{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Particle Filters" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#format the book\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2 \n", "from __future__ import division, print_function\n", "import matplotlib.pyplot as plt\n", "import book_format\n", "book_format.load_style()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation\n", "\n", "Here is our problem. We have object moving in a space, and we want to track them. Maybe the objects are fighter jets and missiles in the sky, or maybe we are tracking people playing cricket in a field. It doesn't really matter. Which of the filters that we have learned can handle this problem? Well, none of them are ideal. Let's think about the characteristics of this problem. \n", "\n", "1. **multi-modal**: We want to track zero, one, or more than one object simultaneously.\n", "\n", "2. **occlusions**: One object can hide another, causing there to be only one measurement for multiple objects.\n", "\n", "3. **nonlinear behavior**: Aircraft are buffeted by winds, balls move in parabolas, and people collide into each other.\n", "\n", "4. **nonlinear measurements**: Radar gives us the distance to an object. Converting that to an (x,y,z) coordinate requires a square root, which is nonlinear.\n", "\n", "5. **non-Gaussian noise:** as objects move across a background the computer vision can mistake part of the background for the object. \n", "\n", "6. **continuous:** the object's position and velocity (i.e. the state space) can smoothly vary over time.\n", "\n", "7. **multivariate**: we want to track several attributes, such as position, velocity, turn rates, etc.\n", "\n", "None of the filters we have learned work well with all of these constraints. \n", "\n", "* **Discrete Bayes filter**: This has most of the attributes. It is multimodal, can handle nonlinear measurements, and can be made to work with nonlinear behavior (in this book we only handled the linear case). However, it is discrete, not continuous, and it is *univariate*, not multivariate.\n", "\n", "* **Kalman filter**: The Kalman filter produces optimal estimates for unimodal linear systems with Gaussian noise. None of these are true for our problem.\n", "\n", "* **Unscented Kalman filter**: The UKF handles nonlinear, continuous, multivariate problems. However, it is neither multimodal nor does it handle occlusions. It can handle noise that is modestly non-Gaussian, but does not do well with distributions that are very non-Gaussian or problems that are very nonlinear.\n", "\n", "* **Extended Kalman filter**: The EKF has the same strengths and limitations as the UKF, except that is it even more sensitive to strong nonlinearities and non-Gaussian noise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monte Carlo Sampling\n", "\n", "In the UKF chapter I generated a plot similar to this to illustrate the effects of nonlinear systems on Gaussians:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qZivlil5InR7S6l6Z+kROCyJXllWo33nnnZibm8vYhol3YvOQbtMHsch2u90p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9nZyezsLN/61rd49tln5fRggPr6+pLXuFyuEg27QlEOEfeEBNJut5NIJOTPKyoq1pgbOBwOcrkc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\nNBrRaDR4PB66uro4e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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pf_internal\n", "pf_internal.plot_monte_carlo_ukf()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the left I created 3000 points normally distributed based on the Gaussian\n", "\n", "$$\\mu = \\begin{bmatrix}0\\\\0\\end{bmatrix},\\, \\, \\, \\Sigma = \\begin{bmatrix}32&15\\\\15&40\\end{bmatrix}$$\n", "\n", "\n", "and passed them through the system\n", "\n", "$$\\begin{aligned}x&=x+y\\\\\n", "y &= 0.1x^2 + y^2\\end{aligned}$$ \n", "\n", "I then showed how poorly the EKF did at estimating the new mean and covariance compared to the UKF.\n", "\n", "This technique of using a finite number of randomly sampled to compute a result is called a **Monte Carlo** (MC) method. The idea is simple. Generate *enough* points to get a representative sample of the problem, run the points through the system you are modeling, and then compute the results on the transformed points. \n", "\n", "\n", "In a nutshell this is what particle filtering is. A bit later I'll demonstrate how MC can integrate over a probability distribution, but you don't need that formality to understand how they work. It's very simple. It is the Bayesian filter algorithm we have been using throughout the book applied to thousands of particles, where each particle represents a *possible* state for the system. We extract the estimated state from the thousands of particles using weighted statistics of the particles.\n", "\n", "\n", "** Generic Particle Filter Algorithm**\n", "\n", "1. **Randomly generate a bunch of particles**\n", " \n", " Particles can have position, heading, and/or whatever other state variable you need to estimate.\n", " Each has a weight indicating how likely it matches the actual state of the system.\n", " \n", "2. **Predict next state of the particles**\n", "\n", " Advance the particles to the next time step based on a system model and noise model.\n", " \n", "3. **Update**\n", "\n", " Update the weighting of the particles based on a measurement.\n", " \n", "4. **Resample**\n", "\n", " Discard highly improbable particle and replace them with copies of more probable particles\n", " \n", " Optionally, compute mean and covariance of the set of particles to get the most likely current state.\n", " \n", " \n", " \n", " \n", "This naive algorithm runs into some practical difficulties which we will need to overcome, but this is the general idea. To demonstrate this I wrote a particle filter that performs robot localization. This is the same robot localization problem used in the UKF and EKF chapter. In this problem we have a robot that we can control. It has sensors that measure its distance to various landmarks, and we can steer the robot. Both the sensors and control mechanism have noise in them, and we need to estimate the robot's position.\n", "\n", "Here I run a particle filter and plotted the positions of the particles. The plot on the left is after one iteration, and on the right is after 10. The red 'X' shows the actual position of the robot, and the large circle is the computed weighted mean position." ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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i54QJqDp1SkzHiwCKX30VPZs3w2az4cKFC9i7dy9effVVbNq0CS0tLejt7YXH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\nIiIiEQMEIiIiIiISMUAgIiIiIiLR/wfnyK8MCoiyuAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pf_internal.show_two_pf_plots()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the first iteration the particles are still largely randomly scattered around the map, but you can see that some have already collected near the robot's position. The computed mean is quite close to the robot's position. This is because each particle is weighted based on how closely it matches the measurement. The measurement (with noise) says the robot is near (1,1), so particles that are near (1, 1) will have a high weight, and particles at, say, (15, 19.5) will have a very low weight. The estimated is computed as the weighted mean of all of the particle's positions, so the estimate is quite accurate. This is partially the result of the random number generation - other runs could produce a poor estimate if no particles were particularly near the robot.\n", "\n", "Several iterations later you can see that all the particles have clustered around the robot. This is due to the **resampling** step. Resampling discards particles that are very improbable (very low weight) and replaces them with particles with higher probability. There are multiple algorithms for this, and we will discuss them later. \n", "\n", "I haven't fully shown *why* this works nor fully explained the algorithms for particle weighting and resampling, but it should make intuitive sense. Make a bunch of random particles, move them so they 'kind of' follow the robot, weight them according to how well they match the measurements, only let the likely ones live, and continue. It seems like it should work, and it does. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability distributions via MC\n", "Suppose we want to know the area under the curve $y= \\mathrm{e}^{\\sin(x)}$ in the interval [0, $\\pi$]. We can compute the area with the definite integral $\\int_0^\\pi \\mathrm{e}^{\\sin(x)}\\, \\mathrm{d}x$. As an exercise, go ahead and compute the answer; I'll wait. If you are wise you did not take that challenge; $y= \\mathrm{e}^{\\sin(x)}$ cannot be integrated and so you can not find the answer this way.\n", "\n", "However, this is trivial to compute using a Monte Carlo technique. Create a bounding box that contains the curve in the desired interval, generate random pairs $(x,y)$, and count how many fall under the curve. Multiply the area of the bounding box by the ratio of points that were under the curve vs the total number of points and you will have computed the are under the curve. As you tend towards infinite points you can achieve any arbitrary precision. In practice, a few thousand points will give you a fairly accurate result.\n", "\n", "Think of how powerful this technique is. You can use it to numerically integrate any function. The function can be of any arbitrary difficulty, including being non integrable and noncontinuous, and yet we can trivially find the answer. \n", "\n", "Let's use it to compute $\\pi$ by computing the area of a circle. This is very easy. We will create a circle with radius 1, and bound it in a square. The means that the length of a side of the box will be 2, and hence the area of the box is 4. We will generate a set of uniformly distributed random points within the box, and count how many fall inside the circle. The area of the circle is then the area of the box times the ratio of points inside the circle over the total number number of points. Finally, we know that $A = \\pi r^2$, so $\\pi = A / r^2$.\n", "\n", "We start by creating the points." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " N = 20000\n", " ps = uniform(-1, 1, (N, 2))\n", "\n", "A point is inside the circle if it's distance from the center point (0, 0) is less than or equal to one. We can compute the distance by using `numpy.linalg.norm`. If you aren't familiar with this, the norm is the magnitude of a vector. Since vectors start at (0, 0) calling norm will compute the point's distance from the origin.\n", "\n", " dist = np.linalg.norm(ps, axis=1)\n", "\n", "Next we compute which of this distances fit the criteria with this code, which returns a bool array that contains `True` if it meets the condition\n", "\n", " in_circle = dist <= 1\n", "\n", "All that is left is to count the points inside the circle, compute pi, and plot the results. I've put it all in one cell so you can experiment with alternative values for `N`." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean pi(N=20000)= 3.1684\n", "err pi(N=20000)= -0.0268\n" ] }, { "data": { "image/png": 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uKMqegRFyFFP8PRCNIBjfj5snT8jmy32DA1IFp/+b1VAA4hMGIF0dr6yswWm1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\nZmZOeNVoF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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from numpy.random import uniform \n", "\n", "N = 20000\n", "ps = uniform(-1, 1, (N, 2))\n", "dist = np.linalg.norm(ps, axis=1)\n", "in_circle = dist <= 1\n", "\n", "in_circle_count = np.count_nonzero(in_circle)\n", "area = 2*2\n", "pi = area*(in_circle_count / N)\n", "\n", "plt.scatter(ps[in_circle,0], ps[in_circle,1], marker=',', edgecolor='k', s=1)\n", "plt.scatter(ps[~in_circle,0], ps[~in_circle,1], marker=',', edgecolor='r', s=1)\n", "plt.axis('equal')\n", "\n", "print('mean pi(N={})= {:.4f}'.format(N, pi))\n", "print('err pi(N={})= {:.4f}'.format(N, np.pi-pi))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I show this to you to help illustrate that you really are computing a numerical integration. An alternative way to find the area of a circle is by integrating the infinitely many circles that lie inside the circle:\n", "\n", "$$A = \\int_0^{2\\pi r} 2\\pi t\\, dt = \\pi r^2 $$\n", "\n", "The Monte Carlo technique does something very similar by summing the infinite number of points inside the circle. Computers can't sum infinities, so we settle for a representative sampling of points and less than infinite precision in the results. Using Monte Carlo for this problem is remarkably unnecessary, but imagine instead trying to find the area inside a fractal Mandelbrot set. As far as I know there is no analytic solution to that problem, yet we could use the code above to find the answer. MC is a powerful tool to reach for when analysis fails. \n", "\n", "This insight leads us to the realization that we can use Monte Carlo to compute the probability density of any probability distribution. For example, suppose we have a Gaussian. The Gaussian has a probability distribution as shown below." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x8alwm4SEBMTExCArKwv37t1DbGwsYmJihPUrVqxAYGAgLl26hPj4eCxevBiB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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from stats import plot_gaussian\n", "plot_gaussian(mean=2, variance=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *probability density function* (PDF) is the probability that the random value falls between 2 values. For example, we may want to know the probability of x being between 0 and 2 in the graph above. This is a continuous function, so we need to take the integral to find the area under the curve, as the area is equal to the probability for that range of values to occur. \n", " \n", "$$P[a \\le X \\le b] = \\int_a^b f_X(x) \\, dx$$\n", "\n", "It is easy to compute this integral for a Gaussian. But real life is not so easy. For example, the plot below shows a possible and realistic probability distribution. There is no way to analytically describe the curve, let alone integrate it symbolically." ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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oewAQ1oYPlwYPlsqXt6UWFSq4jggAQsehQ1KlSlJGhrR9u1StmuuI3KPyCyBs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\nz0FWlt1+XrjQtqrNn3/y2C2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pf_internal.plot_random_pd()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But we have already stated that we can use Monte Carlo methods to compute any integral. The PDF is computed with an integral, hence we can compute the PDF of this curve using Monte Carlo. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Particle Filter\n", "\n", "All of this brings us to the particle filter. Consider tracking a robot or a car in an urban environment. For consistency I will use the robot localization problem from the ends of the EKF and UKF chapters. In this problem we tracked a robot that had a sensor that could detect the range and bearing to landmarks. \n", "\n", "Before I continue let me point out that the filter I am going to develop next is a specific form of particle filter that is intuitive to grasp and relates to the problems we have studied in this book. In other words, particle filters are a family of algorithms. A more academic approach would give you a better grasp of this family and the interrelationships between the algorithms, but as in previous chapters I choose to develop concepts based on examples and basic reasoning, and only once that is done go back and develop the theory. This will leave a few of the steps seeming a bit 'magical' since I haven't offered full explanation for their use. Full explanations will follow later in the chapter.\n", "\n", "Taking insight from the discussion in the previous section we start by creating several thousand **particles**. Each particle has a position that represents a possible belief of where the robot is in the scene, and perhaps a heading and velocity. Suppose that when we initialize the filter we have no knowledge of the location of the robot. We would want to scatter the particles uniformly over the entire scene. If there was a large clump of particles near a specific location that would imply that we were more certain that the robot is there. If you think of all of the particles representing a probability distribution, locations where there are more particles represent a higher belief, and locations with fewer particles represents a lower belief.\n", "\n", "Think back to the *Discrete Bayes* chapter. In that chapter we modeled positions in a hallway as discrete and uniformly spaced. This is very similar except the particles are randomly distributed.\n", "\n", "Each particle needs a weight - ideally the probability that it represents the true position of the robot. This probability is rarely computable, so we only require it be *proportional* to that probability, which is computable. At initialization we have no reason to favor one particle over another, so we would typically assign a weight of $1/n$, where $n$ is the number of particles. We did the same thing in the discrete Bayes chapter. When we initialized the filter we assigned a probability of 1/N to each hallway position. The reason for $1/n$ is simple - the sum of all probabilities must equal one, and of course $\\sum\\limits_{i=1}^n \\frac{1}{n} = 1$.\n", "\n", "We will be operating on many particles so it is imperative that we are as efficient in our programming as possible. That means storing the particles in an NumPy `array` and making full use of vectorization and ufuncs.\n", "\n", "To track our robot we need to maintain states for x, y, and heading. We will store `N` particles in a `(N, 3)` shaped array. The three columns contain x, y, and heading, in that order.\n", "\n", "In this problem the robot can move on a plane of some arbitrary dimension, with the lower right corner at (0,0).\n", "\n", "Here is a partial implementation of this problem. The particles are initially randomly distributed over the space, and are given a random heading. " ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from numpy.random import uniform\n", "\n", "class ParticleFilter(object):\n", "\n", " def __init__(self, N, x_dim, y_dim, landmarks, measure_std_error):\n", " self.N = N\n", " self.x_dim = x_dim\n", " self.y_dim = y_dim\n", " self.landmarks = landmarks\n", " self.R = measure_std_error\n", "\n", " # distribute particles randomly with uniform weight\n", " self.weights = np.empty(N)\n", " self.weights.fill(1./N)\n", "\n", " self.particles = np.empty((N, 3)) # x, y, heading \n", " self.particles[:, 0] = uniform(0, x_dim, size=N)\n", " self.particles[:, 1] = uniform(0, y_dim, size=N)\n", " self.particles[:, 2] = uniform(0, 2*np.pi, size=N)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict Step\n", "\n", "The predict step in the Bayes algorithm predicts the movement of the robot and to update our belief in the robot's position by that movement. How would we do that with particles? Each particle represents a possible position for the robot. Suppose we send a command to the robot move 0.1 meters while turning 0.0073 radians. We would want to update each particle by moving it using the same command. If we did that exactly we would soon run into a problem. The robot's controls are not perfect so it will not move exactly as commanded. Therefore we need to add noise to the particle's movements to have a reasonable chance of capturing the actual movement of the robot. If you do not model the uncertainty in the system the particle filter will not correctly model the probability distribution of our belief in the robot's position. \n", "\n", "If this wasn't a robot, but something you are tracking passively, then you would want to include things like velocity in the particle state and use that estimate to make the prediction. When using particle filters I try to minimize the dimensionality of the problem; if I add 2 random variables I will probably have to increase the number of particles to ensure I have enough samples to correctly sample from the probability distribution of each. This can quickly become intractable. Thus I've excluded velocity from this problem.\n", "\n", "I cannot easily modify a class definition in Jupyter Notebook, so I will show you the method in text. The entire class is in pf_internal, and we will be importing when we run the filter. \n", "\n", " def predict(self, u, std, dt=1.):\n", " \"\"\" move according to control input u (heading change, velocity) \n", " with noise std\"\"\"\n", "\n", " self.particles[:, 2] += u[0] + randn(self.N) * std[0]\n", " self.particles[:, 2] %= 2 * np.pi\n", "\n", " d = u[1]*dt + randn(self.N) * std[1]\n", " self.particles[:, 0] += np.cos(self.particles[:, 2]) * d\n", " self.particles[:, 1] += np.sin(self.particles[:, 2]) * d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Update Step\n", "\n", "Next we get a set of measurements - one for each landmark currently in view. How should these measurements be used to alter our probability distribution as modeled by the particles?\n", "\n", "Think back to the *Discrete Bayes* chapter. In that chapter we modeled positions in a hallway as discrete and uniformly spaced. We assigned a probability to each position. When a new measurement came in we multiplied the current probability of that position by the probability that the measurement matched that location. We implemented that like this:\n", "\n", " def update(map_, belief, z, prob_correct):\n", " scale = prob_correct / (1. - prob_correct)\n", " for i, val in enumerate(map_):\n", " if val == z:\n", " belief[i] *= scale\n", " normalize(belief)\n", "\n", "We will want to do the same thing with our particles. Each particle has a position. We can also assign it a weight or probability based on how well it matches the measurement. We will want to multiply that new probability into the current weight of the particle. If we then normalize the weights of the particles those that are closest to the robot will generally have a higher weight than ones far from the robot.\n", "\n", " def update(self, z):\n", " self.weights.fill(1.)\n", " for i, landmark in enumerate(self.landmarks):\n", " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", " self.weights /= sum(self.weights) # normalize\n", "\n", "\n", "In the literature this part of the algorithm is called **Sequential Importance Sampling**, or SIS, and the equation for the weights is called the **importance density**. I will give it theoretical underpinnings in a following section. For now I hope that this makes intuitive sense. If we weight the particles according to how how they match the measurements they *probably* are a good sample for the probability distribution of the system after incorporating the measurements. Theory changes 'probably are a good sample' to 'are a good sample'. Different problems will need to tackle this step in slightly different ways.\n", "\n", "** AUTHORS NOTE: NO. WE ARE SAMPLING FROM EVOLUTION USING IS TO GET POSTERIOR**\n", "\n", "\n", "And that is the general framework for the particle filter. The particles and their weights represent the probability distribution of our belief. This algorithm works well for multiple objects; clusters of particles will group around each object. We can compute the position of the robot by computing the mean of the point positions multiplied by their weights. If we have multiple objects we have to run some sort of clustering algorithm to determine which particles belong to which object, but that isn't hard. \n", "\n", "This is a nearly complete implementation of a particle filter using the Bayesian framework that we have used throughout the book. It is not quite functional in practice due to some problems which I will address in subsequent sections.\n", "\n", "I want to emphasize that as with the other filters we are using Bayes Theorem in the update step. Compare the Bayes equation to `update()` and you will see why I assert this.\n", "\n", "$$P(A|B) = \\frac{P(B|A) P(A)}{P(B)}$$\n", "\n", "**author's note - need more than 'look, similar' here!**\n", "\n", "### Computing the Estimate\n", "\n", "In most applications you will want to know the estimated state after each update, but the filter consists of nothing but a collection of particles. Assuming that we are tracking one object (i.e. it is unimodal) we can compute the mean of the estimate as the sum of the weighted values of the particles. \n", "\n", "$$ \\mu = \\frac{1}{N}\\sum\\limits_{i=1}^N w^ix^i$$\n", "\n", "Here I use the notation $x^i$ to indicate the i$^{th}$ particle. A superscript is used because we often need to use subscripts to denote time steps the k$^{th}$ or k+1$^{th}$ particle, yielding the unwieldy $x^i_{k+1}$. I'm not partial to this notation but it is what is used in the literature.\n", "\n", "This equation seems to be reasonable, but is it? I will discuss this in the *Importance Sampling* section below. For now, know that the answer is yes, it is reasonable. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Particle Resampling\n", "\n", "I mentioned problems that the SIS algorithm has. One is called the **degeneracy problem**. We initialize a large area with uniformly distributed particles. The number of particles near the robot are likely to be low. As we perform the algorithm above any particle modestly far from the robot will quickly acquire an extremely low weight, and only one or two particles near the robot with has an appreciable weight. The filter requires hundreds to thousands of particles to adequately sample the probability distribution. Only one or two near the robot are not enough.\n", "\n", "This problem is solved by some form of **resampling** of the particles. If most of the particles have a very small weight this means that most particles are not a very good representation of the probability distribution of the robot. The simplest resampling algorithm endevours to discard particles with very low probability (low weights) and replaces them with particles with higher probability. These particles are taken from the current particle set. This means that some of our particles will be duplicated, but this is not a problem because during the predict step we will be adding random noise to each, so they will separate from their twins. \n", "\n", "One way to accomplish this is to sample from the current particle set `N` times, making a new set of particles from the sample. For this to work the probability of selecting any given particle should be proportional to its weight. The easiest way to accomplish this is to use NumPy's `cumsum` function. `cumsum` computes the *cumulative* sum of an array. That is, element one is the sum of elements zero and one, element two is the sum of elements zero and one, etc. Then we generate random numbers in the range of 0.0 to 1.0 and do a binary search to find the weight that most closely matches that number. This algorithm is variously called **multinomial resampling** or **simple random resampling**. Here is my implementation:\n", "\n", " def resample(self):\n", " cumulative_sum = np.cumsum(self.weights)\n", " cumulative_sum[-1] = 1. # avoid round-off error\n", " indexes = np.searchsorted(cumulative_sum, random(self.N))\n", " \n", " # resample according to indexes\n", " self.particles = self.particles[indexes]\n", " self.weights = self.weights[indexes]\n", " self.weights /= np.sum(self.weights) # normalize\n", " \n", "There are many resampling algorithms. Each has different properties with respect to which particles are sampled, execution time, and memory required. I will share a few of them in a later section.\n", "\n", "You will not necessarily want to resample every iteration. For example, if you received no new measurements you have not received any information from which the resample can benefit. We can determine when to resample by using something called the **effective N**, which is meant to approximate the number of particles with have an appreciable weight which meaningfully contributes to the probability distribution. The equation for this is\n", "\n", "$$\\hat{N}_{eff} = \\frac{1}{\\sum w^2}$$\n", "\n", "and we can implement this in Python with\n", "\n", " def neff(self):\n", " return 1. / np.sum(np.square(self.weights))\n", "\n", "\n", "If $\\hat{N}_{eff}$ falls below some threshold it is time to resample. A useful starting point is $\\frac{1}{2}N$, but this varies by problem. It is also possible for $\\hat{N}_{eff} = N$, which means the particle set has collapsed to one point (each has equal weight). It may not be theoretically pure, but if that happens I create a new distribution of particles in the hopes of generating particles with more diversity. If this happens to you often, you may need to increase the number of particles, or otherwise adjust your filter. We will talk more of this later." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## SIR Filter - A Complete Example\n", "\n", "There is more, but we have covered enough to implement a full particle filter. The filter in this form is called the **Sampling Importance Resampling filter**, or SIR.\n", "\n", "The code above has been placed in the file *./code/RobotLocalizationParticleFilter.py* which we will import. If you want to read the source code, comment out the %load command in the next cell and press ctrl-return to execute it.\n", "\n", "To implement a particle filter we need to create the landmarks and the filter. We then execute a loop, successively calling `predict`, `update`, and `resample`. The class includes a method named `estimate` which computes the weighted mean and covariance of all the particles" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# uncomment to view source code\n", "# %load ./code/RobotLocalizationParticleFilter" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final position error, variance: [ 18.00486271 18.00176074] [ 0.00458291 0.00448719]\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from RobotLocalizationParticleFilter import *\n", "from numpy.linalg import norm\n", "\n", "def run_pf1(N, iters=18, sensor_std_err=.1, \n", " do_plot=True, plot_particles=False,\n", " xlim=(0, 20), ylim=(0, 20),\n", " initial_x=None):\n", " landmarks = np.array([[-1, 2], [5, 10], [12,14], [18,21]])\n", " NL = len(landmarks)\n", " pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n", " if initial_x is not None:\n", " pf.create_gaussian_particles(mean=initial_x, var=(5, 5, np.pi/4))\n", " \n", " if plot_particles:\n", " alpha = .20\n", " if N > 5000:\n", " alpha *= np.sqrt(5000)/np.sqrt(N) \n", " plt.scatter(pf.particles[:, 0], pf.particles[:, 1], alpha=alpha, color='g')\n", " \n", " xs = []\n", " pos = np.array([0., 0.])\n", " for x in range(iters):\n", " pos += (1, 1) # robot position\n", "\n", " # distance from robot to each landmark\n", " zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n", "\n", " # move diagonally forward to (x+1, x+1)\n", " pf.predict((0.00, 1.414), (.2, .05))\n", " pf.update(z=zs)\n", " pf.resample()\n", "\n", " mu, var = pf.estimate()\n", " xs.append(mu)\n", " if plot_particles:\n", " plt.scatter(pf.particles[:, 0], pf.particles[:, 1], color='k', marker=',', s=1)\n", " p1 = plt.scatter(pos[0], pos[1], marker='+', color='k', s=180, lw=3)\n", " p2 = plt.scatter(mu[0], mu[1], marker='s', color='r')\n", " \n", " xs = np.array(xs)\n", " #plt.plot(xs[:, 0], xs[:, 1])\n", " plt.legend([p1, p2], ['Actual', 'PF'], loc=4, numpoints=1)\n", " plt.xlim(*xlim)\n", " plt.ylim(*ylim)\n", " print('final position error, variance:', mu, var)\n", "\n", "from numpy.random import seed\n", "seed(2) \n", "run_pf1(N=5000, plot_particles=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of this code is devoted to initialization and plotting. The entirety of the particle filter processing consists of these lines:\n", "\n", " # distance from robot to each landmark\n", " zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n", "\n", " # move diagonally forward to (x+1, x+1)\n", " pf.predict(u=(0.00, 1.414), std=(.2, .05))\n", " pf.update(z=zs)\n", " if pf.neff() < N/2:\n", " pf.resample()\n", "\n", "The first line takes advantage of `numpy.linalg.norm`, which computes the Euclidian distance of a vector. In other words, we compute the distance of the robot to each landmark, and add in sensor noise so that this is a realistic simulation. Uou wouldn't add noise if this was real sensor data, of course!\n", "\n", "The next line predicts the position of the particles with the assumption that the robot is moving in a straight line (`u[0] == 0`) and moving 1 unit in both the x and y axis (`u[1]==1.414`). The standard deviation for the error in the turn is 0.2, and the standard deviation for the distance is 0.05. When this call returns the particles will all have been moved forward, but the weights are no longer correct as they have not been updated.\n", "\n", "The next line incorporates the measurement into the filter. This does not alter the particle positions, it only alters the weights. If you recall the weight of the particle is computed as the probability that it matches the Gaussian of the sensor error model. The further the particle from the measured distance the less likely it is to be a good representation.\n", "\n", "The final two lines example the effective particle count ($\\hat{N}_{eff})$. If it falls below $\\frac{1}{2}N$ we perform resampling to try to ensure our particles form a good representation of the actual probability distribution.\n", "\n", "Now let's look at this with all the particles plotted. Seeing this happen interactively is much more useful, but this format still gives us useful information. I plotted the original random distribution of points in a very pale green and large circles to help distinguish them from the subsequent iterations where the particles are plotted with black pixels. The number of particles makes it hard to see the details, so I limited the number of iterations to 8 so we can zoom in and look more closely." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final position error, variance: [ 8.06115913 7.89412269] [ 0.0029203 0.00299334]\n" ] }, { "data": { "image/png": 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56evogyRLECXR9JHCQcCv7+/XctcLiqM8PDyM8fFxDA8PY3h4GK2trUSfu729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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seed(2)\n", "run_pf1(N=5000, iters=8, plot_particles=True, xlim=(0,8), ylim=(0,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the plot it looks like there are only a few particles at the first two robot positions. This is not true; there are 5,000 particles, but due to resampling most are duplicates of each other. The reason for this is the Gaussian for the sensor is very narrow. This is called **sample impoverishment** and can lead to filter divergence. I'll address this in detail below. For now, looking at the second step at x=2 we can see that the particles have dispersed a bit. This dispersion is due to the motion model noise. All particles are projected forward according to the control input `u`, but noise is added to each particle proportional to the error in the control mechanism in the robot. By the third step the particles have dispersed enough to make a convincing cloud of particles around the robot. \n", "\n", "The shape of the particle cloud is an ellipse. This is not a coincidence. The sensors and robot control are both modeled as Gaussian, so the probability distribution of the system is also a Gaussian. The particle filter is a sampling of the probability distribution, so the cloud should be an ellipse.\n", "\n", "It is important to recognize that the particle filter algorithm *does not require* the sensors or system to be Gaussian or linear. Because we represent the probability distribution with a cloud of particles we can handle any probability distribution and strongly nonlinear problems. There can be discontinuities and hard limits in the probability model. For example " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Effect of Sensor Errors on the Filter\n", "\n", "The first few iterations of the filter resulted in many duplicate particles. This happens because the model for the sensors is Gaussian, and we gave it a small standard deviation of $\\sigma=0.1$. This is counterintuitive at first. The Kalman filter performs *better* when the noise is smaller, yet the particle filter can perform worse. We can reason why this is true. The standard deviation is 0.1. This means that if the robot is at (1, 1) and a particle is at (2, 2) the particle is 14 standard deviations away from the sensor. This gives it a near zero probability, and it is extremely unlikely to survive after the resampling. \n", "\n", "This is *very important* to understand - a very accurate sensor can lead to poor performance of the filter because few of the particles will be a good sample of the probability distribution. There are a few fixes available to us. First, we can artificially increase the sensor noise standard deviation so the particle filter will accept more points as matching the robots probability distribution. This is non-optimal because some of those points will be a poor match. The real problem is that there aren't enough points being generated such that enough are near the robot. Increasing `N` usually fixes this problem. This decision is not cost free as increasing the number of particles significantly increase the computation time. Still, let's look at the result of using 100,000 particles." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final position error, variance: [ 8.03991809 7.93388899] [ 0.00275743 0.00305754]\n" ] }, { "data": { "image/png": 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1Lta4TC5xXV/DWgqQjTV75+1leUmAR8ignIqNyFpR1U1Do+5r9GNPScUsWeYE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i8n2q7rbI2DHsan2mJbbLHKV5in84nhAYAJhurp4Sl1+CK60ZfzS9GPlc8QjJC00jQycy0S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PlAYGnL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\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eTxGjveoqkmnTR1tt4E23IXzy2HKIAppsp8UpbptbPH/wPDbtRmwxd2/2o8UjvOZeQz/0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\n6yxumhsUUSFa3s8tntv6TE4qOED1q6w+9INJWr3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oQBsOQFSAuqthGyucUwtLFIZelc8F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0JKBwSxUYdGDFbrevZDvv5DABIATV2tS42l8JXrKxjZAFufIYzpfVfiVQjFWzQqqoomE8Vd23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\nzz8fr3vd63Dw4EF84QtfeOV1QJ0BHDhwQO3atUsFQaA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seed(2) \n", "run_pf1(N=100000, iters=8, plot_particles=True, xlim=(0,8), ylim=(0,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many more particles at x=1, and we have a convincing cloud at x=2. Clearly the filter is performing better, but if you are running this in your browser you must have noticed how slow the filter ran. \n", "\n", "Another approach is to be smarter about generating the initial particle cloud. Suppose we know that the robot is near (0,0). This is not exact, as the simulation actually places the robot at (1,1), but it is close. If instead of creating a uniform cloud of particles over the entire map we made a normally distributed cloud near (0, 0) there is a much greater chance of the particles matching the robot's position. The particle filter code includes the method `create_gaussian_particles()`; feel free to alter the code above to use this function as in the code snippet below. However, we will be using this in the next section to help with a different problem, so feel free to wait.\n", "\n", " pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n", " pf.create_gaussian_particles(mean=(0, 0), var=(10,10))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter Degeneracy From Inadequate Samples\n", "\n", "The filter as written is far from perfect. Here is how it performs with a different random seed." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final position error, variance: [ 13.65600711 -15.87383315] [ 51.52147194 34.40736373]\n" ] }, { "data": { "image/png": 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Jaed/r6OkQ2/ckxIqa2xJ+Z6pCUnrSmaFsB6mbJXJR/OE9BA3Dm8ck3xuNbfY\nae1IGdxMdObYa04+57O+273qPZrDJpVuhUavwSvzr/DizItSTnXUT9AUURviS4nCRpjeuMd4MqbR\nbzDxJmiKxn5nn77dZ6e1Q61f4x/8r/+Am9+9yZ3rd7jwv1zAw6M1arFZ2+SVuVckwXjSIT66h3x7\nfpIg9EnMzqhDIS7WSSQQ4aeWfkruA1/eWbbKhPQQ6VCareYW1thCVVTOpc6xllnjxuENWqMW9+v3\neX/vfWLBmCRcTsqbW8MWnWFHzquu6s+VsMERKZACWeNPuGO+1d06thA3qhts1jexXRtTMylaRZyJ\nQyqUwgyYKJ6CqqrS8Jw04v6Em7pJpVdh4k1YTC6iovL6wuucz55HV3Uxmek1Xph5gcszl8mEMlzI\nXgB4bvS8llmjYlUo98qMHVFcFNSDtEYtLmQuEA6E+bjyMa7nMnAG9J0+V2euSs3USRw1zgDhQJjX\n51+Xm97PCvgRtl90cdR4H108px0oJ+FvjAf1B9iuze3KbfY6e1i2xUHnABTBYjqewxtn3pCa5pJV\nwvM8yeTHzTgBLcBWcwtN1UgEE2iqRtSI0hw0yUQyGJrBdnNbOsvdUZfHJLZkGV+Ze+VUQ9kcNvlP\nd/4TEyYMnSE3Dm8wmowYOSNUVTBhmqLRHXclgzaajMCDTDjDbHSWhJlgMbH4TGNxdG5fKryE4zl4\nnkdQD3ImeQZN0Xhw+ICAFiAUFYdvxIiQj+RPPRROHraH3UMUFMmy1fo1Kr0KQT2IoRkE9SDbrW1Q\nkI6Vn1J0PRfHE+lUPyg6mzoLwG5rFxSkoZXyjXD2WHB5NONj6iZBPUi9XyegB3hQf8BoMhKsfbfE\nK3OvkAql5PrwU4jZcJahPSQfyRPShL58Ib7Aen6duBl/ilW4V7vHo+Yj6oM6UTNKe9Rmt7kriiEf\nM3+X85c5kzhDpVshoAVYz68TCUS4V73HYeeQs8mzki05GhgDp2oIffgG2Nd3+syKP88KinSse+Me\nu61dXFzq/Tqb9U0u5S/JvWVPbB41H8l95nou6/l1+uM+rueiqZoMHM4kz/BC/gV0TWejvkEymGQt\nu4ahGbw48yLz8XluHN6Q49IYNMhFcnieh6EbPKg/IGJEiBkxFFVhMSn2WNSM0hl3OOgcUB/UOege\n0Bq2qFgV6v06MTPG0BkSMSIMnaFkIQeNgZABRT2ZaSp1S1R6FVrDFplIRjKjZxJn0FWdiTshHAiL\nlPNjqZXGu4qhAAAgAElEQVSHx2x0Vo6hz5SfS52jNWhJtj4bzmJqJn27T9yM8+r8q5xJnmE8GRM2\nwsIxDiYY2ANGkxFDW7CLC8kFzibOgiIcUD+zcHJfmbrJjcMbvPvoXSzHYuJOMDWTbCSLpmjHWEnH\ndWiNWk9lDmNmjPv1+5KtK3aLXMhekGv+eXBch7uVu5StMofWIbVeDUM3WIgvsJRcImpGWU4t0x/3\npZ0zNEMW8J/GhJ4kUvwA8jTGuWyVGTgDUsEUS6klVtIrnImfYS2zxltrb/Heznsyi1ztVfnC8hdE\nMW+vKjO9uqbT7DdJRVLYjk21X5VBrn9eWCMLRVEoxIVTGAqEWEovsd3clja50quwnFwW2d1BE3ti\n0xq25Pc4qfn2be5sdJbbpduCMEPBxSWoB7HGFm5HSJOurl2VTvFRDfqz7KvruaykV57JlJ6awXQn\nIigYdWgNW/Ttvvy97dl8WPoQ4Km6gdaoxX57H03VJDHkf7aCIADnYnOcS57j+4ffJxgQQeaHpQ8J\nG2GZXTvsHvJx6WNUVZW1GLZrS+nQp80E+GvKL/wcToZMvAkvzrx4jJGu9Cp0R11ykRyLiUXawzYR\nI0J72OZO9Q6pUIrWsMVB94CgFpRZcX88fuf//h35mX/z7/9NYmaMUqckfBTbErVCJ8iyT2OfV9Ir\nUvP+UuEldEWQNy/MvMDAHhzLLtw4vIGiiP38UfkjhhMh0TqTPCPXen1Q5zu73+Gj0ke0h21uF2/T\nHXc5kzjDzeJNWsMWHxx8wGZ9UyoyDM2QgeXRrNFpNWSdUYfmqMkn1U9YT63L7/Qn0jFvOa1jG2az\nvsnD1kOG9pCd9g6GZhAxIpKpHjpDFhILNAYNrLFwOhJm4phj6qcoziXPsZxeJhV8wjxLVi0oWMmY\nGaM/Fk5PKBA6NWr1o72IEeHG4Q2q/aqMnBVPsJAL8QVWMiscdg+FpENVSQVTzMfmGU1GhAPhU9Mr\nR41zKBASB/Gow93KXbm5/KLKvt3H8RzuVO4QN+NUe1W6o+5TBX+fBgedA1ojsRmrXWEYdlu7sujL\n0AwWE4vMxea4kL0gxjN1TsoI/HTUdmsbRREGWVM1woGwlLKMJ4ItHTpDwrooLB04A9KhtJhTPcRb\na28dM75HDWmtX6PerzOajKj2qzQHTR7UH9AZd6j1atyv3ufSzCUuZi9yq3iL3rhHb9wDBV6de5V6\nvy4di+cFKUcd9bnYHDEjRiFWkIfq97e/j6Io5NI5GsPGU9KEo+99bD6NEMVOkeaoiTWy+ODwAzRV\nIxQIsdPaIR1Ki/SZbbGSWUFX9WMHQ8/uMXEnLKWXRNo9eQYVwdyGA2Fq/RoBNUAylGTiTjibOHuM\naYAnGR8zYLLd3GZoi8LXD8sfEg6EZbefcCAsi4t8HV04EJb6wLPJs9woiW47rufSHDZ588ybUuPn\n/91397/LR+WP2Gnt0HN6uK7LTGRGBtaFWIFcJEepW0JTNfp2n+awyeX8Zd5+8DaNQYPeuCe6deQv\nH2PcfpjipJOs01HnwGeT9tv79JwetmtjT0SGrjlosppZlZmHgTOQe7MQL9Af92UBoV+cOx+blzrl\n93ffx9AMNFVj7Iy5MnuFTCgjg+yhM2ToCAmPPbG5UbzBx5WPMTSDar/KueQ53jj7BplQhpX0Cpu1\nTb69820CeoDN2iYlq0Rn2KE9bBMLigMyYkYYOSMh93vMQhoDgzutO3S0Dvudfb699208POnApEIp\nhs6Q1qBFzIwRNaJSHjHxJnxc/pi+3T8mLdFVHc/zsD2b+zVR/FjriT16JnWGqCFsVyackV0SVjIr\nKDwpmstEMpxJnCEcCFOIF1hNi6K2slUmFAjJ4rGjzpapm9ws3sQaWxS7RXrjHmvZNTqjDtbY4mzq\n7LHUvb9v/GzZSnqFslXmfv0+EybSQXqW83OapOCgc0Df7osizWCKYCDIS4WXyIazjCdjEWA9Hj9/\nfsNGmNno7Klniu+st0YtWUeVCCaklO0k49wddfE8j+X0MslgUv7+Uv4S1V4VM2DKwGY+Ps+dyh1C\nRojt1jaVXkVIMdoHzMZmsSfCCZx4EzzPO0aM+Kyh/17hQJiBLZoSjCdjWeMxsAeUrBITd8KjxiMq\nffEZp8mQjtpGUxeF+CEjRG/cYzgZ4nkeO5UdEoEEZaUsP+O09zoZzKykV+TaeF4GXWYwJ2MeNR/h\neA4PGw/5uPIxiVACVRVF49utbYKBoJQj2e4Te/yw8ZDd9q7sGuKz0BeyF44Rd9/d+y4DRwTGtX4N\nUzcZ2kPiZly+T9kqo6s6IT0k12HUiH7qTID/3R41H9F3+lT7VVlD1Bw0uTp7lb32Hnerd+nZPayx\nhambLMQX5BpzcRk5I0zdxNANUecWTEribCW9wgf7H/Dtf/9t+Zm/+Pd/EXtigwKaojFyRjKoflaB\n+7Psc7Fb5KXCS7IzSjgQxtRNuqPusYJq13Nl8HTQOeC/bf83HNchFAhx2D1kNbMKCnx98+scdg+x\nbIvt1jYRI8LEm/Cw8ZBsOCt9kc6ow3gyJmpEZSb2aNboWWdNxIhwq3iLvt3nUvqS/PmfSMe8Mqqw\n19njVvEWB90D2d5KVVRGzojuuMul/CWR7hwP+Onln+Zm8aZkB8pWmTfOvHFqGjIVSpEKPrti++RC\nuVW8dSyyPbnIZPcCMyoitGEdDcHWXJkV3U4m7oTDzqHYBIkFttvbcrKPGo2jxj9mxo6xOScNQNkq\nM3SGJINJsQGCcTbrm4SNsNTK+1H9p9WnNYYN/nD/D+kMO4zdMdutbVRPsNAKCkupJZbTy6iKioJC\nISYOuaP67pgZY6O2IQ5Cb0Kj3yAREppa1xMFHP5BWYgVWE4vSxnI+cx5WaR59JmPGlJfxlHtVXE8\nR1Rs90QRZnPQxMam2qtybfGaSEtroqXXnyr8KR42Hsp0+/MOC38dHJ2Lo4xPzIyxvb8NHizOLnIp\nd4n13DoJM/FMJ9Fff9bIYsJEpM8eMzOKopCP5AnoAQbjAaqiiqzL49ZcJUsUf/oZIUVRSAVTvDL3\nyrEODzPRGW4Vb+EiIntd0fns4mcZ2INjGRafmfALBZfTy8SDcXaaOwycAaGAOBgKsQIDZ4A9sblX\nu0ff7tMet/newfc4nzvPx6WPuVe5Rz6WZ+JN0BVdZg784OSgc8Cj5iNRi6Cq2BObQqwgGFdNZyYy\nQ8yI0Rg0SEfSQhMbSqNpGluNLQbOAE3RCGqiBVff7rOWWXsqDfqsObxxeINKr0LUiMqD8+Qh4R/s\nfjcR13NxXEdqfaNGVAYovpRAURThEJlJWbfiz83RjIxka4ctyR737T7XFq/Jg8bvdDSejLlbvku5\nV6YxaIi1Zwj2+8rsFcmwl60yD1sP6Y66ZEIZPql+ItjZjGBnVzKi84e/TxVF4UziDHd373K7cZuG\n2xAp3GGDM4kztAYtumMhPQNYSCwwnoy5MnOFy/nLLMQXJGO5nF7G0J6WtPiHk+uKA9P1XAzNIG7G\nSYVSrGXX5P5ImMe1uQCvL7zOWkY41r4TdvTvTjpbt4q3CAfCOK6DoRt0nS4HrQPpWCbMJ91Shs5Q\nZmYSZoJCrCAd47JVZrslOov40qSYGZMsrB8EnOZI+2xxY9AQeza+wEJ8QWp0QQTSCTMhNeIKipBQ\nnejm4RdEt0Yt/nD/D9lqbknmbj4+f6wThZ+1nY3OClZaebojmd8NKWyILk1bzS16Tg9DNVhMLuJ6\nLrvNXRQUSlaJgBoQ0gvdlIW0rVELa2SRi+TYa+9JltXQDArxgnz/Sq8iOph1SriKS0gPMXbHzMZm\nZa2Irxs/DTEzRmsoOvIMHGH/FpOLPNh/wEH/gPnZ+R8YOB0lU06TbJyWQffXX9kqkw6nZbbQdm02\n6hu0B20GzoBKr0LdqhMIBLDGFrvtXemo+wG5qqjHMjupUErWsT1oPKAz6khZa2PQkG0Fo0aU8WQs\n7avt2lT6FeG0P7bJtmcTDIiz4HmZQf+7bdQ2OOgcMJwMhW2LFwhqQbEmFWSL5KX0EpqiSQfUH59g\nICgzI/lonmggSlAPcjZ1lnq/jq7p/O7/87vyM9/8q2/KTmuKopAJia45vt18XkBxWvbiZGATNaLH\nbKzfrcyf99vF25R7ZeJmnFAgRGvQYjQZsRhf5F7tnsiCjFrYrk06LNqghgIhFBRURaVv9xk4AwBJ\nloYDYfnMz1McHA0sz8XOyZ//iXTM/+vOf+WdR+/IVMR2a1sexr6mO2kmCQVCmLoptZd+RH8mcYaA\nGniqBdmnKaLYae2w1dySxtw/SH0D1Bl1yEVzpIIpWc1cskpEzSgTd0KpUyIbznLt7DXRSzdeoBAt\noCgKc/E5Rs6IhJmQ8hnfaESMyFPMnsuTqLA77rLX3hPFUBHBtikoMuKu9qqEjTAL8YVjLKvPAtyv\n3X9KL3tyXMaTMQ+bIjOx3dzG0A3mokIrXIgWyIayoEIunKNn93jYeMit0i3RPtG15SGynF5mr73H\nQfuAeCjOyBYR+MuFl/HwMHWTfDgvnXyAV+dfJWbGnjoAc5EcnXGHjeoG7VEb13XpjrvMJ+a5dXgL\nayQc9faoTSKUIGpESQVTtIdtwWZFZ3nzzJs0B008PFYyK8e0skcP4KMFN8/TIKqKil23CethXlp9\nict5IXv6NO25jrYP7I17suUfwN2yYDJWs6tsVDfEIWMV2axvEjNisrD1NONvjS0RDM5ckf1+0+G0\nbKt4NMPSHXfZ7+yDJxyHqBlFV3RmYjOy1WIhVmCjtkE2kmW7uc12a5uZ2AyNvijy0RSND8sf0hq1\nhM49IPSJ8/F5/tz5PyfXWWck2Nl71Xuyr7+Hx/nMednZxPVcLNtiLjZHQBPa7fn4vHB8+k3aozaH\nvUPa4zadcYeQHmI+Pv8DtZbXD65T7pWlBvtou63TDvZUKMWl/CUeNB7IrguqqspaB3+PdsYdKr0K\npW6JXES0insWq9UZdejZPVKhFJsNURQ4n5iXwdF+R/Tb9w+2VCglZB3OUDrWcTNONpxFQZGBqaEa\n8vfw5EAxNIPVzCrLqWXBuJlRzibP4nou79x/h+3+NqqhUuqV6I8F2/tS4SU265uYuslyZpmAGuBi\n9qKsa1AVVbLiJ7OSfvZl4k4YjodsNDYI6kHiwbh07i9kLzC0h3LsT9MdH9Xs+j9fz63LfXXS2eqO\nuwxs0T6y1q9hO8LpyYQzvHHmDRRF4XuH35M6/aMSjaOt1sIBYdePtshcSa/wwcEHPGo+omgV2aht\nYAZM6n1xFpV6JcaTMedS5/jGo28cI4TOpc4xckayHaY1thjYA2mn/UDEX3P+z3q2aL3XGDQYTUZS\nOhAxIownY97beY+yVZZ21s9WHNU+H5V5nKwfuFW+RWvQotQt8bDxkKSZpNoXxa8lq0Rz2CQTzvBJ\n7RN2m7tCs9+rEjJEy9iyVaY5bGJqJlEzyoXMBSnXSIVTKJ7ClcIVUsEUAS1AxIhwp3yHvt1HVYUc\nxs86nYQ/734GYDG5yP3afQ7rhwwnQ+yA/VTg9INY4+fpgk+us3w0z05rh9FkJANaUzPRNTG+k8mE\nYr/IvfI90UrYs0XWLzZzaltAv5j7aBvBilVh4oq6n57dAw+RWYvPM7AHgrF+HGAVu0Va4xbtQVvW\nmwT1IJ8797ljmeTTfBpfX71Z3xSdg+IFPNeTOmh/L/itik+Ozcls6sgZcS51jnAg/KQJQTjHv/m/\n/o18jj//1/88nVFHtNAM50iFU1R7VdmS+VntCp83V0flVP45fPQ1vlzTL0zeae+QDqc57B4ycAYs\nxhdlS+beqEd72MbQDUzN5FzqHEupJfY7+5iaSXvcljVDW60tNEWTRMxpzUROwg8sc2ZO/ux/aLvE\nHxd6ox4pM0U6nGbsjPmk9gm6qpM0k+x193hj4Q0pk/AZY4DZ6CzAsWb2/v+f1q4ORNeNu5W7JIIJ\nZqIzvLfznui8oQk9un/p0IelD+UBtd/epxAVBYau51LtVQVjOxS6rkQowf3afc5nzqMrOovJRRYT\ni8cuKvDbcPk4rXVZxRKtmPxLlFqDlmh9ZFU4lzpHPpo/1r7J7xUNooWQi8u9yj25aSbuhL+4/heP\nfcbJcXEnrryUpjls8vLcy2RDWRrDBulQmnwkf+yCBIBkKMn1g+uyCC8SiDAbnUVRFKH5DGdxXId3\nHr5DIVZgLjaH7drMxebkIe23m3NxafQbALLVWC6Sozqo0hl0WM2ucj5zno3qBlEzSsSIcGgdYugi\nZezhsdvZpTfucbZ/lvawzUpqhRdnXwQ4Nu7Pan33rDZyR1tT6arObGj2h253dbR94FpmjZJVIhlM\n0h628RRxGdVWY4uVzAp3K3fpDDtEg1FuFG9QH9a5kr8iD5ZnrevPLHzmqUuajratHE1GfGfnOyRD\nSdlR4VL+Eqqi8vlzn6cxEEU/l2cuE9SCUltY69Xk92gMGszH53nUeiSdbUVR+NzZzwHich8QLfz8\nQkg/HRo346KoR1GkFnIxIQ5jQxcHz7g1ZiWzIgI/q8JgMiAfybOUWqI5aP7Atmz+HM7GZmkMGpIZ\nS4VSOJ5ovXpaG62gHuSvXv2rxy7v8Md7r71H0Sqy1dgSXUnwuF28zUxsRv79eDI+9v7+fNf7denk\nzsfnwUP0+vZEgKgrOtlQlmxUpFd9edDEnRA346K96GNkw1kpRQBkwZavv1dReWXulWNtvnxdsdoX\nznwkIPSk9sRGV3ReLbwq9qtuyrZ0R5GL5Pi9zd+TnRs+Kn/Ez63/HN/d/664aMZzeHf3XeJmnM5Y\nMFR/dvXPgsIz242dNn+ftoVcNpKlalXBg/PZ80zcCRezF5mLz8n0djqcFh2pHvfdrvaqfGbhM0/a\nnT6+4yFhioLifDTPYnyRvc4e96r3pBN02D3kbkXcKeFLNer9OqVuiQu5C+JSr0GTVCgl2OMjbW39\n5zxqp/0LXI613ovNSnt/FI7n8M2tb6IoCpqiUR/UOZ85L53zo2N7stXgfGyeRr9BY9DgUu4S/2Xz\nvxDVo4SMEHcqd/iZ5Z/B1EzWMmsUu0Vu7N8QQYc7FhIhqygKseuPhGwvkqM9ajMTnaHaq/La/Gvs\ndfaoWBV5OdjN4k2ybpZf//DXsWyLmBljt73LxexF+cyntbbVVZ1X5l7h+sF1Kr2KkPKgcjZ+Fg9P\nXkLnt6l8Hp7V2vKZr48WuO3dFvUXirBlfiDc6DdEYG2kMBRD6vnrg7p8ptPaAvqZutnYrGwjiIcs\nPLx25hqaosl2sIfdQ16YeYFPqp/QHDRRPIWALiSFwUAQXdWp9qr86//zX8vv9OX/7cun7ivfhv3O\nxu8IMstzhXwzNgseklQ4bTx1Vee1+de4cXiDTDjDf/xX/5G//q/++nPH+2+89jee+tkv/x+/zJf/\n9y8/Nc9wvLVzLpL7gXNViBXYae/I1qmZcIbF+CKLceFTpUNpbNfmsH1ISA+Rj+S5PHMZgGKnyGp2\nlVgwxnZrm1fnXyUfybNR3eBi7iLNQZNcKMeF3AV++95vkzAS1Ad1Hjx4IC+D2u/s83Lh5We2ovXH\n7CfmgqEfJY4Vf1oihe1XxRq6geM6QocWCPGw8ZCwGeZsQjTMP9p+6TSB/mlN9HVN507lDr/58W+y\n19nj3Z13+eb2N2V7M//gVVC4MntFsh5LqSU0VZPpSEMzZPeCoB4kHU4T0AJPafVO6llPPuvRqNBv\nPRbUg0w8caAMnAGxYEwwQ4qIWqNGVGomTxYJdUddnIlg9P3Wbj27x0J8QbaVO5lGao/aFLvCGCdD\nSSzHYi0jonq/e4nPDvo9mj3F43bpNt1Rl2q/ygf7HzCfmKc5aD6JzB9LbyaI9op9uy9Tm4uJRSmx\n2Ovs8VHpI2xXXF7ktxpzXIexMyZqilZxQ2fI2B1TiIqK+KgRFZ09Hus8B86AyzOXKXaLjJwRfbvP\n2B0T1IPH0r6hQIjrB9efan3nM3LP0/adbD33LBxlNUzdpGyVCRthIoEIqVCK85nzDB1RoNMcNGWP\n7v3OvtDmB+OE9BCJUAJN0ZiNzsqWUc/rxnOSieiMOigohI0w39n5Ds1hU+oZM+EMMSMmawAAylZZ\ntJJTxOU+u+1dEsEE2Yi4pS0dSjNRhHwlG8kSNaJ8ZuEzvL7wusx6NIdNPix9SDaSJW7Ghf4zs8K5\n1DkyoQxrmTV5ycRsdFYw94+zXv7aX04tUx/UCWgBLucuEzNiQtsdKzxTB9wYNGSL0qgZJRfJySJY\nFBjaw+d2OdDV45d3+AXg39r5Fpv1TbrjLu1hW7bzXE2vygupmsMmu61dilaRxqAh9Zt+R6ajTFVv\n3JPkQtyMEwyIItzV7Cq9sZAdfHbhsyyll7icv0woEJKSEZ/xe33hdSKBCGeTZwnpISKBCF9c+SL1\nfl1qM30Wbedwh/6kTzwaJ6AGWE2v8tmFz7IQX+ClwkuSHfLX/FEbutfeE60b7SHNYVPWGVR7VWZi\nMyJ97IyYuBOykSwzsRlxQ+5jKc2nLWB7VmbzpPwA4E+f/dOiyNNMcG3xmsgiPt7f3VGXfDTPTFQE\nTUE9KDthmbrJOw/foTPu8HHpY4q9IhezF9lt7coakPqgjqqotIYtkXGxhBPveKJDUz6SFx2N8IQj\npSii3mfiEDJEmvzkc57WlUS23nss79E1nY3qBpZtsZxept6rSyLE1/MDUkrzPMmG35VHURQ2qhtM\n3AkRI0LCTJANi0vh4sE4zUFTXtQ3mozk/HuehzNxMPUnvaGdiSOlVvdq9/i4/DHJcJLeuMeHpQ9Z\nSi3J1nsJM0HcjDMTmUFTNXKRHPfr9491IvGLLo82VfCZ8+AoSEANcH7xvAhOo3lZs+DLbE5rKxgz\nY0/p8nVVP2YffOmIr2u+tniNUrckL9QzdZPt1jbNgZAqVftVVlOrslZsJb1yaltAnymv9CoyU5eL\n5MTtnJ4HCiwmF59kahA1An724aif42dTA2oAD49QIMRf+4t/jXfffZc/ePcP+Nlf+FmGzlDeN3Ky\nVahf7Dx0htIx11WdVChF3IwzF5uTtRYnWXe/09Kd63fYuLHx3DPuNLx67VX+0p//SySCCcaTMTcO\nb8i+5UclacVukZcLLz/z0i1/Le619xg6Q/zCer8PfCKYIBPO8OLMi4wmI+LBOG+ceYOgFpSXTvlr\ndz23Lu7dGDSZT8zLnv7+/QOapsmbpAf2QLZNPFpT8KxzQ1V+gi4Y+nFhJb0ieum6oqc2CoLpc0ZP\n9LhqQEbZfsTyvGt4TzbRr2xVZBeFcrdMsVPE1EweNR8xG5uVaVH/em5f2+2/11Hoqk4unJOLv9YT\nF5a8OPviU8/hP6vPNPgsvx/l+5cLeXhkIhk81yMXzsmUdUgPya4NhmYIRv4xw3R0DK7OXuXXbv+a\naHeEhqqKwMC/aOA0aIrGcnpZ3s71pZUvSZ3zbGyWQrQgLwgYTUY4rsNh95CJOyGgBrAdm2wky1Zj\ni/Xc+rHI3HaF9jsUCAGChZqLzR1jfUtWiZ32DqupVXHFtgJ4pz6qkKuM2iwmFhk7YxbPLpIL52gN\nWpxJneGwIzqfaKomK9sXEgvHLgy5cXgDF5fWsAUIZqJiVXhl7pVjUbzt2vKCq6OXLNRGtWcyr/46\n8b+b4zn83ubvcTl/GV19ctU4IC+4wROfNfEmtAdtEkYCHpNvuqLLNoSfpqj3JGvkHwylbgl7YtMe\ntdEQ7Gc6lBY3fGpPLg/xizFr/RrtYZuJO8FxxdXGrxReodgtcrt0m/XsupAJeZ68BMo3WPfr4oIc\nF1FAefS7+xcf+a+9UxE3SPr7Yact2nLlwjku5y/zqPlIMvnnUueeYlVOXiLmM/R+NiAfyUtW8nmZ\nEB8nmdu99h65aI6HjYd4iAuwWsMWF3MX5Wu3Wlts1jcl01rpVZiPz7OUXJJsII8v1bJdIb2oWBW5\nRpPBJFdnr4ICgaUAnucdu0r6ZvEmuWiOWk+0Rnxr7S10VUdTNSpWhSszVyRr6SL64isorOfWxQUY\nepxcMIceEBK61+ZfoxAvyO/7LBvqX5jSHDZpD9uymNNnhv1MSiqUEhJCMyGJiXwkf+olLKfheZc3\nPev5js7RSft3s3gTPJFhcD33yZXxVlFKtDLhDDEzxq3iLZKhpJD/TRxGzoit7hYKCp1xh4gZwdAN\nNE2kuTVFIxVKyZZyvXEP13NZTa9K9vRZz3na+gJxOdnbm2+zmllFQVy2dHnmMpVehUalQX1UZ+IK\nIuLkJSsg1tVh91B+rm8D/I46nieK3v3aCRSo9+qgQCwYo96ry65YnWEHFdGJKR6Moys648mY+437\nQg8+6jGYDAgHwjxqPWIptURADXCncgfP87iQu8D/x92bxkaS3XeCvzgy8r5vJs9ikSyymnX3ZXS7\n2zpG3bINW57BeOARJI+gAXaBxcDwBw8G6/F+WcPGrIH1h53dmcUA8liewQgjy4esUVtj63aru7qr\nq1jVJIs3WZnMTOZ9RZ5x7Ic/32NkMlnVsr0Lrx8gSGKRmREvXrz3P37HfmWfB82GaQDCmYGMZmr4\n8ORDbBQ2uJv1zcRNSqBOn13ZLEMz6W/DrjDWcmsECSvvwISJldgKr2aO68qMGvCxvfikdYIT9YSf\n7Zqhodqp4meWfubMOMfUYJNspMxU2sGEdwIPTh5AEiQMjAE6Wgf/7OY/4+ZabLBOXdwTR7lThmEa\nyDfzxKNyBlDulJFr5vD2k7cxG5xFxBXB/dx9XpGNeWLwO/y8cm7AQFtrw+vwcjUuNkrtEgQIeDv9\nNqYD0zBh4r3j9yi+qKdRaZMR3ZXIFWwWN/lZLArUUQNwIZIg2yCVoXKn/LRX9pmD+YlIogTN1PDH\nj/8Yt5K3MB2Y5nNfVItPNUvMNXPkoeIj86uuTmpsVvMgh+zApxc/ze+nq3exWThz0IWJISOrzeIm\nmLwvW58hZ4ift9ZRbpc574WZWh7UDnhs9DQDox9n/J0PzF+ZfgWTvklsFDYQcASQV/NETDNJDSHk\nCiKhs88AACAASURBVCHijAwFKeM2OvaANUPjrWEIIGtzw0S5XUahTcYIXb0LA1QlgEkydTF3jG/m\no+2W0XZk0BnkDOiIK0LZrokLAzfm5lhQCzhuHiPlTSHhTSDbyFKAcurUqQkaYh6qzuRaOe5WuRBe\nODdvo3Pw+tzr9HJKlFkOdAr6Rtvs7B78Dv9QC3e7uI3lGDlgFloUZN9M3kSulUOmnkHQRcFxtplF\nypfClG+Kw3lkUcZCeIGCulOyyW5lFzbZRgGhSYTYQqsAAwYU8RT/FZjl1RUGWwg4ArzLEHAGuDNo\n3BNHuV2mjkHyOhySA1F3FO9n38eT6hP09T4kQSKMrjsylMQA5HZ5UD2AIp7CJ4w+Xpt5Dblmjh9c\nAEiP/TS4YAfBRp2Cnouc5qzEw4Q3gVqbbJlr3Rp3vGNBIWsd9vU+Hp48RKPbwIRvApVuBWEhDF3S\n+eFkDUif1rIdF8gAwLuZd6FB49jXo8YRxwJXOpWhtRR1RbFbJlnIiCuCercOgKqPc8E5Ds+aC8yd\nW+Ps0JBFcmOzuniyzdZ6SBuggHo5ugwIwEmTuj9bpS3O95jwTCDiigxBBdhc383cxWZpEzABAQIn\nKAIATOD5yefPBTI/7pAFGS9Pv4wfPfkRVb9PNeLZ3BZaZAYjCbS+a50aHuUfYco3NfQ8NFNDpp6B\nDh3vZt+FaIqYCc7guHGM64nr5LgqUGKaaWQowWgROZRVSNk7wYjBwJlLpmEaVNnvNeG3+1Hr1hBx\nRRBzxFAf1BEJReBTfDioH8DjIH4BW8Ps2VhxzLlmDlFPlN5tgT6/1qnhpemXsFmgJCjijuC4cYw7\nqTuc8McSh3wr/9RWNXsX0o00Pjz5kCfv1sSGrcmLYC6aofFiB+Mb3Uze5M6/zJFXMzSs5dZQ7VT5\nXuixES+FJQCsK2QaJgRRwGp0larw/SYlVrpGJHtXFMuRZXz98depEumgoNHqqGydx3HXbG3pr+XX\nIAgChzqdtE5QaBV4st7X+6j36pgxZ6AZ2tD7r5nj3S/Zmvvcjc/hjzb+aIh79erMq9gp71BS6Azg\nQfYBN8eb8E1g0j+Jn1/+eRw3jrFX3cNuaRe1Xg0SJCg2BQbIkKuklqCIxG2QRSogMOm8aqeKgD0w\n5NwM0L0dVg+JJOjwINPIYL+yj1dnXuV7bPmQEhFBELBeWEe5UyZ8vDMAm2ijpALAt3a/hbArzM+t\n0YS7q3Xx37b/G/Yqe7gcoqKPYRp4XHyMiCuCgDMwtL40Q8Pd47t4XHyMqDuKl6dfJqUWh5/DvGrd\nGj7IfYAXUi+MLQLWujWEnWHoho6EJ8ET1NX4KjaLmwg7w5jwTPBuSK6VgyzIRNiffAkzgRnkGiSJ\naQomUr7UOfirKIiotqko0uq2kEhQAJpupLnzqCRIKKgFLIQX4JAdQ8Fsup7mewcPOKsHKLVLOFFP\nUO/Ucfkzl/Ha516Dbupo99uYDc7C7/Dji7e/yK/j3/zw3/DizPOTzyPpSfIAfy2/RoUxScaTyhM0\nu03cy9zDQeUAYU8YIXtoCKY3Dp6Z8CbO/t0kidK4O87/3Xr2Rt1RPMo/QkEtIOQKUTGhVyc/mNPO\nXcQdwUZxA5l6BiFXCDCBuCfOteMdNgff31nQHnQFsVGkM79n9LCxvoFXZ16FLMpDCc3fZPydD8xl\nUcbl0GVcDl0GQC/WN7a+gd3qLnx2H8qtMgL2AM9+xo3RB2yaJvwOP/Yqe6THrWvYKm+h2qsi7KRq\nol22YzG8CEmQsBxZHnrg46o14wIf68GbbWZRbpfxbuZdHjiOC0o2i5uotCscuxd1R4deQlmgQya7\nnaVF4ghis7iJpcjSU7FzTGat3C5zW17TNMdu3MBZlaDWoQpyT++RBJvPS89B7/KDLuFLYNI/iZgr\nhma/iaQnCZ/Dh3KnjLnQHLpaF1slwnGtF9ZR7VThU3yotWuYC82h1+rx4I1VLyLuCLLNLELOEK9y\nsQ19wjtBEkyCPDTX7BC0bo4vTb6EhCeB7x58F367H5Ikodgq4nby9vA6E2TM+edIThGA3WbH+sk6\nVwFiBEC24bPN64PsBzzoZIe99SBgay/XzGGvsoet8haCjiDh2UbWLPvbsCuMb+19C41ug5LHbhk/\nOf2TmPBNwC7ZOf51HLbtok7RuEBmwjcBRVA46UjXdcyH51FsF1FoFbhNeV/v83Y+I02WW+WhQNP6\n+ayjoJka+nqfOA2mDtMw0df7VBF3Roc2YbbmNgobMEyDJ4ZhZxiLkUUiYQtddAdduO1u3oGyzjer\nsr599DZvb7sU0ja3iTb47D7km2e27j8O9tQ62N9KgoSXpl9CsVXE9cR13lIFyCrcNE309B4OqgcE\nfThdC1aMIgCuX78QXkCtU4NDduBy+DLWC+tne4OhYau0xTsF7x2/x7tJh7VDrBfWIYkSrkSvUOv2\ndF42i5tcj5lpowNAfVBHzBnDtcQ1kljsN7gTJ3t+1kCf7RGaqaGklrh60sAYcEjOUmSJd6JuJ2+P\nxWE+bY1a9+nN4iae1J5gKbrESXiFVoEH5qODF15MUl/ZKe9wHtB8aB42ycalP1lVci2/RkRWY4Bq\nhzoA9S4ZcgVdQUTcEWi6BkmS4HeSXKpNtpH4gCBT51IgXPKUn/DoQVeQ7xGMYzCuEnnRfbNuWtRF\n0qtFtQj6GoLKMM+OereOkCOEw9ohvrH9DfzclZ/jc5ttZnE1dpVXcK3vCHtH535iju/fTP2KQX1K\n7RKSviQWwgtIN9IIOUP45PwnIYsyvnPwHdS7BHOs9+vwO/y0rlxRqBpVHHXz1DPhdP5wKp0XcUUQ\ndoU5TIW9f0W1iOagibAjjLbW5g6eLIksqkUknAnkO3neHZdFmv96t46AI4Dt8jaHZbKgdzRI7mpd\nfOn+l3A/dx+ZRgbfPfwufmr2p1BsFxF2hTm8hZ0NbD/58ORDHNWPsFfew3RgmlSYgpfgkB3Yr+6T\nUtVp1XU0MLRyMSqdClYTqxyeKokEl9BNnbrCoD1wLbfG9yK2XuYCc0jX03hSfzLE72FjJbqCzeIm\ndFPHQoTWJ+OjsUSa8StYR+CZSILDAhYji6h2qmjrbZgwkW1k4VScCDgD1N0bKYzE3DFcDl2GCRMp\nb4qjGKyj1qlx5Zrj5jFy7Rw8dQ+8Di+CriDfR8fxu2DSXt3Vunhceoxqu4qV6MrQWZD0JvFO5h1s\nFjdR69RQ6VKRSZIkzgNk61MWZJKr7bYoMXMEOMLBLtkBAP909Z9y/tP1xHW8tfMWKZ2JMlrdFoLO\nIMrtMn/O6XoaITF07hn9OOPvPMZ8FJ8jizIWI4ucgJXypZDwJLhyiRVrxsYo/tZpcyJTzxAruVXE\ncesYMRc5cDllJ1aiK1gMLyLuieNG8gZemnppaHGNk8wZ/Rn7/8xqfbu8DXWgYqOwgYPaAVcXcNlc\nXGKpoBagDlT47D747L4hq2grBp1h2mdDpKzitDmxEFkY6x5qvWamwc3Y1KO2xUPs5x45lvnsPngU\nDw9YfXYfbz0CgNpXuUpHyBnCQngBPrsPl0OX8Y+u/iPYRBvXfu3rfQz0AZnYqGXa4BtkDrMYXYRH\n8fCEwaN44HP4uOPljeQNOGQH3Iqb2P3CmSPXsySMQs4QVmIrOKgeQBRELiFp1QvOt/IIOAPwOaii\nbheJoS0KIlw2FwRBQL1XR76Zx4P8A6oaDNooqkWYqklV5Wj0nMTadnmb3B4bx8iqWeyX9ymoB2Gy\nw256ZvOheY7H3q3sYi23dtbRMenzmeNZ2Bm+UNHgWbKB1tHqtdDVu+hpPYScIUz4J1BoFuC0ORH1\nRFFUi5gJzEAdkP77XnUPZbWMcrsMVSfc83HjeEjhx6pio/ZVGDAw6Z/EfnUfBbWA3cou1gvryDQy\nSDfSGOgDeOwe7Ff20eg10Bl0YMLkULWO1sFeeQ8uxUXOfqeV73HyW0yOESL4+/6k/gSH1UM4FSc0\nQ0Oj30ClU4FLcWEhvPBUPOPT3iWmgCEKIl6ZeQVRd3RozpnGOXt+E94JXI5chm7qQxjF3fIunIoT\nXa3LpSwjrgjX+Wbfx1SgBvoA6kDFQfkAfaMPp82JH2V+BN3QUevWsFvZxVxojr/XBbUAp+JEuVNG\ntVPlWPlKsYLqoIrJ+CRXErHOKVMGsWKWdVNHpp7Bk/oTLg13LXENV6JXuHsuU05hmM/Rtfi0NWrd\np3VDR7qZhgAiofaNPkKOEPItgl85bc4hHXP27uxV9rCWX4NH8fCDtdKpQIDAJUYZTlQdqDQv7Src\nNjdinhgWIgsQTREpfwo+xYdsIwuHzYFmr8kDPwECPnbpY5gJzHCSuyiIQ2ZdLpsLU4Epvo6fhqsf\n9WZg8nJdrYtKu4LOoAOP4sFcaA6dQQelDpmQGaAARe2REROb+4/ifimLMpd0lEUiCh/VjriudbPX\nRLVXxdXoVfgdfpy0TpBr5biilU2yoa/3SUWp34JhGpjxzyDpSeK5xHOY9k+jpJZwVDvCdGAaEVeE\nS0SOakIrsoJKuwLN1NDVKfme9E0i6o5yqTq1oqKlteAOurkqmizKtA77Kk+crkSv8I7mqDLKWztv\n4WH+IXZru1zJZ6+yh/ngPJJ+8jZhzs/W/UQ3dUScEc7LWQxTsFrr1jjPaiG8AK/dO/RsmaIJ66Z6\n7V4c1Y7wfu599PU+ulqXnJl1nRvDMWljURA5B84hO8jn4ZQPwd7XP/2//5Q/zy/+6hcRdAbR6DX4\nZxmmgbgnjs6gw8UgrPwKKxY/6o7iYf4h5wQA1Pk/bhwDOC0etEqIeqJw2BwQTIFgvoKAP/n3f8Kv\n480vvomO1uF8mna/zeOxiCsy5CfS6rcwG5jlXBQGq231W8RpMLRz69jvILnUh/mHdMZoKnW1vQke\n2+1WdilREijB6A66kEQJU74puG1uTPunufEYk8Nl6kEA+UckPUlM+iaxGFmEQ3YMeZnYbXbujhp0\nkfM5g421BhTvzXnPCgh/LzHmwHmckSzKcEgOnhVrhoZHJ49QbBchCRLMrIlPzH+CZznjcOAL4QXs\nV/ZJIu5UP9Tv9GPGP4PLIZJyup68fq4yOXotADhExlrFtf5NuV0GkzeTJHI2ZNUA1g5uDVrYKGyg\n0qmQID7os64nrw9hoYeqoILMK1yjra2L5m3KP4Wu3sX3D78PSZB4dj06RiuKDJ7DoEACBF5hseLH\nFUnhEnnsdwEyNah1atzsJt1MQ8Spm2dDww3jBhySA8tRgsvE3DEcN4/5JnE/dx+r8VX8xd5fcOUP\nURDxxsIb5ypD1vtlP8s2s7SpSGe/a60KaiY5UsZcVMXdrm4j7AqjqBaHFHl2y7uo96iyNhOYwaXQ\nJfzl4V8ipIRwSb8EERS0WbHyW6UthJ1hRJ1RIlkpPtxK3qK2nkDcCGt1gLnkFdtFiBDR7rfR6DS4\nFv1FMK1xa+RpI+lNIuaOodymani5XYbP4ePQqaQ3yYMaWZIRtAdRUktoDVoQRML2lDtlvLXzFq/A\nnKtymBq2iluIu+JIV9MoNAlHPTAGOK4fI9PI4ONzH0fcE8dmcRMhVwiyIKPapXZoyBVCSS2h0q4g\n5onBZ/dBN3WOc7dWujVT4zj46cA0KmoFT4QniHqIl7Fb3eVyYeVOGcvRZdyZuMOVlD7K3DGYBMO4\n1rt1vLXzFodqWJ/D86nn+R60HF2GLMjIq/mh+Ym6oyi2ivzZMojWKESur/WxX9vHpeAlfJj/EE2t\niaAZxOPiYyQ99BzrPVJWWS+sI+aKIeqOYimyxJ1ADYNUjh7mH0Ltqch2snDn3ViOLEM3dAScAT6n\n45RBCq0CFEnBanwVpXYJfa0PRVSG9ry/zlq0vqOM2Bj3xjHjn4FNok5Hq9nCZmmTvkMnuBPDBd/L\n3kPUHSWezWkltdatnevIsMFwokylhxE3I64IVqIrXCkr5o4h08ig0W0g4Aqg0q7gUvAS7clj7ovt\nmQziwKTcximsPGtIooSV6Ao+NKkAshJboUTeRVr1jJApCiL8Tv9QN2FcN4jpj1/0XGRR5sZ8sihz\noQEr3I51fduDNiACKW8KAXuAQ6BWYiscr8yUjELOELZKW/xZjX7nlH8KSW8Sfb2PdzPvwiE7yP3V\n6KHT7yCn50jMACeI2CNcoWUxsoh8I48rkSs8+U35U/w6rFA5gPDTxXYRB9UDNLq0l6oDFZIpodQt\n4RIuQYeOdzLv8Io+AK66A5P+95/8+z/BjH8GrX4Lr3zuFeiGjqAzSGpMYzhQskC8j6AriL3KHtQe\ndRUEv4Cr/qswTRNXY1d5Ehlzx0j55xQqoZs6/tWv/yt85f/8ylPXy0tTL5372b/+jX+N3/hffoPO\nOIhD/IpxMJHn4s9hvbDOIR6aTt2Mo+oRJFFC0peEV/HixckXUelUoEjKOeJ2o9fArH8W5XYZTtnJ\nP/v51PNwyA780rVfwn/f++/YKe1gNb6KTD0Dr93LpUXfT7+PaqTKeRCM4MzWMUMZRN1RlDolqH0V\ndaOOP9v+M9yI30BH66DRbaDcLqPZa2I2OIuiWuTnhWmanJsoiwQXup64jveO3yNIm6Ejr+VhCuaF\n0NQp3xQy9QyqnSp6eg87pR1E3BHops4Lf3/T8Xe+Yi4r8lgdaatySUEtoN6rE65TJEOF7x1+DwEn\nMcRrvRrHp7HKM5MtYk5QsiTjUvASFiOLSPlSYyuT1mpgs9/EUe0Ix03KOJnWrSKfWbiKgshF/tWB\nis6gg86gg4QvwV3T/HZyc/uPD/4jOoMOBgbZfM8ESGVmObp8zsJ4nO3wqDbo6LWyeevrfXxt42s4\nrB6i3Cljq7yF2cAsrsaunqtsjeoIT/omeUuV4fhEQRxidlsrj9xwSfGg1Camc7VdRb6VhyIr8Dq8\nuBK7glq7dqaUAdIxZ5rxrNI0MAf47sF30eq38Lj0GLlWDpIoId/MYzY4i3QjjR8c/oBn2Uz3/Gnm\nIdaqIATC+vcNqmTYRBt3aNRN2pznQnPc2txhcyDlTyFdT6NWq6Fn9OD1efGTsz9Jbp1MG1lx4ah6\nxBnfpmlybF7MHcOEd4JXOpr9Jpe1TDfTqHdIq9su2zEXmEPKn0JH68A0zY+ssz5usEpJq9/CXHCO\nV/HmQ/NI+pJDOPt8M48HuQcE77LZsV5ah12yI+Cgd8tv9w+Z7owqwLDExybZqMPQKdL8yQ4MjAFs\nog1hVxgpXwpBZ5AgNaeV2a7WxWJ4ERP+Ce4yOROcQdQd5eonVpWFRyePsFXawlH1CI1+g5SRHCEo\nsoJsM4tKm3Shg44gkj7qluxV9gBczLC3zhdTcDisHuJEPcEHuQ9gl+zoaB0cVg+xVdpCrpVDtpHF\n49JjVDoVeBQPT+5YdSXgDHANcIfswEJ4AUFn8JwTsVWxo6f3yNzk1NSjPWhj2jsNwzTQ6Df4Zwz0\nAXqDHq5ErwAgYpNNtvHDVZEUPCw8xKA3QMAWAOzAXHAObyy8AZtoO6cMYt1j4p441IHKCVaHtUNA\nIA4Oe9+smt9MaWJcF3PcPsVci5mrYsQdwfX4dU7+NkyDihqnqi9euxc+u49XP5maTa1bQ7vfhl22\ncy1yj90Dpo7S7DUR9UShiArC7jBsEkFcrkSu8GtkexAzAJMECXbZDrtkx4RvYqwKCNOjt3Zhxs3j\n6F5t3c/ZWnXKTu69EPPEeHUPAK4nr5NqjM2JuCcOAQJW46u8oj+6dz/L/ZKNVo+UW3x2H6/K2iQb\nya22sjB1E+lmGpqh4UmdXKBvTNzAfHAeP3vlZzEbmB1KTgOOwFAXVJEVnLROEHPHuA8DS8p8Dh/m\ng/OodCpYiiyholZw1DhC3BMnFakW7dO3l26DSXz2jT5xGGDwSjMbd1J3eCeDPZuQM4S1/BpKnRKH\nq054JyCCjGGOG8foDXpw293Yq+zB5/Dh0ckjwtXX9pBpZfC1f/k13H37Lh68+wC/+9u/i4ExwExw\nhjsus2fLsOW7lV2STK2lOUmwPWgTiVOhrprL5uJeAYx8zCrXuqFj7/4e1t9bv3A/v2i89tprWLy9\nyJW/rPvlOBUvr+KFCZNXjgFgKbqEo9oR3IobE94JOG1O3E7dxouTL8KjeNDROvgP//t/4L//D//H\nf4it4hZVzf2TQwZkbsWN+7n78Nl9mAxMotPvwOfwkdLeKYRHkiRIkGiN+ggi5lbcUPsq4p441xPf\nr+6TD4zTj3Q9Dbtkh1MhFT27ZMdB/QDldhmKpCDmiSHpTsKAgXK3jEqngoPqAeyyHcvRZU4m9Sge\nQAAv4F3U4TJMA8fNYzT7TeyX96HYFAiCgIE+wI3kDVIns5/JzP69NBgq98tjZaCS3iTfzJq9Jo7r\nxwg4A2j2m8g385TNnUo0CRC4lCDbMKvdKu7n70MSJZRVsnW/ErmCS8FLeGHyPIkDOA+JKagFkt87\nbUuxnzMLVxZMzwZnkW1kebuNySAV1SJuJm/iq+tfJTdA2U4H3ame8T+4/A+GTATYYOoWal9FzB3j\nVeanXSubt93KLsqdMmIeyhjtIgV9M4GZc99zETzHKvPY1/soqkXMh+eJlGK5jkqngv3qPrpaFylf\nCpIg4UrsCnx2Hwn/B6ZgE2xwKS7E3XEshhf5xjEuwOtoHTR7TZKpslzPXmUPuVYOu+VdZBoZ+i6L\njCUEgtykG2nYJBtv18e9ca46U1AL6Ggd7u7Y1bqIuqNcrm82OAtZIPk+Zl/c6rdwUDuA3KeqiM1t\nQ8wTgyicSSyKgsjNeiZ8Exwvy6rebCNnh/NJ64QrAwRcAVL3cScwFZjixi8nLZIom/BOPNPZjg0e\nXHYr2ChscKv7b+9/G1FPlNsls4pC3+jj0ckjtHot7JR3oOpUXRIFES47OSeyasZiZJGvk9Gksdlr\ncgnFeq+OYrvIHQOdNpL0W4oucdOVmcAMSbC1K8RTaJcRdUXx5sKb8Nv98Nv9WAgvcFtzdsAf1Y/w\n/vH7cNqc6Gk9tHotXE9cx9XEVVTaFdR7dfQHffgdfs4bAAgSE3BQhZrBRawyY9bAcb+6j8PaITx2\nsojvaT16h2Q71otkoHJUI43dZo8CoEvhS5yc5LP78NLUS/juwXeHjGhemXkFYVeYDmblvKujW3Fj\nLb9GQXivgWa3iRvJG/A7/Jj2T0MzNN62bw/aeGnqJbhsLsiijJAzxNvR0/5pXozotDuwS3Z4fV5M\nB6YxF5zjz4+tqdEg0624ce/4Hg7rh8i1yEhoIUJmbyZMHFQP8Oc7f468Sh2193PvczfBi4JB6z4l\nizJ1TER5SHrONE3ubiwKItS+ChMmfz8VWYHaV+FW3Fy5Yym6hLg7jpXYCnwOH9r9NlfKupm8yeVk\n2Rr41OVPDSUmbA9iBmAAeAAvCdKQ6ZnVCC3XzGEpssSDwtEgeRxkyvo7bsXNYSIA4LF7cGfiztBz\nYJBFl40Crkn/5NjCCnue93P3caKecAnSi/aI0QQh08hgvUicoMPaIeq9Oj45/0lIggTN0Og+HUFu\nZse+37p3s8KNW3HzzlBH65ybOwaJnAnMYGAM8KT+BKIgoq8Rydlsm/ApPkymJuF3EIn5XvYeenqP\npDmhI+6OY9I3iaXIEifcHzeOYZNs6GgdKJKCK1GSwvQrflxLXIPaV/Fc7DlUOhU0e03Mh+dRVssw\nYSJdT8M0aZ1pBuG1N/9wk8/Xb/+vv40r0Svn4HBsz2BSwK0edehsoo17BqiainQ9DV3XueQvE7Bg\nUAmn7ISqqVh/bx3b97ZHj+dnjss3L2Px9iLUvopmvzm09saZ+bjtboKtDlTEPBRX9LU+N+vzKB5M\nBaYQdAR5sZApUk1fm8bCrQWUYtSxlCQJe5U9bhxodatl77rH7sFieBHz4Xm0+204ZScVp04x5AW1\ngKXIEhe5YAU3ptSnDlQuKBF0BtHX+kjX0zzo7mpd+B1+/Mziz2AqMIVKu4KBPjhzjjUpGbE6eg6M\nAQ6qB2gP2txh1ak4OSTZLttxP3cfRbXIFbRcsgt9vc8TY5fN9fffYOiiYSW7xdwx9PQePsh+AJtk\nQ6PXQE/vQTf1s98Xhslpa7k11Dt15Fo5tHVaFKyd87c9HLKDyy+pAxV/uP6HvA307977dwg4yRhJ\nEAWcNE7glKgFZDWJYGO0BZVv5X9sYxuADpewM0wOZ+IYXaCnDDb36ToxvqOeKFdqYderGRqOm8c4\nUQmHyw6sW8lbyLgzhL0e0HezJMQqNzXajmWV5u/sfQetQYsw650+12VltrwSJLyTfofzAjRTw3Zx\nmw4IB0ma3Uze5Ao7TPKRtblY1T7XJMOXiDsyBCsQBRGL4UXClp+q9uRVguswgtqoxKJNtOGz1z+L\nolokmSsTvF3Kni2bU3bYTHunkVNzuJm4iYgrwslptQ4pujCVinEwpNExKkNZVItYja+i1j1Vhzkl\n/QGghE2QkW6kARPoaB34XX60ui2EHCHMh+cRd8exV94bq0YySkLlUnUgOIdP8aHcJaJM0EEmLJqu\nIdPIEDFMOsW++ie51Oikf5Lr1Y5rwT6fep4r+mQbWb6e96v7iHvjuBy6zLXqvYqXCGo6qQMx0tdG\ncYObbzCZsVFoDjNXYutRN3WuOe+3+6EOVNgkG8f4+uw+lNQSEp4E4p44GXV1SPaOkaoDzgCXCBu9\nt8PaISb9kyi0Cgg6g6h2q5gNzmJX30Wz18S1+DWIgog3F9/EeoEqaiFn6JyiznJkGevFder8dMto\n9psY9AZoaS0oIQW5Ro6v1SGJQteZRCG7tmKniGa3iWq7isngWYLT03v4rx/+V6iaCptoo06cfxbN\nXhOyX+YQoItgefx6TyEI1j0t6U0iXA+joBZ4EmvA4NAbEQRpy7VyWMutIeEhjeaBMUCmkcFeZW+o\nWzrlm3qm3CL7OTMAi7giXJHGyqdYy68NQ7fGyG6O++xz9336O+l6Gk7ZyUn2Vgk5K0zo+dTzF5qc\nsMGfmVpEqV0ic6HYCv2beUbQZvsRkygtqAVMeCeg66S+ochkpnNUP8Jabo2glgKZ+CU8iSHJnLs8\n4QAAIABJREFUuqg7Cs3UONyAFQwSngSZ6Dxl7gSByP+H1UM0eg34HX46HwQBlV4FKXeK31tBLZx1\nOwHYTNsZVNMiyweQ7OxSeAkumwsexYMv3PoC7LIdJbWEn5j6CTypP0FQD0I3dBw3jjEXmCM41KmJ\nH5MDDTgCQ/P7rd1v4Wby5rn5T9fTPHCLuCKY9BNe/p00wWRcsgubpU2k/CkshckXodwunxF0fVPI\nN2mfFiDgM//DZ/DFX/0iiUG4oryS/nPLP8e/0wr5YNfATOUYCfRe9h4nY46erx2tg3vH96BICsKu\nMPLNPKZ8U2PhWaNqYP/8V/85DBh4++htHNQPcDNxE5l6BgODSLEr0ZWxkp7Wdc+Uh1q9FmTn6Xow\nBRRbRZgw+Z4WcNB++cbCG/jG9jc4zNE0TfjtfuxX95H0JmETbYg4I1gKL52TsbxoaAYJdRTVIgSB\neAEL4QVk6hkoEvnnfHPnm4i4I0PQXEmUsBheBECKLrcnbv/9NxgKeUMXtgJZlhN0EhP+sHoIl42y\n93q/jpg7xoks1xNnuEBmhtEetCk4OXW3TPlScNsoaxxnzz5aDVQkhao1ParGSoLEiRfzoXlOTmKt\noVa/RdquIJt0m2Sjii2o3VZQC9AMDQ7ZgdfnXudB60VEoadVSO2yHQ9yD9DsN7mD4lJkCTF3DA9y\nD2DA4FjdT13+1IVYUM3QcFQ/wnZpGwN9wCuUoiDy9nOtU0N70IbdZieTj1PiTHvQJnORU1LGXGgO\nO+UdHNWO4Hf6qRrliuLFyRe5qyIbo5Wmq7Gr+P7h9+Gxe5BtZHFYO4RTcuJ+7j6O6mShXFSJY+Cx\nE0zolZlXOKFFgABRFKll6DizF2ffEfVEOXQFoEqsz+GDKJwRT9nv+h1+3J64DZfNhcPaIWr1GrWA\nfT6sxle5lCHTNI574tzS3FptGK0eioKIgCOAr3z4FXT1LkqdEiepFttFvoaCDjK8ctvcWIwsPrNV\nbl0zjLTH/n2U9Oe3+zHlnyI8aafMTTxkUea6x7PBWR4s30jeGOrYjGKMrXAMt+JGwksdibg7zolW\nmqGBmUX4HD5uBGGFe7H1PW7966aOereOu8d3CSPYyqOv9RFxReCUnbgav4pSu4Tl8DKS3iRcNhfe\nXHwTdybukAzdKelaFETMheY4QW0UmuOQHWQKZXNhPjyPglrAfGgeQVeQY367WneoEuNShu25WXWQ\nkarZWmPvDLs3wzSwUdzgVaZ0PY0rkSscf70cXca0fxpLETp4GJHPrbjxIPcArX6Ld9tuTdwiKbt2\nic+b0CMZyfnYPFZiK5AECbVeDXczd7nJVraR5XrJtW4NR7UjDPQBdUecflS7VThkB1w2F/ktmCZy\nzRx06NB0DX2jj1sTt+Cz+9A3+tiv7AMYhg157d5nrl9RIOJ6xB2B20aKPK/OvDpU4VYkBa0eGcex\nithJ6wTHzWPIonyuSmYlul8E+xrdg2LuGDpaZ6jKCJxxG1p9In9a1+uz9lMridWtuMdajjMyuRWy\nNlqZHzdGoYR9vc//u6f10Bq08G7mXSLFiyK+vf9tOG1O6vj1mhBFEepAhUt2IafmUFSLkCWZIC6i\nDfPheRgwuBBAR+vgq+tfxcAYIOwKo9wuYz40j+XoMjfwGZ07Ns8A4eof5B6gb/SxVdpCWSWDLUEQ\nENAC6Bpd2Hw21Ho1NLoN5Ft56tyBiIKriVUEHUHcy95DqV2CXbLzDkHCm8Ckb5KfJTOBGbhsLvT0\nHqLuKDfgSngS1P2pHSDmiSHXykHTNbQHbTT6Dax9ZY1f9ye+8Ak4bc6hbpBmaPjB0Q9Q6pS45X3Q\nGcSknyr9LpsLEMknY8I3wd9vdq6apslNkardKkrtEiJuCorbgzZ8dh8SngRcNtcQhOTXf+PXh9aB\nFRq5USBCL6tus0SCrW2nzYn96j7KnTJOWidI19NIeBKwibYhki6DRVlNiNjnME13tmd7HV5U2hWE\nXCHe+b8IgmuYBjZLm9gsbpIpW/UJBEHAjeQNuG1uLpPbHrRRUAuYDcxSoaJTRavfQqFdgEN2wC7Z\nUevW4JSdSLgTSHqTcCtuLgddapcIb36qkDUdmMZydJnPG4tZwq4w5wZFnBFuqnWinlCifnruSKKE\nw+ohdFPHfGgePrsPN5I3cNI6gR12/iz+XkJZXE7XM1uBmqFhv7pPGsvuCPxOPxbDi8i38vAoHq5F\ny16eRq+Bjt7BemGdHOJEWpzTgWm4be5zrmTsxbMuZq/dy23Uu1oXQQexphl2eKu0xTejo9oRjupH\nOKwdYru0jXSDiI9drQtFUpBtZTEfnke9W0dX7+KfrP6TIcUJa5JgZSprJpErTZjn2u/3svfgsrnQ\nGXSg9lW8OvMqJ0ddjV3lhh+fuvypoYzSytS2y3bcPb6Lu5m7KLaL2K/uo6N1EPfESWu4nsaHhQ/R\nHrTxpPYED3MPEfVEkfKl+NxZA6xWr4WN4gY3Zdiv7CPlS8Em2S500GKH50nrBHYbYTx9DoI9pOtp\n+J1+5Ft5ZGoZ6jwI5Gb2+qXXucvcUf0IoiDiUvASVxVg88WTO0eQO8Q5bU6OpQUwRAy0HuZMeaNU\nKkGGjInYBA82AGC7tD3Ugnsa9pvN+4/SP4IgEPs/4opwGcNbE7eoSn8KN7HL9qEk4GnvR6PX4Ju8\nbuqotCvo6oSjb3SHWfwsKBroxHUQBZFgLqfOtW8uvMmNY25P3B5KqC7CuzM4xmZpE+8dv0fGGq0c\naSF3axSoe+hZuW1uNPvNCwO10RYsC/jcdjfyjTz2a/vwKl7EvDGkfCk4bU6U22XMBGfQ03sQBRG/\nsPILnPyT9CbHOnGyYNl6mABEgmYKSB+/9HEEHAHE3aR3m/KlkGvm0NW7uBq9iqnAFJaihDNmB5pm\natwdcvT+rPeWa+ZIklGUMOWf4tXimJscD1+YfOFcUMbfe8XF1SpenXmVaxbrhg6nzUnvQYUUWp6/\n9Dx3Ys238ii2i2QQZgy4i6/P6cN+ZR+GafA5xKlMYNwT55CDvfoeN4LTDA1TvincmrgF4ExtggfI\nFiWoZ61fthcEHUHuVszeRQa9YV1Saxu61W9xBSf2nSzB9jv8Q3vdRRh46x40Lol4Lv4cvr3/bbQH\nbdS7dWyVtrAUIWjW6Ocx+T22n25XtnE/ex8OxQG1f9amH+cG/VEha6PvPduD/Q4/dko7cCkuOGUn\nss0sRIgcFljr1Ei8QJQ5rC3iinAzr/aAjG1eSL2AkCtETsR2H/KtPPEdAtO4m7mLWreGnt5DV+ti\nOjDNCxHjgrIbyRtI19MoqAU0e02CkoTmcdI8gS6QuVFf6+P2xG1kS1momoqD3gH2q/sIuoJcn9pl\nc2E6MI2r0ascU85UWwBAN0k/fCW2MparJQhUFFBkBTP+GRTVIvxOPxySgwoVgSk8F38O3UEX3/69\nb/P5/bVf/zUOg2QyplulLdS6NX5eMQIjMznyKB6Sp9U6lATrA7QGLWTqGVwKX0JnQDCfuCeOTCOD\nUruEWreGRyePUG6XsRSl96Ov9/Gl3/0Sv5Y3v/jm0PlihUbyokNwjksmsoQg6U2iPWgj28pSsmgM\nKKFQi5zfxpR3NFPD4+JjHtew/R04LRg5g9iv7WNgDrBf3gcE4IXUCyiqxbHxkxXvziRd1wvrKHfL\nvOvY03qodWtwKS7O97oUuoR2/4yD5rP7YBomaoManBK5pOrQ4VWIVLoQXsBJi6BcE94JdLUuZoOz\nuD1xewgqzM5JJhlpgvxtwu4wIAAPsg9Q7VYhCiJsoo3gpP4JJD1JZJtZLEWWsFvZRXvQ/htjzP9/\nAWV5WiuQBQMGDJQ7pH6yHF2mhRVbHlLhYK0ipi866ZtEpVtBrVXDreQtlNUyd6q7qD3JroUdguV2\nechNqqgW+X+Yasxx8xi7pV3EvXF47B58++DbaAVoIde6NfzC8i+gp/cw6ZtEtpGF2ld5YG5V+ACo\nLWeaJtd8FiAg4ooM6SNnm6Rx7pAdiHviOGmdYC2/xttYDtmBFydfvHAu2XcxN0xFUjiusNgu4s+2\n/gyKpCDfymO7tA2bZMNJ8wSGYOCHRz+EIAi4M3FnyPCi2CpyCaT2oM0z0kavgRlxZmwL+Nw6OFWh\nAYC9yh7cihvVbhV22Y6G0EC9W8eLqRe5Vq5mkGX0QB+g2Cni3cy7SPlSWDFXMMgMeJt+dJ0dVA9Q\naVd4K5IFSuMc+16eehmVwwp2mjucUJxv5bl809Na3OPmvdguIt1IYyG0wBMKEybqvTom/ZNodBs8\n8WSfZX0/mNoMcNbiZuvdhIlqu4rDxiFen6GOzHJsGROeiXPQmin/FHExTp/TYngRL06++NR3cZzu\nLLvnXDPHyTjs+WUaGfgUH544n2A2MMsr7+NgBuyzunoXmUaGKwcw3KpDcuC1S6+h2W9SVT8wywNI\nQRDgkKiqbIUGsLkbdeJ8ljnTEEP/9HOYjvXl8GXUOjXU+3VEPBEOmbK+V6xiI4syVuOrQ8YyzPH3\ncfExqt0qQm5StbgcuoxmrwngzCDnovlXRGXoXlkbOeaOkcuoIFDlDgRXYXrbmqFx6++13BpPkLZL\n25gPzaPQKgzpVIdcIeiGTvrAgsHdi4tqEYqg4LPXP0sOeiCIzWZxk79TTOWD3fe4d+JZ6i7W90Yz\nNDzMP4QgCrCJNt6G9tl92Kvs8WtmiaxmaPhR+keodqocxnYrdeucCpd1yOKZUZFu6oi6olgvrGMp\nuoR6p861tNcL6yiqxXMwROs7IAkSd9G16sezvzu3/k+LMGzdf5TBMNyiIJJ6iCuI5+LPoaSWCB5y\nqkhz0XDIDnzuxufwrd1vodimQI11uBjkDSATPaYkxfDDoiCipJYw7Zvmz5IZtVn9J5gijm7qKHaK\nKHfKCHvC3C3T7/DDITvw9d//OuqDOhweB/7x//SP0eq1cC1+jfMR2Oel62la0xolh41uA7qp42rs\n6tg1ZYVkMjWqiDuC//J//Bd89f/66lPn95XpV8797GO//DG8+EsvYjY0y306rCo+SW8S7x2/B7ts\nx7RvGs1+E/OBeU6S9ng8HOajSAoWIgv46vpXoRkafIoPf7H3F/j8jc8TH80yDBjnHDAZNJIpqMmi\njK5G/iNWnXR2ppogfhF7JhBI+/2tnbe49GClXRlSw0s30twgEQCWwqQCJQvkMspNk0bip9FRbpfR\n03qY9E/CJtngtDnR6XewXdqG2+7mHg1hV3gIvhlxR0geUwBC9hBCwRCH8y1Hl3EreYtDUK3GW5Ig\nnYMKJ72nDuCnGuUiRFwKX0KxRdAWRSZ5YMEkl9C21sZyZJniTkHAXz35KxgmqUX9TcffamD+/e9/\nH7/zO7+DDz74ANlsFl/60pfw+c9/nv/7L//yL+P3f//3h/7mpZdewttvv/3X/k7rYXQtcQ35Jm1e\n15PX6SA5NcRgmwkAFNUirsauotatIeVLodlr4sPih5gNzCLgDOBr61+D3+nHanyVyxgxTB4AbiFu\nxe6FXWHo0Lmluw4dpTbhS1mFTBIk9DVyVmRKGz29h6P6EZ6LP8flsC5yRWSDSXDF3XEuWZhtZPEH\nhT/AcmwZJbXEM15mhT6KnX3aXGqGhp3yDopqkardpyYtAJlP1No1xDwxtHrkGNnX+wg4A/DavVBk\nBeV2mR8wTFou6o4i18jh7fTbmPJPodVrodqt4sXU+QRh3LBi4gLOADx2In3ppg4RIlK+FKIuwjQy\nE5xcMwdBEDjvoNAucNmnokpShEyuaxT7aHVKuxS8xKXc2MESdUeHTGLC9vCQbCOTSBu3/i6ad1mU\nsRhexL3je/gg9wE8CpkuMGJu1B1F3B0/Z2bDxkX461wrh6AriIe5hzBMAz7Fh/XCOp6ffB42yTZ2\ns2RJx9PMYP46Eo0AwSIYnMMm2iAIAqqdKopqcWzwb02+NwobMAyDywxejV3l+EO7ZMcbi29gu7SN\niCvCA/dnBTKjwTeTLrTe20fBCGuGhoPKAWySDQFHADvlHaR8KVTalTMDFwHYKe8g7o4j7ArjPz/8\nz9wJlbkcruUJx1vpViBBQs/o4d3Mu3h15lUAFOQzI59nzT2bu77eJ4UIXcNrc6/hhfALAIAJ7wSO\nm8e0XgXANEwYMOBVvPDYqdtY6VRglA28NvsafydinhhgglcmHZIDi8FF7FR24HP4kPQksVPewc8v\n/zwAwq4zvkmmnuFyh0/qT/DNnW+em4Nxtuqj92l9b0rtEhRZGcLgzvhJZ3w2MMuvmQXeB9UDbJW2\nIAgC9sp7qPfrXELz5amXx671rt7F/dx91Do1lNtlBF1BxFwxlDtlxD1xRFyRIc3ycYk4k9+TBLrG\ni9aSFVOumRoOq4dD2Nfl6PJTDbFYQGWAqtOVTgWLkUUisNs92D3chd/ph1fxwi7bsRBZwFZxixus\nsORUFuUhe3P+b54kt43P1DPoa3309T6qnSpS/hRPdkYLS4Zp8GeZrqdhE21IeBPYKGzANE2snawB\nJnBj4gaCziAWI4sQIODr/+nr/N6e/6Xn+V7P+Aije99iZBG75V2EXCFcDl/G/dx9OgtOTaasDo1W\nvDrb21lB4scdoiBCFEnTPuAM0BnjO1sDbM3aJTuSviQSJgXFVj6cdRxUDuBVvDhRT9DX+3CKTvzw\n6If4+PzHh37vrw7/CmF3mAwNLe8LLzrgjK8wypOAALrOwBTUnkrPaPJ5wCTZy3KnTO+YWoLH4eFx\nDXDmcAwB/JxjfJqPeiawgFg3dMCkar9LduFEPUHcHUe734YIkfNJrEkFk87UDR0hV4jzria8E5jw\nTtA5z/aIVulCx20APG58mH94lgCbwNXYVazl13A3cxdJTxL1fh33cvfwE1M/wd1RQy5SknLb3WQA\n5XnaHT97/K0G5qqq4tq1a/j85z+Pz33uc+eycUEQ8MlPfhJf/vKX+c8U5bzqyEcdrOrCKpSsosqC\n2uPGMTaLmxy7laln+EvCMLOaQe2ZoIPawn+w9geIuWM4ahzhoHqATy9+mh8mrNrHdHNZVm+YBvKt\nPA4qB1gILwCgFyrqiqKrd6HpVGXq6T3oBhnL3EreQrldRqNLld6BPsBqYhUixLFuWdbBFh4bG8UN\nlNQSr24tRZZQUAt4XHoMgDYLVtV/VlW6q3Xxrb1vQYSIvtHHXm0Pq9FVOGwOassZJierhl1h7NX2\nYIJwcYIgcEtjNsfW4EGRFaQ8KR4QO2QHxwc+zXnRWnFh0oWfWf4MvnPwHYScITL/6dbhVtwwTROV\ndgWZeoYTCBnG1G1zo9wmx0rmIMecE1mgoxnakFNaT+vhR+kf4eXpl/GX+38JEyaWIksUTMSuQhZl\nbDe3EVSCQ9fMdNjHrT9rBRgAx/Fqpobdyi5WE6vI1Ch4eWPhDTgkwjYzYsmzEivrpssSo8PKISCQ\nJFqtV0PAToHjSetkaC0NrbMxASmryDPSL0Dvw/XE9afqJ2umBr/Dj4JaoGqWqcGv+HE5cpkUaByB\nC/Wh2X1V2qSda4oE5Ym4IpAlmZtEALTWX5t9DZUO6Z7fTt7mWuDsYIp5YrxiPXqvFyU3zzpkuloX\nf7TxR+T65gqh3q0j4UngewffQ8gVwm55F9tlqjyLgkhqUG0y2dop7XCsI3e2BZDypVBql5Bv5TmJ\nSTPOHEATnsRQgMHWVNQT5ZAcCORAyd5p3dTxx5t/jFfsr8AhOyCL8pmbImRcS15DWS2Thn+7yCEs\nuqEj18zh5amXeQck28ryeS+oBWRbWbS0FsSBiKbWRLldRrqe5t9xLXENJZXuJ+gMwiE5OE641qlR\n9atVeKat+tOGJEpn3g6n1du54By/ZgZLY8FYvVvHSfuE7MtPbcknfZPc7MSaFH7/8PvYLG3Cr/jR\nGrSQ7CURd5NcIUvSRYj8QB8dUXcUhXaBB9iaqWHaOz2kH881+ccQtpejy6h1Sfo35U1duCY1QxsK\nqAzTwGJkEZV2BXACb+2+BUMgjHe9X8dnLn0GHsWDF1MvjiWUjktcrYmTbuocErMcW0a9Xcel0CXu\n1Dyui5b0Uvv/uHmMaqeKSqeCaruKaf80njSe4KBygDcW3+DEW+swDOPcfI3ufQymkfJRkvBhgXDw\nz8Weo8qx3sXd47sotAroG30cVg+xW97Fy9Mvo9qp8uLOjztEQcRcYI67elqry1yr39R4pZe5d5fa\npaF1wJJT3dTJARpEfNRNkn2GCXzhV75A6jG1NKq9KiLuCLZL21iMLA5VqK3PbpyuvizIeGPhDdKe\nd4tn8ESB7kcSyOEz4AygrJaRdCf5dSY8CYLBnAosaIYGTddI1509r9PndFExRxbp+/t6H6VOCV29\ny2FPPocP84F5WsuCfKEDO9vnR7ue4win44b12kLOEO09Ju2dJ+oJKt0KREFEuVuGx+ZB1BtFd9Al\njsGpdOJqYhX71X1EXX9zHfO/1cD8zTffxJtvvgmAquOjwzRNKIqCWGx85fCjDmvA4bV7sVXcwuPi\nY7w0/RIUUeEPPeVNDUESgDO8MAsgmGxWxB1Bpp7hepTzgXn0DbIPZ7azz8Wfg12yQxAElNtlxL1x\nhJ10gIUcIRhBg//ubHAWESdV7K7ErmCzsImyWsZccI5cCE0N+5V9iKKI2cAsyp0yCs0CbiRvDN2r\n9VoZJCTmjiHpTeKofoSt0hYKrQJl6C6Sfat2qliJraDQKvBq/kcZSW8Sf7775+Q0Kcv82liyE3PH\noJkavnvwXcrwBWAlsgJBENDsN2lj0TUeBLFDmw1JlDAXmoNDdiDiisA76SVdYEv7bdxzZrb3EE6f\nnyfJg5W4O84xdzuVHYRcIURdUYiiCJjg8BmbZMNOeQchZ4gwxTDwQoqqhtZAh6kJrMRWeBCxGFkk\ntrhIxhvvH7/PK18pXwo+yYeD5gFijRjf1FgQwdZfwBFAuV3mQSxvrxka8s08DFBVq9FtIOgM4tNX\nPo2NwgbqHWrfRt3RZyZs40ahVUDUHcVuZZdMMkwd7V4bk95JTnIcF0SMG1aVh3KnzI09BEHAo5NH\nyLfyQ3bzowe4LMp4fe51FFtFTFYncdA4QFktc7yhtbL0UQfbrFllMVPP8Ao6UxZ4moIQ65AAZ9bk\n1gPeqjjxNEgFs2lWNRWdZgdxdxzpWho3UzdRaZMUm27qqKQrmA5NI+AIEJSiW4UkStgobnBmP1cD\nESgoYcpD+VYefb2PSqcCm2TjXRtGelUkhXcSmDlarpnDbmUXIkQoskIGLYKIncYOlgPL5wobsiBj\nNbGKfJOUhhrdBkRB5CpHrHVtmAZO1BOCuQTnKSl3BCBKhL+MOqOQJZn8BgSJJ5YJTwK6oXPMPhu6\nqVM35JQUeJGtunUMddEcARw3jp8Z5GomQRBDrhAG+gDVbpXPSdBFJmoFtcADc2tSqPZV2AQbKVkJ\nElqDFurdOpajywDAq5EwxwcjmqlhNb7K1Xm8di+uxcn6m0GJ7mXvkTHSaVDEkgvd1LFT3kHUHeUJ\nLoOfXbSGWUAFkMHb9eR1PMo/QtARRMQdQbPbREfr4HHxMe6k7jy1M2T9N5bAsGfT6BIf6ZXZV0jS\nVbJzwvy4wbs4Rh93j+9yuElJLeFK+AqmvFMIuUKwS3ZuvmYdd1J3huzexxXodENHtVvla4lxizaK\nG1iMLGKjsMHXw2H1kPZBAD88/CHmw/P4F//yX+DX/udf49fLKvPWYuNAH1DSZhrYLG5CN3VufrcQ\nXuDnwChs6qR1gmvJa1iMLNK7elrUGE2Knk89j4g7gt3KLjymh6tAXQ5fhizK+Lf/27/FN7e/ifeP\n34fX7oUkSYAJDiEa9+zYeWMtnrD35Hri+lBnKdfMEYm3U+a/Px+e5+8l+zsm5WqAYo+gI4i4J85h\nxFaTp4sKHuzz7gh38F7mPQgegZ/fpkjFwNXE8H4wul6t5wBMIN1IQ9PPihUB5+keMdIV4t2l00KG\naZq4Fr9GhZBTRRtFVDg3ACaQ8qQwH5mHYApw292YDkxDERXM+eeQcCfGrvsfZ/x/ijEXBAE//OEP\nEY/HEQgE8Nprr+E3f/M3EY1+9AyDvdQFtYCCWsB72fcw7Z9Go9vATmkHn73+2aGHzdrHVjjBaBbp\nd/qxX9mHbhAOESZlp5vlTWwUNuCz+9DoN3BQO8DPLv0sIu4I8s08HuYf8moow7YzZYCAIzCE2705\ncZO7Y765+Ca+8uFXSH/THkRrQFXMSrfCMcrWAOdm8iaXAou6oyioBRw3jzHQB9ANHZlmBl6bF5Uu\nVR2CziBM0+TBYFEt4qR1guXo8lMd4GSRXNMqnQrssp0H2hPeCY5J1wwNJ9ETLqk2H5rHnYk7VI06\nNVeJuCJ4dPIIa/k1fGL+E+cOz7ngHFVJ2hXu3DmKjQao/b1R2MBabg0+lw9+xY+N4gZWY6uU+asF\nvD73OhySA+lGmsxXRHKNPFGpEswqAflGHnFPHLpOLS/d1NHoNVBql87amKJMVvQtwuVGXBEM9AGi\n7ihVqkwdh9VDQKCW9E55B2F3GE86T+C3kV08k3Jin8egRlY5vsw2JYCyIKPQLkA3yGUv18rBI3vg\nc/jGOtg9KyhnmFJBEBBxRyBC5PJnL0+9jHfS78AwDCQSCThtTlwOX0bYGT5XzbI+A+v/Z0GKJNKB\nzzgdIWeISwlapd3uZe+hqBYRdodR69SgGRpSvhRemHwBBgzYFTuvlt9I3LgQGsMCsIAzgGwzC2av\nPtplYW3V0fthlaNRyNJB9QDfOfgOV+JZy6/hY5c+drbXnPI4mA75syAVUU8UjV4D+WYejW4Dy7Fl\n0reXbLgcvoxym1Ruws4wTlTSq2/2mpgLzsEwjSEoj/WQafaa+CD7AXToOKodwefwIeAM4Bvb3yDr\n8zYZpqzGVzm5URZkfs9M2pGR7vxOP4yWgY36BpYSS9wAaSW2wlvvU74p3Mve42uYBUCFFlX5GEEq\n6ArioHaAy6HLuDN5h5LoU4yqZmjINrJI+pI4UUn9hhHKmIsw2xPYQSkKIuZD83jnyTvOGdQNAAAg\nAElEQVT4sPAhrkSvDLWvR9foUNVs4vbYii97PhCA7eI2V9CBACTcCTypPYHL7uK8nosgZz67D9lG\nFg7ZgWafEi1G8mRdi9H3xxqMsAAh5U0h5aVKrl2yI+lN4p3MO0PdtYAjgKQvCRlEZv/B0Q8QdhIf\nYL+6j08vfhrZZhZ71T1sFjd5d2Etv4afmvspHlD19B7Xb056kih4Csg0M3iUf0RSn70q6r06QVI+\nYndo3GDER3avH54QNHRcF40lHa1eC4uhRTrHM++hpbWwW92FU3HiBccLQwRt60h6k0NBOYNqbZW2\nsFXcwp3JOyi0C4BJRZdqp4qZ4AzvHjwuPiYlLlcUO5Ud2EQbAnbq9PqdflyLX0OpXRobvFqH9T2d\n8E4AwilcpEVSiUwJjsGm2LwKIgWdE74Jzu+5CE44F5jD5298Hn+6+acIOANYii5BERUOEdop70CH\njsfFx8TXCs7ANM0Lu8+jsc+4dToqlbgYXkS5XcZAH/B7sp4PjLvypPYEIWcI1W4V6yfr3A0aOJ/M\njRY8cs0cFEmBy+bC1TjxAcpqmXcHlqPLzyzcsOuywh4FCFgIL/Bixe3k8B4BEFynoBZItx4mpgJT\nWC+sc4lrAFiILGCnsgOn7ITP7kOtW8Pl4GXUujVcj1FixQpwfx356tHx/5oqy2/91m/hp3/6p3H9\n+nX+s16vh1/8xV/Er/zKr+CFF17Al7/8Zfze7/0evvjFL1K2Z/k9NkYZrUwCSh2o2ChuoNlrwq2Q\nkkrYFeZSVZpBzltb5S3sVfbQ0UidhLk8yaLMzXJOWicIOYndfdw4xkp0BfVeHQe1A0z4JuC3k9sU\nIwXE3DHMBee429xccA4uxUVVdE+cE6uYgY1hGvzwnPZP47B2iL7R5+TRttZGoV3AjH+Gu9x97/B7\naPQaGJgDYhQrHq4uUGqXcFQ7ggABPruPnOkkCX67H3bZjquxq5jwTqCv9xH3xGGaJjpaByFHCNlm\n9hyr2qoeEHQG8W7mXbpXiSo1V6NX0R60uTzShHcCXsWLpDfJnbOYuL5hGtit7KKrdaEOVOSbebw6\n8yqXUXxh8gWsF9apGniqljNq0nHcOEbf6OP94/cp4O6SDXTfIJkvt+Lm9sJexYuV2Ao0Q8NR7ehM\nIUTXuMnK1dhVfJD7ALpBmrQOmwMBRwARN8nptfotzIXm+N/Oh+e5GdX1xHVi2Mt2rrQzE5yhw8wd\noxZsrYppzzQWJhfgVtz8Xhkz3irHNx2YxoPcA9Q6NdS6Na7AkG/mufGS2lfhdXjhU3xDDnbjhmac\nuVJulbfgVkjVJF1LYyW6wpVCJFHChG8CXrsXUU+UjJ4UUrdhklXjXG2ta8Vlc0EdqFBkBdulbW7f\n7VbcHAdvlXY7UU+QV/N4//h9KJJCkmPdBpyKE91Bl8tRsmqgKIhj1yULKDuDDlZiKyRR6CR3TAD8\nuvOtPI5qR4i6z9xdnTZ6vqyFDICrQGSbWeRaORimwXkUzEZ6YA7w/zD3pjGSZed55nPvjX3fI3Lf\na1+7utnFZos02S2LEgjCMmjBNiwOYVj6YQgD2QPDIxsGKRswMDYwA8uyfww8sAeWKUuQYcOQxZZI\nk+LSavVS3V1rdlVWVu4Z+75H3GV+nLqnIiIjq5uyBuYhCFRXRUbee5bvfMv7ve/d/F12qjtiv3uC\nYzSKo6PRbzA0hxTbRaq9qnDYPCE+NfcpSfOloOBxejifPM9qfJWPih/R1/ssRhdp9prMhGa4NnNN\nisfYbCC1bo0Psh/gc/kotov0hj3S/rQUE9IUjcXwIh1dsI8EXAEGxkDSowXdQc4lz8lmsmQgiWIp\nxI04fbPPXGZOCqj09T5rsTXCHiGElvAl2Cpv0Rl2pOOXDqTZre1KQTVN0eRapfwpekYPTHGGfE7B\nqOVxeEgGkliWUJX81PynpIpw2BPmlcVX6Aw7WJa4FB+XH+PUnNJJtRml7MzjTnWHXCtHqVNiIbwg\nqQ9tmz5JgWizk5Q6Jbp6V9hNT0ji/31uHxFXhL7e50rmCpdTl0+wW7idbsH8YvZJ+VPEfDFeSL/A\njfkbsml5lMHFZgcZZVJxO9wU20XBwz7BuPKk+kQq4dr7sDMU6qUflT5Ct3TOxoVwTtATxKN5CLlD\nbBY3qffqMhFkIvayqgg2pa3SFhYWV2dEJv1c8hy/ffe3aQwEC0aj3+BTc5/CoTmEEM1TysDT7M3o\nnIzSBrcHbTm3iqKwGF5EU7SprDudgbApnWFHiOsYA/m+IU+ITFBQFtpnIegO8k/+8T+Rv/83/9lv\nSsq+UqfEdmWbB4UHhN1h+kaf48Yx12evMx+eF+qtlsFKdIXV2KoQ0nGKDGfIE2K/ti8oIZ0+or4o\na7E1huaQdDB9Qi0T4Nd//dflc3zjG9+Q67pV2WK7vM1R44igR1AfHjYOMUyDu/m7NAdN3A63bOZP\n+pKigXDiLjYtc4wV7dbxLQbGgPmIOC9n42c5nzzPUeOI947fozfsicZQj2DOinvj/OULf3mqMKE9\n7H1qMxo9Kj/CsIxTGZM0RSPpT2Ji0tf7QvGy+oQPjoVwldvh5k7ujtyDo+tv28pRxind0iXFpoXF\nfn2fZr8plYJ7eg8FhYXwAglfQog+zk0XfYRxJrlaX1A3V7oV2nqbRq9BX+9LYaS4L37ijOZbeXLN\nHENzKJM0cV+coCtI0p/kw+yHdIddqab84uyL/JVLfwWvw0sykJRsfLbfdzZxluFgKJ/vz8LKoliT\nzPR/TiMYDPKv/tW/4qtf/eqpn8lmsywtLfE7v/M7/PzP/7z8+3q9Lv+8tbU19jO5bo6j9hH3a/d5\n0hL4x6AzyEZwg+XAMhlfhoQ7wYO6iJaK3SIHnQNWgivE3DHRkOJJkvE+Kzfopk6pXwIgoAXYae1Q\n7BXZbe5SGVYIuoLCkBoml6KX+Ez6M5T6JXLdHC1dZMgDjgBJd1IurE2Xc7d2l93WrqTfCTlCxD1x\n6oM6dyp3qPQrmKbJkCHnIucIO8Ns1jdJe9IkPAksLBZ9oixVGVQ4bh8TdAep9+tYWKwEV6gOBJ9n\nyBliwb9AxiferdgTWMYnjSeYiEqApVhshAQOvtwrE3PFOB85L7MPD+qCznCvtUe9Xxd8oF5xOC0s\nLoQvTIWclPolit0iuW6OfF80efk1PxFnhLORs2S8Ave5Wduk0q8Q98TRVMH0goWk6rK/r9KrUB1W\naetttpvbWJaFV/XSMlqsh9aJuqPops6n4p9izj9HrpuT62FYBqVeiYQrQcwTo9wv41N9vFt9V0on\ne1Uvr8+8LsR6es+otUbf0X4vu0mr2CtS7BXpmT0CjgCWZQnj4w7j1txSwMWeU4B8N8/jxmMMyyDl\nTVEb1Cj0CnT0DigC49kZdmQQEXAEROOaf4nPpD/z3OxVT+/xVuktVEs4t3W9zqp/lb3OnmyU8Tl9\nRJ1ROnqHlcAKs36BX7b3e8KdwKE6yHVzFHtF+fsKXZFxSvlSck1irhiFXoHd9i6WZVEfiD14NSpk\n02uDGhvBDblXFRQ+qHxAY9gg7AoTcoRYDCyiIWiv9jv7KChU+6LMfCNxQ7L/2GfU3pMKghWgNqix\n4l+R/QGGaVAZiADBMA0eNh6ioRF1R/E7/WhoEgrwuPkYxVLkzx12DmXjOEDSnWQjtMFGaIM3C2+y\nWdvEUgRf7ax3lmW/sC/2c9lzGHFGeNR8RKlX4rAtvvNyVDh3YUeYJ+0nwgl1icsg7opT7BfZbm1T\n6IgM9JngGT4/+/kT6323cpft1jYuzUW9X6cyqDDnm0NDozqoshRcIu6Ks9XcIuaKEXaFedJ6wqp/\nVZwvSyflFpj66qCKpmhshDaoDWtyvQ3LYKuxRcwZI+aJYWFxJniGR81HmKZJfVDHVEw+nRBNkT8q\n/IjaoCZtwqJvkaTnWdNl3+hz0D7AsAwCjgADS6hYelUvLs1FzC2YWjRVk/vPXudqr0p1WBWMCKHV\nMXt91D7infI7Y3biRvSG1Gmwbe7k3j7tu8vdMigQ98SpDQR2ez24PiZkM2rfDFPoPtSHdWLuGGlv\n+rnnc/JM2Wdo9I6wz96D6gOOu8eoqorfIezmin+F7fY21V6Vul7HqTiZ9c/SGDRIuBPEPXE2q5tk\nO1mhGusMYpgGa4E1zkfOc696j93WLjF3jLA7TK1fAwvaRpv7tfv09B4hd4ioUwT+AWeAqCtKyBki\n7opTH9b5w2/+IQGnSAr9zb/1N+U86KZOY9iQ85Dv5nncfCyyhq7IiXt22nkxTZOd9g61Xo2W3qJj\ndNj/b/u885/eOXVOTxvXfv4aN75ygxmPqBAFHAG6RhfTMhkYA1Le1Im9raAw0Afcrd9l0b9I2BVm\nr70nz860++6ll16Sf373XZEQ+N3d36XYF6xjHaPD1dhV1gPrPGk/IeYS1dn7tfss+helY7rqW0XV\n1BN7ozwoS1hNuV8m4oxIBhbbLibcCb6f+z4f1T+iY3TwOXwEHUFCzhAvJV46sX8n9/HoGigoVHoV\nKsMKG6GNE/Z32l42TINbZcH2EnULTQ2f6qOlt0h4E6euv23HR38fFmw1twg7w9SHdYlYUFWVlYBI\nlp0JnqE2rI2dmdF3sr93dM7qwzr36/dRLIWAFsBQDK5HrnMxdnHs522/4V7lHk1DsF4FHAEuRy6T\n9CQpD0QPoX3X3UzcPAHRGp1X+/k2Njbkv4fDp1Oanjb+p9IlzszMMD8/z+PHjz/xz0ScEb5V+xYd\nXXBFHrQOWAosEXKFUFWVhDtBvpuX2E1N1eSFqCmazJqNDofqGNuE5yPn6Vf6oEC+l6c+qJP0JJn1\nz/JyUpSaI84If1L8E1REZqFAgXOhcycWLeVO0eg3pMEq98rUByLwUFXhOFmmRVMXm6KpN4XzoGjP\nHI9+RUAgOoe09BYNvUHGk8FSnjVO+pw+FvwLcg4ACr0ClV4FE1EiDrvClPtlthvbNAdNEeFZ8KD+\ngAvhC5T6JRQUIcygCMen2C0yNIashlYFfq1fOhHUPKgLmEaxV+QH+R+QcCdwaS5q1JhPzI99rjoQ\nl2Ndr7MaWBUOZV/Mh+2s2+tcGVYIOAPEXXFaeovV4ComJgl3Ak0RnNcgDlfEGaHQKxB2hin2irSG\nLRLuhMxydo0uryZeZb+9j2EZXI9dZzG4KMuVxV4RxVJIeBMn3ss+lNcj13nSfoKKSn1YF45B7AZ7\nnT2CziD77X0Aws4wd2t3paJewBkQ/60AloB0XY5dls5AypMi38sLR8YpyunrofUTBmj08OumzreO\nvkVz2CTsDNPUm/g0n3y/x83H+DQfuW5OBK6hDcrVMj/n/Tk8Ds/YGn7SoakaKU+KxlDs57XQmtzP\nFhZxd5zKoEKhKxrrXJqLOd8cVtsi7AyzElzBsixirpi4mC1TQGCeBhC1QY24e7wfwt6TiqKw39wX\nlZHWHiFXiJXACrVBjYgzIufKNAXOG4QaZcqTko2NMVdMOjWGZWBistPYweF+qlTbOeLF+IuU+6LR\naCGwQLYnFGAbgwZVV5VL7ksnLoNCr8CZ4BlxbtGIuCM09aZgCnDFeDX16liAV+lXGJgDsq0sXVM4\nD/vd/RMNqSAowB63HmOYBj7NR4kSIWeImCvGkKGEUC37l0l5UlT6FVb9qzg0B+V+md3mLgv+BZKe\nJJZiEXPHqA3FnGW7WVHx6VcxMceC5a3GFoqi4Ha4STmEY18b1sh4M9xM3JQBYdgVRlVV6aT29B5/\ncPwHkiP7e/nvcTV6VWQQO4fcTNzknbJwvFb8KxR6Ben8XAhfYNPalM7ypL2u9EWwZ+Omh9aQW5Vb\nrIfWAch2s/LM2esy+t15V56t5pZ0HEzFJOqKChVkt1CAnaaCPHo/6KaOq//JCAsS7gSFXoG+3pfB\nzUZw48QdEXFGKPVLHLQO6Bk9PJqHL8x+QajjOqPU+3W6RpeO1WHYHArGHEeAvtFHt3QMRUBSSt0S\nAWeAF6IviEBsWAUFqsMq96r3yPgyMgjTlKfKu90CiqkwH5hHRSXiivC4+Zg7wzuEXWF+5//9Hfmc\nN//KTYbmkHvVexgYLHoXKQ1KxN1x0t60dCptnLV9D512XmrDGjFXjIf1hxz0DmAozuyfdVimRXPY\nZC24xh/n/5iQU1QEh+aQG/EbQhviqeNk33cAF6MXRaDaLbLqXx1zhCfvu9HR03v8p73/xF57D1UV\nXP5OzUmhW8CluMReVTXizjhVb1VUjJwhQq4QCU+CyqAy9n2VfmVMzVRBEUKJjnFoVb6bpzYQvPMd\no0Nr2MIX8LEUWCLujpPriv6QUSd2cg0265tEXVHcmpu4J05lWBFJJXdsbO2mjdpAUGOqqip9FFVV\nibgjBBwBwUOOJZM0wPicWxB2hwUcsv+Us1xvyzMYd8ZJeBPCZxoJIGD8TMOz+8H+74grQnUg5tq0\nTBRLoaE3sLAoD8vS17F/1u53ORM5wzuFd0CBs8Gzokft6Rq4NTcp74gNdIzvh0n/8c9j/E91zIvF\nIkdHR8zMnE77lN4Q+E4bM3hQP+C18GvsVnfRVI1XHK+gqRqXUpe4MXsD3dT5rdu/hamZUpEvYAWk\nGMYoXRNMp33bqe2gPFZYDiwT7opMw2vLr/GlcwIzZXdXf3Hmi5JbOOKNsBhaPEHzZhwb0EI2pfSM\nHsVWEROT0qFgDAm6g5Q7Zc4kzqApGmeHZ2kP2yR8CdlQcj51nnKnzFZ5CwWFlegKFhYOxSFKqZrj\nBH3ai+aL3Dq+JZtiDNPgv370X1FQ8Ck+FEXh5pmbEsc8xxzHzWNKnRK0GcMQJ/1JEr7ECbnsg/oB\nRt3gUfkR7o6bC84L7Df2WUwsMh+aJx1I88WzXxSKgHVDvkPQHSTkC1HqlHgx+SJbpS36llBhU1F5\nfe11Psh+QLVb5bp5XfIMzwRmJLtCtpUlEUhI7OAvpH+BO7k7FPeKnM+cx+/0MzSGLHoWqffqnEud\n45JySe4BEDCIhJWgWBRrkkiJKkUmkMFoiveKE8ewDHasHX7mxs/Q6ous/HHjGHfQTWQvwk5rhxvn\nbzAXFnRNdqk3E8gIXHzyMg7FQcQTkV3f5U6ZZWOZi+mL3Mvfk8FY1Bsdo2yzcZRJRfRiDM0h+Wae\nSDqCNhCS1RdCFyRe+27uLn7Nj2IKgzUTmyEdERz6zpSTF+dfPHHOJhvkfA2fwOA+3bf2nGWbWTJN\nYYQeFB8Qt4QjbVomlzOXARhkB9S6NYL+IENzyHnzvOR2LbaLXJm5wgXzApsFwWsd8UYklnv0jAK8\nffg2+VIeFFiKLlHr10iQkHvyvOe85Oo+ahyhKipXY1clrV8sFpMwm3BDOLHzoXlyrRyRVoSXPS9T\n7VXZLm1zffY6cxEB/VlJrtAcNFm3BDY87A3zhZUvsBJd4aB+gNJUZDY238rjDDj5a5m/xrtH7/Kw\n9JCgIqps8WRczrc9vwkrwe8//H0yMwLrb1oms6FZnOmTa3PNvEb8MC57Ol7zvMZSWOBlRyk7R+3j\nfn1f0KS2BJa66W1yc/0mj0qPwA/JQJI79++IRrL1dUqdklinzLPKmT1G9+Do2b9p3pzaE/D24dus\nudZwa27RROlewel1shxdZtlaFtzMbIg+CF+CiCeCoiqkg2nR/MWLU+n1AAZHAwq7oplZUzXyzTzr\niXWWwmJ97TM3H5qf+syT9n6yOXnybjjtjNjn0LRMrs1dk3M2bT6u6dd4Y+sNUsqzxvBrc6LB3/58\n3+izrC3TLghog1fzklhIcGX+Cndyd3D2nGwoG0JnwxPmc8ufw625BW2idR7TNPnh3g95//h9NjIb\nxJfjlLolXp55me3KNtVuFSNoSFhBUBXiWQoK6751ziXP4dJcpANpQf9bqJBUk0JYZWRE5iI8Lj8m\nqAXJNrMcOY+4lLpEyV/iS2e/xIu8OHUORs/L6Lq8Gn6Vg/oBc805kQRr5an+qMptbvPjjkQ4wdnl\ns4JmtFPhteRrDHUBJ/A6vSxllsb6pLLNLHPMjT3nTnWHu/m7Y2QRk/vn61//ulz7UrhEKBHCr/kZ\nGAMy3gzldhlv2MtcZk5Uzp42Sm4YG2P9QsCJfZ4JZii0nlVvV/VVadvsz9jc5OvBdc5p5yh3ypTa\nJV6ae4mf3fhZPsh+MHV/Tq7BYeMQBYW5kMiurwxXqHfrpAOCjndaVniUJcjT8sg+FsMyiHvjvL72\nOt/Z/g5JJSl7nOzff9p3HTWOOD46FlBYFUKuEF9Y+4Jsvj6oH+BsOk+1Qwf1A46bx2P/nvKL/rP1\nhmD8qvfrhN1hMsEMCV+ClD9FrpWT8xTWw5TbZb68+GVJHfnFDeGzfNx+OO3cj6I+/izjz50u0Yae\nmKbJ3t4eH374IfF4nFgsxte//nW+8pWvkMlk2N3d5dd+7ddIp9NjMJbJYYtQjNKCpQNp6r26ZLWI\ne+PcmL0BCCC/nYWt9+sshhdJ+BJjncSTDs8J3udmlt3qLg5NONKqpZIOioyQ/fl8K0++nedK5oqk\n2Rsd9nfbTV2jzVVf3Pii6KKfF6wSKHBYPwREg1F32OWl+Zeod+tSXc7Gr5c7ZQbmgEelR8R9cc4n\nz1PqlKZeKA5V8Jj+6eGfkmsKTOZSZAm35qbaqwr6o46IWEF0Zdu4OMMyiHgisnHMboyd1lRS7jwr\nv5V6JSEt3W9R69X46bWflhfXvfw9ibkvd8okfAkupi/i0TycT53nR7s/wqEKYYK7+bu8NPfSCcdD\nN3WpUGhjJC8kL2BaJt/Z/g6KohD2htmv77MUXmK3ukvYG+bm/M0xtgrbWKmKKrHSiqII2jafoLKy\n38uhOlAs0axW79aZC82Ra+Vwak40VcPtcBNzxURmVhlfg1JHRPQeTTDRJHwJ4r449/L3UBSFTEjw\n3H/p7JfG2EymNV3a65tr5Sh3yximCA48To/gkY6fIRVIsVvZJewJk21m6Qw7tAatU3lyR/fKKO98\nJvgUDjUxZ3ZDULFdlM16cV9ccPq3S/K7zqfOy7KoLegwyooyMAbEfDGJqT2fPC8p4Ow99tbBW9wv\n3me/uk+tVyPoDRJ3x8ccBofqkA1J+Vae5ciy1B4IuoM8Lj/GoQpqz7Gmw6cd+nZj4zA6xO10S07a\nYrvIwBgIrK4nNCboNHrGR5t6zaw4H/Z+smnz7LWULBsICr/dyi4RT0Q2WZ+2LjfnpzvBwBjbwkH9\nQJ6PoTHksHlIW28TIsQfbf0RC9EF2WjWGDRAgfnQPJlghju5O5I5ZZSu7bQGuGnNaqOjb/S5X7xP\nvVsn6olS7VUJuUNjnzFMg83iphQYsu2vzaJTaBfIBAVs6IPsB1gIdoZip8hGbINAPCAbc3VLiJ/B\ns2DytPm0z1SxXRxrsv+4ButpdKQ2y9efHv6pDJ7i9Tgvzr4oxd6S/qR0dHp6j3cO36HQFgGGQ3Vw\nN3+XRq9Byp9CVUTmtTVojbFK2XjtM/EzuDW3nPv9xj4Pig/4IPcBHb3DncIdOnqH6zPXJTvXZmET\n3dQlHSUIeff12DpnEme4nrlOrpWT72RZFn63nyeVJ2Pv/87hO7QGLVmJdqgCl64GVDkP9nccNA7G\nhIQ+bjgUwaL2D//RP+Rf/B//Yky0xrRMXll8RX72Pz/4zwKGBDypPmE+NC8rb5dTlyl2imwWN2UP\nTWfYkVLzk8HYqF9x1DySXPvZZpazibMnnt9uyTuoH/BB9gOWIkuCJnTQYq+6R9QTJRPIEPFGBLvO\n0/mYRoM8rQkz18zRGrZ4XH6MZVl85eJXZPJvdH5LnRIJv4BqhlwhrmWuSTaeUbKLg8YBK5GVE/Nt\nazzopmADelh8yMX0RYATwjswLq6V9AlmIKfqpNwRUA+bGnPSv5qkObWfyRZJjHqjYCLsEaL/57S7\nyhbZsudi9D4atVP2/VnpVkQw+rTZ2HaubRVl6UT36jg1pwxS7Of+uP1wmv/4PBvyScefq2P+7rvv\n8oUvCFYDRVH4+te/zte//nW+9rWv8a//9b/m3r17/Pt//++p1WrMzMzwhS98gd/7vd/D7/ef/oBT\njOBh45AziTNi8xk6l9KXJIOBLa+7Ed8QQg6qxgszL8gIbHScZmgtS4hs2AwQA2PAg8ID5sPz8vPp\nQFowqDxlWpm8uOzvdmkuLqcvk2/lcSgOeThvzN7APDJlhvK4eUzUE5VqjIuhRRyRZ4at2BaQkp7e\n417hHhfiFzifPH9CWWvasCwL0zRFd7455NWlV9mubDMwB2wWNon74kS8Ed7YeoOLqYsk/AnMvEky\nkBSX51PDPk2hzxYH6A67bFe22SptsRpfxVartGnRdEtnp74j8bwDcyCFX44aRzyuPMbEJOlLynca\nVWictmYO1YFlWUJq3jRkGdDj8LAaXaXerRNwB1BR2a3tjlGg2c902kgFUiLLbxkolpBP34hvUOlU\npDGzLCFbXaFC2BUe49MOuoNUuhXR3W/pgsLLMsi1chiWccKAjbKZjB72vbpo8rUDMztDUWgVBAWi\nIoy53+XnZzZ+hmq3yhfPfpG3D94WtHoOJ/VunWwzS1fv8tnlz06FTNhjlNlEBrJPMyPwzIG/dXxL\nVASe8uPbqpD2frNpLG3mCRCZ2lpXYAUj3ohkJLD30egz7VR3eFR+RGfQYS2+RrVbpdApEPKGKLfL\nhLwhycxis67olk5+Oy+VNfdqe9yYvXGCLeegcUChVeDzq5/HoThQUMi2slJU5Lh5zOdXPi/6D1oF\nYt6YoP57an+mBSf2PJQ7ZTKBzLO1PWWPnUuco9KpEHAFhM6BaXA1c3XqZz/OCZ7cMzbjyHxonsag\ngcLToLJT51rm2omfLXVKxH1x4r74CeXAScdhktFpMmN0NXOV97Pvcy93j4ExoKN30BUdv8tPpVvh\n04uflg2JuqXLPWQnN+w5zrWEY1ZoFQQbVUCou16buSZtrk072jN6UnjKUizu5O7IJMikY/X/x2V6\n0DgYY0Y5ahxxN3dXCKCZAjJ1PXMdgM3ipqTcq/aqXEheIO6Ns13dxqk6sRQBA65OJe0AACAASURB\nVIl5RKAv+aWVZ/zS9jvZttcmAnCqQtiqPWhT69aEY2jBRnyDgTHA5/Rxt3CXWrdGMpDkfvE+q9FV\nFsILkuc97ovTH/Z5P/e+5M+2R6FTEHzlloqqqbLaE/cJYZtR6kALiwupC1IwatJ5Gn2HyX+zWYEm\n9549RkVgzsTP8Nb+W8T9cc4lz1HqlLiSvsK9/D36CN2QJ9UnnEue47h5LDVIJjnygTGufd3UhbbF\nBJxQ3h2m0GXYKm9xNiaYUrpGly+f+TIL0QXRn/OUVcZ+h2mJs8lzfTl9md98+zfRFI2QJ8Tv3vtd\nvnrtq/JOtMWCIu4IpVaJjcQGSV+SmeAMt3O3OWoeUekIOlXd1LmdvT3GsGLPs50gtINHO0E2+p6T\nDrUd0GiqhmYJMoHF8OInYgyzv2OSfWigC3HCgTEg5A7hdwutkfXY+tj+sM/4qNK5fW5H7VTUG+Wb\nd74pIDaWwVZli+XQMh6n0AKJeqPMh+cFMqAlEkmGZZwI5Avtwsfuh9P8xz8PVpY/V8f8L/yFv4Bp\nmqf++xtvvPFjf+ekEzG6ELMBoVpnZyqyzSwxn5BjtTuP4974jz1Rs8FZIu4IvWFP4MAdPkFTaKs5\nPr3MYt6YhHc8b3PajvyoGtboexw3j7mcujymHDl5aC+nL/Mbb/0GR60jTNPknew7aA5NGvzThr15\nav0aMV+M7fI2bx28xc2Fm2yVtkSWNZjiUemRNKpJf1KUclpZ3j96H0uxqPaqfG/ne5xPnh+Tsneo\ngpv6H3znH1BoFwh4AmQbWS4nLxP1RoWoRUzwea9EV+gNewBSsOhO9g77jX0avQatYYuoNyopBp83\nRgUa7Gy+rWhWaIt18rq8fHjwIQvhBWr9Gm8dvMWVzBWupq8Kh7aRA0V8l82OEvFG5OUwE5iR6nkg\n3uH1tdepdqtS8c5eL1V9Zuh0U+eoeSTYDVDZLG8ScUcotkWDkG7oZEIZHFOO3+hh1y2dzaIInMqd\nsqSbszGcQXeQrt4lHUjz4tyLVLtVacg2YhsoiO74qDfKXm2PM3EhMmKXPicvHZuGtNQpUe6WuZC8\ncOp+HpWxB9GJPh8WEILReRmaQ2mw7hXuyT1+3Dw+URYcfZbbudtUOhUa/Qb1vqhSDI2hwMH6Ilim\nJdmR7PdYCC1wPikgX6VOieXoMovhRelA2hkdOxtX6VRkhkVTNFk5snHMK5EVFkILUx25yeDEoYj1\nssuk05yQ0YvRqTr5lZd/hfuF+4BQl8s2sycUKj/JmLwgZoIz1HrivMd9cardKosR0XjGUwGOkCuE\ngWCMUBQReNr6CKNnezQjPzkPpyl0vr72Oq1+i0a/wWeXPkuj38ClufjKxa/g1oQUOYoI5tKB9IlL\ncfJ9VEXIu8+H5sdE5DwOj1yHURXkySTI5HfbSpggaG0/6WUqnQS9JzOFVzNXuZ27LXuY7Dnt6B0W\nw4siSDR0Uj7hpFmIfVvtVmVSIRVIcT1znZ3qjmRTSfgTEqp0NXN1TMp+dH2uZq6yVd4i5U/R1bui\nwmkaqMozewSiGvrNO98UPPXpy/SNPgN9MBZE2lRz6VAaK2eJrP3IuJC6wE55h7nwnFCyRuOluZfk\nvjItk63yFo1eg4g3IquPdmXC5lq/nL58KuXlNJrWyTVcCC3IM5xr5Yj5YlxKXZJrf79wn79+5a9z\nv3CffCvPueQ52Y9ka5DY2dHJYe8v3dTH9uVoBbzcKdPTe+K+cwdoWA1i/hivr70uaRd16+QdPjls\nu2jzbttJg6Q/KZMZfaPP7dxtXp5/eSzZd23mmqxw2QGqicnj0mOq/Srr0XVUVZXZ44XwgqyIFloF\nue7289nIhNPG5JkEJGnDZMLitCpbtpml3Cnj0lxoikZf77PX2KM5aAqce7eCz+WTlQYYTwTZZ3w0\nIWAnZez3ePvwbVHF1txCcwaF5rCJ3+0XbDOKoMn89uNvy36SoTmUjEf2c0tYkeKQ9NiFVkFSNtp+\nm27pOHCcyOb/j47/qRjzTzLu5u9KVTz7he2FsCV97c1iGzI7m25ZlsySTRuTG8l2InRLFxSJ/RYh\nTwhb3CbmjZFr5aSUs815O814fNwmHX0P+PiDcSd3R9A9qi4CvgCHtUP2a/vMBmZJ+pPP3Qz5Vp5q\nVzTDLseW0RQNj+bhtdXXpCOmKqpsVrH5qB2KA6fmxKN5ZKa23CmfuMgelh5ydeYqh/VDHpUf4XF6\naPVbxH1xechSgRRqXiXiEYZrYIhGmXQgTVfvyrVr9pqSQnGyNGpnL21aMVug4XL6MjPBGan8Zf+9\nzcGuoXHUPKLWq7Ff25eqXoqiEPPEqHaqfG7lcxKKYq/ntIzV3fxdySuvKqps8Et703gcnhP7cjGy\niIUIbGxFMMM0yDVzEgt7GkSo1Bbr4tbcXMlcIdfMoSiCIvNR+RFdvUsmmBHiEmhyT9mGTFVFJrfU\nLuHUnJL5ZVpkbxveTDAjJNgt88Q6TO7dyQt11EG2L5zD+iGFdoF8K89OTSjjamg8T9jI5nsudUqC\nitAYslvZZS48Jy8HQzG4X7g/VgkbhX0cN49PBPW2uEy2mcWhCaq/gCvAZnFTZMVVh+SitX/uNEdu\nJjgjqyq6oYOKzN7b2cfJeZk2Xy/Pv4xuChpAaVfyJueT5yUcwv68/TyT3zttbT63/DnuF+6jKApn\nk2dRUcdgG1bUIt/Ng58TsJtpzsS07JDtkE5mjByKg/XYOtvVbVRFnPm4N85yZHkchvM06OkZPWmv\nbYGc0WFDpSbL1Xb23rYx9nenA2lS/tQYltz+zp7eY7O4Oba+pynfTsOPXp+5zhtbb4gLPpDkvWMh\nNmZDC+rdOu1hm5ArhEsTgk4hV0gmZ+J+oRtgO/a2E/3lc18m28pSaBX45r/8JgfuA/5d89/xy//b\nL8t3npbZXwgv8OLci1R6FQEh7LWYC8/xVy//VWmPRjOtfqdfqogG3UHZ6PiNb3xjjApw2vh7n/l7\nJ/7uV//+r/LP/+k/56B+wN3CXY7qR9T7dfw9v4QOjGZbdVPnm3e+ycX0RRyKQwZz9p6zxV5sLYZR\nqIk9RmEVCgobCdFkOAYrG+mJ2W/sS0pZv8sv5d3teR0NnCf3oj1sO2Dz92dbWWq9GmfiZzibPEvE\nExEVMGfgxPdOGz29x3979N8odwVGPOaLcTl1WQgVYeDG/dy1GE32FduiP6rWrRH3xzERTfW28NXo\nsAMaWy/lpbmXmAkKscL9+j671V1CnhAXUxfl2k2zp/b5H507O2HxSWwUCAhJ1BMl7okLambTIOlN\nTuVzt8/oJ01WGJZBrVej0CywFF0a6zu5X7jPxdRFar2n1VtPhNnQrAzERmFFk5n6Pz38UyGU+ZS+\n9X7hPueS54TtHsnmXwhNT2p90vET75jbOLxPonpoZxAcqoPF0MkSyzRDO6kaeNw8ZrO4CapoGtlr\n7HE5dZlyp0y+nT8VQ2rjFqd99+jfTRun4aTsiyfpT/Kg+ID6oE5P79EatsgEM1Jh63lzk/Qn+aj4\nEcfNYzRVw+f08bNnflZm+e3snm7qYzisH3c4FAfr8XWwhMpcyBMSao5PD9loNhPEZTsTnBFGyRtD\nURXBWmGJDPb1metyXZ5UnnDrSAjnBD1BzsbPCi56xcHLcy9PdXxennuZW8e3qPQqHDeOCXlC+Jw+\nnJpTlmjDnjD1fl04ZYpjKhZvEjfXM4RBtasylmVh9S3S3tOz/JoqGBDsZlrDMoi4I0LsaiILNroX\nRuEyduR+WD/kuzvfpdgVGPtSpySifUsfC1xHM9p2RcGGDn3cOl5IXSDXzH3s3jotIzQaOLs0Fw7V\ngcvhYjWyiobgxB11fk97jsuZy6T8KaGs6xVUp41+A4cqMP+KopxwJO3fbWf/RoPuw/ohpXaJd4/f\nRVM0FiIL7FZ3eWHmBWo9wTSwEd+g2C4yG5qVazDpyKX8KZk9tgWprmauyuwN8Nx5mRzZpoDR2MGg\nYRkU2gXe2HpDrulubRdFUSQmfxSCMc1+rERXWImunLA/9u/Pq3k0VZNVJng+tOuTDt0S1aJSt0TI\nLWBHtjz7tFK+7ejaugYfZD84AX2YzP5O4oSH5lBWO0AE/UfNI5yqE93S+dbWt7iYEjRp9vc6NTGP\nFgL2c+I9ntN/ZJfzAW5nb9McNKl2qgyGA9KhNGdiZ2jrbXnuFIfC5fRlFsIL8kyOJhXsNbGbA3/j\nn/2GfI6//ff+NiAcOVuQxQ40dFMHBZajy3z16lfZKm+R8Cd4YeaFseY926m0sARfvGKxU93hUvrS\nqfCpTzrshtLDxiF3sncIe8I0B01qvRr9TH9MUMihOih1SoJatVuTmPdbx7fGesTKXSEUV+lWpLz8\n5NrY6x/3xblfuC/glxOwsmxTqDj//sPfZ78hqFlNy+Sn1356TLzI3peTe/Hdo3dFoqJTwTAFfNDE\npN6r81HxIyyEg2ZZFnFfnKszV8ecu9Psm27q/P7D3+f94/dpDVs0+03SvTRO1UnII7jnHUHxs6MQ\nt9P8hPez7/Pm7puy96bWq3EmcUZ+ZjSoP61Hojfs8ce7f4yCgr/j57fu/BZ/48rf4G7+roTb2g6t\nQ3WIJJGqUOqUCLqDbJW3KLaL/MX1v3hqlWAmOEO8Hhf2rlflsH7ITGCGz618jka/gW7qomdvis+m\nWzoDYyD//rTAx4bSbZe3QREMMjPGDEfNIzRFEA1M6z9xKOO2WTd1MsEMd3N3SXgTwnF/aj9Gm2aT\nvqQQGvTGpT7OZL/hn2X8xDvmmUBGZqaAU50YeJaxmnYgnoctHHUiSh3BlOLUnLjDblwOoUZ1Lnnu\nVAzpx3336DPYTU2j5eqxAOFpxv6NrTfEpak4uHV8i5XYCtvVbTqDDgYG9W6d11dfl5mh0wzBQeMA\nEyFG4nP5MBAqk68svCJ/r93wZ2PIRzf9bm1XNmTYeMITmL+n5VyA+cg8fpefv3T+L43BDEazmfba\ngYA9HLeO2SsJnORidJG+3ue94/dwqk6Omkd8Z/s7RL1RnKpTCPv4UqzH1qdG1qN/dzVzlQ9yHzAw\nBgz0AQF3gPnwPJV2hVqvxk5th8tJ0Sx0O3f71L1jr3OpXRIYcdPAoTqEzLppoPefURlO7kv7/6V2\nCcMyhLy6JSSbR43BaNBoZzYn4TLFlnDGXZqLWf8suiEcd0VRqHarHDQOpu6plD9FrBmTMIZpRm3y\nLKX8qU8UDH/SkfAJuFDUG5XiUKdllOxnUVFJB9Ik/Ul5aY5i/icDjcnAezRQ002dQrsgVUsVRRFz\nayFFue7l7/H24dvcXLhJoSX6RxL+hCyBgnDkip0imiKa3xyMV1ie9zzPm0vd0oVQmKoRcAeodkWF\nxf6ZfCt/gnFktJR7avXiOaX0hDsx1hfxSdZk9LPTGkSxxrG66UCaK5krU+XZ7ayboghWFhtnXmwX\np77PaZLwNpQMRFWz2ClSbBfJBDPUOjXhCPZq0m6nAikJFQi4A7JkPTpvk5CyQqvA24dvk2vlqPUE\nh/uH2Q959+hdIbDmi3LcPmYjvsHy/DLvHb9Hq98i6Any8vzLU9fJTipM3h+jw7Y3x61jZgIz6KYI\nNM4mz0qsvo2n/7kzP3fqHit3yrg1N59d+SxPKk8Iu8O8vvb61HX5cYYtTPao/IigR0DrLiYvyt6S\n0fcdHYZliL4Ay0RBEexYvgTlbpl6ry7x9La8/C/90i8BMDs7KwINRM8UwNnkWerdOglfYgxWBiKx\nYldlNVVUyGzRJ7uasFPbkT0ydqOubup8mP2QH+3/SECULEP0TAw6dIYdsu0sQ11A6wb6gKg3OpYk\net7INrPU+3U0VcOluVAURfQYaIK9aCO+IZ28UYaU0fu60CqQ8CV47/g9Cq0C1X6VWq8m+OxdQUxD\nwC+vz1w/1R/Kt/Lyz3v1PWKeGC6HqPI0eg2+++S7EjriwCGrHCm/UImtdoWY2pv33iQdSPNEecKD\n4gN+5eVfIeAKnPidDtUhVcL3a/vMBeeoDWrcL9znfOo81U5VPs+0c6Eoygk12MmeF4/Dw+trr/OD\n3R/I5Mt3tr9DZ9gh7otz1DjiFy79Ar9773dlcH3UOJKMfrYPZgf2Fhblblkmakodcc/HfXHBfGU9\nhbl2K6fCo/4s4yfeMe/qXQ7rh7JZY9Tp/XGy0pPR4mgG4rTLSEMj7ovLjOVpGFIUxgxFxHsSt6ib\nOj/c+yHvHr2LrYJ2KX1J4rXtzfbu0bsU20XK3bJoDkpdQFEU2v02Xzr7JT4qfESlW+HFuRdxqM/w\n1KPzMhplfnf7u3SGHeYj8zT6Ddk1Puow21jaafP46YVPMx+afy721ePw8NVrX5VKpq+tvHaibD1t\nOFQHn174NKqi4lSdJH1J0oH0mBPS7DdRUOgMO8S8MQzToNqryiDneevuUB2cjYsy/nZ1m5ArRNgV\nptgSmTe/089B44ClyBLJQPLEmtnfvVffkw0rpU6JoTGkO+yiKeLCb1QbrAfWx36vzSxxO3dbKtl9\n98l3WY4sE/fHeVB4IJ/Zlle2mw9H6RJH1yXlT/Hdne8SdAUptUv4nD5cmovusAsIuFOumRs7H7Ji\ncQq8YvKZp33mkzqYk5+bCc6wW9uVF8BabI3l6PIJJ2jaz09jy7BhRXaZe1Kq3ZZiLrVLWMcCxjbq\nzIGoXqxEV6j3BJ2V3y2wpw9LD6n36igokvGo2BHc9hGvEPmwMy42j7Z87qfN26PP+uM0GSb9SfLN\nvPh9iCrAjdkbxH3xE589bUzLxn/cujlUB9fmrn1ieMxLcy+xW9vlQeGBfLbJPWP/eRKrO/ksgLR1\npU6JSrfChdSFsd/3SXDfdkUj6RcUbd/b+R5JX1KImHQrJ+bQZqNI+BJjpej3s+/LPTPqrOqWLh1I\n++w7NSeGZfDu0bs8rjwmE8yQbWXxOX28f/w+QXeQqE8kEhYjiyd6cux5Gq20nsDvPh33C/epdWvo\n6FxJXxHYdMXi1tEtQEjI21ju02BIdpOoYYmEwlpsjTOJM7LZDwSU5Rvf+IakDDRMocb79z/79+Vn\nhsbwxDPadHVJX5Ld6i4ul1CPjHgjxH1xmbXeq++Ra+UYGAPy7bzM9rtUQdOomzrf3/2+hI2VOqLi\nMjSGJP1JfvmXBaTnxRdf5HHlMT948gPaels4oabJZ5Y+g6oKiEqunTsVFjW5d0abEUudEhF3hGsz\n18Se7FXINrLE/YJfuzcQPWeWZeFUnTidThRFEZoZvsTH3nejI+aJscMOPpePvfoeJqZUo5wLz52g\nXh4dNhxls7hJvi1w5iuRFe4V7gn2MEVnq7qFqgk2EXtPj/ZI2A26cX+c27nbDIwB9X4dl+7C6/JO\n/b12RQcE81Gj32C3touFxW51l3OJczT6Df7j3f/I165/bep8FNtFXJqLs8mzaIpG2kiLvVba4nzq\nvEyI2HZl0q8aDahOs68ezcOl1CWR2W/lWImsCNXjp5XahyXBQDNKRJBtZeW8jjLuZYIZCu0Cx41j\nwYT2lL76zf03iXgiODUnZxNnZT+DzWr1Pzq0b9j8Pz9Bo99/JjJwv3yfoDt4Qi7WLqGZlimbVILu\n4Jikrd/llws3Jgn71CBbT/931DhiLbZGtpnF7XDLDPF6fJ1iu8hiZFH+rvPJ88yF5qRzfTZxlmq3\nyh9t/ZF0GAvtAsvRZaKeqHyPvdoe3378bZqDJrqlU+/V8bv8UoralsdtDVr09B7dYVc+e8wXoz1o\nE3aHhXMcXmA+NC9lse15MUyBq/rh3g8FC0p1h73GHgN9IKsAHs3DzcWbY88GotTod/lpDVq0Bi05\nd6qiEnQLOXL7z9MyOyCCH6/Ty35tn4elh8IJG3HuRiXfbQlihypYMZyaU2Qkhh10Q5dY6qE5JNcW\n9G8uzYVu6SyFloS4woScsf1cuikkeh+VH6GoCrNBEe0OjSGNfoOwN0zSnxRqqe4QS1EhUBV0B0/I\nrds48vagTcgd4kziDAf1A7KtLJVehYExwBoKBdCbGzflM6iKKvelLUdfapcIuAJ4NA87tR0CrgCF\ndoE3994k1xbBXlfvUmwXJb2f3cQc9oQJuoO0Bi22KoLLvqN3ME2TxciihFYFXWKtbNls+xw41Oly\n5aNj8izZZ2Xauk1+x7TPpQNpsi1B2QgiO3kxeVG+17Sfr/VqPKk+YbO4ScQbIdfKySabqCfKmcQZ\ngq4gYfe4VPZR44hav8aj0iP6Rp/2sM1x45j1+Lrc20eNIymL7nK4uJK5QrldxuPw0NW79PU+QU+Q\nvdoeD0oP2C5vU+qUKHVLOBQHS9ElVFQupS9xJ3eH1qCFqqpsFjZJ+BK0h205P/ZZdqiCA77QLtDo\nN6bKnduYdxDJgHOJc1ybuSaa+Xgme+5z+WQ53pZzH/0ue9+Pynmftm7HxyKQmJ+bP7EvRr9n1IYO\njAH/ZfO/0NW7lDolPsx+yOX0ZSlzPTrP9nOblslabO3Es5iWSaPfoDvssl/bpzlo0h60iXgisqdo\n2rCzVu8cvcPAHEhp97X4GpVOhc6wQ8AVoKf3MC0Tt8NNe9BmMbwov+Onln4KTdEEFtwd4taRgLwN\nrSG5Zo71+DpBd1DKdbeHbXnv1Ho15sPC9t7O3aZvCLhGa9iiq3fxOX1U+1Xa/TYr0RX6htBmiHqj\np54Tn9NHe9iW7/x//5//t3zWn/1bP0vfEOwiUV+UYqfIh0cfYmExMAaimc4h4BSjv2d0qIrKcnSZ\n48YxXqeXhcgCKuqJ/WOf+1qvJuA5vSrf/rfflv/2S3/3l07YR/teDbgD9PU+lmURdocxTZN0ME17\n0OawccjQGNLRO2yXtwm7wjhUh9ComL2OS3ORb+VxO90MzSFBtziDiqXwqYVPiab5poWqqKQyKb67\n/V3ePHyT5qDJZmmT5rDJQnABj9MjecAT/gT5Vp6lyBJ/sv8nZFtZ2oM25U6ZS+lLstn6SfWJ7FnS\nNI3dyi66peNUnexWdnE5Xc/k7RVYja5Khg+P04PH4SHsDnMxdfETk0zYDEUDfSAqdpqb5cgyF1IX\nWImuoKLidXllA/XoGTxqHEm70hl2aA/b+F1+DuuH1Po12XQ6tIYM9IFgl3tqB+2g0E58rcXXcKku\nHJqDzfwmR60jSu0SxXaR9fg6Xz73Ze7l79EcNKW67tnEWVqDFu1BW8D42kUKLUH9GfAEsLAEgYM/\nfWKv2Psl28rS03vP9p4ikhMxb2zMx9MtnbcP36Zv9OkMO+RaOelX2fa+0hVn3u0UiZOwJ4zb4ebD\n7Ic0B02G5pCu3uVS+tIYXavdZG1n9tuDtmR1s+dVVYTwnW7pVHtVYt4YZxJnmAnOCNuvqFxMXxQ9\nC5aAAq9GVzmfPM9wMJS/y+P58atSP/GO+X57n+6wKyfVtEzpQE0aub3aHkfNoxMOm2mZ1Ho1Hlce\n43aKi7k1bLEWX8Ohik55TdE4mxBR3HJkmdXYKnFvnFcWX8GpOqUTbjvCthNb69W4dXyLnfoOA31A\ntVcl6A6yHl0fM5KPyo/Yr++jW0JdbmgOOW4eC67wp8GBbaB9Th/FThHDMvA4PARcAXmZ2M9hv+Po\npfmk+kRkoLol2eFsmiKj73V6caku1uJrfGruU5/IsUr6kxw0Dvjh7g/RTX3M8Zj8+aOGaK58VH5E\nT++dcIxsg2JYQrQn385LsR23wy3LTa1Bi3q/znx4XsAFNAfFlqASjHgirEfXuT57nb7eHwtKbGd0\nr77H9558j1wzx159T7Kt5Ft5yXG+V91DURQZAGUCGQKuAGcTZ6cGdq1BCwuLkFs0cdmNKl6nl6Xo\nEu6+G5/Dx1JmacwYNfoNqr2qYDnplPE6vYQ8Ibl/Su0SqqrypPKE/do+M6EZGv0GrUGLmC92wtCr\nisp8aJ6V6Aoep4fOoINDE2JGx81jEr4EyUASv8vPg8IDdqo75FqCv34uNPdcx+s0B3zUwZwWGI+u\n/+Tn8q28yDJ7IoTcool62s9O7p/2sM29/D2+v/d99mp7PCw95HHlMaVOifXY+pgjaI9Kr8K7R+9S\n7pbpGT36ep+wJ0zIHZKfnQnOoCka8yEBt3KqTl5bfU3Ci84kzrBd2SbfztMb9tBUjbQ/TcwTI+AO\nkAlkuDZzjdu52/hcPjrDDgf1A84kzuBz+sbmB6A5aGJaJg+KD2gP2wzNIVvlLdwO91iAW+lWeO/4\nPRyqA6/TS1fvsh5b53zyPAqK3Dchdwi/04/f7SfgCtAZdk5dvw+zH8qKiqoIFc58Ky+TALmsYBCY\nnR1vfHxeIHbr+BalTkkoA6sOTISAkQ2vsffoTHBmLHGRb+VP7I1Gv8FmcZPOsMNx65hiS0C3UoEU\n86H5qY653SR76/iWPKemZXIueQ6P5pFJhZA7xFJkCcuymAnO8DPrPzNmw12ai7AnzNAc8u3H36Yx\nEPjWardK0p+kM+ygoLAWW6M9EOtmJ0qOmkd0+h1cDlGpMiwDA4PeoIfX6WU1tsrQHOJz+vA5fbgd\nbvwuv5yjaefE7/bT7DdlMPNv/q9/I9/5F//XX5T9KdmGaDQ+bB0ScAZkpcbv9GMigpDZ4OzUuXOo\nogfI6/DSHXZJB9OE3SeDdFURTB5vH75NV+/ynX/7Hflvf+d//zsnzq4diCkoxP1xPA4PK9EV0sE0\nHs0jM5B2s7+qqjg1J0l/Up4Vn9NHs98kFUiRDqSpdWs4VSfXZq6R8CUYmkMeHj7EwsIdcfOw/BDD\nELDA3rCHT/ORCWYkt7oNA7FpUpOBJBF3hIE+IB1McyV9haQ/OeYkWgjcfdATFLZBEc962DwUjE1Y\nzIXmSPqShDwhtqvb6IZIFjgdTj679Fli3tjYXp0W3NpzPBeak1Wezyx9hoQ/IX2coTmk0WtMTTyN\nJhg9DhEgOFQHT2pPKHfLxDwxmoMmXqeXoDuI1+HF5/ThcTw7H16XV+pzgKAGjPgiaGioqkrKn2It\nukZP7xFwBegOu7QHbX5q6adwaa6xNU8H0jwsP8TvEpVH0zK5MXtD2tTJyu/0eQAAIABJREFUd7eD\nknwrLwP3iCcidWJAoCR2q7tkGwLyY/fVWJbFanRVsL31KlOd9qAryK3jW/icPnE+TYP58DxO1Sl/\n37WZa9IvsP8uHUjTHrYZmAN2a7s8Lj8m6o1y2DgUPPKK4Lm3KbNtmxB2h6UdW4ou0R60BYPY/6Bj\n/hMPZRklwofnNzNMw2EeNA5kiSLpT8oLIO6Pn2gCOK18Oq1EPCo0tFvdZSWyIjdC2vdsk9kjFUgR\ndAdp9BsiUOiKzMsohy8KsoHlTPyMUKfyJU9QG8FJzKdtgGrdmsTQGqZBtVuVtI6KopzKUjMN6mPD\nBiSsJnkBVVFPLZlOCvJMNuf1jT5/+PgPZRa62C7yq5/+VYrt4onS0mxwFiy4nbvNq0uvSnqx0Saw\naWuSbWV5//h9VFVlMbzIk9oTdso7aA5NUjSZiig1hT1hKt0KCV9iTAl0sjxmw1lsOqSYN8ZGbINa\nX8x1jRph10lnM+lP8q2tb6GpGgNjwL3CPT6//Hk0TaNQKhAPxCU8otwr8+Hxh4S9YQxDCAdNsoqA\n2KMbsQ08mgcFhe/vfh+HJhy6e7l7pPwpUr4UD0sPxwyv7dDb5dtypyzliF+YfUHSnU2yj0wbHwcj\nsqmj7Mt6Gi3kie+0dD4qfSQgJgp0jS79YZ+uJi6F3rCHQ3HwxtYbfOnsl8Z+p26Kxu1iu8hmaVM2\ngPmdfnp6b+xZbbiYDUexWXY+yH6AaZn4nX4avYaAI3ijUkwl5hEwqj96/Ef0jT5uh3BOY74Y9V79\nBKbSPp8217llWRKqdDd/VzIiOFQHgqBmHMeO8gz2MLon7UZHO4u3V99jLijUSnPtHG7NTcKfGKOF\nG4V76KZgplHaytSG5ec1iOVbecGnH0xLFqBp4xPBUJ6+c61bEyI1qmikdarO5zLDlNolGr2GaFx9\nWpqudCpCgfepaJTN/BT1RkV/Uisr6ejGxlO6NywwDIM//H/+kD9W/5i58Bx/99f+rqSEzDazKIqo\nGK5F16QDdiV9RZbB98w9ziTOMBec46PSR3hdXtmXkwqMS6rrlk6pVeI//Mv/wG//5m8/d5p+8dov\nnvi7L/3Sl7j+v1wXWXRPlJngjOyB+jjqx1wrh4nJndwdbmdvn4DuAJK15VH50djfn8bOZDOkOBTH\nGDf284bNGuJQBTzCpvzDEll7G/5mwy7qfWEXbmdvE3QHQUFWnzt6h3K3TNQbnQ4nsZB0wYb1jM1p\ntBlxv75PvV9nIbTAueQ5NgubpINpkSjpt7gyd4WZwIxkvLqYFE2QmqLhc/mkmNookcQ0+O3ovNkN\n2gArkWeN2nbVffIM2k3to/f+WmyNR+VHRNwRVJ7qGGBR69RYjQo9kbAnPMagMjAG0tboli5tVDqY\nZjEiKD47eodqt8pcaE7aEFtrYxL2+E9f/6f83v3fQ1EUVqOrbJW3uJi6yHHz+MS7O1TRa5YJZCQk\n7mrmKnfzd9FNnb7R5w8e/QGr0VVqvRrlbpmX517G7XCPEwZYgtZ2lOLW3v+qIigl7eeexic/jaJz\nr77HG4/fQEWVvROrsVXaQ1HJq3fr/GD3B0Q9Yp9tJDbYKm0R88UEvtwSPuit41ucC5577v7/uPET\nnzEv9UsnssX2JI9Gj4BUyBrNro+WKByqA7/LTyaQoT1oj0VM08p6p43JctLAGtDqiyyn3Sx6NXOV\nbDMrI+awO8zAFCT6boebpD/JC7MvSJyfaZmyPK+g4Hf7MUwDp+acmqmezEylAim6w67MttvMBBFv\nRDawXc5cZqAPTkSx0+Yy38pL56CnC+5xRRHZjUnIh27q1Po1buduU+qU6OpdXJqLxcgiYbcok/td\nfv77k/9OtVvFqTpRFVXyDgfdQQkVsR2c0WyOx+ER2UKXH03RmAnOnCiXB9wig3jcPKbWr8n1dqpO\nvE4vfpefmeAMA30gnDILGr2GVNa7krlyIrNnZxltXPtAH8gMz6tLr5Jr5gQFYVd8/tVzr47Na7aZ\nxe10oygK+Wae+fA8uqXj0TxEvBH6utjnNn5ZURTi3jiZYIaV6IoszY0OOxNz3Dxmt7ZL3+jj0USz\nUtQdFbSVjUMOG4cE3AEcqmD5sLN2e/U93jl8R154h41DVFUl28jKqk9n2BFwrMgyc6E5jhpHci5q\n3RoDc0BP741lc+zS/9Aaci9/j86ww2xoVlAR+mJyj087Z7opcLyPSo+o9J5mUzBlVlJBweP0CNo9\nX5yAKzA2L0cNUSXTFI2t8hYqKnPBOZklMjDYrmzzqPQIl8N1AgKmKRprsTU+OP6Ao5bo3i/1SlR7\nVfwOPzFfjEa/Qalb4qB+wJ8c/An1Xh2Xw0Wr38KtuSWMzn5H26m2A/GAOyAuRBQC7gB+l19WD1r9\nFi6HS5ZOFyILRD1Rwp7wiQxrvpWnp/eEuBIm9wv3afQafJj9kO3KNi6ni1K7xGxoVmbU8608rWGL\npeiSEG0adjguHdMyWlxeuTy2HqNVntagJTJkbj+PSo9wak7uFe5RapcEL7Bl8drqa+Rb+amZQXtM\ng7fMBGZAEbzDzUETh+qg3q2zFF2S7z45Kt0KP9z/IfV+nb7Rp9KtMBOc4drMNQHHcYdlhdPr9NIc\nNGkNWrx9+DY71R1cmksGGXYlzO/yk2vl2Knu8Af/6A84uHvA/Xfv87Vf/ZpsFE75U7QGLQLuACvR\nFULuEOuxdbxOLzFPjFQgxSuLr7AWWyMZSEq5+7Wo6Km4mLwoM/w9o8dbB2/RHDZ57833ePLhkxPv\n+XFj6eoSV1+5it/lx+v0yqa80WrytPFxcK9R22Jikvanx7L3//jX//GJ75SB3lNHL9vMSliovd6a\nqtHqi3PY6AtGrIXwAqqicm3mGo1BgyeVJ8R9cXwun2x47Rk9HpYe4tSchHRRrZzPzNMZdGgOm+i6\ngCeuxdfIBDL4nD4ingilTolmv4lLc/HC7Avcyd0R9uFps99iZFEqqab8KY6aR/SGIjsc8Px/3L1p\njGTZdR74vRcvIt6LfV9yr6xcKru27q5mb6RImqRItpqSLRuEx6JESrIka0aSAQ0kwJJAjyT/Gc0P\nSSYo/fDYGpND0yRgWBDItrg0RZHsZjW7qru2riUr9zUiM/blLfHiLfPj5L35IjKyujnSDAhdgEAz\nKzPiLfeee+4530KGdJqlIakkcaFwAQklgYnYBJ6eeJrImhCQkBMIBUIAqFCTjWSxWltF1+xirb6G\n7dY2J06e1mX0Di9kkXWr2Fpi75XBXUOBEML+MHUE5Rg0k+4tE86QYkh0HPloHoVIAZPxSdS0GrKR\nLO+eCYKAYqTI3V3H4+NYra2i1CkhJlPulFASHO4xfA0sJhWjRSSVJGRJxqXCJeTDeRiWgUwoA1mS\nT3SzWQfB7/PjtZ3XAAEIBUJc594n+LDZ2OTSvqFgCDW1hlAghInYBId4towWDtVDxBVyHw0HwpiM\nT3KEwvCzi8txTMYnB7qs3uctCiJM28S317+NneYOitEiJuITXMULAslZp0IpOA5JnE4npnkBQBAE\nRAIR3hl1XAcz0Rn+bv9BVsyH1U281bpsODtwevRabgNHQvGRAidIsnEa0e2HUVFgg5FuzqTOcJOS\nD539EBf89xLRvKokTPJrWPqIVyldcKk5dt+nScOxf2edgYX0At46eAtJhYie6RCRDe8f3kc+kh95\ngh8pxeQx7PEa+RSjxRMMZkEQYNkWttvbmI5No2/3eSuZ/W5aIUJdSKIAypjzo77bSyQbHqPeH/vv\npJLkzq22a0OEiLnMHMZj46hqVSTlJMFc2lt8XriCi5eWX6LK8dEwLAOvbpEEVUpOoapXScrpqLvR\n0Bv46PxHcat8CzXUMB+dH01AFSReFW8ZLSiSgkK0gFw4h7uHdyEKJPmVlGnuBqXgqZKV3k4NU4rY\naG4g6AvChYs+KHmWfCQ11u61MZOcGajaMWfPTq9DG5VAB9poMIq23oYSVY6mnwvbtTl561bpFiSf\nBEEUsFJbGXgWbF6OMoI4nzvPSUOnMelLnRICvgDePf1uXN25iniQYAYdswPHocO14lMQC8ZOJURa\njoXt1jbGomNQ+ypUU8Vceg6CQGROlnj87cbfYim3dKKKX1Er8Pv8KEaKGIuNIdfNwXEcLKQXkI/k\nUVbLtNmLAYiuCNVU0dJbiAVjuFy8zA/Y3rghiceylYcqqT6IgsiJh6yqyOIYe+9euVSvicXwYDr3\nrV4LCTmB3fYuNuubpDyk1Qe6S3EljpXqChp6A3E5Dp/o40RTb0zxdnkASuaYVXwkEMHHFj6GB9UH\nyIay+MDsB7icGvBogisz7MiFc5y89eW3vgzZL2O9uQ64RMb+/vb38fT406NjsUBFl06vw91dXVCM\nYffJYiLzEqhqVS5p1zQGSZKMnOy4DmwM2oC/uvUqnpl8BvudfeQiOSSVJK+AnqZXD4BXTFmF3kt0\nFQUyxHFcBx2tM/IQ805GQAqgoTXwj5f+McnPPkJtaXiwOeMTfHAFl3dAs+EsvrbyNbiui77bx3p9\nHc9NPYd/+Zv/Eq7rnqo4wSqUALgDc6lTOiFDXIgWUNNqSCtpvl6yYdKB9zpBJuQEijHqbD2oPEDT\naFKF/GhIooQnx55EMVrE/TCZr7lwUdfqyIayBK/CURX1qCDndQplEsdsVNQKJmITmIhPcJJvRatw\n1SevoZW36rrV2sJBl+za63odt0q3UIwVCbMtShAEkhIsRArv+L16K+7D0oBMIIDFsYpaweXiZcAl\nhZCaTvOAmSp6ze4EQeAKSd7YxO5FEiW8Z+Y9eGXrFQAk5em6Li9ajboG4KQQx2meLMP71n+5/V84\nhJVJYrJq/H5nHxXtuCMel+Pk+9EtIxvO4tsb3+aytozEyQ6lXOntbfxjhodhGfj8zc9jvbZO3a/W\nFi7kL0AJKNBNHfutfYiiiL7Vh+M4mE5Mo67XUdNrmE3OoqE3ODSHdWv/ruNHPjEfblmPcp/z6ttu\nNDfw/e3vIyWn8ONzPw5Zkk+oqHgTYPbSNhobuFW+xSUKH7XJDCeSzPWQqU0wNrE3IWAteO8m6E0u\n2aSvqBUikdoW5tJzvFrNTmePek5eyUUm61XX6nhYfYhQIMTl2IZVYxjkxwUtxOHW4ijNXfYe9jp7\nWK2uIhPOQA7IuFK4Qq1/JYXx6PjA76ZCKbT2WkilUrDcQRvy4Y1bEiVkw1lejRlW4TgN2pMOkdNr\n02hiJjGDVCjFYQsJOcEhP9vtbSSCCSTkBJkR9JocSmRYBl7dfhUNo4F0hOyyY8EYqupxoLUcix++\n6r06rlav4inrqYG2MLsmra/hxv4NiKIIRVLw6varePfUuwcSpytjVwa0mUcFFC/MQIKEc7lzaBpN\nTpiSJRmST8J4dBz1dJ2z2ZeyS9yxLBfOcXdA26FkJKkkAReYy8xxGbloMIq3Dt6i7kRnD6uNVSxl\nlnjl1vssvHNw2AiC/exRTHo2ZEnGc5PP4UH1AVJyCuey51DRKrh3eA+5MOFPWafIO5jqBCOuRYNR\nTCQm0NSbnFDEEpG4TKo87DNGHQJ9oCppQk7gySId1thm4RPJiloURKSUFM6kzqCu1U91/2Xr0itJ\nysjn5/PH7d7hOOY1ZGG/KwkS0qE0+nYfe+09VDQy0UqFU3ij/gavdrX0Fj5w5gPcYCYbzuLzNz+P\nltFCw2jQhiLNYtQYBStj8Cb2ji7kLmAsOoaG3hgJexlWo/K+83K3jMn4JDeCu1G6gbnUHHVFfDIW\n04sEy+mWT8T5wy4pQ+WjeQ43upS/dMJ99IniE9jv7HM4xGmDJc5qT+Vyi2xsNjfRd/t4/5n347B7\nSGZkCrlKs413lMLKKBiJV95REiWopgrJJ+Hn/vXP4V/8+r9ASknhieIT/G9Z9RkgJZRShwxeDMvg\n5OyEnEDEH8GZsTNcqvZR0nhcivfof65AwgfpUBqGbeALt76AilpBu9+GDz6Mx8axUl3B73369x4p\nI8s+nxnv2K7NpWeHvQwYtED2HRuxeZ0gLcdCq9eC2BXR0BtYa6yhqTex097BmD2G+dj8wKFIEAQ4\ncLgBTE2rnSiglDolTMYmsdcmh/BypzxS8tfr35AJUYd5+MDjPSzmI3nUNdpPMyEimjJ32kw4g/3O\nPknpOoMHptOKf28nDeg9ADGZvhv7N0i+4gh73TJacFxnQIXl2t414vuoB9jv7iMXokP2sGKN7JPx\n7MSzaBktSAIdZljRhF3raTC34YP9G/tv8IKTKIiDOvbdKnwCdVBCfuo4MElM4Fh6WXVVbNY3iXCb\nmkVdr8Pv8/NDdtfsnij6sOIqK8a+Uwdltn4gkuKNIzi4unMV47FxvHD2Bby88TIZhckxKvYddR0Y\nVJgV6Lwu0H/X8SOfmBuWwZMdpobRNtpIKSmkw2l+0rIcC69sv8IxQmvuGspqGb/wxC+cSIC9gXyt\nsYaaWiNGs2NxiUIRp2Oph5NgjLBLrqpVrjxgWiZsx+ZGCt6khV37D3Z/gFe2X6FKpuCD4Rh4s/Qm\nnhx7EpIoca1NNkYtcG+1iMkZsQ7CW4dv4aniU6hqVW6Hzj6HSUYx97C55BxeXHxxpOYuew9swW3U\nN9DsNdHpd+C6Ls6kznDs56F6yKvtAV8AkUAEL8y/gJbR4lbCkiiN3Lgtx8L1/etwXAcNndRuXlx4\n8cRBbZRuNcOns6DG2q33K/fJFVYjnGoylKRk3HEInygcu2amQ2mkw2kExSCSShI1tcZlvZhEpumY\nuL57HTvaDkJSaAD/zK4toSTwNxt/QyYSkTxe330duUgOB+oBbpRuDBz+fhjXNICCXTqc5pU8VmWR\nRAkXcxe5fJN3zk3GJ3E2dRartVVOCk2H0nAc54SBjQgRB+oBVquraPVaWK2vIhaMce1+wzaIsxHO\nwbAMIlWbXdws3ySyWWoOAV/gVE6IYZNkaS6Sg2mbHAvtwkUumkPTaOL5yefx/OTzpz4X9pxzkRzv\nkDHc8FJ2CXcP7w4kIrlI7kT1i82TR2n2p1tpHKqHUPwKHDiYjE1iNjWLB5UHp+IpvfHCK0m639nH\n+fx5XmX34je964utae8GxKqMTP3goHuAvkuVHFEUMZOageM4XOUFOE62a2qN600395scnzw8WJWQ\nXVsukkO5M1jcKEaL2Gnv8PZ0KpRCWjnZzThtMwcouWEW5nAp2cxFcjhUD0/Mk6+tfA3ZcBZ1rQ6m\n3e26RDCualW+ITJuTDaSxYF6ANu1eSxIyAmaZy51XYvRImpaDTOpGXJBHRrMDbaqVtGzeyh3KDYx\nSTcvN6Gm1fCD3R/wavBp65dVbE3bxGH3EC5cjjP2Phs2vJX2ikrFERZfWHGAxc7heMI+k3FKAHrG\njKPAqs23SrfQ0Bsoq2V0zS7GYmPQ+zrGUmN8Dno/z7sWs+Esvr76dZKmDFMidpr0rPczWDeIFQf4\nM5dTlGBrVT53JqITKJVKWIgunIiX3g4dc7AeVTRw3WNStteMarjIlgvneLFglLwne9alDrkTszU8\n7E67lF3CeHScxxb2zk6rOI+SzBzF1ahqVU6iX2+sI6kkkVSSaOiNEyZnHG8tBnA+dx6vbL4Cv+jH\nucy5ASMvwzJw0D3Aw9pDPDf13MBceqdKM+y93ijdINfmI3WXUZywmBxDt9eF5VhcftBbcPvA7Afw\nnc3v4EzyDHfo7PQ6qKgVLnPL1iYr+rDvH8glOmVekHq70TJa6Fk9nMueQ1WtIiEnMJWcwjfXvkld\nB1FAu9fGUmaJQ6EYBFaW5AFDv78Po7YfeYz5y1svYy49B8d18Lebf4vre9fR7rWx196D3tcxm5rl\nTNyr21fRs3pQ/Aq9HJcqPFPxKY4nGpAxg4Pvb30fTaMJ0zK5UgjDep4mWTQM4zD6Bhq9Bm6WbiIo\nBYmpXF3G3YO70Poa1uprMG0TMTmGdq99Qtrv2t413K/ex8PqQzR7TSRDSag9FdFgFMVoEblwDlOJ\nKfhFP+JyHIZl4H88/B+0KTv9AdwkQDhRJmfEvsMv+jlezMtu3mvvYb2xDt3Wsd3YRkWroK7V0TW7\nOJc9xyX6vBKKDI9e1WjD2m3twi/60Xf7gAtMxadwdecqAlIATaOJ2+XbCEpBbgm/kF7AY7nH+IFj\nWKXAdm0sV5fx6tarEERqR3bNLrWU5dNlx8Zj4xSoZApWcTkOx3W4moRmaWgaTUwnpgFQZTEajCIb\nyWImMcPJSK7rwi/5ecWZEWOKkSLHRXZ7XXx99evomB3UO3V0rA4WCgscC39t7xqavSa+ufpN7LR3\nkImQeYbiVzAWGyOMnwdnDJzEvQ2PYaxuu0cY+Wgwyl1NQ1IIkSDh9EP+EB4vPo6D7gHqRh1NvYl2\nr426Xker16K2ZziNc+lzOJ87j4nYBKmABBSU2iU0eg2UOiXUDCL1hqQQZL+MtJzGxcJFbDY2kQ1n\n0el38PLay3Dh4i8f/CVqWg2u62K3vYufXPxJfrAekCx1Lbx18BYAIu44cGBapCwUDoaRDWWP1XQg\ncDyj97l454ADB6v1Veh9Hbqloxgt4nLhMmZTs9hv7xN2+wjXupRd4vPDizlkh5RwIIyLhYuc4CUK\nIsaiY1yO8umJpxEPEnF4PD5+QpHlNCwpe7+u6xLh8eg57HX2cNg9REAKQJEUrNZXUe6WEQqE+PWx\nDajUIfnJhEzdnnQ4jb7VRyFGeNJYIDaAUffGg4ScwGR8kjpE9SYmQhNYmF44oZowjAcfJRELkG7+\n97a/h7pex05rBz2nh3dPvRsAeMJuudbxvToWJ+jPpmbJjdjnw35rH5FgBM9M0OGfyex5D+usQ8FM\n0MKB8IDmNeMlbLe2IftlZBRSKBIgYDY1i0v5S4gGo+iYHRj9Y35EOpTGdmsbiqTgW//Xt/hzeOZn\n6FraZhv7nX2s1dfQ6Xcwm5zluOHt1jbuHd7DG/tvAAKwXFnGRnOD7zMsJnufqeUS/GazucnVYWpa\nDU9PPI0bpRvomt0BXPcv/6+/DEmUEAqEeAcg5A8N8Gq8sXN4/jFOSd/pw7AMVLUq96aIy3FEAhFa\nr3BxqB0SOd4hcnw+SgnvaTF3u0VW94zY3LN6uJi/yD+vEClw7gmbT6Z9LHFpuRbN9U4Z5Q5pnM+l\n5/BY7jFc372Obr+LidgE/D4/HMPBWGgMV+aOi1MM/uLChSRKXKVE8Sv8GS1mFgfWTCwYo2qna+PX\n/tWv4S//6i/xq5/4VVqTfZWr1UjioLzs8D7l8/mwXFmG7dqcH8K4cEpAQSwQG0jK2ZrwSvxJPoKJ\nuq47uE6OyPOsANI1u7zL3zW76Pa7aOqUJ0iihFgwhnyE5Am9SnDeeFvVqnBAhQumBMe4NbfLt1E3\n6gj6g2gbbWTDWQjCSQWt4dhg2mTa1+11Of692WuiqTd50cgv+gc4YQEpgIpawaXCJYgCdZA/Ov9R\nbnH/2u5r2GpuoWN2UNNriAQiSIVSKHVLqGk1NPQGNEuDJBDxnkmrsuLboXqISCDyjrH9AMGRr+5c\n5fypUDCEd429C7fLt+HCRalbQt/p867BlbEriAXJSZx9/zDnL4gg//x/kBhzhoEDjjFjoiDCdm20\njTYPFhW1gobewF53j7Pm387VjAH7faIPaSVNphRanUs0jWJWAxhQZDlQD3A+dx4Pq9ReYooLj+Ue\n42SQ8dj4wLV7qwnsVJtUkuj2SR+0rtU53i6pJGG7Nh5UHkBwBY4FZGL31/au8Xb1XIpMblgVQATh\nWft2n+uPAhhph17X6qReAEqU1hpr2Ghs8CqE9xmwz+/ZPWw2NhEJRpAMJmG5Ft419i46yYeSaOpN\nrNRXYDuUaO9395FVsshFciMVRwA6bd4q3UJNpy6GaqmYTc7Sc2uXeJuI4XUf1VZjz5dt4C2DktGm\n3sSl/CVcLlzm5C5vpYHd30JmAVW1CsuxkFJSHHd7o3SDgpcrHOMZPf/N3mldo/ZbQklAN4mY6zgO\nfy8D9/02/Aamm+y6LlLhFGSfjFw4R2RXo8nf61h0DBAIS56QE3jp4UtwXAcrtRVyGk1M4lb5FubT\n8whKQa6Cwq4bAHpWDzW9hp3WDiLBCBG44OOB/4nCEwObTrlbhk/0EQ5dkCAHZCh+BZFABHcP7+KZ\niWcGniuDWjG5LUmUYNgGvrH6DYiiiLbZxkp1BZOJSRQiBex19lDX6njfzPsG3GS9VSbbtrHV3IJf\noOdt2iaenXgWsiTjY4sfG3i2LIgDOOGs51VK8A5v1fvqzlU09AYqGqlPPDH2xA/VvmQY1b3OHpYr\ny2j0GphLzmF/ZR+2ayMbzmKzvolSp4SLhYsDEK6BOXPkRsta76zbMQyD8j539jfxYBx1kyQmh43J\nWNdhuAvoXVs7rR10eh3MpeaIYOvY5ELsMeoAjhUgvN2QdDiN6/vX0bf7+O9//t/pAOA4eN/vvA+F\naAHFSBHlThmGZaCm1VBRK5hPz/P3wEjjPp8P+Ugeh+oh1hqkOd/W24gqURQiBQR9QY4PnoxPYqO5\ngZpWw5c++yV86c++9Mh39Gc/+Wcnfvb8zz6Ppd9dQsAXgNpXcW33GsrdMhpGA2/svYHpxDQkn4Sa\nXkM+nOfxaBjOpPhIVrFrEIGO2coPV0y9g0EtvK370zg43sE4JQzOJwoialqNr8md1g7HKD+WeQw3\nyzfRNts4lz2H5eoyKt0KPjD7AZxJnjlR1WVreDw2jlavBdMxcXX7KlIhUgFjc4uZrTHnXdelRBou\n7elds8v5Lo5Lutt99NEyWrAci+RN/WGkgqmBe2Pzmdmk+0Qf5tJzVHg5UtzhXW3v3x3tMZpF/grX\n9q7xbqG3G+I1OPN+54F6gPuH93E2eRYCBFS6Fa52xj47GyZ+1oDx35FZFUtCv7f1PTw98TQAzzo5\n+h3G77mDO8iGsxAFER86+yFUVHKpZlVyB84J3DwbA+vesdB3+hyvzaCxDNrZ6rXQ1JpIKESeHcVz\n8iIFDNughF6vIxPOEEcmnBm4v7sHdyHMCAPdbAC4UryCUrfEoavGmgAPAAAgAElEQVS8E9/eIb6B\nKGKjsYH9zj4pe6nE8+K/6xIHaiI+wYt2X7rzJTT0BhS/goe1h1hIL7xjgzZZkvHrz/w6vnD7C9is\nbyImx3Cvcg+RYIS06v0y1L4Ky7bw3un3omW0IEdkDh0btXe3Wq139N2njR/5innX6fITz0H3AAEp\nAJ/oQ1AKYjY1C7/o56zYbr+LG+Ub0E2dE/8+fuHjxwYBGDz1tXtt9J0+FEnhdr0hfwjPTT2HWCB2\nQr1BgMCrx5JIiiytXgsbjQ0OuWEV057Vw0RiglumF2IFxAKxE6om7V4bjV4DO80dMozQa3AcB5fy\nlxCUguiYHdw9uEtmNpaJrdYWd7W7WSYR/YpWwb2De5hOTvPTvvf0xvBk4UCY47qYOgbTFb1TvoNm\nrwkRhAX3+4g4lQ6lT5g7JRVqgbd6LRx0DzAZn0RUjpKObmwMAgT07T4aRgMVtQLFT4THlt5COkwS\ngcNqHuwUftA9oKT3iJjGKiI+0cfhFh2zg9X6KhS/QknokXlLNpI9YZxU1+t4fe91uC6dfOtaHflo\nHrFgDE+PP42p+NSJSiw7/TIFmHwkD9M2B+aC3tdRjBVR1+owugYSwQQmchO4MnYFXbOLRq+BreYW\ndtu7aPVaGI+OIxqMwnZtLGWXYFgGAr4AlrJLXJppWEOeqfr4RB++cOsLZHajVbFeX8fzU88jqSRR\n6pQG3ut8eh6rtVXYro2X11/Gam0VD+sPufzWen0dfp+f9JX9VHWUJRn7nX10zS4qWgV/8eZfQO2r\ngACU1TK0nkbyg4EwVFPFe6bfM6Cj3zW7aBk0F1pmi8xwjjS0c+Ecly/1VhUYvpWtzXuVe3AchxNo\nmnoTuq0j6AviQZXcbjcbm4AArsnurQg9qD5AQ28gH8lzoivT2PZ2IkzbxOdvfh5VrYpD9RA3SzcJ\nKvI2sCE2tppbuLZ3DT27BxcuNpobiAaiiAQi71jdyXEd7LQIBnLQIQJZPpJHQ2/gYf0hJEGig63R\nxFR8Ck9PPM2vj8Uvr/pNNpyldnCsOGC8xFQ2umYXZ1NnyVTHVJEJZdBp0HPLZDJcNYHNQbWvoqE3\nqCJraiPVVtq9NsrdMnpWj0yz/DJC/hBsx0ZVq8KwDIT8IfhEH4qRIjdLmknOoKk3sdXcAgD825//\nt7h77S7uXb+Hn/pXP8XX/cX8ReqoCMBEfALL1WVecPHqDksiEb+6PSpqTKemcdA9QKVbwVh8jMc6\nx3Xwvc3vkaHXd1/F9u3td/S+vWPq0hSkWQkBXwBvHbyF7dY2N67Z7+6jZbQwGZ+E0TcwFh/jilTA\nkeHYUft+v7OPtt4m+/RgdEBqUxTEAYOhX/zNX+Sx0XZt3rWLBqMnYueo+de3+1hvrPPn5rgOLuYv\n8soqq3SmFOLihP1hXMhfQE2todVrodVr8XUXCUYGOhntXpsrlMTlOFZrqxAEgdTGJJnvF9FglDDo\ngoBD9RBbzS1kw1lUtSr22nuIBWOYScwg7A+jqlexUd/AWIxI3H2rj/O580i7acxGZzExfiyFfG3v\nGtpmGxuNDZS6JfIbOPKjWK2v8hjV7DVhOzZh0j17zAc/+kG878Pvw6F6iI7Z4QZ3kk/CnYM7AMDj\n8dnUWey0dnCvcg+bjU3stnfpeWcXeRfsYe0h1hukJ17X60gqSTosHhmLtQ26VlEQ0TSa0CwN03FS\n92BKKczFORqIomf3sNXaIhMrONwRk/kmtHttTCWmuLzf8Lv3xttUKIW12hp6dg9ds4uD7gGen3oe\nbbONH+z+AC5clDtlIolG8/w5Dq97ljO8ukUcLNM2UdWqSIVS0EwNVa0KpgmvWRriwTjavTbvZrP1\n8LBK88GrOLdaW0VFIzik5ViUKwWimIpPoRApIC7Hedcj5A8hHiQzoc+89hmUu2VoloZXdl5B2B+G\n3tfR0BtcW3xUDGPxsd2jtXgpf4nHFIaosF0bfbvPeXOyX+bxnpGmRxm5/YM3GKqaVSxmFhENRlHX\n66ioFQSlIIK+IKYSU0TeOXqYAgQEfYQJvly4jGennoUiKQOtDO9kZRgxhk8N+8P4p4/9U2RD2VMl\niyyXiC677V24rovXdl9DS6dqQavXwsU8yY/lIjmopoqUkiLzCNfFZIIqTt4JHw6EcbN0E7qlI63Q\nyXAhs4BLhUu4lL+EvfYefKKPkyPuV+4Tlmv/BkrdEtdGTypJdM0u+nafn9pYMiKJRKRkrdDHi4/z\nU22pU0IkGCGMXKfCsXptvY1MOIO6Xucn9mHZJtVUUYgVeCI2mZikiqkkY7+7jzf2CELi9/kR9AUR\nV+JIKSnEgrETST6XfQznoFv6gJNcUkkiLaf5d4mCiKA/iHK3jM3mJlfhYNql3gXY7JGTpN/n50TP\n87nzeHr86UcmY95krmt2T0jIFSIF6H0d4/FxtGttyJKMTzzzCQR8AQSlIL6+8nWsNdZQ6pSg2zrS\noTTOps7iYv4i4V4BLgPlhVeJAsk03i7fBkAbwzdXv4mG0YALlyfgrutiKj418Oy8Zi51vY6GTkQ/\nzdSg9TX0nT7ichwds4OETDbzjusgE8pwc47l6jJtUpZJAc7UkVCIBBkLxjAWHSOzpICCht4gfWdR\nxA92fwBFUvCw/hCqqfKq80fmPjLwnNlzLUQKA5JqzPkvG6JDme3YiAfjXKFANVWuOsBMg5jDGzsY\nNIwGT8Rt1x44FLDhNckRBAF1jTopZ1Nn3zahthwLr26/yqUomVRX2B+mdrLHtMUb9Ic3BSbvKAok\ny9azehBFIrwZfQPZcBbRQBQBieaZ17WSxS+2lmdTsyThJgiIB+McrjMMOyh1SlyKVe2rqNUIc5zO\npKH4CT5zoB5wGNS9yj1urDV8UAwHwogGo6hqhCNlCiljsTFU1Arqeh16X0dFq3AMbDQYRd/t40Hl\nAbZb23hQeUBJ8hde5ff2s//6Z3lsqKgVRINRJOQEh3w09AYigQgeLz7OYXguXO7oOBYfg+JT+IbN\nWvUMWmI5Ftbr61h+Y/n/VWI+/+Q83v3ed2MsSlKU261tknq1aG6GA2GkZDIGEyDgytiVAcMywzI4\nP6DULaGqV5EP5/k9sfXgwsUTzz6Bj334Y/iZn/wZDi9rG+0BmVKmMW079gAMwzvXosEodIskRxW/\ngmKsiISc4NBEVsRhRQhGZm4YDeqUCT4ExAA0S+OyrCzBDUpByJIM27Vxv3KfDFeUOJpGk1crlYCC\n1drx3IoEI3zuOq5DxR8lTYUXl+zXGdF2LDaGkD+E2eQsZgWCEDFDLAYLWa2t8v2PuSMzMzkWTwUI\n/NDq3WPYc2roDazWVnm37trONfglP/EWxGMDwkgwgpbRIkKrFEDf6aOiVTAeG6dnfGRa1+13UVNr\nuHNwBz4fdSqY14EsybxrwOIYO9Sz63fhwrAMkte0DESCVDBU/AqigShfU3PpuZEy0t7B4q1qqpAD\nMgRBgGmbSCpJWusuCV+wfMGFi6Xs0kAxgMU+No+bvSYqaoWb/LF3yVSzGnoDcKmAwozQvK7Ho+Cr\nbH6uN9a58R8AXMxfRD5C0o8d89iEix1EbpRuYKe1w0UbgOPOpwMHel/nMWwUhNibUEMAfIIPY9Ex\nTMQnsFJbQVJOQrd0SIKEZyafQd/uYzZ1DGcbnmvsXv7BQ1m8ZI+nxp6CCMJLLeWWcCZBm79XdSUf\nyQ+wskcNL6mCEbKmYlMDbduR8oHhLK7uXMW1vWtwXAerNXISvZC9gFK3hKXsEg7UA4gg9yzWDmPw\nguHWMLsWJrDvE324kL8AAJycmQllIIkkuVfTagj7w9jv7EMJKHA1wvEyYqZP8HFM9TBTeZgkxIhL\n7Gd+nx8fnPsgvrX6LQoaoWMsHiMReiWTHFAy9bD6EM9NPgdZkuG4Di4XLuP6/nXU1Tp3R2SV2ZbR\nwuOFxx/5PhjMQYSIpdwSKt0KKbccwTP43wgSipEib98DFGB3WjsDUATWAmYqE4vZxXfE1PaOURJy\nV8aucMxvI9FAJpjhkIiKSgcc3dKRD9NzSypJTMYnuRMmu9dR7eiaVuPKDHudPby6Q8nLdGIaZbUM\n16G+JYNhnEbQictxWE1SOjAsg8N/ooEoJuITkH0ykkoSY9GxAUlRURD5tZqWiZnkDFdUuHNwh+N8\nmXLAfmcfZ5JksPXM+DNo9BoohAt4YeGFU+Fk3rYoAJzPnccXb38RlmshEoxwctp6Yx277V3Aoerf\n6/uv40L+wgmiUVJJwrSI2GfZg4o/bDCMc02rIR1OY7u5TcG8igE1g1GDBXLLtQakKFlHB6D5udfe\nQz6cx+2D2xAEAT7Bh3QrjWcnnj0x5zJhku6raBWYlkl6+w65eg6b0wy3S4fVb4bHaaTL4bjG/A7q\nGmG163od6VB6wPuBESq9cmTvGn8XxyozBQSA4ggjnffsHu5X7vNY9Pre61iuLqOqETys2WueeB4j\nn71rYbm6jHyYICyMmMbmTy6cQ9/po2kQgZ8pVbhwcageotwtc5O2M6kzeN+n3ocn/qcnMBYdQz6S\nJwfGUAbPTj7Lv/Ory19FNBjlh2YAiAQifN2YjomrO1cBAIVIAVVUcaV4Beey55ANZ3ExfxHAScMy\ny7Gg+BWcSZ5BTa2hEC3wPY7dz+99+vf4fgHghLrJ8PtkGPz9zj5ulW5RB8gnQRKoIDMRn0DAF0Aq\nlEK5U+Zr3Quz8MbfW6VbXLXJdV3Ue3VAIGWidCg9oBYCAK/vvg5BELCQXeCiBwfdA6RCKey2dtHQ\nG3xuPZZ7jENycpEc4nIcK7UVqH0Vdb0O27IhiDR3fAJVKS/mL6LWrZ2YE/cO71GiaBBuOxvKjpw/\nAD3L4T2GrQHXdRFTiNS+Ud9Ay2yhrtbx1sFbvBuVi+QgCbQeZlIz2G5uc+MwJl142D1EUkmSz4He\nggMH7YM2cmdyXDBAFAjGmJATuHt4FwklcUK5Zbe9i4SSQN/uc4fpqlrlhUF+TyPIoW83aloNokBF\ngFulW7hcuIyl7BKHQ57LnMNUfGpgnnkVpRhRNRkiN1ovgZPt/bZrc3z8oXbIO6QM2nTamIxPYjGz\niIpWwUp1hftWnCZPyq6RFc8YkTgejCMbyqKiVeh3jnIHJr4BgOPRvQaP3vwi6AviJxZ+Ak29iX90\n5h8hHUqTl4Po59DP/y/Hj3zFPKRQhZAplzDpLtuxOX6PV8DDWV4pYacqZnYwqnLFSZxHkkhei+th\nMD+rRm41tyD7ZGh9DT27h1gghkKUqloC6MQ8Hh/HVnMLq7VVXC5cRjac5WSs4e/fa+/BgYO+3ed6\nrew0yLoETCNTNVW4AimfJIIJcvWUU4BA2rZPjT+F5eoyr7o8yladnfQgENNbNVWE/WG4AlVl2eJk\nFe2x6BgWM4vY6+xhpb6Ct8pE3Av6g2joDVzMX+RECGZSkZSTdEgSJcSVOMaj44jJsUeaOomCyKv7\noiDiPdPv4RVEr9FNp9fBTHIGfYda+g29AdMx0TW73DADOG7VjoLxDI/hKierdq3WV5FQElxZhhFx\nk0oS4UAYqzur0GwNs5OzHGLBNr9oMMrhGgIEkjT0VBkYwdfbkm7qTViuhZvlm7ixfwO6rWOvvQfL\npYpfq9dCWArjm2vfxER8ghOWAfAqsu3YaJtt+FwfHDiIBCJgxNofm/wxKH4Fl4qXcD53nhvZuCAi\n6UptBZlwBnpfhyRJuJC7AL/Pz41qzqbP8g5VOBDGZpPw0NlQlncZzqbOYiYx88h17u1KBHykHOC6\nLnLhHD46/1EsZhaxXFtGRa3AdE2e1Ph9VKnW+hpEUYRhGRAEgZMyc+EcPjL3kYEkmyXWzCRno77B\neSLz6Xl0rS7X/o8Go/x5GhYpx9ws3URACiApJ0kFxXUQ8oeQDqVRiFAnhxn+bDQ3cO/wHid0VtQK\nMuEMh1mxd9Q22ghIAQR9QTxefBxXxkk9x7RNxIPxAXOaYdKd7JexXl9HUAoOxAx23XW9jvXGOrS+\nxp+DElCgmRpCgRDa1TYUn4JIish/6VAadb3O46DjOjiToqrTvco96JZOyYmHVMUqdxOxCSTlJIeT\nZCNZ2K6N7cY2FL8Cra+hrtcR8AVw++A2bNfGZGIS0UAUr/zfr/B39Eu/+UswbRMHXTLjWquvUTek\n10LX7CIaiMKwDB7jWZU0qSQxmySSb0AKkMa80cJSjvDgfaePbr+L/c4+yp0yQfZEEbqpIypHMR0n\nMvh/+pP/xK/ln//aP4dP9EG3dKowykneOWTVvYBEMn8pOYV8LI+59BzO584jEohgKbt0Iu62jBZ0\nW4ckkuzlYnYRY9ExtI02HtYewnZt5CP5ARgGi+GndXAZtBIC8KDyAO0emU2VuiWIooi/Wf8bLoaw\nXl9HJBDhZMCu2YUsyQOEQVEQMZOcoX/vdeH3+WFaJsZiYzibPstNz1hnxnEdXN+7jv0OQXn8Pj9X\nMypGitzWvapVedyPBCJ4avwppJU0zR0lSVX19gGUoIJGrwHXIe30qcQUzufOo1wi12VWMW/qTWw0\nNnjnznZtjEXGEA6ET1iu950+QV2HugSs0xAOkolYSydc8FRsCj2nh/32PjRT46piZ5JnKAfR6shH\n8kQsTs/i8eLjXJKxolbgE30wLIPyg2CMCK89Ig4vZZc4IZGZYXkr3l4Y5XyGeBXlThkxJYZevzey\nK/xOBuvMa30NAsiVeyo+hahM5n5BKQjVpM4zU0tjIhP3KvcIEmQ0kQ1n4Zf8WKlSNTnoJ0gkw9jv\ntHboMKfX0el1YFgGcdyyi2Sid0Tk32qSjwjz04gFY9D6GubT80jICcxn5jmJk3X4vfsFu/9MKINb\n5VuIBWO0Xvs6Xlx8EaZtkm+IX8Gt0i30nT4ECJw7xWChVa2KdJi6O7lwjlflTcdETa3hUuESZlOz\neH33ddiujUPtEBW1QopuwIm5xuLwP3goC7spL7ZTt3Riytp9njizTWIsOsZ/djZ1ljPdvUHO2+pt\n9pr4we4PsFJfQdtoY6W2gpnkDF8k3onAcZV2j1s09+0+h4u0jBYKUWrRm7Z5wlnNO7ytFNVU4YDc\n8OJyfGCRjkXHkFAS0EwNc+k5aqEdJfFTySlMxCag+BS878z70NJb6Pa7mEnO0MIwOzwoDwd127FR\n6pZwu3SbbI37GmpaDfPped4Ssl0bYX8Y7z/zfiSVJMdprtRW0Dbb1EKUyT1tPEYY6mt710hf+Ygk\nw5zBsqEsfmLhJ9629caIeV4nOXY6zoazuF2+Dd3WsVpfxZ2DO3hYfYhyt4yW2YLaV3GxcJGr1wAn\nHVKZosQwzGC4tbXeWMeb+29yiaa99h53/QPAIT3X9q5hu7wNzdJgBk2Om2dtftM2sdpY5TCD+5X7\niMkxHpQuFy5zSUG24ZuOyTGsh/ohFEnBYnYRNbVGm0qkiJpBGNCb+zdhOiam4lPYa+/h1e1XkQql\n0LN7UHwKHss/hlwoR26oRxU0y7WwmFnk6jXeZ5RUkvjg7AexXF1GKBDiQbEYKZIzaTiNgHgsz7je\nWOfOmk2jiViQDl4XCxdP4P3fbkiiRGYfsQlerU2H0rwVP52cxnh8HNFAFAKIKPWg8gC6paNrdqGZ\nGt5/5v0DFR82WPtUlmTMJmcJ9mX1cbFwEUF/EBv1DdJ3d200jSaK0SK0vobPvPYZIq51D3Hn8A4W\nMgsoRApQJAWzyVnMpeZ4W/xQPYTaV9ExOug7hEtkfJiwn5xX2fwOSkHcLt9Gw2jg8bHHIQoidEvn\nWuQCBLx35r0I+AIDrV+W/Ot9HZlIBhW1grOps1xRiK2h+9X7WG+sY6e1g83mJjKhDFRThdbXoJoq\n1kvrUG0VQoiMUCpaBSk5Bd3ScSZ5BgKIy7FWIy3pgBTgknhs/sfl+MBhlsFdBAjEvWluIKNk0LN7\nKHVLqHQrONQO0bfJPCocCA8k5r/76d/FemMdqVAKb5beJGMpv8LhMrZjD7jSehNKSZQwk5zBWn0N\nhm1wRai4Esf9yn0A4AWJS7lLmEnMYCw+htnkLKbiU1jMLOLf/eG/45/38f/l47h1cAsCiBRo2AbF\nueg4wsEwqUSFcjiTJHnYJ4tP4lL+EiXcR7FtQIXoCEbTNJrckTYhJ9DUm3hj/w1UtArW6mvYbe8i\nHAif4PUMH95Z0Ykp+LR7bZQ6RKiTJRkxOUaQgiP35lgwho7ZQafX4d4K3T4pjg3vT5JIylmTiUm4\nrotCrIC5NAkLlLtlMMUVURCx1dzC7cPbOOgc4FA9REktYSo6hQ+e/SA/XLB17Lr0d0+NP8XnKjuw\n3CiTJ4TjOnAFF2PRMcyn5zmkYn+fTGtYYs4ghZIocZWrQqSAYqwIo29wTsUoCBCrTDPcu+M6uF+5\nj2QoCRcu94LQLR3ZcJYKBoKLtw7e4k7IRt/AM5PPIB6M87VXjBbR6XUgQEAhUkDToAMgK8awvYYp\nTHnhpsPYcGYUxNRlEnKC3KCPjMHeTmmExQG2NqPBKBS/wh1spxPT9D3BOFdmgQBkI1nsd/bRs3v4\nyoOvoGN2qNDUPeRQl+3mNjJKhr9D1mlkED2Wh909uIue08NYbIww96EkhxPtdeh3bdfGZnMTIX8I\nal/FTmsHsl/G3YO7CAVDXCzDC0MZHkklCb2v41z2HH7u8s8h5A8hqSSx0dygjrbRQKvXwuXCZdS0\nGgzL4JBdtpaYwsp4bBy2a2O9vs4Vx/7b3f8G27WJA9itQbd0+EU/ZpOzMG2TzzVvXjMqh/1hxo98\nYl7pEWlxtU7EgIAvABcu1mvrcOBA8SsDCbc3mR5VKWaTmm12db0Ota9iu7lNUlFwTk2mw4EwT7h8\nPh/qWh2zqVlelZ5NzWKzsYlyt4x0OM0xTrZjc8c5lgwOX5vt2tD7Oif1sGC+097BnfIdZMIZBHwB\nfv8hfwgzyRkUI0X8k6V/wt0fE0oCK7UVnqw0tAYy4cxAdY1p+d4+oMSgZbT4KbWhNzim1StlBFBy\nwxa7YRlo6k2Uu2VMxCZQiBYo2B8t/LpeRzRA98GcyGRJfqQcIPuOUe8sHAjjRukGqnoVLy2/hKbR\nxEZzAzfKN5AP5xGQAryKPR4dHwhcjuvwdrTiV0aSNQZkNF0Hr26/ipbRgiiIUC2Vw0CGZbi6ZheN\nemOASJdUklx6T+trKEaLWMgsQPbR/a9UVxAJRJAJkzGFlxPQNbtEDJUC2GptwXVdRAIRhP1hwibC\nRSQYgWVbqOpVji9+fY8k0ep6HU2jiTMpOkQw3K/e1yGIJIEliSRTGZNjpKRxdB9sw6ioFYT8IYxF\nxxDwBfjczEVyaBmtQRJVhDTrWUtT8Ss4lz2H89nzP3RVZ9RgnRKfzwfDNFDqlJAJZzAVn8K1vWvY\nqG/Adm3IkozpxDSv6A0Pb5LENlHLtY6x3ZaBYrSImEzkbdux8ZXlr3Cnxp7d4y3bXDiHkD+Ep8af\nGug2MFnCsegYJy4zPgwj3LH5zTbtsD8Mv+jnSYxl0zxjUmNxOT5w7aVOCXvtPSKMhvM8QfYmqXvt\nPbTNNmpaDX2HJExVkzDILOH7zP/xGdy7dQ8HyweYvDSJtdoa33CqWhUzyRnC8RpNPDP5DPkxHBG9\nvDAhVtxYq69htbaK5yaf4+oWftHPCwP/9bP/FZ/99c/i2hev4eaXb+LGl24MJOUA8O//6N/jy3/2\nZfznP/3P+Mp/+Aq++n9+FT7Rh3c9/y4cdAijzOB1s6nZEwc/1ubOhXO8+s/IaGfTZ2FaJsfyjkXH\nCJoSm+DV3z/4gz/gn/XCL71ABZOjyjQz5XIFFyu1FWw0NlDTa3iz9CYkUUIhSpwTb8HBKzRw0D1A\nx+xgOjHNK/AhfwhrjTW0jTYigQh8oo9XNIet0Ie5OKzoZLs21hpruLp9FZZrodsj6VDWRXNB8SMS\niCAgBbjr7rA9/fCaEQURSTmJs6mz/N2/uvkqanqNlJKOOr13Du6gZVBRxHItOI6DmBLDk8UnB8ip\nAPia8R4g99p7uFW+hYpagQOHYpzjIhVK4fHC4/wz1vbWIPtkTv70dkIjgQj8PppzVa3Ku6qLmUXO\nr3mUiIMkSkgpKYKK2BZkSUbDaMAv+rluPIMLZUIUexQ/cdeGDxksIVdNFW2TPEzOZc9Bt3QyORuB\nd37UYDyPWDA2wPPyvq9RfJbhQhPrPm23tulgIpCYRi6cQ7ffhSiIBLEUgLuHd7Hd2kZDb+BQPUQx\nVkRDbwzA9ubSc0jICYQDYT5/huUZfaIPoiBCCRAURzVVPDf5HA66B1y+0rAMwnCLEkL+EO5V7mG7\ntQ3d0gmedCR5Ouowwu6RdWUAgnsmlSR1Cv0yTNuE45DAARNcYPc66rAoCkTSBsh5vapVUdNq6Fk9\n8vRoUB66Ul+BaZtconMxszggIvEjlZh/97vfxW/8xm/gt3/7t/Fbv/VbmJmZweOPD2KKf//3fx+f\n+MQn8OlPfxovv/wynn76aWSzg9gw701tdDZIbzac5ozmmlaD2lexmF3kmpyjXpx3ogAYmNTs37S+\ndkyyCEQQChyTLFhgZRNeEiWecEUDUbxn+j1YTC9CBJERAyK9SN3S4bgOSp0SQv4QSt0S/ur+X1FF\nAEc/D4QGdEuZprOXbPXG/htYb6zjQDvAWn0NsiQjKkexkF7AeGycKzAwclQhUsCt8i0e3B3XQbff\n5Yt7p7WDpewSyclZJhRJgWEZUCQFk/FJJOQEzqbPcuORK2NXBhRt2r02Jxld37uOslpGSAqh3iOC\naCwY40RcVlmbik9hPjMPo2+MZEYPj1HvjEkg7Xf38dLyS1wSs2t2iaAk+jARm+AJ2o9N/9ipJI+b\npZsIBUInKlIA+PceqtSukkSJw4tSoRSH9JxNneUbSstowegYlExl0nx+iQJJYIb8IZ4MA+A6q+Ox\n8QFYwLBKz3ZzmypaRpuenRzGleIV6n70OqhrdVgOucNatgVFUnhgYffOmOu5SA49u4eO0UFJK2Gl\nusJVRW6Vb5EclHnMjmfPyoGDUreEG3s34Pf5CSIGh8jR/eAFNSkAACAASURBVC66PZpbMTmGXCTH\nq9FvR6wdNU7bXEqdEuJyHNd2rmG3uwu/z4+99h6qehVNvYmqVkWz14QPPoT8IW6fPjyGNXgB4IOz\nHySnN1BckHwS17hX+yoqWgXtXht+H/EYooEoZhIzWEgvnGg9MzJ5wBdARI4QecklciZrx7OuW8fs\n8EOhbulIyiQ1ulolAprW11DuljGTnEFSTh4rsTh9vFl6E039WNaMwTm8sa/da2OtvkaOsAGqlLEO\nFEv4/s0v/husv7WOG6/dwE/96k+hqTUxFiPMdc/ucdgWa+MzJ8hQIITpxDTXDW+bbTysPkTP7kHt\nqyh3yrgydgWyX+bx2oWL26/dxvrN9R9qTgDAxacv4tIzl5AIHasxDOu0e++7Y3YAgENyknISU4kp\nNPUmbNfmJDx2wPZCHf/kf/8T/lnv/eR7UdEqXG3GtE3stncRlIJYq5M0o0/wcbWmSCAy0peAzY2+\n08ehdshxsO1eGxW9gpZOyies28Tmrs/nw53DO1iuLONM8syJCis7TIuiyH0nRIhYzC4SUdxxcaFw\nARW1gqnEFCzXQqVbwUR8gid608lpiBBPJHrewaCF3974NgzLQFohacUD9QCaSRXP13Zfg+yXeaV/\nLj3H72W4W8nigteLw7ANrDXWqFoOOgCdSZ7BE8UneBFlq7yFSq+CpeklfPIXPsn1x1lVfL22jmav\nyaVemcoMgJE5gPfnlmvhUD1EyB9C0B+EaZsISkGuHw4AnV4HmXCGr3lWuGAHB2+OkA1n8bD2EE29\niVyMCh+yT8Zcem5kzH/UGOUr4IWsjSIxDheaTNvEX6/+NapaFQF/AMu1ZfhB5Fbd0rFaW4USoP2D\ndf18gg+KX0HDaJC8byTDq8rZcJbnBd6cynutnV4Hap9gMes1WvcTsQl0ze5A7sMOSIwkyr7bhUvK\nNX0NCSUxoHDExmlFPJbfqaaKhJLAncM7PCezXHI2d+Gi1ClxvmJSPk6qvTlI1+ySGZh6iKbRRKlb\n4opTHaODYqzIO7je6/u7JuZ/r+RPVVVx6dIlfOpTn8InP/nJAWthAPijP/oj/PEf/zE+97nPYWFh\nAX/4h3+IH//xH8fy8jIikcjIz2TkRUmUsJRdQk0jwkEoEOJkoNPGaQROppFZ7paRUlKoqlU0zSYm\nE5OkUhHOwLAMfHX5q9wO3kuU8WodW46FW2VyTpNEiStJwAXGo+PYbG2i3ClDNVV0+h08UXgCS7kl\nwKXrGaXpzD5TFMj6dau5hW6PqmyT8ckBt6vhZ+UlkjK4ykZ9A4kQWWt/Z/M7eP+Z9wMgA4maXuOJ\niuM6jyRGsufZ0lsEFxCICBOX42gbbSBxfE8AuXO5cDmpYth6fJReN/sOpmHsuOTA6IAcQLtml8ws\n+j0Uo0UokoJsiA4FPbuH+fQ8VYWOyFPDeueiIHLr5OHv3Wptodwto6pViYAoUrWYSW8y4sjVnatY\nri7Dho2N+gaEtoCz0bMDBB6vCVXfOcabua57KsmNXQeD8pyJn0G710bMH8OZxBlMJ6fx4uKL+PKd\nL+OmexOO46DT63AIVEpJkb69YxN84IgYV4wWsRfew3J1GZpJaiDpUBqaSTq+Tb2JQqQwQCjbam3h\nfuU+GnoDjV6DzHSOTGZuH9zmFUnmlnmxcBG5cG7AZfSdjlGOeF5ycrlbhk/yYSY+g4AUgGmZKHfK\niCtxCIKAjeYGmkYTrR4RrorR4gkS5zDZlM29jy1+bIDcBNAcLkQLmLfnuQKT4zqYT83jw3MfHvnZ\nw2TyYfdZL6l8q7VFpE+1AghA2B9GUk4SAc22uH6zZVsD1/7G/huYT8+jrtV5nKioFa5J7Z1D7r5L\nRMgjx9OZ5AxWq6s8mfeOmlrjvBTv8FqL17QakqEkLuQvDJClquqxzwFz5dtp7WAyPsnjNQCMx8d/\nqDnBBsNAs6QawIBT4PB9bzY3sVxd5l2duBzHvcq9Y5K4QK6MsiSfcIH++P/8cW5eEg/GUVbLiAai\nnFD6/NTzHBMrCOQE+HaDzQ3LtXCvco/viU2jSVwNQeKyvRW1goXMAi7kLuDPr/05thvbEEURn339\ns/jpcz+N9595/6BShmvhYeUhJU8iVQHzkTzxHqIFTMYm8cz4Myh1SwPzu6pWcT5/nu9Bo56ld1TU\nClKhFNq9Nrr9LkyLHIYnY5MohMnYqmN0EFWiiMmxgX15FEHRcizuxcEcFGcSM1zAICEn8OLCi6io\nFR67JVFCz+7haytf4/rjjARc6pSI7Hr095ZjoabVuGqVV8OfORWzn3f7Xby2/RoECDCTJpq9Ji4V\nLvFrrWk1LGWXkA6lORmUdVWZEII3br1r/F2oqBWC5MUnUFWrJH8qJ3/ouMie36McoR/lrMvGSm2F\nlEKkIMJSGC2hhU6/w+NYNpxFpVvhHg+u62I+O4+H1YeYik9xnxeGI7+2d22APO510vUSslMdcnFN\nKNS9GYsRDMmb+ySUBPbae0jICb7fz6Xn8I3Vb8B1XZi2iVKnhH/22D8b6X1iuRYOOgdo6A1S3Dki\nobP3W1NrmEpMoWW0kFJS/LB49/Au1mpriIfi+M7Gd1DOlPHc5HM8XrM5w67vcv4yvrv5XX4g46Z2\ntVVODv77HH+vifkLL7yAF154AQDw8z//8wP/5rou/vRP/xS/8zu/g5/+6Z8GAHzuc59DLpfDF7/4\nRfzKr/zKoy9UkPDsxLOcrLnZ2OSyS4ytzCaH9+UVIgWuGlCMFHFt7xoP3D2rh+XqMi4VLmGjsYGm\n1sSzU8/CcRzcPriNptGEJEqo63UsZBZONbDJhrNUpXAJn7aYWkQxWsRblbeg9Uimru/0ybnKaKCq\nVjEVmxpYcEx9ZXjYrk0OeaC20EZz41S1GYCYzaztdtA9QEtvIRlKYru5zW2Jb5VuIR/Nwy/6sZBZ\nQKVLVs9e04pRSbM3QWCmMj6BDgAVrYLD7uFA4m25tHl7qyQvPXyJ//9RahWSKOFi/iK+dOdLEAQB\nc+k5vHXwFhw4ECDgbPosNpobsB0bfaePhJLAe2beg4AYoPkgAN9a/xaYbXelW+HBFDhpnezdmNjz\nSckplNUyFjILvIXnJbewQ5gsypjPzGOjvQEffANGLSxgW66F/fY+tbsjBXzo7Idw5+DOyO9n93+5\ncBk3Szex0djATHwG6VAa+Qi9r06vg0898SkkHyRxs3QTMTmGWDAG27GRDqeRDqUJfuGQWylTpciE\nMjzhiMlEtMGRDfqJtSYSxrKu1eEX/ciEMrw9aTuEwQ74iPjGTK0kQfqhk3K2iex39uHA4dh178GU\nbcp+wQ+fSJv2am0VDb3BZbsYuTAWiKGqV/G1la/hY4sfGzl/h9evJB4bBw1bcG81t2DZFlfBiQai\np94D+zvvd7B/8/IkvM+2GC2ShbNjQYCAjJzBenOdk6XvHt7lhkqSKHEllvHYODe+YknETmuHFwQk\nUcKHzn4IL6+9TNA+JY57B/cIbqO1UVMHFS7OZc/hsHtIMJEjNY6EkgDcY2vxQ/UQ6XB6wEiJwaAM\ny8BWY4vs3KNjeLP0JibjkzxeA8CLf/wi/uMf/8eBZ1XqlDCVOJaCfG3nNa5+4b0XlgCNslQffv4T\n8QnUdTq4ZMIZHHQOkAvlOIwvISe4ktFOa4eSKoGS1Y/80kd4Vd52bfKxYPfrkuAAO6gbloGUkkLd\nqKNn9dCze+g7fWTDWQ4XyYazA3bkXnWoaDAKn+CjA20kh4pawVJ2CU8Wn8Q3Vr+BttFGz+nBL/hh\nWRa+tfEtnE2d5QUhdoB3XAcJhYoubC2zBInN/2E1sPO589yApxAdtK4fNSyXDoBVrQq1r6KpNTGX\nnqMKsiDh3VPvxsPqQ+TCOaRDad4pOG3stHfIQM5o0mdAQlpJYyw2NqAmNjzqRh2CLuCX/7df5qY6\nfP88kvYFwBWN2Oc8UXwCb5bexP3D+5hPz/OYyPYZUSDvjq3WFmLBGFduclwHKTmFb298G+fz55GN\nZLlKGFMIeVRSLAkU8xNKApXuyT3n7Uzl+OeMiFtvN7zJJYPgJZSTsZ59PiN85iI57LZ2IQkSzw3Y\n/bLrY3mL5Ry7FgPHBxNepIhPUpFpqOg4fNi4UrzC1etSnRTqWh3T8WkqULgWBFfAd7e+i3L3OHkG\naH29tPISthpbEARSfknKSV5cZLmK5VrwgeSAG3oDr2y9glQohXw0zw9yFa0yoGZ3whDpqJBiVSxY\nlsW5jqZloqJWsNva/aHV3h41/n+TS9zY2MDBwQE+/OEP85/Jsoz3vve9+P73v39qYu6VEuLVh6O2\nN7dvry1zIsgoh04A2G3totQuEeEDpHfeNJqcFf2B2Q+QlrIviFwkh4begE/w8XYYS6ZHDUmU8Fj2\nMZ64LGWXIPkkvLL1CkExJIUznxtaA27KHbifYrQ4cAp1XIdXDOtaHYVwAb1+D5OxScJva3UgNfJS\nBiY9kxFbq68dJ50KmQeNR8f5JHpm/JmTz2uouu3dZM7nzuP6/nVsNDbguA7K3TKeGnsKc+k5XsVg\nSSxw7FC43d7GZmMTuXAOCSWBQ/UQ47FxLnvJ3vfLay9TZckFru9ex1R8CjWjho7RQUAM4MNzH0a3\n10U0GMXPXPoZxINx7Hf2kY/m0dTpMCUIApp6kwdTtlGIgoiPzn/0RNWeyZHlo3kyWQhTIHmy+OSJ\noGk5JJnHTKliAYIHsASMBWwIwIPDB1itrdImrtdhuzavqni/3zuK0SK+ufZN2K6NhkEJ6GJ2kf97\nRa1gOjGN6eQ0T9AWM4vcxfNS/hJPTgDAsA28svUKIoEIGgZpmys+BUpAQT6SHynZJYnksJiP5rkT\nne3YXPqx0yPIgE/wcXiENwF9u8Ek+BhmuKbVuMzp8MiEM4gGo2gYDW6Dbbs2qm2q1jJZtYAUgNpT\nIYYH3XXfyQY4avPzi36uI56QE+TqV77FK9SjKv0s9gxLjHk7buzZso3qzsEdJJQE3tx/Ey2zh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qLWLeGC2rRa1ZI+PNTNx4ZB8th5d52HioJv310nXRVy7RbxF3hEq3IgJAf4aMP6MOMMNNPmeh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\no9QKrE2LQ/FDo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mJukanoFLOx2TvzVRPJynJxWejsYnK7epuIO6IO1f2J485q29n1s3SM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\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SK2tWYrByEHnkEbGdJEP1lfVYWROe8YfPXl61HMurlsOGjY3xjYG6XxGpUEFaLJNJ9/Q+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\nDWMUxqOe2wgrwRjjinbIB3t7z5w540+Sx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seed(3) \n", "run_pf1(N=5000, plot_particles=True, ylim=(-20, 20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the initial sample of points did not generate any points near the robot. The particle filter does not create new points during the resample operation, so it ends up duplicating points which are not a representative sample of the probability distribution. As mentioned earlier this is called **sample impoverishment**. The problem quickly spirals out of control. The particles are not a good match for the landscape measurement so they become dispersed in a highly nonlinear, curved distribution, and the particle filter diverges from reality. No particles are available near the robot, so it cannot ever converge.\n", "\n", "Let's make use of the `create_gaussian_particles()` method to try to generate more points near the robot. We can do this by using the `initial_x` parameter to specify a location to create the particles." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final position error, variance: [ 17.92304664 18.07173658] [ 0.00451171 0.00429415]\n" ] }, { "data": { "image/png": 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bao/RmWta5ToQxRkZGcHk5CSOHTtmWmE6Ojpa1nJ6+816LARBEEZQssVNp9M4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\n28jSNbs8MvIIC9UFWTUHOLd0jnwzT7VTBQvGw+Moi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seed(3) \n", "run_pf1(N=5000, plot_particles=True, initial_x=(1,1, np.pi/4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This works great. You should always try to create particles near the initial position if you have any way to roughly estimate it. Do not be *too* careful - if you generate all the points very near a single position the particles may not be dispersed enough to capture the nonlinearities in the system. This is a fairly linear system, so we could get away with a smaller variance in the distribution. Clearly this depends on your problem. Increasing the number of particles is always a good way to get a better sample, but the processing cost may be a higher price than you are willing to pay." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Importance Sampling\n", "\n", "In the filter above I hand waved a difficulty away. There is some probability distribution that describes the position and movement of our robot. This might be impossible to integrate analytically, so we want to draw a sample of particles from that distribution and compute the integral using MC methods. \n", "\n", "But our difficulty is that in many problems, including this one, we don't know that distribution. For example, the tracked object might move very differently than we predicted with our state model. How can we draw a sample from a probability distribution that is unknown? \n", "\n", "There is a theorem and associated techniques from statistics called **importance sampling**[1]. It somewhat remarkably gives us a way to draw samples from a *different* and known probability distribution compute the properties of the known one. It's a fantastic theorem that brings joy to my heart. \n", "\n", "The idea is simple, and we already used it. We draw samples from the known probability distribution, but *weight the particles* according to the distribution we are interested in. We can then compute properties such as the mean and variance by computing the weighted mean and weighted variance of the samples.\n", "\n", "For the robot localization problem we drew samples from the probability distribution that we computed from our state model prediction step. In other words, we reasoned 'the robot was there, it is perhaps moving at this direction and speed, hence it might be here'. Of course the robot might have done something completely differen. It may have fell off a cliff, been hit by a mortar round or airlifted away, and so on. In each case the probability distribution is not correct. It seems like we are stymied, but we are not because we can use importance sampling. We drew particles from that likely incorrect probability distribution, then weighted them according to how well the particles match the measurements. That weighting is based on the true probability distribution, so according to the theory the resulting mean, variance, etc, will be correct. Magic!\n", "\n", "How can that be true? I'll give you the math; you can safely skip this if you don't plan to go beyond the robot localization problem. However, other particle filter problems require different approaches to importance sampling, and a bit of math helps. Also, the literature and much of the content on the web uses the mathematical formulation in favor of my rather imprecise \"imagine that...\" exposition. If you want to understand the literature you will need to now the following equations.\n", "\n", "We have some probability distribution $\\pi(x)$ which we want to take samples from. However, we don't know what $\\pi(x)$ is; instead we only know an alternative probability distribution $q(x)$. In the context of robot localization, $\\pi(x)$ is the probability distribution for the robot, but we don't know it, and $q(x)$ is the probability distribution of our measurements, which we do know.\n", "\n", "We can **blah**\n", "\n", "$$I = \\int f(x)\\pi(x)\\, \\mathsf{d}x$$\n", "\n", "We don't know $\\pi(x)$ so we cannot compute this integral. We do know $q(x)$ so we can add it into the integral without changing the value with\n", "\n", "$$I = \\int f(x)\\pi(x)\\frac{q(x)}{q(x)}\\, \\mathsf{d}x$$\n", "\n", "Now we rearrange and group terms\n", "\n", "$$I = \\int f(x)q(x)\\, \\, \\cdot \\, \\frac{\\pi(x)}{q(x)}\\, \\mathsf{d}x$$\n", "\n", "$q(x)$ is known to us, so we can compute $\\int f(x)q(x)$ using MC integration. That leaves us with $\\frac{\\pi(x)}{q(x)}$. That is a ratio, and we define it as a *weight*. This gives us\n", "\n", "$$I = \\sum\\limits_{i=1}^N f(x^i)w(x^i)$$\n", "\n", "Maybe that seems a little abstract. If we want to compute the mean of the particles we would compute\n", "\n", "$$\\mu = \\sum\\limits_{i=1}^N x^iw^i$$\n", "\n", "which is the equation I gave you earlier in the chapter.\n", "\n", "It is required that the weights be *proportional* to the ratio $\\frac{\\pi(x)}{q(x)}$. We normally do not know the exact value, so in practice we normalize the weights by dividing them by $\\sum w(x^i)$.\n", "\n", "When you formulate a particle filter algorithm you will have to implement this step depending on the particulars of your situation. For robot localization the best distribution to use for $q(x)$ is the particle distribution from the `predict()` step of the filter. Let's look at the code again:\n", "\n", " def update(self, z):\n", " self.weights.fill(1.)\n", " for i, landmark in enumerate(self.landmarks):\n", " dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n", " self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n", " self.weights /= sum(self.weights) # normalize\n", " \n", "The reason for `self.weights.fill(1.)` might have confused you; it confused me the first time I saw it. In all the Bayesian filters up to this chapter we started with the probability distribution created by the `predict` step, and this appears to discard that information by setting all of the weights to 1. Well, we are discarding the weights, but we do not discard the particles. That is a direct result of applying importance sampling - we draw from the known distribution, but weight by the unknown distribution. \n", "\n", "In other filters you will retain weights in this step. If you can determine the probability distribution directly, for example, it makes no sense to approximate it by sampling from a different probability distribution. The problem you are trying to solve is $I = \\int f(x)\\pi(x)\\, \\mathsf{d}x$, and importance sampling give you the tool to do that when $\\pi(x)$ is unknown. If you can formulate this more exactly, do so!\n", "\n", "**author's note: i don't like this last paragraph; too hand wavey. get specific**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling Methods\n", "\n", "How we resample the particles effects the performance of the filter. For example, suppose we resampled particles by generating a random number from 1 to $N$ and used that as the index of the particle we resample. This would lead us to choosing many particles with a very low weight, and the resulting set of particles would be a terrible representation of the problem's probability distribution. \n", "\n", "There is plenty of room for research and novel algorithms, but research and industry have settled on handful of algorithms that work well in practice across a wide variety of situations. We desire an algorithm that has several properties. It should preferentially select particles that have a higher probability. It should select a representative population of the higher probability particles to avoid sample impoverishment. It should include enough lower probability particles to give the filter a chance of detecting strongly nonlinear behavior. \n", "\n", "I will give you a couple of the most commonly used algorithms; with that information you should be able to search and find alternative should these prove deficient for your application.\n", "\n", "FilterPy implements several of the popular algorithms. FilterPy doesn't know how your particle filter is implemented, so it doesn't make sense to have the resample algorithm generate the new samples. Instead, the algorithms create an ndarray containing the indexes of the weights and particles that are chosen; your class or code needs to perform the resampling step. For example, the robot localization class implements this with\n", "\n", " def resample_from_index(self, indexes):\n", " assert len(indexes) == self.N\n", "\n", " self.particles = self.particles[indexes]\n", " self.weights = self.weights[indexes]\n", " self.weights /= np.sum(self.weights)\n", "\n", "\n", "### Multinomial Resampling\n", "\n", "Multinomial resampling is the algorithm that I used while developing the robot localization example. The idea is simple. Compute the cumulative sum of the normalized weights. This gives you an array of increasing values from 0 to 1. Here is a plot which illustrates how this spaces out the weights. The colors are meaningless, they just make the divisions easier to see." ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cumulative sume is [ 0.1 0.3 0.4 1. ]\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pf_internal import plot_cumsum\n", "print('cumulative sume is', np.cumsum([.1, .2, .1, .6]))\n", "plot_cumsum([.1, .2, .1, .6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To select a weight we generate a random number uniformly selected between 0 and 1 and use binary search to find its position inside the cumulative sum array. Large weights will be a further distance from its neighbors than low weights, so they will be more likely to be selected. \n", "\n", "\n", "This is very easy to code using NumPy's ufunc support. `searchsorted` is NumPy's binary search algorithm. If you provide is with an array of search values it will return an array of answers; one answer for each search value. \n", "\n", " def multinomal_resample(weights):\n", " cumulative_sum = np.cumsum(weights)\n", " cumulative_sum[-1] = 1. # avoid round-off errors\n", " return np.searchsorted(cumulative_sum, random(len(weights)))\n", " \n", "Here is an example:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pf_internal import plot_multinomial_resample\n", "plot_multinomial_resample([.1, .2, .3, .4, .2, .3, .1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an O(n log(n)) algorithm. That is not terrible, but there are O(n) resampling algorithms with better properties with respect to the uniformity of the samples. I show it to you first merely because you can understand the other algorithms as variations on this one. There is a faster implementation of this algorithm that uses the inverse of the CDF of the distribution, but since we will not be using this much, if ever, I won't cover it. You can search on the internet if you are interested.\n", "\n", "\n", "You may import this from FilterPy using\n", "\n", " from filterpy.monte_carlo import multinomal_resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Residual Resampling\n", "\n", "Residual resampling both improves the run time of multinomial resampling, and ensures that the sampling is uniform across the population of particles. It's fairly ingenious: the normalized weights are multiplied by *N*, and then the integer value of each weight is used to define how many samples of that particle will be taken. For example, if the weight of a particle is 0.0012 and $N$=3000, the scaled weight is 3.6, so 3 samples will be taken of that particle. This ensures that all higher weight particles are chosen at least once, and has O(N) running time.\n", "\n", "However, this does not make *N* selections. To select the rest, we take the *residual*: the weights minus the integer part, which leaves the fractional part of the number. We then use a simpler sampling scheme such as multinomial, to select the rest of the particles based on the residual. In the example above the scaled weight was 3.6, so the residual will be 0.6 (3.6 - int(3.6)). This residual is very large so the particle will be likely to be sampled again. This is reasonable because the larger the residual the larger the error in the round off, and thus the particle was relatively under sampled in the integer step.\n", "\n", "\n", " def residual_resample(weights):\n", " N = len(weights)\n", " indexes = np.zeros(N, 'i')\n", "\n", " # take int(N*w) copies of each weight\n", " num_copies = (N*np.asarray(weights)).astype(int)\n", " k = 0\n", " for i in range(N):\n", " for _ in range(num_copies[i]): # make n copies\n", " indexes[k] = i\n", " k += 1\n", "\n", "\n", " # use multinormial resample on the residual to fill up the rest.\n", " residual = w - num_copies # get fractional part\n", " residual /= sum(residual) # normalize\n", " cumulative_sum = np.cumsum(residual)\n", " cumulative_sum[-1] = 1. # avoid round-off errors: ensures sum is exactly one\n", " indexes[k:N] = np.searchsorted(cumulative_sum, random(N-k))\n", "\n", " return indexes\n", "\n", "You may be tempted to replace the inner for loop with a slice `indexes[k:k + num_copies[i]] = i`, but very short slices are comparatively slow, and the for loop usually runs faster.\n", "\n", "Let's look at an example:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pf_internal import plot_residual_resample\n", "plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may import this from FilterPy using\n", "\n", " from filterpy.monte_carlo import residual_resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stratified Resampling\n", "\n", "This scheme aims to make selections relatively uniformly across the particles. It works by dividing the cumulative sum into $N$ equal sections, and then selects one particle randomly from each section. This guarantees that each sample is between 0 and $\\frac{2}{N}$ apart.\n", "\n", "The plot below illustrates this. The colored bars show the cumulative sum of the array, and the black lines show the $N$ equal subdivisions. Particles, shown as black circles, are randomly placed in each subdivision." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pf_internal import plot_stratified_resample\n", "plot_stratified_resample([.1, .2, .3, .4, .2, .3, .1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code to perform the stratification is quite straightforward. \n", "\n", " def stratified_resample(weights):\n", " N = len(weights)\n", " # make N subdivisions, and chose a random position within each one\n", " positions = (random(N) + range(N)) / N\n", "\n", " indexes = np.zeros(N, 'i')\n", " cumulative_sum = np.cumsum(weights)\n", " i, j = 0, 0\n", " while i < N:\n", " if positions[i] < cumulative_sum[j]:\n", " indexes[i] = j\n", " i += 1\n", " else:\n", " j += 1\n", " return indexes\n", "\n", "Import it from FilterPy with\n", "\n", " from filterpy.monte_carlo import stratified_resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Systematic Resampling\n", "\n", "The last algorithm we will look at is systemic resampling. As with stratified resampling the space is divided into $N$ divisions. We then choose a random offset to use for all of the divisions, ensuring that each particle is exactly $\\frac{1}{N}$ apart. It looks like this." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pf_internal import plot_systematic_resample\n", "plot_systematic_resample([.1, .2, .3, .4, .2, .3, .1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code couldn't be simpler.\n", "\n", " def systematic_resample(weights):\n", " N = len(weights)\n", "\n", " # make N subdivisions, choose positions with a consistent random offset\n", " positions = (np.arange(N) + random()) / N\n", "\n", " indexes = np.zeros(N, 'i')\n", " cumulative_sum = np.cumsum(weights)\n", " i, j = 0, 0\n", " while i < N:\n", " if positions[i] < cumulative_sum[j]:\n", " indexes[i] = j\n", " i += 1\n", " else:\n", " j += 1\n", " return indexes\n", " \n", "Import from FilterPy with\n", "\n", " from filterpy.monte_carlo import systematic_resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choosing a Resampling Algorithm\n", "\n", "Let's look at the four algorithms at once so they are easier to compare." ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = [.1, .2, .3, .4, .2, .3, .1]\n", "np.random.seed(4)\n", "plot_multinomial_resample(a)\n", "plot_residual_resample(a)\n", "plot_systematic_resample(a)\n", "plot_stratified_resample(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The performance of the multinomial resampling is quite bad. There is a very large weight that was not sampled at all. The largest weight only got one resample, yet the smallest weight was sample was sampled twice. Most tutorials on the net that I have read use multinomial resampling, and I am not sure why. Multinomial rarely used in the literature or for real problems. I recommend not using it unless you have a very good reason to do so.\n", "\n", "The residual resampling algorithm does excellently at what it tries to do: ensure all the largest weights are resampled multiple times. It does do well at evenly distributing the samples across the particles - many reasonably large weights are not resampled at all. \n", "\n", "Both systematic and stratified perform very well. Systematic sampling does an excellent job of ensuring we sample from all parts of the particle space while ensuring larger weights are proportionality resampled more often. Stratified resampling is not quite as uniform as systematic resampling, but it is a bit better at ensuring the higher weights get resampled more.\n", "\n", "Plenty has been written on the theoretical performance of these algorithms, and feel free to read it. In practice I apply particle filters to problems that resist analytic efforts, and so I feel a bit dubious about the validity of a specific analysis to these problems. In practice both the stratified and systematic algorithms perform well and very similarly across a variety of problems. I say try one, and if it works stick with it. If performance of the filter is critical try both, and perhaps see if there is literature published on your specific problem that will give you better guidance. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Types of Particle Filters\n", "\n", "The idea behind particle filtering is quite straightforward. As a result the idea has been invented multiple times in different fields using different nomenclature. This gives the field a rather unsystematic organization that can be hard to summarize.\n", "\n", "However, we can start with a few basics. The first term to understand is Sequential importance sampling (SIS). This is the particle filter presented above without resampling. As already discussed, it approximates the posterier distribution (the probability distribution after the measurement is incorporated) by drawing a sample of weighted particles via importance sampling. The particles are then recursively updated them based on the measurements. This algorithm quickly generates due to particle degeneracy, but the idea forms the backbone of all particle filters I am aware of.\n", "\n", "The next particle filter is the Sampling Importance Resampling, or SIR. This is the particle filter that we implemented. It improves SIS by adding a resampling step that strives to duplicate particles with higher weights, and to kill of particles with lower weights. Though I have not emphasized this point, this can be viewed as an evolutionary algorithm where the fittest particles survive. Some of the literature takes that viewpoint, so if an article is talking about evolutionary MCMC methods you may want to check if they mean a particle filter. \n", "\n", "The SIR filter has various strengths and weaknesses. On the plus side it places very few restrictions on the problem. You need to know the behavior of the state model so that the particles can be propagated forward. You need to know the measurement functions so that measurements can be converted into state space. Finally, you need to be able to specify the likelihood function. A major weakness is that it performs resampling without taking the measurements into account. This is very inefficient -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] *Importance Sampling*, Wikipedia.\n", "https://en.wikipedia.org/wiki/Importance_sampling\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }