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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using the latest advancements in AI to predict stock market movements"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a **Generative Adversarial Network** (GAN) with **LSTM**, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, **CNN**, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.\n",
+ "\n",
+ " We will go into greater details for each step, of course, but the most difficult part is the GAN: very tricky part of successfully training a GAN is getting the right set of hyperparameters. For that reason we will use **Bayesian optimisation** (along with Gaussian processes) and **Reinforcement learning** (RL) for deciding when and how to change the GAN's hyperparamenets (the exploration vs. exploitation dillema). In creating the reinforcement learning we will use the most recent advancements in the field, such as **Rainbow** and **PPO**.\n",
+ "\n",
+ " We will use a lot of different types of input data. Along with the stock's historical trading data and technical indicators, we will use the newest advancements in **NLP** (using 'Bidirectional Embedding Representations from Transformers', **BERT**, sort of a transfer learning for NLP) to create sentiment analysis (as a source for fundamental analysis), **Fourier transforms** for extracting overall trend directions, **Stacked autoencoders** for identifying other high-level features, **Eigen portfolios** for finding correlated assets, autoregressive integrated moving average (**ARIMA**) for the stock function approximation, and many more, in order to capture as much information, patterns, dependencies, etc, as possible about the stock. As we all know, the more (data) the merrier. Predicting stock price movements is an extremely complex task, so the more we know about the stock (from different perspectives) the higher our changes are.\n",
+ "\n",
+ " For the purpose of creating all neural nets we will use MXNet and it's high-level API - Gluon, and train them on multiple GPUs.\n",
+ "\n",
+ "**Note:** _Altough I try to get into detials of the math and the mechanisms behind almost all algorithms and techniques, this notebook is not explicitly intended to explain how machine/deep learning, or the stock markets, work. The purpose is rather to show how we can use different techniques and algoritms for the purpose of accurately preducting stock price movements, and to also give rationale behind the reason and usefulness of using each technique at each step._\n",
+ "\n",
+ "_Notebook created: January 9, 2019_.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Figure 1 - The overall architecture of our work**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Table of content\n",
+ "* [Introduction](#overview)\n",
+ "* [Acknowledgement](#acknowledgement)\n",
+ "* [The data](#thedata)\n",
+ " * [Correlated assets](#corrassets)\n",
+ " * [Tecnical indicators](#technicalind)\n",
+ " * [Fundamental analysis](#fundamental)\n",
+ " - [Bidirectional Embedding Representations from Transformers - BERT](#bidirnlp)\n",
+ " * [Fourier transforms for trend analysis](#fouriertransform)\n",
+ " * [ARIMA as a feature](#arimafeature)\n",
+ " * [Statistical checks](#statchecks)\n",
+ " - [Heteroskedasticity, multicollinearity, serial correlation](#hetemultiser)\n",
+ " * [Feature Engineering](#featureeng)\n",
+ " * [Feature importance with XGBoost](#xgboost)\n",
+ " * [Extracting high-level features with Stacked Autoencoders](#stacked_ae)\n",
+ " * [Activation function - GELU (Gaussian Error)](#gelu)\n",
+ " * [Eigen portfolio with PCA](#pca)\n",
+ " * [Deep Unsupervised Learning for anomaly detection in derivatives pricing](#dulfaddp)\n",
+ "* [Generative Adversarial Network - GAN](#qgan)\n",
+ " * [Why GAN for stock market prediction?](#whygan)\n",
+ " * [Metropolis-Hastings GAN and Wasserstein GAN](#mhganwgan)\n",
+ " * [The Generator - One layer RNN](#thegenerator)\n",
+ " - [LSTM or GRU](#lstmorgru)\n",
+ " - [The LSTM architecture](#lstmarchitecture)\n",
+ " - [Learning rate scheduler](#lrscheduler)\n",
+ " - [How to prevent overfitting and the bias-variance trade-off](#preventoverfitting)\n",
+ " - [Custom weights initializers and custom loss metric](#customfns)\n",
+ " * [The Discriminator - 1D CNN](#thediscriminator)\n",
+ " - [Why CNN as a discriminator?](#why_cnn_architecture)\n",
+ " - [The CNN architecture](#the_cnn_architecture)\n",
+ " * [Hyperparameters](#hyperparams)\n",
+ "* [Hyperparameters optimization](#hyperparams_optim)\n",
+ " * [Reinforcement learning for hyperparameters optimization](#reinforcementlearning)\n",
+ " - [Theory](#reinforcementlearning_theory)\n",
+ " - [Rainbow](#rl_rainbow)\n",
+ " - [PPO](#rl_ppo)\n",
+ " - [Further work on Reinforcement learning](#reinforcementlearning_further)\n",
+ " * [Bayesian optimization](#bayesian_opt)\n",
+ " - [Gaussian process](#gaussprocess)\n",
+ "* [The result](#theresult)\n",
+ "* [What is next?](#whatisnext)\n",
+ "* [Disclaimer](#disclaimer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Introduction "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Accurately predicting the stock markets is a complex task as there are millions of events and pre-conditions for a particilar stock to move in a particular direction. So we need to be able to capture as many of these pre-conditions are possible. We also need make several important assumptions: 1) markets are not 100% random, 2) history repeats, 3) markets follow people's rational behavior, and 4) the markets are '_perfect_'. And, please, do read the **Disclaimer** at the bottom.\n",
+ "\n",
+ " We will try to predict the price movements of **Goldman Sachs** (NYSE: GS). For the purpose, we will use daily closing price from January 1st, 2010 to December 31st, 2018 (seven years for training purposes and two years for validation purposes). _We will use the terms 'Goldman Sachs' and 'GS' interchangeably_."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Acknowledgement "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Before we continue, I'd like to thank my friends Nuwan and Thomas without whose ideas and support I wouldn't have been able to create this work."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. The Data "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " We need to understand what affects whether GS's stock price will move up or down. It is what people as a whole think. Hence, we need to incorporate as much information (depicting the stock from different aspects and angles) as possible. (We will use daily data - 1,585 days to train the various algorithms (70% of the data we have) and predict the next 680 days (test data). Then we will compare the predicted results with a test (hold-out) data. Each type of data (we will refer to it as _feature_) is explained in greater detail in later sections, but, as a high level overview, the features we will use are:\n",
+ "\n",
+ "1. **Correlated assets** - these are other assets (any type, not necessarily stocks, such as commodities, FX, indices, or even fixed income securities). A big company, such as Goldman Sachs, obviously doesn't 'live' in an isolated world - it depends on, and interacts with, many external factors, including its competitors, clients, the global economy, the geo-political situation, fiscal and monetary policies, access to capital, etc. The details are listed later.\n",
+ "2. **Technical indicators** - a lot of investors follow technical indicators. We will include the most popular indicators as independent features. Among them - 7 and 21 days moving average, exponential moving average, momentum, Bollinger bands, MACD.\n",
+ "3. **Fundamental analysis** - A very important feature indicating whether a stock might move up or down. There are two features that can be used in fundamental analysis: 1) Analysing the company performance using 10-K and 10-Q reports, analysing ROE and P/E, etc (we will not use this), and 2) **News** - potentially news can indicate upcoming events that can potentially move the stock in certain direction. We will read all daily news for Goldman Sachs and extract whether the total sentiment about Goldman Sachs on that day is positive, neutral, or negative (as a score from 0 to 1). As many investors closely read the news and make investment decisions based (partially of course) on news, there is a somewhat high chance that if, say, the news for Goldman Sachs today are extremely positive the stock will surge tomorrow. _One crucial point, we will perform feature importance (meaning how indicative it is for the movement of GS) on absolutely every feature (including this one) later on and decide whether we will use it. More on that later_. \n",
+ " For the purpose of creating accurate sentiment prediction we will use Neural Language Processing (**NLP**). We will use **BERT** - Google's recently announced NLP approach for transfer learning for sentiment classification stock news sentiment extraction.\n",
+ "4. **Fourier transforms** - Along with the daily closing price, we will create Fourier transforms in order to generalize several long- and short-term trends. Using these transforms we will eliminate a lot of noise (random walks) and create approximations of the real stock movement. Having trend approximations can help the LSTM network pick it's prediction trends more accurately.\n",
+ "5. **Autoregressive Integrated Moving Average** (ARIMA) - This was one of the most popular techniques for predicting future values of time series data (in the pre-neural networks ages). Let's add it and see if it comes off as an important predictive feature.\n",
+ "6. **Stacked autoencoders** - most of the aforementioned features (fundamental analysis, technical analysis, etc) were found by people after decades of research. But maybe we have missed something. Maybe there are hidden correlations that people cannot comprehend due to the enormous amount of data points, events, assets, charts, etc. With stacked autoencoders (type of neural networks) we can use the power of computers and probably find new types of features that affect stock movements. Even though we will not be able to understand these features in human language, we will use them in the GAN.\n",
+ "7. **Deep Unsupervised learning for anomaly detection in options pricing**. _Explained later in section XXX_.\n",
+ "\n",
+ "Next, having so many features, we need to perform a couple of important steps:\n",
+ "1. Perform statistical checks for the 'quality' of the data. If the data we create if flawed, then no matter how sophisticated our algorithms are, the results will not be positive. The checks include making sure the data does not suffer from heteroskedasticity, multicollinearity, or serial correlation.\n",
+ "2. Create feature importance. If a feature (e.g. another stock or a technical indicator) has no explanatory power to the stock we want to predict, then there no need for us to use it in the training of the neural nets. We will using **XGBoost** (eXtreme Gradient Boosting), a type of boosted tree regression algorithms.\n",
+ "\n",
+ " As a final step of our data preparation, we will also create **Eigen portfolios** using Principal Component Analysis (**PCA**) in order to reduce the dimensionality of the features created from the autoencoders."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 529,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from utils import *\n",
+ "\n",
+ "import time\n",
+ "import numpy as np\n",
+ "\n",
+ "from mxnet import nd, autograd, gluon\n",
+ "from mxnet.gluon import nn, rnn\n",
+ "import mxnet as mx\n",
+ "import datetime\n",
+ "import seaborn as sns\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "import xgboost as xgb\n",
+ "from sklearn.metrics import accuracy_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "context = mx.cpu(); model_ctx=mx.cpu()\n",
+ "mx.random.seed(1719)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note**: The purpose of this section (3. The Data) is to show the data preprocessing and to give rationale for using different sources of data, hence I will only use a subset of the full data (that is used for training)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def parser(x):\n",
+ " return datetime.datetime.strptime(x,'%Y-%m-%d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_ex_df = pd.read_csv('data/panel_data_close.csv', header=0, parse_dates=[0], date_parser=parser)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Date
\n",
+ "
GS
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
2009-12-31
\n",
+ "
168.839996
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
2010-01-04
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+ "
173.080002
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+ "
\n",
+ "
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2
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+ "
2010-01-05
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+ "
176.139999
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+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " Date GS\n",
+ "0 2009-12-31 168.839996\n",
+ "1 2010-01-04 173.080002\n",
+ "2 2010-01-05 176.139999"
+ ]
+ },
+ "execution_count": 122,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset_ex_df[['Date', 'GS']].head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "There are 2265 number of days in the dataset.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('There are {} number of days in the dataset.'.format(dataset_ex_df.shape[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's visualize the stock for the last nine years. The dashed vertical line represents the separation between training and test data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 637,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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IKXniCBERETkZHJkR1wJVHHVcRORvypH5dJVlWRXLui8iIiKngjKMRERETgFjzHPYw4i2YA+J6o2dEfGsgkUiIiIiUt4pYCQiInJq5FE4PCwYe9jPQ8C7ZdkpEREREZFAaEiaiIiIiIiIiIh4CCrrDoiIiIiIiIiISPmigJGIiIiIiIiIiHhQwEhERERERERERDyo6DVgjDFADSCjrPsiIiIiIiIiInKSVQCSrVIUslbAyFYD2FXWnRAREREREREROUVqArsDbayAkS0DYOfOncTGxpZ1X0RERERERERETor09HRq1aoFpRxVpYCRm9jYWAWMREREREREROSsp6LXIiIiIiIiIiLiQQEjERERERERERHxoICRiIiIiIiIiIh4UA2jUsjPzyc3N7esuyFSroWFhREUpFi0iIiIiIjI35kCRgGwLIs9e/Zw+PDhsu6KSLkXFBREvXr1CAsLK+uuiIiIiIiIyHFSwCgAzmBR1apViYqKwhhT1l0SKZcKCgpITk4mJSWF2rVr62dFRERERETkb0oBoxLk5+e7gkXx8fFl3R2Rci8hIYHk5GTy8vIIDQ0t6+6IiIiIiIjIcVChkRI4axZFRUWVcU9E/h6cQ9Hy8/PLuCciIiIiIiJyvBQwCpCG1ogERj8rIiIiIiIif38KGImIiIiIiIiIiAcFjKREzz33HG3atCm2zcCBA+nbt+9p6lH5E8hnVN6dCdcgIiIiIiIiJ4cCRmewPXv28NBDD5GUlERERATVqlXjwgsv5MMPPyQrK6usu3fa5Ofn8/LLL9OkSRMiIyOpXLkyHTt25PPPPy/rrh2Xbdu2YYxh+fLlZd0VEREREREROUNplrQz1JYtW+jcuTMVK1bkpZdeomXLluTl5bFhwwY+++wzatSoQe/evcu6m6fFc889x8cff8yYMWNo37496enpLF68mEOHDpV110RERERERETKJWUYnaEGDx5MSEgIixcv5rrrrqNp06a0bNmSa6+9lkmTJtGrVy9X2x07dtCnTx9iYmKIjY3luuuuY+/evX6PnZ+fz6OPPkrFihWJj4/nySefxLIsjzZdu3blgQce4OGHH6ZSpUpUq1aNjz/+mMzMTG6//XYqVKhAgwYNmDx5ssdxBw0aRL169YiMjKRx48a8/fbbHsd1Dn0bNWoU1atXJz4+niFDhrhms/NlwoQJDB48mP79+1OvXj1at27NoEGDePTRR11tpkyZwoUXXui6pquuuorNmzd7HGfXrl3ccMMNVK5cmejoaNq3b8+CBQs82nz55ZfUrVuXuLg4brjhBjIyMlzbvv/+e1q2bElkZCTx8fF0796dzMxMn30+dOgQN910EwkJCURGRtKwYUNXRlS9evUAaNu2LcYYunbtCkBBQQEjRoygZs2ahIeH06ZNG6ZMmVLqa3DaunUrSUlJ3HfffRQUFPj9fEVEREREpGzM2ZjKy7+sJSdP/16Xk08Bo1KyLIusnLwyeXkHZfw5cOAAU6dOZciQIURHR/ts45zJyrIs+vbty8GDB5k1axa//fYbmzdv5vrrr/d7/DfeeIPPPvuMTz/9lLlz53Lw4EH+97//FWn3xRdfUKVKFRYuXMgDDzzAfffdR//+/enUqRNLly6lR48e3HLLLa7hcQUFBdSsWZNvv/2WNWvW8Mwzz/Cvf/2Lb7/91uO4M2bMYPPmzcyYMYMvvviCcePGMW7cOL/9TUxM5Pfffyc1NdVvm8zMTB599FEWLVrE9OnTCQoK4uqrr3YFSo4cOUKXLl1ITk7m559/ZsWKFTz55JMegZTNmzfz008/MXHiRCZOnMisWbN45ZVXAEhJSWHAgAHccccdrF27lpkzZ3LNNdf4/U6ffvpp1qxZw+TJk1m7di0ffPABVapUAWDhwoUATJs2jZSUFH788UcA3n77bd544w1GjRrFypUr6dGjB71792bjxo0BX4PTqlWr6Ny5M/379+eDDz4gKEi/KkRERERETqXs3Hw+m7uV+VsOBPTsZ1kWt3y6kI9mb+HX1XtOQw/lbKMhaaV0NDefZs/8WibnXjOiB1FhJX9lmzZtwrIsGjdu7LG+SpUqZGdnAzBkyBBeffVVpk2bxsqVK9m6dSu1atUC7CyZ5s2bs2jRIjp06FDk+KNHj2bYsGFce+21AHz44Yf8+mvRz6R169YMHz4cgGHDhvHKK69QpUoV7rrrLgCeeeYZPvjgA1auXEnHjh0JDQ3l+eefd+1fr1495s2bx7fffst1113nWl+pUiXGjBlDcHAwTZo04corr2T69Omu43p788036devH4mJiTRv3pxOnTrRp08fevbs6WrjvBanTz/9lKpVq7JmzRpatGjBf//7X1JTU1m0aBGVK1cGICkpyWOfgoICxo0bR4UKFQC45ZZbmD59Oi+++CIpKSnk5eVxzTXXUKdOHQBatmzps79gZ321bduW9u3bA1C3bl3XtoSEBADi4+NJTEx0rR81ahT//Oc/ueGGGwB49dVXmTFjBqNHj+a9994L6BoA/vzzT6666iqGDRvG448/7rePIiIiIiJy8rw2ZT2f/bEVgIe6NeSRyxoV237L/sLRCkdz809p3+TspLSBM5gzi8hp4cKFLF++nObNm3Ps2DEA1q5dS61atVzBIoBmzZpRsWJF1q5dW+SYaWlppKSkcMEFF7jWhYSEuAIb7lq1auV6HxwcTHx8vEeQpFq1agDs27fPte7DDz+kffv2JCQkEBMTw9ixY9mxY4fHcZs3b05wcLBruXr16h7H8NasWTNWrVrF/Pnzuf3229m7dy+9evXizjvvdLXZvHkzN954I/Xr1yc2NtY17Mt57uXLl9O2bVtXoMWXunXruoJF3v1q3bo13bp1o2XLlvTv35+xY8cWW0Ppvvvu4+uvv6ZNmzY8+eSTzJs3z29bgPT0dJKTk+ncubPH+s6dO7u+x0CuYceOHXTv3p3hw4crWCQiIiIichp9v2Sn6/3b0zcW2W5ZFgM/X8i1H8wjv8Bi9obCERQFBYGNRhEpDWUYlVJkaDBrRvQos3MHIikpCWMM69at81hfv359+ziRka51lmUVCSwVt740QkNDPZaNMR7rnMd3Don69ttveeSRR3jjjTe44IILqFChAq+//nqRGju+jltSjZ2goCA6dOhAhw4deOSRR/jqq6+45ZZbeOqpp6hXrx69evWiVq1ajB07lho1alBQUECLFi3IyckBPD+z0lyvs1/BwcH89ttvzJs3j6lTp/Luu+/y1FNPsWDBAldwyl3Pnj3Zvn07kyZNYtq0aXTr1o0hQ4YwatSoYvvg/Z25f4+BXENCQgI1atTg66+/ZtCgQcTGxpa4j4iIiIiInLj07Lxit6ceOcbM9XaQKPnwURZuPejaduRY8fuKHA9lGJWSMYaosJAyeQUawImPj+eyyy5jzJgxfosqOzVr1owdO3awc2dhNHvNmjWkpaXRtGnTIu3j4uKoXr068+fPd63Ly8tjyZIlAX6C/s2ZM4dOnToxePBg2rZtS1JSUpHC0ydLs2bNALt20YEDB1i7di3Dhw+nW7duNG3atEj2T6tWrVi+fDkHDx70dbiAGGPo3Lkzzz//PMuWLSMsLMxn7SenhIQEBg4cyFdffcXo0aP5+OOPAQgLCwPsIuFOsbGx1KhRg7lz53ocY968ea7vMZBriIyMZOLEiURERNCjRw+Pot0iIiIiInJqBFKzaNO+I673Gdl5HMjM8VgWOdkUMDpDvf/+++Tl5dG+fXu++eYb1q5dy/r16/nqq69Yt26da0hX9+7dadWqFTfddBNLly5l4cKF3HrrrXTp0sXnMDOAhx56iFdeeYX//e9/rFu3jsGDB3P48OET7nNSUhKLFy/m119/ZcOGDTz99NMsWrTohI/br18/3nrrLRYsWMD27duZOXMmQ4YMoVGjRjRp0oRKlSoRHx/Pxx9/zKZNm/j99989ZlADGDBgAImJifTt25c//viDLVu28MMPP/Dnn38G1IcFCxbw0ksvsXjxYnbs2MGPP/5Iamqqz6Ac2PWdxo8fz6ZNm1i9ejUTJ050ta1atSqRkZFMmTKFvXv3kpaWBsATTzzBq6++yjfffMP69esZOnQoy5cv56GHHirVNURHRzNp0iRCQkLo2bMnR44cQURERORUycnJ4aWXXuKll15yZXeLnG1emFS0HIi3zW4Bo8NHczjiFiTKVIaRnAIKGJ2hGjRowLJly+jevTvDhg2jdevWtG/fnnfffZfHH3+ckSNHAnbWy08//USlSpW4+OKL6d69O/Xr1+ebb77xe+zHHnuMW2+9lYEDB7qGjl199dUn3Od7772Xa665huuvv57zzz+fAwcOMHjw4BM+bo8ePZgwYQK9evWiUaNG3HbbbTRp0oSpU6cSEhJCUFAQX3/9NUuWLKFFixY88sgjvP766x7HCAsLY+rUqVStWpUrrriCli1b8sorr3jUUipObGwss2fP5oorrqBRo0YMHz6cN954w6Pwtvf5hg0bRqtWrbj44osJDg7m66+/BuyaUe+88w4fffQRNWrUoE+fPgA8+OCDPPbYYzz22GO0bNmSKVOm8PPPP9OwYcNSX0NMTAyTJ0/GsiyuuOKKEjPVRERERE5Ebm4uubm5Zd0NkTLz6dytPtdblsUPS3bxx6b9HhlGaVm5HsPQfl6RTF5+8WU6RErLBDpV+5nMGBMLpKWlpRWp2ZKdnc3WrVupV68eERERZdNBkb8R/cyIiIhIaeTk5PDyyy8D9sy6zuH3ImeTS9+YyZbUwv+kPadiJH8MvZR5m/dz41i7pmuDhGg2O9o82K0h73gVxh7aswn3dmlw+jotfxvp6enExcUBxFmWlR7ofsowEhERERERESlDUWF21n/npHgAGlWLASA145irzWa3gJJ3sAjgkzm+s5REjpcCRiIiIiIiIiJl6FCmPSQzKcEOFOUV2COBjubk+93HW2SYHu/l5NIdJSIiIiIiIlKGMrLtgFHFKHtIZq6jHtHR3MADRqUJLokEQgEjERERERERkTLkDAzFRYYCkJdveaz355LGCa73+4/kKGgkJ5UCRiIiIiIiUmaMMdSpU4c6depgjCnr7oicdrn5BeQ6AkTOgFGuY0hadgkBoCGXJPFgt4au5U/nbjlFvZSzkQJGIiIiIiJSZkJDQxk4cCADBw4kNDS0rLsjctq5ZxHFujKMAhuSVjk6jEcva+RaHjV1wynooZytFDASEREREREROQWyc/NdwR9/Vu1Kc713zpYWyJC0IAM1K0UBcE27c060qyJFKGAkIiIiIiIicpJl5+bT5fUZXPXu3GLb3f3lEtf7kCB7WGZugSPDKMf+87Jm1Yrsd06lSMJC7Ef6YT2butZblnViHRdxUMBIRERERETKTE5ODq+//jqvv/46OTk5Zd0dkZPCsiyu++hP9qYfY92eDI4cy/Pb1n1bSLD9iO7MMMp2ZBhdmFSFf99xHv+6oomrbd34aNf78NDCR3tnPaTC5QL+3HzAdSyRQClgJKXWsWNHhg4dWtbdOGVGjx5N3bp1y7ob5cKyZcto164dYWFhdO3a1e86ERERkRORlZVFVlZWWXdD5KTZcTCLlW5DzVIOH/XbtkpMOADRYcFEOAI/e9KzOZiZ4xqSFhkazMWNEmhaPda1X70qhQGjsODCR/tjeZ6BoVFT1zNg7Hz++cPKE7giORspYHQGMsYU+xo4cOAJHf+XX35h+PDhpd4vIyOD8PBwEhMTi+1fkyZNSj5YCX766afTOstGxYoVGTdu3Gk7nz+HDx/GGMPMmTNPyvGGDh1K7dq12bx5Mz/++KPfdSIiIiIiUmj+lgMey/9ZsKNIm637M+k9Zi77jxwDYPz9FxIbYRe9zskroN3I3zjg2BbpqG3krHEEUCfed8AoJ8+zZtLHs+2Z08YvTy6xnpKIu5Cy7oCcfCkpKa7333zzDc888wzr1693rYuMjPS5X25ubkAzU1SuXPm4+jV58mSaNGnCjBkzXOnGmzZt4qKLLmLOnDkkJSUBEBKi27K82Lx5M0OGDKFWrVrFrhMREREREduY3zcWma1s3LxtdGtalYsaJgCQX2BxyaiZru3GQM1KkeR4BXRWOLKUIkODHX8WPivVqxLleh8UZAjae/UQAAAgAElEQVQLDiInv6DIMcJDgsjOtdc99b9VvNqv1QleoZwtlGF0BkpMTHS94uLiMMYUWbdu3TqMMfz4449cdNFFhIeH8/3337N3716uu+46zjnnHKKiomjdujU//PCDx/G9h6QlJiYyatQobr31VmJiYqhbt67PbJvx48fTu3dvKleu7OpLlSpVAKhSpUqRdVlZWTz44IMkJiZSoUIFLrroIhYsWOA63oYNG+jRowdxcXHExMTQunVrZs2axfLly7n66quBwmyrhx9+2O/nNWbMGGrUqEF0dDQDBgwgIyPDY/uqVau44oorqFq1KhUqVKBTp0788ccfru1t2rQhLS2N22+/HWMMFStWBGD37t3069fPdey2bdvy888/l/j9TZs2jU6dOhEZGUl8fDxXXXUV2dnZgO9Mprp16zJ69GjXe4BLLrkEYwxt2rTxe568vDyGDh1K9erVCQ8P57zzzmP27NlAYabS5s2befTRRzHGMHr0aJ/rRERERESkkL+p7d/8bQNvTF3PbZ8t5P8WemYcJcZGEBEaTEyY7/88d2YYhYUUjqJoVK2CRxtnAexjuZ4Bo2y35W8W7wzwKkQUMDpuOTk5fl95eXkBt83NzQ2o7anyz3/+k8cff5x169ZxySWXcPToUTp16sSkSZP466+/uO2227j++utZvnx5scd59dVXueiii1i+fDl33HEHd911F1u3bnVtz8vLY/LkyfTp0yfgvvXv3581a9bw008/sWzZMrp160b37t3ZvXs3AHfccQfR0dHMmzePFStW8PzzzxMREUGLFi34/PPPATvbKiUlhZEjR/o8x9SpU3n44YcZOnQoy5Yto3Xr1rz++usebTIyMujXrx8zZsxg8eLFnHfeeVxxxRXs378fgOnTpxMbG8vo0aNJSUlhwwb7L4isrCy6dOnC5MmTWbFiBddffz3XXnsta9eu9XvNc+bM4fLLL6dz584sWrSIGTNmcMkll1BQEFjq6KJFiwD44YcfSElJYfr06X7bjhw5kk8++YQPPviAFStW0KFDB3r27Mnu3buJi4sjJSWFmjVrMmLECFJSUrjrrruKrLv77rsD6peIiIiIyNlu2Y7DvPv7JmZtSOWFSWs8ttWqbGcLBQX5LqsR4cgwqh5XOFrknIqeI0ecASNnhtGOA1k89u2Kk9N5OStp7M9xevnll/1ua9iwITfeeKNredSoUUUCQ0516tTxqCn09ttv+yz49+yzzx5/Z4vx+OOPFwniuGfjPProo0yaNInvv/++2GyVvn37ctdddwEwfPhw3nzzTWbNmkW9evUAmD17NlFRUZx77rkB9Wvp0qX89ttvpKamEhcXB8Bzzz3HxIkT+c9//sOTTz7Jjh076N+/P82bNwegQYMGrv2dWT6JiYnFnmf06NH079+fBx98ELDr88ycOZN169a52lxwwQVccMEFHvt8//33TJkyhZtvvpn4+HiMMcTFxXmcr2HDhjRs2NC1PHToUCZMmMAPP/zgtwbUyy+/zOWXX+4RtGrVKvCU0YQEO8XVmcXlT0FBAW+++Savvvoqffv2BexMq+nTp/P+++/z4osvkpiYSHBwMBUqVHAdKzo6usg6EREREREpnWyvLKCqFcJd7yNCg4psdxbDjg4PYf6wboSHBBWp2RruCBj9sHQXj3RvRP+P5rE3/ZhHmwoRCgFI4JRhdJZr3769x3JeXh4jRoygZcuWVK5cmZiYGGbPns2OHUWLtLlzD2oEBQVRrVo19u3b51o3fvx4evXqFXAh6iVLlpCXl8c555xDTEyM67V8+XI2b94MwCOPPMJjjz1Gly5dGDlypEeQJ1Br1671CAYBRZYPHz7Mgw8+SJMmTVzD31JSUkr8THJycnj66adp0aIFlSpVIiYmhgULFhS73/Lly+nWrVupr6M4f/31l8dn+P7777Nr1y6OHDlC586dXe2MMVxwwQXFZkCJiIiInGzGGGrUqEGNGjVO66QlIqfLgPNqE+1WrNqXaLehaM56Re7iowsDSolxEVSKDivSxplh9NGsLbw2ZX2RYBFARnYe2/ZnBtx3ObspvHichg0b5ndbUJBnHO7xxx/329b7L8WHHnroxDpWStHR0R7LL730Eu+99x6jR4+mWbNmREdHc99995U4LM67WLYxxmMY1YQJE3j//fcD7ldBQQExMTEsXbq0yLbYWHsqyUceeYQ+ffrwyy+/MGXKFEaOHMnYsWO57bbbAj6PZVklthk8eDArV67ktddeo379+kRERPCPf/yjxM/kueee44svvuCtt96iSZMmREVFcccddxS7n7+C5E5BQUFF+uwve82pcePGHkMKExISOHz4MFD0/rMsS/9QExERkdMqNDTUlakuciaIDA3maG4+s5+4hKO5+dRPiKZ706oM+mKxRztnkWoA90fIyNBgDlH4b3xjoEpM0QCRN2eGEcBPy3f7bTdi4ho+G9gh0MuRs1iZZhgZY4YZYxYZYzKMMfuMMT8ZYxp7tZlpjLG8Xl97talkjPnSGJPmeH1pjKl4KvseFhbm9+U9y1dxbb0DLf7anS5z5syhX79+DBgwgNatW1O3bl02btx4QsdcuXIlqampXHLJJQHv065dOzIyMjh69ChJSUker6pVq7ra1a9fn/vvv5+JEycyaNAgPv30UwDXZ5afn1/seZo1a8b8+fM91nkvz5kzh3vuuYfevXvTokULKlasSHJyskebsLCwIueaM2cON9xwA9dddx2tWrVyTUVfnFatWhVbdyghIcFjFry9e/d6LPu67rCwMI/PLy4ujlq1alGhQgXmzp3ramdZFvPnz6dp06bF9lFEREREzgx/7Uqj7tBJXPH2HLJy8kreQUpkWRbH8ux/i0eEBtE4sQKhwUF0a1qNj27xLM/Ro0VhiYfc/ML/FI70ykaKjw4jJLjkR/cwt4CRrywlJ2UYSaDKekhaF+A9oCNwGXbG01RjTLRXu7FAdbfXPV7b/wu0AS53vNoAX566bp+5kpKSmDJlCgsWLGDNmjUMGjSIQ4cOndAxf/75Zy6//HLCw8NLbuzQoUMHrrzySvr378+ECRPYunUr8+fPZ8SIEcyaNQuA++67j+nTp7Nt2zYWLlzI3LlzXcEO52xhEyZMYP/+/WRm+v6l+NBDD/Htt98yZswYNmzYwGuvvca8efM82iQlJfH111+zatUqlixZwoABA4oE+urWrcuMGTNITk7m4MGDrv0mTpzIokWLWLVqFbfddhtHjhwp9rqffvpppkyZwhNPPMHq1atZs2YNo0ePdtW1uvTSSxk7diwLFixg+fLlDBw4kIiICNf+UVFRJCQkMHXqVPbu3evKJPIWFBTEo48+yrPPPsv48eNZt24d999/Pzt37mTw4MHF9lFERERE/t5+W7OXf/+5jV5j7P88XJOSzrAf/yrbTp0h8gosChyxn/AQz6DNP5pV45HujVzLR92CdEdzCv/D1ztgVL9KTEDnDnMLKu0+fLTI9rdvsGvSZhxTcFACU6YBI8uyLrcsa5xlWasty1oB3A7UBrwrI2dZlrXH7ZXm3GCMaYodJLrTsqw/Lcv6E7gLuMo7W0lKNmLECJo2bUq3bt3o1q0bSUlJ9OzZ84SOOX78+FLNjub03Xff0bt3b4YMGULjxo3p168fq1evpkaNGgBkZ2dz55130rhxY/r06UPHjh0ZNWoUYGcOPfHEE9x9990kJCTw1FNP+TxHjx49ePPNN3nxxRdp27Ytixcv5rHHHvNo88EHHwB2EKt///7cdNNNHgW2AV555RWWLl1K3bp1qV+/PmAP76tfvz5du3alR48etGnTpsQsq3bt2jFx4kRmzZpFu3btuOiii/j9999dwxxHjhxJq1at6N69O9dccw2DBg3yyLgCeOedd/juu+8455xz6Nq1q99zDR8+nEGDBnHvvffSunVrFi1axC+//OL6fEVEREROh9zcXEaPHs3o0aNLHGovJ27epv3c9e/FPDN+tcf6CSuSyVQg4YQcy8snO7cw8BMe6vm4bYzhoe4NqRhl/+dz56Qqrm3uGV5RoZ4jVprViA3o/N4BKne3XlCHCx3n23/kmEc/RfwxgdRwOV2MMUnARqClZVmrHOtmAs0BA+wFJgPPW5aV4dh+B/CmZVkVvY51GHjEsqzPAzhvLJCWlpbmqo/jlJ2dzdatW6lXr55HJocEJjk5mbp167Jnzx4qV65c1t2R00A/MyIiIlIaOTk5rhmIhw0bdlrLMZxtnvt5NePmbfO7/dlezbi9c73T16EzyM6DWfR57w8OZhbWK93y0hUEBRWtD7rzYBZzN+3n2nY1aTR8MgCPXdaIB7rZMyzf+tlCZm9IdbV/9dqWXN+hdol9uO2zhcxy28/dN3d35Lx6lWnx7K9k5uQz7dEuJFUNLHNJ/v7S09Ods4/HWZaVHuh+ZT0kzcXYlXbfBOY6g0UO/wEGAF2BkcC1wI9u2xOBfRS1z7HN17nCjTGxzhdQ4cSvQHw5fPgwb7/9toJFIiIiIiJlKC+/oNhgEcBX87efns6cgZ79ebVHsCg6LNhnsAigVuUoBpxXm7CQIKY/1oXhVzblrovru7ZHemUmNaseF1Af8twmHfLWvm5ljDHUjrerv+w8mBXQMeXsVp5mSRsDtAIudF9pWdZYt8VVxpiNwGJjTDvLspxTaPlKkzJ+1gMMA549wf5KAJo1a0azZs3KuhsiIiIiIme1nYcKa9rEhIdwxG342ci+LXj6p1VsTs3kWF4+YcFBmjm3FLJz85mx3jOH4d0b2wa0b4OEGBokeGb6VIv1zNJvWC2wTKDMY76HmY25sS3BjuBVncpRrE1JZ/sBFb6WkpWLDCNjzLtAb+ASy7J2ldB8KZALNHQs7wGq+WiXgD2EzZeXgTi3V83S9llEREREROTvwj375f2b2nlsa5JYwVUwufHwKQwY6zlrsBRvTUo67pVewoKDuLSJr0fUwDzsVhgbIKKYGc/cuRfOdndVq8IapbXjowDYrgwjCUCZBoyMbQxwDXCpZVlbA9itORAKOOcS/xOIM8ac53bc87EDQfOK7g6WZR2zLCvd+QIyTuQ6REREREREyrNDjoBRq5pxVInxnL24cWIFBl9SOKnL/C0HKU+1bstaStpR5m7c73f7xr2ej5MnmpxVOTqMixsllHq/zJySi5bXrmwHjDQkTQJR1hlG7wE3AzcCGcaYRMcrEsAY08AY84wxpr0xpq4x5grgO2AZ8AeAZVlrgSnAWGNMR2NMR2AsMNGyrPUnq6P6hSkSGP2siIiIiJQ/h7LsgFHFqDDqJ0RTu3IUbWtXZMnw7sRGhPJw90Y0SSws7Xosz389nDPNip2HWZ2c5nd7tzdmcfOnC/hjk++gUfpRz0BN0EkYzpeRXfoZA70zjIKDDK/1a+Wxzhkw2n5AASMpWVkHjO7DzgSaiZ0x5Hxd79ieA3QDfgXWA+8AU4HulmW5/zTcBPzl2DYVWAnccjI6GBpqT3mYlaUfKJFA5OTY/xgJDg4sdVZERETObsYYEhISSEhIUN2c47DzYBadX/mdj2dvLrbdnrRsAKpEhxERGsysJ7ryw72diHfLNvpsYAfX+4zskrNVzgQ7DmTR9/0/uPKduew+fNRnmyxHIMbfDGRHvaaor1sl+oT7dU07u2pK29oVS2hZyD3DaNOLPVkzogfXta/l0cZZH+mA2xBFEX/KtOi1ZVnF/o1gWdZOoEsAxzmInal00gUHB1OxYkX27bOLmEVFRekvMhE/CgoKSE1NJSoqipCQ8lRTX0RERMqr0NBQBg8eXNbd+Fv6ZM4WXpi0FoCXflnH3Rc38Nt2lSODplmNWMAO1Hk/1tSoGEmF8BAyjuWRkZ1LQoVw78OccXYdynLVH1qw5QDXtKvJwq0HeemXtRRYFs/2au5q6y+TPtsRMGqSWIHYiFBevLrFCffrpvNq07BqDC3OCWyGNIDG1SqwYlca0WHBhAQH+XzYj4u0EyLSjuZiWZaebaVYeqILQGJiIoAraCQi/gUFBVG7dm395SMiIiJyijmDRU5rU9JpWj3WZ9u/dtkBo5YlBCAqRNgBI/dZ1M5k7tlBS3cc4pp2Nbnpk/nk5tvBoWs/KCyLu2nfEZ9BFucxLm1SlScvb3JS+hUUZOhYP75U+7w7oB1vT9/IPV3q+20TG2mHAPILLLJy8okOt5cty2LiyhS27s/kgUuT9G95ARQwCogxhurVq1O1alVyc0s/llTkbBIWFkZQUFmPdhURERE5M2Qey+O7xTvJt6CgwOKui+1gQGrGsSJtf1+3z2fAKDXjGMlp2RgDzUsIGFWpEE5yWjark9NpVTPw4VB/V1ludX+Wbj8M4AoWeZuxPpVfV+/h8hbVPdY7M4wiA5zN7FSpHR/FG9e1LrZNZGgwocGG3HyLtKO5RIeHcCwvn8bDp7jadG9azZWJdjop46n8UcCoFIKDg1WXRURERETkJMrNzWXs2LEA3HXXXa4aomJngbw8eS1fzd/hWteuTkXOrVOZ4T/9VaS9v6ygVbvt7KIGCTHEhBf/CHhVq+qs3JXG1wt3MOC82ifQ+78H9wyjNSnp3PnFomLbr0lO9xEwsguER5RxwCgQxhhiI0I5kJlDenYuNYhk494jHm2SDx897QGjtSnp3PTJAvq3r8mwnk1P67nFP6UBiIiIiIhImbEsi9TUVFJTUzXbqptN+45w/kvTPIJFAE98v5JXJq/j19V7i+yT4qdo80rHcLRWAdTDubZdTUKCDCt2pZHs53hnkm8X7fRYnra2+DIkq5LTi6xzzk4WEVb+A0YAsc46Rln26JkdBz0neNqbkX3a+zRnYyoHM3P4aNYWDqogd7mhgJGIiIiIiEg589ZvG9h/pOiD85bUTD6c5Tkj2qj+9jCklDTPB/11e9K59I2ZvDVtAwBNqlco8bzxMeGuwshn+kxplmWxePuhgNpGO4JBv6/bxw6vKemds5OV9ZC0QDkDRumO73dfuud94xyadzq53+srdp7+84tvChiJiIiIiIiUM5EBZquM6t+a2pWjANjjePDPzs0nJe0ok//aw5bUTFfbytGBzXoWGmw/JubkFZSmy387yWnFZ9L8NKQz7RzT2jdKLAy2Xfz6DH5atrvwOI5MrETHlPXlXX6B/b2+MXU9AJmODKnqcXb/l+8MLIh2MrnX5Np2ILOYlnI6KWAkIiIiIiJSzhQEMDxvWM8m9Du3putBPyUtG8uyuO6jP7ng5d/ZecgzEyY2IrAStmEhjoBRfn4JLf/elu8ozGR55ZqWRbY3rlaB7+/txPxh3bihQy2PbQ9/s5wl2w9hWRa7HQGjmpUiT22HT5JVu+1hdev2ZLA6Oc1V+8pZMH334aMnfXhoStpRpq0pOowS4JXJ6/ifWwDOe4iclB0FjERERERERMqZ7NyiwZq68VE816uZa7l/ezuIUS02AmPsjKCDmTmumkU/Lt3tsX+FiMAKirsCRnlndk0pZybNTefX5rr2tYpsjwwLJijIkBgXwTXtajL8yqb0bl3Dtf3aD+axOfWIq+h1QoXAMrjK2oOXJrnevzdjE5mOgFHDqjGAXcR78fZD5BecnO8/N7+ATq/8zp3/XsyibQc9glGpGceKDLHcqYBRuaGAkYiIiIiISDnjPt07wA0davHeTe04p1KUa13l6DDADvBUibGDFcmH/Q+zCjRrJMw5JC3/zB6StsyRYdS2diWCggx92xQGgyp4ZWOFBgdx50X1uaJlosf6HqPnABBkIOpvUvT6wW4Nee3aVgD8unqva9hixagwV9Cr/4d/8sR3K07K+b5ZtBPnrffln9tpM+I3PpmzBYAtqUeKtJ+7aT8P/N8yj+LXE1cm85cjECqnjwJGIiIiIiJSZowxxMXFERcXhzGmrLtTbhz1Chi9cm0rmteIo1uTqjzRozH/d1dHj+3OYWkb9mb4PF5kaDAta5Y8SxpAaMiZX8Mov8BiVbIdgGhTy65T9LqjeDiAvzsxPsYzi8iZhRMTHvK3uX9DgoPo374m0WHB5BdYzN20H4CY8GBCggqv4cdlu094aNrRnHzenr7RtfzzimTSjubywqS1AEVmRAsOMmTnFjBhRTJTVu0BYNmOQ9z/32X0GjP3hPoipaeAkYiIiIiIlJnQ0FAefvhhHn74YUJDAxsydTZwH5L2er9WrvdBQYYhlyRxQYN4j/bOgNFqH9O+331xfRY81S3gIWnhZ0HR6+TDR8nOLSAsJIj6VaKBwmLfgN/gT9taFbm2XU1G9GnuUbMo0M+2vDDGUDs+2mNdm1qVuPG82gS7BY0OnOAU91NWp3gUtHZnWRb7vY6/6rkeriyuA0fs/VbtLswsyj3Ds97KGwWMREREREREyhnnkLT/3nm+q1ZRcarH2cELZ9aMu6iwYGJLEdBw1jA6kx/Ot+63h2HVqhRJkFuA5NYL6gDw5OWNfe4XEhzEG9e15tYL6jLgvNqu9c7C138nVd1qLl3XviYta8bxQLeGrB1xOTUcAcgTLUC9cOtBANo6Zptzl5mTz+5Dnp9bZFgwdRyBrINZdjDJfXjmnhJmtpOTSwEjERERERGRcsZZiDgmwJnNnBlGa3xkGLlnzgQi7CwYkvaHYxhWy3M8h+k916s5s5+4hJvOr1PiMQZ3bUC3JlUB6No44eR38hRzL9J9ZavC+k1hIUHUqmzXynIvQJ2bX8D8LQd8FmT3Z5cjINS9abUi21IzjjFz/T7XcsUoO6hZOcquzeUcrpaRnedqs2znYeT0Cey3j4iIiIiIyCmQm5vLuHHjABg4cKCGpTk4H5JjwgN7ZEt0BIycU6S7Cw0uXW0dZ/tjZ3CGkTMjqGVNz8yXoCBD7fgoX7sUYYzho1vOZfKqPa46SH8nkaGFRbq9+1+7chQLth5kxwE7YLT9QCY3fbLAFQCa+XhX6lbxHNLmS4ojI6hVzTiCg4zHzGsrdh5m3Z4MjLEDdc6gmzNYtXjbIQoKLI66BahmrNvnMVOdnFrKMBIRERERkTJjWRbJyckkJyefcIHdM0VBgcWRHDvwE2htnBoVI/1uK32GkR1IKO8ZRm9P28j9/116XP10DnOKCT+xmc1CgoPo1bqGK8jxd3JunUqu93GRnvdZbcf1OIekjZiwxhUsAhjrmOWsOHvTs9nsmAUtqWoMT/ZozIVJVVxD4ZzHOLd2JW7rVNc1FK1r4wQqhIew+/BRFm476JHRNGP9vjN6qGR5o4CRiIiIiIhIOZKZk+eahtx7end/EmMjPJbjo8Nc70sbMIp1nPO7xTtLtd/plHksj7embWDiyhRmuA1rCpQzEys6wAyuM1GfNjV4oW8Lvr67Y5Ftziyr7Y6A0XKvoWDbD5Rc22jiyhQsyw5MVY+L5J4uDfjqzvNpWj0WKCzQflWr6h77RYQGc6Vj3Y9Ld5GdWxggOpyVy9TVewO9RCzLYsPeDPIUZDouChiJiIiIiIiUI87haKHBhvCQwB7ZqrkFjM6rV5kbzisslB1WyoCRM8C0bk8Gn8zZwsiJa8pd9tcKtwDGilLUtdl1KItbPl3gKsZ8NgeMjDHc3LEOHevHF9nmXsMoOzffNVuasxbR3E37+dZPQNGyLK778E9GTlwDQC+vgJB3Xa6alYpmZ/Vpcw4AM9ankp3nWTMpuRQFxl+ctJZ/vDWbsXO2BryPFFLASEREREREpBw5nJUL2MOE/E3v7i0sJIgujRKoWSmSsbe2Jya8cIiR+zTpgWhVs7AQ9AuT1vLp3K3M23ygVMc4GVIzjvHP71f6DAgt3n7I9d47+6U43yzayZyN+13LgdaIOts4h6TtSc9mv2N6e2Pg41vOpXtTu9D3hBXJPvc9nJXLwm0HXcvt3Ia+QWEGm1NcVNFhl80cWUipGcdIc/w8OIOnaUdzA76OT+bagSJ/wS0pngJGIiIiIiIi5Yhzdqj46PASWnr64o7zmPXEJcRFhnLA8ZAPUL1iRDF7FdXXkd3hbuLKZI7lBT471snw0i9r+WbxTvq89wcZ2Z5BgiVuAaP07MADCGtTMjyWo8JOrIbRmSo+OoyosGAsC/71v1UAWJZdFPzG82sDhYFNb1sPZHose9fX8g7Sxfqo0xUbGUIFR7s/t9jBSmcWXSABo6ycPO7+92LXcoBxV/GigJGIiIiIiEg5ciDTDvbEx4SV0LIoZzaRexHjC3wMOSpOUJChUbUYj3X/t3AnjYdP4bbPFnpMtX4qbdhbGNy5+99LPLbtPFTYB2cB60CsTbHr5lSPi+DcOpVoWLXCCfbyzGSMIcFRnHr2hlSPbZW8pr33tm2/Z8DIvZ4WQHyMZyDUu+C28/zOQKdzZrVqsfZ+vgJGBQWWx7DJ2Rv2M3VNYa0jZZIdH31qIiIiIiJSpqKi/n4zTJ1KqRl2wKhydOkDRk63da7L9oNZ9G5dI+Bhbe58PcQDzNqQyqdzt/Jc7+bH3beSjF++m/lbDhIdVvi46swycTrqFiQ6mpPP/iPHqBJTfEbW4awcdjvq3/z6yMU+M1ukkL9MHud9mXrkGHn5BYS41ch6f+Ymvpi3DbCLXb91XZsi91+Hup5D1JyBIG/eMwQ2rFaBRdsOsetQFr+v20uz6nEkxkWQl19ArzF/EB8dxld3ng/gGkbn5H4vSeD0qYmIiIiISJkJCwvjiSeeKOtulCvODB5fxYADFRsRyqj+rY97/wYJMSzadsjntvV7Mnyu33kwi8iw4BIDN8XZtO8ID329vMh69wDWvoxsUtKyXcspadm0f2EaI/u24JaOdXwe17IsV82dWpUjFSwKQOWoMI9hZ/+8vAlgDzGLjw7jQGYOszak0s1RCHvnwSxem7Le1f6yZtVcs625a1Wzotv7OL8BzX80q+Yx9PDG82rz3wU7WLrjMHeMW0xIkGHNiMvZcTDTlTl24Mgx4mPCiwSMQoI1Ju14aEiaiIiIiIhIOTF++W6++HM7AHV8PGyfLufXr+x6/9q1rejduoZreej8nNoAACAASURBVOO+DMb9sZXUjGNc/9Gf3P/fpczbvJ9L35hJnzF/kBvAFOYFBRaPfbuCV6esY/G2g67hRL+u3uOz/dHcwoyih30ElAAm/5Xisew+ROnFSWt5evxqANrXqYyU7OJGCa73N3SoxX1dGwD2LHqXt0gE8Cgg7h7EA3vYny+hbhlJ/jLZAO6+uD5/DruUsbe25793nU+DBM9hknkFFlv3ZxLkFnC69yt76KIzS+8cR/2knLyS70kpSgEjERERERGRcsI9u6ZO5TIMGNUrrHsUEmx4Z0BbZjzeFYD9R3J4bsIahvxnKQu2HmTiyhRuHLuA3HyL3YeP8vu6fczekMobU9dTUGD5PP7SHYf4YekuPpi5mX4f/snnf2wDKFIfKcRRkyknr8BVy8bfjG2Zx/Jc71fsPEybEb/x5Z/byDyW55otC+zMFynZ4z0au95HhHoWB78wqQoA8zYXBoy8p7tvU6si/vx8f2cuaZzAi31b+m1jjKF6XCSXNatGpwZViAgNIizYM4Tx5fxtZOcWBoOcWXHbD9j3UbMa9mxrOQEEMaUoBYxERERERKTM5ObmMm7cOMaNG0dubuCzXZ0N6lSJLrNzu89s5Sx+nBjrmTHiPnW6u3u+XMKtny3k3d83MXfTfp9tvAsmj5i4BsuyPIYgAVzTrnDGtuzcfA5n+S60DHDA7ZhP/fQXaUdzeXr8avake2a+uGfOiH8x4SHceWE9KkWFck+X+h7b2tS2g0GbUzNd2TvJaYUBo4saVqFOvP/7t1XNinx++3k+h6z5Y4wBr5FlX83fwZyNqUXabj9oF95uXM0uaq4Mo+OjgJGIiIiIiJQZy7LYvn0727dv9xhCJEUDNKfbT0M683zv5q5skkivKegrRpVcBygrJ5//LdvlGiLk5F1QOTTY8NZvG9i47whgF0J+sFtDXry6MAPlaG4+3y3e5fdcGdl5ZOfmY1kWmccKh7Blub0f2rOJZswqheFXNWPJ8MuoHhfpsT4xNoKY8BDyCyy2H7CDM84Mo4saVuHT2zqckv64B34qRNjfo3dNrfwCi/SjdraZs6C2AkbHRz8pIiIiIiIi5VBwUNkW6m1Tq2Kxw4q8gz4/Du7ENe/P81g3ds4WV9bQgn91o5ojCLbPK4AUEx7CO79vci1PuP9CqjraRoYGczQ3n6M5+XyzeKfHfrddUMdV8yntaC5Nnp7C+fU8axQdcQxVS6oaw71dGhR/0VJEkI/70BhDg4RoVuxK47K3ZlPFrdB0zxbVCQs5Nbkpt11Qhx+X7uaLQefx3wU7+H7JLrZ7DWPck57tmkUvLsqe0S2QulpSlDKMREREREREyplT9cB9ogacV9v13j0h7JNb29OudiWevqqZR3v3IWbfLLKDPat2p/H6r+s92qVn53ksO4fBASQ6iidPWbWHTY4MpDE3tmXKwxfx1JXNmOmoreS0YOtBtu7PdC3vPGQHFKK9MqTkxDSoWliE2n1WsuoVT11m3PN9WrD46e60q12Jqo57ZIdXwGhL6hFXzaKKjqLayjA6PuXzt5CIiIiIiMhZqEmiXXPl41vOLeOe+PZsr2YMurCex7r7ujagu6OQ9B2d63Jzx8KgUqjbdObOukWfuRWgdsr3Ko7tPtX6Tefbx3vxl7WudVe0qE6TxFjCQoJKnE1u4157yFK0hqKdVElVY3yuP6dipM/1J0t4iB34i3EMSfOuh+U+RK2SI8MoWwGj46KAkYiIiIiISDlxzPFgW16DGxGhwUWmN3efPt0Ywwt9W7rqHrkHftbtSef/2bvr+CYO/g/gn4vX3YVSaCla3IbbsAkTNpg/Y/bsmSs8c2G+38bcmTxTxgQYztDhUqQUCgVq1L1p08j9/rjc5S7SNm3apOn3/XrxIrlccof0cve9rwDS5sj2/Gdyb8nzIYkhNuuIy6TE27CH74vkqX+nXdWA2CC7y+NDOjZgxOMnplkHG7OLuX9vhYxBkDnDSNskzWAjrUMBI0IIIYQQQgjxEDo913tF7aElaQCgkEsDNNYNkcXriEuBMgtrwLKs0F/ms5uH48gz020yVcTj3AEg1qrE6eV5A2y29/nNwx3u775z3DS3cH+1w3WI88anhNssiwhQw1fVOYE5tdJ+iWGBufm2j0oOXzW3TqPeBJOJmuo7y3OPQoQQQgghhJBuQalUQqlseeJWd8CXzmgcXAx7Aj6zgyfOMOIpZLaXmjWNBhRUNaBSyzXLDvFVIthXhQhRIMdeo+/IAA0CRNlBk/tE2qwzrV8UDj41DR/dMBR3TZCOgNeaA1T9YgKa+2MRJzEMg2uHxQvP06ID8NWtHTMdzR5HQVU+YOSrksNX1LeqQW+0uz5xjHLyCCGEEEIIsWNvTjm2nS7Fg9NSPbYBsTdQqVRYsmSJu3fDY3SFDCOlVcAo1k7PGqXcfplYZmENqrRcz5lgc3+ZMH+V8PqKu8fYvEcuYzChTwTWHL1ofp/94GKYvxqzBsbg0v7R6B8XhCO5Vfhyl6VfUt+YwOb+WKQNXr16EB6Z0UdoTN6ZrH9GQnyVqNTqkW9ucu6rUkCjsASMtE1GKkt0kucehQghhBBCCHGj6z7dgw+3nsXPVmO8CelIfA8jtcJzM4ysg0EhdgI41kElvhHy2dJ6YSIaH/jhM4AAoL+DvjiXDYoVHvu0kH0lkzG4PD3WptStTzRlGLmaXMa4JVgE2AaMogK5/dAbudIzX5UcMhkj/H9paKIMI2dReI0QQgghhJBm5Fc236CXEFcxGE0wmPusdKUMI3tNp637HMUGa1BQ1YDimkZhGT/yXMxRNt+l/aPwyPRUxAT7tNjkmjclTVq6FqChskdvYh1UDbT69+XL0XxVcjTojdDqqfG1syhgRAghhBBCiJVqc48VAAj0oVPmjmQwGPDzzz8DAObPnw+Fovv+fetEDaI9uYeRdcDI7jpWPYwiArg+RXzAKECtgML8OY/O6IOzpXW4f0qKw89jGAb3TXX8uj3uynwhncM6uGh9rPYxN9/2UcmBeqBeRxlGzuq+R2NCCCGEEEIcOFpQJTw2GGmyTkcymUzIzs4WHndn4oCRJ/fNEpekvXltut115KJ1fFVyoVxo88kSAECQqIytX2wgtj02uSN2FQmhPsiraMCE1IgO+XziPtZZeDYZRkpLhhFAJWltQQEjQgghhBBCRKq1etz0xT7heW2jvpm1CXEdnYG7oFXKGbvTwjxFmGiqWY8wX7vrHM23BF1fuWogsovrAABNRi4olhzhb/d9rvblLSPw/b5c3Du5d6dsj3Qe65K0iEC15LmvmnudzzTSNlFJmrM8N2xNCCGEEEJIJ8osrMGQFzYg/YUNkuWltTqH76nTGdBIo5qJizTqPb/hNSANEskc9BPiA0QAcMXgOCEYxhucENwxO2clJSoAz17WH+H+6pZXJl1KeIBlul6Yn0rSGB0AQs1T+PhMowY6VjuNAkaEEEIIIYQAePbP46jU2mYT/X6kUNLTqMlgwuMrMvDsH8eR/vwGzHp3B1iWytZI+/FBFY3Ssy/TlHIZHpiaglkDoh0Gfu6Z1AsAsHBUIgBpuR0ALBiZ0LE7SbxeZIClR9Xlg2MRZNVEfWrfKACWkjQtlaQ5jUrSCCGEEEIIacGR/CpMNPdAWXEwHz8fyBdeO1dWjyqtHiF+KkdvJ6RZJhOLW5fvx6ELlQA8P8MIAB6antrs6/+Z3BvjUyIwMC4IAKDTWwJG6x4cj5ggnw7dP+L95DIGo5NDse9cBW4dm2TTjH1kz1AA5qbXoIBRW1DAiBBCCCGEEEAYZ87LWTobac+sQ5PBhLwKrbD8bGmd9VtxsbqRAkbEadUNeny16xxigjTYfrpUWG7dzLcrUshlGNYjRHguLklLiw50xy4RL/TZzcNRrzMiOkiDivomYfmUtEihD5ifuYdRA/UwclrXPxIRQgghhBDSDizL4u2Np3E4l2vSmxzhh1evGgiZjMHCkVw5TV6lJWBUXmfb04gfFU6IM576/Tje2ZSNJ349JlmukHtuw+u2mpwWCQAI9lW2sCYhrRegUSI6iCtNE0/vu3NCsvCYMozajjKMCCGEEEJIt5aRX41lm7OF58uuH4IB5jKahFCuue8n23IQFaDBv8b1RF5lAwBgWt8oNOgN2HWmHFUNTbYfTFpFpVLh2WefdfduuMWao4XC43B/FcrquP9HU9Ki3LVLHeayQbEI9FGifyxlF5GOoRJl5onL06iHUdtRwIgQQgghhHRL72w6jYMXKjHVnPnA81dbTpETQix9Vl5YnYk5g2JwvKAaAPDkrD74v41coKmmgUodiPPEVZCvXzMI6fHBWHmoAFcPi3ffTnUQmYzB5D6RLa9ISBspZZYgkbiskw8YNVDAyGkUMCKEEEIIId0Ky7I4lFuJdzZxwZ4d2WXCa5EBaqG8AbBkGPFGLd0MgMsG6RXhj0Af7nS6usF2uhohzkgI8UWYvxp3iEppCCGtJ5MxmNY3CqW1jegbY8lk8zH3MNLqKWDkLAoYEUIIIYSQbmXrqVLctny/zfKPbhiKcSnh0CgtE6qsA0a8kT1DwTAMAs1jnDPyqvDB32dw+7iekveTlhkMBvz2228AgHnz5kGh6B6XKEarJuvBvtQ0nZD2+vyW4WBZFgxj6WdkyTCiTFBnUdNrQgghhBDSrRzJq7K7fEBcEAI00oa8/moFrhoah/Ep4Zjez9JXZngPblxzZACXjbQ5qwRvrD+FP44UAOCymEjrmEwmZGZmIjMzEyaTqeU3eImL1Q2S50E+1AyaEFcQB4sA6mHUHhQwIoQQQggh3crec+V2l8eIStHE3p4/GN/ePgrDRSPCE82ZRxNTIyTrvr7uFG7+ch/6P7sen24/66I9Jt4ot0IreS5u2EsIcR0fc9ZnvY4yjJzVPfI9CSGEEEIIAfDPmTLsyakAAPSK8INcxmB4UiiuHhoHhbz5C3Zx9lFUIBdc6h3pL1mnvL4J20+XAgCW/pWFOyf0cuXuEy+SJwoYvXPdYDfuCSHeLTGMC/Bnl9RBbzRJJqiR5lHAiBBCCCGEdBvL/zkvPH58Zhou7R/d6vcaRWVm4sbYT8xMw2vrslyyf6T74DOMbhydiCuHxLl5bwjxXqmRAdAoZdA2GZFboUWvCP+W30QAUEkaIYQQQgjpJkprddiSVQIAWDpvIGaIehK1Rt/oAOFxRIBaeHz3xGRse2wSVtw9RrJ+VKAahDiSX8n1MIoPsd9YnRDiGjIZg0Y91x/t3v8dcvPedC1uDRgxDLOYYZj9DMPUMgxTwjDM7wzD9LFaR80wzHsMw5QxDFPPMMyfDMPEW62TyDDMKvPrZQzDLGMYhsYMEEIIIYQQwR9HCmAwsRiSGIyFoxJtGqO2ZHhSKJbfNgK7F0+RLGcYBj3C/DA8KRSbH5koLC+u0eHe7w/hz4xCl+w/8S4V9U0AgAh/CiwS0lmyimrdvQtdirszjCYC+ADAaADTwZXIbWAYxk+0zjsA5gG4HsA4AP4AVjMMIwcA8+9rAPiZX78ewNUA3uqkPwMhhBBCCPFw+89X4KU1JwEAV6THtvlzJvWJREyQj8PXe0X4Y+W/xwrP1xy9iDfXn2rz9oj34gNGoX50n5uQjhYpygo1mWiKZWu5NWDEsuxMlmWXsyx7gmXZDAC3AUgEMAwAGIYJAnA7gEdYlt3EsuxhADcCGAhgmvljZgDoB+BGlmUPsyy7CcAjAO5gGCawk/9IhBBCCCHEDQ7nVmLa29uwKbPY5rXaRj2u/Xi38HxqX+dK0ZyVGhUgeZ5boUVxTWOHbrMrUyqVWLx4MRYvXgylsvuMlq80B4xCKGBESIdbff844XFepbaZNYmYuzOMrAWZf68w/z4MgBLABn4FlmULARwHwN+6GQPguHk5bz0Atfn9NsxlboH8LwAB9tYjhBBCCCFdw8trTuJMSR0WfXPA5rVvdl+QPE8I7dieMf5qBdITgiXLNtoJZBEOwzBQqVRQqVROlwl2ZdUNegBAsE/3CZIR4i6RARoMiOPySU5erHHz3nQdHhMwYrhvh7cB7GRZ9rh5cTSAJpZlK61WLza/xq8j+QY2r98kWsfaYgDVol/57f4DEEIIIYQQt9GLSgy+2X0evx3mTu/25JTjDVFJ2Ec3DO2U/fn6thF4YGoKxvYKA2AJDrjanpxyu1lVxPM1GbkmvGqlx1ySEeLV+kZzAaPMi9THqLUU7t4BkfcBDALXh6glDABx4aG9IkTrdcReARec4gWAgkaEEEIIIV2WWmG56H7mjxMAgO2ny/Db4QJh+ZvXpmPWwJhO2Z9gXxUemp6K51edwD9ny1HbaOiQ7Vz/6R4AwF/3j0e/2K7ZjcFgMGD16tUAgLlz50Kh8KRLlI5hMrHQG7lLFaWcAkaEdIa+MZYMo3qdAX8du4ipfaOoj1gzPOLoxDDMewAuBzCZZVlx4KYIgIphmBCrt0TCklVUBKtMIvP6SlhlHvFYltWxLFvD/wJAIUZCCCGEkC7MXiGTOFgEAKF+nV/6E6Dhtlmnc32GkVGUVTV72Q6U1+lcvo3OYDKZkJGRgYyMDJhMJnfvTqfQi/6cKoVHXJIR4vX4gNGpolq8sCoTj604ijvslDETC7cenRjO+wCuAjCFZdlzVqscBKAHN0GNf08MgAEA/jEv2g1ggHk5bwYAnfn9hBBCCCHERc6V1eOVtSdR5mHBidaUfAX7dv5d5EANly3TERlGOoNR8vzF1Zku3wbpGHx2EQCoKMOIkE4R7s99B9Q06oUbCgcvWHe/IWLuzvf8AMBCAFcAqGUYhs8UqmZZtoFl2WqGYb4A8BbDMOXgmmG/CeAYgE3mdTcAyATwLcMwjwEINa/zmTl7iBBCCCGEuECj3ojJb24FAASoFfjPlBQAXHr/odxKbDtViidnpSE5wr9T94tlWZwrq29xveRwv07YGyl/NXe6XdcRASO9NBuno/okEddrMlj+7agkjZDOoVHKAXDfZTIZAGPz6xP3B4zuMf++1Wr5bQCWmx8/BMAA4GcAPgA2A7iVZVkjALAsa2QYZg6ADwHsAtAA4HsAj3bkjhNCCCGEdDfLNmcLjwuqGgBwwaJZ7+6QrPfpzcM7bZ9MJhZz3tsJnfkCfONDE3A4rwpnSurw6fYcaJQymExAekKQWzKM+IbGfINjV2q0yjCSdaMJY12d3vz/QS5jIJfRvxshnYE/HjfqTfBVyd28N12DWwNGLMu2eHRkWbYRwH3mX47WyQUw14W7RgghhBBCrGw6aWkPyV/krj12UbLOhsxi5FVoO3x0Pe94YbUwInlYjxCkRAUgJSoAALB4VhoAoLROB1+Ve057VXLuokRn6ICAkVWGUa2uYxprE9fjM4yoHI2QzsNnGAGAtonSi1qDjlCEEEIIIaRVqrSWkqetp0rRqDfityNcH4jL0mOF15b+dbLT9ulsaR0AID0+CCvuHiN5jWEYMAyDyACNUBrW2fiGxk0dEDCy7mFUrdXj421nW1WeR9yLzzhTyim7iJDOolFQVpGzKGBECCGEEEJaRdwjJ7+yAcs2ZyOvogExQRosnTdAeG1LVonLt12lbcIVH+zCR1vPSpYXVXPNt5Mj/MF4YEkWHxDoiICRdYbRqeJavLo2S+gzlV1ci+s+2Y3dZ8tdvm3SPnxJmoouYAnpNBSgdZ67exgRQgghhBAPkllYA41SZtO4ulFvtCmr2nySCwzdMT4ZARol/rdoFG74fC982tgbIrdciwlv/I3BCcF49/rB6BHGNak2mlgMfmEjACAjrwpjeoVBqzPg4+05OJLLTbiJDFS3aZsdjc8w0ndEDyO945KK6gY97v/xCE5erMGCz/bg/KtzXL59V1EqlXj00UeFx92BpSSNLmAJ6SyObio06o2ScjViQRlGhBBCCCEEJhOLD7eewexlO3DFB7tQZ9UPp6RGZ/OeU8W1AICUKC64NCg+CABXulbvZD+dNUcvYsIbfwMAjuRVYeIbW7HuONcf6Z1NpyXrvr3xNBZ+vhfbT5eiptEAlUKGeUPinNpeZ1ErXN/0+kRhNV5anYldZ8oAAPEhPjbr5JTWIa9C67JtdiSGYeDn5wc/Pz+PzBLrCHwAUamgyzFC3G3iG3/D0AFBfW9ARyhCCCGEkG5uY2Yxkpf8hdfXnQIA1DYacKKgGgCX3WMysdidwwUn+KCQWFSgBgAQoFEiQMMlsK/KKHTqBPyZP47bLHtlbRYA4L0tZyTLt58ulTxfftsIpEUHtnpbnYlveu3KkrT5H+/G5zvPCX8vN43uYbPOLwfzbYJ+xHPoqOk1IR6juEaHklrbmyKEStIIIYQQQrq1wqoG3PHNAZvl1326R3g8rEeIkMUyqU8kDEYWmebJZADgI0rljwv2QVZRLZ5ceQwltTrcPzWl2e1/uv0svt+bi/L6JpvXLpRr8fq6rGbf/82/RmJsr/Bm13GnjihJq7ea7jM5LVIIrvG+35sref7PmTKM7e2Zf08GgwHr168HAFx66aVQKLz/EkVvZAEASgoYEdKp3ro2HY/8kmGzvLkS3+6MjlCEEEIIId3YgQuVkue3j+tps87BC5VC+dOA2ECbiWNqpeWUMjbYUh71vlVmkLXCqgYs/SsL58sdl059aNXk2lqgj2f3vOGbrJbVNWHca1uw5LdjqG3US9Y5XVzb5mygEF8lekf446c7R+OxS/vg3esH211v4ed72/T5ncFkMuHAgQM4cOAATKbuURYi9DCikjRCOtX4VEvg/PtFo4TH1j36CIeOUIQQQgghXuRYfjVKahpxvqweBqMJZ0pq8dHWs6jW6u2uX2nO7BmfEo5ND0/E03P7IevFmbhrYrJkvbI6br24EB8E+kgDRuJmobHBmlbva3FNo82yw09Px093jra5kJ7WNxInnr8UgRrptn3b2GC7s4j/HPmVDfh+by5mL9sBluUyTA5eqMSM/9uORV/vb9PnT+sbBZmMwajkMNw7uTeuGByHtOgAAIBGSaf6nkqYkkYZRoR0qgh/NaIC1VApZEhPCEac+SYHBYzs8/58T0IIIYSQbuJMSS0ue3+n8PzqofGo1xmw7kQRXl+fhcznZ9pMMONLwRJDfdE7kmterVHKsXhWXzw0LRW7c8px/w+HUdtoQIBagR5hfkiLDsQm84Q0QFqSFhXQ+oBRqahnxFVD4zCtbxRC/FQYlRyGKwfH4ucD+cLrC0clwk+tQE2jNBPHT+3Zp7P2MkjyKhqgM5igUcrx1a5zAIA9ORVt+vzZA2Nslr09fzDe25KNf0/qLfn/QDyHEDCiDCNCOhXDMNj08ETojSz81AohsE4lafZ59jcsIYQQQghptUMXqiTPfz2Uj1TzBDOWBR786TA+uWm4ZJ0NJ4oAAKF+KpvP0yjlmNwnEgefmo6M/CqE+qngr1ZgSGKwsI5cxkj6sAT5WkrEmowmsCzrcPJVaR0XMJrWNwpvz5eWUjGQvsdHyZ22pscHISOfa8j9/OX9hbvDnipQY79kTqfnAkbiixSD0QRFCxknB85LA0tje4fZrNMvNhAf3TgMJhMrWV5Z3wSFnIHOYEK4v7q1fwTSAfhsBr5kkRDSeQJEx2W1grvhQRlG9lHAiBBCCCHESygVthef4mDO+hPFktculNcjq6gWAJASFeDwc1UKGUYkhQrPBydYAkZGq6CEn0p6epl5sQb9Y20nqwFASQ0XMIoMtA1ezB8Rj58O5AnP+cyo9xYMxV/HL+KaYfFdIuihUcrhr1bY9Chac+wilvx2TLKsor4JkYH2M7SKaxrxxc5z+HR7jrBsw0MThIsde2Qy6f+Hs6V1uPGLvWjUm3D0uRkOg1mk4/EZRtT0mhD34nvw6SjDyC46QpF2yyyswYdbz7h0XCwhhBBCnFdlp0/RhWYaSp8pqRMeT+oT0erthDUTqDGy0gDS8l3nHa7LZxhF2Pm8YT1C8fjMPsJzvldRYpgv7p7Yq0sEi3hh/rbZW9bBIgAOG1/nlNZhzrIdkmDRgpGJSG0myGdPdkkdGvXc+dopc6CQuAc1vSbEM2jMQfdGupa1i45QpN3u+OYAXl93Cs/8cdzdu0IIIYR0azuzy2yWNTd9q8TcQ2hKWqTT2SZqBxe6Cquslj+OFAoNnq3xPYwiAuwHf8TZSuI+SV1NmJ1yP3u0TfbvcH+3J1doOg4ASWG+eOWqga36zGl9o4THZ0UBQj7DhbgHNb0mxDPwGUbrjxe5eU88Ex2hSLsVVDUAANYcvejmPSGEEEK6r18P5mNzFteI+um5/Ryux2c21OkM+G7PBQBAlIMyqOY46vcwa0AMBsQFYsHIRG57RhMa7KT6/3owHxszuRK5SAcBI3HsKUDTdTspNJeRJfbauiybEj8AqGpokjwPaWUACgA+u3kY7p7YCwBwrqxeWN7QZERDkxEP/3QET/1um+3UmZRKJR544AE88MADUCq7R5kcZRgR4hligrg+eOtPFNn0fSMUMCIuRF94hBBCiHsYjCY8++cJAMC8IXG4ZUwPfHzjMMQG2QaCGsxZLKsyCnGisAbBvkrcPKaH09ucPzweADC2l7Tpso9KjtX3jcfLVw4A3+vaXpbTI79kCI8dZRhdrG4UHgf7tj5I4mnC7ZSkAUB8iA/eXzhEeL4juwx/ZhQIz6sb9CipbRT+zXjOZIMxDIOEUO6C6FSxpQzt9q8PoO8z67DycAG+25OLyvomRx/R4RiGQXBwMIKDgx02SPc2TUbuwpR6GBHiXotnpwEADCYW9U2OM3K7KzpCEZdxlJpOCCGEkI5VUqtDnc4ApZzBm9emQyGXYeaAaMzoH22zrlbPnRDzDadnDYhB35hAp7f59Nx+WDpvIN5fONTu6zIZI5SUHbpQhVFLN+Fbc0aT9fhiRwGjOYO4kfHiqWxdkaN+Swkhvpg7KFYyoY7vQ8WyLC57byfGvrJFEjgDnL9JFxXABQ7zKxscrnO+vN7ha8T1+J8BuuFKiHsFqBXCtMJ6HTW+Nx+rqwAAIABJREFUtkZHKOIy6i7cW4AQQgjpyviAQlSgBnJRHZe4bCzQXNJVb872qdRyGSUhvm0rAQrQKLFwVKIk2GHNT82dG7yw6gSKa3R4+neu3yEfrAKA60ckIC7Yx+77+8cGYcfjk/HDHaPbtI+eQtzDKDHUV3gcF8L9uStE2T18SVpJrQ65FVoYTCyO5FVJPs/ZUeytKTl0Z8DIaDRiw4YN2LBhA4zG7nHBtvdcBQAgxk4WICGk8zAMA3819/1Yp7MdHNHdUcCIuAxlGBFCCCGdq1qrx3N/nsDCz/YAAGKtAi/RokBBfAgXqDhRWAOdwYjl/5wHAAS3MWDUGn7mk3C9qC/Ec3+ewLd7uG1HBarx6tWDmi1DSgj1haaL35QS9zB6ZEaq8JgPlI1PCReWHS+oBsuyeOp3x8NEFDLnzrkcZXCJnStzPE2voxmNRuzevRu7d+/uFgEjlmWRbS4PnNQn0s17QwjxN99QqW2kkjRrdIVP2iyrqAa3fbVPeE4BI0IIIa52OLcSr6w9aVPCRIANJ4qQ/sIGLP/nvJBJNCA2SLLO7eN74vL0WHx84zBMTosAAPy4Lw9f7jwvrNORvYGCfbhglHgS1KqMQny24xwAoFiUaeTNwkQ9jHxVCkxMjYBcxuD6kQkAgLeuTReCe78fKcQ3uy/gcG6lw89zdmJctIMslpggDZ6a0xcAbLKYSMepqG+CtskIhoHQX4oQ4j58+XRhVWMLa3Y/dIVP2uz9LWfw96lS4XlXv/tHCCHEs9Q06jHvw3/wybYcDHlhIx79JQOP/ZLhcER7d3KhvB73/O+QzfKeEX6S5/5qBZYtGIKZA6Jxw6gekMsY7M4px2vrsoR1QjowYNQz3B+AZaIqwDVy7m7EmV4apQyf3zIch56eLkzniQzU4Pkr+gvrPPvnCej0tlPo5g+PR0SAGg9MS3F6H64ZFm+zLCJALWS47D5bhtrG7vdv09lqG/U4ml8NgOstpVbQ+TMh7ja0RwgA4Nk/j9Nx0AoFjLzMicJqrDiY79KT6WP51bjly304XlAtWW59t5d6GBFCCHGlz81ZKADQoDdixcF8/HIwH2V17pvm5AkuVjdg4htbYTSx6BcTiL1Lpgqv+TbzXRwb7IMJotInXlt7GLVG70h/m2UGUXna9SMSOmzbnqRHmCWQ12QwQSmXIchH+vc+MilU8rzW3GvqljE98N3to7Dj8cl4/Zp07Fsy1ab0sDV8Vbb/N8L91egd6Y/kcD/ojSy2nS61807iCizL4qOtZzHwuQ24bfl+AJRdRIin+O/svogKVKOsrgnHrK55uzsKGHmZOct24tFfMrDVhV/4ty3fh22nS/HgT0cky40maVCK7vgSQghxlb9PlWDZ5my7r3nr3T+WZbHhRBGKqptPif929wXh8bAeIZKGxvaCAmL/mWKbmRLo07kBI7F7J/fusG17ErmMwSPTUzE+JRzj7ATtANgEkABgUHwQnr9iAMalhCPB3Cy7rWPnfez83+D/70zvHwUA2JhZ3KbPJi3bf75SktkHcFPyCCHu56dWIDUqAABwuqjWzXvjWShg5EWqtZYT6MzCGpd8Zm65VriTe6akDmdK6oTXKs3b4xs2WgeQCCGEEGeYTCwWfb0fSU+uwW1f7ReWzxkYI1lvS1ZJZ+9ap9h1phx3fnsQo1/ZjP3nKxzeiNl8kvvzJ4T64K6JyQCAeyb1wpjkMEztG9XsNsQTunj2AhWukh4f1OxEL34yTXdw39QUfHv7KIclSDKZ7d9Tv5hAl23fV2n7d81nnI3rzf1unU1OXOefs2U2y+JDKMOIEE/B/zyuPFzg5j3xLBQw8iKvrD0pPHZVts/qY4WS5+tPFAmPq8zjeKf15WrfKWDUfo16IzV2JYR0W6uOFmLTSWkwaEJqBD64Yagk6PDSmpPWb/UK+86VC4+v/Xg35izbabPOhfJ6nCquhVzGYNV/xgmTz56YmYYf7hwNVQsDKMQBmhBfJZbOG9iqkettFRmowaLxyQ5f91VTObvY5zcPlzzvGe7nYE3njU8NR4BGGjSa2IdrhB4ZwP0fqNR6Z/aeJzhVxE9FixCWxVHAiBCPcc0wrkT6WEF1t+y15wgFjLyIOI3YFfGiL3aew+vrTkmWvbH+FC6U1wMAqsw/SKF+apdtszsrr9Nh+EubMOSFjXhhVaa7d4cQQjrVmZJaPPAjV/oc5qfC2F5hiAv2wdvz0wEA390+SrJ+Sa1zk0z0RhPyKtw3Nrw1rE9QMy9Ks4WrtE2Y+MZWAFy/m7ZMN9MoLad+VwyOw8JRic7vqJPum9IbqVH+NtkU/moFNfy1MiUtEgpRppG491F7DU0MwaGnp+OTm4YB4P4P+ZonA4X4cVlmVdomt9wAVCqVuOeee3DPPfdAqey4jDd3KKvT4YkVR7H2OHfT9c4Jyfj4xmGYOygGs62yJwkh7jOsRwiCfJRgWaDUyXMMb0YBIy+iEN19rTM3SmyPF1dbghbT+1lS3Ce+sRU7skuFE1t+VKyRIkZt8sb6LCz6+gCOFlSjTmdAg96IL3edk5QYEkKIt9ssyiza+cQUfH/HaOx8YjLC/bmbEqOSw/DDHaOFdZasPObU5z/zxwmMf/1vfLT1rGt2uAPwJeApor4/eqNlUtYLou/ltgZ62tr/pj18VQqsum8ctj46SbJ8SGJwp++Lp5PJGEQGqIXnSeGu7XGjlMtwaf9orHtwPL65faSwnJ+UZ2LdM8WOYRhERkYiMjLSLf9HO9LnO87hpwN5AIAFIxMwJjkMMwdE4/2FQxGg8a7gGCFdHT8EoqKersN4FDDyIv1jg4THBy5UtvvzfESTViakRkheu/d/h4SMolA//iSDAkbOOl5QjQ/+PotNJ4ux4YS00WSjgUrTCCHdB3+j46qhcUJzXusLxzG9wvDQtFQAwKaTJZJR7S1ZeSgfAPDauixkF3tmQ8tKc6n3PZN6gU8yqaznljXqjVh5yNJXYWyvsHZvrzOvy9UKORRy6WnnMPMYYyKlETWnttdzyhXSogOhEZ3nKeUyoWF6DZViuNTZUq7/58z+0XjlqkFeFxAjxJuEmK9r+e9jQgEjr1JQaTlxzsiraleWUbVWjwZzL51pfSMxb0gcnprTV3i9ptHy2WpzvwQT9TBy2u+ipmpZRdLSA53eZL06IYR4rVrz90pMUPP9dOYNiRMei7/3mtNkMEnKpnPK6p3fwU5QpeVLvVVCufeBC5VY8OkepD29Tlhv66OTEOavtvsZzhDfGOosU9IihcfiG13EQpxVxpeMdQY/c38rV2SpO8toNGLr1q3YunUrjEbvumGWbz5OzR8R7+Y9IYS0JNScbVleRwEjHgWMvERNox6nS7g7pj5KOQwmVtI801k5ZdzdkKhANT6/ZQT81QpcPzIRadEBkvXunthLmOrR3UrSyut00Da176RKfNFyOLdK8lqTl50wEUJIc/iLVH918yUaiWG+6BXB9XVp7R3A/ecr0CS6CPfEZpZ/ZhQKPYtCfFUIN5d7//t/h7A7R/p9ntTORsiPXdoHKZH+zTaj7ijvLxwCAAhQK1ySJeWN+ka7bjKaM/iG6PVuChht27YN27Zt8/iA0atrszDt7W2SycH2VNY3QW80oaJeBwCI8O+45vKEENfge+0t+e0YjuZXtbB290ABIy9xJLcKLMulLs/oz/UbOl3c/BdZc/g+CtFBlgaV/moFXp43UHgeoFbgkRmpkJlTa03dKCGmuKYRY1/dgvmf7G7XRLrzzdzlbqQMI0JIN2EwmrDiIFcyJm/FmQk/OYov12qJdemau0puSmoaYTDaHtv1RhPu/+EwAO6mT69IfxissnYTQ30xJDEY34r6zrTVvZN7Y+PDE4WS8s7kq1Lg5AszsWfJVCGjhUi9NG8AxqeE41Nzc+rO4meeWKdt8uyAjbt9vO0szpTUYdrb27Azu8zuOkfyqjDm1c0Y9NwGFNdwAaMgH+pXRIinS46w9BC8/P1dbtwTz0Hf1F7iUC7Xs2hoYrAwNaU9J8S/HeZO3K2/3AbFW9LHR/YMhVIug5wPGHWjDKPjBdXQGUw4XlCD4wU1GBjffFq9zmBERX0TYoJ8kJFXheKaRmw9XWq3LIJhuIlzTXYuKgghpLP9vD8PUUEaTLTqZddWF6sbEOyjwh9HCtAvlsuk+GrXeeH1mKCWx0zzDXrLWxkwsv4+7OyStPUnivDKXydxvlyLq4bG4e35g1Faq0OIrxIKuQw/7ssV1t362CT4qxWQWbU5uWNCMm4a3aNT97uj+KhoMlpzIgM0+NZqKmBn8FO5ryStKyipaYTaqozzk+1nMS4lXLLMZGIx/5PdaDJIz+MoYESI5+vZzgxeb0QBIy9x0NzkeliPEJSas4M+3HoWj89Mc/qzTCYWfx3jRn/uyC6VvKaUy/Du9YPx/pYzePTSPgAAmflucHcKGIn7C/x8IK/ZgJHOYMTVH/2DzMIafH/HaFz/6R7J62qFDIMTgrH3XAUA7i7yhXKt3R5GRhMLuYzBvnMV6Bnuh4iA9vewIIQQR7KLa/H4r0cBADlLZwslyG3196kS3L58P/jkGaWcgd4o/e6YOSC6xc/hM2Nam2FkXYK2/1wFPtx6Bhszi7H8tpEdfiH3xvpTOF+uBQDszanAqaJazHx3OyalRuDLW0cI3+EAEBXIla08M7c/bvxir7Bc0c6/e0Ja4s6SNE+38lA+Hv45w2Z5L1E2Ai+nrN4mWAQAARq67CLE0yVHUMDIGpWkeQGTicURc/+boT1CoBNN1zqaX4VGvXOpxSdFzZft9Re4YnAcNj48EX1juDvDfEmasRs1vRY3/T6S13x964d/n8XxghqYWOCXA/k2r/cM98NHNw7DgLhAXJYeK9zhs84wem9zNgY9tx5vbzyN+Z/sxoiXN7WrHI4Q0n2YTCze2XQaT/1+zKnjxr7zFcJjZyaSOdqH276yBIsA2ASLXr96EJStqEnjp5hUtLKHER8wmtGPK9k+V1aP19edwuHcKqzKKGzVZ7QVy7KSXicFVQ34+UAeWBb4+1QpNp8sQWF1IwDg3esHC+uNSwnHlkcmCs9pohjpaO5seu3p/jp20e7yn/bnobK+CZsyi4WbiSU1jTbrhfur2h1wJ4R0vFirLOcqmpZGASNv0KA34vqRCRiTHIY+UQGSuxqXv78LE17/Gw1O1KOL67Ffu3pQi+vLZXxJmhM73cXViQJGxwqq8fiKDLy6NstuA8TtoiytI3mVNq/3DPdDqJ8Kq+8bj/cWDIFayf1Y6qwCfW9tPI36JiOWbc4Wlv1xpGMvdAghnmFndhmeWHHUqWM5z2Ricd8Ph/HOpmx8tyfXqXKs//52XHh8prTtffEAafDJkUlprSt746eYVDiZYZQaxQ1uEPcH4nsKnS6uxe6zbR8W4Yi9ffxi5znh8evrs3CxmgvGxQVLT1STI/yR8cwMrHtwvLDvhHQUPyHDyD09jIwsA5blgqyedkNMfFP0tast/Twb9EYMeXEjFn1zAD+YS0vL7PzMf7eo80sMCSHOsw7sni/XIrdci+MF1U4nYXgLChh5AT+1Av+d0w8/3DkaCrnMZupJSa0O58tbf4HAl0Y9M7cf4kN8W1yf/7nqTiVptY3Su28/H8jHx9vOYtrb22xOcsRjn8+W2v473DBK2pNCZb67fiSvSrg4dHSA+uVgnvM7TwjpUrRNBtz4xV78dCDPqWyYyvomlNXpcK68HmtEd8dbe7fsgFWA52wLE4Fa8oS5tA0AnpiZZtMn4MFpKYgMaN0UoTjzFJNDFyqbPYHbkV2K7/ZcELIDEkJ9hJscPD67Z8b/bceCz/YgS5Rl2x56owmni2txtKAaABAZoMajM1Jt1jtdXCeM3Y4Osv3zB/kqkeamqVmke/E3N72ub+cE2Lao1xnwTeMwLG8cjqFL/8aCz/bA5OY7kXqjCdnFtajSNiHP/DP65a3Dcd2IRLx+je0NVf74XGiVjRkf4kM/w4R0Id/8yzJcoslgwv/2XcDc93bi5TUn3bhX7kPFtF4oLtgHt45NwvJ/zgvLnCkXK6nlTp5b2/SrO5ak1ekcNxQvq2sSegvllNahpFZns85dE5Lx0PRUVDfohX4VPL4U7cOtZ3E0vxrfLRrlcApHd5pMR0h39dN+S2D41XVZuGZYfIulDaW1Oox7bQt0BhOmpEVKXqvStm4gwtZT0h52Z9uRYXQotxIXzD18HpqWinsm9UJuhRbnzNlOmx+ZiGQnGk0OSQwGwJUHpz29Dtsem4QeYdL3F1U34qYv9kmWBfuqIJcxku+rH/bm4voRCcLz/ecq2n1xdySvCld+IJ2uEhfig5vGJOHjbTk2JT8sC4T4KlvV8JuQjsJnGH235wLSogNw1dD4Ttv2kYJa4XG9zog9ORVIXvIXvrhlOKb2jeq0/eBlFtbgru8OIK/CEvxRyhmhHYO9c2T+GLQnh8tUXDI7DXHBvlROSkgXMyE1AmnRAcgqqkWTwYTD5tYvaTHdM9OXMoy8lN6q/424r1FL+NT9wFY2AeXv1najBCMhwygl0rbZYV6lVnh8zHxn2drY3uHQKOU2wSJA2jcqq6gWr6w9iUXfHJCsw49V3p1Tjvt/OIzcci0IId7pgKghckV9E1YdbTnLaOWhfOjM5clbskokr1k3gLbGsixeWp2J9/8+AwAYk8wdk+yV3LbW6gxLhpP5HgPC/S0j3XtF+INhWt/fw1clvd91y5fSwJDRxOL7vRds3hfkoxQmewJA/9hA1OoMeFdU6ltW1/5+BfwFo1ifqAAE+Six5ZGJ2LN4Kvb9dyrC/S2DC2b0i7bJfiKkM/FNr7VNRjz8cwZOF9e28A7XUSnsT857fd2pTtsHXm2jHvM/2S0JFgHAC1cMEIK6QxKCbd7Htysoq+NuFPaO9MecQTF2MwcJIZ5NreDCJI+vyMA+c/VNd80UpICRl5raV3pHudHOxC1Hqs13n1s7NaY7ZhjVmu8OLxyViBV3j5G8lldhCd7wd5F7iwJLChljt5k4797JvXHVkDgA3EnHJ9tyJK9PTYvEyJ6hwsScPzMKMeGNvzH1ra2t7udBCPF8r/x1Eq+ty0KjVd+ix1YcbTbbx2A04acDjstVWzpOXCjX4nNRj535I7gsg9yKtgWmMwtr8N0eS/BmsPlCa9H4ZMwZFIPPbh7eps8dLLpg4yeQvbspG0lPrkGvJX9h2ZYzNu8J8lHixSsHINhXie9uHyWMwxb3Liqvt80KdZY4KKdSyPDCFf3x5CxuamlkoAbRQRpEBmgQI7qQnDmw5elwhHQkPsOIV+6C4Glr1TbaD2S7I9iy5uhF4fztvQVDoFLIsGBkAhaMTBTWUchlOPfKbOx8YrLQrJ6/YcjfVAzQdOz0RUJIx1GZA0Z82ToA9ImmDCPiRSb3icSexVORbh733tomXSYTKwRDnA0YFdU0IrPQNb0fPFVtox6bMotRYT6JCtAoMTwpFGsfGC+sky/qWcSPph0YFwRfFXf37Mohcc1OAfJVKfDMZf1sll/aPwo5S2fji1tHQK2QS4JQANcf6WtRGSIhpOsqqW3EJ9tz8NHWs9hslSHUZDDhpdWZDt+76WQxckrrIWMs4+fFlv9zHmNe2Yy1Dqb+WJdLjUnmgiqltTqhQXRLDEYTFn29H0lPrsHsZTvQZDQh2FeJZQuGYLw5SBPko8QHC4dier+2lZu8t2CIzbL/23Ta4fo3jk5EWnQArhkWjyPPzMC4lHChZ5K4dNgVgXc+YPTA1BScfmkWbh6ThGBf23+LMaKbByOSQtu9XULaw7qxeqMT2entVW3urZYaIpdk2nVEf0ydwYjv9+YKLRjEftiXiydXHgMA3DUxGZelxyLrhZlYOm+gzboMwyA+xBdDE7mSsxOFNXhrwynUmH/+AzTU+YOQrooPGIn5q7vnzzQFjLwUwzCIDtJAreSCFK3NMKprMgilZa39opOJ/hdd9v5Op/azq3nwxyNY9M0B7DaXG/AHjr4xgXhgagoAIN98h2nzyWIs/StLWG/lv8di4ahEPDEzrcXtWN/liw7U4JObhkv6lvB19GL5lQ0tjr4+ml9FIyIJ8XDiwLM9p4vtZxgVVjXg7u8OAQCm94vC17dZGjdGBaqFz75Y3Yh7/nfI7mdoRRlNQxKDERGghlzGwMQCF6ttL7CqG/Q2zf4Xfr4Xm05KA103j+6By9NjnSo9a05CqK9w3OX3ozkvXTnQZtuRAWqb9Urt9J1zVrH576mlGy+X9rcEy7rriSjxHEMTg/GvS3oKz62ntXYkPmCkrynB3w+Pw4A47hyn0sXnKwVVDejz1Dos+e0YXlotbWBbUtuIxeZgEQDMMAezZTKm2eNWfIiPMLDkvS1nUKnlA0aUYURIV6V2UCbbHVHAyMtphIBR6770+dIHhrHUbrZE3A/C28vSrO/0xwZbUqUTQrmJcvmVDSiv0+EOUd+hMyV1SIsOxNJ5A4WG2M2xzkCaNzTOZp2ZA6Jtot+/HsrHpDf+djh6e1VGIS5/fxfu/u5gm8ZzE0I6R14L5V+OphgtE/XiiQnyES66AGDhyB7wUbZ8AqQVffbX/xoJuYwRMg+eXyXNbDpeUI305zfg8RVHsSqjEElPrkHSk2uEen9eVKAa1w5PgKs9ND1VyKJKf36DsHzvkqlYNK4nnprTFwBw/5Tedt+fFGbbuLatpXfaJgM+3X4WJwqrsT2baxgu/o6wZ1iPUHx7+0hseWRim7ZJiCsxDINnLuuHcb25LEDxzcZ6nQE57Wh835IacxmXCkZEB2rw0pVcRk9lvR6NeqPLJqbd891B4fGfGYVCsJtlWdzxzUHJunzmUEscBZMow4iQrktldS32/kLbrObuggJGXk5jDii0Nq24wRxY8lXKW30X2Hpaj/WdZm82MC5IeJxgHvOcV6FFSa0O4nMbe2UhzrjaTsDo0v7ROPnCTNx2SZJkud7I4nw5N3nofFk9ikQZAR9uPQsA2JNTgcve39mt/q0I6Qp0BiPOl9W32Mi+SqtHk8E2c/SUqEltuL8KDMPg57vG4NaxSbhrYjIWjkqUrG/vZgIfTB7eIwSB5jvkfBlZfqV0vz4wN8b+5WA+ftyfa3dfRyeHYvvjk4Wguqvxx16xqEANnprbD4vGJ2PXk1NwnygTSSw5whIweuzSPgCA4hqdJGjGsix2nSmzW74itmTlMSz9Kwtzlu2E3sgiIdQH01ox3Wl8SgSSI2wHKBDiLlUNXFbP4yuOCsue+v04pry1DRszi12+vcO5lVhzrAgAoGK440+ouYSzoKoB417bglu+sjS2X3f8Iua+t6NNjfiP5kuHkdz85T48v+oEimoakZFXJSz/6tYRTmVDWvc2CfJRIoCyBgnpssQ35QPUCswdFOvGvXEvChh5OY2TJWl8KYKPqvVpeNZBh5oGA97dlO1wFLy3+OGO0ZKTiTjzRcv5ci1OiHo5zRkUIzQ7dcYLV/QHwNXQ946032RNLmNwWbrtAeyhn45g7bGLmPTmVox9dTOMJhY/7c/FyYuW/TpTUifZT0KI+z38cwYmvbkV3+6xnfD11Jy++Ot+S780bZP1aHZWcgEVZp7ANbJnKJ67vD80Sjn+O7sv/u+6dGGddceLbLbD3zgQfw9cOZgLWovLtcrrdDheaLn4qtLaLwnTKOUdmtptPTGtn1W5blywj8O+cX5qBT67eTg+vGEo7p3cG8G+XIDsfJklMHbb8v244fO9eOTnDDz6SwZ+MgfGzpTU4enfj6OivgnH8qvx+xHp9LoRPUKhaKZfHSGe6ngBd27QJOpZ9tvhAgDAIz8fcck2+Ix0vdGEeR/+g3PmILkK3PEnxM9SzlVW14Qd2WXQNhlwvqwed393CMcLavDwz0eQkVflVPZR/1jp8WFHdhm+2nUeyzZzwW8/lRznXpmNyWmR9t7u0DKrnmqjeoa6rPyWENL5xJU2of7tu/Hf1VHo28tplOYMo1aWpNm7UGhJcrg/pveLEu46fbHrnFAWcf7VOc7srsd65a+TqG7Qw0cpR4PeiB/vHI3RydJJZ+Isokd/yQAADIoPwgcLh7ZpmzePScLCkYktXnAMTQzBH/degis+2CUsyyqqFfqTmFjgrm8P2PQTAbgStQGiLClCiHutOco1ouabMM8bEidcqE3qE4HekQFQyBgYTCwa9EaIBzvnVTQI03kAYESSbTmFTMZg3pB4rDtehPUnivHgT0ewPbsUb12bLlzcCDcOROVrfP+j8vom1DbqEaBR4p7/HZKMneYD0Etmp0HbZMQ7m7jvgY6+ZBJPGrt9XE9cOzzeqfeLm273CPNDlbYKF8rr0S82ECzLYusprrxsh/kmyIqD+bhuRCJu+HwPimt0KK3VodhO9lEPO+VuhHR1NY0GVGv1CPJte3+eh386gu3ZZVh13yU2k9j4DCN/tQIqhUySSTntrW2SiUVH86txxQe7sHhWGu6a2KtV2+ZjS1GBahTXWALgm05y57ABGmWbAj09w6U/7y/baZJNCOk6NKJzoBA7Qyu6E7r15eX4/+wtNS5kWRbny+qxzXxiXFXffPNQMZmMwWc3D8cg80Q2cQ8Nb1CnM+CT7Tn4cX+eEFAbaCfIYq83iELWvkul1t6dTk8IbvZ162DRtL7cnbPSuvY3dyXE2/2wL1fIKulI1tO5RieHYowoMM2fsPg4yBzNr7JkxXzzr5EOMxMBYMnsvsLjlYcKJGPv+TLWYNEFYaifCj3CuJKygc9tQFZRjU2fIt41wxLw4LRUzB0UAwCtvpBrq4dnpCI6UINF43ri6bn9kBZtOxCgtXqa/4znzGW99Q56veVVaIWLzXUninA4two+SjneuGYQwvxUGJ8SjquH2ZYSE9JVicurzpTWNrNmy1YeLkBZnQ6v/JVlU+qpBN9Lk8F0q5LOQjtN9wHgzQ2nWr3tOh13fvvEzDTcODoR907mjk989qSr+g61pl8lIcRzJYmCwO2ouNddAAAgAElEQVRtLdLVUcDIy6mFHkbNl6T9mVGISW9uxbvmYE+tzn5D1eYkh3vn3dQKq7tfYX4qmylmgP2mh60tBXSFX+8Zg4mpERjZs/nRzCG+Sgzrwa1T1+j8vzMh3cmF8nosXnkMT/x6rNWZmm113w/SqWV3TewlGSnNl5hpzBmg1o3rL1ZxF1PjeodjQmpEs9uyzn55+o8TKK7h3r/HPAVyWA9LhhLDMLhxVA/h+ZvrHY+vDzEHmt69fgj2LZlqk43pavEhvtizZCqemtuv3Z/FnyC+vu4USmobJWW8Yq+ty7JZlhrlj2uHJ+Dg09Px7e2jEB/SMT2bCOlofLN4nsnEok5UAqtr4ZzSWn6lFoXmCa7i4Sg7skslUxkBIEZu+Zm7u4VgMx88D/dvfXCmXsdtr39sEF66ciD+dUlPIRsfAPIq29b0HgA+WDgUGqUMH93QtsxyQojnEJevUoYR8WrWU9K+35uLhZ/tEe5kL155FINf2IAHfmx/TfrD0/u0+zM8UXm9NAvH3jh7RxTyzqtfH9YjFF//ayTmiyYRiS/4AGDDQxPwz5NThck9dW0IDBLSnezNsWTR1IjGtjcZTC5tGs+yLPbkSDN2RiaF4rL0WIzsGYrFoj5o/EVSg1UA65eDeQCA6KDmJ3PxllqVTKw+ehHaJgMy8rnGr2OSwyWvLxA1zD5d7DjDgA+ey2UMIgNbty+eQlxWcv2nezD/k91211ttLh0Uu9DG6WqEeJorh1iy4/7MKMTmrBKID3ctBYwO5VaizJzB3Kg34rL3dmLsq1tQUtOISq3lJlylVo+zJfXC859uSME9t94AhYK7KTcwvvmS+Y9u5AIzF6sb8dvh/Bb/XPU6A6rM2+cD22H+arxxjaWvm6N+Z60xZ1AMjj93KWYNjGnzZxBCPEM/UcCou7cjo4CRlxMHjBr1Riz57Rj+OVuOradKUKcz4Id9eXYblb545QCntxUbrGl3CVZbsSyLJb8dwzubHN/1bitxmUiwrxK3jE1yuO7q+8ZJ/u6sRzJ2hjTRpI5B8UGSfUiNCoCPSi6kXFPAiBDHTCYWKw5ZLkJqGrljZUlNI4a/tBFP/nrMZduqbzIKd94TQ31xeXos/NQK+KkV+PmuMZKyLh+rGwH8Yz7g5NfKHnQT+0izkKob9PjnTDn0RhaxQRokhEqnj/mrFfjfolEApKPnrx4aj92Lp+C/s/vipztHt/aP7JEiAywBrpzSerAsd0yd3Kf5jC3AcdNvQroacYn9/T8cxh3fHJC8fttX+3G21P6EsmP51bjqw39w2Xs7YTKxqNQ2odL8s/Hu5mwhE5J3roz7nNkDozFqYCqSkpIgk1nOW9Y9OB6O9I60TBd86KeMFs9pThTWwMRyfc/EwWxxFuR7C9o3Opsa3RPiHfgpsQAkE6e7IzqqeTmhJE1vktzVaTKYsPtsud33RASocdUQ53svKOQyYVJYZ8spq8f3e3PxzqZsPPjjYZvpQW3BsiyO5FUJfSompEbgyDMzJA1SrQ2IC8JNoy1lGx1dimGPODXbX63AD3eOgkohw/UjEkTLuYNgdQNd4BDiyPf7ciV9eqobuOPKV/+cR02jAT8dyHPZtvaLtrPtsUk2E3fE7JWklbWhH1lcsA9+v/cSzBoQDYC7+/7RtrMAgJkDYuyW2Q5PCkFcsOU4PzAuCG/NT0dMkA/umJCMUW445rnSkETbfnAvXjkA71w3BD3CfDHU6nVxj6k7JyR3+P4R0hns9WS0tvKQ/YyenWe45vAXqxux7kQR6kVBnD8zCnE4r1KyflZRrXmb9nsHpUUH4pWrbBtIB2oUiAmSnnMWVDbYrCfGn/NYZz6GifqTOFPeRgjxbmN7cd/xzg7T8DY0Jc3L8RlGf2YUClNuAO5u9I7sUuH5tL6ReGpOPxRWNSA9Idhuj57WSAz1xYXyzk/LrxRlAf1+hJv8tWh88yfvBVUN8FXKEeKgkdmGzGLc9e1B4XmYEw3P1j4wHhtOFOOuiZ1/ASEeRatRyjGsRyj2LZmKAFGkPCncFwwDXCjX4kxJbbPNcQnprp76/bjkea05w0ic2fPVrnPYm1OB/7tusFPTJa3dtny/8LilCT1889kK0U0AcXbL7eNaf9wZnBCM/rGBWHu8CLkVWhy8wF3MOTp2qRVyXJYei4/NgaUgn7ZPSvJEGqUcj0xPxVsbLdmq0YEaBPkqseWRSZDLGCQ9uQYAEB/ig+8WjUJhVQMKqxowJNF2Kh0hXZFMxiA5wg85pfUO16lpsH9jTtzfa+WhfPxnSorwvLbRIEwd5FkCRjLs27cPADBs2DDI5ZbjaY9Q235gSeF+kMsYDIoPwtH8avNn1aBPtOPzGT545a+WHqtlMgavXz0I+VUNkr4lhJDu7dObhyPrYg2GdvPvd8ow8nIqheWf+LMd54THz63KxDe7uak4H90wFJ/cNBxJ4X4Y2zu8zcEiAMIUnc5WaVUKkOmgUSmvvE6HS17dgpnvbne4zvrjRZLnznTI7xsTiAempUhGMnYWtcKyTb4fR7CvCnJRuWBkgEaYPvL8qkyXZGQR4k3sNbjmS9DE/TueX5WJdSeK8Nvhgk7bt9Qo7oLoREG1MNmHL51Niw5AopPHYf6YvzGzWPiMqGZ6D83ob8my9FN3/jGuo4WJMgxigzRCTyj+GBpoLumdMzAGchmDhFBfjEoOk3zfEtLV/XbPJbh6qOO76lV2MpTPl0kDTJtOluDVtScly7ZkcVNbZ1hla2sUDNauXYu1a9fCaJQef0cnh2GhqIcaADw8PRUAsOLusVgwkntts9VEWGt8yZqfyvY8d/6IBDw8PbXFgD0hpPvwVyswPCkUMje1XPEUdHbj5dLjmx+3DgDjUsIlwYT26BHqnklp4nI7gBt53By+zKS4RuewLMtgkja0DfPvOh3yn5rTF/OGxNmckIk9einXpHxHdhn6PbMek9/cioy8qs7aRUI8Gj8dK8RXKQQCimoaYTKxKKu1Lf86UVjdru3xGYwPTE1pYU0gPYFrBPv17gsY8fImrD5aiBdXZwKApFystaxvElw+OLbZ9QeLvleanJyW1BXwzXAB4J5JvWya4K64ZyyemJkmHEMJ8UZBvkokRzg+p+OnKopVWJ2LAbBp5g9wNzOvtGp90NxNOZmMwdJ5A/HjnaMxvV8U3l84BJP6RAqfNTWNe5xdYumrZDSx0BmkgSdLhhEVWBBCSGu5NWDEMMwEhmFWMQxTyDAMyzDMlVavLzcvF//aY7WOmmGY9xiGKWMYpp5hmD8ZhunehYYifaIDmu2F8cTMNEmpUns5e2fbFR748TAeX3EUAIQmrbUtjIsXT7N5Z9NpSY09AHy+Iwd/ZhRKlvVzYjqauy0an4z/u25ws80X+SwF3rmyetzw+V6bvwtCuiO+qXNKZICk7Kq8vkkofxDbe872osgZfNaS9UWUPYOsbgT85/vDyC6pQ6ifCo/NdD6I0SvCX/L8ysHN74NMxiDWnHUza4D3TQMSZ4bOHWQbPEuNCrAbSCLE2zTXQL/UTuBcq7PNzLTn1asGIjJA2iuoNdMdRyeH4bObh9v8XCaZs6nzRed28z7chdFLN0uyRfnzm/Zk0hNCSHfj7rMdPwAZAP7TzDrrAMSIfs22ev0dAPMAXA9gHAB/AKsZhvG+PPk2sv5SFmvpTrKzrO9um0yuGzvtyB9HLIEdvuF0fQslVidFJWtf7TqPqW9tE/qTAJaUaTHrEfXeqE5nQP9n1+PDrWfcvSuEuE1BVQOe+eMEAKBXpD/ECZgjXt6EIjt31s+U1EkmKjqDZVnhmNWaEq8kB4H5SakRSIt2PrA9rEeI0ONOKWcQ24ospV//PRbvLxzilY0gx/YOw5S0SDx2aR+HPe4I6Q58mwms2Msw4o9j0XZKWsf1DseLV/THyn+PxVVD422CNvHBLQeMHOEzNGt1BuiNJrAsi6P51ajU6iUBfv5mobf1XiOEkI7k1oARy7JrWZZ9imXZlc2spmNZtkj0S7iNyzBMEIDbATzCsuwmlmUPA7gRwEAA0zp277uO5lJvY1txR8cZ/WMDJVPCrMu6XM06IDUxlUtLrm/hLlfWxVrJ86KaRvxtbsSobTLYLc1yZSaWp3jt6oHoHemP/0zuLVn++rpTOFFYbZPOTYi3M5lYXPLqFlQ36BGgUeA/U3oL4+7FFoxMwH9n95UE5C9WNz+hxx6WZaFtMoI1b6I1pRKOemykRLW9ef3mRybh35N6YeNDE1u1fkyQD+YOivXKfh9qhRxf3joC91odFwnpbiakREie3yDqI6RtMqKmUY9bvtyHu789CJZlUW3uJxlvZ2JubaMeN41JEprHio914f5qDGhHs+lAHyX4Q1GVVo8GUVYRPy24WqvHOnNvyql9I9u8LUII6W7cnWHUGpMYhilhGOY0wzCfMQwjPsoPA6AEsIFfwLJsIYDjAMY6+kBzGVsg/wuAV4+I4lN1rT0+s4/LT/YZhsGTs9KE51V26tldqVEU0FgyO024Oy/OFrKmMxhxtpSrc+8rKjPj+5L8c6Yc9U1GhPqpsGAkN4qeb6joba4bkYhND0/E/Xb6psxZthNPW02JIsSbVTfoseAzS9Vzenww4oJ98PTcfjbr3jq2J+6YkIx9/50GX3PZxod/n7VZj2VZ6I2O+/ws/+c8+j+7HgDAMK0bZw0A/7qkp82y5vqNtMRfrcDjM9Mcfl8QQrqf6CAN9i2ZimuHxWNa3yi8eMUAHHxqmhDs2ZtTgW2nS7HuRBEOXKjE479y7QEC7WTwDE6QltKKA0bzhsQ2W0LfErmMQaD5pl6Vtklobg0ACjl3nrvuxEXoDCakRQfY7AshhBDHPD1gtBbADQCmAHgEwAgAWxiG4W/pRgNoYlm20up9xebXHFkMoFr0K9+VO+1p/NUKvHjlAOH5c5f1w6Gnp+Pfkzrm7qm4gfbIpZvb3QzWnuoGPdafKJI0rF40Llk4AdEbWSxeeRQvrc4Ey0qzA7KL62AwsQjyUeKv+8cJwSA+uJVVxJWrTUyNwHOX98f7C4fgGTsXjN5EpZDh63+NxMc3DpXcQfz5gFf/aBAi8eb6U5JeRHyg6IrBcZgzUNqrRzy6WdvEBa7XHLsoWcdoYjHr3R1I+e9aHC+wfxx8flWm8NhfrWh1EP/JWWn45e4xyH55Ft66Nh0LRiZgShrdNSeEuFZkoAZvXJuOz28ZDpmMQZi/GpHmElbxce2jrZaAuULGIM18jLx/agruntgLD8+Q9lcL8VPhuuEJ6BcTiFvtBMCdxZfBbTtdijpRH0uDkTsHzK/kMkBHJIV6ZWYkIYR0FI/u+say7E+ip8cZhjkA4AKAOQCaK2NjADRXC/UKgLdFzwPg5UEjpSiI44ov5uYorCaubT1Viv6xQS7dxv0/HMa206XCHXUfpRwyGSMpG/thXx4ALovo6mGWPht8dlGf6AAwDINw8/SzSnMqNd+fJCHUF2qF3G7TU280MTXC/Hsk/rc31817Q0jnE/c2G58SLgkKpScECQGh168e1KrPK63VIauIK399c8MpLL9tpOR16wzMuyf2avW+qhQyjEgKBQBcPSxecowjhJCOFB/ii5zSeqwSDQcR935cOCoR/WICcbq4DuNSwh1+zmvXWI6lJpMJCxYsAAAoFM5fntx2SRKeXHkMyzZnw1dlef8LqzNR3aAXyuTEUxAJIYS0zNMzjCRYlr0ILmDE188UAVAxDGPdjTgSXJaRo8/RsSxbw/8CUOtoXW8xNz0WQxOD8eiM1A7fltwqYHSmpA45pXWSSRXtte00128op7QeAIR6dbmMwe3jpAGxR37JgMFcEnLyYg325JQDABJCuMaxwb4q4TP/OFKA7/ZwwZLQbnpS4aOS49d7xgBofkIKId5GJrrrfNKqz1mUqInr8CTpV87joslkI17ehMUrubKM8nrLFKF95ypsRtDvE2Uz3Tg6kXrmEEK6hGHmPkQ5ZfU2rz05Kw2T+kQiMlDTbLDImkwmQ2pqKlJTUyGTOX95cu3wBAT7KlHTaMCzf1rK6Q9eqMSZkjpsNfepDPKlRvaEEOKMLhUwYhgmDEACAD7v/yAAPYDponViAAwA8E+n76AH81crsPLfl+A/U2x71biadarvhhNFmPLWNtz85b4O2ybf1BAA7hifjJHmO++8VUcLUa3VY/ayHULmkfXdptwKLR748YjwnuBufFKRFMZlbtU3Ge02/CXE2xzNr8K+89IAjlh6fDDkMgapUf7oESbt88Nn5wFcVtEP+/Jw21f7cDjX0jxf22TE4Vxp9TQfMLp6aDxeuHwACCGkKxiR5HhqLD9xsbPJZYxQlqY3Oj5voQwjQghxjltL0hiG8QcgvqXak2GYwQAqzL+eA/AruABREoClAMoA/AYALMtWMwzzxf+zd9/hcVVn4se/R82WbEnu3RhjDBhTTA8QeidLIAmksUlMNmzIppGyJJDfBrLJhl1CspDdQApJKAlpQPoSIAQIoYXeTDHginuR5KJqnd8fdzSakWRbkqUZjfT9PM99fOe2ea+OR5p555z3AN8MIaxPnXM18Dzw59zchXZmS6q+R+a36bviP+98OevxtNHlWd/MT6oezi8vOpKf/30pX7zjeQDuWbCa/aeOIrOc0dR0wqjrxNCkPp5BrpBkTndb37ytWzM3SYVoRU091/55IZl57h988FCO6fDN+O7jRnD/549n3MhhnXpRdvX6uO+VtemZF9s88sZ6jthjLDFGvvSbF7g1NfTz2L3GUVRkTQ1JhWHyqM6zoLWZWNm7907btm3j+eeT92z7778/xcU97+FcOXzn71UmVQ3d93aS1Bv5/hR4KHBfxuO2ukI3AR8D9gc+CIwiSRrdB7wnxpg5VuAzQAvwS6AcuBeYH2N0PvABbNmGrSxZv5W9Jo0kxuzhHjvSvK2V7z7QXljxo8fuwaVnzuny2Pcevhsjh5fwiVufZu2mxk5TxE9NveEZ2cUbjLPnTenUS2koGVZSRFGA1ghbG1tMGGlQijHy8VufyuoJ9L7Dp3PKvhO7PH76mIout2fWy9iRNzfWE2PkrwvXpZNFAIfPHLq/ayQVnuGl7b26P3HCnoyqKOVrf3wJIF0Qu6e2bdvGb3/7WwD23XffXiWMTt13Eo8v7jgPTrbM2XElSTvX40+BIYQiYD7wTpJePxFYBNwG3BI7Tkm1AzHG+0kKVG/Pad24RgPwydSiAaKkKNDSxVCm5m2tlBYXMf/Hf+f1te1j35+/4tSsgtXb82ZqlgtIppX+4hn77PD4cSOTNy6PL96YniEDkjc7e01MCtp29Y3UZWfOGdLf+IcQGFFWwqbGlnQPMWmwueOpN7OSRQBjRvR8KOqYEWXsOWEkrTHyq48eyUOvr+dTP3s6vb9yWPJaWr2pkZ8+tpT/95sXss6fXL39b+slaaApL21P5pSXFbNbRjK945DdXLrw2D0YVVHKQ6+t49XVm1mQMZFBm9G9+B0vSUNZj2oYhaQ4ze+AG4CpJEO/XgRmADeSGiom/e0LJ/LVczrX5Fi2YStAVrII4JHX13frum3FrceNHMaXz9p3p1OjZg43u+vFVQCMqijlzk8fy/jKJJm0z6QqLj55Nm87IJk2e/aEkelE01BWMSx5Q7ilsWUnR0qF6Z4FydwImb9GxvfitV9cFLjz08dwz2eOY+zIYUzuMJy17RvtNXUNnZJFlfbek1RghmckjIqLAifNmchFx83ipg8fTmlxfsujnnfodK5570GMHdk5MfTew6bnISJJKmw9fac6HzgWOCnGmDmUjBDCicBvQggfjDHe3EfxqUBNqh7OQdNHddp+/f2v87V3dE4kLepipo2utCWMysu694Zk9oSR6fW6+iTxMW10OTPHZX8DdvHJyexxnzpxE5Oqh3eqUTIUjSgrARrZag8jDVI19cm09te8Zx6tMfLc8lrePm9qr66V+SGpY9LptP0m8ffFG3izpr7jaYyrNDktqbBkTjRSFJKk0c56fOda5u/kY/caz7+/fe6Qrk0pSb3V068B3gd8vWOyCCDG+BfgP4Hz+yIwFb7MMe5tfvXkcj7+06c6bV+8fmu3rtmQSl5kdofekaKikJ7taPnG5DmGlWz/3L0nVVJd7gwaAJWpn8P6zY07OVIqTDVbm4FkSNk7DprG5WfN7dWQtI5mjK3gpH0mMKykiD9dfAynpmoibWpIktajK0rTvx/POnDKLj+fJOVSZu/usMPKEvlTWtwe15iKUnYfNyKrZ5QkqXt6mjA6APjTDvbfCRzY+3A0mMwcN5IT9h7fafufX1rTadtLXYwz70q6h1EP/uhPSM3YsXRDW8Iov92lC8Veqd5Ztzy6hJW17T0jGpq30dpFfSqpkMQYeXlVMn9CXyeJQwh8/4OH8uJXTmOfSVVUdbj+mBFl3P6xo/jX0/bmkyfuuZ2rSNLAt5PKAHmTORmBdYskqfd6+sl5DLB6B/tXA6N7H44Gk+KiwI8vOJznrjiVH19wGIfO6Pxf4xMnJB+WFqyo6zSLWVdeeDNJLJX0YIx8W62itqFVJoy65+RUr4iHX1/PCVffz9amFjZsaeKkbz7Au777cJ6jk3bN08vai11P6OU00DtSXBTSv6cqh5WQOcp1VEUZc6dU8/ET9sx7vQ9J2hVlA/Q9Vebsk2MqTBhJUm/19Ld8MckU9tuzjV7MvKbBrWp4KSfsPaHLOkWfO3UvRleU0rStlZdWbkpvjzHyxduf4zv3vZZ1/H//+VUAnlyy42lTM03oUCNkR0PS1O60uZP4l+NnAdDQ3MrC1Zv59r0LebOmnqeX1tCyrTXPEUq9t35zU3q9v+taFBUFxoxo/z00ymGvkgrch4+eyazxI3jnwdP65HolJSWce+65nHvuuZSU7PpHiZP2mZBe72rWXklS9/T0N3IAbgwhbK+oidU7tV3rtzRlPT5xnwmEEDhw+ijuf2UtzyzdyLxUoezX1mzm548vA+CDR86gcnjvP2BlTvcKUDpAvw0biC45fR8efWM9Ty2t4aWVddz+5PL0vi1N26gu92epwrS1Kfnu46hZY3PyfFNGDWddqh7Y5FEWXpVU2L581r7Avn12vaKiIubOndtn15tQNZwQIEbYf2p1n11XkoaaniaMburGMc6Qpm55yx5Jd+F5qYTRs8trqa1v5sKbn2BVbUP6uCeXbOT4vSfww78tSm973+Hdnxp11viRVA0voS5VcPYfj9itj+5gaJg8qhyW1vDH51eyqbG9g+Hmxhaqy0tpaN5GScbwm+5Yv7mRyuGlA7Yruwa/+tQQ1Yqy3PQ4nDF2BM8trwXgouNm5eQ5JWkoe/iLJ/L00hpOmjNh5wdLkrrUo4RRjPGC/gpEQ8/mxuQDW1uvoqeWbuQnjy7h74s2ZB33+OINHLfXeL76hwXpbZ84cXa3n6eoKHDIjNHc98paAMaOdCx7T1QOS35NLFy9OWv7lsYWlm3Yyj/8z9+orW/m0yfN5uKTZ2fNntKVp5du5LzvPsI7DprKN86zRr5yK8bIx299iv97fhUA5WW5GUX9qRP3ZEr1cN57+G5MG12x8xMkaQhpbW3lpZdeAmDOnDkUFe36F0qTq8uZvH/5Ll9HkoayPvl6P4QwI4SwbwjB7gLarq+es1/W45qtyRC1g2eMpijAkvVb+cZdr3Q67/FFG9nS1F4Qe1hJEeN6mPQ5JKPg9riRjpzsiZGphNGquoas7ZsaWvjh3xZRW59MTX7tvQt59I0k2fete17luG/cx7LUzHSZLv7FM7S0Rn6VMbxNypW1mxvTySKAihxNszx7YiWXnjmHmeNG5OT5JKmQtLS0cNttt3HbbbfR0rKjcqmSpFzqUYInhPChEMLFHbZ9H3gDeB54IYTQ/bFCGlI+8JYZWY8vOHomkBTF3tG01ovXb+E3T7+Zfnzv547rceHqM/efTOXwEt53+HRGOVtGj4wc3nUPjOeX1/CrJ5ZlbXvfDx5l6fqtfPvehSxZv5V/+elTWft//fRylqxvTyItXL0JKZc2bmnOevzSqro8RSJJkiQNbD3tEXQRUNv2IIRwOnAB8EHgMKAGuLzPotOg89Vz9mP+Ubuz6Mozs75p37i1/UNcZYcExfotTfz2mfaEUW+Gc+wxfiTPfvlUrnznAb2IemgbntEDY+yIMkZVJMm9K36/IKvnV5s7nm7vOfT8m7Wc992H0zOq/fihxVnHfurnz/RDxFLX1m9u5N6XV2dtO2fe1DxFI0mSJA1sPU0Y7QU8kfH4bOB3McafxhifAi4DTuqr4DT4fOAtM7ji7XN3WOemoXlbum4OwLbWmK4z8rb9J/f6uYuKdlxbR107JDVk8Jx5U3j8SyczoXLHQ/rq6lsoyyiA/fjijTy9rAaANXXZEyy+tLKOv3T4AC/1l0/c+jRX/SkZ9rrPpEpuvOAw3m8RfEmSJKlLPU0YlQOZ/fePAv6a8fgNYNKuBqWhJ7NXUfO2yC8vOpIbLzgsPVRt8botABwze1xe4hvKDtt9DM9efirXvPcgiooC//2eeVn7j95zLL+66Ei+8vZkOtxXVtfRlOpR1Obel9YAsKkh6Ul21bsOYGJVkni6M6OejNSfFqxs//O1qaGF4/eekNWDTpIkSVK7niaMlgCHAIQQxgFzgb9l7J9ExpA1qbt++dEj2XtiJQD/fvZc5kyu4vi9J6SLWy9NFU/eUa0j9Z/K4e0/97lTqnnq305JP64uL+Ww3cewe2qI4UOvrQegrLiI4/YaD8Dvn11BU0tregjbKftO5NMn7QWQ7n0k9acYI5sb2wupnjbX7zYkSZKkHenpfMI3A98JIcwFTgRejjE+mbH/KOCFvgpOQ8ecyVXc9ZljaW2NWUPHxo4cxutrt6Qfj9/JcCjlxpgR7YXDNzUkH8J3G5NdW+qts8dx3fkHc8hX7+HNmnr+9tra9L6q8tL0NV5bs5k1dQ1MqBqeg8g1VC1ev5VtrRGAi0+ezUr/PXIAACAASURBVEXHzcpzRJIkSdLA1tOE0X8BFcA7gVXAeR32Hw38rA/i0hDVsc5QZoLoiJljOGi30bkOSdux18SRvLp6M2em6kpNHVVOCBCTz+RMrh7O8NJiDp4xmgcXruOxRRuAZPhhcVGgvKx9KNBrazebMFK/uuyO59Prnz5p9g7rqEmScqu4uJizzz47vS5JGhh6lDCKMbYC/5ZautrfMYEk7ZL3Hjadx95YT3V5KdedfzDFFq4eMG698C08uWQjJ8+ZCEBZSRFzJlWl68S09ebYfewIHly4ju898AbQPqzwqFlj8xC1hooYI88sq2HPCSOpHF5KUcYAbJNFkjSwFBcXM2/evJ0fKEnKqR7VMAohtIYQtnWxbAwhPBpCeGd/Baqh6ZjZ43n8Sydzz2eOY+xIh6MNJONGDuO0uZOykng/+cgR6fU1m5IZ0Y7skBiqq08KX5cWF3HgtGoA6lO1jaS+cu9La3jHdQ/z4RsfB6AolST66tlz8xmWJEmSVDB6OiTtHdvZPgo4HPhJCOFDMcZf7VpYUrsQAnYIKAxjRpRxyIzRPLlkI+ccNBWAM/abxDfOPYB/ve05AOoa2gsPt81QVd9swqijdZsbKS0qorrCQu+9cftTywF4fPFGYoy8WVMPwKzxI/MZliSpC62trbz22msA7LnnnhQV9XReHklSf+jpkLTf7mD3TSGEBcDnARNG0hB14wWH8fKqTRySqjcVQuC8Q6cTI1xy+3PMP2r39LEVqTpGW+1hlGXDliZO+uYDjKoo5b7PHd+ptpd27s4XVqXXr7v/dVakEkZTRpXnKyRJ0na0tLTws58lZVAvvfRSysrKdnKGJCkXetrDaGfuBr7Wx9eUVEAqh5dy2O5jOm1/92HTOWB6NTPGjEhvayt83WAPoyx3PLWc2vpmauubWb2pgcnVJjl6IrZVXk/5xl2vAFBcFJhUbXF1SZIkqTv6ur9nOdDQx9eUNEjsM6kqa3a0tiFp9jBqd9eLq/jaH19KP77hwUV5jGZguO+VNRxz1V949I313Tq+saU1vX783uPT68fMHpf+PydJkiRpx/o6YXQh8HQfX1PSIDWhMuntsWT9ljxHMnB89JYnsx4vWFGXHk412DU0b+N7D7zOk0s2prfV1jdzwY8fZ9mGej72kyd3cHa7uoaksHoIcNW5B6S3n7HfpL4NWJIkSRrEejQkLYTwre3sqgYOBWYBx+xqUJKGhrZZ0l5cUZfnSPLvW/e8yuJ1nRNnj7yxnlO+9QCPfelkRg7r61HEA0dra2Sff/sTAHtPrOSuzxxLU0srx151X/sxcXtnZ9ucKqw+clgJEyqHc9rciSzbUM8/HDClz+OWJEmSBquefvo4aDvb64A/AdfFGJfsWkiShoqpo5PaPGvqGvMcSX7FGPn2vQuzth282yieWloDwJambSxdv5V9p1TlI7ycePj19uFmr6zeRPO2Vt6sqae2vjm9feyI7hVB3dyYJIwqUwm2733g0D6MVJIkSRoaejpL2gn9FYikoWd85TAAVtU1cP8razh+7wl5jig/mrdld5352PGz+Ke3zuTYq+5L13daXdcwqBNGyzZuzXp89d2vcPKciVnbunv/G7Y0AUkBdkmSJEm909c1jCSp28aOGJZen//jx/MYSX41tmQX/b7o2FmMGzmMB/71BA5PzTi3um7wzidQ37SNS+94PmvbDx9cxJL12UmkohC6db3X1mwGYI/xI3ZypCRpICguLuaMM87gjDPOoLjYyQkkaaAwYSQpb8pKsn8FdZwOfTBbs6mBGx58g9qtzVmzepUVF1FdkfSMGV85jFkTkqTHqgJOGL2+djPn3/AoD7+2rsv9193/Wnr9P96xHyFAS2vkZ39fCiQ/E4CmjJ8TQPO2Vp5bXsO2DsWNXlm1CYC9Jlb22T1IkvpPcXExhx9+OIcffrgJI0kaQEwYSRowOg7NGswuue05vvbHl/jUz5+mobm9h9H0MeVZx02sSmaSW13XwJ3Pr+TWx5bmNM6+cMXvXuSh19bz/hseo2Vba6f9TyxunxVtn0mV6eLebbOlfeioGUB2T6zNjS2ce/3DvP1/H+Inj2aXzntl9ab0tSRJkiT1jgkjSQNGS2vnZMJgdf8rawF44NW1WT2MzthvctZxbQmjVbUNfOynT3HZr59n2YbsoVoD3aKM2d++mDH07LnlNRz4lbt55I32gtd7jq9kU2qWM4DDdx/DflOT2fSaMpJNtz62hGeX1wJwz4LV6e2trZFXUwmjvUwYSVJBaG1tZfHixSxevJjWIfReQJIGOhNGkvIqc6r4o/7zL1zz51fzGE3u7De1vYBz5lCtT5y4Z9Zxk1IJo8x6PjVbmykUMUY2popQA/zf8yvTQ8u+fe/CrFnQ3nnQ1PRwvDbvP2K39JC0xub2DxFtySKAv722jsWppNTX/vgSDc2tlJUUMWNMRd/fkCSpz7W0tHDTTTdx00030dLSsvMTJEk5YcJIUl5Vl7cnCGq2NnPNnxfu4OjBY/zI9oLf33/wDQCmjS5neGl27Ya2HkZvZPTSWVpAPYzq6lvYkprprSjA1qZtLFhZB0BJUfufoO+8/2C+9Z55ALzv8OlA0rvoHw6YzLDSVA2jVA+jWx9byh+fW5n1PMdffT/fvPsVfvTQIgAmVA6jpNg/cZIkSVJv+W5aUl5NG13eaduWxsL/dvGnjy1hv8vv4tzrH87qYdPmvtSQNIBlG+oBKCnqPAvY1NHlnbZ//NaneGZZTR9H3D+W1yTJrbEjyjh2r/EAPLushhgjb6xLZjO77vyDedsB7UPxvvL2/Xj8Syfzy4uOpKS4iOElSRLtueW1XHnnS3zjrpe7fK7/+Ut78ezPnLxXv9yPJEmSNFSYMJKUV1ede0Cnbbc9uRyA2vpm/vTCSuqbtnU6prsamrfxv39ZmFVHJxfuenE1mxtbeGLJRn799JsAPLlkA++47iE+fOPjXZ5TOby007bq8lK+cPo+nbZ//6+v923A/WDjlia+eXcyxHDKqHIOmDYKgMt/9yJX3fUKr67eTFlJEUfNGpt1XllJEeMr23tgjR5Rll7/3gNvsDE1JO/cQ6bxzJdPYWzGfoDS4sC7DpnWL/ckSZIkDRUmjCTl1YyxIzpt29TQTH3TNg78yt1c9JOn+Pr/vdTr69/x1JtcffernHD1/bsQZc9tamivzfPvf1jAfa+s4V3XP8LTS2v4y8trOh0/clgJFx03q8trXXjsHl1cv70X1pL1W3YpqdZfvv2Xhel7nTqqnJPnTEjvu/7+JOF19oFTGFVR1uX5bcZlDN9rc/jMMVx93oGMqijj7s8cm95eOayEmz98RF+EL0mSJA1pJowkDTiNLa0sWNle1PiWDtOm98Sq2vr0+tam3A1129yQ/VwX/LjrXkVtfvKRI7KGZe3M62s2s6WxhUtue5bjvnE/n/zZ072Ksz/d9cKq9HrFsGIOmDaKy8/aN+uYw2eO2el1xozonFA6fe6k9PrYkcN47LKT+MMn38pDl57IkR16LEmSJEnqORNGkgacTQ0tvLZmc/rxgdNH9fpaVRlFtVfUNOxSXD3R1gOoq94x0D77WZuOw6o66nidFbUNzL38Ln75RDJ8788vre7qtLyaPbF9Wvs9xiU9yWaMzZ657JAZo3d6neKiwEeP24OKsvaC4OccNDXrmIlVw9lvajVVXQzrkyRJktRzJowkDTibGlp4dXV7wmjksOIdHL1jzdtien1lRm+j/lRb38yquiQ59ZY9snvQjBlRxt4TK3ngkuO593PHpbdvL7HU5ocfOpST9pnAbz9+9HaP3dBFce18qqlPhuXtOWEkFxw9E6DT8LOZ4zoPSezKpWfM4eEvnsiIsmL2nVzVZa8jSVJhKi4u5uSTT+bkk0+muLj3f/MlSX2rJN8BSFJHG7c2sXZzY/pxx+FdPdGcmoodYGWOehg98Gr7DGhfO2c/1m9u4pE31gPw8BdPpKy4iKKiwB7jRnDa3ImMKi+jvGzHb5APnD6KH84/DIA9J4xgXernM2v8CGrrm1m3uYlVtQ0DKpFSszVJYP3nO/dnxLDkz83MjJpVZx04hRA6zwy3PaMqyvjbF07c6c9KklRYiouLOfroo/MdhiSpAxNGkgaMPcaP4I21W1i4ZhMtGT2DNjf2PmHU1NKeMFqRgx5Gf3phFZ9K1RM6dq/xjKoo46pzD+Cfb3mS9x0+neGl7cmOEALf+8ChPX6O/aZU8+gbG4Ak6XL3i6tZt7mJ1Zsa2JeqvrmRPlCTms0ss1fR6BFl/Gj+oSxYUcfHT9izx9ccPYASYpIkSdJgZsJI0oAxZ1IVb6zdwrIN2YmdLY29nwEs1z2MLvrJk+n1eanaS9PHVHDnp4/ps+f42PGzuHvBapZu2MqcyVU8u6yGBSthbV3jzk/OkW2tkbqGtoRRdl2hE/eZyIn7TMxHWJKkAai1tZWVK1cCMHnyZIqKrJohSQOBv40lDRjjRpZROaxzHnuXehhty20Po0zTR5f3y3XHjhzGXRcfyx3/chSnzJnIhMqkgPYltz/H0vVb++U5e6quvpmY6iQ2qtxC1JKk7WtpaeGGG27ghhtuoKUldzOaSpJ2zISRpLw7/4jdKCsp4iPH7MFuHWbRAtjS1EKMsYszdy6zh9GKmv5NGLW2Zsc4ocNMaH2pvKyYg3cbTVFRYHxlexHsK37/Yr89Z09sTNUvqhxWQkmxf2okSZKkQuO7eEl59x/v2J8XrjiN6WMqsqZdP2Hv8QDECDc/sqRXs5w1t7QncWrrm/nDcyv4xK1PsWUXei115bJfP88el/1f+vGxe43nqFlj+/Q5tmfsyPa6Pn95eU1OnnNnNrbVLxph7yJJkiSpEJkwkjQglJUkv452G9M+i9a7D51OUWoSrct/9yLvvO5h/vnmJzjnOw9R39S9ukaZPYzWbW7iE7c+zR+eW8nNjyzps9gbmrdx62NLs7bddMFhlOaoZ83Z86ZyzOxx6cebUrWD8qm2PulhNKrcItWSJElSITJhJGlAyexhNKqijJEZNY1W1jZw94LVPLOshp8/vrSr0zvJrGGUqS+TKq+t2Zz1+NYLj+jRdPG7asyIMm75pyOoTtUKerOfh951x8YtXRe8liRJklQYTBhJGlBmjGlPGE2sGsaUUV0Xjn5iycZuXa95OwmjkcP7bpLItno9+0yqZPF/vo2jZo3byRn9Y++JlQA8s7QmL8/fpnlbK397bR2QJP0kSZIkFZ6++8QkSX3g4BmjOWb2OOZMrmLmuBGcPW8qL//p5U7HPfL6elpbI0VFO+7J09jSdcKoq9nYMq9dXlbMvOmjuhVzTapeT3WeZwPbd0oVf1+8gcV5mCntl08s46HX1jFz3Aiu+fPC9PaT9pmQ81gkSZIk7ToTRpIGlOGlxdzyT0ekH7/r4Kn8VxcJow1bmliwso79plbv8Hpbt1PraHtDxtZtbuR9P3gUgEVXntmtoWU19QNj+FVVKmG1uTG3NYyWb9zKJbc912n7FWftyzkHTc1pLJKkwlNcXMxxxx2XXpckDQwmjCQNaBOqhjNnchUvrazrtO/1tZt3mDBatmErf1+0oct9TdvpebSypiG93tjSyvDSnb9xrU0NSct3D6Oq1DC7TQ19OwNcV2KM/OqJ5SyvqedXTyzr8phzD53e73FIkgpfcXExxx9/fL7DkCR1YMJI0oB3x8eOYs6X/9Rp+6rahi6Obvfpnz/dadu4kWWs29y03dpGza3t27c0tnQrYdTWi6miLL+/UitTCaPNOUgYHfzVe9i4tXNPprMOnMI3zzuQSGRYid8SS5IkSYUqr0WvQwjHhhB+H0JYEUKIIYRzOuwPIYQrUvvrQwj3hxDmdjhmdAjhlhBCbWq5JYTQvcIjkgpCeVl24uGUfScCyaxpXYkx8plfPMNTGcWfP33SbL5w+j7pc7dX22hLY3uyZXvD2TpqaE6u1Z3kUn+qHJ70cOqvHkbbWiMxRppaWjsli/7xLbux6Moz+Z/3HURZSZHJIklSt8UYWbNmDWvWrCHGmO9wJEkp+Z4lbQTwLPCJ7ey/BPhsav9hwCrgnhBCZcYxtwLzgNNTyzzglv4KWFJ+nD53EgD/etreHDpjNAB1DV3X6lm+sZ5fP/1m1rYz95/Mx46fRVlx8mvvW/e8yrrNjZ3OzUy2PPL6+h3G1NoaueXRJTyzLJmxbXhpfn+ljh2RzEi2eP2WPn/DvaWxhWP+6y+cc93DXHf/a+nt3//AITx3xal87Zz9u1XvSZKkjpqbm7n++uu5/vrraW7ObR0+SdL25XX8RIzxTuBO6FyANiQbLgb+I8Z4R2rbh4DVwPuB74UQ5pAkid4SY3wsdcyFwCMhhL1jjK/k6l4k9a9r3juPtZsamT6mgp8+tgTYfk+arnoPFadmUystbk/q3PDgIr54xj5Zx23Y0pRev+T253j3Yduvw3P7U8v5t9+8kH5cnuceRvN2G8WIsmLWbGrkhTfr2H/ajguC98TCNZtZUdvAitoGnl2W9Nz6f2+bw6mpRJ4kSZKkwSXfPYx2ZCYwCbi7bUOMsRF4ADgqtelIoLYtWZQ65lGgNuOYTkIIw0IIVW0LULm9YyUNDMNLi5k+pgKAkcOSXHft1mbOv+FRTr/mr2zK6G1U38VQsqry5Jytze37vvvA61nnASxcvanbMT3w6tpOMebTsJJijpk9HoD33/Bon167tr7zN74fPnpmnz6HJEmSpIFjICeM2r62Xt1h++qMfZOANV2cuybjmK5cSpJUaluW9z5MSbnWVtz574s38NBr63l51SZ+9LfFQDLU7J3XPwTAzHEjuPUjR/D9DxzChMrhAIwfOSzrWr98Ivvl/+TSjVmP19Rtv7B2XYceTvkekgZw5gGTgaT3VWZvqe6IMfL62s00tnROuHWVMCoqcgiaJEmSNFjl/9PNznUsxBE6bOuqUEfHYzq6EqjOWKbtSoCScmvksM7T1y/ZsIW7X1zFt+9dSPO2mDquhKP2HJc1bOqjx+3B/KN2Tz+uy0iErNucDOWC9iFszy6v3W4cG7Zk10DKdw8jgLNSCSOA+15eww0PvsHqugZeWlm303P/unAdJ33zAS69/XkgSSDd/eIqXlxR22XCSJIkSdLgld85oHdsVerfScDKjO0TaO91tAqY2MW54+ncMyktNbQt/UnPQq1SYWnrYZTpjqfe5I6nsgtdd5xdDaCirIQr3j6X4qLAD/+2iIaM3jQPLkyGmM2dUsU+k6q4/anlLFhRl55ZraOVNdm9j8qK85+DDyFw0j4TuPflNXzuV88C8LU/vkRRgL9ecgLTRlds99xv37sQgDuefpMvnrEPH/zR33l51SamjirnvTuo5SRJkiRp8Mn/p5vtW0SSEDqlbUMIoQw4Dng4tekRoDqEcHjGMUeQ9Bp6GEmDUlsNo52p6CJh1KYqNQV9XX37sLInFifD0d665zgmVydD2NZv6TyTGiS9b9p63Zx14BR2H1vBYTPHdCuu/rbXpM5l2VojLFq3JWtb7dZmrvjdi9z3SjKN8fqMWeP++PxKXl6V1HN6s6aeBakeSu8/YjeO22s83//AIf14B5IkSZLyLa89jEIII4E9MzbNDCHMAzbEGJeGEK4BLgshLAQWApcBW4FbAWKML4UQ/gT8IITw0dQ1vg/8wRnSpMGrqx5GXTltBzN4VaeKYLcNSWvZ1spPH1sKwLQxFTSlZlrrqg5QQ/M26uqbaWlNhr7917v2p6Js4HTYnDV+ZJfb71mwOl0U++VVdZx+zYMA/O21dTS3tLJ4/db0sY+8vj7r3AcXrgPggKnVfP0d+/dH2JKkIaq4uJgjjzwyvS5JGhjy/QnnUOC+jMffSv17EzAfuAooB64DRgOPAafGGDOnMTof+Dbts6n9DvhE/4UsKd9GdKOH0W0XHcmhu2+/x09VeaqHUWqWtIczEiRjR5RtN2EUY+Ts/32IV1KzqZUWB8oHQO2iTLPGj0ivX/veeXz6588AcPMjS2ho3sZ7DtuNj97yZPqY19Zs5i8vZ88fcPeC7FG9mxuTnlhtM9VJktRXiouLOfXUU/MdhiSpg7wmjGKM95MUqN7e/ghckVq2d8wG4B/7ODRJA1jpTmoF3fzhw3eYLAKoTiWM2oaVlRS3/ypqjZHxlclsaqvqGrjrxVV8+bcv8N/vmcfUUeXpZFFynbIBVwftwGmjuPCYmew+bgRnz5uaThhBMitcx5nhIBl2BvDho2fyo4cWbffae03sPNxNkiRJ0uAzkGsYSdJOHbvX+E7bDphWvdPzqjokjLa1tk+seMzs8cwYm/SkWb6hni/9+nlW1zXy/h88lp5Frc1pc7suiJ1PRUWBL71tX84/YgaQ9DLambYhZ8fsNY7/fs+B6e2Hzhidddy4kWV9GKkkSUnv3ZqaGmpqaki+L5YkDQT5HpImSb3yww8dyqurNzNv+ij++urarH2jKnae1GjrYdRWw6ihORmCtv/UaqrLS6kcVkJZSRFNLa3UNbQXxv7Bg2+k10eUFXPxyXvt8r30t+3N8taVvSdWMmVUOSPKSnhy6UY+eeJszr3+YV5etYmDdxs14HpTSZIKX3NzM9deey0Al156KWVlfjkhSQOBCSNJBemkORM5ac5E1m7KnsXszP23X+g606iKJGG0cWszL66opbFlG9A+s1pRUWDGmAoWrtmcrmcE8MyyGgA+8taZzD969/TQtYGsvLSYMSPKOtVjev8Ru3HqvhOZ/+PHAdhr4kimjCoH4NS5kzg1VTT85//8Fq6//3XO3H9ybgOXJEmSlDcmjCQVtMwhUqMrSrnmPQd167zxI4dxxMwxPLZoA+/7/qO857DpAAzPKGC9+7gRLFyzucvz33bAZKaNLowC0CEEzjtkGt/7a9I76nefOJrJ1eWMqijNGoo3fDvFu0dVlHHpmXNyEqskSZKkgcEaRpIKWuYQqeGlxZSVdO/XWgiB733gEKZUD6euoYUfPJgUen52eU36mJnj2mcb61i7Z59JVbsSds594MikntGYEWXsM6mK8ZXDKC0uYnhpMefMmwLA0XuOy2eIkiRJkgYQexhJKnh7T6zkldWbOH7vCT06b1RFGftNrWZFbUN6W83W5vR65vT04yuHU1ffQtO2ZHhaeVnXvXEGqmmjK7jnM8dSUlzUKan2jfMO5P1HzOhWsXBJkiRJQ4MJI0kF75aPHM4fn1vJuw6Z1uNz22ZD68pRs9p73ATgz589jgUrazl9v8Ks5TN7YmWX20uLizh85pgcRyNJkiRpIDNhJKngTagczgVHz+zVuZOry7MeX/nO/dPr08e0J5MWrKxjt7EV7LaDBJMkSZIkDRYmjCQNaUfOGksIcOC0Udx64RFUlGX/Wjx134ncvWA1Zx04JU8RSpI0uBUVFXHooYem1yVJA0OIMe78qEEuhFAF1NbW1lJVVViFbCXtujfWbmZydXmXdYkamrfx66ff5LS5kxgzoqyLsyVJkiRp4Kqrq6O6uhqgOsZY193zTBhhwkiSJEmSJA1OvU0YOSRNkiRJUt7EGNm6dSsAFRUVhBDyHJEkCcBBwpIkSZLyprm5mauvvpqrr76a5ubmfIcjSUoxYSRJkiRJkqQsJowkSZIkSZKUxYSRJEmSJEmSspgwkiRJkiRJUhYTRpIkSZIkScpiwkiSJEmSJElZSvIdgCRJkqShq6ioiAMPPDC9LkkaGEKMMd8x5F0IoQqora2tpaqqKt/hSJIkSZIk9Ym6ujqqq6sBqmOMdd09zxS+JEmSJEmSsjgkTZIkSVLexBhpbm4GoLS0lBBCniOSJIE9jCRJkiTlUXNzM1deeSVXXnllOnEkSco/E0aSJEmSJEnKYsJIkiRJkiRJWUwYSZIkSZIkKYsJI0mSJEmSJGUxYSRJkiRJkqQsJowkSZIkSZKUpSTfAUiSJEkauoqKith3333T65KkgSHEGPMdQ96FEKqA2traWqqqqvIdjiRJkiRJUp+oq6ujuroaoDrGWNfd80zhS5IkSZIkKYsJI0mSJEmSJGWxhpEkSZKkvGlqauLKK68E4NJLL6WsrCzPEUmSwB5GkiRJkiRJ6sCEkSRJkiRJkrKYMJIkSZIkSVIWE0aSJEmSJEnKYsJIkiRJkiRJWUwYSZIkSZIkKUtJvgOQJEmSNHQVFRUxe/bs9LokaWAIMcZ8x5B3IYQqoLa2tpaqqqp8hyNJkiRJktQn6urqqK6uBqiOMdZ19zxT+JIkSZIkScpiwkiSJEmSJElZrGEkSZIkKW+ampq4+uqrAfj85z9PWVlZniOSJIEJI0mSJEl51tzcnO8QJEkdOCRNkiRJkiRJWUwYSZIkSZIkKYsJI0mSJEmSJGUxYSRJkiRJkqQsAzphFEK4IoQQOyyrMvaH1DErQgj1IYT7Qwhz8xmzJEmSJElSoSuEWdJeBE7OeLwtY/0S4LPAfOBV4P8B94QQ9o4xbspZhJIkSZJ6JYTAjBkz0uuSpIGhEBJGLTHGVR03huSvycXAf8QY70ht+xCwGng/8L2cRilJkiSpx0pLS5k/f36+w5AkdTCgh6SlzE4NOVsUQvh5CGGP1PaZwCTg7rYDY4yNwAPAUXmIU5IkSZIkaVAY6D2MHgM+SDLcbCLJkLOHU3WKJqWOWd3hnNXAjB1dNIQwDBiWsamyT6KVJEmSJEkaBAZ0wijGeGfGw+dDCI8ArwMfAh5tO6zDaaGLbR1dClzeJ0FKkiRJ6rWmpiauvfZaAD796U9TVlaW54gkSVAYQ9LSYoxbgOeB2UBbXaNJHQ6bQOdeRx1dCVRnLNP6MExJkiRJPbB161a2bt2a7zAkSRkKKmGUGko2B1gJLCJJGp2Ssb8MOA54eEfXiTE2xhjr2hbAGdUkSZIkSZJSBvSQtBDC1cDvgaUkPYf+H1AF3BRjjCGEa4DLQggLgYXAZcBW4NY8hSxJkiRJklTwBnTCiGSo2M+AccBakrpF2Z4U/QAAEZhJREFUb4kxLkntvwooB64DRpMUyT41xmiPIUmSJEmSpF4a0AmjGON7d7I/AlekFkmSJEmSJPWBgqphJEmSJEmSpP43oHsYSZIkSRrcQghMmTIlvS5JGhhCMqpraAshVAG1tbW1VFVV5TscSZIkSZKkPlFXV0d1dTVAdWqm+G5xSJokSZIkSZKymDCSJEmSJElSFmsYSZIkScqb5uZmvvOd7wDw8Y9/nNLS0jxHJEkCE0aSJEmS8ijGSG1tbXpdkjQwOCRNkiRJkiRJWUwYSZIkSZIkKYsJI0mSJEmSJGUxYSRJkiRJkqQsJowkSZIkSZKUxVnSJEmSJOVNCIHx48en1yVJA0Nw6koIIVQBtbW1tVRVVeU7HEmSJEmSpD5RV1dHdXU1QHWMsa675zkkTZIkSZIkSVlMGEmSJEmSJCmLNYwkSZIk5U1zczM/+MEPALjwwgspLS3Nc0SSJDBhJEmSJCmPYoysXbs2vS5JGhgckiZJkiRJkqQsJowkSZIkSZKUxYSRJEmSJEmSspgwkiRJkiRJUhYTRpIkSZIkScriLGmSJEmS8iaEQHV1dXpdkjQwBKeuhBBCFVBbW1tLVVVVvsORJEmSJEnqE3V1dW2J+eoYY113z3NImiRJkiRJkrKYMJIkSZIkSVIWaxhJkiRJypvm5mZuvPFGAObPn09paWl+A5IkASaMJEmSJOVRjJEVK1ak1yVJA4ND0iRJkiRJkpTFhJEkSZIkSZKymDCSJEmSJElSFhNGkiRJkiRJymLCSJIkSZIkSVmcJU2SJElSXlVUVOQ7BElSB8GpKyGEUAXU1tbWUlVVle9wJEmSJEmS+kRdXR3V1dUA1THGuu6e55A0SZIkSZIkZTFhJEmSJEmSpCzWMJIkSZKUN83Nzfz0pz8F4Pzzz6e0tDTPEUmSwISRJEmSpDyKMbJkyZL0uiRpYHBImiRJkiRJkrKYMJIkSZIkSVIWE0aSJEmSJEnKYsJIkiRJkiRJWUwYSZIkSZIkKYuzpEmSJEnKq9LS0nyHIEnqIDh1JYQQqoDa2tpaqqqq8h2OJEmSJElSn6irq6O6uhqgOsZY193zHJImSZIkSZKkLCaMJEmSJEmSlMUaRpIkSZLypqWlhV/+8pcAvPvd76akxI8okjQQ+NtYkiRJUt60traycOHC9LokaWBwSJokSZIkSZKyDJqEUQjhX0IIi0IIDSGEJ0MIx+Q7JkmSJEmSpEI0KBJGIYT3ANcA/wEcBDwI3BlC2C2vgUmSJEmSJBWgQZEwAj4L/DDGeEOM8aUY48XAMuBjeY5LkiRJkiSp4BR8wiiEUAYcAtzdYdfdwFG5j0iSJEmSJKmwDYZZ0sYBxcDqDttXA5O6OiGEMAwYlrGpsn9CkyRJkiRJKjyDIWHUJnZ4HLrY1uZS4PL+DUeSJEnSzpSVlXH55b41l6SBpuCHpAHrgG107k00gc69jtpcCVRnLNP6LTpJkiRJkqQCU/AJoxhjE/AkcEqHXacAD2/nnMYYY13bAmzq5zAlSZIkSZIKxmAZkvYt4JYQwhPAI8A/A7sB381rVJIkSZIkSQVoUCSMYoy/CCGMBb4MTAZeAM6MMS7Jb2SSJEmSJEmFZ1AkjABijNcB1+U7DkmSJEmSpEJX8DWMJEmSJEmS1LdMGEmSJEmSJCmLCSNJkiRJkiRlMWEkSZIkSZKkLCaMJEmSJEmSlMWEkSRJkiRJkrKYMJIkSZIkSVIWE0aSJEmSJEnKYsJIkiRJkiRJWUwYSZIkSZIkKUtJvgMYSOrq6vIdgiRJkiRJUp/pba4jxBj7OJTCE0KYCizPdxySJEmSJEn9ZFqM8c3uHmzCCAghBGAKsCnfseyCSpKk1zQK+z6GGtut8Nhmhcl2K0y2W+GxzQqT7VaYbLfCY5sVpsHSbpXAitiDJJBD0oDUD6zbWbaBKMl5AbApxujYugJhuxUe26ww2W6FyXYrPLZZYbLdCpPtVnhss8I0iNqtx7Fb9FqSJEmSJElZTBhJkiRJkiQpiwmjwaMR+ErqXxUO263w2GaFyXYrTLZb4bHNCpPtVphst8JjmxWmIdtuFr2WJEmSJElSFnsYSZIkSZIkKYsJI0mSJEmSJGUxYSRJkiRJkqQsJowkSZIkSZKUxYTRABJCuDSE8HgIYVMIYU0I4TchhL07HDMshPA/IYR1IYQtIYTfhRCmdTjm2hDCkyGExhDCM9t5rv1DCA+EEOpDCG+GEL4cQgj9eX+DUa7aLIQwPIRwYwjh+RBCSwjhN/19b4NZDtvt+BDCb0MIK1PXeCaEcH5/399glcN22zuEcF8IYXUIoSGE8EYI4WshhNL+vsfBJpd/1zKO3TP1fDX9cU9DQQ5fa7uHEGIXy+n9fY+DTS5fayHx+RDCq6njloUQLuvP+xuscvhau2I7r7Ut/X2Pg1GOX2+nhRAeTT3X2hDC7SGEmf15f4NRjtvs3SF5z781hLAkhPCv/Xlv/c2E0cByHPAd4C3AKUAJcHcIYUTGMdcA7wDeC7wVGAn8IYRQnHFMAH4E/KKrJwkhVAH3ACuAw4BPAp8HPtuXNzNE5KTNgGKgHvg28Oe+vIEhKlftdhTwHPAu4IDUsTeHEM7qu1sZUnLVbs3AzcCpwN7AxcCFJNOpqmdy1WbJQUlS72fAg311A0NUTtsNOBmYnLH8pQ/uYajJZZtdC3yE5L3jPsBZwN/75jaGnFy129Vkv8YmAwuAX/XZnQwtufrMtgfwW5LfifOA04BxwB19eTNDRK7a7Azgp8B3gf2AfwE+G0L4RJ/eTS7FGF0G6AKMByJwbOpxNdAEvCfjmCnANuC0Ls6/Animi+0fA2qAYRnbvgi8CYR833chL/3VZh2OuRH4Tb7vdTAtuWi3jGP/CPwo3/c8GJYct9u3gAfzfc+FvvR3mwH/BdwCzAdq8n2/g2Xpx/cju6euOy/f9zjYln5sszkkSfW9832Pg3HJ1d814MDU8xyT73seDEs/vt7OTb3eijK2nQW0AqX5vu9CXvqxzW4FftVh28XAMgr0c7Y9jAa26tS/G1L/HgKUAne3HRBjXAG8QNKTobuOBB6IMTZmbLuL5EWxe2+DFdB/bab+lct2q854Hu2anLRbCGFP4HTggd5eQ2n91mYhhBOB84CP73qY6qC/X2u/Sw0ReCiEcO4uRao2/dVmZwFvAP8QQlgUQlgcQrghhDCmD2JW7t6PfAR4NcZob8y+0V/t9gRJwuKCEEJxCKEa+ABwd4yxeZejHtr6q82GAQ0dttUD04AZvYo0z0wYDVAhhEDyjfbfYowvpDZPAppijBs7HL46ta+7JqXO6XiNtn3qhX5uM/WTXLZb6oPQYcCPe3sNJXLRbiGEh0MIDcBCkiFOX96FkIe8/myzEMJYkt6X82OMdX0QrlL6+bW2mWQ4/LnAmcC9wC9CCP+4a1EPbf3cZnuQfOg5D/ggSW++Q4DbdiVm5e79SAhhGHA+8MPexqp2/dluMcbFJMPjvw40kowQmUYyZEq91M+vtbuAd4YQTgohFIUQ9iLpYQTJUNCCU5LvALRd/0tS8+St3Tg2kHSp64mOx4ftbFf39XebqX/kpN1CCMeTfKC9MMb4Ym+uoSy5aLf3AJUkXfe/QVKv46peXEeJ/myzHwC3xhj/2pvAtEP91m4xxnXAf2dseiKEMBq4BPhJT4JUlv58rRWRfIP+wRjjqwAhhH8Cngwh7B1jfKWnwSotV+8j30nyt+3mXp6vbP3WbiGEScANwE0k9fkqgX8HbgshnBJT453UY/39fmQW8AeSHkt1JHXfriDpLVZw7GE0AIUQ/gd4O3BCjHF5xq5VQFnqzVSmCXTuMbQjq+icKZ2Q+rcn11FKDtpM/SBX7RZCOA74PfDZGKNv0HZRrtotxrgsxrggxvgzkjpvV3QofKhuykGbnQh8PiSzSLaQfHNenXr84V2JfSjL09+2R4HZu3iNISsHbbYSaGlLFqW8lPp3t57Gq0SOX2sfAf4QY1zVy/OVkoN2+zhQF2O8JMb4dOpLkX8ETgKO2IXQh6z+brOY+AJJwewZJJ+52yYFWNzbuPPJhNEAEhL/S5L5PzHGuKjDIU+SFD47JeOcySQV2B/uwVM9AhwbQijL2HYqyaxpi3sR+pCVwzZTH8plu6V6Fv0R+GKM8fu7EvdQl+fXWyD5pijs7EC1y2GbHUkyg0z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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(14, 5), dpi=100)\n",
+ "plt.plot(dataset_ex_df['Date'], dataset_ex_df['GS'], label='Goldman Sachs stock')\n",
+ "plt.vlines(datetime.date(2016,4, 20), 0, 270, linestyles='--', colors='gray', label='Train/Test data cut-off')\n",
+ "plt.xlabel('Date')\n",
+ "plt.ylabel('USD')\n",
+ "plt.title('Figure 2: Goldman Sachs stock price')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of training days: 1585. Number of test days: 680.\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_training_days = int(dataset_ex_df.shape[0]*.7)\n",
+ "print('Number of training days: {}. Number of test days: {}.'.format(num_training_days, \\\n",
+ " dataset_ex_df.shape[0]-num_training_days))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.1. Correlated assets "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As explained earlier we will use other assets as features, not only GS.\n",
+ "\n",
+ " So what other assets would affect GS's stock movements? Good understanding of the company, its lines of businesses, competitive landscape, dependencies, suppliers and client type, etc is very important for picking the right set of correlated assets:\n",
+ "- First are the **companies** similar to GS. We will add JPMorgan Chace and Morgan Stanley, among others, to the dataset.\n",
+ "- As an investment bank, Goldman Sachs depends on the **global economy**. Bad or volatile economy means no M&As or IPOs, and possibly limited proprietary trading earnings. That is why we will include global economy indices. Also, we will include LIBOR (USD and GBP denominated) rate, as possibly shocks in the economy might be accounted for by analysts to set these rates, and other **FI** securities.\n",
+ "- Daily volatility index (**VIX**) - for the reason described in the previous section.\n",
+ "- **Composite indices** - such NASDAQ and NYSE (from USA), FTSE100 (UK), Nikkei225 (Japan), Hang Seng and BSE Sensex (APAC) indices.\n",
+ "- **Currencies** - global trade is many times reflected into how currencies move, ergo we'll use a basket of currencies (such as USDJPY, GBPUSD, etc) as features.\n",
+ "\n",
+ "#### Overall, we have 72 other assets in the dataset - daily price for every asset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.2. Tecnical indicators "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We already covered what are technical indicators and why we use them so let's jump straight to the code. We will create technical indicators only for GS."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_technical_indicators(dataset):\n",
+ " # Create 7 and 21 days Moving Average\n",
+ " dataset['ma7'] = dataset['price'].rolling(window=7).mean()\n",
+ " dataset['ma21'] = dataset['price'].rolling(window=21).mean()\n",
+ " \n",
+ " # Create MACD\n",
+ " dataset['26ema'] = pd.ewma(dataset['price'], span=26)\n",
+ " dataset['12ema'] = pd.ewma(dataset['price'], span=12)\n",
+ " dataset['MACD'] = (dataset['12ema']-dataset['26ema'])\n",
+ "\n",
+ " # Create Bollinger Bands\n",
+ " dataset['20sd'] = pd.stats.moments.rolling_std(dataset['price'],20)\n",
+ " dataset['upper_band'] = dataset['ma21'] + (dataset['20sd']*2)\n",
+ " dataset['lower_band'] = dataset['ma21'] - (dataset['20sd']*2)\n",
+ " \n",
+ " # Create Exponential moving average\n",
+ " dataset['ema'] = dataset['price'].ewm(com=0.5).mean()\n",
+ " \n",
+ " # Create Momentum\n",
+ " dataset['momentum'] = dataset['price']-1\n",
+ " \n",
+ " return dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_TI_df = get_technical_indicators(dataset_ex_df[['GS']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
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+ "1 2010-02-02 156.940002 152.777143 163.653809 160.014868 156.700048 \n",
+ "2 2010-02-03 157.229996 153.098572 162.899047 159.766235 156.783365 \n",
+ "3 2010-02-04 150.679993 153.069999 161.686666 158.967168 155.827031 \n",
+ "4 2010-02-05 154.160004 153.449999 160.729523 158.550196 155.566566 \n",
+ "\n",
+ " MACD 20sd upper_band lower_band ema momentum \\\n",
+ "0 -3.666767 9.607375 183.435226 145.005726 152.113609 152.130005 \n",
+ "1 -3.314821 9.480630 182.615070 144.692549 155.331205 155.940002 \n",
+ "2 -2.982871 9.053702 181.006450 144.791644 156.597065 156.229996 \n",
+ "3 -3.140137 8.940246 179.567157 143.806174 152.652350 149.679993 \n",
+ "4 -2.983631 8.151912 177.033348 144.425699 153.657453 153.160004 \n",
+ "\n",
+ " log_momentum \n",
+ "0 5.024735 \n",
+ "1 5.049471 \n",
+ "2 5.051329 \n",
+ "3 5.008500 \n",
+ "4 5.031483 "
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset_TI_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So we have the technical indicators (including MACD, Bollinger bands, etc) for every trading day. We have in total 12 technical indicators.\n",
+ "\n",
+ "Let's visualize the last 400 days of these indicators."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 452,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_technical_indicators(dataset, last_days):\n",
+ " plt.figure(figsize=(16, 10), dpi=100)\n",
+ " shape_0 = dataset.shape[0]\n",
+ " xmacd_ = shape_0-last_days\n",
+ " \n",
+ " dataset = dataset.iloc[-last_days:, :]\n",
+ " x_ = range(3, dataset.shape[0])\n",
+ " x_ =list(dataset.index)\n",
+ " \n",
+ " # Plot first subplot\n",
+ " plt.subplot(2, 1, 1)\n",
+ " plt.plot(dataset['ma7'],label='MA 7', color='g',linestyle='--')\n",
+ " plt.plot(dataset['price'],label='Closing Price', color='b')\n",
+ " plt.plot(dataset['ma21'],label='MA 21', color='r',linestyle='--')\n",
+ " plt.plot(dataset['upper_band'],label='Upper Band', color='c')\n",
+ " plt.plot(dataset['lower_band'],label='Lower Band', color='c')\n",
+ " plt.fill_between(x_, dataset['lower_band'], dataset['upper_band'], alpha=0.35)\n",
+ " plt.title('Technical indicators for Goldman Sachs - last {} days.'.format(last_days))\n",
+ " plt.ylabel('USD')\n",
+ " plt.legend()\n",
+ "\n",
+ " # Plot second subplot\n",
+ " plt.subplot(2, 1, 2)\n",
+ " plt.title('MACD')\n",
+ " plt.plot(dataset['MACD'],label='MACD', linestyle='-.')\n",
+ " plt.hlines(15, xmacd_, shape_0, colors='g', linestyles='--')\n",
+ " plt.hlines(-15, xmacd_, shape_0, colors='g', linestyles='--')\n",
+ " plt.plot(dataset['log_momentum'],label='Momentum', color='b',linestyle='-')\n",
+ "\n",
+ " plt.legend()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 453,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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O2GDuQmwQIrzUNYXKEw7V5Cugn4gcU9thsBlMFZNbOAGZ6q5RTVmsk4EBItI7Yvllzn6n1aGvuzpkTI4x5kPsBEpt2H2AtTqTsTOLt45Yfhm2JHZOA/YJMAEb2HhIROpa4r8n12cq9jwqss+cgMq5de5x3ZQHmUojll/XyMcpnyCoOuXlt+UZpXV9Lf6ADbDX9EVJgxljCowxX2Anz/FRfXl7edsVNbwH1vlw1PO9x8lAnW+MuRk7QdXAsNU1vV6rcyO2jDr8ca+z7n7nefkXBJ9gMx/PiNjH5djxcec7fSsGvsa+f8WGnVMXbFbopN30aarzM/K9qNJzERHs66g0YvnZVDOBjRNQfREbCH8Em3H7aU2dMMZkGGPGYjOL++1BdqpSSilVLc2cVEoppRruKeyEBT+JyHPYbBk3doyvEdixyxYYY74TkQnAX0WkAzZg5sf+Eb3DGPPaXuzzOOwf0G+JSB9suZ4HW0o6zxgzCVtS/ATwoYj8E1vaeDMQW/0um9xfsGP7fSMiz2MnebgfW4LZZjfbPoWdJf1/IvI3bEAuDhtomGCM+RkbZL4ReFdE3saWlN6LnWAk0mJglIicgw1CFDulkE9hAwbfiMiD2Bmyz8Bmc/3TGBOZuVSFiHzj9G8uNpPwEGy55WqqZj7VxUPY0uRpIvI4dizPy4GTgNtM5Zl668wYUyYiZ2Lv4zki8gb2PsrFTlR0FDaANTdssz25Po9iX0+TnfMow5b8emvZpiHnlSUis4D7RSQXG7A5EZuRVt29sCdWi8gX2MDVemxp+dHY8uUt2BJ5qONr0dhZmO8BXnbuo7HYbMruQE9jzB3Ug4iMw5Ymz8Rmw7bHZtHlYMfZbSp1Ol8n6HYFdqbz9dj33fOwgcjvwpouBk4WkdOxWe65xpjV1R3YGFPlvMLGWVxsjJkWtupTbEB4rBM0X48NtB8PnBuRTfkA9jp+JiJPO+f0GPb3/Hwt1wLgc+ykZs85Y98uwI4venFE342IfAlcKyJrsbOND8HeTzVlub7j9ONY7GRtlTI4RWQedhzaxdj3jj7ABdjy76DTZgy2dP8iY8yE3ZyLUkopVSMNTiqllFINZIzJE5GjseME3gB0wmbu/Ib9Azl8/LeLsOWWV2KzgIqwf0D+dS92uTywdAo24Hc+cDc2i24+TuaMMWaRiJyHzaj5BBso+zf2j+T/7s3+Ov35wgkGPorN2tuKzfppA9y+m213Or+jh7GBvrbYAMuvOCXIxpivROR64C5sWelm4DVsJlrkmHl/xmYivY0tX12JDf6ki8hR2DLsp4AE7ERGtwMv1PFUp2IDdtc526djA4B/3U3pZ03nvlhEjsXOOv4qdlKXZdiZ2mudEKkO+14mIv2xQcI/YjP2fNgy1AXAPdhAeHn7Bl8fY8w8ERmOLXn/j3OMd7D364t7ch7VOBcbMPonNuD1A3AyNvjamO519vsgtkTXhZ25/h3szMrZUL/XojHmFRHZDPwJOwu6YINmYxvQvx+wQdmLsAHnLOw1OM8Ys7MB+6uTepzvCuzr8z5s4LTEWRZ5b9+MvUcmYu/Pb7CB7j3tpxGRM7CB1L9hy/6XYwOTH0e0XSQiJ2JLoj/GfjH1PXDX7ob5cIY1OB17P/4Z+7fbj8Aoqo7regP2/58HscHc2dj3k2pL3I0x+SLyP2A0dnb2SFOddXdjg+dbsGORPhHWxoV9nWg1nlJKqT0iu5/QTymllFJKKaXUgUJEfNjJrb4yxlzW3P1RSin1+6aZk0oppZRSSin1O+BM/NQdm6Gdgs1IVkoppZqVpuArpZRSSiml1O/DaGxp+AnAtcaYyPJwpZRSaq/Tsm6llFJKKaWUUkoppVSz0MxJpZRSSimllFJKKaVUs9DgpFJKKaWUUkoppZRSqllocFIppZRSSimllFJKKdUsdLZuQEQEaA/kN3dflFJKKaWUUkoppZTaTyUAW009JrnR4KTVHtjc3J1QSimllFJKKaWUUmo/dxCwpa6NNThp5QNs2rSJxMTE5u6LUkoppZRSSimllFL7lby8PNLS0qCelckanAyTmJiowUmllFJKKaWUUkoppfYSnRBHKaWUUkoppZRSSinVLDQ4qZRSSimllFJKKaWUahYanFRKKaWUUkoppZRSSjULHXOyjkKhEGVlZc3dDbWP8nq9uN3u5u6GUkoppZRSSiml1H6lWYOTInIfMBroCRQDPwP3GmNWhrVJBZ4CTgESgJXAE8aYj8LapAAvAGc4iz4DbjHG7GyMfpaVlbF+/XpCoVBj7E4doJKTk0lNTUVEmrsrSimllFJKKaWUUvuF5s6cPB54GZjt9OVx4FsR6WWMKXTa/BtIwgYes4GLgA9F5HBjzHynzfvAQcAI5/kbznaj9rSDxhjS09Nxu92kpaXhcmklvKrMGENRURGZmZkAtGvXrpl7pJRSSimllFJKKbV/aNbgpDFmRPhzEbkSyAQGAT84i48CbjDGzHKePyYidwADgfkicig2KHmkMeZXZz/XAjNFpEd4FmZDBAIBioqKaN++PbGxsXuyK3UA8/l8AGRmZtKmTRst8VZKKaWUUkoppZSqg30tDTDJ+ZkTtuwn4HwRaSEiLhG5AIgGpjnrjwJyywOTAMaYX4Bc4OjqDiIi0SKSWP7AlotXKxgMAhAVFdWwM1K/G+XBa7/f38w9UUoppZRSSimllNo/7DPBSbED9f0T+MkYsyRs1fnYDM/tQCnwOnCWMWatsz4Vm20ZKdNZV537sMHL8sfmOvSvDmehfs/0HlFKKaWUUkoppZSqn30mOAm8BPQDLoxY/hiQApwMHI4NYE4Ukb5hbUw1+5MalgP8DZulWf44qOHdVkoppZRSSimllFJKNURzT4gDgIi8iJ3wZqgxZnPY8i7AzUAfY8xSZ/FCETkOuAm4HsgA2laz29bAtuqOZ4wpxWZhlh+nMU5DKaWUUkoppZRSSilVD82aOSnWS8Bo4ERjzPqIJuUz0IQilgfZ1feZQJKIDAnb7xHYjMifG7/X+4crrrgCEeH666+vsu7GG29ERLjiiiuqrPv5559xu92MGDGiyrrqiEi1j6eeempPT0EppZRSzWC730/Q1FR8opRSSimlVONq7szJl4GLgD8C+SJSPkZkrjGmGFgBrAFeF5G7seNOngmcAowEMMYsF5GvgX+JyHXO9m8AX+zpTN37u7S0NMaPH8+zzz5bMZt0SUkJH3zwAR07dqx2m7feeotbbrmFN998k40bN9bYrlx6enql51999RVXX301Z599duOchFJKKaX2SG4gwIzcXLL9frb7/RSHQkSJEO1y0cLr5eDoaNpGRfHtjh2MTU9nfkEBXWJi+FPHjlzeti0xbndzn0KzCRrDgoIC1hUX81tJCQCXpqbSVidKVEoppZRqNM0dnLzB+TktYvmVwDvGGL+I/AH4O/A5EI8NVl5ujPlfWPuLgReAb53nn2HLwX/XBg4cyLp165g0aRIXX3wxAJMmTSItLY1DDjmkSvvCwkImTJjA7NmzycjI4J133uHBBx+s9RipqZXnHPrvf//LCSecUO3+lVJKKbX3LC8s5KUtWxiXkUFhKLIIpXZrS0q4ftUqHt6wgee6duX8Nm2aqJf7ru9zcrhr7VoWFRZWWv7Ahg1c164df+rYkQ7R0c3UO6WUUkqpA0ezBieNMbsd7NEYsxqoNQ3PGJMDXNJY/aqLwrLCGte5XW5iPDF1ausSFz6vb7dt46LiGtBLuPLKK3n77bcrgpNvvfUWV111FdOmTavS9sMPP6RHjx706NGDSy65hFtuuYUHHnigzmNybtu2jS+//JJx48Y1qK9KKaWU2nNloRD3rlvHc5srhvEm1eWlrctLoriIEhcBY/BjyDNBskIBtocCdHB5OTk6kSO98fzqL+TTkh1klJVxwbJlfJ2Twwtdu5Lgae7vtRvmPxkZvLZ1K2e3bs1V7dqR5PGwsaSEz7dvZ0lhIZtKSthSVoZXhFZeL4XBID/k5gLgw0UndxStXB4yQn5WB0t5fssW3khP59FOnbj9oIPwuPalOSaVUkoppfYv++cnzH1A/N/ia1z3h25/4MuLvqx43ubpNhT5i6pte/zBxzPtimkVzzs934nsouwq7cxDDRv76dJLL+W+++5jw4YNiAgzZsxg/Pjx1QYnx44dyyWX2BjviBEjKCgoYPLkyZx88sl1Ota4ceNISEhg9OjRDeqrUkoppfbMltJSzlu6lJ/z8gAY4oljZHQyfT2+Wr9sNMZUWj8yOpnhUUlMKMnh49Ic3snI4MedO3m/Vy+GJCY2+Xk0poJAgFvXrGFHIMCMvDwe3LCBLjExLCys+ctjADdwWlQy58W0INFlS9uNMSwMFDG+JIcVwRL+tG4dH2Rm8maPHhyWkLAXzkYppZRS6sCjwckDXKtWrTj99NMZN24cxhhOP/10WrVqVaXdypUrmTVrFpMmTQLA4/Fw/vnn89Zbb9U5OPnWW29x8cUXExMTs/vGSimllGow40xYIyKEjGFWXh4fZWUxbts2sv1+4sTFbbFtGeKt+cvUcNUFLr0iXOxryQBvLM8WZrC2pIRj5s/n0U6duKdjR9x1rKxobmMzMtgRCNBSPMSKi03BMhYWFiJAT3cMfTw+Wru8tHR5CGLIDwUpxjDQE0t7d+WxJUWEAd44+ntimVyWx9sl2cwrKGDw3LncnZbGQ5064fsdj9GplFJKKdUQGpxsoIL7Cmpc53ZV/lCaeXdmjW1dUrkMaMNtG/aoX9W56qqruPlmOwTnyy+/XG2bsWPHEggE6NChQ8UyYwxer5cdO3aQkpJS6zF+/PFHVq5cyYcffth4HVdKKaV+xwKhEJN37uS7nBxWFxeztriYrWVlFIdClDhjSHpEcAFlYbNrd3JFcW9cO9q5G2fSlt4eH88ldOTV4kxm+Au4f/16vt+xg8/79iV2Hw/E+UMh/rlpEwDnxbTg1KhElgSKyTEB+ntiSXY17KOwiHBydBKDvHH8qziLn/0F/GPTJj7KyuI/hx7KkUlJjXkaSimllFIHNA1ONlB9xoBsqrZ1NWLECMrKygAYPnx4lfWBQIB3332XZ555hlNPPbXSurPPPpv33nuvIrhZk7FjxzJo0CD69+/feB1XSimlfofSS0v528aNjM/MJMvvr7VtwAlKxiAM9sZxjDeBQd44vI2c1RjvcnN3bCoDy/L4V3EWU3bu5IZVq3inZ886j03dHCZmZbGxtJQkcTMsKgERoa83ttH2n+LycE9cO2b5C3i9KIu1JSWMWrKExYcfTqpOlqOUUkopVScanPwdcLvdLF++vOLfkb744gt27NjB1VdfTVLEN/3nnHMOY8eOrTU4mZeXx8SJE3nmmWcat+NKKaXUASZoDD/l5lIYDHJCcnKVEuAvsrO5cuVKsp2gZKK4OdIbRyd3NO1cXlq5PESLiygEAYKA3xhSXG6ipGknZRERTopOoo3Ly0OFW3h32zaOSUpiTPv2ddp+TVERmX4/RyUm7pWApjGGp5ysydOjk4luwuszxBtPn0Qffy7Ywnp/KVevXMkXffvu04FbpZRSSql9hQYnfycSaxm8fuzYsZx88slVApNgMyefeOIJ5s2bx8CBA6vdfvz48RhjuPDCCxutv0oppdSBYltZGfPy8/kmJ4cJWVmkO9UMcS4Xp7dsSb/4eIwxrCkuZty2bQB0dkdzSUxL+nti8exjAa6+3lguiWnJuyXbuWX1agbGx3N4DZ8zcvx+Xt+6lYlZWcwvsEPi3NS+Pc917drkM1x/k5PDgoICohFGRDV9mXWsuLkjti135W/ifzk5vL51K9eHDZejlFJKKaWqJ8Y0bBboA4mIJAK5ubm5VYJ4JSUlrF+/ns6dO+tEL6pWeq8opZQK90V2NjeuXs2m0tJKy+PFhQ8XWSZQ7XajopO5LKYl3ibOhNwTxhj+VpjOrEAhMS4Xf2jRgvPatGFky5bEOdmgM3NzOX/ZsorzdwHGeZzWogVv9+zJhpISFhYUkOLxcHJKCileb6P0b1ZeHqcuXEhuMMjIqGSuiW3dKPuti89KdvBWSTaxLhfzDz+c7rGNV0aulFJKKbUvy8vLK098SzLG5NV1O82cVEoppZRqZOuKi7lo+XLyg0EE6OCKopsBnXkSAAAgAElEQVQ7mqOj4hngicMDrA2W8qu/gJ0mCIAb4ShvPP0bcUzEpiIi3BrblkcLt7IqWMKk7GwmZWfjc7kY2bIlXXw+nt60iYAxtHd5OSs6hSO88SwLFPPPogy+yskh9eefK+3TDRyVlMRVqalc2rZtRWZlYTDI9J07mZWXx+z8fLL9frr4fHTz+egeG2t/+nwkO4HNX3JzGb5oEXnBIIe6Y7jY13KvXpuR0cnMCRSyKFDMvevW8UmfPnv1+EoppZRS+xvNnEQzJ1Xj0HtFKaUU2Fm2hy5YwMy8PA51x/BgfAd8+3AW5J4wxrA+WMYMfz4/+QvYFqo8gc+x3nhuim1b6fzXBEp4onArOSZIkrhpVxZDrsdPupRVtOnh83FHWhqz8vKYkJVFQTC42754RPC5XJSEQviNobfbx1/i2zfLtd8ULOOW/N8QYO0RR9DZ59vrfVBKKaWU2ts0c1IppZRSah/w2G+/MTMvj1hc3BGXesAGJsFmUB7iieYQjx0jc12wlJ/8BSwJFHFiVCIjopKqTArT1RPDvxI7k2+C5G2I5s4L2tKmXZDXJ21mhj+fT0p3sLK4mOtXrarYprV46O3x0c0TQ4q4yQj5SQ/52Rr0szVUxg4TJGAM+U4Qs5/Hx/1x7Ylppmuf5o5igCeWBYEiXtqyhWe6dm2WfiillFJK7Q80OKmUUkop1Ui+y8nhr7/9BsD1sW1o42qcMRT3ByJCF08MXTy7rx5wi5AsHsZ/EE/AL2zd6CG0JYbRHb2MiE7is9Kd/FJWQBdPDCdGJdLLHVPrzNfFJkShCVJmDAZo7/I2+0zZI6OTWRAoYmx6Og936kSCRz92K6WUUkpV58D9Kl8ppZRSqomUlECfPnD00RAK2WXf5+RwxpIlhIATvAkM9ify0zc+/P5ad/W7VZAnTP1i1/iayxZEA3bW6wtiWvJc4sHcEtuW3h7fbgONPnHRyuWlvTuKDu6oZg9MAgz0xNLe5SU3GGRcRkZzd0cppZRSap+lwUmllFJKqXqaMQOWLoWZM2HWLPg2J4dRS5ZQEgox2BPHjbFtGPdcIk//Xwsmf7rvT3DTHL7/NI7Skl0fRVcsiGrG3jQ+lwgjo5MBeGHLFkI6zrtSSimlVLU0OKmUUkopVU9Tpuz694tTcvljWGDynrhUPLiY85Mtb97y2++ntLuugkH4cnwcAEOGFQOw/AALTgKcEJVILC5WFxfz5fbtzd0dpZRSSql9kgYnlVJKKaXqafJk5x+tS/iwtw1MHu6J5Z64dnjFRcYmN9kZdozBndn7z8et9Ss9XHlKKv/7MK5JjzP7hxiy0j0kJAcZ8387Adi83kvejv3nWtWFT1ycGp0EwEMbNmj2pFJKKaVUNQ6sT4BKKaWUUk0sNxdmzwaig/DYEoJJftoHorkrrh1eZ6zDRbOjK9rv2O5upp7W35TP49iR7ebjtxIqxtJsCl9+EA/AqaOLaNU2xEGd7cCcKxYeeNmTo6NTiEGYX1DApKys5u6OUkoppdQ+R4OTv3Miwqefftoo+3r44YcZMGBAo+yrKbzzzjskJyc3dzeUUkrt5374AUIhQ/wjK6F7Aez0Muirrvhk18eqRbN2BSd3bt9/Pm4tnm2Dg9sz3U0WKMzNcbHYCd6OOLcQgEMHlAGw/AAMTia63PwxOgWABzZsINCUUV+llFJKqf3Q/vNpWdVbRkYGt9xyC4cccgjR0dGkpaUxatQoJlfUojWuu+++u8n2He7hhx9GRBAR3G43aWlpXHPNNWTtJhvh/PPPZ9WqVU3eP6WUUge2KVOAnvkUHJGJKwQ81JslXyZVrA+FYMns8ODk/pE5mZ8r/LZ61/iYP33ja5LjLJlrA5Adu/ppnRoEoGd5cHL+gRecBPhjTDIJ4mJFURH/2batubujlFJKKbVP0eDkAWrDhg0MGjSIKVOm8OSTT7J48WK+/vprTjjhBG666aYmOWZ8fDwtW7Zskn1H6t27N+np6WzcuJFXX32Vzz//nMsuu6zG9n6/H5/PR5s2bfZK/5RSSh24Jk8GRm0F4EhJxLUkifUro9i2xQYhN67xkLvDjcdjxxcsyHPhL2uu3tbd0nnRGCMV/Z452Ucw2PjHKc+a7Ht4acWyQ/vbf69ZFkVZabWb7ddixc3Z0S0AO/ZkYVNcWKWUUkqp/ZQGJw9QN954IyLCrFmzOOecc+jevTu9e/fmzjvv5Jdffqlxu8WLF3PiiSfi8/lo2bIlY8aMoaCgoGL9tGnTGDJkCHFxcSQnJ3PMMcfw22+/AVXLuq+44grOPPNMnn76adq1a0fLli256aab8Pv9FW3S09M5/fTT8fl8dO7cmffff59OnTrx3HPP1Xp+Ho+H1NRUOnTowMiRI7n11lv59ttvKS4uZsOGDYgIEyZMYNiwYcTExPCf//yn2rLuzz77jMMPP5yYmBhatWrF6NGjK9aVlZVxzz330KFDB+Li4jjiiCOYNm1ana6/UkqpA8fWrfDxx+D3Q2YmLF4XgBMyARiVkEjvQTby+MsUm2lYPt5kn8GlFYG+nTn7fvbkkjm238NGFhGfGGJHtptlTZDJWH6cvoN3RSHbdQyS1CJIwC+sWXZgZk+eFp1ES/GwsbSUofPns6X0AIzCKqWUUko1gAYn68kYKCxsnkddJ3jMycnh66+/5qabbiIurupsmzWNu1hUVMSIESNISUlh9uzZTJw4ke+//56bb74ZgEAgwJlnnsnxxx/PokWLmDlzJmPGjEGcwf+rM3XqVNauXcvUqVMZN24c77zzDu+8807F+ssuu4ytW7cybdo0Pv74Y9544w0yMzPrdqJhfD4foVCIQCBQsezee+/l1ltvZfny5QwfPrzKNl9++SWjR4/m9NNPZ/78+UyePJnDDz+8Yv2VV17JjBkzGD9+PIsWLeLcc89lxIgRrF69ut79U0optf+69FI45xw45RSYMAE4ZRv4QnR0RdHTHcMRJxQD8NXEOLZnuljsjDfZ/4hSklvZDLnvlk6hJFDUXKdQJ0vm2KDggKNKK85pxreNW9qdk+Vi83ovIobeg3YF50R2jTu5YsGBGZyMFhf3xKWSJG7mFRQwZO5c5ubnN3e3lFJKKaWanae5O7C/KSqC+PjmOXZBAVQTa6xizZo1GGPo2bNnvfb/3nvvUVxczLvvvlsR1HzppZcYNWoU//jHP/B6veTm5jJy5Ei6dOkCwKGHHlrrPlNSUnjppZdwu9307NmT008/ncmTJ3PttdeyYsUKvv/+e2bPnl0RFHzzzTfp1q1bvfq9YsUKXn31VYYMGUJCQgLbt28H4Pbbb6+UCRnp8ccf54ILLuCRRx6pWNa/f38A1q5dywcffMDmzZtp3749YMfU/Prrr3n77bd54okn6tVHpZRS+6etW2HqVPvv6dNh+nQDb9qS7lOjkxARhp5WzCfvJJCxycN9V7YmP9d+97up1WsktriN7AwPE+Z+wM88xBPHTiLRKe/dl4SPN9lnUClx8SEm/zeOmd/7uPaeXNyN9ImxPGuyU3c/CUmVv3U9dEAZv0zxsWx+NKOvLKiybVmwhKXbf6U0UMSgtifidUdXabOv6+Hx8WR8Go8VbmVTWRnDFixg3qBBdIuNbe6uKaWUUko1G82cPAAZJ8WytozG6ixfvpz+/ftXyrY85phjCIVCrFy5khYtWnDFFVcwfPhwRo0axfPPP096enqt++zduzdu965Stnbt2lVkRq5cuRKPx8PAgQMr1nft2pWUlJTd9nXx4sXEx8fj8/no1asXaWlpvPfee5XahGdBVmfBggWcdNJJ1a6bN28exhi6d+9OfHx8xWP69OmsXbt2t/1TSil1YPj4Y1u50KsX9OgB9MqDLoV4QsIwbwIAickhnng7i3ZpATK3eigudEFMDlP9DxCTlAdAbGk3Nhes4asN7zZpf4PBuldahCsfb/Kgzn6SW4boO7iUhOQguTvcLJnbeEHAxeUl3YdXHYSzPJNy8RwPAWcEmJJAEU/NvoFHZ17CJV/14ZGZF/P32ddy/ffH8MXasZQGihutb3tLW7eXvyccxKHuGAqCQS5evhy/zuCtlFJKqd8xDU7WU2yszWBsjkddv1Tv1q0bIsLy5cvrdW7GmBoDmuXL3377bWbOnMnRRx/Nhx9+SPfu3Wsdw9Lr9VZ6LiKEnA/gpoa/nmpaHq5Hjx4sWLCAZcuWUVxczJQpU+jatWulNtWVtIfz+WouVQuFQrjdbubOncuCBQsqHsuXL+f555/fbf+UUkodGCZMsD+vvRZmzYIut9usyWO88cS7dn351rZ9kCfeyqJj1xK74JDJnNXtWjqk2v+L+vvOBeD738YTMk0TiNq01sOlw9px9uHtuei4dlx/RlsWzapbiXR5RmMfZ5IajxeOGGbPZe6PMY3WxyVh43FGWu/7N8RmUVrsYeUi2+/iQAEztn7OvMxplPnL8P3wT7wffsX2vFzeXPIQby19pMp+9gdx4ubOuFTixcXs/Hwe2rChubuklFJKKdVsNDhZTyK2tLo5HnVNhGzRogXDhw/n5ZdfprCwsMr6nTt3Vrtdr169WLBgQaVtZsyYgcvlonv37hXLDjvsMO677z5+/vln+vTpw/vvv1+/i+jo2bMngUCA+fPnVyxbs2ZNjf0LFxUVRdeuXencuTPR0Q3L6OjXrx+TJ0+udt1hhx1GMBgkMzOTrl27VnqkpqY26HhKKaX2L1u2wE8/2X+fcw5sdBXwW3eb/f8HX1KV9kUxawhcfjSMuJU2Zz7P+T3vILmlHXMysexQ4rxJZBVvZmHWD03S35+/91FU4CIUEooKXGRs8vDey4l12rYiaBiW0VgeqCwPFNZHYX7VDy3Z21ykb/Lgchl6D6wcnFyfu4x/LfkzHPIdAAt+sf2JccdyTd9HubL73+g7JZ3iKXfgXz6C4YH3OP6gs7i6z8P17tu+orXLy42+NgD8feNGptfh849SSiml1IFIg5MHqFdeeYVgMMiQIUP4+OOPWb16NcuXL+eFF17gqKOOqnabiy++mJiYGC6//HKWLFnC1KlTueWWW7j00ktp27Yt69ev57777mPmzJn89ttvfPvtt6xatWq3407WpGfPnpx88smMGTOGWbNmMX/+fMaMGYPP56t3SXpDPPTQQ3zwwQc89NBDLF++nMWLF/Pkk08C0L17dy6++GIuu+wyJk2axPr165k9ezb/+Mc/+N///tfkfVNKKdX8Pv7Y/jzmGGjfwXDD6tUEjOEIbxw9PJWz7+dsm8yfpo9ka3A+LYdN5P5THyLa7SO5pc2SzN8RzbCD7DjI32yoPAxJYykPIl58cy5P/ycTt8ewclE061Z6a90ub6eLDeXjTR6+K2jYs78NVK5d7qWsHhNLz5oew8VD2/PW05UDuIudAGiXQ/3EJeyqkijy5/PU7OsoC5Vy8GGbAFgw02Zr+rzxDE25hp//dieLf2pTsY0v42TuGPQiUW7bzhhDVvHWundyH3F0VAInRSVigMuXL6c4GGzuLimllFJK7XUanDxAde7cmXnz5nHCCSdw11130adPH0455RQmT57Mq6++Wu02sbGxfPPNN+Tk5DB48GDOOeccTjrpJF566aWK9StWrODss8+me/fujBkzhptvvpnrrruuwf189913adu2LUOHDuWss87i2muvJSEhgZiYxishq8mwYcOYOHEin332GQMGDODEE0/k119/rVj/9ttvc9lll3HXXXfRo0cPzjjjDH799VfS0tKavG9KKaWaX3lJ93nnwbiMDH7KzSUG4Rpf60rt0gs38Ldfr6YokMehLQbzzPFf0SmpF0BF5uSO7S5O6XQRALMzvmNnSVaj9jUUglVLbHDysCNL6drbz1En2vEYv55Y+zAn5UHNgzr7SW5hg6k7S7PJjPqB+JQSAgFh7fK6Z09O+cyOQ/PZe/HMmrbr//OFv9h/h5d0G2N4ZeG9bC1cTytfe+44748ArFnmJW+n/Zj6wsPJrFwcRXxiiBHnFFTqc/k+xi17nHumjyK/bEed+7mvuMbXmpbi4bfSUp7dvLm5u6OUUkoptddJXcb3O9CJSCKQm5ubS2Ji5fKnkpIS1q9fT+fOnfdKwOz3bvPmzaSlpfH999/XOFnNvkrvFaWUOnBs3gxpaXZIlYUbyjhh4yy2BwJcEdOKM2OqTty2Mmcuv6R/zUWH/gmva1fgbOm8KP58dWvapQV49bNt3PvDGYBwQ/+/VQQwG6W/6z3cPLotUTEh3v8hHY8XlsyJ4i/XtiY6JsRb32ZUylYM9/4rCUz4VyInnlHIrY/Y0uLpmz/h2bm3wPhJsOIsRoxZxPU3tNxtP/xlcOmwdpQU28BiYnKQ5yZk8t0ncXzwqv2M9ejrWfQbYrMyF2b9xEM/X4BbPDx+7Mf0bDGIW89tw8Y1Xu7+ew6t2gb5vytb43Ibnvv3ZlK3Luf1u10s8/bjhRl+vF47i/ed00awuWANx3X4I3cd/nJjXNK9anpZHs8WbSPe7WbVkCG0a+CQNUoppZRSzSkvL4+kpCSAJGNMXl238zRdl5TavSlTplBQUEDfvn1JT0/nnnvuoVOnTgwdOrS5u6aUUup37KOP7M9jj4WXi9ezPRCgkyuKkdHJ1bbv0WIQPVoMqrI8xcmc3Jljg3UPH/0BPk/tmYwNsXKxDYh2PdSPx6ni7j2ojLRD/Gxa52Xal7GcfkHVcagBVi+12x7Sa9fM14lRKaQldCfj4Hn4V5zF1z+soWjIQ9zQ7+/4vPE19mPpvGhKil2ktAqS1CLIhlVR3HFBG3Jz7ORBo6/Ip+/gXeNafrTqRQCGd7qEns71G3BkCRvXeJk/M5rMdDfnMJFHkp6mxxULcZeVci6AH3L/kEZhvz6svupmbhv4HPf++Ed+3PJfjmg3gmM7jGrQdWwuQ70JfOnOZVWwhL+sX8/Ynj2bu0tKKaWUUnuNlnWrZuX3+7n//vvp3bs3Z511Fq1bt2batGlVZvlWSiml9qYfnDlrThxdxtsZGQBcG9sGT9iYyFnFW9lasK7W/ZSPOVlc6KKkWCoFJhuzeqW8zLl7v12BPxE47TwbkPxqQhzVHc4YWO2Ug39afCVb8tcCcFibYbx44hTuPftK23DT0fyw6VMe+/VySgJFNfZjzg+2cuDw40q464kdREUbcnPcuFyG6+7byWW35VWa4O/y3n/m6PYjOavrDRXLDjvKln3/+I2PxbNi8LqD9MqZhbuslLLEJDbGdAEgKXsT7ad8hQQCdEsZwDndbubaOXDtFTdxyNP3E5OZXp9L2KxEhKt9rQB4OyODefn5zdofYwxz8vL4cvt2xm/bxr8zMpi+cydbSksJadWVUkoppRqZZk6qZjV8+HCGDx/e3N1QSilVB9llZczOz6c0FKLUGAbEx9MjNra5u9Uk5s61PzcO2EqZMXR3x9A7YhKcNxb9mQWZP3DjgH9wQto51e7HF2eIiglRVuJi53YXqQfZTMqSQBHPzbuVQW1P4pSDL9zj/q5ygpM9w4KTAMNOL+Ld5xPZvN7LnB9jGDy0pNL6jE1uCvJc4CkhO+EbvlzfijH9HqtY369fNB6PIVCYSkx+H5bKr4xd8jA3DXiySh+Mgdk/2uDkoONKSOsS4NZHd/DpuHjOG5PPkONLqmzTNbkf9wx+reJ5dHYmF8z8F1nuvrxSYgOWBWefxvyeT7F94BEUduzMhDcT+eIVw+WDZzLm2Bnkdbfl8ef2uI3c/P/QPXs7vPcuvT/8gM2nncnyG/9ESWr7hlzWvaqHx8dx3nh+9Bdw3apVzDjsMKJcez+PYF5+PresXs3PedVXYiW63ZyUksJpLVpwRqtWtI2q/2zuSimllFLhNHNSKaWUUrv1eXY23WfN4g+LF3PW0qVcsGwZfWbP5sXNmxs1A3BfkJUFGzcCUSG+9GwBYFREOfeG3GXMzviOkAnSNalfjfsSgRQne3LndnfF8mmbPuaX9K95feH9LMmeuUf9LS4UNq613zd371s5OBkbbzh1tM10fOmRZHKyKn/0W77YSWNMnU//dkdyVZ+HKq2PioZDDvUDcGb0O3RPOYwLe95VbT+2bPCwbbMHj9fQ/wib/XjsqcU8/V5WlcBkdfdM2ucfcfKoo+n5n1d5yPVXoiglKibEWdcUsfGsCyk8+BAQoUe/MvJIYvzmk1l72fUEfTZo7HVFEbz1Rc4+D6YfDK6An46fT+TEc08i7bOJVJs6uo+53NeKBHExJz+fe9au3avHzgsEuH7lSg6fO5ef8/KIRujqjqaPx0c/j49UlxcXkBcM8kl2NmNWraLLL7/wbU7OXu2nUkoppQ48GpxUSimlVI38oRD3rF3LGUuWsCMQoI3LQ093DJ3d0QSM4dY1a7hixQqKg8Hm7mqjKc+aTL0gk8yAn5bi4aiIcRanbf4YgCGpp5KW2L3W/VXMgL1918eu4Z0u4dgOZxAwfp6ec2OtpdK7s3qpl1BIaN0uQIvWoSrrL745l07d/OTmuHnmvhYEA7vWffHjUgCi0hZx+8Dn8biqDqtSno2Zu7YH/zjuM1rEtAVsgHFFzhxCJsSm/NV8+PkqANr2Wsvk9DfZXlxzWfVLC+7mtYX3kVW8FQkE6P3Mowx88HY8JSXk9D2MT899Bj9ezrq8oMo5de9ThstlyEr3VAm2tj1kKOkn/YFRY5J55Z/3k9N3IN6CfAY+dAe9nnuMfV0rl5dbY+31fX7LFiZlNe6s7jX5cedO+s+Zw+vp6RjsGJgvJx7M0wkdeSz+IB6NP4jXEjsxIakrT8WncWFMCzq5oigMhRi5eDETMjP3Sj+VUkopdWDSsm6llFJKVTEnL4/3MjMZn5lJRpkNTo2KTuaymFZ4RTDG8HnpTt4pyebdbdv4MTeX+zp25PLU1GYpRW1Mc+YAGEpHbQbgtOikSmNNBkMBpm/6BIBhaWfvdn/JrWzgdkf2rsxJEeGWw55h9Y6FbCv6jf+tf5vR3W5qUH/Lx5vsEZE1WS46Bv70VA53XdSapXOj+eC1RC65OY/F2T+zbmkHAIYf24OUmDbVbt+jfym8F8/KRVFI2HWYsmkiL86/kxh3HCXBQpg6BYAt7Z9n7JKXOSihGy197arsb9Lql5m88UMARqSO5ohH/0Tbn6cDsGLM7ay87k7aiIs3LsmkVWrVoLcvztCxS4ANq72sXBTFUSdVzsq8pu8jxHoS8Hnj+em4MXQd9xo9X3uG7QOPrPU67isGe+M5MzqFT0t3cNWKFQyIj+cQn2/3GzbA1tJS/rlpE89u3kwIaOvycEtsW/p4qh+uwSNCN08M3TwxjI5uwfNFGfzkL+CCZcvY7vdzQ4cOTdJPpZRSSh3Y9u+/HpRSSinVqPICAS5ctozB8+bx3ObNZJSVkShu7olN5Wpfa7xOcEpEOCMmhUfiOpAsbtaXlDBm1Sq6/vorb6en79eTZsydC/TLZUerAqIQTo1KqrR+UfZP7CjNJCEqhYFtT9jt/iJn7C4X7fZxQY87APhk9asU+Rs2CUr5TN09+lcfnATocHCAmx/cCcBHYxP4dVYez866C9IHAnDaMTXPDt3DyZzcsMpLcdGu4OT24nRcuCgJFhJVlgobjwNg8FA/l/a6jwFthgJQFixha8E6QibEpNUv8+6yvwFwUfc7OPfJN2j783QCMT5mPfkaK2+4G1wuRKB1u2ClyXOq69PKRVFsWuvh2b+k8MX7drKhlr52FTOKG4+H1VffzNSJ37Nt6MkV27f+5Qda//JDjefc3C6JaUlPdwy5wSDnLV1KaahqRuyemJOXxwVLl3LwL7/wjBOYPCkqkWcTOtYYmIzkFeGO2FRGRCVhgBtXr+avGzYccMM8KKWUUqrpaeakUkoppQAbsDh/2TLWlZTgAo72xjM0KoHDPHEVQclIfb2xvJbYiW9Lc/mkdAebSku5auVKXt+6lae6dCHK5WJtcTFbS0spM4bSUIgyYyhzfrb1ejkiMZHBCQkke6uWFDeH2fNCcJ8d729YVAKJLnel9VM3fQTAcR3+iNe1+8lAksrHnMx2V1k3NO0sPlr9IlsK1vL5urGc3+P2evXVmF2T4dSUOVnu2OHFzJ1RyNTP45j4Yida/mE4OcEY4hKCtOtYc1l+q7YhWqUGyM7wsHqJl35D7HHO63Ebw9LOptCfR/rs/jwZ8tChk58/j3ig0vYrd8zjgRnn4fPEUxwoAODCnndxXrfbKGr/GEFvFL+8+C7bDz+qzufdvV8Z33wcx+T/xvH5+/EEA8L0L2OJ9hlOOavIuTaG2du+Jy2+K+06danYNnnpAo647UoQ+OWFd8keckydj7u3eES4Ky6VO/M3MreggD+tXcsL3brt8X7TS0v5v3XreHfbtoplvdwxnBWTwuCIoQvqwi3Cdb7WJImbD0tzeHDDBrL9fp7t2hVXTZFlpZRSSqkIGpxUSimlFNN37uSUhQvxG0Nr8XBXXCo9PXUrJY0RF2fEpDAiOon/leYyvmQ7v+bnM3TBgnr14fK2bXm9Rw+im7EsfNs22HLsJuiZTxwuLohpWWm9P1TGvG1TAWqcoTtSeebkju1Vz8stbi7ocSfPzL2JqZsmck73W3BL1SBmTTI2u8nb6cYbZejc07/b9pfeksfMyT7WLPHRK+oFALr18e/KUAyF8ObnIsEQxu0iFBVD0OejW28/2Rke1q2IqghOArSJPQiA6YvtvdJ7UNUA6fbidKJc0RWByYt6/onzetwGwNK7HmTDOZfYyW7qoXwczPxce007dvWzcY2X155IJjUtQN/Dy3h76V/5bO0bnHLwRZVmF8/t3ovMI4fS7ofvOOK2y53A6NH1Ov7e0Nrl5bbYVB4r3MqLW7ZwfHIyZ7du3aB9GWN4detW7l23jgJnfNjjvQn8MTqFQzzRe9RPEeFCX0sSXG7eLM7ihS12EqnnGyGYqpRSSqnfBw1OKqWUUr9zRcEgV61Ygd8YBnliuSM2lXhX3QNk5aLExZkxKQyNSsI1pdoAACAASURBVODd4mxm+AtIFDepLi+tXB68IngRPCJ4EDxARsjPqmAp20J+xm3bxsbSUj7p04ckT/N8RPl4XgFcvgGAa2Jb08JVuR9eVxSvnvwTc7b9P3vnGV5FubXhe3ZP740QCCEk9NCbVEFQQERBBUVFsCCCFQVFBVTsyOexoHgogiiogCCiFOmi9FATkgBJSO89u8/3Y1JJgAAJoOe9r2susmfeeWfN7B0y+5lnrfUHoe4RdZrTvcw5mZ9d+zW9JfBOCs25DAgafUXCJEBinOI2DQqxcDHjqS4nC8+jB9Hl5eKadoJVrbxIOVTMfw8/BnShRRszvn9up8ObL6HPyUJltVbbP6NnP4b4zuAvbic+pvaDRB9V3JvlomFV+geNonfgCM4XxuJ18hiD/7OBw2/nYPbwBLhiYRKgUVMrET2MFOSqePiZAjr0NDFvhgd7NjvywTQv3v8mg+7+g1l/ZiG7ktYyvs1rOGldAZC1Og5++CVdX3wc/z3b6Pn0Qxx7ZS6JI8dccRwNTRetE/foPVhTpf5k8yusP1lis/FkTAzflrklw9QGHnPwIUxjqNdYh+vdcZZU/F9JOp8mJzPe35+OLi71egyBQCAQCAT/TkTNyX8p48ePR5IkJk2aVGPb5MmTkSSJ8ePH19i2d+9e1Go1t99++2WPYbFYmD59Ou3atcPJyYlGjRrx8MMPk5KSUm3c3Llz6dWrF46Ojri7u1/1OQkEAoGgYXjj3DnOGo14lTkmr0aYrIqnSsNzTv784NacRW7NmOvSmOed/Jni6MeTjr5MdPDhEQdvHnTw5kWnAL5yDWaOUyAOqNiel0ffI0fYX1BwXWrXyTK8/5WZz3fkszE7m7nWaNDJeJ1xp7+2dmHFRefBgKDR1ZrDXIpycbI25ySASlIxNGR8RZ3EKyHpnCKeNm5mrbHN7dQxuj37KEOGdKH7C4/R8c2XGLDwG0Yc+phJfEUblE7dLdpakGw2HDLSagiTAL5/7aSFu9J5uzZx0mKGM1Fl4mSEqcZ2bV4uIb+sYcxr7/HQ09Pw27uD8IXzr/hcqyJJMGdBNvNXZtKxlwlJgqlzcmnR1kxhvorvvnCltVd3mriEY7KVsuP86mr723V6Dny0kNT+Q1CbTXScM42It6ejMteM/0bzgMGLVmoDBVdRf/JsaSm9Dh/m2/R0VMB4gzfvOTeud2GynP46V/ponZGBZ+PiRP1JgUAgEAgEdeKGipOSJL0iSdIBSZIKJUnKkCTpZ0mSwmsZ11OSpG2SJBVLkpQnSdIOSZIcqmz3kCRpuSRJ+WXLckmS/udVsKCgIFauXElpaWnFOqPRyPfff0+TJk1q3Wfx4sVMnTqVPXv2kJiYeMn5S0pKOHz4MK+//jqHDx9mzZo1xMTEMGLEiGrjzGYz9957L0899dS1n5RAIBAI6pUDBQXMT1K6Uj/l6IvjJZx7BXkqUs/XXbisq3gHEKF1ZK6z0lznWHEx3Q8fpsnff/N0TAyrMzPJMF+6nuLVIMsyL+9JZkbIX0zhCMOOHyfFpQgKNdwWH1gj/jxTFnb5yhuTeJR1687LVlMXrcZir/u5Jp1TxMLaxEm12UTAri2orFbyWrRkcysDy9rDb3d0YsttL3MUxfnZoo2ZnIjO7Pz2Vzb9vp/1+8+y7lAi6w/Es2X9HqInvUDJPYPKjqchYP1qgn9YhrqkGICz0VosZglX9+q1K9WlpbT94A1uH9SBjrNfxH+P0s07cfhoYh+9us7kl0JvgDFPFlRcF0mSGBI8DoDf45fXEMrsegP7531N1FPTkCWJ4NUr8N7/Z73Hda2U1590kVQcLirixbi4Ou33W3Y2XQ4d4mhxMW6SmjlOgYw0eDR4LciHHbzRIbE7P5+fMjMb9FgCgUAgEAj+HdzotO5+wOfAgbJY5gKbJUlqLctyMSjCJPA78C4wFTADEUDVbwffAY2BcrvfQmA5cOd1OIeblk6dOnH27FnWrFnDgw8+CMCaNWsICgoiJKRmClVxcTE//PADBw4cIC0tjaVLl/LGG29cdH43Nze2bNlSbd2nn35Kt27dSExMrBBA58yZA8DSpUvr6cwEAoFAUB+Y7XYmnj6NHaX+XBet0yXHvzXFi3Ontby7NJMWbS5f3/BKCdEYeN85iOXGLA5aikkymfgiJYUvyhz57ZycmBoYyMP+/hV1KY02G0a7HY0kIQPHi4v5u6CAo0VFFNpslNhs2IHGej1NDQZCDAbaOjkR4uDAi3FxLLKlgRZI16Mu1iAVaLEuDSbiuernZ5ftvP33I8iyzLOd5tPEtcaz1Ivi7qncsphNEqXFEo7OtSuURzJ2suzUO4S4tWVqx3l1mrvSOWkBWcYx5Twlgcrf35yILpyaMoPUAUP41v4HS0++jafBjy8GrkSvdqRVcxc6OuTj4W3Hggd5bh7V5pZVKkqCgjn95At4y+DobEcuMtJm/lyc8jJo88lczt9xN5EOjwO+hLc3V9SulCxm+j40HNczpwHIC29D6q13kDJoGEUhDVeL0DdAEUczUhURvX/QKJadeofzhTGcyt5HG+8e1XdQqYh54jnyWrenxdIvyOjVv2KTz9+7KAwJw+jr32Dx1hVvlZbnHP15qziFz1NS6Ofuzr2+vrWOtcsycxMSmBUfj4ySxv2ykz/equvTcMpHpeVuvQerTDm8dOYMw728cFBfmxtbIBAIBALBv5sbKk7Kslwtd1iSpEeBDKAzsKts9XzgP7Isv1dlaGyVfVqhiJI9ZFneV7buceAvSZLCZVk+3SDBFxdffJtaDQZD3caqVFC1dtDFxjpd+gvjxXj00UdZsmRJhTi5ePFiJkyYwI4dO2qMXbVqFeHh4YSHhzNu3DimTp3K66+/fkXOl/z8fCRJEunbAoFA8A9gTnw8x4uLcZXUTHS4dKONokKJ2JNK6u7Sj914+79ZNIQBy0+tZZpTAGbZzlFrCYcsJZyylpJoN3O8uJgnYmJ4Iz6eW1xdOVlSQkxJCVfuZayCDCwMgVWNscmK4KlSyTQLT602bG/KBuLyjmJQO+Gq86zz9C6xUbTY9Qe7VX/iZ0/F+E5vCu6+g4LmLdEW5lfUW5QsFqaPeoLRDsX80iYWR/04SlpGKPcJFwtdhqR45VauveYkvSbNxP3UUbav2kxpoyCQJGInTqHAlMMPW5XmNw+2mo5B4wjAmCcL63wekgTBLSzEHVGzqddzDD7xNc6J52i2+ltm8S1D6UKsfSSawnuwurgia3Uk3zYcbUEeR2bPI7OK6NeQ+DRSxMmSIhVFhRLOLq70bXw3WxK+47f4ZTXFyTIyet9Kxi0DKP9Qq0xGusyYjKaokPTeA0keMoL0PgOxOt+4GoqdtU6M0nuw2pTL+Oho/szP56nAQMIdHSvG2GWZyTExfJWqfH6H6Fx5zMEHrXR9k6XuMXiw1VxAgsnER+fP83pw8HU9vkAgEAgEgn8WN9o5eSFuZf/mAEiS5At0B1ZIkrQXaA5EAzNlWd5TNrYnkF8uTALIsvy3JEn5QC+ghjgpSZIeqNqa8MrvNJ0vURdq6FD49dfK176+UFJS+9h+/aCqUBgcDFlZNcddZc2ehx56iFdeeYX4+HgkSeLPP/9k5cqVtYqTixYtYtw4Jf3p9ttvp6ioiD/++INBgwbV6VhGo5EZM2bwwAMP4OrqelXxCgQCgeD68Fd+Pu+Vle+Y5OCDPVfLu3PdGXhXCd36GWuMP3e60nV18rCeAzsNdOtfc1x9oZNUdNU607WsDmOh3cZ2cwHrTHmkmc2sru1vJeAuqQlTGwjVGHCVVOglFTKQZbeSabeQYrOQYDdRJNtxllRoPmhF3m8+PPZyHt9+6oqxVEXjZlYMDpV/dy12M8tPvQ/AyNAncTfUvWNyqwUfEbB9U+WK3+Lgt6UAFISEsX21kuosa7WUBreg7YlI2u4ww447Mbu4kdcmgrxW7cht14m0AUMqpnE8H09JYhH3Fm/lFulPxk5bgspmxabT43n0IMmNgirG/hjzH0qsBTRza1PnDuO1ERxm4dQRZ5Z5TUH980N4HfqL4J9W4LPpN7pykK57DnJwjxvJd9wNQOyEKZwb+ygW1+v3wNLgIOPqbqMgT01mihrncCt3NHuELQnfkVx0Bpvdilp1kdvfKmq7PjuTgubheB/eR8DOzQTs3IxNqyOzRx/Ojp1AZo++NIg6fxkeMHgRZzNx1FrCJ8nJfJKczGAPD15v2pRb3NyYEhvLV6mpSMBTDr4M1rtdds6GQC+peMTBm49L0ngnMZGH/PwIvsJGPgKBQCAQCP53uGnESUmx530M7JFl+UTZ6vLc49nANCASeBj4Q5KktrIsxwL+KG7LC8ko21YbrwCz6in0mxpvb2+GDRvGN998gyzLDBs2DG9v7xrjTp8+zf79+1mzZg0AGo2G+++/n8WLF9dJnLRYLIwZMwa73c4XX3xR7+chEAgEgvqj2Gbj4eho7EB/rQu9dC4sXurMvu0OnD+jpWtfYw3d5Vy0Ik6q1DJ2m8Q3n7jS6RYjmuuTKYqLSs0Igwd36N3Zaykk126jqVpHU7UeF0mNHRk7YEC6rONflmVyZRtyvoaJvylCY//hJXj62Pi/1zzpOai02vhN55aTXpKAu96Hu0KfvHywslwhXKXcegeSzcbXZ+/kYFJTZnb5kQ6xv6LLz0NlMVUbe/CDrzi1bgZNd27njjMqDIX5+P69C9+/d3F+2D0V4qTKbOK2Eb0BuAsU96cNUvsP4cSLb1DSuGlFKCWWQrYkfA/Aw61fQXUNDrpmYUqqe3yMFiSJ7C69iA7swyub1IxXfcOMVt9hyEyvvAxaLRbt9c+k8AkoEydTNTQLtxLi1oaP+v5Kc/f2dc4GKW0UxJ+LVuN8NpagX1cTsO03XOLP4L/7D/x3/0HU5JeIefzZa4rztx+cKC6UGDWhqM46p1qSmOXUiCPWEn435XPIWszm3Fw25+YS5uBATGkpEvCMox8DdDf2QXEfrTObNQ6csJbyXFwcP7drd0PjEQgEAoFAcPNy04iTwGdAe6B3lXXld9BfybK8pOznI5IkDQQmoIiMoNyWX4h0kfWg1K/8uMprFyDpiqItKrr4tgvr6mTUpp2WcWG6Vnz8FYVRFyZMmMCUKVMA+Pzzz2sds2jRIqxWK4GBgRXrZFlGq9WSm5uLh4dHrfuBIkzed999nDt3jm3btgnXpEAgENzkvHTmDHGlpXhJGh5z8MFihu0blNTQlEQN589qaNK8eoOVM9FKSveIB4vYtt6R5HgtW9Y6ccd9lyhd0gBoJYl+tYoudXexSZKEp6Th0EkliSIw2IKzi0yvQUa69k1Bq6sce74wluVRSmWZMeEv4KC5eJkVlzMxtPrsfXLbdiB24lQAkoaPJmn4aDZP92BPkiPe/fox4st3UFkt2PXVOyaXBgRiG/MS9wRsx1HWsKb59/hHx+AaF01umw4V4zRFhVgdHCnFwMHS9mQGtqTxa30VN98FpBbH46r3xFvViA4+/ep8jWojOLxSnCzXVKOP6sjCk59bPkPvb8de0/z1hU+AjTNRlXUnAUI9Iq5qrqKQFkRNnUHU1Bm4nImh6ZoVNFn/A0m331UxxpCRitXJBatT3butlxZLLHzPDVmW6HSLiZCWda/hqpIkOmud6Kx1It1mYbUphz/MBRXC5BQH3xsuTILye/akgw/PFSayLjubX7OzGebldaPDEggEAoFAcBNyU4iTkiR9CowA+sqyXFUkLC/4dOqCXaKA8nbTaYBfLdP6AOm1rEeWZRNgqnL8Kw/6SmpANtTYOnL77bdjLutyOmTIkBrbrVYry5YtY968eQwePLjatlGjRrFixYoKcfNCyoXJ2NhYtm/fjpe46RQIBIKbmuVpaSwoazDzjKMfzio1e7Y7UJhXKeT89YcDTZpXr0dY7pxs28WET4CNr99357sFLnToaSQgyMY/kbiyGpqhVZr7VBUmTdZSPjwwCZOtlPbevbkt+IFa5zFkpNJywTyarP8ByW7H968dJI64D5NP5e2Jp69yjXIy1aBWYy97kHlwtx4XNzvh7ZUYmru1o5FTCCnFZ/ndPYn+9z5U43hmT29+3RvDl++48fuPzowaXMhDPQpqja25ezsWDNpDdmnq1d3vVKFJcwsqlUx+rprcLBWePnaijyoXrGVE/XdTv1rKm+JkptZswlJiKWTl6fncGzYVF93FH7zWRmHzME68NIeoKTOwVUlRbvvhbLyO7OfYK3NJHTi0TnOdP6tBlpX3Y/9OwxWJk1XxU2uZ7OjHaIMnm0z5tFAb6KGru0ja0ASp9dyp9+BnUy7PxMYy0N0dg2iOIxAIBAKB4AKub3XsC5AUPgPuAW6VZfncBUPigRTgwpaYYUBC2c9/AW6SJHWrMm93lPqVexsi7n8aarWaqKgooqKiUNdyQ7hhwwZyc3OZOHEibdu2rbaMHj2aRYsW1Tqv1Wpl9OjRHDx4kBUrVmCz2UhLSyMtLa1CDAVITEwkMjKSxMREbDYbkZGRREZGUnQp96lAIBAI6p0DBQU8flopxXyv3oMIreKW3LxG+bdRU0Ug2be9uqPPVCpVdIUOCbcwZFQxIa3MFOapmfO0N3k5N/R2ogZH/9bz4gM+HNuvu+S42FOK4Nqide3CWqmtGGedO+56H17o/ClqqfrfUE1hPq3+8y6DRvSm6c8rkex2Um69nR3fb6omTAJ4+ihte7IzKufIzlDxznNezHzMh/gY5fpKkkTfxiMB+CNx1SXjTzqnxN+42aWFLbWkxtex8SXH1AW9ARo1VRy18THKsU8fU65xePubSJxspMSYkVLzGfyHByex/sxC/nv8jauev6owqTIZcYuNwpCdSbdpT9B12hPosy6RMVNG4pnKegj7dxouMbJu+Kq0POTgfVMJk+Xcb/DES9Jw1mjk0+TkGx2OQCAQCASCm5Ab/W3ic2Ac8ABQKEmSf9niACDLsgx8CDwjSdJoSZJCJUl6C2gJLCobEwX8DnwtSVIPSZJ6AF8DGxqsU/c/EFdX14umWy9atIhBgwbh5lazaPqoUaOIjIzk8OHDNbYlJSWxfv16kpKS6NChAwEBARXL3r2VuvAbb7xBx44dmTVrFkVFRXTs2JGOHTty8ODB+jtBgUAgEFySVJOJkSdOYJJlumqcGGtQnO6p59Uc229AkmReeCcXlUrmbLSO9ORKES0hToPdLuHuZcPDx45GC9PmJeDmV0DaeQ2PPZzJ5F+H8+6+iZzM3nexEK4LqYlq3n/JkzNROjauurhQI8sQe0IR1lq0rV1Yc9d781avVbzTe3WNJjheh/5m4Mh+hC35HLXJRHaHruxa8jMH5v2XomahNeby9KninCwjJUG5rlaLxMczPTGX5XQMbDqGbv6DuafF5Euea3JZp+7GIdZatx/L/BOLvX5Fw+AWZandsVqyM1ScKxMpbybnpM8lnJNjw19EhYqdSWuJzY285mPZ9Qa2r9pMzMSp2NVqGv2xkYF39yPku0VI1trfF4DEM5XC6dkoHZlpSqyyrDwcyMm40bfo9YeDpOKBsv9vPjx/nhLbP9NpLRAIBAKBoOG40Xc+T6E4HHegpHCXL/eXD5Bl+f9QakTOB44CA4HbZFk+U2WeB4HjwOay5RhQMw/qf4ilS5fy888/X3T7zz//zNKlSwH45Zdf+LVqd/EqdOrUCVmW6dSpU41twcHByLJc69K/f/9qsVxujEAgEAjqn1WroEMHOBhpZ/TJk6SYzTRW6XjeyQ9VWYrv1rVKOZEOPU2EtrbQupMiMlV1T54tS+kOCbeQUZLIl0df4flDEeTf2wUcM7Emtydl+Wz2pW6ixFKZDm60lmC0llyv08VYKvHeNC9KipTbm6gjOuSLVJ/OSlOTn6NGrZFpFl7deZhaHI9ctqNapaGRc0iN/Uv9AtAYSylsFsq++YvZs3gNuR26XDQ2r7K07twq4mRmaqVAlRin5dvPlIeIPg6NeLX7Yjr69r/ofEWFErlZylyNg2uKYKlF55i1dwxPbulJqaX+MhWCy5rinD6mY+5zXtisEs1bmfH2bzjBySrLHLOU8KMxh7lFKbxWlMSK0myOW0owy/Ya430aXVycDPPsRJ/GSr3I8kZB14pdbyBqynR2rthIbusItEWFtPtwFv0euAOnxAuTghSqOicBDpS5J39Z4cS7L3jx6ZwrSzm/2emnc8FPpSXTYuHLsrISAoFAIBAIBOXcUHFSlmXpIsvSC8a9J8tykCzLTrIs95Jlec8F23NkWR4ny7Jr2TJOluW863oyAoFAIBDcZCxcCEePwsgN59hbUICTpOJVpwAcy9KTrRbY9ouS0n3b3Upjmx4DlE7Vf22rTF0tb4bTrKWFPFMmv8cvx2QrJaCphd4vf4Vaa4PTdzEgfzWh7u0r9lsd+xkP/NqS57YPZt7Bp5m771Fe3TOKz45MqyZi1geyDF+85U5CrBZ3LxtanVIbMSWx9vLasScVcahpqAWdvnJ9dM4hnt0+iMUn5mCvInzpszJotnIJ5WpnSeOm/PnVSnas3ERa/8Fcrt1yuXMyO0NVIZiWu+XK0+nXf+vC0b/1te5/IUlnlfi9/Kw4OFVXYGVZZtmpd5GRCXZtjYO2/lJ9y8XJfdsdOBulw9Xdxssf5tS52/SVIMsyf5uLmFqYwBvFyawwZnPAWswJayk/mnJ4vTiZcflneaMoiR+NOZyylmKS7fgGKGJtfq4aU2nNwAY1VRr37E5eV6/ieUF4G3YtW0/kzPcwu7mjK8jD6O1b69jzZeJkj1uV37f9Ow3k56hY+ZUiUB8/oK819n8qGklitF4RXD9ITKRUuCcFAoFAIBBU4aZoiCMQCAQCgaD+iYkBemaR3Ps8AFMc/GikrqzDuHaZM7lZaty9bHTtZwSg+61G/vshREfqyM2SOFyyikORowEnmrc0E+bRibtDn6KT3wDaevVEkiRWpRfz/QJXDn9zF4/elQEGO3bZzsH0P7BjJ77gFPEFlb3tTmXvI6Egmrd7/4he7UBJkcR/ZnnQsaeRIaOvTiza8asDu35zRK2ReemDHL79zJWoI3qijugIbFrTWRhb1gynRZVmOEcydjDv4NOYbUaSi+KwyzY8Tx0n5PtFBG76BZXVQn5Ya3I6dQcgr23HOsdXXnPSZFRRUiTh5CKTVSZO9rujlNwsE7//5MyPi1yI6KHkd2eUJPH7uWU0dmnBrU3urTZfeQ3Qxs1qnttv577hr9SNaCQtD7ScVucY60K5OAmg0cq8Mj8Hv8D6F5qy7BbmF6dz0qaId95aLQPd3enu6oqLWs2OvDy25eWRajZzzFrKMasyTg2EqPXouhkw7/ckM01d4xq19eqJv1NT0ooT+DNlAwOb3Fd/gavVJIweR+rAoTgmJ2BzdEKWwWSU8Ek6RWGLVhQVShW1R0dNKOTvbQ6cOKBn0UduFa5fq0XiVKSOjj1NlzraP4oBOld+MOaQbrHwdWoqzzS+9jqoAoFAIBAI/h3c6LRugUAgEAgEDUBxMSRZjDAjGgCXPwLorql00CXEaVj1peLSeuS5fLRlWaY+/jZCW5uRZYlXF/zEpwdfJidBcX81a2lBkiQeaTOTdt69Kro/3/NoIU2aW8jPVbN0vivJCRo+neVJ4fuRPOcazctdv2J8m9eZHPE+UzvMw0XrTnuf3uhUSirr9g2O/L3NgQVzPdj5W6Vjsy6oTEYsqQWsnK9HhY1J407TW7WXcV7rAYiKrL0pTmWnbjN5piw+PjSFOX+Nw1acz8S05iw9GsGAR0fTb9wwgn5dg8pqITuiy2UdkhdD7yDj5KIIlOV1J8udk94BNgbcqYiy6UmVqcgH0rawJu4L1sR+UZFmXk6FOHlBSndc3jEWn3wTgEfazCTUI+Kq4r0YXr52fPyVYz79Ri6tOtR/rUmbLDOvOI2TtlIMKhUzmzThbPfurGzThueDgnisUSO+bd2a5J49ieralc9btGCUtzcBOh02INZmwvZqFBhstaZ2S5LEwCZjANiasLLe4wcwe3hWiNdrljjzZ6+fGHD/YEK++2+Fa9LLz0qLNhYCgy1YrRK7flNczMFhyjU9uq9uLtp/ChpJYrTBE4D3ExMxCvekQCAQCASCMoRzUiAQCASCfyGxscDkOHC1oopxofC9Fux2zKPfHaXYrPDpLA+sVokufUvpP6y0Yj9ZlvHruYu4U4NIXfckavMZbDY9js42/BvXLiZotTD59TxeedSbbeud2LHBEbtdEfGWzg3lszUuODeqFNc6+vbDw+BXIW5u3FgAuANKXD7+Nlp3rCl6qUxG7HpDeaD0eWQE7qeOobLZGF0+aKmytPINZSb3EnVEEXgi3p4OQFbnHuSHhJN06hacKaRlyRKWLJvNLt9CVKh42G0UX77zI/AJAHaNluTBwzn7wETy2nS40rehGp4+NooLVeRkqAkKsVY4J338rXj6lgmXWWrsdlCpYEDQaJafeo+koliOZ+2lvc8tFXNVduquFCeLLPl8eOAprHYz3f2HMDxk4jXFWxuSBLMXZJOXo6JNp4ZpgvOLKY8omxFntZpDnTsT5uh4kVgkWjo50dLJicmBgciyTILRyK1Hj3IOI4xMJiPFpdZ9bw0azdrYBQQ6h2CzW1Grrv6WOC9HhVYr4+RSe4HT7RscmcFpJFmm3Yezyel8Hl/m0ri50oiwW38ja5cq7+ctg0vo1s/I/JmeHPuXiZMAt+pc+dGYQ4rZzMdJSbzatOmNDkkgEAgEAsFNgBAnBQKBQCD4F7I3zgi3ZAFwR0Igv1pVLPvElcQ4LVnpauJO6XBysTN5Zl6FGdBkLeXLY6/wZ+BqaPkTRN+DbYMi0jULt17SNNgywswd9xWzcZUzdrtE176lJCdoSEnQsuRjN6bOzqOkSCpLXfXkubdz0BsgP0ci+WQjZZLg7VjjBzD7GQdmLoynl6MRryP78TqyH88j+7Br9WxfvU0ZK0lIsozqAveVXa3GlSBzfAAAIABJREFU6BtAXufusAFSEjUUpFlp/OsaNMZSglevACATFRIyqo9kvNtAwsTWTOnwIaFu7chrdZrC4BDy2nYg+bY7Mfn41ct74ulj4/xZLTmZSt3Jclefj78NDy8bkiRjs0oU5Klw97TjqHWhf9Aofo9fxsZzS6uJk+fLnZMhlWnWS0+8RXpJAr6OQUztOK9C/K1vAoOtBAY3yNSct5lYYcwGYH7z5hcVJmtDkiSCHRyYExzMw9HRMDaRpI1tah3r5RDAN7cfQau+NgEwL1vF1FF+ePjY+OSHjBq/IzmZKpLOaZnCZyRrmvKOdTr9Di0imaUcSRgEm0fSq9cw1i51QaeXeeTZArQ6ReQ8d1rLF3s/YlznCbjqFcfh8cw/2ZuykQjfPgQ4BePr0Lhea4o2NFpJ4iEHL+aXpPN2QgLj/PxoYjBcfkeBQCAQCAT/aoQ4KRAIBALBv5BVpSngDe6JLjw03M7ehTay0zWsXlzpJHvs5bwKx16BKYc39o4hvuAUKpWKsTOPs//94cSeUNKfm7e01Hqcqjz6Qj7BYRZC21gICbcQFanj1Qne/LHOibC2Zn75zrnC8deuqxND7y/m750asGvQBUZhfmAY9y94lVG5R+nz8Bb8rfnV5rdrtEgWC3JZDvrhGe/z3ofN2HW0CbfcksdLb2VgcXHFrlaz8NhreBxLJzfRj1NHNRx8fwE++/fgfiISh9OxOBqVufN8vVD5OvF+n3XoNUpK+c7vNl7j1a8dz7KO3TmZagrzVZiMSnUdLz8bGi24edrJy1aTk6HG3VN5X4Y2e4Tf45exP20zWaUpeDs0Ii1JTUayImwGhVQ6Jx9u/So5xjTGtX4FZ517g5xDQ2KTZf5Tko4FmTs8PZkYEHBV8zzg58dLhxNJdy0hsmkGULt4d63CJMDuTQ4U5qsozFeRla7G54Ku5ccPlh9D4l3ry7R+PJD2331C++IDdE3eBNM34fr4syTMmoVPgA0vfxMns/dhaNQFY0oIm3ck0aXZQboFDAZgV/I6tiR8x2/x31Qco5Nvf17ptqhezud60FfrwmZ1ASdtpbx45gw/tqldQBYIBAKBQPC/g6g5KRAIBALBvwyT3c4+71QA2id7Y3CQmfV5FmMmFTB8bBH9hpYwbkp+tXRuJ50bfQJH4GUIYE6v77m37WPM/L9s/AIV8atlh8s35tDqYPA9JYSEK0Jmqw6KmxJgwVwPks5p0ekVV9jab5yxF5uwrjsGyIy6M5Cld+7l2aCN3MtP+FvzManheAsfYiZM4a9Pl/Hr1oPYNcpz1bQkNU9/0I+tR1tg12kJfuR3zB6eyBoNkiRxOvcQub5rAPhg/Y/cY57NXT1O0G28iRZdf8KfVCaMOcPOTUfRf7y3QphsSMqb4mRnqCtSut29bBXdwsvFy/JmKQBNXMNp49UDu2xjU7zi+vz5G2dkWaJTL2OFiAngqvfkjZ7fEuL2zxR7tpoLiLWZcNdo+Do8/Kqdn2pJ4iFzMwCSOqWTZ69el/Pbz1x5/QlvzGUf6eicg/x2btlVHau8TiTAuWhtje3H9ytvrlqjfO6XZY+kn+Evwonm7xHPUuLfiLjbhmDrsJBfGcfCz1tjfvd+3Py+BcAvbSLejoEV8/UJvIvBTR+kmVsbnLVKWvjhjB2sP/P1VcV/I5AkiccdfVABP2VmsjUn50aHJBAIBAKB4AYjnJMCgUAgEPzL+DEjA5ODBTL0dNM6ASaCw6wEhxVedB+1pGZU2BRGhk6qqL/n7mXn/W8yOXVER49bjVcVy0NTCziw00BmmoaebZN5b8D3pH4VSavUY7TpG8Xddit7OU73W31wN/hgf3wy7700iA3GwWQ9sZJp4x7Gp0ycWRu7gGUbv0J3cgLmLa8jm/WoHAqxjRjP8rQN9Cndi6eDPwAjQyextl0e5w6CPaEnqcXPk1ocr5xrbCNs+OPfLRu4uvO6Gjx9Kp2TVVO6y/HysXE2CnIyqj87HtpsPCez/2ZLwnfc5vkCW9c5AUqn55PZ+8g3ZdIzYFiDpXFfDyyyzI9GRaSaExxMoP7aXICj/L356LALcutCNpryecDBCwBZhg3fOWEsVRFzXIdjiyPM2D0SjaSlvfctBLo0r/MxUhLUFc5igDPRWrr1r/55On5AOY9h9xezfoUzf252oKRIRR7hxE9/mXTDiySXxLPgjxkA/PA33HsKpuveYRZOrDo7mRC3SvGuvc8thDv3BpQmS9sTf+KTI8/xQ8wn9A26Bx+HRld3wa4zwWo9Q3XubDDnMTUujmNduqBVCc+EQCAQCAT/qwhxUiAQCASCfxmfJScrP/wSQOMRl+6Im1OahpPODb1acQ5e2BjE3ctOr0FXL+A5OMksfmYdzZd8QXjUTlQnqsRjhzT8iPCOp2moBwBZvQewdcjd/LnOieF5HfBxVNKv405p2fThGORjszDZyxxqTXZhv+chcE8kwudWjLaSiqn7Nh5Jy9FqnlgCqrSuvN75Z4pIxlyi5bP0VgCEtWuYhi4Xw6tKWnd5p26fAFuN7VWdkwDdA4bQ2DmU9j69+Xm5I1aLRKsOJsI6FPPizldJLDzNY23nMLx5/TfAuV5sMxeQJVsJ0Ol44irTuavSrJkEbwRC62gOWoorxMmiAgljqSKCpSRqGNylDV38BnIw/Q8WHn+d2T1X1Fnk3f274ppUqWTsdqmGczI9RU16sga1RubexwvYus6RkiLl2H6BVhwcZUBFoHMItwc/jLdDIwz32MhlEx6njjGPaUxMXkTcvCf4xXEUB476kJygIStNg6OznXnfZdA/aBSbE77DJlswWUsuDPGmZozBk92WQqJLSliUmsqkwMDL7yQQCAQCgeBfiXhEKRBUIT4+HkmSiIyMvNGhCAQCwVWxr6CAfYWFYJFgYwABQdaLjrXZrbx/4Amm7RzG+YKYBovJx5BHq9htqGw28sLbcHz8C9xrWEtT4gkglZJh/ao1EunWTxFDD+wyIMtQWiLx1lQv0iLbgl1LYGgRI6Yc57UF55k2aAafDNjK6z2W0cg5pPpxA2x4+Vmx21SoU3rRp/Fd+OSNRJYlfAKseHjbuZ5UOidVFWnd3v6V709Fx+7M6uKkRqXlP7duY2zTuWxbqzRGGTWhkE3xy0gsPI2LzoP+QaOuxyk0CBZZ5qcy1+SMJk0wqNWX2ePy+PqC/rhyrc7aTRWp3ZlpleJ7SoLy88R2c9Cq9BzN3MVfqXWrNyrLsOt3RdAfeJciCp49XV2cPFHmmmzRxoyLm0znWypF/gK3PcTkHK54PSniHUaHTcE+6ll2Ld/AkVkfka3xoTVRjPj2eT5ZGM7QfR+TVRZ/SZGKVV+5IkkSr3T7L+/1WUdjl9C6X6CbAGeVmvsMyns0Oz6eIuvF/68SCAQCgUDw70aIk/9S+vfvz3PPPVdj/c8//3zTp31JklSxaDQamjRpwgsvvIDJdPl6ZwKBQPC/zOHCQkYcP6682OGDj16N/hKNcFfHfs7p3MNkl6Zh0DjVWxySxYzLmdMVr9P6D+HYjLfZ+vMudq7cxNlnX0B+YCCJNAUkegyo7syM6GFCq5NJT9aQeEbDhu+cyM9R4x9k5dPV6Xz+Yz4TJnrSpVF/egeOoKlry9rjkKBdF8UduXuT4nI7fVxJww2/zq5JAI+ympO5WWoyUhSRqapzskK8zKgpzqkkFRtXOmMsUeHbLAef9sf4LnoeAONaTf9HNsApZ5u5gMwy1+Tj9eCaBOW9b+qqgxilGc6RMldhVmrltS0XJwOcgrmnxVMAfH3sDQrMuZed/0yUluR4LTqDnTGTCpS50zQU5FbeWh8rEyfbdVXuX7pX+ZyXevzNwuOvI8tyzclVKhJHjuH1xw8wg3eJoiWOlNJyqAvvLclkzpdZOFNIwsZ0ks5pcNV7opL+mbf0g3VuBKi0pFsszEtKutHhCAQCgUAguEH8M+9kBP94ZFnGeokn5EuWLCE1NZVz587xxRdfsHz5ct5+++3rGKFAIBD8s9iak0O/yEgyLBa8Cw3wRSiNml78/9m43KOsOj0fgCfbv11R1/FacUo4S5/xd3PL4/ehz0xXVkoS5+4fT3HTSmfjnQ8W4e5lI7iFpUZ6tcFBJqK7Iuj8sc6Rtd8oHcYfeKqgWnfqunDb3UpDnl0bHSgqkIg5poiTYe1vgDjpZUOSZGxWiTNRisuuWs3J8rTuzJripCzD5rWKwJrT5Xme3TGQEmsBzdzaMKjp2OsQfcNgkWV+MimuyelNmuBQD67Jcpo2BfYrzrwjFkWcLE+nByWtu5x7WjxNY+dQck3pfHn0ldpFwyrs+k1xTXbra8TL117hUC53T8pyZTOccnGyUYfToFaaRTkGnmd614WXfGA8aKIG9yUTObR9KzuX/4LXq4Np2cFMRHcTs0K/Jk5uTocnJxCw7Te0BXmUWotZdvKdf1RzHK0kMc6gpNx/mJhImngQLRAIBALB/yRCnPwfZ/bs2XTo0IGvvvqKoKAgHB0duffee8nLy6sYM378eEaOHMmcOXPw9fXF1dWVJ598ErO58oudLMt88MEHhISE4ODgQEREBD/99FPF9h07diBJEps2baJLly7o9Xp279590bjc3d3x9/cnKCiI4cOHM2LECA4frkx/OnPmDHfddRd+fn44OzvTtWtXtm7dWm2O4OBg3nnnHSZMmICLiwtNmjRh4cKF1cbs37+fjh07YjAY6NKlC0eOHLnqaykQCAQ3io3Z2Qw9fpwim412Ggd6bWgJeToCLyJOmqyl/N/hZ7HJVm5pdCd9G99dL3H47dxC/wfuwOPUUSSbFefEcxcd6+5pZ8G6dD5YnkFtfTC69lM6ia//1oWSIhXBYWZ6DymtOfAytO5kpkmoBZNRxbb1jsTcQOekRgtuZd2105MVYcy7ijhZ3q27NudkfIyG7HQNWr2Nxl1OVax/vN1bqKX6E/SuN9vNBWTarfjXU63JqlQTJ63F2GSZrPTKa5V2XoOt7PLr1Q481+kT1JKGvSkbOJG196Lzmkqlii7dfe9QPpPNWiqfp7NldSdTEjTkZKrR6mTC2pnYmrCSVw8Mgs4LkNzOM/3+0Zd9IKBWKx3vXdwhr21HrE7OFdsGBx9FhcwtmRvp9uLj3NG/HbeMGciwj76g2edzyS46f2UX6wbSS+tMC7WeYrudOQkJNzocgUAgEAgENwAhTl4hsixTbLPdkOVyT/Gvlri4OH744Qd++eUXfv/9dyIjI3n66aerjfnjjz+Iiopi+/btfP/996xdu5Y5c+ZUbH/ttddYsmQJCxYs4OTJkzz//POMGzeOnTt3Vpvn5Zdf5t133yUqKor27dvXKb6YmBi2b99O9+7dK9YVFRUxdOhQtm7dypEjRxgyZAh33nkniYmJ1fadN29eheg4efJknnrqKaKjowEoLi5m+PDhhIeHc+jQIWbPns20adOu6NoJBALBjWZnXh6jTp7EIsv01DrzhlMjMuIUx1ajJjXFSVmW+ebU2yQVxeGh92NSxDv1Uu6jydrv6fbiY2hKisnq3IPtP2whu3OPS+7j4CSju0hT5q59qqd6j5tSUKuIaZbtlMg2bGV/I2VZxirLmGVFBJQkGHZ/EQA/LXIhP1eNRisT0tJyhWdYP5QLkOXU5pwszFdhuqAH0aE9Sn5+RHcz825bzUtdvuTVbotp7dWtYQNuQKp26J4eFFSvrkmA0FDglCtas5pC2c5Zm6laWrfVKlV0TQcI9YjgodavMKXDx+jS+mEqrf33Yv0KJ/Ky1fg2stKxrI5k87LP09kyR+yhP5UPdnh7Ex8dfZTPIqdhtJXQ5qHvWbgxlYiQsGs6t3MfvsPYnpHM5zkSHMOQZJmgM0lMiISpe60siX73mua/nkiSxCMO3gAsTk0lw3z9HxwIBAKBQCC4sYhu3VdIid2O8yUcfw1JUZ8+ONXzjTuA0Wjkm2++oXHjxgB8+umnDBs2jHnz5uHv7w+ATqdj8eLFODo60qZNG958801eeukl3nrrLUpLS/n444/Ztm0bPXv2BCAkJIQ9e/bw1Vdf0a9fv4pjvfnmm9x2222XjWns2LGo1WqsVismk4nhw4fzyiuvVGyPiIggIiKi4vXbb7/N2rVrWb9+PVOmTKlYP3ToUCZPngzA9OnTmT9/Pjt27KBly5asWLECm81W7bySkpJ46qmnruFqCgQCwfXjYEEBdx4/jtFup4vGiRcd/dFIUkUtvdrSujcnrGDjuW8AeKbTPFx0HtcWhCwTtuhTWn3+AQAJd93P0ZnvIWu1l9nx0nj62gltbSbulI5WHU107l2Z7hlnNbLXUsRJaylxNiPl8p4asAPlj/J8JA3N1HqChxgwrNRRcEapqxnS0oJWd03hXTWePjbORik/6/Qyrh6VTXmcXGR0Bjtmo4qcTDUBQZXCZbk42aW3EbWk5pbA4dc17oZge1mtSX+djicbNar3+cPDAZsKp2gP8tpncdhaXC2tGxSHo3/jyus8MvRJNv3kyPS5HtxxXxFPvpJfbXxejoo1S5UyA+OmFFD+MS8Xu8+e1mIxw7rlisux1yAjZ/ReaCQtY1u+yMgWT9Wb07XXi/48M3o+L5TM58efImka9xfm2H1sjfuGPcnrGdL0Qdp59aTll/NI7zWA3HYdFTvmTUhbjSMt1HpibSYWpqTwWnDwjQ5JIBAIBALBdUQ4JwU0adKkQpgE6NmzJ3a7ndOnK5sZRERE4OjoWG1MUVER58+f59SpUxiNRm677TacnZ0rlmXLlnHmzJlqx+rSpUudYpo/fz6RkZEcPXqUDRs2EBMTw0MPPVSxvbi4mJdffpnWrVvj7u6Os7Mz0dHRNZyTVd2ZkiTh7+9PRkYGAFFRUbWel0AgEPwT2FdQwJBjxyi02WirceAlJ0WYtNkg9bwiTgYG1xQneweOIMStLRPazqKjb/9rjqPp6hUVwmTMxKlEzvromoXJcu57opCwdmaenJGHJCmuyHXGXF4uOs8aUy6nqwiTADYqhUmATNnKfmsxP9iyMf73AHxwFAal4983v8JZeb3x9Kk8rre/tVqXckmq3F61Y3dBnorTZbUyq4q0/2Sq1ZpsANcklImTQOnOyrqT5eJkuUu1XMgvx2aF1UsU8XHfDj2pRdXTjFd95UJpsYrQ1tXLDDQrEydTEjU8++4GstM1ePnaGDSymIdbv8onA7YyKmxKvabgN2lurXBHH09rTMqQEWRNmcu+Rx8BYEX0hzRbtZTwrz+h76Mjuf22TnSY/SIekQeVopg3GcP0SlOnBSkpWOw35vdTIBAIBALBjUE4J68QR5WKoj59btix64qrqyv5+fk11ufl5eHq6nrJfcvT++qS5idJEvayG8hff/2VwMDq9ZP0+ur5ek5OdesG6+/vT2hoKADh4eEUFhYyduxY3n77bUJDQ3nppZfYtGkTH330EaGhoTg4ODB69OhqdTABtBd8Qa4ab0OlyQsEAkF9YLVCZCR07gwX/ne8ISuL+06dotRup4Vaz0ynRujLuvVmpauxmCU0WrminmFy4Rl8nYLQqnQ4aV15v+96tKr6sQ4mDbuHJut/IOmOkZwbO6Fe5iynWz8j3fopabMm2c4XJRnstBQCcJeXF/f4+NDHzY0AnY4Su50Smw21JKFXqbDLMidLSjhSWMhvOTlszs2FrsqyC9iTD8FqPQN0LvTTupIv29hlLuCItYTbdG4M0bvV67mU4+VTKadWTemuuj3tvKZa3ckjf+mx2yWahFqqdff+J7PDXECG3YqfVtsgrkmA5s0Vo6BpjydMhRibEblUuQdo393I9l+cqjXFAfhrm0NFJ/WcDC0fbH6fj+/+HEmSSDqnYdNq5T7mkefzyDYmk1maTHpJIgfT/gDXL6CgMSnrHwVg1MRCdHrQ4YWb3qtBzrFFOzMpiRpiT+jodIsiXN8X/ixbEr4jOucgkS0exfP2u/Dbsx19bjZN162i6bpVFISGc/b+R0m86/56e5hwrdyidWaJlEWK2czarCzu8/W90SEJBAKBQCC4Tghx8gqRJKlBUqvrm5YtW/Lbb7/VWH/gwAHCy60EZSQmJpKSkkKjsi8Hf/31FyqVirCwynpIR48epbS0FAcHpTvl33//jbOzM40bN8bDwwO9Xk9iYmK1FO76RF12zUtLFZfC7t27GT9+PHffrTRxKCoqIj4+/ormbN26NcuXL69xXgKBQHAz8PDD8P338H//B88+q6zLMpv5b2oqM8+dww500jjyklMAWquKX350QquVsVgUJTMgyIoNIyuj/sPa2AWMbfkio8KUshfXKkyqzCbsZcUibQ6O7Fm8BlnTcLcU8TYT84vTSLCbUQPzQ0OZEhhY7SGaQa3G8wKRpZ9ORz93d54LCuJsaSn3fpNKlCEXTUgJhXYbZ20mzpaaWFKaRVWfVlxpBgZJop/u0g/zrgbPKuKkdy3iZHlNyuwq4uThKind/wasssyPDdShuyo6HTRrBnFxBprKjiRIJfDBMdRfhtK6g1xDnJRlWLfMudoc545681ePjfRqNIzvv3TBbpPo2reU/IA1vL7lgjIw/o9DQWOw6/DwNXLbyOIGOa+qtGhjZuevjsScqPyd9jD40rPRUHYnr2OZehdu736OZLHgdXgfjX9bS+CmdbjGnabD3BkUhLUit33nBo+zLmglFbfr3FhlyuHT5GQhTgoEAoFA8D+EECf/pUyePJnPPvuMp59+mieeeAIHBwe2bNnCokWLWL58ebWxBoOBRx55hI8++oiCggKeeeYZ7rvvvop6kwBms5mJEyfy2muvkZCQwKxZs5gyZQoqlQoXFxemTZvG888/j91up3fv3hQUFLB3716cnZ155JFHrjj+vLw80tLSsNvtxMbG8uabbxIWFkarVq0ACA0NZc2aNdx5551IksTrr79e4YisKw888AAzZ86sOK/4+Hg++uijK45VIBAI6pvVqxVhEuDdd6H12Dw+SUtkU24u1jLX90CdK085+KKRJL7/rwurFlYX0vybmHnr70c4nvUnAGfzTyLL8jU3v9EU5tPz6XGk9xlEzOOKatpQwqRNlllvymOFMRsrMj5aLT+0bk1/jyuvkxni4MChSSGA4pxPMZv5OSuLJampHCoqQiNJ3O7piYtazfcZGfynJB1XSU1Hbd0c/3XFo6pzshYXpNcFHbttNji8VxGCO/f5d4iT26+Da7Kcli0hLg6GJjVnWdApisOLsM2PZG+hG6jcqqV1nzqiI/akDq1Opv/wErascYL4fnx7ajZtnG5n33blQebYyYWEBN5JTO5h9qVuxsexEc3d2lPSoxVbYpS5xjxRel3qmoaVdZ2PPaFFlitd1neGTMRN783QZso9mKzVktW9N1nde3PixTdosv5HnM6fqyZMusRFU9g8vKZV+zoyRO/GT6Yc9uTnE1lYSAcXlxsWi0AgEAgEguuHECf/pQQHB7N7925mzpzJ4MGDMRqNhIWFsXTpUu69995qY0NDQ7nnnnsYOnQoOTk5DB06lC+++KLamIEDB9KiRQv69u2LyWRizJgxzJ49u2L7W2+9ha+vL++++y5nz57F3d2dTp068eqrr15V/I8+qqREldeJ7Nu3L++88w6asi/A8+fPZ8KECfTq1Qtvb2+mT59OQUHBFR3D2dmZX375hUmTJtGxY0dat27N+++/z6hRo64qZoFAIKgPsrKgrI8XaO2kjzzHkJPnkcv0ghC1nsE6V4bo3JAkifNnNaxerHyBb9XBRHK8hsJ8FblNFhGX9ScGtRPPdppPj4A7rlmY1OXm0HPyA7hHn8A58Rzxox7E7Ol9TXNejCK7jXklaRyxlgAwwsuLr8PD8dVdu+IjSRKBej1PBwbydGAg8aWluGo0eGq12MvE3+8zMni/OJW5zo1prjFc8zHL8fK9dFp3hXMyU0nTjz2uozBPjZOLnZbt//ldjKu6Jl9u0gTHBs5GCQ+HDRtAd8iLDzTdefrgORiWyhGXfOifSeZ2X8wm0OkrXZMD7izhlkGlbFnjhJRwKylFjzDp03lYLZ/QKKSYkHClvuSjbd5gQttZFcc6nK9nyzLwbWTl1hElDXpe5TQLs6DRyhTkqUlPVlc09wnz7ESYZ6da97G6uHH2wceqrTOkpdB/zBAKQ8KInvwSaf0HN3jsteGp0tBL68xuSxGfJiezqGXLGxKHQCAQCASC64sQJ//FdO7cmd9//71OY5966qnLdqmeM2cOc+bMqXWbJEk888wzPPPMM7Vu79+/f51rPNZlXHBwMNu2bau27umnn672urY078jIyGqve/ToUWOdqEUpEAhuJM8+CxkZ0KJvKSXTT5LsWIQMDNS4MtLBgyB1pThnt8OCt92xWiW69DEy85NsZFnmq8Nvsin5azSSlle6/ZcI32uvlazPyqDXpLG4njmN0dObvxZ812DC5HmbiXeKU0m1W3BQqfhPaCgTAwKuWVy9GMFlpT0AVJLE0pYtybRY2Jqby7ySND52aYJBqp8egtUb4tQiTl7QEOdgWUp3x55G1P+Cu7byWpO+Wi2TGtg1CZVNcaKjwc9PBx+HExGk42iHBFSjk7Fv8yMtSYMkwf6dyufgrnFFePnZ0GhkrPmN0RW0pvTwnQCYWy3FaB2MQeNY4/PYsZeJZ9/MIby9metVxlGrg2bhFmJP6Ig9ocO/cenld6oF96hj2HV63GKj6P78BBLuup8TL83B6uR8+Z3rmWF6d3ZbivguI4MPmjfH6yapiSkQCAQCgaDhEN26BQKB4H8QmyzzU0YGr587x2PR0dx1/DhzExLIqNJUSpZlSm3/juYb/xRWroTvvgPJ1YLxzWMkOxYhFWjgtba02dasmjAJsPX/2Tvv8CiqNQ6/M9s32d30nhBKEggdoiiCgCBFRRALIopYrnpV9NrrVRTsioJ6bSAoKmJFRQQrICggHZIQIAkJgfSy2d5m7h8bQgs9oei8z8NDMuWck8lmZs7vfN/vm2ckZ50OnV6D42NiAAAgAElEQVTiloaK1tM3/5dFu94D4O4erzWLMKkv302fm6/AnJ+HKzqW5dO/oD4984TbbYo8v4sHbDsplXy00un4o3t3bk5IaDFhsim0osjczEwStVp2Sz4+cFU1W9umMAmtPihAxiYeXE1937RuSYLfFwYFs7P6n/kp3Sc7ahL2ipN5eVBcHPx6gD0BjSAgdaiHdBu7i9V8/GbQFqHXABeJqX70Bpm0TsH74eWe3xGKBgLQeUARPqnpiumCAAOGu0hodXLvm2kdg+Pc13dyD1tqVvPiX7eyqvTHw7ZRNmAoPy5cxbbrb0MWBFp9M5f+owcTvmlti4z5cGSo9LRR6XBLEtNLS096/woKCgoKCgonn7/BGryCgoKCwrHwU00N9+fns9Gxf7GGb6ureXrHDoZHRlLn97PWbqfW76d7aCiDw8PpaTLhlWUcgQAaQSBGqyVKo6HC6yXb4aDQ7aZfWBijY2JQnULPsjOVP/+E8eMBUab1jBwKZBfRgpp+yzP4YnkUX+7y0e9iJ3v0HHu9wAevBStKX3O7jZiEoCDSPiKLH4s+4cZOT9I3acQJj8tYUkTv28YQsqsYZ3wSy9/5FGdy6gm32xQeWeI1ZzluZM63WPiiY0eimyGN+3iI0GiY1b49F27cyA9eK1maEHo2g/+kKMId/62junxvCu5+/e4RJytVZK/RUr5LjTFU4pwzXJz0yzLfemopP4lRk7BXnCwqgq0NfpAd43RcGR3NJxUVcFkJi+ensuJXA6IoM/aOvRYxnbI85K7X8fXMMGRJIKOzl7sH3n1Sxn0spHf2smBu0HfyQP4q+4k/dn9Pcf1WesQOQC0eOgrRZw4j5z+PU953ED3++x9CdhVz3k1XsOHx59l56VUt+SPshyAIXKwN43VXOf/btYv7kpJQi0o8hYKCgoKCwt8ZRZz8hzNx4sT9vCObYtasWSdlLAoKCi2L3e9n3JYtfF0VjAIzItJHG0qUqEGPwDKfna0BN19W7R8lts5uZ53dflR9vFtayjNFRUxMTeXy6GhERaQ8KgoKYMQI8Hig7Uv55EfVokPgkZAE4ob7+OEtiV07NKz8VU/vC4Mi1c/zQnDYRKJb1aHr/R5wDQC94ofwQex6QjTNU2navD2PkF3F2JNT+eOdubjiE5ul3ab4yF1NqeQjUavlm06dCDvF6ZyDIiK4OzGRqbt28YaznOdDk4lVnfiY+l106NTbPdW8fV6Brz8Ieon2GeJCZzgzLUfy/W6+9dSx2u/AIQcjRh9MSSHkJERNAsTEQFgY1NXB8mBtKFJSYEJiYlCcvKCCFW+3BaD/JU5S2u6NZu3U08vn08HtCgpjfYeeHB/JYyWtU9ADs2CLFr8P1Pt8RC9r929+LPqEEvs2fij8gOFtbz5EK3up7nkOv839kR5P3kP8b4sI2bmjhUZ+aPpqQ/nAXUWxx8N31dVcFh19UvuXZJlcp5NNdjthajVJOh2t9HpMLVT8S0FBQUFB4Z+O8oRVUFBQ+AdQ6fVy8aZN/GWzoQaG6cK4UheBWdwrEFyqD2eb381qn4MoUU0blQ6LqCLb72Kdz0mZ5EMnCOgQ8SNjlQNYpQChgkiKSodZULHYV0+O08lVOTn0sVh4Oz2djiHNV+1YkmX8soz2bxRF43TCxRdDpcNP9KRC8rN2AXCXMZY2ah2Eylwyxs7cd8188b6Jcwe5kST4/tPgda3r/hhvb/4fJr2Z8xIvQacyoFMZDtflMVHWfzCb7p/I7gsvwR0T12ztHkiO38V8Tx0A72VknHJhcg/PtWnDT7W15Did3Gsv5k5DLOdqW86HT6MBS3gAa62KtcuDfpODRjiOcNbph0MO8Imrmh+8Vva4bMZoNIyNjeXOxJYTuA9EEILRkytXBsV/CIqTGWYzbbwmCrQ2uKQU9WcpXH2rbb9zM7p6g76TfgFRlDlv8PH5ObY08cl+QkwSDptI0XYNbTv4GveFasO4tsNDvLXhYeZsmcL5SZdh0UUesU2/ycyql98jaeE8SoaObMnhN4lWELlQa+ZLTy3Tdu1qcXGyyuvlz/p6VtbXs6K+nlU2G7YDbE1E4OLISG5PSGBwRISy+KagoKCgoNCMKOKkgoKCwt+cfJeLoRs3st3lwiSIPB6SQIa6afEqTa0n7YDKxP20Gvppjy4Kb4wcwbfuOr7x1LLMaqXb6tVMSEykt9lMsl5PmsFAxHGITkVuN+/u3s300lJq/X7OMZu5ICyMrqGhRGs0RGk0mNVqjKJIiEqF5gwSLxcuhC2RlYiTt1EZGfSOG6OP4DytqfGYi8c4+GZ2KAVbtKxdrsPnFagsVSMYa/B1fJ+OkedwdtyFzTIefdluuj73KBsee75RjDywsm9zY5MCvOEsRwZuiItjWOSRxZOThUGl4ocuXRidk8OK+npecJZysd/CjYboFrMvCI8OipMASa19jZFxZwo5fhcvOUqplYPiztUxMdyZmMg5ZvMpsXzYI07uITk5mDo8zpzIRPcWGLGLIarwRmuEPezxncxdr6NTlofwKInTEVGEtE5e1v+pZ+sm7X7iJMCgVmNYuGM2hdZsPs59kdu7vXDUDZdcNGrvtx43HV+dxNZ//QdPZMtHMg7VWfjaU8viujo22e10Dm3eRYFCl4s5FRXMr65mRX09B8Ym6xFIVelwyxLVsh+bLPFddTXfVVeTbjDwRceOzT4mBQUFBQWFfyqKOKmgoKDwN0WWZaaXlnJffj62QIAYUc2TIYkkqlrOwy9EUDHGEMkgnZn3nJWs8jt4taSEVxv2i0Afi4XLoqIYEB5Om4Y0Ob8kUeTxkO9yUe71UunzUeb1ss3lYpvTSa7Tyb6ywO9WK79brYcch0YQMIoiZrWa1no96UYjaQZD4z+NKFLgclHkdpNhNNI/LKyx4EptQ5VmjSAQplZjVqvRiSIaQSBUpSJGo2k2/zOfJPG8Mx8m70IC4kQN/zbE0FVj3O84c5jEkCscfDPbxBczTCAGRRS5x9skhSfxyNnT0ah0Jzye8A1rOPu+m9FXV4L8ECunfXDCbR6JXL+LVxxlVMl+ErVaprRt2+J9Hispej1Lu3Xj8cJCXty5k++9VvzI3GaIaZFCPZExEjsa/BEHjXRyqgK0PLLEtoAbLSImQSRcVO9Xtdwvy6z1O9Ag0FVtRBQEVvscvOgoxYtMmsHA/9LSGBQRcWp+gAb2+E4CREbCnmDuCR1jeGpRIXK0B+34IuDgcQ4a6WRbtpYR1x2dtcWpIq1jUJzclq1h2AH7VIKKmzs/zWPLLuenok8Y2nocbSwdj7mPzi89SeqXHxP7+69sengy5X0uoCU/nNGihl6aUP702bk3P5+FXbo0i7gtyzJv7trFAwUFuKW9T5YkUUuGWk+6Sk+GWk+yqN2vv10BLwu9Vn7x1LPV5aLvunV807kz/cLCTnhMCgoKCgoK/3QUcVJBQUHhb8huj4cbtmzhx9paIFj99KGQeCLEk3PbjxY1PBqawCqfnaVeG5WSnyrJT7XsZ6nVytJ9hMVwtRp7IIBPPrynXrrXyNZnWkNBCAOf2IU300qF5Ke+Ib3cjdQoYPpkGWsggDUQYKfHs19/TdEjNJS7kpJYVV/PrLIynNKhI6QEgump7Y1GLggPZ2B4OO0MBgyiiE4Usfr9VPl8VPp8VHq9VPl8uCSJOK2WRJ2OGI0Gk1qNT5K4fssW/koKjq1LfgyP9TChE5oWPkdca+f7T0PJXd8gQgp+TOfN5b/nfEio9sQnx8nffk7XyQ+h8nmxpmey8eFnTrjNw1Ep+fjRU8+XnhokIK0hEul0Sec+EI0o8kLbtvQwmRiTk8Mibz1mQcVYQ9QRz5VlmdyAm2rJT29NaJMCS6XkY5nXTle1obEojqiS6X9xy/ocVkt+tvnd5Ac8GAWRtiodMaKG37z1/OC1Ui/vjSYUgc5qA701JnzIfOOupVIOejQmiBrO0oQw31NHALgoIoLPO3Y8KRW5j0T79nu/Tk7e+3WESeSNzLbcUZHD90INQwOmgzxFB45wcsGlp04gPlrSOwejJfM2Nr341DGyF30SL2XZrm+Znz+Du3pMOeY+tl93K1GrlhG6s4hz7rqeqh69yPnPY9R27nFCYz8cY/WRrPU5+Lm2lueKing8NfWE2it0ufj31q0sang2dlDp6a8101NjJOowxYIAElVabjJEM1oXwTOO3eQG3AzesIGPOnTgypiYExqXgoKCgoLCPx1BPsJk8J+AIAhmwGq1WjGb909ddLvdFBYW0rp1a/R6fdMNKCigfFYUTj1FRXDnnVCHl3U3rMMR4UIjC4w1RDJcF3ZaVNCukHys9NlZ5XOwI+DBJu8VATUIxIkaIkQVZkGFRVATp9KQIGpIVmn54tloFn0ZDHlKTfPx6tyK/QQDWZbxI+ORZdxIeGQZuxygTPKxO+CjVPJRKnnZLfkIyDKxooZIUU2234XngIS+RFFDqKDCIQdwyhIBgoLnvgJocyG4VMiTO/D09TJdzvYe9ti3n7Gw8ItgGqHY6Quen6YnPbz7iQ0gECBz2rOkffgOALsvGMraSVMJGJvPK3QPsiyz1GdjocdKbmBv9emxMTG8lZ5+xhSbeHf3bm5tKP18iTaMbhojSaIWPzKVko8aKYDU8Jmqkf0s9toolYLi0QCNiTuNsY1/j9v8buZ5avnTZ0cCTILI4AUd+PLFaM7u7+LRV2tOeLzlAR95ATd1kh+rHKBa8lMq+SiTfFjlgyuG70usRoNOFKn2+XA0IdrHaDR4JAnrPv58Y2Ji+KB9+9PGXiE7Gzp1Cn596aXwzTd798myzKANG/i1ro5emhAeCTk5VcRPBEmWCSCj2Wchw2YVGDcgHlkWmL6wlKjYg39X5c6drCxdxLDW49CIxxdBr7ZZSX//Ddp88j4qb9DEs/CK68i56xH8puYpwnUgv3nrmeosRwR+6dqV/uHhx3R+QJb5orKSGaWl/FxbiwxoERhniOIireW4vCM9ssSrzjJW+BwIwKz27RkX13KevAoKCgoKCmcK9fX1WCwWAIssy/VHe96ZMQtQUFBQUDgi774L83/xw5RNEOGCch3nLWnHyFsOL3idTGJEDcN14QzXBSeXTjlAheQnRBCJFNSHnCTa6wV++z7okymqZHZs07B+hY7u53oajxEEAQ0CGgFC2RutlcHhi8PUSwG+99SxzGcjQdQyXBdGZ7WhyXTdgCxjaxB3tgc8bPQ72ex3HSTwmAQRk6DCIqgwCSq0gkCtFKBG9u8X5ZkiaCm+tSvsDKHVpNIjXr+R19tY9JUBWVJxzfW6ExYm1XYbPR+5g7hlvwKQ96+72XLbfUETu2amQvLxlrOCdf69kYDnWyz8OyGB0TEtkx7dUtySkEC1z8ejhYXM99Yx31t3xHNCRBG3JPGbzwZOuNYQxYeuKpb49hZhCVOrqfP7yRlWwNV2LYNHHn/UZL0U4AdvHSt8DgoDnkMeJwKdQ0LIMpmo8/tZY7ezw+3mLJOJ+5OTGRUV1WhjkO9y8XlFBV9WVeGTJG5LSOD6uDgCsszMsjJmlpUxKDyc59u0Oa2KhbRrF/xIS1KwGM6+CILA62lpdF29mpU+B2t9Dnpoml+Ybw4CDeL+x65qamQ/52hCuVgXRqZKj8kC7TJ9bMvWsmGlnoGXHvzZiTUmc+lRVOs+HH6ThZy7H6Ng9A10+N9LpHz3Oa2/mA2yzMbHnz+htg/FAK2ZTX4nv3ptjMnNZX7nzvQ0mY58IkHrjCuzs/mmurpxW1e1gZsNMSSfgMWJThB5wBjP264KfvLWM37LFiRZZnx8/HG3qaCgoKCg8E9GiZxEiZxU2MvEiROZN28e69evP+Zzlc+KwqlmyCUSP/bfBFm1aN0qvLf0INylZ/rCMk6DzMoTYt6Hocx61UJqmo9OWR7mzwml6zlunnqr+sgnnwRkWcaLjE+WMQjiEaNU90R55m/S8vD1sYRHBZj5U9lR9bXyNz2lVVZGXnniHpNqh52+40cQsnMH6yZOYdfQESfcZlMs8dbzlrMCNzI6QeCRVq24KS6OpDP4XinLMrPLy1lQXU2200me04leFEnR6UjU6dA1CHo6UWR4ZCSjoqL4oaaGMTk5BAiKghJBm4DrYmO5LzkZgyjSY80a7IEAY/QRjNYfe2EgWZZZ7LPxvquyMTJZBM42m0nV64nWaIjXamlnMNDOYCDdaCTkgBuEV5LQniZRj81F27ZQUAAvvggPPHDw/vu2b2dKSQlmQcWjIfG0P0TRsFPFZr+T6c5KdkgHLzYZEBEF8LkEvN/G03dHPPc9e3jB3C/52G0vIMWccdjjjkTUX8vJnPYcK199H09Uy6U2u2WJB2w72dnw84+JiWFiaipphqYXkiAo5l6bm8unFRVoEBipC2eQ1nxQ6v6JIMky77oqWei1IgAzMjK4QREoFf6hyLJMqdfLRrsdvSjS2mAgSac7LTJ3FBQUTh5K5KTCfowfP566ujrmzZt3qodyTMyaNYsbbrih8fuQkBAyMjJ47LHHGDVq1GHOVFD4ZyPLsLTrtqAwKYk8FZbAM1YDtfUi2Wu0R0wXbo7+Vy7Wk9nNizl8bzrh5tValv9k4LoJ9RhDj28xLBCA7z8NRjJdPMZOl14eFswNYcMKPYV5alpn+JvlZzgRBEFAh4DuKN+/90R5Fm0LRu6kph++GrNf8iEjoxG19BrgBk5cmATwh4Sy8rWZaOtqqevYtVnaPJCfPVbedFUgA+eZzcxo354Mo/GI553uCILAuLi4xlROWZaPGP25x5duj0B5tsnEG2lpnLXPwuhbaWlct2ULc901pKn0xxTFVyv5meosZ31DdGrnkBDuTUrikshIorRHHyX2dxMmAc45JyhOduvW9P4nU1NZXFfHWrud/9p3cbcxlj7ao4vOa0kccoAPXVUs8gbf7S0qFY+2asUFYWG8W1rKR+XluCQJZEAPXFXCXx+qkSTNIQOgSx07mPTnOJx+G28NWoZBffyRolVnncfSD7/brzBO16cfwBWXSNFlY/BExx532/uiF0SeCk3kg4Zo4zkVFcypqCBSraZbaChtDAbC1OrGfxa1mkU1NXxaUYEKeCgknqwWiIgVBYFbDdGIwAKvlVu2biXDaKR3cFKmoHDKqPR62WC3s95uJ9vppNrnw+r345EkEnQ6Wun1pOzzf7rRiPk4rFXq/X7mV1fzZWUly61Wyn37v89oBIH2RiPdQkPpFhpK14b/I09Tf2kFBYVThyJOKpwSvF4v2kNMlMxmM3l5eQDYbDZmzpzJVVddRXZ2NhkZJ7bCr6Dwd2VKbinuC0tBgvt0cXTQ6zl3kIufvgph6Q/GFhcnly0y8MojEXQ+282kd4LRjIEATHsynIrdaiKiA1x58/FVu/1rqZ7KUjUmi8T5w5zo9NB7kItlPxr5aqaJe5+rPe2LVRyKom3Bl/NWaYcXJz/dMoV1FUu4L+sNEkLbHH+Hskzb2e8gSBLbx98OgDMxBWdiyhFOPD72FSbvTExkart2p1W6b3NytGnpV8bEkKTTUeb1MiIq6qDrcW1cHD/X1vJBeTmTHbu5Vh/JZbrwI7ZfHPAwyb6bStmPThB4MjWV+5OTTxvfx1PNW2/BhAnQq1fT+81qNUu6deOa3Fy+q67mZWcZDlliiO7kikwBWWalz05hwEuF5GOj30ltg23ErfHxPNOmTeOkPsts5pW2bSn1epGBeeXVPFyUj/vaHSwqSmZY66Yjk6MMCUgEqPNUsqBgJpen33lig97nsxn113JSv54DQMa7r1LW70K23P4AtrYn/v4WIaq5JySOEf4wPnJXs97vpNrv55e6On6pazpSVATuNca1iDC5B0EQ+Jchmno5wDKfnauys1mXlUX0MSwIKCicCJIss9pm44eaGlbV17Pebme39zDvfTbbQZs0gsDA8HBGRUVxeXQ0EYcRD32SxA81NcwqK+P76mq8+2RiikCCqEVCpkLy4ZNlNjkcbHI4mF1e3nhcvFZLK72eZJ2Os0wm7k5K+lsujCkoKBw9yh3gH0pxcTEjRowgNDQUs9nMVVddRXnDA8NqtaJSqVizZg0QjAaJiIjgrLPOajx/zpw5xO+TtrJr1y5Gjx5NeHg4kZGRjBgxgh07djTuHz9+PCNHjuS5554jISGB9PT0Q45NEATi4uKIi4sjLS2NyZMnI4oiGzdubDzmo48+IisrC5PJRFxcHNdccw0VFRWN+xcvXowgCPzyyy9kZWVhNBrp3bt3o+i5h+eff57Y2FhMJhM33XQTbrcbBYUzjTU2G4+UB4tzWL5JoVdIcBJ2/tBg9NSfvxjwtbDt5PoVwUi+Tav0FOQFX2jXLtdTsTu4BrZkgZHjcRGRJPhqZjB6afAoB7qGufbIcUGh8/dFRt54KgzvoS31Tmt2bA1eq9TDiJMFdZv5avv/yLduZEd9znH3pbbVc9b9t9Dp1clkvv48pm25x93W0fCH19YoTE5ITGTa31iYPFbOtVi4LDr6kNfj7fR0ro+NRQI+dFfzvLOU7X430iH+iNb5HDxsK6FS9tPOYGB9VhaPtGqlCJP7YDYHoycP9xEMVav5ulMn7kpMBOA9VwXb/SfvvUCSZd50lfOis4zPPTUs8dmolQO0MxhY3K0bb2dkHBRtZFKrSTcayTAaeTA1iaSNcSDCzJDdlAaavvFrRC2jM+4BYN72t3H6DhYqjpeqHuew5plpVHc7CzEQIOHXhfS75iLafDI9eEMHaqtEtuccf9RUG7WeJ0ITmWNpy8uhydxhiGGMPoJLdWEM1Jo5RxNCF7WBjioD9xvjOO8kRMAKgsAdxlgSRQ27vF6uy8095N+rgkJzUePzcc/27cT98Qe91q5l4o4dLKipaRQmE0QNvTWhXKOP5HZDDPcb43jYGM8thmhG6sLpowklQ6UnXFDhk2UW1tRwy9attFqxgicLC7H6/TgCAb6vruah/Hyuy81l8IYNJP75JyM2b+brqiq8skyiqOFKXQTPhyYxx9KWN8yt+J85lbmWdrxjSuXRkHjG6CM4VxNKnBj82y/1ellRX8/nlZU8WFBAn3XrKHS5TuXlVFBQOMUokZPHiCzLOJuoVnkyMIpisxQMkGWZkSNHEhISwpIlS/D7/dx+++2MHj2axYsXY7FY6NatG4sXL6Znz56NouDGjRupr6/HbDazePFi+vXrB4DT6WTAgAH07duXpUuXolarmTx5MkOHDmXjxo2NEZK//PILZrOZn376iaP1Og0EAnz44YcA9OjRo3G71+tl0qRJZGRkUFFRwT333MP48eNZsGDBfuc/9thjvPLKK0RHR3Pbbbdx4403snz5cgA+++wznnzySd5880369u3L7NmzmTZtGm3anEBUkoLCSabc6+XyzZvxCTIsj6R7YQxgBSCzh5eI6AA1lSrWLtc3pAO3DDlr96YZfz8nhAkT6/jhs72RKiWFGvJzNbTLPHyE4IEs+iKErZu06I0SF1+9N/KyXUcfNz1Qx8xXLPzyTQj52wLc91IZyQmnZyGLppBl2LHt8OJkQPLzxvoHkOQA5yUMp3fCJcfVV/Qfi+k2+WGMpSUENFo23z8RW7v2xzv0I+KXZWa5q5CBfyckMLVduzOq4M2pRq9SMbN9e3pbLEzYto2VPgcrfQ4iBBXdNSGkiFqSVFpKJR+/eevJbyh409di4etOnZR0uRNAJQi81q4dOz0evq6q4mVnGVNMyRiFljXulWSZt1wV/Oq1oQLGx8WRZjTSzmDgoogIDEdhHCwIAndJ6TyY48CbaeNtVwVPhSY1eez5iSP5Yuvr7LLn833BTK7MuKt5fhCVipKLRlFy0ShM27eQ9tJzJK/6hc4vTSR22W98cPUMJj7WGpdDYOJb1XTtdfwrSzpBpJ1aTzv16eFdaxBEHgiJ50HbThbV1jJxxw6ebt36VA9L4W9IQJZ5v7SURwoKqPYHrW0MiHTTGOmsNtBGpaOVSodBOPoFqpKAlxU+O0u9NooDXp4uKuK1khLckrRfZOQewgQV/bQmBmjNtBK1TT7jVYJArEpDrErD2ZrQxu1OOcCugI8qyU+p5OUrTy1/2Wx0X72aWe3bMzI6+jiuioKCwpmOIk4eI05JIvT3309J3/a+fQ8yrT8efv75ZzZu3EhhYSHJyckAzJ49m44dO/LXX39x1lln0b9/fxYvXsx9993H4sWLGThwIAUFBSxbtoyLLrqIxYsXc889wVX3Tz/9FFEUmT59euODaebMmYSFhbF48WIGDx4MBP0jp0+ffsh07j1YrVZCQ4MPMJfLhUaj4d1336Vt27aNx9x4442NX7dp04Zp06Zx9tlnY7fbG88FeOaZZxpF1IcffpiLL74Yt9uNXq/ntdde48Ybb+Tmm4OVKydPnszPP/+sRE8qnDFUer1csH49RR4Pumodnufa0+52R+N+lQr6DHbx7ceh/L7QsJ84WeMuZ3vtBnRqI+lh3TDs89J4rNRWiZTu3Ps4WfqDkcGjHKz7IyhYZnT2krdJy5IFRtplWo+63eoKkdmvB734rptQT0TM/gtDMQO+orUrm/wZj7MjJ5wJE1bSesI9tA/PQiWqubTtv4gxBifmbr8TURDRqk6PSSxAVZkKp11EpZZJbN20b+b8gvcpsG4iRGPh5s5PHXMfGmstnV55mpTvPgfAkZDM6hf+R12nE6vyfSSWeOupkPzEaDS83LatIkweB4IgcEtCAj1NJp4vLmZhTQ01gQC/eA/2FVcLAjfFxTE1La2xEI/C8SMIAjMyMlhjs1Hs8fCWs4J7jXEt9jn2yzLvNVR9FoHZHTowJvb4vBqHDhR5cHAmzF7FBlxs9btJb0K8U4lqRmfcw5Q1d/JN/jtc1GY8IRpzEy0ePx+v6Mn7q37ikYg3mFj/IIHtZTx+Xyu8/uBn9LP3TCckTp6OpKp03GqI4XVXOZOKiojVarmjIRJXQaE5KHa7GZubyzJr8H0qWdRyvSGKrmojmhO4Rzs33WEAACAASURBVCWptFyhiuByXTh/+ux84q6mJBBcOI0W1HTTGEkQtYSJKqJFNe1VBtTH2Z9RUJGmVpHW8H1frYmXHWXkBdxcnp3NFx07cpkiUCoo/ONQxMl/ILm5uSQnJzcKkwCZmZmEhYWRm5vbKE7OmDEDSZJYsmQJAwcOJCUlhSVLltCjRw+2bt3aKPqtWbOG7du3YzLtnzbjdrvJz89v/L5z585HFCYBTCYTa9euBYJRmT///DO33norkZGRDB8+HIB169YxceJE1q9fT01NDVJDNGtxcTGZmZmNbXXp0qXx6z1p6BUVFaSkpJCbm8ttt922X9/nnnsuv/3225EvooLCKaba52PQhg3kOJ1ECCqkpzvjcWho02H/CLzzhzn59uNQVi3V88s3RnxRq/mi7naq/NsbjxERaWXpQIeILHrEXkBW7MBjGkvOuoaiLmk+NFqZbdlanrs3ElkW6NHbzbDRDp65O5LfFxoY/x8rqqN88rz3QhhOu0h6Zy8XXm4lr2YDYbpoYkOC/ogB2U9+1BS4YSH8LxsKL6BwdzmF1g8AODd+WKM4uXjnl3yQ8wzD29zEiHa3Nvsk/Hgo2h68EImpfpoKdCt3FPPJlpcAGN/xMcL1x1YJV/D5OP/6EYQWFSALAgVjbiT3jgcJGFs2ujQgy3zuqQXggeRkjGd6qfhTTE+Tic87dsQjSfxaW8sf9fXkOhzkOp2YVCquiY1lTEyM4m/XzIRrNMzJzOT8dev43Wcn0l3NdfrIZq86uzPgYaqznO0BDwIwq3374xYmATp1gjgMlP0cA0PL+cJdw6OhCU0ee17icD7Lm0qJfRvzC2Y0pno3BzsL1Mx+3QIIPFczga+4kOjKSryoyTrfxablanqt+Ygtay6ifc9Tk5HUUgzUmamQfMz11HDntm2EqdWMPYHfqYLCHuaWVnJbfh51fj8GRK4xRDBMG3bcImFTCIJAb62JXppQcgMuwgQ1iaKmRRcZo0UNz4Qm8aaznN98Nsbk5LCwSxf6h4e3WJ8Kp5Y9ld29kkSqwXCqh6NwmqCIk8eIURSx9+17yvpuDg5VUXTf7eeffz42m421a9fy+++/M2nSJJKTk3n22Wfp1q0bMTExdOjQAQBJkujZsycff/zxQW1G77PqFRJydBNiURRp165d4/ddunThxx9/5IUXXmD48OE4HA4GDx7M4MGD+eijj4iOjqa4uJghQ4bgPcD8WbPPjH/PzyadorR8BYXmot7vZ/CGDWx0OAgXVDzgT+GRjaEIgkzrA6o+t830kdDKx+4iDa9PDAcuBGELRGzDkLgDTffPqG89k0JrNoXWbDwBd6M4ubtIxbIfDYwcZ0d7mOLQe1K6M3t4yOji5dXHIqirDgpSQ69y0P1cN6awAHXVKjau0tH+7Bo+z5tKiT0fvdpAWlg3hre9ubG9z/KmUrk2ixW/XoWgCqAdcTfjf/wYp9/GVel389C2VNROO9FhZqJVNxPVqxdTfyxl/fY4hvo+w9Duc0RBRYR+72Sw2LYFl9/OZ1unsqBwFldl/IdL2tyEeAwpT83NkfwmZ2x+Ck/ARcfIcxiUMuaY25c1GraPu5V2s95i7aSp1HbteULjPVp+99kok3xEaTTcltC0KKJw7OhEkWGRkQyLjDzVQ/nH0Nti4aW2bbk3P595nlq2+l3cHxJPhHh8r892KUCR5KUs4MUqB6iU/PzsrceHTJhazXvp6VwRc2yLEAciCDBoEHz0SQoMKWeV38GOgIdU1cE3cZWg4ur29/Ly6n9TULf5hPrdl0AA3pgYjs8r0L23m+693Xz7UTvyytpz6Vg74++1Un/tW1yf+wwb7ulF5eyXcLT6e1nqXK2PwC4H+N5r5frcXEJVKkZERZ3qYSmcoXgliYu+y+cXyy4AQkpCGLY1haGX+FG3kGaoEgQ6qY0t03gTqAWBO42xOJ0SK30OLt28mcXdutHD1PKesQotT4nbzfL6elbU1/NXfT3ZTid1DZYEA8LCeDA5mSEREUqmzT8cRZw8RgRBaJbU6lNJZmYmxcXF7Ny5szF6MicnB6vV2ig47vGdfOONNxAEgczMTBISEli3bh3z589vjJqEoBfk3LlziYmJwWxumWgklUqFq8EkecuWLVRVVfH88883jn/16tXH3GaHDh1YsWIF48aNa9y2YsWK5hmwgkIL4ZMkrszOZq3djkVQ8XRoIpUrgsJ/Qis/hpADfYFk+vznI+yrRrEzX0fRVg31dSqobo+ruj2BvCG89vVd7JJWsqVmNZ2jz2s8c+okHXlrLAR0VYwZtzct8NdvjXg9MPTKYMGd3PXBiK3M7l56XeBi1qsBaqtURMf76dnHjUoFfYe4WDA3lO++dvOucwiljh2N7ZXY8hne9mbUNiv6gjy838yizfxonmQi8dEL0Mz/i5tGQqjGggy0/ehdLA3FXHoDMJ3xgJ0QdryfSf71Xze2nTLvU9QOO5NCu3CN+xZWFH+D1VmO9o+nyIv4mjZ3fYlO3bBiK0kIgQAaWz1qlwOvJRx/SOjhK2icAEWH8Zt0+exUuXYhCipu6/LsUb2shRZuJ+XbuTiSWlF0+bXBPi67hpJhowicpFXpgCzzubsGgHuTkghVK68ZCmc29yQnk6TTcVNeHjkBN3fbihiqtTBIayFWdXTenku9Nma7qqiUm7ZvGBoRwfSMDBJ1h1kFOgaGDIGPPgrBtDYaW89KvnLXcm9IXJPH9k64mOf6fE2HyLOa3H88fD8nhLxNWgwhErf/t47ouAAXXeVoeC4EK48nDY3Dmmumq20l/isvZMfV49l64514LWGU2LaRb91ERkRP4kNSm21cJxNBELjJEI1TlvjNZ+PK7Gw+79hRESgVmqTK62VORQWlXi9mlQqzWk07g4GeJhPuhve+Py0Nlh6fJON4vzVfBERyv/fw8JRqTJa/R/EllSBwnzGOpx272ex3MXTjRpZ170668eSJpArNz/Tdu7l161YODA/aEx7wW10dv9XV0ctkYkGXLoetFK/w90aZNfyNsVqtrF+/fr9tERERDBo0iC5dujB27Fhee+21xoI4/fr1Iysrq/HY/v37M3XqVC677DIEQSA8PJzMzEzmzp3LtGnTGo8bO3YsL730EiNGjODpp58mKSmJ4uJivvrqKx544AGSkpo2Yz8UsixTVlYGBD0nf/rpJxYtWsQTTzwBQEpKClqtltdff53bbruNzZs3M2nSpGO+PnfffTfXX389WVlZ9OnTh48//pjs7GylII7CaYssy9y2dSs/1tai9otclNOa5PMFVmwJPsTbtN9f5JJkiembnmCBdRZXDt/ApA4PIMtQVy2yY5uGmVMsFG/XsHpBa664KZrzEvcWW3HaBbauD65Wz1++jpFjOmPQhGKzCrzxVBiSJBCfHCCtk7cxArBDdw8aDYy6wcaMl8IYNd7OnrWc9v3yWDC3J2t/i4F1CxEFNSZtUGysBlq9cBXdyoKWCsG4zQeDJ5aBv1Kk9pVvaR3eGZWgovx8CUdKa3Q1VcF/1VVo7PWE4kCwOfF6aIz0bPvxe5i35wHQAxi7z/XZYdnIhT2v4NGzZxBhiKP3raOJXv3nftfQrzfgjo6hPq0Df70yfd9fxgmLlnuK4bRqQpw0aEJ5ud8CtteuJ9mcfsg21A47CT9+R6t5nxKxcQ0AttS2FI0aGxyfIJw0YRLgT5+dXZKPcLVa8VlT+NtwZUwM3UJDuTI7mw0OB597avnCU0sXtYGzNaFkqUMOKVSu9Nl5zVnWOClL0elobzQSr9USo9VylsnEFdHRzRotMnw46HRgezsF3qtkmc/GmEAE8aqDU/9FQWxWYbJit4qP3gwuVN9wr5XouKAYqdbQKEwC2Mddxr/WDeLmxXcx2PcT7Wa/Q/JXs3n2fJFns+z4VaAWtYxseytXpE9AfxIjuJoLsSESzO+U+d1n54rsbD7PzFSKfbQwv9fVcc/27XhkmQyDgQ4hIVwRHU3X0OP31m4JZFlmqdXKO7t382VlZZNFZwC0goBXlhEcKuTJHRh/rgbpThtfzDCRvVbHQ9dH89/Xq4lPDjR5/pmGVhB5NCSex+27KPB5GLxhA8t79Gi2xRuFk8uPNTXc1iBMtlbp6KDSk6bWk6rSkShqsMoBvvPU8aPHykqbjSuys1nYpQtaxT/7H8kpFScFQXgEGAW0B1zAH8BDsiznNXGsACwAhgKXybI8b599KcCbwAUN7XwC3C/LsvfAdv5JLF68mO7d9y96cP311zNr1izmzZvHhAkTOP/88xFFkaFDh/L666/vd+yAAQOYMmUK/fv3b9zWr18/1q9fv1/kpNFoZOnSpTz00EOMGjUKm81GYmIiAwcOPK5Iyvr6+kZ/SJ1OR6tWrXj66ad56KGHgGCq+KxZs3j00UeZNm0aPXr04OWXX+bSSy89pn5Gjx5Nfn4+Dz30EG63m8svv5x///vfLFq06JjHrKBwMni2uJj3y8pAAv9/M5mzIoqoibUU5AYnnG338Zv0Sz5eX3cvS0q+RkBo9CsUBAiPkgiP8lA3zsbUJyL44bNQRo6zo95nbr1hpQ45EHxEOEpSeH39fTyQ9TaFeVokKTiJnvWambF31CNJArGJfiIbCtZcMsZB534lSCG7gE4AGFvnYI52MayyhKHVC+nJGrqxHomgeplDHN2AamM825wpVAhxtB5kQdM2CndkDGnmTsgN1XJz73zooGsjuN1MvsSLr9rNJat0ZPX1kLdRg024lI7d8okLqUPtcYGoQlKrqBNc/CZtYHvdBnJrVtMndijhm/cu5gR0OlQeD2q3i9CdRfhD9k8r6n3LVcgqNfXt2mNrl0FVz3NwJqce9e/S5RTYVRS8vqnpTad1i4JIekSPJveFr19N6tefkPDjd6jdwahySaWi/LwLKB45ulnE02NFlmW+afCavCsxEbMSNanwNyLNaOSvnj2ZV1XFu6Wl/Fxbywa/iw1+F+9RSUeVgduNMSTuIwBm+1284ggKkzfExTGlbVvCTkJEiMUCF10EX39tok1lBAXRNbznquS/IQmHFUHrvbXk122ke0y/Qx5zJFYu1uN1i2R08XDhZc7DHtvnjnCGLF7EEBYxRfsQmY6NPPUDJFSJPHt1GsW2PL7Y9jpLSr7ijm4v0S3m/OMe16lCJQj8xxiH4Cxnqc/GlTk5TM/I4Pq4piNZFY4fWZZ5raSEB/Lz2SPTbXY4oKqKyUVFDAwL497kZIYdkDoqyTJ5TidrbDY2OBxEaTSMiIyk/VHaUR2IOxBAI4pN+tMGZJltTiff19Tw7u7dbG3ICgNo0yDcuJGxywF2Brzslnx4ZZlUUUvxv7sh7zRy3pNlRMcF6HGem8l3RbK7SMPD46OZ9nkFloi/h3WVUVDxREgCj9pLKPIEBcol3boRpXgrn1FkOxxcmZ1NABigMXGXMfagZ1C0IHKjIZoLtGYese3kt7o6bt+6lfcyMpQU738ggnyIVZqT0rkgLAQ+Bf4iKJQ+A3QGMmVZdhxw7D3AhcAw9hEnBUFQAeuBSuA+IBL4APhKluUJRzkOM2C1Wq0HiWlut5vCwkJat26NXn/6VHlVOP1QPisKLUmhy0X6ylX4keHVNMT5CUiSgEoto9PLOO0ik96tpPNZwTWZN9c/yE9Fn6AS1NzV41X6JV12UJs+L/zrojjqqlXc/3wNfYbsfUl+46kwfp7X8GKuccIjJkZ3uBvhz3v5dNreaOik1j5KCjUMuMTJ3ZNqKXMUsbrsZ77e/haSLPNKv++J9epI+GAqGZ9+jGGfF/FZj/3A7jY9qS5X8d2LLsrqTNgI3oNvfqCOS67Z7zFwRN55zsIPn4Uy+HIHl99o4/5rYrBZgyuv4VEBBl/u4NKxdkJMwedeqWMHOdWrGJhyFQDammpkUcQfakJWq1G5nOgry9FVVQBQ06NX8LjaGoYO7IpwwPOzpnMPSi66jNL+Q3DHHd5rcc5bJua+ayY2yc/b35Y36ojegJsFhR8wtNW1hNVYMRVsw1S4DVPBNnLufBhfWNAcvsejd5L8Q3CNzpbaluIRo9l58eV4ok9d0YUtfhcP20vQCQLF555LjDKJUPgbk+9y8VVlJd9VV7PcakUCtAhco48kWaUlx+9iodeKQ5a4NDKSLzt2RH0SI0E+/xyuugriz3VQ+/wa3JLEzYZoLtGFNXl8iW079y+5GIB3L/wTsy7iuPp94f4I/vzFwLV3WrniJvthj/2laC4LvzSy6+tbcNlhHB/yvO5R1s6ZjTc1g5Vli5ixaSKVrhJERN4cuIT40NbHNa5TTUCWmeYsZ4nPBsCDyck826ZNsxdY+qdi9/u5KS+PzyorAeirCaWf1szugJctATcrfPbG6OUsk4lnWremt9nM9NJSXi0podhzcNX4DIOBkVFRXBYdzVkmE+IBv6v1Nhvzq6up8Pmo9PnY6XZT4HZT6vViFEW6hIbSJSQEiWARw1Kvl012O459vO/1CJyvNTFYa6Gd+uC5g10KUCX7UZUYmDAyHp1eYs7yUvbcSmoqRZ64NYqSQg3DrrJz6yPWZrmepwvlAR+P2HdSIweIUKt5JCWFOxMT0Z8iizVnIKAU+TtKltbVMS43lyKPh44qAxNDE9Acwed9tc/Bs47dSMCLbdrwQErKyRmsQrNTX1+PxWIBsMiyXH+0551ScfJABEGIBiqAfrIsL91ne1dgPnAWUMr+4uSwhn3Jsizvbth2NTALiDmai6GIkwrNgfJZUWhJbt6Sx4yyUvgrHMvznXjstWrmzwll6Q97U90+WrqbUJPMitKFPL/qZgQEHjl7BmfHDz5ku3PeNjH3HTMZXTy88EEVEAy6u3FwHLVV+7yATUiDyO3w1Yew8Tqi4/xUlu2NjOt202yqOkyixL6tcVtrfVvmFAykxydz0NiDEzJ7qzaU9ruQit4DqOl+FlJD/nVtlcjUJ8JZ/6eenn3cPD6t+pgD/9b9oeOpO6IIjwoQHh2gIFdLfLIfp0PAWhP8WSzhAa65vZ4LRjix1YnUVauITfQTYpKpcZdTYN185Grlsox5ay5hORswb9+CZctmItf/hdAw4Si+9CrWPTUleKwkobXW4Q3fO9Gv2K3izlGxeD0CD75YTe8L3QBErFuFc85ELDkb6VQlEurZPwJi2fQvqO55DgCRq/8k+fsvKRpxdbDQzWkwwX3BUcqfPjs3x8fzXkbGqR6OgsJJo8jt5l95efxUW3vQvj4WCz926YLhJE9onU6IjQW7He77fRev+LehQeAlU3KTxXFkWea+JcMosG5mVLvbGdfx0WPuU5Zh/KA4rDUqnn2/kszuTScwBeQAs3OeY972twG4I2MGuxZeyfyPQ8Hv596X6uk9KHhfTPp0Ov8LX09tUjzXd3zsmMd0OiHJMnPc1XzeEGF+SWQkb6WlkaS8M54QWxwORmVnk+t0ogJuNERzkdayX9RVpeRjvqeORR4rboJzX70o4m54busQaKPS0Vqlo0zysdHvZF+H2AStljsTE7krKQmDKPLyzp08WlDA8SRS6xBoq9LRX2umr9aE4SgK863+Xcfku6JITffy2tzK/fZtWq3lv/+KRlTJTJ1bQXLbpr1tz1R2Bjy86ChjpxS8nyTpdDyVmsq42NgWX/BxBgLMLCtjaV0df9bXs9PjIUGr5WyzmfPMZm5NSMCkZInsxzankwcLCphXFZxTxIsaXghNxiwe3TNwvqeO6a5K1ILApqys445gVji1HK84ebr9NVka/q/Zs0EQBCMwB7hTluWyJsJ7zwU27xEmG1gE6ICewG8HniAIgq5h/x6UMmAKCgqnLYUuF7MafFg1c1J4cXYlsQkB7kyvpbJURe56HbFJfkJNMrXuCv63PujXOKLdrYcVJgGGXuHgyxkm8jbq2JatIa2jj8I8DbVVKvQGiZjEAMXbNfRRP0627kFqy7oBQS+xmVMsjQLlesNksG9DFFRkRp7NWbEXcl1ZK3q9exMA1rQO5N16D6UDhkITL5PhURJPvFFNYZ6GVu18x6W1dcryYAiRqK1SUVulwhQW4Ol3qgiLCrDyVwNz3jaxa4eGt54J5+1nw5DlYCet0nw889F2nvpzLEX1WxjZ7lau7fAwavEQ6ZeCQH1GJvUZmY2bdJXlJP74HYk/fkfFOXtTDy152fS/Zhj1bdJxxcXjiYplxfoopnggNsqFJf0GnAR9bh3rFjD0p40NZ0pIajWO5FRsrdOwtUnHHbW3im911rlUZ5177BephSgL+FjpC0ZJ/ecYfYYVFM50Wun1LOrShRmlpUzcsQO9KNI3LIzzLRaujok56cIkgNEII0bAxx+D74sELr6xmu9ranhkeyXDFmfQvaeP9l28aBoCnAVB4Or29/Hsyhv4tmA6A1tdTWLosXlw7y5WY61RodHKpHVsWpiUZZlpa//DkpJg4bLRGf9hYMaFiO3r0WplPnvPzIyXLXTv7SFl41J6vjCRdwxGciY8zI4MP7JaTYltO0tKvuKK9AnoVCfPU/dEEQWBsYYoklRa3nBWML+6mp9ra7kvKYkHU1IUK4zj4OvKSsZt2YI9ECBCUPFgSDzt1Qd/JqJFDTcYorlMF85Xnlp+8FhxSxIJooaRunD6a01o9xEJnXKANT4nK3121vic7PZ6ebSwkKklJaQbjfxuDUYo9lAbaa3SYRFURIhq4kQNMaKGejlAQcDDzoAHjSBiEkQsgopklY4EUXPMEbO7dgTfRxJaHSw8ds7y0muAi5W/GZj5qoUn3qg+prZPd5JVOl4zpbDYW88cdw0lHg835eXx8s6dPNu6NSOiolok/feH6mpu37aNHW73ftt3e73Mq6piXlUV03bt4p30dIZFRjZ7/2cif1itDN6wAYckIQKDtRbG6COOWpgEuFhrYb3PyWq/g3vy81nQubOS3v0P4rR5CjZ4Sk4BlsmyvHmfXa8Cf8iy/M0hTo0DyvfdIMtyrSAI3oZ9TfEI8OQJDllBQUHhpPBscTEBZPgrnKwIPbEJQR8vrQ4emVLDB1PN9OwTfHnaVrcBt99BqjmTse0fOGLb4VES5w1xseR7I5++beaxqdWsWRZcu+nSy4PJIlG8XUOi4zIm9B/EmMcTkICMrl7G3lnPa49HIJhKOb9rZ86Kv4fz/G0RWgVFu/p2UHj5tdR0y6LkolFNipL7Ior7+2YeKxotdO/t5o+fjIiizP3P1TYWYOgzxMU5F7hY+HkIn75jxl4vIorB6ImibRoKNoXRKfJciuq3MG/7O+RUr+LsuCEY1aGE6aPpETPgsAUZPNGxFIy9mYKxN++33ZK7CQBzwVbMBVsBaExSqYJ1azpQ3KoNdm8dz2vmkX0uuLqcS+8Ln8GekoqsOTNSo+d76pCAIeHhdFRWuRX+gQiCwM0JCdyccHhLh5PJ1VcHxcnP5gr0qGwPl/+FK9bFV+pKvvpXGpGxfp6dUUVsYvA+eVbsIHrE9GdtxWLe3vAIT/f+9JgmhTlrg/ertI57Rc8DWVA4iyUlX6MWNEzoMWU/y5HLb7Cz+HsjFbvVfPaeiVvHtKMy61yiV/9JlxefIOWbuWx4+Bkesz9HdvVKlu36lgey3qZNWKfjv0ingH5aM8mijumuCnICbp4pLubd0lKeSk3lX/HxJzX9/0xmSV0dV+Xk4JdlOqkN3G+MI0w8/NQ2TFRzoyGakbpwqiU/bVS6JoVCo6Cir9ZEX60JnyyxzGfnU3cN5T4f5VYrWgT+ZYhmkNbc5N+IGRVJKi3NFf+yu8GjOrEJcRLgurvrWP27jrXL9XyzsIjBg6IwqIPPYqunGgGh0apBlmW21a47pKf16YhKEBios9BXa2KBx8qXnhpynU4uy87myuho3kpPJ7KZ/Hw9ksQNW7YwpyJo6RMlqBmis9BepSdZpWWX5GOb380P3jp2ejxctGkT/2fvPMOjqNowfM9sz26STS+0JIReQ0cRBRtSLSBiAxG7FAELggIKiAoiYkFRsYtdUKwgiErvNZSQENJ7sr3MzPdjQzAk9ACJ397XlSuwO3PmzG5m5pznvO/zDo+KYlGzZmj+j6/d7RYLfXfuxCbLtFDpeTggkgbVROmfDkEQGGkIZ7vFxi9FRSwvLKR/ePgF6LGf2kitSesWBOENoB/QQ1GUjPLXBgJzgSRFUazlrylUTut+B2ikKMr1J7TnBu5WFGVJNceqLnIyw5/W7ed88P+t+LkQpDocNN24Ea+iwKNJjL9DoucNjlPuc7TsAAgCDQKbnNEx0g6omXhHJF6vQP/brRzcrWH/Th0PTSnG5RR4f46Zbr0dDB5pYeKdkQSZJT78wxfJ+ftSHQ3iJC5jPS1fe4GQPdv5/af1uMIuTTXS3Zu1vPR4KLfeb6H/sOo9K90usJSKmENl3pxhZuVSI9fcaOPRqSWsy/qJBdsmYveWZyD8MxE2jOaax37i0RsHnFOfdIX5BCfvRl+Qx6q3bLhy7TRsrtChl0RB58sobN+ZlzY9wLrsn4g1xjP3ql8qJhV1AZsicW9pKk4Ufm3blutCz82rzo8fPzWL2w3R0XAs21zVvRBplm/BxPB8Kxx/RJDQws3sxfmUO2yQa0tn9KreuCUnYzu8Sq8Gg8/4ePOfNbPqByOD77Vw56NVs7gOFe/gqb9vwiu7ubf1NAY0HlVlm41/6pk1LgyVWuHVJXk0iHMR9+1ntHh9NtoyX7RaetM4Xm9RyLy2FkSNjgfazuLqRkPP8tO59CiKwgaPjY+cBWTJvoW55gEBzE9M9N9HT8MRp5NOW7ZQ4PFwucbE+IDoC+7f6VEUVrpL2et1crM+pFp7hHMlZZ+GT98IIixK4uEpJVWyR6bcF87uzTrGPl9Er/5Vx4CyInPLI9+grBsH4XvhgY7EmmOwekopcxfRN34E97edgaIofLh3Bt8fepuH273IdXF31Ng5XExsisS3zmK+dxUjATFaLR80b37e142iKNyTnMyHubmIwACdmdv0YdWm3jsVmc+chRWLmFOaQwAAIABJREFUszPj43m6UaPzOn5dZZfVytU7dpDv8dBCpWeqqR76M7ArOBUfOgr4zlVMosHAns6d/dW76xjnmtZdK75lQRAWAAOBXseEyXJ6A42BEkEQvIIgHFsu+kYQhNXl/87hhAhJQRBCAA0nRFQeQ1EUl6IoZcd+AMvp+lhbRFw/tRf/34ifC8G0tDSfMLkpBM3BIDpd4TztPg2Cmp6xMAkQ19TLmOd8s9cfPzOxf6dvwN3hcheNEn233bSDGg7v961Kxzf3pV0LAgzqksKdSx6g5z03Er5tA4IsE7p909meZo3RupObj1blnFSYBF/EaVikjEoNvfr7olD/+d2AyyHQPbYv83r9yi1NHqELT8DK2VDWkHXzRpF91JeWsilnBV/tf42jloNndN27wiLIu7wX27sPY0LuM0wWZuF9fSwH7h9HUVIXlqUsYl32T6gFDeM7vVGnhEmANW4LThRaBgRwbUjIpe6OHz9+ytFqfUVxABo1gnXzwxhTrx4A6inJGBs5OLxPy/tzgiv2iTI25LZmjwGwePdzlLmr+miejL3bfM+OlklVi4t4ZQ9ztzyCV3bTNfp6+ifcW20bXa500rmnA8kr8O6cYFCpSBtyFyu/W8ORQUORVSoaHkhjytZQ2sf0xi27WLB9AhkvDSBy2WeEbN+M2nLG86BLiiAIdNOaeC2wEfcZIggSVCTb7Vy/cyePp6Tgkf8blZdrGpskMWjXLgo8HhJUOsYEROGwiMwYE8ZrU81IF8hyUSMI9NGZGW+MrjFh0mYRWPRiMI/fGcHWf/T8/q2Rg3uqRgBWRE7G+U5OVmR2F6yreF8URBr2/wrRVAAFLWHlTLJsqZS5ixAQSLfs/1drPuXzrR1PVdgr1DWMgoq7DOG8aGpAPVFDtttNn507eSsz87zafSUjo0KYfMYYyz2GiJN6gurLq0yPDvAVIpyelsY+29kVcqwLrC4upu/OnXTZsoWkzZtpt2kT1+7YwfB9+xi+bx+N16+n7ebN5Jdfj1NMsectTAIM0YdgFlQccjiYn5Fx+h38/Ce4pGnd5ancC4CbgKsURUk9YZPZwLsnvLYLeAz4ofz/64DJgiDEKIqSXf7adYAL2HK+fVSV+wS53W4Mhrrja+Pn4mO3+0QOTQ2lFfjxs7ywkI9yc0EBFsfTvpuTAFP1YtiugrXoVAaahiSd07F63uCgKL+UD+b5JqmNmniIiJbQan3Hy81Qsa984hnfzIN5zw4SPn+Per/+gOj1oAgCR268jf0PjscZGXNOfbgUtOzgJjLWS16WmvWr9Vx5g4OogAbc3nQST8yIAFmFSq1gK1Mzc2wYL36Yz8+pH7A1bzWfJr9EmD6G9pE96VFvIO0jep4yBXLnJt/nl9DcgznMN+l0eG0sS3kHgOGtJpNobnvhT7qG+cPtEwJGxcT4fYH8+KllvPACdO3q858MDYU2UgJ/lJSw22ajyYK9HByUxC9fm2je3s1V/XwRWQMb389fmctoFdYNjXBmY5qiPJHcDDWCoNCsbVW/SbWo4YG2s/gs+WVGJ8095b3i3sdL2bZWz471enZs0NGuqwt3aBjbp81l3+inqPfLUrymQJ7uNoSvDyzgqz0v8+CSbaiUbQAogoA1LpGith1IH3grRR26nsMnd/FQCwL9dGau0gbyqaOQn9ylzDl6lL9KSviiVSsa1UA2jkOSWF9WhlOW8SoKFkkizekk1enEJcvU1+mor9Nxpdlcq605HJLE0D172GGzESyomGSMQfSKvDgxjF3lz9iwSIk7Hjlt3MklR1HguUfC2b/L54FgDpMoKVTxx7IAmrY+XnXbYRMoyvfNR2MbeZEUiXlbRvN35jKmdf+M9pE+n+v5fb9kk0lg5lhg/XjuHphE2+5O6psSK2xpBEFgeMvJOL12fkn7iPlbx6FTGegW0+finnwNkajW80pgQxY58lnhLuPhgwfJ93h4plGjsx6P/FxYyBMpKYCvqFKS5syug6s0gfyttrDFa+fe/fv5KynpgkfxXgzy3G4mpqTwcW41sV4niLAi0EZtYHxANEahZjyWAwQVd+nDWODIY15GBo/Vr++3vPg/4JKmdQuC8CZwOzAI+PeSTqmiKNXmLVaT1q0CtuOLknwcCMVXqft7RVFGn2E/TlqtW1EU0tPT8Xg8xMbGIvovCj8noCgKdrudvLw8zGYzMTF1R5jxU3sp9HhovWkTOW43gb/HYpnVlDHTi+k90F5lW0mRGPvH1WRYD/FYxwWVPLzOBkWB9+cG88OnJu4aXcotI30FTkZcE01JoQp9gIzTLjJp6hGee7EFaqfvNp3f5XJ2j3+Wsmatzv2ELyGfvxXIF+8E0a6bk+lv+Yzkl35sYvErwRgDZZ5/p4CZY8MozFMR19SNHJBNXp4Xp2QHUxYEZkHS+zRpZ2FI07F0jr622kHxa1PN/LHMyE0jLAwfezyyp8CRxZ9Hv+XmJo/UOXEvXXIxxpKOWhDI6t6dCG3d8Mj04+f/md1WK522bMGlKHRZ34iNk+LRG2Re/TKP6Po+/0mP5EJzFtFhf/9qYM5TocQ3czNvSf5Jt1MU5Yzuc4teDGb5EhOJLd28/El+pTTXnAwVOzboKCsWfZFkulW0+Wg4jUsE2lmCCcjNqtTW4aEj2PXUjDM+l0vNOreV1x252BSZCI2G71q35vLg4NPvWI5HlnHIMhpBoMjrZWFWFm9lZlLoPX1IoQCMjI5mZkICUbXsfl7m9TJw1y7+LC1Fg8B0Uz1aqAwsmOZ7tmq0Ch63gCAoTHurkHZdq0bwng6P2+d/rboI4TvJO7Q8NSICrV5m8jxfLdipD4VjDJRZ/Ht2hdVCyj4NE26PJDhU4sOVOby/ezrLUhahFrU82v5lrmpwS6V2F84K5pevTIRGSLz6ZR5B5qoRuLIis2DbeFYd/Rq1qGVy1/dJirzqQp/yBUNRFJY4i/jC5fsc74mOZk7jxoSeYcDIIbudTlu2UCpJXKsN4mFD5FmNx/JlD2PK0nEg82piImPreGHAPTYbV27bRqHXiwBcpw2ik8aIujzqtkSRKJa9uBSFpmo9LdR6AmpIlPw3HkVhZNlhLIrMT23a+AsP1SHqarXuh8p/rz7h9XvwCYynRVEUSRCEfsCbwD+AA/gMmFgTHRQEgZiYGFJTUzly5EhNNOnnP4rZbCY6+mQ1mPz4OTsePXiQHLebaElLzpzGqNQKXa6q3mtyXdZPZFgPYdQE0znqmnM+piDAvRNL6T/MWlFIBnxRlMbCbDLtvsFWbFs9R265A21JMYeH3UNJq/bnfMxzwa3I5Mle8mQPKgSiRQ3hovqcV6qv6m/ni3eC2LlBR26miqOH1Xz2ps/EfsRjpSQ09/D0q4VMGhlO2gEt8C9Podw2vt+7b+OgoSPfCK/TOfrairfXZv1I67DLCNSGsnOjb6bRNKmQn1I/4/q4O1EJKsINsdzS9NFz6vulZmV51GT/sDC/MOnHTx2htcnE9Ph4njp8mH3dj9Lsigj2/2XitWdDmPFuAaJIJWHSI7nYXbiepMgrT9rm3m2+679lUuWoyR9T3qNdZM8Kq5EznfAPuc/CymUBHNqrZe0KPV2vcvLj5yZ+/tJIbuaJ05fBxLfox2MLd5EXVg9tUQHBO7cQvno5iT8upfBfkZMqhx1Zra7Vxca6a00kqHS8aM/msMdF7+3beadZM4afZowpKwpvZGYyOTUViyRVeT9EUBEiqlEBOkEkQlATpdKgQaBQ9pIpe9jhtfNeTg5f5uczLS6OMfXq1YpopUKPhz47d7LZYsGAyGRTDPGuABYvDOSPZUZEUeGpuYVsWG3gt2+MzJscwqtf5FVkKZwJ+dkqJt4ZQXiUxMz3CtAbLmwAz+/f+aIZL7/WQbtuLiQJwqO9FOSo2fingR7X+cZ8mWm+v/fYRl5+SHmXZSmLABiT9Ao9699Ypd17Hitj92YdGakaRvWJJjLWS1Q9idAICXOYRGSsxFX97Tzafg4uycHarOW8sHEUU7t9Qqvwbhf0nC8UgiAwzBBGkKjiXUc+i3NyWFpQwHPx8TxwmiJTDkli8J49lEoSzVV67j9LYRJ81eCHG8JY6Mhn0uHD9DabaWMyne9pXRLy3G7679pFoddLI1HLIwFRNFVfmloKGkHgSk0QP7pL+CAnxy9O/h9wScVJRVHOeiZZ3T6KoqQD/WukU9Wg1Wpp0qQJbnfVNBU/fsCXyn3MAsCPn/NlSW4uS/LyEIGk1fH87FbR9jInpqCqA2VZkfnqwHwA+ieMJEBz/pUhj1VuVVstNPjhK748+D1N2Up31rJV35XYRl52T5hKFcf280RSFHJlD6WKRJkiYZHLfysyJbKXHNlDnuylUKka/aECGqp0NFPpaaLSESqqMQkqAgQRAV80iFWRSZVcpEouShQJr6LgRcEbomB4LxOHEx5cZUL5KxwkaNPZxTU3+iJVG7fw8PzbBezZqsMcKhMS4fuMivNVrFwawJ6tBoJ//I3BN/xdMajNsh7mpU0PIgoqGrj7UpCzDEHlZl5BR9zFRYTqo+psKhWAV1FY7falzt3jX5jx46dOMb5+fT7LzWWnzUbIMwfQD2rP3m06fvzMyMA7j6fsuSQHz627i32FG7ml6aPclPhQxXNmxwYdKfs0NGzsqUipbdnBF62mKArfH1rIh3tnYtIEs6D3KkL0kWfcP3OozI13W1myMIiP5gfz1aIg0g76oqBUaoVmbdxE1ZPISFVz5JCG1H0Gfnu/JaMeL8UdGs68yO2s6PYXLw5+g6iWfSvaTfjsPRI+e4/UW4eTdutw3CG1s/BMlErDLFN9XrXnsN5jY0RyMn8UF/NS48ZVIhplRWGXzcajBw/yd2lplbaaq/QM1JnpqjGddhEv2evgPUc+ByUXE1JS+CQ3l0XNmtExsGaqTp8LHlnmpt272WyxECiIjPc0YMfbYbzwhQmbxSc63fdkKR17uGjdyUXyDi3phzTMfTqEJ+enc8S6i2hjI8IMp85sen9uMKVFKkqLVLz5vJnHZhbX9DCnArtV4O9ffXZh193sG2eoVD4f7K/eDeKPZQEV4mRWum+6rolI4/3d0wG4u+WkaoVJAJ1BYfysYp4fHUZxgYqMVA0ZqZUjCG0WkRvvtvJYxwW4JAdbc1eRbUu7oOLkyqUBLHoxmLHPF9P96tP7p58L/XRmGopaFjnySfe6efTgQWYdOcLd0dGMiI6mWUBAlX0eOXiwwibgcWMMmnP80q/TBrPeY2O7187gPXvY1LEjQepLHQd2djgliZt27ybN6SRa1PC8qT5B4qWd3/bWBvKju4SlBQUUezyE+O3T/tPUmmrdl5JTpXX78ePHz8UkxeEgafNmLJLEzUIoa25rSUGOmoefKa4YwP6bHw+/z7u7nsWgNrHo2vWYtOYa6UfIzi10nPQoxqyjAHhRMYun+bTNFF766OQpe2dLgezhC2cR+71OsmQ3Z+pjb1KpiNfr8SgKqQ4Hrhp+lqk8InFqLQ21GuqptNQTtdRXaYgWtVUGrsUFImOHRFJWouLGuy2MeMwXTbivcBNv75xMWtle2PQALF8IjVbDPb1oFNScu1pOolPU1TXa74vJRo+VWbZsIjUaMrp3R1MLomv8+PFz5mwoK6P71q0owIAtCfwwsSFancIrn+dRP953N1YUhYU7J/Fr2icABGvDGNx0NG11w5hwcxO8nsr3w8W/ZxMU6uadXVMq9hnSdCx3tHj8rPvnsAk8OCCK0mLf5DjQLHH36DJ69HFgCDh+z9+2Vsf0R8IRBIVZ7xeQ2MbC42sGkFa2lzB9DAt6/1EhqPYYeTNh2zYC4NXrybx+EPlde1CY1BVndOxZ9/FCIysKnzsL+dpVjAIEq1Q81qABsqKQ7nKx325nl82GtTxSUo/ACEM4vbRBHIsZPFlBj1Mdc6W7jA+dBVgVGRGYER/PUw0bXhLrkTEHD7IgMxOtJJL4ZmuSl4YgS75+1IvzcOv9Fq684XhmSVoKTLwzAq9Ti9D9NZTrxwLwULvZXB93Z7XHOPY3JKp8f1eyJHD/pBL63nphCpz8+nUAb80MoX68hwXf5FWIoFlHVDx8YzSiqPDuzzmERsq88nQIa34OQH3dFLyXzaRP3F080HbWab8LrwcKclXkZqjJzVJRXKAiZZ+GjasNlY7rkhzsLdxwQdO6C3JFHr05CqddJCxS4s3vc9FdwMhUSVH4zV3KEmcRpcrxKOI4vZ4ewcG0N5mwSxIpDkdFAZxpxnq01VQVL8+GUtnLeMtRChUvQyIi+KJlyzpj1yMrCnfv28eneXkYBZEXTQ2or7r0EeaKojDOks4R2c1bTZrwYHlRNz+1m3NN6/aLk/jFST9+/NQOXLLM5Vu3ssVqpYVKT4MFrfjtq0Ci6nuZ/2VelRSjfYWbmPLPECTFy8jW0xjYeNT5d0KSaPLBWzR/62VEScIW24CN1z7AbR+OIo8o+gy28uDkqpEZZ4uiKKxwl7HYUYCd42lXAaJIjFZLmEZDuEZDWPlPpEZDvF5PgsFAvF5PuEZTMeCTFYUMl4tNFgvrSkvZarVS6PFQ7PVikSRkRUEpb7uNyUR7k4l4vR6tIKAVRbSCgEYQ+XutwmFzCZu0BWSfJFLegMj1uiD668yEi8dXbzeu1jPrMV+6yejpxVzV117hWZVjO8K8J6PZ/08CPe7czMB7smkSklRnBqwn4wVbFhs8NibUr8+cxMRL3R0/fvycA48eOMAbWVlEixoipySx868AWndyMWNRQcU2iqKwPvtnPt47myzbYQDEn95C3vgg5ig7QYEaMtPUJHV3MXzmet7bNY3t+X8iIHBP66kMSLj3nO93f/5s4M3nzFx+nYMR48oICqk+TXfBNDMrlxqJbeRh3pI8ZLWNcauuI9d+hD5xd/FguxcAEDweYlf+ROJHCzHv21WpjbxuPVn31mcV/1dbSvEGnrnX44XkgNfJQkceh6XqfRQ1CCSpAxgVEEGkWDORRSWyl/cc+fzl8flPj4qJ4c0mTS7qQtSHOTmMSE72/Wdya1gbDkDzdi4G3WWlay8n/+5Oju0Iczc/wsF1cfCFrxJ1wODROFu/yeyeSyuKBm7OXcmKI0toFNSMSG0in465i6LMIDoN2kW9ejqWvtkUtVph1uJ8mrb21Ph5TbwjgkN7tdwzvpRBd1krvTdpZDj7tum4e2wpN4+wMuH2CFL2aeG2QbS7ophnu32MSjy3iDybReCea2NwuwTmfJJHYquq52b3WNCI2rPynD0dsyeEsv6P44Vl73y0lMH3Wk+xR83gUWQ2eWz84S5jq/ffo83K3KUP4xZ9zURRJ3sdTLZmIAHzGjdmXIMGNdLuhURRFManpPBqRgYiMNVYj3bnKdTWJEudxSx2FtA1MJD1HTte6u74OQP84uR54Bcn/fjxUxsYe/Agr2VmEiiIPHA4kTkjfVEcz7+TT5vOlcUyl9fBQyt7UOTMpUe9gUzo+EaNiF2dJ9xH7B8/A5DRZxA7nn4BuzaI2y6PRZYEHppczPWDq0Zwng02RWKuLYetXl87XQIDmdKoEW2MRhrq9YiXWLSTFYW9Nhv77Hb22+3sdzhILv/3MR8vFdBaHUCooCJYVNNZY+TP2TH89o2vumN4tJcbhtjoN8yGVqcw/OpoLCUqZi/Op3n7um8Rkit5eNiShgTs7ty5Vld39ePHz8kp83ppuXEjmW43fT1h/Nq3NZJX4JXP80hoXlm48MoeVhxZwrJtP5M16zeQdNw842vu7tcdSYI5W+5nXfZPAOhUBsZ3fJ2uMdefdx8V5fQuIlaLwJhboijKV9HvNiujnihld8E/PLN2KADPX/4lbcIvq9Ro2JZ1RP+5grCtGzAn7yLz2gFsmf0GAILXS5/e7XBGRFGU1JniVu0padkOa1wCsu7S+K9J5Yt627y+FNRwUUOUqCGuPLr/QlUI/slVwruOfGTg+pAQvmrVisCLkK66qayMy7dswyMo8EEjAr5pRN+hNnr1t/sKIZ1AiauAh1f0xO4tw6gJJnHbd+z4uhcarcKz7xyhZVsRVXnRjrmbH+GvzKW+HddMgj9mgSkbHm0OujJarcxgz9/1SGjuZu5n+TWa3n14v4bxt0WiViu892sOwaGVJbPfvwvgjedCCA6RmP1BPuNvj8RhExn62iL6d+1OoDbkrI8pOh1oS4vRlpaw5BUtyzc0JmlIIKOeriwQ5trSmbFhBE3M7RmdNLdGxpWb1uiZOTYMUaVw091WvlkciMEos3BZbpVzv5DYFYn9XifJXieZshuDIGISVCSodFyuMdXo2PPH8mtGBH5o04a+tdwr8bm0NKampQEwNiCKXtrapYeUyF5GlqUiA3s7d6aFf8xZ66mrBXH8+PHjxw/wZV4er2VmAvCwKpoPp/q8ua6/xVZFmATQqQ082G423x18k0fav3zuA0hFAVn2mR0BGTfcSPiWdewe/yxHBwwBQUALNGvrZv9OLS07nJ+wViR7ec6aSZrsRi+KzIiPZ1z9+hdsUnUuiIJAa5OJ1ieYmcuKws9FRbyUns6a0lJ2eI+LtN+7irl2rIWbIuNY+XkQBTlqPl4QzKofA7hlpAVLia/aeWKrui9MAixxFiIBV5vNfmHSj586TJBazYImTbh5zx5+1RTSbmghWz8NZ/kSI6OnlVTaVi1q6BN/F0eWPEqWpCOs+QGGXOcrCqZSQag+ChGRLjHXMbTZeOKDW9ZIH8/k8WAKVHhoSgkzx4axfIkJnV7hrjGXc33cnfya9glvbH+c+VetQKc2VDRa2OkyCjv5BEu1zYrKfjyFNzDlABqbBa2llKDDB4j75lMAFEHAHlOfrGv6svexZ2rk/M4UlSBwvS6Y63UXN5qzr85MuKhmri2HX4uLuWLbNpa3bUs9Xc1F1p1IisPBNZt24VEpsDaM5rtimPBlXqVifSdi1oXTu+EQUkp28ljHBYT3qc/sAgcbVxt4/4VY5n6WT3mxYQY1vp/G5jYcSivjn7+eRQGiblxAcExjip15PD5d5v5+MoeTtXz1+3badZJoFlozEVu/f+uLSOva21GtONfzBjs/fWEkdb+WSfeG4rCJiKLC4K59OWUdJ0nyXSzloaT1f/yahsu+xJSWgiE/t2KzXuW/+/30O57HW6DRQNiW9USsX4NWXcxVuQfxCgco2milc1BXVE4HR24chjvUJ7AFJ+8mZNc2lPIL02s0UdqiDdaG8XBCVK3LIfDObN/f68A7rdzxaBnb1ul8n+u7gYx64vwzcc6UAEFFksZIkubCj1n6aYNJlVysdJcxdO9e/k5Kol0tKZCjKArzMjJ4LzsbgyhiVKlYU+5VO8oQUeuESQCzqKaj2sgmr40Pc3KY3bjxpe6SnwuEP3ISf+SkHz9+Li1bLBau2LYNhyxztSOUzEnNSN6hIyzKy4Kv8wgwnfw+rSjKOQuTIbu20nrOdDKvG8DhO8pTwmUZldOBFFB58FZSJFJaJNIo8UxdIauSJbmZbsskV/YSpdHwc9u2JF1Ck/3zYZvFwnarlTyPh11WK5/m5QEQJaoZpY7EsiKCT14Poij/uJF4pyucTHmt8FJ1ucY4IrkYZ0lHATZ26EBn/3PTj586z427drG0sJCGTgPpfbug0cC7P1eN6irIFXlwQDRej8Dzi/Jp0+n4gkuRIweP4iEq4NKlMS771Mj7c3zey/1vtzJsbAZjVvWm0JlNz/o3Mr7j62fclqa0mPBNawnZsx3z7u2Yk3ejsfqKgO0Z+zSHRjwMgOh2Yd6zg6L2nWu8UFxt4pDXyQxbFiWKRH2djuVt2tD2AgguBW433bdu45DTAQdMXP93E+4fY62wSjkVHtmNiFiR9lxWIvLggCjsVpGxzxfRq7+j0vbH0o1bdXAx490CBAEk2YtKVPPmDDO/fWNE02o5niH9uabhbTzQduZ5pTuXFYvc1zcKl1Nk+sIC2nWtPk1/5Z7VvD6mO0pRAgDRDbwsXOYTGNU2K6a0FExphzClHfb9PpKC6Ugqf366HEticwASP3iTVvNnVbQpq9W4g8zIGi2a3DyasZ+hc4Pp1ttJ8zdeptm780/a79Wf/0Jp89YANHlvAS1ff7HKNh6jiZIWbdg9cTplzXwLE9s+yOf3+Ta0YTomvOVGjAhi874opj4cgVqt8Pp3uUTXP7ngXJfxKArP2TLZ5XVQT6tlQ8eOF1TQPxO8sszDBw+yKDu7ynvD9KEM1dfeCM91bisv2rOJ1WpJ7969VgU1+KmKP3LSjx8/fuogWS4XA3ftwiHLRGcFsfqe1khuEb1BZuxzxVWEyeSizYQZYokw+FK+z0WYFN0umr/5MokfvY2gKBhys0i9dTiKRgOiWEWYBF/lVPN5pN/kSh4mWzMoViQa6/X81q4dCQbD6XespSQFBlYSVu+JiWFkcjLpLhcz3Vl06F3CxB6RfP1MNFvX+lIA23a5MNUpLzafOgpRgMEREX5h0o+f/wgLmjRhZUkJ6XoHEfcfJf/thvz2XQBDTvCF+/rdQLwegVYdXJWESYBQQ/TF7HK1DLzDhkYDb79g5sfPTIRHxTDuhld5cdP9XNtw2Fm15QkOIfuafmRf08/3gqKgKyrAlJaCJb5JxXaxK5bTcfIYSps0J23IcDL63oTXWDuipGqSRLWeF00NeN6WRYbLRY9t2/i6VSuuC625iuc2SWLQ7t0+YTJHR8grLRn5QfFJhcl8RxZfJL/CvW2mY1Ab0YiVQwuDzDKDR1r46LVgPn0jiMuucXAsK3/rPzrW/2FAVCncP6mkQlc+Jmz2H2blt2+MePbegFAczwqWkGE9xJOd3zmryvP/ZvkXRlxOkYQWbtp28QmThuxMwraux5ieitpmZV/pdmJKNvN48xAKtk7jT2dfghvWByDh03dpM2faSds3pR6qECdzr7gGV1gk1rgErA3j8QSZK8Tzj14xkvpxMH/84KRbbyfFrdtzeOgItCXFaIsLybIcIl3KwaXwXJP8AAAgAElEQVRV0bLhNXhMx8c7tgZxZPc6btegKywg6MAeNDYrEZvX4f1XRex2v7zHNF6HQuBW32vXBAZzo6kZm62t+PPNidw8qzwSWJIqsnj+C2gEgScDYnjKmkGG203/XbtY0779RbFEqA6r18ute/fyc1ERAj6fzQYqLRZFJkxQ01Zdu8fknTRGAgWRLLeb34uK6FPLU+X9nBv+yEn8kZN+/IAvAi/X7SZco0Htr7p7UdhUVsaDBw6w1Wol1K6j6NbOYFPTuaeD+58qrZK+VOTI4bHV1yMrEtMv/4KE4FZnfczAg/voOHk0wQd9BvNH+93C3jGTcEZeuEllmSzxlPUoWbKH1kYjK9q1I0p76SsA1jRlXi9T09J4PTMTr6IgAv21ZszfN2Tv+gDGTC8+aTGHukKy18FT1gxUwJ4uXWgWUHsM0/348XN+vJaRwdhDh9B7VDgHdScsUODtH3NQl9dX2fK3judH+wqSnBg1Wdv46j0Tn74eXFHcx+6xVFTsrmkaf7SQ5m/NQe30LUB5jCaO9h9M6q3DsSY0Oc3edQ+rLPGCLZs9kgO1IPB206aMjIk573ZtksSAXbtYVVICVjU8msToe9xcPah6n+tM62Fe2DCSDOshrqx/E491XFDtdi4nPHJTFAU5au4eU8rN91jxuGHM4Ciyj6oZeKeFkROqD+yZ+lAYO9br6X7LXnZ2uAybp5QwfQxTun14drYFigJHcvjgjnzi7MnEPNGDZsN8i8wNfviaDs+OO+mu05u+jTD+Ztp1dRGz8ie6TLwfZ1gE1rjGWOMaY4lLxNooAWt8IvaY+mck8B05pGbskChUaoWpbxTQtkvla9kre3hu3V3sLPibCEM9Xu75I2Z9xEnbE7xeTKkHMe/bzdEBgytE0LQB79Mv40MiTFaMkgW1o/J3Ga9JZ9rPasxhMi3nPU/ct59T0LEbex6bgq1RwmnPoy6QK3l4wnqUUkWib2goS1u3vujzrGyXi/67drHVakWLwARjNF01dW8BZZE9j+XuUoZGRLCk1dnPgfxcPPwFcc4Dvzjp5/8ZWVH4vqCA2enpbLJYiNZquT0ykjujomhvMtX5isK1DUVR+Co/n7lHj7LR4ksPCxREWrzTmo2fhdL/div3Tiytkhnmkd1MWzuMPYUbiAtqyUs9l6FVnZ0pf8Ta1XSZeD9qhx1XSBjbn3mJnF7nX6zgVDgVmWesGRyUXDTQ6VjXocMlT2u50By023ni8GG+L/BVu40SNdxniCBRpSNIUCEAdmRKZYkgQYVJrBuRAh5FYYo1g/2Sk1ExMSxq1uxSd8mPHz81iFeWablpEwcdDnSfxOF6L66iCFpRnsi4oZGUlajoO9TK/U9dPK+4c2H/Tg1PDo8kLMrLe7/kVnov25ZGcuFmejUcXGPHs6SUYZ+xjEFHFhFVnFLxel7XK/j7pfcRAw3/qYxvjyLzuj2PPz2+ccyzjRoxLS7unMeMdkmif7kwqXaLeB9rR0NXAPO+yKtWa9uY8zuvbhmD3WshVB/F7CuWEhlQ/6Ttr/rBwPxnQwkwyVzVz056iobdm3WEhEu88V3uSe1zNv+lY8aYcAJMMm/PXkz9BWMILLOjl0WitNFoNSbc5hDc5lAy+t5E9tV9AQg4mkbc1x+jKy7ClJZCYOrBCksAgO1PzODIsBEABO/bRau50/lTl8ohIRe1ItAh9DIShVh0RQUcuHc0RUldAFA5HAhed41UkX96ZDh7t/nGYx0udzJ8XGkl6x6ru4Qn1gwky3aY7jF9ebLLO2d9jHuujaa4QMVLH+fRtLUH0eXEmJ6KKTWFf+bmMD7vWYaMsnDHIxY6PvUI9X/1FSmS1RoO3f0AB0aNQTLU/UXQA14nU6wZuFF4ICaGvmFh7LBasUkSd0VHX1Dv7j02G3137iTd5SJYUDHZGEtT9aUp6nW+pHidTLAeRScIZF92GSEazaXukp+T4BcnzwO/OOnn/5XNZWXcnZzMPnv1q9KtAgK4MyqKoZGRxOn1fqHyPDnidPLggQP8UlQEgBqBHhoTt+hDeX5gA/Ky1Ex7q4D23Sp7ECmKwps7nuD3I5+jVxmZe9XP1DOd3YqyITuTqwddgcrjJr9LD7bMWoAr7OSr4DXFq7YcVnsshKrV/J2U9H9VYW95YSEPHjhAhuv49ykCKgQ8+J69aqCjxshVmiA6aALQCbUzallRFF535LHSXUagSsXezp2pr6+bg1s/fvycnI9zcrg7ORmtS4X7pu7gUNOppwO7RWTvNh3xzdy8+GE+2lq+xlRWLHJ3b1803xdrs9AZfPfcHNsRxq66Bq/s4eWeP5Jgbn1O7ZcUieh0Chqtwi9fGfn0zSAcNhEBmYX3fseAlEVEr/md/bHdaZn5F8ZAhfrxHm6I3UDnJ+IJMNfOe/3ZoCgKnzmL+MrlG9PMa9yYcQ3O3m/0kN3OPfv383dpKXpFxDu+Ld7tZibPL6Rzz8p2KLIi88X+V/hi/6sAtAjtzOOdFxKqjzrlMWQZxg+LIO1A5ayN8bOK6HmDo8r2xvRUGn6/hLzOPRg6ezBZ6Wpm3/07T3503UmPsWfcZA4NfwiAkJ1b6Dl8UKX3Pag5SBOcLZrguW9QpcXh7w8t5IM9M9CrjDzZ5W2SIq865fnUBGXFIkveDuTXb4xIXgG9Qea1r/OIjD2etZNpPcy7u55hdNIrp/2MT8RSKnDXVb7o0M//ycIQUFlzWLtCz0uPh2EKkln0cw6BigVTWgrN35pD1D+rALBHx7J7wlSf6FvH5yDr3FZesmdTnfLSJzSU+2JiaG00Eq/Xo6mByEqnJLEwK4tpaWmUShKxooZnjLHEqOpu5pKiKIyzpHNEdrOwaVMeiI291F3ycxL84uR54Bcn/fy/oSgKC7OyGHfoEG5FwSiI9NWa6aMLJkVystptYZPHViGgAERqNHQIDCTRYMBYXt1NwBdN5VUUPOU/AM0DAugcGEhroxFtDTxgFUXBKklIikKwWl0nRdL3srMZd+gQVklCg8BNuhD66oIxi2oK80TuvT4GUVT4dE02BmPl+/LSQ++weM9ziIg83XUxnaKvPqc+JHz2LiE7t7H1+Xkopyz5WDMc8jqZaD0KwJr27bnCbL7gx6xtlHm9TElN5fO8PAo8nkrvBYgidvl4mrcKSFDpaKk20E9nJlKsPSvC3zmL+dBZgAj82KYNN/i9fvz4+U/ilWVabdrEAYeDZhvrcWhyIpLX98zVG2TmfpZPvbhzL4x2tsiKwn7JSabkJlf24lBkGqq0JKp0NFTpUJ9kPKAocEfPGOxWkflf5VaKCHtp0wOszVpOXFALXr5yeRWfwlPhcgi8MCGU7euqLs6EhEsUF6jQB8jM+SSf7D9y+OF12Ek7AKLIIZN6FOhjKbnrFpL79mWFsIu2ET1OGfVX2zn2fBCAb1q14qaIM1v4tEsSL6WnMzs9HZeiYECk5RdN2bIwmpZJLma+V1BJj7J6Snl1y1g2564AoG/8CO5p/ewZf39HD6v5+UsjASaF4FCJ+nFeki47vngoeL1E/bWC+K8+JnLdnwBkXDeQZ1p9xAfzgunRKZvX+n+O1RzE3/m/073hjWgUEW1pMZqSIorbdaKsmS/VVJefS+LH72A3hrDLksjSvUl8v601pnAV7yzPqVJ1e0/Bet7Y/gS3NHmEqxsNPaPzqSmyjqh45elQDu3VckUfOxNeKK6Rdvds1TL53ggiYrws+im3yvuSBI/e5EutHzmxhIF32HxvKArhq36i7ctTCczJAWD/qLEkP/J4jfTrUrLcVcInjkKiRDXxKh0OZDZ6bPzb8EeFz9f8xvBwbg4PP+tFfbcs82FODs8dOVKxON5CpWeSMZagOpKpcyqWOotZ7CygW1AQ6zp0uNTd8XMS/OLkeeAXJ/38P+HyyrT5eD8H430Dha4aI6MNUVVSS62yxDqPldVuC/skB+filGcQRQaFh3NXVBTXhIRglSSKPB7ssoysKMhAiFpNrE6HThTxyjIZLhcHHA42lpWxvqyM3TYbeR4PjnIRxyiKNNTraajT0aD8d0ujkU6BgTTU6WqdcKkoClNSU5mVng74BgiPBERR/18rl3//ZmDOk6EkNHfzyuf5lfbfkP0rszeOQkFhZOtpDGw86uw6IMvwb4FYUS7a6vNUawY7vA7uiIzkk5Zn4c30H8Ujy+R7PHgUhQiNhgCVil1WKx/n5rIkL4+j/4qwDBZUTDXGklALUm/Wl1dIVIDXEhMZXb/uTqL9+PFzej7NzeXOffswCSJTixL5Yl4oOzfqGD2tmCv6VI0yu1C4FJm5thw2em3Vvq9BIE6lpbFKT6JKR6JaTwNRW1HFdeIdERzaq+WpVwrp1ut4BF6Jq4Axf/SmzF3E0GbjGNZ84hn1x+uB2RPC2PxX5fuyMVDm7jGl9B5kZ/rD4ezerCMsUqIoX0RRBK674ygH60+i4Zpivlu3lhBKKvZdVx8+bw2bLmvJ4B4vkxjS7mw/pkuOoii87cjnF3cpBlHkq1atuDI4GNMJhT88ssyK4mJ+LipiQ1kZ261W3OXz0HbqAAbkRzNrcANkWWD2B/k0b1fZB7HYmceEP2/A4i7hoXaz6d1wSI30X1eQR6NvPiXu208x5PnEMEUQyOt+JWmD72JTw36MHhyFVqfw8eqsioI6x5BkL0//fTNdoq/jpiYPI5ZnQKTuVzN5VAR26/Ex2CPPFHPtzdVnK3kkF2pRe0nGsYeTNUy4PQJFEXjxwzyatfVUu92ajO/RiFq6x/Y9bZu/fBXAwlkhdOzh5JkFhdVu8+vXAbw1M4TQCC/3P1VCxx4uNFr4PHkOy3a9ylN/w7gNcNvENlzRazotw7qc13nWRrIlN8tdpeyRHGRJblwnxFZeFhTErIQErjzNAr9dkliUnc2co0crRMkwQc1t+lB6aYNOupBT1yiRvYwsS0UG9nXuTPP/o4ysuoRfnDwP/OKkn/8XFEWh76r9/CLmgAQh3zZiZh89sQ1OLT26FJkjkpvDkpNC2YsTBafi20eFgBoBleBLU5ZQSJXcpEhOrMqZS5qhajWlXi/S6Tc9KZEaDVPj4ni4Xr3zaKXm8Mgy9x84wAflK79DdaEM1YcinjBAWPRiMMuXmOh3m5X7njzu46UoCjM33MPm3BVc1+gOHmo3+6wGrVF//k7T919nw6uLcYfUXDXNM2G7x8Y0WxYaQWB/ly7E1+HK3BcDRVFIczr5p7SUOUePssNmw4DIJGMMbTWXzm9pj9fBNGsmHhQejo3l9SZNat0CgB8/fmoWSVFovWkTyXY7t+lDuU0fdtEL6ZbJEjNtWeyXnOgEgSvNZuL1eowqFTusVjZbLJRKVUcMWgTaqA300gax9rn6rF1uYsRjpdx4d+Wq439n/sCczQ8hCiqeu2wJrcO7n7I/sgzznw3hz+UBaPUy094spHELNw6biDFQroiEKykUeey2SIoLfB9W4jX/kN7zWtyyA73KSNcta4j6/iAPaN/jKu8KRNk3D5MEuOk2AVW/+xnWbCK6Wl4590QkRWGWLYstXp/wJgDxej1xej2xOl+E64+FhVUyCCIENSMM4VymMTFrXBib1hjo1tvBU3N9qeJe2YNKOJ4xc6B4G6KgItHctmY6LstcfdOVmNJTAXCZQ0m/8TbSbrkDe/1GgG9N997roynKV1VrvbM260de2vQgAJfF9mNs0qvo1AbemxPMD5+aCI2QuLKfnR7XOWjcovL5e2UP6lqSJbFgmpmVS400a+ti9gcFVdaxt+T+wfPr70anMjCrx7c0Nrc5ZXvvzA7mpy9M3Djcwohx1WsTNruX+waEYC/yzb9NQTK3PVCG4fL3eWvnJLyymyAnlOkhSBvK/F4raLFuKwWduteI72ZtQ1EUChQv2zx21nus7PTaORbzfX1ICPfFxnK12Yz5X16LsqLwUU4OT6emku32CfohgoqbdCH00QWjraV2QefDDGsWm702pjZqxLT4+EvdHT/V4BcnzwO/OOmnJvHKMiVeLw5ZJkKjQX8xR/OnYXpaGtPS0kACpraGf3wm3+NnFdHpCtfpdj8rFEUhRXKx2m1hjcdCmeKbROgR0Akixx6VVkWulD6uRiBSVNNYpaOZ2kCCSkeYoCZY9KWRF8je8h8P+YqXfNlLquTiiOSqEDYfb9CA2QkJVUTAi4lHlhmyZw9LCwsRgYcMkVyrq34gNX5YBIeTtUx8sYge11WOSnF67SxNeZtbmjx6VgPY0G0bueyhYahcLvaPGkPyI0+cz+mcFbKiMNF6lMOSi7H16vFqk/9etdILSZnXy427d/sKAyDQSxvINdogmqouru/rYa+LKdYM7MgMDAvjm1atLnqFST9+/FwavszLY+jevWgRWBDYiCjVxRNQCmQPz1ozyZI9hKjV/NCmDZcHV35+KopCisPBlnKhcrPFwlaLhbJ/CZYatwrPc825PjqAh6aUnHgY5m0ZzZ8Z3xGkDWXOlT+dMrX62w9MfDQ/GJVaYdIrhaccM+3YovDiE4F4Er/B02cUiAqtw7szOukVzEJDxg2NJPuomqH9DjO55SdE/fw15uQ9RI+XKQ6A+qZE5hseIyQssSJNuC7gUGQW2vPY4bVTolS/1BwsqOiuMdFSbaCpSk+U6BMej6UAiyqFBV/nUS/OS749k5kb7qFfwgiubXR7zXX02Ny3/Hlaf/k3JCxZTMrt95J9dV/kagxV5z9rZtUPxmqFNkVRWJH+OW/vmIxX8dDE3I6nurzP5KFtyTmq5sk5hXS/2lmlzaOWg0xbO4wBjUcxIGEUKlFdZZuLSVGeyEODonA5xWrHo5LsZcaG4WzL+5MwfTQv9fyBMMPJq7RPuc8XRTxmejG9B1aNFt2au4r3d08nI8MNG0aj2zcSV4lvIX3RLxmER4JLcpBtS2P+1nGkle3l/tIOLJy/HZc5lL1jJ3O0/y2Vs4P+YxTJXr50FvG7u7RijqMCOgcFEafXE63V8ndpKZvLC2xGiWpu1oXSWxuI5j8oSh5jpauMBY5c2ptMbOvU6VJ3x081+MXJ8+C/Lk5mOJ1Ea7X+SeUFZJ/NxoLMTL7My6PQW9mLKUytpp5OR6xORz2tloZ6PU0NBpoEBNAyIABDDYiXDknigMNBlEZDlLZqSoikKLyemcm4Q4d8L7zShMev0LPsExP7d+oQVQqjpxbTa8CFSdeSFAWLImEUVGhO6JuiKFgUmWLFS6CgwiyozklUdCsyS10lfOr0pY7cHhnJ4ubNa8Tz8mzxyjK379vHV/n5aBB4whhNZ42p2m0dNoE7esYgywLv/ZpNaITEppzf6Rx97TkLUfrcLK68vS/6ogKyr7yOTXPeQVFfvEHvancZr9pzCVSpONy1K+Haumu+falwShJ3JyfzVf7xNP8oUU0jUUesSks9UUOsSkusqMEsqGpctMyU3Ey2ZlCiSPQMDuaXtm1r5F7lx4+fuoGiKFy9YwerSkroojbytOniFB4okyWetmaQIbtpqNPxS9u2Z+y5JisK++x2Ps/N5ePcXNJdLijQ0vqV9sx4s6qPnsvrYNLfN3O4dBd944dzf9uZJ2179C2RHD2sYdQTJfQfVn2a+TE+2DOD7w8uBAHqmRozrPlELovtV5Huu3uzlin3+XwZWyS5uOUeC91bZrLWs523djxJkTOXHe8H0DbdTn7nyzhw7xgKulxepwqClMpe0iU3BbKXEkXCrki0Uhtoow6oSLs/hqLAUyPC2b9Tx/W32HhoSgkOr41Jf91IWtk+ogIa8ebVf9aIeBd4cB9tXp7K0QG3cnTA4OMdgFN+vn/+ZGDe5FDim7mZtyS/2m32FKxn9sZRWDwlGEu6Ynt1PWqNwsers6sUgylx5vPEXwPJsx+lWUhHZvT46qy8Ty8UX7wdyOcLg4iI9vLa13lVPNBtnjKeXDOIDOtB6psSmdnjG4J11XtQD+8dTWmxijmf5JHY6njEqFf28Mb2J1h19CsAgrVhDGs+kSvrDWbqqIYc2KXlwaeL6TPkuKB5pCyZiX/2o+MRF8t+jSYiw5eRVNSmA/sefZKCzpfVqevjbMmRPPzkLmGrx0aGXDXl3oDIrfpQ+uuCTytKWssENq42UJgnMugua60vblYdZbLEiLLDyEBat2408hdorHX4xcnz4L8uTnbZsoVDDgd9QkPpFxbGDaGhhGpqRwpBXabA7WZpYSGf5+aysqTqirwKTpuirBUEugUF0dNsRisIFHm9WCWJSI2Ghno9RlEk2W5nj91OpsuFU5ZxyjJaQSBUo8GsVpPmdLLXZqs4VohaTSujkS6BgXQPCsIhy8xKTyf5WEXujxsS9WtDFv6Qi9cLbz7vWw0GGDmhhIF3nnrQXdtZ5S7jdXsuEjAiOpr3mzW7qNFmkqIwIjmZT3JzUSkCLb5tQieNkebt3DRu6ebES2/7eh3THgonqp6XBUuP8Ob2J1md8Q13tniKwU0fPevji24Xl48aQuiurZQ2bclfHyxFuogp1UWyl7GWI1gUmZnx8TzdqNFFO/Z/DUVRWF1SwuKcHL7Oz6/wXT2RMEFNB00AnTRGElQ6QgV1lcnf2bDdY+Nlew42Raad0cifSUkEX0Rx248fP7WDvTYb7TZvxqsoPG2MoctJFtlqCociM9WayQHJSX2djn+Skmh4jpNOtyzTYM0G8nBhWpTIJ49Xf0/Md2Txa+rH3NZ8/EmzE3KzVDzQLxpRVPhoVTamoFPPnYocOTy79jZuSnyIqxrcUq2o9u1iE5+9FYTX4+tX++4+Xz6LJ593N01g8feQsGYNYvmCd1GbDhwYNYbcK66uEyJMeoqaJ4dHEBYp0XuAne7XOCgpVJGyT4PNIjLwTmuFYLf+Dz2zJ4Sh08u8tSwXc7iX2RvvY2POr5h1Ebzc80ciAs7PrkdbVEDzha8Q980nCLKMrV5DViz964y9CkoKRUZc44sS/GBlNubQ6p/H2dZUZm+6jyM/94ffXyayzR7e+aiyV6DTa2fKP0M4VLKDaGMjXrxi2UkFvouNyyEwZkgkuZlq+gyx8uDTpVW2ybNnMOmvmyh0ZpMQ3JrnLv8Ck6ZyZHNpkcjwq32f15K1WegNvu/a6bXz0qYH2Jq3ClFQ0T/hXoY2G4dR45t/f7vYxEevBdPhf+ydd3gU5dqH75ntu9n0TiAJabTQOxwQEQVExV6wHT9sWLAgiAqiggcBFY/YG3YRsaAiRUBEei8hhFAS0usmm+27M/P9sRiIoRMQPHtfFxfZNvPO7OzMM7/3eX5PbxcT32joU7ngwMeoBBWXxV9P6pcfkvHuTNQO/z1LVafu5Ix8mIpe/S+I38eZUCZ5yZFc1Mg+LIq/yeafDTaPh6VS5I3nw9iyRlff5OyBiRYGXX10D9TznafqCtgluQJe6OcpAXHyDPgni5M2n4/kdesaeLxoBYErIiK4IzaWweHhaAIZladEkdvN/Xv2sKCqql4QFIDuGhNDtaEkq3SYDpUt2xSZKsWHRfZRJfuoUnyUyz6KJQ9Fsre+1LkpMAkiTkU+ZuOaIEEkZGECRVOTueHuOm4Z5S8BkGWY/UoI8z/333Q8+KyFS4ZfmBeqP9notfOivRgZmJWWxgPnyIPSJ8vclZPDp2VliAqop7TBszS6/nVzqMQ942obNBT48i0zc94NpveQaqoGDyLHshlRUDEy83mGJt9xymNo/+JTJM/9BI85hBVfLKj3TDoXKIrCC/ZiNvscdA4KYk3nzn9L5uo/EavPxzqrlT1OJ3scjvr/81yuRr95EYgSNdysD+ci7clf0xyKxK9uK7NdlchAz+Bgvm/XjphA5muAAP+zjNu3j2kFBUSLal43J6I7S6WCTkVmqr2EbT4H4Wo1f3TqdMpdav/K87sKeLZ8HxQa+CqtGXrdiUULSZFAURoIir98beKd/4TSupOb/3xY2egz1c5SVhbN58qUu+snQxVFOeHEaHW5yPwvgljwVRAet8Bzb1fSocfhcnFDSRGpH79FwnefofX4RcqajLZkPTqByh59T2of/F38GdsciwFX2Bn9fA2SDx6+PpqiPA3Xj7Qy4oE6Pt31H+blvoFG1DG5z9dkhHc57XGILicpn39A2kez0Nj9vqPFA4ey87EJOOObn9KyHrkhmrxcDY+9WE2/IceuNPLJXkbdCuXZSdz46D5uvt0vsGdVreO73LfYVbUOh68OszaMl/71A/FBLU97+84G29drmXivP7P3hXcryOzmafSeItt+nvrjGmrdlbQO78bkPnMb/GZ2bNQy4e4oYpr5eOenw526f9r3Ae/vfBatSs/Ybu/QNWZgw+XmqXng6hjUav9EgDHo2DqFvryUtI/eIPHbL1B53Njjm/Pr/D/OrTnuBcRrE8JY/pPfw1ynl3G7RK6+s447Rp+0bnRe8b3LwmxXJReHhrK0Y8e/ezgB/sLpipOBVIh/OEFqNSW9ejFrez5/OK1s8No5KHuYV1nJvMpKMgwG3kpPZ0BY2N891AuCX6uruSU7m4pDYm+ySkcvTRD9Neaj+jGZBRVmVCSpGufMK4pCiexlp89JjuRCPPR+vSBSI/u9FB2KTLxKQwuVlhhRgw4RjSDgUxRsikSdIhMmqkhR6QkXVHhQKJa95ElucnwuciQXDkVikDaEPo4wHpjeDBDof/nhoEoU4d+P1yKICj98amblQsMFL0521Zi4XR/JbFclj+zdS6bJRL8TdLk7U9yyzC27dvFtZSUiEPl+OuVLo2nZykNkrMTurVqsNSpeHh/O2uUO7h1fS3CozK6tfuEny/AytZbNmDQhjO36Nh2i/3XKY2g+fy7Jcz9BEQQ2TfnvORUmARZ7rGz2OdAJAp+2bh0QJpuQYLWaQeHhDPrL805JYkVNDT9XV7O4upr9Lhc+RaFM9jLTUUaV7OMaXVijm2S3IrNfcpPrc7FPcrNXclF0RKnQ7TExvJOefl555gYIEODcMyExkS/Lyylwu/nGZWGEoekzvA5KbqbZSxbeo0MAACAASURBVCiUvZhEkQWnUMp9PB5Nj+PZvfmQ4GRxhZMrE47fXExSJGZtGYNHcvFYl9frxZZNq/wxXNe+jX0Da91VTFx9M4W2XDyym+vTHwI4qYqN8GiZOx+x4rSJLJpn4refjA3ESWdcM74ceTmzEmbzxDoND2xSEZqThcZed9L74O9i93Z/bNPnUgfVFSqyt+gIj5JISveydY2O5T+a6NbPhc0qUpSnwRwqceXttczOmsL3e98B4MGO089ImIxas4KOz4/FWFoEQE3r9ux8bAJVXY/f/OhYdOzlIi9Xw7Z1uuOKkw6rjsqcWAAGXmLizzqqn/d/yMayXwEI1UXxZPf3zjthEqB9dw+Dr7Ox8JsgZj0XxsyvyxuVpTcLasmkXl/wzKrr6Rjdv1F2cME+/z1Ri5SGJchDW/6bYvt++iVcQ6ujfLfNknw0S/JSlKdh8yo9fS87+n52eOvYKR7ANe4Fcv89itSP38bRrEW9MCl4PbR/aQIFQ6+hulP3f3w25YlwOQXWLPWL5BNer6QoX82HM0IpL75wY7weGhOzXZWsqKnB4vUSFqgK/UcQECf/B1CLIu20RpLRcpshkgOSm+UeK8s9VnKcTi7eto0R0dG8nJoayJA5BkVuNzMLC3m5oMDfumWvCc30Vox/1U50/OllPwqC4PeMU2m5lKbpOKdDIFmlI1mlY8BfMqZ++s6ELAmktfPQLLGhL6YgQP+hTn741MzeXVoU5cK/jl+lC2Wf5GKl18b1WVms7NSJdOPZ6Xps8/m4ftcuFlZXo0ag/S8t2fxFPMGhEk+/VkVEtIzPC998aObr98ysWmxkwwoDbbu42b3NfzGtjf2eKEMCk3p9TjNzymmNo6pTN2yJLSkYcjXl/xp44g80IfmSmw+dfh+m/7RsSZsmuLEMcGIMKhWDIyIYHOEXDCRFoczj4bXCQqYVFPCpq4oK2Uc3jQktApWKj7VeG1u8Djw0zkhI1Ol4rHlzHmrWLNCVO0CAAASp1cxMTeXarCy+c1sYoDUTr2qaWLFG9vGH18anzkrcKMRrtcxt25YeTVTFZFariV3TjNLL8lkgVnGFYjjueS2vdhcrC7/HVxeK5HuEMT1eRfZq2L7eL052OUKcVBSFdaWL+CRrCsX2A0To4+jX7KrTGueAYQ4WzTOxZqmee58S6ktgAWKMLYhI6sHj5nW81EfhQ8tN+C66rP71kOwd2BJbIhnPn2uuJMGeHf5j5Nq7bLTM8OL1Um9r89msYL75wMxbk8NQa/zbesPddWTbl9YLk7e2fpL+za85o3H4jEEYS4twxsSx66EnKRxy9Rk1T+nQ0833n5jZulZ33Dh58yodsiyQmOatv0eQFRmzNow72z5Du8jeJIe0RSWcv8LQHY9Y2fSHnrIiNZ+/EczIJxqXdyeHtOGV/r8QbTycgSopEipBxcF9fomhRYqPEnsekfo4NCodoiAe19sVoMdFLr6drWHdb0cXJ8scBTz9x7XYPLW8fvEyoqKbsfOJ5xq8J275IpLmfU7SvM+xtkwn77pbKRh27T+yw/fJsHaZHpdTJLa5j8593HgP2UmUF1+4UlCcSktzUUuB7GFBdTUjYmL+7iEFaAICaS3/gySrdNxliOItcxJDtCEIwOfl5WSsW8dbRUVIf0Opv6IobLfZeLOoiDnl5WywWrF4Gxv+nktcksTPVVVcn5VF4po1zDgkTLbYHQkPdMa7x8y7U0O5UJwRVizwC3P9hx49K7JFiheNVsFeJ1JacP4GTCeLIAg8aIwhWaWj3Oulz5YtbLA2felCtt1O982bWVhdjQ6BgWtbsnmaP1B7dIqFiGh/0a1aAzfdW8e0TypISvPicQtsWa3H61aBvprmyTJT//XdaQuTAI7mSaz47Gf23PNIk2zbybLP5+IZWyFuFC4KDWV0wPvlb0MlCMTrdLyUksLM1FQEYKGnlhfsxUywF/Gao4x1XjseFGK1Wq6MiOD5pCQWZGZS1rs3eb168XBCQkCYDBAgQD1XR0YyODwcHwrvOis4U0uoNR4b4+oK+Lf1AO87K3CjcElYGFu6dqV3SNOKB90LE8ApUmp0scF3fE/tlNBMrlP9BK8UsXbaw7y8/mG2b1DhcYlEREskpvkndnMtW3ly5XCmrh9Jsf0A4foYnu8zhxhTi9MaY0YHD7HNfbicImuXNfTYjDI24/nec+jb7ErKDT6GN5vH2rLFAGisNfR88DYuuaofLX6Y4/fpOQ8o2K/GaRfRG2QSD2XOHZnUdOO9Vlq29lBXK2KpVBEd72PwdXa6xw7iypR7GNP1zdPy3EZRCNqfW//Q0qEL62e8y6/f/07h5Wfe1blNJzcarUJVmZqivIaiTlG+mqxNWrwe2Pi7/zvs1u+wmC0KIvd3mMrw1PtIDW1/XguTAAaTwqgJfj/9X742UZx/9PHGmFrUxwsun4Mnf7+S93c8S95e/76uNi9j7O9X8Ormh/2WCSdB94v8+23TH3qOdisYZWhGlKEZLsnOm9vGHvV8VJeSTt7VN+PTGwjev4f20yZy2aVd6PzMaCLX/XHe/FaakkXfGPnyLfNR99lvP/vvAS+63IEgUC+aX8iZk+C3VAP4obKx3UaAC5OAOPk/TJCo4l5jNNOCmtNSpaNWkhiVm0uvzZtZeZQGL2eDco+Hp/fvJ339ejps3MgDubnctGsX3TdvJnzVKjpt3Mi4fftYUl1N3V+6YJ8NXJLE3PJyrt25k8hVqxi2YwffVFQgAW1VBsbq4qgZ3xo8/pP5xpX6+jT5g/vUfDYrmLKi8+9E73QI7M3yR4a9Bx69REKtgeQM/xVt765/RgatThB51hRPikpHpdfLgK1b+bmq6oxvrP5kTnk53TZtItvhIAwVbT5vzaLxfmHy+pFWOvV2N/pMahsvr3xVxuvflPHvx2pp0T2buGv/y4v95hJhiDvlMRiKC4ha/Vv9Y1+Q+Zymve72OZloL6JOkelmNjOvbdvT6rYeoOkZnZDAvLZtuSg0lM5BQbQ2GulqNvNsYiJbunShuFcvfsjMZEJSEkMiIogOZM4HCBDgKAiCwOupqegEga0+B2u8ttNe1jKPlWmOEnIkFwrQJSiIV1NSWNi+/Vk5B7VtroH5/k7j/3WUsafWy+5tR1+PpVLkl1cGgqyBvAGs+aoL73+3DoDOfV0IAvxRNJ/xK68mx7IJrUrP9ekP8/rFy2l2BuW5ggADLvdPHP/pCXckKlHNo53/y7+aXYVP8TJ9w31kVa3DWFyIZDShryyj06THGTj8X7SbMYmotSsR3Y1L0M8VOYdKutMyPaiOkpil0cCjky1odf5Y7Jp7i9Ec+kruajeRvs2uPOV16stL6TH6TvqPGIopf3/98yUDhyLrm6YpoE4PrTv647p1yw+LyNYakTEjonh6ZBS39o9j7XL/+rr1O3bp94VAp95uuvR1IfkEPpt19EmDHRu0LP7WiCTB74XfkVuzjZ/2fUBOjv/4W+6eRJ3HQoWjENcJJgf+JD3TQ2iEhMMmkrWpsS2WKIg82HEGGlHHlvIVfL/37UbvqUvJYNvE6SxavIntT07GmpqB2uWi+c/z6HPfTQQdcYz8EygpUPHWlDDmvBvMc6MisVkPx+HV5SLb1/n340WHzjPR8f576tpqFW7nhRuz9zjUpO2X6mrc/0DB+X+RCzeXN0CTkabWMz2oOb94avnCWcWGujr6bd3KZWFhvJCcTLez0CSozufj5YICXi4sxCb5Z280CLRRG/AoMqWyF4sisdVmY6vNxrSCAkSgfVAQ3cxmMoxG0g0G+oaEnLLHRIXHQ4XXiwAo+Eu29zqdbLHZmFtRQc0RImiEoKaHxsSluhCSVDrWLddjrVERGiEx8CoH8z40895LoWRtcvLLXH/ZdE2VyIPPnhtx92TJ36NBUQTCoyTCo4998k5t42HPDi1zf1tNXcZ2MiN7kxCUioLCprJlhGjDSQ/vXP/+kzF8/7sJFdVMDkpgqr2YbT4nw3bsIMNg4MboaK6MjKSDyYT6FGfTKzweHt67l6/KywFoJRuxP9GWLZtNiCqFOx+p5YoRDYMwSfaxpuQXftr/AUOS76B/ytU0T7ExbIQR+PdRu3meiKC8ffS+7yb0FWVsmvxfioYMP+VlnAk7vA6m2ItxodA3JISfMzMJDnR1Pq+4OiqKq6Oi/u5hBAgQ4AIn1WhkXIsWPJ+fzwfOStqqDYSc4nVrucfK644yFODuuDieTUqima6x+NCUpKYC9ycT0sdKbYKVpytK8I7uwsQpVjr3OTyBKMsw85kwaqtVhEVKWCpV8PszlOr98VxE5hYgmWZBqahENV2jB3JP+ymE65umlLD/5Q6+fDuY7et0VJWL9VUXf6IS1TzS+TUkRWJ18U+8sWUMqQMWs2zeMpK/+oiM914jqCCfoM/fJ+Xz95E0Wta/+gHlfQb4F3AO/Xr+FCdbtW/cSAX8DWPKTEtJezCbrJxq1kTkMEj55PSyCRWFFvO/pt2M59DYrEgaLSG7d2JPPDtejv2HOtm+Xs/ib01cfacNUYRl84047SKCoOB2+ePJsEiJ1LZ/bwVYU3D76Fq2rNax+lcDOds1ZLT3b5Msw5x3zcx5x3+PWFqg5vbRI4gyJjDr91epcoSDIBHVwsbVrSZzSeJNaFX6462qHlGEbv1dLPnWxJpfDXTs2Xiiv5k5hTvbPsN7Oybwya4XiTMl0TN+SKP3+czBHLjxTg7ccAdhOzbT/MdvMBYfxJacWv+e9Pdeo6zPAGrbtD+dXXResOibw7YOOzfqePLOKJ5+rYq45hK/LzQiywKtO7qJTfDfc1uFfWiMYXgdBspLVDRvefYTgM4GqSodYYIKiyTxR20tAwM9NC54AneRAQB/KeAwXSi9NUHMcVXxq8fKIouFRRYLt8fE8FLLlsQ2UQD5m8XCzdnZlHr8QUuaSseVujC6akwYjugCWSP72OZzsNXrIEtyUi776sXKPwlSqXgkIYHHEhI44HLxUWkp66xWBoaFcV98PIl6PVafjxU1NSy1WFhaU8NO+/Fn7iIENRdpzfTWBNFSpWsgvv05o91/qIMb77GyeomBkgI1P38VVP+efdnnnyHvvt3+MbVs3ThQtHlrMamDEQSBlDb+oOPgbjPvbn8agAh9LGpRS5njIJ2jL2Jir88Avxn1pDW38GT395ssOD9bGASRZ0zN+MBZwdJDXqvP5+fzfH4+QSoVvYKDaWsykajTkaTX0z4oiGS9vv67d0oSeS4X+10udtntTCsooNLrRQSGq8PY8WArCrbpCI2QeOKlatp2ObyfFUVhfeliPs6aTLH9AAACAv0TrgY4LVESIHjPLnrdfwv66krqktOo6tLzzHbSKbLZa2eqvQTPoXK879u1wxRonhIgQIAA/1iebNGCz8rK2O9yMaaugHGmOFLVxxccqmUfO31OtvscLPNYUYD74uN5My3tnExupqYCHhWh09uhf2UzZVEumLyTpQvSG4iT380OYts6PTq9zPPvVPLdx0Esm28CZwSIHvr29WfDJYe0YXq/n0gwpyE2Yefy2ASJ1p3cZG/RsWKBkWvubJydqhLVPNBxGrmWrfSKvxwBAVmrY9/t95F/zQii1v5OzB/LiFn1G/rKMuqS0+o/m/LpOyR+P4fq9p2xtOtEdadu1KVkNNn4j+TPzNSMv4iTRbb9LMr7lN8K5mH1VIMZ6Arp4Q+f1nq01ZV0nvgoMauWA2Bp24Etk16mLrXVGY3/ePQc5OD9rzSU7TaxdY2Ojr3c/DLXLwzd/0wN6e087NqiIz3T849oGp2Y6mPAFQ6W/mBi9swQJr9bSWmRmk9eC2bd8sMZqd/ONpOQ7KNT7wEEz7uGKiAq0cLbg389rTi390AnS741seQ7I226uOl/lAZEQ5PvpLBuL7/kfcwrmx/iReO3pIYeQ2AUBCztu2Bp37AJj8ZaQ/p7r9Hq7ZfZd8tIdo8ag2Q4NX96WQafF7Rnd57lmLhdsPQH/zF420O1LPjaROEBDQ9dG8OAKxxkb/H/Hi8a5s+adEtOXlh7O17zd+DoSHnxhStOioJAO7WBlV4bqwPi5D+CgDgZoAHhopr7jTFcrQtnjquK37x1fFJWxveVlTydmMgdsbGn3TRHURSmFRTw1P79yECcqOE2fQS9NEFHDVBDRTX9tcH0P9TYpUr2sdvnJF/yUCx72C+5KZa8TM7P56WDB/EeUaq7oa6OaQcP0sZkIttu568uJwafCrVGQQFCBRVxopY4lYYuahPt1AZURxlPbbXIhkM+MgOucKDVwaiJFl54MJLoeB/Db7cx67kwCvZpGhh/nw/sPyROprRqOIt70JrDs6tv4ZbWYxiUeDOpbfyBpLqsB20i+pNtWUuVqxQAkyaEFsGtkBUZURDZUr6CPZYtjF95Dc/1/oJY07ntDH2qaASB+4zR3GGIZL3XxiqPjZ0+JzZJYonFwhKLpcH7Q9VqUg0Git1uij2NRd1EUcuDhhh+mRxL7jYdQcEy//mwgrgWh4+2g9Yc3tsxgR2VqwEI0UYwOPk2Lku67fQ3RFFo/uNcMqdPQmOzUpPRljVvfoEnvOk7qB6LtR4bMxwl+IBhERHMbdMm0NU5QIAAAf7hGFQqfsrM5KqdO8l1OhlvK2SkIYqLtWY0fxHqFEXhG7eFL11VHJkDeHdcHG+cI2ESDomTQMFOLS2ey4QntkA7K2u3l+Ny6tEbFMqKVHzxlj/WHDm2luYtfdw9rpbdW3UUH1ST2rGWhMjY+mW2CD47ot6AYQ6yt+j46u1gSgvVXHGLrZFoYNIEM2vgcnSqhqXKviAzJZdcTskll4OiYCw6iDOuWf3rEZvXYz6Qi/lALok/zAH83avzrhtB4eDhx2yo4/X4vf8yu7kxmU9siWOtESnO98ec6ZmHY853tj3NL3kf1z8O0UXSN/4KLm5xAymhmSdc7l+JXL+KLk8/jL6yDEmrY/eoMewbcTfKWazeOCi5meErxfmWB1ZE8uNPLZBlKCtUYzLL9B/iRGdQSEq/MIWeY3Hz/VZWLjSSvUXHjb3i8fn8v121RuH+Z2ooLVAx9/1g3pwcSmiERGWpmpAwiXGT5dOegO/Yy82l19pZPM/Ea8+EoRJp1BxHEARGZj5HmSOfzeW/sTjvc1I7nnr2Y8nAISQs/IHUz94lfukCtj39n8MZxyfBi4+Gk7tDyytflTfKeD4XrFpspK5WJCrOx/A7bFw0zMEr48PJ2qxjybf+37Vao9BnkH//fZ3zGiX2PAjNg7KOh5riNM5OvVBodUicXFXbuGlTgAuPgDgZ4KjEqjSMNsUyxBfKu85y9kpuxu3fz/j9+7kkLIwrIiLoYjbTISgI4xGihFeW2WG3s8/pJEarJUGnwynLLKqu5tuKClYdakgyQGPmPmM0ulOYdY4Q1fTRmulz6LGiKKz12vnSVcVB2YMGgR4aE5lqI6u8dWz3OeuzJONEDSlOEwe+jKbou0hcdRqemlndwKz6RKxcaEDyCaS09pB0yBQ9s6uHj5eWoGjsbC5fhsZ4K16HiY3bi+nZOe68KXn+M5sz+QhxstSez7Orb8HiLmNx3mcMbHEjCck+dHoZt0vP3fFziO5VR3bVBuxeK11iLkavNiLLUHRQjWXbRUTYBlDGcsavvIZJvT8nMfjszVY3FQZBrBe9JUXhoOxht89JmeylXPZRKns5KHmo8fnYWFd3+HOIxKo0xIhqWqsMDNWF8ssX/swKUVR44qXqBsLkorzPeG/HRHyyB42o46qUe7g27QEMmqCjDeuk0JcV0/GFcfVZAlWdurP2tY/OaffBjV470x0lSMD1UVF81ro12jM0mQ8QIECAABcGrU0m1nfuzK3Z2fxcXc1bznI+c1XSXxPMRVozKSp/+tBsVyU/uP0l0Z2CghgQGsqgsDAuCw8/p7FRXBwYDOB0Qt5KE0HmDGxPZCFdX8Ci9Wlc1R/mfxaE5BPo0MPFJcP92UUGo8K4l6v47PVght9xbsbab4iT338xsnOjjsXzTCyeZ+KS4XbuHluL7ogO3kcKk27JiSio0IhHJA4IAo6EhhPGW5+dQf72TYTt3ELYji1EbF5HaPZ2Or6wndavT2XR4k0omsbJBwu/MfHB9FCS0ry88F4F5pDjC5R7dvjjzWZJXoJDDws1LYIzEBDoEjOQwUm30Sm6/2kLVwBR61airyzD2jKdjS+9eVazJRVFYZGnlg+dlXg4tP39K9nS1krpNy1BlBl4paPBd3QkkqKQJ7nJkVwUSR6KZC/Vsg8FEPBPnocIKsJENRoEvCh4FBkvCl5FQQZaqw3015iJUZ3bzAdZUYiIlrj6jjrmvBuMzyeg1cskp/n4vydqSM/0IstQsF/D2mUGKkvVxDX3MfGNSuKan1wDnKMhCHDfUzVIPn9W4CtPh6E3ynT9V0MRTSWqGdP1LV7acC+3th6H0yEwY1w4TrtA+x5uOvZwk97ec8xeSN7gUDb95w0KLr+WDi8+hbGkkF