj169esHLiypmEUIIIfUOY26rX5ZHOLir4SV1RQUus44AIP65DuLOTY6EDrAns/ypFCjt4mH4ehH4K9kAAN32jZCj4yHdeEuthV3ddLv/ULWlPkNpDx1pEJh/EKx3PFzXYTRImpO6FStWoF+/fpgyZQoGDBiArl27QhAa7m+xCCGEkCbDXALOZqvULeLBnbBOe7xBFRkR0lxnHQGAYwpQZubqGt2fa2ATRYgpB1X9huWfQe45CCwwpEbirFGmEgjJSaouWnpJSOOnOalLS0uryTgIIYQQUkM8Vb4sD5+dCf7C6QZVXENIO1qp8WLSH+CKClz6OVMx9Es/hOX/Xqmu0GqNeGAHOJvV0VZCI6BEx9dhRISQ2lDpPXWyLCM5ORlnz54FALRu3RrdunWjYw0IIYSQeqqySy+vEQ7ualBJHX9SvZ+OefmAMxWr+zjePnMHgLOaIe5XV/68RrdnM6SBoyB37ev2unBwJwxfLwJns8Iy5SFIg26uhp/gOlkt0K/6UtUl9RnaoGZbCSFVU6lMbOXKlejUqROGDx+Oe+65B/fccw+GDx+Ojh07YtWqVTUVIyGEEEKug9aZOilOXfDM+SDgek1RXIq+mGc+D6YvrZ5n6zsMtvHTND/S8M0ioMxZb9cI+7fD+P7L4HOzwBXmw/j5WxA3r3aJB0UF4C6eBX/yCLisi/a+GqRf/S34yxmONuM4SDc0zKMpCCGVo3mmbt26dZg5cyY6dOiAp59+Gh06dAAAnDhxAl988QVmzpwJg8GA0aNH11iwhBBCCKk8rkBd+VIJbQY++5Kqj3E8rHc+CvHlmY4+/uRRoDAP8AuslTivB59xRjUrx3z8IPe4AabnF0HcsRGseWvYho0Hl5cL3ervHLN1ZdkGj4a47RfHNT77Eowfvgrzk28Cov0rk5CcBOOHr4KTZdW9xq8WwqwogM4A3Y5fwKceASdLqjFMp4fSvBXk2K6wTroP8PK5vh9algAGQBTBnz8N3fof1D/PsAlQWra9vtcghDQImpO6d955B926dcP69ethNJb+1isxMRHTp0/HqFGjsGDBAkrqCCGEkHqGK1SfUSfHdQW/XZ3UKe0SoLRuD7lVOwjn7PvoOaZAPLQH0g0jay3WquKd9tPJ7RIAnofSLh7WdqV7ylhIOOSufV1mIZm3Lyx3Pgpm9IL+158c/eLhPTB88W9YZjwFcddvMHz3nkuydo3xm3fLjZGzWSGcTYNwNg385QyYn5pf5aWR4tYNMHz7HiBZ7XvmSopVcSmBIbBOnlnOEwghjYnm5ZfHjh3D7bffrkrorjEYDJgyZQqOHXNfdYoQQgghdcdlpi6yDZhfgKpP6mI/vkDu1l/VL/y9r2aDqyaC0346uV2Cx7G2IePc9xm9YZ08E3JUB9U13Y6N8Hl0AoxfLnCpIsqqWAlcTE6C6HT0gFbC/u0wfPE2OKsZnKJAOHkEQka6aozlzscAb98qPZ8Q0vBoTuq8vLyQk5Pj8Xp2djadU0cIIYTUQ86FUph/IGSniojXkjmpUy9Vv5D6d80GV02cZ+qU9h09jpW79IESHOZoM0GAbfhEe8PgBfMz/4ISFqm6h7NaXJ5jnvEUzI++5jGxY0ZvKGGRkKNiwHz8Xa7rl34IuKm+WR7+TCqMi/8JjjGPY6Su/SD3TqzUcwkhDZvmpC4xMRH/+c9/sHPnTpdrSUlJ+PTTTzFkyJDqjI0QQggh1cC5UArzC4T11nuhBIWC8TysN0+BEhUDAFDaxqmSFD4rA1ye51/q1gslReAzzjiajOMgR8d5Hi+IsDzwApSAIDCjNyzTnwILCS+9PyAYpllvQ/Gwl5AJAszTn4Q0bALkHgNhfuRVMG/7/jglOAzWcdNQ/NZ3KP7PepQs+B6m1z9D8cerUTLvczBR53gOX3AFhmWfaP4xubwcGN99AZzV9cw9R2wGIyx3PUEVLwlpYjTvqXvttdewa9cujB07Fl27dkVMjP1//FNTU5GcnIyIiAi8+uqrNRUnIYQQQqrIdaYuCEqbDihZ8ANgMQE+fqUXDUYoUR1UlST5k0cg9xpcW+FWmnAqRTVzpURGVbj0UI7vjpJ3V9grUpZJtK5hzVrC/My/4PWvp8FdrYDJfP1hG3QzbEPHgUW0LH1Wr0Eoju8GrrgQLDQC4N3P3Cmt28M6bhoMZY4d0G1dD0gSmK8/WFAopAEj3B96rigwfjAXfG6Wqtty+4NgfoEQju0HFAW20XeAhTUv92cnhDQ+mpO61q1bY/v27Vi4cCE2bdqE1avtpXtbtWqFRx55BE8++SRCQ0NrLFBCCCGEVI3rTN3V/XSiCIh+LuPl9h1VSZ2Q+nf9TeoYg7h3i6pLKWc/nQoveEzAAPusZcn8ryDu3gwlJMK+RFVvcD/Yxw/Mx/W9dGYbMxW63X+oZhZ1O38t/fOmn2B65ROXxE44vNdl36B1xCTYxkwFAEiD68E5eYSQOqMpqTObzVi1ahU6dOiAN998E2+++WZNx0UIIYSQ6sCY2z115ZE7dAJ+XeFoC6mHS5918az9j5FR1RtnVUg2GL5aCN22DapuuZz9dJXFgsNhu3lKtT0POj3M9zwD7zced3uZz82C8f2XYZrzLqDTl962dZ1qnNSxJ6xTH66+uAghDZqmPXVGoxFPPPEEDh8+XNPxEEIIIaQ6lRSpSt0zvREwlF/YTInprGrz6amA1QL9qi/hM2cGfObMgO5/X1ctHoupeg7hLimC8Z3nXBI65usPqeeg639+DVI6dIF1nOdD0IW0ozB88y5wdUkpl58L4cAO1RjrLXcDguYFV4SQRk5zoZT27dsjMzOzJmNx0blzZwQGBrr8d/vtt3u8x934L774ohajJoQQQuoP5zPqKpqlAwAWGAKlzL4sTpYgJv0B3ervHH361d8ATss6KyJuXQ+fRyei69uPQdi3tVL3qgNkMH46H+LR/epuHz+YHnsd8HWtNFnfWCfdh5LXP4P5kbkw3zMLUlw31XXd1vXQ/bYKACBu36g67FyJjIIS06lW4yWE1G+af8Uze/ZszJ49G2PHjkXHjtW3rKE8mzdvhlzmf8QuXbqEIUOG4JZbbin3vvfffx833XSTo+3vX///x50QQgipCc5n1DEPFR2dyTGdwGdddLQN338IjpXOsHGyDN3OTbCNuk1bIFYLDN9/ZD9bDYDx0zdREtMJLCBY2/1lCAd2QnSauVLCImF65l9gzVtX+nl1guOgRMU4qo5KvRPh/dpD4DMvOIbov/8QSkgEdFvUSy9tiWOouiUhREVzUrd9+3aEhoZi8ODB6NOnD9q2betyLh3HcViwYEG1BedceOXbb7+Fn58fJk6cWO59AQEBiIiIqLY4CCGEkIaqsvvprpHbd4Ju56bS55iKXcaI2zbAdtNkTQkGf/ak6hmcxQz9/76GZcZTmuJxsFpgWPqBOtaoGJhm/RvQ+LPVSz5+MD3xBrxff8RRbZNTFBjff1mVTDNBhG3AyLqKkhBST2lO6souYUxKSkJSUpLLmOpO6spijOHbb7/FlClTKjzk/Pnnn8dTTz2FqKgo3HXXXbj77rvB85pXmhJCCCGNhrsz6rTQsrxPOH8KfPoJKG1jKx578qhLn/jnGlhHTgILi4Ru82rwZ09CGjACcnx3j8/Rr/0efPYlR5vxPCwzn2/YCd1VrEUbmB98Ecb3X3Ic0VA2oQMAqcfARvGzEkKql+ak7sqVKxUPqkGbN2/GmTNnMH369HLHvfDCCxg0aBB8fHywZcsWvPTSS8jJycHs2bPLvS81NbU6wyWVQO993aL3v/6gz6L+aEyfRUR6GiLLtHNlhgwtP5+ioIvBC4LFVO6w4jX/xfmb76zwcW2S98D5MABOUSD/Zz4EqwWGC6cA2Gf/0ic+gLyEXi7P0OdeRvzapaq+rF5DccGiAI3lM/MLR8jN09B6/bduL5+J6Y7Cav5ZG9Pf94aI3v/6oz5/FtfOCPekwZRN+vrrr9GjRw907ty53HHPPvus489dunSBoih45513KkzqKnqjSM1ITU2l974O0ftff9BnUX80ts9Cv1t9Dltg67bw0fjzsQ6dgMN71X0cr5o9Cj22D14PzfF8fttV3pfPue33P31M1eYYQ5ufl8Dcpi3krv1KL0g2GFcuBl+mkqcSEATve55CTAUHjTc4MTGw+PnAsOwTVbcSGoFmI8ahWTWuPmpsf98bGnr/64+G/lk0iDWJWVlZWL9+PWbMmFHpe3v27ImCggJcvny5BiIjhBBC6jfXPXVBmu+VY1x/kWobP6308HIAXEmRS9ESlxjyc1VLJivCyRKMH8wFn3LwaiASjIvnuVS7tE55GGhsCd1VttF3wDr+LnXfiEkAbSchhLiheaYuKCgIXAUboY1GIyIjIzFo0CA8/vjjaNu27XUHCADff/89DAYDJk2aVOl7Dx8+DKPRiICAgIoHE0IIIY1MVatfAq776pjRC9abbgNMJdCXOZxc3LoBUt9hHp/Dp6ln4yQvX4imonJfm7NZ4fX2M5B6DganyBCdjkCQY7tCGjBC64/SIFlvvRdKWCTEXZugxHSCbeTkug6JEFJPaU7qnn32Waxbtw7Hjx/H8OHDER0dDQBIS0vD77//jvj4eCQmJuLUqVNYunQpfvrpJ6xbt67C5ZIVYYzhm2++wa233gpfX/Vv4z799FN89tln2LvXvjRkw4YNuHz5Mnr37g0vLy9s27YN8+fPx4wZM2AwlL8shBBCCGmMqnJO3TVybBfILdtCOH8aAGAdPx3w8YM06GZVUicc2Qfh732QO7nugwMA4ZQ6qbvSsTcCOQbd3j/tMQkCrLc9AKY3wPjNu6WxyzJ0eza7PE+JaAnzI3Mbf1l/joM0+GZIg2+u60gIIfWc5qSuefPmuHLlCvbu3Ys2bdqorp06dQpjx45FbGws5s2bh7S0NIwYMQLz5s3Djz/+eF0Bbtu2DWlpafj0009druXk5Kg2NOp0Onz++ed48cUXoSgK2rRpgzlz5uD++++/rhgIIYSQhsplpq4ylRMFEaY570Hc/QdYcDjkbv0BAErrdpDbdICQfsL+GozB+Mk8lLz2GVhIuMtj+DR15cviFtHwGns7WPNW4AryYBs6DkqbDgAAi9kEw4//8RiSEhoB03MLwQJDtP8chBDSyGlO6t5//33MnDnTJaEDgOjoaMycOROLFi3CtGnT0K5dO9x777347LPPrjvAwYMHIy8vz+21OXPmYM6cOY728OHDMXz48Ot+TUIIIaRRUBRwRU4zdZVYfgkA8PWHdOMtLt3WKQ/B+PYzpaX3C/Nh/PAV2IZPhLhtA/iMdCgxnWG+ZxaEUymqe0tatAX0Blgn3efyXNuYqWBBodCv/gb8RXVxFSUwFKbnFrlNHAkhpCnTnNRlZGRAEASP1wVBwIULFxzt1q1bw2q1Xl90hBBCCKm6kiJwsuxoMqNXhVUqtZITesA68R4YVpaeYyucOgbh09Kllvy+rfDKuew4TBsAmI8/LEHlJ2XSgBGQ+g+HkHIQ4ubVEI4fAotoCfO9s8DCI8u9lxBCmiLNSV1cXByWLFmC22+/Hc2aNVNdu3jxIpYsWYK4uDhHX3p6OsLD6TdphBBCSF1xrjhZ6Vm6CtjGTYOQdhRicpLHMcJp9Syd3C5e2144joMc373cg8gJIYTYaU7q5s2bh9tuuw09evTAzTff7Khsefr0aWzYsAGyLOOjjz4CAJjNZnz//fe0FJIQQgipQ4JTsqW0qJ6q1A48D/ODL8J77v2ajyyQo+OrNwZCCCHak7pBgwZh48aNmD9/PjZs2ACTyQTAfoxBYmIinn/+eXTr1s3Rl5KSUt7jCCGEEFLDxP3bVW2p+4DqfxEfP5ifeAPG914En30Jin8QpD5DoNuyFpzN5jJcaZ9Q/TEQQkgTpzmpA4CuXbvihx9+gKIoyMrKAgCEhYWBp4MwCSGEkHqFy73sqE4JAIzjHNUrq5vSuh1K3vwKXFE+WEAIIIpgYc1h+O/HLmPl6HggQ/tB5IQQQipWpWyM53lEREQgIiKCEjpCCCGkHhIO7FS1lXYJNXsMgMEIFhIBiPbfF9tGToYc21UdQ/NWgI9fzcVACCFNFGVkhBBCSCMk7t+haks9bqjdAHge5vufB/P2dXTZ+o+o3RgIIaSJqNTyS0IIIYQ0ACVFEI4dUHVJ3Ws5qQPAwpqj5JXF0G1eAxbWHLYhY2s9BkIIaQooqSOEEEIaGfHwHnCy5GgrzVqBRUbVSSysWStYpz5SJ69NCCFNBS2/JIQQQhoZoa6XXhJCCKlVlNQRQgghjYkkQTykPp+uLpZeElJVpwsk/H7BjHNFUsWDCSEAaPklIYQQ0qgIx5PBlRQ72opfIJ0NRxqMNWdMuO/PXFgVe7uVr4A+YXpIjCHHrKDAytAtVIdXevojxCjUbbCE1COU1BFCCCGNiHBkn6otd+sP8PTll9R/F4pl/N/2K46EDgDOFck4V2RSjTuUa8Om82Z8MigYiZGGWo6SkPqJll8SQgghjYhw5C9VW+7cp44iaVjOFUnYfskCSWF1HUqTxBjD4zuuoMCq7f2/WKLglo3ZeP2vfJgl+swIoaSOEEIIaSyK8sGfSVV1SQnd6yiYhuPXc2b0WpmJsRuyccP/LiO9kPZy1bavT5Tg9wsWVZ/AlX8PA7DwUBF6rczENyeKKSEnTRotvySEEEIaCeHYAXCs9Iut3Lo94BdYhxHVf7lmGY9svwKLbG8fz5cwYm0Wlg0PQY8wfd0G18iczLfhp9MmnC2ScblExmWzfZ2lv47D/mybauwNzfRYPiIE+7NtOJkvwVfHIcTAY9slCxYdKkLZ9O18sYzHd+Thg7+L8HliELqG0OdGmh5K6gghhJBGQnReetmxZx1F0nC8vK8A2WZF1ZdlVjD2l2x8MSQIo1p5ub1PVhh+u2CBRWYY1sIAX139XPxkkRmOXrHBLDNwADgAPMeB4+x/PpbH40BaCTJNMgw8h9vaeSPI4Pqz5FkUrDxtwpaLZrT2FXFfnA/a+Gn7GmmRGRYeKsTCQ4WwKRWP9xE5fDQwCN4ij4HNDBjYrHTf3NAWRgyJNOLBrbm4WKJ+WGq+hIkbc7B7YjjCvJrmPlLGGHZmWnHFoqBHqB6RPk3zfWiKKKkjhBBCGgmX/XSU1JVrS4YFS1NL3F4rkRju/D0Xq24KxeDm6mIcWSYZMzbnYmemFQAQYuDxeGdf3BfnA0kBThZIuFAswyQxWGQGmQExASL6Reih4ytYU1hNLpXI+DylGF+mFCPHUl4mZQRwxdFaeKgQnw8JxsBmBjDGsDnDgq9PFGPDWbOqgMlHR4owLsqIxzr5oZfTjOb5Igl/ZFhgkhisMsPSkyVIydO+pPWNPgHlJoyDmxuw85YIvH2wAEtSilVx5VoUPJuUjy+HBmt+vbrAGEN1bwU8kWfDM7vysO2S1dHX1k/AoOYGPNLRF3GBuup9QVKvUFJHCCGENAJc1kXwlzMcbSbqIHforOlexhj+zLDgr2wbbm5lRMfgxvfljzGGj48W491DhQCAHg7KJTAAACAASURBVKE6HHVKNAL1HPLKFOqQGXD/llxsmxCO8KszP/uzrLjrj1xcKJEd43IsCl7ZV4B/7i8odybKX89heAsj7mjnjZGtjNX405XKsyiYuy8f/z1ZomlWzNklk4Lxv2Tjvlgf7My04MgV98mYwoCf0834Od2M++N88GbfAIgc8M6hIryxvwBVzVdubeuFGR28KxwXZOAxv28gHunoi1f2FWDl6dIKmavSTZh0xoSxUe5nWesCYwxvHijED2kluGJWUCIzKAxo7WXEY1IR7orxgVHkwBjDmSIZPAe08hHAcaW/BEjKtGDTeTM6B+sxurUR+qubDvMsCj78uwjv/e06E3q6UMbpwhIsSyvBt0NDauzvHal7lNQRQggh9Yywdwt0W9ZBadYStqHjwVq0qfge51m69h0BQ8VfatMLJczelYdNV4tULEguwJpRYegd3nj2JeVZFDy87Qo2nDM7+jaeVxfl4AD8OCIEh3NtmLUr35GUZJoUPLD1Cr4bFowvUorxxoECx/47ZxUlUQVWhpWnTVh52oQnO/vi1V4BVf+h3NiXZcW9f+bibJGHADVSGPBZSnHFA6/6LKUYR67Y0MJHwPJTpnLHhhl5PNnFD218BYR7CRA4oNCmIN/KEGLk0S9cr0pkKtLKV8R/BgchrUBCck7pvrxnduVhYDMDAsssJc2zKEjNlxDtL9T6GXdLUorx7+RCl/6zJh6zk/KxILkQPcP02HvZiqyry4Fnxvng3/0CwHEcdl6yYPwv2Y7ZvTAjj6ntvZFeKOGXc+pZVHcsMnDnHzlYkhiMkS2N2HTBjL+yrIgL1OH2dl7gK/Gek/qJkjpCCCGkHhGO7ofXh6/YG4f3QL9pJaTOfaBEx4O/cBp8xhkwUYRt1BRIA0YAV7+MCUcrXnqZb1Ww+EgR0gslGAQOMgN+OmWCSS6dVzHLwD1/5mLr+DAEN/DDnRXGsPWiBU/syMOZChKd++J80CfcgD7hBlwsUbCgzBfwPzMsiP3hEordrJfjOXsSVFnvHi5C1xAdJrateFbKnRN5Nvx2wYIcswyB55BnUfBFSrHbJX3+eg7xgTowBjDYZ4gY7HFzNjOign3Bc1DNdrnT1k/A2Cgv/H7BjKNOM3jXlqKW5x/tvfFGnwC3e/auh47n8OHAIAxdfdnx82eaFIz7JRvDWxgQ6iXgt/NmbLtogcSAAD2HL4cEY1iL2pm1umyS8fr+gnLHZJoUrD9rVvV9nlKMvuF63NLWC7N25ak+2yyzgvf/LvL4vGg/AWeLZNU9NsX+b9tb5FBoK71QZFMwM963cj8UqXcoqSOEEELqC3MJDEvedukWD+8BDu9R9Qmfvglp1yZY7n4GLDgc4tH9quvOSZ3CGKb+lqPpy/f5YhkPbr2CZSNCGuRv8C+bZHx9vBhLT5YgvbDiWauWPgLm9vR3tJ/v5oddmRbsKLM3yV1C90hHHzzV2Q//OVaMT48VocDKoOOBaD8Rbf1F+Os4GAQOJtm+vNW5IMtj2/PQMUiHDhr3OhVYFbx3uBCrz5iRml/xHrVoPwEPd/TF1PbeHgu5pKamIiamNQDgH+3NeGDrFeSW2YPHc8C4KCMeSvB1zKK93ssff2RY8OTOPJzzkCwHGThMausNg8DBV8dhZEsjetZgNdHOwTo82cVPlYwfzrXhcK7NZWy+1b5fcsXIENzQ7PoPL79skrH+rBk5ZgUBeg6BBh4tfQT0CtND5DnM3Zuv+fw9Z3P25ONEvuSyVNiTZl483uwTgIltvVAiMXx1ogQv7sl3XJcZVAkdAHx8pAj3xflUaoaU1D/1OqmbP38+3nrrLVVfeHg4Tpw44fGeI0eOYPbs2di/fz+CgoJw991349lnn6W/qIQQQuo9/fLPwGdf0jxePLwXwpwZYAHB4ApLv7gxbx8obTqoxi5LM2lK6K7ZdMGCdw8X4ekufprvqQ92Z1ow5bcc1d64snqE6vB67wCk5Us4kG2FQeDwRGc/+OtLkx6R5/B5YjAG/XzZJREDAD8dh3f6B+L2dvZZtpd6+OOF7n7IMikIMfIQ3RRDkRWGPzIsuPP3HMdSuSKJ4a4/cnFrtBculcjgwOHuWG90cVOSP8+i4Ma1l5FWoG1p5UMJPnitVwAMFR32VsbwlkZsmxCOV/fl41CODYOvFtho66/+ushxHG5sYcSf48Jw9+ZcVWEOwF4UZtnwEET71+7XzNld/bD2jElTURaTzDBlUw5+HhVapWSTMYYtFy348ngx1p0xu50hbecvYHK0N35IU8+Avt7LH4909EWOWcG87eewKlPv9pcGAJBtVtwu23TW1k/A1PbeeDDBFwFX/y776Dj8X0dfBOo5PLYjz+OM8qlCGX9l21wK3pCGpV4ndQAQExODtWvXOtqC4HkpSEFBASZOnIgBAwbgjz/+QGpqKv7v//4P3t7eeOyxx2ojXEIIIUQbRYFu3X/BXzgNFt4CSmAw9L+tUg8JCAafn1vuYzirBVzWRVWfHNcdEEr/L77QpuC1ffnOtzoEGTi81isA350owZ6s0i/o/9xfgIHN9OgTfv2zGbVhd6YFk37NQZGHL8j3x/vgn73tic7AZgbMiPXx+Kzm3gI+HRyESb/mOPbXeQkcHoj3weOdfV32ZPEchwhvz99RBJ7DiJZG/KtvIJ7elefoP54vYf6B0i/t36UW453+gbirgzq253bnaUroggz24wBGt65akZAWPgI+S9RWOTLEKGDlTaF4eW8+Pjlq34M3NNKAL4cEq/ay1RaDYF9W+Y/fc3BawwxtkcRw66/ZWDUytFJnEjLGcPefufg53VzuuLQCGW8dVCdkCUEiHu7oC5G3/315oq0Nrw9uhd8uWFBkY+gRqsOGc2aX+67x13HYMj4cmzMs+O2CGS18BNwW7YXeYZ73It4Z4wOjwOHBrVccyScHqIrZLEsroaSugav3SZ0oioiIiNA0dvny5TCZTFi8eDG8vLyQkJCAEydO4OOPP8ajjz5Ks3WEEELqDf1PS6Bfu9TjdSUsEiVvLAF/7hTEAzsBmxVKizZgweHQr/0OQkqyx3ulrv1U7UWHCnHJVDrjZBCAeb0CwAAEG3gMb2lEkIHHsEgDBq/Ociy/Uxjw2l8FWHdz2PX9sLUgKdOCyW4SOoMAjIvywr2xPhhQyaV2w1oY8d/hwViaWoIOASIeiPctN3HT4p5YbyRdtuDHNPf716wK8NiOPBzKteHNPgHQ8RzWnjFhmdN4kbOX9u9ztaCNxIDm3jzGR3nV6hltOp7Dv/oGYmacDwqsDN1DdXX6fSs+SIe/JkXgdIGM/dlWHMixIsukID5Ih7GtjVh/1oxX/yrd35ZvZRizIRsfDwrUvL9x7VlzhQmdJ+/0D3Q51iLYKDhmfQEgIUiH1ekmHHMz4zinuz/a+tuX994b5/mXEs4mRXujW4geWy5a0CFQRLZJwd1/lv7CaNVpk+PvG2mY6n1Sl56ejri4OOj1evTq1Qtz585FmzZt3I7ds2cP+vfvDy+v0t9O3XjjjXjjjTdw5swZj/cRQgghtaowD7pfV5Q7xHLfbMDgBaV9R1jbd1RdM3XsCXHreuh/WgK+4IrqmtS5D76KGISl67Pgr+fRLUSHD50KKjza0RcPJLgWRmh5tZLgbZtyHH07Llmx85Kl0glRbbHI/8/enYdHVZ4PH/+eZZbMZJmEhASSQCAQ9k1AAVlERHBFVBZ366617a9vtVbbilrrQqvWurV1raB1qVo3UBRBWRVkFRHCvofs2+znnPePgcCQhISQkATuz3Xl0pmzzHPOMHPmPs/z3LfFi+sreHRlebUhbL/qHcuv+8YdV6/R+MyYWguQN4SiKDw11MMPRaFqyUYO9+L6SpbkBbk+x1Vjb8+s81KapTesNl0SWk4ZDFVRyE7QyU7QmZQdHajleGxUhK2ouXc+w+Jn84v5qSTM//WJI0avPbAxLYtHV1ZPetI1QWdshgNvyGKfz+TLXdWHZF7V1cXQ1Lo/R3ZN4ekzPYz7tCCqN62nR+fmHvUP5I508JwA+MMW8Xalaq5fgd9k3u6AlDxoxZSSkpJGLn3YeL744gsqKiro2rUrBQUF/OUvfyE3N5elS5eSlFR9aMDEiRNp3749zz33XNVzO3fupE+fPsyZM4fTTz+91tfKzc1tkmMQQgghjpT29Ye0W/BJrcvzB41m1/gr696RaWIvKTj00O7gzbI2/GVL7cOoku0m7w30c7QOp5//4OC7kkMrDPEYPNM7UPsGzcCyYG6hxrPbbOz2Vw9ubu0Q5KYO9S94faKVhuD13TYKgwrJdgu7Aq/v1gmYR+8p0RSLf/fz0y22xf58a/EsC57fbuO1XdUDUU2xyHGb9Is3GZ9i0Csuek7lF/ka9204FJgpWDzVM8CwRJPDOyh3+RRe2mlj9n4NE4UOTpOX+vlJPIbY9y+bbbyzN7KBisXzfQIMTGhA8cFaPJxr58O8Q/0741LCPNyt/vNuxYnVtWvXoy5v0T11Y8eOjXo8aNAg+vfvz5tvvsmdd97ZqK9V14kSTSOSdUvOfXOR899yyHvRcjT5e+H34l7xTdRTRtfeKCWFKKVFhPsPI+ame+jqqO8d825V//flLj9PrC48yrrw8Blt6Nfl6MPMpsUFuGD2oWBxaYlGmadjo2UvPPIcm5bF7B1+VhWGmJAVQ+86ip/7wha/WlTMO7XURPv9gDju7h9f47KWZFDP6MfXFAa56quiWjNKAvy2fzwX9s9otDacqt89f8uBQbmV/HpxSVR9QcNSWF+hsb5C4609Ngan2LilRywTsmLQFPj32v3AoZsFl3V2cf0Z1d+PrsDovrCrIszG0jCD29qJqyED6dHO//PZFp1Xl7OuOMTVXd2Ma+RetJvjAnx42Of8myIbaVmZNbbzVNDaPwstOqg7UmxsLN27d2fLli01Lm/bti35+flRzx183LZt2yZvnxBCCFEX29efolQeGr5luePw/WY6xDSsXtlBPxaH+Nn8oqPWTBucEik0XJcz0xwMTbWz5LBsmY+uLGNipxi+3hvAqSn8pm8cHeOO7WdEvs/gnz9WUlJs447UMJ3jdYr8BrctKGbOgWLgT6wp58GB8dzZO7bGuVn7vAZXzS3k+4LqqerjbQrTBsVzY/fWWXOrbxs78y5K4deLS/hku58j38p+bWytLhtpS3Z1Vzed43Su+aqIwkDNPWDL8kMsyy/m3u9KGZxiZ8NhpSRUBe7pf/T3IyNWJyO2YT+3dVXhdwOa7ubEsFQ7GW6NXZWRmwg+w+KSzwqIs6t4QxYlQZPigIldVbi1p5tf9pF/ey1Zqwrq/H4/ubm5jBgxosblp59+Og888AB+vx+nM3I3Y968ebRr146OHTueyKYKIYQQ1YVD2D57J+qp0JhL6h3QWZbFd/uDVIYtBqbYSbCrGKbFh9t8/HFZWVT9KVWB/9c3jl0VYTaUhsl0azwx1FPvunN394vj0jmHev2+3B3gy92HhmB+vtPPrPNSqubo1CVsWlw2p5A1RSHAxss78zi/g5PVhaGqH5UQSc7yx+VlLC8I8rNubjaVhtlablASNKkImXybF4xK+gKgKXBDNzf3DIgjuZUXTE92asw4uw3bysPM2FjJzFwveT6Trgk6L49KlEQWjWxYmoP5F6fw5Jpy5u0J1FrXsMBvMntndHKUKdkuuraguYTHSlUULu8cw9/WHppzW9PNEoD7l5fhtimt9obJqaBFB3V/+MMfGD9+PBkZGVVz6rxeL1dccQUADz74IN9//z0fffQRAJdffjmPP/44d9xxB3fddRebNm3ib3/7m9SpE0II0SLoS75ELTo0osSyOwiOvQyAxfsCfL03QGasxuj2TtLd0cHJ9/lB7vuulG/3R3rPNCVSc60kaNVYiPrhwQnc0avhP8BGt3dwWrKNFbX8yMvzmVz8WQGfnp9MVj167F7bUHkgoIuwgE931J5B8MNt9csw2DVB5/XRSfQ4lslKrUBWnM4fByZw74B4igMmcTYV51ESeIiGy4zVeWpYIhApJL4kL8jrGyuZu7v2eaR6PXrpWoPJ2a6ooO5o7llaSk6CjRHtWmbSpFNdiw7q9uzZw0033URhYSHJyckMGjSIL774gg4dOgCwb98+tm7dWrV+QkICH3zwAXfddRejR4/G4/Hw85//vNHn3wkhhBANcWTGy9DI8yHew3tbvNz0dXHUcLucBJ2cBJ04u0pp0GTWEQGQYUWGhtXkZ91c3N6z4VnyIJKl8a5+cVw5t/Y6ebu9Bhd/VsB/x7Yhx1N7UFUSMHlkZd0FlI/VmHQHL49qnppoJ4quKie0RMGprm2MxoSsGCZkxZBbGuJf6yt5e7O3KkvkQVd1ddXrZkZL1zPRxnU5Lv690VvnumELrptXxFcXpZwUx36yadHZL8XJr7VPSm3t5Py3HPJetByN/V4U+Q3e2uyjfPcu/vTGbVXPW4qK9y9vsEZL5txP8vEZx385VoCfdXPz+JDGqTd1ZJHl3kk2Upwq8/ZU78HolahzQccYOsXpeOwKSQ6VXkk2Ym0qf/iulGfXHb034I5ebq7PcXPD18X8UFRzsHqQpsBtPWN5cFA8