{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import radd\n", "from radd import build, vis\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# may take a while to build font cache\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "radd.style_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dependent Process (SS Race) Model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Initial state of Stop process (red) depends on current strength of \n", "# Go activation (green) assumes Stop signal efficacy at later SSDs diminishes \n", "# as the state of the Go process approaches the execution threshold (upper bound). \n", "# (pink lines denote t=SSD, blue is trial deadline)\n", "radd.load_dpm_animation()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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idxCondttypechoiceresponseaccrtssd
028bslgogo110.59851000
128bslgogo110.52021000
228bslgogo110.54511000
328bslgogo110.57161000
428bslgogo110.50521000
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" ], "text/plain": [ " idx Cond ttype choice response acc rt ssd\n", "0 28 bsl go go 1 1 0.5985 1000\n", "1 28 bsl go go 1 1 0.5202 1000\n", "2 28 bsl go go 1 1 0.5451 1000\n", "3 28 bsl go go 1 1 0.5716 1000\n", "4 28 bsl go go 1 1 0.5052 1000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read data into pandas DataFrame (http://pandas.pydata.org/)\n", "# example_data contains data from 15 subjects in the \n", "# Reactive Stop-Signal task discussed in Dunovan et al., (2015)\n", "data = radd.load_example_data()\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Formatting data for radd\n", "## Required columns\n", "* **idx**: Subject ID number\n", "* **ttype**: Trial-Type ('go' if no stop-signal, 'stop' if stop-signal trial)\n", "* **response**: Trial Response (1 if response recorded, 0 if no response)\n", "* **acc**: Choice Accuracy (1 if correct, 0 if error)\n", "* **rt**: Choice Response-Time (in seconds, can be any value no-response trials)\n", "* **ssd**: Stop-Signal Delay (in milliseconds, 1000 on go trials)\n", "\n", "## Optional columns\n", "* input dataframe can contain columns for experimental conditions of interest (choose any name)\n", "* in the dataframe above, the **Cond** column contains **'bsl'** and **'pnl'** \n", " * in the **'bsl'** or **\"Baseline\"** condition, errors on **go** and **stop** trials are equally penalized \n", " * in the **'pnl'** or **\"Caution\"** condition, penalties are doubled for **stop** trial errors (e.g., response=1)\n", "* See below for fitting models with conditional parameter dependencies \n", " * e.g., drift-rate depends on levels of 'Cond'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Building a model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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idxCondacc200250300350400c10c20...c90e10e20e30e40e50e60e70e80e90
028bsl0.99171.01.00.950.600.000.50510.5252...0.59820.49610.51850.53170.53180.53190.54490.54580.55840.5674
128pnl0.97521.01.00.950.800.100.51770.5318...0.61190.51980.53240.54520.55420.55860.56380.57180.58510.6021
229bsl0.99171.01.01.000.900.000.52500.5377...0.59840.52680.54500.54510.54890.55850.55850.57090.58480.5902
329pnl0.96691.01.01.000.750.350.53140.5452...0.62500.53140.53220.54480.54500.55170.56290.57510.58510.5980
430bsl0.94211.01.01.000.800.250.52980.5585...0.63840.53610.54520.56060.58420.58540.59850.60980.61180.6208
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5 rows × 26 columns

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" ], "text/plain": [ " idx Cond acc 200 250 300 350 400 c10 c20 ... \\\n", "0 28 bsl 0.9917 1.0 1.0 0.95 0.60 0.00 0.5051 0.5252 ... \n", "1 28 pnl 0.9752 1.0 1.0 0.95 0.80 0.10 0.5177 0.5318 ... \n", "2 29 bsl 0.9917 1.0 1.0 1.00 0.90 0.00 0.5250 0.5377 ... \n", "3 29 pnl 0.9669 1.0 1.0 1.00 0.75 0.35 0.5314 0.5452 ... \n", "4 30 bsl 0.9421 1.0 1.0 1.00 0.80 0.25 0.5298 0.5585 ... \n", "\n", " c90 e10 e20 e30 e40 e50 e60 e70 e80 \\\n", "0 0.5982 0.4961 0.5185 0.5317 0.5318 0.5319 0.5449 0.5458 0.5584 \n", "1 0.6119 0.5198 0.5324 0.5452 0.5542 0.5586 0.5638 0.5718 0.5851 \n", "2 0.5984 0.5268 0.5450 0.5451 0.5489 0.5585 0.5585 0.5709 0.5848 \n", "3 0.6250 0.5314 0.5322 0.5448 0.5450 0.5517 0.5629 0.5751 0.5851 \n", "4 0.6384 0.5361 0.5452 0.5606 0.5842 0.5854 0.5985 0.6098 0.6118 \n", "\n", " e90 \n", "0 0.5674 \n", "1 0.6021 \n", "2 0.5902 \n", "3 0.5980 \n", "4 0.6208 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = build.Model(data=data, kind='xdpm', depends_on={'v':'Cond'})\n", "model.observedDF.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Header of observed dataframe (model.observedDF)\n", "* **idx**: subject ID\n", "* **Cond**: Baseline(bsl)/Caution(pnl) (could be any experimental condition of interest) \n", "* **Acc**: Accuracy on \"go\" trials\n", "* **sacc**: Mean accuracy on \"stop\" trials (mean condition SSD used during simulations)\n", "* **c10 - c90**: 10th - 90th RT quantiles for correct responses\n", "* **e10 - e90**: 10th - 90th RT quantiles for error responses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bounded Global & Local Optimization\n", "\n", "## Global Optimization (Basinhopping w/ bounds)\n", "tams()** method gives control over low-level parameters used for global opt\n", "* **xtol = ftol = tol**: error tolerance of global optimization (default=1e-20)\n", "\n", "* **stepsize** (default=.05): set basinhopping initial step-size\n", " * see HopStep class in radd.fit for more details\n", " * see get_stepsize_scalars() in radd.theta for parameter-specific step-sizes\n", "\n", "* **nsamples** (default=3000): number of parameter subsets to sample\n", " * number of individual parameter subsets $\\theta_i \\in \\Theta$ to sample and evaluate before initializing global opt\n", " \n", "$$\\Theta = \\{\\theta_1, \\theta_2 \\dots \\theta_{nsamples}\\}$$\n", " \n", " * For each sampled parameter subset $\\theta_i = \\{a_G, v_G, tr_G, \\dots\\, v_S\\}$ (see table below for description of parameters), the vector of observed data $Y$ (accuracy and correct & error RT quantiles) is compared to an equal length vector of model-predicted data $f(\\theta_i)$ via the weighted cost function:\n", " \n", "$$\\chi^2(\\theta_i) = \\sum \\omega * [ Y - f(\\theta_i) ]^2$$\n", " \n", " * The parameter set (or *sets* - see **ninits** below) that yield the lowest cost function error ($\\chi^2(\\theta_i)$) are then used to initialize the model for global optimization ($\\theta_{init}$)\n", " \n", "$$\\theta_{init} = \\operatorname*{argmin}_{\\theta}\\chi^2(\\theta_i)$$ \n", "\n", "\n", "| $$\\theta$$ | Description | str id | Go/Stop |\n", "|:----------:|:----------------|:------:|:-------:|\n", "| $$a_{G}$$ | Threshold | 'a' | Go |\n", "| $$tr_{G}$$ | Onset-Delay | 'tr' | Go |\n", "| $$v_{G}$$ | Drift-Rate | 'v' | Go | \n", "| $$xb_{G}$$ | Dynamic Gain | 'xb' | Go |\n", "| $$v_{S}$$ | SS Drift-Rate | 'ssv' | Stop |\n", "| $$so_{S}$$ | SS Onset-Delay | 'sso' | Stop |\n", "\n", "\n", "* **ninits** (default=3): number of initial parameter sets to perform global optimization on\n", "\n", " * if ninits is 1 global optimization is performed once, using sampled parameter set with the lowest cost error (as described above in **nsamples**)\n", "\n", " * if ninits is greater than 1 then global optimization is performed $n$ separate times, one for each each $p_{i} \\in P_{inits}$ where $P_{inits}$ is the rank ordered set of parameters subsets corresponding to $n-{th}$ lowest cost error\n", " \n", " * The optimized parameters corresponding to the lowest global minimum across all iterations of basinhopping are then selected and passed to the next stage in the fitting routine (local gradient-based optimization)\n", "\n", "* **nsuccess** (default=60): criterion number of successful steps without finding new global minimum to exit basinhopping\n", "\n", "* **interval** (default=10): number of steps before adaptively updating the stepsize \n", "\n", "* **T** (default=1.0): set the basinhopping \"temperature\"\n", " * higher T will result in accepted steps with larger changes in function value (larger changes in model error)\n", " \n", "### Using set_basinparams() to control global opt. params\n", "```python\n", "model.set_basinparams(tol=1e-15, nsamples=4000, ninits=6, nsuccess=70)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sampling Distributions for Init Parameters" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 304, "width": 694 } }, "output_type": "display_data" } ], "source": [ "# sample model.basinparams['nsamples'] from each dist (default=3000)\n", "# and initialize model with best model.basinparams['ninits'] (default=3)\n", "vis.plot_param_distributions(p=model.inits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Local Optimization (Nelder-Mead Simplex w/ bounds)\n", "* **set_fitparams()** method gives control over low-level parameters used for local opt. Local optimization polishes parameter estimates passed from global optimization step.\n", "\n", " * **method** (default='nelder'): optimization algorithm \n", " * (see [here](https://lmfit.github.io/lmfit-py/fitting.html#choosing-different-fitting-methods) for list of available methods)\n", "\n", " * **xtol = ftol = tol** (default=1e-30): error tolerance of optimization\n", "\n", " * **maxfev** (default=2000): max number of func evaluations to perform\n", "\n", " * **ntrials** (default=20000): num. simulated trials per condition\n", "\n", "### Using set_fitparams() to control local optimization parameters\n", "\n", "```python\n", "model.set_fitparams(method='tnc', tol=1e-35, ntrials=30000, maxfev=2500)\n", "```\n", "\n", "### Using set_fitparams() to set/access low-level model attributes\n", "\n", "* **set_fitparams()** also allows you to control low-level attributes of the model, including...\n", "\n", " * **quantiles** (default=np.array([.10, .20, ... .90]): quantiles of RT distribution\n", "\n", " * **kind** (default='dpm'): model kind (currently only irace and dpm) \n", "\n", " * **depends_on** (dict): {parameter_id : condition_name}\n", "\n", " * **tb** (float): trial duration (timewindow for response) in seconds\n", "\n", " * **nlevels** (int): number of levels in depends_on data[condition_name] \n", "\n", " * **fit_on (default='average')**: by default, models are fit to the 'average' \n", " data (across subjects). If **fit_on='subjects'**, a model is fit to each individual \n", " subject's data.\n", " \n", "```python\n", "q = np.arange(.1, 1.,.05)\n", "model.set_fitparams(kind='irace', depends_on={'a': 'Cond'}, quantiles=q)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting Flat Models\n", "- All models are initially fit by optimizing the full set of parameters to the \"flattened\" data (flat meaning the average data collapsing across all conditions of interest). \n", "\n", "### Steps in fitting routine: \n", "\n", "1. Global optimzation on flat data (average values collapsing across any/all conditions)\n", "2. Local optimzation using parameters passed from global optimizer as starting values \n", "\n", "\n", "- Flat model fits are performed by identifying the full set of parameter values that minimize the following cost-function: \n", "\n", " $$\\chi^2 = \\sum [\\omega * (\\hat{Y} - Y)]^2$$\n", " \n", "- *$Y$* is an array of observed data (e.g., accuracy, RT quantiles, etc.) \n", "- *$\\hat{Y}$* is an equal length array of corresponding model-predicted values, given by the parameterized model $f(\\theta)$\n", "- The error $\\chi^2$ between the predicted and the observed data (**$\\hat{Y} - Y$**) is weighted by an equal length array of scalars **$\\omega$** proportional to the inverse of the variance in each value of **$Y$**. \n", "\n", "### Accessing flat weights ($\\omega$) and data ($Y$) vectors \n", "\n", "```python\n", "flat_data = model.observedDF.mean()\n", "flat_wts = model.wtsDF.mean() \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model.optimize()\n", "```python\n", "Model.optimize(self, plotfits=True, saveplot=False, saveresults=True, saveobserved=False, custompath=None, progress=False):\n", " \"\"\" Method to be used for accessing fitting methods in Optimizer class\n", " see Optimizer method optimize()\n", " ::Arguments::\n", " plotfits (bool):\n", " if True (default), plot model predictions over observed data\n", " saveplot (bool):\n", " if True (default), save plots to \"~//\"\n", " saveresults (bool):\n", " if True (default), save fitdf, yhatdf, and txt logs to \"~//\"\n", " saveobserved (bool):\n", " if True (default is False), save observedDF to \"~//\"\n", " custompath (str):\n", " path starting from any subdirectory of \"~/\" (e.g., home).\n", " all saved output will write to \"~///\"\n", " progress (bool):\n", " track progress across ninits and basinhopping\n", " \"\"\"\n", "```\n", "\n", "### saving model output\n", "* By default the model creates a folder named after the model's model_id attribute in your user home directory (model_id is string with identifying information about the model) and saves all model output to this location (saveresults=True).\n", "\n", "* To prevent the model from creating the output directory\n", "\n", "```python\n", "m = build.Model(data=data)\n", "m.optimize(saveresults=False)\n", "```\n", "\n", "* Or to customize the location of the output directory\n", "\n", "```python\n", "# note, custompath must be an existing path to the parentdirectory \n", "# where you want the model's output directory to be created\n", "# save model output to /Users/kyle/Dropbox//\n", "m.optimize(custompath='Dropbox')\n", "```\n", "\n", "* You can also opt to save the model's observedDF: a pandas dataframe containing all subject's stop accuracy & RT quantile data\n", "\n", "```python\n", "# save model output and observed data to /Users/kyle/Dropbox//\n", "m.optimize(custompath='Dropbox', saveobserved=True)\n", "```\n", "\n", "\n", "### generating and saving plots\n", "* By default (plotfits=True), the optimize function plots the model-predicted stop accuracy and correct/error RT quantiles over the observed data. \n", "* You can opt to save the plot in the model's output directory by setting saveplot to True (defaults to False)\n", "\n", "```python\n", "# save fit plot to /Users/kyle/Dropbox//\n", "m.optimize(custompath='Dropbox', saveplot=True)\n", "```\n", "\n", "### progress bars\n", "\n", "* when optimizing a model, you can get feedback about the global optimization process by setting progress to True\n", "\n", "```python\n", "model.optimize(progress=True)\n", "```\n", "\n", "* The green bar tracks the initial parameter sets (current set / ninits). \n", "* The red bar gives feedback about the basinhopping run (current-step / global-minimum).\n", " * If the global minimum stays the same for \"nsuccess\" steps, global optimization is terminated for the current init set and the green bar advances (meaning a new round of global optimization has begun with the model initialized with the next init parameter set).\n", " * If a new global minimum is found, the red bar resets and the nsuccess count begins again from 0." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, max=2)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, bar_style='danger', max=50)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, bar_style='success', max=1000)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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p4ZlnnuGmm27q67BEREREpBts22ZnZZB3tjWyakcT7+1pJho/9u+6TofB+CGJTClM5mMjkpkwNJFEry5PiYiIyMB1sETrtrIA28pb2VbWyraywIEFSCfG7zEZmu1jaJavByLtWQN+BlhVVcWNN94IwHXXXcd1113X0VZYWMhll13Gc889x+23387999/PBRdcQDwe57nnnuOhhx4C2kvaJSaeOiterj4njyUb6thaamPFo6SPnY87OZfKlY9Qt/kFMsZfQtKwGTjcCcQjrbQUr6Ru8/NEmivImfFFEvPPxOlNxJ+UzriChD5NTgKYpoGJgbMfVHOxrEMTlwuXlLNqewOl5WVY9Tux6zZhJOThLPr0MY9rt9WD6QCXHwxnr99pGopYlNaGKK0NHbaPYUBWspvcNA95aR7y0j/0eOC5EpjS3x0uOflhB9vv+cypUzq8J40YMYJ58+axePFinn/++S4TlFu3buXpp5/m3XffpaqqinA4TFJSEmPHjuWiiy7i0ksvxTTbf1+sWrWKG264oePc1atXM3p0+x7K27dv7zTu66+/zgsvvMCGDRuoq6vDtm3S09M544wzuO6663q05KyIiIhIfxIMx1m1vZG3tzSwYlsj9S3RYz7X6TCYMDSRySOSmVyYwqRhiXjd/eALvoiIiMhxCrTF2LwvwMaSFjaWBNiyL0BT8MSSkQleB4W5fgrz/BTm+hie42dYto/MZNcpW5liwCcoo9Foxx6TDQ0Nh7Tfdddd7Nq1iw0bNnDrrbfi8/mIx+NEIu0ry+bPn8+XvvSlXo35WDz8n6O5+RebqCSHYIuLxMGT8eeOo6VkNfXbXqZy5W+xwkFMjx9P2nASh0wnaeg0TKcXtz8Vf1I6eekefnrL6L7+KP2KaRp4TAOP64NEXGMwhhWPYMdCWJUroaUEu24TcX8WjkEzjzpmvGIF8W1/bn+RNBTH6KvIzx9C0eA0vvbpYQTa4rS0xWgJxQm0xdqftx18HicQan9sOdDWHIwROQkrPW2bjlWZG4pbDmk3DchN8zAky8uQLF+nx5xUDw7z1PwlKnKstuwLHDU5edALq2tYMLN/lnsFOOecc1i8eDFbt26lqamJlJSUjrZHHnmEn/70px2v/X4/APX19Sxfvpzly5ezZs0aHnzwQQBcLheZmZkEAgFCoRAul6vTeACRSISvfvWrLFmypONYcnIygUCAyspKKisreeWVV3jwwQe57LLLTuZHFxERETltVDWEeXtLA29vaeDdnU3HvErSYcL4IUlMHdmekJw4VAlJERERGbgsy6a4uo1NJQcTki3srWrDPs5L8A7TYFi2l8I8P0V5Ce2PuX5y09ynbCLycAZ8gvJokpKSeOqpp3jyySdZtGgRJSUlmKbJhAkTWLBgAVdfffUp+Q89wefiD1+ZwDce386WUgO3L5FQoIGUwnNIGTHn0BMMA6cnAW9iGk6Xh3EFCfz0ltEk+Fy9H/wAs78hTDjcvvLQDlZx8N+m+PZnAI6YpIxXrOjod/B8gEgkTH0gypDjWLZt2zZtEYuGQJTG1igNgRiNrVEaAzEaWqM0BqI0trY/r2+JUtcc6ZHStZYNFfVhKurDvLO9qVOb22kwNNtHUZ6fwlw/RXl+igb5yUo+/X7pysD19xX7u9X/2eX7GXd1/1xFWVhYCIBlWezevZvJkycDsGbNmo7k5FVXXcVtt91GdnZ2exmxnTt54IEHWLlyJc899xw33ngjY8aMYfLkySxfvpzvfOc7/OUvf+HMM89k4cKFnd7v//7v/1iyZAkOh4O77rqLBQsW4PP5iEajrFq1iu9+97uUlZXxgx/8gEsuuQSHQxfQREREZOCxbZutZa0s29zA0s317KgIHvO5g9I9nD06lemjU5halEKiT5ebREREZGCKxS22lbWybncz6/c0s6G4hZa2+HGN5XGZjBrkZ8zgRMYMTmD04ASGZ/tw9ZNKhP1+xvjQQw91lGLtyuDBgw8pAfdRbrebz3/+83z+85/v6fBOqgSfi0f+ewLPLK3kySXlBFweLCtOLBIiHg1j2xaGYeJweXC6vZimg0Svg5svyO/zsq4DSTRuY1kWAEY8/EGDbRHf9mes8mWY+XMwMyeC0wexNqzajVjlb2O37Os01sHzLcvq1h4gncYwDPweB36Pg/wM71H727ZNS1ucmuYIdc0Rapqj1DZFqGmOUNvcvmKyurH99fHeFRKJ2eysCLLzI1+Qk32OA3eK+Bmdn8i4IQkMz/FrtaWckpZuPnSVfk/2P52kpaV1PP9w9YKnn34agPHjx3Pvvfd23IBgGAajRo3i4YcfZsaMGdi2zXvvvceYMWOO+l62bfOXv/wFgOuvv75TKXeXy8Xs2bP59re/za233kpDQwMlJSWMGHFie0GLiIiInC4sy2bTvgCvv1/HGxvr2N8QOabz/B6Ts4pSmD4qlbPHpDA4w6ubR0VERGRAisYstpQGWLe7mXW72xOSbRGr2+P4PSaj8hMYk5/AmMGJjB6cwLBsX7++1t3vE5TSviflVbNzWLSmhpfW1lJe56EpGMOy28tqpvid5Gd4mT8lk4umZnXs6yW9w+UwOv7ObYcHI955H0e7ZR/xbU9xLPdY2A4PAKZp4nL0zi8uwzBI9jtJ9jspzPUftl80ZlHVGKGiPkxlQ4jK+jCVDe1/KurCVDcd2xfhD2tui7N+Twvr97QA7atHfW6T0YMTGFeQ2PEnP8PT61+Wp92+stPr1T+Z0avvL6ee1lD36sgHQsd3Z9XpwOX6YHV+a2trx/PbbruNiy66iKysrC5/ZtPS0sjMzKSmpqbTeUdi2za/+c1vKCsr48wzz+yyT1HRBytVj3VcERERkdOVZdlsKG5hyYY6lmyoP+bvYvkZHuaMT2fOuDQ+Njyp39y5LyIiItIdcctme1krq3Y08u6u9oRkONq9hKRhwPAcHxOHJjFhaCIThyYxLNuH2Y+TkV1RgnKAME2TS6bncMn0nL4ORT4iN81DQ1P7SkXDnwMtJcc9luFv/+fr8XjIS/P0SHw9xeU0GZzpZXCmF0g5pD0UiVNaG6K0NsS+mhD7ato6Hhtbjz2x0xaxeG9PC+/t+WCfy/QkFx8bnsSZI5I5c0QyhXlaZSm9L8HrpLkbm10nevtvmdFAINDxPCEhoeP58OHDGT58eMdr27aprq6muLiYHTt2sH79ehobGwGIx48tgWuaJhMnTmTixIkdx6LRKBUVFezdu5dt27axcuUHNxQcXNEuIiIi0p/Yts3G4gCvrK/ljY111DZHj3qOYcCkoUnMHp/GnHFpDM/xaZWkiIiIDEj7G8Ks2tHIqu1NrN7Z1K1rfABJPgcThiYx8UAycvyQRJXERwlKkT43PNtH8f72cjhGahH2iSQoU4swDAOf18uw7O7vP9mXvG4HIwclMHJQwiFtja1RdlcG2VUZZPf+9sc9+4MEw8eWSKhvibJkQz1LNtQD7YmfM4YntZckGp1CYa6/R75o23b7ncjvbGs8pO3bT25neLaPs8ekMmlYkr7YD0DnjE9j0ZqabvXvr+rr6zueZ2RkdGpraWnhmWee4aWXXmLPnj20tbV1aj/en53S0lKefPJJli1bRmlpKbHYBxPJD1cOsI+3FrWIiIjIKaikuo2X19Xw8rpayuvCR+3vcZnMHJPKnPFpzBqbRlqi66jniIiIiPQ3oWictbuaWbmtkdU7miiubjv6SR+SnuRi8ohkJhcmc2ZhMsMH4OrIY6EEpUgfO3tMKm9tbiApMZnmzInEKldgxLr3Cw/AdvpwZE4kKSkZ0+FgxtjUkxBt30hNcDGlKIUpRR+svLQsm8qGMLsqg+wob2VLaYCtpa3UB45+J3AgFGf51kaWb22EFyAz2cX0UalMH92+h8rxfAlfs7OJp9+qZH9jGCseJx5pw45HsG0bwzDYWmxTvN/LW5sbyE31cO25eZ0+j/R/V8zM7VaC8opZuScxmr61ZcsWABwOB6NHj+44vnXrVm6++eaOfSmTk5MZP348hYWFTJgwgRkzZnDjjTdSXl7erfd77rnn+J//+Z+OVZc5OTmMGjWKoqIiJk2axJAhQ1iwYEEPfToRERGRvlXXEuG19XW8vK6GLaVHL1/vdZvMHpvG+WdkMHNMKj5P/63kISIiInI4tc0Rlm1pYNmWBlbvbCLUjX0ks5JdnFmYzJTCFCYXJjMkS/tzHwslKEX62KRhSeSmegiHM2gJNGMWXIC994Vuj2MWXIDhdJOenkFumoeJQ5NOQrSnDtM0yM/wkp/hZe6EdKB95dP+hghbSgMf/NkXOOqmxLXNUf79bg3/frc9eTSuIIG5E9I5d2I6w3MOv68mQCQa59FXy1i+tZFwOEx9XR0tgWYiH3nPsrJGDMMgKTGZcDiDn/wzzOxxaXzu4/m4XboAMBCMG5LIxdOyeGH10ZOUF0/LYmxBYi9E1TeWLVsGwKRJk/D5Pljtfccdd9DQ0EBBQQE/+MEPmDx58iGTuQ+Xhz0WlZWVfPe73yUejzN79my+973vUVBQ0KnPtm3bjvOTiIiIiJwaIjGLtzbVs2hNDat3NBI/yvU0v8dk9rg0zp+UwYwxqXjd+k4iIiIiA4tt2+woD7JsawNvb64/phu7DkryOZg6sn2xy1kjkxmcoYTk8VCCUqSPGYbBNXPzePhfYTIyMqm1x2CF6rArVxz7GHkzMTPGkJGRicfj4dq5eQPyF6JhGOSle8hL93D+Ge1lI2Nxmx3lrazf28z63c28v7eFpqPUCN9S2sqW0lZ++1IpQ7O8nDuxPVk5dnBip6X4kWicHz63ly2lrdTV1VJXV4sdi2DVbqRy9ybCzfuxoiFMl5fC4cMwUotozpxIS6CZjIxMlm2B+pYI37p8uJKUA8SdVxQCHDFJefG0rI5+/dGOHTtYtWoVAJdeemnH8V27drFz504AvvrVrzJlypRDzq2urqa5uRk49lKsS5YsIRKJAPDAAw+Qk3PoXsy7d+/ueK4SryIiInI62V0Z5F+rq3np3Zqjfs9xOw3mjE/nwjMzOXtMCl59BxEREZEBxrJsNpUEWLyhjjc21FHVGDmm8xymwcRhiUwflcrZo1MYMzgRh0q2njAlKEVOAVNHpjBrbCrLt0IkEqWZWVjeDKzSxUcs92o7fZgFF2BmjCE5OZWMjExmj0tT6dAPcToMxg1JZNyQRK6dOwjLstlb3cbaXU28s72JtbuajrjCsqQmxJNLKnhySQXZKW4unJzJ/CmZFOUl8OirZWwpbaWyspLm5kasum0d/8za6oIdY1jRNmgpwW4pIVa5ArPgAmrtMUQiUSCPx14r58ufGtILfxvS15wOg3s+U8SCmbnc9LONndounprFglm5jOvHKyeDwSDf/va3ASgoKODyyy/vaGtt/eAuNcvq+mfyl7/8ZUcCMRrtXM7Z4Wi/wHawjGtX43aVfAyHwzzyyCMdrz+8N6WIiIjIqag1FOe192p5flU1m/YdubqEYcDkwmTmT87ivEnpJPp0GUhEREQGlrhls6G4hdffb09K1jQffYswgOwUN3PGpzFjdCqTi5JJ9Goe1dP0Nypyivj8JwbTEGhPWLndLuoMAzO1EKt2I3bjLuxgFUY8jO3wYPhzMFKLcGROxHC6ycjIJCMjk3EFCXzu4/l9/VFOaaZpUJjrpzDXz1Wz84jGLDYUt7BqRxPvbG9kW9nhl/JXN0VY+EYFC9+oID/DQyhiEQs1tycny5dhV67kaPfNGLE27L0vYIXqaGYWbreLZVvak9RTRyqxPFB0lYS85+qiPoikd9TX17Ny5Up+85vfsGvXLlwuFz/60Y9wu90dfUaOHInf7ycYDPKLX/yC4cOHM2nSJKB91eVvf/tbXnzxxY7+oVCo03skJycDsG/fPpqamkhJaf95OuOMMzr63H333dx3333k5eURj8dZtWoVP/rRj9i+fXtHn7a27u8BLCIiItIbtpYG+PuK/Sx+r+6o21gU5fmZPyWTT5yZSU6qp5ciFBERETk1WJbNe3ubef39OpZsqKeu5diSkuOHJDJ7XBpzxqUxcpB/QFYp7E1KUIqcItwuB9+6fDiPvVbOsi2QmJhEfX0dLc4p2DmTD+lvGAZJScmkp2fg8Xi0n+FxcjlNphSig/XsAAAgAElEQVSlMKUohS9/agi1zRHe2lTPmxvreXdXM3Gr63KP5XVhAGzbi+H0Q7QVDBPsY9s82a5cgeXNoM4wSExM4um3KjmrKFn/0ZPT2uOPP84zzzzT8dq2bVpbWzslEzMyMvjJT37CmWee2elcv9/P7bffzv/+7/9SVlbGlVdeic/nw7IswuH2n7ehQ4eSnp7O+vXrKS8v73T+5MntvydramqYOXMmbreb1157jenTp3PhhRfyyiuvsGzZMs4991ySkpIIBoMdqy1nz57Ntm3bqK2tPWRcERERkb4UiVkseb+Ovy7fz6aSI6+WTPY5mH9WFhdPy2bUoIReilBERETk1LG7MshLa2t4eV0t1U1HL9/qdZtMG5nCnPFpzBqbRmay+6jnSM9RglLkFOJ2Ofjyp4YwdWQKT79VicfjISc7h7ZQiHA4jGVZmKaJx+PB5/ViOhzkpnq49tw8lXXtIZnJbhbMzGXBzFxa2mIs39rAmxvrWbGtkVAXdykbhoHhz8Kc9AXsUANW5Uqs5hIGDZ1I0uCP4XD5iEfbMOo3YZUtxW7Z13GuVboYM7WQ+vo6PB4PG0tamDQsuTc/rkiPCgaDBIPBTse8Xi95eXkUFRUxb948Lr30UhISur5gdt111zFo0CCefPJJNm/eTDAYJCkpiXHjxnHhhRdy1VVXsWjRItavX8/y5csJBoP4/X4A5s6dy5133snChQupqqoiISGBiooKMjMzefjhh1m4cCH/+te/2LNnD6FQiPT0dMaOHctll13G/Pnz+eY3v8miRYt47bXXuOaaa07635WIiIjIkVQ1hvnHyir++U419YEj3/E/dWQKn56ezdwJ6XhcZi9FKCIiInJqqG2O8Mq6Wl5aW8OOiuBR+/vcJnPGpXHeGRnMHJOK160FP33FsLvakEl63fXXX8/q1auZNm0aCxcu7Otw5BRg2zYbS1pYubWR4uo2KhvCROM2LodBXpqHYdk+ZoxNZeLQJK266wXBcJylm+p5eV0tK7c3cry/OeMVK4hvf6ZjpaVRcD6O3CkUFY5k3qRMvjhfe1EOFNNuX9np9eqfzOijSEROf5pHiYhIf2DbNuv3NPPXZft5a1M98SMUZ8lOcXPR1PbVkvkZ3t4LUvolzaVEROR0E45avLmxnkVrqlmzs4nDFMHrkOBxMHtcGuefkcHZY1LwqgrhKUErKEVOUYZhMGlYslbUnSL8HgefnJLFJ6dk8Y3HtrJhTwNNwQiGo3v7uTgGzQQgvu3PAO37i+ZMpi0Uorhae9+JiIiIiAw0sbjNGxvqWPhmBdvKWo/Yd/qoFK6clcuscWk4TN2oKiIiIgPL7sog/1pVxYtra2kOxo7Y1+s2mTs+nY9/LIPpo1NVaeIUpASliEg31QeiOAlhtVYT3/kcjtxpmDlTMZzHVqPcMWgmVvky7JZ92MEqAMLhMJUN4ZMZtoiIiIiInEKC4TgvrK7mz0srqaw//HeBBK+Di6ZmccXMXIZm+3oxQhEREZG+FwzHee29Wv61qvqoe3KbRnv5+/lTsjh3Yjp+j1ZKnsqUoBQR6aZo3MayDtRbatpDvGk3ONw4cqce8xjm4HOIb/0TRrz9QoRlWUTjqrg9kKikq4iIiMjAVNsc4W/L9/Ps8v00t8UP2294jo8rZ+cyf3IWCV5dXBMREZGBZff+IH9btp+X19UQDB+h9j0wOj+BT07J5MIzM8lMPrZFJNL3lKAUEekml8PANNtLAtgOD0Y8hJkxrltjmLnTsJv2Eq9e3/7aNHE5VKJJRERERKS/2t8Q5o9Lynl+dTWR2OFvTpwzLo1r5uYxpTAZw9B3BBERERk44pbN0s31/HXZftbuaj5i32S/k0+dlcWnp2VTmOfvpQilJylBKSLSTblpHhqavAAY/hxoKQGHt1tjGIaJc8w1OIZeiGXHcLs95KV1bz9LERERERE59ZXXhXhySTmL1tQQO0zVFJfD4FNnZXHt3EEMy1EZVxERERlYGluj/GtVNc+u2M/+hsgR+04dmcKnp2dz7sR03E7tK3k6U4JSRKSbhmf7KN7vxTAMjNQi7JYSiIfATOj2WIYvHQfQGDJIUE10EREREZF+o6SmjScWl/Pyuhrih6lKluxzsGBWLlfOylU5MhERERlwiqvaeOqtCl5eW0P4CBUmMpNdXDQ1m0umZTM4s3sLReTUpQSliEg3nT0mlbc2N5CUmExz5kRilSuwajfiyDv7uMcMRW2eX1ND1LK57aKhujghIiIiInKaKqlp47FXynjtvVqsw1xny01zc+3cQVw8LRu/blQUERGRAWZDcQt/XFLO0s0NR+z3sRFJXDU7j3MnpOF0aLVkf6MEpYhIN00alkRuqodwOIOWQDNmwQVYZUu7laCMV7+HmXXGIXvKvLS2lqWbG/jS/AIWzMzFYWrPGRERERGR00FVQ5jHXitj0Zrqw66YzM/wcNP5+XxqShYulSQTERGRAcSybJZvbeCPb1Tw/t6Ww/bzOA0unJzFVbNzGZXf/Yp1cvpQglJEpJsMw+CauXk8/K8wGRmZ1NpjsEJ1xCtW4Bg086jnxytWYDUVgz8bT1IOMbvzHdOtoTg//kcxi9bU8P8WDGf8kKST9ElERERERORE1bdEeWJJOc8u30/0MHtMDs3ycvMFg/nEmZk4HboJUURERAaOWNzipbW1/OnNCvZWtR22X16ahwUzc7hkejapCa5ejFD6ihKUIiLHYerIFGaNTWX5VohEojQzC6tuO+xfjSN32mHPi+9fjRUOYObPIjkxgbzcJEbk+tizv41dlcFOfbeVtXLLLzZx7dw8vvDJArwulX4SERERETlVBNpiPPVWBU8vrSQY7nrJZGGuj1suGMx5Z2SoOoqIiIgMKLG4xaI1Nfzh9XIq68OH7TdmcALXzxvEvIkZupFrgFGCUkTkOH3+E4NpCESBPNxuF3WGgR2LENv3Bm1mGr6sIhwuL/FoCKN5L3ZbLUZSAY6EXDIyMsnIyGRcQQLfunw4pmnyt+X7eeTlfZ0ubtg2/OnNSpZtaeQ7VxcyYahWU4qIiIiI9KVozOLvK6r4/WtlNAdjXfYZkuXlixcWcP4ZGZhKTIqIiMgAEo1Z/Pvdoycmzx6dwvXz8jmrKPmQbbBkYFCCUkTkOLldDr51+XAee62cZVsgMTGJ+vo6WpxunBGLaKCW6MG+nmRMbwpJScmkp2fg8XiYPS6Nz308H/eBlZHXnJPH+Wek87N/lbD4/bpO71Vc3cbnfrmJ6+cN4vMXFuDWfjUiIiIiIr3Ktm3e3FTPrxbto7Q21GWfnFQ3n/vEYP7jrGytABAREZEBpSMxubicyoauE5MOEy44I5PrzxvEqEHaX3KgU4JSROQEuF0OvvypIUwdmcLTb1Xi8XjIyc5hZ1kjdjyCbdsYhsHgnGR8Xi+mw0Fuqodrz81jSlHKIeNlp3h44IZRXLytkQf+tpuqxkhHm2XDk0sqWLmtkftvGMXQLF9vflQRERERkdOGbdtsLW1l1Y5G9la1UVkfJhK3cDtM8tI9DM/xMX1UKmMLEo7pjv2tpQF+9nwx6/e0dNmelujk5vMHc9mMHDwu3UwoIiIip5cTmTtZls2r79XyyMullNd1nZh0OQwumZ7N9fMGMSjd2xsfSU4DSlCKiPSAqSNTOKsomY0lLazc2sie6jDwQQJx3LB0hmX7mDE2lYlDk456EWTGmFSe/uYZ/PyFEv61qrpT246KIDf8dAP/b8EIPnVW1sn4OCIiIiIip6339zbz9NJKKurDWPE4wbYQ4XAIy7IwTZO6Ri97Kr28sbGeQekePnvOICYN73orharGML95cR8vra3tsj3B4+C6eYO4ek4eCV7tGS8iIiKnn+OdO9m2zcptjfz6xX3srAh2ObbLYfDp6dnceF4+OWmeXv5kcqpTglJEpIcYhsGkYclMGpbM7xeXd2p78MbR3R4v0efk7qsKOW9SOvf/dQ/VTR+spmyLWHzv6V2s2dnEHZcPx+/RxRARERERGdiiMYsnl5SzdHMD4XCY2tpaWppbsGz7kL6mYZCUnEQ4nMmP/xlm7oR0bpg3CNeBrRTCUYs/vVnBE6+XE45aXZwPl56dwxcuLCA9yXXSP5uIiIhITzuRudOYwQkUV7fx3mGqS7gcBpeenc0N5+WTk6rEpHRNCUoRkVPcjDFpPH3HGTz4tz2H7E3573dr2LwvwI9uHs3QbJV8FREREZGBKRqz+NnzxWwsCVBTU0ttbS12PIxVuwm7YSd2sAriYXB4MPw5WGkjaYpPoLm5hczMTN7aBPUtEb52yTDW7Gzix//YS9lhSpTNGJPKVy4eSmGuv5c/pYiIiEjPON65U1NTMy5/KltLu64Q5zDbE5M3na/EpBydEpQiIqeBJJ+T+68fydSRKfz0n3sJxz64k6m4uo2bfr6R+64byayxaX0YpYiIiIhI33hySTkbSwKUV1TQ1NiEXb+V+L7FEGvr3DEewm4pwW4pwapYjmPIBdTYY4lEI0RjuVz7k/cprg51+R4jcn189eJhzBiT2gufSEREROTk6e7cKd5cTMP2N3AN/TgJeRNweCK4EjI6JSk/8bEMvji/gIJMLaKQY6MEpYjIacIwDC6bkcPEYYncvXAne6s+mDC0huJ84/fb+PL8Idxw3qCj7nEpIiIiItJfvL+3maWbG6ipqaWpsQmrYhlWxYpOfYykIZiDz8HMnAgOL8RDWLUbscqWQls99dZMGlptnL5Dk4+pCU7+a/4QLpmWjdOhebaIiIicHFv2Bfj7iv0s3dxAayhGgtfJOePTuGJWLuMKEnvsfY5l7nSQDTQHY9S1RIhbQONzpLXWkVY0F8PhxOVLZXxBAt++opDRgxN6LEYZGJSgFBE5zRTlJfDEVyfy0LN7eGltbcdx24Zfv7iPHRWt3POZQrxu7UspIiIiIv2bbds8vbSyY98ku35r5wtsholj9NU4Bs3sfKKZgCPvbBx5Z9O0520iZZtIzBuP6fJjOt3tpxqwYEYOX5xfQIpf+0yKiIjIyRGL2zz49928sLqm0/HmYIxFa2pYtKaGi6dlcecVhSd8s9RR504f0haJU9MU6VTJDaBh11u4EjJIzp9Ibk4aOWkeRuWr9L10n9nXAYiISPf5PA6+d00RX7tkKOZH5iWvvVfHl3+7hYZAtG+CExERERHpJVtLW6moD3fsmxTft7hTe5fJyY9IGTEHlz8NKx4mFmoCoDDHxxNfnci3FoxQclJEREROqq6Skx/1wuoaHvz77hN+r6PNnQBils3+hjBldeFDkpMATtPAUf46LiNKa0sjFfVhtpa1nnBsMvBoBaWIyEmw+iczTvp7GIbBZ+cOojDXz90Ld9DcFu9o27QvwOd+uYlffGEs+Rnekx6LiIiIiEhfWLWjESsep6W5Bat2U6d9k4ykIUdNTh6UOmImtdtew+H0kp3i5ONnZjK2B0upiYiIiHRly77AUZOTB72wuoYFM0+s3OuR5k420NQapa4linVoXhIDSEt0kZboxDSiWLWbaHFMwcrNYdX2xh4tQysDgxKUIiKnuemjU3nia5P45h+2sWf/B5OK0toQ//mLTTz8uTG6uCIiIiIip7Rpt6887nPjkTYikTiVuzbSVhfsOD5o6EzSujGOJzEL27aprGnhdy/H+N3Lpccd00G9ceOiiIiI9I0Tmb8cr5t+tvGEzv/k5EyCbSEs28Zu2NlxPBS1qG4KE452kZkEkrwOMpLduD5UYtZu2ImVPZlgW4ji6rYuzxM5EpV4FRHpBwZnenns1gmcVZTc6Xh9IMp//WYzK7c19FFkIiIiIiInlx2PABBu3t/peNLgj3VrHF9WUafxRERERPqbyvow4XAIADtYRdyyqW6KUFob6jI56XEaDM7wkJvm6ZScPHg+QDgcoqIufPKDl35HCUoRkX4i0efk558fy4VnZnY63haxuP3x7byxsa6PIhMREREROXlsu/1imhUNdTrucPm6NY7D5e00noiIiEh/E4lbWJYFQKA1yL6aNpqCsUP6mQZkJbsoyPThczu6HizenpS0LItI3DppMUv/pQSliEg/4nKafP+zRVw/b1Cn47G4zV1/3MFr79X2UWQiIiIiIieHYbTfzW+6Ou+9Ho92r9RY/ECC8+B4IiIiIv2N22ECBtG4RVXAINZFXjHJ62Bolo/UBBdHnBY5PACYpnlgXJHu0R6UIiL9jGka3HbRULJT3Pzkn8Udx+MW3POnncTiNvOnZPVdgCIiIiIiH3G8ezV+56mdbNoD+/Y1UTR8GHZLCTHLproxQkvZe6QVzj7msczmvXg8KQzJS2FiYSbf/+zI44pJREREBoae2Gv63md2sWhNzTH3v3hqFvdcXXRc72XbNl/41WbqWsGywJOcS1vd3o52l8MgO8WN33OYFZMfYfhzAPB4vAzK8BxXTDKwKa0tItJPfWZOHvd8prDTnU6WDd97ehfPr6ruu8BERERERHrI8Bwffp8X0zAw0kYSCMXYV91GazhO/Y4l3RrLCtViGgZ+n5dh2d0rDysiIiJyPK6Ymdu9/rO61/+gmqYId/xhO+8Xt2CbbgD8OaMBMID0RCdDs3zHnJwEMNJGau4kJ0QJShGRfuziadl8/5oizA8lKW0b7vvrbhatUZJSRERERE5v00elYjocJCYlEUsdR1XAQfzAFpKh+mIadr19TOPE96/GTCogKTkZ0+Fg+ujUkxi1iIiISLtxQxK5eNqxVTq7eFoWYwsSu/0er62v5eofvcfSzQ0AGKYDhyeBpPwz8Pn8FGR6yUhyH7mc60c5fZiZEzR3khOiBKWISD/3ySlZ3Hf9KBxm51nGfX/Zzevv1/VRVCIiIiIiJ25sQQKJXgdtJGAbbjLHze/UXr9+IdGyFUccI16xAkKNGA4PmZkZDEr3MHZwwskMW0RERKTDnVcUHjVJefG0LO68orBb47a0xfjOUzu5+087aWmLd2pz+VJwu70MmXwxHlf300SOIRdo7iQnTHtQiogMABeckYHLYXDnH3cQO3BLuWXDPU/txOs2mTU2rY8jFBERERHpHsuyeeqtCtbtbsbChdOfSmLeBKKtdTTueov0JBdpCS7sHX8mWrkMM38OZuZEcPog1oZVuxGr/G2MpALMQbPIzMzE4/Hw2XMGYXRrCYGIiIjI8XM6DO75TBELZuby7PL9LN3cQCAUJ9Hr4JzxaSyYlcu4bq6cXLurie89vYuqxsghbcNzfBj4aGqMU2MaEKrHqlh+zGObg2ZhpI/V3ElOmBKUIiIDxNwJ6dx/3UjuWriDuNV+LBa3+fYT2/n5F8YyuTClbwMUERERETlGtc0Rvv/0LlbtaOo45vSmYMdjpI+cS0ZWDkbZ6xBrA8Bu2Ud821N0Wjvg9LXf/Z8+lpTUFLKyMpk7IZ1Jw5N698OIiIiIAOMKEhl3ddEJjRGJWfzupVKeeqsC2+7c5nIYfGn+EK6Zm8cfFpexdLNJJBqhiVkY3nTi+xZ3zJ26pLmT9DAlKEVEBpB5kzK45zNFfO/pXR3HwjGb2x/fzqO3jqcoT+UYREREROTUtmJrA99/ZhcNgVin44ZhkJ2Tixlrps7lwE4vwqrdhN2wEztYBfEwODwY/hyMtJGYmRMOlCbLJCsrk4lDE7lh3qA++lQiIiIiJ2ZvVZD/+dNOdlYED2kryvPz/c8WMXJQ+7W/G8/LpyEQBQbhdrmpNQyM1ELNnaRXKUEpIjLAfOqsLIKROD98dm/HsdZQnK89uo3ff2UCOamePoxORERERKRrsbjN/71SyhOvlx/Slux38u0FIyipaeOtTR5SkpOpra2jxTEFK3vyIf1NwyApOZnMzAw8Hg9zJ6Rzw7xBuJzd34NJREREpK8tWlPND5/bSyhidTpuGPDZc/L4r/lDOu016XKafO2SYfzxjQre2gTJyUmaO0mvU4JSRGQAumJmLq2hOL/+976OY9VNEb726FYevXUCiT7950FERERETh21zRHueWona3c1H9I2uTCZ73+2qONGu7OKknl6aSUejwcrN4dgW4hwOIRlWZimicfjxe/zYjocDEpv3zdJpclERETkdNQWjvOjf+xl0ZqaQ9qyU9x877NFnFXU9bZOLqfJf358sOZO0md0BVpEZIC6Yd4gqhsj/G35/o5ju/e3cccT2/nF58fqDigREREROSWs293M3Qt3UNcS7XTcYcLnPlHATefn4zCNjuNnDE9m0rAktpa1smp7I8XVbVTUhYnELdwOk0EZHoZl+5g+OpWxgxMwDOOjbykiIiJyytu9P8hdf9zB3qpD9438xMcy+NaCEST7j54C0txJ+ooSlCIiA5RhGHzj0mHUNEd4c2N9x/G1u5r5wbN7ufuqEZpwiIiIiEifsSybP71ZwW9f2ke8c7UyMpNd3H/9KM4ckdzluYZhMK4gkXEFib0QqYiIiEjvsW2bF1bX8KN/7CUc7TxJ8rhM7rhsOBdPy+rWdT3NnaQvKEEpIjKAOUyDe68t4r9/u4WNJYGO48+vrqYoz8/V5+T1YXQiIiIiMlAFQjG+9+ddLN3ccEjblKJk7rtuJBlJ7j6ITERERKTvhKJxfvD3vfz73UNLug7P8fHA9aMozPP3QWQi3af6fSIiA5zX5eAnt4xhcIan0/GfPV/MO9sb+ygqERERERmoSqrbuOXnm7pMTt58QT6/+uI4JSdFRERkwKmsD/OFX23uMjn5H2dl8cRXJyo5KacVJShFRITURBc/vmUMCR5HxzHLhrsX7qCk5tA69iIiIiIiJ8OyLQ3c9PONFFd3noMm+508/LkxfGn+kE77TYqIiIgMBGt2NnHDwxvYVtba6bjXbfKdqwv57jVF+D50XU/kdKAEpYiIADAi189914/kw+XpW9ri3PH4dlpD8b4LTERERET6Pdu2+cPiMm5/fNshc8+xBQks/PokZo1N66PoRERERPqGbdv86Y0KbntkC03BWKe2Ydk+nvjqRC6amt1H0YmcGCUoRUSkw6yxadz6H0M6HSuubuOBv+3Gtu0+ikpERERE+rNgOM6df9zBb18q5aNTzvlTMnnkv8eTl+7p+mQRERGRfqotHOd//rSTXywqwfrIHGnuhDQe/+oERuSqpKucvpx9HYCIiJxarjt3ELsqg7y0trbj2Gvv1TFpWBKfmZPXh5GJiIiISH9TWR/m9se3sasy2Om4w4SvXDyMq+fkYhgq6SoiIiIDy+HmSIYBX/xkATedl4+psvdymlOCUkREOjEMgzuvGMHOimCnSdDPXyhh3JBEJg5N6sPoRERERKS/2FTSwjf/sJ36lmin48l+Jw9cP4ppo1L6KDIRERGRvrOxpIU7Ht9OfaDzHCnJ5+B/rx3JTJW9l35CJV5FROQQXreDh24cRYL3g821Y3GbO5/cQeNHJkciIiIiIt21+P06vvSbzYckJ4vy/Dz5tYlKToqIiMiA9Or62vY50keuvxXm+njiaxOVnJR+RQlKERHp0pAsH9/5TGGnY9VNEf73L9qPUkRERESOj23bPPF6OXf9cQfhWOc55byJ6fz+tgnkZ3j7KDoRERGRvmHbNo++Usr//Gknka7mSF+ZSEGmr4+iEzk5lKAUEZHDmjcpg8/O7bzv5NtbGnh2RVUfRSQiIiIip6tozOLeZ3bzmxf3HdJ243mDePCGUfg8ji7OFBEREem/wlGLe57ayaOvlh3SdtP5+Tx4wyj8miNJP6Q9KEVE5Ihu/f/s3Xd0FWX6B/Dv3H7Te0ISUkjovVdBEBALTcoKi4Ai4rq6ujZQEbssuq66YkNXUQFRQaQISA8iGFAQCYTEhCSE3JDek1tnfn/klwsjJCRww6R8P+fkmPvcd+Y+c44kb+Z553lvi8DvaWVIOFvujL29KR2923khpo2bgpkRERERUXNRWmnHkyuTcDS1VBZXqwQ8Pa0dxg8IUigzIiIiIuUUldvw+CencSKjXBbXqAU8Mz0Gt/ULVCgzosbHJyiJiKhOGrUKL81qD/eLVmpZ7BIWr0qG2eZQMDMiIiIiag5yiiyYvzzhkuKkl1GNdxZ0ZnGSiIiIWqWsAjPufSfhkuKkt5sG797fhcVJavFYoCQioisK8zdg4ZRoWSz1fBXe2Xxpey4iIiIiohqp2ZWY904C0nKqZPFwfz3+94/u6BfrrVBmRERERMo5fa4c8/6bgMx8syweFWTEpw93R+92XgplRnT9sEBJRET1Mq5vIG7pGyCLffPTeRxOLlEoIyIiIiJqyo6mlmD+8gTkllhl8Z7RnvjkH90RGWRUKDMiIiIi5RxOLsb9751EYblNFu/f3hv/+0c3hAcYFMqM6PpigZKIiOrtiTuiEeavl8Ve/joF5Wa7QhkRERERUVO0+3gBHvowEeVm+ZYAo3r4YfmCLvDx0CqUGREREZFyth/NwyMfn0alRZTFx/UJwFv3doKnUaNQZkTXHwuURERUbx4GDZ6fEQtBuBA7X2TF25sylEuKiIiIiJqUrw9k4+kvkmFzSLL4tKHBeOWuDtBreSuCiIiIWp/VcSYsWZ0C+5/mSLNuDMXzM2Kh1XCORK0L/48nIqIG6RnthZnD28hiG+NzcTCxSKGMiIiIiKgpkCQJH24/i39vSIckv++GB26NwOOTo6FWCZc/mIiIiKiFkiQJ/92ccdkF/o9MiMQ/xkdCxTkStUIsUBIRUYMtuKUtov60Z9ArX6eitJKtXomIiIhaI1GU8MZ36fjfzixZXK0CltwZg7k3hUEQeOONiIiIWhdRlLBsfRpW7TPJ4hq1gJdntcfMEaEKZUakPBYoiYiowQxaNZbcGYOLF3flldrwzha2eiUiIiJqbewOCS9/nYqvD5yXxY06Ff4zrxNu7x+kUGZEREREyrE7JLywNgXfHsqRxd31arx1b2eM7R2gUGZETQMLlEREdFW6RXpi9qgwWWxjfC6OppYolBERERERXW9Wu4jFq5Kx5UieLO5lVOPd+7tgcCdfhXZGWLMAACAASURBVDIjIiIiUo7VLuKZL5Kx7dd8WdzXQ4MPHuiKAR28FcqMqOnQKJ0AERE1X/eODce+E4VIz61yxpZ+cwarHusJvZZrYIiIiIhaAkmSkJhZgfjkYqTlVCG70AKrQ4RGJcBUaEFBmU023t9Ti+ULuiCmjZtCGRMRERG5Rm3zIJ1ahTZ+ekQHGzGwgw86t3V3trM3Wx1Y+FkyDp0ulp0r0EuLd+/viqhg4+U+iqjVYYGSiIiumk6jwlPT2mHBuyedsYw8M1buPocF4yIUzIyIiIiIXOF4Wim+3J8NU6EFosOByiozLBYz7A4RxRUO2CUNVBodBJUaQHVxcsWDXdE2gDfeiIiIqHmrbR4kiiJUKhUKig04k23A3hOFCPXTY+bwUMS0ccNjn5zG0dRS2bna+Onx3v1dEOZvUOhqiJoeFiiJiOia9G7nhcmDgrDh51xn7LM9JozuFYCYEK6aJyIiImqObHYRn+3Jwv6TRbBYLMjPz0dZaRlESQIgwWqXIEkXxqv17jB6+MDXww07jhVg9shQaDXsqEFERETNT93zIDmVIMDTyxMWSwBe+9aM4go7ckussjGRgQYsv78Lgn301+sSiJoFFiiJiOiaPXh7JPafLHK297I7JPzrmzNY8WBXZ3sLIiIiImoebHYRb21Kx4mMcuTl5SM/Px+SwwIxPwH2wmSYss6iqrIKKq0Beq8QuAV3hHdYL9ikKhTp7YhLEFBYZsUjE6JYpCQiIqJmpa55kFT0B6TKHMBhAdR6CG7BEH3bo8TRDaWlpRD03pC0XrJ7YbFt3LB8QRf4eWoVvCqipqlFFii3bt2KNWvW4NSpU3A4HAgPD8e4ceMwb948uLk1/Gme3NxcfPzxx4iLi4PJZIJGo0F0dDTGjRuH2bNnw2DgY9lE1Lp5GjV4bHI0nv482Rk7nl6Gbb/m49Z+gQpmRkREREQN9dmeLJzIKEeWyYSS4hJIhYlwnN0Fh7USWQVmWOzVTw+ItipUFaQBpRnwLjkMKWoM8iTAarMCCMXne02YNyZc2YshIiIiaoDa5kGwV8kHOsyQyjIglWXAfu4AqoJHwhDUFWq9DVp3fwiCgK4RHnhrfid4u7E4SXQ5La5AuWzZMnzyyScAAK1WC61Wi5SUFCxfvhxbtmzB6tWrERAQUO/zJSYm4u6770ZRUREAwGAwwGq14uTJkzh58iQ2b96MlStXwt/fv1Guh4ioubiphx+GdvbBT4kXNgB/Z0sGhnfzhYehxf26ISIiImqRjqeVYv/JImTnFKDUrIbgHgzBIwyq0KEwZ/0GwbYHKEx3jjfqVAj100MlmuE4sxkqcyFKMBQ6rQ5xCUC/WC/0jPZS7oKIiIiIAJw6W451B89j/8kiVJjtcDdoMLyrL6YODUGXth4ALsyD8vLyUVJcAtF0AKLpYJ3nFSUJWbnFMGeth29sPnxjR0BQa+Dl7YcF49qyOElUhxZ1x3jTpk345JNPoFKpsGjRIsyYMQM6nQ7x8fFYtGgR0tPT8fjjj2PlypX1Op/VasVDDz2EoqIidOjQAS+99BJ69uwJm82G7du34/nnn0dycjKefvppfPjhh417cURETZwgCHhsUjQOJ/8Gm6N6VX1BmQ2f7DyHf4yPUjY5ImoQm82GEydO4Pjx40hLS4PJZEJ5eTksFgsMBgM8PDzQtm1bREdHo3fv3ujale2ciYhaAkmSsDrOhHP5lSi3GSDoLnpT5wGv6GHwih6GopQfYTr8GYxaVBcnL/odIJp+gmDwQ74gwMvLE1/uz0aPKE/+niAiIiJF2B0Slq5LxebDebJ4aaUdW47kYcuRPIwfEIhFU9rhy/3Zzj0npcLE+hUnCyww20QAQFFKHLTu/vAK646gcH9893MOBnbw5jyIqBYtpkDpcDiwfPlyAMC9996LOXPmON8bOHAgVqxYgYkTJ+LQoUM4dOgQBg8efMVz7t+/H5mZmVCpVPjggw8QFhYGANDpdJgwYQLMZjOeffZZ7Nu3Dzk5OQgODm6ciyMiaibCAwyYNTIUn+7Kcsa+3H8eEwYEIyrYqGBmRFQfBw4cwLfffov9+/ejoqKi3sd5eXlh9OjRmDhxIgYMGNCIGRIRUWNKzKzAsdQylJulOsf5xt4AjRrQnlkrK07WcJzdBcEnBvn5BdDr9Ug8V+F8MoGIiIjoerpccfLPNh/OQ3G5HXmlVueek46zu+o8RpQkmC4qTtYo/+MH+Id1QmFhIYxGA+dBRHVoMbvVHzx4EBkZGRAEAXPnzr3k/fbt22PUqFEAgI0bN9brnGfOnAEABAQEOIuTF7v4Btz58+evImsiopZn7qgwBPtcWG7vECW88V0aJKnuG11EpAxRFLF+/XrcfPPNmD9/PrZt24by8nJIklTvr5KSEnz77beYM2cOxo8fj40bN0IUxSt/OBERNSmbD+egpNJer7Ge0TdA7RV5+TftVRDzE1BWWgrR4UB8UvHlxxERERE1olNny69YnKzx46kiVFbZUFZaBjE/4dI9Jy9SU5ys+lNx0qBVoY2XCLGA8yCi+mgxT1DGx8cDADp27FjrfpCDBw/Gzp078eOPP9brnF5e1ftk5OXlISsr65Ii5e+//w4AUKvVCA8Pv9rUiYhaFKNejYcnROHpz5OdsfjkEuxLKMTI7tyvl6gpiYuLw9KlS5GRkQEAzoUERqMR7du3R8eOHRETEwMfHx94enrCzc0NVqsVFRUVyM7OxtmzZ3Hq1CkkJSXBarUCAFJSUrBo0SKsWLECjz32mHOBGBERNY4Bjx1S7LPL/QbDlHz6su8ZrSfQxrcXEjOKkJxtxvvbMms9z+E3rtzhiIiIiFoPpeY36bmVsFgdyE45gaqCygYfb7aJSD1fhQ5ef0AM6oPKKjPSc2svdBK1di2mQJmSkgIAiImJqXVMVFQUACA/Px9FRUXw9fWt85zDhg2DRqOB3W7H3//+d7z44ovo3r07HA4H9u7di1deeQUAcOedd9ZaFCUiao1u6uGHvrFe+DWl1Bl7a2MGBnfygUGrVjAzIgKAwsJCvPDCC9ixY4ezKBkaGorbbrsNw4cPR58+faBW1//fqsViQXx8PPbu3Ytt27ahuLgYqamp+Pvf/45Ro0ZhyZIlbIVPRNQCeYb1qvU9S2l1lyHJYQXAVv9ERETU9Amq6oaTNfOYqyVV5lSfx2KGqcByzXkRtVQtpsVrbm4uANR58+vi9/Lyrvxod3h4OJ555hkIgoDExERMmzYNvXr1Qq9evfDggw9CFEU8/PDDWLx48bVfABFRCyIIAh6fHA31Rb9lsossWLuf7bCJlPbjjz9iwoQJzuLkDTfcgA8//BC7d+/GY489hv79+zeoOAkAer0ew4cPx3PPPYcDBw7gzTffRK9evSBJEnbv3o1JkyYhLi6uka6IiIiUotbVXngUbWYAYJt/IiIiakaq99aumcdcNUd1UVIURVgd3P6EqDYtpkBZUVEBAHBzc6t1jF6vd35fXl5er/POnDkTK1asgNFY/YeX2WyGzWYDADgcDlitVudrIiK6ICbEDdOGtZHFPtudhaJy/swkUtJ9992H/Px89O3bF1999RU++ugjjBgxAoIguOT8Go0Gt9xyC9auXYsvvvgCffv2RVFRER544AGXnJ+IiJoOh7X2lmUqrQEAXPb7hYiIiKjxVS+sqpnHXDV1dR1CpVJBp24xJRgil2sxLV7tdjsAQKvV1jpGp9M5v3c4HPU677vvvov3338fsbGxWLhwIfr06YOKigrs2LEDr7/+Ot5//32cOnUK7777bp2fTUTUGt07Jhxbj+SitKr6Z26FxYH/7TyHxydHK5wZUesVEhKChQsXYty4cY3+Wf3798fq1auxc+dOLF26tNE/j4ioNXLV/o1Wu4ibFh+GxVb/Jx6FwgS0b3P5RcKCZyTUWhUi2nije0wAXpjZ3iV5EhERUcvnqvnNi2tTsOXIlTsp1nDXqyBpVYiNjoJUluGM55daUVRhl43VqgWE+xugUV+6GEtwq+7kqNcbEOqvv+R9IqrWYsr3BkP1qoa6nma0Wq3O7+tTTPziiy/w3//+F2FhYVi1ahUGDx4MvV4PPz8/3Hnnnfjkk0+g1WoRFxeH77777tovgoiohfFy0+DuMeGy2PqDOTibxw3CiZSybdu261KcvNiYMWOwdevW6/qZRERUf3aHhGe+SG5QcRIAxKz9tb4n+LaHShDgZjQgKoh7UBIREdH1N3VISIPGB3jpoBIECL4XFlYVldsuKU5qVEBYLcVJgPMgovpqMQVKd3d3ANUtWGtz8XseHh51ns9ut+P9998HAPztb3+77PiePXvilltuAQCsW7euwTkTEbUG04aGINTvwmoxhyjhva1nFcyIqHWrWdTVWj6XiIjqJooSXvk6FXEJRQ06zmE6CKks8/JvaoxQBXSDp5cXVGo1Bnb0cUGmRERERA3TJcID4wcE1mvsDV184WbQwtPLE6qAboDGiOJKG/LL5A9EqYXq4qS2luIk50FE9ddiCpQhIdWrIXJycmodc/F7gYF1/2DKyspCQUEBAKBPnz61juvXrx8AID09vb6pEhG1KjqNCg/cGiGL7fm9EL+nlSmUEREREREBgCRJ+O+WDHz/i7z1mQCxzuMcpoNwJK2t9X11xGgIaj0CAvwR6qdH53B3l+RLRERE1FBPTY25YpFy/IBA/GtOB4T66REQEABBrYcjdBTySuTFSZUAhPoboNPUXlbhPIio/lrMHpTt27fHDz/8UGehMCOjum90YGAgvL296zxfUdGF1aOiWPsfZzWtYi0WSwOyJSJqXUb39MfqOBMSMyucsf9uycBHD3aFINSy4oyIFGexWHD06FFkZmaiuLgYKpUKXl5eaNu2LXr27Ak3t8vvO0ZERM3DZ3tMWBOXLYv5e2rh66FFXn4hiivsgEoLqDSAvQpi/gmIWT9CKqu9G4YqdCgEv84ICAiAXq/HzOGhnO8RERGRYjRqAc/+JRZThoRg/U/nsf9kEcrNDngY1Bje1RdThoagS9vq7okzhrfBG99Z4OXjB7O1M3xjR6AoJQ4AIAAI9dPDoK29OMl5EFHDtJgC5cCBA7F8+XIkJiaipKTksgXIgwcPAgAGDBhwxfPVPJEJAMeOHUNUVNRlx508eRIAEBkZeRVZExG1DiqVgIfHR+L+9045Y7+nl2HfiUKM7OGvYGZEdDnnzp3De++9hy1bttS6v7darcaoUaPw+OOPIyIi4rJjiIio6fr2UM4lbff9PLRY8WBX/HA0H/tPChBNJpQUF0IqTITj7C7AXsc+4hpj9RMDfp3h7eONwMAAjOjmhx7Rno18JURERERX1qWtB7rcGVvnmJ7RXuge6YHkrAqo9VXwjR0Brbs/Ck5tQ7CHCKNOffkDOQ8iuiotpsVrv379EBwcDLvdjo8//viS95OSkrB3714AwIwZM654vpCQEHTq1AkAsGLFiss+IWkymfDdd98BAG6++eZrSZ+IqMXrE+ONG7r4ymIfbM+EQ5QUyoiILmfv3r2YNGkSNmzYAKvVCkmSLvtlt9uxc+dOTJ48Gdu2bVM6bSIiaoBdxwuwbP0ZWczdoMZ/7+uMtgFGzBkVhu6RHggLDUVgYCBU/l2g6bEAqojREDwjAbUBgACoDRA8I6GKGF39vn8XBAYGIiw0FN0jPTB7ZKgyF0hERER0FXJLLIhLKIQoAVp3f2jcfODRphtibnoEnh1u5jyIyMVazBOUKpUK//znP7Fo0SJ89NFHcHd3x9y5c2EwGBAfH4+FCxfC4XBg8ODB6N+/v/O4nJwczJkzBwAwa9YszJo1y/neE088gfnz5+PMmTOYM2cOFi9ejG7dukEURfz8889YsmQJysvLER4ejtmzZ1/3ayYiam7+flsEfkosQk1NMi2nCjuO5eOWvvXbsJyIGteRI0fw0EMPweFwQJKq/6GGh4ejS5cu8PHxgcPhQFFREU6fPg2TyQRJklBRUYEnnngCISEh6N27t8JXQEREVxKfVIwlq/+AdNEaMb1GwH/mdUKHsOo9krQaFR6ZEIXP95oQlwB4eXkiP78AZeq+EIP6XHJOlSDA08sLAQH+0Ov1GNHND7NHhkJbx/5MRERERE1JWZUdj3x0Grn/v++kIAjQGn0Q6OcNh7kUZToD50FELtZiCpQAMHnyZBw7dgxfffUV3nzzTSxfvhw6nQ4VFdV7nkVHR+PNN9+UHWOz2ZCWlgZAvu8kAAwbNgwvvPACXnzxRRw7dgxTpkyBm5sbRFGE2WwGAISFhWHFihXw8PC4DldIRNS8tQtxwy19A/H9L3nO2Mc7zmFML39o1Jy4ESnJarXiySefhN1uBwD07dsXTz31FLp163bZ8cePH8frr7+OX375BXa7HY899hi2b98OnU53PdMmIqIGOJVZjidXJsHuuFCdVKuAV+d0RO92XrKxWo0K88aEo1+sF77cnw29Xg8xJBiVVWZYLGaIogiVSgW93gA3owEqtRqhftV7LbGdGRERETUnVruIJz9NQkp2pSw+vKsv1CoB2UUenAcRNYIWVaAEgBdffBFDhgzBmjVrcOrUKZjNZkRFRWHMmDFYsGABPD0b9gNi+vTp6NOnD1auXImff/4ZOTk50Gg06NKlC8aOHYu77rqLxUkioga4d2w4th/Nd7Z2zcw34/tf8jBxYLDCmRG1bhs2bEB2djYEQcDo0aPx9ttvQ6WqfeFAz5498cUXX+Dhhx/Gjh07kJ2djY0bN2LatGnXMWsiIqqvrAIzHv34NKqsoiy+5M7YS9rwX6xntBd6RHki8VwF4pOKkZ5bBVOBBVaHCJ1ahVB/PaKCjBjY0Qedw90hCEJjXwoRERGRy4iihBe+TMGvqaWy+PCuvvjXnI5Qq8B5EFEjaXEFSgAYN24cxo0bV6+x4eHhSEpKqnNMbGwsXn75ZVekRkTU6oX5GzBhQCA2/JzrjH2yMwu39g1k+wsiBe3ZswcA4OXlhVdffbXO4mQNQRDwyiuvID4+HqWlpdi5cycLlERETVBxhQ0Pf5SIwnKbLP7oxKh6tdoXBAFd2nqgS1suziUiIqKW5YPtmdj5W4Es1j3SAy/Pag+NurrgyHkQUePgnWAiIrru7hkdDq36wqqy7CILNh7OreMIImpsp0+fhiAIGDVqVIM6Tnh6euKmm26CJEk4depUI2ZIRERXw2xz4PFPknA2zyyL3zUyFHcOb6NQVkRERETK23I4Fyt3Z8likYEGvDGvEww6tUJZEbUeLFASEdF1F+yrx6RB8pauK3dlwWITazmCiBpbzV7cUVFRDT42IiICAFBcXOzKlIiI6Bo5RAnPrU7B7+llsvjY3v74+60RCmVFREREpLyjqSV4dd0ZWczPQ4u35neGj7tWoayIWhcWKImISBF3jw6DXnPhKcrcEis2HMpRMCOi1k2v1wMAKisrG3xszTFubm4uzYmIiK7N25vSsfdEoSzWN8YLS+6MhUrFPZKIiIiodTqbV4UnVybD7pCcMb1GwL/v6Ygwf4OCmRG1LixQEhGRIgK8dJg6NEQWW7k7C2arQ6GMiFq3Nm2q2/wdPny4wcfWHBMcHHyFkUREdL2siTNh7Y/nZbF2IUa8dndH6LjvNxEREbVSJZU2/PPj0yittMviS2bEoltk/bc7IaJrx79KiIhIMbNHhcGou/CrqLDcho3x3IuSSAn9+/eHJEn47bffcODAgXoft3//fvz2228QBAEDBgxoxAyJiKi+dv6Wj7c2ZchigV5avHVvZ3gaNQplRURERKQsm13EopXJyMyX7829YFxbjOkVoFBWRK0XC5RERKQYXw8tpv3pKcov9ppgs3MvSqLrbdq0ac7vH3300Xo9SRkfH4/HH3/c+Xry5MmNkhsREdXf72lleOHLFFnMXa/Gm/M7I8RXr1BWRERERMqSJAn/Wn8Gv6aWyuK39A3APaPDFMqKqHXj0kkiIlLUjBFt8NWP2bDYq/v+55ZYsfXXPEwcyFaRRNdTp06dMGHCBGzatAmlpaWYO3cubrzxRtx8883o3LkzfHx8IAgCioqKkJiYiO3btyMuLg6iKEIQBNx6663o1q2b0pdBRNSqmQrNeGLlaVjtF/ZTUqsE/GtOB3QIdVcwMyIiIiJlrYnLxubDebJYz2hPPDM9BoLAvbmJlMACJRERKcrfU4cJA4PxzU8X9kj6fI8Jt/ULgkbNCSLR9bRkyRJkZGTg+PHjkCQJe/fuxd69e2sdL0nVN8C7dOmCl19++XqlSUREl1FutuOx/51GUbl8P6VnprfDwI4+CmVFREREpLxDp4vxzhZ5+/swfz1en8u9uYmUxH99RESkuLtGhkKtulCMzMw3Y/fxAgUzImqdPDw88Nlnn2HKlClQqVSQJKnOL5VKhSlTpmD16tUwGo1Kp09E1Go5RAnPrvoDqeerZPF7Rofh9v5BCmVFREREpLzM/CosXpUM8UKDCXgY1HhzXmf4eGiVS4yI+AQlEREpL8RXj1v7BchabazcfQ5jevlDpeJTlETXk8FgwCuvvIJ7770X33//PX7++WecO3cOxcXFkCQJXl5eiIqKQr9+/TB58mREREQonTIRUav3380Z+CmxWBYb1cMP993cVqGMiIiIiJRXYXbg8U+SUFblcMZUAvDKXe0RFcxFtkRKY4GSiIiahDmjwvD9kTznirbU81X48VQRRnTzUzYxolYqOjoaDz74IB588EGlUyEiojpsOJSDL/dny2Kd27rj+RmxXOhFRERErZYoSnj+yz+QliPvMPHArREY3MlXoayI6GJs8UpERE1CRKARo3v6y2Kf7jrn3OOOiIiIiOSO/FGC175Nk8WCvHX4992dYNCpFcqKiIiISHn/23kOcQlFstjY3v64a2SoQhkR0Z+xQElERE3G3NFhstenMitwOLlEoWyIiIiImq6MvCos+iwJjos2VDLoVHjjnk4I9NYpmBkRERGRsvadKMRHO87JYh3D3LF4egwEgR0miJoKtnglIqImI7aNO4Z39cX+kxdWuK3aZ8LAjj4KZkXU8jkcDiQnJ6Nz586y+NatW7F27do6j42MjMRLL73UmOkREdGflFba8ejHp2X7KQHACzNj0THcXaGsiIiIiJR35nwlnv/yD1nMx12D1+Z2ZIcJoiaGBUoiImpS5twUJitQxieX4A9TBdqH8mYbkatZrVZ88MEHWLt2LVQqFQ4cOCB7//z58zh8+HCtK0wlScKRI0fQv39/TJgw4XqkTETU6jlECYtXJSMz3yyL//3WCIzs7l/LUUREREQtX3mVHU98moRKi+iMqVXA0tkd0MZPr2BmRHQ5bPFKRERNSvdIT/SM8pTFVsdlK5QNUct17tw5TJ48Ge+//z4KCwtRUFCA5OTkWsdLknTJlyAIkCQJy5YtQ3l5+XXMnoio9Xp/61n8nCRvgX9rv0DMHsX9lIiIiKj1kiQJL36Veskirn9OjELfWG+FsiKiuvAJSiIianJm3tgGx1eWOV/vOJaPB25tiyBvrnYjcoXS0lLMnz8faWlpzlhkZCTMZnOtxyxdulT22m634/XXX0dpaSkKCwuxfv16zJkzp9FyJiIiYOexfHy+1ySLdY/0wNPT2nE/JSIiImrVVu/Lxr4ThbLY+AGBmDY0RKGMiOhKWKAkIqImZ3hXP7QNMDhXvdkdEr7+8TwevD1S4cyIWoZ3330XaWlpEAQB/v7+ePbZZ3HzzTfXeczkyZMviXl6euKRRx4BAKxevZoFSiKiRpRsqsCLX6XKYgFeWiyb2xE6DZsjERERUet1NLUU727NkMU6hbvjyTu4iIuoKeNfMURE1OSoVQJmjGgji234OQcVZodCGRG1HIWFhfjqq68gCAJ8fHywatWqKxYnazNu3Dj06dMHkiQhMzMTv/76q4uzJSIiACgut+GJT5NgsV3YT0mrFrBsbkcEeOkUzIyIiIhIWfmlVjzzRTIcF6ZJ8DSqsXR2B+i1LH8QNWX8F0pERE3S7f0C4e124UH/sioHNh/OVTAjopZhz549zlau//jHPxAVFXVN51uwYIHz+/j4+Gs6FxERXcrukPD0F8nILrTI4k/cEY3ukZ61HEVERETU8tkdEp75IhkFZTZZ/IWZ7RHmb1AoKyKqLxYoiYioSTLo1Jg6NFgW+3J/NuwOSaGMiFqGQ4cOAQDc3d0xbdq0az7f0KFD4eXlBQB8gpKIqBG8syUDv6SUymJThwRj0qDgWo4gIiIiah3e23oWx86UyWJ3jw7DsC6+CmVERA3BPSiJiKjJmjo0BF/sNcFqry5KZhdZsPdEAcb0ClA4M6LmKykpCYIgYPDgwdBorn0qqNFoMGjQIOzYsQPnzp1zQYZERM2XJElIzKxAfHIx0nKqkF1ogdUhQqdWoY2fHtHBRgzs4IPObd3rtR/S1l/y8OX+bFmsVztP/HNiVCNdAREREZEyGjqP2nuiAKv2mWTn6N/eG/fd3FahKyCihmKBkoiImix/Tx1u6RuIjfEXWruuictmgZLoGhQWFgIAIiIiXHbOsLAwAEBxcbHLzklE1NwcTyvFl/uzYSq0QHQ4UFllhsVihiiKUKlUKCg24Ey2AXtPFCLUT4+Zw0PRI7r2Fq2nz5Vj6TepsliQtw5LZ3eAVsNmSERERNRyNHQeNbqnP1755ozsHEHeOrw8qz3UqisvAiOipsGlBUqr1QqdTufKUxIRUSs3c0QbWYHy5NlynDxbhq4R3HOJ6GqUlla3CfT29r7i2F69euH++++/4jhf3+r2ORUVFdeWHBFRM2Szi/hsTxb2nyyCxWJBfn4+ykrLIEqXtqVXCQI8vTxhsQTg399ZMKKbH2aPDL2k4FhaacfCz5JhsV84h04j4LW7O8Lfk39zExERUctwNfMoszkALyYWw2wTne+pVQJend0Bvh7a65k+EV0jlxYob7jhBtx6662YNGkSevbs6cpTExFRKxUd7IbBnXxw6PSFJ7O+PnAeYm8YwAAAIABJREFUL8xkgZLoauh0OlRVVaG8vPyKY/v06YM+ffpccVxVVRUAwGg0XnN+RETNic0u4q1N6TiRUY68vHzk5+dDclgg5idAKvoDUmUO4LAAaj0Et2CIvu1R4uiG0tIyBAQEIC4BKCyz4pEJUc4ipShKeP7LFGQXWmSf9dS0GHRp66HEZRIRERG53NXOowqLSgC9NzQGb2fL/EcmRKJHFO8TETU3Li1QlpSUYO3atVi7di0iIyNxxx13YMKECQgJCXHlxxARUSszfViIrEC587cCPHR7JAK8+AQBUUP5+PigqqoKeXl5Ljtneno6ACAoKMhl5yQiag4+25OFExnlyDKZUFJcAqkwEY6zuwB7lXygwwypLANSWQZE009QR4xGntQZVpsVQCg+32vCvDHhznMeOFUkO3zqkGDc1i/wOl0VERERUeO7mnlU+Zn9sIWOgkebbpAcdmjd/dEuxA3Th7H+QNQcubRAeffdd2Pr1q3IyclBeno63nzzTbz11lsYNGgQJk2ahLFjx8JgMLjyI4mIqBUY3NEHbQMMyMw3AwDsDgnf/ZyDe8dy43OihmrXrh1MJhMOHz7ssnMeOXIEgiCgffv2LjsnEVFTdzytFPtPFiEvLx8lxSUQTQcgmg5e+UB7FRxnNkNlLkQJhkKl1mNtoQWr9ppQZXVA/FNHs64RHnhkYlSjXAMRERGRq5w6W451B89j/8kiVJjtcDdoMLyrL6YODbmkC8TVzKNsDgnn84vhyPsWtooC+MaOgEarhVbtjt/Ty9Az2qsxL4+IGoHqykPqb+HChdi3bx8+/fRTTJw4EUajEaIo4uDBg1i4cCGGDBmCp59+2qU3xIiIqOVTqQRMGypfDfftoRzY7GItRxBRbXr16gUAMJlMSEhIuObz/fTTT8jPzwcADBgw4JrPR0TUHEiShC/3Zzv3SpIKE+tXnLyImH0IEFQosRlQUmlHheXS4qRWLeDlWe2h07j0T3ciIiIil7E7JLz0VQrmvn0CW47kobTSDodYvaf2liN5mPvWCbz0VQrsjuqJztXMoyQJyCmy4P9PgaKUOFRkJ0CylMBms+LL/dmQLrNvJRE1bS7/K0cQBAwePBjLli3DwYMH8frrr2PYsGFQqVSorKzEhg0bMGfOHNx000145513kJmZ6eoUiIioBbq9fyCMugu/tvJLbdh7olDBjIiap1GjRjm/f/PNN6/5fMuXLwcAqNVq3HTTTdd8PiKi5iAxswKmQotzryTH2V0NPoe6451QeYbXOcbmkPDJrnNXmyYRERFRo1u6LhWbD9e9hcjmw3lYui4VwNXNowrLraiyyRepi2d3QRCtyM8vgKnQgsRzFVd/EUSkiEZdhmkwGDB+/Hh8/PHHiIuLw6JFi9CrVy8IgoCsrCy89957GDt2LP76179i3bp1qKjgDxEiIro8D6Pmkr2Xvj5wXqFsiJqvLl26oGvXrpAkCQcPHsQHH3xw1ef64IMPcOzYMQiCgFGjRiE4ONiFmRIRNV3xycUQHQ6UlZZBzE+4dK+kKxA8I6AOHVKvsZsP5+FUZvnVpElERETUqE6dLb9icbJGzZymofOoSosDheV2Wcxdr4K3wQ4xPwFlpaUQHQ7EJxVf9XUQkTJcugdlXQICAjB37lzMnTsXhYWFePPNN7Fu3TpIkoSjR4/i6NGjeOWVVzB+/Hjcc889iIqKul6pERFRMzFtWAjWHcxxvv49vQyJmeXo/Ke9DIiobo8++ijmzZsHAHj77bdRXFyMRx99FDqdrl7Hi6KId955x1ncVKvVePjhhxstXyKipmLAY4ec3zusVbBaHchOOYGqgsoGnSc0cgh8GzB+7lsnGnT+yzn8xuBrPgcRERE1XxfPY5RSM6e5lnkUAFRYRKRkV8JoPYGooD6orDIjPbdhC8aISHnXbSMLh8OBuLg4LF68GOPHj3cWJwHA3d0dbm5uqKqqwjfffIPx48fjo48+ul6pERFRMxEd7IaBHbxlMT5FSdRwQ4cOxfTp051zsc8++wzjxo3DypUrkZ6eXutxpaWl2LBhAyZOnIgPPvgAkiRBEAQ89NBDiImJuU7ZExE1DZLDCgCwlDZ8LuIZ3svV6RARERE1G9cyj7pYzfEWixmmAss150VE11ejP0F57NgxbN68Gdu2bUNxcfVj1jU3swYNGoTJkyfj5ptvBgDs2LEDn376KRITE/Gf//wH3t7emD59emOnSEREzcj0YSGITy5xvt5xLB//GB8JXw+tglkRNT/PPfccioqKsHPnTgiCAJPJhGXLlmHZsmVo06YNQkNDERAQAK1Wi/LycmRmZiItLQ2iWL3vR01xc+rUqViwYIGSl0JEpIian4OizdzgY9Vao6vTISIiImo2rmUedbGa40VRhNUhXmE0ETU1jVKgTE1NxebNm7FlyxZkZWUBuPBDJzIyEhMnTsTkyZPRpk0b2XETJkzAmDFjcOeddyIpKQmffvopC5RERCQzpLMvQv30MBVWr4yzOSRsOZKLu0aGKZwZUfOiVqvxzjvv4L333sOHH34Ii6X635QkSTCZTMjOzr7kmJpFZpIkQavV4qGHHsJ99913vVMnImoSBEEAAKi0Boi2hrUUc9iqoNGzRT0RERG1Ttcyj7qYSmuo/q9KBZ36ujWLJCIXcWmB8tNPP8XmzZuRmJgIALIWruPGjcPkyZPRr1+/Os9hNBpx2223ISkpyVncJCIiqqFWCZg6JAT/3ZLhjG04lIO/jgiFSiUomBlR8/TAAw9g4sSJ+N///oeNGzeioqICwIV53J9pNBrcdtttWLBgAaKjo69nqkREiqvZx3HJ6j+QcAY4e7YEsdFRkMoy6j7wT+y5v0PTdki9x4/vH4hn74xt0GcQERERXawx9qN+cW0KthzJq/f48f0DYXNIdc6jRElCZr4ZVvuFv0lVAhAZaIRGLb/vI3hGAgD0egNC/fVXfyFEpAiXFiiXLVvmXFX/5xauBoOh3uexWqt7UP/5CUsiIiIAuL1/IN7fdhY2R/Vk9VyBBUf+KMHAjj4KZ0bUPIWFhWHJkiVYtGgRDh8+jN9++w1paWkoKSmBJElwd3dHZGQkunXrhqFDh8LDg0/9EFHrFh1sxJlsA1SCANG3fYMKlDa7iNyTuxDdgALl1KEhV5MmERERUaOaOiSkQQXKqUND8HNScZ3zqIJSm6w4CQBB3rpLipMAIPi2h0oQ4GY0ICqILfSJmhuXt3iNiIiotYVrfQ0fPhz9+/dHVFSUa5MjIqIWwcdDi1E9/PHDsXxnbMPPOSxQEl0jnU6HYcOGYdiwYUqnQkTUpA3s4IO9Jwrh6eWJEkc3iKafAPuV25NJEpBdbIHFlo6ilB/hG3vDFY8ZPyAQndtyYQgRERE1PV0iPDB+QCA2H75ykbJmTiNJqHUeVWFxoLjSLjvOy6iGp/EyZQyNEaqAbvD08oJKreY9IaJmyKUFylWrVl2xhWt99OjRwwXZEBFRS3bHkGBZgTIuoRB5JVYEeusUzIqIiIhag85t3RHqp4fFEoDS0jKoI0bDcWbzFY/LL7PCYqt+IsB0+DMY/CNh9I2odfz4AYF4amqMy/ImIiIicrWauUpdRcqL5zS1zaMcooScYovsOK1aqPU+jzpiNAS1HgEB/gj106NzuLuLroiIrheX7hxbU5wURRHbt2/Hli1bLhmze/du3H///fj+++9r3duIiIjoSnpFeyI6+EL7DocIbDqcq2BGRM1DeXl5q/pcIqLGIAgCZgxvA71ej4CAAAh+naEKHVrnMeVmB4orLjwR4BtzA1QQ4a01w9tNAw+DGmqVAG83Dcb3D8TKR7rj2b/EXradGREREVFToVELePYvsVj5SHeM7x8IbzdNnXOa2uZROcUWOMQL5xUAhPjooBIunQupQodC8OuMgIAA6PV6zBweCuEy44ioaXN5i9fMzEw88MADSElJweDBg3H77bfL3k9NTcW+ffsQFxeHVatW4b333oOvr6+r0yAiohZOEATcMTgYb3yX7ox993MO5t4UBrWKk1Ki2owdOxaPPvoopk6det0+89tvv8Ubb7yBn3766bp9JhFRY+sZ7YXhXX2x/yRgtVlRgqEQDH5wnN11SbtXm+PCEwEqrREBXW6BZ2g3+Pv5ok2wP0Z088O8MeFKXAYRERGRS3Rp64Eud8bWa+yf51GF4hC42z1RdWobRFv1PMrPQwuDTi0/UGOsfnLSrzO8fbwRGBiAEd380CPa09WXQ0TXgUsLlJWVlbj77ruRlZUFSZJQVFR0yRh/f38EBwcjJycHv/32G+677z6sXbsWarX6MmckIiKq3a39ArH8+7Ow2KqX2OUUW3HwdDFu6MKFL0S1KS4uxrPPPovVq1fjiSeewJAhQxrts/bu3Yu3334bSUlJnOsRUYs0Z1QYisptAEKh0+qQLwgQfGIg5idAKvoDUmUOJLsFueUC9H7RcAvuCM+wnlCp9QgKCkRoSBC6R3pg9shQpS+FiIiI6LqqmUdZbSEoqpDg0aYb3ILaoyzrOBwFf8BXUwSIFkCth+AWDMG3PVQB3f6/rWsAAgMDOI8iauZcvgfluXPnoNFosGjRIvzlL3+5ZMyUKVMwZcoUrFmzBkuXLkVCQgLWrVt32bFERER18TRqMLa3v2yfgw2Hcq66QDngsUOy14ffGHxN+RE1RV988QWeeOIJJCYmYt68eejTpw9mzZqF0aNHQ6vVXvP5Kysr8f333+Pzzz9HSkoKJElCYGAg3njjDRdkT0TUtGg1KjwyIQqf7zUhLgHw8vJEfn4BytR9IQb1AQDYRREhjgvHqPXuCA4KQLCfB0Z088PskaHQaly6+woRERFRk6fVqPDgbZGYuuw3aIw+UGndYDeXwCdyILQxAyFA3h1LJQjw9PJCQIA/9Ho951FELYBLC5Q7duyAIAj429/+hlmzZtU5dubMmTh//jxWrFiBzZs3s0BJRERX5Y7BwbIC5U+JRcgutKCNn17BrIiarr59+2LTpk3417/+hfXr1+Po0aM4evQofHx8MHbsWIwcORKDBg2CwWCo9zmLi4vx008/Yc+ePdizZw/MZrNzr/EJEybgqaeeYkt/ImqxtBoV5o0JR79YL3y5Pxt6vR5iSDAqq8woLa9CQakFWkGAoNZBpdHB002HXjHe+OuIULYjIyIiolbtyx+zkVdiBQCoNDroPAIR7K2BVrDDYjFDFEWoVCro9Qa4GQ1QqdUI9avec5LzKKLmz6UFyrS0NAC4ZN/J2owfPx4rVqxAUlKSK9MgIqJWpEtbD3QMc0dSVgUAQJKAjfE5uP+WCIUzI2q6PDw88PLLL2Py5Ml49dVXcfLkSRQVFeHrr7/G119/DbVajYiICHTo0AHt2rWDt7c3PD094ebmBqvVioqKCpw/fx5nz57FqVOnkJmZ6SxI1vy3a9euePLJJzFw4EAlL5WI6LrpGe2FHlGeSDxXgfikYiSbKrA/oQhaN6NzjJtehX/N7oD+HbwhCNwzm4iIiFqv5KwKfLzjnCzWu50nBnX0QXpuFUwFFlgdInRqFUL99YgKMmJgRx90DnfnPIqohXBpgdLhqO5b4+3tXa/xgYGBAACLxeLKNIiIqBURBAF3DA7G0nVnnLEtR/Iw/+a2UKs4YSWqS9++fbF+/Xrs3LkTK1aswIkTJwAAdrsdaWlpzsVnV1JTlASAXr164d5778Xo0aMbJWcioqZMEAR0aeuBzuHuWPRZMmwO6aL3gNfv7oT+7ev39zIRERFRS2W1i3huzR+wXzRX8nHXYOnsjvDzvPatR4ioeXBpg+aQkBAAqPcTkTU3vfz8/FyZBhERtTJjevvDoLvwKy23xIrDycUKZkTUvIwZMwbffPMNvv76a8yYMQNBQUGQJKneX6GhoZgzZw6+/fZbrF27lsVJImr1NsbnYu+JQlnsrhtDWZwkIiIiArBieyZSz1fJYk9NbcfiJFEr49InKHv37o309HSsWLGiXu28Pv74YwiCgN69e7syDSIiamU8DBqM7umPLUcu7EW5MT4XgztxzzuihujRowd69OiB5557Dqmpqfj9999x5swZmEwmlJWVwWq1wmAwwMPDA+Hh4WjXrh169+6Ntm3bKp06EVGTkZFbhf9sTJfFOoW7Y8E4/qwkIiIi+j2tDKv2mWSxW/oGYGQPf4UyIiKluLRAOW3aNGzYsAEHDx7EP//5TzzzzDMICAi4ZFxJSQlee+017N69G4IgYPr06a5Mg4iIWqEJA4JkBcr9J4tQVG6DrwdX3xFdjZiYGMTExCidBhFRs2Kzi3h29R8wW0VnzKBT4aVZ7aHVuLSBEREREVGzU2Vx4Pkv/4B4obMrgrx1eHxytHJJEZFiXFqg7NOnD2bOnIk1a9Zg+/bt2LlzJ3r06IHo6GgYjUaYzWZkZGTg+PHjsNlsAIAJEyZg8ODBrkyDiIhaoZ7RnogINOBsnhkAYHdI2PZrHmaOCFU4MyIiImot3t+WidPnKmSxxydFIzLQqFBGRERERE3HO1sycK7AIost/ksMPI0uLVMQUTPh8n/5Tz31FFQqFdasWQO73Y5jx47h2LFjsjGSVL1EYurUqViyZImrUyAiolZIEASMHxCEd78/64xtOpyLGcPbQBAEBTMjIiKi1uBwcvEl7cpG9fDD+AGBCmVERERE1HTEJxVj3cEcWWzKkGAM6uijUEZEpDSXFyi1Wi0WL16MqVOnYvPmzTh48CCys7NRUlICg8GA0NBQ9O3bF9OmTUP37t1d/fFERNSK3dYvEB9sOwvH/3dVO3O+Cqcyy9E1wlPZxIiaKJvNhn379uHAgQPIycmBWq1GREQERowYgUGDBimdHhFRs1FcbsPzX6bIYkHeOjw9LYYLpYiIiKjVK6+y46WvUmWxcH89/nF7pEIZEVFT0GjPTnfq1AmdOnVqrNMTERFdIsBLh6GdfbH/ZJEztik+lwVKoss4dOgQFi9eDJPJdMl7K1euRI8ePfDaa68hMpJ/MBIR1UWSJLz8dSryS23OmCAAL/41Fl5ubFdGRERE9PbmDOSWWJ2vBQF4bkYsjHq1glkRkdJUSidARETkSuMHBMle7zhWgCqLQ6FsiJqm/fv3Y/78+TCZTJAk6bJfx48fx7Rp05CamnrlExIRtWIb43Nli6MAYM6oMPSJ8VYoIyIiIqKmIz6pGBvjc2WxWTeGome0l0IZEVFToXiBsrCwEKtXr1Y6DSIiaiGGdvaBn6fW+brC4sDu3wsUzIioabFYLHj66adht9udsXbt2mHMmDEYO3as84lJQRBQWlqKRx99VKlUiYiavKwCM97alC6LdY3wwH03hyuTEBEREVETUmF24NVvzshiUUFG3HdzW4UyIqKmxOX9ZsxmMz7//HP8+OOPKCwslN38qiGKImw2GyoqKlBeXg5BEPDXv/7V1akQEVErpFGrcFu/QHyx90Lbyk2Hc3F7/6A6jiJqPTZv3oz8/HwIggB/f3+8/vrrGDx4sGzMvn37sHDhQpSUlCA5ORkHDx7EkCFDFMqYiKhpcogSnl+TgkqL6IwZdCq8+NdYaNSKrwUmIiIiUty732cgu8jifC0IwOK/xECv5VyJiFxcoLRarbjrrruQkJAAoHovjssRBKHW94iIiK7VhAFBsgLlb2fKkJFXhchAo4JZETUNhw4dAgCo1WqsXLkSsbGxl4y58cYb8e9//xvz588HAOzZs4cFSiKiP1m1z4Tj6WWy2CMTotA2gPMNIiIioqOpJVh3MEcWmzG8DXpEeSqUERE1NS5dqrB+/XqcOHECkiTBaDSiV69ezpteUVFRGDx4MDp27AhBEABUFyrvuOMObNiwwZVpEBFRKxcZZETPP014txzOrWU0Uety+vRpCIKAkSNHXrY4WeOGG25wvn/y5MnrlR4RUbOQbKrAh9szZbGhnX0weRA7NhARERGZrQ68/FWqLNY2wID7x7G1KxFd4NIC5a5duwAA7du3x86dO7F27Vo8//zzAICOHTvi008/xcaNG7Fv3z4MHToUkiTh2LFjaNeunSvTICIiwoSB8huE3/+SB4fIp/eJ8vLyAAAxMTFXHNunTx9IkoSsrKzGTouIqNmw2kU8vyYFdseFeYW3mwbPTI9xLsYlIiIias3e35aJcwUWWeyZ6TEw6NQKZURETZFLC5RJSUkQBAF/+9vf4O/vDwDo0aMHNBoNjhw54hwXFBSE9957D9HR0UhPT8d3333nyjSIiIhwU09/GHUXfs3ll9pw5I8SBTMiahoqKysBAO7u7lccGxwcDAAoLy9v1JyIiJqTD7ZlIiW7UhZbNLUdArx0CmVERERE1HT8nlaGtT9my2LThgajT4yXQhkRUVPl0gJlSUn1jd9u3bo5YzqdDhERESgqKsL58+edcb1ej3vuuQeSJGHbtm2uTIOIiAhuejVG9fCXxbb+kqdQNkRNh8PhAABoNFfeitxorN5HraqqqlFzIiJqLo6mlmJ1nEkWu6VvAG7q6V/LEURERESth8Um4qWvUiBd1MCqja8ef78tUrmkiKjJcmmBsuZGl7e3tyweEREBAEhNlfedHjBgAAAgJSXFlWkQEREBAG7tFyh7vfdEIcrNdoWyIWoaJImtjomIrka52Y4X1spvuAV56/D45GjlkiIiIiJqQj76IRMZeWZZ7Jnp7eCmZ2tXIrqUSwuUvr6+AID8/HxZvG3b6s1v/1ygrClk1jx5SURE5Ep9Y7wQ7HOh3ZrFJmLv74UKZkRERETN1Zsb05FdKN9LacmdsfA0XvmJdCIiIqKWLjGzHKv2yTtNTBwYhAEdfBTKiIiaOpcWKLt37w4AiIuLk8WjoqIgSRISEhJk8aysLACAIAiuTIOIiAgAoFIJuKWv/ClKtnklIiKihopLKMTmw/I5xJ03hGBAB+9ajiAiIiJqPewOEa98nQrxT50mHh7P1q5EVDuXFihHjRoFSZLwzjvvYNOmTRBFEQDQo0cPAMCuXbtw7tw55/iPPvoIABAWFubKNIiIiJxu+1Ob119TS2EqNNcymoiIiEiuqNyGpd+ckcWig4144LYIhTIiIiIialq+3J+NZFOlLLZoajt4sNMEEdXBpQXK22+/HTExMaiqqsLChQvxxBNPAKh+srImPmXKFDz22GOYNGkSfvjhBwiCgBtvvNGVaRARETlFBhnRLcJDFtv2a34to4mIiIjkXvs2DYXlNudrtUrA8zNiYdByLyUiIiKirAIzVvxwThYb29sfw7r4KpQRETUXLl3CoFarsWLFCjz44INITExEQECA870XXngBd999N0pKSrB161YAgCRJaNOmDe677z5XpkFERCRza79AJJwtd77e+kse7hkdxhbj1KolJCTgu+++u+KYGlcaW2PSpEnXlBcRUVOy63gBdh8vkMXmjQlD57YetRxBRERE1HpIkoR/rTsDi010xryMavxzYpRySRFRs+HyZ6zDwsKwfv36/2PvzsOjKu///z/PzCQz2XdCQoBEQAGRfRFQUAmKC7K7fVqrn7a2ta2t1r3WVj96uRf91bZWrdsXENywgCC7LILgAsi+SEggCSH7nsks5/dHmoFhTWCSCfB6XBfXlXOf+9zzPtelmTvnfe73zapVq4iIiPC1Dxw4kGnTpvHSSy+xadMmrFYrI0aM4KGHHiI2VhvliohIy8nsm8Bf/7MPt6dhM4T9RXVszq6id3pUkCMTCZ758+f7Xho7mcZE/iOPPNKkcZWgFJGzkWmabN9fzbpdZWQV1JJf4qSm3sPeg/6lyi7qEM4do7RFiYiIiJz7jjc/qvd4CbVaSIm3k5Echttjsm5Xud91v7sxnYSo0CBFLSJnk4AmKLOysujUqRNWq5WRI0cec75Pnz689957gfxIERGRU4qNCOHynnEs31zia5v/TaESlHJeM02zSf0aE5RN6a9VySJyNtqUVcH7K/PJK3Hi9Xioqa3DWVdHSWU9TreJYQ3FYgvFsFiJCQ9hW041vTM0hxAREZFz13HnR846vF4vFouF4jIHu3LtZBc6/a4b0DWaGwYlBSlqETnbBDRB+dBDD5GXl8df/vIXMjMzAzm0iIjIGbluYJJfgnLxxiLuG59OqC2g2zGLtHmpqanBDkFEpE1wub28uyyXlVtLcTqdFBUVUVlRidc08XpNXB7/FzOioqIpLLPx4qdZjOwVz+1XphKieYSIiIicQ042Pzqa22uCLRybIwaLLRSrBe6fkK4XV0WkyQK+grKqqoqUlJRADisiInLGhnWPJSbcRnmNG4DKWg+rtpYyqk9CkCMTaV3Lli0LdggiIkHncnt5ec4+NmdXUVhYRFFREabHibdoC+6SXeRkZ+Oqr8MS4sAe3Z6YlO7YO/Vj795qEhMTWbEFSirr+f2N6UpSioiIyDnhZPMjs3Q3Zk0BeJxgteMMSaQ67AKiOvTB46zGFh5LUnISn6wp0PxIRJosoAlKl8sFEPQE5fz585kxYwbbtm3D4/GQlpbGmDFj+OlPf0p4ePhpjfnFF18wc+ZMvv/+eyoqKoiLi2Pw4MH8/Oc/p3v37gG+AxERCbQQm4Vr+ifyweqDvrb53xQqQSkiInIeendZLpuzq8jNy6O8rByzZDuenCXgrqWg1Imr3gOA11VLXXEWSZZ83OXrsXbKpNDsQb2rHkjlveV5/HR0WnBvRkRERCQATjY/OpLXXUt+/m5cnl2U7l5BYs9riUrtRV2lwebsUM2PRKTJApqg7NevH1999RXLly9n0qRJgRy6yZ577jneeustAEJCQggJCWHPnj28+uqrzJs3j+nTp5OYmNjk8UzT5M9//jOzZs0CwGKx4HA4OHToEPPmzWPhwoW88MILXHvttS1yPyIiEjjXDUzyS1Cu2VFKaZWLuMiQIEYlIiIirWlTVgUrt5ZSWFhEeVk53rzVePPWAFBZ66aqzoMjPp34C68iKq0v1pAw8NThLdqM98BKjLoSyhlOaEgoK7bAwK7R9MmLgIx5AAAgAElEQVSIDvJdiYiIiBxrW04VH605yMqtpVTXuYlw2BhxcRyTh7enZ8dIX7+TzY+OVlLp8pXC97pqObTpEyIpp9x2OaGhmh+JSNMFdK31n//8ZxISEnjyySf597//zcGDB099UQDNmTOHt956C4vFwqOPPsp3333Hhg0beO+990hNTWXfvn3cf//9zRrzn//8J7NmzcJqtfLQQw+xYcMGNmzYwJw5c+jXrx8ul4tHH32UgoKCFrorEREJlB5pEWQkh/mOPV5Yuqk4iBGJiIhIazJNk/dX5vv2VDJLtvsevrm9JoUVblKH3EmXax8nrstl2OyRGBYrRkgE1pRLCRn0IEZ0Z8ySHRQVFeF0Onl/ZT7mcfZlEhEREQkWt8fk/2bt4Y5XNjPv60Iqatx4vFBR42be14Xc8fJm/m/WHtwe86Tzo6M5XV7Kqt1+bbHhNqyH1mKWbNf8SESaJaArKN9//30GDBjAwoULefHFF3nxxReJjY0lLi4Ou91+wusMw+CTTz45o8/2eDy8+uqrAPzsZz/jJz/5ie/ckCFDeP311xk3bhxr165l7dq1DB069JRjHjx4kNdeew2Ap556iokTJ/rOXXTRRfzrX/8iMzOTiooK5syZw89//vMzugcREWlZhmFwTf9EXluw39e28LsiJg9vH8SoREREpLVs319NXonTt6eSJ2eJ71xhWT3Jg35CXNfLTzqGNXUYnoPrMT1OioqKsdvtbD9Q7bcKQURERCSYnvnoB+auLzxpn8bzk4a2P+H86EgmUFDu5Mi0o81ikBDdUJXKk7MEI7aL5kci0mQBTVC+++67GIYB4HtDorS0lNLS0pNe13jNmVizZg3Z2dkYhsEdd9xxzPlu3bpx1VVXsXjxYv7zn/80KUE5Z84cnE4nAwYM8EtONoqJieGJJ56gtLSUiy+++IzvQUREWt41/fwTlJv2VZJXUkdqvCOIUYmIiEhrWLerDK/HQ2VFJd6iLb49lSpr3bgjOp4yOdnI2n4w7v1fUGm1422fzLqdZXoAJyIiIm3CtpyqUyYnG81dX0iozXLc+dHRyqtdOF3+qyLbxYRiaXy2767FW7SFSusAzY9EpEkCvgdlIJKNp2PdunVAw8rGhISE4/YZOnQoixcvZtWqVU0ac+nSpQDceOONJ+xz3XXXNTNSEREJpg4JDi7pHMnm7Cpf26INxdwxqkMQoxIREZHmGvyHtad1nae+lvp6D/l7NlNbXONrT730qmaNU2vEYqv3sD27lF35dfzziBegTtf6l079Iq2IiIicm053bnOmPl5TcML50anklTp9P3dLCccs3Y23XX9qauvYd+j4iU4RkUYBL/EaLHv27AGgS5cuJ+yTnp4OQFFREaWlpcTFxZ2wr2ma7Nq1C4AePXpQU1PDjBkzWLp0KQUFBcTGxnLllVdy5513EhmpN0FERM4m1/RP9EtQfv5dIT+5KjWIEYmIiEhrMT31ADgrDvq1R6X1bdY4YUldcVUV/Xe8sFP2FxEREWmrTjQ/avY4NQUN4zjryCt2nqK3iJzvApqgDKZDhw4BkJycfMI+R54rLCw8aYIyPz+fmpqGt0Vqamq48cYb2b//8Buxubm5bN26ldmzZ/PWW2/5kp8iItL2ZfZJZOp/9uHxNhzvPVjLnvymvyEoIiIiZ6/G7Ui8rjq/dmtI85KM1hAHriPGExERETlbnWh+1GyehqSk1+ulvvGhi4jICViCHUCgVFdXAxAeHn7CPna73fdzVVXVCfsBVFRU+H5+4IEHKCws5E9/+hNfffUV3333HVOnTiUhIYHc3Fx++ctfUlurJesiImeL+KgQBl8Y69e28LuiIEUjIiIiralxWxJLiP/+0x5X8/6m8/z3AV6wtjkRERERCZQTzY+azdrw/N1isRBqPWdSDyLSQgK6grJxz8bTMWrUqDP6bLfbDUBISMgJ+4SGhvp+9ng8Jx2vcfUkNKy2/Ne//sUVV1zha7vuuuvIyMhgypQpZGVl8eGHH3L77befZvQiItLaxvRPZO2OMt/xx2sKjunz8Ls7yWgXxqXdY+mdHqUHkCIiIm3I6ezX+Pj03WzZCzk55cQlplKc/4PvXNWBjcR2uazJY1kqsrDbY+iUEsMlXRJ54rZuzY5HREREpFGg9qJ+cuYe5n1d2OT+HRLsGJ4YcnLK6ZqRjlmZ7TtXXOmipMrl179jogNHyPGTj0Z4QwVDu91BaoL9uH1ERBoFNEH561//+rQe3hqGwbZt287osx2Ohrc7XC7XCfvU19f7fj5ZIvNogwYN8ktONurRoweZmZksWLCAhQsXKkEpInIWGXFxPCE2A5e7oYxJtdODx+XEdNdhmiaGYbB9n8m+gw5WbC2lfayd/7kihQFdY4IcuYiIiJyujOQw9uY7MAHiusERCUp37kpoRoLSW1eEzRFLeJiD9Hbag1JERETahsnD2jcrQTn0oli+zyrHYhh447r5EpT1bi+lRyUnY8NtJ0xOAhhx3bAYhuZHItIkAV9nbZpms/95vWdejzoiIgKAuroT18k+8lxkZGSTxoOGBOWJDBgwAIDdu3c3KU4REQm+epeHd5YewG7z/xo0PfW4akpx15bhqinlwIEc9vywm/y8PLILKnjp0338Y34O9a6Tr8IXERGRtmnIhbFgWDCtYUR16IPlv/tO2m0GUa48PHlrmjSO5+B6LFEdiYqOxmK1MuSi2FNfJCIiItIKenaKZOzgpCb1HTs4iRsGtcNitRIVHYUlsRfYGuZHheX1HLnTttXSsGXOCdnCsCT20vxIRJosoCso33777ZOer6uro6ysjA0bNjBnzhzsdjuvvfYa/fr1O+PPbt++PZs2baKg4NgSfY2OPJeUdPJf0keeP9m+ltHR0QDag1JE5CxR7/Lw/CdZbNtfjemuAQ6XHLGEhBESmYQ1xNGwD1VFFt7aIircHamsqiAhIZHV26Cksp4HJ2YQGmIN3o2ItILdu3fzzTffkJeXR3l5OVdffTWXXdawumjRokX06dOH5OTkIEcpItJ0PTpGUF3nwWKPwVJXTWLPazm06RPaxdoxDPDsnAmANXXYCcfw5K2B+iqMiBQSExNIjbfTIy3ihP1FREREWtsjk7sAMHf9iVdSjh2cxCOTu2C1QGq8HaczkYqKSqydMinbOpuaev9FRUnRoVgtJ66eaO2UiWG1a34kIk0W0ATl0KFNq5M9YcIEbr/9dm677TZ+9atfMW/ePBITE8/os7t168bChQvZt2/fCftkZzcsT09KSiIm5uQl+uLj40lISKC4uJiDBw+esF95eblvTBERafveWHSAbfuryc/Pp6KiDEtECoa1YY9ii9WGxdrw1WizR0LSJUDDKglPWRlF5kXU17uAFN5cnMvd13UK1m2ItKgVK1bwyiuvsH37dr/2jIwMX4Ly2Wef5dChQ9xyyy088MAD2O3aX0RE2r4NeyvJLXFisYViC48lMqUXIa5SHKXrGjqYXjw7ZuDNXY2lw+VYEi9pWEXgrsVbtBlv7iqMqI5YUoeTmJiI3W7nthGp2qdaRERE2hSb1eBPN3dl0rD2fPzlQVZuLaWqzkOkw8qIi+OYNLw9PTserjB464gUXvrUSWJiIoVmd1xJQ6Fshe98eKiFqLATpxIsqcMx4ntofiQizRLQBGVzdO3albvvvptnn32WN954g0ceeeSMxhsyZAivvvoq27dvp7y8/LgJyDVrGsr1DB48uEljDh48mAULFrB69WrffmRH+/bbbwHo2bPnGUQvIiKt4evd5Xy5vYzi4iIqKsrw5q7G6DQKI6L9Sa+zth8MeWvw5n5JBcMJDQ1h9TYY1C2GQd20J6WcW6ZOncrrr78ONJTub3TkPMjtdlNQUIDX62X69Ol89913vPfee6csoS8iEkx1Lg9Pf3B4z0mbIwaL6cbeZQRmSTs8OUvA3VAZx6zMwbNjOn5F3W1hDSsD4nsQExtDUlIiI3vF0zsjqnVvRERERKSJenaMpOctXU/Zr09GNCMujmPlViitrCWmy0is4QkUbVuA6aolKSb0+BdqfiQiZyDge1A2x6hRowBYvnz5GY81cOBAkpOTcbvdvPnmm8ec37lzp+9zbr311iaNOWHCBAD27dvHxx9/fMz53bt3s3TpUgCuv/760w1dRERagWmavL8iH6fTSXFxEd7iHVCVi+UUyclG1tRhUHUAb/EOiouLcDqdvL8i3y+BI3K2e/fdd/nXv/7l2ye8c+fOvvnQkerq6ujSpYuv3/bt23nwwQeDELGISNO9sfAA+4vqfMeGYZDROY2kpHZYEnpi6/0LLJ0yMaI6g9UBGGB1YER1xtIps+F8Qk+SkpLokJrKJZ0juf3K1ODdkIiIiEgA/eSqDqQl2PGGxvkqTXS64h7S+l6PPS5D8yMRCbigraAECAtr2HD3ZPtGNpXFYuHee+/l4Ycf5o033iAiIoI77rgDh8PBunXreOihh/B4PAwdOpRBgwb5risoKOAnP/kJAD/60Y/40Y9+5Ds3cuRIrrjiCr744gv+8pe/UFtby0033YTdbveN6XK56N+/P2PGjDnjexARkZbz/b5KDpY5KSkuxnTX492/BFu3Sc0aw9Lhctx7PsES24WSkmLsdjubsyvpnR7dQlGLtJ5Dhw4xdepUACIiIvjLX/7C2LFjAZg9e7Zf38jISObMmcOnn37KE088QW1tLcuXL2fdunUMGTKk1WMXETmVbfurmP5Fnl/bhEvb0S7WzootVqKjoygqKqbSOgBvu/7HXG8xDKKio0lMTMButzOyVzy3X5lKiC2o7/yKiIiIBIzFYpBbXAcYhITFYg0Jx+uswBo2GNMcdGx/zY9E5AwFNUG5fv16AMLDwwMy3oQJE9iwYQOzZs1i6tSpvPrqq4SGhlJdXQ007JvU+OCtkcvlIisrC4DS0tJjxnzppZf45S9/yddff81TTz3FM888g8Ph8I3ZrVs3/vrXv6qmtohIG/fVjjK8Hg+VVRV4izZjuGsb9pVqBkviJRg7puMt2kylbQDJ7ZJZu71MCUo5J7z//vvU1dVhGAbPPfccmZmZp7xm/PjxREZG8pvf/AaATz/9VAlKEWlz3B4vT836Ae8RRQ/axYTy27GdiXTYGNg1mvdX5mO32/G2T6amtg6nsw6v14vFYsFudxAe5sBitZIa37CnksqWiYiIyLlm9lcFbD9Q4zu22ELp1D6N8BA0PxKRFhG0BOW6det4+umnMQyDXr16BWzcJ598kmHDhjFjxgy2bdtGXV0d6enpjB49ml/84hdERTXvF2VkZCTvvfceH3/8MZ9++ik7d+7E4/Fw4YUXMnbsWG677TbttyQichbIOlRLbV1dQ0nKsj0NjVZH8waxNaz8N8v2YCb3p7aujn2HagMcqUhwfPnllwBcfPHFTUpONsrMzKRPnz5s2rSJjRs3tlR4IiKn7b3leezJr/Fre3jyBUQ6Gv4c7pMRTe/0KLYfqGbdzjL2Haolr9hJvcdLqNVCaoKd9HZhDLkolh5pEXo5VURERM45xZX1/OOzHL+2K3rFcUdmmuZHItJiApqgPN4eRUdzuVyUlJRQWlqKaZoYhsHNN98cyDAYM2ZMk0uupqWlsXPnzpP2sVgsTJkyhSlTpgQiPBERCYKDpU6czoZ9p8yaAgwATx1YIpo+iLvWdz2A0+kkv9QZ4EhFgiMnJwfDMLj00kubfW3//v3ZtGkTBw8ebIHIRERO396DNfx70QG/tjH9E7msZ5xfm2EY9OwYSc+OevlUREREzj9/m5dDVZ3HdxwWauEP4zNIjrNrfiQiLSagCcrt27djGAamaZ6683/deuutzXpLX0RE5HS4PCZerxcAw9OQVPQWbcaa0vRkjLdos//1Xi8uT9O/80Tassby9dHRzS9ZHBMTA4DH4zlFTxGR1uPxmjz9wQ9+39VxkTbuG5cevKBERERE2phNWRXM/6bQr+2uazqSHGcPUkQicr4IaIKyX79+p1zObbVaCQsLo3PnzlxzzTUMHDgwkCGIiIgcV4jVwGJp2KjdtNoxPHV4D6xsXoIyd6XvemhYYR9iVRkTOTfExMRQXFxMYWHhqTsfJTs72zeGiEhb8dGXB9mcXeXXdv/4DGIjQ4IUkYiIiEjb4vGaPP9Jll9bl/Zh3Hx5+yBFJCLnk4AmKN9///1ADiciIhIw7ePslJY37DlphCdDZTZmZQ6evDVYU4ed8npP3hrMyv2HrwfsdjspeqNQzhFdu3alqKiIVatWNeu6qqoqli1bhmEYdOvWrYWiExFpnoIyJ68t2O/XNuLiODL7JgQpIhEREZG2Z/baAnbn+e/V/cDEDGxWS5AiEpHzSav/pqmvr29WCVgREZFAyGgXRpjDgWEYGLFdfe2enTPx5K056bWevDV4ds70HRuxXTEMgzCHg/R2YS0Ws0hrGjlyJNCwGnLWrFlNvu7xxx+nvLwcgMsuu6xFYhMRaa6/frqPaufhstMRDisPTso4ZcUfERERkfNFaZWLfy7I8Wu7ul8C/buoMo6ItI4WSVDu37+fJ598kqlTpx5zbt68eQwaNIjHH3+cQ4cOtcTHi4iIHOPS7rFYrFaiIqOxJF6CaftvYtH04tkxA9fXz1O+b90xL9G4vn8dz44ZYDbsX2nawrAkXkJUVDQWq5WhPWJb+1ZEWsRNN91EXFwcAP/3f//H9OnTT/pSWVZWFj/72c9YsGABAJGRkUyZMqVVYhUROZlV20pZvrnEr+3uazvRLkZVD0REREQa/WN+DpW1h1/oCrdbuGds5yBGJCLnm4CWeAVYvnw59913H3V1dfTr1++Y87m5uVRVVfHhhx+ycOFC3njjDXr37h3oMERERPz0To+ifawdpzOByqoKLB0zMbPm+s6blTkc2PUv0sNiiEju7ms3ItpjFn3vO7Z0zMSwhRIfn0D7ODuXdI5q1fsQaSkRERE88cQT/O53v8Pj8fDUU0/x97//nR49evj6rF27ltzcXDZt2sTWrVsxTRPTNDEMg0cffZSoKP3/ICLBVev08MIne/3aenaMYOKw5CBFJCIiItL2bM2pZM56/8VDPx2dphe6RKRVBXQFZW5uLvfddx+1tbVYrVY6dep0TJ9LL72UG264gZCQEMrLy7n77rspKysLZBgiIiLHMAyDW0emYLfbSUhIxJLQHSPl2L0ny7PX+x1b2vU/PEbKMCwJ3UlISMRut/M/I1NUKk7OKVdffTVPPvkkISEhmKZJaWkpa9as8f13vmrVKqZPn86WLVvwer2YponFYuHee+9lwoQJQY5eRATeWHSAg6X1vmOLAY9M7oLVou9rEREREQCv1+SFT7I4smBO5yQHt1yeErygROS8FNAE5dtvv01tbS2JiYl8+OGHPPvss8f0GTRoEC+++CKzZs0iISGB4uJi3nvvvUCGISIiclyDusUwvEcsCQmJREfHYukwHCNj7OFyr0BFzreYXq/v2BKVBlGdMDLGYukwnOjohusv6xnHgK7al0HOPVOmTOGDDz7giiuuAPCtkjz6H0D//v155513uOuuu4IYsYhIg9151by/Ms+v7ebLU7goLSJIEYmIiIi0PXPWH2Lb/mq/tvsnZBBia5Hd4ERETiigJV5Xr16NYRjcf//9fuXAjqdHjx787ne/4/HHH2fJkiXcc889gQxFRETkuH5+dRqlVS4ghdDQEIoNA0tsF7xFmwmr34Kz4iDVBduJTLnYd42t10/BXU1CQiIJCYn07BjBz0Z3CN5NiLSw7t2789prr3Ho0CG++eYbdu/eTXl5OW63m5iYGDp16sSgQYNIT08PdqgiIkDDSoBnPtqL5/A7RiTHhvKLMR2DF5SIiIhIG1Ne4+Lvn+X4tV3VO54hF8UGKSIROZ8FNEGZn58PwNChQ5vUv7Hf/v37AxmGiIjICYWGWHlwYgZvLs5l9TaIjIyipKSYStsAUuIb9k622iP9rrGGxdIhLgmHw8FlPeP42egOhIZYgxG+SKtq164d1113XbDDEBE5pdlfFbAlu8qv7f4JGYTb9X0tIiIi0uhfC/ZTXuP2HdtDLPz+xvTgBSQi57WArtsODQ0FaPJ+XBERKrUjIiKtLzTEyt3XdeLecel0To4mJSWVrl26ERqVTEh4HIYlxFfCEsDESlJsJH8Yn87d13VSclLOSS+88AJ79uwJdhgiIs1WVFF/zEqAERfHMbJXfJAiEhEREWl7dh6o5pO1BX5td2Z2oH2cPUgRicj5LqAJytTUVAC++eabJvXfsGEDAMnJyYEMQ0REpEkGdYvhpZ9exMOTM7iydyLW0DBsYTGEhMcc87JN3wuitOeknNP+/e9/M3bsWCZNmsT06dMpLS0NdkgiIk3y8px9VNV5fMdhoRYemJARxIhERERE2hbTNHlhdhbew+9ik5Zg539GpgYvKBE57wU0QTlixAhM0+SVV16hsrLypH1ramp45ZVXMAyDYcOGBTIMERGRJjMMg97p0fzi2k4n7bdkU7HfqkqRc9W2bdt46qmnGDFiBL/5zW9YsmQJbrf71BeKiATBVzvLWLSh2K/trjEdSdZKABERERGf+d8W8f0+/+f1943PwB4S0PSAiEizBPQ30E033YTdbicnJ4fJkyezePFi6uvr/fq4XC5WrFjBrbfeyq5du7Bardx+++2BDENERCQgQqyHV1HuL6pjZ251EKMRaVl33XUXKSkpmKaJaZq4XC6WLl3Kb3/7Wy6//HKefvpptm3bFuwwRUR86lwenvt4r1/bhanh3HxZSpAiEhEREWl7qmrd/G1etl/b5T3juKxnXJAiEhFpYAvkYB07duSxxx7jT3/6Ezk5Odxzzz1YrVZSUlIICwujrq6OvLw8PB6PbxXKww8/THp6eiDDEBERCYih3WNZufVwmcvFG4vpnhYZxIhEWs59993HfffdxzfffMPcuXP5/PPPKS8vB6C0tJRp06Yxbdo0unbtysSJE7nhhhtISkoKctQicj57e0kuucVO37FhwCNTLsBmNU5ylYiIiMj55Y1FByipdPmOQ20G945LD15AIiL/FfA13FOmTOHVV18lMTER0zRxu93s37+fXbt2kZOTg9vtxjRN4uLieP755/nRj34U6BBEREQCIrNvgt+xyrzK+WDgwIE88cQTrF69mr///e+MGTMGu93uW1m5Z88enn/+ea688kruuusuFixYcEzFDBGRlpZVUMP/W57n1zZ5WDIXd4oKUkQiIiIibc8PB2v4YHW+X9uPr0wlLdERpIhERA4L6ArKRpmZmYwcOZI1a9awZs0a8vPzKSsrIzw8nJSUFAYOHEhmZiZ2u/YFERGRtuvynvHYbQZOd0NSMr/EydacKnp11sNPOfeFhIQwatQoRo0aRVVVFYsWLWLOnDmsX78er9eL2+1m1apVrFq1iqioKK699lrGjx9Pv379gh26iJzjTNPk2Y/24vYcfmkoMTqEX51iP2kRERGR84lpmvz10314vIfbUuLs/OSqDsELSkTkCC2SoISGh1ojR45k5MiRLfURIiIiLSrCYWV4zziWfV/ia1u8sVgJSjnvREZGMnHiRCZOnMihQ4eYP38+n3/+OZs2bcI0TSoqKpg1axYffvih9qkUkRY37+tCNuyt9Gu7d1w6kWEt9uetiIiIyFln5dZSvt5d7tf2+3GdcYRagxSRiIi/gJd4BfB6vXz++efMmzfvmHNLly7ll7/8JZ999pnK5ImISJs3qo9/mdelm4rxevX9Jeevdu3acccdd/Dmm2/y0EMPER4ejmE07PemuZ2ItLTSKhevzM32axvWPZbMo76vRURERM5n9W4vr8zZ59c2qFsMV/SKD05AIiLHEfBXTPfv38/dd9/Nnj17GDp0KDfccIPf+R9++IEvvviCFStWMG3aNP7xj38QFxcX6DBEREQC4rIecThCLdTVN9REOVRez/f7Kul7QXSQIxNpfRUVFSxcuJBFixaxbt06XC4XcDgxmZiYGMzwROQ88P/Nzaaixu07todYeGBihu9FCRERERGBmSvzOVDs9B1bDLhvXLrmTCLSpgQ0QVlTU8Odd95Jbm4upmlSWlp6TJ+EhASSk5MpKChg48aN3HXXXcycOROrVUvLRUSk7QmzW7m8ZxyLNxb72hZvLFaCUs4bTqeTpUuXMnfuXFavXo3b3ZAYaExKhoaGcuWVVzJx4kQuu+yyYIYqIucY0zTZvr+adbvKyCqoZXdeNT8crPXrM3ZQEqnx9iBFKCIiItK2mKbJ2h1l/Ovz/X7tcZEhvLssl4zkMIZcGEuPjhFKVopI0AU0QTlt2jQOHDiAzWbj4Ycf5uabbz6mz6RJk5g0aRIzZszgmWeeYcuWLXz00UfH7SsiItIWZPZN8EtQLvu+mPvGp2O1aDIv5yaPx8Pq1auZN28eS5cupba2ISFwZAnX3r17M2HCBK6//nqio5WwF5HA2pRVwfsr88krceL1eKiureXAoUrcbi+GYWBYQ3GEOdiSXcUj7+3ithGp9M7QHtEiIiJy/mqcP327p4J6lxuvux7TU4+BCXY7W/a62JvvYPnmElLj7Zo/iUjQBTRBuWjRIgzD4Fe/+hU/+tGPTtr3tttu4+DBg7z++uvMnTtXCUoREWmzhnWPI8JupdrpAaC40sWGvRUM7BoT5MhEAuubb75h3rx5LFy4kLKyMsA/KZmcnMy4ceMYP348F1xwQbDCFJFzmMvt5d1luazcWorT6aSoqIjKikpcHi9uz1H73NZZyPVG46xP5MVPnYzsFc/tV6YSYrMEJ3gRERGRIDhy/lRRVUvhoQI8zmrfeZvVoLCw4QVri2EQFR2F06n5k4gEX0ATlFlZWQDH7Dt5ImPHjuX1119n586dgQxDREQkoOwhFkb0imPBt0W+tsUbi5WglHPKVVddRX5+vu+4MTHpcDjIzMxkwoQJDBs2TGWARKTFuNxeXp6zj83ZVRQWFlFUVITpceI6tJncvVtwVqgwwl0AACAASURBVBzE66rDEuIgvl0qCWkXU+7tRUVFJYmJiazYAiWV9fz+xnQ9ZBMREZHzwtHzp7yDh3C76qjM3URNwU7MmgLSYgxMmx0jPBlvXDfKPZo/iUjbENAEpcfTsLIkJqZpD2yTkpKAhr2NRERE2rLMvol+Ccrl3xfzwIQMbFYla+TckJeXh2EYvsTkgAEDGD9+PNdeey2RkZFBjk5EzgfvLstlc3YVuXl5lJeVY5Zsx5OzhLyCUmrrvb5+hqeWaHce3px8vHlfYu2USaHZg3pXPZDKe8vz+OnotODdiIiIiEgrOXL+VFpSRkXuZoq2LcDratimo0O8HcOwgqcOszIbszJb8ycRaTMCmqBs37492dnZ7Ny5kyFDhpyyf+OKy/j4+ECGISIiEnCXXhhDVJiVytqGl3HKqt18s6ecSy+KDXJkIoGTmprKuHHjmDBhAh07dgx2OCJyHtmUVcHKraUUFhZRXlaON2813rw1VNa6qan34ohPJ/7Cq4hK64s1JAw8dXiLNuM9sBLP3rlY6kooZzihIaGs2AIDu0bTJ0P744qIiEjwbcup4qM1B1m5tZTqOjcRDhsjLo5j8vD29Ox4+i+D+s+fyija9QXFu1f4zkfYLYTbrcde6K7V/ElE2oSAJij79evHvn37eP3115uUoHzzzTcxDIN+/foFMgwREZGAC7FZuKJXPHO/LvS1LdlYrASlnDPefffdJs3fREQCzTRN3l+Z79tz0izZjjdvDR6vSWGlm9QhdxLX9XL/iywRWFMuxZpyKZ68NXh2zsRwxFNkGERHR/H+ynx6p0epLLWIiIgEjdtj8sxHPzB3faFfe0WNm3lfFzLv60LGDk7ikcldml2d6ej5U83BbX7JSQNIjA496RjevC81fxKRoApoYekpU6YAsGbNGu69916KioqO26+8vJw//vGPLF26FICbbropkGGIiIi0iMy+iX7HX2wuxuX2nqC3yNlFyUkRCZbt+6vJK3H69pz05CwBoLiynuSBPzk2OXkUa+owrBfdgidnCabHSVFRMXklTrYfqG6N8EVERESO63jJyaPNXV/IMx/90Oyxj5w/ed1OcjfN8zsfE2EjtAl7Smr+JCLBFNAVlP379+e2225jxowZfP755yxevJjevXuTkZFBWFgYdXV1ZGdns2nTJlwuFwA33ngjQ4cODWQYIiIiLWJQt2hiwm2U17gBqKj1sH53OcN7xAU5MhERkbPXul1leD0eKisq8RZtAXcttfUenI6OpJ0iOdnImjoMb+5qvEVbqLQOwNs+mXU7y86obJqIiIjI6dqWU3XK5GSjuesLmTSseeVej5w/VRzYiLu+1nfOakB8ZEjTBnLXav4kIkET0AQlwCOPPILFYmHGjBm43W42bNjAhg0b/PqYpgnA5MmTefzxxwMdgoiISIuwWS1c1Tue2V8d8rUt3lisBKWcVUaNGgWAYRgsWbLkmPYzcfSYInJ+G/yHtU3u66mvpb7eQ/6ezdQW1wCQeulVzfq8qvihlO7+gpS4vmzPLmVXfh3/XLC/ydevf0kvzoqIiJzPmjN3CbQ7Xt7c7Gsa50+ludv9202wWppeptUs3Y23XX9qauvYd6j21BeIiARIwBOUISEhPPbYY0yePJm5c+eyZs0a8vPzKS8vx+FwkJqayoABA5gyZQqXXHJJoD9eRESkRWX2TfRLUK7YUoLT5cUeEtCq6SItJjc3F+CYfUVyc3PPaK8R0zS1V4mInDbTUw+As+Kgry0qrW+zxojq0JeD3848YrywgMUnIiIi0tYcb/50WuPUFDSM46wjr9h5xnGJiDRVwBOUjbp370737t1bangREZGg6N8lmvioEEoqG0qVV9d5+GpnGSN7xQc5MpEz11jlQkSktTX+/vG66nxt1pDmJRitoWG+6/X7TERERM51x5s/nRZPQ1LS6/VS7/GeaVgiIk3WYgnKptq8eTPTp0/n2WefDXYoIiIip2S1GIzqHc+HXxb42hZvLFKCUs4aS5cubVa7iEhraFyBbQlx4HU1lBbzuGqx2Zu+B5KnvhZLiMNvPBEREZFz1fHmT6fFam8Yx2Ih1KrqUCLSeoKSoKyvr2f+/PlMnz6dLVu2AChBKSIiZ43Mvol+CcpVW0upq/fgCLUGMSqRpunQoUOz2kVETldT93R8fPputuyFfdll2KPbU1ucBUDlgY3EdbmsyZ9nlGyha0Y61hALnVJiuKRLIk/c1u10QhcREZHzUCD3o35y5h7mfV3Y5P5jByXxp1u6Nrn/LS9sZHdOQ1LSHt2euuIsOrcLI8Ta/Je0jPDkhnHsDlIT7M2+XkTkdLXqKxG5ubm89NJLjBw5kkceeYQtW7ao9I6IiJx1+qRH0S4m1HdcW+/ly+1lQYxIJLhcLhfbtm1jxYoVwQ5FRM5CGclh2O0O3B4IT77I116T9UWzxvHmrsSI64bFMAgPc5DeTntQioiISHBMHta+ef2HN71/bnEd2YdqsdgankuEJ19EXKTttJKTgOZPIhI0rZKgXL16Nb/61a+4+uqrefPNNyktLcU0TUzTpGvXrjzyyCOtEYaIiEhAWCwGV/VJ8GtbvLEoSNGIBEb37t3p2bMnb7/9drOv/cc//sGkSZN4/PHHWyAyETnXDbkwlpIqN5bQcKI69MESEoYBxHjz8OStadIYnrw1mLVFWBJ7ERUdjcVqZchFsS0buIiIiMgJ9OwUydjBSU3qO3ZwEj06Nr2s/d/mZePxgmGxYrVHEN2hD3Gx0acXqC1M8ycRCZoWK/FaVVXFxx9/zIwZM8jJyQEOb9zrcDi45ppruOmmmxgwYEBLhSAiItJiRvdNYObKfN/xl9vLqHF6CLerzKucvU63soXD4cA0TYqLiwMckYicD0zTpLTajc0Rg8dZTWLPa3H/MIdQmwXPzpkAWFOHnfB6T94aPDtnYs24HsNqJzExgdR4Oz3SIlrrFkRERESO8cjkLgDMXX/iUq9jByf5+jXFdz+Us+z7Et+xzRGDxVOL0Xk0nr1zmx2jtVOm5k8iEjQBT1Du2LGDGTNmMHfuXOrq6oDDD7sMw+DRRx9l/PjxREVFBfqjRUREWk2vTpGkxNnJL3UC4HR5WbW1lGv6JwY5MpHWY5omBw4cYN68eQCEhakckIg0j9tj8uzHDXtOWmyh2MJjiUrthdVagZm/Bkwvnh0z8OauxtLhciyJl4AtDNy1eIs2481dhVmZgyV1OEZ8DxITE7Hb7dw2IhXDOL0yZyIiIiKBYLMa/Onmrkwa1p6PvzzIyq2lVNV5iHRYGXFxHJOGt6dnM1ZOerwmL326z68tPMxBREQShQZY6krw5n3Z5PE0fxKRYAtIgtLtdrNo0SKmTZvGhg0bgMNJyaioKAYPHszSpUsB+PGPfxyIjxQREQkqwzAY1SeBaV/k+doWbyxSglLaNLfbzfjx4/nhhx+Oe940TZ5//nmef/75Zo9tGAYXXHDBmYYoIueZD788yM7cat+xzRFDlB1qbJdhhiXgyVkC7lrMyhw8O6bjOXoAWxjWC8ZixPcgJjaGpKRERvaKp3eGXogVERGRtqFnx0h63tL1jMeZu/4Qu/Nq/Nqu6ZfIzrww6l31lDMcwxHvmz+dkC2sYeWk5k8iEmRnlKAsKChg1qxZfPDBB76SXqZpYhgGQ4YMYdKkSVxzzTXk5OT4EpQiIiLnitF9/ROUa3eUUVXrJjKsxSqoi5wRm83GH//4R+68804MwzhuSdfTLfNqGIZeRBORZikodfLaghy/tk5JDiIckRQW2ikyDIzYLniLtmCW7sasKQCPE6x2jPBkjLhuWBJ7/bcsWSJJSYlc0jmS269MDdIdiYiIiLSMqlo3/zxq3nRNv0QenJTBy3P2AamEhoRq/iQiZ5XTfoJ6zz33sGzZMjwej+9BVocOHRg/fjwTJkwgLS0tYEGKiIi0Rd3TIkhLsHOguKHMq8tjsmJrKdcPTApyZCInNnToUK699loWLFhwxmNZrVYiIyPp0KEDt956KzfccEMAIhSR88WLn2ZRW+/1HUc6rLz6y57M/6aIFVsgOjqKoqJiKq0D8Lbrf8z1FsMgKjqaxMQE7HY7I3vFc/uVqYTYLK15GyIiIiIt7t+LD1Ba5fYd20Ms/Ob6ToTYLPz+xnTeW56n+ZOInHVOO0G5aNEiDMMgISGBUaNGcf311zN48OBAxiYiItKmGYZBZt9E3lma62tbsrFICUpp86ZOncrUqVP92rp3745hGDz44IPceeedQYpMRM4XK7eUsGJLqV/b3dd3IiXOwU9HpzGwazTvr8zHbrfjbZ9MTW0dTmcdXq8Xi8WC3e4gPMyBxWolNb5hzySVJRMREZFzUXZhLTNXHfRru/3KVJLj7ACE2CyaP4nIWemMa9BFRERgmiYVFRXU19cTGhoaiLhERETOCqP7JvglKL/aWU55jYuY8JAgRiVyek63vKuISHPUOD28MDvLr61Xp0gmXprsO+6TEU3v9Ci2H6hm3c4y9h2qJa/YSb3HS6jVQmqCnfR2YQy5KJYeaREYhtHatyEiIiLSKl6Zk43He/hvteTYUH58nJKsmj+JyNnmtBOU1113HUuWLCE7O5ucnBw+/PBDwsPDueaaa5g4cSIDBw4MZJwiIiJtUteUcNLbhbHvUMMG9B6vyRebSxg3JPkUV4q0LY37hcfExAQ5EhE5172xcD8FZfW+Y6sFHp5yARaL/0MywzDo2TGSnh0jWztEERERkTZh7Y4yVm/zrzrx2xs64wi1Hre/5k8icjY57QTlX//6V8rLy5k9ezYff/wxu3fvprq6mtmzZzN79mw6derEpEmTGDduXCDjFRERaTHrXxra7Gsayrwm8OaiA762JRuLlaCUs06HDh2CHYKInAd25VYzc1W+X9utI1K4MDUiSBGJiIiItE1uj5eX5+zza+uTHsXovgnBCUhEJMDOaPfbmJgY7rjjDubOncvMmTOZMGECDocD0zTJzs5m6tSpXHXVVTz66KOBildERKTNOfqPg2/2lFNa5QpSNCLBsWfPHl588cVghyEibZjHa/LMR3vxeA+3tY8L5edXdwxeUCIiIiJt1MdrCsgqqPUdGwbcNz5dpVlF5JxxxntQNurbty99+/blscceY+7cuXz00Uds2bIFj8fDli1bfL84//jHPzJhwgSVgBURkXNGRnI4XVPC2ZNfA4DHC8u+L2bSsPZBjkyk+bKzs/nwww/ZvHkzlZWVuN3u4+5N6fV6cbvd1NbWUl5eTn19Q7nG+++/v7VDFpGzxOy1BWzNqfJre2BCBmH245coExERETlflVW7eGPhfr+2GwYm0UOlW0XkHBKwBGWjiIgIbrnlFm655RZ27NjBrFmz+Oyzz6ioqADgk08+4ZNPPiEtLY0JEyYwbtw4lRQTEZGzXmbfBF+CEhrKvCpBKWebhQsX8uCDD/qSjY0aE5RHv6l7dOJSb/KKyIkUVdTz9/k5fm1XXBLP5RfHBykiERERkbbrjYX7qaj1+I7D7RZ+dV2nIEYkIhJ4Z1Ti9VS6d+/On//8Z1atWsWzzz7LwIEDMU0T0zQ5cOAAf/vb3xg9ejR33HFHS4YhIiLS4o4u8/rd3gqKKupP0Fuk7SkuLuahhx7C6XT65muN/xod3W4YBoZhYLFY6NixI7feemsQ70BE2rKp/9lHdZ3/Q7Y/jE8PXkAiIiIibdQPB2v4ZG2BX9udmWkkRocGKSIRkZYR8BWUx2O32xk/fjzjx48nKyuLDz74gP/85z+UlJRgmibr1q1rjTBERERaTMfEMLqnRbDjQDUApglLNxVz8+UpQY5MpGlmzZpFXV0dhmEQHx/P//7v/3LRRReRlZXF008/jc1m4x//+AemaVJSUsLatWuZP38+brcbq9XKM888oxL+InJca3eUsnhjsV/bL8d0IjnWHqSIRERERNom0zR5+T/7/Pbs7pBg59YRerYgIueeFl1BeTwZGRk89NBDrFy5kpdffpnhw4erHJiIiJwTMvv4r6JcctTDWJG2bO3atQBYrVbefvttfvrTn3LZZZdx8803ExISgsfjwTRNRo4cyYQJE3j++eeZNm0a0dHReDweHn30UVwuV5DvQkTamrp6D899nOXX1j0tgimXqQy6iIiIyNFWbS1l3a5yv7Z7xnYm1Nbqj/FFRFpc0H6z2Ww2xowZw7///W+WLFkSrDBEREQCJvOoMq+b9lVSUOoMUjQizbNv3z4Mw2Do0KFceOGFvvbQ0FC6d+8OwLfffut3Td++fXnyyScxTZP9+/czd+7cVo1ZRNq+t5bkkldy+LvQYsDDky/AatFLqiIiIiJHqnd7eWVutl/bwK7RXNFLe3aLyLmpTbx6kZqaGuwQREREzlhqvINenSL92pZs0ipKOTtUVFQAcPHFFx9zrlu3bpimydatW485N2bMGDp16gTAF1980aIxisjZ5YeDNfy/5Xl+bZOHt6dnx8gTXCEiIiJy/vpg1UH2F9X5ji0G3Dc+XdUHReSc1SYSlCIiIueK0f0S/Y4XbywKUiQizdP4R29k5LGJg/T0dAD27Nlz3GuHDRuGaZrs2rWrxeITkbOL12vy7Ed78XhNX1tSdAi/vLZjEKMSERERaZuKK+v59+IDfm3jL02ma0pEkCISEWl5SlCKiIgE0Kg+8Rz5cuO2/dXkFted+AKRNiImJgaAqqqqY8517NiQUDh06BDV1dXHnE9OTgagsLCwBSMUkbPJnPWH2JRV6df2hwkZRDpsQYpIREREpO16bcF+qp0e33FUmJVfjNGLXSJyblOCUkREJIDaxdjpkxHl17Zko8q8StvXmITcvn37MecaS7gC7Nix45jzjeVhnU7tuSoiUFLp4tV5/vsnDe8Ry5WXaP8kERERkaPtPFDNnPWH/Np+dnVH4iJDghSRiEjrUIJSREQkwEb3UZlXOfsMGjQI0zT58ssvj0lCZmRkYLE0TBtXr159zLXfffcdABERKj8kIvDK3H1U1B5eAWAPsfDAxAztnyQiIiJyFNM0eek/WZiHq+LTOcnBlOHJwQtKRKSVKEEpIiISYFf1icdyxDPYXXk1ZBfWBi8gkSa44YYbMAwDj8fD7bffzrRp03wrI8PDw+nVqxemafLee++xefNm33XvvPMOmzZtwjAMLrjggmCFLyJtxNe7y1nwrf+LOT+/Oo3UeEeQIhIRERFpu5Z+X8LGvf5l8e8dl47Nqsf2InLu0286ERGRAEuICqV/l2i/NpV5lbauS5cu3HjjjZimSUVFBU8//TQvvPCC7/ykSZMAqKmp4eabb2bixIlcddVVPPfcc74+o0ePbvW4RaTtcLq8PPfxXr+2rinh3DYyJUgRiYiIiLRddS4Pf5vrXxZ/WPdYhvWIC1JEIiKtSwlKERGRFjC6r8q8ytnnySefZNiwYb7jDh06+H6ePHmybxWl1+tl+/bt5OXl+c537NiRW265pVXjFZG25d1lueQU1vmODQMennyBVgCIiIiIHMf0L/LJL3X6jq0Wg9/fmB68gEREWpmtJQffu3cva9euJT8/n8rKSuLj40lLS2PkyJEkJiaeegAREZGz1JWXxPP8J3vxeBuO9x6s5YeDNXRpHx7cwEROwm6389Zbb/HZZ58xe/ZsMjIyfOesViuvv/46f/jDH1i7di3mfzdJMU2Tiy66iFdffZXwcP33LXK+yj5Uy7tLc/3axl+aTO/0qCBFJCIiItJ2HSp38u4y/7nTlMvak54cFqSIRERaX4skKDds2MBTTz3Ftm3bjnveYrEwcuRIHnvsMVJTU1siBBERkaCKjQxhULcYvtpZ7mtbsrGILmM6BTEqkaa5/vrruf76649pj4+P5+233+b7779n48aNuFwuunfvztChQ7FYtEJK5HxlmibPfbwXl8f0tcVHhvDr6/SdJyIiInI8f/8sh7p6r+84JtzGz0anBTEiEZHWF/AE5bx583jwwQcxTdP3Zj1AaGgo9fX1AHg8HpYvX866det45513uOSSSwIdhoiISNCN7pvol6BcvLGYu67piGEYQYxK5Mz17t2b3r17BzsMEWkjFnxbxDd7Kvzafj+uM9HhLVqwR0REROSstDm7kgXf+m8D84trO2ruJCLnnYC+6r53714efvhhvF4vUVFR3HfffcyfP59t27bx/fffs2XLFubMmcNvf/tbwsPDqa6u5u6776a8vPzUg4uIiJxlRvaKx2Y9nIzMKaxjd15NECMSEREJrLJqFy/P2efXNuTCGK7ppy09RERERI7m9ZpM/XSfX1vXlHDGD0kOTkAiIkEU0ATlO++8g9vtpl27dnzyySfcddddXHDBBb6SXzabjQsvvJBf//rXfPTRRyQkJFBUVMTMmTMDGYaIiEibEB1u49KLYv3aFm0oOkFvERGRs8+r83Ioq3b7jkNtBg9OylC1ABEREZHjWLihiC05VX5t945L93u5WUTkfBHQdePr16/HMAweeOAB0tJOXjM7IyODe++9l8cee4zPP/+cX/ziF4EMRUREpE0Y3TeB1dtKfceLNxXz6+s76cGtBM2oUaNabGzDMFiyZEmLjS8iLc80Tbbvr2bdrjKyCmrJL3FS7/ESarWQEm8nIzmMIRfG0qNjBBuzKpmz/pDf9XdmptExMSxI0YuIiIi0vObMl47827/G6eHVedl+Y43sFcegbjGtfQsiIm1CQBOU+fn5AAwfPrxJ/UeOHAlAdnb2KXqKiIicnS6/OI5Qm0G9u2Ff5vwSJ9v2V3Fxp6ggRybnq9zc3BZJkJumqcS7yFluU1YF76/MJ6/Eidfjoaa2DqezDq/Xi8ViobjMwd58B8s3l9A+NpSdudV+16e3C+PHV6YGKXoRERGRltec+VJqvJ3bRqTSO6Ph7//3luVSWOHyjRViNfjd2PQg3YmISPAFNEEZFRVFcXEx1dXVxMfHn7K/aTY8rLXb7YEMQ0REpM2IdNgY1iOOLzaX+NoWbyxWglKCqnEOJiIC4HJ7eXdZLiu3luJ0OikqKqKyohLvcX5XWAyDqOgoDhbHUFbr/1LCw5MzCLUFdBcRERERkTbhdOZLTmciL37qZGSveDL7xjP9izy/freOSCEt0dFatyAi0uYENEF56aWX8tlnnzF79mzuueeeU/ZftmwZAIMGDQpkGCIiIm3K6D4JfgnKJRuLueeG/5+9Ow+Pqrz3AP49Z7ZMkplsE0JWEpIAYUcgbArILgqUXiwKLuDeVi3euqBcvdVWlNtaraW9va5IBdRiAVkURTEIRHaRELZA9oQkk32b7cy5f8QMHCD7hEkm38/z8DTnPe95z2/6CHnn/N7ze/tAFPm2GV1/X3/9tadDIKIuxO5w4o3PsnAiuwYlJWaYzWbIkhVOcxrk8nOQ64oAyQqodBB8w+AMSkSFYzBszgqofQOh9gmAIAi4daQJN8SzPBkRERF5n/bMlyqlwaiqqobJZEJKGrD9UDGsjkvJzBCDBkunNb9FGhGRt3NrgvLRRx/F119/jbfeegvx8fG49dZbm+x7+vRpvP7669BoNPjVr37lzjCIiIi6lBsHBkGvFVFvcwIAiitt+CGzig9yySMiIyM9HQIRdSEffJOPE9k1yC8oQGVFJeSyU5BydgGOemVHyQK5OhtydTYKT34N3/iZ8A8fDFlywMdgQohR65kPQERERNTJ2jNfchbsgypmGkrkJNTVW1EnBCi2xPjV7Bj4+aiu8ychIupa3Jqg1Ov1WLlyJZ577jk8+eST2Lp1K2bPno0BAwbAYDDAYrEgJycHKSkp+PTTT2Gz2XDrrbciJycHOTk51xxzxowZbY5jx44dWL9+PdLT0yFJEqKiojBr1izcf//98PX17ejHhNPpxF133YUjR47g0UcfxWOPPdbhMYmIyHvpdSpMHBSMncfMrradx0qZoCQiIo86nlmFPSfLUVJiRmVFJZwFe+Es2N/sNXXaSBgH3wRD1HCotL6ALEOQ7Ug5WYabBgVhWJzxOkVPRERE5H7pOTXYuP8i9pwsR63FAR+NCmqVANhrWj1fAgA46iFd2ArBUobyXuOg0gMafSAAYECUH24dFdrJn4SIqOtza4Jy8uTJrp9lWUZKSgpSUlKu2VeWZQiCgO3bt2P79u3X7CMIAtLT09sUw6pVq/Dee+8BADQaDTQaDTIyMrB69Wps27YN69atg8lkatOYV3rnnXdw5MiRDo1BREQ9y4wbTIoE5dfHS/Hkz2Kh4V5dRETkAbIsY8OeQtceSnLZqeYftgkixH53IDBy/BXtgAQVsosteGHdOWxcPoK/24iIiKjbcUgyXtl4HlsPlijaa63STz/5AIIIZ+H3bRq3IiMFcBjgHz4YKo0vRLUW/zmPW74QEQFuTlDKV2wKfOVxS/076rPPPsN7770HURSxfPly3HnnndBqtThw4ACWL1+OrKwsPPnkk1izZk2775Geno4333zTfUETEVGPMLZfAIy+alTVOQAAVXUOHDhbiRsHBnk4MqIGhw4dcss43FucqHs4lVuLgjKraw8lKWdXs/1V/e+AKmJ8s30Ky21YvvYsXrtvgDtDJSIiIup010pOXkk0REHV/w5Ip9e3akynU0ZptQ1y+ufw7ZUIh6USptDe0GpY2pWICHBzgnLt2rXuHK5NJEnC6tWrAQAPPPAA7r33Xte5MWPG4K233sK8efOQmpqK1NRUjBs3rs33sFqteOqpp2C326HT6WC1Wt0WPxEReTeNWsTUYSHYlFrkatt51MwEJXUZd999t2JPlPZoT/ULIvKMA2cr4JQkVFdVw2lOu3oPpcsIhpgWk5ONvjtZjvTcGgyM9ndXqERERESdKj2npsXkZCNVxHg48/dCrr72dmWXK6uxQ3ICcNajOv84AvokI8SgxoEzFZwrERHBzQnK5ORkdw7XJvv370d2djYEQcCSJUuuOp+YmIgpU6bgq6++wpYtW9qVoPzjH/+IjIwMLFq0CKmpqcjMzHRD5EREXl9uxgAAIABJREFU1FPMHKFMUKacLEO9VYJex9WT1DW4u7oFEXlO8m9TW+wj2ephs0kozDiB+tK6JvtF9BmPtiynWfLGiTb0btnB19r+3Y2IiIi8W2vmOp2lJngcCs6ebtM1dUVnEBCTjIy8CvxvsRX/+3mu6xznOkTUU7k1QelJBw4cAAD0798fISEh1+wzbtw4fPXVV/juu+/aPP6+ffvw4YcfIjY2Fk8//TTmz5/foXiJiKjnGR5nRK8ALYorbQAAi82J79LLMWNEx/ZGJnKH1pZmtVqtKCkpQWFhIYCGtyanTZuGUaNGdWZ4RNQJZKnh95G16mKz/QxRw69HOERERETdgiGy7XOjxvlWw/xL7+aIiIi6p05LUFZWVmLTpk04ePAgCgsLUVdXB71ej8jISNxwww2YO3cuQkND3Xa/jIwMAEB8fHyTfWJjYwEAZrMZ5eXlCApq3TrgiooKLF++HKIoYtWqVdDr+UuEiIjaThQFTB8egnUpha62nUfNTFBSl/DPf/6zTf3NZjP+7//+D+vWrUNKSgr+4z/+A5MnT+6c4IioUzS+Ne20W5rtp9Lw+w8RERFRI5W27XOjxvkWq9YQEV0idsag//73vzFlyhSsWrUKu3fvxqlTp5CdnY3Tp0/jm2++wZ/+9CfMmDEDmzdvdts9i4uLAQBhYWFN9rn8XElJ6+qKA8Dvfvc7FBcX48EHH8Tw4Vw9TERE7TfzBmUyMvVMBSrr7B6Khqj9TCYTVqxYgSeeeAI2mw3Lly9v0/yKiDyvcd9ZUePTbD/J3vT+lEREREQ9jWRr+9yocb7VOP8iIqJOeINy/fr1+P3vf+9aDRIQEICEhATo9XrU1tbi/PnzqKqqQn19PZ599lnY7XbcfvvtHb5vbW0tAMDX17fJPjqdzvVzTU1Nq8bdvHkzPv/8cyQlJeHRRx/tWJBERNTj9Y/0Q0yoD3JKGlZPOiQZu38sw8/GNr3Ahqgre+CBB/Dxxx8jPz8fGzZswOOPP+7pkIgILe9l9MK6c0i7AOTkVCIhLhZydfZVfarrHbhYYUN13g8Iir+x1feeMzoUz9+R0NaQiYiIiFrNnfs2vvRRBrYdav1iS6EsDYnh134GXVFrR0mVchFytMkH+uBYqDQiYsIDMCTehBcXJXYoZiIib+DWNyhzc3OxcuVKyLKMPn364J133sGBAwewbt06vPPOO9iwYQMOHjyIt956C3FxcZBlGStXrkRBQUGH7+1wOAAAGo2myT5ardb1syRJLY6Zn5+PP/zhD9Bqtfif//mfZscmIiJqDUEQMPOKkq47j5o9FA1RxwmCgMmTJ0OWZezatcvT4RBRK8WF6eGr94EoCBCCrn5AJjllmKsa9qgsO/tNm8ZeMKG3W2IkIiIiuh4WjG/b3MWZv+ea7ZJTRmm1Mjlp1KvgoxEhBCVCFAT46n0Q24vl84mIADcnKP/5z3/C4XAgOjoaH330EW688dqrbCdOnIgNGzYgJiYGFosFGzdu7PC9fXwaXpO325suk2ez2Vw/t5RsdDqdWL58Oaqrq7Fs2TL069evwzESEREBwIwryrwevVCF4kqrh6Ih6riQkBAAcMuiMyK6Psb0C4SoUsFgNEA0DQbUygdlpdV2OJwNP1vKslCe8V2rxr1pUBCSov3dHS4RERFRpxkY4485yaGt6isV7IdcnXvNc6XVdjgv22JSFIAQoxZQ6yGaBsNgNEJUqTCmf6A7wiYi6vbcmqDcv38/BEHA448/jqCgoGb7BgYG4rHHHoMsy9i9e3eH7+3n5wcAsFgsTfa5/Jy/f/Nfmt99910cPHgQo0aNwtKlSzscHxERUaM+oXoMiPJzHcsy8NUPpR6MiKhjsrMbSkM2lvgnoq4vKdoPEcE6mEwmCCodVDHTXOcsNgmVdQ5Ff0v6OkgF+5sdMzxIi1fv4cJOIiIi6n6eXRDfYpLSWZ0H6cxH1zxntTuvmj8FGzRQiwJUMdMgqHQwmUIQEaxD0mXPA4iIejK3JigbV82PHTu2Vf0b++Xn53f43r17N7yKX1RU1GSfy8+Fhjb/C2fDhg0AgNOnT+Omm27ChAkTFH9ycnIAAO+9956rjYiIqLVm3sAyr+QdcnNz8fnnn0MQBERGRno6HCJqJUEQcOfEcOh0PyUpg5MgRkyALAPFlTZFX41KQLCfGtLp9bAf+h9IBamQbTWQZSdkpwSd6ECfXj74/eJ+0Kjd+hWTiIiI6LpQqwQ8vzABa5YNwZzRoQjwVUMlCvD3USHAV40AjQWQnRDDr733ZUnV1fOnQF8NxIgJEIKTYDKZoNPpsGhiBARBuB4fiYioy1O7c7DGfR1VKlWr+jf2u7z0anslJiZi586dyMrKarJP4+r+0NBQBAQEtGrcmpoa1NTUNHm+rq4OdXV1bYqViIho+vAQvLk1G40vnJ3Oq0V2ST36hHIvCur6bDYbysvLsXfvXrz55puwWq2uvSiJqPsYFmfExEFB2HMSsNltqMQEWMVA2Eu3Aah39esVoEXjczS5OgdSxr8BWxWE4CQEBAYgMiICkwYHY2icwTMfhIiIiMhNBkb7Y+AdCYq2d77MxZ6T5XBKVlRiAgSfYEg5uwBHw3ypxuJAvc2puKZXcADU8TNd86XQUBPnS0REV3BrgrJ3797IycnBjz/+2KoHVMePHwcAhIWFdfjeY8aMwerVq3Hq1ClUVlZeMwG5f39DSaLk5OQWx/vmm2+aPT9r1ixkZmbi0UcfxWOPPda+oImIqMfqFaDDDX2NOHK+ytX25VEzHpwZ7cGoqCdLSkrq0PU+Pj5YvHixm6Ihouvl3imRKK+xA4iASqVBoSMJMZP7ojr/OOqKzkBrK4GvTgZUOgi+YRCCEiGaBv9UpsyE0FAThvTxxz03R3j6oxARERF1isvnS1qNFmZBgBAYD6c5DVL5WZSV5wCwQ9T4QGfsjcCIJBhjhnO+RETUArcmKEePHo3s7GysXr0a48ePh1arbbKvzWbD6tWrIQgCRo8e3eF7jxo1CmFhYSgqKsI777yD3/72t4rzZ86cce11eeedd3b4fkRERB01Y4RJkaDcecyMB2ZEsdwLeYQsyxAEoV37SKrVaqxatcoti86I6PrSqEUsmxuLD77Jx7oSC3RGNRyWSgTEJCMgJhk6jQDg0u8lURBgMBphMoVAp9Nh0uBg3HNzBEu7EhERkddqnC+t3V2AlDTAaDTAbC5FtWokZNMIRPZVfofSaUQEBARwvkRE1AK3/qt41113QRAEnDx5Eg899BByc3Ov2S8nJwcPPvggTp48CQBuWW0viiKeeOIJAMDbb7+Nf/zjH7BYLACAAwcO4OGHH4YkSRg3bpwiIVpUVIRZs2Zh1qxZ+PDDDzscBxERUWtNGRYMterSQ9+cEgvSc5suK07U2VqbnBRFEb6+voiPj8ddd92F7du3Y8aMGZ0cHRF1Fo1aREyoHrVWCaJaC61/KHyCohERGYWwsDCEhpoQFtYLMTEx6NcvEZGREYgLN+LJn8Xh/ulRfNhGREREXk+jFnH/9Cj89mexiAs3NsyH4hOg8usFjW8Q1PpAaHyDYAqLxID+/ThfIiJqBbe+QTlgwAAsXboU7733Hg4cOICZM2ciKSkJCQkJ8PX1RV1dHTIyMnDq1CnXA7AHHnigwyXFGs2fPx/Hjh3Dxx9/jNdffx2rV6+GVqtFbW0tACAuLg6vv/664hq73Y7MzEwAQHl5uVviICIiao0AXw3GDwjEnpOXfv/sOGzGoBjuSUHX3+nTpz0dAhF5SFWdA3/ekqVoizT5YmisAYVlVtgkJ7QqEREhOsT20mNM/0AkRfnxjX8iIiLqcYbFGTE01oBTebV4+ePzEDV6iBo9AEAlAuMGhSAxwpfzJSKiVnBrghIAnn76aYiiiPfffx+SJOHkyZNIT093nW9MTAqCgIcffhjLli1z6/1feukljB8/HuvXr0d6ejosFgtiY2Mxffp0PPzwwzAY+NCXiIi6jtmjQhUJyq9+MOOJeX2gVnF1JRERXR9/256Nsmq761ijEvDGA0mIDdN7MCoiIiKirkkQBNgdTpwrrFO0P/3zvpg/jtteEBG1ltsTlADw5JNPYv78+fjXv/6FAwcOoKCgALW1tfD19UVUVBRGjx6NhQsXom/fvp1xe1fJ1taIiorCmTNn2nyPL774os3XEBERXenGgUEw6FWorpcAABW1DqSersBNg4I9HBkREfUExzOrsOn7YkXbvVMjmZwkIiIiaoLTKeO1zVmKtsQIX8wd08szARERdVOdkqAEgPj4eCxfvryzhiciIvIKWrWIacNCFA+HdxwxM0FJRESdzu5w4pWNFxRtMaE+WDI10kMREREREXV92w+X4HReraLttz+LhUpkOVciorZwa4Jy8+bNAIC5c+dCFFsuTVdXV4cPPvgAlZWVTGYSEVGPdcvIUEWC8ruTZaiud8Cg77R1RERNcjgc+O6773DixAmUlZXBarW2+lpBELBy5cpOjI6I3OnDbwtw4WK9ou3ZBX2hVbPMOBEREdG11Fgc+NuOHEXb1GEhuCE+wEMRERF1X2598rl8+XKIooiZM2dCr2+5JJDNZsNf/vIXGI1GJiiJiKjHGhZnQESwDgVlDYkgm0PG18dL8bOx3LuCrq/Dhw/jySefRFFRUbvHYIKSqHvINdfjva/yFG23jQ7FyAQ+XCMiIiJqyvu78hV7d+vUAh67LcaDERERdV9uXxoryzIEoXWvsx89ehQA2rQyn4iIyNsIgoBbRpoUbZ8fKfFQNNRTZWdn46GHHsLFixchy3K7/hBR9yDLMl7dmAmr49Lf20A/NR6f08eDURERERF1bTkl9diwp1DRdtfNEYgI9vFQRERE3Vu73qB0Op1YunQp8vLyrnl+9uzZLSYp7XY7zGYzBEFATAxXmRARUc92y8hQvPtVvuv42IVqFJRZ+EWHrpu3334bdXV1EAQBOp0Oc+bMQWJiIvz8/FpVup+Iuo/th0pw6Fylou03c2MR6KfxUEREREREXd8bn2XDIV1a4NUrQIt7bube3URE7dWuBKUoirjnnnvw61//+qpzsiyjoKCgTePdd9997QmDiIjIa8SE6jG4jz/SsmtcbV8cNeO+aVEejIp6ktTUVACAj48PPvroI/Tv39/DERFRZyittuGNz7IUbaMTAzD7ijf5iYiIiOiSvenl2Jtermh79LYY6HUqD0VERNT9tXsPyqlTp+KRRx5R7FG0adMmCIKA2267DWp180NrNBoEBgZi/PjxGDt2bHvDICIi8hqzR4YqEpQ7Dpdg6dTIVpdOJ+qIkpKShnLDt9zC5CSRF/vz5ixU1UuuY51GxLML+vJ3DREREVETbA4nXt+SpWgbGmvAzBFc4EVE1BHtTlACwLJlyxTHmzZtAgC89NJL0Ov1HRmaiIiox5k2PAR/3pLlKhmTU2JBem4NBsUYPBwZ9QT+/v4oLy9HbGysp0Mhok7y3ckyfPVDqaLt4ZnRiDKxnDgRERFRU9anFCLXbHEdiwLw9M/juMCLiKiD3Lqh0K9//Wv8+te/hkbDvUuIiIjaKtBPg/EDAhVtOw6bPRQN9TQJCQkAoKiOQUTeo8biwKpPMxVtA6L8cMfEcA9FRERERNT1FZVb8d6uPEXb/HFh6Bfp56GIiIi8h1sTlI899hgeffTRFsu7EhER0bXNHhWqOP7qBzMcktND0VBPMm/ePMiyjJ07d8JisbR8ARF1K/+7IwfFlTbXsUoEVtweD7WKK/+JiIiImvLmtmxYbJe+kwf4qvHILdEejIiIyHu4NUHZnDNnzuCTTz7B2rVrkZqaer1uS0RE1K3cODAIBr3KdVxR68De9AoPRkQ9xfz58zFy5EiYzWY89dRTsFqtng6JiNzkeGYVNu5Xvh29eFIE+kdx5T8RERFRUw5nVF5VHv+Xs2MQ4MvqgURE7uCWVx2//PJLbNu2DWFhYVixYoXinNVqxVNPPYWvvvpK0Z6QkICVK1diyJAh7giBiIjIK2jVIqYNN2FT6qUHydsOFWPykGAPRkU9gSiK+Otf/4pHHnkEu3btwqxZszB37lwMGjQIRqMRKpWq5UEAjB49upMjJaK2sDmcePmTC5DlS21RITo8MDPKc0ERERERdXEOyYnXNl1dHn/emF4eioiIyPt0KEFps9nw+OOPIyUlBQAwfPjwq/o88sgj+P777yFf/o0YwLlz57BkyRK8//77GDp0aEfCICIi8ipzk0MVCcp9p8phrrLBZNR6MCrqCfR6PUaMGIEff/wRhYWFeOutt9p0vSAISE9P76ToiKg91uzKR1ZxvaLtuV/Ew0fTukUHRERERD3Rxn1FOH9ROYd6cn4cVCLL4xMRuUuHSry++OKL+PbbbyHLMrRaLaKilKtwt23b5irn6ufnh1deeQXffPMN3n33XSQlJaG2thYrVqyAJEkdCYOIiMirDIz2R1yY3nUsOYEvjpg9GBH1BDabDQ899BDWrl0LQRAgCAJkWW7zHyLqOs4X1mHNN/mKtrnJvTAqIcBDERERERF1faXVNvzfzlxF262jQjE01uChiIiIvFO736DMyMjApk2bIAgCbr31Vjz//PMICFB+0b181f1LL72EW2+9FQAQERGBQYMG4bbbbkNGRgZ27tyJ2bNntzcUIiIiryIIAuYm98Jftma72rYeKsbiyeEQBK7WpM7xr3/9C4cOHXIlJrVaLcLDwxEaGsr/7oi6Ickp4+VPzsMhXVo4EGzQ4PE5fTwYFREREVHX9/ftOai1XHqhxs9HhV/fGuPBiIiIvFO7E5Tbt2+H0+nEiBEj8Kc//emq8zk5OTh79iwEQUB0dLQrOdkoMDAQixYtwptvvomvv/6aCUoiIqLLzBppwurt2ZCcDceZRfU4mVODwX24YpM6x5YtW1w/L126FL/85S9hNBo9GBERdcTGfReRllOjaHtqfhyMvh3a5YOIiIjIq53IrsbWQyWKtgdnRHHLFSKiTtDuEq+pqakQBAF33333Nc/v27fP9fOUKVOu2efGG28EAKSlpbU3DCIiIq8UYtBiQlKQou3KL0lE7pSTkwNBEDBq1Cg888wzTE4SdWMXy634+44cRdukwUGYMjTYQxERERERdX2SU8Yf/52paIsL0+MXN/b2UERERN6t3QnKgoICAMDQoUOvef7w4cOun8eOHXvNPhEREQAAs5n7ahEREV1pTnIvxfGXx8yw2LhvM3UOi8UCABg5cqSHIyGijpBlGas+vYB6m9PV5uejwlM/j2O5ZiIiIqJmbD1YjNN5tYq2J+fHQa1q9yN0IiJqRrv/da2oqADQUKr1Wo4ePQqgYR+tph50qVQqAIDD4WhvGERERF5rQlIggv01ruNai4RvT5R5MCLyZmFhYQA4LyPq7r44asa+UxWKtsdu64NeAToPRURERETU9VXW2fG37coKFFOHhWB0YoCHIiIi8n7tTlDqdA1fcG0221XnCgsLUVhYCEEQ0K9fPxgM194vq7S0FAAQEMB/6ImIiK6kVom4ZaRJ0cYyr9RZbrrpJsiyjNTUVE+HQkTtZK6y4bVNyrJkw/sa8LMxvZq4goiIiIgA4K0vclFZd2mxpk4j4jdz+ngwIiIi76du74Umkwk1NTXIz89HcLByL5P9+/e7fk5OTm5yjMa3LENCQtobBhERkVe7LbkX1qUUuo4PnatEQZkFEcE+HoyKvNGiRYvwySefID09HZs2bcL8+fM9HRJRjyDLMk7l1uLA2QpkFtWjsMwKm+SEViUiPFiHuDA9xvQLRFK0X7MlWmW5Yc+kqvpLpcC1agErbo+HKLK0KxEREfVszc25/PQqpF5RgWLptEj0DmIFCiKiztTuBOXw4cORlZWFPXv2YMiQIYpzO3fudP08adKkJsfYsmULBEHA8OHD2xsGERGRV4vv7YtBMf44mVPjatt+qAQPzoz2YFTkjfr27YtnnnkGv//97/H888/j3LlzuPfee12lX4nI/Y5nVmHDnkIUlFnhlCTU1VtgtVrgdDohiiJKK3xwodAHu0+UISJYh0UTIzA07trVaXYdL8XuK8qAPzQzGn166a/HRyEiIiLqspqbcwmCiAqLAKdTDUFs2I4s1KjB4kkRHo6aiMj7tTtBOW3aNGzatAlr1qzBzJkzkZCQAAD4/vvvsXfvXgiCgODgYIwdO/aa12/ZsgVHjhyBIAiYMGFCe8MgIiLyereNDlUkKLcdLsH906P4Rgy51dq1awEAN9xwA44ePYr3338f77//PkJDQxEWFgYfH59m394CGvYe/+CDD65HuETdmt3hxAff5GPPyXJYrVaYzWZUV1XDKctX9RUFAQajAVarCX/abMWkwcG45+YIaNSXdusor7HjT1eUdh0Y7YdFfLBGREREPVhr5lySU4ZDajhW6fyg9gmATuuDD78tuGrORURE7tXuBOXUqVMxePBgpKWl4fbbb8eUKVNgs9nw7bff/rT6RMCvfvUrqFQqxXXV1dVYt24dVq9eDUEQkJiYiKlTp3b4gxAREXmrGSNMeGNLFqyOhi9NhWVWHM6oQnI/7uFM7rNy5UpXArLxf2VZRklJCUpKWt77VJblFhOYRNTwoOyNz7JwIrsGJSVmmM1myJIVTnMa5PJzkOuKAMkKqHQQfMPgDEpEpTQYVVXVMJlMSEkDyqptWDY31vXA7LVNmSivubRnklol4Pk7EqBW8e8kERER9UytmXNJdgtyy2WoDb3hG9YfhshhkO11qPeTkJKmumrORURE7tXuBCUA/PnPf8aSJUtQUFCAHTt2AGh4OAUA06dPx+LFi6/q/+6778LpdEKWZej1erz44ot8mEVERNQMg16NyUNCsPOY2dW25UARE5TkdvI13t66VhsRtd8H3+TjRHYN8gsKUFlRCbnsFKScXYCjXtlRskCuzoZcnQ1nwT6oYqahRE6CzW4DEIG1uwtw//QofHuiDF/+UKq49P7pUYjv7Xv9PhQRERFRF9OaOVdptRV2mwR7aSbqSzNRcS4FfUfPRYlKuGrORURE7tehBGVMTAw2btyIt99+G19//TWKi4sRFRWFBQsW4J577rnmNZIkAQCCgoLw5ptvYsSIER0JgYiIqEeYN7aXIkG5+0QZyqrtCDZoPBgVeZPGEq9E1HmOZ1Zhz8lylJSYUVlRCWfBXjgL9rd8oaMe0oWtEC1lqMQEaDVapKQBA6J8serTC1d137CnEPmlFiyY0BsDo/074ZMQERERdV2bvy/Cx99dRFWdHU7ZCMHPB3JtAQR9KOTqHACAxSahsk5SXBfo4wAyt8JpVc65RiUYMSzO6ImPQkTk1TqUoASA4OBgPPPMM3jmmWda7Dtw4EDMmjULo0aNwrx582AwGDp6eyIioh5hZLwR0SYf5JotAACHJGP74WLcfXOkhyMjb5GcnOzpEIi8mizL2LCn0LX/kVx2qnXJycs4C/ZB8AmGWRBgNBrw6sYLKK22X9Wvqs6BbYdKsO1QCeYkh+LZBfEs90pERERezyHJeGXjeWw92LhFhQBBAKDSQRU+FqrwsZAK9sNx5iMUVyqrV2hUAoL8GhYAXznn2rCnEENjDawCSETkZte1gPasWbPwxhtv4K677mJykoiIqA0EQcD8sWGKts3fF8PpZPlNIqLu4FRuLQrKrK79j6ScXe0aR8rZBVmyouBiCYoqrk5OXmnrwRK8svF8u+5FRERE1J0ok5PXpooYD2f8L2B1KL9L9wrQ4vL8Y+Ocy2wuRUGZFafyajsjZCKiHo07/BIREXUTt44OheayN2ByzRYcOV/lwYiIiKi1DpytgFOSUF1VDac57eo9J1vLUQ+p5ASqaiytvmTrwRKk59a0735ERERE3UB6Tk2LyclG+pgb4RMc6zo2+Kjgq1MpOznq4TSnobqqCk5JwoEzFW6MloiIADeUeCUiIqLrI8hfg5uHBOPLH0pdbZtSizA6McCDURERUXOSf5vq+lmy1cNmk1CYcQL1pXXtHlNvS0NM9LQ2XbPkjRPtvl9TDr42zu1jEhERUc90+ZzpegjudzMKvn8fAFBtkVBdePXcTG87gfCg4TiVXY6zhRb87+e5ADgHIiJyFyYoiYiIupH548IUCcpv08pQVm1HsEHjwajIGyQlJXV4DEEQkJ6e7oZoiLyTLNkAANaqix0ax1p1ESqtrztCIiIiIuqRDJHDW+zTOGdrmMPpOzkiIqKehyVeiYiIupEb4o2ICfVxHTskGdsOFXswIvIWsiy7/rcjf4ioaY1/R5z21pdnvRan3QII/CpHRERE1F4qbcsJx8Y5G7/nEBF1Dn6rJSIi6kYEQcD8sWGKti0HiuF08gsTdVx7vngLgoDhw4dj6tSpmDJlSidEReQ9BKFhH2FR49NCz+aJGh9AdrojJCIiIqIeSbK1vB9445ytcQ5HRETuxRKvRERE3cyto0Lx9x05sEsNyaRcswWHM6qQ3I97UVL7nT59ulX9rFYriouLceLECbz77rs4efIkiouL8eqrryI2NrZzgyTqhhr3KHph3TmkXQByciqREBcLuTq7TePUWiUUlFkBADpjb0i2Oqh9DK2+fs7oUDx/R0Kb7klERER0vXR0X8eXPsrAtkMlre5vKTqOxPDmS+YLhj5QaUTEhAdgSLwJLy5K7FCMRESkxDcoiYiIuplAfw1uHhqsaPt3asf2MyNqLZ1Oh+joaMyePRsbN27ErFmzUFBQgMcffxw2m83T4RF1WXFhevjqfSAKAoSgtj3ckpwyiiusrmPfsP6QrDVtGmPBhN5t6k9ERETUnSwY37a5jvri3hb7CEGJEAUBvnofxPbiHpRERO7GBCUREVE39PNxyjKvKWllKK60NtGbqHMIgoCXX34ZBoMB586dw8aNGz0dElGXNaZfIESVCgajAaJpMKBu/UOukkobHD9VdBU1ehgihyHQ4IMA39YVxJmTHIqkaP/2hE1ERETULQyM8cec5NBW9bXk7INYm9dJEtdoAAAgAElEQVR8J7UeomkwDEYjRJUKY/oHuiFKIiK6HBOURERE3dCIvkbEhV16uC05gU2pRR6MiHoqPz8/TJs2DbIsY/v27Z4Oh6jLSor2Q0SwDiaTCYJKB1XMtFZdV2NxoNoiuY5NA2+BVuuD3mGhGBFvaPFB3JzkUDy7IL5DsRMRERF1B88uiG9xblR14TsI5z9ucSxVzDQIKh1MphBEBOuQFOXnrjCJiOgnTFASERF1Q4Ig4PYblSVsNqUWw9b4ig3RdRQTEwMAuHDhgocjIeq6BEHAnRPDodP9lKQMToIYMaHZaxpKu14qnRyUMAmGiMHoHdYLOp0Od02KxPMLE7Bm2RDMGR2KAF81VKKAAF815owOxZplQ/D8wgSoVUJnfzwiIiIij1OrBDy/MAErbu/bUGlCdkKWZUh2C8rPf4cLn78EIWMDBLn5781ixAQIwUkwmUzQ6XRYNDECgsD5FBGRu7WuJhARERF1ObNHhuJv23NQ+9ObNWU1dnxzvBSzRraurA2Ru5SWlgIA6urqPBwJUdc2LM6IiYOCsOckYLPbUIkJEHyCIeXsAhz1V/UvrrBBkhvKupoG3gL/8MEIDgpEaKgJkwYHY2icAQAwMNofA+9IuN4fh4iIiKhLmjc2DGk5NdhyoAj2mlJI1ho46ivh6yyFTtfM+zpqfcObk8FJCAgMuGrORURE7sUEJRERUTflq1PhttGh+Pi7i662T/ZeZIKSriuLxYIvvvgCABAayv/2iFpy75RIlNfYAURAq9HCLAgQAuPhNKdBLj8Hua4IkKyosasg+YchJL4/DJHDIKp0CAoOQUx0bwzp4497bo7w9EchIiIi6pIckhNp2dUABGj8QiCo1DBEDIE2cgCcpco5F1Q6CL5hEIISIZoG/1TW1YTQUBPnXEREnYwJSiIiom5swYTeigRlWk4N0nNqMDDG34NRUU+Rm5uL//qv/4LZbIYgCBg7dqynQyLq8jRqEcvmxmLt7gKkpAFGowFmcymqVSPh7HUDAECGDL1dRuNOwyqdH/wMQYiNCMCkwcG45+YIaNTcrYOIiIjoWtanFOL8xYbqFIIgQKMPRFRYEOqqK1CtvjTnupwoCDAYjTCZQqDT6TjnIiK6DpigJCIi6sb6hOoxtn8Avj9T6Wr7176L+O8Ylvqjtrnnnnta1U+WZdjtdpSVlSEvLw+yLANo+OK/cOHCzgyRyGto1CLunx6FUQlGbNhTCJ1OB2fvMNTVW2C1WlBaZYWsAQSVFqJaC1FU4YbEADw4I5olxoiIiIiakV9qwdtf5inaxvYPgK9OhYIyX8Wcy+l0QhRF6HQ+8NX7QFSpEBHcsOck51xERJ2PCUoiIqJu7vYbwxUJyq9+MOPxOX0Q5K/xYFTU3Rw8eBCCILTpmsbkJADcd999GDJkiLvDIvJqw+KMGBprwKm8Whw4U4Gs4nqcyKqGs94K9WX/hN8xMRzL5vZp899RIiIiop5ElmWs+vQCrHanq83oq8aLixIR6KdWzLkKSq2wSU5oVSIiQnSI7aXHmP6BSIry45yLiOg6YYKSiIiomxs/IBCRITrkl1oBADaHjC0HirFkaqSHI6Pu5vKEY2v169cPS5cuxfz58zshIiLvJwgCBkb7Y2C0P3LN9bjrtR8V52+IN+I3c5icJCIiImrJl8dKFYt3AeA3ly3ebZxzERFR18AEJRERUTenEgUsGN8bf9ma7Wr7dP9F3DU5AmoVH2hT66xdu7bVfUVRhF6vR3h4OIKDgzsxKqKewyHJ+O91Gai3XVrxr9eKeH5hPESR/5YTERERNaeyzo7Xt2Qp2kbGG3Hb6FDPBERERC1igpKIiMgL3JYcin98kesqZVNUYcPuH0sxfYTJw5FRd5GcnOzpEIh6tPd35SEtp0bR9sS8WESG+HgoIiIiIqLuY/W2HJTV2F3HGpWA5Qv6sgoFEVEXJno6ACIiIuq4AF8NZo9UJiM/TCloV8lOIiK6vk5kV+O9XXmKtomDgjBvTC8PRURERETUfRy7UIUtB4oVbUunRaJPL72HIiIiotZggpKIiMhL3DkxQnF8KrcWxy5Ueyga8lZZWVlMfBO5Ua1Fwn+vOwfpUmVXBBs0WPGLeK74JyIiImqB1e7EK/+6oGiL7aXHPVMiPRQRERG1FhOUREREXiI2TI8bBwYp2tanFHgoGuourFYr1qxZgxdeeKFV/e+77z7cdNNN+Pvf/466urpOjo7I+72+JQt5pVZF2wsL4xHkr/FQRERERETdx7tf5SGruF7R9uztfaFV87E3EVFXx3+piYiIvMjiSeGK4+/Sy5FdUt9Eb+rpvv/+e9xyyy1YtWoVtm7dCkmSmu2fl5eHgoIClJaW4q9//StuueUWHDly5DpFS+R9dp8oxWcHleXIbp8QhvFJQU1cQURERESNzuTV4p+78xVt88b0woi+Rg9FREREbcEEJRERkRe5Id6IAVF+rmNZBjakFHowIuqqduzYgQceeACFhYWQZRkWiwUnT55s9prGZGRjideioiLce++9SElJ6fR4ibyNucqGlZ9cXY7ssdv6eCgiIiIiou7DITnx+48zFGXyQ40a/GYO51JERN0FE5REREReRBAELJ6k3Ity+6FilNfYPRQRdUWnTp3Cs88+C0mSIMsyNBoN5s+fD5PJ1Ox18+bNw+bNm7F48WLodDoIggCHw4Fly5YhMzPzOkVP1P3JsoyXPspAZZ3D1aYSBby0OAE+WpUHIyMiIiLqHj78thBnC5RbTixf0Bf+erWHIiIiorZigpKIiMjLTB0WjLBArevY6pDx6f6LHoyIupoXX3wRVmvDnnfDhg3Djh07sHLlSkRERLRwJTBgwAA8//zz2LRpExISEgAA9fX1ePHFFzs1ZiJv8q99F/H9mUpF2yOzojEgyt9DERERERF1H1lF9Xjny1xF24zhIbhpULCHIiIiovZggpKIiMjLqFUi7rhJuRflxn1FsNqdTVxBPcnhw4fxww8/QBAEDB48GB988AGio6PbPE5cXBw++OADV1LzwIEDSEtLc3e4RF7nwsU6/HVrtqJteF8D7rq55QUCRERERD2d5JTxh0/Ow+aQXW2Bfmr8dn6cB6MiIqL2YIKSiIjIC80b0wt+uktlAstq7Pj8SIkHI6Ku4vPPP3f9/PLLL8PHx6fdYwUHB+OFF15wHW/fvr1DsRF5O6vdiefXnYP1sgdqfj4q/O7OBKhEwYOREREREXUPG/ddxI9Z1Yq23/4sDkH+Gg9FRERE7cUEJRERkRfy16sxb2wvRdvab/LhkOQmrqCe4vjx4wAaSrv269evw+NNnjwZffr0AQAcO3asw+MRebO/bsvGuSv2Snr653GICG7/QgEiIiKinqKgzIK/7chRtN00MAgzRoR4KCIiIuoIJiiJiIi81J0Tw6FWXXojJ6/Uiq+Pl3owIuoK8vLyIAgChg8f7rYxx4wZA1mWkZub23Jnoh5qT1oZPtmr3A94+vAQzLrB5KGIiIiIiLoPWZax8pMLsNgubV3i56PCMwviIAisREFE1B0xQUlEROSlwgJ1mD0yVNG25us8OJ18i7Inq6mpAdBQntVdevfuDQCoqqpy25hE3qSowoqXPj6vaAsP1uHZBX35QI2IiIioFbYeLMHBc5WKtt/M6YNeAToPRURERB3FBCUREZEXu2dKBC7f1uz8xXrsO1XuuYDI4zSahr1ZbDab28YUxYYppUqlaqEnUc8jOWX89/oMVNU5XG0qEfjDXYnw16s9GBkRERFR91BcacUbn2Up2kYlGDFvTK9rX0BERN0CE5REREReLCZUj2nDlPtxvL8rH7LMtyh7qpCQhv8eioqK3DZmYWEhAMDf399tYxJ5i/d35eHoeeXbxY/MisGQPgYPRURERETUfciyjJc/uYAai+Rq02lErPhFPCtREBF1c0xQEhERebl7p0YqjtNyanA4g6U4e6qEhATIsoyjR4+6bcyDBw9CEAT06dPHbWMSeYMfLlThnS/zFG3JiQG4++YID0VERERE1L1sOVCM1NMVirZfzY5BZIiPhyIiIiJ3YYKSiIjIyyVG+OGmgUGKtvd35TXRm7zduHHjAAAXLlzAjz/+2OHxjh49iszMTADA4MGDOzwekbeorLPj+XXncPm2v0H+avxuUQJEkav9iYiIiFpSUGa5qrTr8L4GLLyxt2cCIiIit2KCkoiIqAdYMk35FuXhjKqrSg5SzzBz5kyo1Q373q1cuRKSJLVwRdMkScKrr77qOp46dWqH4yPyBo2lyIoqlHu9/u7OBJiMWg9FRURERNR9OJ0yfv/xedRZna42vVbECwu52IuIyFswQUlERNQDDOljwKgEo6Lt7Z25HoqGPKl3796YOXMmZFnG8ePHsWLFCjidzpYvvILT6cSKFSvw448/QhAE9O/fH8nJyZ0QMVH382lqEb49UaZoWzwpHOMGBDVxBRERERFd7l/7LuLIFVuTPDanD6JMLO1KROQt1J4OgIiIiK6Ph2ZG43DGSdfxkfNVOJxRiVEJAR6Mijzhqaeewu7du1FfX48tW7YgKysLzz33HIYOHdqq648ePYpXX30VJ06cAACIoojnnnuuM0Mm6jZO59Xg9c1ZirbQAA1Kqmx48K9psElOaFUiwoN1iAvTY0y/QCRF+0EQ+CYAERER9RyyLONUbi0OnK1AZlE9CsusrnmS0U+N/aeU+06O6ReA/xgX5qFoiYioMzBBSURE1EMM72vEmH4BOHC20tX21s5cjIw38sF4D9O7d2+88sorWLZsGQDg+PHjWLhwIRISEjB27FiMHDkSYWFhCAoKgt1uR1lZGcxmMw4fPoy9e/ciL69hD1NZbthcb9myZXx7kghATb0Dz649C7t0aeNJUQD8tCLSM0thtVrgdDohiiJKK3xwodAHu0+UISJYh0UTIzA0zuDB6ImIiIiuj+OZVdiwpxAFZVY4JQl19RbXPEkQRJysF+CQ1RBEFQDARyviv34Rz++tRERehglKIiKiHuTBmdGKBOUPF6px6FwlkvsFejAq8oSZM2di1apVWLFiBRwOB2RZRkZGBjIyMvDhhx82eV1jUhIA1Go1nnnmGdx9993XI2SiLk2WZbz08Xnkl1pdbU6HDVrUIOtCLZyX/d1pJAoCDEYDrFYT/rTZikmDg3HPzRHQqLkTBxEREXkfu8OJD77Jx56T5bBarTCbzaiuqlbMkySnDMdPi71UOj+ofQIQFGTAtsMlnCcREXkZJiiJiIh6kKGxBowfEIj9py+Vy/m/L3IxOjGAq1F7oLlz56Jfv3743e9+hx9++MGVfBQEQZGIvFbb+PHj8fTTT2PAgAHXNWairuqj7y669p2UZRkOSyVkayUssMNpToNcfg5yXREgWQGVDoJvGJxBiaiUBqOqqhomkwkpaUBZtQ3L5sby4RsRERF5FbvDiTc+y8KJ7BqUlJhhNpshS1bFPMlqrUd+BaA19oZvWH8YIodBttfB5ichJU3NeRIRkZdhgpKIiKiHeXBmlCJBeSK7BvtPV2BCUpAHoyJPGTBgAD766CMcOnQImzdvxr59+3Dx4sWr+omiiMTEREyYMAFz585F//79PRAtUdd0Irsab27NBtCQnLTXlsJpq4G66gwcObsAR73yAskCuTobcnU2nAX7oIqZhhI5CTa7DUAE1u4uwP3To67/ByEiIiLqJB98k48T2TXILyhAZUUl5LJTkC6bJ8kyUFRhgWSXUV+aifrSTFRlpCBu1FyUqATOk4iIvBATlERERD3MoBgDbhwYhL3p5a62v23Pwbj+gRBFvkXZU40ePRqjR48GAFRVVaGwsBC1tbVQqVQwGAyIioqCVqv1cJREXU9FrR3PrT0LydnwhrHDUgnJWgOxaB/k2nyoEv8DomkIoPIBJAuc5hNw5u2BXJ3TMICjHtKFrRAtZajEBGg1WqSkAaMSjBgWZ/TgJyMiIiJqnfScGmzcfxF7Tpaj1uKAn48aEwcFYcGE3hgY7Y/jmVXYc7IcJSVmVFZUwlmwF86C/YoxymrssNqVVVxCfCXImVvhtHKeRETkjbwyQbljxw6sX78e6enpkCQJUVFRmDVrFu6//374+vq2ebzs7GysWbMG+/fvR2FhIURRRHR0NCZPnoylS5ciODi4Ez4FERFR53l4ZrQiQZlRWIedx8y4ZWSoB6OirsJoNMJo5Bd+opY4nTJ+tz4DRRW2hmOHDY66CqD8DNSBcVANXKy8QPSDKnwsVOFjIRXsh3TmI0B2NlxbsA+CTzDMggCj0YANewoxNNbA8ttERETUZTkkGa9sPI+tB0sU7VV1Dmw7VIJth0owJzkU1fUO156Tctmpq5KTFpuEshq7os3fRwWDvuHRNedJRETeyesKdq9atQpPPPEEDh06BJvNBlEUkZGRgdWrV2P+/Pkwm81tGm/Xrl2YN28e1q9fj6ysLGg0Gtjtdpw9exZvvfUW5s6di9OnT3fSpyEiIuoc/aP8MGNEiKLtH5/nwuZweigiIqLuZ+3uAkXJbIelEqJsg9Y/CKqI8c1eq4oYD1X/OxRtUs4uyJIVZnMpCsqsOJVX2ylxExEREbnDtZKTV9p6sATHzle79pyUcnYpzjtlGRd/WuzVSC0CvQKU1Vs4TyIi8j5elaD87LPP8N5770EURTz33HM4evQojh07hrVr1yIiIgJZWVl48sknWz1eTk4O/vM//xP19fUYP348tm/fjiNHjuDYsWN4++23ERkZiZKSEjzyyCOor69veUAiIqIu5Je3xECturTitLDcik/3F3kwIiKi7uPo+Ur84/Mc17HslCDb66CqK4Cqd3KrxlBFjIdgiLnU4KiH05yG6qoqOCUJB85UNH0xERERkQel59S0mJxsVFnnQHWNBU5z2lV7c5dU2mCXlKVdwwJ1UF25/QjnSUREXsdrSrxKkoTVq1cDAB544AHce++9rnNjxozBW2+9hXnz5iE1NRWpqakYN25ci2O+8847sFqtCA8Px9///nfo9XoAgFarxcSJE/H2229j/vz5KCwsxL///W8sXry4hRGJiIi6jsgQH/x8XBg+2XvR1fbeV3mYMzoU/nqvmSIQEbUo+bepHR7D6bDB6ZRhEwNhaMN1NcHjUHD2UkUWve0EwoOG41R2Oc4WWvC/n+e2eqyDr7X8HYeIiIh6NnfMe9rDofJDTsYJ1JfWtdg3v8x6zfbGeVJdvQVZxXxZhIiou/OaNyj379+P7OxsCIKAJUuWXHU+MTERU6ZMAQBs2bKlVWOmpKQAABYuXOhKTl4uPj4eo0aNAgAcPHiwnZETERF5zv3To+CnU7mOK+scWLu7wIMRERF1T7LUUJpMH5rQpusMkcMVx9aqi4rxiIiIiLyBSuvrmue0V+P1VqsFBaXXTmISEVH34TUJygMHDgAA+vfvj5CQkGv2aXxr8rvvvmtxPEmSMHnyZNx8880YPnx4k/1CQ0MBADU1NW0NmYiIyOOC/DW46+YIRduGlAJcLOeXPSKitpDlhtJkKs3VCxubo9Iq+zvtFsV4RERERF5BEF3znPZqvN7pdMImOd0RFREReZDXJCgzMjIANLzV2JTY2FgAgNlsRnl5ebPjqVQqvPjii/jHP/7RZDlYWZZx7NgxAEDv3r3bETUREZHn3TkxHMEGjevY6pCxelu2ByMiIup+BKFhnyTJ3rZyY5JN2V/U+CjGIyIiIvIKstM1z2mvxutFUYRW5TWPtYmIeiyv2WCquLgYABAWFtZkn8vPlZSUICgoqEP33LFjB7KzGx7gTpo0qUNjEREReYqvToVf3hKNlz+54Gr78odSLJhQheF9jR6MjIjo+mjN3o0nc6rx0OqTsEuX3myMCtFhzbKh+NOmTKRdAHJyKoGqTCB0SKvvLZSlITHc99KxoQ9UGhEx4QEYEm/Ci4sS2/ZhiIiIiJrhrj2rX/ooA9sOlbT+Akc9EuJiYSnPRG6JBZfXitBrRUSG+KCl5VmN8ySdzgcRIbp2RE1ERF2J1yw1qa2tBQD4+vo22Uenu/SLq6MlWXNycvDSSy8BAPr164dp06Z1aDwiIiJPum10L/SP9FO0/XlLFpxOlhgkIiqrtuOZNWcVyUkfrYhVS/rD6KtGXJgevnofiIIAp6W0TWM78/cojoWgRIiCAF+9D2J7ta1cLBEREdH1smB826rJCfYaIDABF8utiuSkKAC9A3UtJicBzpOIiLyN1yQoHQ4HAECj0TTZR6vVun6WJKnd9yosLMSSJUtQUVEBnU6HP/7xjxBFr/m/koiIeiCVKOA/fxaraDudV4vth9uwIpaIyAs5JBkr/nkWxZU2Rft//SIeiRENCzvG9AuEqFLBYDRANERBuniwVWNLBfshV+dealDrIZoGw2A0QlSpMKZ/oNs+BxEREZE7DYzxx5zk0Fb1DfBVw+DvAyloEByCssxrrwAt1KpWpCc5TyIi8jpek1Xz8Wn45Wa325vsY7NdeqjQXCKzOZmZmVi0aBHy8/OhVqvx2muvYcCAAe0ai4iIqCsZ0deIacNCFG1/25GDGovDQxEREXneX7dl48j5KkXboknhmDHC5DpOivZDRLAOJpMJgkoHWCogFexvdlypYD+kMx8p2lQx0yCodDCZQhARrENSlF8TVxMRERF53rML4ltMUs5JDsWIeAN8/AIhC1qYBt7iOmfUq2DQt24HMs6TiIi8j9ckKP38Gn4pWSyWJvtcfs7f37/N9zhy5AjuuOMOFBQUQKPR4LXXXsP06dPbHiwREVEX9dhtfaBTX1q9WlZtx9s78zwYERGR5+w8asaGPYWKtpEJRjx6ax9FmyAIuHNiOHS6n5KUwQMgV2XDfuh/IBWkQrbVQHZKkG01kApSG9pPrwdkp2sMMWIChOAkmEwm6HQ6LJoYAUFoTbEzIiIiIs9QqwQ8vzABa5YNwZzRoQjwVUMlCgjwVWPO6FCsWTYEzy9MwPThJpTWAmrfQPiHD0ZQwiRoVQJCA7Qt3wScJxEReavWLVHpBnr37o3jx4+jqKioyT6XnwsNbV0JgkZffPEFnnrqKdhsNvj6+uIvf/kLJk6c2O54iYiIuqLwYB3uuvn/2bvv+KiqvH/gnzuTmcmk90YgCSQTEqrSsSAoioAiIKuLIFV9dPW3jyJg10WxPauoi20pKlIEAaW6CoIEQaooLQkkpJKQXiZl+vn9kc3AMElII5NMPu/Xy5fJOfee+72cydwz93vnnDCs2HXRWrZ+fy7GDghELJ9QJaJO5GxWBd5Yn2JTFuyjxJvTNXVOQ9Yvygu39vJFwhnAYDSgDDdBFCfCnLIZ5qQ19R/IRV3zjQC/OHj7eCMwMAAjevuhb5Rna58SERER0XUR39UD8Q9G11lnNFmw+pccWATg4uoNYTbBN3oE/AODIWX/DJiq62+Y4yQiIqfmNAnKmJgY/Pjjj0hPT693m4yMDAA1yUlvb+9Gt71u3TosWrQIFosFfn5++Pzzz9G3b9+WhkxERNQuzRjVBTuPFyK3WA8AsAjg7U0XsPyp3pDL+JQqETm/gjID5q9Mgt4krGVKFwnvzIiFr0f9S0XMGNUFJRVGAGFQKpQolCRIPj1gKTwNUXIeoioPMOsBuQqSWzAk3xjIAnr/d7qyAAQGBqBPhAceHhnWBmdJREREdP19sjMTiVmVAGpmnVC4+8PbwxVVyl4QftEcJxERdWJOk6AcMmQIli5disTERJSVldWZgDx4sGYdmMGDBze63e+//x7/+Mc/IIRAeHg4VqxYgcjIyNYKm4iIqN1xVcoxf2IUnlmRZC07k1mB7w7l4f7hIQ6MjIjo+tMZzVjwZTIKym3Xtn9ucnfEd2t4mQiFiwz/e28kVu3Nwb7TgJeXJwoLi6CVD4Al6Ea77WWSBE8vLwQE+EOlUmFEbz88PDIMChenWYmDiIiIOrEDiSVYs892uvyoYDVclR7Q6304TiIi6uScJkE5cOBABAcHIy8vD8uXL8e8efNs6pOTk7F3714AwF//+tdGtZmUlISXXnoJQghERkZi1apVCA4ObvXYiYiI2pub430xso8f9p4qtpZ9siMTt/X2Q4BX49YJISLqaIQQeHPDBZzJrLApnzoiFOMHBzWqDYWLDHNGh2NgtBfWJeRCpVLBEhKMqmod9HodLBYLZDIZVCpXuKldIZPLEeZXs5YSpysjIiIiZ5Ffpsc/1tlOlx/iq8Syp3ojPa+a4yQiInKeBKVMJsPTTz+N5557DsuWLYO7uztmzpwJV1dXHD58GAsXLoTZbMawYcMwaNAg6355eXmYMWMGAGDatGmYNm2ate61116D0WiEm5sbPvnkEyYniYioU3nmvkgcPleKKr0FAFChM+ODLel4Y7rGwZEREV0fq/bk4D+/F9qUDevpg6fGRzS5rX5RXugb6YnE7EocTi5Fen41cor0MJgtUMplCPNXITJIjSGxPogLd4ckcQptIiIicg4ms8Cra1JQWmmylsllwOsPxcDbTYF+UQqOk4iIyHkSlAAwceJEnDhxAuvXr8eSJUuwdOlSKJVKVFbWzHMeFRWFJUuW2OxjNBqRlpYGACgpKbGW//HHHzhx4gQAwGQy4eGHH27w2CEhIdi0aVNrng4REZFDBfuo8NiYbliyJd1a9tMfRRh9QzFG9PZzXGBERNdBwulifPJDpk1ZZJAai6fFNHv9XUmSEN/VA/FdG54aloiIiMiZ/PvHLBxPLbcpe/SurugX5WX9neMkIiJyqgQlACxatAjDhw/H2rVrcfbsWeh0OkRGRmL06NF47LHH4OnZuOkAfv/9d+vPBoMBhYWFDWwNqFSqFsVNRETUHk25KQQ7jxUg+WKlteytjRfQv7snvN0UDoyMiKj1pORW4pW15yHE5TIvtRzvzYmFh9rpPjIRERERXTf7zxTjy58v2pQNivHGw6O6OCgiIiJqr5zy0/aYMWMwZsyYRm0bHh6O5ORku/LZs2dj9uzZrR0aERFRh+Iil9HGC20AACAASURBVPDyAz0w44NTMFtq7twXa414//t0/GNqjIOjIyJquSKtAfNWJlunswZqpiB78+FYdA1QOzAyIiIioo7lYpEOr1217mSAlwKLHopu9owURETkvGSODoCIiIjaN00Xd8y6w/Zp1x+OFyLhdLGDIiIiah06gxnzViQht1hvUz7vvigM1ng7KCoiIiKijkdvtOD5VeegrTZby+QyYPF0Dfw9lQ6MjIiI2ismKImIiOiaZt3eBdGhbjZlb228gLIqo4MiIiJqGbNF4JW1KTibVWlTPmlYMO6/KcRBURERERF1TO99n4akbNtx1ZPjInBDd6969iAios6OCUoiIiK6JoWLDK8+GA35FSOHIq0Rb317AeLKRduIiDqIf23PwC+nbL8JPkTjjWcnRjomICIiIqIOavvRfHx/KN+mbGQfP0wdEeqgiIiIqCNggpKIiIgaJTbcHTNvt53qdc/JYmw9kl/PHkRE7dPGA5ewdl+uTVl0qBvemqGBi5wfkYiIiIga63xOJd7ZeMGmrGuAK15+oAckietOEhFR/fjpm4iIiBpt9h3h6BnublP23vfpyMivdlBERERN8+vZEvzzuzSbsgAvBZbM6QkPVxcHRUVERETU8VRUm/DcV+egN12eVUelkOHtGRp4qDmuIiKihjFBSURERI2mcJHh9WkxcFVeHkLoDBa8tPo8DCaLAyMjIrq25OxKvPj1OViumJlarZRhyZw4BPuqHBcYERERUQdjtgi8vOY8sgp1NuULJ0chJsy9nr2IiIguY4KSiIiImiQiUI1n74uyKUu+WIlPdmY6KCIiomvLLdbjmRWJqDZcfphCJgGLp2sQG86baERERERN8e8fs3AgsdSm7L6hQRg/KMhBERERUUfDBCURERE12T2DAzGqr59N2dp9udh7sshBERER1a+0woj/9++zKCg32pTPuy8KN8f7OigqIiIioo7p5z+L8MXuizZl8V3dMe+qB1mJiIgawgQlERERNZkkSXhhSg8E+yhtyhd9k8r1KImoXanWm/HMiiRkFNhOP/bXW0Mx5eYQB0VFRERE1DGdz6nEP75JsSnz81TgnZmxUCl4q5mIiBqPVw0iIiJqFi83FyyeroGLXLKWVerNWPhVMqr1ZgdGRkRUw2S24IWvz+F0ZoVN+R39/PH/7olwUFREREREHVNppRHzv0iG7oop813kEt6dEYtgH67nTURETcMEJRERETVb30hP/O+9tjf5L1yqxuJvUyGEcFBURESAEAKLN1ywWxtpYLQXXpsaDblMqmdPIiIiIrqaySzw4tfnkVOstylfMCkKfaM8HRQVERF1ZExQEhERUYtMuSkEd90QYFP204kifL03x0EREREBH+/IxI5jBTZlmjA3vDsrFkoXfgwiIiIiaoqlOzJw9HyZTdmkYcG4b2iwgyIiIqKOjp/MiYiIqEVq1qPsjh4hapvyj3dm4pdTxQ6Kiog6s3UJuVh11UMSYX4qfPBIHDxcXRwUFREREVHHtONYAdbuy7Up6xfliXn3RTomICIicgpMUBIREVGLqVVyvD0zFh6ucmuZEMAra88jObvSgZERUWez/Wg+lmxJtynz9XDBvx6LQ4CX0jFBEREREXVQJy6UY/GGVJuyIG8l3p6hgYKzUhARUQvwKkJEREStIiJQjbdnaCC/YnShM1gwb2USCsoMjguMiDqNPSeL8MZ62xtobioZPpgbh64B6nr2IiIiIqK6ZBVWY8EXyTCZhbVM5SLh3Vmx8Pfkg19ERNQyTFASERFRqxms8cGzE6NsyvLLDHh6eSIqqk0OioqIOoODiSV4afV5WC7fP4OLXMI7M2IR19XDcYERERERdUDlVSY8vTwJZVW2n+NefjAa8RxbERFRK+ACLERERNSqJg8PQUaBDt8kXF6j5FxOFeatTMKHj8bBVSFvYO+mGTzvN5vfj7w3rNXaJqKO4/fUMiz80vbpfpkEvDEtBkNifRwYGREREVHHYzRZsPCrZGQW6GzKHxvTFXfeEOCgqIiIyNnwG5RERETU6v5+TwRujve1KTtxQYuXvj5vk0AgImqpM5laPLMiCXqT7XvLyw9GY1RffwdFRURERNQxCSHwzqY0HE8ptym/e0AAZt/RxUFRERGRM2KCkoiIiFqdXCbhzekx6BvpaVOecKYEizekwmJhkpKIWi4ltxJ//3ciqvQWm/IFk6IwbmCgg6IiIiIi6rhW/5KDrUfybcr6RXrixb/0gCRJDoqKiIicEad4JSIiouvCVSnH+3N64rGPTyP1UrW1fMexAihcJDw3uTtkMn7AJaKaJ/UTsypx+Fwp0vKqkVush8FsgVIuQ6ifClHBagzR+CCuq7v1xlhGfjWe/DwR5dVmm7b+Nq4b7r8pxBGnQURERNTuNGWc9cvpYizdkWmzfxd/Fd6dFQulC7/nQkRErYsJSiIiIrpuvNxc8NGj8Zi79DRyi/XW8u8P5UMI4Pn7maQk6uz+TCvHuoRc5BTrYTGbUVWtg16vg8VigUwmQ1GpKy7kumLvqWKE+akw9dYweLu74PFPz6BYa7Rpa9YdXTBjFKceIyIiIgKaNs5yV8lxMr0c4orJbjxc5VgyJw6+HgrHnQQRETktJiiJiIjougr0VmLpY3F4dOkZFF2RTNhyuGbaICYpiTono8mCr/ZcRMKZEuj1ehQWFkJbroVF2E8BLZMkeHp5Qq8PwJsbq3GpRG83resDt4Tgf8Z0bavwiYiIiNqtpo6z3Dw8UCU8ANnlRKRcJuHtGbGIDFa3ZehERNSJMEFJRERE113XADU+fbwXHv/UPklpNAu89JfucJFzyiCizsJosuCDrek4lVGBgoJCFBYWQpj1sBSehig5D1GVB5j1gFwFyS0YFt8YlJl7o6ysHFB6Q1J52ayBNGFIEJ6+N5LrIhEREVGn19Rxlsk7GlU+vSDJy+Di5gMXV29IkoRnJ0ZisMbb0adDREROjAlKIiIiahORwWp89kRNkrKw/HKScuexApRVGvHmdA3UKrkDIySitvLVnos4lVGBizk5KCstgyhOhDlzN2Cqtt3QrIPQZkBoM6DL3A998Ei4hfSG3GSEwt0fkiRhwpAgfhObiIiI6L+aMs4yl6Uj+0ISjNJuBMTfDY/Q3hBmE8JCQ1FaaXLMCRARUafBBCURERG1mYigy9+kvDJJeSCxFH/7/Czen9MTPu5c34TImf2ZVo6EMyUoKChEWWkZLDm/wpJzEJJnN8jCb4UsoA8gdwXMOlgKT8GSnQB9STouFpXAlLsZvtFF8I0eAUnuggD/AIwdEMjkJBERETmFs5kV2HjwEhLOlKBSZ4K7qwtu7eWL+28KQXxXj2vuX984qy5CADkleuhNAkA18v/cDGNlEQI0IyAM5dh3WomB0V7oF+XVymdJRERUg3OpERERUZuKCFLj87/1Qhd/lU356YwKPLr0DLILdQ6KjIiuNyEE1iXkWtdCEsWJsOQegrznVCgGLYA8dCgkhTskmRySwh3y0KFQDFoAU4+/wiRqPrqUpOxDRe5pCH0ZfN2A9b/mQtSxnhIRERFRR2EyC7y+PgUzPzyF7UcLUF5lgtkClFeZsP1oAWZ+cAqvr0+ByVz/mKfOcVZ9yUkAeaV6VBts1/Q2ZP0KWVkyCgsLodfrsS6B4ywiIrp+mKAkIiKiNtc1QI1lT/ZGTJibTXl6fjVmfngKx1PK6t1XCIE/08rx+Q+ZdnXPfZWMz3/IxJ9p5fwgTdQOJWZVIqdYb10LyZy5G/LYByEPG97gfj49bkHY4BnW3/VpP8EFRhQVFSGnWI/E7MrrHToRERHRdfPWxlRsO1LQ4DbbjhTgrY2p9dbXNc6qT2GZAVqd2abMVSFDiK8SlszdEGY9Cgs5ziIiouuLCUoiIiJyiAAvJT5/ohdu7GE7ZVB5lQlPfp6I737Ls9vn6PkyzFuRjHc2pWHvyUKYDdUwVZfBWFUKU3UZEtOLsPdkId7ZlIZ5K5IbTHQSUds7fK4UFrMZ2nItLIWnIakDr5mcrOUbfQtc/SLhpZYj0N0CS+FpaMvLYTGbcTi59DpHTkRERHR9nM2suGZysta2IwU4m1VRZ93V4yy7NSf/q0hrQGmV7fqSCrmEMD8VZJIEmKo5ziIiojbBNSiJiIjIYTzULvjwkTgs+iYFu/4ospabLQJvbbyApOwKPHNfFCQILPspGwcSS6HX61FcVARtRTkMV01JlJ1dCkmS4OnhBb3eH+99r8fN8b6YO7oLlAp5W58ekdMZPO+3FrdhNlTDYDAjN+UUfGNGwrcJ+/ppRiLn0Bcor66C2nAKob79kZhRgnO5Onz6Q1aTYzny3rAm70NERETOrzXGPNfLzA9O1Vt35Tiruqiq0W0azQIX8i4nNNWGU4gMuhFV1Tqk59ed6CQiImopJiiJiIjIoVQKGd6YFoPuIW74/D+2CYbvDuXjVEYFugWqkFmgR1FRIYqKCiFMBlgKTyE39TT05ZdgMeogU7iiR1QkJJ9olAf0gbaiHP7+Afj1LFCsNWDBpCgmKYnaAWE2AAD05ZfgGd6/Sft6drm8vb780hXtqVstPiIiIqKO6spxVkvU7q/X65BTpG9xXERERHVhgpKIiIgcTpIkzBkdju7Bary6LgW6K74ZmZJbhdRLVVCKKlSVF8BSlARL1m7I1IHwjRkJz/D+kCvUMBurIRWfhiU7Aabcg5B1vQOFoicMBiOAUCzfdRFPjO3muJMkIgCwrg9rMeogVzQtsShXXt7eYtTZtEdERETU2V05zmqJ2v0tFgsMZss1tiYiImoeJiiJiIio3RjZ1x9dAlzx/FfnkFV4+UO1EIAebpCEBSJrL1yiJ0IeNtxmakgXlQcQOhTy0KEw5xyEOfkbWHRFKMdNUCoV+PUsMCjGG4NivNv+xIjISpIkAIBM4Qqzsbrmb7eRzIbLU4zJFK427RERERF1dleOsyzG5k/NWjvOkslkUMplrRIbERHR1ZigJCIionZFE+aOr57ug8XrU/HzyWKbOpl3FBQ3LYIkVzbYhjxsOADAnLQWFld/FEkSPDw8sW5fLgZGezGhQdRMLV2z8ZU153H6ApCZWYboqEhIxaeB0KGN3l8qPo2YULeanz0jIFfI0C3UG316BOAfU2NaFBsRERFRrbZcp3rRNynYfrSg0dvfMygQLz8YbVd+9ThLaDNQpTcjp1iPK+ebkACE+qrg7lr/8heSZwQAQKVyRZi/qtGxERERNQUfgSEiIqJ2x8PVBQ/cEoIAL0XN1yevcK3kZC152HBInt1gydoNYTKguLgIl0r1OJWhvR4hE1EjRAWr4aZ2hUySIPnGwJyd0KT9LRcvby/5xkAmSXBTuyIyiGtQEhERUcd0//CQpm1/U93bXz3Oqis5CQDBPsoGk5MAx1lERNQ2mKAkIiKidulwchm8XGWwVF2C0Jc2qw1Zl1sgmaphKTwFrbYcFrMZvyU2ry0iarkhGh/I5HJ4enlC5t8bebnZKEnZ36h9zTkHIbRZNb+4qCEL6A1PLy/I5HIMifW5jlETERERXT/x3Txwz+DARm17z+BAxHWte3r8K8dZ8OuFS1qZXXIyyFsJT/U1JtTjOIuIiNoIE5RERETULqXlV6Nap4MwG2A8tQLm9P9AiKs/YjdMFtAHACBKUyCEQLVOh/T85q/FQkQtE9fVHWF+Kvj5+cMIBVy734mcI19dM0lZu65sLXm3OyDJVQgI8EeYnwpx4e7XO3QiIiKi6+b5+3tcM0l5z+BAPH9/j3rra8dZ7p6+MEIB//i7beoDvRTwdrv2al8cZxERUVvhGpRERETULl0q0UOv1wEARGUuzOVpkEXciZpVUxrJpWY6IlGVBwDQ6/XILdG3dqhE1EiSJOHuAYF4KbEUMldveIT2hrGyCDmHv0BZ6l506XUHXIL61vzt/vfbz5aL+yG0mdY2ZGE3QfKLQ0BAAFQqFabeGsZ1ZYmIiKhDc5FLePmBaEweHoJNBy4h4UwJKnRmeLjKcWsvX0y+KQTx9XxzspYkSbixhxf2niqGi9rHOs4qSdmHQC8FfNwV14yD4ywiImpLTFASERFRu2Q0C1gsFgCAZP5vUtFUDSia8ASvqdpmf4vFAqO5ad/CJKLWk5pbhfe/T4feaIGLqzeE2QTf6BFQeQbANW8vxLm1MJ5bW/fOLuqaJ/r94uDt443AwACM6O2HvlGebXsSRERERNdJfFcPxD8Y3ax9j54vw9IdmRACNuMsb78gKHL3WD8b1YnjLCIicgAmKImIiKhdUsglyGQ1s9ELuQqSWQdL4SnIQ4c2ug1hrARc/Wv+D0Amk0Eh5xPARI7wW1IJXlh1HpV6M4Cap/wV7v5QKhVQKfsAYbGwFJ6GKDlf861nsx6QqyC5BUPyjYEsoPd/pxsLQGBgAPpEeODhkWEOPisiIiIixzuQWIKFXybDYKp5GLN2nOXl4YpqZW+IwBiOs4iIqN1hgpKIiIjapRBfFUrKXAEAklswoM2AJTuhSQlKmXswFENfgaU4CZApoFKpEOqrul4hE1E9Nh64hH9+lwbLVV9g7haohpvKHQaDPwoLi6CVD4Al6Ea7/WWSBE8vLwQE+EOlUmFEbz88PDIMChdZG50BERERUfu092QRXlx9HqarZooZEuuD8ioP6PU+HGcREVG7xAQlERERtUtRQWqkX3KFJEmQfKIhtBkQ2kyYcw5CHja80e1IMjnkAb0AACXVEoQATGYLXOT8wE10vZktAh9uTcc3+y/Z1U0eHox590XhTKYW6xJyoVKpYAkJRlW1Dnq9DhaLBTKZDCqVK9zUrpDJ5Qjzq1kLidONEREREQH/+b0A/1iXArPFtvzv90bgoRFh+DOtnOMsIiJqt5igJCIionZpaE8f7DtTAk8PL5QH9IEp9yAkUzXMyd8AQINJSmHSQXJxtSvXGQX2nCrGfYtPYMKQIIwdGIgu/vbbEVHLVerMeGn1ORxILLUplyTg6Xsj8cAtIZAkCf2ivNA30hOJ2ZU4nFyK9Pxq5BTpYTBboJTLEOavQmSQGkNifRAX7g5J4jTNRERERFsO5+HNby9AXDVDxYLJUbh/eAgAcJxFRETtGhOURERE1C71jfREiI8Ker0/tBXlkHW9AyJtGyAsMCetheXir6jwGwbPLv0hV6phNlRDKj4Ny8X9EBXZkAUPgDziLkjuIXZt55cZsOynbCz7KRv9u3ti7IBA3NHPHx5qDo2IWkN2oQ7zv0hC6qVqm3K1UoY3psXgll5+NuWSJCG+qwfiu3q0ZZhEREREHdK6hFws2ZJuUyaTgJf+0gPjBwfZlHOcRURE7RXvwhEREVG7JEkS/joiFEu26OHvH4BC0RMWXRFE7kEAgNBmIudcks0+MaFu1p8tl45CSErIIm6Hyt0fJiGv8zh/XNDijwta/PO7NAzR+GBkHz/c3MsXPu6K63dyRE7st6RSvLz6HMqrzTblQd5KvD+nJzRd3B0UGREREVHHJoTAJzsz8dWeHJtyuQxYNDUGo28IcFBkRERETccEJREREbVbg2K8cVOcDw4kAgaDEeW4CRZXf1iydkMyVde7n3BRQ9b1Dsj8e8LLTYXQEE/06uYBuUzCzuMF0BksdvsYTAL7z5Zg/9kSyGXAgB7eGB7ng6GxPogKVnO6I6JrEEJg1d4cfLozE5arphrrGe6O92b3RKC30jHBEREREXVwJrPAWxtTse1IgU25Qi7hzYc1GNHbr549iYiI2icmKImIiKhde+TOcJRUGAGEQqlUoEiSIPPpAUvhKagNp6EvvwSLUQeZwhXwjIDkEw15QB9ILkr4+wfA3z8A8V3dMX9iJJQKOf42rht2/1GEHccKcDJdW+cxzRbgyPkyHDlfBiADQd5KDNF4Y0isDwZEe8Hfk0kWoitV6814fX0qdv9ZZFc3ur8/XvpLD6hVdX+LmYiIiIgapjOa8dLX55FwpsSmXK2U4d2ZsRgS6+OgyIiIiJqPCUoiIiJq15QKORZMisLyXRfx61nAw8MTxcVF0LoMQKjfDTbbyhUSJEmCp6cX/Pz8oVKpcHO8L+aO7gKloiY54ql2wcRhwZg4LBiZBdX44XgBfjheiJxifb0x5JcZsO1oAbYdrXlauWuAK/pGeqJfVM1/EYFqyGT8hiV1TheLdJj/RTJScqtsymUS8Ldx3TDttjB+A5mIiIiomcqrTJi3Mgl/ptk+XOnj7oIP5sYhvhvXliQioo6JCUoiIiJq95QKOZ4Y2w2DYryxbl8uVCoVgoOCcT67FMJsgBACkiQhPNgLaldXyORyhPio8NBtoRgQ7V1vu90C1XhsTDc8eldXnM+pwt5TRdhzshhpefVPHwsAWYU6ZBXqsONYTcLSw1UOTRd3aLq4I7aLO2K7uCEySA0XuaxV/x2I2pt9p4ux6JsUaK9ab9JLLccb0zUYyqf5iYiIiJott1iPp1ck4sIl288nob4qfPRYHCIC1Q6KjIiIqOWYoCQiIqIOY1CMNwZGe+FUhha/JZbiQr4ewOUP5fGRfogMUmNYnA/6RHg2+ltbkiRZE4yPjemGjPxqHEwqxeHkUhxPLYfeaL9m5ZUqdGb8nlqO31PLrWVKFwlRwW6ICHRFtyA1IgLViAxSIzbcvVnnTtSeGE0WLN2RiXUJuXZ10aFueHdmLMIDXB0QGREREZFzOJtZgWdWJqFYa7Qpjw51w4ePxHFtbyIi6vCYoCQiIqIORZIk9I30Qt9IL6zYfdGm7q0Zsa1yjIggNSKC1PjrraHQGy04ma7FoeRSHE8pQ/LFKpgt4pptGEwCyRcrkXyx0qb8yHvDWiVGIkfJLdbjxa/P4XRmhV3d7f388coDXG+SiIiIqCX2nirCK2tS7B6U7N/dE+/N7glPNW/pEhFRx8erGREREVEDVAoZBsV4Y1BMzVSx1XozzmRV4M80LU6ma3EqXYsKnfkarRA5h/1nS/CPtedRftWUrnKZhCfHdcPUEaFcb5KIiIiomYQQWJuQi4+2ZUBc9UzkqL5+eG1qNFwVfBCMiIicAxOURERERE2gVskxMNobA/+7tqUQAjnFeuu3Jc/99/+F5cZrtETUcRhNFnz6QxZW/5JjVxfso8Ti6Rr0jfR0QGREREREzsFkFnjv+zRsOphnV/fwyDA8MbYbZDI+CEZERM6DCUoiIiKiFpAkCV38XdHF3xWj+vpby4u1RqTnVyOzoBoZ+dXIKNAhI7/agZESNU96XjVeWXseSdmVdnU3xfng1b9Gw8dd4YDIiIiIiJxDaaURL6w6h2Mp5TblchmwcHJ33Dc02EGRERERXT9MUBIRERFdB36eCvh5KnBjDy9Hh0LULEIIbP4tDx9szbBb/0guA54Y2w0PjQjjk/xERERELXA+pxLzv0hGTrHeptzdVY63H9ZgSKyPgyIjIiK6vpigJCIiIiIiG8VaIxZvSMX+syV2dUHeSiyeHoN+UUy+ExEREbXEnpNFeG1dCnQG24fBQnyVWDInDj1C3RwUGRER0fXHBCUREREREVkdTCzBom9SUVxhv47q7f388dz9UfB245SuRERERM1lsQgs+ykLK3ZdtKvr390Tbz8cCz9PjreIiMi5MUFJREREHdaR94Y5OgQip1FRbcIHWzOw9Ui+XZ2bSoZnJ0Zh3MBASBKndCUiIiJqrrIqI15bm4IDiaV2dZOHB+OZCZFQuMgcEBkREVHbYoKSiIiIiKiTO5hYgje/vYD8MoNdXe8IDyyaGoPwAFcHREZERETkPM5mVuD5VeeQW2K73qSLXML8iVGYOCzYQZERERG1PSYoiYiIiIg6KW21CR9sSce2owV2dXIZMPuOcMy6Ixwucn5rkoiIiKi5hBDYeCAPS7amw2QWNnV+Hgq8PUOD/t25vjcREXUuTFASEREREXVCv54twVvfpqKg3H6tyahgNV5+oAd6R3g6IDIiIiIi51GpM+Otb1Px0x9FdnW9u3ngrYc1CPZVOSAyIiIix2KCkoiIiIioEyksN+D979Ox+0/7m2QyCZg+Mgxz7+wKlYJrHxERERG1RHJ2JV5afQ4ZBTq7ugdvCcFT4yO43iQREXVaTFASEREREXUCZovA5oN5+OSHTFTqzHb13UPUeOWBaMR383BAdERERETOw2IR+GZ/Lj7ekQnjVVO6uqvkePGBHrijn7+DoiMiImofmKAkIiIiInJyydmVeGtjKs5mVdrVyWXA9JFdMPfOcCj5BD8RERFRixSWG7DomxQcSi6zq4sOdcNbMzSICFQ7IDIiIqL2hQlKIiIiIiInVaEzYdmP2Vi/PxcWYV/fM9wdz9/fHXFd+a1JIiIiopb69WwJXl+fgpIKk13dvYOD8OzESLgq5Q6IjIiIqP1hgpKIiIiIyMlYLAI7jhXg452ZKNYa7erdVDL8z5huuP+mELjIJQdESEREROQ8qvRmfLQtA5t/y7Or81TL8cKUHridU7oSERHZYIKSiIiIiMiJnEzX4r3v05BYx3SuADCyjx+euS8SwT6qNo6MiIiIyPkcTynDovWpyC3W29Xd0N0Ti6bGINiX4y4iIqKrMUFJREREROQE8sv0+HhHJn44XlhnfYivEvMndcct8b5tHBkRERGR89EZzPh4ZybW779kVyeXAY/c1RUzRnWBXMbZKoiIiOrCBCURERERUQdWqTNj7b4crP4lB9UGi1290kXCtNvCMGNUF6hVXPOIiIiIqKX+TCvHom9SkVWos6vrGuCK16ZGo0+EpwMiIyIi6jiYoCQiIiIi6oBMZgu+P5SP5T9lo7jCfp1JABjV1w//754IhPm5tnF0RERERM5HW23C0h2Z+K6OtSYB4MFbQvDE2G5wVfKhMCIiomthgpKIiIiIqAMRQuDnk8X4dGdmnU/tA0B0qBueuS8SA6O92zg6IiIiIucjhMDuP4vw/vfpKNLaPxgW5qfCKw/2wI09OPYiIiJqLKdMUO7cuRNr167F2bNnYTabER4ejjFjxmDOd/iXdAAAIABJREFUnDlwc3NrcnsGgwFffvkltm3bhoyMDCiVSkRHR2PKlCmYNGkSJIlzyRMRERHR9SWEwJFzZfj0h0yczaqscxsfdxc8OqYr7hsSDBc5x6hERERELZVTrMP/bU7DgcTSOusnDw/GU+Mj4Map9ImIiJrE6RKU77zzDlauXAkAUCgUUCgUSElJwdKlS7F9+3asWbMGAQEBjW5Pr9dj9uzZOHbsGADAzc0Ner0eJ06cwIkTJ/DLL7/gww8/hEwmuy7nQ0RERESdmxACR8+XY9lPWfgzTVvnNq5KGabeGoppI8Pg4ep0Q3wiIiKiNmcyW7B+/yV8/mMWdHWs8901wBXPT+nOGSuIiIiayamyalu3bsXKlSshk8nwwgsv4Pfff8eJEyewatUqhIWFIT09Hc8++2yT2ly0aBGOHTsGPz8/LFu2DL///jt+//13vPHGG1Aqlfjpp5/w2WefXaczIiIiIqLO7HhKGf7nkzN48vOzdSYn5TJg4rBgbH7+BvzP3d2YnCQiIiJqBb8llWLqP0/iw20ZdslJF7mEOaO7YO2z/ZicJCIiagGnuYNhNpuxdOlSAMDcuXMxY8YMa92QIUPw73//GxMmTMBvv/2G3377DcOGDbtmm1lZWfjuu+8AAO+++y5uueUWADXfzJwyZQpMJhNee+01LF++HNOmTYOXl9d1ODMiIiIi6kyEEDiWUo6Vu7JxPLW83u1G9vHDE2O7ISJI3YbRERERETmvrMJqfLAlA/vPltRZ3y/KE8/f3x3dQ5q+hBQRERHZcpoE5cGDB5GRkQFJkjBz5ky7+piYGIwaNQq7du3Cli1bGpWg3LBhA8xmM2JiYqzJyStNmTIFH330EYqLi7F7925MmjSpNU6FiIiIiDohs0Vg3+lirNpzsd41JgFgeE8fPHJXOHp182zD6IiIiIicV5XejC92Z2PtvlwYzcKu3lMtx1PjI3Dv4CDIZFznm4iIqDU4TYLy8OHDAIDY2Fj4+/vXuc2wYcOwa9cu7N+/v0lt1pfMdHFxweDBg/Gf//wH+/fvZ4KSiIiIiJrMYLJg57ECrP4lB5kFunq3GxrrjUfu6oo+EUxMEhEREbUGk1lgx7F8/Ps/WSgoN9rVSxJwz6AgPD62K/w9lQ6IkIiIyHk5TYIyJSUFANCjR496t4mMjAQAFBYWoqSkBL6+vg22mZqa2ug2a49PRERERNQYFdUmfHcoD+sSclFYxw2xWkM0NYnJvpFMTBIRERG1BiEEEs6U4JOdmUjLq65zmz4RHnh2YhTiunq0cXRERESdg9MkKPPz8wEAwcHB9W5zZV1BQUGDCcrKykpUVFQAAEJCQurdLigoyOb4REREREQNSc+rxrcHcrHjWAGq9JZ6t7u1ly8eHtkFfaOYmCQiIiJqLX9cKMfSHZk4ma6tsz7QS4GnxkfgrhsDIEmczpWIiOh6cZoEZWVlzTo9bm71L1KtUqmsP9cmH+tzZb1ara53O1dXV5vjExERERFdzWIR+C25FOv35+JQclm928llEsbcGIBpI8PQI6T+cS0RERERNU1KbiU+3ZmF/WdL6qxXukiYOiIMM2/vAjeVvI2jIyIi6nycJkFpMpkAAAqFot5tlMrLc8WbzeYG27uyvjFt1h6fiIiIiKhWRbUJ248W4NsDl5BVWP/6kmqlDBOGBmPqraEI8VXVux0RERERNc35nEqs2JWNPSeL66yXJGDcwEA8eldXjsOIiIjakNMkKGu/yWg01r9+j8FgsP7cUNLxyvYa2+a12iMiIiKizkEIgT/TtNhyOB+7/yyC3lj/NK7+ngrcf1MIJg8Pho87x5NEREREreXcxUos35WNX07VnZgEgFviffHE2G7oEcqZK4iIiNqa0yQo3d3dAQA6Xf1Ppl9Z5+HR8ALXte01ts1rtUdEREREzq2kwoidxwuw9XA+0vKqG9y2d4QHHrg5FKP6+kHhImujCImIiIicX1J2BZb/lI2EM3VP5QoAfSI88OT4CNzQ3asNIyMiIqIrOU2CMiQkBH/++Sfy8vLq3ebKusDAwAbbU6lU8PX1RUlJCfLz86/ZZlBQUBMjtpWRkQEASExMxPTp01vUFhEREbVvPXv2xIsvvujoMJyGI8dRQgiUV5tQVG5EaaUJQoh6t5UkCb4eLgjyVsGYL8fqo8DqNoyViIjIWXAs1bqc4Z6UEALaajMuleqhrap/GSY3lRyhfiro81zw/lGpDSMkIiJqH9rTOMppEpQxMTH48ccfkZ6eXu82tQOuwMBAeHt7N6rNI0eOIC0trd5tao8XHR3dpHivVlVVBQDQarU4cuRIi9oiIiIi6kw60jiqoAgocHQQRERERFfoSGOpltIC0OY6OgoiIiICnChBOWTIECxduhSJiYkoKyurMwF58OBBAMDgwYMb3eaRI0dw6NChOutNJhOOHj0KABg0aFAzI68RHh6O7OxsuLm5ISIiokVtERERUfvWs2dPR4fgVDiOIiIi6lw4lmpdHEsRERF1Hu1pHCWJhuah6kAsFgtuu+025OXl4dFHH8W8efNs6pOTkzFx4kSYzWasXr26UQnF1NRUjBs3DkIILF++HLfccotN/Zo1a7Bo0SJ4enril19+4TqURERERERERERERERERNcgc3QArUUmk+Hpp58GACxbtgyfffYZdDodAODw4cN47LHHYDabMWzYMJvkZF5eHsaMGYMxY8Zg9WrbVYB69OiBiRMnAgDmzZuHXbt2QQgBk8mEDRs24O233wYAzJ49m8lJIiIiIiIiIiIiIiIiokZwmm9Q1nrllVewfv16AIBCoYBSqURlZSUAICoqCuvWrYOvr691++zsbNx+++0AgCeffBJPPfWUTXtarRazZ8/GyZMnAQBqtRpmsxkGgwEAcPfdd2PJkiWQJC6sTURERERERERERERERHQtTrMGZa1FixZh+PDhWLt2Lc6ePQudTofIyEiMHj0ajz32GDw9PZvUnqenJ9asWYOvvvoK27dvR0ZGBmQyGXr37o3JkyfjwQcfZHKSiIiIiIiIiIiIiIiIqJGc7huURERERERERERERERERNR+Oc0alERERERERERERERERETU/jFBSURERERERERERERERERthglKIiIiIiIiIiIiIiIiImozTFASERERERERERERERERUZthgpKIiIiIiIiIiIiIiIiI2gwTlERERERERERERERERETUZpigJCIiIiIiIiIiIiIiIqI2wwQlEREREREREREREREREbUZF0cHQE2n1Wrx9ddfY/fu3UhPT4fBYEBQUBCGDBmCWbNmQaPR1Llffn4+Pv30U+zbtw/5+fnw8vJCv379MGvWLAwePLjBY+7cuRNr167F2bNnYTabER4ejjFjxmDOnDlwc3O7HqfpVJrTZ3v27MHjjz9+zbaTk5PtyiwWCzZs2IBNmzbh/PnzkCQJkZGRmDBhAh566CEoFIpWOS9nVlFRgRUrVmDXrl3IzMyEq6sroqOjMXHiREyePBkyWd3Pd6SlpeGzzz7DwYMHUVJSAl9fXwwePBiPPPIIevbsWe/x2Gct15w++/rrr/HGG2802G6XLl2wZ88eu3KDwYAvv/wS27ZtQ0ZGBpRKJaKjozFlyhRMmjQJkiS12rl1Ju+//z4+//xzDB48GF9//XWd2/B65vwa8zo4c+YMVqxYgaNHj6KkpATu7u6Ii4vDlClTMG7cuHrb5vtt25k0aRLOnDnT4DZPPvkknnrqKZuy3bt349tvv8WpU6dQXl5u7dtJkybhnnvuqff9lX3bOTTldbV582Y8//zzjW574sSJePvtt23KeL1vW+fPn8fKlStx6NAhFBQUwM3NDX369MHUqVNx++23N7qdF154AZs2bcL69evRv3//Brfl2KDtNKd/+bm46TiO6hw4zuq8mtv31dXV+Oqrr/Djjz8iIyMDJpMJERERuOuuuzBr1iy4u7vX2Rb7vn1p7liJ91E6vtYaJ+fn5+Oee+5BaWkpVq1ahSFDhtS7bVv0vSSEEK3SErWJ9PR0zJ49GxcvXgQAqFQqyGQyVFdXAwAUCgUWL16MCRMm2OyXlZWFBx98EIWFhQAAT09PVFZWwmKxQJIkLFy4ELNmzarzmO+88w5WrlxpbV+hUKCqqgoAEBkZiTVr1iAgIOC6nK8zaG6fffzxx/joo4/g6uoKDw+Pets/cOCAze9CCDz99NP44Ycf6jzeDTfcgC+++AJqtbrVztHZXLx4ETNmzEBWVhYAQKlUQggBo9EIABgwYACWLVtmN3g7efIkZsyYYf378PT0REVFBYQQUCgUePfddzF27Fi747HPWq65fVZ788rNza3eC2tISAg2bdpkU6bX6zF79mwcO3YMAODm5gaTyQSDwQAAuPPOO/Hhhx/Wm8imuh0/fhzTp0+H2Wyu94YKr2fOrzGvg82bN+Pll1+GyWQCALi7u6OystJaP27cOPzf//0f5HK5zX58v207JpMJN9xwAwwGA/z8/Op9P5w9ezbmzJkDoObmx4IFC7Bt2zYAgEwmg7u7O7RarXX722+/HR9++KHdjRD2befQ1NfVzp07sXjx4gbbNBgMKC8vBwDMmTMHCxYssNbxet+2Nm/ejFdeecU6frv6vf3hhx/Giy++eM12tm7divnz5wPANROUHBu0neb2Lz8XNw3HUZ0Dx1mdV3P6Hqi5ZzJr1ixkZGQAANRqNYxGo/V9oHv37li1ahUCAwNt2mHfty/NvZbyPkrH11rjZACYO3cu9u/fDwANJijbrO8FdRhGo1HcfffdQqPRiFGjRolff/1VmM1mYbFYRFJSkpg2bZrQaDSiV69e4vTp0zb7jRkzRmg0GjF58mSRkpIihBCirKxMvP7660Kj0YjY2Fhx9OhRu2Nu2bJFaDQa0bNnT/Hll18KvV4vhBDi0KFD4rbbbhMajUbMmDGjTc6/I2punwkhxJNPPik0Go1YsmRJk4758ccfC41GI/r16ye2bt0qTCaTsFgs4qeffhKDBg0SGo1GvPDCC615mk7FbDaLSZMmCY1GI26++Wbxyy+/CKPRKAwGg9ixY4cYMGCA0Gg0Yv78+Tb7lZaWiqFDhwqNRiPmzp0rcnJyhBBC5OXlib///e9Co9GIPn36iPT0dLtjss9aprl9JoQQEydOFBqNRmzYsKFJx3zhhReERqMRQ4cOFfv27RMWi0UYDAaxYcMG0bt3b6HRaMTHH3/cWqfYKWi1WjFq1Cih0WiERqMR06ZNs9uG1zPn15jXQUpKioiPjxcajUY8+eSTIjs7WwghRHl5uXj//fet+y5fvtxuX77ftp2kpCSh0WhEfHy89e/tWj755BOh0WhEXFyc+OSTT4RWqxVCCFFSUiI++ugja7+/9dZbdvuybzuH5ryuGmKxWMTs2bOFRqMR9957r6iqqrKp5/W+7Rw5ckT07NlTaDQasXDhQpGfny+EEKKgoEA8//zz1vf2Xbt2NdjO1q1bre8VGo1GnDhxot5tOTZoOy3pX34ubjyOozoPjrM6r+b0vV6vF2PHjhUajUaMHj1aHD16VFgsFqHX68V3330nbrzxRqHRaMQTTzxhty/7vv1o7rWU91E6vtYaJwshxOrVq63bazQacejQoTq3a8u+Z4KyA9m2bZt1MHH27Fm7ep1OZ02GPfXUU9byzZs3C41GI2644QZRVFRkt9/TTz8tNBqNmDp1qk25yWQSo0ePFhqNRvzzn/+02+/cuXMiLi5OaDQacfDgwVY4Q+fT3D4TQojbb79daDQa8eOPPzb6eFqt1pqMWbdunV39vn37rPGkpaU1+Xw6gz179ljfpOu6ONf+PcXFxYm8vDxr+b/+9S+h0WjEyJEjhU6ns9nHZDKJBx98sM4kGfus5ZrbZ0aj0Xpz8eoHBBqSmZlpfe9LSEiwq1+7dq31PbesrKx5J9UJPffcc0Kj0Yi+ffvWe0OF1zPn15jXweLFi4VGoxFjxowRBoPBrv6ZZ54RGo1G3HnnnTblfL9tW999953QaDRi/Pjxjdper9dbb47UdWNMiMvX2l69eonS0lJrOfu282jq6+paVqxYYX3Pqb1ZU4vX+7Y1fvx4odFoxHPPPVdnfe2DnXPnzq2zvrq6Wrzxxhs2N1waSlBybNC2WtK//FzceBxHdR4cZ3VezRkLLV++XGg0GjF8+HBRUFBgV79x40Zrkqo28SEE+769ae61lPdROr6WjpNrpaSkiL59+1rHCfUlKNu67zkXTQeyb98+AMCQIUMQFxdnV69SqXDvvfcCAI4ePWot/+abbwAA9913H/z8/Oz2e/TRRwEAx44dQ3Z2trX84MGDyMjIgCRJmDlzpt1+MTExGDVqFABgy5YtzTwr59bcPquoqLD2RV371Wf79u3QarXw8vLC5MmT7epvvfVWxMfHw2w2W6f1IFu1UwP17NkTAwcOtKuvfc2bzWYkJiYCqJnyovbv7KGHHoJKpbLZRy6XY+7cuQCAn376yToVBsA+aw3N6TMASE1NhcFggIuLC2JiYhp9vA0bNsBsNiMmJga33HKLXf2UKVPg5+eHyspK7N69u6mn0ynt2rULmzdvRlxcXJ3TINfi9cy5NfZ1kJqaCgCIj4+vc72T2ulJcnNzbcr5ftu2kpKSADR+HHP8+HFUVFQAQL3TC02ZMgUAYDQa8ccff1jL2bedR1NfVw1JS0vD+++/DwD429/+hh49etjU83rfdv744w+cO3cOHh4eWLhwYZ3bzJs3D88//zz+8pe/2NX9/PPPGDNmDFatWgUAuP/++695TI4N2k5L+pefixuP46jOheOszqs5Y6Fvv/0WAPC///u/dU7HOG7cODz77LN4/fXXbaZ2Zt+3Hy25lvI+SsfW0nFyLaPRiPnz50On02HevHkNHrOt+54Jyg4kPj4ed911F2699dZ6t6mdK7x24FFVVYWTJ08CAIYPH17nPj179rS+QdXOPwwAhw8fBgDExsbC39+/zn2HDRtmtx9d1pw+A2oGHEIIeHh4IDw8vNHHq+2zQYMG1btINfusYS+99BISEhKsN6yuZjabrT8rlUoAwIULF1BQUACg/r+zIUOGWOfpvzIZzT5rueb0GQBrsrJ79+425ddS22e1/XI1FxcX6wLj7LNrKygowMsvvwylUol333233r8DXs+cW2NfBwDg7e0NADh9+rR1zZQr1b5OIiIibMr5ftu2at9jG3vzRK1WY8KECRgxYgSCg4Pr3CYgIACSJAGwHTexbzuPpr6uGvLOO+/AaDSie/fumD17tl09r/dt5+effwYAjBgxAj4+PnVu079/f8ycOROjR4+2q9u0aRNyc3PRpUsXrFy5Eo8//vg1j8mxQdtpSf/yc3HjcBzV+XCc1Xk1te8vXLiAtLQ0uLi4YNy4cXVu4+rqikceecT68FUt9n370dxrKe+jdHwtHSfXWrp0Kc6cOYPx48fj7rvvbvCYbd33Li1ugdrMrFmz6n3Sqdbvv/8OAAgJCQFQ82SwxWIBUHMTvj4REREoLi5GSkqKtaz256ufJr5SZGQkAKCwsBAlJSXw9fW99ol0Is3pM8B2wHHgwAFs3LgRiYmJMJlMiIyMxPjx4zFhwgS7xbCb0mfnz59v8vl0FsHBwfUO2tevXw+gZkHpPn36ALj8FKokSYiKiqpzPw8PDwQEBCA/Px8pKSnWpDX7rHU0tc+Ay08e9uzZEzt37sTWrVtx/vx5yOVyxMTEYOLEibjjjjvs2qvt78b02ZXvqVS3F154ASUlJZg/fz40Gk292/F65twa+zoAagbmO3bsQHp6Op599lksXLgQoaGhqKqqwtdff42NGzdCkiQ88cQTNvvx/bZt1b7HhoSEYOnSpdi/fz/y8vLg7e2NgQMHYubMmejatat1+/79+6N///4NtnnixAkIIQDA5j2ffdt5NPV1VZ/ffvsNe/fuBQDMnz8fLi72H4t5vW87V47JhBDYsWMHtmzZgrS0NKhUKgwYMABz5syxS5jUCg4Oxvz58zF9+nSoVCqbp//rw7FB22lJ//JzceNwHNX5cJzVeTW172u379atG9zc3HDu3DmsXr0aJ06cQFVVFbp37477778fd911l92x2PftR3OvpbyP0vG1dJwM1OQeli1bhuDgYLz66qvQ6/UNHrOt+54JSieSlZWF7du3A6gZdAJAfn6+tf7KBNjVagcftd8Cu3Lf+m76X11XUFDAN6ImqqvPgMsfxE6ePIk5c+bY7JOdnY1ff/0VmzdvxscffwwvLy9rXW2fNaavq6qqUFlZCXd399Y5GSdWVVWF1NRUrF27Ft999x0AYMGCBfDw8ABw+d/dx8cHrq6u9bYTHByM/Pz8Ov/O2Get61p9Blz+O/vxxx+xdetWm/0zMjKwe/dujB07Fu+88471G5aVlZXWp0kb6rOgoCAAtu/BZG/NmjVISEjAwIED6/z2ypV4PXNeTXkdAMD48eORkJCA7du344cffsAPP/wAd3d3VFVVQQiBiIgIPPvss7jzzjtt9uP7bdvJzc1FaWkpAGDhwoU2H4Byc3ORlJSEDRs24O233673Se66fPbZZwBqrrf9+vWzlrNvO4fWfF19+OGHAIBevXpZpye6Eq/3bevChQsAah4me+SRR+yexE5JScH333+P999/v86Hx1599dUmH5Njg7bTkv7l5+Jr4ziq8+E4q/NqTt/XvgcHBARg/fr1eP3112E0Gq37ZWdnIyEhARMmTMBbb71lM8Ur+779aO61lPdROr6WjpMrKyuxYMECWCwWvPnmm/Dy8rLp57q0dd9zilcnodfr8cwzz0Cn00GlUlkHpldOy6BWq+vdvzapcuX2lZX/v707j4/p3v84/soqQuwahNZW0XKLCopYWkrVUumPLpTal9raWtoqtRStq0otpfZrqVJyqVRVrbFTe2srri00tohskkjm90ceczojM1llEvJ+Ph55PCZnzvnOd+Z75pzPfNcoADw9Pe0eZ7nWnuWxkjp7ZQb/9I6Ii4vjnXfeYf369Rw/fpwtW7YwYMAAXF1d2b9/f7I5o81lllJZq8zS5/Dhw9SoUYN27doRGBiIq6srkydPtprX2/w5pvRdgZS/ZyqzhyctZQb/fM8SExPp168fmzZt4vjx42zYsIFOnToBsH79esaPH28ck95rqrl8Jbnz588zadIkPD09+fLLL5P1fH+Q7mePp/SeB5C0ru/kyZMZOHCgsS0qKsro8X3//n2io6OTHafrreNYrvdbokQJpk+fzv79+zly5AgLFy6kSpUqxMXFMWzYMA4ePJimNBcuXGj8GOvZs6fVNFMq29zhYZ1Xx44d4/DhwwDJGjzMdL93rIiICABmzpzJjh076NixoxGXLV++nCpVqhAbG8vgwYMf2ggNxQaOk5ny1e/ilCmOyp0UZ+VeGSl78zX43LlzjB49mgoVKrBkyRKOHTvG1q1bjfXl1q5dy4wZM6xeT2Wfc2T0Xqp6lEdfZuPk8ePHc/nyZd5++238/f3T9JqOLns1UD4G4uLiGDBggDGn9IgRIyhVqhTwz9prbm5uxlzytphHB1muQWB+nNL6BZbrtlmu8yYpS6nMIGnYdvXq1Rk5ciQjR46kQoUKuLu74+PjQ//+/ZkwYQIAwcHBBAcHG8epzB6+q1ev4u7ublyU4+PjGT9+PCtXrjT2ScvnDvqeOUpayiw6Opq6detStWpVpk6dysCBAylTpgzu7u6UK1eOESNGMGjQICBpiljz9AaWn39ayszWui6S9LkMHTqUmJgYhg8fnqbp+HQ/e/xk5DwAiImJYcCAAUybNo1XXnmFoKAgjh8/zqZNm+jevTtXr17lo48+YtasWcleD3QeOIK7uzsNGzakdu3a/PjjjzRr1oyCBQuSN29e6tWrx7Jly6hUqRL3799n4sSJqaa3Zs0aYz8/P79k0+erbHOHh3VeLVq0CIDSpUvzyiuv2NxH93vHMjeG3Lhxg27duvHZZ58Zcdnzzz/P4sWLKVOmDPfu3TNGv2aWrhuOk5ny1e9i+xRH5V6Ks3KvjJS9+Rp869YtSpcuzbJly6hduzZ58uShVKlSfPLJJ0aHrfnz53Pz5k3j9VT2OUdG76WqR3n0ZSaO2rRpE6tXr6Zs2bIMGzYsza/p6LJXA+UjLioqit69e7N9+3Ygac1Dy1FC5tbs+Ph4o0ecLXFxcYD1iWfuPWE59N/ecQ8eK/alVmYAEyZMYMWKFXTs2NFmGq+99pqxtsQvv/xibFeZPXwvvvgiR48e5dChQ2zYsIGXX36Z27dvM3LkSNasWQOk7XMHfc8cJS1l5unpydSpU1m9erXNKRAAevToQeHChTGZTGzYsAHAagrftJSZysu2GTNm8Mcff9C4cWPat2+fpmN0P3v8ZOQ8gKTpmjdu3EjLli355ptvePrpp3F3d6dMmTIMGzaMjz76CIBp06bxv//9zzhO54Hj+Pv7M3fuXJYsWULBggWTPZ83b14GDBgAwNGjR7l69ardtL7//ns++eQTTCYT5cqVY/r06VZTT4HKNrd4GOdVeHg4GzduBOCtt95Kdi6Z6X6fPTw9Penfv3+y7fnz5zdmm9m2bRv37t3L9GvpuuF4GSlf/S62T3FU7qU4K/fKbNn36dPHatkby+3u7u7Exsaybds2Y7vKPudJ771U9SiPj/SW/c2bNxkxYgQuLi5MnDgxxRG0D3J02auB8hF2/fp13nnnHXbv3g1Aly5d+Pjjj632sZz7O6UFUGNiYgCsblTmY1P6AWj5nK2bnFhLS5mlVa1atYCkaRrMVGYPn6enJ87Ozjg5OVGuXDlmzJhhNGhNnToVk8mUps8d9D1zlLSUWVq4u7sba2+Yv2eW19S0lJnKK7nDhw8zZ84cChUqxLhx49J8nO5nj5eMngenTp0yGheGDBlic5/OnTvj4+NDYmIigYGBxnadBzmLn5+f8dg8St2SyWRi8uTJjBkzhsTERCpWrMjjbvs1AAAgAElEQVTixYspUqRIsn1VtmKW2nm1detW44e2vdGToPu9o5k/72effdbu2lU1a9YEkipKLly48NBeU9eNrJfV5ZsbfxcrjpLUKM7KvR4se8vrbu3atW0eU6BAAZ5++mkAqykiVfY5R0bvpapHefRltOyHDx9OWFgYPXv2pHr16hl6TUeVvWumjpZsc/bsWXr27Gn0hhk4cCD9+vVLtl/JkiWNx6GhoTz11FM20zMvfvrEE08Y20qUKMHRo0cJDQ21mw/L54oXL56+N5HLpLXM0srLywuwviCUKFGCmzdvpqnMvLy80tV7Qv7x7rvvsmnTJq5du8b169eN79nt27eJi4uzGuZuyfzZP/g9U5llvQfLLKWFni2Zb7KWvc8KFy5MWFiY1WLjD7JV1pJk5cqVJCQkEBMTQ9u2bZM9b567/vDhw9SvXx+A6dOn6372mMnoeWCufCxRooTV1OiWXFxcqFGjBiEhIVaVnLre5izmOAaS/1iOi4vjk08+ISgoCIDnnnuOOXPmULhwYZtpqWzFLKXzCmDz5s0AVK1aNcXpEHW/d6yiRYty586dFNe5sRwp8jBGUCo2cJysLt/c+LtYcZSkRnFW7vVg2RctWtT4P6XrcIECBYB/GqpAZZ+TZPReqnqUR19Gyj4kJMSYuXHFihWsWrXKav/ExETj8YABA3Bzc6NGjRrGOrSOLnuNoHwEHTlyhA4dOnD16lVcXFwYN26c3YausmXLGsNsU+qJePHiRQAqVqxobDP3nknLccWLF7c5vYAkSU+ZXbx4kf/85z9MmTIlxd4t5nnhLS8CaSkz83OWZS3/OH/+PNu2bbPqgfsgy888LCzM+CxNJhOXLl2yeUxkZCS3bt0C0v89U5mlLCNlduLECRYuXJhsEfgHmcvM1vfMcsqjB6nM7DOPYI2NjeXmzZvJ/syBdHx8vLEtPj5e97PHTEbPg7CwMMA6oLbFfK5Y3kd1vXWcoKAgZs+ebTQG2WK+vgIUK1bMeBwTE0OvXr2MSrNGjRqxePFiu5VmoLLNLTJzXkHS2ig7d+4EoHnz5qm+nu73jmOeovPvv/+2u8+dO3eMxw+WbUYoNnCcjJavfhfbpzgqd1OclXtlpOzN12CAa9eu2T0uPDwcyF3X0kdJRu+lqkd59GWk7C1njgsLC0sWJ9y+fdt4Pjw8nJs3bxrXAHB82auB8hFz6tQpevXqRXh4OB4eHsyYMSPF9QbMLeAAe/bssZum+cQ0T48CUKdOHQBOnjxpdZJaMk9Vam+aAEl/mV29epUJEyYwe/ZsDhw4YHOfxMREozzN5Qv/lNmBAwesFja2ZD7OsqzlHx9++CG9e/dm3rx5dvcxN4Q5OztTokQJSpcujY+PD2D/e7Z3714SExNxc3OzGlqvMsu8jJTZiRMn+PLLL5k+fbrdisfIyEiOHj0K2P6e7d271+Zx9+/fN767KrPkvvzyS06fPm3378033wSS7ivmbXXq1NH97DGT0fPAPPr5+vXrXLlyxW76f/75J4BVD1Fdbx1n+fLlTJkyhblz59rdx9xQ5OHhwbPPPgskVaQOGDDAKIfXX3+db7/9NtXe2Crb3CGj55XZ2bNniY6OBv6ZBiklut87jvne+9dff9mtPD148CAAhQsXthoNkFGKDRwno+Wr38X2KY7K3RRn5V4ZKfsaNWoYs3zt2LHD5jF37941pna1jJ9U9jlHRu+lqkd59GWk7EuXLp1inGC+TgAsXryY06dPs2TJEmObo8teDZSPkKioKAYMGEB4eDh58+Zl7ty5vPTSS6ke17JlSwBWrVpl9C60NGvWLCDp5Ctfvryx3c/PD29vb+7fv2+z4v/06dNs3boVgLfffjtD7+lxl5Eyq1mzJoUKFQJg7ty5NtfLW7ZsGSEhIbi5uREQEGBsb9KkCR4eHty6dSvZ8G2A7du3c/LkSVxcXHjjjTcy+e4eTw0bNgTgl19+ISQkJNnzcXFxxnemdu3aRlmZv2eLFy+2mhIDknrsm79DrVu3tpqbW2WWeRkps4YNG+LqmjTL+Zw5c2ymO2PGDKKjoylYsCDNmjUztrdo0QInJyf+/PNPmwH+ihUruH37Nl5eXrRq1SrT70/+ofuZ1K9f3/iBPXPmTJv7bNq0iTNnzgDWo6R0vXWcF198EUiaQWL//v3Jno+IiDC+r23atCFPnjxA0jrB5utqx44dmTBhgnGtTonKNnfI6HllZq5wd3V1pUqVKqm+nu73jtOiRQs8PDwwmUxMmTIl2fMxMTH85z//MfZ1cXHJ9GsqNnCcjJavfhc/fIqjHg+Ks3KvjJR9gQIFaNKkCQCLFi2yGjllNn/+fOLj4ylYsCANGjQwtqvsc47MxEqqR3m05YY42WX06NGjM52KOMT06dPZsmULAOPHjzduMKmpVKkSGzZsIDQ0lF27dlG9enWKFy/O3bt3+fe//01gYCDOzs588cUXxigwACcnJwoVKsSmTZs4dOgQrq6uVK1aFVdXV/bt28egQYO4e/cudevWzdRaio+zjJSZi4sL7u7u7NixgytXrnDhwgWqVatG/vz5iYyMZP78+Xz11VeYTCb69etH06ZNjWPz5MlDQkIC+/fvZ/fu3RQvXhxfX1+cnJz47bff+Pjjj4mNjeX//u//rH7AyT98fX0JDAwkIiKC7du3U758eXx8fHB2dubPP/9kyJAhHDlyhDx58jB16lRj+otnn32WVatWERoayqFDh6hVqxYFCxbkxo0bfPrpp+zcuZO8efMyadIkq+lTVGaZl5Eyy5cvH+Hh4Rw9epSTJ08SGRlJlSpVyJs3L7du3eLrr782bvBjxozhueeeM16vSJEihISEcPLkSbZv307ZsmUpX748CQkJrFq1igkTJpCQkEDfvn2NNV8k7bZu3cqff/6Jj48Pr7/+utVzup/lHvbOA09PT2JjY/n99985efIkf//9N88++yxeXl7cu3ePwMBARo4cyf3792nevDndu3c3jtX11nF8fX356aefiIiIYMeOHZQuXZonn3wSFxcXjh49ysCBAzl//jzFihXjm2++wdPTk3PnzjF06FBMJhP16tVj8uTJODunrS+lyjZ3yMh5ZWnNmjUcOXKEcuXK8e6776b6errfO07evHlxd3dn165dnD59mhs3bvDcc8/h6enJpUuX+OCDDzhx4gQFCxa0WbYPunv3LosXLwagffv2lChRItk+ig0cJ6Plq9/FGac46vGmOCv3ymgsVLVqVVatWmXUmTzzzDOULFmSmJgY5s+fb3RY+PTTT6lWrZrxeir7nCMzsZLqUR5tDztOBoiOjmbBggUABAQEULp0aavnHV32TiZb3dAkx4mLi6Nu3bpERkbi5ORktcixPatWrTKGdJ8+fZp3333XWHPAy8uL6OhoEhISAPjss8/o2LGjzXQ+++wzVqxYASQNDXd3dycqKgqAcuXKsXz58hTnq8+tMltm48aNsxpenT9/fmJiYowy69ixIyNHjsTJySnZ6w4cONDoyeDu7o6Li4sxqs/Pz48FCxYk61Uu/zh69Ch9+vQxepa5ubnh5uZmTAvm5eXF5MmTadSokdVxe/bsoU+fPsaaHwUKFCAiIgKTyYSLiwvTp0+32UitMsu8jJRZfHw8H374IRs3bjS2eXl5ERkZiclkwtnZmQ8++IBevXole72IiAi6devGsWPHgKSAISEhgbi4OCCp19KUKVOSfT8ldeZ7Tu3ata2ugWa6n+UOKZ0HiYmJjBgxgtWrVxvbvLy8iImJMaYeqlevHtOnT7casQ663jrSqVOn6NmzJ9evXweSX5eLFy/OvHnzqFy5MmD9/SxQoIAxwsOebt26WVWcqmxzh/SeV5YGDx5MUFAQ9evXN36Qp0b3e8cxmUx88cUXRgcxSLq2R0REGI9nzJjBCy+8kGpaV65cMWLuFStWWC2v8CDFBo6RmfLV7+L0Uxz1+FOclXtlNBbav38/7733nnHd9fT0JD4+nvj4eAA6d+7M8OHDdS3NwTJzL1U9yqPtYcbJADdu3MDf3x9ImgnQPKXrgxxV9hpB+Yg4efIkS5cuNf6Pjo5O9a9Tp04UKFAASFogtW3btsTHx3P79m3Cw8Px9PSkVq1ajB492hjubcuLL77I008/TVhYGGFhYcTGxvLkk0/Svn17Jk6cqEVw7chsmTVs2JAaNWoQGRnJ3bt3iY6OpnDhwtSrV49PP/2UTp062awMcXFx4dVXX8Xb29sos4SEBCpUqMC7777L6NGjFTikokSJEgQEBODi4kJ4eLhxwS9btiwBAQH8+9//tjk1WJkyZXj11VeJiori9u3bREREUKBAARo2bMgXX3xB3bp1bb6eyizzMlJm5s+9QoUKxvcsJiaG4sWL07hxY8aNG8err75q8/Xy5MlD27ZtyZcvn3FNdXJyonLlyvTt25fBgwenuUeqWEtpBCXofpZbpHQeODk50aRJE6pVq0ZMTAwRERFERkaSL18+atSowYABAxg6dCgeHh7J0tX11nGKFStGQEAAbm5u3L17l4iICJycnChXrhxvvvkmX331lVUP3a+//ppbt24BEBsbm2rMVKNGDasfUirb3CG955WlFStWcPnyZWrVqmU10iolut87jpOTEw0aNMDPz4/IyEjCw8OJjo6mVKlStG7dmokTJyZbV9SetIygNFNs4BiZKV/9Lk4/xVGPP8VZuVdGYyEfHx8CAgIwmUzcuXOHu3fv4uXlRc2aNfn000/p3LmzrqU5XGbupapHebQ9zDgZUh9BaeaostcIShERERERERERERERERFxGHX3FBERERERERERERERERGHUQOliIiIiIiIiIiIiIiIiDiMGihFRERERERERERERERExGHUQCkiIiIiIiIiIiIiIiIiDqMGShERERERERERERERERFxGDVQioiIiIiIiIiIiIiIiIjDqIFSRERERERERERERERERBxGDZQiIiIiIiIiIiIiIiIi4jBqoBQRERERERERERERERERh1EDpYiIiIiIiIiIiIiIiIg4jBooRURERERERERERERERMRh1EApIiIiIiIi2S4hISG7syAiIiIiIiIO4prdGRARscVkMrF582Y2b97M4cOHuXnzJrGxsRQqVAgfHx9eeOEFWrZsydNPP+2QtDp16sT+/fttPufk5ISbmxteXl6UKlWK+vXr06ZNGypUqJDh9/+g0NBQ2rRpQ4kSJQgMDMTFxeWhpZ0VOnXqxB9//MHq1aspX758dmdHREQkS4SHhxMUFERwcDCnT5/m1q1bODk5UaRIESpXrszLL79Mq1atyJMnT3ZnNUeLj49n8eLFXLt2jREjRqT7eF9f31T3cXFxwcPDg+LFi+Pr68trr71GkyZNMpRWShYvXkydOnXSfVxCQgLvvPMOJ0+eZP369ZQqVSpT+chq06ZNY+bMmfz73//mtddey+7siIjIYy6lOpnUHDhwgAIFCjzkHOUsioUcT7GQyMPhZDKZTNmdCRERS5cuXeLDDz/k+PHjKe7n7OxMQEAAo0ePxt3dPUvTSm8w7OLiQpcuXRg8eHCmGxNNJhPvvvsu+/btY+nSpdSqVStT6TnCqVOneP311/H19WXlypW4ublld5ZEREQemvv37zN37lzmzJlDdHR0ivv6+Pgwfvx46tat66DcPXrefvttDh06REBAAF9++WW6j89oRVrz5s2ZMmWKVayWXZVy06dPZ8aMGQwYMID+/ftnKg+OcO/ePVq0aEF4eDhr166lTJky2Z0lERF5jKmBMmWKhRxPsZDIw6EGShHJUW7fvk27du0ICQkhf/78dOnShUaNGuHj44Obmxvh4eEcO3aMJUuWcPjwYQAaNWrEd999h5OTU5alZRkMHzp0yOo5k8lEbGwsN27c4Pjx4yxcuJBz584B8PrrrzNhwoRk6aXHypUrGTlyJC+//DIzZszIcDqONnLkSFauXMngwYPp1atXdmdHRETkoYiJieG9995j9+7dAFSoUIEOHTrg5+eHt7c30dHRXLp0ibVr17Ju3Tru37+Pq6srU6ZMoVmzZtmc+5zppZdeIiQkJNMNlDVr1mTu3Lk290lISODmzZvs27ePWbNmERoaCkDfvn15//33jf2ioqJsHv/7778b8Uzv3r3p3bu3zf08PDzS3Tnt7NmztG3bliJFirBx40Y8PDzSdXx2WbduHUOGDMHf35/58+dnd3ZEROQxllKdTGry5cuXFVnKURQLZQ/FQiKZpyleRSRHmTt3LiEhIXh4ePD9998n67lVoEABypQpQ4sWLRg6dChBQUFs376dX3/9lVdeeSXL0rJkK7jNnz8/RYsWpXLlyrRp04b333+fLVu2EBgYSKVKlejatWuGPo+7d+8yefJknJ2drQLGR0H//v1Zs2YNM2fOpHXr1pQsWTK7syQiIpJpw4YNMxon33zzTUaNGmVVCVO4cGF8fHyoW7cuHTp0oGvXrkRGRjJ06FCeeeYZ9a7OQi4uLilWQhYoUIDy5cvz0ksv0bZtW27fvs3ixYvp0aMH+fPnB+xXYlpWlLm5uT3Uys6xY8cSHx/Pe++998hUyAG0atWKuXPnsnPnTn799VeaN2+e3VkSEZFcIDc0OGaUYiHHUiwkknnO2Z0BERFLmzZtAqBJkyYpTivh7OzMyJEjjTWdgoKCsjSt9MiTJw9ff/01Tz31FACzZ88mIiIiQ2nNnz+fO3fu4O/vT8WKFTOVL0fz9vamZcuW3Lt3j2+//Ta7syMiIpJpv/zyCxs3bgSgTZs2jB07NsUe4s899xyfffYZkDQN1NSpUx2ST0mZt7c3b7zxBpA0SuDEiRPZlpddu3axb98+ChYsSEBAQLblIyOcnJzo0qULAFOnTiUhISF7MyQiIiJpoljo4VAsJJJ5aqAUkRzl+vXrAMTGxqa6b6FChXj55ZepUaMGJUqUyNK00itv3rz069cPgDt37vDTTz+lO42oqCiWLFkCJI3QsMXX1xdfX182bdpEZGQkM2bMoGXLllSrVo06derQpUsXgoODjf1DQ0P5/PPPeemll6hatSr16tVj0KBB/PXXX3bzsXnzZgYMGECDBg2oWrUqfn5+tG7dmnHjxnH+/PkU30P79u0B+O9//2tMHyIiIvKomj17NpDUg3zIkCFpOqZNmzaULVuW4sWLYzKZSExMTLbPvXv3WLZsGZ07d6ZOnTrGPbpnz56sWbPGbmVHp06d8PX1ZezYsVy8eJFu3bpRvXp1/Pz8aNeuHWfPnuXKlStGvHD8+HHWrFlD8+bNqVq1Kv7+/nz00UdWaSYmJvLTTz/Rq1cv6tWrR9WqValbty7du3dn7dq1NvNvKTY2lsDAQLp06ULDhg2N99KnTx82b95sM/8hISFAUrxgzuuVK1fS9PlmlGW8d+PGjSx9rZSYz6m2bdsaneUsmT+j8ePHYzKZWLVqFW+99RZ+fn7UqFGDgIAAli5dinnllri4OObMmUPr1q2pVq0aNWvWpFOnTmzdutVuHk6ePMnIkSNp3rw5zz33HNWrV6dJkyYMGTLEGC1sT4sWLfDy8uL8+fNG472IiEhOlda4aPr06fj6+tKqVSuioqIYOXIktWvXpnr16rRo0YL169dbpXv9+nWmTJlCQEAAzz//PP/617946aWXGDJkCL///rvd/JjzsmHDBoKDg3nttdeoWrUqL7zwAj169CA+Pj7LPgvFQv9QLCSSfTTFq4jkKKVLl+bs2bNs376dPXv2ULdu3RT3nzx5skPSyoiXX34Zd3d34uLi2LlzJx07dkzX8WvXriUqKgpPT08aNmyY4r7Xrl0jICCAS5cuGdvu3bvHnj172Lt3L1988QXly5enV69e3Llzx9jn1q1bbNiwge3bt7N8+XKeeeYZq3SHDx/O6tWrrbbFx8cTERHBmTNn+P777xk/frzdXm41a9akRIkS/P3336xcuZIBAwak6zMQERHJKc6dO8epU6cAaNq0Kd7e3mk6zsnJidWrVxvTZj3o1KlTDBw4kIsXL1ptv3XrFsHBwQQHB7N06VJmzpxp9zVv3LjBO++8Y3TOArh8+TJPPvmk1bY1a9awdOlSq+Ms83Xr1i369etnrM1tdvv2bXbu3MnOnTv54YcfmDlzJkWKFEmWjwsXLjBgwADOnDmT7L1s3bqVrVu38tprr/HFF1+ke22ih82yc9YTTzyRLXk4d+6csZ5WixYtUtz3/v379OnTh23btlltP3HiBCdOnODPP/9k6NChdO3a1ThPzfbv38/+/fsZM2YMb731ltVzgYGBjBgxIlkj+JUrV7hy5Qrr1q2jffv2jBs3zma+8ubNS+PGjVm3bh3ff/99qu9DREQkp0gtLoKkdRv79+9v1Uh1/vx5nnzySeP/9evXM2LEiGRrN4aEhBASEsK6det4/fXXGTNmDO7u7jbzsnfvXlauXGncj8PCwnB1dcXNzS3T79MexUJJFAuJZC+NoBSRHKVdu3ZAUiNYt27d6NmzJ6tWreLq1avZmlZGeHp6UqFCBSD9i7jDP1PN1qtXz24QazZp0iRCQkLo06eP0fPu888/x8PDA5PJxJdffkmfPn1wd3dn4sSJ7Nixg23bttGvXz+cnJyIiYlJ1kC7fv16o3HyrbfeIjAwkD179rB582a+/PJLihUrRkJCAqNHj7aq/HxQo0aNrN6PiIjIo8iy9/sLL7yQrmPtNU6GhITQvXt3Ll68iJubG7179yYoKIh9+/axevVqY+qt48eP061bt2QVX2YbN24kLCyMsWPHsmvXLgIDAxk7dmyy+GHp0qVUqlSJ5cuXs2vXLqZNm2Z0oIqNjaVHjx4cPnwYV1dXevbsyU8//cT+/fv5+eefee+993B3d+fQoUP06dOHuLg4q7SjoqLo1q0bZ86cwc3NjV69erFu3Tr27NnD999/j7+/P5DUAcvcU37u3LkcOnSIUqVKAdC6dWsOHTrEoUOH8PHxSddnnB7nzp3jv//9LwDFixenWrVqWfZaKVm3bh2QNJNHanlYvXo127Zto3nz5vz444/s3r2b+fPnG59TYGAgHTt25Ny5c7z//vv89ttv7N69m0mTJuHl5QXA119/bTWzyNWrVxk1ahQJCQnUqVOHRYsWERwczM6dO5k3bx5Vq1YF4McffzSWTrClcePGABw4cEAzZoiIyCMjpbjI7Pz58+zevZuOHTuydetWfvvtN0aPHm3cI7dt28aQIUOIioqiWLFijB49mi1btrBnzx4WLlxI7dq1gX8awexZvnw5TzzxBPPmzWP37t3MmzePvn37Ztl7VyyURLGQSPbTCEoRyVE6d+7MwYMH+e2330hMTDRGDgCULFmS559/Hj8/P+rXr2+s8eiItDKqVKlSnDx5kvDwcO7fv4+ra9ouu3fv3jVGL9SoUSPV/WNjYxk7dqzVVLBvvPEG//vf/1iwYAF37twhb968rF271uq9Dhw4kLNnz/Lrr7+yb98+4uPjjR56GzZsAKB27dqMGTPGOKZIkSKULl2aJ598kg4dOnDv3j02bdpEhw4dbOatRo0arFixggsXLnDp0iWrnoYiIiKPigsXLhiPy5Ur91DS/Prrr7l58ybOzs7MnDnT6NQDSRU1VatW5emnn2b8+PGcPXuW7777jg8//NBmWv369TPigGLFilGlSpVk+7i6ujJr1ixKly4NQPPmzY3nFixYwIkTJ3BycmLGjBm8+OKLxnMFCxZk0KBBVKtWjd69e3P06FFWrFhBp06djH2+/fZbY6rWr7/+mmbNmhnPFSlShO+++45OnTpx6NAhFixYQPfu3fHw8ACSRpma85cvX770fYgWEhISbDbimkwmoqOj+fvvv9m1axcLFy4kOjoaSJotIrWOYFll+/btAFSrVg1n55T7DsfGxtKqVSurDmX+/v589tln9O7dG0iqRP3qq69o3bq1sU+bNm24e/cun3/+OeHh4Rw/fhw/Pz8Atm7dSlxcHHnz5mXOnDlGeUBSZWX16tVp2rQpd+7cISgoiKZNm9rMmzlWNZlMBAcHG1P8i4iIZAV7HbZs8fDwsDtrQ0pxkaV69eoZa4oDRp1GXFwcY8eOJSEhgWLFivHjjz8ana7Mx9WpU4cPPviAX3/9lbVr19KmTRuj09aDJk+eTM2aNQFo0KBBmt+jJcVCioVEHjVqoBSRHMXFxYVp06bx/fffM3PmTG7fvm08d+3aNX7++Wd+/vlnACpVqkSvXr2sAo+sSiujPD09gaQg5c6dOxQrVixNxx09etRY48nX1zfV/YsVK2YzAKpduzYLFiwAoFmzZjYbYmvUqMGvv/5KXFwcYWFhxtQe5pERd+/etWq4NKtZsybffvst3t7elC1b1m7eKlWqZDw+fPiwGihFROSRFBERYTy2Nb1peoWHh/PLL78A0KpVK6vGSUudO3dm/fr1HD58mOXLlzNo0CCbFW2tWrVK9TWrV69uVMI9aNmyZUD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" ] }, "metadata": { "image/png": { "height": 289, "width": 916 } }, "output_type": "display_data" } ], "source": [ "# build a model with no conditional dependencies (a \"flat\" model)\n", "model = build.Model(data=data, kind='xdpm')\n", "model.set_basinparams(method='basin', nsamples=1000)\n", "\n", "# NOTE: fits in the binder demo will be much slower than when run locally\n", "# set_testing_params() sets more liberal fit criteria to speed things up a bit\n", "# comment this line out to run fits using the default optimization criteria\n", "model.set_testing_params()\n", "\n", "model.optimize(progress=True, plotfits=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Accessing model fit information\n", "\n", "### Parameter estimates\n", "* optimized parameter estimates are stored in the **poptdf** attribute\n", "\n", "```python\n", "# display the model's poptdf\n", "model.poptdf\n", "```\n", "\n", "### Goodness-Of-Fit (GOF) stats\n", "* critical information about the model fit is contained in the models **fitdf** attribute (pandas DataFrame). \n", "* **fitdf** includes optimized parameter estimates as well as goodness-of-fit (GOF) statistics like AIC, BIC, standard (chi) and reduced (rchi) chi-square\n", "\n", "```python\n", "# display the model's fitdf\n", "model.fitdf\n", "```\n", "\n", "### model predictions\n", "* optimize also generates a **yhatdf** attribute, a pandas DataFrame containing the model-predicted stop-accuracy and correct/error RT quantiles (same column structure as model's observedDF)\n", "\n", "```python\n", "# display the model's yhatdf\n", "model.yhatdf\n", "```\n", "\n", "### save results post-optimization\n", "\n", "* if you set saveresults to False when running optimize and later decide to save output dataframes\n", "\n", "```python\n", "# to save output dataframes\n", "model.poptdf.to_csv(\"path/to/save/poptdf.csv\", index=False)\n", "model.fitdf.to_csv(\"path/to/save/fitdf.csv\", index=False)\n", "model.yhatdf.to_csv(\"path/to/save/yhatdf.csv\", index=False)\n", "```" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " idx flat pvary acc 200 250 300 350 400 c10 ... \\\n", "0 avg flat all 0.9811 1.0 0.979 0.948 0.574 0.093 0.519 ... \n", "\n", " c90 e10 e20 e30 e40 e50 e60 e70 e80 e90 \n", "0 0.612 0.519 0.531 0.54 0.546 0.558 0.564 0.573 0.585 0.603 \n", "\n", "[1 rows x 27 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.yhatdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting Conditional Models\n", "\n", "- Conditional models can be fit in which all parameters from **flat** model fit are held constant except for one or more designated **conditional** parameters which is free to vary across levels of an experimental condition of interest. \n", "\n", "### Steps in fitting routine: \n", "\n", "1. Global optimzation on flat data (average values collapsing across experimental conditions)\n", "2. Local optimzation using parameters passed from global optimizer as starting values \n", "3. Global optimzation of conditional parameters \n", "4. Local optimzation of conditional parameters passed from global optimizer\n", "\n", "\n", "- Conditional model fits are performed by holding all parameters constant except one or more **conditional** parameter(s) and minimizing following cost-function: \n", "\n", "$$\\chi^2 = \\sum_{i=0}^{N_c} [\\omega_i * (\\hat{Y_i} - Y_i)]^2$$\n", " \n", "- where $\\sum[\\omega_i*(\\hat{Y_i} - Y_i)]^2$ gives the cost ($\\chi^2$) for level $i$ of condition $C$\n", "- $\\chi^2$ is equal to the summed and squared error across all $N_c$ levels of that condition\n", "\n", "- Specifying parameter dependencies is done by providing the model with a **depends_on** dictionary when initializing the model with the format: **{parameter_id : condition_name}**.\n", "- For instance, in Dunovan et al., (2015) subjects performed two versions of a stop-signal task \n", " * **Baseline (\"bsl\")** condition: errors on **go** and **stop** trials are equally penalized \n", " * **Caution (\"pnl\")** condition: penalties 2x higher for **stop** trial errors (e.g., response=1)\n", "- To test the hypothesis that observed behavioral differences between penalty conditions was a result of a change Go drift-rate...\n", "\n", "``` py\n", "# define the model allowing Go drift-rate to vary across 'Cond'\n", "model_v = build.Model(kind='xdpm', depends_on={'v': 'Cond'})\n", "# run optimize to fit the full model (steps 1 & 2)\n", "model_v.optimize(progress=True)\n", "```\n", "\n", "\n", "### Typically...\n", "\n", "* ...you'll have multiple alternative hypotheses about which parameters will depend on various task conditions\n", "\n", "* For instance, instead of modulating the Go drift-rate, assymetric stop/go penalties might alter behavior by changing the height of the decision threshold **(a)**. \n", "\n", "* To implement this model:\n", "\n", "``` py\n", "# define the model allowing threshold to vary across 'Cond'\n", "model_a = build.Model(kind='xdpm', depends_on={'a': 'Cond'})\n", "# run optimize to fit the full model (steps 1 & 2)\n", "model_a.optimize(progress=True)\n", "```\n", "\n", "### compare model fits\n", "\n", "* To test the hypothesis: threshold (a) better than drift-rate (v)\n", "\n", "```python\n", "# If True, threshold model provides a better fit\n", "model_a.finfo['AIC'] < model_v.finfo['AIC']\n", "```\n", "\n", "### How to access conditional weights ($\\omega_i$) and data ($Y_i$) vectors\n", "```python\n", "# replace 'Cond' with whatever your condition is called in the input dataframe\n", "cond_data = model.observedDF.groupby('Cond').mean()\n", "cond_wts = model.wtsDF.groupby('Cond').mean() \n", "```" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, max=2)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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\n", 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" ] }, "metadata": { "image/png": { "height": 289, "width": 916 } }, "output_type": "display_data" } ], "source": [ "# build conditional model and optimize with drift-rate free across levels of Cond\n", "model = build.Model(data=data, kind='xdpm', depends_on={'v':'Cond'})\n", "model.set_basinparams(method='basin', nsamples=1000)\n", "\n", "# NOTE: fits in the binder demo will be much slower than when run locally\n", "# uncomment line below to speed up the demo fits (at the expense of fit quality)\n", "model.set_testing_params()\n", "\n", "model.optimize(progress=True, plotfits=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idxpvarynvaryAICBICnfevdfndatachirchilogpcnvrgniter
0avgv2.0-457.9562-454.2138689.046.048.00.00326.8991e-05-461.9562True5.0
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" ], "text/plain": [ " idx pvary nvary AIC BIC nfev df ndata chi \\\n", "0 avg v 2.0 -457.9562 -454.2138 689.0 46.0 48.0 0.0032 \n", "\n", " rchi logp cnvrg niter \n", "0 6.8991e-05 -461.9562 True 5.0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fitdf" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idxCondpvaryassvtrvxb
0avgbslv0.4075-0.78440.21231.13950.7191
1avgpnlv0.4075-0.78440.21231.10290.7191
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" ], "text/plain": [ " idx Cond pvary a ssv tr v xb\n", "0 avg bsl v 0.4075 -0.7844 0.2123 1.1395 0.7191\n", "1 avg pnl v 0.4075 -0.7844 0.2123 1.1029 0.7191" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.poptdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Nested optimization of alternative models\n", "\n", "* Typically you'll have multiple competing hypotheses about which parameters are influenced by various task conditions\n", "\n", "* Nested optimization allows alternative models to be optimized using a single initial parameter set.\n", "\n", "```python \n", "model = build.Model(kind='xdpm', data=data)\n", "freeparams = ['v', 'a', 'ssv']\n", "depends = [{pname: 'Cond'} for pname in freeparams] \n", "model.nested_optimize(depends=depends)\n", "```\n", "\n", "* After fitting the model with **depends_on={'v': 'Cond'}**, **'v'** is replaced by the next parameter in the **nested_models** list **('a' or boundary height in this case)** and the model is optimized with this dependency using the same init params as the original model\n", "\n", "* As a result, model selection is less likely to be biased by the initial parameter set\n", "\n", "* Also, because Step 1 takes significantly longer than Step 2, nested optimization of multiple models is significantly faster than optimizing each model individually through boths steps" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = build.Model(kind='dpm', data=data)\n", "model.set_basinparams(method='basin', nsamples=1000)\n", "\n", "# NOTE: fits in the binder demo will be much slower than when run locally\n", "# uncomment line below to speed up the demo fits (at the expense of fit quality)\n", "model.set_testing_params()\n", "\n", "freeparams = ['v', 'a', 'ssv']\n", "depends = [{pname: 'Cond'} for pname in freeparams]\n", "fitdf, poptdf, yhatdf = model.nested_optimize(depends=depends, progress=True, plotfits=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examine Nested Model Fits" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelIDidxpvarynvaryAICBICnfevdfndatachirchilogpcnvrgniter
0vavgv2.0-459.4273-455.6849200.046.048.00.00316.6909e-05-463.4273True2.0
1aavga2.0-405.0658-401.3234260.046.048.00.00962.0765e-04-409.0658True2.0
2ssvavgssv2.0-422.5899-418.8475384.046.048.00.00661.4414e-04-426.5899True4.0
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" ], "text/plain": [ " modelID idx pvary nvary AIC BIC nfev df ndata chi \\\n", "0 v avg v 2.0 -459.4273 -455.6849 200.0 46.0 48.0 0.0031 \n", "1 a avg a 2.0 -405.0658 -401.3234 260.0 46.0 48.0 0.0096 \n", "2 ssv avg ssv 2.0 -422.5899 -418.8475 384.0 46.0 48.0 0.0066 \n", "\n", " rchi logp cnvrg niter \n", "0 6.6909e-05 -463.4273 True 2.0 \n", "1 2.0765e-04 -409.0658 True 2.0 \n", "2 1.4414e-04 -426.5899 True 4.0 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compare GOF stats for the three models\n", "fitdf" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelIDidxCondpvaryassvtrv
0vavgbslv0.4075-0.79800.21481.1820
1vavgpnlv0.4075-0.79800.21481.1533
2aavgbsla0.4036-0.79800.21481.1689
3aavgpnla0.4075-0.79800.21481.1689
4ssvavgbslssv0.4075-0.76540.21481.1689
5ssvavgpnlssv0.4075-0.82920.21481.1689
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" ], "text/plain": [ " modelID idx Cond pvary a ssv tr v\n", "0 v avg bsl v 0.4075 -0.7980 0.2148 1.1820\n", "1 v avg pnl v 0.4075 -0.7980 0.2148 1.1533\n", "2 a avg bsl a 0.4036 -0.7980 0.2148 1.1689\n", "3 a avg pnl a 0.4075 -0.7980 0.2148 1.1689\n", "4 ssv avg bsl ssv 0.4075 -0.7654 0.2148 1.1689\n", "5 ssv avg pnl ssv 0.4075 -0.8292 0.2148 1.1689" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compare param estimates for the three models\n", "poptdf" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AIC likes v model\n", "BIC likes v model\n" ] }, { "data": { "image/png": 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9gM+qL37xi9lvv/2SJKNHj67fZbehN954I3/605+SJKeddlr958ccc0w6dOiQmpqa3HvvvZvMmzNnTp599tkkyXnnnbcrlk8TVFtbm8svv7w+xJ188sm5/fbbM2nSpEybNi1vvvlm5syZkzlz5qRnz55JkiOPPDIlJf4YAwAAwGeFv8Vvp9LS0lxzzTVJkldeeSVDhgzJ/Pnzk6y/7fXJJ5/MZZddltra2vTu3Ttnnnlm/dySkpJcffXVSZIxY8bkrrvuqr/99ZVXXsmgQYNSW1ubE044Iccee2zBV0Zj+c1vfpPp06cnSX7wgx/knnvuyVlnnZXDDz88rVu3TvPmzZMkH374Yd56660kyRe+8IVGWy8AAADw6e3Rt6nuqHPOOSd//etf8/Of/zyTJ0/O5MmTs88++2TNmjX55JNPkqyPJT//+c/rQ0qdgQMHZvr06Rk/fnx++tOfZvTo0SkrK8tHH32UJDn00EPz05/+tPBrovE888wzSdY/A+6iiy5qcNyECRNSW1ubJOnRo0chawMAAAB2DjFuBw0bNiwnnXRSxo0bl2nTpmXx4sXZa6+9ctRRR6V///75p3/6p5SVlW127o9+9KOceOKJ+eUvf5k333wzq1evTpcuXfK1r30tgwYNSps2bQq+GhrT+++/nyTZd999Gxzz3nvv5e67767/vZ1xAAAA8Nkixm3BFVdckSuuuGKr444++ugcffTR2/Udp59+ek4//fTtmsvupXXr1kmSefPmZeHChfUvCanzzjvv5PLLL8+KFSuSrL9VumvXroWvEwAAANh+nhkHTcTJJ5+cJFm9enW+/e1vZ+LEiZk5c2ZefPHFjBo1KgMGDMhHH31U//bUI444osFdlwAAAEDTZGccNBEXX3xxnnrqqbzxxhuZP39+rrvuuo2Od+vWLXfeeWfOPffcJG5RBQAAgM8iMQ6aiJYtW2bcuHEpLy/PpEmTsnDhwrRt2zZHHHFEzjjjjHzrW99KVVVVli1blkSMAwAAgM8iMY6dolP73ft/paKur1WrVrn22mtz7bXXbvZ4hw4dMmfOnELWAgAAAOx8u3dBoTBXXdDwG0ABAAAAWM8LHAAAAACgIGIcAAAAABREjAMAAACAgohxAAAAAFAQMQ4AAAAACiLGAQAAAEBBxDgAAAAAKIgYBwAAAAAFEeMAAAAAoCBiHAAAAAAURIwDAAAAgIKIcQAAAABQEDEOAAAAAAoixgEAAABAQcQ4AAAAACiIGAcAAAAABRHjAAAAAKAgYhwAAAAAFKRFYy+A3cOy/3tPat5b2NjL2GVadO6Qdtdc0djLAAAAAD7jxDh2ipr3FmbtosWNvYxdpqaxFwAAAADsFtymCgAAAAAFEeMAAAAAoCBiHDQh/fr1S/fu3XPRRRdtdWxVVVX69OmT7t275/vf/34BqwMAAAB2lBgHTUj37t2TJHPnzt3q2PLy8qxcuTJlZWW58sord/XSAAAAgJ1AjIMmpFu3bkmSpUuXpqqqqsFx7777bh555JEkyYUXXphOnToVsj4AAABgx4hx0IR07dq1/uct7Y674447smbNmrRr1y6DBw8uYmkAAADATiDGQRNStzMuSSoqKjY7Zvbs2XniiSeSJIMGDUq7du0KWRsAAACw48Q4aEKOOOKIlJSs/2M5b968zY657bbbsnbt2nTq1CkXXnhhkcsDAAAAdlCLxl4A8D9atmyZgw8+OJWVlZu9TfXll1/O888/nyS58sors9dee20y5uyzz87MmTM3+qysrCzt27fPCSeckCFDhqRz58675gIAAACALRLjoInp1q1bKisrN7sz7vbbb0+y/q2rAwYM2OR4TU1N3n777bRt2zYXX3xx/efLli3LlClT8uijj+bPf/5zJk2alBYt/PEHAACAovnbODQx3bp1yx//+McsWbIkS5cuzb777pskmTRpUv2OtxEjRtTfzrqhioqKfPzxxzn++OMzbNiwjY5VV1fnq1/9ahYsWJCKiooceeSRu/5iAAAAgI14Zhw0MZt7iUNNTU3uvPPOJMnxxx+fk046abNz33jjjSRJ7969NznWqlWrtG3bNknSpk2bnbpmAAAAYNuIcdDEbC7GjR8/PpWVlWnWrFlGjBjR4NxZs2YlSXr16rXJsaeffjqVlZXp16+fZ8YBAABAI3GbKjQxhxxySFq2bJnVq1enoqIi1dXVKS8vT5L0798/PXv2bHBuXYx7/vnnM2PGjCTJihUrMnv27Lz66qs5/fTT88Mf/nDXXwQAAACwWWIcNDElJSU5/PDDM2vWrFRUVGTs2LFZtGhRSktLc9VVVzU4r6amJnPmzEmSPPjgg5sc79OnTy677LK0a9dul60dAAAA2DK3qUITVHer6uzZszN27Ngkyfnnn5+DDjqowTkVFRVZvXp1vvrVr2bOnDn1v1555ZXcdtttmTFjRi666KJ88MEHhVwDAAAAsCkxDpqguhj34Ycf5qOPPkqbNm3y3e9+d4tz3nzzzSTZ5C2pn/vc5zJgwICcddZZqa6uztNPP71rFg0AAABslRgHTdCGL3FIkssvvzz77rvvFufUPS/ub2Ncnf322y9Jsnz58p2wQgAAAGB7eGYcO0WLzh1S09iL2IVadO5Q6Pf169ev/vlv22prMa5u59whhxyyY4sDAAAAtpsYx07R7porGnsJe7Ta2trMnj07rVu3zuc///lNjk+YMCEvvvhiOnbsmK985SuNsEIAAAAgEeNgtzBv3rysWrUq7du3z+jRo+s/r6qqysyZMzNz5szsv//+GT16dPbee+9GXCkAAADs2cQ42A3U3aK6aNGi+hjXrFmz7LPPPunSpUuGDRuWCy64YKvPnQMAAAB2LTEOdgMDBw7MwIEDG3sZAAAAwFZ4myoAAAAAFESMAwAAAICCiHEAAAAAUBAxDgAAAAAKIsYBAAAAQEHEOAAAAAAoiBgHAAAAAAUR4wAAAACgIGIcAAAAABREjAMAAACAgohxAAAAAFAQMQ4AAAAACiLGAQAAAEBBxDgAAAAAKIgYBwAAAAAFEeMAAAAAoCBiHAAAAAAURIwDAAAAgIKIcQAAAABQEDEOAAAAAAoixgEAAABAQcQ4AAAAACiIGAcAAAAABRHjAAAAAKAgYhwAAAAAFESMAwAAAICCiHEAAAAAUBAxDgAAAAAK0qKxF8CudeSRR270TwAAAAAaT7N169ata+xFAAAAAMCewG2qAAAAAFAQMQ4AAAAACiLGAQAAAEBBxDgAAAAAKIgYBwAAAAAFEeMAAAAAoCBiHAAAAAAURIwDAAAAgIKIcQAAAABQEDEOAAAAAAoixgEAAABAQf4fwbAW/UDlN6EAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "image/png": { "height": 414, "width": 625 } }, "output_type": "display_data" } ], "source": [ "# Evaluate all nested fits and plot fit stats: AIC & BIC (Lower is better)\n", "# According to AIC & BIC, the model with execution drift-rate (v_E) free across levels of Cond \n", "# provides a better fit than models with free threshold (a) or braking drift-rate (v_B)\n", "gof = vis.compare_nested_models(fitdf, verbose=True, model_ids=freeparams)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Troubleshooting Ugly Fits\n", "\n", "## Fit to individual subjects\n", "```python\n", "model = build.Model(data=data, fit_on='subjects')\n", "```\n", "## Other \"kinds\" of models...\n", "\n", "\n", "* Currently only Dependent Process Model **(kind='dpm')** and Independent Race Model **(kind='irace')**\n", "\n", "\n", "* Tell model to include a Dynamic Bias Signal **('xb')** by adding an **'x'** to the front of model **kind**\n", "\n", "\n", "* To implement the **Dependent Process Model**...\n", "\n", "```python\n", "#... with dynamic bias: \n", "model = build.Model(data=data, kind='xdpm')\n", "#...and without: \n", "model = build.Model(data=data, kind='dpm')\n", "```\n", "\n", "\n", "* To implement the **Independent Race Model**... \n", "\n", "\n", "```python\n", "#... with dynamic bias:\n", "model = build.Model(data=data, kind='xirace')\n", "#... and without:\n", "model = build.Model(data=data, kind='irace')\n", "```\n", "\n", "\n", "\n", "## Optimization parameters and cost weights...\n", "\n", "* set more conservative fit criteria\n", "\n", "```python\n", "model.set_basinparams(nsuccess=100, tol=1e-30, ninits=10, nsamples=10000) \n", "model.set_fitparams(maxfev=5000, tol=1e-35)\n", "```\n", "\n", "* Inspect cost function weights for extreme vals\n", "\n", "```python\n", "print(model.cond_wts)\n", "print(model.flat_wts)\n", "```\n", "* If the wts look suspect try re-running the fits with an unweighted model (all wts = 1) \n", "\n", "\n", "```python\n", " model = build.Model(data=data, weighted=False)\n", "```\n", "\n", "* Keep in mind that error RTs can be particularly troublesome, sometimes un-shootably so..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import radd\n", "radd.style_notebook()" ] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "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": "3.6.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 1 }