4P3kbBkOHsHDMJT3jkccfpccPmVXpkSWDlQiPDbz99P97T5c/M3cHX2VGpICJaZvL7lWRv1fLd7CA2/G7gkuF2goIV8mp31Xt0dm2VysYcKC9RISkS+dbdtAxpe87Hf6a0OmQXsMZqRVaUgO/9BY7QVI0hLmQEQQgGamtrawk+C/6K5wO/7irD7jm9mTxJUVjusbLIU0uu1PCiIAJRGg2hajV6UWS3w4H7OMeUGoG7DVFcqg1usuBUVhTyZQ+RghqzeFgoLZQ8HJTcpKn1ZC8KZtbzoXhcfk8YRREwmGSmfVJxwlR2t1Ng9a96vngzmIpSNSPH1jDsZr/o6ZU9/GfdXeysWotHcsHsZZA3AK66k9CeC7g27QGuSBnZJNt5ung9cFOfeCSfwHsLSomKk6h2lTF+5TWUOfJpYc5gSt9vMGv9qfDj74oke4uO0S9UM2BYw1nK96eFsOxHIw6b/ypvCvYR+kQfiqT1RBkSePWihQRpQ8/5NjY1XkWhQHJTLvuIENXEiBrMgtjgmD24T80jN0YjSwJ3janhyr94TG4sW8rktXfQNeYS7m7/AjHG5mc8rvjFP9Jt3P3+LIH7H2ffrfec1SyBv5Llc/KcrQgPCjdFR/Npq1an7NcZIECAAAEufGRFYUZBAa8VFjaoLogS1MSqNOzw+eOHmampjE5I+LuGCUD79rBjh//vGS8rvN18J3ujqjDnmZmVFMc9Q2Jxu0See6uSDkfxtzuXKArs2qJl/mdBrP9Nj6IING/pZey0apqnNIxXS2wHmLbxPtqEd+fu9i+c0nq0lmqa/ziXpG8/py4plfUzP6x/LWnup5T36ocjIZHp48JYtdhf5praxsNzb1c2yqDctUWL0y7QuY+bz98I5psPzFx8pZ2Hnzvsv+702bF6qs8oFhLdLmSdX4gQfD5SP3mb/Tf/H5KhaRreHA1FUXjPWcECjz8ra3B4OI8kJHDNslwckYdiZIeK1ho9Xc16OqlNJKu02BWZtV4ba702dvlcOGiajLrWKj1DDtlwqc+iCONUZL5xVfOzuwatIJIkagm1GEjR6cgM19BCrW2wfpdT4JXxYcgKPPRsDSHhTbO9sgz/nRjGbz8badXBzdTZx+/K/O7UEBbMaZgE0PdSB49PtZzQclXlsNPqrZdJ+eJ9BFnGGRXDrz+uqj/mjsaBHDWP3uS3tkpt42HG5xUnt2FNxN4sDWNujUatUXh/YSmhR9nvTofgbz4lSjz5+1Xk1mylZ9xg2uR+yYczQul+SS37B7XB4irnw8s2EaI7d5VYTYGkKIyo3YcLhR1du9Iu6PSTQAI0HVarlZCQEIAQRVGsJ/u5QOZkgBOiEgQu0YVwiS6EEsnDH14bu31O9kluahSJMq+XMu/hjDyTIJIgarEqElWHZgbbqg10UhvpoQkitoln/URBIFnV2OgjQaUlQaVFkuCtKX5hsn13F/c/XcOs58LI2qzjxUcimP5ZOUHBRxdUf/g0iDnvmuvFOG2IheLkycBjAKgFDdsrVuFTvMSaEtGle8jPA7GsGzXujxGa0I/oeOzN0jDruTDad3dz8ygrBuPh7cnfq0HyCZhDJSJjJVw+B8+tGUGZI58YYyKTen9RL0yCv5t09hYde7O0DcTJwgNqfvrSf8LX6hTUGgW7VU2fHT/j69qVMkc+s7aOYVy3986bjNHTRSMItFTrSVag5KCKNRt0eL0CQ26w13sIzX3fjCwJdO/v5IpbGvuYdo0ZyIt9v6V1eLcz2h+Cz1cvQBYPGsbufTkUXXYVtpZpJ/hk07LP52KyrRgPCsMiIvgkIEwGCBAgwP8soiAwtkULHktIYInFwkelpfxcVUWF7KPC50MEPsjI4M64uL97qKSl+cXJhAR4YJRAu91pDC6xUJdUx8w/wnG7RJIzPLTv8feXNgoCtO3soW3nanZs0PLKU+EU7Nfw+K1RPDyppkFpa5FtPwdqszhQm0XriG6n1OXaExbOvtvvZd9t96C2Ha4SCdqfS4cXx6MIApbMzpTk9yeCfqwU+7F3VxgvPBTBs29W1ceZu7dpeebuSGRJoPcgB5Wl/nglPdPNl7tncFnirYQbYjGoTRjURy8dPxFqu43Wr08lYst6Vnz2E4pGi6JWk3vXg6e1vJPlSGFSAKa1bMljzZsjCgJvy125fd5+uKwUgiSysZPtsvMpVYQIKuyKxJFSslmlondwMJlBQWQYDCTp9fUWUnZJotzrpczjwaMo6EURnSD4/xdFXLLMvIoKltXUkC25yHaU8rGg5lJdMF3UJpJVukZ2VIqiYFNkyg9VBVXIXmoVCasi4VYUYkQ18SotcaKGcFFNmKDCi0Kh5GWP5GKeqxqL4s98dCkS2yUnBDv5HcAOWgR6aYK4WBtMO7UBvQGemlnd5N+BKMJtD9fy289Gdm/TUV0hEh51dOEza7P2sDDZ7wWoSofsa/ljsZGeA130vfT43dMlo4msxydSOGQ4LiTmjgAAIABJREFUHV8YS/Elw44rTAIc3Hf4nnbvLi2lhSpiEyRsdQJvvhBKh+5uLrvOcWobfQr82fOgzyDnUYVJoP63uijvS3JrtmJUm7knczJ77P7v11JiIEQbSaWzmN8Lv/vbk2pOFZUgkKbWs8PnZLXVGhAnL3AC4mSAUyJOpeV6VXj9Y4vso0aRsCsSLkUhXtTg2GNk8bwgRoyyEhwmIcNRPRzPFYUH1DjtInqjzLNvVqFSwdjp1YwZEUVJgZoZT4Yz4b9VqI74NSgKfPW2mTnv+jNpTVGVODJn4un0Ab9V2bnDdz86tQFBEBjVcRpJIW1IDm7Db04jry2GDM9dXNvD3KCz9ZriBfxR9CN3tptAlCG+ybbP7YJXngqn+KCavFwNa5frGTWhpr673Z9+ky0zvAgCfLJrCvnW3YTponmu9xeNmtmktvVnQOzd1VBEXrnQPzPdqbeLp2dWkbVZx7P3RfLrvAjGDP2YV/IHsbZkIQsOzObylv9usu37O3A6BOZ/FsTib41UlR0+MJx2getH2ijKV7NqsX9/3HR/HYIALp+DN7eNZUSrscSYWgDQJqL76Q9CUWjxwxzSPpzFytk/+D0lBYGc+8ec0badDgckN5PsRTiR6R8Swtdt2qAJCJMBAgQI8D+PWhQZEhHBkIgInJLEYouFxdXVDDv03PnAVVfBggXw6qug18OlHfREf5RE+dX72ZJZBK3DGX6rdK6aWZ80md08vPpVOa8+E8a2tXpmPBlO8UEr14/0xx1dYwdybdoDzMt9g1lbniA5uC3NzCmnthJBwGc+XDUmSD7KevcnZvUKwrdv4j42cR+vIKFivdiD57ZNYMroAUz4bxU+H7zyVBiy5N9xq5ccbiRSHPY583Nm8lvBd8y6eBmaoyQRnAzRq5bTYfKTGEuL/I9Xr6Cs/6DTWtap8Fdh8oOMDP59hNB+43AV4x5Jo+TNVF6bb0foUMNii4VlFgu1sl/0yTSZuCEqiqEREXQICjqje6H7mzWjyO3mg5IS3iwqoszr5UtXNV9SjUkQaSFqEREQBKiTJcplH84zzNZM0euZnpJCc52ObXY7W202th36Z5UkVnjrWOGtI1bU8Igxhlbqs5PBGhEtk5HpIWeHlnXLDQy5oXFCgNspMGuSP9HikuF1JIzw8MXuO/H8uhtWPMurkxXqmv/IRWkXn1Akr23Tnt8//cl/M/jnGDatJeaPZeTcPbqBP+vBvQ3vlVYtNnDtXTa+ftfM6iVGdmzQcem1jrNybtm6VlffqHXojScuJ8+1bAXg5laPE26IJTref5yWF6u4scUN7NuxnWUFcy84cRKglcrgFydra7knvunusQOcewLiZIAzIkxUE3bEYSRJMPrpcAoPaNAbZO563Mpf22RIEnwwPYSoOInht9vOejCYu9PvoZPS2luf9RYSLjN+ZjXj74xk6xo9H78WzF2P+zOOFQW+eNPM3Pf9wVr4sJlUd34MRIXe8ZdzVco9aFWHZ9IubnF9/d/JGf4M0vw9OrrEDKzfNkmRmJ01hTJHPlvKV/B0z49oG9GjSbbvizeDKT6oJixSQq1WKC9WM+n+SB57sZp+Q5yHxcnWXnKqN7HggN+QfHTnmUdtYpN6qGP3gRwtkg9Uav8++f0X/wXwomEO1Bro0MNN9/5O1q8wsOyDHtwx+mm+yZ1FfFDLJtmu08HrgaNYJp00igKL5xn58u1gaqr8B4tao9Ai1cv+bC1fvRNMl75ufvzChCwLdOvnpGWGF5/sZdqGe9hc/ht5tdnMHLDkjLp46kuL6fjCWGJW/wZAyy8/ZPcDT5z+hp0BByQ3E22F1Cky3c1m5mdmYgg0vwkQIECAAH/BoFJxVWQkV0Ue36ftXHP77TBiBPUxoCDAfZEJPL+7AlrVwX+3UmeMRFFCzrvKj9AImYmzqvh4ZjDzPzPzxZvBlBxU8+AkCyoV3NLqCXZXbyKrai3TNt7L9H4/NYhRT5W6tNasfeNzDMUFSPM3cvCdHQwQV5Au59CL1Rh1XnZu1PGfx8NJV+WSWFyIJj6TeybaeXtKKCUFavQmLz/VjQEBrkm7/5SFSdHtIn7JTyR//THhO7YAYI9vzrYJ06jo+a/T3rZT4XNX1TGFSQCtFn75BXbtErhpaBCCEMRDCQm4ZZkNViuRGg2tTKeXKXosmul0TExKYlyLFnxVXs7c8nJ+r62lTpLIlo7uoR+r1ZKk15Oo0xGj1RKp0aAXRfY5neQ4nex3Oik9lLEJEK/V0sZkYmh4OKOaNUN3aCK66xG2Z4qisKGujtmlpXxZXk6pz8vTtkLu0EdyhS70rPyGeg50krNDy5ql+kbipCzD7JnBlBSoiYiW+PdjdZjM99EjbjCzw6axLisbqbI178xQ8cHwTN4YuKLeXsAne1GLjSv6GlgmSRKZLz1DSO5uEn6eR+GQ4VR270t1x27k7/ULoskZHg7kaFm1xEDvS5wsOJTRWFejoqzIn03ZlFhrRP470b/uITfYyGjvPcEn4MFOMxiSfAcJZn+XsOh4f25vrUVFt4jhfCg+z4HaLPbXZl1w3pOt1Hpww2rrSVcPBzhPCYiTAZqUNUsNFB7wn+RXLTFw56PWRibEOzfq6tPuC/areWBCTYOsxaZmb5Z/PGntGnZcbpnh5eHnLUwfG8H8z8wkp3tJTPfx1dtm1v92aPbv0seo7voqQZoQ7uswlb7NrjjuuhKSfag1Cg6b2OBipBJUjOv+Lm9tHUduzVaeW30LY7q9TffYM5v93b3N70sE8MBEC207e3j3pRCW/2ji8zeC6X2Jk/27D4mzrbykhXViZObzVDqL6Rjdr8GytNWVtJsxiQ46A9M0iRx0x+P8tTVBl2awN1tHSYEarV6me//DAdAdj1rZtErPxpV6Lr9pFK8PuIZgXTjHQ2OtQV9WgtrpQOVy4YyJw57Ysv61xG+/QPT5cEbHYW+eiC2x5QkNqQE2/K7npcfDue1hK1fddnqG1Et/MPLWFP/FPibBxy2jrPS8yIVWr/DSmHDWLjMwfWw4ZcX+O5zr7/aXQn2dM5PN5b+hVel5oOO00xcmFYUW339Fu1eeR2Orq+9AuffWe05veWdI3l+EycUdOhB8Dj0uAwQIECBAgKbgr3Nqt9wg8nynDjA2By6q4H1PBbsUJ7fqI4hXncEs5zHwKQo7fA6yfS7qFAmbIiEBYYKK8EPe1okqHfGiplGGnUoFdz1uJb6Fj3dfCmX5T0Y693Hxr8FOVKKax7u+waO/XUa+dTefZb/EXe2ePePxOuObsyQmgzcIo31XF9MnbSd6zW/0js9kwaMyW9fouZlPmcN0vJUGLB915fLrLuPtksuZq52FS/DQt9mVXJo44pTWK3rcDBraE32131tQVms4cOMdZD8wFslgPMGnm4YF7hq+cVsAeCc9vZEw+ScdOvj/HYlOFOkbena913WiyB2xsdwRG4tPltlss5HvcqEAChCiUpFsMNBCpzupyWRFUaj2+VABoZoTW28JgkD34GC6BwcztWVLRubkMLeigg9dlezwORlpiDrtxj1Fkocq2YdREDEKIhGiGp0g0vNiJx/PDGHnJh3WGrG+2VJxvopZz4Wxa4tfAL//GUu9J2qcKYnxfd5k7Qt1TL0f2Hw3uuhK6JwIRv/nJ6+9E6univHdPyDK2OyoY0KlIvvBJ8mc/iymwnzSPnmHtE/eAeBy4GeGsvLuT5g+Lpz9u7XMfCa8vqM5+JNkYhOOX1J+KigKvDU5lOoKFc2SvNz5yMkLcimhmfV/B5kVjEEyDpuIqzKS7rGXsrr4J5Yd/JqWmc812XjPBRmHJmRynU7KPR6itU1/Dg9wbgjcZQZoMmTZ78P3J1VlanK2a2ndsaEouGPD4RnUZfNN2GpFHp9azQlsPU6bPVn+E1R6W0+j1/oMcpE30src94N5fVIosuwXlQRBQRj8GNre79Ez7lpua/MkEYYT+yapNdRn2R3I0TSYKWsZ0pbJfecyY+MoNpQuYer6kTzU6WUGNL/utLbL7YLXJ4WiKAIDhjnqO9jdN76Wzav0lBWpWfajkbw9hzt1i4LI8KhriV25lOgvR1OXnEbu//k9e3ymIBIWzUeQZepz9MaD+6Uw4kw9GMk15PQfgcGoIHg9NF/wHckuJ19k1FKbZcE8SUXnK7V4TUHUts5kfetIIg1xhPq0pH7yNoayEsK3bcJ8ILfBduTefi+7Hp3g33+2Otq+9mKjbXXExlPTtiOFl11JyaBhjV73eeHDGSH4fAIb/9Cdtji5foX/ILzsWjsjx9VwZIw26pkadm/TUlLgP2127OUivZ2XvZZtfJM7C4CHO71CRniX01q3segg7V8cT8zqFQBUZ3Zmy3OvYEtOPa3lnSk1so/JtuIGwmRIQJgMECBAgAD/ADIy4M4b1eQsacOwWwuZWLiP1YcamFykNXOzPoKoo2RUnSyyolAse8nxudjpc7DBZ8emnLjMVoNAlKgmTFATJqoIFdSEiyoiRDW9r5UoKVDzw6dmtq3T8a/BfsEjXB/DQx1fZvK6O5i/7z26RF9Mh+gzzzDMz/Vvf1K6F2dcM/KvGUEa8PRrVUx+OBLZLVKnC8PsthC9biXR61byHs/wrBkqQrVkz32xPosu5dN3Ccrfh6zWgCCgtttQ2+swlJciSDIrvljg329aHbakFBSNhrzrbiP/6ptxR0Sd8bacLGs8Nt5z+puaPJ+UxN3neYmoWhTrhcLTRRAEIk5ClDwawWo1c9q0oV9REY/t28cGn52tdQ6u0oXSUWOkRpaoln1YFIka2YddkYkWNTQ/1BeguaglWFRRJHn4wlXFKm/D+F0Ev3AfriXuUg0liyPYsELPwKscLJpn5IPpoXjcgr9ab0xto27eAD17mhl8nY2F3wRhXzieexdCRns3Y16ysL92B1ZPNTM2jmJK32+OmkUJUNbvEip69CVu2UKi1q0kav0qjCWFAMiItO3ioUMPN9tWa7hxx3/4TLiNoG7xbF+vJ3entv632hT8vtDAmqUGVGqFR6dYjtkl/k8OWnMI0oY2svECf/Zk3h4tZcUqLk6/ntXFP7Gi8FvubPvMMffF+UiQqKK5qKVA9rDGaj3vsvcDnDyBO80ATcb6FXryczUYTDKZXd2sX2Fg5UJDI3Fy+3q/ONl/qIPVvxpYv8LA21PCGP2CpcnH5HEfDq5S2zZOeZcUiZDL/ov4R0fk3cMQBIW+lzm5YWQdjrABJIc8ePLlMYqCvqKUlhmh7M/Wsn+3hv6di5H0+vrZXp3KwJPd3uONrWNZVvA1b2wdS1pox/oU+5NFU2NhwSww51m507ydscZlRN67C0UQ8JqDufqOz5n9agizXw2huXsfXTTr6bF4AwlbtxK1fhWiz78vatPb1IuTsk7PtvEvoquqoGxzHfb1hfTjd8w1FrrXLGQN6UQP8V9ctbU1dJr0OADt/xxUBfCB/8+NwwYxvnI1ySFtmJwxk1bvvNpg/O6wCHwGI5LBiCfssCeV1xzCwWHXoahUGEsKMRXkYygtwlhajLG0mLrk1Hpx0liQR+dnH8MVFUNFvsKcghqCsLE8awia2lvwhoRxKigK5GzzC9kDhjn4a5wWHCbzwMQapoz2j/eGu+vwSm5e2/IosiLRJ/6KUzKj/ystP3+fmNUrkHQ6skeNZd+IkY1TPc4RkqIww1FKpeIj3WBgUfv2AWEyQIAAAQL8o/joIwABaM7QmFAm5OXxU1UVyzx1rPPaedQYS1fNqZXmVss+fnbXsMRjxao0LOWM1mi4PCKCBJ2OcLUaURAo8XgodrvZ43Sy027HJkkUy16K8cJfKkE1CKRfUwXbktmxsWHTh66xAxmcdDsHarOINp1+R+wjOXBocjsprWGX8PbdPUz9uIL83LEsuewBQg/uJWTlL4g/vkOH/VYS6iBMbeSg7nAGYczKX4nasPqo61FEsUEn7o1T38QTFtGwtPYs41FkvnFZ+NZdjQLcGxfHM4mNrY8CNEYQBB5MSKB/aCij9+5leY0/8/TP7NMTESyosClSvUNmhsGAXZap8fmwSRIlspcS2YvqiZ1gaceaZUbqakVmvxoCQIeeLkZNqCEm/til0yPH1tI8xcfqXw1kb9GSs13H57OCeX7sHJ5aeS05lk18umsq/2434ZjLkHV6ioYMp2jIcFAUDqx38+J9Zszh8Eqolz6DnLRYvZDnmMRE5Xl22IdxF8+Sm9W0JdIrDvlMXnNnXb0V1/F4a9uT5NZs4/Eus+gVP7TBa9HxEnl7oLxEzWV9+hOii6TWXUlW1To6RPVt0nGfbVqp9RR4PKyurQ2IkxcwgbvNAE2CosDX7/mzJi+/yU7rjn5xctUSA/83pra+bNteJ9SXWd/6kJWLr3Dw7P2RrPjFwIgHa4mMOTPzZodNYONKPT0GONHp4UCOv1N1SLhEVFzDi1aZo4BXNj5IjmUTXGMg9sB4xl97F4n1XuLHzoDTVldiLC5AV1WJobyU8G0biFy/Cn1VBd+OqQBMHNijoc1rU0j8YQ7usAgc8Qk4mrXAGRPPf8NS+KY6gxeTcvh5/0fc22EKketXEbZjC+YDe9DU1qCps6Kps6K11gKwaMmm+vW3Gv0IQ7cv9T+oA74+PDZvUDCDn7fz7UdBWGtUvMqjXOH9Cd48/B5ry3RK+w+iomfD0u78624F/N/nmy+EMvw7Lb1U62gtZZFnbMXdvf0l3YIsU9ZnAJJOhzsyhrm/JOGqkxnUr4RmIbVUt0tGFNaRXb2Bh9dcRfTQvkTFdsTSvguW9l3whB299NtnDmbLCzMbPKe22wjJ3k5Y1jYqu/Ssf95UVEDElvUAHFmI0cm+Fe/ls9gyaQYll1x+zO/wr5QcVFFrUaHRKqS0aZxlC9Ctn4uHJlnweaFNJw+fZL1MQd0eQnSR3Nt+ykmvC0UheE82aqed6o7dANh9/+PoK8vZPeoJbEmnaGjfxHzsqmSnz0mQSsX37dqdVJlPgAABAgQIcKHS0Wzmx8xM1lutPJSby/q6OqbYi7lBF84N+vBjNjOxyRL7JTf7JTc5kosNXlt9l2aDKNLVbKZncDDDIiLoExJy3KYosqKQ53JR4HZT4nZT6vFQcujfNpuN7XY7WaG18NpWyqZnUFGiahDb3tVuIipBjUr0B92Kopy2/5+iHJE5mdFYAGmZ4aVlhhcQqUtJx5KczDMpy6kp2cGT0Y/QJahhDH1w+E1Udu2N6PUiKDJeUxA+kxl3WDi2lunIRxiGu6MaZ3idTbJ8Tt5wlFEs+7fzluhoZqWlnXf+o+c7mUFBLO3QgR8qK3khP586SSJOqyVOqyVWqyVOpyNYpWKf00m2w8Euh4M8l6texL8iIoLJycm0P9RtWVEUyr1esux2ZhQU8Et1NUzZycYXW7HxVf8xcv1IK7eMqjth/wK1xn9/evlNdnK2axh3RzQrFxkY8UA7Hu70MlM33M0P+96hbUQPusddeuKNFQT2FoVTQRjx6S6gip4XO9k+LYZfPYO4RFpCx6z5bORHPtwxElXlQ0iRx7e9Oln+nDTo0rdxluhfyary34upRe1RK7uObIqjEtXc234KEfpY0sI6NclYzyWtVAaWYA34Tl7gBMTJAE3Cpj907M/WojfIXDnChjFIxhwiU1utYucmHR16+E+gWZt0yLJAfAsfUbESUbESSZkV5O2I4pHp8zBcOpV+CcO5Nu1BjBrzCdbaEHudwMR7I9mXreXKW+u463FrfTOctLbeBheubeUrmbFpFHUeCwZ1ELe1H8/g625DPIk4pOOkx0n8Yc5RX5PVatITa4BwDuRo0GVUAaCzVKGzVBGWta3+vW2Boq8mMDjd3xUt/b2ZRG1cc9TlKoLgr5sXRTxu2L3XTBx6JLUOX2pzLJmdqWnTHlmjQZFldlmXETNIjXXuTYRhYXVQMqZusWja9adkwOATlgoLAtwzvobig5Gs3NSXlfRl0GA7Gk0NAK7oWNbO+rT+/UulUBbPM7GxuY3/G+MXUydV92Xm5tEU2Q/wr+5/0MJcQaw+l+Sy7dwc9viJd/QhfKYgqrr2pqpr7wbPW9NaseGlt8hdYOGPFSG4Q8NRHG5Ge14h07mTupSMk14HQPY2f0ZvahvPcZvqDLzK4R+X7GV75SoA7u8w9YRemygKobu2Ef/rAuKWLiCoII/q9l1Y+fEP/uWZQ9g47e1TGvPZYJWnjvlu//f8catWtG5iQ/cAAQIECBDgfKV7cDC/d+rEY3v3/j975x0eVZX+8c+90zMlvUJ6SAi9V+mgFEWQInZRV10L6lp2da2rP9vaUVfFdRURG4ggSpNepPcWQkkIKaS36TP3/v6YFEISSChC9H6eJ0+emXPm3DPtzrnf877flw9zcvjWWcxiVxk9NUY6qg1YZYkiyUOO5Oao18FJyVNvjCv8/flb69ZcHRyM5nTj9TMgCgIJBgMJhoarHm+rqOCtrCxm5+fDtHRW7ktk8imOQ6dn+ry34xEijfGMb/NXNGLzPNgKT6qoLBdRqWWi4xuOzjpSuocgfTiB+jDUoobHenyI1V1OrKUtRaf1PTH6umYd//fAK8t86yjm+6poyUitlult2nBdSIgiTJ4jgiAwLjSUcaFNS8W3er2k2Wz4iWK9AkKCIBCu1RKu1TLA359bDxzkm4J8eOYAJFq53hDEDXfWr9x9NlI6uenYy8GezXp++srEHY+N4pqEu/jp6Ke8t+MR3g9cRYD+7PPPPOITCWMTfecAk0Xm5jnxZIgzWVG5jzafTid6yXzu8s7AMe570qY9TsaEm88rK6qsWKSkUIUgyMS2OXvU5LcHfZlrQ6MnNZrWDT5xEqBfVNODOi432qp9578t5eW4JAltM869CpcPijipcN74oiZ9XicjJ1mxBPqiH/sOt7N0rpF1Sw014mR1SneHXrXeGz3GHiBjTyiVv02mss9jzE3/gF+Pf8uNbR9jROyNTSou4rQLvPRQMEcO+BZfS38wMvnuCtL3V4uTtZFw8w9/whf7XkJCIimgM3/v+UkdE2TRYSdoz3b8D+7DP20vgXt2sO6zH2o8b2ytYpAFAXt4JM6gUJzBIZSltKewZ3+KO3Wjldd3ciw6qWbZ1zMJUpdgzM7CLycLv+ws9AUn0ZYUobbbGJNyT81x868YijMkjPKkVJwhobjNlqo/f1wWf6rV1W8/tjDXNo+AYC/vzcmvMYUGn2D2xtb72LhxESSZwDCCAfZ1jL93E7fd1LpJ72c1Gg38/d/FPHFbKHlZaoaNtTXat3NvB0vnGtm1sdZPNDmoG+8O+ZX5R2bw/aF3OV6RxvGKNMryTAwxnX/lOmdwKGm9x3LfyxFUIPLgwyUs/NrI52m38OEjvxJ5igAb990XFPQZiDUmvtHxDu70fVbadmk4avJ01KKGVwf8yOa8pfSJHNlov8Dd22i19Ccily/CLy+75n6vTocjJBTB7Ua+TCITCyQ3H9rzAfhHTAzXNXFxqaCgoKCg8EdBJ4p8kJxMb4uFhw8fpsTj4VdXOb+6Go7Iidfr6W42081kYkRgYJ3KxheS7mYzX6amsnGfh6OhxfwYl8U1cisMDayTDxZvY2XWHAB2Fazlqd6fYdQ0fV6Z6b5LxFZxnnobtqWOAv6793nWZs9nTPxU/tLpRQBCDFGEGC5vj8ZqCiU3b1rzaqpc3xERwZuJiUqmyO+MUaWim/nswSgaUWRWu1SyflCzPjgHbj7OWjGXdu4wOqkNzRaTr7utkj2b9Sz9wY/Jd5dza/un2Fe0kaNle5m5/xWmdXvrrGMcP+z7jpwqElZHIlbQlu2vfsAzxx/gkQOP0tm6m9h5X/vEyfOguo5ARGsvBr8ze00eKNrC7sJ1qAQ1E5MfbLBP9XwLclq+JBQlavBDxCZLpNlsdDSZzv4ghcuOlv9JVLjk7Nqk49AeLVqdzLhba42MB1xlY+lcIxuXG7j7H77iIjs3+3ZmcsM+RZYnIQgCo0cFsOTjSioKwxhtW8TOqLvIsR7lP7v+QZ41k9va//OMx3e74dXHgjiwQ1cTsXkyW82yH4yk7632m/QJTlZ3OYsyvkBCYljM9dzT6f/QqvSoK8qJ+vVnIlYvI3TTWtSOusbFwds3kVPldXhs0q0cu/423JaGq/EZkImM9pCbpWbNIgOjJkt42vpT1rZDo8/B7rHySOdj3DTlBQJ0jftknDimZt5M38n23n+W1hEmAdZn/8TG3EWoRS3DU8YT/PROCnb0YMqE5gmT1VgCJd74Kp/8bDUJbRvfoevY04UgyBw/oqE4XyQozDcvjUrHxOQHGBoziYPFWygodDD7vnu5z65lyr0VDL3xMJ8feIGb2j5OpKlx4bAh3G54/fFgKspEWse7GTzGxta1eo6ladkq9uIafLuplrR9dHrtGbxaHfv+9iwZE2+mofyPA1XiZGrnpomT4BMo6+0yynKd8dt8/iGRK5cA4DH4cXLAMHKGjebkFUPx+l0+UYleWeYd60msskRvs5l/xcVd6ikpKCgoKChcMm6NiOCGsDBWl5Yyv6iIbRUVhGk0ROv1xOn1dDWZ6GoyEfg7ClqiIPCyuS1TCrZiC3XwH1s+j/iF1xNnUgK7cUvQ18x6sSv7Bj7P055JPNdnFgH6UDIPqzm4U8uI62w0FlxU6zdZu/aTZZmVWXP4bO/zVLrLEBFxeu3nlT5+KXDLMv+qzOG45MKsUvFJcjJTwn/fVHKF5qMSBNZNSOa73EAePpZOjsvFc9ZsQgQ1PTRGYlRaKmWJcslLkKiii9pInEqL2MBns0tfJ3Ft3GSka1j0vZFJd8rc2+ll/r31PnpFjGjSfI5XRU7GJDV+fVTZtzfdD2zjnU7v0ulvbc7bS776exnfgNXC6XyTVhU1GTOZML+GrwNPTeuu5mjZPhYd+4IwQ2smpUw7r/n+ngiCQLRKS5rXwX5FnGyxKOKkwnkhy/DtJ74drysnWAkIrhXL2nVzERjipaRQxbzPzXQfdZjraNQBAAAgAElEQVQTR3wRihlBH1HkuIIQQxRBxmCuneJl1vuQtrg/7365nJ+OzmDm/pfZenI516c8gl7t1+gclswxsmODHp1e4tnpRWRnqpn+fCALZpkoKfSdbNtUFcMxaiy8PWgJW08u54pWY2sWU9E/z6XTa7UmyPawCEo6dKUspT2lqR0p7tqrps0dcPZCK8kdXeRmqfn03wHM/Z+Z4eOsXHNjbVTp6Xy060lWn/iBPYXrea7PrEaFutkfWpC8Aj0G2ukzxFGvfWDr8ZyoPEJKYDd6RAyDzsDoc6tcXY3JLGM6gzAJYAmQSEx1c3i/ll2bdAy5pq64G6QPp1/U1cxfbsJp1dU8l8Wr1BTHtWJ9dgb+ZTH07Gliwq2us0ZVyjJ89H8B7NmiQ+8n8dirxajUEBntS0/Iy6o9tbnN/hR2603o1t/o/PKTRK5YxO5/vIQ1NqGmT0WZwIljvh/8tmcRJ/cXbWZn/mompTxUJ03KePwY0T99T/TCuaz77xzsUT5D+qzR1+HxM5IzbDT5fQch6RtO17rUzHeWsM9rxyiKzEpNbVYqmoKCgoKCwh8RjSgyPCiI4UEXxi/uQjC6vxZxQDukN3eyhgo6ugyM0PnX6SMIAseXjEUu8ENY/jrH2sfx5Lrx3NfpTd5/ZCwnT6hRa2k0KybzUG2lbpfXwdrsBfx89DOOlu0FIMG/Aw90eYOEgMY33i9XFjhLOC65CNVo+K1bNxIbSaNXuDyZHBnKlSEBPHXsGP/Ly6NQ8rDYVVav30yK8BdU9NYYGaq1kKLS11z3CQKMv72Ct/8ZxMLZJq69uZLkoG78Z/jaJlWpLisWKS3yXWNGJ9S3dqimTQcXXsy8aZ/GO53za+5PmP1f1NZKDv3loWY992OnfC/PxMHirewqWHPGqEmoTesuK1HhsAvoDTInrZksy5xNuF8ME5MfbFEbD9Xi5D5r89P9FS4PFHFSodns2qjD7YZu/Z3s267lwA4dao3M+Nsq6vRTqeDmB8qZ/nwgX//HzJxNh4BuqKP28upVn9ZJ/bjyOhvffmLhyAEtR/Yaua7zfQTpw+kZMeKMwqQsw7J5vuizWx4sp20XF4ntXHw53VIjTIa38tQRBQ0aE9eUxSEUbaeks88cOGvMdUT/NIe8wVeSN3AE5cmpDUbXNZWpj5YRGOJl5U9+lBSq+P5TCz/NNjFmipVrb6msF/E4OfkhDhRvIc+ayT/WjuPpPl/QJrBLnT6H92vY8KsBQZC5+YHa1CKnx45HdmPUWBAEgZtSHz/neZ8Pnfs4Obxfy85N+nriJPjeq6U/+N7LvsPsbF+vo3h/J9j/ITJQCizLgGVzJDoPzmHaPz0EBze8QJj3uYnl842IosxjrxYTl+z7cY2oEidzTxEn7VGt2fDxtyR88xnt3nuFsI1rGDpxGEduupP0qffj9g/kYJXfZKs4d6MCMsDx8jTe3vYgBfZsDA4395b3JOy31YRuWos540hNv+iff6hZcOQOH9OswjyXgj1uG7MdPoeo99q0Icmv8e+cgoKCgoKCwqXDbIZehgA2fpoA9xxlhr2ANmo9capaax2nAzat9NkMyaXR+GffSG70LJ754r9wwuf/+Os8v0bFyYz02sjJhUc/Y+b+lwGfr+WUlEcYm3h3k0Scy42TXjffOooBeDMxUREmWygBGg0fJifzRmIiK0tL+bmoiFyXixCNhiC1mv02GytLSiiTvCx1lbPUVU6kqGGMLoArtRa0gkj/EXa+eMdLcYGKAzt0dO7jrPOZ9speVELDkY7Hj/quM8Jbe9AbGk+vrrYVO35UXSP++e/fTcd/PweA4PWQds/fmnzNmdFEcTKrIh2tSs+g1uMJ94tutJ/JLONnkrBViuTnqIhJ9NA1bDBaUcdJ23GOle8nwf/CVhu/mMRUBY3sV8TJFosiTio0i+xMNS/cH4wkCbSOd9dU4R4+zkpwWH1RZ+hYK0vXHyZtWU9c26b47hsUQitT3ehDS6DEgJE2Viww8vPXRtp2djE4ekJNu8NjY+b+l7mi1VjaBvWo8aE8ckBDZroGjVZm0BjfAkujhdHXW5n9oc9bp017F98cfAuTNoBJmqF0eOclolYspjSlPatnLwJRxGP2Z81XP1+w1ykgSOL2R8q56YFyNq/SM/d/Zo4e0DL3MzOrfzHwznf5mMy1P2atzIm8NmA+L268laNle/nnwsdI2LSYnH2tuf6eCkZNtjLrfd/zGTjaTlwbnwhX6ijg/zZNRa828mzfL5tteH4h6dzbwdzPzOzepEOSYPl8P/Zs0XHTA+WER3nZu01LdoYGvZ/EtBdKKMxTMfM9Cy6ngDnxIPuZRfGm4XBkJLtWtuaR0pXM/Cy5ZvzcymP4aSxs3nuML6dfA0DyjTNZbZhHQMndJAV2romc3JdeQLE9nyBDhO/BosjRG+/iZP+hdHjzBSLWLqfNFx8RcGAvGz7+hoM7tYSTx8LKycRNKQZRhVerRdJqkTRaJK2OLW2DeKLVAhxeK6OKIvnsxU8QvbUl0CWVioLeV3D82inkDWpaSsjlwH6Pnf+z5uABJoWGMjUi4lJPSUFBQUFBQeEMDB4MG1+LptWYUrJbF/O6NZc3zTE1/pPb1upx2GozIJKOTCdkACz96iaqV58HdurIOqquF/nldEBOpm+BH5fsJtFyPcsyZzMi9gaGx96IRXv2DKLLEVmWmWHPx4XM4IAAblZSuVs8fioVY4KDGRMcXK/NKUmsKS3lq5MnmVNQQK7k5lN7AXMdxYzXBTJaF0BiqoviAgO5WSo69/E9TpIlfs38mh8Of8jrA35qsODl8cPVxXDOLBIGh0kEhfoE0KMHNbTr6mJThIvVI008t7iSth+/TcC+Xex+6hXska3OOJbb5bP3grOndY+IvYGe4cORObMvpV9WBv9WvY2Zk5h+6Qf3jUGv9qNr2GA25S1hY84vLUqcjFb5roP32Rqvk6BweaOIk38SQpcvQhMUSmn7LmfvfAY2rdQjSb7dneo0WJVa5rrbG04dfn/nY6T1ngcHVsGJvgD07qcG6qftjpliZcUCIxuWG5iaX1bjWwiwOW8pvxz7nF+OfY5FG0RiQEcS/Dty/JtpQBh9htgx+9eegEdOsjLnMxMuh0hl2HLm73mLv6+HoRteRO12I4si5W1SUdtteIwXz5NCo4H+Ixz0G+5g8yo9n77uT0GemvkzTdx0f91I00B9GC/0nsPjL20gb+kUDnp8u7kzXgtgxQI/jhzQolbL3HBvOaWOAladmMvCo59RaM/BrAkgrzKDaEtyQ9P4XUjt4kKrlygpVPHYzaEcrSpOlHFIw6ufF7Dke1+E66BRdgxGmehED/98t7jq0aHI8sMcLN7Kd8ueYcfrL1C+YwiH9+WT1N6N3WPlwRVD8chu+OkjkEVos5CDSbdzMBt0KgMPBHYmNMpXeMlZGM79yzozLuVORsffhrlqIW2NTWDTe18QtnY57d99mdK2vh/cAzu1OPHQpXAtFDb8/IpKwTEOOob04+bB7yB/MoDKqFYU9BlEft+BFPboh8d8cUzwLxYHPXZerMzGgcyVgYHMbNu2RaVvKCgoKCgo/BkZPBhefVVAfrktIR9sJUdy8W5JPn8P9PlPrlnsy4DoMdDO1jUGdq7z5/9ue4ulh32+5gmpLo4e0PLZzCxG3rObGEsKQbpwNCod+3aJSJKAJcBLYIiEIATz4bC1LX59sMltZavHhkYQ+E+bNi3++SicGZ0oMiIoiBFBQXyQnMzMvDxePX6c404nnzkK2eKx0irJBGsMdTKuZGQWZcwkz5rJN2lvcnen/6s3drU4GZPUeEp3NZHJBRQXRJC+V0u7ri7mHJrO1j6VFAnw5jIVEetWEDJhCPsf/AfHJt/WqC9l1lENXo+AySIREn72oqJnqjgeuGc7bf/zJmG/raY6nKJ43hrWPuAL/ugTNconTuYu5sZLlJF3LlSLk+k2m1Kxu4VyScVJQRCeBK4D2gJ2YAPwd1mW06rag4AXgCuBaHyywY/AM7Isl50yTgzwATC0apzZwGOyLDe9ssUfGZeLti88ga6ogOKO3Thyy93kDBtNoy7YZ2DLal+KyK3TyhBEWPmTHwNH2WoMdU+nb+RofstZxPjnt7Dk6Z54PQLtujX8tiSmuknt4uTATh1L5hq54a+14l2UKYEh0ZPYmLuIclcxO/JXsyN7Eyz7FwBJQ7YBPg/BMmcRkt7LDX/V8cO3duJLp/LjTxBbBuCmoNcV7HniBSoSU5r9/M8VQYDeQxzIwKt/C2bBLBOjr7cSGFI32vSXLyPJ+2Wq70bcCqI6HaFw6Z01VchDB/zMu8de5ND27XjlqlRmYyzP9PmSVqYELiUaLbTv5mLHBj1HD2jR6SX0Bl+RnFcfDWL/dl+60VUTGw61FwSB1OCePDelJ+/ss7NqoZEv37fwwn+KOFGRjlrU4CkLhF23AzD4hiNEpPwNtailbZAvPT8sUkCt9eJxabEX+/P1wTeYl/4hI2JvZGLyg/jrfDur+QOGkT9gGILHg9sNh/drcaNh2SMfYko0gyShcrsQXS4O5q1jzZGvSQ+GMfFTmdrhWdSihmULf8MZEnbxX9iLxHa3lX9b87AjMzQggB87dEB/nkbdCgoKCgoKChef/v1BrYacA1qY1g7e2clGVQUzs/2Y6O/PtnW+9fpN95VTUaIibY+W1x4PQpYFuvVzMHKylZcfDmbHslh2dOwH6qpIrOzuMGsxAP4pexEEn5jZ0oU8uywxw14AwBPR0bQ1Xj4FCRUuPkaVir+2asWdkZF8npfHo0eOsMdjJ3PCfvi1cx1xUiWouKPDczyzfjKLM2YxKu62esEfmVWVumPOEjmZVZFOunEN8Czbt3i59hZ4vOdHzNj9DNP5hmUJXr5fGkqH9AI6vf4soRvXsvnd/zU41ql+k419HfcVbUJAoF1wrwbbBY+H5E/fJfnT9xC9XmRBYEPACNaWdCWppwVN1cA9gweyaBZ81D2Nk72PE26MOePzvFwIFtS+it1IHLLZ6KAUxWlxXGo5eRA+UbEPMAKfWLpUEITqX4yoqr/HgI7A7cBI4L/VAwiCoAJ+BozAFcAUYALw5u/yDFoC5eUU9x+EpNYQtGc7PZ+4l0E3jyH0t9U+I8AmUloscnCXTyQbONrG+NsqeW9OPhPvbLzgSo+IYXxy5W9M6j6B6XPz+fDHkxj8Gj/mmCk+4WrxHCPuUzTMpIBOPNTtbb4YuZPXB/7EXzu/SvuC18ARCP6Z9L+idpHxbdpbTF3SjS/8g+kztDU/LCwitgxsEVFsfv0jNnz09e8qTJ5K78EOkju6cDpEvv/UXKdNlmHlQt9O960PlTL55YWMvDWHt78poGNPJ5FxNnK738mB4s14ZQ/JgV25r/NrvD146SUXJqu54iqf12SnXg7e/T6fp6cXodXJ7N6sx+MRaNPBdcaq39Xc8NcK1GqZXRv17Nqko01gF74cvZfxxfvAoyO5o4uHxk1kStu/MTH5ATqE+KJyRREiWvkE3wkh7xDv3x6H18ZPRz/lr78OYPWJeXWOI6vVHD2gweUUMAXIWG8ZS37/IeQPGEbu0FEcGDqQv4QsYEYPCL7yfv7S6cUaP5qWKkzKssxCZykvWXOwIzEkIIAFHTtiUIRJBQUFBQWFFoHJBE89BcnJ0EkMIOCbRADmGU4ya4MXt8tnvxSX7GHEdb61dbUX+6jrrXTv7yAgxA22UAIy70Kr0sPRIfDFCrCHQNRmYm98F6989gitlsA3jiKKZA/xej3/jI291NNRuERoRZG7o6LY0LUrcXo95X4u+GA7Jyrrfs47hvSjd8RVSLKX/+17sU6bLDetUneBPYfnN9yIK/F7APb+Fkh5qejL9ur6Bvd1fo1DoSKdbijgnZu74TaayLnqmroHOoVjaWeu1O2VvXy86ymeWncdy49/12CfhNmf0vbjtxG9XrJGjePXBet4pv88nuRVfkq9v6Zf8rpNjDwMP34Lrd98Arwt4zxQXbEblNTulsolFSdlWR4py/Lnsizvk2V5FzAViAG6V7XvlWV5gizLP8myfESW5RXAP4FrBEGo3uK4EmgH3CzL8g5Zln8FHgX+IghCy8qxvFiEhLDvlfdZungzB+9+GLfRRMCBPfS77yb63TsF85G0Jg2zba0eWRZISHUREt540ZBNuUvItWbU3DZpfBUEDX4yBuOZxdA+Q+0EhXopK1axfll9k+r0nRaOrehPZP4dsO0eAIZcYyXE6PONCdy5lXELdyLg2/lZ2UbDgc5JpN31ECt+WEXuiKvPq9DN+SIIcMs0X9DvkrlG8k7UCkLHDmnIy1Kj1cmMmmzjxtRHGZt4F61iPbz4SSHvzcnjjj738Wj3D5g+dCWvD/yJK+NuwqC+fHZ/h4218fnyXF74qIiI1l7atHfz4PMlNe1XTWiaQXF4lLcmwnLWdAuSBB6HjmVzfL4v426taPRtrC6KE2ofyFuDFvNc31nE+7fH5iknSFcrKLq9vhTwnb/5ogvad3Ph9NrYXbCevYW/AWDRBvJs3y8ZGXcrN6X+vRmvxOWJV5b5xF7Ap/YCJGBqRASLO3XCqAiTCgoKCgoKLYoXXoC0NNi1Cw693BrjunBQwaK2mRBhZ8BIO4Lg2zg2GH3r9rAoD936O1CpYfi1DgCMa98lamYZzFwBLgvte1iZ+U0Yjw18sdGCIC2JDK+Tn5ylALzfpo2yGatAR5OJzd26kaoxgslLXtd8pNMubW9v/zRqQcP2/JXsyF9Vc3/hSRW2ShGVWqZVXMNp3TZ3BS9suIkiRy6tEx3EJNvxegTWLam9tr0y7iYe6v4ugkrFI0nbmfTiQLJGjqtpT/zyY3pPu43Wv/yAPj/vrMVwVmXNJassjTinmZFl0USuWETCV58SsXJxTZ+MSbdR3Kk7W1+ezvaX38fWOhajxffEreW1slBBnwEsvro3AKPnraP3w1NRV5TTEmitUoritGQuN89J/6r/xWfpUy7LcvXZoC+wV5blnFP6LAF0+ETOlRd8li0UZ3AoaX99jGPXTyX5s+nEfTeT0M3rEd1nj2QD2FyV0t1rkKPRPiWOfN7d/ggalZY3Bv5MqN+ZzX1PJWTzejq/9HfS7B4cyHj+pUfY2pOT/QZT3K03ed5Qnrs3BI+nrio1ZUoQfllHaP/uy0Qt/4V+egOhfztIueBALWo4dLXpnFLYLxYde7jo2s/Bjg16Zn9o4W8v+8S7DVVibLf+jgajSzUqHWMT7/pd53ouBATV/XUfMNKOzVrCkQNaBo1u+i7WpLsqWD7fj/R9Wu4ZE06reA+V5SKR0R56D2n8MxgZ7dvdyzuhRhAEuoYNplPoAHYXrKNjaP+afp/seZod+auxLf4VsHAi4l1uXvQcHslFSmB3Xhs4H4B2wb0aTY9oSbhlibdtJ9ngrkQAXktI4LHo6BafqqWgoKCgoPBnJzRU4NcxyfTbYkNOroAZWynRBFAqWQgwqBkxzsaCr0yMvbmyxtJu2LVW5vzXTHaGT/QQVTKDRtn569OlaHUtrxJ3Q0iyzEe2fCRgQkgIoxsonKLw5yRUq+WZhFhuTNuPNCKPgoI4wsNrr78iTfGMTpjKgiOfMGP3M7w9ZCk6lYHjVSndrWI9aBr5mny+7yVOVKYTrI/gub5f8Vuek8/eMLByoR+jr68VzQa1Ho9a0PDmtvs5rCmiwlPmKzgly8R//yXGE5lErF0OQKKYRC4RxHwvwvZIdj5fmyTa85E76LJ7Of8uA523AphU01YZHUve4KtAEPAaDKz9/Mc6gTqmKnGy8hRx0m0JwPuv79jabwFdX3iMiHUr6H/P9az/+Bs8Zn8uZ6ordu9TxMkWSbPFSUEQRHzp1dcBcYAMHAPmAF/KcjPyhOuOKwBvAetkWd7bSJ9g4Bng41PujgBOntpPluUSQRBcVW0NjaPDJ15WY26o3x8VV1Awex97niM33kn4+lWUte1Q0xa1ZAHFXXvhCKv70jkdsPM330vWa3DjwtB/9z6PzVNOorFTbaXkhvB6CduwCrclgJLOPr9AW6toTFkZ1LhDuIAfjxD74zcAfDf4RTyep/EP9NLGL5NJuR/TJeoEfaZtxXwsHUGWkUWR7JHXonF78DdfvguQmx8oZ8cGPWsW+TH2pkoS27lrxMl+w+2XeHYXnqsm2IDmhdcHBEvc82Qpn7wWQEGemoI83+nq2lsqGvOKBiCitW/f4nT/mK5hg2pue2UvW/J+pbQIyPCl+J+IeA8kF6GGVkSZ4ps118sduyzxqjWXXVVG8F+lpjIprGWmpSsoKCgoKCjUp09XFdPT2/PAkT2QaGUxRawoL+ZmfTC3TJMZMMpGUrvagITIaC+3PVxG+j4NPa5w0nNQ3eKSfwQWOks56HVgUql4JynpUk9H4TLjuvAQxK1qpFAXa3McTAzX1Wm/PuVh1mXPJ9eawe6C9fSMGF6b0t2I3+SegvUszfwKgEe6TyfUrxUDR9n5/G1/0vdqOXFMTev42ojL/q2uJimwM+F+0bWDCAK/vf8lMfO/JWzjWvwP7iFJOkwSh2EvVJbXvU6RT6QRW+wLzpAFAWdwGPaISGyRrbFFtkJts9YWgD0tKKFanKwor3u/SlCRPWo8lbGJ9H3gFgIO7KHvA7ey4T+z8fpdPll7pxNTHTmppHW3SJolTlYJiAuA0cAuYA8gAKnA5/gEy3GNPf4svA90wucb2dCxLfi8JffjK5JzKg39kgqN3A/wJPDcuU2zZTL30MfY3E7C/WJIDepBkCECe1Q0GZNuqenjdyKT7k8/hKRWkX3VtWSOv5GSTt1AENi9WY/TIRIS4SG+kXDybSdXsC57ASIi93V5rcFUEJXdTsyCb0mcNQPjiUxO9hvExg98J3BbqxjWzfger8GP2R/5c3xdOXfE/cxIYQnmY4fZeiQKgIl3VXBX67X0eeh1OFE79sl+g9j38NNUtEm9gK/cxSEx1c3gMTZW/ezH/97y564nSsk5rkajlek5sHHx98/GkGvs9BthZ9s6PeuWGlCpYOjYM//YRFaldeedaPz0phJUfDhsDT/MsTIHEUtsJpP63UO38KFEGeP/UNGEkizXCJNGUWRehw6MCAq61NNSUFBQUFBQuMDcP1nPsLQebNcX8V5hJpsqKvjMUUiBzsPUdiH11jfjb2vcN76ls9tt43NHIQCvJiTQWq+/xDNSuNzQiSIxByLI6HWCdZpSJhJep92osfBQt3cxqPxIDuoGnL1S92+5iwAYGXdLjSd+QJBEt/4Otq4xsPInP26ZVjdFuo4wWYU1NoED057kwLQn2b3IwdqnjpAQUcJfHszHq6v7WX5xXAR7ijNJ6TCFcVe8gtxYSGcDmBpI6z6VwpQU5r7xMhMfeQLL4YOYMo5Q1q5Tk8f/valO606325WK3S2Q5kZO3g4MBIbJslwnXVoQhKHAj4Ig3CrL8szmDCoIwnRgLDBQluUTDbSbgcVAJTBeluVT1bE8oPdp/QMBDadFVJ7CK/iiNKsxU0fm+uMxL30GJ22+p6hX+TGt21v0i7q6Th+Vw05J+84E79pK7PxviZ3/LZWxCRR17cWxzH6EMZkeg4wNev05PDY+2vUUAFcn3kViQMc67eYjh2i1eD5xc2ehKykCwGXxpyIhBSSpJu26qIfvJJ58r4ZP1oXx64mRzFiUh8fq5t1x4QiCTP8RdhyF4Ry9/jZc/oGUtu9CaWpHnKF1f1Aud256oJwNy/Xs267jwxcDgaqU7rP4cv7Z0Omh33AH/YY3TbStjpzMy1Ihy1CQq+JYmoZegx11Prt+GjM5O3zV564aEcg1LSBl/lxY5Cpjl8eGnyiyvEsXelsUK14FBQUFBYU/Km1TBNoSwg0xwbyZlcXjR4/yk7OUYsnD/X5h+P0BfCTPxkmvm3/bcpGAW8LDuS8q6lJPSeEypV9pJBmcIDO4nGIpmCCxrjzSObRu3FTmkapK3acVw5FkCVEQ+UvHF+kQ0ocuoYPqtA+92sbWNQZW/ezHjfeXN5gFVu4qYc2JeYyJn1pnI2H/8VCWkcig7jZOjC6p85hSZyGfmrfhNcHk7nc2S5iEhtO6qzlensYTa8aiVemI/OBLVJJ8WQuTACGCGgMidlki3W6nvfHyjfJUqE9zxckbgJdPFyYBZFleIQjCq8BNQJPEyapIzOnAeGCwLMvHGuhjwech6QTGyrJ8ukLxG/BPQRAiZVnOrbrvyqr+2xo6rizLzqr26mM0Zbotmivjriez/AjHyvaTVXGI17fcy8Q2D3JD6mM1EY4VSW1Z9795BO3cQuy8r4la9hOmzKOYMo/yMN/wM23pOagrAJG//kziV59iD4tEkCXycrezoCSHIJeaaHkeG9+fSHlKOwA6v/gEcT/MrpmLNSqaI7few/Gx1+M11C96A5DU3k1KRxdpe7QsmWtEq5VxoaNjDydBoRJloR3Zk9qxwce2FEIjvIy7pZLvZlhI3+fb5ek34txSusslL+leByWSh3LZi12WCRRVhIpqIkQtrUQN4mXyObfLErleN2Wyh1LZS7bXxXGvi2zJhQBoBRGTIDJIY2GQ1oy6mfMOjfIiqmScDpFNq/S8/3wgleUi/3iriD6neFV63LXFcHoM+GNGq+Z6Xcy0+6IGXktIUIRJBQUFBQWFPwmCIPBYTAxROh23HzzIencl28tsjNBZGKMNIFz1x/CVPJ1iycMr1hwqZIkeZjMfJyf/Ka71FM6NXuFGZu+xIHcsZ7mrnEn6xrOLjpceJuNwCACxiW4yyw+yMut7tuYtZ3jsFMYl3YsgCPUCgAB6DnJgskgU5avYs0VHlz7OOu1ur5NpK4ZS6iwg0hhH9/ChNW0HdviuE9t2ctUb96Q1k2BDJP7aIOIszc8eNFl8QTENiZNRpgRUoppyVzGbIr2kBvesabMc2o8tsjUe8+V1bVFdsfuQ18E+q1URJ1sYzRUnOwFPnKF9ETCtGeN9ADPndrIAACAASURBVNwIXAtUCIJQbVJYJsuyvSpicingB9wMWE6pwF0gy7K3qn0/8KUgCI8DQcAbwAxZlltGWanfgVvbP47V5cErefjywCv8ePhj5qRPJ9eawaM9PkAUqk5IgkBx114Ud+3FnsdfIGTrBhyL9+JeuodCczQdevhOpOaj6QTv3FIzfm3ZGw9QgLa8dlenpH0XYhZ8T36/QWSNvo7cYaOR1Wf/6I2+oZK0PUEsmWPEHODb1Rkw8vf3j3DLMjbZi02W0AsigeKFqyM1/vZKls0zUlKo8qV0V4lkXlkm3esgy+vCIIgYBRGDUPujUSp5yZFcnJBcHPI4OCGduaiRURBJUenpovZjuM7yu++ay7JMutfJElcZ61wVOBt1XKhlt8fOd85iJuoCGaK1NFmk1GggNNLLyRNqXns0CFn2Pe7XH/3qiJMHd2mxVYpYArwktW9aUaiWhFeWec92EicyQwICuK9V04tTKSgoKCgoKPwxuDE8nEitlvvT0zlgs7HAWcovzlIe9AtnkPbCCQuVkpfFrjIKJQ8yMtXlEWVABXRW+9FDY0QrXJw0S1mWWeWu4FN7AVZZIlyjYV779kp1boUzkpQETI+CjuX86ipjgi6wwYCOXGsGj/04Dcm9C9Q2njvQj3xHbVzV7oJ1jEu6t9HjaLTQe4id5fON7NlcX5zUqHQMan0d8498zNcH36RL6EBUohqvB9J2+8TJ1K7OeuOmBHXno+HrKXMWntPzP1PkpFrU0C1sMGuz57Mlb1mNOGlJ20//uydhjY5jw39mX3ZFcqrFSaVid8ujuSpLEI2nSlPVFtiM8f5a9X/VafdPxedh2Z3alO3Dp/WJBzJkWfYKgjAG+BBYD9iB2cBjzZjHnwaVqOb29s8Q79+B6TseZWfBGvKsGUSZEur19Zgt5A0Zyeu/3MgGDFx9TSUaTRkA2SOvpTI+Cf3JXDyCzIqyFTiNRga2vxOPyUJlTK1Rb/aoceQOH43bEtCsufYbbufzt7yUFKooKVShUsv0GXpxItxkWSZDcrHPYyfNY+eY14VV9mKVJVynCWnhooZ2Kj1Jaj1RooZIUYuMTIUsYZW9VMgSlbIXtyzTSqUlVqUlRFA3uGtr8JO5dVoZ7z4bRLery9mkLWO71cpOj40KWarX/0y09fMjQa8nRKPBqFKR63KR6XCQZrNhlSS2e2xs99j41lnMGG0Ao3T+F1RobQir7GW1q4KlzjIypNrdvmC1miidjnCtlkS9ng5GI6lGI2pBwOb1sttq5a2sLE663Xxgz2ees4Rb9CH00RibtPsd0drDyRNqZFkguaOLQ3u0bF+vp6RQJDDE97puXeeLmuzW33nGAjstlZ+cpRyoMoH/LCXlsomcVVBQUFBQUPh9GRIYyN6ePVlaXMyrx4+zuqyMd2wnsckSo3TNW5+fjkOWWOgs5QdHCTYaX7sucZXjh0gvjZFIlYZgQU2kSkuySo/mPNcoGV4nM+2FbPf4ghi6m0zMTE1VfCYVzkpSErA6FB5M56TRwx6Pnc4av3r9Io1xTA371leVN2wf+Y5jqAUN3cOHMih6PJ1CGiybUYfqQjgFeQ1feIxPupclGbM4XLqLb9Le4qbUJzh2SIPDLuJnkohJbNjnUhREAvXnVuiyWpy0VQp4vdS7JuoVcSVrs+ezKW8Jt7R7EkEQEGQJWRQJ3LeLHv+4n43vfVH/gZeQ6OqK3UpRnBZHc5UJFb7QuMbwNmdMuTqkqfH2VfgK25xtnONA/fhphUYZ1Ho8GlFLvH97Io1xjfYrLxXZvNr3wz5sbO3ugzUmHuspAmQSdyPLMkUNLC68Bj+8hvon+bOh0cBVE61885FvV7drXyeWgOYJdk2hVPLwkT2fje4z766YVCqsXi8nJTcnJTcr3RVNPoYegXBRQ7ioIVBUYRBEdIjYkCgY6iaqTxqbNE42nXIO9Vep6G2x4JAkSjwerF5vzZchUKMhxWAg2c+PbiYTff39CW7EY8QjSeyyWllbWsrHubkctNn4zlnMd85iWoka2qkNxKp0hIlqAgQ1uZKLY14XhZKbUFFDlKghQFRRLnkplb1IQJCgJkhUUS57yfK6yJbcGAXR9/wEFdmSm6NeJwc99pooSZ0gMDksjHuiouhnsZxRZBwVHMyDrVrxcU4Orxw/To7bzWu2XOJVOnqo/eio9iNJrauJAM2X3Kx2VbDdbcUgiFROLYEkE1EpLuIHWylY6kfJF61Z/Ysf4271mb9vW+v7XHe/4o+X0p3ldfGVw+ft+lZiInGN2CcoKCgoKCgo/DkQBYGRwcFcGRTEtPR0PsjJ4WN7AWWyl4m6oGbb6NhliUXOUuY7SymTfZWCOxqNjA8JQSUICFXHFIBCt5vvCwo44XSyyl0BpySs6BHoqPajt8bIAK0ZXRMjK72yzDGvk/nOEta5K5EBrSDwfFwcj0dHo1YKYSg0gbg4EF0qpF/D4doclrnKGhQnAcqzfN6lfTvHMKDnx3QI7otF1/Qik6GRvu9JQW7DQl6APpT7u7zOm9vuZ86h6bQL7sWJnWMASO3i4vSP9MHirSQGdEJTJcadC8YqcVKWBWyVAmb/ukE53cOHoha1ZFce4URFOtGWZMraduC3D2dzxR3jCd+wivbvvsy+vz1zznO40FRX7N6nRE62OJorTgrA54Ig1I8p9qE7z/ko/I70ixpT57ZX8qA6LZJu7WIDHrdAfIqL+JRaXdrpsfPD4Q+Z2OYBNCrf234x/FyummBlzqdmPB7hoqR0r3FVMMOeT4UsoRYEhgcG0t9ioZfFQphGg79ajb9ajUWlQi2KlHk8/FZWxrqyMvbZbKTbbBy229GIIkFqNYFqNUEaDUFqNSpBYL/NxkGbDYcskym5yJTqe4UAvvJNQDeTiVFBQYwKDqa32XxBFlZqUaS72Ux3s5lprVszr7CQfx8/zqaKCrIlN9mui5vSnOrnxz1RUdwSHk5QM0ya/VQqHomO5s7ISN7IyuLNrCyOeZ0c8zr53umzDfBDxF9UkXt6WnuyDZILyQFyPMDQMhiay7c7gmjnNlK6IYCsoxpElUzXfn8scdKXzp2HG5mRQUHcFRl5qaekoKCgoKCgcJkgCgLT27QhSKPhxcxMvnEUs8ZVwU36YPppTA2u5yVZ5rDXyX6PnWLZQ7HkYafHRmVVlk+CXs+/4uO5ISys0UyNNxITWVNaysrSUrKdTrJdLrZXVJDvdrPFY2WLx8oXjkKGa/0ZrrXQSlVfcLHLEhtclWxyV7LPa8d6SpbR5NBQ/hUfT4pf8wMiFP686HQQHQ2ZP0fCtTlsdFspl7xYxPoCYnUxnJS2+nrX0U0hLNJ3LZ3fiDgJMKD1tewr2sjijC95e9s02mwZBUC701K6Fx79jE/3PItFG8Qbg34hzK91s+cDvmAgvUHCYRepLBcx+3vrtPtpzHQJHcDWk8v5LXcR0ZZkAMpSO7Ljhbfp+fe/kvTlx5QntSVr7KRzmsOFJq5Km0iz2bB6vRgvo6hOhTPTXHHyiyb0aValboXLgx35q5mx+xme6TuzTiTl8gW+H/hhY2uFwVJnIf+38XbSS3dSaM/hwa5vXrR5BYZI/OXvpaTv09J32LkVi2mMuY5ivqyKLutiMvG/lBS6mM1nfIy/Ws3I4GBGBgc3+TguSeKYw8Exu52jDgcFbjeVXi+VXi9mlYoYnY5ovZ7eZjMRuour74uCwITQUCaEhlLsdrO+rIz1ZWUcttvJcDjIdbmI0+vpYjIRr9eT6XSSZrNR5HYTptUSXiUu5rpc5LpcWFQq2huNtPXzo8Lr5ajdTo7LRaLBQFeTiR5mM11NDS90m4pFreZf8fE80KoVPxcVsaK0lJUlJWS7XNiQsEkSAjAkIIApVYviY3Y7eS4XRpUKf7Wa7aVWfi4rxN61mL9XFqParAGCGDnRWmME/UfhB2cJ6V4n/ioVMxQTeAUFBQUFBYXTEASBf8XHE6vX8+TRo+S43fzblkeUqKGPxkRPjREvMse9Lo54nWxzWymVvfXGSTEYeCo2lhvCwtCcZUNdFAQGBwYyOLDWAUySZXZVVvJzURH/zcsjw+FgnrOEec4SWokaemiMmAUVblmmQHazwVWJ4xS7JbNKxZWBgfwzNpauZ1nDKyg0RlISZC43E+s0kamrZJWrnLH6+k51xw/7roNik84tsKM6crKkQIXHDepGYjbu6PAcaSXbOVa6j707fMJaatfaAJeVx+fw6Z5nARgdf/s5C5PVmPxrxUlfImxd+kaOrhInf2FyykM19+dceQ1phw+SMuNdOr/0dyoS21Davst5zeVCECSqCRRUlMhedlZW0t//8vLEVGicZomTsixPvVgTUbh0SLLEl/tfIcd6lKfXTeKlK74n0hhHxiE1Rw9oUatlBo7yCYMVrhL+sfZa8qyZmDUBDI+ZctHnd9VEG1dNvLBRkwscJTXC5JMxMbwQF3fWRdW5ohVFUvz8Lrud3CCNhmtCQrgmJORST6VJhGm1TI2MZGpVJGClx0OW00mOy0WKwXBWX6Gr77Pyc0gG8tACPH85SkCnCm4ZYgFq3/diycN+j53cqtT9StlLlKglTqUjTqUlStQ2O+3p9+Swx8G3VZ/r99q0UbyWFBQUFBQUFBrlzshIJoeG8taJE7yRlUWO180PzhJ+cJbU62tRqRgWGEiSwUCUVkuKnx9XBgWhOo91kSgIdDWb6Wo282RsLAuLivhPdjYrSkt92T3O0nqPSTIYuDU8nKuCguhmMinp2wrnTVISLF8O7Y5Fktk2nWWucq7RBdTZ4He7IOe4TzqJOUdx0j9IQq2R8bgFigpUhEfVFwIBtCo9T/T4iE9Wz2RHmT9qjUxSOxd2j5VFx75g1oHXALgm4S6uT3nknOZyKiazTGFew0VxAHpGXsmNjsfpGzmqXtvBex/Fkn6QyFVLaPPZ+2x589Pzns+FoI1Kz2aPlS3l5Yo42YK4INUwBEGIBYzAQVluZhUPhUuOKIg802cmz6yfzInKwz6Bsv93LP3Bt/PRY6ADS6CELMv8Z9eT5FkzCTW04vm+s2llTrzEs28+i5ylfObwVTR7Pi6O5+LiLu2EFM4Jk1pNqlpNqtHYpP4PjTfy85XtYH823HeY0t4F3GsvprVTS4ioJsPrbCTtvtavRI1AtEpLjKglQtQQodIQLfqKHmkuUvXJppLhdfKCNRsPMDY4mFvCwy/pfBQUFBQUFBQuf8xqNc/FxfFw69YsKipiflERS4uLsajVdDAaae/nx/DAQAYGBKC9iEKgShC4NiSEa0NCKPN4WFpczPKSEjyyjFYUMalUjA0Opr+/v5IVonBBSUry/Tf8Fo5fuyNkSS7SvA7aqms927Mz1EheAaNZIij03OQOUYTQCC+5WWoKchoXJwEiTfFc4X2NHUCb9i4OVfzGa5v/QoXbJ9gPjZ7M1A7PXpDvQrXvpLURcdKiDawTMVkHUWT7v94maebHHLrrwfOey4UiUa3ziZMVTa8RoXDpaZY4KQjCbUCgLMvvnHLfJ8CdVTfTBEG4SpblrAs4R4XfgUB9GC/2/45n1l/Picp0Hv9wFpXf+qqOjRjvE2dWZc1lQ85CREHFEz0/bpHC5I+OEj6vEib/ERPDs7Gxl3hGCr8XQ4dChw4CJ5a35pUn/XjKvp8Sj4cyr70mg0EAuppMdDQaiTcYCFSrOWizsauykt1WK5Veb43v5amo8FWGS1TriVfpSFLpaKPSn1c0QXPI8rp4rjKbClmil9nMzNRUZeGuoKCgoKCg0GT81WqmhIcz5TLY3PRXq5kUFsaksHOrQKyg0Byqxcnj+9VMDgvj87w8lrrK6oiTmUd8OdgxSW7OZ4kdGunxiZNn8J2s5sAOn+9q2y5OduavocJdSqQxjuva3M/QmMmIFygwwuzvEycrys5tPI/ZwsH7H78gc7lQtFH5sse2KuJki6K5kZP3Ap9U3xAEYSQwFbgVOAC8DzwH3HWhJqhw4ZAk+OQVf1RquPPxsnoVvwL1YbzU/zse//wdCr59D4DBE7Lp1h9OWo/zyZ6nAZiS8jfaBF56P4nmIMsysxxFzK1KU3k8OpqX4+MVAedPhEoFW7aAywUWSxC3ePuyz2qt8dtM0OsZHhhIiLbhineSLJPpcLCrspI0u53DVX+7Kysp9njIkFxkuGojL4MEFUO0FgZrLbQWNY1+1hyyhE2W8CIjyaASQIuIThCaVLEy0+vk+cpsymQvXU0mFnfqhL/6ggTFKygoKCgoKCgoKPyhqRYn09PhnYhIPs/LY42rkut1bsJVGrxeWL3QZ88Vm3h+hURrKnbnnX2tfmCn75okoWMJ+9ylPNr9A/q1uhqVcGELvBjNPnGysuLM18XrsxeyIedn7ujwLMGGRgpuShLt336JyrgEMifcfEHn2RySqsTJQ3Y7pW43Ac0oyqpw6WjuFWwysPWU29cCC2RZ/gpAEISngP9doLkpXGAO79eweI4JgOgENyMn1fVxlGU4tDGaii//C5IK2n+L8ZpfEYTnybefQC1qSA3qyYTkBy7F9M8ZryzzkT2fZa5yAF5NSOCJ6GhFmPwTotf7/gCMKhW9qiqzNwVREIg3GIg3GOrcL8syWU4n2ysq2FFZyfbKSjaUlVHs8TDXWcJcZwkmQSRBpaOVqEUnCGgQKZTcpHudZEsuGivJEyVqaKPS005tYLDWXE+s3OO28aotF6ss0dFoZFnnzgQqP74KCgoKCgoKCgoKTSIhwRfEUFYG8l4LQwMCWFFaymxHEY8YI/hyuoXtG/RodTJXnmcdhBpx8iyRk6XFItkZvjV9l+4aBvi/cl7HPROms6R1V7PgyAzSSrbRPrg3oxNub7BP5PJfSJr1CbIo4goIInfY6As93SZhEVWEiWryJQ/bKysZGli/wJHC5UdzxUkDUH7K7X7AZ6fcPgpEnO+kFC4OW1bXFsf4/B1/uvV3EhblRZJg43I93//XzLE03w5NavcKUh7cyC0dfdGSHUP68e6QX/FKngu+W3MxccsSb9tOssFdiQh8lJzMX6KiLvW0FP5ACIJAjF5PjF7PuNBQAJySxMKiIj7LzWVpSQmVssRuj53dNFxxXgWoBQGVIOCWZdyyT67MkdzkSG5WuyuY4yjmVkMI/TUmimQPW91W/msvxIPMFf7+zO/QgSBFmFRQUFBQUFBQUFBoMn5+cMcdMGMGPPSQwIfLE+izcztr3BVELo/kxy98leAfeK6EhJQLEzmZf4o4WZQvYreKtI731Ny3fqkvGCIm0Y3Zv7EwhguDqYlp3b0jryKtZBvb81c1Kk7mDh9DxnU3EvfDbLo/9SAb3/ensGf/Cz3lJpGk0pMvVbKlokIRJ1sIzRUnM4HuQKYgCCFAe2DdKe0RQNkFmpvCBWbLGp846WeSsFWKfPCvAG57pJxPXvHn4C4dAHqDxMhJVq6/uwKD8bE6jw/SX3oPmuZglyVeteayy2NDKwh8lZrKRMW7RuF3QCeKTAgNZUJoKE5JYp/V+v/s3Xd4XMXZxuHfbNFqVXYlq7jLcu8N27hQTQudQAq9JRBqQoAAoSYQaiihfwQSQguEkoSS0IuJKcYG927jLhfJkryq2+f7YyV32SoryeW5r2svW3vmzHkPSRx4eGeGGVVVrAwGCcbj1MbjdHC5ONDnY0xmJvnbLSWPWUtZJMK3lZV8U1HBs+vXszoU4sGa9TyGYeteyx/l5vLSwIGkOveef2kgIiIiIrKnuOsueO01mD4dZr/u4/QJebxaUsKr8RKgCz+9uIJDj9t5k0FT5HVKBJAb1yf+vt1auPmiPIrXOrn7LxsZMCJMsNbw+l8SgeixP61ucK5kychM/HNFdeWuw8kReYfyAnczd+PXROMRXI6dNEUYw+wb78ZTXkrnzz5g3JXnMeP3D1B03KmtUfou9XF6+CpSpX0n9yJNDSdfAJ4wxgwGjiBxOvd3W12fAMxNVnGSPMXrHKxYnILDYbntsVJuuzSXWd+kcs0ZicAy1RvnlHOrOOHManxZe/+B6zU2xh1Va1kYC5LucPDmkCEc1aFDe5cl+yGPw8EBmZkckJnZ6HucxpCXksJxOTkcl5PD9QUFPLh6NfeuWkVNPI7LGAanpfGjvDxu6tGjzQ7eERERERHZ1+TlwR13wFVXwU03wSmX9YJDNmLHlDPo4nWccWnT/vl4XSxMmY0RtZYYljTjwGecZNZt1Viy3om1sHaVi/WrE5HMw7dl8/CrxbzzcjqbSp107BbdfDBta6o/EKdqN8u6C/2D8KfkEAiXsqh8OoNzxu50nHW5+PaeJxh9w+V0/vxDRt/0SzKXL2Xhpdeyw6EXrah+38lpFRW7GSl7iqaGk/cBacBpwHrgJ9tdPwh4JQl1SZJ983miM7L/sDADRoQ56/IKnvuTH4CDjqnhwmsC5Hbc+0NJ2DaYzHK5eH/YMMY2cl9BkT1RmtPJrYWFXNG1K0WhEP3S0vC04f+5i4iIiIjsyy67DJ5+GubNg7/+wQu/7AKnFbHp7GVETAEedv333hFrmRKp4t3QJhbEgjsd48gEDnMT/jyfQLlj84ncAOtXu3jqriym1m3FdtZlFbTFjk3pdXtOVu1mWbfDOBiWdzCTi95iVvH/GgwnAeKeVKY++AyDHruHvs8/Rd+/PUnRD06msnf/pNa+K71difxjZShESThMXgOHnsqeo0nhpLU2Dtxa99nZ9e3DStlDfD0p8T/OMYcm/qA8+ZwqfFlx8rtEGTI6vKtb9yrbB5MfDx/OqCZ0rInsyTq43dpXUkREREQkydxuePRROOqoxAGaD08o5PaUEtaGw/ytdiOXpjW8Pdg3kSr+UlNCiU0s23YZQ8/UVDwOBy5jqIhGKY5EqIrFcFy0nPj/8ti4zrn5RO6BI0MsmOFh0n8Tp4IX9o1wyLEtX0beGPUH4uzutG5ILO2eXPQWs0omc9bA63Y92Olk/q9voapnX6zD2abBJEC6cdLF4WZtPLFV1nE5OW36fGm6JoWTxpg47PRg2QpgEfBHa+2/klGYJM9zqzYwPRUgn9F14aTDAUec3LLTxvY0y6JBHqhZz9p4RMGkiIiIiIiINNoRR8C0aYll3gUFbnqVDeDo2bN5PxzgAHcaB7ozthm/IRbhL7UlTIsmll93Sknhks6d+UWXLnTxeLYZWxGNUvD11wS61cL4UorXOVkwIzHmRxdW8m2fCO+/npj/7Csr2mwF9OZwcjfLugGG5x8CQDQebXjfye2sOuX0bX7OXLoQRzhMYNCwZlTbNH2dqayNR5imcHKv0NRl3Q3tZJoFHAi8ZIw531r7esvKkmQJRKNctXQpsZsiuH+8huoCP4lD1/cd1lr+Gw7wXN3Jxd08Ht4aMqRJe/yJiIiIiIjI/m3UqC2/P6pDB67t1o0H16zh8Zpibk130cfpIQq8HSrn1WAZYSxuY7iue3du7tGDtAYOqfS5XFzWtSv3rloFZ65i6Xd9WbsqEccMGB5myOgwxUUusvNijD5k58vCW0N9OBmscRCNgGsXeWOutwsvHDsbn6d5ZzmklG1k3K8uwFNawqyb72X1ya278LaPy8PnkUo+KCvj1h49MNqnf49mrE3e0fTGmCuA86y1DW9AsAcyxviAQCAQwLeP7U1YE4txyGOrmd5/FXgTf/Ac5s7kYm8eGY69/3TfmLU8VVvMR+HERren5OTw7IABWvoqIiIiIiIiLRKKxxn73XfMqk50R+YZF25jWBuPAHB4VhZP9u3LwPT03c61PhSi2xdTiDktWbcPZdOkHAr6RHj09eJWfYddicXgR6O7AvDcJ+vI6tB651C4KgOMuuUqOv3vYwCW//R85l5zK3FPaqs8ryQe4YqKlYSxvD5oED/Ob3hpviRPRUUFfr8fwG+tbfSJRMluFv4Q6JfkOaUFUo2TonsL4dyxHBvvhAE+j1RyTdUqFkbbZh+LxiqLR5kaqeKtYDlP1RTzYPU63g9torjuD34gceJZXaAetnHur1nHR+EKHMDDffrw7yFDFEyKiIiIiIhIi3kcDt4cMoQf5eaS5nBQYqOsjUfIc7t5YcAAPh0+vFHBJEAnj4exgU4AbDpqLQCDRoRarfbGcDohPTMRSFY3Yml3vVCs6VlCNNPPN396loWXXgNAz9ee57CzT8C/YE6T52qMPIebUz3ZAFz7/ffUxGKt8hxJjqYu694dL9B2PciyW8Eg/Pzn8NlnHt46eAAzgl04a/58lgWD3FS1hpM8WZzkySK3EftFJFt5PMr0SA0zo9UsjAY3byC8tcmRKqgtIdM4CFlLGIsTyHW4MBjWxyOkGMMrgwZxWl5em7+DiIiIiIiI7LsKvV7eGDKEmliMj8rLWRcKcXp+PtnNaIo5z9Odr+Lr4KBSGL+RASPaaHPJXcjwxamudFC5mxO7AeI2zh+mnMucjV/z+BGf0Sm9R9Me5nCw6JJrKB80nJG/vxbf94s49LyTWHTJNSz++S8hyUuvT0vN5tNwBatCIe5btYrbe/ZM6vySPMkOJy8GZiR5TmmBtDS4664tP49N8TFj9GguXbyYV4qLeSu0if+ENjHBncHJnmz6ulqnpTpmLTOjNSyM1lIUj7A6FmZ1fNtTwh3A4PR0BqWl0cfrJcXh4KPycr4KBKi0W9rLY8CGeCLIzHQ6eWvIECZmZ7dK3SIiIiIiIiJpTien5Oa2aI4JBWnwSkc4ZgPcOZcV0Tys9bfrfojpvjgUQVXF7mtwGAfBaC3ReJg5G79sejhZp/iQI/nsjU8ZdveNdP34v+RO/YKl5/4i6Uu8PcbBhd5c/liznvtWreKCTp3o6d23zuDYVzT1tO6HGrjkB0YDvYFDWlqUtC6fy8XfBw7krPx8Hli9ms8DASZHqpgcqWKAM5WTPFmMdqfjMdv+m5OQjVMUC7MmHiFi43iMgxRjWB+LsCIeYl0sQqpxkGWc+B0usoyTLIeT4niUj0KBnXZGjs7M5NgOHTg8K4sDMzPJdG37zZWmMQAAIABJREFUX8nbCgspj0RYHQrhczrxu1zUxOOsCAZZEwoxweeje2rrBKoiIiIiIiIiyVJQANzfH2qdcMpa3kwpYWNNLb9O64SrnQLKzLpDcaorG9fFOSR3HAvKpjK/dBpH9zir2c8NZ3fg2z8+xZpJH1Ay7rBW23tyvDuDoS4vc6K1nDl/PpNGjCC1gYOLpP00tXNyZAPfVwDvA09aa1e2rCRpC8YYTszN5cTcXGZUVvLwmjW8UlzMwliQhTXrcWEY6EqlwJHC+niENfEwxfEoLTk+qYPLxam5uQxJT6d/WhqjMjPJT0nZ7X3Zbvc2LfPZQFePpwWViIiIiIiIiLQtvx/86Q4CD/djlD+D2ROX8EWkCm9tMZd789ulgzK9LpxszLJugIEdxgCwoGxqyx9uDOsnHrvlZ2vJnvUd5SNGt3zuzY8wXO7N57qq1XxTWcnFixfzwoABOr17D9OkcNJaO7G1CpH2MzIzk+cHDuTeXr14cu1anl+/ntWhEHOitcxh241uc1wuBqSl4XO5qI7FqInH6ebxMCw9nYFpadTG42wIh9kQiSR+DYdxG8M5HTvy47w8/RsKERERERER2W/16AGzZ8P5OV0oHJzCD+fO5aNwBR0dbn6c2qHN68n0JVqQGnsgzoAOo3HgYH31SsqCG+iQ2jFptQx84o/0ffZxvvjLG5QdMDZp83Z2pnBdWmdury7ipQ0bGJKezg0FBUmbX1ou2XtOyl6ss8fDH3r25I7CQhbX1vJRWRkrgkH6pqUxsO6T14hORxERERERERHZ0TXXwIsvwplnQm5uLo/27cuVS5bwUrCUbOPiiJTMVu/qi1vLv0PlrImFyeyQOG28qpHLutPcmfTwD2R5YB4LSqdxUNcTk1aXZ2MxxloO+N01fPbaR8S8aUmbe7g7jYu8eTxdW8KNy5bRweXi4i5dkja/tIzCSdmBMYb+aWn0T0veHwQiIiIiIiIi+7vzz0986l3RtSvLamt5aM0aHqvdwMfhAGel5jDI5SVO4uyH72MhFsZqWR0LE7aWCBavcXCIO5PR7nTcTQgzIzbOozUbmBypAmDw6LXwTBZVjVzWDYml3csD85hf+k1Sw8m51/6OvG8mk75mJYMeuYs5v71r9zc1wfGeLNbEwrwbDvCLxYtZHgxyZ8+eOLTEu90pnBQRERERERERaSf39+6NyxgeWbOGBbEgt1YXNeq+ryJV+I2Tg9wZDHB5GeBKJc+4Guy8rLYx7q1ex5xoLQ4gDizoUwJdahp1Wne9EfmHUVJbRJ/s4Y2+pzGimT5m/u5BJlx2Jr1efZ51E49j49iDk/qMi715ZBonr4bKuGfVKpYHg/ytf39tQdfOjLUtOeJk32CM8QGBQCCAz+dr73JEREREREREZD9TFApx76pVPL12LeGtspqeqamM8/kYlZmJz+nE43Awv7qa5zdsYH04vM0cecbFAe40RrrS6edKJds4iQEfhgO8GiwjYGNkOJ38a/BgHly9mg/Ky+GLHAb+sx/3/G1jG7/xzg27+yZ6vv4CNZ268tnrHxPNyEz6Mz4NV/BEzQZiwMF+P28OGULOVgfxSvNUVFTg9/sB/Nbaisbep3AShZMiIiIiIiIismeoicWojcdxAikOB2kNdPVF4nHeLyvjo/Jyvq6oYGZVFdHtMp5M4yAFB6U2CkAfr5dXBw3igMxMFlRXM2TqNOIG8h4YzDN3hnf2mDbnrKlm4k+PJr1oFStOO4tZt/6xVZ4zO1LDfTXrqLZx+nq9vDt0KH20vV2LKJxsAYWTIiIiIiIiIrI3q47F+HzTps2B5aKaGuoTnzy3m98VFvKLzp1xO7bsL3nWV0t4JVyEY1Uarw/tgrMJ+y8W16yhJlJBoX9Qkt8Ecr79mjHXX8rsG+9i7dHJ29dye6tiIf5QtZYSG6VzSgrzxowhWx2UzaZwsgUUToqIiIiIiIjIvqQ2FmNRTQ1rw2EO8fvJdO147MjsZRGGz/0GfFGuTevEISmNW0L92ao3eGTGrxmSO547D3o92aUD4KytJeb1tsrcWyuPR7m5ag1r4xEu6dyZp/r3b/Vn7quaG042/jgmERERERERERHZK3idTkZkZnJ8Ts5Og0mAXvlueKMbAM+VlBNvZANb/WE4i8tnEIm3znLwbYLJeLxVngGQ7XBxRVpHAJ5et44pgUCrPUt2TuGkiIiIiIiIiMh+KCMDLszqClVOStND/On9GI3JJ7tl9MGX0oFwLMj3m2a3XoHW0uNff+eokw8mbfWKVnvMYJeXie5MLHDp4sVEWzEMlR0pnBQRERERERER2U/95WE349cnuicn55Tw4mO7X9ptjGFQzlgA5m6c0nrFGUPnT94jvWgVA556sPWeA1zgzSXDOJhVXc1jRUWt+izZlsJJEREREREREZH9lMMB75zfDU/MCf2q+PeiMCXrd35C+NaG5k4AYM7Gr1q1vgVX3gBAt/feJHPJglZ7jt/h4rzUXAB+u2wZX2p5d5tROCkiIiIiIiIish/Lcbu5qrALAPbslfz31bTd3jMkdzwAC8umtdq+kwCBgUMpOvpEjLUMfOL+VnsOwFEpPsa50wlby6lz57KitrZVnycJCidFRERERERERPZz13bvjtsaGFTJ+/NjBGsNAGUlDv77j3QqA2ab8d0z++FL6UAoVsvS8lmtWtvCy6/DOhx0/vxDsmd/12rPcRjDr9M60cvpoSQS4cQ5c6iIRnc6tiQc5u6VK/nd8uVEtEdliyicFBERERERERHZz+WnpHBe504ABE8o4tN30giUObjl4lyeuS+LP16Xs82h2Q7j4NxBv+X6MU9R4OvfqrVVFfZm1Uk/AWDg4/fRqFN7minVOLg5vQvZxsm8mhpGfvstrxYXY60lHI8zedMmfrFoEd2//pqbly/njpUruWzxYmwr1rSvM/qLB8YYHxAIBAL4fL72LkdEREREREREpM3Nr65m8LRpEIf8G0bhD3pYMjdl8/WfXbuJk8+pbpfavOuKOPKUQ3BGwkx6+T0CA4e26vOWRoPcVb2WchsDoI/Xy7pQiOqtEtpCRwqr4mHiwB2FhdxaWNiqNe3pKioq8Pv9AH5rbUVj71PnpIiIiIiIiIiIMCg9nWN8HcABxePXs2RuCpn+OKddUAnAi4/5WbnU1S611Xbuytzrbud/f3uz1YNJgD6uVP7PV8iZqR1IxbC0tpbqeBy/cXKoO5O7M7rxp8wCfuHNA+C2FSt4bt26Vq9rX6TOSdQ5KSIiIiIiIiIC8HFZGUfPng21DtznjeXOBwP0Gxrhrqty+HZyKoX9wtz/YgnuuobKxeUzmFH8OQd2Ooae/kHtW3wr2RSPMjdaSzdnCj0cKRiz7f6bL9Zu5J+hcjzGsPDAAyn0etup0valzkkREREREREREWmRI7OzGZyaDt44h//f9/QfFsEYuOK2cnxZMVYsTuGLD7aEb28u/TOvLHyAqes/bNM6U4vXQyzWJs/Kcrg4OCWTQqdnh2AS4JzUHIa6vISs5ably9ukpn1Ju4aTxpgbjTHTjDGVxphiY8ybxpj+243xGGMeM8ZsNMZUG2PeNsZ0225MgTHmnbrrG40xjxpjUhARERERERERkUYzxnBdYXcApuWVEraJPRazc+Mc+5PEfpPffpG6efzQ3AkAzN34VZvV2O+ZRzjq5IPo+uHbbfbMXTHGcGFqLgZ4pbiYbyoa3TQotH/n5GHAE8A44GjABXxojEnfaszDwKnAGcDBQAbwH2OME6Du1/8C6XXXzwB+BDzYRu8gIiIiIiIiIrLPOCs/nwKPh002xifhLUHbqIODAMz4KpVYNPFdfTi5oOxbSmqK2qZAY3CGQvT7y2Nsc4R4O+rlSmViSiYA1yxdqtO7m6Bdw0lr7bHW2uestfOstbOAC4ECYBSAMcYP/By41lr7sbV2BnAOMBQ4qm6aY4BBwDnW2hnW2o+Ba4GL6/aSFBERERERERGRRnI7HFzXPdE9+e9QOdG6oK3P4AiZWTFqqhwsnJ1YsNo1ozeDc8YSjYd5bt6dbVLfstMvIJLhw7dsMZ0/fa9NntkYZ6fm4sHwVUUFb5SUtHc5e4327pzcnr/u17K6X0cBbmDzxgXW2rXAXGBC3Vfjgbl139f7APDU3S8iIiIiIiIiIk3w886dyXe7KY5HmRxJnNbtdMLI8SEApn+ZWNptjOHnQ+/AgYMv177DnDZY3h3N9LHszAsB6PeXR2EP6VLMcbg4NTUbgDtWrmznavYee0w4aRI7ij4EfGGtnVv3dScgbK0t3274hrpr9WM2bH2xbnx4qzHbP8tjjPHVf4DMJL2GiIiIiIiIiMhez+t0cnW3xJEf/wyWE68LAOuXdn/3hWfz2F7+wRxTeA4Af53zO2LxaKvX9/3ZFxFNSydr0Tw6/u/jVn9eY52YkoULw9zqamZXVbV3OXuFPSacBB4HhgFnNmKsAbaOxXcWkW8/Zms3AoGtPmsaX6aIiIiIiIiIyL7v8q5d8TudrImHmRJJBG0jx4cwxrJicQqlxVtipbMGXkfHtAKOKDi9TWqL+LNZ/tPzAej/zMN7TPdkhsPJaHcaAC9v2LCb0QJ7SDhpjHkMOBmYaK3dOihcD6QYY7K3uyWfLd2S69muQ7JuvJvtOiq3cg+JJeT1n24NjBMRERERERER2S/5XC5+Vdc9+XKwlJi1+LLj9BsSAWD6Vqd2+1KyefLI/3Fy74twOlxtUt/Sc39BNDUV3+IFZKxc1ibPbIxD3YkFuq8UF2/uOJWGtWs4aRIeB04DjrDWLt9uyHdAhMRJ3vX3dAaGAPWbGHwNDKn7vt4xQKju/h1Ya0PW2or6D1CZlBcSEREREREREdmHXNu9OzkuF2viET6uO7n7gPql3V+mbjO2rULJeuEOuXx3z5N89O4Uqgp7t+mzd2WUO500HKwKhfgqEGjvcvZ47d05+QSJ07fPAiqNMZ3qPl4Aa20A+CvwoDHmSGPMSOAlYA5Qv6HAh8B84EVjzEhjzJHAA8AzdcGjiIiIiIiIiIg0g9/l4rbCQgD+ESyl1sY37zs5c4qHSGTb8XEb5+u17/L7r84mFK3d5dx/vD6b687NI1Rrml3f+sOPIZSb3+z7W4PHOBiXkg7Ay8XF7VzNnq+9w8nLSCyrngSs2+qz9QYFVwNvAq8BXwI1wEnW2hhA3a8nAMG666/Vjf9Nm7yBiIiIiIiIiMg+7NIuXeiVmkq5jfF2qJxeAyL4smIEaxwsX+TeZmzcxvjbvD8ws+RzPlj59wbnLC128NVHaSyZm7LN4Tot4SnZc/Z4rF/a/VpxMZF4vJ2r2bO1azhprTUNfJ7bakzQWvtLa22OtTbNWnuStXb1dvOsstaeWHc9p258qM1fSERERERERERkH5PicHBPr14A/DtYTgVRevRLtEyuXrZtOOlyuPlx3ysTY5c8SSi28+7J+dO3BJJff+ptUX2u6irGX3omR584AU9pSYvmSpahrjSyjJPSaJQPy8vbu5w9Wnt3ToqIiIiIiIiIyB7uJ3l5jMnMJIjlo3AF3XtGAVizfMd9JicW/IQ8b1fKQ8X8d9mzO51v/oyUzb//dnIqkXDza4umpeOursQZDlH42vPNnyiJnMZwcP3BODq1e5cUToqIiIiIiIiIyC4ZY7iya1cAPgtX0K1XfefkjuGk25HCT/r9CoAX59/LpNX/3GHMvK06J2urHcz6pgVLu41h6bmXANDztRdwBHe912VbOTglA4D/lJYS1tLuBimcFBERERERERGR3TotN5d0h4O18QgMSpxBvP2y7npH9ziL4wrPx2J5dPrVfFn0n83XKgOGVUsT9x10TA0AX3/SsqXd6444jppOXfFsKqPbB2+3aK5k6edMJds4CcRifKql3Q1SOCkiIiIiIiIiIruV4XLxk/zEydiLupYBUFzk3Olp28YYLh72B44sOJ04cUqD6zdfWzAz0SXZtTDCsT+uBmDqJC+xaPNrsy4Xy08/H4CerzwL1jZ/siRxGMNYd6J78l8bN7ZzNXsuhZMiIiIiIiIiItIo53fsCMA0U0lGfhhrDUUrd1zaDeAwDi4f8Ud+P/5lTu590ebv509P7Dc5aGSYQSPDZGbFqAw4mPtdy07tXnnqmURTU8laNI8OM6a2aK5kGV8XTr65cSOxPSAw3RMpnBQRERERERERkUY5NCuLwtRUaojjP6kY2Pm+k/WcxsmI/EM3/xyOBZk3PbGke9DIEE4XjD08CMCUT1NbVFvEn82a408DoMdbr7ZormQZ7PKSaRyURCJ8EQi0dzl7JIWTIiIiIiIiIiLSKA5jNndPBg+tDyd3vu/k9opr1nDDx2ezdEEizBw0KnFE97gjEgfYTPnU2+LV2N+f8wum3/Ews266u2UTJYnLGMa40wH4V0lJO1ezZ1I4KSIiIiIiIiIijXZep04AlHavgJzQLjsnAb76KJVfnNCRV//iZcWcHGzMhdO/joWxfxKzMYaPDZGSGqd8o5OVS3c91+5U9ezD6pN+TNzTsi7MZBq31b6TVku7d6BwUkREREREREREGq2X18uBmZlggAPLWLN814Hih/9Op3iti0/+NgzHa28BEOv+GX+afiWXf3wo761+hgEjE6d2z5qSvFDRRMI4a6qTNl9zjXClkYphTSjEtMrK9i5nj6NwUkREREREREREmuQHHTokfjO6jHWrXUTCOx8Xj8OSuYkDcLzpcWKRRJA5+sBUMt1ZbKhZybNzb2dR9r0AzJqSOBTns9VvMHXdh6ysWNisbsOOkz/hyB8eRv8/P9Tke5MtxTgYraXdDVI4KSIiIiIiIiIiTbIlnCwnbmHtqp13T65b5aK60kGKx/LU2xs44YwqBo0M8cuzx/PMMVO5bPi9FGT2Z+S4RLo5d3oKoaDlqVk3cvfUn3HVZ0dx0xensSIwv0n1WWNIX7uaXq8+h6dkQ4veNRmOTvFzTX5Xfta5c3uXssdROCkiIiIiIiIiIk0yNjMTn9MJvij0q2T1MjeRCLz5QgYLZ6ZsHrd4TuKwnF4Dwvg7xLn4hgB3P7sRf4c4qa40flB4Do9M/Jirjr2Y7NwY4aCDuTMNB+RPpLd/GCkODwvKpnHN58fx7NzbCceCjaqv+KCJlA4fjTMUot+zj7fKX4OmGO5O48r8LvRLS2vvUvY4CidFRERERERERKRJXA4HR2ZnJ34YXcaqpS4evS2b5/7k54Ebszefur24bkl3vyGRBucyxuB1pzF8bAiA+VN93HDg0zx4+Ls8cdRkxnc+nriN8fb3z/B/s25s3DJvY1h4xXUA9Pjn3/GuK2r+y0qrUjgpIiIiIiIiIiJNtnlp95hy3n4pg8nvJ7oCN653sWReomNy8Zy6cHJoA5tSbmX4uERX5My6fScBvNGu3HDg09ww5mkcOPhm3fsU165pVH0bxxxEyYEH4YyEGXb3jYkNMGWPo3BSRERERERERESa7Jj6zsnBAYImEfx16h4FYMonXkJBWLEkEVI2Kpys65xcttBN+UYHT9/r55xDu/DOy+mM73I8V4x8gAcO+y8d07o3usa51/6OmMdDpy8+pf8zDzfl9aSNKJwUEREREREREZEm6+n10tPtBScwspzzfhXgnCsrAPj6Ey/LF6YQixqycmLkdY7tdr4OeXF69I1greH68/J499UMAGZ+nQrAkQU/pUtGrybVWNFvELNuvg8AT1kpNOPkb2ldOz9KSUREREREREREZDdOyO/A40VFDLxkPacO6UCwxuBOsaxb7eKjNxPLvPsNCWNM4+YbPjbIyiVuSta5MMZiraFo5Y7x1ZySL1ldtZTje56/2zlXn/Rjqgp6Uj58VJPeTdqGOidFRERERERERKRZflC3tLusRwCweNMtI8cn9o787J1EONm3EUu66407InFvbqcotz1eCkBxkZPIVlMsLp/BrV+dzrNzb2dD9apGzbtNMBmPYyINH9AjbUvhpIiIiIiIiIiINMvhWVl4HQ42xKN8H0vsGVkfMMbjiXbJXZ3Uvb1BI8M89Eoxj75ezIjxIdIy4sTjhnWrt3RP9s0awfC8Q4jGw7ww/54m1evZWMz4K85h4BP3Nek+aT0KJ0VEREREREREpFkyXC5Oyc0FYFK4EoAxh9XidCX2djTG0ndw4zsnAXoNiJCWYTEGuvZIHLBTtGJLOGmM4cLBt2IwfLn2HRaWfdvoubPnziR/yv/o+/xT5H01qUl1SetQOCkiIiIiIiIiIs12bseOAEyOVBK1lky/ZcjoRBdlt55R0jKafwhN18JEOLlmxbb7Thb6B3FUjzMA+Ouc32MbedDN+sOPYflPE/tUjrrlKjylJc2uTZJD4aSIiIiIiIiIiDTbMdnZ5LndBGyMmdEaAI48OfHrqEOCLZq7a2FiSfjaFe4drp014Do8Ti9LNs1kfuk3jZ5z7jW3Eug7AE95KcPv/K1O8G5nCidFRERERERERKTZXA4HZ+bnAzApXAHAocfV8sjrGzj78ooWzb15WfdOTuzOTs3n0G6nAvD+ihcbPWfck8r0Ox8l7nLTedIHdH/n9RbVKC2jcFJERERERERERFqkfmn31Eg11TYGQI8+UdwpLZu3a8+6Zd3LXTttcPxB4Tn08g9hRN6hTZq3ot8gFl52LQBD7/8d3rVrWlaoNJvCSRERERERERERaZFRmZkMSEsjjOXrcFXS5u3cPYoxlpoqB4GyHWOsPlnDePCw9ziyx+lNnnvJ+ZdRNmwUoZw83FUt6/CU5lM4KSIiIiIiIiIiLWKM2dw9OSlSmbR5UzyQ3yXRiblm+Y5Lu+uf3SxOJ9Pu/zOTXvmAin6DmluitJDCSRERERERERERabGz68LJedFaSuPRpM1bf2L3zvadrFcTqeS95S+wuGx6k+YO5nci5vVu/tlEws0rUppN4aSIiIiIiIiIiLRYj9RUJvh8WODLcPK6JzeHkysaDif/vuCP/Hn2Tbz5/Z+b95B4nN4v/JmJpx+Dq1JLvNuSwkkREREREREREUmK+lO7JydxaXfXHhEAila4GxxzdI8zAZiy7n021q5t8jNcNdX0+sffyFy+lOF338hOT9+RVqFwUkREREREREREkuIn+fk4gCWxEOtiyVkiXX9i966WdRf6BzE4ZxxxG+O95c83+RnRjEy+vftx4k4n3d5/i+7vvNHseqVpFE6KiIiIiIiIiEhSdExJ4ajsbAAmR5JzanfXHolwsrjIya62hDyp90UAfLDiJULR2iY/p3zEaBZdei0Aw+69mfSVy5perDSZwkkREREREREREUma+qXd/wtXYpOwPDo7N443PU48bli3uuHuyTGdjqZjWgFVkQCT1vyzWc9afOEVlIwej6u2htG/vRxHONTcsqWRFE6KiIiIiIiIiEjSnJqXh8cY1sTDrIi3fGm3MdCt7lCcl5/08fGbaaxb5dxhnNM4OaHXzwB45/u/Ni8YdTqZftejhP1ZZC2cy6BH7m5R7bJ7CidFRERERERERCRp/C4Xx+fkADApnJyTr/sOSYScUz718vjt2VxxakdWf79jF+VRBaeT5vLRNaMX1dHmPTuY35npt/+JuMtNTdfuLapbds8ko712b2eM8QGBQCCAz+dr73JERERERERERPZq72zcyMlz5+LB8GdfIVmOhpdjN0YoCFMnefl+gZsvP/JSss7F+b8OcOr5O+5rWR2pIN3d8nzHu66I2s5dWzxPvWHdsuiZm560+fY0FRUV+P1+AL+1ttHJsDonRUREREREREQkqU7MyWFMZiYhLK8Fy1o8nycVDjm2lguuruCksxKB5Jxpnp2OTUYwCWwTTLqqKnHWVCdlXtmWwkkREREREREREUkqYwz39uoFwIfhABtikaTNPXRM4pCa+TNSiO5i2g01q3l27u1E4y17dub3izjsnBMY8YfrQSuQk07hpIiIiIiIiIiIJN0R2dkcnZ1NFHglWJq0eXv0jZKZFSNY42Dp/JSdjonFo9z8xY94+/tn+GDFSy16nrsiQNqalXR7/y16vvpci+aSHSmcFBERERERERGRVnF3z54AfB6pZEUslJQ5HQ4YfEDigJw53+48nHQ6XPyk368A+Meih6gKb2r288pGHsi8X98CwJAH7yB71nfNnkt2pHBSRERERERERERaxWifj5/k5WGBv9VuJFkHM9cv7Z7bwL6TAEcVnEFBZn8qw+X8c8kTLXresrMvouioE3BEI4y5/hJSylu+j6YktGs4aYw51BjzjjFmrTHGGmN+uN31DGPM48aYNcaYWmPMAmPMZduN8RhjHjPGbDTGVBtj3jbGdGvbNxERERERERERkZ25p1cvUoxhVrSG76I1SZmzPpxcMCuFSHjnY5wOF+cOuhGAd5c/x6bQxuY/0Bhm/v5BKgt74y1ez9A/3tr8uWQb7d05mQ7MAq5s4PqfgGOBc4CBdT8/Zow5ZasxDwOnAmcABwMZwH+MMc7WKlpERERERERERBqnt9fLr7sl+sierS0hkoTuye69ovg7xAgHHSyeu/Ol3QCjOx5J36zhhGK1vLn0qRY9M5qewfQ7H8U6HHR7/y06TfqwRfNJQruGk9ba96y1t1hr/9XAkPHA89baSdbaFdbap0mEmaMBjDF+4OfAtdbaj621M0gEmUOBo9rgFUREREREREREZDdu7tGDfLebtfEI74Wav/9jPWNg6OhE9+ScXSztNsZw+oBrAHhv+fMt654ENg0eztLzLmHDQRPZNGBIi+aShPbunNydL4CTjTFdTcJEoB/wQd31UYAb2BxVW2vXAnOBCQ1NWrcU3Ff/ATJb7Q1ERERERERERPZzPpeLu+oOx3k1VEZJPNLiOeuXdu8qnAQYlX8Ew/MO5Yd9LsHtaLjLsrEWXHEDUx57gWCnLi2eS/b8cPJXwHxgDRAG3gcut9Z+UXe9ExC21pZvd9+GumsNuREIbPVZk8yiRURERERERERkWxd27szIjAyqbZzfVq5hZQtP7x4b+IG8AAAgAElEQVQyJrHZ5KJZKUz9PLXBccYYfj/+75w54Deku30teiaAdbkSrZsA1uJbNK/Fc+7P9oZwchxwMokuyWuBJ40xu1uybYBdbWBwD+Df6qMDdEREREREREREWpHTGN4aMoSBaWmU2ig3Va1hXrS22fN1KYgyfFyQaNRw969zePZBH5EGGjJNfZgIROINnKDTVLEYw+65mcPOPp6O//s4OXPuh/bYcNIY4wXuBq6x1r5jrZ1trX0ceBX4Td2w9UCKMSZ7u9vzSXRP7pS1NmStraj/AJWt8AoiIiIiIiIiIrKV7qmpfDFyJAf5fFTbOL+vKuLrcFWz5jIGbnmklBPPStz/9kuZ3HN1Drs6b2dp+Syu/uwHTF7zVrOeuQ2HA2coiCMWY/QNl5I989uWz7kf2mPDSRJ7SbqB+Hbfx9hS93dABDi6/qIxpjMwBPiqDWoUEREREREREZEm6OB289Hw4fwwN5cIlj/WrGv2ITnuFLjougC/faiUFI9l+pepfPNZw0u8v93wCWuqlvDn2TdRVru+ua+QYAwzb7mP9YcciSsYZNyvLyBtzcqWzbkfatdw0hiTYYwZYYwZUfdVz7qfC+o6Gj8H7jfGHG6M6WmMuQA4D/g3gLU2APwVeNAYc6QxZiTwEjAHUD+tiIiIiIiIiMgeyOt08sbgwVzSuTMW+HNtCf8IljZ7vnETg5xybmJh7AuP+Ik2sLz7x/1+SW//MKoiAR6feR12V22WjWDdbr697ynKBw8nJbCJsVf/DFd18zpB91ft3Tk5GphR9wF4qO73d9T9fAYwDfg7iYNxfgvcDDy11RxXA28CrwFfAjXASdbaWGsXLyIiIiIiIiIizeM0hv/r1487CgsB+EewjI9DgWbPd9oFVfg7xFi7ysUH/0zf6RiXw81VB/wJt8PD9OLPmLah5b1tMa+XqQ/9hWBuR3xLF3HArVdBfPuFwNKQdg0nrbWTrLVmJ58L6q6vt9ZeaK3taq31WmsHWGsfslvF2tbaoLX2l9baHGttmrX2JGvt6nZ7KRERERERERERaRRjDLcWFvK7Hj0AeKq2mAXNPCTHm24589IKAP7x50yqK81OxxX4+nNir58B8Pqih1vcPQkQzO/M1AefJuZOodPnH9Fh9nctnnN/YZLxH8DezhjjAwKBQACfr+VHyouIiIiIiIiISOPFreX0+fN5o6QEv3FyX0Z3OjndTZ6nJhLnqlu8lHhDZC3NopPLRU7HGCedWc2AEVtO6d4U2sgvPhpHOBbktnEvckDHiUl5j67v/ZuIL4vig3acb1i3LHrm7ryjc19QUVGB3+8H8Ndt19goCidROCkiIiIiIiIi0t6qYzEOnjGDmVVVOIGRrnQOT8lkvDsDp9l5F2S9WhvnT9XrmR6tJlr/5aIMuHQUYHA4LKddWMXpl1Tgrss8n517B29//zRHFZzBlSMfaMU3S1A4uXPtveekiIiIiIiIiIgI6U4nbw8ZwnifjxjwbbSaB2rWc3/NOmK7aa57OVjK1LpgsqPbjccY6F/F6U+t4rATaojHDW/8NZMbzsujYlMiDju1z6XcMvZ5rhhxf6u8T9rqFYy4/TeYSHj3g/djCidFRERERERERGSP0D01la8OOID5Y8ZwU0EBHmOYEqnmr7UlDe4NuSQa5L+hTQC8OWQI6yZM4KLOnRPXBm/g6jvLuf7+UjKzYixbmMJ/X0l0L2an5jO605GY3XRlNoeJRJhw6Zn0ePMfDH74rqTPvy9ROCkiIiIiIiIiInuUgenp3NWrFy8NHIgB3g0HeLMugNxazFqerC0mDpydn88pubkYY7ime3ccwPRoDStiISYcFeSi6xMngX/+Xhrb55wltWv5+4L7idvknLJt3W7mXH8HAL1f/itdPvpPUubdFymcFBERERERERGRPdKP8/N5qHdvAJ4PbuTJmg2UxRO7Skas5bVgGctjITq4XDzUp8/m+3p5vfwoLw+AN4PlAIw9PEiqN8761S4Wz9ly2E4kFuK2L3/K64sf4ZnZtybl9G6ADYcdzZILLgdgxO2/wfX9kqTMu69ROCkiIiIiIiIiInusX3fvzvXduwPwYbiCSytWcEdVEecFlvFqqAyAB3v3Jj8lZZv7rqu7Z3KkkpJ4hFSvZezEIACfv5u2eZzb6eH0/ldjMLy34nlenH9P0mpfcMX1bDxgLO7qKjr+7ByoqUna3PsKhZMiIiIiIiIiIrJHu693byaPGMEEn48wlunRGmqJk+92c1NBAed36rTDPWN8Pg7z+4kBL9WWYq3lsOMT4eAXH3iJRraMPbz7j7h8xH0A/Gvpk3yy6rWk1G1dLr6990mCOXnUTjgEnM6kzLsvMclqVd2bGWN8QCAQCODz+dq7HBERERERERER2QlrLe+XlTGvuprDsrIYlZmJYxcH2nwVCHDIjBnEgV968znc6ednP+hEoMzJLY9uZPQhoW3Gv7LwAV5d9DAuRwp3HvQaAzqMTkrdKeVlDBjai5656UmZb09UUVGB3+8H8FtrKxp7nzonRURERERERERkr2CM4bicHH5TUMAYn2+XwSTABL+fO3r2BODPtSUUmRCH/KAW2HZpd73T+1/D+M7HE42HuWfqRZTUFCWl7nB2h6TMsy9SOCkiIiIiIiIiIvusGwsKODo7mzCW+6vXM+6EKgC+mZRKbfW24abDOLjqgIcp9A0iy5OLy+He2ZSSRAonRURERERERERkn+UwhhcHDqRTSgqr42Fm9SqmY9co4aCDedNTdhif6krj5nHPcddBb5Cdmt8OFe9fFE6KiIiIiIiIiMg+rWNKCo/37QvAO6Fy+h6a6J6cP92z0/F53i5kpGRt/vnbDZ8QitW2fqH7IYWTIiIiIiIiIiKyzzstN5exmZkEsQROWA3AgplbOifjcfjXcxnMmrJtYPn+8he5c8r5vLbokTatd3+hcFJERERERERERPZ5xhju690bgHldSqFbDUvmpRCuO7B72uepvPCIn3t/04HKwJa9KH2ebADeW/481ZFGH0ItjaRwUkRERERERERE9guHZWVxQocOxA24L19GNGJYMi/RPfn1J14AaqsdvP1SxuZ7xnU+nu6Z/aiJVvLe8hfape59mcJJERERERERERHZb9zTqxcGiIzfCP0qmT8jhUgEpn6eunnMf17J2Nw96TAOTut7OQDvfP8Moaj2nkwmhZMiIiIiIiIiIrLfGJqRwVn5dadwn72SBTM8zJ3moabKQVZOjMJ+4R26Jw/pegr5ad0JhEv5eNU/2qnyfZPCSRERERERERER2a/c1KMHBuDQjcwvj/DlR4kl3eMm1nLGJZXAtt2TLoebH/a5FIA3lz5FNB5pj7L3SQonRURERERERERkvzIoPZ3TcvIACJ66mkn/SQNg/JFBxk4M0rN/onvymfuyNh+Yc2TBT/F7cqmJVrGyYmF7lb7PUTgpIiIiIiIiIiL7nVsKCxK/mVhMtGMtGb44g0eFMAbO/WUFxlj+914a15+XR9FKFx6nl9+Pf5lnf/AtvbOGtm/x+xCFkyIiIiIiIiIist8ZkZlJ/7IccAJnreLAw2txuRPXDjgoxG1PlOLPjrFicQrXnpXHskVuevoH4XF627XufY3CSRERERERERER2S/9qkNd9+Sx68k+dcM210aOD/GnfxTTd0iYYI2Dz95J23zNWsuG6lVtWeo+S+GkiIiIiIiIiIjsly4c6yf9ky7ggLcKVzMnUrPN9Q75cSaelPhuwxonAMU1a7jy08O59vPjCMVq27zmfY3CSRERERERERER2S95vbDi6r6c6M8liuWe6nUsi4a2GdO5WxSA9WtcAOR4OxOOBamKBPhq7X8b9ZyZUzw8+bCbBQuSW/++QOGkiIiIiIiIiIjst3I7GF4fNpBD/X5qiHNP9VqCNr75eseudeFkkRNrwWmcHN3jLAA+XPH3Rj3j07fTeOCuFF56Kfn17+0UToqIiIiIiIiIyH4t1enkrSFDKPB4KLFR/hEs3Xwtr0sMh8MSDjrYVJqI0o4qOB2HcbKgbBpfr313l3PH4zDzaw8AP/hB673D3krhpIiIiIiIiIiI7Pey3G6e7NcPgLdDmzYv73a7IadjDNiytLuDtxMn9boIgEemX83qisUNzrtsgZuKTU4yMizjx7fmG+ydFE6KiIiIiIiIiIgAJ+Tk8JO8POLA/9VuIGYtAJ261YeTzs1jzxt0I0NyxxOMVXPP1J9TFQkAsHSem4ryLZHb9K9SAZhwWAy3u41eZC+icFJERERERERERKTOI3364Hc6WRIL8X44ETh22u5QHACnw8V1o58iz9uV7pn9cOBg5hQPvzknn9uvyKEu12TGV4kl3YdOjLXti+wlFE6KiIiIiIiIiIjU6ezxcE+vXgC8FiwjaOObD8XZsFU4CeD35HDvIW9yw4HPkObO5J/PZgLw/YIUvp3soarSsGhOCqBwsiEKJ0VERERERERERLZyUefO9E5NJWBjvBvaRKfuOy7rrpfj7YzDOFg6382caZ7N3z/1RIDZ33iIxwzdekboVmDbrP69icJJERERERERERGRrbgdDm4tLATg36FysrolDsfZUORq8J63XsgAoPuI5eAMUbq4N888kbhvxPhQ6xa8F1M4KSIiIiIiIiIisp2z8/Pp6/VSaePM7loKQPlGJ8Fas8PYDWudfPmxF4Crf5tOv4mzE+NXdAHA9PmADdXr2qjyvYvCSRERERERERERke24HA5u69EDgPcoIy0/DMCGoh2Xdv/n5QziMcPwsUF69Y9w9a+6g4nXTVTLO7Fz+NusJ9qs9r2JwkkREREREREREZGdOLNjR/rXdU96Ty8CdjwUJ1hr+OhfaQD88PwqADp3j3HIMUEAOg1aSb/8gRxWcHQbVr73aHihvIiIiIiIiIiIyH7MaQzXdO/OJYsXE5xQAo/13OFQnJVLXARrHWTnxhgxbsvekhdcEyAl1XL86R3oPfAdhnXLauvy9wrqnBQREREREREREWnAj/LycBlDdaca6F7N+u06J1ctdQPQo28Es9V2lDn5cX75+030Hhhpy3L3OgonRUREREREREREGpDjdnNMdnbih4klO4STK79PhJMFvaJtXdo+oV3DSWPMocaYd4wxa40x1hjzw52MGWiMedsYEzDGVBpjphhjCra67jHGPGaM2WiMqa4b261t30RERERERERERPZVZ+TnJ35zRDHri7aN01Z/nwgrC/qoQ7I52rtzMh2YBVy5s4vGmN7AF8BC4HBgOPAHILjVsIeBU4EzgIOBDOA/xpgdj04SERERERERERFpolNyc0nBQI8aNnhCxGJbrq2q75xUONks7XogjrX2PeA9ALP1ovwt7gLetdZev9V3y+p/Y4zxAz8HzrXWflz33TnAauAo4IPWqVxERERERERERPYXPpeLE3Jy+HfpRmKHlFBWkkZepxgV5Q7KNyb647prWXeztHfnZIOMMQ7gBGCxMeYDY0yxMeab7ZZ+jwLcwIf1X1hr1wJzgQm7mNtjjPHVf4DM1nkLERERERERERHZF5zZccvS7nVrEpHaqrol3fldonjTbHuVtlfbY8NJIJ/EEu3fAu8DxwD/Bv5ljDmsbkwnIGytLd/u3g111xpyI/D/7N15mBxVvfDx75k1M0lmsm9ASMK+yGYAQS6LIKBxB8ENCeC9AvIKKqK44tUryFXu9QoiKhBcwA1cAAFFAWXfJLIEZA8kJCHbTCaT2c/7R3VN90xmkplkZmqW7+d5ztPdVaerTnWfrq761TmnagrSq31YbkmSJEmSJA0z8yZOpLipCKY3cM/ipJWkXbq33mAOTqZl+32M8X9ijI/FGC8CbgJO38x7A7CpcPWFQHVB8gY6kiRJkiRJ6lZlcTH7Nk4E4KGmeiDfcnL7HezSvaUGc3ByJdACPNVp+iIgvVv3MqAshDC+U54pJK0nuxRjbIwx1qYJWNdHZZYkSZIkSdIw9ZGdkuDk6tlrWbOyiMXP2XJyaw3a4GSMsQl4CNil06ydgZdzzx8BmoG3pjNDCNOBPYF7B6CYkiRJkiRJGiFOnJ1rH7dLHXfdV9zerXs7W05usUzv1h1CGAPsWDBpdghhH2B1jHEx8N/AL0MIfwPuAI4F3gkcDhBjrAkhXAl8J4SwClgNfBt4HLh9wDZEkiRJkiRJw9608nKm143htTF1/PGFJupqiygqimw7y5aTWyrrlpNzgX/kEsAluef/CRBj/C3J+JLnkQQcPwYcF2O8u2AZnwJ+B/wKuAeoB94ZY2wdiA2QJEmSJEnSyDFv8gQAVmxbC8D0mS2UlWdZoqEt05aTMcY7SW5es6k8VwFXbWJ+A/D/ckmSJEmSJEnqNx/dZQI/fmwxzF0NRZGZduneKlm3nJQkSZIkSZKGjIOqqihvKoHqFtillpk72KV7axiclCRJkiRJknqopKiIA0tyN8Y5cLV36t5KBiclSZIkSZKkXvjwTsm4kxy4mjm7GpzcGgYnJUmSJEmSpF54x5QkOBl2Wcf66eszLs3QZnBSkiRJkiRJ6oUZ5eW8Y+JEYoCvr1/KktamrIs0ZBmclCRJkiRJknrp57vtxhvHjKEmtnJB3RJeb+u+e3drjNxSs5qG1tYBLOHQYHBSkiRJkiRJ6qWqkhJu2Wsvdqmo4PXYwhfrXuXJlg0d8jTGNm5tXMsn1r3MJ155np8uX55RaQevkqwLIEmSJEmSJA1Fk8vK+NPee3P4Y4/xYkMDX6x7lXll1cwsLueplg38o6We2pi0lpxQUkJLjBmXePAJ0Q+FEEIVUFNTU0NVVVXWxZEkSZIkSdIQUtPSwrnPP8+PX3tto3nbl5fzme2249Tp0xldXJxB6QZGbW0t1dXVANUxxtqevs+Wk5IkSZIkSdJWqC4p4Ue77ML7J0/mgpdeoiwEDhs3jsPGjePfqqspLXJkxe4YnJQkSZIkSZL6wNETJnD0hAlZF2NIMWwrSZIkSZIkKRMGJyVJkiRJkiRlwuCkJEmSJEmSpEwYnJQkSZIkSZKUCYOTkiRJkiRJkjJhcFKSJEmSJElSJgxOSpIkSZIkScqEwUlJkiRJkiRJmTA4KUmSJEmSJCkTBiclSZIkSZIkZcLgpCRJkiRJkqRMGJyUJEmSJEmSlImSrAswmNTW1mZdBEmSJEmSJGnI2dK4Wogx9nFRhp4QwjbAq1mXQ5IkSZIkSRrito0xLulpZoOTQAghADOAdVmXZZAZSxK03RY/G/Wc9Ua9ZZ1Rb1ln1FvWGW0J6416yzqj3rLOqLeGQp0ZCyyNvQg42q0byH1gPY7ojhRJzBaAdTFG+7yrR6w36i3rjHrLOqPess5oS1hv1FvWGfWWdUa9NUTqTK/L5Q1xJEmSJEmSJGXC4KQkSZIkSZKkTBic1KY0Al/LPUo9Zb1Rb1ln1FvWGfWWdUZbwnqj3rLOqLesM+qtYVlnvCGOJEmSJEmSpEzYclKSJEmSJElSJgxOSpIkSZIkScqEwUlJkiRJkiRJmTA4KUmSJEmSJCkTBieHuRDCoSGEG0MIS0MIMYTwnk7zx4QQLg0hvBpC2BBCWBRCOKNTnjtz7y1Mv+iUZ3wI4achhJpc+mkIYdxAbKP6Vg/qzNQQwoLc/PoQwq0hhJ065SkPIXwvhLAyhLA+hPCHEMK2nfLMzK1nfS7f/4UQygZiG9X3+qjeuK8ZQUII54cQHgohrAshrAgh/C6EsEunPH2yLwkhHBZCeCSE0BBCeCGEcPpAbKP6Vh/Wmc77mdi5ToQQ3hBCuCt3bLQkhPCVEEIYiO1U3+lhnfmP3P9Pba4ubPSf0pP/HuvM8NCHdealLvYzF3XK47HwMLG5ehNCmJD7b3omdxy8OPd9V3dajsc0I0Qf1plhc0xjcHL4Gw0sBM7qZv7/AMcCHwF2y73+Xgjh3Z3y/QiYXpA+3mn+tcA+uWUdm3v+0z4ovwZet3UmtxP7HTAHeDewL/AycHsIYXRB1v8F3gt8ADgEGAPcFEIozi2nGLg5t65DcvmOA77TP5ukAdAX9Qbc14wkhwGXAW8C3gqUAH/q631JCGE28Efg7yR175vA/4UQjuvPjVO/2Oo6U+AUOu5rrklnhBCqgD8DS4H9gf8HnAt8uu83Sf2sJ3WmEriVZN/QnU3+91hnhpW+qjMAX6HjfuYb6QyPhYedzdWbGbl0LvAGYD7JvuTKdAEe04w4W11nCgyPY5oYo2mEJCAC7+k07Qngy52mPQJ8veD1ncD/bmK5u+WWfWDBtDflpu2S9Xab+q7OADvnpu1RMK0YWAV8LPe6GmgCTizIMwNoBY7JvX5b7vWMgjwfABqAqqy32zTw9SY3zX3NCE7A5Nx3eWjudZ/sS4BvAYs6resHwH1Zb7Np4OtMbtpGx0OdlnsGsBYoL5j2eWAJELLeblPf1ZlO8w7PzRvXafpm/3usM8M3bUmdyc17CThnE8v1WHgYp03Vm4I87wcagZKe1gmPaYZv2pI6k5s2bI5pbDmpu4F3hRC2CYkjSAIJt3XK9+Fc0/InQwjfDiGMLZh3EFATY3wgnRBjvB+oAQ7u7w3QgCrPPTakE2KMrSQng4fkJr0RKAX+VJBnKUkgPK0PBwFP5Kanbsst/439UnJlqSf1JuW+ZuRKu6mszj321b7koMJlFOSZG0Io7bPSKwtbUmdSl+b2NQ+FEE4PIRQeEx8E3BVjbCyYdhtJoHNWH5ZfA69znemJnvz3WGeGry2pM6nPhRBWhRAeCyF8sVP3XI+Fh7ee1JtqoDbG2JJ77THNyLYldSY1LI5pSrIugDL3SZJulK8CLUAbSUumuwvy/Bx4EVgG7AlcCOxN0vwYYBqwootlr8jN0/DxNEl33AtDCB8H1pM0CZ9G0oSc3POmGOOaTu9dTr4+TMu9bhdjXBNCaMI6Mxz1pN6A+5oRK9f1/xLg7hjjE7nJfbUv2ShP7nUJMAl4rU82QgNqK+oMwJeBvwAbgCNJusxNIt/lchpJq6fOy0jnvdgHm6AB1k2d6Yme/PdYZ4ahragzAN8FHgXWAAeQHNPMBj6Wm++x8DDVk3oTQphI8l90RcFkj2lGqK2oMzCMjmkMTuqTJF1T3kUSPDgU+H4I4bUY4+0AMcYfFeR/IoTwLPBwCGG/GOOjuemxi2WHbqZriIoxNufGNLmS5KpOK3A7cEsP3t65PlhnRoie1hv3NSPapcBebNyStitbsi/pnCd0M11DxxbXmRjjNwrmPZYbE/4rFIwHh3VmOOpNnenM/czItMV1Jsb4PwUv/xlCWAP8JoTwuRjjqjRbF2/1mGbo22S9yY0BeDPwFPC1TrPd14xMW1xnhtMxjd26R7AQQgXJILqfjjHeGGP8Z4zxUuCXJIOkdudRoBlI77S7DJjaRb7JbHxlR0NcjPGRGOM+wDhgeozxWGAi+asuy4CyEML4Tm+dQr4+LKPTVeFc/lKsM8NSD+pNV9zXjAAhhO+RXCA7Isb4asGsvtqXbJQnt4wWknFPNcRsZZ3pyv1AVQgh3b90V2fYzHI0SG2izvRET/57rDPDzFbWma7cn3vcMffosfAwtLl6kxuu6FagDnhvjLG5YLbHNCPQVtaZrgzZYxqDkyNbaS61dZreyqbrxh6596XNxu8DqkMIB6QZQggHkoyJcG+flVaDSoyxJsb4eghhJ2Au8PvcrEdIAkppV1xCCNNJuumm9eE+YM/c9NTRJAP8PtLfZVd2NlFvuuK+ZhjLjXN8KfA+4C0xxs6B6r7al9xXuIyCPA/34ABPg0gf1Zmu7EsyJu7a3Ov7gEM7jQ93NMmdLl/amm3QwOpBnemJnvz3WGeGiT6qM13ZN/dYeEzjsfAw0ZN6k2v99ieSMdffFWNs6JTFY5oRpI/qTFeG7jFN1nfkMfVvAsYA++RSBD6Vez4zN/9OkoHiDycZB2U+yXgFZ+Tm70DSLHguyYCpbwcWkbRoKi5Yzy3AQpIu4m8C/gncmPX2m/qlzrw/V1/mAO8m2ald32kZlwOvkIx7sS/JOBiPpXWG5E7Nj5N07d03l+8V4HtZb78pm3rjvmbkJeD7JAdOh5Fc0U1TRUGerd6X5P7b1pOM5bMbcCrJQd5xWX8GpkzqzDuBfycJWO5AMv5bDfDdgmVUk7Q0uDaX7725PJ/J+jMw9UudmZb7v/pY7v/r33KvJxTk2eR/j3Vm+KS+qDMkN6BIj4NmAyeQ3Bn39wXL8Fh4GKXN1RtgLEmLtn/m/nsK83hMMwJTH9WZYXVMk3kBTP38BSfBgNhFWpCbPw24OveHuYHkxhWfJndbeWA74C6SZuKNwHMkAzxP6LSeCcDPgNpc+hkwLuvtN/VLnflk7o+yiWSc0q8DZZ2WMQr4Xq7e1AM3Att1yjMTuCk3f1Uuf3nW22/Kpt64rxl5qZv6EoH5BXn6ZF9CcuD3aK5uvQicnvX2m7KpM8CxwD+AdSQneI8DZwMlndb1BuBvJK0PXgO+Su7YyDR0Ug/rzAU9yLPZ/x7rzPBIfVFngP1IggpryZ9fXQBUdlqXx8LDJG2u3tD9cXIEZvWmTnhMMzxSX9SZ4XZMkwagJEmSJEmSJGlAOeakJEmSJEmSpEwYnJQkSZIkSZKUCYOTkiRJkiRJkjJhcFKSJEmSJElSJgxOSpIkSZIkScqEwUlJkiRJkiRJmTA4KUmSJEmSJCkTBiclSZIkSZIkZcLgpCRJkiRJkqRMGJyUJEmSJEmSlAmDk5IkSZIkSZIyYXBSkiRJkiRJUiYMTkqSJEmSJEnKhMFJSZIkSZIkSZkwOClJkiRJkiQpEwYnJUmSJEmSJGXC4KQkSZIkSZKkTBiclCRJkiRJkpQJg5OSJEmSJEmSMmFwUpIkSZIkSVImDE5KkiRJkiRJyoTBSUmSJEmSJEmZMDgpSZIkSZIkKRMGJyVJkiRJkiRlwuCkJEmSJEmSpEwYnJQkSZIkSZKUCYOTkiRJkiRJkjJhcFKSJEl9IoQwP4QQc+nwLoq8o+4AACAASURBVOaHEMJzufl3djF/UgihMTd/7mbW9c4Qwo0hhOUhhKYQwuoQwl9CCB8OIZQW5IsFqTWEsCaEsDCEcEUI4U19sd2SJEnacgYnJUmS1NfWAad1Mf0wYIfc/K6cBJTlnnf1/jTAeTXwB5Jj2U8DRwEnAwuB7wNndnrbb4CDgEOADwA/Ad4E3BdC+G7PNkmSJEn9IcQYsy6DJEmShoEQwnzgauDHwIeBaTHG2oL5PyUJTlYBK2OMh3d6/+PAFOBlYGdgeoxxQ6c85wHfAr4aY/zPLsowDdgxxnh37nUELosxntUpXzHwQ+BU4MwY4+VbvuWSJEnaUraclCRJUl+7Lvf4wXRCCKEaOA64qqs3hBAOBPYEfgr8CEjzF+YpBT4HPA18vavlxBiXpYHJTYkxtgJnASuBz24uvyRJkvqHwUlJkiT1tVqSrtSnFkz7INAG/LKb96TduK8CfgHUs3HX7rnABOD3sQ+6/+RaZd4OzA4hbLu1y5MkSVLvGZyUJElSf7gKOCCEsEfu9anAr2OMG403GUKoBE4E7o8xPpXL82vgsBDCDgVZZ+YeX+zDcr6ce5zRh8uUJElSDxmclCRJUn+4C3geODWE8AZgf7rp0g2cQDIOZeH8q4AAnNKfhcytQ5IkSRkxOClJkqQ+l+t2fTXwEeB04F8xxr93k/00oAG4NYQwLoQwDvgn8BIwP3fzGoDFucfZfVjU7XOPS/twmZIkSeohg5OSJEnqLwuASSTByau7yhBC2Bk4BBhFEnxcU5BmAdsAx+SyPwysBt4dQtjqFo8hhArgKOD5GOOrW7s8SZIk9Z7BSUmSJPWLGOMS4L+BG4FrusmW3vTm34EjOqW3A83kbqwTY2wGvgXsCny5q4WFEKaEEN68ubLlWmNeCkzMLVOSJEkZKMm6AJIkSRq+Yoyf725eCKEE+CiwKMb4427y3Ai8K4QwOcb4OkmwczfgayGEA4BrgVeAauBQ4D+ArwL3FCxmagjhTSTjS44F9sytd2/gf2KMP9q6rZQkSdKWMjgpSZKkrMwDpgEXbSLPD4H3AScBl+TGsjwlhPBbkkDk/wLjgXXAY8Dn2LgL+fG51AbUkdyh+z7g9Bjj/X22NZIkSeq1kBzfSZIkSZIkSdLAcsxJSZIkSZIkSZkwOClJkiRJkiQpEwYnJUmSJEmSJGXC4KQkSZIkSZKkTBiclCRJkiRJkpQJg5OSJEmSJEmSMlGSdQEGgxBCAGYA67IuiyRJkiRJkjREjQWWxhhjT99gcDIxA3g160JIkiRJkiRJQ9y2wJKeZjY4mVgH8Morr1BVVZV1WSRJkiRJkqQhpba2lu222w562TPZ4GSBqqoqg5OSJEmSJEnSAPGGOJIkSZIkSZIyYXBSkiRJkiRJUiYMTkqSJEmSJEnKhGNOSpIkSZIkqVdaW1tpbm7OuhgaQKWlpRQXF/f5cg1OSpIkSZIkqUdijCxbtoy1a9dmXRRlYNy4cUybNo0QQp8t0+CkJEmSJEmSeiQNTE6ZMoXKyso+DVJp8IoxUl9fz4oVKwCYPn16ny3b4KQkSZIkSZI2q7W1tT0wOXHixKyLowFWUVEBwIoVK5gyZUqfdfH2hjiSJEmSJEnarHSMycrKyoxLoqyk331fjjc66IOTIYRDQwg3hhCWhhBiCOE9neYvyE0vTPdnVV5JkiRJkqThzK7cI1d/fPeDPjgJjAYWAmdtIs+twPSC9PYBKJckSZIkSZKkrTDox5yMMd4C3AKbjM42xhiXDVihJEmSJEmSJG21QR+c7KHDQwgrgLXAXcAXY4wrMi7ToLK+aX2384qLihlVMqpHeYtCERWlFVuUt765nhhjl3lDCFSWVm5R3g3NG2iLbd2WY3TZ6C3K29DSQGtba5/krSzN38GssaWRlraWPslbUVpBUUgaQDe1NtHc2v2YD73JO6pkFMVFxb3O29zaTFNrU7d5y0vKKSkq6XXelrYWGlsau81bVlxGaXFpr/O2trXS0NLQbd7S4lLKist6nbcttrGheUOf5C0pKqG8pBzI3R2tub5P8vbmd+8+ouu87iPcR7iP6H1e9xFbltd9RMJ9RO/zuo9IuI/YsrzuIxLuIzbOm5a5u3oZQmivD2ne7vQqL4Gioi3Pe+opp/KTn/yE//iP/+D7l3+/Q/7/d9b/4/LLL+fkk0/myquubJ9+7733cvhhh3PUUUfxx1v+CNBeJwHa2tq4/vrrufSyS3nsH4/R2trKnDlzeN9x7+MTn/gEkydNZsGCBZxyyikAFBUVUVVVxc4778y8efM4++yzqa6u7nY7RorhEJy8Bfg18DIwG/g68NcQwhtjjF3uNUII5UB5waSx/V7KjI25cEy3896+09u5+UM3t7+e8u0p3e6UDtv+MO6cf2f761nfncXK+pVd5p07Yy4P/ftD7a93v2x3Xq55ucu8u0/enSfPfLL99f4/2p+nXn+qy7zbV2/PS+e81P760AWH8vDSh7vMO6lyEq9/9vX212/7+du46+W7usxbWVrJ+i/kD4CO+9Vx/PHZP3aZFyB+NX9Ac9JvT+I3T/2m27x159e1H2B8/KaPc83Ca7rNu+LcFUwePRmAT9/2ab7/8Pe7zfvi2S8ya9wsAL74ly/y7fu+3W3eJ854gj2m7AHAN//+Tb5219e6zfvgxx5k/232B+C793+X824/r9u8d5x8B4fPOhyAHz7yQ866pfsRGG764E3M23keAD9//Oec8vtTus37q+N/xfv3eD8Av130W074zQnd5r363Vczf5/5ANz23G2847p3dJv30rddyicO+AQAf1/8d4645ohu81581MV89s2fBeDR1x7lgB8f0G3erx72VS44/AIAFr2+iD0v37PbvOcedC7/ffR/A7C4ZjGzvzu727xnzj2Ty+ZdBsDK+pVM+faUbvOevPfJLHjPAiA56N7U7/743Y/n1+//dftr9xEJ9xHuI9xHJNxHJNxH5LmPSLiPSLiPSLiPyHMfkRgp+4i22MailYu6jSiNHzWeHSbs0P76H8v+0e1yq8ur2WniTu2vFy5f2G2Qf2zZWHaZtEv768dXPN5tgL2ytJLdJ+/e/vrJ159k1YZVTJ0xlWt/cS0nnXcSoyqSCyehJXDdddcxc+ZMABatXNQeLP7O97/DCaecwO+v+z23PHILM2fOZK+pe7Uv94zPnMGV37uSD/77B/nopz7K5GmTeeXFV7j+p9dTX1TPxV++GICqqipuvvdmdpiwA2vXruXee+/lwgsv5Oqrr+aee+5hxowZ3X5GI8GQD07GGH9Z8PKJEMLDJIHKecAN3bztfOCr/V02SZIkSZIkDQ67vmFXlixewh233MHb3vc2AG6/+Xa222475syZ0yHvhvoN3H7j7Sy4eQGrXl/Fjb+6kU+c+4n2+Q8++CA//N8f8umvfZoPfuyD7dNnbDeDAw89kA3r8q1WQwhMnjqZ6ZOmM336dHbbbTfe+c53sscee3Deeefxs5/9rJ+3fHAL3TVnH4xCCBF4b4zxd5vJ9yzw4xjjt7qZ31XLyVdramqoqqrqs/IOJna16H1eu1rY1WIod7WwO5b7CHAfkXIf0fu87iPy3Ef0Pq/7iIT7iN7ndR+xZXndRyTcR/Q+75bsIxoaGnjxxReZuf1MRo3K/7brm/J1qaddtcuKiygtKaYoFNHS2kZTaxsxtjGqNN9tusNyC7pqV5aVbFG37rVr13LoYYfyxz/+kT/96U8AHH300bzzHe/kzjvvZNy4ce3duq++6mquuOIK7n/gfm666SbOOfscnn3uWUqKk7pz9tlnc9VVV/H6ytcpLS3tshzFRcUsWLCAc845h9WrV3coE8A555zDVVddxZo1ayguLu5yGYNNWgdmz57doQ4A1NbWpt3Uq2OMtT1d5rALToYQJgJLgP+IMf6kh8utAmqGc3BSkiRJkiRpa3QXmJr1+Zs38a6uXfah/Zi313QAbv7na3zi2kc5cPYEfvnxg9rz7Pf1P7N6/caB5pcumtfr9c2fP5+1a9fy4x//mG233Zann36aEAK77rorr7zyCh/72McYN24cCxYsAODNb34zJ5xwAmeffTYtLS1Mnz6d6667jqOOOgqAt7/97SxZsoSFCxducr1pcHLt2rUbzfvBD37AGWecwfLly5kypftu9YNJfwQnB3237hDCGGDHgkmzQwj7AKtz6QLgeuA1YBbwTWAl8NsBLagkSZIkSZIGtUmTJjFv3jyuueYaYozMmzePSZMmdcjzzDPP8OCDD3LDDclogSUlJZx44olcddVV7cHJGGN7S+QtlTYY3NrlDHWDPjgJzAXuKHh9Se7xGuAM4A3AR4FxJAHKO4ATY4zrBrKQkiRJkiRJI9FT/3lMr99TVpzv4nzMHlN56j+PoahTkO7uz3V/U6Gtceqpp3LWWckNli677LKN5l955ZW0tLSwzTbbtE+LMVJaWsqaNWsYP348O++8M3fffTfNzc3dduvenEWLFlFVVcXEiRO3bEOGiaLNZ8lWjPHOGGPoIs2PMW6IMR4TY5wSYyyLMW6fm/5K1uWWJEmSJEkaCSrLSnqdSgqCkyXFRVSWlXQYb3JTy91axx57LE1NTTQ1NXHMMR0Dqy0tLfzkJz/hO9/5Do899lh7WrhwIdtvvz0///nPAfjQhz5EXV0d3/9+13e976obd6EVK1Zw7bXX8p73vGejsShHmqHQclKSJEmSJEnqE8XFxSxatKj9eaGbbrqJNWvWcNppp6XjJ7Y7/vjjufLKKznrrLM48MADOe+88/jMZz7DkiVLeO9738uMGTN47rnn+MEPfsAhhxzC2WefDSStLpctW0aMkbVr13LffffxzW9+k+rqai666KKB2ehBzOCkJEmSJEmSRpTuboh85ZVXctRRR20UmAQ47rjj+OY3v8mjjz7Kfvvtx7e+9S3e+MY3ctlll/GDH/yAtrY2dthhB44//nhOPvnk9vfV1tYyffp0QghUVVWxyy67cPLJJ3P22Wd7Y2aG2N26+4t365YkSZIkSdq0Td2pWSNDf9yte2R3apckSZIkSZKUGYOTkiRJkiRJkjJhcFKSJEmSJElSJgxOSpIkSZIkScqEwUlJkiRJkiRJmTA4KUmSJEmSJCkTBiclSZIkSZIkZcLgpCRJkiRJkqRMGJyUJEmSJEmSlAmDk5IkSZIkSZIyYXBSkiRJkiRJw9r8+fMJIXD66advNO/MM88khMD8+fMHvmC9dMEFF7DPPvtkXYw+ZXBSkiRJkiRJw952223HL37xCzZs2NA+raGhgeuuu46ZM2dmWLKRzeCkJEmSJEmShr399tuPmTNncsMNN7RPu+GGG9huu+3Yd99926c1NjbyyU9+kilTpjBq1CgOOeQQHnroofb5d955JyEEbrvtNvbdd18qKip4y1vewooVK7jlllvYbbfdqKqq4oMf/CD19fXt74sxcvHFFzNnzhwqKirYe++9+c1vfrPRcv/yl78wd+5cKisrOfjgg3nmmWcAWLBgAV/72tdYuHAhIQRCCCxYsICXXnqJEAKPPfZY+7LWrl1LCIE777xzq8o8EEoGdG2SJEmSJEkaFmKEAY5jtaushBB6/75TTjmFq6++mg9/+MMAXHXVVZx66qntQTyA8847j+uvv55rrrmG7bffnosvvphjjjmG5557jgkTJrTnu+CCC7j00kuprKzkhBNO4IQTTqC8vJxrr72Wuro63vve9/K9732Pz33ucwB86Utf4oYbbuDyyy9np5124m9/+xsf+chHmDx5Mocddlj7cr/4xS/yne98h8mTJ3P66adz6qmncs8993DiiSfyxBNPcOutt3L77bcDUF1dzfLly3u8/b0t80AwOClJkiRJkqReq6+HMWOyWXddHYwe3fv3nXTSSZx//vntrQ3vuecefvGLX7QHJ9evX8/ll1/OggULeNvb3gbAj370I/785z9z5ZVX8tnPfrZ9Wd/4xjd485vfDMBpp53G+eefz/PPP8+cOXMAOP7447njjjv43Oc+x/r167nkkkv461//ykEHHQTAnDlzuPvuu7niiis6BCf/67/+q/315z//eebNm0dDQwMVFRWMGTOGkpISpk2b1vuN72WZB4rBSUmSJEmSJI0IkyZNYt68eVxzzTXEGJk3bx6TJk1qn//888/T3NzcHsADKC0t5YADDmDRokUdlrXXXnu1P586dSqVlZXtQb502oMPPgjAU089RUNDA29961s7LKOpqalDl/LOy50+fToAK1as6JNxMXtT5oFicFKSJEmSJEm9VlmZtGDMat1b6tRTT+Wss84C4LLLLuswL8YIQOjUZzzGuNG00tLS9uchhA6v02ltbW0A7Y8333wz22yzTYd85eXlm1xu4fu7UlRU1KHsAM3NzV3m7U2ZB4rBSUmSJEmSJPVaCFvWtTprxx57LE1NTQAcc8wxHebtuOOOlJWVcffdd/OhD30ISAJ9Dz/8MOecc84Wr3P33XenvLycxYsXd+jC3VtlZWW0trZ2mDZ58mQAXnvttfZWmIU3xxnsDE5KkiRJkiRpxCguLm7vol1cXNxh3ujRoznjjDP47Gc/y4QJE5g5cyYXX3wx9fX1nHbaaVu8zrFjx3LuuefyqU99ira2Ng455BBqa2u59957GTNmDCeffHKPljNr1ixefPFFHnvsMbbddlvGjh1LRUUFb3rTm7jooouYNWsWK1eu5Etf+tIWl3WgGZyUJEmSJEnSiFJVVdXtvIsuuoi2tjZOOukk1q1bx9y5c7ntttsYP378Vq3z61//OlOmTOHCCy/khRdeYNy4cey333584Qtf6PEyjjvuOG644QaOOOII1q5dy9VXX838+fPb7zo+d+5cdtllFy6++GKOPvrorSrvQAmF/dFHqhBCFVBTU1OzycopSZIkSZI0UjU0NPDiiy8ye/ZsRo0alXVxlIFN1YHa2lqqq6sBqmOMtT1dZlEfl1GSJEmSJEmSesTgpCRJkiRJkqRMGJyUJEmSJEmSlAmDk5IkSZIkSZIyYXBSkiRJkiRJPebNlUeu/vjuDU5KkiRJkiRps0pLSwGor6/PuCTKSvrdp3WhL5T02ZIkSZIkSZI0bBUXFzNu3DhWrFgBQGVlJSGEjEulgRBjpL6+nhUrVjBu3DiKi4v7bNkGJyVJkiRJktQj06ZNA2gPUGpkGTduXHsd6CsGJyVJkiRJktQjIQSmT5/OlClTaG5uzro4GkClpaV92mIyZXBSkiRJkiRJvVJcXNwvgSqNPN4QR5IkSZIkSVImDE5KkiRJkiRJyoTBSUmSJEmSJEmZMDgpSZIkSZIkKRMGJyVJkiRJkiRlwuCkJEmSJEmSpEwYnJQkSZIkSZKUiUEfnAwhHBpCuDGEsDSEEEMI7+k0P4QQLsjN3xBCuDOEsEdW5ZUkSZIkSZLUM4M+OAmMBhYCZ3Uz/zzg07n5+wPLgD+HEMYOTPEkSZIkSZIkbYmSrAuwOTHGW4BbAEIIHeaFZMI5wH/FGG/ITTsZWA58CLhiQAsrSZIkSZIkqceGQsvJTZkNTAP+lE6IMTYCdwEHZ1UoSZIkSZIkSZs36FtObsa03OPyTtOXA9t396YQQjlQXjDJLuCSJEmSJEnSABvqLSdTsdPr0MW0QucDNQXp1X4qlyRJkiRJkqRuDPXg5LLc47RO06ewcWvKQhcC1QVp274vmiRJkiRJkqRNGerByRdJApRvTSeEEMqAw4B7u3tTjLExxlibJmBdv5dUkiRJkiRJUgeDfszJEMIYYMeCSbNDCPsAq2OMi0MI/wt8IYTwLPAs8AWgHrh24EsrSZIkSZIkqacGfXASmAvcUfD6ktzjNcB84GKgAvg+MB54ADg6xmhrSEmSJEmSJGkQCzFu6r4xI0MIoQqoqampoaqqKuviSJIkSZIkSUNKbW0t1dXVANW5YRR7ZKiPOSlJkiRJkiRpiDI4KUmSJEmSJCkTBiclSZIkSZIkZcLgpCRJkiRJkqRMGJyUJEmSJEmSlAmDk5IkSZIkSZIyYXBSkiRJkiRJUiYMTkqSJEmSJEnKhMFJSZIkSZIkSZkwOClJkiRJkiQpEwYnJUmSJEmSJGXC4KQkSZIkSZKkTBiclCRJkiRJkpQJg5OSJEmSJEmSMmFwUpIkSZIkSVImDE5KkiRJkiRJyoTBSUmSJEmSJEmZMDgpSZIkSZIkKRMGJyVJkiRJkiRlwuCkJEmSJEmSpEwYnJQkSZIkSZKUCYOTkiRJkiRJkjJhcFKSJEmSJElSJgxOSpIkSZIkScqEwUlJkiRJkiRJmTA4KUmSJEmSJCkTBiclSZIkSZIkZcLgpCRJkiRJkqRMGJyUJEmSJEmSlAmDk5IkSZIkSZIyYXBSkiRJkiRJUiYMTkqSJEmSJEnKhMFJSZIkSZIkSZkwOClJkiRJkiQpEwYnJUmSJEmSJGXC4KQkSZIkSZKkTBiclCRJkiRJkpQJg5OSJEmSJEmSMlGSdQEkSVLfixHq66GoCEpKkhRCfn5rKzQ0JGnDBmhsTPJUVORTSUmynHXr4PXXk+WVlXWdysuTdW2JtrbkcUveH2Py/pYWaG5OUlsbjBmTlKm799TXw6pVsHp18rq0NFn/unWwdm3HtGFD8lkUFyefV00N1NYmn+eoUR1TSUkyr6YmWdeuu8Luuydp+vT8dxBjsuy2tuTzGzUqKUNPNTTAihUd09q1yTaUlnZMhd9R+ry0FOrqku1fsyZ5XL062daxY6GqCqqrk1RVBRMnwpQpMHVqso1NTUmqrU3WW1OT/7zWr0/KGEJSnhCS1NCQrLOuLvmc6+qS9Y0ZA+PGJesaNy5Jo0Yl32VTU8fHysqkLOPH5+t04TpGjYKZM2HChI71PSsxblyOtO5VVCSfbVlZUn8bG5O8FRWDo+ySJEkDxeCkJA0C9fXJiXoaXClMafCoMDU35wMPTU354MSGDflljhqVnPSPGZOc/KYBqu5SurzC4Et1dRJQmTgxCWAsW5bMKy9Plt/amg9qtLYmgYPKyqR869cny0rLUVmZDyT1JrW25re7qSlZXmEArXMqKkreV7iuGJPp/ZmKi7ue3taWD9ysWZMP4BQ+r6/PB2DS1NKSfM6dty8NkKX1In2eBvja2pLv6dVXk+UWSgOVMSbr25ySkuQ9TU09q8fpNqcKAyzp88IAaHNzUs/SehtCsn1pILDwMYTkM2lpSepE+rylpfvylJUlwZ80tbUlQaGVK5NA0ECrrobddku2+7nn8gHM1HbbJfNnzky+0/Xr88G8wud1dRt/t+po9GjYfvskzZyZfH7PPgsvvZQEX7fbLtm3pfuXwv1sCEmAdPz4pP6tX5+k+vqOj+vXJ99lGrgdMyaZlgZe01RcnNS/0aOT33ttbceypvuJVGlpsv6ysuS3Wpggv/+trEzKOGFCxzRuXLKNY8fmf++dl5H+rgpTV9M6/xabmjp+Xp33Q533SV39ljf3mAbrR41KvovO5S9MIXQMwKfPJ0zo/uLE1ooxqTfp/1NbW1KOsrJkGwovQBT+l6UXQNS1GJP61diYTw0N+QtOad3tqj53VbdLS7t+hI7LT58XF+frXUVF/nl5ee8vFqT/sYX/683NHbctTU1NybrTdaaPIcCSJbB4cXKMtbnjt65+T4X1sbg42ZY0pduW/m76sm5u2JBc0CzcFxSuuzCVlnoxRlIixHRPPoKFEKqAmpqaGqqqqrIuTr9obc2fHLe25v+QoOuTvfR5Y2O+BUhtbT61tORbKjQ2Jn9C9fX51Nyc/LmmgYo0NTUlJ4Zr1iTvLfxzTA8CRo9ODvALH6HjuqHjugtTS0t+fW1tsHx5PmiTtqwobGVRGERIX6efWXrg2Tl1N724OGlVMn16cmCcBnu6O0hKt7m0NDmZqalJPr/CwEpXj5ua1/mxpiYJVCxblnz/69YlJ08hbHxwU1GRnGBNmZKc8KQHLI2N+ZOtGDc+YSkvT/KPH598X4UHjV2VuavnqfQApfCx8/PCA9jCA8uyMpg9G3bYITk5a27OB3jGjk3qU1tb8t6GhmSbClNtbcfXjY0dv+vunm9qXueTl8IAVnFxUsZVqzoGFaWBlv6O0/1+Vyork9934cnWpgKDQ0FZWRJYCiG/v6iqyrfeS1NFRf5/sbw836IQ8idfaWpuzrc6bG6Gp5+GJ59MgpGF+7q+UFqa32dPmZL877S1bXyBozDoXZhGj84HtNIgV2Vl/v8o/e+vqUkCusuXJ9tYqKKi42dVXZ3/3+4cRCi8YJLuk0eNSv6TOrdWbWjo2DI3/T9dvz7f2rOlZeP/k7q65D9/sCspGfq/n8EsBJgxIwlOFxfnL26NHp3UvYqK/DFkYWAV8nVuw4akntXU5I+NN/cbTo9TustXVNQxiNrdY2GdT48by8qS3+nEick2dBfQ2dyp3abmpxdwli5Nfu+wcUvsoqL8/rLwsbvn6WPaSrysLH8M3zlINxiVluZbkKcpvWhRV5fUi/Tcoa4uqS/r1m3+exhs0jrWuR4Wvi4u3nRwuLk5Od9Yvbrn600D+4XnhJtLIXR9HpYm6LrHQOHzadNgxx1hzpzkO62oyLdiL/zvbG5O/pNefbVjeu215HMZOzZ/bJQGYqdNS/Y/hWn69OQcMe0hkZ4TFxXle12k+6F16zr2aChM6fT0fC49v02D0Wlqbc2fuzU25r+jadNgp51g553h0EPhyCOT/2KpL9XW1lKdHChXxxhrN5c/NeSDkyGEC4Cvdpq8PMY4rRfLGNbByZkz4ZVXsi6FpJ4obMGYpsIr6WmLs9LS/AFUSUlywDNlSsegwIYN+ZY76QlCV6m1tWMgIw2+jB2bHJAtW5YEJiZMSNaTBn4bGpIDookTk3klJfkLFKWlSZBj1Kh8V860i3FPUucLB2lgLG3ZWXhBor6+4+uuWkmmn0lvW2z2tpVnV+8PoWN31fHjOz6OG5fftsKTgeLijhdg0u1ube3YqqOi6NJrrwAAIABJREFUIvnO0pMGSL6nbbdNHmHj7xzydam8PN+aBJIyF37Gra0waVK+bhVKg/2FqfBkJVX4vLk5v+z0wH7MmPzJRuEFssLHtOVRd628ios7njzX1XW8qFZTk0yfNClJEycm2zRQLTYaG+Ff/4JFi5Lvd6edkpOisrLkc6urS1r2LVqUtJaprOx4oa7zRbuJE5N6NZAtTmJMTobSFnHp5z3YbNiQHPe8/HKSFi9O6nr6ma9bl5xcLlvWcR+bpra25ARwzZrk+ejRSUr3Q4XPS0qSE8wVK5LvsDDwmqbW1qQO1tUlv/dp05KT4dbWfNf29KS7sKV1U1M+2FV4oS5tvVhfv/GJ66pV+eDIunXJOrpaRjoUwuZS51bKaQChcyuvztPSYR66+i1valr6v5bug9LWkd2lNCBS2P0/PRHX0FZamg8mpccF0PVF68L9YHqBprA+tbRsHDAubBxRVrbxUCd9XYfSIF/nAFy67s4tkVtbk6DWzJkwefLGv8XOwd/uflup9OJjmnrSe2Jrt7dwX1C47sJyjSQhJN9lY+PGPSeyUlYGBx6YHE9srlFNmtKhXiZOTC4AzZmTv2Cb/m46/yZXr86fs0Dyevny5HOors6fS0ycmJRJQ9tID04eDxxVMLk1xvh6L5YxrIOT22+fHJhDcjBdUpL/c4CNT/IKnxd2iSu8alhamj8BLy9Plpu2lEwDJw0NHVtT1tcny0x3Pun4U2nrt/QPOe0qVdiNDfLBksJuToVdHtMD4pKS/EE7JCfnU6fmu5R213KvsPtnjPkrT2kLt86pq+nNzcmO9rXX8q06Ol89Lrwil253U1OybWmLk8IATWFLz94+hpCcGE2fnpwMjR+fv8IHGx/krF+fnGAtX56cSKVdUApbHnYVQGhoSE6kVq9OPvfuWqhu7nnnoEZ3XXYg3+2r8Err+vXwwgvw/PP5+pZ2gU2vHqb1Ot2mdGy19Hnh6/SAqrvvvbvnha/Tx/RPunPAq7g4+U1MmpT/fLfW+sYWvnHzU5wwdzv2nTkegA1NrTS2tDKu0n98SdLwFWPSpfSll5LgdDqOZ2lpcmyQDiXRObiadqVNA5yVlflW0mm32e7++9OAWHps3dUFt5aWjcdQLezy2928wmOtNAC9bt2mjxc2dyyxqfkTJyZBsalT860kC1Nr68ZBjK5ed36eDhGSBo87t4YrDNqlAcm+lAbkYfNdiWPMHxenPWsKL3S1tuaHq0mHToDk+Do9X0p7RqXD6gy2rsttbfkWq53rXFev07rY1UUCyLfeSy+OburiWWtr113ce5oKz9O6SpD/PaZ1rvCxoSG5QPX888k+oq4umVY4NEBhvU6HAtl223yaMaPjhae0DseYXPhaujQ5H1y6NP+8c1C28NwnVVrasVdD594N6fN0qJD0/LZzcLqoKH9eUzg0wauvJhdCH38cbrstOW/qC5WV+X1ccXH+InxTU7LtvQmGjx6dH/qnpSXZJ+20U9LSNT1XKi5Ozp2mTk0eO7c27y6lsQ2H2OhfIz04+Z4Y4z5bsYxhHZx86aXkB5520011dWVD0tD3ud/8k18+/ArXnHoAh+08GYDf/uNVzv31P3nTnAl87N/mcPjOkwn++CVJkjTCpQHp/gokt7UlvZBeey0JYk6enATw0gsizc357t0DJcYkUPnAA0ngtqtGNZ2HaWhqSgL0q1cnDVpeeim5GNQTheMrV1Xle2PV1OSHfevr4W+6UlycfP5TpuQfKyuTMqxa1XF8+8KeFTvuCPvuC3vtlTT6SQOcDQ3J5/jqq/mLGCHkG7usW5c0/Fm2DPbeG044of+3MWtbGpwcLjfE2SmEsBRoBB4AvhBj7KPrAEPfrFldTzcuIQ1P+8+ewB8WLqWt4OLTE0tqaW2L3PPcKu55bhVztx/PZ4/ZhQPnTMywpJIkSVK20ptr9Zeiovz40J2lLYYHWgjJ2JM777x1y6mrS4Jv6bBEra35sTFLS2GbbZJAZNr7Me051lk6rMmqVUnAL211vWxZEvx74YV8C9fm5vxY2KtWdX1D0cKW6Gmrb0ge0/sxbKm05WZ5eTKMTE/b+334wyMjOLmlhkPLybcBlcC/gKnAl4BdgT1ijKu6eU85ULgLGAu8OlxbTkoaeWobmqka1fEo6+VV6/n5A4u55t6XaGxJLk3+8KQ3cvQePR6iV5IkSZKGlBiTFqJpq8/CtGFD0mU+DTh2Ht++ri4ZE/yxx+CZZzZu4TluXNIgLB3WAfI3Wh0zJj/M3EEHwYc+NOCbPuBGbLfuzkIIo4HngYtjjJd0k+cCNr6JDgYnJQ1VMUbWN7UypnzzDeKX1zbwjZsXcePCpYyrLOWWs/+N6dUVA1BKSZIkSRqampqSVpsrViTjGO+wQ9Ii1l6peVsanBx2Q4HGGNcDjwM7bSLbhUB1Qdp2AIomSf3mgRdXc+R37uTvz25+4JepVaP4zvv3Zq9tq1lb38w5v3iM1rbhdaFKkiRJkvpSWVlyQ6R99oGDD05aRBqY7BvDLjiZ67K9G/Bad3lijI0xxto0AesGrICS1A++f+fzLK9t5LYnezaASllJEf/3gX0ZXVbMAy+u5vI7n+vnEmowizFy/wureOCFVbyyup7m1o79VRqaW/nkdf9gty/fyu5fuZU9v3ob1z24OKPSSpIkSRpOhvwNcUII3wZuBBYDU0jGnKwCrsmyXJI0UB5/tYa//et1iosCHz90hx6/b9ak0fznu/fkM79eyP/c/ixH7jaV3aY7tMVIVN/UyhV3Pc8dzyQtb8uKi5j/5ll8+q3JKOkf/+kj3PWvfKvcyWPLedfeMzIp61BxxzMruPLvL3LErlM47ZDZWRdHkiRJGrSGQ8vJbYHrgGeAG4Am4E0xxpczLZUkDZDL70paPb5r7xlsN6GyV+99337bcOwe02hti3zl908w3MYhVs+MLi/hux/clwNmT6CsuIim1jZ++LcXePv//Z1TFzzEXf96nYrSYq6aP5e/n3cEN551CKNz45u2tkW+e/uzrKhtyHgrBocNTcntIF9ds4G7n1vJ7/6xJOMSSZIkSYPbkG85GWP8QNZlkKSsPLeijlueSLpyn3F4z1tNpkIIfOWdu/O3Z1/noZfWcP2jSzj+jQ7DO1K0tUWKipKBcqpGlfKrjx9EW1vkr0+v4Au/fZwXXl/PC6+vZ1RpEVfN35+Ddpi40TK++cdFXHn3i/zl6eXccMbBlBQPh+uePfdazQZ+et/LrKxr5PV1jTzy8hqumr8/b99zGjX1TbzTFqaSJEnSJg354KQkjVRtbZH/vf1fxAhH7TaVnaeO3aLlzBhXwSeP3IlbHn+NXbZwGRqavnXr06ysa+KCd+3O2FGlABQVBY7afSr7z5rAN25+igdfWs2F731Dl4FJgI8etD1/fPw1PvZvc0ZUYDLGyB8WLuXLv3uC2oaWDvOufXAxl5ywD2e9ZVP35pMkSZIEEOzCByGEKqCmpqaGqirHW5M0+K1e38SnfvlY+ziAvz3zYPadOX6Ll9fS2kYIgeJcK7plNQ1MHlve/lrDz8q6Rg69+A7qm1q5av5c3rLr1C1eVkNzK6NKi/uwdINbc2sbn/nVQv6wcCkAe21bzTF7TGN0WTHTqkdx5G5TKR1BgVpJkiQJoLa2lurqaoDq3A2oe8SWk5I0xDyxpIZ//8nDvFbTwKjSIr7xnjdsVWAS2KjF22nXPMTy2kYufN8beOvuWx600uA1aUw5PzxpLnc/t3KrApNAh8DkK6vrufyu5/nKO3YftgHLGx59lT8sXEpxUeCTb9mJM4/Yodtg5K1PvMb1jy7hM0fvzK7TvAAqSZIkdWZwUpKGkIbmVs74+SO8VtPA7Emjufwj+/V5wGNtfRPPrahjh8ljaG1r69NlK3tr65sYV1kGwCE7TeKQnSb12bJb2yL//pOHeXrZOl58fT0/OnkuY8qH16FGjJGf3p/cc+/co3fZ7Fivv/3HEv781HJ2mjKGXY81OClJkiR1Zp8jSRpCrrjrBV5ZvYFpVaP43Sfe3C8tscZVlvHPC47mhjMP5tg9p/f58pWNltY2LvjDkxx1yd94bkVdv6yjuCi5wdKY8hLue2EVX7jh8WF3B/iFr9bwxJJaykqKOHH/7TabP70hzo3/XDrsPgtJkiSpLxiclKQhYn1jC1fe/QIAX5y3G9UVpf22rvKS4mHbJXckqqlvZv7VD7Hg3pdYWdfIvc+v7Ld1HbzDJBacsj/FRYE/LFzK7x5b0m/rysLPcq0m571hOhNGl202/5G7TqWspIhXVm/g5VX1/V08SZIkacgxOClJQ8To8hL+cNYhnHXEjrxjr4Fp0biuoZkr736RRxevGZD1qe81NLfykSsf4O7nVlJZVswPPvJGPnrQrH5d59xZEzj7yORO1V/+3ZMsHiZBubX1TdyYuwnOR960fY/eU1FWzM5TxwDw9LJ1/VY2SZIkaagyOClJQ8isSaM595hdCGFg7qL97due4es3PcUP7nx+QNanvvfV3z/J40tqmDC6jN+cfjDH7jltQNb7iSN2ZP9Z46lrbOHsX/6D5tahP37pcyvqGDuqhN2mV7HfzHE9ft8uU5PhF55e1uMbFkqSJEkjhsFJSRrkYoy88Hr/jBG4OScdlLQO+/Oi5TzwwiqaWoZ+gGkk+eVDi/nlw68QAvzfB/Zl9xkDd0OW4qLAJSfsw9jyEv6xeC1n/vxRGltaB2z9/WHurAnc+/kjueIjb+zVBYJdp40F4BlbTkqSJEkbMTgpSYPcn59azpGX3MXnr//ngK97xyljOWznycQIJ/7wfvb86m0cd/m9PLXUFmCD3RNLavjy758E4DNv3blP78rdU9tNqOTSD+9HWUkRf35qOWf87FEamod2gLKspIiZEyt79Z5dDE5KkiRJ3TI4KUmD3GOvrCVGenTzjf5w/tt35eAdJjJ2VAlNrW088vIaTrziPu59LrmpysMvreYDP7yP33e68ckrq4fHOIND1cW3PUNTSxtH7jqFMw/fMbNyHLbzZK48eS7lJUX89ekVnP6zR2htG1p3rV6zvonrHly8xeXedXoSnHxx1Xo2NA3t4KwkSZLU10KMQ+sEoT+EEKqAmpqaGqqqBq7LmyT11JNLa9hmXAXjKrMJUELSvXzx6nrO+80/eeDF1ZQWBw7aYRJ/+9frjCkv4Z7Pv4XqilIaW1p523f/zguvr+f+849kWvWozMo8Ui1du4E3f+uvxAh3ffZwtp84Ousice/zKzltwcNsaG7l6+/eg5P6+aY8fen8Gx7nugcX8779tuGSE/bp9ftjjLzxG7ezen0Tfzjrzey1bc/Hq5QkSZKGitraWqqrqwGqY4w97m5ny0lJGgL2mFGdaWASIITA9hNHc82pBzDvDdNpbo387V+vUxTgXfvMaM9XXlJMdUUppcWBJ5bUtE+3JeXAueHRV4kRDpw9YVAEJgEO3mES5799VwBuXPgaQ+ni6P9n776joyq3Bg7/ZtI76SSQAin00Ku00AQBQRRBRMVyFVFRsVy91qvXT7F3wK6oCCg2VEB67yFAQoAkpJCE9J5Mpp3vj4RIlJIyk5kk+1mL5cpk5pwdDDPn7He/e3cLcMPN0ZbZA4Mb9XqVSkUX/+rqSZnYLYQQQgghRF22lg5ACCHExW2IO0dnXxfC/dwsHUodjnY2vHdTXyL93TidU8p90eF0C6hbdf76zN74uzvi6lD9MbMvOZ+5n+7jliGhPDGpK/a2sjZmLkajwqqDZwG4cUCQhaOp6+bBITjZ2TC9b4dmmzhvCrcODWVGv461v8+N0TXAjT3J+SRkSXJSCCGEEEKIC0lyUgghrFBybhkPrTyCUVH4fv4wenbwsHRIdajVKh4cF3HJ74f5utb5+mBqITqDwme7znAotYD35/QjyKthQ0VE/RzPLCatoAJXB1sm9Wpv6XDqsFGrmHlBwrRSa8DJ3saCEdVfUxKTcMHE7mwZJiWEEEIIIcSFpHRFCCGsjFZvZOF3MVRoDfQN8vxHVWJLdF90OB/fOgAPJztizxYz+d0drI87Z+mwWqWoju3YuGgkr94QhbO99a5B6gxGRry6mdkf7SGnRGPpcC5qxf40/jiWhVZvbPKx+od4cc+oztwyJLTpgQkhhBBCCNGKSHJSCCGszOsbTnI8owRPZzvemtUHG3XL2f56OeO7+/PbwuH0CWpHiUbPPcsP8d9f40yS+BF1hfu5cU2vAEuHcVlHzxaRV6YlMafMYpPoL6dKb2DxugTu/eYwu5Pymny8cD9XnpzUjYk9rauaVQghhBBCCEuT5KQQQliRTSey+Wh7MgCLr49qdZOuO3o6s+qeofxrRCcAPt+Vwsxle8gvq7JwZC1fdomG19YnYDS2jEEz/UO82PF4NG/N6oOtTfXliNGosCUhxyqG5WyMz6GoQkd7d0dGRPhaOhwhhBBCCCFaLUlOCiGElTiTV85DK48AcNvQECb0aJ0VVva2ap6a3J1Pzm/zTi9i5tI9ZBRV1uv1Px/J4P3NpzmcVmjmSFsOrd7I9A928cGWJJbvTbV0OPUW5OVcJ/G3bHsyt39xgM92pVguqBqrDqYDcH3/DiarXi6q0LI7Ma/OFHshhBCNtysxj2d+Ol5nUeuj7Unc/+1hEnPKLBiZEEKIhpDkpBBCWIEKrZ75yw9RqtHTP8STpyZ3t3RIZjeuuz9rFgwj0MOR5Lxybliym/jMfw4LKSzX1rnp+P7QWV7fcIoZH+7mlk/3XfQ1bY29rZp7R4fRu6MHQzp7WzqcRqvUGQB4+89TFJRrLRZHVnEl20/nAjCzv+kmnn++K4U5n+zjy90pJjumOeSUath6Msfs50nKLeO+bw/z85EMs59LCNH6nMouZe6n+1i+N5XDaUW1j2+Iy2bt0SyuX7KbQ6kFFoxQCCFEfUlyUgghLExRFJ744Rgns0vxdXPgw5v7YW/bNt6ew3xd+f7eYYT7uZJVrOGad3fwzE/Ha7+/OymPsW9uY8m2pNrHJvcKYHJUAHY2KnaczmPyezt4/pc4q9gK3Nw0Nck8gLmDQ/jh3mF0qZkK3RI9NDaC7gHulFbpeXfTaYvF8cOhsygKDOrkRaiPi8mO2y3AnWAvZ6vssXleYk4Z49/czoJvDnO2sMKs53pxbTy/Hc3i5LnS2scURUFvkD60Qogri/R34+1Zfbiubwd8XP96X33ymq70DW5HcaWOmz/Zx8b4bAtGKYQQoj7axt2vEEJYsV9iM/klNhNbtYoPb+6Hv3vr6jN5JYHtnFh9z1DGdfNHraJOcq1KZ6SgXMsvRzKp0lcn4mYPCuaDOf3YtGg0U3sHoijwxe4Ufj/WtqZ/f703lYlvb69NIKnVqtrejS2VWq3i6cndgOqfLzm3+bfk6QxGvtmXBsCNA0xXNQkwsWd7tj8ezZPXdDPpcU2ps48Lkf6udPZ1qU1+V2j1lFfpTX6uV6+PYkbfDvQJagdAiUbHPcsP8cofCSY/lxCidZrWpwNvzepDiPdfC0n9Q7z49q4hjOnqh0Zn5J6vD/HJjuRWs4ip0Rn4bOcZfonNtHQoQghhMi37LkYIIVq47BINz/4cB8ADYyIYGOpl4Ygsw9PFnk9uG8DeJ8cyvW+H2sd7dfTg9Zm9WbNgGA62NnVeE+ztzHs39eXBsREAPP9rHCUaXbPGbQl6g5GXfovn6Z+Ok5Jf0epuToaF+zCmqx96o8Lidc2fpPr9WBZZxRp8XO2ZEmXdE8/NQa1WsWRuf35acBXhfm4UV+iY+8k+HlgRY/Jz+bk78uasPrX9dQ+mFLAhPptPdp7h92NZJj+fEKLlO1tYwZ1fHCC7RHPZ5znZ27Dslv7M7N8Rg1Hhf7+d4P5vYyj720JLS0xYPrnmGC+sjedo+l9b2RVFkX7GQogWTZKTQghhIYqi8OSaYxRX6ujVwYMF0WGWDsni/NwdcXWwrf3ax9WBG/p3xNne9pKvuXd0GJ18XMgtreL19SebI0yL0BuMJOeWMffTfXy84wwAC0aHce+o1vd7859rumKjVrE+LpvvD51ttvMqisJH25MBuG1oKI52Nld4ReMYjQprDp+1ql6LiTmlGGomvfu4OtRW4aYXVnD0bDGbE3JM1t+1QnvxKswxXf25e2RnABatOsJLv8VzrvjyCQghRNuRV1bFrZ/uZ1NCDo9/f/SKz7ezUfPqDVH899oe2Nmo+O1YFmNe38pdXx7gpd/iue2z/Tz3S1zt8xVFYe4n+9icYN3bwO+LDifQw5HBF/SY/jEmgynv7eTJNUfbxEKtEKL1keSkEE1QqTWwJSGHJ344SmJO6ZVfIAR/3ZjrjQqdfFxwsFXzxo29sWvhW3ItxdHOhpem9wRg+d5UjlxQSdAafH/oLKNf20LXZ9Yx5o1t7E0uwMXehiU39+PxiV1RqUwzSdqahPu5MW9YKACPro7l3U2nm6W6ZU9SPnGZJTjaqZk7JMRs5/n1aCaLVsXy/C9xFFdY/iayQqvn+iV7GPfmNtIL6vaZ7NnBgwk9/AFYc7jpiWJFUbjl0/3c9tl+UvLK//H9x6/uwqhIXzQ6Ix/vOMOIVzdz5xcHePmPE6w6kE6p3HQL0SaVanTM+3w/yXnldGjnxCvX96rX61QqFbcNC+W7u4fS3t2RnNIqNp7I4eMdZ9h2KpdfYjNr+9ymFVSwMzGP+csPk1lUac4fp8Eu7MUb7ufK9sejGd/dv/ax85PJV+xPZ/yb26TPphCixVG1xFJ2U1OpVO5AcXFxMe7u7pYOR1g5jc7A57tS2HIyh5i0QnSG6n9Di8ZHsrBme6kQF3M8o5gX18ajNyr8cO+w2sdzSjT4tbE+k+awaOUR1sRkEOTlxNd3Dq7Tf6qlWh93jvlfH+L8R7WdjYqoju1YfH0vwv1a7uCb+jDWbOteVlPJeEP/jrwwrcdlq2ibat7n+9l6Mpdbh4bwwrSeZjuPzmBk8rs7OJVdxrxhoTx/bQ+znas+vth1hud/jSfU25lNj4zGRl034f1nfDb/+uogvm4O7HliTJN6m246kc2dXx7EwVbNjsejL/repygKW0/msmRbEvvP1J2028XfjV8fGN5mhoYJIcBgVLjts/3sTMzD28We1fOH0tnXtcHHqdDqOZJWRFJuGcl55Xg52zOldyCdagaflVXpefz7WEZH+nHjQNP2HG6K/LIqZi7dw8KxEXVa3/zdvuR8nlhzjDM1Cz9Tewfy7JTu+Lo5NFeoQghBSUkJHh4eAB6KotR7240kJ5HkpKg/g1FhwTeHWB/312pkoIcjY7v5M6NfB/oGe5JdomFXYh4z+nW0YKT1ZzAqdW5EK7UGnOzNs5WxrTuVXcqEt7bj7mhL7HMTWmXFmyXll1Vx3Ye7SSuowMfVga/uGET3wJb7nn70bBGzlu2lUmfgpkFBPDAmAn93x38kjlq75XtSeO6XOIwKBHk58cqMKK4K97nk89/eeIpfYzN5dmoPRkX61vs88ZklXPPuDlQq2ProaLMnt3eezmPup/uws1Gx+ZHRBHk5m/V8l6I3GIl+YyvpBZW8OL0nt1ykYlSrNzLk5U0UlGv54vaBjO7i16hzVWoNTHh7G+kFldwzqjNPTrryYKBjZ4uJSS8kKaeMn2MzKarQ8dQ13fhXzfZvIUTr9+q6BD7cmoSTnQ2r5w+lZwcPs51LURSruj5TFIU7vzzI5oQcIvxc+W3hiMsuzmh0Bt7aeIqPtydjVMDN0ZbHJ3ZlzqDgNnf9IISwjMYmJ2XZWYh6UhSFp386zvq4bOxt1Dw/tTtbHx3NrifG8OL0nrWJyXFvbOPR1bEcO2v9TanP5JUz9b2dbEnIASApt4yJ72znm32pFo6s9Tg/7RYg0t+Nxdf3Yv3DI63qwre18HZ14Pv5Q+na3o3yKj0aveHKL7JSmUWV3PnlQSp1BkZG+vLitJ4EtnNqkzcWtwwN5cs7BhHo4Uh6QeU/tnhX6Q11tvq6OdqRlFvOPcsPciCl4GKHvChbGxUu9jZc3b19s1TdDo/wYUSEDzqDwlt/njL7+S7ll9hM0gsq8XKx54ZLLKrZ26qZWjMcaM3hxvfJ/HBrIukFlQR4OLJwTP12GvTq6MGtQ0P577Se/KcmmfnOptPkXGEYhhCiddgQd44PtyYBsPiGKLMmJoE612fZJRpW7E8z6/mu5MvdKWxOyMHeVs17c/pesWrc0c6GJyd146f7rqJXBw9KNXqe+ek4M5fu/scwICGEsCaSnBSiHjQ6A6+uP8mK/WmoVPDO7D7Mu6oToT4udS5i/N0dGd3Vj14d2+FgZ/3/vN768xTxWSUsXpeAoihsiMsmNb+CZ3+OY9upXEuH1+LtTc5nxKtbOJRaWPvYrIHBBHg4WTCq1s3P3ZGV9wxl+Z2D6BfsaelwGu3NP0+RW1pFF383PpjTt0nbaFuDERG+bFg0innDQnnl+qja9939Zwro9dwGPqkZEAQwZ1Aw7o62aHRG7vjiQL2HuET6u7Fkbn+emnzlaj5TeezqLgD8eCSDhHOmGTbTEAnnSnjmp+MAzBsWetmq+fO7ATbEn2tU38ek3DKWbqtOMDw3tTsuDg3fnn9D/470DmpHWZWeNzZYLqErhGgeKXnlPLI6Fqh+j7q2d2CznbuoQsuo17bw5JpjxGVapuCgsFxb+1731DXd6Nq+/rtBojq246f7ruKFaT1wc7DlcFoRL/4ab65QhRCiydr23Y4Ql6EoCodSC/jPj8cY9NJGltSs2r44rSeTegVc8nUvz+jFmnuHEelv/f3gXp/Zm4VjI/ji9kGoVCrmj+rMjH4dMBgV7v/mMGn5FVc+iLiouMxi5n2+n9zSKj7ZkWzpcNoUDyc7BoR61X59PKOYn2KsZypyfdw0KJgZ/Trw8vW9cHO0s3Q4VsHVwZbnr+1R2xsM4PdjWWgNxjqJPSd7G/b9ZxwDQz0p1ei59bN9l5z4fCCloM773MhI32bdXh3VsR2TewWgKJh90vxK6YthAAAgAElEQVTp7FKWbkti5YE0yqr05JdVcdeXBynXGhjS2Yt7R19+6ntURw86+7qg0Rn54/i5Bp27rErPUz8eQ2dQiO7iy9U92jfqZ1CrVfz32h5MjgrgwXHS41mI1sxgVFi06gilGj39Qzz5zzXNt3AE0M7ZnvHdq9+rzt8DNLePdiRTWqWnW4D7RVtuXImNWsWtQ0P5dN5AVCpYeTCdDXENe/8WQojmIj0nkZ6TopqiKGh0xtrKkQ1x57h7+aHa7wd4OLIgOrzBFwcanQFHu5bTw1GrNzLn470cTC2kd1A7vp8/VKZIN1Cl1sDU93eSmFPGiAgfPr51QIv6HWhNzhVrmPzuDvLLtXwwpx+Toy69sCBanoRzJdjbqOn0typ2gOJKHbOW7SHhXCnX9g7k3Zv61vl+hVbPuDe2kVfTR3FY2KX7WJpTUm4ZE97ajsGo8P38oXUS66b09E/H+Hpv9fZEZ3sbfN0cSM2vINjLmZ/vuwpPF/srHuODLYm8tv4k/YLb8cO9w67YnqKsSs9Xe1L4eHsyhRU6HGzV/PnwKIK9LdNfUwjRcny5u7rfsKuDLRseHklgu+bfdXK+F7FaBZsfGU2oT/MN2ssrq2LE4i1U6gx8fOuAOpO5G+Pl30+wbHsy3i72rH94JD6uMiRHCGEe0nNSiCZYdSCdLs+s45mfj9c+NjLSl/bujszo14Fv7hrMzn+PaVBiUqs3snhdAlPf20ml1np63yXnlvHmhpPoDcaLft/eVs07N/XFw8mO2PQi2TrXCP/3+wkSc8rwc3Pgndl9JTFpQX5uDkzr04Gojh6MiLRM8kmYT9f27nT2db1okszDyY7XZ/ZGparuq7gvOb/O90s1ejr7uuLn5kDfIMu1AAjzdWVGzfTVH5rQz/FijMa/FqAn9wpkTFc/Ovu6UKE1kJpfgauDLZ/eNqBeiUmA6/t1xN5WzeG0IjaeyLnocwxGhfVx53hgRQyDXtrIq+tOUliho7OPC0tv6W/yxOTao5l1evsKIVq+zKJKXl2XAMDjE7tYJDEJ0D3QneguvhgVWLa9easnl2xNolJnoHdHD8Z1a9wQsgstmhBJF383jIpSO81btB0anYGXfz/BxvjsKz9ZCAtpeMMfIVq49IIKnv7pOAtGhzG4szcACgpavZH0gr+29zna2bDz39GN7vVWodWz5vBZskuqeOWPE/x3Wk+TxN9Ub/55irVHs9DojZfcItOhnROLr+/F/K8Ps3RbEt0D3RnXzQ9ne3nLuJJNJ7JZvrd6oNAbN/bGq543/cI81GoVz0zpVqcq+vyOAWscShSTVsiawxnMGRxMtwCp5G+qnh08mD0wmBX703julzjWPjC89j3d392R5XcOIre06rK9FpvD2G5+rD50ltj0IpMd84MtiZzIKuGtWX2ws1EzNMyboWHeKIrCwdRCNsSdY2LPACIa0IKkvYcjdw7vxJKtSbzyxwmiu/j+4zNSBTz/SxxZNVvpO/u68MCYcKZGBZq8d+orfySwdFsS1/XtwJs39rbKf9NCiIZ7b/NpyrUG+gW3Y+7ghm9nNqUF0eFsOZnLD4cyeGhcJP7ujmY5z/GMYuZ8vJdIfzeGhnnXXks+MqGLSd7bHGxt+HBuP9wcbPEz088grNfH25NZtj2Zz3adYdU9Q+nbgvuyi9ZLMg2iTTmeUcysZXso1xrIKa3i94XDUalUTOoVwLAwH9p71P2wbsqNVDtne169oTe3fbafL/ekMjzCt8lbMpoqJa+c349lAXBdTaXOpUzsGcDNg4P5Zl8aC1fEoFJBsJczId4udGjnxB1XhTboprYtOFes4fHvjwJw5/BOjIjwtXBEAqqTkBcmn97eeJrMokpenN7T6qpal+9NZc3hDDQ6A6/N7G3pcFqFx67uwu/Hskg4V8p7mxOJ8HfFyc6Gsd38UalUVnGT1juoHQAns0up1BqanCxNy6/g7Y2n0BkUpkQFMLHnX+0MVCoVA0O9GNjI7eP3jg5j5YF0knLL+e5AOnP/tqNArVZxy9AQCsq0TOkdSO+OHmZLGg4P9+HTncl0aS+fRUK0Js9M6Y6rgy0zBwShVlt20WFgqBcDQjw5mFrIygPpLBxrun63iqLUvj8GeTlTotFzMLWQgzWDFAeGejIiwnS7PsJ8XS95ftF65ZRqWFIzkE5nULjvm8OsXThCCiiE1ZFt3aLNKK/Ss3BFDOVaA32D2/HBnL61H8jujnYEeTmbvLfiqEhf7hzeCYBHV8eSUVRp0uM31LLtSRgVGNPVr15VWc9M6c7M/h3xdXNAUSA1v4Ltp3JZsT+NkkZMa23NdAYj9397mPxyLd0C3Gun8Arrkl5QwQdbEll96CwzPtxNar51bW2a2T+Iyb0CmDM42NKhtBpeLvY8MiESgHc2neb+b2MsNtzgUgI8nPB3d8BgVDhugqmwwd7OfHTLAB6dEFknMWkK7o52PDwugtkDg2oX3IoqtCTlltU+Z8HocJ6e0p0+Qe3MeuM7PMKHrY9FM39UmNxgC9GKONvb8tTk7lYzXPLmIdWfySsPpGMwmmZeQ3U/y50cz6h+z/dwsmPtA8N5eUYvJvVsT/cAd56b2sNs723r484xc+keKrR6sxxfWI+3/jxNhdZArw4edPJxIbNYw4PfxZjsd1kIU5GBOMhAnLbi8e9jWXXwLO3dHfnjwRH17rHVVFq9kRuW7ubo2WL6h3jy3d1DLDJgJrtEw4jFW9AajI0aupBXVsWpc6WkF1aQUaRh3rDQ2hW3Q6kF9AnyxMbCq9uW9OLaeD7deQY3B1t+fWB4szZNFw2zKzGPhStiyC/X4ulsx7qHRpptm5awDnqDkX99dZCjZ4sJ8XamT5Anz07tbumw6rj7q4PEni3ixWk9mdDIadaWMvLVLRSUa/n89oGNrsg0BY3OgFFRpAWJEC3U6exSwnxdLV4t+XcanYHB/7eJ4kodX9w+kNFdmt4D8pZP97HjdB6TerZnydz+Joiy/iq1Bka/voXskioWjA7j8Yldm/X8ovmcyi5l4tvbMSqwev5Q3B3tmP7BLip1Bp6e3I27RnS2dIiiFZKBOKJFyi2t4sOtiWxJuHhjfVP5NTaTVQfPolLB27P7NFtiEqoHzLx/U3WPl0Ophby90TIDZj7ZkYzWYGRgqGejpsH6uDowLNyHWQODWTQ+sjYxeSq7lDkf7+OznWdMHXKL8dvRLD6t+flfv7G3JCat3FXhPqxdOJyu7d0orNDx9E/HkYW61s3WRs3ntw/i0DPjWbPgKqtLTAK8e1Nf9v1nXJMSk4qiUFzR/FXtzvY22KhV5JRUNfu5z0vMKWX6B7t47uc4i8UghGi8nFIN0z/YxYwlu8kvs9x7ycU42tkwo191O6QV+9NMcsx3Z/flpkFB/G968/ekd7K34a1ZfVg0PtKk29SF9XnljwSMCkzs0Z6BoV50ae9Wu5tky0nz3n8L0VCSnBQW9dDKGF5dd5IYEw4B+Lus4kr+8+MxAO6PDmdIzRCc5hTs7cziG6IAWLYtmcSc0mY9f2G5lm/3VV9MLRgdbtJjH04tpEpvZF3cuTaZ4EnNL+ffP1T3mbxnVGeubmEVT21VgIcTb83qg61axZ/x2fx+7JxF44lJK+R/a+PrbI0VbYsp+p/GpBcx8P828p8fjzXr+/FvC0dw5NnxTI4y7Rbyhsgr03Iyu5TVh85yqKZfmxCi5YjLqC6uUQBPZ+vrhXfToOqt3ZtO5JBTomny8Txd7Hl5RhTerg5NPlZjDAvzYeHYCKvrvS1M67GruxDdxZcnJv1VHds/pHoYzqlsueYU1kWSk8Ki/nttTzr5uDD0goThnqR83t982iQ3Voqi8J81xyjV6Okd1I4HLbg6eE2vAMZ180dvVPjvr/HNeuP48h8nKNca6B7gzuguph3SMnNAEO/M7sPKu4e0yZ5fP8ZkUFalZ2CoJ49NkD6TLUm3AHcWRFcn65/75TiF5VqLxfLVnlQ+2XmGZdusqxeiaH6KomBsZB+or/ekotUbqdIZm/X92Eatsvj7/5DO3lzTqzo5uk2qQYRocaK7+rHlsdG8MTPK6rZ1A0T6u9E/xBO9UWH1obONOkaV3sDG+GyrW8w3GBUScyRR1Rp1C3Dn89sH1dnVFeHvhlpVveuhUmuwYHRC1CXJSdHsLmy+G+7nyuZHRjE0rDo5mV2i4c4vD/D6hlO88kdCkz+81xzOYMvJXOxt1Lx+Q1STpm+bwjNTumFvo2bH6Tw2nmiem6fdiXmsOlh9EfXCNNM31rZRq5jWp4PF/24t5cGxEbx6QxTvzO7bZv8OWrL7osOI8HMlr0zLs7/EWeSGoahCy2/HsgCYMzjkCs8Wrdnzv8Qx8KWNbG5Eq5OCci1rj1b/Ht0ytG3+Ho0Ir55quysp38KRCCEaw8/NkXA/6xiCczGzBwYB1YUUjbF0azJ3fXWQJ344ZsqwmiSjqJIp7+1k1rI9FFfKsMu2wNXBlvgXJrLtsWic7KVyVlgPuZMWzSqvrIrxb23j5yMZtUmAC5Nl/u6OPFpTfbZse3KjVyYBcko0/PfX6t5TD46LIMIKJv6FeLtw14jq6d0vro1HozPvapVGZ+DJmi3tc4cEN6rXZENU6Q1sbWMVKyqVihsHBBHYzsnSoYhGcLC14dUbolCrqnvTvrXxdLPH8MPhDLR6I90C3Ond0aPZzy+sR1mVnrwyLUca0epk1cF0tAYjvTp4tNnfo6tqkpOx6UWUVckEWiFagqXbkth2KtfSYdTLlKhAvr1rMF/dMajBr03KLeODLYkADI/wMXVojebr6oBWbyC/XMu7m5r/GkiYx8u/n+CZn46TXlBx0e/Ldn5hjVpNclKlUi1QqVRnVCqVRqVSHVKpVCMsHZOoS1EUnvrxGMm55Xy4JQmd4eIVSncM78SjNY16/7c2vlF9XY6eLWL44i2UaPT06uDBPSOtZxLZfdHh+Ls7kFZQwWe7zDtEZtayPaTmV9De3dHsk/jKqvSMe3Mbt39xgJ2n88x6LktLL6hg4YoYiwyeEKbXN9iTF2sa0r+76XRtf9bmoCgK3+5LBWDO4GCLb40VlnXXiE6sWTCM+8c0vDfwr7GZQHVftLb6exTk5UyQlxN6o8KBMwWWDkcIcQUHUwpYvC6B2z7bz4mseg90tRgnexuGhfvUbjuv726L8io9j66ORWswMirSlykW7M/7d/a2ap6d2gOAr/akXDKZJVqO4godX+5JYfneVPn/KVqUVpGcVKlUs4C3gZeAvsAO4A+VShVs0cBEHT8czmB9XDZ2NirenNUbe9tL//rNHxVGrw4elGj09Zqkm1taVWeLRWGFDq3BiKOdmletYDv3hVwcbHliUlcGd/JicCfTVTJuTsjmmQv+rhRFITm3HIAXp/fE3dHOZOe6GFcHW4aH+6AocP+Kw6Tlt84PQ0VRePC7GH6JzeSpn6xnW45ompsHh7CwJiH09E/H2J3YPAn2w2mFJOWW42Rnw/Q+gc1yTmG9urZ3p1+wZ4MrGtILKojLLEGtgqt7+JspupbhqrCard3N9G9YCNE4RRVaHlkdi6LADf070i3A3dIhNUipRsfdyw+x5QptOEo0Om75dB8xaUW4Odjyv+k9rW4BaVSkL8PDfdAZFN7885SlwxFN5O5ky2fzBjJvWGht67S/O5RayMylu7ln+cFmjk6IS7OejE3TLAI+VRTlE0VRTiiK8hCQDtxr4bjavAMpBfz7+6NEv76VR1fHAvDQuEh6BF5+y5mtTU1SUa1iQ3x2bT+2iyko1zJjyS7u+OIAR89Wb4Ub3MmLLY+OZsfjY6zyYmd6nw58d/cQ+oeYJjl5MKWAO744yPK9qWyIzwagSm8kuqsf/57YlfHdm+dm9bmpPegd1I6iCh13Lz9IeSvcVqdSqXhhWk/6BLXjqcndLB2OMKGHx0cya0AQRqW6Aqs5rDmcAcCknu1xM/MCgmi91sdVT5sf1MnLYpNfrcX5G7Hd0ndSCKul0Rm468uDpOZXEOjhyDNTuls6pAb7bGcKf8Zn8/gPRy/ZpimvrIq5n+zjcFoR7o62fH3X4Ga7vmiof9fssPrpSAZxmcUWjkY0hUqlYliYD89fe+lZA7ZqFQdSCjmUWtjM0QlxabaWDqCpVCqVPdAfeOVv39oADGv+iMR5X+5O4b+/xnF+/o1KVT2xur5brM9P0n1302kWrYzlqz2pDOnkxaBO3vQLaYezffWvr6ezHaHeLqhVqtqbe0c7GzpdMJXM2ph6xXRAqBfvzO7D8YxiRkVWT+N2tLPh3Zv6mvQ8V+JoZ8Oyuf2Z+v5OEs6V8tj3sXwwp5/VrRA3ht5grK3A7dnBgx8XDGsVP5f4i0ql4qXrehLd1bfOzcPzv8Th5+7APSPDsDHhBNEqvaF2gMl1/TqY7LiiZdt6MoeNJ7IZHu7DxJ712/q37nh1cnJij/bmDK1FGFZTORmfVUJBuRYvF3sLRySEuJDBWL0D5WBqIW6Otnx++yA8nFre4ty9o8M4nVPKncM71al2L67QsflkNr/GZrHjdC46g4Knsx1f3zX4isUZltSrowfX9g7kl9hMFq872ai+msLyCsu1eNbjcy/S3413Zvchws8NRVHknkZYhRafnAR8ABsg+2+PZwMXvUpXqVQOwIWlBZaflNKK6A1G/vfbCb7YnQLA5KgAru/Xgf4hXg2++Lg/Opz9Z/LZm1zA/jPVfyARW7WKuUNCaleEXp/ZG1u1qsVVjBSWa1m2PRkHWzUPj49s8Osv/DCZ1qcD0/pYPsHR3sORpXP7Mfujvfx+7Bxf70vjliEte3JswrkSHvg2hicmdWVst+oqVPkQb51sbdR1EkJH0otq38uGdvamb7Cnyc619WQuxZU6/N0dahMqQsRnlfD13jQSc8rqlZzMKdVwKK268mGCJCfxdXOgi78bJ7NL2ZOUz2Qr6u0mRFtXodXzzE9xrI/Lxt5Gzce3DqBL+5Z5G2Zvq+b9Of1qv96TlM+bf57kUGphbWEGQI9Ad968sU+L+DkfndCFP45nsf1ULrsT8xgWLtcmLcmR9CJmf7SHe0aG8dC4iMveqzjZ21jFfaMQF2ot27oB/t6UUHWRx857Eii+4E/jR0KLf7gwMfnviV15/6a+jOnq36hVUXtbNSv+NYRNj4zi5Rm9mN4nkAAPR/RGpU41hL+7Y4tLTALEpBeydFsSy7YnkVdW1aDXbknI4boPd5PdiIFB5tY/xKt2e8iLa+NJOGf9Tc4v5VBqAdPe38XpnDJeW38So7F+zc9F69C7owev3RDFwjHhdRKTpmgw/mPNlu5pfTqYtCJTtGzX9q7uPbrvTAFZxZVXfP6f8dkoCvQOakdgOydzh9ciDAs/v7Vb+k4KYS12J+Zx9dvb+eHwWVQqeHNWb4Z0vng/vJZIpYIDKdWJyUh/Vx4cG8HGRSP5beGIFpGYBAj2dubmwdUFBW/L5O4WxWBUeOan42h0RtILK6SIQrRIraFyMg8w8M8qST/+WU153svAmxd87YYkKE0ir6yqdtLtO7P7mGRFRqVSEebrSpivKzcNCkZRFM4WVuJg1/Jz69Fd/LhxQEcmdG+Pd02y9Uql9YqisHRbMq+uT0BR4P3NibWThq3JncM7sSsxjy0nc7n/2xh+vX84TvYNG/JgaRqdgcdWH6VKb2R4uA/vzO5TO6FRtA0qlYqZA4LqPJaYU8aEt7bRN9iTW4eGMKlnwGUHfF1McYWOzTVN9KfLyrW4QEdPZwaFerE/pYBfYzO5e2TYZZ8vW7r/6aowHz7flSJ9J4WwEku2JrF4XQIAgR6OvHJ9FCNr2hC1Fv1DPHlxek+iu/jS0dM6+0rWx/xRYXy7L439ZwrYl5zP4FaUQG7NVuxP41hGMW6Otjw5qX498ZNzy9hyMhdvF3um95VrUWF5LT67oyiKFjgEjP/bt8YDuy/xmipFUUrO/wFKzRxmm7FiXxpag5HeNX1LzEGlUhHk5Yyfm6NZjt+cVCoVr97Qm3Hd/WsTkq9vOMmEt7bx8MojfLMvlcJybe3zk3PLuH9FDIvXVScmZw8M4ukp1jmURaVS8drM3vi6OZCYU8b1S3bz+vqTbD+Vi1ZvtHR49fL2xtMk55Xj5+bABzf3a5HVucL09ibno1apOJRayIPfHWHCW9s4kdWw6uC1xzLRGox0be9G90DrG9olLGta3+rPz59iMi/7vOIKHXtqEnBtfUr3hQZ19kKtguJKHcUVOkuHI0SbdjClgNfWVycmbx4czPqHR7a6xCSAnY2aW4aEtOjEJFS3Z5o5oCMA729JtHA0oj50BiMf1Py/WjQ+El+3+t2vHEkv4sW18Xy7P82c4QlRb62hchKqqyCXq1Sqg8Ae4G4gGFhq0ajaoJuHhKBWq4j0d5Ny8kYoKNfy2c4UKnUGTmWX8WNMBs//EseoSD9yy6qITa+eRm6rVvHctT2YOzjYqv+efVwdeHtWH27//ADxWSXEZ5XgvMuGvf8Z2+BKs+Z27GwxH+9IBuB/03u2yGbtwjzmDglhQnd/VuxPZ/neVFLyK7juw128MiOq3ivPh1KqewReJyvV4iKu6RnAcz/HEZ9VwunsUiL8L74lsKBCy7BwHwrKq+js69rMUVovd0c79j45Fl83B6v+jBSitavUGnh41RGMClzfryMvXdfL0iGJepg/KoyVB9LZcTqPmLRCk/TbrtQaeOWPExzNKMZgVNAbFFwdbPF2tcff3ZF5w0IJteJhptZsfdw5soo1eLvYc9Og4Hq/LrLm2uJ0dqkMxRFWQaUoraN/mkqlWgA8DgQAx4GHFUXZXs/XugPFxcXFuLtLBYuwHEVRyC6pIi6zmGMZxfwZn01c5l8VWTZqFSMifHhgTDj9Q7wsGGnDZBRVsvN0LvvOFNChnROPTOgCVP+8sz/aS4S/K49O6EI758ZPVU3LryCnVMOA0Kb/vegMRqa+Vz1xfEpUQJ2G50JcqLBcy4Mrj7D9VC4AtwwJ4anJ3epM7rwYRVGIyywhwKNl9ssV5nfXlwfYeCKH+6LDeOzqrpd9rsGoSN9SIYTVURSF1YfO8tnOM6yePxQ3R1nobSkeWx3Lz7GZPD+1B3MG1z/hdTGKonDrZ/vZcfrSfYD93Bz44d5hBHm17MpTS7h+yW4OpRaycGwEixowYLVSa6D7c+tQFDjw1Lh6V1wKcSUlJSV4eHgAeNTsVK6XVpOcbApJTgprdiq7lHXHz+HuaMvkqMBW9cGRlFvG2De24WCr5ujzE3CwrU7oVGoNONqp672CV6U3EP3aVjKLNSyd25+JPZvWe235nhSe+TkOT2c7/lw0Ch9JHonLMBgV3tl4inc3V2+p6drejffn9CPc75+VbLIyLerr19hMHlgRQ0dPJ3Y8Hi2/N0KIFksWUFqe8wM3/d1N00Zry8kcHl0Vy5PXdMPbxR61WkWZRk9+eRVf703lVHYZnX1d+H7+sDpDT8XlxaYXMe2DXdjZqNj1xJgGtz0b9doWUvMr+PauwTKdXZhMY5OT1r2vUrQYh9MKuWHJbtbHnbN0KK1OpL8bC8dGMO+qTq0qMQnVFzxL5/bjyUldaxOTVXoDt322nyd+OFbv3pQOtjbc0L+6P46LQ9OG7pRqdLy9sXpC4aLxkZKYFFdko1axaEIXvrh9IN4u9iScK2XqeztZc7junLVtp3KZsWQ3OTUX/EJczrhu/rg62HK2sJJX/kjgwsVko1HhvU2nySi68jTvtiqzqJK7vzrIDUsu2n5cCGFGGp2BSq2h9mtJTLY8/u6OJktMQvUQ0O2PR3ND/45Ed/VjVKQvk6MCuHVoKF/dMZhAD0eSc8u544sDVGj1Jjtva/f5rjMATI0KbNQ8hgi/6q3dJ7NlBIewPElOCpP4bOcZDqYWsiHuUgPShfgnVwdbJvYMYN5VnWof23+mgIOpBaw8mM6tn+2jqEJ7yddfeLN+35hwYp+dwIiIpjVZX7YtmfxyLZ19XJjdgL4tQozu4scfD47gqnBvKnUGFq2K5bmfj6MzGDEaFRb/kUBMWhFLtiVZOlTRAjjZ2/Ds1O4ALNuezDubTtd+b1NCDm/8eYop7+5AozNc6hBtmqujLRviszmYWlhnsJwQwvyWbkti7Btb2XRC7gtagwMpBXy1J6VRr7twEc3F4eLjLtp7OPLVnYPwcLLjSHoRD313BKNRdndeSU6Jht+OZQFw+wX3Ug3Ro2Yo4/GMhg12FMIcJDkpTOL5a3swf1QY80d1tnQoooUbEeHLp7cNxNXBlr3JBUx9fydHagYBnacoCj/FZDD+re3sTqzuX+Nga4OH81+9jPSGhk8EP1es4ZOd1UNwHp/YFTsbeYsUDePn7shXdwxm4dgIAL7dn8bJc6Wo1Spen9mbGwd05N8TL98/UIjzbhwQxHM1Ccq3N57m9fUnAfB1c2BoZ29mDwq+Yn/Ttsrd0Y5XZvTiu7uH4NzEinohRP3pDUbWHs0is1hDpSyetHjxmSXcuGwPL/waT1xmcb1fV6U38NB3R4h+bSu7ky7da/K8cD83PrltAPY2auIyS8gulV0mV/LdgXR0BoWBoZ706ujRqGNE1bzuWEbRFZ4phPlJz0mk56QQ1ujkuVLu+uoA6QWV2KpVPHZ1F3oHtSMus4TfjmZyOK36QzTCz5VfHxhee4OuKArfHzrLGxtO8cK0HkzoUf/+k49/H8uqg2cZEOLJ6vlDpcebaJKN8dnkl1cxa6BU4Iqm+WBLIq+tP8m0PoG8M7tv7eN6gxFbWUQRQlgZjc7AL0cymTmgo1xLtQILvjmEm4Md/5ncDQ+n+g01Oles4aGVMaTkVbD1sdH1XkjbkpBDjw7ujdqi3NZMfHs7CedKeX1m79r2Vg2VU6Jh0P9tQq2CY89ffcnqViEaQgbiNIEkJxuvSm+o7RUohKkVV+r4z5pjtVsWLuRsb8N90eHcObxTnQsencHI7Z8fYGdNReXD4yJ5YEw46nr0O/rtaFMiyqQAACAASURBVBbvbjrN/83oRf8QT9P9IEII0URrj2aiVqm4pleApUMRZnS5oVkVWj25pVV09HSWHn5CiGbT2IUwRVHILauSRKMZpOSVM/r1rdioVRx6ehztnBs/RGjI/23iXImG1fOHMjDUy4RRirZKkpNNIMnJxnt45RGySzQ8NbkbPQIbV04uxOUoisKK/em8+edJHGxt6B7oTq8OHtw4IIj2Hhe/2NEZjLz02wm+2J0CwOyBQbxyfVS9zyer/EII0fKVVen5M/4cGYWV3D8mwtLhXJbeYOSurw6yKzGPjp7OBHs54+Jgw6MTutDZ1xWA19ef5P0tiQzt7M2Ku4dYOGIh/ikmrZCoju0ked6K6Q1Gnvk5jtuvCiXS381s59EZjKw6mM7R9GIW31C/a/i2pEKrZ+OJHFLzynlgbNM+3/711UH+jM/mmSnduXN443pXCnGhxiYnpW5XNFpybhk/H8nAqICx4e39hKgXlUrFnMHBzBlc/62xdjZqnr+2B90D3Hnyx2N8dyCdkZG+9ao4ksSkEEK0DhqdgYdXxqJSwbyrOuFqxdvV3tl0mq0ncwE4k1fOmbxyAOYOCalNToZ4OwMwvru/ZYIU4jJS88uZsWQ3od4urH1guGwPbaXe25zIiv1prDuexcp7hv4jQZlVXMlH25N5aGxknV7wDZVZVMmzP8dhMCrMHNCRAVLRV4ezvS3X9g40ybGiOnjwZ3w2x85K30lhWfKpIRrtf7+dwKjAmK5+jW7CK4Q53TgwiLSCCt7fkshTPx5jQKjnRbeWvLY+AU9ne24eHIKTvbQpEEKI1sDH1YEAD0eyijWcyCqx2u1quxPzeH9LIgD/d10vQn2cSc2vQKs3EurtUvu86/t1ZFKvAGwvqErT6AwyFElYhS92p6Ao1Ul0SUy2XvOGhbL1ZA6xZ4uZ8/E+Vt0zpHYBBeCl306w9mgW6QUVfHLbwEafJ8TbhfmjOuPr6iD3mWZ2/u/3aEb9Bx4JYQ7SRV00ysb4bDYn5GBno+Kpyd0sHY4Ql7RwbATdA9wprKjuX/n3VhZnCytYti2Z//12gvgs+VAWQojWpEdgdbueOCu96corq+LBlUdQlOoWJHMGBzMszIebBgVz27BQAts51T5XrVbh6mBbm4xcsT+N4Yu3sCvxypNwhTCnEo2OVQfSAbjjKtkW2pp5utjz5R2D6BbgTl5ZFTd/so+k3DIAdiflsfZoFmoVPDw+ssnneuzqrsy7qpPMN/ibdcezeH/zaVJqKuybqlcHDwaFejGmi98/7pOEaE6SnBQNptEZ+O/aOADuHN6ZsAtWy4SwNva2at6c1Rt7GzUbT+Tw7qZEDMa/Pnj93Bz53/SezOzfkf4h1llVI4QQonHO98M+nlnvlkfNprBcy11fHiS3tIpIf1eem9qjQa8/ea6UvLIqXl2XIDeUwqJWHUinXGsg3M+VERE+lg5HmFk7Z3uW3zmIcD9Xsoo1THhrOw+siOHZn6vvD+cOCTH5LIJzxRp0BukjBvD13jRe33CK9XHnTHI8b1cHVs0fytNTukt7K2FRkpwUDbZ0WxLpBZW0d3fkgTHhlg5HiCvq2t6dRyZUr+C+tfEUsz/aQ0xaIVCdvJw9KJjXZva2ZIhCCCHMoGeH6hvkQ6mFaPXWc2ObWVTJzGV7OJJehIeTHe/P6dfgtiL3RYfjbG9D7Nlik92kCtFQVXpD7QDCO67qJMmNNsLH1YFv7hrMyEhfDEaFX2MzScwpw8vFnkfGdzHpuRLOlTDtg538+/ujGI2yEDOtTyDRXXy5ukd7S4cihElJclI0yLliDUu2JgHw1ORu0lNGtBh3j+zMyzN64WJvw4GUQq77cDfJNdtQhBBCtE4DQjxxsbfhTF45T6w5ahUVhmn5FdywZDeJOWW0d3dk9fx/DpWoD183h9rJqq+tP4leqopEMyuv0nPnFwc5W1iJp7Md1/XtYOmQRDPyd3fkqzsG8dvC4UzvE4i3iz3/m96zSYNwLiarWENemZY1MRksXpdg0mO3RDMHBPH57YMI9XG58pMboKxKT2JOqUmPKURDSHJSNMiXe1Ko0hsZEOLJlKgrTz4WwlqoVCpuGhTMuodGMqRz9fbtdVJpIoQQrZqniz3v39wPG7WKNYczePPPU5YOiXVxWZwr0dDZx4UfFgxrVGLyvH+N7Ew7ZzuScsv5Mz7bhFEKcXlFFVpu/mQfOxPzcLa34YNGVP+K1qFHoAdvz+7LoWfGc00v098fRnfx49XrowBYtj2Ztzeeki3eJnYwpYBez6/nzi8PWjoU0YaprGEF2dJUKpU7UFxcXIy7u7ulw7Fa5VV6hr68iRKNno9u6c8EKSUXLZSiKBRV6PB0sbd0KEIIIZrBygNp/PuHYwC8MbM31/fvaNF4MosqMSoKHT2dm3ysxesSWLI1ieguvnx++yATRCfMSW8wsu9MAWfyynF3smNyrwBs1C1rK7RGZ+D6JbuJyyzBw8mOL24fSN9gT0uHJVq5pduSeOWP6srJMF8XnprcjTBfV9ILKjlbWEF6YQVnCytxd7Tjxek9LRyt6VVo9Xy3P52re7anwwXD0kyhuEJH7xc20KGdE5seGVU7eE2IxigpKcHDwwPAQ1GUejf9lj25ot5+OHyWEo2eUG9nxnbzt3Q4QjSaSqWSxKQQQrQhswYGk15QyftbEnlv82lm9Otg0d54gSa8sZzZvyNLtiax7VQu2SUa/N0dTXZs0XQ6g5GUvHIiLqiQve2z/ehreud9vTeVN2/sbZJEdXP5v99PEJdZgreLPd/+awhd2je++leI+rpnZGc8ne1YvO4kSbnl3PHFxav87hr+18T4Kr2BHw9nEN3Vr8W/N247mcsLa+P5ck8KWx8dbdLPMA9nOw4/Mx4vuT8SFiTbukW9GIwKn+08A8Adwzu1uBVeIYQQQrRt944Ow8FWTUp+BSeymr+vlsGokF5QYfLjdvZ1pX+IJ0YF1hzOMPnxReOVaHTc9tl+Hl0dW/uYrY2a4RE+RHfxxcXehv1nCpj09g5+OHTWKnqiXsmf8dl8tScVgDdn9ZHEpGg2KpWKWQOD2frYaO4Z2RkHWzWOdmrC/VwZ3cWXW4aE8OSkrtw6NLT2NfvPFPDEmmNMfW9ns/37KtXoOJ5RTGp+uUmPe74d1YTu/mZZXJPEpLA0qZxsI6r0BhxsG1+efSS9iJT8Cjyc7LjBwluhhBBCCCEaysXBltFdfFkfl82641l0D2zeVj7bTuVw55cHmdY7kLdn9zXpsW8c0JFDqYWsPpTO/FGdZWKylUjLryAmrQgbtQqNzlC7VfKLmu33afkVPLQyhsNpRTyyOpYfDp/lxek9CfN1tWTYl7UnKR+ork4bFelr4WhEW+TuaMeT13Tjsau7YKNWXfb9TlGgT1A7ugW4mf198bv9aby+4RR5ZVW1j70zuw/T+jR9UJRWb2TziRwAJvY0b2s1RVHkM0RYhCQn2wBFUfjXV4fwcrbjuak9GrWdtX+IJxsXjSIptwxne/m1EUIIIUTLM71PB2zVavqHejX7uY+kF6Mo4OPqYPJjT44K5Plf4knOLedwWiH9Q5r/5xP/1LODB0tv6Y+3i/1Fe7gFezuz6p6hLNuezLubTrM7KZ9Jb+9g/qjOLIgOt8q+b89O7c7QMG9GRvpYOhTRxtnaXHkT6MhIX0ZG+mI0/lU1GZ9ZQqVOb9L3yRKNjpd+P0GpRg+Ak50N7k62RHf1M8nxdyflUVqlx9fNgb5B5unvmltaxaOrY0nJLzf5tnEh6kOyTG3AsYxidp7OxajAzsR8Xp7Ri/HdG94zMtzPlXA/613JFUIIIYS4nEm9Aphkhmmy9bFofCQz+nYwy0RjVwdbJvVqz5rDGaw+eFaSkxZ0rlhDWZW+9pr5StWFtjZq7osOZ2pUIM/+cpytJ3N5d3MiP8dm8sK0nlZRnajVGzEqSm2ytDH3EUJYkrqmJVl2iYY7vjiAg53apAm45XtSKdXoifBzZc2CYbg52lFcqcPd0c4kx18flw1Ub+lWm6m9WjtnO/Ym51OlN5KcV27VFdyidZKek21AVMd2/HDvMCL8XMkrq+JfXx2s7R95JcczitmbnG/mCIUQQgghWp/dSXks2ZpU+3Woj4vZhjLcOCAIgLVHs6jUGsxyDnF5heVabvl0Hzcu28PxjOIGvTbY25nP5w1kyc398Hd3IDW/gts+28+D38Wg1RvNFPGV6Q1GHloZw62f7adUo7NYHEKYgqOtDUWVWlLzKzh6tmH/Ri+lQqvnkx3JANw/Jhy3moSkh1P1fzOKKvl81xkOpRY26vgGo8Kf8dX9Js25pdvORk1URw8AYtKKzHYeIS5FkpNtRN9gT9YuHM6tQ0MAeGFtPK+tT7hsY+BSjY77vz3MnI/38tvRrOYKVQghhBDCrBJzyvhqT4pZzxGTVsjcT/bx+oaTpOWbfhDO3w3u5MXYrn7cFx2O3mi5ZFZDbTqRzZNrjjHv8/1MfHs78z7fz/q4c+gN//wZckuryCyqrPPY4nUJ/BSTwbliTXOFfFG5pVXM+3w/p3PKsLdR1yYmGkKlUjGpVwCbHhnNncM7oVZVT/u2t7XcLduBlEI2ncjhSFqRRQZJCWFKHs52jO1WXfm79mimSY757b40Cit0hHg7M/kilfkfbUviv7/Gs/pgeqOOfzitkLwyLe6Otgzp7N3UcC+rX7Bn7TmFaG6yrbsNcbC14b/X9sDf3ZHX1p/kgy1JHMsoYd6wEEZF+v1jAreNWsWAUC90BoWrws37RiiEEEII0RxKNDomvbO95vrGxyxb1xRF4eU/EjAqMKaLLy4O5u8dqFKp+HTeQLOfx1RKNDqe/zmONTF1J4wnnCtl68lcAjwc+eDmfrU3yx9sSeS19SeZPTCIV66PAiCzqLK2MlWlguHhPtw4IIgJPfybNAiyoQ6lFrDgm8Nkl1TRztmO5XcOIsjLudHHc3Ww5Zkp3ZnaO5COnk4mjLThhoZ5s3HRKFLzKxjUSdoFiJZvalQgvx3N4rejWTw5qVuTtklrdAY+2l5dNblgdNhF+2Be3bM9J86V0je4XaPOse54ddXkuG7+2NWjz2ZT9D2fnGxklacQTSHJyTZGpVJxX3Q4Xi72PPXjMbafymX7qeoLwBBvZxztbLh1aAhjuvrjbG/L6zN7U1CupZ1zw4foCCGEEEJYG3dHO0Z38UNvMFKlM0+F4daTuew/U4C9rZr/Te+JtxmG4LRkh9MKeeDbGDKKKlGr4ObBIfQIdMff3ZF9ZwpYeSCNrGIN+WXa2teE+bqiUkFRRd2txXdc1YlDqQXEni1mx+k8dpzO46pwb76+c3CzDHT4Zl8qz/0ch96oEO7nytK5/U3Wo71P0F/JjCq9geV7Upk5IKhRVZlNEeTl3KRkqxDWZHQXX1wdbMks1hCT3rQBYisPpJNTWkWghyPX9e140ecMC/NhWFjjBkgpisL6uOrk5IQe5p3SDdCvJoF6KruUsio9rg6SLhLNR37b2qibBgUzqJMXK/al8f3hs2QVa8iq2Q5TUK4luotf7QWdVyOmewshhBBCWKuPbulvtsSVwajwyh8JANw+LJTAds1b+VZWpWdjfDZ+7g6NviE2p12Jedz15UEqdQaCvZx5a1bvOsmB6K5+PDQugi0JOXQLcKt9fHQXX44+N6G2nxtAYDsnnp3aHYC0/Aq+P5TO0u3J7ErMJya9qLbq0ly2n8rlqR+PAzA5KoBXr4/CxUw38/d9c5iNJ3LIKtbwzJTuZjnHhbYk5ODqaMtAC0y2F8KcHO1sGN/dnx9jMvg1NqvRycniCh1vbzwFwL2jw8zSfiEus4SzhZU42qmbZTiWn7sjHdo5kVFUydH0IoaFW99niGi9pOdkGxbm68rTU7qz98mxfHnHIN67qS+v3RDFvGGh6I2X7kUphBBCCNGSmbOi7seYDE5ml+LuaMu9o8PMdp5L+Xh7Mg+tPMInO+o3/LA5bUnI4fYvDlCpMzAy0pffHxxx0cSAo50Nk3oF0NHTuc5jbpeZfBvs7cyiCV2YUtPz7bv9aab/AS6QU6ph0aojQPWi//s39TVbYhLglqGh+Lo50LODu9nOcV6JRsfjPxxl5tI9/BmfbfbzCdHcpkRVv0/8diwLQyPve9/ZdJrCCh0Rfq7cNCj4is/PL6vi5yMZl5358HfnqyZHRfriZN88rSrObz+PSZehOKJ5SXJS4Ghnw6hIX6b2DmTmgCBm9Oto9n4WQgghhBCWlldWxSOrYmt7ejVEcaWOn49kkJpfDlQPLlm+J4UX18YDsCA63CJtcaZEBdDJx4W+QY3rb2ZO+84UoNUbGd/dn49v7W+WLYM3Da5OEvwam2W26dJGo8KilbHklWnp2t6N56Z2N/sW8lGRvux4PPqSW0dN6YtdKeSWVtHJx4WRkVI5JVqfERG+uDvakltaxf4zBY06xsSe7ekR6M4zU7pftNfkhTQ6A8MXb+HB745wOqes3uc4n5w055Tuv+snfSeFhci2biGEEEII0SZ9vTeVHw6fZU9SHj0C3RvUV69Uo2PN4Qwe/O4IEX6u6I0KZ/KqE5U9O7gzb1iomaK+vAh/NzY/MqpZ+i021L8ndqGzrwvX9e1gtoXwASGehPu5kphTxi+xmdw8OMTk51i6PYmdiXk42dnw/py+ONo1T0XThecxVz84ncHIN/tSAXhoXESzDhYSornY26qZ2LM9qw6e5ZfYTIaGNXz466BOXvx6//B6DdRxtLNhaJg3mxNy2BB3jkh/tyu+RlEUnrymG+uP/3979x5nVVU3fvyzZpjhPgPIHUQQvAEqKipewktlapaaWvZkj5qaZld9MuvxV1paPpVZlplaPd61TDMf72YF3sALYogggtzv9xlgmPv6/XEOOI4zMGc4M3vmzOf9eu3XcPb+nr3XgS/rbL6svdZKjt9nQMbta66D90gVJ6cv2UiMsU1+lyg3OTxOkiRJHdLFE0cypFdXlpeUM/Hn/+LCu15v8iiaob27ccHRI+jRuRNzV29mwdot7Na9kGtPHcMjlx7VagWrhrTVf0yGEPjs+N1b9AmdEAJnH7o7AH96dUnWzz9t0QZ+8WxqnrkffnoMo/rvvMiQbX+ftYqjf/pPXlvYvBFfO/Ls26tYVVpB3x6dOWnsoKyfX2orTh03BIAnZiynorqmWefIZKXvE0anCox3T1nEvCaMngwhcNw+/fmfMw6guFvrLYI1elARXQryWL+lklkrSlvtupLFSUmSJHVIXQvz+eN54/nIXn2JEZ6bvYrP3T6Fl+etbfQ9decLm7h3P1668nhuOnscPz59LJOuOJYvHjG8TUyPU11Ty4OvLWHG0mTnDauqqeVnT7/D8o1bW+2anzl4KIX5eby1rISZy0qyeu4HX1tCTW3k0wcO5qzxLf+IdUMefXMZG8uquOSeaSxZX5bVc989ZSEAnz9s9xZZ4ENqKybsuRuDirtQWl7NP2evbvL7bpv8Hjc+O2f7lB5NdcqBg9mrfw9Wb6rgc7dNYdbytln4K+z0/uI7T72V+ZQnUnP5jSNJkqQOa9+BRdxzweH847+O4WP7DSBGuPKvM9hSUd1g/LWPz+a7D89gw5ZKAIq7FXDquCF84fA9drhgS2u74dl3+c7DM7j+yXcyWoAh2/76xlJumfQeZ/7u5WYvPJGpPt0L+UR6jrYHX8/u6MnrP7M/Pzp1DD8+fWxiI1R/fuaBjB1SxLotlVx09+uN5mqm3llZyisL1pOfF/iPw3e+wIfUnuXnhe2jJx9+Y1mT3hNj5M6XF/Lrf87jvTVNnzsSoEfnTvz54iO2/909+/YpvNnIojNPz1zJV+6dxupN5RldI1tOTi8s9q85TS/aSrvK4qQkSZI6vJH9evCrs8cxpFdXlqzfys+fmfOhmIrqGp6euYI/vbaEmcuzOyIv286ZMIzC/DymzF/HizsYCdrS9h7Qk8NH9OFLR48gP4NHIHfVeUfuwfg9evO140dl9bx5eYH/PGJ4ooXoroX5/P4/x9OvZ2feWbmJb/35TWqzUPi9Z0pqrskTRg9gUHHXXT6f1NadcfAQjh7Vl08d2LQpDGpqI98+YR8+deBgjhyZ+WJRfboXcv9FEzhkj96Ulldz7v++ypyVmz4QU1VTy48ee5unZq7kvqmLM75GNhy/b39uPecQHrrkyESur44pJPk/qW1FCKEIKCkpKaGoqCjp5kiSJCkhk99dw7n/+yoAD158BIeN6POh4w9PW8qvP39QEs3LyA8fe5s7XlrIAUOLefSrRyU20i/GSG2kVYuTkFpVO5M54RqzbONW7nxpAZd9fG+6Fbad9USnL97A526fSmV1LV89biRXfGLfZp9rxtKNnH37VMoqa7j/osObVXiR1DRbKqo554+vMH3xRvr37MzDXznyAwuyzVpeyi2T5nHDWQcmOn+x1BylpaUUFxcDFMcYmzx/gcVJLE5KkiTpfd956N88+PpSAHp26cQ3P7oXF35kz+3Hq2tq6dQG5pXcmbWbK5j4s39RVlnDLV84ePujeh3R//17OQcOLWaP3bpn/N7z7niVSXPW8KkDB/ObNlaUfmT6Ui77878BOHn/gawoKWfJ+q1M2LMPV3xin+2f95HpS7l10nwOGFrMj0/ff/t8ki/MXcM9Uxbx7KxVAOw3qIgnv3F0m11UScoVG8sq+dxtU5mzahPD+nTjkwcMYuzgYj55QMftp5UbmlucbPt3VZIkSVIruuqTozlgaDEAm8qruem5uSzd8P7CI+2hMAnQt0dnLjx6BADXPj6LjWWVrXbtO19awP889c72uTmTdP8ri/nGA9O56pGZzZp/8/yjRrBn3+5886N7tUDrds3pBw3lK8eOBODJt1YyffFG1m6u4PEZK/jYjZP5d3pOuwl77saCdVuYtmjDBxa6ufzBf/PsrFWEAKcfNIT/PW+8hUl1OKtKy7lt8nssWNv4IjerSsu5/fn3mJ/hXJON6dWtkLsvOIzd+3Rl8foyfjfpPZ6auSIr586Wm/85l4/dOJl5qzftPFjaRW3nuQRJkiSpDSjuWsDfLj2Kkq1VrC+rpGRrFb27FSbdrGa5+JiRPD5jBfPXbuHKh2dw6zmHtHjxaVN5Fb/6x1w2llUxqn8PzjwkmVWttzl6VF+6FeYzqn8PqmoihZ0y+/zH7N2P5y4/JiuPiLeEK07Yhz7dCtlUUc3eA3rQp1shtz0/n9WbKth/SKrIPqi4K7eeczCV1R8szo4ZXESvrgV87fhRjOrfM4nmS4m76pG3eG72auau3swNZx3YYMzfZ63iJ0++w1MzV/LIpUdl5boDirrw0CVH8pfXl7BuSyWjB7WtpzinLdrAvNWbefKtlXzjo/YPalk+1o2PdUuSJCl3zVxWwum3vERVTeS608ZyzoQ9WvR6v3ruXX713FxG9uvOs5cd0+pzTTakpKyK4m5NX8SmuqaWpRu2Mrxv5o+BtxUlW6so7tp2VpCX2qrpizdwxUMz+P4pozlm734Nxpx/x6v8a84arvjEPnz1uOwutNVWvTRvLas3lfPR/QZQlOAiYGpffKxbkiRJ0oeMHVLMlSemFku59vFZvLuq5R7RKymr4o8vLADgso/v3SYKk0BGhUmAG559l5N//QJPzGhbj1lmwsKk1DQHDevNs9+a2GhhclN5FS+9tw6Aj48e0JpNS9RRo/py+kFDLUyqVViclCRJknLcl44awcS9+1FRXcs3HphOeVVNi1znL9OWsKmimn0G9OTksW1vYYfZK0q58K7XeH3h+g/sL6usZs2mCsqranjm7ZXcOvk9yipriPiUmdQR1J22oab2g3/vf/r0O1RW1zKyX3f26t+jtZsmdQjOOSlJkiTluLy8wC/OOpCTbnqed1Zu4vonZ/PDU8dm9Rq1tZH7XlkMwLlHDm9zczTGGLny4RnMWFrCc7NXM2HPPhw+YjemzF/HG4s2UF2vIHHB0SM45YDBCbVWUmurqqnlzpcWct8ri7jvogkM6dWVl+et5d6pqX7t2lPHdrgFo8qrarh7ykL+9c4a7r7gMArayYJwan/MLEmSJKkD6Nez8/bFHl6ct5YtFdVZPf9L761lwdot9OzciVPHtb2iXgiB2754CGccPJROeYGp89dz0z/m8uqC9R8qTB6x525896R9E2qppCTECA+8tpiF68o45dcv8ORbK/jOwzMA+MLhwzhyVN+EW9j68vMCtz8/nynz1zF5zpqkm6Mc5oI4uCCOJEmSOo4nZqzguH370a0wuw9Rffnu13l21irOO3I413x6TFbPnW3LN27lrpcXsryknMNH9GHiXv0Y2rsrmyur2VJRTb8enenkCCGpw1myvoxL73uDt5aVbN83pFdXnrlsIj06d8wHT697fBZ/eHEBJ44ZyK1fPCTp5qiNa+6COO2+OBlCWAjUX3LwpzHG72ZwDouTkiRJUjMt37iVo3/6T2ojPHf5REb175l0kySpWSqqa/jxE7O5e8oiAO694HCO3qvjjZrcZvaKUk666QUK8gOv/vfH6N29MOkmqQ1rbnEyV0r/PwB+X+f15qQaIkmSJLUHtbWRP7w4n6qayFePG7VL53rg1cXURpiwZx8Lk5Latc6d8vnRqWM5cexAqmtihy5MAuw3qIjRg4qYtaKUx2Ys5z+PGJ50k5SDcuVZhU0xxpV1NouTkiRJ0g689N5afvLkO/zi2TnMWbmp2eepqK7hgVeXAPDFCcOz1DpJStaRI/syce9+STejTTjjkKEAPDJ9WcItUa7KleLklSGEdSGEN0MIV4UQHGcsSZIk7cBH9urHFw4fxnWn7c/eA3o0+zyPTl/O2s0VDCzqwgljBmSxhZKktuCUAwYRAkxfvJEVJVuTbo5yUC481n0T8AawATgMuB4YAVzY2BtCCJ2BznV2+eyJJEmSOpwfn77/Lp+juFsBI/t15+xDh1HgIjKSlHMGFHVh/B69eW3hBp6euZLzgeGYawAAHphJREFUjxqRdJOUY9rk3UMI4ZoQQtzJNh4gxvjLGOPkGOOMGOMfgEuAC0IIu+3gEt8DSupsS1v8Q0mSJElt2JaKahavK8v4fZ8YM5C/X3YM5x45PPuNkiS1CSeOHQTAU2+tTLglykVtsjgJ3Azst5NtZiPvnZr+uaNZva8HiutsQ3e9yZIkSVL79OaSjXzsxslcev80ampjxu/PywsUdmqr/7SQJO2qE8cOBOC1RetZXVqecGuUa9rkHUSMcW2M8Z2dbI39bTgo/XPFDs5fEWMs3bYBzZ8BXJIkSWrnhvTqypaKamYuK+WeKQub9J6Zy0q4d+oiyqtqWrRtkqTkDenVlXG79yJGeOZtR08qu9pkcbKpQghHhBAuCyGMCyGMCCF8FrgN+L8Y4+Kk2ydJkiS1B/16duaKE/cF4OfPzOHdVTv/v/tf/v1d/t/fZvLjJ2a3dPMkSW3AyfunRk8+NdPipLKrXRcngQrgc8AkYBbwI+D3wOcTbJMkSZLU7vzHYcM4Ys/d2FJZw5fvfp2Ssqodxn/p6BF0L8znvKOGt04DJUmJOik97+Tc1ZsdNa+sCjFmPqdMrgkhFAElJSUlFBUVJd0cSZIkKRHrt1Tyqd+8yLKNW5m4dz/uOO9Q8vMCAJsrqpm9opRDh/f5QHyf7oVJNVeS1MqmL97A/kOK6ZTf3se6qSWUlpZSXFwMUJyeRrFJLE5icVKSJEna5u3lJZzxu5cpr6rlI3v15ZQDBtG/qAtXP/o26zZX8NQ3JzJst25JN1OSJLUxzS1Odmq5JkmSJElqb8YMLuaGsw7k6w9M54W5a3lh7trtx4b06sqGskqLk5IkKWssTkqSJEn6gFMOGMyefXvwzNsrmfzuGt5dtYkTxwzkmlPHUNSlIOnmSZISdNUjb/HC3LXc9aXDGNG3e9LNUQ6wOClJkiTpQ0YPLmL04CIu+/jexBgJISTdJElSG/Duqk0sXl/GawvWW5xUVliclCRJkrRDFiYlSdt8/fi9qImR8Xv0TropyhEWJyVJkiRJktQkE/ful3QTlGNc+12SJEmSJElSIixOSpIkSZIkqcleX7ie/3nqHV6etzbppigH+Fi3JEmSJEmSmuzxGSu48+WFlFVWc+Sovkk3R+2cIyclSZIkSZLUZIeN6APAqwvWJ9wS5QKLk5IkSZIkSWqyQ4enipNzVm2ipKwq4daovbM4KUmSJEmSpCbr17Mze/btTozwyoJ1STdH7ZzFSUmSJEmSJGXkmH36AfDom8sTbonaO4uTkiRJkiRJyshZh+wOwN9nrWLDlsqEW6P2zOKkJEmSJEmSMjJ6cBFjhxRRWVPLo28uS7o5ascsTkqSJEmSJClj20ZPPvj60oRbovbM4qQkSZIkSZIyduq4wRTm5zFrRSkzl5Uk3Ry1UxYnJUmSJEmSlLFe3Qo5YcwAAB6a5uhJNY/FSUmSJEmSJDXLWeNTj3Y/Mn0Z5VU1CbdG7ZHFSUmSJEmSJDXL0aP6Mqi4CyVbq3ht4fqkm6N2qFPSDZAkSZIkSVL7lJ8X+NmZBzCkV1f27Ncjo/cuXlfGso1bOWLkbi3UOrUHjpyUJEmSJElSs31kr34ZFSbLq2q48dk5fOzGybyxeMP2/c/NWsXlf36Tl+etbYlmqo1y5KQkSZIkSZKyorY2kpcXGj0+c1kJl973BovXlwFwyB69tx/755zV/HX6Moq6FnDkqL4t3la1DRYnJUmSJEmStEsWrN3CT596h5KtVTzw5QkNxlRU1/D1B6azeH0ZA4u6cPWnRnP4iD7bj59ywCCKuhRsXwFcHYPFSUmSJEmSJO2SboX5PDtrJbURlm4oY2jvbh+K+cMLC1iwdgv9enbmmW9NpLhbwQeOHzmyL0eOTI2YrKiu4bbJ89lQVsnVnxrTKp9BybA4KUmSJEmSpF0yoKgL1542loOH9W6wMLls41Zu/uc8AP775H0/VJisb+ayEm78+7sAnHLA4A88/q3c4oI4kiRJkiRJ2mVfOHwP9htUBMDU+euY/O4aVpaUU15Vw4+fmMXWqhoOG96H08YN2em5DtmjD2ceMhSAS++bxntrNrdo25UcR05KkiRJkiQpq377r3m8MPeDq27n5wV+eOoYQmh8wZy6/t8n9+OtpSXMWbWJs2+fygMXTWBU/6avCq72wZGTkiRJkiRJyqoRfbszpFdX8uus3H3h0SO2j6xsil7dCrn/osPZd2BP1myq4OzbpzqCMgeFGGPSbUhcCKEIKCkpKaGoqOl/SSRJkiRJktS4GCObKqopq6hhQFHnJo+arGv9lkrO+cMrzFpRyvg9evOXS45o1nnUskpLSykuLgYojjGWNvV9jpyUJEmSJElSiwghUNSlgIHFXZpdUOzTvZA/njeergX5vL5oA//37+VZbqWSZHFSkiRJkiRJbdqg4q5ceuxIAK5/8h3KKqsTbpGyxeKkJEmSJEmS2ryLJu7J0N5dWVlazq2T3ku6OcoSi5OSJEmSJElq87oU5HPVyfsBcOvz81m4dkvCLVI2WJyUJEmSJElSu3Di2IEcOXI3Kqtr+d5f30q6OcoCi5OSJEmSJElqF0II/PJz4zhun37c8NkDk26OsiDEGJNuQ+JCCEVASUlJCUVFRUk3R5IkSZIkSWpXSktLKS4uBiiOMZY29X2OnJQkSZIkSVK79fTMlfzp1cVJN0PN1CnpBkiSJEmSJEnNMW3RBi65dxr5eYF9BxUxbvdeSTdJGbI4KUmSJEmSpHbp4GG9OP2gIfTs0omxg52qrz1yzkmcc1KSJEmSJKm9qqmN5OeF7a9XlGylf88uH9inlpeTc06GEK4KIbwcQigLIWxsJGZYCOGxEMKWEMLaEMKvQwiFrd1WSZIkSZIktb66Rcja2sj5d7zGSTc9z32vLOK9NZtJamBeVU1tItdtb9r6Y92FwF+AKcAF9Q+GEPKBJ4A1wNHAbsBdQAC+3nrNlCRJkiRJUtLmr93MipJySrZWcdUjMwHo17MzF0/cky8dNYK8VhxN+d9/fYsFa7dw5Un7cujwPq123famTRcnY4xXA4QQzmsk5ARgNLB7jHF5Ova/gDtDCFdlMoRUkiRJkiRJ7duo/j15/orjuPeVRbwwdw1vLN7Imk0VXPfEbCbNWcMNZx3IwOIuLd6O0vIqHp+xgq1VNfh0+Y616ce6m+AIYOa2wmTaM0Bn4JBkmiRJkiRJkqSkFHcr4KvHjeJPXz6CGVefwHWnjaVLQR4vzlvLiTc9z1tLS1q8DY++uZytVTXsPaAHBw/r3eLXa8/ae3FyILCq7o4Y4wagMn2sQSGEziGEom0b0LNlmylJkiRJkqTW1qUgn3Mm7MET3/gIY4cUsbGsissefJOK6poWu2aMkftfWQzA2YcOIwSHTu5IqxcnQwjXhBDiTrbxGZyyoVlNQyP7t/keUFJnW5rB9SRJkiRJktSOjOzXg3svOJy+PQqZt3ozv/3nvBa71oylJcxeUUphpzw+c/CQFrtOrkhi5OTNwH472WY28VwrqTdCMoTQGyig3ojKeq4HiutsQ5vefEmSJEmSJLU3vboV8qNTxwJwy6T3mLX8/aVKFq8r480lG1ldWr7L1/nTa6lRkyePHUivboW7fL5c1+rFyRjj2hjjOzvZmpoJU4CxIYRBdfadAFQA03bQhooYY+m2DdjU/E8kSZIkSZKk9uCksQP5xJgBVNdGrnx4BrW1qQdvb33+PU777Us88OoSAKpranng1cV85paXKK9q+iPgpeVVPPpmammUzx82LPsfIAe16TknQwjDQgjjgGFAfghhXHrrkQ55FpgF3BNCOCiE8FHgBuD3rtQtSZIkSZKkukIIXHvqWMYOKeLKE/clL72UdlGXAob06krv7gUAVNdGfv2PubyxeCP3peeP3Jnqmlq++cB0yiprGNmvO4eN6NNinyOXhBh3NDVjskIIdwLnNnDouBjjpHTMMOAW4HhgK3A/8O0YY0UG1ykCSkpKSigqKtrVZkuSJEmSJKkNizHudKGaP726mO/+9S36dC/k+e8cR4/OnXZ4vqv+NpP7X1lMl4I8HrhoAgd1sFW6S0tLKS4uBijOZNBgmx45GWM8L8YYGtgm1YlZHGM8JcbYLca4W4zx65kUJiVJkiRJktSxNGUF7TMPGcqIvt1Zv6WSP76wYIex97+6mPtfWUwIcNPZB3W4wuSuaNPFSUmSJEmSJCkJnfLzuPzjewPw+xfms35LZaOxJ48dxKHDe/ODU0bziTEDG43Th1mclCRJkiRJkhrwyf0HMXpQEZsrqrn9+fmNxvXuXsgDF03g/KNGtGLrcoPFSUmSJEmSJKkBeXlh++jJe6cuomRr1QeOl1VWb/91p3zLbM3h75okSZIkSZLUiOP37c8+A3qyuaKae6cu2r6/qqaWT9/8Et/603TWbnb5k+ayOClJkiRJkiQ1Ii8vcMmxewJwx0sLKK+qAWDq/HXMW72Z5+eupbCTJbbm8ndOkiRJkiRJ2oFTDhjMkF5dWbu5kr+8vgSAj+zVj8e+djQ/PeMAiroUJNzC9svipCRJkiRJkrQDBfl5fHliavTk9x99m+qaWgD2H1rMx0cPSLJp7Z7FSUmSJEmSJGknPjt+d3brXgjAnFWbEm5N7rA4KUmSJEmSJO1E18J8bjjrQE4aO5C8EJJuTs4IMcak25C4EEIRUFJSUkJRUVHSzZEkSZIkSZLaldLSUoqLiwGKY4ylTX2fIyclSZIkSZIkJcLipCRJkiRJkqREWJyUJEmSJEmSlAiLk5IkSZIkSZISYXFSkiRJkiRJUiIsTkqSJEmSJElKhMVJSZIkSZIkSYmwOClJkiRJkiQpERYnJUmSJEmSJCXC4qQkSZIkSZKkRFiclCRJkiRJkpQIi5OSJEmSJEmSEmFxUpIkSZIkSVIiLE5KkiRJkiRJSoTFSUmSJEmSJEmJsDgpSZIkSZIkKREWJyVJkiRJkiQlwuKkJEmSJEmSpERYnJQkSZIkSZKUCIuTkiRJkiRJkhJhcVKSJEmSJElSIixOSpIkSZIkSUqExUlJkiRJkiRJibA4KUmSJEmSJCkRFiclSZIkSZIkJcLipCRJkiRJkqREWJyUJEmSJEmSlAiLk5IkSZIkSZISYXFSkiRJkiRJUiIsTkqSJEmSJElKhMVJSZIkSZIkSYlo08XJEMJVIYSXQwhlIYSNjcTEBrZLWrutkiRJkiRJkjLTKekG7EQh8BdgCnDBDuLOB56u87qkJRslSZIkSZIkade16eJkjPFqgBDCeTsJ3RhjXNnyLZIkSZIkSZKULW26OJmBm0MIfwAWAH8Ebo8x1ibcpjZlS+WWRo/l5+XTpVOXJsXmhTy6FnRtVmxZVRkxxgZjQwh0K+jWrNitVVup3cEfd/fC7s2KLa8up6a2Jiux3Qq6EUIAoKK6gura6qzEdi3oSl5Izc5QWVNJVU1VVmK7dOpCfl5+xrFVNVVU1lQ2Gtu5U2c65XXKOLa6tpqK6opGYwvzCynIL8g4tqa2hvLq8kZjC/ILKMwvzDi2NtaytWprVmI75XWic6fOAMQYKasqy0psJn/v7SMajrWPsI+wj8g81j6iebH2ESn2EZnH2kek2Ec0L9Y+IsU+IvNY+4j31e8j6rZJ78uF4uT3gX8AW4GPAr8A+gLXNfaGEEJnoHOdXT1bsoFtQY/rezR67OS9TuaJ/3hi++v+N/RvtKM5Zo9jmHTepO2vh980nLVlaxuMHT94PK9d9Nr216N/O5pFJYsajB3dbzRvX/r29teH/v5QZq2Z1WDsHsV7sPBbC7e/nnjnRF5f/nqDsX279WXNFWu2vz7pvpOYvGhyg7HdCrqx5b/f79zOePAMnpz7ZIOxAPHq9zurLz7yRR6a9VCjsZu/t3l7p3Tx4xdz17/vajR29bdX0697PwAuf+Zybnn9lkZjF3xzAcN7DQfgqn9cxQ1Tbmg0duZXZjKm/xgAfvLCT/jh5B82Gvvqha9y6JBDAbhp6k1857nvNBr7r3P/xbHDjwXg9mm387WnvtZo7OOff5xP7v1JAO576z7Of/T8RmMfPPNBzhpzFgCPzH6Ezz702UZj7zj1Ds4bdx4Az8x7hlMeOKXR2JtPupmvHvZVAF5Y/ALH3XVco7E/+9jPuOKoKwB4Y8UbHPaHwxqNvfqYq7nm2GsAmL1mNmN/N7bR2G8f8W1+fsLPAVhcspgRN41oNPbS8Zfy20/+FoC1ZWvpf0P/RmPPPfBc7jztTiD1hbqjv/dnjj6Tv5z1l+2v7SNS7CPsI+wjUuwjUuwj3mcfkWIfkWIfkWIf8T77iBT7iBT7iJSG+oi67df7Wn1BnBDCNY0sYlN3G9/U88UYr4sxTokxvhlj/AXwA+CKnbzte6Tmpdy2LW32B5IkSZIkSZLULKGxoaotdsEQ+pIa2bgjC2OM28c0p+ec/FWMsVcTzn8U8CIwMMa4qpGYhkZOLi0pKaGoqGhnl2iXHEadeayPWviohY9aZB5rH9G8WPuIFPuIzGPtI1LsI5oXax+RYh+Reax9xPvsIzKPtY9IsY/IPDYX+ohcf6y7tLSU4uJigOIYY2lT39fqxcnmyLA4+TXg50CvGGPjvcYH31MElORycVKSJEmSJElqKc0tTrbpOSdDCMOAPsAwID+EMC59aF6McXMI4VPAQGAKqTknjwN+TGpBnCYVJiVJkiRJkiQlo00XJ4EfAefWeT09/fM4YBJQBVwK3Ehq/sz5pOac/G3rNVGSJEmSJElSc7SLx7pbmo91S5IkSZIkSc3X3Me6W321bkmSJEmSJEkCi5OSJEmSJEmSEmJxUpIkSZIkSVIiLE5KkiRJkiRJSoTFSUmSJEmSJEmJsDgpSZIkSZIkKREWJyVJkiRJkiQlwuKkJEmSJEmSpER0SroBbUlpaWnSTZAkSZIkSZLanebW1UKMMctNaX9CCEOApUm3Q5IkSZIkSWrnhsYYlzU12OIkEEIIwGBgU9JtaWN6kiraDsXfGzWNOaNMmTPKlDmjTJkzypQ5o0yZM8qUOaNMtaec6QksjxkUHH2sG0j/hjW5ottRpGq2AGyKMfrMu3bKnFGmzBllypxRpswZZcqcUabMGWXKnFGm2lnOZNw+F8SRJEmSJEmSlAiLk5IkSZIkSZISYXFSO1IB/DD9U2oKc0aZMmeUKXNGmTJnlClzRpkyZ5Qpc0aZyumccUEcSZIkSZIkSYlw5KQkSZIkSZKkRFiclCRJkiRJkpQIi5OSJEmSJEmSEmFxUpIkSZIkSVIiLE7muBDCxBDCYyGE5SGEGEI4rd7xHiGEm0MIS0MIW0MIs0MIX6kXMyn93rrbn+rF9A4h3BNCKElv94QQerXGZ1R2NSFnBoQQ7kwfLwshPB1C2KteTOcQwm9CCGtDCFtCCP8XQhhaL2ZY+jpb0nG/DiEUtsZnVHZlKWfsZzqIEML3QgivhRA2hRBWhxD+FkLYp15MVvqQEMIxIYRpIYTyEML8EMIlrfEZlV1ZzJn6fUysnxMhhP1DCJPT90TLQgg/CCGE1vicyp4m5syX0989pelc+ND3SVO+d8yZ3JDFnFnYQD/zP/VivAfOATvLmRBCn/T30pz0/e/i9J91cb3zeD/TQWQxZ3LyfsbiZO7rDvwb+Fojx38JnAicA+yXfv2bEMKp9eJ+Dwyqs11c7/j9wLj0uU5M//qeLLRfra/RnEl3aH8D9gROBQ4CFgHPhRC61wn9FXA6cDZwNNADeDyEkJ8+Tz7wRPpaR6fjzgB+0TIfSS0sGzkD9jMdxTHAb4EJwMeBTsCz2e5DQggjgCeBF0jl3U+AX4cQzmjJD6cWscs5U8f5fLCfuWvbgRBCEfB3YDlwKPB14NvA5dn/SGphTcmZbsDTpPqGxuzwe8ecySnZyhmAH/DBfua6bQe8B84pO8uZwent28D+wHmk+pE/bjuB9zMdzi7nTB25dz8TY3TrIBsQgdPq7ZsJfL/evmnAtXVeTwJ+tYPz7pc+9+F19k1I79sn6c/tlr2cAfZO7xtTZ18+sA64MP26GKgEPlcnZjBQA3wi/fqk9OvBdWLOBsqBoqQ/t1vr5kx6n/1MB92Afuk/x4np11npQ4CfArPrXetWYErSn9mt9XMmve9D90H1zvsVYCPQuc6+7wLLgJD053bLXs7UO3Zs+livevt3+r1jzuTu1pycSR9bCHxrB+f1HjhHtx3lTJ2Ys4AKoFNT88H7mdzdmpMz6X05eT/jyEm9CHw6hDAkpBxHqpjwTL24L6SHmb8dQrghhNCzzrEjgJIY4yvbdsQYpwIlwJEt/QHUqjqnf5Zv2xFjrCH1j8Kj07sOAQqAZ+vELCdVCN+WD0cAM9P7t3kmff5DWqTlSkpTcmYb+5mOadujKuvTP7PVhxxR9xx1YsaHEAqy1noloTk5s83N6X7mtRDCJSGEuvfCRwCTY4wVdfY9Q6rQOTyL7Vfrq58zTdGU7x1zJnc1J2e2uTKEsC6E8GYI4ap6j+h6D5y7mpIzxUBpjLE6/dr7mY6tOTmzTc7dz3RKugFK3DdIPUq5FKgGakmNZnqxTsx9wAJgJTAWuB44kNRQZICBwOoGzr06fUy54x1Sj+ReH0K4GNhCanj4QFLDyUn/ujLGuKHee1fxfj4MTL/eLsa4IYRQiTmTa5qSM2A/0yGlH/u/EXgxxjgzvTtbfciHYtKvOwF9gRVZ+RBqVbuQMwDfB/4BbAU+Suqxub68/8jlQFKjnuqfY9uxBVn4CGpljeRMUzTle8ecyUG7kDMANwFvABuAw0jdz4wALkwf9x44BzUlZ0IIu5H6Hrqtzm7vZzqoXcgZyNH7GYuT+gapR1Q+TaqAMBG4JYSwIsb4HECM8fd14meGEOYCr4cQDo4xvpHeHxs4d2hkv9qpGGNVen6TP5L6H54a4DngqSa8vX4+mDMdQFNzxn6mw7oZOIAPj6JtSHP6kPoxoZH9aj+anTMxxuvqHHszPS/8D6gzHxzmTC7KJGfqs5/pmJqdMzHGX9Z5OSOEsAF4KIRwZYxx3bawBt7q/Uz7tsOcSc8B+AQwC/hhvcP2Mx1Ts3MmV+9nfKy7AwshdCU1oe7lMcbHYowzYow3A38mNWFqY94AqoBtq+2uBAY0ENePD/8vj9q5GOO0GOM4oBcwKMZ4IrAb7/8PzEqgMITQu95b+/N+Pqyk3v8Op+MLMGdyThNypiH2MzkuhPAbUv8xdlyMcWmdQ9nqQz4Ukz5HNak5T9XO7GLONGQqUBRC2Na3NJYz7OQ8aqN2kDNN0ZTvHXMmx+xizjRkavrnqPRP74FzzM5yJj1N0dPAZuD0GGNVncPez3RAu5gzDcmJ+xmLkx1bQXqrrbe/hh3nxpj0+7YNIZ8CFIcQDtsWEEI4nNT8CC9nrbVqU2KMJTHGNSGEvYDxwKPpQ9NIFZW2PY5LCGEQqUd1t+XDFGBsev82J5Ca7HdaS7ddydhBzjTEfiZHpec3vhn4DHB8jLF+kTpbfciUuueoE/N6E27y1IZkKWcachCp+XA3pl9PASbWmx/uBFKrXS7clc+g1tWEnGmKpnzvmDM5Iks505CD0j/r3s94D5wDmpIz6dFvz5Kaa/3TMcbyeiHez3QgWcqZhuTG/UzSK/K4tewG9ADGpbcIXJb+9bD08UmkJow/ltR8KOeRmrvgK+njI0kNER5PavLUk4HZpEY15de5zlPAv0k9Ij4BmAE8lvTnd2uRnDkrnS97AqeS6uAerneO3wFLSM2BcRCpOTHe3JYzpFZrfovU470HpeOWAL9J+vO7tX7O2M90rA24hdTN0zGk/ld329a1Tswu9yHp77QtpObz2Q/4EqkbvTOS/j1wSyRnPgVcRKpgOZLU/G8lwE11zlFMarTB/em409Mx/5X074Fbi+TMwPR31YXp766PpF/3qROzw+8dcyZ3tmzkDKlFKLbdA40APktqddxH65zDe+Ac2XaWM0BPUiPaZqS/d+rGeD/TAbcs5UzO3s8k3gC3Fv4DThUEYgPbnenjA4E70l+cW0ktXnE56SXmgd2ByaSGjFcA80hN9Nyn3nX6APcCpentXqBX0p/frUVy5hvpL81KUvOUXgsU1jtHF+A36bwpAx4Ddq8XMwx4PH18XTq+c9Kf3631c8Z+pmNtjeRKBM6rE5OVPoTUzd8b6bxaAFyS9Od3SyZngBOB6cAmUv/Iewv4JtCp3rX2B54nNQJhBXA16Xsit/azNTFnrmlCzE6/d8yZ3NiykTPAwaQKCxt5/99V1wDd6l3Le+Ac2HaWMzR+fxyB4Znkg/czubFlI2dy+X5mWwFKkiRJkiRJklqVc05KkiRJkiRJSoTFSUmSJEmSJEmJsDgpSZIkSZIkKREWJyVJkiRJkiQlwuKkJEmSJEmSpERYnJQkSZIkSZKUCIuTkiRJkiRJkhJhcVKSJEmSJElSIixOSpIkSZIkSUqExUlJkiRJkiRJibA4KUmSJEmSJCkRFiclSZIkSZIkJeL/A0Vi0OtHEkfeAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_technical_indicators(dataset_TI_df, 400)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.3. Fundamental analysis "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " For fundamental analysis we will perform sentiment analysis on all daily news abou XXX. Using sigmoid the end result will be between 0 and 1. The closer the score is to 0 - the more negative the news is (closer to 1 indicates positive sentiment). For each day, we will create the average daily score (as a number between 0 and 1) and add it as a feature."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.3.1. Bidirectional Embedding Representations from Transformers - BERT "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " For the purpose of classifying news as positive or negative (or neutral) we will use BERT, which is a pre-trained language representation.\n",
+ "\n",
+ " Pretrained BERT models are already available in MXNet/Gluon. We just need to instantiated them and add two (arbitrary number) ```Dense``` layers, going to softmax - the score is from 0 to 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 643,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# just import bert\n",
+ "import bert"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.4. Fourier transforms for trend analysis "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **Fourier transforms** take a function and create a series of sine waves (with different amplitudes and frames). When combined, these sine waves approximate the original function. Mathematically speaking, the transforms look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$G(f) = \\int_{-\\infty}^\\infty g(t) e^{-i 2 \\pi f t} dt$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will Fourier transforms to extract global and local trends in the GS stock, and to also denoise it a little. So let's see how it works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_FT = dataset_ex_df[['Date', 'GS']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "close_fft = np.fft.fft(np.asarray(data_FT['GS'].tolist()))\n",
+ "fft_df = pd.DataFrame({'fft':close_fft})\n",
+ "fft_df['absolute'] = fft_df['fft'].apply(lambda x: np.abs(x))\n",
+ "fft_df['angle'] = fft_df['fft'].apply(lambda x: np.angle(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 638,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(14, 7), dpi=100)\n",
+ "fft_list = np.asarray(fft_df['fft'].tolist())\n",
+ "for num_ in [3, 6, 9, 100]:\n",
+ " fft_list_m10= np.copy(fft_list); fft_list_m10[num_:-num_]=0\n",
+ " plt.plot(np.fft.ifft(fft_list_m10), label='Fourier transform with {} components'.format(num_))\n",
+ "plt.plot(data_FT['GS'], label='Real')\n",
+ "plt.xlabel('Days')\n",
+ "plt.ylabel('USD')\n",
+ "plt.title('Figure 3: Goldman Sachs (close) stock prices & Fourier transforms')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As you see in Figure 3 the more components from the Fourier transform we use the closer the approximation function is to the real stock price (the 100 components transform is almost identical to the original function - the red and the purple lines almost overlap). We use Fourier transforms for the purpose of extracting long- and short-term trends so we will use the transforms with 3, 6, and 9 components. You can infer that the transform with 3 components serves as the long term trend.\n",
+ "\n",
+ " Another technique used to denoise data is call **wavelets**. Wavelets and Fourier transform gave similar results so we will only use Fourier transforms.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 639,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from collections import deque\n",
+ "items = deque(np.asarray(fft_df['absolute'].tolist()))\n",
+ "items.rotate(int(np.floor(len(fft_df)/2)))\n",
+ "plt.figure(figsize=(10, 7), dpi=80)\n",
+ "plt.stem(items)\n",
+ "plt.title('Figure 4: Components of Fourier transforms')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.5. ARIMA as a feature "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **ARIMA** is a technique for predicting time series data. We will show how to use it, and althouth ARIMA will not serve as our final prediction, we will use it as a technique to denoise the stock a little and to (possibly) extract some new patters or features."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 499,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ARIMA Model Results \n",
+ "==============================================================================\n",
+ "Dep. Variable: D.GS No. Observations: 2264\n",
+ "Model: ARIMA(5, 1, 0) Log Likelihood -5465.888\n",
+ "Method: css-mle S.D. of innovations 2.706\n",
+ "Date: Wed, 09 Jan 2019 AIC 10945.777\n",
+ "Time: 10:28:07 BIC 10985.851\n",
+ "Sample: 1 HQIC 10960.399\n",
+ " \n",
+ "==============================================================================\n",
+ " coef std err z P>|z| [0.025 0.975]\n",
+ "------------------------------------------------------------------------------\n",
+ "const -0.0011 0.054 -0.020 0.984 -0.106 0.104\n",
+ "ar.L1.D.GS -0.0205 0.021 -0.974 0.330 -0.062 0.021\n",
+ "ar.L2.D.GS 0.0140 0.021 0.665 0.506 -0.027 0.055\n",
+ "ar.L3.D.GS -0.0030 0.021 -0.141 0.888 -0.044 0.038\n",
+ "ar.L4.D.GS 0.0026 0.021 0.122 0.903 -0.039 0.044\n",
+ "ar.L5.D.GS -0.0522 0.021 -2.479 0.013 -0.093 -0.011\n",
+ " Roots \n",
+ "=============================================================================\n",
+ " Real Imaginary Modulus Frequency\n",
+ "-----------------------------------------------------------------------------\n",
+ "AR.1 -1.7595 -0.0000j 1.7595 -0.5000\n",
+ "AR.2 -0.5700 -1.7248j 1.8165 -0.3008\n",
+ "AR.3 -0.5700 +1.7248j 1.8165 0.3008\n",
+ "AR.4 1.4743 -1.0616j 1.8168 -0.0993\n",
+ "AR.5 1.4743 +1.0616j 1.8168 0.0993\n",
+ "-----------------------------------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "from statsmodels.tsa.arima_model import ARIMA\n",
+ "from pandas import DataFrame\n",
+ "from pandas import datetime\n",
+ "\n",
+ "series = data_FT['GS']\n",
+ "model = ARIMA(series, order=(5, 1, 0))\n",
+ "model_fit = model.fit(disp=0)\n",
+ "print(model_fit.summary())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from pandas.tools.plotting import autocorrelation_plot\n",
+ "autocorrelation_plot(series)\n",
+ "plt.figure(figsize=(10, 7), dpi=80)\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pandas import read_csv\n",
+ "from pandas import datetime\n",
+ "from statsmodels.tsa.arima_model import ARIMA\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "\n",
+ "X = series.values\n",
+ "size = int(len(X) * 0.66)\n",
+ "train, test = X[0:size], X[size:len(X)]\n",
+ "history = [x for x in train]\n",
+ "predictions = list()\n",
+ "for t in range(len(test)):\n",
+ " model = ARIMA(history, order=(5,1,0))\n",
+ " model_fit = model.fit(disp=0)\n",
+ " output = model_fit.forecast()\n",
+ " yhat = output[0]\n",
+ " predictions.append(yhat)\n",
+ " obs = test[t]\n",
+ " history.append(obs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test MSE: 10.151\n"
+ ]
+ }
+ ],
+ "source": [
+ "error = mean_squared_error(test, predictions)\n",
+ "print('Test MSE: %.3f' % error)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 640,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the predicted (from ARIMA) and real prices\n",
+ "\n",
+ "plt.figure(figsize=(12, 6), dpi=100)\n",
+ "plt.plot(test, label='Real')\n",
+ "plt.plot(predictions, color='red', label='Predicted')\n",
+ "plt.xlabel('Days')\n",
+ "plt.ylabel('USD')\n",
+ "plt.title('Figure 5: ARIMA model on GS stock')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As we can see from Figure 5 ARIMA gives a very good approximation of the real stock price. We will use the predicted price through ARIMA as an input feature into the LSTM because, as we mentioned before, we want to capture as many features and patterns about Goldman Sachs as possible. We go test MSE (mean squared error) of 10.151, which by itself is not a bad result (considering we do have a lot of test data), but still we will only use it as a feature in the LSTM."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.6. Statistical checks "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Ensuring that the data has good quality is very important for out models. In order to make sure our data is suitable we will perform a couple of simple checks in order to ensure that the results we achieve and observe are indeed real, rather than compromised due to the fact that the underlying data distribution suffers from fundamental errors."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.6.1. Heteroskedasticity, multicollinearity, serial correlation "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **Conditional Heteroskedasticity** occurs when the error terms (the difference between a predicted value by a regression and the real value) are dependent on the data - for example, the error terms grow when the data point (along the x-axis) grow.\n",
+ "- **Multicollinearity** is when error terms (also called residuals) depend on each other.\n",
+ "- **Serial correlation** is when one data (feature) is a formula (or completely depemnds) of another feature."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will not go into the code here as it is straightforward and our focus is more on the deep learning parts, **but the data is qualitative**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.7. Feature Engineering "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 456,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total dataset has 2265 samples, and 112 features.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Total dataset has {} samples, and {} features.'.format(dataset_total_df.shape[0], \\\n",
+ " dataset_total_df.shape[1]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " So, after adding all types of data (the correlated assets, technical indicators, fundamentl analysis, Fourier, and Arima) we have a total of 112 features for the 2,265 days (as mentioned before, however, only 1,585 days are for training data).\n",
+ "\n",
+ " We will also have some more features generated from the autoencoders."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.7.1. Feature importance with XGBoost "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Having so many features we have to consider whether all of them are really indicative of the direction GS stock will take. For example, we included USD denominated LIBOR rates in the dataset because we think that changes in LIBOR might indicate changes in the economy, that, in turn, might indicate changes in the GS's stock behavior. But we need to test. There are many ways to test feature importance, but the one we will apply uses XGBoost, because it gives one of the best results in both classification and regression problems.\n",
+ "\n",
+ " Since the features dataset is quite large, for the purpose of presentation here we'll use only the technical indicators. During the real features importance testing all selected features proved somewhat important so we won't exclude anything when training the GAN."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 396,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_feature_importance_data(data_income):\n",
+ " data = data_income.copy()\n",
+ " y = data['price']\n",
+ " X = data.iloc[:, 1:]\n",
+ " \n",
+ " train_samples = int(X.shape[0] * 0.65)\n",
+ " \n",
+ " X_train = X.iloc[:train_samples]\n",
+ " X_test = X.iloc[train_samples:]\n",
+ "\n",
+ " y_train = y.iloc[:train_samples]\n",
+ " y_test = y.iloc[train_samples:]\n",
+ " \n",
+ " return (X_train, y_train), (X_test, y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 398,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get training and test data\n",
+ "(X_train_FI, y_train_FI), (X_test_FI, y_test_FI) = get_feature_importance_data(dataset_TI_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 399,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "regressor = xgb.XGBRegressor(gamma=0.0,n_estimators=150,base_score=0.7,colsample_bytree=1,learning_rate=0.05)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 482,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xgbModel = regressor.fit(X_train_FI,y_train_FI, \\\n",
+ " eval_set = [(X_train_FI, y_train_FI), (X_test_FI, y_test_FI)], \\\n",
+ " verbose=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 401,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eval_result = regressor.evals_result()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 402,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training_rounds = range(len(eval_result['validation_0']['rmse']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot the training and validation errors in order to observe the training and check for overfitting (there isn't overfitting)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 403,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(x=training_rounds,y=eval_result['validation_0']['rmse'],label='Training Error')\n",
+ "plt.scatter(x=training_rounds,y=eval_result['validation_1']['rmse'],label='Validation Error')\n",
+ "plt.xlabel('Iterations')\n",
+ "plt.ylabel('RMSE')\n",
+ "plt.title('Training Vs Validation Error')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 641,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(8,8))\n",
+ "plt.xticks(rotation='vertical')\n",
+ "plt.bar([i for i in range(len(xgbModel.feature_importances_))], xgbModel.feature_importances_.tolist(), tick_label=X_test_FI.columns)\n",
+ "plt.title('Figure 6: Feature importance of the technical indicators.')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Not surprisingly (for those with experience in stock trading) that MA7, MACD, and BB are among the important features. \n",
+ "\n",
+ " I followed the same logic for performing feature importance over the whole dataset - just the training took longer and results were a little more difficult to read, as compared with just a handful of features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.8. Extracting high-level features with Stacked Autoencoders "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before we proceed to the autoencoders, we'll explore an alternative activation function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.8.1. Activation function - GELU (Gaussian Error) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **GELU** - Gaussian Error Linear Unites was recently proposed - link. In the paper the authors show several instances in which neural networks using GELU outperform networks using ReLU as an activation. ```gelu``` is also used in **BERT**, the NLP approach we used for news sentiment analysis.\n",
+ "\n",
+ " We will use GELU for the autoencoders.\n",
+ "\n",
+ "**Note**: The cell below shows the logic behing the math of GELU. It is not the actual implementation as an activation function. I had to implement GELU inside MXNet. If you follow the code and change ```act_type='relu'``` to ```act_type='gelu'``` it will not work, unless you change the implementation of MXNet. Make a pull request on the whole project to access the MXNet implementation of GELU."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def gelu(x):\n",
+ " return 0.5 * x * (1 + math.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * math.pow(x, 3))))\n",
+ "def relu(x):\n",
+ " return max(x, 0)\n",
+ "def lrelu(x):\n",
+ " return max(0.01*x, x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's visualize ```GELU```, ```ReLU```, and ```LeakyReLU``` (the last one is mainly used in GANs - we also use it)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 642,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15, 5))\n",
+ "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.5, hspace=None)\n",
+ "\n",
+ "ranges_ = (-10, 3, .25)\n",
+ "\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.plot([i for i in np.arange(*ranges_)], [relu(i) for i in np.arange(*ranges_)], label='ReLU', marker='.')\n",
+ "plt.plot([i for i in np.arange(*ranges_)], [gelu(i) for i in np.arange(*ranges_)], label='GELU')\n",
+ "plt.hlines(0, -10, 3, colors='gray', linestyles='--', label='0')\n",
+ "plt.title('Figure 7: GELU as an activation function for autoencoders')\n",
+ "plt.ylabel('f(x) for GELU and ReLU')\n",
+ "plt.xlabel('x')\n",
+ "plt.legend()\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.plot([i for i in np.arange(*ranges_)], [lrelu(i) for i in np.arange(*ranges_)], label='Leaky ReLU')\n",
+ "plt.hlines(0, -10, 3, colors='gray', linestyles='--', label='0')\n",
+ "plt.ylabel('f(x) for Leaky ReLU')\n",
+ "plt.xlabel('x')\n",
+ "plt.title('Figure 8: LeakyReLU')\n",
+ "plt.legend()\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note**: In future versions of this notebook I will experiment using **U-Net** (link), and try to utilize the convolutional layer and extract (and create) even more features about the stock's underlying movement patterns. For now, we will just use a simple autoencoder made only from ```Dense``` layers.\n",
+ "\n",
+ " Ok, back to the autoencoders, depicted below (the image is only schematic, it doesn't represent the real number of layers, units, etc.)\n",
+ "\n",
+ "**Note**: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last layer in the decoder. We achieve this creating the encoder and decoder with same number of layers during the training, but when we create the output we use the layer next to the only one as it would contain the higher level features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 459,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "batch_size = 64\n",
+ "n_batches = VAE_data.shape[0]/batch_size\n",
+ "VAE_data = VAE_data.values\n",
+ "\n",
+ "train_iter = mx.io.NDArrayIter(data={'data': VAE_data[:num_training_days,:-1]}, \\\n",
+ " label={'label': VAE_data[:num_training_days, -1]}, batch_size = batch_size)\n",
+ "test_iter = mx.io.NDArrayIter(data={'data': VAE_data[num_training_days:,:-1]}, \\\n",
+ " label={'label': VAE_data[num_training_days:,-1]}, batch_size = batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 478,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "model_ctx = mx.cpu()\n",
+ "class VAE(gluon.HybridBlock):\n",
+ " def __init__(self, n_hidden=400, n_latent=2, n_layers=1, n_output=784, \\\n",
+ " batch_size=100, act_type='relu', **kwargs):\n",
+ " self.soft_zero = 1e-10\n",
+ " self.n_latent = n_latent\n",
+ " self.batch_size = batch_size\n",
+ " self.output = None\n",
+ " self.mu = None\n",
+ " super(VAE, self).__init__(**kwargs)\n",
+ " \n",
+ " with self.name_scope():\n",
+ " self.encoder = nn.HybridSequential(prefix='encoder')\n",
+ " \n",
+ " for i in range(n_layers):\n",
+ " self.encoder.add(nn.Dense(n_hidden, activation=act_type))\n",
+ " self.encoder.add(nn.Dense(n_latent*2, activation=None))\n",
+ "\n",
+ " self.decoder = nn.HybridSequential(prefix='decoder')\n",
+ " for i in range(n_layers):\n",
+ " self.decoder.add(nn.Dense(n_hidden, activation=act_type))\n",
+ " self.decoder.add(nn.Dense(n_output, activation='sigmoid'))\n",
+ "\n",
+ " def hybrid_forward(self, F, x):\n",
+ " h = self.encoder(x)\n",
+ " #print(h)\n",
+ " mu_lv = F.split(h, axis=1, num_outputs=2)\n",
+ " mu = mu_lv[0]\n",
+ " lv = mu_lv[1]\n",
+ " self.mu = mu\n",
+ "\n",
+ " eps = F.random_normal(loc=0, scale=1, shape=(self.batch_size, self.n_latent), ctx=model_ctx)\n",
+ " z = mu + F.exp(0.5*lv)*eps\n",
+ " y = self.decoder(z)\n",
+ " self.output = y\n",
+ "\n",
+ " KL = 0.5*F.sum(1+lv-mu*mu-F.exp(lv),axis=1)\n",
+ " logloss = F.sum(x*F.log(y+self.soft_zero)+ (1-x)*F.log(1-y+self.soft_zero), axis=1)\n",
+ " loss = -logloss-KL\n",
+ "\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 479,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "n_hidden=400 # neurons in each layer\n",
+ "n_latent=2 \n",
+ "n_layers=3 # num of dense layers in encoder and decoder respectively\n",
+ "n_output=VAE_data.shape[1]-1 \n",
+ "\n",
+ "net = VAE(n_hidden=n_hidden, n_latent=n_latent, n_layers=n_layers, n_output=n_output, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 480,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "net.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu())\n",
+ "net.hybridize()\n",
+ "trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .01})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 481,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "VAE(\n",
+ " (encoder): HybridSequential(\n",
+ " (0): Dense(None -> 400, Activation(relu))\n",
+ " (1): Dense(None -> 400, Activation(relu))\n",
+ " (2): Dense(None -> 400, Activation(relu))\n",
+ " (3): Dense(None -> 4, linear)\n",
+ " )\n",
+ " (decoder): HybridSequential(\n",
+ " (0): Dense(None -> 400, Activation(relu))\n",
+ " (1): Dense(None -> 400, Activation(relu))\n",
+ " (2): Dense(None -> 400, Activation(relu))\n",
+ " (3): Dense(None -> 11, Activation(sigmoid))\n",
+ " )\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(net)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So we have 3 layers (with 400 neurons in each) in both the encoder and the decoder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 500,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training completed in 62 seconds.\n"
+ ]
+ }
+ ],
+ "source": [
+ "n_epoch = 150\n",
+ "print_period = n_epoch // 10\n",
+ "start = time.time()\n",
+ "\n",
+ "training_loss = []\n",
+ "validation_loss = []\n",
+ "for epoch in range(n_epoch):\n",
+ " epoch_loss = 0\n",
+ " epoch_val_loss = 0\n",
+ "\n",
+ " train_iter.reset()\n",
+ " test_iter.reset()\n",
+ "\n",
+ " n_batch_train = 0\n",
+ " for batch in train_iter:\n",
+ " n_batch_train +=1\n",
+ " data = batch.data[0].as_in_context(mx.cpu())\n",
+ "\n",
+ " with autograd.record():\n",
+ " loss = net(data)\n",
+ " loss.backward()\n",
+ " trainer.step(data.shape[0])\n",
+ " epoch_loss += nd.mean(loss).asscalar()\n",
+ "\n",
+ " n_batch_val = 0\n",
+ " for batch in test_iter:\n",
+ " n_batch_val +=1\n",
+ " data = batch.data[0].as_in_context(mx.cpu())\n",
+ " loss = net(data)\n",
+ " epoch_val_loss += nd.mean(loss).asscalar()\n",
+ "\n",
+ " epoch_loss /= n_batch_train\n",
+ " epoch_val_loss /= n_batch_val\n",
+ "\n",
+ " training_loss.append(epoch_loss)\n",
+ " validation_loss.append(epoch_val_loss)\n",
+ "\n",
+ " \"\"\"if epoch % max(print_period, 1) == 0:\n",
+ " print('Epoch {}, Training loss {:.2f}, Validation loss {:.2f}'.\\\n",
+ " format(epoch, epoch_loss, epoch_val_loss))\"\"\"\n",
+ "\n",
+ "end = time.time()\n",
+ "print('Training completed in {} seconds.'.format(int(end-start)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 505,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "dataset_total_df['Date'] = dataset_ex_df['Date']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 517,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "vae_added_df = mx.nd.array(dataset_total_df.iloc[:, :-1].values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 523,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The shape of the newly created (from the autoencoder) features is (2265, 112).\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('The shape of the newly created (from the autoencoder) features is {}.'.format(vae_added_df.shape))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " We created 112 more features from the autoencoder. As we want to only have high level features (overall patterns) we will create an Eigen portfolio on the newly created 112 features using Principal Component Analysis (PCA). This will reduce the dimension (number of columns) of the data. The descriptive capability of the Eigen portfolio will be the same as the original 112 features.\n",
+ "\n",
+ "**Note** Once again, this is purely experimental. I am not 100% sure the described logic will hold. As everything else in AI and deep learning, this is art and needs experiments."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.8.2. Eigen portfolio with PCA "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 534,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We want the PCA to create the new components to explain 80% of the variance\n",
+ "pca = PCA(n_components=.8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "x_pca = StandardScaler().fit_transform(vae_added_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "principalComponents = pca.fit_transform(x_pca)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 536,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "84"
+ ]
+ },
+ "execution_count": 536,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "principalComponents.n_components_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " So, in order to explain 80% of the variance we need 84 (out of the 112) features. This is still a lot. So, for now we will not include the autoencoder created features. I will work on creating the autoencoder architecture in which we get the output from an intermediate layer (not the last one) and connect it to another ```Dense``` layer with, say, 30 neurons. Thus, we will 1) only extract higher level features, and 2) come up with significantly fewer number of columns."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.9. Deep Unsupervised Learning for anomaly detection in derivatives pricing "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "-- To be added soon."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Generative Adversarial Network (GAN) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Figure 9: Simple GAN architecture"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### How GANs work?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As mentioned before, the purpose of this notebook is not to explain in detail the math behind deep learning but to show its applications. Of course, thorough and very solid understanding from the fundamentals down to the smallest details, in my opinion, is extremely imperative. Hence, we will try to balance and give a high-level overview of how GANs work in order for the reader to fully understand the rationale behind using GANs in predicting stock price movements. Feel free to skip this and the next section if you are experienced with GANs (and do check section 4.2.).\n",
+ "\n",
+ "A GAN network consists of two models - a **Generator** ($G$) and **Discriminator** ($D$). The steps in training a GAN are:\n",
+ "1. The Generator is, using random data (noise denoted $z$), trying to 'generate' data indistinguishable of, or extremely close to, the real data. It's purpose is to learn the distribution of the real data.\n",
+ "2. Randomly, real or generated data is fitted into the Discriminator, which acts as a classifier and tries to understand whether the data is coming from the Generator or is the real data. $D$ estimates the (distributions) probabilities of the incoming sample to the real dataset. (_more info on comparing two distributions in section 4.2. below_).\n",
+ "3. Then, the losses from $G$ and $D$ are combined and propagated back through the generator. Ergo, the generator's loss depends on both the generator and the discriminator. This is the step that helps the Generator learn about the real data distribution. If the generator doesn't do a good job at generating a realistic data (having the same distribution), the Discriminator's work will be very easy to distinguish generated from real data sets. Hence, the Discriminator's loss will be very small. Small discriminator loss will result in bigger generator loss (_see the equation below for $L(D, G)$_). This makes creating the discriminator a bit tricky, because too good of a discriminator will always result in a huge generator loss, making the generator unable to learn.\n",
+ "4. The process goes on until the Discriminator can no longer distinguish generated from real data.\n",
+ "\n",
+ " When combined together, $D$ and $G$ as sort of playing a _minmax_ game (the Generator is trying to _fool_ the Discriminator making it increase the probability for on fake examples, i.e. minimize $\\mathbb{E}_{z \\sim p_{z}(z)} [\\log (1 - D(G(z)))]$. The Discriminator wants to separate the data coming from the Generator, $D(G(z))$, by maximizing $\\mathbb{E}_{x \\sim p_{r}(x)} [\\log D(x)]$). Having separated loss functions, however, it is not clear how both can converge together (that is why we use some advancements over the plain GANs, such as Wasserstein GAN). Overall, the combined loss function looks like:\n",
+ "\n",
+ "$$L(D, G) = \\mathbb{E}_{x \\sim p_{r}(x)} [\\log D(x)] + \\mathbb{E}_{z \\sim p_z(z)} [\\log(1 - D(G(z)))]$$\n",
+ "\n",
+ "**Note**: Really useful tips for training GANs can be found here.\n",
+ "\n",
+ "**Note**: I will not include the complete code behind the **GAN** and the **Reinforcement learning** parts in this notebook - only the results from the execution (the cell outputs) will be shown. Make a pull request or contact me for the code."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.1. Why GAN for stock market prediction? "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **Generative Adversarial Networks** (GAN) have been recently used mainly in creating realistic images, paintings, and video clips. There aren't many applications of GANs being used for predicting time-series data as in our case. The main idea, however, should be same - we want to predict future stock movements. In the future, the pattern and behavior of GS's stock should be more or less the same (unless it starts operating in a totally different way, or the economy drastically changes). Hence, we want to 'generate' data for the future that will have similar (not absolutely the same, of course) distribution as the one we already have - the historical trading data. So, in theory, it should work.\n",
+ "\n",
+ " In our case, we will use **LSTM** as a time-series generator, and **CNN** as a discriminator."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.2. Metropolis-Hastings GAN and Wasserstein GAN "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note:** _The next couple of sections assume some experience with GANs._\n",
+ "\n",
+ "#### **I. Metropolis-Hastings GAN**\n",
+ "\n",
+ " A recent improvement over the traditional GANs came out from Uber's engineering team and is called **Metropolis-Hastings GAN** (MHGAN). The idea behind Uber's approach is (as they state it) somewhat similar to another approach created by Google and University of California, Berkeley called **Discriminator Rejection Sampling** (DRS). Basically, when we train GAN we use the Discriminator ($D$) for the sole purpose of better training the Generator ($G$). Often, after training the GAN we do not use the $D$ any more. MHGAN and DRS, however, try to use $D$ in order to choose samples generated by $G$ that are close to the real data distribution (slight difference between is that MHGAN uses Markov Chain Monte Carlo (MCMC) for sampling).\n",
+ "\n",
+ " MHGAN takes **_K_** samples generated from the $G$ (created from independent noise inputs to the $G$ - $z_0$ to $z_K$ in the figure below). Then it sequentially runs through the **_K_** outputs ($x'_0$ to $x'_K$) and following an acceptance rule (created from the Discriminator) decides whether to accept the current sample or keep the last accepted one. The last kept output is the one considered the real output of $G$.\n",
+ "\n",
+ "**Note**: MHGAN is originally implemented by Uber in pytorch. I only transferred it into MXNet/Gluon. \n",
+ "\n",
+ "\n",
+ "#### **Note**: I will also upload it into Github sometime soon.\n",
+ " \n",
+ "Figure 10: Visual representation of MHGAN (from the original Uber post)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **II. Wasserstein GAN** "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Training GANs is quite difficult. Models may never converge and mode collapse can easily happen. We will use a modification of GAN called **Wasserstein** GAN - WGAN.\n",
+ "\n",
+ " Again, we will not go into details, but the most notable points to make are:\n",
+ "- As we know the main goal behind GANs is for the Generator to start transforming random noise into some given data that we want to mimic. Ergo, the idea of comparing the similarity between two distributions is very imperative in GANs. The two most widely used such metrics are:\n",
+ " * **KL divergence** (Kullback–Leibler) - $D_{KL}(p \\| q) = \\int_x p(x) \\log \\frac{p(x)}{q(x)} dx$. $D_{KL}$ is zero when $p(x)$ is equal to $q(x)$,\n",
+ " * **JS Divergence** (Jensen–Shannon) - $D_{JS}(p \\| q) = \\frac{1}{2} D_{KL}(p \\| \\frac{p + q}{2}) + \\frac{1}{2} D_{KL}(q \\| \\frac{p + q}{2})$. JS divergence is bounded by 0 and 1, and, unlike KL divergence, is symmetric and smoother. Significant success in GAN training was achieved when the loss was switched from KL to JS divergence.\n",
+ "- WGAN uses Wasserstein distance, $W(p_r, p_g) = \\frac{1}{K} \\sup_{\\| f \\|_L \\leq K} \\mathbb{E}_{x \\sim p_r}[f(x)] - \\mathbb{E}_{x \\sim p_g}[f(x)]$ (where $sup$ stands for _supremum_), as a loss function (also called Earth Mover's distance, because it normally is interpreted as moving one pile of, say, sand to another one, both piles having different probability distributions, using minimum energy during the transformation). Compared to KL and JS divergences, Wasserstein metric gives a smooth measure (without sudden jumps in divergence). This makes it much more suitable for creating a stable learning process during the gradient descent.\n",
+ "- Also, compared to KL and JS, Wasserstein distance is differentiable nearly everywhere. As we know, during backpropagation, we differentiate the loss function in order to create the gradients, which in turn update the weights. Therefore, having a differentiable loss function is quite important."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### _Hands down, this was the toughest part of this notebook. Mixing WGAN and MHGAN took me three days._"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.4. The Generator - One layer RNN "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.4.1. LSTM or GRU "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As mentioned before, the generator is a LSTM network a type of Recurrent Neural Network (RNN). RNNs are used for time-series data because because they keep track of all previous data points and can capture patterns developing through time. Due to their nature, RNNs many time suffer from _vanishing gradient_ - that is, the changes the weights receive during training become so small, that they don't change, making the network unable to converge to a minimal loss (The opposite problem can also be observed at times - when gradients become too big. This is called _gradient exploding_, but the solution to this is quite simple - clip gradients if they start exceeding some constant number, i.e. gradient clipping). Two modifications tackle this problem - Gated Recurrent Unit (**GRU**) and Long-Short Term Memory (**LSTM**). The biggest differences between the two are: 1) GRU has 2 gates (update and reset) and LSTM has 4 (update, input, forget, and output), 2) LSTM maintains an internal memory state, while GRU doesn’t, and 3) LSTM applies a nonlinearity (sigmoid) before the output gate, GRU doesn’t.\n",
+ "\n",
+ " In most cases LSTM and GRU give similar results in terms of accuracy but GRU is much less computational intensive, as GRU has much fewer trainable params. LSTMs, however, and much more used. \n",
+ "\n",
+ " Strictly speaking, the math behind the LSTM cell (the gates) is:\n",
+ "\n",
+ "\n",
+ "$$g_t = \\text{tanh}(X_t W_{xg} + h_{t-1} W_{hg} + b_g),$$\n",
+ "\n",
+ "\n",
+ "$$i_t = \\sigma(X_t W_{xi} + h_{t-1} W_{hi} + b_i),$$\n",
+ "\n",
+ "$$f_t = \\sigma(X_t W_{xf} + h_{t-1} W_{hf} + b_f),$$\n",
+ "\n",
+ "$$o_t = \\sigma(X_t W_{xo} + h_{t-1} W_{ho} + b_o),$$\n",
+ "\n",
+ "$$c_t = f_t \\odot c_{t-1} + i_t \\odot g_t,$$\n",
+ "\n",
+ "$$h_t = o_t \\odot \\text{tanh}(c_t),$$\n",
+ "\n",
+ "where $\\odot$ is an element-wise multiplication operator, and,\n",
+ "for all $x = [x_1, x_2, \\ldots, x_k]^\\top \\in R^k$ the two activation functions:,\n",
+ "\n",
+ "$$\\sigma(x) = \\left[\\frac{1}{1+\\exp(-x_1)}, \\ldots, \\frac{1}{1+\\exp(-x_k)}]\\right]^\\top,$$\n",
+ "\n",
+ "$$\\text{tanh}(x) = \\left[\\frac{1-\\exp(-2x_1)}{1+\\exp(-2x_1)}, \\ldots, \\frac{1-\\exp(-2x_k)}{1+\\exp(-2x_k)}\\right]^\\top$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.4.2. The LSTM architecture "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The LSTM architecture is very simple - one ```LSTM``` layer with 112 input units (as we have 112 features in the dataset) and 500 hidden units, and one ```Dense``` layer with 1 output - the price for every day. The initializer is Xavier and we will use L1 loss (which is mean absolute error loss with L1 regularization - see section 4.4.5. for more info on regularization).\n",
+ "\n",
+ "**Note** - In the code you can see we use ```Adam``` (with ```learning rate``` of .01) as an optimizer. Don't pay too much attention on that now - there is a section specially dedicated to explaoin what hyperparameters we use (learning rate is excluded as we have learning rate scheduler - section 4.4.3.) and how we optimize these hyperparaments - section 4.6."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 416,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gan_num_features = dataset_total_df.shape[1]\n",
+ "sequence_length = 17\n",
+ "\n",
+ "class RNNModel(gluon.Block):\n",
+ " def __init__(self, num_embed, num_hidden, num_layers, bidirectional=False, \\\n",
+ " sequence_length=sequence_length, **kwargs):\n",
+ " super(RNNModel, self).__init__(**kwargs)\n",
+ " self.num_hidden = num_hidden\n",
+ " with self.name_scope():\n",
+ " self.rnn = rnn.LSTM(num_hidden, num_layers, input_size=num_embed, \\\n",
+ " bidirectional=bidirectional, layout='TNC')\n",
+ " \n",
+ " self.decoder = nn.Dense(1, in_units=num_hidden)\n",
+ " \n",
+ " def forward(self, inputs, hidden):\n",
+ " output, hidden = self.rnn(inputs, hidden)\n",
+ " decoded = self.decoder(output.reshape((-1, self.num_hidden)))\n",
+ " return decoded, hidden\n",
+ " \n",
+ " def begin_state(self, *args, **kwargs):\n",
+ " return self.rnn.begin_state(*args, **kwargs)\n",
+ " \n",
+ "lstm_model = RNNModel(num_embed=gan_num_features, num_hidden=500, num_layers=1)\n",
+ "lstm_model.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu())\n",
+ "trainer = gluon.Trainer(lstm_model.collect_params(), 'adam', {'learning_rate': .01})\n",
+ "loss = gluon.loss.L1Loss()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will use 500 neurons in the LSTM layer and use Xavier initialization. For regularization we'll use L1. Let's see what's inside the ```LSTM``` as printed by MXNet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 417,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RNNModel(\n",
+ " (rnn): LSTM(112 -> 500, TNC)\n",
+ " (decoder): Dense(500 -> 1, linear)\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(lstm_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " As we can see, the unput of the LSTM are the 112 features (```dataset_total_df.shape[1]```) which then go into 500 neurons in the LSTM layer, and then transformed to a single output - the stock price value.\n",
+ "\n",
+ " The logic behind the LSTM is: we take 17 (```sequence_length```) days of data (again, the data being the stock price for GS stock every day + all the other feature for that day - correlated assets, sentiment, etc.) and try to predict the 18th day.\n",
+ "\n",
+ " In another post I will explore whether modification over the vanilla LSTM would be more beneficial, such as: \n",
+ "- using **bidirectional** LSTM layer - in theory, going backwards (from end of the data set towards the beginning) might somehow help the LSTM figure out the pattern of the stock movement.\n",
+ "- using **stacked** RNN architecture - having not only one LSTM layer but 2 or more. This, however, might be dangerous, as we might end up overfitting the model, as we don't have a lot of data (we have just 1,585 day worth of data).\n",
+ "- Exploring **GRU** - as already explained, GRUs' cells are much more simpler.\n",
+ "- Adding **attention** vectors to the RNN."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.4.3. Learning rate scheduler "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " One of the most important hyperparameters is the learning rate. Setting the learning rate for almost every optimizer (such as SGD, Adam, or RMSProp) is crucially important when training neural networks because it controls both the speed of convergence and the ultimate performance of the network. One of the simplest learning rate strategies is to have a fixed learning rate throughout the training process. Choosing a small learning rate allows the optimizer find good solutions, but this comes at the expense of limiting the initial speed of convergence. Changing the learning rate over time can overcome this tradeoff.\n",
+ "\n",
+ " Recent papers, such as this, show the benefits of changing the global learning rate during training, in terms of both convergence and time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TriangularSchedule():\n",
+ " def __init__(self, min_lr, max_lr, cycle_length, inc_fraction=0.5): \n",
+ " self.min_lr = min_lr\n",
+ " self.max_lr = max_lr\n",
+ " self.cycle_length = cycle_length\n",
+ " self.inc_fraction = inc_fraction\n",
+ " \n",
+ " def __call__(self, iteration):\n",
+ " if iteration <= self.cycle_length*self.inc_fraction:\n",
+ " unit_cycle = iteration * 1 / (self.cycle_length * self.inc_fraction)\n",
+ " elif iteration <= self.cycle_length:\n",
+ " unit_cycle = (self.cycle_length - iteration) * 1 / (self.cycle_length * (1 - self.inc_fraction))\n",
+ " else:\n",
+ " unit_cycle = 0\n",
+ " adjusted_cycle = (unit_cycle * (self.max_lr - self.min_lr)) + self.min_lr\n",
+ " return adjusted_cycle\n",
+ "\n",
+ "class CyclicalSchedule():\n",
+ " def __init__(self, schedule_class, cycle_length, cycle_length_decay=1, cycle_magnitude_decay=1, **kwargs):\n",
+ " self.schedule_class = schedule_class\n",
+ " self.length = cycle_length\n",
+ " self.length_decay = cycle_length_decay\n",
+ " self.magnitude_decay = cycle_magnitude_decay\n",
+ " self.kwargs = kwargs\n",
+ " \n",
+ " def __call__(self, iteration):\n",
+ " cycle_idx = 0\n",
+ " cycle_length = self.length\n",
+ " idx = self.length\n",
+ " while idx <= iteration:\n",
+ " cycle_length = math.ceil(cycle_length * self.length_decay)\n",
+ " cycle_idx += 1\n",
+ " idx += cycle_length\n",
+ " cycle_offset = iteration - idx + cycle_length\n",
+ " \n",
+ " schedule = self.schedule_class(cycle_length=cycle_length, **self.kwargs)\n",
+ " return schedule(cycle_offset) * self.magnitude_decay**cycle_idx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "schedule = CyclicalSchedule(TriangularSchedule, min_lr=0.5, max_lr=2, cycle_length=500)\n",
+ "iterations=1500\n",
+ "\n",
+ "plt.plot([i+1 for i in range(iterations)],[schedule(i) for i in range(iterations)])\n",
+ "plt.title('Learning rate for each epoch')\n",
+ "plt.xlabel(\"Epoch\")\n",
+ "plt.ylabel(\"Learning Rate\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.4.4. How to prevent overfitting and the bias-variance trade-off "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Having a lot of features and neural networks we need to make sure we prevent overfitting and be mindful of the total loss.\n",
+ "\n",
+ "We use several techniques for preventing overfitting (not only in the LSTM, but also in the CNN and the auto-encoders):\n",
+ "- **Ensuring data quality**. We already performed statistical checks and made sure the data doesn't suffer from multicollinearity or serial autocorrelation. Further we performed feature importance check on each feature. Finally, the initial feature selection (e.g. selecting correlated assets, technical indicators, etc.) was done with some domain knowledge about the mechanics behind the way stock markets work.\n",
+ "- **Regularization** (or weights penalty). The two most widely used regularization techniques are LASSO (**L1**) and Ridge (**L2**). L1 adds the mean absolute error and L2 adds mean squared error to the loss. Without going into too many mathematical details, the basic differences are: lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective. The impact of the two types of regularizations is quite different. While they both penalize large weights, L1 regularization leads to a non-differentiable function at zero. L2 regularization favors smaller weights, but L1 regularization favors weights that go to zero. So, with L1 regularization you can end up with a sparse model - one with fewer parameters. In both cases the parameters of the L1 and L2 regularized models \"shrink\", but in the case of L1 regularization the shrinkage directly impacts the complexity (the number of parameters) of the model. Precisely, ridge regression works best in situations where the least square estimates have higher variance. L1 is more robust to outliers, is used when data is sparse, and creates feature importance. We will use L1.\n",
+ "- **Dropout**. Dropout layers randomly remove nodes in the hidden layers.\n",
+ "- **Dense-sparse-dense training**. - link\n",
+ "- **Early stopping**.\n",
+ "\n",
+ " Another important consideration when building complex neural networks is the bias-variance trade-off. Basically, the error we get when training nets is a function of the bias, the variance, and irreducible error - σ (error due to noise and randomness). The simplest formula of the trade-off is:\n",
+ "\n",
+ "$$Error = bias^{2} + variance + \\sigma$$\n",
+ "\n",
+ "- **Bias**. Bias measures how well a trained (on training dataset) algorithm can generalize on unseen data. High bias (underfitting) meaning the model cannot work well on unseen data.\n",
+ "- **Variance**. Variance measures the sensitivity of the model to changes in the dataset. High variance is the overfitting."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.4.5. Custom weights initializers and custom loss metric "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Coming soon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.5. The Discriminator - One Dimentional CNN "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.5.1. Why CNN as a discriminator? "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " We usually use CNNs for work related to images (classification, context extraction, etc). They are very powerful at extracting features from features from features, etc. For example, in an image of a dog, the first convolutional layer will detect edges, the second will start detecting circles, and the third will detect a nose. In our case, data points form small trends, small trends form bigger, trends in turn form patterns. CNNs' ability to detect features can be used for extracting information about patterns in GS's stock price movements.\n",
+ "\n",
+ " Another reason for using CNN is that CNNs work well on spatial data - meaning data points that are closer to each other are more related to each other, than data points spread across. This should hold true for time series data. In our case each data point (for each feature) is for each consecutive day. It is natural to assume that the closer two days are to each other, the more related they are to each other. One thing to consider (although not covered in this work) is seasonality and how it might change (if at all) the work of the CNN.\n",
+ "\n",
+ "**Note**: As many other parts in this notebook, using CNN for time series data is experimental. We will inspect the results, without providing mathematical or other proofs. And results might vary using different data, activation functions, etc."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.5.1. The CNN Architecture "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Figure 11: High level overview of the CNN architecture."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The code for the CNN inside the GAN looks like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "num_fc = 512\n",
+ "\n",
+ "# ... other parts of the GAN\n",
+ "\n",
+ "cnn_net = gluon.nn.Sequential()\n",
+ "with net.name_scope():\n",
+ " \n",
+ " # Add the 1D Convolutional layers\n",
+ " cnn_net.add(gluon.nn.Conv1D(32, kernel_size=5, strides=2))\n",
+ " cnn_net.add(nn.LeakyReLU(0.01))\n",
+ " cnn_net.add(gluon.nn.Conv1D(64, kernel_size=5, strides=2))\n",
+ " cnn_net.add(nn.LeakyReLU(0.01))\n",
+ " cnn_net.add(nn.BatchNorm())\n",
+ " cnn_net.add(gluon.nn.Conv1D(128, kernel_size=5, strides=2))\n",
+ " cnn_net.add(nn.LeakyReLU(0.01))\n",
+ " cnn_net.add(nn.BatchNorm())\n",
+ " \n",
+ " # Add the two Fully Connected layers\n",
+ " cnn_net.add(nn.Dense(220, use_bias=False), nn.BatchNorm(), nn.LeakyReLU(0.01))\n",
+ " cnn_net.add(nn.Dense(220, use_bias=False), nn.Activation(activation='relu'))\n",
+ " cnn_net.add(nn.Dense(1))\n",
+ " \n",
+ "# ... other parts of the GAN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's print the CNN."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sequential(\n",
+ " (0): Conv1D(None -> 32, kernel_size=(5,), stride=(2,))\n",
+ " (1): LeakyReLU(0.01)\n",
+ " (2): Conv1D(None -> 64, kernel_size=(5,), stride=(2,))\n",
+ " (3): LeakyReLU(0.01)\n",
+ " (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
+ " (5): Conv1D(None -> 128, kernel_size=(5,), stride=(2,))\n",
+ " (6): LeakyReLU(0.01)\n",
+ " (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
+ " (8): Dense(None -> 220, linear)\n",
+ " (9): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
+ " (10): LeakyReLU(0.01)\n",
+ " (11): Dense(None -> 220, linear)\n",
+ " (12): Activation(relu)\n",
+ " (13): Dense(None -> 1, linear)\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(cnn_net)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.6. Hyperparameters "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The hyperparameters that we will track and optimize are:\n",
+ "- ```batch_size``` : batch size of the LSTM and CNN\n",
+ "- ```cnn_lr```: the learningrate of the CNN\n",
+ "- ```strides```: the number of strides in the CNN\n",
+ "- ```lrelu_alpha```: the alpha for the LeakyReLU in the GAN\n",
+ "- ```batchnorm_momentum```: momentum for the batch normalisation in the CNN\n",
+ "- ```padding```: the padding in the CNN\n",
+ "- ```kernel_size':1```: kernel size in the CNN\n",
+ "- ```dropout```: dropout in the LSTM\n",
+ "- ```filters```: the initial number of filters\n",
+ "\n",
+ "We will train over 200 ```epochs```."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Hyperparameters optimization "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " After the GAN trains on the 200 epocgs it will record the MAE (which is the error function in the LSTM, the $G$) and pass it as a reward value to the Reinforcement learning that will decide whether to change the hyperparameters of keep training with the same set of hyperparameters. As described later, this approach is strictly for experimenting with RL.\n",
+ "\n",
+ " If the RL decides it will update the hyperparameters it will call Bayesian optimisation (discussed below) library that will give the next best expected set of the hyperparams."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.1. Reinforcement learning for hyperparameters optimization "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Why do we use reinforcement learning in the hyperparameters optimization? Stock markets change all the time. Even if we manage to train our GAN and LSTM to create extremely accurate results, the results might only be valid for a certain period. Meaning, we need to constantly optimise the whole process. To optimize the process we can:\n",
+ "- Add or remove features (e.g. add new stocks or currencies that might be correlated) \n",
+ "- Improve the our deep learning models. One of the most important ways to improve the models is through the hyper parameters (listed in Section 5). Once having found a certain set of hyperparameters we need to decide when to change them and when to use the already known set (exploration vs. exploitation). Also, stocks market represents a continuous space that depends on millions parameters.\n",
+ "\n",
+ "**Note**: The purpose of the whole reinforcement learning part of this notebook is more research oriented. We will explore different RL approaches using the GAN as an environment. There are many ways in which we can successfully perform hyperparameter optimization on our deep learning models without using RL. But... why not.\n",
+ "\n",
+ "**Note**: The next several sections assume you have some knowledge about RL - especially policy methods and Q-learning."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1.1. Reinforcement Learning Theory "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Without explaining the basics of RL we will jump into the details of the specific approaches we implement here. We will use model-free RL algorithms for the obvious reason that we do not know the whole environment, hence there is no defined model for how the environment works - if there was we wouldn't need to predict stock prices movements - they will just follow the model. We will use the two subdivisions of model-free RL - Policy optimization and Q-learning.\n",
+ "\n",
+ "- **Q-learning** - in Q-learning we learn the **value** of taking an action from a given state. **Q-value** is the expected return after taking the action. We will use **Rainbow** which is a combination of seven Q learning algorithms.\n",
+ "- **Policy Optimization** - in policy optimization we learn the action to take from a given state. (if we use methods like Actor/Critic) we also learn the value of being in a given state. We will use **Proximal Policy Optimization**.\n",
+ "\n",
+ " One crucial aspect of building a RL algorithm is accurately setting the reward. It has to capture all aspects of the environment and the agent's interaction with the environment. We define the reward, **_R_**, as:\n",
+ "\n",
+ "$$Reward = 2*loss_G + loss_D + accuracy_G,$$\n",
+ "\n",
+ "where $loss_G$, $accuracy_G$, and $loss_D$ are the Generator's loss and accuracy, and Discriminator's loss, respectively. The environment is the GAN and the results of the LSTM training. The action the different agents can take is how to change the hyperparameters of the GAN's $D$ and $G$ nets."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.1.1.1. Rainbow "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**What is Rainbow?** \n",
+ "\n",
+ " Rainbow (link) is a Q learning based off-policy deep reinforcement learning algorithm combining seven algorithm together:\n",
+ "* **DQN**. DQN is an extension of Q learning algorithm that uses a neural network to represent the Q value. Similar to supervised (deep) learning, in DQN we train a neural network and try to minimize a loss function. We train the network by randomly sampling transitions (state, action, reward). The layers can be not only fully connected ones, but also convolutional, for example.\n",
+ "* **Double Q Learning**. Double QL handles a big problem in Q learning, namely the overestimation bias.\n",
+ "* **Prioritized replay**. In the vanilla DQN, all transitions are stored in a replay buffer and it uniformly samples this buffer. However, not all transitions are equally beneficial during the learning phase (which also makes learning inefficient as more episodes are required). Prioritized experience replay doesn't sample uniformly, rather it uses a distribution that gives higher probability to samples that have had higher Q loss in previous iterations.\n",
+ "* **Dueling networks.** Dueling networks change the Q learning architecture a little by using two separate streams (i.e. having two different mini-neural networks). One stream is for the value and one for the _advantage_. Both of them share a convolutional encoder. The tricky part is the merging of the streams - it uses a special aggregator (_Wang et al. 2016_).\n",
+ " - _Advantage_, formula is $A(s, a) = Q(s, a) - V(s)$, generally speaking is a comparison of how good an action is compared to the average action for a specific state. Advantages are sometimes used when a 'wrong' action cannot be penalized with negative reward. So _advantage_ will try to further reward good actions from the average actions.\n",
+ "* **Multi-step learning.** The big difference behind Multi-step learning is that it calculates the Q-values using N-step returns (not only the return from the next step), which naturally should be more accurate.\n",
+ "* **Distributional RL**. Q learning uses average estimated Q-value as target value. However, in many cases the Q-values might not be the same in different situations. Distributional RL can directly learn (or approximate) the distribution of Q-values rather than averaging them. Again, the math is much more complicated than that, but for us the benefit is more accurate sampling of the Q-values.\n",
+ "* **Noisy Nets**. Basic DQN implements a simple 𝜀-greedy mechanism to do exploration. This approach to exploration inefficient at times. The way Noisy Nets approach this issue is by adding a noisy linear layer. Over time, the network will learn how to ignore the noise (added as a noisy stream). But this learning comes at different rates in different parts of the space, allowing for state exploration.\n",
+ " \n",
+ "#### **Note**: Stay tuned - I will upload a MXNet/Gluon implementation on Rainbow to Github in early February 2019.\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.1.1.2. PPO "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **Proximal Policy Optimization** (PPO) is a policy optimization model-free type of reinforcement learning. It is much simpler to implement that other algorithms and gives very good results.\n",
+ "\n",
+ " Why do we use PPO? One of the advantages of PPO is that it directly learns the policy, rather than indirectly via the values (the way Q Learning uses Q-values to learn the policy). It can work well in continuous action spaces, which is suitable in our use case and can learn (through mean and standard deviation) the distribution probabilities (if softmax is added as an output).\n",
+ "\n",
+ " The problem of policy gradient methods is that they are extremely sensitive to the step size choice - if it is small the progress takes too long (most probably mainly due to the need of a second-order derivatives matrix); if it is large, there is a lot noise which significantly reduces the performance. Input data is nonstationary due to the changes in the policy (also the distributions of the reward and observations change). As compared to supervised learning, poorly chosen step can be much more devastating as it affects the whole distribution of next visits. PPO can solve these issues. What is more, compared to some other approaches, PPO: \n",
+ "* is much less complicated, for example compared to **ACER**, which requires additional code for keeping the off-policy correlations and also a replay buffer, or **TRPO** which has a constraint imposed on the surrogate objective function (the KL divergence between the old and the new policy). This constraint is used to control the policy of changing too much - which might create instability. PPO reduces the computation (created by the constraint) by utilizing a _clipped (between [1- 𝜖, 1+𝜖]) surrogate objective function_ and modifying the objective function with a penalty for having too big of an update.\n",
+ "* gives compatibility with algos that share parameters between value and policy function or auxiliary losses, as compared to TRPO (although PPO also have the gain of trust region PO).\n",
+ "\n",
+ "**Note**: For the purpose of our exercise we won't go too much into the research and optimization of RL approaches, PPO and the others included. Rather, we will take what is available and try to fit into our process for hyperparameter optimization for our GAN, LSTM, and CNN models. The code we will reuse and customize is created by OpenAI and is available here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1.2. Further work on Reinforcement learning "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Some ideas for further exploring reinforcement learning:\n",
+ "- One of the first things I will introduce next is using **Augmented Random Search** (link) as an alternative algorithm. The authors of the algorithm (out of UC, Berkeley) have managed to achive similar rewards results as other state of the art approaches, such as PPO, but on average 15 times faster.\n",
+ "- Choosing a reward function is very important. I stated the currently used reward function above, but I will try to play with different functions as an alternative.\n",
+ "- Using **Curiosity** as an exploration policy.\n",
+ "- Create **multi-agent** architecture as proposed by Berkeley's AI Research team (BAIR) - link."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.2. Bayesian optimization "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Instead of the grid search, that can take a lot of time to find the best combination of hyperparameters, we will use **Bayesian optimization**. The library that we'll use is already implemented - link."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next part of the code only shows the initialization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 629,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Initialize the optimizer\n",
+ "from bayes_opt import BayesianOptimization\n",
+ "from bayes_opt import UtilityFunction\n",
+ "\n",
+ "utility = UtilityFunction(kind=\"ucb\", kappa=2.5, xi=0.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2.1. Gaussian process "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 6. The result "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from utils import plot_prediction"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " Finally we will compare the output of the LSTM when the unseen (test) data is used as an input after different phases of the process."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Plot after the first epoch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 580,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_prediction('Predicted and Real price - after first epoch.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "2. Plot after 50 epochs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 611,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_prediction('Predicted and Real price - after first 50 epochs.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 623,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_prediction('Predicted and Real price - after first 200 epochs.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " The RL run for ten episodes (we define an eposide to be one full GAN training on the 200 epochs.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 636,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_prediction('Final result.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### As a next step, I will try to take everything separately and provide some analysis on what worked and why. Why did we receive these results and is it just by coinscidence? So stay tuned."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# What is next? "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Next, I will try to create a RL environment for testing trading algorithms that decide when and how to trade. The output from the GAN will be one of the parameters in the environment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# About me "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "www.linkedin.com/in/borisbanushev"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Disclaimer "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " **This notebook is entirely informative. None of the content presented in this notebook constitutes a recommendation that any particular security, portfolio of securities, transaction or investment strategy is suitable for any specific person. Futures, stocks and options trading involves substantial risk of loss and is not suitable for every investor. The valuation of futures, stocks and options may fluctuate, and, as a result, clients may lose more than their original investment.**\n",
+ "\n",
+ " **All trading strategies are used at your own risk.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " There are many many more details to explore - in choosing data features, in choosing algorithms, in tuning the algos, etc. This version of the notebook itself took me 2 weeks to finish. I am sure there are many unaswered parts of the process. So, any comments and suggestion - please do share. I'd be happy to add and test any ideas in the current process.\n",
+ "\n",
+ "Thanks for reading.\n",
+ "\n",
+ "Best,\n",
+ "Boris"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.6.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}