ugxHPC7y3VM3X9ji4+0+ZmysZFl+kDPaOvj36Ma5mdASzr9lWfxQHGZXRRhdVdCVSCF3j0Plh6IQt3wTXTbFrStkx+tkxWmMSXdyTY6r3sO6W7KW8F4cDwmzhRBCiCZiWhZXfVXEkrwgv9i1KGrZwoRufLUnltc3Fh5zQHd4cgOIBDmTs138v76xjTrHR1EUXjsriZ9KwsTbVdLdGoZpcduCYt49IvPkuuIw64qje+NcusKFHZx8sC163Vs7BDmvRztmbPRSEjS5pUdsVWa/ORck89D3ZXy200+yUyU7Xic7XifNpRGrK8TaVPq2sZF2nEXAhaivGF1hcraLydkuLMs66ab0KIpCnyQbfWrIOtsz0cb28jB/PqynvTJssaYoxJqiEB9t97OhNMQjp3tOZJNFDSSoE0IIIZrId/uDVRkkLypYEbXswzan8bcV1YsY902ysa44RE1xXq9EnUdOT2BUeyd7vQYL9gYo9Juc18HZZMOhFEWJmq+mqQovjEgkaFp1znnzhq1qZQfSXRpXp4fp097JWe2rp2h36SqPneHhsTMap/1CNKaTLaCrj7v6xbGuOMz/ttVcQuT5dZV0jNW5tackUWlOEtQJIYQQTeTgjyBPqJKRJeujln3S5rRq69/U3c1fh3ooDZqsKghSErQoC5pUhi1yEnRGtXOgHRhu2M6lMTn7+MogNJSuKrwyKok30728tdnLkrzgUUspHG7aoHicZuPPrxNCNA1FUXh+hAe7Bh9v89c4suB335aS4da4oGPdJVNE05CgTgghhGgCpmXx0YGgblzRanQOpeHf4E5nkystav0hbe08cnoCAAl2lVE19GK1JJqqcE2Om2ty3BT4DWbv8LOmKERJwKQkYLKuOMQeb3TpgYHJNi7vHMPmTc3UaCFEg7h0lX+NTMIcYZHnM1ldGOSm+cVUhCMBngXc9HUxV3QJcFZ7ByPbOU7qBEYtkQR1QgghRBNYtj9YFdRcVBg99DJ1+AgubR/D+1sjQV/HWI1/j07CrrXOoV3JTo1rcqKzbRqmxfy9AWZs9PLVHj8dY3VeGpV0UiRUEOJUpSoK7Vwa7VwxvDpaYeqXhVVDxX2GxSsbKnllQyUxmsL0IQnVvhdE05GgTgghhGgCB4de2sww4wtXRy2zDx7OK12T+EXvILmlYcZmOEk8ye5qa6rCmHQnY9Jbdo+jEKJhxmY4eWKoh/9bXFJtmc+wuHtpCed3cNLGKUmNToST6woihBACiKSozvMabCsPY1pSueZEiwy9jCQRGVH6Ex7jUA0oM86Dmd0DgAHJdiZnu066gE4IcWq4vpubvw5JwK1X74H3G/Bm7qHvPsOMzBG25JrUJKSnTgghTiKGafHJDj/P/FDO8gOFqeNskXTVQ1Lt3NwjlnaSCr7JLdsfZLc3UnLgwiOyXhr9h4Iq74EQ4uRwU49YrujiYklekNc2VPLJjkNZcV/dUMnPe8eyz2sy9ctC1hSFOLu9g1fOapw6f6eCoGHxzA8V/KZf3FHXk6BOCCFOEp9u9/GHZaVsLTeini8PWSzOC7I4L8hrG7y8OCrxmIbEmZbFmsIQZSGLEWn2UzKld10sy+L7ghA/FIVIdKh8tjPyo0axzGrz6cIDhjVHE4UQosm4bSrnZDjpn2xjzq59BA/kSNpSbjB/T4C//1DBmqLIjcav9gS4Y2Exb5yd1KDriWlZLNwXZH1xiGSnyuj2DpKaYIhn0LCYs8vPfp/JpOwY4mwnPghdtC/A/1tcwobSsAR1QghxKnh7s5fbvimmrkEtRQGTy+cUcnf/OO7pF1eVHr82uyrC3L6gmAX7IrXWhqfZeXNMG+LtcocVYGtZmDc3efnvFm+1YBrg8vxv6eTPr3ps2ewYvQedyCYKIcQJk+zUmJAVw7uH1ae8Y0Ex+3zRmXBn7fDzzA8V/LJP7YHK7kqD2xcUs7k0TFa8Ru9EG3ZN4YOtPnZVHvq+VYCBKTYmd3ZxQ3c3eh3XtfpYXRjkjgXFrCsOA/DkmnI+Gp9M5/gTFzr988cK7vm2tN7rS1AnhBCt3CfbfdyxoOaALt6mUBaKXmIB01eVs7ogyOtnt8FRS8bF97Z4+X9LSigNHtp+4b4gl3xewHvnJp8y88D8YYuPtvuwgGGpdjJjdXZXGjy6sow3N3lrrc+mm2Ee2vpu1HPhgSPAIXWchBAnrxu6u6OCuiMDuoMe/L6MHok2OsZqlARNOsTqpB2YHmCYFlfNLWRVYaR3b7fXYNGBm4tHsoDl+SGW55eycF+A10Y3PMtuWdDkmR8qeHJNOYeX49tVaXD+rHz+Nz6Z7h5bg/YdNCzKQyaJDrVa+ypCJjGaUnWj9du8APd9V/+ADiSoE0KIVm3ebj83zC+KuvhoCkzt4uLOXrF09+hsrzB4Z7OXx1aVRwUgn+8KcMs3RbwyKqlaj91935Xw/LrKGl9zRUGIC2bn8+7YZNLdNQ95sSyLXZUGTk0hJab1zh/bURFm6heF/FgSrnouO15jd6WB/7COuR6Vu/jNzk85o2wTcxN788dOk7gybzFdfXlV61iqSnDi9Sew9UIIceINaWunh0dn/WHfmzUxLJj0RWHVY7sKTw3zcFVXN69uqKwK6I7FR9v9/GFZKY+c7ql1nV0VYX6/rJQvdgVId2sMTrHTO8nGl5vtLF6yN+q7/XD7fCYXzCrgvXPb0D/ZfkztWlcUYvIXhez2GqQ4Vc5OdzA4xc7aohDf7A2wtdygQ6zGU8M8DEy2c9M3xdRQ4/2oJKgTQohWanl+kKu+KqqauwCgKvDyqCQu6XSoNygrTue3/eMZkurgxvlF5PsPbfDhNj93OUp4cqinam7DzNzKWgO6g34sDtP7nX0MSrFxboaTzvE6CXYVC5izy8+s7f6qRCG9k2yMy3AwOt1J70Rbq5kcvyI/yNS5hew/4i7z5rJDV/xeFTv54/b3uTR/GeqBvtIe3j2cX7IGV9AXtV141AVYaZlN33AhhGhGiqJwQ3c3dy+t3tN0aadD9TmPFDThF4tKMCz404qyo76GS1cYk+5ga7nBD0XRwd/z6yrJdOvc3is26nnDtHj5p0oe+r6sqmh6bmmY3NKDwWfdYVFhwOScT/K5JsfF3f3iae/W8IUt8v0GbZ0azhqygAYMixvmF1VdE/P9Jm9v9vH25ujzsKPC4LI5hXSJ19lZER1Zvj46qc62SVAnhBCt0KbSyF0/bzj6Vt7fz/REBXSHG9nOwTcT2nLxZwWHXcTg1Q1eEuwqfzgtnp9Kwty1JLrmkF2F358Wzxe7/Cw8bPiLBSzLD7Es/+h3U38oiiQQeWJNBQDtXSp92tj5vz6xDE11HMthnzAfbvNx2zfF+I5yq3RkyXo+WTMdl1l9SFDnyn1Rjy27g+CE6xq9nUII0RJNyXbxwPIyKg+7Rg1pa+elUYl0jtP565ryGrczLfjlouhrUKyu8OQwD5vKwpQGTPon27mwo7Mqccnm0jAXzM6PGuZ533el5PkMbuzuJsWp8eF2Hy+sqzim3r/UGJUnh3qYuzvAKxsO3egMW5Hr5pubvLh1laJA5HVTnCr/GpnI6CMSkf19bTkbSo/ea3m4TWXR697e083FWXUP25egTgghWpk8r8GlcwqrLiQHPXZGAld3dR9123YujffPbcP4Twuq7hoC/G1tBbN3+AmaVtTQkxhNYdb5yQxItnNLj1iu/aqQL3YHjqv9e7wme7x+FuwNsHRiWzrENv2laFdFmHy/iWVFgtEkh0pWnFYt81rItHh6q42Zu4uq7aNTnMbOCoOwBZpp8GLuyzUGdDUJjb0MKzG5MQ5FCCFavHi7ys+6uXl2XeRmnktXeGa4B1VRuHdAHGUhk7c2e1GIfB9vrzBqnZ9872nxTM521fpa2Qk674xtw/mzCqp64Cwi17W//1BBnE2Jmhtelw6xGpOzXfy8VyyJDpXzOziJ0RWeO3AsBwUMCBiHrsP5fpNJXxTy7PBEpnaJtHdTaajWALY++iTZeGBQQr3WlaBOCCFakZJA5KKx44ihGXf3i+O2nrG1bBUtM1bn/XFtGD8rn+LAoQtdTXcSnxiawIADcwdidIU3xrThpZ8qeXuzl9V13PF0apGLXm2XUm/Y4s8ryvjnyLqHlTTE7kqD97d4eWeLj7VF1dvav42NX/WJ5eKOMSgKbCoN86vFJSzJqz4J/v/6xHL/wHgqwxZL84K0X/YZ2ZV7o9YxOnTBbJeJ7dt5Uc9brliCF1zRuAcnhBAt3B8HxhNnV9hebnBTdzddEyLfrZqqMH2Ih+lDDs17+/eGSn61uKTaPnom6tza4+g3KwH6trHz+tlJTP6ikMMHsJgWNQZ0cTaFaQPjyfHYWLY/yIbSEHZ/Gdf1b8+gFFvUIBgqyQAAIABJREFUDT9FUXh4cDwDk238aUVZjZmODwpbcNuCYraWhzk3w8kDy0sJHLZ6slPlHyMSWbQvwLYD8+hGtXegKZGhp4df2126wsujEmtNZnYkpaSkRMq6i2aTm5tL165dm7sZpyw5/y3H4e+FYUbqyn25y49TV5jUOYYuCTZWFwa59qsith8R0F3d1cUzZ3qOud7P8vwgl84poKyWO5jXdHXxzPDEWrffU2kwZ5efVQVBioMmJQELb9gkO17nwo4xnJ3uwBe2+HJ3gLm7/KwtCpFbGo664CrA1xen0LeNnfXFIT7d4ScrTmNcprPBNYEsy+LB78t4em1FnSUeIDLEpjxkVRvKCqAr8OQwD9fmHPajIhTEdc81qIWHkqCEzhxH4ObfgaKgL5iN499PoYQivXj+635N+OwJDTqWk5V897Qc8l40Lzn/hzy+qoxHV0b3as0+P/mYhul/tM3HLxYVH7Vn7sIOTh4f4qmW6Ks+70XItHgj18v0VWXs8UZ66TSFeic1+dfIxFp7HUuDJvd+W8pbm704VIUXRyVyYcf6Z0uWoE40K/kya15y/luO3NxcHO068eTqcj7Z4afAHz20cmiqnZUFwWpZuc7NcPDGmDbYGliXZ1dFmEdXlfOfI1Lz90rU+fLCtsTUMOn7eAQMi9Ef7Y/KJnl2ewcTsmL4zZKSqoDPqcG4TCfdPDaKAybFAZPAgaumcmA/+X6TAr9JyLS4tJOLaQPjsWsK01eV8cjKhg93Oai9S+W10Umc3jb6B4Xti/dxzPx71WNLt+GdPhOrTWrVc0r+XvTv5mOmZ2H0H3rcbTnZyHdPyyHvRfOS83+IZVlMW17GMz9Ebsj9rn8cvxsQf8z7qQyZvLvFx7/WV/DjgTpzCXaFK7tE6tgd7DE80rG8F6Zlsb3cwKEppMaofLTdx63fFEclLjvS2e0dvHdumzpvwBYHIuUNakq6cjQS1IlmJV9mzUvOf8sxe9UmblvnOqZx/0NT7fx3bBvcDezROtz64hAPryhjzi4/PTw2Xj87iay4phmhP2enn8lfFta94jEalmpnUmcXv15SfQiPqkAPj45dUwib1Dgc83BntXfw0qhEkp1HlGMI+HDdfSVqaXHVU8GxlxG8+heNcgynCvnuaTnkvWhecv6r23xgKkB2wvFdgyzLYlVhiAK/yZlpdlz60a+Vx/teLNwX4Mq5hTWOfnFqsOSSVDo1YfFymVMnhBDNbHelwa/WOY4poLu5u5s/n56AvZ5j7evSI9HGG2PaEDYt9Ab2+tXX2AwHI9LsLKilkGxDLc4Lsjgvep+JDoXf9otnYqeYqqK2ABtLQvz9hwre3uwldODOqseu0CPRxojYCu4Z3j6qdp+6YzP68m/Qln0dFdBZdiehi65q1OMQQohT2fEGcwcpilI1J/xEGJ7m4JuL2/LPHytYXxKuGmXSxqly/2nxTRrQgQR1QgjRrEqDJpO+KGB/MPoOYrxdYVyGkx0VBt/uPxSoxGgKT5/pOWomsOPR1AEdRC60Dw1OYPTH+dWWuXSFNk61Wo2ehrCpMOPsNgxPqz4fI8dj49nhiTw8OIGt5WHSXBppMSqKopCbW1IV0CkF+3DM+Dv6qsU1vkZo3OVYCU2T6EUIIUTrkhWn8+gZtRc+b0oS1AkhRBOzLIuVBSHWFoUImhYhE0qCJrklYZYXBKsFMDd0c/PYGYd64X4sDvHeFi8VIYsbu7vJ8dQ8H6A1GZBs5/LOMfx3y6Hiq+kujTfPSaJvko3l+SHm7fETNCPprpMcatT8Pk2J1ASKtanc821JVP28g54a5qkxoDucx6EywFHDnVwjjG3Oe9jffxUl6K9xWys2nuB5U+p5xEIIIUTTkaBOCCGa0IaSEPd8W8r8PfWr7XZBByd/GZIQNfSvZ6KNngPrV6emNXnsjAQ2l4VZWRDinHQHzw1PJPXAEMnBbe0Mblu/YTPvn5vM3UtL+PdGb9Vzv+wdW2fNvpooBftIm/8hrueWohYX1LiOpagYPfoTnHQLuOOO+TWEEEKIxtaig7onn3ySjz/+mE2bNmG32xk0aBDTpk2jZ8+etW6zfft2+vXrV+35//73v5xzzjlN2VwhhKDQb7C13GBnRZgleUFe+amSGjLl1+j0FDsvjUqKCuhOZslOjXkXtaUyZB5Xshe7pvC3YR5GtXPwyQ4/Q1Pt3Nj9GAO6UBDH639DXzAbt1XzG2Zk9yQ0+iLC/YdCXPMMrxFCCCFq0qKDuoULF3LjjTdy2mmnYVkWjzzyCJdccgnffvstiYm1104CeO+99+jdu3fV47rWF0KI+tpaFmZlQZA4u0qKMxKMfL7Lzyfb/XVmVazNOclh/nlOWqOXEGgNGiN7p6IoXNrZxaWdGzDX0DRx/OtRbN/Nq3Gx5YolMOU2wiPPB/X42yqEEEI0thYd1L3//vtRj//5z3/SoUMHli5dynnnnXfUbZOSkkhNTT3qOkIIcSzCpsVTa8p5bFV5vQuNHm5oqp0eHhu6Gkl40jlep5tHp5vHRsGOzbQ5Mn2+aBJKWTEE/JG6cqqK/e1/1BjQWXYn4TPHEpz4M0mGIoQQokVr0UHdkSoqKjBNE4+n7mEv11xzDX6/n+zsbO644w4mTJhwAloohDhZbSsPc9s3xSzdf+xp+DPcGn8+PYGLOzprLTpa8+ytk4BpgGGAEY781zRQDAPLGQPOpsngGcWyUPbvQdu2Ae2n1WjrV6Du3RlZFJeAkZmN/uOKqE0CCW2wJl5HeMgYiDn2eXlCCCHEidaqio9ff/31bN68mfnz56NpNd/RLiws5M0332TIkCHous6sWbN44okneOGFF5gypfYsZbm5uU3VbCFEK2VZ8GOFyod5Gp/t1/GZdQ+NVLDIdlm0c5q0c1jkuE3GpRg0ZyecXlFGQu5q1IAPX7uOVKZ3xtIbMYOmESZ+yzqS1i4ldvsGtEAgEryZJgo1X2IsFPIHj2b3uVOhlkAXANMgZdlXpC75HCUcwpfWkcr0TgTjk3CU5OMo2o9ihKnI6k7BaSMx7U4wDRJ/+I42a5YQs3cbesBX+/6PEIpNYOP1vyPoST7GkyCEEEI0nboKo7eaoO6+++7j/fff57PPPiMrK+uYtv3Nb37DkiVLWLy45jpDovnk5ubW+Y9UNJ2T8fwX+g3m7wnwxS4/C/cFqQybZMXpZMfrdE3QGdnOweAUO5oCS/KC/Gt9JSsKgsTaFIa0dTC4rZ3KkMnqwhDf7Q+yoTRc4+u0caj0TrJR4DcoD1l09+hc2DGG8zKdpMQcewTX4PciHELd9CPa5nVom9ah7t6G5YrFzMzGbNcBbcMatDVLUUyzahPLZsfI6UtwwrWY3frW62XUXVsgEMDM6gpaZJCHUlyA7fN30Rd+jlpecuxtBwJX/pzQuEmHnggFI3+WhbpzC46Zf0fbuble+7LiEgidOQ595SLUvN3H3BbLGYPvvr+zIVj3xVMcn5Pxu6e1kveiecn5bzla+3vRKoZf3nvvvbz//vt8/PHHxxzQAQwcOJA33nij8RsmhGhWlmXxyQ4/723xsaUszI6KMCXB6vepigMhVhZEEpg8urIcj10hzaXxU0l0wPZjcZhXNlTW+brnZjh4dngibRsQvDUWJX8vtnkfoX8zu8aAStu6ofZtQ0H0dcvRflyB/84HMAaNrHVddXsu9nf+hf7DMiBSmy182nBQNfSFn6GEG5YY5iD72//AyO6JlZSC483n0FYubvA+lfJS7J+9U+d6lqqC3YHiP9SDZ2ka/jsfwuzYFWTkhhBCiFamxQd199xzDx988AEff/wxOTk5DdrH2rVrJWmKECeZtUUhfvdtCYtqKDpdl5KgRUmw5h64o0mNUflt/zhu6OaudW5ck7Es1N1b0dZ8h7b2O7T1K1FqSb1fX4pl4nzhT/h//ShG70HRy/L3Yn//VfQlX0S9jlJRhu2bWfVvtqKCpoGqgX7gvz4vihE5/4ph4HzmfpSgH8Vbd0DdEJbLjdGhK2anbhg9TsPI6QN2O+q2XLT1K1DKSwkPPQczq2HXGCGEEKK5teig7q677uLtt99m5syZeDwe8vLyAHC73cTGxgLw4IMP8v333/PRRx8B8Oabb2Kz2ejbty+qqvLZZ5/x0ksv8cADDzTXYQghGpE3bHL/sjJe2VCJeQIGj6sKjM1wcl2Oi7EZTmwnsIacunEt2vqVaFvWo279CbW0uMH7MtKzMDOz0TauQS3Kr3peCYdwPv0H/Lf+PjK0MhjAPust9EWfRw3ZrIvlchMePJrQmeceGqKpajWWANC+X0DM3/946DhLCuvcf2jYuYTOvQx1z3bUrT+hVFZgpaRhpmag7tuJbc57KP5DxcctTSM8fDzBcZOw2nescd6emd0DM7tHvY9RCCGEaKladFD30ksvAVTLXHnPPfdw7733ArBv3z62bt0atfyvf/0rO3fuRNM0srOzefbZZ4+aJEUI0TpsLAlx/bwifiypvZfNpkI3j41z0h2ck+EkK1Zja7nBptIwi/MCfLnbT3EgEg0qwLhMJzf3cGNa8G1ekLVFQWJ0lX5tbPRrY2NAsp1ExwmuTWaEcbz4GLYlX9ZrdcvlJtx/GEaXXpiduqN4K1B3bkbduxMrNo7woFGYnbpFAhvLQl8wG+fL06u2V4J+Yp7541Fe4cDr2B0owUDUc2ZyGsELriA8fDzYHfU7vIEjCI6fXOtQScvujPTqKSpGx66RuX/d+0der1M3OPPcatsEx12Ofc57aOtXYnTsSmjcJKyUdvVqjxBCCNHateigrqSk7on3L7zwQtTjK6+8kiuvvLKpmiTECWVZFiET7Nrx9Q6VBEyeXVfB2qIQQ9vamZTtIt2tVb3GHq9Jntcg329S4DdIcqgMb+cg7rCi0JZlURgwyfeZFPhNSoMmcTaFJKdGG4dKmktFbYQhiaVBk+KASUXIoiIU+W9l2GJHeZjHVpVTGa7ePXdOuoP/6xtHdrxOakz1dmTE6oxo5+Bn3d2ETYvl+UG2lIUZluYgK+7Q1+DYDOdxt/+4WRaO15+uV0BnZOUQOnsC4SFngyMmetkRwymrKArhkecT8HtxvPFsvZpkpGcRnHIbRq9BaOtXoq9YiFJaRPi04ZG0//qxX0qCk25B2xRJ7nKQZbMTnHQzobGXHXuR79gEgpfecMztEEIIIU4GLTqoE+JkYVoW3+4PsrowRMCIBGqGZdHGoZIRq5Hu1kmwK+iKgqrAt/uDfLrdx+e7/JQGLTLcGj0TdXon2bisk4teSfVLR29aFv/Z5GXa8jIK/JGhdJ/v9PPg92WcmWantNLBtm/3Uh6qHijZVRjRzkFOgs664jCrC4OU1pCE5KA2DpWLOjqZ2CmGXkk2CvyRANC0INWlkhajkWBXouaiFQdMPt/p55u9ATaVhtlcFqYwUP8hf53iNB47w8O4zPoHY7qqMCTVwZDU+vUqnWi2D1/HNv/jGpdZdidGj/4YfU4n3Od0rLSMBr9O6NzLwVuJ44NXa13HTM0gePHVhIeNjQylBIw+gzH6DG7w61bRdfw/fwDn336Ptn0j4R4DCFz3a6x2HY5/30IIIcQpRoI6IRrBzoow+T6TirBFZcgkaIJhWhgWfF8Q5MNtPvZ66x+sHGlXpcGuSoM5uwL8bW0Ft/Rw8/vT4qN60gDCpsW64hBrCkOsLQqxOC/ID0XVMwlawMJ9QUA78Ki6oAlzdweYuztQ4/IjFQZMXtvo5bWN3lrXidEUUl0q7VwalgXL8oMYDZwXN6lzDE8N8xBrO8FDI+tJKdiHvmoJSlkJlqZFkoU4YjBT0zHTMrGSU0FRI/XcKsuJ37gK+5JPsc/9X9R+zKS2keGHnbtjpmdVlRNoDKEJ12Ilp6F//w1KUT5KcQGKtxwzozOhcZMJn3FWVTDXFKykFHwP/QsCvmo9jUIIIYSoPwnqhGigQr/BO5t9vLnJy9oaAqemYlrwjx8r+Wibj0mdXWgqhExYUxji+/wgFTUMT2wpfIbFtnKDbeVGg/fh0ODxMzxcl+M68Rko61JZjr50Lralc9E2rj2mTbNreM5yx+G7azpWelajNK8aRSE8fBzh4eMOe1Hr6MXAm4IEdEIIIcRxkaBOnNJMy2JDSZhl+UFKAyaGBSaRoYdpLo3UGI22MSqxNhW3rlASNJmz08/nuyJDBkMN73w7bnu8Jk//UFHv9Z0aXNXVzff5QVYVRgeh8TaFzFiNtjEaiQ6VlQVBttYQeLl1hXYujWSnSoJdoTxkURww2VVp1DiEsyHsKrSN0YizKcTalKpzH2tTyHDrTO0SQ5eE+g0/PVHUnVuwffkB+uIvUIL+RtmnZbPj+79Hmi6gq01LC5SFEEIIUScJ6kSrsSQvwNxdAXyGhQJoCmQn6Jyb4STNFRkiVhwwWZIXoMBvEm9TibdHggFNUdCUSI/W5rIwm0rD/FgSYmlekKJjmMN1PNy6wrkZTjJiNWxqJPPiPp/J7kqD3ZUGvrBFyIzMt0t2qpyT4eCCTCcDElU2exVWFQT5y+pydlQcey/X+R2cPHJ6QlVSkPXFIdYUhfAV7GVMr45kuLWoXi/LsthYGuaLXX7KQhbdE3T6J9vJitNqTIYSNCy+3hvgg60+5u724wtbpMSopDg1FAX2eQ32eU18NYy17OnROb9DDEPT7HSJ18lwa2jHWzbANA7VICsuQAkGIRQEu4Nwn8EY/YaAzQ6Asn8P6o5NmJmdsVKPMkfNV4lauB+lrBiltBilYC/ato2o2zeh5u85vvYewXLH4b/lXsycPo26XyGEEEKcnCSoEy3eptIQv19Wxuc7a+8BGZBsw7JgdWGolhliTUtVoFeijXi7gltXcGiHAsk4m8LodCdjMxy49HrM//JVoq1dhr5yMfrr34K3kqQBw+h/zkQuu6Qv01dX8OwPFdQ2yjLFqTK4rZ0+STZ6J9no38ZGpktF3Z6LkluA2b4DPVIz6JFoI9c0yYw94mvA50VxOOjmsdHNU78eMbumMDbDedTskZZlURayqgK88pBJ7yRbVPbJ+lIK81B3bI48ODDnSynORy3Yh7pvZ6Qwd0VZjdva5n+M5Y7D6Hka6vZc1P2HArJwvyGEzpuCmZiM9tNqtA1rUPfuQM3fU+v+jsbI7onR87QDDwyU8hLUfTtR9u1CLY9k97VUFXQbvqRU9D6DMLv0Itx7MMTGH/PrCSGEEOLUJEGdaLCAYaEpkWyCtTEti7JgZIheccCk7MB4RfVAwFNaqRDnNWjjVMn3mawtiiT42FNpUBE2KQ2YzN0dqDWAOWhlwYmb03a4nASdK7u4mJztor37GBJKGGGUvN0o4TCWrqOYBtr6VWgrF6P9tArFiK7Dpi//Bn35N9jbZ/HweZO5bsIY5uwNURGyqoLYdLfGGW3tdIo70Ovm86JtWIU+bwnaysWoJQVV+zOTUjC6DyDT58dh11F8lShF+aiFeSi+Siy7g/CAMwkPH4eR0wd193bU7RsPFa1WFFBVLEcMVowLnG7MzE6Y6Z1qHb6nKAoJdoUEu0o3Ty3nJeBH3b0NpaQApbwUpaIUwmFwxmA5YlAL8yLnaOfm+p/rmtpSWY6+7Otqz+url6KvXnpc+zaTUgiddRHhoedgtW1/lBWNSKKUA+crNzeXrl27HtdrCyGEEOLUJEHdKeZgkFV0IMgyLAubqqCrCgHDotBvUhQw8YctHBo4NQW7pmARSdBRFjRZnh/k2/1BfioJoyuQFaeTHa8Ra1MpOVBj7OBfadCqo+csBlbuOzEHX4t4m8KQVDvZ8Tq6Ggk2K8MWeQd6lAr8Bt6whTccyWbZt42N8zKdjMt0kpOg1z9ZhxFG+2k1+nfzI9kGy0uPua3anm1oL0+nV9p/6Hrpzwj3PSMSIIZCKPl7UVftRN2zHW3jGtStG1DMmoeWqkX5qIvnkFzL6yjBALZvv8L27VfH1D4zNT1S6DotE7VwH0pBHoRDWAlJWJ42VX+mpw04Y1B3b0PdsQl1+ya0HZtQ9u1CsZpxomIDWYqK0b0foTGXYJx2Zv0yVDZhVkkhhBBCnFokqDsFlARMPt3h470tPhblBQgYEGME6OHdg8MMEVR0gqqOBeiWic0yMBSVQj2WfHscXtUBioJqmTjMEG2DZaQFS7gwVEaxHsvaUCabylwn5FiGtLVzQQcncWX7Sdidy3dlOm+aHSi1uSMrWBajYsrpF2uQr8ayV4mh3FAwLFCMMA4jSGdnmG4xJl1iDLokOema7EZ12lAqy1GL8lGK8sEwsJISsDomYiUkYsUngf2wumbBAEpZIeq6Xah7d6Dk7QYscMVixbixnDGg20C3oxTno/20Cm3DGhR/7en+j4W6byfO5x9qlH01JjVvN/ZP32zuZgBguWIxeg3EyO5Z9X6o2zZi+/arqIDa0nSs5FTUvN1H359uw2rTNhKgxidiJiRhZnTC7JiDmdk5+t+HEEIIIcQJJEFdEwsYFgV+k8qQSWXYwlvpI2bLOjybVtNm54/YA14UVUNRFRRVw1JVLEUFLJRgECUUQDENfIlpBNI7YWV0ApsDKxTECgawykuxykrQyosJhgxKtBgKVBf7tVhyY1L50dGOFWE3Xct3MaB8G9dUbGNA+TZ6eHej1XP2WVDR0CzzqOtvcySzJSaVcs1Jheak2OZmU0wquTHt2OZMQbcMXGaQGDNIjBH5r9MMUqzHsiWmLducKYTUmv852s0QQ5Qi7ksvZ4RvK/q7i9C25wJwDfAMUJqSiaHZ8BTvQQ0cmntnKQo4nBAKohgNT6MPYDljsFyxKN4KFL/vuPZVFzM1g/CAYYQHDEOxLPS5H0Z692rpeasPy+HEzOiMumMTSihY+3qK2uJ7yyxFxczuiRUbB4YBpokV78FKTsNMaYeZnoXZqVv1HrMR5xG84udo65aj5u/FTGmP0b0v2J2ouWuxz34b7YfvATC69sbo3g+zSy/M1AysxDbSuyaEEEKIFkmCugOK/vc2Wnkkq51aVozqqySkaARVG0FNx5fQlmDbDKy0TAxnDGHTImSYVHgDeMsr8JVXYC8rIqlsPynl+0jwlaKFg+hGiAQzTFsrhMMM4zRDqA1I5ZFYsBNylzXBkdfNbtUdDGUFCsgKFNS5Xm1MFPLt8fgcsZiuWBy6ittfjtNXhsNXjmId/Zwl5O+s8XnFsqCRAjDF72vUYM5yxWImpUTmz4UNzORUjL5nEB4wDKtdh6h1jR4DCBbmYf/wdfQFs+sd3JntMjG6DyA8YBhGjwGR3qRQEG3TOtSdm9lfVExKh6xI72JCUqQnKi4BdeNabAs/R1/+DYqvMhIodeyKmZ6FpelgWSimAX4fit+LUrQfbf0qlPDxz22sKs4dl4AVmxDp8Qz4UQI+UFSMbn0J9zsDYhMa9gK6jtFvCEf+qzZz+uLP6XvggQlqyyxqLoQQQghxJAnqDujwwQvN3YRTmopFarAUgqVQ3tytaTpWXALhfkMInz4ao9fASMBS323bpBK44W6C50/F/r9/o6/9LpKmX9MjQwgTkrDaZWKmZWJmdMLo1g8rsYZZczY7Ro8BGD0GUJCbS2INyTnM7v0JdO9P4MbfQjhUlf7/qLwV6KuWoK1bDqEQVnIqZnIa2B0oJYUoJUWoJQUH/r8QxVtxKFjs0AWjQxfMzGyIOTFDeY9KAjohhBBCtCIS1J3CzJT2WPGeSHZBIwSWFRmupmmR5BvlpZG/A70vlqKArmPFJkQCiLiESMbEvTuOa1hgQ1iKgtm5x4FsiVujevKsGDdWbDxKZRmKt/KwbVRwOLBsDnA4IoGKYUTmxwUDWA4nVlJbzKS2YLOhlJeglJVE6pKVlURlpLRUNdKTlNwOs10mZloHsNtRvBVQWREpQB0KRc6rZsPM7oHRvT9metZxF3e20jIJ3PYHAse1l3pSlPoFdACuWMLDxhIeNrZp2ySEEEIIIaJIUNcM9sam8mO73mxK783uhHRCYZNA2MA0TFQr8qcAlt0Jdjs2TJIKdpJauJ200j2gKIQ0GyHVht/hptLtwe/24LTbaIuPFNNLsq+YxKJdxBbsxlZRgtW2PWZWTqRXpGNXjA5dwB1Xd2MtK9IbpOu1zycKBSOBXVkJ+L2R4XilRaj7dkVqcpUUYul2sNvB7sSyO8Bux9LtBPbuwlVRjFpc+9BNS1GwEpMjx5DSPjLXqf9QrISkyAq+ykjNMiUS8FhxnkOBUzgMQX9k2KGmNzygsizwVqB4K7BcseCKPe7gTAghhBBCiMYgQd0B/8kaS5EzgQqXB687kXBMHG7NJE4xiA/7iC3ei6doN8ml+7CZYVBUFFXB1G1YTheKy43ijiOU3A4jOQ0tuS2JcW48sQ40ux1Lt4HNBrqNOE3nDOCMY2rh4AYdV+jAX4MpSt1Z/Wx2zA5dGrT7qtpcwQBKRRmKtzwSPBlG1ZwqKzb+6MMUY9yY3frWvEzXQY9tUNuiKAq447DqEwgLIYQQQghxAklQd8BFD/6+yfZ97GlRTkF2B1ZSClZSSnO3RAghhBBCiFZFsgEIIYQQQgghRCsmQZ0QQgghhBBCtGIS1AkhhBBCCCFEKyZBnRBCCCGEEEK0YhLUCSGEEEIIIUQrJkGdEEIIIYQQQrRiEtQJIYQQQgghRCsmQZ0QQgghhBBCtGIS1AkhhBBCCCFEK6aUlJRYzd0IIYQQQgghhBANIz11QgghhBBCCNGKSVAnhBBCCCGEEK2YBHVCCCGEEEII0YpJUCeEEEIIIYQQrZgEdUIIIcRR9OnTh2eeeaa5myGEEELUSoI60ahuv/12PB4P06dPj3p+wYIFeDweCgsLm6llJ7+D597j8ZCcnEyXLl248MILefHFFwmFQs3dvFPW7bffzpQpU5q7Gae8wz8fh/+tWbOmuZt2Ujh4fu+8885qy6ZNm4bH45HPQTNYtWoVSUlJjBs3rrmbckqQz0HLdKpchyWoE43O6XTyzDPPUFBQ0NxNOeWcddZZbNiYfCT6AAATmUlEQVSwgTVr1vD+++8zfvx4Hn30Uc477zwqKyubu3lCNKuDn4/D/3r27NnczTppZGRk8L///S/quyYcDvPWW2+RkZFxXPsOBoPH27xT0owZM7jxxhtZv349GzZsOO79yQ3CujXl50CIo5GgTjS6ESNGkJmZWa237nCLFi1izJgxpKam0rVrV+69996qi/Zrr71G165dMQwjapubbrqJqVOnNmnbWzuHw0Fqairt27enb9++3HnnnXzyySesXr2ap59+Goj8OJo2bRo9e/akXbt2jB49mrlz50btZ+PGjUydOpUOHTqQnp7O2LFjWbduXXMc0kllxYoVTJw4kc6dO5OZmcn48eP57rvvotbxeDy89tprXHfddbRv355+/frx9ttvN1OLTy4HPx+H/+m6zuzZsxk1ahSpqan07duXP/3pT9WCiIqKCm655RbS09PJycmR4Zg16NWrF507d+aDDz6oeu7zzz/H4XAwfPjwqufq+zl48cUXufrqq2nfvj0PPfTQCTuOk4XP5+Pdd9/l+uuv5+KLL2bGjBlVy7Zv347H4+Hdd99l/PjxpKamMnjwYL766quqdQ6OsJkzZw5nn302KSkp1a4VorrG+hz8/Oc/r9a7ZJomvXv35tlnn236AzlJ1dRr9+ijjzJ06NBq67zwwgv06NGDjh07cscdd+D1ek90c4+JBHWi0amqygMPPMCrr77K1q1bqy3fs2cPkyZNom/fvnzzzTc888wzvPfeezz44IMAXHLJJZSVlTFv3ryqbSoqKpg1a9Yp0X3e2Hr27MmYMWP4+OOPgciFYtGiRbz44ossWbKEK664gqlTp7J27VoA9u7dy/jx41EUhQ8++ICvv/6am266qVqQLY5deXk5U6ZMYfbs2cydO5c+ffowadIkioqKotabPn06559/PgsXLuTSSy/lzjvvZOfOnc3U6pPb3LlzueWWW7j55ptZunQpzz77LB9++GG1IOL5558nJyeHr7/+mnvvvZeHHnqIjz76qJla3XJdc801vPHGG1WPZ86cyVVXXYWiKFXP1fdz8Pjjj3PuueeyePFibrrpphN2DCeLDz/8kMzMTHr16sWUKVN46623qvW0TZs2jVtvvZUFCxZw1llnceWVV7Jnz56odR544AH+8Ic/sGzZMgYNGnQiD6HVaozPwXXXXceXX37Jvn37qraZN28eeXl5coP7BFiyZAnr16/nf//7H6+++iqffPIJ//jHP5q7WUclQZ1oEueeey5nnHEGf/rTn6ote/nll0lLS+OJJ56gW7dujB8/nmnTpvHiiy/i9XrxeDyMHTuWd955p2qbTz/9FF3XOe+8807kYZw0unfvzrZt29i6dSv//e9/efXVVznzzDPJysrilltuYezYsbz22msAvPTSS7hcLv79738zcOBAunTpwpQpU+jbt2/zHsRJYNSoUUydOpVu3bqRk5PD9OnTcTqdfPHFF1HrTZkyhSlTptC5c2d+//vfo+s6ixcvbqZWnzy+/PJL0tPTq/4uv/xy/vrXv/KLX/yCq6++mk6dOjFy5Miqm1KWZVVtO3DgQO666y66dOnCz372M6ZOncrzzz/fjEfTMk2aNImVK1eyefNm8vLymDt3LldeeWXUOvX9HEycOJFrr72WrKwssrKyTuBRnBxmzJhR9eN/+PDhxMTEMGvWrKh1brjhBiZOnEhOTg6PP/446enpvPLKK1Hr3HPPPZx99tlkZWWRnJx8wtrfmjXG5+D0008nJyeH//znP1XbzJw5k/POO0/ehxMgLi6Op556im7dunH22WdzySWX8PXXXzd3s45Kb+4GiJPXgw8+yNixY/nlL38Z9fyGDRsYNGgQqnronsLQoUMJBoNs2bKF3r17M3ny5KqubpfLxbvvvstFF12E0+k80YdxUrAsC0VRWL16NZZlMWTIkKjlgUCAkSNHArBmzRqGDh2K3W5vjqae1PLz8/nzn//MggULyM/PxzAMfD4fu3btilqvV69eVf+v6zpt2rQhPz//RDf3pDNs2LCqYcgQmf87aNAgVqxYEfW8aZr4fD7y8vJIS0sDYPDgwVH7Gjx4cFXvtzjE4/Fw4YUXMnPmTBISEhg+fDiZmZlR69T3czBgwIAT2fSTypYtW1i6dCkvvfQSAIqiMHnyZGbMmMGECROq1jv837WqqgwcOJCffvopal/yPhy7xvocXHvttbz88sv8+te/pri4mFmzZjFz5swTfTinpG7duqFpWtXjtLQ0li9f3owtqpsEdaLJDBw4kIsvvpj777+fu+++u17bHByaMG7cODRNY9asWYwaNYr58+fz3nvvNWVzT2o//fQTWVlZmKaJoih89dVX2Gy2qHUkYG56t99+O/v37+eRRx6hQ4cOOBwOLr744mrzt458bxRFieo1Eg3jcrno3Llz1HOmaXLPPfdwySWXVFtf7oY3zNVXX83tt9+O2+3mvvvuq7a8vp8Dt9t9opp80nn99dcxDIPevXtXPXfwO+TI4Lku8j40TGN8DqZOncoDDzzAkiVLWLNmDcnJyYwZM+ZEHsZJR1XVatfTcDhcbb3WeB2WoE40qfvvv58zzjgjanJ1t27d+OCDDzBNs6q3bsmSJdjtdjp16gREEhpccsklvPvuuxQWFpKamsqIESOa5Rhaux9//JG5c+dy11130bdvXyzLIi8vr6pn7kh9+/bl7bffJhgMSm9dI1u6dCmPPfZYVXrx/fv3k5eX18ytOrX169ePjRs3Vgv2jnTkHdrly5fTrVu3pmxaqzVq1ChsNhuFhYVccMEF1ZbL56BphcNh/vOf/zBt2rRqpQxuvfVW3njjjaphmcuXL2fUqFFAJOhbsWJFVE+eaLjG+BwkJiZy0UUXMXPmTNasWcMVV1wRNcpJHLvk5OSqHAIHHfm4tZKgTjSpzp07c/3110dNLr3xxht54YUX+M1vfsNtt93Gtm3bePDBB7n55ptxuVxV602ePJkJEyawfft2LrvsMvkiq4dAIEBeXh6maVJQUMDXX3/Nk08+Sf/+/fnFL36B2+2uGtr65z//mX79+lFcXMzChQvp2LEjF198MTfeeCOvvPIK119/PXfddRcej4cVK1aQk5Mj8+qOU3Z2Nu+88w6DBg3C6/Vy//33S+DczH77298yZcoUMjMzmThxIrqus379er7//vuoZCnLly/nySefZMKECSxcuJC33nqLF198sRlb3nIpisKiRYuwLAuHw1FtuXwOmtbnn39OYWEh1113HUlJSVHLLrvsMl555ZWqpGOvvPIKXbp0oWfPnrz00kvs3LmTG264oTmafdJprM/Btddey+WXX04oFIrKYCoaZuTIkTz99NPMmDGDM888k48//pilS5eSnp7e3E07bvIrWTS53/72t+j6ofsH7du3591332XNmjWMGDGCO++8k8suu4z7778/arthw4bRrl07fvrpJyZPnnyim90qzZ8/n27dutG7d28mTJjA7Nmz+d3vfsesWbOqhtA899xzXHXVVdx///0MHjyYKVOmsGjRIjp06ABE3p9Zs2YRCoW46KKLGDlyJP/617+i3kNRf6ZpVo3Lf/bZZ6msrOSss87ihhtu4Oqrr64676J5jBkzhnfeeYeFCxcyZswYxowZw1NPPVWtntQdd9zBunXrGDlyJA8//DD33Xef9GgcRVxcHPHx8TUuk89B05oxYwYjRoyoFtBBJLv0jh07mD9/PhDJfvncc88xfPhw5s6dy8yZM0+KH7ctRWN8DkaMGEH79u0ZPny4JAxqoMOvw2PGjOGee+7h4Ycf5qyzzmLHjh0nTXZdpaSkpGUPEBVCiFZs4sSJdOrUiSeffLK5myKEEECkTl2/fv2YN2+eJEJp4Xw+Hz169GD69Olyg7uBTpXrsPTUCSFEEygsLOTTTz9l0aJFnHXWWc3dHCGEEK2IaZrk5+fz/9u7t5Covj6M448p/TW9kNRGKrXwkAVlVFR0gNQixCKNoKybIiKiSArDqS4qo8yQoRCTpBOVUGQEBqFUI9Fhig5QkUUHy6SDTkOTTQermXkvquGdzLf+b5ru+n5gbtb6zdpreTM+7LX3Ki4uVkhIiLKzs7t6Sobzt/0Os58KADrB/PnzVV9fr+XLl2v69OldPR0AgIE0NjYqJSVF/fr1U2lpaZu3MeLH/rbfYbZfAgAAAICBsf0SAAAAAAyMUAcAAAAABkaoA4BfZLFYlJqaqpiYGMXHx2v27Nmqq6vzq/F6vSosLFRycrKio6OVmZmp27dv+/obGhq0bNkypaSkKDo6WikpKdqwYYPevXvnN05+fr4mTZokk8mkoUOH/pb1AQCA7o1QBwC/6Ny5c1q4cKFqampUVVWloKAgZWVl6eXLl76a7du3q7S0VEVFRbJarYqKilJ2drZev34tSbp3757cbrcsFosuXryorVu36tChQzKbzX7X8ng8ysnJ0Zw5c37rGgEAQPfFi1IAoIO5XC7FxsaqoqJCGRkZ8nq9Sk5O1qJFi5SXlyfp89lDiYmJ2rhxoxYsWPDdcXbt2qVNmzbp4cOHbfpKSkpUXl6umzdvdupaAABA98edOgDoYC6XSx6PR+Hh4ZI+b61sampSWlqaryYkJETjxo3TpUuX2h3n9evXvjEAAADaQ6gDgA5mNps1dOhQjR49WpLU1NQkSYqKivKri4qKUnNz83fHePz4sUpKSrRw4cLOnSwAADA8Dh8HgA60Zs0aXbx4UdXV1QoMDPy/xmhubtasWbOUmpqqpUuXdvAMAQDAn4Y7dQDQQVavXq2jR4+qqqpKAwYM8LWbTCZJkt1u96u32+3q06ePX1tTU5OmT5+uwYMHa+fOnQoICOj0eQMAAGMj1AFAB8jPz/cFuqSkJL++uLg4mUwm1dbW+trev38vm82mMWPG+NqeP3+uadOmKSkpSbt371ZQEJspAADAj/EfAwD8ory8PB0+fFgHDx5UeHi47xm60NBQhYWFKSAgQEuWLJHFYlFiYqISEhJUXFys0NBQzZo1S5L07NkzTZs2TdHR0SosLJTD4fCNHxkZ6dvKWV9fL5fLpWfPnunjx4+6ceOGJCk5OVk9e/b8zSsHAADdAUcaAMAvau8Nlfn5+Vq9erWkz4ePb9myRfv27ZPT6dTIkSNVXFysIUOGSJIqKirafX7u+vXriouLkyRlZmbq/Pnz/7MGAAD8XQh1AAAAAGBgPFMHAAAAAAZGqAMAAAAAAyPUAQAAAICBEeoAAAAAwMAIdQAAAABgYIQ6AAAAADAwQh0AAAAAGBihDgCAn1BYWNjuQfMAAHQlQh0AAJ3oyJEj2rFjR1dPAwDwByPUAQDQiSorK1VWVtbV0wAA/MEIdQAAAABgYIQ6AAC+YbPZlJqaKpPJpOHDh2vv3r1taioqKjRjxgwlJSWpT58+GjFihCwWizwej68mMzNTNTU1amxsVHh4uO/zldfr1c6dOzVu3DiZTCYlJCRo2bJlcjgcv2WdAIA/Q1BXTwAAgO7k1q1bmjlzpiIiImQ2m+V2u1VUVKSIiAi/ul27dikpKUlTpkxRcHCwzpw5o4KCArW0tGj9+vWSpLy8PLW0tOjp06favHlzm2utXLlSBw4cUE5OjhYtWqQnT56ovLxc165dk9VqVXBw8O9YMgDA4AKcTqe3qycBAEB3MW/ePJ06dUpXrlxRTEyMJOn+/fsaO3asPn36JKfTKUl6+/atevXq5ffd3NxcVVZWqr6+Xv/8848kafbs2aqrq9PNmzf9ai9duqSpU6eqrKxMOTk5vnabzaaMjAxt27ZN8+fP78SVAgD+FGy/BADgC7fbLavVqoyMDF+gk6SEhASlp6f71X4NdG63W06nUw6HQ+PHj9ebN2909+7dH17r2LFjCgsL0+TJk+VwOHyfr9s5z54927GLAwD8sdh+CQDAFy9evNC7d+8UHx/fpu/bNpvNpoKCAl29elUfPnzw62tpafnhtR48eCCXy6XExMTv9tvt9n8xcwDA34xQBwDAv/To0SNlZWUpPj5emzdvVv/+/RUcHKzr169r3bp1fi9LaY/H41Hv3r21Z8+e7/Zz0DkA4GcR6gAA+CIyMlIhISF68OBBm77/bjtx4oRaW1t16NAhxcbG+tobGhp++loDBw5UbW2tRo0apbCwsF+bOADgr8YzdQAAfBEYGKi0tDRVV1ersbHR137//n2dPn3ar076fCTBV62trSovL28zZmhoqF69euVXK0nZ2dnyeDzaunVrm+98fU4PAICfEWg2m9d39SQAAOguBg0apP379+vYsWNqbW3VhQsXlJeXp5iYGNntdpnNZoWHh2vfvn2yWq1yu92y2WxatWqVevTooebmZs2dO1dxcXGSpMePH6u6ulpOp1NOp1N37tzRkCFDFBsbK4fDobKyMl2+fFkvXrzQjRs3VFlZqRUrVshkMmnYsGFd/NcAABgBRxoAAPCN8+fPa+3ataqrq1Pfvn2Vm5ur58+fq6ioyHcH7eTJkyooKNC9e/cUERGhOXPmaMKECcrOztbx48c1ceJESZ+PPli5cqVqamrkdDrl9Xr97sIdOHBAe/fu1e3btxUUFKT+/fsrPT1dixcv9nsDJwAA7SHUAQAAAICB8UwdAAAAABgYoQ4AAAAADIxQBwAAAAAGRqgDAAAAAAMj1AEAAACAgRHqAAAAAMDACHUAAAAAYGCEOgAAAAAwMEIdAAAAABjYfwCUgf+/dQrL2gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "portfolio_benchmark_ho = plot_return(df_test,'Out-of-sample: Performance of the SDF Portfolio')\n", "\n", "\n", "print('Portfolio growth factor:\\n'+\\\n", " str((portfolio_benchmark_ho.iloc[-1] - 1).round(2).to_dict())+\\\n", " calc_metrics(df_test)) " ] }, { "cell_type": "markdown", "metadata": { "id": "Q7CaBzbsyunS" }, "source": [ "## Conclusion\n", "---\n", "In this exercise, we estimated the SDF model for BTCUSDT using a GMM method and calculated the sample portfolio performance on the in-sample and hold-out time intervals.\n", "\n", "The results of the model can be interpreted as follow:\n", "\n", "Through the entire year of 2018, the BTCUSDT showed poor performance (-34%), resulting in the next-to-zero Sharpe ratio. On the same interval model portfolio showed a 300% growth but not without high volatility (Sharpe is still less than 1).\n", "\n", "On the hold-out dataset both index and the model portfolio showed significant growth by 284% and 1108%, respectively. The Sharpe ratio for both index and the model portfolio is higher than 2: 2.7 and 3.4, respectively.\n", "\n", "Given the results from the model run, we can confidently conclude that our SDF model portfolio achieved better performance than the index with preserving the higher Sharpe ratio." ] } ], "metadata": { "colab": { "name": "2021-07-20-intro-to-crypto-hedging-via-sdf.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }