{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "from IPython import display\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compressed Sensing Tutorial\n", "###### Written by Miki Lustig, Translated to Python by Frank Ong and Jon Tamir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this assignment we will explore some of the basic elements of compressed sensing: Sparsity, Incoherent measurements and the Sparsity based reconstruction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sparse Signal denoising\n", "\n", "Before we start with compressed sensing, we’ll look at sparse signal de-noising. There’s a strong connection between compressed sensing and denoising. Here we'll attempt to denoise a sparse signal that is corrupted by random noise.\n", "\n", "Generate a length-128 vector, x, with 5 non-zero coefficients and permute them randomly using:\n", "\n", " x = np.array( [0.2, 0.5, 0.6, 0.8, 1] + [0] * (128-5) )\n", " x = x[ np.random.permutation(128) - 1 ];\n", " \n", "Plot the resulting signal using `plt.stem(x)`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add random gaussian noise with standard deviation $\\sigma = 0.05$ to the signal, $y = x + n$ and plot it\n", "\n", " y = x + 0.05 * np.random.randn( 128 )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many approaches for denoising and regularization use the Tychonov penalty to estimate the signal from noisy data. Specifically, they try to solve:\n", "\n", "$$\\hat x = \\arg\\min_{x} \\frac{1}{2} ||x - y||_2^2 + \\frac{\\lambda}{2} ||x||_2^2$$\n", "\n", "This optimization trades the norm of the solution with data consistency. The nice thing about this approach that it has a closed form solution, and finding the minimum is a linear problem.\n", "\n", "__Q)__ Show that the solution for this problem is\n", "\n", "$$\\hat x = \\frac{1}{1+\\lambda} y $$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "__A):__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe what happens when we plot the result for $\\lambda = \\{0.01, 0.05, 0.1, 0.2\\}$. Use plt.stem(xhat) to plot the results." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot the resulting signals (xhat) for lambda = [0.01, 0.05, 0.1, 0.2]\n", "# Your code here:\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Q)__ Is the solution sparse?\n", "\n", "__A)__\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sparse Signals and the $\\ell_1$ Norm\n", "\n", "Instead of Tychonov regularization, which penalizes\n", "the $\\ell_2$ norm ($||x||_2 = 􏰆􏰄\\sum_i |x_i|^2)$ ), we will use the an $ \\ell_1$ norm ($||x_1|| = 􏰄\\sum_i |xi|$) penalized solution. Specifically we will solve:\n", "\n", "$$ \\hat x = \\arg\\min_{x} \\frac{1}{2} ||x - y||_2^2 + \\lambda ||x||_1 $$\n", "\n", "It turns out that this is very easy to solve. Because the variables $\\hat x$'s are independent we can\n", "minimize each of them separately by solving \n", "\n", "$$\\arg\\min_{x_i} \\frac{1}{2}(x_i - y_i)^2 + \\lambda ~|x_i|$$\n", "\n", "The solution to each $x_i$ has a closed form. \n", "\n", "__Q)__ Show that when $ y > \\lambda$, the solution is\n", "\n", "$$ \\hat x = y - \\lambda $$\n", "\n", "when $ y < -\\lambda$,\n", "\n", "$$ \\hat x = y + \\lambda $$\n", "\n", "and when $ -\\lambda <= y <= \\lambda$\n", "\n", "$$ \\hat x = 0 $$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__A) Derivation of soft-thresholding:__\n", "\n", "\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a function `SoftThresh` that accepts $y$ and $\\lambda$ and returns $\\hat x$. Plot the output for $t \\in [-10, 10]$ and $\\lambda = 2$. \n" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def SoftThresh(y, t):\n", " # SoftThresh -- Apply Soft Threshold to y\n", " # Usage\n", " # x_hat = SoftThresh(y, t)\n", " # Output:\n", " # x_hat = sign(y)(|y|-t)_+\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot the output for t in [-10, 10] and lambda = 2\n", "# Your code here:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effect of this function is often referred to as soft-thresholding or shrinkage. Describe what happens when $y$ is small compared to $\\lambda$, and when $y$ is large. \n", "\n", "__A)__ \n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply `SoftThresh` to the noisy signal $y$ with $\\lambda = \\{0.01, 0.05, 0.1, 0.2\\}$, and include the plot for $\\lambda = 0.1$ with your report. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot the resulting signals for lambda = [0.01, 0.05, 0.1, 0.2]\n", "# Your code here:\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Q)__ Is the solution sparse?\n", "\n", "__A)__\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Frequency Domain Sampling and Aliasing\n", "\n", "As we mentioned before, there is a strong\n", "connection between compressed sensing and denoising. We'll now explore this connection and the importance of\n", "incoherent sampling.\n", "\n", "First, let's set up the undersampled data. Compute the unitary Discrete Fourier transform of the sparse signal, $X=Fx$, where $F$ is a Fourier transform operator:\n", "\n", " X = np.fft.fft(x);\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " In compressed sensing, we undersample the measurements. Recall that compressed sensing requires an\n", "\t\t incoherent measurement matrix. One good choice is the undersampled Fourier transform. With this choice, we are\n", "\t\t measuring a subset of the Fourier transform of our signal, $X_u = F_u x$, where $F_u$ is a Fourier transform\n", "\t\t evaluated only at a subset of frequency domain samples. This is an underdetermined system for which there are\n", "\t\t infinitely many signals that yield a consistent solution. However, we do know that the original signal is\n", "\t\t sparse, so there is hope that we will be able to reconstruct it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compressed sensing theory suggests random undersampling. To see why, we will look at equispaced undersampling\n", "and compare it to random undersampling. Undersample $X$ by 4 by taking 32 equispaced samples. Compute the\n", "inverse Fourier Transform, filling the missing data with zeros, and multiply by $4$ to correct for the fact that we only have $1/4$ of the samples.\n", " \n", " Xu = np.zeros(128, dtype='complex');\n", " Xu[::4] = X[::4]\n", " xu = np.fft.ifft(Xu) * 4\n", " \n", "This is the minimum $\\ell_2$ norm solution (why?). Plot the real part of the result." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__A)__ Describe what you see:\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Will we be able to reconstruct the original signal from this minimum-norm solution?\n", "Now undersample the data by taking 32 random samples. Compute the zero-filled inverse Fourier transform and\n", "multiply by $4$ again,\n", "\n", " Xr = np.zeros(128, dtype='complex');\n", " prm = np.random.permutation(128) - 1\n", " Xr[ prm[:32] ] = X[ prm[:32] ];\n", " xr = np.fft.ifft(Xr) * 4\n", " \n", "Plot the real part of the signal. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code here:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Q)__ Describe what you see. Will we be able to reconstruct the original signal from the result? How does this resemble the denoising problem?\n", "\n", "__A)__\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the important part, so say it out loud: __By random undersampling, we’ve turned the ill-conditioned problem into a sparse signal denoising problem.__ However, the “noise” is not really noise, but incoherent aliasing that is contributed by the signal itself. Therefore, we might be able __EXACTLY__ recover the sparse signal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Reconstruction from Randomly Sampled Frequency Domain Data\n", "\n", "Inspired by the denoising example, we\n", "\t\t will add an $\\ell_1$ penalty and solve\n", " \n", " $$ \\hat x = \\arg \\min_x \\frac{1}{2} ||F_ux - y||_2^2 + \\lambda ||x||_1 $$\n", " \n", " In this case, $\\hat x$ is the estimated sparse signal, $F_u\\hat x$ is the undersampled Fourier transform of the\n", "\t\t estimate, and $y$ are the observed samples of the Fourier transform (of the original sparse signal)\n", "\t\t that we have acquired. Now all the\n", "\t\t variables are coupled through the Fourier transform, and there is no closed-form solution. However, the\n", "\t\t problem is convex and so there __is__ a global solution! We will arrive to the solution iteratively by\n", "\t\t applying soft-thresholding and constraining data consistency. Let $\\hat X = F\\hat x$ be the Fourier transform of $\\hat x$, we will initialize\n", "\t\t $\\hat X_0 = y$, and implement the following for the $i$'th iteration:\n", "\n", "\n", "1. Compute the inverse Fourier transform to get an estimate of the signal, $\\hat x_i = F^* \\hat X_i$\n", "\n", "2. Apply softthresholding on the signal $\\hat x_i = \\text{SoftThresh}(\\hat x_i, \\lambda)$ in the sparse signal domain\n", "\n", "3. Compute the Fourier transform $\\hat X_i = F\\hat x_i$\n", "\n", "4. Enforce data consistency for the measured observations in the frequency domain, that is\n", " - if $ y[j] = 0 $, $ \\hat X_{i+1}[j] = \\hat X_i[j] $\n", " - if $ y[j] \\ne 0 $, $ \\hat X_{i+1}[j] = y[j] $\n", " \n", "5. Repeat until $||\\hat x_{i+1} - \\hat x_i ||_2 < \\epsilon$\n", "\n", "This is a Projection Onto Convex Sets (POCS) type algorithm. It is not a state-of-the art compressed sensing algorithm, but\n", "it is intuitive to understand: at each iteration, we alternate between enforcing data consistency and\n", "promoting sparsity. \n", "\n", "To implement the algorithm, we store the randomly sampled Fourier data in `Y`,\n", "with zeros for the non-acquired data, ie we set `Y = Xr`. Then, we initialize the estimate of the Fourier transform of the signal to\n", "be `Xi = Y`. The core of the iteration can then be written as\n", "\n", " xi = np.fft.ifft( Xi ); \n", " xi_st = SoftThresh(xi.real, lamb); # Enforce sparsity\n", " Xi = np.fft.fft(xi_st); \n", " Xi = Xi * (Y==0) + Y; # Enforce data consistency\n", " \n", "Note that we take the real part of $x$ before soft-thresholding because we know that our signal is real.\n", "\n", "For the general complex case, the `SoftThresh` function has to be modified to return $$ (|y| - \\lambda)_+ \\frac{y}{| y |}$$ instead. __We will need this modification later when we deal with complex-valued MR images!!__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the algorithm (at least $100$ iterations) to the undersampled signal with $\\lambda=\\{0.01, 0.05, 0.1\\}$.\n", "For each $\\lambda$, also make a plot of error between the true $x$ and $\\hat x$ as a function of the iteration number.\n", "\n", "It is really cool to see the evolution of the intermediate result. To plot the signal at each iteration in python notebook,\n", "you can use the following commands within the for loop:\n", "\n", " plt.clf()\n", " plt.stem(xi.real)\n", " plt.title( 'Iteration %d' % i )\n", " display.clear_output(wait=True)\n", " display.display(plt.gcf())\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize Y and Xi\n", "# Your code here:\n", "\n", "\n", "# Initialize lamb and niter\n", "# Your code here:\n", "\n", "\n", "# Loop \n", "# Your code here:\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, repeat the iterative reconstruction for the equispaced undersampled signal by initializing `Y = Xu` What’s wrong?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize Y and Xi\n", "# Your code here:\n", "\n", "\n", "# Initialize lamb and niter\n", "# Your code here:\n", "\n", "\n", "# Loop \n", "# Your code here:\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__A)__ What's wrong?\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Beyond Nyquist Rate: the Phase Transition Curve\n", "\n", "We've seen that with the combination of sparsity, incoherent measurements and a sparsity based reconstruction, compressed sensing can exactly reconstruct a $5$-sparse signal with $4 \\times$ undersampling. But how well does compressed sensing work with other sparsity level and undersampling factor? For bandlimited signals, we have the Nyquist rate guiding our sampling strategy. For compressed sensing we will instead look at the __phase transition diagram__.\n", "\n", "The phase transition diagram is a 2D color plot that can characterize signal recovery performance. On its horizontal axis we have the undersampling factor $\\delta = $( Number of measurements $n$ / Signal length $N$), and its vertical axis, we have the sparsity level $\\rho = $( Number of nonzeros $k$/ Number of measurements $n$). At each point on the 2D plot, we assign the probability of exactly recovering the signal as its value (with a probability of 1 being what we want), so the resulting plot is a color plot.\n", "\n", "In general, there will be a transition between a success region on the bottom right to a failure region on the top left in the phase transition diagram. This is because given the same number of measurements, more nonzeros in your signal will also make the recovery harder. Similarly, given the same sparsity level, less measurements will make the recovery harder.\n", "\n", "For compressed sensing, it turns out there is a very sharp transition between success and failure in the phase transition diagram. Below the phase transition curve, we recover the signal __exactly with probability almost 1__. Above the phase transition curve, we can recover the signal __exactly with probability almost 0__. This transition curve essentially generalizes the Nyquist rate. Instead of depending on the signal bandwidth, our sampling rate now depends on the sparsity level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "The above figure shows a compressed sensing phase transition diagram from random Fourier measurements. This figure is taken from the paper *Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing* by Donoho and Tanner. For each point on the diagram, the authors generate 200 random instances of the compressed sensing problem and then calculate the frequency of success as the success rate. From the diagram, we can clearly see the phase transition curve, splitting the diagram into a success region and a failure region. The phase transition curve is theoretically justified, but the details are beyond the scope of the course." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following part, we will empirically generate a simple version of the phase transition diagram. Instead of generating $200$ instances and assigning the probability of success, for each point in the diagram, we will assign the normalized mean square error between the recovered signal and the original signal for $8$ compressed sensing problem instances. We will consider a length $128$ signal and a $32 \\times 32$ discretized phase transition diagram. For each $\\delta$ and $\\rho$, we will do the following four steps:\n", "- Randomly generate a sparse vector according to $\\rho$\n", "- Take random measurements in the Fourier domain according to $\\delta$\n", "- Reconstruct the signal using the iterative reconstruction algorithm\n", "- Compute the normalized error with the original sparse vector. \n", "\n", "For convenience, first create a function `x_rec = cs_recon( Y, lamb, niter)` that takes in the randomly subsampled Fourier measurements `Y`, the threshold `lamb` and the number of iterations `niter` and returns the iteratively reconstructed signal `x_i`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Compressed sensing reconstruction function\n", "def cs_recon( Y, lamb, niter ):\n", " # Your code here:\n", " \n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, initialize the phase transition matrix as a $32 \\times 32$ zero matrix. We will consider a scaled version of $\\delta$ and $\\rho$ by $N=128$ and loop through $32$ steps from $0$ to $127$. For each $\\delta$ and $\\rho$, you should generate a randomly signed $k$-sparse vector with $k = \\rho \\delta / 128$:\n", " \n", " # Generate signal and spectrum\n", " k = np.floor(rho * delta / 128)\n", " x_orig = np.append( np.sign(np.random.randn(k)) , np.zeros( (1,128-k) ) )\n", " x_orig = x_orig[ np.random.permutation(128) - 1 ];\n", " X_orig = np.fft.fft(x_orig)\n", "\n", "Then generate a randomly undersampled spectrum $Y$ with $\\delta$ number of measurements and reconstruct it just as we did in the first part of the exercise. Use $\\lambda = 0.05$ and $30$ iterations. After reconstruction, add the normalized mean squared error to each point of the phase transition diagram:\n", "\n", " phase_transition[rho / 4, delta / 4] += np.linalg.norm( x_rec - x_orig ) / (np.linalg.norm( x_orig ) + 1e-5)\n", " \n", "Do the above steps $8$ times and divide the resulting `phase_transition` by $8$. Your resulting code should have 3 for loops.\n", " \n", "Generate the phase transition matrix and plot it using `plt.imshow()`. Note that it may take a while to generate the matrix. If you are impatient, you can put a print statement after the first for loop.\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done\n" ] } ], "source": [ "# Initialize phase transition matrix\n", " \n", "# Initialize lamb and niter\n", "# Your Code Here:\n", "\n", "# Loop through iterations\n", "# Loop delta and rho from 1 to 127 in 32 steps\n", "# 1. Generate signal and spectrum\n", "# 2. Random undersample\n", "# 3. Reconstruct\n", "# 4. Compute normalized error\n", "\n", "# Your Code Here:\n", "\n", "\n", "\n", "\n", "\n", "print \"done\"\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Plot phase transition matrix\n", "plt.figure(figsize=(6, 6))\n", "plt.imshow(phase_transition[1:-1,1:-1], origin='lower', extent = (0.03125, 1-0.03125, 0.03125, 1-0.03125), vmax = 0.4)\n", "plt.colorbar()\n", "plt.title( 'Phase Transition' )\n", "plt.xlabel('Measurements (n) / Signal length (N)')\n", "plt.ylabel('Sparsity (k) / Measurements (n)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Q)__ Do you see a phase transition? Is it as precise as the paper figure? Why and why not? Does it match your expectation?\n", "\n", "__A)__\n", "\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that because we are only doing one problem instance at a single point, this is a very coarse version of the phase transition curve. Luckily we have ~50 people in the class. Each person will generate a different random instance, and by averaging, we can get a more accurate phase transition diagram.\n", "\n", "Save the phase transition matrix with `np.save()` and submit it along this python notebook. After collecting all the reports, we will average the matrices from everyone and hopefully get a more precise phase transition diagram!" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#np.save('phase_transition',phase_transition)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Medical Image Sparsity\n", "\n", "Even without noise, medical images are generally not sparse. However, like natural images, medical images have a sparse representation in a transform domain, such as the wavelet domain. Here we will use the [PyWavelets](http://www.pybytes.com/pywavelets/index.html) package to perform wavelet transforms. You can install the package with the command\n", "\n", "```\n", "pip install PyWavelet\n", "```\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pywt\n", "plt.rcParams['figure.figsize'] = (16, 16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PyWavelet package does not provide nice functions for visualizing the wavelet transforms. To do this, we need to define functions that stack and unstack the approximation and detail coefficients, as well as scale the different levels when displaying an image. We provide you this functionality with the code below" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def imshowgray(im, vmin=None, vmax=None):\n", " plt.imshow(im, cmap=plt.get_cmap('gray'), vmin=vmin, vmax=vmax)\n", "\n", " \n", "def wavMask(dims, scale):\n", " sx, sy = dims\n", " res = np.ones(dims)\n", " NM = np.round(np.log2(dims))\n", " for n in range(int(np.min(NM)-scale+2)//2):\n", " res[:int(np.round(2**(NM[0]-n))), :int(np.round(2**(NM[1]-n)))] = \\\n", " res[:int(np.round(2**(NM[0]-n))), :int(np.round(2**(NM[1]-n)))]/2\n", " return res\n", "\n", "\n", "def imshowWAV(Wim, scale=1):\n", " plt.imshow(np.abs(Wim)*wavMask(Wim.shape, scale), cmap = plt.get_cmap('gray'))\n", "\n", " \n", "def coeffs2img(LL, coeffs):\n", " LH, HL, HH = coeffs\n", " return np.vstack((np.hstack((LL, LH)), np.hstack((HL, HH))))\n", "\n", "\n", "def unstack_coeffs(Wim):\n", " L1, L2 = np.hsplit(Wim, 2) \n", " LL, HL = np.vsplit(L1, 2)\n", " LH, HH = np.vsplit(L2, 2)\n", " return LL, [LH, HL, HH]\n", "\n", " \n", "def img2coeffs(Wim, levels=4):\n", " LL, c = unstack_coeffs(Wim)\n", " coeffs = [c]\n", " for i in range(levels-1):\n", " LL, c = unstack_coeffs(LL)\n", " coeffs.insert(0,c)\n", " coeffs.insert(0, LL)\n", " return coeffs\n", " \n", " \n", "def dwt2(im):\n", " coeffs = pywt.wavedec2(im, wavelet='db4', mode='per', level=4)\n", " Wim, rest = coeffs[0], coeffs[1:]\n", " for levels in rest:\n", " Wim = coeffs2img(Wim, levels)\n", " return Wim\n", "\n", "\n", "def idwt2(Wim):\n", " coeffs = img2coeffs(Wim, levels=4)\n", " return pywt.waverec2(coeffs, wavelet='db4', mode='per')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we dive into wavelet transforms, we need a nice image to perform the tests. The provided brain.npz file from the webpage has a very pretty brain image (note it is complex-valued!). Run the cell below to load this and other data, explained later." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = np.load('brain.npz')\n", "im, mask_unif, mask_vardens, pdf_unif, pdf_vardens = \\\n", "data['im'], data['mask_unif'], data['mask_vardens'], data['pdf_unif'], data['pdf_vardens'], " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's an example of how to compute a Daubechies wavelet transform, display it, and reconstruct it again:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reconstruction error: 2.37826805594e-10\n" ] }, { "data": { "image/png": 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3d/XDP/zD+uVf/mUdHh7GQ4Z+67d+S4888kiK7Wacrly5osPDQx0fH2t3d1dL\nS0vx4BSctRM+83ak/vc16eAFXSRw8vXKQUr9fj+uE3QuawPcjvt6429ezsjn0qI8DDtJW/1at+Xu\nGJvNpl7/+tfrrW99q9773veq1Wrpqaee0rVr12IgAGCtVqtqNBoqFot6+9vfrqefflqXL1/Wk08+\nqaeffjoG2R5wkAXxLKG02EOI3aHv9MEBLtk21gvgGYBQr9dT4Jzy1n6/nyKSyABSbeD+slgsajgc\nqtFoxLXJPRuNRgT49Am/y5yzFik9hdgCaDWbTfX7fTUaDW1vb6ter2symR0gRYZyf38/BUQJFqWF\nLaVfSZJoZWVFlUpFN27cUKPR0MHBQQwm3T44gUc2HwKBsQ4hqNPp6OjoSEdHR6rVatGuZUGSE2pZ\nskNSrJ6CVGNe+Nn9b7lc1sHBQXwe8+BzSp8A4mfJ1kmnY7usz2SOGA/HR1ls59lNaYYRwHbSIovm\n/saDFSeKPCNIW8jOO2EGxqKd0kJPJKVAugdO2F2yXtg77uuZNR8fbBi4FF3CxzabTR0dHUU7zM/4\nddZG1rZjD7AvXrHhxDoYiz35tI9Ag7HN9tkxjCdPCJTdNtEnfsfuQYJh0xknz8w5BuFZEF6e7aVv\nzIePJ332efTrfE6wJQTkjol8CxP2hnE5LTvOvZzEop/oiG+rAiMz5nznxc6vcWztZ9lQleFZc6Re\nr6d8GH/D59NHD64ZJ/fhL4brGJOXg+vu2SthpMUiZZGhNPzLMgNS+nRCX+CSUk7Js1HHx8cpBhO2\nggml1FRa7CUslUrRMaKE0p0HyPAcjBDlS/1+PzpqJlZSyniy6H0xeIbRgwkYXQ9+vB0OkmCWUWgv\nVXJlRWlwnJ6Zpa+0A8NEkJUFmcwBC8uN02g00uHhYYpBQokpC4C5kpQqkXLSgM/5nzmt1Wra39+P\nmUCuRyfQMzcevpidpGCufH6ZJw/iMSKSUmCXclnIDRj2er0enak7S54BW+gAH/by8PAwPrder6cO\nPckGnllyxTNWBMrMZZY9BEBzb9YJzP5ksjh8ZX9/PzKs3k/+eZkJZXGsYc9sMReSUuU6Z01wBuVy\nWZcvX1aj0dDy8rKef/557e7u6vHHH9fOzk50HOx/pFzW9ZVMz/HxcQRTkEYEDF4C52PqukebqtWq\nms2m9vb2UvrhmTHs0+3btyUtHI3rpaQYvDhQ8wANPZUWbCvXYeMkRWDhthFyBufrjC5j22639aEP\nfUjvf/8VqxWWAAAgAElEQVT7I0GEnh4dHaUy+ZPJRNvb25pOp/q1X/u1aKsbjYbe+MY36tFHH03t\nn2T9OsPsfcQ+sq4dZGJbmCNsIOvg8PAwEkNHR0cxyMYX8FxA3eHhYfRngEjsPKVSBKTT6TRVJeHg\njAALO4xujMdjtVotlUolHR0dxewzwWuv14v+rdFoaGVlRTs7OyliicwpIKfX68U1vry8rO3t7dhm\nglyAzdHRkbrdrra2trS8vBz9G4cvYeOKxaKWlpa0tbUV9QoAz+FOjCm6BSh2u8iY036AWKfTiVUD\nIcwOx2o2m3FcvWrEgyy30e12O84n8wjRfFYrQ14K23kwxXpmnbi/Zqw8o8/84Xe8uoh7e2Y0GyiV\nSqUUJnJ84UmCbEDj25F8HWUBu/tu1pKUDhRd0De3CSEE9Xq9qIPYVUmpvdfePyfoGEfsqvcPn01G\nmfkiSHL/78Ga2wju74S+pNTPtMUzmp5owNYytu12WwcHB9GWdTodbW1tRazoWJOsbqvVimQdwRcY\n2QkqsCJl8+47nIw4Pj7W0tJSHG9wWTbmwP6QteYaxoOkFDrqe2RJHmDDuTc+zUkEaXFWBLbRM8Ye\n7Pf7/WjTvbrI7RL6ho+g0gqMBk6HUMjiOnTb8bDHIllcx5y/HFx3TzOlfjiDZ5ycOfdSSmckUTIc\nLgEUQMwZAC+b9WCAwfVJ4RksFpwybC+BHovMGW6U1lPe877GycHwYkilBWtMv30fDT/TbkmpvQVc\nwz25B0YX1iTLcmFYAIkYcdrvz/Fnw7C7Q/d9gdwLw8G+WLI/0iKYZB4pwWFxV6vVCAQYS2nmjJwR\ngwEHcI5GI7VarXgaKUbAWULvt5dS0Cd3cK6HzLEbNkAm1zkb5RljxtHLMnDWrgNcxxx5YA2b62Po\nxoa14iQIz6LtlMbhTHFazrA6oeBsrK9HAiBIEmfoWKfupE5jcn1ss2Uf8/udmexBmO8pLZfLesMb\n3qCv//qv1wMPPCBJ+vSnP63RaKT19XXt7u5qOp3q3Llz6vV6+pM/+RP96Z/+qba3t1MMLk7s/Pnz\nunnzptbW1uIaoGyRtYxtm7cjRWQ5+bK6uqrd3d0UMeXZA/SJ4BMQj23KZp8cqPO7Eym+Lp1o43u+\nlrxNALhyuRyDlhBCZHhhrz0LSd+5d9a/sP79e5BIgCTsMiCFe0L6eXaBf2QzfRwKhUIsW/UqAt9D\n1e12457XZrMZwbPb4Ha7HX0RBEK1Wo37UUMIWlpa0nQ61d7eniTFbGKSLPbwE2hjX+gPz3YQxrPw\ni4VCQaurq6mgzf0pxOdgMNDy8nIkajudjorFora2tqK94llkR7EfJycnWlpaiuW2vu/KT9qt1+ux\nPNjtEGSnBzZOAGOLPauNX5UUg1IPHCC5O51O9MPMDePgwNsDFNqA3Z9nic+MrZPuHtsxxi+E7Qia\nWO/SYvuIB0qsTbc10+k0leFCL9E1sBnPQq/Rf+7Dde43+Z5XV2WDaHwa69wTG1yXDQDdDqKDXnIs\nLfa90m5pcdgaz+QZ4D6wBpUKzI0HlJ6scRvrZCHklVfdgU0gywlCsb0EKB447+3txcpDgjbsDO2i\n6qLf78dKAyoVPbCn7zwX0syrCJ34yh6k534VnAqWhIgHx3lgBimC3nqSAv0EU7PWHZvxfAhTzzw7\nyUpyBl/r1UFcRyLM7ZDrJNiM7zGOfhgRiSmPuWiLt98zvfT5NFyHrjJ+LwfX3dOgFMXB0HtgBivl\ndfhuXDzF7cxQkiyypZ51YnGzqD2oQHld2X3zMxNBdmDe/juCAtrp6XjYF9rjCwAn7+31EiP64tk9\nFjHPkZRSCmmxsJeWluICI1Uv3XnUN8bNM4D0hWd7v7zfGDCyLePxOJ6GuLy8HOfEF3Or1YoGHtab\nbGChMHv9RKPRiM6D/jJmZHQclABImeter6dOp6O9vb2U0aevOCHGmLmSFtlsDwoBbq4rfIaTwRFS\nVsbPsGMAKhhH1093tB7U0h5nJlkT9N1LkjFkrmc4VMYfAMuc0wcPQOijZ3dwfDzT1yc64WwhOuTB\nj2fKHKA4CzeflzMD1AhKq9Wqvuqrvkpve9vbVCgUNBgMdOHCBX3DN3yDPvjBD+rhhx/WBz7wgbj2\nP/rRj+qP//iPdePGDUnpE5ovX76s3d1djcdjXbx4Ub1eT7u7uxHI3H///To4ONDOzg5tSM2zr290\neW1tTcvLy3ruuefiXKOnq6urunHjhpJklsVNkkS7u7spPWNtYF/REfTD5x1hvTpo5O/870EqOsxa\nRq+/7Mu+TJubm9re3tZ4PI7ZMmfE/eANJ1EIBrETOGuCR8gy7DInunL9aaDSiS76gf3KgnKqIZw0\ncPLBASNzx/0bjYYODw9TQXOpVIr3xDd5OaKTAYwzr9EBoPf7/ZR/4TPIDk6DLxaLcT/rdDrV/v6+\nJMVTdbGDBMAhhHiS7WAw0MnJSXyHLAEsdmg8HqvT6Wh/fz/aH/b4ZzOMjFmtVkuVxpLtoA+eCXP9\nZNwIij2oZK7QY372gIMgn8xxr9eLY0vwSjn14eFhipA4S7ZOemFsh0/zefHgwLM8/O4EgaRTsR3Z\nO3yTkxokBzyD55gSgkValBV7oIYu8iwH8R5MO15wXEVQyzj4830N+lg5FuS5VMyggx480RZsgwck\nnligz2Ttve1OHCCs8263G9cAbyCgIgwskSSzV4k5tq5UKpFMgjSSFqcJk3wgyzcej+MaPzg4iPMN\nVpxOF6+22t3djeRONvBm7D34clyNzaePjAF2kTYS6BIUhzDLXhMbOGF6GvEgKWUXGF8PcsF12WCV\ntjFWXvaM3eHAOp6F/ad/6AT3A3+6j/EMs+N/T0548OltREc9HpD+7Ljunu8pzaaEGQQH5QyGlN6P\n4CW6AAHeCwbQga2S7jwBC/CXZUMAGRgad15uXHkO12XZZ/7uDtRLEDxrizCZzlQ4iHRwSXCDkiEo\nDIDN+4pTcKMNAKMUhkwkAILvOWibTCbqdDqSpJWVlViyQMnYyspKKhMHwGq1Wgoh6ODgIBUIunLz\nPLLUMNMXL17U3t5eZOD9UCsc33A41HA4jKeHlstlNRoN7e7uRhacbArgxTMqPv7ogGeW0Cc+Qw8w\nCK5jGD4Hr058MGfZOUdHeKYfBEW5Ct/xzJPrOG0+7dAExskBL/PjgQE6IymyuqwxAnrGx0tKuIaT\nNn09uSF2h+3G+KwGpeVyWa9//ev1pje9Sevr69rY2NBb3vIWfehDH9JkMtHb3vY2feQjH1G5XNb6\n+ro+9KEP6T3veU/MdCEOtHEWfrw72w7QdbfxDrQcDFG602w243pzkFOr1WJ1w9LSkm7duhUDmW63\nq8PDwztO3n2BsUgRgt4O1z23kzg3AJwDTNdBzx6wvt2eZDO3tAf9ZI58T1s2qHM762PKGiJAcYLJ\n7+UZEC/Zw3cQAPteeieJADWsF89QAJxgzr0kNetLAcVsfwDwUP4L4CbIJsgHOPt7Pxm3JEm0tram\n27dva2lpSZPJRMvLy7p27Vq8fn19Pdrtfr+vSqWidrutra2tVKUKAAmQhV/HV21ubsaMz/7+ftR/\nJ+nK5cX+TubNiQkCcEmpslrsk1ezcGjTdDqNei8pkoyOUZzoOTw8jH4S0I1v5V5nydZJaWyHLSBI\n9WwRc+XZNCckPPgEd/h30GEwWlaveZ6Tq+43Hdu5r3f9y2KYLAnnmNEBPOJZOWkB2Fk38/FKjZWX\nf/r3nfxFh7J2xkvTuQ5/7UEoa41nSGm9bTabajabsdrjwoULGgwG6na7KezDmHugDJb34I++sy4J\nNrGV9Xpd/X5f7XY72lNwHd87ODiIeJD+Hh8fRzsH3vAKSq8Uy+Jo5s39hOMucDLjw+dZH+X9ws5k\nq88YX8aD+zvW8vJwx518BuYFx00mk9RWPAiY0xJB3j/WEb6Cz7O4zmMw+sQ2C3yKV6xmiRofH+nu\ncd09DUpha+g4YNkDS886oiCeZXIj4dkvFpgPrCtdtoSThYDz9CDiNFCHw/S2ORPo+xecGXTWDiDi\nE5xlrwhyp9NpDCTckM/HMhpjz855Ns+VEmV14CelA376zMKHGafMqtFoxGxmp9OJYJgxJHijPeVy\nWXt7e/FESNoIEwXQ8SAfB9HpdPTBD35Qa2tr6na7KcDM7zs7O5pOpxE4kD1gvvf39yNrz36j/f39\nFDilnX5asQerXo7hgJE5yQaW2fHmPm4snMxgQXMtY+/Mqt9HWryH0OePuWUsuSfjfXx8HBlJ+u2l\ni6wPjD9ry/WCezuop72eCYIsIqvK+OE86QvGlyzUWQJqBKWFQkEPPfSQ1tfX9eY3vzkC5nK5rE6n\no5s3bypJErXbbTUaDT3yyCN617velQJWrM9araZaraa9vT1dvnxZN27c0Orqqra3t1UuL96p1u/3\nT13XDo58vbnuMPduA32+nRwjOPJDvXgOa8KJC+7N3/jngN377YGogwgvO0afvITedSx7L2fTvUQK\nn8DvHKC0v78f9fny5cu6du1avA5/lLXfjJn7HII+zwo50F1aWorVIKwzxsmBDesFQMSapkyftnrZ\nYfZgO/c9gBSyhj4ulUol9bohaXHabr/fv6PyolAopE7frVQqsdSyVqvFg9JWVla0v7+vtbU1PfHE\nEynQdu7cOd28eTN1KAsVJ9hjMszT6TRmUBqNRtQBMjruDxx0MRb4Kg+a0BXmEaINrNBqtVJbKrgH\nB4wB0vmcexL4TyaTM/tKGMd22IwstvNgkvWIvntFgbSoqHDw64SPlNZngkpPTADuWWf4UE8SSIuE\nhQeipwWEjvnAntwXAsfPgMBPZ6ue/HpsD4SiHw7mh/2A/zxh4ngYu+TBD9lJso20n1OsGbtOp6NK\npaKlpSVVq9VYJcEa3tnZie/q5NVP2A2wHNURZEeZP/aNhhBi9c1zzz2nr/iKr9DOzk4k6lZXV2Og\nCVYAuzGHBK3gZub19u3bcc3yd/rNGmc+COhYkyQpQgiRiHO8jU305zGnXiHmW9PAdR6kkqF3fOiY\nyQ/fcn/KfHrlEIE4SRf8OdlJfmeteCWdX+vkqhPF7mdov5eVf65x3T3PlErpTBKT4hvjmTwXBkJa\nKIo7RL8Ox3FaeYaDPQ8euK8rHobEy4A8EM1mQVmI2YwsjLkHrQ6UPADNBo7OjCG0i8WHwrjBnkxm\nJ7jhnDGeGDOu5Xsoo7R4ETZAQZLa7XYs2eU6mGdORIThxggxzjDslBgPh0P1er340nuY6NXV1VTQ\n/uSTT+rKlSva29vTjRs31Gq1tLa2plarFecWnQAYQAYcHByoXC7HwzU4QZTFglMg20NQxmJ29sr1\nba7LUYeLxUWZGd/xUhHXT2dDs+sQ3fGsooNyd9o8n/Xi5ZC0j3u6XnumDePJ9Q7E/H1TDhKya9d1\n2ceG+2WDbnQV3UdHzuLpu/P/1Wg09O3f/u161ateFfcRSzNgdHBwoMFgoFe/+tV65JFH9JnPfEYf\n+9jHUgGhNAMnr3rVq7S7uxuDKkDBZDLRhQsXtLm5GXWbOcgKY47jwHl0Oh0dHBxE0s5thQe2biv9\n9U28CsC/x/We5XP7iy57MJf9nGdDfg2HQ7VarVjy6YDW7bmDVycsASNZW+r9wslj4yBqCBw9u+G2\n38lSt9eAO6+eweZ6VY+vLwc23lZ8CToBAKQsjvHneQA+Dthg7TtA5lmTySRmGlqtlur1eizNxU9L\nSvngCxcu6NatWyoUClpZWVGpVNIDDzygzc1N3bhxI4Imf/UV7SWgf/WrX60nnngi+gMCyqtXr+q5\n556LcwaYBwCjyxCfHM4kzezl3t5eDIo92PRggTVCAIONdBKGoJOstM8jfcuWi3J6PfPnVV9z/Tsz\ntk46HdthY07DdlnyhpLCrG1wOyAtDqjKVjP5tVls53Ps95YWJ/H61hX8OetQSmfB+J/149gOG+AB\nMDjQ2+HJD0jirE90HMqzCG4YMwIqD7adOIT8pgICbNFqtVQul7WysqIQgpaXlyPhT4DlJMrx8XHc\nIw6+5SRuSu0hnyjL9a1LvLqq2WxqbW1Njz76aMzobm9v6/DwUJcvX1az2UzZ0WKxGN89SmUIVRJU\nxvH6JmzD3t5erI7Iluw6mcQ4ZUlTJwrAVe6Pmc8s3kb3ncRA32gHuoVkfQj3QzewmdkzaHgWusE9\nwKPeP/QSstD10Nvg9tETHE70vlK47p4Gpf7uIwIPB/Is0CwQcqA7v5ckpQxANmXvxsrZACn9fkwc\nLgscpfMyimzWwZ/lGVyu8X0nznJ5X719klKBDe334IK+u1F1Y4lyOcArFoupU+M82PfFwtjAhi0v\nL6vT6ShJZuVijUYjBqWj0Sh14NBkMollWYBiP8SEfUSUBfurDQh8Kb8lY8S7BT/2sY+lTjWGwABQ\n09aDg4MIQDy7OB6PI1grl8vq9Xqpg5UGg4GOjo4iIGeu2fdEwOzGHv3y/V6Iz5H/nbH302lx2MwV\n7c1WBDgpEUKITjS7jrPsHsYFJ47e+3rKGmf0ESIHY4WeeWbeDakzhm7k3SgyLtwPnS0UCmcuexDm\nr4SRZgD5wQcf1Llz5/S2t71NGxsbWltb08rKip599lldunRJTzzxhPb29vQHf/AH0WZI6dLb1dXV\nWLmwtramra0t1et1Xbx4UYPBQI899pgODg5S69udKfOAXeK9vQ6y3flhn9xeo5/sWUcPvCSIdksL\nph/iLcsSu+PGF2TJROxSr9dLPSNbpsX9vBTXnaQ0e1/szZs3U/tgsbcemPoa4BlZh+8BNM/ygN4F\nu85n3Mf3PfHzdDqNFQ2MM6cweoabzB02mO9T8eHEk1cI+f/eX4hIfz/0eDyO+8a4Z7VajQHocDhU\nu93WxsaGms1mzE5Iiifr8hyewZhhAzg4aTgcam1tTcViUefOndNgMNDly5c1HA7jK2hu3LgRD03h\nvpVKRb1eT5Ji1QwgFn9RKBQi6cu+WPbfItzPK0QISsEGXuXFtYwzJAGkAM/0Q67O8ithXgzb8fcX\nw3ZOpjn4diDs2E5aHMTi2C77O4GAY8hs2SV64sSCpDuCX8dLgHL++fYxD455hmdLs6WS2DTHoYiT\n1D4WklLrNdtuArUQQsRwEJjgiEajESvTwCdgOwgUxonD1kg4kAQ4PDyM8wxe8j3B3K/VakWyq9ls\n6hOf+ISKxWJcs9hQbB/404NFz/xx0BzBD4kP8OlgMIgn1mNXk3kSCJ1wAgO/yPMdy+EjPDB0DOPz\nTHbXdcwzqdlqSzCXrx/03hMJHtNge0/DU+gW/spJSHTDcR0B6Gm4znWaNqGr2cSZt4Pf7xbX3dOg\nFMViQfOztAACABaCRQxPFihlwQjXMnCeuZEUFdLZUGtbaiK9XDMLMLiXZ4f85+z7TbMMnhsM/u5B\nsoMoZyqcVfdUPouG/rsxpB8EUAAd7zcBXr1ej6+lWF5eVqVS0draWtx3ymLD6MOIAQB4trNMOObx\neBxZfYwBY0gmgv7SXncUvgAokel2u9Eg+KtKML6l0uwQi1arlWKeRqNRzLiMRiNtbm5GNnA0GsU9\nrDyLPUbohrNnroeMpxMlzBkMZzaLhDHy05T5jN95ruudB5PuFLNtAFR5Sa07YQflXnY1Go3uAHWQ\nGx74OsvrBzW488z23cE+fTmre0rtd507d04XL17U6uqqPvWpT+m1r32tut2uNjY2tL+/r49//OOp\n7Djf87kkKLnvvvvidWRbsxlA10vWj++7Yl6SZPFOOCfJcKToGn9zcNbtdiN77oGmB23O4Drph755\nVsLXuesKeubfc8DL52x7cOAqLUiaer2uQqGgXq+XYpd5Pr+Px+P4ovTs2vG15rY6y6Z7ObCTSU4W\nYWewy243nQhz+0Mwmy3nY1zchro/cOADOKLs9/j4OJ4+78Sv73ti33O73datW7e0srISsxSUelEJ\nAxjkb5JS+zyxo056dLvd1Kmc2JQkmZ3IeuHCBUmzA5V2d3fjO38hLEejUdwSQmBKRkVKV40wr/g1\nB/DMnQdKDvA8c+F+jDFjTCk7hgTFNp61qhDpdGyXJTNfCtsxhr5mHBe4HnvAICnqNr7EySj+Zw7B\nX/N2x/u4b/a55Zl8l+DQ1zvbyzhbhH4S/GT322Z9oG9BY9yw9acFCt5efnaCEbvNtoFut6t6vR4D\nQxIMPva0m72f4DX6jo9ZWlqKyQYCQLJ/Pv5k8JyoBJv5+HjQVKvVtL6+HklM2k9gDQ6hMoeKOZ7F\nswuFgg4ODrS7uxsDVogrJ9aZe8dxTox6MOgVYY7VnEBxMhXs7XqVxYVZPeAezD26RRt8DWBjPObJ\nkiJO+EJsZBMfXkJPf9x/g08d10lKnWHjevjZ4LoviKAUxfRDAPyflM4q+t+cmXB2kwmjhMAzBb5w\nGbh5myQtFl4WLDkgYhG7srKwHERIShkWQI0fmU6AJKX3CPJdN3jOwrkCZoMfZ3y8pMQzhz4WhUIh\nlm8sLS1FNovTDDlwAsMFi8arGA4PD+OCIagjiANswPzxPTcgLDqE4IYxZ648C0ObskE+r0IgmKUM\nhHJjFl+xWEwBFdrPiaUbGxs6PDyMJSPuWDD03j7PaGaBMKyb99Xnj7lmLiFp0DHvp+ufM/foZtag\neODhAQO/Z4NoD0Cc1HBH6hkuL+9wR+nXeZ/ZewCpxBjQ37MelCLF4uwdi81mM66VXq8X9fEu751a\nNx40ujAfpzm/JEliGT0kBQ7LgVO3243r3jOtODyCBj9hOmu7uBdz7oDOyRC+Iy3K3r3Kg75KSjlf\nJ/Oy2Q7vy9LSkvb391PBLoED+6TIdElKBTesV+YQW+fz4e8cpu/00Uv7HfhweqSk1CEnrBlfq/Tf\nsy7MM8CX/2G+fT88thV/4ZmDJJnta+Y9iVSxYL8Zc+Ydu8W6xyd4n5z1971c/vzpdBpLdsnKOOHr\n8z0cDrW8vBz72G63tb29HfeXTSazQ5b29vbivmPfluKAtVAoxPfE0k70zclV8ITvE3VyyINT7B72\n0NccfZBmJNJZsnXSC2O77BpBZ7E57jc8w+m4wwNMsB3jyrVOGPhbHXi2lA4esttkHNt5qb3jRkkp\n3UePJcXScmySV6Y5fnUSzH09ttOTNNKi3JRxcpIOPfWgwAMiXsNEZU2pVEodXofOUk1WLM72KfKa\nMrY17ezsxICfcnzsofcTH+aBm1/niQa2U3mw7RUKjDukE3O1vLwcEwrsI+dnxyUQtPv7+5pMJrp1\n65bG47H29vZSyScfc/yatIg9nPxwQsQrAdAtX/deZYl+oCtOhjJ2CO2hTD1L5HopNgGhpNSzXd/x\n0aw518EsxmPusGnobvb5HryD65yU+2xw3T0PSqVFiYy0qIN3NiELbDA6zqih+FnAjNOWFiAGo8Zz\nWfjOTmAsMGx87mCbwfYSXM96ZlktZ/ocYPB3N8R8jxduuxFlgTM2bpicWfGsA8bZWUcARJIksdzK\nWTQOr5Ck/f39eC3A1Dfw7+/vxywJc0e5GYAF4O1Gl5Iw5t11AqYc8MvCp9yXIAx2HzbNGVnYQAzy\ndDpVu92Ox5qzcDDaEALsn9rZ2dFgMNDOzk7MrDpL5MYW/XSDwfijY17Lj15kAbpnPdyY+Bh5iQab\n9D3rCvDk3t4Od/TZd946M5Z1Jg6MnXhxgsf/xrp0w+fgknn2NRpCOHPZg9OCUg+QpNOzbNnr/boQ\nQiSN/FAcSqqYJyfi/BkOXNbW1lKHBXEv7Ae2zHWBtrDnuNlsajQaxZOus2yxB4vu9BywOzh0u+pg\nkP54BYpnerHJWcaZPmGHX/e61+mTn/xkPEgs2z+exb0dQDox02q11Ov14rp0cOFMNuMtKe55BGz4\n2pVmJ1/2er24t46sIgAQ0OyHG3nASbkogRb3YX27z2NN833aS1YCe8jaJqtAfzjVkwMC2X+GH3A7\nhUB68Q5KxpIT4AHJ2AL6iX9nnAG0BDSXL1/W9evXoy45wcu93R8DImm3B9kQoF4S6T4dgpHAG31D\nb7Dh3BcyGnvP/c+arZNeHrZjnhkT1tlp2C7rb9xvOJjmfvhz1t1pgYSDd9eJbAAJhsKPel+yOuyv\nYsLuePuydlZaBMlZvIsPxzaynriPk+FO8haLxfj6liRJtLKyEgNSSanMqJ9HMBwOY6l9tVrV3t5e\nDN4IQqmkgdwBj3nJJ7oO+cQz/NActo1Q+osOsGb98Di2hzBW4Df/d+7cOR0fH8cqC7ZckUUkYTKd\nzl5dxYnx+/v7sQLQsbeXkLO+8RUQeG6THftjT12/CObB6Z6ldX2njxBujvfB8Y4/PRHFz06s+Fzz\nz7Eaths9dxzpuDRLKHsg7n7TiaHPBtfd06DUX+btYERKv4yWDqIUzgx5Voj/vQzAWXwGzg2EgyQm\nE4DuBgxF8xptnJ6Xb2IYuN6DaDeCzuo7ePEDnLKZLGeNHez5OHBf2urMLPfy9+9VKrN3EK6srMQX\nm/N8jrMPIcSN4gSgZLoODg7iiYQYDYIcFiUOwIM+spS8OsAXJIZqOp3G101waAWb2Xd2dqKisxAd\nkAKQAS8ArpOTEz300EORaYWxq9frkYXjtEiM5WAw0Pb2tk5OTrSxsZHat+FZQnRLWpxOix4QWOME\nmCv+dwDsTDEMlLNU6FS21JEg3cE7hhM98WP1eT7OM7s+HDC4Q0DHyYignw66+B0DhQHEeaIHDrIB\numcNqAU76EhSBLU4HoKKJJmVNPX7/dQc8l03+OfPn9fR0VE8jfDcuXPa2NjQl3/5l+vGjRva3d29\nw5Ewp7QBu5ElRNCPcrmsdrsdQRYkUVbnsWf+UvaHHnoodZqq95/nu2N1hyotAlbs/dLSkra2tqIz\n7Ha78VU5gMFsuakHH26DKVc7Pj6OVR0E11lg68FoNrhkLD1b4my6rx8nqvy1B9LCrnNPbCbBHsLz\ns6XIzvKzrnnFAu96pi201bOA2IdSqRRfWM/4YcubzWZq3hkr7pU9NIO21Gq12D4nUN0fQn4OBoN4\ncEc36BYAACAASURBVN7Ozo4KhYLa7XZsJweqYCMBsvhnAk/8H2QlmRhOfoeM412IkKS0PRvU4Du8\nBJnsgZTOfPCZVxm43UWnufas2TrpxbGdJw2kBbZzH+1ZSintjwDCTvY4tnObl12r4BGwHfbVbYOv\ncQ60Yh14YoHn+Zrzag1pcRATz3U/7m11v+4BigehXtovKeIaMBzXcS4HOG9lZUXtdlsrKyuRLDo5\nOdFwOIyVL9iO7e3tSMaHELSxsSFp8a5Ntms1m807SG/WAuuZpIYH2eiEpHguCRV2YMudnZ1YBpzN\nIPocYN/QGRIqBKtU9xWLs2okiDnGdjgc6uDgIAbhm5ubKTIBvXCsT0KC+fP5YD6d7IVE8+ojKe17\n3f85mUr2nPgCu0XbPAh2v+3j7QSb2yP/LIvrJKXOv0HXnNB0XOf+lvZyzWeL6+75QUdMDkaJ8iAP\n0hhwJjhJFvue6DCfM9hueDw4lNL7FJydAjigiB4QYDSkRTDhGSsUjUni5eVZg5kFPbRXWmz8d6fI\n31AAJhvmiTFxxs4zxvwP00QQRlnu2tqaLly4oCRJ1O124/tVeeZwOIwlrMPhUDdu3IjGZjAYpErc\nGFfYF14JAPjgORz9DeDwLEt2DxAgyA/Y2NjYSJWVsP/BGXgYK2dgvRyCzCmvtCE7QGkLbZ5MJpFJ\nw5Dt7e3FMuQse+8sG7rkdfq+B8WBr7QA4v6OT9dvz8S4s3Vd8qyW67WDAidEPMD1TKqvJwy/l1kx\n3/6OPmfAncl2csQNvgMLb/9ZA2phftARc95qtXT//ffr9u3bfK7RaKTBYKC1tTVNJhNtbm6mgkWf\nE06lHo1G2t/fj3NbLs9eLcOrMdgHlCXgsjaCICZbNgwQ4rUE6BF6gh3mGeiIO2GfW67FlnkGREqX\nEmUzC36kP7pFdQeAhuex1gm8HAR6cMZ4YN+zDtfHzB07+yQBuE48OSnKuDjQwM4w5/QPp849AH20\nl0AKMIoN41ruAaDw7A1z6UEpvg5SqVarxdd+9fv9WBrJHHCGgAdaHnQ4AOdz9lB6RQljIC3OOPAy\nNGkGbAG+2M92u63hcBjnmLnxUkmCY15jsbm5qdXVVQ0GA00mi3eYMxeUabO9B/uM3pycnESf6LYU\nspP7+CtefA6yZXuMGVtX2N92lmydtMB2TtqScZ5/HvEKNgaf6djOsanbKq/o8fJCabGnWlrspZSU\nWn/4IsdXHlwwhx4gMt9JkqTwF8Qwz+E+npzg747T6LMH2R78eLWbk1+eifO2cl90v1ar6cKFC/F1\nfVRneEC1t7cXqyS2trZiIFIsFtXr9eL1HjRLiq8FxO83Go3oPzi9nfUmzexbs9mUtHhFC+W2vAsV\nfHXjxo24LvwtDvgT9wn4C+af9pRKJXW73bgtpt1uRyKSNV4sFuNBgHt7ezHZcXh4GCuPnERDh9xX\n+fOx99hp+unJFj+d2HXFiU/3M8yrZ58hK9BH7JLjSe6JzrmdBW9hn1kzjuuwufgIT4B9PnDdPQ1K\nKTGg0wxOoTA7gZOA1FPEDKwDm6yiZDMMDK5/R0of++yTy7MYePZAZifWg2kH5hgv3xNEP7y0ycuq\nPPik3Sg2oMEViv5iyHiuszA4R57LePr7RdfX16PjODo6iiw+zDZZyaOjI+3u7sZnUcpB1pXx63Q6\narVaMWj1wJ2Dh7rdrrrdrkII2t7e1u3bt7W8vKytrS3t7u6m9nadO3cu7h1iv8Dt27e1u7sbT2lk\nPGDzQwixhFFKg2DmCsfCceidTkflcjme+MhLoh2MYcCvX7+uwWAQT+91cOlgn3nMBm1uMDxz4A6L\nzFT23Vk4JJ93yB1JKSMJQ8v8AGTpB/dz/UN3PZOCQ2JeHID7Bn5p8bJwB59uKAl60QsniJCzBtSC\nle9WKhVduXJFhUJBzzzzTArYFAqzfd2vetWr9JGPfCSVuUMgedjvg4MnMCMTOBqNYgWDj78He/O2\nRfDmIKhUKml5eVnj8TiSL+PxOEXweaZKmp2cenBwoEKhkDqCP1sR4MDI24Pz9SDOg3L8xKVLl3T7\n9u3oVA8ODuI75er1egysnHnmNTcefGKTIdE8EMwSQmQ6EA+0KOFFv92nsY79DACC7GxJXpIkarVa\n6vf70X773mvsivse93m+j9f750EbbWC8GR+yiPglhLV+fHyslZWVmGUhoIDIcAIZBh9i0gP1YrEY\nSa7Dw8PUKZPuo5l37Be67+OB7ngg4tU57XZbe3t7SpLZHlkOYWENEShhD52sw756Bor9afShVJrt\nncOmZokeSn6x1zwT8E+G6CzZOunusV12b51nexxHSenXdyCu1+hXFqj7nvfTEgGONZwMcwLJq+48\nUGVueXar1YokPYQ+2Xv/Dj7VA1hpcSaKtDh5nzZDNOGTveSf/tfrddVqNXU6HS0tLanb7UpSxEhg\nWM7RuHnzZgzKer1eigiCPGH91Wo1LS8vRx0mYPQydsqDyYDeuHFD0gwTPPfccxqNRrH8tF6v6777\n7tPJyYnW1tZULs9e/eTYyscHTAnmY47AqhBb4Du2aPHqGw4DJDjFn4DdyNLySpr9/f1UYOk22H0I\nWMZLd7EdzKkna2in65wnpVw3+Y77QLArfpog1fvD2Higjq/2gy49ieEYknY6CeBxl1ftoZ9eEcm9\ns2TPF8We0lardccrDzwI9b/TTpy7s64eDADwvF7fAXI2w4QiSQsnDRsAUPHUNW1x505g4YAbFt03\n2jsAZfLos5etYfz8uR7csPiyhtaDCgctLM4QZoeVtNttLS0txRKLcnn2ehQUkY3uzz77bAQGsEoE\n6YBh9rUBAlZWViJrjrEg0CwUCrr//vtVKBS0t7cXy0UODw9jhiVJZoeu0H8WEJnUpaUlFQoFbW9v\npw5cwQGurKxoPB7H/QJkhTltzY0Z7CHP5CAkjilvt9txvwIAxvea8g/gngV96AtGi/l3UOXz6Ydp\nFYuLA1QwNBiyLCvm7Ko7ciTL2PK7GyFnjVl/AC8/yTWbNcVwOZsopd+lSv8w1l7K5wEHbThrQI2g\ntFicvd7iTW96k97znvek7BOOtN/v69u+7dv06KOP6tlnn70j+3zp0iVJsz3PBAe8MxOdcUeAnjHW\nbsfcIXqWzEkk5g67BHvu9hVnBOD2ChYAId9xlpa28hx0N2svkUpl9kJ3wEmz2YxZsGKxqLe//e16\n97vfHW2sM7/FYjG+8mB3dzfaesgoyriwJ6wp7LTbU9YIbX/ooYf05JNPxmDciUUPAAET5XI52hP0\n3xl59zEeaPoeJc8Ssh1A0h0lrA6GCISkRSYHG8u+IsA132Xv6/nz5xVCiARltpIIYE27vULI7aGX\nJrfbbZ2cnMQSW2yKX+u+GL3yQwIp5wVwQ6yUSqXUGQh8DplCdoZKHfpOH9APSAJ0CEDLGDNHELCs\nZweE6CD6TX+m0ylB+5mxddLdYzv02LP5YDtfb46JWJunHXjmWUTWE/rvVSfZajPsk6QUUSqlsR33\ny2aMIJ/8cDNsn1cs+fe4D30lQ+8nRmMLyehTesva5eAfgkbOAyHI63Q6cbsVAXOpVFKv19NTTz0V\n18J0Oo12AQzSaDTU6XQkKQa5KysrkhQDQ16hd3x8rEuXLsXD8nZ2dmKgSwXLaLQ4hIj98tjWWq2m\nWq2mer2u3d3d+P5n5mk6nUY7TTbTyXTsfTa7DWFYqVRSuI7EyXQ61crKSsSfvV4vnl7//PPPS1Lc\nIuLrmHlk/zD9oDqAte72W1qQBxBrvq0LG8+6QJd5HraQDLoTG1yPHwSfsT6ysQHnDEBAYK9YO04W\n+bMgHr1NrwSuu6dBqZdXOEgnwGC/Fc6E6xCfANL2DpKdecKYOajyYM6BNse2e4mstDi4B+copd8Z\n6uWmAAUcEEDDQQ5GwdlEn1Da6CwZSsUzmHTGwzNmjCOLsdVqqdvtxvdD4ei5//b2tkKYZTivXbsW\n28u+NUAQhwedP39epVJJq6ur8ZTDYrGo3d1d7e/v6/r163EMue/+/n4EHrVaLYIIz+jBOgE2Dg4O\n4vv5+B/j6dmXRqMR2XSYtmazGR0BbDflWhgPL+nl1Q+M14ULFzSZTGL2yUsnBoOBbt26pY2NjfgK\nGQwNwEtaOFsMsC9yDxicqfcMpbNy6Bv65KwZBoLPvCwOYw0DSimVtw/9wzgBWn0fThaknxaMsh5P\nA9a+t4J7wciyns8aUCNzUK/X9eCDD+r4+DjqzOrqqjY2NlJkwjd90zfpxo0b+uQnPxn1AF3gCH/K\nqyTp4sWLOjw8jCVQ6FOWsczaTmwFbHKpVIr77Nw2YquwaZ7NQNexndkME7YtywCjL4DO7BYL7zOA\n4/DwMJZJop8c7OQO28Eea5y+klXGtkmKlSv0AyBE8Ij986oYhOygV2w42EbHmZMQQgyIsL8e3DvY\nHY/H8b3LBE6sPYJbwCxrlIPkeM/n7u5uav2x9n2/D/aPftEf1mSn04l+Yjwex/XKPDkxmNUR+uXg\nxttJAIu+esYhhBBPI0YvnUxz4svBItUjjh8AqA6gyJoyrth5SXFLCLaKvmEHaTOZJ9YJ/UqSJB6+\n1+12I1nBffED8/3jZ8bWSZ8dtsviUHwBa8dthnTnAWrgFAiyLLbDljg5zByy5gikeSYYy7EaWUbu\nyfNYs952gDjt9e1WHuRkzxFhfWO78N9kMsER2JJqtap2u60LFy7EwAfCjnG7deuWqtWqNjY29JnP\nfEarq6vx/Z1kIkejkZaWlrS6uqputxvXXgizLSdkD/f29rS7u6vd3d1IpLJOmBcw78nJSeoEbgio\nEEIk8n0evAKF30lGTCYTbW1tRfsAQQTuImjFn7DGeH0MBx+12+1YIciZKZDCnikF30k69UR82uF4\nB0yHPcUn0ZdsQoH1gficM5ZegcG90V/+BhZ00s9L2mkHNgl7xnMc17HO3LZ7gMnfIZYlRZ1F/x3X\nMU53i+vuaVCKcS4UCqn3gDKJGAJnDaT0O+twcjBpCADCNwpjqKT0q1aYXF8UKKqzDGx65/5MLAEx\nbeN/DLFnW6WFEqIAHgijlPSZd0GxPwyAhiP1AJZ7YfjOnTunfr+vc+fOKUkSra+vq9vtRhDD/cfj\nsTY3N9Xv93XlyhU988wz2tvbi+VqsMjlcllXrlyJBxTdunVLFy9elDR7fcrBwUFkGmHQnCxgXxPB\nPdkRwAEgm889uGdTvoMeFiHgkXtyfDnADxYOhogSNEqSAcPOblYqlbgvgz0JkuLeCBY6m+S3t7fV\n6/Uic+YHAMGq4TQBNSzi0zJXAEbKozGKTnxgkD3j6Rl01g73B6i58cGAOFPsc0bbAaSAL2cyAXTo\ntfeH+Qb44nzpH8EaeoO+nSWghq178MEHtbW1pfvvv1/PPPNMtDFLS0va3t6Oc9VsNvWa17xGjz32\nWJx3B1/ZUjIvySkUClpaWlKv10uRBu4EpfQJ5Fn237Nd6CMVH3zXWVXPrKMXWXuLbrqjk9KHmABk\nsJ2sb9anO07WNw4YH3D16tV4wBJj42VKnvWAZPN3R7pfmE6n0fYyRjzT28e1PrYeFDughrSBXHPG\nnesIQD2zDLnJOFNK6lU3ACsIO77npfIQbp4lkhT9b7fbVa/XS7H4KysrsRy6Uqmk9jFDBng/6QPz\n4hkk2sp8SYsS4UKhEF/ZhY2mQsbHGX+HANA8mGcNUEpIQAi56IEItpoxdAKOjCvVQ9hPggIH0bye\ngmCVDJZXvLg9TZJYnnxmbJ10OrbzjM4LYbvTSDMnltxmOPAm8+ggHt2C+HGSiHs4OYy+8hk4wbPf\nbkNZY+ic67a3H1KL7QXYXewRuuDkkOsjNsqTD5A1rJelpSUtLy/HszGkxeFEvPcaMu6ZZ56JPkRS\nJFWq1apWV1fVbrcjuXLu3Dnt7e1pY2Mj2g/6d3BwkEr8+P5E1g3bEBhjSZH4wX5TqUcA736IIPz8\n+fMRf0C8FgqF1OGUYHN/dY2UPkQOUk6aEbkc7ome8rqnZrOpg4MDbW5uanNzU0dHR9rf309VCaIn\n2QPy3B/SX+IJT2iQ4fa5R2+8mgCbwZsjsFGsI2nhD4hJsPlO/GLvPUPaarUi/s/iOhJHXj3i5B+E\nHViZdnqiBQL55eK6exqUeoaEAMRfKs5AMmh+CA6CA8EJcD1/h5FzxswNHL+zMAj6isWiDg8P4/cI\nqFBAFI19P75PhsWEw/bnZEtYPKuVZfkxBFmmgs9x5gAWxgwGCKN19epVFQqz/WooTb/f197eXtx7\nubW1pW63q83NzVheQZ9rtZpWV1fV7/fV7Xa1tramEIJ6vZ6ee+65yLY5gMLADgYDDQYDnT9/Pjr0\nw8PDaEi3trYiE8l+NPpKEEdb+v2+pBlwYhGiOxgeAipelAxDVq1WI6NXLs9e/M5412o17ezsxIM9\nAAsEhK997WslScvLyxEUsT92MpnEkrbt7W09//zzKVCO48I4AmxZsBgXPodMQc88MwMI5NRbACkn\ntgLccWqe/XcH6iVOPIvP0UN3EjgwZ8OyR7l7ACItMmAYJZyrB6i0j78xDmfxoCNp5gjX19d169at\nWBJfr9fVbre1s7Ojdrut/f19Xb16VeVyWY899tgdhn5+vxjIQ9Iw/jDt6LRnq1zfXFi7TkSwDldX\nV3V4eBgdme815rmtVksHBwcRYPnnHoB6VhTd9myU20PXe2whfXA77vY1m5nMBooABq51e8vzPfPm\nJWIeeAFendjJlmKx9QNQzH3xbQAEWH7ai+0j4J1MJup2uzo4OEjtU8r2mQCwUqno4OAgRSxxSBBj\nwEFYZFlqtZo2NjZiho/SNjIpq6urUS+y5dR+3gInX5LlARgDhCaT2btDeS1DlvBgzOi7Z6p83vAJ\njEOn04l7V/kHYMS+c1I1to1MC4FJtgS5WCyq3+/HvbbYbcCk+31KnKXFVgpJsazXCTrfM4z9PEu2\nTnphbAdgdmznQQ3YwbOQ6By2zTOHkBFeYUQiAZ3JEq+O7bg/GTcyoNm9sNzLq63wiUg2KyulAzTa\nkU14zMcr6gh6SzWX4wL8NQcN3X///VpbW9PS0pKWlpbink3OATk+Po4HQlarVT377LNxnLHvlOke\nHx9rfX09Zg6fffbZ+F3wECROkiTxzABIJMjx6XSaemMD5Fu/34/jyvqQFA/9AoPg1yB0vKqiXq/H\nElvG0RM2x8fHOn/+fAzAwS7sbcW2Enytra1pbW0tbj9jbLEDW1tbkqQbN27o9u3b8bR2P2WedhIw\n0l78l9t8dAc/ynew347rGBv3x15VwHpwX5UlZrKkDevHDwLFJ4DjqBzBn6GTrqO+HnzfPD4HP/rZ\n4rrSS13wSgsDzKZwZyKZEATA41lSHDegwU+sYpC4DmUDaHg2EwPBxOJEcWyepmayyPLxM4wGhhbj\n6YGMp8AdeDAW2T16HjT7IQ6eHSAgdqWFOWIPZrlc1ubmZlTEo6OjeOoYC6Hf78e9pWTpBoOBOp2O\nGo2GXve610UmfnNzM4JGgn+UD2fAnpK1tTUNBgN1u11dvXpV/X5f/X5fq6ur+uZv/mZduHBBxeLs\nYIBLly7F/Qg7Ozt64okn9MQTT+jo6CgGw7Bl58+fj8+Bidzb24vjCitWLM5KD9kjSilKkiTa2NhQ\np9NRpbJ4z9X29nYEZdPpVJ/4xCfiIR8YYUgBAn+Az4MPPqjr169HQkNKn7QsKeq5Axgcj2ccWdgA\nMWeVyWJPJpNU+aKvBfQRxtcdPusCPWe++dmBgRtz1p+/AsJZcNYHhg8AR7u5j5/y7PdwlvEsSaPR\n0Ld8y7eo1+vp6aefjkAcRjpJknii67Vr1/Qd3/Edun79ujY2NuIYebBFpoU582CB+1LC5dlJt3Me\ntDmp42U+t2/fToEjnyPPtLJesD8e2ErpcwF8S4TvzeP6bCCN7WYdUEYJWcjYZG24k3kAJw8uPUBh\nfJwE9aDV2WkPJjnF3AMb7oud9kC6VCqlHDk2ClviJCjbF2Dp6SPlaOynhZCEFOW7vD+WtkEAAJBY\nr+zPZc8a13qGk9JuQHupVIpgEFDjtoFr8LGQJWT+8cHs5ScDSxaXQKZYLKb8JGvAQZSf4MvJ5UtL\nSzo4OIggnYNSKNEkE0vGs9Vq6fLly/EcBWkW7GKnPLjGp+N7IP1cV3xvr6QYcB8cHKQCWl+3Z03u\nBtthJ3xPL7bIA0x8HnPHnDjWY92gKx7Iuh/DdoIH/SAi37fNGmbt+n5R1ra0qF4j0JYW28X83k4u\n0j63eTzHS/tZe+C9YnF2CCOB5vr6uo6Pj3Xz5s0YNOzs7KReeSIpZWt47d9oNIqluhcvXlS329XW\n1pZu3bqVCn4gY8bjccREzBtB/uXLl3XlypVI0L/+9a/XpUuXYla03W6rXC7HirLr16/rySef1OOP\nP656va4LFy5Imp2VUK1WtbKyot3dXUmLd7tTOcE8QIZBtF25ciXi2Z2dnRi0raysKISg/f39VIXh\nrVu3Ig49d+5cDMYIMsF6bHdjnykEi2fbnTTls2wGkTVBQOe4zqs+pcUWCnTEP2M8nRTDziDgRjAr\n5H/W5ztxJClV1fBCuI51i+10XMd+2T8LrvuCOH03y4jTIbI8nvmUFqlkBxRuLAhcnA3ln4MejJaD\nIrKOpKaPjo5imhtx1ov7uAHkZwyHs9RMomcQUE4U239H6XkeY0TfaTvjSI3+xYsXValUdP/990eA\ncOvWrWhYeN8mJbPONFLqRKnva17zGl29elUnJyfa2tpSr9fT1taWbty4oUKhEEETpVAY5+PjYz38\n8MPxMBDG5IEHHoilJgCYCxcuxPp9Al8yoCEEvfOd79Sjjz4aFxnEASANY+7ggLnCGEuK5dA4O7Lo\nSZKo1+ulGLp+vx/Hh8C6Xq/rypUrKhaLWl9f13A4jCcWk1Hq9Xra2NhQr9eLQf1wOIzGzMGhpGgY\nHEA7UYFhcKbRAxWCA2deMTgOYmGHPcvuGQf/PuuPseO5jA9BJ8YM1pBnAxYwtvSV6zBcnn3FBszX\ny5nJHpA5+NZv/Va9733v0+bmZiSSyBzhKCeTWYnkO97xDn3kIx/RE088EW0AeoD4OyC9rJRMkgM9\ndzJu87GJZL4BAMw1eun2xjP/SZLo4sWL0eFji6V0cMjv9IF20i8CDvrH+uS57hvQmRBCDKYIDHnH\nMGPpAS7rDNvCc7G12Cf6xvdDCDp37lwERrTfK1lguR1Eu32WFvvK/Nncy0vYCKywa/Q7m6EmAGY9\nu79k7FdWVuJZAQBg1j7AF+B4cHAQ7SHBLWAcOwe48WwhGVYvx2S+ASrcC0DGHjYvC+fgFk5YRx+9\nhIz7uB5C8EpKneVAgELGoVQq6YEHHtBzzz2XyniT8SG4QZ8AwFT8QOZS0utkrI8t3+FAKHSQw2bQ\nr2q1Cjl6ZmydtMB2VL1JaWznRPtotDitWnphbIeuS4pr37Gd4z90gu/4uvFTtv2MCuyvz43bstMI\nPbbDsM69MsgTC7SBzz2T6kEp45HFu4xfsVjUysqK1tfXYyBHBRhr/ODgIK4dyMrpdBp9BXoMNvzK\nr/xKtVot7e3txWTA1tZWfC8780N2maqs1dVVLS8v67777tPq6mrMkK6trUXM0Ov1dOXKFZ2cnGhv\nby9mc/FPkvT444/r3e9+t65du6alpaXYRuwgJKQTC9KChAT3sk8fQq5YLMYqB7aV8dl0Oo0HX7KW\nOSiq3W7HPfScNgy5xV7anZ0d9Xq9aD/J2NMut++e+Wb+bZ1EIgS9xv/gPxzX+ZqBQKUyhvgAm+ix\nBmuDtQdpBg5Fhx0XemyDvmZxHX1Alx3XOWnDON8trrunQSmT4UGYp8Nhh5ylRklxQrBR2ewOxkZa\nnKiGsvJMjCQTjSMhOwoAAUzgUF0B+N+Daw+ecaJu8DCI0sLA0S4+43vZjcseRDB39Lter6vb7eqh\nhx6KGZRms6lyuaytra0YVI5Go5hV9OCCcUaWlpbiuwCPj49169atCAiy77FDwev1ui5fvqyv/dqv\n1aVLlyLw6Ha7qdc0NJtN9Xo9TSaT+AJ7iAA27fP6GALDp556Sr/5m78ZDQIn/Pq8hhCi02KRTSaT\nCIKcNOB6AjXYea6hnIMylnK5rFarpUKhEE8a5uhxsh+ME1lnWEvGh+ylA3GyOL7vGOCN02VvEuCX\n9qNvBIdugCA1CDQc8DvgdSeKLrsxciCcnW8vC3GnS5Dg6xviAENLf1nbnj07S0CNoPQ7v/M79Ru/\n8RvRtvHuXdhZnHGj0dAP/MAP6Ld/+7f16U9/+g6yodPpaG9v7//n7s1+5Mquc88vphwiIjOGnElm\nFocqsSYWJbFUg6ySZKgkueSy7IIhu21A6Paf4Le+/0i/NRr3rV9s2A3DcMsNw7L0ooYGDyq5BhaH\nZJI5RWRMOWdGxH1I/lZ85xRlWyXZ1OUBCJKZEefss/cavvWttdcOm5fOPEij7JU0Kkfk82mAxe+X\nlpYSgUO73Q7CC3lF33B6yFYmk4mSLmwEz0Hu0FH/fToYBJACMvgcgITSKZoPzczMqNlsxr0dXCKX\n7hfQbe6Xng8INSdesMnY7nq9HlkIfIiP3//mdwSF/f5Z4w4HH04skiWBUPCOjl6d4WtHIJS+Xzqz\nTYMsfAPzyX5WzwCQGa3VaomtMXyWAI6xEpBhb7DryAprii1H3r2Mrd/vR+MqumJKSmS4seu8E+tO\nxoR7e2UT8umZBvap7u7uxnjdhu7v78e2D2SGuXaSjd8fHR1FNhDilGyzA2qX5+FwSJXLE2PrpH8b\n2zmB4fuAmf80tsMHSiM75o37sKUETsg7ZAfZaHSYe0NSgFmQSwImLxv3EkTGgs3yJANEI3YLGfe1\nd11124Ft8Xtit9Czer2uK1euhI4ib91uN/BGv3+2PWp7ezvG6YEg7zA/Px9jPjw8VLPZlKS4t5dM\n+37Ea9eu6Y033gi8Sbl9q9VSPp+PY/kajUb4ETq6T09Pq1QqaWFhQTMzM+r3z7ZoHR4e6rvfy+MO\ngAAAIABJREFU/a7+/u//PnH0C88nwcH6SKOtA9gBfGmv14v9reBaL09NN3biD7huenpaMzMzmp6e\nDlkjyF5dXY1Kuk6nE/gX2+XHNkI24EOYe6+ewUchE8wzMQGflxSy4qROqVRK9DtgPZEn5JA1f1SJ\nvD/DEx3IjpO2abl3rAkmdVzH/X5RXPfYg1KcBaDKy7i8DGpsbCzhxDzb6GBZGrFyUvLoF2fwHbQ4\nmPMyC2fRGKczBQgjAY00arrgrDGLjkODMZ2amkqc5emA3g0V43OWzdk56cyYEETW63WdO3cuMn6N\nRiPO9mTDOKW8gEo3uDwD9hhQQbYsl8slSp8+9alP6eWXX9b58+ejFNbZyePjY7VaLWUymajTh93h\n3b3sGmKiXq9HUI0MdDod/cVf/IU++uijUBbYcIATWUFplOGr1+s6PDyMcbNO3umX8TDPOCZK5WBn\nZ2dnY10rlYoWFxeDicSA0ZV3Y2NDrVYrHKJnGWClMEA8y8/Dddnh/s7gYnwwLuiC71Pj/uzzcQCN\nY/YSDs/cOmPtxgk58fmCOYTcQXYf6nsie8W90FsHhA/n6IkBaplMZjgxMaFr167p3Llz+s53vhME\nAyyqs6Pz8/O6dOmSfvKTn0QWlXUl++D7U2DRIR44lgkSycYRtsSDUS9/u3r1qobDYYCUubk57ezs\nKJfLRZOHZ555Jsp6O52OZmZmdOfOnZBnbLiTcdLoGBIPTpEdz+R5xQhy4Ww9hIikCC78GdgRgjxs\nte9j5tlOKqaBIpcHEwTLJycnqtVqUV2BjYQUw7e5n5JG2VLuD1jmPZgDB2EekLrfIjBCB9PbQdA/\nD2TRUSl57jcBgQcTlKt6JjpdaYGt4WeUM2N/WSPfA++lyDyb9QSgAd7K5XJk7iEDGZMH5+gEa+8Z\nBidS0DP2p2GHWX8qZOgnwFxMTEyo0+kksvBOBrkP93LTwWAQASvvyz0JqJ4kWyd9HNthp5wo8cqF\nNLZzPWSN+Tf2BT128lkaybxjMNbcAT56jC1Ep7wBGHKEvKYDaGlkb/jD/uZisZgoR0/jNreDUpJM\nQ0cYWzab1fnz51Wr1VQul3XhwoUI3Pb397W1taWdnR0dHx9rb28vdI09zZ6dg/xOV0F46fTJyUns\nrXzttdd07do1VatV1Wq1sC/4eci5RqMRWIHne2CNjuZyudgHOxwOo4Hk4eGhfvazn+mv//qvYyyQ\n6mAr7BHEHdUkhUIhzolmTcB1JEDwrV7pkrZDnE4hne3nnJub0+zsbOAyKhtWV1e1tbWlZrP5MWIN\neXcimC0bnDzhWA3bhByxVm5j3c7hzzwm8H246BgxFc/x+Xfs5USwB5Ke1SXuAWsyb+kg+VeF6x57\noyMU3FPCksLop89yQ7hYFM+McaHI3nDCy1s9CySNMkEomjPcLAbG0NkDacTich/GJo0ABZ9nwZzV\n5m8v8/FAAWFKszCUGQNUx8fHNT8/r1qtFueQHh4eam9vT41GQ+12O9g09lfChuE0PPuWlgvPihWL\nRc3MzOhzn/ucbty4EUowOTkZjRy2t7dDaPv9vtbW1tTtdjU+Pq7t7e1w+t4dD6NBN8parRZlBnSX\nOzk50dbWlv78z/9c6+vrEVgvLCxoeXlZ09PT6na7unr1apT57uzsqNFoqNFoaH19XXfv3g05Ye4B\n9oeHh2HoDg4Oohtdq9UKYJ7JnO0zkM7AcqlU0vz8vJaWlhIycHJyog8++CC68hJsYjAHg0Hi+Ama\nAbgjx0F71sedMM6f9cGYYEAYozsdDLGTNxguD1hcNyEaCG4BkWnAjcGFBPIsMPKAbHtg7KXqD+Xh\niQFqmYfdKBcXF/WVr3xFf/VXfxVr3O12o7spFRpvvvmmbt68qXfffTfWkAs9X1tbS1R9PHyOJiYm\ndPnyZd28eTNxvqz08TOacSjunKSzIKJQKGhmZkYPHjzQhQsXIngaDofRcKxSqajRaGhvby/k1eWI\ny3+edqieLXcZhTDygHl8fFxLS0taW1sLwAtbPjk5GQQf8iUp8X6zs7Nxvh4/42Icj6p28ffxjNvY\n2Nm5rl7Z4ySTH5GCT0Kn0H/PeKAHgC/G6ORp+p3Qc4ANdoGjtGq1WoBE5h3AS2MQthx4FY9XfqSf\n577LA3juC8GXPuSe+WM+8NtenXP16lVtbW1pd3dXR0dHAUDxnZAyjMcD5qOjI1Uqldi3Shk0ti6T\nySTIQAdj6KM3V/GGIMijk8Xus9M+Hzkhc+ZBgpOgOzs7T5Stk5LYjnlyYod19K1DHoQ5Qe7khzQK\n6iAv8NdgvUdllLgfMoAeuG1hXbmvpMCN0qgCyd4xQdq6P4ecSPtDvu+N3fiOB44Ei+h0sVjU8vKy\n6vV6ZATJkJK163a7Qdb4/nDmCkxFJUK64g5Sf2pqSleuXNErr7yip59+OsgVmktyrjyZsZ2dHe3t\n7UXWsNfrRZNNZB9d4Gir4XAY502Xy2XVarWYq+9///v64Q9/GAHn9PS0zp07p+Xl5ahifPHFF+MM\n1Ha7rfv372tjY0O3b98OH4Wc5XK5xJnYJIQc42EDpBFpxzvUajUtLi5GKTFyeevWLTWbTbXb7ZA5\nsvzYY4jCvb29RPUNc46d9UScE4rYUN9iyJjxhy7vjM19KXPvRK/LOrKHv0rHUsg+fsNJoTSugyj8\nZXHdYw1K/YwcBk1XUmlkCADFKDtAnEViAfg8TCj3w7ECCDyQ9YASg+WBHwbKBd2DaAeECADO0g0h\n93ADDUvCxfe8jBNnmWb8hsNRGejU1JRmZ2ejJr5cLiufPzskmeNKyFCenJwk9sfSzMKzx5J04cKF\nuA9NUzgv6uLFi6rValpZWYl9VJlMJg6nB1Dw3VarpfX19RDQXC4XNfYABYDEwcGBarVavB9Cz7+l\nM4X98Y9/rH/4h3+QJL3xxht69dVX41m5XE6XLl0KgIWhZmM42dKtrS395Cc/0U9/+tMo0XWmnmd5\nJoIujjBwyMjk5KRWVlY0MTERLGM2m40S3o8++kjtdjscDmsB4AE4pkGeb06HzECOpBHYTYNs9Mrl\n2j/j4NLJDvTF2TEHocgp9wAUOsMmKTLYOGKMHnOMvMFoO+B9+P8nBqhlMpkh9uHZZ59VsVjUzZs3\nowpjeXk5zvadmJjQxYsXtb6+Hm3tH94j1pBDzAm+CBLJ5E1PT2t7ezvB0LujScsKv3Ob6uQK8oX8\nEWg5aeHBjsucXw7e3EH6Fo1+vx9dwqngwBZQGonDp2rASR50xRlrLt6FZzoQ5EL/se/uD/i/E5Zu\nlz2T86hgH4CO/3LwTakfQDQNgr2s0ZukeWMmfs48crQBWQUINnRTGpVXevbDmyEhQ75/joB3amoq\n3vX09DTRgMrJXMCVNMosuTwCVI+Pj3X58mV1Op0Ai2TGseWMzytJnAjwNfaMvWdufL+g2x3WiGDc\nQZpnU5Efz8YwpvQWCwJRiAvWD114WH3wxNg66dHYDv/587Adfi6dqXc5TRO5aWzH2vkeOfQwnYn0\ngJSf4Zcc0znBwDj4bpqw4rMQRV7Vhw6gw0428Wwnx6kMKRaLunz5cpSSU13X7Xa1ubmpvb29OP5r\nMBgEkQJWlhTkPAHahQsXND4+Hk23PKGxsrKimZkZzczMxNnAlLCOjY3pwYMHsYVid3dXH3zwgfr9\ns20dU1NTajQagYXRA7ftHKmXyWRUrVaj3wlzfXBwoD/7sz9Tp9PRuXPn9Du/8zuan5/X6uqqTk5O\ntLS0pAsXLqjZbEYlBcE4jcmGw6Hef/99/eAHP9Dq6urH/IL7ADCI7zsFB2HP6YtSLpejJ0Cv19P+\n/r7ee++9IASQEeyxd9PFRnqQ6OSly6f7TidksEPIGHKE73Wf4WQMNtuznPgN5DZ9L0/QSSMy6PT0\nNNEPBULDt4Exz58U1z32oBSH6Olf767mjsMnz4G6B4pMLA7Fy5LIOnpmxzOw/nyEgd+TNnfw4wbS\nHbFnonz8AAfeDcH0oNXT3xgXZ5v57NjYWLBNCwsLmp2dVa1WUz5/tnF7c3NT6+vr6na72tjYCJaX\nTCfNURCuiYkJPfXUU/rSl76k5eVlNZvNUFDKD3K5s03f8/PzKpVKun//vu7duxeMD4p76dKlAJWZ\nTEarq6vBruHct7a2lMuddZLjgHdkgDK0QqGgUqkU4LNWq2lhYUHb29tqNBr6zne+o1qtpm9/+9va\n2dnRgwcPItN4dHQUbBoH0KOcrN358+c1Pj4em/t/+tOf6t133/3Y+hN0otSU58B4ktUfHx/XysqK\nFhcXdXp6GoHw6emp7ty5o+3t7Shj5nsAP2QlbWzceXEh+9Ko7t8ZYDdazHe62RaXy5wHna5v7vCR\nSX6ffiY65mUoDjqYU2f1HFRIT+ae0od/K5c7a5DVbrdD1nGKyCUkTdpJ+bp56ZEzpdhDB1a+ng6i\nHmX73c74ejnJwX25n69del15Ds7YQZoTMC7Dnkn0izJbgKFnH9LBpwPQdPCRztxyP4JFz6T5uNLv\nnS65SvsonulZZgAY9/dgyfc+cR8AK9lTz9x4MIYdoVQNkHB8fBxNrDxgTZNMkMEQdhBiDqicXQfQ\nMH4CUn5GIOwEm1dyEPgxJp7FO7G3DJ/hhIATAZ65JPhgXQ4ODgJcIpuUy3uZt1d24DuoQqLRIaSp\nrzF6yth4f688KhQKUcpMIOXPfZKDUq8Uk5L9MhyvpbPOXjbodscDC+bPyRovG3QiyUsN/TPoMNgu\nvY2KvYlpEpj7I28eeIPtfD88Y3X59DH6u0tnQeT09LQkaXFxMcpdeZ+1tTW12201Gg31er1EQyLe\nAWxClcH169f1xhtvqFgsqtlsamZmJt6NrPVgcNaEcm9vTzdv3ozziAuFQvTQoOEbyY2PPvpIg8Eg\njjcjQAMPcUaq+x6ylHSoXlxcjGMUj46O9IMf/EAffvih3nnnHV25ckXvv/9+ghjg3NB6va6jo6No\nJgnBncmcHRUzMTGhRqOhe/fu6fvf/36ioSAEK7LIfcGP6YoGsrXYNuaL7Vk0DvWkmG/N88SBxxr4\niYd6k7D7abLNfQad1bFL2FdPzCH73DtdueCZzjSucxlH7tJbQ9JYw7/7y+C6x3okjLNOkiKVzV4N\nDI4bLwIpJhFnhuOSzhSNDmvOirPADtDcQDHBfIffYbQ8c4rBSQeUzojAkjpwdFAAm+Jnn3oZM0qD\nA3dFGh8fV7FYVLFY1OLiYjTcOTk50f7+vnZ3d7W5uRnlugSVnoFAsC5evKg333xTFy5c0NbWllZX\nV2Mu6Kw7Nzenvb09zc/PR7bw9u3bcfQJhnB/f1/Hx8ean5+XdNZxELB1cjLqtgiA2tvbU7/fDwDh\ne8gwEp5V3d3djXmYnp7W1atXdXJyEpmhw8PDCJJpVtHtdnVwcKBKpZIYRz6fj7Po6vW6vvrVr+r1\n11/Xd7/7Xd25cycyQpQIwnzyt+85Rjbu3r2r3d1dLS8vx884t6tSqWhtbS2aTnlmShoZFgd+Xjrh\nBpWsrzcnQYad9XeZcoAN+MbYuIFMBxOANinZIMKBOSAWQ4dRGg7jfKp4P2eWnZRBjp7kq9/va3Nz\nM+bADb10ZocAseng0QMvMghORvDzdIDGc6VRNz13kh74OsGW/u6jMosOztIBrTtb7s2zkRm3jZ5p\nRW4JZAkYeSd32nzGAxWeK308WJydnY2ma+4T0uw0ttZLCNEDJ4MINlg79MIbWjiYJQBk7hkzz0n7\nBMZJxgMyFOAJo41eMh/ci2YuHgyxZryDl5jyc0AHvpQ5IJj0veisHe+TPs/b11ZSohrD1441854D\n4AK3V26Lsb+sD2c8UpLI+wAUPVNChufk5CTelblCFmjU4mXUZL5Y53S2mbVgTanQQT/Iuj6pl2M7\nSBPHdlSxOVmDDKJTbnccxON7WEMv504HAfhF1390MZPJRIYrfZyLNOrXII3wIe8ESZQm6sCkyK1/\nH4Du48JmOrEBuV8sFqM3iJNGnU4neoSgx340GLhjYmJC169f11tvvaXj42Otra0FNjw8PNT6+nr0\nH8hkMjp//rx6vZ4++uijaFTkOk/QyJYAsC7+HZzGtqderxc6gU1kPXO5nFqtVgSsNK/M5XLRLHNx\ncVFbW1uBv7AP6BHnKPf7Z3uBOVItk8lEsHbu3Dk999xzeumll/TDH/5QP/7xj6PkliAWe0riirmm\nBLpSqejevXva39/XyspKJCoIvGmSxFmmHo8Q0GEn0AePadJJKMgb7Ffap+ZyyT2j6cAVDOVENp9J\nE9vIH7iO+fM909hG/zzf+Xm4zomWXxTXPdagFKVkEghYnGnEoeCAvIwIoyYp8dk0A+3ZGWepGIOz\nYIB+FImL4MHBV7rkwx0OwQo/Y2HYa8HCUR/OPKQzCAiA125TSlGr1TQ/Px+CTkDa7Xa1urqqXq8X\npaYe9GIQJycn9frrr0fp67/+67/GONrtdnQQzGazWl9f19TUVDBTjUYjEVjCllM2vL+/r0uXLklS\n7A1CsNmvw/4fWDCMH5k9yjGy2azm5uZiH+fe3l5kTSgZIchL781Cuff39+P4GQwacwHbLZ2xlN/4\nxjfUbDb1ve99T/fv349Am2MROOsOJzsxMRHvPD4+rp2dHfV6PT399NNhwMmcnj9/XsfHxwF0pNEh\n2xgrfuYA22XpUaAdOUFmCWLJxjpYZf3RH+aJ77nuAK74DgAYmWJMHqQ60eREDLrihpjmS8i669yT\ndnlQxdxhZzwY9Hnl82l75k7oUfd2Z+SOyJ0K3/Pfp8fKs/iuj88/mx6zlNxf5+NCXp119QCOeyBr\nZEI4k9LHw16ffr8fwYhn0pg7f3632/3YvLjjJOvIkQLMI2SYs8xkPqjuwH7yt7+7M9VOajrJCjDy\nMkYnhLzixkFAmhQl6PMsjvtWdJnMnTd9QY6QTQ+y/H50ZfYSfieLy+VyNCBhfiHzvJKDcUC+OlDC\nxvJ5voPt8+0WTgjgH/zsbn9HbA5ZD38HAion2JzE9sopD1aRX2nU8Z85As84Uedbd5606z+C7Zx0\npdIqXUnmeCgdbDqZ5eSuZ6L4P+viRJgTaunGSMPh8GPHP7n/xAe7PWM/Iv8HV0xNTYVsSsneC/4e\nmUwmtklRjUa56OnpqTqdjh48eBABKfND8OKlnfPz8/rKV76iy5cvR6VaoVBInHgwNjYWSYVKpRJ9\nL6gqc7tBEHp8fKxer6cLFy5E+S97VCXF0WAkKSDGJSUCE3QQO01Zr3SWBOD8VewHeuxVLO6D8CXd\nbjeq6+hCzP7Qz372s7p+/bq+973v6YMPPlCn00nIix8dODY2pqmpqThXlSTHz372M125ckUzMzM6\nOjo7MaJUKmlubk6SYo8tdtH9kCcF/GdOmjjZ4iQi8uJYikwzusa9PE5xWUOOpBHhwvr61jhphCM8\nSPWSXidMH4XrvKHeL4rrHisC9IDTnSqCTBAJeMCIMTEYE2l0ADKNCpzRdoA1GAwCiLMY6WDQBcad\nkYPBNPvmpSp8H0fHgsGuYbjYtI6BJnADELjB5X2KxaKmp6e1sLCgarWqXO5s3wGfa7fbunnzpra3\nt0OhOSpGGgG5iYkJPfvss7p+/bo2Nzd19+7dmEOAX7PZDLCAEd7f39fa2lrsRUWxMVg489PT0yhX\nhYWivASBhZmmdJH1w3EB0pAB9n2WSqXImAJUAUOSIogkUCTjisz5AeZkLjzLKp3tqf293/s9vfzy\ny9FsieCWQ52RU2euYDMPDg70wQcf6MGDB4kAmkYClUolnK8TJbCwDtjTcueb0fldmkBxB8s7YdR4\nHk7Dm2ohv8g9OoYMe7YMFhRQwTs46PPgyMkT13HG5qVNT9qVDsg8MGQO0own32MdsUN+v3RgwpW+\nj2dG/fK19oDOg1YcGu3+kUl+7+SJZyH4fzowTAev6UxIuVwOoEC2Af0kAMQBkp0HdEkjOU+P02Xc\ng3hkmmwszdbQZ9bA18PP7x0MzipK0uvh+86kpC/yzCH3wCYAdrLZ7Me6s3uG1dca+4d+4QfJSHEA\nPGWpvDNnMQPWfewO3L3RnftjPoOPADySScQXM/dUdTBu7HulUomsLn5kcnIyqo04LgkQzhjoWgww\nKhQKQdqSEfJmTc7iO/HGPjkqtIrFYthG7CNz7wEpa8k7e3MrfLIH7p45Tevik3T9R7EdugEmQj7S\n/gJyiu84ke2kDxnPTCaTkDV+774UTJMmCfk84+VijE4qDYfDRMmujxVc4FlF5sbtLPo7PT2t6elp\nLS0thUzRtOjg4ED379/XzZs3tbOzEzJI8yNJoRu1Wk0vv/yy5ubm9P7770f3dOxJu92OTK5vg2i3\n22o2mwliC90ngKHRUbPZDBnmSDzI6LGxsThu0Ct3WFe3p+iTZ7339/ej068Hr17ltr+/H7gRXcZu\nQ0YxL1TFYFO+9rWv6etf/3okdJAzMq28BwQathUbdOvWLa2uribOLJ6ZmdHy8rJmZmZij7z7U2yD\nkyPIcTqryGeYW+QTf8fnsGsEsO5TPEjlnswjdgkZ9Wwrz30UrnNSwAmfNK5zwuCT4LrHnpZwJ+dG\njMkGgOO8+Bwv7RONAnkQ56WJgCs/TsHZU2fDeRbABmBOzTkTze+dLWDB+f74+HhCCSWF4jgDCKvq\nDtTfr1wuR6388vKyKpWKisViOPMHDx7o3r17waJls9nobsb3UIynnnpKr776auzPRLiOjo4iiCOr\nQDkpBokxdbvdOHidfQY4hnz+7Hy49fX1WGf+JnuJsUBBIRto1uIAhT2pzE0+n9fs7Gwiw0EzDfaC\n0JGP1t0YFgxcJpOJn6OIrVZL7XY7ugffuHFDX/va1yLbCIj18ZycnGh2djYyuwTCg8FAm5ub2tjY\nCEPHMSAcfE3ZDcExso1RRD/cgSPHXkLojBvOECfPnCFv/J818QZKHoDybH+GBw+53OjgZzc8jJe5\ncqDA2qA7kDR8/0ku302z4lLSCXmgxsVncSAe7KU/484+/fN00MrvWTN0AFuIcwcIkilLB3HYZSdP\nHvVeDh7SJW8etBYKZ10aeZdSqaRqtRrvzdYF9q4Ph8PE8SNpcpHvIH8+D14hA1nJcwl0vFmDEyzY\nutnZ2SA4HXQyl54Z9X3VAFz8ERUkZDIAAIAS9yHoJRlMiENfSwIq3md3d1c7OztBkLFvilJVAJxv\nR0Dn8aH8n3fgvfG9jJd5p8QWgERDDK8Cwd5jG/BnZK48GE7vxwRIekadNSPbDHkwOTn5SPvEO2Nz\nvUsmATV/Ow5gHSA0GRe+DblG7qampsInM7dPcqZU+rexne//Q/75nAeZ0sjWUbHjIBtwjM+CpMhk\nMtrb20sElt7p1JMNdLqdnJxM7H/0xEPa74H3sH0EA+gEesT7uB/2DBbvTxNJEg4zMzORCdvf39fq\n6qoePHgQVVfFYjG2bFE+SqPJF154QZcvX9b9+/fV6XTi/cEAHkDj+1utVpwNik+mUy3Z64ODg0gC\n7O7uqtFohF/ArrB1Cj1Hd/AlZH95d6oR+Hwmk9Hi4mIcrYPOUFmAvhFgd7tdSQryCZuBrLB+ZDwb\njYYODg504cIF/e7v/m74EGwSVWzMc7lc1szMjA4ODiJLzHytra1FEItNLZVKsR0M/fYjW9KEjPtN\n5M6xk/ctwaZ5sMrcOAntONB/5ljScbMTymlsiK1FflxPqWpM47p8Pv9L4brHmpZAeR2AkwkCGDi7\n7SUUUjJDyaLCVKYZNH7vpYUO4PyzacFxBtiNKJ9N3xM2jeAHIzYcDhPn6jmLngY1PjYMH2d3njt3\nLkqgCoWCms2mtra29ODBA+3s7AQIqtVqmpub02AwUKvVUrfbVaFQUL1e12c+85nE+Bg3YKvT6UT5\nmrN9h4eHmpubU7PZTHTdcmME+EMRJUWjIOYWJYa1k0YbzXFGlH/VarXYHO8sPsYYgwS7TbDLvkwI\nANbOL8ACSgQrSfvxarWqq1evamlpSX/5l38ZnYW9NIFmFsgxpRGwjffv31c2e7bfbGFhIQDm6emp\nGo2GisVijCHtjJ1ddOPlRgen96i1dF1zxhqZxgA6eJBGTJwzmK6zfj/GzNr7c/r9fnROdfYcGXc2\nkfd8Ei8PQqUkW+6/cweAffC9KHxXSpbaOJHlQa87HA/IeDZr5sGuZ80AI8PhUHNzc+r1elG2T8CC\nE8L5+Vq6LXM7iM4zbrfXZEdzuVx0lqSM04/4YN8Q38eB9vv9OALBHbzPs88//oWLeU0zvF5JQMUO\nNm9qakqLi4u6fft2IruYfh5AhHWgxG9iYiLKj32c2DyqQZhvz7A6QOde3kSIeebnBPFk7qrVahCJ\nBAoEm/QHaLfbYbu5D77Cjz1wMsIzSpISGQhJcR9Ix5OTE01NTWl7e1vSqIkh8uTfBSMAtJFbz3DT\nDATgxjiQQ7eBHmzzM8A/YByAPhwOo/mRd01H/vEjAGv6I/AOXLzLk3ghOz8P27mvIiBJl9Z6VQMJ\nA+TT7R2fJVB0v+QkrJdae6kvgY9nvNN2WlLCjmGT8PXIBHqHXkPGQMzyLPfdkPC1Wk1PPfVUkESZ\nTEZra2va3t7W2tpanGddKBRUqVQ0Ozur4+PjCLTK5bKWl5f10ksvqd1u6+RkdH47mA67hP3gHFUn\ntY6OjjQ9PR1H9jiOOD4+DoxGcyOOHwQnEExDFoGVfB5ZV8peK5VKbDfjSMO9vb0oJWarF4QGmGd/\nfz9sF7KD/cZe0UUYnJbJZKLD8Ntvv62/+Zu/0d27d0PPyQqXSqUo3XXchC3Z2dmJqpnz589rbm4u\nns9xheA+r7TAF+Ov/N5SsqqKruzoFJjdM5Fpu4KP8HtxYft8+4P/nLG4TUS/0GN+hryjC78qXPdY\nM6WeCWLhmSQCj1xudDZken+HTxiTgZMoFAqJ8g5n1JxtQ1AAzDwHIAFYQbnJtsHGIUi+P8IDUxw1\n7+KBJ0orjRSfEisCToBRJpPR8vJylDTh5GDAb9++HWUdXnK2v7+vvb29cPr5fF7nz5/AKVUhAAAg\nAElEQVTXwsJClMTxzhgracRys5ncm0+dnJwEgIKdcqDBmuF0Op1OAAQMK+y9g2UcB2yaNDomx9d9\nZ2cn9pr6fgoCzqmpqWBvABKU3nLYMw6IIBTW3evss9msGo1GnAn5zW9+U5VKJcH4ArIxuGSPMpmz\nM7to2NFut9VutwNUU04HO+t7ht3AuAFCydmXnA5cMHQARUCn65vLvTPBzvL6eiCzBNKAA3SO7Csy\nzeXO3JuaEFzgnL1U2bNwT9rlRBprxh9JMRd0RST75baMjoxkxwaDger1eoLA4t84CSnZMdYvbAQg\nrFqtRnMNZNi7i+7s7IQthkXmPl7m744wXf7m8+EBjDtt2FaIDn4Pe8290T3fq4cto7IkDSjTY/Ms\nLSQkgZBnJAlgnDDK5XIBGNjH7yV+2Bjm3gNwt9OQZhMTE4kzgAG8PJcxMXePIgDc33FvMngXLlyI\nahknPxgLY6ZkjaBie3s7MqpkATOZTNhefAi2i3F6ttEJXoI4iAfsL7YVkMW7sqXDg0n8I/ZjMBho\nb29PnU4n1q9cLoee8H4efHoJGu+azWYjkIWMlRQN8QDE7XY7ZIoyUQJ55tUzZYBofgYofFIv8M0v\ngu1cV1yH+JkHT9zb7Royj2/ygBZ/4+PDR+GPaJbjmBLiLQ3OH2W/0H0CJ3wqJBA2wwk5miAuLi6q\nUqkEoXF0dKR2u61Wq6XV1dXYMsRWKBpnbWxshP5xnJiTPa5r4CXWg/knuBsOR/tgvfKNbVTYIye1\nut1uzDk/Zy6l0dFcjyJg8vmzo1VoZnlwcKD33ntPmUwmKje8rJt9p+gbuklDJeYFgp4gl2ZIVFUU\nCmdnwq+urmp/f19vvvmmrl27FtWA2E+CRsp+HdvSLIn702OlWq2qWq2qUqmoVquFHcG/OGGJ/CBD\n2DuXZ/SD2INtGdiVtM6hY/77NAni7+d+G1tNFtRxneNM7sUWPnAblX6/LK57rEEpi0Tkni4DcHbL\n2R0cDUbMAS4/81p2Ly9DgZwld0dCTTvfTWeDHEjyHRwkn2cBCApgcjwrweI7qIClJdDjCIDZ2Vk9\n9dRTGg6HOnfuXMJorq+v6/bt27F/hqBlYmIi9pEeHBwEQzQ5OamFhYUAWQAeFMeDNgLvg4ODOByZ\n/ZKsHePFePGuzFU+n4/AmbI8AlaCP+bIg2MvFSQYZn7PnTsXLdI9k4cTI2sKA0mpCaUG0lmGgk35\nnvkbDkd7HFivo6OjCDD/6I/+SJcvX4719n3AZFZrtVoiA9tut9Xr9dTtdvXBBx+ETE9PT6tSqahe\nr2tiYkJTU1ORiXS5w6h42RoyTvmkA1XYPMA9cs08OTuHHkijBiLubL0Ex42ZNCJ4PHjyDoYE9m5c\n+R3O2MkdyueexMsJBLct0tk8FotF1et1zc7OanZ2NlGWCdinPAtiYGlpSb1eT+fPn088wwkFD4I9\nWyglG4Ig9zMzMzo+Pg4b6PfE5uKMm81mPJsAMH1/f9dHBeMEMZ5p9Wy9B3L8jC7d6KB/3vX1+Pg4\n5gyH7USM3wNCyxs75HK5qAYh0OU5BK+tVivekfNVkXne2wNiB7dkL9EnMs7FYjH2xaNTVIEwD96N\nGD33JknoI35hbGxMt2/fDnvKekAskO0l8GT/+3A4VL1ej0YtTsSyZ8wBOb4A/43P9oAeX4HtLRaL\n6vV6cRg9Zbic9UmJs3f05ZluzwDs6f2hrAOZI59vZIaMkZf/4gcp/YXcJXiFCMVf+Nx6UI++Yj+H\nw2FkZJ7UK43tvMwR4JzGdoB/7AxrRmCLzngFFvKR9mcEAeAaZNHBtmMxPuPjBtd5ZQL2yW0mNsyD\nGA8kkE3kCvmbmJhQrVbThQsXIjCFqOn3+7p9+3acs4k98e0UXmY6NjammZmZ0FVseDabjWc3m83Q\nEW9M6MkBmhaxXmQZOR0hl8slgudMJhNbDgjGPXMJJgQfOcmH/nP84MWLF/XCCy9EtR9YDvuJvuXz\no/4GjJe1IADlvfx3BIPIBxUa7XZbN27c0BtvvKFqtRp2ivcicUCgCS7tdrvq9Xpqt9va3NyM/iqU\ngi8sLGh6elpTU1NBqrCW2FL3cVKywz5y5YEqNgk7iJw63nbCDT1LkyjgQcd16IUn+fBl3uTPKwog\nZdEn/Mkvg+sea1CaBkr8G+Fzh87ESwqhT4Mud0DS6LgFBCGdXeLybBRNg5zZcIfmrAPjZuIJkPiZ\nPxvHxJjSGba0AaVEgwY5uVxODx48iDLiTqejd999V2tra3GsSj6fj468dC9jz+jx8XGcNbW0tBQO\nFPaWPQUYZoRWUsKI1Ov12JuEg/YGBLw3zUkkxZ6NwWAQAARHBHhy0sHr+z075Ee6nJycaGZmJpyK\nb7QvlUqRqcQR+vE8gFOMMUAEoMT6wRxubm6G8bt3756++c1v6umnnw6WjO9glAFUMHtHR0fa2dmJ\nPa7vv/+++v2+lpaWtLi4GAwm2RIu5NWzXpLCCcFMUR3gJeQYTnQGR4QewBjS+ITxuz5iNHkGz+N3\nZDGYUwfdOCQ+6+ws98RZIR+s+5N8OXvv1QWVSkW7u7taW1uLUnsCCj6H3kgK+apWq1FKjy64M/Os\nu2fH3AFh2+r1uprNpjY2NkL/pFGWl4DYA82trS298cYbIdvYAWwb3/WyIJ7v4/Wfe0bV7aOXl9Ex\n1YM85IrvpbOZDhjJgvm+RfTKiSlfN29Ok868SFKr1YpxAJw9sHd9Yo2wf0564kcYh6QIUCEssd+s\nH+vlQJs1IUCTznwiwIEMBWM9PDxUvV7X9vZ2ZG3L5XI0FDk9PY1zCH3vuW+XGQ6HUaniPrtYLIau\n47vZT8/a5HK5AH3ZbDbuQymiVx05mcj53ATJ9BbwbKw37KjX6zGnZEYpsatWqyHD/j1sk2dE8ZPs\nXeUdpqamIvhyzMHnc7mzTsHe7fNJu/4tbMecoiuOzdJdo9PYDvvjlWlpcO5ZVHyjpET1nNvA4+Pj\nCHKwG2kCSVJC/9P2zINQdIXPuN/EthWLRZXLZU1PT+vo6CiOEykUCtra2tJPfvITra+vazAYxBan\n2dlZlUqlsANbW1thDyYmJmIvKmRMuVzW7u6utre3A+u12+3IQqIrXimBDoE5wJ7Mk6RoYkT1Hc8b\nDs9ONeDflL4j95xOQLA8PT2tQqGgarWa6IherVZjbr3xFfbI7fbc3FzgE55NlhfyFBvlVRLs3ycx\nsbW1peeff17f+MY3dHJyEv1TwO5U2rGti/VuNpsRjzx48EC9Xk9zc3O6fPlyJEcODw8Dq7pN9D40\n6IiTDsxX2m8Tr3hM4Z8BD/MZcLX77TSuc6KGQBx55WfMH1hPGpEk4Dof3yfFdY91TynOzSfXnTPB\nYBpQD4fDYIGdmcewpcGQLwr3J4DxTCdKB4PHZzz1jIH0syFzuVw4Qhy8b0SmVh3gA6jwgJsxuuEu\nl8uan5/XjRs3VK1W9eDBA33/+98PY3FychIGAmNKELe5uRnvQ0BQKpW0uLgoSVF2QWCBclIeQHYV\nlg2igM95syiAkTsEFN/ZeNp1MyeZTCZKg/f29oJRAjTy+3K5rH6/H44co8S7OrtNCcnu7q6ksyNe\n2u32x8ZJWRwOkGNqYMZZz0KhkDB8OMC33npLvV4vAHy73Y7A++DgQPPz89rY2Ah5Yz7YQ3rz5k09\n//zzGhsb08rKiu7evRs1+siOlAwImO9+vx97p3xDOk7cAz8HxZRUOJD1fbru8JFVZJTMAWvj2bhM\nJhP7yrxcBSeLHPJ8SBmYOs9c4ECetMsDf+YQO3P9+nXdvXs35sWbPyCL/f7ZniDfB3L37t0o/b52\n7Zr+6Z/+KfZneWmiy6BnHaXkkRydTicCOOTPHTklmcjTYHC2feDv/u7vInuQLtPxLARyh4ymx+R7\nvnBqOEVkFFnLZs+6RWJjsZnIEiXuzWYzkYFFXglA+bw7WuYNe0K2VVKwvvv7+6E7khIgAFvMOrh+\necCOnvg+Wyc1GRPZDo5xYPzMrz+PvfFeSoWPItja3d1VpVJRp9NRv99XuVwOO01Z6vb2tg4PDyMD\n7sRBt9uN9yqXy+HPCHi73W5k+gGl+G9vQAII42f4M0nRcObk5CQqBnZ3d4O0w55BHDJnZIKoYAFb\nSKPyzp2dHRUKhZhTSnMBtPitqakpjY2NqdFoaGpqKj6H3GIfHYghI8wrHd15dwIj1v1JvfCt6DGV\nWmlsRwYc/UQPwHZuG8AGHhi63jrB7OSYk6zoK7qILGKTIPucCEbXfMyMhf31+DjkjnE7QcjYi8Wi\nZmZmtLKyohdeeEFjY2O6deuW/vZv/za2UPT7/SCMIIjIyh8eHoaOgVcqlYqq1WrgNpp15XJn54GC\n/wjYsEnsBQffSAr5d4KQd8IWEFiCkTkKDwJscnIyyKxcLqfp6enQVTBcu91WrVZTp9OJniHYECoL\nfe7BxpD4e3t7Wl9fV6lUSmCITqcT6390dBT2is+BDWmmSeJib29Pi4uL+vrXv67vfe97GgwGkXQA\nb1UqFbVarUQWt9PpaDAYqFqt6qOPPgpy6vr16/rZz34WRykSYCKfTtx6jIKd8JMlqBD1KgHmBWyI\nvPt64WedBEfW0Yn0mdL8jT9irfFxBwcHCd/5q8Z1jz1TSpCAIPshwRgTFoL9hplMJowcWRiclJeF\nwqyn69r5LMbOWTcmHIVPTyygyFlQD3IxItwTw8AeUN6lVCqFMnvq3J/NfsStrS0dHx/r3LlzqtVq\nIeCwvMViUbOzs7p06ZIGg7OmRp1OJ7KVmUxGly5d0vXr13Xjxo3YjwAzyB5RSj4ILhHKXq8XpV3Z\nbDaAAUCBwBGhowyKcpBGoxEO2MstHpU5wIhh8FHW3d1ddTodjY2NxebzfD4fDBlzz3NqtZoymYw2\nNzdDZlDMvb091ev1WHNvXER2mTVlDBhuGgsUCgV961vfis5sp6dn54hhmCm5gzXn6JterxeZ1NXV\nVdVqNS0tLcVZV+yDwhBxeTm6n8+LgXFDBdtJIMEc+v2Yfy85Yn5Ye/6QMUBeKC33AIlGDg4uCKwA\nxhgsxoIupMv5ntSLeeAqFou6cOGCbt26pa985SsJ0sBlFZvDelGxgUxfunRJ5XI5cZSKAzGe7SWl\nTlQsLy+r1WpFJUGa1IO0c6fnAVgmc1aO7dkzPsOzfSyMGwCKDNAEDvnDLlBGy5goiWd8ONLhcBh7\nerC//H55eTnmx/erM9/cA31wu4T9Zj9Vq9UKYot59C0STjB6RhhQhV3hu+gX2YTd3d3IIvC+kHDo\nF3ON3sCWk7Hj2aenp/E9fGqpVIqqjMXFxdC/RqMRzVL6/X4cR0GwB0HiHZEBkOVyOQI+BygEp9ii\nSqUScgCwI9jlnZCzg4ODuM/p6anm5uYSpA32jPesVCpho5HNsbEx1ev1xJq7jONHkQW3ecw9cgn5\nDIGJjFD6jSxLCr/kJeaQvZ1OJ2HDn8QL2WQ96Oadxna+Bw4ZQo8gY7AT4CRsDDIFFpOSgJvsGGsC\nOcAfbCwZHWSeo0E8gwW+IBnBliOCI/DpcHi2TxR9gZCRFBk2AufhcKiNjQ0VCgUtLi4GGQ0GpuHP\n3NycLly4ENiDIIPA46mnntJrr72mq1evRjNLz3wReDlRgB/HV6APzDN4BbtP8LK3t6fd3V3t7e1F\nUJbNZtVsNmM+mSt+RxDL70hEUVUgnWH0+/fvhw0fHx+Pig3sKRgjn89HUF6tVsPOEPD7diyyjf1+\nXw8ePIjKCXwp1Wztdjts4yuvvKJPf/rTQVJhGwnUiFOQBTKyvV5PpVJJN2/ejEz47OysqtWq6vV6\n4FBpRIpge3O5XNgEsJb7B/SC77GOThw48e1VACQrIOzA9awF+pDeagjZ5nEV+uIkURrXgVU/Ka57\n7OeUohC+ORsn5kEpoIEsHcrj4B0H4EEIWTkH7/xNdg0F9ZIiQAhnDsGScV8WE3afoMXBP5vKERDP\nTrGoLJw0argAs/SpT30qyjW2trb07rvvSjrbu9TpdHTnzp0ASFwERs6Qz8zM6Itf/KK+9KUvRaBI\nYEppBeNjbniP/f19zc/Pq9lsBtt/eHgY510x77Ap3N+N/mAwiM7AjUYjjlvx0jTYMww2exnIDLDW\n0llgi1FstVqamJiI/Q6ebaZkzMERWY9WqxVdb13RUFxAkSsj4yPAWlpa0h/+4R/qxo0bwboDoNgI\nj1GemZkJeaHsZXNzU1tbWxoOh3r55Zc1Pz8fQalnkTwDhTy53PJ/DDMgyY0RF2voBhUwKI3KrtAT\nAC/j4b4OBiB2MEA4aC6cu9+bygHKU1nndKbtSbrcWXhmqVQqaXV1NcqekEkP8Gjtj5y5Hfzoo4/0\nk5/8JJ5DwJf+dzpzPhyeNZeBZIEoAzCguzhwBzXYbcaL7GM7eF/+MBbG4FUkAHbv5Mf3JQVpRsWM\ngykp2dG42+1GNsGD69XV1bD3zHO6PBn7jnzzfhxNgK5JZzYLewAYdf+CLjoAQie8myOEFv9nTcli\nstecDp6eaaJsFHBAsAnYYE8o4ycAg7Qi87K0tBQZmqOjIzUajURWg/I8wDagd3p6OnoF7O7uJiqP\nvKyRbRLHx8fqdDqR3cf2IGPIAoFMoVBQrVaLBkOAZMC7B5c8hyDZCeGtra1o4AK+eNT2Ee8Qjw9B\n1yBNGDMkETJMZgY7Rud65Nx1msqhJ3X/vPRxbEe1RxrboXOUX6exHTYFbIe8Y4Poo4H+YJ+wEcgZ\nmTJki7Fw8Wwy89LIfiFPHqiS8eb52DZ8N+SJ+0jOHK5Wq1pZWYmExEcffaS7d+8Ggb2+vq719fVo\n2oUNZzsSuCmXy+n555/Xb//2b+vFF18M20eAwZw68Y4NyGbPyuOpXOM7p6engSmxFZD9w+EwMd/g\nLxrgbW5uJoIZrxZhHqgWY15brVbgz2KxqFu3bmlsbEzr6+thRyCFHKM4HmcPPk2K+D/VEMgCekfH\nXtZtMBjEd1qtlmZnZ/X222/r7bffjgoJ5IZmmdgNbCJE6fb2tnZ3d3Xr1i0Nh0NdvnxZy8vLyuXO\ntgbgc5hzT8IwV/gWr7QBszkR57jOK3J4L4JJ9It5gAzEBjoecIyGPEAIONnK+qHjjuuQu0+K6x57\npjRdwoQgw6ji5FgAgoN06QaBK4YCBeSenlkA7DBhCC/GA4YOR+md1JhcT4l7aQOOD6OZXhgWFCGH\nZfB9ELVaTbVaTYPBQPPz8+EY2cMJU5TNnpXrUqrWarWiZLJcLmt8fFy/8Ru/oT/90z/V4eGhfvrT\nn2pvby/27Xh5AswJZ1DBDhJwLiwsRJkUG8iZizTzTfkXJbreunswGMSeVlgwP9Qd5fHyAAd53HNm\nZiYUwVk/SaHQe3t74czoQkkph5dQkOVkHQGgNOMhYzUcDqPUjX1OFy9e1FtvvaUvfvGLkaFhLPPz\n85qZmYkyY0mxX6HX66larerevXuRSa5UKlpcXIysF89GpiA+MAJkW8gyeOYRJ43cefaEOSYjC7HC\nGrrsMv8YJNdTaRQ4ID+wbn4PAmZnUgHGgDkMME7/Sbs8OGOenn76ad27dy+CBmf/CdT8e2NjY2H7\nKEPCDiwtLUV3bmwd8+7BG/OLTgG4qEDA/vH7XC6nCxcuRJnm2NhY2Ga/Nxdg0v+43UVePbCVRudj\negmrNNq7SZDAd/wdHbRyL2Sc36HzjMOzAq4X6AvZBWwc45NGjZUIzvANjAdn7qw4TtzfieoawBr3\ndlYbkm5vby9Ye2m0ZYE5437Y2Exm1DnZx+RHmFBpcv/+/Shp87EMBoOwl9lsVpubm5GJ9eoa1n04\nHDXZy2ZHnd8lBZiFzJqcnIwgAfmemJiIgA97R2XJYDAIwE0TLmyKZzcJRPk/5wo60ebzju8gMPW1\nwa4+/fTTYXsB8VzIJfbR17dSqUSgzXjpnE+TwSf1+rewHX7VM0/oiO9B5HLfQHDljaPSekflATIA\ndoSogpBl3SEq0iX82AuvxvCtV1514RVHBDx8DixZKJx1V5+dnQ2CnMx7r9eLvZ6Uh9JZPZ/Pa2tr\nK0qLS6WSZmdn9Sd/8id66623dPfuXd28eVOZzNkRK+BfGh5JCsJqZ2cn9m9COpFNHQ6H0X0YG0IQ\nC67GdzNu/AlzWKvVdHh4qKmpqcj24k+y2VH3X8gnqixoGpTP56OsFvLJn+vBKPPL+NiCsru7G4Fi\ns9kMfYego8cISRdiBWxqs9nUwsKCXn75Zb399ttaWVmJ7Pjp6amq1aqWlpYSOJrAHVzXarW0vr4u\nSZqdndXFixcDl7vtQS/cL/IHO0bc4DgNDIhsSgpZRt6Q3XQM5LaQOAZ943s803ECOFAaVVs5rkNG\nvHz+k+C6xxqU4shJF+PkC4VCADUvefDfw1rBzHi9PZPPYjCRDopZDHckzlwCslFYhMKzRVLy4PX0\ne3nAx/dwuCwyygKYIDjLZrOq1+vhGGlWBNOEs4et7/f72tnZifcsFot655139Oabb+rDDz+Mkgua\n88Bk4DjIhDAWUvls1qYlOWyIpAAoGGqfewLRjY0N3b59OwwHIBqjxLzAPrGezJGU7C5J98SpqamY\nDzK2XIBrXzMcDMDCld/BKXMgKeFckCU6+iJzdPn9/Oc/r9dff13PPvusstlsNBiggYEHZ9JZYM+B\nzuzTxZHyt5ejkUlmfZgXn0vWExlArpE75N8zWhghmEj+EIB61tWzATgxnG46m4oTwvCxns4IEnjz\nLk4YPWkXQBbbMz4+rvPnz2s4PCv3+tGPfhSlgh6YckQMtg2H441bjo6OdOvWrdhLwzxCUkgfL+3h\nd91uN2wpQQFggc9RViUpwAjPwE46kEnbSJ7vWVoPVB1Y+n2cISaz7kyw398drTtBJ0DcMSLjzuLy\nvsgtGRO3k4yfn3t2LD0HzAvvjy7SEIr9+5558yyI+xjsLvaVe0IC8sypqal4Z+wmDULQQ3wgZ49i\nU7C/Hjw4IUAgure3p263G1U9zsIzdggn7DR+DVtIEExvBEAda8Y71Ov1yHbga5g/1oLv1+t1PfPM\nM1Gm7cdnOJjyjA++i7XDtvFO+Xxet27dSjSYcmCHDECyQihAGKXL4yjrBMQ/qZeTnGA7iDYCAa+W\n8S1YrLVjO69ScrLT/Rzf59+OJRyDIV/SqNkMsof+IteSEtiOINf3ZWK7wKf8DBIC8oFqLqoNIOVp\ngki2EXkE44ERsEG1Wk3f+ta3NDExobW1NR0fH0cigcDIbSVJDA/a+Qwlocwp48KmsZ2LDLJnTdnK\nQH8SP8nBjzKh1wTjRM8cAxcKheheS7DbbDajAgMb3e12P0a841vZHgAh5gQEZAa2TUoeYQeJhB1t\nt9uqVCq6ceOGvvzlL+vatWtRqt3r9bS6uhp+UBpVPfT7fW1sbKjf72tra0utViveF92nggXMh20F\nezEel18n+5FbcIKkkHf3M1TnOBnN+mGPCVw9AJaSZ2R7hZVXw3B/dAsf62XKnwTXPdaglAnyMioc\nF4YFQfcyHWkUqTO5TCbO3R385ORkOEkWwp2FpERZB3v+PAMqjerzuVwpWBgMFiw7ezKl0WHgGF4H\nZ7nc2b4tumAuLi6qVCqp1+tFcAuL44Eoiw0jjFP87Gc/q8997nN68OBBMN8Y7FarFUYB5XX2iXfG\n0HGUi3fpIkDCgEIgOGMpnYFp9qO6cSZ7wTi4twdWjBtQOhgM1Ol0IkMMCPdsEsCU4N/LF7xMjiAJ\noJUuL8CY8V3ADQCXw5739/dVq9VUr9f1yiuv6Omnn9aFCxeCMcXZsXdiamoqnEu73dbx8bHW19e1\ntramqampyK7Oz88HEeMy64AUeXRDhq546QbOBR3jvVyvPJBhjZlDDB1B+nA4TFQZoFvcw4NdnsP+\nND57cnISjC465Kzfk3als4Tnz5/Xj370I73yyivhEACrzAeBw+LiYpRYe0t6nPnk5KQ+85nPKJfL\nBTPtOuHMp2coARmcWUzlhpTsSO66CwHnZIY7Pp7hoJF/e/DkZXBewkn5G8A0k8kkui16kOesOT9L\nE17ovDPLgE3GxvpIIyfq/gYbA/DAWfO8K1euBGEgjToSOhHFu+dyucie+fYRLvaiA/QYG81UqOrh\nfdHRdNmcgwYCQTIE2BT/HkRwmjhz0EFQi/1OM+isBfekg2OlUvmY/4WswsZR7UMFADYCAhZAz3zx\nrEwmE9UrZIGmpqZ05cqVRPmb2yPuQWl0NjsqwXUSz31TNptN9E3gHfE1BE4AYhpEOUEujap4pqen\nf6GStv/ZLmTLsV02m/0YtvM18svJHnTZKzMIjMhwum9EJtErD0qcfHd7THDsWXLuhf663yKI2N3d\nDRyJfUHWvZqPssbFxUXNzMxEgEOQtb+/r52dncC7+FI/xWB8fFwTExP66le/qsnJyejQjp3s9Xpq\ntVqBNQh6IAHYP+nZMd8b65UD2HjGAFlDYAfOAjOcnp6GnuOrvBu7lDzDfXx8PPSWxmvZbFbz8/Pa\n3NwMm4RNADujk5LiGCcqffBn09PTyufPSoMhljx4ZJ2whdhT8DrECQH6pz/9ab3wwgu6dOlSzA9+\nr1wuhw9jjrrdrra3t9Vut7W+vh5VIQsLC5qfn4/xImtgNTA9z3Dsyvi9kgi75bjL9cdtD7ENmVSv\n+sE/QGB4QOr4mu+gO2TC07jOdfyT4LrHGpRKSqSYpVHrfZwFE4MR4ju8MNE5L+81274gzpzzHC+F\nwrAweThxPusOGTADAEgzxJ6hxThJCoPDgrG4DuYqlYpmZ2eVyYyah7CHs9FoBFPtgTXGHeN99epV\nvfnmm7p9+3a0/Oaz1MRjgD3ogY0n64ySY9R9by7OwAEXxhxlp9EDAowictixZ3H4HkaK4BrGEONO\nSQrv6nscYC8JwgBsTmBg1Jgvz5iyFoAp9nxkMhlVKpUoq5VGDamGw2EieL5+/bquXr2q8+fPhwPk\nT71eT2TEaWpyfHysjY0NbW1taXx8PM4tLZVKmpmZSWTFvKyCoN6JHRyGH4vge6bMrUAAACAASURB\nVOVwFu70HKjzGQddZPaQcWlULu8BigPwdDaLfcxe0oETyOdHZd9P6j4rZBxne/HiRUmKfdY4B0qy\n3U5QKtTv9+PsNM960QCs0WhEeZQ7Ja/IkEZOBbDDHiLkkvVLf9eDON4HkE9WwG2zP8t1DVnENrNX\n1Vlj39IgjUCqZzKl5FFh/BvbC4jzMREIMW7eh8+4X/FsmNtIgkTWElvqWThAK+vkRA0Bo+sZ78Rz\nybCwln4eHOvGuzioh+0mawfxix3g+TS04H3Rb0AlF/fzSgeyIOg/92StvCwSkDI9PR02CVtDIxHk\n2bPcLgOnp6OyYy/BxgZRzjscDrW5uanbt29rY2ND+fzZMRrIMHLI/JycnKjb7Sb2OQLGsFEnJyex\nXxo/im+SRsGXk5yUBfq6MTeQ5mTBnuTLAxdphO0I2NPYLm1jvDwTnZH0MVnzKhTHfK5vnlzguTwD\nLEc5uZQ8ZzENxrkP93VyzjEl74gNmp2djUCHsaMD29vbUapOsI1+Qb7t7+/rC1/4gm7cuBGNLAnc\n8vl8NJ1kTtBj8Fo2m41jWmgIhE7gn32umGP2+4OHstlsHFODXcO+YNcg3tE9gnnem4o9aZTsyeVy\nqtfrQdgT9FHVB15hTdmeRSIok8kkTpVwEgwf5Uc3Mc9eosx3CNwI0J9//nm9+OKLunLlSuBO3gG8\nCCYfDAZx5Ey73dbt27ej7BoiDIznPgJ/y314NrINccL7e4JMUuBnbKP7SdaU36NP3kjM/XS6ms77\njriOksF3XIdf/KS47rGX7/qF8yeT6AJP4wBptDcKgeMPbMXPq492oOxgK30ffufGh/uxZwVB5uce\nlHmpF2VUPFNSAjRiEFFINp8vLCyo0+nEWVQHBwfa2tpKCC8MGMZlYmJCc3Nz+uY3vxmMPKwtDZyc\n9fJMFc4WUOjzNxgMPsa+8Z4orwMNwDNlAl5ah2LQwKlarSa6M0oj58L8HBwcaHd3N4JVz5qwrsyt\nryNrgpJKClaS+fM26LBSGCjYvEqlEuvpZTLUz+MgxsbGVK1W9eqrr+rZZ5/V7OxssMN0rWPdyToe\nHZ2dUXZwcBCdeynzqNVqMWfMiTsAWDKMqAerrPdwOAyg5WwvBsv35jgbxvwDmgkgvawKHcZ5oV/o\njOsWxpffeUmXA/Un9fLgBrBaKBTi6KZSqRR7j30v6eTkZDQ5wnlXKhUVCoVo5MV3KfdFFz0TkA4o\nmXPWABnAibhNgFhzO8iF83fyzX/PvaTR3mQngrCF6DX3dLvt5XB0peWZ3A/bgQxh48iAuRw6iehZ\nXQ/iPXu9srISewmZD78Xe4e8SY5n6NJzDkj0slE6W7L2OHYnh3gn7g8pSJWKNzfC1nhGiAwu40Tf\nPRviFT+8KzYRUDs/P68XXnghGszxeeScQNcbSu3v74csAn5YA2kUgLD+EKKebcH34IOxSZCprl+Q\ntd5JlTkiIGUtKCVlHPl8Ppp+DYfDaEZGkO1ZIPf1YAInPqvVahBNY2NjiW7uvm/ySbuYF2kk+/h4\n3+qRyWRi/6GTXE60oAt81/2KV/JgMyDIPBhG5hzX+Tg9U4oOOB5AXp3IoZTcg2cCTkh5ggOSDbOz\nsxofPzt+pNPp6OTkJMpUnbSDiOcPGbvf//3fjz3gyD9H6vF/L7n1CoV+/2y/ONktaXQkmDQK8Jlr\nt4fS6LgbepuwtgSt7A0ulUqanJyMyrl0p1rHNAcHB2o2mzFWtlYhB/1+P7p+u1/BV4yNjanT6QTR\ncXR0FKXzfI51J0tKv5VarRYYh/fDVoGf3S5+9rOf1YsvvqiLFy+G/QUHsv4eKO7s7KjdbqvT6QTB\nCK6DAOB7zLWToWlfNBgMIg5CJ3yu0vGRVwa6fWWc/Bzs6Akc1wF0iO86sezP5ZmQFZ8U1z3Wc0pR\nAldqDIuXIfX7/URw58yYNMru+CHpLDILxuR5Vs2ZEZSUSXShSC+2lDw8HYMLIPF78Vmv4/bOX7xj\nLpdTpVIJRqXX60WAl8vl4qxN2EaABX+Xy2XVajW98847UfrpZ6P2er3IqDj7B6gj8Gd+cPyejaHE\ng/Ezj85Upc8iovQX4AW4872zudzoDCnKPDEQvAMloCgFc4wRprMdAJUMZrpkxRWN/bIOXAEPrCcd\nmAF5BAZezsu78Z1qtaobN24on8/re9/7XgS+yBzv4uVL7KeoVqtaWFhQvV4P+SbzQiOAtHOFqeVy\nhtWz/a4vHsh7sxIcOHIDMObduI/v7+GZ/l0PdDxT5STG8fFxgAeMbJr9e1IubFs2m9Xc3JwajYYW\nFhaiYRbZQkmRFUKW9/b24iBv9C+bzUYTtv39fa2trWliYkJbW1uJKgTWxC9k3MEYzslLR5Ep72Tr\nQaaDvZOTkwCXPMP1zQMR7kWQhYzQ/RT5QcZhhSnZZ1zuvCGVnJxCrpyEcfDLHPiWCtcv3uH+/fsR\nPDJ2nwP/nqTIcj5qzpwgQ7cAbIAqnz/Wx5v2MZeUe7vPZB64l28VwXdICiDLfk3vB9But6NLebrj\n7+npaZTJct98Ph9ZEM9weoUFAQbBGNU42BK3q5C5ZBCwkZ7h9SCdihTP3JCpJAOEn+D+HpBTUYPN\ng1SVFGc7Atbp3Is/cEDG/ZgvD3iZR2QPe/ekBqZpbIeOpysVCJQAz3zOM6zYOml0RJXjMw8GkBEI\nVMd9Hlz6/x1gS8ku3NIoKPbfMS4CNUgRP+MTncnlcpqbm1OhUAgb5rINcYK99/3TY2Njmp+f18WL\nF/UHf/AHajQaIft0hvXvg3XBb5BYpVIp8JDjEewKz8T2OVbG/zsehchyWw2Jio45zvNKCxIDXvbO\nHldfc9dRx+XgQtYinz87PhFsAVGK7PFMJ5HArthPzmvNZrOJY9gcA2YyGV27di3k7f79+yHrkBME\n89gajq5ZW1vTlStXIiAlGZUmEBkf7+ElzDx3MBgEiYY9xKa4j2DdCGg9nqHqiHVizrC1fjlZgh13\njAfB4NiQd3SZ/o9e/26mNJPJ/J+ZTGYzk8n8s/2slslkvpPJZN7PZDL/byaTqdjv/lsmk/kwk8n8\nayaT+dq/c+/ES/Ezb97gWRxnWXHCGDpn391oUbLkv8M5A1g88ndj58rq7DcCgnHyzBxGT1JCGFB+\nBALhRXFpe0/g5WckbW5uRqMhByv8f25uTq+88op+8zd/U+fOndP29ra2t7fjXbjv7OxsfBdWi//7\nvi7+ECQyxxyGPDk5mZhv1tAzMTBTkiLgZI1hE8noofDMc6EwOp6CdfFAyxklZ8x4tssNxtLLrWDt\nPeBiPh3IUgrDe/vaUjJLlpRzVAFIExMTun79ut54440AT8w3pbsuj3TXvHv3rlqtViKAq9frIU8e\n/Pu+qrTxcaeLI8lms4myIi4Mnztbgm4PPLzsnPni5xhkB4bci4wcuoMj8CCZ9U0HUP+V13+mrXPW\ncHp6Wqurq8rn89re3v5YhodgaTgcJoJWz2rxb+TpH//xH7W8vBwt6h1UuV3j3xBlvl+PDJJnal3/\n/J6SEtkxAoFMJpM4U1QaBaHIKHrk6z02NhZ7Lb0KBjs0HA7jvGa3U3wWWULH8vmz7pV+bqcHsQSy\nw+Ho8HE+44CY+aD0ysuQPPjHhw2HwwCETvwAitwOoiew+cPhMHE0GvrrWSfALxlP7JiUtKvIk+9Z\n8tI1CA2AqWf4CEhZm3x+tOcf/fVjFRyUsiZkcuhajp91whbbR1WH22DWknH6fl4PZJgf/BhzRRdg\nr7oC+BFAe8djSVGi+FC3w/65r6Y6hXt5gExgzPwD/LyUG7CJ33mc138ltmNNwXaetXFbgy9Bz93n\nPArbuQ4iD5A8zLU0CkL5nAeO7jvBhqyXZ/Zchty/YTclRTYMG1UsFjU9Pa3hcBjkBCR6JnN2jrpX\nEaSTKVevXtX169f1+c9/Xt1uN7rwgo3AT+Vy+WNZtcFg1OwLvAGWYQ7YGkbJPTqKr8In+TYBnktG\nmvfGPjIGSnEdD1ar1dDnUqkU5fNkeg8ODhJ7Hj024IKYxZby3vRuYc2YQ5cx31JF9ZrHEcjHYHC2\nZY1kDvtIB4OBrl69qi984QvR6Zgg24NDbD3Z1rW1tcDk6EOlUkngZOYeQiddkUNQig/1ZN6jfDW2\n0n8PYch9IYmYH8aB/PA5HwtzhL11ggXZcjvwi+K6/0j57v8l6eupn/3vkv6/4XB4VdLfSfpvDwfw\nvKQ/kPScpLck/R+Zf2M0Hoj65nAcnTTaN+OlHIAcd0QOhDFWzmijTEyaO0eCHYyCs3oO0PwIDoTC\nlcL3vTr76oy5B8o8q1gsql6va2VlRYuLizo9PY1SXRgZz2zwZ2xsTNeuXdNrr72mxcVFzc3NxbmX\nZNZQPILiSqUSxpEgwvfPEIjjgHk3QCsssjfpYJywxs7cE9x5trhSqaharUZ3R0opOKcLYEvnZdYU\nthujAtigfAQZAQQAsmC73Di5bBGsuWNgHjBkDj6lETja2dnRxsaGNjc31Wg0tL6+rvv370dp8nPP\nPRed2zC6ZErTpcXHx8dqtVpaW1sLZ0VAWywWE1kg5MYddTprjSHzYBq9Yy1x6h6gMy+sKXJO5soD\nBTdanjVwQ8jnPQONAfcMha/NY7r+02yd25der6cXXnghSrSQYwJP36tOQIGT9eCVtcKpZrPZKNV3\n4Oeyy7pgDxcWFiJLS6DBc/guTtTXUxo1G+H/kFXeQZZ7+FjS94I19sDXM50euPqe14frEP/HuaPP\nzWbzkbLPeOh07CWxjwLMAElKmhzw4PTTwYbbe/dZPn/cy8d2cnISZxoD+tAbAmln5QEYdOdOE7j4\nUth75hrQ6cEtssRzKAsGbABMydK6fcU+ZzKjahnGwfPc7mBXIGA98EQn/LxISDhAr6QIuJ1cJgNP\npQ3ZKewMPoJAkp9hM90GQlrQuRg76WvLupOxkEbZCwAw8+J2zsvdH+P1WLAdfsKBrDTCdv5v5ptH\nMd+eCHDc4dhOUsIX8ntsC/dk/dJEq29L8JL3NAGDb0OOwBSFQkH1el3z8/N65plnVK1W41gWSLZW\nq5Xo14CMlUolvfbaa3ruuec0MzMTAclwODp3GLvkDcXw4xApbp/cNhOwggtzudF2MM824puYSwgw\n7I9nmqm0wQcNBoM4Em9ycjK2D9BzxCvjBoOBVldX456OTbwaBBLAKxwJYnO5XCL54FUWbnuxgcxT\nGotI0tbWljY2NrSxsaFOp6PV1VWtrq5qMDgrT7506ZJu3Lihubm5sFd+ZA3jpYqk2+1qdXVVm5ub\narfbQXgxJ8giONvtOGvFeFkv922sl2/5QJYYjxM+jNnfn3XEZ2Cz3GY6rkP2fevYrwLX/btB6XA4\n/L6kVurHvyvpvz/893+X9HsP//1NSf/3cDg8HQ6HdyR9KOmVn3dvByCu3EwUE4HxJuOCc8PwsyA4\nGS8Z8AsDw/O8NCCTGW1uhtnxEhCcXZot4Pksavrz7uhYnEKhEE59fHxcpVJJ8/PzmpqaikN4Udzb\nt2+rXC4H84sAFItFvfzyy3rppZdiHyqHK8N8OQvO2aKAEErtmEvKfQkwDw4Owhkzd4PBIEpO+Q7z\ngZLzB9Z/amoq5tb3/JIZLhaLoYSlUkm1Wk0rKysx9xyeTtkDrKAzpygDa8c80X6cLIEHmNT4Myds\nqEc+KC3lZzQOwdkcHx9ra2tL3W43mlYQgHP2H8Hpa6+9phs3bkQ5yPHxWYc2DDyGBuDVbDbVaDQ0\nMTERZ58SyFcqlUQmyR0l+1i4kHEPWjEUTuJIo2yBBxIeFEkjsI+e4pzRM3fWbji5N7LjWQN0iu8z\nJ4/j+s+2dcw3urq7uxvZHIx2pVJJVC3AgAOQWHd3oKzH7du3lc/no7rBM7CsSep9o2wcUgfyiKOO\nCASxiZ6VSFcyEDRQquYkEONJB7ae2eTfAEK+x79pvsblDhK5JqhE5jwok0Z7FwkYfByAMeTW7Zsk\n7e3tBXh1Zt3fMV0eytp7IEOmDt1xoF0qldRut5XJZBJHd2HPpbMtEYwRQoLyW2SHkl/XJ+adrCdr\n6D6LQCuXO+vkTMDoGUnmi3khkPXSRsZRKBQC+GJz3M9iB5gjCEmexT5rJ4YhIJBJsq3IBD0H2J9G\nObrrkcsQuAF8gW2jnJzKFTLNXvXhmS1+ns/no9wem8kcTk5OJipEHuf164DtkCFsvwctEA38Dt8r\nKYGpJAVZ4RUP6YQA43Jiz7EaZCABFjqOfKaDW/TXyQnIdMprOU1hYmJCvV5PjUYjcMXW1lZ0b4Uk\nyefzmpmZ0Ze//GWtrKwE9mu320HGOyEiKY4S4/8Q4GRtyax6dQ02wqs/3N4xf1TpQMqRqSPzms1m\nVSqVYm1LpZLGx8dj7z/6X61WNTs7m8C96NT4+LgqlUokUBinn7lNAOfrj1ywnmBLtzsTExNRwSCN\n9piSDHH8xP/b7bZarVbi2EWq4ba3t3Xnzh2dnJzo05/+tD73uc9pfn4+4TOwS5D4VPPt7u5G0mhu\nbi5weLVajVMZkC+PQ6hsYe3QDU8QQK76fDk2Y77dBns/kXTygTmWkpVO6C0EDPPzq8R1n7TR0fxw\nONyUpOFwuCFp/uHPz0u6Z5+7//Bnj7wQLIwHDhtD42UzTAigyT/jrII3g2AyXNEICgmaPMvjXQ0B\naYACDKCPyZtBpEuJYJsfztHHAmuyuXTiItg6ODhQp9PR+Pi43n///ciIMVYY/tdff13PPfdcjJ9j\nYobDoVqtVhwa7Iwj3T2r1WoE7wQ50ihoJ0BEWCV9bN9mu93WcDiMIAxQiZHBqHugRyYH4feMBIq4\nt7cXTGKtVktkimZnZ/XMM89E+3DWio6kOAvWimMU0mVuMLeebWV/KXM0OTkZBjifzycOZGYNAYcT\nExNqNpvxnNPTU7XbbW1ubgbo/63f+i29/fbbsW9YGh0Wjywyn8zt4uKiXn31VX3729/W0tKSdnd3\nE3LoGSU3SswtQA2GzQML5okgHYbSyzK8GkEaNblCH8mcwKhi2PgbIIo+4PwA9siXBzC/htevxNY9\n/H44KRhuLyHs9/ux3wiZJ8PlBAD6yTrkcrloxsL5j55x8IxjOigkQOI7fAZWnsYKjI+1cjaWi5+h\njzhLD4yRKYABcoiDc7lwJ4rta7VaifND0yCK+XXSxP9mDP59D/iz2VGJJv5pMBhE0zGahLCW6Xt5\nVQXPQh+dveZ3DrTIlhM08jln9JkjWG6eLZ35lt3d3US/AbIR2Nx+vx9Hrjh7jhx59hXiDsBG86F0\ndtIDUN6RPWP+HgTXZA+xVQR/HthiDwjuIHI9CwbRhm3HJ/NdsAQgmHfmHFPGRZd4bJk3PUJv8vmz\n45kAXIBkD5Q4Userd2gGRRmzl5+zbr9m138Ktstms4/Edug/3/HtVvgaMAi4SRoBXZdr96MEGOAy\nt4NemcQa8F30wIlY5MYTGgQFvIM0SqDwnmyZIoNJhvP09FSrq6uRTICsz+fzqtVqevPNNzU9PR3P\nY7zdbjfKSdEL3y+NXxgOh6pWqyqXy1Gd5uS7vyMEDvp3cHAQ1Vxe6kulFoQ/x79Io34VTnChJ062\n08WaoIxEQaVS0cWLF/XSSy8FFuv3+2q1WoG/3YYSgJJAITudDqrxRxzlBHbzJmMuA2Rat7e3g7zD\nnvKO7XZbzWZTzWZTnU5Hr7zyit555x1dunQp8A1ZYuQK2W82mzo8PNTMzIyuXbumP/7jP9a1a9ci\nqEOO0A3+TTyDD/VeMY4BmB/iIWl0DCU6BiGITGGTsIXoofth921e3ce7kZ3+VeG6X1X33U+EJhFA\nAsI0M+0MlTRivXHU7lRxkggQIBkQ5qw2zAXPxLGhdADo09PTyMrFiw5HB/cSBJCdJDjEWWIs+Ww+\nPzpMHLBAJpBzL2lo9OGHH6rVaml6ejoAAnPx3HPP6amnnor9jjA5x8fH2t7eVjabDXYOFg0Wvlqt\nxnvAwMDgMlbAD5lGMqOUcp2enm3s52xU5tuNPg03cODz8/OJ0hLYa/aSEdz6ocso4uLioorFYmxu\nP3/+fICZQqEQ56BCTORyOXW7XWWzZ+eZpo2Sgztn5QGJNLZADskcwTBNTEyo3W5HgM1aFwqF2FeK\njN6/f1+dTkf37t3TpUuX9MYbb0hSYu/YYDCIPRVkE09OTnTnzh3dvn1b7733nnZ3d3Xu3LkE8PY9\nNbyXs1rdbjeMMGuI7DtwRhfYC4NOSKNAg6DEy9wcXGGYuLevoQMDnCFrhdPiGR4k/Zpen8jW8X4n\nJydaX18PFhsH6kDIs0ONRkOdTieaWVy4cCFh93AugBUOWidI8yAUWZFG6z45ORmstoNknDB6yr2Q\nA1h01hkZpAwsMWH2TII/J+/4g5Nzm43NIDDhueVyWdevXw+g6pnVTGa059pL+zxzyeXvxTg8owYg\nOzw8VLlcDtvthJqkBFmTdsboKZ8nS0u1CQDX7ac/gzlkjjn3joAIsgw7BenoJZMEhAQL3ggF2+m2\nh7VgvpEJgmhkFXvh5xwSFPi8eoBAea1nSnlXL6nGp7DPzIkFABBzx/v40VPpd2Mc9GcYDAaJoy7w\nRekGLowHWfMGUZISoHU4HEZQi530I4Owz2S9/ie4fiXYzrcY8HvHdp4dwld4MIlcIi8OigHtPFNS\nAk/yHN+XjMy6fXQiym0bpDtZeifMpFEAXqvVgpgoFosReE1NTanRaIRevf/++xoMBnEsC35xYmJC\nn/nMZyJbz3YKMoh7e3ux/YpsPJiMo/z4N2R9oVCI/4MPMpmzUwTGxsYC42Fn0XnOhmZ+6bibyZxV\ncHAUCLYZrOvk5enpaWBEyHkwF/a0Vqvp0qVLmpyc1Kc+9amoJCiXy7G9y6shwZoE42S5Cfo5/5Uj\n9ZCFNPZx8pIERKFQULPZTGy12t3dVblcDnzKuu/s7Gh7e1sbGxuanJzU1772tdh+hk3CvnhA3u/3\ntb6+rps3b+rDDz9Us9lUvV5PlDSnzwTHnvnagUV5f0/U4eez2WzYN2ISj1nwKehWWgcghpgzaVQB\nIeljlarYuF8W133SoHQzk8ksPBzkoqSthz+/L2nZPnfh4c8eeTGxDlZgmnh5DBoO1DNFKJEz75LC\nceMUMHZS8owdv2ClvOsswB6FZvI90ykl2TQ/J9CdEOwaijExMaHp6WlNT09rdnZWknTnzh3t7u7q\n3r17arVaWllZSRwaPjk5qeXlZV2+fDn2EzB3fuj86elpHBxcLpfVaDS0v7+vvb09tVottVqtcP6e\nMcMx8/4Eb8w5TiItuBhYDCTZUi/1xdF7c4xsNpso1wCocDkwmZqa0oMHD/Qv//Iveu+997SzsxNH\nZfiaMkYAtZfcoaCwidKIZOBvjHS/348ScbIO+XxelUpFu7u70YAGpwWrT8CNEsLmNZtN3blzRysr\nK7r48IxKL4tEwfn75OREm5ubYYDYu+DlNl7yhCF0PZIUwJx7st7MG2uM02XfCIYFgI3hR44BmZ4x\n8z2jMGbOhgOQPWPG95A7byTza3L9Smwd8wAAbjQase7MtYMD5oeSQXf0zlTyPZqYHR4eqtVqhR45\noedBjqRYL4gUKXlsC00guPyebm8ZB6DQnZPbDmm074v7eOYEG8rnAAC+NYCs3N7enh48eBDt/Z0U\nI3jBkZOF8KoLnoMdZ8ye9ZiYmNDCwkL4Hw6653tOOro/8Gyfg2Lei3d1R49uoDNkGdwu+/4gP+fy\n+Pg4mmZAvkFIAtbGxsY0PT0dFTAE+ewDc2KLuSfoo0MmGX7m2huSUPXjQQfAHz/FnDnAQpa8ORfn\ncLMm09PTQZ76d9O2hXn3s/x4xunpWZdT5hN94nv8nn+77RsMBol9bI4r0plY7Dlg0ctR0Tfm3Ym8\nX6PrvwzbcYY6lTZpbOdEGr9DF5wY9SoEX3cnQiUl1p7gyoMd7g1ukRTj4XNOPjne9AoEMqSTk5Oa\nm5uLY096vV5Uv7FvnLGUSiVdvHhRS0tLGgwGarfbymazqlarkUjp9/uB48jsrq2tRWKCsz0hq8AD\nZFHJLA+HZxUwBKa8AzLrzagymUyQYOAzfJikOIKK8l50lGNpmGeCaN6DTGy5XNbOzo7ee+89/fM/\n/7Nu3bql3d3dwE9u87CHYCrWfn9/P5HlZhua23iq4pAHJ+xOT09Vq9U0HA4jiKYSDnKAbDQkLX6x\n0Whoc3NT/X5fX/7yl8NOu98hEAwFe7ivVFL4Ly/J9Yww70BgSZIHrAZBw/3BVvgPEhLgWWm0/5Qx\neum7Z9DBIO43nezEFrrP+FXguv9oUJp5+Ifr/5H0vz389/8q6S/t5/9LJpMZy2QylyQ9Len//7kP\nz47KYB2cu9B70ISyIUgYE5RaGp196iDMwZw/D8PC4hDMsqgemHjpF4GVd6HFQRJEc38vL/buiOVy\nWVNTU3G+EnXvGK/Lly+rWq0mWIZCoaCnnnoqYYRdGNNHSTjrkmY4ABbehABB7fV68X2Y/DRAAmTB\nNHlAicLAelEW2+v1EoH09vZ27F8dDAZRxuDg4fDwUJ1OR71eT7du3QqhJ3OLMZ2eng5AQebOwXOv\n11OxWAzDiIzQXMZL/MjIekYC48F6ENimz4r1zMxgMFClUonS52w2q+3tbX3+859XrVaL43J4D8bA\nntPj42Pt7OwE+7myshKOwjf0e3bF93chJ4Agz8YhI8gQ8+3/diDg/0ZnKJXC0QHeeXdk1LMhri8E\nGB7I/Bpc/ym2jrnA8F+5ckWXLl1KVHDwh73h2WxWX/jCF4K0Ojo6O9NWGjUZYr263a4WFhZCjpzc\nSf8bO5bNZnX+/PnQR2mUwXDijhJjyBq3aS5Pg8HoPGOexZoit8iqAw1kx4NKlznPIlK2Vq/XA7zQ\nJA4Ax1gLhULinGSXVy8fhPCjPHRqakrLy8saDs8y1efOnVOn0wnQg/3z/Wu8I3LvDty3fGAvnaAB\nEFC2i5P3DKWfQwhg90AMYEplzf7+ftgoAqCTk5PoZoufRF6wJ4eHh1GaT0l0nwAAIABJREFUR3aB\n9f8f3L1Lb2TZdbb5xpXBuEeQDJLJTDIzqypLVaqyJbsEDwwLwtfd38A/wROjYXjSf6T7X3hgoAc9\nMQwDPTAMyTAgwwYkF1SyLLnkrMqsvPESjAjGhXdG9CD0rPOeU6mWSv7U2eYBEplJRpyzz97r8q53\nrb02oBw54OgEfCrliQS+HujTuAhgjSx40HFxsTyPu9vtxrYSMu8e/FJi7Fkxt1mMieDfCTW3NZeX\ny2OGyDTgl534wZ6fnJwESYK+eKYIG0eghb3ztcO/cF/e6Q1fv1Vs53t/kTMnQslYObbDHzPXJBiw\na8iUzyf2jOyRYzJsFMDYSV2vwEAGHau5X0InXcelpUxC7NC5mrOji8ViJAVevnypm5sbbW5uBpHG\n+6ysrOjdd9+N5nc8l9JynktJvdtVkhLud13Oydwii5DY2F7ILCq/wL40CGN+SaqcnZ1Fc6XLy8vY\nMoKekgig6g+dQKeoGqSr7WAwiPWjNBUdd7Lbu5pje8BvHHfI9gASM9gWMBCYHjvsxBgE3HQ6jdJl\n5hkSEIzHfnuwHdtK7t+/H4EtyRls7MXFhY6Pj7VYLDQYDCKY3traiu7u7kOcvIeIQCeQGd+WhZ/3\nxk9uF7NNDD2m8Sw6so998koE/jAWJ29+Ga77qtevcyTM/ynpHyU9yuVyX+Ryuf9V0v8h6X/J5XL/\nLul/+sX/tVgs/k3S/yXp3yT935L+t8WvgTQx0ARnpM49A8qCebYOpfOyWSYPwfdAjEXy57EoBCoo\nFo4JRwp76wvoWVgXGIwuz6rVamFYaHqwvr6ujY2NCHpevnypp0+f6uTkRFtbWyHolGVhnB89epR6\nXxT96OgoxkypAI4dphtl9vIpFN6Z6/X19TCCCB3Cz3gpK8CA4qAxogDAer0ejBNjnUwmGo/HOj09\njQ7BudyyVp4gbzQahbE6PT0NZfbSNoBYqVQKZrFUKkVpFPMxmUxUqVSiDIx7YvgAaKVSKeROUoAs\nMgbz+TzOj6WkhQtgxFmizvTiZGG2ZrNZdOPD8dzc3KRIDvbWHh4eBpu8trYWwXjWQOVyuWge5U4d\nQ+2ZUMbhe9IA/Q6oua8bSNcxGFAAL4adezlwZ134PvfDQXhXwDd1/TZtnQdZ19fXevr0qb73ve9p\nbW1NkmIenTy7ubnRj370o6gIYF1wHARI5XJZjx49UrVa1atXr4LcYThu66Rkjm9ubqI5EmP8xTzo\nF++YCiwB+QSQLnuNRkN3795NkWDukDwLgt3xINezEYzRCR6cMP+G0Lm4uNCrV680Go1CT+7evatO\np6Ner6dqtRrj5lnMHQ0mKpWKdnd3w7bduXMnmpSVy2UdHh4GOXl2dpY6gxRAyPs1Go0IftBlJ4iw\nPfw7GwBha3y+fd+pA0/v7NpoNGJLBffiOQCkVqsVlSuLxSI6sOPHyuVl47nBYBD+CmDt4Iq1428A\nNmXg+BhAI+9O8MZeTGSabCpEAvKLT5CS801dPl1uyR44aYZ8OPGIPWYbBlUtDrIo30OGC4Vlwz1+\nh30m2Md2uX4xJ1RO+RpDALCmb+r6/wLbMd8eQLDW2CXfxuDkN/rF+oC50KdfvEOKIAAfesaJYdJ3\nA1lkjzTVUQQlUrrcn+cwXifi0BlwY7lc1r1797S+vh4VYgcHB3r+/LkWi4V6vZ5ubm5C7njXtbW1\nCFaZJ/Y0HhwcpOwj/tzHgY+QFKWoTqBBGNfr9Qga0RkILinZ10u/Ay8r9bkGx6ysrIS+TKfTqKjg\nPGl6J5AAGAwGqef2+/2wg07c0uQNoof3J+NZLpejb4pX37ENicQBRIXjQSfvpWW2Evnw7DlrSiBO\nPMF8MieM7fr6Wu+88456vV7gU8aBXNIz5OjoKAhWiDFiHBJk4CT6zfjl68P7uK6hK8iHZ4jRB8bv\nQTCJLGIudMrxhMsT2A2dex2u4/m/7vXrdN/9k8VicWexWKwsFovdxWLxF4vFYrhYLP7nxWLx7mKx\n+O+LxWJkn//fF4vF24vF4r3FYvG3/2/3puyFwBK2EgDiJVdSUorIRZbPS8yYfMowWFgv2cHAMFGe\nIbL3iAwgDBg/J7Bk8XHO3ANFoYwAZhiGDkWu1+uq1+s6Pj6OUlMCFYwGgVcul1Oz2YwgFWHkWSgF\n/0Zw2JPQarVizofDYRh7FI5nehaDAAcFQIip0cdZ4PiZFwxKLpcLBovysVwuFyVgBEyw3jTHKBSS\nRksoNOtBsNTv9+OZdDH1cwe5mIPr6+vY7yEpNu6TbWBdYQABWFIC6jHGrCn3xTEBmk5PTzWdTsMQ\n80wvh9ja2op7I+dkVmELmcN+vx8yC5Pv+oNxxemS7SdYREe8RAfZIlDg+RhDWEk3poBsdIrSHQcB\n2Wws6+kkEjqOY8Pg8R5v6vpt2jopKUklqPvWt74VmUXAKraQuXQQxfwzd8xnpVLRs2fPtL+/H/NN\nkMT6YutwLNzDu/4Bwnge9wIwIvvcu1gsanNzU8ViUR999FF0Dc9mvT27wVoTEOEksc2eDSHo4B6+\nD4zAam9vT2trayoWl83IOp1OMPCQPZQw8R7o+2AwiO6yr1690mAwUKvV0pMnT2IesMvYLfTHwQm2\n0QN2z6o4oTOfz8PGe/aP9wZgsA9eSkq/ebaX+08mE7Xb7bBz6HutVovML1nlwWAQMsZnx+NxAC6v\nrJjP57GnCuDMHHqG2wk7CEUvn8QGUr6H3cCvnJ+fx/MJ8LkvwR/zhp2kX4CUNIvD5zMudI25dpmC\nhMbHYit5b+wsa0DgwhxIUrfbjff25kWQbwSk2FA/pgmQ7Z3S38T1/wdsh9zTI8H1igw7a+fYzjEf\nQRN+nywRNucXYw+8xu+xafQNIbtD1R06ijxiq9gGRGCCXiJ/xeJyf2mlUtHnn3+uyWSSIp3JhiJr\n5XJZd+/e1dHRka6uruK4uPF4HFVSBGpUfOV+UR0B6UvA6H0+ZrNZyBj71wkUqGJrtVrxjmAx3nHx\nC8IcXJPNFDv2hojz57MWYMpcLqdOpxPZTJouSclxeCQs3E5j212m0EfmgTXCz1FlSHaX74HrsAME\nnaPRKLaLIYu9Xi9Ip8Vi2cUbe+jBH/icNaYHAYTbbDZTt9tVu92O6hNwHfZnfX09AmgpOR6MNSC4\n9SQTtoZgkLUjmHYcQAzEsWHYcLLcWVzH97g3tgv7CrGL3ZPSHbf/M7juf1Sjo9/oomkAwkO9Piwt\nRoksDMbMFwEngcMgE8TeOAIJAihnmZgsJg9AjeEk6PM0P+UeOFOEBieJwCMgtVotDC+gql6va3Nz\nU81mU0dHRzo+Po4yTTIngNf5fB5s9e/93u8FWABMeVdeGCEUDkOFsT07O4uW3Qg1IARDMp/PQ/EA\nRVzlcjmUl9Q88wKwJUjP5/MRILOGGDgMNkYGcOABC2vnRg5jD7uULaHwMgkv6+AZBJxSco4sToCM\nuHcOxjmiiDBpgFyyBpQRekkWpccXFxcaDAZRDgKxUiotzzFFLpxlohRosVh2Oea5tVotzv0iqAbo\n4TDJVBBkQyIAbB3oo0/MO+QKMsF3mHt0BnBK8wJ3UrwbnyXLgvP14McdfTZLd9suz6KgZ//+7/+u\ns7MztVqtADlONGCvvIkBzkBSdFesVqva3t7W2dlZ7Hv0oBCA7mNh7i8uLrS5uamdnZ1YB34P+0oW\nHl3nXovFQgcHB7pz547Oz5eHkaN3UlL+4wEZ9wdAAuqc1EMXnAThM4Ag9OnFixfR0IOGFfgViCPk\nnioI7skY0KFSqRRluoANgiWaNy0Wi5TPcX/EO+PMfZ7c3wyHwy8xzwBtHD9EW6VSSWWW+DdrQdks\ncsMRUnwOQtGziOPxWHt7e3F+YKlUCtCH3QUkYS88u4sOcz8CYcgsAlC3k75/FTs7nU7j+CJshROE\nUlI+yTOwZdgniEzmnnGBISAHa7WaTk9P1W63U8SH76e6ublRu90Ov4Otr1QqqtfrUcILQcEFeQOZ\ngN1ERtk2gi5Uq1WdnZ1F08HbeKGDnoF8HbYj6KdDrOMJ1w8PbvAxvkXJyQ8nTlzPCEj53uXlZTRJ\nlBQNhNAXKdnO4PYUn0j1BPa8Wq1qbW1N9+7d0+rqql6+fKnZbKbDw8Mgor06AvtSr9f11ltvKZ/P\nRyCJD2DMbled/HGMKymSBU7SFIvL/hqrq6uaTqdh28BH2Akq3dwOc3/wJceZScn5wV5RhvyDYbDH\n2GfIV55xcXGh8XisSqUSpy1gkwjG+L4nXZzYLpVKEWzSJJBKON+33e12U3ua2aLgSSqCNuYI/Mk8\nlsvlWCPwLPIgLf3Z+vp6YEgCWjKe2FB8wMnJSVS4+dE6YEL8RrVajd4PkFrIKD4KmfftNawdc4VN\nZ8zYP8YOrvPstZM+2E4vh/bqAvT1P4Pr3mhQykB5QRbQmeJGoxFgF5ZGSjZmoywYI+6Vrbt3UA5w\n8JSzszm+1873DbH4OD6eyXNZAJhXWDEECON7586dKKUaDoc6ODjQ1dWVHj16pF6vp+FwGKUKCB5M\nCwKGYPE5HDKGGKYMxhalmUwmsQ+Acgveh98DfOncCxMEOCW48yAQp+uEAawwbBWCPRwOI3CEWcrl\ncpEJdSOL0G9sbEQ5AQaFDm3dbjeUy7sfMz7fm4sh8T2pKDqG3ptLYYhqtVrMA38Dkt3hwpJjcMiY\ns+eDQKJUKml7ezvk1GUfg4qs030Phm19fT1kj2Da2U4vFWRfA3LjBABBB7IPYePBBEYJow1YdEPG\nZ5zkIUiFuUMWPFsGeYGuuE24bZdnKnF+7HlBDwBqyDCVEZxRnM04jMdjffjhh0GMYKs8CPRnezAj\nJftSad7w8OHDVMbO96m4o8H+IH/5fF4//vGP4129hNKZVmSI+yMjklJ2+JeV8fFeBBAEBvP5XP1+\nP0o8CfQBSdnMsbPktVpNrVYr9loi1+gUf6RkD47/zDNw2TInv3hfXw/uB3jBhgCssOG+7ugM2Rnv\nMAnIxIY5yUh1CwDn1atXajabERjcuXMnsnl0FydzQklyp9MJ2wcgYb2Ydz82BgKU4BQi0BsZYTsA\nvE5IUKnE77GfuVwu7GG5XA59YewATwAxQfLKykpkYZykgZyA1CDQPT8/j4CTbqOXl5epo8a4sGOQ\nJsg+64b9Z77wj7f1wm54iV8W2xFIePkhxD+YxIEwmXT3jeirl5U7AIf05d6O7bIYQ0qXOuL/vXTe\n9yJiI8m20fehVqtpPB7H0Xz1el3vv/9+YCmIY2lJaOzu7qbsLVgSTOz4EplB5sE8kkIn2BJFBYdX\nhkgKnOJbG9BpbCX2g8DMZZlqLWwKWJT59+NqsL/4fXRaWvqf9fV19Xo9jcfjIBza7bZWVlbiBAkq\nvsBVnq32vbboK99hPGAiCHL+jS+EuGIN+Tf76yEMvdmbJ2W80qVSqWhjYyOCc4iPk5MTjUajwOJk\ntUloVCqV1Dmu2Swkc+olw06K+HFLjgfxC9hPx1/oHr4QPfIEVbbcF9tFbEPyiRjNA2GPwf7LBKWA\nDinpWMiEw7QCPDAOGC0EVFIEG84e+OZcSSkw4aCBRfZMlae+cUQINoyzB7wYXYyrA08yVaurq+p0\nOtrY2IhyM4JJAtfBYKAnT56o0WhEC2rOVNvd3U118SPIxgmiLJx1yr7SLJOV7YAGaOEMJUmxPxPF\nAjTBcrvBJ3ABUGBMKb+h2Uan01GtVouMuKS4P0rj5U6sb6/XiwYu5+fn+qu/+ittbW1pfX09DG+r\n1YpsE0ATAFQul3VwcBDMpJRkO5y9JoDD+DoglJYGv9lsRsBISY6DM4CXpCitgXlD0WG5OKOMkh5n\nd914EEQfHR0Fq5rN4HumxgE4mVTW2zNV+Xw+ynwxgN58wYMRz/7A4AImPQPke+cwgswl7wkYJUhl\nfnFiXkJ/Gy8P3GezmWazmba2tr407/P5shyy2WwGoOIgdmxPtVqNfejPnj0LgslLcRzkc29k8eZm\nuQeHo2RGo5EKhWUTIcbQ7XajJT77gJyR5eBwd9DIGnrhgZt/1mXeM/uSUmDU7Ti6hN31LR1eFkbg\nMxgMojuiB9H8IWCjNM5Bh4PbrOwyPwAH3s+duJN9jJl1yB5RQKMlSanz/PA/VEfgMynBB6wBIPL5\nfOx/dJLUG86wJx67CPlBppCMJwCTDPNkMtHKykqqlB/iz0EtbD+VOYBAsjSANWw/60tZI3JJcOvE\n6sXFRbyfZ/xdpplr9M3tkNsbaRnM+/46SiYBt7lcLkgb1hiy3KuivOwP2wdeQBbBLvifN12++9u8\nvIybtcpiO/yvBwuetQZUg+2Qc2ylk2zYAa/C4ffIHPaItQc/IYtkCRk38gzgB3Q7uQAxcufOHa2t\nrQUBfnJyEtt4yuWyHj9+rE8//VRS0qsCPPfgwYNoLsPfVC94Fq9UKoWdoucIwSE4E8yJz0f+sBOQ\nON5bAyyKvjFH7vMJRsB1VB8Ui8UUoXpzcxOZP9aIBAD4AuJta2srdOnjjz/W0dGRdnd3Y96o/ACX\nFYvFON7QtyZRGcn7et8PSZF5JTYgUUKWHLvL3OBbwOTIK5UZ+AtkGz0H/7RarcDtkFGMDawFYcAa\nsj1QUoqYcB8qJWW2vn3M7YqTA16V5P1PeAby7KQH65z1vdkKUvD563AdQSrj+qq4rvirP/Lbuzwi\nZ+AAEyJvF2SMh5QcVIwz9uyLlJT9MNEOhDyg9NIxKSm/onTHm07wWYyaf54xMR4vJfHShu3t7XDs\nT58+1fHxcWQgF4tFAAV/5sbGhr7+9a9rOBxGoISgIPQoLr/38qJut6svvvgiMgLeKMD3F3iZhDer\n4A8sPMEM2QUYEZg15shBgO8pYG5x9ASnuVwu9jgABgBs3W43znR96623dHBwkGraxPxiQD0zPhgM\n1Ol0wkggR74vhPmYzWba3NyMM8R4NwJDZAOWF/bQs+owb2QAPYuby+XUbre1tramVqulzc3N2ANH\noOigDadzfHwc59bu7u5qNBoFg8k40Cn2englAYwYzRcwcl7263riIBpW0rNvp6enQVK4nrAeOAwM\nFdkHnBoyxdzj5G9rppR3xEawNshKtVrVaDQK/aP6g/JNZ3MBRQAJZ1RZOynpiOeXz72Du/F4HCCJ\now0A4Og5eoZssA8Ilho94LnZgBjw6FUezsDjEL0ixoElvgK7ze+5Fw7aG9dIyQHilObxzqwLY/fK\nGbf1/JvP429w2NgaPsNaoFPOcHsmCPuOPWUts2XuDiABycwhQRDP5IgFJ4rI0PhZcTwP+SJ7BbDE\nTkhJgMH+KOwZ707Qx7gJ8pARbLIfU8O8k1n1xkiAT9aMTCfACXmUFNtYpKSrfC6XizMLkXcnB7zc\njgoY7gvRRwBN9gVZ8Mop3s8D8ZOTk5TuMDY+B5iGVLitF/rnGRnmDn3IYjvWiKDAgyRwFd9/Hcbg\neV6B4FlxbA0yiKz4M5BVAk6eh13jc44f6vW6arWatre3QwYODg50eHgYpZwXFxep4+/AiA8fPtTa\n2lpkCn0MnF3PuyCT4MdarRbbJmjalV0DMCKZUSoGkD/fPsV+bs8ukyUGJyG//JtM8Hw+j/Jnvk9V\nGL6M/4Mp2J7QbDb1Z3/2ZxoOh+r1eqnzQMFR+IDBYBCnOUAc0jVYUvQVoMwX0pP3B7N44M690V3i\nD+SMyjr231PZJym2kFF6u7W1pZcvX2pra0sHBwcRfJIBZQ8oweZgMNDx8bE2Nja0tbWl4+PjwNFZ\nLOa4jsoAJy+dRGV9XUecWCPR5EkGr9rimQSTjF1KGsz9tnDdGy/fxVljgACx7L90ECYljToQGH9h\nWMvX1TO7k3fAxHcIYHBkOEtYHinJvkrJPlSMle+HKBQK8X+EoVqtanNzU5ubm8rn89FYo9/vRzMf\n2Ombm6SzKSUFZBIBMq7cBE7MoRvVs7Mzff7551HW4bXqNzfLtt+MF5ZuZWUl2C4CGAwPjhjF8GwB\n7KKDM7ITjHk6nQbrjcFhzXwv5WQy0dramk5OTnR2dqbDw0N9+umnmk6n+uEPf6h+v69erxfj53mU\n91B2gbFE2WjDzTtgjJ3tJ6gCMMKKAT6kpNTL5RDHgVxUq1Wtr6+HQ2Z/EmUtDx8+jPMFcXww6G5o\nMPL9fl8rKytqNBp65513UoCL4BfDBEPmjGehkHQFdUfL2nAvd+gAQ+QLQ4cjz/7MqwyQQZ7ve2cd\nHCK3sJG38fI5h1AD0M9ms2if74CV5l04e8/CARayQZSTMci0zynPwJY54z+bzVJn7J6fn0fpNfbG\nsw4Es5RLujxI6Sy7B5d8jrFjM7xs37+DzeXdPOj2AFVK7DKfZ5wetDrYxGH7+Px5/N+JSACbs9G8\nj3/Ox+Ay4OXDPm7ew30ccw94h4RCz7NyhO53Op0gAJz05Z29UQVkJiQHBBOVHLwjZ+sxTmfPmRff\nC4o+Q7A5YOEdPbggCMa24N/cn+CPHHDxe8/OUZXjDfq4/FxWSFQCY8+MY8N9zfBd+GpJoT/z+Tzs\nOViCTJwD6SyWuY2X460stgOk42NZa2yFE0HINxjBiX+e42AccO04zINZB+z4YrI72Emv2sv+4R08\nw9RsNgPbnZ+f6+nTp9rf3w9dpBrDfSsdVTc3N4PY5f7oEE2NqIjA5rOHfjqdRnd+qlykpAqgUqlE\nZpEKKyexslky36OIXXR9rlQqcboAmLZer8faMvc8G7IV3OSYq1Qq6ejoSJVKRT/+8Y/1k5/8RP1+\nX59++qnq9bru3bsXc80Yc7lc9FcpFAoRHBJkM6eOd9gTC24m2KN5FD+jOhCbQrUhwTfd3SEFOp1O\nCq9Qilyv17WxsaFmsxlbDbBn7FVm/tEPPzlib28v8AF4HNuD3WCbhb8na+vJOsd1jBMc5/LEPBD4\nOm7D9jn5Q6Lkt4Xr3igCZBKYEBwODgQnC9jm585YOuiG6XUmzEERjpn/kwEDcOH0UByyQwAtFkVK\n9lgB/PmeAyGYh1qtpq2tLfV6PZ2fL8/dPDo6iiY8MA2wsrwvRnlrayuVkifQktIt1zGA8/k8ytBw\nnHTfZQ6ZVwyGAySE0MEi7+ZOnjFi1HiWB8zcF2NA9ocmGyjRYrFs7kHn3kKhEAHkz3/+c/X7/Qhu\nc7llFpWN317i4WQBe6EoxSK7l3V6vs+UbBRgi6zDfL4sX8MA3Nwsy4QpwYRJBLCimIVCQY1GI8rz\n1tfX42zIXq+nR48eaW1tLcbjjBPGlCBhMBhEiWapVIqmHJ65Qq+kNOAj2MdoOICkTIn3clnwQIT1\nc5bOmTacDrKBTjl4x5hSWo7B8izYbbwATrwnRhsn7nJLGSBnFnNmLTLC+vA5z0yy1qylOzPG4WvD\n83GSHjhISUaN+0LYMH6XLbfd2D9fTwIvQJO/jwcY+AS/PGgjOOd5yBw2mvnBNvleW+6fDdSZCxy0\ns8f8W0p0y5lnv08WGHDxM+Se0lCAB+9CZpr7ousezGUBpR8BhK04OjoKn8F6IR/ObBNcUU2BDfPy\nWMbBfPicIgMOHr0ZF+/O931/HkAZMgKw5cElgYQT2NjeLNmczbhKyZl9vheeMmTkHXvm5DS+hiZ6\nyCR66IAQwM7aEeSDOebzuU5OToIwpYT8q2QP/qtdXj2AXEsJ/qCElfnxJAE2Dbnh++gOZL3bHq8W\ncrvi5JHLD+uPfXN9ZhxOOKHX/m7sL97e3g4CfTgcRpdWgjUSAk4AU8Fw//79VObY8RbZKeaHeZnN\nZjo+Pg5yzbc9MX4q2Jw4kRRBG4kP7IcThnwef++EebvdDiInl0s6kpMNrdVqqZJ95ok+KeAkqvEG\ng0EQ/ScnJzo/Pw+fR5AJZsjiOo5a5L7Mq8see9JZB8dy2MPJZBIkFtj68vIyspaezEG+wJWM586d\nO9GJfnNzU++99552d3fDFmGPvI8GMjwcDgPXVavV1BGSrLv7EveXjEdKN3vlWdhWdAKbiZw4Se7J\nBt9j7T4xmxREb1kjAlbm/DfBdW98T6mDIMACL+HMs0+c7z3ixTEaCAwGw0EDoAUlk5L6bc9MOYD0\nLBeGy9lUzzzxbMaCYSA1D7gYDodxBifOFYWjcQXGptPp6J133kkpCEZ5sVi2ZqZ7bKm07JqIYaAN\nOO8kJSwxgi0tBQ/DBKPlmVdKkcgcU3uPMULgWQ9JKVaZskQPgB3UOlB0R8Lv2RdBIx4vg5xMJnEU\nBUaCMg3ARamUdAojq8NY+R17XZANDIaXYRWLRY1GozBszuz73i7WpF6vB0iuVqt68OCBCoWCPv30\nUw2HQ41GI/V6PX3jG99Qs9lMKTzfY24bjYaurq70+PHjkH/f7I6+oFfMpQfKvLfvwcLoZLM8vCNG\nxw0kTT4geciEMHc8y2XCx0ZW1xlMz37c1stBqDsG5isbuDrox/H6vqrsPT1Qy2YY/ffO9mc/5/bS\nAyBk5XXj57PZ53vAy98ecPI5fuYEyOveAd/g88X30QcPVNy2ePbE2WJ/B+bG7+1s8+vmwZ2tj8UD\nct7NwUz2mBlITUhJbABjczlALpgTGHlKTyVFaR9z5iVblAFTYoceA26p6CB7KimVdUBHITaRG894\nUl7Oc/iM70Pn3RuNhur1esi3B5GQhIBODxSwz6wLFUVuh1w2AG3YPN7VwRRZEjIJpdKyYRMVMGSf\nWV8PIhgXwJb1gpB00tYbk9zGy7Gd4y7XceTIyQDPUjK/HhCyhmQApaSqAh/N5aSKE2Gux2zRgcxw\n3+k41J/Nfel6vr6+Hnjk+PhYJycnccqCB8G+bxB8ura2Fns+2ULhOIKjidDzcrkcjRMJ7sBrVPSB\nj6QkGEU3kWmfo6xPQF+diMGmIc9sheA5YDN06XXkEvvWGS+fBUfzPO5NSTPzSLUg9gk5kBSN0ihR\nJsHjW+n8eSQjwLeDwSDGBaHU6XRCTvAnrVYr9pODY/b29rS6uqrHjx9rOByq3++rXq/r61//epwW\ngXxAFHBfkilPnjwJnMaeVPQiG5TzOcd1zBtr7v7Y/TqyzXp77x0mXcMxAAAgAElEQVS3scgVsuPf\n4/6uw8xrNjP9m+C6NxqUejbJI22vWcZQoYBcfJYFZpLJ3NAkydk6L5/wko5sWZeXN7CwlDZR7uuB\nHt8nGMaYSsvzLtkLyX6DFy9ehLLBxnrAhfBOJhNtb2/rvffeUy6Xi2NjmBtnI8gGOmAtl8vqdrup\nwITD1dmnw/sTwLIeMCXcnyAE4UVhAHaw+7AsXi5GJ0M/PwlA4GABUEcwhXHrdrvhIDz4Rw7m83kw\na274C4VCgB2IBcblmRfuy3tMp9No/sJepoODAx0dHSmfTxpbQUgQQDsApoPmfD6PbnL7+/t69uyZ\njo+P9eTJE33++ecaj8fa3d3V+++/HwE/Rtrfg8ZKw+FQp6enWltbi6DfM9e8h5Q4ZeQeEOmsn4Nt\nN2AOzp3pYt28mQfPZCySwjBlyRucP8YPZ4T+AkZv4+V2geDB/+9ZTEmpbGUWwP6q72c/z/8dyGUD\nUCeXsvfKBp+sfTaj6b/3+0pJeacDErIfTgYS1ACQsmV+2Hwp2evE9wB0XNyf73jGmc95hQCf8Yy2\n9OXjbfBJjA3wjK3JVh04EUcpHzKfz+fVbDbD7mNzsbduC7A9kGw+j+g5Ps7Bt2cFYcMlBclGNoqS\nNd6LYKHVaoWP9MCMOeK4A7qzQ/S5X8cO4NeLxWVndEAkIHyxWETjOsaM3WUufQ+zZ5LZaoG+Efwx\nBifB8/mkOsvtJMCRoAVQ64SE77vyTpwAYWTAtzQwTv59m6/XYbus34FcB3e4jXISBh0BFxK0EMR4\nNh0f46QB/kZKtj04yHcSgsoF91vogO91zOeXDW06nU6QOf1+Xy9fvtTNzfL8Z/Z7Evj4lqBKpaK7\nd+9GCSpdoZFFqrYkRcMf8BZEOqcO4ENp0lUsFkMPmHcaCrFHdWVlJTAKVYXYRvQAHOk4h8CDtfRM\nM4QCFYbsP3UZgHhbLBbRQO/qatl0COzm3W0hcDhOB9vMljfGSiWcJ1d4FwLN6+vrwE8c/TWZTDQY\nDEK2FovkbHnWy7P8frYqYx4Oh3rx4oVGo5GePHmin//853r16pXK5bL+4A/+IPqPEEcg4wTq19fL\nM2knk4larVZqvzP21XEvsu64DpvuZG62QoTn+lp49h98zOc94HRc50QksoGtxq5SifOb4Lo32ujI\n2TD+TwADE4Ji+QZrKZ19wQhJ6cYH7Fsgavfy4OwkOYvqqW5PU0vpjooEcygvRg4Ql8vltLa2Fobn\n5uZGBwcHGo/HUTLEESKMCYGnnLTZbIYBhnFkDJSBedcw9kDCSuH8cd7cm3njIHJvBoIhWywWqeDd\ngSjjJHj1gMyFMpuVYw1YIxhPlwHeEdba9wlTzopCOwAZjUZREobD8vIX7u8BJA6GYItAmpKbVqul\n0WgU8+jg7/LyMoyhZwtwFtKyXKZSqeizzz5LNWHijD5KR95++23NZjP95Cc/kaTUs+jSSHAOUPUG\nCRgH9APdwYgAjim5dbDspSDoQlaWuWCW0SvfL+bBLIbcvw/pglw5Y+h7PG7jhYNhnviZkwJ+oWfZ\ngNDvhRz6M6R04JkNHPmuZ/gcoPm/+Vx2bD5uJ/2kxI46KGXcniXNvpfLmssT98zKqD8vm2X1eXPb\nlWWNmQvm25+X/YzbD3f67tSz8+o6kQUD2BqCGlh/fgZBBqkHqQYhmAWevm+f8fi+dzIwkJ0OHLG/\nAG32/RNQLRaLaAKSzZx6EzSCRfw0dpXnQxKzjovFIppquV8vFArRFMaPUPAtO1KSESiVSuFHnCRD\nDpg31wWAErLBeLDnvDv+iIZINI3CRyJT2FUyDFmS2jOAUuLfvfnUbbp+Gbaj8RXyISn8CeuJXWNt\nuQf+lnVyP4/84HtcT1kHtzMOyFkTJ3DIVHqWFiyEX4UYLhSWRwb1+/3QT3SD82ixibxbqVTSzs5O\nHDNCpQLzw1Yjgg23sfhJgjP0zf1roVDQeDxWoZA0OvIg00uZ3V5zUXngVVRUEjBWcBj3bzQaUYZa\nr9clJb7Ixwd2oHybeadygkZmEHM0eKJbLl3SmWNKkAksOaoEG8WaQqYdHByoUqlEksezwcwFCSSu\n+Xyuzc3NyEZXKhU1Gg31+/0U1jk9PQ0MvrKyoq2tLb3//vv6p3/6p1Q1AIQX90JuLy8vtba2puPj\n45grx5aetCLwhsDBp7hvAke7LvjlhBHzib5ISvk3xpjFC9mSe8f7vwmue+N7Sp1JIQih2YwvAsbd\nwYGU3usCMykloF5K2DHYCikBQvzOv8vkYiizQMoNLYwOCu2Lz36DXq+nSqWig4MDHR8fh3Kg5Cg2\nnbxY6MViEcoPKHEF8HJnyhc8pU5JaLfbVbFYTLX25v15T89GEIDR1QwHAztMtgsnw9pksxi8o4Nx\nztK6vLzUaDSKRkaz2SzeHQfOmK+vr+O8WtYMho33Z+y0EmcPCk0meF8v0cUISYoA18u5qtVqdDFG\nLiWFzOIwCWhxNmQJAIr7+/upspyrq2UTL5oTAIDff/99bW9vS0oyMswfIOn8/DwCWlhaHBHfg/Ag\ncMYJeXbJsykO1Dzjxrrxt4MAsiAYWnTTHb0zlS57kBbIlpeD3+YLtt8DFykd6GA7XD+zv5OSEuzs\n/dFV7uvBGT/3dUbP/BmeLXdbl3Vq2feQkv2Z/P51wSfPz97H3wmnRmbPP+cZRsb7uneXEpvvZJoH\nvp6BwaZl55NxZDMyjNfH6cGtr6+/M4ANHcFmOlghG4F99lI4Kdk/2Wq14udk9LwzbTZLNZ1OY2uD\nlJzRCQAaj8dB5rH1wZsNAqDy+XzYOUgyMqT4UkAR2QfmF5vhv0c+AOX4SK/A8TmGrX9d5jqXywVZ\n62tOIJQlKjxAIUjknQlo8U80LGL+kVvKlTl6AZCXz+cjYPagAd9wW6/XYbvz8/OYzyy2yxJsXO57\nvKMo4NuxSNZOOgmP3Lp9ywZ7rsu5XC6Ifj4H2C4Wi1pbW4u9pJJ0cHCg0WgUesY+ZOxJrVYL8pr/\nQzKDqZxYZl8284duUaLfaDRCjgiCnWRibnwP52KRnHSA7EIouV5IiiZ7XuFBJhP9rtfrOj8/j2Cq\n3++HLRiNRlF1RjksuulVIFTvFQqFeDf8Xy6XiyN1yGbzLl4lAbY5Pz//Ugk0c4ouenDMWKjyYO3B\n2/l8PjLnlAxjX0ulUuoYRScdkBfm+sGDB/rwww8jlvDKRWTx5uZGg8FAi8UiqitdHsFu2D3m3beg\nML9Oynqc4NU/ju/QU2SbueV7njVFT/B9jus8ZvrP4Lo3GpT6hEkJgMARwRRjJJzRlZSaXAcONzc3\nUSqay+VSh6w70OMeOCg/GkBKM+b83xl8D9C4+G6pVNLdu3fjfM7BYKD9/X0dHR1FZy5n5PweMIKV\nSiWOMrm6ukq1B3dQ6SlzjNTq6mqcE0fASxkIjRa8hh8FcJbDS7gwYh50SomSeKCLMXf2mIwdgRJK\ndXp6Go6ceUDp6Xy8s7Ojbrcbdf84GYJZxgRIIUsMIBiPxzo+Po41JCjL5XJhaFlr/rjzLBaL4QSc\npUcGmDPm10Gwb/AHOAK+VldXNZ1O49D5arWqd999N+YMsoLvIdsvX76MDHexWFS32429Cc4MMqce\nKHqQg7Hxn0lfDjaceWPN3Qg6E4c8MTYPcAHeOCUnBnDGt/XK7nXCVjmQytoadwS+ZQBH5uWNrF02\nS8g6v865+Ho7I5oN8niGB5XohX+f3zMe/zdkjwedvgfU7RrBB3PiupbNRGZtoZcHZwNgbCNgg3nB\nL+DsXYY9YPHxO2AE5ACyeGcnijwIIhPJHNJXAMACucU8ASKxa/zetxEwJtYDf8jPKRMDKJBZwJ4y\nBwSZq6urcUYfYA7ghyxCRqK3dG+u1WoBhnnv7FrzXlI6eGa/LeCPsxBrtVrKjhGIYKd5hvtjJ0vZ\ncsA6O0HIz/kuPhkgh4/I5ZbHonH8w/X1dQDzYnHZv4AghHe8uLiIc05dHznr9bZeHtxJaWxHNszt\nA1iINXbCNKuPBEuSAudBYrh988ozSlz9Z247fKzoMQ1nsr6xWCxqa2srAsWDg4OogOt2u2o0Gqkg\n3Eu4qXryzriXl5cxL8grskHmHdIHTOC4bHV1VdVqNU4i8ODIExnYkOz92cMONmP9yMahG2R1wdRs\nOUNfarVa9P+goSdYjfszZnS70+lEYOm4DLvmtuLq6irwHoQ9vVbITpJd9oZOyBAkk68nfg1b6VlI\nz+aS2MHXjUaj6AtAI6PJZBL3uLy8jOqSZrOpvb29KOkG22KHkGvsLXPPVgjwkmMuGna6XZHSZCzy\nCvbypBv+GRuNfIDH3LeiE44jsvEC9t5xHb6Y+/261xsNShEEnJpnUjw75E46y47jSD3QROF8H4gD\nZmeGYIE80MUwEGC6UPsmbYSW73N/SjsajYYkxRmTL168iDbatJymDIsgjXFiFGG38/mkuzDsDYAC\nhfVSj3w+r/X19RBwhIm9BJzpiUHyrDDGiTIKNwyuzHzWy6XcsFBWimGHBZeStv84mPl8HueSEqRM\np1P95Cc/0T/+4z/qiy++0ObmZhizXC4Xgf1oNIrMKR3ccrnlfoGjo6MAipSdYLx5pssFrFF2HxGZ\ne/7ms4CjbMAAu47BY98A7wbDBECBCWy1Wtrd3Y01htnDaXW7XR0cHMQekbt376aytNwfHQFwIlfI\nM47X5dtLoDx7lA1CAKhupAjgubeXwiMrsNee8fZsGvp8Gy+few+EsCPoFXYMx8v8Mn8e7HipJmvm\nTXL4PPdgzx5Eh2cxsK/u5NzWMk7uma0gYS0lvZZcQAa5FwSL248sKOTi9x74OsHiQNZBlD8LAMD/\n/T2c0MmOh3EyN04u+B45wCrVE/zc15P7lsvlCPaygTqgzbd0OJjm31RQAHqkhCAk00o2wstUfb0d\nLHBfjgMbDochT+hrq9VK2RTuTakv3XO95wD/lhTNfpzMIpAjm4rPwa/i8/AdgDSyIPN5skeNzChE\npweX6BOyie/DDtODIp/PR6UN9guiwrPWzCX+rVQqhb13PYIU9qwHAcZXAWr/1S70BN15HbbzbKeU\nkCpO+CBHUjKnfAYZzNpHvsOcO6bCF/Fzxke1GECaIMfJWGkpw97tf39/X8fHx+r3+3GiANVgyGW2\nUgm759uPkDF8OQEg2flarRayO5/P1Ww2o+KB76yurkaTHK+YAH9wxjDVYpRAM9/+fLeBHqyS8ICY\noqGXd6hFB9FzmlT6uj1//lw//OEP9cknnwTOlZKMpLQ8EmcymUTA5ns9CXglxb03NzdDZwnE2TpH\nYsTXgp87fgbXsQ0Qn0AscnV1lTrahQSLxxD8ezabaTgcajabqdvt6oMPPlCr1UqRj9g45Ib1pmEp\na0FsgExjgwkKPeOKjfeEHb7DdQxcwHv7vOCrIXP898ig4zr8BHPFXDiO+HWvN4oAMQAekfN/ACqg\nwaNwZ6ZxcgRVOA3KnxAqFsnLBp2Zd4fNhMKg+oLiMJ19AOiwALA/BE2LxUIvXrwI1jUbNFPSJCk1\nDxgL3hc2NmtUeU8vE3L2B4ACO4dgO4vdarWiay17j9jfS8CR7UrogaczNB5YkM2hwxxOnONfAKCM\nAYBLF+Jms6lvfvOb2tjY0NHRURhF1gHDNJ1O9erVK11eXkYHtJOTkwAZMIdsKPf186yilJS3wmIx\nRpyVz7nLkGdaKAEpFovBqs1ms2BRyaASlPteLMpJkD3mBAdJYErpMwwoMuXsGH9j3Lxc3YMASrbd\n8GQzcOhJ1vDxee/Sh35zYbx8LC4n6LUHI7ftwth7KSZsJfYFOc1mrz148Uw06yAlQRIOSlLIv2cf\nkV3WnIwZa8kf7AwXtpbxuAMDMLguuN19HWkCGeJOENmVkuwKn3sdyETe8vl8dIX09wcUkh1F5ijH\n5N0pv/RAlnd2hjkLZgEI/M71ivfy4N2DOvSJf6MTBGQOIjxzjA8olUpByC0WyZ4siC6AJ7IECKXs\nFptTKBSC8PNsiu93pDLFfZ2UgD5JqRI73t1BnaSUrWAPLX6RIOTq6ipAOHPH1gwH5p5pZZ09KFws\nFuF33Le77WKsrCGNTDY2NkLW3ebl8/mw2f49gmMnShzbsO7YYNft23j5vHA5cYuesecMnICPQC58\naxF6ii9E17EpyB6ZG+bYg1HHKb5WEPbuu/g/OkxzoXq9njpb/uDgIOyoY0uCFd6HrKFjTD4P3vIt\nZ57wwG9gx8ECBIjtdjtwH5iS5xWLxWi2eHV1FXs/mTcSANgL7o9NBOdkMYOPQ0oyhPgS9JduweBR\ner289957+trXvqaTkxPN5/OoQMAGcK/BYBDHxLAFDhvFO9Ot1xMJ+DD0kiw1YwbX8i4EmOBAz676\nflz2i06nU00mk2hmhb3J5dJ7L6+vr6NJJrbeyRVscrvd1hdffBEBOOQYsuHyy3o4weryxHthUwl+\nkS0psZFeCeRJAv5Gl1y3+Tzvw3OziQXil6+C695oUMrkITheOoQxgSVAoDE+RPluZBBGGBEcLM7e\no3cpKXNicp09RyHcaDlD4kbIgQls7Z07d9Tr9bSysqKf/exnOjo6ivMpJ5OJptOpGo1GGCkEFbYE\nhaM8CEODoZaWAgjzg9PGeBEIbm5uBqsmpbviFYvFAAbMCawNARVlBLyrB0oYSBhwnAQdFSk5Zo54\nR783BpxglT1SdKPb3NzU7u6udnd3VSwWNRwOUwEihgPWkQwlwSFAhpIJnovR8uwI8uUZotcBePZJ\nMI+sGdllmPrT01MdHBxoOBxKWgbY7iyRF4w45AEb6h3cY+CR5X6/L0na2dnR9va2ms1mMKMewOBs\nkRe/Jw49G6ggY/zNWGms4uVDGCMp2Z+CbBEIsEY4GmkJMjkrzJno23ox5x6AZY27Oxp02oE592Bt\nACzYToAScy+lSa7T09M4Lw/bgb3DPjI2ryjxYIz/Z+WIZ/j40CsPLNE9Kcki+l4YJ2G8AoXnehCO\n/jBWadkMjM8QyOJjuL/bP3fCgENJqT0/PMuzplKSDWKOAY3+HQ84fb3y+XzY64uLiwBD/jn+Takc\nY6KByeXl8lgJ/AJbM5iH3d3d2BqBneHZbO3o9Xq6uVnuZ6KCp9PpxL2wAfhDByCAMErtyMJD3uJH\nWRsym8zd1dVVaktHtswxu7eWqiKeTZCCnKE7BCDohZSU8nowyz08iB8MBrq5udGrV68CxOdyObVa\nrQDpEJPe5wJ9IiuK7FxdXcW9kX23u7f18sDcAx2qNJgfKpKkBNu53fCkgaQgkcB4XhXiNsqBsdsv\nSSm5BNvRlAiMgJzzB31cLBba3t6O5kU/+9nPdHZ2pjt37gTpfXZ2FtulPHDK5/Nx+gFHxGDzV1ZW\noqQZPEQnbHwl2BLdomdIs9kMvfGKJMht5oBKmWKxGBURNO5BVtFtsB2NlAhs8/m8Dg8PY0scnWyl\npBrIT25AR0qlkhqNRrzL2tqatra29ODBA62trUUlGD7Ry+fBw1S1ecYSe8v38Cee0GHNCRyxhQT0\nyA8l/VxUtmG3OI3h7OxMg8FAR0dHurq6UqfTiWd7Nhbbi829d+9e7DN1ApLvrqysBF7e3t6O7X/g\nI3w+NoweLxCUTnQ5oeb6QVLOq1WyHYvdx7r+IVdZXOex0s3NTVR28syver3xoJRgzvfG8IIYIJyW\nZwG9dI3/szAItDdLQnAxWkyolD5WAQYN5hYlJRhF4ZhsgA4OZ21tTQ8ePNDe3p7y+bz+7d/+Tfv7\n+5EFzOVyURrCHxzodDqVJLVaLUlLxWy1Wl/aByApJVi8m3e2PT09VaVS0XQ6DbYRY0bGFZBBRhRW\nBxBC+RM18+zv8QwlwNTn8vT0VJ1OJ8XSQxCgiOVyWevr62F0C4XCl9iwn/70p/rnf/5n/e3f/q2e\nPHmiVquly8vLeCfWgbNbs1kYD/J9nybGCDlBwQhsXW5Q/Kurq2gyVK/XY48pXeeQHT4zGo00nydn\negEIJ5NJqiRpMplEFsIP3K7VarG3inWiwdF8Po/MK/PtRyF51gXdKpVKajabqb03WbAPaZDNeOGU\nnfChfMnlEjCBM8rlkj1EGCgYPwwcckcQ4WDiNl3okzOLDpIpy8fJOLDic06ueUUCTpi1xQmyrthW\nJ/PYD+XZHC/z94BM+vKZf1mG3DOcvAe6zX0hk/gswJz/O6nIXLmMotfIG99j7Dc3y+7mUlJmhhx7\n4Mph5wARgB1O1YFKNrjHzvj7SklADuniexWd9CSw8cCNtXDSky6w7XY7FQj72Lg/c3J9fR1HHVxe\nXurx48dh2yE2PduJbXDSFfDl/gCZ8ndnrpz0wJYSzBIgu6+gyoa5J3tBkOp+p9vtBnh0csaDYoAY\nfhkgn8vlorQQf+4BD/colZZN8/ysV0qSGXehUNBwOIx3JJOLz3dA7E0J/Z0pxfQjaG57VQgZzl+G\n7ahgACh7+aTbCSeP2Z7gDSad2EIu0Fvsg5cOEyQjTwS5q6urqaovrzzJ5XLqdDp6++23de/ePZ2d\nnenx48caj8dBWJCRm0wmUb2ADZlMJjo/P1e73Vaz2Yw90jwPPXNCmIqw6XQa80hnXoh/5hFbXC4v\nzzGlqzU6e3FxEZk9cA5bxsiQUknBu4P9mDv2SG5sbMQ5xvhxL++lyRiED5iN6rXHjx/rBz/4gb73\nve/pH/7hH2I7FV11sTvgPCdzSVB5w0/wUa1Wi2oHKi3AT8gCc8tnCR4hmzi9YTqdqt1uR2kxTazo\nTeJkZz6fjwyppCCtarWahsOhBoNB7CftdDqRlfbvOhE9HA4DR3rShGMlsR+sDZgQOy6lj59zAhbd\ncwKDd2HN3c9mq3iQV/wI/s23oxWLxf8UrnvjjY48C8XP3OnhHFBgXpZJdCBOUALbAAtLIMSE831+\nJiWdYgFGklL75DCOgCkEmuvq6krValXlcll3795VqbRsKe3dXinnGA6H2tzcjOfSCpw6fIzQ0dGR\nPv744zB2HA4+Ho+jPDiXSzKvkgL0dLvdGDuOEiGRktIyHKjv9WRdCOokpYAG7+LBhSsDY4TVcwYH\n1un8/FyHh4dxsDhyAIPfbDb1ne98R48ePdL5+bmePn0aQJLxE9RLSdfEk5OTMEYoCw6MZ3imCaYH\nBfYyOebi5mZ5oHGxuOy654EDBhkZ4xmcDwszR9lus9mMAJJgkfIP3xuC0fRz95ANQCesbK/XCxKF\n9yGYlpIgAgIEGffMUD6fD0fqBsSZOZwW4NF1zI0n657NgDKvrA8Gj8+ji7fx8oDSQbW0nGPK5qVk\nb6DrzeuYXCk5O82DCxy2Zz3JGkE8eFkZcu5y7M/1KgLsnweZrDnv5MSHpCir410lRcUCdtizWvzN\n5frm2SXmFNuWJWPY0uAZscViEZ0hq9Wq7t69G13P0W/kGV3HoaNb+CrG5mPCVpONdIDN3wSjBIHO\nNpfL5fAlUlLuWigUInB2XwfAAfgCDJgzt11UlrAu+ByeQ+CEjpPNoeGgywqy44G8pPA12DYudN3P\n6fMyMohMSUGmkmEkOPDyV4J6fLiXmM3n81TndM7IBlxDTkLQ+r9ns1k0BvRAAbm8f/9+BC9OFDlm\noGKBLI+TI+gk9vq2Xq/DdlJCYrNNBb0kU5mVb/QJW4MPc3BOFRlgGlvpPslPAZAUxBD+yIkuZNvx\nY6VS0dramu7cuaOzszONRiP1+/3QKYif2Wym7e3tkFuy7ew3xn+fnJzEMXFgu8ViEXaJUxcYMzZ8\nf39fjUYj5avRPz57fn4e93PSD6wC1vD5dTKaY+qccF8slmcH01xSUhBH2ATeCzvUaDRiXCQF6vW6\nfv/3f1//7b/9N5XLZR0cHEQ13nQ6VbPZjOaPEBaQGpTNg1fBr/V6XYVCIWzP1dVyz6lXT0oKQoJ1\nxpYzH4VCIXxDt9tNVYgwT/yc9eD8ZqoRwXNgNLAt++HZlz+dTlOkGgRDqVTSZDLRxcWF1tfXtb6+\nHmNjPbDFYCfPqJMkwNZhW91/EpxjB70iBiIJXMh8oSvoNJeTktzvP4vr3vieUiaPwIEL5+wlZl7j\nD1OM0OC4pOUEAeqdgXO2FeMFQMyysASwGE1nvbmfO7harRZNdBCOg4MDPX/+PBhbMnJeo05mYjAY\npN69WCzqd37nd/Ttb387FQAxNoIVQAgZOTqQLRYLHR8fRwYGRUABnWlyELJYLGID/dXVlZrNZirL\n7PPLGABaKDmlIhgFDDbzxtrf3NyEAvL/er2ubrerjz76SPV6Xdvb2zo6OtKPfvQjffe73w1iQloy\nUmtra7FmnBEF6GPvaKlUUr/fj8CLNQC04ST5G0dDJoESlvl82U2XEhQuZxe5jzfNAITgoGezWSq7\nSwc9OiYTyG5sbMT8IicXFxdqNpuaz5fNAgBnlAvROARCx4E7GSzkxnVJSrqZImc3NzexH4SAiEYP\nXurDeW4waNwH3fPSEj7vmSP2pSGTt/HywIZ3ZU2z2TcvzfW5xOC7k8V2AvA9MweDCgC5vr7WnTt3\ntLu7q/X19QDG2awgn0deYa8Zu2dNAQYEr9lqBd5dSgI4gL47Mmw11QeevQeIMA7PWDFXPN8Blh/C\n7hnjfH5Z/j4ejzWdToPcyeVycWQXpCLVMQTRUtKIiXVgTPgL37vDH9aNoB2gQpYFm8m8Hh8fq1qt\n6uTkJErzOQAeeZjNZrH9wg+fx28iW/hHnxfWkSwKNhXQJCl+Xq/XU3sEkQ+C/m63GxU9vD9nMFMO\niZ9m7xuZYcCQpGjEQqkg8ufVH17O68e3XF9fh43i3pC9+CH3edhI5p17oUfZjrn5/LJXBMDQiVgC\nWnSSi46k2HOCesZ8mzOlr8N26AtzQcYOUsoDQ7LjyC4/Z72QRcr83WZmCQ/u66WpjBHf7GQpRBtl\no+VyOYIOSPMXL17EGerIJbrBeHjWYDBIYcdisagPPvhAb731ViQ6vDP4eDyOgHg8Hke1xGQyUafT\nUT6f19HRUfgNSvzBLRBiVD9gWznNgS1hBLRZWa9Wq5pMJlxgStQAACAASURBVKleLswFDcLAh5S7\n0z8FW3Z6eqrRaJRK7LTbbe3s7Ojtt9+Ohp+ffvqp/uZv/kafffZZBMJk/8Av2Kh6va56va7j4+PU\n/nIaZBYKBbVarRTZml0LaamX/X4/RYxjn7yLsJSQF1KyZ9bJXmSxWCzq5OQkzj51+eEYKez/nTt3\n4tmesUcXXr58GVlx9vl7ZQ0VizwDv+H4wu3W64gb+seQiFtdXVWj0Qic6wkdSeEfpeRIICeGwHWs\nA/dE/7/K9UaDUgJPhJYFoqwPhtEzRQADBBUg4h3cYEGkZLMyE8eEceE0AYiwnKPRKFqCE7RhtFgQ\njAxOiQxfvV6Prmw7Ozv68z//c7311lsaDAYp0OMMBWP1OSDYY/8ghtYBkAcji8Ui6v6dEcchExzz\nLhh27gf4Ipjysl4paczEfcnuMP5utxtsEYpAQM/6zOfz2FfhzNz+/r6eP3+u//iP/9Bnn32m73//\n+/q7v/s7/fSnP9W3v/1t/dEf/ZHu3LkT5a84MfYVENzwvsPhMDJOlBFTwoYiOjBDwZhnHCPsIuCC\nYG1jYyNVj5/PL7tTZsEK8wd497nHYDIvvv+CcXrWDMAH4Lq4uAhHJSV7Fzyrz3oDallzjKKXszAP\nyCd65hmS8XgcoNYdEIGWg0eaFgCkPUODs3SyxwOj23h5sIb8+v4b5kBKspyue5JSQZKkOMCdYMe/\nt7q6GhUZyPvKyoreeeed6F4Is57L5aLtPWQENpY1oqSXdSI48X3x3pQBW+kZCt4f8Md98QOAB4CU\nN6PANuXz+RirtNTXdrsdQagHq1KS7Xc7jx4QeCKnAIqPPvoogDOAjtI47EMWUPj48EvoMTrMenrJ\nqKTQFUp48VnOhns5Kc9i3WHeOd8OMODMOXbPA3cyqJSc0RyOyiFINmyTlIBA/FShUPiSvSVbw/sA\n3LAfWd/tfgUbif3Ex0BeeQBBBqZYLKYa9ZG9ojMx8srnIQ2xX8gcdozGI4x7dXVVJycnevjwYYAs\nABw+gjFAAPrWJI4HYQ2c5L2NVxbbOQhHNhzQZrEdmTJ8CRdy7cQqGWh8F7LjjTElhV1iKxL6m636\n8v3Xju0ajYZWV1f19OlTnZ6e6hvf+Ib+9E//VO12W4eHhxqNRoEx6bJKJYMTc6VS0lgSvZHSZ3tf\nX1+r3W5Hxp+MFrYH2+q64E0W2S6Ffu7u7kbfC56Jret0OoFxWRPPNp+enqrX64W9QtZzuVwEzF45\n43vBB4OBXrx4Ebjuk08+0Xe/+119//vf1/b2tv74j/9Yv/u7vxsNjSDB+D5+i6B3OByGXBGcESRj\nX7ClXt2Bz5UU74yeOjHL+zluokkU2Aqbi25DpHqzLmywlJSs53LJ2Z1eQisplUC5ulrul/UO4bwD\naygpPuM+j3uiA2TqkQfiBIJwZGg6naa210lJQE5mGJ/HuHhGtvIGIoexfFVcV/zVH/ntXQ7yCSwQ\nHIQDcJIto3GjzsI4c83CSEmwh+LA8njpJIrl7EaW9UfYnDXO5/NxEPLdu3e1sbGh8/NzffHFF3ry\n5InW19f1F3/xF5IUAaSPzUEbBhoHuba2puvra+3s7OjZs2dqt9vqdrspBgpgi4IR9Dnow0gSNFCS\ny7ywP4AAC2FiPCiTZ2GYEzbKI3gILY6GNfKyN4DBdDoN9pkgS1IYTWTi2bNnoZDlclmHh4exbwMn\nBBFBIM0zeAeAA2VZgC3eicCMjsDs5WC9YA6RA0qx3WnC/ON4GDOMJ0aP73lQzB5ggE2n09HBwUGU\nggBokNX5fK7RaKTpdKqDgwNtb2/HXjAMhpRk4jHKXlHgGSuXCYws4/OAEgNORpm5ZU4wusieB2A4\nTwAKuooz5fe39WIOHTS5jXMDT8DHugPs3nrrLT1+/Djuhw5jE9HNSqWSCioIHufzuT755JOwpVQ9\nnJ2dReMpz/QD6tn75yWNzgIjU9wXGXGWmvf0igveA7lBj/0z2Dn+TbBHJv7dd98Ntp+KD/4tpRtQ\noA/eqMGDO2Tz7//+77WxsRFAyQEz+oCucE8u7Irvs2JtAdEExLDJjIW54/7ValWz2Syylcz7ykpy\nnie2FzsjKaobINf4LrrI79A/bLez7dyDd6S6xIPv4+PjFIBEHrz816tknGShyQ3zyLyyb585vLlZ\nbnvAFuLHmdtcLhdVJnt7e/rss89iHQCmHhBRRkimi8ySb3kgaKbXA2v48ccfRyaaYNr9ITLOHDAu\nsjHIuxNQt/Fywom5c2wHzsKeYKfAZh7EIoPYMbCh6zty6wEl2A6fhw5iP7zCge8TnEnJ0Su9Xk87\nOztaW1vTeDzWs2fP1O/3dXNzo88//1yStL6+HvaX54EdLi8vU+XkYJvz83O1Wq04+9ZLM70qARlf\nLJZb0xqNRuiipMh8Ekgyh5RN3twsG5n5XnXHfp7BBwdLCVHKvsqVlZWoyACvsD3NM25UvxEASUo1\nnywWi+p0Onr27JkkpcpPB4NBZF+RD9YCW42dGQwG4Z+QB+wmcoCdx5Y5rsNHgkmYB8bJXld8i5Rg\nVzLIYEyfA/y2JxfA4DSQw04zZsdpFxfLs437/b663a4Gg0F0S0bGwN74EzAl64YMg+NYB5oQSQo9\n40Iv0CdwHcEv8ovtR5+5j+M6J3u+Kq574wjQBY3gEuOOUnkA4JPvzSEIbgmKUDoAPcqD03AGge85\no+7dpHDCnh1ioZ0Vp+R3NBpFYIWRQJA9kKNmHfDWbreDwXj06JG2t7cDMOCUpaXwsPcJYSXYYjwA\nsX6/r3a7rVevXkVzHsqeGT+CS2AIa45wI1DcM5/PR5DGfCLEMFfe7AQngoMGbPA8DDRKyecw8IDr\nfD4fm99hdmCAnLmRFB3wYIfYK+Rg2TM6kqIkFwXGSAE8PNtxdXUVTsXnCLkdj8eRVeF+dOalJAPH\njJNBhjqdjjY3N/XTn/403gsnwBEwyPlsNlOz2VSn09Hq6qpGo5EkxZ4UL490QgHHhVEEtDvg9gxX\nsVgMEOd7FjHCyADfQa8IQpg/HLaDUIwc83obrywRRfYHe+JAH73xwAcQS7OJQqEQ2Yajo6PITPAc\n7AB2DieBnYM4oGEGTrVQKGh9fT06D2b36jn4dqaYQIc1BYBngwjen3UmwCVIx1b4vCFjDiwJVt5+\n++0Ahi6D6LXbMWd0AXXu5F3u8/l87HnP5ZZ7fjwrx9/OjHvAjv/B1mTJTS+r9UwmpWPIAcGPM9sA\nS4gvdNkJAGw1eohOIhsOwvAXzJGTDuxXRna5F/LCOL3yIp9flkSOx+PUtgSvnOBzgBhvZuQBAjqR\nBd3eARcZyefzevnyZap000kQqlbIpmLTWC/eiYPpvVkMNtIJNs8OkeXGDzAeAmxvGIOdZU5v6+UZ\nNLAd8/U6bOf7QCGpvKERIFhKqsbwgf5Mgg2y1U5MuC3wcl1kDnlCxh3bXVxcaDgchr3NVmKgc+g2\nNhd5hfjY29sLPIcs0ieELB6kBjJ3cnISvpLqjdFolJJT9j1iF1xOfWuAlPQI8SAW8ooxY4upzMnl\ncrENCVKcQJQ5pELHy0AlBb4B856cnMQcojN+NqtnEUmkeBb3+vo6sJfbNean3W7H9gGy1VJCDFNJ\ng+8BkztxBIZDZ5vNZorIAhtip5nn6XQaRJq0DOIajUZsw/jBD34QGJxycBIzZOeHw6HW1tYio85z\nIJCRAfdz6A4xCz7HMRU+iIQDmXBkgHdHpt2nQky6z/xluI61+E1w3RutlQOIOIgCfOF4fW8MhgJH\n42WGUnr/AA4GQM9i8Dn/PIEDztkNFQqI4niAitMvFotaX1+P1tzPnz/XbDYLhgnmiEwwiwZbA9Di\n0OXd3V3t7OxoY2Mjnt1sNlUsFqMFv5TsZVpbW9PFxbKF+OHhYewnmM/n2tjYUD6fV6/Xi32nZBI9\n8+ClaIwPdidbm+8BOME2IKdSqWhjY0PtdjtKyTDKOH4Og0YxuDAIGCDPJlICRZCLwaPzrO9d8KwQ\nJWQAeMoLmHcU2ceCc+h2u6ksk5dHsPbIBA7XZYmN9wBoym+QSTa/Ayj5fr1ej7IYDw7n82VnTOae\nDm0EhZubm2q1Wmo0GhFgu8FwptmNBN/n4v0o16Gkw8uZ+BlGCJ1B/7zcD13HSHmmWEpKhDH0t/Xy\nTDnzRVmolHQSzAJzyseKxaL6/X7MFYdzI2uU+TDPUhIMY9cGg4FOT0/jfgQ7rFcul4sSKSkBCqur\nqyG/yISUXtds6ZKXREmK77hsZBlwtz2UqOEMJQVbL0mdTifOJqYMF1lGLtFH3t/317pDdyfu2xgg\ntyCEskeeeJACaOT9/T19ftBddBowzF5tAIdnXBk/PgA/wjxRueCZJGw9wZjrPPYeEEelBOVhyB/k\no3+HOcOmO2hF/7vdbjDu2GYnDZkX7CnP5OeU0GZthFelQAayBvP5PIg/fAM+tVAoRMdcB5KSQrYB\n6VnMwHpTHoh9dyLAq3V4P9aatSCwcQxwWy+wHfaBYID1Oj09DaKBNcU3SsmeVLcd2DHwwNVV0kCI\ny/0L/pSfI6OSUkQKn8FeoWeUKRIcXF5e6sWLF5IU56hjH7EFTkBhX/P5fFRF3L17N4JS5B2bgqwy\nf8ViUY1GIxpfjkaj2PtM8FoqldTtdlM4GrvBO0vJ3n8qMxxLQ5gQbGB3SSBgOyqVitbX16NxGskH\nAkl+5kQz9gVbcnV1leo8fHOz3AIF2QoG9YoutxnoDaQ7Y+N7+CjelQCb7GU+n4/SbWw9vsXnBZx6\nfn4egSj4D9s9m83CX2Pz2fuKjiOv19fL7WUkhvw7bos8cEeutre3tba2pk6no+Pj47BJHggi09gj\nx2KeQCuVShEsIxPe44P59jhKSghp5pPnub92+SM4RmeZ41/neuNWkSynpMjWEXwghM748H83Lv59\n2FpJIeRMKAbLfwaw5l4Otr38y7NnDi4LhYI2NjaiDfXR0VEApJubpC4d4+OZQwTL2dpGo6HNzU19\n8MEHwbBKS2Xb2trS3t6earWaDg4OtL+/r9lspuPjY93c3KjT6ehrX/ua3nnnnRTr4kEjAA/WhUAS\noVxbW1O1Wo3AkswqxkZKG2LWxPeANBoN7e3txViYW0lhfBFon3+cGHsKMEYwoRgFAIqkeEeXBScf\nMO5klAG/Xl4hJUonKTKIrFE2gOc5zq7hxGiuwecxugAeFBsg5oE3czIYDHR5ealOpxOOlHFfXydH\nStBMpN/vBzgk207QISXO3h2wgyL0BKPF/3kOY2SOeH/fj+HVA04OIHf+86yB8rVExm7b5fPvxBZk\nhJSUxfBzAAIZgkajEWWcsP3IN47YswCe/SJgJXtAho5KjHq9ngJ36Ag6SbmSEwsQPVJSkpjN8sK2\nSkkpLv/GobotdUfK93yOcOqlUikaFGE3AQ/IJvdAhnH2AEYnQV22WSf+T0t/ty2sJ7LLnHB/D7R5\nH+aHcmuez9517Gq73Y659mwTBBP3m8/ncSwUf+jcyZ62+Xye6jqPXlJBAllGltqJA2+QRyaTMWCz\nkDUPKldWVuJoHikJZt1+894etCBrzWZTpVJJ0+k0BbbJsHplgQNibCUyDkBFPrOl8tgbJxF8L6iT\ntJAFHjRh+zzAJrNCoNFoNGL9KBsk+31bq0K4vMEJ6wLh4ljObRlr4X6G77MOWVLMgbPbFWyo39MJ\nKyd7pXQghP3c2dlRt9uNPiGj0SgqF8j+I/Nsr4E8Qd8J+rrdrnZ2dvTNb35TUnJcUKPRiPPGi8Wi\nnj9/rtFopIuLi/Dt6+vreu+99/T222+rXq+rXC5rPB4HIY7uoUP01/Cmh5x5Sd8Tmg3h350E5/0J\nHOlIvb6+rgcPHuhrX/uadnd3IzClYo29keg7uIQxcRwiGA7yh8y4Jz5YX8flkkKOwLFuw72xE3Ps\nPob7My5/Bs/FNrOVq1arhT57hQe+E5vuPUuwLTRryuWWJ1mcnJwEiYBcOwZlXBcXFzo6Oop7d7td\nnZ6eRjdeKR3fuL9yUkJSvBOfx0/6lkb3WfgC7GmWVCZWwI46rvO4DBnKZul/1fVGy3cBzV6aCdvi\nE4vgYcykxABJSu338VIkKb1nE2eGUUK4PW3NPVFMWB+encsl51GWSqVobtTpdDSZTPTZZ59pPB6r\n1+sFuwUYdMYFBgzlQrm73a4+/PBD7ezsqFQqxVmenOF0dXWlRqOh7e3tYALJuMJ2X10tD/RFyDxT\n63OCAszn82CRnfWlsQcBKKUcklLG3Nlnsgs0drq8TM6ookkRSk9QBqOPUWDcHqzAQvlakCHFODnb\nA+Po3Xil9J5I9qN4EOkGDSbez25i/tnP5eDWAzmMEJmd2WymfD4f+0YwbJRL4wCOj49jvDs7Ozo4\nOIjPtlqtCOgBWxg5jAJd6yaTSRAF7owdBGYDcgJxz/ZgsCgvxOniDDFC3B8HlC0bxBhi2H0OKFn0\nUtHbdiGXTphlg3jPBvAdHEkutzwnbzgcql6vRxk4tg4Q4A1GpHTJbbVa1f7+fjzLs1gQP05e3dwk\nWwomk0noPk6QKhbPnjt5IiVVKe7IsOHYWi/hxaYhO+4UmT/2RCHX2BwPOGDRsxl8/Eq5XI4SWIIQ\nZ3j96JJCoaDDw8MUaJP0Jb/AnHumxt/VqxT47mKxiLnlnQg0fb+pz4PvJ+PdqQihxNjJCPezngFw\n2eBYHO5JEzVkE1338kSfH2ygA03Wy4kY1pcMime08AFcyCf2CnDl5CNkKSWxvnXHP+sl25A9/N/Z\nfz/iwzM+6BiZEkkpfWM8Thzmckk38mKxmOpqSmB6W0t4fx1s57rpuuJVO6wh4NexnX8f/UMG8evZ\n7KEHoK/DdlTolctltVotraysqF6v6+XLl9HbwhtGErB5GSxds7kPlWKdTkd/+Id/GKWl6BDVDPn8\nsqLt3r17qTJmsCj4p9FoxM8o+0XPfFsBckfZKFk8/Dt6QrDmOFpKsv1OhDUaDW1tbanf76vT6UTl\njaQIPvAvngjy6kP0X1L08fAO3+gS75FNSLldZ4yQXOzfZW28woJ74xvYzuXBJjaU+cDu8PN6va6D\ngwN1Op0gsBiLE1R0Qy4Wi0Ew4Lf39vb0/PnzWCMnlNGbk5MT1ev12J5Fd9x+vx/2x+1wVn/AFm6/\nsOVOALH+6Atbi/g+BKLvPf5VuI77sx5fFde90aCUyQKYMcEYIYCEZ/ekNMDDELDAzm5ywYp6hg0l\n5f5edsPfnvHJsnMEg/V6PQT0008/jSzp6elpZKzOz881Ho9T5zZxDhtZDgRkc3NT77//vlqtlo6O\njpTP57W+vp5qkJTLLbuRzefzYHMeP34c4xqPx6FYGExvCICRqdfrGgwGYewRJgIKZzp7vZ6Ojo5S\n7AjzT7CK4YS93traCqbw9PQ0shpS4qwwMjh3jDgMHaCcclfv2ojsSEk2kHUiU8y7wwg6AeENFPgs\ngaakCPYwrJ4xJVDNjgdjms/no3wblhCZAoQDCjGG/Pv09FTb29uqVCr65JNPYowcE8G+p1Jp2ahr\nPB7H0REEeXTsc13BILG/xXWGfyM3/IEB9SwHOgAoRH8AfxhDf0+CHwAqhiybeXH5vE0XIIA5doLF\nZQ5ggBzB1GKvsGEAZz4rJUAQJ+kXawCY8q7j2DjWCgfHPhfkSUpssZcO4cCkxDl6dYaU7FNBJh2M\ncy8nBZ00Yf6wq+4fGDt6yvf9bw/yAZXogTeCcDBFNgT9yeVy0eADx+xr5LbMCRcvSeNe6AtbADyA\nxz9gT9zvMAcEaoB87C4gADDnGTkAFr6mXq8HoPXMUvZ9nHBkHtFxb/ZD1pXgkJ/zfCcNfYsD8kom\nnu7ivDdgj7H53jFJsdeL92VuvXQUX+2NoVg//AIkCxlk7BRj5z6sPX0q+LmXyPvle+shjWnYlP3s\nbbrcjmf18ldhO/wI6+3ZF0gjJz1oSun7s7EdYAmv+MlmU933+J7lbrerzc1N5fN5PXv2TJPJJKrX\nCBbwv2z1ub6+jhJyKgHIEL777rva3t6OcdM0aDwep8bBEXTj8ViXl5c6ODgIuaTRETqH/fMyZSrb\njo+PU8Qf2Ay5w760Wq2wqwQiUnJ0D7aQypRqtaq3335bL1++1NXVlX72s5/F95ysZM2Yd2wi9/et\nCHyO96KLrus65DVVFZToelNLsI3rKRiG7HGj0Qib4s/1yiDvPOvycnl5qW63K0nhJ9zOkWTi86PR\nKHD3bDZTo9FQr9fTxx9/HM0DwWpnZ2dBIEjLHiej0Siw48rKSvTRqVarQRZiI3lPxsaFfEDIYdfB\n406GMyf4EHygbyvK4jon+BzXebn9V8F1b9Qq+gTxcm4o+L9nLBFq39Tr4NjZAU9vOwvPs5199oYg\njMlZHybV/12r1dRqtdTpdOIwZRw+nyUTC7smKcAIAeX19XXsSf3oo49ULpejS1av19NisSy1hW2k\n62qhUNDR0ZGkZI8N7AzBGAAUcAuogs31ZgDe1toBGA7fSzIo+/X7U8Z1fb3solav17WzsxNdzChV\nJuPGusMU4RQIrJh/wDTAxuUhm5XA6SA3BP2UU5CxRt6QCww0mU3kxPcrsG/LGX7GwDOZLy8JhN3F\nyXqwTGakXC4HS0+Gk3NvOZOMuadpE/ckYMDx8s6SUuCRcdKd2cGCVxO4/jnIzQJ/Bw0EFK4jyDoB\nLcYtl0vK9NFd3sUDkdt0MZ/okpdMI8N8DrmlmoPMVbvdTmWzsUPMW7ZRnIMw5h8Wn8DWMwWAAymd\n2WQ8OF8cIUEXcuXBi4OSbGbEg59sltgzgx7IE8hmM7DIGf/3IC5LgPEcgh4HG7yr2w/mwu0bNsuD\ncCcvs5kF5J3xZ1lrz/S4b8oG554J8QyxpNR5fr7eBFfn5+e6uLiIah5AkGcvmRuyJwA7ByJOUhKM\nsT/ZyzLRYQ/Ukf/se3uWgr99fxv3yf6eZyDz+BHWFF84m820urqq4XCYOg6BrP/JyUmAO2wdPQ0k\nhT7hk5hjACVZF7d5yAhrkS1bxZaiI7fx8mDJs0m/DNtl5TyL7aREx51IwedgU6S0zcGG8hkH3FzI\ntleKdTqdKHM9ODjQ0dFRVHiBP9nPSl8IMAk/A880m01tbm7qW9/6VvyMXh34eTDF/v5+YDnKN/Hv\nfGYymURAhO5hl/HZ/B5dgLQi4Kb0mDn2hpM+d56gmM/nOjw8VK/X08rKih49ehS+6eDgILrUQhKw\nxtgNTw45hqBBI5UdHNkDtvQMJ7qPL1pdXY0KhKwPmc/nQciRQPLqIvwjDai8IoR7+NqAcZBN3ok5\ngMhCJnkW9p+GgsViURsbGzo+Pg4/yPvR9R75IihEpghwISLdZxAQOo7ju25LqSxxjOj64PPo1T+O\n8/BFyDxz9D8C173RPaU4QCnZZCwp1VjBA03PbDno9fp9FgNFchbJQZeU7PPxDCsTT6aO8iecHgIJ\nq9LtdpXPL0tkOSTZ9xvBbjNGSVHihOL2ej0Vi0U9evRIjx49SpWbkk6HlfX9lTAmzMvq6qrG43G8\nt7O0GId6vZ4qHUTx2A9JuWutVtP29nYw7Bi5cnl5kDTnuS4Wi1QmbWVlJc4d/eKLL3R4eKhms6m9\nvT3t7e2lSm68xIJxSkkWm5KWlZWVOOC6WEy6zFGu9bosN8F1o9GIA6OROdaawBAns7q6mjoji7UD\n4JGd9ECXMRFgsuaME0Dn2QHAGYwf60AJIsDp5uZGd+7c0WKxCAdIFvry8jK1H4S9HK1WS61WKwwb\ne2cxEi6LzKEHSC536BnzikPh568LPngf1pO5BoCgawBMZNSN6229nD2E4GEdmD93eqzh2dmZNjY2\nVCgUAqBISVbBgwEPCv2ezPlgMIhnehko6+UkHc623++HHHAWpOuQ224v7wF4Z4MTLneU2RJI1xV0\n2cu7PCj2PTY8z4NgxoRMwzgjp57JA0xnt4Lgf3g3J2eQc/74vKIf6LtnY6WkvHWxWJ4B7HsmAYkO\nDj1YBURhfwGjV1dXmkwmUabL/jHsBvfCD0GcskYErcwNgIr54t2YfwI05h7gzv2azWbY72azGRke\ngnZsKvf28kyCR7qpelYEuyYpwKt/F59JV1wwAXPEkWJOkPBekEEEOxwsf3V1lZID3pu5cvICWfbs\nAfa4Xq+H37qNF9gO7MZc0XXXSX/wFp9lHcB26IyUdE0F8xCIukxlfUiW5HEfDbbzbs78nC64nK3L\nxTPw617tAPGO3Ozu7qpSqehb3/pW7MGHnCXrhTzR7ZVtXXRaRWYg/HlPz1blcrmYW+QR7FqtVrWy\nsqJWqxU4ZnNzMypwsJfz+Tz2c7MmBMhkFy8uLvT555/r+PhY4/FYe3t7evDgQWAjzy5Crvs2H59D\n1pfj8hzv8x5ZWwMeXV1dVbvdjmaMbIFy30P2l4AUG8HcOIEAQddsNgM/gauwA1QbIh/ua8Av+Bj+\nj7wyj+y73dvbk5Q+ag3yAHsIeTCdTlWr1YIUq1arsY83iykd11H1xrv4uBg7/vB1uM6JWb6H/Dlx\n7Ikhgmh8DPr5VXDdGw1KHXxLibJjoNzBeL2ylDhzZ/E9ywmgcNbAo3VXxGwDBGeZCTQAlACgSqUS\nHbHOzs50dHQUBq1UKmk4HKpYLIaSX1xcqNFoRBbu/Pxc29vb0axmZWVFDx8+1F/+5V/qX//1X+NM\nU2eUeT57THGQCAH7CzF+7HVdX19XpVKJw5hpmNLtdvXgwQPt7Oxoc3NT9+7di+ZGl5fLw4ylJGuC\nchIYoaAoxWKxiOYUZI5fvXql4+NjtVotffDBB3r48KHa7XYKwPm8wbIh0BhCWCTWh0ytj8sJjNXV\nVW1sbGhrayuO1kHmJKXmjX0W3gzGD2ZeX1+PUl4pMZYYNH7miktpNvvDnC0H3MCOOdkCyOee9+/f\nD6fijTtwRLVaTZeXlzo8PNT+/n7s8YBkIAtL0Esm2vULo4LhJzOE7HnJKAGEB/fMK5/1jA4gk7X0\njCvP9AAq67xuy5UNqrIBv6SUsa9Wq1pfXw+9Oj8/rRIoFAAAIABJREFU19bWVqoiJGs3vcmOB1nY\nTRrqIIPIggc7/Ax2G6BNGS8VHc4A+5ryc3TYgzVAg1cmIB8Entg6KSl5BjAiQ8iOs+40QOJdmBvk\n3DOhnhHlHRgf/3cSlGBGSvZOu7574Mn7OVnq4DsLWhw4tFqt1Px5pUeWTAUIufzUarXQ+0qlosFg\nEOs7nU4DYLGHEjliTpy84Pf4PeYem8e8oO++t4mADrs8GAzinpz76llZwDakFnOEzrD2l5eXEUBT\nyu6kihNg2G5sLLpXq9VCHplbD3rAI7PZLDKm3EdK9vN61pU54TPzeXJGHz/jfZB5MMVtvZgbJ/ux\nUVLS84H1B0PwO4hhfAPrS+Dj8+nkCbrFz50cccIV+WYdwDNUSnW7XTWbTY1GI+3v70tKtlZQfg1+\nIPNJ4HZ+fh4NHr/44gvV63Wtrq7qr//6r/Xq1avI3jM2JwJPTk5CvqXkqKTj4+NogAYh3m631Wq1\n4v5sJavVarE39f79++r1enr48GEEOJeXl3HOqnenltLH2/izLi4uNJvNNJvNNBqNolpvMpnowYMH\n+uijj7S3t5eyweBegj7sGv4oW1bNXnTmle97sJTP59XtdnX//n3t7OyE3lGB5vYCbEfAXiqVovNv\ntVqNucO/4jcvL5OGZ4wPn0OVGSQdNorLEyxUO4KXkbuVlRVtbm7GiRq8q5Sc2cpJHoPBIPqxsEeZ\nxA9BqfeLYQxemYO/I1DE9znJ4ck0LsdivwzXoY9SOk7wSpCviuve+KYGnB1OGKH00luMEcac7/F5\nhIcAhuyXGzqMGgLsJaMsCODHf46R83R9qbRsq9ztdnV2dqbj4+M4nJj7cm7oaDSKUlTuOxqN4n1r\ntZoWi4W+853vqNfrqd1uR816qVTS8+fPYyM4gS0MF6UeGACEJp9fdtOEpT09PdXp6akePHigXq8X\nAefJyUmcR0jwQkCFAkEQAHhh8mjWgMGnXIXS5Vwup8PDQ21uburZs2dxJM2HH36ora0t/cu//ItG\no1EouwOt2WyWCjoR/FarFYEdQI35Rj7a7baazabu37+vjY0NXVwszxZrtVo6PDwM4OWH0UNC+BEE\nnIEFmwkQJbDj5wRx3qUOmeQdyuWyJpNJHPlwdnamVqulm5ukHbukKAlH1kajkbrdbhhpB8oO5ACL\n/X4/2qvD9sG2+eUGA93CUHFvKd11GgPogBxdZA1db7wJleuPBx0AAUqHJMUem9t2MY8YdEpbWD8v\nS4LoaTQaevr0aez/oImalDgJZzQ9SJD0pb3DuVwu9hJ6eSH657bVgxMCG0obG41GHPqOTcX5ecbd\nKzKyQbRXg0hKNW8AUPJ/ggPABfuNHIB6lQHy7ftCfQ8vjtwdNvPgQSUBiJMA7NGHgPFyXt4Zn8V9\nAJ/8nOwdss8FGZDP54PE8uZS2Uyzl04D6rxyhoCY/1MSR6DOO/rYuO9sNgs5ckDp6wyw9wwz+5qQ\nacp7PeiEeIMMQI59rAR+yDe2hN+hAzzfMwG+L5XMAiV6Xv7NHAGOmVsva2s2m5FRJXNFqR/v6/KL\nb3BMwTFubOHxbPttvn4ZtvNEAXIkKWTT96x7GSE+z7GdZ25YTwff+BrWCXvgASwyBZ4A2xF8UR6L\nPNH0hTFKCVlFsoAM5927d/Unf/InevX/cPcmP5Kl57nfcyIiI3KIecjMqqysrqqu7lZ3k2KDIkg2\nLECCbZk0F7ahhWDAAO21DdiAV75/wbWX3nhp4MIbw/BGXhiwYVyBEgyIgigLbElkd5PVXUNWDjFH\nzpExeBH1e+M50cXL5iX71kUFUKiqzIgT53zfOzzv8w7f4aGKxaK2t7djsNHR0ZGur6+DKNna2oop\nq91uN3Qtm81GpRcZNKrnRqORarWa3nzzzUjekMWkp/Ty8lKDwSB0AnLHA0eq09gPiHNwBKQmVYHj\n8WKADQM1q9Wqvv71r2t9fV2fffZZahiYn1nPWvskXl7op09OR2eo4Lt796729/fDD1xfX2s4HCpJ\nkggCscH0YjI5fTJZnG3KcT7YzXw+H2TCzc1NtEcRGJJ1ZL3G43Fcm/PjKactFovxb4JkSXF8DwRD\nvV5Xq9VKnRGO/cGHY187nY4k6eDgICYoU/niz+GZSdcRx3X4MvwQesH/wSH4DtbNiRN8B9h7lUz+\nbXHdKx90JC1L+wiKyMSsAnyYcWlZNoYSwRawOCgCG46RWnWwXvLhxg0h9dIy7rVcLkcAurGxEYwR\nTh8w4o4bQMjBumSj8vm83nvvPX37299WkiT6/ve/Hwbib//2b4O1RQhplMZIkq0cDocx0ZcAYTqd\nRl9np9PRzc2NfvGLX6jb7UZ2kjXY3NxM1bwTXFAC4aVLXvYiLQ9GhkFHoGGWSqWSer1ejBVvtVr6\n8MMP9fHHH6vX62kwGISDANBQIugZJRjoSqUSSgz4Y7rdnTt3tL+/r+l0cbTAeDzWxcVFZH291KBQ\nKESfrhMQyBjlys5EZbPZAFmUYLHWnqUiiCQwQEl5Lj7j94RxI1Pb6/VUq9VUKBTU7/cDJPMeaQlW\nT09P1Ww2dffuXbXb7QC4q07fWS4cs4N2l3PPYvJ/7gHw6IYIkMlzIYfIKSwin3lZhvt1LWnDYeBA\nsD+eoUTW8/m8tre3dXx8nJqoigNHXvi3pCCJPCtJ1gcZcSbUWU+XK/a/VCpFOSnsLGWd/X4/tY/0\n/gC40VcPRJE1qlocoBCQsD6eRcL+wzBzgLlnxjzTu2oruA8/6oN74vP+GWw/9wHwdVuJ3LL+gCAH\n2gBf5FpKD19C17kHgDiAydl5zkcl8MSPAE6LxWIE5UmSRHYCEIWMkA3wtfUsqKTUwKTVzDJ2h+sQ\nNAPOWSsyT9hY7Cff4XvqmW3WlNkH9IXSQ4eOOA7gs4BGADZDjQBZ9P3xeYAmGQ/2hrVEVk9PT8PO\nea/wqr10LIFuoFPsC+uGzKI/r+PLZR48hN6zH45TsF++tp4hd12CqPHydgC1z2qQln3dXrXg5bO8\nlwC4XC5H1QWyx4AatzmuQ+BAevXBB/l8Xh988EGUr7/xxhtxv//wD/8QwzCRzVqtpp2dnRTZC6GS\nzS6P0iPgK5fLevjwoYbDobrdbmQv8cFgYHA1wfpoNIr1hlgfDoepclTwtqQgh1hH1nk0GsUAoXv3\n7imXy+lrX/uaKpWKnjx5EvNO2B/2ENIHm8V38G+wFXi0Vqtpd3dXDx48CJwEcQdJAY7DFp+dnUVF\nD1iQ3noSRuBaqkdYb+6TdWT2irQkNxg6RWBNxhYbxffiz3mP22oP1p2IRW8k6ezsTLVaTffu3Utl\nT3mtDk104tezpVzP/b/HPFTseCscz+/r4TrFHnhyh8/+NrjulVJ1Xsrl/QNJkoSiAoQJDlkcBBBj\nR5YLhpzgliAQdsEDRQycl4nA3DLowQEIC0wpbJIkGg6HevLkSThlDJKXXwE2xuNx9AsA7IfDof7k\nT/5Ew+EwAre1tTV99NFHqf7QbrcbGc+nT5+q0+l8oe/l/Pw8SjR4tvPzc3300Uf6yU9+oj//8z/X\nL37xi5iqKaXL9VgT1kpaZsgonZ3P56ljBjzd730dsDg49qOjI3W7XQ2HQ02nU9XrdX3jG9/Qw4cP\no8SZfWPNYPEcTAJ+uf/r62vt7OzonXfe0Xe+8x3t7e0pl8vFxGKMFA4Q4I4RIJPnh6UzaAoW7urq\nKs6bo6xnc3MzGu1RRi//BZBQngFjf3Jykgr0MBaeZUWuWN933303QA0BANObMcwXFxcaDod66623\n9Id/+Ifa29v7ApnCs/I3WSYMDDLrATb3gC54nywy4s6AzwHKAG4erPqgHs/i8T2v48uf1XVmtY/G\nZYoJj7lcTtVqNWQCZhfH6v0t6AfOlaoKvmd13VeJBOTh7OwsMliTyUTD4VC///u/HyCGUlF6rv3z\nADYv3/FgyIM7zzTAMHtw61mscrkcJag8s6Sw57DX2GlnkAFDTkwCZNFVB8981vcGHeH6a2trqlar\nUZ7mmUZ03gNmr2yYTJZne3JfgCMyLdlsNsASFSIEdAAazmfl85R4OQj0jCT7sgqGWAMPHgiaPZAH\nXJDpILvhVUqsFdfJ5/OpmQCepaTqgj4pMjvYlNFoFM+C3PId7BH3ByC8vLwMuQdfsN6OIQhgt7a2\nIrhw38LPWD9kjiAJH+73gl11gO9ZXDJdXhL8Or5ehu2QXyc9kFUG7LDOEGFO8CIrBBOnp6eBfQDH\nvNczOdgJbArVSU64gBEJHDKZjI6Pj3VwcBCVbhA8yDWkytbWVpS2ErgdHR2pWCzqhz/8YbR2gdc+\n++wznZycxDN1u93oNXzy5EkQKoD5+XweZ1xC0JBB+5u/+Rv91V/9lX70ox+p2+2mSk69zebi4iJI\ncWkx2RUMMRwOo0w3m023RWCrIbmw0ej32dmZTk5O9PTp00govPPOO3rnnXe0v78fOAGM7gQe/ooq\nQJIA6GahUNCtW7f04Ycf6vd+7/e0sbGhRqMRQ6LI1kJYesKEjKjvOUOUvB0M2aAdj/v1zDxtb+gs\n94aNZ/+9BNdtupMog8Eg/MSDBw9ij/E97DfrcHZ2pm63q1qtpj/+4z/WW2+9lbJhnpjiO8EHXhXj\n5Ji0PPISPIdt8uSSE8WeDfVkBLogfRHXSUvf/5vguleaKcVoYBjYTGlZyrGaduYFkHJG3tkwZ7Ik\nhdPy7JuXbADGyBrBJGGsMH6cGUR9/y9+8YsQInoCYPwwkAiMAyVpwQh/4xvfUKVS0aeffqpqtapS\nqaSjo6MUY4XAIGBk/1g/Sod5D4JdrVaDQfrmN7+p9fV1DQYDSUp9nvJAwB+KO5lMgtWpVCpxbh2Z\nitVsKwASYfcSQX42Go10dnamnZ0d1et1ra2tqdVq6ec//3mMRveR3jgMSndns8Uwn/F4rDt37uj2\n7dvB0pGN3traUqfTCQNMJngwGKhYLKrf70cWlXPJCAR4DtaR93HMTi6X0/HxcbBtKLCXJVGSw1Aq\nss9nZ2eqVquR+XBwSAkwIBn9WFtb0/379/XjH/84elFwPMg4mYGbmxt99NFHajQaOjs7U7PZ1PPn\nz1PnGDozjJw6g4yc8zeyinxQgkH5srRk9dBBnADPjcyuZrEc9Dsofx1frDN7y5qzZqz5dDpVq9XS\nwcGBbm5uVK/X1Wg0VC6X1ev1dHl5GQEq4OHm5iZII2nhYJG91QwkoIBSI+wsGYlfldmjxB/b40Ex\nA3XcqXoGaTJZTLOkzMrLQFdlDgdJCwTlaWSM2+12XAPWGt2jmsMHDblsrgaz+BBnjXl+yrAAMx4o\nc11YazJvrONqGTTfBaglOwLBJynWkiMn+C4fyIQuAQSwtQDp0WgUvoiD1jnvzkk/ggG+18sdJcVe\nevaKdaCahbWGBPYKEUmpa/peAcIIzi4vL3V1dRVDUSiR9SnSACgyqN7vtfpdYACfGSApMv2rWWx8\nlKQA2y4z3GupVApCxGXbiWx0yStf/PxtgD89iY5nXrfXKrYjOJXS2I41l5TSFa/qYf0kpfwVPsNJ\nCr7HWyN4D4EGVQnYHzBWs9lUsVhUpVLRbDbTkydPUgmJm5ubyJphS7BXUvqop3K5rD/6oz/S3//9\n3+v4+Fhf+9rXdHV1pefPn8f9E8hAeEMEXl1daX9/Xx9//LFu3boVOAw72+v14gia3d1dvf322xF4\nejYaLMvz5fN59Xq9sGUEZ8wgWe3Zpp1NWp7J61kwCLebm5uodjs/P1er1dLe3p4qlYp2d3f16aef\nRmIDfFAqlTQYDKKcH6zG0M1bt27pjTfeiKrE9fX1sEdgSbLH9Hli+6nC8AznahCLrKGn2WxWvV4v\n7PLm5maqPNztDdfChlxcXKhUKgWO5N7APNPp8rxvJ6zu3LmTamfAL0ImYL+y2ax++tOf6sGDBxoM\nBtrd3Y22PPC4+2qwF/riOAM/wu+Y9gteoP0CH+5kJX4A++nEKrbQcR1k3G+K615pUEoJBYAAYUHo\nkySJIzxYdGe33Cl4CQb1/Bg9+mz8uqTtPauDwXMG05kBsmC7u7vRc3hychKMORssLbMZ0gIkrq+v\nx1lXGLJbt27phz/8oQ4PD6M0gTHRBH4YCASWQ85ns8V4bsoUAKcIDYEd5WAEo2SGi8ViDFgajUbR\nO4PSnJ2dhXHjfpwFhykiAGMvLi8vVSqVwuBiaGG92cPhcBgsGcOfjo+P9fjx4wCgklLfyZTiUqmU\nOmCaLDlGi4OGR6ORTk9PdXBwkCp7I9AkOEI+6CFFTggUG41GGO7Dw0NVq9UvAJ6trS0Nh8PYc+55\ntafMyzu8TAxWrtFoBKvoGWymP9I3xjUlRV8yoItpxa4vEAZcG6NKZg3jwv2gj+joKglBlsWDDK90\nwFABDvge9AgD59k+AmP2/HV6ARDcIXppo1eAlMtlHR0d6d69e6FjP/3pT6OcCWfBUASYZoJPB3KA\nCrJaJycnKWeB7SXTSHAAoQDwQA6azWaq/xMyxIEgTpB9pKeaveV7peVURWQZZ54kiXq9XtiNYrGo\nw8PDAAxUnLCua2trcZZyNptNHVxPkOVHCvDc3JOD17W1tbhf1oY1hWx0wtKrYjzL6v6EYAX7yj0A\n6ui5oV8W38X7HAx6tpXqn5OTkwBXgNPDw8O4P/bKs/QOYFkf9LTVamk0Gn0BeLPW2CXIAwgHZ/Gp\nGJlOp2GbCMgJRvG1XJ91Rqbxv9g8aXk0hmfnJcV9kDEnuEeGPPPvhC974/aUM0XxZe5XIUkItqk8\nwoYCPpEBnhcfzedY89fx9auwnVfPsC6O7Vw+pS/24uODJMVkabCdtAwYHBuik+6bpGVPNnimUCho\nb29PjUYjBjXWarU48s/JYPa3XC5Lko6OjlKVS2+++aa+973v6dGjRzEjpNPpqFAoqNvtpsgdZNSr\nA588eRLkBpURnU4nKlSwOevr6+r3++F78busR7/f1+3bt3V2dhYJCj9qhL3A5lIlUK/Xo6QY/AAm\n4juYCjubzdTr9ZQkSVQKSos+yo2NDVUqFX3yySdqt9tBsqHjOzs7YTMqlYr29vaCFCTr6a0NEGD9\nfl/tdjt0aTQaqdlsxvwBdBtMRybWiSGfBM7kY+xnkiQxM2Y0Gn3hTFh0G+Le2xbw0VS6+Jm1rB8k\nH8EsiQqvLgCnkTyh/Y0su+M6ZAT/jQ/B9nNP0tLHem80suAZcjAePpN9x4Z+WVznmfsv83qlQakz\nrAATBwI3Nzep+nd/SBYYthkGnxS7N6dTVuAZA2fnHEDg7DBelHKikLAmGxsbOjw81OXlpd55551o\nTM9kMjE1E4G8d++e+v1+DElAIT/88EOdnZ3F4BsCsU6nE6wVSkcNO8YWFrbZbKparerRo0daX1+P\nMeaAJ89gYEBgzL0MidJfym0py8IRsJ6AB4SSY2jq9Xo8G8NQcNaUxxaLRQ2Hwzh0GvKAYyY407Tb\n7Wp7ezsV9JfLZd3c3KjVasX6kck8OzuLASS5XC41Wa7dbsdgKRwew4ooFwFMEUhjXDDY5+fnevLk\niUajkb71rW+lev08eEP5PAuLUUFhATuw+wR88/k8dTj0aDSK7DRDq3BCfBcN/BcXF6rVajG8amdn\nJ5UFp7wJ0oS980w44E9aZgBwVJAgGD0H1jggL/F1II7hZj0Zde/DTnCUOKDX8eUZGYIXD9oouS2X\ny8HQw4R2Op1UPynTICEj2DsP7DhnGFvqg5WwHX5UBk7Ey3K4L5wOthd5b7VaqV4R7PMqMeG/95Jb\nZApmlufjeVbLy/k9ARDv4z2Hh4dx7x7USQof4E7XMzEAZq4FyHP214NY5J9982mr3r+KffW1gWDC\n3gPOp9NplCjzO0lRdgaR5hO/yaCgk95jh20EhLDGkHDYQ/waZx7WarWYHo/semYfvWWtsXvIBr36\nAB7ANgEe3+ktMtgZygc9WKXSAnvL/TLvALkETGLjPLvh/WcuNzwL/vHq6iqeHVmo1WrKZDIxQI41\nZV/5DmSU72L9IJYpU8bmrgZgr9PrZdjOfcNsNtNwOExhO28xYH3IJPnQFAIMr8jxCh3kiT14WTZb\nSve+oWPIzKNHj5TNZrW/v6+jo6Moc+RMTMikSqUSA4uonrp165b+7M/+TJ999ln0BEoLm9ftdl+K\nqag6Arednp7q4cOHkcWCwB+NRtra2gqd98y+tKzgILDJZDKRUJhOp+r3+ylS3RMe4GqC5PF4MUyp\n2Wx+oRwb3wKGw98Ph8OoGqQSp1Qq6f3339fnn38e04FHo1FkfElwoGfolrTsfczn84Hr8F1+xnAm\nsxjAhH3Hd5F4AEP78Ljr62t1u111Oh3dv39flUolhkMxn8XPDcVOs5bIlfsC/Cb9wvl8PhJJkGON\nRkPn5+eRzMAn8F3FYjGei/ugAhMbT+abChBpiTHwfV4BBdbFlrIu+C8fboqdRB6p9PRsLPvkxDB+\n5bfFda980BGpe88CeFCIIeIhYb6k5ahhT3U760JJAZ8DpGPoCPhgqp1JYHNxugRC1WpV29vbIdDl\ncjn6Dgn0PIN4584d7e3t6eDgINixra0tPXjwQB988IEGg4GazaaSJAkGxIUEZ811+/2+SqVSlOJe\nXl7G0S+j0SjWhxIhmLxGo5FaLxTv4uJC1Wo1mtL7/b6y2WwIk48/Z80JWADCKCWACEVjD6nBpyyY\nPl/YaxQXgsEJhFarFf27gAmcSLVaDQWCeQcArDbiP3nyRNVqNZg8wBNZBYJwSmx9xH+SJHr48KG6\n3W6cezqbzfTJJ5+kStycZTw8PEwZMklRxgGY8jIyDB+Gjef3TKbLOSQJRoHSvfPzc7XbbVWr1SgV\nxhnzAjA4U4wzJkCX0kcxoTMwapA7lDmSTVrtRUHHCX683KRYLEavC4AO3X7dXgAlacn+A5R4/slk\not3dXf3sZz8L/WGNAfmsPQ6BgJ+1Ze3JLE0mE9XrdW1tbenZs2epSgBnkyuVSjhSruNs52Qy0eef\nf65OpxNgg5eXynpwJy3LZn0d3IYTGHvgid11MNlut8MuEFhwr26fkDlkV0ofx+GZamwZ9+S/83JQ\nSann9ayLlylyb/gZMji8DxAMWIKkhPTc3NyMTJ9XDPnxA36NTCYT9swDYJ4Vn0Eg69k9r5aByed+\nCfyofCHo5rkkRdnweDwOfXa/THAL8JEUpB/y7qXOBOTel8d7CNIhSzl/VFoew+Myx7FkHlh6uTDf\nh0+SFIEB1wNYQfyRZYIYJAB2+0nG3clevqtUKgX569m81/WF3WcQDHqK73VsB6ngFQyOVTxQAcBT\ndQVYhvygusnL+H0KKv6L78Yul8tlbW9vq9VqxfDF7e1tHR0dpchbt4nvvfeeCoVCJARILjx8+FCV\nSkXtdjuOLfn444/DbpHFguBCHwaDgRqNRgQHR0dHcZwd61CtVqNyxMkP8AvVadj/ra0ttdvtCNJy\nuVz4XQh/iG7WC5/s9swrCD3wlxS6iW5ApGOfKMl/5513IikxHA71zjvvBOFOBhVCCjLw+vo6AmDW\ngRLk4XCo8Xgc8kCV0Ww2i6NuyJJOp4veX4YFIU+cYb+7u6uNjQ09ePBAP/vZzyJJQHYTH+kVZZeX\nl2EzNjY2orIH3MZ9l8vlWE/w8Gqbg1cogQPBp+PxWIPBQJ9//rnuvRh45PvjOI17w+bi18iqojO8\n320ROodtB2/ii5BBz+C7vfxd4bpXXr4Lu+ULuzpZ1B05C765uRmGBgBDEDedTqNc1QEW7wWEoNQw\nuSw21wDAEcCcn5/r7t27kc1D6BiKA+MPuMlkFmWX//iP/6iTk5MU4/UHf/AHcQwLI7YJoGB1YGkJ\n4jjoHAVDYJ4+fRrZOQwaxposCA6yVquF44cxymQWpbEAJUnBhuVyuRjrj9JQKkA2EoDMhDPYPLIo\nMCmUEkuK68KUIewwZjj2e/fuBWFBMMpeUZJCDxKGEXmYTqc6Pj7WbDaL81cBgNTqcxQMDCBngmLY\nz8/Po4Rld3dXz58/j6Edb7/9tjKZTLCfrDPnhLKWMIGsHWw7BoX9wgmzv+wRfRVkWMgK0AA/mUyi\nhJvgnkCf7FGlUlG/39fNzU0ALAA564GxJJOKfuGYPMOXyWRSI91xWugPAQDyxef5P44COfVs1ev4\nYr3d/hDwuLxKiiw7vSOA8NUSnNWsnwNd5EtaZgToq3ayhb1Gj9kHwAoADwb4wYMHevTokba3t3V+\nfp7KoiIv6NPLSvF4Ru+l8dJj7gHHzAvH7/aM8irkFpuL7XaH6H2fPDcVNvgW3ss9eMbG14zyOew9\n+uD74e0i3ss5mUyCEff+HYIwyFEHgawXvcIOHpADJnQTeOHbIA5pa+Aoqmx20SpSKBRSpaccSYHP\nBZywrqvklZfsz2YzNRoNXV1dqdFohL8C9APAYf+995dMB2Sb2xzsraQISsg8enXAeDwO2+pAFRmD\nfCbbwVBB9oWAklJ3gG+pVArZYK096PTgkmDIq7V8j9lXJz1fx9eqbr8M20kK4sxBNBkXB9fYTEmp\n3l73uwRW8/k8rgGhgk55dgvATun8vXv3otR0fX1dx8fHgQGxZ5AdEO+ffvqpOp1OlNI3Gg1973vf\n0yeffKJWq6Wbmxs9f/78CxNjCVaYMk32bzQahR+dTqc6OTnR9vZ2HOOFnQA7IvfYbC+HppIM357J\nZGJ4JnYfXEXlGWvGvzc2NoJgOjk5ifYi9IZhkFTGYQ8J6glAs9ls9LYPh8OYySJJzWYzkjA+zwP7\nMRwOUxUrJDakJXFOIgL95A/tY41GI0qA8Wfg1VqtFt/V6/W0s7MTASHElrSMHcBlXnItKTD41dVV\nCssTPIKjwXisB1g6l8tFBrher2s+n6vZbIasILPEHyTKIGjQMfyck7NOlrHG2HjiALf5YHf0EDmR\nFFiVvQbX4RN+W1z3SoNSgA7MC87byy68zAUhRjABDjhFFohF8LIGFs6NFwvuPVIIrisHQQNKRiCC\nwaKEAbYAAHh+fq6Tk5MIjGq1WpSg7u/vR5BncuxJAAAgAElEQVTKfRAIZrOLMznv37+vTz75JDKC\n+Xw+QMfz58+jlJX7xnnDziOYXuoqKVXXTgmglxYSMLLulIqQISE7BwsJQ+ZTILkWAg5jRWbUyxlq\ntVoEU4AJGCgycQg3jp5etsFgEODBAzZJMfIcEEHGASCHkR8OhxHk44B8YhyZTab9MZq93+9HsMhR\nFRwOz7qxnp6l4XnYD8AlBo5eQcrUkAeywRAP9I2Ox2M9ffo0AFm9Xo8ymWKxqF6vF+wioJryQQJ8\n9It7IYCWlmWX3At6hC4S2BJc8XMHzh4AU0aK3hNwebb1dXt5ebcHOQCjWq2marWqx48fB+A6OTlR\nv98Pcovze/f29vTs2TNJS7CG7LueEVRxwDlHBbFX3l/p5b38ofoDRzUYDNRqtYIZbzQaKSKIvfTM\nuYN3Z4E9g+rgHOcMqCWYxO7wzJKCmCRY8AwrfgSQKqWP0vGKHN8PdJT7ZC08KAaMoT9UORAc4zO4\ntvfRIgM8qzttgAzPxLM6AOfnnsEmy4Bd9+/04Brb5oCWdWDKM+CXYM5BjF+boB9yF+DZ6XTieQBF\nrA3VNZDQrLNnz9hbz3Dh86hyAmjyjNh8sqrSsn8Pf8G6QRjid1kjAmzPhmHvV5+HUjXIIuwVFVoE\n2GQAfVIn9m88Hqtarf4OLcy/Xa9VbAd54f7Awapn4JET101khH3k+vgm1pcSR4Iw9ohgAmznwD2b\nzUYv6cXFhUajUVSRIasE2F6lcnBwoE6nExn1yWSiO3fuRHUKWT7PrEGc0Jrkfm88Xkw9v7m50c7O\nTmBKdCyfz8dwILAN5IbrKy+fmEu2DwIS3XDcDZnNvpBF43gW9hMd4L3gVo4tBJcwjA9sxB6AJZNk\neV41lRhra2vq9XpR+QW+82QDx97QroeN4EWVHzaaYI8srOOQ6XQxrA67hrwxJPTevXuaTCY6Pj4O\n/wMOBvs6jmJNIB8oYwZPexCYyWTC7joW8inynE3KOffEIEwMJzsrLYlqj508I488EW+4n3PS3DGc\nl+Fzz6zdy3CdtBwY59juN8F1r7R+BGVdLRebTpfj3alHxnm5I/EeIUAFn/MgA4CA0mHkXEBQTE/R\nAxjdUeIgu92uJKVKschUwfLs7+/r9PQ0NvXq6ko7Ozv6wQ9+EMJP3yX/7nQ6+uUvfxlT2t57770w\nWOVyWdVqNZSs3+9rOBxGqhwARdkEbD7MWLvdDqNHJo11GI/HwR5TksXgCwIrzuFiDZ1FwpChoJSN\nAhJw6mR0aEhHISUFCYAiwKg7o1QoFHRycqKLiwsdHBzo/Pw8nonMab/fj9Hp1Wo1FJy1ob8V4Or7\ni+Ki0AAR+lpWg4Dr62s9fvxYP/7xj2PK3Pb2duxTvV7X7u5uOFDPwmMw3Cn5msKUlctl7e/vh2Gn\ntI7SO4Cul1EQ+JZKpWB2YQ8p3cMoE6TwQj+4Xy/3lZaHO2PAHGRg7HhhiHE+6CClzeg990KJyev2\nApSxjrCjkiKLAvmUyy2Ged26dUvSAkiTHUBWm81myg7iVDwQnM1mUR7V7XbVaDSC+ACk0It4fX2t\nVqv1hYCJrA4lpYVCQa1WK+wG13c77g7KnSP77JUbkHF8F9+9traWCmApV0O+5vO5Tk5OotfSM7AE\nPOgta+Q9a176xP99f3g+d9KAIyeteAbe587aA2939J7dmU6Xwy+4plfnABo9q01A6oEmGRcqPcrl\nsm7fvi1p2Y4ymy3PLkaH6V+6uLhItbuQbcVO4x9ZL3zEbDYL4E9gjH47qcA6YPdYYwIF9J89wBbQ\nysKxO5KibYFKA2wK2SrK+NAL1oO1k5bVH5SPY5/wMVTncL/8Hn8BOEd+edbT09PIXLCXEJl8L/aa\nSprX8eXYTlriJOTVsR0+CeJOWpYYrmK7TCaTOuPUdYPAxPXYg1uwmRP23CO44/z8XIeHh6kghb+9\nAoR2KZ7r5uZGb775pv70T/80SHXworTI7rbbbbXb7RiWuLe3F/dSqVQiaXF1daXBYBBnpELQUxrp\nvYX4+uFwGMEY1Q/e5w5uAP84WQ9GZN0lhd5Ky7OwCYr5jFcZeoUHmIR94PNO7BSLxdBVz+h1Oh2N\nRiN1Oh31er0UFh0MBur1euFf6NUFm+E3ICnYW3yiV6N5gIROUzlDxnk4HOqv//qv9dFHH+ny8lK7\nu7vRStZsNrWzsxPBNTIPPnbCkawjWGk6nWp7e1vFYlHNZjPsOutHOyABNfiJUt75fB5DmpjZ4iXY\n+C7kX1r6Ha9gomrBsYNXKHnpruM6J0id8MPuk6zzKojfBNe90qDUBcYDB98MShNwiL44GDeicZwn\nQkuQgRLzGWfnnDUA6JOhgumUFEczSItJa7ArgCAU6/LyUsViURsbG1HiS8nt2tqa7t27p52dHZ2f\nnwe7y7j5w8NDdTqdMEadTifYJnf61MZTbkBp0nw+j3I2jIb3aFI+S7/BT37yE7Xb7ZhWS5kXx6qg\n4IAulMtLPz2Dyu89a0wwhHFGgKvVasr44qyZMnZ+fh5sEABCWrAw19fXOjo6SpU9n56exrNzDXeK\nMGoA3PPz8yivADCx78gkTBsMKYYe5wDorFQqunXrlorFoiRpNBppMBgEyETearVanLFFxpjgDFmD\nSWW/KB27c+eOKpVKZApw7pVKJbL3rBUyR0bAHQzGwSf1AhKlpcFBZgiYMejIIcEFABX9Acix154Z\nQv/IPBOE40glpQa8vE4vB0dexuhrDNC6c+eO3njjDU2ni140zyJMp9OwP+ikn8WM46dqhH2eTCYa\nDAZhS2DAkZNMJhMO3zML2Ga+g15p5IxyL158t1e18OwAK3eCnjFhLarVamry7HS6bF3wciIcdL1e\nT5E5klJ67FlT10cP9LA/+B3PPng1CveJbcQ+enbYCR4CRyd50PW1tbU46sqJPmwloIFnxgbyfL7O\n/E2QNRwO9fz589irzc3NGJBSKpVizdB7fKlnvPP5fGQ/WBvIBXQW/8ezAoyRBWTIsxn4MPwYfoO1\n5l6ReaptAJ+QOMgWgbYffA850Gq1Uj26kKIE5W7TXc7QNeSfz/A+1g4yFx1zIAwu8Uy6l9E5efe6\nvSB50I+XYTv2DXIM+ZOW8sj6efAP1iFz57LvGR0H+5PJJGQT+WF/Go1GzAnhvE1whLcTQOYRnGGP\nwGXvvvtuyB7HixQKBQ0GAz169Ejdblf9fl9nZ2c6PDxUkiRBMEsL38d8kcvLyzg1ANIIzEig6QkD\nbBIVLX/5l38Z+uttNvRQskeeyMB+suYEPN6WRtYPQoFqNoJpfMlgMIhBRPycqjNwD3aCBAaDKTud\nTpC4JBoggcAXrBFZRWSFgBT5AIMif+g/rSnSsuwUOZSWA1RJMLDnTGUnMJeWsQE2DUzsrREkSCRF\nG0Uul1Oj0YhKO5/nwKkUyMZsNou4gKQT6+EVApCd2Bl/ER/58YdeoeJkqScmwPr4I2wr+ycpqgL4\nPfv7r4PrXmlQ6qy+3zyOj4ViUZxldOMDAIEJ5tpeo46zZVMcHJEpKhQW57DRKwXYLhaLwU6sra3F\ndFyyoAin11LncotzKSk/gAl/44039OjRo6iTZxLXL3/5y2C2eGZKrSRFEErKH8M6Ho/V7/d1dHSk\nTqcTGUPWh3H2vq7c/3e/+11997vfjSFICCuOnrIoDLKXKcCGSMszvNzREGDCKnvp3Wy2mLw3nU5j\nMq5nkvhun0JGj2av14tnSpJEg8EgeqBQFADzaDT6QgkqIM1LdwArKBP36IDJs4VJkgRoJsgiWAOk\nSAsw2W63NZ1O47iHfr8fz4mRxFFLCsNWq9W0v78fQJqsGcMcKMmGLQUQ+lqiM2TlWE8PGKR0fyLG\nGyDvzCJrxDoiY54xADwCRHiPs804jdWSD8D76/hivz0rSEkj5U9MdUR3Hj16FLrs2UUcKD1xvkfO\njCLP2A6yWsgaQ1ewN+gxAN71kawWfToAEXrRXT6QO+wrIMfvz4lI/55SqRQMrBNVPLuDVMDVcDhM\nkTIOgtEDGHcPhgkEpeVEZF9LL1V3YhGb4Fk9XoAiJ2KcHPOsAftB64O3HUwmk6gyIaPHWnAtQIIT\nVNgEfs+z+P4nSRK9a71eLxUwQt4xIZPsFK0ZUnpoF5ka/DeDiHK5XASDZLIB+jc3Nzo9PdX19XUq\ny0qg7/7YSTD2lL1AJgiACDzwF5PJRP1+P46rYN/d/hMwuq3jZ/TT8n7AP/J1dXUVR1c4MPayccqF\nZ7NZ+MNV+XgdX06S/Sps54AXjAT+wEY6ceVlvYBuT1hQ0YEsScvEB+QJukVlAz6RrBstDvhp8CQ6\nxd4xZBJ5KZfL2tvbi/PWt7a2VK/XNRwO45QGfCq4rtPpaGNjIwbXMNMCAmw+X7QIPX36NHWcE7bU\niWH8f7vd1ubmpn7wgx/ovffei2QJxLX36vpas848o1efeGUAGAhCgGdZrS65urqKMltsCuR/LpeL\nVippgc1IrrDHvV4vfJ/LCbYMf8jzsIa8CHKJAdBByCvXSffL0iKrDSkiKfCk2yEwNBlVcD6yT/CN\nXYEQLJfLevPNN2PSd6PRCBmkgorEC7aRSkgIQoYokVzCD2JTnMRxjO7VMmAvJ2Wxe+6j2S8CX8d1\njhcga5wUR29+U1z3yst3CRoJZhA4ggVKDGBzEGxp6ZQxYDg1jJUzASg0yowj8kwQ95DJLM9JwkHn\n8/k4B+nk5CRYCgcAAE02gE0kMLh3754ePnwYPYNkF58/f57KbkrLkj2yiAwFopeSe8JQSIqSXgJ3\n+gYAKhgGmOj5fB6MHwE5mWVpGWTzHIAiZ6JZQ8pwJEV5KOwOe41Cw6CwP/Rq4Xy4ppcFEtgxDIDA\nFJlAkbiODyggmPYyU0DodDqNTCXT1nBqsD3IB0CI4HMwGETpMOQBRhPD5TLt6+ssorOAvGc+n+vo\n6EjtdjvK25IkiQE2fB/y78wmZMj6+rr29/cDnFL+i8FAlxxwugPwoBaHTpkLcuoMm5egSEqBNQIo\nrsEaEyA7a/u6vlZ1gECVskGyXKPRSCcnJ6lqD+SVIA/ShgBOWhInXnZ6fn4eZ+zNZrMg5KTl5FJ+\nh+OSlqyqg3/sCDJCmSOf9z+eqYCkAiB5YMh98IdeKdaLLJu0LAEku0LmiTXke3Gurq/IpZNB7IU/\ntwclPDfEkYMgnC3X8koDsgZezuyBK9+BLQUo+VAhADzlUPgiwDtEq4MCSA0CTHrO0MPJZBI9dZBn\n+E363QDO+GV/LgeVDng8qMBn8x3eS40NcRssLc8dBTARDI/H4wgyneT053GCj2BwVcbRCQJs9IVA\nwLOWqz16Li+ANP5NuR9rwhpxn5TVOaHkJB2B0+v4+lXYjmAIOXG777IGnuL/BA3IJ7IJBpEUMuO2\nFR1yOaECi9LxjY0NNZtNdToddbvdANiUrqND2FWmrrKv1WpV77//flSecM7txcWFPv744xQGcVm9\nvLyMwM2HHpFlWw3ijo+PQxc8cERfIVK4D84uxi+QXZzP59G6BQ5Fp9FbCDu3p9gf8MCq7DuJx34S\n5LivI8vH9fv9vi4uLqIVjf50xyLYO/aZNXByj+vznBsbG5EtdkLNe2GJFSDtkFEPyldJ3Ha7HSQ6\nn89kMpHJxteBT8kSI4vHx8d6/vx5+HVkA5n0zC199FyP49Du378fz8V3ogdOGDoeW82C0hb3st5i\nr/pxbCgpEjfoxr8K16225nyZ1ysddMTLy7pg1gDxzrhLyz4NWB9n1/g/ZZPOTiPUXq7EJmC0vCSH\ngUWwBJVKJQDTaDTSzs5OlOu6sdzY2FAmk9Hx8XE8H+n8b33rWyH0KOpnn32mbrcbUwFzuZwGg0EK\nTJyfn8fIaQ5yZq1QMJ4JY45SvmyYjZfGeRaDtQX0ASJ8SAFKwvsBEvP5ovSZKYur6+2ghWuOx+Po\nSUDI8/l8HG2Dkdnc3IxS5rOzszhKxpWOxnsvX6AEzFnB6+vrCMhxnJSDIBNepoBCOQDkd57tQV48\nMMtmszHplgAdAMoeEFT0er0og4Thm81m2tnZiV4v1hZmElbRg0IC2tFoFAzb+vri0GUcEmAKmfCS\nFfaI9ZcU+4ujcWPl7K0HBRA0/JuMIPpEkOwGyzOrr9uLoMIDM2k5sENaHtt0eXmpzz77LPST4yd8\nQNVkshi+AHAh4HUWnH8j57zXbQB64QMPnOX0TGoutxgkw8HqsLfsG1kEQIPbGM/ES0rtuwN2bCCy\niNwgq/wcHdve3tbJyUkqu8n3+zo7Ew244oVss4YeRPI71zVsBevrA0lepg8e9Pr/Gd4D0IUExMkz\nbduvxf67z/L1A6ziF/FvkoIExf/M5/PIMLBX7Kd/p+8lP/cKJK5PMMx3JkkSNg3Zxa+sZqyxzQTm\nPtCN67k98azm6v45AT2dTqO/30tDAd1kqgGIgEzAN1keroWeYP8JErxiwckjZARfmssth5q8rrbO\nX6vYbrWKx0u/WWMH9KuVWwQ52DJsFXuKXIEvXD+QY3QA3eM8X4LNRqORCmzZf6o4qPLCH1arVX3j\nG98Ignk4HKpYLOrp06epLCR7z6Ah8CP3Ua1WU+cD4xe4DwIb5NhtNjrF/Xn5tOsaMus4wEl7Bq1h\nKzyh44QTuu7kTTabDdyJvXQynmtKCqKCfw+Hw1Qvth8V5n7JS0IJRHO5XBw1w7N6KSu+gXYZfIrb\nOHCpk6lra2vq9/spcoTspVf/+SkL3BP/p40Lu0xMgszl83k1Gg09f/48Tobwe+IzZEXPz88D/xFX\nQJDgJ1lXiFTPMvMckqIyxxM7yDW2ye0rRAKBMxjvd43rXmmmlId1MO9BFIvjAAJQx+ckpRyUtBR4\nZzsQQpSFIBamSFJKiD0FTs13JrOYusvmo8h8P4YBcARYobdle3tb3W43mqNdQclOwsaQXfNeSUmp\ns0hhsDDGPqQI4YBNhEHnWREYWDbYfT4vLQ+cJ5NKORZ/vB4dQ4oBhb1nTdlHfo6RxjEh/AAelBDl\npHSXAI1pu1yXjAqAgF472NP5fB79J8iBgwwpPZjCSQkHwrw3n8/HWVyARMA/5XYQGGSpfegU6+5l\nDbCUyAalIYwI397ejqwybK8bTM9meFDIHuKsYTX9WAKX/dU+IAfafMfLDA4ZBw90CNw92EUm+bwb\nRO7/dXthzwBGnpGXFH0z4/E4hqhJSjl3ADel64BmL9txmWVf8vm8dnZ2Yn/ImLt99ZJDd0DoFk7N\nKzloc3AHRQYIZ+r2/WUZTA/Y6IHm/au9epB/rGcmk4mjv7geQIqfebDK515WvumBhGfjHOTyDNw3\nsk7W8WX2gj3wz/BdTu554IUeYL+8hweg4USrgwm/NsN92HdsM33mEKAeyOGL+Bn9Y752znr7wC5k\nwG06z4vMumyxTvgkgKcHs26LALb8nGfgPZ6Zd9nmPEOO1qIEGmCHLLle+bmtBEPYeUih2WzR2uN+\n3uXHM8nId5Ishs/xvtf19auwHXbM98cBsMuG6xt2kj3xDJX7XuTNbccqoYz8rq+vq1qtBhEzGAzi\nyCQIf+7F7SS4EFm5e/eustnFWeG3b9+OwIxMKPuMDwbfUjHHGlCuivx6hha7R7uQB9r4VAgsJ2d4\ndghqH2jkSQyqqaiKcxvo5Zv4CGwq7UMcs0SFGs/MdamwyWQycVYoNol7Yz08cYNs+H5TSSEpyoBZ\nK8gs8CXPz9qBbVeDNTJ+2Hqem5koBOrcp/cUg+u8MoCs93w+j++dTCZR/cFAzvX1ddXr9fhO7gMd\nAS/wIivMfXJfjv+8LYXPOq5b1dGX/WF90Et8o+M3/vyucd2vfWeSJHeSJPmXSZL8Y5IkHyVJ8l+/\n+HktSZL/O0mSj5Mk+b+SJKnYZ/5ZkiSfJknysyRJ/oNfdW0UC1YHppyFcOb4xXXjfR6EYkAQAl8M\nlBN2h8BKUlzbnZ4HcdLyjEmEi+ERmUwm6sw9gJYUA0Aotdzc3NT7778frEuhUFC9Xg8FpI8AA87k\nYBz8zc2NRqNRil2cTpfnHGEkYSwY9MMQB2l5jpszuM6C+dABACVKRskXAo5DhiUBZMOesXcYIlhO\nFA7BJbAjAHUmMEmSKF2eTCY6OTmJ+yEb7iAMQLiqdH6wOyXGfN5lClmD0XIDu1oiiAwyOZnSsrW1\ntQgW2VNAG0Ep6xcKmFlOy51MJhHE8r3T6TSm5a2vr6vf76cyUG4Q2D9JERBTztZsNkM++T4vB0Sm\nfSgS6+EAi/Vlr/nbyyy95wt20zNlbihXHeCrzB58lbbOHYwDHf7kcjm1220VCoXob3JAt8pY39zc\nxFAM7w2iZAnQm8/n42w0WHrv0XQ76iSeExz87WRPLrfoGaRkFBvkn3FQgdzQG+nZeXTaBxkhG6uZ\nKZ9siK1ydng14CGIwCZ5xhXbwXf5HrEuBC5OtqAn3pu96vB9DT1IdQLPgbcPZvPSJ8rnWScH2Z41\n8u8im0H2h2CL76MaBHLL74MglH3ED+BTPHvlfVjSknnHF3OkSi6XCwLMe3I94MP+u5/CfjiJDODy\njJrrVD6fjynWgO0kScLmFYtF3b59O+y/y7+TE6wBNg1QDKh3W4VtI9hwMob/u+x5VuNVvb5KWyf9\nemwHqP5V2A658qwnn3XyGpKNliSu4dgOnXKyzsnkJFmUQD59+jTsZqfTCVwDrrq5uQlMhxxUq1W9\n++67UbVSKBS0t7cXJx1AbnipPBlS7Bd6g8xmMpk4WmhzczMGHDJvgowf18S/sq48M+sHBhqNRkE+\nki305Az66G1s+G+vwHI9ACti+/E5ZPGwN1QKrLYqkWxgOrWXEqNPXNMJMQbvQXRA+DAdF7liPago\n5Ho8m2cksbXgKTBjJpMJO0kpLi1VYMdVm8jvsWecXEFGX1rgpG63q62trUiCoAvS0j9IikQT2VL8\n2vb2dsiz203+sA5ejg2h58EvNpDWB8fy2CkfCMc+YR9/l7juy4SvE0n/7Xw+f1/Sh5L+qyRJfk/S\nfyfp/5nP5+9I+peS/tmLBX1P0p9JelfSfyjpf0pWvfWLFzeOgjiwQbHIeLqRQhHZJJy8O3d3rASC\nOKbpdDmGn43ESXA9rkFzcqFQULvdjqmtBHfcO0KA8HBPhUJBd+/e1f3794Oxon8MZoQBNavBNP1a\nZNAAFxhKgBTBFkpDOYcz65SyEDz4OHpAA0qP8YAlcqHnmp6h8QCK//MHQ8HaUAKN42YaJMaaEgjK\noAFiDgC4NqATxs8ZTp6LwBJjiiGSljXyrAtK7Iy3ZynIMDCtmCnLvo5ra2sxEKtQWJxhurW1pVar\npUqlokajEdP+6vW66vV6ZOLL5XIE6LBqzhRSksKzEsTi4HK5nLa3t3Xnzp1wvrVaLUrieBY/wJz1\ncdCLIZYUWRMvw5IU4JUyK5yYM9s4HkAtsuEGCoZ7tbTkFb2+MluHfZDSo+FZMwYIZbPLM+9WyTcP\nFCTFsBr+eACH7OTzeVUqlSgBdvLAbSr7CpjD5mJPvf/FS/r98+6UPChFj5Af7Dbf56QSE1RzucVk\nQvTQbSv+gUAbm+1lwjhTz8j6v50l9l4vbAA2022NB7+sDdd1coz9kdLEAv9GBjwYJCCfzWap6dro\nrpOJ6BdAHb3l3xzl5dUP+AYHUh4wrIot+s6zkMVIkiSCKg/g+e7xeJwiM/E/ZGA8uHCgCFAmk4mM\n8sIm+Vo6SMVGrYIxfMR8vhwEc3BwEPMAPMvKZwnOybq7PcMP8j4mhyLfviZeNu2lzd6L9wpfX5mt\ne/H+3xjbobfInmM7MBy4zL96c3MzRYRubW2lbI6TAuwJtgbs8fz58yCi6TVFl5ENevf4/kKhoPv3\n76vZbGo6TZeczufzKK1crRhCT0g2kGnzoCFJlud408PJ2vkUa4Jk7slL8V1/SQzM5/MYDIkfdl13\ncgx7yHs8IeL2dBUn3dzcRAANSQpW4N7Amz49mb9Xz1fluXzwGbIA0Q9mhAhjHSj1ZV3Zf3wYdgr8\ntL6+rkqlEsEnvi2XW5wlvra26NvlT71e1+bmporFohqNhur1emTfOU+02Wxqc3Mz5ILsrvtH/C62\nGfuDLa/VanrjjTcCG1ar1ZgpwR5B2HqCx4lsJ4CQP28DkZbT273FANnFf0NSkOhzjI59/m1w3a8N\nSufz+dF8Pv/7F/8+k/QzSXck/ceS/sWLt/0LSf/Ji3//R5L+1/l8PpnP559L+lTSt1/65Znl4CKY\nFwf54/E4mAscES+ECCfN/yltoNTAM59sEgvJprijdyBD0JjLLUZhn5ycfKFsAQZpOp2mRs/7EI7d\n3V1ls9lQGC+fG41Gce4aoI97WWXAmNCIE8V4rq+vx0QxGDU+g5LxGYzpKmgjYPfz2VAMjBSGhMCO\nNa/Val/Innogg8GVFGy4AwqyutwjxojAmVIXymBhtgh0nDkFrGFw2RtGdUtLB8d9+NQwaanABM8E\n08ghZXVkT+r1egSsPmGRgQXr6+tqNBpx1AVAGPkgCPZyMtYK0MaaONCRFIYUZz2fz+M8tuvr6zhX\njHX2LBDPj8HhXtAND/zRHwaiOCnBXgK6XbZwUrBs/kI3WfdXDNK+UlvHcwLGANnsNYE7wZiX9Hh1\nwWrGFDuA3GN/kJXpdDHhmsPPuQdsr2fHvUwXBhkAgj5zVjKMNDYHGXFbK6UnM7sTxuYSODtjjez1\n+/2U3sGW88zcM47VdWk+n4d9cOfKXgACYHO9RNWJT4CZs78OmPERrVZL0hKMs8c4aRw6OsLeuJ54\nGwDPi2N3+ZGWZXEuGwA0gJg/g5f3kUElKMB+eHYa/+Vy5wQI+2S6E5kF7AvZD16w8k6C4bvYc+8l\n9WsDkhzgU17HPUO4DIfD+P/5+XncK0Eve5HNZlODSQDip6enQe7il8j+eiaHoAV/wz17lhCZRkeR\nTbefr+L1Vdo66ddjO+zdKrZDt8BlrB9+mIAO/XUiwjNdrp/sDTiB76TKaWNjQ4eHh6lAbDQaBU5E\nbrgfx2q3b9/W9fV1BBpOinFWugfi0v+PERcAACAASURBVNJfoo9gO3wkWAbCmmonzzgig6VSKUW2\nu4/nOdE7L33nM47TwKb8HlLdKxHYI2woZBK2DrIa0k5SVJBNp9PUdHWSLZDqzAtZzW6SGcRuQHYw\n7NGDJM9+g5/B6Nw7+I6gj8CPIBe7TFDpmWD2ArvF9FxsEKXA4ED3x+zR1tZWquLFCXsf+uaZ/bW1\nNdXrdUkLO8rxbdKyLcDtGuvkRA+fRdf8+mBrT+ax7y/DdWTU8SMuH57U4Xt+k9dv1NSQJMk9SR9I\n+mtJO/P5/PjFRh9J2n7xtj1JT+1jBy9+9oWXl9yiJBgwBJcgCYD84vviD79jgV/cZ8pZ81mcpYNo\nB0gebCHMjUZDrVYrHDGGiu9ezV6xQaVSSZlMRrVaTbVaLUowLy8v1e12U/ewtbWlRqOR6nfJ5/Oq\n1+sqFoshwAgsAWSj0UhtvLN4nIXEekhKOV4AnrQYgU0QxroBNLkurDEGhOvBIHpAheL7z2hi956v\n6XQafZI+BKJYLMa9UwaGwSXgZVjQKrj17AIM22QyiSl3rA/noGK42DuAhjO7GEMv0WD9OEcLMgAg\nw5hu2DrPZkpKTWjM5RbDEugRrNfrUZZCuY47YGfu6U8g43l1daWjoyPN5/PovYDFg5DxzJqDcEnh\n0D24YE0w/gQjXj7E386q4SgcQCCrDji4Dzeer/r1u7Z1ODrW250/ARvlPNfX11GuDphmX5yhJkvo\nJfcOfHFIz58/j2niyDagO5/PB0mGfgGYPShzW+vEBKywpAhyeB7P0jkj7Pab+yeIcxLOqy1WCRR3\ndA5sAT+U93P/2H2cLuCOa3GvyPfq39w/79va2kqV3TnrzL/9qDDk2kEn9h6/RQaNzwDcfYAU9tPJ\nP9YF9n7+IkvjQZKz5z7Z04kRJyghXj3LJ6WnzPK9gCruEV9MGS2+3AEh6+8Dnrgm6+akJcQB6yIt\nS9ogg+mhZ72wnUmSxHmGfCdkBz7Hs+Ucd4Q8s2fIIrYZ/8+6sL8E9U7MsL8EQZCs/za8fte2Tlpi\nO2l5pq4Px6IiyglqtwsA31VdlxQ2DhIbecM+OQG/GohiQyGKyXLOX2TMPCDDdqAv2BF0a2dnR9Vq\nNabGXl9f6/DwMOzPdDrV7u5ulGzi8ymF5/qUjuZyucA69Xpd+Xw+1YsJxqhUKlF9hp3mumBRdIT2\nCo648QQKdhUcjI9y28zvCM5ZB9rOsC18n9vb3d3dIBjAf+Bbgj/+YMfAbrlcTr1eL3wUNgR5Al+g\nhzwzpFapVIrKQh+qBLYlMMNnevaXvWAKsJ8bz7o5iYk/Ifh0vW82m0EoMEALXLe1tRXHTUqKgUg8\n3/X1tYrForLZbOpsW06fIG7gfrDpXun0Qo9DB9EpaRkXefKOteD+kRGPk6iW8hgBu8d1fxtc96WD\n0iRJipL+d0n/zQtmbbVI+DduBqOMwBfTX87A+EReMoAokmfApCXbxsZkMstpbq4IlIgCmrkGDtLr\n1Jmqls/nU4fVn5+fx70hHN7nVK/Xo+l9/iKLBSglCOz3+7p161YolpeJck84WowG5QUY/vX1de3s\n7ISgYLR5PkoCAWCetWF9YI4BR6y9GyMACFPGYIk808h1pWWjtfd0zGYzVSoVlUqlCJIA2T5hFEXx\nAUx+0DKf45nZexhALynY3NxMDU/a3NyMyYvIC2vvjtKngUKg4NxQVh/4Ui6XQ5adtGA/KAGp1+tB\nWDBEy40D2XkcD1nWVqsVI98dgEnLRn56cZ0lhe31EhafXgeAd6fk2TQYNnQWAOgAwo0ZDp49IYuB\nHBGg4wRgKXFur/L1Vdg6aVnWjj3z0iAGpqytrUWwgz1bLftE31hDgC7gC0cHc42+8MI5YQN8MiIO\nx7Ok6Db7fnl5GeVS3/72t1O2D5lDfrz8jqAFu4Tscn3kiGdGbrAl/B5ZczlFznlO3uul69jm1RIn\nD3ilJaDmfciptOyR80mRBEQOfJzk4W/AON/pmW38Bb6Hsnue1fUEPcJHQYaRnRyPF8dVYRsBFDyT\nlzVyfQIGwDN74v4CQAtQ90m56C3XY52dMEiS5XEcEJmQv9ISsGKLKpVK+GgfKOLZp/Pz88hI0VNI\nlopJldhivhvbVK1WValUUlUnBBMEzfgSsAhkA8AWHca+ObHp04L5uQdoqyXKr+L1Vdk6/AR6sIrR\nWEMyaqw5uIw1Ww1I8VGe9WOYkGcI6QFkz1Z1E5KCs0SHw2GccAAmZco/mXbHCQQr+Favcru+vlav\n1wui//79+zFk0ntDHaNJStnzSqWibrcb9pnACOwCBnTSirMxpaWdmk6noQNcP5fLxQRX5BE9RNdY\nH7fLXmGHfEvpsmsygfTBegLAzw0Fkx4dHen6+lq1Wi1VMuty4M8CacU6S0rZFh9Qxt6gh47TvUKJ\n52NGCEQV5NV0OtXOzk6qWo1YZDWwgzCgHYtyX+Tb59rg9yl3JuvMuoNv6TseDoex1uA07sNjmIuL\niy9UtqxWgCJLrBXkoJM5nsyTFn4RueA92D3s/u8C132poDRJkpwWhut/mc/nf/7ix8dJkuy8+P2u\npJMXPz+QtG8fv/PiZ194eTCB4WDx+D0GngVyx+wb40ymtCwV8Wwof5wRAZDg6Mk+UH4pSYPBQO12\nO5hNBAAWxxlm2BlAEv2DPip6NlseD0KQdX5+rlKpJEkB+i4uLmJYkR8kTMlckiS6detWNIuPRqMA\npBifarUaDrVSqQQQIdDFoBHcU/oASGGNK5VK3AOAiqAcxWasN4YORsYBDmW0TNhtNpsRHJ6enobh\nIsvqg4mclMAww+axth6goVic+Xp2dqZSqZRaR+9x8iFYniUk80CQSKkJ34MjYM2azaZubm5UrVZV\nq9W0s7Oj27dv64MPPtDu7q5qtVqwXd1uV71eL5wpU44JXsvlsh4+fBhB7NXVVeyzZ40wij4tE0a0\nWCymMlq+LwSp6MXa2lpkJgCTGFDOUiRQQDYkBXiHQDg7OwtngN4y6Orq6iqGNWCUkQ/PyryK11dl\n63CurLVP9kQfm82mbt++HWcN84IUYb9xMjDFPs0aRhmQ4sEsDprfTSaT6Gf3Ule3A4A/svCXl5dR\nHipJf/EXfxHOzb+L++QPoNGfCd3j+zxr6mVI/nL2nt97BlVa9phBGGGfvXzUg1rkD6CCrUbGWRcn\nT3G22AvsgVdW+N7zxwMVfkfmBP0hqMOf8By8H59JO4dPS8c3AIImk0kcLUOLB/fi2V0nPjlCCiDr\nhAXVFjc3yymgAGdsEESlB6XeQwZZgC2hxBadgNQdDofhT8kAE7giXz5BnICV6hjugTW/ubmJzClr\nw5p5VQyglym5gG2yWgBy7w3F/iHjvJ/7xkbzfgKKV/n6qmyd9KuxHfvm2WYvE8SWOLYDr6GT2B8+\n78QX/pmMGTiRPSPYAT91u1212+1U9QK2h2wme8l9sJ/NZjOGQGIbJpOJ+v1+BKS83+8ZjFkoFCJL\nz4uExM3Nje7duxcE89nZmVqtVqwJLU/4EIIayD8fWDOfz1Wv1yPwoBcQ/+CVZdhGdBH/wCRhr5qi\n9F1aVkngi8hAg6/JOlLGCvYiEAVbol++n9g87o0Xa8ERJGRva7VaPCPBqmdDwZHz+Tyyk2AanpH9\nhKQ6Pz9Xs9nUeLyY4l2r1bS3t6fd3V29+eabevDgger1esgfZBn2eWNjI1URubW1FZ/hqD9Kxn2o\nE5hptWKjUChE4Avm94QCe4Ktp4Ta8ZjjPcg/93ngXwg6hudh99jbzc3NuEeqCOjT/dfBdV82U/o/\nS/qn+Xz+P9rP/g9J/8WLf//nkv7cfv6fJkmST5LkvqSHkv7mZRdl8RA+dzgIKCCElzPP7khxjoAT\n35zVazhzCasAQwGzgQMvlUq6fft2fNYdHEpCsHd6eipJcTQLrNjp6WmwtDy3l5Ry9Ec2m42BOAy/\nyefz2t7ejk1mqiqsXKPR0IcffhiAlkAOYJHL5dRqtcJBEkQTHMAIcv8ARAd9GHmABWySpOiJxRi6\nkeh2u8rlllPiEHYC/kqlkspIogQAi2w2G4ErGUsy4BgW9oO190wADgXmj6FSAA8pPV0S1p9AM5PJ\nqNlsKpNZHPBM8MjeewkYBpYjfOr1ejBorVZLl5eX+uSTT3RycqLT09MUO0mGBxne3t5Wq9XSe++9\np7ffflvZbFa9Xi8VTKLwDvi8/K9QKGg0GkXGgGdmXwleYMkweOiLB+sECugM78HgeJmnM6rsB+AE\no1csFiNYcKYPWXvFr6/E1knLYS3SkhCAZWy328pkFkecOFniWTzAkGcgcRrYhmw2q36/n6oMITND\nBhYHU61WU5mr2WymTqeT6jvBjnrmEdBVqVRS5/NKywoBdNmnersseJku8gbLzvpIyyoRyDBJqbXg\nd15ChL4zbRLbhM3kuQBekkKO0WecNt/lVSr4KqovKHkmaGHNHRSwfgA/1ofADfkANLOuHqB7Vtkr\nPgBcq7MJIAgJogGYDlQoz2UQB/dAjxw21EEaxJNnUgjKaLcApPJ9kLBksK6uriJbw7WxZwR32HH6\nviCwWDu+g71jfQBY7Ln7j8FgELaGDBtBEoE23+P9XQQQyA1gzokGMAOEK98ByGXfyBThD1/h6yuz\ndavYjiod/IgPEfOAU0pjO2wY8s81eUFkoVf+wsfzewhf2muKxWJML83lcqkhL9gTMkP4X3xnqVTS\n+vp6VMp5pRD+HNnodDoRlGA3wLpM2SWQ8Tkl9+/f19e//nVtbW3p/fffj+sT1DCoCTKIQHcymQTm\nJWPb6XS0u7sbBMF4PI7+TtZbUuBfMApENq1r2K5OpxNEttvnXC4XAxt5fogt7DO2BfsjKVr10DFs\nLX7Js+O0nFxdXUVQxrokyaK8FUzmeogsYR89K0pfKX4UO8Sk+aurKw0GA21vb6cywuPxWAcHBzo+\nPla/39doNEod7eOkSKvVUrVa1VtvvaX33ntPW1tbQcg5AYgfohyb4B//y3dwXq7bQPYQ3wvuBv8h\nG5lMJkgaT77gyx3XYb84UUJKn6+O7vI84ETHdY5Nf93ryxwJ8+9I+s8k/btJkvx/SZL8XZIk35f0\nP0j6kyRJPpb070n67yVpPp//k6T/TdI/Sfo/Jf2X81W6+8ULw++ROSCAh6EpGBZLWk7u4xoOkEkT\ne7/GbDYL0MAi5vPL40FYVK6BccLp9Pt9ff755xGkekM6QQ+bwr0y5KbVamk8HqvX6wVb4Ep3dnYW\nGbputxslraVSKdh3lM0P6QXknZ+fq1qtamdnR5nMYpR4rVZTPp/XYDDQeDwOEJnNLs7Sur6+1vHx\ncRjVTGbRK8r9e3kCgM77Ycg4ZjKZOHydcmrKUmDFYPhZHwxMsVjU/v5+OCYUByC5Wl5HCQBGkM9h\nTAE2ZFoBJJubm9GbW6vVglHEAOGgPBh1EMr3NBqNCKbdYGJ43ZFeX1+HgTo9PdXPfvYz9Xq9YP55\nL+UhyDD7eXp6qnv37gXDRCCAvAEokTv2D6AIUUNGrFgsqlwuByNKWRIOCmYOuUQfeXbAARUJfL8b\nG/Zh9fPID5/ncwQ03ru1mmH6N/36Km3daraAgFRS6B4OBKAEOeTlo6yXByiQDtgknKoTS57l4296\ncFYDDzK5BC+etZ3P5/rOd74TjgtG2u8PkOcBKgEOdsSzEDg/SakhEW6DCV7IPLBWAHwns6bTaYAJ\n5Hxzc1PVajUy/r4+2B/Wjz8898tsDeWJq8c8cU1/r68f+gs4BTyhezwXIJpSO2yxE0H4AfeXk8mi\nf5/sDENYWDdIS2lJksznc52enqrRaARJhT46cLy+vo6jySiHcwBC4EjGoFarxXAOSXE/2Cj8L2Rw\nkiSR1SXIxo5Rvos9cuJAUkzSxW5Dju7s7Ojk5CT0jyDXs15kgbCfkDfoHSX1xWIx/B5Ak4CEoAK9\nOTs7C7Kae3Vbyne/qtdXaeukL2I7aUkmIasE/zc3y2EvyH2SLMvNIRwgT31oHnLiAaEPBMQ28J0E\nfexbu93W06dPg8QfDAbRosW+QXRgY5HLVquli4uLmOkAKcMZlbRO0PJw+/btFJG2vr4eWS78KC1c\nfFez2VSpVIrEQbVaVT6f18nJSZBMBAoEi77u2DYymzwnuoi94Z7AQLVaLX4HbsWWgoH4Q5YZgn1n\nZyeIcjAugST4nMAcXE45N/pGQMw+QxChoxBp6CG2kYo4Momz2SyeG/tEII7vI4nDOl1fX8cRLh54\ng085wubZs2c6PDzUYDBIHaVF9Qf4m3aQp0+f6t69e4FLi8Wier1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xy6et1mq1VBkwzeqU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pPdUU2RpYdVkzOOEfH/6T/9p/o3/+bfNNl1b8o3+T924Ec/+lFtbm62zNrBwUHt7e01QjEJ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\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Ogvbk5ERHR0eq1WoRZPR6vciAuu5jL5BR7t1tBvdEdgsMSFYdDEYVGaQNdlTK416v/EJP\nLy8vdXJyop/85Cc6OzuLqignmAnYfc3Bq6wxciwpzmFtNps6ODjQJ598og8++ED379/Xzs6O7t+/\nr42NDT1//lxnZ2dqNpu6e/eukiRRvV5XtVrNVebhy9AZdNCHPHoQ6DGBHys1m83i2CbWB9LWSXh0\nkJeTm1LWIkPlYr/f13A4DOyEL/Fg/eLiIufjsA/Yln6/r8lkEokrKZuGj23Hx2OvCWj5PogSCMM0\nXc2yaTQaQTBQyuzy7n+ji2AsbC0+yUmZN/mRdZJyHdfx+Z+G67y15G1e73zQEX+7o5QyxgeG1Flo\nnO06OAbYYDC8xAOhhXVG2aVsKuLV1VWUlT579izAYqPR0MnJSe4YlIuLC7XbbZVKpegNbTQaGgwG\n4aiXy1V/KmUdvV4vB0oBkIBLDAoZVn8Wpsc6A8i6EPRSqkLQ6AFcsbiaMnZ5eRkK6YM/WAPA7Ppw\nhtvb28jMrGcHyVBzrhsCzpEj8/lczWZTn3zyif7+7/9eT5480fX1tX7rt35Lk8lE9+/fj/4ADApG\njHJV9g3DDTtJSSzOhf2GDfQR2UyrpRzWg1OeA3kD7KDIKCRnzgLgkS2MuDN4GLDr6+sYeFQqleLY\nGsCPyyHOEIPByHnkFyPIWHr2SVIO4C8WC+3t7enm5ibWTFL0wgL4kEUct2c2+ZkTPPwecMW68Mz8\nzjPFzoJT+uKO2oNj7ul9e7lz9EoAfs4QFtbDe+EB4OuDuNhrZ0GRVSkDhQR7gOJarRbVGMg/Ot1u\nt+Ma6M0664puEHQQ8NTr9ehRQk6QCcrskW3k+vr6OlhwZ2NdJwF+zi57YAoLz3RhL8OluoRsHoNu\nAHyAQtYRWyNlU21Zm52dHXW73fBL+BtsJu/zjCZ2cJ1UBQDh08jKEXjz/Ogh1SLYF8+OYmtodcGO\nAQTwGZ4ZcTuD3XS5IzO4sbE6Hoj9cwDDvno5HvfsAQfX4J6wGXwOcC9lZB17gF9CB3genpXrID/Y\nNo6QcBKRagOeVcoGXGGPnbQms+G/k5SbHIqfdWDNi6ye+0uvKnqfg9J1bOd+DvkCd7i/dGznskOg\nii55WbxXDkjZunuGC9m8uLiI6eOl0urM5+FwGIQ3lQVUYH366af6zd/8TV1fX+vFixdRBTKfz9Xv\n9+N9vV4v7oH7hgyZz+dRFYBuePaO4AIb40kYqjWwFQR0YMCbm5sYusP9k/RA5tEx8I4Ti5A12Ble\nniHb2tpSs9kMu1oqlfTFF1+oWq1GwFkul/Vnf/Znevz4sY6OjrS/v680TdVoNGLvGbTHdbDbYDmC\n51arpZubm5iYjN3BRhK0eaCJfUCvkQ2qhjybzPOhk0mSxHF8yJUnoM7Pz7W1tRWDitw3+vAjr7Dx\nMl9sGDMLIAjAuOAAL/F2YtWrkMhMUwXi5cDIG/bNg0b0gKQVdpz79mwpGM6vz/f4+mG/kc+fhut4\nnrd9vdNMqQcbUsbGe9mGC45nW9ypeJaHzcT58B5JOVCw3j+FMWFC7uHhYZSu0pMAaNzY2AglKpdX\no+YpgZ1OpxEswTzBqEjKAYDJZBLBws3NjbrdbtTKX19fx/+9hM5ZJO+rgumX8g3WHihwb7zfFZp1\ndcPEv1Fu7p+sBEaHiZ4ATAIh9gunzCHqGxuroRV37twJJgnWslarBdhI0zRKRKSsBIGGerLiBGoe\nNF5cXESA7ECM9wMQ/Lm9xAqDxOdRagJtDDuf51lhnijxZe1hX29ubjQYDIIlZa/ZH7KmGEhIC+6L\n4AO5AKSSgfGMWavViu+oVCrq9/thLPxQaYymlJWZOuPFOnk2E2CKw/CqA9YZ3WMdIVB8vcjceCD8\nPr4I4HDSUla2A0GATuGgCLpYa5h45AU5huWmpw85gdBCjwhYyWx66TrAfDweR+kZn/XMHvdDCR7M\nKf15yIQDRGTOX8gnTtadGDaL6+P8pWxYBrbQicbhcKjJZBIy5UE/xAhgtFDIhh9Vq9Xoi2TEP8+B\nTyCbCQAEPHgwC5nIsV0eKC2X2UAp9sMHJUkKMMkLPfSeUwJHfz6el0qZdbbby8U8O4AeaGqPXgAA\nIABJREFUe0+/2+7FIushcoAC+EKuqbLBt0L44ifZN/dd+GDuDWIWG4ZPhgDxsllAvmdKPZPp2TmG\ndSEzgEkPrtlvsivL5TJ0x30oewkR4+uBLiG3BM1UKaHvBA4e5L6PLydEpNex3XpQ4PbfM6msu6RY\nb/RMyg934/PersD+4Ecnk4kODg5yfXrz+aq9hT30WSYciefnn0PoS4rhMjwL9zuZTIIQmUwm6vf7\n0SOKnrsuStkQPHwBv7u8vIwz7ovFYkwlJ0ACz6EH2ETslPsUr6Lyck72AZJnc3N1BB42CgKPZ63X\n61oulzHIsdvtqtlsqlgsam9vT2maRmnzxsbqJAGwJ3vDESlpmgamJLGD3lHZhb0EFxEfeBsUuFLK\nHyuIrIBhsYn4LuyClB2hxbp7pQlZSi/vp0IOPAfuRy6oTiNLD9bFb4CdHGc5ZsemkWzy6p4kSaLP\n1CsM0B/su2dd2XuvXCEmWa9UwJaC5UjI8X3cHwm0XxSue6dBqafEUTgvR8BosxBeVul/eHC+08sD\nWGDA1XqpDkJ5eXmp6XQaQ4Bevnypfr+v2WyWq5X3EgAAV5qm+uKLL9TtdrW7u6ubmxsNh0NdXl6q\nXq+HYSIQRVg5/qPX6+VKkjy9zrQxnkPKhBaWBdYfY4aiSNk4Zxzn5eVlKAbGDLCCgQOMeBkxAsmE\nOa4PILm8vAzjwB5xT2ma5spSMPw4b/aB3tJOp5Mrz2P9AUzeZwILCrPGz3AwMD6UWwBy19l6Jzzo\npXVl5Y8333vwTCZCUjDyZGmYssuaA3aRQaaxEfBTBulBx3w+zwFu+h5YB8AlBs5lz50fmQQMKDrD\nd5RKpdwkTF8rDygBc56d8cwzBtADMCcEfKol73NA8r69cJoMJpjNZrEHTJll/9bL552JrdVq8W/0\nCKftpZDe44c+AwaplpBWAWyn08mVEQGY10txIEFwVp5VROex34AIn/7nYAmShAoCnDR65TrM+jng\nwpZw3zwL1yIAR06x/5PJJFosJpNJBDMEQAywIzDCLnMNZ6CRYfSTDJhnerEVyDnX82DRM4WegUYn\neTZ8pJdVAfQB0MjWm+TImXsIvEKhEIfUY+8dvGHX2VeABn2v/MHGAljZBycnkRdAKcG1Z5I9q0jJ\nK2vg/hziGbuLrPFs8/mqrB2SjyCS9Yao45qskU9CZwCTkyDoFfeFPkBQ42fRPfd3yAO+0MnA9+2F\n/1gvz10fMoh/cB/s2I69R8eQKWTJB8g5CHasBx4Ccz19+lTD4VDz+Vztdvs1UoF7A4MeHx+r3+9H\nQMq8Ecq7wWcMGSIIZUAO94TN9+t4kIWdACt4XzTriY2UskGEECVpmsY0cwgg10vPyjnRCBlH4IN+\njEajuGe+wwP8crkcWVJ6YUkuOMF6eXmpyWSiVqsVMz2QETLPvJhXwjonSRLZbWSJZwaTYJ9YT+w3\nlYHIiA8D4vrYUWwjthi/4sG9Y0/Wgr3z8mp8HtgdmSBp44OY2Cev0mNdICGdbLi8vIygFNIDeQAf\n4xecjFxPIGBj+bnfD4Qh+uvEslc0OaaEKMd+/0Nw3TtHgIAHAgBeXqpFgOWMppSdW+cBFi8cmpRP\nNTsjgfHyKWSwEoA7sl47Ozu5kiVegIfj42OVSqUoN03TNFe3TrkGmVBnYsvlcrD2GB3OYKpWq3E+\npSsXyopjrtfrufIxgn2MGUYGo4NSowSsD4bJGTiE8vz8PIK0vb29eH6E0rN/MIseFHpAU6vV4nnJ\nbhK8k2VsNpva3t6OCXnsC3tHsHxxcREN9ZwHNp1Og3ED4JTL5SgZ8Yw5z0dwTLCMY4CR4v4Bggzm\nkBSBPrJHmWShUFC1Wg2D5QAHGd3Z2YkgmBI+fkdWmPVDvsbjcYBQDCuGdLFYqNPpqNlsBsPZ7XY1\nmUxy/bfrZRusqxshJ3soN3I2zcuX0UcpO9yeDIq/h5+TrUauv0rfwX9sLycl2COydt7n5mWSgAps\nCBl35BMZZu8x/JTFAsogMtC70Wik6XQagGMymShN0zi43Ce8ApLeVOKDbOI0PRuC7cTZEXzwd6/X\n03Q6VbfbzZUJY7OkbNI23ytlUwSRNa6DA4Y5Rj6RLSpiNjc31Ww2Yx/SNA0ACThbJwqkfP8bPgMb\niX46g7xeIuX2z/WHfVoulzFUb2trK4I+sjLoMaBwsVjEsTzYMf6NfGFTkC3sMYDj4uIigi8py0Ai\nQ/R3YYeoACLj4QAeOSyVSuGXnGxGhtAD+kndzzuj71k0D36x3QAwsvr4H/eBBAuQPV6qho9F1zwD\nh54xld/tJXoDKQOg454gFMnaEIyQiUNevO3ifX39h7AdfnO9hQjdx4e6jcMGYJOkDBPyvXyO78J2\ncn18uvd+Qsbhk/k9Ov/48ePAONidSqUSpZMEvlSaQEQQ6HL8C4Ea94NeUamwWCwiIUHwyuwTkhUk\nXJw49GCCABtcyVAlAixwkPc839zcRBa3WCyq0+nkEi5XV1cRaE8mEx0fH+eGcUqKwZLFYlHtdjsC\nVGzScrkMbMZeUcEAJmKva7Va+JPBYBBJHJJGBMusObZNyir5SAphQ/GtzDwA63og7meBknQhWF0u\nl+FLqKqp1+vhM9gnH+BEogB5gTjz9UeuqeyjYs5bK/BV7Mnh4WEE6ufn54E98a/4CCkrwwZHIAuu\nP+wb6wL2xC4iB47nIFM8m5okSdyDVxN9VVz3ToNSNsdZMh4Kp1sur8Yfs8gImBsyKSvP8vIEhB6j\nBjBwoEDA4c6cJmUyi51ORw8ePMgxd6VSKYBHkiQ6PT0NZmM0GsX0Uw4rJigoFApqtVra2dnR4eGh\nms2mms1mpOYZWlKr1eK+EEY3MNKqBG48HueAP4dC+7AdBJAMJMJEiYGn8VFOz8Rg5GDFKLktFotq\ntVpxL5VKJdaFQBvQs7+/n2M/Hzx4EICZvymLYG+9PALA6r04gCIpC2gxXvweNonylVqtFoqKkiJD\nrA2OzsdlI48AXowu9+MBL/9nxDnZGYwZ10JueQaCc/o3ms2mGo1GjtGnHG6xWOjk5CQMlmesICgA\n6x70AkQx5oADN1zeq0WQgM5g3HGsXHcdSPJcGHR03J0j+8te41jfxxdg2VlDLxVCpvgb5yUppxOA\nLcAwWWccDfq5numGXCEYJtjA/kH28X3YBg/KAEmMol8ul8FmIwc+RRD5AxigG9hqz87d3t5GWSP6\nDYCX8pNO10kdvy4OkWOo0FFJkQll0jilY5ubmznGn/vE9vK9Ozs7EbDD7lNuh30iqGNAmmd7JUWm\nG2aZ9Yccu7q6CpsOIGKvIRvQQ2SI4JmA0YMkSkjdZpZKpdg3nt+rQdjXq6ursD3eeuFZR8BVrVaL\nDHSlUonMBtUArI+kuM+rq6sg4QgQmMVA0MezsMbeg0uZOnIkKTIFDrpns1n4VWwTpYbsH+CbPn5K\nOBnsB8DFH0ACURWDjntJ5Wg0ysm6pCh7xM++r6+3xXacFUrGSFJUN6DnyLv7fvTa/TPXAcBTDcD1\nkEd6A8mUfvjhh4ElPKOLnzs9PY0WrF6vFzhnPB6r3+/H8XlUnVQqlRj02G63g/Brt9uqVqtB8CDn\n4BBkerFYnRZwfn6uyWSiTqeT6zfvdDo5X0KAhR77wEontJni7uuNnpAUWSwWGgwGqlQqUY3VarXi\nuzlai/Yf9I6fgfmcEHDSFF1Ft8EYPAu4yY/HobqPjCb6iEwRHIMPIfYgKfCNyAdrORwO4zNU9+CP\nPUHhg4cgI5bLZQSDTkrynPjk2WwWpde7u7sql8uq1+uq1WoR1II1WSvIx/Us8N7eXi6ZQjaY9SUA\nhhxET6Ssx9ZJPA/W/TpOACErrIvjOt6HvXVch/yhU18F173ToJRJshia9VI1r/NGqVgsKStfcLBC\nRgc2m0AHUOasCoJGVoy/qalnKMZisQiBkrLyWYJjGIRKpRJs7eXlZQSkBAqHh4c6PDzU5uamOp3O\nG9PhpMxh6nGmAAJXTC+1414QMgAN/aUYbRckH4rk5bG1Wk2VSiX2gZJD1o4+DFgW35OdnR3du3dP\nlUolxnInSRJTZTFG7XY7DALGar1nDqPWbDZz4ImBH8ViNoIbxaC/lEwSwS5sJKAF+SHbx/XIiMKk\nc18E8dVqNcpycbJ8D89H3xl7t7Ozo/F4HKUcMPcAvkajEcSEs4zeLwYYw3kQSHi2BgLh5uZGH374\nYfybkhEcMYAUJ+DsIGvBeuH8HVy9ifHFEHrmFMfiTBm9IzhEZBLA4bL0vr0w0ug764vzBHSUy+UA\nrOyZpMgwOaFGwIKcUM5I0FAsFnXv3r0ALjhZKjaazWbYLJwqMg1zj1xwT1wHR+jyTo80jsmZXq9u\ncDKDrB5g0UEspBQECD3mTmYB8N2pcm9kHABj2LdOpxMAk3sAbPgZq6wh9oGfsw8EYui+l8q7DeJn\ngG3PAHspWqVSCd32ybGsP/rF+mxurs5WZEgf604Wtlgsqtfr5RhvMub4Be8PZRDacrkMG012ge8l\nyMRHbm1taTAYhD/FV3Ov9MDDpCPfgEFsITaX1gMP5hhk50AX2fJKl/UMpGfRuYf5fK6zs7Nc5ojA\ngT+TyST8Y6lUUq1Wi0wI94Tvc3BNBhxiF3uH3FPOiUy9ry+wnZSdgfkmbMegMbLyTkg5ie1ZPfwH\nsuOVJo4JIXkgvQgAi8VibrjQwcFB6C8BD9eXpJcvX75GDHkfdbPZ1J07d7S3t5cj4Ak2Ib8kBQ7g\nnEp8JPeOvmDrCoWCzs7Owt5Xq9WoLsF3e2UGsumVfQQ8kELgU7Ak3wPukZSrIMOHN5vNOAoGPC0p\n9BF/RhLDM6X0YlI1AKbBDnHfy+Uyjgn0ikhJUaWAXeUaYBWeydsikmRVVg3hj81wkhMyEcKRSgcw\nNNik0WioWCxGAE6LFiRWkiRqtVqRAKjX69rd3Y09JdjEn/t11jO33pefJEkcefgrv/IrUe0GtsP/\nrZPc+FIIHSd+y+VyzpZ5uwdywfqwzvhD+vndF/2HcB0Jh7d9vfPyXYICL1XEWcNwk73ByXq/IYuJ\nUvN7jAzgCQMHyAAcoEB8H0z28+fPAyR6+RXss4NIgiSOLfGA8P79+yFsCK4Po8EQXlxcxLAS7pvn\n4B4JjintAgR46W6ptJruyhmrBJfrwShGzEvhEFIvEVsusylhgFQHHZQkkPElM0bwBiijNJAAand3\nN0qcAOaAFwAzAg8gG41GMawAkOBH8EAmwHJLWcbcr+E9TA50yDIlSTaFkiDSmTRKwRikgMPyTBbl\nxHzHxsbqiAFYMYAKIA8SguCOc89QcpS7XC5HFr3b7eZYVu4HnWKQFuXUlBt7NhvmzDMp6BLlft6b\n43rGtTBQgCwAMEbM+5WRIydV2CdY1/fx5SVmrC0yhj5SYiopgiVAgxNK6BW2DbnDsTo5sbm5qdPT\nU93e3mp/fz8cFzaRXhdshrQChIAP9Jf7BSg4631+fp5j7l1eAQzYFzK02G7+v1isBh6hw7DIyCVO\nlQAPtpvKAZ6XkkuCVmwr61qv16P6gICLYB8/1Gg0lCRJAArkleAQ+cYeAqpouaDEjv0jW+lBG39j\nm6vVamSn3a/xb3SOZ3KCiCnj9MxXq9VctoHeUQIwBtOxZwDbJEkic1UulzUajeJ5yIAwLA+dpm9r\nb28vQA9ZDJ5ve3s7MkTIsoMzdILrbmxsqNvtBtAqFAoaDocBkJF39guiglYWlxn2CpCHrWItPYMA\n6IXghGzx4TMMCWM9uZak3PdyXTCNlwl/1czBf6wv9IKKgZ+G7QiSeC+2krUkWGPPJcWa4ruxR05U\n0R7hLTiSdH5+/loSwsll7Ce4hX5SEgbc+9HRUcg1mA4bR/Yce31+fh5l7Ts7O7nWAmSDxAcyha/v\ndDpBTiKv9+7dC9KD6+LbeR9yiFwS/KZpGhgC/0sQis6ABdvtthqNhlqtlra2tqKVDNvjtmk+n0dQ\nxp5jX7AxfrY8BCCzRAgqx+OxDg8Pc8EzfoXgFzwPmUWAi36hY7QkoMM87/X1degyOo+f8ipBEi88\nI+eGgpk8hmAAFT8HW3HiA3iVJIFXdZLJLxQKevToURCrVKHgi5FLguHz8/PAgPgkZBu8h07wwp8j\nI973z7Mjb6wJOse6guvcx3mFyj8E171TFAjg8bp/jJYPwOEFEPbUuiuaM2KeeYEphxXw0kUEkt6d\n4XCom5ub6CWBfS6Xy7pz546Wy2UItPfeXV1d6Uc/+lEIOkYOUEOWt1KpqNPphDFM0zQCMAIbFJbs\nAo6ZxnoCGXd260Fcp9PJATVAFetIWQtB5ObmZmQouRcYZwxLo9EIAfdyCcpcMcYYqmq1GqWofAes\nFEEqa4UxYY8JrmGYMDqAUKb88rwE6kw7A7wUCoX4f5qums99ep2TIpKCuQKkwtSSfec9lFrC7k8m\nkyAkOLIFo8hnKBn0enwMqDPBfr4tx9iQAQAEnp2d5crHvdwTEEpAQ9k2Ro+AhGvzOUADQQLOF0eL\n0eNaZJX9O9FrnBSGlBfAzuWfgAMH/T6+3HHg/HE0TM3FuHuGG/uBrhcKhQjYWF9KgXwoBjaqXC7r\nwYMHqlQqoU+SAnTTt00wgExQpguLizMESCGT2Bwv9UZG/LgUSBPsFjKDvcGGpmkaQ+f4PMwzWavF\nYhGlllRFOLkEAcLPvPLh5uZGL168UJIkUQ7FMIxyuax+vx9M/HQ6jayOB+OUTrH+ABqmXVPqylrQ\na88zAJ4lRUDE/uMz0CteXMv9AmsBwMDGTyaT2FMyO97L5dlsyuWwdYPBQLu7u7lMh5e2FovFANcQ\niKwRZBzVLpCbVAyRKYB0wz45w48MkZ1cr+ZAP6Ssb51nZ32RfWRuZ2cniCApG2jE81MR5UTscrk6\nzk3KcIeX5PLc6DZ7ijwQTEmK8mzANOWE+LL38fU22M4zKOgFOsO+evbOyQA+i+zhP9EdZFZaycl0\nOlW/3w8stFwuo+KrWq3q6Ogo+kEbjUbYFAKQbreba/PCVyJj0orwotWMSgB6U9eHyYBjAf7gRgf3\nyBlZRirB9vb2okXDbamkKIuW8kEEWNVbc9Bt9BCcjN2kBYF75H42N1eTeff29lSpVLS3txfYjLJ4\nT+QQFIPTJpNJtLKVy+VoAfPzjqlwwecRVEIegR2H/x93b9Lk2H2eez6YEshMzImcmcUiWRIpS7It\nhSSGv0BHdIeju1feeONvcFcd0fdT9O6u7ubuOrxse9O2oxX2gnbQokmKYlESWcWacwSQQM4YewH+\n3vOcQ9JXoqQudyKioqoygYNz/v93eN7nHf6npxGgsbbYJeKArEwiX/hXsA2+FvxChSD9oufn5yED\nyBz3wd54uxO+Fn85HA5DpjwxVCwWgyjM5ZLBTlyba3g1E/4LYsb1BTvD36wBPovYyINicBi4xIkG\n1oc9/PdwHWv8u+C6l56aIBjgQQFAGBwH0FL6eBcWKwuIURwpmZALCOYF4HKlKRQKqtfr0bSN4AJo\nXnnlFa2trYUgNRqNEMiVlRU9evRI8/k8GHZYOgwwmTL6dTBYMCoAsPX19ehFcBAD40+QRkDJ88xm\ni54pyocYTISiAXoArS70BEoAU1j16XQahgbFcMfAPVIrD0sC2Ds/P4/epJWVFW1sbERplk8dw4Cg\nsG7IyWw4iwWwcGIABUKxkJV2ux3DXDw7AEhFcSjLgdUF7AFC6deF8QKIE5jjpJBFL6vGuXm2nsAU\n54lxIrvtusGzAaA+++yz2EMMqrTIzO7t7YWcXF9f6+nTp+HYYFZxyJ6xkJTKZGRL66VkeiTOEl1z\nZ+B6y3rjCFgjJ50A9ZLi/m7bC5nFbnmGGtuGPUInIUsIhGBO6dPzgAv5lZIznnHgJycnMdwHYMe6\nMxmx0+nE8BlJMWBCSkrBYK4nk8W5w1QeeJDhQAcni604PT0NcoTnouecqd6SokwNp4180MZAkOJZ\nAGfk8/m8tre3I5AngIIYnE6n+sEPfqBut6vJZBI9YsViURsbG5EpYTKnDyrDqXvFBORM9vB5d8bo\nKN/P+7DpgMzJZBJtAZKCyGOPKTNGDyHjAEVSMvEQ/4LNQQ/JgrjPBdhhYyRF9gp7MJ/Pw84hw9gP\nMtS+Hl5xw3559QWgBZ9DYMzasG4eKDhz71U9+A7ukfV2kEipJ5UvrBHywxEYrBdyj57xHegF+swf\nSWEzseXT6TRaeM7PzwML4Btu88v9Fz7ay/h9pgO4hP3HVzi2c1mSkrOfqdbhO8AFyKmkCKTQL7JN\n0gIj3r17N0o8eS/VDvV6XR988EH82092QM4bjUaQMp55InDK5Ralne12O6o8yuVyikwH43lvvbe2\ngDWOjo5SpBLvyxJzlPuDGSAzfbAS9tLXyu0XWIc5J2RBfVBhLpeLoYp37tyJ6hu3L5CePA+tblS1\n+IkT6IVjD34nKchNfJK3LaB7+EN8JTYATOstINg0ZrrwfPgFfC8Bn8skvg1Z8SpK7CeYjtYrD5SR\nJWz/8+fPI/HEe0kYMdem1+tpOp3q8ePHMaDKA2EPzh3vsp/ey46c4k/ABJ6UcFyHzfe9cVyHz/ld\ncN1LDUp5IM+SAYZ9IbJ14rzcIRHdewDgU3UBdAACNh4HIS0CM5qXYbsJhgh+W61WakAQSkFA9tFH\nH6W+iz7ETqejw8PDCHwAlYPBIEp2ATAIPYEPvwPkkaX17Iln7xAI2A6yjAAvBxWwOLDiBCuAxkaj\nEQJFRhXDAaBEECllwAgBJCktqVarWl1d1d27d6N5nGeEraMZHwMmJU6LZ3UlQzl8Eq47MYJySRGw\neyaaNUEJXd5wqqurq9H7QIksWSiYUdYL4OIlZoBMykj4fowGyo9xg4lDP0qlUmSukHvkk/1mmFG7\n3Q5QiLFz50rQ4NkkJ3186AfPj+65zrlDpGSF7+Rv9sDLbwCi2WoGKT0x+7a9ADEEMoB5gLw/O4DZ\nmU0CLioNJKUcoKQYNrOyshLTrdG3p0+fhs75fhOQUuFAVs17vJAJQAjyVyqVotzd7TOkGAd6Ewx4\nRYWUnBd3c3MTA7hms0ULBdlLMrRkHKUEpBB4I5OFQjLk4/j4OOyKpC/pNWWp2AOyir1eL2xRt9sN\n/4Ctcvllr9CxSqWSGkqGbeO9DrjI8HDfANFcLhfrhI0A+LD2VBpQpkXP2srKSipDCJnqWXBAqu8p\nhCn+B1IWsOey6xOhWTspmWSP3DigIgPDz/APTjCT/SYApuxaWtjFwWAQPoU1Y12xi5Cc0mIaOmvO\nuuKHyJY5IcR+sM5OYJKBduLb79/t/Xw+jz1AbvEvlBdC5DiWuW2vr8N26ItjOy+Ll5KSP9cN9qpQ\nKASZzHvBKwT6yCbkWLFYjFJ0SUHmQnLho9rtdgrbVatVra+va3V1VdfX13r+/HkqC0ibw+rqqg4O\nDqKnjkEytERAamHXJcXARXQBLEKFHuQJupSdb+E4EJzhxLz30POs2RJNX28qsVhHsqQM3UM/sJ1Z\n+S0Wi9rc3FSn00n5LMgCbBDB9Hi8GHa2v7+vfD7pU+Q+vHLCM4HoGN9P4InNIFOMneHeJAVWwYbQ\nFwqG9zJjJqFLCt+FX8OucVSO20Ywqc8xcf/lmVr2kDXP5XLRL+y2BdzJHBrsE/JMEgO98WQIe0RQ\n7UNRseX+XnBdtsoIzMf7vVoEP+q4DrmSfntc99Kn77KJCN18Pg/l4AEdCPvLy2q8r42FpDEZIc5m\nRT0zQWYO51WpVOKs0qurq6i/39zcVK1WCzBYqVT0/PnzcP5PnjzR7u6udnd31Wg09K1vfUu1Wk29\nXi/AnLRQEAJSGLNqtRrnB/oaOcgnk0cm0lk0BIvnYXAR3+VGybOeBLw4DjK6rmgIMApDtoCAdDwe\nx1ETlLNmU/+UADebzWAG6amjDIH7QynJrCAnBL8YahSgXq/H5+jF5DMoIgyrKyLGAKAPsHU2n30A\nCJJN5/xUDBigB6cBwyYpxpi7s8U5eLm3y7YzUJS7kJ0lq4FDQne8NJZ+OY7p8WlzOCofkc6+QgA4\nC+bDDQCByFy2DJlsCA7Ywbg/G9cH+HoZ3217ebYKAJHVKxwONs91zwEEQRMBEw7P9Q8ATC8U58NR\n4gjp02q1Yqok3835jAAH1yXXZ4AAVQkAcGQSOfFsL2CPqaXY4kKhoF6vFwyzM+zICmvHdxIEYu/J\nbq2urmptbS0VuPLs6NqjR4+Cpad/H9CJbkH6EfhiV9ADL4UqlUo6Pz+PsjOy0ugU6+bBILbFq2kk\nhU12gpF9oKwMW4JMEWTTz+m/xz57eSCB/WAwCLIMsI39d/vFHrK/ECYeTIxGo2ifmEwmUc4GkeLy\nTxaBdQF8ATw9c8pzeFCITcHOAfhpvfCsAPLo1Qb4A/wSZCoygo30YJt7IKjy0mYPbrmXXC6X0jkP\nim5zQCp9Gdt5pgo/6mvslTguE8iuV2bNZrPw9w7Es9dDPqiS4L4kaX9/X8PhMLJ1lUpFu7u7gemQ\nvadPn4ZP+tWvfqW9vT11Oh11Oh29/vrrKhQKXzqnlOw4FW3SoqWm3W4HeQF288CBth8PxMB5BAnY\nQ6rieK+T6tgj9ISBk/zfK3DAKo5D3P7P5/M4XoVKK+QfW4YtB8Ozxqw7dgU/D4abz+dxpikYzIlL\nqhdYH76X6jYwO98H6Yh9A7MgH9kMaS6XSw1Ug/zg7GzuR1LYDoIv7CI4FnnjXijZhzzBz7P+yDtn\nRLOW3W43Rf663+PzyA4VlwwudAzG97GOBNvYI+Ih+vAhDNEZZINrcs8eX7GH/n/X5W+K6156ptRT\n9SwUAN0Bqy8GQsDDusHysgOco5QMvCEQw8FJSYkUzBIBHYpJTyKZwFarFUONCM4AUP1+X3//93+v\nvb091et1jUajCCic3e52u3EtjCKOjqZnGIZcLimvQolKpWRwjjtrSSln4AYCZcKIYrxQbMqNCLwA\nljhtwBbBl5T00zjgY815DoD10dFRPJ8HPTgs9onvoGwBRcvlcqlpvwTsOHzPonr2pVq+AAAgAElE\nQVSg7gaJNaHc2Y0zf/z72HOAFxlSQK+vozOegCzuk7Xw/jGfJOglszCtGFmyJwxSuX//flQTYOCR\nHyYVU8JJOZE7b/aD90jJwfYYW/SKdSCL4WXvyBv7jx7jiHnPdJr0LrMnyIgHLFzrNr6wX76nkgIM\noJvoHPYCmfJ+cM80QGSht6zrixcvIuj81a9+laocIUPOXkAisf/IJTYWp0Uggy0h8CJQw/Gho15y\nzhr40DYpKR8aj8cxoGxpaSmyjw5guAfkl9IsABlMPoy7B6tecsdQseFwGEN7ut1uZK0lxf+91Niz\nC4AeQKaXMTup5cCaShbKy7hv9h6b5wSjt6Hga3wgFnaEzCK2h+OoWGsIWh/UxzPwt6QUCXF9fR3l\nYPhFByz4Ug+mvarHqzA8EMa2EWQD3hws8+yeWZeSI3i8VM+rD9Afb+04Pz9PZUvQH0o4+T9YQ1JU\nwIxGI9Xr9cig8X3eUuEkElgFOSWrBPZgD7189Da+stgOwOvVTewfa48cYSNdf7AXUkIc8znsZDYj\n42Acu0SpOdlW5ImS0mazmRqk6P3/z54908OHD7WxsaGtra0IPEmegE9OT09VqVS0trYW1WGSIqjh\nfrFr3APYrlhctBGwXuPxOCrYSGpAdCCvyBpZQwIOSEmGdtHOACZBnsHaXhXggQTBnv8M+yop9Izg\nxPGYvwesid4QKHnbD3vtFRZgDfQXwt2Pp8JmgKXQ12xZOLgOXS0Wi2q32yqXy+p0OqmKQnC2Dxri\nd+wlfhK5ZDaNV8Nh/yEusF/Y38lkomfPnkU7B/dFUL6xsRFVLhwFiT93XOekNWsoKWw6fgY9wcdC\nIrFGfBa/60En64svcizhGd5viuv+Q9B1LCCgDQHD6LBoUuL4WCTex2IhdPybRZUUDtCzFnwvC4rD\nQ2HIkuJEl5eXtbe3F6wKhgN2v1Kp6PHjx/r88891cXER47zJYHBNJkwSpMHcwsQQtHnZkGcFABH5\nfD5q32G8PVBzAfd15dkBSF5aAgvna+OZriy4yvYewmQRUPpQEhQQ44YieFkrGRj2bD6fpwyoZ9C5\nR+/3YX3YZ3qDcDJka50N4pkoneGZyRYBmslgOADifukb4PucrfXMPM/Fz1z2nETxMggHQufn53FO\nLMwbJcatViuc7mAwiAoC7tFZMkgYJwY8APH9cDaVe3Jn44DS99YzguwNAFRKSA0Ht7fxRWmk/03A\ngbMuFAqpoWcYeww+euYgfzweR+848gCJwvEBUpI5wxY4a4o8Z/u3pIVutVqtsKtk7XDS6ICXYrv+\nuMzh1ABn2Htkip/7yHkpOSrMSSPubTabBZtNBm48HscwDIIBZBOSrV6v67vf/W6UCWNv6dMslUox\nmMKzYdy3P4eXr5MBdHIMMseDLC9/c2A2Go1Szz+ZTOKcUQ/8uQ6AV0rbUtaO72JAjwdlDuAgAwgQ\n6/V6BKRcFz0GaEiKzKxnCqVk8ipAV0r8rweQ+Hve77JKW4tnLNkb74XDbrrvAZAil/wMWXGSGL/A\nOozHYx0fH8c+X19fq9lspnqaswCM50Sm/Vnd5zGEicqa2/4Ct4HjfC9ZF5/KzRoiI541k5JACDlz\njIcdcQDssst3OjFA29fV1ZWq1areeOONCDAJkLADlUpF//RP/6SzszP1ej0Nh0PN5/MYTCMtsBH4\nAhuAfcQXci/IKMEiz0eggk/3Fip0ySsbvPqC+8b28XOSIfhZvp91RZ88oIEs5fruj8CHzAzhLHYI\nP7CmB43+DGAKDxo92+3YguQEwTN2FByIDwMDkp2jyg5cCOmETXdCnp/xOcd1YOt6vR6yhq1wwsQJ\nP54L4gPCCrvMs3CPo9EoBm15fML5szs7O0FegOvQgWwg7sEguA487HrAvTg29bgIzMde8pweE2Bv\nsbFeNfNNcd1LDUodYLgBctAKYCNAxan6AyPggB8WxJ2/A2/PhiGw2TpolJQacgDI5eVlNC4DIACB\nGJerqyv97d/+bQgxioJCVyqVaKrHEHsmkvv2abheDoMB4xmkpJ+GdeE+cdQwJZQLoKwoAKCX75tO\npzo5OQlBxxF7r4OUOACMI0rlWVOMzGg0inHsOB7PsJF1Ye8kpbLRADsYbJQGZXFW3aeXQQJgzDgP\nkawnz0fWGNlEpui5xQFxzxhQyrml5BxC2HrkLntmGwrtpbsYbZ4JOaDfD0MCQOY99BR4phs5IWvF\nOjgQ5vN8v2c4vScDecCoZ4NQJzocJLiBAwQ6sEc3fC+d/LhNL99/SVGS5RkCbBN9N9gN/o2+sreQ\nYOyPs9Ts4WSyOCB+bW0twIoTDtwHthQgRSBVKpV0cnKS2l/vi0cOkYFGoyEpGbbjZAZ9SvRA0ncj\nJeBDknq9XthCKTnuQErOnURvPHvqNs1Bl09FxEbt7+9HTz1/I498j5eFecaT7+M7pKRUDftUq9Xi\nd54BAKw4+PTJhB40AujIQPhwHnSLaiBkhd5WyEquB/Ai4IS48mw5PVKz2SyAtttxCAgALvsPyPSK\nE3qTsbvcr/e7o+/IL7IoKYaz8NzIFDYLu8X/+R6IXSk5ooWMg/f4QjyjT04QIUOAzHw+H6WeyNNs\nNotn8dkVfJ59JTPkhA390579u22vr8J2kr6E7Rha4zr8VdgOe+Zl6dnKBf64n3GfhU/CZiIDEDJU\nojl5nZWrwWCg9957LzAm348toKefgNef1X0q/s/xEevB9fgsszA8CGDaOsQ/ARDX5r7ASj4cDSzg\nBDu4Ez3lftF9bwVD5z2Z0+/3NZ/PYxAo30v7xNnZWWBHdBzsy4AhAleu70QoWVPPojLXAFmBYGw2\nm6nBmOw5FTiOR8E8BHHsT6lUisSF+0xazZBrZN3vw6tKnDBlv1k7T14UCoWY/O5EJj36+DZJEfyz\nbz7V3G0iL+wRz4U+oXPZxIPHTHyOz/BzXm7/eUa+55viupcalPJwzmLg0D37iRMHoPnAGxbVFwzj\n5WArm8JHCGE+PbM1GAxCOIbDYRwTg2GUpHv37qVAHMMjcNyUdrjjpgyE5+TcJ/qWAE25XC56QBEQ\njG2j0Uj1ImSzjgibpK8c9MRnERQHcpRmAtJQCLKuKJ+DQe4DR48BY+1w5OyNl9vxO2eWeeZsSTbs\nD0DTmdIs0+OgBcdBrxp7PhwOY6gAgRtGFANUKBQCaLIWDiqRnel0GqU3TFgD4PJ7B45ky1FU5I4s\nC4aL5yYopnz34OAgCJBCoRBBaavV0sOHD/XBBx8EMAfQQnzgjJ19ltJloVljBEiAVHECgn1gv5xN\nwwm4bCLP6Kcz17c5KGX/IcBYG4aL+VALB+weSEIMYQ8YvAChhR56YEnvSb/fT7G6yDFkTKPRSJX0\nEKhkqyr4Dj/iYDpNzp9DJthfHCL6s7W1JUkRCHA9gApEn7PN0+k0lcEH3HPvHiCyNthZr+pAp8mY\nDgaD0B36qDxzAIjwdXe5hpACTDEJEXuOg+dZs8SOl5FiM1lXgLAzzfg69hiwBTjkOblv7B3PzvNP\nJpMo6yMQwEZ4yT7XJtNMUIgP9t5yJ1fc9g4Gg7Bf2HAHNgTnfDfP4bbDZZuAOOuDHLBia+bzeRCt\n/I08Uyru1SweaCAHYAx8l+sDdhT7JSUEBvLK3lKWzWcp77utL8d2YLd8Ph/niju2k5JsFXrr2M7X\nibWGCGPvkBvslpRgO+yIpDiyCHIanWXfp9Op7t27F1gkl1uU5DIxd2lpSQ8ePEgN1fLSfe610WhE\nyTy4BfvkuAfdLRaLkcSghP78/Dz0E9kF/EPo0ddMRYiUtNNgf/2aEF1etYGuevUB68f3sY+j0ShK\nTNFlcM3KykoMYCTgQ0e8RQnb4sE00975PfpOMsqDetdTbBsVNqenp/H8nNvMc3jWnZJmbA/2nLhg\ndXU1KiNZZ+IK5ADZA1dJSb843zuZTAIHEx9w7/Qc4wP29/djr8Cc0+lUW1tb2t/f13vvvRe4kSy9\n4zqq3jwodVyGz5HS54wjm2BDfCZ6iS56sAzh6rgO8hm9/qa47qUGpdkbRcBwpl53746MDKKnwB0Q\nS0nflGdqEHaMl9ecO7hH+VlUmAiMBKBse3s7pUhMWltaWhw38uDBg/huFJIyTwwJn8MI8nwASjJj\nnJVUKCwa69fW1uI+MSxSUhbqoBbQSsZZSkooMIoe8HnwAViizBhBRlEBhrB8gJps6h5DRc2993zB\n2mNkcFoAJwCNgwMHC7BOfI4A28G7pAgwi8ViZDxbrVaU4OGUXFkJKk9PT1PZKDfSyG2n04mgwntn\nYQcxKMgszgxARqkO/SzIcaFQiO9/+PBhBK8A4OXlZXU6nQCNOzs7Ue6B4QKEoSsYbs9gIp9S0nuB\nQXe2GdDF2niQy/NL6ZH9yAV6ipHnOTwbdhtfWWYc3UUXCAR5L0El2TQcBnLvwA17wXsgYTgPjp66\nbDkmoIB9IVhxHfcgyku5PABG3ieTSfROe+YOeaC/inMx3eYRQHqGmL+xSVm5oRwyywxj27LZfmSb\nrK2TcBcXF1pdXdXm5mboMwE2zhx9wfdgm/Ed3nuOLBPEeEkbwMbXi+el1833mOwLARz7wd4jI9hq\nB04uJ8gW1/dgl8CbvaOSh2Cd9UY2qDBxhtzXBd2nzBtSk3JGz4BBtmE7uC8fjoTdcGDnVUW8h98B\n1AqFQgzWQq8AyZJCxpFpJ47cxiO77vdYzywwdV8K6Ya+eYDtMnvbXo7tAK3olKTQlWw2lOw8Pgk/\niG+R0uf5SsngPNYWfXVdIwjyYG08HkeASDlqsVhUs9mMYTeQ52dnZ0Gs9fv9ON7HfeTS0lIMmOPn\nfoamlBA4rAkVFl6J1W63U0SaV4hJCRkCTga3sB5O4CHnEEMkGCjTB6sRkIJDIBAkBUnkWHkymUTF\nBYMUPctL0JUtFeVanpWD1PdKEvYOezcajeIeWF/eQwsDMtLv91Wv11O2CxyIn4Mg4/xQSvPxCRBJ\nzFph3oC3ynE/YElwnaSQMSryOp2OWq1WzCHgPfivp0+fRlBJCxnVdp1OJ0jf119/Pcg07icbzLt8\nsc5ZXOcEh5PhYFECavyMJ+ScxHQfA9nhAeo3wXUvPVOKM2AhssY+y5a5MXdmFMVCmTFCnqJ2poRr\nsfAEnzAlBKCSol8UAIOjoRwNZh5FJ9j413/91/g9gOTm5iaYt2KxGOW0bD736wK/vr4eAiElBh1H\n6M3tDkwp54S1oLwBxgwjDnOM4nNvCDtHjSB0sJHOaHkfgqTIOnIt/k9mje9kD3AeKDyOxzOXCL6U\nTNAdjUbBagFCPSPBc5+engboRQZgqz0AZO3cecGqocjsv8sPTqLdbqdKv/2oh3w+H0cOwQxjeMhW\nwSLyYl0BUpz1mAWVOzs7qtVq+su//Ev9+Z//uebzuQ4ODkKGut2u5vN5NPRzDfYMRwfoI4jiecn0\n4URYC2SIfclmqNkPPoMueAmiD08C1N+2F/16yAz/R5fy+Xww0O4Ibm5uwhHCijvrKyl0wKs9/Jze\ner2uVqsVIAu7gZ6USsmQHC9lxx4DbHA2OGjfX4CJZyu9kgLWnAm3UlKW6wQIThmZRNdwoMgdzDP2\nHgCAXmAb0HW3/7RYVKvVVAZ6PB6r3+9HAON2gTVz5tdJT7LB2Lps4OQBqWduCEixjTwnk7GLxeR4\nM4JWKdEz1hCbw8vJNSlhtD3rMpvN4kgLsk1kKH0SpmcSfQgSQAg5wo9y3052AZbI4AKMuT/vPUR+\nyEwSAEsKW4mtIPCj4sDJVGYzQAhAqDjIJijBxnMvrB/vZyYEPXRO5vkkYkCZn4nrcsPf/LmtLycp\nsSXeHgIh5foKQHY75mSBpFhj5In1xdbxOa7BvkP2eCsTckzmEd0iEJEUwJoAkCnMP//5z79EZjCd\nm5cfncK9OD6UFmeoO4hvt9tRTkywjJ/kvglYHBt4MA5e9Wor5I7n8z1aX19PtWj58TP1ej2VzSsU\nChHMDofDFHZHL6n64RoEl5CiUrr0FfzGz8Cn2fOOwX9OQoF3yRTy3izR55WT3BuZbc/o8X989Xw+\n19nZWRy5x/XAxby8MoZgjevT0oL/Zp29PeHo6ChFvGDTX3nlFRUKBf3FX/yF/uqv/kqVSkVPnz4N\nQpTWGubTuM3yNhd8EjLLs/Jzr4Jxcge/gL7xO9c/Kn7Yb373TXHdSw1KPZDCcLuBwtE6K8LvPWvH\n4sCkAmZw3LD/gC4MmC86DhJnRQkvIOn8/Fxra2thECTp7t27UaLhTB9sA0pWLBYjAwbb6uWrrnQ4\nOcrTfKBIs9lMTcNkSiuZFkomyuWyms2mptNpDL+hdJhgCgVHwRwUXV5eBrvH2gwGA+3u7sZByvRe\nEEg6Az6bJYeWZ5kpHI87amfaJYUB8DIBL1P0TCX7w/p5UE85caVSicwIP0Mhu91uSgk9aOBcKK7r\nQTxOjN5iz/7SY0q/TK1Wi0wHxoPfeybVM1IYee8BePLkiYbDYQS6+Xw+jr/gc++9957eeecdvfPO\nOyHTOJT5fB4lIgTM6BL64JkKB9zO9nvJHPfN511XkX3kSkp6kP16AHe+6za+AB6sLWwoTlZKzh0l\naPFSGIJW3lculyMDBFvqduj09DT2//j4OKo4yJ4DnkulxUAf7FOplEyxJdBCD3FclJYhUxCA9Xo9\n9RytViuCsNPTU11dXWkwGIQ8ASABh51OJwID12VsO4CBSggnZ9BPaSFjHHXCengQwGCORqMRJfwE\nVfwbQgu5xU9Qni8lY/65LwAvfsX3XUpXDHiPkAMnfufHSfBMzrDzOWxPNuvBi7XDdnHkWD6f9Jxy\nr5AKs9lM+/v74SuzxzRgr9knruelgvg31o33IVtOIktJkM16uy/j55ARACE+B3HCcCvP4LI2AErX\nt8lkEpktAKJXs3DWL37YMyHI0Ww2Sw0ncZ+HXXPQyzMTQNzWF34S/c62AWSxnWc/wTdcw0lNJxYI\naFhjr7zwQIyAEKKE1qjZbBbHK7Xb7ThObTKZ6I//+I9jYi2ZOnS7VFr02TebTZVKi95DMuk8G2Xb\nELeOdySlEigkONAVSEKCXMgyqqI4npDnyeq8pAhOuR/exzqB/8Ayu7u7McCOwJ/qMz83GaKPsmcp\nwQ0kdLI4j332ewAX8DlfG4g2kgheWgv2hkjyAIs2unw+r/39/fCHZFvxo7TMYbe5L3rq8Y8e1F1d\nXYV/428SCqyxpAjICUrBrMgEdtRnyBwdHaX6QiXFPpMM+eCDD/Tuu+/qH/7hH+L0DCf6ac+h/QU9\nQ/adEMIekw1mD73Sg3V1++zxEvvH79k77h/9w57/NrjupQelBJcEZP4Q3vTrpapSktWSkqE0XNPT\nzDA7bIYz55JC+BASgLaUlEmcnp5qOBzGmH0M13w+12uvvRasGoEBgLNUKulnP/uZ3nrrLe3t7cVk\nt4uLC/V6PUnJYAt3pCjk1dVVnIVZqVQ0HA5Vq9VSxrHf74cxhomBocgKBGUJKKr3FqCsgEyABUpU\nKpX05MkTFYuLSa8w7JQj5/P5CJgQXCkZ6w3bSBYAIMneMIiCawFcCXRXVlaimR6l4bk8aORe/bw+\nDCrgm59nAwAyNLCGDsQLhcW4b1hXz8A7KAZ0ra2txTryMw6jx2gjQz7xE4DvRqtUKung4EDdbjeC\n4fF4rGazqVarpZ2dndjXR48eRbbnq5jRcrkcTCzXxnEAkjzDhfF348z68Ts+D2GEg/CSImfS/H18\nlmCD99+2F4NbAKjZ8jT2AiAH4MjlcuEsIVbQLYYdILeclUy2HjABGTcYDGKSI/fAZNFqtRr9Vt6P\nytRW9s6BgJdZEUhhYyRFaSkAnHsi4MAmI0vHx8ehE+iCyx72Lets/X68N5fsCMMm0EdKtThCAVAB\nAOUeCOq9nJBgXEqCb/QCP8I9Ahh4P3aRgBOijQyN+yVAe7ZUUUocvzP+UjKQjOwO60YgynN45oMj\nBmD3sd/Ly8upI3cgHbHdrCH3ivzxPNhD76WVFNkir5BC3qSFv+DMbia2k90G3GXJToId1ow9xFaX\ny4uD7r10l88OBoMUMUJgjTwjTwBUnhk9AYCyLvhcZBFCHPDHsyO/t/X1ddiOZ/86bIetwBc5jnFS\nwCsmssAYG1oul780zRRZAdt1u10NBoNoIcLfj0Yj7ezsBMCfzWZRpomOf/LJJ3rzzTe1t7enO3fu\nBKlFNRSy7xVnXl1Sr9fD5lxeXsZRg4D+brcbSZZOpxPksVdMsMbIJkHfdDqNqd1gKzAphBF6eXZ2\nFqRluVyOyjwSI56woSqGahn2jGFM7IMfNVKv1wNPoidU3jkmJZkxnSaT4bFZ/B6ZoXIDe8LvWRsI\nUSogXee9CgI77JU9XtrqJNxoNNLW1taXEglgF/bBK074LnAn9pfP9vt9HR0d6ezsLNajXC5rc3NT\nW1tb0Xrz4sUL7e/vRxWLxzncq1cpeZKF+MCrjrDh/A55wraSVSXgR3d4Lv7vuM5JZnTeifLf2HZ8\nE4Pz+3rhOHgIf1geCMUBFEhJ8EeJg5cPAVA8xe8Z1ayAs1E+HdOF+erqSo1GI0qobm5udHZ2ptls\ncWxKrVaLEgc2FkZ6PF6Mlic1//z581AEMp3cA4IM+9fpdOJsSkoIOO5jPB7r8PBQvV4vghwp3TdA\ndpZsKN/JHxQa4AdAJAMxn8+jUZxBT/l8XoeHh7q8vNTh4WEE62RWYXXIwgCems2mBoOByuXFMThe\nwoZyONtHEOoBujetezkf++zlqJeXl3EeFM8rLUAkZVgEocgFSgywmU4XZ/thzKbTqdbW1iLDuLKy\nEuCfNeOeAFaARcAcRgSgwvvI5EoJywi7SX/yw4cPY2pjsVgMZpZJpoCeYrEYZbuwrMiZZ4EBxBgT\nD8Bx4pLidzhZ7pcsLXJEIIphZV88u1MoFALkAYox4mQdvCTmNr1YW3eIMJ1krNAJJz4AEsgr8r+0\ntKRWqxW6v7a2FvvJPgCyvUqCIIxSqmazGSQTsuvAkX0EeDsZyL1JikCCoWHYYmwQ8ry3txc9Wvy8\nUFj0/QE6uWdIRSoDvASLygvkmmAK3ZcUvVhOYtGbIymIGzIc2KtqtRpg14kS7g89B1DzXQBXJxsA\nwx5M4csArbyX60iKrB0AlXvh+9Hl6XQaaw9wkhISaDabaTAYBHlIJpBnzuVycfSKpNBB3gMo9Uw/\npIJnkYvFxYRbKoXQY3SdNSRTTMBNhQn7SoksLS48583NTeqIK8AgGIGsMd8P6Od7wBa+L6PRKEhQ\nz1xyPchNAhgySfgbKlrQtdlsFtVL7AU66FjGM2K39fWbYjuvwuHlAb8TzmA8fs5+omMEpf6dEDDg\nOuwaeK7ZbMZRHJPJJDJtFxcX2tzcDLvogQrE4LNnz4KMevHiReBOetTPz8+Vy+XiCCACt1KpFESR\ng32q3g4ODiJpgW0bDAbRnygt7D0+HuyGTfJgic96Roy1cSJ5OBzq8PAwWn3Oz8/V6/VS5bfgqbW1\ntbDTUlIxgk9hvSGT2G8w2urqqrrdbtwvpa8El77XBMLs8eHhYVT3OUlEtQQ23YdJUcbP93kfJrbM\ns39kXNF1MB54jL5y9puqPjLeBN9LS0uRTe/3+0Eg8h2z2UxPnjzRyclJZFyXl5fjMxzLJS1w3eHh\nYdgYkhMe94Drwc0eO2GnPCmHfwfXkQBir5EPcIUTBNgwbKUPrnJSiXv0lrT/3uulU3UOROjLBBg5\nm+plMBgHnB0O0sHAfD5PHflB9hBHjoD5d8BouLDNZjP1er0IVOjd8qzH+vp6bMxgMIgyIlicx48f\nq16vBzNPEFEsFiMYdvYDFrpWq0VAydpg6BEugiQALA4QJYadkxJA4GWBgBpAIIKIQaGeHobZBZ/v\nGQ6HkhTnMSHI7OtgMFC1Wg0QABBlDxxwYTy9HAWlQPFQLt5LQzuGyMsjCABLpZJ2d3dDVrgOCurZ\nFciF8/PzKOlBfjzzQ5O8Zwddad1w4aQpibm4uIjfZ8F4s9nU2tqa1tbWVCgU9POf/zwOzUbxp9Np\nZEoZ704A7YOz3DhhoJwl9PInDAn3jzHBqTgjhrPwANZBAsEqk7QxwgRWrBOOAgN3Ww+UZ+2z/2Y/\nPINGYIDeeskMzvv6+lqnp6dxjZOTE5VKJa2tranT6UhSjNmnF9kd0nQ6jQzSxcWFHj16FAABWebl\nWXTAJEEQ2dl8Pp86fuj6+jqAADZ2Ol0M6iJYbTabYS+opkC2/dgMgB9ERrfbjYC10WiEw0S3WBMq\nN66urmJIyXQ6jf5q/g8AYHIkWTVKO5msnR2q5IwwFSYAM/qiPHDFDvjaUrXAHg+HwyApSqVSEEt8\nF4E3IHBpaSlaTQBL2MXr6+sYmobN88oOyFWAG2uCLfBgi+fAtmNjeHaCW/qofA4D9gZCigACwIcN\ngVApl8tBCALomZQpJX2DBH4EH/l8Xpubm+H3ptPF8Lnl5eXwnxAc+GBK4llvfCCDBDudTjwnugQu\nIJjCv/A7yAaCYwJYiD8+4/2Ht/GFLDl+yWI7ZBXCC39JYsDto5Mz4AivakJPCPyyGJKZDRDg8/k8\nsJ3/nMw2xD77CwHPHpLlguyBkMLW4AdzuVy02TCVF3nHZs3n86juQl7ItlN5B5aB/CoWFxN/vcrG\nfT067G0UHqxKydnQYEvPaEEWYyv4Pm9xcLvAgKTxeBz3yjr4sXVgZYjWQiHpT+W67Ad6AgFELy0+\nB6IITLqyshI6jX6RLcSWgZlZa54D+ZIW+PP4+PhLmT6SLn6P2ALsnvfCInvFYjGOqiHb+umnn8Y5\n4k7Ut1ot7e7uhi/An7LuVN7gK53o4VrgOk/kQWIgVxCxWVzHs/JZno/3SYrkEj6bewPXoOffBNe9\n9PJdHB+RPH0c/I6/Udrsonkw6mCaCVUYBS+plRKnkGVcUD6+t1QqaWNjQ5eXlzo4OIj+UhiZ+Xyu\n119/Xdvb23Fvy8vLceDy8vKyHjx4EJ/BAML+wazAUuC4pUXGb21tLTJ7fNQT4qkAACAASURBVH4+\nn+vZs2fqdruRrZpMJhHEAAB4dj8gWlJkyjAmsG2NRiOa/BGubDDM5wBHLqww+wApGG4MPQbn5uYm\nprXhCHgPmWfKSs7PzzUYDKKUC3Znc3Mz+nNRCC9DQDEpScMIlsuLg4hh0XkeHCCBJ4E6xhZjj6I2\nGg2trKyo2WwGiCKAzcom9+wlghAmZJgJ8GHSKEN5/vy5Hjx4EIEAxoUsKcOsjo+PdXZ2FpkSJxcA\nBwSoZIGQUS/3YO34NwbMKwg8S4TBR6bRI4wljhZADOhwY83fPrzhtr0gc9hn1pLMHgEkfZ3oEy9A\nEiz90tJSEBWug+wT+nJ9fR3lU+wdcse5kAx1w/kA8LCzyO/V1ZWurq7UbDaD+SUYofKDUuBaraZ+\nv68XL16o2Wym7qlarUbJHKAUXfNz4JBZ9I6AGECxtLSkw8PDkDMY8UqlEhMs+/1+6NTl5WVkpiuV\nij7++ON4D3IuJdNovdoCu+ABNnqATfZMJASml4zxDK6XBF/Yfo6F8HVxcE3w6n3sEF7uHwjAisWi\n2u12qrRKSo4ogkXn3s7Pz9Xv90PGHAASkGIr+DeBFz3OXumBPeC5eS+2GHslKbLd0gLwS4ujU6gU\nwsZgg7Ehnk1DlyA0jo+PU9UcyI338HnGFhzB5zhSywNySAbfT/cfELJUo5ycnKT6tAmAb6utk9LY\nDv/6VdgOvAa2kxIAnU00gAPBK/gxsJ1jQwISr6KjTYbse7FY1MbGhobDoQ4ODsInYkfy+bzefPPN\nVGawUqlEBrRSqeizzz5LkTAEMGQ1yYri+6gEmEwmajabobduZz777LPQX6q5tra2IkD0BAs6BR7N\n5/OpycAEKqwhAaNXbTgZTRaOYJX1xRazrmTXsDFetUCFA4QE/8e/X15eqt/vByHKeaPVajUVuC8t\nLUUSh+wo1wFzEay32201m81UBQJBKLYTu4QPJfEE/nZsksvltLGxEfcEXkNuwEfYDL835A18OpvN\nYrrw6upqDN68f/9+zCXBxtdqtZgXQXkv8oYdk5KBcPhNKtogT3lufDT4C//g/iyL6zyLjN6BB5BT\nAn8nGZG/3xXXvdSgFMFnQwhEnDGVkv4EHoxFZPMxMt7AjVJISekgBg0AhzDxt6ekKeVwxpjv8dKk\n+Xyufr8fDGkul9PJyUncx2Qy0cHBgZ4+fRp19T5UiHIApsceHx/r8vJS5XI5WB/KknhelHh5eTkM\nGsCDz+EsccTO9kpKTbhljTEo/J++ouvr6whAHchgsPw8vFqtFoEgRglFIbhptVqpwJ/Abz5fTJs7\nPj4OoExPGyBmb28vgm+MxMrKSgT3GElXQkmRqc3n8/E8rAMvABNZDklREsKRK7Ct3B+M7ZMnT/Tx\nxx/r+PhYH374od5//309ePAg5I4AAhadEp92ux3GAbC8ubmpzc1NXV5e6v33349nQj/IolerVR0d\nHaUc4NnZWeqIHeTDDTHyR6CBs/HeDJdvAllKXXBSBC84LClhyDBQfA9g04MnZA4WlN/fxpdnHzH2\n7Md8Po8SS7KlOH3K/yBkTk9PtbS0lBqwlsvloqri+vpav/zlL4OUgzTwEjj0gACoWq0G6QXocBlw\nNrlQKASRAqtL6Vmz2dTR0VGQPdg3D5YhwQiAOdTdS62oPiA48il+7lTRPz6PvBOMEKAiU9gKMoR/\n9md/pjt37sQxNblcLgIa9030UHkQ4mVyXhKYXTdACXoFaPYSLPYbR18oJEfpAG6wvfgQwD0kKLqK\nD6REeTQaxZAUAiIGcEB0edtEs9mMNaOUkUnADmBchjk+g+dnD5AlfCpgl+8iiMa20LPnAYVnW2mB\n8MwW+8T3jEajGKrFfsPUY7vooUNm/DxuZ/trtZrOz89TZ0+7fcT+8UzcL3sAmJaSgW9gGr7/tr6+\nDttRKeDYDpmWFAS4V+H4GiML/PEgAdIPn0QWiGDQMzoEbpA66A5loKPRKKZEo3+53GK+CIHc9fW1\n9vf3tb+/H8eG8OxOJpHR6vf7KpVKarfbkTRgXgDPhq0hIIOov7y8DJ9P0kFSyLCUTOF1f4w8E2Q3\nGo1obwBLoudScpwM9owAHRKFoImKEk/OOE6BDCBQY4273a6Gw2HYF3DC6upqnAzAHA7H/cgImA99\n9fOWvbx+MpnEfniVCPvvlVwEklSOkSGfTBZnjL777rs6ODjQ/fv39fOf/1zvv/9+yLk/Pz7BSQsS\nCNPpVK+99loMC/3nf/7nVGYS7FWtVtVqtXR0dBS2n4pIiF2qCkj4sOeuUySyyBBjuwimkX0fkMq0\naOIWr4YDp3ANvhPf/vvEdS81KAUwITQAWIYlkJ0j7YxThcFBCCgbdENQLBYjuMPgOXOEIMEw4Gj4\nHUp6cnISQeDZ2ZmOj49jw3F6kqJJHCGqVCr67ne/G9MdHz58GP2pZBMQSkmhjK+++qo2NjYCoMJc\neD8rGQuCLg/YZ7NFzT4N80tLSzEpbXl5OXoqnXkBZLgwI1QwkI1GI0ARQBFFxEBeXFyo3+9H2Zuk\nyHKyDmQwfOAOiuxsFQAbJ1Gr1SJr7Mw4RhonxP1j2FGk+XwefXcYWoJlFMn7BTAs9HBeXFzotdde\nU7FYVKfT0fb2dpRjXFxcqNPp6O2339bOzo5+/OMfxx4itxg575chuGUgBqxcpVLRycmJfvrTn+r0\n9DSCebJM6+vr2tnZ0draWhjl09NT7e/vpyazlctlDYfDKPGDpcSRszYwjw64vSSefcIIoVtOLPBe\ngBnrCcHj+uYlXAQtksJJ38YXAArHA0CAucfeoA+AJRwyjqHZbEpKT3Ul+HLAjs1zdh2wABgmAKZX\nz8vPudcsiVMuLyZ7U+nAlF8yCKXSog8Pe4P9Qv6xkwQpngHknrwNwdl37Jmztr5mXjbmWToyALQB\nEOx9/PHHurq6ip55B530x7PukIPF4qJnDBs3Ho+jgoFsK5/DhnrbiaQUMHegBNBgzxhs5SQsVQle\n0u12HEKAeQiQVAyh4rOUWg8Gg7CP3At6zvO5vScwlZKycg6H91LcLGuOzkvJcTWejcWW0sYC2GJN\nx+NxkAmeJeJ5JaUqd5yA8VJawDGfJZhG7gG4ADoCUuwleAOwht30ah/vfc7lcpEB5mfoPMHBbXx9\nHbYrlUpBsFFtQKWcpCBMnVBHnx3rObbzzLmUTHaXlNorZAM5PD09jUEy/X4/JoPzXQxlq1QqUZoK\nlrl7925Uq9D6QJavXq/r8vIy1RNYr9e1sbERwahnJCFiIEucyOA4LynBrx50Ehhid13HqVw6OzsL\nAgwMSsDGaQoEj7zA0uwX80MgtVhX1qjT6UT1DGQZf3N/2G0wKTYTn0Lv683NTRCmJDim02mcb+2Z\nSt5DexzfxQuZ44+kaHWQpKOjI7VaLbVaLa2traler6vdbkfbw83Njd5++23dvXtXf/Inf6I7d+6o\n0WgE6YXv8diCPfZ45s6dO5IWePjv/u7vdHh4GPcAMdlsNtVut7WxsRGDpM7Pz9XtdnV8fBw+z/Ec\nawLudWxJhtJxnVfs8X78E9eBgIHk8QoViAHHdZK+hOvYb+m3x3UvNSiFSfENhWl00AWzjvFH2XB+\nXhrEdd3g4UgpAcCx4Wy4JgwpYB3mBMYbJgtHBFhYXV3Vzs6O1tfXw6leXV3FcSOU6V1eXur+/ftq\nNpt666239MYbb6hSqUQJKGCu3++r3+/H/SJgFxcXocyUhFCCAJOPoHsPHyALIFKtVjWbzeJeYbLo\ne/FpnlxXUoADAmBGlGPMMNyDwUAnJyfBghIMExBeXl6GMSUAwjHwO9htyrY6nU7ICQYRwwD4QwnI\nzLE/rAVsIUE8SgYQAcRQhvzs2TP1er0AeZ9++mmsf7lcjnKkzc1Nvfnmm2HEBoOB6vV6ZBCYnAz4\nxkliWL0ev1hc9ML94z/+YwyywhlTilmtVvXKK6+EfADe2GfAfrfbDfIGXUDG+X7WjHuRkmMk2GsH\nWr4HXDdLEDlTKqUJIJwdeoYe87vbWtIGePI1YcDG0tLi+CEy0rwHB01QOR6Pwwl9+umnYRvOzs7U\n7XZjYBZBLABuPp+njnjC4aEnBIHspZM9VIYQAE6ni55MeoU98wVpReBLkDWZTCL4IThirD73SasE\nRA4gCRvhlScADQjBXC4XfZ9Ut3BfBFPIIIEbfamHh4dRdo9NxM6iWwAt790nG1YsFlOZBjLi2HPW\nHNspJVNiceS8PAiEaMK+eYkYwRJ+DJ/JNX3vCZYg29hrfu8ZcPR4NBoFeUB/GwDTs3v5fD6y5tw/\ney6lzz9m/Vgjz7iyhllWHv8BEAYcecYE3YBARB64XyfJINR4D38IVJ1khtDEZhL88/08L9fHd3sg\nBkHie4Pfgji9ra+vw3aQHQBf9NszfP8etnO/Q6k8RA46TvmhE9LeCsD3+3CZ8XicOkYKe1er1fTt\nb387WgCoYqCnE7Kq1+vp/v37unfvnr7//e9rd3c3SueRHdoXmODNd0DKkyxggB3lsQRR+GvWA9tc\nLpeDTALj4Lf5GznmHsjIum6yriQ2IORdlrGvyDS4a2NjIxIIkEfYvEKhoMPDwyDDGGQ2ny/OSN3b\n2wsSE9vhJfdUYFBRiAxAsksKO4BtdQzoOGs8HqvX6+no6CjIkOPjY11dLc78LBQKarfbQQh+//vf\nV6vV0uXlpc7OzlQul1NViY5tIJIhxyhPBpNPp1O99957evToUcqfQqa2Wi298cYbqYwj+Lder8f1\nDw8PQ254TidbPIjEL2Hb2TuPYRyTeFKC/2P3vUJJUtjpr8J16PQ3wXUvPVPqYBbnS6kRxt3Ze09F\nsxBslpQwI1xPUgReBB/OwPPCyRH9Y0zPz891fn4eg0L6/b663W5kImGg8vm8vvOd70SGjsCULNmn\nn34aoKvX68V0tm63q2fPnkUvK46PEk13es4gesksyukT0uhlIOADEOFoKYdFcGCkHIB5aQgZE0Ae\nZboYNYABoNEHGmEk5vN5DAuAoWHvOW6Hw8nJDlNqNp/Poy6/Wq3qzp074YAATignwMr7lWADZ7Pk\nwHnvpyCw474w+p7NQf76/b4ODw/jWTY3N8NhcB9eqlQqlbS1tRV7x/6SqT09PY1y5/Pzc3344Yd6\n+PBhfJ5SGrI3d+/ejRKYcrkcY+1PT09jX9kLwCtGAbnHKfpxEV6WAWj1wUSsnZdxkGHixTWQNQyk\nByqAAoJcnL0HsrfthYPI5XJRMgPLS4UB6+UlZNhCWEspOdBdSgJ+Agx6NVlHdBOH4XpMyQ/v9f4Y\nz2zgZL10SkoGW1G+KyUEC7rJZ3ywAmvAvnv5D9UxbodxqAAnrgeoIOuKI3USE0CD8yWDCsu8vLwc\ntoSgk54e1hy5nM/nsV74HP7vdo73Yu+zRA2gA7vi2R6vJHHw5eWLgDNKWlknQGa5XFan04l9dfsF\n6cj+0F8G2CCIBTR6Ka6fj4gsUtXEfuC/8QGQKZSHIeNSAp7I0vPy0mX+j70ksEXe8FnYadaW/0OS\nFAqFsIX4PA9+Ce7JwOFDsaMMR8FOeiuR+0h8PyWIrD/ZiNlsFr7zNr/+PWznWWTsgRNyju3QZX7m\nwSX2h357D2ad5OL/ns2RFLby5ORES0tLOjo60vHxcRzpQon16uqqvv3tb6vVakUGiQByeXlZT58+\nDeL79PRUJycnGo1G2t/fj4m22LDhcPglnMFzI8fIv1f1EaTy7JA1Xr5MNZ3bW2zWdJoMtEQPPdMl\nKbAi1ThS0n9NcoMA2PXw7OxMh4eHId8E0JD7vV5PxWJRR0dHmkwmcWpFFpsuLS1pb29P9Xo92o9I\nPCAj2ETP/FHJ5QSElLRQgOOazWaqbBVbNBqNoi2Kycfn5+dx7CF+huo98KuTZl6BQ3DNgD3W49Gj\nR/roo48isQWexM6sr6/H/jI1maMokSFwExWZXg0oJa2NXuLr5Cr77LgOn+fX8RYEKcEv/Ay7yJ44\n2YSt/qa47qVnSt1gA4IlRbDpmU2cPsoBIIHpxBB6uYikCHCyQstCOVsAO58NeBCGSqUSZW9nZ2eR\nVSBY2draivI2WCmynisrK3r77bdVLpf14sULHRwcBHtIyaiUlJ/AHg+Hw8jAcW+AIcr2AK/0R3mm\nkEA0W8KWfW7WnWwBZaPez+Qj9HHeXlaA0yF9zxTN0WikVqsV5WQIPYoOw0RgyvdzXAXy8Oabb6rZ\nbEYZDSCPZyLIxKhjIM7OzoLNZl95Dt4PoETmCoWCTk5OwijBLnmJXK/X09OnT1Ng3rOerAvy5pkP\nD+4w6M+fP9f9+/c1GAwkpY+hwGi3Wq2U8x2NRpGJIigGnKFDGP6sQfcsrQNcD2TQLcCbM2c4RNYO\nncb5s8Zki9BPAALr7XtwG19kjKRkWif7BwEGKcWeoaPsyXw+19bWVmTLpcRZXF1dBbHjJTfoNY6B\n7P5sNotzKD1LyL7SOkDpLNUkyATkGXvOHkNo+bMCLp248YCTz8HyozeQffzfGWGCWj/7j3X1wT60\nSfBcrDslxSsrK3rzzTfDl2DrnQQgCPOgGXkmS4ouY/sImrx01MsM3d7w3bPZLKpruL4HTOgSdhvb\nhz0BzObz+Si1QwcpUfVsOOAEO+4ZTIBFvV6PoNWzNLx4H8EqssHzA96drORZPNPA84xGI21vb4d/\n4D4B0IBUBoFwTz5kCjsIMANI08MGwQLZwv7wfgJPD7YhBggQkBXAJeuKrT07O4vAA9DJPV5eXobt\nu62vr8N2yCRgGfzmJBhr6mSm+x58ez6fTyUFpGSgD7LsE47BPvye6iHIBvSZUxTAM8y4aDabcTxg\ntqrt7t27+slPfqLBYKDj42MdHh6Gfc9iFJICtGEx+Adcgl0Ac0pKkTRScp7kZDIJApwAD1+BTanX\n64EvqY7jGEOujT0Cl1AxwL22Wq2whyQHIMIopyah4EkC1gm9pTLIq1sgx3d3d4PsowUN2XDCIdvC\nwHpISmFQ7hf7hF8F47948SLWijWgeqbb7Uag7YEWgSDXJyjEfnlPqxOJx8fHeu+993R0dBS6D2Ys\nlUpaX18PcpQ1oZSbazt56ENakRH0hTXiudyvEkSjA+hRNnDk5wSVXqGAPuKPiBPAHI6judZvg+te\nalCK0XIWxIEsgaeXgOA82XgWB+MHq0tQybVQUth2ADWC7c4R54IhRODdmRGQzueLISWz2Uzb29t6\n/fXXU8EwG1sul/Xuu++qWq2q2+2GEfLsCRkSDBgsIWy1A1SUj6AEpUUhPQj3YRouWJR0EeTCFmGg\nKceFiST7yfO5M+A9/J51rNVqOjs703A4jEmGfm/j8TjKWWD5yGS4gV5eXo7DhCm3AGhJiULyjLPZ\nLEgBlAIlJYgiU8wasm4YZd4Lo0rpCllKhhft7+/H2aCsBcYKuSXTAAB0w7G8vKxGo6HHjx/rk08+\nUa/XC1lDJiuVijY2NvTDH/4wAgnKNjHksHmsIwbUS8qkxMFhcPk3gexsNksxeBhr/u0lc77XPCvX\nQHfQJ3cUDny9yuG2AjXK4aX0qH0pCVw8mPcziiHBKG1lcihAWkp6gzwDg8xTlghLDfh3wMfAB/QQ\nW4OTQz4o/XeHlXWE2DWysm6HnNWXlNJzyDnIJmQB5hd5k5L+Mbd9lNJxNEk2kIJVx1/A8n/00Uex\nJrD0s9ks1TuE/hCAFgqF1LnBXN/9jetDNisEsPK1xA5g28jyuh/i2lyXfSbLiR3j+ugYAJdruM/j\nhY9wwoCedKo0uB//7ul0GiQs5ZDYeQgKADe+TUompCKn6AaDArkuv/OMMqQFzwj5x8+ZSg9Apmef\n76AvGCzgpXL4SHQJmcOPe1kaa5oNdnxvKWcE7zgwvK2v/x6282o45A0fAUB2bMfPsDHuVyRFKauT\nZI4P+YyU+D8n1cgsYhO8sqtYLOqP/uiPdPfu3bB5+Ff08d/+7d9Ur9fV6/Wi7YbKNU+W8HwerLEu\n2AywHX6eZ2POBWtDMMjpAlmCz/EIrVa0TWELS6XkPEwITAgZ7AYBKtckM0nQKSlardBbkg0EtZ7x\nBpeQUKnVamq1Wjo+PtbJyUm0b2FPuU/PumOjIdHAI2AmZMZJEIgqJ9wPDg6ilxiSClLt4OAgjmzB\nTxGAYduxMU6kENCxbgcHB/rkk090cHAQWNurNVqtln74wx+miAamyiPnxBv8nvV3O+5yDZFKPCQp\n9sur5ty34UOkxK575dzX4TpfD9YAn8W1/n+TKYV9R5myJZVS4gicOfLMEhuCk5CSrJ+UTJX14I/r\nO/PEonkmyf8/Go0CrJHNJWjmO6SkBA7giGG5vr7W0dFRHO9BsOeBIMEEQIvsHtdHMDCKBFv0MBEw\nI1gEKzyvl0FhHFhXZ1NQbACnC14ul9POzk4cSYKyetbU7xdhd9aQgI//Y9BgEXkxGZIysLOzsyhz\nZC9QAoJxAkvWh2BZUpTpsFdefoByA6AoPeH59vf3U4EwASmycXJyol6vp06nE0NQPGuCIUI2YOwB\nz5eXl3rx4oUePnyYkmtKI8vlsl577bU4Y5Eg4Pz8PJ4Lw0/fLuuZBZEOhAHt6FR2z12PkCnkBqPp\n/4dxRMZYe89SoXvIGAbQM3G37YWueomTM5nILOuCHs1mMzWbzXDusOq+vxBy6APlkK6LntX5KgfG\n9aSkBUJKxsFLSekvOjIej4Mp96wIz4hO+b1wf4VCIRWUYD8gkiiPQmc8cyklVQeSgkyRkvJx7CuZ\nEPSPZ+b5b25udHBwoO9///tx78g6gJT9kJKMt/dU8ZwQi9geSmyxuQ60AazoHMEk+opc8Kz+7J4Z\n9owBvWdZG+9suO89AZT7RwfqTjzQCuEZXjIrlAUSuDqhwX0AopAT5AuZ5XfII5lF9x2UsCFHTgLw\nXZQfYjPxh54x471u413uCZ75vfcDO5HngZeTBZRAA4QhI1lDjrm5rQSc9PXYDt/rts+rCaRkJgh6\nyO9JVPB5ZJrv8BMKkHlkims55sN/ge2m02kQzm6rCViwDVRuIZs3N4uz6J89exbPXigU4lgjKr+8\nnJuKMUg8B/3oD/K/srKiTqcTtgW84tVh4Caek2thoxgEJElra2tx3WKxmDqGZH19XdVqNUr1WQts\nHn6IfQHHEARBjEmKSjkqTMCt9KuSId3Y2NDh4WFcmzXJ2jgpmb5McOlVfD792ys6ILO87BrbRaad\nsup+vx/6O5vN4uSM1dXVmDRPQsuzkUtLS1GF5BU92PRf/OIXUc5L0oWAdnd3N+wuuI6geDqdRjn5\nYDCIUmhvTXOs4CSMEyEeLPJZlxV8g2NjdNF9geM6ZBZc53YQ+/xNcN1LPxKGFDTBA4GUs/AAAMo8\nEQqckwMfB9oIrwuhLzKbJiUlDM5mcT0EAiEuFotR5+3Hxpyenur6+lq7u7upoNb77p4+fZpKt2P8\nCBgZw032lQwLLI+DeZzpfD4P50egAuuBE+VZXUA8c4BxAVhQYoHReOONN/TKK6/EeVlMKGs0Gl8K\nSLNDN3ztveTGWWYCKS/JQMEqlcW5g5TK8Ry8nz0F1Dvz6srIsSkoPs+Mo/MsKUEu68tadrvd2DeA\nS6PR0MnJiU5PT9Xr9bS8vKxms6n19XVdXFyEsXKWDrCNjD148EC//OUvo3wIZ03Jzebmpra3tyPj\ncHl5qcFgEIG7lxGSkQL8Imv+jFmiAX1xp+5ZFdbTg8zs3+y5g1wPWHHKvj/IMkb8tgalHmiy7z50\nBsCFvnip0WAw0Hw+j/JqMlLumJBVnC5sNkGOs7jZbDfXZso1oABZwLZ6D/l0Oo2+Fq/k8OfAjvIz\nQJb3lWG7ABTYJCk565bAD1AFWJKSLKqTkvgVgJAPx0DeyDRja8gKUMqHj4Hk4bNevuR9XawrUwbL\n5XKKEMPOstYAXh/EhH/L55PJ1ZBi7DG6mC1FRMZ8j9ln1tLJLre9fv6qgwuC7FqtFmtCoOWZC/fX\nrCVA1ttxXBfYIypCvFXE/T73TiaZPXd8wIufYQs96EYOPIiBiOPFPeEzHY+wH8h2lijGF3uAzVrM\n5/MgMwlQISxv6+s3wXYEU5T2eibT8QOyxt5ksZ33uzvh5D7LfQ0YEh9FWTc2k2NSvLLl9PRUhUJB\nOzs7qbYoz+CSCcP/QfaReKCSg7VBl77Kr4JT5vN5qozVA2FsWJb8R65Z+/Pzc62srGh7e1uSgoCs\nVqva3t7WxsZG6tx3jiVh0JIHKNzX5eWl1tfXNR6Po8SfwNUxHUkHcCyYhOsUCgU9e/Ys8BHP5gka\n9iCLUzzDTYAIjobQk5I5B+ierxm6SIsWWPzq6kqtVis1/bZQKKjRaES/Pq0jVChhz9nPQqGgg4MD\n/exnP9PR0VGqfJZJ9QzS4jnn88VRaAyaw/+CWbkuWVhsP74Av/hVMQ72nd/5z/g/L8d17suRL+we\na861fh+47qU2cPmN8gAYEMA3gMQNEw7VF8EZOD5HtpH0M9E7L4A3Gw6j4ulwWFOao9k8psTWarUA\nWTitjY0NHR8fR2+JswoPHjzQW2+9lXKMnEfoZQ4EHtPpNPoiYcI900KPDqW1GAfKTTEMsMDORo7H\n4xh97cKGM2g0GnHtk5OTuJ98Ph9DQnK5XEz2RTE9Q8i9MkiJKbwEle7YAWNkRx2gM82XXl7vVWK/\n2Hf6JlwZr66uYsomBsiNm4MLQCz746QIMsNe87m1tbXoP725uYnzDtvtdjhTHwRCsAnBcf/+fZ2e\nnqYYddZ6ZWVFP/rRj2LAAJnVm5ub6F2B3KD0BcPsQ18khTw7C81z8z5n3Ry4E6hgdLwckWuhpxhJ\nD1ZZO36OQQWkO6l0217OWqJrUgIQWPcsa44jQh4JVOjdcdJOUjCwOFvsJHaVLCqZSEqCuZdiMZnU\nzX16qa879SzJwH7yb+5lPB6r2WyGfAP2sMno1vLycpRaor8ehLE+ksKmoKsEs04YORmGLpHNRYZd\nlwuF5Oy8LAh0Us0zadhQBpF5JhWABSiHUDg7O4sSZUpNs86cwOj8nTGRGQAAIABJREFU/DyOEHJC\nkes72ejDPorFYgAfbKUDEd4jKWwWLD/Ake8aDodaXV1NHbfAdZFVgj5AWjbg5t4d/CC/6PxXlesR\nfBMUelUH/oJqAYA2mQrsOuuCLcQ/IaPoiWe5AYPsHwEL4Bdd8yDLsxM8H4EDMuQZLQ+Ib9sri+3c\nBoHtwCpeHo3su/57BYknHcB2kiJZ4DYWWcOG4iORfa5FdQZYg0E0HFMENpSkra0tdbvdVLIB4uyz\nzz7TnTt3UlUcnHlM4oIKK+QJ+0vmklJd96/NZjOSFVSocD0wK3Yvi2PBXKPRKE4O8HPdx+OxXrx4\nETaABA/BHnoE4Xh2dhZtXQxpfP3110OePaPJfaKnBHX4DmaGvHjxQp1OJ3pSkQH0nvWhosZJB67P\nsVToPJ8FC4OXfV4De86/wdn4Y2kxeO7o6EjT6aJkGJnwIaTcH0fB4Ktms5keP36sw8PDkB8nGPL5\nvH7yk5+EHcKGStKzZ89ShCiZXGIh5MiTC9gej4eoUEG/PKiXFDbfcR3rm42pWD902/3j7xPXvfSe\nUhgSBJEHcgAlJeVaHnXztxt3Z0AkpZyTZ/M8GMKxAAjZHByKZx14H0HkaDRSt9uN72Zq1507d8IQ\neiDDeU+waNwHgMaZFg/AZ7NZnC/F/SHoZFQ8S+JG3deBOn6MIMLnpS985ubmRsPhUJ9//rkODw91\nfHwcDe3j8TiOSyHIw4FTAuUN5wym4D48wJMUQSuZS7/G2tqa+v2+bm5udHFxkQJvPD/PC+vK86Hw\nAAJAIyATMEPfhaSU88JIw7hTkoIRZojSbLYYKMAY8NPTU3W7XS0tLc7gY8rt1dWVjo+PUyXWz549\n07Nnz0IecdIAzW9961sp4EaQgnGj3IPMcaFQiIyXEw1fBQI9+ERPsqyZZ8FY++x72Vu+P5t1wcG6\nvPIzjDq/u40v1iTbX+F9w5BerCsla2RtyKRxlASZo0ajEUAE5w0g8H5Nt3NXV1fRSyMlIClLvjg4\n4voAIAAgjC+yCZEmKc7R3dzcDBCWzeZJCjLGg2DPvrmjQ2Z84i7BB0y0Z66QQQclAAoCC7IZEEI8\nlwcUzg7TgsB0RohN1qxer2s8Hoe9BdR4nzwvACFygO64znqWE1mCJHBd43OQWZCRLn88N/spLVol\nlpeX4+cAXHyJlAAOwCk+FlkFTF9eXn5paqaDSg/g3G57KTH3DHDk5wQ13DcgHPlmboGDJLKnXIN1\nhKwEICOP+BmCbv9uJ4wISpANMna8Fxvn7UT+3J7lvW2vLLbL+pcstnM8hq7yu+w1fS5DtjLHCQKv\nkuL7kXmwHX4Q/XNi9+bmJgbT4Fs3Nze1s7PzpcBoOp3q8PAwgmPmAJTL5bCF6JfbaMgPBg8RrIOF\nisViVEIhj0zGdnlErgkeIB6lhW2nei6Xy0W/+uHhoZ4+faqjoyMdHBxEexIDHAmA0UEyqRBPrD3P\n4VUIbrPIKNJGgc5guzY2NoIMRDakpAQcnDufz6O1A533agcSPL7OfG82W45Nx/+yTmDjwWAQ2LbV\nagVGpmd4MpnEsX9gwfPz87jeaDTS559/rs8//zxwOfeIHHPmqQeNo9EoVZkDSQLmpA8e3/tVlZA8\npydtpCST6XGTt+a4f+OFXDmuYy2dUOK92DwP1vndb/p6qQgQ440i839n1hyUACD4rDtJFhKHz88Q\naHfofA6hJqL3BebljNbFxUUqwCQwnc1mkQWA8Wm1WnHe0dXVVdTtX1xc6PHjx5FNQ1jn86Q8r16v\nf4mRx+A6AOBzXiLkwSYMIcCG5+M9BGmcG0WpB89LJgADDhj2EmEmv3mmA+CAQQHAAFLYSzcwGBJK\nXXmufD6vTqcjSQGyAMrNZjMFMgAEKIsPwsBg+nUd/GDEUGR3arDbyCkOi+ww68F6E7j2+331ej0d\nHx+r1Wqp0+moUChoc3MzrvP48WM9ePAgSAdfu1xucT7rd7/73ZisWSqVNBgMohQIg0h5kLNm3sOC\nDHg5rw+eweggH04C4eDcEaODrD33630vnoXmOyWlJi67k5DSBNNtejlokNLHYcxmyZnBUjKaX1Kw\nt8hsuVxWr9cLuykl5zi7fXO7CeAHHLuzhkl19hRyhnt0vfWsOPeL3chm2t1xPnnyJDXJFAeMU8QO\nA+wJPLMOFZkF8HFvsPeeVeNZAZ7YUMgcvpfD7tFBJz0pk2KgB07edQdSi3WHfERXfCqsVx0AKLA/\n/A0wodrBdZp1kJJzTbFj2DSu4T7Os0ZeqgY4c7IREtR9sJ+5ir7SL0ZgB6kCOMR/YX+oZIGoZW+p\n+qBnlDVDL7yaCd/hWXQHdT5ExNeRffd14vrYbHz5dDoN3wK550QhsgFmQaYdd2DDsYHsGfcJkXFb\nX1ls56SWky74Ce9BdRvmABnfLyWErRPMThw52e2Ejus25a0EnQSEEIRkhU5OTqLyqd/va319XfV6\nPWwnZaGnp6d69OhRBHCOnfC/fu69E5EO5n2CK3YR+fHee3SN52O9qJAiqAaDssY+RAm9IiPMPRPI\nekn7cDiUlD6GzDNr/Bw9IkDEPoP5yJTzjOgoa1Wr1VJHEkLMSkrdZ7VaTZ2bypE57L1ne91OSIry\nYHSTLOd4vDivNp/PR4BOlpokRL/f18XFhV68eKGlpSV1Op3IrOP/er2enjx5ElUxyCjEQrVa1Q9+\n8IPAblRL+nm1UnKGLXaaZ0WOCUjxE/P5PIJr1yH0zAkicAbktxM7fB59xXZzT66bX4XreM83wXUv\nNSjF4XmE7guIkcBhY+CkpCzQHTTMAkAIB4rwZ7OhbKiXc6EcUjKNkOAxn8/HsS1LS4tz7jhLiGtR\n67+ysqK33norMoCUW5XL5Ti7CpCIMaLmHKFcXV0NBtvPP+UMNFeyXC6X6qUBjMD2UArgGV6YJ0kp\ngfO15Pd8BkCD4aG8CqVzNq5er6vdboeQeg8sDgFnAuhCUZwxv76+1vb2dgQxsKyUvuHMMG44QfaN\nteNoAPYKA0awCxkA2OF+2RtkjXsiGCdIZd8wTIDk+XyuTz75RDs7O/rTP/1T3blzR7u7u3r69Kk+\n+eSTyDDjMJD7RqOht99+O3qVK5VKDLo5Pz8Px0GW1O9xOp0GQAOQOrh03cFJeAkH++Sl8OgUcjif\nL/oJcJroXjZDzwvADJgAqHo26LZmSmElndmWksEbninzgWSe3XHG0rMsk8lEGxsb8W/WFNtGyTx2\ngGoLHDWBF3uKbhIQ4Fy4JvYBYI1NzOVycZQMhJcDGI6swbHm8/kIgLOgCLvvmQ70ivvGptNfDbjg\nGp4tdFafe8KejMdjPX/+PHpbmWa5t7cX+4T/cfLAB5VkM4roCM/BM/AcbpewyZTlQxxJSjHnyIGz\n48gTgG4wGMT61uv1GKxBMMizeDbCgX02i8k+offsP7bes361Wi1KryFSvbwQ+wFA4Z4Y0ueljPgE\n5Ao7jc8oFApR7gjp4n6I58R2cx/YVq8QIIiGUFtZWYkjuejnxqd64Mm+oJf4AOQZ+XUb6FktD1Rv\n2yuL7aSkr94TDFQhOQHl5Itnoh3vuG8FN6CP7Dl4zoNV9h0CCFICu+uZybOzswgqnNxeXV3VnTt3\nQm6RgUJh0UMItgPvIeOQaOgc/Zv4fIhChiqB2dwGYyORP2wM9pagxIlzAuqzs7M4q5PhPuAXSDc+\n50kRP/LDs8qFQkGtVismFnt7Fzrh2V4Pusvlsl599dWw13wfCSJwgpMM2Bs+z5qznv59YHeIJZ9D\ngHwik+B2D9awHVTC4UewTcjr0dGRJOmHP/yhXn31Vb3yyiu6vLzUxx9/rJOTkzj2xs+NLZVK+tGP\nfhT+Ert3cnISeH08Hms4HEZCx+0F9+JZaW+lIAbwLLPbGvwxPgN7zP3hv7NtD34Nx2nsgfuS3wXX\nvVQE6I4Kw5XL5UKRnd1xJiz7fs8uObsL6OB9znLiWN0Q8nNfTNgSfu8pc8o0lpaWNBgMInNGNmt1\ndVXtdjs1SIfv/8UvfqFcLqdWqxXXotzU0+vuEBEy/rjS8aLcwoEk5RMovbOW8/mikR4FILj1YKPf\n74fRRxEAGmTwKLmVFkEbPZYA03w+H46e+8QYYVx4fpjDVqule/fuaTqd6t69e1pbW4t1KhQKEXRm\nS+LYe9bPy23ZO/bXy/3m83kARc9iA1Y5n2swGAQQ5zkuLy/DMZydnQXTiuGaz+f65S9/qXa7rZ2d\nHX3yySf64IMPwgF59hbneO/evVSWZzwe6+TkJFW6wuecfcexeGYL2WYvpOTcNi+DcgeIDHqWJgsu\nPbP8VX+8fATD5sEZMsCLPbytL3TY+x+lJHh3x4yDofIBR+EZOlhXdID34Qhns1nIrZcWzedJ6ed4\nnAy74TMwtx5o+Z5hf7Ah6OVXlSx6aaTrt8sSoMiDHxw/Ou5BB7oJ+QRBgv33LLz3NvpAHXyKZxpW\nV1fVaDRUKBS0v78f4BibS7UMvgYdl5QK/Pg+D6IJjHhm7CwgI+sDnPhDz/gZug7Rg+xgtxhYB1ih\ntw3/yTqRnZQU30VQ69ODPcNIcMA9AzYZ/OdgCBbes1x+3xAgAGz3B4AtbBLZU54XffBMvk/ZBVxP\nJpMICrCHnimlXxYfRBYc3YHMIQOBLgHCvLeUYXaz2Uz9fj9AHVkcyr3dNt/G12+C7dBBx3ZS4gOQ\nE8d26Bxr5wQB/8YuenUANoO9kRRl5q5vBETYk1KppOPj4yDAyaitra2p0WiEnEDyXFxc6Ne//rXa\n7XZkCSHEkTmyUNhDfDB/IKWoBtvf30/55lqtFvpFsEOFiScKOLnh0aNHce47E4G9lYI18gyzE3hk\nfXmGdrsd++MYjD9UzeBLfCAefmNra0t37tyJM163t7fDLjEdmFYVz7KxBth4fEc+n1ej0Yj7Zg19\ndkKpVIoA1bPEyMVkMtHJyUmKVMU3cKyhr+v5+bl6vZ6Gw6FGo5E6nY4uLi70zjvvxHXcP4I/7927\nF/uHXJKdZQ4Ae+FVSJPJJHBlthLKq2Gyve7ch2MxbLSTbFnSMIvl+Fk2C5rFdZJ+J1z3GweluVwu\nn8vl/i2Xy/1fX/y/lcvl/i6Xy/0ql8v937lcrmHv/c+5XO7TXC73SS6X+x++7pqesZQSxWDjWSwc\nDn97uZkHiw4ApHS/FgbBHbArIc4Tdg8wRO0838WQHYQDoM41AQ0Y1Hv37kVZJo6v3W5rNBrp3Xff\n1fLycoyadvYOI1Sv1+P/GCwMNN9DFoKyDQeAMGsIr98f64sg8Ww8D88tJeVm9Ar5xE0fpgOQpRYf\nEOdHuXjZIUabbA0GolKpaH19PZzA5eVlZI6bzWacrYUDYl89u+cAwjPI9B0BIGE82X8MKoDCQS9O\nNTulFOBLWS2ZTUp5CC5Go5GePn2qDz/8MMUowWZi8F555RV95zvfiUb/paUldbtdnZ+fx5ECADb2\nxA0dwM3lnb9Ho1GU9LjBdpZZSgaBEfwC4pAbHDyyyX6gZ+ijB0I4DByWAxUPmF/m6w9l61hDMv3o\nHeWW6C1OhgwX5Y0EqziXpaXkPLtut5siML64N0mLQTXonjuQi4uLCF4AYOgrQ0QAL9gOL48E8FNy\nT0ACKYKDu76+jj4t1oB/Y8sJ2vjjrCyMMgAVYIJtozyWkv3JZBIZ2Gxw5UEd616pVLS9vR1ZtOfP\nn0tSTMf0jC2ZWQeW3BP6w3M5YeN+BBsFA57NsqFPTgxBQnjJLaw64InAns/QkkEpmpToPzbeM4gA\nJ+6t2WyGTWFPXcf5DDaD3/kasxbcKwEv+0LrQ61WC5tN5RL2gHVC3jwbkK1IcSKAeysUChoMBqlq\nFs+UMgCGEmR8EpUC7l+wq2CMrB92u+w2kD32Y22+ql3o/+vXH8LWSV/GduyPY7svrvklu5DNxKN7\nklJyC+nEtVhr9Iy9A1vx/egVgQjfMxgMNBqNwp5w3wTCBJ6QF7u7u5EQYB5Ip9NRr9fTBx98oO3t\n7SgDd7LO8RJZufF4MTAN2aeKpF6v6+zsLHTYK26kZNo61+VaYB63Q91uN4YTUZLKOmEvkFX0nedm\nKjkYz+Wbdc0GNmRFkQPKedvttra2ttTv97Wzs5MqbW61WpFZBRM6hgfPsW8kRMgsSopMtyeYeHkw\nisx4dRFBNhlq7BHk3tnZWRBS2M5cLhcTev/mb/4mWjfy+cXxQZ1OJzDv2tqavve97wU2XlpaUr/f\n18nJifr9fsQY7A24ANtCsO1412MZr1zExuDzwWLYedfVbIk413PZdVKR9/JcX4XrPPj/Q2VK/5Ok\n+/b//13SP8zn8zcl/T+S/vMXi/VHkv5C0nck/Y+S/kvOEYa9eFAExDcZYecB+Z07JISOBaZe3zfK\nGW1YXX6Hw/JsEhuX7TUBSHj5AuVDsCUEqmzq1dWV1tbWUk7Vnd/z58/1L//yL9ra2tLGxkZkRJgU\nW6/X1e/3o+a/UCjEAcqNRiPFsOPcMUoIZbPZjO8keGKNPBOB8aFGn9IPv28YKgJ377et1+uazWbB\nFsFg+bhoglVq7DnQeXV1Vevr68FmwzrhVHhGmDYCHLKagCEORyd4AgjUarUod221WilmDyM3m82i\n/KzVaklaDP9oNBoBTgA6ZC7K5XKcSUqGCwIC8AoTWygU1O129dFHH+mv//qv1e12AyjCWCE/tVpN\nP/7xjyPwL5VKUdrhg1kkRRk3QBCw6AQMeoaeLC8vh+PF+XiVAYALUgZjRIZPSgYbZeXaSwv57myW\nCEeKvnnm4D9I9uD3bus8m488AJi8JAuwgCPHWUsKssRLYmgjAHAjL8gIOg/pIiVHUeXz+QhYWX8q\nIrge90uJuWe5yUT1+/2wK1QecKwR+yylJ/M5eYFcQ8hwfTJbyCGgiP+jy3xHFrCS9VheXo4193V3\nkg8SibXBHnyxz6nMn5eiZX2WlAx8cPIUW+ogFGCFPaGMDp0CoLMmPkGSn9O7yb24+BEMZolS1ozA\nENvmQA3iFV8JSCOLyPNDRPCM6HSxuDgU3skG1t6HpbBm+Awy/6y1B4jID7IvJcSm+zWele/hWvyN\n3GEPHR9Iigwy5Eej0QhSlzYJl2GXGdYaHwzZ6BkJz5r9B3j93m2d9GVsh7xISasVus0++xohL+g0\n5dVgD5cDyAwy73yfBy8u/2ApdJD3Q8ohg/RZY0d9v8AoYD30H725f/++PvzwQ+3t7aWGXoIba7Va\n+FLkYTAYaHl5WY1GQ9VqNQj+VqsVdoFydV/TLFnFuvN7qrsIUFlb74MmoABPsF6tVivei/9nHRn4\n48QAe8JZpO12OwbxgeWYu0IQCQZDn/Ad2D16drn3LMFK+TN+BhnDTq+urkZCg7klKysrcV9gL8d4\nzWYznrlQSAZHUl5NAF8oFDQcDtXtdvX++++nJkLjC1nTYrGoH/3oR0GeLi0txfEz+DpIkPE4aZeT\nFGXUYCTuU0pIS0gWb4EB1/n6IINuq/Hl7K3/m+t57ERgD0mHb+D6HnP8trjuNwpKc7ncK5L+J0n/\n1X78v0j6b1/8+79J+l+/+Pf/LOn/nM/nk/l8/kjSp5J+8lXX9TIYzzgSMOJAYXYRAjdwLAxgyuu+\ncYD83xlnFADD55lDFhwWidIxgKUzvX5Y+WQyicllUnIUyebmZgg5LBQlYg8fPtRPf/rTlMFot9vh\npGFoBoNBsE0EPpQH+707a8/POTcTNs17EADB9LGxrjhWWDSGWbgz9QCbfZGUKgWcTBY9bax/tVqN\nkjUUAnYIRcSQSdKLFy8ioCPQdVDFMzp7Q6mXpFjLVqulq6urKKXFmXimt9frBYhBLjudjnK5XADU\n4+PjME7s+9rammq1Whw6vbKyonq9nmLOK5WKtra2gjlC9nEGvk7f+9731Gg0NBgMVK/XVSgU1Gw2\nNZstBmo5a4jh8WfC0bJGnpEjwPChMoAC1oP9RwcogeR32WCSNSdIQl89E+pZQBw8wMGzRy87U/qH\nsnWSoowPeceWkFH39WSdPEOADrJ/TPL+wQ9+EPLPfo3H4zjrTEpAP84d4EHA62AP+4JMYDdxTIBw\nsua5XC6ObLq+vtbm5mYMPcAmwPoXi0W99tprQfoB5rCzfoyV91MB0MjWQnZ4tYQHup4RIxOGPefZ\nIMHu3LkTpVnX19dxLiekI+X0+BL6QAHDvs4QZdhT9pTeTp+0yz4DSgABlPV+IY8hBzDr6Cn34a0C\nFxcXcX2IPde7fD4fQR+2jr70r8quQOoRUANQJKUG8AG0ISYhurB1ECUQLfgej2vw+0wBZd3p68Q/\nYlfd7nuFE8+G/QMkehYBG42MMyW62WxqMpnE0BLPFknJRFCO9PEMHPc0nU7j2I3BYBBrwNoih1zz\nZb3+kLYO/4ZNd1DqJA46TtBGEAD+wA879sCmoAtOlkKAeIabgAe5dbIN28O9It/4YkgP+tY9GCkU\nCup0OnHsG76Wdol33nlHv/jFL+Le/fxzvgd7jh1hGmulUtGdO3d0cHAQtopnB4vyXNhR5J0ZE1dX\nV2FLCFL5brL2BGQQmmAygpvhcKhGoxH2YD5PTpjwI7E8geCVfZPJJKa8F4tFtdvtqOp5/PhxYEwP\nYiANsZnoL9/txwpWq1V1u93UWaRkcweDgcrlclTpSYlfbbfbEfhWq9UUeUtWmgREo9FQrVaLs1ux\n1eg29vHRo0chW0yjH41GOj091Wg00t27d3Xv3r04ix5SdD6fx7wF5BP7Cdak8gQZcFyHvCNHtOs5\nCYutwVZhg7ytA+yB/+APuM4Jacd1YFr2znEd9/eHyJT+H5L+N0l+AurmfD4//OJBDyRtfPHzXUlP\n7X3Pv/jZl7/8C4NAXTcKh3Lw8M7CsChS0nPg6WnS677QPrDGa+aldHmGOzX+jcIXCouSNhSZJnjO\nMLq6ugrBajabAVpubm60t7cXwStGjkBDkj788EP9+te/1traWqrPZj6fq9VqqdlsRtqeUdX0N7Tb\n7WAoyK5iDDA4OENABBllACYEgLQo4UWJ5vN5qtyI++WMQGe7yfZQGsv1l5eX457oLQC0UaLibDgs\nIQ7jyZMnWl1djcAZ+SCIY19xZvQ/oCSM04b9qlar8cwwaEtLS5Gt9QmQfm0PrEajxZhuKRkUwu9x\nQBAnOAPKNH72s5+p3+9/qfQZcuD111/X5uamXrx4oZWVFfV6PZ2cnOjo6Eij0SiOxgHU0RSPIc7l\nclH2Qiab7D2DCP5f7t6sN670Std8d0wkg4yJwVmUlBoqJ6fTLlf6FAoF3/R008C5PjeNPtfnJ/Qf\n6P4bhb5oVN91N9AFNGoyXC6j2mWkLWfaqRyklCiJQzAGBmcyGLsvQs+Kd+9Uumyn3SowgERSZMSO\nvb9vDe961/DxXg96PMMgTXu1CVjdILmxg80kU0FfNX8nqPGScN6HfqCXyOxrfv1RbB3BEM4ZQ54k\nScg7w1Fefk8G1MP6UtLP2heLRT1+/DhTTgi4dlLBnQZgj3JySWFPLi8vozyc/aAUFnvilRmXl5eZ\nibXFYlFbW1vRX0pA2O1242zjTqejw8PD0FcAfr5fFruBfcDuENR4ZiJPpknT4W0epDohORwO1ev1\ntLe3F9N7Z2dntb+/r1Jp0hKB3tA3xPcBCnu9XpQmj8fjmGTebrclKfrOIBfr9XrmWC/kwgnQer0e\ne0lgTGkWTh8QQmsBQRX/zc3NxYwAQD8VKoeHhxmw4VkTbAX2C4BGBQZySZUI/cxU8LBugFbef3Fx\noeXl5fDtZLE8i813sk5c29sEuCZBBfLsrQze299oNHR4eBj+l7XGdiKz/A3bzIwFZJ+AgYAYe+YE\nHFkXSEbwAPdZrVYDsPOMr/n1R7F10rT1A4LDsR0vz9rnM6DuY5BVyuadOMUeeNAJpmNPIeKxrewV\nAYik6BlEh9FjZAFcQEUHMnbr1q3AbPR5J0mitbU1JUmif/iHf9De3l4kIfCf6LmfKMD3rq+vR2bv\n+Pg49BhC0UspwXasqw/E9KCbyhlIK2Tf+zx9gA4DyyDVwTBUhHU6HUkTvTg/P4/eT+wNROvFxUXg\n15WVldCP4+NjLS8v6/DwUP1+PwJH1oHJ3twH9olyYHwTOJNnoXe2VqtFML26upppe/EKL09O4C8P\nDg7Cf7EmBM8+hwGfNB6Ptbu7q263m/FLXPf4+FiLi4t68803tbW1pWKxGJni7e3tKK3mGZywwP8U\nCoXwsxBt/N9jCmTBA3nkxBNVXrno73Hcy/qWy+XwefhOSRlcx3vBdayDVwT9tq9/MyhNkuS/l7Sb\npunPJf2mcXHpb/jbK18EMrAsGBnYYUowWTQAnZRdEP88Agtwc8OI8/FSRc8wwDTByGLknOWlLh5H\nS2kFk3jPzs7U7XZj4AFszzvvvKNbt25lAixJMfH1pz/9qR4+fBifg13wHozT09MAanye4Abg1el0\nQnh4Pow0DtZLsAiyADk4CGkKcDHsGFXuGUfC4e4INYZNmgY7jNZmjzwj6e/FKHW7XfV6vXDwrVYr\ngA/vYe8wQtKEDWeYBHJxfHwcmVeMGr2f7CPT6FB8jJFP1aTXFXYJ9ooAj2AAAFSpVDJA5uTkRM+f\nP4/AjQmnGKDNzU29//77wYT6ETHsda1W02AwCMPlZRysq59/S4aHvUSWWRsPftzwAwzQP8Ckl6YD\nPDBu7K0HSIAOWG2eCXmEGPHs3et6/TFtHc6Enk1pWtLLsUoOymBuCf6wX5Ki58SrOABdAHMHJbyY\nAs7Eahw/ezAcDlUsTnreASDcNzaTo63Q+1qtpuPjYy0tLUUABZCAafZKlaOjo8wwISZAOslE4MFE\nXSdEkHWCOK7P+kCIwXrzb0AIDhuW2ktEWX9K69ALyIJ8GSrPQaYAwm9ubk79fl8LCwtBkvL86CY9\nsoA4AC2+gYCUI7rQbQd8+CHWHQIM/SSLgT1iAJtnwPMtEYB85MJ7SKXpObEAOB/e52V/flwWLSQH\nBweZ8q+5ubnQBwhYqgBOT0/V7/djwBxErR87JimCbYLrJEkTodVVAAAgAElEQVSiL21mZiYyKIA9\n9hQswDOimxC9/M1xBQQn9tBBm5dzY7/BEYeHh1EO6plmWjBex+uPaeukKbj+OmwHIQa2Q94kZfyT\nV+egv5BYTk7h1zyjzfuRScd24EdKHsvlcugu12DPKOM9P59Mu/fTEEqlkt58801tbm4GEYQt4Nzy\nf/mXf8k8B/qFXUOXvGIG4pkqDKqknAACQ3jlA4NyfKAU60+A4y0EEFVko1lbqrvoid3d3Y19g4A/\nOzsLfIudJOCRpudoY28Gg0GUEu/t7anZbEYwj86Ap/AhZD4pu2Xt+E4SNAsLCxH0cv9U8fV6vVin\ny8tLNZvNwH/YdVq2rq6uogKIYIpqONdpMqZgn88++yzWHptCMNdsNvXnf/7nsUeQbcg8LW2Hh4eR\nOcefI/ezs7ORcfaMNPfjFSPIvFePsP+ssyftkCX0ED9DksVJHw9sHQ8SlxSLxZAPru0x3G/z+m0y\npX8p6T8mSfJI0v8m6b9KkuR/lbSTJMnqS6Fbk7T38v3PJd20z2++/N1Xv/wlOPUaeZwlgY0DGxaa\n9+IoPWOKoNAc7hvCwrBYAGrPkOaHT3iAAXjGYTEumixioVDQ/v6+dnZ2IgCDmSoWJ/X51Wr1K2ff\nwUZ99NFH6nQ6wYwAIHhOnBqDT0qlUvwMmwQb64wQ7DzPyHOm6XQimqfaAcPu6NvtdlyPgNbP2gLg\n4gggAy4uLtRoNML4AYgdkAMKUHCC5WfPnmllZUUHBwfa2tqKwOfy8lL9fj8Mj6RMLT9GDOfWaDTC\n8KHMPhgA5aR3aH5+PrKrrBVKSgDOUTcYLrLgkrS6uhrBb6VS0dramq6urvTo0aOM4SqXyxoMBuFk\nbt++HXsiKQbEwKCS4UAvvDzS2SiCfAwCGQb2Db3A+PDCmPkzAbw8m4MOoL+sjbPAXpZH0AOrx3ci\nH+igkxmv6fVHs3XIPs/abDYzZ5JR/kmwgx3EAeIsCVakKVlGjxT7ViwW1ev1MjaU0lEvbcPeIiM4\nUsrYAZXYaJwLk7lhwgEfzjpDQjlxglwg0zyDDxCDkENHy+VyZDEpMUP2IIcIGtFnd9JeboyMQugR\nxBGo02MEEHT5JMiTpkfBYIPo+QcwQmphb/ARTh5BonlVkKSw3dLE3hMEUrrN92N7ANCARGww3+NT\njVlvWHdAJlkjfJ1X9fCdBBRcJ1+xgp8FEFar1WDXITucsPTySmliv3Z2dsIWE+wRmAN2vbyOHjh6\nWSE/CPjJai0tLWUGhhAQ4KvIOGDL2AsIykqlEkNwyNajr2530ZVKpRJHPSAzPhwL+/+aM6V/NFsn\nfT2288SCZ8E9+8w6uU/gc+y9Yz56dl/ec+gCeuDYzktTwXSQsciGpMy545Sl7+3tqdPpBNYhI4Tt\noHoA24BevXjxQj/+8Y+DsAU3gbOwiZIim0lATHAKAVcul7WyspKxJcgcFUvY7tPT07Cj4OVWqxX7\nQkDJZwhMKEel8m0wGITeYW98gBpr4QQAmBache5AfmJ3eDZJYU8h7fCN+C3um/1DbvCP+Eh6VWkV\nwH+wH5T18jn8Vrvdjt9xvCLY6fj4WDdv3oznnpub09LSksrlsra3t7W7uxvBHfK6u7ury8tL3bx5\nM1PRCEEHrvMBgWB69otKOvA+QSoJPZ8v4NUvBJIeP3BfrLeTRB7bYOch4/y6js/AElQKQDJ59asT\nyb/t698MStM0/Z/SNL2VpuldSf9J0t+nafo/SPq/JP3nl2/7HyX9Hy9//j8l/ackSSpJktyRdF/S\n//uqa/PwGCAE29PxsLIssJd0YLwcGOMsXThgiHAkzqDx8uEDZCapW0f4yRj6whMoEWwwjAIWlODh\n6upKm5ub0X/grDiMx/Pnz/XJJ58Ee0PWD8Gg/pwgst/vZwwu90wKPU3TKMkD7CwtLalQKETpHSw7\nTgFmzZlFHAdZXPqPfA8vLi60v78f2Q1J4SzW1taCjaLkDKbcyyEIxiVFUMi1CcB6vV4wiDCMXlKI\n0mG8ONpie3s7Gt2Rm7wc8ux8jn8jO1yXfSU4AOQ4kF9cXNT6+ro2NjY0Ho/14MEDPXnyRNIEVGL8\nuJ979+5pbW1N0vQIjeFwGOwiTCTOi2nLyCfg250MQBHd4F6ZMOfZUgIgZI1r8rwASJwB78VReJCD\nvjqbTWkgvXbIMPcpTUtAXtfrj2nrcBSsr+uuZ+zr9Xqm1wy2U5oeYeDVDMvLyxkHJU2HTXiGgO8l\nSPQA4+Wzh6xgs2Cn+SyTepkSjQyzpwB4nKQ07S/nXkajUYA6gBnADwACSMROOjDjXrGxyBuZOQhH\nSmrRS2SQ7Ch2giCKydYnJyfq9XoxxCRf2QGwwx7yN8pGeQaekc+QsWNdPIPBNTzLBuM8HA7DHrn8\neKkrDLvvcaFQyPRIoWf4r8FgIGl6bAoAExAJeUc2l312neUenUyem5sLn4Iu4ysA/Xnww/2hIwyW\n86oL7DvBBmd183zoiJMI+JrBYBBEg+MNbBj7RnDLvQMI+U58AD4JP1ksTo8aAqNgj32CKNlwACfE\nzOt4/TFtnfRqbIfsSNOpnY7tnJxBdtyOeckm152dnY0eQ8eC7EVed53EwWahh44/qJzCtqCTlGVj\nO9GfmzdvqtlsZki+o6OjIMy++OIL/fKXvwyfTT87BIXjCvoQsaW1Wk3z8/OZKeesA0Te3NxcBM8k\nRLDXflSTZ62lqV9ynWAfWMuzs7PIAFP9wP5gJyDgarVaBtcVi8XMUEYCNyfFC4VCVFFQGQTpxNGK\nXlXgGTpmAKCPZHvRMewxJB62X1IGs0ECECxjsxnCJ02C5mazqc3NTa2vr6tQKARmh5z0gLBSqWh1\ndVXvvfde2EaCYk66SJLs2drYCO6de8GugO2kKaHJzxCYkKb+zKw1NstLwL3yBb+C7vB+x3XECm4f\nvcXjm+K6bzJV5H+R9N8mSfJQ0n/98t9K0/RXkv53TSa6/d+S/kv6NblbygY8mveeKkABDwcD5kqE\n42JzKKnw8i6YFowOhoeF92CTzSWoAWi54/MMEueYYoR6vV4oHRtFJmE8HuvWrVuq1+uhHJQrUDL3\n4sULff7559HfgJOmdwiB5RlZC4QaRfOpu7AvlUolc74mIIhgnfUjU0NmkJJjsogcJg2AoUyCfSQb\ndnBwECXNfCcslysG+8zr9PRUBwcH2tzcjGDX97TT6cQh8c4S8h2UebCfMHKLi4uhdBhzMihcGxCE\nQvMeSnHJRvsgEj86ADlaW1sLB/TZZ5/pyZMnwdR5z+f5+bnu3bun//AfJjMjGLCAY4I1pNwEw41z\nwbknyfS8RowSoIv1xSEfHh6GQWMf3OljDAEEHtRQTo0xcqPoRg/HhazyHnQJuSPQ9XKrf4evb2zr\n2BPWi8ACAIM80s/p9g3QhqzTayJNM1ZeJTEajaKk1I+4wJaS8QRQ0dPqGWz0DXAPaQNIgbQBiJyf\nn2t7e1vj8TjAkzQFIlSeuCPECc/Ozurw8DCmIqK3V1dXQRAC3GDfWRNAEEEdmTKCFnfo6DelhTDO\nyLiz0d5Lz16wbhxo7lU87CcAABZZUqY30ttJCDLdhnBv3A/PzjUrlUpUgXg5IiCb93sZt2cWKPUD\nwCAP2GRI1tnZ2Vh7QKwHAx4weqWE+1yyzW6bPRDnP3ws601/GXYZeQGU8fIA3UGPgzovl2cdvEIA\nXeIZAVv4TreD+HOfWIxPAw8AZHlOKgnYJ3Tfcc2/s9c3tnXSq7GdPz/ZQtYAn+eZZ8/M4c+c7GS9\nyUw7TpSmU7PRC/YewsB1C5LW5xz4eeO0ZzEci+ABmZmdndXGxkYMuKHsl6ApSRI9fvxYz549y9hA\nbAj/xh54exBkJD7W+z3df2DvkH0fUob9x744qYRuUZbMv7H3JFzIGEoTDHHr1q3Q95mZmQgspWmr\nE9lmaXpMFT6JCgiwAH2W2HfHG+ggmBXykjXIV3S5TXBM5GQZ8kFbG7iXoJp9Yd2SJIljCpMk0d7e\nnj755JPIlvP5NJ0MLlpaWtJf/uVfqlicDkUC1/EMEAzImSdGWEuGEYKvkBHHAGmaxn55Ag87jb1y\nnJXHdcQ1kHD44VfhOk8eOq5DHr4JrvudgtI0TX+Ypul/fPlzL03T/yZN07fSNP3v0jQd2Pv+5zRN\n76dp+k6apv/P110PA8ADkwZGEPgZgMLCAnYJPNgkFMkjfNhUACEsEP/3gBZBcOGl1wSjiBMEHMAg\n4NgxwqVSSZ1OJxgSn9a6sbGRKQEisED4Hj58qF6vp6urqwgu2XgCAcpnuRcACv95nyPr5QH54uJi\nGHkYYn7mSBqEjr4mlJ9+DAclXAM2W1L0jVQqFS0sLMQgJkqX2Ldut5thLmHF6APg3lAuspQQGLB7\nZBgBexhsSoTp8UCWUEKezUsUeZHNmZ2dzfSy0CvKtQCMXgaWJIk+/PBD/eu//muUf6GsZFLeeust\nffDBB5HtYOouB2aT1Z6bm9P+/n5mSrEHlhhe7jkPHpFdZ7GcOXU98lIOD2Q8u8qaA+4hYlg7XwO/\nF66H7vhnuPd/D68/tK3DPrz8TKwlRtvZZmnahyMp+jrYQwLFYnHaQ+RAA3tA/48TdpJCXpEdL4fF\n2Xn5Df3VDvzJrEkK3cexDQaDTNDBcwM2YJKd8R2Px3GAOKQRjgx7iu3FNwC8kG/AC5/zoAfmnmfn\nefJgq1qtxkA0bCU+xEuACdI5/5JMGdnEq6ur6HHCZrvfYS0gHSgXozeHoJe982oQ1zGux2fSNI0S\nQkAExCb3jN8DjOHv+D4AY57dxg9L0+m22EdsD1lKAly3P3lCBr+HL3Fb4gFnmqYREPrzA46wwQTu\n7BcVQmTrPXvi9g0CkvvjPcgL14DQ5PnRJ3SB52Fd3R8RTHmPLt/3ul9/aFsnvRrbQdI4Qcr+oc8E\nT6/CdgQGLj9eHfHyHkM+8PEO8tl3dAnyyXsBaWPwgBpcQLao0+loPJ6Wv1N1trKyEmXn+ORicVLl\ndHZ2pk8//TTOscSuegZSUpTSl0qlIAxJbLBerBH2wzGrn8jAcTbYgkqlEsf3IeP8TVKchYp/gjgn\n8cDfVldXlaZpJFgYnsmsgGKxGBPN2Ys0TbW3txdBH8fUYI/QffTfs8fePoAdJagdDoeZ7Cu+EBvg\npJSTVeA1Wtuwf9gy/u7VP+j/l19+qR//+Md68eJFBjOOx+MY4vSDH/xA0iTDSpXg9vZ2VP0xb4Dh\npWQsnVSBmPF2CScT2XuvuPHqKvenPD9/Z9/Za8+UgkXwe3k86WQt9swTTd8E173W8xcQOs/SsICe\n/XIABegA/PN7SZmsH8ZHmgJkDzj5Hg9MUWTuzdlZ2BIWl3ItNobe0fF4HFN55+fn1e/3dXx8nCnD\nYow4ABABBbwUCgU9fPhQe3t7USaHsqC0rBHC4JlcT8EztGZxcTGcK5kNwBEOg89gjPhOjCZM8+Hh\nYQzsYB8kBStMWdvx8XEAXBg8SrvyGTkCXqYak+3k+Rg04O/HWEIa8KKHya8rSbu7u2EIuGfALCDK\nGUdKf2ERMU6AWX7v4KhSqQTr9bd/+7f62c9+Fj0iGDgc9vr6ut57771Ya85ThRU9Pj5Wp9NRs9lU\np9PJTJ4DtGOIHeggx2R+yuVyGFoH94AqDJVPcfMSKGf5WTNnvzGYsKLosd+LM8Dopmdo8iUf1+2F\nw8WueNCRJEn0SXn5uaRg9svlchAZd+/ejWtdXV1FBQB9j97+ICnaEHBekFvIPAEKZWoEvE545cvE\nAYRk/7Bd3jbBcyO3kGTuvLy8m+zrycmJFhYWQrc8cOaafAf6ICmzduiGpAgM+Sy26PLyMoI0+qsu\nLy9j+JL7I5dd9xkAF7fD5fKkJ9ZH//O8Hohgm7lXfAm2BH0AbPId2B/sPNkaKlkIcD3TyrN41pM1\ncfDP/Xm5LrbAS3pZV2zD3NxclDWzz8iAnzGLL5Om7R2pZe2dqGDtPXOPDBIwcC954MReU42EjHiZ\nLYNtpGnPLWvqw5QIsnmxTt7q4kEN35Uv52Ngk5Mo1/X1KmyHLHoGy7Edev4qbOftJWAU/AoyxPtc\nZx3XIA+eSUI2aRHwTDi2CTtxdXUVPaWSYhAXWGhhYSGwHbpYKpVidgCEza9+9Svt7+9nykmRN/qM\nFxYW4sx4jokpFouZs5N9PgcDugg0eDZpWqGWJ4Y8e4bdJ3MH2YQOuw06Pz9Xv9+PNZWmgbSTZOwN\ndpUKiFarpXJ52n8PbuBkC6ZWU9WH76KHt1AoxDwRMClJDvxFpVKJ6kBwEH4Mn0SVDjEDpKOfacp3\nMDzz9PRUDx480D/+4z9GiTXZbhIyCwsL+v73vx/fWa/XY8owCZz9/f1oLWCNfXgkNgaZ9eCS9XKS\nEvvnwbyvvdt/5Id9499eCePr5bgOfXSdct8I4fRNcN3rPRRQ07MTWVCAhRsymAgWG0XAEfMzgRNO\n0dPvOCKMEkYHwCBlD0l3pXWD6kGBGz/YNwDmaDRSv9/XxcWFHj16FMYRh3Tnzh2trq5GyQrAnrKB\nq6srffrpp9ra2oq+U/oo/exMZyUQKkAEJTLS1IBiwAhGpYnj4LgXyrscGKHwHLdCQOsZO9YNxhgA\n7AEMQS4BGgEge3t2Njlbqt1uR5kMpX3ci7OlDlSvrq7iPDscvpcZYAzcCUEmMIjEg3OuvbKyEk7H\nJyZLiowvRsmB3t///d/rk08+CTYPg00T+/z8vD744IMwsBhRZA6jPDMzOWeL/lLKlFB6svn82wEZ\nACBfIuQBqGfkMOo8mztxfoehY889o+BZBNdTAgCu7wQRhsszttfxhW5K0/JCB9TONHqWmYBvNBpF\nth4nRiYIWaXMHhsKe+3EDE7bHQXOlKwhuu02+FX7DoHijtCDVwd2fI9fl+EXHhQRmEDm8RmyxYAi\n1gCHiW1FprDX6ABOnefGNszPz6vVakUPOwE5w9GcvPFAyDP9njVkb6XphGBegD3AAUGfl8Y6Eee2\nnUAbO+GVKjw/wRTXZE/wCQBnZIfP8P0eYPI7Ai2eBfKENhqXT0Amz8GaQI5QsoZ8MZCIjBCyCXtP\nKayTHDwL13Q9YU9ZcydMIW1dNsiyI3eFwnQiuFcXOZnDdZFxyAxp2ieMXfWAFLCNjXNi9Lq+vg7b\neZbPCQ+XdwIRfnbi1/0Z18pjOy91lLKgO//9Hsx4+SGygh8Go3D0x9HRkV68eJHBqQsLC7p7967a\n7XZgOsh0iDYw4fPnz5Wmk4n02EHOtwVPFIuT0s+5ubmYFUKyge8DN4DVXO69JBd8cnx8HGXAyCz2\nBfuBTmMzx+NxptwW4lCa6hn6QkWZVydw1NLGxkZgU4hNMoRkLLGTYBNIfveRfCdYaHFxMYJ4bCQ2\ny9sJ2HtmJXjFGZVFjil93UajkX7961/rJz/5ibrdbmBNWkWwse+++64ajUZM85ayJxHQRki5siek\n8hlHAmrPiHJPJKgIMMFZHgB6pQJyDtHntjDv37GHeVznCQf/PnBdHuP8PrjutVtFD/icIfA0PS8M\niTNLbBbvR6Bx2s64eYqerBcAwq8BM+tshd8Tn8MZcY2Dg4NwtIPBQL1eT6enp1paWopxzjjQarWq\nGzduRPCFoKGoOMetrS396le/0mg00vr6ut555x2laarl5eUwvrBihcKkP8zrw3HQrDFrQl+C/4dh\n57Mw7ggbQMpL6HAeXqYiTQcYYGg9q9JoNIL14hkODw+jqZ0MSalU0qNHj7S8vByGiRI1lIDskCuR\ng1xXltFopOXl5QjwAWQoNs+IASyXy1FSuL+/H3ID+AWw4CToc/35z3+ujz/+OFPOhdxyjb/4i78I\nY8Z9j0ajjJFK0zSyNl46hMHiXnl29hsdkJTJarNvAE32zskeJxDIpBPUYLDYVy+BdhYWI8Q64YS8\nnNcBAvqX1/fr9HKmGnll/Qh8cHyeRcTGEXQx8ZtggPIsSRHkSVOCwQcE5bMOBLrYXsrUycSxT8iV\nZxlhm91+I+/SZD89C4X9RSZ80AmAz7OrV1dXQZ559oPv41pcnyoUGG3kCB0F1PJsZAKq1apu3ryp\nRqOhVqsVZN3W1lZUKwCaCEqo9uA5qeLwapL80SF8nkCZF/eCbXDCjawaOgip5xVErDtrgO9C5/x7\nWG8nDrDjVJ2QsXDwjrxgeyACAC/uM7xKg+/1oN3LI5FHbAV+Hdlj/73yxytCAESemUQ2nST2gP78\n/DwzfMvlDcLE788zaw6y3E45cc264svpq2KtJWVs7HV+fR22k7J4S5pmOL0NyslN/IP7FmQC0I6c\nEND59Z1Yc/1zbCdNh3GBGfj8cDjM9P+RKfRpr+xru93W6upq6Bx20wccjsdjPX78WF988YUKhYJu\n376tO3fuqFCYTMZGdigbB0e5jfDgWlL4ZbCWk1tOmHtWmrWhQoW1Rb/QSbK0fJ8Pt0vTSXl9s9mM\n/QGvXVxcqNfraTwexykMhUIhBlYyYI7nQk/Ay+5TeG7P5OE/ILAgiVy/2Vve71VB2D4CVLAh8cH8\n/HzYxv39ff3whz+Mc2NJfPmQo+9///t64403AnNBoEGyQkSSPXeM7sEwuN7tmxP5w+EwyAjki/tx\nX4Lt8nW4upoeI5a37/geT+7x3eiz7zskC3vj2BQ9/11x3WsNSllMfyAANosPWPIAUlKACw9eERTY\nVjeGnplw1oEXQu6ZH3d20lePkuE/H75AHT3vHQ6H6na7kfnyTN/q6qpu3boVU9I8kyBNe222t7f1\ni1/8Qh9//LEuLi50586dCM4Ap4XCpFyLhnyemeelCdvXNk3TAGLj8TgMoJe3cC+U+TmA8CDFQWPe\nCVOu6qUmZAed4efAdJSv1+vF8Cj2DuBxeHiYIQ44foBnQ7EIApGZy8vLyKiyxu646A0BDKOYtVot\nhlo5s0ogwUS+Fy9e6PHjxxEs8rzIUJIk+u53vxtBMcALOYGBRRaSJAlQ6uvuDeSAUWTO5dGBIg6F\nANezdwQ/GDQ+632QrB9OCifioMwzq24QAdOANmTBjRf7cR1fXq7HmpK5ehVYcl3Nl9hApMzOzmpu\nbk7b29vRv+jBDxUAfDeMPY7HCRaCG8+0ekYd5+S2k2u4bfXBQTg7vtP1oVKpZHr90DXAGIEr15Cm\nMoqcOmOL7cbhOsmH7OEXCEwAXDdv3oy9GI1GcYQTtolAkuv4cDH2Df8BQQg4gmTg/eisE1rYHYCr\nrxP6zrMTiPJyJh2ikXX0wU34Tw+MPcjLgwc/0op7wwaxl05QcD3klXvyjG6j0cgAGtcDfs96YAeQ\nASdmnHDADrK23vbjYLNYLAYgBMwR8EqKtRmPJ+dBYrvQPTLoHnyyzuwl8kL/HXbfMwoeJF/noNR9\nwm/CdpS3uw/GxrlMImO+n54hddIC+yZNjwvkvU6iSFlbzN5xnz73A7/s5EWv14ujSzjNQJrI0t27\nd3Xnzp3Ya4aGEUQRmHz55Zf68MMP9fnnn6vRaERPKt8DOUPwRLBDFYcnTZxAmZmZUb1eD+INuyRN\nB35JyhBe6HK+Dx38zVAwBss5pkmSJM4BRRd8Vker1Yp7HI1GcYQKPgwcy7wESvshdbx8mD2ERB2N\nRtGr6gSkB1JkSqk0Qh6QQ7Ks4DaSL/jA09NTffTRR5HlTJIkjv2SJvHDG2+8obW1tZB3TsLY2dnR\n1dVVBM58Br8MrnN5xT5iP9gPl29PRjix5rhLmrYvslce7GP3ePZX4TrXWa7H9ake/EPiuteeKXVg\nDDuFwHnZhmd0eHlWCMAM4Hbj5RkaaVryAZDyTfMMAwLgA248+EIxvQQgSRLt7+/HtQqFyRS3wWAQ\nSuws4eLiot5++221Wq1wlmw0DhE27uc//7n++q//Wn/zN3+j3d3dMKCSgh1kQBH3RLlVmqaZMlgy\nBZRrkc1DuPiclymfnZ2p1WrF9Vg3Z7S5F1gwhNePCTg8PAyQQE8orCMKsLe3pydPnmSOECGLgWFg\nH2CcAMI4LMqnkCvYO76fZwJceKaRzyFHKGe5PJm4zHEUaZrGETf9fj8MFwZvfn4+hkeNx2N973vf\n040bN+J7r66u1Ov1tLe3F4aIqX2sDxOMMabOcAHwkTdJsV7oDMANp+TZGy/d8D4AB80OMD1LwDX8\nhf7yfvSCgRC+npIyRvA6Z0qlaQ8dNon/E/hJU4NPMIcdZE9YP15pmsY5xOwZMuxyUqlUMmc+Apg5\nN3l2djZzCDv36/eEk2JvPRPkQcz8/HwAoiRJtLi4GLpCoAiw8moKnhFASibWg14cuZMyBEKsk5dR\nSgoggk0HYEkTsPmzn/1Mw+Ew2iz4TphzHLmXPnF/TkoS7Lnt4L78fgnC/UgpH+ThBAXAjn33DI4T\nVlwHPXZ5wdayf9g6DzJpKRgOhyoUCmHDIDLQUcoMWTt/uezh35w4INOE3QFEcj2AHNlU/JiDaeSR\n7/NyNff1Xk7uIN5Lmn1aKXIFqeMT48le4TMdTyBPeTBHeWW9Xo+sjGfjPCi/ri+eU/rN2M4zMqyr\nV9mwr8gsiQF0hXX1bCov3uuBkgNnBhJxn8gBsgf+gBAhm4is0xbFfXFPnKjwzjvvxCkF4A+vQpmd\nnVWn09GPfvQj/dVf/ZU+/PDDGHLJWtCm5dgOIh7Z83JbfzbIMfy6T8/HnqCfVO25/kKaFgqTslwI\neIKner0eFTGU+TP7AAzjVXmnp6f65S9/GXiQ9USnLy8v1Ww2M4GzB3IejDnm5j0+w4T3Q8556wgE\nBD6MM4k5DcMDviRJ9ODBgzhGS1IkKRj+dOvWLb3//vsZOSRDSnDJGbR8LxVx2CAnranW8GdFd9AX\nfIr7Fq7D/mHTPHnH/8HATnxK07YTJ8bRK7+XPK7zNqFvgutea1Dq6XU3QCg1i+Mgyx2dM2I4D2na\nTwcDwII4I8T7/LtZcA/k3IkD4LkmRgCn6E6JcgiO82BozePHj9Xr9SJQXVpaUq1W0+rqarAsZMlg\nEclgYpQePHigf/qnf9LTp08zoAFGDafOOhwdHanf70moXrMAACAASURBVEe5KA4Xo0C/o7OTGCAm\nbMLE03tFgzVlIZeXl2q322q323E2J+ADgHNwcBDfAUgkeOa5YSNp3GeNuW8cBoEU1wEscx0UTpoe\nc+MsLCSGTyfFCAMqCRgwsICmcrmser0eTOrFxYX29vb00Ucfxb0jD4wCl6TvfOc7un//fub+Dg4O\nNBwO1Wg01O12oxSIDAEBarFYjH6HvK5gUPJlwrCoXAsQiUPBgZIpwbg4eHIWDx3x7IrfDzqV1wWc\nkQdXnqFAN30vrtsLnfGsjqToO2FPpOmREbCrgArs18HBQbDfXk5fr9cztoyMFQ7DnR3lUe5YXVax\nexAeTlD4QCwnfZBFSpPoXcJmIIME28hRvuwUmwc55wQUk26xP+5UYdsZBkJpM4QOjDzPh8wid0zd\nZUAbBNjZ2VlMEodMSJJpb5D3V7JvrA/PyFpL0/PxfOo5euWZVUg/ru2gF5vvWUbW0IkNSRn9xU+y\nngRayFexOJ0hQMBNcAqocyYeu8TvpWlGGLDsQQDySb8w9tKzp/gFr/Lg5d8Fy49do+zYZZEX+sB6\nkLWCcPMqG/elgEtIDHTCM3hOguTnLXjlAdcH6HpFy3V7sR7uW78O2+UDdNcZSRnZR3a93J+9dH/m\nZYfSVJfAInzWdZrvggjC3iGHlIleXk6GPV5cTKb3HhwcaG9vT48ePYoWriRJdPPmTVWr1TgGEHtO\nlh5Cmazm6emp/vmf/1k//elPtb+/H3ICaeXBEus0GAyi59Ur0tDzYrEYWUqCF9obIMtYQ+wHVQFp\nmsagzbW1NS0tLWl9fT2GeDID5OTkRN1uN4g11vrw8DBISK/mun37tnq9XuboETAvR8MgEz7c0k+e\nIIOMfVxeXla73Q75oJLtVboGXsUeYQdLpVL4ANbj4uJCH374ofr9fpCSVCEhQ7du3dL3v//9WLNK\nZXJE0f7+vsrlcgw6RYaxffgDt9noDbpB4OeVh+w9z4HMO67Dt3E//h1e/cJ7kCvHD1KWBEBO8rjO\nicg/BK4r/dtv+eO9EEYekIAR5fEgVJo6OwfAHix66QCOB6fjjJBnCqSpk2IzMWj+HWw6fyNYcobD\n+/swFAyNgIEtFArqdDpqtVpqtVo6PT3VzMyMVldXJU17ABFiFGt+fj6EuVKpaGdnJ/owl5aW1Gq1\notwLo4dDJQiF+cdRzszMxPErCDfGHqMAIE7TSbnxyclJHCJMOQKgo9Pp6K233opzmNrtdigyoI7G\n/HzpKCAP4XWFQ1GPj4+DycRokPVAeebm5uJ9DnoJ6LzWn+xmrVbLGPF8wEYg6hlEvrNUKunhw4eR\nZQHM8nfW7k//9E/1rW99K3opKpXJmbEHBweanZ3V9vZ2gBsMNE6GewEwMVwmz37h2AlCHByjS+iQ\nPwt77g7c1w2AQCDNtRzIAdpgatEPdGVubi6cHfrpei1Nz7S8ji/k2Ekkr/DAeVOVAQCSpgFimqaZ\nCX2QZ71eT9KUoUUuvZwM2eE+ILo86EX+88yqBzsAGcrQ+AwZWKZPS4pMEQGYD3ch+IKdBhTy3NgN\nrwzARuMEAUHYPcrL+v1+ZI4h3AjKCb6Qe6pLsBtLS0va29v7SkDpfkialqz58QEw0z6t3UGaAxZp\nqqdJMhlaxzRHwKsPouP3Th7hy9gPMk+SImj3abnYMWwTdpu9Yi9pjfAsJOsI4GV/3Dd6III9whZQ\n+sx944f4bm/FYd2Pjo4yNhy54TMLCwuZjCaAkfVmfXguL1/Hp/I+7DYBE/vPehNoEHh60ON9wdi/\n2dnZmNAPUON3aZpGEHFdX47tPLjzwSwuL/4+ZECatguwT9g+gg/0WVL4NNqD/F6Qu3yfJ36Pa2Gj\nkSGXJ+6Hs8TpO8TuVCoVPXv2TJubm/H7paWlsKmPHj3S1dXkiDoCH4huqrfG47E+//xz9Xo9vfHG\nG4ETyTY6jsUuemXf2dlZEPAHBweZ50cPKVMnAOSIGGmiR0tLSxoMBqFfDCVaXl7Wzs5OVPORGfXs\nnROVVLpgB/ELEAHYv+Pj47BTkD7geXTSMQqygt0oFosxuRc5GA6HcUYr5BM2FIKYo2nAwjwXFYEv\nXryIgFRSVD04Zrpz546++93vRsUf1XfMF2G4Wb/fjyBOmh6jhizOzMxEa5jjI2QfnIAPQDYJXl1O\nScY5SYh9h8DxjDm6hI9CByEouBfXPUlR7eMl738IXPfag1JnAAAcABMv/+BBETAvxZKmBsmNDaOY\ncULu2ClnctaYqJ7NQdG4T8+yStk+VAeE/gzn5+cx2bbX60Wp7MXFhfr9vtbW1lQoFKIxfjQaheIf\nHBxkmser1Wpk8gqFyfCd09PTOEaFINCPNkGYWas8o8h0W2nKUDogZkgRz0iWxhUEg87+HR4eRh8o\nbGKaTqbMbW9vx1AgQBqGmjX1vjKMLgOHMBxMawSM8Yw4JRrQ6S9zgMh99Xq9DPCQFNkcHCaMppe0\nYGjn5+eD1WRPrq6uIkPE+r377ru6ceOGnj59qsXFRc3OzmpnZycAULfbDdBGQI2MenlePrOAQXKZ\nZT+QZUAqa+M9Wc5esw7okusVugXbyecxhv55Z69ZDy+TZq0xmnyPZ/euY1kbDgByhmfFnnk21MGq\nl9gg57Ozs5lzNSGHADgEgW5bCUIAYF6G6KAxD/bRUe4Zp0TJOqVa3DtynySTEsb19XVtb2+H00R/\nCGqpWiCoxPl59h0Qz/0jU+6Ar66uogQYUIOdctvntoosxWAw0NnZmer1uhYXF+McQfaMe5YUskx5\nqaTMGhOgUF1DNsR7wtzW8GwEYAzWIKME4EMn8jrr4IV1IZs8Hk977/Fz2G0yx86qeyCAbXd7hOwQ\njPNZ9oyqFWfb3V/CmHOcA/aIQM4n8/JMHI1BFYCX4EHUOHHGfgG88MeUJ3NONTYMG8U9k7ECH+A7\nnEzjusg5oJZAAVKae3Ps4LrrJMd1ezlwl16N7XixDhALr8rmexYGLOTTnrEP+F8+z9/xYdLUpyHv\nToggy8gH8uxAHHAOuVKpVLS/v69qtaq5ubko08QWLC8vx897e3tRyskU/2JxcvoBPv309FTPnz/X\nycmJ1tbWtLm5qXa7HXYMW0ullesneuTr48FBo9HQycmJBoNB3Ds2m/3yABe59hapJJmcf9poNMK2\n4SP6/X6m3xV/5uQl+wRJc3V1pd3d3ZgcDGGDrmDLqHjz0yhIvlAuPzMzo8ePH0dm1VvCqGyBWMUu\n+n6ORiPV63U9ePBAn376qY6OjqKE2Y/2mZmZ0f3793X//v3AabVaTd1uN6qPLi8vI4EAZiX5wlq7\nv8c+OvHnwSV7in9jz9AtD9bxodhlJ8H5OY/rPAhmTfKkkNt6ZA0fhL7yHnT1d8V1rzUoxSnxwtEW\ni8Vgu7ykyZ0mi0VQwmZyzUqlEkyRv9fLMTBYsCbSNNtAtgsnh6HyYNjZbN5HkMVzoawoAo6TcofR\naKRarabRaKSFhQXdv38/gsxicTJli74HDBvZlIWFBZ2fn2tnZ0enp6fqdDpaX19Xs9kM4IlBoKQU\nAwNTTWkqAZg7/jSdTH/FKFBC7L1IHBPj2cxOpxPgxwOjcnlyUPHBwYFarVbsj09ExGERHLKPnklp\nNpsxqATQQFkiTfIYID4DCHalX1xcjDIGV+JCoaBarfaVcgkATrlc1rNnz/R3f/d3XwnwnTGv1+v6\n7ne/q3a7rZOTkwDJvV5Pg8EgSA3O5eIF0YAsOymCA+G52Dsv1cMwkL1kPT2YxOD59dyJc32yVqwF\n68N/GCt0R1JGFyE6uB7OwktycET5gOw6vdgTyl0uLy8jU0OpIIESewVD7aAIe5h33u4InBXFjrH3\nEEyUVi0sLGg4HEavtDs8z56hZ9g5HBiOifd4dvXGjRuR7ScoJLuAfCLfDG/ywTHot2cMkVfki+BE\nmtoR7JdXhRSLxSAEnLW9ceOGHjx4EICXYxq4Zycc2Udn9QHIXBcAzZ6iYwSM6IOz12RayuWy+v1+\n7KdnRDxTze+8AsgJA2malYPUJBjz92NPAN4EDQTdDNNCNrlX7CsZXNdZMjqS4oB6/95KpRIl0gCZ\nfM8oa1Sr1cIOehYhr1fIN+y/6xQzEyqVSoaERKawO8w6wN7xe/QB2+gD8MbjcZQ3QgBDIGAD83bO\ns8yeOblur6/DdgB69OtV2M4TEN7u4+tHm4tnN72sED/spK1ny923I1dgFsr2wXlO7GLfsINpOpnV\n4dPsd3Z2Ak8ySLLVaunGjRtBgJVKJQ0Gg2g1cBtIwHZ8fBzBaa1WU6PR0NraWhBXp6enmp+fj88w\nqA67NxqNMkEdcnhychLlt2QQeWbH1OwT1VadTkdffvll2AWvflpfXw/7C7EAVga/opvsF+uHTQED\ne8Dj8uKYBvlAN6k4Gw6HarVaQc5ht8GixWIxMCk+ybFKv9/XT37ykzh+B5IDfEqA9d577+n27dsh\n40mSqNPpRLKF9SIBwnf1+/3AYy7vyCu/c/IU+Scm4Lkhc/Fn2CswLf7C5R35oJITmeU++E6vnHM9\nQe6dcMrjOq8m+X1w3WsNSp0F8/JIwA/vYZPcmTiDxf/ZQAdWfnB3Hlxg5NI0zfQNufM9Pj6ObBub\nwEbi+GFdPGjlGl56cHp6GuVsvKfb7YayEBi+9957+vjjjyVNjpk5PDwMALO0tBRsyfLysvb393Vy\nchIlJdvb21pfX9fNmzdVLpfjQHPARrFYjClerD8BCoYNpfYsaLFY1O7ubiarTUAKAByNRtre3tbW\n1pYWFxdVLpfV6/W0vLyso6OjyAZXKpUYguSZUUo3GAhAaQfBJA5eUgRjGAmewdlOvsuzjWRRuW+X\nHwgC5BBATZlHuVzWJ598oi+//DKMIcaSM7Y4H4vsKKVfGJZut6ticXIO4nA4DEae/YUp9aySGx/2\nBOeRLw1HPiXF8+NsHIx61swJDw9+cejoj1cjuD5gFHFG3CfgzRlvHAXf42V8zq5etxdrliSTHhHk\nF2YdNtbZXC+xBDBxFimsMITHwsKCdnd3Q64kZZwEZJZnHyH/sLc4FmwAJYtk1WB4nSwpFApBEBEk\n85mLiwutrq7qyZMnQZpUKhUdHh6qUqloaWlJ3W43nhc7X61WNRwO41xmdIfvoASNnz3zSJaRcihk\nkfXiu8gE7+3tSZr2vlB2Va/XA4xwbwBVZBzfQpAI0Gi321EtQaZPmgbVgBKyrQSMTlz4wffoNusv\nZY+SgRz0SgbKvNFtJ8ywjYBfgL7vAwDP79urjADdXiHBPiBjVLJgdwhmJUUGBp85MzMTmUz0H5/G\nOkDqQaDU6/WQE9YYuYbcZH0JirFlyChrSuVLuVyOSaoelLAHrBEBte8NfwezcL/cE/4N/XWAfd1e\nr8J2rL/jKGQROZWUsS3ohPs+Pg8JgX/h916NQ3aP92K7SHqgg9gNSTGch6CI7/QKBL7Pjz5ptVqZ\nY1foQ0TPaNHi/PI0TWPSPvaE7GStVosKrKdPn8ZE79PTU7Xb7RgmB/FDZha94v7AAF55SPUXP5N8\nwI+T3Ue32KfHjx/r+fPn+sEPfqCTkxN1Oh1Vq1XVarW4Pw+Q+T4wlFdzEVyBiblf7Bc2lf05ODjQ\nwsJC2J5yuRxnxEsKLO0l+Pjaq6urOGYHggICliF4u7u7+uSTTyLgY295NnpANzc39fbbb6tUKoV9\nStNUnU4nrnl+fq69vT2VSqUgoXk/sozNA+N53MJzE/C7vcLG+HPyO/TASWye13Hdq4g0roEt9ww7\nPg986vMc2C9wnSctfl9cl+BA/v9+JUmSIlA4Nl5ukFA83zwe0tPQLAgvnDlK4OWdXtrjaXSyCAQa\nXtKBEMDEY2z9TDwyS84q4/R5ThwcfQI879zcnJrNpur1uprNpobDoXZ2dvTxxx9nMlCNRiOc4XA4\nDGAGoEW4m82m1tbWdOfOnVgPQBcKDSt/cHAQDtKB5cXFRQTRBBQYzMFgEKVvo9FIb7/9tjqdjj77\n7DN1Oh3duXNHa2trevr0qe7du6e5ubk4kP573/ueHj9+nDk8GIVIkkT9fj9AJSV6a2tr2tvbC0Xm\n/85Ie7aJda1WqxGYAwb7/X6UgwCyMGCsnzPZ+/v7evjwobrdbsgRRhbjyh6VSiW98847un37djhC\nsqH0qknKlIIwpU5Spl9UmpYLIePsI70jx8fHAd4xTBAjOHd/VmSTvzkbij5J05Im+gZ8ShyOz/UU\necGAezDE97lh5fsc5AN+X97btWkuTZIkdRbcs9+1Wi0mM+O8PWu+tLQUZ+Qid1dXVxoOh0rTNEqC\n2NdmsxmsquuPByLsFbJFgMHwBj+KBNKAKgSIOGki6zg1nBmBZak0GXD2ne98Rx999FFGFry8HLBE\n9oPnwE57poqXEzXSNCuDnKMLAFP0kJI3nG61WtWdO3f08OFDLS8vS5J2d3czJZ+lUilTVcK9c11p\n6q+Ojo40Nzener0eIMbXgr5VB0usmaQAaABWntODI9c3fJKkjB7xb/SR7/es5fz8vAaDQZTFstYA\nR3wj5CzvIShDHgFwBGDVajUYfWyiB3KUKkPCcP8Qwp694TuQO9bKyQbWB/ALGINQA9g54POeXIAW\nBFGhUIjSRmzd4eGhZmZmYuYBdpGAlOwoOsHecO+tVkuDwSD8idvGlyXL18bWSa/Gdl5xgDw0m80g\nZJAVdAE5dh3n5Rls/u14DN+MHUEG8I3YAPYS8gRsx/d5tYR/L/oBZvHsaaVS0eLiYiQypIlMrK6u\nRonuycmJnjx5oqdPn4YNrlQmE8zRNYg7ZM6PImk2m3rvvfe0srISQ7jAQCQ2IH+Gw2HY70ajoV6v\nl2mFoOqOJAPTZ9EzznH++OOPY3bB6upqVNi9/fbbcYTe7OyslpeX9ejRI11eXmZOXyBryzORaGDf\na7VaVOwwrA4bjF7zGfYA/Iy9oNKh2WwGpuL9l5eX4RvZ0263q+fPn+vx48dB4rH3pVJJ/X4/5CZJ\nEt26dUtvvfWWlpaWQm7Pzyfnl0IKnJ+fRysaA56ogMG3gePQEU8QQEpg86TscXyeocc/IHvIN76P\noBDfQYwEDmOCMNckgGUNnEgiOcFa4B+chHbddbv6u+K6195TijPyn2GdYf29VpnNkKblhiyANJ3U\nli8RwVCR+fPSKS/t8gwoTbzukN0Z58/GdHBNUONZDxjU0WhyLhJlHwTenp1cXV0NELOzsxMsPcEB\nz8E9wyCzHsfHx/rVr34VZ5Zubm4GEMKgIIiAFWdcyL5IEwFdXFzU48ePwwAsLy/r+Pg4AMZnn32m\nTz/9VJICNKMMT5480Z/92Z/p7OxMg8FAW1tbWl9fD+NFkOOlYJQKY3AJVDnfk+AQMEWQDalACdzc\n3JwWFxeVJJPG/16vF8wh6wjLh3JxyDEBNs7ISxz4HYMrxuOx1tfX9Sd/8idxHh/nl5F9wsj0ej2l\naRqBPUyrs1HISb4cQ5qAclhTPyzaWWCeT5qO50Z/MMAE9l5S6NlUHAalc+gse+Z9GGRXAKY4EgCb\nyxj7604fw/q6SLI/9os9waiTtZOmQ0EouXfWvtPpRLBAkJEkSfS+XVxcqN1uR/nS8+fPVS6XM8OC\n0BEPtrxHdGFhQdvb22EPkBuAH0QacuJODvKIoBr2mkCKv+HUsD0AejJTZKlg+8licB1K3JFLBzXY\nPkqS0B0AMWvB8xOoJUkSoG08HscQssXFxVgfbJwTRV6tQOBBUJMkSRwWT7DvJVQAQs+wSopA1StP\n2APv55GmcxW8PNbBNVkQACc2lWtQvkWATdAN+PFBcfhNMjn4IECJB8gEi14+S4YV4O3BMfrPHkLO\nIj+0d5AFlibEiffCYmepIHgV8QMZSesI9+6tD870S9PzK53McZ+ODL6qxHA0GkWwjQwxR4HvAzBf\n11ce2+XL2ZvNpg4ODjJ65NjOK3PwG+wJOAE/DE5AtsEGTiA7aQXRgl5AhICfsBlkRZ3M5V6xjV6G\nyt85K352dlZLS0sR6A2Hw8hych+DwSBsJH2I+E0vOeZ3BDg/+9nPlCSJbt++nRmSiV1IkiROa0De\n/JxeKsbwJ51ORzMzM2o0GqpWqxoMBup0OvrWt76lH/7wh9re3lalUgnSCd3b39+P4ZbPnj2LKb1P\nnz7NlE3jM66ursJ3QYBS2bG0tBRkEjajVqtldJ31r1arqtfrmp2dVaPR0McffxwVLugWhCL+ltap\nFy9eRFk0PshbK/CPYLN6va47d+5oc3NTSZLo6OgofC82igQNFUXYhsvLy6g4cYwLYYquIPs8O/4W\nm+YDp5wUxCe4nHhywTGGJwOSJIlkEH/Hj7LOBNR+qgd6l6ZpZmgWso8txT//PrjutWZKYf1Regy7\nM6DUxMN8sZFeRoZx90xRPovjYNtLPQAPgBBfWGcy2CAcHAEDSgeLwHc4K8pmY1QwrgAwyhG457W1\nNb355puSpB/96EcBsra2tsIBShPHiTPGmSL4CF2SJFpbWwtjQWaOUhk+g3PHaVJWSKAGi0wg1Gw2\no8Tk888/1yeffKJ2ux3rcXR0pM3NTd28eVMvXrxQtVrVG2+8oWq1qp2dHY3HY73zzjsxUpxgkTI5\ngsvxeByZzsvLyUTfi4uLYKoAocgQgA5wU6vVtLa2FsaBIQNkZAmKSqWSut2udnd3tb+/H0wlDoM1\ngTRBRiRFJpeMMAZhe3s7UzIk6SvnAA6Hw0z5JDKOLGHI84Ei++YZJAwHRk7KZlo9GHmpgxmD4o4V\nOXcCyAMSSujQTQAxz8BzoH9eyrO6uqp+vx/gGsPGe69b9iBJkpTBQIB0SAUP2N3JSNPBHzgsyss9\neOQ4IYbozMzMaGNjQ91uN4Ilz0QUCoX47jSdTNQeDAbB6mKHJQW4Rxak6XA3sk1kND3wQm7X19e1\nu7sb2XZJ4Th5XvQWxhXbxTpR9YBOsC4EcWQVsU/YBC+D5rn5mbItfA0DRRhC0mw2Yx2Ojo7CF7id\n5TpkFrG7rAc2lcEcXsLIf57BwR7AXiMLAEof5EIwid5gy7lHQADBJ3rr4MAzCE7CMkWZ69Hbhv57\nuSrrTIYln6WuVqsReGAL2F8yWicnJ2o2mwF0IavIZrntkhStJ0zERF8AQb7G7BcBIfeFPngPKPYL\nu16v18Nme5UQsox88Tv+AywCJLl/1grdghR+uSfXxtZJvx22K5Umx2p4yxT66VnNl9f7ik/xMl6I\nYilbNoxf8sQFuoM8YVfBHuyjZ/rRHy+pZK/5LknxjPzMUEt8aL1e1/r6utbX13V2dqaf/vSnmp2d\n1WAwiLYegH+hUPgKEch9kwWtVCq6e/eu6vW6arVa/I1AFDwHWUdAx3NguzwYpwd2dnZWT58+1Ucf\nfaTBYKBWqxXPdnp6qm9/+9u6vLzUwcGB3n333Ti+7+nTp3r33XclSf1+P3pfIcl6vV4QiJT7OiGJ\nL5Km52Sz1pKiojBJJudfr62t6cGDB0GOtlot/eIXv9Da2poODg60uLiofr+v/f19bW9vRzUOpJwH\nXfg4r4pZW1vT/fv3Y5Lv5eVkeNGLFy9Uq9XCJpMsoiWL0l2wndt/MBRygQxjH70Mnfd4Fp/fgweR\nRw8w0TsPUJ1Y4Tuwh3w/gTBBM/GKJzbwzdhT7OPKykqcvOHEze+K6157+a4zlTh5Hhyn6ZkcNlRS\n/M6zS3lGLV++AQOAgEnKDBTxOmkcnbPIvLhPd/IIiRtFGBhnvxAEB6GFQiEG+CwsLKher6vdbsex\nMQcHB3r06FEoFr0CXoIMCGU9AbCkzm/cuBFZRYSGen4U34WV7EG1Wg3gSxAB07W1taUXL14EE9lu\ntzU7OxsHSpfLZa2vr2t5eTn6wKSJYdvY2NDc3Jy2traC5ePeKUUmcK9Wq+r3+2q32xGo+tEyrMFg\nMIjrkm0FKG5vb+v4+FhbW1uhQNIkc9nv96OMGYACSKvValFWgTwC3jY2NrS5uanFxUVJk36yw8PD\nKPUdDoeq1+saDAbq9/vRqwDYxDhhPLyEBGPiTL7LJy9+71kHMjcE185YEgxyfT6PTvHdZBO85JHS\nZjdMXAs9QS65BrLlmSb0G+DsOvTyOtcGqCUvy3cBVt5nhj4kSRJBPoACHWbt0XW3eWdnZ2o0GpGB\nLJfLWltbi6xf3tZJ05K68XisZrMZ7KvbR0CiBxIEnp45G48nUwkpr2TKINl8SjJ5P3qEEycwoUQ8\nb+slZYILSim9lYI1dftCdp/79rJNaaJfS0tLQZih96yzZ3axiQBGZ+6x4WRfCKrQDQI1bAj7MD8/\nHwEjgNHLt1knyAfAjzQNPr2kFX2iQgX/49lOXvye0nEnorC7+cwfZJcH5nyP2wwHMshKPtjA/vGC\n+SeAH4/HWlpaUqfTiefzyox8+SV+ln3zIB758jJMZBt8kabTKb+ezQBQsff54MD7Vn0f2E9Ktsmi\nQCC77X4J2K+NrZO+iu28ZDaP7RxDofvSdKK1B5FJMu1LQ8/BjdguHypTKBRCZ51Q5brIDj7LcZvL\ntjTtk375fCFzEK4QeF4Cif2bm5tTrVaL1qd6vZ6phBsMBvrkk0+CSKRiBhknu+n2zqtYCoWCNjY2\nND8/H7MvSqXJmZvci/fa0y8+Go20trYWc1GQTQYtbm1tRaUISYjRaBS2slaraXFxUZVKRe12O/pd\n+/2+3nvvPb148SJISZJMYKxSqaRGo5GxEQxPAotgZyuVyZCy+fl5tdvt2G+yrKPRSI8fP44p76VS\nKeaf9Pv9OCcUnMI12UP8UJqm6vf7StNUtVpNGxsbMReE6hn8Ksc6np2dxcRdZJhKO+QN++nVJdgZ\nKi3cR7v/88CTv9dqtWjDc1n14NJtuvtxSEivfnOy0eMj9PHrcB265C8wgk8Z/11x3WsNSjHeLCRA\niYDIF8aBLEDBs0K8HwVg4z0IdPaAjeZ9GE+CUYwJ2cFisRgOHAPoJWGeVSUoe/mccRA8CkPQgLDy\nfI1GI0qkeO/m5qYWFhaipOTx48fRVI0jxGnDd4LyPwAAIABJREFUaPMds7Oz0XPW7XYjQ9NsNjUz\nM6O1tbVoasfYYNQArsPhUHNzc9rY2NB4PA6j8vDhQ+3t7UWAS4ltu92WpDCiy8vLevHihRYXF/X+\n+++HMFP2cOvWLfX7fT1//lwLCwtaWFiI4U4EhUtLS7He8/PzWllZybA+1Wo1Bkbt7u7q3r17kXnt\n9XrqdDoBeIfDYWQ/h8Nh9OaxBzg7jAdy4ywv5WKtVivKdZGtk5MTHRwchPPY2NjQl19+qefPn0cJ\nGX24yIEbEUmZzKXLtss1suVlGV7m5PLon/cSYYINQAPPIE3LORgugIw4AeT35tlZSpM8qyZN+95Y\na9c11lzStcyUeuYFkiQfjACqWAcn2nzvuA6lVJQCA2TIku3v72cYTq5PH4o07ZMiAAHsEHRh75BT\nB0K8D11xu3h5eamVlRU9f/48yCbssZQ9Ow+bztoQqHFP2GnIGcr0sIG8cPqj0aT1g+DEy+AkBTM/\nGAwyvakQYAAgbAb2jewiINeBjWd1vCfX+xzzGWcHnUmSZLJzZNKc8AGEsCZ8nooZQDHZY8hW5In1\n8yEuyIdXOlAi63ruGXzIEgIAbAz345lqB/+evQR4e4YRW+AluIAcbB2lhfgobCf3zjU8OG42m0Gc\nIANUouBTut1uBPqU3vp8BfTFsQS66tVTZJ39MwTxXoGCXr/MEF8bWyf929gOPUV2/G95bOe+zklO\naTooyf1JHttJ2fOAHZh7tYK3M9GCQvbM9Q1biZ2DkHIZxXZiS4vFyVFPHBlzdXWlpaUlra6uBhF0\ncnKix48fa39/P0P4MmjIhy9h5xcXF+N4F1qQGo2GarWaWq1W2AQfcMm6jUajaFW4ceNGEPaj0Ugf\nfvhh2NhCoaDhcKjLy0stLy8HUcdU7/F4rLt370Y1WpIk2tnZ0d27d5WmqZ48eaKLiwstLy9HMIc9\n4izpUqmker0eRKETetxrt9tVu92OOGA0GkUigfYn2reYUwIZ6tUKTkxAHGHb8EWFQkEffPBBHDlD\nyTT+5vDwUPfu3VO/39fnn38erVmHh4dBIORJVSfvwWroBzaW+IV74/mRcyfiPLZx7IhuIZf036If\n2C2OBoKgy1cvOSbl2l5Bip2WprjO3/tNcN1r7SnNgxsHR2ykD2FhQTEYAFzvg/LSCgd3CAX/9oyi\nOw93shgqFwaAF5uNwDtj4YwZbJn3IODIpGkT8+npqYbDYaZ/rFwua2dnR7dv31a9Xo/eiUajoW63\nq8PDwxgxTanY2dlZZB0vLi7imgCBQqEQWb5f//rXWllZ0dLSkmZmZiKbeXp6Gj2lZFKZEvfw4cMw\nhOwBhh5lge2anZ3V5uZmsHRkeAGFe3t7+va3v61OpxPB7eHhYbDNc3NzOjs70/HxcQx4oo+AwJ9+\njUqlot3dXXU6HW1vb0cvZ6lUiiCX/caJOPNO8O7yyOQ0jBhGfnV1NbKjGBWMINM7kZsnT57EFD5J\nGgwGmcwN6+alkHl2DJlgfTFmzroB3jA+PBcGEFIFxyZNjSUGyHWN5/LSKoIID16RK4wssuvlfl4a\n7zIvKfOdXs5y3V70KLG3yCI2A313lpi1pfQFJ+ByWq1WVSwW1Wq11Ol0wumdn59rfX1dvV4vABKM\nOMDZy0O9D7HX64X+AvAhPhzgYQsBcjhBHBRHFuCsK5VKBD+8nKQDqJJF9awpDg8fwd8INJIkiSOe\nzs+nU8/pr3JQwsRFAhvkG32nD8mDZfTv/Hw6pZX7JNBxfeVoAMrb+Tv6ig6xv5S5OUj2IB8wyz6w\nHp4h8soKz2YSLEGQ5oN9AkDWlzXlmAZ8G7LyKp3meu4f2VsnxAj8vZwNmeaank3El9DHysAg1sB9\nPaV1lPg6lsAfepmaX5droodpmkbPrr/XyXHAGyQgawmx6TgDTEBQ7kT2dXw5tsuTTOxH3va/Ctt5\nawPXdcDvxJk0xXpkzKWpLcUXYk/9eujqeDyOHmYwAr7cSVbPeiHL6KMH29gJKsz29/e1sLAQwxZv\n3LihdrsdZywzR4XzPpFZ+qGxwZLi+ZA3MOvFxYX29vZ069YtLSwsRG8rZyF3Oh3Nz89H2erW1pYO\nDg4iMHXSzSsB0O1qtRoVaJTGVqvVsDEEiLdv344KQGxbqVQKTDwej2NoZpqmev/99zMkKTjp6OhI\n3W5X29vbcdIEmPvw8DCCxdnZ2ShV5tSGUqkU1Qr0s2ObfYASn9/Y2ND6+noEzIXCZKrvYDAIv3V+\nfq7t7e2Y3F4qlaI1DGyGT+F589jK8Z9jfoJxnh9sBgZFlzxjif920prg0okX7tVtoeM6f3kw70QA\n+oiPcB+ALrBGvy+ue+09pThXZz0xEpICTLOoHqDi1AhK85F5PiWOsPhG816yhAQsOFLYFmd4fQMR\nDK7JxpAp9LNWX8UeehbEg41arabLy8sokaAUd3FxMYKFL774IrIhlPs5ACIY5vtQEGkCXJjExqHF\n8/PzkibZk0ajkTm2ZGFhQZ1OJ8DRixcvokeGo3NQMIJHgAWBLSxWq9VSq9XSs2fPdOfOHd2/f1/P\nnz9Xr9eL73awx3Er/K5SmUwX9qzb4eFhAHCMsoMKegnIojiJgSEYjUYx6RnZ8Sz34uKiSqWSWq2W\nVlZWVC6XI+N7enoafZKUXJIR5XvPz88zfTSeLUJu2SvWztlZ051wGpAgXh7F7zAQBA4E5GRBXqVv\nfCdGjfvyLBTrBphFrxxoOyjhuRwISNMzM50lhwS4TtmDJElSjhriOV/+PkM0sH5kUXG8HthAdJFl\nYVI3E5ixg4uLixnHJk2Py8DWAQw9+GLvvByR/fUgMZ9F9ICEz1Ni7+Dcy5rcQcPk+5RJaZrpJwPp\nffrz8/OZozg8sCcD4eX9Z2dnQX6hm4uLi5FpIHtYq9XU6/UyGc588M46o1vYC/wGJaMeXPM87AX3\n6kftIA+sKQE76+eZJc80sha8B1DB8ToQag5mPLvp+42e+7XcTrCWAHAP5PDXEBweALi88f0OpMj4\ne4DIWlHm7xlIr/bgc/jrdrsde5ymaegHsu/92ugWw/HQO5cnsmsAe+/DIyPPffgAJ/aN7ImXARcK\nBQDytbF10u+G7ZBZ/i5le+KwfZ5ZkqZkFu/1azsQduzl1WrIoqTQW2wR8oWOYp/QGycYnIhANwim\nuCbYlQwjA79arZYajYZarZakyZmmYJnPP/88TlcgIKLcE1tBoA2RRjCdpmn4hOXl5cAKrDsYEgxw\ndXWlL774Qm+88YY6nU7gmEajodFolLGjtVotiCVmoVxdXUV1xttvv62trS1J0gcffKDxeKwXL15E\nDycDrvBry8vLGgwGgXFqtVoEPpBAvV4v5oCAOyENOUHi8PAwyELH17RUkFRx+cFOQQpUq1W12+0I\nogeDgZIkiUn2PG+32w0bTGaUgNfJOv7uMQr6gI9HhpA5ZFb6aisV13QilWsgH9gybBH+BhvvFXTI\nqrfk8H2eLOE+fhOuQw6xd3wf2OJ3wXX/Lsp33dGwGDhUBiBICkCAwrNAAC0E0gNRNhBAiNJ6RtQX\n2cs0YFtgbj3VLk0Fmw0DwPG9XMd7KqQpq+zlJw4+isWims1mBrDNzs5G6SpGjOlt3W5X/X4/Ai8v\nubq8vIwz3byHA0eAA4VpQZHW19eVJJNyVAKZ8/NzvfXWWxHwwdx5uQCGgF5Myj9xAACEjY0N7e3t\naX5+Xm+++aY6nY6ePn2qarWqe/fuZUoPEXTOkuW7yJLSq3V+fh7PC3gHUGL4pInxJ0gEbLCPXkYI\nIJufn1ez2QxygOwU2djDw0MdHR1FiRgkwWg0ivsDNHvztzQ9zBjjRCbJQbJnpXCgBJ3ICLKGjLFf\nL3UtjBVOxDP3yCuBqxs0vtsDJ74XJ49jg+X2EhBn2/z/sG1k8BwYX8ee0kajEcSWZwO9Vw/dubi4\niH309fPsQK1WC0dMYOLDEMj8M0wpX9FQLBYzvcYEsAsLC5m+RGzVaDSZmEnPDZkF7hVnjyzPzs5q\ncXFRW1tbobvIJ0wvZU/YRuQR4N5oNAIYIBvcCzYV+8zPZL74jDvmq6srraysZM7CJMDgmnldsT3M\nMOCvImPK5cnkWs6DJXB1v+NzChyse7UD++0BZJ6I8s9dXEyGM/l5xOxBmqaZwXb+WQfx0nRwHtlG\n7xWmjwnbQwDJ5zxLzCuf7XKb4vfCvyEDCAQhQ8l2O6Bk/fAPkMDsiWMEfLMDMCci2Rvex/04GMS+\n5YeHQcZ4xZU0LdFHXhcWFjI+Hll76TOvja2Tfjdshyyi875vvNcJVnQzj+28WofA8eW9SFJG3sF2\neXto959JhngSxGWQ37H/4CCvwvLgbzwea2FhIRIDXAfCe319PQhjejl7vZ6ePXsWmNNbxsAQPnvF\n/QtrAT49Pj6OftbLy8vQ5yRJonwWXEeZsAf0jn/JPtL+dHFxEckGgsV79+6pVCrp+fPnGgwGunfv\nXgR5tKShn2APgtHRaBSlxxcXFzF/g+CWRAWEIhUwkGAenKFvnggCHzabzQhEkySJGQtM2GUWSKVS\nCSxP8ocWPWTIq0bwD+wRmVqvVEOG8DnIPX/DLnkFnP/efZUHmMgltozECjrguI6f80Gm4zpv5ZCy\nSQv28A+J6157UMqCIDBufHhwnJ2XSji76uVgnh11Z86G4JxgDBAaDxpZSIyKNA1S+LcbKc8ocB2/\nN2fAeQ/PTLM7z+rCVS6X1Ww2MxngVqulYrGoer2uVqsVQPTs7Ey9Xk8HBwcxrppjFDwYYj0BrYyr\nZmosjlJSZBml6dh0BvrwO4SfLCskAkwQ2V7KiN0poQTtdlv7+/saDoe6c+dOMH3cE44Ag4McUHLn\n2RhARrvdVrlcjlIZ70XmCAofxuRAhrKEpaWlKBumR4MSFXoMKCdDZik3wWgwvRNQ5QAOYATjCwD0\nbIkDNweinr3y8gwHVLCPPizFJzd7Rpi9wmhjcJzcyINwadrY7mVQ6Itnc/y9rDn35IHuxcUF93Ft\ngFqSJClHDgFgAG0eqHt5jLP1yDWfgUiRppUkMM+eCaW6AdnI20ZsEJmclZUV9Xq9DKkAI81gMByq\ny6D3U5IFWFpaCvuCvQUcAAzzwSA2FqZ1dXVVW1tbGYfpQYU7fAITAivIFu6p2WyqXC5rf38/018G\nw836pWmaGRjHv7Hb3guML2ENAQDYY/bPA2fWDp8BiCRzh41zUsqH9XiQ7VUVnh2irxSgiT/ifpzA\nctCG/XFiAN3Gj2HrxuPpsVSS4sxkwBf+BRYdPScbybEz2AKeH1vEvlB+xwAkAjw/uJ57AlD5WeIE\njZShQ1xQueDkEPtOltiJONpFqDzKl7SzlpCMHvRwPbLHfnTRS1B/bWyd9GpsJ00r2CAo8tjO9xN5\n9BJBdAac5KQKds/JO3TD98exXV6ePXPuGU7+xr3/Jp8H0eQljn6fyFq1Ws30oVKxxjAkBggdHR3p\n6upKOzs7gSWYWUHVCPLIi+xyvkxfmlYXYE/RbarluG9KasGhYCzH0th1sEGSJEHYkxVN0zRmjzBY\nzfUv7/f86JOjo6P4TreLtVoteto5VpEEA/flpJVjGbLVHH3D0TJkmg8ODqKvN09wUvYLIU+mNUmm\nlWmeNJMUtsP3B6yKH3A8xP16ia3bfGk6JA8bnQ9GPfB2XIf88/0Eiq/CdZ5QcPl1GfbqrVfhOnT+\nd8V1rz0o5YFe/i6Eh0UBIPl0RDYNAeBF0OiRv6TM9Xg5E8CmEHhxbcCSBxFuDD1Di7Fi49hw7ofn\nc4PMphNA5NmIJJmk/TnXytlyMqcoP444TSflRGTx6D0FHLAuFxcXWlhYiPXH2OP8WXdnRFgngAMM\nGgYPkOTT2chgsje8h4ATgITxWVpaivIMZ1FZayck2BOyNc583r9/X41GQ/1+X51OJ9gtGH6yB+wX\njB9r3Wq1tL6+Hk36lBoBrrrdrrrdbhhjrs89YSQlZQw5RsCBqGf8pamSe4kkxsZlyWUboI8TdCfq\ne+iA3vuiyBh55pRSPmefff/zhI6XgODsPSOE4XKDjZFEr4rFIiXU1waoJS/LdyV9BaRiJ2AWvR8Q\nx+KDo3BQDmrL5XIECARahUJBy8vLcWYmJZdePsx3ECDOzs5qd3c3UzFC8Fuv11WtVvXo0aPQYxyu\npAD9VEkA3skUejnQ+fl5kG3IDXLm9p3PuczwQgYp3cKeHB8fh13za6+srMTh6pVKJY5eIdvPfSHD\nZPxgppF1bLj3yLkDpuIE4OtZlnwJFi8CHnQLZtoz4pKiisJJUOyeAxEnbglC84GrA3T3p8ihv4f7\nwme6T8QvAcycTPZyQg9OkOl8hitPxvHdEGnsHUEzATTAOUmSAIpe4ujlmnyXV4W4vfOstQ+1QlfJ\n4nopILLoA3DcX9LmAmGCXU/TlOz2tbF10hTbOfn/KmyHPFFFwd8c3PNZ9FjKBol5ffJqAydsPNDE\n5vq1IbyQY59v4jjPn8Fl1H/nGUUvq4To8mompq3jXzlblOBubW0t/OfFxXT66+Xl5MxNLyV1na7X\n6+EzwLVkHbFRnmFlLbADYAFsAu+j1xydyGeuJWV6HrHh1WpVMzMzcRoAtouKLi8vJtmAPjsZ2263\ndffuXY1GIz179ixztJKTaDwHVTEkQZiqS7takiRhf8nIbm9vh42jCs+z3fitfOzk9+/VZcww8BJs\nbJDbPl7uUzy5gIx6wgx5BEPmfYs0PbqMvXLiBj+PjccuI0uSMr7bE4V8Fjv5h8J1rzUoJdomcONn\njL47Vv8/zs1ZZgfpvPxzvtAssKRMpsaBu6QMCCSgwAk72+CO2EvZPOvmgYWDOr9n7vHl+sTfGOzD\nBgPYAEEwb8ViMcrnKKGA7YEB4j5x3qxRkkwOW261Wtra2opn5vnTNA3gx3O6AiCI3AeOBtDBgBBG\nkaO0HuTAhnkg5ACE8kHAgWdQMRxpmurGjRt699131e129eTJk8j++J7hmLgPzkJttVpaXl6OqXfN\nZlMbGxt68OCBBoNByB4BKQCGc1Mx5Fyb9XPmSZqWEyFXONK8HHjA6ZlTZIZ/u3P0TCdG0EkL1pd9\n8wxLPuvqwJ7rugFCd71EPQ+A82Ui7JsbYw/Mr+P03UajIUmZPjnWI5+VYc9cvqVs/zoOgQCJoMHL\nsnH6OFL2EHCQd4Tes4QTZSgNlRCU83uJmpNt6PHKyor29/cjK+Tkn6Q44gmZIqgmY8kaYWvylS08\nN2VJkuKecJToJlPNu91uPDPBHt/h5yqSxfMgEtsBkYqMo0eeyYak8qNYHAjgP/I64TaA/fTvRUb4\nm+8dPoySWUAPRIT7NNYvb0vYP+QKWQS0Y8t4VrLe2BRJmZYBnt9nK4zH48z989yQGtyLl5KhI9LE\n3viEXMgE7KlXTuE7AKXO4nM/kHaAwJmZGVUqFQ0Gg7CZDODBNmL3+b+TD54pJrvNenmATgB03Qg4\n6TdjO/Carx9y4f4xXxXwdTjVg0r+jS90ueUaTpw6IUKwBmYDJ3A/0tRHSdP+aO6VfYXMQr/ZZylr\nv9EbjkHB/koTO0b/4szMjBqNRuAzKs6YT3F+fq5OpxMZVbJ4jgMkxeTa/f390BUP4sC14BfWCPl2\n/OnEmRPsENr5BIFjRPRNmto4qifwJ8wyQV/5/czMjL73ve8pSRI9efJEOzs7EWxDJPJMPsSIBEmz\n2YwjcChj7nQ6evbsWRC1/X4/kyjjGD8nUD1gY489i+xklSebnBDkOo6DkUVsucuq4zW37d6y47ER\nL/cLHqyyrp748ySaP0Me1/Gz/9739ZviutcalPIQnrX0yN2zRO688+yMs5w4BwdKzibnnYhnBHFi\nMLLcizPKnurmHnhxXR9M40LHveSFA2UloJSUyUoiWJQySdNx2igrddwYDwAVAG80Gmk4HAZQpGyO\na1ByRvZVUhg6/s11vWyDNSqVSjFMg56kPOvIYeqABGlyniBrTlBE5oahEpIC8HmfMYYPA8K+Ly4u\nan5+Xru7uzo8PIx+2rOzswg2+Y5CoRDT5OgbgAlknwHi4/E4ShjJZKLcnMkEI/WqTAjlgZ5BRR7y\nWVJeBKwYfpw08uH6wB65s+D96AIGm33Myybryr0AOH2YCffsGThAfR7o5QkXJ3/yLwf01wmoJUmS\n0meITgJ2sUPoGOtIqaAzjuisVzgcHR1F3zasLEAd/a7X6xoOhxmbws84j7xjwtlSKuyEUT4D4QO3\neH+73Y7BZQ7wcKjIM7aNMnjKuvJVBnkHja3me6XpyHoCF8i68XgcGQb8CM/hLK+3ThD0OEnjLRC+\nL+5nvOqEbCwkmgf8gBUHwOynD81xwCF9FRij6w4+pOmZqrzyJF+pVIp+Xd7r5biAnvF4HK0OZDiQ\nH+SENSKgza+Nl1x7ZsD9FHaF/WGtK5VKyC6/o4TXJ+dicy8uLqJ828k5rkUriWd63Q5DxJRKk4ma\n+CZAOGeNulwid7wcs1xeXqper8e5guwlz3vd+uelfxvbEXDyN2mK5fJ67zLuJJD7Fc+yub5g0wqF\n6RFcPqDLCTUnQ/z3vI/fuU9lHwHynqFzDHp1dRU65sGiJ0kYGEmWExuVpqnq9XoQ/Nh+1gVyz3tQ\n/XslhZ2hnJaqMMejDPfBDhEUSsrM8YBgAY+S+ZQmJwug09jwPEHgpbaeWABjOMnl5CIYaGVlRRcX\nF9ra2go7Rq/m1dVVJEUgGvmPjDRkCYSQpEjagPs5WoaMLTjJk1/sGUPqnNx32WKdPfHiftYDf5d5\nZNbjl7we+Gc8m8qeO/mAf+BvxA35CgAnipxYdZyPf3NCyK/hQaknT35bXPdag1IHBtJ0IV/+PTZG\nUmSKYE9e1f/mvUTO0PmGOfsLUIAt8mACAwmA5J74P8rsjLYLo9d4EzTbs0vKnqsHE+vsmjtUd+TU\nwPN3B03FYjGAabVaVavVCmMyNzenvb29COYoTeCYEowVAuSZFITYD3VHaCnhnZ+fV61Wi8EUx8fH\nUbPP0TUALwYhOcD0PiEMgTNugIJqtRrGAAfjAKlWq8WkRmeomAiMA6BZH9lBBllvJnBeXFxoMBjo\n/Pxc9Xo9wBABK+sI4HbHiIwCUPxnd3DO4ntGBePlDhUASfbLM/UuL5S9uVFBrshIsc7IPMEne+7X\nRWYxnG6E8nrBe72HlRd6RWDk2RVJ1zYoRYbRb8r6vLfRAREOBd3DmfJ6FfnGemNz2BtnopENB1f0\nqQA6PAuAncNheT8V4Af9oaduZmYmABIyzz2gx6PRKMpnsYUEPjwfWUue0WWMrBi6hC0EtN26dUvH\nx8dxNp6X3HJ4OHLsQSAMO3uBnvlgL4Awz58kiVqtVvQa5UlV7s1L3/EJPA9DfdgD2GUyjdgzStsA\nuoASiAkAHD1p2JbZ2dmYIulVIshFvV7X0dFRrD22iwyN2xq3JWQ6kQ8AYalUit5WfJuUDe4dbBEQ\nkyHFt/AsBNVOjuaDbTIaZNHxBQBgrokceRYBQpRrQSDy/cgOZZZMXnfSgb3ys2DRGewsOlksFpk/\ncG1snfTbYzswBvLg2RlPGDiR5yRqHttJ02oJZIJrOFErTafae+CUz2R6OTc2yAlE3o+d9SDVkw3I\npSdC8oEFMs10Xn5HBjdJkpguS/A1NzcXpFq5PDlLk0CJ5MDh4aEuLy8jAPPncqKPdYV0wdZQ8UZZ\nsfsIfAhVB0mSBOHjbRyeAPFqCT7Dz5S5eksANhq7laapjo6OVK1WY53orURnW62W6vV69Mp6Ekua\nzD2hMuPg4CCGpHlp7MXFRRz5Anb2bKg0zUx6lh/b41lV7D5BKHIhKao+kCMn8jw76f6iWq1GYOsZ\nVeQJGeZ5sYUud+5Lub40Hb7Ey8kLsAd6DTZ2TEkM8k1w3WsPSvMROAuFEQKME+jBsOQBl4OWl9fP\npMsd6EjTZmN/r2fHfNPc+fN+XvmSSBTHy4P8O2Cj86wK94bgOHPkWTEEoFKpxHls3gsB4OG9s7Oz\nWlhYCBCHImGQEazxeBwHiJ+dnQU7fHl5GT2eOGmytawhAQ6gjfVhTRiAwb2dn58HAMhng2DRKpWK\nZmZmdHBwEEpOdhaAQtbIAQgliIBGWD6cE2uB3GDYMBzHx8dxVpcH2zw/5ccYKAJUaRrEYdjIevGs\nAEqAHHvByzM9fi3fKz6LAUQX3EFDiHBPGDaXNTdcGEkvv3C7wPcixxhHrpknh9zY8XkCUc/SOlPu\nYOG6ZQ+SJEk5bBxZ8YwWcu/AGlvwqmDLmUr2AHYVhpsAjL3i6JiX95Mp/5GmQSsknQ9Xc0DnYIlz\nLKVp+Sr7SfDCBFXsyXg8jkDB9jruAVLHB18QrGJrsXVMewa4YYeoiNjY2NDOzk70a3oZJZ8HaCKf\nBMGQQT7dWFJm3dEdbKITBL4mEHHugyAPTk5OwlagPwAb79HENgBIPRAnW4CPdIKP5/YA0IlTnhXQ\nAwDt9XoZO4UM+JRMnkea2hl6fN0PuW9n3Y+Pj8NXuEw5CYK8ObjjvdhDKoEAXQSMgGPkHeAq6SsZ\ngNFolAGAyCJtMG4feQ9kBOWHDLfjHp1IgVQii8v6SLq2g46+DtuxFr8vtsuDZHCRNMV2AGUPuNgT\nzyC5nnl2B7nxKgZ+D3HLM/EC2xH85cG/BwxO8uUrpTw76WSMV1nQTnF+fq6lpaXAMdwjz8Z6npyc\naDgc6vj4OM5M515Za57f+/851xz7B57CZjtxAwY9OjoKe4hN5tnBAV5ZAFnoCQn8E7iPAJS9IzM3\nHk+mGZ+fn8d5rG6LnVxnzgoTnwkaIf+YuwKO8QQN9///tXc2MZpl513/n/qc6qq3q6tmuu0onpCg\nSDZBSMnGimQkIiSsAFKSVZQNShBZE7GAxLBgG1aIDStAssJHhJCSmFWcKGLBIthR7MSCMBglNmYY\nj6dnuro+u6reqsvifX/n/u7pt9s9M+6pj9wjtar6rfveez6e5//8n49zLliJQ4atXORkIotgLGXR\nrBG2ysG8RUmK8/PzGjB20NpyjPwbo9DHT7udAAAgAElEQVSZdmsYfcButAFTdA7bwrXmIMi3eR1z\nYQfdAZz3w+uu3CltFXf+t4GDZoLkEqWkd4zaCENbosZkJcP3T0H0AJk2IuDoL/fCgCL4Lk1CQMgY\nIHR8p6219/y7RMAlqTaQjJl7UgZLyRHzRoSaUxEpV4XgAY6TyaRmDojyEmEDaEwcWQteP7C8vFwd\nR9aom2cQccoAf0De60SEGecZ0KBflPBCbLme5yWzkkfAkrlYXu73Ht27d68+K0l1tM/Ozuo64gBD\nciGTzhjx4mbGwDr4wBOTGu/P4h4AmI0ga++MDYaojbQ7ogYQGARsALgf5MtRsFLKIMIJ4KB7rT6a\naJuc2VDQT8pg+BvRXAI8yA0Ou4NJyNhtImqllI6yKww7e5BdwuRqBDv2OFK+jjV2FH55ebnqugN7\nYN/du3cHRIR7eS8ilQM+8Ma4C0aCt2QHGQflXPfv38+jR49ycHAwCP4kM8K1ubmZd955Z1DSiMO0\ntraWj3/843nrrbeqowIpI5vIvVZXV+sWAMaTpGZvKcGifMtVF0kGxITyePYg8f457AfzTeQf7E9m\nOM38oi8QQkfIMdQuJQYrkgxe3wLBMpFzhsNBKQeiIAqMmyAZh22BYYyd9SQgZtkjkMSBJK0zDIaw\nrq56oQ+vvvpq3cPMHIBnfsc1OECwETm3A7OofNyVQuAfmNt1Xe7fv5933313wA34G7jjjBxRf943\nzVpRFWXnGnkH0wi60QefBMw+U7IcOPC3CeuSF+N2kFVzu7aiDRm0c9tWtHGtAwHwGEg6sgYX87rb\n7iLvLmFvDypyZozxILNgA9eWUgavZsNut5ybsdFwWAigE9BmvqjY8uub4AxJ6j5VdB98ZA+q14G5\nBBOooAMPfBYB8g2vAtdcPusANfPASdxwOV5fZbxpuTL32d3drZUnzANOJ0EeEi4EXU9PT2spMSfq\nMi6qJZw0evLkSbUpPBfcaRNGBBQJlnE960b/sSd2WMF5n9rL+O1ngDP4CrZp9MG2A9mgn8wj+sDn\nXncHYNrAAmNpA4HwbmQCfYVrcN2H5XVX6pQycQA90RKXBzH5KLejAAY3l9pihF1C62ivBcuKiTOI\n4nqSTczn/R9k7xBGjBGOpOvN7SQvytQaWOwMWWBwfmyYIUs4poAIkSSEBAVaW1urL5FH4Pkc8uza\ne2e/yCQQDcIpY64XKSIkm9Jq+mhD4fkDTA4ODsKrNFjj+/fv18wHspNkUCa6urpaD6sATHBAIQWU\n2QGQjNl76CDUPgX3+Pj4qUNi2rUwkNnBgzxyje/B/PK9pHfw+ecyDF+HjLDuBpnptC8N5xkGGjuz\nyDL654guAOnnOXvmQJLLnkxyHf12MABiT5Z7fu2tIWqQNLDExg1n3cYYOYKwOWjgtUP2cQpZdwJn\nSQaOg3XSRI5SaoI1Seo+Qu6BkYbg8Qw7UDzzwYMHefToUS2TbL9Lpo/PcBwnk0klQg8ePMhkMsm3\nv/3t6uTgBFAahjOJA2QSeO/evRwcHDzltLQZS+YBgrG9vV1P2XZG0rh+79697O3tDaLVSWqGkX7h\noDAHSR/oNKYaO6yLS0tLg/MNII00y4qzss6WE9gjOIkjS3YVmwGZhrQhV2A5sss9kU1kyOTGgQ6q\nYXzyLWvkih5w0U48wUHjJ89E/pgTV//4Gch1uycLWQT/IK8078nd2dmp7wAvpdR30RJwxEGFEDJH\nVMgQdMXW2Y7P5/jWYF2ymNvx2jtwyNkdnKukD8InPe44q+Ssp50IB+0IEhDkog/IAs8leGeuOO//\nINFg58EcEhvHNc4KLeJqcE5vBzOn5DMwHCd5bW32Siv4mE9wTlIr1KguIwBn/MExtI1JUs/6YO8k\ngUWcRA6oZI89SQjOL2Eb1f7+fk5OTrKzs5OkT1r4sDO2c4FtOL7m4hw4SeCKYDrBUGwE/8ehxM6x\nD57AH5lGki5ra2v11S5U8CWpDjtym/RnatAX8AvcMnaD8cgJa+mgh6sm7YTCA8yhsBfYL8uagy/w\nupaDtgkSl2ybvxnnfJ1tVFudgn1ycBd9QLY/DK+7Uqd0dXV1sMcj6U+sMjixQF58p8SZRASd8iwv\ntsky9+J5fMfpfowbEaCk30+HkWbu+JzF5nkYcu7pA5BYKMpHTBrJVAA6LnUia+AoXnsYzvr6en1H\nnSPBRN14juu/ne26d+/eICr84MGDvPvuu7WPgIhLS9qyZSLsLiMhWkVGhlMKMTxcu76+XvelMV+A\nB4rp0jQ+39/frxv2p9NpzXLwdwBsY2OjZoIBGjKxSSroYdRWV1crwfWeFRwMOwvIpYFnUXQLAgco\nkLEwiAEGLmXEGNN3Z1+5N+O1wwio8Le2vKwNqnA9UbA2yowseDwOFpl4AtTMmR0nB0Xo623cU+pX\nILEu6L4JNbhCxtm45hIuZ1EJ7iAnH/vYx+rJhDiOEA2cQXBzZ2cnKysree+99+qa2fEkoMT14DWB\nDuMm6//jP/7j+YM/+INaVkUwB1mA5KDzBI2QKcovKb8FBxxVRjcJmIADZCvBcHCEcnyqTcBxsmPc\nH/xiXYiqM8+OGicZZDJwkI0HlJextg68lVIqGeR6HxoFdllPCbixJwrcoPR4aWmpro2dTdYTYmcs\nYN6n02k9FAv9x0YnqXYacoxcYr+Qm8PDw0pqmR9XITFfzGGSKu/8nsycEsgs+nJwcFDvSSYI59oO\nDDqxu7tbXwUEmWqzVV3Xn2uA42O7Ciexzi4ikMZY5paGzDsISJDkNmFd8uG5HfPYBvJtP9DppJ93\n/u7AP/aRdUJGwM6kdz5YU2wj1xPEgEs6S9Q6AsiO9/CBb76WnwQsHByzw43MOXuIY8c92b/N/DIn\nSaqO7u/vZ3d3tx6iRPmvHRqcip2dncH4yUa2h+qBmbwr9OHDh3n11Vfrda2j6Cwnzz87m71yb29v\nr+oYp++ik5w4jN6cnp7WoCT7Z+Es+/v7NchxdnaW7e3t2s/pdLY9BV5ivHKAq81QuoTfHMuBQjul\nlmlwDayCe7eBPMu+cQO5wibxGbLAuiPvYK05p2UfXMM2mlMiNy5LtzOdZICRlm/sx/eC112L03cB\nnvnnNUrtdxA5wosycr0VmetYTBQNpUM4MeQISru3ACDjeia83ZdARLd1TjCcZPWcsSDC5EwTgkYD\nGO0Q+/Okz5QRlUBQIVpkT4lsQSqZH041NLAByo8ePcqdO3fy+uuv5zvf+U52dnbqWA3cGxsb9dAi\nomqOGi8vL9eTdclevPHGG/nkJz+Z5eXlPHr0qL40Opmd4Pbaa6/VwAJ7XOk/Av/OO+/UExgdSXeW\n+/LyMg8fPsxkMqkABhFlPYnoAfSAForbAo0zHwCVSxPJkBDhJ/pPvwECZBYD64gYwAsYltKXtB0c\nHAwccmfMTD69NwdQI3pPiabLPJwR8vpiSLkXRs/EwODnrBL3RZfoh8s9HM12FPs2ETWwDqNsg4cc\nOKCF8UgykH2TLTALjGCfEFURDpDwDFdwzPtVTwblwCWwlUoFR1tdcre1tVVfmwFRRCa/7/u+L2++\n+WbW19erg8lYqdJYWZmd3GgsI1uFDL/++uv1oCIcZggcMoejifH71Kc+lW9+85s160uA7ejoqBIy\nO1fOytkpRgchTw4eJMNqAfSCIAJ7/VlP7xG2rfCBGNiLJDXAx/xYd8no4Iyjz9g5iC7rh41h/p31\n4ffj4+OKF36VCXrZRtAZg/dmMv/OwNJfvmMZPD09zfb2dtWDrusGFTDGJ7L24I0z38gymOVx3r9/\nP2+//XbFeaqwWD/bS9bnlVdeyePHj6vcw0Mga9gDnoPzzfUrKyuDw7l4poPjtvW39ZUw343bEYBw\nprLVKVcA2aZg75PhKaNgp7HRNjcZnoyKDMDL2jMZ4AN2JN0nbLQdXQ5hRI7Rfe5t+XMAgzlyQDrp\nDwUDM5hHMqiMl8Mtt7e3c3R0VMvK0b+VlZWKnfv7+3n11Vdr4oS5Y87BEoJbdkzJxp6cnFTbvr29\nnbOzszx8+DAXFxd5/fXXq+1YX1/P5uZm3nvvvbpP1tyKrRDr6+v17Qzm7Px0pQVOLlvUWD+yvg8f\nPqxrSLCMSjnWA57ozB9zD993xSH8hODXyspKtSPOZNqhZD2T1O9xoCEBFOyAgxist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D6X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\nxXf9LIOsIzh8ZqdEsjR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Wim = dwt2(im)\n", "im2 = idwt2(Wim)\n", "\n", "plt.subplot(1,3,1)\n", "imshowgray(np.abs(im))\n", "plt.title('Original')\n", "\n", "plt.subplot(1,3,2)\n", "imshowWAV(Wim)\n", "plt.title('DWT')\n", "\n", "plt.subplot(1,3,3)\n", "imshowgray(np.abs(im2))\n", "plt.title('Reconstruction')\n", "\n", "print 'Reconstruction error:', np.linalg.norm(im - im2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now evaluate the sparse approximation of the brain image. Wavelet coefficients represent both space and spatial frequency information. Each band of wavelet coefficients represents a scale (frequency band) of the image. The location of the wavelet coefficient within the band represents its location in space. What you see are edges of the image at different resolutions and directions. Threshold the wavelet coefficients retaining only the largest 20% of the coeffi- cients. You can threshold im W for the largest fraction `f` of the coefficients with\n", "\n", " m = np.sort(abs(Wim.ravel()))[::-1]\n", " ndx = int(len(m) * f)\n", " thr = m[ndx]\n", " Wim_thr = Wim * (abs(Wim) > thr)\n", " \n", "Plot the masked wavelet coefficients. What has been thresholded?\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Code to threshold and display the wavelet coefficients\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reconstruct the image and display it. Compare it to the original image qualitatively and by computing the difference image. What has been removed? Examine the results when you retain the largest 12.5%, 10%, 5% and 2.5% of the coefficients. What, in your opinion, is the sparsity level of the image? Provide a reconstruction and difference image to support your argument." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Your code here\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Your Explanation:__\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compressed Sensing MRI\n", "\n", "We'll now explore 2D compressed sensing reconstruction from under-sampled data. The brain.npz file contains additional data, which we have already loaded\n", "\n", "### Non-uniform random sampling\n", "An important aspect of random frequency-domain sampling is matching the power spectrum of the image. Since the energy in many images is concentrated in lower spatial frequnecies, more samples should be allocated there. We have provided two 3-fold undersam-pling masks for you. The random masks are in the variables `mask_unif` and `mask_vardens` and were drawn from probability density functions (PDF) given by the variables `pdf_unif` and `pdf_vardens`, respectively.\n", "Compute the 2D Fourier transform of the image using a centered 2D FFT. Multiply by the uniform mask, divide by the appropriate PDF (called density compensation), and compute the zero-filled Fourier transform:\n", "\n", " M = fft2c(im);\n", " Mu = (M * mask_unif) / pdf_unif;\n", " imu = ifft2c(Mu);\n", " \n", "Display the image and the difference image compared to original image. Is the aliasing white\n", "random noise? Repeat for the variable density mask. What happened now? Both use a similar\n", "number of samples, but which gives you a better reconstruction?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fft2c(x):\n", " return 1 / np.sqrt(np.prod(x.shape)) * np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(x)))\n", "\n", "def ifft2c(y):\n", " return np.sqrt(np.prod(y.shape)) * np.fft.ifftshift(np.fft.ifft2(np.fft.fftshift(y)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Your code here\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Your Explanation:__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reconstruction from Random Sampled k-Space Data\n", "\n", "Extend your 1D POCS algorithm for 2D images. Add another step of computing the wavelet transform before the soft-thresholding and the inverse wavelet transform after the soft-thresholding. Make sure that your `SoftThresh` function works for complex-valued data, as was mentioned earlier. \n", "\n", "Reconstruct the images from both the uniform and the variable density under-sampled data. First get an idea of reasonable values for $\\lambda$ by examining what would be thresholded. You can do this using\n", "\n", " Wimu = dwt2(imu)\n", " imshowgray(abs(Wimu) > lambda)\n", " \n", "Don't use `imshowWAV` for this, since it will scale the different wavelet levels differently. You want a significant number of coefficients to be below $\\lambda$, but not so many that too much detail will be lost!\n", "\n", "Start with the variable density data, and experiment with several values of $\\lambda$. You should only\n", "need about 20 iterations, but start with fewer while you convince yourself it is working! Compare\n", "the result after soft-thresholding to a zero-filled density compensated reconstruction, the original\n", "image, and a the original image soft-thresholded. As an initial image to the POCS, use a zero-filled\n", "density compensated reconstruction, as it will converge faster. Show the image, and the difference\n", "image for the $\\lambda$ you find the most effective.\n", "\n", "Then try the uniform density data. Run for at least 50-100 iterations, since this converges\n", "slowly. If you want to speed up the convergence, start with a relatively large $\\lambda$ so that the recon\n", "will converge rapidly. Then, decrease $\\lambda$ using the previous recon as an initial image. For example,\n", "you might divide $\\lambda$ by two every 10 or 20 iterations (this is called continuation). Show the image, and the difference\n", "image for the $\\lambda$ (or the final $\\lambda$ if you use a sequence) that you find the most effective. Don’t spend\n", "too much time on this, the point should be clear by now." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }