{ "cells": [ { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt, seaborn as sn\n", "import pandas as pd, numpy as np\n", "sn.set_context('notebook')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generalised Likelihood Uncertainty Estimation (GLUE)\n", "\n", "GLUE is a framework for model calibration and uncertainty estimation that has become popular in recent years, especially within the UK hydrological community. The approach is well documented in the academic literature ([Beven, 2006](http://www.sciencedirect.com/science/article/pii/S002216940500332X), for example, provides a comprehensive overview) but it is also controversial, in the sense that many authors consider the method to be both [statistically incoherent](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract) and [computationally inefficient](http://www.sciencedirect.com/science/article/pii/S0309170807001856). \n", "\n", "The more I learn, the more I'm inclined to agree with those who feel GLUE is **not** an appropriate tool for model calibration and uncertainty estimation. For anyone who has yet to make a decision, I strongly recommend reading the literature on the subject, including the exchanges between leading proponents on both sides of the argument. For example:\n", "\n", " * [Mantovan & Todino (2006)](http://www.sciencedirect.com/science/article/pii/S0022169406002162) then [Beven *et al.* (2007)](http://www.sciencedirect.com/science/article/pii/S0022169407001230) then [Mantovan & Todino (2007)](http://www.sciencedirect.com/science/article/pii/S0022169407001242)

\n", " \n", " * [Clark *et al.* (2011)](http://onlinelibrary.wiley.com/doi/10.1029/2010WR009827/abstract) then [Beven *et al.* (2012)](http://onlinelibrary.wiley.com/doi/10.1029/2012WR012282/abstract) then [Clark *et al.* (2012)](http://onlinelibrary.wiley.com/doi/10.1029/2012WR012547/abstract)\n", "\n", "Two of the reasons GLUE has become so popular are that it is **conceptually simple** and **easy to code**. Such advantages are not easily ignored, especially among environmental scientists who are typically neither professional statisticians nor computer programmers. Although most would-be modellers are aware of some debate in the literature, many lack the statistical background to be able to follow the arguments in detail. What's more, many understandably take the view that, if the issue is still a matter for discussion between statisticians, either method will probably be adequate for a first foray into environmental modelling.\n", "\n", "The aim of this notebook is to provide an introduction to some of the key issues, and to make it easier to follow the more detailed assessments in the academic literature. We will begin by comparing the frequentist, Bayesian and GLUE approaches to **simple linear regression**. \n", "\n", "I will assume familiarity with frequentist **Ordinary Least Squares (OLS)** regression, and if you've worked through the previous notebooks you should also have a basic understanding of formal Bayesian inference and the differences between e.g. Monte Carlo and MCMC sampling. I'll try to provide a reasonable overview of GLUE, but if you're not familar with the technique already I'd recommend reading e.g. [Beven (2006)](http://www.sciencedirect.com/science/article/pii/S002216940500332X) for a more complete summary.\n", "\n", "A much more comprehensive and detailed investigation of the limitations of GLUE is provided by [Stedinger *et al.* (2008)](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract). \n", "\n", "## Three approaches compared\n", "\n", "We will consider the following:\n", "\n", " 1. **Frequentist OLS regression**. This is just the usual approach to linear regression that most people are familiar with.

\n", " \n", " 2. **Bayesian MCMC**. A formal Bayesian approach, exactly the same as introduced in [section 7 of notebook 4](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/04_MCMC.ipynb#7.-Putting-it-all-together).

\n", " \n", " 3. **Monte Carlo GLUE**. A \"limits of acceptability\" approach using an *informal* (or *pseudo-*) likelihood function. The most common implementation of GLUE uses Monte Carlo sampling, similar to some of the techniques described in [notebook 3](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/03_Monte_Carlo.ipynb).\n", " \n", "It's worth emphasising straight away that using **numerical simulation approaches** such as Bayesian MCMC or GLUE to solve a simple linear regression problem is a case of using a very large sledgehammer to crack a very small nut. It is extremely unlikey that you would ever use either of these techniques for this kind of analsis in practice. However, if an approach is going to generalise well to more complex problems, it's often a good idea to check it works for simple problems too. \n", "\n", "Simple linear regression is just a basic form of parameter inference: we want to infer the **slope** and **intercept** of our regression line, subject to a particular error model. The simplest form of linear regression assumes **independent and identically distributed** Guassian erros with mean zero.\n", "\n", "$$y = ax + b + \\epsilon \\qquad where \\qquad \\epsilon \\sim \\mathcal N(0, \\sigma_\\epsilon)$$\n", "\n", "We will start by generating some synthetic data based on the equation above and we'll then use the three methods to estimate the **regression parameters** and associated **confidence intervals**. The reason for doing this is to **check that the two more complicated approaches gives results that are broadly consistent with the simple frequentist method** (which is very well established)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Generate synthetic data" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3Fe2dwP7KlRk6NIXVq//HtddepyAuUU8ZuYhEpH2LlpB7UQNG7/iFpwAHuOWW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\nHk+xGbpOjAoO9bP71MfuK+3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Choose true params\n", "a_true = 3\n", "b_true = 6\n", "sigma_true = 2\n", "n = 100 # Length of data series\n", "\n", "# For the independent variable, x, we will choose n values equally spaced\n", "# between 0 and 10\n", "x = np.linspace(0, 10, n)\n", "\n", "# Calculate the dependent (observed) values, y\n", "y = a_true*x + b_true + np.random.normal(loc=0, scale=sigma_true, size=n)\n", "\n", "# Plot\n", "plt.plot(x, y, 'ro')\n", "plt.plot(x, a_true*x + b_true, 'k-')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.title('Observed data')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Frequentist linear regression\n", "\n", "There are [several ways](https://en.wikipedia.org/wiki/Linear_regression#Least-squares_estimation_and_related_techniques) of performing simple linear regression, but the most commonly used is the OLS approach, which minimises the sum of squared model residuals. OLS regrssion under the assumption of **independent and identically distributed (iid) Gaussian errors** is so widely used that many software packages make the analysis very easy - so easy, in fact, that people often forget to **check** whether the iid assumption has actually been satisfied. In the examples below we won't check either, but that's because we *know* our test data was generated using iid errors, so we don't need to.\n", "\n", "### 2.1. Fit the model\n", "\n", "We'll use `statsmodels` to perform the regression in Python, including estimating 95% **confidence intervals** for the slope and intercept ($a$ and $b$, respectively). We must also estimate the error standard deviation , $\\sigma_\\epsilon$ (we'll ignore the confidence interval for this for now, because it's not provided by `statsmodels` by default)." ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.955\n", "Model: OLS Adj. R-squared: 0.954\n", "Method: Least Squares F-statistic: 2066.\n", "Date: Sat, 07 Nov 2015 Prob (F-statistic): 1.14e-67\n", "Time: 20:14:14 Log-Likelihood: -206.23\n", "No. Observations: 100 AIC: 416.5\n", "Df Residuals: 98 BIC: 421.7\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 6.1723 0.382 16.176 0.000 5.415 6.929\n", "x1 2.9965 0.066 45.454 0.000 2.866 3.127\n", "==============================================================================\n", "Omnibus: 2.018 Durbin-Watson: 1.824\n", "Prob(Omnibus): 0.365 Jarque-Bera (JB): 1.445\n", "Skew: -0.251 Prob(JB): 0.486\n", "Kurtosis: 3.309 Cond. No. 11.7\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\n", "\n", " 2.5% 50% 97.5%\n", "a_freq 2.865716 2.996540 3.127364\n", "b_freq 5.415049 6.172266 6.929484\n", "sigma_freq NaN 1.922188 NaN\n" ] } ], "source": [ "import statsmodels.api as sm\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "# Add intercept for model\n", "X = sm.add_constant(x)\n", "\n", "# Fit\n", "model = sm.OLS(y,X)\n", "result = model.fit()\n", "\n", "# Regression summary\n", "print result.summary()\n", "print '\\n'\n", "\n", "# Key results as dataframe\n", "freq_df = pd.DataFrame(data={'a_freq':[result.conf_int()[1,0], \n", " result.params[1], \n", " result.conf_int()[1,1]],\n", " 'b_freq':[result.conf_int()[0,0], \n", " result.params[0], \n", " result.conf_int()[0,1]],\n", " 'sigma_freq':[np.nan, \n", " (result.scale)**0.5, \n", " np.nan]},\n", " index=['2.5%', '50%', '97.5%'])\n", "print freq_df.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2. Plot the result\n", "\n", "We can now plot the **median** regression line plus the **95% confidence interval** around it." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GBjhe72v3vNdttJuTbnksXLKZ61Al3jeXk7b6TSw6zwcPTW8izWsHnI7D3NFs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\n5jpUEZGMGbPLzyJv7qTglVeI7t8HhcUZP366jnr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot predicted\n", "prstd, low, up = wls_prediction_std(result, alpha=0.05) # 95% interval\n", "plt.fill_between(x, low, up, color='r', alpha=0.3)\n", "plt.plot(x, result.fittedvalues, 'r-', label='Estimated')\n", "plt.title('Frequentist')\n", "\n", "# Plot true\n", "plt.plot(x, y, 'bo')\n", "plt.plot(x, a_true*x+b_true, 'b--', label='True')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The estimated \"best-fit\" line is very close to the true one. Also, if our 95% confidence interval is correct, we should expect roughly 95% of the observations to lie within the shaded area. This proportion is often called the \"**coverage**\".\n", "\n", "### 2.3. Estimate coverage" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coverage: 95.0%\n" ] } ], "source": [ "# Data frame of lower CI, upper CI and observations\n", "cov_df = pd.DataFrame({'low':low,\n", " 'obs':y,\n", " 'up':up})\n", "\n", "# Are obs within CI?\n", "cov_df['In_CI'] = ((cov_df['low'] < cov_df['obs']) & \n", " (cov_df['up'] > cov_df['obs']))\n", "\n", "# Coverage\n", "cov = 100.*cov_df['In_CI'].sum()/len(cov_df)\n", "\n", "print 'Coverage: %.1f%%' % cov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coverage from the frequentist approach is correct, as expected.\n", "\n", "## 3. Bayesian linear regression\n", "\n", "For this problem, the Bayesian approach is significantly more complicated than the frequentist one. One of the real benefits of the Bayesian method, though, is its generality i.e. it doesn't necessarily become any more complicated when applied to challenging problems. As demonstrated in notebooks [4](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/04_MCMC.ipynb) and [6](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/06_Beyond_Metropolis.ipynb), the Bayesian approach is essentially the same regardless of whether you're performing simple linear regression or calibrating a hydrological model. It's worth bearing this in mind when working though the following sections.\n", "\n", "### 3.1. Define the likelihood, prior and posterior\n", "\n", "The likelihood, prior and posterior are defined in exactly the same way as in [section 7 of notebook 4](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/04_MCMC.ipynb#7.-Putting-it-all-together). Note that for the likelihood function we're required to explicitly define an **error structure**. This was not necessary for the frequentist approach above because `statsmodels.api.OLS` implicitly assumes iid Gaussian errors. For more complex error schemes, we'd need to specify the error struture for the frequentist analysis too." ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import norm\n", "\n", "def log_likelihood(params, x, obs):\n", " \"\"\" Calculate log likelihood assuming iid Gaussian errors.\n", " \"\"\" \n", " # Extract parameter values\n", " a_est, b_est, sigma_est = params\n", " \n", " # Calculate deterministic results with these parameters\n", " sim = a_est*x + b_est\n", " \n", " # Calculate log likelihood\n", " ll = np.sum(norm(sim, sigma_est).logpdf(obs))\n", " \n", " return ll\n", "\n", "def log_prior(params):\n", " \"\"\" Calculate log prior.\n", " \"\"\" \n", " # Extract parameter values\n", " a_est, b_est, sigma_est = params\n", " \n", " # If all parameters are within allowed ranges, return a constant \n", " # (anything will do - I've used 0 here)\n", " if ((a_min <= a_est < a_max) and \n", " (b_min <= b_est < b_max) and \n", " (sigma_min <= sigma_est < sigma_max)):\n", " return 0\n", " # Else the parameter set is invalid (probability = 0; log prob = -inf)\n", " else:\n", " return -np.inf\n", "\n", "def log_posterior(params, x, obs):\n", " \"\"\" Calculate log posterior.\n", " \"\"\" \n", " # Get log prior prob\n", " log_pri = log_prior(params)\n", "\n", " # Evaluate log likelihood if necessary\n", " if np.isfinite(log_pri):\n", " log_like = log_likelihood(params, x, obs)\n", " \n", " # Calculate log posterior\n", " return log_pri + log_like\n", " \n", " else:\n", " # Log prior is -inf, so log posterior is -inf too\n", " return -np.inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2. Define limits for uniform priors\n", "\n", "In the `log_prior` function above we've assumed **[improper uniform priors](https://en.wikipedia.org/wiki/Prior_probability#Improper_priors)**, just as we have in all the previous notebooks. Below we set allowable prior ranges for $a$, $b$ and $\\sigma_\\epsilon$." ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a_min, a_max = -10, 10\n", "b_min, b_max = -10, 10\n", "sigma_min, sigma_max = 0, 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3. Find the MAP\n", "\n", "The [MAP](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) is the maximum of the posterior distribution. It gives the most likely values for the model parameters ($a$, $b$ and $\\sigma_\\epsilon$) *given our piors and the data*. It also provides a good starting point for our MCMC analysis." ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 206.230122\n", " Iterations: 162\n", " Function evaluations: 295\n", "\n", "\n", "Estimated a: 3.00.\n", "Estimated b: 6.17.\n", "Estimated sigma: 1.90.\n" ] } ], "source": [ "from scipy import optimize\n", "\n", "def neg_log_posterior(params, x, obs):\n", " \"\"\" Negative of log posterior.\n", " \"\"\"\n", " return -log_posterior(params, x, obs)\n", "\n", "def find_map(init_guess, x, obs):\n", " \"\"\" Find max of posterior.\n", " init_guess [a, b, sigma]\n", " \"\"\"\n", " # Run optimiser\n", " param_est = optimize.fmin(neg_log_posterior, \n", " init_guess, \n", " args=(x, obs))\n", "\n", " return param_est\n", "\n", "# Guess some starting values for [a, b, sigma]\n", "param_guess = [1, 1, 1]\n", "\n", "# Run optimiser\n", "param_est = find_map(param_guess, x, y)\n", "\n", "# Print results\n", "print '\\n'\n", "for idx, param in enumerate(['a', 'b', 'sigma',]):\n", " print 'Estimated %s: %.2f.' % (param, param_est[idx])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's reassuring to see the MAP estimates are close to the true values. However, as we've discusssed previously, these numbers are't much use without an indication of **uncertainty** i.e. how well-constrained are these values, given our priors and the data? For a simple problem like this, there are much simpler ways of estimating uncertainty using a Bayesian approach than by running an MCMC analysis (see [notebook 8](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/08_Gaussian_Approx.ipynb), for example). Nevertheless, the MCMC approach is very general and we've used it a number of times previously, so for consistency we'll apply it here as well.\n", "\n", "### 3.4. Run the MCMC\n", "\n", "As before, we'll use [emcee](http://dan.iel.fm/emcee/current/) to draw samples from the posterior." ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Average acceptance fraction: 0.6815\n", "Autocorrelation time: [ 20.51629294 36.9195641 3.85606414]\n" ] }, { "data": { "image/png": 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tfyMcOzbz0ifmp6VcLtNsNlWrTTqdJpfLYrc7KBYL7O6GyOUyajCp4nZ5MFar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njwmFQsf6WavVwnvvfetQXdf1juXDwwe3FtHrkgqFAuVymUqlQk9PjzExI5VKGl3fBwcDuN1u\nVlZWiEQiRCLfb7ufHo93n9dzA2EKhfypBFh64KgHkiBDhGclk0kTj8fJ5XL4/YPnaqULCbCE6KBc\nThsq2VkUfBwWi4U7d+7y/vt/xNra6rkMsGZnVUKhED09PfteQE+SHmB5PB6sVit2u92o56nX68YQ\n0KuoXq8TjUZwOByMjY0f6WcjkTDptJa9O0wWKxwOYTabDjU5Y2urhs3P14vL5cJmsxGPx2k06rhc\nLuNm4/r1GywszNNsNnb9vv7+gX2HwbcWuvf17a4J67TNAvfNz81sNmOz2WSI8JTpGcNyuczGxvqR\nvwdnSQIsIQ5hfn6WaDTKW2+9s+8dVKcyWLru7m48Hh+pVOKVDwba0Yd/CoVCRwOscrnM/fv3uHRJ\naVuEvVUul8NsNhnDtm63m3g8ztLSIrOzKl/96tePNSHhNMRiMer1BsPDI0xPzxzpZxuNBul0mlKp\ndGCAlcvlyGQyDA4OGp3U97O1VYOeDfR4tJuKvr4+I+M2Pr4ZrLlcLm7evHWk97D1Z+H0ZhJu9sDa\n/rlp7/l0i+2/7LYuVbS4qNXxnZea1H0DLEVR/iLwl1r/dACvAQFVVTOt5/808D8ANeBXVVX9Zye3\nq0KcnbW1NUqlEslkct8Lei6Xw2q1tJ1uflw+n49kMkEmkz5XvbEymbQxC7LTLSdisSjpdJqFhblD\nBFgZXC63ERi7XFqAtbGxRqPRJJNJv7IBlt7Zf7/FwvdylCGtcFgbHjyouH0rp9NJIpEgndYai+oB\nbE9PrxFgHWYdzsPQf/dpFbrrx+vO48Jut5PJZKjValitp5OfyOVyPHr0gNHRMSYnp07lNV8legar\np6eXVCpJOBw60nF6lvYdzFRV9V+oqvqeqqrvAZ8Cf31LcNUF/BLwx4FvAD+vKMrRzwLiSyEcDvP+\n+z/i+9//Ht///vf44Q9/cOqdmY+rUCgYX/JoNLLndo1Gg0Ihj9vd2aEwn0/rBq9fyE5aOp3qSHuI\nWCxm/PfWu9BO0IelksnEvr+7UChQq9WN7ApsZhej26FSbgAAIABJREFU0Zixzauo2WwSjUbo7u4+\n1qLK+jDeYT77WCyOybS5Jma7fVlfX9tW4K1nd+LxGBaL2cgy6YXhNpttz5mBR2W1WrHZbKeWwSoU\nCphM7QKs06/DSiTiZLNZnj9/xqeffvylK7LXP+urV69iMrFnu49X0aFCcEVR3gCuq6r6X2x5+Cow\np6pqurXNj4GvA7/V8b0U51axWERVnxEOhzGbTTidLur1OsVikXg8fqwLx2nb2jgxGo1w5crVttsV\nCnkajWbHhgd1+nI7x1mC5KjK5TIfffQThodHuHnztZf6XYlE3PhvvV9Sp+jDUs2m1sxyYqJ9YKAH\nYltndbpcLprNJqlUgqGhkY4Hf52STCaoVqvHvlvXM1gHvb9arUY6ncTn690zKzM/P8f8/Bw9Pb28\n/fY7gBZ8NJtN4vE44+PjxrCN2+0hEAjQ29vX0aEcl8t9akPl+XyO7m7HrnIAh0MPsModqbM8DP0G\nwOv1Eo/H+dGP/nDfMgWTycTly1fOVa3SfkqlElarBa/Xx9DQMMFgkO9+99/u2s7j8XD79uuvVDb6\nsDnObwN/b8djXmDrGT8LHG7hNfGl0Gw2+eSTjygWtRXpr127jtvtJpfL8v77P35lL2w76YGCtu85\n8vl825oW/WLe6QDL6XTS1dV1KhmsYlFb4HZr9uk46vU6qVQSr9dLqVQ68t+6VCqRy+X2bKWQzWax\n2+3UatXW2o23226nTzrYWv/ldnsol0vGhetVranRm3QedUUAnT5MfVC2JR6P02zCwED7odZkMsHC\nwhxAa4gmTCAQwOl0US6XKRTy2wJYk8nE7duvH2uf9+NyuVoZy8KJBje1Wo1KpdL22NMzWKe56LN+\nc3L37puEwyHW19eBvZvlZrMZFhbmOxJgBYMbrYkJh2vOa7V2cefO6x1dSqhUKhrLFF28eJlqtUq9\nvn2ihFZvmOKjj37CnTuvdyxz+rIODLAURekBLquq+sMdT6WBrUe5B0hyAL//dKJ+cfaCwSA2G1y8\nqPDaa5vZkN5eBz6fA4fDfODx8CocL41GicHBHq5cucLDhw9pNAr4/bv7scTjTXw+B1NTwwwMdHa/\np6ZGiEQi+Hz2A3sUvYxaLYfPp53MurvZNrR2FLFYDLfbzszMBPF4nEwmc6S/5ccfa1nP8fH3dgWs\n5XIZh8PC8PAwZrOZ9fV1UqkUfv/ubOjycgOfz8H09Mi26f0ulw2LRft72e2mV+I42+nRoywDA16u\nXJk6diZocLAHq3X/71k4vIzP5+Dy5Sn6+rZvV61WefhwFp/PyWuvvcajR4+IRFa4dm0ah2OIe/ea\n2GwmpqaGT/wznJgIkM3G6O7uzN9rr9+RTqfx+RyMjQ222WaA1VUHTqfl1I4Zmw0GBryMjvYzOtrP\n669f33f7hw8fsrKyApReugZuYSGFxVLf1RpjdnZ2S+NeM+Pj47jdbqrVMvV6Hr//cD3mKpUKz58/\nZ2NjwwjifD4f7777LqAFu05nF4OD/a3P28PkZPsbjpWVFR49esTs7BPGxsZ2ZfmsVisXL148tdo5\nOFwG6+vAH7R5/DlwSVGUXiDf2u4XD/pl0ej56T8jXs6DB09Jp4t4vYO7/u6FQpVgML7v8eD3e878\neCkUCoRCCQKBAGazk3S6yPPnC3g8u09cKysh0ukipVLnj/NGo4t0usj8/PqJNshcX4+RTmsnTlVd\nOrCotlgsYrFYdgV9L14skU4XMZkclMtNksk8q6vRQ/UwqlQqzM0t02g0efp0ngsXprc9H4tp+zgw\nYMbn6yGdnmNtbY1qdfew0epqmEqlRjZbJZvdrCvLZkskEhnS6QKZTJFwOH2q/XVyudy+NYjlcplg\nMM7IyAix2PELu8tlrb5qv+NxdnaZcrlCrWYlGs0SiUSMovd8Pk86nebixYt0d/fg8QywsrLMZ599\nzvj4BJFIkmw2S7lsOvHvaqkE6XSRlZUQVuvLZYn3O7eEQuHW97i5a5tcrkY6XWRjI05v7+mcm4LB\nGC6X+9Cfr8vVRzqt8uDB05fOJK6vR6nV6rzzzjvGY6VSiXA4SU+PCYvFQq1Wo1YzMTIyzaNHD5ib\nW8HpPHgyzvLyEvPzs1SrNbq7u7HZbJRKJeLxNYaHg8ZoRzpdxONpHPj+HY5epqev8ejRfR49et52\nm1SqwMzMpSN9Bi8TSB8mwLoMGFVliqL8OcCtqup3FEX5m8B30Yrl/7mqqsFj74n4Qsnn88TjcXp7\n+9oOmXV3Ozpel9MJ+XyeDz98nxs3bhEIDBn1V729fdjtdnw+H6lUsu0solwuh81m62h6XLdZh5U6\n0QBra4+fZDJxYID10Uc/odFocPPma9vulhOJOGazid7eXuMzLBYLhwqwwuEQjYZ2NxuNRncFWPqy\nLB6Ph4GBAWw2W2vYRPvdTqcDj8dLvV6nUMjT09P+ZN9sasehPoR52I7nnfDZZ58eath0cPDlOlc7\nHN2k06k9O6AXi0Xy+TyDg4NGlmxu7oUx3N1oNBgYGGB6+iKgNWsNBteZn59rLYFTplIp4/WefI+z\n02rV0K7JqG5zuZzTGSLUFtRuHKmuyOfrwev1Eo1GKJfLxz4fNZtNisUCPt/24bZKRTtHjI9PcuXK\nVcLhMA8ffsbz509pNpskEol2v26bcDjM8+fP6OqycuXKVSYmJjGZTKyurvD06eekUkncbjfFovY5\nH3ZWtt/v5+tff2/X36fRaPDppx+zurrKhQszp3YzdWCAparq/7zj37++5b//NfCvT2C/xDm3trYK\nwMRE+w7eDoeDbDZLtVo9VN+d05JKJUkmkywtLRIIDBknC70VgN/vJ51OE4/Hti3boBXuF06sjcJp\nzSTUT0xWq+XAE2W5XDYCss8++5SZmYuMjo5Rr9eMlhIWy2bLCq0W7+B90NfDczicpFKJXceIXuCu\nr/c4MjJKMhniwYPPAG2twW9+849RKORpNvce5rRYzHR3d79UgLW6ukIwuHlfOTo6yujo2L4/k06n\nKBaL9Pf3MzIyuud2FouVQOB49Vc6vXZFq2PZHWDp9YVbm3dqNU5u7ty5ywcf/Jh8XluyKRAIYLPZ\nuHBhhhcvVD744H2ePXuKw+E8lQuWw+HAbDadeICVz7dv0QDaMJPVajm1ZqP6TWi7YG8/o6NjPHv2\nFFV9xq1b7esTD1IoaPWYOz+HcrkCYGStA4EAV65c49mzpwSDG1QqldaySVpgHolEtk0UApibmyUS\nCfOzP/unGRwMGIFgT482zJ9KpRgbGzfqB4/S9qarq2vXNaXZbOJwuFhZWeLJk8fbzt1er7ejbXW2\nkkajouPq9Trr62vYbLY9C3S3nvhfhQCrVCqxtrbKp59+zOLiIsHgBlevXieZTNDV1WUU1fr9g8zN\nzbG4uEgmkzF+vlKp0Gx2poN7O3a7ne7u7hMPsPQp4IODATY2Nshk0kb2bCf95O/3+8nlcsZMM50e\nlOp/68NkLIvFIslkgr6+Pvr6+pibmyORiBsnxGg0SiwWw2q1GCf+6ekZKhU/sViWWEx7PpfLGhfi\nnQFWs9mk0WjQ3d1No1EH9KzF0TKDzWaT2VmVarWGyaRlxAqF/IEBVjis9baamJg6Vn+ro9jshdU+\n4xKPa5MZ+vu1914ul6nVtA7sxWKRer1OvV7nwYPPCAQCXLt2gwsXpnG7PajqM2w2O7VajbW1Vaam\nLpzoezGZtFnIJ90Lq1gstgL89hddLft+OhN09IkYRw0ARkZG+eijn/D5548JBIaPFajr71FfFkmn\n34RtDdgnJiYpl8tEoxFCoRCzsy9IJpMEAsM8efJoV9uXFy+eU61WSafTZLMZ5ufnePvtr+D1+rBa\nLcYC4Ppx+7LLIy0vLxGNRlhcXCAcDqEomzPBrVYLb7759p7nuZchAZbouHA4RLVaZXp671Ts5hTy\n0pktoaKrVCp88MGPqVarVCoV3G4XuVye73733+D1+radnLxeHw6Hg3Q61baGRr8DOwk+n49wOHyi\nC96Wy2VsNhsDA35WVpb5+OOP+OpXv9b2BK+f/P3+QW7efI2FhXlj+MBstjA2pmUv9UCoUDj4orR1\nPTyv18vc3FwrezJEPB7n3r1PWFiY4/XX3zSGtLq6uhgZmcLlytLV1dUKsHJ7Blj5fB6bzY7d3m2s\nl3ecC2YikaBarTExMcnVq9d4/PgRGxvr+waloH0/rFbLqSw2vbmkTfvgNh6P093dbQzjbx0e03u+\nXb16jVAoRDgcxmKxGMPBoVCQqakLhMNhNjbWTzzAgs2ZvIVC4UjDZo1Gg0ajcagCZ20o20G1WuXR\nowcAvPnm28bzdrudXC53Ku0i9mp4ehBtSahuyuUKv/d7v8vIyAhWaxeDgwFGRkYOlWnfa6i0UtEz\nWNuHHi9dukx/fz/vv/9jurq0zzkej1GtVunp6eXqVS2oKZXK5PM5wuGwMYzZbMLS0iKvvXYHn6+H\neDxOpVIxMlj6TdpxbWys43B0c+vWbeLxGCMjo3i9XqrVKgsLc9y79ylvvfVOx8sEJMASHadfJPe7\nkz/sFPLTsLa2SrVaZWrqAoHAEOFwiLW1VTY21vF6fbtORm+//ZW2U/stFsuJ3AXpvF4twEqn0ycY\nYJVwOJz09mrLnZTLZUZHx7h69dqubTdP/i66urpQlCttf6fD4cBk2h7E5PN5Y13ArUKhoLEens1m\nw2azEYtFaTQaPH/+lHJZqxnaGjRVKhUWFxeJRNLkctnWmnpm43V3ZhW1AMvWmvVUA9pn15rNJk+f\nfr7nMbq0tEgoFDKG1wYHB9nYWCcajex5HGQyaQqFgjED8qRt9m3azGDNzr6gVNKyU5VKZdsw5dYL\n+sLCPF1dVsbGxpmYmOSDD35MMLjBxYuXcTgcxGJRhodHyGRSbGysn/h7Aa2+KBQKkU6njhR0PH78\nkFQqxde+9o19P/dGo0G5XMJut/Phhx8Yn1sqlTSm/m+2aiifeM8l/bjUA+Wj8Hq9eDweLBYzHo+P\nYrHA2toqa2ur9PX1bQsa29kruNOHR+323bOZ+/r6W7VTRSyWzZUI3G638Z3I5dZxudyMjlpZWVnC\n4/FhtVoJh0OtMoJe4vE4qVSKYrGEyfRyGaxsNkM2m2VwcJDJySk++eRj6vUaw8MjxgSdZ8+e8tln\nn3Ljxk0sFgtms7kjoxESYImOy+Vy2O32fU8+Bw1dvKxms8nS0qKRmrZaLUxOXth1x9lsNlldXcFq\ntTAzc9Go47l9+w5PnjwGdvchstvtJ1LIfpCtDUdftjanHW02UB273Y7JZCKXy1IulwmHQ20DrM36\nkP1P/iaTadukhlqtxne/+28oFosoylWj83ej0SCTydDT08Py8hKFQh6v10ssFuPJk0fkcjlKJW0I\nS79I1mo17t37BJOpSjpdpNGoG1mkS5eU1gVm+988n9dquPr7/VSrFSwWc9sMTyaTNmoJ21laWqTR\naBCPx4jFYvT3D2A2m4hGo3vOVNKHB7fWgJykrUPxsNkjaaut+6IHwY1Gg2KxyNDQkPFZT05O8eTJ\nY1ZWlhkaGqJSqTA5Ocnq6gqxWJRKpbJrNmmhUEBVn1GpbH4Pb9y4dezvj54hTqfTDA+PHOpnms0m\nsZg2Gy4ej7dtXdBoNNjYWCcej7G0tITFYmFsbJyhoSFCoRDBYNAIsLYuQXTSAVahUMBsNh05wGg0\nGlSrFS5duozH4+Gdd75iNIVV1Wet7Ov+9a/6TeTO4E7PUu/MYOl6e3tbQ801IpFIqw5zc//12s4L\nF6b5wQ++D5h48823mJubY2Vl2Riu1iZnFLHbu1+qYa1eIzkyMkpfXz8ej4dwOGx8F7VtNggGg8zP\nzxnH1c2bt/atkTwMCbBER+ld2vWL5l52nvg7LZVK8uKFuu0xs9myaxgjEtGG3CYmJrFarZTLZbq6\nuhgdHSMWizEzc/HECiCPaucwTqdt1js4WF5ewul0Ua1WW+vNpXZ13c/nD3/ydzgcJJMJGo0G4XCI\nFy9Umk2tSHxkZBS32025XCabzZLJ9JBKacOveuNTraeaDb9/kIWFBZpNbcjns8/ukclkuHlTwWbT\nhprr9Qb1eo27d99sW+CuL8g9MDDQuqDOUy6XcblcvPbaHWM7bYmkArdu3WFiYnLb70inUzQaDXp6\neslm08zOqrzzzrv09vYRj8f3nL0VDoewWMwMDHRmjb6DdHV1YbVajL+tHkhevHiR0dFxzGbztqBI\nP7b04dWtS+cMD48wO/uC9fVVo2fRyMgo/f19hMNhksnEtmAtl8vy6aefUC6X0a+PzSasr68deeFq\nncfjxWw2GcfHYeTzeWo1rdYuHA61DbBU9TkrK8utCSxaZ/rbt19ncHCQROL7hEJBrly52rpZ2Mxg\nnbRCoYDD4TxygJHLZY2ZuNpkjyYmk4mBgQGSyQC5XI5sNrNtckO7125XMF4uVzCZ2LMfX2+vdjyY\nTCaSySQ9PT3bhvgSiThdXV309fWRz+exWCxcuDDDysoK6+urTE5OYTJpgVipVNw1i/GogsENurqs\nxrF8/foNVldXqddr1Go1Go0GfX19rYy5nampC5jNZiPQexkSYImO0k/QWzs7t2O32zGbTYeqyzkO\n/Y55cnKKQCDAvXufsLq6sivAWl5eBjAuoPqF0e8fxGq1sLGxTrPZJBLR6k/efvsrZ7aSe3d3NxaL\n+cQKbPULhtlsZnVVu5P0eDyk0ynC4fCuAKtQyB/65O9waAsDF4vFVoAEb7zxJsVikVKpRK1Wx2Kx\n0t/fTyAwxNTUBSqVCqr6nCdPHhIIDHP16jWWl5dIp9Osra0ZBfFDQ0PcunXL6Bc1OjpKJBLB6/W2\nvQjoC3JPTk6Rz+ewWKyUSmlWV1eZmbm0Za3CKM+ePcPpdDM5ObVtaCkej2M2m5meniES0WqQgsEN\n/P5B4vE40WhkVydtvfA+EAiceO3OVg6H08gebtal+doGxnrGJJNJYzKxLRCs1Wr4/YPMz88xO6ti\nMpmM4eT5+XlWVlaMespiscjDh/epVqtcuXKVyckpqtUqf/iHf0A4HDp2gGWxWPB4vGSzaRqNxqGG\nWbfWSkYiYZrNG9uez2TSrK4u43K5GBkZxW63c/fuG0aWeGhomJWVZWKxGH6/f8t6hCfbqqFarbbq\nl45e16lPhrFYzNTrWjZSz7bpNx3ZbHbPAEtv0eDx7B7qrlTKdHXZ9vzeb725rlTK5PN541grFAoU\ni0UCgQDJZIquLitdXTbMZjOTk5PMzs4SDofweLzE4xHM5u3Zr6NKJOKUSiVGRzcbj/p8PW2XaMvl\ncvuWOhyHBFiio/QT+EHFgiaTCbu9+8QyWNWqVojp9Xrp7e0jEBhmY2OdWCxmFBdnMmmSyQQDAwO4\nXK5WWr1qDCvpM+m2DqmUy+UTq386DIfDeWIZLH12UCIRo1ar89prt1lcXODx4wcEgxtcvqwY2+on\nf719xEGczs1WDSsrS3R1Wbl9+3W8Xi+LiwutGWJO3G43PT29xsnb5XITCgUpFgskEgmWlhZJp1M8\nf/4URblKf38/N2++tu1k73Z7iEQiZLNZ+vv7aTabLC4u4HA4CASGyOdzeDxe/H4/3/jGewwNDXPv\n3idUKmUSibgRYAWD6zSb2oX06dPPuXHjpvEaWsBtZmBgAK/XSzgcZHb2BXfu3AUgFotuC7CSyYQx\nw/K4awseV3d3t9ESRQ/O9xraKhQKNBoN5ufn8Pl8RCJhY03B99//IyqVKs+ePaXRaODxeOjqep/V\n1VXW1lb43ve+u239SZMJbty4adRidnV10d8/QDQa3XO5KV0mk+bTTz+hXq+1ftbGO++8ayx8nU6n\nyWTSh1oSJZ1OE4vFcDgcuFwuEokEg4ObE2uePXtGswlXrlwjHo9tK/oHGB7WAqxQaAO/39+2ru0w\n8vk8H330E2q16rb3tFeG/LgtGmAzwPL7BwmFQuTzeeNvrtcWbZ0FvVOpVKLRaO6aQQha0LRf0bnb\n7aGry0q5XCIYXKerq4tQaINUKkkoFCQU2sDlclEqFfH7B1stezKMjU2wsDDP8vISAwN+NjY2WvVX\nxx9B2NjYAGBk5ODhZLvd3vHrkQRYoqP0KdQHZbBA++KkUolD34nmcjmePv3cmFrf3d3NtWs32mYp\nKpUKa2urXL6s3Y1MTEywsbHO06dP6OvTLrp6P6WJiSlgM4OjB1AzM5fo6rLR29tLLBZjbW21dXI5\nuwDL6XSSy+Xa1ru8LO2kWiccjtDT08PU1AVqtRpzcy/Y2Nggl8saJ+ejnvz1Oo5YLEIiEcfr9dLX\np/XJ2mvxbNB67PzZP/vnyWTS5PN5kskEdns3gcAgt2/fwe8fJBqNcv/+T1CU13C73cbFMZfTAqzP\nP3/C+voaAHb789aFY/P4dDqdeDxecrksiUTcyGZqC5Sb6e3tZX19DY/Hw+TkFLlcjlwuZ2SiHA4H\nExNTLC4uEI/HcLlcxOMxGo0GpVKJ+/fvGcOSXq9327Dbadg6HL9ZV7P7oqUHzc+fP6dQKDA6Osrn\nnz/B5XIxPj5Bo9EkEAhQr9eNGYQjI6Ot5YpW6eqy0d8/YHw/hoaGd82UHBoaJhqN7pvF0icXRCJh\nxscnaTTqFAoF0ukU3d1D9PT0GMN57QKsSEQ7fvXvx+PHD1lbW2F6+iK5XI5wOMSVK1OANlyZSiUZ\nGhpiYGCAtbWV1uezGVj09PTidDqJRMLU6/UjtR3Zvl9h4wau0WiQz+dJpZLb/ha5XM5oURCLxajV\nascqUchk0lit2k1iKBRq/d21bGSpVGJjY51kMrltVqXJZGJiYhKn07lngbt2E1rD69373GMymejv\nH6BUKhMKhWg2YXl5BbPZzNLSIolEgp6ePrq7u7l69RrJZJJoNMr09IxxI2w2m6hUKtRqlSOfb0Oh\nIOl0mmq1yve+9/s0mw0slp2Ldjt55513t2WSu7q6yGazh74eHYYEWKKjDpvBAm2GUzKpfeEPUyy6\nsrJMMpkwvizpdJpyucL16zcwmUzbXjMajRKJRFhcnOPChQuttLCPDz/8gJmZS8bJ1+PxGDUZO4s3\nnU6ncfHX35feZO+s6Cf+YrHQ8QCrXC6TSCRwOl1MTk5isVgYH58wairC4ZARYB11+rh+kVhbW6NQ\nyDM9ffHQw2ROpxOn08njxw/p7e1jbGyCfD5Hd3c3ZrOZWCxKtVolHA7idl/aEmDlWjVDa/h8Pny+\nHtbXtaL1rbVZDocTm00b8kgk4kZ9UTgcIpNJMzY2wdLSAqr6rLXwrdbaYevkhwsXpllZWTKGCZeW\nFllaWjRmpPr9g4yOjtHT02sspO12u0+le/zWlijFYrE11Lz7sy8U8pTLZVKpJL29fXz1qz9FKpUi\nFosxN6dl327cuMWtW7dZXV1mcvICVquVoaFhI3gIBAKMj7dvLgxaRsVsNu0bYK2trRpDwW++GcDt\n9vDgwX0j+6YP77RrkxIKBXn48AEWi5mhoZHWDNMFo7h5ZWWZe/c+wWyukkoVyOe14WK9L1KxWMRq\nteyqnxseHmF+fo61tVX6+weo1Sokk8nWcLP1UEGA3mzz9dffIJfLce/eJ9uy0foqEvpCxlqgkDpw\ntt9O9XqdXC5LT8/mKhpbm7Oq6jOy2YwRiG7N/lYqZW7dur1ni4bNGYT7T1K4fv0mY2MTxizcN954\nC5PJRLlcYmhomK9+9WtYLGacThc//OH3icdjTE/PMDSkBVilUplMJkUikSCZTB4q4PF4PJTLFR4+\n1Fpr6A19p6amts0IrFQq5HI54vH4tj50+nuqVI4e1O1FAizRUVrRovlQd11b76wPulA3m03C4RA2\nm41vfvOnMZlMPHr0gGAwyG//9m8xNjbOe+/9MWN7/eSbzeaMvkRaLUoJs9nE1772jdY+bH6RdnYo\n3mrzy3c6HZz3stlTqtC2juBllMslYrEoFy/2MDo6brze9PQMS0sLLCwsGLPjNhsgHjbA0rZbWJij\n2eTAJXh2KhQKBIMbeDwepqYu8PjxI1IprfBezw7FYnFmZi7hcrkxmWB+fg673Y7L5eL119/AZrMx\nM3OReDy2LTjSj1Wt91GNbDZDqVQikYhjt9tZWJjj5s1bzM/PG0NWHs/2JrpaM1qtNujyZYWlpUVm\nZ1/w4sVz8vk8Xq+PpaVFYNH4Gbvdzje/+dNH+hyOY/OYyVMqFfdcOqhYLBKLRSmVSkxOTjIzc4lM\nJk0kEmF29jk3b762Lbur07KGHoLBDUKhoFGjtDWLpDtomLBSqTA7+8Jo2BqPJ4w6MH1IzunUAuJ2\nhe7xuDZEabV2sb6+Rj6fw+Vy8e67P0WhUGBxcYGFhXmGhwcwmbR9u3z5ypYgtND2mB4aGmZ+fo7n\nz58BMDs7R6WizZQDeOedr+z7fWw2m6RSSZxOJ93d3dTreoPbovH848ePqNcbXLx4EYfDSTabZWNj\ng0gkfKSGtNlshmZTy5bqAZI+stBsNikU8gwM+HG5nNy+fccIwh48uG8Eyns1OD1oBqHOarXS19dH\nb28v+bwNm62LeDyG3d7NyMjItsym17u5/Fh/fz9dXVYSiTjPnj0jkYgzNDS8bTZfrVY1+tdZLNZd\nWSiz2cTt23eJRsPYbDbeeuudbT+fSMT55JOPSSS2B1j6e+rkKIUEWKKjCoX8oYYHYWsvrINrGRKJ\nBJVKhfHxCeOO68aNWxQKBe7fv0e1WuXNN9/eNjwEWlHs0tIit27dplgsYLVaWsW5jl1Fmu06FOv0\nL99pLZGxF/2E2ek6LG0qe4xSqcTY2Pi2z+DChRk8no9YXFwwGjweNYNlt9tpNpusra3S09OL1+sz\nMhJms/nAO+LFRa0wfnp6ZksNiVZnks9ncTq7SKc314h0Ol08efKYO3fuGsEVaMHzzun9TqezNStK\n24d4PE4ymTBaPVSrVWKxGO+885V999Hr9ZJOp7DZbLz22m0KhQKxWJTx8QmuXbu+bdtgcIN0Ok2x\nWDzxWar63zKZTLZd+kSXTCZZXl7CarUay6ujO83DAAAgAElEQVR4vT66umwkk6k9g+nu7m48Hjfr\n6w0SicS25aXeeOOtXdtvDhMGGRwcYmlpkXK5hMvlJp/PtYbRvHR1aX/Tri7tb7e1Psbn8xGNRnfN\n1kynU1gsZr7+9W8Sj8d5/lxbyqe/v5/BwQCff/6EVCpFs9k0Mm1aK4nnNJsNisVS2yacbrebq1ev\nkc1mKRQK1OvaAsVer49MJk0ul9s3wMrlslSrNWNtSf2Y02aqlozawuHhYSOwTaWSlEpF1tfXmJq6\n0HZN13b0+iufz4fFYqG7u9vIYGk1dk0GBvw0m01U9TnT0zOMj08wMjLK7OwLwuHQPhmsvW9Cd9L7\nhFWrWt1eOp3G4XBw6ZKybbuBge3Ljw0ODrXasmSp17W2MZOTU/h8PjY21pmdfUEryUyjUWdiYpK5\nuVnm5+d4/fU3WqUDfhKJOA6Hc9fx3tPTi8Vi3lYvuPU9dXKUQgIs0THFYpFarX7oi+5RWjXozUuH\nhjangZvNZi5duswHH/yYaDRiLBAKWubKZNJOMqFQkKGhIdLpNFNTF7BYrGSzmV3NIPe7O9Ob6uld\njI8jGo1it9teqhnp1gzWYaXTKRYW5o27bb2uqK+vH59PGyL45JOP+eEPf4DD0W10YNcNDAwQCAwz\nNzdLOBzkwoUZCoUCJtPROkxXq9qwSrlc5vHjh9vqP6amLuw5e6dYLLKxsYbL5SIQGMJkMtHVZSWd\nTlMqlVrNQrtoNrXgKBAIYDJpJ8qenp5D9+kql8s0Gg0SiThzc3NUKlUuX1ZwuVysri4zPj5uBHft\nunhvnZ01OjpGIhFnYMDP1NSFXRm7er3eKtTOnEKApf1+Pavbrq7m/v17fPjhBywvL3Hnzt1t+6sX\nOu9Xc+R0uhkdHeXyZQWTycT6+hqJRPt2Ffow4eLiAnNzs8bFUh861ZrAelr71mw1rTRvuxHr6ekh\nGo2SSqWM2X61Ws0YGjObzfj9foJBn5HtdTqd+HxeisUCuVyulVHclM/nWF5eYmysfYPkiYlJotEo\njx8/wOfrodlsMjQ0RCaTplwuk0wmWF1dMYaYtwoGgywtLbRm9dVoNpvMz8+RzeZYXJxncXGBt956\nm6tXr7O6usL/z957NcmVpnd+v/TeV9ryNqsKVQAKDXQD3dM93TPkcodmR0FSq2AE90oXitCNQhFS\nKLSfQFcbwQtJEdzgirEig2a0JGfJ4cxwHIGGLfjyWVmVld5771MX5+QBCoU2wyGpVUQ/dwASmSdP\nvud9H/M3R0cHojSKsFfs7e3y3ns3zxWFkUiYUqmI3792bg2NC4/xPiNgAgv0+/3X5DecJJMJTk/j\nDAZ9fL5JvF4fJyfHJBIJ+v0eKpXyQiI13iO/TIen3W5hMAiio5VKBY1Gw/Xr7174vy6XwEwVJDRc\nGI0motEo7bYw3puYmCCVStLr9QgEjlCpVNhsdhKJGIFAgFKpxGDQJ51OA0jm8K98DM+vd7lcjtVq\nuyCn8k8xpfgqwfoq/tHiFf7qy1Var2NDPi/G40GNRnOhumy1WpjNQjWbSqUk5la9XkGv17O4uMTu\n7g6PHj1Erdbg96+RSiWpVi8mWO32eYXiVCrJ/v4uN29+IFXR/9CHL5/P8+zZEywWCzdvvv8Peg94\nZXj7ZeQtBoMBweAx0WiYN/d84TALotFoWF1dI5/Pks1mMBqNVCplKVmQy+UolUrW1y9xfBzg+DjA\n/PyiZCfy84BBzWYzKpWapaWVcybg5XKZcPgMs9ksdZcE6nxMJCPUGQ5HLCwsSgeM2WyhUChIScPE\nxASVSkysgt2SQvt4XPVFMfbe6/f7FAp5gsEjNBotq6vrmExmnj17wuHhAQ7HhKj83+X9989bCL2e\nYAnfSwArvw2IPV57tVr1nGhstVrBZDJ/JgV+7KP4enJXLpd49uwJa2uX3iq+KWDVZJTLFXQ67bkE\nSxhNvSSfzxONRiiXS1y6tHHhd9VqNTSb9c8UpxTWjU48II3I5XIODw9Ip1MXkkuVSoXT6RKlPyzM\nzy/icDhoNOoSjT8YPMZut4sYHIHY0Gq1JBC4XK5gNBqeE92tVCqMRuftqsrlMiqVSvrOdruD6elp\n5ufnWVg4T67Y29slEAgQCByxuLh0Lpmu1arkcjnOzk6ljmupVJTwUt1ul1DoVEoS34yzsxDlcpls\nNsPZmdCtikaj9HodcrkMpVIZl8uFSqWWEsrRSCBjeL1eUqkU6XRK+n2FBC1Ir9enVCpx5cpVSXKh\nWq2iUiml8avBYKRQKNBsNqTOlNPpJhIJ02q16PX6ZDJpSYizUCggk/HWQnC8/433w9djNBpRrVYk\nZ4d2W1DEH8ulXL/+7luLHbPZgl6vJ5fLsr39kHK5xN7eDo1Gg4kJFysrq+zsvOTJk22GwyFLSyvU\n6zWi0YhEmDEYjMjlMiyWV8/O+D6+rTs+/p7FYkG6p6/2+K86WF/Ff4HxikH4ZZllX66DVSgU6PV6\nzMzMSg9PJpMhkYgRCoXIZjOk02nu3/+URqNGtVpjd3cXg8FAuVyiUikTiYR5771bzM/PS+OZN3WK\nxpvH+FBOp1MMBoLC89KSgDn5h4wIxx0beHWPPiv6/b5UASuVygsH7bjbUi4X2dvbfY1RqWN5eUV6\n/Wg04tGjB9RqNQwGA+vrl6QNuNvtUioVyefzJJNxHjy4x+npCWq1Go/HSzB4TDB4LH3m1atbLC0t\no9GoCQaP+aVf+hXa7fYXism+GbVaDZ9vkg8++FAameXzeWKxKLVajf39XYxGIyaTmWDw+NxhZTAY\nziUPY7+ysUrzzMwMkUhSMi8eb5JvJgODwUDEtJzXolpeXqFUKpJOC9o7+XwBq9WKzzeJVqtlYmKC\nfD4vjb8AcrnsOQFSITF6RX8fv9ZmO59gZTJpDg8POD09OYcBSSTi7O3t4na7uXr12lvv4fb2I2q1\nCg7HBG63B4/Hy9HRIb1en8PDA+x2x1sPFKVSRSoVxOVy0+12JMB1IpEgnU5jtdqoVKqA7Nyoq9vt\nUq1WWVxcYjgUAOjjDsHr8QpMXcdoNOJ2ezg6OiCdTr8Vb3fp0ibz8wvnPmusT7S7uwPA4uISpdI2\nxWIRrVZLMpmUQOCDwYDd3ZeUSkXm5xdQKpVUKiXpfcbX3mq1zuF9NBqt1FVpNpvnJDM8Hq/Uvdre\nfsSHH36dUOiUaDQsiXZqNBq2tq5x796nJJNJCZPX7XYkB4tbtz648H1lMhmNRh2VSo3ZbGV2do6F\nhUWKxYI4Kh+h0+np9/vY7XZmZuY4OQmiUChwu4UEq1QqSc9AqST4YJpMJhqNOk+ebONyuZHL5TQa\n9XOF6Hg/ft2f02q1IJO90tSLx+P4fJP4fJMUCoXPHCWPi1Ct9uIaC4fPJHFnvV5PpVImFosyP7+I\nwWAgGAxc+D/jKBYLBAJH4u9nkRw4Gg0Bh6ZUCsr6fv8qbreHp08fs7TkZ3p6FpPJxMzMLLVa9Zz0\nhEDoeHt3eGJigmDwmELhVYL1jzGleDO+SrC+ii8VR0eHkvkrCCOdN9lC47HVl02wFAqFVOl8Xrw5\nHoxEwhLYNJVK0O12GQz6hMNnUgWcTCawWKx8+ukddDod/f4Ag8GA0Sh4c9VqFzVgOp0ucrlMOpTH\nh1Amk2ZlxY9KpfoHPXy7uy/pdruoVEp6vf5nqnyHQicEg0Hpz0ajkfff/9qFJEuv1xMMHjMYnO9k\naLVa6cAf+285nU6uXNk69zq1Wo3b7cHt9jA7O8vt2z+jXm9gMpn55jf/BTqdTsKjZTIZYrEobreH\n+fkFjo6OpI1QrzfQaDSQy+VfODKoVgWF7LE8Awib6osXT1EolFgsNgaDIc+fP8Nud/D8+VN0Oj3f\n/vZvSurZr9+H8SF6chLAaDSJ7+sgm81Sr9fpdDri93iVELdaLZ4/f0qtVmN+fuGcrlen02U0gmw2\nRyIRF/WfbAQCwjrr9/s0mw02Ni7jdrt58OD+OUkHENazXm+gXq8yGo2oVMoYjUZpzFKtVjg42Jcw\nMq1Wk3K5TLvdptGos7Pzgn6/TyaTIR6PXSgA+v2+2L2Rkc1myWazHB0d0OsJeKB2u00gcCjhp8aR\nzWY5Pj7i6OhITFZ058azFosFj8fLYNAXBSAL1GpVUexRSFhXV9eJxSJEoxHm5uYvrMlxt2dcQIy7\nzWNx2dc7fd1ul6OjA5rNFjduvHth1FoqFUWlbwdms4VKRWA1hkInLCwssbq6xmAgWCJFIhHu3Pl7\nbt58XwK9v26n8/qfBYB3nc3NK6hUKmmtqdXC8x4KnVKv11Cr1QQChwwGA0KhE+RyQXl/zPoMhU7F\n5D7OkyfbVCqCF2OlUsFoNErfx253sL5+iUajIRZNwt5y+fIVPB4vkUiYw8MDarUqGo0Gr9cnEXWi\n0Qg6nR6VSiXKBsjOJQ/ZrLAXr6ysolDIefnyxTnrl4kJp2hkfEo0GmFn5wW5XJb5+QVxtC90mXq9\nHsPhgFKpKIrgejg83KffH7wVc/d5MArBA1SGzWanWq2QTqcplyuiIHDpwutfj8FgQDh8hkajwWKx\nSEzXYrFEMhlHrzewuXmFjY1NAgGhu7y0tMJg0CeZFJi7489ttVqo1Wq63S5ms/mtnydg/JTncFiv\ng9z/seKrBOur+MJotVoi+FWBUqmi2+1wfHyE2+05N6P/eTSwxqHT6aS2dalUlDYOgFzOQLHYIJtN\no9VqsdnsHB8HODsLSZWkXC4Ilh4dHRKLRVleXsFud9DrdVGp1GKCcZWnT5+gUqmQyWQS2+tNvZNu\ntyM9ZLVaVRozNZtNaRP8Mh2sMeZpNBrR6wmHosvlQq83EA6f0Wo1LyRYo9GISCSCUqnAbnfQarWo\n1WqkUskLflh6vYFqtYLT6eSTT77JYDBge/sBJyfHeDxe1Gq1tBm/2al5M4xGE8vLfiKRCGq1BqvV\neo4+/+jRQ0kN2e9f4+joiN3dl1itNpRKJX/4h/+eXq/P2torEHe/3yeXy5777FQqyfPnTyVNKYEt\nFmA0GqHVaul02szMzBGJhDk9PSaXy2O328nncxfU90EYN5bLRaLRGCaTEb1ej8MxQTabJRI5Q6FQ\n0Go1ePr0MTqdFq1Wx/FxgG5XsPlIJOLnOn67uy9JJhN0ux2SybjUiRnjOsb3vVwusby8gl6vlyQd\nXk82zGYzqVSKbDZDvz+QxoO5XI6dnef0+wO8Xi+9Xg+dTkelUub27Z9JSuEul0tMuBrYbPZzxcq4\nKJiZmWNqaprnz5+yvf0Qv3+VGze+zs7OS1KpFD7flNS1OTsLEQwG0GgE8Uyn0yUllkISWMHlcvPo\n0X1GI7h58wNGI9jf3+PmzfelLqLH42E4HBKJhN+6Jl/vkozD6/VRLBZJJhPSb1goFNjf35UKlXw+\nf25E+rrS91gCQXgOUrTbHXy+SQmrZzab+eEPv080KrgxRCJhFAolodApk5OT0ojWbLZK793vD/D5\nfCwuTvP7v/9/MRj08fvXRDHMFNVqDYfDSblc4ejogHq9IT27w+FI6koOBn3qdcE8WS6XU61WUSgE\neYfhUOiwxeMxkXChF/eCLhqNTeqY6PUG2m1BOsPrnaTb7UqWVJWKQCrQaITRrNFokhJ3mUxGLpcV\n9woBb/bRRx9LXR+ZTIZarSYUOhX9WIV73WjUSaWSUqdrXEhaLDZqtSrJZILl5RVcLg/JZOKtHayx\nTc5F+5wOlUoFu90uERvM5m2y2Swff/yNLwTFRyJhQqFTrFYbMzOzFAoFlEqhIFUqVQwGfRKJOMlk\nnNFIWG9+/yqZTJpkMkm73cZoNEp6aeOO6md1sGQyGXa7g0wmIxF3XoHcv0qwvop/xkilBDVcv3+N\nqalpqYMUDp+d6wKMLRF+HhsQrVZLtVql2+2yv793Tq+lVNJRqQgt7KmpGdLpFGdnIQwGA++8c4PB\nYMBwOMJms+F0Onn+/CmDwUCsfK34fF4MBiMejw+Xyy1RuMdsr1qtisViZTAY8PjxIyKRsMRwGW+k\n49FQJpNBpVJTq9W4f/8uSqWKGzfeJRqNSG12pVLJ9PQMZ2ehc4mi0Wjk0qVNMhnhsG42mxdwObmc\nYJY7MzPL2to6rVaLu3dvc3YWunCYyWQCbk2v10udgcXFZY6ODjk+DrCxsXmOSfRF0Ww2JJ2wNztR\nPp9PUmCemZnBaDRSLpexWm0EAkdUq3URFyaXRmGnpyc0GnWGwwlpBJZKpVAoFJIOVC6XYzgccPny\nVRqNBuHwGVarldnZj4nFooCcTqfN6WkQj8d74bqOj48wmy00GocolQp2dnaw2z3S54/fI5VKcnh4\nKNG3xx2FSCRMNpvF7XYzHA7Z29ul3xd0nHQ6HaPRiBs33mNmZg6rVZD42N/fJZfLSVpIsViUSqV8\n7rcUOjkpotEow+EQi8VCPB7j4GAPuVzO1tY7uFwuQqET9HoDtVqNSqVKq9VkZmaW1dVVgsFjAoEj\nisUCPp9Pwl2lUklisShnZ6fY7XbC4TP29/cpFovU6w06nRY7Oy/5y7/8f8ROhQy5XI7L5ZYO4snJ\nKZaXVxgMBrx8+ZxisUA2m2F3d1e0iHmH0WhEOp2mUChQKORRq9USmzAaDRMOn11YkzqdDqVSQblc\nplgsYLPZ0ev1RKMRjo+PJMFfALlcxtTUNPF4jGw2cy7BetVRkHHv3qdUKhVOT0/EAst6TpHb6/Ux\nNzdHuVym0aiTTqex2+1Eo0KnLZlMMDHhQKfT0W63yefzUmJ3cnKC3W6n1+tJLE+1Ws1gMOBrX/uI\nn/3sx1QqVWZmdGxsbF7woiyVSqRSSWZmZpmcnCKdTmGxWLl0aYOpqWn6/T6PHj0gHD5Dq9VSr9dE\ngdxXJB29Xk+73abb7TI5OUUulyWXy2GxWEUJkrG5u9BNrFarYhEro9ls4na7pQLxbUzcfD6PTAa/\n9Eu/AozE7nheGvGqVMLxb7VaaTaFfW1s8D4u7l6fWgDEYhEmJpwXOpjj170uXdLr9dBo1F9IMun1\nekQiYdxut4iNymOxWLh69R1evHhKMpmgVquysXEZp9PFcDhgff0SCoVCgj00Gg1MJiOlUolSqYRC\nIXy3zyOQjBOsYrGAXq9HoVCgVCro9XrUalWMRtMvbIv2VYL1VXxhJJMJUbhPwCtMT88QDp+JYoNz\naDQa+v0+7XYbh+OzzUPfFuMKI5/PiaDGCQnvNDFhIp+vIZPJMJnM0lhwc/MyOp1OGh0K7V61VO2M\nK32fb0pihTkcEySTCarVChaLhVhM2LgsFiu5XFbyjxvboYyr35UVP6VSURTZNFIul0Sgr5Kf/vRH\nkonsONLpFLValX6/L1VyJpMJtVottdzfxgBMJgWl8clJ4eDS6XR4PD6SyQSZzPlDaEwKeL06m5mZ\nJZlMkEjEmZqaolarolDIzwnsfVbU63VGo6Fo7KrmxYtnkmWKIFGQw2w2Mzc3j9frIxoN0+12CAQO\nUKtVrKz4sVqtXL/+LuVyiUKhgNVqw+v1cvnyVbrdLum0wD786KOv4/NN8dOf/giZTM7+/h7NZpNm\ns0Eul8Pj8dLptNFqtayurom4jqNzY698Pk8qJeDjzGYLTqebeDxOtytU1j/96Y9pNOosLCyIifYk\nVqsFu90hmjNXiUTCJJNx3G43Z2chSV7BarVx8+YHPH36hEDgkGaziVar5b33brG+vsH9+59yfHzE\n0pKQiBcK+QsJVjqd4unTbWQyBf1+D7Vag0ql4tq1d6TXlkolSXFaLpezurrKt771q9jtDlZW/Pzw\nh98nGAySy+VeW1tCghWLxcUR30gUjMwQCgWRyeRkMhmR3i8k4CqViunpWRKJJCcnx/h8kxweHnB4\neCBazVjF9aZlbW0Nt9uLVqshnU6zu/uCTqcrJVPCmvQSjUbZ3n7E1tY1qlXh8/3+VWw2O7lcjseP\nt6VxeLfbodfrYzSa0Om0KJVK5ucXMJnMYkKRPdcFHCdY4wJBSIjron6W4xxeU6FQ4PH46PcHOBwO\nhsMhfv8aExNOnj17Qjqdptfrce/ep4BQKAqdsDbT024++OBD4vEYhUIOuVxOr9fFYrGJyZyDk5Mg\ng8EAtVpFLpdlcnIKn29SPNjt6HR6qtUKS0vL1Go1zGaL1DlRKpVsbb3Dw4f3pY6kx2M+9xzr9Xo6\nnRadjiCNUijkyOWyTE/P0Gw2cToF+YJarcbc3DyJhLBnjSEVryczb0a/36dSKWE2C0WTw+Fkb29P\n7JzqxXsuJGe9XheFQkUymUCn02GxWDGZTFJB+noIyf2rPb7VavH06WOpe/a6d+XnYaBej1gsQq/X\n48qVLVKpFI1GXcSpzVKv16jXa1QqFVQqJe++e150VavViljbovibnbC9/QCNRkcsFqFer2EwGM/d\n93GMv0exWJDG8Wq1hlwux/3796Rk+ReJrxKsr+Jzo1Ip02g08Hq9Em5jbHJ7cLDP2VmI1dW1L23y\n/GbY7Q6xyhUAkB6PV8LXWK0mer1X3bBqtYpMhmQoOx6Dmc1mcZOQASOq1SpqtUq0iUiJgnJuyYtw\nrNxeqVSYnhaSon6/T6fTlVhBY2CtyWRmYsJJJpNBq9WRyaSZnp4hHo/SarX54IOvsba2DghA0YOD\nPXZ2XjIzM8POjgBsVyoVfPTRJ1Il9ybdvdPpkMsJ5sSvM3fm5xdIpRKcnZ2+MUYZ2528qgxlMhmr\nq2tsbz/i8eNtjo4EwHO5XMJgMNLtdjEYDOcqstFoxGAwoNlsoFQKLf9CQejWKZUKFAolw+FA8oVb\nWlpmcXFR7JzsMRgMuXnzBmazmWKxSK1W5cmTx8TjMer1GqenQemAqFSExNZkMpNKJbBabZJVTi6X\no1Ipk88LyUSpVJJMV4VOgcAOtduFQ/TwcB+ZDFGKocPly1cYjdqcnASpVKqUyyUmJiaYmprh6dMn\nKJUqPvro63Q6HTqdDiaTWfTZy3Jycszf/u1fE4/HuHbtOouLS7hcHgKBIxH4r8Hj8fLs2RPeffcm\nKyur7O/vkUwKMgRHR33pdxBshYIkk0nK5TJ+/xqTk1MolUoWFpbO6RiNaeeCVo+OublFacM3mcz8\n1m/9a7LZrERiGI1G/Pmf/wlOp5MPP/wEuRxRrFJGsVhkeXlZVN6fJp1Oc/XqNaampkUAvGA5UqvV\nOTzcpdvt0Gy2sNuFZOL09ASZTM7c3Dwmkwm9Xo/VauPly+e43R4p6QcBe3nv3qdEImcS86vbFeQw\nrl69RrFYJJvNkM/nMBrNfPTRxySTSTweN1NTM0QiYRKJBJCg0WiQTqcwGAzSMy/Yw/Rot4Xu8cbG\nJvV6jWw2S7ksMEudTpf0nExPT4tJUgGtVofP58NstuDxeER9p2mJ3VmtCiOshYUFLl9eRaUy0e8L\n2M1EIi4Cu4WEwGg0ks8LtkfjRKpSqVAsFrhyZQulUonJZKZUKqJUqmi3W/R6vQsWTFevbvHkyWOG\nwyFqtfoc+FwulzMcDul0OlgsFmw2O4VCQep0W61WSZ9uvMaq1SqVykUj7tcjFDpFJpNJelfja1Eq\nlRwdHXHv3h1isSh7ezvEYmFu3/57er0OodApi4vLfOMbv/TW981k0jx79lR6Tsd/V6vVyGTSLC+v\nSKPiwWDwpb1Kczmh07a+viF2Ixt4vT7p2pVKJRqN+jP1qex2B7FYFKVShVyuIB6PMzk5JUFCUqnE\nWxMso9GIRqM5R1xRqzVkMilmZua+tO7Y58VXCdZX8bkhbIZCN+j1mJyc4uwsRDwexWg0StT0n9f6\nY2JiAoVCzulpkKmpmc/cNEajEbVaBaPRJLXFx52qfl8YFTocTkqlIkajSUyOTJTLJUKhEOl0kkaj\nSS6XFQG9QmemVqtJis/9fo9yuUQ+n6der+Hx+ET7D48IPI7SbLYoFouo1YJwpsvlljb7tTUzwWCA\nXC6HzWbD71+lWq2QSqXIZNKio7uMRuN8gpVKJRkORxfGLmM2VjqdlgyRm80mhUIBnU4rMZjGYbPZ\nWV5e4cmTR+KIJEU+n2N1dV36bRYWFnG53MTjMYnlJdDBDYxGwhhKoZDz4Ycfo1arJTzCT3/6U37v\n9/4dJpOg2D0eE3Y6bZLJOicnxxweHtBqCQe3Wq2h0RCsV2KxKMVikbW1dbrdHqmUME65fv1dhsMh\nP/7x32EwGOh2u2SzWVqtljRaXF9f586dv+fRowdSNdlsNvF6fTx//pR0OkkgcMj6+jLdbpdUKsHi\n4hKffPJN0ukU7XaLhw/vYbG8UrXW6XRotVqi0Qj7+7sUi0V8Ph9bW9fY2nqHZ8+eSGu62WzicExQ\nKOR5/vwZ09PTDIdDzs7OCIVOaTYFu5VxgjDWXhtX25ubVy6sZQEHJhM9MYdotTpWVlYk/JJMJkMm\nk50D6L548Zx2u83Skp+vf/1jQNCvGg6H3L79Uwkr5XZ7mJhwoVarpbUQi8WYm5vD6/XRbnew2Wy8\n++4t/H4/vV6XZrPJyoqZW7c+kIqAqakpbt/+KXq9/lzHot3uiLIFdT799DZTU9Oo1WoikYh4KE6c\nY+2N8XixWIx+f3BOe6rZbJLNZtnb2z3XKeh2uyiVKubmhFGWwzFBqVQUfezykkSGwWDAbLZgsVgk\nYLzBYCQSiUh6Vu+//zXpfUulEhMTTt555wZOp4lcrobX6xVtjcLi2hC+/xifZjabuHLlKjqdnidP\ntsnlspIGms1mI5vN0um0aLfbEmkHhKJt3AEaA66r1Qp7ezsS7mo0GlEqlRgOhyiVSpxOF4VCQbpH\nFouNfn8g/rYjZDKh2zLW+nobrqlSKRMMHkvuFePfYmZmFqfTKYLnDWIHTofd7kChUKBQ6HC5XJyd\nnVIo5N9qSG63C+PWcrks4ZbK5ZL4nDQudK/gi42a+/0+1WoZs9mKUqnE718Tx5AuEW4i4Nv0ej1y\nuVz63NfD4RASLGHdKMUunRav9xJyuVzqhr4tTCaT5PmoVCpRqZTk83k6ne6Fbtk/JL4wwfL7/f8r\n8BuAGvg/AoHAf3jt3/5H4L8FxintfwWMifIAACAASURBVBcIBI4vvstX8f/HGA6HovWF5sLob9zF\n2t/fY39/T/r7nzfrH29UT58+YX5+6TMVvRuNujQSGke1WkWr1RKLRdDpdExOTkpslX5/QCh0SqlU\n5MWLZzgcDkqlEsFgQBSSPKXVapBIxIlGo6hUKglknslkiEYjVKtVSqUCN27cRC6XEQye0Ov1UCjk\nLC+v0m63CIfPmJ6ekSQVtFodGo1KdGZvs7zsJ5VKkUolRYV03YUOViIRRy6XvVXDaH5+gXQ6LXX4\nqtUqhUIOp9P11lGjYHXRx26fQKUSrklgdNlErM0OMpkg4Hh8HKBYLJHLZcQu0AirVc7s7Jy0eWs0\nGj788GPu379LsVhEJpNRrVZEIUcXg8GQfr9PrVajUMhz6dIGv/qrv0Gz2SQcPmNz8woHB/sUCgLm\nKh6PolDI2djYRCaTSYfSeCM+OzsFXrEEzWaLyIYKIZPJkclkKJVKqtUKpVJJTJpz1GoeGo0G1WoN\ng0FPKpVCq9XwzW/+Mo8fb9Pt9rhyZYVKRcAIZTIpdndfMjk5zcqKH6fTxeysAMQWEtkGNpsNt9uN\nRqPB4xESXYFZOgKEpLTRaFAsFiVPvjEGr9/v43Z7JEuP16NWq4k4xRHJZJLhcCCO1VSS2vWbh2ej\n0cBqtXLlijAqbTabPHx4H61WAPDfvXuHwWDIxsYm7XZTLH5i5PNFiT1rsZgpFIpkMlm+/e3fxGIR\nTJMNBiNLS8tSt2x8DxQKQWtqrH01GAw4OjrA4/Ehl6cJBgNYLBb8/lXy+bwE0H49lEolPt8UkUhY\nBNtrePddQTBzbBfT63WYmppCpVIzHA4JhU5EWYY4/X6f+fkFdDodtVpNwl2enYWkcb7NZucv/uI7\nmM0W8vk8Z2cher0+m5uXiUTCzMzMMhgMaLVaF/axsQbTyYmA9Rsf3u12i0ajgU6nl7pOLpcwTi4W\nizidTinxrFSqdLs9ieZfr9fZ3X0pSTvE4zGKxSJ2u4NkMim5BvR6PZLJJCCYWy8uLnF0dMjh4QH9\nfg+rVcBinZwE6fW65HI5ms0mS0vLUhf+zRgnNrFYhEuXNqXfw2azs7FxmVRK0CXb3Lwimi7b6Pf7\nuFxuLl3a4MGDB/zsZz/md37n31x47263g8fjkSyHLl3aoFQqUa1W6Ha75yRHxmNMne6z2cUCqL8i\nFsfCvVSplExMuKTRtUajpVarSqSaSqV8IcGy2x3IZIidcqFAzufzmExmrNYJian7NqazwSB0KsdF\nUrPZol6vYbFYqdVqb1X1/3nicxMsv9//MXArEAi87/f7DcD/9MZLrgH/JhAIPP+FruKr+C8ycjnB\nRPdttGxArF41kjeU4D/182GwQNDoGQ5HyOWfDSh8fRwIwmio3W5TLpew2x04HA663Q6PH2+TSCTQ\narW4XC6Rzi34eYEMu13AiRiNRobDAeVyCa1WGAPF41FGozE4U4PJZKZcrhCNRul0uuTzWUlR2uv1\nIpPJCAaPCYfPWFpaFivyDEtLK0xOCoeKwWDAZrNTKhUlXEwkEubJk20Gg6EkpOnxeN5akZrNFjY3\nL9NutzEYDPzkJz+iUikzP79Ep9M+x4TMZrNUKhVRUNXPu+++x/7+vngwb9FqtTg7C1EqFZHL5Swu\nLjMxUaZeF7RmcrksN268d4G1F4/HcLu9TE3N4HK52d5+KAp/LqBWq1Gr1czPL5DP5ygWC9y/fxeA\nSCRCPB6jVCqSSqXY3n7EJ598k5UV/7lEXKvV0u/3xP8TxmKxSoD5Xq+HXi8whjY2Nuh2e0SjYZFS\n7qbVanLlylWWlxfIZEqUy0V6PaGSff13yGRSjEZDlpf9KJVKvvOdP0WhUNJs1imVirRaTSYnJ6nX\nqxJzUKDwa2g06rz77k08Hq+ENQGhW/DixQtMJhMGg55iscizZ08BYYx7587tc6bS48jnc6LIZI96\nvYbT6cbhcJBOp6lUypTLJcxmYTTtdrulboper5NGLgcHAp6m1+sRi8UIh0MMhyMWFxfpdjtkMmli\nsQgul5vl5VX293fFNSQk+H/yJ3/M1tY1Tk6CEm7s+DjAlStXJcPdiQknZrOV4+MAHo+XVCpJrVYV\nhWlztNsdFAolPt+kJFS6uWmVhIGr1SrNZoNMJsvz509JJBKsrq4hk8ml8VixmKdYLKHXC6O4eDxG\nrVblypUtkskkyaTAWFxbW5e0jvR6A8lknIWFRfR6IZlutdp0Oh0SiYSIkbKh1Wo4OjqUWInwCl7w\nemg0WgkgP8Y1hUKnaLUaaUwnk8mYmJjg7CxEPp/D6XRK3aF4PEqn06Hb7dFutzk83Gc4HLGxsYnV\nauPu3Tv4fD4+/PBjAKkbk81muHv3NsfHAf7Tf/ozrl27TrFYlADfQmLQolqtSpOEarUqkjE8F76H\nsO4E651Op0e93uDRo4dS4ry/vyt2Yp+gVMql+3F6GsRud/DBB79MJBIhHI5wcnLC0tLSufcWnBGE\nRCeZjOPxeCTmI5wX1B1j5T6rg5XNZiUpFniFh3pTakOj0dJo1FlcFK6lUqlcKERVKhUWi6Djls/n\nKRRyHB2N6Ha7EmjebLawvr4uQSHGIRSbQ0ntv1Ip0+v1MBpNPzfc5W3xRR2sfwHs+v3+vwLMwP/8\nxr+/A/xbv9/vAb4XCAT+t1/4ir6K/0/ibayJRCIGcI6582b8PCaknxVCcoVU0b4t3kywCoUCJydB\naQw4HA44OQmys/OCXq/H7OwsvV6P0WhEt9vBavVgMBhoNpvSCOL4+JBOp49GoyEWi5FMJimVSpye\nBlGp1FQqFRKJONvbDwEZuVwGuVxGp9MlEgnz0UcfE4mEiUYjzM8vkMsJCY7NZuejjz7hxYtnHB7u\nS92m4+Mj4nHB3kGQixAeYJVK+bnmx6+PDgVcgRyFQsZgMCKXy6HTaVGrNdJowW63MxoNmZycJh4X\nNJ3GWkTT09P0el1xzKlieXkFjUZLt9shGAySyWRElXah2uv1eoTDIebm5llcXEQuV6BWq6lUymxt\nvSOxl1wuF/V6XfROE6w2zs7OyOWy4kFtYTQaYDSaLiRwGo2GWq2G0Wjk4cMHOBwC+1AQmO2KitkD\ngsEgRqORdrsjql4rpfHE8vIyx8dhHI4JPB4vXq9Xkpu4evUaf/M3/5nvf/97qFRqGo063W6Xa9fe\nQalUioD3pIjhkFOv17Db7Xg8HpGOX0Mul1841MbssdFohFKpolhMiF2WPlqtILQ41hN7PQQngQoT\nE048Hi8zMzO0WsLoY3PzCgqFnGQyIYLSfayvb0hkAJPJRDKZoFAoSIQQ4UD2Mjk5ic1mZ319XRzj\nCPT/wUAYaQpyAD6RQVuTOpJy+atO4sHBPjqdXtT4mmN/f4fDw30ARiNQqwWci8FgZGZmhnZbGJn3\nel2ePn1MNpshmUzSbDbpdDq0Wk1GoxHhcIhkMsFwOKDRqItjKaU0IhJwQ3JyuSzVaoVIJMz09CwG\ng4H9/T3u3r2NUqmk0Wjg968hkwlJ0NraOjs7L/B6hdHo2dkplUoZu93BzZsfcHCwRyQSlg7uNxNe\ngV2YRS6X4XA4UCqV1Ot1McF0SRIGZrOFTqdDv9+TMEhOp5N6vUosFqHbFTpO9bqQAHq9PiYnpyQG\n9vT0LMPhgIcP77O87Gd+foHhcIjT6SaTyTAcjjg9PWV6ehqr1co3vvHLbGxsMhwORYNoHZOTUxwd\nHXL16tZnMvNarZZklZNIxDEajSJbUCY+YyZarTZ7e7u4XB5UKiWtVlt0WVDx8cff5E//9P/mzp2f\nMTc3d04vrdvtIJPJWFxcIpvN8qMf/ZBYLMpgMMBms9Nuty/4y77ZNUqlhLXx7NlTWi0BsrG2to7B\nIEjYjCU3xp03uVxGvz/EZLLQaNTeavANsLV1Da/Xy+npMZ1OG51Oj883KcI70hwe7kv35fWo1apE\noxFmZmZwuwXbo9FohFqt+rnhLm+LL0qwnMA08OvAAvCfgdcNw/4E+N+BGvCXfr//1wKBwPd+4av6\nKv5Zo1wu8ejRQ2Zn51hdFewjWq0W+XxOZJS8XaztHyPGLvM2m4N+vy8yT7TioTeiWBQA3Xt7u9Rq\nVbHTFGd/f496vc7GxmX6fWFsJ3hMWen1+mg0WpRKBU+ePKbRaKJWC3ThMQC30+mQzxdRq9WiP1kD\nvV6HyWRBoRDGn5OTUygUgrK11WojHDaSSqUkenehkGd6eobT0xMSiTjptMCA2dy8gs1mY2vrGk+e\nbJNMJgkEjgiHwzgcdgwGPZcubZyjrn9WDIdDHj68T71eYzQSMCJms5larY5er+fFi2eAYFStUChF\nocgiJpMFmUzG9PQMe3u7xOMxlpdXSKfTHBwc0Gw2RJ0joevUbDapViuEw2F+//f/Tz744EO+9rWP\nCIVO6fX6+P2rUmL0ppL3cDgkEDhibW2Nmzc/oFQqUiwWePToIaPRiKWlZZaWlsXEpsPz50/R6w3S\n5p3JZMhmM7TbLY6ODrDZ7NJGvLS0RDabYzAYUCwWWVnxY7PZcbs93Lv3KUajmeFQ6PC43R7pYNvY\nuCxdn06nw2QyUSwWRc+4AsPhiOFwgMkkjJWq1Qqh0CkOxwRyuQy/fx2z2Uy9XmUwGL4V+wHCqCeZ\nTOByuaVR8enpCe126zMZSKVSScLQNZtNSWF7ZmaWzc0ryOVy7ty5TSBwxA9+8LeAjHD4jEajRqFQ\n5MmTR4xGQ1ZW/GSzQjdlddWP1erg4cP7BAKHHB8fi/IZSBgTm81BPp/D5XJjs9lotzvo9Tqy2Ryh\n0AlXrmwRj8f48Y9/gEajo9FoMD09IzLsenS7XXw+HwaDkcFggNGo5/g4yKNHD8hk0kSjUXZ2dtDr\nhTU6PT2Lx+Oh1WrhdLp49OgBVqsdp9OF0+nC4RA6QM+ePZFGqZOTU5hMJiKRCNVqhY2NTQqFHIlE\nQsTi6Gi1WgyHA1KphMjGTGMw6Gm1mszNLZBIxEWShWD+++jRA6nL8nrndDQasbPzEoVCyeLiEqen\np9Jaarc7rK6uolQqKRQKUhKeyWQYjeD+/bui1lWcWq0mJUaRSJSZmRlWV9cZDocEg8fI5UJSkkol\nGY3g5OQYvd7A06ePCQYDyOUySTPu+DhANpshEDiUjIxNJgu1moCpUigU57TG3gyh41XBYNCLhvcj\nvvGNXxYtcXro9UKXMBqNolZrJMjFWPx3dnaWxcUVTk6O+cM//APRMsgosoEFPOb09CxyuZx8PicC\n921i4taUEqw3MViDwYC9vR1R/iNPJBIBhKSy2+2hVCoZjZD23nFiNna1EBJ74Xl8U79Q+He1+Pyp\nMJnMOBxONjYu4/evit00YS980yeyXq8TDAZ5+vQxVqtNKhgFbOSXs9n6vPiiBCsPHAYCgT5w7Pf7\n236/fyIQCIw9LH4vEAhUAfx+//eALeBzEyyn84tp41/FP2/k83EsFh21Wh6zWY1Go+HwMI7ZrGNr\na/2f5DcT1NcH1Go1dDoFW1sbIqOtSCJRuuDpVSikUKvVtFrCRlksprHbTXzzm1/j/n0Bi3Lpkh+n\n00a9XmcwGODxTDA7O8Xs7JTkazYa9Tk7C7K5ucmVKxtMT09z7do1/viP/xi9XsvsrFA1/+7v/i5T\nU1P86Ec/Qi6Xc/36db7zne/Q67W4fv0qZrOOSiXL9evXKZXSFIspSqUMDoeFy5f9OJ0mnE4TDoeR\nZDKJVisnn8+ztbVFsVjE7bZ9qfuaTCaRy/t4vROSmvnUlEukbs+h1Wopl8tsb2/jcDhYXp7l8LDJ\n/LwPp9OE3b5CJhOlXi/gcBjY26uys/OETqeD2azj9u3b1GoC2HdtbRmtVsvR0RHf//530WrloqGt\nnXfeeeVPN8ZOjKNaLaPTKVha8jM762Z2djyOEej+U1NTxOMK3G4Bs9Ht1ul2Xx0SzWaZVCpKuVzG\nZDLw3nvXGQwGxGIxarUSSuUInU6DUjni44/fp91uc+fOHYbDLsNhm3a7SrFYRKOB1dVFPvnkE+kg\nFayHOjidNpaWliQskcPhoF6vk81mqVYLDIddzGY9a2tLKJVKbDYbarWaaLSBxaKj260SiwnrxmKx\nUK1W2dnZQaUaMTvrY35+no2NDe7evUuzWWFmZoZ3391iYeFVMjoYDKjX69y5o+b+/fucnZ1wcnJC\nv99hdXWZ994TxritVpvRqM36+gqhUIi/+Is/oVQqYTAYKJVytNttrFYr3//+X5FOp0ULIh+Tk5MY\njRpOT4/odBrMzPiwWk2USnl+7df+JcvLy3z3u9/l6tWr3Lp1i+3tbQBcLoG9FgodEY1GKZUE0/Rv\nfetb+P1+1tbOe/a9ePGCQGCXUCgkFkUaZmZ8lEo5TCYTN27cQKcTGH2CgGeaaLSJUinD43GwuDgj\njsMFVtjkpEvUhhO02Or1OsvL87hcLlwuF7/zO78lst/uUSwWMZu19Pt9VCr4wQ++S6lUxGKZY2rK\nw82bNzEYDPzZn/0Zz58/5P333+FXfuUTbt++TbfbZX7e99rh3GY4bDE/P8nZWZdkMsKLF8K+YzRq\n+Na3fomDgwOyWcET0+Ew0e1aSSbD9PtNvF4vcvmIwaCLTDZEo5GjVsuYnHQxO+vm9PQUtVpgx83M\nuMhmo1gsQsKxs7PN2dkx/X4Hk8mI0ShgspRK6Pc71OslWq0K+XyCuTkv4XAHl8uMxaJDoeh/5t6h\nVA6Ry4f4fF5isRgOhxmn00Q+38Fi0bG8vIRGI+PwcIe9vRS3bt3CYtHj9TrI5+NMT0/z27/9r/jO\nd74jWmmFmZubIxIJiGxPHdPTTtbW5pHL+3S7i6yvr/Py5Ut0Orl0XWr1CJvNwPS0oCj/+PFjWq0K\nc3M+tFo5TqeVyclJ/vzP/xybzcjsrA+fz8fU1NQ5qITVqsdgUDMxYUar1RIOh9FoRlitF79/u93G\nbNbT61nQ6ZS4XBa8XhvT00KS/9FH770V6hKPn1IqlYjHT7HZjBgMWkqlHEpl/4LN1c8bX5Rg3QX+\nB+Df+f1+H2AAigB+v9+CMD5cA5rAN4A/+KIPzOVqv9AFfxX/+HF8HKZWEyqOx493WF5e4enTXVFk\n0MThoQA8FrSQFmg2BSXyt5m+fpnI5XI8e/bk3N+tra1zeHjAkyeCD5nb7ebWrXfI5wUD2EymhNfr\n5dKlTarVCplMiZmZWVKpIuVyg1pN0KkymycAFWazmeFQxeTkHIuLyyLGpk293iISiVCpNPjVX/2v\nMJnMotFvV8QubYqtcB25XI1eTyYyd/rE42kajTYqlQG12sjZWQK3ewadzsrz50/IZnOi9IBeWuet\nVh+NxozTOcmLF3uUy5/SbDaJxTKA5gIo+M14/vyASqXF5uYNqWWdTqfIZF7Qbo+Ynp6m1RoSi6XQ\naIwkEjmCwTAymYZWa8jEhBODQVDA/+M//nN+9rOfEI8nkMlk/OhHP5G0p7a23pUwFC5Xld3dHf7m\nb37I1tYW7713i0JB6CTm83nJ+mcch4cHaDRqPJ459vZOaDRqNBpN4vGo2NU4oNvtcfmy0Nm7dOkd\nOp02/X6fXq/H2VmCUCiKyWTE45miWm2yvOyn14NPP73N7OwsKyvrnJ6G+O53/xa93sDpaQSvdwqD\nwcju7g5WqxWNxkyt1qbZHNJqCfc/EDgiHs9gNFrxeGZYW7sksfRgrDyuZnf3JUajlatX3yOTSZPJ\nZLDb7TQaPbrdHIlEFqPRRDSaFs13Bean1+ulXq/z4sUB8XiWfD7P+rqg/fXo0XPK5RZzc/N0u11+\n8IO/JRQKsre3h06nR6s1MBxCvd7izp17HBwc0+126XY7mEwWFhYWODk5o1DIo9XqUKt1NJtdut0e\n8XhaxCjFUKk0DAaQzRaZnJzGZLJiMFhYXb1MNpvBbLazuLhGp9NnOFSg1Zoxm11MTi4QjUaYnV2i\nUKhQq7WYm1tmMAgilyux2dxMTEyd27OHwyF37jwgHk+jUKipVhtYrU7sdjs3bnxAo1HH650jEDjk\n6dMdpqdnsNvtlEo1BoMRsViK2dllzGbBVkgulzM19SoJjcdjnJwE+eVf/hXkcgWRSIJ+/yGXLm1g\nNNo4PDxBqzXS6/U5Ojrg9DQksvSGzM+vMTkpjIU3Nra4c+cOf/RHf8a3v/2bVKuC92AqVUKtVuN0\nmtjbOyafr1IuNxiNlGxtvSeBxw0GI2azi3Z7j06ng9vtZmVllXT673j5co/NzSv8xm/810xNfZ9y\nuUo0GmdmZkHEfOZ4/HiX58+foNPpsVjc5HI1zs4EHJXNZuPFi32OjoJ4vdPMzMzSaDSYn1+g0ThA\npdJSq7XZ3w9iNme4fv0GlUqLe/cek8kUaTR6LC6+/Rw9Pj6j1xvR6Qzp96FYrBKJZETNtRbdrgyX\naxq1Wk+hUEYm02CxTPDs2Z5IaiqxsbHJb//277K7KzgDnJyEefFiDxAIM+vr1yiX2ySTeSYmJmi1\nhlQqLeLxHDqdjdFoRCgUo9ls8sMf/oxyucRgMMTn86FWm2i3k+I6G+B2T+FyuXE6pzCZHFQqHUDo\nlNVqVU5OImSzRc7O4jgcE0QiKdTqF0xOTl347oIxeJ1ms0Oj0eH0NAZo6fcVFAo1zs6Sb53G+Hxz\nFItVyuUm3e6IQqFCtzskHs/R7yt/oQbD5yZYgUDge36//yO/378NyIH/Hvhv/H6/MRAI/Hu/3/9v\ngZ+Jd+THgUDgB//gK/kq/smiVqsSi8UutEdBmKvv7+9hsVhotVokEjGCwYA4o3efU/LNZDKEwxEa\nDUH47m0U9C8TJyfHyGRIYEW1WsPU1DSZjMDSWlkRxlFGo5FWS3BoFwx3naKoosAOdLs97O7uEA6f\nMTHhZH5+EYvFwuHhPoPBAKdzgsGgz9LSkvRgjUYjkWnXIxg85uOPv0k6nUQmEzAYCwuLhEKnonL5\nLAaDUdR4qol+gkJSOTs7Ty6XIxIJYzSaiMfjFAoFCTcRjwv3e3f3JaenJ1LnpNPpiAljing8/rkJ\nVr1eo1Qq4nA4zuEBnE4XCoWMFy+eUalURFuWPnq9gUePHpJOpzGbTZTLZY6ODpDLFYRCJ9TrNUKh\nU9RqNZcubVIul1le9vPrv/5taTQ8Go24fv0Gf/3X3yWbzVCv1ykWi1SrVUl5W6fTsbzsR6vVUiwW\nxPGsQvLte3X9DUljRqvViDY0SVQqtfR5odAJtVoNrVYjgoKtBALHGI1GJienqFTKtNsuPv74m9Ko\nrt/vs7S0wpUrV3E4JohGw+zt7eF2T0vkA+Hz65ydhUgmkywsLJJOp1hdXT83Xuh0OtTrNUwmE1ar\njcFgIOmejT0Qnz59LHZEPdTrDWZmZkTV9XWcTifdbpe7d+/w6ad3aLVarK0JQO5IJMzJyTHz8wtk\nMoIhebvdkrTJPvjgQ3EUnxctbY4BGTKZYHTs83mxWCz0+32RyeiVKP8CfsfJYCBU2dPTsxSLBU5P\ng7RaDZxOF9vbD/D5hAOsXm/QajVRKl9JCfj9q9jtdmKxKIuLS6ysrDI9PcOjRw+IRMKSTMLrMdY9\ncjpdfP3rn/CDH3xPwuG8++57nJycsLf3UhyLB+j1ejQadXK5rMgCRWImy+UKHI4JbDYbOp0OmUwm\nMQCtVhtTU9M0GnUSiTgWi4WtrXfY29ulVCoSjUY5Ojqk3W7hcExQLBa4e/fv0WiEtXXr1tcIBoOE\nw2f8wR/8voTv/N73/prJySnMZg13796h3x+wsrLKwsJ5b8pxrK2t02w2JaKP0WhmNHpl6zUmw9Rq\nwvOhUimJRiOcnYUYDIZMTU1x9+4dhsMhL1++wGazMRjMEgwGSCTimM0WXrx4JgmD1mo10Rz+hQj8\n17G5KfhfCqb2KUKhE9555/oFN4hOp0OhUKDfF7wpV1ZWGY1kfPrpbek1er0er1eQJFGplKytrXF2\ndiZi3mYknJJMJhPH1QqGwyGVSoBGo4lcLuPRo/uSfpTFYqVarUl6euN9Kxw+Q6lUYTSaMJlMTE5O\nSWNipVLB/PwCP/nJ3+H1ehkOh/zkJz/m137tN1AqlaIkR5hyuUQgcCSyqI/xeKqEQqeUyyVyuaxk\n7uzz+ZDJ5GSzaXK5jIj9a3N0dCgKlArrvVKpvDXBMhgMTE3N4PF4xe9aZjAYfK4K/JeNL5RpCAQC\n/8vn/NsfAX/0C1/FV/FPFt1ul6dPn3ymv1I+nyOfz6PVapHL5SSTgomsXq/na1/7iNnZOYnxEwwe\nc+/ep5TLZfr9AZubV8hkMhIDTK3WfCZ9eByZTEZ6KN5M0K5evUa/37+wsMcA91gsSrvd5ujogHa7\nhcvlJhw+I5tNYzAYkMsFEc9CQcCcyOVy9Ho9JpOZXq/HyUmQfr/P5OQ05XKJ09MTcrkcS0vLaDQa\nSVen2+2wvf2QbDaLQqGg2+0SCgmikwaDkVarid1ux2QS2FdyeQatVvB6i8dj56796OiITqeD1Wpl\nbm4BhUJGNBoVE4XeudeOLT42Ny+LFPooIOjhCNYvgqyF2+1hNJLx8uVLXrx4jkKhEKnGf49crsBi\nMTM/v0Sr1eT+/bu02x0RoC5oyiwtrbCwsMT29kPcbg8Ox4SksZVMJkQQdV4Elwvm1EJimkWjUYvS\nESkuX75KsVhgZWWVK1eu0mjUkcsVIgPHIG30MpmMmZkZfL4pfvKTvxMTtxpWq40HD+6Ry2XweidZ\nXb1EPB5DJhNGoy9fPqPdbpHL5fjrv/4ryuWSaOSqQalMcXx8hF6vp16vE4mcsr39lJUVP8HgMTMz\nM8jlCs7OQmIXz0Cr1SKTSZ8jDTSbDapVYeMVMFd1yRZFALuqyWQEL8zZ2Vuk00kMBgPvvXdLGmWM\n8UNKpZLl5WUmJ6cYjQTw/v7+Lg8fPpCwTw7HhHR/7t37lGg0jEajxWq1U6mUJE+7SqVMNBrF718n\nFotSrZax2WxoNFpGo5EI1m9w6dIGq6vrZLNpEadWkAx+W60mHo+X1dU1UqkESqUKu91OMBiQwN71\neo2dnR0sFjPNZoNA4FAE+gq4ZY1EpgAAIABJREFUvjdxZ7FYjE6njc/nY25unt/8zX/No0cPUKtV\nnJwEefr0CaVSUSpYbtx4T8QejUQdtK5ozWSh0agTj8fOPTMCqFtHt9tFoVBw5coWDx7c4+BgX0zW\nmmg0akwmEw6HA5VKxXvvvc/MzCx7e7vs7r6kXC5x7dp1PvroY/7jf/wPoqXXKr1el1AoiE6npVDo\nUqlU8Xp9LCwssry88tb9ymKxIpcLz9dwOCAaDVOv1zg7C/EXf/EdMpkMVqtVPJgrbG1dI5VKotUK\nqvfz8wvUalUKhYKk0wTCoT4WwjQYDGi1Oq5fvynZeB0dHZJOJygUity58zNu3fqAW7e+RqPR4Pbt\nn3L37qf8+q//q3PXWq/XpARJp9Nz5coWhUJeWp9arVYq6AQ5Gd3/y957vsl1numdv8o5h66uruqc\nG+hGIxMkRYocUqI00ow9tsfX5cu7+y/tB39Yh5n1eLweSyONJCoxgABBZKAbnXN1rNSVcw774T11\nSAjSBI3kD7t6P5EECFSfOue8z/s89/27SafTZDIpKpUyCoX4M3pMKIVCwYULF5mbu8Dbb78ja0GN\nRhM7O9skEgm0Wg35fI7d3W1UKiULC4vk83lKpSKjo+O8/fY7UjGW5vHjhzSbTaampkkkzllZWWFs\nbEzGbnz/+3/zEn/L4/FQKAQ4Pz+X9cGVShmNRofX20cqlZAI/248Hq+kORVJG8lkUnYeivtRSS6X\n+7W6yF4ndWAgwP7+ruwoLBaL/+CE4R9afwCN/p6XiFix/E4cCb/N2txcp16vMz4+js/3qhtwbW0F\ns9nCrVtvoNfruH//C6kIGXjlRDcyMsrm5jqFQl4aATx7RSs1NDTMxMQk5XJZziQ0Go1yV+Hw8ACF\nAkZGxvjVJaJaXh07Fgp5kskETqcLm81GIDCIy+VkYWGRbrdDuVzCbndydHQMIPGs1DidbgIBUYQd\nHR3KNPPr118jkTinUilzeBiS4050Oh27uzu0Wm1OTo558WKJbFYI4QOBQTQaDXa7Q84PGxoaZmNj\nnW63y+LiFZ48eURfnxA722w2qaDJY7PZGR+fIBQS0SfdbpfT0xPy+TyVSgW1Wk2n02Fvb4d2u8PS\n0jPm5xeJxSK0Wi0ODw9euh7RaIRMJk25XCQYHMRkMslZdWazmUAgKLuXxscnqVYraLU6Tk+P8Xi8\nXLy4QLPZIJfL0Om0ef78CUajSS7C1WoVExOTkghZjAW0Wg1+v5/p6Vm50ynYWGnm5i78WjAhIHdG\nzWYL3//+35DNplEolNImpGd3d0e6vkFWV5dl0OLW1hYqlQKj0USpVGZ9fQODQcBBA4EAZrOVVquB\nUqmi2xVF+PHxCel0GqfTwcBAEKfTgcViY2RklNnZWW7f/oS1tRWKxaLczQ2HzygUCly8OCK51Epy\nxmOxWMTtdjM7O4ffP8B3v/unHB0dcvv2J/zX//oXUqErhM5arU4Gi3a7XSKRMEajYCgtLz+nv7+f\n4eFRPv/8jtxl2d/fJRAYlMamFzk4OJAKKZeE4OhydnZKvV5Do9FSq9VZWXlBsShcje12m1QqwZUr\nV1lYWMRms6PRaCgU8vz85z/l8eMH5HI56vU64fAZuVyOXC5DoVCSMviGODjYp1AoSFFS4p7JZDKE\nw2EODva5du1L2GKpVJKfB4dDZBv6fD7ee+8bnJ6e8OGHP6ZYFM+q0WjE4zHKBPHBwSHef/8D4vEY\nT58+Ip1OcuvWmwDSgU1Ac9vttmxGmZiYxGAwcPnyFUKhAzqdDna7Ha1Ww/DwCB6PF6fTzc2br+Fy\nuWm325ILscDq6gsuXbrM8PAoBoOR7373T2i3O7LzsFLJEoul8Hr7/t4g4qWlZ7KoPJfLEolEGRsb\nJ5/PsbW1TjgcRqVS02jUyOfz6PU62u0O1WqVRqMuoV+6pFJJstksrVaDRqNBq9XGahXdSbdbHHCE\nBOICnU4HlUqJyWTEYIhIz1sEo9EkdcDaPHx4n2vXbkiMM6E3jETC1Go9TZcZn8+Hz/cqzkGEUbfw\nej0UCgXpkKiSXMdn5PP5l3hhCoUCg8HAG298TSruNTL889q1G/Khd21thYGBINVqmXq9icPhJJcT\nma89vl0vfubzz+8Aouum0WhwOh1yd79WE6aI3oGoWq0QDodZXLzCxMSUlJGZpK/PR7stBO/DwyPE\n43GCwQDtdpt2u4Xf78dgMEoh6JHfGOPW25t7+0G73cJut/9WyKFfXX8osH6PK5NJs7LyArPZzK1b\nb/yzgyP/qevs7JREIoHT6WRsbOKVX+92u3I3ptd5mp2dY3d3W4YufnX10s6dTherqy+4d+9zut0O\nc3MXZTL29vYmP/7xD1GpVPIYyWQysbh4hZ/85EccHYUwGg1sb2/JsTRfXUqlQmYreTwOul01e3s7\nnJ+LvDOlUgi7R0fHMZnMEuJBJY92QJxIeq3pUOiAdruFSiUy0IaGhslmRayLy+WWMxX9fj+ZjEIK\nfJ5gYmKS73//b2g0GtRqNXksolIpqVZrNBp1+vv9FAoFnE4XkcgZly5dlk9sIDYLq9Umjx41Gg2d\nzqYcL/LkySMymQyNRh2v1yfrFGKxKEtLzyQ+iwDgTU3NoNVqiUTC7Oxsc3h4yNjYON/97r9AoRAu\nszt3PsPn6+P99z9ApVKhUChwOATx+Wc/+wnFYlF26gkuGDKnq9Vq4XQ6GRgI0Nfno1wu8ejRQ9nG\nHAwOcuvWG/KJLhAI8LOffUgsFpPE1xkymSxqtQqDwYjJZMTrFY7GbDaLTqeTAmwdjI6OkUjE2d/f\nx2QyMTk5xfr6miT+78PvDxCNhvF4PAwODlOtluXPLrLvxNi6XC4xNTXL5OQkfr+HH//4Q6xWK35/\nAINBT6vVoVQqyqPn09MTyuUyzWZL3lTX19eIRiN88MEfk81m5LBxp9NFKLRHPp/H5/NhtdqkAiNL\nqVSUI05AOLcMBiPZbJZ79+5KxPEv4zcAvvnNb2Gz2QiFDpicnOLb3/4Ojx65ODo6oFzudUTNTE/P\n0N/fL3WylIyMjKFQKDg42Ofzz+9QLpcZHR3DYDBIxOk6//2//zd0Oh1qtVo+zBWLRc7OzlhdXeX5\n86cS+0yN39+PSiXiRMxmC4ODgzgcTq5cuSZ/1mg0zI9+9EN2d3cYHBymr6+PdlsgBvb2digU8hwc\n7LO4eIVoNEIodCARvQtSIHGZdDpNPB6XOiMKBgYC9Pf3Y7fbSSTOSafTPH36BBAbvtlsZnHxCgqF\ngnK5RCqVkp2bdrtD/ny1Wk3SUkXl8WcgEEShULyEOgmHw4RCBzgcDimOKcXCwiVCoQP+8i//E5lM\nkkZDjCLv3bvD2NgYBoOJsbFx2T2Wy+XY3t7CZrPJXKbx8XHee+8brK2tsrW1idlsRafTSS7YPaxW\nOwqFgnq9jtlsQa1Wo1KpKBaL2O126dkqE4+LYtbn87G4eIWnTx+zv7/L3NwFGeJ7enqK3e7E7XZT\nKORYX19BpdLQ3z/Aysoyf/EX/4nr16/z9tvvolKpOD095vw8zvDwTRlR8utWrVaj24X+/gHOzk4p\nFksMD4/gcrkIh884OTlmZWWJdrst71kKhULOXe12u2xsrGEymTk42JcPK6enJ/z4xz8gm81KmI9B\nmRun0Wi4dGkRp9NFuy0g0Gq1ErfbzdOnT9BqtXJMkHBvq6hWBSFfq9VJDucjXC4X6XRaOtRe5eBg\nn3Q6Jd0X4qBdLBbRasWBZHR0nFDoQErgSMvdua+uXoEVjUZJpVJoNGK0+Q+FVP9j1h8KrN/jCoVE\n56FUKkkvhIF/4P/43a1yWbT7NRrNb9RKieDiFv39X36uoaFhfL7+V4jq4qUWwWw2MzY2Tjwe5/79\nz+WNRqlU0Gy2iMViJJPnuFwuMpk0AwMBSqUiv/zlT1lfX5cCWvvRaDTyja5SqRkY8GM0mmi32xKw\nr0i7XadUqhGNRlGpVNjtDg4PDzAaTXg8XjldPpcTNOFUStj58/k81WqZWCyMUqnC6+3D7fbII7hO\np8POzhbAV1r3OjqdLna7nevXbxIKHXD9+k3OznzkcgWGh0fQajUcHR2i0+mp1xtYLEo5h3BzU9jT\ndTqdvJlXKhUOD0OUSkW5mydcawWq1SqxWAyz2cz5eYLV1RXeeeePmJiYwuvtY21thWg0isfTx+Dg\nsLx5KJUq7t79jEQixvXr1+VxFggrc19f/yucqWq1islkYnZ2jpOTE7a2NshksgwPj8hF8MWLCy+9\neHrBtSLupiGJdcUm0263pXgdIxaLEBzv7u5I392XeY5Go2D3NBp17ty5jclkwu/30+12iUYj5PM5\nyaF0IgcET05O8e677/H553dptZrcuvUG5XKJtTUhrDcaDSSTggrf6cCDB/cYGRlheHgYr7cPn6+f\niYkppqenqdfrtNsduSs6MzNHPB4jGAwyNjZOt9tlZWWJWq1OLpfBaDTJzC2Xy8XmpsguFGOeIg8e\n3Ae6TE5Oo9VqcLu9UiadBavVSjKZZGdnW7KRK5ifX8RkMnJyciKNOhPU6zXm5i7I3VqtVk+pVKHV\natNsthgcHOL69Rs8ffqYTCbD+voqCoVCGm36uXXr38jjXZVKRaFQoNlsSjiEGqVSQY6QunhxgY2N\nVer1JgqFAqPRgNvtRalUks1mWFtbZXR0DKfTycbGmvy9ZbMpKpUqh4chPvzw7zAaTezt7RCLxWg0\nRNcwmxWdMa1WR7vdYnt7k3D4DLfbzcTEJMWigD4KzZfoeNntws4vQpvreDweut0uT58+IZfLyYV4\nr/hNJM5fuZf1ej17e7sYjUZcLheLi5flImB0dEymv4fDQk7w9tvvMjg4RCKRoFarUq/X2dvbQ6NR\nYrU6pfFfknK5ImtCewe1UOhAKrD7UanU2O0O/H4/druDr33tbSwWC8FgkIWFSzx8+IBOp8ONG6+h\nUqmIxaK89dbXmZqaptvtcvv2xxiNYrT8859/yObmGj6fnz/5k39Jf7+f7e0tzs7OKJVKmM1mvN4+\nDAYDpVKJfD5HJBIhmUwyOBjAbnfSajXZ29thdnaOUknwrVZWVuh2O8zMzNHf76darZJInOP3D7w0\nGehBQN1uDz/5yd/RbDa4du0GOp0oLI+PjyTHoA2FQikXUAJWW0Wn06PVahkYGKBYLHJyciQVmA2O\njg5JJJISc7CMy+UiGBySXbkgYMLFYpG+Ph+Tk1N8+OGPJYe40Aj6fP1cvLiATqel3W7L2JdcLsvY\n2Bhms5mJiSlJYF8hn8/J2r2+vj6USiUmk5larYpCoWBkZJSNjXWePXtCPB5DrzdgMpkkXp5fklgk\nCYX2iUTOpOsgQKV/X3fzH7P+UGD9npa4KYQIsFQqyFEMv8rv+H2taDRCu91hdvbCb+R59MZ7PR4N\nINO+fzXio1Qq0e0KensiIXL5dDodQ0PDLC5eBUR8Tq1W4Re/EKQOj8eLwWCk2Wzx4MEDuWs0OzvH\n1NSMLDgsFvOSqPKSTFzO53Oo1W0eP17GbDZz6dJl5ucv8R/+w/+JQqHEYDAQj8fkDESfz8fly9eo\n1aqSMDuF2+1hbGz8lRMLQKVSIhKJUKmU0Wp1zM8vUKmUCQQC1Ot1Tk6OMJvNXL58jSdPHmE0GlCp\nVFQqFfR6g8yEEdemSLPZwuPpI5FIsLb2gnZbdE9yuRx6vZ7zc9ExsljMMoBRp9Nx4cI8x8c/kYsI\nQYKfwOl0k8/nsdvtTExMUijkZR6PAOIJXUevmxOJCCaQiMMRIdMi807JxsYqsVgcl8spBy/7fH18\n4xvfoFgUQbpPnjxiamoGlUqIs1UqtRx/0263SKdTElXbz9LSc46PDwmFQng8HlwuF36/n6GhYQnI\nWOP4+Jh4XLh26vU6iUSCwUGRNfnRR7+QQ36z2RyVShmlUoHDIToVKpUatVpFsZgnFNrH7w+QSMRR\nKJRydM3MzAUqlRKrqyt88slHDAz04fUKp1e9XkOvN7C4eOWl77zVanH37m1KpZJ87+n1BhwOEZGR\nSCSkg8kOPl8/tVqNUqmEy+Vha2sTpVKJzWbDarXSbndIp5PyiK9ebxAK7UtYDSUOh5NMJkk4XOXz\nz+/w4YcN6QDR4saN1+SYo95Bxm63U6tVuXfvDqWSgICGQvsUCnnK5Qpms4XXX3+dK1eu8fjxQ8Lh\nU4LBIVQqFUqlkoWFSwwMBCSgZ4wLFwRO4smTR/z85z+V9IgGisUSpZIgY5fLFSkOKkehkKdYLMhA\nV6H56/LFF5/TbDZRKhXYbDYGBoJSJ7bN0tJTOfWg2WzT3z/AjRs3mZ6e5d69OywvP6darUlA0Sbx\neAy1WiOFI+fkQkapVNLtdmi1mnS7It1AqVSQSCReKbB6I+uhoWGuXr3+0gYoEgXG2NvbZXZ2TmKM\nFchkmuzsbEvawiT1eo2xsWm+/vVvMD9/icPDA0KhA+r1OpcvX5Fdx+12C5vN/tKY9Kt/XywWY39/\nH6VSJXVX3DIvLJ1O8+jRfXw+nxQL1MFms5NKJSkUCoyOjvPOO+8xPDxCqVTE6/Wyt7fH6ekJs7Nz\nuFxuDAYjnU6HQqFAvV6XYoeCzM8vkM/n2dvbYWNjFegSj4vooqGhEebmLrC3t8vp6THttpAOXLly\nTS6yep3X09NjCWOCVKR2MRgMHB6GGB8f5/r1m/J+JSQIEarVCpFImLm5eSYmJjk8DHHhwjwTE1Oc\nnByTz+eoVqvMzl5gYmKSTCYtJxL0nsHj4yPa7SZDQyMSEsRCX5+PxcXLEgRXjBR7h0er1Uqz2aLV\nalKtVnn99Tfl78Dn62d3d5tw+FR6v1pYWFik1WrJP6fBIELJd3Z2KBaLko6xTbst8h2NRhOJxLnE\npauhVHbJZMQheXz81cnPP2X9ocD6Pa1e92p6elpy3wlK7asvjH/+arVa8qmkt3piVCECLsrsonw+\nh0ajZWxsnFQqKROMe2tra0MuvOr1usQ7EieYnhOnVqvQ7XZwu93SC1fcRhqNhr4+H8GgIFMHAgFO\nTk4oFosYjXq63Q5+/wDz8wtMTc3g8XglYF2K1dVlVleX6evzfSU7SsnS0pKcPffw4RdYrTY0Gg3Z\nbEYqYETUTS9otlDIU69X0Wi0eDxeHA4n9XqdXC5HtyuKRoPBwGuvvcGLF0usra1Qr9eZnZ3j9PSY\narXKwcE+7XaHmRkRICziGgR0sdPpUioVXzINfJl/2GR1VYA/FxevyBv99PQ0g4PD8u/3+wfIZjOk\nUknu3/+CYrGAWq2mUimzv78n6Q5qKJVC1H779sd81QB64cI84XCY9fU1WfyeyaQllouO58/VNJsN\nQqEDEokEzWYLt9sln9KMRiMLC5fJZMTn9ni8JJMJlpaekc/nCYUOMJmMDA2N0O126evzEY1G+eyz\nT6T8MQHs9Hq9LCxcwu8feOXw4PcPSHDVI/R6A319fTidLjKZDNFohEajjt8/QKfTRqMRRHmfr596\nvU4sFsVud6JQKCRnXwutVodCoWBx8QqZTFbq1vWh0WglwGuZ2dkLlMsl1tdXSKdTpFIJgsEh+ZlT\nq9V4vX3EYjGOjg4xGAxUKlUWFi7JkRqxWIzV1RXi8Sj1el2Gox4c7KPRaPja195GqxWO0Egkwubm\nBplMRoqOEfedRqOh1WrRbrcwGIxoNBpSqYTcnYlGI3I6QqvVJp1OsrT0jLOzEzodMYrvdETB4fP1\n4/UKAW+n0+GLL+6STCbRaLTys/TgwT05sqk34jw6CnHp0mXcbg96vYGJiQnee++bPH36mKdPH9Fu\ntxkbG6NQKHB4eCDhFFwcHOzh8bhlYKVWq8HhcMqBx5VKGavVRjA4yOFhiHD4DI/Hg8VixePx4HA4\nOT+P4/X2YTJZpA3MSD6f586dzwiHT3G53Jyfn1MulwAFqVSCSqVKJBKWQ8KnpmbI5TJyF6HRaPDF\nF5+ztPSMRqMuh/LabDa5SB0ZGWNkZBSbzYbNJkaR+/t7aDSC7be6+kIKVQ/w2muv8fbb78gi82Ty\nnM3NE2kkpeXo6BCVSs3o6JhEdy9SLBZlrVixWGR9fQWTyUyz2SSbzeH1erl8+Qoej5ePPvo5h4ch\n/vqv/wq3283p6QnJZIJkMkE2m8VmE9DMO3c+pdVqSwWdKARnZ+ek8GcPe3s7dLtdWRd44cJFFhev\nUK1WWV5+yhdffMHq6oqs8xodHef+/c/pdkW3z+WykkgkeP78KVeuXJM6RVXa7TZ7e7sYDAY6na40\nghdE90JBOO+++jwLZMI+mUxaAp8KR5/RaJQ/j1IptJUGg4np6VmuXbvB8vISu7s7xGJRlEoV0WiE\nQiGP0+nCbLawtbWBy+Xi3//7/0MyftwjmUzy9OkTDg9DkgHLIENTE4lzgsFB+XNpNBo8Hq8cb9VL\nI9HpdEQiYb744nP5fe12uxkbm8Dj8VKv18jncyQSCYrFotxkEJouBVNT06+4NH+b9YcC6/ewcrks\n6XQal8uF3e7AaDQRiZxxeBgiEAj+2o7Kb7tKpSJPnz55KSOt3W6ztraCySRs+79ubW1tUK1WGR+f\nkJlG6XSao6Mj2u0WQ0MjbG6uU61WMRpN0jhIvDDL5TKtVputLUHd7s2qG40G9+59TjweRa1Ws7a2\njs1moV6vEQiI04jJZGJnZ4dqtcb09AxDQ8O43W6uXr3O8vIS8XgcjUYIqlMp0QEJBIKYzcL1NDw8\nSrVaIRqNkslkOD8XnY319VVOT49ZX1/n2bPHaLV6nj17jEKhQq1WSnRfN0qlErVazXvvfUMa79hk\ncaPVapXn/BaLBb9/gGJRBEoXCgW0Wh06nZZkMiFvZL3vO5kU2VpqtdB6ra+vEAgMIlrNLbLZLFtb\nG9RqNcxmMzqdiOI5Pg4RCh3S6bRl12LP9dbX1yfxXoTI1O1243S6eP78Kd1uh/HxcbzePprNJsvL\nyzidTtRqsSGKAGkVSqXIULt58zUSiThqtYrFxSvcuvUG1aoYAxkMBi5dukyhkGdrawO73YbL5cFg\n0HP58lXq9SrPnj1jZ2eLra1N6WTZxWazSyL0EqenJxI9X02325WCox1YLCnOzk65cuUarVaL09ND\njEbh6KtUKhKV3cLMzAU5M1CAPp0MDg4yP7/A9vYWly5dxmazcfnyNba2NtjYWMPt9jA7e0HSsTVI\nJhNYrVbq9Qa7u9vU6w202ifMzy9w8eKCjF3I53M8efKIarVCpVLG6XQxNzeHzWYFFFLkSYnj40Ma\njSa//OXPKBYLzM5eIBgcIh6PUKvVAQW7uzuk0ynZyeTz+SmVCjSbLdnpVq83ePHiOUNDwxwdHRGN\nRjg+drO9vUU6naJaFXEzwsloY3p6lmw2LSM1rly5ilKpIpk8p1arcX4eZ3JyitnZOfR6PcPDIxKw\ntEkwOEipVOL8/JxwWIycVCqllFhg4623vk632+XFi2XC4TOsVivVak06qBiwWKxSsaySA6xF/I7o\nCN258ykOh4PvfvdP+elPf0Kj0cDjcUvCaDcXLlxEq9XJh0pB2O/HYDBIwmovLpdHGo/qyGbTGI0m\nBgdH6Hbb+P0DPHv2jIODXfz+APfu3UGhUBAKHbCzs0Oz2ZCo6DFarRYKhRgNmkxmdne38Xq9sjjZ\n4/Gi1erodrtSFzmOSqVkfv4Sf/7nf06hIN57er2e6ek5Hj9+zOPHD6nX6zx+/AilUoFGo2FjY41O\np0uhUECjUWMwGKlWq2g0Oq5du4FGo0WrFcLvXpFy6dJlKS0iSSKRkDEt4vl2Uy4Lgr8YnRokl/Y+\nx8chuajsGW9EvqgY/+fzeVKpFLlclmBwmJOTI2l8b6Pb7eJ0urBa7Xi9XoaGhlGpVGxubhAOn/H8\n+VMWF69Qq9WIRiMynqRSqZLLZaSfSSOFVL88aTk6OqJcLknaLb/kVlTwxhtfe8nAVSgIELTBYMBq\ntXHjxmvcvv2JbLYSkoMaXq9PDmz3er243eLd7PcHyOcLsvkCkA6fFWq1ClarlYWFxZf2UL8/IBd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NJ9rsJutxGJhDEY9AwNjUgShSH+uesPBdY/cyWTSZ49e8LW1ibVaoW+Ph+HhwfSGKwgiTnL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\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tKu6S9jRrYWUyGUwmWrwzzwpd1PSkvmuxWAyz2XSspILL5dIELXOHPkY3R35QiYinFbPZ\nbPhlBgJBzf/v9MMIZ43FYsFqtRjWPse5Wjwsfn+g7WS10+nE7XaTSiUNyZGzblw/LTbb2Rk+S6FR\nyZcSoda7SSAQMAIrvan7sNWJLjzXbqqpv3+AnZ0dxscnHsn2NouNPmr0MegHbWyVPB70ycpisUgu\nl8Ht9jySLIPeUF8o5NnZ2dEmqQ4X4s1k0gSDnScSpHS7PaRShwdvujnyl4nZ2QuMj0889UGj3e6g\nViswPX3usVlzNdPR0cnm5gZbW8L8+mn7vnT7LjEIdLpFqAywJF9KdIuG5hKEbgtzWICljyy3s3eY\nmJhkYmLyrDfToFVs9NGil0hlifDpRFfxTiYTmrHzo5my00VNP/74Q2q1Xcxm05GZLKvVyvDwyTIe\nusaSGNVvzVCUSqUjDY2fVaxW61MXLLRjcHBI0x87KJz8OOjsFAHWzk4EeDozWKB7GsoASyI5gM/n\n5/XX32gRujsug/UwontnhdPpxGw2kc8/+gDraVFxl7RHLxfvXYAezUVbzyDV63XGxyeYmJh8YNuc\nw19bBE/5fP7ABVQvvzxtvTdfFR7lQvEk6OfXRkP0pD0ue7CTcphlz8MgAyzJl5b9J/bjDpxkMmn0\nCDxuTCYTLpf7sWSw9prcZYnwaUQv3ek9TKdxCTiK3t4+zp+v0dUVOvN9Xg+e2vWy6AKVj9p2SPJ0\nove4FovFpzLIPskw1EmRAZbkK8NRB04ul6VSqTAw8ORELl0uF/l8njt3bh/aG6GrJjdfEKPRKJHI\nNhcuPHeinoq9AEse/k8jTqcTk0ms8OHRTBCC2JcelZdjcwZrP3t+fF+uEqHk5HR0dFIsbj6VAZbM\nYEkkD4HJZMJms7U9cPYsIx5/eVAnGAwSi8UMw9fDaDQazM6eN/5eW1shFosxNDR0oglEvUQqM1hP\nJyaTCadTrPAdDscjl+14FLjdbkym9kMb+vTY03hxlTweurq6CIc3H9ni4TTohti6wvtpkAGW5CuF\n3W43MjjNPMn+K53JyWl6e/s5bJKrXq/z4YcfGBcoHb2UlE6nTxRgyR6spx+9hPI0XoBOgtlsxuVy\nH9hXQQRdwmhX7n9fVfr7B2g0Go/c2/Nh6OzsZHp6+kyGAGSAJflKYbPZKRYLpFJJEok4xaIQvovH\nhYL6k+4LOW6ixuVytegL1Wo1Q7wvnU4d9rQW9jJY8gL3tKL3YT2q/qvHgcfjIRqNtoy71+t1CoU8\ngcCj1XqTPN2YTKZjjbGfFGazmYmJqTN5LRlgSb5S2GxW6vUGH3/80YH7hoaevtXUfjweD7FYzLho\nNWcImj28jkKYXVseua+X5OHxeER/0rOawQJ9SjGqBVRBQGSvGg3ZfyX5aiADLMlXit7ePkqlEoFA\nkFAoZPSBmEymZ+Kk7/X6iMVi5PM5gsGOlibiYrFIpVI5VkBUBGey/+ppZmhoBLPZ8sS0is4CPRtc\nKBRaAqzm+ySSLzMywJJ8pRgcHHpqU9MnQb8w5XKtAVZnZyeJRIJUKnWsv1u1WvnSqWh/2bDb7YyN\njT/pzTgV+j7WvAiQDe6SrxKyRiCRPEPoPVr6RSuXE36LuqfYcX1Y9XqdWm1X9l9JHjl6RrhZC2sv\ng/X0Z4slktMiAyyJ5BlCX/nrgVU+n8dmsxl2KscFWHsaWDLAkjxaXC4XVqtlXwYrj8mEzKBKvhLI\nAEsieYaw2Ww4HA7y+bwxkeX1+rDZbHg8HjKZ9JHPlyrukseJ2+3Zl8HK43S65ICF5CuB3MslkmcM\nr9dLsVgkk0nTaOz1ZQUCQarVWouMw350kVWZwZI8DtxuN7XarmbwXKNcLssGd8lXBhlgSSTPGHqZ\ncGdnR/tbD7CEZtJRZULdJkgGWJLHgV4KLBTyRiZLlgclXxXkFKFE8oyhN7pHItva30IrKRgUo/Cp\nVOrQSUmp4i55nOjB/8LCAmazqeU2ieTLjgywJJJnjGZ9oea/fT4/FouZra1N4vFY2+fq/lqyB0vy\nOPD7A5hMe1ZUJtPeQkAi+bIjAyyJ5BlDz1gBWK0WXC5hq2IymRgeHjUyW+2wWq24XC6j2hR3cgAA\nIABJREFUnCiRPEq8Xi9vv/1NI7C3Wq3HCuFKJF8Wjg2wFEW5DuijSUuqqv5S031/DPhrQA34LVVV\n//Ej2UqJRGJgt9ux2+1UKpUDgo2KMoOizDyhLZNIDqLvrxLJV40jAyxFUZwAqqq+0+Y+G/B3gatA\nAXhfUZR/parqzqPYUIlEsofH46VSSch+FolEInlKOW6K8DLgVhTlO4qifE9RlK813TcLLKiqmlZV\ntQq8B7z5qDZUIpHsoQdW0nJEIpFInk6OC7DywK+rqvrTwH8C/J+KoujP8bNXOgTIArKxQyJ5DHR0\ndACyYVgikUieVo7rwZoDFgBUVZ1XFCUO9AObiODK1/RYH5B8FBspkUhaGRgYxO8PGJINEolEInm6\nOC7A+kXgIvDnFUUZQGSt9BGl+8C0oigdiEzXm8CvH/N6pu5u3zEPkUj2kPvL4cjvphX5fUgeBLm/\nSB41pkajceidWiP7t4ERoAH8CjAOeFVV/Q1FUf4o8KuIUuNvqqr6jx75FkskEolEIpE85RwZYEkk\nEolEIpFIHhzpRSiRSCQSiURyxsgASyKRSCQSieSMkQGWRCKRSCQSyRkjAyyJRCKRSCSSM+axmD1r\n4qT/ELgElIH/WFXVxcfx3pJnh/2+l8DfREyx1oHbwJ9XVVVOZXyF0dwk/paqqu8oijJFm/1DUZRf\nBv4MwiP111RV/f0ntsGSJ8a+feV54P8D5rW7/6Gqqv9C7isSTS3ht4BRwAH8GnCPMzi3PK4M1r8D\n2FVVfQ34r4H/8TG9r+QZodn3UvvvlxBel99SVfVNwAT8/JPcRsmTRVGUXwF+A3EShDb7h6IofcB/\nDrwG/DTwNxVFkU7DXzHa7CsvAn+36fzyL+S+ItH4U0BUO4/8DPAPEDHKqc8tjyWDBbwO/AGAqqof\nK4py9TG9r+TZwfC9ROyXfxV4QVXVd7X7/w3wU8D/84S2T/LkWQD+OPB/aH+32z92gfc1f9SqoigL\niMz5p497YyVPlP37yovAOUVRfh6RxfqLwMvIfUUC/wL4v7V/m4EqZ3RueVwZLD+Qafp7t8nTUCKB\nNr6X++7PIb0uv9KoqvovEal5HVPTv3UvVOmRKmm3r3wM/Jeqqr6FaD/46wh7N7mvfMVRVTWvqmpO\nURQfItj6b2mNjR763PK4gpwMrb6FZlVV64/pvSXPBnNoQZWqqvNAHOhtut8HpJ7AdkmeXprPIX7E\n/rH/XCM9UiUAv6uq6uf6v4HnkfuKRENRlGHg+8A/UVX1n3FG55bHFWC9D/wRAEVRXgFuPqb3lTw7\n/CJab57me+kDvqsoylva/T8LvHvIcyVfTT5vs39cA95QFMWhKEoAmEU0qUq+2nxHUZSXtH//BKKs\nI/cVCYqi9ALfBX5FVdVvazefybnlcfVg/S7wk4qivK/9/QuP6X0lzw6/CXxbUZQfI3wvfwGRxfoN\nrZHwLnt1cslXG32S9C+zb//QJn3+HvBjxALyW6qqVp7QdkqePPq+8p8Cf19RlCqwBfwZrSwk9xXJ\ntxClvl9VFOVXtdv+AvD3TntukV6EEolEIpFIJGeMbDSXSCQSiUQiOWNkgCWRSCQSiURyxsgASyKR\nSCQSieSMkQGWRCKRSCQSyRkjAyyJRCKRSCSSM0YGWBKJRCKRSCRnjAywJBKJRCKRSM4YGWBJJBKJ\nRCKRnDEywJJIJBKJRCI5Y2SAJZFIJBKJRHLGyABLIpFIJBKJ5IyRAZZEIpFIJBLJGSMDLIlEIpFI\nJJIzRgZYEolEIpFIJGeMDLAkEolEIpFIzhgZYEkkEolEIpGcMTLAkkgkEolEIjljZIAlkUgkEolE\ncsbIAEsikUgkEonkjJEBlkQikUgkEskZIwMsiUQikUgkkjNGBlgSiUQikUgkZ4wMsCQSiUQikUjO\nGBlgSSQSiUQikZwxMsCSSCQSiUQiOWNkgCWRSCQSiURyxsgASyKRSCQSieSMkQGWRCKRSCQSyRkj\nAyyJRCKRSCSSM0YGWBKJRCKRSCRnjAywJBKJRCKRSM4YGWBJJBKJRCKRnDEywJJIJBKJRCI5Y2SA\nJZFIJBKJRHLGyABLIpFIJBKJ5IyRAZZEInniKIrybUVR6oqiXHrS2yKRSCRngfVJb4BEIpEAvwss\nAZEnvSESiURyFpgajcaT3gaJRCKRSCSSLxWyRCiRSCQSiURyxsgSoUQieaQoimIF/irw7wITQBn4\nBPgfVFX9vvaYbwN/GriiqurNpuf+WeA/0563DfyviDLit4G3VVV9V3tcHfhN4J8Cvwa8AKSB/x34\na8AM8HeB14AM8C+Bv6KqarHpvbzAX2raThuwjihf/veqqhbO9IuRSCRfamQGSyKRPGr+PvDXgZj2\n7/8L+BrwHUVR3jrsSYqi/E/APwIcwP8GfAj8DeBXD3nKK8B3EAHYP0QEcv+N9tz3AJN2exIRtP2N\npveyAn8I/HfAJvAPgN8CXMB/Bfz2g35oiUTy1UZmsCQSySNDURQ/8GeAH6mq+o2m2/8xIov154Af\ntXneVeAvIIKqn9SzR4qi/A7we0C75tELwF9UVfXvaY/9DeA+8IvA31FV9Ve02/8GIjP1J4H/Qnvu\nvwe8DPyaqqpGAKcoyl8B5oGfVxTFqapq6SG/ColE8hVDZrAkEsmjRD/HjCiK0qvfqKrqZ4gy3J88\n5Hl/Wvv/X20uzamq+q+Bf4vIRu2nhMhQ6Y+dA+KIYOzvNN2eRQRe3YqiOLSbPwN+Cfifm19QVdUc\n8DliMdp51AeVSCSSZmQGSyKRPDJUVU0pivLPgf8AWFMU5X3g3wC/p6rqvSOe+hIiMLrW5r4PgJ9s\nc/u6qqq1fbflAZeqqjv7btczUQ6grKrqPDCvKIpTUZSvAeeAKeBF4C1tWyxHbK9EIpG0IAMsiUTy\nqPnTwKfALwBva//9bUVRPgV+WVXVL9o8JwTkD2ksDx/yPvlDbq8ct4GKopiAbwF/GQhqN0cQwdwK\nMEv7rJlEIpG0RZYIJRLJI0VV1dr/z96bxziSZ3d+n+B9k5kk86q8j2Jm3d1dVX3NaE5pNdIasrSS\nD8laGV7bawteyFgsvLAEDwxL/seADRjrNbySIXuttaSFhF0B49Ex0sxopqePurMqK6syMpN5kEkm\nbzJ43/QfwYhKVp5VldXV1RMfoNFZDB4/BiN+v/d77/veE0XxfxFF8SIwAfynwHeAq8D/1xWYP00e\nsAQCgYO8Rq6XMMx/Avw2cjjwp4EhURSHRVH8e0DoJXyehobG5xzNg6WhofHSCAQCk8gi9w9FUfy2\nKIph5Oy83w8EAt9F9mZNHfDS28AbyEbYjaeOvf0ShvrLQBP4ua7uClA9W/PIIULNg6WhoXFiNA+W\nhobGy6QK/FPgtwOBgEl5sPv3MHIphdgBr/u/uv//HwOBgHXP674C/DwHZxG+6DgNwMBTj/93yF43\nkOtiaWhoaJwIzYOloaHx0hBFMdatZ/WPgYeBQODPgTZyGG4e+B9EUSwEAoGnX/dJIBD4P4D/AlgM\nBAJ/CQwCv4Bcx8oHtF5weHs9Un+A7Bn7MBAI/AmybusryF60BLLh5QPWX/AzNTQ0fkzQPFgaGhov\nm/8G+C+RdVW/BvxnyFXWf00Uxf+++5wO+71S/xVykc8O8A+Rw4X/BDnECHCSyuqHebp6Pk8Uxf8d\n+EfIZR3+AXLWo9T9/3/efdo3TvB5GhoaGsAxzZ4DgYAReTKbQE5n/h1RFL+15/g14H9G3gnGgP9I\nFMXaSx2xhobG555uzayGKIqZA479S+BXgQFRFFOf+uA0NDQ0TsBxHqxfAZKiKP4Eskv/f1MOdMWf\nvwv8x6IofhH4S55oFTQ0NDRehF8FUoFA4O/vfTAQCMwga7CWNeNKQ0Pjs8xxGqw/Af60+7cOOctG\n4SyyO/0fBwKBC8C3u5WTNTQ0NF6UP0KuS/W7gUDgZ4ENYAhZg2VADh9qaGhofGY50oMlimJJFMVi\nIBBwIhtbv7XnsA+5M/0/A74OfK2b4aOhoaHxQoiiGEGu5v6vuv//r4G/g+wpf08UxX39CzU0NDQ+\nSxybRRgIBMaAfwP8c1EU/3jPoTSwLoqi2H3eXyKLUL9/2Ht1Op2OIGilZDQ0NI6nO7XsZRj497r/\naWhoaHwaPLfRcqSB1RWafgf4dVEUnzacNgBHIBCYEUUxCHwR+D+PHKUgkEwWnnesGj9m+P1O7XrR\nOBHataLxLGjXi8ZJ8fudz/3a4zxYvwm4gW8GAoFvdh/7PcAuiuLvBQKBfwD8YVfw/qEoin/x3CPR\n0NDQ0NDQ0PiccGSZhpdAR9s1aJwUbZepcVK0a0XjWdCuF42T4vc7nztEqBUa1dDQ0NDQ0NA4ZTQD\nS0NDQ0NDQ0PjlNEMLA0NDQ0NDQ2NU0YzsDQ0NDQ0NDQ0ThnNwNLQ0NDQ0NDQOGU0A0tDQ0NDQ0ND\n45TRDCwNDQ0NDQ0NjVNGM7A0NDQ0NDQ0NE4ZzcDS0NDQ0NDQ0DhlNANLQ0NDQ0NDQ+OU0QwsDQ0N\nDQ0NDY1TRjOwNDQ0NDQ0NDROGc3A0tDQ0NDQ0NA4ZTQDS0NDQ0NDQ0PjlNEMLA0NDQ0NDQ2NU8Zw\n1MFAIGAEfh+YAMzA74ii+K0Dnve7QFoUxf/2pYxSQ0NDQ0ND4zNNq9Wi3W7ve9xoNL6C0bx6jjSw\ngF8BkqIo/mogEOgDFoEeAysQCPxD4ALwty9lhBoaGhoaGhqfaeLxGPfv36PT2X/M5/Px1lvXPv1B\nvWKOM7D+BPjT7t86oLn3YCAQeA+4DvwLYP7UR6ehoaGhoaHxmSebzdLpQH9/PwbDE9OiUCiQSqUo\nl8vYbLZXOMJPnyMNLFEUSwCBQMCJbGz9lnIsEAgMA98Efh7491/iGDU0NDQ0NDQ+w9RqVQAuXbqC\n2WxWH49Ednj4cIlYbJfp6ZlXNbxXwrEi90AgMAZ8D/h/RFH84z2HfhHwAX8O/FPglwOBwN9/KaPU\n0NDQ0KBYLFCtVl/1MDQ09lGt1hAEeowrgIGBQXQ6gVhs9xWN7NUhdA4KmHYJBAKDyNqqXxdF8ftH\nPO/XgPkTiNwP/zANDQ0NDQAajQY7OzuqYLhcLhOPx6lUKjidTr785S+/2gFqaDzFd7/7XTqdDl//\n+tf3Hbt16xaxWIwvf/nLOJ3OVzC6F0J43hcep8H6TcANfDMQCHyz+9jvAXZRFH/vqeeeyHhKJgvP\nNkKNY8nnJQwG4+cuvu33O7XrReNEfN6ulY2NddbW1noeMxqN6HQ6JCnBzk5qn6dA4+R83q6XzwLx\neAan033gebVY3EjSJg8frjE7O/cKRvf8+P3PbxAep8H6DeA3jnsTURT/5XOPQOOF6HQ63Lp1A6fT\nzfXrb7/q4WhoaJwC+XwegEuXLmMwGDEaDbjdHjY3g6ytrZHL5RgcHHzFo/zxpNFosLoqMj4+jtPp\netXD+UxQr9dptztYLAcb/X7/AAaDnlhs97UzsF4ErdDoa061WqXZbFEs5l/1UDQ0NE6JQqGA0Whk\neHgEv9+Px9OHIAi43X0ASFLuFY/wx5fNzQ12dsJEIpFXPZTPDIrA3Wy2HHhcr9fj9w9QKpXI56VP\nc2ivlONChBqfcSqVMgCNRpNqtYrFcvAFrqGhcTj1ep1sNnvkc/R6PV6vF0F4bknGiWi1WlQqZfr6\n+vcdc7vdCALkcjmazSZ6vf6lj+fzQLPZpNVqvXBYtVarEQptdf/Wkg0UqtUasF/gvpfBwWF2d3eJ\nRqO4XO5Pa2ivFM3Aes3Zm1FUKpU0A0tD4zl4+PAByWTy2OddunSZ4eGRlzqWYrFAp8OB4SeDwYDD\n4SSXy/DDH36fkZFR5ucXXup4Pg8sLd0nl8vx5S9/9YUM0o2NIK2WnHigGBUaT4zNo9Yfv9+P1Wol\nHN5mdHQMh8PxaQ3vlaEZWK85igcLoFQq4vV6X+FoNDReT/L5PCaT6dA6PeVymVBom1Kp9NLHUijI\nIuHDFiC320MsFkOnE9DrY5qBdQKy2Yzq5bdarc/1HuVymZ2dEDabjVarRbVaOeVRvr4cFyIE0Ol0\nBAILLC7e5fHjZa5d+/xrhjUD6zWnUun1YGloaDwbjUaDWq2G3+9nYmLywOeUSiVCoe1PpQaVYmAd\nls7udrup1Wq02y2sVtuPZYXsZ6FcLtNoyE1ISqXSsQZWvV4nkYjvezwej9Fud5idnSMUCpHP5+h0\nOlqIlpOFCAEGBwfx+/0kk0l2d6Mv3Rv8qtEMrNecpz1YGhoaz0axqHiMDk/HVkIfn4bXolAoIAiH\nj8fj8VCv16hWq/h8fjKZtGZgHYFisAKUyyXk+tiHs76+RjgcOvCY0+lkeHiERCJOLtehXq9r5TJ4\ncl+cRKIyP3+OTOYDRHFFfcxoNOHzHf27vI5oBtZrTrVaxWw2IwgCxaJmYB2HLCCuYLfbtZ2nBvDE\n82u32w99jl6vx2QyfSoerGIxj81mR6/XH3jc4XDSajW7xoIc/hodHXuhz1xdFYnFdnnnnfcwmUwv\n9F6fNQqFJxnW5XL5iGfKlEpFBAEuXry875iSeKCEwqrVimZgIXv9DAZ9Tw/Cw7DZbExPz7C2tsaD\nB/fVx999973PnfhdM7BeYzqdDtVqBafTjdFoIJVK0Ww2T3SR/7giiiuEwyGMRgN9ff3Y7U90LsPD\nw5+LujaNRuPI43q9Hp1Oq9CioGxMjhPdWq1W1dv1sqhUKjQaTbzeo3fzBoORWq2GTqcjk8k89+e1\n222Wlu4Ti8UAuWHv562+Vq+BdbyMolKpYDZbjgxfPfFo1nB/vmyC50Le6J88wWpqagarVdayZbNZ\notEIpVJJM7A0PjvIOowONpsVk8lMKpWiVCridns+9bF0Oh0KhTytVot2u0On06HdbtPpdPB4PJ+Z\nXV4ul0WnEzAaTSQSCSCx51jutS/Wevv2TdLp9JHP0et1vPPO+z8WWTwn4SQhQpAXVUmSqNfrL83L\noxh7x7UTMRgMmEwmjEYj1WqVUql0pAcunU4jSfvLUKRSabLZDBaLhWq1SqGQ/xwaWAX19zpOp9pu\nt6lWK3g8+0tk7EUxsLRSDfI5q9frzzSfCIKgGrAWi5VoNHIi4/d1QzOwXmMU/ZXFYlWFm6VS6dQM\nrN3dKBsbwQOPTU/P9OzwHj9e5sMPf8To6Cg6XW9ow2Qy8e6777/yEhKdTodSqYjT6eadd95leXlJ\nLRa4vb3FysojqtUKJpOJhYVzr8RQPSmtVotYbJeRkTNqqLNYLJJOp7FarYcu0LVaDUmSkKScZmB1\nKRaLWK3WQ0NyChaLfI8p18jLQPG2OByHe1JbrRYGgxGz2ayOOZvNHGpgJZNJ7t27zWFtZwcHB5mf\nP8cPfvD9z10RyEajQaVSwefz0Wy2kKQsh/XfVYyvTodjNW3KhrFW00o1KOfgeed3Ze0qlz9/WZma\ngfUao+hBrFarOrmeZiZhOBymWCzuW0zq9TrhcLjHwFpeXiaVSnH+/AUGBwcRBB2CIFCpVAiHQzx4\ncJ9r166/Ut1TqVSk3e7gcDjodDrs7kYRBNDrDdhsNnK5LIlEDKfTTSQS+UwbWNvbm6ytrVGv15ma\nmgZkgxhgbu7soeGNbDbDzZs3tIzTLkoG4UkEtsoCUqlUX1ooQ/GmHeXBqlTK2O02JMmshnoP02EV\ni0WWlhbR6XRcuHAJo7H3XtbrdXg8cnV4i8Wituj5vKAYrE6ni1qtRi6X7eqweg3YaDTC0tIDJien\nALDZjs403Gts/7jzROD+fOUvrFYrgiCHZj9vaAbWa8xeD5aiJTpNjUipVMRms/HFL36p5/Ef/vBv\ne9y57XabVCqJ1WrF7fYwM9Pba6rRqBOLxVhbW+Xs2cCpje9Z2Zv+Locz24yOjnH+/AUKhTwfffQh\nQ0NDJBLxF9rJdzqdHjGt2Ww+dV1cMpkCIBTaZnJyCkEQ2N2NYjDoGRg4PMSjXCdaxqnMScOD8GSn\n/TIX1UKhgMGgP7KUQKlUxmAw4nA4aLVamEymnrBws9mk3W7TarW4d+8OjUaTS5cuMzQ0fORnu1wu\nEokEtVrtMxPSf1GUe97lcqlz1kFC90hkB0Atz2C1nsyD9WkkPXzWUTxYz+vV1el0WCxWLUSo8dlC\nqYFltVoxm80YjcZT80zU63Xq9Toez34vjs1mI51Oq4J6ScpRqZRxudzkcvt1HufPX6RQKLC5uUGp\nVNwnsDaZzAQC8y9deP1EzOwkl5N7uSnfz+l0YbFYSKfTOBwuCoU87Xb7xGNqt9tsbW2SyaSRpBzN\nZks9Zjab+dKXvnJq3rtms6nqaarVKrHYLhaLhUqlwsjImSNDXSaTCZPJpHmwuijn4STh0ifC5pez\nqLbb7a6Gsu/I5ykbq/5+L5VKBb9/gGQyQalUQpJyLC096Hn+1NT0ieoNKQZWPp/H7/c/8/jj8RjN\nZpMzZ0af+bUvC8Uj53A41fvv6YW8Wq2SzcqJAplMukdycRg6nQ6TyXRsiLDdbrO8/PDADY3BYODS\npSuvfdamcj88rwcLnqwprVbr2FD964RmYL3GKBOtMhnY7Q4kKftMhsFhHLWzt9sdpNNpymU56yOd\nTlOv13E6nRSLxX0iYIPBwJUrb3Dr1s2usHw//f3ely6u3Rt+iUblHeveMKDP52dnJ4zDoaPd7pDP\nS2r45DjW19fY3NwA5MXa5XKj0+mQpByFQoFisXBqGYrpdJpOB0ZGRtjdjbK9vaW+90kWUrvdQS6X\nOZXr5HXnpBmE8PLCQvF4HLtdzqiSW+Qc7U1TPDBer490OoXVKht+2WxG3Tj4fD70ej1Op5Pp6dkT\njcPplMOehYL0zAZWuVzmwYNFQL4Gleuq1Wrx6NFD6vX6vtdYrTYWFs69VNlAsVhAr9dht9vpdOQW\nN09vLuLxmKpPy+VyDA1ZsdkOTxhQOElW6cZGkGg0gl6v6/menU6HVkv2/I+MnHnGb3U0jUbj2Ezi\nvcghuuf/DZ5osJ7f6yl7DNNUKuUTeZNfFzQD6zWmWq1iMplUi99ut6sagxcVMB+18CgCUCWtNhaL\n9iwMB6V6OxxOvvSlr9BsNnsez+fz3Llzi3Q69dINLCWbyGQyIUkSRqOh5/spBpYyRkk6mYFVKpXY\n3t7EarXuqyO0sxNmefkhkiSdmoGVycjhoNHRMVqtFvF4nEIhj9lsPlGrJLvdTjabOZXr5HVHWSD3\nlus4DLPZjE4n9HRPePHPL7K4eBefz6emuR9XouGJB6ufdDq15/EK5XIZQYA33njrWOO50Wh0e+vJ\n3tZ6vUY4HKJcLmE2W470RO0ViguCwNqaSLstP7Z3Y5JIxIlGo4e+T19f30ur5t1ut7sbGzeCIKhG\n09MerN3dXQQBbDY7kpTF5XIRDK4f61U3m81IkkSj0cBoNO47Xijk2dwMYrVaee+9L/TIBHK5LDdu\nfIIkSadqYJXLZT766AO1X+JJGBkZObDm10lRMilNphcxsJ4I3TUDS+MzQbXaezHuFbq/TANLWYzK\n5RKdTodYLIbVaulWOE6QzWYONJYUt/pevF5vt4bX8Y12X4Rms0mlUsHr9dJoNCiVSvuMEZ/Ph04n\nqC7vk+qwRPEx7XaHQGB+3/dzd4vk5HK5Fy4GqZBOpzAY9Hg8fQiCQDwep93uMDw8cqKd6GleJ687\nSgbhSTVyZrPlVD1Y0aicxZrL5Wi3W9jtdgYGBo58TblcxmQyqeFtJRxdrVaoVMpYLNYTeSbD4RBb\nW5s9j+VyWfJ5CbvdzuDg0IHnJZNJc/fubXURbzTqVKtVPB4PrVa7Z2OieNSuXbve4y2uVqt8+OEP\n2dgIvjQDS0lqUTZ+er0es9nco8Eql8tIUg6fz4fFYqVarZFMJrHZtjGbzYf2poQnxUZrteo+A6vT\n6bC8/JB2u8PCwvl959HpdCEInHpSwe5uhFar3WOwH0U8vqv+Rs9LtVpDEJ4/ixCezEl7O5McxutU\n6/HIUQYCASPw+8AEYAZ+RxTFb+05/h8CvwE0gSXg10VRPCQZWOM0qdVqtFrtHq3AEwFzAXgxb1Cx\nWFR3dU+zd4HO56XuQu1kZOQMqVTyQB3WYQiCQH+/l3g8fmwtn+NotVrUajUsFsu+BWZvyFOSZMPp\n6SxBvV5Pf7+XVCpFp9M+dOJZWnpAvV5jdHQcQRBIJpP09/czODi077kOhxODQY8kvdgkplCpVCiV\nSgwMDCAIAh5PHx5PH7lcluHho0XMCr1C989XzaNn4VkyCBUsFivZ7OmEVzudjmpghcPbDA0NMzEx\neaSRrBQXdrk86uaqXpdDNJVKhWq1Sn//0TWcFOJxuWH022+/q5ZWsdvtPH78iEajST4v0d+/3yOa\nzWZotdq43W4MBgOffPIxpVKRr3/9p0ilUj3Xei6XVTMV954vu93OyMgokcgO8XjswHvnRdkrcFew\n2exqeBwgFtsFYGhomHK5TKvVVhf5zc0gZ86MHir4V0Ji1WqtO6/k1KKvsh5OYmRk5MBwqxy+dVEo\nSKfaz3B3dxe9Xsfly2+cyAiRx5l9oTHUalVMJvMLfYcnHqyDDSx5I7/L9vY2kpTj6tXrJ/LWK1Qq\nFba2NpmdnTvQ2/iyOO4X+BUgKYrirwYCgT5gEfgWQCAQsAK/DVwQRbEaCAT+EPi7ynGNl8veDMJa\nrdb9tzw5xGKxE+suDqNUKmK12g4UHMo1g3SUy2XS6TTVahW3243D4cTl8iBJ2WfaZfh8fuLxOKlU\ncp+BVa/XuXv39rFi0larqTZ09fv9vPnm1Z7jews4KgLxg8ow+Hz+roElZwI+rSfb2Qmri2IqJYdn\nBAEWFs4dOC5BEHA63eRymVPZeSkhob1hpEuXLlMoFI4sHSC3CCpTrdbI5yUymTRbWxsH7joFQcDr\n9b324tvjUITHzxKSsFotZLOyB+ZF+/+l0+luseA2yWSSwcGhY8NFlUplT3FhOdyteLSUjcNxGXAg\n3w+KmH3vdTM4OMTOTphKpUw+nz/QwFLqFV26dIVCIc/29jbZbJpUKkU6nVLPS7OoRqQ5AAAgAElE\nQVTZpFDI4/H0H2iMTk1NE43uEAyuvxQDS7lX9mra7HY7i4t3+d73vofd3k8stotOJzAwMEg4HAbk\nQqM6nUCz2WJ9fY3z5y/se+9qtUq73aFarRKNRtjcDO6rqG8ymQgEFg4dn8vlJp/Pn5o+U9nsDg8P\nn3ieUcrTlMvl597c1mrVFw7rKRv5vR6se/fukM3Kc3Wn0+5JHEomE89kYD18+IBMRq4VNz4+8UJj\nfRaO+xX+BPjT7t86ZE+VQhV4VxRFRZBgAD5/hSw+ozzJ3LDw0Uc/UkWksViMRCLBwsI5tW/Ws1Cv\n19na2iQU2sLr9R3a8dxms1MqFclm5ZYdLpcLm81GX5/sTcnlcif2DCjGQjqdYmJisufYxkYQSZK6\n+pcnk3S73e4uKB31PPh8fsrlMslkEknK9RhQym7W4XCou9aDMiT7+uTQhrKjl6Qngt9qtcrq6gpG\no4HLl98kHo+xuxthfHzyyAnG4/GQzWaQJOmZJoWDOMjAslqPznoqFot88smHakin0+kQCm2TTCZU\no/RphoaGuHz5jRca62mh6H327pDb7Tbb21sMD488d2jiWQTuCnuF7i9iYK2uiiwvL3UXlg7NZguv\n139sBpWyw1c+W0lYsNnsZDIZHA7HicaVSMitcZ6+t+X72K6GCg8bgyDI150oPsbhcPDFL36J5eUl\n8nlJ3ZgUCgU6nYPvM5B/z76+fnZ3d9na2lJDoy8qugbZEI3FdnE4HD06SrPZTDqdJhqNotPJBtHg\n4CBGoxGdTke93sBkMjM8fAZJyrGysky1Wunx+iq6q0Ihz9raGtlshuHhEbxeL6OjY+j18rLqdruP\n3KQonrXT0mcqWrehoZOHXO12+Vp5XgOr0WjQarVfuKyHwWDAaDSq13exWCSRSGA0GtVeu/39Xs6c\nOcPHH3/4TKHVeDymGr/ZbOazY2CJolgCCAQCTmRj67f2HOsAye7xfwTYRVH8m5c3VI29KBdiuy13\ndHe73Xi9PjqdDvfu3eX+/UW+/OWvPvP7iuIKorhCNLpLu93hwYP7WK3WfWJvm81GPp8nkUggCPJu\nTJkwNzc3yOWyJzawrFYrDodjX+hFLlK6jc1m4/33v9iTmXTr1o19Xq1SqYTFYiWXy7K5ucGVK2+q\nx4rFAoLwJERotVoPnPyUiVSn09Fut8nnc6qBtbIih07OnTuP1+vF6/Vy7tz5Y7+fosOSpNyRBlan\n06FYLBxaaRpQK7U/y2SYTqdotdr4/X48Hg8mk5larUa1WuHChYv7nh8MrpNKJV9qlmG1Wj1Qb2E0\nGnuM1U6nw61bN2m1mly79jZ3797B7/fTarUIBtfJZNK89da15xrDkxT+ZzGwTqdUw40bn3DjxkfM\nz59jcHAQnU53otCecs6UHb9y30CHcrmE1Wo9kYEVi8XQ63X4/b16L4fDidlsptlsqB6xg8ZgschG\nUC6Xw2q1MjAwQCTiwWKxdsuISOTzSimU/Ykijx4tEw6HqFYrPH78iOXlh+oxr9fLxMQkOp3AlStv\nPVfJiO3tLdrtjlo4VEGx2/r6+piePkehIKkGSbvdotVq0m4bcDqdDA0Nc+PGJ4TDYfr6+nE6ndhs\ndmw2G16vl/7+fjKZDD6f77kaFT+ZF6QX1mcqITSj0fhMIe9e4f/R57lcLu8zupX74Fn6EB4+Fpta\nGDaZlLPN5+cX9nl1HQ7niUOrrVYLUVxBpxPQ6/WqR+zT4lg/YiAQGAP+DfDPRVH846eO6YD/CZgF\n/t5JPtDv//xkCLxKYjE9brcVj8eC223l/Pk5pqenuXBhjmh0C1Fc4qd/+qvHpnzvpVgsUi5n6euz\n89Zbl5idnSWRSCBJCebmxnueOzY2SDq929WweBgdHcDvd+LxWNjYeESnU8XjeXLTNZtNbt26deDC\nJAgCmUyMaDSK2axTJ55isYjTaeHNN99gcPDJ5HXnzh2gzvz8ND6fbFRKksTu7i7NZoVGo0y1msdq\nFdTFU69vMTzsw+k0YrMZOHPmzKHX4tBQP81mk0ajgSA08PudxGIxqtU809OjvPnm8UbVXpxOI5ub\nK+h0jSOv/8ePH7O+vn7ke9lsBiYmJp7pPgqHG7jdVr7wheuqYdZuV4hGo8zPT+3zAJlMHYLBIIJQ\n27cAnwbtdpu//uuPDkzdBzh//jzT03J1+o2NDdrtCoIAW1srtNsV0ukonU4Ht9tKo1FCdqY7e85J\nuVzmzp07WK1Wrl69euDndDpVvF4nMzOjJ/aYdDp+IhErNpv+ueeyZrPJzs4GgtCmr8/B8LAPvb6N\n02k69j2TSQG328rY2AB9fU7GxwfJ55NYLGbMZh1Wq57x8UHc7sPfp1gsotM1mZubZHi41/jx+52s\nrLjI560YjR08HkuPZqXVamGx6PD7/dhs8ucp99LIiI943IvRqEOvbwB13G4rc3NjPZsZWUcm4fe7\nGRoKUKsVyeVyjI2Nkc1maTQaDA97u5qoEn7/9DOd33q9TrGYZmion0uXAj2bhGzWgd0uZ16fP98r\nYI9GBWw2M0ajgYmJIfr6+hga8lKtVhkdlbWK09NTXL9+HZB/x1arwsDAADMzz177y+u1I4oO9Prm\nC6+LqVQKi0XHxMRUz1x5HCZTm60tKxaLcOwYfvCDuwd6jtxuK6Oj/hf+DmfO+IlE6jgcBhqNIh6P\njYWF6X0b4cnJEUKhEFarcOz6trq6iskk12IslUpEo1FsNt0LaX2fheNE7oPAd5DF698/4Cn/Anl2\n+/mTituTyZfbjf7HhUgkiSRViMWySFKFel3XPbcCZ89e4IMPfsi//bff5hvf+NkTv+fi4l1yuTJ2\nu4tSqYjfP0oikUMUN/D7x3p2xrUaxGLprhHkpFZ78tu22wY2NnbY2NhRnx+Px4hEIhiNhgMXs3K5\nRCwWIx5PMzd3Fr1eTzod4/Lla5hMLvW919fXCAaD9PX1MzY2p06eDoePoaFJPvzwAwTBhCRVuHnz\nPhcvXqJWq5FMSgwODhIM7iBJFYaHjYdei/U6ZDJ5TCYTW1tRpqcL3Lr1gHy+woULU891DVerbba2\nokxOHv7a1dUtymVZPH8YOp0Oj2fomcawtRWl0WhSLrcpl+XX1esCklRhayu2z6tmMNiRpAqPHgUR\nhOcvHngYuVyWTz65jSDocLlcCILA9PQsPp+PcDjEJ5/codUyYLFYuXnzHoKgw2g08tFHt3A6ZWN3\nfHyCubmzbG1t8OGHt/i5n/sGqVRRff979+5SrVYQBAG3e4BYLIbX61O9IfV6nZ2dBD6fj1SqSKfT\n6WnVcViYqlRqIkkVIpEUHs/z6YbS6RSRSAy93sjFi1c5d+483/3uX7Ozk2Bw8OjfVbnvy+U2zWaB\nWk3o/rtJKpVDEMyUSi3q9cPfJxhcQ5IqTEw4D7yOSqUG29sRCoUqJpODs2cDqlexWCwiSRWczg7r\n6+Gee6lcbtFq6cjn82xs7CBJOSwWK5JUA554mzOZNMmkxPj4BCMj0/T3r+F2+7l69Rp/+7ffo1BI\nodNZKBQybG3tMjQ0+UznNxhcI50uMD+/QDrdW5IhnS5QLtdpNBr7vnskkqRcrmOx6MlkSmSzZbze\nYUZGzuD3+xHFFTY2wkxMPDHayuUGsVjm0Psxn5fY3NzA7x84UF/XahkIh2PE49Ize4sVjRvIHjtJ\nqmCxuJ9pbmg220hS5UTXXjSaxGQyq625FHQ6HTZb/wuv7dVqB0mqEAzusLUVwePp33ftgHzOlOcd\nVUokl8ty584DDAYjHs8QxaJ8va6thZ6pGO6LGI7HebB+E3AD3wwEAt/sPvZ7gB24DfwnwA+B7wUC\nAYD/VRTFP3vu0WicGEmSutW75ZDBXkv+7bffZXHxLsvLSxiNhn03rsfTt08ELkk54vE4brcHnU5H\nuSy7jycmJnnw4D7b21s9Qm6bzUa5XKZQyDM4ONizI5idPUskElb/3Wq1CIdD+Hxe3njjrX0CzHa7\nTa1W59atGzSbDQqFAhsbQYrFHHZ7n6qf2vvZV668se976fV6HA4HlUoFs9nM7m4En8+nnqO9FdwV\nL9lB2O0OMpmM2pstl8uRz+fwePqfu6yBx+MhHo9TqVQO1EspmhWv18v8/OHC2GelXq9TLpf3hQ2e\nZIIW9xlYHk8fRqNRddOfNvF4DEnKMzc3183ekhfB2dk5+vv7uX37Jrdu3UCn09Nstjh/fgGbzcaD\nB/dJJhPU6w2mp2c4ezZAo9FgZydMKBTCZusnlUqxuHiHdrtNJpNRM94MBgM7OyHeeec9HA5nNxzd\nUnfHDx4sEovF1DEelCgBsgYrnU4Rje4wOzv3XNqTYHCDarXG2NgY5XIJg8GgitWPo1QqYTDo1XEr\n12Oz2aRer9PpdFSPk9J772mi0Sh6ve7AlkrlcpmdnTC5nJwRZzAYqFarvPfeF9TjIBugSrhF0S3K\n5S6M6PV6Mhm5GO5BiSRKseGBgUE1cw/g/v172O0Otre3efjwAcPDZyiVCs+U4dZqtdjc3MRoNB4Y\ndqtUKrjdHur1+j59aaVSwWg0YjQaaDQaqrfd5/MxNDSMJEndbg1PxOztdufAFi+1Wo21tVWi0R2a\nzRa7uxE1vLgXuYK5nHn5tGb2uO99587tnt/XYrE8s+72pNdevV6n1WrjdDoZGzt8A/giKOdme3ub\nTodDQ8N7tWuHGUqpVIr79+/SbrdZWDjfzRCXz002mz22xlsiIYvoXzQp6TgN1m8gl2E4jM9PTfvX\niEIhT6PRwO8fIJNJYzabe9yoRqOR99//Ij/60QeUy+WeMI/szUmSyaR7MoTW1lYBOHv2LPfvL6oZ\nhENDw+pEsTfF1W53UKnIk6PcC/GJgeX3+3tujlBom7GxcaanZ5ibO3vo97JaLUSj0W56eB8Wi4G+\nvr6eScNoNDA3FzhUPOpwOLsZWYOEQnKTaQWn08XW1iaCwJGiUuW7KDfX1tYmnQ7PpG1QSCQSxOO7\naoqx2WxStXJKO6J2u61qIQ7K2jqITCbN/fuLanVqq9XG9evv7BNJKy79pxe6o5qDC4KA3z9ANBoh\nn5dOvbFxJCJnYZ47d4Fz585z584tUqkUxWKR/n4vs7Nz/MVffJt8vsB7773P6OgYnU4Hq9VKoZBH\nEFDrrM3OzhGLRVlZWcFkcqpC66mpGUqlEsHgGtvbmywsnMfj6Wdx8R7vvPMekUgEUVyhUqkwMDBI\nIhHHYrHg9fpIJOKHlugwGAxdTVuLaDSybzd/EpaXlyiVShiNRvJ5iVarhc1mJ5/PHbmoxmK7FIvF\nnjpZSousSqVCrVbDYHjy+y8vL5FM7q8vVywWGBub2HettNttHjxYxG63Mzk5RS6XQafTUygU1I3B\nEw2YjVhMLgmg3EtKqLlerxEMxmk0GtTrNYrFInNzZ1VDPpGIYzQa6O/vVzNxAYLBIAMDfoaHhwmF\nQhgMRpxO16ECbLmfaKtH47W+vsrt2zf50pe+euD3K5XKjI3J3nhRXMHvH8BgMNButymXS5hMJiwW\nK6VSUc2WVL6fz+dna2uTlZXHagbq+voq+XyBZrOJyWRCp9NTKpVYX1+lXq9jNJpwuVzE43E2Njb2\n9WlNp1Nsb2+Tz0u8+eZVJiYmabfbbG5uEI3uMDU1w+xs72vk16XJ5bK43R71vD6PVg2eFFg96tpT\niom+SDuc41A2nkrbosPkCU6nC51O6NGDlctl1disVqsEg2sIgqzhU+4Xh8OJ0WhQ338vnU6HtbVV\nHA4HOzs7ZLMZLly4+MJtn16Pal0aPSiNXV0uF9Fo5MCFf2HhPOl0CpPJzDvvvKfeOD/4wfdZW1tl\nYGBAXczl9Oo0Xq8Xh8PZ04NQEATGxycQxRXC4ZBaeE+v11Ov17FaLT1Vkp9GmSz0et2+DMGnuXz5\nDTWtuVIpI4oPsNk8z5TNpuzo7XYHFy9eUltGGAwGBgYGePjwPna748hsLUXorjwnFJKLDh41gRWL\nRTY3N5iZmVV3Yq1Wi4cPH9BoNCgWS2QyGTY2NqjVnmiPjEZ5cl9ZeUytVuXy5SvU63WazWZ3N31w\nzZbd3V3q9ToOh4NGo0E+nyefl/btYJWSFE8bScrvdVjT54EB2cBKJBKnbmDFYlFMJqM6gcr101LE\nYrvMzs5x5swYgiCg0+nUBsX5vMTg4BBGo5FGo6FW25eLQc4Sj4dIJncIhbYwGIwkEnGSyQTJpBzW\nkJsfNymVSty7d4dPPvm4e/3aePhwiXa7w8TEJJOTU9y9K29CDqrQrTQJr9frBxpYT1c4f5psNtP1\najQwmUxEo1Gy2ayaLl+pHJyd2Gg0WFl5jF6v25f6r7SuarVaPZ+Zz8th7pmZJyVbUqkUyWSCdDrJ\n+voaU1PT6nW+svIYSZKYmZmjv9/L7ds3MJmM3dclGRsbV8OoJpOZYrGAx9O/p8dfGVF83DXaBfR6\nHTqdjnw+x/LyEu+//0XK5VK3Z6ZcFFe5/kZGzrC8vIROp+Onf/pn+KM/+lc8fvyIQGCBcHib0dHx\nHu9xvV7nk08+ptVq4nK5GRuTj4viCtVqrcdgKJVK3L9/j1ar1Z3b+pibm+PmzUW1YnulUqFSqXRF\n/iaKxSKlUkltswOyp85g0PPw4X2mp2eYmpqhWq2ysxPGarVhNBpJpZJsbgZptztMTU0zPj6BwWBg\nZeURkpTHaDT2JDM4nU7S6TTJZIr79++xtLSIxWLFaJQ3kMo98TQbG7JW89y5cy98f56kVIPSvcBk\nMhEOh9jc3FDrr+l0OiYnp164NNDe8iJ2u/3QaIFOp+vWEJP7xQqCwO3bN3tC/EajgTfeeKtnPlTq\nBiaTSbWheavVolqtsr6+ytraKqlUkqmpGQYHB49tjn4SNAPrNURplaJ4cQ7yxlgsFs6cGSMU2iYa\njXDmzCg7O2HSaXmCvXXrBnq9gaGhYR48WGR7exOv10sotA301gYaHR0jGFwjEtlRDaxSqYjZbO5O\nBgbMZnkRezoDbnc3SrVaZWJi8ti6SoIgqLsYJRvq6doyx6HclMVicV+GX6VSodlsHSuMVL6DXKqh\nQySyw7lz5w/1epVKJW7fvtmtadRSDcKdnTCNRoOpqWnOnBnFYrFgsVi4evVa97zJ4ZRqtUo4HKZY\nLHHv3l019KnU5xkdHd8Xxstk0hiNBt577wvEYrs8eHAfSTrIwFKKqvZOwgaDAYvFcmjTZ69Xrmqf\nSMQPnOCfF7lydh673aG6+gcGBjEY9ESjEWZn50gk4szMzNFsNlVDKp1OYzAY+Mmf/Dusr6/17F6n\npqa5ePEs29sxqtWqmpWXyWSYnp7D6+3HYDB0G3pbSSQSlMtFLl68gtVq5fHjR0xPz6gTqmx8JqlU\nyhiNvedtd3cXk8lIs9kkl8uRy2VVD8q//td/SLPZUjsCzMzM7asEHgyuk83msNttDA4Os7MTZn1d\n3vA0m01u3PiYN998a5/HcW1tlVqtxtzcnGqARaMRXC43DoeDdltOylCuHaWIqt/vV9PS6/U6weA6\nQ0NDGAxGgsF1wuEQVquVTqdDPp/H6XSysHCOtbVVzGYrjYb8vk8MLNmDVavV6HSehAfr9TqPHy9T\nqZTx+fz4fHLG6le+8jVWV0U2NzfY2Aii0wnqbw5PSmUov1mjUae/38ulS5f50Y8+4Pbtm5RKRUZG\ntpmfX1A3acHgOktLD7DZbIyOjqleiY8//ohkMk4iMU00GsFisbK4eJfV1RXK5Qqjo2PMzc0xMzPD\n0pJIKLSF2WymWq1SrVax2+2YzWby+TyVSlltswPywq50Tuh06IZPK7RaLR4/XqbValEsFtHr9UxN\nTeN2u9Xr9NKlN9jdjaDX67l48TLFoiyDeOuta9y/f5dqtUaj0SAej2Mw6PmZn/m7xGK7pFJJksl4\nj+eoVCqr2Yunsfmx2210Ou1uyY2D58ZYbJdIZIdyuYzNZkOv1+FwyPdvpVJmbW2NYrHI+fMXn7tZ\ns1wgWqDd7hybXONyuZEkiWKxQLFY7HqiB9TX9fd7D9yoKAZWLpdlcHCIW7dusr29xcZGEJCzTKem\nJjl79nRkGpqB9ZrR6XTIZuWCaUoW1mEGw9TUNDs7IYLBdfr7vayurhAKbdNsNgkGg+h0emq1Kqur\nItPTs8RiMWKxGDqdrmf3YDAYcLs9Pd3O5d2xGZvNhs3mIBLZYXl5iYMqDOh0wr506ZPg9XqJxTIU\ni8VjtU/K7kWn09No1NUCqHsz5J7Uwjr4fCUSCYLBNfL5PDs7Idxud3cHLuF2ew6sNF8ul1Xjymg0\nEo/HqFQqWCwWtre30Ovl3Z3JZGJ8fIxgMMj9+4u89dY1dWw6nY6RkTMMDg4xMDCAXq9HrzdQKOTV\n32R8fELVwFWr1W7o148gCOokqwhe9yJJEiaTifX1NVot2VjR6w3Mzy9gt9sPPE8g/+ZKVfvdXbm+\njtKz7mmerjV0FJKUo1Ip91TI1uv1DAwMEY1GyGYzap0yk8moLlDKAtrX14/b7SEajfRcF1artRuu\ntjAwMMjW1ialUpG+Pg8LC+dIpVJUKmUmJ+X0/7m5APPz8xQKBXK5HDqdDovFQjweJxhcY2dnh4mJ\nKSqVCn19/ermIBbbxWq14fX6qddrRCKRbiX9HBsbG7jdLgKBAIVCgbW1VWw2m2q45XLZbmP0Klar\nnStX3iAWi3D37h3efvsdQqEttRvBpUtPesPJOrMQDoeDyUnZY1YqlVhaeoDb7WZ4eIRGo9nVybRU\n46/VavX0WHz8eJl6vU4gMM/Y2DibmxuEQtt7Cq46uHz5DfR6PX7/ADabjWq1gsfjIRrdodVqc+/e\nHer1OpVKGUHQqYbg+voazWaLqalZNSTj88ke35mZWXZ3o2xtbWA2y4uoUsetVCohCPJ9NDg4RLvd\nYX19jS9/+WtdHc09rFYrZrMZUXyMxWLB4+nj/v17hEJbDAwM0W7L2qB6vd7V9+X45JOPyOWyBALz\nXUMlpW4ezWYTy8vLBAILLC7eRRRXgCfFY/v6+kinU937wtajmywUCmxvb1Ovy5tGm81Gs9mg1ZKL\nCHu9PqanZ3o8+oWCRD4vMTk5zcZGEFFcod1uk0gkMBhWaDZljeCbb17lgw9+QDqdZnFxkUqlws5O\nmFKpV0O5sbGO1+vn+vW3T3TPHYfNZmd7e5tkMnVom6ZoNEI8Hqevr4+JiUmmpqbV+7der3Pv3l12\nd3cplUoMDQ3jdLqeq56ZIOio1eQeqXt1YXs33yBvGMNhuRVTKLSNTicwP3+OUqmEKD4mm81y8eKl\nfe+vbAgymQxWq5V4PEYyGWdgYIC+vn5arRZu97PXjzwMzcB6zZCkHM1mi+Fhr2owHGZgWSwWRkfH\nCYW2uXnzExqNprr70OlMVKsVNjaC1Ot1+vv7qdWqFItFdZLfi8Mhu7KLxQJut4d8Po/FYsFqtWG3\n27q7U92BIsuBgcHnKgYpe23WyGTShxpYSp2kvXH1tbVVms0m5XIJp9PJ5OQUQ0PDFItK3aMn56tS\nqRCPx4jFdpEkCUGQPYOFQpHt7U1cLg+1Wo3Hj5dJpRK8//5P7Jls891stSpnzwYwGo0sLz9kZyes\niu3HxycwmUxUq1WSySSFQoHNzQ22t7f4mZ/5d/D5fOrY5+bO0tfXr3rQZmZmqddr3L+/yO5uhPn5\nBQRBUJ+vhHjtdjtGo2Ff3aJKpUK9Xken07GzE+455vF4GBoaJp1Os7m5cWAl+oGBQVKpVI+O7TAc\nDgcjI2dotVpkMhkKBUltR2I2W9Qm2LJ7vr7P/T4ycoZoNML29hbZbEa9jpQK+LlcVvUuyAt+BEnK\n9VwXyu+r0+mRJKmb9OCkr6+fRqNJLLbb3fnLBWn7+vrVe6hcLvHhhx9QLBbZ2trg4cOHbG9vcv78\nJS5dusTFi5fJZjNUKhUmJiap1+vk83ni8V3m5xeIxaLd38LB1avXKRYL3LjxMQ8fPuiGYHKsr6+q\nXl673cbU1DTnz19kaekBW1ubrK+vIwiyV6fRqO9rnqt4ZOUiu7JGLJvNUqvVWFq6z8ZGEJPJhN3u\nIJVKEgqFKBYL3fpYNmKxGB5Pn9qOZ3Z27lDvZF9fHy6Xi62tra7wfItyuUoul8VsNhOJ7AACHo+H\nYrGgGoAejwdJyvHVr/6k6snQ6/XMz59jcfEulUoFv9+v6htLpQI2m139zS0WM9FohIGBAX7qp77B\n7m6UXC7LN77xs9y+fZOlpftYrXZWVh5jMBiw2axUq1WGh4fx+fysr6/icNi7HSVyJJNJLBYrNpuV\ner3OysojBgYG2N7exun0ce3adXWjajIZqdXqDAwM8ujRMtvb8ma0Vqtw9ep1IpEId+/e7ib2DDA6\nOsqlS1eOvTdu3bpBOp1mZOQMiUS8Wyleh8lkZnFxEYfDgcPhJBrdwev10Wq1MBqNTE1Nd6ukP6k+\nrjQHB+HU9FDJZIJMJoPH4+Gdd9498DmPHi3jdrv56le/vs+7ajKZuHbtOsvLD7u6zefvr7izE6ZY\nLGAymfYZZy6Xi7Nn5/F6vapHfnNzo+u1HiIYXO9el/J5crlc+2QpbrcHvV6n/g7r66ucOTPGT/zE\nl7BYLNy6dZNEIo7ZbMLlcr9wHUDNwHrNUMKDXq+Xzc0NdDqhZ5f6NIoXS96dWcnnZYF8u90hl5MY\nGRnlK1/5GhaLRfVwpNNpRHGFVqupijL3iqIVA8tqtWG1WqnV5Ey1M2dGDyxc+bwoYbGjqu9ubgbJ\nZjO43R61KnGhkCedTnfHmWNp6QGrqyK5XIZYLEan0+nZfQGqcHp2dg693kAwuM7W1hb9/V52d6NM\nTU3TarUJhbYJBObZ2QmzsvKIVqvN3NzZ7vEWq6si4XCoO0HQE9JoNlvMz59ja0sOl3zrW3/GL//y\nr6ph0HQ6pbqqQU5W+NKXvoLfP0A4HEKScng8faoGr1fL4SKT6W3HoyzCijbn+vW3EQSBGzc+IZlM\ncPnyG2xsBNnZCTE5ObUvu/HMmVE1RGcwGA50/Xc6HdLpFPF4jNVVUT2XDpNiNwQAACAASURBVIez\nmwUoN9ZOJOKMjo4Rje70aKsU+vv7MZvNPH78CI+nj6GhISqVCtlshp2dMM1mi6Eh+fvubaC9V4Sq\nTOyNRp1yuUR/vxeLxYLJZO6GsS1dwXYZvV7X9Uo+wmIxEw6HMJstjIyMsLLyiFqtRjqd5ubNj0ml\nEpw/f1HNMpybO8ujR8tYLGYajSaJRFw9ZjAYqNXk3nQXL15hcfEOn3zyEZ2OrAsZHx+n2WwxMODB\n4XB0dTw1ZmZmefRoGYNBz/j4BD6fj/HxSfW7ydX6bXz44QfUatVui5UiqVQKl8uJx9OHyWSi2Wzg\n8/kol0t4PG7cbo+atafX67hw4eKxXoVIZIdYbJdyuczmZpBKRW7F4/cP4HK5GBwc5MaNj7vhXont\nbTkJ5OzZeXZ3I+RyORqNRk8G1uDgYLckRkoND9ZqNRqNJn19/eom6sqVN7lx4yOWl5d45533GR+f\nYH19nVgsxuXL8rEf/OD7bGwEGRgYwucbYHc3wurqqjq3DQ2dYW5ulng8jt8/QDabYWZmjvX1dUKh\nkBoW39kJYzKZ1MQbOdFH9oRkMhlarSYLC+fZ3t7kj/7o/6VQKJBMJpmZmcHlch0qX6jX6yQScdWr\nnslkePjwAcWi3M5KNmjSTE3NEImEWVg4RzabpVQqcvnyFQYGBlQ9ZrFY6GbCyRugxcW7jI6OYTKZ\n+da3/qyb9S3/niMjowe29XmaVqulZj4WCoXueTAyPj5xYNYnyNe13e44VCah0+m4ePESMzOzFAp5\nCoXCcxXjPUxU3mjUSSQS3L59E5/Ph81mZ3c3iiDIMgpZMycbVbOzZ3n48AGrqyt4PJ6e76TT6ZAk\nCVF8TDAYRJJyvPHGm5w9e7arhV3m8eNHLCyc00TuP46k02kEQY4lF4sFHA7nkROmxWJhYmKKnZ0Q\nNpudRqPJ3FwAm012fZ87d5633upNR1fCXuvr6zQaTebnF3q0TXLF8Tyjo6NMTk6p3pHx8dNN31U8\nFodNZMVigWBwHbPZzFtvXVUFyRaLlY2NIHNzZ7HZbIRC20QiO2xsbLC+vkqpVGR4+AwDA/KkL4fm\nBns0YpcuXeE73/kLNjbWMRpNDA0NYzQaiUTCqsDZaDRy6dIbqltdr9czNjbO4uI96vUaFy5cxGaz\nUSqViETC2O12rl27ztWr1/iDP/i/icdj3L9/T9XOlEpyFtPk5JRaNiMajeD1yvWh0ukUHk8f2WwG\no9HQM9m53R4ymUxPg17Fo1Wv13C53KpXSEkN73Q6zMzM8vDhEpubG/s0azqd7kRZcmfOjKqLislk\npq+vT/0tyuUyH3zwAxKJOENDw6RScq+6pzVhgiAwPDzC0tJ9BEFgcHBIzQpSQjuKQel0ug5soF0o\nFDAajZRKskBZyTSs1WrYbHYslgLNZoNoNMKFCxcpl0sUCgVmZmYRxRWazSZjY+OUSrJxNjY2zs5O\niLW1Nb773e9gMBgxmUyMjo6xsRFUPXTB4Dr37t3pZiKauX37Bk6ni06nQ7PZJBwOc+XKG1y58iZr\na6s0Gg21915fX58q6LVarTidTgYHB2k0mj2Te61W49atG5TLZaxWq9ovUBnTO++8Rz4vYbPZGRgY\notVqY7fb+drXfopqtUo6ncJudxxbYLFcLvPo0UPa7Q56vb5bQqXG9PQMqVQcQZDlA4ODQyQSCW7c\n+BiDwaBmDive1Vqtus9gv3DhEpHIjloPSglNtttyn7mRER8Oh4OFhfMsLT3gwYP7nDt3ge3tbZaW\nHnS97HICSF9fP+fPX+D69bfJ5bJ873t/gyiuqJtGQdCr2YAulweDQcfCwjmCwXVWV0V+6Zf+XR4/\nDrKxEcRutzM4OESlUu7WXpLY3AxSKBQpFPJUKhWSySTZbJqzZwNcuHARUVzh0aOH9PX19xiS5XKJ\nbDajSiU6nTZ3794hHA4BAoIAkpQnkYghio9ptVrd9j0pxscnuXDhEuHwNmtra8Riu/T3e4lGI93s\nWR2xWIyxsXH6+vr5y7/8Nj6fn3PnLlAqFYlEwszMzB4bLbh9+1ZPeQej0cj8/IKaEHQQtVqtmyV5\ntEdHlozYXkpvyXxeYmVlpZt5mqJQKKgRl2q1wvDwCG+//S6CIHDp0hXu3LnJ/fuLvPvu++TzeR49\nesj8/Lnu5ldHvV7HYpGlBfF4HJ1Oh9lsodVqMzExcWAZk2dFM7A+o2xtbWK12tRFAuSdRy6Xxel0\nqT2gTtJk8+zZALOzc3z7299Cp4O33rpKLBZjbs7HG2+8te/5NpuNt99+l9u3ZQHg6OiY6iUrFguU\nSqWuWNyF2+1haekBHk/fqWebgbyo7u7u7tNhdTodNfvr3LkLPdleSsi0WJTrSgUC88zMzBIMruLz\nDTA8fEZ1xcu79A02Nzd6PlfRYOh0ehYW5DpMw8PDLC0tEYlEmZ6e5s03r3az01qqd0dulhtCEATs\ndifxeJzHjx+SzeYYGTmDKK6QSMTVEFM4HOqmD8vZcaOjY0xNTXdDignC4RDXrr2NIEAqlWZkZJRy\nuczAwECPYa0Ixvc26JUkiWq1gslk6hHJ+3x+tReh0kMyEgkzNTV9ZE/Do1AMj6ex2WzdTCm5EXCp\nVFJ/x3g81vNcg8HQ1bmVMJvN6s4zGo3Q19evGoiyHsPGw4cPmJ9foL/f2w0Jl9X2Ja1WC4fDgcFg\npFIp43K5sNvt5PN5stkMgiCwuHgPkEs9LC/LAu14PIYgyBmvyo78+9//G0KhEKOjY4yPT3RbQvWp\nmp18Pk8otE2hUKRYLBIKhdTfQK83MDY2TrFYIJ1OdRdaOHNGNjKU77S6uoIgCBgMRgYHh4hEImop\nFVFc4a/+6s+pVCoMDg4xPDys9vm7dOkKOp2c0Xv27DyJRIzFxTuYzRa11Irdbj9x5erV1RXa7Q6X\nLl3G4+mjXq8Rje6yvb1FoVBQBeE+n5/Z2TkePnyA1+vlC1/4CeDoVkJyxucT0b8icF9dFSmVSupc\nNDJypltrLEokEsZg0FMo5Lti/DZ+/wATE5O8++77nD0bAGQv0b17d3E6XfT396l1sJR6fR5PHwsL\n5/mrv/oLIpEwzWaTQGCBjz76EZubmxgMRjodOdQdDm936+BZabfbzM6exe/3c/v2bYaHR7h69To7\nO2F2d2N8/PGPyOfzPck9drscKrXb7aoAW9Zcxel05B6qSp22QiFPrVZDp9PhcrlZWrpPo9Fgc1P2\nLM/OniWbzXT1ciFWVh6pi78yZ1y8eImPPvqAaDRKNBrZl1ixl3xe6q4hcuhc3swMsrq6iiRlD22P\nVatVD80U/7Rwudxcv/52d5PfZnh4mHv37iIIcos3s9nMo0fLgDwPz87Osba2RjC4rmZJ3rjxIY1G\nA7fbzblzF5idncVud/D22+/icrkIheZYWVkhFttldHTs0Czuk6IZWJ9BlBAdyJ6J2dk57HY7kpSj\n3e7Q3++lWDxaf3XQe8oZT/0MDY10C2ge3pfJbDYzMTHJ8vJDMhlZCB2L7fb0i3K5XOqCcdreKwWl\nGWwkstMN3+kpl8usrYlIksTIyMg+YeZeb5tCuVyiWCwxMTHJL/zCL5HLZQmHQ/uy6MrlEltbmxSL\nBZLJRLdxa4eNjSAzM7PcvXuHUqnIL/7if0CjUeeTTz7C55OLUsq1VMRuP7YGt2/foFKpsL29Rf//\nz957/UianWl+v/DeR2REZKT3tlxXdVdVu2pyetg9JDWzM9AuIGFu92L/BUF30s0KEKCLuZCwGs0u\nFqMdLKVZkiKH0+xmu6ouk1XpfWZkZEaG994bXZwvvq5kNc0YQbMSD9BAV2VWZpgvvvOe932e3+N0\nyugLjUa4N1OpFPF4nNlZiywgH5z8BmLtRCIhjRbsFIt5Mpm0/Lq8vAbF7UAU3u/3ZR2UUqm6wtfy\neIY4ONjjo49+hs/nY2ZmjuPjIzY2XrzivOn3+5KTrCWPC3+bpVKpuH79BhaLGCmdnpYlvUSdXq/D\n1tYG8Grn1eFwotPpSafTeDwe9Ho9iUSC4eHAlZN5tVolm83x5Zdf8P7730GhaMq/V/Cg1Fgsg1Fi\nDr3eK51QtTSbLU5OjuXMO7fbg16vk66rEwD8fj/NZpPx8Qm8Xp+UTeeTR76D63J8fIJ+H/b3d1Gp\nlMzMzDM9Pc309KxcAOfzOfb2dtjZ2ebp08eUSkU6nQ7n5yF8Pj+zs7N89dUjksmElHHpJhqNEo1G\naLfbfPTR31CvN5iammZsbIJyuSQVjGZMJjONRp1+X1w7SqWSQqEI5FlZeVXk++tWNpuVhMxOGcD5\nzjvv8emnn9Dv91EqBXZhbm4Bt9uNVquT7fGD92aQSfeyZf5XrWq1Kjlow/T7/SsHqKWlFbRaHefn\nIer1BgZDC7VajdlswWKx4vEM4fd/PWa22+2Sk8yD3y+K136/z/l5iGg0ytjYGIVCjuHhAIlEgseP\nH2Mw2KSO1qF8PzWbzTx79oRw+ILr12/Q64nuiJBGWFlaWqbd7nDz5m2q1c9RqdSsrl7HaDTSbreo\nVity2LPJZKbX6zE0NITd7sBoNOB0uvjqq4d0Oh1WV69JetcG9XqTRCJBKBTCbBYaumfPnrK6eh2z\n2UIiEafRaGA2W3C53Dx//gydTo/ZbCYcPqfZFB3kUOjs1xZYkYjQKM3Ozl/BzryMavhlvavAonT/\n3qHq/5AlsCYxUqkkCoVCGvl/3UlbWVlhd3eXbDbDwcE+x8dHtNtCd7q4uMzBwT5ra0+l+4GFbrcn\nITmqeDxebty4xdlZkGazgVotDnGFQp5YLMr09Oxv1cD4det3BdY/wTU42Qt3Up4XL9aufN3pdMnF\n0W+bwn5xEaLZbKLX6yiVing8Q1xcnMt2329aA6ePEBQXKZdLnJ4ey3+v1+sJBk8kPZOCL7/8nNu3\nX/97d0G+abndHtRqlXSjvJTcjBn6fVHg/TITCIQrRqlUXCmwLi7OaTSarK5OoNfr8fn8r+iAGo0G\nT558xczMLFqtjnQ6I4M8K5Uq6bSw7gucwnMcDge9nqD+DgqhdDotEa2Fq+nyMkyz2WBmZpaFhUWM\nRhMul0tirxxRqZRlDZlarbpyEh4dHSORSEgUfDfFYkHutP1yMLDRaLwidK/VanQ6XTqdtvT9XxdY\n7baw62s0GrkNbrVaKZVKr1DzQWiq1GrNb001rlQq7O/vcnJyLNHKaxwcHGCxmAmFzhgfn0CvN8hi\n65fX5KRwWp2dBfF4POh0WsnF9XXh93JEyP7+HtVqBbvdRKPRw253yJuEWq2S+GBlms2GlKNnQKEQ\n19Xdu/ewWKyEwxcEAqNAj+fPn9FutwkERimVivLYrtFoMDMzJxegAzdSsVjAYDDS6/WYmppGq9XK\nY5/BEgWaVXK+XVCv1+j1+hwdHXJ8fIjT6aLValEo5Nnd3eHNN9/FZDIRi8U4OzujVquxsrLKd7/7\nXwCiuy2YTk3a7RYul1vSYtno93vSNdf8O20O/X6fo6MDFApYWFi48n4IWG6CWCxCv69gdnYWu93B\nixdreDxD2Gw2stksXq8Xg+G3D8OuViuk0ym63Q5ms5VisSC/viqVivn5Bebm5mUUwtzcPP1+n2q1\nit1uv3LIGLgnNRo1IyMjRCIRrFYbiUQMm83K0JAAD3s8Q9RqVTodMYKt1Wqk0ymcTicWixWNRkso\nFESn0+F0Cq1PvV4nFDqj3W4TjUaJRCKcnBxzdLTP9eu3eO+9b1Mqldjd3UKv//o67ff7+P3DWCwW\n3O4h0ukUc3PzhEJn5PN5FhYWuXv3Lfb3d4lEwvLfmUwmLi5Csn5MrVZTKhWYnZ3jgw++i8lk4vT0\nLykU8jgcdk5PTymXS6jVmldMIi+vblcQ5fV6/a9MdvimAqvREMXyP0ag869b5XKJ4+OjK4alAeVf\np9OhUolOZqlUvvIY0+kUFosVs9mKRqOWGwJiajDCxsYLLBYznU6b4+MTCoUcCwuLrK5ew+l0cXYW\nlA/aNpso1AXj7Zv1aH+X9bsC65/gEqRjDa+//gaxWIRPPvlYEiqLjW5vb0cWpP82HSwxnsiiUChI\nJOKcnh5z44ZoxyeTiV9ZYA1YVKenJ7jdHkwmM4lEgtPTY4rFIjs7W5LIe5JYLCLfrH6VIP3vswwG\nA/fuvUU0GiEajZDJZDCbzUxPz+D1+r5RfyaysUxUq18XCwMB9subx8ur0+mwsfGCZrPJ/PwCQ0Ne\nXrxYw2Qy893vfp+trU3J8r/M06dfEQwGZf1OtVphb2+HpaVlikVRvHq9XkqlosT2aaNSqSkU8vh8\nfrpdEdFy69ZtfvKTH9NoNDGZjGi1WhKJOG+++Q5TU9M4nS7MZjPJZILVVeFWCocvGB4e/sbC2moV\nG127LYTl/X6fZrOFyWTCYBDjjtPTE0KhM6xWK1arjVarSTKZ4O7d+7+yQ6VWf3N+5K9aZ2en7O1t\nU6/XMJstkkMqSjB4ikqlIRAY4d69N9FqtRQKeba2NmVStOhSmCkU8uRyWRSKr5lggxWNRjAahZ5t\nYODI51vE4ykpPqqKwWAiGDylVqsSi8WpVMrE43E0GjUGg5Hl5RX5NRTdCRtLS0s8e/aMWq1KqVSQ\naOvCTVQqFclk0vJGNBjr5vN5SQfSY2RkVOoCvNq9MRqN3LnzBn/5l/8Oo3GC3/u99ykUCsRiMbLZ\nrFRcmCgUivzN3/yY27dfp1wuYTQaWVhYuuJWG2jPvF4/+XwOj0e4Pd1uN9VqRYq6qgOvMlN6vR77\n+3vy6y10Yl3JGFCTjAgx9vf3WFm5JumtvDx79phqtUoul2d7e5Nbt26TyWQYH5+QOVler1d2tg02\nZUAG5w7e34HWUYjGhfbFZrORyaSvRNcA0gjLT7lcZHx8gmw2i8lkwufzX7kmBTOtjVKpxusVWr98\nPovX68fn80ng1DUJ8yCcuisrq1itVg4O9hkeDhAIjPCLX3xMrSacou+88wC32y3FWAkszdcGB71k\nmihxdHRAIpFApVIyOzuHXq8nk0kTiURIpRJMTs7gcrlIp1NUq1VarTY6nZalpRVGR0cJBo+xWm2c\nnp7yxRefyZ99u91Ot9vFarXSarVRKlX4/cOyXrFarVKt1igUSsRiERQKQTcPh8PfWGDF4zE6nS4T\nE5OvfJ4Hhe03xf40GqI7/P9UB6vdbnN0dCgV8KKLOJBcmExm/P5hWeKwtbVBq9VmfHyC6ekZSqWS\ndA9LEggEaLc7JJMJ8vk8i4t6pqZmOD8PYbGYGRkZ4/T0FLVaQ7VaQ6FQcHR0wOnpMYVCnnK5hN3u\noFqtcHJyJHHhfrsGxq9avyuw/omtgRW80WjIhHWv14fRaEShUMgdjlgsKll9vxneORg5qVQqKdtJ\ngAQVCiVKpYpGoy6LZa1W65V8qUajIQt4DQYjkcgler2emZlZ9vf3ePHiOXa7nUAgwPT0LKOjY7L7\n7dc5/lqt1m+EjX7TMhqNzM7OMTU1zcVFSBIidslms68Uh8LmKwSfOzs7hMNhVCqVZG2Gk5MTzs6+\n1ls5nS6Wl1fY3t6kVCoxOjrGxMQkR0eHaLUCCeB0utBoNITDFzidbr797d/nz//8f6bRaHLr1m1S\nqRTR6CWZTBar1SLrG7LZLNPTM1y/fpOdnW2ZaTVYQsSvJ5mMo1KpcDgc1Gp1NjfXSSaTdLsdYrEo\n29tbPHnymHg8Sr/f55//8//qGwueQYFVKpWkqI8qarUGm81OoZBnb2+XSqWC0Wjk3Xff4+Ligm63\nQ7FY/LUU57/rqtcbWK02vF4/KyurmM0WacMr4HQ6ZWRDuVxiff05nU4Hm80h61MsFivVapWTkxMy\nmZR0QBACfp1O8MU0GjVvv/1AhgQeHe1wenqK3z+C2z1EMHjC6Og4Pp+ffh95Q8rlMrTbHaLR6Cti\na7d7CIfDgU6npVgsEgwGZZp7tVolnU5dsX07nU4uLkKk0xlarQb1eh2FQnDTvvjiM0wmE1NT0/Jm\nVywWabVajI1NYrFYsVjE565UKlGr1ZifX+QnP/kRJycnUhyUTnJvquWuMQj3pFarlREfer2ed955\ngEKh4OLiXOry5r7RLp9KJWUre6vVJBwWuW8qlQqbzcZrr91hfX2Nfh+ePv2K5eVV4vEY1WoVt9tN\ns9ni8ePHVCpVVCoVy8ur7O3tyKNrrVaLSqWUMS/FYoGnTx/Lom+lUsHq6nXcbg+ZTIZarcbo6Ljs\nMPymZbWK7tbPf/638t/5fFdF1AO6v0qlQq/X4/X6CIXOMBpN3Lx5i16vh1aroVot43a7SCaTcids\n8Jra7Q6CwRP6/T5jY2OyrjGfz6PXG5ienqZUEly51167zeTkFJub61xeXmK32+WROIgR8tHRAel0\nmtu335B/TyolPtf9PvJ9xeMZotVq02jU2dsTusKlpRW5Kz41NSXhMoy0Wi0Z4TEzM0er1eTx48d0\nu20WF5fY3Nxgbe0py8srLyFYFrHZ7EQilygUSN3aq2vwuHO57CvMwq9jcl4tsPL5HOvrzzEaTbjd\nHpxOlxzXZDAYf6v7/dHRIdFoBLPZzPz84pXu1GAcKDS32xKIVU00eimZgfLkcjmZEVcoFBgbm6DZ\nbLK3t4NCoZC0ah1arSa///sfoFAoruQvVqtV2u0OdruDaDTKs2dPJRbi5e9chP9fWgPw38Cpt7u7\nTafTxmw2c//+WxwdHZJOp7h9+3V6vR6NRoNKpczBwb48Mun3+3S7Xfr9AXrATyIhRnztdpuhIS9K\npYJ4PMZrr93h2bMnHBzsyTfys7PgFXdWOHwh83lAdJTa7Razs/O89da7aLVaUqmU3Mr9VY6/UqnI\ns2dPcLs93Lhx6+/1+sRiUVkjM1i/DODc29uV2VGxWFTiAploNluMjY1dQQ10u12i0YgEAhSC+MXF\nJRnwZ7UKEX8ymaDb7RKJRFAqVYyNTeD3B3A6nVy/fhO9Xs+nn37CxsY6lUoJh8NJMHiCVqvlxo1b\nGAwG7tx5XQqwFl21fD5PIhFDo9Gg1+tRq9UMDwfI5/OSqDWI1Wojk8mQyaTp9/vkcjmazSbHx0e8\n9963X3l9Bs68UqlIKHTG8+dPUau1dLsdOUJmbGycubl5Wq0WFxcXsl4kmYz/g6MuBqtWq2K12tDp\ndKRSSUqlkowJGWxcgn6/Rrvd4dq16/j9w/T7fT799OMrYvXj42MymRS7u1tUq8L6XSgUeOedB7Lj\ncn39OScnJyiVSgwG0VUQ3ZgRpqamqdVqTExMotFoCIXOKJdL5HJZueCvVqvo9XryeZG998Yb97h2\n7aZsnW+32+TzOaLRCDdu3EKtVtPtdsnn8xwcHEhOs4ZE7m+h0+llTVAmk8Fms6PX66TrqH9FOwSi\nODAajUxNTfPuu++xv7+H1+sll8uyvb3FzZu35A7DgDg+NDQkjwArlRJDQ0NSNIy4DtRqNYXCq9mG\nA8fvm2++RTqdlsKZRZew34etrXX6fSRmU4KtrU1AdBIqlTKTk5M0mw12dra4du0GXq+XZDIuGVGE\nq1mvN8garEGOp8/nQ6VSk0zG2dvbYX5+kVwuQ6fTwe/3Mzo6LrPNfhkVMDIyQq1Wpdfr0+v1JGNJ\nT8aVgCgWlUqFNAbW4/f7JWagyGwNhc4wm80Ui0Xsdie9Xo/z8zOWllZQq1UUCnlOT9uUyxVsNitO\npxutVkuz2ZTuiUUODg7ka3vQ0W806jgcDt54496VEbrBYEClUlOpCGewxWJFpRLd7H6/j0qllsfe\nq6vXmZycolQS+ICRkVG5y+10ulAqBYS00WgQi0Xle9j4+ASnp8fkchmsVisffPA9QqEztrbW+cEP\nzFSrwiV7cLDL7dtvkEqlGB0d/cZCyWQyyV3EQYzMYA3GvS//u2QyicGgZ2dnWyLYlymVSlcwMyqV\nktdfv/trzU+tVotEIobRaOT+/bfY2togmUwCYmQ4iANLp1OUy2X8/mFGR8cIBkW3r1Ipc3x8xNLS\nMlqtjmIxh88n9JYnJ0cEg6dotVqSSVGszs8v8O677+H1+uj3+7L2r1Kp8NZb7/BXfyVGr/1+/7fS\nEf6m9bsC6x9pDd6QQYfJYDD8VpAyccOso9XqOD4+Ym9vB7PZzNjYOOHwOUajmZmZuSsXnsjbEhf9\nIN1dhJQOyOAq1GoRwbKzs0U8HsPjGUKpVLC4uIROp5M7JLdv32Ft7ekVmORAwApCIGy1WvB6fSwv\nr6BUKvH7h68ECw9OrwPHzjeR1/f392U7bCRyKTvO+v0+jUZDKtzaV2jkSmWLXK6MQqGQtVcAi4tL\nqNVqzs9DhMMXtNstVlaukctlZe6ScNvVmZmZ48GDb7G29pSxsbErxV2322Vt7Smbm+sYjSaWlpbZ\n2HjBwcE+8XiMkZExarUKJyfHXF6G6XS6dLtdSiUxrmg2m0QiF8zNLUo3/i5Wq416vY5Go+HmzVuy\nHk2pVDI2Ni5R2lVsbW3y4sUa0WiEfn/gRFTSbLZIJpMUi3nGx6d44403+O53v8fQkJ9YLMJf/MW/\nkUJ/y/IGW6vVqFarcj7Xw4efk0ymODk5xefzk8vlqFTKvPPOA7kYNRgMmM1maUyTol6vyaMAhUJB\nIBD4e7tCBwHK3W6H58+fcX5+DogxzmDDFjFE4r0cjIVEyLSXWCzK4uIyHs+QlKcpNqhsNkutVpWC\nXQvE43GCwROJBq7A7/dL7KonmM1mYrEonU5Xekw1uUgYmCKi0Qijo2M0Gg0pNUB0F71eH2NjY0xO\nTmIwmMhmMwSDp2xtbWK3O3C53ORyWamTpJNE8gZZ11ir5TEaDdy8eVuKOklTLEIsFkOlUr1yKh4c\naKxWGwsLCwSDQQqFIkNDPprNNSqV6kvfO4g+sr/klq3Ir59Op6fRaBAIBOh0OmQyGVnMXK/XyeWy\n2O0OzGYL5+fnKJVK7t9/C73ewGeffcLW1ibXrt1geXmFyclJtrY26XQ6uFxOKpUKer2BhYUl1tef\nU6tVabVauN0e4vG4NL63yBFMlUqFZDKB1WqV46M8Hg+bmxs8f/6M/hNUXgAAIABJREFUVCqNTqdn\nfHxCzr7MZNKvFFhWq43bt1+n3+/z9OljyuUSz58/u/I9woGplonqWq3ImavX6+zsbElRS6KbYjDo\nqVaF5X9ychqtVk80GpGxFIPxHMDGxgvW1p6Sy2Wl69CC1Wrj8PBAgqoWyWazjIyMykHNg/u+VqtD\npVJSKpWk/DybpJ3tYzabaLcFg090D+289todzs/PWVsT5phw+ILZ2Vn6/Z7cRYpGI/L7Pjk5yRdf\nfEqv18XnG6bdbvLGG3f54Q//Ex999DPJESlG6D/+8Q/xev18+OF3r2ShAoyOjhIIjOD3D1MsFonH\nY1e6WIO9JpVKkkqJMbxAYoiYqOnpGSYnp8hkMpRKRfmQHw5fyJiEX6XfjEQu6XZ7jI2NU61WSSZF\n6Hq5XJad3Ht7OySTKXQ6HZVKlXD4Qj4c53I5arUa3W6XL774lGQyweLiEu12S9KuFfH7AySTKUwm\nIzqdTorM0ssHj1DoTA5wF+DRER48eI/bt+9842P+u6zfFVj/SGt3d4dYLCr/2WQy8frrd6+0SF+2\n8w/W1tYmhUJedsc1my2q1SRjY+NkMhlMpjrxeIxyuYzT6aTVanF2dopKJbLkTk9PJE3J6ituun6/\nL7svTCYzRqMRs9nC0JBXKnRExt6tW7fZ3t7C4XAwOTklb9zNZpN4PMrS0jKBwIgEEoy/8jwymTQa\njZrx8QkODvbJ53NXCqxoNEKxWJCF2kdHBzgcTkwmE6HQGScnx9RqNSlfTyO/VlargVarj8FgYGZm\nllwui8FgkEeQHs8Q6+sviMfj5PN5Li8vSCQSzMzM4vP5aDabXFyEJMeN7hXRr0qlYmlphS+++IxY\nTMSz6HR6ajWxYQ+eg1qtYnl5hXq9LheW4+Pj/PCHf83R0REgWtjf+c6HrK6KiJNBbhmIDTQcviCR\niKPV6vD7h0kk4iQSccrlMmq1inQ6LYP8kkmhlRiMs+LxBIVCkbm5BQKBUZLJJEdHh7z22h1KpSLP\nn6+RzWZxOByEQmd0Ol3Oz4M4HA5u3rzFzZu3SKVS9HpXNTmzs/Ps7m5TLpd49Ogh2WyGkRExKq5U\nyty5881RHANB8cun3MESndU6drtIBvjpT/8v3O4h7tx5g36/S6vVxuUSG77oXFx1n3q9IjKnUMjj\ncrlxudy89todiQckAJilUolo9JJPPvmI4eFhnE4Xd+7coddTsbW1gVKpYGVlFZPJTD6fo9ttU6/X\nZbyJ3e5EpRJ0+4FZwGy2cHFxDiB1eQUTp9fr8uDBe0SjYYLBMw4P95mYmEKhEGR1cQ0H8fsDTE1N\nk0jEicUiOJ3CkeRyuZmZmUOj0bC+/px4PP7K53RQYA06TwaDnouLEH6/H4NBT71eo1QqSvlrg++1\nS2YK1RVjwqDAGh8fp1KpEotF5AJroHEZGRmR3+MBqHgQRdJsio1JxGVZuHnzNZ4+fYzL5WZ4eIRi\nsYBOp+M73/mQVColaQ8F3DKTSTMxMSnrsILBU1lTk0wmaDabNJtNFAoFl5eXVKsVxscncLncOJ0u\nFApIpzMy3PiX19nZKcViEbfbjcEg0CJarY7R0VFAwe7ujvy9qVSSWq1KtVrlb/7mp4AYBYsIoQ7l\ncoFwOCoX4js72ygUCjk02mq1yQkC/X4Pq9WK2+2RAoQd7O3t0u2KryuVKo6O9rm8vKDd7nD37n2U\nSqUUN+UkmYyzuLiEzWYjHo/K+sdCoSCPqUGYWsQYtkGn00GpVFAoFJienqVarcpjV3Gg1lCpVGg2\nG5hMZlns3u+LGKJBV9VstmIwCOd3Op0kl8tgNH5tQhKTh75cYB0fHxKLRX+pwKrTajVlR+fZ2SlW\nq02Sgwgt1AD38DJWSKVSEQqdsb+/y7VrN+h0Oqyvv5A/9/1+n8vLMGq1OHQMWHStVguNRsPS0grT\n0zM8efKYQkFE31SrFRKJBPV6jX5fHC6npqZ58OBbPHz4OXa7A71eL8thRkfHMZlMjI0Jdtjk5CRe\nr/dKDI/JZKJQyJNKJeUC2+FwSkiZ37kI/19f9XqdeDyKwWCQHEFN0uk06+svuHPndZRKJaenJ5yd\nBeVxyODfDdqRm5vrqNVqrFYriUSCdDqNRqOhVCpLELpxVlevk06nCIWEs2jQIjYYTPIp8OU1EM6+\n++57uFwuTk5OMBgMuN1udDodiUSMhYVF1Go1breHbrcjOVoiEosnIhUsM/R6ffL5PGazhUIhJ5/U\nKpUy9Xodv98vO9ViMXEaFIHBSo6Pj6QiZVUWNe/sbDE/v8DR0SE7OyKoeOBy7PeFM0Sh6NFstiWb\ntgGfz8f09CytVpNGQ9yEPvjgu5ycHEl6tRxqtYaJiUmSySSLi4uk02ni8Sjj45OvdGQymQwPH34u\ndR5FxMvwcEAaAfolS/UjiRbs480335aLJr9/GJvNRiKRkO3Sq6vXXymgB0G3gAwd/elPf4zVamN5\neQWXy83FxTkOR5d33nnAxMQUT548otVqYTKZsdlseDxDRCKX7O1tY7VaSafTnJ2d4fV62d/f5fT0\nlGKxJJGwb6LRaMlkUhQKeWZn57h+/SYff/y3ryAphoaGeOedB/T7Pba2tkgkErz//nf4+c8/Ym9v\nR46bEZoZoQGqVMq8ePGcbrfLu+++98rzrdVqsqYnHL6gVCqxtLTM228LTtLADfTLq1QqkkwmyWQy\nnJ4ec34ewuPxEIlc0ut1JcG3ibk5YUD4sz/7n2TelMlkxudzkc2WsViEY+z73/9nNJsN9vf3qNcb\n6PU1fD6/tHEp8Xr9xGJRzs/P6XQ6KBQKgsEgjUYNjUZDq9WSw8bNZgsLC0v0emITfPPNt9FqtWi1\nWnq9HrFYBINBL3NzLi4EVPKXhcaxWBSFQvFKcHexWJSDwDUaDS6Xm3w+TyQSZnR0DL3ewNbWJjab\njXw+L48BAcl99zW/SK/XUSyCz+cnkUhImh9xIIpGo6jVKtk9W61W5OIKoNPpotGoOT8/4xe/EKO4\nvb0d2u0Or7/+Bq+9dod4PEYweMrQkBen00kqlcLlEl2qra0Nudvc63UJBk9IJpOsrz/HYDBI2X0m\nucvfbIqIG5fLjUajwW53UijkvlGrWSoVOTsLyt2paPSSXq8vdVdEYTQIrW632zx69CVKpZp3330P\nu90pv/4iH9KATqcmlytiMpmZmppme3uTer0hFyx2u51CoUClUkGr1aHT6VhYWJLz7QTKpSqNJtWc\nnJxIUOYFzs6COJ2CMTU7O0ej0SQej+FwOCiXS7hcbhm78/IyGAwsLS1TrVZoNoXWqlz+ehpQLouA\n6Hq9Lv18JWq1IOxPTEzJRdHU1BS7uzt8+ukneL1D/NEf/QkbG+vs7e1Qr9d58ODbckfp0aMv5WJj\nQOtPJpOUy18LvJvNJvm8OPAUiyLjcnAveVkwX6/XiUYjTExMolarmZ2dI5/PE4/HUanUJBIxHj78\nUgq8f51arUq73WZxcZler0c8HiWdTuHz+fF4PFy7dkMC5JqYnZ2T7wfdbpREIimNk628/fa7tFot\nXC4Pd+/eZ3x8UgIcf0a5XJEdxB6Ph+vXbxIIjNBut9HpdPT7fQ4PDyUJTZNSqcTc3DzT07P/KLDU\nX1tgzc/Pa4A/B8YBHfDfHR0d/filr38f+G+BDvDnR0dH/+Yf/Ij+M1yC4yKy4wbt/52dLWKxmOS0\n6xIMBrm4OKNer/FHf/QnAHKordi8FLRaLYkw25TcfUN0OhnS6TTvv/+BtDEI1k0ul8XhcHJ6ekog\nMMLa2lNUKpUcl9FoNDk42CedFq3VarVKr9eVAziHhwOEQmeSoK8gi1DPzk4pFIReazA6KpcrdDri\nz/1+j2w2RzabxuPxkk6npefgwWw2o9Vq2djYoFAQuX46nV6CIM5TKgmXxvDwMLFYjCdPHvPJJx/R\naDQYGxuXRx29Xk/KUfTTavWIxaKcnh6Tz+dRqdSSQ+dSEny2+fDD7zI1NUOr1cLn86HV6iSjQF0a\naQZeKUBrtRobGy+kyI0hPvzw+xgMBnnsMTQ0xPb2FiaTkUQijkKhoNvtyCNGIeL2cXYWRKVScv36\njVcKh263y+XlBXq9npWVa7hcLra3N9nd3SadTtLrie8R8R5epqam8Xp90vMQNxmtVsvy8goOh4Pt\n7S2Oj48kinedgwPhBvN6fVitFgKBEW7dukMyGcdoNON2i8iNXq+HwWCUxdwvL7VaTSAwSiAwTLfb\n4/nz5ygUCnq9HvV6DZ1Ox+HhPm+++Ta5XI6trXXabeEIy2TSOJ0u6vUa7Xab/f1d4vEY+/sH1OtV\neeR7eRnmb/9WdBHMZgvf/vb7KJVK6vU6sViEWCwm3+SVSiFAzefzNBp1+dSezWZlzc3nn4uxiMNh\nZ3p6hvv338Tvd/Ls2RYnJ0eMjIxis9lQKOwcHOzJJ1GFQkm/D91uh5GRETY3N3j69DGVSpnnz5+x\nu7vN0JCXvb1dzs9D8qiq0ahjtdokE4CCer0ub3qDYGVQEItFCYcvuLy8wOl0yM45AZG0SrgJPQ6H\nS46SyWQyNJtNWZelUgmNXywWodPpMDIyhtFoJBaLyq+R0/k1PXzguKxWK1gsVhQKoa+0Wm1UKhVi\nsSibmxvSPehUvmbr9RrhcJihoSFOTo5ptVoyQFS4LYUIWGxcbkqlEr/4xcfMzs7R6XTY3d2SXH0Z\nNjfXmZiYJJMRo9SbN18jErkkFoui0xnkg+eAKzY5OcXt23d4+PALPJ4hWdowEO3H4zE5KBpEJ35n\nZ5ter0+/3yUWi2I0GpmYmOTsLCg/fp1OT7/f48svv+Dy8hK/38/7738gfy5fvFhDpxPGgUhESzgc\n4+BgD7/fT61Wo9EQmrkbN25hsVglzWSFWq2Gy+W60tWxWq1EIpeo1RqsVpucxpBKpSmVSjJIdnFx\nhY2NFyQScV577Q5ut4der4/NZqdcLr0yDVhcXOLZsyecnh4zOjpGuSyK1Hv37ktO3BPGxydYWFiU\ndEQ9NjY2sFiszM9/7ZDW6w2srT3FYDAyMjKGRqPl5OSIg4N9/uqv/nc+/PAP5CipSqUiR2yJcVqS\naDTKwoIosCqVisxuGx4OcOfOG2SzGRKJBK3W15m1InbrklqtyrVrNySi+nWePPmK8/MQGxsvuLy8\nRKMRGaG9Xg+dTsfY2DiXlxd0uz0pEsrArVu3USgUsm41FovKE6Lp6VlZVwUKgsFTWq2mHOdTKBQk\n45bQ9e3v70rAV5G1+7Of/ZRoNILfH0CrVRGNxiU+XQmtViPLQL5J6vJ3Xb+pg/VfA+mjo6M/nZ+f\ndwCbwI9BLr7+R+A2UAMezc/P/+jo6Cj1D3pE/5ktIX4W2XMvW4yXl1ep1xskk0mazSaFQk4OKb24\nOMdgMLKx8YJsNiMzqhwOO7VaHaVSQbEo3CqlUkl2zdVqNfmEGw5fcHx8KBOWByGbIyMjkpASOeal\n0RAcl2azyfLyCu12Wy6wxCnAxtTUjAQpzNNud5icnMJud2AwGGi1mrx48VzKHzQQj8d5/Pgrvve9\nPySTSctcIRDYiEIhj0KhkDheOSwWCyqVio2NF7hcLm7efE0ScIsgVq9XwBZbrRaBwCi5XFYaPSzw\nwQd/RLFY5F//6/+ebDbH974nWEBra085PDzg6OgQh8OBWq2RRe2VSpluV7hCGo06kUgYh8NBv78g\nn7YODvbI53P4/QGuXbvOxMSE/N61222ePXtCo9GQRKZK4nFRDM/MzGE2i1O/iA/JSH/3ais5k8nQ\n6XQZHR3H5XLJzsfl5RXS6bScl6fTCb3IxcU5ExOT0phGdeU0OTwcIJfLSQ7QBpmM4AcJDpFdHjmc\nnp4Qj0dptepMT0/T7QqCtMlkIp1Of2N3QIwqRuj3++zv71IoiGsiFouhVqvI5bIkEnG5azI1Nc3Z\nWZCLi3P29/ckwfw52WyWarVKKHSKXm+QQ6jz+QIbG+vU6zWUShXtdksSCA/QDCoJGOvD7XaTTqfY\n2tqUkR/vvPNAzk/7/PNPCQZP6fV6vP32uyiVSlqtNgaDgUIhz8LCEnfuvC6PLx0OJ5eXohjvdgUT\nbFD4HB3tc3YWxOUScUlqtZqhIS8ej4d0Oi13WqpVQQK32UTRkkol8Xg8JBJxGfKYzWb4i7/4X9nd\n3SabzRKNRq7oGtVqFc1mC5fLyVdfPQREF2qAX3kZDun3+zEahUh5dnaesbFxmacEXHn/XtZhDdyX\ng3F7qVQikUigVu9Rq1XI5fLYbDbOzoIUCnkSiYQ0yhLC5EE4ei6Xpd1uyUgUv3+YQqFAOHxOMHjK\n6OgoFxcXNBot+v0eBwd7EgusL0GSj2Sq+vj4JCMjI9y48Rr7+7uk02lZIG4wGK+4Iz0eDzs7W6yt\nPaXb7b4kaO/S7/ekqK824+Pj3L//tkTUd/Ls2RM2Nl7QajXlrqvVauXevTevFC+1Wg2j0ciNGzcZ\nG/Oxvb1PNHoJ9GWnWjQqDi+De2GhkKfZbDA05L1yQLNYrBSLRZRK4b4caIhEERQkn88zNzfP0NDQ\nS5DonqSVNUjCezGyfLmj6XS68Pn8bGxs0Ov1UKuVErZHS6fTwWq1YzCI4lKn06FQKGg0asTjUfnA\nPsA1WCwWdDotR0cHgEgPyGSyXF5e8NFHP+P9978jaWAFXsdms+PxeNBoNJIGVWSRXlycU6/X0en0\nMoDa5/NRKBRIJOJSUoCZaFQcCuLxOC6Xm0BgBIPBwJtvvs3W1iZ+f0Ca0MS5uBAsN5fLTTB4yrNn\nj2k2xfWkVqslSUNXmtZUsVqtjI1NEAiMYDabuXPndf79v/+3xGIxDg/3cTicDA155HQGh8PJ/Pw8\nsVhUinsSoOy//uv/g+PjQ0qlMkql6PTq9cK4NXDGz87Ov5I68fddv6nA+o/AD6T/VyI6VYO1CJwe\nHR0VAebn5x8C77z0/f+/WIK23GFmZuaKqF2pVHLz5i22tzc5Pw8xPj6JxWLhyy8/4y//8t8xOTlN\nKBSSU97Hx8f51rd+j/39PQqFPH7/sByPoFZr+Ou//gGplBj7dDrC1TTYBFwuF2q1mlAoSKvVJBAY\nQacTXZNB2v35eYhsNofJZGJ6eoaVletcvy5iNgY3jr29XbktHAqdYbc7WF29hsFgpFKpEo1GpPwz\nMW548uQrOevr5Zt+Op0mGDzhX/7Lf0Wv16NWq7K29hRAFiqPj09QrVYwGg0YDCLWY2XlmhS/sSPr\nrSKRS8bGxqW4kQrxuKDJC2tzC1BQq9WIxWLk83nJ0VJhdfUanU4Ht9tDNBrl44/F2GtmZo5ms8HZ\n2RmFQl52GcbjMdlVMtic/P5hiaKckJ6T4CotLa1IehmNrBkqFouo1eorWWCpVEI6GWrIZDKk0ymp\niBxBqVThdrvpdnvY7XYuLs45Pj5melqIWvP5ghxNEYvFmJ+30umIKB2r1crIyAjT0zOMjo5jtzvY\n398lkYgRDJ7QaDRQq7WyzTwSuaTZbMpFysvvVafToVQqyBq3jY0XaDTCMNHr9ZifX2Vj4zmxWBSP\nZ0iG80Uilzx9+pjZ2TlMJjNqtZqpqSnJeVWXSNzilGmz2VEoFCSTCWn0KzY9h8NJIBDA5/Nf2Qjd\nbg/tdot0OiWRt+MSCFMQtwdROYMNIxaLMD7upVwu4/P58Hi+3gh9Pj+HhwfS56ZDt9vh4GCfVqsN\niGiaqalZ3n//OxwdHWK12lhZucbDh1+QTCap1Spsb2/IbtmBkUIcGJ7LGrpuVxQDw8MjNJtNhocD\n3Lt3X/o8pKRRZY07d+4wNDREqVQiGDzFYrEyPT1zpUC32x1YLBaCwVNZ15jLZXn99buviIWbzSa1\nWpViscjQkJd6XUBJV1evkcmkKRSEAabVUjM5OSGHAYfDSiqVCnNzCzidDpl6rlQqee+93+PFi2fS\nSOUGOp2OQCAg5z/Ozs5hs9no9/u43UNEo1FOTo6APolEjGQyTqNRZ2pqWnptREfA5XLx7NlTwmFx\nkGg0arIxJpfLSlEwh3KnVaFQ4HS6MBqNEm7mnG63i8ViIRqNMDIyitksRny/+MXPuby8xOXyYLXa\nmJmZuaIHEiakGjabA4fDydRUgM8+eyhBZcX9UnRgE2SzGZ49e8rW1jqRyCWjo2NyIPRgDQwlXq+P\n+/ffxuPx0G63pTxKIfIfdJSE/T8ih5Y7nS75sDI44A4SGCqVitRB7koj8yyxWByTSejC7t27j0aj\n4fBQFE07O9vUanWOj49Rq4UjWRz6LyWzxgQrK6s0GnXOz88kLp2ZUCjEj370n+TRcaMhCozh4WH8\n/mHC4QsePXpIrVZlff05vV6P8fEJzGYzjx59CQj5w9bWBmq1Rh6p2mwWDAZx37LZ7HKBUioV0Ot1\nksnEx+7uNvm8QIkMHofJZJKKmjJPnnxFIhEnl8tx48YNbt9+/crIPRy+kHNkp6dnJDF/nZmZWdLp\nlByHdffufarVCgsLi1gsVgqFHOl0Uj7wQp/R0TFmZmZotdosLCzwrW+9j0ql+jux/37V+rUF1tHR\nURVgfn7egii2/puXvmwFii/9uQz844fR/RNfFxfnqFRCTPfLS6PRSPocwUZqtVrk8wUODvY4OTll\nZmYWq9VGr9fj+vWb0nhih5GRMe7evYdOpyeVSqFQKDg4OJBzAJvNOk6nG79/WDo5Cvfa5uYLotEo\nb72lk1Loj6URVAulUiU7YzKZNBsbL5iZmZEFpc1mk2DwhHa7Ta1W4/DwgNXVa8RiMUDktQ0PD5PN\nZtjYWJfSyE9ZWVnhrbe+DgQeBG8Own+Hhrzs7u7Q7fYYHR3j8jLM+fk5q6vXCIfDZLNZ/H4B+ZuZ\nmZUfn9C2xDg7CzM1NSOd9BX85Cc/ptNpS/l2WSqVMicnR3Q6Hbxe4VwbjFpBgEX39nbJ53NcXoZ5\n9uwp5bIge6tUKjwer4yYeHnZ7XY0GtEVMxhEN+bk5JiHD7/k2bMnOBwuVlevcXCwd0U87vV6+fDD\n7zI05OX8PMT+/h65XA6PxyPzV0RhCCMjY+zubkli4tt8/PHf8vHHH0lZhU0OD/cBMfKZnJzi8eOv\nqFSEy1KhUPL66/dk+/6tW7cpFgvs7u6gVKrQ6XS4XEIw+/DhFxIwsS2xfxzy483lcpLweZR0OoVS\nqcJgEHogg8HA4uIiw8PDnJwc4/cPyzq7crlEpVKVu6sjIyPcvXufn/3sJzidbt5++x2CwSDLyyus\nrFyj3++zvv6cX/ziExwOB3fv3gOQha5utwej0Sjz17RaLY1Gk2hU6DLE5tqT4LfCAl8sliiVimSz\nGdTqHv1+/0pxJd4PHzqdXhbHFwp5eZR07dp1dDotJpORcrmAxSIEwblclpmZWb766iGbm5tEo+Iz\nkEjEKJWE5jCVSkoiYhP1egOtVsvt23cwmUx8/PFHuFxu3nzzbTQaDb1ej2r1fyOdFp2IQqFAJHLJ\n+vpz7t69J1/3g1Wv1+SuYy6Xk/Q+FY6Pj16x2Hc6HQ4PD0kmExJss8v4eACj0UQksiEfSkRBqpUd\nnQOa+NBQWt7cFQols7PzJJNJRkfHr+hFB6/l5uaGPHa6uDiXOh5q1taeMTY2RrPZlET3Si4uLjg8\nPMTn87G+Ljh0+XyWXC7P6ekJfv8wWq1ePpheXl5gsZglm71K5vwtLa3QarU4Pj7E5/NL2rBdOp0O\nPp+fUOgMp9PFxsY6zWaDxcVFtFrtletc6CxBp9OytvYUr9eB1+vj6OiIbDbL7OwcJydHjI+PMTIy\nQiwW4eLigl6vJ+U/XgWg9vs92u2OxNwShZxOp5M1c+vrz0kmRcST3W4nGo2wtbWBTqcnEAig0YhD\nTqlUlPlOAzRPqVSi1+tTq9VIJOISODPHzMwsd+68QbVaYXFxGa1WSyaTxucblpA8XgKBEXky4nZ7\n8Pn8BAKiO+1wPOf8PMTbbz9AoViXi6ZWq4nL5ZLTA3w+PxMTk/LrbDSa0Gq1DA8HWF5eld+vSqXM\n9vamFC+Tw253SM7rKjs7W/IhuVQqEY/H5DH35OQUw8PDWK02GWQ7NzeP0+lif38XnU4n0djrTE5O\n8eDBt+UM0UGUzenpMSaTwBeJA3MQlUqNRqNhZmaOFy/WCIfDjI6KHN3BWHV3d4fDw0NWV6/zxht3\nWV9/Trfb4+zslGq1isFg/K0TK36b9Rt/0vz8/CjwfwJ/dnR09B9e+lIReHkuYgF+dbidtDyef5gq\n/5/SSiQSaDR9pqdnCQRc3/j1SiWLzWZAoehiNKoZGfGRz6fpdDpEIiGMRo0kZk/zxRcfcXl5TiAQ\n4Ohoi4ODTYxGDc1mE7fbxsrKvDR+iLK0tESpVCKVSpHNljCZ9DgcdrLZLJFIiFJJXNyBgJ+lpSUe\nPXok3bCUxGJher0Wa2tJ9vc3JaeLcBWOj49TKmWpVototQrK5Qz9fp+LiwvK5bJ0MnTRbFaJRqOs\nrT3GZjNRqxUwmUwolR2MRh1qNWxuPmVqagqVqsuNG0usrKzw2WcNqtUCZrOabDYOdFlcnOHBg3vs\n7+8TjUYJhY5Qq9XodDry+TxnZ4coFF2uXVvmq6++AvpYrSZqtTLFYpt0Wtjrh4e9qFR9ut0mX3zx\nc6xWKzs7LySHnh6NRkk6nUap7OPxuPB4PCwuLvLhhx/K7XZAEui7XjnB/PSnPyWRSBCPx6lWq0xM\nDLO8PCvrhM7PRRfqo49+jN/vJxQ6ptWqUSplGBkZYnl5GYPBwN7eHlNTI4yPewmFtIyNDTM6Oko8\nfk4wGGR2dgqPx4NS2ZXgiSbW1r6k3a4xPj5CPB6nUEjz6NEncmSLKBbtaDQKQqGg5DitUK/X0GgU\n6HQqyuU83W6NZlNkOCqVSpLJC2w2A9PTI4RChxiNWubnZ5iYmGBtbY1/+2//Fz744AM6nRoHB5uc\nnx9JI4gsKlWP9fUn2O12/vAP/5ChIRvFYpbhYQ/Dw24ymRhzcxP4/eIxKpXXePbsIcViSr4PhEIh\nYrEQhUKSqakpDg4OJJaRivn5KQKBAF9++SXNZp379+/jdrtukCECAAAgAElEQVT5/PPPpQK4j1LZ\nJZOJ88knSWZmZlhamn6lCJmfnySRCLO9/ZxkMobdbiedjkkAwgaRSAidTsnw8BDj437y+QRvv/02\nh4db9PttvF6n1Bmzs76+TjodZ2JihLt3b+N2u3n48CF+v59bt24RDAZZWJilXC5zerqL3W4nl8uh\n0ykZGnIRiZxJZO4q7XYDr9eFRtOlUhEIErEZhfF4hFbx+9//Dp99JjSU9+7dkzsfgyWcZGVSqRSh\n0KEkDp5nd3cXg0HFa69dZ2tri6mpKd599125e6lSdWm1/Dx48Lb8swwGA/l8nmSyyfLyLNeuzV/5\nXU6nkcvLU7LZGBaLhUQiTKGQot2uUyzmcLmusbCwwLNnz7DbRVe73W5z69YtNJqvo5b29vZQqVS8\n+aaw7/d6PUnor2RoaIj794ULL5vN8vz5cyqVLCqVipmZcb797W/TbAq4Zjx+zsbGEyqVCjqdCr/f\nS7fbpdttMDs7wdDQ1xTudLqBzWagXi+iUCiIxer0+y3U6j7NZoVvfestwuFTUqkUf/zH3yccDpPP\np7BYLPyLf/HPcTqvvu65XAyn04LTaXllT/N4LBQKCbLZLI1GgZmZUba316hUCty48RbT00KnOzRk\np98XnWWfT2SVVqt5UqkUzWaT73znO+zu7vKjH/0Is9nA9PQ4ExNiXNtqlZmYmGN6eoyxsTHC4TA+\nn5Nbt5ZJJC44OVGi0cDwsEt+fK+9do1E4hKlss0f/uEfcHBwwOjoKDqdgsnJSebn5/nqq68olQp4\nPGMUi0V0OgVer4vZ2Vnu37/P6uqq/DwDATfxeJjJyRHicXHP/NM//RdcXFzwySefyLKKSqVCNpvF\naNTR67XR69U0mz1MJh02m4nFxRm+//0/4Ac/+AHxeJiZmRkmJ0cwmxcIBAIoFE1CoQP5Gu33+1it\nBu7duyd1RZ9hsxlQqVSk0xHee+894nE39XqBft+DzWZgYsKP222mUEhisRi4dm2B+fkJAgE36XQa\nm80gHQhn/1FrlN8kcvcCHwH/6ujo6NNf+vIhMCtps6qI8eD/8Jt+YTr9atbZf46r2+3y6NEazWYD\nq3XolefV6XR49OgZ6XQKrVaH0WjA6/UxNDTC7GyXg4M9FAoVY2PT3Lx5i3q9wdbWBul0DqvVxcHB\nKbFYUgbfCbDlEOFwjHQ6z97eESaTEaPRSr+vQq83otebcDoVKBQaisUqGo2RsbFZyuUWx8dB2u0O\nJpOZXC5NpyMEx+l0nnBYiLgtFie3bt2jUmlQKtW4efMufv8wu7vbgIZCocLhYZCFhUVmZpY4Ojpg\nd3eXUCiM3T5EMHgpuU/slEp1trf36fWEQ9HjGSWdLmOzDRGJ7PL550/Y2TlAodAwNjZLOJzi+fMt\nXrx4Tjab5fd//wP++I+/xw9/+Df0+30qlTIOhxOPJyCF3o5xeHjA9vaWjMjodvvU6y2CwXNSqSR2\nux2320MgEJAYVGpqtRp7ezsS/0ZDNlukUmnj8VylG9frfX45asTl8hONprHZ3DQaXQ4OgvzJn/yX\n8olufv46ZvOX7O/vsbGxI7lCHeh0Jlot2N8/lcZUXRYXRwkGIxSLdTodFa2WErXaiFqtZ2lplVar\nRa/Xo9Wqsb19gMViwWi04XL5WF8Xjie324/ZLNrmjUabZrOJ0WgllyvS7SrIZIoSmE8tdRRz/If/\n8B8JBEbxen3Mzs6xubmOSqUiEklTr3dQqXS0231u336Ln//8M4rFPD//+acEAiLfrdNp0+120OkM\nVKtCYzg/v0Cx2ODiYo1crsT16zc5Pj6nWKwDevmz0W6rUKl0hEIRzs8T6HQ61ta2pHFgkYcPn8rj\np0wmTbFYpdWK0G73cLv9+P0TUm6dA7PZTrPZ48aNu5TLn1EqZanV2pTL4r+Xl8Pho99X8fjxM7LZ\nPM1mB4tFFGQ6nYlms8n5eQS/34/XO0Y4nOCzzx5zcnJOuVwnHk/j8QTw+caw2c7Z29thd/eA27fv\nc3JyTjyeYXx8lm5XTbFYx+sdIZXaYm/vBIvFwvl5iFQqKY0dlDQaXTQaHf2+ks3NPZrNq9eZxWLh\n+vXXSadT/PjHf8v29qZ8/V2/fvPKONVgcDA8PMHTp8+5vIzzzjvv8vHHX1AqCfioXm+l0VjHanVj\nt/vke1evp8LtDmA0iusnmUzy5ZcCOqzV6mg2+5yfx648LqVSKUFGdxgZGaHV6hGPX9JqdVGrdezu\nHlAqVWk0OgwNBbDZ7Hi93lfAwg6Hj8PDA0qlJuPjPur1BrHYKUqlivHxefL5AeBRz9zcKs+fr5FM\nCnbekycbUhGlZn//hFQqJWWCKlldvUkmk+Hw8IS33mqwsyPGZm63m3A4SSgkCPaTk1PMzU1wfh4l\nHk9Tq7VIJPLU6x2azR6ZTIWLizjJZI6hoRE6HfUr9/ft7UPq9Tb5fIXDw9AVLRnAyMgMl5cJHj16\nxhtv3CMYPKfb7eNyBeSf1e9rODsLotcbmJ2do9tVYzY7KZXqFApVCoUqKpUBvd6E2+3D6fThcgVI\nJHLkcmm2tvYoFksUi3XOz0Nsbx+QShUpFKooFFqSyQz5fE3+fR7PKNVqi42NXebnr1Ottjg8DNJs\ntsjndzg9DVOpVDg42OPzzx9JaAo3Op2ZXk+F0eggnS5TqVTY3Fzn8PCAra1dCoUK7XaLXk8p3eO9\nfPjhP5Md76lUiulpge2w292MjU1zcLBPJBKmVmvx5ZdPpNfxQEpLmKXZ7NNslslmD4lEwrTbAhgr\nQKgtHjz4Fr2eluPjC46Pz3E6nfh8fvb39/j44y9RqVQEg2FOTy8AGBoaJZHIc3ERQ602/N/svVeQ\npNmZnvek974qs6oyy2SWr2rve3owDhgMgMFiF0Qsgyu6oIIXumOELnVBXfBCiiAvGAoxVuSSQaNd\nUdwlwIHHGIztrjbV1V3e+6r03vtMXZyT/6AxABbkSqugtOdmYrq6KzLzP/n/3/m+931eul298rlY\nrX0MD0+Qz1epVNpfuNZ/kYLrz+tg/Q+Isd8/nJ6e/ofyz/4IsOzs7PzR9PT0fw+8i9Bn/audnZ3o\nf/Er+a9s7e3tUq0KncGvEsLt7e1KWndUcjVcvP/+ezSbdXK5nNQHDTM4OEQwKASuxaKgib/xxpsc\nHx/RaDTodmFkZITbt+9KmnGbbDaD3W5naMiPzzdAvV7j4OAAh8OJWq0mn89htVrln+/icgkmT7Uq\ngGxDQwGGh0eV0NbDwwM+/vhDHA4HP/7xD9nf32VoyI9areboSFCMQ6EJpqamSSaTTE1N4/cHuHr1\nGn/8x/8Wvd7A3Nw8P/vZT8hmM0xNzeByuWk2m8zMzDI+/jkhfGjIz8bGGh988C5HR4dMTU1RLpd5\n8uQxS0uLJBIxmk0ReRGJRGSn70wWsnYuXrzE+PgE4fA56XSGaDQs/00dUDM6OoZer2d/38ClS1f4\nxjd+B6fTSSQSlgC8Du22KHK8Xh9HR4ccHx+h0+moVqvU6zXOzs6UqCGDwcDrrwtbs8vlplAQ4b4u\nl5NI5JyjowNlzKpSqbh79x5qtZpPPvmYer1GrSYcPaHQOM+fL3F0dMSdO3dxOJyEw8IV43A4lIdm\npVJhaMhPOHyGSiVs99lslmRSAP7S6QztthiHBQIj3L1774V9J/QN6wQCAb72tW/gcrl5+vQJ0agY\nNQh9lo7T01POz8+IRqPY7SLktlQqEQyGMJlMPH78kEAggN1uY3BwiPHxCYaG/HQ6XdRqFSqVSrKz\n0jQaTd555z8SDE5gNpsl8DOF2WxWRpgg3HidTodYLMrjxws4HA4ajQYTExMsLj4hn88roFiLxUp/\nv1eJxymVCvz4xz+QNPggodC4gnAYH5/g+fP0r81KE0JWIzabjampGalZyuH1DjA0FCCTyRAOn3Lj\nxm3Gx8dJJhOEw+fodDouXLjA2FgIq9XK9es3uXXrNv/4H/9P5PN53nnnP0nshFphjYEYpW1ublAs\n5lGrIZGIEo/HGRwclMyzmBwbltjZ2eLChQuSn6ZCrVbjdovvKnRptVpSp1RmdXWFra1NPB6PjGIS\nDuPHjxfY29uj0WiQTMbx+Qa4cuU6MzNzLC09BQSJvbfK5ZLCp+qtnZ0tNjY26HYF9FFkFL6YTdls\nNjg6EmDj0dFR/uAP/jYLC5+xtbVJPp+lWq2g0ahJJlMYDHquXLmuhImD0GZub29y6dJlRV/ZI8ur\nVHD58rUX9gsIt64IbY5KAf+x8rOerODmzds8fPgArVZLPB7n4GCX8/NTJRLI4RBMq7OzU2ZmZrl0\n6Qqjoz7qdaFPzOVy/OQnP1JcbY8fL/DgwWecnoqQ9YWF+9y8eVvp/h0fH/Ho0QLNZoO9vT3a7Q5v\nvPEVhTcGPeTCBVZWlnny5JEyeu9x/nqvS4wDO3g8fRwdCbNBMBjk6OiQTz75GLVag9FoolarsrW1\nxtLSIhqNFovFovDP6nUR+i3ArlE5Bu5yfHzCp59+RDAYxGq1Ybfb6e/vIx6P0Wg0GB4e5ehIsN3U\narWMtbJy7doNYrEoBoMetVpLrSYkKcKZWmRx8Ykiw+ihTMrlInq9gQ8+eA+/38/JySl7e9sYjQY8\nnn4J3lYxMjKG2+3h0qXLSvSb1WolGo2Qz+cZGRlhfHwcn29QYkky7O/vUa1WGBsLYTZbqNdrSid3\nd3cXgMnJKZxOF7u723z00Qf09fWzv79HIhFnbCyo6OCOjkSD4JeDnHsxYT1upPh8Rv7C48I/T4P1\nD4B/8Bt+/iPgR3+hV/Bf4crlspyeHitZYz3dyC/+fHl5idXVFbmpfayurlCv15mdnePwcJ9Op83I\nyAi5XE5x3SUSccWCnUjEicWi2Gx2EokEi4tPmJ+/oDj76vW6Yv/udKySIF2WNvQ2V65cU+CaU1PT\nMmn9iIODfYaG/JjNZpLJpPz9MapVoZva3FynWCxzfn6G1WpldFRszmvXrsuu3Kfs7GzT3++VvBiX\nIlwvl0vk83kmJ6eZnJxkf39PyeMTkQhRmccXZWnpKalUktu37/Do0QPu3/9Mvh+HRCg8RaMRCAMB\n2xvn9u27mM0WVleX2d7eZnt7k40NMbMfGJjg2rXrXL58lYODfep1wVcplYoKQLK3gsEQ9XqDcPgM\nl8vN6uoKyWRSgvEOFAxBbzmdTgYGBjk/PycSiTI+HuL69Zt88snHbG5uYjZblMiZcrlMNCr0PhaL\nmXQ6TSaTVpLe1WqhX4rH45LwrFKMBSLqo8Px8TE+Xz+HhwIaWqvViMeFdd7hKKPVauh29ezv73Lv\n3ssvAD/NZhPtttCG9EShg4NDpNNpbDY71WqFyckpms0WzaaID+rr68dms1Gv17DZ7GSzaVZXlwmF\nxvH7/USjUR49WiAYHFfGpt1uV+HYQBeNRhSoY2NB2u029Xodj6efRqOh0OtXV1ekEDjBs2dLdDod\nXC4XfX19MvDXTq1WR6/XMzk5xdCQn3fe+S4ajYaxsSBPnjwmmezgcnkk1DNCLBZldnaenZ21Lzyc\neyuTSSusKavVRq0mCgudTsvg4JAEO3awWMw4HE6F+6bVCk2H1+vj5OSY9fVVzGYzFy9e4cmTBSqV\nEkNDfu7du4dOp5fg2ALZbFZmQhbpdoXpwuPpY2ZmnsHBIc7OThXHlXAlJjg9PVE+23BY2P/NZguN\nRgOTyaxQ2dvttlLQ6PVGKpUS2WxO4h6qdLsdotEoVqudTz75kK0tUTT9Yterh0PpORCz2Qy7u6Ij\n/sYbb35B0N1b4fA5y8vPyGRS6HTCHOLx9LG1tUm5XEat1pDP50kkYmxsaDCbLbz11teV/bK7u02l\nUmFra4vLl68QiUQUp6bH0/dCgdJbhUKeTCbF2FiQyclp+dD/XIDcu+ais2ljdnaOTqdNMDiO2Wwm\nl8tyfHzEs2dL5HJZ7tx5SWr2xHf893//b/DDH77D/v4eTqeLWEyMkGu1Gv39XkZHx2TH5jk3btyk\nVCry8OF9NBo1s7NXFJTN+fnpF17/wIAInQ6Hz3E4HJLzlVNMRQ6Hi0JBmGMcDkF5t9vtdLtdtFqN\nIgI3Gk2USmXu33+AWi04Y6HQODMzc/T39/Pyy6+wsbEuhe0+9HqhYTo8PCCRiPP48UMmJqakjlXN\n6ekJ77zzPQIBPwcH+xQKwhxy5cp19Hod3W6X+fmLxGIRtre36O/3Mjk5qUCNm82mgqyoVIrEYjEK\nhRzxeIzV1WVWVp6zsrKspE3Y7Tb0eiO1WpXt7S3sdgdOp4vnz5/RajWZnp5FrVZLeLEJlUo4J3v7\nRavVEggM43K5aDZbHByISUA4HCadTjE8PIzT6ZJonjo9V6jL5eL8/JRSqcjIyKjitI/Fouzv7yn3\nKbvdwfT0DGq1imQyyebmBmdnZ3z1q29x/fpfjOb+V6DR/8zVE/51u+J09eGH70t7copEIi5DR0U2\nk8FgYHp6jqtXr/LkyWMmJialK8/E+PgkrVaTcPicra1N1tZW2dxcZ2xsjFqtRiIRp1arYzI1sVoF\naXZhQZyqcrkc5XIZr9crLdViPORwOCgWi7TbTRk90yIajdAbdblcbvR6A6mUKCay2QxLS084PRUZ\nan6/n7GxEMWiyHd6/PgRly9f5fr1G2i1WumSm2RnZ5v9/T3m5sQDIxw+JxwOUy5XKBQKivtLBObu\nUyoVqdfrbG6u02g00em0dLtdxUEVDp/j9wekCP4Mj8dDrVZjc3OTiYk5bty4gcPhYm9vj7OzEyqV\nirQuC1JyMDiukLltNjuRSJh4PMLz50uSPdPH9PSscjNWq9U8ePApy8tC6JnL5Wm1GgSD44RC40xM\nTOHzealUqvyH//AnfPDBu1y7dpN8PkcqFafTaeH1DtBs1llaWqRarWK320kk4kSjETqdLoGAX4Zg\nHyqZaJOTU8zMzJFMxnn+fIlUKsH4+KRiEddqdXg8bhnMvCrZSWIvZDI56bgbIBaLkUpF2dhY55/+\n038imVleNBo1JyfHSqxHbw0N+el2uxwdCfu4iF+a4Kc//RHNZpO33vo6TqeL73//e9LmLm52weA4\n1WqF5WUhlp6bu8D16zcASKdTnJ0JkGi73aHRqJPNpshmUxwdHVIuVygWi8RiUbRajZLx6PeL19Lp\niDiNRCLOe++9i1arYWJikmKxwPT0jAz2Plbcizdv3paFcw2n08Xx8RF9ff0S9Krn5Zdf5vw8/oXs\nPYAnTx5zfi6o37lcjqmpaeketTI1NcPx8REmk5FcTsSEiMKmKSGabhwOkat4dnaKw+FgeHgYl+sb\n7Oxsk06neP/998jnc6TTaYxGAy6XG6PRKIGeXVQqNa+++voLnKKlpUUqFRFovba2Qrfb4+G9uCqV\nCru7OxQK4nRvNJpIpQRk12hU09fnpd3uMjYWpFjM43R6sFotQJd6vUGpVFFQJVevXkOtViudj14H\na29vl3Q6yaVLV17oNv/yymTSeL1izHh4eMB77/2MTCZNvV5ncnIan89HuVyiWCxQLpc5ONjn+fNn\nmExGYrEYa2urFIt5RkbGcDgczMzMolKpWF1dZmNjlY0NABXBYIhgUBhndna26XZhenr2BZxBJpNm\na2uTQkEAdrtdcbjQ6XRKqsPIyBitVotqtSo1QGaq1SqbmxuEwyY5wkZ2yGLE4zHMZnFodLk8eL1e\nvvzlN1lfXyUajbK+vkouJ67z6GiIN954k4WF+8RiUZLJxAsZfr3u3+zsHN1uh0BgmPPzM9k5FQWW\n6JrX0Gq15PN5Op0uHk8f29sbynfE5xuQkVYFdDotWq1OYT59+9vfUT4Pi0UgLPb29vB6vVy5co1m\ns8HJySnPnz//hcioKtFolPff/xlGo5FWq00+n2VnZ4t8XgRe9/f3Kx1Pq1V0sKvVCjs7W3Q6HS5c\nuKjk9DmdLkqlCoHAsDS8dCiVivh8A1y/fkNCczOSy7ghX6tAtAiJRornzxcBNTqdllKpwPLyksJe\nbDQa2Gx2hoaGODk5plwus7GxxtCQn6GhgFJIJRIJDg72JPcwIA9PopPeaDT58MMPGBsbIxgcx+l0\nKrFzKpWYHPl8A9y8eZuTkxMymTRTU9NMT8/+2u/Cb7v+qsD6z1jdbpeVFfGwMZvNCuZf3LizGAwG\n7HYHKpWKfN7E1NQ0r7/+ZQ4PD3A6XVy5ck3pprz++pdZX1/l5OSYf//v/4Sjo0M6nTbRaIRMJoPP\nN0ijUWNoyM+NG7eo1xuKM6Xb7SrZfeJLIUKH5+cv8PDhfUqlMjMzc0SjEU5OSrLLIMY6s7NzJBIJ\nDAY9s7PzPHnyiEajSqPRpK+vn9de+zJDQ0P883/+z9ja2uT999/lwYNP6XYFHG94eIStrU1OTo6x\nWm08erTA9vYmm5sbSvh0Op0iEokoOXfHx8f09/ehVmsIBAIy5seA19sv4zS63Lx5m42NdaLRMG++\n+TVp7U6Rz+dwuz2k0ymOj4/I5bL093up1+tYrTbefvtbdLtdzGYLpVKBGzduodfrOT8/xeFwMjk5\nSSDQczcJB14+nycSiXJ+fopKpUatVlGvNzCbLdy6dVtxyvVcWaenJ1y6dAW/P4DfPyz/vrgx9k5M\njUZdAYQKfIIIlu657AQR/ZLCa/rpT3/EyckJKpWG6ekZarU6zWaT8fFJMpkU7XYHlUrNm2++xdra\nKkajidHRUdxuDx6PW477hPbi8HBfZp3ZyWQyEgkRl2BLE9FohPX1VcLhc8rlEo8ePVAcpvV6nfff\n/xnhcJj9/T2Gh0e4ffuOfOhEUKs1VCplEok4z54tYrFY0Gq1PH/+jFgshs83QDabIZ/PEY1G0GhE\nGoHJZGJwcJBWq021KjAadrsNt7uPo6Mjcrk8TqeDwcEhmk0BonW53EQiYXZ2tjAaDTx7tkS9LnIm\nj4+PlIDjaDSi5FkeHR3IU76aVktNpVJR2v0guoXLy8LBFggEUKlUcgRzxNbWphKJ09fXz+7ulsyK\nE//u+PiIWk24z+LxONvbG0pxajKZuXfvS+zs7LC3ty2dhDrUajXBYIg7d17CarWh02kxGIxfGF8W\nCsLiH4/HsVot+P1+Xn31dWU012wKrdvOzjYrK2K8OTAwKB1v04RCIUk3F7iRl156GZvNxtOnT0il\nEjx/vkSrJbAcpVKRn/70J+h0et544ysKQdxqFc7m5eVnGAxGrl278WvzUwXsMcL09AxTU9MsLj7h\n2bMlTk9P0Ot1fP3rb6NSqcjl8ng8fezv7xGJhPnDP/xfuXr1mmQmhRWndDqdIhqNMDoaJBqNotfr\nMRgM1Go19vZ2cDqdMi4qQ19fn1JcZbMZid3Isb6+Kjlzw8zPX2BsLMj+/j65XI7FxUU2Nzfw+4fR\n6w3cuXOX69cFIqNWq+F0mkinS3S7XUZHR+U9EsV524OiqtVq5ucvUqlUiEaj8oFvY2xMYHd6bttu\nV5Dig8EQrVaLhYX7dLtd7t69x8WLl2XH5UzZX709ZjAYUKsFaw7E2PDk5IRcLse1a9dlV00ob0T8\nk41IJEIymeD09JSRERE1ZTZbiMWi6HQ6xscnlGI4EBjl/PxUCeB2Ou2EQqK75/H00Wq1JJ9M7BnB\nFBRB1DabA4NBFKyNRhODQc/YWEgChIvSMV6nWi1z585LxONREokEZrOZ6ekZvv3t7+B2e3j0aIH9\n/V1isQjz8xf5vd/7DouLj7h79yU+/vhDNjfXqdcbVCpV9vf35OHBhFarI5vN4PV6mZiYUPZhLxXg\njTfepFarUSwWeP58CRDuY5/Ph9VqxWAwMD4+STQqeHU7O1uMjYW4cuUaQ0N+6VSusrBwn+3tTUol\n0Y0bGRnh5s3bf2EGFvxVgfVbLeHQOWR1dZlwOEyr1VCcY8PDI1LfA9PTMwwN+dFoNAwODjI1NYNG\noyEaFREZ5+enxOMxrFYLx8eH5PN5Go0GpVIBj8dDMBgik0mTzWaxWIQV+vLlq+j1BhwOp4JNcDic\nlMslJS9Mp9PhcDgIBIa5e/dlNjbWsdvtmM1marUamUwGjUaN2Wzh6tXrHB0dKNyd69dvSm1WB6/X\nx8jICE6nizff/Bp7e+Imabc7Ze5WnpmZeYLBEGtra/yTf/I/o9EIOraI0bHQ3+/F6RQZeDqdjtXV\nFbrdDo1Gk2pVsKWKxSIGgwmHw0mlUsbnGyQUGuf993+GRqNldnZO8rCekskUuXPnnhLGefHiJRwO\nJx988C5mswWHwylvmE4ZFyIy1aamZnA6RdfrwYP7MlT582taLhclUdnG7du3ARWBwLBSXIHQWng8\nHhKJBFqtEIrPzMzS7Xbxen1otVq+970/Ix6PMjw8JvVK45TLZfmQEO65QGCYaFSMs4LBEKVSiUaj\nKSNaYHHxCfv7uxQKRebmZvF4+hkbC5HJZGQnZACXy8nVq9eVsUqxWGZ2dpaXXnqZSCRMNBqh0Wiw\nvS2Cqmu1Bh9//CEul5t8Psfu7g46nYFAIECpVJQP7CHS6SR7e7ukUknlpAiiU/vuuz+V0S59dDpd\nNjY2OTw8ZHBwkLOzM4XQ7XQ6UKvVnJ+fk0rFJFXcwdraqnJajMWi+P1DWCx2IpFzybIyotEIbVss\nFpMhxwJo+eGHPyeVErEZuVyWRqPB9eu3lOiXg4MDGo2GTCgIMzw8xMHBoczv/LzAEuPfBDabA5vN\ngdvtYnx8kmazyfLyc7773T/D6XTQbnfQ6bQy09NHOHxOvd5gamqKbDZDIhGnXm9QKETJZJK43X1o\ntVoGBgb50pdexWy2sL29TTab5t69L30hY/EXV7MpTuaBwDAqlYpMJiPZa3kcDqcSw1MqFTk6OpQP\nrGmsVmF2EewxC/v7e2SzOXy+AV5//ctYrVZqtSoPHnyGTqfH7/cTCr3C6ekJCwv3efDgM9nl2FH2\n+fb2Jul0htnZ+d8YDZJKCZfjyMgoPt8Aw8MjxGJR7t//jP7+Pl5++TWWlhZxu7UKZqJWq1GplEil\nElSrNQYHh5S8TZ1Ox97eDo8eLeDzDfDmm1/DarVQKGT8uD8AACAASURBVBR4+nSR+/c/kzR+lM5f\nu91mZWWZRqOOyWRkbCxErVZVcAQ2mx2tVsPc3JyULqTQaLQ4nQ7MZpMC5wQhXDaZPhcz12o1wuFz\nNjc3SSbjDAwMKvBXjUbD1avXWVpapFwuMzAwpJD37XY7pVKJTqetFFi7uztKh0dozq6g1WqxWm1K\nGLJKpSKVSmKxmDGZTEQiYdRqFa1Wm8PDAywWK/fuvcLh4b78rpm5e/ceH330IclkjNPTU/7sz/5P\nbt68BYhD47NnTxXifI8ZFwqFCIfDNJuCoTc9PUc4HFGmByAK7Z/+9Edks1mKRZFPCUKuUCwWFG2a\nyWRmbW0NnU6H1WqV8URmvF6fkhhRLpepVivcuHELr9fH8vIzCoUCZ2dnVKs1DAYjT58+QafTMzEx\nyUcffaAEiHe7ojPeaglBu9Vql1IRDVqt0K55PH2k0ylyuZySnlEo5BWkRjh8xvb2JqHQODabHYNB\nYDTy+ZzsgG9hsVhJJOLKtReHpy3ZRLDzxhtv/hcH3f/y+qsC65fW3t4ue3s7dDodisUie3u7ShBk\nsVhCrVZhNptRqdQMDfnZ29tVRjK9Dgkg5/85PvzwA9bXV5VOQDKZIBSawOPxKK3oq1ev43A4JPnW\nzOrqMmazhRs3blGr1fj004+xWm20Wg0KhRzZbI6rV69y8+ZtJVi1p6fo/b9giIzL5PGWwl3p7+8n\nHo9J3ZeadruNy+VhdHQMt9vN+voad+/eU+jWHo+HGzducnJyTCwWp1wu8corr7G5uc7Z2Qk+3wAT\nE2JsGAyOY7FY6Ovr5+tff5vz8zNOTo4Jh8OUSgVFE9RjMWm1WprNFoODfoUc3dcnRJjT0zPMzs7y\n0UcPWFj4jG63i9Fo4MqVazx9+phKpYrfH5CnuTiXLl0BRGegXC7h9w/jdrvJZoU+zOFw4nSKL41O\nJzLkrFYbzWZTmgNUpNOpF/ZCKpXE7x+W48oNJiYmMRgMrK8LvUM2myUcDqNSwaVLV/nSl16R17nC\n7u42x8cn+Hw+hodHiMejyrhrZ2dLamtMvPzyl4hGo6hUIgbJZLIwMDDA+voaCwv3+fTTj/F4+rDb\n7Qqvy2g0YDYb2dnZ4caNW7zyymt0Oh2ePn1CIpFgYmISs1lE/PTyIgWyI0A+n8dutzMxMUmhUMDt\nFqGm7XaMwcEhQqEJUqkkOzvb5HJZjEYTV65cxePp4+nTx7TbHVnYuhDaKy0+n+AWFQp5ksm4ZHC5\nMBj0uFxuMpkMuVyWUqlIMBhCo9EyPDysgDFrtSoqFTIKpYvFYuHs7FSyzQYk+d3C7OwcMzOzNBoN\nnj9/JoNrLTJSxsvW1j6Li48kFFNLtVrl008/IRqNUK/XyWRSjI4GMZstqFQqWq0W+bygUZtMZtRq\nFWdnpzK8d5tUKonT6VQCjr/5zW+hVgsNixi7nbO0tEin02F4eJRkMo7FYn2hSP/F1dNe9UwtPt8A\n+Xye4+MjlpefUSwWCQQC6HQ69HqDHFsJ9+HW1hblcgmr1UY6nUSt1kgNV5HR0aDsHDq4efM29XqD\nVqvJ7dt3mJ2dJ5vNSKt8Srlm1WqFBw8+Y3d3G41Gzd27d3/jfbH33RChzCp0Oj3VahWfb0ABWWYy\nAiEj4otsGAwGPvzwAx49eohKpeLmzdvodA0lILnTabO7u4vZbGZvbxeNRrCveiw+t9vNq6++oRwG\nT09PqNfrhELjMji6wUsv3ZNu5lUymQyrqyu02y2+/vVvkkolWVp6ysHBARcuXPqV4eS9NTYWJJ/P\nk0zGqNcbTE5OvVBwGgwGXnrpZR49ekihkFPE+0IakFAiZw4PDzg7O8VkMmEwGGW4t4+BgUEZj1Mk\nl8tisViJx+NKHFMvw+/p08cUiyUuX75CMBiSfKYSXq+PaDQqQb0q6vU6u7tb+Hw+tFqdLNw62Gzi\nHifGiDpcLoGjcTqd3LsnkByffvox0GV8fIJut0M4fI7dbmd0dITBQT+gQq/XSW1VkYEBL9PTszSb\nLbLZjBKYXS6X0WrrXL16nYmJKTqdDh9//HOFa3d8fEQsFmNzc13GMPURjYbZ2lpncnJaulLrWCwW\n3G4PmUyay5cvMzU1q+w3MbrPKTT7ublZUqkUh4f7/Nmf/Qdu377D2FiQixcvsbT0lOXlZ6TTadRq\nDd1uF7VaNDv6+vpZXV2m1RKa0F6KRO97uLz8jG5XdAlLpf/7SAd/VWDJVavV+OSTj1hZeU673SGb\nzSgz506nS6vVRK/X43J50GgEeqCXWxQMjuPxeGSeVY1qtUI2m+F73/uPlMsl2u2W8qCp1aoYjUa5\nqVw0Gg3y+azU/QxiNps5Pj6m02krI6B6vc7Gxhr1eoNEIiaFta/8SlFobxRRqVRl2Og0y8vPODk5\nxuPxKPqLSqWCRqMlEjlHrVZx48ZNOp0OJyfHUkTYxOl0cePGLd544yucnZ3y/vvvkkoJyrHfH2Bg\n4ASfb1BuZJUUyotW8urqCtFoRBLYC9RqFV5++RWGhoZ4+nQRq1XQvwuFPDqd6ASl0ylsNitLS09l\n3piOjz76jFKpiNvt4d69l/nwww/44IP3aLVaXLlyFbVazeLiI2KxKDdu3CaZTNBqtenvF9lcP/7x\nDxSydk9MDihdH9EdqTM0FECv13PlylUcDqciyq/VhJmgF88SCIzgcDjweDykUim63Q7tdleJzCmV\nSqytLcvYmTVisQhf//rbuFxuPvvsE5aXn8n4HZWiu7LbHYyPTygk44ODA/b3BZHdbrcTDIbkeMxK\nsVgkkRChyFqtloWF++zv7+J2i/13/foNgsEgGxvr1Gp1ZYykVqvw+QZJJhNKrMrnSwTkVqtlzs6E\nxk+lEqORRqPB2toKbrdHKYBEoLMYi1qtVgYGBqXDtYPdbmdmZo5r166RzeaUdny1WpFQ2UH0egOg\nUgpHAL3eQDQalqG6s4rA9tq1G7z++peVhy+I/MTLly/z4MF9Wq2m/F3iQbKzs8W//Jf/GxMTkxwd\nHbGy8gy1WiNz3YpoNMJlq9FoCIXGaTQaiuljc3ODpaWnJBIJms0G3W6XfD5HrVZlfn6eW7fuKNT/\n09NTfL4BHjy4T61WRa/XMTs7TzKZ4Pz87AW9FQh5weHhAbVajaWlJ6jVIgppZWWZbDZDsylE7JHI\ni8DiZrPB2dkZ3W5Hjsd9aDQaJduvUqlSLpdYXV1mdnYOn0/EDR0dHXJ+fs7s7Dx2u4PR0THy+RxP\nnjzCYrEBXR49WqDbhcnJXx9sKw5obdLppNItf/bsKalUivX1VQmwdCoCYpEjd51AYJhEIk4ikWBr\naxOLxczExKRCbe+NJ6empiWMtU2r1aTT6aLTCdF3vV5nZeU5R0cH+P3DHB7uywLcytraCiqVioGB\nAfr6vJycHMv4piOsVoHGELq0AplM+gWq+69ava770tJTjEYjv/M7v/eFv1OtVsnnRbRNr1izWsX+\ntdnsxGIxfvCDd2g2G4yPT8jiqcPm5gY2m51ut6NAYUW4sYiZicWipNNpRdtrNBq4c+ee5M0V0et1\nCnR2eHgUo9FItVojGo3Saom4rEwmhVqtIZfLSLhxGp9vUHaKTaysPCcaFfyyVqstTQc7mEwC2uvx\n9HH79h1+93f/Gjs72zx7tkilUmVkZIRQaOIF6Gy5XGZh4T4/+ckPSSbF9T042Adge3tLyR+FroRM\nt9BqNTgcLspl4eDVarVsb28RCIzg8VQ4OzvF6XTxjW98i/n5C4qsYXp6hlwuy/37n8lkggLtdotM\nJs377/+UJ08eSp3YABsbG2SzWUZGRnE6nczNXaBUKtJstpicnGJ3dxubzc5Xv/q1F66rYEeecXJy\nRLFYIBI5JxQax2Qy/cY989us/18UWI1Gg0qlTLvdodVqUamUicdjcqZep9uFZDKuhHe6XKK97PH0\nYbUKh16z2cBgMMgsN6SepUg+n8fhcKHT6dBohJ7DZDKiUqnpdIT2pq+vH4fDgdlskbRoE5HIucxa\nG6NeFyGUP/nJD+l0hAU/m83w8ccfYbPZuHFDCLUFudrMnTt3f2VxBSibolYTLWqfz4fBYCCdTktS\n8BrJZByVSk0+n6dSqSg5WzabXWqmDjEaTajVKK3ZXofNYDBwcLCHwWBkenoOn89HKpVkaMgvNRhZ\nOp02GxtrWK1WvF6fjKDp0Om06evrx2g0yhuzm0uXLpPP5zk9FR0Lt9vN/fufkMtl8Xr7abe7UjRb\nIpe7QDabodGoMzMzq0T2lMtl0uk0h4f/B/PzF5RTy+Ki6Oj4fD6CwZB8sIsVDodpt1t0Ol2SySQ+\nnygSYjER7XJwsC+p6OJELXQQKm7cuMU3v/kt0ukUBwd7fOMbv8Pq6gorK8/5R//of+TChYt4vV6u\nXr2udPn+zb/5V4q2CJDRMjqMRoPUvxgwGo1otTol13F+fp75+YuYzSYlugGEbuMHP3iHUqlIf/8A\njUad9fU1QOQkikKiIMnebTod8Zneu/cl/H4/RqOBer2ukNRrtRqFQg61WqPk8fX39zM9PcOFC5co\nFPJsbKyxsbFOPp+nv78Pq9VGJpORLju97FAKTcbc3AVeeeVVBgaGlMy+Wq3KxYuX2N3dkQaQtEQF\ndJWu4unpKeVySRavfXznO3+dWCwmO6fRL4zcrFYbLpdbUv/bRCIRBgcH2d3dRqXScHJyQjh8hkaj\nYWZmmgsXLtJut2Xk0YHye3r5dm63R8liLJdFJl+tVlPy4+bnL2GxCGZWMpkgkxEn7K985Suym9TG\narWRSiV58kTEQvn9fqX7IqCTNTQaNeFwTCnwSqUSgcAI8/MXyOVyXL0qnFyNRoNWq8Wnn35EtdpH\nX18/+Xye69evK8HdlUpFgkWvSGNBGb/fj81mo91uE49H6XSEg3B6eoZkMkE0GmFoyM/t23eJx2Ns\nb29JV2nzBYQAiAfp1tamoke7dOkyT58+IZfLYTDoGRz0SymEAJLOz1/Abrcr45XBwSHeeOMrFAo5\n/P5hvvnNb0mH8DMymTQDA4MSKjmujNREYXiK0+lkYWGBUkmQ83u6vImJaRKJqIRheonFBGS4lx1Y\nqzU4O9skk8kwODjI6uoKAwODv5XdXozRdXi9Xik3eFGP1tNCCfNCbx8KnY6IEcpSrVYJBoOKyade\nb7K9vcXu7g7pdJKVlRVFq9dsNrl+/SaxWJR6vcbc3JzUdJmVHFKNRoXXO6BEoL3xxgz5fB6nU8gk\n9Ho9/f1eHj58gEajIZPJYrVa5X1PUO8TiRiRiDgslstFGe+kY3n5OYODQ0rqg9frVYjt4fAZIyNj\nysTg8PBAKbDE+LyOSqXCYDCwt7eD2Wyl2+1yenpMKpUEhHxG4A6GGRkR0WjpdAqdTqdkN5pMRhYW\nHgDw5S+/qUQ5DQ4O4XS60Gg01Os1ut0u5+dndLtd/P4A2WwWt7sPlQqlkNdqNYRCIQYH/fh8g9jt\nDubmLrC0tMje3i5Go1FBMPyiCej09Bir1SpdsIKif3x8xOzs3J+7Z/689ZdaYPVykgBpzxVun180\n/fy2+T89e++LS41K1ZUPdPF7BWsDNJrel0VFz1XX+x29h5darUat1iivUaPR0Ol0ZMSJTsl6EoJx\naLXaqNUiC8/hcGEyGbFYrOj1RvR6MbfW6/XYbDYSiQQajYir2dhYJ5VK0WzWJXOpydOnT2S2Vz/z\n8xfp7/ei0+kxmYysra0QCo2jVquw220vJM333ker1VLejzj5FJQUdJFYPsLs7ByZTIazM8E/qlRK\neL0DzM9fxGazo9FomJqa5tmzJY6ODrHbnYTDZ7z33k/pdDqcnp7KXLJhDg8P8PsDmM1mnjx5iEql\nlp2HKNWqcC1duHCZSCRCpVKj21WxsLDA8vJz9vf3KRRyDAwM8ZWvfFXhXHk8fQwNBYhGY7JDdQWf\nL8DR0T7RaJxWq8Xg4BBer4+XXnoZEC4j4SaLcnYWp1QqMzw8QqvVwmazEwqFePvtb70gWBQapQoj\nI6N0OuKLK8KT28TjMZxOJw8efEoikeDq1WtYLBaq1Sp6vYgg0uv1PHy4gEql4bXXxAjjnXe+y87O\nNtVqmb/1t/4eMzOzvPzyKzx+/IhkMkEkEqbVatLXJzpRuVyWbLbCysoyFy9eZm5unOPjIwwGI9Vq\nGYdDRHmcnp68EAhrsYigabvdgd8/xJtvvsXPfvYTMhnhYCuXi5Lv5KdWqzIwILSAc3PzAHi9PuLx\nOEajCYPBQD5/QjYrRh6XLl2WQbyWX3hIDmIwGDk9PZHdXOjv9yn6snK5TCqVoFgU3YtLl66Qy+XQ\nanWo1SoODw+oVMrodDq+8Y3foVgscP/+Z1itNkZGhtnd3SWbzWCx2AgERnC5hHV9aWmRkZExisU8\nn3zyoTJSANEt0Ov1dDptGQUT5vBwh/V14W61WEwMDAzJh+WAEl8iOmw2/H6/HE9WqdVqis4um80q\nY8N8Po/L5ZJxHjZZrAhAcC6X4+joEJUK/vpf/2+kkPcBmUxaQYx0Om1CoXFeeullmfcmeE+XL1/j\n5OSEaDTC/v4uOp2WgQFxvQwGIxaLmVBIaGOWl5+h1eqULo/gt3UkydqO3z/MxMQENpudo6NDqtWK\nYoHvdNoUCgXy+ZzM3huXRWeYy5evyuIkil5vYGHhAcvLzxkeHn0h0Pj09ITzc+EwjsdjPH5cxWAw\ncPPmLaxWO319Ta5cufprR6IgkDW9gPRcLsfo6Bi7uzsy6NzN2FgItVqt6OYmJiY5Ozvl6dNFxsbG\n6HQ6tFptdncbWK1WvvWtb3N6eoLFYpOh3kYymbSUPIBer0WtVlMo5LFaRWB2s9mU4/7Ar32dmUya\ndDqN1+vFZDJzenqidKkE8LdBOHyOSsULbC+RkyicmU6nC7PZzO///t+QQeCfEYmEWV9fo1QSnCi7\n3S6F2KJjGAqNy46pODRub29zdHRIvV6nWq3SajXlOLutFKdqtUpB2iSTSfb2/hP1ep0bN25hsVgY\nGvLzwQfvY7eLw8rs7DwajY6hoSGZNrGF0Wim1WogXK4q7HY7arVG6VhrNBquXbuOw+FkYGBIOrPj\n0j18Srvd4vbtu3Q6HeUw12638fuHZYxVHq02wsDAEENDfp49W6TTEU5Pgd5IEwqNo9PpSadT9Pf3\nK4e+3uo1C/R6PW+//S2Ojg75/ve/h9lsYW7uApOTk/ze732HaDTC1tYGqVQKh8PB5OS0Eq/ldnu4\nefM2T58+wWq1odfryWazSkezUhFB190uCpev02kTDp8RCo3/xrHyb7P+UgusnjD8/431S8y8X7t6\nBZlKhbyhqzAY9HS7OlQqNa1WE7VaFGQC0GjAZrPKalutdKiGhgYZGRljcHCISqXCzo5oT3q9Ioy0\nWq1QLleo16sKK6mXUi5EwjauXbvG0dER5XKV/f19BgeHaLVaJBIJbDa7pHtbWFx8rIz+QEADq9Wq\n4owBYdWfmZnj/v1P6HQ6HB8fKnldsViUZrOpdNtKJaET0Ol0HB8fK/ll29sb2GwOXnnlVcm7mmJ6\nepb79z+lWi2zvb1NpVLGbDZjMJjw+Xxy1i0Kx+3tLSKRczIZYZ3N53MsLS3Sbreo1ep4vV7sdju3\nb9+l1Wpy795tJiYuEI/H+OM//rcKJLXH7drd3SYQGObevS+hUqn5d//uXxMOn2MyGZmamsZiseD1\n+tjd3VY+h25XIDW63S6zs3OYTFYajYby8OoF+sbjcfr6+rh8+QoXL15Gq9Xx8OF9/sW/+EMFISBy\nvobZ3NxgbCyIRqOh2xW281wuw87ONsPDIzSbDUKhLzM9PUtfXx86nY6dnR02NtaALv39/Yqer16v\noVZrmJwMYrXaOT09IZvNKAXW8fERTqeLsbEQpVKRf/bP/hdqtRoqlZp4XNDJNRoNzWaDtbVVPJ4+\nfvd3/5ry/oX9Oq4Icre3t0in00xPz3LhwqVfecCJx2NMTc3Q19fHe+/9jPv3PyGbzcr4Ew0ul1sp\naD2ePiVv8nM8iNAaTkxMkstlef/9d8nnc+j1BhoNcU8IBsf40peEts/lctNoNBSeTjQqLP49R6bZ\nbMbvD9Df7+XZs0V5rVyK1bzTceFy1ZSiWphBrEqHwu32fOGQYjAYGR4exmazEQyOYTSauXv3Jdrt\nNg8fPuBP/uR/x2y2YLWa8XoHlczD8/MzBgeHmJ+/xI9+9H3J8jFKYO0pDx58yvj4pDS3WOW1HmJn\nZ5OVlRXcbhexWBxYUbIWQ6EJjo4OicfjmExmhoeHOTw8pFgsYjabJEpk8oVrpdNplS7Q0JAfo9FE\nJpMhm80qRod6vYrVaubJk0ek00lKpbIiUE6lkkQiEaamppSu29bWBu12G71eHBQzmTTdLjx9+hTo\nMj09+0IA7y+vRqOhuCWLxSKZTJqxsaD8TjQVQ9CL18EguVLnNJtNWUQkKBaLVKsVvvvdP6VQyDM7\nO4fd7iCbzVKpiHupMFPEJJ/PqTjRTCYzyWTiV3bpPt/jQvQ8Pj7B48ePqNWqLwRw91avy9NbKpVK\nBsJHlVGsWq1me3tThguvk8mkKZVKjIyMMjk5jcMhxlR7e3sKI1AcUMzKZKRYLKBWq6XBROBMIpFz\nzGYz3a4o+qrVKsfHG9LwpMVsNqPV6kgmEyQSMUqlAhqNhpGREV555RWWl5ep12uKESmTSSsGnsFB\ncSBbX1+lXq8zOCikH7FYlEajSTwe5cc//qHSIe3r65PGBwHMNZnMirHi7be/yc9//j7NpjDxPHny\nmJOTY5ll6pKh0n5GRsb4+OMPpRN05IXsyGKxINEXDeXPCoU8kUhEPo+7bG5uMDg4iNvdh9c7wNmZ\nCLUfGRnDaDSyu7tNLBbl7be/xe3bImYqEhGyEJ/PR6fT4ejokG4X9HqdMnrV6fQKQuY3mVV+m/WX\nWmB961vfIpFI07vxfg4tRP63KwsbEE2kz7tUvXuJRqOm01HR7XaArtK1aTZbco7fptMRYZwqVVfR\nTzWbLWm7byr/RmhK2sqpsDe20en0dLsddDo9BoOedrtDp9OiWq0pjgadTsvMzAxOp1OOlez09fWj\n04loBotFEHEDgWEWFu6jUiGdP/vU63WmpsRsOZVKEQpNkEjEmZ+/wKuvvs5HH/2cnZ1t3n33XYkg\nMCnOk54zTzxURZet1WrjdApRsehe5SXqwYfLJTKubDY70WiEbhdu3brNyspzdDq9Ej/w3e/+KZOT\n0/L9RKWGYYzR0VH8fj8//OEPCIfD9PXVJdspKz/LDrOzcxQKeVZWlkkkEoyMjFIo5Hn33Z8BSDCm\nKGzK5Qomk5m5uSB9fV458mtisYi2eK9zI9yGAnh58+ZtgsEQT548IpGIYTL1AJ4ZgsEgiUQCvV7P\n3/k7f48/+qM/pNEQyfA98OEvL4PBwNhYEIfDidvtQafTU68LwGU0GkGr1aJSqZQQUhHarVeEqZub\n66hUajQaNc+fL2GxWLhz5yU0Gg2np8fs7e3SaDSIRCKUy7s0Gk2mp6dlwXqIyWSm0RDMnEKhoEA7\nK5UKp6fHTExMEQiMoFKJTmkmkwFE5+373/8e3W6Xa9dusLS0iM1mw+cbIJGIE49HmZubR6MReodO\np0s8HmNvb4eJCQGP9PkGODk5ZnNzQxZhy9hsVq5fv/GF4qrZbLK4+IS1tWU6nQ6rq8ukUiny+awc\nUXqZmprBarWQSCRwOBx0Om3MZhM7O1syRFgcWN58802Ojg45PT1BMJrqfPLJR7hcbtlxKLC/v8fu\n7i5ut4vBQT/Vao1QaAKLRVCoe8L0SqWMwWBQwmNFEaAnEAgwPDzM8PAI8/MX6evr4/nz5yQScUZG\nRohGIxgMBrxeLwcH5S/si/7+fkZHBTtJsILeVU7j+XyeWq2K3R4kFovQ7XYwGk1SKL7D6OiYNK04\nCQSEBmRnZ5uTkxOpwREnazF60uDx9DM+PsHU1AwrK89kpmCdR48WSCYTnJ2JbrHoRnVpNhs0GmIs\nMzIy+oVrZTIJ2rt4H176+vrY2Fjn5z9/XxH51+sNOh3Y3NykXq8zPj6BwWDg7bd/h1wuy7NnSwQC\nw5KsHWVyclpCVg8RBdU0BoOJ3d1tstkcZrPpN04czs5OODk5xmQyEo8LrU6vo6VSCZZWs9lEq9Uq\n97d0Ok21WpXvRcWrr75GsVgklUoSDoepVMpUqzWZ/vBIOViq1SqOjw/pdDoEgyHK5RIGgwGDQY/D\nIQwi8XiMQGD4V77WVCqJVquRrrQCwWBI6fqq1Wp0Oj06nfZXusssFqGNVKkEk0mEEe+j1Yrr7PX6\nCAQCvPXWN/D7h4nHY4opKZcTAcY9/ZwoGC7w7W9/B5VKpTjeVlaWmZiYkPzEFru7O6ytrQBqjEaB\nXDAYDLRaLdRqjRSdt+l2O7JbJswNokA04PMN0Ol0+PDD96VObp1ms06tVpPIiQhra6vs7m6Tz+fl\nAbRLLjfBxMQkz58vUSqVJOHdzsjIKDqdXmqQa3i9PgWxsb+/j8kknILT09NKN1zowsJMTEwwMDDI\n4uJjTk9Fd1fskc+hxvV6jUgkolDro9GIhFBn6O8XMphSqSSNTyIhQ683cHp6yrvv/oQ/+IO/hc83\nyO7uDgcH+2g0Gs7OTpXDtclkYnh4hHy+ILuBN78Qf/Rfsv5SC6zvf//7/49nETabTZLJhEwgz9Js\nNpUWb6fTUToVvRFfJpOS44EMnU4Hr3cAp9OBSiVm+j0nQrlcIhYTEEn4nKVUKpUUUvXnkEMVxaKI\n9nA4XGxsrEkxdIdI5JxqtcryclHGSiRQqdRSw9LC6XThdnsYH5+QrkIj2WyWbDaLVqshGAzR7XaZ\nmZllaWmR3d0d5uYucOvWbWVDGgxGTk6OCYXGX7gh9Dae1WqTqIAG7fYFLBaLElsRCIyQTCZIJhOS\nD+SlUBCaHrVag8Ph4PBwn0KhwMDAFmdnpyQScfR6g1KU/M2/+beJx4U2QuSeiQe9Vqslm00zOjrK\n3//7/52MbDlmZeWZor/R6XSEQhOEQuNEIoesyA//0QAAIABJREFUre3w7NlThZHTarVkQScAgzdu\n3FI4S5VKiYkJEVvz1a9+/dfuEZVKJfULBqk70tBqNTEajaRSCRmKbZSjDRvdbheDwcCbb75Fs9mg\nUCjKk9IIer2egYFBtrY20Wg0jI9P8uCBEOYXCgUFfihu8OKU7Ha7sVgsClZiZ2eb4+Mjfv7zD9Bq\ntWg0Gvb395TrVyjkSKdTfPjhBzSbTRmPVOfChUt4PB4uXrzMn/7pvyedTuF2exTMgM8nnEf/+l//\nS0KhccbGgqhUaiXt/uOPP6TRaHDlylX293cxGk14PH243W40Gg2xWJTnz5fI50UArRhBOdHpDLjd\nOi5fvorBoCcUmsThELmPZrNZ5j8G2Nzc5OOPf06jUeejjz7ka18TodrXr99ib28Xm83O7OycQrwf\nGwsqjtyRkRHUajVjY0Gq1ZoUgjd4+HBBhpzHqdfrUtTtxe22MTY2gcfTR6FQUDqPnU6XtbUVHj9+\nxPn5Obdu3WZ4eFSx2f/iEtqTcxYXH3NyckIoFGR0NMidO3fR6Qx88MHPFNeo0SjEwWJEs8vBwR73\n7r2C1Wqn0ahz4cJFgsEQz549ZXHxMSMjowwN+RUjjMvlZnp6hosXL2M2m9nf3+P4+JBcLsfy8nPc\nbjdDQ36JUUhRq9VQq7U4ne4vjC2EkeEAk8ksg+H7gK4i3u102tK4oMLpFB2EdrvDBx+8x/DwiHIQ\nFMDRFGtrK+zv78rXPMTZ2Rlutxuj0YzZbGFoKMDxsXAHP3786Fd+x8Lhc9bWVshmRQGh1WqoVmvs\n7IgHnF5vUIqkRuPzTrvoHMQUBtL5+TmvvfYGIFAR4p63LWUIZ0pXSKPR8OzZEleuDPMHf/A3WVgQ\nWXqVSlnqSIXJIJcTDLD5+QlUKpPCguvp2XrXd2Rk9Nd2L7rdrtLBFpgNkRSg1+txOJwcHh6QyWSY\nnZ3FYChz8+YtZmfnKBZLaLWiY1er1dFo1GQygotlNlvY2hIctosXL71w39ZoNDx8+ICHD+8TDI5T\nqZQJBEa4cuUKY2MhNjbWGR4eZmjIz+PHC4rrcGpqmkxGUPprtQrn52dMTk5is9m5cuUaPp+P+fkL\n/Mmf/Du2tjZpt0UxZjAYOD09Vop+EctTIpuNs7paYX19TZHHzM9f5M6du3LPHOD3+7l16w4+3wCF\nQoFCISdDo8VIX+CKzohGI7KT7MXn89FqNTk+PubTTz+SMGo9ly9fpVqtUioV6HbFPne7XXg8/bRa\nLXK5LFqtjlwuRyaTRq1WMzc3j1qtQqPRMTjoZ39/j83NDT799CPqdWFcikaj7O5uYzKZGBgYlDrc\nhHRkaiU+w/oXjsmB/w+K3HU6naS8+gGk26khNUodqc/qKmLynjMqm81wdHRAqVTG6XTgcvWcIh3M\nZitWq4VqtcbW1obkJzn4+c/fV2jWuVxWmfXbbHZcLpfUBuQkwTpAtSochg6HQ86AVWg0ml+wxIsb\nrU6npVKpUqtVabc78kvZZW9vl93dHUZGRjk5OZKakabkwzxXNCqRSISTkyP0egMej2jhV6tVnj59\nglqtJpvNyE6coLO/+eZbGAwmNjfXaDabzM1dkK19H2+99Q2Ojw8ZGRnF7XZLYbITjUbDwsICdrvg\nqdTrArQ5Ojoqu0EGut0uW1sr5HJZnE4nKpVKZl5dUjooAMVimVwuz9hYkL6+PoXifOnSJc7PxcnX\nbrczOztLu91hamqGw8MDfD6fUlBVKlWy2bR0zZjlqOZXz88rlYoCwBQdQrPCI7NabahUGtRquHZN\nUMtF8dvB6XTh9/t5/PgRFouZGzduKSMOEW0U49q161QqZba3N+nr6+POnXvkcmnOz8MylsYrsR8J\najUBL9zb26VcLsvu3zClUpn33vspbncfVqvgHT18uEA8HiUQGCEUGqfdbiuoh6OjQ7mPtqjVaszM\nzLK3t0d/v5d8Pk88HqNarUjgqxebzYrVapNwXD0ul0sp/k5OjlGrVWi1Oo6PjygUCoyNBVGrNQwM\nDPCVr7zF7u6udCuJTp7QgwV45ZXXlM+40WhITIKadrtDs9lgd3eXyclpmk3xs0AgwLe//R0SiThr\na6tYrVYmJiap1+svBAQLHZSVJ08eMTg4SKcjxlavvXaDYDDE+vo6UMdgsJFMJkmlklQqVdkd0WAy\nmTk+PkKv13H79l3m5y/I7/shqVRS2Ze5XI61NTEiuXjxIn/37/63ijbxwYP7dDpdBgcH5ajfh81m\npVqt8vDhfVot8aDO53NKPqXL5cLp/L/Ye4/gyNLzXPNJ730m0gAJDyQ8CuWru8k2RVK0EmWuFJLu\nndDyRsxuFrPTfiJmo1nciFEoQlchTkh3eCmJIkXTrG6yTfkqmEIVXAGJhEsA6b13s/hPngK6qrtp\nYiY0uvpW3QWbyHP+8//f977P66DTaVOplKnVqvIaILIRhQOti/8QaJg8oOSP/uhPsFpthMM7Ep0/\nLTl3z7v9isUii4uPqdVqlEolac1oUavV0evF2NPr9ZFOZ9BqNUxNzWAwiJ9dKAid5k9/+iP+8A//\nGKfTxQ9+8H2q1YqkSRT600ajSX//APV6nUZDfF+n00k+nyOfz750fwm8zZakw1Gj1Wqp12uYTGbp\nECNwESKMWi8xlDpoNIJSbrPZ6O/v5+Bgn3v37kqQ5gXcbjd9fUFUKiXJZJKjo0MuXryMy+Xi7t2P\nabfF2L/L2RLQWTPNZgODQVDcu/mEjUaRgYEQS0uPSKVSJJNJ2QnrdnteGl2erWQyKa/ZZrPQ1+7s\nPMfhsGEwGHj+fEvKVlxgd3cHg8HI5OQUDx8+kJlZq6srRCIREok4ZrOZVCrB48cPqVZr0iZ7iWBw\ngGw2zUcffSCv9UajiVarTTCowG53kUymSKUS1GoVisUCJyenxOMJTk9P5c2RQqGgVCrK9yUgZ/kN\nDAzyzW/+DplMmnQ6jU6nZ3v7OcViHqPRzJtvvkMg4Oef/ukfpA1XBZVKLWeWvvXWO3g8Pezs7GC1\nWrDZurmvgn3YNbYEAn0SS+8Z8Xgcu90ub7xfe+0NGo06Dx/eJxjsx2g0kctluXv3Nv39Qpep0+mJ\nxRyMjIwQCk1htVo4PDyU8lHb0nsnHKYqlZpWS2za+/v7efDgPt/97n/jq1/9Oh6POHyazUKLqVSq\npIOA6MpvbDzDZhPay5s3v/Qb87D+zW2wPlldp8PnidXcbuGiEILE4jkQ2dnK53NEo0f4/QF+//f/\nkKWlRUlgl5BypNpkMkJYKqz/enw+L++882WWlxfZ2dnGYrHS19fP7OwcJpOZH/3oB+zsCLHv2tpT\nFAoFrVaTRqOFwSBAk52OQs5R2tzc4Nmzp/T2Cs3IvXu3+fjjD+TXazAYyOfzJJMJXC43KpWaTCYj\nwf8qdDriQXFyIgTpodCkDEddWnpMPB6T8+kcDgdPnmQxmy1YLBaePFkmGAxy8+ZX+C//5f8gk0nj\ndntIpVLUauKE2m53JOihCE7W63W88cYXyWbTFAqC9G4ydVlK0G638Hq9vPXWO8zMzMl/a7GgCyBn\nT4+XiYkpVldXZIfk2Zl9l2LvdLoktlPxle/55uYGGxtrUp5blvX1Z9jtTmkUl8FmExvS/v5BHA7x\n/btjRpEn5mB2dg6VSn1uEXa53Gxvb7O9/ZxCIc/JybHEChKLvNVqla3QqZQYfS4vizzGoaERKQpD\ng88XwOfzSXFEO5IAOyYBFXvw+fzs7+9RqVSka6SOWq1BpVJhsVhpNBp0OsLBNjn5ZcLhHdbWVqlW\na5TLJVKpJFqtmtdee0NCZWhkoKKg8Auxb6vVlB6KBroHgYsXr9DXJ07KKytLbGysSYBEYQy5f/+e\nfEIXwbMxRkfH0em0WK02Wq0WkUgYq9UmxXQ0iER2ZT1TMpnAYDBQLBYpFoskEiI8udlsSpgSEcky\nNDQisXtEEPDY2Dinp3sMDU2Sy2Vptdrya41EIlitVgnW6kSj0XD//j2ZZm6xWCRmTptKpUoyKfSN\nb7/9Jfn6arfbLC8volar+NM//Z+wWKw8eHAXgDfe+CKjo+Osrj7h+PhIEl2nefDgDkNDIzQaDbRa\nHc1mi83NDSkPsiIZCV6gMrpC/EIhL53Os5Imr87AwJCcSXn2ayqVCouLjyQXcoGTkxPW159JI5oe\nhoaG6e3tY3x8Qv49RP6j6I6JJIgcx8dRvvvd/yaFoh+i1xsIBgfkTubAwIBEAi/z5pvvoNVqMZvN\nVCplbt78ykv32OLiIzQaDUNDVQlQnMFgMKLVapmfv4DD4WRlZVmSg7y8kXG5XFy6dIV4PMbf/d3f\ncuvWT7DZbAwNDTM9PcP09Aw/+9lPiUQiHB9HqVTKhMM7UgSZMAg4HA62tjY5PDzA4XDQ3z8gX2cH\nB/skk1F+/vNbxGKnJBIJ2RDg8/nlfMpPq+6mMh6PUSqVUCgUFIsl1tbWef/9W3z44S/k6K5SSbi0\nHQ4nZrOZfD7H8+dbMm5CONvrpNNpOh0BJW40Gjx8+ICHDx/QarXY3xfXcBcmXSqV0Om0cgTZzMwc\nuVyGRqNOKBQil8uRyWSkIHeL7JTtsujEKL2fiQmRATg5OcX4+ASPHz/gO9/5r8RiMXp6ehgYGEKr\n1fL06ROUSrVEje/IB5JGoyEdNk2USkXeeONN1tefsb6+RjAYZHBwGFDQaNRIJpNMTExQq9VwOh1k\nszn29/cYGhrm+DjK4eEB5bIwHrlcLjY2Ntjf38PhcGCxWDk62iadTmO1iig04fYTB7eeHh9jY+Mc\nHoo8yMuXr8rvVZcz9+TJCvfu3cVkMlEuV1Cr1RiNJgqFvMxz83h62Nzc4OjoQBofNl59AfwK9W9+\ng/WrlM1m58aN18+Bxur1BsVigVKpRK3WXeA6hMNhSqUSv/u7v8/k5BTf+953aTSaOBwuQEGz2UCj\nMTM3N09fX1CCrp1InZ3uqLAsAxDn5i4Qj8ckqKFeRv4Hg/10OlAulxgdHWVx8RHPnq1K6IICg4OD\nGAwGWSwseEZVjEYzoMDhcGE0GsnncxgMBumh0ZGotsLFeDY49dq1Gzx8eJ9cLifZzpMcH7/gEz14\ncJ/DwwN+67e+wVtv3SSZFLE7pZIYOQrXyhTXrt1gY2ONYrEo6do0jI2FWF5eoVqtMjg4zPh4iHq9\nzs7OcymH78XMu1DI8+TJhjxXV6s1Eu7CLrXxFfIGCIRdemRklERCnN4EqPC8uymfz3FwsCcv6qK9\nnMPlcqHV6qnVUuj1evr7+5mampG/7uwGC4SF+JMB3+12m62tDR49eiB1LYR4fXh4CI/HK4+s8/mc\n3M2z2x2Uy2UpoyyH2+1Gp9Oi1WoYGxsnGj0iFJpApVJTLhcJBvtlq3Q3TmVoaIRgMEg32X5ra5Pj\n46gc2qrVajEaLYyNTdDT4+XgYJ9UKsWjRw8ZGxtHpVJSqZQJBPpwOByYzRZZqFsqFWUERk+PV3Zh\niVb8DPfu3ZHcN1ZpJJA9o6cUh4Ovf/2bOBwOWT8BYlSu04lMzCdPlqWNoQBQ+v29ZLMZvvOdv5HH\nOd3q7e0lkYjjcrnQaNREIrsEAr14PB6GhvycngoI7/Pnmzx5siyJoNPyJi+dFjmJgNw9OT6OShqZ\nIBsb6wSDg0xPzzA3Ny//3LW1pxQKBaanZ2SL/sjIGJubGxweHjI7O0exWCAeN1Or1VlbW+Xk5JSR\nkXH0ejU9PT52d7f5+c/fw+l0Uq1WcTpdn2CRCQ3YH/zBH3H79kc8evRAGjnZGBwcJJVKUi6XSSZT\nOBxOuSMtAI4e1tfXSCQEBmBoaJgbN94gmUxxcLBPLHZKPp+XHJLi+guHd6hWa0xPz3B0dMjq6gpO\np4tisYROZ5BxKoIx5KdYLEoMM628VhQKBarV6rkYoHw+RzIpCOoiGzXPwMAACoXosJZKJaanZ1Gp\nVMTjMRwOJw6HU1rDBCC1Ky73en1885vf5p/+6Xv84z/+d9m0YDAYpfSIFHfufEwg0ItarWJwcFAG\nd9psdlKpFM1mk0wmQy6XpRtQ3N8/wMOHH3N0dCpvWMxms+QW16JQcA5E2a1Go0G1KrL8qtUqOp1O\nCom243DYSaWS3L79oeRQHiIaPZI2CSJlohsb033dFy9eptVqkc1mOD4+xm530N/fj9PpkkwFaTqd\nFlarVdarCpezg3A4LMsMxsfHsdudRKNHOBx2IhGh85ydnef4OEq7LVzx5XKR7e1t+vr6pA1Ejfn5\nBTQaDV/5ylfZ29vl6EhQ4/v7B/D5hE7LZrPxzjtXCQb7MZvN7O3t8i//8s9EIhGi0SMGBgZlJ7fF\nYsVut2O1int6enqGTqdDPp/nxo3XOT09IZsV96nBIJy929vPpXW1l1QqyeDgMJlMhmQyht/fi9vt\nIZGI43a7mJiYQqFA3lj39QVRqzVUq1Wy2SyJRIJisShfKzabjdnZeQ4PD2UNXacj4s9UKhWJRJx6\nXRisbDY7PT0+UqmUxD1svXQN/Kr17xusT1TX/nq2PhnCWqlc5P33f8azZ0/567/+KynoNkWpVMJo\nFBdNMpmQxgV73L9/h0QiIQVRBrBareRyWeLxOCaTSYoccbzkbAKkrDtkGNzFi1eo1wUEcW7ugpx7\n1u3WpFIpPvjgfT788BcEAgHZ/ty9Ga1WKyaThUKhIAlJ29y9e4fBwUFJV6JkZ+c5tVqNZrPJ++/f\nYmdnB41GQ6GQp9MRgLyPPvo5druTdrsltaLNuN1uJiYEv6nTEVyZnh4vdruN5eVFzGYz6XSCkZEh\nWUws4KtVjEajFIGyQ6EgNDZWq4GxsXHpJFiUhaFd2Gr3ASWy9+JShEyBcFg4LgVjqE9esDc3RXDs\nyMgokUhYPs2UyxUp27FOoZCTOowvLN35vAhA7n6f+/eFDqibM6fVaolGj8hk0oCC/v5+OTh1bm5B\nDhZeXHxEJLJLb2+QWq3GN77xLd599yfs7obx+fxcvHgZlUq48oTY2MLwsIgDqVYrzM0toNPp8Pn8\nKJUqVlaWaLVaZ0ZN4+zt7ZHPi+ilTCYjX2+tVpsrV64yP3+Bu3dvk0wmabdb2Gw24vG4NLJtYLVa\npWw/HclkEp1OT7FYYHp6GpvNJiNMDg8P5JNsf/8A77zzpXPolN3dMArFNoODQ/j9AXkspFKp6XTa\nzM1dYHv7OWazmVqtSiYjuFj379/m9DQmncYn8Xq9ZDJidDE3d4HdXXGwGRoa4fnzLT788OcUCkXa\n7SqlUo3l5SWi0ahkclHS0+PlC18QJ2un08mFCwtS6LmW999/j83NTTY2NuTfXWhBdllbe8bYmDAG\nLC8voVaruHTpivz6+vsHOD6Ocnwcpa+vj+npWfL524AQmavValqtppzN2NsbBDrE4zEpDLzF5ub6\nS/e7QqGkt7dP6tSKaJFUKkV//wDRaJStrXV6e3t5/PghlUpFonF3JNCqG4+nhzfe+CImk4lgsI/d\n3bA8KqrXm0CHQKCXfF7EVnWz815krBrkCCcBxGwxODgk3UtCIN51BzabTba3nxOPx5icnMLn88sb\nWIvFTDabk0J3BYJia2uT3d0wV65cw+v1vQQ3FRu185mNY2PjfOlLX+bevbvUanVKpQLF4j7NZgur\n1Y7RaESr1eHxuCVRuFjv7HYRJ6ZQKCiXS+c266enx4CQCgjH7rjcQYxGj4jHhXvzbGUyaZaWHtNs\ntiRxuRhddyn/weAA29vCtebz9fL7v/8fSKXEGDMWi3P//l2USqWkUzSRzWZZX19ne3tLNuP4fD48\nHi+jo6MSUR2ePXtKLHbK1avX5ZG2Vqvl/v17Evi4D7vdgd8vIMIi8syHxWIhFJo4B7wV7tYG167d\nkJA0MZ49W2VmZo6BgUEuXFhAoVDxxhtvcP36a/LBW6vVyciEeFwgZ+bnLzA+PkksdsrMzAzBoADk\nqtVq3njjCzx4cI+HDx+g02mZnp7B7XZTKhUler0VhSLI1avXCAR6qdcbEgA4RyqVxGq14nS6MBrN\ntFpNkskEHk8PHo+HhQUhHdBotBweHnDlylVMJjPh8A7ZbEY+sE5MiKDmWq0m5UnOMD4e4p13vszf\n/u1/lXiPEdrtDvW60H91Oh1KpQLVaoXt7S3ef/89/viP//Sle/RXqX/fYP0aZTAY+NrXvonZbGF1\ndRmPp4eLF6+QzWZkWrt4OI7QbrfZ3FwnnU7S29vP3Ny81FHKs7+/J+Uuefmt3/qapA367LakSqVC\nr9fx/PkWer1BGhse4/EIrker1ZT1UOVyiXq9xt5ehEKhQKPRwGg0yuLEQCDA06dP+eijX1AqiYfH\n3t4u6bSwsabTKcLhbaLRKIFAgOfPtyQNV4bbt28zMjKK2WyhUChSqZSo1xssLy8Sj8fQ6bScnJxw\n8eJlxsZCbG//QLKHpyRC9osuYSx2jMVi47vf/XtqtbpEDx7h6tUFlEojmUxaOhULM0EqlWJyclrW\nnImxSEX6+yhJp9M8e/YUtVpNNHrI7OwF2u0WmYxwVtbrNSqVKtPTM+zv70loBDGWzeVER6u7yW61\nWhQKeWw2BwqFgkwmLROrRdhzVY7IEFb/fi5cWOD9929Rq1XlE7/b7ZYAgDGMRvEQ6OsLMjY2LjF7\nLLIOKJVKUK83JP1NhGazhc/nk5xsYuQ4NjaOWq2SnHqiAoEA4+Mhms0Gly9flRySQiz88OEDIpFd\nLl26wm//9u9y794dqtUKExOTZDJpotEjyuUSW1vrNBpNyaSgkISfYoP4/vu35J91cnJMLpdnbGxM\nCrvtyAtyu92Wg9BF91FNIBDghz/8Z9rtNjduvC6hLiLSoaOXVCrN7dsfUyjk0Wp1TE1N8frrX8Dj\n6eHOnY9otztUKhVsNsH+CQR6JRq1iLZyOCyUSjVUKjWDg0P09vZx+fJVxsdDEi6kjF5vkB/q9Xqd\njY1n0rg8QCKRoKsLSqfTbG9vEYud0Gy2SCYT9PX1nRPGKxQKpqamefDgHouLj1AqVTQaTYLBft56\n6yYbG2vyhlyEk1dptVrs7GxL18HpuczEs9XbK15bpSK0ghMTk4yNhfjpT39EJpPm3r3bVKs1zGYT\n1WpVjonJ5/M8e7YqBwB3x7KpVIpgcIB4PEa93qBWq0t5fgYpcFiLyWQilUrg8wWwWCxEo1EKhQJ+\nf4B0OiXHmVQqFX7xi/dZWVnk6OiIWq2K0Wji3Xd/LIewBwK9DAwMSKR/C06nC5/Pj9VqYXd3h+98\n52+kvLsXZTKZ6e8fQKUSRh6LxSrxlZp4PF5u3vwKsdgpx8dRNjbW5eiV3t4+4vEYqZRIKigWC6hU\nagm3I0ZI4h4uSNyzCuHwDv39/Rwfx0mlkvT09NDfP4jH4yGXy0q5oDby+ZwMT15ZWZY2sm6Jfq4i\nk0kRiUSkMaSTdrvN0dERCoWKbDZDJBLBYrGgUqlYXX1COLxNINDHt771bVZWFiVzTlQamwnnuVKp\nIBgUrD3xe4uOo9frldanLIFAL1arhY2Ndfr7B3A4nPJ61Ww2XgJpdqsr2u50kLIVH3P79sf84hc/\nR6lUUCqV8Pv93Lz5FRln8fz5ljSitGOz2Tg6Egera9fEBuzBg/vYbHYGBgalblyWZrMlTRuseDwi\nyqfT6chuwjfffIenT1fleKduRaPChCU2tx2mpmZkPa5CoSAYfOEAFTBvhUS+F5pAm80uoRyOpO68\n4M+ZTGbZMfsP//BdnjxZpl6vYbXaUKvVkotzkpGRUQmuu0a5XOLmzS+/8v78VeqX2mCFQqFrwP+2\ntbX19if+/U+B/wVoAX+9tbX1f/7Gv9H/T0qlEpDJN998+5ydtJtluL7+jKMjLV/+8m9JbVb7SyC1\nK1eucf/+HU5OTkinX7COPq+uXLkudXpyHB7u8/z5JmNj49TrdQmc6GZqaoZms4HJZGZqalpCGeiY\nm5uXyeRXrlyhVCrJRgCbzUqn08Hr9fLbv/277O1FuHXrXanTZpFO3n1Eo1F0Oi0ej4cvfOFNms0m\nDx7c4/j4mHQ6JZ1ABhkZGcPtdlOr1bDZhFCxm/Hl9fbIZPVWS/BuVCoVZrMFo9Eghcp6SSQKmExm\nksmkTP+uVmvyjdkF8jmdTsldOEcsFsNutzE7O8f6+jNWVhZRq9UolQrGxyd4992fADA7K3KnuvEK\nm5ubFItFBgaG5L+16Nq9GA8eH4vT7/T0rPx+CUdmjYUFiySWL0rasfMtZr/fz/b2Nvv7ewDy63j7\n7ZuyFi6fz7G3t8fw8LCs5ert7SUUmuTk5FhyBFUZHw/JI7Bms4larcZms0vdqhZer49SqUQ+n5ev\n10hkF6dTbB77+oIsLz/mvfd+yslJTBJdW3C7vSgUSOn2JTyeHpRKpQS+fcEQ0mg0hEIhBgYGJYFw\nWeqS1Flff0atVmNgYBC1Wi1l1B3JhPFSqcjeXgSnUwA++/r6ODo6JB6PyeRmMUbIsLT0mEQiIX9v\njUYjaV6K+P0B9vYiXLhwkTfeuMqDByvSibhGMpmir69P5t00Gk2s1hcPnUhkl0KhyPT0DN/61u+w\nubkhsZLq8mZ3c3ODZDJBtVrl7bdvvkT2ttns9PcPsLsblt/Pg4N9zGYzExOTvPXWO6hUKhYXH7O7\nuyOP6CqVMhMTU0xOTp2DVnY6He7c+YiTk2P6+vrRaMTY/PLlKygUCnp7+0ilkkSjUYlBpiYaPZKC\nzDtoNFri8VPi8ZhkUtjj+PiYel1IAZLJBPV6jXQ6JXHymuTzObxen3TdNHG7e6R4ngMA6bCkY3w8\nRKGQ58c//hcikV3a7Q7h8DatVpuJiUlZt9ql84uOkVgXnU4narVaMhvsE4nsfWJF68gPSuEKdTE9\nPSsfmrqlUikZHw/Jm+GpqWnUajXtdotIJEKz2ZS1U2IdMZHJZCmVimxurmO1WuRN4czMhHRvi66F\nyyUyFkdHx3n27CnR6BEffvgBtVqV3d0wnU6HwcFhotEj8vkCbreLk5MTiXdlkycCuVwWq9XGL37x\nPtHoIUNDIxiNRjnb9vT0hKWlx3JiQTfWEBuMAAAgAElEQVR30eNRotPp8HhecLbi8RjNZouBASEL\nEPeicKhPTk6xurrK0dEBe3u7xOMxVlefkEoJdtTw8IjUBTLKI8fuc0roNtVMTU2ztPRI6vZqsFqt\nvPnmO/J93uU3djptisUi0agYH165ck3mnxkMBuLxGPv7ezIgFcRGT4xBL6FUKtna2kCj0XDp0hVZ\nu9oV3ndL6NmE/KHVajM2Nv6pNPWzmbsg4MOCSWam0RAYC7EhPJICwGf4/ve/x87ONmq1WsrF1MjX\nnclkxGaz4/X6yWazjIyMnoPu/rr1uRusUCj0vwL/ESi+4sP/OzAFlID1UCj091tbWy+Dh/4N11kO\njEKhYHw8hMvllroau7z33s/w+XxMTk6+9LVdHEI4HGZ4ePeX3mAFAgF5t61UKtnbi7C0tIjBYMRk\nEnE809PTHBwcMD+/gN1u5/btD9Hp9ExNTcst7v7+QSwWC5ub6wQCvZyeHmM2W/jyl38Lp9Mpz7lf\nZCc65RGT3W5neHiUsbFxjo+PCYUmmZycZmVlGZPJyNTUNO22GEecnJwwNTXDxISAWe7tRZienqGv\nL0in0+G9996lXBYdhv7+AU5OjiUIZ41cTmRuHR7uo9frOTw8oN1uyeHVXX2U0+nEbLZgNgsuVCaT\nwuPp4erV6ywtLcohsQqFQuoeGAkE+ujt7eP09EQKLFbIJ/tundVfdeNHuiembmWzGbRaHcPDI5yc\nnHB6eiKR1c+PO7zeFxusoaFhEomY9O8+YrEYPp8Pp9NFNpuVdRDR6BFarY5cLoPBYMBms3FyckKx\nWMBms0udi5ysN7ty5RogOjT37t2WA6ILhTzb29vE4zFGRoTzslqt8ejRfYrFshyVFAj48fkClEol\nTk6i7O/vSZ1DNTqdViLZ64jFTpmfFyHQyWRSFos+e7YqRTAVsdvtLC09lgCVHX7nd34PrVbDkydP\nJKdSgYODfQnNIPLWGo0GR0fiOtzf32Vra1MC9/YSDm9Tr9ep1+vodFr0egONRoOZmVnZ6j08PIpS\nqeTWrXcJh7cZGhqWxpNiRNKtZ8/EPTA/vyBRt/dRKMRmeW9vTxaJl8tlQCmPp89WN3pHpRLLaLtd\nZmNjDbfbIwulRVzHFE+frrC3F6FaLZPN1nG73QSD/S9xpCwWG8+fi3SCmZk5iVR/LOlL4lSrVTwe\nDy6XW3LKaiX9oguHw0kiEWNh4RI+n5/33vspmUyGiYlJrFYr779/i3bbLLPd3n77pswW+7u/+45E\n6k6Ty4kuaqcDOzvbWK02Fhcf02w2KJcrBAIBJien2N0No9fr+drXvikLzY+ODqnXhdtNuEzFQy8c\n3qFYLHL16lU5bBvEmtlut4lGo2SzWY6ODlhbe0oul+fSpcsSP04rscNcqNVqwuFtSqUSU1PTuN0e\nKRoqJoOVT09PePp0Ba1WjNNPTo6lcZDQ//l8PnZ3d2m12lSrgq/06NFDhoaG0Ov19PUJxMjDh/ep\n1QR4MhgM0moJyHOhkJd0gCLdoys4r1QqqFQqPB4Po6Oj1Os1Ll68wqVLl1EqlQQCAeLxOJHILoOD\ng9TrdUTIux+/34dCgTwaBNElhhfRPN0NdyKRYG9vj2ZTbITv378nZ6uKg7IYXR4c7ON2e1AqFVgs\nIpRaMLq2CQb7yWQy8iFhaGiYdrtNqVTi0aMHcle+yzV0OBwUi0Varca5a9bvD7C7G2ZzcwO1WkV/\n/wA6nY4nT1YA0QEDsTm+ePGS3LVVq1WUSudZdKWSOEx3DShnNbafrO7a2tXKCcOOCq1WS7PZkHh7\nooROS8R6zc0t8PWvf4vhYdEZE/E++4TDAueg1WolvddvzsCCX66DtQP8HvCdV3xsFRCJlmczaP4H\nL5fLxbe//fv84z9+l9PTUy5duozVKsJLu1qPo6NDwuEdKUBVzcbG2ithj5lMmpWVZVk7AqLVGwwO\nSJofoc+q1WoYjQbqdZF9VS4XMRpNPHhwT74Yg8F+nj5d5d69uxSLQkMEQvchxlM5SiWRx7e8vMTx\ncZRWq43L5WZkZISxsRA+nx+NRoPFYqZcrrC4+Jh6XcRYXL16nVQqwcbGBk6n+JpIZFcmQn/ta9/E\n7/eztxchk0nT1xeU8t7E+EevN0jOp14eP37E4eEhuVxFovomUas1HB9HyWQycjehOyLrOhJB8IwS\niQQHB/uMjIwyMTHBnTt3ODjY5/Bwn3q9IZ8UBZPKLLlN9bTbHTKZlCyS7H7/LlFajIHOgx6774Og\nLre5e/c28XgSvd5AqVQ6p4MzmUwkEnGGh4dJpYRguXtNmExmLBYlSqWCZDJJpVImn89TLpcIBoNM\nTEyhVCp4/PixhFwQmr1sNitvsLa3n5NIxEmlUkQiYWw2u/zzhbkiisFgwGAwolAoWFi4jNVqxePp\nodkU46OTk2NKpSIej5d2W4hsxQZGCOtNJjOJRIJCIS/rv6LRI1IpQeivVCo4HK5zuhen08XGxjqN\nhhhhHB7uUy6XiUR2OT09YWHhkhRvsY/ZbJG6oG6mp2eZn1+QYqKsPHr0gEgkQjab5cqVa0xOTtLX\nF+Tg4EC6xoPY7Q4++OB9wuEdrl9/nc3NdZRKBUNDojNZLpfZ3d3Fbrfj8Xi4d+82arWK69dfp16v\nScYMPcPDQt+0uvrkHLD28PBAZjGVyxVGRwWSoV6vc3BwwKNHD3G5hD7RYDCQSCQl4WyYw8MjTCYT\nbrdH0mwa5WtZRFtl5U28QqFgbe0p+/si3y2RiDM7O8/CwiUpDWCTVErE81gsFmnDesDKyjLj4yX2\n9w8l7ANSvmqCeDzO2NgYCwsXpe5Pm7/+679iaWkRj8eD3+/H5/PSaIxwenrC8+fCFduNBurr6+fK\nlatUKhUGBwcZHh7l8uUrNBpiLL29/Zzj4ygKRQez2SI5ze5SqzVwuVz09gZRKBTEYqfn/qZOp5Oe\nnh46nbYULXUo55SOjo5Loex1iROnRq1WyQ9XkWrxwjDgcrnlaJSbN79MLpej0agzNhaSs13X15fR\n6Uw0myIjNRzeYXt7i2KxSC6Xk9Im6hgMRgKBgBzVVCoVcTqFRnJt7ZnMTupuXtxuD6OjY9JhageT\nyShnWo6OjjM/v8B3v/v3bG5uACJnVXT1/TKLq/s+JpMJbDbbuZiv4eEROh3BFbxx43UuX74qpR6s\nMDw8TKPRYnRUGGQKhbyUMKKmUBDu90qlwocf/oJUKoXb7ebg4ACFQvnSZqc7avT7/Vy8eAmVSjiF\nj44OWFx8yOTkNMFgP319QVKpJHa7g6GhYcm8kpTTFrqRNE6n89x7JOCseVlaIACiTZxOE9msYIOd\ndYl/srodrLNdzm5H/+LFy9RqApq6vLzE/v4e0egR1WqNq1evy5srEBt8kaDiZ3v7OScnYqOaTqcY\nGRn9zN/hl6nP3WBtbW39YygUGvyUD68Bi4gO1j9sbW3lf6Pf5t9Q6XQ6rl27wfb2NrHYqdTGP/85\nRqORCxcWOD4+JhY7feWYcGtrS6aCn42lmJ+/IIlSjej1ehqNBplMht3dHdbWnrK7m5PhjGIB7/Ds\n2TOq1QrZbBaVSkmz2WJoaAS93kA2m5XZJe+++2NpY+UiGAzi9we4efPLmM1m2VVSKpXIZNJYLFZM\nJhMXL15Go9Hg9we4e/cOe3th1Grh0jCZzBiNxjOjDI388O2SuMUYUdj39Xo9X/ziW1gsGhIJITrU\naDR4PB7i8VMpXijB4OCgfDN2R3gg2C5PnoiOwcjIKNmsyCXTaETIqtFoxOv1oVAosNnsuFwuajXh\nJInF4pyexmTRZi4nhLomk0kWJXedfN1KpVLodDrMZrPkFMzSajXO6AFC8ucKcCkcHR3R0+NlYGBA\njnDpjgZsNgfZbJpUKkUmk8ZoNLKwcAmj0cjo6DhLS4tsb2/JlvTuJjCbzXBwsC8Fz2bpdEQH0WgU\ngl2Tyczz55vSKV6cilUqJS6X51wsRTfKwu8P4PP5mZ2dw2538PTpqqyByWQyPHnyBK/Xx/b2Fvfv\nC16RyPNUYDQacLk8siYwnU5JGAqfpKvxSKYLm2RgsJJIJLDZ7IyOis3Hzs5zJien8Xg8tFotLl68\nzOjoGP/0T/+dfD4vjy4LhTzvvfceuVwZp9NJJpOht7ePWOyU73//H0gmhbZoZ2cbl8stC78vXLjI\n6uoTms0Wc3Pz8gb4xo3X2d+PsLcXQaVS4/H0kEolKRYLcqBzsShYZ2q1GovFIiMleno8rK6uEI0e\nEovF5I14q9WSxktZqtUyP/rRDyXEhVJGO6TTSXK5HIuLD9neFjqhaPSIS5eu4PcHKJXEGHZkZJSP\nP/4QlUrJ3NwFDAYDAwND7O/vsbm5wePHDzk83OfZs6eUSmVOT4/RaDSYzVYJ13IqjYzD/OAH3+fp\n06d4vV7+83/+n+UDFAgtnaCvZ4G2pO1sSOYOHR6PwA+c5Ub19QW5c0dk8NXrdRwOYd4ZGRllcHCI\nQqHA2tpTXC43LpcLpVLoaFotcS186Utf4datd6WooFNOT08Ih7cBsYZptTppJBbn1q13uXBhQZYa\ndO8h0RlSUCwWiEaPcDodJBIJOp028/MLss5ViLfFI7BaLZPJdAXrQSYnp7DbbXQ6QoYgXHENCoWC\nTHsvFvP4fH6GhoZ49EgwBl9//QuMjYXk0VWnI8KEQTCZrFYb4+MhHj9+zP5+RHLPpqUYHzW3bv2U\n7mhVr9fLQdrdstsdXLp0hXw+L4+6jo4O5ZH5yckxQ0PDfO1rX+fBg3tSSLTo4PT0eGk0GhwcHKBS\nKdFoNAwODrKwcFHGf4h1OsrDh/ex2+289tobDAwMyj/f5/OxsrLM+vqaZGQRRheH4wUANxzeAWBi\nYuJTOVKiU5WTpQXFYlH+98PDAwwGw0sTgLP1ooN1foOVTgvGY2+vMHhYLAIvJDBKFubnF175/XQ6\nHTMzs4yNjVMsljg83HtJ4vHr1K8tcg+FQnPA14EBoAz8X6FQ6A+2tra+91lf5/G83Gr/t1oOxyz1\nutDj6PV6dDrdGWeGlvHxcbRaLTduXObdd9/l5CTCxMSg/PXxeByoYbMZ6HSqn/jbWYDzbUy9HiKR\nBpOTY+TzaXZ3d1lff8LoqBgndrVKOp0YeViterLZKqVSllKpRE9PD1NTU1JUxBCBQIDV1VUpWsYn\n/+7f/OZXWFlZ4fBQzcHBAVarnvX1JarVKtHoPp1Ok1qtzO6uEMVfvXqVq1evsry8zPFxBIPBQLmc\npVbL0WqV6HRquFxWQqGhl66P7ultY2OFcjnHzMwkhUKWaDTCV7/6DgpFg0DATSDwYvM5OTnMvXt2\nCoUUdrsetbqFzWbga1/7Gmq1mp/8RITLejwWtNogx8cRaTPi4Xvf+x7p9Akej4Xj42M2N1cZGxvD\naFTSbJYZHAwwNPRC7FwoFNDrlfT19eHxWHj+PIPNZsbn85DJZPjxj7/PV7/6VblLEY8fkk7HsFj0\nzM5OMDk5zN7eFj6fV37t4+MDPHwYI5k8weGwMjo6wMCAOAlaLCE++shNPp/C7TbT02OnXM6STB6x\nsrJCPp/h5s2b2O2CDH358hxTUy90DHfu3JGjd7qVzycwm7WMjQ3Q19cngRNd3Lhx41yr/M03r3Pr\n1i22tqr09wtn4E9/+gNZLzU6Oip1hXz09DhQKtsYjTo8HgGODAaDLwEcQ6Ehtre3CQQCbGwMks1m\n8XpdrK2dkkrFKBR8PHny4NzXGAxaymUFyeQxDx58JDuifD4frVaFfL5CrVYEmhSLGaxWI263lcPD\nMIeHYRYXF8lkMrRaFUDH9PQYc3MvHmQezyWuXp0nGo1yenrKwsI0u7u7JJNRhoauoFa3KRRSXL58\nAZ/Pd463Njc3wdBQkO9///uoVCpmZmYkTIGCDz74gPfffx+AUilHrVaUA6fb7TZmswmXy8bm5jP0\nejWtVo3JyXF+7/e+xdDQEH/xF39BpZLnww/f5fDwkN7eXtbXlzk4OECvVzEyEuTatct0Oh3i8Tj5\nfAaVSsWlS/OMj49jMKjY3RUZpOVylpWVh2xvbzAyMsCf//mfy/iQs+X1nteNHh4esr6+Tr1eZ2FB\noEyMRiVW64v7dm5ugocPb5NIJHA653E4TJTLGdbXM9RqNTSaDhcuTDEzM4NGo5FiU55wdHREInHE\nzEyIzc2nrK8/QaFQoNfrmZ+fp1QqcXx8zNHRkdSdeEiplCUUCp0b4Z6enqJWC8TO6upjdnd3JW1O\nmXB4TbqvNzEajVJnfkyKpRJd0rMSARERtcrS0n1JwN2kp6eH4+M91GolExNjjI6OUquVpUOwhcXF\ne3Q6HY6Pj7l161+wWCyYzWbW18X3NZt1VCp5mk1hVDAYdBiNWkmr1mFwcIDBwUG8Xu8ro4gajQYG\ngwqvtwePx8LhYRWbzcClSzPcvp1FoajT39+D3f42i4uLFApiRD4y0s/MzAzLy+KayWTi+Hw+xscH\n5PsykUhwdBSmXM7R6dSZn584p730eCz09Xl48uSJND4XG9D9/S38focE8K4SCg0xMvLp4dr9/V5K\npTQ6XQePx0KplMJmM+BymUml1PJ6+lnV02NHrVZis+lYWlpiZ2eTcDhMpSIcmaKhUOXP/uzPsFr1\nEtbm5QSH82XhtdcuE4m4CASc2Gy/2X7lN3ER5oAKUNva2mqHQqE4Ylz4mfX/dlTOv7aamLjwqR/L\n5WpAjUBgmFqtxb17j5iZuSx//MGDZXK5CmazmWg0wcFB/NzN/8laW3tOLldhbGycb397kB/+8J/J\nZjO022rMZgejo1N4PD08fHgft9uNxeJGqdQDYTyeAN/61rflzgYgOZmaqNVGksnzErxAYJi1tecc\nHZ1ycHACiAxJjUaH1epEpdJSLNbweDwUCjU+/PCerAOq16totXqePFmTxwWCX6NhefmZ/DNsNiO5\nnKCtP3smQJPz8ws4nV62tsIsLq6RTOYJBALnrqtyuY1WayIWS/PDH77L6uoKarUGi8UtCR/FjD0S\nOaHT6ZBI5DAajQQCA5RKVZaWnvL66yd88MEvSCSyqFSH/PM//1gieQ+e+1n7+3vkchX6+nRsbESI\nRKJ4PL2Uy3UymQLh8D4ffHBHfgCk0ymq1QbZbB6TycnubpTt7T08njIq1YqM+Fhb26JUqmA0WgHt\nuZ/pcPSwsbHG06fPabVU3L+/yP6+yI+0Wq3cufOARqNJudxgaekZ1WpHjv1QqQxsb+/JjBe1WoQf\n2+120ukSFkuVBw+WSSbj2O1eNJoDLBYbDodDymS0UKu1MBgsWCwtSS+hwmAwMzt7kdHRcaanZ+QH\n1tlKp8svXbNqtYlcrkI0uoLF4uTixesShFQ4L51OH1qt0OAI11AHu72HQqFCJlOg1VJQr9dxOm18\n9avfxmg0ShqcDSqVJqOjw4RCk+zu7tDpaCStWB2/vw+/XwA0fb7BV65LRqOT4WGxcY9EjtnainB0\nFOfx44eoVGqmp31MT1966etcrl6OjmJUq1W+8Y1vy53nwcEJarUmp6enOBweJibmZEep3e7A6xWc\nsuPjBA6HQ4psGcTh8JPNVunrG2Zn5zmLi09ot9u4XH6i0RhKpY5WS8nm5i4Ohxej0UgymQWU1GpN\nHj9ewePpo9FQ4Hb70ev1DAyMc/v2fdxuH3/yJ3/G3buLZLNp2fHn9frQaDTkcll5fKRQKHC7PUxP\nXyKVSlKr1djYWOfBgxVMJhPR6BGRyC6lUolCoYzZbMPnC1KttqnVKtL7rWZkRDg7s9kqIMZ8fX2j\nNBpKtre3JCK30AwZjUZUKh1arQmPJ4DZ7MBmc0nC8jxWq41qtc3Y2CB2u41arU4+X8XjCTA9PcvG\nxjOOjk5IJtPs7R1SrQo3sZAZ6Gi31ezvn5x596IvvZ+NRoOtrTCpVBKz2YLb7efo6BRQ4/UGyeer\nlEp1yuUaz5+HyeXymEwGisUyyWQGrzfI6Og4SqVYu202A3Nzl8hksoyMhKS4nzFJfjDMyIjY7H9y\nze1WLpcll6tgt4vn6c7OgUTcV9FqqdjfP2Z0NI9CoWB6+hKx2CmRyC6VShmtVqwnWq2ZlZX7aLWb\naLUmyQXbYHNznVZLBLer1QaePn3+ysig0dEXnMBUKsXS0iPee+9DCetSYWrK/5nP+lZLTS5X4YMP\n7jA8PEK5XCGXqxCLZeT19PP2CrVah2Qyxb/8y88ol8sYjXaUSi3Vaot6HarVJiqVjq2tCOl0CovF\n9kvtP+p1BblchUjkmN5e5W/UFPpVNlgdgFAo9MeAeWtr669CodBfArdDoVAdodX6m1/7N/kfuCwW\nC8Fgv0wm7mqIumwUm00Ex2YyaQyG3ld+j3ZbhJnqdDqGhoZRKBT8zu/8Lltbm/j9fgYGBIQvkxGR\nG8PDIzLnp5vxd3ZzBcLlIajs5y+wZFLklanVGt5++0uyGPEF3fee/LmXLl3BaHyxKTQYDBwfH8s6\nrk5HuFocDtdL4Zoiwkb8t4gaqnDx4kXMZhMfffQRDx7cpVAooNVqicfjsuvDZDLhcrmIRqOUSiJE\nWWysDtnZ2WZjYx2XyyU/OPb2IpRKRaamiuRyWZ4+fcL3vvd/s7UlTrmTk1MSM6yFy+WWx5LdINZm\ns4nVamN9fY1ms0kg4Mfj8TA/v0AkEpYBhyAWo8uXr+Lz+Tk6OuC9934mjWdfvO5Op0Oz2ZQiQwZe\n0keMjYVYX1+TUB16arUahUKeUCiEz+dje3ubRqMut/03N9el8WWanZ0dSdB/3hHXzU5cW3tKJLKL\nXq+X3Y4gwtaNRpNkPhAogeHhEZRKFScnUfR6AzduvPFSlMvnlc1m58KFi9y/Lzpr8fgpk5PTjIyM\nMTExeW480W63efZsVQ7j7oZld3Uy3VFOX1+Q69df5969O/h8fhKJGHt7EcktZ+PChUtMTEx86rjg\nVRUKhVheXiKTScspD+VymcPDg1d2fq5evS6PqbsdgFqtQiDQR6slQni1Wu05ThGIMW/3+lWp1Njt\ndjlftZtOMDAgHLojI6N88MH7GAwiKH19fY18Po/dbqdcLkuZlXVWVpbluCOHw8nBwT53794mkYjR\n19cvOcWQUA2C5L+3FyEQCLC9vX3u9xsaEoDgQKBXhhDH4zFJY7dGp9PBZrMzO3uBubn5l17fZ9XQ\n0LCUDddmenqGx48f0Ww22NnZIRKJyKP5vr4gV69eZ2VlWdL8xHn8+D5Xr75Gf79wX/b29nHhwgJz\nc/OoVBqePFmm3X7hAp6bm+OrX/02arVKNoK8qiqVCmtrTwmFJmRMhMAW2AiFJnjttTfknM7+/qCc\nI9poNBkYGMJkMvMnf/If5TF9t65cuYrH40GExqsIBPrY39+TNXOfVd3OkdFolDNlAwHxt7HZ7BQK\nBXnzqVAo8Pn859b2mZlZPJ4ewuFtae0oYrVaqdfreDw9DA+P4PP52dvbZXc3TG9v30vrxdlyuVzM\nzMyxuvqERqNIT0/P50bMOJ0u5ubm2dzcYHtb5HB2TQLw2fqrbun1ennM2H2eiftKh9/vl7EcYnyv\nwGD49JHj2XI4nKhUyv/vsgi3trb2gNek//77M//+l8Bf/sa/xb8XMzNzRCIRPvzw5wSDAyQSccrl\nkpzt12w2SaVScsbiJyuVStFoNM7ZvoPBfnZ3wySTSaanZ4EXrouz82273c7Jyck5QTYgJ9Wfbb8X\ni0WWlh6hUCgIhSbObR66FY0e0my2cDqdzMzMnvuY1+vn7t2P0en0zMzMUiqVsFisBIP9cnp9tzwe\ni3zi2NnZJhzeQalU4/P5cTqdHB0dUq3WsNvtrKwsyuynrraqSzNWKBSMjIzi8XjY398jkYjT1xeU\nT2bCxXSIx+PF6/USj8dZXl6kUCjS29sr6SN0ZDKn/OQnP2Jra0N264iPCcu8AKe+oJ43GnUUCgiF\nJtBqtRQKBSlyRS0Fqu5LOIMBvvjFt9Hr9SiVQmw/PT0j6TMEnfgs20YAZM1SV0ZBNpump0csjD09\nXhYXH1Mul/D7e5mcnGJzc53Hjx/SbncwGAwsLFx8iSIOYiT9wx9+HxAokKmpKVqttsx3SyRi8gN8\ncHCEubl5FhcfodPpJHxG5aXv+cuU1+tlcnKaUqmEwWBkd3eXYjFPf//Auc/b24twcnJCu92SDwWp\nVAq73Y5SqWRp6TFut5tQaBKbzSYR6RWcnp6gUAhdmFKpxGQyyhq7X7bsdgdvv32Tk5NjdnfDslN4\nfX3tlZ/f6bTZ3t4kHN6WRv2iHA6HrMfa3BTZkcPDI3KXK51OS1mfakqlIhqNhkePHlAoCMdlqVRi\nYGCI4eERaV1oYbEI+v7eXoRw+DlqtZpEIs7Y2Dh9ff28++6P+dnPfsrU1DRarY7j42NyuQzFYhm7\nXdD7Q6FJXC6X7ARdXl4ikYjjdLpk9+3a2rNz3DUB6n3hMp6amuHy5SsvaRR/lep26DudNkqlUqKr\na0inUzJXy+PpkQ991WoVhUIphWXvcfXqdVmcDUg6PQ9vvfUO/f39NJstWq0mFy5M0W6/zIr6ZN2+\n/RFPn66gUChxOByUSgJd0tPjZWZmFrfbTS6XweF4sdZ5vX6Oj6MSC6whmxnOlsjJ1Mhg327GY1eL\n9FnVPXCZTGbS6RSAfP3Y7XaOjg7JZrOfuskRGYBqLlxYkA0SomtoYmBgQAbqdjodIpFdDg8Pzh10\nXlV+f4B6vc7+/p58cP+88vsDUo6hOPQK/aYSu91+Ttj/adU1Hk1NzcgpExaLlXQ6TTQaRaVSMjg4\nzM7ONkql4iV47KeV2Wzm5s2vvHI8+6vWv4NG/5XUxMQkt269y97ePnt7wmLqdDrY3xf/LXLJaszO\nzr3y609Pj6VsLHHKnZqaRqUSttmufXpgYFB2XXRdGCBOPScnJ2xsrJ37925k0NkNViSyS6cDc3Pz\nL3W8umU0msjn869sLZtMJvr7xcPg0aOH8r+f3Ri++nuKm6NcLmE2WxkYGKBSqUgar+s8ffqEZ8+e\n8tprb6BWq6V8rJK8AFksVolz4pH+Sv4AACAASURBVJNzuLqLhsvlRqlUUqtVMZnMaLVZ9HqDnB92\n+/aHtNttdDq9FHCbk+jLAs8wODhMtVoln89hs9np7e1jZmZWYq7EpbGC6Hz5fH5aLZEq7/X6GB0d\n4+rV6y+dlrob3efPt6T4h4LsFBSvw0s4HKbdbqFWq6nXa4yOjstW62q1gsGgp79/gE6nw+bmBna7\ng4WFi6+EEHYjO9xuD5OT01y7dl0+TXZ1WOL1aJifvyC/96+99gblcpnbtz/6pU7fn1bVagWXy82l\nS5dYWVlhc3MDvX5VPghotVp2d3fQarW8+ebbGI3CnbW5ucHo6Bjf+MY3+PjjBySTSdLp21gsNur1\nOltbG4ACv1+MjLoL8a9b6XSacrnC7GwvN268TjwekzsKn6wusNVms8kRLqOjYywtPZLz7zY3N9jc\n3MDlcmMymajVajLvqlQqkUgkJK6ZuD9SqSQXL15CrVYTiYTJZNI4HA5Z2L+6usLmptBIXb58Fa/X\nx8aG6HZGo0cSHNhEsVggFJpgZmYWjUbDxsaa/OBOp0UXy+Pp4dq1G/J7EA7vnOumivSDGiA6rr29\nvb/R5upsra+vo9frcbvduN0eGUFQLBakLD9B6m+1mhJ408b29nP29yP09PjOpWLodDo5A/PatesA\nuFyWX2pcFA7voFKpuXHjdY6OjhgdHefq1esy6gCQN0Uulxu9Xk+n06HT6eB0ujg9PSWfz8qO326J\njrjY7IgotrJkljh/mHpVdb/WaDRyeNh9Voi1odv5yWTSLx1QzpZgTim5dOmK1CUXsVhnpwiDg0Mc\nHu6zuxsmGOz/zC4WCHOR1+v7THH6J0uMjEeZnJzC4/Fw8eLlz/8iqYaHRxgYGDyn6+yiayqVikTG\n90iThYa8ee9mfXY+6To7U0qlEqfT+Rtvsv59g/WvpNRqNf/pP/2ZPH7oCi5VKhWlUpH9/T22tjb5\n+OMPuXr1+jlhbavVYmNjnUQiTk/PrAS4E2nn/f0D7O9HCId3yGTS7O6GicViEq35EIBKpUwkEpaB\niWq1Gr8/gEajkfObQLSmT06imM3mT91cAZI4U/mpm6bR0TFZ3Ari4fl53JHuhqNUKkldKiVGowmb\nzSbH4uzuhtna2mR6egaTyQzEZXdXd5P4aR08Ades4fP50euFu3NiYhKVSsmtWz+lUCgyOjpGPB7H\nYrEwP3+BRCJBKpWUuT56vV7eXImfKbpE+bzIGezGk2g0ggCv1WpZWLh0hrDcoVqtntPZdX/vQuHF\nBgtgYmJKcrDZ5Iec2Wzm9PQEpVJJvd6Q37eBgUH5Af6qBUMALj/m4cMHtNstVCoVjx49YHZ2/lxH\nMxY7RaVSyqG5gNQZMqJWq+SO569T5XIFpVKBy+Xh9de/wMbGGkdHh/Jr2N3dkREBomNilrg8bYxG\nExaLhStXrhGLxdja2iAWO2Vzcx2HwylvpM86TX/d6t4zwWC/5N4b/NTP7enx8vjxQywWqwxMrNfr\nPHr0QM6N65oKstkMY2MhFArk6yyXy3J6ekxvb1DK7xPXiU6np1qtcvfubY6PT1AqVTK6pNtFdTqd\nzM3Ny7lsHo+HwcFhnE4XBwcH1Go1VCql3F1Tq8X4UKFAPpwEg8Fz90l3hNgF29ZqNdRqDRqNFrVa\nxeTk9Mt/hF+jTk9PyGREV3Zh4YXGLZfLsrGxzvLyEgaDAb8/QCaTlniAYt0RTrFLslyg3W5TqZSJ\nxeL4/cmXshM/q+LxGCcnxxJLSonVKjrtLpfrnNu7WCyi0ajl72u3i810o9Gg0WiQTKakqCRxrzUa\ndQmWihSVZqRcLuFy9b90mHpVlctlVColBoOBdDqNwWA44xQW0WupVPJcusInqwv57CJqugzBs6XV\navH7ezk8PJDSLD5dYi3MACvEYjGuX7/xmZ/7yeoe5MWa/avVJ00zZ+9xn8+PxWKlXq9RLpflDtb6\n+jNOTk74vJqZ+c0PZP++wfpXVOK09uqNRrvd4datdzk42KfT6TAzM0u7LXbgBwf70qy8l6GhYSKR\nXfb2IkxOTqHVaunvH5Q3Vicnx+TzBbLZ7LkHYjA4QLPZlP/fZDJx6dJljEaTvAHodq+Gh0c+83UM\nD48yPDz6qR9XqVSf+z0+WV0xcKlUlB/ozWZLvpFF0HOco6NDvF6fvOBEIrtyqKfZbJG7DWdPiDqd\nTu4iic3FQ7RaNePjIdRqNR988As0mpo8djy7gJTLZTloW6VSnRu9dTVQ3c5Ol6p++fJVqUPgObeZ\nWl8Xm4qzo9fuBuv4OHquU1KrVWk0GjidDqLRIxKJBKurK1QqVcmurjvn/vmslnsyKUKQRcj0jETQ\nT3D//h1CoUk5IqVYLOL1el9a1EQGnIVCIUe73f7ck+6rqlIpy2wug8HAxYtXJKJ5kFjslGw2h8dT\nkzf2FouFfD6PQgF6/YvDhtfrlQwMu2xtbcj5dN3u5G9aJyfHqNWqTx3Vny2nU2QexmKnuFyCC9ZF\nM3Qz/7pJCt0HYjDYz/r6GslkQu4yTE1NS4cd8X42GiItoFKp4vx/2jvX2LbS9L7/eBdJUaQkUve7\nZR3LF9kz4xl7ZjudmexutlsUSJH2S9AARZo2bdEWLdpiUWyL7ZcAG6BIPmTRAM22xSJB2w9bZBsU\nySZpu9POjHdu9tij8UVHknW/UBLvpESJ1344PMeSSEqURUuW/fwAw+ZFPMfie97zf5/3ef5Pi+aW\nnc/nWViYK3U22ObmzTeN7ZJ0eptr117hvfe+AWir/Pn5eUZHR0s3Ie3muvtmXCgUjPGq09joIRwO\ns7mZwuv1GX5hdruD4eERdna2CQbLb1x6cnwt2z75fB5VncBsNqEoe82Z9Xy9eDxuiC/NmHaR2dnH\nZLMZtrbSrK0FS9tbj0seXrOAVlWon7OOHm3aTzab5fPPPyWfz5eawsdob283etzt/j1tbqbw+Z5U\nMevehuFwmImJR6jqIyNvs1DQzE216zdHf/+AsbDS59n9i6n9aHOgm2QyQTab3bPgAfD7AywuLhCL\nRfdY++z9DH2bsXK7Jh2v18viojZ3VRNNuVyOu3fvGNXJ4XB4z3sTiXhZ4/bd6H5otYyPw9DnX6vV\nYtiHaG79KSNfdW0taPh0VcNisRy6q1ILIrDOCF1d3Zw7N0wul2N1dYXbtz+jvb0Tq9VKKLSO2Wzm\nvfe+Tnd3L8HgKsvLiwwNncPhcHD+/IjRsBWKmM1am59qTE6qLC0tMjmpGiFbzfF4CZfLdWD06lmh\ne9xsbW2Vbpqaj4q+YjGbzVy5MsYnn/ychw/vc+XKWCm3aqnkYfVoz+fp3jk6emhd/7uhwUkmkyEY\nDJbaxFjJZDJ0d/fsmTxcLldZfoWO0+nEZrOWettNEg6HsFjMeDxNZfkR6XTaiI6o6gSZTIaREaW0\nZWknkUiU3fB8vuaSi3qS9fV1vvpqHL8/QENDA9vbO4YAP4yZmWnW1oJcunSZb37zW1itVoLBVR4+\nvM+DB/f3vLfad683ME+lkocmuO4nl8uRyTxJytc/L5VKsrS0SLFYxOVy4nDYjTxBj6cJs3kVr9dn\nREJ1LBYLw8Pnef31G8bWbWtr4Njh/lxOc/L2+XyH3pgAI8F4YWHecLa22TTvomKxQDgcYXNzi2IR\nUqlNwuEwqVQKs9nM2toa8Xic7u4e4yapGwNns1ruWTq9xciIwvCw5szf3d1jiPB33tG6mmm98bQF\nlN6sWUv4b+Pq1VfKxLJOc7PWyWG3mezuKLLXqzVT7ujooL29y9iurcbk5ARtbe1GJ4BqaIaQWl5a\npeuqoaEBp9Np3LBtNhuDg0O4XG4ymTssLs7jcDhYWdGqATUvrRU8niZyuSypVNJYxG5tbfHJJz83\nHOB3MzMzzdLSErlclv7+ATo7O418zt2kUkmKxXJxoCijhEIbxGJRYrEora2tpUb0U5hMWgK1XkgS\nCm2UFhPaDT+RSBhN1fej95n1+VqMDgX7vRN1gRUKhQ4UWHoU7CB2R9Arkc/nuX37M+JxLUofCoUM\nMQmagL19+/OKv+NqxzoOmsdhu7H7A9oCLJvNGfeDQqFIf//AgVuo9UIE1hnB7XbT0NBAJqPdOPP5\nPJnMDrlclsZGDyMjCj09Wk7A0NA5Hjy4z9zc7J4qnvv3xwGTkXdRjYsXL5HJ7BjJ3nrX+EKhaFQo\nngZudyOxWIRCoYDP10wiEd9TbeLxNNHfP8js7AyRSJjLly+TTCbo6upheHiYZDJJIhHHYrFUFUV6\nU9+dnQy3bn3I9PQkXV09XLlylXQ6zciIQjqdLm1LmAyjPe1vS9lE6/NpJof69qve82w/enRQaz20\nzOzsDNFoFLvdRmNjIy6Xm97e3orRoVRqk48/vkUgEGBkRPOt+fTTTygUcmXv3U86nTZy7y5dumKs\nojs6OvF6fczNzRqTvd1ur7qq0yfHUGjjwPHR0OAsG3vptF4V9US0jIwo+Hw+I7pw7twwMzOPefx4\nirGxa8Y2TF9fH9lsrqxvHWjXgT75HxRRrZWVlRVyufyR8owGBgbJ5/O43W58Pp/Rc9PlchMKbRjv\n29raZHz8SywWC729fYRCG0xNTWI2m/njP/4JfX39hMMh5uZmyOWyqOoE8Xgck8lsiAnQqnZHRy9i\nNlt4/HiKVCpp5G+Bll+lN/WtJq4Ao1VWJBKpKLCAkhjUUhji8Rgul6vUZHfvbSWb1Rzu19bWWFtb\nO/R3prnoV49w60U5Wmm+q/QzDtxuNxaLjZmZGS5dukRPTx8zM9NkMhmj79/uJPLZ2Rmy2Sw+XzNW\nq/a70Lz8ligUCvj9/pJfobJHXOmFADs7O8a1sf+67+7W2nA1NjYyPT1dMpMNlyr0unC73TgcDnZ2\ndlhd9bG4uMDi4gJ2u52VleU93+luUqkUk5Mq7e3tRgRmv4jSKlDNhEIbVRPOt7Y2a4roan05TWWL\nO531dW0h0NXVxeXLY3z00Qd7olWJRLwUZQscGPW12+1HXphV49q1V/c8ttns2Gw2Eok4S0uLWCzm\nuuUKHoYIrDNES0srKyvLDAwM8O67v2BsTcHeVYzWt22apaUFmpqaSv0GHxnlrIcNLpPJxNjYNT7/\n/DNCoZCxUtJ7wp0WLpeLaDTC1tYWw8Pn6enpKRNKQ0PnDIEyOHgOr9fHwMCAUaqcSiWJx+NVJzCt\npcV5cjntpp1IJOnsLHDjxpsUCgVMJhMff3zLyOXaj8/XjKIohvAbG7u2J/m7UvXezs4OKytLOJ1a\nqyCtEfMXe1aCkUiEWCzKhQsXy1asjY0efD6fYYKo50VtblZOvt7N7Kzmin3u3HBZUYLT6azabHU/\nevRpamqqrLR/Nw6Hg7fffmfPzV2PuuxeTWuO2nvPJxwOsbq6yuDgEM3NLbzxxg0ikQjT01PE43Es\nlr1Rpd7ePj777BM2NjaOlHhbDb0Bsp5PUwtOp7Oskha0rczdwge0Zr6qOkEul8Pna+b69TdYX19j\nZmaa6WlNLK2trTEzM0MsFsPn8xGPx/nqq/GqxzeZNFuPzc1Nstkss7MzAIeu3vUtqmg0YuSZ6Tdk\nPX8nkYjz+PE0iqK1KxodvVi1tL2rq5tIJMza2tqBycXae7sOFH96UY4u6pLJBJ9++rFRqJHJ7BAM\nBtne3jE6NkSjUfx+fyniVCSVSrG0tEBDg5MLFy6QSqUIh0OEwyG8Xq2TgdvtZn1dq1LbvWi4f3+c\nSCRipBnorY8q4fHoTeKXsdmsvPba62VbbX5/gNu3P2Vubo5vfvNbRk5SJQqFPF5vE93d3QQCAZqa\nvGVRKItFy6cLhSrnnKXTaXK5fE1RWLPZTGOjh83NZMWcLn1bsL9/AJPJhM/nY2Vlxcj9C4e1IiOt\nbc7RLFzqgd45xOGwl7zA0nR39xwYYKgnIrDOEIpygdZWzQjwoDwXs9nM0NA5Hj58wPj4l8bzbW1t\nNZfQWiwWbty4aTTeBC3C8DT5NfVCF1OxWJSent6ypEzQEvQV5QLj41/y1VdfGnk98/NzrKwsV12J\nVaJYLGI2m4wJymw2E41G2N7eprm5pdRvTsvhKBQKbG1pVV+ffvpJafVbfSvEZDLR2tpKe3sH8/Nz\n5PMFIzpot9u5ceOmkROXz+cNwXz79mcMDw8bDZt1WlpaCAaDpX6AGTo7O0in06TT6arbAIVCgTt3\nNMuN3V3unwav18f58yPs7OxUfU8qlSQSiRAMru7Jf3gisA4uox4eHuHOnc+ZnZ1hbOwazc0thodR\nNBqlufnJ96RHFltaWpieniYUClW0FDkKy8tLmM2mYye+VmNgYJDGRg87O3vF+/b2trHVNDExgdvt\nxmw2MTp66dAFj8vlYm1trdS8e4VQKERLS8uhPkNav0rnHtd/s9mMzWY1okAPH96nUNDG7f7cpEq0\ntLQemFtUK3paQCwWo7Ozi4WFBYpFGBw8R09PL199NW74wI2NXWV6eoqPPvqQeDyGqk6wtbXFxsYy\nMzOL9Pf371kseTwew4/r9m2tynn3Im5+fo5IJFJzxZvX68Vk0gx9r19/vWKURqus7ufhwweYzeay\nz81ms0xNTRKNRjCbLZw7d54bN24e+B36/YHS4njD2BrW0SOQuyPGB9HYqOU7bm5ulkXqYrEoVuuT\n3FOfr5mVlRVisWhJYIUwmcqjbCfF9vY2DkcDLpfLmGd6e2tfIB0XEVhnCLvdXnMEqbe3D4/HQzwe\nJ5lMUiwWjlzlo61Ijtfssp60tbUzO/sYVX2E1+utGA0CjEl3amqSdHrLSLw2mTSRGQi01SwUrVYr\nxWLRKJ3Wq64GB4fKSq9BW/GrqmpE/Q5ieXkJh2OCfD6Hw+Eou3Hr0QCr1crFi5fo6enh3r27TE9P\nl8w1n4yF5mZNYOlNjzs7u9na2iIWi5YaDm8YPdh0IpEIKyta7kc98hEOK1xIp9N8+OH/ZWFhvqLA\ncrsPFlh+vx+n00ko9KRCyuPxEI1G+OlPf0pbm/aZDoeDt976S9jtdux2B42NbiKREMHgqpHv1dnZ\nVVV4RiLhMj+ifD5PNBo2eps9Kw6qpo3Fonv6o128eLmmKIR+Q52aUgFq3i7dnYcVDoeZnJxgfn4e\nt9uNqk6wtrZOT0/PkcxE60FTkxez2UQ8rrWuCQZXcDqdKMoFo3nvnTu3SSQSxONxwwohkYjR1OSj\npaWVTCZDW1sbijKKy+Uyvtfd0SWtAX2DMVdsbm4yNaX1DdV9BQ/D4XDwyivXcbvdVdMSAEZHL/Hw\n4QMePXpo5NQBpWbj9wiFQlitllIFo//QeVmLqD2qKLB0m4daxg5o0emVleVSL84nAkuzzUjh9/uN\nyJa+da+L31gsSlOT90DbiWeJ7s2nRxibmpqOVOF4XERgvcD4fM3PlUA6Lo2NjVy+fJV7977g7t0v\nuHnzraoX7sWLF7l16wPcbjc9PT2lViQdR77Qd3Z2mJxUCYdDdHZ2sb6+Zkx0lWhubuHmzTcr5gTt\nJp/PsbS0ZJiynjt3/lDR19Tk5dVXr/PZZx/z4MFXNDQ0GBGBlhZthbi8vIjNZqevr5+JiUc8fHif\nW7c+ZG5utuzztAqqAtevv3EikUmn00l7ewfBYLDk36Sds56DVYsRYEtLK8vLS4ZTtcPhIJlMUChk\n8Pv9ZLM54vEYjx9Pc/78CFtbWiJ4sYiRaA5aovfly2NGOT9oImpi4hFLS4sVj51Ob9PZ2XloYvCz\nwmZ7MnZ1c89a0N+nVd16q47d/eh5WPfu3WVrawubzYbJhOHNZ7NZuXr1lRPPyTSbzaVWOXEWFuZL\nTet7jfNoavLyxhs3+eKL28zPz1EoFMjlcqRSCQYGBmlpaSEQ8NLa2sXmZqrkfL53W04TNl/i8XiM\ncZlOb5HPF7h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Nx7IaNWoWcTQiIiJ/UrJQQlx99TV+0XsgUhIZYyKttYezvQ8H+gJ/AD9aazdlK6sFNAPq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\nhe9amZfnpuf99NPlZGdn065du+BojdWrVw9O3gPw8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import emcee, corner\n", "\n", "# emcee parameters\n", "n_dim = 3 # Number of parameters being calibrated\n", "n_walk = 20 # Number of \"walkers\"/chains\n", "n_steps = 200 # Number of steps per chain\n", "n_burn = 100 # Length of burn-in to discard\n", "\n", "def run_mcmc(n_dim, n_walk, n_steps, n_burn, param_opt, truths=None):\n", " \"\"\" Sample posterior using emcee.\n", " \n", " n_dim Number of parameters being calibrated\n", " n_walk Number of walkers/chains (must be even)\n", " n_steps Number of steps taken by each walker\n", " n_burn Number of steps to discard as \"burn-in\"\n", " param_opt Optimised parameter set from find_map()\n", " truths True values (if known) for plotting\n", " \n", " Produces plots of the chains and a 'corner plot' of the\n", " marginal posterior distribution.\n", " \n", " Returns an array of samples (with the burn-in discarded).\n", " \"\"\"\n", " # Generate starting locations for the chains by adding a small\n", " # amount of Gaussian noise to optimised MAP\n", " starting_guesses = [param_opt + 1e-4*np.random.randn(n_dim) \n", " for i in range(n_walk)]\n", "\n", " # Prepare to sample. The params are automatically passed to log_posterior\n", " # as part of n_dim. \"args\" lists the other params that are also necessary\n", " sampler = emcee.EnsembleSampler(n_walk, n_dim, log_posterior, \n", " args=[x, y])\n", "\n", " # Run sampler\n", " pos, prob, state = sampler.run_mcmc(starting_guesses, n_steps)\n", "\n", " # Print some stats. based on run properties\n", " print '\\n'\n", " print 'Average acceptance fraction: ', np.mean(sampler.acceptance_fraction)\n", " print 'Autocorrelation time: ', sampler.acor\n", "\n", " # Get results\n", " # Plot traces, including burn-in\n", " param_labels = ['a', 'b', 'sigma']\n", " fig, axes = plt.subplots(nrows=n_dim, ncols=1, figsize=(10, 10)) \n", " for idx, title in enumerate(param_labels): \n", " axes[idx].plot(sampler.chain[:,:,idx].T, '-', color='k', alpha=0.3)\n", " axes[idx].set_title(title, fontsize=20) \n", " plt.subplots_adjust(hspace=0.5) \n", " plt.show()\n", "\n", " # Discard burn-in\n", " samples = sampler.chain[:, n_burn:, :].reshape((-1, n_dim))\n", "\n", " # Triangle plot\n", " tri = corner.corner(samples,\n", " labels=param_labels,\n", " truths=truths,\n", " quantiles=[0.025, 0.5, 0.975],\n", " show_titles=True, \n", " title_args={'fontsize': 24},\n", " label_kwargs={'fontsize': 20})\n", "\n", " return samples\n", "\n", "samples = run_mcmc(n_dim, n_walk, n_steps, n_burn, param_est, \n", " [a_true, b_true, sigma_true])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Blue solid lines on the \"corner plot\" above indicate the true values, while the vertical dotted lines on the histograms mark the 2.5%, 50% and 97.5% quantiles for the parameter estimates. In all cases, the true values lie well within the 95% **credible intervals** (a \"credible interval\" is the Bayesian equivalent of a frequentist \"confidence interval\").\n", "\n", "### 3.6. Get the confidence intervals\n", "\n", "As with the frequentist analysis, we can also plot our median simulation and the 95% credible interval on top of the observed data. First, we'll extract some key values into a data frame that we can compare with the frequentist results." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5% 50% 97.5%\n", "a_mcmc 2.857336 2.977162 3.109976\n", "b_mcmc 5.594186 6.292076 6.954500\n", "sigma_mcmc 1.667850 1.933849 2.220527\n", "\n", "\n", " 2.5% 50% 97.5%\n", "a_freq 2.865716 2.996540 3.127364\n", "b_freq 5.415049 6.172266 6.929484\n", "sigma_freq NaN 1.922188 NaN\n" ] } ], "source": [ "# Print estimates and confidence intervals\n", "mcmc_df = pd.DataFrame(data=samples, columns=['a_mcmc', 'b_mcmc', 'sigma_mcmc'])\n", "print mcmc_df.describe(percentiles=[0.025, 0.5, 0.975]).ix[['2.5%', '50%', '97.5%']].T\n", "\n", "print '\\n'\n", "print freq_df.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Bayesian and frequentist results are very similar. We can also sample from our MCMC simulations to derive credible intervals for plotting." ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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asrBdLoyJk7H7n/qDS1cXPb+tQgjRUaaJc+0a1JITUVkr2tdyo7aCqKpY9O9Z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\ncQ449UzW9w183+CljcOYeN0neXt7x5sTPPHtFVR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Store output data in lists\n", "conf = []\n", "\n", "# Pick parameter sets at random from the converged chains\n", "for a, b, sigma in samples[np.random.randint(len(samples), size=1000)]:\n", " # Simulate values\n", " sim = a*x + b + norm.rvs(loc=0, scale=sigma, size=n)\n", " \n", " df = pd.DataFrame(data={'Sim':sim})\n", " \n", " # Add to conf\n", " conf.append(df)\n", "\n", "# Concatenate results\n", "conf = pd.concat(conf, axis=1)\n", "\n", "# Get 2.5 and 97.5 percentiles for plotting\n", "conf = conf.T.describe(percentiles=[0.025, 0.5, 0.975]).T[['2.5%', '50%', '97.5%']]\n", "\n", "# Plot predicted\n", "plt.fill_between(x, conf['2.5%'], conf['97.5%'], color='r', alpha=0.3)\n", "plt.plot(x, conf['50%'], 'r-', label='Estimated')\n", "plt.title('Bayesian')\n", "\n", "# Plot true line\n", "plt.plot(x, y, 'bo')\n", "plt.plot(x, a_true*x+b_true, 'b--', label='True')\n", "\n", "plt.legend(loc='best')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The edges of the **credible interval** are a little jagged due to our limited numerical sampling, but if we ran the chains for longer and used more samples to construct the intervals, we could get a smoother result. Nonetheless, it's pretty obvious that this interval is essentially identical to the one from the frequentist analysis. \n", "\n", "### 3.7. Get the coverage\n", "\n", "As above, we can also calculate the coverage, which should be roughly 95%." ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coverage: 96.0%\n" ] } ], "source": [ "# Add observations to df\n", "conf['obs'] = y\n", "\n", "# Are obs within CI?\n", "conf['In_CI'] = ((conf['2.5%'] < conf['obs']) & \n", " (conf['97.5%'] > conf['obs']))\n", "\n", "# Coverage\n", "cov = 100.*conf['In_CI'].sum()/len(conf)\n", "\n", "print 'Coverage: %.1f%%' % cov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. GLUE\n", "\n", "The GLUE methodology is a little different. First of all, GLUE typically makes use of **informal** or **pseudo-** likelihood functions, which do not explicitly consider the error structure between the model output and the observations. Within the GLUE framework, it is permissable to use *any* scoring metric (or combination of metrics) to evaluate model performance, with the emphasis focusing less on what is *statistically rigorous* and more on what is *physically meaningful*. For example, it is very common to see GLUE analyses using the **[Nash-Sutcliffe efficiency](https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient)** as an indcator of model performance. GLUE also takes what is often called a \"limits of acceptability\" approach, requiring the user to define a threshold for their chosen metric that distinguishes between **plausible** and **implausible** model simulations.\n", "\n", "The methodology usually goes something like this:\n", "\n", " 1. Choose a metric (or metrics) to indicate model performance. **Skill scores** such as Nash-Sutcliffe are very commonly used.

\n", " \n", " 2. Set a threshold for the chosen skill score above which model simulations will be deemed to be plausible. These plausible simulations are usually termed \"**behavioural**\" within the GLUE framework.

\n", " \n", " 3. Define prior distributions for the model's parameters. These are usually (but not necessarily) taken to be uniform, just like the ones we used above for the Bayesian analsysis.

\n", " \n", " 4. Sample from the **pseudo-posterior**\n", " \n", " $$P_p(\\theta|D) \\propto P_p(D|\\theta)P(\\theta)$$\n", " \n", " where the likelihood term is replaced by the **pseudo-likelihood**. Just like the Bayesian approach, the sampling strategy can be any of those described in previous notebooks (e.g. Monte Carlo, MCMC etc.). However, the vast majority of GLUE analyses make use of **simple Monte Carlo sampling** i.e. draw a large random sample from the prior, then evaluate the pseudo-likelihood for each parameter set.

\n", " \n", " 5. Any parameter sets scoring below the threshold defined in step 2 are **discarded**; those scoring above the threshold are labelled \"**behavioural**\" and kept for further analysis.

\n", " \n", " 6. The behavioural parameter sets are **weighted** according to their skill score. The model simulations are then ranked from lowest to highest, and the normalised weights are accumulated to produce a **[cumulative distribution function (CDF)](https://en.wikipedia.org/wiki/Cumulative_distribution_function)**.

\n", " \n", " 7. The CDF is used to define a 95% **uncertainty interval** or **prediction limit** for the model output.\n", "\n", "Some key points to note are that:\n", "\n", " 1. The use of a pseudo-likelihood function means the pseudo-posterior is not a true probability distribution, so GLUE **cannot** be used to generate a **marginal posterior distribution** for each model parameter. The basic unit of consideration in GLUE is the parameter **set**.

\n", " \n", " 2. The prediction limits (or uncertainty intervals) identified by GLUE are **subjective** and have **no clear statistical meaning**. For example, they are **not** confidence bounds in any true statistical sense: the 95% confidence interval is *not* expected to include 95% of the observations.\n", " \n", "We will discuss the strengths and limitations of GLUE below, but first we'll apply the method to solve our simple linear regression problem.\n", "\n", "### 4.1. Define the pseudo-likelihood\n", "\n", "The range of possible metrics for the pseudo-likelihood is huge. In this example we'll use the **Nash-Sutcliffe efficiency**, which is very commonly used wth GLUE. Note that other metrics may perform better (see below), but a key \"selling point\" of the GLUE approach is that we shouldn't have to worry too much about our choice of goodness-of-fit measure." ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def nash_sutcliffe(params, x, obs):\n", " \"\"\" Nash-Sutcliffe efficiency.\n", " \"\"\"\n", " # Extract parameter values\n", " a_est, b_est = params\n", " \n", " # Run simulation\n", " sim = a_est*x + b_est\n", " \n", " # NS\n", " num = np.sum((sim - obs)**2)\n", " denom = np.sum((obs - obs.mean())**2)\n", " ns = 1 - (num/denom)\n", " \n", " return [ns, sim]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2. Set the behavioural threshold and sample size\n", "\n", "We next need to set a **behavioural threshold** to separate plausible from implausible parameter sets. Choosing an appropriate threshold can be difficult, as it is rare for our skill score to have any direct physical relevance for our problem of interest (i.e. what is a \"good\" Nash-Sutcliffe score in the context of linear regression? What about for hydrology? etc.). \n", "\n", "If we set our threshold too high, we will identify very few behavioural parameter sets; set it too low, and we risk classifying some poor simulations as \"behavioural\" and biasing our results. In practice, many published studies start off with a stringent behavioural threshold, but are then forced to relax it in order to find enough behavioural parameter sets to continue the analysis. This is sometimes argued to be an advantage, in the sense that GLUE allows rejection of **all** available models if none of them meet the pre-defined performance criteria.\n", "\n", "For now, we'll try a threshold of $0.7$ and we'll investigate the effects of changing it later.\n", "\n", "We also need to decide how many samples to draw from our prior. For this simple 2D example, Monte Carlo sampling should actually work OK, so we'll choose the same total number of samples as we used above in our MCMC analysis. Note, however, that for problems in a larger parameter space, we might need to draw a *very* large number of samples indeed using Monte Carlo methods to get a reasonable representation of the posterior." ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns_min = 0.7\n", "n_samp = 4000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3. Sample from the prior\n", "\n", "One of the main advantages of Monte Carlo GLUE is that it is usually very easy to code (and to parallelise). Here we're drawing 4000 independent samples from our priors." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_s = np.random.uniform(low=a_min, high=a_max, size=n_samp)\n", "b_s = np.random.uniform(low=b_min, high=b_max, size=n_samp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4. Run GLUE\n", "\n", "For each of the parameter sets drawn above, we run the model and calculate the Nash-Sutcliffe efficiency. If it's above the behavioural threshold we'll store that parameter set and the associated model output, otherwise we'll discard both." ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 163 behavioural sets out of 4000 runs.\n" ] } ], "source": [ "def run_glue(a_s, b_s, n_samp, ns_min):\n", " \"\"\" Run GLUE analysis.\n", " Uses nash_sutcliffe() to estimate performance and returns\n", " dataframes containing all \"behavioural\" parameter sets and\n", " associated model output.\n", " \"\"\"\n", " # Store output\n", " out_params = []\n", " out_sims = []\n", "\n", " # Loop over param sets\n", " for idx in range(n_samp):\n", " params = [a_s[idx], b_s[idx]]\n", "\n", " # Calculate Nash-Sutcliffe\n", " ns, sim = nash_sutcliffe(params, x, y)\n", "\n", " # Store if \"behavioural\"\n", " if ns >= ns_min:\n", " params.append(ns)\n", " out_params.append(params)\n", " out_sims.append(sim)\n", "\n", " # Build df\n", " params_df = pd.DataFrame(data=out_params, \n", " columns=['a', 'b', 'ns'])\n", "\n", " assert len(params_df) > 0, 'No behavioural parameter sets found.'\n", "\n", " # Number of behavioural sets\n", " print 'Found %s behavioural sets out of %s runs.' % (len(params_df), n_samp)\n", "\n", " # DF of behavioural simulations\n", " sims_df = pd.DataFrame(data=out_sims)\n", " \n", " return params_df, sims_df\n", "\n", "params_df, sims_df = run_glue(a_s, b_s, n_samp, ns_min)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that with a two dimensional parameter space and Nash-Sutcliffe cut-off of $0.7$, only about $\\frac{1}{20}th$ of the model runs are classified as \"behavioural\". This fraction would decrease *very rapidly* if the parameter space became larger.\n", "\n", "### 4.5. Estimate confidence intervals\n", "\n", "Using just the behavioural parameter sets, we **rank** the model output and calculate **weighted quantiles** to produce the desired CDF." ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GE0Otfd3fGrQ/xlKd7eG8VkkSE10kcISHXOcQa2oio7acumOnUXRvj97DgQhF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\ntvSYJiumFbKlYFqPm3Niqll/7y5aL79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U6lNERCSCNDlLREQkghTQIiIiEaSA\nFhERiSAFtIiISAQpoEVERCJIAS0iIhJBCmgREZEIUkCLiIhE0P8DiXIaot4nFzkAAAAASUVORK5C\nYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def weighted_quantiles(values, quantiles, sample_weight=None):\n", " \"\"\" Modified from \n", " http://stackoverflow.com/questions/21844024/weighted-percentile-using-numpy \n", " NOTE: quantiles should be in [0, 1]\n", " \n", " values array with data\n", " quantiles array with desired quantiles\n", " sample_weight array of weights (the same length as `values`)\n", "\n", " Returns array with computed quantiles.\n", " \"\"\"\n", " # Convert to arrays\n", " values = np.array(values)\n", " quantiles = np.array(quantiles)\n", " \n", " # Assign equal weights if necessary\n", " if sample_weight is None:\n", " sample_weight = np.ones(len(values))\n", " \n", " # Otherwise use specified weights\n", " sample_weight = np.array(sample_weight)\n", " \n", " # Check quantiles specified OK\n", " assert np.all(quantiles >= 0) and np.all(quantiles <= 1), 'quantiles should be in [0, 1]'\n", "\n", " # Sort \n", " sorter = np.argsort(values)\n", " values = values[sorter]\n", " sample_weight = sample_weight[sorter]\n", "\n", " # Compute weighted quantiles\n", " weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight\n", " weighted_quantiles /= np.sum(sample_weight)\n", " \n", " return np.interp(quantiles, weighted_quantiles, values)\n", "\n", "def plot_glue(params_df, sims_df):\n", " \"\"\" Plot median simulation and confidence intervals for GLUE.\n", " \"\"\"\n", " # Get weighted quantiles for each point in x from behavioural simulations\n", " weights = params_df['ns']\n", " quants = [0.025, 0.5, 0.975]\n", "\n", " # List to store output\n", " out = []\n", "\n", " # Loop over points in x\n", " for col in sims_df.columns:\n", " values = sims_df[col]\n", " out.append(weighted_quantiles(values, quants, sample_weight=weights))\n", "\n", " # Build df\n", " glue_df = pd.DataFrame(data=out, columns=['2.5%', '50%', '97.5%'])\n", "\n", " # Plot predicted\n", " plt.fill_between(x, glue_df['2.5%'], glue_df['97.5%'], color='r', alpha=0.3)\n", " plt.plot(x, glue_df['50%'], 'r-', label='Estimated')\n", " plt.title('GLUE')\n", "\n", " # Plot true line\n", " plt.plot(x, y, 'bo')\n", " plt.plot(x, a_true*x+b_true, 'b--', label='True')\n", "\n", " plt.legend(loc='best')\n", "\n", " plt.show()\n", " \n", " return glue_df\n", " \n", "glue_df = plot_glue(params_df, sims_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results are clearly a bit different to the output from the Bayesian and frequentist analyses presented above. The predicted line is not as good a fit to the true data and the confidence interval is wider at the extremes than it is towards the middle. Nevertheless, this result seems superficially reasonable in the sense that it does not obviously contradict the output obtained from the other methods. Overall it is likely that, in a decision-making context, all these approaches would lead to broadly the same actions being taken.\n", "\n", "### 4.6. Coverage\n", "\n", "For consistency, we'll also calculate the coverage for GLUE, but note that *GLUE confidence intervals are not expected to bracket the stated proportion of the observations* (see above)." ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coverage: 98.0%\n" ] } ], "source": [ "def glue_coverage(glue_df):\n", " \"\"\" Prints coverage from GLUE analysis.\n", " \"\"\"\n", " # Add observations to df\n", " glue_df['obs'] = y\n", "\n", " # Are obs within CI?\n", " glue_df['In_CI'] = ((glue_df['2.5%'] < glue_df['obs']) & \n", " (glue_df['97.5%'] > glue_df['obs']))\n", "\n", " # Coverage\n", " cov = 100.*glue_df['In_CI'].sum()/len(glue_df)\n", "\n", " print 'Coverage: %.1f%%' % cov\n", " \n", "glue_coverage(glue_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the results so far, you might be thinking there's not much to choose between any of these approaches, but let's see what happens to the GLUE output if the behavioural threshold is adjusted.\n", "\n", "### 4.7. Changing the behavioural threshold\n", "\n", "The Nash-Sutlcliffe score can take any value from $-\\infty$ to $1$, with $0$ implying the model output is no better than taking the **mean** of the observations. What happens if we **relax the behavioural threshold** by setting it to $0$?" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 509 behavioural sets out of 4000 runs.\n" ] }, { "data": { "image/png": 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GeeNd1zoeSYAWQoSN0tyEdvIkaktTT9tHCczh0H/kO9yIteliMi/smcWMvA6y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\nkSL0//7BHep/go2dAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Coverage: 100.0%\n" ] } ], "source": [ "ns_min = 0\n", "\n", "params_df, sims_df = run_glue(a_s, b_s, n_samp, ns_min)\n", "\n", "glue_df = plot_glue(params_df, sims_df)\n", "\n", "glue_coverage(glue_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what if we **make the behavioural threshold more stringent**, by setting it to $0.9$?" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 32 behavioural sets out of 4000 runs.\n" ] }, { "data": { "image/png": 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loe6tWRdW1DYH4VqNA28DFeVEfLCST1d6+J37YbYyCVUxmTilB2OuLCVzjj1r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\n84i88zY5SxeT/eYa+gOPlFzDKZ8YxoAPWxAq9e9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Coverage: 73.0%\n" ] } ], "source": [ "ns_min = 0.9\n", "\n", "params_df, sims_df = run_glue(a_s, b_s, n_samp, ns_min)\n", "\n", "glue_df = plot_glue(params_df, sims_df)\n", "\n", "glue_coverage(glue_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Interpretation\n", "\n", "How should we interpret this very simple analysis? \n", "\n", "Just to reiterate from above, the confidence intervals derived from GLUE have no formal statistical meaning, so we should not be surprised to see the **GLUE 95% uncertainty interval** bracketing anything between about **70% and 100% of the data**. Nevertheless, you may be surprisied to see just how much the GLUE confidence bounds depend on the (arbitrary) choice of behavioural threshold. In essence, it seems we can set the uncertainty to be **almost anything we like**, just by tuning the cut-off. How, then, are we to interpret the uncertainty limits presented by GLUE from the point of view of **decision making**?\n", "\n", "As a related question, it seems reasonable to ask, *\"What's the point of a 95% uncertainty interval that has no intention of bracketing 95% of the data?\"* Frequentist \"confidence\" and Bayesian \"credible\" intervals have a very widely accepted meaning in statistics, and it is rather confusing that GLUE presents something that looks so superficially similar, but which is actually nothing of the sort.\n", "\n", "A further difficulty with GLUE concerns the method's ability to assimilate more data. If the above examples are repeated using a larger observed dataset, the confidence intervals for the parameter estimates from the Bayesian and frequentist analyses will become **narrower**. This is consistent with the (fairly fundamental) idea that more data provides more information, and therefore allows us to make more refined estimates. This is not necessarily the case with GLUE: as described in some detail by [Stedinger *et al.* (2008)](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract), predictions from GLUE do not necessarily become more refined as the amount of data increases.\n", "\n", "## 6. Methodological inconsistency?\n", "\n", "In addition to the issues mentioned above, there are a number of features of the GLUE methodology that are difficult to reconcile.\n", "\n", "### 6.1. Limits of acceptability *and* likelihood weighting?\n", "\n", "GLUE is often described as a \"limits of acceptability\" approach, because it defines a threshold below which parameers sets are discarded as being implausible. This is in contrast to more formal methods that use the likelihood function to weight *all* parameter sets, in such a way that poor sets are assigned lower weights then good ones. One strange thing about GLUE is that it implements a limits of acceptability threshold **as well as** weighting the behavioural sets according to the pseudo-likelihood.\n", "\n", "If it is possibe to assign a physically meaningful limit of acceptability, then surely all the acceptable (i.e. behavioural) parameter sets should be assigned equal weight? (**Generalised Sensitivity Analysis**, a closely related technique to GLUE, does exactly this - see e.g. [Young, 1999](http://www.sciencedirect.com/science/article/pii/S0010465598001684)). On the other hand, if \"acceptability\" is better considered as a **continuous spectrum**, it would seem more sensible *not* to set a well-defined threshold and to weight each parameter set according to some continuous measure of performance, exactly as done in a formal Bayesian setting. Doing **both**, as is the case with GLUE, implies that the informal likelihood function cannot be trusted to distinguish between good and bad parameter sets.\n", "\n", "[Stedinger *et al.* (2008)](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract) have demonstrated that, when used with a formal statistical likelihood, GLUE can produce results that are \"correct\" in a statistical sense. However, they also point out that when this is done the behavioural threshold becomes unnecessary, because the formal likelihood is capable of effectively separating good from bad simulations. Of course, GLUE with a formal likelihood and no limit of acceptability is **no longer GLUE** - it's simply a Monte Carlo-based formal Bayesian approach, similar to **[importance sampling](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/03_Monte_Carlo.ipynb#2.2.2.-Importance-sampling)**. Such formal Bayesian approaches pre-date GLUE by several decades and their limitations in high dimensional spaces are very well documented.\n", "\n", "### 6.2. Fewer assumptions?\n", "\n", "It is often claimed that GLUE makes \"**fewer assumptions**\" than more formal statistical approaches, because it is not necessary to explicitly specify and test any particular error structure. However, although GLUE may not require an explicit consderation of the errors, just because an issue is ignored does not mean it will go away. Commonly used pesudo-likelihoods for GLUE, such as the inverse error variance or the Nash-Sutcliffe efficiency, all make **implicit** assumptions about the nature of the error structure. In addition, many of these metrics have unintended or undesirable characteristics, such as not properly accounting for number of samples in the observational dataset (see section 5, above).\n", "\n", "Choosing an arbitrary metric without understanding the assumptions inherent in that choice is not the same as making fewer assumptions - it's more a case of **acknowledging fewer assumptions**. \n", "\n", "## 7. Computational inefficiency\n", "\n", "A less controversial but nonetheless significant drawback of the standard GLUE implementation is its **computational inefficiency**. Unlike concerns regarding statistical validity, there is really no debate to be had about sampling efficiency: standard Monte Carlo methods are expected to be *extremely inefficient* in high dimensional parameter spaces, and the standard Monte Carlo version of GLUE is no different (see [notebook 3](http://nbviewer.ipython.org/github/JamesSample/enviro_mod_notes/blob/master/notebooks/03_Monte_Carlo.ipynb) for a discussion of why this is the case). \n", "\n", "It is of course possibe to use GLUE with *any* sampling strategy. [Blasone *et al.* (2007)](http://www.sciencedirect.com/science/article/pii/S0309170807001856) implemented GLUE using an MCMC approach, which resulted in vastly improved sampling efficiency. This method has not become popular, however - perhaps because the most appealing aspects of GLUE are its conceptual simplicity and ease of coding compared to e.g. MCMC.\n", "\n", "## 8. Is this comparison fair?\n", "\n", "A common response from proponents of GLUE to comparisons of this kind is to say:\n", "\n", "*\"In the Bayesian example you used exactly the right likelihood function, so of course you got better results. If you'd used the correct likelihood function with GLUE, GLUE would have worked better too. In reality, though, you never know what the true likelihood is.\"*\n", "\n", "This is a fair comment in some respects: GLUE *can* produce more reasonable answers when used with a formal likelihood function. [Beven *et al.* (2007)](http://www.sciencedirect.com/science/article/pii/S0022169407001230) have even argued that, *\"the formal Bayesian identification of models is a special case of GLUE\"*. This statement is difficult to accept, at least in part because two of the key characteristics of GLUE are:\n", "\n", " 1. The use of an **informal likelihood** and

\n", " \n", " 2. The setting of a **behavioural** threshold.\n", " \n", "As noted above, [Stedinger *et al.* (2008)](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract) have demonstrated that GLUE *can* perform correctly using a formal likelihood, but they also showed that this makes the behavioural threshold redundant, in which case the method is just a very outdated formal Bayesian approach - one that can **no longer reasonably be called \"GLUE\"**.\n", "\n", "On the whole, the analysis presented here is consistent with the approaches recommended for the three different methods: Bayesian and frequentist analyses require us to think carefully about formulating an appropriate error structure and to test to see whether those assumptions have been met (refining them if necessary); GLUE, on the other hand, does not require any explicit consideration of the distribution of the residuals.\n", "\n", "Of course, in the examples here we knew the exact form of the true likelihood and we used this to advantage in the Bayesian and frequentist analyses. An advocate of GLUE could argue that we never have this information in reality - which is true - but not knowing something doesn't mean we can just ignore the issue. What's more, it is perfectly possible to *test* whether the error assumptions in a formal analysis have been met, so although we never know the true likelihood for sure, we can at least say whether the data are consistent with what we've assumed. There are plenty of papers in the literature where the authors *do* achieve reasonable results using a formal statistical approach, so adopting an *ad hoc* methodology such as GLUE seems unjustified. What's more, using an informal likelihood function does not remove the problem of specifying an error structure - it simply hides the assumptions being made implicitly by whatever goodness-of-fit metric is selected.\n", "\n", "Statistics would be a great deal easier if we could perform robust inference without having to think about things like likelihoods and error structures. GLUE is appealing partly because it **promises a great deal for very little effort**, which is attractive to environmental scientists bewildered by statistical notation and seemingly impenetrable mathematics. Unfortunately, I'm increasingly of the opinion that the claims made for GLUE are **too good to be true**, and I'm inclined to agree with [Stedinger *et al.* (2008)](http://onlinelibrary.wiley.com/doi/10.1029/2008WR006822/abstract) that,\n", "\n", "*\"If an arbitrary likelihood is adopted that does not reasonably reflect the sampling distribution of the model errors, then GLUE generates arbitrary results without statistical validity that should not be used in scientific work.\"*\n", "\n", "Don't take my word for it, though - please read the papers cited here (and elsewhere) and **make up your own mind**." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }