{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(sampling_conjugate_step)=\n", "# Using a custom step method for sampling from locally conjugate posterior distributions\n", "\n", ":::{post} Nov 17, 2020\n", ":tags: sampling, step method\n", ":category: advanced\n", ":author: Christopher Krapu\n", ":::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Markov chain Monte Carlo (MCMC) sampling methods are fundamental to modern Bayesian inference. PyMC leverages Hamiltonian Monte Carlo (HMC), a powerful sampling algorithm that efficiently explores high-dimensional posterior distributions. Unlike simpler MCMC methods, HMC harnesses the gradient of the log posterior density to make intelligent proposals, allowing it to effectively sample complex posteriors with hundreds or thousands of parameters. A key advantage of HMC is its generality - it works with arbitrary prior distributions and likelihood functions, without requiring conjugate pairs or closed-form solutions. This is crucial since most real-world models involve priors and likelihoods whose product cannot be analytically integrated to obtain the posterior distribution. HMC's gradient-guided proposals make it dramatically more efficient than earlier MCMC approaches that rely on random walks or simple proposal distributions.\n", "\n", "However, these gradient computations can often be expensive for models with especially complicated functional dependencies between variables and observed data. When this is the case, we may wish to find a faster sampling scheme by making use of additional structure in some portions of the model. When a number of variables within the model are *conjugate*, the conditional posterior--that is, the posterior distribution holding all other model variables fixed--can often be sampled from very easily. This suggests using a HMC-within-Gibbs step in which we alternate between using cheap conjugate sampling for variables when possible, and using more expensive HMC for the rest. \n", "\n", "Generally, it is not advisable to pick *any* alternative sampling method and use it to replace HMC. This combination often yields much worse performance in terms of *effective* sampling rates, even if the individual samples are drawn much more rapidly. In this notebook, we show how to implement a conjugate sampling scheme in PyMC and compare it against a full-HMC (or, in this case, NUTS) approach. For this case, we find that using conjugate sampling can dramatically speed up computations for a Dirichlet-multinomial model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probabilistic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep this notebook simple, we'll consider a relatively simple hierarchical model defined for $N$ observations of a vector of counts across $J$ outcomes::\n", "\n", "$$\\tau \\sim Exp(\\lambda)$$\n", "$$\\mathbf{p}_i \\sim Dir(\\tau )$$\n", "$$\\mathbf{x}_i \\sim Multinomial(\\mathbf{p}_i)$$\n", "\n", "The index $i\\in\\{1,...,N\\}$ represents the observation while $j\\in \\{1...,J\\}$ indexes the outcome. The variable $\\tau$ is a scalar concentration while $\\mathbf{p}_i$ is a $J$-vector of probabilities drawn from a Dirichlet prior with entries $(\\tau, \\tau, ..., \\tau)$. With fixed $\\tau$ and observed data $x$, we know that $\\mathbf{p}$ has a [closed-form posterior distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution#Conjugate_to_categorical/multinomial), meaning that we can easily sample from it. Our sampling scheme will alternate between using the No-U-Turn sampler (NUTS) on $\\tau$ and drawing from this known conditional posterior distribution for $\\mathbf{p}_i$. We will assume a fixed value for $\\lambda$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing a custom step method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding a conjugate sampler as part of our compound sampling approach is straightforward: we define a new step method that examines the current state of the Markov chain approximation and modifies it by adding samples drawn from the conjugate posterior." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pymc as pm\n", "\n", "from pymc.distributions.transforms import simplex as stick_breaking\n", "from pymc.step_methods.arraystep import BlockedStep" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "RANDOM_SEED = 8927\n", "np.random.seed(RANDOM_SEED)\n", "az.style.use(\"arviz-darkgrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need a method for sampling from a Dirichlet distribution. The built in `numpy.random.dirichlet` can only handle 2D input arrays, and we might like to generalize beyond this in the future. Thus, I have created a function for sampling from a Dirichlet distribution with parameter array `c` by representing it as a normalized sum of Gamma random variables. More detail about this is given [here](https://en.wikipedia.org/wiki/Dirichlet_distribution#Gamma_distribution)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def sample_dirichlet(c):\n", " \"\"\"\n", " Samples Dirichlet random variables which sum to 1 along their last axis.\n", " \"\"\"\n", " gamma = np.random.gamma(c)\n", " p = gamma / gamma.sum(axis=-1, keepdims=True)\n", " return p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define the step object used to replace NUTS for part of the computation. It must have a `step` method that receives a dict called `point` containing the current state of the Markov chain. We'll modify it in place.\n", "\n", "There is an extra complication here as PyMC does not track the state of the Dirichlet random variable in the form $\\mathbf{p}=(p_1, p_2 ,..., p_J)$ with the constraint $\\sum_j p_j = 1$. Rather, it uses an inverse stick breaking transformation of the variable which is easier to use with NUTS. This transformation removes the constraint that all entries must sum to 1 and are positive." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class ConjugateStep(BlockedStep):\n", " def __init__(self, var, counts: np.ndarray, concentration):\n", " self.vars = [var]\n", " self.counts = counts\n", " self.name = var.name\n", " self.conc_prior = concentration\n", " self.shared = {}\n", "\n", " def step(self, point: dict):\n", " # Since our concentration parameter is going to be log-transformed\n", " # in point, we invert that transformation so that we\n", " # can get conc_posterior = conc_prior + counts\n", " conc_posterior = np.exp(point[self.conc_prior.name + \"_log__\"]) + self.counts\n", " draw = sample_dirichlet(conc_posterior)\n", "\n", " # Since our new_p is not in the transformed / unconstrained space,\n", " # we apply the transformation so that our new value\n", " # is consistent with PyMC's internal representation of p\n", " point[self.name] = stick_breaking.forward(draw).eval()\n", "\n", " return point, [] # Return empty stats list as second element" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The usage of `point` and its indexing variables can be confusing here. This expression is necessary because when `step` is called, it is passed a dictionary `point` with string variable names as keys. \n", "\n", "However, the prior parameter's name won't be stored directly in the keys for `point` because PyMC stores a transformed variable instead. Thus, we will need to query `point` using the *transformed name* (hence, the `_log__` suffix) and then undo that transformation.\n", "\n", "To identify the correct variable to query into `point`, we need to take an argument during initialization that tells the sampling step where to find the prior parameter. Thus, we pass `var` into `ConjugateStep` so that the sampler can find the name of the transformed variable (`var.transformed.name`) later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulated data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll try out the sampler on some simulated data. Fixing $\\tau=0.5$, we'll draw 500 observations of a 10 dimensional Dirichlet distribution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 10)\n" ] } ], "source": [ "J = 10\n", "N = 500\n", "\n", "ncounts = 20\n", "tau_true = 0.5\n", "alpha = tau_true * np.ones([N, J])\n", "p_true = sample_dirichlet(alpha)\n", "counts = np.zeros([N, J])\n", "\n", "for i in range(N):\n", " counts[i] = np.random.multinomial(ncounts, p_true[i])\n", "print(counts.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing partial conjugate with full NUTS sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't have any closed form expression for the posterior distribution of $\\tau$ so we will use NUTS on it. In the code cell below, we fit the same model using 1) conjugate sampling on the probability vectors with NUTS on $\\tau$, and 2) NUTS for everything." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sequential sampling (1 chains in 1 job)\n", "CompoundStep\n", ">ConjugateStep: [p]\n", ">NUTS: [tau]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5226dc9fa11e4f8d8ae5761bebd51d0f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 26527 seconds.\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (1 chains in 1 job)\n",
      "NUTS: [tau, p]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51f002774dad471b9c0eecca302741d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 1 chain for 1_000 tune and 1_000 draw iterations (1_000 + 1_000 draws total) took 104 seconds.\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "traces = []\n",
    "models = []\n",
    "names = [\"Partial conjugate sampling\", \"Full NUTS\"]\n",
    "\n",
    "for use_conjugate in [True, False]:\n",
    "    with pm.Model() as model:\n",
    "        tau = pm.Exponential(\"tau\", lam=1, initval=1.0)\n",
    "        alpha = pm.Deterministic(\"alpha\", tau * np.ones([N, J]))\n",
    "        p = pm.Dirichlet(\"p\", a=alpha)\n",
    "\n",
    "        if use_conjugate:\n",
    "            # If we use the conjugate sampling, we don't need to define the likelihood\n",
    "            # as it's already taken into account in our custom step method\n",
    "            step = [ConjugateStep(model.rvs_to_values[p], counts, tau)]\n",
    "\n",
    "        else:\n",
    "            x = pm.Multinomial(\"x\", n=ncounts, p=p, observed=counts)\n",
    "            step = []\n",
    "\n",
    "        trace = pm.sample(step=step, chains=1, random_seed=RANDOM_SEED)\n",
    "        traces.append(trace)\n",
    "\n",
    "    # assert all(az.summary(trace)[\"r_hat\"] < 1.1)\n",
    "    models.append(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the runtimes for the partially conjugate sampling are much lower, though this can be misleading if the samples have high autocorrelation or the chains are mixing very slowly. We also see that there are a few divergences in the NUTS-only trace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to make sure that the two samplers are converging to the same estimates. The posterior histogram and trace plot below show that both essentially converge to $\\tau$ within reasonable posterior uncertainty credible intervals. We can also see that the trace plots lack any obvious autocorrelation as they are mostly indistinguishable from white noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
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QWFgY9foSBBH/MEdj14BoTnh9IldzEZPjjS1bOJJdQOdONHeoDyR2EQQRl3DO8c13wOzXOXbtBnr2BP42NT5FLj25LRju+D3Dr3/F8cLLHC++zPHFPOCeu4A+veO77gRB1I/a2lqsX78eN954o2b56NGjsXLlSsvfXnjhhaitrUW3bt0wZcoUjBw5UrO+qqoKEyZMgMfjwUknnYQ//OEP6N27d9SPgSCI+IN7Q29DENGCk9jVqOzcLT7Jfbl+kNhFEETcsXETxwsvcaxdB3TrBjz5GMPYMfEvculp355h2pPADz8Cz73AcfOtHBeczzHlRoaMjKZ1LARB2KO4uBgejwf5+fma5QUFBThyxDiVWmFhIR577DH06dMHtbW1+Oijj3Dttdfi7bffxrBhwwAAXbt2xdSpU1FUVISKigq89dZbuOyyy/DRRx+hc+fOpvXJzc2N2rERjQf1YzCce5GRUYfMTAdyc5uGj18k/ZiRUQMAyMp2IiPD41/udiejsJDMvRqD5nA+Zh11IyPDg/Q0htxcV2NXJ+rEex/K8z43l0IV1AcSuwiCiBuOHuWY+SrHF/OB/HwRk+uM00Uw+KYKYwynjAWGDgFen8Px7w+ARYs57vsTMGxo0z0ugiCs0YvznHNTwb5r167o2rWr//9Bgwbh4MGDmD17tl/sGjhwIAYOHOjfZvDgwbjooovwzjvv4IEHHjCtR3FxcT2OgogHcnNzqR8NKCnhqKwEUlxAcXH8308j7cfKSmFaU1ISyJAHAN98B5w2qeGPe9FiDuYARo2I/zaPBc3lfCwtFeeXuy7+zq+yMo7s7Mjr1BT6UJ73xcVVjVyT+MWOYEmvAwiCaHRqajjefJvjsis5vl4AXHUlMPdthrPOZE1a6FJJT2e47RYHZrzCkJ4O3Hk3x9PTvaiqIvtwgkgkcnNz4XQ6cfToUc3yY8eOoaCgwHY5AwYMwK5du0zXOxwO9OvXDzt37oy0qgTRpLHjXuXxJNA9Nk4OpbIKqKgAtm3n2LEzTipFoK6Ow+uNXn9It1lvnLnP7tvHsXQZcPQYjT0iNCR2EQTRaHDO8fU3HFdczfHqbI5Ro4B332K46XcOpKcnhsil56ReDLNnMlxxGfDJZ8DV13EsX0E3bIJIFFwuF/r06YOFCxdqli9atAiDBg2yXc7GjRstg9FzzkNuQxCJTCixq6qKY8G3wIEDiXGPNToK3ogBlbbvALZuSzBBsQnz7ffAz9ZhIcNC6mZR1M+iQnm5+Kw+0bj1IJoGJHYRBNEobNrMcevvOR56hCM7B3j5BYZHH3KgTZvEFLlUUlIYptzkwD9eYkhJAe64i2P6c2TlRTRt9u/fj5EjR+LRRx9t7Ko0Otdddx0++OADfPDBB9i2bRuefPJJHDhwAJdeeikAYPr06fjTn/7k337OnDn46quvsHPnTmzZsgXTp0/H/PnzceWVV/q3eemll/DDDz9gz5492LhxI+6//35s2rQJl112WYMfH0HEA6HumNLl77BxqLwmh5GFTUNY3ZSVcZSXm7d2TU3s6yA5eIijtpaelcwoLolOOVu2cuzeI757PdbbNjSy95tYGF+ikaCYXQRBNCjHjnHMms3x+RdAXi5w358ZzjqjacflipQ+vRlefxWY/QbHv94HlizluP/PwKCBza8tmgL6zHihWLJkSYxqQsQ7Z599NoqLi/HKK6/g8OHD6NmzJ2bNmoV27doBAI4cOYIDBw74t6+rq8O0adNw6NAhpKamonv37pg1axbGjRvn36asrAwPPvggjhw5gqysLPTu3RvvvPMO+vfv3+DH1xTgnKOqCpQMJJFpbpqHwfHaMew6fpzD7QZatozsXFi6THyeNsl4fU0tkJ4eUdFhUVcnEhe1yAGGDY39/hoazjkOHwFaRdBPbnd0T4adigc9h3XMyYZGCryMTHYIG5DYRRBEg1Bby/HBf4E5b3HU1QFXXAZcfSVLWHdFu6SkMNxyM8MpYzmeeIrj93dy/OYSjhtvYEhJad5tE2/ccMMNQctmz56NrKws/OY3v2mEGhHxzBVXXIErrrjCcN1TTz2l+X/y5MmYPHmyZXn3338/7r///qjVL9HZsRPYth04eSQnwStBsevBV1oKrF7D0b9ffGd1drs5HA7zl39G7mR22mCFz7XNTKyqLw1p2QUAVQkar3v/fmDDJqB3L4527cIbp253jCrlw+sFnM7Y7sMucsw3w3fkRASQ2EUQREzhnGPhYuCllzn27gPGjgFum8LCvpEnOn37MLzxKjBjFsd77wNLl3I8cB/Qqxe1U7xgJEbMnj0b2dnZIYUKgiAaFploq7YOyGjcqhBRYMNGjtRUoGuXwD0xlNAjda2aWuHKWF4BZGfFsJL15JvvrK2WOAdcyUBRT2DterGsIYOHm1n31Daw2BVvAdOjhRQzZUyqcKirE5+OGFk7xaPYBd1QLC3lqKgAzS8IDWQASBBEzNixk+OuP3Hcez9HcjLw7N8Zpj7uoBuRCampDHf83oHnpjNUnQBuuoVj9hveqJunE7FFjV21c+dO/PnPf8YZZ5yBkSNHYv/+/SFjW40cORJTpkwJWl5ZWYlXX30Vl112GcaNG4dTTz0Vd9xxB1atWmWrXo8//jhGjhxpuv0bb7yBkSNH4osvvvAv++STT3DPPffgwgsvxCmnnILTTz8df/jDH7BixQpb+wSACy+8EBdeeKHhuilTphi6h3LO8cknn2Dy5MmYOHEixo0bh2uvvRaffPKJ7f0SzRs5IXbSk25CsG+/sNRTCTdwdiQiQkNTUqr9X82u5/UCYI3nvnX0KAyz/ZVXNMz+eZwGTFdZs5bj2+8iq2BysvisqQ3/t1LsSoqRGUs8CYxmll0/LReWcYlAJFk1f17JsWVrHJ8cjQQ9AhAEEXXKyjmee9GLa6/n2LgJuPMPDG+8xjBsKIlcdhg6hOGt1xlOPw14400hem3dFmMbdSLq7N27F7/73e9w/PhxnH322TjnnHOQLJ9mw6S0tBSTJ0/G7NmzkZOTg4suuggTJkzAxo0bceutt+K7774LWcZZZ50FAJg3b57h+vnz5yMtLU0TJ+rvf/87jh8/jmHDhuHSSy/F6NGjsXbtWtx+++34/vvvIzqWUHDO8dBDD+GJJ55AaWkpzjjjDJx//vk4ceIEnnjiCbzwwgsx2S+RWHh8QZXj2GuNqC8h5nX6CXpTELv0qMfg9QpjFmayPtasWgOsWq1dlpEOHDiABgkaL0UOHgfCy/++5li3PviYDx0G6iJ8XJPHVV0d/m/lPpNiZH0Vj2JXIhNJex87ro211hQpK+eorubYtp1H7UU/uTESBBE13G6OTz4DXpstTIkvvAC4/lqGnByabYRLZibDX+5jGDuG42/TOS65tBSTb2D4za8BpzN+21MEhY7fgBrp6ekNFrNlzZo1uP7663HjjTdqlu/fvz/ssqZPn47t27fjgQcewLnnnutffvPNN+P666/HU089hZEjRyIlJcW0jMGDB6NVq1ZYsGAB7rrrLo3wtmnTJuzcuRNnnnkm0pVIw3PnzkXbtm015Rw9ehTXXXcdXnzxRZxyyilhH0soPvroI3z55Zc477zz8Oc//xlJvlfVdXV1uO+++/DPf/4Tp59+Onr16hX1fROxobSUIy0NcLka7tolJwvReFwuLuYoKQW6dI7fa28sKC7hyM6K33tOqEmvfsIorV+aEnqxy+EAHIqg0dAT/2PHtf/n5QKVVaJtXa6GqUO8aB0HDgJ9+0SvPNnXkcRAq/NZg8XK1dB+fDyOZctFyJJYxZ0NZeHn9fImn/QqXgW9mhoOxmJ3L1/6U+C71wv06F7/MknsIgii3nDO8cOPwMxXOXbtBoYMBv5wG0PXrk37ZhMPnDKWoV9f4PkXk/HyP2rx3ffAn++Jz0kX5xxnnXUWfvrpp9AbNxIjRozA559/3iCCV35+Pq677rp6l1NSUoKvv/4aQ4cO1Qhdch9XXHEFnnnmGSxbtgxjxowxLYcxhtNPPx1vv/02Fi5ciPHjx/vXSWuvM888U/MbvdAFAAUFBRg/fjz+/e9/48CBA2jTpk09ji6YDz74AGlpabj77rv9QhcAJCcn4+abb8aPP/6IL7/8ksSuJsRPy4HUFDEBaiikZVc0ZsbLfxafXTrXv6ymQlUVx/IVQLu2QO+Toleux8Nx4oR4oVNfQnWtfjLsHxNxCDeZ3apiF+cQbozQLYsxSU7AbdJ2Tt8l2tMAlj/xKgBECzleIzlMv2VXjGb2ds+d3XtE/YtLgNatYlMX/zlh0lAeT+xilzUU8WRJp/L9j+IzFsku9Nah0XpMJ7GLIIh6sXYdxyszRDroLp2BaU8ynDwqvjMeNTVycxmeeyYT739QjBde5Lh+MsfVVwJXXg4kJ8dXO1O/B+jRo0fEbosqGzZsgMfjQW1tLV599dWg9Xv27AEA7Nq1y1LsAoQr49tvv4158+b5xS6Px4Mvv/wS+fn5GDZsmGb7ffv24c0338SKFStw5MgR1NZqg4kcPXo0qmJXdXU1tm3bhoKCArz11ltB692+lFO7djVxW/1mSHUjBbFevwEYOoTH3bWyMThxgiMlxTzbn4rM7lYR5XhM69aLYPGTJti3vlDj12zaxLFnH3DaJBbasks3QY9nscusbuqk1+MRcYrUiXxDTIqTkszFLimu6Ns6FsSrABAt6tOG0gUyVo9g8Sg0mtXJ4wnEP2uqxGN7xxq9U0iyiUrldnNs/gXo0AHIzQ1dLoldBEFExK5dHDNeFRZdhQXAvX9iOOuM+HV3aOowxnD6qQzDhwIvvMQx+w2Ob74VVl59esdHmzPG8Pnnn5Mbo4+8vLyolFNWVgZAuEWuWbPGdLsTJ06ELKtr167o2bMnFi1ahPLycmRlZeGnn37C8ePHcdlll8Gp+EDs2bMHN9xwAyorKzF48GCMGTMGGRkZYIzh559/xsqVK4PEr/pSVlYGzjmOHDmC2bNnm25n51iJ5o20NKmoBHbsBHr2aNTqNDoeD8eiJUCvImGtFZIYXSZLSmR97FtfqELQnn3KihATQr1AE89iiVnd1GP3eoWYod7CGmJObBUQ3y92xXHbNhX8mm4EnSp/Ul0NfPU1x/DhQHZW9E7ieOpfWRczN8ZYidoeD4/JHKe4mCMrC0hKCpQdbntHEtA+3qjUTR2STATL8gpg/wHx16lj6HJJ7CIIIiyOHuN4fQ7HZ58BaWnATZMZLrlYZBIkYk+LFgwPPsBw2mkcT0/nuPlWjksu5ph8A0NaWuP3AWMMGRkZjV2NuMbhm+F5DJ7IKgzMKGR7Xn755fj9739f7/2fddZZeP7557FgwQJccMEFfhdGGcBe8q9//QtlZWV4+OGHg9wbp02bhpUrV9ran8PhQJ1JsJzKykrN//JYe/XqhTlz5tgqnyCMaAhLk6aE1yv+oqxPh4/vNhXOZM5s8mpl/XD0KMeWrdpl7jjO82LWHuoxSjdGVSQMFax9337rSbDHw+FwhLDKtihCBkSPZ6u5aGPmclpf/CJOBMKSrFKV7z3Qvn1AdhQ9/ePJ0oiHEAVjMRbLyjiWLgMGD+TIz4/es3ZdHcfyn4GWhcCA/oHl4bZ3Ipx/NbrEDGaGv+He25u4RytBEA1FVRXHa697cekVHF/MA359MfDePxmuuoKR0NUIjBrB8M4chl9dCPz7P8DV13EsWx5HTyOEKVlZWQCAI0eOBK3bvHlz0LLevXuDMYa1a9dGZf+nn346nE4n5s2bhxMnTuD777/3W3yp7NsnTCjGjh2rWe71ei0tzPRkZWWhuLjY74IoOXHihN8FU5KRkYHOnTtj586dKG+KqdOIIBrrjbO6V7pDRT6RjvYkV05gwpmcGW3r9XJLAxh9IHUgenGljh6NXqYwiR2xS7oxqrpUqNNrw0a1rOCNF3wLLF9hXYbVGPBbdjXAaR4vgkusrJzqJXbpfhPttrJbXkNGsrByY4w2UkTcsze65cq+9hnwBy23S1MVuzjnOHZMdGSQJW6U+pfELoIgLKmr4/jPfzl+cznHnLeAU8YA/3yL4fZbHZRlsZFJT2e48w8OvPIig8sF3Hk3xxNTvSgri5MnQsKQjIwMdOzYEatXr9aIPZWVlfjHP/4RtH1+fj4mTZqEtWvX4p133jGcsKxbtw7VNvOVy9hcq1atwnvvvYcTJ04EWW4BQOvWrQEAq1drc82//fbb2LZtm619AcBJJ50Et9uN+fPn+5dxzvHKK68YuiP+5je/QXV1NaZOnWq4fv/+/RFltCQah7iYoDJhwXL0WDxUxpqyMo7/fc1jdh23PYmKUVNJq6T6i13WY8vIRdITBcuu2lqOlauBQ4frX5aKXcsuprPsMusnzjnq6rhumfG2JaXis66O4/jx4I0sxS6fq1FDWFJGei3xejnKyqM3oGMudvHwrcf0wkB1NXDgQPSOOS6u4z5CuTHGwoJTirrSDTtayHbVt2+47R1PbqbhsHMX8PMq4NgxHnydN822Gd4+yI2RIAhDOOdY8C0w61WOffuBYUOBKTcx9OxBAle80a8vwxuvAW+9w/H2u8DipRy33wKcfhoFjI9XLrvsMkybNg2TJ0/GxIkTwTnH4sWLTTMM3nPPPdi9ezdeeuklfPHFF+jXrx8yMjJw+PBhbNq0CXv27MFnn32G1NRUW/s/88wzsWTJErz22mtwOByGYtdFF12ETz/9FPfddx8mTZqEnJwcrFu3Dr/88gtGjx6NhQsX2trXr3/9a3z66ad48skn8dNPPyE3NxerVq1CRUUFevTogS1btgTtd926dfj888+xZs0aDBs2DAUFBTh+/Dh27dqF9evX49FHHzXMFEnEhj17OTIzRLKMcGnISVJtLceKlcCAfsHrtmwR8Z5GDOcRx7LhnMf8mnrkqPg8egzIzo5eubIfvF5g6zYOp9M6q6/ZhKK8nGPnLqBvn8juL1EVuywmPU4DscssyHo4yIl0tCeXpmKX8t3jFZNujWWXye/WrQcOHgreh1WctNVrRBa9CeO4Jn6Q1SnckG6MkV5LtmwVWQJHj+JIT49CFtAYXdPU8ezxiOtZUhLgcoWus75Ox46Lv7w8jpSU+D3mSAgV2yzUWPxlC0fnTh64XGHs1LevuigLaaax+sK8vkTj2tYYyPeZ1TWAPtpFtCz3yLKLIIggVq7iuPEWjoce4UhPB555muHZvztI6IpjXC6G313vwBuvMnRoDzz2JMcdd3Hs2RtHTyiEn4suugh33XUXMjMz8fHHH2Px4sU455xz8Pjjjxtun5OTg1mzZuG2225DcnIy5s+fjw8++ADr169H165d8dBDDyEnJ8f2/sePH4/09HS43W4MGjQILVu2DNqmqKgIzz//PIqKivDtt9/i008/RVZWFmbOnGkqyhnRvXt3PPfcc+jVqxe++eYbfPHFF+jSpQtmzpyJzMzMoO0ZY3jwwQfx+OOPo0uXLli4cCHmzp2Ln376CS6XC7fffntQ1kgitmzaDCz/ObLfarPJxeZ65HZzlJRwHDkisgfu3q1dz1jADcVtHD7OFurDd2kpx5q1vMkEBlZdpI4eBY4buPmpmE001m0QIkrEeUiiFLOLc2sRxuEMXub1Ajt31c8F0cwSo76YtoeyH69HjGV133v2iliq+nNLL3RZ7sOHDKEYdGx23BgbwLIk0iaX1jjREgRiFgBdaUOvF1i4GFi4yN5vzcZjaWn962VVfqMQ4hwM1T+7dgPLfw5PtYqZwCmPRb883AD1DSI2R78R/HG5eLBFntGtdfFSjh07w9sHWXYRBOFn126OOW95sWgx0KoV8Nf7GU471V6aciI+6NqV4eUXgE8/B16ZwXHNdRxXXwVcfqm9t4NEeCxZsgS5ubkoLi72L2vbti2WLFkS8reXXHIJLrnkEsMyjUhNTcWVV16JK6+8MvIKK2UtWLAg5HZDhgzBrFmzgpb36tULkydP1iyzOu6hQ4caZlc0ctuUnHrqqTj11FND1pGIb9TnY7cbcBqIEPVl/Qbg8BGgZ3d72x86zJGfp81+ZQf1WHbuEvts1QpoFawVxx2qZZf8s7N9OMKHHSKJ2WUkUkTixggIK5+KCmGZFgl+QSKGYtfadRw9uovEP0ZujKpl15Gj4i8rExg5wnofkcZd4hzIzgI6dhAxwNRJqBS7yhoixGKEbS6PO1pPQLHSt9X+kVYudgU6K7HL4F2WKZWVImurnnjS9P3XMs25EfjH6toSqWATK7HLa3I9Cbe9G8KyMib78J2UnBuUH+TayWGQwykkZNlFEISfG2/mWLMWuHUKwz/fYjjjdEZCVxPE4WA4/1zRh+PGAa+9znH9ZI5Vq+PoaYUgiCZBfd/m6gNsxwJpkWIQ4g2AdvJeUQmsWSus1cJFPZYsn1FiKAupxsLr5fjhR47Dh0Wl9WJXqL6I1eRWilA1NbBtYWVq2WX1c9061WVJlwQ2LKQFRSwtuw4eAjZuMt6GMRGvs20b7bpy3SQwyUBUDlVnq/hBublAmzbML25JpHh94CBw8FBsnzHUeoUTg0t/PNu3c5SW1sO6L0YnhzrOjQQnK0LFY7OL8t7OVvmmxHAoeA3GqXo9txIITRJDB8E5x9693H//i9XhmLVr2JZdDWBZGYt9yHuz18CyS982dvtOD4ldBNGMqariePOtwNXrVxcB7/+T4bLfsqj4+BONS14ew0MPOPDM0wy1dcBtf+CY+jdvvR7yCIJoPqxew/FVaANASxpC7EpJEZ9VJmKXikxvXlsb/n6MJib6Y6qs5KisjO01tqLCvPz9B8REvrpaxEHZslUsV8Uujx3LrhDrI534yMnNxs3AN9/Z+41RXbxeWLoz6WPeZKQHvlfX2NuvYV1M3I7qi76+MhmtemxS7AKAzp2sy9OLUvL3dtC3p5cHrKL05aovROsjIoZbL4PExSF/J49/2w7gp+WR1yNWwkJ9BFSz35aV28+IW1PDcTg4SbRvB/Wrz759HJs2ReesMRKc9+5T1lv0j10XuP37xTVKusSHKz4Z4XZrXaiLi7k/a6y+vfYfCK9s9T4UC3dDUW70y7RyY9TvjsQugiBs43Zz/PdDkWHxnbmB5TdNdiA7m0SuRGP4MIa332C46kpg3nzgiqs55n3JY3ZDJAgiMTCd+IRBQ4pd0rJLP7djCIgEtb4H5uTk8PejER583/XHtGhJ+FYZRpjFfj94kGPxUphmlly/QUzka3xinmwbrtTX6wmeELrdIgvkvv1iw1Dz40hvH1YB0k33ZbDMq8TsMqqLOjnt1gXo0CHwfyRCp0SKUtG+feon03Jip+7GywPjQj8+9MMlErHL9Jh4YAdG5frrYPPx8eBBjuLi8BtQ/YVVLpaqKo7VawLx9FS3t2g890TDsOvYMY6aGm1B9RHRzH7r9ZpbvAIijuKWLSLm27r18IsvQeWEecz67TdsEglCooE3xDlo1ha1tRy79xiv0yPPcymMR+N8/3GhVuBf/jOwdZtx+eq9939fc+zeY10Bfby3WBBrN8agmF2644j0uk1iF0E0MxYv4bjqOo5nnuPo1hV4+QUSt5oDKSkMN/3OgTdeY+jQAXjcF8A+1A2UIAiiPjSE2CWp9lltGT3sy4m4fKAOJXbV1nLsP6C9PmriJ8VI9AjFCd8xyqDbZtT4JmnSfU8NUC+tuzTl+ibEe3yTQVP3Gima1dOyKxwMLbs81m2vTra7dmWG2RkjIVZujPr29IuCeuHWTOwyOT5VXLQrWBi5Mcr9WZ43Nstfuz7ChBc6KzczNm4SYoEMzu4XuzzAtu2B7aqqIozfFAUx4edVwE/LtMu8XsXSJUysxmOtiUXMkqUcS5YCO3eLbJXy+hlu+Ybbx9CtzqOcg1VVwckZjMaG2M7+PqR7rl9Yi6CeeqwyOYYqf//+4GVl5dw/hr0NIHZZlbttu3hZEi7y+uTlBu6nuuJqSOwiCMKKg4c47v+rF/fcK1KOPz2N4bnpjDIsNjO6dmF4+QWGP9/NsPkX4NrrOea8xVFbS6IXQRDRpyEewuUkxiiWCwCN2YsUgUKJXevWCyspswmxXNoQsVJUpHhl9JZbdZGp1Yldco3HI9pJn71LrpeihpysVlYax9aKNPtXRGKXsnsZK02N2aWuLykRVstBk+0oPer4BaMo3zL148jMekW2XygLObcbaNsGaN0qsMxKgLCyeOLKfvv0Bjq0N97OSFQ5fpzju+85yk1ibHk8HGVl9hpT79JpBlOsRdRtS0u1bmwLF5tbSFph55y3bE/fOr07rdcLJEVgcSrKDF6W7LPCqzO4VnDOUV4RcP3mXutYV+GKV7GLccU11/mFi4G164DMDLEsOSm4fzZs5Fi4GPjRZmZLAH5x3BMDcdvoeVtfvoOFvmQt/Ukcv/73jSF2bd8RWZnyGKWFmwq5MRIEYQu3m+PduRxXXsOxbBlwy80Mc15jGDWCgUXy1Ek0eRwOhvN8AezHjxMB7K+9gWPlKhK8CIKILupVJVaWXWZCgYrfjdE38QslFsjttLFQ4Hc9Ui2lYoHZ5MqpBHjXowpgciLtz37pK09atgXV27deDRgsP1cYWOFEOvmLpL1UiyTV4kIvdlVUcCxbAWzdKiylHAyYME6s01vM1NVFdgDRtPRQ0YsJ0uXOyMoKCBYNRdIBjpISsYHHI1wO1e2sLLs8Hmu3UFlMSgpDryLjZ0e9GxIAlJYJEUy1qFLZuAlYusxYANAj6+Vg2nF0+DDXiNL6MSzXGIlxkWR3q68bo9l10OuNXJM16rMUn6unkUhg1FeWWQzDrY+pW2X9Gk+toxTcjx0XbdemtXiJob/G7DOwigK0guS+fVybYEGfNTaKJ3xllVFltP96OeAII3Ox2t7RFOZkoH6vl9u6duut7EKiG/Bpinuy/jjIjZEgiCDWruO49ncc/5jJMXI48M5bDJdfysJOt04kJnl5DA8+4MCzf2fweIDb7+CY9rQ35sGVCYJomtTWhh98XX0Ibyixy8waBgi4QoSaEEhRRd1u/wHg+x+l9ZC9csLFX55ZPBrfcmPLrsD3Wt1xyjbyi126+EV6EUXtt7Ly4Ppt2w78sqVh7hVqG/uFDEXskm0ij62kVKxLTob/eUcvDkU6cYqVG6ORVY3R+SLrbfSu8pctwLIVIkmCxyPGsLqdXoBQ+9+juIVq3HX9A8O87kU9tXUT5XEsXMT97rZm535Zmfg0El/0yGo5ndpzfvVaYNHiwP96F1B53GZZPcPFa2NCb+li66uPvkndHiAjI/z6mO0vRVqBGohdRgKYpdgVrhujyfb1vQeo/e7WvYhgTAhEdvehbrdhk7AQCxSo3cbwnNDhdosMuGaWig6LFxUqsvxwXK/VPUbzBczBgyJQ/85d2nLN2yC88vUvndLS1H1o1xlZKNraR2Q/Iwginqmp4XjpFS9uuZ2jpka4LD7+qAOtWpLIRQQzbCjDW28wXHUF8PkXwNXXcyxfQYIXQRBaFocZfN3r5ZqsZ3bjPLnd4QWw1k9urB72/etCFK+KKhJpnVJRocSuirKAJyfmZgYQcr2crKpv0tUJrJx06LMHqtt4LawBQrV+WTmwa3fg/+07OLbvCN1nkUzE1LqpMV70wqC0hHD7AvBrJlK6x5+aGiEK/e9rjmNhuLKZusrWk9pa4YLV+yQgL1csU62tJLL/jMQuaaVUVSXqqRe79GPKayJEayf25vuTdOzAUJCvHVsVPhe5o8eCy1TRuxxa4tvG6QwIHXLSbfRzfXw5o4l4RGKXjd/YEruU8VlTw3HiBFCQD/TsHn6dDN0Yk8U54LZp2WVZfpjnrZcDW7eJ80svqtYHozHrYD5LTofW6s/rtY4jZdQG0vLMoxNIvQbnhJ6SUmFRa+bSlySvT3XBFlBGbogaodq4yKDf6L/XF3me1dVZ3y8ceks4m+gvK1ZiF8XsIggCALBuPcd1v+P41/vAry4E3npduCwShBUpKQw3TXZgxisMaanAHXdx/P1Zb8QBXAmCaJoYBfyVmAU6Ni9L+7/dB+H1G0QAa322MhXOuT/ejl5E0z/sG03UQ13ZpFASKuNXtN0YPSHK9bsXekVQ4AXfBmJq+YUQKGKXV/eplmUweTGy7LLDtu3mrmoqkYgL6m/8FndeBCx3dKKXtFJS+13vxlhbGwjyfySMrKN2xdJwqa0VmTPbtWVo11YsU62tJFZilxyzqgurul1VpXaSHZQ8wuCYVNdBlaFDgH59Av8nJ1sLKKbnfhhil9wmKSlgYWdUrurGuHVbwP3K0JovkvGonBsVFcYFyLoeORKc+VrWWbXckcH0W7TQimB2n96NrhdOJ+BKtm/ZZcSgAeLTTjPV1gYsXuvqAvHRzETVSNC4MarXLyl2OYQQ/8OP3NRFtUd387p8/Y2I4xZkDagTo0pLA66zBw74RD3l2u12B+6jnIu4uPL8dHuCzxW1ff3nXBhujHaTN0QK59q4WkH3WN+xHTwoPktKOHbstGEBqXzv3En0TZfOQhi0cmOUVot2ILGLIBIEj4fjtdeFNVddHfDCswx3/sGBtDQSugj7nNSLYfYshssvAz7+BLjmBpHCmyCIxIdzEch3zdrolGcUV8gOcpISKiDuylUiALZ+0hLkxmjw+1CTayPLLqPf1zd+j8rBgxx79obYr295TW3AgkAev8z25XAqYpeuviqrVouMXur+9PGO/Pu1aDCjAPZmRCR2KW2hsezSlSmFwhMnxGRbtewycmOU25tlMjTC78Zo/yfBZXh5UJvV1ASSCTgVCzX9jqRwYRRzzi92+YKOJyUpMdsAbN6ic9VSCJV9U99+uS0YWrcOLHT63McqKznWrOVB48dM5JAlnDgB/O9rjkOHrQTuwL70brmaMhXB9uChwPJIXVf1qOfm4qXA3r3GdT54iGPVGvjPaYnRuJNWK2lp2rauT2hdh0OIkEbCllVmQABo1RIYOxrIzxf/h7p2e70c3/0QaG/V6jNIVPVtv/QnjuPHwzuTPAbCGZOWXUyMjdpaIfgaZTAEAufZvv3G5+KWrYHTzihRBOfAT8sDweG3bBWf6lj85jtg+QrxffNm4LsfAtfUutpAYgAj/OM8nOuS2sYxiiNZroiH+uuFrOu2HUB1NcfBQ8C2bdb3DX05HTsAyckM3bsxOBzGQn/rVsD4U4C+fWAbErsIIgE4eozjjrs45rwFnH0m8ObrDIMHkchFREZKCsMtNznwyosMTqeI5fXa696wJjQEQTQ95IO9dD0yQz7AVlYauxtWVgZPIIDw3+pbPSdLq7HauuDMgPqJ2f4DwGGd9U4o0UUKB0ZWP1z5faRZCSXV1SLOS1UV1wRSNrUoM1guj1dOah0s4H7it0Az+F1JKbBunXY7M5HPSigoDyPIdzhWB6WlHEePaoUTv8WdGqAegWWSo8esxS63J7D97j3A7j327m/RcGP8eaWYDKvU1gaLXcXHzctQEwx17yaXiU85kXY6hbVEy8LA76Q1G2Dsrmj6PcQjpRS7NmwEDh0Gykq1683OfdlHpb7YXdIyxAhZFVXsUq0ZJbIdPF6gRU5gudEYjkSs1v9GjWnnrysP7K+6WrtOdb/zb+87HumKZ4dduwIuuIaWXY7g+GaSUJZdRT2B1FQlkVWIdrK6thtZdlVXi3bbsNG63KCylP2oYhf3CvFQPef12S4l8jzbtVuc+3rBlAEaKy3AOiaWPt6eXC/HxZ594lOOh527A0KYEV5lLNjB7dYmsgjXKtcK/1DU9X8o62mvV/wkdHyywHdVmBedoN22tlaIt8nJzHbbACR2EUSTZ9ly4ba4aRPw4F8Y7v2TA+npJHQR9advH4Y3XmU45yxgzltC9DpwgAQvgmhq7N6jyzRlgl0hQk4yFi0R7oZ6Fi0Rb72D3AttCkNW1kjQrWMsuN76yWhFpfnvzZAP73KiYvb7+sZuOnhITMr27dc+7Ju6MRosl8vUYMoyTk+ogPdmllz67T0WFk1q4ODaWmPrnOUrOFb8zMOyiPppObBytXHMLo83+Nj0x2AldoFrx6fqoqOnrCzgkmRk6REuxSXBy6QbIxAYB79sDT6HBg8M/m1GuviUAkZlZaCcpCSGXkWBbdUxpokTxI3HtJ2YXYBwO3J7tEKTSmBsahtOL65a7Ud1Y5QCg/xUj0stU11uNwmAnoOHOL79jvvjOQUH+jevq/r955UcGzdx/3VQteySY9fhgG3fxV+2Aj+vMq+Dw+fGarTOrhsjYKg7BGHnWg2I9vZ4uF9gCjd2mKkbIwJujBK9yChJUsbElq3AsuXB26ju4oC5GKzWSf8bsS4cq1fttvbFruBzOdrIMuW1Jihml+7epYqalpiIXQ6mPQ6vl6POHRAqSewiiGYA5xxvvs3xx3s4clsAr81kOP00ErmI6JKWxvDnexx49GGGHTuA637H8fUCErwIoimx+Rdz9yWVcMUuK2prg7MnGU02Lfdjoz7MoD523mzbtewyI1oxu/xWHFxM4iX7DwirLz1G9Q4Kpuw1CFAf4njlBPzYcaC42L4bGqCdlKxZK/6++VbEqZEUlwDHi+1bHajxIo3ELo1lFw8sU9HE7NL1p5fbiyVUU8OxdBmwabPvdwYxfOqLx8Ph9girBUA76dPXKz8/+DnPH5jf1+cnfBNMOamX5QLaMaYZEyEsu0I9Xeqzl+rrrY5NI2wJ4dK9S7FW2u1zl1Mn235XV1/Ms9QU8yLtXMu2bBFuf1IgshugXh98/9hxYO8+xXJHTR6giH0aiy+b48xQ7HKYi10encikPyaNK6Uj9HXOrtjl9gDffAssXeb730TsKiszdnHUnLO6a7Be7DKzKtLHwtJbgKkvUIxevJi1hRxLaoy0cERx/TkSsdgVxaQpfvFaJyrr20A/ZuX6UGKX2jyqtap+3MpxIq9l4bj3kthFEE2QmhqORx7neHU2x5mnA7P+wdCpEwldROyYOJ5hzmyGLl2Ahx7leOpvXsvg0QRBND20k1zz89tq4qP+rlIJUG8kSoXC6qHd6k12fbOleTzc0m1PFVrspGO3RLGscuomYVJgUTG07NIF6zaKZ2XaJgZui8t/DhaO/JY5QckAuOa30r3U7TF2h7XbRGrmLSOxS7VE8poco9VkkXvtCZVykrX/gBDg/G6MEMe+c1fA4idS1FhU6qe6f8BYcOrQPhAvxy/GeLXlOJSZqJnYxTks9btQk0tNnDEEuwwKcZEH9b/eEsyOZZfcV00N97snq/GNZBEeXx9bxWSzc03yjzm/qGyvv81cwIxidnEutmeMaZcj+LpSW8uDAuMbnVdOC8su/YuEIFc+fdywEIdsdV7rRWUTjVXD0mXAipXByz2KEKS3qHI4tOeOWUIVO7Gw9G7q6vEdM3Evlm2oimzhvAzRW5HZFbukO6a/nFhYdvnqJsUmWcfiEo7ych40lv2WXWG4MWrQjVu9ABiOZVdS6E0Igognjh3juO8Bjo2bgFtuZrjst1o1nCBiRevWDC8+B8x5i+PNt4FftnA89ojIHkUQRNPHbtYsu+tqlLe6yclhPPgbCElmGG1jy3rIYkLw80oRz8oM1YXOa/BAHg5Sh+AGYpdhFkmDelsF6zazevLvQ5YRQjA0s8zxerWipFV6eqNlnHPDZxi1vmqZqvCgd2PUH6M6ITKy7LIjvKnbyIDUcvnevcINysGAjh1Dl2WGPoaTWlfVAkcv2pw2SfygpERUUh90XD+e9MtM43TBYLlNsctrInYBwdeNLVu536XTjugkqyL3pe5DbTPVnYxz6xhY4YhdZoKv2TgyC3dlFLNLFeWM4h+JwOsc27YDhw8HCzlGAkdGOnAsyEKGG7p+W4ldepcyI6xfgAS+Rysbo5qRUzawgwW3XX4eMGgg8NUC8f8pY4z7KzUlIMyo7eMxELvUlxBqbEqja2Q47z8sxS6LcvYfMC4nKoSw7JKxx7KzlP2rYpdFIH7AvH0czHjcyP2TZRdBJChbtnJMnsKxYycw9QmGyy9lJHQRDUpSEsPvrnfgb08xHDgI3HAjx+IlZOFFEE2JkhKRKl1vHeA1ERj02F2nvtV1JkVg2WVjAmXk6mjLsstinZXQBQRbBXm9HDU1XJP5zS7MQuxKMnglbdQmsl2N2lcfxN2MoAm8iWWXfv8ej7a9Q/Wx/vdmkx2rSaKDiUyUhw5rt7V0xdI9Ktm17LKqnxQIzCxI7OJ3FfTNymRcGkDr+mtmkeIwcS0yGj92xC4jl0a7ll0nfJNbM7FLrePOXYHvYcXs8u1LjTvlcARv542SZZfelcu25ajOjVGiBjCX2a5VUU7fBHL77duFG6TheDOoU2amaBe1zb/5DvhxYfBxW8XOMrMO0+zeylJWFX/CFGIOHeY4cSJQuDyWJGdwfzBH8DkislsGWjQlxTi4ed++ge+lZYGxKS04zY5vnxLT0WgshXPP07+0MBKrrZDjJ5pujBJVZARC3E+44sYYwrLLKpakRuyS1qq+vgunbUjsIogmws8rOW77g3gj84+XGMacTCIX0XiMGsHw+iyGtm2Be+4V2RrDCcRJEETjIUUZfaBsu1ZKdjNvqfE61MlJKPRC1sFD3NRtWh97BrA3oarP2++gwPteYNv28DOLAYGJuBrnRGIkVqgTgBSXtj769nUp1nShrE/0x2RmjWJo2aUKf4bWXIGF+tVm/WAWI4cBSEnVblvnFnHGZL/LpyMzVxeHIzhmFwAsWSqC6GvqYVyEELtkkPR6il0yQ6Jf7GAMQwf7ylbGt9nxmIlgRhNCKzdGq++hnjj1k2CN1ZUiFpmNQ1vno86NUW/J5t9McUELZdllJ0C63rLLqK7/+5pj/QZlnHOtSKbGoFPFCOmG6fWau2gZZQTUo/5ekpLCDH9XWxeeZZedCPWW9wul7FDXBz1r1gJLliq/Vyx89L9yOoLjcaWlBZdpdB6luIKX+ffphenxl5QqbrMG97dwEgF4PMJS7KflxvXcvVskidqyVSsASvxWV9F8FJf3Yp1l1ebNYsxL9M8OkcTs0hDCsispCUi26Z9IYhdBNAG+/Y7jrj9xtG4FzHyZoXs3ErqIxqdNG4Z/vMhw7tkiW+M993KUlpLgRRDxhExNr6KP6eNfbpDW3QirwPHq7zRiV1L4ApOIjcWxdl1wqnb5ICwnaRrXLzsuUfW4VOktVDye8N/CS/xuhF57dVK30buUqFZADgeQnq78LkTbB8fiEp9t24hPM6sWvRujEepE2q4LmF5wSU4C0tOAtm2N23r5z4E4X4FYVYH16vckZ7BIBwDlFSKIvorZsZ04IWLlAEBVCFcdK4pLONauF99VCyR/DCwbYpeRNRQD4HQGr3AaWEBZfbfah6ZcXZ+o1kcyzo/+vFEJ55z1i12+/nYlG4ujMmaXVXyfUMJATQ1Hebn4rk/6oK+X6k4mY3AB4uWC6gJrZoHJ/GKncflWXcCh7duB/QNlGZ37+jrohUM7box79goLYY/HOsuqFPQA47qEuu65De5LRuOIOYL7OjU1eDuj64fLQuzSxxlTOXwEyM721dNAONVbOOr3c1JRIMOhx6OzVtR1+OYtwLoNwups3frgfflfnFhc68ONtatPOCFF7SDrZx44tmUrArEb5TOA6u5pVP6gAdrlQdaN0vXXb9nFcMpYW4dAYhdBxDsff8rx14c5TuoFvPQ8Q0EBCV1E/JCSwnDvnxy49x6GVauA62/k2LSJBC+CiBdkanoV/VtSiTrpsArQblcIU10YkiJwY/QogkTVCREnUFpIyLrKSZr6ljcWAXpVONfuQ52ohl2WrlwVo/ZSJ3my/6qrxUuxigrhuuRyicmu6gpi1iSmll1ciEu9igJ1MQrE7vGGzpqpEbtMXLr06F0j09KA0SczpKZqG1q13PDo3H/Uia8+M6ORJZ1hPUy2USd7NRbnSijUYNbOEGJXQb5xGUaTd/147NZFfJqNN814NtomTLFLbbdkaYFowyLU0o1Rty8pqDl1Qro6QQ91boYSan9cFGibUBklzeqrx8gNURXl9PU9dtzc+skf88+r7YPCQrFCjnU9QWKXbgzrXYCNytixQ3y63dYCS6jg6eG8ePD42klNfiJxsGArRyPrWH34F5dLhOgw3aeFRaK6D6Pxre/rdJ2lWfv2DCedJL7rx6reSk1fJz0Ok2u55MABju9/FJku7aLGvwPMRcGKSu34k7+rc4v79jffAfv2Be+XcyFW6+e2QW6MBs8sDiuTTQUSuwgijvnPfzn+9neOUSOBZ//OkJVFQhcRn5x7DsM/XmZwMOCW2zm+/IoEL4KIV/wPjibuMgBwwsL9QD9BVCdiZpPHpKTQokjQfnTWTrt2CwsJ9aFZigFGkxor6mPZdehQcFD2SOOkqPGFuFdMJoYNEcusYnABgQf/Y8cCol9uDjBuLEN+PtPE6zGboDMGbNrENfGT5PaMBayDausC9enYAejfL1DHkJYZimCjPyY7ll0ej1a4Ul2OWuQEl20UxFgvdhlZdhkRSjjNzKhP32sLN7Ts8pU9cjjQs6dxOdJySlu29v+uXRlSU3QWXJofGH+3G7PLynoqRRG7omrZ5RMSkpOMLdPk/hwO0U8tC4E0naVPqLFrlLRDL/qaJWIwE4CMXiRoLLt0bblho4hRZ4Rsd87NhQijQ9T3g97dThWE7MTssvuCIdQ1LRRer0XsOgPLLjsWtycVWa9fuNhazOvUUXzaidmlWttK5PF4dPc7TdIFm/dOBmFVZWTRLQX68gp7ZQEIcmNskWPu8qlva/kCSlrAHjkqLLy27+CBax+HoZCujzVnZo1uBxK7CCJOee/fHM++wDH+FOCJR5nf954g4pWingyvzWTo2xd49HGOGbO89U7JThBE5BhZA+zew1Fa5vvHIOuXpMYisKyVYCHLSE3RbmPXsquqivstwsxEhLLyYDfGSMSun1dybNoc/jWqzi0szdTMgMGWUfbKlZMoaS3GALRowZCVaTzBUZe5DCxmnGbtoFQnSTdh2LMPQdTVaUWUnbtElkpATNjkhMfrCS32LPnJsBri98rxVFRwFBf7LPeU5XqXmT69A1YMLhfQt7f4LoUhRyjLLmcYll0hji07O/L4b0GWHMp3OamT1mppaeaWDA4Hg8vXV1ZxbIJEixCui+ryUE+gqanw10GPHKduj7koIs9jS1HN91t5rkuBRu8iLb/X1gbqP2okw4D+DMOGCjF50gSgXdvwBHgzyy5TscvkWPXXVs651rLL4DcHDxqfp/5rEAdSUozXy3NJfR7TX4utHtVsBai32Y5yv/l5QFam77dhil0OB9C7V/A6hyNYKJTnUauWQJvWxmXasco1q2Kb1kBurvjuNrhW6F0brWKI6V8cqKd7kOusUR25uLYdOCgsuk2fv8Npb53Y5XQGjlePXoTUj0fGgB07RXzLgwcDVbEzuzWzRrcDiV0EEYe8O5fjxZc5Jk0EHn6QITmZhC6iaZCTw/DM0wwXXQi880/gvge4JjArQRANR/Dbe47NvyiWBXqXMuX/GgvLrqCJkoH1g4xjIpGp4g8e4qisNHCH83Ds3sPx0zJlmYn1jSps6WOJ2IVz4R60Z69+uf3rlZzcu932JsCG9VC+qxNep0lAf87FJLFzR+FiyKATuzRuHopllyp22Wir8nLhEqlS5otd5GDaDIDhWuypqO20eKmIvQUEz8fUCWlKCkMXn1ue0xmoi8fji1Vl4A6mfnf62iVU8OTVazjWbzBf7/Blf4v0nY4a1wmwjtkVakIuBSUpUCYbWV/oRAuzxAJG1l+hLbsYxo4xXifjJnk8MJ1oyzFsJxuj3rIrycSyq6bGZwmknBMpKQwtWjA4HEwEOfcCR45wfPMtD5lkx20idhmdp4uXmo8LvdtrXZ1W+DSykjOLC+ff1sSySxWq1HqG41IeSuz6/kfrlyMqcjwXFADt24nvwa7N5juT8RHbtQseKB4Dqy8p7Pfvx9C3j/HgssrWKdHXUV77k5ICInSFgcWUPoFKkhMYPUq7zG+dp7smqePWrL/04pl6/Pr6RDKT1N8/jKzn/PvWCVH68VhTE8hyKY/HzM1YHXO1tRwbNgX2Hy4kdhFEnPH2uxz/mMlxxmnAX+9nln7kBBGPJCUx3HWHA3ffybBkKXDTLRz79pPgRRANjX5Spn/4DXpbrFp2GbjamGWd0ric+crIytJuI7NnrV0HLFqCoGxSv2wBNv+iDZRsFrQ9WQlILSe84b7xVYMmq4QzCZQP84cOBzJcSmyLXYqbofrgbyZ2ycl7jx7C4tuh286ps2bye4uYuMeYUecOWF74y5OfDq3rjddrnvHOLDCxxExwCmkpolgcqdn+mENpQ01baN2yamutXXUBMUaMrDUkSU6xv/q6sErU+jocIiyAHRFIXV9QAOTlwp/NUbMNjIUs/T9G1l92BAGHg6FL5+BxIy08rNwYrdpZX8UgN8ZkmFp2WVndOZgYv1u2iv2HEmzUCbqmXiZDvLLSePkJnXAlxXL/eRnGY79q2RVK7CouDiw3ihtmhh3LrqPHrNfnthB1lWKXUzlP7cQqVNeZxbJyuSJzY5TNPWig+Tb6OrZvLz7NXhw4FQEe0Pat/lyW2x49BqxYqdRLORaz/lItqDm0ZfstuHWITLQRvI2BtdgVnA1U+79aH1msHbHryNHAcrLsIogmzocfc8x8lePMM4D77yWhi2jaXHgBw7N/Zzh2HLjxZo6fV5LgRRANif4BXWYVM1uvCS5vIAQkJ4sJvn5iamQtop/w6h9S1cxlZeUcew1cdMwmqmrQZTPLrtwWxr+VvzdDdTsRGe3Mt5UP82oAZokdsevIEe7PWiXdnuxYdqmTA49HK0wyO2KXU7uNGdI6T1p4ySIcLNDe7jrRT2aTPmlF2LLQeL1RAgV9fYHgseCfqzGtG5AqupkJNA5HmHFrTHA6fdZzUbq1GU2C7ZYthZqWhcCQwQwZGcEda9eNUW9tCNjXX7p3Y2jVSrtMjdllVwTeuYtjx87AxmVlHGW+ybI+QH1Skkngc4h2MZ2c+yy7zOKS6YVaMzdGO0kM/Ps0aMiaWp1lV5hil7RGNTpOtc9Xrw1dZyPsiF2q2N+hXfD6/v2EMKMRf0zELqO6Sas7s5hd7dsB2VksSAizI47IdivIN294fZ2kyJSZYby96roLBPrUwYL7Sf5/9Kh2uVlsMgD+k1QjcHLttT0oNpyvDps2A19/Y1F28G4CdXIGnycybqJ+uVWGS819KYTYFUlQehUSuwgiTvjmW47pz3KMHQ3cew8zTBlNEE2NwYMYXp3BkJcH3Hk3xxfzSPAiiIZC/4BerY8Vo9teY9lVE/yw6kwSE0t9HBKjrElqIN4O7YInHWoZJSVGtTe37FIrXmcSs6tLZ+MyT5vEkJ1lvE7uU5KcLOJDmRHqYX7fPo7SUlHZEyc49h8IVLyujmPVmkAcHil22bHsshLrNGKPidilt/4yI8M3kRs1gqFjB+U3Dp8VBRNjyo7YlZpqvB6ARtQQdeVB4oXeVTI/T3zm5Qbaw60LZG82L4o0c6YepzPgEmkHt5trLCq8Xq1IE2QZobRZqDrLWG16i0oVvWihEanNRDCb+1fRnxcuV8DdNlRbyd1s2Qps3RZYvnSZsKAEFBfPuoAgzQ0suwAxJszqzpg4PrNMkHqxwEzsMrNKM3JrM4p3VFFhnY3RCq83YClmJXbVJ36qPlh4yO0NBCYpksiXKA5n4Py04xYq7xf6ZBUSeX3RC0RmYlevIlXxD3wdNBBo3Spoc3i9gXh4mZlA27a+bVsbd5bq4g5o+9ZM7KrT3VetzmVJji5cgHrN07erflzV1HD/nxn6MuQ1T0WK2/p7tT5upwblvmTUgpp7Vz3c5AESuwgiLli+guPRJzgG9Bcxusiii0gk2rVlmPEyw5DBwBNPcbz2ujesuDgEQUSGXiAIzqKoW6+KSHXBAaeTnKHFLrkP1YWhVy+mmXS4XMZxvvTs2WvsaqZmrZJ10QfmtkrbbnX50a+zuhsnJZkLKlu3ARs2AT8tF/9v2Ais3xBw39S/xZfZAdW4PXYsu/RBl/WWXV5lwiB/ZseNMcUFzUs3vYjEGENKqpi82hG71Cx4+jbbus0geLaFiy0A5OYyTBwvgvnLA/PohA0ry65ooAbEtiMmzP9fLZYtD/yvz9inn4ymp6nrrJ8LBw8Ugfotnx91q2wFqzexerJC776UnBwQb0O1ktF6fdv63RjdARGFI2Dh5OXaibZpjCElwYQRZiJM0HUzDDdWoyDypaXa89puWzudIpbXwsW+3xlsI68B+myLRoQ78zDrS6P2djoZmCPwwkV1Y9RjJB6qQqORgCX7Ur9vs/OmW9fABUvdoiCfoV9fg3hgHtF3I4cDI4aJcq0swfyWr3qxy8AN0Gx8WsVWlGMwNQ0Y0C+wTL3mhRK7SktFvLXvfwy9HyBw3ddfV+W9V39eWIl16kuYUG6MehEwXEjsIohGZtMmjvse4OjcCXjqCcq6SCQmGRkMf5vKcN65wJy3gMee4KitJcGLIGIJ100SgwKJWwgKXq7Nxgf43Bh9YpcqWGuEK5MU4f6g4Qi2hgkSMloEvks3PyDgMsKVoOher7G7odGESLpb6CcBW7Zw/O9rcTxBcZR05YwepcSuYsFtJNG7ZcqJi4w/orf6KCkV7RrKjVFv2dWxo3a93rLL/wZdqYOdlPbtda5I2nhS4jM1JSB2GbYDN7bsMrLy0oufdlybpBjnjx/m0bq4mrkBRduyy6x+Rsgg/xIryy6jzG1mpKcztGljfWD6tRpNy+R27F8eRpvprU2SkhSxK0Q7GbWjeg0Q57pWhNX3AfdqRSVTCz/FIhCwFv8BVXDRrgjH6smoT48dE+dJKMuugvzAedm5U7AFksMpLFH79QksY0y0h/4FhRFm4rAdN0ZNPcxEZqbbJoyYXbKNPSaWrcxE7LKD1W96FSl1ZEBWFjN0pRs5XPu/PAZ9zC5msD8zQc7hAE4ZA41VrR6GwHWdc+01L9S5pr8WGaH2jdH9AzAfNxkmLp6A8hLGhthlZ+xaQWIXQTQiu3Zx3P1njrxcYPrfGDIzSegiEpekJIY/3cUw5SaGL78C/ngPR1kZCV4EEStCuYdYxewCggUMddJqNsGTgoP+AV7uq7Aw2C1Gv1+HAxg6xLdOedBt20ZMmLw6ixiHM/iBO0knUvXuBQwe5KuLrs47dwfXU37Py9PGoElPZ5qJlZr1LjMTKCyAIVLgqfIFrTayXigpDbbs0mez1U8O9IKO2g5qO3MeEO5UAc8ortDI4UDXrtr+05crj0mKXQ6HeTwiBmGBIDESu5avCHw/cSK4fawyPsr2qK0F1IyDkVp2VVZy7N4d+t7kVMZdOIKHJJRlVzhilx2C3BhtZGOU2dPCeTpNTmYYOzrwv7xuHDgArF1v/VsjUcUosLWK7APVWkQVu8zGQZBIFsKaUBVc1OtLOB6C6bo+bdNaWF1VndCe+0bIlw1yG/355mBA2zZM41rn8Fm92bHsMhOH1etIRQXHt99Zu75ZlaN+d5iIXUaWcrKNvZ7A9StDcZV3hGg7SwwGd+eOQFHPwCr5UsUM/UsRKfIbWXaFstL0V4uJ7KGhXJONrs1A8LjU79UsgYKKeg7IsRfkKmrS5i6X+XFq+tym2FXU07quZpDYRRCNxOHDHHfew+F0As/8nSEvj4QuIvFhjOGKyxgefZhhwwbgpls59u4lwYsgYoH+YTfI/VC3vX7SYSR2JSWJiZNRUHq5D/lQnNsiMAmQotFJvcTDuVH2NInTGZhMagLG+9zGuFf7GyOXGP3kIzVVsQgxueR4vTzYjZExdOumK1t5S6939WzV0rhsOWmQxev7Qj9ZczrEtosWA7V1Wis6dUKjP06NZZeyv+pq0Z8OFjpos6EbkkGcr/R04WZaVi4md0aiQnW1EB7UQM5G+1cDxi9bAWzfoV1vZaWg1lfNdBdpzK7lK4DNW6y3AQIB6gFrscvr5YZZKTm3tuwysxqMFMZ0FpU6t2X/cuU3x44HfhsOqanMfw47ncwfbD9UtlMjsUvNIqinW9dA3USMPw6Px+diLEXpEJZdZuivn6rlmDqGw3FjTNL1aXZWILOfURurMQOlGzmgtYySGB6PbxvZv1bxve1k3Ny3X7iVff+jQQB0H2Yu5PokGf5+s2HZVVMN/O9rjqoTgbYfPizgHu3v6wiUDaMm6dGDoWOHwIsNvYt0UBm6dTk5MMymalZG505CYDMq0+p6zVTREPasdiVGMeUkBw8K7wu1a+T1SD9O5D7Dik4SRswut1u41XfsENk8mcQugmgESks5/ngPR1UlMP1phnZtSegimhcTxzO88BxDeTlw860ca9eR4EUQ0UYvEISy7NJP2vRxsKTY5XabT5rd7sDvhg5hmDie+X7L0KsXg8vFgtwY9QIOUwQZ1cLHwQKWXRqxyxk8iXM6hcuh/7eqe4ep2GUcwNs0wLVDa33Dvcbber2BgOuy3qrYlZwUmPD66+s7fg6gpjpQKbdHa1ViGf+FiQnp2nUcxcUiqLtV6nj/zwyOQW+VAWjd1dLTg9/wcy7ErtRUkUVr2BARV0oiszzawY5lV9DyCC277LrN2HVjXL8B+Oa74OV6y66gvozyo6G+ndTTYJeJdaPZb+0wYnigv/UT9hHDte52VvsuKQm+Fklatgy0067dIv6bzE7qMpmcS/TtHbRvUzdG7fGEEvBU9O3AHIFkHnIMqfVq24b53a+TkgPt4LQZ+0kuk8LUsKGBdXqh3soiK5xjNBu3mnNEjS9lw2W5QrFCkseUlMT8VqKxcmOU497M3c6ojNGjxEsdhyNw/5L9blZEj+4MXboYl6m3Upb1keWZWXZ5PMDuPVwTy86/HdO+GFCpquJYu17EmVT7wi+06i0KDcSu8aeIT/UerHLkqPBusuvGWB/hn8Qugmhgqqo47rmXY/8BYNpUhu7dSOgimid9+zDMfIUhOxv4w50cXy8gwYsgosmiJVrflaAJS4gYNcm6rGp+sUsXU2mfEp+qrs46sC5g4Mbo0U5mnU5F7DKx7FL3n5ISPKF1OoXLYeDHga9mYpfHY+ySFCQSyJhQTm3mOS83nlh//Q1w+LD2t3pBRR+4Wp20qBYUbncg856sg1ld5feDh0TdWrQQ4lwo9zjDSbOavMxXbo5vEp6aImLb6NvJq4hdgAgmr8aVMprEmWElJqn1TUsNtLHZpDukiGTzsczMjbG4mGPL1sAk89Ah499XVGhFvOBYPvbqEQ5m2Ri1Gxksi6Auahwx/Th1JRtnMzXKHFhTC2RnB28LCFcpufnOXQG3y7LywATZ7jgIFSfOHyeQa48nnCcX/T5V61BpfaXvd3nddrkClmHMwJrV6Djlolpf2ep1Rn+dNg3k73Nd37adY/ce42302xuhtpPM0AkAa9cZJKjQ/9bEAlJ/roeyWjXCUsTyrfOYvMgwKiM9ncHpZHA4zS27inoAQwfr9mVy/lsdk94dVN328BFg8y/iE9AK661aGY/bX7ZwbNgovldXa/vCL3YZWE0DIjOuJDlZVEpzD1YoKwd+2SrqbJVFFNBai0cCiV0E0YDU1XE88BDH5s3AYw8zDOhPQhfRvJGZGnv3Bh56lOPtdwMTBIIgootVFkVAvOlV70p6MSI5SSzzeLQPwXv2AR6PKEy6EFmhF7v02fwcDmOxS7XsUuvuchlMInUzE3W9ldilyUrnL0u7nZyXJSXpxC6PuSYgM0r5LbuU9nO7A20t21W1sqjzucDV1vKgtlKDdQPaiYO+v10u4frTuZNJJX3YtexKSmIYOlhY6SQlsWD3JS6yr5mloI9WVkS1vsOHhS7fjruWHcwsu6Tosv+A+N9MqFm9Vifm6ho+WvUMlK8b+2Hcauv7tJqerv1fH2tIwnWujh6PGPNWwa6l63CLHOU85+ZuVxIzd7t9+0TCCn3cOGn5yi2yj4YiK0tYWKa4AnWT1xA5FvTnn5rRVHVjDBK7jEQDnWWXer3SW8uYtVNSkugTvWuxGWblSLGlb2/h5irrX3VCZAYEALeb+88bFTV2Y4qBSCr7Um2DU8bYq68l0tUyhBtjKNdv/fqOHRlyc/XXbt35byF2+U9dppTNjbeVpaoacn6esbXkrt1AcYn4Xl4BHDocWGcmHqelMYw5GUGWafXB6QiIyx5PZCKmhMQugmggvF6Ox6dy/LQMuPfPDKNPJqGLIAAgO5vhmacZzjgdmPkqx9/+bhzfhCCI+nGiWvu/epZ5vRzHi4ECJcC6fkLnTAoEGlczpAEBqwQ7b2GlW8zRoz73Cp9bEFPWm1l2SaFMY9mlWDyYoU7A1N9qYu94jYUw/1t5+Xsl46Tqyte+fWhrHP/b6jrF/QOB+uuzdwGBiep3P4hPq/ZV968PSp2cLCZUjDH07qVdV9Qj8N1uzC4AyM1l/kDEejeoOp+7q95qLVT8mnDRC3HcYLnR/k2xeftRXcl+XhlYLo9Xugnpj1/FKnB4LNwYNbH2TI4zEOjd2tomHPSJG5KSjAU0r1e7Lzn2M9INNvbhcjHk5YpkC14OtCwEBvQPCDvhWnbt8FmH1dRo16vZGCOdfDscDIMHBYKOM2Zf7EpN1bkxBgn8wfuTy+rqhKCuCqpBll0m7WSWGdYMs7aR401aDKv1lW2/abNx0ow65T6gCp9+109fWapgZDe7vZXorlpNhSt2Gb0gCC+rpbFVJBC4BzFo62UodinHIElJMbastMJvRWdwrGlpzHbgfX2ZRj9Tx1x9xa56GIURBGEXzjmee4Hj6wXAbbcwnHUGCV0EoeJyMTxwH9CuLfD6HI6DhzgefwTIyKBzhSBihfrwe+yYeKjMbSHiaQDBD5hJSYG36hW6TE51dWIyZseNkTHx1njlaqBfHx7I5ufUZnN0OHiwZZdP7FIn6i6XqLcVRpZdLXKAsrLAcn25gZhdDPl5HB18cbXkJk4nkJPDMHoU97trHD1mPZvxKm+rU1ICgoicxPrFLqXthUVXoFwr9z8z90dA2y/t2jHs3sP9/ahaH4WauJlNPMwm6WbxVkJZdiUnAW3aALv3wN/2ofar1s1s7qUGLg8nk54e1Y1RCHscDgfTBDIHrF0wreKDxcKyS8Vs4s0hjuXrbwLL6hssPz+foWsX7rcOcjrF+Q0E94MqrEjBSe9SffJIY5fdFBfQv584ZzMyfBaRJoKivn2DQnaZZGP0csAVweRbdVtT3e9k20pBR4gcgdrIb2lpog4uF5CeISyiVIws1WQTud3B562+T63cGI2yx5phKtJK92/ffozELrOg96oobGTlV59zxY57Yig3Rn2fiWXB5di93KjXeKPrrRrfTRXSjLZVY81JRBxF7Xa1tda1k/cqM4vISLAjduljeoYLiV0E0QC88Sbw3w+BKy8HLv0NTd4JwgjGGK6/FmjbFnjqbxy33M7x9FNAy5Z0zhBETPA92x44wLFug/huFdMlyRlwR9JncvJbdtlwY3RqhJzAm1sHAzwITIaSnDo3RmfAOkWdiKakiMlzUQ+O6hrhoqHHKM6LzBAn0bsxqgweFHwdksehxiUJaTSkxCHJzAxMIJJ0YpfGjbE2OCC/GerEQT/J109u1TKNrBBUjGJ26dHXyy92mVgQGJWTmSn6trJKTGCLerKQKee1k8qA+ZLZpMxvWeaAGHA67FpfqG6MgIhxk56uZO3zLY9Y7LJXjbCw48bIubZenTpG58VTy0KtK5zsB4cj4CLo9WrjmOmDzUv09ZFjwOUKWC9l+6yn9FaokqCA7CFiGHq5cLNzu4EkC7dKlfQ0YNDAQJIGPaoboxpLLz8vIPCmpwlhKylJ/H7cWLH8+HGduGJk2SXFWIOXEPr/zc7rcGLrAcHXj5aF4tOrWMTq96e+QDBCFUBTUwM/5DoBLdr4Y/JFYNmoBqUPV6wZfXLgu9H91H9tZ6Etu4zErpQUbZ28XnHvNCI1Rbijt2kj/o+mxalZzC55HNKNuT5iF7kxEkSM+e+HHK/P4TjvHOCmyTRpJ4hQnHm6cGs8dAi48RaOLVvIpZFoerz77ruYOHEi+vXrh1/96ldYvny56bZLly5FUVFR0N+2bds0282fPx9nn302+vbti7PPPhv/+9//6lVHOVGQ8VIArTugU/eQnZwsrDCTk4RllkptnbBithuzS/2uf5hV468YWXZ5PNp5aYovQG7Hjgw9ezDk51vfa+Vx6+vp0U20QxHuJBAQx+p2i5TuqrCYbOHGWFfHNZM9Kysb9XcylpGsq97NRJMkQPlu5I5iFX/GbHmtiVAR2E/wslaFgYDHdidVphZcJsv1bql6zO44E8aJOEAyO57Tqd23FGa8XuNPI6zErmhaUAA+F08bAer37wfWrA38HyqhgV2sAqKPHiX2o4/ZJS277FiL6svMywPatwO6d7P+jUTfHnrLLgBYuSp4P6HqlZ7ODIUuQLqhMQwZBAweGFg+eBBDYaH4zbChwMjhwb8NcmM0EqlVN8YQ4paVZVc4qNtPGAd/fGK9e7HGtdwb/FsV+TJl0EDtcrO4iuFgmY1R2U+4+/Afp4EFWzh10sdl1GynxOzisBa7tO77TFMvrxeo0YU58K/jwGmTGNr6kk2Ea0U35mQRb86wbJNroxrGwOOtn5hJYhdBxJCvvuZ49nmOU8YCd90ZmT8zQTRHBg9ieOUlBqcTuOX3HEt/IsGLaDp8/vnnmDp1KqZMmYIPP/wQQ4YMweTJk7F//37L382bNw8//vij/69z587+dStXrsSdd96JCy64AB999BEuuOAC3HHHHVi9erWtOhlmPtN9AlphQhVzWhYKqxtAiEtGMbvkxD2k2KXcCjduFpmZjCyL9O4zMgOZ/gFZig+W+zSw7NLXc/du7SQ/lM+JZeBgE9TJvNonessujTtinVYA0IuQKupjRvduAcHLSCBTLVcyM4BhQ8yD1xu5jFltAwTEH7PxYDTJ5DCeJFqhf7ayii0DBCZO4d5VGBNxgPwuRE7tPqQw4x+fvh1YxTtyu8WEevhQg/2FWT87aMQuk21KSgNBqoHoWXKYiS0cQhBK94lqRmJXqPhCRmPG4WA4qRcztUrTjw+/27LvfykAtywUIRaAQDyp8nLr+khCCQOyDnl5zDTGlMvFkJVlZLal/ddIEPDH7DKIpWj3/ApX7DKzEtULWuruZVubvUCQLp7peuHV4Fzv0E4EwbeL3VhcEYtdBskEIqlTm9bG22ksuwzGgNvAsku/j5qagLXY6FHaftC7loYrPKWlMf+zgyQzI1AnMzdGwJch2VM/4Z/ELoKIET8tEwHpBw0EHnqA+c2PCYKwR9cuDDNfYejQAfjTvRyffkaCF9E0eOONN3DxxRfjkksuQbdu3fCXv/wFrVu3xty5cy1/l5+fj8LCQv+fU5llvPnmmzj55JNx0003oVu3brjpppswcuRIvPnmm7bqZCjMcO0noJ1Uqt+LegbccFJcwQ/OdXWKm0qoCZ5BXTRvshWxS534qjG7JElO2Lq/at5iK26MKseOhyxGg2EslRDuLmrd1WxYciLqt34IitmlbGszZhdjzC9eGglOsq6njBGp4lu0YOjR3bgt1WO1awESSqgwmuSok59YvR+UAkS4iX/lcavuWBqxq1q7Xo4z/bmiTtjdbmHRlJMTfLDRylYpYSwwvk6c4Dh2zN7volUPfyZBAyss+b/Xqw0Mr1p2jR4FDB1iXLaRZVco9IkD9MNB9luvIqBFC/FdBsrv0MHePkIO4fpYJOktu4zK8i1z1wXOT+neqd/erntyKLTXikCh+uuu3rLIal/uOuP1gbiKgWW9ejG0aWO/YS2vMzZEfjPU8S7PeTMr11B1mjQB6GMg4DFmLi5KvF6gqor772+9Twrex6IlwOZfAvWO9tO2/njatROfZrHQNGIXuTESRPyxbj3H/X/l6NoVmPq4+dsagiCsKchneOk5hhHDgaee5pj1mleTIYog4o3a2lqsX78eY8Zo856PHj0aK1euNPmV4MILL8SYMWNwzTXXYMmSJZp1q1atCipz7NixIcuUyIfHNq2BUyeK70ZiV5JGgFFiUSm3MaPscnWK9VGoyZFZjA6v7i29vhz5hlzup0d3YNwp1vsy3KeJZZeeUJcaQwEpxG84D0zsVJdROSmU8W1UwbA2RMwu+RsgeOIgtzUSnMxEPyPUYzWzUtf3q9crJnpmYqRZ1kejyXA4hOo3f9uGeSuRx61OzNPSGEYME8eid2OU9dCPCa2LqvlELiZin68uPy0PJKIIRbQC5TPG0LM7MHyY/N9XJUWwKCsHjir1qqmBLzsrQ3o6Q24LE3fACATSpCRdVlJ9jC4lCLgck14O5GQztG4V2NH4U4SrllW99KgB6iPFjhuiLN/LA+fw8GFCPNHv2mwchrpO6vcV8pgd2k9Aealh8FvVnd3MGq8+WFp21UPs8l/HHECXLsIFMy/PXiF6t1eZRVfvUiwsu8S2LpfxuerxAKvXiO+ZGUC7tsz/W/12ar1NiWDM6vtN7tvrsRa73B4xdutznlCAeoKIMtu3c9xzL0dhITB9mrn5NEEQ9khPZ3jyceC5Fzjeegc4cJDjvj/Bn+6eIOKJ4uJieDwe5Ofna5YXFBTgyJEjhr8pLCzEY489hj59+qC2thYfffQRrr32Wrz99tsYNkzMDI8ePRpUZn5+vmmZkgxf6qoWOQycc2RmOpCXl4yMjBpkZzmRm5uE9PQ6ZGR4fXVJQUaGMKfIzVW/u/wvbgoK3Cgt05owJSc7kJXlREZGHXLzkpCba/7E3CLHjYwM7e9btHCiuEQsa906Gbm5DhQU1KG2LqDy5OUmo0ULD8rKODg4clskIT/f+slcrX9ysqh/enoNOIDcXCcyDpubYjFfG5iVWVAQvO5EtQcZGeaBmDIyGHJaJCMjoxYF+UnY69s2NzcFF17AkeQUE5uUFI6MDKGe1NVxZGa2QEZGHTp2cKJ1a+3j+/hxwGdfiDrl5bvgSg5cG1vkupFxzIOCAgdyc7VmBYUFtaio5CgocNkKs6COCyNa5NShvEJrxpTiCt4+K6sOlVVeZGc7kVESaP+ink506ezE+g1ulJV7kZ3FkJsbwn/NoG45ObVgDo68PBfS0gLHdeH5YmZ89KjXso/0tG7lQLt2Dv+YTk8X5efnJyMn24HcXGDnrlokJzPk5iYjPb0Wbo8413Jzk5GWWuOf7GVkZCA7m4ErykpWtvFxpqYGxoBZm4dDdnYdGOO+c6EGycmwlZEyN9f6fA6H3NzA99pacXxOhzi+tDRxHaqsCmTcS05myG2BkOMgJ0dck3Jygse5FdU1gfM1O0dcdzIzRf9mZDiQkeFFXp4LnIsx40oWE/Hc3Fz/mGvZMgXV1YG+Usk26dvMzDrU1HqRmyv2GQmVldprTX6+K+i5qLY2sE1urrjeS44c1V6Hs7IcqKzy+rYNjDfGvMjIMElpqSDHUl6+y3Dcdu5Uh0OHvf7rjUu5xmVmijGWlRl8b0hxBYTk/HyXJoZVRoboq9xclyZRiB3kvTEvz/z6xxyBY8/NtR5b+utjbq4bJaUepKcx5OW5kGeQOCVUGXrOOYvjm2/rUF3DfduJa9yYkz3IaeFAeXnwtS093YGyci8yMnyZi31lZ2fXobomOGhWQUEKMjNqNCEE1Po4nNrxoK7r1LEOWVlMM84AIEd3b2jRIgkZGW4kJ4txF9yuYh8Z6WK7UM8UVpDYRRBRZP8Bjjvv4UhLBZ59miE3lybjBBENkpIY7rpTZIP5x0yOI0c4nnwcyDaKY0EQcUBwHCFu+kDdtWtXdO3a1f//oEGDcPDgQcyePdsvdoVbpqSyshKAyKhUWSlizRQXM1RWcpSWAYcOAceOiXUAUFxchcpK7v9eVSmm5SUllf6JVE0N928vOXpUZBCT+0ixEKMrKoN/X1ERqENdnahjixyOLVsD25SVie1Ky4S1kzwW6+MXx1JaWumfJFX4llVVIqgeeoqLg1O5qe2jJzWFo2UhkNsCWLsuEGtGwr1A8XGx37JybburaNo4IwPHj5egslK4IBkds/84Syo1llQVFaKc6urg3xX15CgrA0pKTNLV6UhyijFjdNyAcb9yb/D2VVViu0ql/VvkAPl5DGVlQKWvzg4Wun8lap907cJx8CBQXV2FaoOgy+XlwfU0I8kpsuIlJzMUF4tlZWUctXVAeRng9Yj61dVxHD0q6ltWxlFZJRI/FBczlJdz1LnF5LqyshLJSdpxl5xkfJzqGDBr83CoqOD+c+bECeEayxDawK28zPp8jhS3W+3nKhw7HjjetFTgRLVwDU1NCz0O5DivqLA/ZoDAWAOAkmLA6RDXxsoqoKRE9FNpaaX/XD3hALKzs1BcXKwZc0bXRMC8b+UYLC01t5QMRXGxdp+lpZVBVpRlylivqtLWRX8eqNdgdbxVVNg7X/r1AXbsAirKKw3L6dyJo03rwPXmxAl1fAPFLRhKSo2vIVUnAseotleXzhw7dwHV1ZWoqbHfjrm5uf57o9X1r9yi/fTo7wvyOud0RHYdM6PqBPdn8S0pqUR1tbD4qq0Bygzab/PmwDleVRko26hfk5xifUWlSIrSpbNI8qDWp7ZW/C7FBQwcoF3Xswd89Q8+LnVf5b7zyeEQ5ejbR7b7dz8Etjdqw1xVPTeB3BgJIkocP85x510c7jrgmb8ztG5Nk3CCiCaMMVxxGcMjDzKs3wDcchvHvn3k0kjEF7m5uXA6nTh6VOsjdOzYMRQUFNguZ8CAAdi1a5f//4KCgqAyjx8/brtM6RagBmHmXDxMqsGo9Ui3CHU+ZuQSd6Lafswuo7hWquuEFNVSdRmcHI5ANkZ9nUJhld48XDq0s8r0x9C9m8gIaRRjhXPjODOh6iZjF4WKXaJfL9vKyBUpJSWQ8c0OQwaL+F5292223549RCD8Qumy6QQGD1LKqacbY3o6Q9eu5j8OJ9hxn97wWwRKPAbxhVJSzN0Y9QHqw8nkF03UmF2yX3r2bPh6mJVbq8TqSvfFxnJ7tLHtzDByjbODOj45fJNs39xd9iNjAc8tr9f4umE3Q2k0OaETco36SV2kPxf1LoDhZGMcNCB4WcuWwKgR5sm4pCuq0f7854ivTqNGBNbJ5BoMwcJgTg7DgP6xSwCmCf4e5v1CxlaM9hiQgf7btQVSU7XHbeTGyE2+GzWZ/tqYlha8D5eLYeJ44JSxDNnZ9to9yOVWujGGiNnl/59idhFE41JezvHHeziOHweensbQuRMJXQQRKyZNZHhuunjLfuMUjpWrSPAi4geXy4U+ffpg4cKFmuWLFi3CoEGDTH4VzMaNG1Eo1QAAAwcODCrzxx9/tCyza5fAE2OQ2MWCsywZ4TQQu4wmn7W1gfTwoR7u9dZOgJjQtCwMBIA2KkdmnrKK72KG0WQoUrGrVy+GSRND71yNySXxepXYZFaxYnTHXm1b7NIWKgM72w2MbIXTaR2D1Kg9jZa5XCIQvhxbwjUseBIc7iQxKzP0NkDoiVOSMxDE2zDgs0Fsm5SUQIB6Txgxu4DwslXWB6aYcbnrhNVGxw6hx3GsBJsgsUvxlMvJCezXKJOoWVnhah76spf8FPju8QQEllBZ+exmKI0mWVna/w1jdinL9C8PQmUttSrXyCWvPseqJnVITQEyMwOFyUx+9cnIFynqsduNXSapb+xBM2S/FeQHr8vLBdq2QVBsLyOsRKZQMeXU67UdjOJvWtVD39YUoJ4gGpGqKo4/38+xazcw9QmG3ieR0EUQsWZAf4ZZMxhy84A77uL45FMSvIj44brrrsMHH3yADz74ANu2bcOTTz6JAwcO4NJLLwUATJ8+HX/605/828+ZMwdfffUVdu7ciS1btmD69OmYP38+rrzySv82V199NRYuXIhZs2Zh27ZtmDVrFhYvXoxrrrnGtB4n9UryCwBBogNDkItXmm4yBACdOolP9WHTbPIpXU1CiUhdOkMbGBpCYBjQn+HkUYF7aCgLikiD1ub5PB9iPXkyEgU5h19wsAr8rRetZF+FK9Cl+PpUxkCKJUZ1s5ogmk0CI+nXieMDgc9DEWrilJMTsF40qqO82+jFLi8X7ozS8sLrBTye4HuTfv/hZKusD4yJ8VdXx+Hl5vuNdT0kcoy3aRO8LjMT6NhBbhe6rEiyMQLaPuS68EUaq5MQFj7htpEd685QtG7FNJaWhtZNyqJWLbWr/IkykqzrYiyiMYwepdtVmAejbi7d8rzewHVx5HCRCVOKXXoLyYYm0qyUUbfs8n0aZzVm6NObIcV3bkvrMineqy8E7FhURSs5hVqu0xk68H+Q2FWPezXF7CKIelBdLYSu9euBRx9mGDqEhC6CaCjatWWY+TLw8GMc0/7OsWMnx61TWNhvnAgi2px99tkoLi7GK6+8gsOHD6Nnz56YNWsW2vnybR85cgQHDhzwb19XV4dp06bh0KFDSE1NRffu3TFr1iyMGzfOv83gwYPxzDPP4LnnnsMLL7yADh064Nlnn8WAAQb+JAYYvbGtUdyGRgyD3yVh0IBAxsUunRm6dNaWpT6Idukstt20Gaj2TVhCPdy7XAzt2gEbNgWEACMBTV+O06l9aI70QXxAf2GJZhTPKZoYCT21dcDSZeI7A3DySPP26txJWGPs2Qvs9w2XcCdcnTsBOdlokBiidi27JKauXxFMbMK57ocan06ntaWQ0ykm3qogKa3FvN6AZZf83+j3Kg0tdsnz3q7YFUtReMK4QHv0KhLXEUAmNoAvHpONOjLtp100bow6XbKkNPBdLTaUBZVKqPO1vn0sLC3NX/ZJgSi3hVHMR/GZ5ASsjHzNjk24JBrve8ggoCJEnC+13CNHgZISDq5k3svKYsjKErHJGgu1jmG7McZIZeE2wgXI605mphjH2dkiG6R6PN26inNLHed66pMFUUW2RXoaMGyoiFcpMXSndGjHVn3cGEnsIogIqakRQtfqNcBDDzCMO4Um2ATR0GRkMDz1BPDKTI733gd27+F4+K9aE3iCaAyuuOIKXHHFFYbrnnrqKc3/kydPxuTJk0OWeeaZZ+LMM8+MqD56sQsITCJzsgNvzwGgoMD6/FEf4rt3EwG4gUAMmUjcA40mBkzxuxo2RCSqSHJyZX34+xH7YkhKApxOjtwWQKeOwIZNWrfOnGygY8fIyg/sx3o9Y7DM2NyjO0N1NceevYFl4VoJMMZsZQGLBhFbdunjufiOUS8+RAszi0GvEotL1sFIUB0xTAQvV5HbeTyKS5bXWOwKsuwysZTUT/jqiyxNjvMUmwkeozXhNUINqN6hPcPOnRzVNUKIS/eNHTtWif5+CrOuQiQN3cZa0SN4J+q1SsVuG8eKnGyR/OGkXsHr1PEOmJ9vVtecsaOBHxYGL8/LC/+6U1enteySZNp0T44F6j0mKcR9bcgg7fkeqbVhKOxYBUrXaSl2AcEZzNPTGYYNBY4c4di+QwhQ+tAG0RLc5X0gNVXUw8F40Do9I4cH3IrJjZEgGpiaGo77HuD4eSXwwH32YncQBBEbnE6G229x4N57GJavAG6+lQLXE4REb6GiPijL1OJDhwS7zVmhfziVFiJSPLP7YDpCcTsLJQxlGrhj1vfOm5IiLLILC1lQWcOGCjeh+qDPjKbHjmVacOyS+H3eiJpll26sRptQolx6uuKCZNDcGRkM7dppV6gBlyXFJcD3Pwb/Xl+mXQurqMAVscuuZVcDzhZlUtqUFDEpHjUCKLIRRL8+p4UMhm413NRJfzj7amyxKzmZYdhQZiiq690YzbByT9QHLw8HfbEyeYe+ffUJIhqScALU5+UxzYuiSFxVW+SE3sbKjdG/ja9v5TnuNoiTKSksZDjpJPG9Ti92Rencl0KhmvRBYtauWVnM3x71qQeJXQQRJrW1HH99iGPZcuD+PzOcflr8PngSRHPi3HMCgesnT+FYtpwEL4KQGAV7B0RcqXAFFP32LpcQnqpsujFK1ExOoYJQy/qqD8bRnITrJyTRzu7VslD8afZh43ehBDPJ4IHCLaUxMZo0h7KGMEKKPzK2WrQxzBrpq2fnTsI11x/HyG7mRN92VVXB69JStRN4/STVTgD2qGDixtiubYifNeBjbru2DKdNCoQjyMy0F5qgPpNh/YsAq22A8Nw6U03ELplENxpimD4Wl12MLLuGDRGu1Q2B/jyUlpANKa6Goj7ZGKVFYpvW9n8zdAgwaYL1NnbcGGUcvNa+fbdvZ12mjNepPwWi9W5Ftp1RJmWre4R/LNTjcZ7cGAkiDGpqOB56lGPREuDeexjOOpOELoKIJwYOYJg1A7j/ryJD6u+uB666Ir6tIQiiIQiylvH9b5QxMFwYY0h2cb/FSCRx8+y4/Imyg5dFg1hP6Af0Z9ixk+PwkcCycCd1VmJWfj5DvkF2robEaPIViUtrYaGwtomVO7pRu8t6tmolxrN0l7V765CbrV4bvK5rV2DbNmVbXZnOBpqNyZhdtbWiDaSQ2vskhn37zWeT8SQ+mFGf89f/W4sJtcaN0fd9QD/j/Y4dDZSXA6vWGGctBEQsxPbteFSslvr3i6wMf8yupMD/LVo03LOS3vXT4xV1MGrT8acYuwTHGo0oE+Z5mp7OcNqkcPfHQo5lOxZjXTozdOrI4XDYq4MYhxzpuiyOUbPs8rWd7EO7sdCcOouwiPYd+U8JonlRWSlcF1euAu7+I8O559DkmSDiERm4/u/Pcrw6WySQeOAvQHYWnbNEM0bvxuhbHInljRGu5MAkOhJCCWSxFrvq7RNpgyDhJMx9du0S39cwI9HGaoKYkiLcdrp30y5njMU0To8apyk5CahzB+opLQ/kOLM7xszGfWqKsOxgAHbsCi5z0EAgt4EEBsbEUdfUhmdR1JCWXY2JXbdZh+9a1bKlccOkpjKkpiKkyNCY7nmA/ZhdDYW07DISPxqrrepj2RUrjLLBGhHuS95RI4JdqqPV6nqxy267ntQLSNkhklVEvO/If0oQzYfiYo67/8yxdRvw4APCxJogiPglNZXhL/cCffsAz7/IccONHI88KN5gE0RzJCNdfEqXJfmwGa2MUSkpIvtWuOLZiOFAaWno7aRbYbjZGEePso5XEmtGDlfipunqm2gGp+FadjEmAiQ3Jif7xsf2Hb4FvplkRqYQwuzG0/IHo2YiOHReLnC8WLgTMcbQpg3g5U5s2Kjt94L8hrSkCVh22Y3XBTQNyy5JJMKc343RYhu1DRLlvNVbdjU23MKyKxaMHiWEXyvUfm/Q2Ho2iPZ5aWRJG619+F8mhGnZlZLCDJMrhLXv+v2cIBKfgwc57ryH48gRYNqTDCNHJMhdjiASHMYYLjxfBLd98GGOKbdxTL4BuPxScmskmh/JLmhe1EjhJdJ4QaNGaCclaT73h3AfjrOzGLKz7G8fboD69HR753qsrghZikWp/rJjd1I3oF8Sjh2LYqVihNGkOd4miHpcLgaXC+hVxJGZCbRoIZbntmAYP85+OXLcezmQnxc4rwzPh0a6/Uivsbq68Pol0S27YhmzK57J9Qmy8trd2JZdngaO2ZWezpCebr2N6moZiXt+LIlpO0UQXN8KvzuiUcyuGKtRTUirJ4iGZ8sWjptv4ygpAZ6bTkIXQTRFTurF8MZrYuIyY5aI5XX0GAWvJ5o38m4WqWtGZqY2w5ecMMV6YtxQAepjgd6yy+4+27d3omPH+H/+MOqP1NSGr4dd8pV4SklJDJ07scgTE+h+JoUDI2s3v6VfZHuKGGnZ5XbbF7m7dom/Sb4R9RFq7MTs0rhdJcjsuUtn4JQxjS92tSwU54K3gS27mjoNIQpGax/yvi0TM9i17IoGCXK6EkT0WbiI45bbOZxO4OUXGPr2oasvQTRVMjMZHv4rw/1/Zli/Hrj2eo4F33Lwxn6VSRAxJtTEIWpujD5LkViLG5oH4yYUoB6I3LKrqWA0aYlXsWvieBEvK1posi06AsKBkRWQX+xq4P6XNipud+jzvvdJwIRxQLeuCTZILWhull2MMaSksODkJQ3IhHFA/36iTb0eIXjF43UxOQ594aKdMdh4H9Eqh+GUMUCf3sHlkthFEA0M5xzvfyCC0XfuAsx6hcV9UFiCIELDGMPZZzHMfpWhTVvh2viXB8nKi2ieyIfNSN0Y9WT4Aoq3bx+d8szQWHY1sVuzfpLclGIh2cHhYBg1InhZPOJ01sOKywDVak9aUAHGfcyCvjQQvv3V1YUWu5zOQLbGRMcqZlfnTtptgKZ33QlFY4pLSUniPHQ6fNkYEX/XxSGDgFEjG7sWATrE+B6rEs2xkZLC/PcDcmMkiEbixAmOx57geOEljlPGAC8+y5DfgMFDCYKIPR07MMx4ieH2WxmW/gRceQ3HZ1+QlReRmOTkiE/9m2mvDE4cpbeq2VkM48YCrVvF9p6pPhg3tWyM8ZLNK5YYBTluDphZdlm6MTa0ZZci6oSyVGmqgk69AtQbPAL06C5WagLUNwG3zrBoRMsuicPhy8boiT/Lrrw8YQEXL/QqarhEabESHsmNkSAagV27OCZP4fhqAXDTZIZHH2ZITY2fixtBENHD6WT47SUMb73O0LMHMHWaiOW1Zy8JXkRi0aO7yAiYlqa9n3l8GQqj+VbV5Yr+PbP3SUDrVoH/1fhB0ZwUNcTdXi98xJsFQ7QZMayxa9BwqGMxlGWXHGyNGfspSWfRqZ9wJvrYDJdEjNkl8YcsiwOxi6PpCq2JSKyEx4a07IpDD1SCaHi++ppj2tMcaWkiEP3gQXSlJYjmQLt2DM8/A3z6GfDyPziuupbj4l9xXHsV02RRI4imisPBkGWQ7dDty4rkjPMnwXZtGdq1NV4XzQfx/HygvCJ65RmhFxTizYIh2mRnJ/gBKjATyy7VdVW19urWBWjZMnS5gwdGT3hSRQS9ZdfoUUBNDbB0mfi/yY3NmAeoD2TlczgB7g3epm/vphnPy042yljjF7u8wYk8iMYjVtcBp5OhZSFH2zaxjz0W5484BBFbyss5nnuRY/6XwMABwMMPMhSQ2yJBNCsYYzjvXGDMaGD2Gxz//gCYN4/jhuuB889tPnFLiOaF1zdZi8fAu3aJpvVJ925Au7bAwsXRK1OPvr5NTlAgTNG4uTFrN0YA6Goz8Hs0Q2mo400fqy8lhSElBfALOk1McGjTBjhyFOjUMfzf2hV7HEy4fzsdgNtA7GrTpmme0JmZQG4LoGfPyH7fr0/9hTIpdnm8ZNkVT8TyHjWgfwO5YjbIXggiDlm2nOOa6zm++hr43fUMz00noYsgmjO5uQx3/9GBN15j6N4deOY5jiuu5vhiHofbTe6NRGISaxeCpgJjDOnpsX0GaC6WXS0LgZ7dG7sWDYumL03cGFsWio2ysxuuXhpUVzyT817Wt6mNzeRkhiGD6xdbKeRd3ld0vCZdiBSnk2HoEIbsCK3ZW7dm9Rb6VMuupmgdl2hYumE3MRLgEAgiPEpKOJ58yos77+ZIzwBm/YPh2qsZWW8QBAFApFp/bjrD09OE+9cTT3FceS3HvC9J9CISj6YsdsXiQbxNa2HlEAv09U20SbNkQH+GTp0S89jMMHNjVF2yWrVyYtIENJqLvGplZpaYIj1NfDY1sas+hGPZBSSGABBvOByAxyMEx+Y09uIVmdgmEe5RTfgRhyDCw+vl+Hwe8MoMjqoq4JqrgKuvjK8MGwRBxAeMMYwaIQJ7L1oMzJ7D8fiTHLNfBy65GDjnbCAjg64dRNOnKYtdsZgU9e0Tu/O6OWRjbK6oAghjATdh/WhqzMmjOv7MxmJmJlBRKYSH5oJdsUtu1xzP46KewKFDQElpbMp3OIDaWvE90RIANEUGDQSqqhq7FtGhCT/iEIR9Nm3meOEljjVrRWyue/7Y/N46EgQRPowxjD4ZOHmUEL3+9T7HCy9zzJ4DnHcOx68vZsjNbexaEkTkNEWxKzUFqK5p7FqET3OcJDcXgiy7lO/xgnqum43FXkVAejqa1X3NH3w+lNjl68t46tOGomMHho4dgP99zU2tAuuDKnZRgPrGJymJNZ67dZRpgo84BGGfAwc4Xp3N8eVX4sb9l/sYzjw99pkfCIJILKToNfpkhk2bOd77twhk/+8POCZMKMfZZ3IMHZIYJt9E88LpbHpjdshg4MiRppc8Qs3oRiQW6mMlY0CKC6hAfAkjdiy7kpMZunVtmPrEE3bOTNnFzVm0HnNybI4/twVw+Ij4TpZdRDQhsYtISEpKOP75L44P/iMCHV57NXD5pbEPPksQROLTq4jhoQcYbr6R47//x/HFvDp89TVHm9bAeecCZ58FSnZBEDEkPZ2hU6fGrgVBBBAvOoRcwhjQtw9w9BiQlhY/9wI1KD299NXCmI2YXQ4heDXntovVeC4oADZvEd/JsouIJiR2EQnFsWMcc9/j+PBjYQ579pki02JBQfO9MREEERtatWSYchPD3Xe1wCefFuOTTzlmvSbieo0YwXHG6QxjTgbFBSQIgmhGOBjgcjG0bdPYNdESC/ezRMJOzC7KFBgbXK7A93iyhiSaPiR2EQnBgQPCrejjTwGPGzj9NOCqKxk6dqBJJkEQscWVzDBxvPjbt5/j08855n8JLHqEIyMDmDBOCF8D+pObI0EQRKITr5Ypzdn9LiQGfoyjR+k2YeRiFyvUsUmPSUQ0IbGLaNKsWy9Eru++F28CzjoTuPJyhnZt6UpJEETD064tw02/Y5h8Pceq1cD8/3Es+Bb49HOO1q2A007jOPM0SpBBND6njGleGdcIoqGI18k6iV3mMCa0Lql5ORwICn3CWGwywBLaeIZkPUdEExK7iCaH283x40LgvX9zrF0HZGUBV1wOXHwhuSsSBBEfOBwMgwcBgwcx/PEP4po1/0uOf/4TePsdjl5FHKefyjBhPFBYSNctouEh99rGoUtnYMfOxq4FEUvi1bJLJHSgBAlGMJAbY7wQr2Ix0TQhsYtoMhw5wvHJZ8Ann3IcOQq0bwf88Q6Gs86IrwCgBEEQKikpDJMmApMmMhQXc3y1QAhfL7zM8eIrwID+HBMnMEwYB+Tm0rWMIBKZ7t0YduwkwSGRocl600MGqPcHqjc4RcmNsWGgmF1ENCGxi4hrvF6O5SuADz/mWLgQ8HJg5Ajg7j8yjBzRNFOmEwTRfMnNZbjkYuCSixn27hUujl9/w/HMcxzPvQAMGsgxaSLDuLFATg5d3wgiEcnPA4pLGrsWRKwgV7emh77P0tODt3E4Qlt/EfWHxC4impDYRcQlxSUcX8wDPvqYY99+IDcXuPxy4PxzGNq0oacIgiCaPu3bM1x9JXD1lQw7d3F8vYBjwTfA3/7OMf1ZYNhQjkkTGMaOATIz6bpHEInC4EF0Picy8erGCACpKeKZmtCheHhmZQKDBhpswijuWUNAYhcRTUjsIuIGt5tj6U/AvC9FfJu6OmDwIODGyQynjAGSk+nhkCCIxKRzJ4YbrmO4/lqObduFtdfXC4AnnuJITgZGDBcWX6NHBQfNJQiCIOKHeHZjHDsmjivXiEiti3OgsNA4pmF2Nll2NQQkdhHRhMQuolHhnGPzL0Lg+uproKREvHH61YXA+edSxjKCIJoXjDF07ybi+tx4A8fmzUL4WvAt8ONCjpQUYNRIYfF18igKMk4QBBEvOByA1xvfll2EMTJWl8zIaETPHnS/bQhI7CKiCYldRKNw+DDHl18JkWvnTsDlAsaOAc48nWHYUJkxhiAIovnCGEOvXkCvXgxTbuLYsBH4agHHN98C337HkZYGjBkthK/hwwCXi66bBEEQjUVGOlBeEd+WXYQxjAmhEiCxsrGhmHdENCGxi2gwyso4vvm2Gh994sWKn8UblIEDgHvvYRg/jmLSEARBmOFwMPTtA/Ttw3D7LRxr1gqLr2+/A/73FUdmBjB2jHB1HDqEXhgQBEE0NOk+sau2trFrQoQNC7goktjSuFBcNCKakNhFxJTSUo4ffgQWfMux4mfA46lE+/bADdcxnHEaKNg8QRBEmDidDIMGAoMGMtxxO8fKVcDXCzi++wH4Yj5HTjZwyikcE8czDOhPFl8EQRANQccOwKHDIrYT0bRgUCy7GrUmBImNRDQhsYuIOgcPcSxeAnz/A8fPPwMeL9ChA3DFZcAF5+egZWEZGF3JCIIg6k1SknD9HjaU4a47OZav8MX4+gb45FOOtFRg6BCOUaMYRg4HWrakay9BEEQsaNGC4bRJjV0LIhLS04HSUvGdpiiNC8XsIqIJiV1EvXG7OdZvABYvESLXtu1ieccOwJVXABPGM3TrKuLP5OYmobiY7iIEQRDRJjmZYdRIYNRIhpoaYfG1ZCnHoiXADwuFf0a3bhyjRgAjhjP0PokC3BMEQRBEfh5w9Jj4TjG7GgeXS7gAk0EEEU1I7CLCxuvl2LYNWLES+Hklx+o1QGUlkJQkYnD9/laGUaOADu3pYkUQBNEYpKQwjBwBjBzB8IfbOfbsARYvFS8l/vU+8M4/OZKTgd4ncQzoDwwcIGKCpafTdZsgCIJoXmRmBr7TXbBxGDEMKC9v7FoQiQaJXURIyso4Nm0GNmwENm7iWLsOKCsT6zp1BE47FRg6WLjSZGTQLYIgCCKeYIyhY0egY0fgt5cwVFVxrFoNrF4jXla8Oxd46x0OxoBOnTiKegK9ihh69gA6dwJycui6ThAEQSQuScmB72RY1DikpjKkpjZ2LYhEg8Quwo/Hw7H/ALB9B7BjB7B9B8cvvwB794n1jImJz7ixwKBBDIMHAgUFdEcgCIJoSqSnM5w8Cjh5lLh+V1cLV/S164DNmzlWrgTmf8n927fI4ejQQbzc6NCBobAQKCwQfwUF4gGVIAiCIJoqSWoGQLqlEUTCQGJXM4JzjhMngIMHgf0HgQMHgAMHOA4cEP/v2RNIl8wY0K4t0L0bcN65DCf1AnoVkYsLQRBEopGayjBkMDBkMCCf8o8f59iyFdi1G9i9h2P3buEG+ennPOj3mRkcWdlAVhaQlRn4dLmA5GQg2QW4kpn47vtzOMR9xsHEJ3OIPauxUpxOYOxoIC2N7jsEQRBE7HAqYpeDbjkEkTCQ2NVEkcJVRQVQUSliZvm/VwDFJcCx4xzHj0P8FYvP6mptOenpQJs2QNvWwle6SxeGrl3EG3x6W08QBNE8yctjGDEcGDEcUF9znzjBceQocPQocOSICOh79ChHeYWItVFeDuzaJe5HtbVAnRuoqwPq6oJFMjs8+jDDxPFROSSCIAiCMCRJmRGTGyNBJA4kdjUCnHPU1AiBqrJKEaoqxPfyCqCykqPCt7zSJ2JVKJ9VlYDHa74PxoDcFkBunsgw0r49kJcL5OYytGolxK02bYDsbMp6QRAEQdgjLY2hYweRbTdA6HsI5xx1dYDbLUQwzgGvF+AAuNf3Pxff5S3J6QQKC+n+RBAEQcQWp5NB3JG08bsIgmjakNgVBm43R9UJITRVVQWEqsoqsUz8H9hGrq9SP6tCC1UA4HSIzCAZmUBmhvjeurVwDcnw/Z+ZyZCRoSzLCPwmKxNISqJJAkEQBNH4MMbgcgnXxvT0xq4NQRAEQRiT4mrsGhAEES3iSuyqq+MoKwu86ZVvedU3vpp1XPedA5mZbhQXc7il64QbqPO5UrjrAi4VtbXAiRNATQ3HiWqg+gRQXSPc/E6cEJ/VNb7l1UKk0rsAGsEYkJEOpGeIz4wM8WCfny++B5YJoSo9LSBOqcJVaipZXBEEQRAEQRAEQTQUlBGQIBKHuBK77rmXY/mK+pZSGtbWrmQgNU1c2NJSgRTfZ2amyDQl/8/IADIymBCydGKWFLfS04C0NBKpCIIgCIIgCIIgmhrJyTSPI4hEIa7ErpsnM2we78vOpGZqcvgyYzDjdQyBzE5ZWZmoqqoIZIFKEr7XyUkiI5T835UsBC7ho00QBEEQBEEQBEEQBEEkAnEldvXqxdCrV/3KyM11obiYBCyCIAiCIAiCIAgiNC0LRZgbgiASh7gSuwiCIAiCIAiCIAiiIRnQn4wlCCLRcDR2BQiCIAiCIAiCIAiCIAgiWpDYRRAEQRAEQRAEQRAEQSQMJHYRBEEQBEEQBEEQBEEQCQOJXQRBEARBEARBEARBEETCwDjnvLErQRAEQRAEQRAEQRAEQRDRgCy7CIIgCIIgCIIgCIIgiISBxC6CIAiCIAiCIAiCIAgiYSCxiyAIgiAIgiAIgiAIgkgYSOwiCIIgCIIgCIIgCIIgEgYSuwiCIAiCIAiCIAiCIIiEgcQugiAIgiAIgiAIgiAIImGIa7Hr3XffxcSJE9GvXz/86le/wvLly239bsWKFejduzcuuOCCoHVlZWV45JFHMGbMGPTr1w9nnXUWvvvuu2hXvVkQi/6ZM2cOzjjjDPTv3x/jxo3Dk08+iZqammhXPeEJp2+WLl2KoqKioL9t27Zptps/fz7OPvts9O3bF2effTb+97//xfowEpZo98/777+Pyy+/HMOGDcOwYcNw7bXXYs2aNQ1xKAlJLM4fyWeffYaioiLccsstsap+QhOLvknU54JI79FE7Jk5cyYuvvhiDBo0CKNGjcItt9yC7du3a7bhnOPFF1/EmDFj0L9/f1x11VXYsmWLZpva2lo89thjGDFiBAYOHIibb74ZBw8ebMhDIXzMnDkTRUVFeOKJJ/zLqA+bBocOHcLdd9+NESNGYMCAAbjggguwbt06/3rqx/jH7Xbj2WefxcSJE9G/f39MmjQJL730Erxer38b6sdmCo9TPvvsM96nTx/+/vvv861bt/LHH3+cDxw4kO/bt8/yd2VlZXzSpEn8+uuv5+eff75mXU1NDf/Vr37FJ0+ezJcvX8737t3Lly1bxjdu3BjLQ0lIYtE/H330Ee/bty//+OOP+Z49e/gPP/zAR48ezZ944olYHkrCEW7fLFmyhPfs2ZNv376dHz582P/ndrv92/z888/8pJNO4jNmzOBbt27lM2bM4L179+arVq1qqMNKGGLRP3/84x/5O++8wzds2MC3bt3K7733Xj5kyBB+8ODBhjqshCEW/SPZu3cvHzt2LL/88sv5lClTYn0oCUcs+iZRnwsivUcTDcP111/P//Of//BffvmFb9y4kd944418/PjxvLKy0r/NzJkz+aBBg/j8+fP55s2b+R133MFHjx7Ny8vL/ds8+OCDfOzYsXzhwoV8/fr1/KqrruLnn3++4fWHiB2rV6/mEyZM4Oeddx5//PHH/cupD+OfkpISPmHCBH7vvffy1atX8z179vBFixbxXbt2+behfox/XnnlFT58+HD+zTff8D179vAvvviCDxw4kM+ZM8e/DfVj8yRuxa5f//rX/MEHH9QsO/PMM/nf//53y9/dcccd/Nlnn+UvvPBCkJjyz3/+k0+aNInX1tZGvb7NjVj0zyOPPMKvvvpqzbKpU6fyyy67LDqVbiaE2zdyQlhaWmpa5h/+8Ad+ww03aJZdf/31/M4776x/hZsZsegfPW63mw8aNIj/3//9X32q2iyJVf+43W5+6aWX8vfff5//+c9/JrErAmLRN4n6XBDpPZpoHI4dO8Z79uzJf/rpJ845516vl48ePZrPnDnTv01NTQ0fMmQInzt3LudcvDzs06cP/+yzz/zbHDx4kPfq1Yt///33DXsAzZiKigp++umn84ULF/Irr7zSL3ZRHzYNnn76act5BvVj0+DGG2/k9913n2bZbbfdxu+++27OOfVjcyYu3Rhra2uxfv16jBkzRrN89OjRWLlypenv/vOf/2D37t247bbbDNcvWLAAAwcOxKOPPoqTTz4Z5557LmbMmAGPxxPV+ic6seqfIUOGYP369X73qz179uC7777D+PHjo1b3RCfSvgGACy+8EGPGjME111yDJUuWaNatWrUqqMyxY8eGLJPQEqv+0XPixAm43W7k5OTUu87NiVj2z8svv4y8vDxccsklUa1zcyFWfZOIzwX1aSuicSgvLwcA/zV77969OHLkiKYPXS4Xhg0b5u/DdevWoa6uDqNHj/Zv06pVK/To0YP6uQF59NFHMW7cOJx88sma5dSHTYMFCxagb9+++P3vf49Ro0bhwgsvxPvvv+9fT/3YNBgyZAiWLFmCHTt2AAA2bdqEFStWYNy4cQCoH5szSY1dASOKi4vh8XiQn5+vWV5QUIAjR44Y/mbnzp2YPn063n33XSQlGR/Wnj17sGTJEpx33nmYNWsWdu3ahUcffRRut9tUgCGCiVX/nHPOOTh+/Dguv/xycM7hdrtx2WWX4cYbb4z6MSQqkfRNYWEhHnvsMfTp0we1tbX46KOPcO211+Ltt9/GsGHDAABHjx4NKjM/P9+0TMKYWPWPnunTp6NVq1ZBD9+ENbHqnxUrVuCDDz7Ahx9+GOtDSFhi1TeJ+FwQSVsRjQfnHFOnTsWQIUPQs2dPAPD3k1Ef7t+/H4C4LycnJwe91CgoKMDRo0cboObEZ599hg0bNuCDDz4IWkd92DTYs2cP5s6di+uuuw4333wz1qxZg8cffxwulwsXXngh9WMTYfLkySgvL8dZZ50Fp9MJj8eDO++8E+eeey4AOh+bM3EpdkkYY5r/OedBywDA4/Hgrrvuwu23344uXbqYlsc5R35+Ph577DE4nU707dsXhw8fxuzZs5vsQ21jEu3+Wbp0KWbMmIGHHnoI/fv3x+7du/HEE0/g5Zdfxq233hr1+icydvsGALp27YquXbv6/x80aBAOHjyI2bNna8SUcMokrIlF/0heffVVfPbZZ3jrrbeQkpIS3Yo3E6LZPxUVFbjnnnvw2GOPIS8vL6b1bg5E+9xJ5OcCumY3DR599FH88ssv+Oc//xm0zqgPQ2FnG6L+HDhwAE888QRef/11y3st9WF8wzlH37598cc//hEA0Lt3b2zduhVz587FhRde6N+O+jG++fzzz/Hxxx9j+vTp6N69OzZu3IipU6eiZcuWuOiii/zbUT82P+JS7MrNzYXT6QxSUY8dO4aCgoKg7SsrK7Fu3Tps3LgRjz32GADA6/WCc47evXtj9uzZGDVqFAoLC5GUlASn0+n/bdeuXXHkyBHU1tbC5XLF9sAShFj1z/PPP4/zzz/f7+ZTVFSEqqoqPPjgg5gyZQocjrj0uo0rwu0bMwYMGICPP/7Y/7/RW43jx4+HVSYRu/6RzJ49GzNnzsQbb7yBXr161bu+zY1Y9M+ePXuwb98+TJkyxb9eZgfq3bs35s2bh44dO0ah9olNrM6dRHwuiFZbEbHnsccew4IFC/DOO++gdevW/uWFhYUAhKVBy5Yt/cvVPiwoKEBdXR1KS0s1lgjHjh3DoEGDGugImi/r16/HsWPH8Ktf/cq/zOPxYNmyZXj33Xcxb948ANSH8U5hYSG6deumWda1a1fMnz/fvx6gfox3/va3v+HGG2/EOeecA0DMIffv34+ZM2fioosuon5sxsSleuByudCnTx8sXLhQs3zRokWGgy0zMxOffPIJPvzwQ//fpZdeii5duuDDDz/EgAEDAACDBw/G7t27NWlId+7cicLCwib5QNtYxKp/qqurgwQtp9MJLhIpxO6AEohw+8aMjRs3+m8MADBw4MCgMn/88Ue6+IdJrPoHAF577TW88soreO2119CvX7+o1Le5EYv+6dq1a9D1b+LEiRgxYgQ+/PBDzQSXMCdW504iPhdEq62I2ME5x6OPPoovv/wSb775Jjp06KBZ3759exQWFmr6sLa2FsuWLfP3Yd++fZGcnKzZ5vDhw9iyZQv1cwMwcuTIoGt73759cd555+HDDz9Ehw4dqA+bAIMHD/bHeZLs3LkT7dq1A0DnYlOhuro6yGpLziEB6sfmTFxadgHAddddhz/96U/o27cvBg0ahPfeew8HDhzApZdeCkDEpDl06BD+9re/weFw+OMcSPLz85GSkqJZftlll+Htt9/GE088gSuvvBK7du3CzJkzcdVVVzXosSUCseifCRMm4I033kDv3r39bozPP/88Jk6cqHnrTlgTTt8AwJw5c9C+fXt0794ddXV1+PjjjzF//ny8+OKL/jKvvvpqXHnllZg1axYmTZqEr7/+GosXLzZ0uyCsiUX/vPrqq3j++ecxffp0tGvXzh+bID09HRkZGQ1/kE2YaPeP/joHANnZ2QAQtJywJhbnTqI+F4RqK6JxeeSRR/Dpp5/ilVdeQUZGhv+anZWVhdTUVDDGcPXVV2PmzJno3LkzOnXqhJkzZyI1NdUfgyYrKwsXX3wxpk2bhtzcXOTk5GDatGno2bMnxWtsADIzM4Ou4enp6WjRooV/OfVh/HPNNdfgsssuw4wZM3DWWWdhzZo1eP/99/Hoo48CAJ2LTYQJEyZgxowZaNu2rd+N8Y033sDFF18MgPqxORO3YtfZZ5+N4uJivPLKKzh8+DB69uyJWbNm+ZX2I0eO4MCBA2GV2aZNG7z++uuYOnUqzj//fLRq1QpXX301Jk+eHItDSGhi0T9TpkwBYwzPPfccDh06hLy8PEyYMAF33nlnLA4hYQm3b+rq6jBt2jQcOnQIqamp6N69O2bNmuXPYAKIN1/PPPMMnnvuObzwwgvo0KEDnn32Wb9VHmGfWPTP3LlzUVdXh9///veafd122224/fbbG+bAEoRY9A8RHWLRN4n6XBCqrYjGZe7cuQAQJKpOnTrV7xY3efJk1NTU4JFHHkFpaSkGDBiA119/HZmZmf7t77//fiQlJeGOO+5AdXU1Ro0ahaeeeopeEMYJ1IfxT//+/fHSSy/hmWeewcsvv4z27dvj/vvvx/nnn+/fhvox/nnggQfw/PPP45FHHsGxY8fQsmVL/Pa3v9XEfKZ+bJ4wTv5hBEEQBEEQBEEQBEEQRIIQlzG7CIIgCIIgCIIgCIIgCCISSOwiCIIgCIIgCIIgCIIgEgYSuwiCIAiCIAiCIAiCIIiEgcQugiAIgiAIgiAIgiAIImEgsYsgCIIgCIIgCIIgCIJIGEjsIgiCIAiCIAiCIAiCIBIGErsIgiAIgiAIgiAIgiCIhIHELoIgCIIgCIIgCIIgCCJhILGLIAiCIAiCIAiCIAiCSBhI7CIIgiAIgiAIgiAIgiASBhK7CIIgCIIgCIIgCIIgiITh/wEep/KXrbQIBwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for name, trace in zip(names, traces):\n", " ax = az.plot_trace(trace, var_names=\"tau\")\n", " ax[0, 0].axvline(0.5, label=\"True value\", color=\"k\")\n", " ax[0, 0].legend()\n", " plt.suptitle(name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to avoid comparing sampler effectiveness in terms of raw samples per second. If a sampler works quickly per sample but generates highly correlated samples, the effective sample size (ESS) is diminished. Since our posterior analyses are critically dependent on the effective sample size, we should examine this latter quantity instead.\n", "\n", "This model includes $500\\times 10=5000$ probability values for the 500 Dirichlet random variables. Let's calculate the effective sample size for each of these 5000 entries and generate a histogram for each sampling method:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "arviz - WARNING - Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)\n", "arviz - WARNING - Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summaries_p = []\n", "for trace, model in zip(traces, models):\n", " with model:\n", " summaries_p.append(az.summary(trace, var_names=\"p\"))\n", "\n", "[plt.hist(s[\"ess_bulk\"], bins=50, alpha=0.4, label=names[i]) for i, s in enumerate(summaries_p)]\n", "plt.legend(), plt.xlabel(\"Effective sample size\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, we see that while the mode of the ESS histogram is larger for the full NUTS run, the minimum ESS appears to be lower. Since our inferences are often constrained by the of the worst-performing part of the Markov chain, the minimum ESS is of interest." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum effective sample sizes across all entries of p:\n", "{'Partial conjugate sampling': 435.0, 'Full NUTS': 288.0}\n" ] } ], "source": [ "print(\"Minimum effective sample sizes across all entries of p:\")\n", "print({names[i]: s[\"ess_bulk\"].min() for i, s in enumerate(summaries_p)})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we can see that the conjugate sampling scheme gets a similar number of effective samples in the worst case. However, there is an enormous disparity when we consider the effective sampling *rate*." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum ESS/second across all entries of p:\n", "{'Partial conjugate sampling': 0.016398236944603167, 'Full NUTS': 2.757810535058409}\n" ] } ], "source": [ "print(\"Minimum ESS/second across all entries of p:\")\n", "print(\n", " {\n", " names[i]: s[\"ess_bulk\"].min() / traces[i].posterior.sampling_time\n", " for i, s in enumerate(summaries_p)\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The partial conjugate sampling scheme is over 10X faster in terms of worst-case ESS rate!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a final check, we also want to make sure that the probability estimates are the same for both samplers. In the plot below, we can see that estimates from both the partial conjugate sampling and the full NUTS sampling are very closely correlated with the true values." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", "axes[0].scatter(\n", " summaries_p[0][\"mean\"],\n", " p_true.ravel(),\n", " s=2,\n", " label=\"Partial conjugate sampling\",\n", " zorder=2,\n", " alpha=0.3,\n", " color=\"b\",\n", ")\n", "axes[0].set_ylabel(\"Posterior estimates\"), axes[0].set_xlabel(\"True values\")\n", "\n", "axes[1].scatter(\n", " summaries_p[1][\"mean\"],\n", " p_true.ravel(),\n", " s=2,\n", " alpha=0.3,\n", " color=\"orange\",\n", ")\n", "axes[1].set_ylabel(\"Posterior estimates\"), axes[1].set_xlabel(\"True values\")\n", "\n", "[axes[i].set_title(n) for i, n in enumerate(names)];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Authors\n", "\n", "* This notebook was written by Christopher Krapu on November 17, 2020.\n", "* This notebook was updated by Chris Fonnesbeck to use PyMC v5 on December 22, 2024." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last updated: Sun Dec 22 2024\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.5\n", "IPython version : 8.27.0\n", "\n", "pymc : 5.19.1\n", "numpy : 1.26.4\n", "matplotlib: 3.9.2\n", "arviz : 0.19.0\n", "\n", "Watermark: 2.5.0\n", "\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -n -u -v -iv -w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":::{include} ../page_footer.md\n", ":::" ] } ], "metadata": { "kernelspec": { "display_name": "default", "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": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 4 }