{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "41ebfdf9-72f9-43b3-9373-fffad6eb49ac", "metadata": {}, "outputs": [], "source": [ "using Distributed # For multiprocessing\n", "if nprocs() < 8 # If not started with multiple processes, we add some\n", " addprocs(8-nprocs(); enable_threaded_blas=false)\n", "end;" ] }, { "cell_type": "code", "execution_count": 2, "id": "002cd541-3c41-446a-8092-f6a48de22ac5", "metadata": { "tags": [] }, "outputs": [], "source": [ "import CSV\n", "import TypedTables: Table, columnnames\n", "import Printf: @sprintf, @printf # For converting numbers to strings\n", "import LinearAlgebra: BLAS\n", "# The BFGS optimizations use BLAS operations, but they are individually small so it's actually more efficient to run BLAS single-threaded.\n", "BLAS.set_num_threads(1)" ] }, { "cell_type": "markdown", "id": "affc1ba4-b8c1-43cc-9dfb-b3256faa5445", "metadata": { "tags": [] }, "source": [ "Load the isochrone table using `CSV.read` into a `TypedTables.Table` structure. This contains an isochrone table from PARSEC that has 26 steps in [M/H] and 71 steps in `logAge=log10(age [yr])` for a total of 1846 unique isochrones. For this example, this isochrone table actually has more steps in `logAge` than we want to use, so we will only use a subset of the available isochrones. In particular, the spacing of `ΔlogAge=0.05` dex results in the most recent time bins (`logAge < 8`) having too little stellar mass to measure robustly." ] }, { "cell_type": "code", "execution_count": 3, "id": "93643d0d-4f2e-40c3-b8b7-c8e81e4e5bf8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Table with 50 columns and 709192 rows:\n", " Zini MH logAge Mini int_IMF Mass logL logTe ⋯\n", " ┌─────────────────────────────────────────────────────────────────────────\n", " 1 │ 2.4652e-5 -2.19174 6.6 0.1 1.17111 0.1 -1.436 3.5769 ⋯\n", " 2 │ 2.4652e-5 -2.19174 6.6 0.105145 1.2127 0.105 -1.411 3.5791 ⋯\n", " 3 │ 2.4652e-5 -2.19174 6.6 0.109821 1.24828 0.109 -1.387 3.5809 ⋯\n", " 4 │ 2.4652e-5 -2.19174 6.6 0.124 1.34512 0.124 -1.321 3.5865 ⋯\n", " 5 │ 2.4652e-5 -2.19174 6.6 0.129464 1.37867 0.129 -1.298 3.5885 ⋯\n", " 6 │ 2.4652e-5 -2.19174 6.6 0.140418 1.4407 0.14 -1.252 3.5923 ⋯\n", " 7 │ 2.4652e-5 -2.19174 6.6 0.149107 1.4856 0.149 -1.219 3.595 ⋯\n", " 8 │ 2.4652e-5 -2.19174 6.6 0.183261 1.63379 0.182 -1.1 3.6048 ⋯\n", " 9 │ 2.4652e-5 -2.19174 6.6 0.197245 1.68445 0.196 -1.057 3.6085 ⋯\n", " 10 │ 2.4652e-5 -2.19174 6.6 0.207292 1.71805 0.206 -1.029 3.6111 ⋯\n", " 11 │ 2.4652e-5 -2.19174 6.6 0.210274 1.72762 0.209 -1.021 3.6119 ⋯\n", " 12 │ 2.4652e-5 -2.19174 6.6 0.225868 1.77492 0.225 -0.977 3.6159 ⋯\n", " 13 │ 2.4652e-5 -2.19174 6.6 0.246817 1.83218 0.246 -0.925 3.621 ⋯\n", " 14 │ 2.4652e-5 -2.19174 6.6 0.261505 1.86868 0.261 -0.893 3.6246 ⋯\n", " 15 │ 2.4652e-5 -2.19174 6.6 0.285683 1.92332 0.285 -0.84 3.6305 ⋯\n", " 16 │ 2.4652e-5 -2.19174 6.6 0.307343 1.96739 0.307 -0.796 3.6357 ⋯\n", " 17 │ 2.4652e-5 -2.19174 6.6 0.33773 2.02284 0.337 -0.737 3.6427 ⋯\n", " 18 │ 2.4652e-5 -2.19174 6.6 0.355357 2.05211 0.355 -0.705 3.6465 ⋯\n", " 19 │ 2.4652e-5 -2.19174 6.6 0.366732 2.07002 0.366 -0.685 3.6492 ⋯\n", " 20 │ 2.4652e-5 -2.19174 6.6 0.404464 2.12461 0.404 -0.62 3.6576 ⋯\n", " 21 │ 2.4652e-5 -2.19174 6.6 0.436853 2.16644 0.436 -0.569 3.6643 ⋯\n", " 22 │ 2.4652e-5 -2.19174 6.6 0.453571 2.18648 0.453 -0.544 3.6677 ⋯\n", " 23 │ 2.4652e-5 -2.19174 6.6 0.457007 2.19048 0.457 -0.539 3.6683 ⋯\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table = CSV.read(\"../../../../Work/Resources/isochrones/parsec/jwst/nircam_nov22/table.dat\", Table; comment=\"#\", delim=' ', ignorerepeated=true, \n", " header=[\"Zini\", \"MH\", \"logAge\", \"Mini\", \"int_IMF\", \"Mass\", \"logL\", \"logTe\", \"logg\", \"label\", \"McoreTP\", \"C_O\", \"period0\", \"period1\", \"period2\", \n", " \"period3\", \"period4\", \"pmode\", \"Mloss\", \"tau1m\", \"X\", \"Y\", \"Xc\", \"Xn\", \"Xo\", \"Cexcess\", \"Z\", \"mbolmag\", \"F070W\", \"F090W\", \"F115W\", \n", " \"F150W\", \"F200W\", \"F277W\", \"F356W\", \"F444W\", \"F150W2\", \"F322W2\", \"F140M\", \"F162M\", \"F182M\", \"F210M\", \"F250M\", \"F300M\", \"F335M\", \n", " \"F360M\", \"F410M\", \"F430M\", \"F460M\", \"F480M\"])" ] }, { "cell_type": "markdown", "id": "f4453708-dcec-4859-a337-8d1ee10da379", "metadata": {}, "source": [ "Check the available columns in the table" ] }, { "cell_type": "code", "execution_count": 4, "id": "7b453c29-3569-4d6d-a121-3d87df302d1d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(:Zini, :MH, :logAge, :Mini, :int_IMF, :Mass, :logL, :logTe, :logg, :label, :McoreTP, :C_O, :period0, :period1, :period2, :period3, :period4, :pmode, :Mloss, :tau1m, :X, :Y, :Xc, :Xn, :Xo, :Cexcess, :Z, :mbolmag, :F070W, :F090W, :F115W, :F150W, :F200W, :F277W, :F356W, :F444W, :F150W2, :F322W2, :F140M, :F162M, :F182M, :F210M, :F250M, :F300M, :F335M, :F360M, :F410M, :F430M, :F460M, :F480M)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columnnames(table)" ] }, { "cell_type": "code", "execution_count": 5, "id": "5b1f0e60-a550-4751-a30e-3f4f4596efd8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "71-element Vector{Float64}:\n", " 6.6\n", " 6.65\n", " 6.7\n", " 6.75\n", " 6.8\n", " 6.85\n", " 6.9\n", " 6.95\n", " 7.0\n", " 7.05\n", " 7.1\n", " 7.15\n", " 7.2\n", " ⋮\n", " 9.55001\n", " 9.60001\n", " 9.65001\n", " 9.70001\n", " 9.75001\n", " 9.80001\n", " 9.85001\n", " 9.90001\n", " 9.95001\n", " 10.00001\n", " 10.05001\n", " 10.10001" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unique(table.logAge) # These are `log10(age)`, where the age is in years." ] }, { "cell_type": "markdown", "id": "28ecab1d-69c8-4bb4-8ab0-e5eec8282e09", "metadata": {}, "source": [ "For the most metal-poor PARSEC isochrones (e.g., -2.2 [M/H]), there are multiple individual isochrones with the same [M/H] but different `Zini`. This can be seen as `length(unique(table.Zini)) > length(unique(table.MH))` below." ] }, { "cell_type": "code", "execution_count": 6, "id": "d36ef094-3948-40be-9cec-1b8d1ddbacc8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "26-element Vector{Float64}:\n", " -2.19174\n", " -2.1\n", " -2.0\n", " -1.9\n", " -1.8\n", " -1.7\n", " -1.6\n", " -1.5\n", " -1.4\n", " -1.3\n", " -1.2\n", " -1.1\n", " -1.0\n", " -0.9\n", " -0.8\n", " -0.7\n", " -0.6\n", " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " 0.0\n", " 0.1\n", " 0.2\n", " 0.3" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unique(table.MH) # These are metallicities, [M/H]" ] }, { "cell_type": "code", "execution_count": 7, "id": "61d75162-ffd1-4588-877b-ee18eda68985", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29-element Vector{Float64}:\n", " 2.4652e-5\n", " 3.1035e-5\n", " 3.9069e-5\n", " 4.9184e-5\n", " 0.00012351\n", " 0.00015547\n", " 0.0001957\n", " 0.00024632\n", " 0.00031003\n", " 0.00039019\n", " 0.00049103\n", " 0.00061788\n", " 0.00077741\n", " ⋮\n", " 0.0024432\n", " 0.0030686\n", " 0.0038518\n", " 0.0048313\n", " 0.0060543\n", " 0.0075779\n", " 0.0094713\n", " 0.011816\n", " 0.01471\n", " 0.018261\n", " 0.022594\n", " 0.027842" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unique(table.Zini)" ] }, { "cell_type": "markdown", "id": "b73ea4b3-50c3-4619-bbc9-f7dee8596655", "metadata": {}, "source": [ "For bookkeeping purposes, it is simplest for each isochrone to be identifiable by a unique [M/H], so we we will use the `Zini` values to calculate new [M/H] values. These are now *initial* metallicities. " ] }, { "cell_type": "code", "execution_count": 8, "id": "faa7cd65-752f-4fb0-8635-154a8a35a680", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29-element Vector{Float64}:\n", " -2.8000208992140845\n", " -2.7000110527475605\n", " -2.6000175983197713\n", " -2.500009794651941\n", " -2.100012070548602\n", " -2.0000162211614816\n", " -1.9000073196768041\n", " -1.8000170782347087\n", " -1.7000105737090876\n", " -1.600009209339709\n", " -1.5000150464034467\n", " -1.4000147226611754\n", " -1.3000117782255178\n", " ⋮\n", " -0.8000106239270766\n", " -0.7000142637673\n", " -0.6000162328319801\n", " -0.5000166309399301\n", " -0.40001148522876806\n", " -0.3000154566956319\n", " -0.20001378845202142\n", " -0.10003154653740932\n", " -4.821637332766436e-17\n", " 0.09998742559427602\n", " 0.19998649952690642\n", " 0.299992911269662" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: MH_from_Z\n", "# Rewrite [M/H] column. \n", "for i in eachindex(table)\n", " table.MH[i] = MH_from_Z(table.Zini[i], 0.01471)\n", "end\n", "unique(table.MH)" ] }, { "cell_type": "markdown", "id": "32a19a5f-43bb-4744-b9be-195185940352", "metadata": {}, "source": [ "## Observational Model \n", "\n", "We'll construct a model Hess diagram from the populations in this isochrone table. First we need to set up our observational models for photometric error and incompleteness. You would typically measure these from artificial star tests but we'll make up some models here using functions built into StarFormationHistories.jl." ] }, { "cell_type": "code", "execution_count": 9, "id": "1577b33d-fb20-40f2-b24d-9e2773bcc7d2", "metadata": { "tags": [] }, "outputs": [], "source": [ "import StarFormationHistories: Martin2016_complete, exp_photerr" ] }, { "cell_type": "code", "execution_count": 10, "id": "40c54453-9882-493d-ad23-5a3edac215f8", "metadata": { "tags": [] }, "outputs": [], "source": [ "distmod = 25.0 # Distance modulus \n", "@everywhere F090W_complete(m) = Martin2016_complete(m,1.0,28.5,0.7)\n", "@everywhere F150W_complete(m) = Martin2016_complete(m,1.0,27.5,0.7)\n", "@everywhere F090W_error(m) = min( exp_photerr(m, 1.03, 15.0, 36.0, 0.02), 0.4 )\n", "@everywhere F150W_error(m) = min( exp_photerr(m, 1.03, 15.0, 35.0, 0.02), 0.4 );" ] }, { "cell_type": "markdown", "id": "c98e8a43-e6b6-4d39-acf9-c7904be3bf06", "metadata": {}, "source": [ "We'll make a few plots showing the adopted models. Below we set up PyPlot.jl (a wrapper for Python's matplotlib package)." ] }, { "cell_type": "code", "execution_count": 11, "id": "f40a687d-fcbd-481d-8707-d107e7e96cf0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import PyPlot as plt\n", "import PyPlot: @L_str # For LatexStrings\n", "import PyCall: @pyimport\n", "plt.rc(\"text\", usetex=true)\n", "plt.rc(\"text.latex\", preamble=\"\\\\usepackage{amsmath}\") # for \\text\n", "plt.rc(\"font\", family=\"serif\", serif=[\"Computer Modern\"], size=14)\n", "plt.rc(\"figure\", figsize=(5,5))\n", "plt.rc(\"patch\", linewidth=1, edgecolor=\"k\", force_edgecolor=true)\n", "# https://matplotlib.org/stable/gallery/images_contours_and_fields/interpolation_methods.html\n", "plt.rc(\"image\", interpolation=\"none\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "6a4b1a66-34cf-495e-8e9a-101edb617d3f", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(0.0, 0.41895850114873723)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmags=20.0:0.05:33.0\n", "\n", "fig,ax1=plt.subplots()\n", "ax1.plot(plotmags,F090W_complete.(plotmags),label=\"F090W\")\n", "ax1.plot(plotmags,F150W_complete.(plotmags),label=\"F150W\")\n", "ax1.set_xlabel(\"Apparent Magnitude\")\n", "ax1.set_ylabel(\"Completeness\")\n", "ax1.legend(loc=\"lower left\")\n", "\n", "plotmags=20.0:0.05:33.0\n", "fig,ax1=plt.subplots()\n", "ax1.plot(plotmags,F090W_error.(plotmags),label=\"F090W\")\n", "ax1.plot(plotmags,F150W_error.(plotmags),label=\"F150W\")\n", "ax1.set_xlabel(\"Apparent Magnitude\")\n", "ax1.set_ylabel(\"Photometric Error\")\n", "ax1.legend(loc=\"upper left\")\n", "ax1.set_ylim([0.0,ax1.get_ylim()[2]])" ] }, { "cell_type": "markdown", "id": "828b9bf7-4502-474b-9cab-1ab877bb63b8", "metadata": {}, "source": [ "We put a maximum on the magnitude error in the model because most of the time one would place a cut on the photometric catalog in either signal to noise (e.g., SNR>5) or magnitude error (e.g., error < 0.4). From a practical perspective, if we allow the photometric error model to grow too large, it will hurt the efficiency of `partial_cmd_smooth` for template creation later." ] }, { "cell_type": "markdown", "id": "6af00e21-3a97-4bc1-ad61-4138ca22d67d", "metadata": {}, "source": [ "## Template Construction\n", "\n", "We may now define our mock star formation history. We will use `partial_cmd_smooth` to construct the templates for a Hess diagram model with F150W on the y-axis and F090W-F150W on the x-axis, with an initial mass function model from InitialMassFunctions.jl. We'll construct all 1846 templates, one for every isochrone in our table, but this may not always be necessary." ] }, { "cell_type": "code", "execution_count": 13, "id": "b483e8fe-724d-4dc2-b648-c476ec4356f4", "metadata": { "tags": [] }, "outputs": [], "source": [ "# \n", "@everywhere import StarFormationHistories: partial_cmd_smooth, NoBinaries, RandomBinaryPairs\n", "@everywhere import InitialMassFunctions: Kroupa2001" ] }, { "cell_type": "code", "execution_count": 14, "id": "b79bdea6-d984-4457-8510-e93584b9d3d7", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Some additional setup\n", "edges = (range(-0.65, 1.75, length=100), range(distmod-7.0, distmod+3.0, length=100)) # The bin edges for the Hess diagrams (17,28)\n", "imf = Kroupa2001(0.08, 100.0) # Initial mass function model\n", "binary_model = RandomBinaryPairs(0.3) \n", "unique_logAge = unique(table.logAge)\n", "unique_MH = unique(table.MH)\n", "template_norm = 1e3 # The stellar mass of the populations in each template\n", "\n", "# Now define the subset of unique logAges for which isochrones are available\n", "# that we actually want to use for our example.\n", "# As stated above, the isochrone file we read in has a uniform grid spacing of \n", "# `ΔlogAge=0.05`. We will only use a spacing of 0.1 dex for logAge < 9 if isochrone_cut is true.\n", "isochrone_cut::Bool = false\n", "if isochrone_cut\n", " logAge_cutval = 9\n", " selection = vcat( findall( <=(logAge_cutval), unique_logAge)[begin:2:end],\n", " findall( >(logAge_cutval), unique_logAge) )\n", " unique_logAge = unique_logAge[selection]\n", "end" ] }, { "cell_type": "code", "execution_count": 15, "id": "683f1421-60b9-42d4-a8d5-d7a06230c790", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of final isochrones is: 2059" ] } ], "source": [ "@printf(\"Number of final isochrones is: %i\",length(unique_logAge) * length(unique_MH))" ] }, { "cell_type": "code", "execution_count": 16, "id": "912a394f-be4a-41e9-af43-2071cc96b14e", "metadata": { "tags": [] }, "outputs": [], "source": [ "# # Constructing the templates; single-threaded with push!\n", "# templates = Vector{Matrix{Float64}}(undef,0)\n", "# template_logAge = Vector{Float64}(undef,0)\n", "# template_MH = Vector{Float64}(undef,0)\n", "# # We don't strictly need to save the initial masses (m_ini) or the isochrone magnitudes (iso_mags), \n", "# # but we'll use them later in this example so we'll save them now. \n", "# template_mini = Vector{Vector{Float64}}(undef,0)\n", "# template_isomags = Vector{Vector{Vector{Float64}}}(undef,0)\n", "# for logage in unique_logAge \n", "# for mh in unique_MH\n", "# # Combination of a logage and an MH defines a unique isochrone.\n", "# # Pick out all entries in `table` that match this combination; these will be row indices\n", "# # into the table of the stars in this specific isochrone.\n", "# local good = findall( (table.MH .== mh) .& (table.logAge .== logage) )\n", "# # Chop off the last entry in the isochrone because its the 30 mag weird thing that parsec does.\n", "# local m_ini = table.Mini[good][begin:end-1] # These are the initial masses of the stars in the isochrone, in solar masses.\n", "# push!(template_mini, m_ini)\n", "# local iso_mags = [table.F090W[good][begin:end-1], table.F150W[good][begin:end-1]] # These are the absolute magnitudes we want from the isochrone.\n", "# push!(template_isomags, iso_mags)\n", "# # Create template and push\n", "# push!(templates, partial_cmd_smooth( m_ini, iso_mags, [F090W_error, F150W_error], 2, [1,2], imf, [F090W_complete, F150W_complete]; \n", "# dmod=distmod, normalize_value=template_norm, edges=edges).weights )\n", "# push!(template_logAge, logage)\n", "# push!(template_MH, mh)\n", "# end\n", "# end" ] }, { "cell_type": "code", "execution_count": 17, "id": "2fd9ae3b-f4a0-4e90-993a-b1337481c96a", "metadata": {}, "outputs": [], "source": [ "# Constructing the templates; multi-threaded with pre-allocated arrays\n", "templates = Vector{Matrix{Float64}}(undef, length(unique_logAge) * length(unique_MH))\n", "template_logAge = Vector{Float64}(undef, length(templates))\n", "template_MH = Vector{Float64}(undef, length(templates))\n", "# We don't strictly need to save the initial masses (m_ini) or the isochrone magnitudes (iso_mags), \n", "# but we'll use them later in this example so we'll save them now. \n", "template_mini = Vector{Vector{Float64}}(undef, length(templates))\n", "template_isomags = Vector{Vector{Vector{Float64}}}(undef, length(templates))\n", "# Multi-threaded, nested loop iterating over unique_MH (outer loop) and unique_logAge (inner loop). \n", "# Using `enumerate` also gets us index `i` of iteration.\n", "Base.Threads.@threads for (i, (mh, logage)) in collect(enumerate(Iterators.product(unique_MH, unique_logAge)))\n", " # Combination of a logage and an MH defines a unique isochrone.\n", " # Pick out all entries in `table` that match this combination; these will be row indices\n", " # into the table of the stars in this specific isochrone.\n", " local good = findall( (table.MH .== mh) .& (table.logAge .== logage) )\n", " # Chop off the last star because its the 30 mag weird thing that parsec does.\n", " local m_ini = table.Mini[good][begin:end-1] # These are the initial masses of the stars in the isochrone, in solar masses.\n", " template_mini[i] = m_ini\n", " local iso_mags = [table.F090W[good][begin:end-1], table.F150W[good][begin:end-1]] # These are the absolute magnitudes we want from the isochrone.\n", " template_isomags[i] = iso_mags\n", " # Create templates\n", " templates[i] = partial_cmd_smooth(m_ini, iso_mags, [F090W_error, F150W_error], 2, [1,2], imf, [F090W_complete, F150W_complete]; \n", " dmod=distmod, normalize_value=template_norm, edges=edges, \n", " binary_model=binary_model).weights\n", " template_logAge[i] = logage\n", " template_MH[i] = mh\n", "end" ] }, { "cell_type": "code", "execution_count": 18, "id": "e259cfbb-14b6-474b-b057-62cfb9ee6dc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.10001 -2.8000208992140845\n" ] } ], "source": [ "# Write single isochrone to file\n", "# Some extra info here https://discourse.julialang.org/t/formatting-float64-output-with-csv-write/23479\n", "# let idx = length(unique_MH)*70 + findfirst(==(first(unique_MH)), unique_MH) # 1821\n", "let idx = length(unique_MH)*(findmin( x -> abs(exp10(x-9) - 12.59), unique_logAge)[2] - 1) + findfirst(==(first(unique_MH)), unique_MH) # 1821\n", " logage = template_logAge[idx]\n", " mh = template_MH[idx]\n", " println(logage, \" \", mh)\n", " # local good = findall((table.MH .== mh) .& (table.logAge .== logage))\n", " # open(\"isochrone.txt\",\"w\") do file\n", " # print(file, \"m_ini F090W F150W F277W\\n\")\n", " # for i in good[begin:end-1]\n", " # @printf(file, \"%.10f %.3f %.3f %.3f\\n\", table.Mini[i], table.F090W[i], table.F150W[i], table.F277W[i])\n", " # end\n", " # end\n", "end" ] }, { "cell_type": "code", "execution_count": 19, "id": "c7993fed-6e57-46b9-a8bc-8aa88a889e52", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Sort the template_logAge and template_MH so we have a guarantee for later\n", "permidx = sortperm(template_logAge)\n", "template_logAge = template_logAge[permidx]\n", "template_MH = template_MH[permidx]\n", "templates = templates[permidx];" ] }, { "cell_type": "markdown", "id": "20dcd127-b83b-4829-bcac-d52b960fa9f5", "metadata": {}, "source": [ "We'll plot a few templates for reference." ] }, { "cell_type": "code", "execution_count": 20, "id": "4eb63679-b3d4-42ae-a449-ae7e36af0638", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# When displaying the templates we will divide them by template_norm so that that units of the templates are\n", "# [N / M_sun], or expected number of stars per bin per solar mass of stars formed.\n", "# Plotting in absolute magnitude here rather than apparent on y-axis\n", "# display_templates = (100,1495,1821) # Before option of using isochrone_cut\n", "display_templates = ( length(unique_MH)*(findmin( x -> abs(x - 6.8), unique_logAge)[2] - 1) + \n", " findmin( x -> abs(x - (-0.1)), unique_MH)[2],\n", " length(unique_MH)*(findmin( x -> abs(exp10(x-9) - 2.82), unique_logAge)[2] - 1) + \n", " findmin( x -> abs(x - (-1.0)), unique_MH)[2],\n", " length(unique_MH)*(findmin( x -> abs(exp10(x-9) - 12.59), unique_logAge)[2] - 1) + \n", " findmin( x -> abs(x - (-2.8)), unique_MH)[2] )\n", " # findfirst(==(first(unique_MH)), unique_MH) )\n", "\n", "fig,axs=plt.subplots(1,3,sharey=true,sharex=true,figsize=(15,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.075)\n", "\n", "im1 = axs[1].imshow(permutedims(templates[display_templates[1]]) ./ template_norm, origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2] .- distmod)...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=1e-8), rasterized=true) \n", "im2 = axs[2].imshow(permutedims(templates[display_templates[2]]) ./ template_norm, origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2] .- distmod)...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=1e-8), rasterized=true) \n", "im3 = axs[3].imshow(permutedims(templates[display_templates[3]]) ./ template_norm, origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2] .- distmod)...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=1e-8), rasterized=true) \n", "# im1.set_clim( [0.5, im1.get_clim()[2]] )\n", "for ax in axs\n", " ax.set_xlabel(L\"F090W$-$F150W\")\n", "end\n", "axs[1].set_ylabel(\"F150W\")\n", "axs[1].set_ylim(reverse(extrema(edges[2])).-distmod) \n", "axs[1].set_xlim(extrema(edges[1]))\n", "# Label the templates with their ages and metallicities\n", "axs[1].text(0.95,0.95,(@sprintf \"Age: %.2f Gyr \\n [M/H]: %.1f\" exp10(template_logAge[display_templates[1]])/1e9 template_MH[display_templates[1]]), transform=axs[1].transAxes,ha=\"right\",va=\"top\")\n", "axs[2].text(0.1,0.95,(@sprintf \"Age: %.2f Gyr \\n [M/H]: %.1f\" exp10(template_logAge[display_templates[2]])/1e9 template_MH[display_templates[2]]), transform=axs[2].transAxes,ha=\"left\",va=\"top\")\n", "axs[3].text(0.1,0.95,(@sprintf \"Age: %.2f Gyr \\n [M/H]: %.1f\" exp10(template_logAge[display_templates[3]])/1e9 template_MH[display_templates[3]]), transform=axs[3].transAxes,ha=\"left\",va=\"top\")\n", "fig.colorbar(im1, ax=axs[1], pad=0.01)\n", "fig.colorbar(im2, ax=axs[2], pad=0.01)\n", "fig.colorbar(im3, ax=axs[3], pad=0.01)\n", "\n", "# Optionally, plot photometric error bars along the left. Errors are small in this model so they don't show very well.\n", "plot_errors::Bool = false\n", "if plot_errors\n", " # Set up points to show the photometric errors\n", " error_points = minimum(edges[2])+1.0:0.5:maximum(edges[2])-0.5\n", " error_points_x = repeat([minimum(edges[1])+0.15], length(error_points))\n", " \n", " # Plot the photometric error points\n", " for ax in axs\n", " ax.errorbar(error_points_x, error_points, yerr=F150W_error.(error_points), xerr=sqrt.(F090W_error.(error_points_x .+ error_points).^2 .+ F150W_error.(error_points).^2), ls=\"\") # , fmt='', ecolor=None, elinewidth=None, capsize=None\n", " end\n", "end\n", "\n", "# Optionally, overplot the isochrones themselves for comparison\n", "plot_isochrones::Bool = true\n", "if plot_isochrones\n", " alphas = (0.5,0.5,0.5)\n", " for (i,t_i) in enumerate(display_templates)\n", " local good = findall( (table.MH .== template_MH[t_i]) .& (table.logAge .== template_logAge[t_i]) )\n", " local iso_mags = [table.F090W[good][begin:end-1], table.F150W[good][begin:end-1]] # These are the absolute magnitudes we want from the isochrone. \n", " axs[i].scatter(iso_mags[1] .- iso_mags[2], iso_mags[2],marker=\".\", c=\"orange\", s=1, alpha=alphas[i])\n", " end\n", "end\n", "# plt.savefig(\"template_example.pdf\",bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "id": "195fa6ee-6810-4ea1-8391-15bf967eac30", "metadata": {}, "source": [ "## Building a Model Hess Diagram\n", "\n", "We may now use `construct_x0_mdf` and `calculate_coeffs` to create a mock star formation history with constant star formation rate and a reasonable metallicity evolution for a dwarf galaxy. We will use a `LinearAMR` model for the metallicity evolution with a `GaussianDispersion` model for the spread in metallicities at fixed time." ] }, { "cell_type": "code", "execution_count": 21, "id": "b9922650-38c6-45ce-b64c-8acf60deb156", "metadata": { "tags": [] }, "outputs": [], "source": [ "import StarFormationHistories: construct_x0_mdf, calculate_coeffs, LinearAMR, GaussianDispersion" ] }, { "cell_type": "code", "execution_count": 22, "id": "6052ae80-5598-4640-82e8-1999ddd817e1", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "71-element Vector{Float64}:\n", " 0.3546754852763317\n", " 0.39795243976854205\n", " 0.44650998135479464\n", " 0.5009924391101048\n", " 0.5621227621472895\n", " 0.6307121127124647\n", " 0.7076706298151519\n", " 0.7940195062200948\n", " 0.8909045390546783\n", " 0.9996113338407332\n", " 1.1215823636987226\n", " 1.2584361100895634\n", " 1.4119885390804652\n", " ⋮\n", " 316.1121376527206\n", " 354.68365207520725\n", " 397.9616030676007\n", " 446.5202627454242\n", " 501.00397502014556\n", " 562.1357056512223\n", " 630.726635562751\n", " 707.6869247211959\n", " 794.0377894053578\n", " 890.9250531259797\n", " 999.6343510072705\n", " 810.7874311842759" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set overall stellar mass of complex population and metallicity evolution variables\n", "stellar_mass = 1e7\n", "α, β, σ = 0.1, -1.87, 0.1 # Metallicity slope, intercept, and standard deviation at fixed time\n", "# max_logage sets the highest bin edge for the template_logAge\n", "# if template_logage was [6.6,6.7,6.8], I would set max_logAge=6.9, \n", "# representing the rightmost bin edge. In our case, our oldest template\n", "# is quite old, so we'll make it either the Hubble time or the maximum of \n", "# template_logAge plus the isochrone logage grid spacing, which is 0.05.\n", "# max_logAge = min(log10(13.7e9), maximum(template_logAge) + 0.05)\n", "T_max = min(13.7, exp10(maximum(unique_logAge)+ 0.05)/1e9)\n", "max_logAge = log10(T_max * 1e9)\n", "# Get initial guess for stellar mass coefficients.\n", "# We have to divide `stellar_mass` by `template_norm` here because `template_norm` is the total amount of stellar mass in each template, \n", "# since we passed `normalize_value=template_norm` when constructing the templates with `partial_cmd_smooth`. As such, when computing \n", "# coefficients, we need to normalize out the adopted `normalize_value` for the templates.\n", "x0_mdf = construct_x0_mdf(template_logAge, T_max; normalize_value=stellar_mass / template_norm)" ] }, { "cell_type": "code", "execution_count": 23, "id": "3924dea5-5ec9-4def-a509-83aad446ab4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0000000000000002e7" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check sum of x0_mdf * template_norm; should equal stellar_mass\n", "sum(x0_mdf) * template_norm" ] }, { "cell_type": "markdown", "id": "e6ff5afc-47bd-4c4e-9d7d-7aa06a0af66a", "metadata": {}, "source": [ "The above coefficients are the amount of stellar mass formed in each age bin of `unique(template_logAge)`. To construct the complex Hess diagram, we need individual per-template weights, which we can get with `calculate_coeffs`. To use this we construct `MHmodel` and `dispmodel` with the coefficients defined above. " ] }, { "cell_type": "code", "execution_count": 24, "id": "a280e026-ba10-4f83-bc4b-365adb17a74a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "2059-element Vector{Float64}:\n", " 2.0774487661255844e-116\n", " 1.2259099971069627e-106\n", " 2.651625167725607e-97\n", " 2.116285080371619e-88\n", " 3.8714448827814896e-57\n", " 2.0772592204779136e-50\n", " 4.1081211720955453e-44\n", " 2.9810605289610284e-38\n", " 7.975095776546787e-33\n", " 7.843403705649588e-28\n", " 2.8356607021243346e-23\n", " 3.773991167821277e-19\n", " 1.848166988977186e-15\n", " ⋮\n", " 3.479788626121187e-18\n", " 1.445217171479718e-22\n", " 2.20783391878389e-27\n", " 1.2406132585250893e-32\n", " 2.5626436899898873e-38\n", " 1.9497482102204813e-44\n", " 5.45289352873606e-51\n", " 5.628156079458227e-58\n", " 2.1193303072699266e-65\n", " 2.958180570282522e-73\n", " 1.5159136200760118e-81\n", " 2.853524813787374e-90" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MHmodel = LinearAMR(α, β, T_max)\n", "dispmodel = GaussianDispersion(σ)\n", "x0 = calculate_coeffs(MHmodel, dispmodel, x0_mdf, template_logAge, template_MH)" ] }, { "cell_type": "markdown", "id": "cf8c1697-2991-469e-91e1-053cd21e46c1", "metadata": {}, "source": [ "Let's plot the cumulative SFH and mean [M/H] of our model population." ] }, { "cell_type": "code", "execution_count": 25, "id": "945738c9-4ecb-4039-ac86-20f388b52d5d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "([6.6, 6.65, 6.7, 6.75, 6.8, 6.85, 6.9, 6.95, 7.0, 7.05 … 9.65001, 9.70001, 9.75001, 9.80001, 9.85001, 9.90001, 9.95001, 10.00001, 10.05001, 10.10001], [0.9999999999999997, 0.9999645324514721, 0.9999247372074952, 0.9998800862093598, 0.9998299869654488, 0.999773774689234, 0.9997107034779629, 0.9996399364149815, 0.9995605344643594, 0.9994714440104541 … 0.6741419731491223, 0.6343458128423622, 0.5896937865678199, 0.5395933890658052, 0.483379818500683, 0.42030715494440796, 0.3495384624722883, 0.2701346835317526, 0.1810421782191546, 0.08107874311842757], [0.0007301391778410239, 0.0007301391778410239, 0.0007301391778410242, 0.0007301391778410241, 0.0007301391778410241, 0.0007301391778410237, 0.0007301391778410241, 0.0007301391778410241, 0.0007301391778410242, 0.0007301391778410241 … 0.0007301391778410239, 0.000730139177841024, 0.000730139177841024, 0.000730139177841024, 0.000730139177841024, 0.0007301391778410241, 0.0007301391778410241, 0.0007301391778410241, 0.0007301391778410241, 0.0007301391778410239], [-0.5003989802960737, -0.500447558059937, -0.5005020632066869, -0.5005632189862201, -0.5006318368982151, -0.50070882746022, -0.5007952122896429, -0.5008921376599632, -0.5010008897110451, -0.50112291151538 … -0.9466925595632899, -1.0011998230888992, -1.0623590537122782, -1.1309764552665305, -1.2079609175110564, -1.2943433956348684, -1.3912720224823167, -1.500024267978308, -1.622041778524316, -1.758945498471188])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: calculate_cum_sfr\n", "unique_template_logAge, cum_sfr_arr, sfr_arr, mean_mh_arr = calculate_cum_sfr(x0, template_logAge, template_MH, T_max; normalize_value=template_norm)" ] }, { "cell_type": "code", "execution_count": 26, "id": "4d851844-a437-46d9-98f6-8a3e908464df", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject Text(457.3650849792141, 0.5, '$\\\\langle$[M/H]$\\\\rangle$')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=false,figsize=(10,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.35)\n", "\n", "axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(cum_sfr_arr, 0.0), label=\"Input SFH\" )\n", "\n", "axs[1].set_xlim([T_max,-0.1])\n", "axs[1].set_ylim([0.0,1.1])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "axs[2].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mean_mh_arr, β), label=\"Input SFH\" )\n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "9229fcf8-d85d-44a3-93ac-9cdb6fac1606", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9-element Vector{Float64}:\n", " 0.0007301391778410239\n", " 0.0007301391778410242\n", " 0.0007301391778410241\n", " 0.0007301391778410237\n", " 0.0007301391778410238\n", " 0.000730139177841024\n", " 0.0007301391778410244\n", " 0.0007301391778410243\n", " 0.0007301391778410236" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at the unique values of the SFRs\n", "unique(sfr_arr)" ] }, { "cell_type": "code", "execution_count": 28, "id": "a6cfda68-21bf-4add-a072-0767642ddb38", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,ax1 = plt.subplots()\n", "ax1.bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFH\")\n", "ax1.set_xlabel(L\"log$_{10}$(Age [yr])\")\n", "ax1.set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "ax1.set_ylim([0.0, ax1.get_ylim()[2]])\n", "ax1.legend()" ] }, { "cell_type": "markdown", "id": "8a9a2a22-cf33-4107-a0c9-8f566f8ea0c0", "metadata": {}, "source": [ "Now let's make a model Hess diagram with this SFH. We can simply write `composite = sum( coeffs .* models)` if we want, but we also provide the `composite!` function that is more efficient." ] }, { "cell_type": "code", "execution_count": 29, "id": "9d767186-dd61-4ee3-b8ce-357e1bae9f43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "99×99 Matrix{Float64}:\n", " 0.0 0.0 0.0 … 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 … 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 5.40348e-64 1.1489e-17 1.68644e-14 0.0 0.0\n", " 8.79792e-62 1.85824e-15 2.695e-12 0.0 0.0\n", " 9.51394e-9 6.42556e-8 1.85539e-7 0.0 0.0\n", " 4.08655e-7 2.63458e-6 4.70605e-6 … 1.36809e-60 5.05902e-67\n", " 9.09277e-6 3.88866e-5 5.59208e-5 3.30775e-27 4.88989e-28\n", " 9.4817e-5 0.000273266 0.00036055 8.82855e-17 1.02195e-19\n", " ⋮ ⋱ \n", " 0.093715 0.124497 0.169652 8.50135e-5 4.59165e-5\n", " 0.122256 0.162978 0.209807 5.83147e-5 3.40225e-5\n", " 0.158895 0.209166 0.254177 3.97421e-5 2.38911e-5\n", " 0.197845 0.256004 0.268525 … 2.62775e-5 1.58235e-5\n", " 0.214431 0.265278 0.240223 1.66153e-5 9.8891e-6\n", " 0.188775 0.22464 0.197867 9.97523e-6 5.84905e-6\n", " 0.143641 0.168571 0.208804 5.67619e-6 3.28961e-6\n", " 0.114504 0.13959 0.298203 3.07097e-6 1.77149e-6\n", " 0.102223 0.151286 0.407505 … 1.59535e-6 9.23375e-7\n", " 0.104158 0.205221 0.499652 8.12093e-7 4.74249e-7\n", " 0.109607 0.262972 0.53131 4.19252e-7 2.46846e-7\n", " 0.116197 0.281354 0.470248 2.29721e-7 1.35386e-7" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1 = sum( x0 .* templates)" ] }, { "cell_type": "code", "execution_count": 30, "id": "aec656ce-cbcb-4fb0-a20b-b8f87217c63c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "99×99 Matrix{Float64}:\n", " 0.0 0.0 0.0 … 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 … 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0\n", " 5.40348e-64 1.1489e-17 1.68644e-14 0.0 0.0\n", " 8.79792e-62 1.85824e-15 2.695e-12 0.0 0.0\n", " 9.51394e-9 6.42556e-8 1.85539e-7 0.0 0.0\n", " 4.08655e-7 2.63458e-6 4.70605e-6 … 1.36809e-60 5.05902e-67\n", " 9.09277e-6 3.88866e-5 5.59208e-5 3.30775e-27 4.88989e-28\n", " 9.4817e-5 0.000273266 0.00036055 8.82855e-17 1.02195e-19\n", " ⋮ ⋱ \n", " 0.093715 0.124497 0.169652 8.50135e-5 4.59165e-5\n", " 0.122256 0.162978 0.209807 5.83147e-5 3.40225e-5\n", " 0.158895 0.209166 0.254177 3.97421e-5 2.38911e-5\n", " 0.197845 0.256004 0.268525 … 2.62775e-5 1.58235e-5\n", " 0.214431 0.265278 0.240223 1.66153e-5 9.8891e-6\n", " 0.188775 0.22464 0.197867 9.97523e-6 5.84905e-6\n", " 0.143641 0.168571 0.208804 5.67619e-6 3.28961e-6\n", " 0.114504 0.13959 0.298203 3.07097e-6 1.77149e-6\n", " 0.102223 0.151286 0.407505 … 1.59535e-6 9.23375e-7\n", " 0.104158 0.205221 0.499652 8.12093e-7 4.74249e-7\n", " 0.109607 0.262972 0.53131 4.19252e-7 2.46846e-7\n", " 0.116197 0.281354 0.470248 2.29721e-7 1.35386e-7" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model2 = similar(model1)\n", "import StarFormationHistories: composite!\n", "composite!(model2, x0, templates)\n", "model2" ] }, { "cell_type": "code", "execution_count": 31, "id": "fcb096ff-4a03-4119-ba63-010857138d46", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1 ≈ model2" ] }, { "cell_type": "markdown", "id": "fc8e9b28-b772-4b13-89ac-98a182fcf71e", "metadata": {}, "source": [ "Let's plot the smooth model." ] }, { "cell_type": "code", "execution_count": 32, "id": "ce2d87a4-3b13-42ef-92c7-223deb160f2f", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,ax1=plt.subplots()\n", "\n", "im1=ax1.imshow(permutedims(model2), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5), rasterized=true) \n", "ax1.set_xlabel(L\"F090W$-$F150W\")\n", "ax1.set_ylabel(\"F150W\")\n", "ax1.set_ylim(reverse(extrema(edges[2]))) \n", "ax1.set_xlim(extrema(edges[1]))\n", "fig.colorbar(im1)" ] }, { "cell_type": "markdown", "id": "3d7ff261-5b9e-45f7-82dc-47d7c9c89b49", "metadata": {}, "source": [ "## Sampling Methods for CMDs and Hess Diagrams\n", "\n", "The templates are constructed such that the 2D histogram pixel values give the expected number of stars in that bin for a population with total stellar mass `normalize_value` (in our case we defined the variable `template_norm=1e3` and used that as our `normalize_value` for the templates). As such the pixel values of this complex model also represent expected numbers of stars per bin for our defined SFH. We may, therefore, simply Poisson sample it to obtain a reasonable observational realization of the Hess diagram:" ] }, { "cell_type": "code", "execution_count": 33, "id": "db0239d9-90d8-49a8-9bd9-81634becee2b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "99×99 Matrix{Float64}:\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " ⋮ ⋮ ⋱ ⋮ \n", " 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 1.0 0.0 1.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 1.0 0.0 0.0 1.0 3.0 1.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 1.0 0.0 0.0 0.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 2.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 3.0 1.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Distributions: Poisson\n", "import Random: AbstractRNG, default_rng\n", "function poisson_sample!(sample::T, model::S, rng::AbstractRNG=default_rng()) where {T <: AbstractArray{<:Number}, S <: AbstractArray{<:Number}}\n", " @assert axes(sample) == axes(model)\n", " for i in eachindex(sample, model)\n", " sample[i] = rand(rng,Poisson(model[i]))\n", " end\n", "end\n", "poisson_sample(model::AbstractArray{<:Number}, rng::AbstractRNG=default_rng()) = (sample = similar(model); poisson_sample!(sample, model, rng); return sample)\n", "model3 = poisson_sample(model2)" ] }, { "cell_type": "code", "execution_count": 34, "id": "e2b55299-0164-41aa-853f-6acb6ae0471d", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=true,figsize=(10,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.0)\n", "\n", "im1=axs[1].imshow(permutedims(model2), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5), rasterized=true) \n", "axs[1].text(0.1,0.9,\"Smooth\",transform=axs[1].transAxes)\n", "axs[2].imshow(permutedims(model3), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5,vmax=im1.get_clim()[2]), rasterized=true) \n", "axs[2].text(0.1,0.9,\"Poisson-Sampled\",transform=axs[2].transAxes)\n", "axs[1].set_xlabel(L\"F090W$-$F150W\")\n", "axs[2].set_xlabel(L\"F090W$-$F150W\")\n", "axs[1].set_ylabel(\"F150W\")\n", "axs[1].set_ylim(reverse(extrema(edges[2]))) \n", "axs[1].set_xlim(extrema(edges[1]))\n", "fig.colorbar(im1, ax=axs, pad=0.005)" ] }, { "cell_type": "markdown", "id": "5fa33827-24e8-4943-91f1-85a22f25a208", "metadata": {}, "source": [ "To sample the color-magnitude diagram (CMD) directly we could use `generate_stars_mass_composite`," ] }, { "cell_type": "code", "execution_count": 35, "id": "d3527a3e-f946-477e-8d21-10c009aaf205", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.258217 seconds (4.99 M allocations: 1.160 GiB, 2.10% gc time, 330.25% compilation time)\n" ] }, { "data": { "text/plain": [ "(Vector{StaticArraysCore.SVector{2, Float64}}[[[0.602836006504568, 0.0]], [[0.39302224462286917, 0.0]], [[0.23803436535211742, 0.0]], [[0.582718591113463, 0.0]], [[0.2460596471398033, 0.0]], [[0.5183840215795765, 0.0]], [[0.19349118370276422, 0.0]], [[0.14077858777398147, 0.0]], [[0.2343139581240549, 0.0]], [[0.10344269027686466, 0.0]] … [[0.2330929571602882, 0.0]], [[0.11220097719935143, 0.0]], [[0.23611223391744898, 0.0]], [], [[0.1448442646372922, 0.0]], [[0.19577632322532562, 0.10848111799421307]], [[0.5517387274199562, 0.0]], [[0.25371143407679136, 0.09157683493949993]], [[0.3935897584019476, 0.3043456637814438]], [[0.12497087468662814, 0.0]]], Vector{StaticArraysCore.SVector{2, Float64}}[[[29.75407066007857, 28.94842275578724]], [[30.43600238030495, 29.544724592386764]], [[31.183081682641994, 30.17837387704299]], [[29.810491848067613, 28.994536566272675]], [[31.159438690863233, 30.113012576540363]], [[30.023442500915433, 29.14105822191084]], [[31.538889923258857, 30.43178267205469]], [[31.991730156170455, 30.841211602781687]], [[31.288735429848266, 30.1654040070757]], [[32.42996938220384, 31.236934920780904]] … [[34.06020114831955, 32.8760097524788]], [[36.205513559757, 34.817908402151716]], [[34.172501392340315, 32.938177781026184]], [], [[35.633039223392366, 34.16878192226898]], [[34.7325216855572, 33.26128838100799]], [[31.910873178576573, 30.696055504848744]], [[34.37072335359247, 32.906487897924265]], [[32.90688840891596, 31.507070344019855]], [[36.70129516180108, 34.865867348381755]]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: generate_stars_mass_composite\n", "@time starcat = generate_stars_mass_composite(template_mini, template_isomags, [\"F090W\", \"F150W\"], stellar_mass, x0, imf; \n", " dist_mod=distmod, binary_model=binary_model)" ] }, { "cell_type": "markdown", "id": "88dde706-f9c1-4048-9e4c-2530a42023b9", "metadata": { "tags": [] }, "source": [ "We'll concatenate the per-population samples returned from `generate_stars_mass_composite` into a single `Vector{SVector}`. " ] }, { "cell_type": "code", "execution_count": 36, "id": "52cfc0bd-6487-43d6-b3c3-66e24448488a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "11861263-element Vector{StaticArraysCore.SVector{2, Float64}}:\n", " [29.75407066007857, 28.94842275578724]\n", " [30.43600238030495, 29.544724592386764]\n", " [31.183081682641994, 30.17837387704299]\n", " [29.810491848067613, 28.994536566272675]\n", " [31.159438690863233, 30.113012576540363]\n", " [30.023442500915433, 29.14105822191084]\n", " [31.538889923258857, 30.43178267205469]\n", " [31.991730156170455, 30.841211602781687]\n", " [31.288735429848266, 30.1654040070757]\n", " [32.42996938220384, 31.236934920780904]\n", " [31.72263584080963, 30.553042978165735]\n", " [30.490518477110953, 29.36310340972399]\n", " [29.170889106898827, 28.34224075628732]\n", " ⋮\n", " [32.185606416301624, 31.078823738979988]\n", " [34.8313039807046, 33.64098825127086]\n", " [35.6368099543945, 34.384924433089544]\n", " [34.06020114831955, 32.8760097524788]\n", " [36.205513559757, 34.817908402151716]\n", " [34.172501392340315, 32.938177781026184]\n", " [35.633039223392366, 34.16878192226898]\n", " [34.7325216855572, 33.26128838100799]\n", " [31.910873178576573, 30.696055504848744]\n", " [34.37072335359247, 32.906487897924265]\n", " [32.90688840891596, 31.507070344019855]\n", " [36.70129516180108, 34.865867348381755]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# starcat_mags = reduce(hcat,reduce(vcat,starcat[2])) # To make a 2D matrix\n", "starcat_mags = reduce(vcat,starcat[2])" ] }, { "cell_type": "code", "execution_count": 37, "id": "d28eb80e-5812-493c-a5e3-fd772e90bc40", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of sampled stars is 1.19e+07\n", "Stellar mass of sampled system at birth was 1.00e+07\n", "Present-day stellar mass of sampled system is 5.93e+06\n", "Absolute magnitude of sampled system is M_{F090W} = -13.30\n" ] } ], "source": [ "println(@sprintf(\"Number of sampled stars is %.2e\",length(starcat_mags)))\n", "println(@sprintf(\"Stellar mass of sampled system at birth was %.2e\",stellar_mass))\n", "println(@sprintf(\"Present-day stellar mass of sampled system is %.2e\",sum(reduce(vcat,starcat[1]))[1]))\n", "import StarFormationHistories: mag2flux, flux2mag\n", "println(@sprintf(\"Absolute magnitude of sampled system is M_{F090W} = %.2f\",flux2mag( sum( x -> mag2flux(first(x)), starcat_mags) ) - distmod))" ] }, { "cell_type": "markdown", "id": "8a6557a5-ecdc-4f9b-8c09-348999e786c8", "metadata": {}, "source": [ "The number of sampled stars is very large as we have not imposed a faint-end magnitude cut, which is supported through the `mag_lim` and `mag_lim_name` keyword arguments. However, the sampling is still very fast." ] }, { "cell_type": "markdown", "id": "ce6d273c-cde8-4d2d-8408-e94073ed9238", "metadata": {}, "source": [ "Now we'll mock observe the \"pure\" catalog with the same observational models we used to construct the templates using `model_cmd`." ] }, { "cell_type": "code", "execution_count": 38, "id": "ba1600c9-2729-472e-b23d-8f3d93fabcec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2×214975 Matrix{Float64}:\n", " 27.8238 28.9519 23.2301 26.6281 … 29.0557 27.0229 27.4408 21.6945\n", " 27.2647 28.1453 23.5305 26.232 28.6 26.2704 26.7373 20.5931" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Construct new / read cached catalog (for reproducibility)\n", "import StarFormationHistories: model_cmd\n", "read_obs_mags::Bool = true\n", "if read_obs_mags\n", " if binary_model isa NoBinaries\n", " obs_mags = CSV.read(joinpath(@__DIR__, \".ipynb_checkpoints/obs_mags.txt\"), Table; delim=' ', header=1)\n", " else\n", " obs_mags = CSV.read(joinpath(@__DIR__, \".ipynb_checkpoints/obs_mags_binaries.txt\"), Table; delim=' ', header=1)\n", " end\n", " obs_mags = permutedims([obs_mags.F090W obs_mags.F150W])\n", "else\n", " # Apply observational effects to \"pure\" catalog\n", " obs_mags = model_cmd( starcat_mags, [F090W_error, F150W_error], [F090W_complete, F150W_complete])\n", " # Concatenate into 2D matrix\n", " obs_mags = reduce(hcat,obs_mags)\n", " # Optionally write out to file; use .ipynb_checkpoints to prevent commit to github\n", " write_obs_mags::Bool = false\n", " if write_obs_mags\n", " CSV.write(joinpath(@__DIR__,\".ipynb_checkpoints/obs_mags_binaries.txt\"), Table(F090W=obs_mags[1,:], F150W=obs_mags[2,:]); delim=' ')\n", " end\n", "end\n", "display(obs_mags)" ] }, { "cell_type": "markdown", "id": "bbe08dca-ac72-4e10-ad08-8904216f83fe", "metadata": { "tags": [] }, "source": [ "Now we'll compute the Hess diagram with `bin_cmd` and compare with our smooth and Poisson-sampled versions. " ] }, { "cell_type": "code", "execution_count": 39, "id": "0ed75236-08cb-4e7a-bca9-f8bf3a5d2875", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "179044" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Total number of stars in the observed CMD space\n", "count( obs_mags[2,:] .< maximum(edges[2]) )" ] }, { "cell_type": "code", "execution_count": 40, "id": "0975e57e-ef82-4304-85da-6994142db7f1", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "99×99 Matrix{Float64}:\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " ⋮ ⋮ ⋱ ⋮ \n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 1.0 0.0 0.0 0.0 1.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: bin_cmd\n", "model4 = bin_cmd(view(obs_mags,1,:) .- view(obs_mags,2,:), view(obs_mags,2,:), edges=edges).weights" ] }, { "cell_type": "code", "execution_count": 41, "id": "35b9d228-2262-42dc-85bb-c9441347141f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=3,sharex=true,sharey=true,figsize=(15,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.0)\n", "\n", "im1=axs[3].imshow(permutedims(model2), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5), rasterized=true)\n", "axs[3].text(0.1,0.9,\"c) Smooth Model\",transform=axs[3].transAxes)\n", "axs[1].scatter(view(obs_mags,1,:) .- view(obs_mags,2,:), view(obs_mags,2,:), s=1, marker=\".\", c=\"k\", alpha=0.05, rasterized=true, label=\"CMD-Sampled\")\n", "axs[1].text(0.1,0.9,\"a) Sampled CMD\",transform=axs[1].transAxes)\n", "axs[2].imshow(permutedims(model4), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5,vmax=im1.get_clim()[2]), rasterized=true, label=\"CMD-Sampled\")\n", "axs[2].text(0.1,0.9,\"b) Sampled Hess Diagram\",transform=axs[2].transAxes)\n", "\n", "axs[1].set_xlabel(L\"F090W$-$F150W\")\n", "axs[2].set_xlabel(L\"F090W$-$F150W\")\n", "axs[3].set_xlabel(L\"F090W$-$F150W\")\n", "axs[1].set_ylabel(\"F150W\")\n", "axs[1].set_ylim(reverse(extrema(edges[2]))) \n", "axs[1].set_xlim(extrema(edges[1]))\n", "\n", "fig.colorbar(im1, ax=axs, pad=0.005)\n", "# plt.savefig(\"mcsample_example.pdf\",bbox_inches=\"tight\")\n", "# fig.colorbar(im1)" ] }, { "cell_type": "code", "execution_count": 42, "id": "7a164970-a20e-4f70-bf9b-7bc2b314f97f", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=3,sharex=true,sharey=true,figsize=(15,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.0)\n", "\n", "im1=axs[1].imshow(permutedims(model2), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5), rasterized=true) \n", "axs[1].text(0.1,0.9,\"Smooth Model\",transform=axs[1].transAxes)\n", "axs[2].imshow(permutedims(model3), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5,vmax=im1.get_clim()[2]), rasterized=true) \n", "axs[2].text(0.1,0.9,\"Poisson-Sampled\",transform=axs[2].transAxes)\n", "axs[3].imshow(permutedims(model4), origin=\"lower\", \n", " extent=(extrema(edges[1])..., extrema(edges[2])...), \n", " aspect=\"auto\", cmap=\"Greys\", norm=plt.matplotlib.colors.LogNorm(vmin=2.5,vmax=im1.get_clim()[2]), rasterized=true, label=\"CMD-Sampled\") \n", "axs[3].text(0.1,0.9,\"CMD-Sampled\",transform=axs[3].transAxes)\n", "\n", "axs[1].set_xlabel(L\"F090W$-$F150W\")\n", "axs[2].set_xlabel(L\"F090W$-$F150W\")\n", "axs[3].set_xlabel(L\"F090W$-$F150W\")\n", "axs[1].set_ylabel(\"F150W\")\n", "axs[1].set_ylim(reverse(extrema(edges[2]))) \n", "axs[1].set_xlim(extrema(edges[1]))\n", "fig.colorbar(im1, ax=axs, pad=0.005)" ] }, { "cell_type": "markdown", "id": "c0cda9b1-e1a8-4233-8240-04acf99a6b4f", "metadata": {}, "source": [ "The mock Hess diagram created by Poisson-sampling the smooth model (labelled \"Poisson-Sampled\") and the Hess diagram created by sampling stars via `generate_stars_mass_composite` and mock observing them with `model_cmd` (labelled \"CMD-Sampled\") are highly consistent. The Poisson-sampled method is more efficient if a Hess diagram is all that is required, but if you need individual stars (e.g., for forming a star catalog for injection into images) then you should go the CMD-sampled route." ] }, { "cell_type": "code", "execution_count": 43, "id": "9bfa581e-21dc-46e9-8e4f-adafbfda7392", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "99×99 Matrix{Float64}:\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " ⋮ ⋮ ⋱ ⋮ \n", " 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 1.0 0.0 0.0 0.0 1.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Choose which sampled Hess diagram to use for model fitting; choosing CMD-sampled instance here.\n", "data = model4" ] }, { "cell_type": "markdown", "id": "9206cc3e-9e5e-4b98-a50b-b3476bae832c", "metadata": {}, "source": [ "## SFH Fitting: Constrained Metallicity Evolution\n", "\n", "Given that we constructed our model population with a linear [M/H] AMR, it will be optimally recovered when fitting under the same general AMR model. This can be achieved by using the generic `fit_sfh` function with the `LinearAMR` metallicity model and the `GaussianDispersion` dispersion model to fit the SFH simultaneously with the AMR.\n", "\n", "Note that in real data, the parameter σ (which sets the width of the metallicity distribution function at fixed age) can be difficult to constrain. As such, it is often preferable to fix σ rather than fitting it. For the purposes of this example, we will allow σ to be fit to show it can be recovered robustly in the case of synthetic data." ] }, { "cell_type": "code", "execution_count": 44, "id": "3c2c7ad0-1e05-4558-9bd4-87546b2990db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StarFormationHistories.CompositeBFGSResult{StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, PDMats.PDMat{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}, StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, LinearAlgebra.Hermitian{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}}(StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, PDMats.PDMat{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}([2.850774072838053, 0.8885139642281051, 0.8586842430495469, 0.9985800064679068, 1.2684221807000682, 1.505945373414648, 2.5976083311258162, 2.154861156313259, 2.649083126744581, 2.4706015115739013 … 976.8268469572694, 348.35479851402687, 853.1626495076866, 766.7648029319765, 966.0888723410521, 873.8142165871213, 945.28862820743, 0.1003007979961255, -1.8706256011936075, 0.11460351512553132], [1.109062644613079, 0.8535570741360422, 0.8289434824461341, 0.9546698357907253, 1.1980402736061035, 1.417161914247759, 2.113961007817644, 1.984717522939562, 2.3448621016113065, 2.122752741484948 … 279.2318702716069, 193.14285198173874, 140.10379549981528, 146.96812089249374, 107.62954061901138, 73.79276670158454, 50.12389195255788, 0.000511866499438965, 0.005634723941002937, 0.0023396312734710466], [0.15135143907313653 -0.15337626370572194 … 2.781147860094574e-5 -0.00023181188605877277; -0.15337626370572194 0.9228616874188966 … -1.6261302428596908e-5 0.000229992634540546; … ; 2.781147860094574e-5 -1.6261302428596908e-5 … 3.175011389131167e-5 -2.1396190244436507e-5; -0.00023181188605877277 0.000229992634540546 … -2.1396190244436507e-5 0.0004167724074764922], * Status: success\n", "\n", " * Candidate solution\n", " Final objective value: 1.474753e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 6.96e-10 ≰ 0.0e+00\n", " |x - x'|/|x'| = 1.01e-10 ≰ 0.0e+00\n", " |f(x) - f(x')| = 2.27e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 1.54e-16 ≰ 0.0e+00\n", " |g(x)| = 8.01e-09 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 3 (vs limit Inf)\n", " Iterations: 114\n", " f(x) calls: 288\n", " ∇f(x) calls: 288\n", ", LinearAMR{Float64}(0.1003007979961255, -1.8706256011936075, 13.7, (true, true)), GaussianDispersion{Float64}(0.11460351512553132, (true,))), StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, LinearAlgebra.Hermitian{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}([4.503006051366787, 2.309907151940367e-23, 1.6217568662788475e-24, 6.139998279470846e-21, 8.509206679847138e-53, 4.7806033254260056e-64, 6.533423420804538, 2.4741048291067253e-45, 8.754625663304788, 1.8238830235398162 … 1333.4049688999933, 110.77712625978579, 890.3590046072976, 757.7130653654132, 978.433862196377, 873.6680517499103, 946.5902458076688, 0.100257361634675, -1.870423274970492, 0.11462347088972348], [1.0447303397810428, 2.4240757955799605e-14, 1.792283603836572e-15, 5.917455735482319e-12, 2.3554796524897527e-43, 1.627181204209429e-54, 4.377095065877323, 5.7428760920151245e-36, 8.916001021713885, 6.4907982118238845 … 367.53170532752455, 245.4780946919635, 148.89410285715286, 158.1954866514361, 112.33422083582451, 75.03212802649716, 50.565054788739026, 0.0005277096523322279, 0.0058090114106535175, 0.0024464765047667813], [0.053827393839936455 5.4653653567417385e6 … 3.744304244849339e-7 -0.00012401033319806772; 5.4653653567417385e6 1.1012941778095706e18 … -66592.69662337165 309332.8477563253; … ; 3.744304244849339e-7 -66592.69662337165 … 3.374461356910277e-5 -2.0525624213333692e-5; -0.00012401033319806772 309332.8477563253 … -2.0525624213333692e-5 0.0004557075474841134], * Status: success\n", "\n", " * Candidate solution\n", " Final objective value: 1.681799e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 1.21e+00 ≰ 0.0e+00\n", " |x - x'|/|x'| = 8.30e-03 ≰ 0.0e+00\n", " |f(x) - f(x')| = 4.55e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 2.70e-16 ≰ 0.0e+00\n", " |g(x)| = 9.93e-09 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 5 (vs limit Inf)\n", " Iterations: 159\n", " f(x) calls: 451\n", " ∇f(x) calls: 451\n", ", LinearAMR{Float64}(0.100257361634675, -1.870423274970492, 13.7, (true, true)), GaussianDispersion{Float64}(0.11462347088972348, (true,))))" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: fit_sfh, LinearAMR, GaussianDispersion\n", "mdf_α, mdf_β, mdf_σ = 0.15, -2.25, 0.2 # -0.15, -0.2, 0.2 # initial guess for the metallicity evolution parameters\n", "mdf_result = fit_sfh(LinearAMR(mdf_α, mdf_β, T_max),\n", " GaussianDispersion(0.2),\n", " templates,\n", " data,\n", " template_logAge, template_MH;\n", " x0=construct_x0_mdf(template_logAge, T_max; normalize_value=1e4))" ] }, { "cell_type": "markdown", "id": "e2263c26-6ef7-4acc-8c00-90c339d2a509", "metadata": {}, "source": [ "We will now calculate the per-template coefficients using `calculate_coeffs`, which we can use as input for other methods like `calculate_cum_sfr`, which computes cumulative SFHs, SFRs, and <[M/H]> from the fitted coefficients. We can use our result from `fit_sfh` directly in the call to `calculate_coeffs`. If we call `calculate_coeffs(mdf_result, ...)` it will use the MLE result for the best-fit values. We can alternatively pass call `calculate_coeffs(mdf_result.map, ...)` to use the MAP result instead." ] }, { "cell_type": "code", "execution_count": 45, "id": "edb1af7c-bda9-4742-8b3d-aebef7d57528", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([6.6, 6.65, 6.7, 6.75, 6.8, 6.85, 6.9, 6.95, 7.0, 7.05 … 9.65001, 9.70001, 9.75001, 9.80001, 9.85001, 9.90001, 9.95001, 10.00001, 10.05001, 10.10001], [0.9999999999999998, 0.9997223365797941, 0.9996357959350763, 0.9995521606845409, 0.9994548996835425, 0.9993313562416706, 0.9991846782053404, 0.9989316729530312, 0.9987217909688955, 0.9984637721071453 … 0.6822235721188221, 0.6297872944804435, 0.5911544532677411, 0.5581273304933472, 0.4629850721646168, 0.4290555560651026, 0.34595810491628587, 0.2712757441835005, 0.17717935714510769, 0.09207045766633874], [0.00586863745638014, 0.0016301919287618856, 0.0014041321211743748, 0.0014553161445431005, 0.0016475488817316382, 0.0017433464407234844, 0.002680082415934947, 0.001981498616478578, 0.0021710512085235975, 0.0018045843372968095 … 0.0009877350533333091, 0.0006485825521496454, 0.0004941733867156731, 0.001268767565839232, 0.0004032610513698563, 0.0008802303020104885, 0.0007050609307013549, 0.0007917381293718258, 0.000638239365265749, 0.0008512616689354861], [-0.49690460933667785, -0.4969533331715629, -0.4970080022129338, -0.49706934188555635, -0.4971381661293689, -0.49721538819995675, -0.4973020327868817, -0.49739924961066845, -0.497508328678869, -0.4976307174036449 … -0.9445412966864771, -0.9992127163361006, -1.060555004805502, -1.1293786844477323, -1.2065959651554437, -1.2932378469418548, -1.3904571670533703, -1.4995365618335161, -1.6219214403011222, -1.7591499050664972])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mdf_coeffs = calculate_coeffs(mdf_result.map, template_logAge, template_MH)\n", "mdf_coeffs *= template_norm\n", " \n", "_, mdf_cum_sfr_arr, mdf_sfr_arr, mdf_mean_mh_arr =\n", " calculate_cum_sfr(mdf_coeffs, template_logAge, template_MH, T_max)" ] }, { "cell_type": "markdown", "id": "9f83529b-e92b-4bff-b8be-099ed6244cac", "metadata": {}, "source": [ "Of additional note are the uncertainty estimates, available as `mdf_result.map.σ` and `mdf_result.mle.σ`. These are essentially standard errors derived from the diagonal of the covariance matrix of the fitting parameters (discussed more below). The `mdf_result.map` result contains the maximum a posteriori result and is often very comparable to the mode of posterior samples obtained via Hamiltonian Monte Carlo (provided by `sample_sfh`). We don't generally recommend the use of `mdf_result.mle.σ` because the estimate of the covariance matrix obtained during the BFGS fit can be poorly conditioned when best-fit SFRs are 0, but we provide it for completeness." ] }, { "cell_type": "code", "execution_count": 46, "id": "b9e7ffc0-0777-416d-b3e2-55594e48b1bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "74-element Vector{Float64}:\n", " 4.503006051366787\n", " 2.309907151940367e-23\n", " 1.6217568662788475e-24\n", " 6.139998279470846e-21\n", " 8.509206679847138e-53\n", " 4.7806033254260056e-64\n", " 6.533423420804538\n", " 2.4741048291067253e-45\n", " 8.754625663304788\n", " 1.8238830235398162\n", " 2.2735852230753333e-30\n", " 6.09274530204169e-22\n", " 8.949039764457536e-21\n", " ⋮\n", " 421.22221930344364\n", " 126.94594002529965\n", " 1333.4049688999933\n", " 110.77712625978579\n", " 890.3590046072976\n", " 757.7130653654132\n", " 978.433862196377\n", " 873.6680517499103\n", " 946.5902458076688\n", " 0.100257361634675\n", " -1.870423274970492\n", " 0.11462347088972348" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mdf_result.mle.μ" ] }, { "cell_type": "code", "execution_count": 47, "id": "0a655ae9-e026-4b19-b39d-7cec8d8e631e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "74-element Vector{Measurements.Measurement{Float64}}:\n", " 2.9 ± 1.1\n", " 0.89 ± 0.85\n", " 0.86 ± 0.83\n", " 1.0 ± 0.95\n", " 1.3 ± 1.2\n", " 1.5 ± 1.4\n", " 2.6 ± 2.1\n", " 2.2 ± 2.0\n", " 2.6 ± 2.3\n", " 2.5 ± 2.1\n", " 1.3 ± 1.3\n", " 1.3 ± 1.2\n", " 1.1 ± 1.1\n", " ⋮\n", " 400.0 ± 200.0\n", " 340.0 ± 200.0\n", " 980.0 ± 280.0\n", " 350.0 ± 190.0\n", " 850.0 ± 140.0\n", " 770.0 ± 150.0\n", " 970.0 ± 110.0\n", " 874.0 ± 74.0\n", " 945.0 ± 50.0\n", " 0.1003 ± 0.00051\n", " -1.8706 ± 0.0056\n", " 0.1146 ± 0.0023" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Measurements: uncertainty, ±\n", "mdf_result.map.μ .± mdf_result.map.σ" ] }, { "cell_type": "markdown", "id": "06f15be4-d029-436f-b778-7682c6e065a3", "metadata": {}, "source": [ "### Sampling from Inverse Hessian \n", "\n", "Another thing we can do is draw samples from the posterior approximating the posterior distribution as Gaussians and the inverse Hessian as its variance-covariance matrix. This is preferred to using the standard error uncertainties in `mdf_result.map.σ` as samples will include covariances. There are additional notes in the documentation on this approximation, but this sampling method often reproduces results from more expensive Monte Carlo sampling methods fairly well. In short, we are approximating the posterior (in the transformed fitting variables `θ = log(coeffs)`) as a multivariate Gaussian in the region of the MAP estimate. For a multivariate Gaussian, the inverse of the Hessian matrix (which the BFGS algorithm from Optim.jl computes for us) is an estimator for the variance-covariance matrix. In concert with the MAP estimate for the parameters, this gives us both the means μ and the variance-covariance matrix Σ needed to define our multivariate Gaussian approximation of the posterior, which we can then sample from using `MvNormal` from Distributions.jl.\n", "\n", "`N` samples can be drawn using the methodology described above by calling `rand(result, N)` where `result` is either a `CompositeBFGSResult` or a `BFGSResult`." ] }, { "cell_type": "code", "execution_count": 48, "id": "a460acba-94c8-41be-8b0c-b1c087e9375b", "metadata": {}, "outputs": [], "source": [ "import Statistics: mean, median, quantile" ] }, { "cell_type": "code", "execution_count": 49, "id": "826a6997-220f-4be0-981f-9c2239a3a861", "metadata": { "tags": [] }, "outputs": [], "source": [ "# # This is effectively what rand(mdf_result.map, 10000) is doing.\n", "# import Distributions: MvNormal\n", "# import LinearAlgebra: Hermitian\n", "# import Optim\n", "# mdf_dist = MvNormal(Optim.minimizer(mdf_result.mle.result),\n", "# Hermitian(Optim.trace(mdf_result.map.result)[end].metadata[\"~inv(H)\"]))\n", "# mdf_sample = rand(mdf_dist, 10000)\n", "\n", "# # Transform the variables, noting that the stellar mass coefficients `mdf_sample[begin:end-3,:]` \n", "# # and σ `mdf_sample[end,:]` are fit with logarithmic transformations so we have to transform them back.\n", "# # α and β are optimized directly, without a transformation.\n", "# @views mdf_sample[begin:end-3,:] .= exp.(mdf_sample[begin:end-3,:]) .* template_norm\n", "# @views mdf_sample[end-2,:] .= exp.(mdf_sample[end-2,:])\n", "# @views mdf_sample[end,:] .= exp.(mdf_sample[end,:])\n", "# mdf_sample" ] }, { "cell_type": "code", "execution_count": 50, "id": "59c5c477-0c83-4728-a324-e48c30f00b7a", "metadata": {}, "outputs": [], "source": [ "mdf_sample = rand(mdf_result.map, 10000)\n", "@views mdf_sample[begin:end-3,:] *= template_norm # Correct the stellar mass coefficients for the template normalization\n", "# Calculate the cumulative SFH for each point in the sample\n", "# and find the 1-σ range\n", "mdf_cum_sfr = Vector{Vector{Float64}}(undef,0)\n", "mdf_sfr = Vector{Vector{Float64}}(undef,0)\n", "mdf_mh = Vector{Vector{Float64}}(undef,0)\n", "for x in eachcol(mdf_sample)\n", " tmp_coeffs = calculate_coeffs(LinearAMR((@view x[end-2:end-1])..., T_max), GaussianDispersion(x[end]), @view(x[begin:end-3]), \n", " template_logAge, template_MH)\n", " _, mdf_1, mdf_2, mdf_3 = calculate_cum_sfr(tmp_coeffs, template_logAge, template_MH, T_max)\n", " push!(mdf_cum_sfr, mdf_1)\n", " push!(mdf_sfr, mdf_2)\n", " push!(mdf_mh, mdf_3)\n", "end\n", "mdf_cum_sfr = reduce(hcat, mdf_cum_sfr) \n", "mdf_sfr = reduce(hcat, mdf_sfr) \n", "mdf_mh = reduce(hcat, mdf_mh) \n", "\n", "# Now calculate quantiles\n", "mdf_cum_lower = quantile.(eachrow(mdf_cum_sfr), 0.16)\n", "mdf_cum_med = median.(eachrow(mdf_cum_sfr))\n", "mdf_cum_upper = quantile.(eachrow(mdf_cum_sfr), 0.84)\n", "mdf_sfr_lower = quantile.(eachrow(mdf_sfr), 0.16)\n", "mdf_sfr_med = median.(eachrow(mdf_sfr))\n", "mdf_sfr_upper = quantile.(eachrow(mdf_sfr), 0.84)\n", "mdf_mh_lower = quantile.(eachrow(mdf_mh), 0.16)\n", "mdf_mh_med = median.(eachrow(mdf_mh))\n", "mdf_mh_upper = quantile.(eachrow(mdf_mh), 0.84);" ] }, { "cell_type": "markdown", "id": "e7105901-b13c-41b3-bacd-b166f5afcf04", "metadata": {}, "source": [ "We can look at the sampled values for the AMR slope α, intercept β, and MDF width σ:" ] }, { "cell_type": "code", "execution_count": 51, "id": "ec80d39d-5192-4eda-a318-ed66d7e2a1de", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.1, -1.87, 0.1)\n" ] }, { "data": { "text/plain": [ "3×10000 Matrix{Float64}:\n", " 0.0992832 0.100068 0.101225 … 0.0998089 0.100756 0.100894\n", " -1.85934 -1.86814 -1.88047 -1.86539 -1.87355 -1.87784\n", " 0.110859 0.114228 0.112381 0.110796 0.113548 0.11746" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "println((α, β, σ))\n", "mdf_sample[end-2:end,:]" ] }, { "cell_type": "markdown", "id": "0685f66d-cb49-43e4-ad34-8e6ee360b6a5", "metadata": {}, "source": [ "Note that `cum_sfr_quantiles` is available to simplify the above process of calculating SFH quantiles from samples." ] }, { "cell_type": "markdown", "id": "423a6482-a2ee-4e43-8c07-ea358e4f1bde", "metadata": { "tags": [] }, "source": [ "We can look at the marginal distributions of the metallicity variables using the \"corner\" python package:" ] }, { "cell_type": "code", "execution_count": 52, "id": "59db177a-855f-40fa-a419-90795e3df8c9", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@pyimport corner # Easy marginal distributions\n", "fig=plt.figure(figsize=(7,7))\n", "# fig.suptitle(\"Linear M/H\")\n", "corner.corner( permutedims(@view mdf_sample[end-2:end,:]),\n", " fig=fig,\n", " labels=[L\"\\alpha\", L\"\\beta\", L\"\\sigma\"],\n", " quantiles=[0.16,0.5,0.84],\n", " max_n_ticks=4,\n", " show_titles=false,\n", " title_kwargs=Dict(\"fontsize\"=>17),\n", " label_kwargs=Dict(\"fontsize\"=>25))\n", "fig.subplots_adjust(hspace=0.0, wspace=0.0)" ] }, { "cell_type": "markdown", "id": "3ffabed6-f019-4f34-9950-4534bcbfb115", "metadata": {}, "source": [ "We see that the inverse Hessian has captured some covariance between the parameters. Now we'll plot the cumulative SFH and metallicity evolution." ] }, { "cell_type": "code", "execution_count": 53, "id": "8a63d181-7d1c-44a3-b2b1-84dff2441054", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now plot cumulative SFH and MH evolution for the metallicity evolution model\n", "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=false,figsize=(10,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.35)\n", "\n", "axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_cum_sfr_arr,0.0), label=\"Result\" ) # This is MAP result\n", "# axs[1].plot( exp10.(unique_template_logAge)./1e9, mdf_cum_med, label=\"Result\" ) # This is median of samples\n", "axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(cum_sfr_arr,0.0), label=\"Input SFH\", ls=\"--\" )\n", "axs[1].fill_between( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_cum_lower,0.0), vcat(mdf_cum_upper,0.0), alpha=0.3, fc=\"k\")\n", "\n", "axs[1].set_xlim([T_max,0.0])\n", "axs[1].set_ylim([0.0,1.05])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "axs[2].plot( vcat( exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_mh_med, mdf_result.map.μ[end-1]), label=\"Result\" )\n", "axs[2].fill_between( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_mh_lower, mdf_result.map.μ[end-1]), vcat(mdf_mh_upper, mdf_result.map.μ[end-1]), alpha=0.3, fc=\"k\")\n", "axs[2].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mean_mh_arr, β), label=\"Input AMR\", ls=\"--\" )\n", "\n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")\n", "axs[2].legend()\n", "# plt.savefig(\"example_cumsfh.pdf\", bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "id": "81616642-1c3d-43de-8899-f318cac7f743", "metadata": {}, "source": [ "The above plots show that we are able to measure the cumulative SFH and age-metallicity relation simultaneously and that they are statistically consistent with the input model. We will now look at the SFRs in each log(Age) bin. " ] }, { "cell_type": "code", "execution_count": 54, "id": "ca6df6c4-8d99-4862-a574-d6666dd5acd4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72×2 Matrix{Float64}:\n", " 0.00398107 3.33067e-16\n", " 0.00446684 3.54675e-5\n", " 0.00501187 7.52628e-5\n", " 0.00562341 0.000119914\n", " 0.00630957 0.000170013\n", " 0.00707946 0.000226225\n", " 0.00794328 0.000289297\n", " 0.00891251 0.000360064\n", " 0.01 0.000439466\n", " 0.0112202 0.000528556\n", " 0.0125893 0.000628517\n", " 0.0141254 0.000740675\n", " 0.0158489 0.000866519\n", " ⋮ \n", " 3.98116 0.29039\n", " 4.46694 0.325858\n", " 5.01199 0.365654\n", " 5.62354 0.410306\n", " 6.30972 0.460407\n", " 7.07962 0.51662\n", " 7.94347 0.579693\n", " 8.91271 0.650462\n", " 10.0002 0.729865\n", " 11.2204 0.818958\n", " 12.5895 0.918921\n", " 13.7 1.0" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[ vcat(exp10.(unique_template_logAge)./1e9, T_max) 1.0.-vcat(cum_sfr_arr,0.0) ]" ] }, { "cell_type": "code", "execution_count": 55, "id": "64bbf9b0-e9a7-4db9-816b-4b924a57d346", "metadata": {}, "outputs": [], "source": [ "# Construct an interpolator that, given a fraction of total stellar mass `x`, will find the lookback time `t` \n", "# at which the fraction of stellar mass formed *more recently* than `t` is `x`.\n", "import Interpolations: extrapolate, interpolate, Gridded, Linear, Flat\n", "# recent_sfh_interp = extrapolate(interpolate((vcat(exp10.(unique_template_logAge)./1e9, T_max),), 1 .- vcat(cum_sfr_arr,0.0), Gridded(Linear())), Flat())\n", "sfh_quantile = extrapolate(interpolate((1 .- vcat(cum_sfr_arr,0.0),), vcat(exp10.(unique_template_logAge)./1e9, T_max), Gridded(Linear())), Flat());" ] }, { "cell_type": "code", "execution_count": 56, "id": "c2467b5c-8cd3-4d38-b121-5ceea99e37c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.247410788638181" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log10(sfh_quantile(0.001)) + 9" ] }, { "cell_type": "code", "execution_count": 57, "id": "141780b5-7a2d-4dba-903f-c4b097b4cdb2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.149038152771352\n", "7.247410788638181\n" ] }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now plot the SFRs for the metallicity evolution model\n", "fig,ax1 = plt.subplots(figsize=(7,7))\n", "ax1.tick_params(axis=\"both\", which=\"both\", labelsize=16)\n", "# ax1.bar(unique_template_logAge, mdf_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", "ax1.bar(unique_template_logAge, mdf_sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(mdf_sfr_med .- mdf_sfr_lower) .* 1e3, \n", " (mdf_sfr_upper .- mdf_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"Result\")\n", "ax1.bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "\n", "ax1.set_xlabel(\"log(Age [yr])\", fontsize=20)\n", "ax1.set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\", fontsize=20)\n", "ax1.set_ylim([0.0, 9.0]) # ax1.set_ylim([0.0, ax1.get_ylim()[2]])\n", "\n", "# Draw vertical lines to denote times at which `x` fraction of stellar mass was formed more recently\n", "for q in (\"0.01\", \"0.001\")\n", " laq = log10(sfh_quantile(parse(Float64, q))) + 9\n", " println(laq)\n", " ax1.axvline(laq, ls=\"--\", c=\"red\")\n", " # ax1.text( log10(sfh_quantile(qn)) + 9, ax1.get_ylim()[2] - 1, L\"$\\leftarrow$ \\n $ %$q \\text{M}_*$\", transform=ax1.transData, ha=\"right\", va=\"top\")\n", " t = ax1.text(laq - 0.02, ax1.get_ylim()[2] - 0.5, \"\\$\\\\leftarrow\\$ \\n \\$ $q \\\\, \\\\text{M}_*\\$\", transform=ax1.transData, ha=\"right\", va=\"top\", fontsize=20)\n", " t.set_bbox(Dict(\"facecolor\"=>\"red\", \"alpha\"=>0.25, \"edgecolor\"=>\"red\", \"lw\"=>2, \"boxstyle\"=>\"round\"))\n", "end\n", "ax1.legend(fontsize=20)\n", "# plt.savefig(\"example_sfrs_hires.pdf\", bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "id": "e8ddecee-3c5d-4322-bf1a-a22beb1317e3", "metadata": {}, "source": [ "It is important to note that there can be strong covariances between the SFRs (or stellar mass coefficients) of neighboring time bins, as their templates can be very similar. We can calculate the correlation matrix of our posterior samples to examine these correlations." ] }, { "cell_type": "code", "execution_count": 58, "id": "7f313c41-360d-4d6e-aacb-f7bfec859222", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5×5 Matrix{Float64}:\n", " 1.0 -0.285024 -0.19777 -0.13857 -0.0998224\n", " -0.285024 1.0 -0.0331039 -0.0373696 -0.032796\n", " -0.19777 -0.0331039 1.0 -0.0411748 -0.0425882\n", " -0.13857 -0.0373696 -0.0411748 1.0 -0.0591129\n", " -0.0998224 -0.032796 -0.0425882 -0.0591129 1.0" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Statistics: cor\n", "cor(mdf_sample[1:5,:]; dims=2)" ] }, { "cell_type": "code", "execution_count": 59, "id": "fa7875f1-42a5-455a-a32a-6d7fc99bb753", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Young stellar pops show minimal correlation\n", "plt.imshow(cor(mdf_sample[1:10,:]; dims=2))\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 60, "id": "b863c189-67c0-41a3-ab8c-7e8c7aec6089", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Old stellar pops show greater correlations\n", "plt.imshow(cor(mdf_sample[end-20:end-3,:]; dims=2), clim=(-1,1))\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "id": "81bf2870-3f58-4c3e-a249-a60794e76b0d", "metadata": {}, "source": [ "It can also be instructive to look at corner plots of the samples to examine their covariances," ] }, { "cell_type": "code", "execution_count": 61, "id": "d4ccdaf6-5bd0-4843-94bc-c1dc45e564ed", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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46aWXGB0dJRAI8MILL7B9+3ZisRiVlZXE43Fg9nb+qSKRSHDdddcBkEwm57y+dOlSli5dqgK5prOzk02bNrFw4cI5rwshhBBCnK6OaFs/kUhQW1sLTAfGcDisepuOj48DcOGFF7JgwQK8Xi/f/e532b59O/v37ycYDNLc3Mx5553H+eefz913380XvvAFYrEYGzZswOFwUFNTw4IFC6itrWX//v1ks1lgejvfaDSeUiumxWKRvr6+Q17X+rtqh8zcbjd2u11Ni5r5WCGEEEKIM8ERhVOt8f7MPqcABoNBrQ6effbZdHZ2cuONN9Lb2zvr+YODgwwODvKb3/yG1tZW7r33Xn73u9/xxz/+kXg8Tm9vL8lkEqfTycTEBJlMBqPRSHNzMy0tLdjtdhXUTscT7E1NTaoDwvbt27npppve6FsSQgghhHhdHVE4nTm9aXBwkHg8rtpAjY6OArB8+XIee+wxent7qaqq4qabbmLlypVUVVXxyiuvMDAwwG9/+1v6+/v54Ac/yCc+8QleeeUV/vjHP/KNb3wDr9fLhg0bCAQCtLS0YLFYqKioYNOmTdTX16uQerpqamqiqalpzlIAIYQQQojT3RHVnGq0ZvLl5eVq0pPBYFDXTSYTAIsWLeKyyy5jyZIluN1u1qxZwyc/+Un+8Ic/cMkll5DL5fjOd77DBz7wAa6++moee+wxnE4noVCIV155hc7OToaHh5mYmCAajbJ582Y2bNjAli1b6O/vP6VqUIUQQgghxGs7qglRxWIRh8OBXq8nm81SUVFBZWUlExMTTExMUFVVBUB3dzc9PT0MDw8D0zWrkUgEgI9+9KNceumlfPOb36Sjo4PzzjuPL3/5y/ziF7/gxhtvJBqN0tfXh91up7S0lPr6enp7e+nv72f+/PksXLiQRCKhJkmdCo35hRBCCCHEoR3VyqlOpyMejxOLxaioqKCuro6SkhIAYrEYZ511FgATExMUi0WsVitWq5VcLkdJSQklJSXkcjn+9m//ll/96lesXbuWVCrF5z//eR566CH+8Ic/YLFY8Pl87N27l7179xKJRHC5XJjNZtUFwGKx4Pf7T7mT/EIIIYQQYm5HvHKqjRDV2hul02lMJhOpVAqY3tJ/9tlnAWhvb8doNJLL5ejq6sJonP12yWSSWCzGN7/5Tf7v//6Pr371q6xfv57y8nL+8z//k4997GMEg0F27txJLpcjn8/T3NxMaWkpzc3Ns17rZFs11el01NbWMjY29qrXtc9wqMNdtbW1p+XhLyGEEEKIuRxVOM3lctjtdhwOB2VlZerUPkyH0wceeACAiy66iP7+fjZt2sSGDRsAqKyspKGhgbGxMfx+P8VikYaGBj72sY9xyy238L3vfY/vf//73HLLLVx11VU89thjais/GAyi1+vp6urCZrOxaNEiPB7PSRfehoaGCAQCfPvb32bdunXYbLaDHmO32xkYGHjV19Ce88c//vG0PgAmhBBCCDHTEYdTbYSo0WjEZrORTqfx+/1q5TQQCKiZ8Oeeey5+v5/BwUH1/GAwSDAYVP+u1+sZGRnhX/7lXzj33HO54YYbeOCBB/j+97/PZz/7WXbu3MnIyAh/+ctfcLvduFwuvF4vqVSKP/7xj1xyySUsX75c1ZxqK7tvVA3q0NAQ7e3tamVZOzwmhBBCCCFe21GFU7vdjt/vp66ujsHBQex2O0uWLKG7u5sf/OAHLF26lD179vCjH/2I97znPWpr+4YbbqCqqoqRkRESiQSrV69Gr9fzhz/8gW3btqlpUZ/5zGf4j//4D771rW/xpS99iZ/+9KeMjo6q51VUVLB9+3aqq6vZvn07Z511lgqk2squz+dTAfX1DKmBQIBEIsH69etpb2/H7XbT1NT0ur2/EEIIIcSp7KgORMF0SHW5XNTU1NDf38+yZcuorKwkmUxis9kwm828/PLLPP744+qwUj6fZ2pqCqfTiclk4rnnnuPXv/41bW1trFu3DofDwfDwME888QRXX301AN/4xje46667+MQnPoHFYiEUCvGHP/wBv9/P6Ogoer2ePXv20NXVxZYtW9SqLqAOSr0RE5ZaW1v5yEc+wnve8545e5Ymk0nWrFnDmjVr5ryurURfe+21vPDCCwwNDZ3wexZCCCGEeKMd9Wl9h8NBVVUVxWIRp9NJLpfjzW9+Mzqdjs2bN/OhD30ImJ4TD9O1qH6/X73Grl27ePnll+no6OChhx4iHo/z7ne/G4vFQkdHB06nk4suuohsNss//dM/8d73vpetW7fS1tZGPB5n7969TE1N4fP56O/vZ8OGDTz//PPs2rVLraK+kSNPi8UiW7ZsYcuWLRQKhYOuFwqFQ16vqKgAoL+/n/PPP5/29nYJqEIIIYQ47R31yqmmoqKC8vJyqqurufjii1V9ZV1dHTDdTqq0tJRsNsv+/fvV89LpNNFoFIBsNsvDDz9Mf38/l112GQD/+7//y8c//nHWrFlDJBLhhhtuoKysjEcffRSTyaSeHwwGmZycRKfT4fP5CIVCTExMANOruz6fj4GBgVOu1VRjY6P6+b777iORSBAIBN7AOxJCCCGEOPGOOZw2NTWxZMkSFixYQElJCTU1NQDs2bOHc845B0C1TBoYGODFF1+kv7+fUCgEgMViobKykmKxyKZNmxgcHMTtdhOLxXjggQe4/fbbqa6upre3l+uuuw6Xy8Xf/d3fAdP1nTU1NQwMDKDT6Vi2bBllZWVEIhHi8Tg+n49oNEo0Gj3lwulMixcvfqNvQQghhBDidXHM4TQej2M2mzGbzSSTSbUd/cQTT7B69Wpg+oT+tddeC0BXVxd1dXUqLKbTabLZLGVlZQB0dHSoetPHHnuMgYEB/uM//gOHw8GLL77It7/9bT7zmc9gMBgIBoP8/ve/Z9euXYyPjzM1NcXExASpVAqLxQKA0+nE6XSedH1QhRBCCCHEwY45nMJ0T866ujpcLhfz5s1TK5+//e1vcbvdjI6Osnv3bqqrq8nlcjz55JNzvgaAwWDgvPPOY9WqVeTzeT73uc8RiUT44he/CMAPfvADpqam+OxnPwvA+Pg4Q0NDxONxvF4voVCI/fv309PTg8PhoLW1ldbWVgmnQgghhBCngGMOp1VVVdTU1GC326mvr6etrY33v//9lJeXE4lEyGazOJ1Ourq60Ov1auToTNFoFJ/PB0z3Bd29ezdr165l3rx5pNNpbr31VhKJBJdeein5fJ7rr7+e66+/nuXLlzM5OUkkEsFoNFJTU8PExAT5fF71GS0Wi7P+T9vu11ZuD/x3IYQQQgjxxjnmcKqd2vd4PCxYsIDVq1dTU1PD6tWrsVgsTE1NMX/+fIxGI2NjY5x77rnquS0tLej1eorFIvl8Hpjehq+urqauro7LL7+c5uZm0uk0TzzxBP/2b/9GW1sbPp+PT33qU9xzzz2YzWai0SgvvvgiuVwOm83G6OgogUAAv99PR0cHAwMDJBIJEokEAwMDqiYV/jrx6kSEU7fbfcgG/Md6XQghhBDidHNctvVhesWzpaWF+vp6mpqaWLRoERdeeCFms5kdO3Zw4403ArBx40aWLVsGwMjICGvXrqW2tpbGxkaqqqrU4SkAo9HIwoULAdSo1HvuuQeTycTGjRvx+Xz8wz/8AwDDw8Ns27YNv9+P1WolEokQi8UYHBxkYGBArY5aLBY1blVbrT0RLadsNht+vx+/3z/nazscjsO+rpU8dHZ2sm3bNmkpJYQQQojT1nELp8ViUa1OWq1WWlpaWLVqFWvWrAHgueee421vexsAe/fuxWq1ksvl2LFjBwaDAbvdTnl5OXq9nm3btrFt2zYmJydVn1S3201PTw/pdJorr7wSgC996Utcf/316HQ6YrEYyWQSr9fL5OQkRqORiYkJNWYV/jo4oKWlBZhu0g/g8XhO6vn1brcbu93OunXrWLVqlfQ8FUIIIcRp67iFU51ORzwep7y8nEQigcFgoFAocNFFF2E2m1WP03e+850UCgXy+TxOp5NUKsX4+DilpaVcfPHFAJSUlOBwOOju7mZ0dBSA5cuX43K5cLlc3HjjjZjNZrZv345Op+OKK64A4IUXXlA1pIODg3i9XqxWK9XV1VRVVRGPx/H7/SdFk/4j0dTURGdnJ1u3bmX9+vXS81QIIYQQpy3j8XwxLei1t7czMjJCeXk5fr+fN73pTbz00ks8/vjjzJ8/nze96U28/PLL5PN53G43gUCAV155hR07dlBSUoLf7ycWi6nVwbe97W14PB71PjabTa16lpWVcdlll/HEE08Qj8eJx+PY7XaGh4fZu3cvQ0NDXHHFFbS0tDAwMEA6ncbv91NVVQWgnqPdv8PhUIepjsTQ0BCBQECt9KZSKRW2n3jiCbV6q0kmk2oF+HCuNzU10dTUdMT3JYQQQghxKjmu4dRut2O324nH44RCIbLZLG63m9bWVsLhMH19ffT29jI2Nsa8efPo6+sjEAhQV1dHIpEgHA4TCoVUg36dTsd73vMe3vzmNxMIBPB6vQB0d3dTKBTUdveiRYuA6UlTWmlAJBJhbGwMk8lEY2Mjq1atwm63EwwGKS8vB/56GGpycpLy8nK1onqk4XRoaIj29nb1PLvdTkVFBRs3bgR41fGlx3JdCCGEEOJ0dNzCqU6nUz8nEgncbjfFYpHm5ma6u7sZGBhg+fLl7Nmzh6mpKTUzftOmTXi9XpYuXcq//uu/smfPHtUeavXq1Vx66aXA9DSo+fPnA/Db3/4WmN7qLy8vZ+nSpQDEYjG8Xi9GoxGXy4XJZCISiTA1NcXAwAAej4e2tjaMxumP7XA4iMfjalVWW/k90vrTQCBAIpFg/fr1tLe343a7qaysPIZvUwghhBDizHRcV041Ho8Hv99PQ0MDOp1OHYTK5/MsXryYDRs2MDAwwKZNm7jhhht45JFH2Lt3L9/85jdZvnw5ra2tnHPOOXOGxBdffFGF05tvvhlATaWC6dVTo9GIyWSiUCig0+kYGhpiz549nHXWWdjtdoxGI/39/cB0n9YD606P9nBUe3s7K1euBJC+qUIIIYQQR+GEhVMtoOZyOdxuNxUVFWzYsIFkMsmll17Kjh072Lx5Mw888ACXX345HR0dDA8P85e//GXWa1VXV7N48WKsViuNjY08/PDDAHzoQx/ioosuIpVKzQqTLpeL8vJyFixYQCgUIp/PEwqFGB8fn9WmKhqNAn9dLdW29E+FA1JCCCGEEKerExJOtS1+bdtc+2c2m6VYLFJSUsI73vEOEokEe/fu5U9/+hPz5s3jk5/8JPl8nrGxMXp6eti9ezcTExNMTEzMev22tja+853vYLPZyGazvPjii+paIBDA7Xaj1+ux2Wz09vZSXl5Od3c3NTU1VFVVUVJSAkyvkDocDnw+H9FoFIPBgMfjOaoDUUIIIYQQ4tidkHCqcTgc2O121fqosrKSdDpNRUUF4+PjrF69Gp1Ox/79++nr6+Puu+/mwgsv5P3vfz/f+9731ISnHTt2MD4+TjqdJpvNcsstt2Cz2YhEItx+++3cd999AJSWlpLP5xkeHsZsNmM0GrFarRiNRvL5PMlkksnJSTKZDPPnz8flcs1adU0kEuRyOQmnQgghhBBvkBMaTrU2TVr/06VLlzJ//nxGR0cJhUJYrVaWL1/O2rVreeqppxgaGuLZZ5/l2Wef5VOf+hRNTU00NDRQVVVFJBIhnU4Tj8f5/Oc/r3qnjo+PA1BbW8u5555LR0cHJpOJTCZDPB6nqqqKBQsW0NDQgMvlwmAwkM/n6e3tZcWKFQCqrZTBYCCdTh+3hvyv9TrHcn3mcAJpMSWEEEKI08UJD6daP1KXy0VtbS0AoVAIm81GTU0NJSUl6PV6SkpK2LVrF4ODg4yOjpJKpeju7qa7u/uQ72E2m6moqKClpYVkMondbiedTjM1NaWC6Zo1aygrK1O9RGOxmPpZp9OpWlOLxYLRaCSTyRzzZ9dKGY739ZnTomA6wHZ2dkpAFUIIIcRp4YRv68fjcUpLS3E4HBSLRQDmz59PPp/nnHPOUaEyGo3S2trKwMAAXV1deL1estksPp+PYrFIRUUFqVSKYrFIaWkpU1NTANTV1ZFOpzGbzdTU1GC1WgGorKzE7XZTKBTo6+ujtraWqqoqfD4fZWVlqu505r36fD5VOnCy0qZFaQ3/161bRyAQkHAqhBBCiNPCCQ+nc51+12bba7WdiURC9TUtLS3FZDJht9uxWq10dnZit9uZP38+4XCYdDpNXV0dY2NjAJSXlxMMBjGZTFRUVBCPx6mpqaGmpoZ8Pk8gEECv19PS0sLOnTtxOp0ALFu2DIBisUgikVCrlBaLRQXf13LgVKjXi0yLEkIIIcTp6oSG0wPNPMXv8XjUdKZoNIpOp6NQKBAOh4nFYjQ1NVFZWUl1dTXj4+Po9XqWLFlCsVjE6/Wi0+nUFn4qlcJqtRIIBLDb7WQyGUwmE8lkEpvNRmlpKdlsFoPBgM/nw+12k06nSafTAPj9fiwWCwaDAaPReFg1p3NNhXK73ep6KpXiPe95DwAPPfSQWtE9XtdnkvpTIYQQQpwuXtdwOtPM6UyJRELVoba2tmKz2aisrGTevHn09/fT3d2NwWCgrq6OlpYWNm7ciMPhIBqNYrVaKSsro6ysDIfDQaFQIBqNkkwmyWaz1NTUkMvlsNvtGAwGqqurSaVSBAIBLBYLw8PDlJWVkU6naWlpweFwHFbN6VxToWYGw3w+z+OPP65+PtCxXgepPxVCCCHE6ecNC6d2u33WCmV9fT3pdBqHw0FlZSWVlZXYbDbMZjOpVIp4PI7JZGJgYIDq6mr0ej3pdBqDwUAsFqOtrY2SkhJyuRw+n4+pqSlqamowmUw0NzdTKBRYtmwZwWCQQqFAPp8nHA5TVlaGwWBQwfRIzZwK9XqT+lMhhBBCnG7esHCqbfHDwQenfD4fuVyOdDpNdXU17373uwF46qmn1Db9W97yFtU2SguVyWQSmK4bbWxspLa2lsrKShKJBHa7HY/HQzweV/+uPe9IJkO9UXWmr+bA+lPZ4hdCCCHEqewNC6czHRgODwyrMH1w6eyzz2Z4eJjKykoMBgPz588H/toP1GKxMDk5ybJlyzAajep1/H4/+XyeeDxOa2vrUd/na9WZvpFki18IIYQQp4OTIpwe6FCn/FtaWigUCgSDQRVGLRbLrANO6XSa0tJS1Wc1kUhgsViO+b5eq870jTTXFv9zzz130t2nEEIIIcShnJThdC5aGYA2cQrAaDTi8XgAVBjVGulrjwVobm4GOOTWvfa6cz3mwK38N7LO9FC0Lf65VlEffvhhPB6PBFUhhBBCnNQOK5xqp8VHRkZwuVwn9IZeSyAQIJ/PqzGj2v9lMhkSiYSqJ/V6veTzeQwGAyaTCYBMJsPk5OQhX9dgMKja1SeffJJMJsPNN99MKpUCwGq1ks1mGRkZOeR9alv/AKOjowe1pzrW64ei1+t5+umnCYVCBINBbr75Zq644gp1//feey+VlZXq8dFoFJie3HUmKBaLRKNR6urq0Ov1b/TtCCGEEGIGXVEb23QImzdvZu3ata/H/QjxuhkeHqahoeGNvg0hhBBCzHBYK6dtbW3A9P+YH6+V0wMz8czT+6/2+Jkro4lEglwuN2tr/3gZGRlh6dKlbN++nXnz5gHMecr/dKF93qP5/cbjcerq6gDwer0HfTfHev1EiEQiNDY2qmlhQgghhDh5HFY4NRgMALhcrhMSTrXxoYdq6VQsFme99+E852hp71NdXa1+drlcqi7VYDCcVgF15mc80t+v9t+G9vwDv5djvX4ivdZfiIQQQgjx+jspDkRpp+rj8fhBtZWvFiAObOJ/Isy1yqfd5+kUToUQQgghThYnVRP+QwW+k2GV63Du80yj1+tZvXq1+vl4XxdCCCHEmeWkWDmduTWvbfcfuG0/s9XTGxUOZ773XPdzMtzj681ms7F58+Y5r2ktuO655x7cbjc2m+2Ini+EEEKIM88JD6eHc/BprkB64Fb/4Wz9zwyHdrv9uK+2aoeytPexWCzE43FsNhs6nW7WPWr3cyYFVfhrIPX7/VxzzTWzpmlJr1UhhBBCvJbXbeX0tQ4wJRIJOjo6yOfzVFdXU1VVNWsL3eFwMDAwcNDrzAyqhwqwx0oLpFooTSQSBAIBFZxnnuR3OBz4fD6i0ShOp/OYRqaeSuYa7/rkk08CcM0116heqzJaVQghhBCv5nUr8psZHOPxOD6fT60wAvh8PgKBgGqS73A4VIsov9+vHmcwGBgeHp61QqlxOBwYjcYTslI5cxSqdo8Wi4V8Ps+uXbvo6OhgeHgYj8dzwg9qnUwSiYQaKzs8PKzGu27dupXOzk7+5m/+ho985CNUVFTw/PPPs379ehXsD3z+zMEDQgghhDgzHfPK6Vx1lnP19Z95mOjA7W+fz8fw8DBWqxWn06lWHmc+1ufzkUgkCIVCNDQ0zAqh2vsdeIJ/rvs42hZU2r0AxGIxMpkMVqsVmD7Io9PpSKVSDAwMqMfP/I7mKjM4HWpUi8Uig4ODs/5s5njXeDyuri9fvvygutOZzz+MeRBCCCGEOM0dl3B6YHulmQFQC2UHbsH7fD7C4TA7duxgbGwMk8nEggULaG9vJx6P09/fz8TEBPPnz8fj8ZBOp7FYLJjNZoLBoFqhPPC9Dvz3w7nfV+P3+1VPU+314vE4+/fvZ2pqivLycqqqqgCoqqqisrKSiYkJxsbGMBgMWK1WzGYzgUCAJUuWHHSwS1pTCSGEEELMdszhdK72Sq9W+6kFR4BkMonP56O7u5upqSnq6upU+PP7/WzatEk97uqrr1avPzY2RklJCYODg8TjcQKBAOXl5eoe/H4/+Xweg8GAx+M5KKQeSTuouYJjIpGgq6tLhdNwOIzD4aCrq4vm5mbS6TQTExMUCgX0ej3FYpHq6mrcbjetra0HBVIJpkIIIYQQf3XM4XSuZvgHhi5tuzYejxOJROjr68NqtRKLxfB4PJSXl9PU1EQymWTfvn1MTExgNBrx+XzU1dXR2dlJZWUlVVVVKoACRKNRCoUC6XQau92uakK1A0tzBeQjGT96YP1qsVhkcnKSyspKotEolZWV1NbWMjY2RiQSYcuWLTQ0NFAsFnE6neRyObX1HwgE1P1LIBVCCCGEmNsxh9O52jUdWH86c8U0k8mQSCQYHBzEYrFgtVqprq6moaGBLVu2EAqF0Ov1OJ1ObDYbDoeDYDBIT08PdXV1NDY2AtMrmGNjYwCUlJTMqglNp9O43W4VLo+2pZTdbsfn8wGo7ftEIkE6nWbRokWUlpZitVoxGo2MjY0Ri8VUcK6pqaG2tpZsNovdbldtp7RyBJ1Oh8/nk219IYQQQogZXpdWUjPbKjU3N9Pb28vU1BTj4+OUlpbicDgYHx8nkUgwPj6OxWJhyZIlWCwWIpEIw8PDTExMsHPnTiorK1mxYoUKdCMjI3R1dQHg8XiIRqPAdEDWAuXRmhmqtfDb19dHPB7H5XJhMpnYvn07o6OjBINBdDodwWCQQqGAz+cjmUxSU1NDY2MjY2NjpNNp/H4/drtdVlGFEEIIIeZw3MLpzJPWxWIRnU6nwt3IyAijo6MqkNlsNiKRCIFAQG2PT05O4nQ6KRaLFItFstksACMjI4yMjJBOpwEwm81MTU2xaNEi+vr6GBgYwGazkcvlWLp0KQaDAZh9Ev5wBgHMxW63zxqp6fV68Xq9JJNJCoUC0WgUn89HoVAgkUhQUlJCWVkZ4+PjpNNpgsEgkUiEYDBIWVkZlZWVWK1Wamtrgb+uMJ8sE7COhk6nY8mSJYd1fa7v/bWuCyGEEOLMckJWTpPJpDrYFIvF6O7uJhQKqVXOQqGgAmdJSQn5fJ7JyUnGxsZUS6ZCoUBlZaU6MOVyuSgpKaG+vp7q6mpaWloYHR2ltLSUiYkJXC4Xw8PDLFq0CIfD8aqHmY4kADocDhUktS34srIyVW6QTCaZmpoiGo0Sj8fVISiLxUIwGGR4eBiv14vVaqWsrIzm5mZaWloIBoOcffbZaqv/VD65b7fb2bt3LwDbtm075PWZOjs7AXC73XNeF0IIIcSZ6biG00QioZrpWywWxsbGKBaLFAoF/H4/NpuNWCwGgNVqxWQyqefl83mi0SjJZFI9Np/P43a7MRgMqg41HA7T2dlJMBgEwGQyYbPZyGQyJJNJdS+Tk5Oqif9cI0ePJABqzzUYDMybN49YLIbRaGRoaIhsNks6nSYSiZDJZKirqyMSiWA0Gkmn08TjcSYnJ8nlcthsNsrKyigWizz11FM4nU7e9KY3zdriB1SP11MppB4ut9uN3W5n3bp1gEyLEkIIIcRsx7UJfzweVyfop6amKCsrY2xsTG3dW61WCoUCRqORxsZGpqam2LhxI16vl97eXmKxGPPmzcNqteL1eqmoqCAQCJBIJGhra2NsbIxEIsG8efMYGRlhyZIlKuwYDAYaGxvV2FOLxYLf75+1KgkHn8A/FK3PqfZ69fX16HQ6ysrKGB4eJhwOE4vFKC0tZWhoiP7+flV/mslk1Mn90tJSGhoayOVyeL1eNSGptbWVnp4empub1UQsv9+vhg7M7K96umx5NzU10dnZSSAQoLOzk3Xr1hEIBCScCiGEEAI4AU34nU6nmvLU1dXF2NgYgUCAeDxOTU0NLpeLTCbDxMQEP/vZz+jt7Z31el1dXZhMJgYHB9m0aRNTU1MUi0Xmz59PU1OTas1UWlpKV1cXbW1ts7bZn3/+eRoaGtRq7MDAgGolVVVVdURB78B+pOl0mmKxSCAQwOv1sn//fsLhMF6vl61bt6q6WI025hRgcHCQhQsXMjU1hU6nY2xsTJUvtLa24nQ6gb+2ukqn0weF1JNxJTWRSLBmzRoAfvKTnxzy+ubNm7Hb7TQ1Nc0Ko9dddx1Wq1VdF0IIIcSZ67g24bfb7bS0tOD3+/H7/SSTSUwmE7FYTB2A0ul0DA4Oqq15g8HAe9/7Xt72treRSCT44Q9/yPbt2wmHw7PeR1tZXbhwITqdjomJCVKpFA6HA7PZTCgUUmUEAAsXLmRiYoKRkRFqa2vVQakj+nKM01+P9vmGhobIZDKkUilyuZw69BQOh0mn05SWlvLpT3+ahoYGTCYTmzZtoqOjg5deeolAIEAkEmHBggXodDr0ej1+vx+dTofL5WLBggVkMhlcLhcrVqwAUGH3ZK5FLRaLdHR0ALBv375DXn+18aR9fX2HvC6EEEKIM8dxb8I/MDDAxo0b0el0FAoFDAYDbW1tbNu2Da/Xy/bt25mamgJg2bJl/OhHP2L+/PlYLBYArrzySvr7+3nuueeorq5m9erVbNy4kU984hNMTExgMpk477zz1OGpaDRKPp8nFouRzWYpFovY7XZWrFihViP7+/vVdKYjWZnzeDwqiPp8Pnw+H0NDQwQCAcLhsArfXq8XgG9/+9tcfvnlanX3zW9+MyUlJbzyyiv8wz/8A4FAAJ/PR3l5OQaDgWKxSCqVYvPmzej1egwGA2azmR07dlBWVobH41FlCidjMAUYHh5WP990003Y7XbcbvcbeEdCCCGEOJUdtyb82qqXttUdjUapqakBpttB/eEPf1CroVarlU9/+tN87nOfw2w2E4/H1SqlXq/nrLPO4qyzziKbzeJ0OtVq6S233MLIyAjPPfcczc3NFAoFzGYze/fuJZlMYrfb8Xg8mEwmFUQTiQStra1q5VSrQz3csOdwONQhL62+NhAIsH//fgKBAAMDA6TTaS655BLe+973Eo1GSSQSPPLII2zfvp3+/n4ikQi33XYbv/71r9mxY4dq1G+xWKitraW6uhq9Xs/ChQuJRCLYbLZZo1dP5ppT7WAawPPPP09jY6PUjwohhBDiqB330/oAlZWVWCwWRkZGCAaD/PznP1dh8dOf/jQf+chHcLlcmM1motEov/3tb/nd737H9u3bueyyy7j99tsPCjh///d/j9Vq5eabb2Z8fByHw6EmM6XTaYxGI06nk2w2yyuvvEIoFOKCCy5QK6Xayf1IJILf76elpWVWQI3H42oalNZjVTuhr9Wv6vV6NRHKZDJRLBbVCftbbrkFnU7H/v37+fCHP3xQLe3u3bv51re+xbve9S5+//vfs2vXLtLpNAMDA0xMTFAsFjGbzZSWlqrhAcFgkC1btgDTp9xP5tpTgOXLl5+09yaEEEKIU8NRhdOZtYFamya73U4gEMBqteJ0OhkaGmL//v08+eSTxONxlixZwr333sv8+fMBeOWVV/jZz37G73//+1ktoB588EEeeeQR1q1bx4c//GHa2toASKVSXHfddezcuZMf/OAHTE5O4vP5aGhoAKab88+fPx+v18vU1BQDAwNYLBZcLpc6COVwOPB6vZSVlalQeWCnAYBCoTDrs3V2dlJVVUVtbS3hcBiHw6G6DWSzWQwGAwsXLuTBBx/klltuIZFIUFVVxZvf/GYWLlzI3r17eeyxx7jtttv45Cc/yS9+8QscDge/+tWv+Na3vkU0GuWVV14hm81yzTXX0NTURCaTobOzE4vFgtvtxuv1snTpUvU9nYoN+4UQQgghXssxrZwmEgk6OjrI5/O4XC68Xi/d3d10d3fz/PPPs3fvXlKpFKtXr+b3v/89Op0Oo9HIvn37+MAHPqBWKuvr67n66qtpb2/n5z//OTt27OC///u/eeSRR7jjjjv4wAc+gMlkwmAw8JWvfIXnnnuOnTt38pe//IXzzjuPqakpKioq2LJlC7lcjkAgQD6fp7GxEZ1ORzgcRqfT0djYSF1dHel0es7G91qNqrZyarfbGR0dpbKykkwmo3qWDg4OqrGqAGeddRZWq1UF05UrV3LXXXcRCASora3liiuuoKKiggceeIC7776bH/3oR6xZs4a/+Zu/4YknnuDHP/4x69evZ/v27QwNDfHe974Xk8lEJBJhyZIl2O12SkpK8Hq9uFwu4vE4kUiEwcFB3G636pN6qhseHmbx4sVv9G0IIYQQ4g2kf+2HvLp4PE4+nyeZTJJMJgmFQni9Xrq6uti5cyepVIpLL72Uxx57jIqKCgA2bdrE1Vdfjc/no62tjV/+8pfcd9993HTTTZx//vn853/+Jw888ACLFi0iHA5z6623cumll7J+/XqGhoYwm838/Oc/p6amhlgsxksvvUQoFGJ0dJREIkGhUKCxsRGHw4HBYKBQKKDT6YjH4wwPD6vm/Nqq48y+pw6Hg6qqKmw2m/r3xYsXU1tbq07gR6NRbDYbZrOZfD4PwOLFi+nq6iKRSFBaWsoPf/hD9Xlhui73hhtu4Dvf+Q41NTVkMhk2bdrEXXfdxY033sgXvvAFfve731FSUkIwGOS+++5jx44dqrF/U1OTGo2q1aum02k1QlVbBX4j1dbWvup40ubmZpqbmw+67na71XcNsHLlSoaGhk74vQohhBDi5HVEK6fa3HuNFu6y2SyZTIbx8XFCoRB9fX3kcjkWL17M3Xffrfqa/sd//Ac/+tGPyOfzLFiwgC984QuYzWa2bNlCOBwmHA6zePFi2tvb+eY3v8n//u//8j//8z/s3LmTnTt38rnPfY62tja+9KUv8cgjj3DFFVeok/+rV6/G7XZTUVHB8PAwxWIRn89He3s7NpsNg8GgZt3DdA2q1WrFZrMxODjIrl27qK6uZt68ebM+s8fjwePxkEgkSKfTzJs3j2g0itPp5LnnngOgpqaGrVu3qp+1cZyjo6MMDg6yc+dOdDoddXV1fPSjH8XhcOD3+/nFL35BX18fF1xwAffddx+//e1vuemmmxgdHWXr1q0sXryYQqHA8PAwl19+OQBlZWUAtLS0qJVnh8NBsVh8Qw5NaeHyj3/845ydEOx2OwMDA3M+t6mpiX379klDfiGEEEIoR7ytf+B8epvNRklJCYODg1itVvx+P93d3ej1en784x/jcDjYvXs3H/vYx1RIecc73sFVV11FR0cH999/v1oNhOmT/H/3d3/HNddcw9VXX81NN93Eb37zG5555hl27dpFT08P3/zmN3nppZf41a9+xbve9S6i0SgjIyO0t7eTzWbV6mQoFKKzs5OVK1eyfPlynnnmGQBCoRAwvaqn0+nU9vzExATLli0jk8mo+9ECn8PhoKGhgb6+PlwuF3v27FFN9hcsWKB6fNbW1s4qD3j88cd56aWXZn2HbW1tfOtb3+Liiy/mM5/5DJ2dnVx77bXceeedbNy4kb//+7/n+eefZ/v27cRiMQKBAOXl5bz97W9XJQl2u121mTqVHdiQXwghhBBntiPe1p9Zp6np6OggHA4TiUQYGxsD4IorrmD16tX09vbygQ98gIGBAcrLy/na177Gv//7v/PUU0/x7//+7yqYageOUqkU999/Px/60Ifo7u7G7Xbz8Y9/nHvvvZd///d/B/56IOvCCy/kgx/8IACxWAyTyUQikcDhcKiAqtPpSKVSwHRtaGtrKxUVFXR0dKjRqDabDavVytlnn33I2s1AIMD4+Dh+v5+SkhIVQnfs2KEOZmmrmTDdQuull15Cp9PR0tKi+rn29PTwgQ98gGeffZa7776b6667jmKxyO23384zzzzD448/zsqVK8nlcgSDQVWW4PV6SSaT+Hw+Ojo66OjoYHx8fNZ7zvX78vl8p3yIFUIIIcSZ4YhXTmdOhCoWi0xOTpJMJolGo+j1etVC6YILLlAhbHJykiVLlvDxj38cp9PJxz72MZ566ikA1qxZw7JlyygtLaVYLNLV1cXLL79MKBTiO9/5DsuXL8dgMBCLxVSQ9Xg86mT9ueeey3333UcsFqOiogKLxUJFRQUlJSWMj4/T19dHZWUlkUhE9UEdHBwkHA4TDAZpbGykvr6e5uZm1cLp1SSTSaampigpKaGiooL58+fj8/l48cUXufLKK4HpaUf9/f3A9FY3wKJFi1i1ahWZTIZFixaxc+dORkdHueuuu3jwwQf55Cc/STqd5tFHH+UjH/kIOp2OK6+8km3btpHL5Vi7di0ul4v9+/eTTCbZv3+/avRfXl5OSUmJ6kig0VZ8Dzz0dbxpwf+GG25gy5Yts2pIte/swgsvBODZZ5991etaGzIhhBBCnNmOaOVUp9OpgDMwMIDf72dycpKxsTGCwaD6M4DLL7+cb3/724yNjVFfX8/dd99NOBzmrrvuUsH0qquu4p3vfCfJZBKHw0FJSQnNzc3cfvvtOBwOJiYm+PnPf05FRQUVFRUqCJWWlmK1WrFarbzlLW9Br9cTjUbZuXMnQ0ND7Nu3j46ODgYGBkgkEkxOTuL1ekkkEuRyOWC6h6jZbAaYdSjq1cTjcWw2GzU1NaruU3tOZ2cnTU1NWK1WUqkU5eXlwHTDf7PZzKWXXsrOnTuZmJggFouxfPly3vGOd2Aymejr6+PWW2/FYDBwySWXUCgU+MQnPsGb3/xmYLoE4bHHHqO/v589e/bw4osv0tXVRXd3N6Ojo6RSKfL5vPruBwYGGBgYUAMD4vG4KgU4EbRV7I6ODtWCa6ZCocCWLVvYsmXLIa9rI06FEEIIcWY7qtP6fr+fcDjMvn37SKVSmM1mampqGB4eJp/P097ejsfj4fe//z0Ad9xxB/v37+cb3/gGe/fuxWq1cvnll3Peeeep18xms2r1zOFw8P73vx+AX//616xfvx6YXiUFeOqpp9RBqNLSUtrb24Hp6VQdHR3s3buXUCiEyWRSW/Zms1kFVZguIzAYDFRVVeHxeF5zrGk8HqesrIzzzjuPSy+9lPr6ekpKSigvL6dQKHDPPfeoPqS9vb3s378fgNbWVkpKSma9lk6n48ILL+Rzn/sc7e3tFAoFHnnkEcxmMytWrCCRSPCpT32KG2+8EZgOfv/zP/9DZ2cnw8PDTExMEIlEyOVyeL1eIpEI0WiUwcFBotEoExMTDAwM4PP5sFgs0g9VCCGEEKeMo+pz6vF4CAaDlJWVEYlEaGlpQa/X093dDcBFF13E//zP/xCPx6msrOSFF17g/vvvp1gs4na7ufrqqwmFQvzhD39g//79xONx1Vu0paWFbDaLXq/noosuYuPGjfznf/4n6XSaG264gba2Nnp6erj33nv56Ec/SqFQYPny5ezdu5dsNqu2s00mE1VVVVRVVVFeXs7Y2Bh6vZ6lS5eSSCSw2+04nc5ZoVRbBZxri1krZ4C/ttCqrq7m7LPPZuPGjTzwwANcffXVbN26lW3btqnwnMvl2LJlC6lUiuHhYSKRCCaTiWeeeYZsNsuyZct429vexuOPP86TTz7Jl7/8ZYaHh9m/fz/9/f285z3v4f/+7/8IBoPs2rVLrRhbLBZisRipVEqFb71eT0NDAwaDAYvFMv0LPoxV4aMxNDREIBBQB8GEEEIIIY6Ho1o59Xg8rFq1CqfTSXV1NXV1dYRCIVVveu2116rVzjVr1vDHP/6RYrHI/Pnz+fCHP0xDQwP79+9n69athMNhFUxhulwgGAzicrlob2/nnHPOoVgs8rvf/Y6ysjJuuOEGAB5//HGcTiclJSXq9P3o6CjZbBa3262mQ01NTREMBgmHw+TzeSYnJ2lubqampuZVa0xfLZx6PB5sNhuhUIhYLIbZbKa+vp6ysjLS6TSBQACAvXv3Eg6Hgel+pNqY03g8TjAYZHx8nHg8TiaTYevWrYyMjLBo0SKKxSI/+clP+PGPf8zFF19MLpfjoYce4r3vfS9vf/vbyWazvPTSS+zdu5exsTF0Op1qj2Wz2SgtLcXj8bBkyRI1GetENOgfGhqivb2dVatWcdNNNx3X1xZCCCHEme2owmkikcDn89Hd3c3IyAiACqCXXnopmzZtYufOnTidTtauXatC25o1azCZTACMj49TKBRwOBzU1dWp/qJ6vZ7q6mpgevu7trYWQP3TaJxe7NXqOuPxOJs3bwame4zW1tbS3NzMypUrqaurw2q1kslkVK2pVt+qBVOtRnNmID3UFn9VVRULFy6kqamJqqoqSkpKOOeccwB44YUXKCsrw+v1qvscGRlRLbS0zw6o1U6AXbt2qc80OjrKpz/9aW699Va+8pWvAPCTn/yEr3zlK7zjHe8gn8+zf/9+Ojs71evEYjEikQhOp1Nt4Wuh9ESc1te6HKxfv57nn3/+uL2uEEIIIcQRbevH43FSqRR+vx+/38/Y2BiBQIBoNMqLL74IwDXXXMM///M/A3DTTTepiT/aNvSePXsIh8Oq9lOb5OT1eoHpgKm9vslk4tlnnwXg7LPPxu/3s3v3bgDmzZtHIpHgnnvuIRAIYLVaueiiiwgEAthsNhKJBBUVFRSLRTKZDLFYDKfTidfrpbKykqVLlxKPx9Wpf613KHDQifKZ7HY7LS0tZDIZCoUCkUiE888/n87OTjX1atu2bXR0dFBeXs7k5CQ9PT2UlJTgcDjQ6XQUi0VVutDc3Mzg4CBdXV20tLRQKBQYGhri5ptv5r777uP8889n06ZN3HTTTdx77708/fTTxGIxJiYmSCQShEIh/H4/RqORZcuWqS4KGp/Pp4YGaH1Rj1cNant7O4sWLTrm15lJC91ut1v6nwohhBBnoCNaOdVOuyeTSWw2GxUVFRQKBbZv304ikaC9vZ2RkRHi8ThLlizhhhtuUFv21dXV6PV6PB4PExMT6jV9Ph/5fJ5UKoVOp+Oss87i7LPP5uyzzyYYDJJIJKiurubqq6+mtrZWPXfZsmU4nU4eeughYHpldXx8nGKxSE9PD6Ojo2oVVgueer2ekpKSWQevnE6nWnFMJBL4/X6SyeScn19r2h+Px3E6nXg8HiorK0kmkzQ3NwPTh5c++tGPAhCJRLj66quB6T6stbW1asU2n8+TzWYZHh5WK6oDAwNcdNFFOJ1OfD4f//Vf/8WXv/xlHA4H27Zt4/vf/z633347ML16+fLLLzMxMUEwGGR4eFh9Lu1z+P1+tcoJc/eoPR7cbjdut/uYrldUVGCz2Vi3bh2rVq2ivb1dRpkKIYQQZ6AjCqd2ux2j0ahmvcN06NTCz5IlS9R2dmtrKwaDQW1pt7S0qNeZ+TOgGvcvWrRIrVomk0k2bdoEwPXXX4/BYACmywEAGhsbpz+AfvojaK2onE4nZWVlZLNZhoaGSKfTOJ1OWltbmTdvHqWlpdjtduLxuFoFbWlpUX+Wy+Ves+emw+HA5XKxYMEC3va2t7Fy5UoaGhrUiq3f7+fcc88ln8+j0+lUV4L+/n5135pCoaBKDrSV1He9610AbNiwAaPRyBe/+EV0Oh0///nPmZiY4LLLLiORSLBv3z5GR0cxmUy43W5GRkbo7+9nYGCA8fFxBgcHKS8vV6um2rjZ41mDqo1i9fv9c77u4V4PBoPs27ePrVu3sn79ehKJhCoHEUIIIcSZ46hqTh0OB6lUiu7ubrZt26ZOyOv1eiorK4HpQzNDQ0N0dXUB0/WW4XCYnp4e1a9UUywWcblcalVzZGSEJ554gmQySW1tLUuXLiUQCBAIBNT2v9b3VKs9NZlMzJs3j5KSEmw2Gw6Hg3A4TCAQwGKxYLPZqKuro6KigvLy8lmrh1qghOma1tdqK+VwOHC73TgcDtVD1OFwsGDBAgB++9vf8ta3vhWAP//5z1x22WXodDqmpqbmnOakbcNXVFTQ1dXFxMQE1dXV5HI5fvjDH9LY2KhWTL/73e9yySWXsGbNGiYnJxkfHyeZTJJOp+np6aG7u5vNmzfz4osv0t/fr4L/qaCpqYmVK1eq1mBCCCGEOPMcUc2pNupTO3W+c+dO0uk0kUgEALPZzMKFC4Hpk/OVlZWqtrSmpkY15IfpGtK+vj516n3t2rWYzWZKS0sxGo3s3bsXgE9/+tOcffbZRCIRHnnkEdWiqa2tjXg8rg5k6fV6FZq10/Qul4vR0VGqqqooFosYjUY8Hg/ArFU8bcXUaDRSVVVFJpN5ze9CC+Rms5nJyUlcLhd2u536+npGR0f5zne+Q11dHV6vl+3bt3PeeefxwgsvMDo6SmtrK29605uwWCw899xzmM1m0uk0drtdBeO2tjYmJibYsWMH73rXu3jve9+L3+/nnnvu4Wtf+xrbt2/nuuuuY8eOHbzyyitUVlYSj8fp7OykUChgNBppa2vD6XRSWlqqwvhc06JkOpMQQgghThZH3Oc0nU6TTqeZmJjAYDAwOTmpaiZ7enpYuXIllZWVBINBnnrqKVpaWujt7WXDhg0sXrxYvY5er2f16tUMDQ1hNBoxm81ks1leeOEFBgcHAVi9ejVvfvObCYVCfPnLX2bLli0AfPCDHwTg7W9/O0NDQ6qZfiqVUo3oFy5cSENDAx6Ph2QySWVl5azRq4Daaj7wzw+XVrOqlSPYbDZ6enqYmpoiFouh0+nQ6/U8+uijtLS00NTUxNDQEP39/YyMjLBkyRJ0Oh0VFRXAdF2qRgvI2sowwMc+9jE1qjWbzXLnnXdy9dVXE4/H8fv9ZLNZstks+XyekpISFixYQDabJZ1O4/f7VX/XA8sqjiWcJpNJNbr1iSeemHM86bFcF0IIIcSZ5YjDqcPhIBKJkMlkKCkpwe12q9XMPXv2kM/nueqqq/jFL37B/fffz/LlyxkeHmZ8fJxYLEYwGKSmpoZMJsPk5CROp5NMJsPIyAj79u0jk8mg1+u5+OKLectb3sKWLVv4yle+QigUwmKxcPvtt3PuuedyySWX0N3djcViYfHixVgsFhKJBPl8nvLyctxuNy6Xi6GhIWpra0kkEmqF1OfzkUgkVKN6bUKUthp6uLSg19jYSHl5OT6fj1QqxQUXXMCmTZuIRqM0NTXNaifl8XiIxWIkk0l27typtvvdbjd6vV5t+2u1tR6Ph7GxMVwuFzA9ESsUCjE0NKTGqObzeSorK4nFYlitVmprazEajbhcLtLpNBaLhWAwqOqEtRCu9V49FoVCgY0bN6qfj/d1IYQQQpxZjvhAlFZnuXTpUtrb29VWtF6vV9v9H//4x7HZbPT29uLxePj0pz+N0WgkFouxe/dunn32WSYnJykrK6O5uZliscju3bvJZDJUVVXxwx/+kDvuuIOJiQk++9nPEgqFWLhwIZs2beK2227je9/7Ht3d3RgMBhYsWIDNZiOdTuPz+bDZbDQ3N6upVS6Xi0wmo+5dO7BlsVhUvah2Cv9IaM/R6XQYDAbi8Tgmk4lCoUBrayt/8zd/g8PhYGhoiA996EO8733vw2QyqRXOd7zjHeqzBwIB9u/fz9jYGOFwGL/fz/DwMAAXXHCBOu3udrvVymIqlaK1tRWYDnXd3d14vV41cMBisZDJZMhkMuzYsQOYDrFaWQP8tZxBCCGEEOJkccTh1G634/F4cDqdLFiwgHnz5uF2uyktLQWgq6uLsrIyPv7xjwPwm9/8hlWrVvHzn/+cSy65hKqqKtLpNGNjY2zcuJH//d//Zd++fRSLRd7ylrfwxS9+kbq6Om6//XZ+8IMfkE6neetb38of/vAHli5dSrFY5IUXXgDgnHPOYcWKFdTW1qqt7Le85S2ce+656uCTy+Vi+fLl6kS+2+2mqqoKg8HwmgefjkRzczNtbW1cfvnlvPnNb+bGG29k7dq1APz0pz/F6XTy5JNPcvnll5PL5Xj00Ue54IILuPbaa2lpaVHTqzo6Oti/fz8AV111FcuWLVPvMTAwwOjoKEajkXPOOQebzaa6GOTzeaampohEIkxOTqr2VgaDgaGhIV5++WWSyeSsqVhaWD+e34MQQgghxLE44m19TaFQoKenh0wmo+bMa+Fq/vz53HDDDfzmN79heHiYX/ziF7zrXe9ixYoVXHnllQwMDPDQQw/h9/vJ5XKUlZXx//7f/2PRokW8+OKLfOlLX2JychKj0ci//uu/8vd///dkMhnS6TT9/f2EQiF0Oh0lJSVYLBZSqRRlZWVUVFSg0+lIJBIEg0FKS0tpa2tj0aJFswKYdpp/rsNBR8vj8bB8+XKGhoaYnJwkHo9z6aWXMjQ0RG9vLz/5yU9Yv349H/zgB7n88sv505/+xC9/+UsaGhp45zvficFgYOvWrRgMBlKpFAsWLOCtb30rfr9fjWD9/e9/D8All1xCSUkJqVQKu91ONBpl3rx5jI2NYbFYsNvtRCIRysvLqaioYN++faRSKdU3VNvO1yZJHc4BMCGEEEKI18MRn9ZPp9MAhMNhrFYrU1NTRKNRdSjqkUce4UMf+hAul4t/+7d/48Ybb+Txxx/nvPPOY+3atarPZklJCZlMhtHRUWpra2lvb2f37t3cd999ACxevJj/+q//YsWKFWzYsEHVVf7sZz8DpltHTU5OMjo6yoIFC/D7/VRVVan+mNp2+8yt+5mO9hDUXGa+ttlsxuVyUSgUWLBgARdccAEmkwmv10skEuFHP/oRq1at4utf/zpf/epXGRkZ4Z577uF973sf55xzDjqdjnQ6TVVVFU1NTVRUVLBz5042btzIo48+CsD73vc+SkpKVB9XmD7cFY/HyWQyZLNZlixZQigU4oILLqCvr4+enh7MZrMKplrtrTZ84HAMDQ0RCATUFKcTTaZFCSGEEGeeIw6nxWKRdDpNQ0MDyWSSfD5PoVDAbDYzOjrKtm3buPTSS7n77rt55zvfyZVXXskTTzzBv/zLvxz2+9x000187Wtfo7+/X40GnclgMKjSAu3wj9bjNJ/PU1dXRyKRoKWlhcWLF8+5ba2VKBxpremhzJw41draSiAQIBgMUlVVRVlZGQ8++CBDQ0Ns3boVnU7HX/7yF77whS/w/PPP88ADDxz0el//+tfVCFTNmjVrePe7302xWOSf/umfVD2t0+kkGo2SyWSwWq2MjY2RSqWIx+PU1taSTCYxmUz4fD41xjSdTh/W0AGYDqbt7e3qsVqJxIngdrux2+2sW7dOvVdnZ6cEVCGEEOIMcETh1G63q1PzDoeD888/X53Wb2pqIhKJ8Pzzz9PZ2clll13GDTfcwE033YTD4eChhx6iWCxSVlY2K1ja7Xb6+vqYmJggHo9zww03cPvtt/O73/2O2267jUQigdFoVL06dToda9aswePxUFVVxdq1a/H5fCxcuJCSkhKMRiORSIRVq1aplkkzZ82fSA6H46A2TRUVFSooX3HFFfz5z3+mWCyyZcsWPvWpT3HJJZfQ0tLCrl27sNvtagzqK6+8Qn9/vypvWLNmDRdffLGaHvWTn/yEp556CqvVysKFC6mvryebzZLJZPB4PGok7O7du1VbK+0wldZ6Spv4dTg1p9oY1PXr19Pe3q5WM7VJW4dypNebmpro7OxUq7Tr1q0jEAhIOBVCCCHOAEcUTrWxnVr7IZ/Ph9VqxWg00tPTA8CFF17I1q1bGRoa4oEHHuD3v/89t956K3feeScOh0NNkEqlUqqVUzwex2q1ArBjxw7e+c538tJLLwGo1kgVFRXE43E8Hg9NTU3EYjHS6TTBYJBzzjmHhoYG8vk84XCYJUuWzLmVD6iWUlp/0+PtwPdMJBI4nU7cbjfXXnst4XAYm81GZ2cnW7duZevWrbMePz4+zgUXXMC//uu/snbtWkZHR1m4cCGFQoFt27bxpz/9ibvvvpvf/OY36v0SiQSDg4NMTk5SKBQwmUxUVVWxd+9eGhsbyefzzJs3jwULFlBVVcXAwIAqB2htbT2imtP29nZWrlyp/l0rj3g1R3u9qalJwqgQQghxBjqqPqd2ux2/3080GkWn0+F2uwkGg4yNjZHP51mxYgUVFRV0d3cTjUb56le/yj333MNFF13E8uXLWbx4MWazGZvNhk6nIxgMks1m2bRpEz/+8Y/Vyfu6ujpaW1vV9nE0GsXtdlNRUUEul6O8vJyamhoqKipoaWkhHo9TU1Oj7nMuWr3l8ao3fS0rVqwgHA6zaNEiYLpe9Oyzz+Z//ud/2L17NwaDQZVHZDIZBgYGGBgYYP369VitVtasWUNvb68a2zpTaWkpTqeTiooKLBYLZWVlmEwmKisrcblcTE1NEQqFKBaL2O12ysvLcTgceDwe/H7/rLZSQgghhBAng6M+ra/VV8L04aV4PE4sFiOTyajQ2draSjKZ5Nlnn2ViYoIHH3yQBx988DVfu7S0lGXLlvGmN70JQNWTTk5OksvlcLvdWCwWKisr1YrrwMAAdrudqqoqtU0813b+8TwI9VocDgc1NTXMnz8fq9VKMBhEr9fT0NDA29/+dlpbWykWiwwODuJyuWhqamLnzp0MDw8TCoWIRqM899xzwPRELZvNRk1NDcViEZvNxsKFC/F6vdjtdiorKzGZTNTU1FBZWYnBYCCfzzMwMEChUGBoaAi9Xq8OSWnBtFgsvm5lD0IIIYQQr+WowqlOp8PhcKhw5XA4yOfzxONxkskkxWKRmpoa1RT/kksuYXh4mEwmw/j4uKod1RrAm0wmdDqdqi11u91cfPHF2Gw2JiYmSCaTmM1m5s2bR6FQoLq6GoPBwJIlS4jH44yPj6v30uo77Xb7nFv4J2o7fy4z30ur8zSbzepEf21tLQMDAxgMBoxGI2VlZcybNw+bzYbJZKKvr4/JyUlKS0vJ5XIqhGpdE7xeL/l8nmw2S6FQUONhJycnaWxspLS0lHQ6jdFoxOPxoNfr1Yl97Z6O9btIpVK85z3vAeChhx5Sf1k4Xtc1cnJfCCGEODMc9crpTA6Hg0wmQ1NTk1rJi0aj5HI59Ho9Op2ORYsW4fF42L59O+FwGJgOR4lEQo0zNZvNJBKJWVOQstksqVSK+vp6SktLaWxsPCj0GQwG1UbKYrGoQzqv9xb+ocTjcTVitampCb1ez+TkJG63G6fTycKFC/F4PKrcQQuj6XQas9lMIBCgWCxSUVGB2+0mk8lQVlbG+Pg4JSUllJeXU15ezsjICLFYDL/fT0NDA83NzZSWltLc3DwrsPv9fvL5PAaDgWw2e9SfK5/P8/jjj6ufj/d1ObkvhBBCnFmOKpxq28B+v1/VLi5atIhwOIzJZCKTyVBTU4PFYlGTowwGAyUlJTQ2NmI0GmlpacFoNDI+Pk4ul1Nb1tFoVLU/qq2tJRgM0tTURENDg6o9nVn3qnUO8Hg8s1ZKtcedDMFUuxeA2tpatTpYUlJCf38/uVxOBe/58+czPj5OIpFgdHSUbDZLOp3G6XRiNBqpr69XQwdMJhPz5s2jWCxSXV1NdXU1er2e/fv3UywWVd/T8fFxstksbW1t2O12LBYLgUAAi8VCMBhUh6Pm8nr3Nj2QnNwXQgghzizHtHLq9/sJh8MEg0FWrVpFWVkZFouFoaEhstksTqcTu92uDvxks1mMRiPV1dVYrVYVpvR6vRopCtOrZWeddRaVlZWqZVSxWFQTpWZu1Ws/a6UGM4Po67mF/1pm3kuxWMTtdlMsFnnzm98MTJ/q12pm4/E427dvx+12k81mmZqaIplMks1mcTgcvOlNb8JgMBAOh+np6SEWi5HL5TAYDGrAQSqVIhwO09bWxsTEBAaDAZ/Pp/5SoLWsMhgMcx62gte3t+mhHHhyX7b4hRBCiNPXMYVTj8dDMBikrKyMeDxOQ0MDIyMjLFq0iEAgQCAQoLa2llAohN/vJ5vNUlJSwsTEhNqynjdvHpFIhHQ6zdTUFFarVR3qWbJkiWrNpK2KAmo19mQKn0fK4/GoQ0k6nQ6fz6eCN8D8+fNV8329Xq9qRuvq6tThsFAopEojIpEIqVQKq9WKwWAgHo+rhvtnnXXWrPDrcDgoFosHrTQf6NV6m75RZItfCCGEOP0dcziduXo5czWzq6uLdDrNyMgILS0tlJeXo9frGRsbw+12k0wm0ev1asxnf38/JpOJXC5HSUnJQW2OtJrNw20af6qZqwTBYDAQjUaprq4mlUqRTCaxWCyqK0JtbS2FQoHR0VEAstksbrebc845h46ODvX9ejwe9Z1r4ddut+Pz+YhGowfdy4Fb+Qf2Nn2jzLXF/9xzz50UwVkIIYQQx8dRn9bXaIF05rZ7IpGgoaGB7u5uampqsNvtlJWVqdrJyclJUqkUJpMJt9ut+p729/dTU1PDwoULD2qifzLVjx6ruYYDzFWSoAVJp9NJS0uL6i07MTGByWTCarWq8gftuywtLaVYLLJ69WpisRglJSXA9CqoFvAPNdXpZNnKfzXaFv9cq6gPP/ywCuISVIUQQohT02GFU+0U9cjICC6Xa87HFItFkskkgUCAVCqF2WzmrLPOwmq1YrfbCQQC5PN5IpGIqi9NpVLodDq8Xi8ul4uzzjpLNY6fmpqaFYq0Q1jpdJpQKDRnwDtehoeHgemgVlZWdsLe53CYTCaKxSLpdBqTyUQikaCiooJsNks+nyedTtPY2KhO4u/fvx+Xy4XD4VD33tPTg8ViIZ1OY7fbVUmF1oIqlUoB8OSTT6rDWHfffTcLFiygoqICvV7PyMjInPenhViA0dHRg0LvsV5/NXq9nqeffppQKEQwGOTmm2/miiuuAKanit17771qGtmBtNXiUCh0WO91qisWi0SjUerq6tDr9W/07QghhBCHpCseRgf2zZs3s3bt2tfjfoQQJ8jw8DANDQ1v9G0IIYQQh3RYK6dtbW3A9P+4vdrK6avRsm8ikVCHck72rfmRkRGWLl16VJ/3cJxs38nhfN6Zf4c5We77aB2v3288Hqeurg4Ar9c767s41LXDuX48RSIRGhsb1UQ3IYQQ4mR2WOFUa/HkcrmOOpxqzzuR2/HHi3avR/N5D8fJ9p0czuedGU5dLtcbfs/H4nj9frX/v9Be68DDbK927XCunwin8u9MCCHEmeO4TIg6Gq9VTTDX6NFT2czPWywWD2qRpQ0WOBUCxIH3/GpOhc8ihBBCiJPL6xZOXy1szuxfCn+d/nQyjR49nhKJBD6fD0A13I9EIvj9flpaWk6Jz6q1oNK6CAAH9Uw9nf5i8Wr0ej2rV69WPx/utcO5LoQQQpypXrdw+mphU/vzyclJysvL8fl86rrRaDztwo3WHF87pV5VVaXGsJ7KQXxmYHU4HKflXywOZLPZ2Lx585zX/H4/99xzj/r5wNZWh3quEEIIcSZ73cLpzD6lxWJRtZ7SVk21pvvpdJpcLofRaFSN47XAeqoFnblKFxwOB3q9XrV+isfj6rPPNblJ+7OZXq/t8pkHtw4Mmk6nU42VPfB+Dvxdv573fDKYq1esTLISQgghDs/rFk61/prw17ATj8dVr86qqip1+lsLNslkkoGBASwWC8ApF05nmhnw3G43w8PD6PV6FcS1oAevvsr8Rpl5P8CsSV3aPWtjUWdu68fjcaqqqtS/n4p/wTgaM8e+Aqxbt45AICDhVAghhDgMr0s4PXDlbWBgQIVSgPLycjW1aGaI1ba7tcedqiuoMDvgJRIJDAYDk5OT6vNqwU/751wlDW/UIbEDp3Np9+j3++c8FBWPx4lGo7NaTs0sW9CC66n6u9QkEgmWLFkCQEdHx0Hfw2233XbUzxVCCCHOVMccTufaup4Zomw226xDP1pQGRsbo7a2FoPB8KohRfszLdDkcrlZAfVUCjYHBrzR0VE18UkL54BaSdW2+rUSCJ1ONyvgaquWOp3uuB5GOnArf+b9awHKZrMRCATUvWi/45mrvU6nk3Q6TT6fJxAIzBqterKtDB+tYrHI4OCg+nloaIhAIEBnZycAY2Njh/1cIYQQQkw7biunMwPSgdvAfr+f0dFRampqVB1eQ0PDawaTmauoMB26tJrUUyXYzPxetMBps9lYsGAB0WgUg8GgVknj8TiTk5PqcRqt7nZmDaMW/OHgbffj8f1oB5zS6TT19fUqEGv3o71XaWkpOp0Oh8OBz+djcnJSbedXVVXR0dFBoVBQ9w0HB/XTwfDwMKtWrVK/I5vNRjKZVNe1wOp2u2V7XwghhDiE4xJOE4kEHR0d5PN5qqurcTgc9PT0MDk5iU6nw+l04nK5SKfTpFIp8vk8BoMBq9WK1+sln8/jcDhYuHChes0Dg4u2EqgF1NLS0uNx6yfcwMAAg4ODGAwGFi5cSFVVFTabjaqqKrXtPTg4SDKZxGazUV5ezr59+9i3bx82mw273U4ymSQQCDA0NEQ2m2XVqlUqnM5c4dS+k+MV/JLJJOFwGIDm5mb8fj+Dg4OEQiHKyspUK6wNGzbg8/mwWq3kcjksFgttbW00NzcD0/99DA8Pqy3+qqqqgwL4qS4YDKo60/b2dux2O+3t7cB0UF23bh3w18NRlZWVb+TtCiGEECet47Ktrx1s0kLU8PAwXq+X8fFxdXCmqqqKbDarejrm83mGhoZIpVJMTU1hs9nw+Xw0NjZis9koKSnBYrHQ0dGByWSiqakJj8dDPp9ncnJyVtA5kiB2vE6+v9b2t9bPtKOjg2AwiM1mo7a2lkQiQTKZxG63EwqF8Hq9bN26lfr6empqanA4HOzZsweLxUJ5eTlWqxW73U4kEmFqagqj0cjExATLli0DZrdw0lYmtX8eaW2n3+/HYDCo8BwIBCgUCmrlc+fOnWzZsoVEIkFTUxMtLS3kcjl27txJOp1mcnKSyclJampqqK2txe/3k8/n8Xq92O12gsEg5eXlJBIJWlpaTss6y/b2dlauXDnrv4lt27aRSCTo7OxUh6MknAohhBBzO+ZwqoU7g8GgVgOz2SyBQACbzcbU1BS1tbXs379f1SpWVFQQi8UYGRlhamoKs9lMSUkJZrMZj8ejTu8Hg0HGxsZIJpPkcjkAent7MZlMWK1WKisrj7q28ngdLprZp9Visai62pGRERKJBOFwmFgshslkYnx8nHA4TDweV+2kent7SSaTDA8P43a72bFjB+FwGL1er2ahT05OYrPZKCsrI51OU15eDqCeF4vFaG5uVr+Loz31PzExgV6vV8HRbreTTqeZmJjA6XTS0dFBZ2en2sZ3uVzs3buXnp4ebDYbuVyOXC5HMBiks7MTvV5POBzGarUSDAaprKzE5/NRUVFx2m3rH0pjY+MZ81mFEEKIY3VctvX9fj+xWAyYXrULh8OsXr2aTCZDoVCgt7eXQqHA8PAwIyMjKphlMhlMJhM2m41isUg+n8fv93POOeeo/zF3uVwEg0EmJyfZunUrDoeDVCqlgk9NTc1R3fPxOpSjlRp4PB61Urh7924cDgcjIyNEIhFisRh2u53R0VFisZhaPbVYLOTzeerq6igWi3R0dBCLxYhGo6xatQqLxUIsFsNsNuN0OnE6naRSKcxms6pBLRQKavv/1e7tWD5fsVjEZDKxY8cOEomE2rbXWmF5vV6y2Swmk4lFixbR3d1NsVhkbGyMcDiM3W6nUCiwYsUKDAYDpaWlpNPpWau6QgghhBCaIwqn8XhcreZptKbxhUKB8fFxmpubqaioIJFIkE6nVXgyGo1UVFTQ19fH5OQk8+bNI5fLkUql8Hg8xGIxQqGQWnmbP38+lZWVmEwmDAYDo6OjVFRU4HK5KC0txWg0Yjab1cSlI51NfzTBbWZJwIEn5LUV02QySVlZGcPDw8TjcQKBAFNTU1itVsLhMOXl5WSzWbxeL3q9nrq6OnQ6HTabjUKhwNDQkPp8XV1dmEwmGhoa8Hg8jI+PU1JSQjabJRgMEgwGGR4eVrWsc93bzFP/cOgyBq1eeGBgQK36ut1upqamVGuo2tpadDqdOonf2tpKZ2cnmUyGSCSiAv/g4CDl5eVUVVVhsVjYs2cPdXV1uN1uMpkMXV1djIyMsGjRIvX70L7LQ5VMnExhVqfTqXZQh7o213f+WteFEEKIM9URhVPtJPJMOp2O5uZmhoaGyOfzhEIhAoEAwWAQt9vN2NgYo6OjGI1GKisrCQaDRKNRhoaGyOVypNNpEokEsViM0tJSKisriUaj9Pb2qrBktVpJJpOUl5dzzjnnEAgECAQCZDIZDAaDOlXudrtfswZVCwLHazs/Ho/j9/sJh8MEg0EWL15MX18ffX19RKNRpqam8Pl8FAoFcrmcCsVTU1NEIhEikQj5fJ7m5maMRiP5fJ5YLMbY2BjpdJrR0VHy+TyZTAaHw0FdXR2VlZWYzWY2b95MIpFgYGCAVatWzXlvR1LX6fF4SKVS+Hw+vF4vlZWV2O12JiYm8Hq9FItF5s+fz8TEBJOTk0xMTNDa2qrKOCKRCJOTk2QyGcrKyvB6vcRiMRoaGhgdHaW/v1+1T9Lr9dTU1FAsFjGbzWQyGRYvXqxCtva5tD63Wrutkymc2u129u7dC0zXlb7atdd6rhBCCCH+6ojC6cyRmzP7Xno8HuLxOIVCgYGBAUZGRlSdqN/vp7e3l1QqRW1trdrSraysJJ1OEwqF1KqbVkuqBTmYXs2rra3FaJy+1UAgAEB9fT3j4+MEg0GmpqZwOp3q1D+c+IlEM1de4/E4W7duxWw243a76enpobu7m3Q6jc1mQ6/Xq9AWDAaxWCwEg0GMRiORSESd1NdWpXU6HZWVlRQKBcxmM4ODgwwODpLL5bjiiivUxCyz2azCbUdHx6xwfmCQO9waW4fDQSaTQafTqUNYe/fuJZvNYjabsdlsaoCCXq+np6eHaDSqamkHBwdJpVI4HA6qq6tVDevExAQjIyP09fVRV1dHY2MjoVAIl8tFV1cX5eXl6iCW3+9n3759qqtDY2PjnEMJTlXSVkoIIYR4dUccTmc21G9paVFb11oQSyQSjI+P09vbS11dHW1tbej1eqLRKJlMhkAgQDKZVPWZWgApFotEo1FaW1vViE+j0Yher1ctiyKRCL29vZSWlrJ48WJ1H8FgEJfLNatf6My+nyciqGrB3OfzEQqFsFgshMNh+vv71QSo+fPnUygUGBwcpKamhsnJSfR6PXq9nmKxSDgcRqfTMTw8zObNm1myZAl1dXW0tLQwf/584vE4FouFyspKBgcHKSsrY2xsDKvVqnqk1tfXA7Bv3z518Mbj8cxaMdXCXllZGU6nU30X2mNmHqDSTutbrVbGxsZIpVIUCgWamprUpC5tNTMSiVBSUsLQ0BDd3d1ks1n1nvF4nL6+PtWxobq6mpKSElXiUVlZSV1dHaFQSJU8VFdX4/P5VOutyclJli5dqj6T1gHhUL/L49WN4XAd2Hj/UNxuN3a7/aC2UhJQhRBCiL86onCqndLWxor6fD72799PNpvFarVSVlamVkqHh4dJp9NkMhlefPFFhoaGZr1Wf3//nO/xwgsvUFJSQkNDAytWrGDx4sX09vbi8XiYmJggEomg1+vZvXs3qVSKaDRKU1OTCkYzVzO1f84M08cSUGcGnXg8zsDAAH6/n3Q6rdpkRaNRQqEQ27dv55lnnsHr9apV4Nfy8ssvYzAYePnll9m2bRtOpxOLxaJaZ8H0inFnZyd+v5/6+nrq6+spKSkhmUyyf/9+fD4fS5cuZenSpcB0WNNaRIXDYZxO50GTtjRDQ0PqLxgWi4VUKkVPTw+hUAij0UhJSQmdnZ1s27aNbdu2EQ6HZ4XBlpYW/vEf/5Err7ySX//619x33334/X4ef/xxTCYTDoeDJUuWYDKZ6OzsJBQK4XQ6qaysxGKxsGvXLvWXmvr6eurq6mbd48k2Waqrq4v29nb1HdjtdtxuNzC9Ur1mzRoANm/ejN1up6mpic7OTgKBADt27ODDH/4wb3nLW9i9e/dp2VZLCCGEOBpHfFrf4XCoFVNtTvzOnTtVDWEkElFTnKLRKNu3b2doaAij0cgll1xCQ0MD9fX1xGIxtW0ci8VIpVL4/X727NlDLBZj3759jI6OsnnzZs4//3zVqiidTuP3+0kkEtTU1FBWVkahUGBqagq9Xk8ikWDx4sXAX2fAp9PpWbPdjwdtVTOTyZBKpQgEAmqb/le/+tWs0ZUWiwWj0Ugul6NQKFBRUaEa0dfX17N27VqKxSLr16/nlVdewev14vV6sVqtNDY2YrVa8fl8lJaW4nA40Ol0TE1NqYNoVVVVVFdXqxC7d+/eWU3utXIMbbIUTLen0vqjaiUTiUQCm82mphvZbDbC4TCTk5Ns376dwcFB9uzZQyaTUa9dV1fHkiVLuPbaa7n22muxWq0AfPazn+Wzn/0s69ev56c//alqkbVz505GRkaYP38+NptNHQpKJBLs27eP8vJy7HY7559//kGB7XAPsR2vNmGvJRAIqGD6/PPP09jYqFZBte4L2s+apqYmmpqa1PSovr4+GV8qhBBCzHBUraS0AONwODAajWSzWdLpNIFAAL/fT0NDA2azmcnJSXVQ5Ctf+QrXX3+9CguRSASXywVMT1HasmULO3fu5JprrqG2tpaf/vSnbNmyhWg0SiAQ4K1vfSvJZBKXy0U4HCabzTI8PEyhUKCyslLVnOp0OnK5HKOjo1gsFpxOpwrTxzJrXvt55oEqgOXLl/P0008zOjpKOBzG7/errffbbruNv/mbv2Hp0qVEo1H1nHA4rD67tvIaj8f5+c9/ztTUFP/93//NL3/5S+LxOPv371fvPzExQSKRYPXq1ZhMJkpKShgYGFCrkkajkZGREVpaWujv70ev11NfX69CmraCq9UIR6PRWZ+xqakJp9OprjU3N9Pb26sOZWnBtLy8nPe+9728//3vp7y8nLKyMgDGx8dxuVwUCgWeeeYZXnzxRfr6+rj55ps555xz+MIXvsBzzz2nVkAvvvhidbAtl8sxNTWFy+XCZrOxdetWFi9ejNvtVt/5zJZZM38XB3ojVliXL19+UqzmCiGEEKe6owqnOp1OjbLUVu5GRkbIZDIqGOzfv5+BgQHy+TzXX389H/3oR5mamkKn07F9+3Z27drF2NgYe/bs4bnnnpu1GvfQQw/x8MMP89Of/pRvfetbhMNhfve731FeXk5TUxNtbW3odDpVtxkOh3nrW9+KTqdTTfvtdjuBQICSkhI15/1YzZwpD9MhKJlMks1mVYnBE088AcA3vvENrrvuOnV4qVAosGHDBn7xi1+oQKqVG+TzeQCWLFnCl7/8Zb71rW/x+c9/nt/+9rc8/vjjGI1GysrK+NOf/qT6xK5evVqNFg2FQqTTaUwmE263W9VnVlVVqdVUt9utQqpOp1MHpxwOB6FQCJgOrQaDAb/fr4YjlJaW4nQ6eeKJJ8hkMrz73e/mq1/9qhqVOjU1xS9/+Ut+9KMfkUqlMJlMxONx9ZoAGzZs4OGHH+aRRx7hzjvv5Lvf/S5er5dHH32Us88+m4aGBvU52tvb6e/vx+FwEA6HueSSS9R3PbNO9tVopScny9a/EEIIIY7MUTfhHxwcVIdgVqxYQWNjIzt37iQQCDAxMaEmH11wwQV85zvfYWpqinvvvZdf/vKXB9WfwvTsdq2h+0MPPcS5557Lhz/8Ya6//nruvPNOHnzwQTUeUzvtr9PpsFgsuFwuamtrGRgYIJVK4fV6qa+vV8HVarWyZMmSYworWmujWCxGd3c3ZrOZYDCIXq8nnU7jdDrx+XwqZK9bt04dIBofH+czn/kMf/7zn+d8bYPBgMFgoKOjg+uvv561a9fyrW99i0984hN84hOfUGUJ//Zv/8add96pepy63W7MZrMKr9rhsoqKCnVyf2BgAIPBoEaGaquN2gSoRCJBMBhU9+Lz+dRYWZfLRVlZGRaLRdX0/uu//qsK3Nlslq997Ws8+OCDB32mkpISLrroIkZHR1V95caNG/niF7/IhRdeyE033UQoFGLr1q1EIhFWrFih7mdychKj0YjX6+Wxxx6jtLRUHRDTfheHGl17svVDFUIIIcThO6JwWiwWVa0pTPeqLC8vp7KyUvXhzOfzvPLKK6RSKS655BK+8pWv8NOf/pS77rpL1dmVlJTQ0tJCU1MT9fX1tLW1cc4557Br1y4++9nP8r//+7989rOfBaZrGu+++25uueUW7rnnHu6//34GBgawWq0qgOn1eqampnC73QwMDGA0GlWvzXA4TD6fV/fs8/kAqKqqOmgV7lAnu+PxOFarlX379jE1NUUqlSIUChEOh6mvr1db04AaHDA2Nsa2bdv4zGc+QywWQ6/X8+53v5umpib1/nq9nvnz5xOLxfjVr37FE088wSuvvMJll13GHXfcwQc/+EHV4/XKK6/kzjvvJJvNqnCZyWRYtGiRmtZUWlqK2+2mubkZQE2u8ng8c9Y2+nw+/H6/+v1qv1ftMNvExARlZWXo9XoAUqmUqrP953/+Z7Zu3YpOp+ODH/wgbW1tlJSUoNPpsFqtlJeXk8lk+NKXvkRfXx8f+MAH+P73v8+FF17Is88+yyc/+Uk2bNjAnj17MBqNLFy4EL1ej8FgUKfftZKMhoYG7HY70WhUjUKVECqEEEKcfo545VTbttfa4oyOjtLb28vk5CQvvPACzz77LKlUire+9a3cf//9PPDAA9xxxx3AdND4wAc+wGWXXcb+/fupra1Vc+lLS0s5//zzqaqqwufz8b73vY93vvOdXHjhhSxfvpzW1la+//3vE41Gefjhh9m3bx+rV6/GYDBQVVWlRp9qY00zmQwrV66kqalJHQTy+XwMDAyoelktnB7qAM2BNabV1dWMjY0xOTlJOp3G6/WSyWRUEIXplkE2m42nn36aL3/5yySTSVpbW/nSl75EW1sbvb29NDY2AjA6OorL5cLlcvH+97+f//f//h9f//rXef7557n99tuZmJjg05/+NGazmWXLlrFo0SK6uroYGhqipqZGdUOorq7GarXS19eHzWZT9zxzSpTmwOlWB9LpdOp3qw0M0EoPzGYzLpeLf/zHf2Tr1q1YrVZuv/12zj33XPr7+6mtraWjo4NcLkd1dTVms5nbbruNz3/+8+zatYtLLrmEv/3bv+W6667j/vvv53e/+x233norO3bsoLe3l8svv1zVvWotqAwGw6zfgcFgmHW4SwghhBCnj6M6rR+PxyktLWVgYICtW7cSCARwOp289NJLhEIhli5dyv3338/u3bv50pe+BMCNN97I9ddfj8fjIZ/Ps2PHDr73ve+xa9cuamtrufDCCzn33HP553/+Z+666y46Ozvp7OzkrrvuwmKx8LGPfYwvfvGLfOc73+Hpp58mHA4TCoVobm5my5YtpFIp5s+fTygUYmxsDI/Hw9jYGKtWrZoVQpPJJAaDYVawOZwDNFqYHRoaYnJyknw+z8TEBNXV1WSzWUZHRxkfH0en0/GFL3yBW265hfvuuw+Ac889l09+8pOUlpby8ssvq7KAaDSqJi9ls1nWrFnDddddx3/8x3/ws5/9jHvuuYfvfe97ZDIZvvnNb6LT6fjHf/xHPvWpT+H1epmammJ8fByTyaTae2njRzds2MDatWtZsGCBWkWd+fvTPm9VVRVTU1Pq++np6SESiTAxMUEul1PtsrRt/cbGRn75y1/y9NNPYzab+drXvsby5csB8Hq9fO1rX2N4eJj6+nr+5V/+hfr6eqqrq7nvvvv46le/yu7du/nzn//Mn//8Z1atWsUDDzzAD3/4Q2699Vai0SjPPPMMjY2N1NTUqGlhk5OT7Nq1C4fDQW1t7WFNAns9aSNdZ9Imp2k/H8lzhRBCiDOZrngYfWwikQilpaXqlLnWDH3Xrl288MILqgfnD37wA3Q6Hffffz/V1dW8733vIxQKsWLFCj7xiU/Q0dHBli1b2LVr16w6x5kqKiq46qqrgOk+klqfTYBly5bxgx/8gA0bNvDVr34Vs9nMFVdcwejoKB6PhyuvvJJoNEokEiGbzdLa2qoa2jc3N+Pz+VT7pJaWFvWeB66cjoyM0NjYqCZPaeLxOK+88grPPPMMyWRShVGbzcZ9991HsVjkjjvu4PHHH2fz5s3odDouvfRSLBYL27dvZ3R09DXbBrW2tnL99dczb948Ojs7+e53vwvApz71KW699VZ1KGnPnj20traydOlSkskkNTU1aptfC+CrVq2iubmZ5cuXqxGgMwcVaMLhMIsWLeLpp5+mv7+foaEhTCYTNpuNzs5Ourq6eO6556ivr+fJJ5/k/PPPJxKJcOONN7J48WKsVitbtmzhwQcfnNXT1Ww2c9FFF1FWVqZKIM466ywKhQIPP/ww8XickpISvvGNb7BgwQI++9nPsmvXLgBcLhfLli3jrLPOIhKJ0NjYSEtLC7lcDo/HQ1tbG01NTTgcDmw2Gzqd7rBbSM38/WpdE47Gtm3bWLVqFVu3bmXlypWv23OPlPb/v8f6eYUQQojXwxE34dfpdKrVUD6fJ5fLEQ6H2bx5MzB9EGj16tV8/vOfJxQK0djYyMc+9jEKhQI/+clP1Cqd2WzmnHPOYenSpWq86d69ewmFQjzwwAMsX76cG2+8kTVr1vD000/z7//+7+zevZsvfOELrF+/ngcffJB9+/bx4osvYrPZMBgMahpRJpPBYDAwMTFBKpUiHo+r0ZhacDkwyLxamNEeBzA8PEwgEFB1t8lkkkgkwpYtWygWi6xbt47BwUHVdP1d73oXQ0NDsw5ClZeXYzAYKC8vx2KxMDU1hcfjIZlM0t3dTX9/Pz/5yU+44447eN/73gfAd7/7Xb773e9y/fXX09LSwg9/+EMuu+wy1S5qwYIFGAwGKisrCQQC6PV67HY7qVSKiYkJ9u7dS1tbG+l0WvVKtdvt+P1+crmc+suF1i82FAqRy+VYunQp1dXVbNy4EZgOzo8++iiRSISWlhZuvPFGtmzZgs/n41e/+hUATqeTuro6gsEggUCAv/zlL7O+z+3bt/PjH/+Yt73tbdx5553s3buXW265hR//+Mc888wz3HvvvXzrW98iFArxwgsvYDAYKBQKpNNpCoWCOvgVDAaJRqO0tLSoFcqTrUm/EEIIIY6c/mie5HA41BjMefPm4fF41NZkXV0d6XSaDRs2APB3f/d3WK1WHn/8caampigtLeXGG2/k7W9/OxdeeCGVlZU0NTVxzTXX8LnPfY6//du/xWQysWPHDu655x7y+TyXXHKJWkF85ZVXyGQy/OhHP1Jtj7RT8T6fj3A4rA7vDA0NsXv3bvx+P4FAgIGBAfUZBgYGiEQis1YQ56IFnsHBQUZHRxkYGKCqqory8nLMZjMdHR2Ew2Gqqqq44447eOSRRwD44he/SFNTkwrtZ511FjfffDMf/vCHOe+887jqqqt461vfSmtrK4sXL2bFihW8613vwmazMTk5qe71fe97H+ecc4767ABr1qxR5RIjIyMAalpUdXU1NTU1VFdXUygUiMfj7N27l6GhIRKJxKzQpvVG1coehoaGsFqtWCwWdVhr0aJFVFdXAzA2NqZWkisqKjAajeTzeX7xi18A06ud8+bNw2azcd555/HOd76T0tJSPB4Pb3rTm6irq6NQKHDXXXdRUVHBt7/9bTXK87bbbmN0dJR/+qd/4tlnnwWmD2gZDAZSqZQaZet0OikUCthsNgYGBlSpxsxpXRJMhRBCiFPXEYXTYrGotqUdDocaO2m322ltbQWgs7OTRx99VNUz6vV6Xn75ZR599FEAVqxYgclkYnJykk2bNvHHP/6R7u5u/vznP/PCCy9QX1/Ptddei8lkoqurizvuuIOOjg70ej11dXXkcjmefPJJFi9ezBe/+EUANdtem/leX19PVVWVquUcHR1VvUD9fr+a7qQFmXg8js/nOyioaiuk6XQau92uVoq1WfMWi4WJiQkA/u3f/o1HH32UqakpKisrcbvdPP/886rNVFVVFfv372fLli2Ew2H+9Kc/8bvf/Y5QKERvby+9vb3k83l1UOqll16ip6eHnp4e9WcvvvgiExMThMNhrrnmGmw2mzqUZbfbVfjP5XKMjY0xNjampniFQiGSyaT6Hc5VXlBWVkZNTQ3Nzc0YjUampqbo6uqipKQEmB45q01A6u7uZnh4mMceewyv16sGHvj9fnw+H8lkkkAgQGNjI2vWrKG6upply5apQ1vf/va36evr48Ybb1RDCj7wgQ8wMTGhfg9a3abWo1Wn0xEOh1XrrJqaGlVioq3kv54n+FOpFAA33HCD6kShSSaTrFmzhjVr1hx07bWeK4QQQpzJjmrl1OfzMT4+Tl9fnzpcpM0UHxkZYc+ePQBcfPHFVFRUqH6oHo+HtWvX4na76ezsZOvWrXR1dbF582aeeeYZNm7cyL59+2hra+Oaa64BYOPGjaRSKRwOB+effz4wvTVsMBj4x3/8R97xjneQzWbZtWuX2nbXVgC1+rpCoQBMjxH1eDw4HA5cLhctLS3Y7fZZ28EzaTPmtZGtTqcTl8tFX18f+/bt4+mnnyafz/O3f/u3vPOd7+Shhx4C4PLLL8flcrFv3z4AVq1axdjYGOl0mnQ6zdatW+ns7GRkZISuri7cbjc1NTUArF69GoDdu3djs9morq7m7LPPBmDXrl3YbDaMRiOVlZVcf/31wHS5QVdXF8lkkomJCfr6+ujt7SWTyWCxWNTWeGVl5azPp31ubVtfa8/ldruxWq0kEgkmJiYwm82YzWYKhQL5fB69Xk8sFqOkpIS9e/cCsHTpUgqFAg6Hg2w2yzPPPMOmTZvYs2cPGzZsIJlMYjQaWbVqFQBPPfUUf/nLX3A4HNx5552Ulpaye/du7rnnHnU632Aw0NPTQ6FQIBqNEg6H2bt3L93d3YTDYQwGAxaLRa2Kj4+PH81/zkdNC/gdHR3qvzFNoVBgy5YtbNmy5aBrr/VcIYQQ4kx2ROE0kUioufahUIjdu3ezZcsWhoeHGR4eBqbrErVZ7Vqzdi2kLVy4EJ1Ox+7duxkfHyebzWI0GlUYSaVSdHZ2UiwWWbhwoQpsXq8XgAsvvBCA5557DpheWbviiiuA6TAQCoXw+Xz09vYyPDxMXV0dbW1tVFdX09DQwJIlS1SI1lZLta1u7aDQTHa7fdaf19XV4XA4KBQKBAIBFere8pa3UCwWVVBbvnw5u3fvJhAIYDQa1Wl2QH1/mmw2q74fmJ4SVVVVRTwe5wc/+AEwfRBMp9PR2dmpVmoBFi9eDEwHnUwmg9frxev1MjY29v/Ze+8wucq6//81vc/szu7M9r4pm0oKIYQSQBQQUGmiz6MgAgoI+igqYv2iIEoRGypFiQIqiqCogLQAgSQkkL7Z3WSzvc7s9N5/f+zvvp3dbCCBhKLndV1c2eyZcu5zTjjv8ynvD/F4nMrKSul7WmwnFY/HZe1sIBCQv1+1ahW1tbVUVlbS2toqj53D4aCmpgaANWvWyO999dVXMRqN8vgD+Hy+KVOvVCoVqVSKV155hVQqRV1dnTxnf/vb3/jpT3+Ky+Xii1/8IjDZVKdWq1m4cCHZbJbOzk7C4TDxeJxQKEQsFpOTtUZGRnjllVcYGxvDYrGg0+lQUFBQUFBQeG9zyOI0m81K0SZGWBant+fPny/F6djYGN3d3VK4Ajz44IM8/vjjFAoFdDoddrudyspK3G43KpWKaDTKU089xc6dO6W47erqYnR0lNbWVjQaDT09PezevZtoNCq7/g0GA3V1dahUKrLZrGxWam1tZe7cuTQ2NhKPx+nv72f37t0yFRyLxTCbzbhcLsxm85SUd/HvY7EYJSUlzJkzhxNPPBGXyyWFmc/no729XY5nDYVC3H///QCUlZXx2GOPMTQ0xJYtW+ju7gaQ3fAA+/btY8eOHbI+9KijjkKlUvHcc8/x29/+lkAgQFtbGwD/+Mc/iEQiRCIRuZ8Gg4GqqioMBgPxeByr1Uo+n2f16tUsX75cOhPs2rVLrl1ETEtLS6ecX5ic1nXOOedw+umns2rVKrRaLStXrpTfL1L769atkyn/gYEBstks4XAYmGx4E530Go2GcDjMiy++iMfjwWKxsHDhQgAeeughrr76atxuN0cddRSJRIKvfe1r3HLLLeh0OiYmJhgaGqKsrAyTySRrnROJBCMjI/Ic2O12OaK2t7eX3t7eN6wnfrMMDAywZcuWKQ8Vb4Vt27bNODVNQUFBQUHhv5FDTusHAgEsFgtOpxONRiP9L0dHR4HJyKkQL+l0mmg0SqFQkGlb0cADk1FDv9/P8PAwExMTUmyNj49TXl4um290Oh1ut5vGxkZpu7Nly5b9Jhf19/cTjUbJ5XKEQiECgQC9vb1y9OiWLVvYtm2bNOsXQkcgIsOiBlC4E4h57aLWsaamhkAgINeeyWTo7e0FJidPuVwu+vv7gcnaw56eHiKRiGzcUqlUqNVqCoUCer1eHtdIJILT6WTWrFly3vz69evRarWceuqp8u8OhwOHw0FJSYncbyEOE4kE0WiUkpISCoUCarWaBQsWyJpNES0OBALyIaPYB1aUMYgJVIlEgmAwSCwWw2azySYlcQ6WLFkCIEfFCiupdDot610NBgMqlYp0Os2mTZvwer2UlpaybNkyNBoN27ZtY9u2bfy///f/UKvVPPXUU9jtdu655x5gslRkx44djI6OSlGu1Wrl8AIRHRYiXIj3IyFOBwYGaGtrY9myZVx22WVv+nOKSyyOP/542traFIGqoKCgoKDAmxCnxZG2+vp6GhsbSaVSMgX/0EMPydS51+uVkTUhFEQ0dDrFDTqiAUikhovTtSKCuG/fPgDe//73o9VqiUajJBIJstksmUwGo9FIJBIhm80yMTFBOp2W3d2ZTGZK+lxEDKfXYIrfiZGnMFkjODw8jM1mk8I4n8/z3HPPAdDa2orZbJbiQ61Wy9epVCrcbjcajWY/4/XiNXZ0dMho87HHHgvArFmzAKbsmxDqyWSSeDxOLpfD5XIxZ84cGhoaGB4eZnh4mKGhIYxGIw6HQ45NLS0txWKxTBnjajabZdOYcAuoqKhg8eLFuFwueV52795NW1sb0WiUjo4OmpqayOfzZLNZKVyLEdZW06mrq5P1tMFgkHnz5nHGGWcAcO+993LBBRfIEoCxsTHZmOZ0OvH5fNjtdqqrq8nlcvT19cmorM1m2+/B43AhyjkeeOABXnrppTf9OeJYwuRaRamFgoKCgoLCfzuHJE6LazBFlDEUChGJRDj++OMBePLJJ2UdoxAUGo2GbDZLLpfDYDDMWBsoxKnRaMRoNNLf3y8FbTweZ2RkhJ6eHik4uru7SSQSuFwuLrnkEmBSsKrVajKZjBRquVxOemNWVlZisViorq6e0ghVPF++OJIohGlfXx8dHR3s3r2boaEh+vr6cLlcsqwhFArxxBNPAJN1qYODg9L+SUQxVSoVhUIBn88na0TFf4JsNsu2bdtYu3YtAKtXr2bevHnyGMNkRDKTyZDJZKitraW5uZlMJiOboaxWqyxjELPoh4aGyOfzzJkzR4ryA9XYWiwWOf1qcHCQsrIyFi9ezNy5c6mvr0en09Hb28snP/lJANrb25k3bx5Go1GWRAgxLshkMrI7XURvPR4PyWRS/t7n89HR0SGb3h566CGGh4elAM/lcjQ0NMjaV51Ox969e+no6KCnp4dAIEA8HsdkMtHQ0CAfPMQ1VOxSUCzw3yxtbW1TaonfCqKGV0FBQUFBQeEQxWlxpC2RSFBdXU0oFJLRMqfTCSCjfoFAgNraWpmiTiaTNDY2cvbZZ8u61GJ0Oh0LFizgqKOOYvHixTIN3tjYSElJCWVlZfI7ROTMarXyzW9+E51OJ8eBirGemUxGNkr19PSQSCRk5LZYjAKyyUaktMVrfD6frHHdunUr4+PjxONxgsGgrK/MZrNyitUJJ5zAkiVL+OQnP4larSabzXLWWWdRWlqKWq0ml8uRy+XI5/OyichoNLJ8+XIcDgfbtm2jUCiwfPly7rjjDs444wyWLFki6xtFBDSbzXLffffR09MDTArz3t5eRkdHpThTq9XSt1Sk5kUD2EzjP0X5gs1mkw8UQ0NDhMNhNmzYwNjYmCxDADj//POBSYH6ta99DZiMIp9yyimynhYglUrJKLjZbJYWSyaTSR43nU6H1WplxYoV1NXVEY/Hefnll1m1ahUwGVn1er0MDg7i8/no7e1lbGyMoaEhcrmcnBAlLKxee+01xsbGZkztHw5xKhDuBoe67WC2KygoKCgo/DdyyGn9WCyG1+vFaDQyPj6O2+2mpKSE8vJyKeosFgtLly6lUCiwdu1aTjvtNAwGA7lcjh07dvDII4/IhiiB2+1m2bJlss40EAgwPj4uO7dhUvg89NBDAJxyyinyvXfddReZTIaSkhJKSkqwWCw4HA7MZjPhcFhOKxKTogBZGykEWiAQwGAw7CdmSkpK0Gg0aDQa0uk04XCYcDgshSZM1tmKbvgtW7YASPN5gOHhYXQ6HVVVVVNEG0wKQuHzuW7dOmKxGPX19Vx44YUy9f/II49Io/vzzz+fxx9/nNbWVunzqtVqpWg3GAxYrVY0Go38nWCm9U3HbDbLqUvj4+OyJthqtcp6W5gUpDfeeCOlpaX09fXxwgsv0NLSAkzWxep0OjkFS6vVYjQapTcuTD6oPPfcc4yPj2O322WkWaVSSQswrVbL/Pnz5fmKxWJEo1H27t2L2WwmGo3KyHM4HCYajcoouEajIRgMzpjan6nE4M1gsVjwer3yoeZgt03fPv2aUFBQUFBQ+G/mkMaXFgoFaeWTTCZxu90kEgkpOITY1Ov1XHbZZVx11VW88sorHHXUUXzlK19h06ZNhMNhXnnlFXK5nBQtIiIai8XQarX09/ezc+dOYDJ9mk6nicfjPPzww3R3d2M2m7n44ouJRCJ0dnZy2223AbBo0SL0ej3l5eW0trbKGkTRuGS1WnG5XFI0CgN+g8Egjd4tFosUPC6XC41Gw/Lly/H5fOh0OimIGhsb5edWVFRw3nnn8atf/Yo//elPzJo1i5KSEmw2G+Pj43R1dUnzfhF1FtTU1JBIJNi+fTvZbJaKigrOOussIpEI4+PjPPXUU/ziF78A4Morr+R973sfF1xwAclkkvLycurq6mhqasLhcODz+WhqaiIej+N2uwkEAvj9frRaLVarVaa+xdqKz+v0n2OxGPl8HpVKhU6nY/bs2TgcDux2O3v27KG9vR273c61117Ld77zHdauXcvs2bPlMYVJcWkwGNDr9fLaUKvVDAwM8NprrxGNRrFYLFxwwQUA0qdUlIXo9XpUKhU1NTUMDw9jNBrle2w2G5lMBo1Gg9VqpbS0lEAgIBvSxBpnEoaKGFRQUFBQUHj3csiRU4vFQiAQIBqNMjg4iM1mI5lMolKppCiZmJjg/PPP5+yzz6ZQKPDnP/+Zuro6zj//fL71rW/xsY99DJhM97a1tVFXV0dbWxtz5swhGo3S3t5OV1cXMDm+c8mSJQwNDfGTn/wEgE9/+tNUV1djNBr59re/TTqdloMAcrkc4XCYiYkJHA6HbIAym81YrVZpPVXcCCQcCKanui0WCy6Xi8bGRpYtW8ZJJ51EY2OjFGDRaBSYTM9ef/31LFq0iHg8zj/+8Q9ZpwmT3ebC/1OI3Pnz53PKKaeQzWbZvXs32WyWyspKvvOd73DMMcdgtVr55S9/yY9+9CMArrrqKr7//e/T3t5Of38/Go2GRYsWYbFYCAaDGI1GmpubKSsrY2JigrGxMTkRSoi+eDwuR74WUyyWhTsBTIrDVCpFeXk5arUak8kkBfnOnTv517/+xSc+8QluuOEGAPbu3ctJJ50km7dEtDOVStHS0sKpp55KLpdj/fr10sT/Bz/4AWeeeSZz585l7ty5U85RbW0tZrNZNsH19fURCoVkDW6hUGBwcJDh4WHS6bSsJXa73cyfP19aSxWvq3h9CgoKCgoKCu8+DilyCpMp0fLycqLRKK2trUQiEUwmEz6fj5aWFjweDw888AAXXHAB3//+99m5cyd9fX1cc801nHbaaVx00UV87GMfY/PmzXR3d7N161ZgclzndNra2jjppJP45S9/yZo1awC44IILuPLKKwH43e9+x0svvYTJZKK5uRmr1Sotm9LpNPl8XtZIRqNRIpEIZWVleL3eKcKl2IGgGJEeFiK2vLxcNtqEQiHq6+vZt28fP/rRjzjllFP42c9+xqmnnsqGDRt46qmnqKurY86cOVJo6/V6tFot8Xic9vZ2adqvVqs59thjWb58OYVCgQcffJDnnntOlh5cdNFF3HTTTahUKtl45XQ6MZlMJJNJOWteCOvR0VEaGxsxmUzodLop07Ki0eh+652pBtPtdsvfJxIJ3G43HR0dOJ1O6urqGBwc5NOf/jSXX3451157LZ2dnTzwwAP885//5KSTTuKYY47B6/WyefNm/H4/mzdvZvPmzfLzm5qa+OAHPyjLBGBSzH7rW98ilUqxcuVK5syZQz6fl04QogZZjJGNRCIymgqTE6Xi8TixWOxtGWGaSCSku8ATTzwxJSL7etumb//+978PTLo0wOR1JuzYFBQUFBQU/ts4ZHEKSGEXj8flDHe73c6KFSvo7+9nZGSEq666iieeeIKbb76Zn/3sZ6xfv56///3vrF27lrPOOov58+cza9Ys2tvbicViMu1rMplobW2lsrKS8vJyvvKVr7BhwwYAPve5z3H11VeTSqUYHBzk29/+NjApLt1uNw6Hg3Q6TaFQkKbter0ep9NJS0sL9fX1U2yjiv8UnffFiMhfsQ9oLBaT/q6rVq3C4/Gwbds21qxZw//+7/9yzjnn8Kc//Yk77riDk08+mQ984AOsWrWKYDDInDlzKBQKPPnkk0xMTOD3+6mqquLMM8+kpKREfk4oFAJgxYoVfOELX6ClpYVUKkU2m+Xpp58GJqOKbrd7She/RqPB7/dTWVlJPp/n6KOPBiYj2SI6KqZcFXOgGkwxhAAmBXQqlSKfz2O1WnnyySfp7+/n7rvv5uGHH+YrX/kK559/Pn/5y194/vnnMRqNrFy5kssvv5zHHnuMgYEBYrEYDoeDD37wg7S2tkq/VZicAnbPPfewY8cOzGYzP/3pT6UrgahDFmNk9Xo9uVyOZDKJ0+nEarWSTqfx+XzyfBaXLRQ3ucHUMoa3Qj6f54UXXpA/H+y26dudTidms5lPfOITwORx7+joUASqgoKCgsJ/JYckTuPxOKlUSooEr9eLx+MhFothtVpxOp0sXbqUQCDA4OAgl156Kffddx9///vf+dvf/sZ3v/td+vr6+OMf/wjAnDlzOOmkk1i4cKGcKS/S3vfffz8///nPyWQy6PV6/t//+39cddVVwGTzywUXXEA4HMZisVBaWkqhUGBgYEBGCrVaLcFgUJraFwuy4sYg0bAzk0Cbbrc0MTGB2Wymt7eXwcFBObkoEolw0003ccwxx3D99dezY8cOOjs7eeyxx6iuruaEE06gpqZGDiiYP38+VVVVwGTKv6amhoceeoi///3vwKSn6Y033siZZ54JTEbZTCYTP/3pTwkEAtI7NZvNynpSs9mMw+GQc+gXLlwoa2hhcoRsMpnEZDLJ7xZieyZELa54//j4OOl0mmw2i9PpZMmSJRiNRoaHh/H7/Vx//fX87//+L3/4wx+47rrr6O/v5/nnn6evr48PfOADvO9972NiYgKj0ci8efPk8bTb7fzrX//ixz/+MfF4HK1Wy2233caCBQuAyVKB3/72t8BkJHvu3LmEQiHS6TQ6nY6GhgbmzZsnR982NTWhVqtlDWppaemUB5G3I6J6qNTV1dHR0cHExAQdHR184hOfYGJiQhGnCgoKCgr/lRyyODWZTDKaaLFYpK9mPB4nHA7T3NyMzWbjySef5JVXXuG0005jzZo1nHPOORx11FE8/fTT3HfffXR2dtLV1SVT3kuWLOHEE09k7dq17NixQ37nMcccww033CA7wXO5HFdccQWvvPIKpaWlzJo1SzY52e12gsEgTU1NRCIRORO+pqYGk8kkLZwCgQAajUZOfTqQYCl2IBCUlJRQV1eH1+tlbGyM2tpa6WBwwQUX8IMf/ICf//zn3Hzzzaxbt46RkRHpMCDQ6/XU1NTgdDoZGhrC6/XK6Nqll17K9773PekV6vF4ePbZZ3n55Zdlx35dXR2zZs1iyZIlNDY2otVqqaiokJ/ncDhIJpPS6kpgNBpl45dobps+dEAg6mrFsbFarVRXV2MwGPB6vRQKBUwmE4sXL6a/v5/Nmzfz4IMP0tPTw5NPPslf//pXfvzjH9PX18fdd9/N/fffz/Lly0kmk2SzWXw+H6Ojo7KGFSaHCtxyyy2yZvWJJ57g0ksvJR6PU1ZWRmNjI7lcTtYSq1QqSkpKSCaT+P1+8vm89E1NpVL7Nb6JSPi7kfr6ekWMKigoKCgocIji1Gw2S1En/t7Q0IDX6yWZTJLL5TCZTJhMJkpKSvj973/P4OAgp59+Otdddx0rV65k1apVnHLKKezZs4eenh6ee+45nn/+ebZu3SrrT1UqFccffzynn346Ho+H66+/nsrKSo4++mg2bNjA008/LefJiwib2+3G6XRit9sJBAIyfT1nzhyqq6tlvaLNZiMej8t0eGNjoxwoUBxphMnIXnFUVXhSZjIZ6urqZLNRW1sbDz/8MIFAgCuuuIKVK1dy7bXX8rnPfY59+/bx6quvsmfPHrxeL6Ojo6TTaTn/XeBwOLj00ku57rrr2LlzJy+88AJPPfUUr7zyypRzMGfOHGpra+UI2ebmZsLhsDw3TU1Ncv9ElLt4TcW1tkK0zRQ1FuNLBcI7taOjA5vNRiwWI5fLUVZWRnNzMzqdju3bt7NhwwZOPfVULrnkEtasWcNjjz3Gww8/jNfrZd26dft9j0ajobq6mvPPP5+rr74atVpNNBrlnnvu4cYbb5T7OWvWLGbNmkU2m8VqtcrpULFYDJfLRSKRIJlMMjg4KJvWilP78O/IqbgWFBQUFBQUFN59HJI4tVgssrEG/u15ajAY5FjJiYkJ2tvbqaqq4n3vex/bt29n3759UmgIVqxYwcUXX8zvf/97enp6+MMf/sD69etZsWIFZ599Ns8//zw333yzbHYBZDOQSqXCZDJhtVqxWq0Eg0HpOypmrsOktVRtbS2BQGCKhVRfX58Up2IdIqpWLOSm15yqVCrMZjN2u53GxkasVisNDQ10dnZy3HHHsWvXLkZGRti4cSMXXHABp556Ktdddx2XX345oVBI2lR1dnbKyGtVVRVLly4lEonw4IMPyrrdYqxWK2VlZVRWVlJaWko+n5flCJWVlYTDYRkRnD17NjabbYqNkhi/WuxGIAz3i62zxO8PhBjLGovFqKqqIplMkkqlZLnD3Llz2b59O8PDw9x4440YDAZOO+00rr32Wmw2m7SDqqmpweVy4XQ6aW1tRafTkU6niUQiPPDAA/zqV79idHQUmKwdrayslJZUDocDm82G1WrFYDBQV1cn64vb29txOp3SvaG4OapYbAvj/0NhYGBApt0VFBQUFBQUjhxvqiFKIOoSA4GAvPknk0mCwSADAwOYzWbOOussnn32WTo7O8nlcrIZZdOmTWzatIlrr72WpqYmmpqamDt3Lk888QS/+tWv5HcYjUYqKiqwWq14PB6i0Sj19fVotVoWLFhARUUFs2fPJhwOk8vlpGCdM2cOc+fOlRE+IVI8Hg/xeBybzSajiNNT2PLgFKX84/E4u3fvJhqNkslkiMfjGAwGTCYTJ554Ii+++KIUxS+88AKjo6M888wzPPPMM5x00kmccMIJLF26lNbWVhKJBNlslnA4zLPPPsuXvvQlhoaG5Peq1WrKysrQaDS43W7a2towmUyEQiFKSkpIJBJy6pZGo5GNYDabjebm5ilrEONXRTf/G9VcipGfB3qtOGYajUaOIIXJpp7q6mpUKhWDg4NEIhGi0SiPPfYYjz32GDDZ7Ga32+W+VFVV0draitVq5emnn2b79u3yezQaDRUVFcyaNUuuu6SkhMrKSlwuFxUVFZSVlVFaWkoqlaKhoQGYtAUTAlpcn9Ojw8XWWQfDwMAAbW1tsvxBOFYoKCgoKCgoHH7ekjgV4kWj0RCJRIhEIiSTSfR6PSUlJaTTaUpLSzn66KNpaWnB7XbLaU2ZTIYdO3YQi8Xo6OiYEpESoyyXLVuGWq2mUCjQ1NTE8PAwZWVl0j7IarXicDikYXxZWZmcTiVqYoW3qEajIRaLyYhpcQe32WzGZDKhUqnk/HWYWnMqRO7AwIAUpSUlJbJusqSkRE5Fqq6upqOjg3379tHd3c3zzz/P888//7rHUqPRUFVVJY+XxWJhz549ZLNZqqurZcmE0Whk0aJF1NXVkcvl0Ov1lJaWksvl5PkoFmUwKQqLyzFej+Iostls3i+Sajabqa+vx2KxkEgkGBwcxOl0kkwm0el0JBIJKisrqa2tpa+vj/7+fjlWNpFITBGG7e3tPPPMM/LvKpUKh8PBwoUL0ev1ciqVx+NBq9VSKBSw2+04nU5cLhfxeJytW7dSX1+Py+WSPrdDQ0MYDAZSqRTxeHy/9P6hji+dmJggHo/zwAMP0NbWJq2eDlQSUXysXo/DNalKQUFBQUHhP4m3LE6FCBRoNBry+TxOp5Pu7m7Gx8fJZrO43W7pP6pWq6mqqiKdTkuxEgqFSCQS1NTUUFdXByCbb5LJJPF4XFobqVQqbDabFC6ZTAaHw0FVVRWLFi2iqalJCkwR0RWCpNiWaDoiDSz2ScxrFyKvoqICj8eDWq2Wdavl5eUYjUaCwSBDQ0PE43H8fj8Gg4F58+bJoQWZTEZaOun1eul56nA45KjRiooK6YQQi8UoLS1ldHSUwcFBamtraWhooKqqisbGRtnBb7PZqKysJBaLSfN9YZzvcDhkBPRgoqbiuMwURS4WqaJZq6mpidLSUnp7e4lGo0SjUSoqKvB6veh0Ourr67Hb7eRyOdLpNP39/bIRS6vVks/n8Xq9ZLNZXC4XtbW11NTUkE6nGR8fR6vVolarsdvt6HQ6nE4nlZWVcrSqELzJZBK1Wk0ikSAajWIymUilUpjNZgwGw377/2ZFYVtbG0uXLt3vWL3ecTwQb7RdQUFBQUHhv5W3JE4FojEqkUjQ19eHRqOhpqYGi8XCwMAAY2NjWCwW/H4/ZrOZfD6P3W4nGo3KSU4iGtbc3IxarSadTuNwOPB6vTJFLNLCs2bNIpfL0dDQQEdHB4ODg1RWVnLqqafuJ6qEmbwQKU1NTXJbsd+l2HeDwcDExIT8vYgkarVaGhoaZDTW7/ejVquZmJigvLyclpYWjEaj7BrXaDSyLCCXy1FZWUkymWRkZASNRiMtmXK5nGy8qqyspLq6mkKhIGs4S0pKKCsrw2q1smrVKumJmcvlpEeoWHMul2NkZGSKl+nrpegPdC4PRryJ41BSUoLRaMTn85HP56mpqaGhoYHy8nI0Gg2jo6NotVoSiQSzZs3C4/Gg0WikuA6FQsTjcWkjZbPZsNlsFAoFufZcLodarcZqtQKTnfg6nY5CoSAfVMT1ISKoovxgJqH9XohYKob8CgoKCgr/rRwWcSoQIz1jsRg2m42Wlhaqq6sJBAKMjo5K0VVbW4vdbsdgMNDd3S2jSFarlZqaGhwOhxRgp556Kr29vQQCAbRarfQwdblcVFVVMTAwgMvlorS0VNaTCoEiGphEJPL1BJpIhU/v5J4eSRSNVV6vl87OTuktWhwVbW5uluuJRqPSWcBisfDKK6+gUqnIZrOUl5eTzWYZHx9HpVLJxp9wOCybzBYsWEAqlWL27NksW7ZM2iXt3r0br9dLNBqVa7bb7bLudbp90uH29yw+rsIxIRKJYLVayWQyuN1uVCoVdXV1jI+P43A4mJiYwOfz0dfXR3NzM+Xl5VitVjZv3kwsFmNkZIRcLie9UEtKStDr9QwODpLP57FYLOh0OsrLyykvL98vXT89ij/97+8FRCmJYsivoKCgoPDfylsWp8XpUovFItOwarWahoYGTCYTiUSCZ599lnQ6jdVqpaqqikKhQHl5OZFIhEwmIzu//X4/DocDlUrF7NmzMZvNGI1G8vk84+Pj+P1+AoEAFRUV2O123G43yWSSWCzGzp07KSsrA5Dpf7FfM4mU6fsu/hweHp7y+5neK7rhPR4Pe/fuJZPJkM1mMZlMsi4zFApJ8epwOAiHw6xatYq+vj7ZVCW65XO5HIlEgpaWFjKZDENDQ7Ir/dhjj0Wr1aJSqWRKHSZrSdPptIwKTxdrcOjG8wczd774uBZPkfJ4PIRCIXQ6HT6fT0bJ4/E4VquV8vJy1Go14XAYp9OJzWZDr9ezfPly9u3bB0yWcqhUKtmlX1z6Ia6ZYgssESEVjgTF9bZHWpgmk0nOO+88AP7yl79gNBoPatvrba+vr1cM+RUUFBQU/qs5rJHTYt9Tl8slaz09Hg9Go5Hq6mrZ5SyERnl5uUyTh0IhWUtaXV2N2WymtLRUpmHT6TSBQACr1So7vg0GA06nU3a2p9PpGUXaG1EsQg827Suihz6fj2w2i9FoRK/Xk8/nqaqqoqWlhX379hGLxRgbG8PhcMg07Y4dOwgGgwQCARoaGkgmk8yePZtMJkN9fT1VVVXSNH/6oABRj1osDA9knTRTU9ORoNimqr+/n1wuJz1lATm1SUTFYfJ8mkwmdDodJ598Mh6PB4/HIyPYZWVlsgRkcHBQjrgFpjS2Ff/dZrO97mCFw0kul+Pxxx+XPx/stjfarhjyKygoKCj8N3NYxamgtLRU/iw65O12OzabTabr0+k0c+fOZWxsDKfTyfDwMIlEgoaGBilMAWlTBcixmyaTSXZLV1dXU1ZWNkWkvd01hfX19ZjNZtra2qRQBWRXv91ul6M7haD1+/1y2pR4vVarpby8nEwmQ1VVFXa7Xa7rQFFe8QDwetZJbwciwi38U0UkUzykGAwGNBoN8O+muUQiQSqVkrZQ9fX1clCBaBozmUxUVFQQCoXQ6/X4/X7mzZsnha8Q46LRrVgkv1XeLd6mSv2pgoKCgsJ/E4dNnBaneoVgEEJFNLm43W5p3C9EV3V1NcPDw5jNZkKh0JToqsFgYHh4mFwuh0ajkXWmIhooup0PpeHncK8XJgWRSC+LNcJkBFGn06FWq3G73ej1ehnNXLp0qXyd1+uVY0eFgb5Y1/SoZ7F5vmjoErWl8G9v1rcjWir2RyBKDkStp9in4ppf8bAyOjqK0+mkvr4et9s9xVFBiG63243H4yESiUgfU+HkIM63KGlobGw8rOt6N3ibKvWnCgoKCgr/jRz2yOl0oSgihYAcE5rL5aRAFV3w4r2xWIxwOEwgEJA351wuNyWFfaDvejdQ3O0uGpYAOULVYrHQ19cHTApZk8kEIFPZjY2NUyZSHQziuAnrqHcDxQ8p08+TRqOhpaWFVCo1JdIrBKkQ7cXvb2lpmXJci8W4+N3hjJofyNv07USpP1VQUFBQ+G/kiKT1pzM97SzsjmDmJh6v10tpaSkWi0WKlXeL6DoUxLhPjUaD3W6fEjmESQEnamffSir+7awtPVgO1Clf3OUvmF5Pm81mCQQClJaWykiweL34XCHGzWazFKuHo5xheip/urfp2830+lMlxa+goKCg8J/O2yJOi9PO0+2OpguqYhP66TPR303i60BMT/eLyF+xaCoeBCDsrkQa/FCE+LvteBxsp/9MEW9RoiCEZ3Fpw3RLrOnvn15K8mZ5N6TyD8RMKf5HHnlE2qYpQlVBQUFB4T+FgxKnopt4aGhICsuDpVAokEgk5ISnYh9R0X1/IILBoHyfGC/6djA4OAhMipWSkpI3/Tli7SMjI3IN8O9UtN/vx+/3A0w5Rul0+q0t4BA5XOt9KxQPRBDm+uLYCd7o2LzR9SQQ633yySflgwLA3r17icfj/OQnP2HWrFk4nU7UajVDQ0Mzfk7xGFRRN30w2w5m+3TUajXPPfccfr8fn8/H5Zdfzumnnw6A0WjknnvukTZq0xGRenGt/adTKBSIRCJUV1dPsV5TUFBQUHhvoCoUq4IDsHnzZlasWPF27I+CgoLCYUGM/VVQUFBQeG9xUJHT1tZWYPJ/9ocaOS2mUCjIUZXFNYnF+nim7Qf7msPF0NAQ8+fPf1Prna71Z4r2xmIxuf+A/PlAdaPiM4/Uug9mvQc6/sVrKI4GHmgfD+b4HGkOtN4DHeeD2eeDOUcHuv6PNG/lei5GWLcBjIyM7FcrfKBtB7P9cBIOh6mrq5sSFVdQUFBQeO9wUOJU+FMW14u+GQqFwpT3i5t88c0/lUphMplkE1Hxe2Hy5u/3+6Vv5lvZnwMhPvPNrLd4Pw801z6ZTMo1AlPW+3rCR+zL4RZ0B7PeNzpH4k/RnHSgc3Mwx+dIc6D1in2bvr6D2ecDvXf6a2a6/o80b+V6LkZcr+KzpjswHGjbwWw/ErzbarIVFBQUFA6OI16QVSgUiMVicu598e/Ff8JiSozzFB3aM1UciI5/Ycv0bqW4iadQKJDP5+VxgH/7kRavtxhxbIQvbPGxe6cR+wzMuG+i7rP4HE+n+PhMf+1BVJocUYrXJ67beDxOX18f4XB4xn2e/l5x/b7Ta1FQUFBQUHiv8bZ26wurn+IIFEBfX5/0uxRMH8dZPBVJ/PluFKfFwwg8Hg+BQECuwev1YjAYsNvt0rRfCNC+vj5cLpf8vWB6p/o7RXEUSqTziy2cAGkXVjwhzOv10t/fLy2kis9f8Zqmj159Jyg+dzOtb6aHIq/Xi9frnbLPxUMFiq/f93okT61Ws3z5cvnzwW47mO0KCgoKCgqCt0WcThciQnCJqFQulyOVSmE2m/ebmT5dmL1bRel0xH5GIhEikQjxePyAEV+v10sqlcLr9e4nTqcfuwMhhOGRPj7Tv6d48tP0KVUwubZQKEQ0GsXlch3wPE5/gHk3MNOxn77f4twNDQ3JcatC3L5bHiwOFyaTic2bN8+4zev1ctddd8mfp1tbvd57FRQUFBQUinlbxGlx4wz8+6YfCASIRCJoNBrmzZs35T3FQla8vjiy9l6IQlkslimepgKR2hcTolwul5yYNT0NfLAG+9PF3eE+PtNHpRZ7kYrIolarlcb4+XyeQqEgJ3y90fSqgxXhbyfTr1vxszgW+Xxenjun00kulyOdTuP1euVappeozFRnXfz79yIz+cMqY1YVFBQUFN4sR1ycFt90xQ1ZeH56vV4SiQRWq1UKtumjT0V6dePGjWSzWebMmbOfkH23UjyOs7iEob+/n3A4THV1NcuWLZsixIVInWly1uvxdog7kaYWIkSI0lwuRyAQkOdqeif6woULZ6yrnb7/7yZheiCxKGqki2uoxWjdWCzGxMQEkUiEaDSK1WqV42hnKlF5J5vCDifFo14BZcyqgoKCgsJb4m2JnE5H3JQBysrKSKVSMp0/MTGBwWDAZrPR2NgITKYJh4eH8Xg8WCyW94w4hckoaSQSwWazYbFYMBgMRCIRGX0UxyEcDtPe3i4nEomaVfj3pKnXY3qU70ggmtEGBweZmJigtLSUxsZGUqkU+XyegYEBEokE1dXV2Gw2DAYDgUCA8vJyHA7HEd23txOPx0NfXx+hUIhgMIjBYGDRokW4XC4p1AFZR32gFP97Le0fj8flv73du3fvd7195StfedPvVVBQUFBQELxlcVqcnpwpZVkcHRJTnsRNuTjCJkSaRqORE6mKI1Q6nY6amho5ZUl8h5gmNP27pt/8Dkfa9ECp2JmmG830etEUVVpaSqFQwOl04vV6gckJUZWVlaRSKRmFK66/fSfEy/R1iegsgF6vJ5PJTIkCZrNZTCaTnOIUCAQoLS2V50N0r6tUqjdsgHon0twH01lfHP33+/3EYjFSqRSjo6O4XC7sdjtVVVUyCl5MIBCYUrpRHO2e6d/Ru41CoUB/f7/8eWBggImJCTo6OgAYHR096PcqKCgoKCgciCMSOS0WHsXNT+LvExMTwOS88OniFCbHltpsNinQDAYDixcvJpFIUFdXN+P3wL+7/oEDplKPxBqLxVUikZhSohCPxwmFQtLnMZFISA/MhoYGxsfHSSQSjI2NEQqFppQBzFSveiCOpKApXqsoN+js7CSZTLJt2zbsdjsajYa2tjYCgQCJRAKv1ytFqvDWnH5dhMNh2ckvSiDerRG14n0XkWyNRkM2m6W9vZ1AIIDP52PFihVyfblcTgpUEXUuFqzis96LYm1wcJBly5bJ0gaTyUQikXiH90pBQUFB4T+BwyZOi2/e01PZw8PD+P1+SkpKGBsbw2KxkMvlZIq4rKwMr9dLIBBAo9FQW1srBWyxeXdJScmUCTvTvTKnd8NPF8aHQ6SKdQqxMb27XAjqRCJBKBSSUWCDwUBHRwc9PT2MjY3R0NBAeXk5qVSKYDBIX18f8XicaDRKVVUVFouFpqYmmUKeyWaq+DsPhwiPxWIkk8kDdtIXH8eysjLGxsbo7u5Gr9fT0tJCQ0MDGo0Gj8fD6Oio9HdNJpP09/fT29tLLpfD6XRSW1tLPB4nn8/j8XgoKyuTQv+drsWc/uAhPE7Fg8/0euDx8XFyuZx8uBCfMTQ0RCaTkQMjUqkUuVyOzs5OOS1JPMBML+F4u9wX3iw+n0/Wmba1tWE2m2lra3und0tBQUFB4T+AwyZORSRseioTJm/Ue/bswel0YrPZ0Gq1GI1G+vv7sVgsjI+P09fXh1qtRqPR4PP5pBBQqVQEg0H8fj+hUIhZs2YB/xYI0xttii2MxPZUKnXYOtmnWyaJ7yoWxKlUivHxcZLJpOxY93q90lbKarUSiUTYt28fzc3NpFIptFotyWQSjUbD+Pg4NpsNj8dDV1cXXq+X1157jWXLlskGm5n2qVgoHyiy+3prj8fjFAoFvF7vlO8RDxxDQ0OyHjiZTEohG4/H2bdvH1arFaPRiEajwWg0smvXLpLJJM3NzfI8BwIBSkpKKCkpkUJuYmKCVCo1xS9VCP93QphNP57TBz8UCgU8Hg/JZBKv14teryeRSJBKpUin04yOjjIyMkIymcRoNNLR0UFpaSnJZJJ4PI7dbqe3txej0cjAwABms1leUzPZUInz8G5M97e1tbF06dIpmQ9ApvrLy8uVxigFBQUFhUPiLYvTYuNyYUg+NDSE2+2WQnNwcJBIJMLExAR1dXUYjUay2Sy5XI6RkREpZkSkce/evZhMJuLxOK2trUxMTDA0NMTY2Bj5fB6NRkNpaSlarRaXyyX3QdzEvV4v4XCYVColjd8Pl9ARnyUsk8R3iS51g8GA2WzGbreTSCRIp9MYDAYZGYtEIpSVleHz+RgZGWF0dJRMJoPP5yMajaJSqejr68Pr9VJdXU04HKa3t5dYLIbNZsPlcu0nTotrQYVQFuKmr68P4KAcAEQdZXFEuLh5LZfL0dHRIQW30WikUCgwPDxMIpGgp6eHyspKHA4HmUyGvXv3EolEKBQKHHXUUeRyOYxGIwaDYUqUEaC0tFR6wcL+Dx1vB8XXsrheVCrVlAeeDRs2UFJSQiAQYPv27fh8PnK5HFqtlpKSEjKZjBSu/f39GAwG1Go1Op0OlUqF1WoFJkfY7tu3j4qKCmpra6mpqQGYItAB2Uj2TkeTDwWTycQnPvEJ4N+2UmVlZe/wXikoKCgovFc4bJFTYaezYcMGotEoGo0Gk8mEVqtlYmICn89HIpEgGAwyNDSE2WzG5/PJJplUKoVeryeVStHd3U02myWRSKBSqWRdXjabpb+/n3nz5pFKpeT0pend7EIo53I5+vr6aGxsPGRrptdbZ3GqNx6P4/f7yeVyhMNhVCoVBoOBYDCISqUiHA4TjUYxm81Eo1HUajX79u2jt7eXcDiMwWCQgtZqtZJOp2lvb0etVpPJZDjuuONIJBJkMhkmJiamdPiLGs2ZJjYJgRWPx9FoNNKi6o3WZrfbpwh5j8eDx+MhFAqRSqVIpVIyZa3X6/H5fAwMDODz+TCbzezatQuLxSJLOIxGI319fcyePZuqqipGR0fp6uoCJgWaiD77/X5MJhM+n4+ysrKDcig4UkwXgOL4btu2jcHBQflQsWfPHsbGxmRzm8/n49VXX8VoNMooeFdXF6lUCqvVit1uJ51OM2fOHJkWX79+PYsXL6a0tJTy8nLGx8cJhUK0tLRgt9vlg8h7qbN/y5YtxONxOjo6pK2UIk4VFBQUFA6Ww9atn8/ngck03sTEBF6vF7vdTigUYnR0lFwuR6FQIJFIEI/HaWxsJBQKkUgk0Ol0jI2NYbPZqKioIJ/PS3HW1dUl7XrUajWFQoGOjg56e3vR6XSUlpYyODjI3LlzpfgSozKLx6IerkYbEcHq7+9naGgImGxyymazRCIRdDodqVQKmBzTODExgdlsprW1lbGxMV544QWy2SyBQEBGID0eDxUVFdTV1aHRaDCbzQwODpJMJuno6KC6ulpGXtvb22XHdzwel539MHWue7G11EzCdHonvvhTuCEUp2n9fj/RaBSHw4HVapXRv76+PpLJJKlUCpPJRGlpKfv27SMYDGI0GqmrqyMej1NZWcnmzZs59thj5fCFl19+mZUrV8rpWbNmzUKj0aBSqcjn8wwPDwNQU1MjJ0sVn8MjHUkUxyQej+P1etm3bx87d+5Ep9ORzWYZHx+ntLRU1hZnMhl6e3ulv6fNZiMUCpFOp0kmkzICOzY2RjabxW63o9VqMRgMhMNhxsbGUKlUdHV1YbPZZAlBceS62ND/nTDzV6lUB7RxK95WX18/o1uG2P5uLE9QUFBQUHj38JbF6XSRUFNTQzqdJhAIsGvXLvL5PNFoVAqYUCiE1+tFq9UyNjYmI0wiKidqUqPRKD6fj9HRUXp6eqiqqkKj0aDRaOjo6CCTyVBeXo7b7ZaTlqan74trJg/XDVHU1vb29qJWqwkEApSVlUnjdY1Gw9jYGBqNhuHhYdRqNUajkXw+T3t7O88++ywAmUwGh8NBTU0Ner2eWCyG3++XaX8RKRWNUdXV1Wg0Gvx+P3a7HZPJJJtrDAaDFJVi7WK94vgcDCqVaorVlziWmUyGfD5PKpXC6XQyNDREMpkkFovh8Xjk6/x+P+FwWIpynU5HKBQiHo+j1+vZvHkz4XCYzs5Oecx6e3ux2+309/dTU1ODwWAgk8kwOjpKJBJh7969LFu2jIqKCul7K87DkY4kxuNxdu/eTSAQ4KWXXmJoaIhUKoXdbsdisUibrCeeeIJUKoXP58NqtRKLxSgrK5PXfTQapaSkhGg0SjgcplAoEIlEqKysRK1Wy4yCwWCgvLwcrVZLY2MjJpOJ/v5+2VxYvP6Z9vVIp/3NZjPt7e3AZHT0QNve6L0KCgoKCgqvx1sWp0IkvPbaawSDQaqqqqiurqa9vV3aBFksFhwOB3v27GHTpk2MjIzwzDPPyM9QqVTo9XrMZjOdnZ3odDqMRiMOh4NkMoler5cpYlGrmkqlMBqNVFdXEwwG5b5Mr//UarWH3Z5oZGRE1tS2tbUxMjKC3+/HbDYTDAalaNVqtYyPj5NKpXj88cdlNFDg9Xrp7u7GarXS0NBAOBwGIBQKYbVaSaVSDA4O4nK5aG1tZXh4GKvVKi17RkdHcbvdspFsYmJCugMcjEARgkZ8Xn9/v3RYEFG6WCxGRUUFwWCQcDjM5s2bpYAMh8Pk83lZdpDNZhkaGiISibB161b5PUIw1dXVUVFRQWlpKSqViuHhYVKpFOFwmLlz59LQ0CAb2dRqtUyHz2RRdLgnYs3UHS/soETzl3CTCIfDxGIxKisrefTRR9m6dSsqlQq1Wo1WqyWbzZLJZIDJaKder8fhcEibqUKhQC6Xk+NPzWYzmUxGOh/Mnj37dc/XTAL0vZT2V1BQUFBQeD3elDgtTiUWe5cGAgFGRkaYO3cuVVVVjI2NUV1djV6vZ8+ePbS3tzM4ODjj54nIqZiuA5Mzu2tqalixYgWlpaWEQiHZAa1SqdBqtTJSt2fPHoaGhvD7/ZSVlVFeXk5NTQ1arVY2CR1q17Ooh53up1pdXU1fX58UIDqdDrVaLb098/k8TqcTvV7Prl272LFjB5lMBr1ez2WXXYbNZsPn89HZ2SlrdNvb22XKvqGhgWOOOUY2XZnNZvr7+4lGo8RiMYLBIE1NTTINv3jxYmKxGNFolGAwiN1ul7ZPr+dOIASN8KqMRqPynBbbGQl3BRFFLCsro6enh/Xr16NWq2VqPxQKTfl8sf+jo6MyyiqEtvCrVavVLF++HLvdzp49exgdHaWyshKr1Upra6scAzo9+ls8wOFgUtszlTEU/1z8YNPY2IjRaMRisWC324lEIsyZM0dGkIXl1p///Gdee+01+Vm5XE5eE4JsNiuP8bp161ixYgUVFRXU19fLGuKWlhZyuRxWq5V4PE5PTw9lZWW0tbVNWbfH4zlgmcrhFusKCgoKCgrvFG85ciqETH19Pa+++iowKXIikQhqtZpsNsvTTz/Nxo0bSafTaDQabr75Zs4++2zKysrQ6/UMDg5KYTs0NITX6+W5555j8+bN7Nmzh3Q6zbHHHiuFqE6nk81Tw8PDGAwGGd0yGo0kEgmcTicwGQ3M5/O43e7XTYvORH9/Py0tLTNGpYRwFc1Lg4ODDA4OEo/HGRsbI5VK0dnZKQXbqaeeyg9/+ENaWlpIJBKybnN0dJS1a9fy5z//mWeffVY6HuzatYv6+npmzZpFMBgkl8sxMDBANpuV0bbi+t5EIkFHR4fcP71e/7qpYFEDWSx0rFarjJx6PB6i0SihUAidTkcwGGRwcJCBgQEpzJLJ5JTP1Gg0nHXWWXzqU5+ira2NyspKCoUC69ev57HHHuPBBx/E6/Xi8/no6elhxYoV1NXVEQgEpBuDEHJLliwhFAphsVgYHR2dYtQPzBhBPNjU9vRIrHifmGjl8XhkbXQgEGDTpk3s3r2bYDBIaWkpXV1dtLe3y8+55ZZbOOWUU+QDgslkoq6uDqvVSnd3N0NDQ9x6661s2bKFp556itraWpYsWUI+n5c1tscdd5wsYygpKZmyXovFQjgcPuAoWHEuj7QwjcfjHH300QDcfffdB9y2efPmKeK5o6ODRCLBJZdcgk6n22+7goKCgoJCMW9anE4XAvX19VRXV7Njxw45c3zbtm3s27dPNgi1trbyi1/8guOPP55kMiltg6qrqykUCoyPj3P++efjdrv59re/zQ033MAtt9zC8PAwo6OjZLNZVCoVZrOZUCjEnj175Cz6SCQiu52FWEwkEuRyOVmPeah1eQMDA9hstilTm4pvqnv37pX1n5lMhkAgQDQaZceOHbLUwOVycdNNN/GRj3wEvV4PTEbT/vnPf/Laa69xyimncNFFF3HRRRfR39/Pww8/zK9//Wt6e3vZs2cPAwMDjI+PU1JSItPcwo5JWFA9//zzGI3GKeb/LS0tcsLUwdLY2EgymaSvr4/h4WEGBgYYGxtDq9USDAbZs2ePtE4SwvSWW27BarWiUqlYvXo1dXV19PX1kUqlyGQy6HQ6jj76aE455RSuv/56vv71r/P73/+e8fFx1q9fz+rVq/F6vWQyGekBWl5eTlVVFel0Wp4zUU4gZtjPlMI+2NS2iBQXv0/Ue8Kk76pwYNi1axd79uyhv7+fZDLJ0NAQ3d3dJBIJysvLue2227jgggtkmUnx5wHMnTuXZcuWceqpp3L11Vfz0EMPMTAwQDAYpLa2VpZQ9Pb2Mjg4yOjoqJw8NT4+jtvtpqqqCpj8d/JODiooFArs3r37DbeJaHR5eTlms1naShW/VkFBQUFB4UAckjgV0UKYOjUIkOl4jUaDXq/n2WefZXx8HICmpia+8IUvcMYZZ+BwOGT6dGJigkcffZR//vOf7Nu3D5i8Ad9zzz0sW7aMz3zmMzz44IMMDw/z/PPPy/1YsGABdXV1UqxOTEzImeYTExMUCgWGhoYwGo3odDoaGxulwCwWL8U3yeIpPSI1K0RtsZ1PLBZjYGCAkZER6U1aX18vSww2b95MNBpFq9Vy+eWX8+Uvfxm73c7Q0BBdXV2sXbuWhx9+GJ/PB8Ctt97KqlWr+NznPsfixYu57LLL+OQnP8maNWv46U9/Ku2JdDodNTU1rFy5EpVKxb59+5g3bx6xWAyfzyeN8S0Wi+zub2xsJB6PzyheCoXCfml9MS/d4/Gwd+9ema4X6WSdTofBYJjS+f/hD39YRoGffvppPvrRj7Jr1y65vbS0lJNOOomrr76a1tZWvvvd73LWWWdx+eWX4/P5ePbZZ1m8eDFNTU0kk0lMJpO0lDIYDLL5qLe3l2Qyic/no7GxcUYBOj21XVxHWvxQcaCUuFarleUThUKBcDiMVquVNk8jIyPs3r2bbDbLKaecwh133EFlZSXRaJS+vj7uvfdeHnnkEZxOJ/PmzWPu3LmsWrVKRhS///3vc/bZZ/O5z32OUChEd3c3y5cvp6qqis7OTnw+H3q9nlAoJB8KxEOZy+ViZGSEkZERaREmBPA7kcrv7Ox8w9fU19fT0dHBxMQEW7du5bLLLnsb9kxBQUFB4b3OIYlTEdkSDA8PEwgE5H8wOWJ07dq1jI+PYzabue2227jwwgvRarVEIhFUKhVPPvkkd999Ny+//PK/d0SrxWq1MjIywjnnnMOdd97Jhz/8YW6//XY++9nPylq/fD7P7t27CYfDzJo1S0bcTCYTNpuNQqEgbam8Xi9Op1OKL5GuFtOIiiNQYuwo/NsWSxj4e71eGQ1rb2+XlkHRaBSPx4Pf76e9vZ2NGzeSSCRobm7m17/+NXPmzGHLli3ccsstvPLKKzKCDFBWVsbSpUtZu3Yt69evZ/369RgMBpYuXcqqVau49NJL+exnP8uvf/1r7rrrLgYGBujr62N8fJxFixahVqvZvn277NTv6enBYrEQCARoaGiQ0dXXG906Pa0fj8dJJBIMDQ0Rj8dlrWk2m2VkZIRgMEgkEuHvf/87gPRn1Wg0dHZ2ctVVV8n35fN58vk8gUCARx99lL/97W+cd955fOlLX+LDH/4wlZWVnHPOOYRCIV5++WXpbxoIBNizZw+9vb243W4WLlzI0qVLZa2lyWSSDxHTnQmmr3H6lKXpwxrEe8X7RClDMBiUwx1effVV6fG6Y8cOstks5513HnfeeaccTdrf389ll13Gzp07gcnRnnv37uVvf/sbMPkwdc4553Deeedx9tln09bWxsc+9jH27t3Ltm3bZBRWOCKo1WosFot8CBB2VNFolFwuRyKRoKWlRTZovV21psX14pdddpksLXk96uvrqa+vn7GpTUFBQUFBYSYOWZwWk8/n8fv99Pb2Mj4+Lqc3icjZTTfdxAUXXIBWq6Wrq4tf//rXPPLII7IjW6VSceKJJ7Jq1SrOPPNM1Go1X//613n++ee5/PLL6evr41vf+hbnnHMO6XQanU7HZz/7WX77298SDAYpFAqyjlWIIY1Gg9PplB3sZrOZgYEB4vG4vJEWN5UUjyMVaXAROS0vL5ejTwE5YjSTyaBWq6murpZm+uvWrSORSHDKKafwwAMPYDab+f73v89tt902xQP22GOPZfXq1Zx99tno9Xr27t3Ln//8Zx555BF8Ph8bNmxgw4YNrF27lqeffpovfvGLXHXVVTz99NN85Stfob+/n127dpFOp5k1a5aswXU4HPj9fpxOJxaLhUWLFsluf4PBgNvtpqmpacr5E3WNQjSLUZwmk4nZs2fLyHBPT48cECCmPun1en7wgx+gUqnw+XxcdNFFxONxli5dym233UYmk8Fms9HZ2cl9993HunXr+POf/8yTTz7JTTfdxCWXXMLjjz/OZz7zGdkMZjQaaWlpobKykmAwiNPplCltYeKeSCRkRF745R5ImB1qk5DFYpFR4FQqRVdXF+Pj43KSWSqVoqWlhbvuukv68D7xxBN89rOfJRwOY7PZ+PznPy9Hz7766qusX7+eXbt2sWvXLn784x9z9dVXy/N5+eWX8/TTT7N161acTifz589Hp9MRjUbJ5/MsXLhQNl/Z7XYGBgawWq04nU7sdjsajeZtHfMqov0AL730EnV1dcpoUgUFBQWFw84hidPiiJPFYkGtVlNVVUUsFkOj0RAKhdi0aZO02Tn//PMZHBzknnvu4Ze//KV8r9Pp5MQTT+Tcc8+lqqpK1jYCXH755VRXV/P73/+em266ieHhYW666SYSiQQOh4ObbrqJrq4uNm7cyPr16/nIRz4i90d4h8Kk8M1kMtIQXtj3VFRUTDEzFxEz+PfEJWGuLz5XjCsVxyASiVBdXY3P56O0tJQtW7YQDofl/o2NjfF///d/vPjiiwCcfvrpvP/972fu3LmyDGHPnj3ApNj61Kc+xcUXX0xHRwdDQ0PccccdbNmyhYsuuoi7776bbDbLaaedxvLly7n44otZt24dW7duRaPRUFZWxuLFi9Hr9bhcLkpLS+Uo0Hg8Ti6Xm5K2fyOnArPZTFtbGzAZsfN4PDgcDjo6Oli/fj0AS5cu5Y477mD27Nm0t7fzxS9+kYGBAVwuF5deeilDQ0OEQiGcTicGg4HLLruM//mf/+GnP/0pHR0dfP7zn+d3v/sdN9xwA8888wz33Xcft956K4FAgPb2dvR6veyUj8fjjIyMSM9UvV4v63nFwwVwwPT9oTTemEwm5s2bR19fH36/n2w2S1VVFVarlU2bNgFwwgknSFeDW2+9lV/84hcUCgVaWlq45pprcLlcRKNR5s6dy4knnsjHPvYxurq6+Pvf/05PTw8333wzv/zlL7nhhht44IEHuPnmm/nxj3+M3+9n27ZtHHvsseh0OlkmIkoMMpkMs2fPRqPRUF5ePsVR4Z1I6x911FGKM4CCgoKCwhFBfSgvLjazN5vNzJs3j/r6ehYtWsTs2bPJZDJSwHzxi1+U9kBCmC5fvpwf/OAHPProo1x44YU4nU4ee+wxHn30UR588EHZKHPttdfy5S9/GZVKxZo1a7j66qtJp9NSVK5Zs4aGhgaSySRPPvmk3Ob3+2XqWzQUqVQq7HY7TqcTjUYjTenFWoQlkYhACSN6QG4X3fEej4dIJEJdXR3hcJhkMsnAwIA01v/mN7+J0+nkiiuu4MUXX8RgMHD99dfzzW9+k5KSEjQajewCF7WVuVwOg8GA0WikubmZiy++mLvvvhudTsc///lPvvGNb0ixUlFRwaOPPsrVV18NwKuvvorX65X+p2KyUF9fH8888wz79u2TaxbjVoWYE+ssXq/L5UKtVsuRrDabjUwmQ39/P08++SQAV1xxBY888ghtbW0UCgVuuOEGdu7cid1u55prrqGsrEx2qOv1eoxGI7lcjuXLl3Pfffdx5ZVXYjabefXVVznvvPO47777+MxnPsMrr7zClVdeCcC2bdvw+/2oVCo8Hg+dnZ1S/BefQ41GIwWaSN9PZ/oaXw9xvoV1VzqdlpOchEftGWecgdVqZfPmzdx5550UCgXOPfdc/u///o/a2loMBgOJREKOpVWr1Zx55pn84he/4Mtf/jJNTU0Eg0G+/OUvs3v3br7zne/w0EMPUV5eTiQSYcOGDUQiEex2u3RI2LFjB319fYRCIcrLy+VYXnHOFJGooKCgoPCfxFvu1hfROeEHKepSP/nJTwJI4fa+972PL3zhCzI929XVxc9//nMmJiamfO4TTzzBLbfcwsc//nFKSkq44YYbePTRR3nyySf5wAc+wIc//GHOPPNM/vrXv7J69WqCwSBr164lm83KmsXq6moMBgMqlYqenh4pkoS35vQ1ANJntVjgiJSquPmLyUiFQkGmbru7u4nH4zQ3N3PZZZfx+c9/nk2bNmGz2fjhD3+I2+3m1ltvZefOnYyNjclZ9y6XS9oCzZo1i+rqapYsWUJTUxMrVqzg5ptv5itf+Qpr1qyhurqab3zjG5MnTKvlM5/5DD//+c+l2b/P58Pv9+N2uwkEAoyPj+NwOHA4HJhMJhntjUQi+5nMi3Mojkc8HpcuAGJ0qEijA3zve9+Tx+C6665j3bp16PV67rzzTkKhEDfeeKMs6zCbzTQ1NbF06VKam5uxWCyce+65fPSjH+X6669n48aNfP3rX+evf/0rP//5z7njjjtIp9P8+te/xuv1MjQ0RG1tLZFIhFAohEqlorKyUtYviocGsZbpIu3NdLR7vV4ef/xx9u7dSy6XY/78+bS3t8tJZsuXLyedTnP99dcDcN5553Hdddfx6quvks/nGRgY4Pnnn+c3v/kNvb29VFRUsHz5chYtWsTKlSu55JJLuOaaa3juuef41Kc+xfPPP88pp5zCiy++yKpVqwgGg/h8PsbHxzEYDIyOjsrofzAYJB6P093dLT1kD9Ue7XBQVVW1n9hXqVQ0NDTInw/lvQoKCgoKCsW8qW794k5vIY60Wi0vvfQSAOeeey65XI5wOMxTTz0FwIoVK+jr68Pr9fLMM8/w0EMPkc/nKS0tlabnAwMDDA8P8/nPf57/+Z//4QMf+AA//OEPue222xgZGeFvf/sbf/vb3zj55JP5wx/+wG9+8xsuvPBCAoEAO3fu5JhjjmF0dBSVSsVJJ52EWq0mFAqhVqvlhCmfzyfr5ESTk4jITo++iQinx+NhYmJCRtOEf2s4HJberp/73Oe47777uP/++wH4xje+wfDwMN/85jfx+/1TjmMul2NsbEyWMojGMLVazYoVKzj33HNpamriW9/6Ft/97ne55ZZbOO2005g/fz6pVIqNGzcCyNpDUdMaDodJp9MYjUacTifV1dXkcjn5ACCixNMdF4Q4HR8fl9OMHA6HnHZkMpnkz/39/eRyOf7yl79w//33o1KpuOqqq8hkMvzgBz9gaGgIjUYjj5+oJ/3Tn/7E0UcfzRlnnMGiRYu4/vrr+ec//8mDDz7Ipk2beN/73sfdd9/N17/+dTZs2MDu3bvJZDKsWLECr9dLIBBg1qxZqFQquab+/n4aGxupra2V6xGlGcXrm25aX1zmMH1O/cDAAIODgySTSbLZLL29vbLZb968eQSDQe6880452Wv+/Pl897vfpb29ndHR0f28XyORCN3d3fzxj3+kubmZr3zlK3z84x9n165d9Pf3c+mll3L77bdTWVnJj370Iz796U8zNDTEunXrOPnkk8nlclRWVlJbW4vJZMLr9TI+Pk5fXx9qtXqKOD2YYQRvBeFe8Y9//GO/cgmz2SyjuYf6XgUFBQUFhWIOuSFKNM8UT4UKBAIy/Qpw8cUXYzab2bJlC/39/Wi1Wk477TS6u7vZuXMnf/jDHwBobm7muOOOw+v1MnfuXJYvX85zzz1He3s7Dz74ILNmzeKss87ihBNOYNOmTWzfvp01a9awdu1a1q5dy+mnn84tt9zCtddey+DgIDU1NSSTSZlWLS0tlen4lpYW2dBRKBSoqamZPAD/vzAVa7JYLKTTaQDZiZ5KpaS3p9lsJpFIkE6n2bdvH4lEgtraWj72sY8xb948AD772c/y/ve/nyuvvBK/309paSnz5s1j4cKFlJSUsHfvXtxuN8FgkO7ubnQ6HUNDQwwMDLBx40aGh4f54Q9/yKc+9SnWr1/PM888w6c+9SmefvppysrK2Lt3LzA5mcrpdMraS1EqIEoRampq8Hg8eDwe2traZKSxOGI8fbxrWVmZdCgIBoNotVpsNht6vZ5sNksoFMLlcnHfffcBcMEFF3D88cfzox/9iKGhIXQ6HR/5yEdklLO7u5v169cTDAZ56aWXZJmH0+nkfe97Hx/60If42te+xq5du7jmmmvYtWsX9957L6eddhrBYJDnnnsOt9stx7Jms1npnmA0GslkMiQSCSorKwGmnMvp57f4OhZ/FrtPFJdxJBIJWUfd1dUFTNba6vV6fvvb3wKTtcTi/Aj0ej1Op5M5c+ZQUVFBX18fuVyOnTt30tPTw5o1a/jqV7/Ktddey7e//W2ee+45/vjHP3LttddKi6177rmHrq4uKisrKS8vp6ysjNraWuLxOMFgkEAgQElJyYwd8O+E/6mCgoKCgsLh5JBqTuPxuBQHooPZbDYTCAQIBoPSj1E01Nx0000AnHbaaaxfv56bbrqJH//4xwDU1taybNkytmzZIjuiNRoN73//+znmmGMAWLNmDYlEApVKxaxZs/jyl7/MSSedBCCbls4991xgUrTYbDZpJzUyMkIikSCfzzNv3jxplC/Em1arxe12S8EmoorFaW5hleN2u6moqKChoUEK03Q6LcVBc3OzrAk0Go1ceumlAPT09ACTIkZ4rer1eqxWKw0NDSxevJiFCxfyoQ99iKuuukqWQgwODkrbrZtvvpm6ujp6e3s599xzCYfDsjwhnU5LQRWPx6UJv0jzixS92Wymt7dXnsdihwKXyyWjWhUVFcTjcf785z/zz3/+U6azW1tbZTQUkD61AKtXr+Zf//oX69evR6VS8aEPfYiqqirUajUVFRUcd9xxfPzjH+fKK6+UpRBf/OIXZTS5qqqKu+++G41Gw8TEBKOjozQ3N8u0+d69e6XHaTgcxuPxkM/nZZObWq3G6XRis9mkrZLH45HrE/XFxRRbZ02/vgFmz57NUUcdJdcaDocBqKmpYffu3fh8PiwWC0uXLpVR6Tlz5nDttddy8803c9xxx7F48WIqKytpaWnh/PPP5/LLLwcmpyd5PB4ZRQX44Q9/KB/YPvCBDwDIBr58Ps/Q0BAvvPACkUiEiYkJTCYTKpWK2bNnM53Xq79VUFBQUFB4L3DI3fqiXnLbtm3s2bNHpjFFw8jcuXMZHx9ny5YtPP/882g0Gvx+P1//+tcnv1CrZfHixajVap544gnZ+dze3k5FRQXHH388q1atYufOnfT19fGFL3yBL3zhC2SzWWkED5Mp6Gg0KkWqWq2WkVMhSMR7+vr6cLvd0n7nQGnF6WlumOymF5EojUZDOBxmZGREdnHDZL2qSM3X1dXR09OD3++XwiWVSpFMJtm2bRswmdYXry8UClLoOZ1OysrK8Pl8vPzyy3JU5ze+8Q2+/vWvs2PHDq655hq+9KUvAZNjYjOZjDwnosNbNPTMnTuXcDiM3++ntraWWCwmG2hmqtEsKyvjpZdeYnR0FL/fz9FHHy0nUYkUcTabpaOjA5g02N+7dy8PPvggMCnqEokE3d3dFAoFXnvtNQqFAnv27GHPnj1yXwcGBrjyyiu58sorpTCurKxkeHiY1157jZUrV/KpT32KO+64A6/XSzabRa1Wk06nGRoaorKyEo1Gg8PhQKfTUV9fj8VikVHKVCpFPB6fMiygmOnDFUQ63OPxMDg4SDAYJBgMolarZdkHgN1u5x//+AcAixcvZmRkRNbjlpSU0NnZSWdnp0zlh8NhWlpa2LBhAzA5YGJkZIQHH3yQc845h5NOOon29nYeffRR/u///k9OABP7Jnx8hb+piOBqtVrmzJlDQ0PDfqn84nMrth2uiUzi3/onP/lJXn31VXkcYfLfyYknngjAiy++OGVb8XvPP/98HnjgAWpraxUbKgUFBQWFGTnkbn0RbYRJQTg+Pk5/fz9bt24FYP78+ZhMJu666y5g8iYubs5HH300N954I5/+9KfZs2ePFKYwGQUcHBxk/fr1tLW1cfXVV6PVatmyZQu7du2SY0RF+jYWi0nvTfj3ZCq9Xi/rEn0+H1u3bmV4eFjWiup0OiYmJqZEl+LxuBSIxWlulUolBauYHOTz+YhEIuzbt09G/zQaDe3t7cCkOHe5XLL+zu12U1NTQyQSwWq1YrVa6e/vp7u7m7Vr17JlyxaSyaT8b/78+cDkBB6xnsrKSu69917UajWPPfYYo6Oj6HQ6UqkUe/fuxev10t/fL2tohTl8e3u7nHJkMpmmrFOI1OJO9q6uLoLBICaTiZqaGhKJBJlMBq/XK8s5LBYLoVAIgIaGBtkQZrfbmTVrFq2trbS2tsrXbNu2jfb2dukdm8vlsFqteDwe7r33Xmw2G3V1dbS0tADw05/+VNqGicjiyMgIyWQSj8fD2NgYoVCIWCyG1+vF5/Oxbds2+vr6MBgMpFIpzGYzpaWlU67dmdwJhDgXxyUej8thD6KMo9jmrKKiQhrtH3/88XLaGcDY2Bgej4f+/n42bNjA9u3b6e3t5aWXXiKZTJLL5WSkc8OGDYyMjGCz2bjuuus488wzKRQK3HzzzXLEbTqdplAo4Ha7qa2tlQ954+PjsjFPRO6L97/YXUJwuAzwhcjdvXu3rHMW5PN5Xn31VdkYNh2n0wlAb28vxx13HG1tbQwMDByW/VJQUFBQ+M/ikMRpMbNmzWLWrFk0NDSgUqnkTbq5uZk9e/awceNGNBqNFAJLlizhhBNOkEb3xVObisXJwMAAXq+X1tZWVq9eDcDjjz8uv7ekpARACkMhejQaDfF4nNHRUbZu3crIyAgejwe9Xk8ul6O1tVWmwxOJBIFAQKbwD5TmFvsnImcGg4GmpiZqamrkJCqYjJY+/fTTADIdLHwxRQezIJ/PMzIyQkdHB9FolJGREbq6uuQN/YQTTgDgtddek/6rAMuWLZOp4S996UtccsklwOQceNHAk0wmOeqoo2hpacFms5HL5QgEAlgsFhobGzGbzVNEuUiBi3Mh5sPX1dVRWVlJRUUFiUSCRCIhaz5tNpucFCQiloAcJbtnzx7uuusu1q9fzyOPPCLLCUQqGibrVO12O8PDw1x55ZWEw2EuuugiSktL2bFjB9/5zneAySlEs2bNkiUkMClyxIOJMKwfGhri1VdflWUmYi0HW3Mpzr8o4xBlH06nk0Qigd/vR6PRcNxxx8n58UcddRS7du2SHriiWWnXrl3ymoTJiOGmTZvIZDLU1NRw9NFHk8vl+N3vfkcymUSlUrFgwQJ5rRx99NGYTCYSiQSvvvoqg4OD2Gw22ZjX1dVFKBRCp9MRi8VIJBL09fUxNjYmGw6FUBVML194Jyh2yrj33nuJx+P7OXUoKCgoKCjAIYrT4m59t9vNypUrOe6445g3b94UcferX/0KmOzQ3759OzCZthW1c3/+85+n3MALhQLJZBKDwUChUOC3v/0tr7zyCvX19ajVatrb23nttdcYGxtDrZ7c5fHxccLhsBw5qlKpsNlscq59IBCQs9hnz54tJwmFw2FpTF88XWemxhmYFFXl5eXS+NxsNlNTU4PT6ZQ3V5vNxsjICEajEavVys6dO3nllVeASdG2c+dOYrEY7e3t/OMf/5DCWojggYEBNmzYQDgcJpFIUF9fTy6X495776W3t5fBwUG6uro466yzqKmpkfZCc+fOlaNFYVIIiVGbKpUKq9VKS0sLarWaoaEhfD6fFG3FjgtCvMRiMUpKSshkMvLYiQlc4vzq9XrZ+Ga321GpVNIbtaenh3/9619TxrRqNBp0Op28bmBSLJ166qmYzWY6Ozu56KKLiEaj3HLLLajVah5//HGeeuopMpkMN998szzfOp1ODlrQ6XTSjisajRIIBBgZGWFoaIhcLrefIf/rIc6/2+2Wx83pdOJwOGTa/pRTTqG7u5t0Oo3VaiWXy/HCCy8AkyI9HA7LaLmIvpvNZtRqNcFgkBdffBG/309TUxNmsxmv18sdd9whswUw6QOsUqlk7XF3dzednZ0ySh8IBNDr9VRUVEzxeDUYDASDQQwGgyyDKK45favd8QMDA2zZskWe97fK3LlzD8vnKCgoKCj8Z/KmIqcTExO8/PLLrF+/nrVr18qGKJiM/og0vhBPNpuNmpoaenp6pEm8IBaLMTQ0xOjoqEzR79u3D4PBIMdwAvT19VFZWSkjp8J/VKRBc7kcnZ2d0qtUr9fL5pFgMEhfXx/btm1jZGSEQCAgo2Oiq7k4FVqcBhVpYFH/WVFRgV6vJxAIyNS12KeWlhbKysrIZrMkEgnUajVutxuj0UgoFCIQCEwpZUgkEjL9HI/H6e/vx+12c+qppwKTKfGWlhasVqtMVYsGsD/96U/cfvvtcl+6u7uJRqP09fXR09PDxMQEtbW1ZLNZ/H4/AwMD8rtFxM1iscj6TJiMepeUlFBWVrZfGl2Qy+XkA4Hb7aatrU1Gh9vb26UANRqNlJWVUVpaSnl5uYyMq1Qq6SRw2mmnyTKHdevWsXjxYs444wwAHn74YQwGAyeddJIUa+vWrWNgYID+/n4ikQjRaFTaZhkMBnw+H3q9/pCipsCUZjiYLFcZHh5m3759UpxecMEFcqqXaBAT13xdXZ18QIDJBxLRNGcwGIDJ63Xr1q0kEgnp6vDEE09QW1srv2PevHk4nU5uvPFGjjnmGMLhMHv27GFsbExGxx0OB6WlpZSUlMjIut1uZ+7cudjtdll7XFyy8VbE6cDAAG1tbSxbtozLLrvsTX+OgoKCgoLCwXLI4tTr9bJx40aGhobYvHkz3d3dbN26Vaal9Xq9FGsVFRXAZONOseATTR/TEcKmvr5epoBFjaT4rCeeeAKAY489FkCm3EXntogoabVadDqdnKC0Z88efD4f+XxedrAfiJnSoGazGafTyY4dO9i9eze9vb3k83laWlp4//vfL+tOu7q6ZOlCPp+f0oxSWlq6n/ekSJer1WrKy8sBZIlEdXX1lNd2dHRwzz33AJPm78uXL+eiiy4CYHR0lEQiwb59++jp6aG9vZ29e/cSCAQYGxtjaGgIr9c7pd62eDoWTKbC6+vrKSsrk7ZhooFM7NtPfvITli5dCsBjjz1GoVCQ7y9GNPJEIhFZcgCTolUcg9LSUpYsWQL8+4FAnO/W1lb5WWJEbSqVwuv1yuaopqYmbDYbOp0OnU5Hc3Mz6XRaWmsdSjo7FovJ415WVoZGoyGXy8nodjQalfvU29tLWVmZXEdxpFggIsVim1qtltecEM56vR61Wi3dKX72s5+RTqflaGAAh8OB2+2mUChQW1vLwoULmTdvnizJAOTErOL/DpeN1MTEBPF4nAceeED6GCsoKCgoKBxJDtlKqrOzE41GQ6FQoKysDJvNhtVqlVZDyWRSeoiOjIxQWlpKoVBg69atpFIpWa84neImitraWvbt28fIyAjDw8PSM7O9vV12uZ977rnSKgomb/7V1dVSoMLkjV1YS8ViMWpra6WN08TEhKzTK667hKld3MXNJslkkpGREUZGRqSIOvbYYykvL5cRzb/97W9EIhEp2Lq7u+nv75fpcxGBFRQLIq1WS19fH1u2bAEm63p7e3vx+/10dXXx5S9/mWQyyQknnMAPf/hD4vE4V155pWyWicViU8ol9u3bh9frJZ1Oo9Pp5PhUEV2Ox+PShQAmRZOImtrtdhwOB7W1tTgcDk477TQAfvvb37JgwQIMBgPd3d289tprU6LBAtE1n0qlCAQC7Nu3D5is292xYwc7duwgFArJEodkMsm+ffvkUIMzzjiDZDIp9xEmo66lpaXo9XrKysp4+eWXefbZZwkEArjdbkZHR6X/7sHYKcViMfr6+uRY1/HxcSn67HY7DQ0N0uR++/btzJ49G4PBQCgUkiNb4d+WYdPXL6ygRKmARqNhdHRUOlu4XC6Gh4c59dRTKSsro7e3l7vuuosNGzawYcMGdDodJ598MmVlZcyePRu9Xk95eTnxeJzBwUHGxsZkxNfj8RxR+6i2tjZZT62goKCgoHAkOWRxWlJSgt1u55RTTmHBggUyciVEok6nk9GwoaEh3ve+9wGTkT2n00lLSwttbW0yHT+dmpoalixZwuzZs2XD0Zw5c1i8eDFr166lUChwzDHHsHjxYhwOh4ww5XI5GT30+/14vV5ZX5rP53G5XNTW1tLQ0CA9SsWceyFkZkqDFvtGFgoF2UwlBEZDQwM1NTV87Wtfkw1B4nsAQqEQdrsdt9vNqlWrOP300/eLatXU1LB69WqqqqqYP3++TBGfdtpptLS0MG/ePP7yl78QCoVYuHAhf/rTn6isrMThcOB0OmlqagJgz549UmiLumCz2UxFRYUc5ypM9ovrbUVU1Gw2SwFYUlJCTU2NLJ0wmUw4nU7S6TR79uzhwgsvBCZT+SISbrFYOO200zjmmGOor69n0aJF+3mkajQaOXc+FArJh5XKykq2bdtGPp+nra2NBQsWYLVaZTMQ/NvXVZRJDA8PywhvKpUiHA4zNjY2pWTjja5nMdY1Ho+j1Wppb28nFArh8/mIRqPyM5577jlMJpO8tru7u2Ujk8/nY86cOZxxxhm8733vk9F8mBTUxx13HKtXr6a6uprly5fjcDiAyTKKhoYGqqurZfnKpk2b+Ne//gVMPrCEQiGSySTj4+NUVlZis9nQarU4HA6MRiMwWfISDoffFm9TUX99qNsOZruCgoKCggIcojg1m81UVlYyZ84cLBYLJpMJnU6HXq+X/qOxWEymKTds2MCCBQvkzPv29nbZWFN8cxWCaNasWbI20e/3y6ai888/n0wmw0MPPQTAJz7xCblPTz75JIDs3rbb7ZjNZvR6Pel0Grfbjc1mkw1Mw8PDsrFErVbT0NDwukKmuFnK6XRy4oknsmjRIunReNdddxGJRGhqamLx4sXAZJTt4osvRqVS4fP52Lt375TIcGlpKaWlpWi1WkpKSmQjDEzWmSaTSZxOp7QeKvZF/d73vkcqleIrX/kKK1eupKmpie7ubiwWCwsXLqSiooLy8nJOOukk2traqKuro7Gxkfnz509Z44HWPDExQX19PfPnz8dqtTI+Po5Wq2X27NlyTOjGjRu5/PLLMRgM9PT0UF9fj16vJxaL8dRTTxEMBmW076ijjqKpqYm5c+dSW1srLYU8Hg9r164lHA5TXV3NsmXL+N3vfgfA//zP/0zZpx07dshzkc/nSSQSjI2Nodfr0Wg01NbWEgqFiEajchDEwaS2zWazNO+Px+Ps3btXCt+ysjJZ02o2mxkcHORb3/oWZ555JgAPPfQQs2fPxul0Eg6H6erqYu3atfT19WE0GnE4HBgMBmpqaqbYWuVyOekTe/LJJ1MoFLjvvvtYu3YtMBmJFw8byWSSQCAgS0jEaFmr1UokEpEd/MJC60hPhBKTw7xe737f9Xrbpm+f7oGqoKCgoKBQzCGb8Nvtdvr6+hgYGOCVV16RYzOtVqtM0wobovHxcXbt2sXZZ5/N/fffz8TEBM8++yyLFi2SxvH5fB6dTofVasXv99Pf308ymaSrq4tcLseiRYuYPXs2zzzzDBMTE7hcLlasWEEymaRQKPDXv/4VmKxTFR31JSUlmM1mWVtaW1srpyjZ7XZZb1mczj5QDarouobJyM+CBQtIpVLo9Xra29uZmJjg9ttv51vf+hYrV65k27ZtbNy4kc985jPU19czMDAgU+7ZbJby8nLy+TwlJSVyP0VjjcPhkB3g8+bNk41IYvqUxWKhqamJSy65RIoZlUqFw+HgIx/5iGzGamlpwWKxSL9TUXsrUs1iXSJyKlwH4vE4JpMJrVZLWVkZ7e3txONx9Ho9mUyG+vp6duzYwfbt29Hr9VxyySX86le/YtOmTVxxxRX88Y9/lHZHarUar9dLXV0d+XxeRvuCwSCvvfaaHCXb2trKBRdcwNatWxkdHaW0tJSTTjpJ1qim02lpJdbU1ITVasXn89HU1ITD4WDRokVyf1OpFHa7nbVr19Lc3My8efNeV7CZTCbcbjc+n4+xsTHsdjvBYFC6ELhcLmKxGGeddRZ/+tOfWLNmDd/73veoqqqSlmUf/vCH2bx5Mx0dHbI0QUTfzWYzJpNJZgDUajUvvfSSLFVYtGgRX/3qV3n22WcBuPzyyznvvPPkg140GpW1q4lEAq1Wy8jICGVlZZSXl8vSEbvdLh8Wi2ucp9c3KygoKCgovBc4JHFabGDu9/tJpVLSA1P8ftOmTVx99dWceeaZ/OY3v2Hjxo3ceeedLF26lOuuu45wOMyrr74qO7M1Gg27du3i73//u6z77O/vByYbRm644Qay2ayMmn7yk5+krq4Os9nMN77xDQYHB6V4HB4eprS0lLKyMoxGIzabDbPZLBt8hIdlcXd2NpvF4/HIrv2Z5rALEadSqWTK3O/309DQQHt7O7/5zW/4zGc+w/vf/35+9atfsW3bNurr67n11lsZHBzkpptuwu/3s337dpYuXcr8+fNpa2uTRvkOh4Ph4WGefvppOa71ggsuYPny5bS3t0trrpUrVzIxMcHatWvR6/XMmjWLkpISTCYT0WiUZDLJvHnzKBQKclxrsX/n2NiYrKMVIrX4ZyHYxXGoqamhv7+fQCDA4OAggUBA2jk9++yzfOMb3+DRRx9lfHycTZs28Y1vfINgMMj9999Pd3e3dGEwGAyYzWZSqZTs9Ac47rjj+M53voNGo5GG+5dccglNTU0yujYxMcFTTz0F/HsAQDqdJpPJUFVVRV9fH1VVVTidTjQaDUNDQ2i1WgYHB2loaHhdcSpssCwWCzabjZaWFoaHh8lkMiQSCVpaWti1a5eMwHs8Hh544AG++tWv8sUvfpFt27YxZ84c7r33XrZv305PTw+///3vGRwcJBaLsXz5crLZLHa7nfHxcXbu3ClF9wc/+EGefvppnn32WbRaLT/60Y/4xCc+Ia2yxKQwj8eD2WymUCiQTqcxGo1ytK14WInFYsRiMVnGUXzNHulo6ltBRJDLy8uVaVEKCgoKCpJDEqcCt9vN7NmzyWQyWCwWPB4Pxx57LH/+85/52c9+xlFHHcUVV1zB448/ztDQEF/72tf40Y9+xJVXXslf//pXenp6eOWVV+jt7aWtrY0XX3yRQqFAY2OjTIGm02mOP/54du/ezY9+9CNZ13fFFVcA8J3vfIef/OQnAHLEpFqtRq1Wo1KppJl5SUkJwWCQ0tJSaUYvEAI1lUrJutLim3k8Hsfv90/pRp+YmGBiYgKr1cqCBQvweDx4vV6uvfZaHn74YRobG+nr6+POO+/kIx/5CMuWLeOBBx7g+uuvZ/v27bLZSZj2T8fpdHLhhRdSVlbGY489xnXXXSeF2De+8Q1Z6iDqH3O5HIlEAr1eTyKRwOfzkc1mZdq3ubmZqqoqysvLpXgptk2yWCyyxlWINLH2lpYWCoUCo6Oj7Ny5k5KSEiorKxkcHOSGG25g9erVfPvb3+bLX/4yGzdupKuri8997nP88Ic/5P7772fLli0MDAwQj8flg4fRaOSEE05g9erVMqWeSCRk6v7888+fcjx+//vfk8vlsNlsNDc34/F4CIVCGI1GGbUXwwPcbjc6nY5wOCxN34sfPGZCCDm3243JZMLlctHZ2YnJZEKtVuNyuRgZGaG+vp5MJkNXVxeZTIZrr72W22+/nYceeojOzk4uueQSTjzxRFauXMnXvvY1urq6ZBS8GIfDwcknn8yqVav41re+BcBNN900pVRl7969+Hw+tFqtjDwbDAasVivBYJBCoYBGo5HT2XK53BQnAOF9OtOI2rdCIpGQVl9PPPHEfuNLD7Rt+va7774bs9ks12w2m+no6FAEqoKCgoICcIjiVHS9x2IxGhsbcblcPProo+h0Ok488UQ6OzvZuXMnV111FXfffTe//vWv+ehHP0pHRwfXX389q1ev5qqrrmLz5s389a9/xePxSDucFStW8PGPf1xGip588knWrFkjrXiam5v505/+hNlsZs2aNdx+++3A5EhUtVqNw+GQ8+5tNht9fX0cf/zxuFwuOfIUkDVxIuUqbqJer3e/WezCqL/YuD4ej6NWq2Udps1m4/7772ft2rU88sgjXH311Xz5y1/mj3/8I6WlpZxyyimoVCqWLFlCU1MTW7duJRgMksvlSKfT0nrLZrPR2NjIWWedhUajYe3atdx3333k83lOPPFEfv7zn1NaWsrPfvYzYNJbs6KiAo/HI83oE4mENIhPp9PU1NTI0gExena6iBG1tMXnGCZrh/P5vIxU1tTUkMvl5NSvgYEBLr/8cm6//XZ+/vOf8+1vf5vh4WFuvPFGjjvuOBoaGvjf//1fgsEgPT09OJ1OCoUCDoeDiooK2tvbUavVZLNZWYssbKzECNV8Ps/9998PTKb0xeSq7u5uWa+s0+kIBoNkMhnKysqYO3euXGexIf2BRFpxw5sQpzDpVdrX10d5eTk6nQ61Wk0kEiEQCHDrrbfyzDPPYDQa+fGPf8z27dv5wQ9+wFe/+lUcDgcLFizAYrHQ3t6OSqWS57e2tlbajj3++ONEo1HmzJnD6tWrpcvC+Pg4t956KzAZUayoqKCkpESWaQixHIlE0Gq18pyIhwpR030wDWGHSj6fl4J7pvGlB9o2fXtNTQ0dHR1MTEzQ0dHBJz7xCVnrrKCgoKCgcMiR0+KbufwQrZZEIsE555zD8PAwfr+fa665hvXr13Pvvffy6U9/mm3btrF9+3YWL17MiSeeyGc/+1k6OzvZsGEDH/jAB/jiF79IKpXi3nvv5eGHH5Y369raWs4//3wuv/xyGhoaePbZZ2XEyel0UlJSIqdBGQwGWltbyWazlJWVMTQ0JMWYiNyVlpZOiaaJ7dNnsQNT6lLFerVaLel0Gr/fL70r582bx9atW/nqV7/Kww8/zIc+9CEee+wx7rzzTp555hk++clPcs4559Dc3AxMNkwJEeT1epkzZw6AFFm33HILzz//PAAXXXQRN998M3a7nS1btsiIqxiDarFYOProo6moqECn0zE8PEw2m0WtVjN//vwp3qler1dOyio+j8W1icVz571eL1qtlmg0SiwWo6WlhVgsxpIlS4jH4+zevZtrrrmGW2+9lVdeeYXrr7+eBx54gJdffpkNGzZwyimncN5558ka2P7+fv76179OmTT04IMPyp/f//7343A4pEfqZz7zGXp7e7FarajVajo6OqitrZXnuLS0lNmzZ8tGKDGqVYiyYgE+EyI1Ll4jnB/E71pbW1myZAnd3d2y1lOMKb3mmmu44447OP300zn//PMZGxvje9/7Htdffz1nn3229KgdHR0lEAjQ0dEhv2/37t0888wzAFx//fXU19fT19fH17/+dV544QUpMMWAgUWLFjE4OCiPv6ijbmlpoaKiQjZ/iQdHh8Pxrk7nw2SNuCJGFRQUFBRm4pDFaXFK2Ov1yg5+jUZDJpPh5JNP5rnnniMQCHDJJZfw61//mnvvvZcbb7yRjo4Otm3bxrZt2+TnVVZW8uqrr3LVVVcxNjYmI6mnnHIKl1xyCaeddhpqtZpkMsnjjz/OpZdeKtOYDQ0NNDc34/f70Wq1VFVVMXfuXEwmE6Ojo3KaUTAYlBFFl8s1JY1fnOaeqQPZZrPJCNzIyAh2ux2r1UptbS2RSIRcLsfq1avp7e0lEAhw7rnncuedd1JWVsYf/vAHurq6+OY3v4lWq2XlypWcfPLJRCIRXC4XBoOBfD5PTU0NwWCQO++8k2effZZCoYBWq+Uzn/kMp556Kn/605/weDzcddddRKNRGfGNRCLU1dXhdrtpaWlh9+7daLVaDAYDc+fOZd68eXJOe19f35RUb/GaRUNWMWazWZZARCIRrFarFP8qlYqKigqCwSDd3d2cc845nHPOOXz+85/nwx/+MLfddhsbN27kmWeekSKsGI1Gw9FHH00gEGBoaIhYLMaxxx7Ll770JQqFAn//+9/59re/zcTEBEajEbvdTqFQIJvNYjKZqK2tpa6uDoPBIKO+whjf6/VKL9mDqbmc/hoh8MTvmpqaSCQSNDU1odPpMJvNvPLKK7z44ouceOKJXHHFFfzud7/jiiuuoLe3l69+9avodDrq6uqw2Wzs3r17ivdsMRdffDFnnHEGO3fu5IILLiAQCACTZSolJSUsWbIEi8XC4OCgdKGor68nGAxSU1Oz3xjQd3uNqYKCgoKCwsFwyOJUpMMnJibweDzk83nZPBOPx7FarXzwgx/kH//4B5s2beK8887ja1/7Go899hgbN26kvb2dTZs2sX37dtklXYzL5eK73/0up556qozuPfHEE9x+++3s3r0bmJwWVV1dTWlpKVarlWg0SmVlJXV1dTidTnK5HAaDgUwmQyqVwmQykcvlaGhowOVy7SdGRZPUgbqbxevNZjMGg4Gjjz6aoaEh2UUdj8f5n//5Hx566CF8Ph+XXHIJt912Gx/72Md45pln+Mtf/kJPTw8vvfTSQU3ZaW5uxmAwcPfdd/OLX/xiv+MjhgmUlpZK8/hcLicFt8vlIhqN4vF4pN9qcaOMWMv0qPJMuFwujj/+eAKBAIlEQtZ6Llq0iIaGBsbHx9m6dSuPPvooL7/8Mj/5yU/49a9/zVNPPcXTTz/Nxo0bpaOAw+Fg2bJlXHTRRZSXlzM8PIzL5SKdTpNKpejp6eFLX/qS9Pm0WCycfvrpUhzX1tbicrlYuXIldXV1hMNhRkZGZC2sSMvP1LFe/Lvin6eLUSHiBblcjvLycmw2GzabjYaGBqxWK88//zzBYJDbb7+dNWvWyNT0X/7yF5LJ5BRj/qqqKpYuXYrH4yEQCJBOp7nwwgv5whe+QGdnJxdeeCGBQICSkhI++MEP4vf7qaurY/Xq1WzdupXh4WGampo45phjMJlM1NfX09jYOCX6XXwu3wvNUAoKCgoKCgfiTXfrWywW0uk02WyWQqFAPp8nlUrhcDgYHBykoqKCfD7Pnj17uPzyy3nuuec488wzOeecc4DJbv/169fz8ssvy25drVbLqlWrcLvd7Ny5kz/+8Y888sgjDA0Nye+0Wq3U19dLhwCRyp09ezYLFy7EZDLh9/vR6/UYjUYSiQTV1dXSCF98zsHeuEUqVrze6/XKlGRnZyfJZJJsNovX6+VDH/oQTz31FMPDw3zhC1/ghBNO4LTTTuPBBx8kHo+zdu1a1q1bRyQSIZvNEo1G8Xq9soP9qKOOYnBwcIqwEeuASS/X5uZmwuEwarUam81GNpulv78fo9FIOp2WlkrF+y8aZEQN7fQo8YFstIT4gcnyilgsJqPHoiyhvLwcvV7Pli1b8Hg8XH755fzzn//kox/9KBdffDEwaYkkaiJTqRRWq5VYLMbQ0BAvv/wymzZt4oknnpBRc61Wi9lsZu7cuZSWllJdXc34+LjsrB8eHsbn85FIJGQEV5zj6XXDgmLRNtMaRTZgumdoOp1Go9HQ0NDA2NgYXq8Xq9XK0UcfjcfjYffu3fh8Pn7yk59wzjnn0N7ezujoKIODg3g8Ho466ijpY1s8OSwej/PPf/6T6667Tl6vs2fPJpFIYDKZsNvt6PV6AoGAdFOora2V+yf2VwwgECUqxet5K+J0YGBA1oQqKCgoKCi8nbypbn2YFDSzZ88mFAphs9koKSmhoaGB/v5+Ghsb5VSmffv2MTY2xiWXXMLHP/5xuru7aW9vZ/fu3fsZ0y9fvpxbbrmF7u5uOWsdJv0hm5qaphjqh8NhTCYToVCI2tpa5s6dS319vfy9yWTC5/Oh1+uloDociNpUi8VCMpkknU6TTqc56qijyGQyXHjhhfzlL3+hv7+fdevWsW7dOr75zW+iVqupr69n1qxZXHjhhXzsYx+jtLSUYDBILBbjF7/4BXfeeafszG5qaqKqqgq3200wGGR8fJz6+nqMRiMajUb6XBYKBVwuF2azmSVLlshyBjGjXeyriKiKv08/lzMx3c0gkUig0+lIp9PMnTuX6upq+vr6KC0tlc4Ko6OjfOQjH+G4445Dr9ej1WqlwC8UCvT09NDb28vg4OCUCKY4zw6HQ44JFQMchFF/W1sbMFnHKSLHNpttv3XNFD2cqVa6eI3Fx0R4hqpUKurq6vB6vbL2eGJigkAggM1mw+12YzAYGB8fZ3h4mEcffZTNmzdz6qmnsmDBAmbPno1OpyMajWK1WpmYmMDr9bJhwwbuvPNO+dBltVqpqKjAbDbjcrlkM1MkEqGkpISysjKOPvpo3G43fX19DA0NUVVVRWVlJRaL5aBKVA6FgYEB2traZERdWJEpKCgoKCi8HRxyt36xoLBYLCxdulRGbtrb26mqqsJut6NWq6mrq+O4447jF7/4BT09Pdx0001TPk/4X8ZiMQKBwBR7JfF+s9lMU1MTLS0tMmpVU1NDPp8nHo9P6br3er0kEglKSkpIpVK0tLQwMjIim1PeKsU3/lgshk6nIxaLSTeAlStX0tXVxcknnywtfrq6uqS9k5jj/vTTT/Otb32LVatWyUipOK5ihGlbWxtOpxOPx4PT6aSiooLZs2cTj8fRaDRSmNXU1FBVVSU7xIX5vXAhKBZqwh7pzawbJqOkr732Gg0NDfL4qtVqGZVduXIlv/rVr/D5fPzjH/94w881mUwYjUYsFgtz5sxBrVZTVlaG2+0mFApRX18vhzQ4nU5pNh+LxQgGg3L6lRCSgpmih8XnrjiyXFzeUCgUpvwMk7ZpiUSCsrIyWltbSaVS0iLL5/NhsVhkOcuDDz7I0NAQa9as2W+tGo1GRkAFOp2Ompoali1bhtfrxel0Mn/+fOx2O1VVVdKVoLGxUTZDie8KBoNymtpMJSpvhYmJCeLxOA888ABtbW0ys1FcPjATb/S9r7dd8TxVUFBQUBAckjgVc8yLI1KBQEDWMwKMjIwwMDBAXV2dvKGdfPLJvPDCCxgMBmlKvmDBAiYmJkin03KKjkajobW1lZKSEsbHx0mn03g8HoLBIOFwGLfbLSOQxxxzDN3d3fL30WhURqjE9ClARkwPR/3d9HIA0TAkygXEDVan02EwGLDb7dIMX6fT4fF4iMVijI6OEolEZEc+IK2p6uvrMZlMBAIB1Go10WhUprOFV6tINVssFurr61Gr1UxMTEzxcRXnw+PxEIlEpFXVGzGTyNNqtZSXl6NSqVi6dCn79u2jtraWQCCA0+kklUqRy+XIZDKsWLFC2kQlEgkMBsOUa6WkpAS1Wi3tkUKhEOFwmHg8TiaToaamBo/HI0felpeXy9Q1TIpGtVpNbW0tJpNpiuARQlwcz+nR0OL9mGm9B3ItKC8vp7+/X0Y29Xq9HCeq1Wqpr68nFAqxatUqent7yeVyBINB6SJQPJlLo9FIUWoymaiqqkKn01FRUSHrWaurq0kmkwwNDZHL5WQaX6/XA5NlEqIR7EjWlra1tbF06dIpx3B65Plgtr3edjEYQ/E8VVBQUFAQHLI4FfO8xc0ml8vh9XrlDVaYldvtdhobG4lGo6xatQqDwUA4HGZ0dFT6WWo0GiKRiLSEyuVyMl1eWVmJzWbDarVOqWvN5XJEo1F27dpFY2MjixYtApAiVgg4l8t1RMc3iuk88O+mGkCmvYWpu8FgkGn2fD5PfX09K1eupLOzk9HRUaqqqojH49hsNtnQFYlEqK6uxmAwUF1dLaNqYqjAnDlziEajUhQNDw/Les158+YBU62xxEPFwcybn75Gj8czpaZR+NsWN1Y1Njai0+kYGBjAarXS2tqK2+2WnrBirGooFKK8vBy/34/ZbCafz+NwOKT3bDKZlMIeJmfLiwh1JpNhz549tLS0kM/nZQ3rq6++it/vZ/bs2TLFrdVq5QPD4cJgMMjJTJWVlYyNjWG1WqmqqqKnp0eWPdTV1TF//nySySQDAwPodDp6e3vJZrNUVVXh9/uBSSsllUpFa2srtbW1nHDCCQAyi5BMJqmqqiISiVBfX09paSmpVGrK2NLXm2z2XqG+vl7xPFVQUFBQmMIhiVPh+ylqEGEyEiTsfkpLS8lkMiSTSWw2m4wWNTc3k0wm8fl8WK1W7HY79fX1JJNJMpkMWq1WRolsNpu86Z500kl4vV727dsnZ75ns1lUKhU2m01GB4UQEVHTI3Wjnl4jKcRvLBaTneNOp5PFixfT2dkpI71CHOr1egwGg4y+zZkzB7fbzfj4OBqNhsbGRllTKj5bNIBNTEzISUbixm02m2VzUTKZlCUSxU09Iu38ZiYGiRrG4lpN0c0u6l5jsRjl5eVEIhGMRiMOhwOXyyXHswYCAeLxuHxwEEMR/H4/RqORaDRKQ0ODHD9qMBhIJpMycixS1/l8HrVaTT6fl+fZbDbT1dVFOp1mcHCQZcuWyf18o8lQh4KwFAsGg7J5TXithsNhOYZW2E5VVVURCoVwOBxotZP/xOLxOBUVFbKUYenSpahUKtLptBy563a72bp1qzz2zc3NRKNRQqEQgUCAefPmEYvFmJiYkBHjw9H89E6jeJ4qKCgoKBRzSOLUYrFgt9unmH273e4pdZglJSUypfraa6+hUqmoqqrC6/UyMTGBw+HghBNOwGazYbFY0Gg0cipUPB6XHdjV1dVotVpaW1tJp9MyLV5TU4PdbqehoQGz2TxlJKcYY/pWa+4OFWFYb7PZ0Ov1pNNpRkdHGR8fp6mpiebmZmmZVVdXR1lZGWVlZYRCIYLBIDabjcrKSsxmMzabjUQiQTabpbm5mVgsJj0udTodRqMRn89HdXU1FsvUqU/F0cLiRqADNUId7NqKz68QvsLMXwxBELWn9fX1MoputVqBydrSuro62VUfi8Voa2ujs7OTyspKSkpKWLhwIeFwGJgU/UNDQ4TDYex2OwsXLpT7E4/H8Xg8sqZWXEN1dXUyunswk6EOhZk8X4V1mt/vZ3R0lGOPPZa+vj60Wq20LotEIjQ3N+NwOAiFQkQiESorK+U1ajKZ0Gg0OBwO9uzZIy3Z7Ha7bLh69tlnpYm/sAWrqamR/8beTmGaTCY577zzAPjLX/4iXSTeaNvBbFdQUFBQUBC8qW794qaL4ughTLW2qaurIxqNMjo6SmVlJYVCgZaWFurr63G5XHi9Xvx+v2woCgQC2O12otEoWq0WvV6Py+Vi9erVBAIBZs2ahdfrZfbs2bhcrim2QTOloI8EM3WCC/EivrO8vFxGgisqKjjhhBNIJBJ4vV68Xi9VVVWUlJTQ3t5OMpmUkca5c+fKRqpMJkMul6OmpgaXyyWF6uDgILlcjkAgQFVV1RThJM6F2A8h0rRa7QEtlt6ImRpsph9f0eyjUqloa2sjEAhI/9mFCxfi9XpxOBzAZATU6/USj8dpaWmRNcGDg4MkEgmcTicOh4OTTjoJQIrv4uNe/N0LFy7cL41ffC0cTvEmyiKKRbCoJ9VqtVRXV+Pz+QgEAtJFobKyklWrVrFv3z48Hg+FQgG73S5dDGbPno3P50Oj0UyZ4CUa2BYuXMi6detwOp1ye/FxeD1/3kPljeyjcrkcjz/+uPz5YLcdzHYFBQUFBQXBm7aSEhQKhSmNMxaLhXA4TF9fnzS8t1qt+Hw+GhoapjSx+P1+WRawcuVKOjo6MBgMBINBaXgv0n3T034z2SFNt9U5EhzIR1KIxHw+j9vtlmUMTqdzSse4yWQiGAzi9XoJBoPkcjmsVitz586lrKxMRsNElFKr1VJWVoZarZYCUzQ4vdEaD3dkTYghUd4gon/ie6qqqojFYoyNjREOh1m4cCHxeJzy8vIpnfZCBGk0GqqrqxkeHpaeryJyLgS3cGUQ5QSillkMQJhpfdOvhcO9foGI2hb7y5aUlGAymWSNqHCvEA1f4XAYnU5HNpuloqJCPmR5vV5cLteUfx/5fF5GS8Uo3sPRjT8T7yb7KKVzX0FBQeG/mzclTosjNYVCYT8R5PV6ZdOGy+XC7XbT1NREoVCgr6+PSCQiU/zBYFDe2IUdktfrxefzUVdXB3DQTS7F+3EkmqGmz2Iv/r1ArVZLz9Hp6fCysjJgMsWp1+sJh8MsWLBAzkcXok9YBgmrrOIobXH0bvp3T9+ft1pzeaBjOL2rXXyPeFDRaDTU1NTI7xaCR0T7ROe5cBBwuVxs27aN6upqSkpKpDOAOJ7T62jFMIiZ1le89sMlTA90HET3vtg34U5RfN7EvtTX11MoFPD7/dKxoKamRmYcxOuLrb6KfxaR5yPV5Hcg+6i3E6VzX0FBQUEBDkPkFPYf/1mceny997hcLunfWBx59Xq9sqbyUETG29G1fLDfMT3CFYvFZHOMmIne0NBAPB6fknKPxWKy6UY0CB1uH8sjTWlpKYFAQPqfFjsHwP4C2+Vycdxxx+HxeKSQne5RKv482PP7dnWwT99HYEodsEBEIkW0V5BKpeT5fjd03U+3j3o7malzf926de+YWFZQUFBQeGc4LOJ0OtPnthebwE+v2xPM1HRTXFf3XkasobgGtLy8fIpYicViMkUubJJmEj7vdsQ5FIK7uBmreI799BpYo9FIQ0MDExMT+6Xj383XQHGpQ/G+H2h/xevz+byM/ooo+XSKBwEcKeH6bhtTKjr3lSiqgoKCwn8vb1mcvl6asbhWENgvRS0oFh/C53F6w9V7heklD8VMLzsQxyeXy0krKYvFIksgik3lZ/r8dxsHSrWL4yDEubB51u7OgwABAABJREFUEogHF/H+I9HMdCSYfi6Kz6/X65XG/eK8Ti//gH9nEIQ120yff7gbnwTvpjrT6ShRVAUFBYX/Xg5KnIru2qGhoYOaUS/EiM/nIxKJkMlkqK2tJZVKzXiDFa9PJBIMDQ0RjUblZKV34mY5ODgITN68S0pK3vTnFK+reNRqMpnE7/fL4+PxeHC73VPWWygU8Pl8UrgeyeNwuNZ7IKYfB+G7KrxAU6mUFGAmk4lQKEQul5Om/YebI7He4geRZDJJd3e39K+d6Toufn3x9ZFOp6e8pnjbm40ei/U++eSTMioPsHfvXuLxOD/5yU+YNWsWTqcTtVrN0NDQjJ9THN0dHh7ebzrXgbYdzPaZUKvV8t+F0WiUUVSj0cg999wja7inI8okxMCD/3QKhYIc3PFmxhMrKCgovNtQFaaH92Zg8+bNrFix4u3YHwUFBQWFN8Hg4CC1tbXv9G4oKCgovGUOKnLa2toKTP7P72Aip4eLmTqf3w6GhoaYP3/+m1pvsdaf3nH/buWtrPeNeKfO4etxJNf7VjkSx+vNrvfdeO4Ohjez3lgsRnV1NQAjIyNThljM9Ps3et/bSTgcpq6ubkpUXEFBQeG9zEGJU41GA4Ddbn9bb+bvlHAQ3/tm1lssTu12+7u6RlTwVtZ7sJ/9buJIrvetciT2582u9912bA6WN7Ne8f848T4hMg/0+zd63zvBe+H/NQoKCgoHwxHp1gemdGa/l6Iub4aDqIw44OunTz56L99gipu4/hvO+6FS/G/ivXSu/5v+LSsoKCgovPMcUXF6uCf0vNn9eDturG9WlBUfp3erXdKhcKDz/p8ocA51Te+Vcz19Xe+Wf8tHErVazfLly+XPb/T7g92uoKCgoHDoHDFxeqgTeo6UeHm7bqyH8j3Thex/yk2/2PpKTDQS/CcKnENdkzjXMGmr9W4V6tPX9XrX6H/KQ4fJZGLz5s37/d7r9XLXXXfJn6dbWB3ofQoKCgoKb54jKk4PdLOa6YZWfEME8Hg8wL8NyN/Kfrwdgmim7zlQmlusVfh9ijTvezHFL9bo8XiYmJigtLQUu90+Y2QwEAjsZ77/XuZgrq3i4yPeUzze990o6Gbymp3JuzYej7N7925yuRwVFRU0NTW9Q3t8ZJjJB1YZBKCgoKBw5Dli4vT1mB6ZEWIVJiNuxSM832pE5u2I6BzIfB5mjq6JNadSqSlp3lgsRjgcxuv1SuP29wKxWIzx8XHpK1lVVTWjsJ4+xvS9zsFcW2LQgvDuFR6vYgLauxGxrgNFhcW/11gsRi6XI5FIvEN7emQonpoVj8d54IEHAPjEJz7BxMSEIk4VFBQUjjDviDgF5BQoj8cjR5ZqtVoZcZs+wvO9yIHS3MJQfXrkzWKx0N/fTy6Xw+PxvGciURaLBY3m/2PvvcMkrcr0/0/lnKs6p+me1JNgAiBRhrAKZsE8uCoIblDQnwH1q7K66rq7orhrRAUUkWURiQoGcpphEhO6J3So7q7u6so5p98ffZ1DdU9PZJgZ3Lqvi4ueqq6q97znVJ/7PM/93I8Kg8FwyN95PepQX+01irWdzWaxWCwHjB9OzTV+qPkSYwJobm6WkcXXs2Qjm82ybNkyKpUKkUhEEm6DwcCNN94oG5Ec7HUAAwMDp7SWuIEGGmjg9YLjRk7nViLDbHIGzHrO4XAwOTlJKpVCpVLN2vxFy8f6178eNz0RWSoUCsCBVf1zu/6I9pEiajw3JdzU1HRE6f76SDQce/T4SKrLa7UaBoMBt9vNyMgI+XyeYDA4a/7E588XgTvVdaiHusb5WszWr3GBQqGA2+2edR+CweC80g44sZZAc+e4fs6ampoO+F3RZlWsaZPJRDabpVKpyPtwqh845kOtVmNsbEz++84776S/vx+j0Uh/f/8Rve5oXTsaaKCBBhqYH8etvHSuZrT+MdFTvZ5o1kdJjUaj1JZms1lCoZDcJOe+59zPFJHXUxEmk4lCoYBOp5OG5mJs9T8rFApJSJqammhpaZHEQEgcUqnUEY/zYPf9aCFkBl6vd1b7yXqIaxdm7fUp3kPNnyBFp3J6G15Zq4fST5fLZcbGxpienpYHiXo4HA6y2ey83wHgsPf4tUT9HAkJQjKZnHfOxDWL76pOp5P6WTGP9XrqU/m7eTj09/ezZs0aOjs7T/alNNBAAw38n8Nxi5zOlwY0mUx4vV58Ph+RSOSAP/SiOGZu6nCuHvVwxOBUjbyJCHD9WISm1Gg0StIKr0SbRDRVkFdgVueX+aq856aexWfNd3+PBvXFO/X3OBgMEgqF8Hg88jM8Hg+RSGTWdQKziF39ddZLOU7FuRM4XARQjMVoNM5K/YqIdz3hLBQKUt5Rf0+mpqbQarUy4nwiUf8dE3NyJAcGEf2FGSN6j8cjxysOnXMjw6fyPB8pBgcHAXC73Q3taQMNNNDAa4RXTU5FKqu+xWH9Y9lslmQySaVSkcUgsVhMboI9PT0YjUb5mvpK4bk4GAk72ZtefTqvVqvJSKK4NrfbLZ8fGxujWq3Kf4uK6LlEdS5RqI9KCTIkMPdxg8Ew6/ljTRPPJdcCoVCIYrHI+Pg4MDNngqQmk0kmJiaw2+2oVCo5v5lMBq/XO6tKXbyvuH/11/l60KPCbGnGnj17iMfj8nqFPEOlUuFwOGS2QET8hbuBwWCQ9+VEQ9xfMQf1a6tWqxEKhQiHw3IN1x8GxfdZjDcQCJDL5ejp6aGpqemAor9TeR6PBAaDgQ0bNgCvVO67XK6TfFUNNNBAA397eNXktJ6EwewIILxCWs1mM0ajkcnJSQAmJyclYe3u7j7gPSuVygEkZz4LJrEJ1j92slB/L4LBIKlUCovFQk9PD6FQSEYTBXnMZrP4fD4KhQJ2ux2XyyV1qqJgTNwLOLh1kfi8WCxGJpN51fZb9RDzOFf7OzExAUA6ncbj8bBs2TIymQxbtmxBo9GQy+Xo6+ub5URQqVQYHh6mo6NDEtr6CLH4DDGnpxKpqSfL4t/16y0UCjEyMoLZbJYaXJVKJde/kHCI1+7du5dcLkcul8Plcs36vbmfMd/nHW/Ua2fFQSmTyfDUU0+RTqepVqt0dXVht9txu92z5tPpdAIzEfX6QkaxXk+VOXy12Lp1K9lslsHBQVm53yCnDTTQQAPHH6+KnM6Nhgl9pMVikQRDEJBoNEo4HJbWM/F4nGKxiM/nkxGWvr4+qdEUqc7R0VGam5vxer14PB4KhYJ8n7npyBO9CQpSDAcSqnpks1n27NmDSqVCq9XS0tKCyWRi69atjIyMUCwWWbx4MXa7HZiJplYqFXbt2kWtVqNYLNLT0zOLxMz1RDWZTKRSKQKBANls9rhYUYnPmO/+mkwmNm/ejN1uJxAIALBr1y6mpqZkYZfoOy70lIKEi7kTbg2pVIp4PE6lUsFgMBwQFT8Voqj1hw1g1joPBoMMDw+TzWaJRCI0NTWRSqXw+/04nU55CDGbzZKkjo2NyahjffRRrJ/675LJdHBbp+OFTCbD9PQ0fr+fjo4Oenp68Hq9FItFuXaFHZZGo6FUKmGz2eRBSqfTYbfbaWpqmlVIdbA5OxXm9GjR2dn5urnWBhpooIHXM141ORWpZ4BwOEy1WsVisUiiVigU8Pv9wEzaT6vVotVqMZlM5PN5AIaGhqhUKiQSCdra2qjVarjdbqLRKNlsll27drFw4cJZJGxqagqXyyWJ8YnWLtZqNUkggAMIlclkIpfLEYlEiEQi8vfb2trIZDKEQiHi8TjJZJJYLIbFYqG7u1uOYWpqCpVKxfT0NG63m8nJSSKRCHq9nomJCVwuFw6HQ5I8k8mExWKZVYD1au+HmEOYIcxCgmA0GvH7/eh0OhKJBDabjWAwSKlUolKpEAqFcDqdJBIJHA4H1WqVWCxGsVjEbrejVCpnpYaj0SilUgmLxTLLuUGkm+sLu04GOZhbhZ3NZpmcnKRcLqPT6dBqtdhsNkKhEM3NzRSLRbxeL5FIhJGREVpbW9Hr9UxPT8sDjV6vJ5PJyHvT2tpKJpOZt1HBiZCvmEwmpqenKZfLjI6OEovF0Ov1OJ1Ouru7patGIBCQ689ms9HZ2Ukmk6G5uRmz2UxPTw/BYJCJiQk6OztZsGDBvET0VIuMKxQKli1bRj6fZ2Rk5IDHxc8He93Bnm+ggQYaaODo8arIaX0ULZPJ4HA4KBQKMnISDAaJRCJYrVaCwaAsAlIqleRyOVlAYTabyefzaDQadu3ahcViYXp6GoPBIFOf8Xgcj8cjN1GNRiOr3uur/U9kREYQQpGaFoVOMLP5Tk5OolQqGR0dRafT0dzcTCQSYWpqSupOK5UKpVKJXbt2USwW6e3tpbe3F4fDQa1Ww2q1olAoiMViGAwGkskkVquVbDYrSYxIBS9YsEBGcI/H2MX9FI0RkskkW7ZsIZ/P097ejlarxWq1olQqUalUNDc3y0jyvn37gBm9ZVdXF0qlkmw2S7VaJZfLUa1W5fzFYjH8fj8ajYbu7u55C+tONokR68tkminySyQShMNh2traqFQq2O12PB4PmUxG6k5HRkaw2+2USiVcLhc+n48XX3wRj8cjXQ68Xu+scYlGBeI7JAqr6r9Tx7q2M5kMVqv1oM8bDIZZkVARSQ0EArS1tZHP5xkeHiadTqPX67HZbJRKJbq6uujp6SEcDjMwMIDf75eHKEFOD9aI4lQgpjAjX9m9ezdbt25l7dq1Bzx+uNc10EADDTRw/PCqyWn95lJfTCGeE6las9nM8PAw4XCYbDbLxMSEjMS53W6USiV+v590Ok06naa1tRWNRoPVasVut2O324lEIkxOTjI6OopWq6WtrQ2tVntAiv9ERWREwZDX68Xr9ZLL5XA6nbIAzOfzsXPnTgqFAh6Ph1wux/79+8nn8ygUCjweD3a7Ha/XSyqVolQqyeiL0WjEZrNRLBZxOp00NzeTSCTo7OyUhHy+8R4PUl5P8OtTtKFQiHQ6LSOJy5cvl7rYVCpFIpGgVqtJHaqYy/3796NUKpmamgJm1saiRYtwOp14PB6mpqaIRCIEg0GUSiXt7e2zPvdgutcTCVH4FAqFGB4eJpFIyAh1MpkkGo3KqO/09DSBQIBSqUQul0Ov1xOPxxkeHqZWqxGNRnE4HExPT1OtVlEoFDidThl1rK+WDwaDUuoAvKq1fSirKqEhDYfDtLa2ksvl8Pl8+P1+pqam2LNnD0ajkXg8jlqtprOzE7vdjlqtZnp6mlqtRiQSkYcOQB5WDiXROJlz2kADDTTQwKmJ42YlJboDpVKpWVE9tVrN2NgYTz75JNVqlXQ6jUajwWAwUCgUcDqd1Go1kskkk5OTaLVaqWlzuVwoFArsdjt+v59wOAxAtVqlVqsRj8epVqsYjUaam5tnbYCvxWY3X1V+/fiLxSKpVIpoNEoqlSIUCqFUKikWi9RqNSYnJwmFQigUCsxmMw6HQ0bccrkcarWaVCrF+Pi4lDckk0meeuopNBoNq1atwul0StsisfFPTk5Ksno8jNznamdFNFTM6djYGHv27GHTpk04nU4WL16MRqORKWshy2hvb2fbtm1MTEwQCoXQarWYzWbp27p+/XqCwSBtbW3EYjGampqk1EOMrZ6Ynsg0sDDYF3rZ7u5uPB4PExMTRKNREomETM0PDQ1hsVjQ6XQYDAbS6TT5fJ54PE5zczPbtm2jUChQq9Ww2+1SQyokGIlEgoGBAZkZqL/3BoNBkl5xH+p/PposwdzmAGI9i8j/8PCwlFYYjUY0Gg1KpZJ8Pk+1WiWfz6NWq2UbVqVSSSgUolKpMDk5SSKRQKlUkkgkZIHf3r17MRgMkmDPXVunUmq/gQYaaKCBUwPHjZwK+6RIJCK1h4lEgmQyye7du5mamiKVSqFUKqXXo06nw2q1kk6nicfjRCIRLBYLbrcbm80mN+9IJCI1jqLwwuVyUSqVZHRWRLGOZ6X6oZDL5WZFF8XGK9Kira2tqNVqSqUSxWIRjUZDJBIhn89TLBZJJBKYzWbUarW0ZhKpVBGJW716NSMjI4yOjqJSqXA6nSxatIipqSkqlYokLTqdjlQqhdfrldZNrwZzI11CVwszBCcQCLB79268Xi/d3d1Eo1GWLl3K+Pi4bCowPj5OS0sLKpWKYrFIMpmkqakJnU5HpVJBr9cTjUbp6+vDZrPhcrnw+/1SJjGfR+aRkpjjJe0QZFvoaJuamtDr9XL+arUaiURCkra51yeKiSqVCgqFAp1OR09PDwqFgnK5TH9/P9FolKGhIUlom5ub0ev1LF26dJaUoD4dXn99R0PuDvY7mUyGdDotyWY6nSYQCBAKhWhpaZEepm63G4vFQigUklKMSqWCyWQiFouhVqtJJBJSqpLNZvH7/ZTLZRwOhyTf87kQnGxks1nOOOMMeTia+zjASy+9dMB363DPN9BAAw00cPQ4buQUkB6boVCIarXKxMSE/H+tVpNaU7VajV6vl9EpEZFKJpNSvzgyMkJbWxvBYBCVSoVSqUSn09HZ2UlPT88sH1S3233CC6LqiYHH45Gb0t69e0kmk/h8Prq6uujr68NkMhEOh9Hr9TLlKSLKTqdTFowJTeDIyIj0fjWZTCgUCkqlElarlUKhIH0xxWcWCgVJbuulFceK+mIk4aRQ3xggmUxSLpdl5X0kEuGpp56iXC6TSqXI5/PyoKLRaKQmuKenB6fTycTEhLwPWq2WRCJBR0cH6XRazjfMHABEAdnRSBaOhrQdisiKg0d9JqCnp4fJyUlMJpN0KXC73RgMBnw+n2xOABCPx1GpVOTzefleQpOdy+XI5/NSr+rz+eQ6NxgMbNu2TVbNC1cMj8dzQCX8qyF39Y0CkskkCoUCo9FIIpEglUqRy+VIp9MAMuIryGi5XGZycpJcLkdnZydut1sesuLxOD09PdhsNnbu3Ekmk+Gss86adw5PBWIKM1HkgYGBQz4+X3vSwz3fQAMNNNDA0eOYyGn9H2GRPhaPiUpkhUKBSqUiGo1itVql/+Pg4CCVSkVqMsvlMgsWLCCbzdLU1IRWq5XV7UajkUKhQCqVktFUEYlVqVRYLBZZjCGuQVzHfGb4c6NOxxpdq/eEtNlsUjcoSMT27dulNAEgGo2i1WqJx+N0d3ej0WgYGRmRWr1sNoter0ev16PVaimXy+RyOQYHBzn99NNZtGiRjKaGw2FJUAWJa29vn2XWf7C5qp+vo4EodJuampKFTitXriSVSlEul5mYmCCRSMi0vEqlQqFQEI/HZaQ8Ho/j8/koFotMTU3hcDhkZNRut7Nv3z7Gx8dpamqSB5FKpSKJ8dFGCI/kdwX5nu+9a7UaBoNB9lUXxT5GoxGlUkkgECAejwMzxK25uRlAFr+JDIJSqSQWi6HValEoFAQCAVQqFVNTU0xNTWGz2bBYLHI+xVjD4TC1Wg2Px0MoFKJQKMjobf04j2btiu+HWAP1UXGHw0E0GpUyBoVCgVarlUVQwu5KHFLEQUs4FYhDlIiUF4tFtm7dSiAQwOl0yuKv+mYd4v0aVe4NNNBAAw3U45gjpyLqAjOVxUajkXA4TKlUIpFIyBTe9u3b2bt3Ly6XC5PJRLVapVQq4ff7ZSFQLpejUCigVqtRqVTUajUqlQq1Wm1WVf/U1JT0fQRwuVzS1L++Vae4lrGxMRl9E88JvBoNo7DQEr3RRdvOSCTC/v37ZWQwGo3i8Xgwm83E43EymQybNm3CZDLxxBNPEIlE6OjowGKxoNVqZcQzFotRLpepVquSsNRqNVlEI8hDOp2mq6sLk+mVPu2Hm7OjJeQmk0lGwvft24ff75dFPrFYTFbbVyoVpqenGR8fl+4BolhNo9FI54IFCxZgs9lIp9MyahyLxWSauFAocNppp2EwGORci+itkIMcTxyMyNYfbOCViLiIGouivmAwSF9fH1qtlomJCcLhMMlkkkgkIoumwuEwmUwGlUpFKBRiz549FAoFIpEICxcuRK1Wy3ulVqvla7Va7axI+FyLqaNFOBw+oJ2s3++XTSFSqRSZTIZ8Pk+5XGZoaAiFQoHf75ffTavVKm3BhPWUkCRYLBasVivRaBSz2YxKpZJ2VGq1mh07dmCz2fB4PLS3tx+XKH8DDTTQQAN/ezgmcioKRUTURZi+i6iSy+WiqalJFo8kk0nC4TDd3d0MDQ2xb9++WWl5v99PV1cXzc3NWK1WUqkURqORarUqi6hyuRwqlYpSqUSpVEKj0VAsFgkGg6TTaRYsWCB1mGeffTZjY2N4vV4ZvZxroXMsKdF6slLv+5lOp9m+fTvVapVUKoXdbqdcLmMymRgfH5fpX6GrTaVSJJNJAMbHx2XXnfHxcbxeL/F4nIULF2I2mwmHw7S0tGCxWGTUSbS+1Ol0UpsYDAbnbW0qrru+XaYY/9HA7/fjcrlkRC8Wi0kj+Wq1itvtZseOHezZs4dcLnfQ90mlUpJ8jo6OMjU1RW9vL3v27CEQCEirLFEsBtDa2iobF8yH+SrAj+Tgkc1m531f8X5CQzw4OMj09DQ7d+7E4XBgNpvR6XTS5mznzp2Ew2GGh4cJhUIyEyCKBEVkcHp6mlgsht1uly4UQjctrNYGBgaIx+NoNBry+bx0MFi2bNmrToHP1fEKGzefzycj1blcTtqiZTIZlEolCoWCQqHA/v37iUQisghKQETTAdra2mhrayOXy5FMJlGpVPT19R2gWxYHG2BWNPhkQXQ8OxoMDg4ecq030EADDTRwbDgmcioih5FIhFwuJ9tTCoLq9XrZu3cvMGMnI0zUo9Eou3fvlh6fIiWeSqXYt2+fTDMGAgE2b97M4sWLJalsbW0lGo0CyAp+mCF8Qtuq1+tlwYbRaMRsNlMqlWTktB5HGj2sT4sL0iOilIFAgGKxSDgcljZJS5YsYXh4GJVKRSaT4Y9//KPU7dVj3bp1vPnNb+Zb3/oW8Xhcpm0TiQQws+GvXr0ap9OJ2+2WGk6bzUa5XJbFKeK6BMGpb2EqIq7ivlarVZn+n+s8IN7HYrFIMiUOIcFgUPrQrlixgmQyydatWwkGg+h0Omq1GsPDwwwODgJw/vnn09TUxPDwMFNTU7S1tbFw4UKeffZZpqenGRwc5LTTTpMV/oFAAIfDIVPle/fuxWq1olarWbZsGfF4nJaWlgMq+OvT03PttI7k4DHXWqn+PtQ3H1AoFLKorVgsksvlsNvtnH766dx7772MjIzw3HPPyYNQIBCgs7MTs9mMy+UiHo+TTqflmiiVSvT398uMwdTUFFarlaamJvx+v9StVioVpqamZJGOaNJwrCRVrFtBUsVYtVot4XAYjUaDRqPBZrMRCATQ6/V0d3eTzWbZunWrHN9cmEwz7YkDgQBTU1Oo1WpJvOPxOC+88AILFy6ks7OTjo4OKVVQqVSMjY0d8F4nWoc6Pj7OmjVr5L9F+9mDwe12YzQa2bBhw4m4vAYaaKCB/3M4JnIqNo9CoUA6nZZRUKFX8/v9+Hw+kskkpVKJarXK9u3bJbm86qqr+PrXv05LSwuBQIBPfepTPPjgg4yNjc3arF588UU0Go006a9UKpLoCZlApVIhGo3K37NarbIAB2ZS7fW+kUeLualwETkNh8NEo1GcTqfc0EXhS6FQYGhoiE2bNlEoFGhtbWXRokWyReeHP/xhrrvuOlQqFUuWLOHqq6+WUbhVq1bR3t7OH//4R/bt2ycr+kX1eyqVolarUSgU5L/FnPh8PukrW1/EA2CxWGS7zXpyKkiv+FlA6GdF5bXonJNOp8lkMtx2223yXggolUq+8pWv8LnPfU46KIh1otVq2blzJ5dccgnxeJwtW7bMannZ3d0tu4vBjHm/Wq0mEAigVqvZs2ePPGRkMhlJ4OqdEuq1jEdqrVSvd6yPjKvVaikjUCgU5PN5WfAkKvXz+Tz79u2TcyAi26FQSB7ODobJyUnOOussEomELJwSnxOJRGhubsbtdktrtVgsRnt7+7wOBkcq1fB4PKhUqlmRdxFJz2azckzj4+OSvArbMJiJcH7oQx/ife97H01NTdjtdvR6PeVyGY1Gwze+8Q2+9a1vMT4+Tj6fZ9GiRaRSKZxOJ/l8Hr/fL9eYcGswGAxMT08Tj8ex2+1YLBYWLFhw2Lk7ngiHw7MioFu3bqWrq+ugv9/V1cXg4CDhcJht27ZxzTXXnIjLbKCBBhr4P4NjIqfCkFykPQWhCIVChMNhxsfHCYfDDA0NEQqF2L59O/l8Hp1Ox6c//Wm+8pWvyChMc3Mzd955J4888gj33XcfVquVRYsWodfrufvuu3nxxReJxWLs3LlTRpc8Hg+FQkGm9zs7O/F4PPT19cnioEwmI4tSIpGI7N5TjyPZ0Ot7qnd3d8vIsNDOCr/LarWKWq1mYmKCXbt2sWvXLgqFAqtWreLBBx/E4/Gg0WgApP4Q4G1vexuPPfYYv/jFL7jgggv40Ic+RKVS4dJLL2Xjxo1s2rSJdDoto1CZTEZWSE9OTuJ2u/H7/RiNRlQqlaz8F5urw+FArVbLorG5xSdiDsW8CoImLIUKhQIOh0MSs9HRUR577DGy2SxdXV2sXbtWSi7+8R//kfXr1wPMSvsKrFy5kgceeIDrr7+eHTt2kE6niUQiDA8PMzo6yllnnYXL5WLlypUkk0lZjDM2NsaCBQswGAy0trbi9XpnFQgda7RtvnS+iIyLSLRoFqHT6XA6nUxPT1MqlaQjQyqVore3lzvvvJPTTjuNcrnMrbfeypNPPsnExIT0sxVEV6lU8txzzzEyMsLTTz+Nw+GgUqmgVCrxer2yc1S5XEav19Pa2ip11cLPtr497dFqp0XUWWiEe3t7yefzWK1WpqampAMDwMjIiCz6+sxnPsNXvvIVKVUolUpotVoAGWX+f//v/5HNZvn+978vdeJCU5vL5eT1l0olli5disvlQqlUHnCN4mAk/GVPVNq/tbUVrVY7i5gqFAq6u7vlzwJdXV10dXVJUtva2too7GqggQYaOE54VVZS9S0Wc7kc2WyWkZERXC4X+/fvZ8uWLTLdfdFFF/Ev//IvMpoiCF+tVmPLli0MDg5Sq9VYvnw5GzZsQKFQcOWVVzI8PMyNN97ICy+8QDqdlv6Qw8PDdHV1YbPZcDqdqFQq4vG4JFhCEytSx8lkUhKtWCwm+9IfLakR1fXxeBy/349SqZS62LvuuksWuwCsXbuWu+++WxIdQeJjsZgkCcJG6e///u/p6emRm92PfvQjLr30UqLRKC+99BKALDARvqbFYpGdO3dSLBYxGo14PB75s7BkEhu8mCcx5vpOXoIoAwwMDFCtVonH4+h0OlmglkqlGB0d5fHHHyebzbJixQruv/9+rFarJCnT09P88pe/5P7776elpYU3vOENLF26lI6ODlwuFwC9vb08/vjjbN++nRdeeIHt27dz//33Mz09zYsvvsh73/teGT0vlUoyCpzNZhkfH59FruYWCB0Pf1NBBPP5PNFolD179tDc3Ew4HGbfvn3odDpaW1tRqVRs3LgRgM997nO0tbXJCOpll11Gf38/f/3rXymVSrLD19lnn01zczPpdJovfelL3HvvvUQiEZxOJw6HA4PBQEtLC8lkEpfLRSwWk366whlBjLm+APBI13G9m4XRaJSG+4FAgL1790rpx8qVK9mzZ490W/iP//gPrr76ank9MOPp+etf/5pt27axatUqrrvuOlasWMH111/Peeedx7XXXks0GmV8fByLxcLy5csZHx+XFloDAwPY7XZaWlrQarW0t7fLIspgMMjevXtlkeCJIqcPP/zwrPS+uE9er/egrxFk/eGHH24UdzXQQAMNHCccEzkVEQKxIYpinB07djA5OcmuXbt49NFHZVegf/mXf+G6666T3aFqtRp79+7lj3/8Iw888AA+n0++97333sumTZu4+eabUavVrFu3jscee4zvfve7fOtb3yIYDPLAAw+wZs0a1Gq1bLkYi8Voa2ujWq3KHuGiY49Wq5UtRGH2xn441Jv610sOEokEg4ODxONxent7+cMf/sDLL78MzGgDP/7xj/OhD31IRsxUKpUkgSLCunHjRh588EGef/55crkcb37zm7ntttswGAz09PRwzz338PnPf56xsTEikQiVSoXnn3+e3t5eyuUypVJJVlCLgjCdTidTshqNRlZFwyta4bkpcKGv2759OzCTcq3H2NgYIyMjPPPMMyQSCU4//XQee+wx7HY7mUwGhULBgw8+yDe/+U2ZAga48847gRlt8Rvf+EbWr1/PW97yFqxWK2vXrmXlypVYLBYefPBBrrrqKgKBAHfffTeXXnopJpMJm80mI1nFYhGfz8euXbtYvHgx7e3tB6S1j0cXqUqlIg8dwWBQSh7i8TihUAiYIc0bN24km83S2trK+9//fhkNHx4e5qabbuLRRx894L3PP/987rrrLqxWK7fccguXXnop1113Hfv378flcskIq9vtRqPRYDKZZPRfaH+bmpo466yz5HseDREX1fr10eZgMIjf72fPnj0ygupyuZiengbgv/7rv/jwhz8MvCK1eOCBB/jUpz4lierw8DAPPvgg119/PR/96Ed505vexDPPPMOGDRvYtm0bzz33HH6/X3YHa21tJRQKEYlESCaTLF68WGY3RLRar9ejUqletUNBAw000EADrz8cc+RUkIJsNkuhUGBgYICBgQHGxsakWbtGo+Hhhx9mxYoV3HHHHQwODhIMBtm9ezf79++X76XVannDG95AU1MTv//977n77rvZs2cPP/3pT1mxYgUqlYrPfOYzXH755Vx55ZVMTEywadMm1qxZI9PdHo8HpVIp/SZFNE+YvAtboqMlLfU91YPBILlcDo/HI1OZIsImKvKvv/56vv3tb8tIrkA2m+X3v/899913H9u2bZPV+vV49NFHede73sVdd92FzWbjggsu4MUXX5S2Uhs2bOD+++9neHgYtVot25gKiYWI0mm1WorFIqFQiK6uLsLhsHQymK9ZgdABiihpT0+PjLIGAgGy2SxWq1UW5tx4442yIK1arfJP//RP/Pa3vwXAbDbzd3/3d+j1ekZGRti9ezeJRIIHH3yQBx98kJtvvpnvfe97XHzxxfLz3/72t3P33Xfz/ve/n1AoxFNPPcXKlSsJhUJ0dHSg0+mk1lWpVMoOU/UtMAUhB47ackqsZXEPRGrbYDBgt9tJJpNMTEyQTqel36loivCDH/yABx98kKeffprt27ezfft22RHqkksuka4Tf/7zn3nmmWf41Kc+xQ9/+EMAPvKRjzA1NcW//Mu/EIlE2L17N/l8HpfLhV6vR6PR0NzczK5du+T8trS0EI/H6evrO+RY5lvrQirR09MjHR5E5N3lchEMBgmFQrz00kukUilWrlzJ+9//fvn6oaEhbrnlFh544AEAzjrrLD74wQ/y4IMP8te//pWbb76Z//7v/2b9+vW8+93v5uGHH+ZLX/oSd9xxB0NDQ9LDdWpqing8jl6vR6lUsnfvXpYtW8bY2JjUlHd3d9PT03PKmPQ30EADDTRw4nDMJvyZTIbp6WkCgQCpVAqHw0GtVsPpdLJ582YAPv3pT9PS0sJ73/tenn322VnvodFoOPfcc7nkkkvo6uqSqbs1a9bw7//+72zfvp3169fzla98hQ996EOUSiWWLFnCo48+ypVXXsng4CADAwNoNBosFgttbW2YzWbGx8dlV6IFCxZQLBax2WxUKpVZEcSjJarCS7RQKDA6OopCoZA9w4WtEMykrWOxGDBjvzQxMcH999/P7373u1lV+zqdjsWLF7N8+XLWr19PKpXia1/7Gi+++CIXX3wx3/nOdzjvvPOAmUid3W7nRz/6EXv37mVwcBCfz8c555xDOp2ms7NTttVUq9XSa9JgMBCLxWa1QxX3WcgTMpkM4XAYmIkaCvsq4V1aKBTI5/N0dHRgMpmk/jWXy1Gr1bj22mu57777UKlUvPvd72bDhg1YrVZCoRBOp5NKpcLLL7/MyMgI999/PxMTE1x55ZW84x3v4E1vehPnn38+TqeTdevW8ctf/pKrrrqKyclJDAYD3d3dxGIx0uk0zc3NtLS0yHS02WyWPrYul0tqMQX5PpoUf73WdNmyZfK1VquV4eFhqtWqjNAPDg5KzeQXv/hFbrrpJnbv3j3r/dasWcNHPvIRFixYIJsTnHXWWXzta1/j97//PVqtli984Qvo9Xo++clPEolE+O///m8mJydpbW2V8pOuri5aW1vJ5XLy0LFo0SJcLpfsGOXxeGZpHQ8VPS4Wi1gsFvmcsItSKBRccMEFbN++nd27dzM8PAzMHEKmp6dJp9P827/9Gw888IB02njb297GBz/4QZRKJddccw0XXXQRt956K16vl8cee4zHHnuMc845h7vvvpuVK1fyxS9+kWw2y/DwMGvWrJESAnEw2rdvnzwAmc1mSUyPh1TjSHHVVVdhNBp5+umnZbo+l8txwQUXAMx6XEAc2K666io2b958wPMNNNBAAw0cPY6KnAqDbvGzMOcWVkgKhYLnn3+ebDbLkiVL+MIXvsBvfvMbnn32WfR6PVdccQW9vb20t7djs9lob28HXumqA7B69WoeeeQRPv3pT7Np0ya++MUv8utf/5rvfOc7XHjhhXR1dfHHP/6RDRs28Oyzz7Jt2zbOOuss9Ho909PT5HI5SeZSqRSrV6+W0U+x2SWTSUKh0BFFZuolDJ2dnYyMjJBKpWQP8VgsxvDwsCSe7e3t5PN5fvCDH3DffffN8k/s7Ozk/e9/Pz09PaxduxaNRsPw8LCsQv/e977H1772NUZHR/nIRz7C//zP/3DGGWegVCpl9fhdd93FBRdcQDKZ5IUXXsBut6PT6XC5XLLgSpAZtVot24nq9fpZVddiDkUEGJDOA1u2bMFqtVKpVKhWq+zYsUO6CYh7olareeKJJ7jvvvtQKBR8/etfp7+/n5aWFmCGCAkpQ09PD5dccgkbNmzglltu4d577+WBBx6QEbje3l5uueUWrrjiCp5//nl+/OMf4/V6aW5ulj6bIlq6aNEiWltbicViPPfcc5jNZk477TTZ5rPeYP5IU/xzdZsiI6BUKunu7mbjxo1EIhF27dpFpVLhrW99Kz/5yU/YsGEDu3fvxmazcdFFF/GGN7yB008/nUgkIjW2Qjf7xje+kS984Qt8+9vf5n/+53/I5/P84he/QKPRcNNNN1GpVPjxj3/Mjh07aG9vx+FwsGnTJrq7u4lGo3R0dLBs2TJcLheRSASz2TyrVerBxlKPrq4uVCrVLNcJ4YsbiURQKpX4fD6y2SyrVq3irW99K9PT0/zwhz/k97//PQAXX3yxJNT5fJ6XX36ZxYsX8653vYt3vetdvPjii2zdupVf/OIXPP/881x00UX86le/4o9//CMf+9jHGB8f55577qG5uVkWCZbLZRQKBblcjvb2dunpe7Tz+Goh2pAKAi5+Foft+scFhIZXaLUbaKCBBhp49TgqciqMxWOxmNRzCsPwdDot0/YajYaf/exn6HQ6fvOb3wBwzTXX8NGPflRu2i+//DKPPvoo9913n9S6ORwOrrjiCt7xjndw991387Of/Ywf//jHDAwMcMUVV/Dv//7vfOxjH8Nms/GLX/yCJUuWkMvliMVijIyMUKlUSKVSLFiwgEwmI6vZ3W63LLaAGVeBudrL+SDIeH3URvhPCi2gSI1ms1kuueQSzjjjDK6//nruv/9+YCZCetFFF3HppZdy0UUXSd2qIJL1WLBgAQ888AD/9E//xEsvvcT73/9+7r77bk4//XT5O319fbz3ve/lF7/4BdPT0ySTSfR6PX19fWSzWcrlsrR/EgbvoqJfq9XK7k31lkn1no5ClxoMBmlvb8fn8+H3+6WdFCB///bbbwfg3e9+N29+85sP8KysR7lcplgscv311/ORj3yEn//852zdulXqWf/5n/+ZLVu28M1vfpM//OEPjI2NkUqlUKlUs9pjRqNR9Ho9kUiEcDhMOp3mtNNOkw4D9XM6n4RhPtTbMoniPIPBII3vm5ubefLJJ6lUKpx11ln8+te/ZvPmzTz55JMolUruuOMOrFarJOZCpzoX69evp6mpic9+9rM88MADFAoFbr/9djQaDd/73vd47rnn2LFjh9RiptNphoeHsdlsUkbh9/uZmpqip6eHjo6Og47lYOMUvsHBYFBGZ0dHRwmHw6RSKXkYs1qt8mcRSb3hhhu47rrrqNVq3Hzzzdx9993Sv/Xiiy/mPe95D729vZx99tmce+65XH/99QwNDbF+/Xp++ctf8txzz7F+/XqGhoaYmppCqVTidDql48X+/fupVquyC1q9w8Zr0R2sgQYaaKCBUxNHRU6FXZHRaGRiYkJW09ZqNQKBgNzEvvCFL7B48WI2b97Mxo0bUSqVrF+/nsHBQcxmM9lslm9/+9vs3LnzgM944YUXiMfjrF27ljPPPJM3velNfP3rX+eJJ57ghhtu4Omnn+ab3/wm7e3tNDc3EwgEZCtIs9ksC66USiWlUol9+/ah1WrZs2ePrFKvj7LVajXZEUf4ZgoIMl6fVmxubiaTyVAsFhkbG2Pnzp1kMhkuvPBCfv3rX/OXv/xFEtNrr72W888/H41Gw69+9StuuukmkskkWq2WFStWsHbtWrq7u+UGXK1WWbp0Kf/2b//GZz7zGV5++WXe97738bOf/YxLLrkEmEm9v/GNb+QXv/iF9FAVBV/CiUCr1crCML1eTzweJ5/PMzY2hs1mk+b5gpgK0imizYJk5XI5hoaGsNvtmM1mGWU1Go0MDw/z0EMPAXDaaaexe/dupqam8Pl8DAwMyKp3YS0WDAYplUosX76cT3/601x99dVcddVVWCwWNmzYwNjYGDfddBM33XQTX/rSl7juuusYHh7mtNNOI51OY7VaiUQi+Hw+YrEYtVpNFoTl83nZf76+JeiRVHnPLaiamppi3759NDU1SXP6QqEgx/PWt76VWCzGl7/8ZQDe+MY3ks/n2bJlC3/+859lkwlBjC+++GJJ8nK5HAsXLuTLX/4y3/72t3n00UdlFHbx4sV8+MMf5rOf/SyFQgG9Xo9arSYajVIul9HpdHKdCm31wdwKBA5HVDOZDO3t7dJpQ+iXYSabIe6rOHSo1Wr+9Kc/8fOf/1x+dw0GA7lcjj/96U/86U9/oquri2uvvZYlS5bwjW98g1/96lc899xzfOxjH5Oa6ze96U2y2cCaNWvI5XJUq1VyuRyRSASNRiM14w6HQ1rDies+VbF9+3Y6OzsP6ZHaQAMNNNDA4XGgyeAhYDKZZKVvW1sbxWKR5uZmnE4nTz75JLFYjBUrVvCJT3wCnU7HXXfdBcCFF17I4sWLZRrwS1/6Ejt37kSpVHLppZdy+eWX85GPfIQlS5ZQLBb513/9V0ZHRwFoaWnhhz/8IZ/61KcAuO+++3j44YfRarWcccYZAJJAFAoFcrkc5XJZeoIajUamp6epVCrSO1GMA5Cbr/DNrIfRaJwVfRNEfHR0lLGxMV544QWpTbznnnswm82yQv3yyy/nzDPPpFQqcdNNN/GHP/yBZDKJUqmkWCyydetWbr31Vr7xjW9w++23s3//fvR6vfRyvfnmm3nDG95ANpvlhhtukDZGOp2Oyy67DLVaTTqdJpFIUCqVmJ6eZvv27fh8PmknVavV2L17tyxSE7pMQG74uVxOkifRQaharaJUKhkcHGRqaopkMilT/AqFgo6ODn7yk59QLBbp6elh9erVOBwOQqEQ//mf/8lPfvITHnroIR566CFeeOEFJicnpa/t7t27+cxnPsPevXspl8u0tbXx1a9+FYAf//jHTE5O8p73vIezzjqLTCbDww8/zMTEBF6vl4mJCaampmQxXaFQoFqtSvJUqVTo7Ow84ogpHJg2NhqNstgomUwyPDzMiy++KK3P3vOe97BlyxY2bdqERqPhQx/6EEqlkp/+9Kfy9zKZDIlEgqmpKX7961/zm9/8Rhr+q9Vqzj77bP7lX/4Fi8XC5s2b+e53v4tGo+GDH/wgOp2OfD7Prl27pP5TePUK6Uo8Hicej0tP1LljER3Z5vr6zv0uezweenp6cDgcKBQKzGbzLFswsV5E5b7FYuGhhx6S392LLrqIz372s7zjHe/g7LPPRqvVMj4+zk033cTAwABGo5Hvfve7XHDBBRQKBd7//veTTCZ54IEHcLvdpNNpHn/8cdmW2Ov1smvXLtnyWDh/hMNhkskkXq/3kGPKZDLSX/VEQWSCAM477zz6+/sZHx8/YZ/fQAMNNPC3iKMipwIiPdjZ2Sk3ShE1ve6661Cr1eTzee655x5gxuP0M5/5DP/8z//M1772NbxeLyaTiX/4h3/g7/7u7+ju7mb16tUy4lIsFvna174m31OhUHDVVVdx3XXXATOFCQDvfe97gZk0qtVqpbm5GYVCwcTEhKzsFoUXwWCQdDpNMBiUm5iwwBKb8NxIlPAOrSc60WgUtVqNTqeTEcaVK1diMBgIBAL89a9/BWZkDNVqlf/4j/9gz549aDQa3vrWt3LjjTfy3ve+l8svvxy73U6hUOCpp57iW9/6Fp///Od54YUXgBk7px//+McsXbqUcDjM5ZdfzhNPPAHMpDgFWU+lUsTjcRKJBPl8nnA4zOTkJC0tLTgcDpRKJYVCAZfLxaJFi2Z1iRLpe5GG9ng80tJIr9fL6upcLic9Pc855xxGR0f52c9+NmsOtm3bxo9+9CPpa7lo0SLOPvts3vrWt3LZZZfx5S9/mS9+8Yu0tbWRTCa58cYbZfTtwgsv5JxzzqFSqXDzzTejUCj4yU9+gsViIRaLsXHjRmkIHw6HKRaL2O12urq6pF5XRP3quycdKUQBG8yYqXd0dNDV1SXJ+9DQEAAf+MAHMBqN/Pu//7u8brfbzY9//GOCwSBGo5GPfOQjvPvd7+ZTn/oU69atA+DPf/4zH/nIR7jlllv40Y9+xGOPPcbSpUv5+c9/DsD//M//sHv3bhwOB1/5yleAmbR7IpGQPeotFgvValXqpIWNVz1E0Z7H4zligl6v7azv9rZu3ToUCgWPP/445XKZvr4+7Ha7PDSed955XHDBBahUKhwOB+94xzv4/Oc/z5IlSyiXy3zjG9/g8ccfR6lU8q1vfYs1a9aQTCZ5xzveQaVS4eGHH5bzOzg4SKFQYGJigv3795PL5aR0RtiviUYIhyOn5XL5hJLTzs5O+fPPf/5zstmsLDBsoIEGGmjg2HBM5FSQNpjpS71p0yZGRkYAOPvsswkGg9x5553E43FsNhv/9V//xZ///Gfy+TwWi4U1a9Zw4YUXSuP1QCDAc889x8aNGzn//PNpbm4mEonwuc99jre97W3ceuutDA0Nyer1p556ilgsxsUXX0x7e7u0ErLZbFitVnQ6HWazGZPJRLFYZGJigomJiXl7p4vuSf39/Xg8HlnFLiCcCQSpLRaLZLNZenp6pN/kpk2bmJyc5Cc/+QnlcpklS5agUqm4/fbbGRwcRKPRcN5556HT6WRbS4fDwWWXXcY73/lO1q5di8FgYHJykmuvvZarrrqKF154gVAoxLe//W2Z2n7ve9/L//7v/5LP5/nyl7/MunXriMfjDA0N0dLSIqObMGMmLxwMPB4Py5cvp7+/fxZhicViMp0rxmoymZiammJqagqbzUZLSwtms1k2U3jHO97Bpz/9aSqVCueffz5ms5m77rqLr33ta7IQ68ILL+TMM8+kt7dX6kX37dvH0NAQb37zm1m8eDGlUonvfe97bNmyhZGREd75zncCcNddd7Ft2za6u7u57bbbpCVXKBTCbDZjt9txOp0YjUZWrFhBX18fOp2OSCQiNbeHIjDzQbTwFFG3arUqU9oi/Qzwvve9jzvvvJOXXnoJnU7H8uXL+fnPf84TTzyBQqHgjDPOkGtr//79eDwe3v72t9PU1EQ2m2VwcJAHH3yQ733ve/zHf/wHnZ2dXHjhhVSrVT772c8Si8W46qqruPrqq6VUprm5GZfLJWUaMKNXzmazDAwMHJDGb2pqkv8djpwKU/5CoYDT6ZRFXzDTSCCXy3HfffcBM9/rsbExWeCnVCrZsmULW7ZsIRgM8sQTT/DUU09x9tln09/fT7lc5rbbbuNDH/oQTz31FNdccw3Lli0jEonwpje9iVQqxW9/+1t0Oh2BQIBNmzZJz95kMikLLfft24dCoZBd4YBZ38d6CHJ+sszwly5delI+t4EGGmjgbw3HRE4FwuEwu3bt4q9//SuVSoW+vj4WLlyIQqHg5ptvBmaq1wOBACaTicsuu4zPfvazvOtd7yKZTMq0pCCOghh++tOfllXqQ0NDfP/73yefz3P++edjs9lIJpNs3boVg8HAtddeC8CePXvw+Xyy9aOoLk8kEqRSKenzKSA0iWITVygU0p9VRD5Ez21BOPbu3cvOnTsJh8MMDw+TSqVQq9WUSiUGBwf51a9+BcAVV1xBNpuVUdD169ej0+lobm6mubmZ/fv388wzz3DPPfewdetWTj/9dK688kr6+/tRKBRs376d//3f/8VgMOB2u/nmN7/JJZdcQrlc5he/+IXsFHX77bdjs9mIx+O88MILVKtVisUi+Xxe9kr3eDx0dXVJL9R68qbT6aRPLSCjPslkkmq1SjgclhFE0RZ18eLF7NixA51Oxw033IBGo2Hr1q3UajWsVisrVqwgnU7L+7R161aefvppXnrpJUniP/zhD8/qlJROpznjjDM477zzKJVK3HLLLahUKi699FK+//3vA7B3715GRkZkq9RcLkc6naZWq+FwOKTPaaFQmJeUHY7QwEwEvlarUSwWCQQCRCIR9u3bR6FQYPny5ZxxxhmSrAnLrOeffx6AJUuWsHjxYux2Ozt27GDv3r08//zz7Nu3jyuuuIJ3v/vdrFu3jtNOOw2Y0Var1Wq+8IUvoFarefLJJ9m8eTMajYZvfetbOJ1O4vE4uVwOk8mEz+eTxVKlUolAIEA6nT5sqvtQyOVyjI2NyaYLiUSCaDQKzERG9Xo9O3bsAOBNb3qTvDcqlYpqtSplJoODg7z44os888wz/OlPf+KDH/wgb33rW9FoNOzfv5/vfve7pNNpfvjDH7Jq1SqSySR///d/z7p167j99ttl213R0lbYdk1OTpJMJonFYrLNbigUYmBggJGRkVnetmIum5qajpmc2u32WcWBAqKo8mA43PMNNNBAAw0cHY6ZnIpWpbFYTJIb8Qf6rrvuYnp6mvb2dpYtWwa80omovpe2SIO//PLL/P73v+eBBx5gZGQEh8PBNddcww033CBNzCORCGq1mje/+c0A3H333QB88IMfRKPRkMvlZIFQT08PKpVKpvSFFREgU4P1ZEVUeos0v0C9FrFQKOD1eikWizLCNjw8TLlcxmQy8fTTTxMMBmlpaWH9+vXccccd1Go1urq66O/vB2YiVSMjI+zatYvx8XEqlQrj4+M89dRTqNVqzjrrLK6++moAaVsEM5EyEVmsR1dX1yzrJuEt6nA4cLlc+P3+WTq8TCYjyZsYk8PhkJt5NpuVmslisUhLSwsKhUJGDk8//XQZmV2wYIH8bKfTKedYHADy+TxPP/20dHDwer2MjY2xceNGKdOw2+34fD6uvfZaBgYG+NKXvoRCoeCBBx7gqaeeAmZI4Fvf+lZgpmo8FosxMTHB448/zqZNm9i/fz+jo6NUKhV5v4LBIKOjo7NI21xiLiDGvm3bNkKhEMViEb1eT0tLC263W2plhb5Z6F0vvfRS9u3bRzQalZ66Yo4TiQQjIyMkEgn27NnDX//6V+x2O729vVx55ZVSDzwxMUFXV5ccn5BK2Gw23va2t8nviDDHHx4eZnR0lFKpJNdsKpU6gKQdDMLNoP6epFIpbDYbPT09GI1GGZ0V3y/RcrdYLMoDSltbm/we12o1KUEAmJqa4q677uKss87iox/9KG1tbUSjUe69915sNhs//vGPaW1tZWpqih/96Edcdtll/Nd//RcAPp9Pti4dHh5GoVDg9/uJx+NMTEzINraVSkXamh1P/PWvf5VuFgLCD3bu4/M93/A4baCBBho4PjhmciqIjlKplEUUIhUpOuBcddVVchMLh8NEo1GZCvT7/Tz77LPEYjGKxaK0GnrxxRe5++67ee6550in05jNZmCmO834+Lg0xL7//vsJh8NoNBouu+wyYMbI2+fzkclksNvtkgQ4nU5ZkDU8PMyOHTsYHByULSHnpvkFyRa6PYPBgMlkoru7m2w2S0dHB06nU6boL7zwQn7xi18AMz6Qf/nLX3jssceAGU/R7du3s2vXLm6//XYeffRRSqUSGo1GppOHhob43e9+RzgcRqVSSQL9+OOPMzExQTAYlGl1o9FIPp+X0VHR+rWjowOr1UqtVsNms5FIJMhms+zfv59YLMbu3bupVCqy6Ee0SLVarZKghcNhRkZGMJvNuN1uGVUWBTHnnHOO7ISl0WiYmpoiGAzKTVlEWEdHRxkeHiaXy6HVamVkSalUEo/HufPOO5mcnOSd73wnnZ2dpFIpvvjFL1Iul7niiiuAGduiSCRCKpXic5/7HDCj983lcoyPj6NUKtmzZw87d+6U0fFdu3axc+dOSYjnprzn02EKt4ZisSiL6kQ3Ko1GI9dve3s74+PjJBIJFAoFtVqNJ598EpjpZiSi+b///e+lhlXYhe3bt49f/epXjI6OyqgzzHhjBgIBLr30UgDZAjefz0syLOy0RKOHfD7P+Pg4Op2OZDIpK+2PBIKciyyFyWTCYrGgUqmoVCq0tbWxaNEiYKb17NTUlJTv7Nq1S/rkmkwmkskkQ0NDPPXUU/I+m81mWUh38803k0wm+bu/+ztgRvry2GOPMTExwXve8x4Avvvd77J7924+8IEPcO6555LL5di+fTtKpRKVSiXb7xYKBcLhMIlEArfbTXNzs+xy9WowPj7O1q1bGRwcfFXv00ADDTTQwPHFUZPTej9J4bkooku5XI5nn32WZDJJR0cH73rXu2Qas1qtkk6nZVel4eFhafk0F9u2bZvltwgzGjer1So1qalUit27d2M2m7nxxhtRKpWMjIwwPDyM3++XnaOMRiOpVAqtVovBYCAcDlMqlUilUpJUCeIi0vyCrLndbgwGAwqFQkZ+m5ubqdVqbNy4UXb/OfPMM0kmk7S2trJq1Sr27t0rSWJTUxPT09P4fL5ZlfGlUolYLEZTUxMqlYpkMsn+/ftpbW2VBGFqaooFCxbQ1tYmX2c2m2U6VUgOYCYlXSwWKRaL8iAAM2Q2Go1KL1jggCpvgVAoJL1bvV6vlB8ImcP5558vbaccDgd9fX309vZKrZ1CoaCvr09W9+t0Otra2kin09IOCWZS9IJAf+ADH5AtQh955BE+85nP4Ha7GRkZ4ZlnnsFgMPCGN7yBj370owDSc1VE1GKxmKxkVyqVBAIB8vk8SqXygAjYfDpMoWdsaWmRrVJTqRTRaHRWsVhXV5e0kxLyDEHae3p6GBsbw+v1yt+v72SlUqkol8sMDg7i8XikZ2mpVKK9vZ21a9dy7rnnUq1Wefrpp9FqtaxduxaYSbWHw2HGxsaIxWJUq1USiQTBYBCLxSK7Y9UX+c0nX4BXDltCr7p161bC4TDZbFaS3ObmZux2O/l8nv/v//v/WLhwIfCKxAVmivXEWhZrQ6lUks/nsdlsKJVKYrEY27dvp7+/X/r0PvLII7jdbi699FJWrlxJJpPhd7/7HWq1mu9///uo1WpisRiTk5NybkXLWEDql49ET3s4jI+P09/fz9q1a9mwYQNGo7GRmm+ggQYaOEVwTOS0XC7LDjHZbJZSqYRCoZCenzCzkajVavbt2wfMWK6ICCswLzkSEPYs+XxeEqre3t6ZC1Yq5fPFYhGAtWvX0tPTA8xs5kKnJnxPe3t75Yaaz+fRaDSyYEdsdAfb8HK5nExrCxsqoXeEGX3pqlWrAGTLT5HuzufzUospxixIh4DwIoVXUswilSrkEoCUBmzbtk0Wdolqd41GI4uEYIZIKBQK2fJVpJzF/Z8rYxCRN6PRiMPhkB6fmUxmlk5Xr9fL6mRB1OAVk3bx+eLeeDyeWa8Xh5jOzk55KKlvK7t27VosFgsrV64EmGXtJXxeBQFXKBS4XC4MBgMul4tSqYTP55NNE9xu90F1pnPh8XhYt24dXV1d2Gw2GVkul8vyOoUdGcxIQ0SVOiDXqBi38OkUZE6QUYF6gi8g1q94TnQr0mq1RKNRuZbUajV6vZ5sNivlB01NTUdkI+XxeGYR2UAgIDtCTU9PUyqV0Ol0nHfeeWi1Wh566CEZ/X344Yelxdb27dvJZDJotVqp1xXQarWzOpDBjFsHwPPPP08gEECpVPL+978fgOeeew6YKSZ605veBMzox/fu3cvQ0BBer1daTSUSCXntIyMjBxSEHQ0EKb/zzjvZsmUL27Zt48Mf/jAXXnjhLMlALpfjwgsvPODx+Z4Xc9dAAw000MCrw1GZ8IdCIaLRKCaTiXg8TqVSwWAw0NbWJiubRarb7/fj9Xrl5tPT0yP1jDBDMqvV6rwt/+x2O36/XxbmCLsgoXkTpK1cLpNKpdDpdJLQiV73mUyG4eFhLBaLTGWPjIwQDodZt26dJANzfSJF60qYIaMi6hUMBhkfH5ekQLzO6XTidDpxu92Ew2G2bdvGggUL0Gg0Uqcq/FLL5bK04hEoFovynhiNRvbu3Us8HgeQ+spCoUBXVxcajQa/38/AwAB9fX1s2bIFmCE5PT09qNVqWTji9/vp7Oxk+fLlOByOWZ2TvF4vOp2OQqEwKyUsetkPDw+jVqul7ZMonEokElJDPDY2ht/vx+fzsXXrVmCGtI2NjclDg/B1FfdSSCd6enoYHx+nVCrx0EMPUSqVZPTS6/XK14i0vl6vl1HuSqWC2WzGYrGg1Wpl+9ZIJEJbWxs6nQ6n0ylN68XY64tkRCSuvgOYIG27d+8mnU6jVqupVqvYbDbC4TD79u2Th5BisYjP55P3a//+/dIWTKwpsXbhFdKq1Wrx+Xwyql0sFhkcHJRFbDBDdorForQNczgcUmKRyWRYu3YtY2NjUtvqcrnk/cpkMjINf6jIojiMCWKZy+Xk96dSqbB8+XJGR0fZvXs3u3fvli1TU6kU/f39DA4OMjk5iUqlwmw2E4/H5Xe53u0ik8mwfft2YEZ24vP5uO2223jPe97D4sWLAdi6dSuBQACDwcD555/PI488QjqdxmKxUCqVZMajUChQLBbZsWOHnFNRCHcs9mEC/f39rFmzhkwmI3XOc9uXzvf4fM+LQ6OQCbjd7oYhfwMNNNDAMeCoIqdCE5nNZmVEo7m5GZVKJaMlgpyKKKLQSlqtVqxWK0uWLGHJkiW0t7fP25JQq9VyxRVXcNZZZ8n3uvjii+ns7KS1tRWNRiMLQOx2OxaLhS1btvDyyy8DSKunVCrFyMgIk5OTshNOPB6nWCwSi8XmTW3P9UkcHx8nk8nI1LHf72dwcFAWZsDMxt7Z2cn69euBmSrsRYsWyQhpOBzm7LPPlmMVPcy1Wq0kmzATLRVpS3HPXC4XXV1duN1uXC6XjBiPjo5iMBik5lFs5KLoKRaLkcvlmJycZOPGjUSjUZRKpex2JYipiJQKeDwestksTqcTu90ui6VExPW5555jyZIlslvUli1bsFqtkkxrtVqam5vl+yWTSfmcIKxdXV2sX7+epUuXMjg4KCPrV155JU1NTTgcDpkuV6lUWCwW1Go1jz76KIDswy6qutPpNKVSCbvdTq1Wo6mpCbfbfUi/T0EO662nhFuDzWaTlfDi92CG0AsCl8vl6Orq4p3vfCd9fX1Uq9VZv1ssFuclMjabbZaX6IIFCzCbzeTzedm/3el0smvXLtnL3uFwYLFYSKfTmEwm2YbWbDbT09NDPp+X2ul6K6n6MdcfQIREZfny5Zx33nksWbKE3t5eacBvMBiIRCI0NTVJ/ejZZ58NwEsvvcS73vUuenp6qFQqBAIBli9fPmuMokgLkAV34vAEM00YrFYrq1evlgVnw8PDGAwGqR0XLhvFYhGNRkMgEECn01EqlaRcREhsxJo41gjq8YSQWGzYsIG1a9c2DPkbaKCBBo4RR0VO3W63rJRVq9XYbDbWrl3LypUrZeFSqVTijDPOoFar8aMf/Yg1a9YA8MQTTzAwMCCjSDBjeO50OtFqtej1evR6Pf39/eRyOX79618Ti8VoaWmR1cy1Wo2vfOUrpFIpli1bxhlnnIHP55MFFna7XRYwiWprlUpFNBolm83S0tIiq9nnw1yfRK1WSzabRafTEY1GZSeq6elpudn+9Kc/JRQK8clPfpKmpiZ8Ph833XQTl112GQaDgVAoxK9+9Ss0Go0kqMLySaS53W43K1euJBgMcscdd5BIJHA6nbzrXe+Sv/+d73yH6elp7HY7Z599Nk8//TRPPfUUGo0Go9HI5OQksVhsls+rIKqbN29meHhYpsmtVqssKJnrC9nZ2UlLSwttbW3kcjmsVqvUDd96663cd999fOxjHwPgG9/4Bj6fTxLx4eFhisWi1MwK5PN5FAoFra2tLFy4kG3btvHTn/5UFvZ86lOfYvXq1QDcdtttUkssiqO+/OUv87Of/Uy+R3NzM729vdRqNVwuF83NzSxevJiuri6ZehbzOZ9cQ7gLzO0A5vF46O7u5o1vfCN2u50VK1ZIPe2ePXtYtWrVLOsuhULBhz70oVmEHGYIoM1mY+nSpXg8Htra2nA4HESjUR5//HFgRr/rdDoJh8PceOONjI2N0dHRwVvf+lauvfZaMpkMTqeT1tZWUqkUhUKBfD6PWq2mtbVVan0zmQyBQIDBwUEGBgbm1ZweqmBKNNTo6elh0aJFrF69mpUrV9Lf3y/lKX/9619ZvXo1+XxeGuqLNsTCSktARA/1ej0dHR3AzN8EcXgU3sAKhUKuO0HkhS9vtVqVTTRGRkZk8eXq1avl4UhEk8UaPhXamnZ2djI4OMiWLVu48847G4b8DTTQQAPHiKNK67vdbiwWi6wSbmtro1KpoFAoZrWU/MhHPsKWLVt48sknufzyy1m3bh2bN2/G5/Nx77330tXVRS6XQ6lUSsKmVCrJZrPs3buXb3zjG8BMmviaa67h1ltvZWxsjGg0SiQSQavV8pOf/IRcLseGDRsIhUJYLBbe8pa3MDo6ikajYeHChbjdbnK5nIz09Pb2UqlUKBQKjI6OHqCNA6RxP8yQOIvFQigUkgb/IrK0Zs0aNm/eTCwW48Ybb+RnP/sZd911F1deeSWTk5P88Ic/5Oyzz2bz5s0kk0mSySR6vR6PxyOr2MXneTweJicnZRvOrq4urrjiCjQaDePj49xyyy1SHvHZz34Wo9HI5z//eWAmsmY2m2WUU7ShVKlU9Pb2Uq1WGR8fp7m5mWKxeIAPpNFonKVt7e7upru7m7GxMQYHB0kkEixevJh4PM6mTZv453/+Z376059y0UUX8fjjj/O///u/fOxjH2NycpKJiQk2btx4QDTNbDbLtPTTTz8t0/sOh4NPfvKTUj5wzz338Mtf/hKA//zP/6S9vZ2f/vSn0jN3zZo1OJ1O1qxZQ6VSwWazsWTJEmw2GyqVCq1Wy9TUFAaDQepD5yMtIgItiFn9+hbyDOGN29nZiUKhIBKJEAqFWL9+Pffffz93330369evx2KxcPXVV/Otb31Lvo/QhpbLZWlyn0gkqFaraDQa3vzmN7N8+XJGRkb42te+ht/vp729nTvuuIM//vGPDAwM4HQ6ufzyy6nValI+IyQr0WiUdDrN2NgYy5Ytk7rearU6S8pQ33b3YDAajVLSsHbtWpLJpGw88fa3v51HHnmEiYkJYrGYnPP77rtPEmcR+RfQ6XQYDAaUSiWVSkW6YqRSKRwOBxdccAGRSIRAICDXXaVSkc0P2tra2LdvHyqVikAggNFoZHh4WLb1hRk9rojOLliwAJPJJLWoouDrZKGrq6uRym+ggQYaeJU4qsipSAlarVYWLVpEb28ve/fuxev1yo1mz549LFmyhA0bNgAzrRsvuOACfvCDH7Bw4UJqtRpjY2MEg0GKxSLt7e3S8D2bzUrtXWdnJ//8z//ML3/5S/7yl7+wf/9+IpEIGo2Gb3/726xZs4Z/+qd/YteuXbjdblasWIHf7ycajVKpVOTmJQqOLBYLBoOB6elp0um01L8eqpDE4/HI/uNms5n+/n4MBoPcMBcuXIhOp+OJJ57gySef5Nxzz+WBBx6gs7OTYDDInj17+Pa3v81ll12GSqWSXYdEyjgajTIxMcHWrVslMb3yyiv5/e9/T1tbG7/61a+45ppreO6551Cr1fzoRz/i7W9/O295y1vYuXMner0ek8kk21w6HA5qtRoKhQKLxUJrayvt7e2cc845uN1uTj/9dNlwYO5/9XMs5lkQwHK5zMKFC3E6nWQyGT7/+c/zla98RRK5P/3pT3zve9/D6XRSLBZl6tblcsmU9MjICIFAgHK5THNzMx/+8Ie55ZZb+OAHP4jdbuf222/n5ptvplar8YEPfIC///u/Z/v27Xz1q18FZoij1Wpl2bJlsjLc6XSiVColGcjn88TjcSlbqCemBxvv3PWdy+XYs2cP4+PjjI6OEgwGZVbgD3/4A5/4xCeAGa3kpk2b6O/v5/LLL+c3v/kN3/ve9+ju7gZm5BtTU1NEIhEikQjVapXW1lbuuOMObrzxRmw2G1/84hclMX300Ufp7e3lv//7vwGkjZXBYKBarZLL5TCbzVLSkEgkZEGQ2+2mu7ubpqameaUMByOn9fejqamJ/v5+VqxYIQu+qtUqb3rTm9DpdFI68PWvfx2DwSAPim9/+9tnkbFCoUA8HicajTI0NMQTTzwhddZf/vKXufDCC1m6dClDQ0PSlqylpUUeBIW+fHJyklQqRT6fl7KUgYEBmX2ZW5xU72N7MslpAw000EADrx5HFTmFVyJRoVCIoaEh/H4/2WyWZcuWsXHjRkKhEJdffjmf+MQnuOSSS/jLX/7CzTffzBvf+EauvPJKNBoNjzzyCNu2bWNyclJWfZtMJi666CJaWlq48MILKRQKfOlLX2J6epru7m7+8R//kdWrV9Pb24tGo+FjH/sYDz/8MDqdTmoVm5qaMBgMWCwWWXxkt9ulL+LAwABut1sWXIiISywWm5XiFZFTAaPRSE9PDwqFgtNOO01GIIVHptfr5etf/zrvec976Orq4t577+Wd73wnU1NT/OAHP+Cmm26it7eX/fv3s3//fuldKXSilUoFpVLJ+eefj8Fg4IILLphFkvv6+viP//gPdDodl1xyCcFgEIfDQXd3N7lcDrvdLm2QrFYrhUKBWq2GxWKR0ePOzk5ZLHOkEDIFofs844wzeO6555iamuLv//7v+bd/+zduuOEGtm/fzqc+9SnOOecctm/fjs/nO+AeilT0kiVLeOc734lSqSQajfLVr36Ve+65R66B66+/no9//ONMT0/z4Q9/mGq1it1uZ8GCBaxYsYIFCxYQDAbZv38/7e3trFq1ipaWFunVqdfrUalUsgf9kUJE3sLhMJVKhXg8TqFQQKPRyLX91a9+lbvuuosbbriB73//+zzzzDM8//zzvP3tb+fiiy9m3bp13HHHHXz5y19m69atkiR1dnaybNkyVqxYQaFQ4NZbb5VNGpYtW8aPfvQjuru7uf7664nFYqjVarq6urBYLDQ3N/OXv/xFapH7+/vJZDKyKExESoWE4VghIqgmk4mhoSGam5sJhUKoVCrOOeccdu/eza5du0in09x1111885vfZPPmzTz44IOoVCoWL16MRqORRWSFQoHJyUmpCV6/fr3UZe/Zs0dG/i+++OJZMpD6wiylUkmtVpOexel0WtqEOZ3OA6zCRLT4ZLUvbaCBBhpo4PjgqMipSPeJNF2lUsHlcqHT6ejp6eG9730v9957L4FAgB/84AcsWrSIt73tbfzxj3/kqaee4qmnnqK1tZU1a9bgcDhk15mOjg7e9773odVqGRwcZOvWrfz2t79lenqarq4u7rnnHvR6vbQHuvrqq/nLX/6CRqPhjDPOoFQqUavVpLdkU1MTbW1t0mxc6BCNRiORSITFixfT09Mjx+NwOGaltgXmVnN3dXXh9XpxuVyk02k8Hg9nnnkmwWCQiYkJfvjDH3LFFVfQ1NTEHXfcwTvf+U4pU1i8eDF9fX0sXbqUYDDIwoULUavVbN26FZVKxfDwMI8//rjU37lcLi699FIuueQSzGYzv/vd7/if//kfyuUyZrOZlStX4vF4mJiYkCS0VCpJcirsldxut4wkii43ByNtoVBolh2OxWKhvb2dkZERSqUSBoOB1atXs3PnTsbHx/nmN7/JV7/6VW655RYmJyeZmpqio6ODCy+8cJadlc1m45JLLqGlpYVwOEw8HmdwcJCf//znUgf7lre8hZtuukmm/wVBtVqtdHd34/F40Ov1shuPIMxC15jNZg9oHnA0EN6u4j41NzcTj8dxOBy0tLQwMjJCKBTigx/8IF//+tf53e9+xze+8Q22b9/O73//ex566CH+7u/+jre85S1YLBaph06lUgQCASYmJmRjBoH3v//9/L//9/+oVCrcc8893H777cBMJNFoNMq2niJaL/TA4rsj1uXx1FwajUZOO+00CoUCHo8Hh8MxywfV6/Vy7bXXcs011/CWt7xFNhcQhW0KhYLu7m4WLFggv5PVahW9Xs/GjRupVCp88YtfJJvNcuaZZ/KNb3yDSqVCMplk79690ifWZrPJBg6LFy+WzR5cLhdOp5P29nbglUMFICUrx9qp6WCk9nBkt0GGG2iggQaOL446ciqsihKJBCaTiTPPPJNisYjX66VcLrNy5UpaWloYGBhg//79jI2NceONN/Liiy/y1FNP4ff7eeSRR4AZe5nzzz+faDTKrbfeyujoqOyuAzMFEo8++ijd3d0Eg0Huvfdevvvd7zI+Po5er6e5uVkWigiD90WLFrFgwQKUSiV2u51Vq1bR1NQkSZAghaI6uz5COTetn81mZYW70HNGo1EUCgWxWExW3K9atYoXX3yRr371qwwMDPCJT3yC888/n9tuu40Pf/jDMh15JDj//PP55Cc/ybnnnovD4eCHP/wh//iP/ygjkR6Ph3POOYdcLieN9x0OB8PDwyxYsACFQoHVakWr1UpSXm+9Va9FnAshb9DpdDKaDMiismKxSHNzMyaTSaZrv/Wtb/Gd73yHHTt28POf/xyfzye7VtVj48aNNDc3k8vlSCaT8vHOzk5+8pOfcOaZZ2I0GhkdHeWqq65i27ZtWCwWOjs7MZvNsvtQpVKhVCrR0tLC6aefTk9PD1u2bCEQCMgIuUjbC9nGkdoMGY1GSqUSarUatVqNRqNBpVIxOjqKw+GQ6eTPfe5zrFq1in/4h3/A4/Hwn//5n7z44ov84Q9/4A9/+MO8720ymdDr9bI5xCc/+Ukuu+wyfvKTn/Db3/5WOjTY7XapLRVWT0KzK2Qa+XyeRCIhLb6OBzGtlzqYTCaam5tRKpXyHkxNTUmCGQqF+Pa3v01PTw833HADCxcu5NFHH+Whhx6SzQi8Xu8Bn/Hggw/Kn9euXct9991HPB7nE5/4hIyeCyQSCVpaWujv75eduyqVCk6nE5fLJQm5aMEqrlvIVo4W4r2O9PEjfb6BBhpooIGjx1GR02w2i9VqRaVS0dfXh0qlkn+cRVtHo9HI8uXLMZvN7Nq1i0Qiwb/+67/yta99jY997GO8+OKLvPDCC+zcuROfz8dvf/vbWZ+hUqlYtGgRq1at4tprr2X79u18+ctf5pFHHpEEzWAwsGrVKhnxLJfL2Gw2nE4na9eulUTCYDCQzWYJBoOSvNZHmeptlWCGxNlsNvk5InpV//tut5tYLIbNZqNSqeB2u1m2bBlTU1OMj4/zm9/8ht/85jecfvrpvPOd7+SWW27h9ttvJxqNUiqVJLEQKV+Hw0F/fz+LFy/msssu46KLLqJarfLQQw/xgx/8gB07dgAzkaTm5mbWrFnD8uXLZepcdOppbm6mpaUFpVJJU1MTixcvxuPxUC6XZ23khyIyarUah8NBKBSiUqlQrVbp6+tjaGhI3gvhy6pSqXjmmWeIx+Ncd911fPzjH+euu+7i4Ycf5k9/+hMtLS2sXLkSm83G5s2beemll6TGUMzhu9/9bq6++mr27t3LX/7yFyYmJvjTn/5EMpmU/rELFy6UETGbzUYymaSzsxOj0Uhra+sh12swGCSVSmGxWGQjgoOhPtq6fft2VCoV/f39RCKRWYexdDqN3+9nx44d/MM//AMdHR18/OMf533vex/33HMPL7zwwixN9ooVK7jkkktYsmQJ8Xgcs9lMKpXiBz/4AV/4whekdlKpVGI2m+nq6sJgMMjGEsPDw7IrmVqtJp/Pc/bZZzM6OiotzZLJJKFQ6KilDAeDyCYIOYvD4WBgYIAlS5bQ3d3Ns88+y/j4OF6vlxtuuAGn08lHP/pR/vjHP+L3+9m+fTu7d++mXC5jt9splUps2bKF3bt3k8lkuOiii/jud7/Lbbfdxne+851ZzQvUajUGg4Hu7m5sNpt0Cdi9e7e0WhPjFJkPYYsm5vxU0pw2PE8baKCBBo4eiprwfjkEkskkNpuNoaEhent7ZcRR2Dblcjlefvll/vKXvzAwMCAroMPhMKOjo9KIux46nU7qBUVqslQqoVKp0Ov1BINBnnjiiVkbjbCIeuMb3ygN0EWLQ71ez4UXXkhPTw96vV4WVCxatEgWiog06NzImiCpooWpz+ejs7OTeDwuxyJIbi6Xk60tRZTQbrczOjoqN+yxsTFJeHU6HZdeeqn0DTWZTLS1tXHuueeiUCjw+Xzs3LmTffv2kUgkqNVqskMOIDsmLV26FJVKRXt7u4zACd9HtVpNrVZj9erVsqq7q6vrsDo8cR/EeBOJBBaLRY4VZiJDQkeq0+lobm5meHiYiYkJ0uk0zz77rGwGADPV/ueccw4rV67kbW97Gw6HQ7aT3bRpE1u3biWdTjMxMcH27dtJJBIHXJfH4+G0007D4XDQ1taGzWajWq3S1NREpVIhHA7jdDpZvXq1JK7ZbFZqJgUEyZ5LTuvHW1+tLyQVg4ODhMNh2YZ37969bNq0iXQ6jcPhYNeuXezevVu2SoUZYnXxxRdz2WWXyQh7JBLBZrOxatUq7HY7Tz/9NPv27eOvf/2rTF9bLBbWrVsn51Cn01Gr1Xj729/OypUreeyxxySpdTgcnHnmmdJCSsyPiHbXV7TX42DjPRjEd0JANCEQTSlefvllIpEIzzzzDF6vd1ZxktvtpqOjg87OThYvXswVV1xBa2srW7ZskX7EAwMDsyKrBoOBnp4ezj77bIrFopTj5HI51q1bR29vL6lUCpvNxhlnnCHXtSDnogmHyWSSDTNWr14973jHx8cJh8MMDg6yYcMGtmzZIu3ujidEe9T67muDg4OvCUEVf5+PdH4baKCBBk51HFXkVBC7+jSpiF5otVr6+/uJxWKkUini8TiZTIalS5cSj8eZmJiQZEihUFAoFHjppZcO+5kqlQqbzUZbWxsqlUp+ns1mw+12E41GJUEslUro9Xq5sYpe2YK01G/o4v/i8fmiiuJ6hdZOmH8bDAbZ3jOZTDI6OkoqlaKtrQ273S6Lr2KxGJlMhocffviAcWk0Grq6umTrz7mwWCyoVCo6Oztpb2+nra0Ng8FAPp8nnU5jNBrp7e2lUCgQDAbp6+tj0aJFhMNhmb4XBVD1HajEeA6W7hbzKzSDoVCI3t5e8vk8xWKRUCgk73s6nWbp0qWUy2WGh4fJ5XKMjY0xNjbGb3/7W77yla+wevVqSXbn83wU0XZBLOp9VXU6HRqNBp1OJwm5Wq1myZIlWK1WzGazjIh7PB5ZsR2LxWTk72g6B4n5Fs0JFAoFqVSKcrlMX18fPp+ParWK0Wikr6+Prq4u2dGrWCzy6KOPymYBh4No89nV1UVHRweFQkHqOp1OJwaDgXQ6TW9vL1NTU6xZs0YWvc2Nkh5JVPxoIO6ZaG9bq9VYtGgRRqNRWsbpdDo++MEPks/nef7559m2bRvxeJxwOEw4HJYH0ptvvpmFCxfK19VDrVbT3NyMQqGQxVTiACI6hYVCIalbFTIGnU7HxMQEZrOZqakptFotJpOJpUuXHuDbW4/5CKP4G5HP56Wv7u9+9zvZUvhgjwsc7Pmuri55yBFEOBwON6KnDTTQQANHgKMip4cqpFGpVCxYsACbzcZjjz1GuVxGq9XS0tLCeeedx9DQECaTiVQqJc29X3jhBflanU6HTqfDbrejUChksVW1WsXj8bBo0SLK5bJsdVmtVnG5XLS3t7NkyRIsFgvd3d0YjUYCgQAmk0l2bKnvPT7fJn44AlOf/q9vlejxeKRPazqdpqmpiZaWFv70pz9RKBSwWq34fD4SiQRKpVK2CxWFVsPDw9KX1OVyYbfbZQtIYaljsVhk//T6NpEulwuVSkVXVxctLS2yQEQ0Sai/djFusWkf6l4cbM5XrlzJzp07SafTOJ1OmpqapPzh/PPP58wzz6RarfLiiy8SiURkull0Pqp/P9FiVDQyEL3np6amsNvtsuhF2FGJynSYIRROp5Pu7u5ZBwvhnSo6QwEyDXy0ECl+i8Ui5z0QCGA2myVJTqVSKJVKeQ1Wq5U9e/YwOjqKVqtFoVCg1+vRarUkEgnK5TJOpxOLxYJCoaClpQWtVsvZZ5+N2+2WLVGTySQmk0l2a1qwYAEXXHABPT09sw5J9XN3rK07DwcxNqPRSKVSkdHzrq4uFAoFPT09JJNJ6ZYRjUbJ5/MyoplIJAiFQuzfv18eQoxGIx6PRzZQEFX5nZ2dnH322ajVaiYnJ8nlcuTzefm9GB0dxWazEYlE8Hg8dHZ2ks1maWtrI51Oy0NK/bqcC2FXd+edd9Lf3z8r1V6pVKReWHjkHurxI3m+4XnaQAMNNHBsOOqCqPng8XgIh8OySvbcc8/F6/XKFot+v58FCxZgMBikoblI0Y+Pj7No0SJGR0dRq9X09vYCM8U5er2eZDKJwWCQFf4+n09uKqJyW/gyitS9+G9uBOVYo0v1JGBuJfDKlStRKpVyEzebzXR2dqJUKrFaraxbt45KpUJzczOTk5Po9XpcLhc7duxgcHCQ7u5uDAaDlB7E43ESiQRdXV3SkqdcLqNUKunr62N4eFhGBTs7OykWi/T29kojdmE/JNK780XVjibSJgiFiDCNjY1RLpdZsmQJWq1WRsRFZLurq4tUKoXL5WLfvn2Mj49LwiD8V1OplIxC53I5Fi1aJImG2+3Gbrej1+tl28tcLkc6ncZsNrNmzRo5ryLKKwiqzWabJds4VoiKb/HeIpLr9/txu92SKKZSKVpbW3n55Zdpampi2bJlskjN7/fLTlsjIyNybddqNelL63K5WLx4MW63m2q1Sk9PD2q1WrYS1ev1LFu2TI5XaECPZ5T0UBDfIZPJxMDAANVqFaVSydq1aykUCrLTlmhOsWfPHrRarTyYKhQKmUHo7OykVqthMplwu91Sy6vRaCiXyyxfvpzVq1ejUCgoFoukUik8Hg8rVqxApVJJTa7L5ZL+wyIiX2+DdSTo7+9/TVL5h0NDf9pAAw00cGR41eRUGHh7PB5CoZC0fDn//POBmWiF3W7nkksuwWAwEIvFGBkZIRgM0tzcLFOz5557ruw77nQ6MRqNWK1WaUXkcDgYGxuTFdvd3d243W7MZjPpdFpGMA8WRTrW6FK9jKFWq8nIp4imZrNZli5dysjIiIycdHV1YbVaWbVqlbTBEdEjUby1Zs0alEolFosFu91OZ2cn09PTaDQa3vCGN9Dc3MzatWsZGBggkUjI93Y4HKRSKTQaDYVCQbbXFNcjIrqCoM037iO9F3PN+ZuammRaVUS7BXHU6/W0tbVhMplkNX5XV5ecv127djE9PU2lUmHVqlWUy2VSqRQ9PT0sXryYSqVCJpNhcnKSQqFAe3s7nZ2dtLW1MTk5KavG517Xq5nbw423/n0XLlxIc3MzFotFklOhO87lcvj9fqxWKw6Hg3g8zmmnnSa1sk6nE7Vajcvloq2tDZ/Ph1qtxul0SsukSqXCGWecMasLUqVSIRgMztLLvlZR0kPdE0EoD1Zc1t3dzeDgILlcTjYtaG9vx+/3s2LFCpqbmymVStKRQNiyZTIZKf9ZuHChJOHNzc2kUinMZjNOp5POzk7Z2W2uG4NY86/G4/W1htvtxmg0ysYkr6X+tIEGGmjgbwHHJXIqIDYJpVKJw+GQfbFFlE8QG0CmBAuFAlqtVpJOv99PW1ubjKCKivxUKkW1WiWVSklvywULFtDU1DSreOe1xsFSyO3t7dJc/x3veIf8/VAohFKppLu7WxKvarXK9PS0TE+KTaqpqQmr1UqxWGTp0qUYjUa6u7sJhUJSU5lKpfB6vTKKKz7/eOsO54OolBaf09TURC6Xk+SxUqlgMpno7OyUbV4rlYrsMCVS1YsXL8bpdJJOp0kmk1KOsWvXLmDGOslkMsluS+IezE1nn0iIVL/JZJLp9XQ6LX1k165dSzweZ2RkRM7NG97wBkqlEl6vl1qtJl0mFi5ciN/vx2KxYDabZSOF+mi/2+2WOt1j8W19Lcc/F4JsDg4Oks/nsVqt9Pb2YjLNNLQQRXbxeBy9Xk9rayvlcpnzzz9ftj8VWZZgMEhraysqlYqmpiaZBZhPI30i1vzxwHz602eeeeYAaUEDDTTQQAMzOK7kVKQdxWYiCnTEhiv0e6effjqALLpIp9PSiH7hwoXYbDbWrVs3y4c0m82yZMkSVCoVHR0dckMSn3miIDZEkUIGZLqy3sEAkN2GRAq7o6ODbDbL6OgomUyGUqnEkiVLZKFLvaZVvIfH48Htds+K5InNvr7jk0htnojx13/O2rVr5cFDkCyr1YrH45HNGtRqNe3t7VIXK8iHOLSICmNRDGM0Gjn99NNnje9kE5H66LkgyrlcjqVLl8r17vV6yWazKJVKlixZwpIlSzCZTCxcuBCfz0dHR4dcq+LeiKYRc8dWH6U+FQjYkURsTzvtNCllEIcYUdA0NDTEGWecQSaToVgsYjabMRqNnHXWWfLwJfThJpNJHk7rpQXHck2nCoT+dL4o6n333Se/5w2i2kADDTRwHMnpXBNvsWmIFODBDNHrIzIGg4GJiQlcLtesbkaZTEZG0ea+/tXqC48Gc1O99RDkMJvNEg6HZxFJmO2j6fF4aG5unqUhFBv0fOnJQ6XoTwbmzvV8cywqogX5Etc91xmh/meRDhZ94utxKo1dXEdra+usa/J4PJx77rmzSBbMjvB7vV6amppmrfuDSS9OpL701UDck5aWFvr6+g4qIxFEVRRMud3uWXruQ32/Xg3q7aNONuqjqKFQiHe/+928+c1vBhpEtYEGGmhA4IjIqdA7+ny+V+2jVywWZ3WBqn9co9HQ29tLOBxmYmJCagxTqRSlUomOjo6Dvv54YmJiApjZ1EQl+aFQbxUrUtnCGFz8JzxZBYSBvPDDrFQqTE1NHbTS+LXE0Y73cCgWizJqrFKp0Gg0B8xZ/TjFz6VSCaVSedj7IMjNsUaLj9d44/H4rOvQaDRS+zx3vgUZgZnuR6LA7EjW86td88d7fg+F+mutXwOi+5ZosSuaXUxNTcnfEffk1UKM99FHH6VYLPLxj39c+tHq9XpKpdIBXczq/ZQnJydn2a7N9/jhXncoiEYZTU1NPP744/JvwMc//nFJVPV6Pbfeeisul+uw7yc6ZEWj0cP+7t8CarWatO5TKpUn+3IaaKCB1wBHZML/0ksvceaZZ56I62mggQYaaKCBw2JiYkLWHzTQQAN/WziiyOnChQuBmT8Gr0UHEtHt5WjsYF5L+Hw+li9f/pqNdy5O9viPdbwn+7qPFSdqfk+V+3Oi1/OR4rW6P/ON91SZi9cCB5vfTCYj/ZKnpqakRGruY/U43POnAkQLYyGZaqCBBv72cETkVKTXrVbra7K5Heo9D6ZVfS0hrudYxnss13uyCcOxjvd4X/eJmutXM7/H8jlwctbx3Ot4rcc7F4cb82t1LeJ9C4UCKpUKk8l00r9jryUONr/i77Z4zmQyzftYPQ73/KmEE1lv0EADDZxYvKaCnUwmQzAYnNWn+1jeQ3Qzej3g1Vzv8bhfpzION77X21wfCUS7Xa/XSzKZ/Jsa21zMnd+TPZ/is//Wv1cNNNBAA39rOK5WUnNxNG0yxe/PjbScbAuho0X99R5ttOxo79epjrnjP9z4Xm9zfaSY2/72aF53sqKtx4K58/tqvgvHA2r1zJ830fIVTowX8qkCpVLJunXr5M8He+xwr2mggQYaONF4Tcnp0ZINsbkFg8FD2uycyqi/3mAweERkU2zcMLOhvp7GezCIaGE9KTjceni9zfWRon5MYp7/Fg8rc+f3SL4LrzVpPdaDwd8CDAYDL730kvy3sNT66U9/itvtPqAV83yvaaCBBho4GXjNyenRbAhicxP95F8vm/LBUL9ZzzVFqNdLCRKiVqtP6TaMR4P5SMHfKvkEDjm/wmoqFAod1bp+vUWSDzW/BxvLa0nAy+UyWq1WaidfL/fxtcD4+Dj9/f3S+qrRQrWBBho4lfGaktOD4WDRkvro2utpUz4Y6sdXP6a5XoivNxJyJDgWQvp6S2PPB9HVTIxhbsOCo5nn1/N9mItDjSUWi83qBna8UH/YO5qI9d8iwuEw2WyWO++8E4ANGzYQDocb5LSBBho4JXFSREWHK5QwmUwH9BN/vRc11EsWQqHQrHHMN97XG+bOz7GM6WQX0LxaZLPZQxY+/S3M86vFfN9jh8PxmnyWx+PBZJppkTw9PU0wGHxNPudURTabpaenh56eHnK5HAA9PT187nOfA5CPHew19Q0GGmiggQZOJE5K5PRII0giVSo2/ddTUcPcKODBJAuHSgefqpjvmo9HevZI18WpEmGdGw3/v6xvPBzEvRL3aL6iqdfiM/+WLaQOh1qtxtjY2AGP+/3+I3rNEfRnaaCBBhp4TXDCyemxEIvX46Y/X+XywSQLc1PBryeIa4dXX8z1enM1mG+O4fDjOFXI9YmEuFcwe50czZwf7T0TkT8RrT5ZrgENNNBAAw0cHU54Wv9oU7eC/KhUKnp6euYtqDgV0/0mk2lesnYwyUIymcTr9R4wjlNpfOJaxKZfn8YGTljK+mD39kRDXAcgU8biHhxq3l7v8oUjwXwyj0KhIH8+2rk7lnsmtN3iOwfI9RoMBk+Z71UDDTTQQAOzcVLS+gcrgJgvqhEMBkmlUlgslnk3tFMlijYXczfguSmybDYrCY3YuOvTnQJzN+WTGfWZey1er5dKpSJJR70FWD0OJgM41rGc6PHXarVZ0W1BekQrzHqbJHhlHc83n+L6D1a5/nqN6s299vq1YjQapW1RIBAgHA6zbNmyoxrjsaT/62Uz2WyWgYEBQqEQKpWKjo6Og85PAw000EADJxevuQn/fJvtwQog5iOa2WyWSCSCSqWa9/1O5Ur3uSn8ufrEVCoFzIyhp6fnsESmvqiqXipwojD3WoTUQlz7wXws66OKgtgd6lBxKpK0+Q4J4v+iZ7vNZjto+roeBxvXqXrQmov55mc+iYP4WRD7bDZLLpfDYDAc9WHr1awFQUwnJibI5/P09vZiNBopFAoUCoVT/n6/VtizZw8GgwG3292o2m+ggQZOKZywDlHi3/DKpj2XuMCBUVWj0YjL5cJoNM6Koi5YsAA4te12RLo+FAphNBplpCaTyTA2NiY3ajj4OOojdTATnQyHw1QqFfn8icJ81ziXoNQ/HwwG2bNnD5VKBYPBQDabnTW3c8nbwYpmTgWI9QowMDBAtVpFqVQeUKQnfqde4jB3nR9sTKfyQase85FoEUEuFAqzMgJGoxGv10sqlUKlUtHU1EQkEpGOFa/1PIdCIbZt20YkEkGv1+NyuWT/+Gw2e8qtsxOJa665Bmh4njbQQAOnHk5YhyixoQnvwVqthtfrxev1YjAY5Oag0+kIhUJyY6svZjgVrWDmRpHqU9gmk4lQKIROpyObzUpyJlKLAO3t7cArqe9DVeuLNLJIpZ/MDXUuUa0n0bVajVqtJseZy+VQqVSkUimy2Szt7e3zNhwQa6T+Xp0qqDfSr1arZLPZWZFgscYrlQqxWAx4JVI8N0p+KHL6eiBJc0l0rVaT9yGVShEOh2lvb5cpfYH69SsOV/UHlPoU/PGKnIdCIYrFIslkEpVKhdPppL29nVgs9rorsjxaKBQKFi1aRKVSYe/evfLxZcuWUSqVuO222/B6vbM8TxUKBcuWLZOvb6CBBho4GTihHaJEVEmkpWGmXZ5KpZL/FmRubtpvLlE9VXCoVKzBYJBkFF6RM4jHHA6HLKgRhHy+sc01chf/P5XuA8zW0TY1NUlyuWjRIknQVCrVIdPdgtCIiOSpBpPpleKaudHRTCZDLBajUqnMIqMAFotl1r9fzzjc2jMajXKORfGcxWKR903cC0He69f38baN83g88rBjMpnI5XKo1Wo8Hs8B5PlvDeFwmMnJSbLZLFdffTVGo5HOzk52794tf2duC1Oj0Tjr+QYaaKCBk4ETVhBVH/0UZK6+stnr9crolPj9g8kCDpciPZGaxblRpFAoJNP4InJaqVQkKRMQxE3ck7lyhUN93qlKcAQBzeVy0swbmCVhENFHOJB41GsVT7VUq4jmCe2kx+OZRWxEitpoNBKJREgkEsTjcdra2rBardLUvL56/W8Ncw+PXq+XXbt2odFoWLJkySxpxMFQr2U+HvfI4/HIuQoGgxgMBsbGxohGowBUKhUmJiZYu3bt39yc1HeF6u/vb2hLG2iggdcNXhNyejBjebG5w0w6T/w8MTGBSqVicnISm82GUqmcRVzrierk5CTpdFpKAU52YcncawiFQhQKBSKRCAsXLpS6NnFdQi8aCARYsGABLS0t8rXZbJbR0VFgNvE+1Yz6D3Y9JpMJi8Uyy3kgFAqRTCbx+XxYrVZKpRJOp1OmxQXm6jLnpvzF75yMQqn6aN7U1BRms5lQKCQPGGKcIkoo0tY6nY5YLCaLbgB5X+ZGDF8POJTrQj2ELjoUChEOh9HpdEQiEWndFAgEZnUsqsfxzgyIaH4kEqFYLAKwf/9+wuEw6XSazs5O4EC99N8S+vv7WbNmzcm+jAYaaKCBI8ZrGjmdm+YVmjxhPVSpVJiamkKr1ZJMJsnn80QiEVpaWmZFVgXEpi4ikYfaTF6rft2Hg8fjYc+ePeRyOWKxGG63W5KWVColianJZCIej9PX1zcrYli/cYt7JmQNIirr8XjmJW8nGnPn12g0Si/asbExCoUCRqMRq9UqiUy1WpV623rXgYPpMusJ6cmqZq+P5nV2dsrIaf2hS5DR6elppqamKBaLmM1mXC6XTPNbLBZisdgsc/gj+eyT5Vww32fP1YPWHxp1Oh1+v19GJY1GI263G7vdjsvlki4GKpVKppPrCbpCoTju4xTfqYGBAbRaLfl8HpVKRT6fx+FwkMvlSKVSh43ovh4h/o685z3vYefOnRiNRrLZLGeccQYAL7300gGvmfv837LsoYEGGjh1cVzJaf1mdrBCkPoCoUgkQqVSoVgs4nA48Pl8soBB2BPVExGxgTQ3Nx/W8P216td9KIjry+VyJJNJIpEIMEPCzGYzbrcbi8WCxWIhEongcrkO2OiLxaLcuOvJmNFoZGxsjOnpaXw+H+edd95Jj/SI+a0vYBLR4XQ6TTqdpqenh2XLlhGLxRgZGZHaYaPReID1kMViIRwOy/aJgpzX/97JiHAdSTRPEPF4PM7w8DA6nY5FixYRiUQkSSgUCkSjURl5PVJyerLspeb77LkSFDEnuVyOoaEhotEoer1eFh8tX74clUpFOBzG5/OxePFili1bJg81RzquYyXpJpMJlUqFQqFgYGCAjo4OFixYwIoVK/D5fPh8PqLRKK2trYeV1LxeMTIyIqPetVqNgYEB+fNcHO75BhpooIETgeNOTuvJlCAc2WxWWseI03sul6NarTIyMoLH46FQKKBUKmVF/0svvYRCoaCzs5Pe3l6pSxVFRofaoE4kialPR8NM2jafz8t/i0hTqVRi2bJls6x1hoeHSafTkqQEAgHgFfKdyWTYtWsX+XyelStXYjQaKZVKmM3mWWTtZEXWTCYTxWKR/fv343a7mZqaIhwOy+eam5uBmXuk1WrJZDIoFAomJydZtGgRk5OT8nmRyhcSD0He6udSrKmTifr7LfSLO3fuxGazoVAo5D1QKpUMDw9jNpvR6XTY7XbS6TQ6nY5KpYLH45m1+R8sxX+yCPmRfLa4/kwmQzwep1qtYrFY0Ov12O12bDYbIyMjGAwGpqamSKfTFAoFWltbyeVy2O12qU02mWY7XdRHaIFjLpIyGAwsW7aM0dFRUqkUmzdvJhKJ0NHRwcTEBIVCQcppli5dOisi3kADDTTQwMnBcSWn9ZuZSNE1NTVJMpZKpUgmk2g0GtLptLTdSafTtLa2YrfbcbvdBAIBmf4NBAKS5Myt7D/UdZyozbw+eiiinkJrKgq8YrEYGo2GYDAoNXa5XI5MJoPf78dsNpPNZnn55ZexWq3SEzSbzTI1NYVCoZBFG/UFY+LzT6Qx/9zoeC6XIxKJEI/HaWlpkf6fra2ts8hksVjEZDIRiUTQarUA2Gw2qckVUfVwOIxKpWLRokXAKxZO4oBzstLb9YV5wrvW4/Hw7LPPSsusJUuWkM1mKZVKtLa2AlCtVkkmk7hcLlkQBsjDWr0d1Xw4mQVwB5NX1GdB9uzZQzabpVKpYLFYcDgc2Gw2CoUCCoWCSqWCz+djYmICgHw+j8lkIpFIEIvFWLBgwbwEeG5nuPmKpI7kYFZPNFOpFPF4nHK5TCKRQK1Wo9FoACiVSoyNjZ1S9mUNNNBAA/9XcdzJKSD7r4tNV6fTMTk5SSKRQKPREI/HUSqVFItFFAoFiUQCm82G3W6nq6sLjUZDKpUiFovR2dnJxMQES5cupbe394CNaG4hzcmIHooiIK1Wi06nk1ZRwWCQpqYm2tvbmZiYYMeOHYTDYdxuNz09PZRKJemZ6ff7yeVyaLVaBgcHZQS5ubkZn88nyazYzOurvjOZDIVC4YSkfzOZVxoL9PT0kMvlSKfT6PV69Ho9tVqNlpYW3G43qVRKdqGxWq0YjUai0Sherxe9Xg/MyC8EARkbG8NqtUryWm/1czSOBq8FJicnMRqNMsqv0+nYu3cv09PT5PN5PB4P8Xgcg8HAaaedRqlUwmq1yrEtXLhQujVs3ryZffv20dXVRWdnJ263e1aU+GSu54NBEPR6gihIuSDa5XIZh8Mhv8/JZJLBwUEMBgNarRa3243VasVqtVIul6lWq/j9/llFgTATNRXSEPF5Vqt13u/+kaz5YDBIpVLBarWSTqdJJBJUq1XMZjNdXV1Uq1UmJibo7Ow8aQegBhpooIEGXsFxL4jKZGa6H+3btw+dTic3JEGw9Ho9Op0OjUZDJpPB7XbLIqnh4WFOP/10rFarjMRUq1W0Wq20H6qPlByNwfnxxtx+6zBj3RKPxykUCvj9fjQaDdlsFrfbjVKpZHx8HL/fj91up6OjQ163sHwRG/fevXtRKpXS+kVEi4UWUxTo1BeNHcx+6Xil/evHG4vFcDgcBINBjEYjDocDhUIhSer4+DhKpRKv10s6nSaXy7Fs2TKUSiX5fJ5sNsv09LRsQBAOh6UcwGazST1qfTT4RKN+vOJ+C+P4np4egsEgyWSSpqYmcrkctVqNqakp1Go16XRaVqmL9P/k5CQKhQKXy0UkEpG/39bWhtfrZd++fXR0dGA0Gk/Kej4U6g9D2WxWHhKEbVZPTw8vvfQSwWCQQCBAd3c3KpVK+mpGIhGWL19OPp+ntbWVQqEgmxl0dXUxNjbGtm3bcDqddHd3S116LpeT2vH59Lli3QMHXSvZbFam7/v7+ykUCoyOjlKpVEin02QyGSwWC21tbQQCAaanp1GpVFJq8H8Fg4ODAI0CqAYaaOCUwHEnpyaTiWQySTKZJBaLodVqmZ6eZnp6mnA4zIIFC2RkTFQyt7a24vf7MZlMeL1eNBoNExMTZLNZVq9eLQuIisUilUqFlStXyuhSNpuVG+GJ1mCKKKIgGUajkXQ6ze7du7HZbLPSuMLGJhAIUC6XmZqawu/3E4vFsFgstLa2YrPZiMfjNDU1EQwGaWtrIxKJSBlER0fHQfu2H2y8x7OgRpA00ZcckFpLvV5PLpdjamqKarUqDyBerxeVSsX27dvRaDQEAgEymQwtLS3s2bOHaDRKR0cHJpOJvr4+WlpaMJlMB7RpPRkNGMS9g5kKc1FlL9ZaV1cX09PTZDIZotEoVqtVkje/3084HCYajbJu3ToZYc/lcvT39/Piiy+SSqUIBoPk83kUCoWUfahUKiKRiHQ1gPk9YE+kdEWn06FWq2Uho0qlwuPx0NbWht/vR6/XEw6HsdvtDA4O0tnZSSaTQalUAjNR1ra2NrZv347X66VUKtHX10exWCQQCJDP52e5ccTjcTo7Ow85TvFcvXfyfPKASCQideCJRIJUKoVCoUCpVBKNRmX0OpfLsXHjRlauXPk3bS1VD7fbjdFoZMOGDcCBpvwNNNBAAycDR01Oj2RzdDgcuN1uFAoFXq9XRkdLpRIjIyOYTCZaW1vp6uoikUhIorN3717i8Ti5XI5cLkdrayuTk5OsXr1a6tpsNhtqtZpYLCYjd3M79ZxIj9NQKITdbqdQKODxeBgZGSGbzaJUKnG5XDIdPT4+TiAQkNHDaDTK+Pg46XQahUIhe5ILa63m5mbGx8dRqVS0t7fT3d0NzNi7LFy4UBJ5QZQONh9HU1Aj0rSHIgOCQAgN4P79+4nH48TjcUqlEtlsFrVajVKpxG63YzAY5Dg1Gg2VSgWHw0G5XMbv9xMMBvH5fJx22mmy77nwDD1WneGrRX2kUK1Wo1arCYfDVKtVxsbGZAQ7n8+zf/9+pqen0ev1WK1WWltbKZVK1Go1qtUq4XCY4eFhzjrrLLZu3Uo4HKa3t5dgMEgoFGJycpJ169ZhMBgwm80AkqBVKhXZSay+AO5EyTfq73MwGJTFQwCRSEQWNor/WywW2bZ2dHQUjUZDuVymp6eH9vZ2WRiVz+eJxWK0t7czNDRES0sLhUJBknLx3Rea9cPhcGtcp9NhtVpllLRQKFAsFmW3qGQyKZsm9PT0SJ373wpBbW1tldpbhUIh/5YoFAq6uroYHBwkHA4zODjIhg0baG1tRavVNgrDGmiggZOGYyKn822O9ZW7ohjm2WeflQSkp6cHm83Grl27pN7L5/MxPT2Ny+WSvqBCa2qxWHA6neRyOTZt2oTL5ZJFJvWG58lkErPZLDdyj8dz0PaYhxvX3DFlMhlZwHGo9ysUCnR3d+N2u+nt7ZXau3Q6TSgUYv/+/XJjt1gslEolAoGAtMzq6+ujtbWVTZs2yehTJBIhnU5jtVqx2WxSqwkwPDxMNBqVm/nChQsPupEeDYk7GOmZTweZyWRQq9Uyoi2It9/vp729Ha1WSywWo1ar4XQ6SafTUneoUChoamoim83i8/lkYZyIqgaDQSwWC263W5qkC5JfKBRk3/bjTRzq17DQ/IpDQCaTkYSzWCzi8/kkwUwmk1itVrmO9Xo9ra2tVCoVWfyzf/9+AoEAExMTjI+PYzQapQZ7amqKhQsXAkgP37ntTuv/fyJI09zvuTgcxmIx7HY7iUQCi8VCPp9n27ZtDAwM4HK5aGtrI51OMz09LaU7CoVCNh1oamrC6XTS1NREMpmUa3vJkiVoNBpyuZz0i50vcjwfDvWcWGfC4UIUPLW2tjI9PU21WsVgMDA0NEStVkOn09Hf3y9lM69nciqioA8//LDM4Ai3kHp0dXXN6hz18MMPN0z7G2iggZOKoyan9ZvjXEInok0igrRx40YCgYCs6k4kElQqFarVqtSYVioVyuUy+XyeRCJBPp9Ho9HIAohEIsGuXbvweDyUy2WGh4dRqVSUy2W6urpk9buIOAJSL3Y0Uba5FdkwIxkwGAwH3aQymcysCJfJZKKnp0dWl7/88sts2rSJaDSKyTRju5RKpRgZGWF6ehpAbvCTk5Oo1Wr0er0spsnlcjQ1NaFUKqlUKrISXKfTSeLb09Nz3BoOHIzUC11vJBKRxWkejwev10s0GpU+ikJ7OTExQS6Xw2w2o9frUSgUWK1WGbESUXRxSBGp8JGREdRqNalUisWLF2MwGCRJqMdr3WAhk8nIAhlARr5bWlpwOp14vV7Gxsbw+XxSf2u329m7dy8KhYJYLCaJlUqlYnp6mmg0Kl0NdDqd1Je6XC7ZoCAcDpPL5Vi6dKm0Epu7fuvT+6+lJrf+ey4OBqlUSkpPFAoFoVCIfD5PIBCgUCiQSqVIp9Oy2CkWi5HJZLDb7UxPTzMxMYHBYMDlcqHVamXho5CtmM1med+Eu0U2m533MDLfvRGPiYMrIIvYdDodXq9X6k8nJyfJ5XKUSiVZ6BUIBOjr6yOfz1MoFLDZbCdFRtFAAw008H8dx0ROxR/peq2XKIRKJBK0t7fjcrno7OxkZGRERpeKxSLRaFT+8Rc+n8uWLcNqtcrOUOVyGbfbLS2VSqUSmUyG0dFRvF4vxWIRvV4vi01qtRqVSgWv10tnZydGo1FWtR9pCrR+Mxb6UKPRKNPcByMKKpWKRCIBzHgxCg/WbDZLPB6Xm7pSqaRarZLL5SiXy8RiMRKJBEqlklgsRi6Xw2g0Yrfbpbm5MGu3Wq0Eg0FsNhsmkwmPx8POnTtlhxudTifJMXDMm+nB/GOFXtLv96NSqXjqqafQ6XQEg0H27duHQqHA6XSSycy08Uyn06hUKuLxOO3t7RSLRWq1GkqlklwuJ6UbFouFZDKJXq+XHq5+v18eLoS2VYxbzIPwuzwYjoVQ1L9GRNpEi9V6G6xkMinJul6vp6urC7vdjlarZWJigsnJSZRKpTxQWK1WIpEIg4ODmEwm1Go1FotFykE6OztpaWlBpVJRKpWw2+1yLg+1fl/r9H79/fb5fAwNDZFIJDCbzbS3tzM5Ocnk5CQWi0X6lQLyAOJ2u5mcnKRWqzE5OUk+n5c2Y9VqlUwmw6pVqySRHRkZoaurSxLUUqlEpVLBbDbPe2iab/zisXpyKsYSCoWIRqPUajX59yOfz8vDXrFYJJPJsHPnTvl+69evBw6eUWiggQYaaOC1wasqiJobRfX7/TJ1KXSFTqeTVCpFIpGQxTSCzEUiEaLRKMViEZ1OJ/WVIprS0tIiI4ZqtZqJiQlcLhcWiwW1Ws2OHTuYmprCarXi8XhkKrS+reaRpvjriUwsFpOPWa1W4BUiXm+439TUJKMyIyMjxONxduzYgdFoxO/3k0gkKJVKcjMMh8Mylb9p0ybp69nX10dTUxMajQa/3y/7z09MTNDW1oZSqUSj0Uj5AIBWq6VareJ0Omf1sofjt5nWm6KbTCY6OjrYtWuXLFbz+/3SJiiXy8molyAhCoVCShkmJycpFAo4HA6ZFrdYLGi1WimBmJqawmg0Eg6HZVpcoJ5sHm5sx0Lc6l8jqtCF1jOZTOLz+TCZTORyOQKBgPT0FMV+wmJKzEkwGESpVKJWq6nVahQKBWnEb7fbgRl5isFgoLe3V2pOVSoVqVSK5557Dr1ej8FgwO12HxAlfS3T+3OdCrRaLcFgkHg8zsTEBKFQSFqIxWIxnE4ntVqNaDRKtVqVWsWenh5efvllotEoXV1d5HI5FAoF8XicWq3G+Pg4bW1tDAwMsGTJEtLpNPF4HKvVSjQalTrU7u5u+b2rH+98RYFi/ubD9PS0PNy5XC4Z4Qfk35RKpcKWLVvQarX09fXR39//uiWmogjsqquuYvPmzRgMBnK5HBdccAEATz/99LwFUFdddRVGo/GgzzfQQAMNvNZ41eRUpLBhpvo+mUwyNDTEzp07qVQq6PV6pqensVgsWK1WqSuLRqPs3buXQqGAz+eT76nRaDAajfT19aFUKkkmkxQKBWkvpVQq6ezslGR27969tLe309XVhcPhwOVySSsitVp93HrQi41PpC+FF6PQ0hUKBbZs2UKxWKRQKMgNfmJiQvq5isfvv/9+aY2Vy+XYtWsXHR0dLF68mHQ6TalUkgVfYiO32+1UKhWSySTNzc0yFazT6WhrawNma3EFiZ0Ph4os1j8nNnmv10swGKRYLFKtVgmFQqRSKarVqtScTk5OYjAYiMViTE9PMzk5SSaTkWMXpEVEQZVKpbTJUqvVRKNRUqkUdrudJUuWkEwmZfQ5HA5LSyGRzhdEfL6o9rEQt7mvWbhwIYVCgT179hAOh1EoFAQCASKRCNPT0ySTSfr6+hgdHSUUCsl0v8/nk/3klUqlPJjVajUikQiZTAaVSoXL5aJYLFIsFvn/2Tvv8Eir8v1/ptdkUia9Z5Nt2couvYMiICBtsQDfnyhFQVEEQWwgqCBKESxIEWmC9LYsRWDpbC/Z9N6T6b2X3x+5znEStrO77Orc18XFZjKZec973vc9z7mf+7mf/v5+lixZgtVqxWQy0dPTI5m8vLw8RkdHqaurk04G4nj3ZsBks9nknIs0vNvtJhQKodVq5eZErVYzODjI+Pg4yWQSr9eLwWCQOlubzSa9fOvr6+VGRlznoVCIgoICvF4vVquVSCTCwMAAkUiEcDiMxWKRbLxgtPPz84lGo1LSM70Bhch8CASDQRn4CvZ7fHxcBqYiu6HVamVxZmlpqfy9kFh8ntZmuwOxuWxtbSWVSgGTG8e1a9fKf28Non3ptn6fRRZZZLG3sUespESQ1NjYKKuz3W43Pp8Pg8GAx+MhFouhVCplilgEpjk5OZx99tkolUq6u7t5//338Xq9NDc3Mzo6SmFhIVarFZVKhVKppKOjA4PBIItu1Go1wWAQr9cr+3gDMh0sjm97ad5d+T0gGVChNQ0GgwwODkqLHMEaBoNBnE6n1JOKlKbQKT7wwAO8/vrr3HfffdIHtqamRhYbGQwGybKNjo4SDoepra2V/oyi0tjtdlNfXy9TwkInu7352pl0sfjZ6XRKex+v10txcTHpdJqWlhYKCwtxOByyb3xXV9engoNM2O12GZgnEgnKy8vx+Xyy2j+dTuNwOCguLqazsxOr1SpZ1QULFsgCpczU/vTWlrsTQEz/G7ERycvLk8HVxMQEgUBAFrP19/djt9vxeDwMDg7idDqBScN+wSqrVCoZoAt9tfD/FG087XY74+PjMggqLi5mcHBQeqiaTCY8Hg8zZszYaxrIzM8NBoN0dnYSDAaprKwEkBkOg8EgmWShI1coFHR1deFyubb5+Q6HQzbaEG4bMMlYjo6Okk6ncbvdDA4OSlupgoIC4vG43ACLbmG7mikwmUxEo1EMBgOBQEBW6gNccsklhEIhnnjiCcno5+fn09zczIoVK6SdnXjGHagsahZZZJHFgYTPFJyKBQ0mU2wiGGxqaiKdTtPZ2YnZbMbj8dDX10cqlcJkMklvUIC//OUvnHrqqTK4eO+99/j973/PO++8I9mNWCzG8ccfj8PhQKvVEovFcDgczJo1S2r7RkdH6e3tJT8/H51OJ03C0+m0TM2KAqJd1e9l/l4sUkqlUhY2hcNhxsbGiEajWK1WqSVVKBQMDw8TCASmfN7ChQt5/PHHqamp4bjjjuOss87inHPOkf6Rc+fOpaamBp/PR21tLRqNRjJSwvRe2PQIdlH4QprNZlkYti1sj1mcLtVIJBIUFhYCk5Y86XQas9mMy+WioKAAk8lEU1MTr732Gn19fcRiMWbPns3XvvY1Zs+eTW1tLdFolHg8jtPpZO3ataxZs4aVK1cyNjYmWbPq6mpcLhdWqxWdTofT6cRut9Pb24tOp6OhoUHKCUSVfF1dnXz/dMup3YFgu/v7+2UwpFKpZGpXrVaTm5tLSUkJNpuNgoICPB6PDOSUSiVXXnklGzZs4N133/3UBqGnp4fi4mLJnIqUudlsZsuWLQwPD1NaWkp1dTUHHXSQlJQI6yqR2t5TQVKmbCPzGrfb7VM8TTdt2kR/fz/pdFpuNIVcJx6P093dLRsHZOLkk0/m5z//OTfeeCP//ve/cTgcVFdXU1xcjFqtRqfTkUql8Hg8UvohCgIXL15MOp2murpaNrKA/5jxZ16jOyqQE5uBWCzGxx9/LG2lAA477DBOO+00fvSjH3HOOefQ39+Px+PBYrHw8ccf4/V6ZeMAERxnA9Qsssgii72LzxycZlruDAwM0NPTI/VqgUAAl8vF6tWrCYVCdHR0TPn7yy+/nGXLlkltGcDRRx/NiSeeyFtvvcV1111Hc3MzXq8Xk8kkU/nRaJTGxkYGBwfJyclhaGiIZDKJy+VCr9dLN4DMBSSTbZm+sOwoDZz5+8HBQbq6utBoNGg0GgoLC2lubiYcDss0t9DSdnZ2EggEOO644/j2t7+NUqnEaDRy5JFHSn1sKpXi+OOP5+GHH+a8886TldHB4GT3rE2bNmE2m5mYmJCVzvX19ZjNZoqKiujp6cHtdtPd3c38+fNZsmTJDhfP7bFuW2MQRfo0kxnzer1MTEwQCoVYu3Ytg4ODAJxxxhn8/e9/lyb8MNm3XATMp512Gmq1mrPPPpvly5dLKylhT5RIJCS7HgwGqampkY0WYNJqqbu7G5VKJVvBCmcH2HanoJ2FKOwLhUKkUimKi4tJJBIolUqpHxZFTh988AHDw8NEIhHy8/N55JFHOOGEE6SrxBtvvIHBYKC4uJg1a9Zwxx13SFN4kf6fM2cOarWasbExEokEZWVlHHPMMcybN0+OwWg0YrPZGBoaQqVS0dDQIOfks0Lcq/AfDafwKQ2FQtLiy+12E4lEGBoaks0RRGEjQFVVFQ899JDcmCqVSsxmMxqNht/85jf8+9//xul0snLlShobG1EoFLILVCKRQKvVMnPmTBQKhZTjFBcXk0wm2bJlC3PmzPmUrEHMl5B8bA1C/ypY6JqaGjZs2IDH4wFg1qxZqNVqZs+ezYoVK/jSl77E4OCg3FR6vV4WLlzIggULZAYhFAr9z3WQyiKLLLLYl9hjBVE2m401a9bQ3t6OSqWSBURtbW2EQiFZhd7a2ko0GuW8887j1ltv3eZnn3jiiXz00Uccf/zxrF27VhZRifR4W1sbjY2NUsOmVqsZGRlhxowZ0iS+vb2dZDJJcXExtbW1chHeWnEJ8CmmK3OcJpNJ2iWJPveiP7yovvf7/RQVFWG322lvbyccDnP00Ufz7LPPyg47gFzQM3H66afz4IMPcvXVV+N2u1mzZg0mk4nS0lIqKyvlsfX19aFWqznkkEMkAye6anV2dsq+85mazF1FZopXjKejo4ORkRFsNpus2o/H4/z73//G5/OhVCr5xS9+wU9+8hOUSuV2U/sKhYL77ruP008/nfXr1+Pz+QiHw1JjLKyWVCoVAwMDNDU1SfcGwU4LPeT0Y/6srKLJNOmSIKyFhL3T+Pg4drudZDJJX18fzc3N8vuPPfZY/vjHP9LY2Cg/p6mpiaamJmKxGBqNhrPOOosTTjiBa665hvb2dkZGRqQ2d+nSpbKYSHjHiqBxw4YNcqOXTCYlg7inAqPMDWamPru0tJR169aRTqfxer2UlpbS29vLxMSEvBcFvv71r3P77beTn58vq99hclMCsGjRIu69915uuOEGJiYmaG1tJTc3F61WK1nJmpoazGYzsViMeDwuN57j4+MEAgE2bNjA3LlzgamtTHdWXxwMBqVEQqFQyM5fQrYwNjbGa6+9xj/+8Q9++ctf8sEHH+D3++nr66O3txeLxUJNTQ0ej4fy8vL9mj0dHBzE4XDQ3t7+eR9KFllkkcVuYbeC00yz8oGBAakDi8Vi2O12EokEPp9Ppr3VajWPPvoohxxyCFqtFr/fTywWY3BwkI8++ohFixbJop5wOCx1lslkkpqaGtauXcvExATxeJx4PC57dLtcLsxmMyMjI5jNZoxGI319fZSVlWEwGBgfHycej6NWq2lqapJBdGZVtmBWpmstt4ZQKCRtk5xOpywOKioqoqOjg5ycHBQKBQMDA4TDYb7whS/wj3/8A5jUWooCI7fbjcFgwOFwTNGVnnDCCaxbt44//OEPPPjggwSDQXp6emRq22g0snjxYtra2tDr9ZSXl2Oz2dDr9ajVamKxGO+//z4ej4eamhpKS0vl2ESB0466vggZRKYzgVioRZtNwZZu2rRJaiQfeeQR5s2bh9PpZP369RQXF8tAWVTsw2QFsdlsxmQy8cILL/CDH/yA559/nu7ubvR6PWazmdraWrq6uigvL6eiokLKQvLz82XnoJkzZ2I2m6WNlk6nw+12Y7VapbxkRwgG/9NkIfM8zZ49m82bN6PT6WhpaSESiTA+Pi5T2ps2bSKRSFBRUcFvfvMbvvKVr+DxePD5fMBkgY3wdRV2UgCLFy/mo48+4sUXX+S+++7j448/xuFwsHr1agoKCqiqqpLXj8Viwev1Mjo6itPplAWCFotlj3q8ZgZ3QtYgPIeFTlOpVDI8PMyaNWsIBAIceuihXHTRRdTV1VFXVydZbbfbjdvtluMNh8OS1TzttNM4++yzefLJJ7nnnnvo6uoCkLIMod3V6XQkEgkpZRGFkIDsHiaC00xZwo4gnkvBYFAy+qLt8Zo1a7jooosYGxtDp9PxzW9+k2g0ypo1awiHw7z66quEw2F8Ph+zZ8+W490fC6QGBweZM2fOp+y0dgfC0ziLLLLIYl/jMzGnAwMDbN68mXg8zsyZM8nLy2NoaIh4PM7IyAh9fX0A3HXXXRx77LFEo1G0Wi2FhYW89NJLXH311bLQ5emnn8ZisaBUKlGpVASDQQwGw5QAR0BoD0XVrvAKFfo4UVgjOhTNnDkTmGrSndn2E7buczodwWCQ3NxcaZvT0dFBJBLB4/FIW53Nmzfj8/k4+OCDeeihhzCbzbS0tNDW1obT6WRgYIA1a9bQ2toqq7EPPfRQjjjiCM455xxqa2u57bbb+N73vsef//xnHnnkERkYim48lZWV0rg8EomQk5NDVVWVbKcZi8XQarWUlpbS2toqfUerqqqmtHrdGgRj53A4GBsbkwGySLd6PB5sNps8/qOPPppHH32U0tJSbDYbl156Ka+99howmeo96KCDmDdvHocddhiLFi1Co9HIxT0nJ4cnn3yS5cuXc8stt7BmzRoikQjvvfces2fPpr29Hb/fL23JFAoFZWVl1NfXywIxoTcUbT53JVjIbLIATNmcJJNJaY0WiUSkfEI4S5x77rncfvvt0kZpeHiY5uZm1q9fz6ZNm+jo6MDv93PGGWfwm9/8hqqqKpLJJFqtlmXLlnHmmWfS3d3NZZddxvr160kmk9KWq7e3V1pKKRQKcnJypBY5Nzd3jzlQKBSKKe4G/f39sk2r8Pd1u91s2bKFDRs2EAgEOPbYY3n66acBZObA5XIRDAZpb29nYmJCSjrmzZsn51pIWr71rW9x4YUXcuWVV/Loo4/S3d1NUVERRqORzs5OFi5cSF1dnbTYAuT9HAgEJNMpsCO9uEBRUZG0aRP3d35+PkajkbvvvlsWsIXDYf72t78Bk4GzaOspurqp1WpmzZo15XrZn4JTh8NBKBTiscceo7a2ljPOOAOlUjnlPYJ9nw6RnRDFYgcddBDt7e1TukdlkUUWWewL7HZwKvppCwsgYQ+jVqvxer309vYCcP755/N///d/hMNhhoeH8fv9vPjii9x1112S+di8eTPLli3jySefpLe3lzvuuIO3336bSy+9lOuvvx6NRkNNTQ3HH388K1as4Ic//KEMRBsaGojH41itVrRaLQaDgaGhIenNKFgc+I+rgOjbnrmwZAY1opJ4OkymyY5Vw8PD9PT0kEqlsNlsqNVqwuEw7e3t+Hw+ysvLefLJJ9HpdPzgBz/g4Ycf3uZ59Hg8vP7667z++us8+OCDPP744yxevJjy8nLuvPNO7rjjDoaGhvj973/P3/72N5mCFab14+PjpNNpmeIXXZaEP6FoOanX6/H7/TsM3sQ5Eml2u92ORqNhYmICvV5PNBpl9erVeDweFi9ezEsvvSQbIVx33XW89tprqFQqUqkUQ0NDDA0N8eKLLwKTTNVJJ53EpZdeygknnABMBkinnXYaJ598Mu+++y7f+MY3cLvdtLS0UF5eTn5+Pg6HA6fTyfz586mtrZWLqEqlkpsWwSbuSqAgCp7EuAHp/uBwOPD7/VN+LwLTK6+8kltuuYVwOMyjjz7KDTfcIB0IpuPFF1/k9ddf5/vf/z7f+ta3phzf/PnzeeuttzjppJNYs2YN3d3dMu1st9upr6+XjLDNZkOn08mN1p5GMDjZWCCZTKJWqykoKACgvb2dLVu24Ha7Wbp0Kc8++6wM6Ht7e7nnnnv44IMP6Ozs/BSTWVxczHe/+12++c1vTnFXUCqV3H777axevZqOjg4cDge5ubmSkS8oKCAnJweNRkN9fT0ajQa1Wk1hYeEUK7HMzeaO2OTi4mIaGhooLS2VQbXQFX/44YcAPPLIIzgcDp5++mnefvttHA4HZrOZQCBAb2+v/K6KigoqKiowGo07zdLva8yZM4eDDjpIOkgICE3x1lBdXS2t09ra2rjgggtkEVsWWWSRxb7EbgenNptNsqCi65PD4aCiooLW1lYSiQT19fX88Ic/5C9/+Qu//OUvP8VIfvnLX+aUU07hZz/7GZs3b+bYY4/F4XDIRe6+++5jZGSEe++9V6Y0L774YiYmJrjlllsYHx+ntLSU0tJSCgsLZdqvpqaGZDIpA63x8fEp1b27EshMt5ESqVadTicr6FOpFD09PXg8HoxGI48++ih5eXn84Ac/4LHHHkOpVNLY2EhZWRnFxcWUlZVx0EEHUVFRQXNzM0NDQzz55JMMDg5y4okncs011/C1r32NiooKAMxmMyeccAJ/+9vf0Gg00vPUZDJRUlIi9ZGiTWg6nWZsbEw2LKiqqpIm+TsaszhHVVVVsgBKBE1jY2OMj4/T29uLUqnk7rvvJhic9DK99dZbeeKJJ1Aqlfzud79j9uzZOBwONm/ezPr16+ns7GR0dJQVK1awYsUKampquPLKK/nGN76BTqcjEolwxBFH8PTTT3PGGWfISvHGxkbZXUwcg+gCJhbelpYWfD4fCxYs2KWUtyi8ErIAEZj6/X7UarVk5jUajSz4+s53vsMvfvELwuEwN910E3fffTcw6c87Z84cFixYQHFxMfPnzycWi/HnP/+ZzZs38/vf/54//vGPfPnLX+aMM87giCOOkAzoPffcwwknnEAoFCIQCGC1WmloaEChUMg2rwMDA9KUPxQKEQqFqKmpmcL4fxYGT9gt+Xw+Zs6cyezZswmFQrzxxhsyfX333XeTSCTw+/0sX76cq6++WhYWAZSUlMiWs3a7HZvNxq9+9Stuu+026urqaGxsZPbs2Zx33nnMmjWLF198kS984QsMDw8zPDzMwoULGRwclE0OYrEYgUCAVCpFKpWSmQAxx8KtYnsFUZmwWq2UlJTI8xQKhfjkk0/wer3o9XosFgupVIrrrruOnJwcXnzxRdlOWTiEOBwONm3aRDKZZPbs2ds0/D9QUV1dnQ1Gs8gii88duxWcCgN6hUJBOp1GoVAwODiIWq1m/fr19Pf3o9Vqefzxx3E6nVMC0/LyckpLSzn11FM5/fTTAfjjH//ItddeKzWOp5xyCkuWLOF3v/sdy5cv56c//Sm33XabZLF++tOfEggEuOeee2hpaSGZTKJSqaSnZG5uLjqdjvr6eqn5E8yY0IjubGo0M20oGDwRyKhUKiKRCK+//jp2ux2tVsvf/vY3li5dyttvv81jjz0GwI033siSJUsoKysDJnVhIvCcP38+Z5xxBhdccAFXX30177zzDr/97W95+OGHufHGGznnnHPQaDTyb71eL62trTJ4USqVuFwuVq5cSUlJCUajUdpM5eTkMGfOHIqKiqa4KmwPmYutyWSSPpw9PT2MjY2xbt06YDJIO/jgg/F4PPztb3/jzjvvBOAXv/gFX/rSl/B4PDQ1NXHsscfidDqlb+lDDz3Eyy+/zMDAAFdffTU333wzZ599NldddRU5OTkceuihPProo3zta1/D4/HQ0tIidcplZWVs2bKFkZERNBoNFouFqqoqWlpayMnJobOzc0oV+654ggp5gMPhoLu7W3ZBisfjvPHGGyQSCc4991xuuukmtFotzz77rAxML7vsMpYtWybntKOjQwZQt99+O729vdx99920tLTw/PPP8/zzz3PyySfz+OOPo9PpmD17Nr/97W/54Q9/yODgIAqFggULFmA2mykvL5cbi/7+fnJyckgmk9JjF/ZMelnYJKlUKkZGRgiFQvh8Psl633DDDcyYMQOtVstbb73FFVdcQSQSYe7cuZx55pkce+yxWK1WuTmMxWL861//4umnn6arq4vW1lZaW1t58cUXicfj/OY3v6GmpoaXX36ZL33pS9hsNlauXInFYkGj0ciGBAUFBbKtrsvlkhtQUeC4KxtNUWAotNfpdJq3334bmLwPCwsLmZiYQKlU8t3vflc6MgjWNxwO09LSwqxZs0in0/T09LBgwQKpy9zf9KdZZJFFFgcqdjk4Fdo0YeuUm5uLxWLBYDDQ29vLpk2bAPj973/P/PnzOfroo4nFYhx55JH84Q9/kHqnzs5OOjs7efvttzEYDFx11VVs2rSJE088kSOOOAKYZGKENm3hwoVcccUVwGQqWCz6y5cvp6uri7y8PIqLiykoKMBgMGCxWNDpdFNshjZs2EAsFpM903cGmczUqlWrsNls5Ofno9Vq6enpYd26dVKT+cILL3DooYeSSCT42c9+BsA3vvENzjrrLAYHB0kmk3R0dDA+Pk4sFpOpSwCLxcLtt9/Ov//9b+68805GRka45JJL+Mtf/sK9997LoYceynnnncdTTz3F+Pg40WgUs9mMXq8nGAySTCbx+/0UFBSgVCpxOp1otVq5sO9MYCoKhMTniWry/v5+nE4nHR0dhMNhqqqquPHGGwF45pln+OUvfwnAd7/7Xc4999xtfv7MmTO5/vrruemmm7jnnnt44oknmJiY4O9//ztvvvkmzz//PLNmzeILX/gCf/7zn7n00ksZGhqirKyMuro6GaAIT1GTyYRWq5W+mQUFBVIzm+nTujOBW35+/hQLpVQqJa3QhE/tJZdcgkKhoKenhx/84AcAXHrppfz4xz+Wvr3ToVAo+PKXv8ypp57KRx99xPLly3niiSd47bXXWLZsGU888QQ6nY7LLruMf/7zn6xevZqenh5Wr17N0qVLaWxsJDc3V3qkii5oIpUtivyi0ehOB6hbC9rFBkRobMfHx3nhhRdwuVzMmTOH7373uwQCAe6//35++tOfkkgkOOKII6Ts5N///jcbN25k9uzZXHjhheTk5HDKKafwrW99i87OTjZt2sSqVat44YUXeP/99+WxNDY28tRTT3Hcccdhs9kwGo3k5OTIIDyVShGJRMjLy5OsbKbX6K4GgzNmzOALX/gCPT09jI6OymPJbJX7xhtv8Nxzz3HBBReg0Wh45513UKvVstDz+eefZ2RkhIaGBkZHRxkbG6OmpoacnJwpwfLnHaiGw2FOOeUUAFasWCH1pNNf29rfTPdmziKLLLLYl9il4NRutxOJREgmk9LSZ8aMGXg8Hvr7+3nxxRdJJBKcdtppnHvuufzhD39gw4YNGI1GvvOd79DW1kZubi4TExM8+OCDbNiw4VM6tddff53DDz+cU089leLiYq644gr+9Kc/cd1111FTU8MxxxxDLBYjLy+P3//+96xcuZJgcLK14GmnnUYgECAcDlNSUkI6nZYFFKKTk8/nw2KxyFRlJlO4tUp2wSjBZGGGXq/H5/NJN4LOzk5gUoe4dOlShoaGuP3222ltbcVkMnHCCSfQ0tLCe++9xwsvvPCpIGbWrFmcd955NDU1kUwmOfzwwznooIN4/PHHeeqpp9iwYQPnnnsu7733Hvfddx9z587lpptuwu12s3r1ahYuXIjRaMRsNpNMJkmn08TjcaLRqGzXaDQap1hpicpsAfFvUSAEkx2pvF4vAwMDUrogmLo77rgDhULByy+/LIO0888/n8MOO4y2tjZsNhuvv/66ZFtnzZrFmWeeycKFCwkGg5SVlbFs2TK++MUvMjg4yC233MLg4CBf/OIXeeyxx1iwYAHnn38+Y2Nj3HDDDYyNjZGbm8ucOXPkcebl5aHVavF6vbI7lvADFayi6Cy0o1S/CGRHR0cJBAKk02nmzJkj/WZFG0etVsvo6Cjf/va38fv9zJkzh1NPPZW2tja5Yevs7GRgYEB2lRIODwqFgpNOOonvfOc7LF26lOuuu463336bU089lfvvv5/a2lruuOMOjj/+eEKhEIODg9TU1LBu3TopMxCFfMLOzG63yyBoV9jTrQXtQ0ND9Pb2MjIyglKpZOPGjVI3/uMf/5j29na+853vSOb84IMP5pxzzuHGG2/knXfekfZob775pswenHjiiSxatAiYbKdbV1fHCy+8wIYNG+T8RKNRDjroIC666CIeeughJiYmmDt3rtQa9/T0UF9fT1lZGYWFhcyZMweHw7FbbHE6ncZqtVJRUcFRRx3FU089xSeffALA+Pg4fX19PPvss7z44ouk02luvvlmvv/97xMOh/nkk09k5sbr9fL+++/jcDgoLCxEp9Oh1+slc5yfn79fFEqlUineffdd+e9tvbatv8kiiyyy+Lyg3PFb/gOHwyEZU6PRyMTEBKtXr6a3t5d//vOfuN1uDj30UO666y6cTie33XYbAFdccQW1tbXEYjHefvttrrzyStavX086nWbBggU0NjZSVVUl+3e/+uqrXHPNNWzZsoUrr7yS0047jWQyyUUXXcSWLVtkxXdtba1kKPv7++nq6mLdunXS5kilUjE0NEQ6nZbHLRhTsbiFQiHZk14EuduyYRHdbYSWct26dbjdbhYuXMj111+PSqXi6aef5tFHHwUmWdPy8nI+/PBD7r33XkZHR9FqtVNsnTo6Orj55pu54oor+Pjjj6W11Pnnn88bb7xBZWUlQ0ND/PCHP0SlUvHDH/6Q5cuXS63vhg0bpMm90PzF43GCwSDpdJpEIoHT6aS/vx+fzzfFKkuMXVTnGo1GRkZG6OzsxO12k0gkcDgcRCIRyYhffvnlHH/88QSDQS6//HKSySSnnHIK1157LQqFgmeeeYbvf//7vPzyy7S2tuJ2u/nkk0/4yU9+wiWXXMKjjz5KW1ub9Hw98sgjeeSRR2hqasLtdnPFFVeg0WhQKpVce+21/OQnPwEmda+iGlwwzj6fj2QySTgcRqfTSfZLNFwQZvHbswcTFevFxcXo9XrS6bQ8b6LPu7ge8vPz+fDDD9mwYQNms5mrr74ai8WC2WzG7Xbzpz/9iT/84Q88/fTTPPvss6xYsYJ3332XlStX8s477/DrX/+aoaEhjjzySO666y7y8vJYt24dV1xxBclkkvnz5/OjH/0IgN7eXtra2vjggw9YtWoVw8PDGI1GAoEAb7zxBitXrqSjo0MGQTvDjAsIfalgUAEMBoPMBDQ3N0sLrUMPPZTDDz+c888/n3Xr1qHT6bjmmms45phjuOWWW3jttdeIRqMUFBRw+OGHk5+fTywW46OPPuL222+Xzwy1Ws2CBQsoKysjkUjwwgsvoNVq5b188803U1RURDgcprW1FbvdLgvBRBAlAnO73c7IyIgcj7hvtzfPYq5hsiBLo9FgMpnkNSO6Yr3zzjuk02mMRiOpVIo//vGPhMNhDj/8cCmnMBgMRCIRmpubsdlstLW1UV5ePqV18ucdmGaRRRZZHMjYpeBUdOMpKipieHiYDRs20NXVhd1uZ2JiAoBf//rXGI1Gfve73xEKhZgzZ47Uln744Yf85S9/IZlMUltby7XXXstll10mg5tbbrmFr3/968ydO5dIJMI999yD1+vl1ltv5ZBDDiEQCHDuuedKthLgwgsvBCYXhMHBQbq7u6Un6vDwsKy8z83NpaGhgcrKSgwGg3xdsKn9/f3SA3VrwWmm/ZRer8dkMkmWcfHixWg0GsbGxrjrrruASV3ssccey913380999xDKpWiqamJH//4x3zjG9/g5ptv5vrrr2fp0qWYzWacTid/+tOfuOeee6RBeElJCX/6059Qq9W8+OKLPPPMM8CkH+rLL78sjyuRSJBOpyktLaW2tpaysjKKioqk/ECwO9MXTZvNxvj4OA6HA/iPxjQSiWCz2fB4PJhMk33dxTn5v//7PwD+8Ic/SH3hr3/9a/x+P7fddhtPPfUU6XSaxsZGLr74Yn72s59x1FFHYTQaGR4e5sUXX+Tiiy/mpJNO4q233gKgoKCAv/71r+Tk5NDf3y+rpwF+/vOfc8QRR5BMJqXVUVdXF0NDQ3L+hA/q2NiYZEpzc3O3Wayyrc3HrFmzKCoqQqlUsmbNGtkaNplMotPpJPMHk0yxYGRTqRTPPvssmzdvRqPRUFVVxeLFiznyyCNZvHgxJ598MqWlpQSDQb773e/S19fHvHnzeOaZZzCbzXz88cdceeWVpNNpfvCDH5CTkyNTyG63W3ZTgsnir/7+fiYmJggEAtjtdvr7+wF2ujhHsK3inArPX+FVHAwGZbHZ5Zdfzs0330xnZydWq5UnnniCgoIC7r77bhwOB8XFxZx44olcdtllHH/88Xz961/npz/9KZWVlYRCIW688UZZNKVQKOT1c+ONN07pOJafn88XvvAFOad6vZ6KigqqqqowmUyyI5gIXAOBgAxGM+/hHQWoJpOJ8vJySkpKpEZYo9EQj8d54IEHpOdwTU0NhxxyCBqNhk2bNtHa2soZZ5wBTKa+LRYLiUSClpYWurq62LBhA0NDQ9hsNhwOx5TA/0BGW1sb69evlwWBWWSRRRb7ArscnBYVFREMBmWRg/AXjUQiANTX1/Pee+/xxBNPAHDqqafS1dXFww8/LBnFJUuWcOihh9LT08Pbb7/N4OAg//rXv3jooYfQarV8+ctfprCwEI/Hw09+8hNGRka4+uqrWbBgAW63m4svvhiv10s4HEav11NTUwNMsqFCl5ZKpRgcHJTVv6LbkWB9Myt8R0dHJYMibJoEMvuti8WmqqqKI444Qi5uTqcTn8/HtddeSzAYpKGhgerqan784x9Lz8958+Yxe/ZsaZ2zdu1a+vr6mDt3LmeccYbsfvPwww/zf//3fzKtajQa+drXvgbAVVddRWtrK5FIhKamJubNm0csFmNsbAyNRoPL5ZLWUjqdjry8POlxGI1GpZ/ktszL7XY7hYWFRKNRUqmUlAkICymAsrIyVq9eLb0gzznnHN5++23OOussyWp/9atf5bjjjiMYDPL2229TXV3NlVdeydlnn838+fMxGo14vV7uuOMOfvazn9HV1YXL5eK0004D4NZbb5VBcjgc5vvf/z4APp9PLvw6nY5YLEY6nZaSBq1WK+exqKiI2tpaSktLP6UvzgxORdOBvr4+7HY7ZWVlUscqrgcxfzabjXfeeQeAww8/nK6uLjZv3syvfvUrySxXVFRQX19PVVUVJSUlNDQ0UFBQwJFHHklRUREul4tvfetbvPvuu1itVlno98QTT/CrX/2KSCTC2WefDUz6h86YMYPc3FzMZrMMxIWvr9jUjI2N8fHHH2/TIigTghkW7KlOp8Nms9Hf309NTY0sChLuCRaLhaeeegqYzAQ8/PDD/PSnPyUej1NSUsJhhx2GUqnkpZde4pFHHpHtTY877jhyc3MZGxvj+uuvp7u7m/7+fo466iiqq6txuVxcf/31kvEMBoNSgiO8RGfNmkVBQQGxWEx6zDocDrkRFsgcy44CQqPRyFFHHcVhhx3GSSedhNlslp2sXn31VWkz1dvbS1VVFYceeig5OTl4vV6WL1/OQQcdBEym9vPz80kmkwwODvLoo4/y6quvsnHjRnnv2mw2eb4PRBgMBi644AKWLFnCnDlzsgFqFllksc+wS8Gp0M7BJFshem5Ho1HS6TSFhYWUlpbyzDPPkEqlOPHEE1m4cCEdHR08+OCDACxYsICjjz6ajo4OfD4fg4ODrFixgg8++IAtW7bwzjvvYDAY+MpXvoJCoeCtt95i06ZN5Ofnc//991NQUEBnZycPPPAAOp0OnU4ndW2Dg4OyH7Zo4Scq7Pv7+9m4cSObN2/G6XTKoMNut5OXlydT/sXFxVOC03A4TH9/P8lkkomJCdauXcuWLVsYGBiQHYFE+vfVV19FqVRy6aWX8ve//52xsTH0ej1f/epX5ecaDAZcLpfsvLN69Wr6+/ulGb/QLT733HPk5eWRl5fHN7/5TZqamvB4PNxxxx2o1WrUajVf//rXARgZGWFkZIT169fT2tqKz+eTi7rX62XlypVs3ryZjo4OybS1trbKzk2iSC2RSGA0Gqmrq0On00nmbnx8HJg0zS8uLuYf//gH8XicY489loMOOoiXXnoJu92OxWLhqquu4vDDD2fTpk28+OKLfPzxx/zzn//k/vvvx+l0csghh/D73/+eL3/5ywC8/PLLrF69mpycHK666ir0ej1r1qxh/fr1aLVadDodZ599NvPmzcPv9+NwOBgfH6elpYXW1la8Xq90a4jFYtIuKzNdPz3FOp1hFPZRQq9aUlIiC6wEw75w4UI+/PBD4vE4jY2NLFq0CK1Wy/r163nvvfeAycW8v7+fd999l5dffpk333yT5uZmuSE69thjqaiowOfz8eijj5KTk8OXvvQlfvrTnwJw55130tnZyTXXXINKpWJ0dJT+/n6USiXhcFgWB/l8PkwmEyMjI1RVVcnU/raC060xxUKHLPxFRYW+uP5gcpPwyiuvkEgkWLx4McFgUFbvFxUVUV9fLwP2lpYWhoeHefPNN0kmk+Tn53PqqadiNpsZGhri+eefp6CggJKSEq666ioAnnzySdnhTK/Xy4Ybfr+frq4uVq1axfr16xkYGGB0dJR0Ok0sFqOmpmaKLZrJZJJj2V46XTy/TCYTeXl5FBYWYjabgcnMSiqVkhKRaDTKyy+/zObNm4FJn9RkMsn69eul16zb7SYnJweHw8HHH3/ME088wQsvvEBfXx99fX04HA4pGToQsX79etatW8djjz0mHVqyyCKLLPYFdik4FSgqKqKiooKCggIUCoV8aM2dO5d0Os1HH30EwLJly3C5XPz5z3+WnZpOOOEEFAoFgUCAdevWsWbNmim95kUKsLKykiOPPBKAW265RbamvO6664DJtLJIPQpmUZjuGwwGkskkOTk55ObmMjAwwLp162hvb8flcske26JXutCvZvofwn+qmkW3IJ/Ph8fjYWhoiJ6eHpmWLC4ull1zDjroIFKpFKtXr0ahUHD++efLTjcwyWCOjIzw0ksv8fjjj9Pe3s5HH33ERx99RCwWkwVGn3zyCWNjY8Bk2vHWW28F4LnnnqOjowOYrDyG/7BhQhPn8/mkldTExAQul0uaa9vt9imBWOZiHo1GpUawvLxcBnniOE477TTC4TDLly8HJlO+MKkHBTjppJMoLy+ntbWVdevWyQIr4R7w7rvv8uqrr5JKpTj99NMlU3r//fdLc3PxmpAwwH+6C8FkUZJSqSQej+N0OnE4HJSWllJSUoJWq5Xv2xkNopjrYDAoNyc+n4/R0VEpHxB95A0GgyxmE+e9r69P2oXl5eVJ7a5arSadTuP3+2lvb6e1tVWy2ZdccgkqlYq2tjZZjHPuuefKlPFTTz3FzJkzueiiiwCkNVt3d7fsVJSXlzeleE9oObdV+LW94Mhut/Pxxx9LQ/zVq1eTTqflxkPIR84880w5J8cccwxlZWVEo1Gam5ulDAUmN2ovvPCCrLD/wQ9+IGUS4rMWLVrE0UcfTTqd5vnnn5d/KzaM6XQam83GyMgILpdL2rSJlr0qlepTLOm2NiLbQkVFBYWFhSxcuJCioiJ8Ph9KpZLBwUEZJAtmXqFQMHfuXM466ywAaVkmLM6E3nlwcJDVq1fzzjvvyHkfHx/HZrPt9PW4P0F0eBOFiFlkkUUW+wq7HJyKIqLZs2czf/58adYNk7Ywzc3NjI6OolarKSsr46677iIYDEqN17vvvssDDzxAd3e3DO50Op1kMAKBAF1dXfT09DBv3jzKyspwu918//vf55133mHOnDnU1dXh9Xq5+eabcbvdnH766cyfPx+fz8fY2Bhms5mFCxdy8MEHE41GGR4eZmRkRNpLVVZWytSwCExFuluk8WFyURd6N1FYI6QEQt4Ak8G6kCzMmDGDJ598EoDS0lK6urr4xz/+wcqVK/nnP//JU089RXd3twzoM9no9vZ23G43FRUVpFIp7rnnHnp7exkYGCA/P5+jjz6aVCrFr371K8LhsFzMlUol9fX1FBUVkZOTQ2VlJalUSrZq9Hg8qFQqKVnIycnBbDbLTYEYr9DTDg4OSqsr0cYU4IILLuCpp56SFfelpaV0dnZKZjUej/Pkk09y3333kUwmMZvNNDQ0cMQRR8ge7GNjY/zxj3+kpaWFmTNnUlxcjMvl4v7772dsbIyjjz4agGeffRafzyc1tSIALi4ulkVIol3p8PCwDCSHh4dZt26dbLywNWQGazabDb/fj9FoZPbs2RQUFBCJRGTrWwEhnxBztnr1ajnOqqoq+ZlGo5GqqirKy8tloLxu3TqWL1+Oy+Viy5Yt0ov117/+tazuP+644wD44IMPiEaj/PjHP5aG+x6PR7ZJjUajkvXLycmRXb9EC1fhFZyJbWlRRdcrUdiTqR1ftmwZy5cvl5KY1157Da/Xi8lkIjc3F5fLxcaNG+U5Fiy3Wq3G6XTy+OOPMzY2hs1mY8mSJQDcdtttvPnmm1PG+/rrr0tXAxHci+K+goIC2dlJo9EwNDQk29Z+1lR5TU2N1AUffvjh5OTkyMKrnp4e2fkpFAoRi8VIJpOsXbsWk8mERqOhv78ftVpNUVHRlM11JBKhpaWFd999l/fee49Vq1ZJtn9rc7O3kek2sr3XduX3WWSRRRZ7G7sVnCYSCQoLC2loaCCVSsnU58yZM2ltbQUmA9V0Oi2LNY4++mja2tro6enB7/dP+cxEIiF1X6lUivLycubNm4dGo+HSSy9Fo9Fgs9l44YUXsFgsfOMb3wAmffpExe9RRx0F/McyStjv+Hw+AoEAsViMuXPncsghh8hxCB2m+DvxX+ZYAWpra6U9U25uLqWlpQwNDUlWrba2Fp/Ph16vZ968eZL9FVrGsbExqeGcDpPJJBdCobNcuHAhAJs2baKsrEwWYn37298G4KWXXkKj0VBYWAhMBk5OpxOVSkUikWB0dJTW1la6uroYGBiQxyM6CgmJgbC8EYHV4OAgAwMDjI2N0dvbywcffEBrayvhcJgZM2Zw5JFHyhT2KaecQn5+PqlUinQ6jcFgwOPx0NbWJsdWVlaGRqPhgw8+oK+vT46/u7tb2u+Ul5cDkwxhbm6unB8xFwaDAafTKTcyCoUCvV6P2WzGarUSi8UkA+71etm8eTNer1cWc20NW1t4hX1TOByWrDsgr8tkMinPU05OjgzMlEqldKKAyeCkr6+P0dFRQqGQ3HS53W6am5sJhUKyA09bWxvRaBSj0cjixYsB6OrqQqVSUV1dzbJly4DJAFrob0X1elVVFQaDQQZGdrudaDTKwMDAp1g6Md7MFLNgjIUmWdwzkUgEk8nEmWeeyRtvvAHAYYcdxscffwxMZjRcLhfDw8MymBMV98lkUrKooVCIDRs2AJOSCIvFQiwW4+OPP8ZkMkkfzc7OTpRKJSqVipdeekme65GRERkkCe9e0YpXq9VKicnWKvV3Jo0uzvkhhxzCvHnzprCD8Xh8Svtfs9nM+++/z9DQkJRQlJSUyI2DKPiEyevT7/fzySefsGHDBmw2G263m3A4TCgU2qcMqrBIm96mefprO/qbLLLIIot9jV0KTsVDSwSjBoNhSio1EonQ0NAAwMDAgOzGAsigT3RvyoRoTwiTD3cRrLndbpYvX048HkepVMpq3ubmZgAZxD355JP89a9/BSYXHaEP/eSTT3A4HLLvfE1NDdFoVGoXM9ssJhIJ7Hb7lBRlIBCQxuANDQ3k5+djtVpRKBR4vV5SqZQsdsnNzZWsydKlS4HJAp5wOLxdFiIQCMjgMTc3l/z8fNrb24HJdG0mRDp//vz5KJVKZs2ahVKpJJFIEI1GZcARiUQIBAI4nU45PtFhqL29XXpyisBGzEc8Hqenp4eRkRG2bNmC1WqVwUsgEJgyN2K+CgsLUSgUhMNhotGoLBKDyUBLzFUmBGO6ZcsWNm7cCMCxxx4LwL333gvA7NmzqaqqApC+i6KDkVqtxmw2k0gk0Gg0pFIpTCYTfX19UsYwe/bsKYtrZhCTOR/FxcXyOvX7/YyMjJBMJiksLGTmzJlSj2u322UAuXnzZg499FBUKpX0fxUs9nTvSBHcZt4nYr4LCwtlYd6///1vYFIWolQqCYVCsphOmO+r1WopOTCbzcybN0+e76KiImlQv63KdfG6CJICgQAGg4HZs2czc+ZMeexLlizBZDLJ+7ympob58+cDMDEx8SnWMh6PEw6H5b2jVCrR6/Xk5uYCk5sRr9eLUqnkmGOOYf369fzxj38E4KKLLkKpVPL//t//46233sJgMJCbmysLkwoKCqisrKSqqko6UZSUlMjMhbh3dzU4hcnraebMmZx00klcdNFFUrsOyGwA8CmtZWlpKRdffDH19fXE43FWrVrFddddh0qlIp1Oo9FoiEajDA0N8dRTT/HOO++wadMmuru7t8vo7+/IVu5nkUUW+wq7bMIPSHsdURQkGLGJiQkaGhowm80EAgFWrVpFXV0dLS0trF27FoPBIAPFTDN6YRwPkwtGb28vq1ator29XS6EJ510ElVVVQwMDEhG58ILL8Tr9Uqv08bGRiorK7FYLFMKRJRKpWTihFG7SGuLwEyk6MQxTIdgx1QqFW63WwYjIjj42te+xn333ceKFSv43ve+xymnnMKKFStwOByS5REBYWZ6T6FQoNVq0Wq1NDQ0sGHDBnp7e6Vp++DgIMFgkIKCAqnPW7ZsGdFoFJVKRVNTE83NzcTjcSwWCwUFBVKTp9VqZXo5EAhMYXtFy0XBIMMkS1RUVITb7cZisVBUVMSRRx7J+vXrmZiYYHh4WI5XdNjxeDyUlJTItGVJSQnz5s2TnZbEvApYLBZOOukkVq5cyYoVKwA47rjjOPLII9m0aZPUcP7617+WaWxh31RaWir1ppndoERAFgwG0Wg0W63QzzSez4Rg50QgKwqPlEolVVVVVFdX09raysjIiAygOzo6iEaj1NbW0tPTQ0dHB0qlEq1WSyKRmBKgRqNRFAqFvLacTqc0txetXePxuHS3OP3004nH4/ztb3+TxULz58/HYDAwODiIUqkkFovJ7INouyr+E84SQpOZGaALqYY4B+FwmK6uLjQaDdXV1TK4nDFjBg6HQwbR8XicZcuWsWXLFjweD8PDw1POoWijK1oIa7VaYPK+a29vl6zrGWecQSgU4o477iCVSnHmmWfy7W9/m/PPP5/XX38dvV5PSUkJ9fX15OfnywInv98vbaUE65+5kRQer7tj3yR0qqLF7wMPPCCdFzLHlxmQj4+P89prr1FZWcnw8DA2m41HH32U73znOzz00EOysMzhcOB2u4nFYsyZM4eKigqKi4ulk4X4fvG82VoTkP0BVqsVo9HIBRdcAEzeM21tbTIDkEUWWWSxp7HL7UsFwuEwXq+X8fFxuRh1dXWRn5/PIYccwttvv819993H2WefTW9vr0wfNzQ0MGfOHBnoeDweVq1aJdOiBoOBp556SrIfhx12GP/v//0/yR4Ju505c+bw5S9/mauvvprh4WHUajVz5syRDKbb7SaZTMpOQslkkuHhYSorK6VeT6QFp7caFMdSUlJCcXGxrPBtbGxk9erVdHZ2Sv1hc3MzLpeLn//857zyyiuMjo7S0dHBjTfeSH5+Pk8//TR+v59AIEBlZSVz5syhsbGRpqYmyVQWFhZis9lob2+XRV5LliyRBWFDQ0P89a9/pb29HZVKxQUXXIDRaKSvr08GEF1dXeh0OkpLS6mvr5eBWGVlJVqtFqvVSigUku0vhUYxmUxKG7CCggLUajWrVq1ifHycZDJJLBZDr9cTDod57bXXpOXV2rVrGRkZoaamhoULFzI+Ps7AwAAVFRV85Stfob29nYqKCsbGxrDb7TKYtNlsPPPMM5KZOu200/jVr37FRx99xHe/+12SySRf/OIXOf300wmFQvziF7+QxWapVIqRkRFqa2vxer3EYjGsVisOh4OSkhIsFgtWqxWDwfCpwCyzDa2YX/hPQCDem0qlJMtnt9ulsfqWLVsYHBxkzpw5tLW18fvf/55zzjmHt956i3Xr1sliNJgMyjID1MMPP5yysjJ6e3ux2+1yg/DNb36T2tpa/vznP0vbsAsuuACVSsWf//xnAMrLyxkcHMRut6PRaDCbzfh8PsbHx+nu7ubggw+W4xDm8aIb2PS0bHV1NSqVimAwiMPhIBqNEo/H6evrkz60MGmbZTQaZWvd3t5eLr/8ct566y3effddxsbGyMvLo7S0FJi0YhPyherqag466CBaWlpobm6W1e6NjY3U1dVx6623Eo1GOf7447n77rv5+te/zocffoher6e2tpaZM2dSX1+PyWTC6/Wi0WjIzc0lLy9P3qeZQWhmEZRgUXcGmfMuNicLFizgxBNPxGazyfs7cy5FxzGPxyM7ZQmMjo7i9/t55JFH+M53voPD4aCqqoqhoSHa29sZGhqSPsRbtmxhxowZlJaWsmjRIrlRFNhTrU8jkQjnnHMOMKnh1uv1W31tR39TXV1NW1sbDoeDtrY2LrjgAhwORzY4zSKLLPYadik4FT6hpaWl2O12ysvLqa6uxm6309nZyb///W/a29v57ne/y/r162lpaaGvr4+jjz6azZs3Mz4+TldXF11dXcBkuq+srEzqDzPtSmbMmMEZZ5zBKaecQiwW4/HHH+eFF16QBth/+tOfeOihh7j//vtRKBTMmDGD/Px89Ho9brcbo9GIxWLhqKOOkql5rVYrzfQFpjNsmRA9vIEpi4VgOUdHR/H5fHzzm9/knXfe4a677uK8887jmWeeYdGiRcybN4+qqiqeeuop+vr6GBoakubxH330ERqNZkqgBJMs0NKlSznttNMIhUL861//4rHHHpPs5jXXXENJSQmdnZ18+ctfZmRkRNoeiWPN1MxptVpmzpxJTk6ODLDFmAW7LIIQ0f2mpKQEr9eL1+vFZrNRWFjI8PAwN954I5988gmHHHIIq1ev5vLLL+eCCy7ga1/7GoODg7S1tckiEJVKJSUHIvidPs4TTzyRX/ziFzz88MP86U9/IpVKMXfuXG699VY6OztZtmyZlDjk5eVhsVhQKBSSbbJYLLKjVlFREVVVVVLHurXgVPyc2YBBpJ4BhoeHZU93jUaD0+mkoKCA8vJyRkdHOe+88zjvvPOw2Wx0dXVxxx13sGzZMhYvXixN+wcGBuScCjZx3bp1U4pmAL70pS9RW1vLU089JT1jb7jhBnJyclixYgUjIyOyi5Hf7ycnJwelUklubi7JZFJqNjM7QwnWNBQKSX1xJkKhkLx3Y7EY4+PjuN1uabovAnFxfi677DLeeecdPvzwQ6677jqOOOII3G43mzdvxuPxEAgEJOMYj8dJpVJ0dXWxZs0aGahrNBrq6+txOp3ceeedABxxxBHceeedLFu2jFWrVkkf1wULFkg/4K6uLpxOpyzsyzx/ophxelcsEbjuTjGP0WhkyZIl2O12fD4fH330EQMDA9LJQWiORTGhsPdSq9UUFxdTWFjI3LlzmTdvHg8++CBnnnkmQ0NDXH/99dx2220Eg0EGBwdJJpPYbDaGhoYoLCzE6/UyNjYmP7ehoWGP6T2TySSvvvqq/Pe2XtvR38DkpiMzGBXacqvVmg1Ss8giiz2OXQpOjUYjubm50sTb6XSycOFCmd7q7Ozkj3/8I1dccQV///vfuf766+no6OCNN95g1qxZLFy4EJ1OR0dHB52dnbJHfSaUSiXnnHMOxx13HFu2bOHRRx/ljTfekFXEixcv5oYbbmBgYICrr74amEz3VlZWotFopMZUrVZLhtRoNKJWq4nFYuTn508x7N7eIhAKhUin09jtdmpra6Xd0dy5c9FoNNTW1vLwww8zNjbGt7/9bZ544gmWLVvG008/zW9+8xu+8IUvsGjRIr73ve/x2GOP4XQ6GR8flxY1IojR6/Xk5ORQXV3NmWeeid/v57XXXuO3v/2tZJAXLVrET3/6U+bPn897773HhRdeiN1uR6fT0dTURGVlJQMDAzK4FCyb2WymqKgIq9WK1WqVTFMmyyZS79FolJKSEunjKPxr58+fTzweZ2JigquuuopbbrmFm266iXfffZcHH3yQzZs38/3vf5/nnnuO9evXT+n8Iy80tZrc3FzKy8s57rjjyM/Pp7m5mXPPPVcG7MuWLeO3v/0t4XCYc889l46ODvR6PfX19RQXF2M2m+XmSATY4nhFx6NYLEY0GpWFcduzF5quTaysrMTj8TB37lxcLpdkZ0844QRWrFiB0+nkoYcewmKxUFtbS39/P0888QTz58+nrq6OxYsXE4lE+POf/0wwGJyipYbJLkiLFy9m1qxZWK1W/v73v0vt5Q9+8APOP/98wuGwlDYIh4Pi4uIpNlFarRaLxSLtvjI7JQmLMLvd/qmNVyKRkJISUSSYSCTwer0UFxdTUlIibcdEYd7PfvYzfvOb3/Dee++xadMmjj/+eOrq6qT/qWAYp59Li8Ui240KrbTVauX0009ndHSUQw45hFgsRl5eHrm5uVitVnkPFBUV4ff7CQaDFBYWUltbS35+Pna7fQp7On1exe+mb/h2FkajkTlz5kh7ueOPP56nnnpqSpGk3++XkgqVSoXFYpEyms7OTjo7O8nLy+Pggw9m9erVbNmyhQceeIDLL7+ccDjM+Pg4iUSCWCyGyWSip6eH0tJSKW3K3DjtijUWwMaNG+W9tLeQTfFnkUUW+wKK9E54svh8PiwWC16vVwangDS27+np4YMPPpAG3aeccgonnngiRx11FC+88AJ33XWXZM8qKyv50pe+BEwyND09Pfh8PmKxGIFAQBYVBAKBKXrFkpISbr31Vmpra/nZz34mvVTF4lZdXU1OTg6RSASj0UhpaSkmk4mCggLMZjN1dXWyPaWQGBQVFcnUfmZ6f3h4mKqqKkZHR2VVslgoNm7cSCgUor29nb6+Ptrb21m/fj2BQICjjjqKX/ziF9xwww3SwxIm2VnRL16tVlNSUsLcuXMpLi6W2r729nbGxsZwuVxT9G2NjY1cc801fOMb3+Dtt9/m9ttvlxXzojtWTk4OdXV1lJSUSLaurq6Oww47TC60olBMvDcTYrzt7e2SXfb7/XR3d7Ny5UpCoRBer5c333yTeDxOU1MTt9xyC+vXr+fXv/41iUQCi8XC0qVLWbhwoWzlGo/H8Xq9kl1Kp9M0NzfT0dEhU9swWf3+y1/+kssvv5znnnuOa665homJCVQqFTNnzqS8vFwGyHl5eUSjUdnNSKfTyXkWBT6xWAyHw0F+fj5NTU2fCtIy5zc3N1fOvUjb2mw2nE4n77//vqz+t9vtshJesHgFBQXSUxYmJSlLliyhpKSEqqoqKbmYPXs2VqtVGsk3NzezYsUKGVRecMEF/PWvf6WlpYXrr79edqGaNWuW1EIvWrSIRCIh9cBLly6VzgY6nU7qMzOZUzFuMd7u7m6USqXUPq9fv56uri4GBwdlYY9oGvC9732P66+/nmAwyNq1a/nBD34wpUiooqICi8WCw+GQDgfpdJrc3FxZCChgNBo5+eSTGR4eZvXq1fJ1rVbLjBkzSKfTlJeXc+SRR9LQ0EBeXp68bkRBlJCk5ObmbjfbkTle8bzaFYhnwYYNG+Qm+oMPPqC3t5d4PC6fY9N1qJkQ1xFMXhPd3d1s2rSJ008/XbLyQh+u1WpZvHgxBoOB0tJSDjroIEpKSgiHw3JTsqM0vxhv5ve3tbVNaTIQCARkUD/9tenj397vAdngRKT4161bJztn7QtMX4+yyCKL/z7sVnAqHuCijanT6WRiYoK//OUvMmUv0NjYKAt73nzzza2meLcF0TVp6dKlKBQK3nzzTVatWgVMPvSLi4upqqoiFotRXV2NVqsllUpRXV1NcXGxDHAEqyXanApjfVHUIoplRBCVubg5nU6ZVoVJXdvg4CBjY2P09fWRTqdpaWlh1apVklE56KCDyMvLY3BwcIqF0s5Cq9Vy+OGHc9ZZZ5Gfn88bb7zBm2++KVkvtVqNwWDgoIMOwmq1UlpaSm5uruzs5HK5sFgsHHLIIZJxGxgYkN2PRHAq5tDj8TBr1iw++eQTPB4PWq1W6hpFh61AIMBHH33E6OioZHdOOeUU5s2bx4svvii7Cu3KGOfNm8ehhx5KZWUlq1at4pNPPpFjFD3qGxoaMJlMNDU10djYiN/vJ5VKUVRUJCvdy8rK5PzApEbX5/NJi7HpC2zm/EYiETn34lyJQiuHw8HKlStxuVzyOo/FYvT19UkvWJiUoAi/1F1BdXU1X//617FYLLz55pusXLmSdDotK/vnz5+PXq+nsrKSpUuXYjabZSq5srKSmTNnTrmOtxXAiPFu2rRJ6kRNJhMDAwMMDQ3J7mJbtmxh8+bNcn5ra2s566yzOOmkk6ivr+fOO+/kgw8+oKWlZYc+owUFBcyZM4f6+np6e3v58MMPgckNVUFBgQzOioqK0Gq15OfnS/u44uJi2bVNOBpEo1EZpO2ITfwswamAuDcGBwdZuXKl9ILt7++Xcp7Mser1+imsqlqtlhpTwUanUik0Gg16vR6dTofBYMDtdpOXlyc9e2fMmCGLP4WXr2hAsi0mVYz3vvvuY8mSJTLVvrVAc08EpwLr169nyZIlPPbYY8yZM2efpfizwWkWWfz3Y7cKokQwJzRsMJla/9a3vsVbb71FIBBgYGAAt9s9RWNqMBiwWCyy64pGo5EeiaJQQ6QsTSYTPp+PDRs28MYbb8jFUPhK1tXVUVRUhF6vR6VScdhhh0mZQFVVFbNnz6a7uxufz0dZWZkMTAXLNT21v60Uv1gQTCaT1K6KIoFoNEp+fj4NDQ1YrVY+/PBDRkdHWb9+vfx7EVwkk0kUCoW0fRL/1+v1WK1WLBaLZHQdDgfvvvuutFASUCqV0obJYrHQ1NQk25Tm5+ezaNEijEYjLS0taDSaKZ2uxCInfhZjFvMoYDabZYGRXq8nnU5LtlcYrAuLqBUrVsiK+8rKSulFGQqFZAAtrKj0ej0KhUIWq3k8HtavXz/lXIkxNjU1MWvWLEKhkGR/RLvQuXPnSja4sLBQBmdChyokJ3a7XbJO28PW5l6cF6vVyimnnEJ3dzeJREJaaVVVVeFyuWhtbaWzs5Oenh75t+K6FoU0Iq0vLIb0er285v1+P7fddtuUQK+6uloWds2fPx+j0ShT7pkLv9CUbq2gb1sQOlURhFdXV8tiICEJaWpqQq/X8/7779Pf38+dd97JnXfeSUFBARUVFbIArqenB6/Xi8vlIhqNolQqSafTqFQqIpEILpeLDz/8UAalwpf3yCOPZM6cOXR3d2MymSgtLSUajVJYWIhKpZLSDRGYCYmOeCbsK2QWSsGkrZvIBAwNDfHQQw/R19dHLBbbqowlkUgwPDw8xdlAp9OxcOFCKVEqLCwkFosRj8eJx+PYbDZ0Oh1Op5PS0lLGxsbYuHHjlAwBsM05nzVr1j5lMLeW4n/uueekjCib6s8iiyx2F7sVnGYWHogqVJVKRX5+PrNnzyY/P5+DDz6Yzs5OHnnkEbq7u2UHmMx037aQaTMlYDAYKCsrw2KxUFpaikajQafTSe2nMCUPh8OyteLs2bPxeDyy3aQ4buHVmfmQ39bCl/k7EaiKAE4U48RiMSorKzn88MMZGBhg+fLlDAwMEI1GiUQiDAwMbHOs0Wh0Soo7E0LLZrFY0Ov1sk2nQqGgsrKSRYsWUV9fL7WWMLkACgZVBMEw6aGa2fYxU7snCkisViuRSEQa4ws959DQEB6PhwULFmCz2Xj//fdRKpWSFRadmXYVarUatVqNVqvFZDKh1WqZPXs2S5cuZf78+USjUWm6nkgkaGxslIVdmQt15v9Fk4EdpX4Ftjb3Wwv6dDqd7CjW19fHmjVrZCFff38/0WhUanS3pXnMdEbIhOiQtmTJEnJzc1EqlVRXV1NQUEBpaSnl5eWyu9l0m6xdCdjMZjMmk2lKpbrYYDY0NMh0vFarpba2lpUrV+J2u3G73bhcLlwu11Z9a7cGseEULYTnzp1LdXU1NTU1cjyRSISqqirJ5Gu1WmKxmPSo3ZXAe29AuHTMmTNHHosoELz++usZGRkhGAzS3t4uu75Fo1G5CRAbUovFQiKRQKlUMjY2ht/vlxs58Tw0m81Sc9rW1kZpaSnDw8Pk5eXJgrCBgQGMRqP0Z94eW74vkFnFb7fbOfvsszn55JOBbKCaRRZZfDbsVHAq0tLDw8NT0iherxeFQoHD4aCgoIBUKkVhYSFlZWUUFhZSXFzMQQcdRFVVFbm5uWg0Gnp6etBoNAwODkq/R5hM/1dXVxOLxQiFQigUCsrLy/H5fDLggEmtWyAQQK/Xo9VqZYDj9XopKSmhtLSUUCgkK9BLSkoIBAKkUikZNGQGD7FYTDKKAqKoYHBwkLy8vE+dD8H4Zlbz+/1+kskkpaWlnHfeeYRCIVwuF0NDQzLAikQishBJeKZqtVrcbjfRaFTqY9PptFx40uk0brebRCJBXV0dFotFMisGg0Hqcn0+n2wwIOZCdNYS3yeYH/GzgLCvCofDnxqvw+HAZDJJX1edTkdNTQ1ut5vS0lJmzJhBMplk8+bN2Gw2XC4XKpVKLppCh+jxeGTHJJPJRHl5OSeccAI5OTm0tLTIILqmpkam+kXrVFHcldmSNfP4HQ6HZFOFaf72sKP5FRDfIVK0wumhoqKC4eFhEokEubm5HHfccfh8PoaHh/F6vRQVFaFUKqXGNJ1Ok0wmpXuB8Pv1+Xzk5OSg0WioqKigvLxcFgPl5+djsVjkmEdGRqQbxa4W/IjxKpVKGWyKuRGdiwRLKzTPIuMQDocZGBiQgZhoPiGkH/F4XGYvYJIhTyQSlJeXS/1oYWGh7PRksViYMWMGM2bMkAFfVVWVPA6RCci8V6ffn3tqfncVsVgMjUZDU1MTTU1N8hwODQ0xNjZGfn4+4XCYzs5OhoeHJQsqnnXhcBiTySQ32KK1spATiY3BxMQEW7Zskc+MgoICBgcH8fl8pFIpaZsWj8exWq1SUjM+Pj5lk5iZEREdt7b2WiZ29PvpUCqVcjP49ttv43K5cDqdXHLJJTJQ1ev13H///bKj3WeFkE5sjbH+b0Q6ncbv91NeXi4zUVlk8d+OndKcrlmzRhZfZJFFFllkkUUW+xZDQ0NUVlZ+3oeRRRb7BDvFnIqWpENDQzslQBfayUzN1oGE4eFhmpqadnq8O8L+fj729Hg/C/bFudqX490f5n5fz+/nPeZ9Nd7Pe5wCmeNVqVT7xTHtTYjxdnR0MGvWLOA/UjAhSRodHZXSkOmvHWgQdRSZRZ9ZZPHfjp0KTkXKLjc3d6ce9p93gPNZIY5/Z8e7s5+3v2JPj3dPHMu++I59Md7P+3xmHsO+mt/Pe8z7aryf9zgF9qf7d19AjDEzWJs+bmGtJtauzNcOVOyv7W2zyGJvICtgySKLLLLIIosssshiv8FuVevvSUw3wP9vxf/KOHeE/8XzcCCN+UA61gMF+/Kc/i/Nn1KpZOnSpfLfwKd+3tp7ssgii/0f+0Vwmkgk9lg/6f0V/yvj3BH+F8/DgTTmA+lYDxTsy3P6vzR/BoOBNWvWMDg4SFtbGwDPPvvsFNsq8Z4sssjiwMLnHpxuzwA/E9NNBQ40/c3OjvO/Hf+L5+FAGvOBdKwHCvblOf1fm7/BwUHmzJkjLbBE69asr2oWWRzY2C+C0519kIq2kvtLympXUmj7yzF/3vhvTm1u63sPpLnfmWP9X0odC3yWMe/L8zS9OcV/+/w4HA5CoRCPPfYYABdccAEOhyMbnGaRxQGOzyU43d0HvUhZ2Wy2HXZ22hfY0fH8Ly7iO4vMcyN+3lPnaV+nUae3g/0s33sgXDPbGqfNZpNtY3e2Q9eBgGAwSH9/PzqdDtj1gG9fzandbicSiWC323f7WA8khEIhTjvtNABqa2vl66eddhrd3d2y6cDcuXMBaG1t3WFTgSyyyGL/wOeiEM9c3HYFmZ2Kdufv9zR2dDy7O87/BWSemz19nsS87IuFeXqg9lm/90C4ZrY1TrvdTjQaxW63f05HtncQDAZlO+Ddmdt9NaeJREIGprt7rAcS0uk0Y2Njn3p9bGxMysDS6TQDAwMMDAx8ShqWRRZZ7L/4XJjT3dFFiT7X4m/3B13Vjo7nf03/tSuYfm725Hna12nUTFbss37vgXDNbGucRUVFkjn9b4IY6+7O776aU7VaTX5+vvzO/fkayiKLLLLYHvZ4cLozKazP+uDc3x682zqerb1+IKRt9wW2psucjgPhXG3r2Hb32Pfnse4Iosf6tnAgzOfW8Fnv43013qKiIlQq1X7NumeRRRZZ7Az2eFp/Z1JYwWAQm832P/UQTafTUrvm8/n+p8YusKvzfiCkuLeFz3LsB/r9sa3jP9DnM3NM++tYgsEgPp+P/v7+/e7Yssgiiyx2Fns8ON0Z3d3uPtgP9EXbZrPh9/txu90HFHO0p7CrC+e+1I7uDrZ3PX6WY99fA5+dxbaO32QyEY1GJeu4v2Jr8zp9TPvrtSnOsU6nmxJIH8jPzd3Bxo0bGRwc/LwPI4sssthN7PG0/s6muXZWg7Wnq6H3NnaU7jMajeTk5Oy3x783YTKZZMFG5sK5K7KI/Qnbux4zj31X09kHgu50R3C73Z/SnorxHwj38PRjnD4n+7NkR1SkZ15/+/s53xMoLCyU/z7qqKMwGo2sW7fuczyiLLLIYnexT6r1p+/cTSYTxcXF8oG/vV39nq6G3tvYHutVXFxMaWnpFF1e5vi3V016ILAfOzpGk8lEbW0tubm5mEwmbDYb4+Pj2Gy2A6aSNp1Oy/9gMgjbEaZfEztznsT9sT9hZ65V8bpOp8Nut2+VPd3f7+GtHeO25sRms9HS0iLPy+fNeNtsNiYmJnA4HPK1A+Gc7w4UCgX19fXy5+rqahobG6mvr+fBBx8kFArhdDqZO3cuc+fOPeAat2SRxf8y9km1/vZ27jva1e/paui9jW2xXpluA5nIHP/2PPgOBPZjZ47xQJjDXYGojt4etuZMsL/P5daws9fqdIZ8epC3v495V44x0z6rtrZ2v5jTcDiMwWA4oJ6buwOj0cjTTz/NkiVLMBgMGI1GOjs7AVi/fj0w2b60paXl8zzMLLLIYjewT4LTrQVsmbqz7e3qD7QH6+5UZ4vzYLfbt5vi3h8Wvu1he8e4tZSnYKL25zFtC6JbGYDFYtnue7fmTLC/z+XWsLPXqtFo3G8Ctb2NTPusHV3LezvtL+amuLgYo9H4X3/us8gii/9e7NXgNPNhPN1iRhQH5eTkUFdXt9Ofc6A+cLc1BqPRiNFopL+/f7vnQ/ydSK0eSOdCuBQkk0lUKhW1tbWYTCY59gMRmdev+Hl7czJ9/g+UucuEmC+73f4p5jdzfEajkWAwKM34D8Sx7iym22dt7T4Xr9ntdpLJ5E4983YHGzduJD8/n5KSkr3y+VlkkUUW+wp7VXO6pzRY+4OW67NiW2NQKBS7pIXan8/Fto4tGJzssOPxeKYUQ4mx7+o5+DyxtWPdmTnZn+dtZyHGvjUN4/Tx/bd2i9oRtjbP4rVQKLTXvzscDu/V79ifEAqFWLZsGTApZQiFQjQ1NdHU1CTPQzgclq/t7fOfRRZZ7DnsVeZ0e+nLXUnpHqhp0EzsaAw7ez7253OxrWMTP+fm5k75+UDG9Pna0Zzsz/O2q9jadTp9fP+t3aJ2hK3Ns3itpqZG/rw3UFVVtcNGCP9NSKfT9Pb2Tvm5tbX1U+8Trx0oRZdZZJHFHg5Ot5a63Fkt6dbSYel0Wmr7DsRU6PRU5/bS8rsSqO+spnNvYVvfNX3uBKan7wXzKN5jt9tlIHOgLKzTg9JtHfeuLIj7o3xFXK+AHOP25l6Mt6ioiKKiogOGEd8TyNTRZzqT7Ox8ftb5NxgMe+Rzssgiiyw+b+zRtP5nSV1m/q1YEEVguj+mQ3dkByTes60U354ez748T7vzXdtL33/WFPDOzMWehtDR7my3rwM19R8MBvH7/fj9fnlvbusYha4yFAodUFKNPQVxbjI1ueL1nbk+P+v8Dw4OMjEx8anvzyKLLLI40LBHg9NtadF25sGc+bfiIS3+LhqNbrfQ5PPw/9yZhSRzTKFQSDJQavUkYb0jf9ddGdfWzv3ewq58VygUor+/n76+vk8t1kIDVlRUhE6n2+0U8OcR1Akd7fRrU4xN/BcKheQma3vXMex/fpQiGFWpVLLwa3vjCAb/e1tn7sz9KObPaDRO8b/d2fPyWeff6/UyMTGB0WjcqWdMFllkkcX+ij2a1t9aGikzcMj8XWa6MzN1b7PZ2Lx5M3q9nsrKSvLz8+UDf2vY1ufvbWxLQzhdiiACLsFmRKNRABwOh/TIFIxTZlp7V8e1L1N425rnzGMV/xbMm/g7Ucnt8/kYGBiQKX9Rwb+7x/N5zb+4LtPpNHa7nfb2dvLy8lCpVNKI3mg0otPpthl4ZJ67vZkKt9vtqFSqnT5PIgAXxyWqzTNbkIo5hU/7m4rXdyTNORDS0JkbZoHppvyZ8hWdTidfN5lMDAwMEAgECIVCzJ07V35m5pg/6/jD4TD5+fnSgD8UCu1Vd4Asssgii72FPV4QtTXd6fYCB8GsCZuh4eFhwuEwyWRSMgAmk2mbC/bnVWiyvYVka4GlOM5oNIrf7ycQCBAOh8nNzcVut+PxeHA6nTt93jK/a39Y2KfLMiYmJojFYhQWFqJSqab4LoogJplMYrPZKCws/EzH/3mMXXxnpin9wMAAXq+XcDjMzJkzGRoaIi8vD9i+l2/mudveJuyzzvOubuIyr0FRpS+CT7vdTn5+vtRPC4jNWOZrO7KN+7w2mLuC6fevqA4Xm6rp85O5ORPzGggESCaTksnc02PW6/X4/X4ikQhDQ0OYTCbKysr2yGdnkUUWWexL7JXgVDAM4kEt2ECbzSYZQrGICXZmdHSU8vJyCgoKACgoKJjCIsLWq1w/76BsW5jeWzxz0QqFQsRiMblwFBUV4XQ6ycvL+1RgD1PP2/TCm329sG+LIZ2+IIfDYcLhMHl5eeTk5HyKYaqtrcVms+FwOCSbfKBh+gbCaDRiNpslo1ZeXr7VFLg4h5mIRqPyvdtiFz/rPO9qynj6sRiNRoqKihgYGJA/w2SQ5Xa7yc/Pn5LO3lkP289rg7krEGMZGBhgdHQUvV5PYWHhlGtezE8mo2qz2UgkEvKah6nFdOI9e+I5VlhYSH5+Pps3b8ZgMKBSqVCpVPK79ufzuztQKBSUlZUxNjYmfxaOCJkQr/2vaaCzyOJAxl6p1heYzgYJZikUCn2KYRE2Q2VlZRiNRpkKDAaDJJNJ2R5wf3/AhkIhyS7BVPmCYJ+ExlIEI5mV/GJ8mX8nCoYGBgZkwUlNTY1cBPfFwiOOJ5MFy2QOp28kzGYzZrNZahWnb1gyU/mJRGKvHvvewnQXgtra2imbLtFwYDozKmQNo6OjUgIAkEwmAbbZhOGzzrPoYrQjTHcYEIu6kC0kk8kpbLcYl9vtxul0Yjabp9hITTepn34M++sGczpsNhtDQ0Mkk0n0er2USIjnVCgUkvM7PWNisVi2Om4RvO6Je7iiogKPx4NarWZiYoLCwkJCoRD5+fn/lcGp0WjklVdemdK+tL+/H5javlS8lkUWWRw42OPBaaa2TgQqotUhgM/nw2KxyAVv+sIkFsbMBdztdpObm4vRaNzvtVOiECYWi7F48WLg05q7zIVLBDci0BHnZbpuVVSy22w2GazvavvPPS0BmB4wicDV4/HIoCvze6LR6KeC1M8SdH3ekobMIjfRMhKgv78fnU4nr1lxrICcR7fbTTKZZGxsjBkzZuB2u6foFKfj8xjjdIsvoVkNh8Ny09Hf3y+DMqGhBqZkDcTma39P3W8P4r4UUoaysjJ5//b392Oz2fB4PMyYMeNTwakIQvv7++W5zNzI76kiuJGREVpaWojH4+Tn58uAbX8qsssiiyyy2Bns8YKozCKJzFaHYhEXC5j4OfPhnZnyz9QlulwuXC7XFKYCtu+7uDexM0GRxWKRx9ne3o7D4aCqqoqDDz5YnhuhtxWL+3S2ZVuLuWCedzU4+yyp4cxe8mIetva9YkypVAqDwcCSJUvIzc2VC7JI5QtN6uzZs6f8bleD7emWPfviOhDnfWvFXjabjY6ODhwOB0ceeSQmk4nW1lZcLhcFBQVUVFSgVquxWq3odDpUKhXRaJRwOIzRaNyvfF4zGXv4T2q7sbERk8lEf38/HR0dJJNJmfkQ17X4G3Fvi2vcYrFM+Q4x7/DpAqPPA1uTrQiYTCaampqora2ls7OTvr4+FixYAExe91qtVkpU+vr6gP+MSejKh4eHmTlzJoDcyO+pOW9ra2NkZASbzUZFRQVlZWXy3hJjOVBY6iyyyOJ/G3ulWj+zYj0YDOJwOAiHw2i1Wvx+PwaDgVWrVsmip4qKCoaHhzGbzcAk6yLSvqFQCIvFQiQSQavVyoBVfB/s+cKCHWF7QZ5YaEKhEA6HA7/fT0dHBwqFgkQiMaVLjM1mo62tDa/XS1lZGUqlUjKOYpEXAb5g4kSlL/zHAWBnx/5ZWUrRglFsMKanf00mE7FYDK1Wy/j4OHa7Hb1ez+GHHz4lME0mk3R2dpKfn8+GDRumLNYiRSyYqu0F4NN1rnvrOpjuLCGY0UyLJZHyjkQijIyMoNfrGR4eBqClpYVYLEYkEqGmpuZTgf2GDRuIx+PSRH1PaRB3d4wCgrEPhUKMj4+Tk5Mjr7/+/n4GBwfp6+ujoqIChUJBIBCgp6eHQCAgWWO73U5HRwcGg4Hq6upPfcf0AP/zDpy2JlsRrLbb7Zb35djYGLFYjM7OThYvXiyfXSKtPzExIVtoFhcXEwqFGB4eJicnR37+nmY0XS4XdrudWCxGMBhk/fr1hEIhSktLKS4unlLAtj+c68+KcDjMhRdeCEAkEiEcDnPMMccA8Mc//lG+fvDBBwPw3nvvyXssiyyy2L+x1zpEZQYzOp2OZDJJLBYjJyeH5uZmaV6el5eH0+kkHA5TWFhIeXm5TH2K4gOr1UphYaFkF9vb2xkbG5ui6duXD9ptBXli/IKt8Pl89Pb2kpOTQ2trK4WFhbz22mtoNBqZBo1EIkQiEbq6umhoaMDj8VBeXo7D4UCn08n/j46Okpub+ymJwK5WX+/ueRLflZmuzZRriLGLDYjP50Or1ZJKpQAkywYQj8fRaDTSSkqMwWAwfKpyf3tB5/Tx7I3rYDqTJpwlotEotbW1kk188803cbvd+Hw+ioqKpLxhfHycgYEBSktLgclA1GAwyFaTQgoj2C0R+Gae833JeGUy5DCpoxVpa6GNFZiYmCAYDOJ0OlEqlWzatIlAIIDJZKKmpoaioiI2bNjA2NgYGo2GxsbGT82RyWRCpVLtl33PBTssnk8jIyM4HA4aGhqIx+PY7XYsFgt2u51AIIDNZkOn05GTk4NKpcJgMDA0NMTmzZvRaDTU1NQQi8WIRqMyYNyTiMfjFBQUMDIyIjeHgpGvq6ujrq6OwsJCeb0d6MFpKpWa0po0lUqxdu1a+bP4v3hNPIuyyCKL/R97PDjNDCREuk4wTKFQiL6+PgKBAJ2dnaTTaWmxkpeXh16vB6C3t5fm5mbcbrdMAy5ZsmQK0xQOh9myZcvn0rt7W4HC9PGLRT4SiRCNRrHZbIyMjFBcXEwkEqG+vh6VSkUikUCpVDI0NCTT4G63m+HhYVQqFZFIhGAwiMFgoKamZqu6zX05ZuHpKY7D4XAQCARQqVSMj48TCASkvnRkZISPP/6YoaEhlEolWq2WgoICfD4fZrOZVCqFx+MhEomQl5c3xbFAfO/OVDXvrfMg5lSkpv1+v5QjZPp4wqSONJFIYLVaicViDA4OMjQ0hNVqpbS0lEgkwsTEBB6PB4fDgdVqxWAwEI1GsVqt0g81s8J/bzLCmYFvpsxEsISCHQ6FQoTDYYaHh+X7WlpaaG1tlRITr9eLSqXC6/VSXFyMXq+fIr/Jzc0lNzeXYDBIS0uL1F6aTJOa6v2hKE4cr2BNRZFTLBZjYGAAj8dDPB7H6XSSm5uLy+WSqfrR0VHUarXcNMPkc29sbIxwOEwsFpPsqrDiEtje82RXNiZCA51MJhkdHSU/P5/BwUFmzJhBV1cXxcXFVFVVSWeI/4YAdUdob2//vA8hiyyy2A3sseA0nU4TDAYZGhqiqqpK6kbFYisMoYuKihgeHqa8vBy3243X6yWRSOByucjLy5OaKRHcmEwmtFotH374IRUVFTIYFR6h7e3tlJeXA5+2rtlX1iGZBuwAs2fPxmaz0dLSIlmMSCSCWq2mqKiIqqoq+vr6ZOFURUUFQ0NDlJSU8NFHHzFr1iwMBoP8z2azEQ6HGR0dZe7cuVPS47B1i629McatsbbRaBSn00lXVxclJSWMjIzg9XrRarUYjUZ6e3tpaWkBYMaMGTIgEyxsLBaT+lNhfZNZLJJper41xmdvM4tikyUY7FgsRl5engwu7HY7w8PD8vzk5eUxMTGBTqfD6/WiVqvx+/2MjIxgMpkYGhoilUrR29tLNBqVLKmYS5EhEOf8s0gxdoTMwFcw2OJ+NRqNJJNJnE4nyWSS3t5eCgoKWLt2LRqNhnXr1skNh91ul6z+zJkzSaVSU3Snubm5zJw5U8oEMnWsYlzTrdf2JTKLMAUrDpPXm7iGxbWZTCZldgCQmxCPx4PJZCISidDc3Cw9fg0GA7m5ufI55nK5CIfDzJgxg4GBAcm01tbWfup5tasbk0QigUqlYnBwUHqqFhYWEovFgMliVIH/FvZ0WxD30cUXXyxfGxoaYvbs2Z/jUWWRRRY7iz3KnIZCIcxms2SSRFpsZGQEjUaD3+9Hp9NRUVFBV1eX3L1rtVrC4TAOhwOj0Ug8Hqe4uJhDDz2UGTNm0N7eLrWrlZWVWCwWmpqaMBgMU5imzOBJLLZ7M3DJ/Pz29na6urooKCggGAyyZs0a7HY73d3dVFVV4fP5JFNkNBrJyclhYmICv9+PQqFAo9HQ3t5OKBQiHo9LuynRaWhsbAyFQkF/f79k2ESqf1+lfoVUQdh6iaKs1tZWJiYmZJo+EolQXFyM0+mU/ecFmyZS3larlXg8TiQSIR6PEw6HKSsrk5sYcQ2JIEbokKcHMHuTWYT/sFoTExO0tbWRn5/Pxo0bCYVCqNVqEokEGo0Gu92O2+1mbGyMuro69Ho9BQUFmEwmXC4XbW1tRKNRDAYDer2eWCyG3+9Hr9fLoEF4/Gam+TOv570xNvH54t4RUhnx3SqVis7OTsLhMIODgxiNRhKJBIFAAJfLBUwW//l8PsLhMN3d3VitVqknLykpwWg04nA46OzsnDLO8fHxKTZaQiayLwOm6bKNnp4etFrtFFY3Ly9PMqeC5S8qKsJisTA6Oio3Y6lUCrVaTTQaxeVykZ+fT2NjI0cffTQOh4NPPvkEjUZDXV2d1JWLTcnWsKsbE4PBQGtrK2NjY0SjUWKxGC6Xi6KiIlQqFT09PeTk5FBUVITVav2vZk+rqqpoa2tjaGiIo446CgCn0/k5H1UWWWSxs9gjwal4wIsFVAQQIgUo0mJarVayFEqlUqbFBCMhWDixIDQ1NdHX10dzczPj4+PMmDFD+ijOmDGDuXPnTgnKbDYbPp+P9vZ2aaOypz3+tqartdlsuN1uefzNzc309/czNjaGwWDA5XJhMBgYGBhAoVAwMDAgmU+9Xs/Q0BCVlZXodDoSiYRMkSaTSQYHB7FarbL7S3NzM/Pnz0elUknGeG8HaAIm02TVsd/vZ926dcyePRuHw4FSqSQej8tA2WAwMD4+zujoKMPDw6RSKUpKSsjNzaWzs5Px8XFKSkpQKBSMjo4Sj8cxm818+OGHFBYWUltbS11dnVxkBbu4rXTovlhgBbs7OjqK3W7H6/VKaYbH4yGVSjE4OCjZt8rKStRqNePj43i9XmKxmGQULRYLVqsVjUZDT0+PDGiqqqowGo2MjIzI793a9bunArjMzxF6yemtggcGBhgZGZFFbELDaLFY0Ov1Uuog7gFxrsbGxkgkEjIQb29vJxqNUlhYyOLFi7Hb7VLHWl5eLptw7OtgKXPDZTQaycvLIxQKSb9ip9NJb28vIyMjsv2o3+9nfHwctVpNMBgkPz9fPpd8Ph/5+fnyWjUYDPz73/8mEolIuYTQPopGFKFQSGonhWPD7kh2XC4Xo6OjssNeNBqlt7cXv99PdXW1vN/MZjMmkwmfz0d/f/8B4R+9O6iurqawsPDzPowssshiN7BLwWkwGCQSiWy1EEVoxkQ6v7+/n87OTnw+Hzk5ORgMBkKhkNSZhkIhIpEIBoOByspKxsfH0el06HQ6WVGpUCjw+XwoFAqUSiUqlQqXy0Vubi59fX2yqCRTnzgwMMDw8DAajUZWDE+3rxHYltn4tiCkC9N1pdFolJKSErxeL7m5uTJIUSqVUk+q1+tRKBTE43G6u7sxm80YjUY0Go08fqvVyowZMygoKMDj8dDR0SGZU6PRyPDwMGq1mlAohNVqnXL+ga2Oc3usqvidYCm3Nb8CwkHhww8/xOfzyfcaDAZKS0sxGo1YrVYGBgZwu93SYSE3Nxez2Sx1tC6Xi1QqhV6vx+12k06nJfMUiUTIz89HpVJRVlaGx+PBYDBgtVplcZ1onSmkAZnV/XsDwWCQ3NxcPB6PtAiLxWLk5+dTWVkJTKYMA4EASqWS0dFRCgsLpWF7KpXCarWiUqnw+/0UFxejUqlwOp2y2CbTxcHv9+N0OqUeFcDv9xONRqmoqNgrAVxmkJ9Op1EoFFIjnZ+fTyAQwGAwMDIyIjcTJSUlUi/r8/nQ6/WEw2FSqZRs89nd3Y3RaGRsbAy3200gEJBsOCA7GRUWFkomT2x094UsR2y4RPGhx+OhoKCA0dFRnE4nHo+HRCKBwWDAbDYTiURwu92Mj49L2YVOp2Pp0qUMDQ2RTqfleQkEArS3t6PT6TCZJjvllZSUSLcLUeAp5hv4TK18hQtAPB4nlUpRWFgory+n00lBQQFGo1F+hxj3fyt7mkUWWRy42KXgNBQKSb3gdCZnug5yaGhIsokWiwWv10t7eztGo5GGhgbC4bAMRgsKCmTnqFgshsfjAWDjxo0UFRWxZMkSRkdHZQWs0HP19/dLHZEwQbdarbJyVhhl7+jBO11LuT2IsQopQXFxMX19fUxMTFBVVYXf75fMiEKhkJYzVqtVSh4KCgpkSlNUrQvPxNLSUiwWC8lkEpPJhMVioaSkhLy8PBnchsNhWQ0uqm+31QJ0e6xqpqPC9PkVv9/WORGMp2A2i4uLicfjMsWZn5+PxWKhtrYWh8NBX18f4+PjKJVKdDod6XQalUolU/sGg4H8/HzKysowGAw4nU46OjqwWCwcdNBB8jgEw6VWT166ovvL3vbILCwsxGw2y5asQjs6MTGBw+GQmlSPx4PZbKarqwu3243ZbKaoqIjq6mpGR0dJJpO43W4MBgPhcFgyaiKTIJBpqxYMTloTeb1e6WqxN9Pf4XBYdkNKp9NyHAqFArVaLVnCaDSKSqWS9ktKpZLZs2dLplhUUItrory8HLPZzNDQEE6nE41GQ319vdx0iGspMwuzt2EymaTjR09Pj9x0Cd2m1+uVwfaiRYswmUysWrUKj8fD2NiYtMabMWOG3BwODQ3hcrkIhULSRqqiooL6+vopmxDBzvb19RGJRCgrK0OlUmG326Xrx67MscfjkZtlYe0lCrjUajWjo6MEAgEAKQ/a1zKKPY28vDy5XgBTNu3bek8WWWSx/2OXglOj0fiprj8CQivY09MDTOrJhMZubGyMdevWMTExgVqtlmlCrVaLyWSSbQ+FvVQikcBut5OTk8OiRYs44ogjmDNnDkNDQzLYFYGCSqWaUpFpMk0aZTc1Ncmft4VML9adYRBExxxgimegw+GQaTKhOc3NzZXtRh0OB7FYjOLiYhobG+nr65Np+lQqxfj4OPF4HJvNJhkzmAxMNBoNNpuNwcFBnE4nM2fOxGAwsGHDBgKBAFarlfr6+m3KF7aX9ha/y+xSldnoYGsG9yJtLSryXS4X4+PjxGIxWcGcSCRobGykqqqKqqoqNm7ciM/nY2xsjNzcXAoLC9FqtUQiEdLpNA6Hg3g8jslkoqqqCqfTSUtLC8lkEo/HQ2lpqdQm6nQ6ueiLoE34Se6t7mGZbWJFJbTVamXjxo2MjY0RDAZJp9NEo1GZ2hW6TJPJhFKp5I033pBBQ25uLl6vV7JuwsN2/fr18hopLCyUjSmE369CoZBp5T2djp0+3yLoFpraWCyGWq0mPz9fOk/4/X4sFov08xX3czqdpqCggPz8fKxWKy6XS7KOZrOZzs5O+vv7sVqtKJVKUqkUJpMJq9UqvURhquxgb+iqMxt/iGImr9cr2XrxTBPX9EcffURJSQnpdBq/3y83021tbZJZ7e/vJxAIoFAoKCgooLKykrq6OgoKCmRgWFxcjM1mk61ghT4XJu9Bv9+P3+/f5fGLayaRSEyRkYgMVE5ODjabjbVr12I0GtFqtRx++OEHbHBqMpl46623ZPtSsXnd3nuyyCKLAwO7FJyaTCbZCUZApLp1Oh0ul4t0Os3HH38s359KpWhvb8fpdKJQKPB6vSiVSkwmE4FAgNHRUYqLi2WgVlFRwcjICFqtltHRUUwmk2RURIBjsVg49NBDmTlzJu+//75k0YR2TVgu7chHMFOOsDOG2KLSWCwmIm0rNIgNDQ0olUoKCwvx+/2YzeYpbFh3d7dsNmC32zGbzZIF83g8GI1GqT2zWq0MDg5SUVGBVqvF4XDIdH84HJYBQygUwuVyTekwJeQKO2KExeuimnf6/GYGpD6fT6ZjhW+lz+eTxt9KpRK32y29H5PJJOPj44yPj5OXlycDssLCQmm5JGQafr8flUrF5s2bZRCfTqcZHx+nvLxcsuzCAidzIyHYdGGjs6vBy7YWfVF9P717kc1mIxKJSMZWq9XS19dHSUkJGo2GUCiEXq/H6XTKIGHNmjX4fD5yc3OxWCxyPMIgPR6PMzExId0rRPBtNBql/EPoEx0Oh5zv3UnHTpeyZPqaZt4DKpUKhUKBQqEgnU4zOjoq097ifR6PR863mEO/3y/tslKplLz+lUolJSUl6PV6PB6PDNSqqqoYHR1l4cKFmEwmkskkQ0NDshmF8JLd07pq4bAhnANE8Zbf75csamFhIR6Ph76+PpnJEPIck8kkNbV+v5+Bgcrxj9oAAQAASURBVAGSySSRSASXy0V5eTmBQICCgoIpWtPBwUGpZVWpVLjdbhkYi/tcdJCD/9inbW/8IvNhMBikpAAmn4d9fX3Sys/r9dLc3Ex5eTnj4+PU1tbS3t5+wLKnGzduZGho6PM+jCyyyGIv4DMXRInFLRqNotfr6evrQ6lUMjw8LP05nU4nWq0WQC7sIlhJp9OSrRAsik6nk56QkUhELhTxeJx4PE4ymeS9995jYGBAVk2LdLdgaTJbQW4tAMm0K9rZ9JmwwhEFX1qtVrYmFUHG6OgoPT09JBIJWlpaZHeo/Px8KXMQPpjpdFra0CQSCemHqFQqpRZvbGxsCvuRl5dHMpmUkgARAHV3d0sGaFcZ4a3BZPqP96WwSxJMpsvlkrpLs9mMTqejv78fhUKB3W7H5/OhVCrlfFdUVKBSqUin0zidTpk2FbIAg8FAMpmUUoXKykrKysrIycmRi7Tb7ZYds8TiazKZZFHcdCeBnR3v1hZ98XnBYJD+/n6ZAp03bx4AmzZtwuFwoFAocLlcuN1ukskk8Xgct9uNUqmU13NPTw8ej4d0Oi1ZYkCOTaPRMD4+LjdhsVhMWgBZLBZqampkMFRXV4fJZJLs5WcNKDJ9TYXdkSjQEdKRWCxGIpGgtbVVdosqKCjA4XDI9wYCATkuUUwkJAhCIys0x42NjfL+zM/Pl76o4ry73W6Zhs3Ly5vCEAs5zZ4KUMX9DMjsRCKRYHR0lO7ubgoLC1EoFLK4SPjTiuecQqEglUoRi8XkeUwkErIwsqSkhI6ODrnpEgHrIYccQm5uLpFIBJi02hL3u9BbZzL14pmzrQ20CE4dDoc03/f7/YyNjUkHAeGKoVAoWL16NQ0NDbhcLsmm7q2sw97EscceCyDlXFlkkcV/Dz5TcCoWb8GaDg8PEwqFJAPh9/tlyk+k7jJTZxMTEyQSCXJzc2UXKcG6pVIpWSiQk5MjGRjhG1lVVSUX/IKCAmbNmiX/3u/3T9GsZbYkFA9hEbjtSgvB4uLiKQtpSUmJTCv39PQQDocZGhpiYGAAm83G6OgowWBQBpHhcJhoNCptdwYGBtBoNKTT6SntEjUajdS6iTS/VqslFArR1dUlq/eLiook86NUKmlvb5fpbsEI747hdmbgPjAwQFdXl/SjFYytqNAWgbpKpSIQCJBOp4nH49KYHyAQCMiAtbCwUGr5RAGNMOcPBAKyUKqkpAStVotSqaSgoEAGPC6Xi/nz58tjFUG0yWSS1+KujHVrsofM8xeLxXC73UxMTKDRaFCr1QwPD0v2WBSRieBUtGwV17/ICgi7NNFesqysTOoArVarDB4ESylS+1uTaYj/f9YATdwXoVCIsrIyWltbWbt27RRfzJGREVQqlWxBHAgEyMnJQafTEY/Hcblc0vgdJjcRIv2v0WiIRCJUV1fjcrmkK0FDQwN+v59AIEAikSAvLw+lUkldXR1WqxWTyUROTg5DQ0NotVpaW1tl8LEnC3jEZs5ms2Gz2cjLy6O5uVlmQpLJJOFwWDYHCQYn7exElX4ymUShUBCLxdBoNOh0OrRaLSqVSrbwVSqV+Hw+/H4/GzZsoLy8HL/fzxFHHCHPXygUYs6cOXLs0+9f4VaxrUyQeNYNDQ3J7mypVErefzDpgZpMJmULVSEv0ev1OByOva7Z3hv485//zN///nc0Gg1FRUWEw2FOOeUUAFasWCF13ZdeeimA3AxkkUUW+z8+U3Da2trKunXrUKlUFBQUyKIXwZbE43FpGeT1eqUvoGDhBIRgv6CgALVaLSv6hb+nSKk5HA7pQejz+VCpVFRVVcluNOXl5ZINEA9cwf6JNJqoRN6eFnNbmO6JaDKZWLBgAS6Xi7GxMTZt2iR1lxMTE1M8OSsrKyXLGYvFZN91AZfLhcVikSyWMDfPycmRBWVms1kGS/n5+Xg8HvLz8yksLJTsSaZxu8VikQEIsFWj7+nIdCQQqUjhwykW4Pz8fMlaRyIR6aRQVFREIBCQgb/Q7AWDQamFE/IFi8VCaWkpDocDs9ksmWfh/yiCPFHZPDY2xujoKHV1dXJDMX0+YDI4mi492drcCWQGeSLlnXn+RPAyPDxMV1eXlKMIx4RwOIzX6yUvLw+XyyVT4R6Ph2g0ysTEBBaLRQY0Xq8XQFa919XVEY/H0Wq1MoCNRqMMDg7Krmo1NTVTWN09mYYVOmOhgRwdHWV8fFzqW9VqNYODg7IKPdNhQjCZ4toQQXk8HketVlNeXk4qlZLtasfGxuS1ImQTyWSSrq4ujEYj4+Pj5OTkAMiGBh6PR5ref9Z+9JmShnQ6TTgcpr+/X3oNCzZa+AsDMshTq9Vyw2UwGEin06jVamKxGD6fD5/PR3l5uQysi4qKpHxBq9WiVqvJyckhlUrJzmhjY2OyycGMGTNwOBwMDQ1Jd4rMjfD2xpy5ETcajaRSKaqqqrjyyivZsmULjzzyiPSgVqvVUpqSSqUoLS2V2Yd95ZCwp9DQ0MC6deuA/7Qmfffdd6f8nEql5HumS1qyyCKL/RefKTgdHR1laGgIlUqFUqnE6/UyOjpKe3u77PgjzNaHhoZoaWmRjEB5eTmnn346M2fO5M4772R4eFgyGAqFQqat0+m01GyJzjWJRIKSkhKKi4vp7e2ltLSUwsJC6e/n9XplYRIwxU6ltbV1SuvEXYFIHbe3t08Jmj0ej9RbCkua3t5e6e2YSqXYvHnzpz7vggsuQK1Ws3r1alpbW2XgIlhXYV4upArCZkswmFqtlqKiIpRKJUajEYPBIIu2RFCzq7Db7VOsfsS5jEaj2O12XC4XOp1OHqNIDXd2dk7pXZ3Z2Wn65wOyoYBOp5P61UgkInW1Go1GngPhICCq2zPnI7OIJ5lMSpZJ/D5TN5v53q1hax2wTKbJam6n08nIyAhjY2N0dXXh8/kYHx9ny5Yt23RKUKvVhMNhWbAlxj1v3jyam5tlVXd9fT16vV5qGgVrnpuby8jICOXl5TQ2NlJTU7Nb0oVMiOBDWIiJKmbRh12023S5XDidTlnoJdLaQmYi2NWqqir+8Y9/sHTpUkKhED//+c/5+9//jt/vZ+3atVJfXlBQIH1RlUqlLOwSVf+9vb3k5ubKNLa458XG67P2o8+8FsR91d7ejlarxWazYTQa6e/vx263MzQ0hM1mw+v14vf7P9VaNZORFPcsTGrKdTodZWVlANJuSjyrwuGwzAYItn9kZIRQKCTPq2CxBYsK/9ks7UhTLTT8ANdccw3/7//9P3Q6HTNmzOCXv/wlgUBAPiMTiQQ9PT2sWbOGvLw8eX+LZ+O2zt2Bxq5mkUUWByY+U3BaXl5OSUkJgUAAs9mM2WyWjJvofjM8PExvb698wDc0NHDDDTdw1llnodFoALjooot4+OGHee2119i8ebM08BZ/I6QACoUCs9lMfn4+w8PDUq8mWkoajUZKS0ulB6N4qGu1Wpni8Xg8OJ3O3XrQmkyTnaDa2tqk4bgwoBddnbxeL52dnVMWrelYunQp1157LV/5yldIJpOyIv+NN95g+fLlvPjii3KhEjo2m81GYWEhBQUFVFVVydaSExMT1NXVyTSokBII9tBqtZKTk7NTi7oIPkWKXlSmZwaQbrebnp4ehoaGCIVCsq84TGpw582bx9y5c2W6M5FIUF9fzxe/+EXKysr4y1/+wvPPP8/4+DiBQEAu9BMTE2i1WpxOJ7W1tSSTSWkIH41G0Wq1WCwWIpGIZNhMJtOUtpeiWEzIGGBqGlj8XwRW0xEMBhkfH5fNE8Si3d/fLzs8eTweWfTj8/mkef6sWbNktqCiooIjjjiC008/ndtvv522tjbGx8c5+OCDueqqq1i8eDH33nsvP/rRj2Txjfg+UfWtUqlIJBIMDw8Ti8VkNXtPT4/MOghHil1Bpp5WVIobDAa6u7tlsaFg50WQODExIVPwAlqtlpNOOombb76ZWbNmoVKp0Ov13HPPPVx88cXceeedvPHGG/J8CXZSFH6lUinZBU2wpEK7LuyzwuGwfG3GjBm7PNbp4xb3USgUkoGz0L6vXLmSaDRKT08PGzdunLLhUCgUNDY2snjxYr70pS+Rm5tLV1eX9LOtrKyku7ub3/3ud0SjUYaHh1EoFASDQamhF4V70WiU0dFRBgcH5XWdTCax2WzSI1cEt9OPf0cbE3HPAZxxxhkMDw9z7bXX8tprr3HooYfS3NwsO9D19/fj9XrRaDSEw2EKCgqorq6murpaSoO2trE7kINT0ZzFarVSXV39eR9OFllksR3sVnAq0iNWq5WysjKGh4cl8wXIKtfu7m4ZJNTV1XHNNdewbNkymSLzer10dHRI38SvfvWr/OlPf0Kj0dDR0SEZwM7OTh5//HHpK9je3k5NTQ0qlUoGCKKdomCGRDpfsDwixS90bDtrxTM9FSTscDQaDTk5OYyPj9PW1kYgEGBiYoKOjg6i0ShLlizhkksuoampibq6OgKBAJFIhI6ODiorK5k5cybBYFCmDIVe6mtf+xoDAwPcdNNNPPnkk5LRUKlUrF27lmg0yieffCL1bTk5OfL8CfuawsJCNBqNlEYI6x/YfvGX0MIaDAaqqqrYsmULXq+XoaEhTCaTDIaNRiNKpVIGmAA33HADF198sUzLivQnQFtbG+3t7XzyySecf/753HLLLbS0tNDS0kJvby8fffQR77333pRq/HQ6LR0L0uk0NTU1Mnjr6emRLKNWq5UslCgWCwQCsme5SANnbkbExmI6BAsuitWi0ShjY2Ns3LgRu91OIBCgtLQUj8fD+Pg4AMcddxwPPvgger1eVpW/9NJLvPnmm/ztb39j7ty5nHPOOSxatIiGhgby8vKIRCJccMEFVFRUcP755xMKhUilUtKkPRAISL22YMorKiokKyz0x9sr9Nva5itTsgGTRVnRaJRwOCxlIT6fj/r6ejo7O3E4HPLYxPd+8Ytf5Mwzz+TYY4+VOtDBwUHGxsZkYc2sWbO49957CYfDtLa28tJLL/HMM88wNjaGx+ORtmHJZBKz2Sxt1ET3KXHfBwIBPB4PZWVluN1uVCrVbgdIIsgS94I4DxaLhYGBAYLBIB0dHTJoLCws5Nxzz+X444/niCOOQK1WywYYer2ek08+GZiqCZ01axaXXXYZsVhM2suJdH46nZbymGQyyYYNG9BoNBgMBoqLi6Vvc2FhoSyI6uvrA/5zz9rtdpLJ5DafXcJZoby8nJdffpnrr79ePn9XrVpFRUUFJSUl9PX1YTAYZAHf+vXrsVgscmMgrqPpzUYO5MAU4OKLLwYm14a2trZsgJpFFvsxdis4FQuj8O/0+/0MDw8zMjLC+Pg4fr+f3t5eUqkUBoOBa6+9lquuuopAIMCLL77Ic889x7p167aa+i0vL+eOO+7gjDPOIB6Pywf/z3/+c9566y2uvPJKRkZG6OjooLi4WHpHlpSUyAVMtIjMzc0lJyeHmpoaCgsLZUHTrhTOiDS5eGgLu6Da2lry8/Pp7e3F5/PJCu5UKsXpp5/OP/7xD6mv+/jjj3n44Yd55ZVXpug3y8vL8Xg8kmVdvHgxl1xyCeeddx4PPPAA1113HXfffTfPPvusZHw/+eQTTKbJrkxlZWVoNJopgboIakQ3GFGEIgpfxLFnajZFYCIYaMEalZWVSR/TgYEB2QghGAyyZcsWaaXzwAMPcPbZZxMMBlEoFGzevJm3336b9evXs3r1asbGxuT5vPnmm7nwwgu59tprOeecc2QA29nZySOPPMKdd97J6OgoHo+H6upq8vLyJFtbXFzMzJkzZeOBdDpNaWmpDIhVKtWnGOJdYciNRiNVVVXyOtLr9fT390v5SiQSobe3V9pIfe973+OYY47hq1/9Kg6HQ9oIZTKzvb29vPLKK8Ck1c/BBx/M0UcfzcUXX8zpp5/OCy+8wDnnnCM3YWKjEo1GpSa1qKiI0dFR0uk0lZWVOJ1OrFbrFDYwU4qwLZbL4XDIgF1oYEWRk0KhwOl0Eo1GaWtrY8uWLTKwOfLII7nooos46aSTKCwsJJFI8MYbb7BixQrefvttBgYGpmziampqOPTQQzn99NP5yle+wpFHHslNN93EmjVruPnmm3n//fel/la0N43FYqxbt04y7cKxwGw2U1VVhcPhoL+/n1AoRFNT0y6nmkU6X1wnQubjdrvp7u5m5cqV8nl08MEH88QTT6DX63nooYe49dZbGR4elo4ES5Ys4Rvf+Abnnnuu1IcDLFu2jMrKSpYtWyZZfJPJJIs5BQMeiUSIRCKUlJSgUqnIzc2VbUdFMwpAsswOh4O5c+dSW1tLf3//NgNUcd3NnDmTq666imQyyaJFizjvvPO45557ZFtcYbknUvwiQE4kElitViorK6UzhviOAz0wBfjggw/o7+/nggsuwOFwZIPTLLLYj7FbwalYAI1GIzk5OeTl5UmtotCOplIp5s2bx1NPPUVubi433XQTDz/8sGzTB5Mp8YaGBhlsiUDga1/7Gl/60pc499xzOfjgg2lsbEStVnP66afT1NTEiSeeyMjICHa7XTITTqeTvLw8AoEA4+PjJBIJhoaGpIWTKNIqLi6WHWGmF9Vs7QE8MDAgvSfnzp0rCz46OzvRaDQ4HA6pF0ulUlx44YXcddddPPfcc7z66qu8++67U7xOhVl+LBabou0E2LBhA5dffjk//vGPuf7667nmmmu49957ueOOO3jzzTe59NJLpSG4CGJE5a2wofF4PNhsNumGMHv2bAoLCyVLNj3Nndkhqrq6muHhYbq7u2lubqayspL6+nrZclIc8+joKIlEgvLych5//HEOPfRQEokEzz//PPfccw/Nzc1TxqVSqZg9ezZGo5E1a9bw97//nccee4zLLruMn/3sZ+Tm5lJfX8+tt97K3Llzueyyy6SWtb6+nkgkIl0a1Gq1LLzx+XzE43GsViv5+fmYTCa5ibDb7buVijSZTJSWlkptqUgDCwcCEcDcdNNNLFy4kPPOO+9TmtOqqiq+/OUvc8QRR7BmzRpaWlpYv349Ho+H9957j/fee49nnnmG5cuXc8IJJ7B8+XL+7//+j6GhIdra2qSFUUFBAX6/n56eHqLRKOPj48yePVvasmWygZl62kypQyYmJiZkVbwolBsZGZGWbh6Ph66uLtLptJQOvPLKK5x00knAZOeoRx55hF/84hcyUBPIzc3FarXS19fHwMAAAwMDPPXUUxQXF3P++edz5ZVXctxxx3HMMcfwl7/8hWuvvZZoNEpLS4scp8fjIRKJ4HA4qKiokKyp8PIVG09gq4H5zmw0P/74Y6nfFJraDz/8UM7rFVdcwQ033MA//vEPfv/733/K1B1g3bp1rFu3juuuu45vfvOb/OIXv5Dn+/jjj+fNN9/k8MMPJ5FISAcG4egg2M3i4mLcbjexWIze3l7ZpMBkMjF79mwpPxHdxDKfXdvaXIsiLmFHVlNTw6OPPopKpeKYY47hn//8J/feey+jo6MyQBVFW4lEAp/PJwsyKyoqaG1tZXh4mMrKyj3a8OHzwqJFi7JG/FlkcYBgt4JTsSjW1tYCMDw8LCtwRbefhoYGnn/+eT788EOuvfZa+fAvKSnhzDPPpKGhgfnz50s7ntLSUiKRCPfffz9PPvkkr7/+Oq+//jow2crxkksu4ac//SmlpaU8/fTTnHHGGdIUXKvV4vV65eJbXV0tO1NpNBrJsMJ/TK2F0XtmECOYlUwWKLMoyWazoVKpUKvVNDc3097ejt/vl2n9o48+WmpJP/jgA/kZOTk5HH300Zx11lksWbJEfqcI4mfMmCGDuxUrVtDb28vPf/5zXnnlFf76179SVlbGySefzL/+9S9OO+00mf4uLS0lFothMBgwGo3yHIsq682bNxOPx8nJyeGcc86RWrLphRaZ3WmsViutra2Mjo6i1+tla82CggJaWlqw2WzEYjHmzp3Lv/71LzQaDf/85z+5+eabZbCt1+uZO3cuCxcupKmpidLSUlmctWrVKv71r3+xZs0a7rnnHp588kl+8pOf8JWvfIWioiKWLVtGbm4u3/rWtwgEAgwODlJYWAhMbmYy+76Pjo5KWyYBsYjvTCoyGAzK4D7z2hY6YmHzU1JSgs1mk1ZXF154IUcddRSnn3460WiU4447jjPPPJOioiL5eXl5efLaPeecc0ilUnR2djIyMsJ9991HR0cHRx99NM8++yxNTU18+OGHnHTSSbJhRXFxsRyXy+WSKdhoNEpubq6Uqgg7pMwNh2DednQPC11rOp1mYGAAp9NJKpWS3bsqKio45JBDCIVCpNNpfvKTn/C3v/0NmAxGjznmGI4++miKi4upq6uT82K32/noo4945ZVXsNls3HnnnTz22GPccccdnHrqqVxwwQXMmzeP888/Xxrhw2QBWV9fn2xGYTQaqaio4MgjjyQSiVBeXj6l/WdmYJ4ZpIoAdjrERrK7u1s2kFi1apXUTN9+++3MmjWLE044gS1btgCTLhvf+ta3qKqqorGxkUQiwcsvv8xbb73Fli1beOCBB3jyySf54Q9/yMUXXyxt7X70ox9x2223EY1GpfRJr9djtVplgwOtVittyoLBII2NjcyePRur1YpCoaCqqgqtVkssFpuyic7cXGdCZBCEbKW0tFRWqkciEY4++mg8Hg/PPfectDATm12fz4fVamX16tUYDAYWLFiA3+9n8+bN1NbWcsIJJ+yWxnlvY3qb2621vd1XrXCzyCKLPYfPbMLf09OD1+uVqfZoNEp1dTWvvPIKr776Kj/60Y9IJpM0NDTwzW9+k2OOOYbm5mZWrFjBvffeS09Pj0y9i3TgSy+9xL/+9S82btxIR0cHgUCAO++8k7fffpt7772XuXPnsnz5ck4++WTcbjfxeFwygCqVisbGRhm02Gw23G63XICSyf/P3nmHt11db/yjbUmWZMt729nOIiEhEEjY0KSMMFo2BQopG8r4AaHsDaWUsMomrLIpMxRIGAlZZDjLe1vesixZ1rLm7w8/9yI7DiQhgKF+n4cnWJbku773nnvOe94TkVIv8F3WsuAtGgyGAdnVwgDv7Ozk22+/paamRla9qq2tlVzE7OxsXn75Za6++mq++eYbjEYjp59+OkcddRSFhYVs3boVn8/HW2+9RUNDgyxvKbwoSUlJzJgxgyuvvJK3336bu+++m7Vr1zJnzhxuuukmLr/8cg466CCee+45zj77bFwuFxs3bsRisRCNRolEIrjdbqmSoNfrycnJIRQKEY1GpVZkPOdUHOYiHCgktoQiQnNzM42NjXR0dNDZ2SlLih566KG8/PLLJCQkcO211/Liiy8CYLFYOP3008nLy2PTpk3U1dXx7bffkpqaygEHHEBxcTHFxcUsWbKEr7/+mvvuu4+mpiauueYann/+eR5++GFmz57N0UcfzfLlyzn++ONlIYacnBwZjq6pqZG6o6K6khAaFzJPwmj7PuNU0BziD3qRuZ2UlERdXR3t7e1otVq2bNlCIBDgkEMO4bDDDmP+/Pn09fUxa9YsHnnkETo6OkhJSaG0tJRt27ZJ2SzBUzWbzRQXF3PKKadw8MEHc+GFF1JXV8cf/vAHPvroIyZOnMgHH3zAoYceKhUvhDyTSqWSYurr168nHA5TXFxMSkoKEydOlBWF4qs9iTrv8UhMTJS0B+Fl3meffVixYoX0tHu9XpnBf9RRR8mkxauvvpoXXngBgEsuuYTDDz+crq4uPvroIxnBgH4qyNy5czn//PM5//zzqa6u5oEHHqCmpoazzz6bffbZh7vvvpujjjqKtWvXcuqpp7Jhwwb5bGZnZ7Nt2zaUSiU6nY6xY8dKYz1eXimexjDYe7wz4zQ1NZVoNCr3q5KSEskdvuaaa/jPf/4jL5VWq1Umba5YsYLU1FRcLheTJ0/mtNNO45prrmH16tXceeedlJaWctdddxEKhbj55psB+L//+z8++ugjysrKKC0tBb6T21IqlSiVSqmfKpL/hGKCoAMMLpsbH8ofal2LsRHGaVJSkpy/6upq7rvvPhoaGrBarRiNRun9FtSf6upqSctRq9UyAiWKZ8DwytwXEoLxGPyzGL8RjGAEvy7skXHa2dlJQ0MD1dXVfPXVV7Lqj6iK89prr/Hmm29yyy23AHDkkUeSnJzMs88+y0033bTD9/X29rJ9+3a2b9/Oxx9/THJyMgsWLODuu+9m6tSpvPvuu9xyyy1s2bKFuXPn8tBDD3HRRRfxzjvvcMQRR8i/nZCQIA21goICEhISCIVCuFwuaVjZbDbJFxV6oSIrXnhf4kOWgnfl8/koLS3F6/XS2tpKT0+P1PQEeOCBB3jjjTd49913UalUPP/88+Tk5PDII4/w1ltv7bLG3vTp0zn11FN56623uOeee/jmm29YtGgR77//Pk888QQnnHACjz32GJdeeilOpxO/3y91YYWRkJ2dTVpampQoCgQC1NTUUFtbS3JyMtOnTx+goSj66/V6qampoaamhrq6OtLS0ujo6KCxsZGWlhYikQhjx47ltddew+VyccIJJ0jPzF/+8hcmTZrEP//5T8nJjIfQH8zMzOSUU07hxBNP5PXXX+ezzz7j4YcfprS0lKOOOorTTz+de++9l2nTprFs2TIOOugg3G43PT09hEIhtmzZIkW3ASk/1NjYKHmizc3NA8KeOztIhbdZaGgCsu1NTU3S6CkpKSEQCJCXl8fZZ5/NX/7yF8LhMHPnzuXhhx9Gp9NRUlLCM888Q3V19U7nNjExkfPPP5/TTz+d559/ngsvvJDKykoOO+wwnnnmGY4//njefvtt5s6dK6uNpaamotPp5H92u51oNIpKpcLhcMgSq8LQTk5OltWeBiOedyl+FjxdwT2Ml5c655xziMViXHXVVZJDffvtt/OHP/yBO+64gzfffHOAXrHA22+/DUBxcTHHHXccjz76KO+99x6vvPIKW7Zs4dhjj+WCCy7gnnvuYdmyZfztb3/j8ccfl4l/iYmJJCUlkZaWRn5+vizWMFQS2GAjdXAfBcRzKjSQt27dKvVcr7jiCp566ik8Hg8qlYrp06fT3d3Nc889N2S/DjzwQO644w4OPPBA3n77bd555x1uuukm/vnPf3LKKacwfvx4dDodV199NRdccAG9vb0yAgFImkpCQgK5ublEo1GpWexwOHYwQr1e705D+fE48sgj+fLLL6XBXV5eDvTvr88//7zkfguakdgDRYKc+Hf79u2ydLJaraa8vJzs7Gzy8vIwGo20t7fjcrmYMGHCHkt7jWAEIxjB92G3jFOv1yurxbS3t8vSjJFIhMrKSgD+9Kc/8e2330rD9NBDD+Xbb7+ViUDQ78HIzs4mJyeHrKwsGVZqa2uTMk1Llizh5ZdfZv78+Vx88cU8+eSTPPfccyxbtowrrriCUCjE+eefz8KFC3n66afxer2kpqbKTF9RZUpkBet0OlnvvKmpiezsbClTJAwd4X2J76/ggYmqTGq1mp6eHnJzc2lra5NJKmq1mkWLFgH9vDWv18sJJ5wgDT8hgWWxWGRmt5CJikQiUnexpKSEkpIS8vPzue+++5g9ezaPP/44a9eu5YADDuD+++9n4cKFOBwObrnlFgKBgORE6vV6otGorHPv9XolZ01IcalUKioqKgb0URgY4vC32WwySUtIeokD7c4776S1tZWzzjqL8vJyjEYjl112GRUVFTz99NNAf+JPQUGBFLH3er2yBGp7ezuPPPIIjz32GPvvvz9//etfefLJJ1myZAmffvopr732Gps3b+aTTz4hLy+PRx55hHPPPReXyzXAA+J2uzGZTHL8XC4XqamptLS0SG3UlJQUaXgKD7joJ/Qbp/G6qc3NzZSWlmK32yWnURSWUCqVXHnllVx66aWEw2Hmz5/PiSeeyDfffMOLL77Ipk2bgP6MdlFPXayXcDgs680vXryY5557jssvv5zHH3+cq6++mq1bt3LKKadwwQUXcMstt3DjjTdy55130tnZKaVvhCC/yWSS3rX29nYaGhqkt1isZ6PRKOkPg/sLSAF8kRAlKjoJbdFAIMCMGTOYMWMGt912mzRML7nkElJSUjjhhBPk8z5+/HgyMzNlKU+hZGC32ykvL6e8vJwHHniAWbNm8dxzz/Haa6/x/vvv8+yzz/Lhhx/y2GOPcd999xGJRHjyySdxOp14PB6Sk5Nxu91s27ZN0n8cDocUrU9MTGTixInAd5q6Q3nJxKVQzGc0GsVms8nkoPPOO48nnniCUCjElClT8Hg80pMrKEJ5eXkEAgHsdjtNTU2sXr2ao48+mqOOOoqFCxfyhz/8gc8//5yvv/6aSy65hA8++ACA+fPnS4qI2WwmKSlJGoQGg0GWJHW5XIwZMwaTyYRKpZLSUvHG985C+fEYP348hYWF1NXVAf0XrFWrVvHuu+/S3t6ORqNh8uTJaLVaNm3ahNPpRKFQMGrUKGpra+UaERcUn89Hbm4uSUlJbN68Gb1ez/777y/LSdvt9gGUkl/akzqCEYzgt4PdMk5F2Fyn00mvYWJiogz9Go1Gbr75ZmbPng30G6qrV6/G7XaTm5vL5MmTmTlzJomJiSxbtoyUlBSCwSA2m43Ro0djtVopLi4mOTmZzz77jNLSUj766COSk5M588wzefLJJ7n33nt57rnnuP766znxxBO57bbb+Pzzz6mvr5d17EXIPBgMSk+STqcjJSUFt9tNYWGhFJP3+Xw7eGDiJXJSUlKkwWa1WqmoqKC9vZ1IJCI9ERdeeCHPP/880WiUQw45hDPPPJNzzz2Xrq4ukpKSOPXUU3E6ndJAKisrY9SoUUB/IQNxyAp5rQ8//JCmpiauvfZa3nrrLQ488EAef/xxli1bxqJFizjjjDO49tprcblcPPTQQ7jdbmw2GwkJCcRiMak5W11dTSgUoqenh+zsbCZNmiQ9jWvWrJH6qILz19TUhNFoJCsri2AwKMuF1tfX09fXx8EHH8yxxx7L7bffLhN3brjhBkpLS3n//feB/tCiSGLKy8sD+sOWRxxxBOFwmOrqatra2qitrWXNmjUEAgHuu+8+LrnkEv7yl7/wl7/8hfLycp555hkWLVrEiSeeSElJCYsXL6a0tFR6alJTUyUfVKfTEY1GaWlpGVCswGAwyOpYg9cx9NMY4mkOIqGvsrJSerFsNhsAixYt4qWXXpLjcOutt7JmzRqeeOIJGQ4dPXo0EydOpLy8nLS0NFlFaNq0aZJaIXi7zzzzDGeffTavvvoqf//733nppZd49tlnKSgo4KabbqKtrY1nn31WeoSFEH1RUZHUQd26dSv5+fl0dHSQkJAgOajCQBP9Ev2NNx6EbmZ3d7eU9PJ6vTJ8e+mllxIKhXjssccAuOGGGygqKuLBBx+ksrISjUbDEUccwYQJE1i3bh3Jycl0dXWRmZnJMcccg9frlQoWZWVlfPvttzz66KM8+OCDHHroofzzn/+kqamJG264gWOOOYa///3vNDc389FHH1FeXo7X65Vi/cXFxYRCIcrKyqQHOSkpSRpwYo6F5zg+rB/vbfX7/dTX17NlyxYATjnlFF5++WVCoRDTpk2juroar7e/3PCRRx5Jb28vOTk5QH+lodNOO43Ozk4++OADysvL+fTTTxk3bhyzZs3i3nvv5aCDDmLdunW0tbUxatQo/H6/rBwmFBFycnJkCV/B8c3JySE7O5t58+YNKDgiLo27mvCVlJTElClTpHEK/WU8hSc1FApRUlJCcXExJ554It9++y0NDQ3U1tZK+oRIPFMqlTJZq7W1lZKSEqqrq+XFoLOzU8q4DU6y/LmM1WAwyDHHHAPAO++8A8DJJ58sfxZRo/jXBIRXeUTzdAQjGJ5Q7s6bvV4vFRUVVFRUyDKUwlsJ/ZIzIqNZr9dz1VVXyTDpBRdcQGFhIYmJid/fIKWSKVOmcM0113DaaacB8NlnnxGNRlEoFCxatIgJEyZIORuj0ciNN94I9IerREWm1tZWyTdVq9Vyo8/NzcVisWC1WoH+w8vtdtPQ0LCD18Vut0v+ligtKQ6Zrq4uyZE777zzpJdq5syZUosV4OKLL5aGqEA4HGb79u18+umnAygEInlm8eLFWK1WWltbeeWVV8jIyOCVV14hPz+fQCAgE8Vuv/12pk+fPkC7Efor01RWVmKz2aS2ZE1NDeXl5VitVind09bWJqVtoP/wAjjppJM44IADSE9PlxnUAKeeeirhcJiXXnoJgOuvv55wOMy///1vAHJychg3bpzkH8aPqzDUxo4dy/XXX8/111+PWq2mpKSEzz//HOjPpr3kkksA2Lx5sxyXBx54gDFjxtDX14fD4SAYDOJyuaSXTmiEZmdnk5ubK+cK+MGqQsKDJQTTRYlNwWMW1bjOOussWeXr5ptvRqPRsHz5cqqrq0lISGD//fdn+vTpUle2srKSdevWYbPZiMViKJVKxo4dy2233SaT7NasWYNGo+Hqq6/mnnvuAeCxxx4jGAzyyCOPYDQaiUajOBwOqTkqRO3b2tro6OigqakJs9lMVlaWjAAMXs/CYyo82A0NDTQ2NtLW1oZWq8XtdkudYBE52HfffamuriYYDGKxWPjTn/7E8uXLqaysRK/XM3/+fIqLi4F+z+Xnn3/Ot99+y7p166RxMnHiRK666ioWLVqESqVi1apVrFixgunTp/Pee+8B/Zx1Id0lKD+hUEgWPOjq6qKqqoqamhpZ3jY3N1cmyRmN/eVCTSYTBoNhBw1b4TG12Wxs2LCBhx9+mHA4zKGHHsqnn35KKBRixowZVFRU4PV6yc3N5bjjjqO+vp7Ozk75TAikp6dz2mmnySIXwngVmf9Go1G+dvXVV9Pd3Y1arZZJUOJSICT40tPTGTNmjJRvEtz45ORk6QXfVRgMBpmYJ1BfXy+5rfHQ6/UccsghHHvssRgMBnnBmjNnDpFIhFAoJPe0QCCA2+1mw4YNPP7446xatYqKigo++eQTXnvtNd555x2ZDBkvZfZTIxqNsnTpUpYuXSoLSsT/DOzwWmpqKgaDgbPOOosZM2ZQXFxMU1PTT97WEYxgBLuH3fKc+v1+gsGg5Lx1dnZKbU2ACRMm8N///heAKVOmsGrVKqmFKbLdBZ/N5XLJ8JrH45GcxAMOOECG4jIyMkhISKCjo4Ovv/5aGrb7778/FRUVfPDBBxxzzDGcdtppPPDAA5Lvp1arUSqVUlDdYrFIkWulUimTj8QGKkS4m5ubGT169ADPi0jC6OrqkrXe4zOCzz77bLxer5QoaW5u5r///S+xWAyTySTDZQ6Hg+bmZsrKyuQYAFRWVtLc3My4ceNIT0+npKQEgCOOOIK33nqL5557Tno8jz76aJ599lneeecdjjvuOEKhENdffz2nnXYabrcbrVYrvYZdXV1YLBbGjRuHx+ORVZPq6uqYPHmyPEwaGxsHbOQ6nQ6FQkFycrKkH4gDety4cbz66qt0dHSQnJzMmDFjuPjiiwkGgxQUFKDRaGhqaqK3t3cHI2HDhg1YLBYOPvhg1qxZI+dx1apVPP7441itVqxWq/Rax1ezUavVnHzyydx///2ynK0o9NDb24vH42GfffaRHD7h4ff5fBQVFQG7Vlc7OTmZjIwMYrEYsVhMUhmmTp0qE2VycnLo7u6mpKSEpUuXAjBr1iy8Xi9bt26lvr5+gM5pQ0MDr776KhMmTCA7O5utW7cyatQotm/fzpIlS8jNzSUYDDJ//nweeeQR2tvbef755zn//POZMWMGK1asQKVSYbVa6e7uloUHNBqNzPhWqVQDBOoHS4YJ49Tn86HX67HZbPJZEmvDYrGQmJgox8lsNrN+/XqgXxdz5cqV0vM0Z84cfD4fq1evprKycsB67unp4ZFHHmHOnDlkZWWxdu1aoF87dO3atdx///3cf//97L///jLEvX79eubOnUtOTo6UWsrJyaGjo4NIJMLGjRsZP348xcXFMvogqsMJTjggK1rF6ycbjUZZXveJJ56gp6eHzMxMqqur6enpYfz48ZSWlhIIBBgzZgwA7777rvx8VVUVKSkpzJgxQyY2eTweKbuUlpaGy+XilVdeAfqTyNRqNevWrePVV18FYPTo0fT09GCxWKSR6vP5ZAlii8WCUqlk7dq1FBYW4vf7SU1N3W35Jr1ez8yZMznrrLN45JFH5HMdT12B/udcXJ5HjRrF0UcfzZdffonD4WD16tXMnz+f9vZ2uReJRMRAIEBVVZWkCEyZMkXKxLW3t8vyw7W1tcRiMcaMGTOgD8MhmSo/P5/y8nK6urooLy8f0TwdwQiGKXbLOE1NTZVZtA6HQ4qUCy/Vtm3bpHHR2NgoeXh9fX2sXr2aWCzG6NGjMZvNbN++nWg0ukMpSRF2CoVCrFq1Sm6wQrN01apV0pDdsmWLTDQYN24c1dXVMqlBeL08Hg9JSUnSw9jV1UU4HMZqtVJQUCC9Zh6PB61WS19fnzzMRSk/4SGuq6uT9cA7OjoApIi22NyWLl3KrFmzgP7NuLS0lKlTp1JbWys9foNRU1NDXl6e1O3cvn0769atA/pDV6WlpUyZMkV6ob/99lsZ/ovnjzocDlkOMRAIkJCQQEZGBtnZ2djtdpKSksjKypKHuShRKvQRc3JyaGxsZMuWLTLMKzzR0C8o/+WXXwLIEo5iHLRaLXa7Hb1ev9MKTD09PWzevJl9992XlpYWampqgP5LT1VVFePGjeOtt94CoKWlRdYh/+qrr/jiiy+A/oM2HA5LbU7h3WloaGDs2LEDdE7jPUjxclHxnkRxcAtKRXJyMvX19bLiEfRnPwvtVqfTSUlJCfX19XJcXC4XTU1NQ65n8ZmOjg5CoRA+n096y7du3UpKSgo9PT1UVFTIghNLly6VYWbRzq6uLmlwJiQkMGrUKJksJSgM4vM74ycaDAZUKhV5eXkyCTASiaDVagmFQgN44Q8//LD0OHd0dPDZZ5/JS0p9fT29vb1SG3UoNDU1oVQq0ev1tLa2yktad3c3NTU1NDQ0yHUiEti+/vprlEql5FzGVzZTKpXsv//+koYgeMaD1SfipdHE/H744YeUlpZKY+vUU09l8eLFaLVaxo0bJ+lA++67L2+++eYOfREXy/T0dEpLS6UHfebMmTQ1NfH888+zatUqoF+I//333+eKK64A+jP0A4EAZrMZg8Egje+ioiKSkpKYPHkyVquV9vZ2SQHIz8+XVdl2x4hLS0tDpVJx2GGH8Z///GfIxEToz9wX/FeRvZ+UlERPTw/RaFQqqSQlJXHLLbdQVVVFJBLhnnvu4fnnn5fP7bZt22S1v9zcXDo7O8nIyJAlmcVcxJdDFWtYePHT0tIGJGf+HMarKNMqMBLiH8EIhh8UsV1wKbndbiwWCz09PZjNZjo7O1m6dClfffUVlZWVOJ1Ompqa8Pv97LvvvrS2tkqek8lk2oH3J/QqBfk/vglms1lWxBFGb0FBAXl5edTU1MjvTUlJ4dZbb6Wzs5MlS5bQ3Nws5VkSExOxWq0yOaioqIgTTzwRs9mM0+kkFosxduxYsrKymDhxohR89/l8FBQUEAwGycvLkxWR4LtM9tdff52WlhbKysokH/Hcc8/l8ssv59xzz5VGjPAKAdLIElnHg+t2p6SkkJCQgMPhGCBjpVarycrKIhQKyX5rNBruuOMOKioqZHlTQIrwi0MQ4OCDD5Yh9sLCQiZNmsRBBx00IANYeHCmT5/O8uXLpWEqDpht27axfv16afTfdNNNPPTQQ/T29soKNyIUGd/u+MSy+HkXBp2ARqMhJydHlgoV43XdddcRCoV49dVXaW5ulmtJo9GQkZFBeno6Op2O6dOnE4vFpDLDvHnzmDBhArDz6lDNzc1yPen1elpaWmT4vaOjg3Xr1tHU1ITNZpNGaF5eHmazWXrPRNh7qLUd30eRhBYvbC/6eO2112IwGHjppZeoqqoC+nmDc+fO5ZNPPiEcDqNSqUhJSSE5OZlAIEBhYSHZ2dmMHz+eqVOnymx7IY82VJ9Ff3t6eqQqgcjyt9lslJSU0NnZyYoVKygrK5Me4wMOOIDm5map0SkoAD80p1qtVnra4n+n0WgYM2aMpJpAv/f8nXfeYd26dZx++ukEg0G0Wi1FRUU4nU60Wi1Go1GG2zMyMigvL5fydPvtt98O1Yzi57e1tZV3332XyspKPvnkE6C/UtCRRx5JIBDgoosu4sUXX8Tv90uO4mAIybJ4JCcnS/1RMc/77rsv4XBYclrVajVFRUVSPio1NZXx48eTnp5OcnIyWq2WrKws+f9i37HZbKhUKpKSknZJW3Tw/JaXl/PCCy/w9ttv72Cgij13ZzCZTCxYsIB58+bJJDiXy8VJJ53ESy+9RCgU4r777uP++++X0SKVSoXJZJJzJZ7pmTNnkpOTI5MjRSKpKCQgPPyij52dnbLAyPdRcUR/P/30U373u98ByDLK8REBsc8Nfk2gqamJ4uLiAQmDw7Gs6eDzdwQj+F/AHovwGwwGGS63Wq3k5eWxdu1aNm3ahEajkZusOLxF0orImAWGNGDcbrc8ALVaLQqFQlacEd9zxBFHoFQquf7666Uxp1KpmDp1Kh6PB4PBwOjRo4F+XlJRUZEMQRkMBmkExovPp6Wl7dAeIT4uKqq43W6pOTl16lQikQitra0sWbKE9evX8/TTT7N48WLefvttaZiqVCrpTRvq4BOFCwSERqnwEAoDWK1Ws99++5Gfn8/tt98uv0un05GamsrkyZPlBq1UKklOTpbeQpF5KygJg/scL94vkk0cDgeZmZmy4pbX66WpqYm7776bs88+m88++0xmPOt0OoxGozT8h5pXMReifWq1mlAoRCgUkgeoVqvlwAMPJBqNct9998nvUSqVmEwmxo4dK43vSCQijVShjWm1Wunq6sJut8tKXN/ngRGeRAGtVkteXh7Nzc0Eg0E0Gg2xWIz29nZsNhvZ2dn87ne/Y/ny5TIqoFKppHcsvo/x8yv6oVAoSEtLk/Ny//33S0NBo9FgNptxOBx8+OGHQD+tJTU1lUgkgsFgkEZfSkoKBoOBhIQE+SwKD9WuQFzGdDqdTFpraWkhFAqRnp5Oe3s7W7ZsYe3ateh0OllNSPRRtD8Wi+3QX0BymsV7dTqdLJcqvFQ5OTlcccUVHHvssbz//vv89a9/JRQKyQpdJpOJnp4e9Ho9ZrOZcDhMY2MjEydO5IgjjqC9vZ2EhARqampQKpUkJiZK41xAeO40Go3Ur/X5fHg8Hv785z/zxBNP8O9//5ujjjqKNWvWDFkNCr7jYguvJ3ynJarX60lISMDpdEpZNWFciUiFMOIKCwspLCxkv/32k+Me7zUUc2k0Gnfw/O8OCgoKOPXUU5k2bRr33nsv9fX10gD7PsPUYDAwbtw43nvvPZYuXSovKRMnTmTx4sWypO6JJ57I/fffTzAYlFQMq9VKU1OTVFooLS2lvb1dGq25ubny0piWliaVO0SxCnGxEB5zYfTHj0t89GNvYKgQ/8qVKykuLh7xoo5gBL8wdts4FaGXoqIiaeSZTCYCgQANDQ00NDQQCoWw2WyccsopJCYm8uabb0rDSeB3v/sdubm5VFVV4fF4pDc1MzOTKVOmUFZWJg/p/Px8fv/73zN//nwOPvhgFixYwIoVK4DvvDTCu5Gbm0txcTF5eXkyQWH8+PGYTCZZmk8YZWq1Wm548ZIo4mA1GAx0d3fLevNCoiorK0uKqwtd1dLSUp544gmef/55Lr/8cu68806WL18uD4PrrrsO6M/Ob2lpkUa4xWKhoKCA3Nxcpk6dytVXXy1lnFJTUznmmGOYN28eRx11FBUVFcyZMwf4zlMajUZlEo44fFNTU8nKymLfffeVeoVjx46VlAv4TnA/PgyqUqkYPXo0KSkpUjs0EolgNptRq9Xy77z44otUVVXx1FNP8eKLL8oD5YYbbmD27Nls3rxZyuWIC0laWhrBYJCbb755QPg7KSmJmTNnctxxx3H66aezaNEiqS8puJYqlYq0tDRycnI48MADpQpBUlISmZmZ6PV6DjroIHp7e8nLy5OH+g+FBo1GI2azWYYbhVyT0HUMBoNkZGRI4f/W1lbJJ3zooYf473//K6WNAJYsWSLL6mo0GsLhMDqdjqKiIjIyMsjPz+e5557j8ccfl20Q2qnPPPOMNI6EBmZqaiqJiYn4fD6USiWjR49mxowZMmPaaDQO8BLvCkRSjpjLtLQ0KZUkkgUFbSEQCNDX10draysPPfQQW7Zs4bPPPpMeboGTTjqJadOm4XK5ZMKS1WolJSWF9evXc+utt8r3jh49mgsvvJALL7xQ6rZeeumlsg9CWqm3txeFQkFCQoI08ISI/ty5c7Hb7bS3t9Pc3Exvby+ZmZk7jIGg6mRkZAzgkTc3N/O3v/2NL7/8kvLyciZNmsQDDzzA8uXL+eqrr6ioqKCxsZHCwkLGjx8vqSaxWAyNRsOsWbM4/PDDWbJkCTabDb/fj0KhkFx3s9lMcnKyLP8rKmgJRYW8vDzJhY5fiwLp6el7rB8qnuuZM2cyY8YM8vLyePXVV/nggw/o6Oj4Xu61z+eTBjb073/HHXccN998s0xA02g0TJs2DbPZjNvtxuv1olarZQlUMe6iip3gQ3d3d+NwONi0aRObNm0iLy+Pjo4OWltbaWhoYPr06cyYMQOj0SgT+gRVJZ5DnJ6evtMiC3sCEeKPT5QSfX/33Xd3uCCMGK0jGMHPg10yTsXhKzxKIpszPT2dww47jEgkgtVqJTc3l02bNrF+/XoaGxsJhUJceeWVLFy4kDVr1mCz2fjyyy/ZvHkzFouF888/H+jndInNT+hVigzKk08+mbvuuotoNCr5jPEi0hkZGTgcDsxmMxkZGey3334ykcrn80kvjEajkeUnARkKHMwRFNJWgOT5hUIhEhISsFqtjBs3DoVCgV6vp76+nrKyMurr67Hb7TgcDlpaWsjOzuZf//oXNTU1nHTSSXi9XsaPH88+++wD9PMhRQUfl8sl++73+6Wn+f777+ekk06S/Nne3l7pPRaZ3zk5Ofj9fmmIC+PCYrEwevRocnNz2XfffeVmHgqFZIg8HkI+yOfzYTabpVdHHKZGY39Jz7y8PJYuXUo0GsXtdrNw4UKOP/54Fi1axDfffENnZydWq5XDDz98QH337u5uUlJSpMRNQkICt9xyC+PGjWPy5MkolUqZ4SsMNIPBQG5uLtOmTZMep5ycHJRKJTk5OQNko4QHdMyYMej1ejmnojb5YIj5bWpqIikpSVbRCoVCaDQaJk6cSFpaGgUFBbJyklKppKKiArfbTXJyMnfeeSfXXHMNFRUVnHHGGUB/SPGQQw6R/Y1fz4AssQtw9NFHc/3115OTk0MsFmPx4sUAjBkzhqSkJJKSkqSHVMifTZw4Ucq4Ce9zX18fCoVip30dqr9CsN9gMNDa2kokEpGya0KLV6FQUFdXx7Zt2wiFQhQUFHD00Udzww030NTUxLZt23j55ZfZsmULSUlJzJ8/Xz4zwhvm9XqlYbHffvtx//33y6plIlogvO/QX5c+GAzi9/uxWCySyy2S1IRaRnNzs/z/aDRKdnY2Go0Gp9OJ0+mU/W1ra5OheqGBC/3Pn91uZ/z48ZSXl2O32+nu7mb69OkyzA79kY7u7m7eeustkpKSePLJJ5kyZYpMSBOlXIuLi5k4caJUvkhMTMRsNjNq1CiKiooYN26cvDiKJL+hnsM9xeD5FRDc1ptuuonjjz+edevWyQIbdXV18ln78MMP0Wg0bN26VV6iAVauXIlerycUCsmxEwmuYl2Ks0E8Q4DkPouKWMFgUOYpWK1WKisrpT5xT0+P3GOF5GB3dzcajUZe0ru7u+X+L6p7AZLqBAPXkfhZjPng14aCUqnkiy++kEb0woULmTdv3g7vS0hI4JlnnpHP988BcSaIc++3jlgsRm9vL9nZ2bJwxQj+97BLnNP169fLJJ8RjGAEIxjBCEYwgp8SNpuN3NzcX7oZI/iFsEueUyGxYrPZfhQhe3Ad++GK5uZmJk2a9KP7u7v4pcbnp+zvcJzzX2p+vw8/5Tj9mP4Ox/n7Ifyc8zscxmdX+jsc2rm3IPpbWVkp8wh+7X36Pnzf/Hq9XrKzs4F+ypigag1+7fveP9zgdrvJy8uTyZsj+N/ELhmnImnEbDb/qM1+uBgCPwTRzh/b3z39uz83fsr+Dsc5/6Xm9/vwU7bjx/R3uIzP7uDnnN/hMD670t/h0M69BdGXzMzM31S/dobvm9/4hE6z2YzRaBzyte97/3DF3k6AG8GvCyOEjhGMYAQjGMEIRjCCEQwb7JGU1M+F4VBR5OfCr7Wvv9Z2/9z4rY3Tb60/exPDaWyGU1t+CvzW+/dDUCqVzJw5E0BWWwwEAkydOlXqa+/s/SPJRiMYzhj2xqnI4v6tbzy/1r7+Wtv9c+O3Nk6/tf7sTQynsRlObfkp8Fvv3w9Br9ezfv36nRYUEBq7g98/ghEMdwzbq5O4Eff19f1mNh0hrSMEpuMhNB5/bX3d03Z/31gMZ+xpu3+t8xuP+L7/FvrzY7GztTDcxmZnEmO/BYixBn6V+8neQldXFz6fj1deeYVXXnllQJnkEYzg14hh6zkVQuHDaZP/sfi+W/6vNSy1p+3+tXo89rTdv9b5jUd83+Nr2v+vYmdrYbjNtdBt/S1CjLUoffpr20/2NoqLi3/pJoxgBHsFw9ZzOty8D3sDv8U+7Sl+rWPxa2333sD/ct+Hwq9hPH4Nbdwb+F/p52D4fD4KCws59thj5WuipPfvfvc7Vq1aJQvaxL+/sLBwr1baGsEI9jaGhec0ntQuaigPN+/D3sBQfRpcA+H75DOGC/lftNnn8+1xe37pPuwpfup2D5c5Hgq70qa9sTZ+buzOMygwnOcpHqJ9goKwJ+3dk/H5OeD1egkEArJPw3kefirEYjFZOVBAVK/q6upizpw5kn+an58/4P27UH9nBCP4xTAsPKfx4TGfz/c/yR3y+XzY7fbv7Xf8OP3S8Pl8NDQ0yPraI9gzDOYtDqc5/jGI78evgV8snr9d9Sb92uZpb7V3OO3Pdrv9VzUHPxdE+WmAZ599doR/OoJfJX4x43RnyRW/tk1/b8Hr9eJ2u2loaNhp34dT6EpwgvdmwtpwNmJ+qrYNXu/DaY73FMJrKtbGr+GZ7uzspL29nc7Ozl16/69tnvZWe3d3nH5KOByO31TC7E+BCRMm/NJNGMEI9gi/WFg//sBKS0vDYDAM+N3/2oZjNBqx2+3odLqd9n84ha5EO/Zmm4ZzktRP1TZhvP0U4/lLYahkxuE4pz8Gv7Z5+rW1d1eQlJT0m+zXCEYwgl/QOBWHMvSHZ35rm8yuctIEf8toNFJYWDjgEB+uvLahOMG70tYfes9gQ204YVfbtqtzFv++9PT0vd3cXwxiPTudTtLS0gAGrOf4n38JDJ6fwT8Px7X3SyOeYypUGn7o2f859i6Xy0VOTs5P8t0jGMEIfln84pxTu90+IEz0awgB/hC8Xu8e8THjDZXOzs4B8ijDHT9ES9iVMRH9H44GQnzbvi/Ev7P1+1vmlg7ul4gADH7fcOjv4HaIn4Eftfb2Bu3jx37HD63LvUFLGeoZHWpuf475jkajA3j6w5kWNIIRjGD38LMap7FYTP4nNq/BCQjx3Kj49/5aNh3RXp1Oh9PplB6E3UH8gTl4LIZbhqVok9FopK+vD51OJw3rwYdVPEd1OPZlVzHUwRs/DvHh7MHrXayHeE7mrwXxa3CofsH3c5GHgxj84PnZVS7m4D6L9b2zcdgT/Njv8Pl88vODn6/4795ZH/b0eRxqDH8OTm4wGKS3t/c35djYXSgUCiZOnMioUaN2eG3ixIk7ff/EiROHjerCCEYwFH6SsP7OQjp2ux273U5aWpr0QBUUFAADOYziO6C/DFtnZye9vb2YTCaKiop+iibvFcQbomazGZVKtVMO6feFvcTYWCwWaciJLFnxe/Hvz23cDDa2jUaj5AvH84YH8zOHCgOK138tBlp8uwfzKYeiY6Snpw8YExHqHoqTOZwxuH9iHQoPWmdnJ319fT/IndXpdL84hWfw396dtohEL6/XSyQSwW63U1hYCHy3X1kslgGf2Z2Q94+ltRgMBlQqFQANDQ3AwDC8aKPwNoqL5J6Ox/d95ueYY7/fj9PpRKVSkZ6eLtUWxJnyvwCDwUBpaSmbNm1ixowZA14D2LRp05DvH8EIhjt+MuN0sDdFJPz09fVJAzX+4I6/xcV/Pv49g//GT81p2l2IdqvVammAD3Vwx793qMNoqD4JT4fP5yMxMZGcnJwfdZDtKUS7nU4nOp2OxsZGUlNTAQYYXIPbJvokqApOp5Pk5ORfpA97ivj5jeeJDvaKNjQ0oNfrBxjuMLBSTzwnc7hj8PPc29uLz+eTgt5Go/EHk8XE8/99CX/DDfF7TPwlWRiAoi/i/4e6bAz1nO/s2f+xe5nRaMRkMmG32+nt7R3wnQaDAYPBIOWXhLyQ3+8nOzubvr6+X93zGI1G8fl81NXVYTAYiEQiO9BJRjCCEfw68ZOE9eNDOvEbcVpaGjqdbshDOT60JELEXm+/7qnRaESlUv0gzykePwUVYHAobPBrg0NZBoMBo9FIJBLZgYs5FH0hGo3uNMRmNBpRKpXykPmlvG6i3WlpafT19RGNRuVBKNo0mJfm9Xqpr6+nvr5evk98XhgAv4YQ/85ClfGv+3w+/H4/wWDwe8OcwhAQa2c4U1fi2y4MIJVKJdc18INScEajUc45/PL8wPjnLBqN0tnZSWlp6QCJpJ31xWAwUFhYiNlslmMyuL67WM+/RMhbzJHJZJJ/Q+i4Qv9cGQwGdDodWq2Wvr4+0tLSBuxH0Wh0wHM7HNelx+Ohp6cHg8GAz+fboc8jGMEIfr3Y657ToUKcgldqMBikYSo2ysHeJfG+eG8M7FgfOj5MNVTlk+/zTP6YvrndbhnOG3wYD04UEIb2UB4j0d74ijrx4VLx9+LHMTU1VYop/1Ie4/j58nq9NDc3o1QqyczMlK8NFcYUBuzgtofD4QHz90Ohz58bg9vxfW0Raz0lJWWH5ClgQJi/s7OTrq4uIpGI/PzgtTVcMLjfRUVFGI1GKioq0Gq1+Hw+DAYDjY2N+P1+UlJSZLh7MMRzPNSz9EtAUI0cDgcqlYrm5mbGjRs3YP7EpQPAZDINCJXHX6oaGhqIRCKoVCrZp5875C2UNAT9Kf4SMDiyoVKpyMjIkP0R9CGxN9vtdhobGwkEAowfP55JkyYN+Td/qWe1vLwcnU4n199wTab8KeHz+dhvv/0IBAI7vAbw9NNPD/l+gPXr1+80MjmCEfzS+FHG6c4MkXijUmwekUiEjo4OgsGgDCMJDprg4YlNRhh0gj+0szAx9B8IIpQz+Pd7O0Q1lKG5s9B9PD9RbADxvxvMHxXhYKfTyfjx40lLSxswjuFwmK6uLjweD5FI5GcLv33fwePz+aQ3HKC9vZ36+nqKiorIzMwcYIirVCocDgdtbW1YLBYyMjIAqKqqAmD06NE7jMdw0Dwd3I7vW/OCquB0OvH5fJSWluLz+bDZbPT09DB69OgB1VuSk5MHJA4Nx7B3/DqOh91ux+Px0NLSQmpqKoFAgM7OTlJSUiSlYTAGP5PDob+CagQQiUTQarX09vbuML+NjY0D5n1wFEQkAXZ2dlJQUEBDQ8MvanSLeWtsbMTj8ZCYmEhqaqqkVfX29hIMBncwTsRabmlpIRKJ0NzcjF6vx+FwfO/f+iWe1ZqaGhITE1EqlWg0GoABcnzD5Rn6KRGLxSgrK/vB18rLy4H+s0j87tcQrRrB/y5+tHE6VOLLYCNNGJ99fX1otVpaW1vJy8uTRqvwwHR3d6PX60lMTESn00mjJ95TJ/6G+HlnmcE/xeYkwnlDeUAHc2QHb/KCtO/1eikrK8Nut5OSkkJiYqL0JgWDQTQajaQyDB5HwasaTHH4KTHUHItNLS0tbUCCW09PD4mJibS3t2MymQaEbUXbPR4Pfr8f6DduVSoVwWBwQBa/SDyBHRNMfm4MNQ9DrXlx2VCpVJKfGAgESEhIoK6uDq1WK9e5wWDAZDLJsLBYM4PX1nDAYMNbrGfBf05OTiYcDqNQKPB4PJI7LtZIPJd8sLdxOPRXrOHc3FyMRiMNDQ3SSxrf92g0SltbGwkJCTidTkaNGjWAp+nz+XA6nZhMJoLB4ACj++esTR9PNwqHw3R3d6NSqfD7/fh8Puks6O7upqenh0gkIrnD4nPw3fNaUFCAVquV9dqH6stP4QjYFaSmptLb20ttbS1ms5m8vLxhc6kdLkhJScFgMHDWWWcBoNfrf+EWjWAEu4YfZZx+n0dzsGdReEV9Ph9JSUnyZ5vNhlarJRgMEgqFgH4jDfofpPhw8c4ywH+uW/JQ4vPi78eH/4TXQhxyfr8fvV4vN34RylUqlZLmIIywrVu30traKg1hMWYCbrd7B4rDT4n4vsV70cS/8fzh3NxcqqurpRdNqVQSiUTw+/2kp6djMpkoKCiQIbh4b3l8X4U3XBhvu4u9GWYc/B1DecrFf+Ki5HA4aG9vl5eIGTNm0NTURH5+PiqVCpVKJROKBB0kfkwHX8J+yv4N9X2DqTlOp1PynAFaW1tRKBQolUq0Wi2jR4/G4XDgcrlISEiQ8ziUh3nwmP1SBoQwENPT0wckt6WlpUm6QTxHNjExkZSUFBwOB3q9Xo5JX18fFosFr9dLTk4OfX19pKSk4HQ6pXRW/OVlMBf7p/Dy2e12KioqgP5nEvpLfVZWVqJSqRgzZoxUEhHebrFHi+dOPJ9i/x0qarKrdJefChaLBYfDgcVikUZYSUkJgUCAqVOn/uzt+SVgs9m+9/d5eXmUl5fT1dVFeXm5NFJHMILhjh9tnA61KQk+WWNjIwDNzc3U1NRgMpnIysoiOTmZpKQkaZh2dHQQCARkooXFYqG7u5tIJEJjYyN2u12G+4fbbTj+gBmcDZucnIzBYJBc0YqKCvR6PSaTiYyMjAF9KSsro66uDr/fTyQSIRKJyINQeDHWrVuHUqkcUgnhpxqX+O+ur6+ns7MTl8uFVqsFkJ7fkpISmpqa8Pv96HQ6NBoNqamp9PT0SF5iPDdMeAsFbQO+k7iJRCK0trZiNpv3qM17w3uyM8NhsKdchE67u7sBsFqt0kgFCAQCjB07lsLCQskX9vl8RKNRGSrt6urCYDDQ29srw//x7Rjchr3tHRpMxYmnyoj5EH2HfjqC3W6nu7tbXiizsrLIz88nEAjgcDhwOBzSy5+XlzeAEiO+6/uMs5+SxzjUd3d2dkquZXNzM15vP586NzeXSCQiDe+EhAR5eRbjItY09M+lTqfD4XCQmJhIY2PjAE96V1cXEydOlP3f28+yz+ejoqJChvNHjRrFuHHjaG5upru7G4PBQHNzM1qtlkAgQHJyMi6XS9KsSktLCQQCTJ48Wepkxkcy4scv3jv8U87XzrBt2zYCgQCBQAC3282qVavweDxYLBZsNttvnoPa1NTEvvvuK3/W6/VSOSUe+fn55Ofn/5xNG8EIfjT2WkLU4HCsyOT2+/00NDTg9/ux2+3EYjGCwSA9PT2Ew2E8Hg8AoVAIrVYrb/pqtRqFQkF9fT05OTky1B3/t4bim/7cm2T8ASMMaOElEaFqu91OQ0MDCoWCxMTEAbxar9dLe3s7TqdTaidmZWXh9/tpaWlBo9HIg89gMOB0OvF4PDKRIxKJ/Cz6r/GJWzqdDrfbjc/nk0ZkQ0ODTJ6YOnUqZrOZQCCATqcjFApRX1+PUqkkNTWVtLQ06UkOh8M0NDTQ3d2NxWJBpVLR09NDUlLSHrd1T8OM8SHLeG3dwsLCHTxFwnva1dVFZ2cnlZWVpKeno1AosFqtqNVqWlpa8Pl85OTkYDAYpKc4JSWFnp4eurq6iEaj0gvp9XpJSEiQ62ZnRujeDqMO9o4PdTkQz1skEiEUCqFSqQiFQnR3d6NUKrHZbJjNZpRKJT6fj1gshtvtJhqNEo1G5aUznoIj+jc4oXEo42dvYvAzC99xT8XfrayslPtOIBDA4/HQ2tpKamoqsViMlStXkpiYSFFRkbxcGAwGySMWtCXoN2Krq6vluMTvFfHj/mMvHOK7tVotzc3NqFQq6uvr0el09Pb2yhC+0WjE6XTS2tqKWq2WSVFOp5Oenh40Gg2lpaUD9KgH899hR63fnzucHg6HZTLbihUr2GeffYD+C3NSUtJvPrQvpMAENm3aRH5+/oCLxAhG8GvFXs3WFxtUX1+f3PSF1Edvby/5+fk4nU6am5ul5Ie4sSckJKBSqXC5XITDYUKhEH19fUQiEVpaWsjMzJSh0ngh88F80597kxSGiqgGJQxJcRD4fD6ampqk8aHVaolEIlRWVtLV1UV7ezs6nU4abzk5OSgUClQqFV1dXZLrpdfr0Wg0JCUlSSM2MTFRejB/SsQb/MJQCwaDNDQ0EAqFZF89Hg9KpZKkpCSSkpKkUe73+zGbzbhcLgKBAB0dHWRlZUn+k9/vR61W097eTnJyMhqNBpfLJUOKu4uf4mLS0NBAZ2cn6enp0vPldrsJBAJEIhFisRg1NTX09PSQk5NDT0+PzAD3+/34/X6qqqro7e1l8uTJ9PX1oVQqcbvdMjksOTlZJqmIA2Zn0lV7q3+DQ/hebz83XMyfMFBEAlRnZyfd3d00NTURCoWwWq10dHSQkJCAy+UiOzsbu92OxWKhra0NpVJJKBQiPz+ftrY2efkU/RB/74eMn72JwcZ9vBGp1+upq6vDYrHg9/tlFvS2bdskZ16v10tDVqVSybEYNWoUsVhMrmthgDY2NqLRaEhMTCQUCg2ItAzeu37MJTueS5qVlUVjYyNut5ukpCTC4TAajYaWlhbcbrdUz0hMTJQGqaA6CO9jXV0dJpNJ0jREe9VqtaQhxVNQfm5jsKurS17qk5KS6OvrIzs7m4yMDNra2nC5XHJP/i0bqQLxyZYjGMGvHXtdSkrwrJKTkzEajXR3d0ujShy6brebrq4ukpKSsFqtBINBgsEgycnJ1NbW4nK5CAaDFBUVyU1z27ZtFBUVSU6Y0+kkNTV1h4zYn3uTFIdHb28vDQ0NVFVVEQgE0Gg0qNVqzGYzoVAIs9mMRqOhtbVVepDb29vR6/X09PSQkZFBMBjEarXi9/uJRqNoNBpp/DmdTnJycvj0009xOp1MmDCBP/7xj7INPwXEASkuAkJ8vqysDIfDQXd3N319fYRCIXp6ekhNTSUajeJ0OikpKZEJF6mpqfh8PqxWK62trWi1Wvx+PwUFBZIrJgyErq4ueZER1YREW34ub/hg2SDxmjDCBerq6vB6+2Wy/H4/CQkJlJeX093dTUNDAxaLBY/HI405QU/RarX09PTIi5mAoEgI7/RgsX+BvRkOH+wV0+l0cr56e3txOBxMmDABQPaxq6uLxsZGIpGI9IoGAgHJvYxGo1RVVaFQKKSh2draSnp6+gCP1s44rl7vd9XRfgoIwxAGeoQNBoOUwtqyZQsA3d3dpKWlyXUdDAbZf//9USgU+P1+3G63zBYXfRbPanFxsbx4ZGRkoFKpyM3NHVCZaWf82z25ZIs1arFY6OjoIBqN0tXVRV1dHcnJydTX1xMIBOQ8CnqCxWKR86pUKqXEmdfrJTs7mylTpkilEOEVHuxRHWwA/hwRrK6uLsLhsKSPiItgSUkJo0ePxmg0kpSUNCzpYHsTWVlZaLVaeblQKBSyStZQyXeD3z+CEQxH7HXjVIS1xH9iE6+qqmLixInEYjH8fj9arRaTyURiYiItLS3Y7XY2btxIRkaGzPru7u5Gp9MRCARkiCotLY22tjaZ1Q67p0W5txAfAjYa+6WSOjs7UavVeDwedDqdPNC6u7spLCyksbGRlpYWDAYDbrcbQHI0m5qaMBgMbN68GYvFIr1xWVlZKBQKkpOT8fv9xGIx6S0VB4Roz4/dbOx2+wAlAHH4AHI+BdcwGAySmpqKWq1m27ZtKJX99RzMZjMtLS0EAgFCoRAej4fc3FwsFgs9PT14PB70ej2FhYVoNBp6enqA/k1UiKELQz2+As/PybONN8aNRiPRaJTU1FT8fr/kFdvtdgKBgOS9ZWRkSKNK0BSErNCqVauIRqMDwuFZWVnodDrq6urYunUrFosFq9Uqqw+JRBvRnsGcPzE2BoNhp9XVdmV8Bl/mRB+8Xi+lpaWoVCr5DNtsNnQ6HcnJyVitVpqamrDZbFgsFnQ6HQqFAovFIg1xEeUQFbOE4Zeeni4LMsSv4Z/j+d3ZWLa2tpKUlERHRwcdHR14PB55MRT0pJ6eHvx+P5s3b5YRIsFLhf6LeVtbm1SgEP8mJSVRUFAwICM+3lu8swsG7J5ahcFgQKlUykuPyWTCZrOxfft2qSrQ3d1NXl4eCoUChUJBLBajoqKCcePGUVRUxMaNG+nr66Oqqorc3FxCoZDsX0tLC8AAg29n7R9K83VvQ9CE7HY77e3tbNmyhYKCAkaPHk0gEGDChAn/E6L8H3300QDuqcFgkMUxduX9IxjBcMReM07jJUXUajUVFRV4PB7C4TButxu/3095ebncPIPBoJRo6enpobu7G7fbjdfrlZum8MJYrVbsdjvZ2dm0traSkZEhQ+lr1qwhFosxZcoURo0a9bNtRPH8Wq/Xi8PhwGw24/F4BvALI5EIbreb1tZWWlpa6Ovrw2Qy4ff76erqQqVSSUNdr9cTDofp6OiQkkSCoykONqfTSXV1NWPGjBnAuRXt+DGH+85kwYSxEg6HsdlsBAIB9Ho9ycnJMrRfV1cnq8w4nU60Wq3k2bpcLoqLi0lISECn06FSqbDZbESjUZqbm2lvb0etVpOSkkJKSgpKpVJmRAveY3xd+p+KtiGMvHiDSfCHfT4f2dnZtLW1YbfbsdlsNDQ0UF1dLfuVlpaG0+mkr68Pt9stw8AOh4NYLCbDqw6Hg9zcXOx2O06nk66uLjIzM2XCik6n24GDGS/nBAyoSDWUJ3JXx2bwehGfb2xsJBqNEggEZIhU0FOMRqPUzezo6MDv9xMKhUhMTCQxMZGOjg6MRqN8xqH/8qTT6XA6nVRVVckLllj/fr+f6upqcnJyflJ9UDGWLS0tci2mpaXJNenxeAgGg7S1tUnZJWFIC8+i0+mUnseUlBRcLhe9vb0oFApCoRDBYBCTyYTL5ZIG3YwZM3Z4ruLX2OD5Fpej3RFJNxqNmM1mbDYbKSkphMNhNm7cSGdnJw6HA6fTKZ8dq9UqQ/cZGRnYbDY0Gg0Oh4OysjI5l/Hc2eTkZFQqlTT44mkJ8Q6CeIM/Ozv7J4tk9fb2Dlij4XBYJpuKpMv/lZD+CEbwW8Ne9ZyKTUBknkciEfr6+qSotdVqJRQK0dXVRUJCAl6vl1AoRCgUQq1WYzKZiEajkqcWDAaJRCKEw2Hy8/NxuVx0d3dTUVEhw4Ner1eSwoPB4M9WcSbeYOjs7GTbtm2YzWays7PRarXU1dXR19cnPS5utxu3241Go5GeJhHSDwQCaLVa1Go1PT099Pb2Si+aCLE2NDSQlpbG1KlTpXdZjO/eSqgYqsxi/M8i41gYHD09PZjNZvx+PxaLBaVSKS8NOp1OeoCFB9lgMBAIBHC5XJLyIXQjRWJGQkKC9FyJS4lINBFt+j5v04/1vA0+ZMUciyQh4cUXqgwqlQq9Xo9arUapVMrEE8Gh7unpwWQySb1TlUolBdoNBgPd3d0kJiai1+vp6OiQnrKhDMZ42bH4RJ74ef8x/Rd9t9vtNDc343K5SExMxGw243Q6USqVdHR0yEuIePZisRgJCQlEIhFpIIXDYWm0CuO9p6eHmpoaHA4HWVlZeDwexo4dK59bER4XPNefCmL9Cp1dr9dLdXU1Xq+XtrY2WltbJa9aCNZ7vV6USiVmsxmFQkFGRoaM/NTV1aHX6wkEApICIDjmVqsVl8sl+crw3YVHcHmH8jCKy9juQHhGRXEEn88nk+3ExUtEalQqFQkJCdIwd7vdlJaWUlpait1uJxqNYjabycjIwO/3ywTFwRXORCKYzWYjKSlpQPENcbH8qeYyGo0O+H+vt19hISkpCYvFItVihlvFtRGMYAQ/jL2arQ9I48Tv90s9RLVazeTJkyksLKSqqkomUNTV1REMBhk1ahSpqakYDAZisRibN28G+sM2Op1OcotMJhMajUYab2lpaZKvqFQqZdKGQFpa2pCcvb0BYTAYDAba2tpkGFCv1+PxeOTGKCqXmEwmfD4fer2eUaNGEQgE8Hq9FBQUEI1GJb+2tLSUUChEcnIyycnJtLW1UV5eLo3QiRMnylC78Nw0NDQMkNra0xC/mAOBeEMtISEBv9+Py+VCoVAQDAaJRqPU1NSg0+mIRCKSMxwOhxk7dqw0wEVI2+l0Sk6e4OWKwyw1NRWj0YjD4ZBh1HA4PKA9Ygx2xsPcGx7Vwd/T2dmJ3++nu7ubaDRKe3s7Go0GvV5PZ2cnKpUKq9VKYmIizc3NRCIRcnJySExMlNJJ4vKUmppKbW2t9KoJHua0adOkSPqKFSs47LDD5LMgIhLxxqi4qMRnU//Yw1dEKoRRKTzjWVlZ9PX1yYQel8uF3+/HZDKRmpoq14NIohIRgYSEBAoKCgbo3UK/99FsNlNbWytpDVOnTkWj0dDW1iYl1vYGTWVnfRT80u7ubtra2lizZg3d3d3ykig0SlNSUiT/NyMjg76+PvR6PVOnTpWeX4fDgUajwel0Mm7cOFJSUlCr1fj9frxeL62trZhMJnlBh36PnzB+hZReUVHRgHkUl7Gh9GJ3Brvdjt/vp7KyUl4EzWazjEYlJCRgNBoZM2YM0WhU7o+BQACfz0djYyMdHR243W4SExOJRCIoFAp0Ot2A/Uyn08k8AUFVER5l4VWNN6xFaea9TdkQZZNFXkJSUhKtra2kpaWhVCpRq9VYrdafjQ70S+Hss8/GYDCwYsUKSZ85+OCDAeRr3/f+EYxgOGKvGafCu7dp0ya8Xq/0fona6729vbS1tdHQ0CAztu12OwqFQpZWE+FMcStPSEiQMi5WqxWr1UpmZiZ6vR6z2cyoUaNoa2uTngqNRiNDncFgEIfDsVeSRob6nDAWKisrZbhMp9NRU1NDW1ubNKBFRmk0GpWySmVlZXg8Hmpra2V1pHXr1pGYmChDVeFwGKPRKEODra2t8tAWB2AoFJLlI4VEjBDTHmy47Eo/u7q6dioPI7KYy8vLUalUTJgwQVaA6unpobOzE5vNJg11YYRkZ2dL6Svo96Y0NjZiMplkMoPZbCYSiRAIBMjMzKSzs5Ps7Gy6urooKysjKyuLcePGfa+00N4y0uK/Rxj8Ho8HrVZLWVkZoVAIl8uFRqOhqamJjo4Ouru7SU1Npbm5GbvdPqBvokpPJBJh1KhRQL/HORQKSbqL0WgkMzOT5uZmmXAm9F8H96exsZGenh58Pt+AGu8/BvEhZkGvqK+vp6qqiu7ublJSUrBYLFRUVNDR0YHX66WoqIhAICA9+OKZ1el00iOuUCjo6OhAo9HQ0dEh+bGxWIxIJEJmZqa89MRisQGXDmHQAAOKFOyN/vp8Ptxut+xLU1OTjG5AP7dScMVbW1ulxJnf7ycYDFJbW4vRaKSjowOfz0dXVxe5ubm0tLTItSK0imOxGHa7nTFjxsh2m0wmacy3tbVhNpsHqI7Er8HdvXT19PSQkJCAw+HA6/Vis9mor6+XXPiUlBRisRgdHR1UVFRgNBqlJz4YDOLz+aROc3NzM//973/RaDQygqBSqZg+fbrkzQtpvMTERFQqFZFIZEDp1vhLz+AksB8Lp9PJhg0bpBMjGAwSDodpbm4mGAzKCNPeiiwNV4hypMKTHI1G2bBhw4DXvu/9IxjBcMReMU4FAb6pqQmHw0FLSwuNjY3ycBGyQ3q9XnINAVwul6wYJQxLk8mEx+ORsifhcFh66US1IRG2gX4hYpfLRVFREfn5+VJOY+vWrTJJZSjjdE82qvjPCWHt2tpa3G63zPTt6uqS3hbRR2FYbtmyRQpe/1BdY8HdTU9Px+/3S2FzrVbLpEmT8Hg8qNVqsrOzpZelvb2dtrY2EhMTcTgc5OXlDZkZvDN0dHSgVCpldRhAVgVqampiy5YtbNq0CaPRKL2I4qAXMkNdXV3y8jEYIrHLZDJJ2Z6uri5ZHjE7OxudTsc+++xDa2urNGyEcSKSsoaat7114MV7nwVcLhdlZWU0NDRQV1dHLBbDYDBQW1srxdbb29spLy/H7XajVqtlmF8oL4jvsVqt9Pb2kpGRIT2KLS0tNDU1Samhvr4+SQGA78rmAjJDXKvV/qiDNv6iJTRdBc9Z8BNFIqNarSYQCNDd3U1NTQ0+n4++vj6ysrLo6emRBqfT6ZSKFKIuOyDnUKxDQePQaDRMnjxZjntTUxMqlYp33nmHrq4u9Hq9zDo2Go0/2rgQ0m4dHR1y72lsbJTay6FQSCoUeL1egsEgra2tNDY2Sr1QrVYrJdHEviS8rQqFgvXr16NWqykoKMBisaDVaqUWp6ByCMNNeCMFtzzeOI3v4+70WRTtqK6uprOzk5aWFnp7e1Gr1TgcDtra2qivrycYDEpvKvRfGoWySCwWk1ERUUxDcGeNRiM5OTmS2mGxWCgoKJB8cBHeH4q77nQ6pXZw/B4zVJ93Baeffjqffvop3d3dco9Uq9U0NTURjUZpbW3loIMOkhdnQCpPjKC/utTIeIxguGKPjFNxcIusZfiO86PT6dDpdITDYanbKfTmBL9QcL4EOjs7ZdhQbGKhUAiTyQR8t3mLSiDC+BEGqlarpbGxkaysLKnHJ6Rb9qaXLf5zohCAQqEgJycHtVotQ5eioIBSqcTj8eB0Oqmrq5PagtDvDRo/fryUFhKJQz09PWzZsoXm5maZDS9C/Gq1moSEBNxut+TqdnR0MGXKFBQKBbW1tdKTmZaWNkCeaXCVn6EgZL8AmZCRmppKY2Oj1PAU3u3S0lJcLhfp6emo1Wrq6upwuVwAHHjggZKiIKoiQf+6EeHTMWPGkJOTIzl3sViMzs5OOjo6MJvNuN1uWWkqXmtzd4ztHwuhUdvT00Ntba0M4ysUCjo7O8nMzCQWi9Hb2ytF1gE5NwIZGRk4nU5cLpccP61WK7Ung8GgvIyMHz+etrY23nvvPcxmMwUFBSiVSrq7u7FarWRnZwPscaJJfO11UaZTGG2DExMjkYgM6YqQtgihdnR0sGLFCoLBoFyXGo1mgDaoWO8ajYbc3Fw8Ho9UmxB6oKmpqTLRbPLkyVRXV9PW1iaN/HjjdG9QNiKRCBqNRu5FOTk5BINBaZQKQ0xEfcR4CVUJkdCp1WplFr7P55Ne4Gg0KgtUCENWJMc1NjZSXFxMcXExXq9XqnqIwgX19fU7GKi7s8Y3bNggZfjE5UaE7EWiFyDnB5De4s7OTlJTU8nIyJB0KRGVMRgM0rMv+peXl0dCQoL0nALS0TB43xW8VK/XK9dEPJ9bp9PtUa7A/fffz3333cfXX3/N+eefj9/vl9rP0WhU8qLXr18v8xhE8Q/Bl/1f4qOmpqbKsD/AvvvuS0VFxUj1qBEMS+yRcSp4UI2NjahUKjweDwqFQnLxoN9L1N7ejtFolIT71atXS/HxAw44gKOPPprRo0fz9NNPs3Llyh0M140bN6LT6Rg7diwJCQkyE1zc8MUBD8hMU1Fyz2AwSON2MN9pT42a+HC3+Bvjx4+nq6tLZjO7XC7Ky8vZvn07LpdrgJEyefJkrr76ag4++GApzyJ4Z9CfLa/Varnhhht46KGH2LZtG729vYwdO1Z6HBwOh/Sc6fV6FAoFpaWlWK1WKXUTjUYlx1Vkf+9KOVBRb1ts3H6/n3Xr1mGz2YjFYqSlpcmQn0qloru7G5/Px4YNGwgEAhiNRu677z7OP/98QqGQlIoSyRWff/45f/7zn3G5XFRXV8tkIIPBQEJCArFYjC1btmCz2eQhu99++8nDL74sqBiPvV0ZKz7ELS5fNptNJrT19fWhVqsHJGC0t7cTi8UoKiri4Ycfll4xh8PBrFmz2H///Xn88ce59tprJZ1FZEebzWYyMzNl9SBBE1AoFFLdwWKxYDKZiMViTJgw4UcnmojnVxgG4l+XyyUvSAaDQYZiBZc7ISGByspKeQkB5GEXz/WeMmUKJ5xwAiUlJXz55Zd4vV6ZMCiMw+zsbLKzs4nFYrS1tckDs7CwUHqm8/PzSUxM3KF85o9BX18fiYmJMgFPqC6Ul5dTUlIi50HgmGOO4ZxzzmHjxo188sknbN26VV7I29racDgcZGZm0tHRQTAYxOVySf67oDIkJibS1dWF2+2WiUoigUrQR8QFwGAwyPK1uzu/1dXV0tu9fft2KXcmsP/++3PmmWficrmoqamhvr6erq4uHA6HjHqIuY+//Gg0GjQajSzNq9frJQ9dqE0YjUZGjx5NX1/fAINPoVDsQKOK30fT0tLkpXN3Lx8iQnHCCSfQ1NTETTfdJGkUer2esrIylEolBQUFhEIhotEofX19UoPb4/H85Ml3wwn5+fls2rSJ4uJiABnpGzFORzAcsUfGqQgDxpdOE2E8pVJJMBiUWd0lJSVS06+vr4+xY8fy+uuvM2bMGOmlW7BgAVu2bOGDDz4gLS2NCRMm8Nlnn/Gvf/2Lvr4+ysvLJdcSvvP+iM18MAk/Xj5kqBKnPwbixt/Q0IDD4cDv99Pc3Exrayt1dXV0dXXxzTffSKPUbDZz6KGHcvbZZ3PsscfKZKLvw1133UVHRwevvvoqtbW1RKNRCgoKZGaxCKNarVbC4bCUmorFYmzfvh2Px4Pf72fOnDmyxJ0IA38fxo0bJ+tt9/X14fF4qKiokBnn4vIhDnC9Xi+LDqSlpfHZZ58NCBOJJIW+vj5UKhXz5s1jxYoVXHbZZaxYsYLm5mbMZrNM1AgEAjJZRHhp6+vr2b59u6xrPn369AEemL0NMb+1tbV4PB7q6urkhUjwAp1OJ7FYjA0bNkjqyrnnnstDDz0kw88iISEUCqFUKrnsssvw+/089thjMqwM/Qaw8HQLvp5Go8Hj8VBQUEBGRgYajUZyeMVnfsyhKp5fkUzo9XqpqakhOTmZ7OxscnNzqauro7q6GpfLJbmGwjuuUCg4/PDDOeuss/jjH/9IU1MTq1evpqamhiOPPJI5c+ZIAf5wOMwJJ5zAl19+SSAQICEhAb1ez+zZs0lOTsbhcNDT0yMNb71ez/jx4wkGgzKZTlREE1qdP+Y5Fhc1i8Uiw95tbW1UVlbK52PChAkccsghnHfeeUyfPh3oN1LvvPNOampqeP3111m6dCkbNmygvb2drq4ukpOTSUtLw+VySUpHYmIi4XBYJoUK6bjy8nIyMzPJyMiQIv1CS3ZnF8ld4cmnpKRQW1tLSUkJ7e3tQP/l4ZRTTmHhwoXMmjUL6N+rBV0nHA6jVquprq5m0aJFvP/++/T09EjJsFAoJCNEwlMK/c6HnJwcmpqaZMZ/X1/fgASu+LaLvyMoOuICAj/eK/7SSy/x8ccfM3XqVLZu3YpKpZLryu12o1KpKC4ulhGkhISEHTSE/1cM1JEqUiP4tWCPOaeiJOPo0aNpbW2lra2NQCAgk2psNpvcIAVmzpzJq6++OkAiSHAWs7OzufTSS4F+g27OnDkcc8wxXHbZZdTW1krvYHFxMSkpKSgUCpnhLmgALS0tBINBZs+eDUBFRYWUafkhr+GuIp4/VV9fT3t7O9FolI6ODkKhEBs3biQUCjF69GgWL17MrFmzUKvVA0oGdnV1UVFRwdatW6VnDZBcVoB//OMfTJo0iRtvvJH6+noppzVu3DjpnREakdFoVCbdGI1GKZi+bds2+d3xeqg7g6ARNDQ00NzcTDgcxmKxYLfbaWlpkd5SEb50OBx0dHSgVqt5/vnnJZ8S+isCLVu2jM8++4z99tuPBQsWkJeXh8lk4r333uOTTz7hnnvuobS0FLfbTU9PD+np6RQWFpKVlUVKSgq9vb0kJyfLDFyBwYlAXq93QPLMnhw08R5Tp9NJY2Mjer1eCn2LBBaRrPbNN9/I0PM//vEPTj31VGKxGB6PB7fbLdvg8/mkB//Pf/4zF110EatXr6akpITS0lLeeust6XnyeDwYjUZZbctkMmE2m7FYLIRCIelhDAaD1NTUMHXq1B+l4yjCrc3NzTLBbuzYsSQmJqJQKOSlQIiyi0Swp59+miOPPFImM6lUKundjQ8bCi/lCy+8wNy5c7HZbNjtdmbMmMHatWsZPXo0CQkJtLa2YrVaZUhZePyEUZqQkEBWVhbAbssrxcNoNNLQ0EBra6uMdpSWlvLNN99gt9uZPHkyL730kiyz7HA4ZAKgmIfs7GwuueQSrr32WpYtW8YVV1whE+GEHJZWqyUrK0tWk9JqtdhsNkwmk9Tx9fv9slDBYM6pKB0b3+5d4cmnpKRQUVFBe3s7CoWCu+66i7POOoukpCR6enro6emRSUNiTQpN1oKCApYsWcKLL77IX//6V9xuNxaLRXobvV4vU6dOJTc3F61WS3t7O8FgUOq9GgwGtm/fLiMuwiMc3/6dtVu8Tzx/u7qe/X4/Tz31FA888ADQvzZEdEUYy263m+rqakKhEPvss49MrhRt+iGP7Z4mz45gBCP4cdht41RsIEJzVCRGCH5PY2Mjzc3NMhninHPOYdKkSRQUFHDggQfS3d3Na6+9xsqVK9mwYYMUuBbYb7/9OPPMMznxxBPZb7/9WLt2Lffeey+PPPII3d3dlJSU0NnZSW5urjzMRHtaWlqwWCyUlJQASE/jpEmTftQgxUNsUH6/n7a2NmKxGCqViry8PL766ivpRXnvvfekzEkoFJLjsmHDBt58802ZYABw+OGHc/HFFzNr1iyZBANw7bXXEg6HueWWW7DZbDQ3N9PU1EReXh4ajYZYLCbLXVZVVZGYmIjVagX6aQ6BQIC+vr4BvL3vgwjXi38TEhLYd999ZTlOEd4XJQIFD+++++5j7ty5uN1uGhoa+PTTT3niiSdoa2sD4LPPPuPuu+9mwoQJnHnmmSxcuJBjjz2Wo48+ms8//5zbb7+dyspKbDabTCibO3euNIb7+vqor69n2rRp8rAQHhqRWCGM4j09RAQHU3h/srKycDgcpKSksHXrVnw+n0y6+O9//4vf7yc1NZXXX3+dOXPmUFVVxWeffcY333xDfX39gJDvqFGjKCgoIC8vj+LiYiZOnMjcuXMxGo1MmjSJ2267TQrcm0wmqYfr9/upra2VmplC9kcYFkKeaHf6q1AodjDuBR80EomwcuVKYrEYlZWV0jjcvHkzwWCQxMRE/v3vf3PYYYfR29tLfX09Tz75JO+//z4OhwPo539fcMEFXHPNNVitVtRqNZmZmbz22msceeSReDwePvzwQ/Ly8rDZbDKRRiTZCOM+PmEsFArR2toqn+M9NRgMBgNJSUk0NzdLtYx169bR0NCAVqvl+eefJzMzk7/97W88/fTTA8L7BoOB4uJiJkyYwNixY9lnn32YMmUKa9eu5e677+b555+nr69PisJv2bJFluC1Wq1oNBpZ976oqIj09HR56RNREYvFIj2TgocpvMXww9qn1dXVfP7550C/J//iiy8mEAiwZMkSvv76a9avX4/NZiMrK4tJkyYxdepU/vSnP0kNU41GwyWXXMLatWt5/fXX8Xr7dajD4TCxWEwa0GKdCokxkWQmKqgdffTRAy7aQ83TYJmwPUlSbWlp4R//+IecH7vdzpQpU9i2bZssshCLxYjFYpKuMGnSJFmaN74a287+7q8hyz8pKUl6wgVSU1N3+v7U1FTC4fAAes4IRjDcsNvGqQgJBgIB1Gq1THBRq9UEg0E6OzulNNTf/vY3brjhBrq7u/nnP//JzTffTFVV1fd+//r161m/fj3XXXcdd911F5dffjl33303Z555JldddRUrVqygpqZGhqqFvE/8pp2fny/pBdOmTcNsNu/RxjJUOU/BzYpGo5KX1dPTw7Jly6iqqkKpVPLvf/+bcePG0dPTwz/+8Q/+/ve/D+B+Qf+GPXnyZNavX88XX3zBF198QWJiInPnzuXwww/n1FNPxWw2c/3115OSksJLL70k+Z+tra1kZmbS29srxftFqdh4rUWr1UpbW5v0kIlwcLw0jzD0oD8jX3i+tFqtlKgSxpHb7aasrAyXyyUTDq6//noyMzP5/e9/z+bNmwfUnk9LS2Pfffelq6uLzZs3U1FRwc0338yjjz7KpZdeynnnncfJJ5/MggULePnll3n88cfZtm0bW7Zsobu7m4yMDKxWK5FIRJa8FGVtg8EgU6ZMkQefMNj29ACJT7gSiEQi1NXVyTHo6uri888/lxGDjz/+mNLSUg466CA2btw45Pe2tLTQ0tLCypUrB7z+l7/8hcWLF3PDDTfQ1tbGU089JS9qmZmZ0iCLxWKYzWaplTl69GimTp1KV1eX5N8K7KrRNliRQHi7SkpKZBRASJJVV1dLWZ433niDGTNm4HK5uOuuu3juueck5zs5OZm8vDy2bt3KE088wQsvvMCcOXM47LDDOOyww9hnn314//33WbhwIU1NTdTX19Pd3U1WVpa81IgLW25uLvn5+WRnZ5OamkooFCInJ+dHF5wQHllRc76kpITq6moAHnzwQSZNmsS1117LU089BfQbayKRxufzsXHjxh3m+YQTTuDee+/ln//8J11dXTQ0NHDSSSfR2dlJZ2cnlZWVFBUVSS+eXq+nu7ubUaNGkZ2dTUZGBr29vaSnp+NwOCRHUhikgnPs9XqlKsfOIHjKAAsXLsThcHDSSSexbdu2Ae9ra2ujra2NZcuW8a9//Ytrr72Wq666SnLfH330USorKykpKSEtLY3e3l6pSSvGLysri9TUVLn/lJWVodVqGT9+vCxzDN85M8SFaGdrdE9C+w8//DCRSISDDz6YK664gtNPP51t27axYMEC3n//fUkvE1Grvr4+eWGcOnUq06dP/0EViL2RiPdTY/ny5QPKkYq9bCiI323atIkZM2b8XE0cwQh2G3sc1he13kUYPxaLYbFYKC8vp6uri8mTJ3PVVVfxzTffcMEFF8hNU6VSMWXKFMaNG8f06dOZMGGCzPJ0uVx88cUXrFy5kqqqKq677jqcTidXX3016enpvP/++1xzzTU8//zzNDc3SykaYfgJbT6VSkVmZqYUohbYXY/L4E2rs7OTpqYmAHmYJiYmEo1Gpbf2xhtvZPr06bS3t3P++eezbNkyoN8TUlhYSEFBAcXFxRx66KEkJCTQ0NDAypUrWbp0KS6Xi08++YRPPvmEF198kVdffZXs7GyOP/54zjzzTFatWsWVV15JTU0NLS0tsvpLLBajsLAQv99PRUUF48ePJz8/n6SkJNxut8wyF8Lw6enpA/QHhUEpsouzs7NlVvrWrVuBfoNp2bJldHd3A/D73/+eyy67jAceeIC7775bjllCQgLjxo3jgAMOYP78+fI7dTodX375JW+++SadnZ3ceuutPP7449x0002cfPLJ/P73v+e0007jggsu4O2336ajowO9Xo/VaqWjo0MK/dfX11NXVycVGWbNmoVer99BmmZ3YTQaKSwsHJAM5fP5cLlcWCwWHA4HK1euxOv1kp+fzxtvvMENN9zAe++9B/QbBlOmTGHu3LlSiSEpKYmqqip6e3ul3mR7eztbt27l6aefxu/389BDD3H77beTkZHBHXfcQX19vSxoIPRBPR4P48ePp6CggDFjxpCeni7lysSFaU+MNmGcOhwOGhoaZBZzamoqTU1NVFRU0NfXR2ZmJv/5z3/QaDQ8+OCDPPbYY9JTevDBB3PEEUcwZ84cVCoVq1ev5tVXX6W0tJTPP/9cevKmTp3KHXfcwZdffsk111zDe++9J5UBRHJNNBqVnFaDwSC994WFhTL8LeZK9PGHnmmhqiFkjiKRCD6fj4qKCjZt2gT0exlPPfVU7rjjDv71r38BcMstt7D//vvLaleVlZX09PRQV1dHVVUVNpuN2tpa3nvvPT7++GP+/Oc/83//939MmzaNl156ifnz50u6iqh2p9frJc1Io9GQk5MjK4yJ51N4TEVVtHjdYVHgRHjRB/dXVOUTBUp+//vfU11djdVq5eijj2bmzJkUFhZKrdyPPvqITZs2ceedd/Lcc89x6623csopp6BWq3nrrbf44x//SElJifxOAI/HIytmRaPRAQL40WgUvV4vq4U1Njbi8XhkApXX65XV/cT+HP/8iffsqmD/f/7zHwDGjx/P66+/zoknnsibb77J0qVL+d3vfsenn35KNBolPT1dKlBUV1fLksKD9XMFdpbANYIRjODnw24bp+KBFoe28K55vV5efPFFWltbUalUPPPMM+h0Oq699loZujrvvPOYN28eFouF0tJSGXro7u5Gq9WSnp7Osccey3XXXccjjzzC448/zr333gvAlVdeiVqt5uGHH8ZkMrF48WIaGhrkwaZSqWTyRnp6OgkJCRgMBurr6/F4POTn58ts8109vOvq6sjPz99hg7LZbGzevFlqPG7ZsgW3282MGTO4+OKLUavV3HDDDSxbtgy1Ws2NN97ItGnTJG+uurpaamMmJCRw5ZVXcvnll1NSUkJ9fT3PPPMM27dvZ/bs2VxxxRUsXLgQhULBnDlzWLFihRyX5ubmATJA4gApKyuTtczFYSgOxsFhwXh1BLPZTFJSkvzO7u5utm7dKoWuBYd4yZIlzJs3j0suuYSvvvoKtVrNeeedx7Rp05g2bRoqlYqqqirJ8/V6vaSmpnL88cdz2GGHUVtby6OPPkpzczNXXHEFTz/9NIsXL+aAAw7gmWeeweVySU+0kMMRRkZvby9ut5uUlBSSkpKkh8/hcBCJREhMTGTixInfO7/x3t2h0NjYKPUhTSYTSUlJvPrqqzgcDrKzs/nwww956623eO+991AoFPz5z3/md7/7HWPHjgUYkAErssKhf53n5uby7rvvcv311/Pyyy+Tnp7OokWLuPHGG4nFYtx55500NzdLkXO/3096ejo6nU5ytEWSn5i7eJmwXVnb8YevwWAgJSWF5ORk7Ha79KyI8rsZGRm89tprvPjii7zyyisyK7+wsJBFixZx0EEHSVF6gGnTpnHiiSdSXl7O119/TWlpKStXrmTr1q2cdNJJ3HrrrTz11FMcf/zx3H777dJQipdoE21ISkqSGe1CdmmwYdbZ2fmDz7Qw2kUyX2NjI5999hnBYJATTjiBe+65h3/+85/cf//9AFx//fWcfvrpVFRUSBWS9PR0yd92uVzk5+dTUVHBPffcw6pVq3jqqadwu9288MILHHrooVxzzTU8+OCD2Gw2LBaLjEbk5+dLj15zczNWq5WmpiZycnKYOHGiVMyAHXV9jUYjvb29O6VzCCrV4Ycfzt/+9jeqq6tJTU3lhRdekBfXf/zjH8ycOZOFCxdyxhln8J///IdHH32U1tZWLrzwQlwuFxdffDEpKSn897//5dhjj2X9+vUoFAqpAxsIBAiFQtTW1krtYp1OJxMpKyoqMJvNWK1WaXgLvq/Yp4UiweAKWLtzwYrFYowdO5ZvvvmG8vJyJk2axLx58/jvf//Lpk2bmDt3LitXrmT79u2SXpKbm0skEpGX9qKioh0M072dRDuCEYxg97HbxqnYRLRarQzpu91uNm/eTE1NDQC33nor06ZNY/HixZSWlmI2m3n55ZeldxX6OZsffPABq1evlmFytVrNH/7wByZNmsSVV16JTqfjoYce4t5772XDhg3ceOONzJw5k9tuu42tW7fy5Zdf0tnZSU5OjpRBEfW7R40aJavt1NTUUFNTI0OFQm5qMAaHuUWVF3GTB+TmKjZngNLSUqA/zKRSqXj77bdZsmQJCoWCxYsXc/DBB8v3Qn+i1p133kl1dbWstjRlyhSOPfZYLrroIo466iiuv/56SkpKuO+++1i3bh1vvPEGOp2OhIQEbrvtNiKRCPfdd59sr1AOyM3NlSX7HA4HRUVFBINBWU978GabnJwsk4lE4o9Op5Nj4HA46Ovrk5zO7OxsjjzySDZu3Mi7776LQqHgxRdfZMaMGTK7++OPP2bLli04HA5pkB166KHMnDkTg8HAySefzLHHHstTTz3FCy+8wPbt2znllFP45JNPmDRpEi+//DJz5syhtrZW1sqOLxcpZKfWrl3L1q1bSUtLw2KxSAWBHzrYBhunYt69Xq/0znq939VZr6+vl2v7+eefJy0tjSeeeAKAu+++mz/+8Y80NDTI74tEImzYsIHKykqysrJIT08fwLE76aSTCIfDLFq0iH/84x9kZmZy+eWXc+ONN/LVV1+xcuVKKcMmQstCx1ZIdBUVFUmZMBHe39WoQLwBYDAYSEtLY+LEibL/vb290gi99957+fvf/87SpUsBGDVqFH/5y1848MADaW5u5o477pCFKPx+PwcddBCXX345EydOlHJRXV1d3HXXXbz77rvceuutBAIBbrvtNs444wxWrVrF2WefTWtrK52dnYwaNUrqVQYCASwWC5FIhJSUFMm5jE+AMxqN0rs4+PkVc22322lubpbjUldXh9vtRqFQ8K9//Yu33nqL++67D+i/BJ977rkyotHW1kZXVxdTpkwhNzdXKoxAf1b/k08+ycaNGznvvPN47bXXOOGEE1iwYAFnnnkmDz74oBT393g8WCwWAoGAvHi0trZKSTFRMUtc9uPl0+LnVUjExV8wRH8FRaGwsJAHH3wQhULB448/TlFREXfccQdvvvkmAGvWrOHf//43Z5xxBqeeeiqnnHIKd955J0uWLOH666/H4XBwww03kJyczCeffMK8efPYsGEDTqdTSreJggRCAH/06NFYrVYcDodUEMnNzZWXYRGREHMp2h9PpYk3UOOpVIPXdfz86vV6tm/fDvTvwwceeCATJkygoqKCgoICcnNzpV6t0+nE7XbT3d2NwWBg2bJl5Ofno9frycvLk5eiSCQyoGLXcMdf/vIXEhMT+eSTTyQta/78+QDyNQHxu3jptxGMYDhij8L6YpPs6OiQtd+FEWO1Wjn55JPZtGkTt99+OwBnnnkmXV1dUr9y+fLlrF27dkhJpXvvvZeenh4OP/xwDjroIJRKJQ899JAMEc6dO5d77rmHK664gi+//FImrYjwoygvKaqgCFHwrq4uMjIyZJh/qNDR4DB3Wlqa5DK2t7dLL2xTUxNGo5GUlBTWrFkDwB//+EcmTpzI8uXLueqqq4D+0LdGo+Gjjz6isrISv99PWVnZAA5YJBKhtLSU0tJS/vOf/3DRRRdxyCGHcP3111NVVcXdd9/Nl19+yRlnnMHTTz8tS/Wddtpp3HfffYTDYZKTk8nNzaWvr09WfBH6isL7J8L18Ru/OAjiw2ter5eWlhbJ9RTfI4wrkeTx+OOPAzBnzhzUajVbtmzh3Xff5YMPPhggMSbGWoRQMzIymDx5MlOmTGHatGm88cYbXHXVVVRWVjJ//nzefPNNxowZw5NPPsnRRx+N0+mUiWXxZWo9Hg81NTXEYjH2228/srKypHasONBEuczBiO9v/LwDMiO+vb2dxsZGamtrqaioIBaL8Yc//IExY8bwxBNP0N3dLblrNTU1VFZW4na7+eSTT9iyZcuAAzQ1NZV9992XOXPmSG7Y5MmTueiii3jyySf5v//7P6LRKOeffz5nnXWWpA/k5OQA/bqh2dnZ2O12ysrKGDNmzADvmjAAdsWDKNa/MOgEvaOzs5P29nbphY1EIuTm5qJQKFi6dClKpZLrrruOuXPn4vV6ueqqq4bk2b7xxhu8++67HHzwwRx33HHss88+QD8HcvTo0fz973/n/vvvZ86cORx44IFMnjyZP/3pT9x3332ykIXJZEKpVNLa2kplZSVWq5XU1FTZ58EJcCKaI7yo8WMvDG6R3FNeXi6rBU2dOpV169bJ5/XPf/4zhYWFnHPOOXz77bcD+vX555/z3HPPcfDBB3P44YcPKLgwffp0zj33XF544QUuvvhiJk2aJJOMYrEYmZmZUppJKFoImaOcnBxMJhNpaWn09fXJ8ReXjviwvhCNt9vtA+ZatEVUZfviiy8AOPTQQwkGgyxcuFDuUxMmTKC1tRWHw8Gjjz7K+++/zyOPPMLChQtRKpU8//zzPPDAA6hUKm688UZ0Oh1vvPEGxx57LOXl5bS3t5OTk4NGoyElJYWenh6p16tQKGQpY+H9HzVqlKQoAIwZM0aWe25paZE0CxFV2tmeHL+u4+e3sbFR0gtCoRDPPPMMp512GjabjQ0bNnDUUUfJSoPiYtHa2gr0UxRaWlqks8VkMslxF5cg0abhDPEcxpcv/frrrwe8JhD/uxGMYDhDuScfEt6WjIwMHA4Hvb290nt4xhlnYDQaeeCBB/D7/UyaNInjjz8em83Gv/71L2699VZZWcZqtXL44Ycze/ZsTjvtNKZOnUosFuPxxx/nq6++AvpvhZ988gnHHXccKpWKlStXcuutt/L73/+ek08+WSatNDQ0SDHxQCAg5adE7WqR6SsMxPb2dul9ERBeGIHi4mIpKF1RUUFzc7NMGmlra6O5uRmXy4Ver+e2224jFotx22234ff7OfDAAzniiCP48ssvWbRoEUuWLOGNN96Qhun48eM59dRTOeKII1iwYIE0LhcvXsymTZsIh8Mcc8wxPPzww+h0OpYtW8att96KXq9HrVZTXFzMPvvsQyQSkVJeQgJJ8AdHjRpFUVERKpWKlJSUAZ5ThUIhDwKx2ft8PinHVVZWxvLly+nq6qK3t1eG9EePHk0sFpMJPgcffDB6vZ7W1lbeeust/H4/aWlpFBUVceihh3L00UdTXFwsDceOjg6WL1/Oww8/zFtvvUVmZiZPPfUU48ePx+FwcMkllxCNRpk7dy7XXHMNAJWVlWzdupX29nYCgYD0BIgqPAaDYcBrvb29NDQ07DR8P9g4hX56g9FoxOVyUVdXR2NjI+3t7bS3t9PS0oJareb2228nEonIhJnTTz8dq9WKxWKhoaGBBx98kDVr1kg+aH5+vtS3/Oyzz7jtttt49NFHcTgcqFQqrr/+ehYuXAjAokWLKC0t5fjjjyclJYVQKCR5juXl5VI/Mzs7W2r7xh/68R7EnUEYNdBPz4k33m02Gzabjbq6OrZs2QLAeeedxxtvvAH0e3unTZuG3+/nxhtvlAfivvvuy9y5c6VhnZWVRSgUYvny5SxatIilS5ei1+vRarVcdNFFnHjiiUSjUf785z/T0dGBVqtlypQpAFKeqKmpCYVCQV9fH6WlpXz11VdUVVUN8GYJuaDBSTVqtXqH+e3r6yM1NZWuri5aW1vlWj744IO59tpriUQizJ8/nyuvvJIPP/xQGqYpKSnMmDGD2bNnYzAY6Onp4cMPP2Tx4sVEo1GMRqOkzFxzzTUUFxfT3d3Nv/71L5KSkuSloampSZZ8FRxUUWSiurpazklVVRVPPPEEX331FTU1NdLwjn9mxXM72GgVGDNmDJs2bUKlUvHHP/6RJUuWsGbNGpRKJfPmzWPBggWccsopnH/++ZhMJpqamrjuuutQKBScf/753HrrrQA89NBDVFdXS23Sjz/+WF5uRURBSFbpdDp6e3txuVzyYiqqmnV0dNDc3CwNPrVaLf8ViZqietRQnPGh1nV8f8UlJSkpiVGjRgHw9ttvc8IJJ6BQKPj888857rjjgP6oS319PdXV1VRUVFBfX8+KFSsoKytjw4YN+Hw+SZWJN4pHMIIR/PzYLeNUeFjEoa9SqbBarRQUFMiH+rDDDqOiooL3338f6PdGfPTRR1x55ZVUVFQA/by03/3ud1x88cUceOCB5ObmMmrUKI455hhmzpwJ9GeMfvTRRwAUFRVx22238dFHH6FSqfjiiy+khIsoJxgOh0lISECpVBKNRmU5UFH/+dBDD5UJQmVlZTt492CgF2Yw0tPTUalU0iMSiUSklNLhhx9OTk4O//rXvygrK8NsNvPAAw8QDof5+OOPgf7Nc/LkyRx++OHMnTuXAw44QPJiJ02axFlnncW0adOA/sxhQQOYNWsW99xzD0qlkpdffplnn31WtunYY48F+ksQxmIx+vr6cLlcNDY2smXLFkpLS6mrq6O1tVWK28fPY3woG/oP0UgkwtatW1m9ejW1tbW0tbURiUSkV+awww7j1Vdfpbe3lzFjxjBu3Dj6+vr4xz/+QTQaZdSoUZx++uky+UvI1lx66aX87W9/4+ijj5b9/PTTTykvL8dsNvPwww+TmppKZWWlTEq5+eabGTduHNFoFJvNJsuriuSY/fbbj+nTp5ORkSFL2Nrtdtra2mQ4dFchJHuE0SDKOgrPeWFhIaNHj+aFF17AbrdTUFDAkUceCUBtbS3PPPMMbreb3NxcjjzySC677DLmz5/PueeeyyWXXMKkSZOIRqN8+umnnHfeeXz11VcoFApuvPFG5s+fTzQa5eqrr0ar1XLuuefK762urqayspKvv/6azs5OKYck5lJcpuI9S/X19ZKaMBiDD3qfzydpNZFIBIfDIUN+06ZNY9WqVQCccsopRKNR7r77bmpqajAYDFx++eWcccYZTJgwgQkTJrDPPvtwzjnncOuttzJ69Gj8fj9///vfOe+882hra0OhUHDHHXcwbtw4Ojo6OP300/H7/fz+97+XuqA2m02W3BS859raWsrLy2VlJZGMI8LbYi2L53ewcRqJROT+1NPTI/s3evRo2Zdbb72VhoYG6VU6+eST+d3vfsf8+fM54ogjOOecc1i4cCEGg4GGhgb++te/yuRA6E8mvOSSSwB47733iEaj0mssaEC9vb2yipTZbMbv99Pb2ysvl42NjXR3d9Pc3CyfSTG3oo/ADkZrfH/Fep0wYQJ2u53ly5ejUCg4+uijZXtUKhWzZs3ihhtuwGQyUVVVxV133UUsFuPCCy/ksMMOo6+vjwsuuEAWE8nNzeWee+6R7RFtcjqd9PX10dnZSX19PaFQiIyMDEaPHs2oUaPQaDTSKzo4RB9/yYo3BIea050ZpyKpT6PRMH78eKZNm0Y4HGblypX86U9/AmDz5s3k5+dLPWhxia+vrycQCEgOraiuJxRZBpfZjsfgOfm1ory8nE2bNslE3xGMYLhgt4xTn88nSxpWVlZKHp7gnQKMHTtWJjEdeeSR1NfX8/TTTwP9B/yxxx7L5MmTUSgUrFy5kjfeeIOamhq+/fZbNm3axKRJk2QlkxdeeIFFixaxZcsWamtrycrK4vjjjwfgpptuIjk5WRpogUCAlJSUAQlAiYmJeL39gvlr166lra2N6upqyeODgRut2GjiNz+73U5jYyNGo5EJEyYwceJErFYrOTk50mDbf//92bhxo8xaP+ecc+ju7ubjjz/G7XZjMpk47LDDMJlM1NTUUF1dzdKlS/n0009xOBysWrWKNWvWMGXKFIqKiggEAtx999288847lJaWkpGRwV//+legn8/79ddf4/P5OOSQQ4B+Ie2cnBySkpJk1ZloNMr27dulhNPq1avxeDyyv0K+x+v1SiNbq9USCoVkeE7ISYmMe5VKxezZs6WBvGDBAiorK7nnnntobm5Gq9XKMGBpaSkff/wxn376KV1dXSxdupQVK1ZgMpmYPHmyPCxuuOEGNm3ahMPh4OqrrwZg8eLFVFRUEA6H5Zh6vV4pFC9qtAuvjpDkSUxMRK/Xo9frZSLJDx0eIgHH6XRSU1OD2WxGq9VSXFxMfn6+9CyKEpWPPfYYAGeffTYVFRV88MEHXHTRRXi9XlJSUjjkkEMIhUI8++yzPPXUUyxbtoxwOMzcuXP54x//SFFREdFolMWLF/Ptt9/S1tbG+eefT2JiIhs3buSZZ57huuuuY/To0YTDYTwejxRC3759O83NzVRUVFBSUiJ1QuG7kL4IeYuM6qEQz83ctGkTJSUlJCQkoNPpZHgU+ikOvb29mM1mDAYDzzzzDCUlJWg0GubMmUNNTQ3PPvssn376KX//+9954IEHJDdx/vz5HHXUUSQkJFBeXs4111xDRUUFTqeTW265heTkZEpKSrj44otlCFn0A/qN6EAggE6nk9WTBK9alNyMRqMDQtxDwWg0Sv3ctrY2Ghoa6OvrY9y4cXz55ZdAf3GQsrIyrr76aiKRCKmpqTidTpqbm/n8889Zvnw5drud3t5eDj74YAwGA7W1tfzlL39h06ZN1NXVUVdXR0FBAYmJibS0tPDVV1+xYMECuTep1Wo0Gg0JCQlSG1TowIr5UqvV6PV6qcgASEmqXfXiiUtLUVGRpN5MnjyZcDjMZ599xhtvvEFHRwfr1q2jtraWY445BqVSydKlS3n66afp6OjghhtuwGw2s2nTJm677TZcLheRSITjjjtOqpNoNBppVDqdThQKBYFAgLy8PNLT0xkzZgxWq5Xp06djMpmw2WyUl5fT0NBAQ0MDnZ2d6HQ6aXzGX5oGey2/zxAUxqmQisrKykKj0dDU1CQVPZqampgwYQJqtRqfzyf/VmpqKkqlUnpUGxoa+Oabb6RnXVxY4y97oi27Myd7G01NTWzatElKNu4p9Ho9Z511FjNmzKC4uHjEQB3BsMJuGacGg0F6agTnr6amhtLSUlmdyGq1Su/DX//6VzZs2CBv0zk5ORiN/SXxqqurKSsro7q6mpKSEr744gs2bdpEbW0thx9+uCz/+M4778hsS4vFwtVXX41arWblypXU1dVx2WWXAf2bU1NTk6zz3dLSQm1trUwWstvtBINB8vLy8Hg80psoNpz4jWawcSo8denp6TLrd+3atTKb+ZRTTuHNN98kEAgwZcoUTjzxREKhEOvWrQNg9uzZLFu2jG+//Zampiba29ux2+20t7dTUlKC0+nE6XTS1tbG2WefzahRowgGg7zzzjtYLBaUSiXnnXcec+bMIRwO8/TTT6PRaJg1a5Y0yAVfMBwOE41GUalUtLe3U1ZWRklJCS6Xi23btkn9u/jEJ5GkplKpiEaj0jjJzMxErVZLMf0DDzyQsrIyWc1p3rx5skStQHV1tdTJ7Orqor29ndWrV1NZWUlFRYVMAhOeY5vNRklJCSqVipNPPplZs2bR19fHY489hkaj4cgjj5Reu9WrV9PT00NpaSk2m01eloSXTfQhKSlJZu/v6uEhONOdnZ1YrVZZQUhQP3Jycvj444/xeDwUFhby+9//nmAwyEcffYTX6yUpKYnjjz8ek8nEhg0bpMHf0NDA9u3bMRqN9PX1ccIJJ5CdnU1fXx+ff/45ycnJFBYW8uc//xno9yYnJSXx0ksvoVKpcLvdRKNRPB4PPT09VFRUsH37drZt20ZbW5ucT3HgCj64KOkbDyFTFG/IOhwO6TEOhUJyHAAp/zZz5kxSUlJkuHvBggX09PRQVVVFRUUFTU1NdHZ2Yrfb+eKLL+ju7kan05Gbm8uiRYtISkqS5XiTk5MpKiriySefRKlU8v7779PU1MQ555zD6NGj6e7upr29XYbCxWVJeLKEUaNSqVAqlZKK8n2wWCwyGUZEJObNmyejGocddhiVlZWy4IFareaLL75gxYoVrFu3jjVr1rB8+XJZ1vPQQw/FaDTS1dXFmjVrSEpKIikpieTkZA4//HAAvv32W84//3ymTJmCy+WitbVVRjWEoSsqLAUCAbxeL1qtlv3335/DDz9cJqi1t7dLSsCuXLbE3JnNZjlWBxxwAGvWrGHr1q00NDSwatUqWQ44OTmZ0047DeiXZkpMTGTMmDEsWrQI6E8AFFqmZrN5QIhcyDMJNQ1xmUpISKCxsZGNGzdSWVmJz+fD4/Hgcrlwu91EIhGp0yvWbLx3dHAof1dC7L29vZSXl9Pd3S252m+88QY333wz0F8IRHi2hRyWzWajqqqKpqYmmpqa+Oqrr+T+Jy4ogibU0dEhjer4C9TPzUdtamqiuLiYGTNmcNZZZw1IdtpdbNq0iY0bN/LKK6/ICMoIRjBcsEecU6PRSH5+PhqNhpaWFhkmi/fCQX/YTCQxCA+IQDgcllqJarVaekE2b95MJBLhoIMOkiLBzz777IDwkqh3vWbNGqZPn05ycrL0qInykqKij9BRFGEfk8lEYmIiSqUSl8slN0chHF1fXz8g/A3IxCDxfTabjcrKSgAuueQSEhIS5K3zwAMPBOD+++8nEAiQnp7OtGnTBiQ7iBr10M+BFZudGKdDDz1U/i4egsh/0EEHAf28rjFjxgD9h5Lf75eC8aLqjF6vx2AwoNVqZRhRJAupVCpZF1vMq1AEmDhxIunp6VIkXMynaFNWVpY0ZgWE5iogNVih37vR29sr+avQf2sXYUahBatQKLjooouA/kuJSJh74IEHpIyOyFjv7OykoqKCjo4OOjo6WL9+vdRGFQbqrmbcCuMyGo2SmppKMBiUlb+EYT537lxJS5k9ezYqlUryQqGfe6nT6Whra5PrRoQz16xZI5NwlEqlPDzFmhZ9j//MzJkzpVdUrVbLRLe6ujrJLxbC7kICSvxXVFS0g0SO+B7B9xP9NhgM0oMXDodlqHrBggXyb4q1KqTBRGKJeF2lUjF+/HhZ7vLDDz+UiVdJSUmcccYZALz55ptyDc+ePVtmfgtOr/jeWCwmkxlDoZDkkYvkGZfLRVZWllQr+L7SvF6vl6ysLFluWazP7u5uwuEwEyZMoKCgQPJsTSbTgD1MrGFxCRMJW0JWKv7ZFZ8Xn1Or1cyZM2fAd4n/xCVCjL/gbsKOahIGg0E+mz9knIrnU/ybk5Mj50nsP4MF2ufNmyd5o6JIys4UTQStSfQjMTGRzMxMtFotWq2WWCwm91an08mqVatobGwkFAoxatQoxowZI+Wyurq6hvSI/pCx+kNIS0uTSgJarVauv9dff50TTjgBQBrUHo+Hzs5O6SxZsWIFdrudzz//nC1btrB9+3ba2tqk4gkgIzY/pnTwnkJU0HvllVfYuHGjTDTdE+Tl5bHvvvtSXFy8F1s4ghHsHeyWcSq8fUIkWqfTSR1IIfUTX45UHMjis6Lmu+APxmIxtFothYWF5OTkoFQq8Xg8/Oc//6GsrIwJEyag1+tpaWnh1VdfpbGxkcbGRsaPHw/AihUrcLvdMtlJGFY6nY60tDSMRiM5OTkYDAZZOWnSpEmyPjQgN0efz0dnZycdHR0DEoSEviT0H5o2m43t27fjdrtJSEjg7LPPxm63S+PU7/fz6KOP8u2330pD5KOPPpKGlkaj2aHUnKg5HgwGpcA39BsvdXV12O12VqxYQV1dHQkJCSxYsIBAIIDf7yczMxPoz8IUm6eQpklNTZUGqlarJS0tjZ6engFGYlpaGikpKQAyo1kcBOPHjyc7O1se6ELeB/qpBG1tbQNK4AneIvQbW+Jzgs8lyghWV1dTXV0ts5grKytZu3YtFRUVJCcnY7VacTqdvP/++7hcLqxWK9dddx3Qr+Uo6t0Lr3hNTQ02m42enh4KCgooLCwkMzNTJrPtDGLevV4vJpOJAw44gNzcXMaOHUthYaGcT+hPnhHGqdlsprW1lfXr1xMKhTAajUQiEcrLy2WoWCQbpaWlEYvFpOh8bW2tvJT19fXR0tJCZ2cnmzdvBvovAMFgkGAwKNeoWq0mLy9vQKUzEbIUHvBd9RAbjUZp9Op0OnJycohEIiQlJUmlAqVSKcPs0O9pEsUbAEpKSvD7/QMy+8UY6nQ6enp6eOONN3A6nWzfvp1QKERBQQGhUIi7776buro6Ojs7pSHX1tZGIBCQe0d6eroUoBdlPUeNGkViYiJ+v5+kpCR5sdqZlxi+o2yIS5NOp5PlQUVUY+7cuVRVVbF69WoAWfcevns24hU7VqxYQU9Pj5zDYDBIa2srra2t0iMqINYGIHWcMzMzSUtLIzk5GYvFgtHYr9O83377MWPGDCZMmCD3G/F+YQTtioEm2i5C06FQiO3btw/gsAJs3bqVbdu2SS1jcSlavnw5DodDaqYmJibidrvlmhR7Y0JCgiw5K4p+pKSkDLgctre3o1Kp6OjoIDk5WaoSiKIKQrZsZ2tXlB4VCbi7ontqs9loaWmR2sKvv/46hx56KGPGjKGzs1PurZFIhKKiIpnQmJycLDVcu7q6aGpqorKyUnqrU1JSMJvN0igVSVwixP9z80+Li4vZd999ycvLk0ll8RjqtV353QhGMFywR55TEeoQt+e+vj652ONDA0KYXnDGmpubsVgsTJo0SW7ioVBIVjwSXrjm5mZSUlKoqKiQm21paSkWiwWLxSLF5pubm6X3Afo35La2Njo7O3E4HBgMBhkOE8aj8NwITqrNZpOeVrGxiu8Tn4+X7YnFYvIQLSoqIiUlRYpsQ//hILwwsViMrq6uAQa7MNTivaLCM6tUKlGpVDITWoTYEhISpED48ccfT0ZGBnq9no6ODhlSdzgcUkdWq9XKDGWTyURWVpZMvhKHkIDIAIZ+Y0XUaRbcxXjvXmNjo/SeCY5pbm6u9LzGS4NFo9EBtckFgsEgKSkpJCYmSn1C6PcImkwmLBaLFLBft26dVCcQGd0i09lut+N0OqVh39DQIMOjwADO3s4ODWGYCu6b4KHl5ORQUFAgJaTS09PJy8uThrhCoaCoqEhGDIQWp8lkktxrn89Ha2ur9MKJRBiTySQ9V1lZWZITJ6R+Zs+ejVar5csvv5ReW6E+oNVqMZvNpKSkoNFoyM3NpbCw8AfL8wqDYnBotLW1lbKyMjo6OrDZbNIgiUajUrcS+nV5LRaL9BbW1tai0+nYb7/9pLHX3Nw8IJFHXIBycnJITU2VF7Jvv/1WejuFgSfUL0RIOhAI0NnZidvtxu12y/ruTU1NeDweVCoVhYWF0tARAu+D1TfEM11YWEh2djYul0v2X1ABDj744AEJcPGXLbvdvoPB6ff72b59u+T7ZWVlSU91MBiURq6QtROqFmKPEWFwUfo5ISGBMWPGYDKZGDNmjNRnFln5wjAdKjFoKIjxjy9tazKZdiifHIlEUKvVuN1uuWdAv0c5PT1dRgRSU1Mxm82o1WrUarWcM7/fT0NDAz09PXKfUKvVaLVaWltb6ejokGVNGxoaZJIifFeNLSMjY8DFYk+TjMT+Jfb9+KS+xsZGtFot//jHP4B+2aWZM2fKql+BQECG7QWXuaysjPr6eik/lZCQIKlhDQ0NlJaWyvXmdrupqKigvb1dcvh/TsTr+w6WCBxKVu77fjeCEQwn7JZxKkJRwIDwd3JyslzoNTU1MlHnoYceQq/Xy6zmjo4OPvnkE1577bUBId8BDVIqSUtLk2EL6PdUHX300UD/5rlkyRKgv6711q1b+eyzz1AoFGRkZMikDnE7FOGj7u5uSktLWbduHY2NjUC/MZaXl4fT6cTv92MymZg4ceKAMolCf1B4YdVqtfTilJeXc9NNNwFw1FFHAbBlyxYOOOAA2Tchpj4YIuyt1WqxWq1Eo1Gqq6t5/vnn6e7uxmQycd5559Ha2srf/vY3mpqaKCgokElDVVVVzJs3j6amJskH8/l8JCYmEgwGUSgUWK1Wxo4dKyWHNBoNbW1t0miF77yHgJR38Xg89PX10drailKpZPz48ahUKj788ENcLpe8XFx22WV4PB5JQxjcPxHqUygUqFQq6QWrqqriiy++oLa2lmg0yqRJkyRV47///S+bN29GrVZLLtwLL7zAKaecIudM0ECEtqIw2np6eujo6BiQSPFDXDWv1ysP8o0bN/L111+zbt06bDYbZrOZxMREOjs7+eKLLzj77LOBfprJtm3bGD16NGq1WupxhsNhaUQLiLlIS0ujra2Nl19+md7eXtLS0liwYAEVFRVcc801BINBjjvuOKZPn86XX37JH//4R0KhEBaLRfbXYDAwceJEWdbV5/PtoPM5lBdHzG+84QX9RmFTU5N8ZsaOHSuNlIsuuohjjz2WoqIiOjo6WLhwIbm5uUycOJFQKMT69etZu3atNFgFwuEwarWaCRMmoNPppJKB4HOee+65hEIhrrrqKmw2GxkZGcybN0+GJ0U0JjExUV5EW1pa2L59O1u2bGHdunUyzC1gt9sH1HMX2Lx5M42NjXR2dmI2mzGbzXK/ETzr5uZmEhMT5fMg1u5Q0Gg0aDQampubCQaDjB8/Xj7rDQ0NXHrppVKw/7DDDuMPf/gD69atIykpiZSUFGmgm81mTCaTzGgXnjiHwzGkzufuGGyCGiHWtM1mk4mbAklJSeh0Onl5fuONN6iqqkKpVHLCCSdQUVEhFTMuuOAC+blgMCjzCURf/H4/3d3d9Pb2olKpqK+vl1n84jURKRNybQKDDe7Bz6sooCA45T+UFCUSB4PBIE6nE41GI8P4Ylx0Oh2XXnopgIzeiX6Ew2GCwaCMxmzfvp3KykrKy8spKytj69atcj3FZ/SL0tu7qxAy3DCSuT+C4YQ9qhDV29srQ4FWqxW/309eXh4tLS1s3LiRCy64gFWrVrF69WqMRiOzZs2iqKiITz75hPb2dnmrFV67eN6ikA2C/k38iCOOYNq0aWg0Grq7u7n77rtxu91SvFvIhaSlpTFr1iwqKipQKpVkZmbKzFKLxTKgFr3f72fChAmyfGp5ebn0AInM7XgILUmDwUBmZiaZmZnMnj2bzz//nEcffZSCggIWLFjAU089RXV1NfPmzeOMM87gtddeIxQK7UA0V6lUcnPXaDSyIo/wekydOpW5c+cSDoe5/vrrcblcFBUVsWTJEulxPO6442TCw6hRo8jKykKhUEh+mU6nIyMjg1gsRnZ2trwxx5e6FAeBMF5SU1NlCLCjo0Mm4wiu6bp161i0aBH3338/99xzDw0NDbz11luceuqpKBQKyR2N76OYXyHx1dbWJufbYrFwzDHHUFBQIIsRPProo0B/CckJEyZw3XXX8dJLLwH9fGPhXU1LS8Pj8UhN1Wg0KktwCm+M6PP3eQiEsQv9h6EotJCSkiIF0j0eDw8//DBvv/02b7/9Nl9//TXXXXcdRx55JLNnz2blypXY7XY+/fTTHaTIRO1xYTgpFAomTpzIvHnzaGho4Oqrr8br9TJ79mwefvhh2tvbOeecc2Tm8dixY2WyoZAL0+v19Pb20tbWRmlp6YBqQk6nU2YZD5YaEnxBMf/BYJDU1FTC4TChUIji4mIZBn777bdRq9Xcd999XHrppVRVVfHKK69w2mmnsWzZMlavXj2AgiJgNpvJycmhr6+Pr7/+eoD80VVXXUVWVhZ33303ZWVl6HQ67r33XkwmE9988w2ADNNnZ2fT09MjDcfu7m7Ji/3222/p6uoiNTVV9nGo0rwqlUoKvbe0tJCZmSm9d+np6bS1tbF27VopZbdly5YBnOn4i3O8ioFCoeCYY47h0EMPJRKJsG7dOm666SYcDgcTJ07kqaee4sILL2TNmjUYjUb2339/6Z1NTEwkMTGRjIwM6QXPy8uTuqYAM2bM2KFUrRjDH4KIyJSVlZGRkUFHR8cOvESdTkdlZSUOh2NAtGPevHlYrVYpIXXUUUdx4okn0tfXh8/n46677pIGZ2rq/7P33dFxVtf2e2Y0vWi6NLKaZVm23LBxoZkADoQSQgoxhF+A0CEBUl6AhIQ0kpfGS0JIQkjIoyQOkEJ5xNRQHAM22AYbF/U26pree/n9MescvhlLtlwkjcTstbysMpq57bv33HP22ccMvV6P4eFhRKNRlJeXQ6FQQKPRIJvNQqFQcFWvSCTCHGKC0BAlCkzh81porBZeNGUyGbefEssAsJLHDTfcAJvNBr/fz2OwYsUKfOpTn8Kdd97J5aN1Oh0MBgO6u7uRSqW4KmA4HEYikYDNZoPBYIDJZIJMJkNNTQ0UCgUqKiogkUig1WpRWVnJHknh5bjwslGMMJvNUKlUuPzyywHk9ovW1laOYJVQwkzgiIxT8lhotVrU1NRAIpEgGAzC4/FwaPfll1/G+vXrcf311+P3v/89/v3vf2NoaAjXXXcd1q5di4qKCjzxxBN4/PHHmVNUiBNOOAEbN25Ec3MzDAYD3nzzTbz++uv4xS9+AbfbDZFIhF/+8pf45je/iS1btkAikUChUKCnp4dLH9KtmA4wKvcYi8WwbNkyWK1WzjAGwDzVQtCGCeQyJQcHBxGNRlFRUYH58+ejt7cX3//+9/HUU0/h5JNPxttvv42//e1v+O53v4vm5mY89dRTednswAdlUYH88JvBYMD3v/99mM1m3H///awx2dTUhGeeeQZWqxXvv/8+LrvsMjgcDiiVSlitViQSCYRCIej1eqRSKc7w93q97E0g8XbKlKUkMOHnE4dTIpFwFSav1wudTof6+nrs3bsXXq8XP/7xj/Gb3/wG1113HcbGxvC3v/0Nt99+O2pqavDPf/4TwWAQqVTqoGQRwrx583DVVVexURCLxfDYY4/hnnvuQSKRwEknnYRvfetb+N73voc///nPEIlErImYzWa5vKDJZEI0GoVSqURTUxMcDgdCoRDsdjtX0xnvYCBjnJLChOEwpVIJv9/PFZJOPfVUPPPMM3jjjTfw2GOP4f7778d5552HgYEBPPfcc/j617+Oiy++GL/73e9YqUAIoUFzyimn4Hvf+x5CoRB++9vfcqixubkZTz75JDQaDa699lqMjY1BIpFgwYIFkEgk7A2qrKxkrz+VgCTtU+Lh0hoeTxeSOItkCBgMBlitViSTSeZQWywWnHLKKdi2bRueeOIJ2O12/PrXv8bVV1+Njo4OPPzww/jmN7+JU045BZs2bTqovxSKJyiVSpx77rnYsGED+vr6cNddd8Hr9UKj0eBvf/sbVq5ciXvvvZfl5pLJJIu26/V6VFZWwmazQSwWQ6vVIpVKQSwWY2BggKMicrkcZrOZLwY0v+Xl5dBqtdi+fTt74MiDRobMSy+9hCuuuAJf/epX8etf/5q5v4X7Es3jihUrcPXVV+OjH/0oXnjhBTz66KNoaWkBkHtOr7rqKnzqU59iTi1d4Ds7O5FOpxGNRrFw4UKo1Wp+TmkdUziaDDainBxJKc14PI6VK1dyYintcwSFQpE3Z2q1Gueffz7Wrl2LHTt24LzzzkMkEoFer8cf//hHGAwGbN68GXfeeScrHQjLG5eVlaGiogIWiwXJZBIajYYvU5WVlWzoyWQyTj6iRDiv15sXpSo05MYzVulSReNNSi6xWAwKhQIrV66ERCLBueeei3POOQetra148MEHmTazYcMGZLNZVFRUYHR0FB6PB+l0GslkkvmmIpGI+bXZbBaBQABNTU3Q6/UsTbh48WIkEgm++KvVai7FSucFJSsejvt+LIjFYrj44osB5JJIyUgv/NmhXl9bW4vW1la4XC60trbi8ssvh8vlKhmnJcwojsg4pYewvr6eDwKfz8d6kCTl8e1vfxvz5s3Dhg0b8NZbb7GGIIXe6+vrsXLlSng8HjYkamtrYbPZYLPZUFdXhz179uAPf/jDQZ4ZtVqNCy+8ENdddx36+/s5VEyapmVlZZDL5TAYDFzOs6amBo2NjRzeJk+Z0+lkHhodGIWgzbOvrw89PT3w+/2cSPKxj30MTzzxBPx+Pz772c/il7/8Jfr7+zE8PIxbbrkFNTU1WLlyJVauXImWlhYWySePRTKZhEqlQlVVFaxWK8RiMe6//350dHRw1uvGjRvxta99DVarFdu2bcPnP/95BINBKBQKrFq1ivtssVhQV1cHi8XCWaoymYyTdXQ6HSoqKjjJi8ZSaEBGIhHU19dztv7evXu5sEFZWRkWLVrEHLKrrroKP/7xj/HjH/8Ydrsdd955JwwGA5YvX45YLIa+vj7m1paVlUGv17PHU61W43//938hEomg1+vhdDrZu3zGGWfg61//Ou68807cd999AHIeP5vNxh4LKqkaDoc5nC+RSCCTydDV1cVVqgpDhnS40cGqVucq/NDPjUYj1q5di507d0IikXA29wknnID33nsP//Vf/4Ubb7wRv/nNb3Dbbbehq6sL3/72t3HhhRdi/fr1OO+881hBQC6XI51OQyqVYvny5dDpdGhvb8ctt9zCNdBFIhE+8YlP4Ac/+AH0ej2+/e1vY8uWLVCrc6Vx6+vrEQgEUFZWBqPRiFgsxt5/pVLJxkA0GuVw/ERFJADkJZWEw2F4PB74fD7o9XosXLgQXq8XWq0WSqWSk0K2b9+Offv24dprr8Xf//53DAwM4Oabb0Z1dTU2bNjAHGmPxwO3280eM/J4Lly4EB0dHfjqV7/KHvOmpiY88MAD6Orqwg033MD8T71ez8ZNPB5HKBSCWq1mjzl5r2j+tFotJ9YIQb9vbm5GLBaDWp2r/JXNZmGz2TA2Nob29nZUVVVheHgYDz74IN58801cccUV0Ol0zFuk9xGLxWhubsb/+3//D36/H08//TS+9a1vsSdQLpfjhBNOwNDQECfukRfRZDIhFotBq9WirKwMJpOJ17JGo2GqBIX1TSZT3mVJuO8eDrW1tczLraurg91u52pyo6Oj8Hq9rB27bNkyzJs3D8FgEHv27ME///lPfp9Fixbhe9/7Hvbt24cbb7wRL7/8Mo8DeXyTySR7Devq6ridmUyGNZfnz5+PkZER9oJT4iXBYDBwVb+J+JGFxioAjmx99atfxa233opoNMpG2dtvvw21Wo29e/fi7rvv5v1NLBbj05/+NM4//3yce+65eP/996HVavmyIgSF98ViMe+vIpEIZWVlMBgMzPGmfgO584HWrVqthsPhYI3qQ0VvhPvS0Riw6XQazz//PH890c8O9Xogt3ZKxmgJxYQjDusTwuEwJ/tQ4k4mk8HOnTsxMjKCoaEhDqVbLBbOhKSSkEDucK6qquLM7/b29nE5qMuWLUNdXR1MJhP27NnDJRWlUinWrFmDeDzOhxfJfCxcuBCjo6OQy+VIJBIwGAwcaqZNwGKxwO12c2h9og2CwuFjY2Osc0pGwSmnnILdu3djbGwMt912G6677jq0tLTg+eefZ/28QyEUCqGjo4MlXAinn346PvOZz+DAgQP44Q9/iEAggJ07d/Jh29DQALPZzOO7YMECLh0prEZz2mmnsdeIOF60GZIXgEDeyQULFnDmvsFgQE9PD9LpNCorK6HT6dDa2gqn04mvfe1rOOuss7BgwQK88cYb8Hq9nBAiBMmG+Xw+9r4QKOnHYrHg8ssvR2trKz71qU+xIUMJYU1NTSgvL+diCxqNBjabDTKZDHV1dTAajRgZGYHNZssLzZGXpjCMSHA4HAgGg5xJ7PF4mGZAa3XVqlVwOp0YGBjAAw88gEcffRRXXHEFqqursWXLFq5kRmu6cA1TZjhBIpHg7LPPxpIlS/Dee+/hmmuu4XA6kDN25s+fz4ciZa/rdDqWZCMFirq6Ok5emwyobaSrqVAoIJFIsHr1asjlcrz//vuIRCJYvnw5lEolJ5z9/ve/h8lkQlNTE3p6ejA4OIjHH3/8oPcPhUJM2wGAt99+m79evnw5FixYAK/Xi09+8pPcZqlUiqamJqxfv56lwsRiMc9TTU0NMpkMlwQVRjgoyWg8TzEA5ooT53NsbAz19fU4cOAAhoeHcdppp2HHjh1obW3Ft771LYhEIlb8kEgk0Ol0EIvF2LdvH2699da8vlZWVsJsNmNgYIA1YCUSCebNm4cTTzyR5aoSiQRnnGu1WphMJpbvIRrOeNJNxJufiANbiNNPPx2bN29GV1cX7rrrLmzevBl79uzh5C25XA6ZTIZgMIh3332XOf2EE088EUuWLMHg4CCuvvpqjqpIpVI2pFevXo3+/n6+yEilUpbCymazkEql7MWkcDehMNwdDofH9fRPFmeeeSauv/56/PWvf+VELZFIlBf6F4vFOP3003H11VfD7/fjM5/5DDweDwwGA1asWMEJskajEaFQiL3dpBlNCaIkTajRaNgwFSYXkYe7vr6eKRkkS3c4xRDal4op/E9rxmw2l4zWEmYER2WcUliQbtKRSIT1MCsqKmCz2dDZ2Qmv14tkMnlQogLVeXc4HLw5EHQ6HXtKSJuzra0Ne/fu5ddQGJMyx1UqFaqrq9nrYDQaUV9fz1xTyrovDK+QZ43CXhMlLpGHrampCeFwmDmUdPM88cQT8frrr8Pn8+F//ud/sGrVKjz44IMIh8PYvn07h9io9Cb9Az6QXxKJRFixYgVWrVqFdDqNxx57DF/72tcOao9Op0NtbS00Gg2CwSCsViuMRiOamppQW1uL7u5uqNVqzsyncJOwBrvQe2YwGPL6T4khfX19UCgUyGaznJkbjUahUqlw8sknY8uWLQgGgyxkrtPpsHz5chb/J49FMpnkeuKpVAoWiwVr166F1WplIyASiWDbtm341a9+xf1UKpWcuEFeQVJgqKurY/kXSqhSqVRYvHgxfD4flEolv6/D4TiIfyrk2QlB68FoNLLep8fjwdDQEEtCUZWiP/7xj1xMgHRXh4eHeU3Q4UxySDKZDCtWrMCJJ54IAPj973+Pl1566aA2lJeXs1QSedqqq6tRVVWV5/mkkLUwHHokhxtdVMRiMa+NqqoqDA0NYXR0lNdYfX09tm7dyt5EMgLq6+shlUrZCywEUSUqKipYP3ZkZAT79u3Dvn37+HUikQiVlZXQ6/VQq9UYHh7m5ElK1DrppJNQW1t7kPYw9UE4tzTXhTI51dXVWL58OUZHR1lhpLKyEqOjo3jrrbeg1+thNpvR1dXF6528ooX8c5vNhtraWiSTSezZs4cv2iRtVVFRAZPJhFQqxQlYJpMJiUSCjVNKkCLu/vEyTMiQ2LdvH372s5/hC1/4Ak4++WT885//ZNF7MjiNRiOam5tRX1/PUa9C3UzSXjWZTJx46PP5WAKurKwMTU1NrDu7aNEiDA0NwW6381oVevKFVZWOh0aoxWLBxo0b8cILL7BnXJi/AOT21//85z+cyAWAn0lhEYtgMMj6unRukYoHjVF3dzeXxKZ12NPTA7vdjoaGBixZsiTP8SH8v3AfEnpLi8kwLfFPSygWHLXnVK1WY9WqVcyXIo6RQqFgvgoR6bu7u+FwOBCLxRCPx/mAA3IPL2WOUhUc4nAJQQc88QtjsRiqq6u59J3RaITVauVKJb29vTCZTFi8eDFkMhlzuApDKMRHPVxf6UYsFovR398Pv98PhUIBq9UKvV4PpVKJnTt3wuPxYPfu3VyH+4QTTsCpp56K0dFRDA4Osh4nebDKysrYsN68eTM2bdrEn0tVXCwWC5dBPemkk3DaaaexV8bv92PdunWorq7msOfChQuhUCiwePHiQ/LWaGMcT/OOvIh04FE1LKHua39/P3w+H4aGhhAIBA4rCG0ymeB0OjmsVAiVSgWlUolFixaxrBaQ81BVVFTA6/WiuroaZ5xxBkKhEJLJJBYuXAgg50ETen3p0BnvMBQaHEKpHhqTpUuXIpvNYtGiRex5N5lMXNhg165dcDqdCIfDeZ5BjUYDs9kMl8uFUCh0kHxPT08PnnnmGf5eIpHAaDTyxU4ikaCmpgZyuRzNzc1oampCdXU1C87TuFP4UHjZOtLDjSqeEWgMiNpByXCUqNPV1YVQKIT+/n6Ew2H09fXx3xGXOZ1O8zNeyD0FcgksdKGl9VhdXY1AIMB7yKpVq5gzKZVKUV9fj/nz5/NnptNpvmwdKnmGQOu/srISqVQKCxYsYEOBqsn5fL6DlAxIQojk5ShpcWRkhL39AFhKrrKykse0srIS1dXViEajkEgkEIlEWL58OVNsSCtTWNnseOATn/gErFYrhoaG4PF4uMzw4sWL0dTUxN5NkUiEdDoNu93OvHYAnFCp0+lQXl6OZDKJTCaDyspKJBIJ1NbW4tRTT8XChQtht9tZRpD4wNXV1fD5fKiqqsrTeCVMhSG2cuVKnHPOOdizZw9isRhGRkYQCoWgVCp5jwTA+4nZbGZnAIHOB1LEMBqNvL/X1tbCaDQyT1eogqBUKrF3716WzxJ6v2mOgXyjHEBekhu9Rpg4OJMYj3/6xhtvoLm5ueRFLWFacdTGKZB7sCwWC5dEzGazWLVqFSKRCEZHRzlxKBwOo7OzE5FIhIWNo9Fo3s2VQPJKSqUyTyC8pqYGwWCQ5WXEYjHq6+tRX1/PhgndiLu6ulhfkXhOVLqx8FCbLCiUQwdOR0cHjEYjlEolli5dynJX77//Pl566SXY7XZEIhFs376dyfjjIZVKcb1pAKyz2tzczGHD8vJyDA8PQ6PR4Oyzz2b5ngMHDnDoiUrDBgIBTgai8BK1fyJO13ieRPJ89PX1QSwWY8GCBcwRDYfDKC8vR319PWQyGXsNA4EAurq6OLlGLBZzmI+SrYAP6AMKhSKv+pTNZoNer0dDQ0NeIoXJZGIvX319PcxmM0sOqdVq9l6RoUQeYSoLO16iW+EYCCG8xKxZswZ6vR7vv/8+l17UarUYGRmBWq3Ge++9xyLyhSFtiUSS580h3ppMJuMLxIIFC1BdXQ2Xy8Xc3EQigdWrV2PRokVcJY3C14dq95GCDDeq6U5KAvQ5TqeThdYbGxsRDAbZ+0heR2F2ciHIoNbpdJg/fz6L4qtUKlZVsNlsGBgYQCwWw7x58zBv3jw4nU5OghIe3nTJGi8UTBGQeDyep81LBhFdjMrLy9Hc3AyXy4V0Os3JMbt3786L4gjpMUKIxWJUVVWxKsG8efMglUpRW1uLdDoNjUaDFStWoLGxEW63m73ghW2lMP9ESYNHA3rmOzo68O677yIQCGB0dJSLR4wHmn9SINHr9RgcHGTZObosZbNZ1ib1er1IpVKor69HKBSCVCpljrRKpcLAwABqamoOyyE9HlCrcwldVHJ4ZGQEHo8HKpWKlRpsNhv6+/uhVCrR0NAAh8MBn8+H8vJypsiYzWau3FdWVsaFAhYuXIiBgQEuKX322WdzNcJQKITq6mo4nc5DcktpbXq9Xtjt9jxnAV26DqXIQHv4nj17DsrBmAoQ/7TkRS1hJnFMxinwQdg7HA5zwsrg4CB0Oh0n5cybNw/d3d3Ys2cPZDIZXC4Xy+EQxysQCDDnp7m5GalUClqtlqWgiM9ECRANDQ2oqKjA4sWL89oTDodZIqquro45lsfKb6K+kmeMxL2Jh0fGK3lbxsbG4PF4MDg4iFgsxtwlIaGeJFYymQyX9FOpVFizZg1OO+00dHV1AQBn2gMfeLwoZEYheTrEhRmjRxPuFXoUyItYX19/UBgKyIXyKXOV5GQoQ3VoaAgymYznaWBgAN3d3XC5XGywkTFdWVnJB0VNTQ3S6TQWLFiAmpoaTu7SaDR5lWKE/dJqtXncQ6FH+FClLSeC0AtHY11VVYVgMAiHw4Guri5WiLjiiiugUCjg8/nw9ttvIxQKwWq1wmw2Q6lU8qHV29vLVXWCwSCi0SgMBgNWrlzJ3tP29na+jJGXguaD2kSc6qNF4fzSmJHuMBlOQmN4zZo1WLNmDSKRCPOPtVotLr74Yrz44ovw+XzIZDLQarVwu90YHh5GWVkZTjrpJJx44olIp9Po7++HXq+HVCrlJLb58+fDYrGwqP9pp53GUYKJPMMTrWf6OUUTCn9Of5NKpTB//ny0tLQw//Pkk0/GlVdeiVdeeQWdnZ0YHR3lalcmk4lLD/v9fhgMBqxbtw6NjY04cOAAP/fz589nbc2qqirMnz8/j989UZuPpyfRYrFgeHgYixYtQiAQwMKFC1nej6TWSOFAaJhrtVqsWLECy5cvR3V1NZRKJXp6enjNEdWEZAPpudBoNGy80kWRChJMF0QiEc466yysW7cO4XAYe/fuRW9vL7xeL1wuF9PKampqYDKZWKGD9hkqZLFs2TIEAgEuyarX69lbarPZ4PF4uNSzRqNhGauamhqsWbOG2yMM3wtBhWsAcBEJMloPp8hAxinph5MxPdU4lBdViJJHtYSpwKSMU+LRkdF5KJSXl7NQdSQSgdVqhU6ng9lsxqpVq7B06VL4/X7E43EOLymVSrjdbk6yIqOyqqqKs8vpwR0aGkIsFmPOaaHXTyqVchWkRCLBVW+EIF7RRKDbKR2oBArF63Q6NDQ0IJlMorq6GmVlZYjH45BKpZyFC+Sqk1AyRHl5OUKhEAKBAJcVlcvlCAQCiEQirNOo0+mwYMEC6HQ69poR15EkT6RSKbefsk2F34dCIWQymQm5lZPt70Tjls1mMX/+fOYbj4yMsFIAGTYejwehUAhisRhNTU0488wz4Xa78e9//xv9/f0wm82QyWTwer2QSCRobGxkjikALiuYTCY5OYvmm/pF/5eVlSGZTMLj8cDv9/PGTXxXlUqVNxaH6y+FW4V/R2HexYsXM9+up6cHJpMJJpMJlZWVeZ5+q9XK9btdLhdXASJ+Z3l5eV41MiAnoSbUnI3H47x+x2vTZHG4/gqfB/oceu78fj+3lTxLlJRmMpnYQ20wGKBQKDAyMoLu7m7WPl60aBH8fj97jEkz0mg08lppbGzkxCCfz8f9JM/3RG2daN6IL1zYX51Ox5Gduro69rCRQUbqAH6/H9XV1Vi8eDH8fj8SiQR0Oh0b6zabDTqdDgqFAqFQiD2pxEX0+/15+46wT+NRaA63Hx0OwvlNJpNIp9M488wzodVqcc455+D8889HLBZDJpNBKpXC4OAguru7odfrUVFRgerqahiNRthsNr5UlZeXc5tpboCcjiopa9BzFolEWAJsOjDReiZlAmGkhEryKpVKRKNRpkfRPFCUhS7CpJ8L5Piq4XAYDQ0NAHLnHxVIoTGTSqX8PJLRT/QBAlVL8/v9/De0toXP9dDQ0LjrhNbzj3/8Y77MisViPmMJlF8x3s8Ih/rdeKDoRTKZhEKhYC+qEAqFAg8++OBBagxHCzLiqd9zHdlsFsFgEFVVVewkKAEQZccTGi3Azp07sW7duuloTwkllFBCCSWUUMKHCgMDA6iurp7pZhQNJuU5bWxsBAAu6TgVoEpFhaUJZwKDg4NYunTpce1vMfWvEFPR3/FQLGNQmt+pxUyPxdH2d6bbfbQ4lvmdjX3+sPa3vb09T5prrkLY30WLFgHIlUUnO4S44VVVVfz9bJnL8RAIBFBTU5NXQrmESRqnlDVN9amnAtNxaE4W1Jbj2d9i6l8hpqK/h/qcmUZpfqfn82YKR9vfmW730eJY5nc29vnD2l/SmZ7roD4KjTXh14VjQDkcsx2T1TP+sKBEcCihhBJKKKGEEkoooWhwzNn6JZRQQgkllFBCCccTYrEYa9asQSKRwIEDB7BkyRKUlZVx0hCpJJSSiOYmSsZpCSWUUEIJJZRQVFAqlXjyySfR3NyMDRs2AABrJNfW1mLnzp0z3MISphKlK0cJJZRQQgkllFB0cLlciEQi2LRpEzZt2oRIJMJSZiXMbZQ8pyWUUEIJJZRQQtGiUPi/hLmPkue0hBJKKKGEEkooKkQiEVx44YUAckVm6OtoNIpIJJJXvryEuYeS57SEEkoooYQSSigqZLNZjIyMHPQ1fW+32/nrEuYeSp7TEkoooYQSSiihhBKKBiXjtIQSSiihhBJKKKGEokHJOC2hhBJKKKGEEkoooWhQMk5LKKGEEkoooYQSSigalIzTEkoooYQSSiihhBKKBkVjnIbDYTgcDoTD4ZluyrRiLvd7LvdtKlDs41Xs7TteKPVz9mOu9m2u9ms8iEQiNDQ0HPQ1fb9kyRIsWbIEIpFopppYwhSiqIzTVCr1oXjohJjL/Z7LfZsKFPt4FXv7jhdK/Zz9mKt9m6v9Gg8qlQr/+Mc/AAAGg4G/ViqVUKlUOHDgAA4cOACVSjWTzSxhilA0xqlarUZZWRnUavVMN2VaMZf7PZf7NhUo9vEq9vYdL5T6OfsxV/s2V/tVQgmFKBoRfrVa/aF84OZyv+dy36YCxT5exd6+44VSP2c/5mrf5mq/SiihEEXjOS2hhBJKKKGEEkoAcuVLN27cCCBXvpS+pvKlS5cuxdKlS0vlS+coisZzeiQIh8MIh8Nz4hY5G/syG9s8kyiN1+wYg9nQxunCbByL2djmo8GHpZ/ZbBY9PT0HfU3ft7S08NclzD0Uref0UFmJc4kUPlFfijkrc7w2F3N7pxKT6fdcWq+TwXhjMhvGwOFwYHR0FA6HY6abMuOYTc84tcvhcBT9GjtWhMNh9PX1IRAIzOl+HgptbW3Ys2fPTDejhClGURunE200c4kUPlFfivkwH6/NxdzeqcRk+j2X1utkMN6YfNjGYLZjNj3j1C4Ac36NhcNhyOVyxOPxOd3PQ+G6667D+vXrZ7oZJUwxpi2sP5lQROFr6OtCzMZwxkT9n6gvhf0vplBOYRuobQBQXl4+7t8UU/uPJ4TzNFf7eDgI+03fA/lroVjHRNh2q9V6RPvTbMRk+zDe74t5rXu9XlgsFlit1kn/TbH14XBwOp3QarWQSCQfavmkN998E21tbbjuuutmuiklTCGm1TilW/ehNn96DR0UcwWT6b8Q4xmAR/L30wm6zR/Ka1HM7T8WCOdJGFacLXN3PFDoUTvcWigmHMmeMxfm8Vj6MJm1PlMwGAxH/DezbT7JO6xWq2dVu483Vq5cOdNNKGEaMKXG6WQ8oZP1lh7qb4oJxH8CkHfYTbZvE6GYvRbAB56LiUDtBnIH2/Fse7GMx3hzHA6H4XQ6EYlEUFdXN+7fFUv7JwOhlxzAuM+tw+FAPB4vysOzsP3AwaHgQ0U5irFPR4LJRGQmsx6LbT+aaP+ZaL3S17NpPuPxOJxOJ3+vUqlmVftLKOFIMOXG6UReCdo0yOt2JN7SYr7xhsNhBINBAAdvhMcSLixmrwUwec+FcL6Pp3FaDOMx3vyFw2Gk02nI5fJx/yYcziU40O+LZT4nAo211+uFwWAY97klz47wElIs/Spsf1lZ2UGh4ML1VBj6n42YqA/jPTuTeZ6KbT8q3H8Kzxfhep3MnlyMEF7wJRIJ0uk0+vr6UF9fP6v6MVmIRCLYbDaMjIxAJBLx5V5YrtRms5XKl85RTGlClFqdT6rPZrMIh8Po7e1FS0sLAoEAgPE9F4fKCi1832KCWq2GVquFVqsFAPT29qK3tzevLzQO1MfCsOhkPqOY+n+49lB/A4EAXC4X4vE4gPHHZio+/3jjcOtTCLVaDYlEwn0e770KExyO5P2nGzTWFotlwkS+cDjM/RWu60P1a7r6TO1XqVTwer0Tvk74O3o+KSO8GOdlItC4TpTJPtGz4/V6Jz0nM7kfZbNZqNVq9tQLozOjo6OIRCLjrtdifsYmgkqlQjweR29vL9xuN7xeL1/05yJUKhU2b94MIHf56OvrQ19fH1QqFZRKJQBg8+bNH2r+7VzGtOuckmcxFAohGo2ipqZm3Ncc6iZezDdelUqF+vp6ADkC+3heVCC3eQaDQWi1Wlit1rxQKIBDelKLpf+H8ygVeoTtdjvS6TT/fqKxOVJM9XiMRz2ZrKdIrVbDYrEgEAjA6XTmvQfNtU6nOyisOtOeqMNhIi8xeYGpT0Jj4VBe8+nqM7Xb4XBM6M0GcrxZmi/gA2NV2P5iCGcfDjSuAA4yzgqf3UJv48DAAKqqqgAc2qM/k/2PRCJwOBxwuVwHeUaFKGzjbHjGCqFWq6FSqSCRSDA4OIjq6mqYzWYAx58qVUIJM41pC+sLQylarRYjIyNIp9O8ATqdzjzO0KF4YPR9MT+MTqcTdrsdwAfcIGH7I5EIBgcHodFo+HAQGmuzYeM81AZPhkowGEQikcDixYthNpv5IKF1QCjcXIvp4C/s50RcNYfDweu48MAfHh5GJpOB2+3mCxklDhUa9sXMhRsv5N3X14dIJAKVSsVeYHotkHsW5HI5JBIJyspyW07hfM9En8fjKQrnSyaTcTvT6TRGRkYQiURgNpsPingU41wBH4wrKSc4HA5EIhFEIhEOhQvnkebP7XZDJpPxXBar8eNwONDa2gqfzwe1Wo3GxkY2uIUXyUKKyWTWWzHtQQCYb0oX/MHBQaYrTOYSUUIJswnTGtYHPvAsGo1GpNNpeL1eDk84nc68jMTxwjTkgSnGMFs4/EHyi91uh9/vBwBYLJa88AsdDiKRiA9Al8uFaDQKoPjC9hPhUO2kudm7dy/8fj/a2tqgVqshFouRyWQQDocxf/58zJ8/HwAOCjkeKdVhKlHYT+HhR8hms7Db7RgZGUFbWxtTFshDp1Qq+UJCCXMTjd14719MKAx52+12tLW1oaurCxKJBPX19QiHwxgdHYXdbmeDx2q1siE+ng7qdPeZPG3CPYS8hkqlEtFoFB0dHQiHw/D5fNBoNIhGo8z1A4pfV5PGlS7IDocD7e3tCIVCHLoXekvj8ThUKhWqqqpYsmi8Z7FYwuIul4v3WqlUimAwyHND640uRIFAAH19fXke48MZp8WyBwFAR0cH5HI5VqxYgYULFyIej2NwcBCDg4Po7u6G0+ksmrYeD0SjUVxxxRUAAL/fj7Vr12Lt2rWIRqOIxWIAgCuuuILPzRLmFqbUc1p44yTicjgchkqlgkwmg81mg1arzfOYUXg7nU7D6XRymFz4vmTQ0S2yGA6Iws0sEAigvLw875Yu9FCYzWaoVCq4XC72LI2ntVhsN/gjQU1NDRKJBPR6PcLhMKLRKPNOhX0t9GIUk/dwohB24UEQjUaRSCQgk8nYC67ValFWVsZkfqVSCbFYfMiDsRjme6I20HNJfVer1dBoNBgbG0Mmk0EkEkE4HOZ61yqV6iDaQjHMLbUhHo+P6xWvq6tDW1sbNBoNIpEITCYTurq6YDAY0N3dDZlMBpVKxZer2QC1Wg2fz4exsTEkk0lYrdaDaBbUf6fTmecJP5Qg/0w/o2azGZFIBBaLBdFoFKFQCA6HA1arNW9ead8tjMBN9KwVwzoVgpw5tJcYjUbE43GEQiHYbDZ+LoulvceKTCbDJUrT6TR27drFP6eSpS0tLchkMjPWxhKmDtPOOQVyG4LBYMgjMtPm0Nvbi2AwiHg8jmg0ykYNeVyIYwTkPB8zHXYaj49IYUEgdzgL20X9k0gkWLJkCcLhXKKQ1+tl/tB4n1EsB4EQE7WLjBOJRILa2loOnYbDYchkMgCATCbLM9gpZEXf0xwXU3+FoHkjQyUQCECn08FisaCuro6NN2F4nw6Vw63T8ULn022sTtQG8vr7fD4O5S9evBhKpRKhUAiDg4OQy+XjPtsTfT/dGO+ZLRxnaj/RNOx2OxKJBN5//32YTCbo9foZa//RQqVSwWg0wuFw8EWC5pA8icAHfPhEIgGVSpV3kRJeyorBa1xXV4eenh5EIhF4PB5UV1cjGAxiYGCAeahA7jkkj/5kjeyZXqeFMBqNMJvNcDqdaG9vh8vlgk6nA5BzhBRztKWEEo4U02qcCkNILpcLQM7bRAaL1Wplb6hEIkFVVVVeFrPL5YLT6YTJZILVas1Lupgp40342RQ+czqdSCaTGBoaOqiaBx0GWq2WQ6IqlQpms3nCZJHxbvDTbbCM93kTHewU0lcqlbBYLGycqtVqRCIRqNVqJBIJALlwuNPphM/ng9vt5vcizyP9nfDrYtiA1Wo1nE4nZDIZvF4vRCIRotEoNBpNntEDfLBGhoaG4PF4oFQqUVNTk2e4HsqzOFXre7Jeo2w2ywaLRCKBVqtFMBiEw+GAyWSCy+WCx+MBkKsKNTw8jJqaGkQiEaTTaUQikTy5G/J6EKZbCkZICwKQ5+0VXhwtFkvePIZCIYjFYsTjcUgkksPOxUx6wIWfTd+r1WrU1dVheHiYL9CVlZUcChfOUTQahVKp5O/p8k9jR4ap0Fid7n6Gw2FIJBL+ure3F0ajEYlEAqFQCOl0GtFolGkoE8kZArl1WwwRi0MhHo/D5XJhz549aGtr4zNDKpVCp9MxBQMo3kt9CSVMFtNunKZSKUQiEcjlcrjdbkSjUYhEIiQSCQwMDLBhajKZIJFIYLFY0NfXh8HBQXbfjxcWHe/gnorNpvA9xzPQgFwYIpvNIhAIYO/evdDpdFAqlVx+LhKJYO/eveylEYagCjFRWHk6DfLxPk/oYaLkJ0pyUiqVfID39fVhYGAAJpMJAKDX66FUKnmsVCoV3G43e8nV6g+Spchg8Pl80Ov10Gq1MxpKJa8ueZ0kEglqamqgUqk4yYT6RRn6FosFXq8Xu3fvBpBbG0I6ynjjWmiQT8U8T8ZrRIZkJBKB2+2G1WqFxWKB2+1GIpGARCJhwxTI0RjkcjlfMv1+PxYsWJD3GRT9EHrtxuv3VIHGMx6PIxgMwu12Q6lUYmxsDOFwGHq9Hi0tLRwuTqfTUKlUWLduHdxuNwBwAgphPG/s4fRrp8oYcjgcaGtrg16vR2VlJQCwMU50C/r80dFRGAyGvIuxMEGP+iJcy0LDtFDzdjqNokgkAqVSCYVCAbVaDaVSCblczsZ3MBiEQqFAIpFgzrvQWC9UlijUay02Y1Uul6Orqwt79uyBx+NBKBRCVVUVbDYbNBoNABRlhK2EEo4Gx2ScjrchTxSOpdfQ74lfCYAPAAqFk8AwGaDktUmn01i2bBksFstBt/WJNv/j/bAKPUjkWRGGwwKBANxuN5qbmwF8kFlJxhrxuLq6unjM6H2EY0jeZaE0VWHlKRqD6aA0jOfNE34/MDCAUCiEuro6WCwW9mg4HA50dHQgFouht7cXq1atglgsZuOTEuAog51C/JTxTjQIILdOhFzVmQAdemSk0CWKDn1K8FOpVBgcHGRuotvt5ovZwoULoVQq4XK5Dprj8TBVczuR0Su8ZNHrVCoVTCYTe2fS6TTKy8thsVigUqnQ0dEBo9EIAOju7kYsFuOQo9fr5a/p/R0OB4aGhmAwGDhKQp91vDGRF5G+l0gkcLvdbIQlk0kAOU+pRCLhEL7FYuHQMNFQhNEgIUc+HP5A/F0ikUw4zlNhTDidTiQSCfT29rJxSt7D9vZ2jIyMIJPJQK1WQ6FQQKlUMs+f9hK6LAMfRAnI0BYar7QnxeNxVgQoxFQZeSqVCrFYDAaDATqdDoFAAK2trQiHwxgaGoJcLkd1dTXGxsZQU1MDh8PBF1vaxyORCGw2G/dzOiIWRwuNRgO32w23243e3l7Mnz8fo6OjPMdAcVAtSijheOCYjdPCm6bQkDAYDHm3arPZDLVajWAwiJGREVRXV7PHibwwOp0ODoeDvydDRyqVoqmpiUPEk6lAMxUeJ/IgSSSScSuOUKjX5/MxSV2j0TDfkDw2JpMJbrcbCxYsgMViQTabRSQSQV9fH9LpNHp7eyEWi5FKpeB2u2EymfK8bULDfTo2UJVKxZSFlpYWeDwelJeXo6KiAmq1GjKZDEqlkj2p5BmXy+UwGo3o6emBVquFz+dDTU0NjxUZZ1Stx+l0cpk+yrZVq9WorKzkA1IYLp8OrwbNDX0ueb8Jg4ODSCaTUKtzgvsajQaDg4McVoxEIvB6vZDJZJg3bx4aGxs5aiCkPBCEl7ypNMQPd6kr9Ii5XC7uN1226H2qq6uRTqfR1dXFz65EIoFMJkMoFEI4HOZxpOx+rVaLRCIBm82WR9853hDuU0C+d4n+CT2KtOdQpAMAz6dWq2VvPj2XxGv0er3IZDKcjAPkDN8joescD5BnWyKRoK2tjdvo9XoRDAY5UpXNZlnMnH5PBnYkEkEoFILVasXSpUvH5WvS2JEk2kT9mCojj543vV6P1tZWdnL09vYilUohFotBLBajoaEBCoUi728jkQii0ehBJaYPFbEoBk+qQqGAWCzmczSRSMDn8yGRSGB4eBinnHJKSZS+hDmBYzJOCx9eYTiWfkZeTjp81OpcxmggEMDWrVsRDoexaNEiyGQyVFZWcoIT6QyGw2E0NjYeVM1laGgI0Wj0kB6XqdhEyINEmo2Fm5lKpcL+/fs5VCQSiTA8PMzGHWVcArnQIB2MDoeDvY/BYBBWqxWJRAKRSIQraZF3cTpCvhPB6XTC7/ezDiId6lqtFhUVFXwoCw04lUoFm82GUCiEkZERyGQy+P1+hMNhKBQKSCQS7N+/HzabjfuoUqnyvDiFfZ2qA2+8aAD9E2p4CqkY7e3t2Lt3L1atWsWGJyGZTKKrq4sNm/nz58PtdnMYTi6Xj6u1ebwKFBwNhB594ffpdBrt7e3s6VSpVFxYwW63Q6vVwu/3I51Oo6qqCosXL0ZbWxsGBwd5DUciETidThiNRpSXl2Px4sVT3s/xjIzxwtL0+2AwCJvNxpfjtrY2uFwujI6OQq/XQyqVYnBwkOWmKNTtcrkQiUTgcrn4YlXotS1s11T0mZ6X119/HWKxmKV2IpEIamtr0dXVhcHBQcRiMSSTSTZm6urq+AI4ODiYFxGYCJPZf6ZyjyIDVavVYnR0FL29vZDJZJBIJEgkEhCLxXC5XJxs2tvby39LkYBDvXdhNOt4l14+ElDhmmQyiUwmg/Lycr78DgwMoKKiArt378b69evnjPdUr9fD5/MBwLgJw7MxMbGEyeGYjdNCQ2n+/PkIh8PsXWtqagKQ4yR2dHSgqakJJpMJIyMjcLlcEIvF2LdvH0vupFIpuFwuDguSZw3I3e6dTifcbjfzwKbS4zIehPJH430uhWrtdjtOPvlk1qIbHh5GQ0MDfD4fc/Sqqqpwyimn8OZ34MABuN1u1hisrq4GAA45mkymI6I0HM9bPhUVoAS2hQsXstc7nU7nqSZQZmxfXx+2bt2KSCSCuro6JJNJmEwmjI2NIZ1OY+fOnYjH44jH45wkZDKZ2EtzOC7mdPAwhTQOIEdfIK+nWq1GVVUVXC4XKioqOIOdLhj19fUYHR2F0+mEx+NBXV0dc1QHBgbyLlwk3QOAuaxCPuZMQGjMATmtQaKj+Hw+1NbWAsiNSTKZxN69e1FWVgapVAqxWAyn0wmlUsmGUCwWw+rVq9kwN5lMBxkAU2GwFV406Hu6EJL3sKqqivU/BwcHIRKJ0NPTg71798LtdkOhUKC2thZ6vR7l5eXstQqHw2yUDgwMYMWKFXl9Ga962lSBxrGjowMejweJRIJ1pCl0Pzw8zGFvr9eLaDQKj8eDJUuWwG63Y//+/YjFYtDr9cwXLpRhKvwn/PzCfWeqjHCn04lYLAa3243u7m6MjIwgFoshEAggm81y0Q86U4CcgQfkDPHKyspx18V4YyqssgWMT6U61Pscj/3Ybrejvb0dQI4uFggEsH//fvT29kIulyMQCEAikRyU3DZboVar8eqrr2L16tUwGo1MpQHAz+yrr7466/tZwvg45oQoYeatUMd0YGCAJaEUCgW2bduGsrIy9Pf3Y/ny5ex5dLvdsFgsXH1FLBYjkUhAJBJBLBajt7cXWq2Wb41kyEilUqxbt27a+Ye0uVC9eGH4FciF9vx+P/R6PVQqFUQiEXvOiFM7NDQErVaLVCqFxsZG1NfXw+/3Y2RkhCVByMsqk8k4PLxz5040NDSgsbGRP5c+u3CTPZ6exXA4jLa2Nvj9fg7XkjfGYDBgeHgY6XQaPT09fJNVq9V44YUXsG/fPohEIkilUqxcuRIjIyNwOBwYGRlBNptFNBrlEL4wUaaurg7xeBxDQ0Ow2+3MZaXw+lQd+kIvjzAZyO/3IxaLsYxSMBhkUXa9Xs8Xjp07d6KsrIy9w0NDQ2hvb4dIJIJcLodIJMLSpUsxNjaG3t5exONxZLNZNDc3QyaToaKiAiqVCpWVlXn9m84M98Iw+OjoKHw+HxeN2LlzJ5YtW8aZ64lEAt3d3Ugmk0ilUlCpVNBoNKxnGwgEkEql0NPTAwBoaGhANBrlCmrknZTJZNBqteMerMfjcKd+0Rp79913WW+Z6ETEV1QoFPB6vRgZGYHP52OKjU6ng9VqRUNDA3p7e5FOp+FwONhDaTAY+DkR6k5Olwc8HA5jbGyMhehJBcPtdmPp0qUIBoO8L4tEImSzWbS2tqK6uhp9fX3o7u7Gtm3bIJVK0djYyEaqzWbjy9Sh+jWdPM33338fY2Nj6OzsZLoVRaJoXoQ8aLVaDb/fD5/PxyoikUgELS0tSKfTqKioyOOkEved9gTSrJ6ISnWovh+PcaGk0H379iGRSHBSVDqd5kIvmUyGHSKrVq2a1otRCSUcTxy3bH2h0RAOh5FMJjk8PTY2hkgkgq6uLixduhQA0N/fj9bWVigUCqTTaXg8HgSDQWSzWSgUCsyfPx89PT3w+Xys7SYSieBwOLBw4UIsWLBgRhNjiB8aDAbzPFyZTAYymQzZbBY+nw/vvvsuWlpamJNIIadwOAypVIrnn38elZWV6O3txdDQEEQiEUKhEHp6epDNZhGPx+H3+zEwMIBFixblSaMIOajjGaTHyzDt6+uDTCZDJpNhTp1IJILP52Nv8NjYGOLxOLq7u9HS0oJoNIq9e/dibGwMavUHPGMA6OnpYXqE2WzmTd9sNqO9vZ0zbg0GA0KhEGQyGRsyUx1aE14+6PtEIoFwOAyv18thUJFIhKGhIYyNjbFXamRkhEX2s9ks3n33XYjFuSJsSqUSTqcT2WwWPT09cLvdGBwcRDabhc1mw4EDB3DGGWcwX7MQ5LlWqVR53vNjfQYKw870vuTRjkQieOeddziJLRaLQaVSoaenBxKJBE6nEyMjIxCJRFAoFAiHw4jFYnA6nfB6vezZUqlUSCQS7EmuqqqCx+NhkftAIIBwOIzm5uZxPT+FBvPRGKpCb+nw8DDcbjfGxsZgNBrR0dGB/v5+9Pb2Mo9Rp9MhFArB6XRyxKCrqwvl5eUYGBhAWVkZc6qrq6s5kUqpVPJlVKlUsud9urxZHR0dGBoagkqlQllZGXp6enich4eH0dPTw3NgNBphtVoRCoXQ3d2Nvr4+2O12SKVSyOVyiMVifm6NRiPvTUBu7Wk0Gl6TwkvydPRzYGAA27ZtQ0dHB1KpFIxGI4xGIxobG7Fr1y4kk0n4/X5Eo1EMDAwglUoxz/m5557jy7SQXkRi/lR1KhKJYO3atQdFCI+UQ3y8xqWlpQXDw8MIBAJQKBRIJpO8x7hcLixcuBAejwcbNmzgSz9QHHzZI8WePXswMDAw080oYYZw3LL1HQ4H+vr6kEgkoFQqYTKZMDg4yDzKsbExhEIh/Pvf/0ZTUxMCgQAbIqeeeiqkUilisRjC4Zx23djYGEwmE8LhMEZGRqBQKBAIBPKMwL6+Pg4tT7ckjVCoWqvVchIBbR4UMjtw4AAnGiSTScybN4/Ld+7atYszZVUqFcRiMSwWC3bt2oVt27ZBrVZj2bJlyGaz0Gg06Onp4fKJGo2GpZkK54GoDsJb89FsTiRJQwZ1VVUVZ+wODAwgm83i1VdfhdfrhVqthsfjYSMyk8kgGAwiGo3C5/Oht7cX3d3dsFgsrPVpNBqRTCYhl8s5E9Vut8NoNKKqqgp9fX1IJpNYuHAhbDZbXmhtMn2YTJ8P9xpK2nK73XkUEqfTiUAgwAcFhfMB8BwHg0HodDpotVrU1NQgFouhu7ubL2FlZWWw2Wyc7EceaZlMlhfCUqvVzPWlzyi8mBwtCo0+oYZlKpXisPeBAwegUCjQ398Pl8sFvV6PtrY2dHZ2IhaLQSqVwmq1IhgMcsb7wMAA88Ip6pFKpfiiQpw50tPU6XTsHTpULfSj9UIRbcLlcsHn80EqlUKlUsHn86G1tRUdHR0IhULw+XzMKabIRSKR4AS/HTt2oKKiAmNjY7BarVAoFLDb7ejp6YFcLoff70d1dTUbuT6fD1VVVeN62qbCYCDuJe2NSqWSDfHBwUGEw2Eeg2AwiMrKSiSTSQwPD8Pj8fDF0Gg08qWDjO1wOJc/QNx5pVKJxsZGAPkXJeobMHX7cTweh8fjgcfjQTgchsfjgUKhyCuMkU6nEYvFIJFIWOmDLvcjIyNobm6G1WqF2WxGNBplg5SSP6PR6EHzNlF/DtXP4zEGyWQyr40UvROJRMhkMnyJJnk3IZ92Oj3axwtnnHEGgJx0ZFlZGc4880wAwAsvvMDlS2+44Qa88cYbHOYvYe7gmI1T0r8DcmEH4sJQYoTT6czLwAZyRo9UKuXsXZILCofDMBqN8Pv9vAFSOJSEr4GcfmRXVxfKyspQU1MDj8eDqqoqFl/WaDRYvXo1t/F4bY7UXofDgc7OTqTTadTW1kKlUqGtrQ1jY2McNuvr64NCoWCvg1qtRiAQgM/nQ3l5ObxeLwKBAMRiMXw+H0wmExYvXoxAIMA/Jy1No9EIj8cDr9cLp9MJh8OBlStXckIGSaJYLBa4XC7WgyX+L/07Uo+j0+nkxAKTycTheKPRiEwmg7fffhtdXV3weDxIp9NQq9WclU4Ghkgk4r5LJBKUl5ezB7azsxOJRAKJRAJVVVXw+/1QKBScvd3T0wOLxQKFQgGLxcJhtsIEIprnwrmezIZ8uNdEIhFkMhlotVqYzWa+aHV0dEAkErExJpVK2bCMRqMYHR1FKpVCIpHA4OAghoaG2COTyWTYMxePx2Gz2RAOh/H6668jnU5DLBZjxYoVcLvdaGxsRDgcZpoHyTcJD/5jQaHRRx43ei7FYjF709LpNEKhEOLxOJLJJBvlJpMJXq8X3d3dzH2jNUj1vquqqpjGQRnvKpWKLyp0kUqn0xgZGWHjlUKt44WPj6TvtD7IWKPSiJlMBjU1Naw9S7z3YDCIiooK3ofKysqQyWQwMjICnU7HGeHBYBB79+6F0WhENpuFVquF1WpFMplkA3Xx4sUIh8O8F9JFmsZUGE4+FhC3mUK9w8PD/BwS9SIej0Oj0bB3P51OY/fu3ZBIJAiFQhzqDwQCnDMwMjKCl19+GWazGTqdDuFwrtJbVVUVysvLOaxM0Qa6uMfjccybN29KDSKpVMo8Z4VCwcaqWq3mSz/tu36/H01NTXyBSKVS6OjogNVqhclkQiQSYVUGs9kMjUaTp708097HYDAIo9HImtlUUCGVSkGv16O3txehUIgvHkDuzKI9mBwXs8VA/c1vfoNbb70VmUwGlZWV+M9//gMgv3zpu+++i/feew81NTXMgS9hbuCojVN6UCnTlbxKHR0dcLvdHMqSyWQYHBxEd3c3b5rl5eXMVYvFYujv74fT6eQbeiQSQSwWY85lNBrlUCpxqKgNgUAANpuNif+0EQFgUWbKkj4emwt5iN1uN2KxGMsIeb1e+Hw+hEIhDA8Pw+fzcUg+lUpBqVQilUrBbrfDYrFgbGwMsViMM97lcjmcTifGxsaQSCRgNpuhVCrx7rvvwmg0Qq1W8+FB4UMKi9rtdtTX17MhVSi0faQeR/o7IFdmlLJ4aYP3+XxwuVyIRqPMzYrFYigvL0cwGITFYuExIG3PcDiMRCLBHnCNRoNsNovR0VEeP51Ol1dpiULhPp8P27dvRzgcRnV1NZxOJwYHB2E0GrFkyZIJvWmTCaVN9BridNbV1bFG6+7du7k/SqUSvb29fBCQ1Bd5RclgTyaT8Pl8PL/k+Uwmk3zIk8eN1oxQvsnr9TLvbfny5SgrKzuuPDIhjSEczmXkd3R0sMdcJpOhvb0dfX19EIlEKCsr49f29/cjkUiwgZ5IJKDX65HNZrk/lAilVCohk8mgVquZuxuNRtHZ2Yn29nYsWrQIRqORizPQ5baioiJvTR4N35ioKVRW1Ww245lnnsHevXs5ikHPM+0pADgpKhwOM7+WLk1KpRJlZWVcNnNkZATz5s1jClJjYyMbqrSO9+7di1gshiVLlkCj0eRJjR2PZLCOjg7odDqUl5cjm80iFotxchPNBxnh8XgcMpkMY2NjvF9mMhkkk0mIRCKUl5dj27ZtiEajyGQy8Hq9iEQisFqtMBqNfInSaDRYvHgxXxj7+vqwY8cOSKVSmM3mQ+qfHiuGh4c5MpNIJNh7COT2OjpvhAUzRkZGUFtbC4VCAYPBwNxwUl/Q6/WQy+UYGBhgSUDSESWj+0gLgRwvo1alUkEqlSISiSCZTMLpdGJgYID5pzS3drsdW7ZsQV1dHT8r9NmBQGDWJExRMvV4oKghAKxfvx4qlQqtra0lA3UO4ZiMU9rsKeOTNtuhoSEEAgFOjKDfi8ViyOVy3gDp8CZPK/FOyTAQi8UQiURQqVQcJqUbv1arRWtrK7xeL6xWK9asWQOtVguZTIZYLAa/3w+v18tZ1MAHYv+FxiphMuFfl8sFr9fLigF2ux0Oh4PDKKlUCoFAAC6XCwqFgosHJBIJxONx3jQp1K9UKpHNZiGRSOByuTAyMsJeADpMDAYDe5ekUilkMhmSySRz28ggILkq4IMELboxH65/4/XXYDBweIvoCQMDA5BIJOwVJK8ZeVui0Sj6+vqYryeVSvm1iUSCDdlMJsNrgQ4RSp6hqmEqlQrNzc2ccDI6OsrJJiQzRtq5wAc8SWF/D2fEHG7OAaC6uhpdXV1obW1FIBCATqdDNptlxQkAHLamttMFgrxTFIaji0o2m2UPOyUHBgIBVq3w+XwcJne73awNO9UenLGxMXR1dbF0Eq0fSgYiz2YsFsvLhKZnNhgMIpPJMEc1k8lAJBKxN0upVLJXrqenB0ajkQ2CbDaLZDLJl7XCEqFHahyQ55/2KiqO0NraiuHhYV6LZOAQkskkkskk+vv7udKbRqNBJpNBWVkZJ/ERFSeVSrFmqM1mQ3l5ORvi4XAYr7zyCnbv3g25XI7KykqIxWL2TlPy4/EIuZaXl7NMFPBBdTXibdOFn/pIIO9aLBZDPB6HWCxGJpPhaA3RLxYuXAiDwYBoNIpEIoH+/n5IpdI8z2JHRwfvXY2Njfx8HG9uNJCTEers7ORLFV38gBw9hS66tCcKKSV0uTQajWzoEPddLBYfRMcQVoU70kIgkw2pH+65tlgsSKfTyGQyrOdKxjidjXK5HKFQCG1tbXjyySfR3d2Nyy67jKMPQq3oYjdODwWSHASAP/3pT7juuuvgcrlKxukcwlEbp7QZqVQqDiv39vZCJBJBo9HA4/Hwhkebm06nQ2trKych0CHg9/t5s6RDQiqVcqYlZdOSRIjBYGBvGx0+8+bNg8vl4kOLQsikp0nkfuJMAhOLjY8H8qiMjIwgHo9zdiRlnVNfKCxGBxHJC1EpR/JiEmKxGEQiEVKpFKqrqzkRQaVSsdYr3ZZDoRCL95OBp1bn5Izi8Tjfhsfjex1pMolarebs3b6+PoRCISSTSa5BnclkWAeSNDnFYjEGBwc5I5o4fZRYQwapkAcG5DzclZWVGBsb4zAkHe6pVAoajQapVApisRh2ux2VlZWQy+W8QQUCAQ7hkYfyaMopCrPzaQzT6TTa2tpYRiuTyfBFrKKign9mNBrzPMXRaBTZbBbl5eVQKBSQSqWora3F6OgopFIpLBYLf53NZtnQT6VSTAswm82w2+0Qi8Xwer04++yz2VgkOs3RekCEB6FKpeK+V1RU5HlCab1SsYWWlhbmLY43dmT8pNNp+Hw+GAwGvkwSB1mtVmNsbIwTiihZkJQ6FAoF5s2bhwULFvB7H406Aa1DACx1RmFPtVrN3kuqehWNRtlYpb8n+P1+NkapMlEymURVVRXEYjGMRiOkUikGBgYQCASwbNkyqNVqvPvuu3jzzTfh9XpRWVmJZcuWYcmSJbwnCUuBHquxUFFRgd7eXmQyGTidTuj1etZeDYVCeTzFQggLnlAhCb/fz4YQ8TtPPPFEVFRUoLW1FXv37uXIh16v52RJvV6PNWvWAABHE8ajZRzJBauQGw2ALw2jo6NIp9P8XFGFLOL8lpeXM41EJpPB6/UyvSEajaK/v58N18bGRpbsq6ysRDgcZi8qUZyEof7JYLLzezgj1mQycUEFUseoqqrC7bffjh/84AfsQa6urkYoFILdbkcqlYJMJsOVV16ZV+FsNhumhSCt5BLmFo5Ltj7J7MRiMeaVZbNZDAwMIJPJsNeNxIMB8I2acMopp+D//b//hzfeeAObN29GJBJBd3c382VcLhdCoRD0ej3zUBUKBRQKBSc1ALkDZeHChVyRqb29HX6/H2azOU/wm/4Ph/PFxicC3TopcausrIy9mVR5hTzEg4OD8Pv9LFEihEQiwYoVK3DSSSehtrYWmzdvxrZt2/i2C+RCynR4k+c5GAzC7/fD5XLxQSiXy1kMnQ5YIH+TC4fDnOVdqB9Kry2cC+FYJJNJFlinbOz9+/dzAphYLEZHRweHqQGw9mcikYDf74ff7+f3HR0d5a+FmZhDQ0MoKytDLBbjQ0Gr1bJnhi4VFLpbsmQJ1q5dmxeyBXLhPPLmC4X8x5vbcDiXWV74e4fDAYfDwWFp4jeLxWK+TJHnn7yIJN1TCMpspj6SrBZdWCiiQBIwOp2ODbqenh7odDpWCBDOJyUaHe1BI1wHKpWKvYF+vx/l5eUQiUSsryuRSOD3+zE6OsqSWWKxGJdddhm+8Y1vQK1WM9eajIS2tjb8/Oc/Z13Gffv2Achp9lZUVPC4kweS5OEAMH2HkrCqq6tRX1+fF56cqE/EKwU+0KSUy+VwuVx8wdRqtXyhJA+gw+HIKzX81a9+FUuWLIFWq8XQ0BB+//vfY//+/dwXmsdUKgW5XM4VeigDXCaToa+vjzmrgUAAVVVVGB4ehtlsZi8chV6Ph7FAnOHOzk5W0CBNXkJVVRUymUxe1S4gZ0R2d3dDKpXymqMLKPGnq6qq2JOcSCRQVlbGexJFvdTqXDU3Ksfb19c3rpA/rb/DVfkjCA088ha2tbWhp6cH6XQaGzduxKOPPgqpVIpdu3bhsssuQ19fX55zIJVKcfspokH/ezwemM1mXhcNDQ3scSX+rlqdU1sZGRnJKxl6OByub0JnwqGoVwqFgpOMk8kkmpqa8Mwzz6CmpgZnnHEGLr/8crS0tKCzsxMSiYS93xR5ueSSS/KiL9NR9rqEEo4WxxTWp5AeSav4/X688847CAQC6O7uZu8LGaRAzh1PIXiXy4WxsTEsXrwYzc3NKCsrww9/+EP84Q9/wF/+8hf84he/gN1uRygU4pANeQ8VCgWampo4IWlgYIAN1WQymbcpV1RUoKam5qAQ75E8mGq1GvX19XC73Zx4lUqlOKRHfML+/n7mHQK50NOGDRuwfPlynHbaaVi9ejWcTid6enowNjaGP//5zxCJRPjb3/6Gbdu2Ye/evejv78/z3kQiEZZyIiNQKpXC4/Egk8lg8eLFaGxsxOrVq/lwjkQiMJvNzEmKRqN5nlUhT5MMDiEcDge6u7vR2dkJjUbD5f/sdjtGR0cxMDAAj8fDXkQgp1951VVX4Stf+QpisRi2bt2KXbt2wWw2Y/78+ZDJZHjppZewb98+vnhIpVLWl6SwPHkTQ6EQG/qkc0uai2T01dXV5XkDyLNIYdxDhbCIO0q/L6zURe0jigLJg/n9fvYCUt81Gg3WrFmD1atXo66uDlarlZ+HwcFBVisg43p4eJj1UGldp9Np6HQ6WCwWThyTy+XsMSHPJR34wproRxo2pfkHcgbEwMAAenp60Nvbi7KyMvT29rJmL80PkDNKL730UnzjG9/AkiVLeI1SOC2VSkEikWDt2rX43Oc+h2effRb3338/3njjDQA5HWCie+j1en5WfT4fkskkrFYrhybJ0KXPJu7toYxTel7o9SRKvnv3bjbaAoEA3G43AoEAUxOAnGbvJZdcghtvvBF9fX3MfTUYDHjmmWfQ29uL//3f/8XmzZsRCoVYGkskEmF0dJQTvegC3dXVBa/Xi5UrV+KEE05AOp3myBEV2Cick2PxLlJyYm9vL3p6etiI27BhA66//nqceOKJGBsbg1QqRXl5OUwmEyfy/fa3v8XPfvYzjkgRJ3xsbIwdDkRbOXDgACeyaTQaJJNJrtJEia0tLS1QqVRMf2pra+OLiFByihQcJsMNp99Ttv1rr72GZDIJs9mMn/zkJ+xdX7NmDd555x3ccsst+Mc//oFoNMr7Je2lIpEIer2eozoikQgGgwEulwupVArvv/8+NmzYgPLyci4aQdn/FNE7XqB1ezg+OUXZ6Dy95pprEAwGcccdd0AkEuGcc87hPAW6EHm9XoyOjuKxxx7DsmXL2BFDF/655kUtYe7gqIzTbDbLm0s0GoXb7UYymURPTw8GBwc5YYYeopNPPhmf//znUVNTg0AggJ07d+Jvf/sbh9zeffddfm+RSIRTTz0V5557Lh544AHY7XY88cQTaG9vx8jICIesqO51bW0tewYzmQz8fj+6u7uZblBbW4tFixbBarXmCStPNqGiUPwcABKJBIvOj46OMse1s7OTD7qNGzfic5/7HABgy5YteOutt/D444/DbrdziBHIHfZnnXUWzjnnHFx11VVYu3YtAoEAnnjiCfzjH/9AR0cHh0IpA9jr9aKjo4OTdSicYzAYeIMVekRaW1uxbNmycY2YcDict9nSjbqjo4M1XCmkNzw8DL/fz6E0OvzOOOMMfPnLX0Z5eTnee+893HTTTUilUlCrc6UF7XY7du7cCQBYsmQJ7rjjDixduhQymYwlpH72s5/hoYceQjKZxNjYGMrLyzlE2N7eztxiIOeBjMfjbLCuXr2aDTePx4Pq6uq8rP6JNl+VSsW8xmw2i5aWFuzfv5+T6AYGBnDgwAHmgWazWRiNRnR1dfE8b9iwAV/5yleg0WjQ3t6O9vZ2vP/++ygrK4NIJIJWq8Xpp5+OCy+8EI2NjdBoNLj//vvxxBNPcOiRuKj0+uHhYa6y5fV6YbfbUV5eDqVSCZ/Ph/b2dlZwkMvlzIc70hC/0+nkAhGJRAItLS38LFE4lhL+ysrK8PnPfx5f+9rXUFNTg23btuHZZ5/F9u3bMTIywga7wWBAVVUV5s2bhxUrVuCUU07Bk08+iQMHDuCRRx7B3/72Nw6JCyMpGo2GoyRarRYejwd6vZ69cclkkhU4Juqj0OAmKJVKeDwe7Nu3D/39/RyGJqULKhpw4403YunSpXj11Vexfv36cb19y5cvx1lnnYX/+Z//gUgkwpYtW/Dyyy+zzBglGQmz4C0WS54HfHR0lHmLJpMJSqUSdrs9L3GTMFmuIqG1tZX1cxOJBKRSKe68805UV1fjn//8J6677rqDxmfDhg244oorcMMNN2D9+vW46aabmJpAe2h9fT3GxsYwPDyMt99+mw1KMk7VajUWLVqUN9ZjY2P8GRRaT6fTfMkar3CIsN+TMcop4rN8+XI0NjbCarVixYoVOOGEE/CFL3wBmzZtwpe+9CXcfffdeP311wGA6UH0fEajUealZjIZaDQadHR0IJvNYseOHVi/fj2USiUkEgkqKirg9/uh1Wp5zxDqDwufvyO5WEw27K9QKKBUKnntDg0N4dOf/jQXdyFYLBaYzWa0trYCyJ2p7e3tePzxx1kLVqVSYenSpaivrz9q2sx0QCiHVVhq9lClZ0uY/Tgi41QYBqWF4fP5MDIygt27d2Pv3r0sDwQAn/vc53DFFVfg+eefx1133ZUX3gVynooLLrgAIpEIiUSCMz3feustvPXWWwByXs9zzjkHX/3qV3HKKafgkUcewV133YVoNIq2tjaEw2Ho9XpotVqWOKI2lJWVIZFIMP+LwmxHI05MGqZDQ0Nc7q+1tRXt7e3sQSFKwXe+8x1s374dX/ziF/MqORGkUinq6+uhVCqxd+9evPrqq3j11Vf59zU1Nbj88suxdetW7Ny5E9/97nexe/duAOCEkPLyciSTSfY07tu3D9lsFsuWLeNwlUQigc/nQ319PQKBAHsvhEab1Wrl+aLxoPAsJWyRR5iqRA0NDSGZTKKxsRG//e1vsXfvXnz5y1/G0NDQkSwnGAwGzJ8/HwsXLsTNN9+MW2+9FR//+Md5s6dNV5h1SzxWlUqFV155hT3pFouFD9VYLHZIQ40MDzKECB6PB2KxGG63GxKJBO+88w46Ojogk8kQCoVYpieVSmHp0qX46U9/iu3bt+Paa68dlxoxUZ9PPvlk3HzzzXjooYdYIFw4/lKpFO+//z5LDo2NjbGu7/z587mCmtFo5DnOZrOHzcKlBCFhWI8kvlKpFOLxOFNzyFBNpVJobm7Gww8/DIPBgPvuuw9/+ctfOKO9EC6XC52dnQCAxx57DEDuUP3sZz+L2267DT//+c/xxBNP4C9/+Qt27dqF0dFR6HQ6HmfiYUqlUk6WJPkpUgQZ75kdz8gBcolqIyMjHHaOxWIsNxSJRLB69Wr85je/we23345f/epX/Hf19fUwm80sO7R//37s27eP6QkAsGDBAlx++eU48cQTsXXrVmzatImLMYTDYaaCGI1G5lFTtEGn0zFflTzwwrUIHLlwu91ux5tvvolQKAStVotHHnkEP/zhD7Fnzx5+jcVigVgsRjgcRigUwmuvvYbXXnsNZWVluOaaa/Daa6/h73//Ox555BGex7a2NqbIULQIyF2sFQoFRCIRBgcH8xI1vV4vli1bxh5m0mUuLMk73r57JEb50qVL2fB0OBx45ZVX8Morr+CBBx7AXXfdhVtvvRXPP/883nzzTVx77bXML6USyuFwmJUkvF4vMpkMR92USiVzODOZDEt+0f5JhilFdsizX9gH+l74O2F4/XDnD+1XFOEgjc8DBw5geHgYVVVVWL9+PcrKyrBlyxZO9qOkrng8jo6ODjz++OOorKxEeXk5Fi9ejLGxMTQ1NaGpqWlS1IrpBtHICIUXK/r+vffem9Z2lTA9OCLjlHRKqcRgS0sL2tvbMTg4CIfDwdIyCoUCDz74ILZt24bPfOYz7CmUyWRYtGgRli1bhkWLFmFwcBCvvfYatFotGhsbsX79etxwww0YGhrC9u3bsXXrVoyNjWHTpk3YtGkT6urq8IUvfAHPPvssvvzlL6OzsxPBYBBjY2Mwm80oKyuD0+mEWq1mDqpMJuMSoUKtTMJkNkKqBkWSWMlkElKpFMPDwxgZGWHx9PPOOw8333wzrr32WjZKy8vLccEFF/DNXiwWY9myZZzs0dLSgldffRXvvfceWltbmVP0k5/8BPfffz9uvfVWPPXUU9i1axfuuusutLe3s6dUoVCgrKyMQ+9ut5u9adFoFIsWLeIDT6/XI5FIcLsocYi4YcLx6O3t5Wxxkitpa2tjTb1kMonly5fjN7/5Db7xjW/gnXfeAZAzulevXo1169ZBLBZDLBZzdiklUu3du5fXjNfrhdfrxXvvvYenn34aP/zhD/H888/j85//PB+o2WwWYrGYaQ2Dg4Ps+RCLxdi7dy9UKhVWrlwJs9mMZDKJioqKQ3pjCr1iNL+06et0Ok5eI0F54tZlMhmsWrUKX/7yl3HzzTejv7+f19nSpUtZ7kmhULAETyKRwNjYGHbv3g2v14sXXngBQM7js2jRItTX10Mul+Phhx/mUrDkiSc+J+k5AmAqAikVUEWtw4XphOucLiZkzFOIlSg5ZDBv3LgRv/71r/G73/0O9957Lx8IWq0W69atw6mnnoqKigpUVFRwpn0kEkFnZyfee+89HDhwAD6fj5/hCy64AN/4xjdw880346mnnsL3v/99tLS0AMhVhHE6nVi4cCF0Oh3TdIQZ7kS70Ov1zPujuaX+F4qP63Q6mM1mHoPh4WFEIhGo1Wr8/ve/x9VXX80e8vPOOw8XX3wxGhoaMDo6ylxmmUyGlpYWvPXWW9i5cyc6OzvR3d2NX//61wByoeQnn3wSL7/8Mu677748Xd9wOMzhdJKOGhgYQE1NDRoaGthzTUmlR5vZPjg4yJzuO+64A9/85jfR2dmJ8vJyfOITn2BjZfXq1fjsZz+LoaEhPPzww/jPf/6DtrY2/PGPf8STTz6Ju+++G++++y727NmDL37xizhw4AAA8PzSHqBQKBCNRtHT04NQKMTlM41GI1OpqKABcLCnayLDbLJG+Wc+8xl+lq6//np87GMUicayAAChS0lEQVQfg91ux2OPPYb33nsPd955Jx566CH86U9/wllnnYW33noLy5cv5/3T4/Gw11Amk6GtrQ0mk4nzGQKBABcwqKqqYpoIAK4OODY2xtrME/VByK8lJQrhayZz/gBgpRKqCEXP6EUXXYRPfepTaGtrw+joKOrr67F37948SUWap97eXuh0OnR3d2PVqlVMkVi+fHkpxF9CUeGowvqRSAS7du3CY489xpzGVCrFD8PPf/5z/PSnP+VNbdmyZfjc5z7HGbx79uzB3//+97xkoffff5+/rqurw7nnnosHH3wQPp8PW7ZswYsvvgi73Y67774bNTU1eOihh3DnnXdi165dnFBCMk2Dg4PQ6/V8yA8NDTEHim7ukyWhk+FCbSc9U41Gw0oB6XQaa9aswdlnn41Pf/rT7EG96KKLoFKp8Oyzz+L//u//8oyi2tpaLFiwAMPDwxgeHkYoFMLatWvxi1/8Ap2dnXjooYfQ09ODH/3oR/jZz36Gj3/84/j1r3+Np556Cn/84x8RiUTw/vvvQ6fTQafTcXh337597F0UiUSYN28eqqqqYLPZuNqJ1WrlRI2+vj5ORAmHw5xtTx6msbEx2O126HQ6DA8Pw+v1YtWqVbjkkktw7rnnIh6PQ6lU4ktf+hIL6VPoj943lUpBKpVCKpXirLPOwte+9jVEIhH09/cjmUziueeew5tvvolvfOMb2Lx5M/7whz/g8ccfx7333otAIACZTAaj0cjZ7m63G/PmzWPDsbu7Gw6HAzU1NWhubuZDUBiuKkwAEs4vhfOlUilzz3w+HyoqKrgIwBtvvIFMJoPq6mosWrQIV199NQDAbDbjuuuuQ2VlJVpaWjiDmVQiDAYDqqurMW/ePNxxxx2orKzEa6+9hmeffZY9cRs2bMBDDz2Eyy+/HOeddx57LClMKpfLodPpkEgk4PP5EI1GUV1dzd5aEnonTJToQOucZMfq6+sxNDTEhQX6+/uxe/dujnDccsstuPbaa/GJT3yCqTeLFi3C9ddfD71ej/379+OFF16A3W6H2+1GJpOBQqHAggUL0NjYiM985jO4++67ceDAAfz973/HW2+9heeffx7PP/88zjvvPPzsZz/Da6+9hq1bt+LGG2+E1+vlxDiTyQSZTIbm5ma+TJIMEKlmUOIcceuoFK4wEqBWq2EymXDKKadw6VTy+l522WX47Gc/i/7+fhgMBvzoRz/Czp078bWvfY2LZxAkEgkaGhqwePFifOUrX8GCBQuwc+dOvPjii9ixYwd27dqFSy65BJs2bcLq1atx4403IhaLYWhoCCaTCV1dXbz/pNNp9Pf38zOq1Wr5uRMmSB1pWJ84tU1NTXjggQf4s9esWYOnnnqK959NmzbhO9/5Ds444wxcdNFFuOKKK7Bz507cd9996O3txc0334y7774bX/3qV7F582ZcfvnleOuttyCVStmDTUohlOhKSgdEz6iqqmJpKlLqIGUEYf/GU52YrAdv//79iEajMBgMePDBB/HYY49h4cKFaG5uxmmnnYYnnngCnZ2dOPvss3HvvffiC1/4Au666y7cdttt0Gg0MJvNLIMmkUhYRUQqlXJugVqtZupaNptFdXU1awBTaWaZTHbIiIWQX0sFZYR9Pdz80n5VW1uLE044AV1dXQiFQrxGNRoNvve977GTAMgZpPPmzeNIH71HJBLhhM5t27YhEolwJGbhwoV5Mny0PksoYSYgyo5HqixAIBBgDT2xWIyBgQHs3r0bb7zxBidnOBwOuN1uXH311RCLxfjf//1fWCwW3HrrrfD7/bjvvvvytPWAnH7kqlWr2NgbHBzMy3Bvbm7G7373O6jVOfHgjo4O/PSnP8XAwAAWLFiAl19+GRdddBEOHDgAqVSKpUuXsndXp9NBJpOxgfHRj34UlZWVmDdvHntcKJQZiUTgdruhVCrZ+7Zo0SIMDw9jcHCQq07Z7Xb83//9H4eq3nrrLa468te//hWnnHIKUqkUzj33XPz3f/83XnnlFfzwhz9kI5iSBoSaioUwGAz46U9/isWLF6OtrQ0PPPAAh/RFIhHuvfdeLFq0CF/+8pfR0dEBILc5Wa1WrtJENbVXrVoFrVbL4XObzQaNRgO5XI76+nrOcg+FQli1ahXefvttRKNRvPHGG5zgAQBvvPEG9u3bh2AwiFWrVuE73/kOLr30UiSTSaxZswbf/e53YbPZcM899+Bf//pXXnZwISQSCT71qU/hs5/9LMLhMJYsWYJsNounnnoK999/P6LRKM4880w8//zzePHFF3HttdfC6/VCKpViwYIFCAaDzM9Lp9Mspq3X67Fw4UKsWLECq1evzpPWAvK9NF6vFzU1NRgeHoZEIkFPTw/zsyijlyoevf322yyybjAYcNttt+Hb3/42RCIRH+x2ux2/+MUvDuJ+jYeFCxfi/PPPx6pVq7B9+3Y88sgjSCQSOPfcc/Hkk0+ip6cH559/PoaGhmA0GiGTySAWi1FRUcFyaBqNBsuXL8dFF12ExsZGPuyEBo0wuWJwcBA1NTXMl6MLV3d3N4e829vbsW3bNs70vffee3HDDTfglltuwYMPPojy8nLcfPPNuOCCCzA0NITvfOc7vP4IJKNGEIvFOP/88/H5z3+ePaG///3v8fTTTyOTyWDFihV48cUXYTKZsG/fPpx//vmcNEPh549+9KOoqKiAzWZDY2MjjEYjJz+S0erxeGCz2VBZWQmr1ZrXX4lEgpaWFuzZswddXV146qmn0NXVBaPRiFNPPRWbN29GZWUlfvSjHyGVSuGLX/wie+spYY1UFQgajQZ33nknPvKRjyAcDsNkMuFrX/sa3nzzTchkMjz55JMoLy/H5z73OV5jJI1ms9lYXs9qtUKv16OxsZHDyA0NDfjsZz+bR70gGaSJPKrUX7qkNzY2oqurCzabDUqlkpMHdTod5s+fzwVRqC+33347NmzYgFgshnfeeQf33XcfGylPPfUUTjvtNNx66634+9//zuuPxiYQCMBgMOCEE07g8D1JvVVVVbGxSsafSqXCmjVruMqZy+ViTduJDLxCDyv1FwDrxRaCDNLbb78dL7zwAsRiMVpbW1FZWYn169dj3759nPQF5CJJcrkcNpuNuaQSiYQjXlKplL3BpMawbNkyuN1uppUJQ+NEgRA+h0KHCIC8velQBjn1d3h4GP/6179w7733orW1lef7qquuwiOPPMJzTPxnAHyhoKRXSu4k/WGZTMaXrjPOOAMqlYr7LEyeAo5dq3ayoP7++9//ZqrNX//6V3z+858HADz55JMAgIsvvhgAcNddd+HUU0/Fpk2b0NzcDLPZPKv0Tsm+8vv90Ol0M92cosFReU6Jl0ibCnl0AODSSy/Fpz/9aQDAvffei+HhYdx9992sV1lfX4/a2lqIRCI0NzcDyMkLLV++HEBOaiYUCuHhhx9Ga2srbrnlFtx7771QKBS4+OKLsWHDBpx++uno7u7G008/jcceewwnnnhiXoY+cegMBgPrkPb29rIeKJB/m3U4HHC5XJBIJByaAj4oXQmAq53I5XJWIiDu1a9+9Sts374dqVQKTU1N+OUvfwmRSIRXXnmFPZWnn346Vq5cibKyMi6R19LSArFYjBNPPBFisRiPP/44hoaG8KUvfQmXXHIJvvvd7+LCCy/Eu+++iz/96U949tlncfvtt2PLli3Ys2cPbrvtNtx///0Ih8NsGFDIijxPEokEAwMDbBwQP5NAhh8AzvAkz5TT6YTb7cbAwACCwSAUCgX+8pe/4JJLLkEymcS5556L22+/HeXl5Xj66afxz3/+E5lMhmtVkyQS6ciOjIygt7cXTz75JF577TXccMMNWLJkCRt6F1xwAS644AJs2bIFDz30EK699lq8/PLLOPnkk7nSEm2qFAaVy+VIpVIIhUJ8IFNt7UgkwhWkxsv2jUQi0Gq1kEgkWLZsGYBcxvPWrVsRDoc52Y0O6p/85Cf4xS9+ASBX0/mss87Ca6+9hvvvvx9AjiO1YcMGplxQ8YC6ujp0d3dzxnhnZyfWr1+PTZs24aKLLsLFF1+Ml156CX/4wx9w/fXX4+c//zk+//nPIxgMQqlUsgJFNBqFQqFAKBTCkiVLEI1GuS/CkH6hJ4YO73A4VwnJ5XLB6XRyiUniYNIBdtJJJ+GGG25Af38/Hn30UQDAfffdB6vVis2bN+OXv/wlc7o/9rGPsbSb1+uFWq1GRUUFduzYgX379uG5557DSy+9hJtuugn/9V//hXvuuQdXXXUVrrzySuzduxcPPvggvvnNb2L58uX48Y9/jOuvvx6xWIwVJrq6ulBdXY3R0VE0Njairq4OKpWKL42kWalUKpkqQyCRfQp3UylVIFdV5rXXXgMA3HrrraisrMS9996LbDaLhoYGbNy4Eb29vViwYAGy2SwnLb3yyisYHBzEt7/9bZx33nm48cYbYTab8dBDD+HWW2/FSy+9hEsuuQR///vfsW3bNlxwwQVoaWnhTHaPx8Pc3lAoxJf9+vp69uYJjRWiddAeJSw6UWjQZDIZ6HQ6dHV1QavVoqKiAnv27IFGo8HatWuxatUqiMVinHHGGdBoNHjsscfQ19eH733ve3j99ddx22234brrrsOVV16Jr3zlK3j66afxxBNPYMOGDdi0aRPC4TCee+455gAD4DA3lZcmz3w2m8X+/fvZ4KYsc6oCSGLw9Lfj6RKTce5yuWAwGLjPQiiVSkQiEZx66qlYvHgxEokENm3ahFdffRV9fX144IEHsHHjRuzYsQN//etf8Y1vfAO/+tWvcPbZZ7OXlIw8t9vNEQtSlHC5XKisrERFRQUkEgmcTifS6TROOOGEPN1Qep4oqY0MVGG50ELDldbiZL3jarUa69evx5tvvon29nY+m/7yl79AoVAwLem0005DZWUl808puZD2dxo3UmCg3Ik33ngD9fX1WLFiBT7ykY9g1apVnIRsMBjyjGlhm6bKYM1kMnj++ecB5JKQ6WtyXtH3v/jFL6BSqXD55ZcDQKla1BzBERmnJD5tNBphs9kgk8lYsJuSZ1599VVEo1E0NTXBZDLhi1/8IlKpFBYsWIAzzzyTD8rR0VH861//Qnd3Nxt55eXlOPfcc2E0GnH++efjhRdeQEtLC6666irceeedHJq4+uqrcc899+DHP/4xLrjgAlx66aV47LHH+BarUqkwb948FveWyWTMVbPb7di6dSssFgvfsjQaDYcCJRIJyyZRhSvyrtL3er2edQLLy8vR1NSEn//85wCAE088EV1dXXC73diyZQv/LJVK4Z133kE0GsWOHTtYuB7IZfNXVFTgggsuwO7du7Fnzx48/vjjeO6553DNNdfgjDPOwI9//GOEw2G8+uqr2LhxI7Zu3Ypvf/vbeP3119Ha2gqPxwOTycQeDeJMUpYwZc82NjayAZNOp/OMcUrmGB0dhc/ng9FoRCAQYB7bjTfeiEceeQQtLS3Q6XS47rrrsG3bNrz00kucwGY2m9HQ0IBsNovt27fnyYjp9Xqcdtpp2L9/P7xeL+655x5oNBpUV1dDJBJhyZIluOmmm/DrX/8ad9xxB9auXYu6ujpcc801ePDBBzlMLhKJoFQqIRaLYTKZOJOdNnm73c7zdKgkIVpPVH7XYrGgr68P0WgUoVAIgUCAk8MWLFiAUCiEzs5O6HQ6XHjhhXjhhRfwxz/+EUAugWbZsmUIBALYunVr3ue8/fbbsNlsuPTSS9Hf349t27bhzTffxG233YYbbrgB1113HX73u9/hW9/6FpYuXYqzzz4bQI7jZ7FYOFEoEAigrKwMBoOBx9VgMDB3tzAhA0Aex5YSW6j8IXlZxsbG8tY9edO/+93vIpFI4IQTToDNZsN9992HzZs3AwBsNhuampqwfft2NpAJOp0OH/3oR9HU1IT33nsPvb29+O1vfwufz4eNGzdCJBKxB/q+++7DjTfeCJVKhTPOOIONGJPJxJSSPXv2YPHixVxClRIlRkdHuaJRYRuAD2TYFAoFEokEV2Ki5zwUCjEX9Y033mBjVaVS4T//+Q+USiW6uroA5JIU3W43l+rcs2cPXnzxRezatQu//OUvYTAYcNNNNyEej2PLli34zGc+g4cffhibN2/GJz/5Sezbt481bKlAiUajgdPphEajQSwWg81mg8vlwpYtW3DmmWfyfFK0hRIDKRoynkFD897Y2Ijdu3dDLBZzRIcSh5qamhAIBHD66aejsrISb7/9NrZs2YJ0Os2eqgsuuABPP/00Nm/ejLGxMdTW1uInP/kJXnzxRU56stlsGB4ehkKh4CIFVHiis7MTyWSSvas1NTVcKIMcBIODg7DZbHlex8K+0PMnDIUTbrzxRvzhD3+ASCRCPB7nNb1w4UJ0dnbijjvuwG9/+1t89KMfxY4dO/Doo4/ipptuwkc+8hF8/OMfx3PPPcdC/Wq1mjWL1Wo1dDoda9gSdYA8v/Pnz4fVas1LdnK73RzRoedOrVaPa3gWXiCPhOu5aNEi3HjjjThw4AAnAqXTaYhEIohEIgQCAXR1dcHn88Fms6G+vh7vvvsuO4/mzZuHcDjM39fV1aG/v581UcvKyiCRSKBSqdDT08PVs9LpdB6/ViKRHFWhk6lATU0NWltb4XK50Nraissvv7xULWoO4IiMU3poqdJIZ2cn11AHwDqW9FrSPAVyXqXXX38dNpsNIpEIb7755kHv7/f7sWXLFpx11lmstReNRlnC5Oyzz+aSj0AunLdz507m29EhHI/HOVmISPuZTAY7duyAXC7H6OgoV1P59Kc/DbPZzNWFiNsHAP39/RCLxVxVxu/3I5FIcNIStfn111/nBJ4dO3bgsssuQ0dHBx+EPT09iEajqKiowODgYJ5hShgbG8Orr76KpqYm9rIEAgH89re/xcknnwyZTIbVq1fj1VdfxdDQEF555RWce+65fFumEqISiYTLFBJPqqysjL+22+186EkkEvZAEYjTSJmslJkPACeccAK+853vAMhpmpaXl6O9vZ0NUyDHC9y/f3/eewrfm7LQgdzN+L333sPq1asRCAQglUpZUzASieCFF17AZZddxp5LKm2rVCq5PKxcLofBYOCSopQYYzab2RM+0QZKY0BeHOJwut1ujI2NccgbyG3idFMno27fvn08x06nE62trRMqFoyMjOCVV15BTU0NVCoVQqEQnn32WVx//fV8WMfjcWzevJmLMQC5kA8lV8ViMchkMhbMF3L6KMGN2iJMuhBycMViMcviEFczEAhwnXIA2L17N1paWvDcc88BAJfJffnll7ldlNw2nlEYCASwfft26HQ69qwBuez9Sy+9FOFwmEPNbrcb77//Pj7ykY8w/5kS90jXsaenB4FAAP39/XC73WhububCGsRl1Ol0B81zWVkZzGYzotEoLBYLhoeHeT8ijrtIJOKSxGTwE6dQGC7u6uriix6VWgVy6gSvv/46LrvsMgC5EOO7776LYDCIn//859i2bRtuuOEG3HrrrQgEArDb7byGyVNHHuw9e/ZAIpFwJTzy5lssFtTU1PAl4lASaXThJFUBpVKJYDCIt99+O29+li5dCp/PB7fbzT/ftWsXG2n/+Mc/AICTtMxmM/7+979zeN7tdnMxiWg0imAwiFQqxcU5aIwUCgUn31GyItWBp3OD1khhf8gw12q1eSFlmhf6+2w2i3fffRfxeByLFy/m/aqlpQVut5uLJxA3+sCBA+jt7QUA9mxSkhe9P/H4qU/79+9HRUUFFyOgsSZPKtFvAOQZrUDO6VLYr0JjdbIQi8VYuXIlPvvZz6Knpwc+n49L6NLFs7+/nxM1qdx1IpFANpvF0NAQPvaxj2FoaAgHDhyA3W7Hj370I+zYsQPPPvssRkdH4fV60dnZiQULFkCr1bJu7549e7By5UqMjY0xt7+wb0IciZzWsaK2tjbPGCWa1mwL8ZfwAY44rK9Wq+HxeDA6OspeJrFYDKVSCb/fz6Hv3bt3IxQKsWeHkqNIJmgiBINBPPnkk3lyJSeffDJsNht+9atf4emnn+ZDeN26dfjud78Lu90OAMy1pJs2adiJxWIupRgOh7lsocVigc/ng1ar5bAkVcMBwGFdEorv7e3letsmkwnhcBjJZBI33HADzjvvPPz73/9GT08PrrzySnzpS19iXidx84SSQeNBuKkAOQPwhBNOwJ/+9Cfs2bOHjaezzjoLNpsNp59+Or+nyWTimuYk6dLd3c1i8WazGT6fjw8CSgyijQsA3nzzTQwMDMDr9SKVSrHuH5H4b7rpJlxzzTXcni9+8Yuora3Faaedxl5SId9pPAgLKlRVVWH16tX417/+hZ07d6KtrY1/t3btWsRiMdZ9BXJeQspal0qlyGQyCAaDqKmpQWNjI/PhqLQpVQkr3BiFRgclBjkcDhw4cADvvPMOXC4Xl4dVKpWIxWJ47bXXcOqpp6K8vByDg4O48sor0djYiNraWvT39yMcDqO7u/uQ80uVp4Cc4XTKKafgqquu4sSGFStWwO1247e//S2AnBFMGfqZTIbDpSKRCA6HA52dnaiqqmKZK5vNhra2NhYXp37T82Y2m7FgwQKuUe9wOCCRSGC1WjE8PMxj6/P5cNFFF+HGG2/EL3/5S7z99tu4/vrrMX/+fC4QMZGUFGF0dDSvGphGo8HGjRvxzDPP4IknnuCD+6KLLsKpp56K1157DRs3bmR9ToVCwUl7JAEVDAZhs9k4sUipVObV2KYxpvmlSA95fWgMhoaG0NPTA5VKBafTiV/96ldYuXIlli1bhv379+fJJRHosliIZcuWYdmyZfjHP/6B119/nT2tCoUCn/vc5/DRj36UE1WEyXYAeJ+iywHpR3d3d6OxsRH79+/HwoULxy1TKzzwheu5rKwM8XgcK1aswL59+xAOh7Fjx468Ng8NDXEyIkGv1+OEE07AHXfcgVdeeYX3zcsvvxybN2/GpZdeyvsiVcejPpSVlfElIRQKIR6Po6KiAlarlY1KhUIBiUSC3t5e9sxTqJ7mWAj6O6HuK4H6K9RSBXIGCRmiIpEIZ599Nr7+9a+jv78fIpEIN998M+655x78+c9/5j7TBYhAnFkqKS0SiWAymXis6JJTUVHBlZcAsMeULsP09aGSbY8WKpUKF110EXp6evDXv/6VL5VUsU8YrSI9YcLKlSthNBpZunDdunW4+OKLcdttt+HRRx/FjTfeyJSd3t5e+P1+jtzI5XJ0dXVhxYoVSKfTXAmQkvgKw/1HmtB3PECOplKIf/bjiHVO3W438zhJckOlUmHJkiXYsWMH2tvb0dzcjNHRUdbJA8A37vE2IiHoQROLxVCr1TAYDNi9eze2bdvGr7FYLCgrK+NNVyKRwGQyoaGhgUvwqdVqxONxrt+tVCpZtL6qqgrz58+H0WjMe7DI00beskgkgvnz50MikSCZTLIKAHkuysrKOHv4xRdfxDnnnIOOjg7Y7Xb85Cc/gUQigdFoZI+eWCxmT+d4IP1T8lr29/fnlb/U6XS4/vrr4ff78ZnPfCaPcF9TU4OBgQGUlZWhvLycw53E36Pa7tXV1RwuI8OENhWPx4NEIoF58+YhFAoxL4kO0aGhIfzpT3/CJz7xCWzZsgXd3d3o7u6GWCzGvHnzOPmDDNtCUGayTqdjmaW77ror7zU6nQ4NDQ1ob29n4X6ZTMaGlc/nYy4t8Tlra2vZU2gymbBw4UJYrdYJs00LEygog1gqlea9Px3CJpMJe/bswbZt21BeXs5ZsHv37uU201zQeBeC2ksJNrFYDP/5z3+4zd/61rewZcsWPP7447zGm5ub0dnZCbFYzNWUKioq2GimohJKpZIrDun1+gmTS+h7OvgpPBwKhdDb28tlccPhMLxeL/74xz/iqquuwrPPPovBwcG8vtC4FhpxBFJnIM+Kx+PBww8/zL9fvHgxvvnNb+Lkk0/G//zP/+CnP/0pRzxMJhNHMHQ6HT/Ler0eZrMZ1dXVHN4WSqE5nc68ClF0WAO5RDSxWIyFCxcilUrB6/UiEomwsbhnzx6IxWLuV2GeKH1Pa9hqtfIB/vWvf51fR2H06upqfOc73+FQsFKphMFggNlsRiqVglarZY808TRJcUSv13PpZ5vNhurq6nH5iuMZp9T+vXv3QqfT8V4iBO1B5FSgCwmtRyBXLKOqqgqPP/44GzeUle71ejnJifZaKoGr1Wqh0WhQUVGBJUuWcHlniUQCiUTCpVtXrFiBRCLBxl0kEoHL5Tooo3880GWLaFMEqiimVueKNvz73/8GkDNYbrrpJjz00EOctKjRaGCz2fgCJRaLWXKstrYWfr8fZWVl0Ol0qKurQ11dHSeeVlRUIJFIQCaTsSTYeJ7QqTTKlixZgm9+85uwWCx49NFH+Uweb98liMVitLe3c5TvwgsvxE9+8hPejy699FJs3boVf/3rXxGPx7F8+XI+f4iCIqSsnXDCCTjxxBORTqdhNpuxfPlyJBIJzt9YsWLFtHhNhaitrT0oxP/GG2/MykSpDzuOyDiNRCIIh3O16Mk4fe+99zA6OspajHRT+a//+i+Ew2G88sor6O7uZhLzpz71KXzsYx/DwMAA4vE4c6mopNyCBQvQ19eHRx55hOt1Azmv2TnnnIPzzjsPt912G5xOJ0QiEW/AqVSKaw7rdDpotVpOkKioqMDixYsxb948+Hw+LvU5Hg8oHP6gYtK6deuYD6VSqbjEYlVVFYfDSO/P7Xbj3//+N55//nls2bIFv/vd7xAOh/NKg95333345Cc/yXqOo6OjXIa1qqoKOp0OO3bs4FsfkOP2kfbiGWecgUcffRS33HILgFzIjsK9Docj70AlbhzJI5G+bGHFjfnz57MBYbPZmE+USCQQj8fZo0Zh73g8jqeffhrvvPMONm3ahH/9618sDg0Al1xyCX7+859jaGiIPRNk8AK5rMs77riD2yCXy3HKKafgzDPPxLnnnostW7bgzjvvBAD2hJIhQsYoqSesWLEClZWVWLt2Lex2O19mhAb3obimwAeHuVqtxsKFC3HSSSexdzQcDkOr1fKhS+U8/X4/fve73+HVV1/Fiy++yB41qVTKup6JRIJDn01NTbjnnnvwhz/8gQ96iUSCM844A5/85Cdx6aWX4sCBA/j617/OCRrRaBQdHR3sCZFKpTCZTKirq+PMTporqjsPYNyiDwQyasg4zWazsFqt2LFjByeskWEeCATg9/vxj3/8A21tbfjLX/6C//znP6xeQPj1r38Nk8mESCSCQCAAm82GV199lat9CQ0nUtS46aabcNVVV0EikWDr1q34wQ9+ACDnbSQaDfHeZDIZFixYALVajSVLluDkk0/mykXC+aM5FEqFqVQqpiHRZeOdd96BSCSCxWLB0NAQvF4vrrjiCjgcDqYkAcDTTz/N5YFjsRj0ej1WrVrFnGrykFK7Tz31VFx44YXYuHEjxsbGsGbNGu5zeXk5y3VRUhtx4a1WK0wmExfWIA61VCrlyyElBhEKvXHC9ZxMJnH11Vfj4YcfRiAQwEc+8hHcdddd2LNnD+sg19TUYP369fj+97/P/GhKzDz77LPx0Y9+FI8//jgeeughAOC9WSKRcLlWugzOmzcPFouFC6BotVpUVVVxXoLJZIJGo2FeN7V3vL2XaCa0504E6u+aNWuwa9cuLF++HJdeeikWLFiAqqoqnHHGGQByZ8aVV16JO+64A8uXL+ciG2VlZUilUrDb7chmsxz6bmhowKJFi6DRaJhm1NDQgIaGBqjVaqxatSrPQ9jW1sYJp42NjQeVH50Ko0y43hcsWIDrr78eixYtwubNm/HUU08dZJySUwjIRV/I+fOlL30J//3f/w2xWJwn+3fPPffg9ddfx/DwMOx2O+9XJB8WDoe5QiJVqYpGozAajRgeHobNZkN/fz+MRiOfbQBY1YdKOxOmIvRPIf7xvKhPPfUUV9AqGarFjUkZp7S4qdqEXC7HokWLeJKDwSB8Ph+2b9+OnTt3csLF7bffjptvvhlutxvXXXcdG6mrV69GQ0MDh3UokQnI3eqJD7Ru3TpcccUVqKmpwfz58/P4iEBOioqy0K1WK5qamuD3+6FWqzl7V6PRoKqqipM9KisrYTabOYlkPNADHI1GIZPJ+OGlijF0cGYyGS5h+tBDDyEQCGBwcBDXXHMNvvCFL6C1tRV9fX247bbbAOTCaR6Ph0NHy5cvzwtvjY6OMmdxwYIFeOCBB1BXV4dUKgWFQgGn08k3f4lEguXLl6OiooK9LQqFAjKZDDqdDkajEY2NjQByDyUZ3LTZZDIZ9gaTYXnWWWfxBSMSiXA5RJ1Ox94Q8lbE43HceuutuPLKKzE0NISvfOUrsNvtcDqdGBkZgcPhYA9iMBhkD5ZQP/J3v/sdTj/9dCgUCs6SFRYJqKioQG1tLVf4WrhwIZLJJDweD/PwaBOsqalhCkZnZyfS6TR7agpBF4v+/n5ed0DOA3rGGWdg6dKl6O3thcPh4Mx/m80Gt9vN9IWGhgacdtppuPPOO7Ft2zZ85Stf4fFMJpMwGo2s++r3+5mKcvrpp+NTn/oUTjzxRPYa+Xw+pgSQQV5eXs7KFiaTCRUVFVCrc4L75MGnMZJKpbyW/X4/6/tS32l+ybCgeclmszy/K1aswJIlSxAMBtHW1galUsk0i1gsho0bN+LjH/84xGIxenp6cMstt3D9+aamJr4IRiIRbsvq1atx9tlno7y8HHV1dVixYgXkcjnS6TSvAyGNZf78+ZDJZLBYLNBoNFAoFJg3bx5qamqwfPlyLrtIZU9pvCeaX+J0UmJkVVUV1qxZw2Vo//Of/3D1rfvuuw8DAwO44IIL+PC1Wq18caMIwsjICO8/X/jCF7B+/XqceuqpkMvlHEIV9on2m1AoxHPY2NjI4WStVouGhgYuY6vRaPhZicfj8Pv96OrqYkF/WtOJRILHWcgbBXLi9DU1Nbj77rsRCARgMpmwfPlylqkhY4IuVV/96lfxhS98gSslCd9TJpNh8eLFHL6nhKempiZWGNBqtQgEAhCJRHmqJiKRCMlkko1zomMAYA8bPae055Bhfqj9mdbzNddcg127diEYDOKcc85BJBLh/dNoNOKNN95gWk4h/1OhUKCyshLV1dWoqanBwoULWZatq6sL9fX1WLp0KSffqdVqJBKJvKiITqeDx+NhZQ6VSjVu1ORYQf0t3K+A3OXnlFNO4ZKtjz32GFpaWtDV1YVwOIzm5mb8+c9/5vEkRQWNRsPPICULArkL7Lp16/DMM88gkUiwsUvRMLlczg4PKj4TjUbz9jhSTxGuXZrrYDCI6upqTjCORCIH7dVEgxPSgoQyfYW8/qGhoXGpgmKxGK+99ho8Hg/cbjeuv/56nHfeeQDAhYIo4WsmQRdt4aV/LiObzSIYDHKFvIkwKZ3TnTt3Yt26dce1gSWUUEIJJZRQQgklfPgwMDDAdLTxMCnPKXngSC/zaBAOh/m2NJ5kyES/mwkMDg5i6dKlx9Tf8XA0/ZyOsTne/T3aNk/XOpjJ+Z2JtX60/S1sa7E9pxPheM0v6VcCyJPpKjYcSX9n65wKMZn+zoV+Eo5mfgmzub+7d+9GPB6H2WzO0+aea6D+tre3Y9GiRQBy6iBkdw0PD/MaJqUI+tlsBEnLURLhRJiUcUoZ3lQq82gQi8WgVCohkUgOeo9iq4pA7TmW/h7qfY8Ehxq344Xj3d+jbfN0rYOZnN/pmM9CHG1/C187E20/Ghyv+SUprFQqVdR9PpL+Fs5hsfbpUJhMf2fr2h0PRzO/hfzX2QTqozDxcLbN2ZFA2F+C8GtKChXK8tHPZjMOVxp34oD/UYLI+4UZomq1ekpkNeYaCsdvto7bRHyxDzOI/D+eoPhswGTX4kR7wGzE4fpczH0dr22zdT85Ggj7/2Hp9/HsZzGs7Q/DnJUwPo6qfOmhMJG22XRLSsxWFI7fbB03SvIq4QNQgYDZuuFOdi3OhL7hVOFwfS7mvo7Xttm6nxwNhP2frrrwM43jOb/FsLZnq/f3aCAWi7FmzRokEgkcOHAAS5YsQVlZGScN0e/p67mO426cEjfiw7ARTAXmwvjNhT5MBT4s4/Jh6SdQ3H0t5rZNBz7s/T9WzPT4HapYz1yEUqnEk08+iebmZmzYsAEAuFBIbW0tlEola39/GDAlxmlpMzh6zIXxmwt9mAp8WMblw9JPoLj7Wsxtmw582Pt/rJjp8fswzp3L5UIkEsGmTZsAAJdffjlcLteHUpP1uBunU4HprNF7LJgt7SwWzPbxmu3tPxJ8mPo6Hj4M/Z+tfZyt7Z4pzJbxCofDiMViRd/OqUBzc/NMN2HGUTTEhUORr4Xcl2LGsbazGAjoxxOH689smdeJMFH759o8Asd/roptjOb6Wh0PhX2erX2kdjscjqJaU8UKh8OB0dHRQ1aTKwb09/cjEAh8aOYzEongwgsvBJBLKKavqShQJBJBfX096uvrDyrBPRdRVMbpRBvjbMm0PNZ2ztbDYSIcrj+zZV4nwkTtn2vzCBz/uSq2MZrra3U8FPZ5tvaR2g2gqNZUCccGqs4429bj0SKbzXLVLuHXwt/b7XYuuzvXUTRh/UORr2eLW/9Y2znTBPTjjcP1Z7bM60SYqP1zbR6B4z9XxTZGc32tjofCPs/WPlK7heHqEiYGKRcU+zjpdDpUVlYWfTtLmBoUlXF6LB7HYubQTLZ9xdr+o8VE/Sn2+Srh6HAk81psc09tEXoR5yqE8zSXpHo+THN4NJht865SqUpz+SFG0YT1jwXFFiIsRLG3b7ox18djrvdvIsz2fs/29k8Wc7mfc7lvx4rZNjaRSGRWtbeE44spN06nI/Gh2LlSR9O+YksYOVYI+1Ps83U4HG5uZnv/jhbCfs/G9XuoeZuN/SkE9QGYm5V3yDP4YeIpHglm2/ouzeWHG1Me1hfe1qZqkRVbiLAQR9O+6Ri36YSwP7O9Wsvh5qbY1+NUQdhvh8Mx69bvoeZtLjyP1IfZXHf9UAiHZ3cFtqnGbFvfcrn8Q7uXljANntMPqxfpWDHXxm0u9Wcu9WWqMNfGaC70Zy704VCY6/2bShTj2BVbe6YaIpEIDQ0NB30t/P2SJUuwZMkSiESimWjitGLKPaelm8/RYa6N21zqz1zqy1Rhro3RXOjPXOjDoTDX+zeVKMaxs1gsRdemqYRKpcI//vEPrF69GgaDgb9WKpX8+wMHDsxwK6cPx904nUzGbqHkRylzO58PRmHv2ZDVPtk20usIxdyn44EjHZfC1xXD3B9JG45He2e6z+M9g8fyXjPVl2P57PH+dqbnZSKEw2G43W4Ak5uvYu3HdGO2nDV9fX2QSCSwWCxzkoZSwqFx3MP6k8kIFL5mtmUQThXC4TCCwSCCwSCPxWwYm8m2kV7ndDqLvk/HA0c6LoWvK4a5P5I2HI/2znSfx3sGj+W9Zqovx/LZ4/3tTM/LRIhEIkc0X8Xaj+nGbDlrhoaGEI/H4XQ6Z7opJcwAjrtxOhnuivA1xch1mQmo1WpotVpotVoei9kwNpNtI73OYrEUfZ+OB450XApfVwxzfyRtOB7tnek+j/cMHst7zVRfjuWzx/vbmZ6XiaBSqY5ovoq1H9ON2XLWzJs3D3K5HBaLZaabMi2IRCLYuHEjgFz5UvpaWL506dKlWLp06YeifOlxD+tPJixAv+/r60MkEkFdXV3Rh5Ami8O1/1AhlcLQ1GwYg+PZxtka+j+WNUt/Ew6H0dvbC6B4Krgcr7DwZH8/U9SGwmeSfkZtmmz7hZjJ+ROuKYfDkff9RHQq4e8KQ6jFsBbHg1qths1mA5BTh+jr68sLAc/2s+RIMdn9U61WY/78+XmvL0ZaEZC7gJDndK6H9rPZLHp6eg76Wvj7lpYW/nquY8YqRNHGGQ6H4fF40NTUBKvVWpSSFkeCidpPDzuFVIDchkBVMAKBAJxOJ+rr66FWqw9afLM1O4/m2eVywWAwIB6Pw2AwjDu/NHZer3fC1xQjxpvziX420eFRuC5mQ7+FGG/u6OeF/ZxoXMY7IKdjLxCOPZDzUMjlcgA4qD19fX3j/q7YUNhW2meEdKpgMAitVsuGymzed51OJ4eA6RwpnKsj6WOxGGeHg7Cd4z2DKpWKz47CPh1qPIphPfT39yOdTkOtViMSicx543QitLW1QalUQqVSzXRTphVTapxO9IA7HA7Y7XZIJBJIpVKk02n09fUByN2OHA4H4vH4rNwo6aGf6GEHAK1Wy6+l/9va2uByueB2u7F69Wq+MTqdzllDCB/P+HI4HGhtbYXf74fRaERTU9OE4SMaOwrjFPvcC/srFJ8v/Jnw9YFAAN3d3azht2TJEn4+CtdFMWG8Z1nYV/rfYrEgHA7z8yuXy/OeB1oTwud7ooNwomfpeEM49kBOX9Hr9UIikRwkTC6XyxGPxwGAvZLU/2IyZMLhMNLpNLq7u1FdXc3t8nq9UKlUcLlcCIVCiMfjeRGcYmn/kcJiscButwP44ELscDiQSCSwatUqAB+sJ+Dwc1cMxtlkQO10OByIRCKIRCJQqVQ8z0C+J73QCTJR/4phPfT39yMSiUCtVmPp0qUz1o6ZxnXXXQcAnLX/YcGUG6fjPeBOpxMSiQQmkwn19fVoaWlBOp1GJBLhzaPwUJstONQB5fV6YbFYYLFY+DabzWahUqmgVCqRTqcRCoX4xlvoDSh2TOQ9E4lEyGazKC8vHzdsSCjWg34iUH8LDVMSAh8vPOp0OiGTyeDxeKBQKPL6On/+/BnqyeFxKO8wzTf1mQT4vV4vIpFIHmeM+ip8r4kOwukI59Pn0tjTzyQSCeRyOZxOJ9LpNIDcpVKn0x1kUAMoOkNGrVZDIpFAr9cjnU5zOw0GA89XNBrl57SQUjRbPIcEetYCgQA7OgBAr9fz19QXYYEIYPy5KwbjbDKgdsbjcaTTafYU0zyn0+k8Y9TpdPLZeiiFg2KY93Q6jUwmA7PZ/KEzzIR488030dfXh8svv3ymmzKtmFLjtPABp1C1xWJhj6BKpcKSJUsQDofhdDpx4MABBAIBNDY2or6+fiqbN6UYb3M3GAz8+2w2y8a4Wq2G2WyGx+OB0Wjk1wvHaTZArVajr6+P5V1oQwTAN/rDHYCzxWMB5K9voWc8Ho/D6/WOewCoVCpYrVZYrdaDxqOYMd5hTQc9kOtzeXl53mtVKhUfjoWHHV3U6PXTPQ7ZbHbctSY0YJxOJ1QqFSKRCNxuNyQSCc8bgLy/K7b1qlarUV9fzx41ah8lJQKARCLh3wm5qcDseA7D4TBisRi3W61Ww263I51OQ6vV8vkxXvtp/c3Uxeh4QXi5I8+p2WxGWVkZVCoVBgYGoNfreS+qr6+fNbSGZDIJsVgMnU6Hurq6GWlDMWDlypUfSuN8yo3TwoVNoWriQNFDQ+FAr9cLqVTKfz9bUbi5CzdBOhAo9EbhKJvNBolEAiDnbSQjZraADvJEIsHhGAB5Xilh4slEB6DQcJktoPktLy/nkKJSqcybe+LAjUfTKIbD4FBtKTRcaB4BsNe08LXCPhfOMV3UZrLf4XAYAwMDqKmpGff3wn6l02kEg0FOugFw0PgUG8gj7HA4EAgEYLfbYTab+cLkcDiYwjAe/WIielIhdWem+k6JMlqtlvcTlUqFUCiESCTCxim1d7z1VwzP2/GAWq2GxWJBKpViyozL5YJMJoPP50NlZSW/Dpg44Y9QDJcTkUgEvV4PpVKJcDiXMDrbS1+XMHlMefnSQlCoemBg4CCtNavVikWLFqGyshIqleogvtdsAnkpxnuQCr1smUwG6XQaPp+PD4nZCpVKBYlEMi55mzJqx8bG8jw5hWMk9DAXMya6gEQiESiVSkgkkrzDgPiKh1oTxTD3h2tLOJxLphkbG4PL5ZqwT+S9o1C48OdCKsRM9TsSiUCj0eR5D4WHtlDuTqvV5oX6i2WuJgO1Ws1hX+F+K/SkjicfRRco4bjQfBXTGEQiEfT19SEQCEClUkGj0TBdYbz1dai9eTaD+gUAwWAQ6XQaiUQCVVVV/Bq6MAYCgUPOXTGMkVwuZ2fN8dIfLmaIRCJWnxCJRKirq0NdXV1eMrTNZjvoZ3MVU56tn81m4XQ62TtIEHor6AGgED+Qk5kaHR3lbNJCFJOniVDYJjLQhCFEoceJvKjxeBxKpRImk4kNmomy9Yut30RPoPAhkAvnCz2Dwr6Q0RYOh2G32w8KbU/ksSlGCNsqnGOVSpVXeq+3txculwvRaBQmk+kgD3KhZ32mQV418sAA+TxgMtZIeYHWOs2zMJFvIqNV+HP6jMLQ8lRA+PwIaTPC51N4oRCGQ8k7R7+bzqTNyTz3xOUvvBjSeoxEIuPuL8L1R/0XUheEFzDyNptMphk3XoCc15TE+AcHB6FUKjmzuVgpF8cbhWsjHM5xpisqKvI84bRHCZP6SL6uMOox3jM63edOKBSCVCqFyWQCgDmv7alSqbB582YuXyrkThM2b96ME088cfobNwOYFikpp9MJv98Pp9OJ8vLygw4tocFWmEAxHuj2l06nIZFI8riN9PuZMOAKN3K60RMPCABcLhdvDgMDAwiFQjCZTFCpVCy1JDRcKFReyG0spg2XPGlutxtKpTIva1StzpHw6XcSiQTRaBR79+7lg1K4HorF6J4MCsPXAwMDnGxCRiiQu/UPDAwgEAhwcg39X3iAFkPfqQ1CaavCLF/63263s+c0HA7zXGs0mrzsabX6A2UCIcgImi6JJofDkSehZLVa8/jfQ0NDXDZRr9fzWhby36l9gUAALS0teaFy4d5DfRf+3dH2bTLPfSAQQCwWQ319PSdU0gUwEokgFArltamvrw8OhwNWqxVLly7N+wyhgSuk2ZC3GZh53UlSvAByhozP50MwGEQikQCQr1lLVDIA6OjogEwmy7twFMuzN1kUUmso4uZ0OuFwOKDRaPI0Qqn/arU6L6mPnvFIJJIn3zeewTvd505XVxeSySRisRiampqQTqeZs1/C3Me0SEkRv3R4eBhjY2MYHR3F8PAwli5ding8zhyhQCAAsViMwcFBlv8Qvo8wOUgul2N4eBhVVVUHPTAzZcDRQwx8EApzOBxIp9PweDzw+XyQSqUsoeXz+RCPx5FMJtHY2IihoSF4PB6k02kYjca895TL5XmbESWfTDeEm6JwbCUSCSQSCRKJBGQyGex2O5RKJTQaDZxOJ/r6+mAwGGA0GhEIBNDW1gaJRMKH4nR4zaYKtMkPDg7C6/Wio6MDiUQCWq0WdXV1UCgUCIVCKCsrQ39/P4eqTCZT3sYvHINiGge6YAh5iZFIhDPZQ6EQWlpa0NPTg0QiAZ1OB5PJhEgkgsHBQXR2dkKn0yEajWL16tUAPjBYib4TCoU46Wi6n9u+vj50dHTAaDRieHgY3d3dkEqlOPnkk5FIJDgqQAlexHG02+08BjRnQuNXrVYfN93eyXjWac3R89nR0cFJJcFgkMfa5XLx+5BKCmFoaAjRaBR2ux11dXV5F0+h0VoMnHBSMhkcHMS+ffsglUp5zxkYGEA0GmUVFIpajIyMwOFwcHJbISe62J69iSA0LLVaLXuxd+/ejQMHDiCTyWDJkiXw+Xz8fDU0NOCEE06AxWLhdQAAZrM5zzlA7z8ZRY2pRGtrK6svnHTSSWhsbCyKdVfC9GBKjFMKGQm5lbFYDDKZDCMjIyylMzw8DJPJBJ/Ph3A4jFAohEwmw8aN2WzmjcNut8Pv98PtdqOmpgYSiQSLFy8GcLCnZTofJOENU6VS8W2V+q3RaNgw9fl87NmIxWIIhULQaDSQSqUs/E0eNb/fj7GxMUilUmg0GtTU1CASifDhSGHU6eae9PX1Ye/evZDL5TCbzdDr9dwe8mQDwNjYGIfXkskk6+4ZjUb4fD6o1WrI5XLodDq4XK48b1bhuE40j8VAcaA2kBfY6XQimUzC7/dDJBKhtbUVOp0OUqkUtbW1kMlkcLlciMViAD7QBSUPXaFHdaYgpBuQp1ToVWlpaUEoFOL5DoVCGB0dRSaTgU6ng0qlgtvtRjabRTabxdjYGORyOYfohFEPIEf3EIvFrHlcGA05nv2KRCJsoKnVauzduxderxdutxvJZJINbMLIyAj0ej2SySSkUilXtDObzczro3VQmNkfDofzDtSjvYRN5m/MZjOvq3Q6zRJSCoWCObOZTAa9vb0Qi8X8rEWjUb5Mh0IhjIyMwGq1sreN9i+ZTIaGhgbu10xHb9RqNfr7+7Fr1y74fD6IxWJYLBbm8Y+NjaGiooI92yqViqkYtG4piqVSqYouInUoELUGQB432Ofz8SUlGAxCoVDA7XYjFouhra0NHo8HVVVVCIVCMBqNqKioyPMe0/sAGDfJcTrhdrvh9XohEolQVlbGl/25img0iiuuuAJA7vxfu3YtAGDr1q38miuuuAIqlQpbt26d8xn8x9U4HY9L19fXh8HBQdYsW758OcRiMaxWK5RKJeRyOWKxGOLxODQaDdxuN78HhSHI8HM6nQiHw6iqqoJOp8tz7xcaKtP1II3HVXM6nXllWQ0GAw4cOIBEIoGamhoYDAak02koFAr2aACAz+eDRCKB0WiEx+NBIpHA8PAwP5AqlSovDFN4q50qQ0343pFIBKlUCrFYjBNEiPc1ODjI7Z8/fz4kEgksFgsGBwfR1NQEmUwGlUrFpG8AfEgQ91YYHj1U9RL6N1N6uMI2kEFZVVWFSCSC4eFh1NTUQKfTYWhoCIODg7BarTAajVCpVBzeD4VCsNvtkMvlfPh7vd688TnUZ0/lOi98b+HBRQLuQM7763a74ff7kUwmIZPJ0NLSArvdDovFAqPRiMWLF8PtdqO8vJzDh8KohzADt9BDOxX9AnJexra2Np4nukyRLqZCoYDH44FcLoder+d16vP5oNFouH1CziNdMIAP+HHCPaq3t/egS9jxhsFg4AiMRCJBXV0d7HY7dDod6uvr4XK5sG/fPjidTigUCthsNo5u0OVarVbnhVAHBgYQi8Xg9/tn/LkTQqVSIZFIQKVSoa2tjZOBgNxBb7FYUF5eDpPJxCLuZKQKPcZCfupsMEyBDxLWhBxoitKp1WpoNBreR+hCFggEEAgEEI1GUV1djUAgAKlUipaWljz1jKleo5MFJfHR5WHdunWHPPNnOzKZDJcnTafT2LVrF/+cQL8X/myu4rgbp0KeGpDjjSQSCUgkEpx66qkHkfWF0lHJZBKZTAbRaJQXJBkqlIUpk8nGzQ6e6VB+PB5HIBDA8PAwG2FkpEajUVRUVECn03HxAZJcooPZbDYjGAzyQUhGu9Fo5BuS0OtD3NXpKD8ofG8ylD0eD2QyGf5/e18e3nSVvf9ma5OmSdMmbZruG6VlKRRQQBB1BEVgxAUcR3QclFkcHZVRHPEZFR3cZ+TL6CiOG844MiIuqCgqCIwga0sppXvpnqRt9qXZk98fec4xqQURcRl/Pc/jY+mS5H4+93Pvue95z/sKhULI5XJGA202GwYHB5GTk4P8/Hw0NjbCbDZDLpejpKQELpcLPT09SE1NhUajQXZ2Nnw+H6OGsajZicYSi8h/200Zw3EIY5NnILqIJiYmYnBwEHa7HTabDeXl5Rg1ahQkEgkGBgaQmZnJv0MbAen8UtkxFAqdklTKdz3XYxNxp9OJcDjMnDbiMnq9XmRlZcHv93MlRCaT4ayzzoLP50NWVhYsFguOHz8OlUqF0tJSfm3aaIHhXXzO9BjT09NhNpsZxU5OTsa0adNgMBiQmJiIzMxMps3QAQuIIjk2mw1tbW3weDxIS0tjehFVOLKysuLE7b/LtYiUMuhzE2ImFArR0tLC90yhUMBkMqGpqQlCoRAymYxdpOhekJGEXC6HWq1mpJuQxh9CM5RcLkdlZSVsNhsaGhpgMpkQCASYPqRSqaDT6b50kC8oKOByvtls5kT120Lrv60Yug5Qk5NcLkc4HEY4HIbP50NmZiasVisyMjJgMpmg1WqRlpbG1cxQKMSOaLGHq++bapWWlga3241IJAKDwfCln39fe/5IfDdxRpPT2FKDXC5HfX09/H4/+vr6oFarcfz4ceh0OqhUKvh8PmRnZ8clq2azGVKplFGmwcFBVFVVccKWlZXF5eHhkNLvY5ISqksoMY2NNgC/3w+PxwMgigrl5eUBALKzswF8UVY6cOAAent7MXbsWFRWVjKRPSEhAX6/n98HiG48AwMDcQjbtzn+oa+dn58fh+K63W6kpqbCYDDA7/fDbrcjJycHjY2NOHz4MBISEnizNBqNMJlMEAgEyMvLg1gsZsTDZDKd0LFmuM9DjlPfZgzHIYzl/sY2XYhEItjtdphMJhw5coTRi5ycHMhkMhgMBoRCIU7kCFmnchwdaoaL4RoUvuvqABCde0KhEBqNhg8UTqcTWq0WIpGIKRvp6emYPHkylEolBgYGEA6H0dnZCa/XC6PRyChyrHQa/Z++/rbQudiqDiEzPp8POp0O7e3tjISXlpZCLo9aC9PniC2N6/V6iEQibnYEogeVWNWN2PsWS5X4NoLWX7fbjX379rHWMDXAtLa2IikpCR6Ph+lT1LxHCTjNv1gVA5JGI27jd/HcnUrQM1dUVISDBw/C4/FAKpUiLS0NCoUC06ZNg8ViQV1dHfR6PSoqKvgeUIKqUqnQ3t7OAMnXscn8PpG7WBoJ/VutVsPlcmHfvn3w+/2wWCwYP348RCIRcnJyEAgEkJKSws1+kUiE1R0I/ff5fDyfv+/ELzs7G1arFQ6HA11dXTCZTF9qYBtJTH+8ccaT08LCQu5+tVgscLvdUCqVsNvtkMlkaGxsRHJyMqOFcrkc3d3dOHjwIKRSKQoKClBYWIhQKIS6ujrmpObm5nI3NPG8KBEkTtH32cWXlJTEVqwWiwUajQYajQa1tbXQarWc5Bw6dAh5eXlQqVQsp2UwGNDR0QGhUIiuri7IZDJ0dnZCr9cjEAhg0qRJvHGT+xJtnN8FJyj2tUnQm2RpQqEQmpubkZOTw5t7W1sburq6oFKpEA6HYbfb4ff7EQgEEIlEuPxNiEZ3dzdMJhM0Gg0nbKf6eb7tGMohpLJ2amoqcytj0QahUMiNbu3t7SgsLIyzkGxsbGT5JaKpDA4O8iFkcHBwWLHpWJTg2xSiHm7DpU1ALBbDZDIhFArxNejp6YFCoUAgEIDT6URLSwvkcjmX8BsbG2G1WmG1WhEOh+FyuZCbm8sl87a2Nm7+I7UKQpa/DXQulnpEfuy0ntAhMhQKwWg0IicnB4ODg2hra+NqQEZGBhwOByfixBWne9za2vol3nSsheS3uUa53W6YzWaYTCaIxWL09vYyb594sVarFR6PBxqNBqmpqQiHwzyHhxorAFGO+cDAAJKTk+MqGidC1b7LhK2rqwu9vb0wmUzIzMzk58NsNrM9KzWt2e12ZGZmshpMbGInlUr58Px10MLvE7mjwxutnampqcjIyIDT6eRqTTgcRmJiIkaNGsVUMb1ej0gkAq/XC6lUytfJ5XIhEAigsLAwTjHn2xjXqc6R1tZWvsYulwt6vZ4PtAC+1XVwJL7/+FYaogjx1Ov1EAgE0Gg06O3thdPpREJCAp/EaaGrr69HX18fhEIhCgoKoFar0dnZiXA4DLfbzQghbSJNTU3Iy8tDe3s7lEolS4p8HxM19kEjVAIAuru7UVZWBq1Wi76+Pub7WK1WBAIBaLValpAiJEIqlcLr9eLIkSOoq6uDQqGAQqGATCbj7tPk5GSoVKrvjQ9ESRUhvnTfSC4rMTERgUAA/f39sNvtyMrKQkFBAbq7u2EwGJCfn4+EhAQ4nU5UV1cjNzcX9fX18Pl8KCkpwaxZs770nt8nQpGUlMT3id7f6XRCr9czKnj48GFOAIBoc4/P50NbWxtTL0QiETfUUMd+QkIC1Go1fD4fBAIBPB4Pi2cPHet3hRLEcqgp6OBHnDaah1arlX9ncHAQtbW1XP1QqVSwWq0Qi8XciZ+WloaZM2cCiHICbTYbUlJS4HK5+JrRpkhldTqUnalxkxEEjYtoFX19ffwenZ2drKyRk5MDABAKhfz35FtuNpuRlJQEr9eL4uJiHDt2DHK5nLnjTqczrvHm275/dNgjvmhZWRn0ej0MBgMLmLtcLiiVSoTDYajVamRmZjKK3NTUhNTUVBQUFDCabDabmWpFMTQpi30+v8uEjWS/zGYzsrKykJWVhdraWhiNRojFYiQmJsLhcMDpdCIQCODIkSOoqKgAAOZ8q9VqvsfA10MLv0/kLpb+IpFI+MAUDochk8kQDodZKowqU0TzSElJgUKhYB6xwWCATqeDVCqFzWZj3nXse5zJMZ7qHKEDhFgsht1uR1dXFwoLC09o+vG/HkePHv2+P8IPKr6V5NTtjlqnWa1WKBQKJCcnIzs7G5FIBEePHoXD4UBCQgKAaCmXSgsymQyBQABVVVVwOByQy+VIS0tDKBRCbW0tO9KUlZWxyLDdbucOTOKmUnybnexDFQliN3PgC5MB2vSIO9PR0YH29nZoNBoIBAKEQiGkp6dj1qxZEIlEaG1tRXt7O1JSUiCVSqHRaOL0MakkTO8fO8YzncTFCnXH8rYKCgpQVVXFTTFisZg3r+zsbNbaEwgEcLlcOH78OC9yeXl5kMlk8Pv9iEQiMJlMiEQicDgcsNvtjBrG3sfYaxxL4/guYigBH4h2UobDYezfvx9GoxFmsxkCgQBqtRpSqRQ5OTmcILhcLjgcDowdOxZyuRzNzc1xmr9+v587rGk+2+12psdQfFdjprlqtVrR398fdx+IpiISiWAwGOB2RzvByWWJkvOioiI+qDkcDubkEr/R4/HwuLOysvjgRp3DFITSx0rInYnrIJPJYLfb2brT4/HA6/VyVYYaiqRSKcRiMR8YiFNsMpmgUCi4QgRE6Q4VFRUwmUxx14yair6LJCYpKQlCoRAKhQJarRY9PT3w+XxoaWlhJRSxWMw84Y6ODubLW61WXreJF08IlUQigU6n+xL9IvYAEXufvis+KvUfuFwuNDQ0QKFQ8OHJarWiq6sLfr8fycnJCAaD3Hw4a9Ysnq82mw1lZWVsUvB17tP3yceMpXkNDAygt7cXNTU18Pv9EIvFSEtLQ3t7O+x2O1pbW3n+ikQiiMViGI1GqNVqqFQqjBo1Cn6/HzKZLM7u9EweNE6HlkTz1ev1cmXD5/MNq5f8Y4iLL76Yv6Zq0v/PcUaT0/7+fuZKBgIBiEQiZGdnQyaTwWq1wm638wSrqamB1+tFTk4O855aWlrQ1dWFjIwM+Hw+FBYWcpmUFpLs7GyWkaKS3PHjx+MEvGMf3DMVQx8ucsEhRK23txfd3d1ITU1FZWUl0tPTWYC/tbUVKpUKdrsdvb29kEgkCAaDqK6uhlqt5s47t9uNQCDAHbS0OHg8HiQkJLAe6uDgID777DMAQFlZWRx6cSYRGlqsgS+6qIGoZI1MJkNfXx8GBweRmZmJUCiElpYWRnoTExMRDAZZ59FutyMhIQFHjx5lqZ78/Hxu8iI3kP7+fkaJqfSUlpb2vfGg6H7HWiBKJBIcPHgQDoeDm2RkMhmjbaFQiIXKI5EIQqEQOjs7YTQaYbFY0N3djXA4jJKSErjdblgsFm5mSUtL48aE7wMxjt08KNkZHBxEV1cXrFYrVCoVl7ntdjs32dB902g0rMhQX1+P9vZ2hMNhaDQauFwudjEivrLFYoFEIuGKChDv306J7cDAwDduMqLX1Gq1KCoqAgDo9Xo4HA709PRAq9Wiu7sbFouF7R337NkDl8sFuVyOhoYGaLVapmjY7XYMDg5yOXTy5MlcHk9OTmaKyndZ/o59vaNHj8JisWBgYICbFcktig4fNCdHjRrFcneBQICft4SEBOTk5DDCSmvMUJmh2PWBkvFvu6GGuPg9PT0wm83M849EImhra4PZbEZxcTHsdjvzxkm2j+bo4OAgOjs7mS7zvyDwPlyiZzQaYbVaYbFY+JBLAEBfXx9X7ACwTJpKpUJFRQW/RigUQlVVFaOvY8aM4T0MGN504VTn8enQkgjxpgMI6SkD+FEmqE899RTuvfdeCIVC5OTkxHHZKVQqVZwqxY85zugoBwYGYLPZGFmghdHpdMJkMsFoNEKpVHJ5Ye/evdw4QZt8f38/Ojs7MXr0aLS1tTGEr1arodVqkZmZiZycHN7Um5ubIZFIYDAYIBKJ0NfXB5FIdMYn79BT5MDAAHdHklxLKBTCsWPH0NXVhczMTMhkMvT09PBGZrFYIJPJ4Ha74ff7WalApVKhrq6ONzq1Ws1OSyqViukQoVAIDQ0NUKlUcDgcAKJSPrGuIKfC2fy6Y7ZarYwykfA1AP68zc3N0Gg08Pl8MBqNaG5u5tM7EFVkoIYfl8uFYDDIqBQlflqtFmazmZtUjEYj/H4/NBoN8+K+TR7UySK2vEUokVQqRVVVFYuce71eGAwGpKenIz09nT8zidMnJiYiISGBeVQWiwVtbW3Q6XTo6enhJrOhlppDnZm+raBkItbZizajwcFBRCIRSKVSDAwMMBpMhhF6vR5WqxUGg4GvDTmgNTc3M0Kem5uL/fv3M3eTEnnSYwS+MK+gBIj0VWPvxTcZY6x1I0VPTw+8Xi/cbjcn23a7HdXV1YwAE7/a7XYjOzsbQqEQZrOZD8+tra2wWq3cHe/3+1nEfqi8HvBld6YzEXSgMRgMsNlsOHr0KHp6ehAOh/mzkUObw+GAy+VCc3Mzzz1K4oLBIHNOqfJBIvbDIaNUTYk9OHwX5f3U1FS0tbVBr9ejs7MTwWAQEokEEokEqampiEQi6O/vZ5oC0RqoCkUWvDQHv28O7akG9S8QZaSpqQldXV1MozMYDBAIBEhISOB7TWV80rEljWkak0ql4opCQkICjh8/zlrFsW5vQ6/Dqd7n06FAeDweTsTIuMXtdqOvrw/AF7a8/wsHilOJioqKOKoU7bOxsX379hH70lONWJQDiJb+qInEbrezULfb7eZETK1Wo7W1lZt+BgcHIRAIGFELhUL88NhsNpbiKS0thVqtRk9PD4vyx0qhAMDx48e5HH4meZlDH67YJCISiSA/Px8WiwU9PT2orq4GAIwaNQpisRgGgwFSqRQSiQQlJSWsZ6lUKpGWloZgMMgkdbPZjLa2NtaBVavVSElJgUAgQFNTEwoLC5nekJiYiIKCAtZEpUazM8UTksvlvLES9aK/v5/L0OTs5ff7YTKZoNPpeAxyuRxisZhRc0oISJSfHIOOHz+OkpISRjZCoRACgQA7veTm5jISC3x3lomxlIZYc4XExESkpqZyp7PL5UIkEkEgEODSWSAQwODgIBoaGrgbnDrxVSoVkpOTEYlE0NjYCKPRiOLiYvh8PrS2tgJAXOMJORB9m8L0QLwFLQnlU/OTQqFAeXk5Nwc5HA4Eg0EkJiait7cXCoUCfr8fUqkUwWAQdrudeZvUrJGQkMBNGnSQSUtL46RCoVCgp6fnlFDH0xkbPRNisRg+nw/BYBB1dXUwm82srmG321FeXg6j0YjW1lbe3Enc3uVywefz8cadmZnJdA6bzcYJQF5eHkQiEYLBIFpbW2EymZCWlga5XA6ZTAaBQMDVAofDwaX/M3Fvjxw5go6ODvT19SElJQVpaWlxpVGVSoXU1FQW1lcqlXA6ndi3bx/S09N5TQkEAlCpVPB6vYwumkwmmM1mlJWVfckE5HQ40t808aNmJ7FYDKFQCKlUyocrsVjMJeyUlBTY7XakpKRAr9dj3759KCsrQ25uLjQaDVMZhiZd30Y16ptG7LpkNptZyoyeQXp+FAoFCgoKEA6HGWAgY4ZgMMhNyk1NTYhEIlCpVMjIyEA4HIbf70dqaipsNhusViubqcRa28ZWdk7Fcvx07jEZvBgMBojFYlitVtZGJi3pgYGBH01yOhLx8Y2SU7fbzXpx1GWblZWFnp4eZGZmMkIaiUS4hEKJFW3i1MlKBHWBQMB2j1arlV2Surq6mLQtEAiQlZXFmn2UDNAp0W63o7u7+4ws+MOVUAAwQgbEl78p8aTyXmJiIguUk42hVquFUChETU0NqqurefM3m83o7e2FQCBg2Y+8vDzWqSPbOeLmBgIB1NfXIy8vD16vFxqN5owiFjRmur90iKDmAurwJCTCZDKhvb0dJpMJKSkpaG9vh8fjgUAggEAg4AY3n8/HJeGCggKkpKRAq9Wy1h4QRaPT09MxZswYvg90/WORqNifnenNI7aDmd67paUFLS0tjEAR71apVDLHraenh5OOxMREeDweBINBpKWlYdKkSQgGg2hra+MudpFIhOLiYm72S0pKQmFhISMDjY2NUKlUX7qnZxLVkcvl3LgFRBv6ent7OekkZYy6ujq29ySqidvthlAohNsd1TY1Go2cxJGWIpX0FQoFPv/8c0ilUgiFQowePRoCgQA+nw8ymQxZWVkAvnwIiR0r/ftUSolKpZKfCbFYzI1d5Dvf29vLP7fb7ejp6WGjCaJp0GEMADfbRCIRzJo1C5mZmaw73N/fj6KiIkaPic9qsVjYkYq0j/V6PWw2G9LT00+I2n3doMNEbCOMSCRCR0cHvF4vgsEgkpOTEQgE+FBAbkLE6ZsyZQqvWRKJBIFAgPVAaS0eGBiIO7gO9xzGrpcn6+w/UWPVqVyL/fv3M0pMBw5qoKHEur29nXnDvb29yM/PZ3rV1KlTOSGnzxNbISFd4q9Kvr5rdJWMEQ4dOsSygs3NzWhpaeFmxczMTIjFYuTk5HBDHFWtaB2neykUCpGZmckUGoVCga6uLnR1dbHc44mQ8Vjd5jMdPp+PXeOIQtXb2wuxWAybzYbU1NRv7b1H4vuPb5ycxpbJjh49CplMBpFIhOPHjwOInvSEQiHa29sxMDCArq4u7tZPSEjgZh+9Xo9wOAy5XM7IklAoZCtTtVqNtrY2/rvx48ejuLgYNpuN+W0ZGRlISEiAQCCAyWRCfX39Ny7vxy6gAOKI//Q9SmLa29vh8/mQn5+PY8eOob29nXmX9FrkskKSL7RJ9/f38/UIh8NIS0uDy+Viy0+6zsRPpTKrRCJhj3p6D0IPvkmCShs4Jf0AWHTf5/PBZrNBr9cjNTWVm4IikQg75QiFQoTDYdhsNojFYoTDYUaMYjcQ4hInJyejrKwMPT09fB0AYNu2bXGeytRtHevq8m2VD+m9PB4Pl0cp4Sbh6p6eHggEAshkMpZOo2skFArjkiNK2Ii2Qad+KgubzWaUlpbGWWtSGR0AiouL4zbQWJ7fmUhOqdJAXtxerxdOpxONjY2cUJnNZuj1emRmZkKpVLIOId3fYDCIcDjMvE0qP9rtdohEIvT390OtVqOpqQkpKSlcFSkuLoZKpYLZbIbZbGZ5uNhGw1hN1FO558SVG4riEb+OqAekvUr8V6pihMNhOBwOBAIBTuCEQiHEYjEyMzPhcrm4dC4Wi9kRjDreqfM5KSmJKRFk0qBQKJCeno7k5OQv0QxON0hPmMaoUCjQ3t7OB16PxwOj0QitVsuHRqFQyLQFrVaLHTt2IDs7Gzqdjp3saE1TqVQwGAxISUmB2WxmC8yTPYcno6YMd1++zrNMCgNUwaEm21h+pVarZXtaUlyIRCKQyWRwOp3Iz89Hf38/+vv7kZ+f/yWJQlJzoIR8uM/1XVAYYiMpKQm1tbWora1lzVqj0cjWyZFIhGXp6Nmlv6PPKpPJuGrncDggEAggEomQmZmJ2tpaKJVKCIVCpKamYs+ePfz3sX0WNG99Ph83x52JoM87NEjxh5LsysrKHwSafabC5/Ph/PPPBwC89dZbuOKKKwAAH374If/OkiVLIJfL8dprr7GRyY81vlFyGkuIr6+vh9PphMFggMFg4I5Xsknr6uqCw+GAyWTivyHXB3oQIpEI0tLS2Juc9BBjfdsjkQh3nPb19cFmsyEcDqO4uBhSqZRLbDabDUCUt5Kbm3vaKOpQviGhn4T2ejweTjhbWlpYXsnlckGr1bLkkFwe1XOlZJwWUEJoSO+RSjKRSAR2ux16vR67d+/mUkpiYiJyc3OhUCi4WaGsrIw/H532T7cURR3IjY2NEIlEzBUcGBjgZJJE42MPECTdQidxqVSKjo4OlpPyer1sD2k0GllWyWw2w+l0QigUorW1lRuJcnNzuXx67Ngx/OQnP+EO9liPbKIPfBuoaW1tLaNOvb29cDgcSExM5AXS7XbzNUhLS2NuLjmxCAQC9voGonqKdXV13JCj1Wphs9lgt9uhVqsZaTebzXC7o57sra2tcDgcTNno7++P4wGfKVkVKhdSFcDv90MikTCK1tTUxPeLKAperxf9/f28OQaDQWRnZ/OzR88yhd1u51KwQqGAUChEIBBAX18fxGIxVw+Iq15RUcHvRQLwQ5HTkwU1RVJSEYlE4Ha70dzczM1ANpsNLpcLTqcTHo+HeXd0oDKZTHwoJDc0sk82mUyw2+0sKaVUKnmts1gssNvtKCsr48MICfyTdBxxPb8q+TnVIF4sJdDEfY1EIohEIqwXSbQphUKBlJQU2Gw2lkIjNQa6p/39/RCJRGhpaeE529bWxhUuStxpfSKaU2y5/0SWtKdDBYgNQuI9Hg87dNF9ogZUaqYl/U9KysggY3BwEN3d3UynKisrQ3t7O/Ly8uI44CdbT0+HT3m6QUAIgQFE2aDEm0xNqDqZmprKXGNSDyHKSSgUQn9/PyQSCfR6PbKzsxEMBpmykZOTw+v8jh07MHbsWH5G6KAxtDnuTI0RiFYni4qK0NbWxmMzGo2sz0sKDd+3k9WZikgkgl27dgGIHvToa6JCkU48AEycOBGNjY1s6vNjjG+cnNKEoNMULczV1dVxizuRtfv7+9mOjBCM2DAYDJBIJPwgEbqSkpICmUzGPD9KEORyObxeL3JzcyESidgWs6enh+kB36RsRn9HiQMZBwDRDdJmsyEUCsFsNkOlUmHfvn3cWCCRSJgfQ92GJ/PEtVgsjN5otVreQIAoGuByubiLX6vVshJCRkYGIpFIXKntm5xm3W43oyR0vUmHEgCXaoGoKoPFYoHP54PH4+GmGHqIAPDmB4A7EGmBpA59iUTCCZFareamBqvVyuj55MmTmb86lId6svLh1x177H9EYxAIBHA4HNygRQcQUhkgNJm0A6nrmXiklDiQE0tsgkkdxDqdjhO7jo4OTsAtFkucHFEsD/hM81DpgENuOwcOHEB3dzdfC6fTyRSG1tbWL6F+bW1tXL2wWq2sNhEKhdjylixCxWIxuru7IRaLUVhYiIKCAj4IBINBPhiROxfFqd7j4aSB+vv7odfr0dXVxdSKwcFB+Hw+1uc1Go2MSFEMDAxAIBAgOTmZGx0pObNarZDL5VwGJ9oNJQjUGJiWloZwOMxORTKZDCaTCY2NjUhISDgjvOK+vj643dHubYFAgNbW1rgDglAo5HlLJgLhcBihUAhisZjNFJxOJ88Del06CCYnJ/OBOyEhgRNYlUrFTRyxpf2hJeHhYrjS+FeVy2n9JaDAbrfH/YyeOaIeyWQyBINBtoU+ePAggsEgGhsbEQ6HkZOTg97eXhQUFMDhcLAcICWoJ4rvMjEibvioUaMYKe3v72fd3UAgAL1ez79PBy0gqm5Awvx0sKRrQ5xwnU4HuVzOCaJarUZdXR2ys7PR398PlUrFlcnhgIEzQXGgQyVx13t7exEIBJCeng6HwwG/34+EhAS43W7uvfihcIK/rcjLy0N1dTXKy8sBREE3k8k0kpyeLGgyJiUlsSuSXq9n1IdKnaFQiMtksaRuACxGTkkIIad2u525MYFAANnZ2bBYLIhEImhubmbUrrS0FK2trcjJyUF/fz8yMzNx9tlnA0Bc9/E3CTodp6SkcHmDHuCuri40NDTA6XQiOTmZ9Rm9Xi8jqxRFRUXIzs7mZoUxY8Zg8uTJUKvVeOmll/Dcc88hEAigp6eHLfUoaYmVZiLkgtDFgoICfqiptB57ov8645fL5cjMzOQGJb1ez+gOIeJUjpdKpSzkTPeaXmPatGnQarXYv38/n36pxCiVSrkULBKJ2EGKmhjo+2q1mpEQKiOLRCJ24YrVWoylX5zuAkmvA0Qb2kQiEfLy8lBXV8d6nFQGJFeZoYlMbNBrBYNB6PV6TgBowaWNkpDB1NRURqFJvcJqtUKv16O+vh4ajQbd3d2MKJ6pBZnMBDo7OyEUCmEymdDT04OWlhZugBIKhRCJRGhqamIeplwux4UXXogZM2ZAJpPhtddew759+wCAE1dqrACA5ORkpKWlcSVFpVIhLS0NWq0WjY2NkEqlKC0t5coCNUYRt53e81TiRKVWn88Xx8OkgxEt+JTM5efnY+nSpSgrK8Of//xnHDt2DE6nE62trYwqy+VyFqnv6upiZFSpVMLr9WL37t2YNm0al9ULCgoAgA9S1GhkMplQUVERN38pTnUe7927F9u2bUN7eztz9om7fuONN2Lx4sVIT0/HQw89hDfeeAMul4sPhpFIBAkJCcxDJQSSEutwOBzXuZ2VlYXRo0dzckuHdAIIYq/7qXz+4UrjX1UuJ4AiFApBpVLh/PPPh9vtxsGDB7miRkggAKbP9Pf3o6GhAcnJydxsSlW54uJirhLR4bC7u5uF6b/voHI60TDq6+thNBqh1+v5mczIyOAD0J49e7jT3mKxsA6uTCZjoIga/0hrnK55MBhERkYGNBoNV8S6u7uhVCqRk5MTx0WlOJ3ndGjQ/SKeMwXJhJGhC+lhk2HEjz1BpcPS/y/xtZPToY0Je/bsYY05t9uNY8eOcaLa1dUFtVqN9vZ2PqUDUevNK664AlOnTsXEiRNZvgWIIqfV1dXYuXMnNm/ezE0Zfr+fO4Op+5ISHIPBgK6uLrjdbu4yBOJP71SqAL6eOD/9DX0+ehC7urpY59But+Po0aPw+XzIzs6GWCxmWR4AmDRpEm644QbMmTMHubm5TDwHoigkfb169WrcdNNNWL16NV5//XUEAgG0tbXB4/Ewcpyeno7e3l6o1WoEg0FubiBOpslkgsVi4U5o2mTy8/PjrslwQRqUsUGoL228RqMRDoeDjQQikQg8Hg/6+/t50Vy9ejVuuOEGLtFTJ/rGjRvx6quvMjLV09MDIFquKSkpYa9nsVgMh8MBg8EAn88Hi8XC5ayysjKEQiE+4dN9jU3ET5cDFos+06GmqKgIXV1dSExM5I2Qmv9IuBwAb3BSqRTTp0/H3LlzMXPmTLhcLtjtdrz99tv417/+xZwwQuvEYjFLtojFYlRUVPC/yRGNeJzHjx/nxkGZTDbsGE4Xuejs7ERNTQ0nvlRxIOm3YDAIi8WC3t5eFse+8cYbsXLlSqaYAMC1116Luro6bN68mW1sa2tr4Xa7ec6KRCIYjUZW5IhEIhgYGODGK0oa3G43Dh8+zEYchC6fagxFdYjv7PF44hrMpFIpGhoa2Bo4PT0dDz/8MC699FK+zueffz6efvppPPHEE7wZ0nwBwI5LFouFKQHUOHb06FGkp6cjKyuLDzYkFUelVbFYjL6+Pmi1WkavCVk/VSmxlpYWno9WqxV+vx/p6enYsmULiouLeSxr1qzB448/jscffxxPPvkkz0kg6pxEzyGhplSlIAc7kUiEnp4etLW1obi4mPVwYyss9GycaoI6XGn8VMrl9BmffvppLFy4EBKJBEajEcuXL8fbb78NILr+ED8/VjebuIvp6ekoLS3FlClTkJ+fD6/Xi56eHvT19SE/Px/Jycmw2WwoLi4+6fX/tqOjo4MVIajyRAYmFosFAPDggw/i1ltv5eeR9s1nnnkG69at4/WL9g46YPl8PnR1dbEIP0njTZkyBZFIBEqlEvv37+c5TJSIoaBALNXom6KodHC8+OKL8emnn3KvCmm30hptMpmYdzycLNt33bA2EmcmTis5JYI7baIGgwHHjx9nwfmkpCQYjUb09vZyeROITtyVK1fijjvu4DISAG40AKIE9ssuuwyXXXYZnnjiCRw+fBi33347Dh06xBqhtMiQxiQhFzRR09LSsG/fPj4likQiJk+TqDAJwH+diNWXs1gsaGpqYnkdk8nE9qRNTU3sELRq1SrcfPPNXDoBouXYWI4QJYSRSASjRo3CK6+8grvvvhuPPfYYNmzYAL1ej4GBARQVFXHCtn//fkyaNIm1QAnuJ69kek16gGNFzOk+Dn1YYxcZ6sTMyspiPc+Wlhb2sh4cHOSudOoSTk9Px7///W+ce+65zF2jMVZUVKCiogJ/+tOfsH37drz11luorq5GU1MTl9aolE8IncViYaSYCP8ejwdz5sxhXmxnZ2cc1QL45hwwKr3Txme1WtHc3AyDwcDNa93d3YwMPvPMM7juuusY6SbpKLqOiYmJWLBgARYvXoybb74Z3d3dcWVCEt33er144403kJycjDFjxiA5ORlZWVlQq9WMrPb19SErK4sbhobG103MYxus6LBH95g840nDlK7HwoULsWrVKp5ztFkC0SRt6tSpmDp1Km8uNTU1WLZsGWprawFEu6yJskKHpo6ODpY2SktL47UltnowtLz/VRHbRAeAjTOoGtDd3Y2+vj4+LAHAjTfeiNWrV3/pOZLJZLjvvvtw/fXX47777sOGDRvirh0QfX7D4TDzkK1WK3JyctiNR6/XIy8vj3lz9LqFhYXcDEqbu1gsHpbveLKNlpKF7u5uTkw/+OADjBkzJm6NpXXxz3/+My677DLcfPPNqKmpgcVi4UoVHWr7+/uRlZXFHGvqdk9ISGCueXp6OlM2iHsa66oVm2DTnKOIBRCGjuerkgmDwYBwOIyrr74aV111FdNvdDodXnvtNXzwwQe4+eabWTOZDsv0TEokEmRmZqK8vJwbwADENe6RUUzsXDpVJPhMJ0SUgFEJX6/XY8+ePXzIv/XWW7FixQreF4Ho2jtq1CisWbMGS5cuxZ133omdO3fGNfElJCTA6/VyRYcOJ5FIBBaLBXK5HC6Xi1HSoqIiJCQk8LNrtVoZvImlGhE//nTX4ocffhgzZszAsmXLGOghubqWlhaUlpbC6XTCYrHwgQnAl7RPv+uGtZE4M/G1k1O5XM4LfHd3N99shULBhHu9Xo/a2lqeUAqFAosWLcKdd96JUaNGAQAaGhqwdetWfPbZZ9wUEwwGMX/+fNxyyy0sdD116lRs27YNV199NbZu3cpcNK1Wy0gA6drRYgUAmZmZ6O/vx7Fjx9i9Z9y4cewPfiJR4aERy1ujhMxkMsHpdKK5uTnugSZdw1AohOLiYmzevBklJSUAwPpy1MUf22ygUCiQlpaG+fPns8ZkaWkpXnnlFfziF7/AkiVLYDabmbrgdrs5OaMmne7ubqjVakZ/UlNTmQRP9yFWjop0RWO1YOmwQK9P/wFgCSWj0QiJRML0C0KpFy5ciMcffzyOA9PR0YGamhrodDqMHz+e79NPf/pT/PSnP0UoFILX68XTTz+N1atXs1xVQkICJ5y0uREa093djQMHDkAqlaKnpwfBYBBTp06FQqH4xg5CAwMDzP8TiUTsXhUIBNgkAkBcY8vf//53LF68mBHOffv24fPPP0d1dTW6u7sxZ84c/PrXv0ZOTg7mzJmDI0eOYOvWrejr64PD4cCbb77JSRshWEajEeFwGDNmzOBmIOJP05ynA8JwHdCEYp8Kz49MDwBwsrljxw5uiiIUye/3Y9q0aXj00UdxzjnnsFUpWRJv3boV9fX1uOCCC7BkyRJOBIAoeX/37t1Yt24dnn76aVbsAKLrABBN0rKzszkRIh5nVlYWJk2axNdfLpd/aeP5Kl5iLL+XDha9vb1ob2/nQ4hKpcKzzz6LSy+9FAKBAO3t7XGIMEV+fj5eeuklLF++HJs2bYLf74dAIMD27dv5PpJpAenfBoNBHD9+HBkZGcyrNhgM3BR49tlnx312apii+3mqpe6MjAxugkpKSsJ7773HUmx+vx/bt29HTU0Npk+fjvPPP58P7Xv37sWTTz6J5557juXQSD+UOtvtdjvk8mgTq1wu57GRdnVRURF3iCcmJvK6RvsFXfvY5/mbPq/hcBh5eXmYN28e5syZg/z8fIwbNw5TpkzBtGnTsHDhQsycORMTJ06EwWBgruLg4CCXhEnRgKTAxowZA5FIBJ1Oh0AgwNqpbrf7a6ljnOw+nW7iSpKEoVAI1dXV+Pjjj+MS00cffZTpIx988AGqq6uh0+kwevRolJeXY+zYsfjkk0/wzDPP4K677uI1VSgU8vMaCAQ4adVqtdBoNEhOTobX64VOp0N+fj4SEhK494F0jKn5ivYQOhB+VbPUya7FokWLcOWVV6KlpYW/Rz0INpsN77//PhwOB2bNmsVNwWQSM5TSNpKY/u/FaSWnBQUFqK+vR09PDwwGA3ek22w2OJ1ONDQ0YHBwEBqNBg8++CAWLlwIn8+H//73v3j55ZexdetW1NXVDfv6jY2NWLt2LS677DIsW7YM55xzDoRCIV555RX8/ve/x8aNG5n0n5eXh+LiYgQCAXR0dMDpdEKtVmPUqFHc7U5OTEqlEj09PezrTpqoFCd6QGIXGeKkDQwMME+MGkeSkpLQ0tLCzjDvvvsu5HI5jh49ik8++QR79uzBnj17OMEZLrKzs/H4449j7ty5vMFMnz4d27Ztw7XXXotjx46hv78fCoWCNwAqbzidTn5AgehDXFFRAQDcAU4akicKaiQj5EWr1UIgEKCmpobHCkRP8GazGeFwGGPGjMHq1atRWFiIffv24e2330ZLSws+/fRTtLe382sLBALk5uYiMzMT2dnZyMnJwfz58zF9+nTceuutmDNnDpYvX469e/dy+VwqlTIqIxQKkZKSAqPRyPqaJFvW2dmJcePG8UIYi3Cnp6ef8iZAtAa73Y7S0lJ4PB7s2bMHx48fh81mY83IUCiEwsJCbNy4ETk5OXj77bexY8cObN68GUajMe41q6qq8MQTT2DOnDmYOHEiSktLUVhYiAsuuABKpRK33HIL/vjHP+KFF17gBFcsFjNS1dvbyyU0Mi9wOBxsdUrjo6CxDrcx0vhiE53ExETU19fDZDKhqamJjS28Xi+8Xi+amprg9/tx6aWX4tlnn0ViYiIsFgt27dqF3bt3Y/Pmzejq6uL3ePvtt7Fy5UpcdtllmDhxIqZNm4ZRo0YhHA7jt7/9LZYtW4Y333wTf//731FTU4O+vj4kJyezJJrb7WYeL/F1AbCA+FDR7RMdtGKDkB29Xo+jR4/ymCwWCwKBAM4991w8++yzyMnJwebNm/Hoo49yoqnT6VBQUACZTIaUlBSoVCqMGzcO5513HlauXMkVkvvuuw8PPPAA1q5dC5/PB4fDgZSUFPj9fnaHCwQCKC4uRmNjIztSZWVloba2FtOnTwcQpVcMdUOLHe/JNlqbzcbr6l/+8hfodDocOnQIL774IjZv3hzHfc/IyMCCBQswceJEzJw5E7/73e/w61//Gp9//jmee+45fPjhhwgGg3yYp3UzISGBm1ZlMhnMZjPzouVyOTweD7RaLebOnRv3ualSQInpmXB7y8vLw69+9SssW7aMD0sUV111FdatWweFQoH77rsPN910ExITE5Gens6atlKpFJ9//jknYwUFBSyHRlanjY2NkMvlSE9PZxc7sv88WZzsPp0ukpeSkgKTyYSPP/4Yn376Kavd3Hrrrbjlllvwn//8B++//z62bt06rBzTjTfeiEcffRTLli3D2WefjWXLluHYsWOsL05jpueJaFMmk4mdwUj7lxzGaE+IpY3R+EhX+GRxsmtxzTXX4OjRo0hJScH06dOxdetWAF8cSlpaWrB3716uYFVWVsLlcvGhJ1bC7Luy1P2mEWu7PpwFe1JSEsLhcJz28o81TqshijilVOalcnZdXR0OHz6MYDAIjUaDjz/+GHl5eXjyySfx6KOPxjWNCIVCnH322Zg9ezbLejgcDqxfvx5VVVXYtGkTNm3ahIsuugh/+MMfMH36dPzrX//CtGnTcM8997DPbnd3N7Kzs5GcnAyNRgOn0wmbzYbq6mruEk5PT2euUWzXfyw3JnYixwb9HIjyL2mCHz9+nBtcqOxJqgHvvPMOPv/8c7z88svYu3dvXAOYUqlEdnY2cnNzkZ6ezhqWhw4dQm9vL5YsWYLy8nIsXboU1157LZRKJYqKivDZZ59hyZIl+PDDD+HxeOLUAFJSUuDxeDAwMIBgMMgJOukUdnV1ISMjA1arlbmawz2gxDvs6OhgfUaj0Yiamhq+PgaDgT/z0qVLcckll+Dxxx/nJpjYEIlEKCsrY3tLEnameOqpp3Deeefh97//PX7yk59g+/btuPPOO/HMM8+wuHZhYSGCwSASEhIQDoeRnp4OvV4PlUrFGrGx6DAAdigbrjnjZJGens6vc+TIEVRXV+PQoUNs7EDUFa1Wi/fffx8mkwkXX3wxJzJAFLGurKzEWWedBbVajY0bN+LQoUPYunUrL65AdCH985//jAsvvBD/93//hwsuuABLly5lS1syacjNzWXHsISEBEYX7XY7J6ZD0YcTbYx9fX0QCoVxSRxZyx47dowbfsRiMYLBIFcGxo0bhxdffBFOpxNPPvkkXnjhhbhEJzExETNnzsTo0aPx3nvvobu7G//+97/x73//m39+zjnnYMGCBbjkkkuwePFiLFmyBIsXL8aWLVsQDoc58QbAVQiSRWptbeWDFekLfx3aApWXKTHt7OzE8ePHEQgEcOWVV2LdunVoamrCsmXLeB4TXYik8YYLtVqN3/72t7jjjjsgl8vx8MMPY/78+bj88svhcDhYASAcDsPpdMJut8NqtaKwsBApKSmsRKJUKrF3716mQKWlpWFwcBDZ2dnD8jBPNO433ngDwWAQV1xxBRYtWoQHH3wQ69at4yRfq9ViypQp2L17N/r7+/HSSy/FjeXBBx/Er371K1xyySVoaWnBs88+i3Xr1rFNq0qlYmUJqVTKFIK0tDRGJiORCHp6eqDVajFz5kz+rLHrbewcjaVGfN2k4b///S+mT5/OqP7EiRPR3t6Ojz/+GBs3bsSRI0fw3HPP4brrrsPzzz/PewI1JQJRpFAul0MoFKKvrw+hUAhKpRJ6vR6RSIQdibKystDU1ITRo0fHza1Yk47YMcQeAGN/Tl+fDpInEonQ29uLtrY2HD9+HOFwGLNnz0ZraysqKiriEpbs7GzMmjULfX19rKzx4osvYvv27Vi7di0uvPBCfP7551iyZAnef/99dgIjFzSXy8Vd/cnJyWhqakJ/fz/vW0C0mqZWqyESibjSReM9VXmpk12LQ4cOISkpCY888ggKCgowMDCAqqoqpoEJhUJusNVqtcjPz0dxcTE3A1NvxzftRfiugp4pitivgS+uVXV1NSZPnsxVJ41G86Ps2hdEhrbODxOEAtjt9rgGhg8//BAHDx5EIBCAz+fDhg0b4Pf7UVBQgHfeeQdlZWW45ZZb8MILLwCI2jJOmjQJM2fOxNlnnw2v14uDBw+ip6eHyxXnn38+dDodnn76abz77ruc0Op0OsyZMwdz585FaWkp1qxZg9dffx3BYJBRudLSUuh0OrZqo85gpVKJjIwMqNVqJCYmYtSoUXEe9B0dHWhtbeXTs0QiQW5uLvR6PSNNbrcb9fX1OHr0KNra2lBfX4+Ojg6YTCbuRK6srMSTTz6JlStXxiVr06dPx/jx4zF58mREIhHuFKYGrkmTJiEnJwfr16/H66+/DqfTCSB6CFi0aBEWLVqEOXPmAIgS3p944glO2JRKJUQiEbKyspCcnAypVAqpVIrKykpMmjSJNSNLSkpQWVn5JXTJ7Y7qs1ZWVmLPnj1obm6G0WjkxpT9+/fj8OHD/Hv0wPz6179GQ0MDPvvsMwBgH3SdTseOYJWVlVAoFKwnaTQa2VmrpqYGH3zwQVxJecWKFVi8eDGee+45/PGPf0QoFGJpKaVSCZVKhYkTJzI/T6fToaioCOFwGCqVKk6EPCEhgYXOh256PT09yM3NZWF4WqySkpLQ0dGBw4cP47PPPsPAwADq6upYkofK6h9//DF2796NVatWwefzQaVS4ZxzzmHNPXL/IXRGJpOxVh0131ASPGvWLNx7770477zzsHnzZixevBjAF2XupKQkqNVqqFQqJCYmorKyElqtFnl5eSgoKEBhYSFzu06EVNB49+zZA51OFye2397ejpqaGjQ0NGDfvn2sqtHc3MxSbrt378bevXvxpz/9iTfjlJQUTJw4kRsUSOt37NixKCkpQVdXF+rr69HU1MTzGYgi6HfccQdWr14Nr9eLX/7yl3jnnXcAROk/ubm5bPNLVZqJEydixowZPDYqxysUCkZEKORyOaxWK99fQvYaGxtx7NgxHD58GHV1dTAajcjKykJDQwN27dqFJUuW8IHvyiuvxPz582G1WuPcnZKSktDb24uqqiocO3aMkwGdTod7770X1113HaRSKT766CMsWLCAn2FKMoVCISQSCdLT01FSUoLk5GSIRCKmvHi9XrS1tWHatGkYPXr0KaP+dH/pULFu3To8/PDDfBCcM2cOFixYgIGBAdTU1GDUqFHIzMxEY2MjampqUF9fz+jT9ddfj7Vr10IqlUIsFmPXrl244oorGFGn7n2ymM3JyWFdVOryz8jIQH5+PiZMmACZTAaZTPYlXnhsSZhK/KeCtMWO9ze/+Q2ee+45PpxKJBKUlZVBq9Xi8OHDMJvNEAgEfGiYPXs2+vv7WS2ESvZqtRrJyclQqVQQCoV8gCdJv/PPP5+pDSQFBkRd1FwuF//90DF81XN5qkHjbW1txaFDh7B8+XIYDAbk5eUxKAFEkxSlUslof1paGqZOnYoLL7wQ27Ztw1133cUHrTvvvBMPP/wwQqEQ7rzzTvz9738HEJ2vdH0KCwuRk5PDTYyksU0mOSkpKUhNTUViYiIEAgE725GBwelqi9N4AeCCCy7A3XffDSAKOtx7771xVVfiPut0OsybNw/FxcVQq9WYMGFCnITZcIf4H0rQeHft2oVZs2Z95e93dXWxnTQQvWcNDQ3/Mwnq0HzyRHFayWk4HEZnZydee+01PmFu27YNLS0tyM7OxtatW6HRaHDLLbfgzTffhEAgwK233orKykp0dXVh9+7dOHjwICd1Q6OiogJLly6FQqHAtm3b8M4778SdCseMGYM33ngDVqsVN9xwA5qbmwFEHXRI+DslJQVTp06Fx+Phxo7s7GwkJiaipKSE9TaBqCQJlfxjk9PW1lbIZDI+BTY1NWHPnj1oampCV1cXdu3axdSAZcuW4eqrr8Y111zD/sYXXXQRJkyYgD179qCmpibO7eZLN0IgQGVlJW644QbU19fj/fffj0MZ1Wo1fvrTn+Luu++GzWbDokWL0NPTw4lpUVERfD4fazEmJydj5syZ7OpUXFwch2TQgzowMAC9Xo+LLroIn376KXOGjEYjmpubcfjwYRgMBtjtdhw7dgxCoRC33nor1q9fzyWen/70p5g3bx5efPFFHDhw4EvatUMjJSUFM2fOxE9+8hPU1dWxrA0A5OTkYM2aNVAqlfjFL36Bvr4+JCUlQavVsiSPy+VCTk4OFAoFxowZw53XPp8PGRkZLM10og7n2OSU+M7UnFNVVYUtW7agsbGRyfYGgwGdnZ0QCAT429/+hl27dmHTpk0AgAkTJkCpVGLfvn1xepInCrlcjtmzZyMSiWDr1q1cjpw9ezb+9re/4Z///CceffRRSCQSjB07luW11Go1pFIpzj77bIwePRolJSUYM2YMN7tROXi4MdN4jxw5wgkd0R/279+P5uZm2Gw2rkQ0NTXB4XAgISEBf//73/HJJ59g48aNAKKIzC9/+Uvs2LED+/btO6GEFiVhOTk5bDPY0tKCqqoqAMDll1+OZ599FgDw/PPP47777mPXs1GjRjFSotFoUFBQgMWLF2Py5Mk8Z0OhUFxyShQFsVgMv98fd/jo6OjA9u3b0d3djdbWVnzwwQfw+/149NFHUVBQgF/84hfw+/2YPHkyZs2ahU2bNqG7uztuPAKBAHl5eRg/fjzmzJmDoqIi7N69G+vXr2e5m1mzZuG1116DUqnE/fffz/N4zJgxzBckMwXqyne73SguLsbZZ5+NUCjEzlulpaXMi/wqdZHYzVwikaCwsBDNzc1IT0/HVVddhSNHjmDv3r1fei41Gg0uuugi/Pa3v8Vrr72Gf/zjHwiHwygvL8ff//53nHXWWQCA2tpaXHnllWzdSmYnarWaS6xEsaFDolQqRVpaGh9aZ8yYgYGBAebZk82zRqM5JSWRE433REEH2Z07dwIAU0oWLFjA4ygqKuIyLyW4SUlJGDNmDJxOJ8LhMPLz83H55ZcjKyuL6WsikQgulwt9fX1ISUmBRqOBWq1GUlJSXEI2NBkaLjk6lYSJxrtv3z4899xzePnllwFE18qenh4UFBQgEolwH8XQ0Gg0OP/883H55Zfjo48+wj//+U8AwNVXX41169YhEongn//8J2677TYA0UOiUqlkhQeag6FQCDk5OUhJSUEoFIJGo8HZZ5+N9vZ2lnZSq9Vwu93Q6XQYO3ZsnLXtqSaGsfe3vLycD4Xz5s3DggULsHz5ch6rRCJBaWkpK9YUFxejuLgYl19+OWbOnPmDTEaHxtdNToFogmoymdDQ0IBrr70WVVVVmDRp0rf8Sc9MnGpyelplfbKhJC/w7u5utLS0QCKR4D//+Q/S09Nx55134s0334RIJMIf/vAHWCwW3HnnnewQBXyBtkUiEd5k6urqUFtbizvuuAPz58/H6tWrce+992LHjh04evQoNm7ciPr6etxxxx1466238Nlnn+H+++/HunXr0NbWxkLBycnJMJlMCIfDTKJOTU2FUqmExWLhrlpC2pKTk6FWq7n8DURPJPRzQsRcLhdaW1tZ/1Eul2Pt2rUsHdTf34+CggKsWbMGBw4cwNq1a1nmQyAQIDMzE0KhEBqNhmVPBgcH2bigpqYGF198MR555BFIJBJs2rQJH330EcxmM9avX49AIICXXnoJO3fuxPz581lzsru7mxUQaLxKpRLTp0+HTCaD1+tFVVUVysrK+FpTRz5FYmIiu94cO3aMETGHw8Fi8o899hheffVV2Gw2VFZW4rbbbkMgEMBdd93F4ywqKmKXKJJDIq7XsWPHYLfbsWXLFnz++ed44YUXcMstt2D9+vXYsGEDenp6cPPNN6OhoQEHDhzgRMhgMKCmpgbhcJgb3HJzczF+/HjmFpN7SSAQgFarHZazExuE3pA+Y2dnJwwGA0wmE5d/bDYba3+++OKLaG5uxqZNmyASifDzn/8cH3/8MY4cOQIgmrhNmzYNfr8fpaWlfKLNyMiA0WhkNHbz5s04++yzsWPHDjz99NPYuHEjtm3bhoceeghr165Fa2srNm3axG5SpAcrFovZ3jU7O5s3PErWQqHQsCWrWBtPakyhRhWXy8XyYCaTied1ZmYmXn/9dRw4cAAbN26ESCTCddddhwsvvBB/+tOfeHMoLS2FVCpFfn4+hEIhWlpaYDAYWJuVBMHLysqwceNGbNmyBffeey/efvttFBcX4+6778aKFStQVlaG66+/HoODg3y9bTYbzGYz/H4/3nvvPajVaqSmprK+aywlhxId4nlSJCUlsW0j0Rf8fj90Oh0WLFjAzV3nnXcerr32Wtx6663weDxISEjgjZkE9Ts7O9HZ2Yldu3bh3nvvxbnnnospU6agtrYWTzzxBP773/9i2bJl2LhxI+69917s27cPe/fuRVVVFSdtiYmJCAaDcDqdbA87MDCAI0eOsAMOJaqUyKvV6pNKScU+wwUFBWhuboZCoeAEmTSGMzMzUVJSgu7ubuj1ephMJrz22ms4duwYHnroIeaPNzQ04PLLL8dbb72FadOmoaKiArt378Ztt92GLVu2sAQT0RU8Hg9SU1Oh0+mQm5sLoVDITngkaO92u7mxLiUlJc4w5ZugihQ/+9nP+AC3adMmNlm455578Mgjj2DdunWYO3cuPvroI8yfPx89PT18+KfmQSC6Bvb19cHv90MqlcLj8TCdiygafr+fE325XA6ZTIZQKMR7GyWoQ5Oi4crKX6fUXF9fj/feew8AMHnyZFRVVUGhULCUklgsxsyZM9n+uq+vD4cPH4bJZMKmTZtgtVrxj3/8A2PHjsU999yD//znP7jqqqswe/Zs3HTTTbBYLHjggQfgdDohEAjQ3d3N/Nu0tDRkZGSwa2EoFMLcuXNRVlYGj8fDn5/UVvx+/1eO/VSira2NG0W3bNmCRYsW4bHHHsOKFSvQ3d2NQCDAboZELTGbzZBKpTAajXEVph96+P1+zJ8/HwDw73//G0uWLAEAvPnmmyzjduWVV/L3/leQ0tON00pOSYydHE+obDdr1ixMmTIFv/71r/Hqq69CKBRixYoV+OSTT3ihlUqlmDZtGpKSklBeXo6enh4MDg5i8uTJEAgEmDVrFurq6rBz50689957OHToEFatWoWpU6di4cKFuOqqq3DhhRfik08+4TLxU089hWAwiBdeeIE3FovFgvr6eshkMmRmZkKj0cButzNHCohK4FAjRnFxMZd4aZEhj3gq3wwODjLPlrokn3rqKVx66aV46KGH0NHRgYyMDKxfvx79/f14+umn4XQ6uVv9rLPOgkwmw9GjR1FUVAQgmvRWVFTAaDTivffeQ11dHT788ENUV1fjpZdewmOPPYa77roLe/bswW233YY333wTf/3rX5Gbm4tt27bh0ksvxeHDh1m+hhJCiUTCiEtvby86OzuRnJwMq9WK6dOnQy6Xs288JeCdnZ3MT/J4PGxy0NvbC5/Ph3HjxmHBggVYsWIFhEIh1q1bh5qaGtx1112w2+3IyMjAddddh4KCAnR2dqKgoIA3QeJq2Ww2iMVivPjii+jq6sI111yDRx55BEuXLsXtt9+OCy64AF1dXVi7di3uuecevPvuu5gxYwZqa2thNBo5AacN7dChQ5g+fTo7EfX19UGn0zGaPlxpiTZz6jD1+XxISkrisi5RTMhFBYiqEaSmpuLRRx8FAKxYsYLvc25uLi666CKew0eOHIHVasXRo0chkUiQk5ODvLw8piQ8/PDDOHDgAF599VU89thjmDlzJm6++Wb897//hUAgwIsvvoiDBw+is7MTNpsNycnJbPFaW1uL7u5utLe3c7nU6XTC7/ez1vDQDSC2/CMSiVjnkLjLVqsVra2tbK8LAC+88AJ0Oh0eeOABANEyYG5uLm677TaWl7nqqqtQVFSEuro61oDMycnBpEmTMDAwgGPHjkEkEuE///kPGhsb8Ze//AUrV66EWCzGihUrsGbNGsyZMwfnnXcefvrTn+KRRx7Bbbfdxgmn2+1GOByGxWKB0WjEnj17UFZWxgcsis7OzrhkYWhFpru7m5UnCBG99tpr8de//hUOhwOlpaW46qqr8Pvf/56lg66//np0d3ejqKiIXXgkEgneeecd6PV6rFy5EjfccAPmzZuHX/3qV5g8eTJ+9rOfYevWrXjggQewatUqbNy4ETNmzGDXPEqUyc1IqVRCqVTC7/ejtraWUc/8/HykpqbywYl486eSnNKzfN555+Hw4cNoa2tDUlISZs+ezRqQLpcLEydOxN69e/Hqq6/iyJEjWLJkCR555BHs2LEDS5cuxYEDB7BgwQK8+uqruOiii1BQUIDNmzfjP//5D6699lpOrP1+P6PWRIMiRDsnJwfFxcWsJkLKB7GoZyzyHZuwnQztovGeddZZOHjwIPLy8jBt2jT09/djwoQJGD9+PO644w6+19dffz3Wr1+Pf/zjH3jjjTewZcsWTJkyhdd/kvMLBAJwuVzscBYIBOD3+9HY2AidTgeZTAaJRAKdTsdJNlEZDAYDVCrVSd0Ih+NXUgXjVNQ1WltbYTKZIBKJOLEm17asrCxcd911SEtLg16vx/jx4wGAraafeOIJbN++HZ999hkWLVqEqqoqPhTPnj0bAHDPPffg+eefh16v54SQrF99Ph/MZjMKCwtZxYH4216vF5mZmRg9ejQDArm5uV9ChwF8bddCApEAMM9dqVTiySefxG233caJslAoRDAYZFUPakrMyMhAUVER5syZE+ey90NEUsPhMD744AMA0USVvqa1LRQKfel7P+b4WskpEeCrqqpgMpnQ1tYGmUzGHXvjx4/Hrbfein/9618QCoVYtWoVNm/ejKqqKkgkEi4/C4VCfPDBB3yhAeC9995DcnIyRo0ahcrKSkgkEuzduxcGgwG/+c1vMGfOHPztb39DVlYWKioqUFtbi9dee427MK+99lq88MIL7GQTCoW4E1ij0bBUCPlOUwdxb28vJyjAFyV+4AtHCrIqFIvFMJvNjBorlUpcccUVqK2t5RLlpZdeil27duHxxx/nByE7Oxuff/45N9fEdiEDYOkknU6HhQsXYseOHejr68Pll1+O22+/HVOmTMGCBQvwzDPPoKmpCS+//DJuueUWyOVyfPjhh5g3bx6qq6uh0Wg44SJUuLe3Fy6Xi7UoFQoFDh8+jJkzZwIAC0wDYEergYEBJCQkICsrK0725+qrr+byLpVY//jHP8JutyMrKwtSqRSffvopjEbjsE0kVIafOnUqLrnkEnz00Ufo6OjAH/7wB1x11VVYtWoVbrvtNtxxxx148skncf3110OtVuOmm27CTTfdxA5ctBD19fVBIBBw0wvxeKnLv6OjA93d3SgrK4vbGIYmazSv+/r60NPTwwLi1CAARN2ifvGLXyASiWDOnDlsJqBSqbBw4UJs27YNR44cQWNjI/8NBYmgKxQKlJSUYOLEiThw4ACeffZZ3lhFIhG6u7vR0dGBsWPH4v7778cNN9wAkUjEHDCPx8Mou8fjwYEDBzBv3jymPcjlchiNRh4zJXC0YZCUl9vt5gSOUGPSDSZkuqKiAj//+c/hcrkwduxY5Ofn4/bbb2ddXa/Xi3feeYcl4GKD3Lx0Oh2mT5+O2bNn4/3338c///lPZGZmYubMmVi4cCE2b96MG264Afv370dqaiouu+wyrFixglULADA3kHijSqUSMpmMdZZpk6FqTqz5BQCmPNjtdvT398Pr9SIpKQmVlZW47rrrAERpQitWrOBkJRAI4PHHH4+jaRDthhDinp4ePP/885wY6HQ6rFixAqtXr+bD429+8xu89dZbuOCCCziRCwQC6O7uZgUGckOjxIYqDbQmAGB5OUK8aS2mTTa2QkCHi1GjRuGZZ54BEC2Nvvvuu3j33XcBgNFTIMrpq6urQ3d3N373u99h7dq1eOyxx3D33Xdj//79uPLKK/Haa6/hiiuuAABcccUVWL58OdasWRMnuh8MBplnTSgvucclJyez7BRp+lLXPo1lqMZybBPL0ISC7i89Z11dXVi+fDnKy8sZqT/rrLPw0UcfYe3atfjLX/6C9evXY+vWrWhtbcWYMWOwcuVKPPjgg6x5ShJtQqGQAQ4y3iB9ZaIv+P1+pKWl8XpL/Q2xn224zz1cUkTfOxmqSK/5/vvvAwDGjRvHSDu5rOn1ejz22GP8mmR7XVZWxpJan332GVauXImNGzeisrISGzduxKeffsqSWQKBABdffDFefvllRCKROOcsEvx3Op3QarVISUlBXl4e6uvrGeyhRqmSkhKmh9G1IMrN100KY1mHXq8X8+bNAxCVp1uzZg1uu+02GI1GflZVKhX8fj8MBgOOHDnCdq1NTU246KKL+F5RVYTmamwCfaJ79UOOH2NzlPDr/PLAwACamppYy5T81Enwt6+vj5ufzjnnHFx88cU4evQogC9OTLSBDBcul4tLqiKRCJmZmfyzbdu2sWsNnRqo6SgYDPLCS/8mD+VwOMybgNfrZX3OpKQkJCcnY/To0cztoqCvLRYL7HY7l6t8Ph9CoRAjxSqViuWGiBNLDSQ00YmjBIDtEocGuZVEIhHmu9L3iR9ktVohlUoBgHlUQLQL9q677gIQTaap/ETWb52dnWhra4PD4WBDAvIlBr5AlQCw+HZpaSmjAXQKBaJlwUOHDgGIboKffPIJJ7Y5OTkAoo4tJ+puphImJZaxD//mzZvZsYTmwqeffspuSnRfadEMBAJwOBzYs2cPtm/fzhaypD+anp7ORgADAwNxGyDdX7k8KhFjMplYjJ3cY2w2W9xBoqqqijf+xMTEOMFnCqPR+KXElKKtrY3LzbHNIa+//jo2btzI3E0qwdL1dDgc8Hg8CAQCrCsbDodhMplgMBjQ09MT529PklexIv803s7OTpYGIr1eQtjJJx6IHiK6urqwZ88eANGN4N133+XPf9ZZZ7Hc1HDzOdZ4w+l0snICAHz44Ydwu938bPf29uLTTz8FEEWGCF2jA6/P54Pf74fJZOKGkI8++ggbN27ERx99hL179yIxMZGTA6PRyPcGiCYugUCADUIAYObMmTh8+DB3ZpNmLABWwBjKH45EIujq6opzhQIQ15wROxdoHSwvL8fFF1/MYwmHw4xCUQc/zYkZM2agsrISBQUF6OvrQ25uLsrKyrix4/jx46ivr48TN4+9v8AXiUysScSJ3MTI3S12M3vppZcglUo5GQWADRs28Nc+n4+NEUhRgcwH3G43S6C53W589tln2LFjB2pqahAKhfgZICF5p9OJjo4O5otTQhCbxBCKTr8XO17akIcbV2ziu2PHDl7baE6T7F4kEuEDAf0nFovh8/l4DSD6Fn2P3NuoYU+hUDAqnp2dze8be48oqMOfvk/XisY/XNA97ejoiLuftA9OnDhx2L+j8ZFFLhA1Lti7dy8rh5BhjNvtxuOPP86uWuQgJRQKWfNUp9Ox1um5557LEnCDg4NcNh96/070vVONEyGEZGjx/PPPM2cZAFcbwuEwenp60Nraiq6uLuzduxdHjhxBe3s7jhw5giNHjqCpqYmvf0dHByvSDHfffqih0WiQlJSEa6+9FpMnT0Z5eXlcr8r/cnwt5JQE3kn6qaOjgx8qi8WCDRs2YMGCBXj//fexe/du3H///ZgxYwZ2797NuqAnIm0DUQTRZDLhX//6V9yJSSAQ4NJLL0VdXR3+8pe/4NixY1AqlVi+fDlsNhsWL17MG5xMJmN0JRKJwO12s62kUChEZmYmfD4f+vv7kZGRgbFjx/JiD4BP/PQ1lU5JS5RcRex2O7q6urBw4UI88cQTGD16NJqamvDkk09i6tSpKCwsZKL4wMAAwuEwI1RDm0hoMST5IoqcnBzMnj0bTz31FPbv3888njvvvBM+nw9btmzhMhUAXkhIdimWi0jNC8XFxVxyGRgYQG5uLuuRkq95W1sbVCoVu2kROk5dnbt27UJTUxNeffVVbhQ4cOAABAIBUlJS4swAYkMmk8FiseD555+P2/yFQiEWL16MJ598kpPxc889FzNnzsQvf/lLbN68GUA00U9KSoJEIuFFsa+vDzKZDE6nEzKZDL29vaisrGRVgIGBgTjJJblczgk1zQ1K/gYHB5krJ5PJmHfodruxa9cujBo1Ci0tLXj//feh1WrR19eHlpYWtLe3QyqVIjU1lYneQyMrKws2mw2HDx/mhJI2PELdr7rqKsyYMQMfffQRfvazn/HcSExMZIexcDjMqDY1JpKzFhDddGnMQ8PlckGhUPAhS6/Xsz0uzXdyBXrppZdwww034KWXXsK///1vXHjhhVAoFHA6nfjwww+ZAzvcfJbLo44ytbW12L9/P38/OzsbWq0WN954I2+45557LubOnYstW7bg+uuvh8fjgUQigVKpxODgIFJSUiAWi3nDidUNJX1Km82GsrIyuN1uOJ3OOOSU0PFgMAitVovOzk58/PHHmD17dtzcpftGfzt0XMSjO3ToEK8VhYWFmDdvHg4fPoxNmzZh27ZtPP477rgDbrcbN954I89fmUzGr0vd5eFwGDqdDmPGjIFOp+OfFxYWstkHoY1Wq5Xv84k2e5rfW7du5edw9+7dcb9jtVpx4MABmM3muIONRqPBtddei9WrV/Oakpubi1tuuQUAsHXrVtxxxx18gJLL5RAIBIwIk41pQkICmpub+TBDCWthYSEsFgvGjRsHIIoE19XVcTNiLMIYO56hLlmUnJKFMEUkEoHZbEZNTQ0fUHJycrjaM2vWLMyfP5/pJQDYLhkAJ2M0HlJToKpGQUEBr1tqtToOySXd6aGJNa07sYDMUHkjQhXpmgxF7Wi8sbSIffv2cTVl7969X5oHZFNaVVWFXbt2AYjO4UmTJuGvf/0r+vr6IJFIsGrVKjz99NN45plnOIEl8xaqRpD165gxY6BUKpGSkgKJRMLmIMnJyayLGssFp30lIyPjjPCKY4MO72q1mhPYgoICdHR0xD23gUCAnc927tyJyZMnIyMjAw6HAzabDRqNBjKZDC6XCw6HA1lZWUhKSvra9IPvK/Ly8tDQ0BDXHPXZZ5+hvLz8fx5F/dqc05KSEkZvkpKS2BaPFor//ve/WLVqFVavXo2tW7dCqVTimmuuQWJiIg4fPozm5uZhN28AcY0MGRkZmDJlCkKhEHp7e/Hee+/xIq9Wq/Hhhx9CrVZj7ty5aGlp4YWSRJQjkQg7WchkMi6dWq1WeL1e1j4EvuACkTMLfY7ExEQ+lfX19TEHJxKJIDs7G3a7HQcOHMADDzyATz75BH/84x+xYcMG7Nu3D2q1GpdccgmOHTvGJ5kTdTYT0gtEF6KKigqIRCIcP34c69ev59+bOHEi1q5di/7+fpSWlnIDEgAuQ1FyLZfLWWaDSoTEOSOeE42f/MqzsrIY1fT5fFzyTkxMRDgchs1mw+OPP44tW7bg5z//OZcGKVEi5PFE4fF44vy6y8vLsXz5cvj9fqxcuZIX8GXLlmHZsmWYN28e2tvbWfeTStIajYbRPio70f0vLS3lzz10URxaFhwcHGS1A/pMLpcLOp2OHccIpaDE56abbsK6devQ19cHsViMlJQUmM1muFyuE6KmALjcSEFl3IaGBiQlJeH//u//cN111+HgwYO4+uqr4fV6GUUm5D/2/qamprIyhVAoZL7fyTaC5ORk/hl9ZlrY7XY7vF4vWxm+9NJL2LJlC/Lz83H//fdj+/btyM/Px4wZM7Bt2zZGVoaLWNRBIBAgOzsbkUgEvb296O3tBRBFFO+66y4+lKxatYotM2nTTUpKgkqlQlZWFgQCAXN3KVlPS0uDTqfj+0f8YnrOqNs/MzOTERHi7z700EPYsGEDHnjgAT74jR49mnmpQ5/VcDjMyHlRUREWLVqE6upqPP3003EJ7bJly7Bq1SrU1dVhypQp6OjoYA6jSCRiVJmQxpSUFH7mIpEIcnNzOdGhIKoOgK+U6DEajaisrMThw4fh8/kwZcoUVFVVxR32Yw+OCoUCc+fOxfTp07Fz507cc889bAP6u9/9DqtXr4bNZsPChQvx0Ucf8T2ljnx6hlwuF4RCIXJycuD3++NQW5vNhvz8fPT392PixIkYHBxEeno6I29EoRkuCJ0ktDF2bpG5wuDgIBQKBbq7u9HY2AgATCMjMOTWW2/FY489hocffhh//vOfAUTXTEJZiV9KexqtOVRREAgEnAiTPBYANuXw+XzDurUBUemzjo4O7uaPTWBjD80nahqi5JRAk+zsbCQkJMDj8aCsrIzHLJVKMW7cOLS3t8dVTiQSCdLS0jAwMMCVr+LiYixcuBB//etf4xp2idZCDVYKhYJdpzweD3JzcyGTySCVSjmxS01NHbb5iyqMp1siLykpQVtbG1fLLrzwQixduhTTp0+Hw+GAVqtl+9bk5GRUVFSgo6ODKykKhYIrml6vFzU1NbDb7SgsLIRSqYTX60VdXR3kcjmmTJnCdq3FxcVfuj8n46p+U4mqE839U428vDzk5eXFoajA/57E1ND4WslpXl4el4b1ej00Gg3S0tLg9XqRkpICp9PJZZC3334bt99+O9ra2vDKK69gw4YNuPnmm5GamoqOjg5s2rQJx44dQ3NzM+RyOQoLC5GVlYWCggJGNVatWsWyGUD0xDht2jT88Y9/xIQJE3DnnXeipaWFH6q0tDT4fD5IpVJkZmZy1/60adOQkZER5/kbiy6daFKVl5dDoVDwAqxUKpGXlwexWAy9Xs/8tU8//RQNDQ1Yv349Fi1ahJUrV6K5uRkffvghNmzYAJ/Ph+PHj0MgEMBisaC2tpZ9vjUaDcaNG4eSkhKUlJTgJz/5CR555BH84x//ABCdYJdeeil++9vfsk7qxIkT43ywZTIZUxOys7NZniErK4s7FsvKyhhVJUQ1MTERmZmZnIxrtVpYLBbIZDLWhPX7/ejq6oJAIIDNZsORI0dgMBiwc+dOPPzww9iwYQP0ej0kEgnWrVuHnTt38mZPfDpCuJKTkzFhwgTs3LkTR48eRXV1NbKzs7FkyRL09/ejqKgIa9aswYUXXohFixahvb2dkZjExETunC4qKkJiYiL/N2rUKNYnrKysRFJSUhw6MTRiOZj0c0JQk5KSoNPpkJycjISEBFRXVyMtLQ1tbW0wGo3IycnBxx9/jLvvvhtVVVUwm81YtmwZi+bX1dXFLTYCgQCpqalQq9XIyspipJ6kqM477zw88sgjLAOyZs0aFgpPTU3ljZHkmAoLC1mNIDc3FzqdjvVdTzZmIIos0GZOc0YqlSIjIwMpKSl8uPN4PLBYLHj22WfxyiuvoKioCLfffjs6OzuhUChw6NAhbN++He3t7WzHS2XqtLQ0FBUVoaCgABMmTMDYsWO5RCwUCjFz5kwsW7YMixcvhlAoRH19Pe677z6+H2KxmPmzOTk5yMzMRGVlJdRqNXQ6HdRq9ZfchWI3htjDJR34vF4v8vLyOAGlTeqDDz7Ajh078NBDD+GJJ55AU1MTfv/733N3uslkYtRWoVCgrKwMeXl5GDduHK644go+nGk0GvzkJz/BLbfcgmnTpgGINsx1dHRwNYEQ4dTUVHi9XmRkZHBylpqaCoVCAaFQCJfLBaVSyfM99l6S/uaJguR+7rzzTrzyyiv4+OOPcejQIbz99tvYv38/qqur2QKZROtLSkqQm5uLVatWMTXqrLPOwkMPPYQLLrgAQFRbmRJTQteBKHpLzybJS6WmpjLHdPz48QiHwxCLxZDJZBg1alQcdYcMTwYHB+NKs0M3+6G8TAqVSgWbzQadTsdUouTkZFY7IMRw3bp1uPHGG2G32/Hwww8DiCZsseYdCQkJyMjIQHp6OqsJRCIR3lMISdNqtXEKLsSdHZqYDm3yIlR56MFiuKTuRPeYvj8wMIB58+bhnXfewQUXXIAFCxawrCMlnyKRCOXl5Zg3bx6effZZljurqKjA5ZdfjoULF/KaQxU9oVDIkmDp6ekYO3YswuEwV1tI05RQ7timp+G4tMR7PV3u5qJFi/Dqq6+iv78ffr8f27ZtQ3NzcxytjaoVV155JdNNTgQCDQwMsNkAmX6QdTEh8n6/H52dnTCbzUhKSkJpaSnziglRBuIbbU9XiYDimyanFCdDUWPjfwVRPaXklE7dtAilpaVBq9Vi0qRJ0Ol0aGpqYsHqnp4eDAwMYNKkSdi5cydmz57NP3e5XJBIJNBoNDjvvPNw6aWXAojyRGhxcjqdUCqVcLlcjLCOHj0a69atw6hRowB8gWJQGSIzMxMLFy5EJBJBe3s7IpEIJkyYgNzcXKSlpUGtViMtLQ2hUAgOhwNSqZR5nbEWphT0PYfDwWPPyclhrh+hpkeOHOGSWX9/PxwOByoqKvDee+9xd7rT6eTNVavVAohypajJwW6387iIX0jj/vnPf44HH3wQQHRxo89D6OTZZ5+NYDCI1NRU1pSj8ZH5ACWZNpsNFRUVLJFCizxdE7q/hEbFmhuQ9Awt9iRK/tBDD+Gaa67B7NmzEQgEkJ2djZtvvhk333wzBgYGuPRiNBqZY+j1erFjxw6+1tRsAAAvv/wyysrK2BoPAFvPEbc5MTERY8eOZQ5Ufn4+88CKi4t5MSHNwuHuLyUtoVCIGyJI65TmBakUxM43o9HItrBbtmzBDTfcgA8++AByuRxz587FL3/5SwDgJjuSt6KFgJpn6MA1f/58/OMf/2D+LM0HIMpdnjBhArvZUHKUm5vL9qVyuZwXVgCcXA8d89D5TEmXTqfDuHHj4PF4YDabkZKSgubmZjQ0NMBisfBh8/zzz8dTTz2Fa665BoODg8xLPueccwBEnWJIKszpdLIShdvtZpROrVZjx44dUKvVjMwA8ShecXExUxboecvMzERSUhKmTJnC987pdCI9PZ1RX1KoiB0nXQ+lUolQKISSkhI2LLDb7WhpaYHNZoPb7cZ1112HhoYGvPXWW3C73SgvL0d5eTkMBgNXFWLnM0nnAFFptWuuuYab9Oi96ecZGRkoLi6Oq2YUFRWxliZ1e6elpTHtiND4vLw8Rstovp5svZLJZIyyr1+/HhUVFSynN2PGDFxyySU8x2LnJN1nIKoHev/99w97PRUKBcaOHctjI5oCJaSkwBIMBlFeXo4JEybwHCBKRnZ2NifRoVAornOf3odQbrrPNPah8zs3Nxc2mw1Lly6F3+9HVlYWzj//fIjFYixfvhwAsHz5cixYsAAOh4P51kCUH5yamsqHA1r3ysvLmbZ19OhRrjzl5uZicHAQGRkZSExMZIqLXC6P21soYsdAjoqk1DDcPaQYOpdjrwu5VHm9Xm5CCgQCuOaaazB+/Hj09/ejpaUFGRkZmDt3LpKSkuDz+bBmzRoAYOUF4AtuOxBtnpNKpSgqKmI6VFFREYM8ANiJiZBSSuTItW+4cdEe91VjHhr0u0uXLkVhYSHuvvvuOIqI0+mEy+ViiTggSvGgtTMcDiMcDjMfGgD3cwgEAni9Xr73BGQIBAI+HIZCIdTU1CAlJYV1plNSUthxit7jZHPz6wTNydiDV6xxCVHNYn9O3xsaKpWKDVtkMhmjqLEhk8nw6quvxmm9f5dB4/gqif1TEuE/FdHjkRiJkRiJkRiJkRiJkRiJr4ru7m4+8A8Xp5SchsNh6PV65p/82CMUCqG1tRUlJSVx3K8fa4yM98cdI+P9ccfIeH/cMTLeH3f8/zZekiTLyspiitBwcUrJ6UiMxEiMxEiMxEiMxEiMxHcRX0vndCRGYiRGYiRGYiRGYiRG4tuMkeR0JEZiJEZiJEZiJEZiJH4wMZKcjsRIjMRIjMRIjMRIjMQPJkaS05EYiZEYiZEYiZEYiZH4wcRIcjoSIzESIzESIzESIzESP5gYSU5HYiRGYiRGYiRGYiRG4gcTI8npSIzESIzESIzESIzESPxg4v8BYB/PRwq8h+wAAAAASUVORK5CYII=", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig=plt.figure(figsize=(7,7))\n", "corner.corner( permutedims(@view mdf_sample[end-20:end-10,:]) .* 1e3,\n", "# corner.corner( permutedims(@view mdf_sfr_sample[1:5,:]) .* 1e3,\n", " fig=fig,\n", " quantiles=[0.16,0.5,0.84],\n", " max_n_ticks=0, # turn ticks off, illustrating shape only\n", " show_titles=false,\n", " title_kwargs=Dict(\"fontsize\"=>17),\n", " label_kwargs=Dict(\"fontsize\"=>25))\n", "fig.subplots_adjust(hspace=0.0, wspace=0.0)" ] }, { "cell_type": "markdown", "id": "a0025533-b1a6-4c83-a6d4-7e73e2939f7a", "metadata": {}, "source": [ "We can also look at the overall mass-weighted metallicity distribution function that results from the combination of our star formation history and our age-metallicity relation." ] }, { "cell_type": "code", "execution_count": 62, "id": "481bb820-9d92-4855-a7cf-016b8091bd45", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject Text(27.219312263257578, 0.5, 'Probability')" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Make integrated MDF plot\n", "import StarFormationHistories: mdf_amr\n", "MDF_x, MDF = mdf_amr(mdf_coeffs, template_logAge, template_MH)\n", "fig, ax1 = plt.subplots()\n", "ax1.bar(MDF_x, MDF, -abs(MDF_x[2] - MDF_x[3]), align=\"edge\", linewidth=1, edgecolor=\"k\")\n", "ax1.set_xlabel(\"[M/H]\")\n", "ax1.set_ylabel(\"Probability\")" ] }, { "cell_type": "markdown", "id": "138cba13-ccce-412f-af32-bcccfb984cf6", "metadata": {}, "source": [ "Overall the resulting fit is very consistent with the true values. We do, however, see that the SFRs at recent times (small log(Age)) are slightly higher than expected. This is in large part due to our grid being equally-spaced in log(Age) with a very fine time spacing ($\\Delta$log(Age)$=0.1$). As such, the most recent time bins contain very little stellar mass. The total stellar mass for all bins with `logAge < 7` is " ] }, { "cell_type": "code", "execution_count": 63, "id": "4a1f482e-764d-44f5-bf30-522ffea5b758", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13123.389328137404" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Equivalent to sum(mdf_coeffs[template_logAge .<= maximum(unique_template_logAge[1:5])])\n", "# sum(mdf_result.map.μ[1:5] .* template_norm) \n", "sum(mdf_result.map.μ[findall(<(7), unique_logAge)] .* template_norm)" ] }, { "cell_type": "markdown", "id": "3065b8ab-cbe9-47b6-b193-c053f0a12c3a", "metadata": {}, "source": [ "which is of order 0.1% of the stellar mass of the system." ] }, { "cell_type": "code", "execution_count": 64, "id": "c8dceb41-843e-4d7b-b657-965ceebec58d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.13123389328137405" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(mdf_result.map.μ[findall(<(7), unique_logAge)] .* template_norm) / stellar_mass * 100" ] }, { "cell_type": "markdown", "id": "ab01c087-bcff-437c-9690-aa40620f819b", "metadata": {}, "source": [ "### Examining the effects of observational systematics" ] }, { "cell_type": "markdown", "id": "35931c06-375e-49ee-b85c-063b1ad6a8b3", "metadata": {}, "source": [ "Uncertainties in properties of the galaxy that we assume when constructing model templates (e.g., distance, MW foreground reddening, etc) should additionally be taken into consideration. We consider here what happens if we model the population with incorrect assumptions about these parameters." ] }, { "cell_type": "code", "execution_count": 145, "id": "e46bef8f-417f-4d3f-8b89-006b06fb28d7", "metadata": {}, "outputs": [], "source": [ "# Try to use distributed rather than threads because threads use single global GC,\n", "# distributed has a separate GC for each worker and so is more efficient for expensive iterations \n", "# that have a lot of allocations" ] }, { "cell_type": "code", "execution_count": 146, "id": "759e8601-db66-4448-9651-e2dd4ba853d2", "metadata": {}, "outputs": [], "source": [ "@everywhere include(\"fitting1_synthetic_systematics.jl\")\n", "@everywhere using Distributions: Normal, Dirac, Uniform\n", "@everywhere using StarFormationHistories: RandomBinaryPairs, Martin2016_complete, exp_photerr, LinearAMR, GaussianDispersion" ] }, { "cell_type": "code", "execution_count": 147, "id": "287c663f-5eac-4b5c-adc6-8585f56c7a2b", "metadata": {}, "outputs": [], "source": [ "# # 16 seconds\n", "# # Basic function, which we will Monte Carlo sample on top of\n", "# @time synthetic_systematics(template_mini, template_isomags, [\"F090W\", \"F150W\"], \"F150W\", stellar_mass, x0, imf,\n", "# SVector(0.1, 0.05), [F090W_error, F150W_error], [F090W_complete, F150W_complete],\n", "# edges, LinearAMR(mdf_α, mdf_β, T_max), GaussianDispersion(0.2),\n", "# templates, template_logAge, template_MH; \n", "# dist_mod=distmod, binary_model=NoBinaries())" ] }, { "cell_type": "code", "execution_count": 148, "id": "ca0eaad7-1ca7-42d6-81c0-5f00293ea086", "metadata": {}, "outputs": [], "source": [ "# # \n", "# # This works but we can't reuse [F090W_error, F150W_error], [F090W_complete, F150W_complete]\n", "# # because we didn't define them as @everywhere above so we have to redefine the functions here\n", "# sys_samples = distributed_systematics(Uniform(0.0, 0.15), # A_v distribution \n", "# SVector(0.4788075325936103, 0.21031702893243048), # A_λ / A_v from DustExtinction.CCM89 \n", "# Uniform(0.0, 0.7), # Binary fraction distribution\n", "# RandomBinaryPairs, # Binary fraction model\n", "# Normal(distmod, 0.09), # WLM distance modulus is 24.93 ± 0.09, Albers+2019\n", "# 2, # Number of MC realizations to run \n", "# template_mini, template_isomags, [\"F090W\", \"F150W\"], \"F150W\", stellar_mass, x0, imf,\n", "# [m->Martin2016_complete(m,1.0,28.5,0.7), m->Martin2016_complete(m,1.0,27.5,0.7)],\n", "# [m->min( exp_photerr(m, 1.03, 15.0, 36.0, 0.02), 0.4 ), m->min( exp_photerr(m, 1.03, 15.0, 35.0, 0.02), 0.4 )],\n", "# edges, LinearAMR(mdf_α, mdf_β, T_max), GaussianDispersion(0.2),\n", "# templates, template_logAge, template_MH)" ] }, { "cell_type": "code", "execution_count": 149, "id": "76b1c9e1-a24e-46da-9358-21a272423dc5", "metadata": {}, "outputs": [], "source": [ "# Threaded version seems to be working fine but might be less memory efficient?\n", "# 02:27:28 for 1_000 samples\n", "using Serialization\n", "run_systematics::Bool = false\n", "if run_systematics\n", " sys_samples = threaded_systematics(\n", " # Uniform(0.0, 0.10), # A_v distribution \n", " Uniform(-0.05, 0.05), # A_v distribution \n", " SVector(0.4788075325936103, 0.21031702893243048), # A_λ / A_v from DustExtinction.CCM89 \n", " Uniform(0.0, 0.7), # Binary fraction distribution\n", " RandomBinaryPairs, # Binary fraction model\n", " # NoBinaries, # Binary fraction model\n", " Normal(distmod, 0.09), # WLM distance modulus is 24.93 ± 0.09, Albers+2019\n", " 1_000, # Number of MC realizations to run \n", " template_mini, template_isomags, [\"F090W\", \"F150W\"], \"F150W\", stellar_mass, x0, imf,\n", " [F090W_error, F150W_error], [F090W_complete, F150W_complete],\n", " edges, LinearAMR(mdf_α, mdf_β, T_max), GaussianDispersion(0.2),\n", " templates, template_logAge, template_MH, T_max, template_norm;\n", " x0=construct_x0_mdf(template_logAge, T_max; normalize_value=1e4));\n", " # Serialize to temporary file to avoid rerunning\n", " serialize(joinpath(@__DIR__, \".ipynb_checkpoints/sys_samples.serial\"), sys_samples)\n", "else\n", " sys_samples = deserialize(joinpath(@__DIR__, \".ipynb_checkpoints/sys_samples.serial\"))\n", "end;" ] }, { "cell_type": "code", "execution_count": 151, "id": "1688c698-5340-44ac-a55a-733f82da19ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1000, 3, 71)" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "size(sys_samples)" ] }, { "cell_type": "code", "execution_count": 152, "id": "393dd0f1-8cba-489d-9edd-d8e29e18a231", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "71×3 StaticArraysCore.SMatrix{71, 3, Float64, 213} with indices SOneTo(71)×SOneTo(3):\n", " -0.588667 -0.490768 -0.391009\n", " -0.588698 -0.49082 -0.391074\n", " -0.588733 -0.49088 -0.391147\n", " -0.588772 -0.490946 -0.391228\n", " -0.588816 -0.491021 -0.39132\n", " -0.588866 -0.491104 -0.391423\n", " -0.588921 -0.491198 -0.391538\n", " -0.588983 -0.491303 -0.391667\n", " -0.589053 -0.491422 -0.391812\n", " -0.589131 -0.491549 -0.391979\n", " -0.589218 -0.491681 -0.392185\n", " -0.589317 -0.491836 -0.392415\n", " -0.589427 -0.492019 -0.392674\n", " ⋮ \n", " -0.909922 -0.858302 -0.809898\n", " -0.958019 -0.901732 -0.851322\n", " -1.01568 -0.949865 -0.896544\n", " -1.08698 -1.00234 -0.944032\n", " -1.16296 -1.06186 -0.994633\n", " -1.25128 -1.12907 -1.04993\n", " -1.34973 -1.20519 -1.11066\n", " -1.46066 -1.2906 -1.17749\n", " -1.58889 -1.38885 -1.24962\n", " -1.73515 -1.49938 -1.33212\n", " -1.89258 -1.62312 -1.42633\n", " -2.02071 -1.76149 -1.52974" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute quantiles on statistics\n", "# sys_quantiles = [quantile(view(sys_samples,:,1,i), SVector(0.16, 0.5, 0.84)) for i in axes(sys_samples,3)]\n", "sys_cum_quantiles = permutedims(reduce(hcat, quantile(view(sys_samples,:,1,i), SVector(0.16, 0.5, 0.84)) for i in axes(sys_samples,3)))\n", "sys_mh_quantiles = permutedims(reduce(hcat, quantile(view(sys_samples,:,3,i), SVector(0.16, 0.5, 0.84)) for i in axes(sys_samples,3)))" ] }, { "cell_type": "code", "execution_count": 153, "id": "4f8a945d-5083-41b8-9fe9-a8f08c823863", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now plot cumulative SFH and MH evolution for the metallicity evolution model\n", "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=false,figsize=(10,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.35)\n", "\n", "# axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_cum_sfr_arr,0.0), label=\"Result\" ) # This is MAP result\n", "# for i in axes(sys_samples, 1)[begin:min(10, end)] # This plots individual MC results\n", "# axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(sys_samples[i,1,:],0.0), alpha=0.5, c=\"orange\")\n", "# end\n", "axs[1].plot(vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(sys_quantiles[:,2],0.0), label=\"MC Median\")\n", "axs[1].fill_between( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(sys_quantiles[:,1],0.0), vcat(sys_quantiles[:,3],0.0), alpha=0.3, fc=\"k\")\n", "axs[1].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(cum_sfr_arr,0.0), label=\"Input SFH\", ls=\"--\" )\n", "# axs[1].fill_between( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_cum_lower,0.0), vcat(mdf_cum_upper,0.0), alpha=0.3, fc=\"k\")\n", "\n", "axs[1].set_xlim([T_max,0.0])\n", "axs[1].set_ylim([0.0,1.05])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "# axs[2].plot( vcat( exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_mh_med, mdf_result.map.μ[end-1]), label=\"Result\" )\n", "# for i in axes(sys_samples, 1)[begin:min(10, end)] # This plots individual MC results\n", "# # axs[2].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(sys_samples[i,3,:], sys_samples[i,3,end]), alpha=0.5, c=\"orange\")\n", "# axs[2].plot( exp10.(unique_template_logAge)./1e9, sys_samples[i,3,:], alpha=0.5, c=\"orange\")\n", "# end\n", "axs[2].plot( exp10.(unique_template_logAge)./1e9, sys_mh_quantiles[:,2], alpha=1, label=\"MC Median\")\n", "axs[2].fill_between( exp10.(unique_template_logAge)./1e9, sys_mh_quantiles[:,1], sys_mh_quantiles[:,3], alpha=0.3, fc=\"k\")\n", "# axs[2].fill_between( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mdf_mh_lower, mdf_result.map.μ[end-1]), vcat(mdf_mh_upper, mdf_result.map.μ[end-1]), alpha=0.3, fc=\"k\")\n", "axs[2].plot( vcat(exp10.(unique_template_logAge)./1e9, T_max), vcat(mean_mh_arr, β), label=\"Input AMR\", ls=\"--\" )\n", "\n", "# axs[2].set_xlim([max_logAge, axs[2].get_xlim()[2]])\n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")\n", "axs[2].legend()\n", "# plt.savefig(\"example_cumsfh_systematics.pdf\", bbox_inches=\"tight\")" ] }, { "cell_type": "code", "execution_count": 71, "id": "14e39b79-1907-461b-a10e-d54fbf6ab9f1", "metadata": {}, "outputs": [], "source": [ "# Reset workers; this is a hack for what looks like a memory leak\n", "# rmprocs(workers()...)" ] }, { "cell_type": "markdown", "id": "193b74e0-94ed-43f4-a364-50cea9b8c769", "metadata": {}, "source": [ "### Normalizing Templates to SFR\n", "\n", "It can sometimes be convenient to normalize the templates to uniform SFR, rather than uniform stellar mass per template. Below we illustrate this gives the same SFH result as above, where we used templates normalized to uniform stellar mass per template." ] }, { "cell_type": "code", "execution_count": 73, "id": "bf55a189-a4a0-4cf3-bbf8-073d2cfb1511", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Compute change in time [yr] between bins of logAge\n", "template_δt = diff(vcat(exp10.(unique_template_logAge), T_max * 1e9)) # exp10(max_logAge)))\n", "sfr_norm = 1e-3 # Star formation rate in solar masses / yr to normalize templates to\n", "# Now normalize the templates:\n", "# SFR = stellar mass / dt; sfr_norm = A * template_norm / dt; where A is a multiplicative constant\n", "# since we originally normalized all the templates to contain stellar mass = `template_norm`\n", "# Solve for A = sfr_norm * dt / template_norm\n", "sfr_templates = deepcopy(templates)\n", "for i in eachindex(sfr_templates)\n", " idx = findfirst(==(template_logAge[i]), unique_template_logAge)\n", " A = sfr_norm * template_δt[idx] / template_norm\n", " sfr_templates[i] .*= A\n", "end " ] }, { "cell_type": "code", "execution_count": 74, "id": "5ba27393-d9d9-473e-8fdc-b85142af4c4d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StarFormationHistories.CompositeBFGSResult{StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, PDMats.PDMat{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}, StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, LinearAlgebra.Hermitian{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}}(StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, PDMats.PDMat{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}([5.868637456495999, 1.6301919286436621, 1.404132121156659, 1.455316144503296, 1.6475488817518376, 1.7433464408243413, 2.6800824160601975, 1.9814986164877264, 2.1710512084894056, 1.8045843371876287 … 1.2687675658226878, 0.4032610513755984, 0.8802303020011871, 0.7050609307161256, 0.7917381293684235, 0.6382393652599907, 0.8512616689352823, 0.1003007979961189, -1.8706256011936246, 0.11460351512571251], [2.3227544277017618, 1.564756825220588, 1.355986502803152, 1.3929134706972448, 1.559662201005966, 1.6450614671703379, 2.16605426074187, 1.8408017947739217, 1.93443677920176, 1.5667262007918659 … 0.3587619908555255, 0.21626858118954428, 0.14667787532725948, 0.14235715989285164, 0.08890076785355186, 0.05357449900121989, 0.043888063400377236, 0.0005143614199603658, 0.0056157558066597885, 0.0024086238123192825], [0.15665058853934372 -0.1590903668155928 … -6.369702159636971e-6 -0.0002910340279704332; -0.1590903668155928 0.9213321623811233 … -2.9011209107967457e-5 0.0003305351165404875; … ; -6.369702159636971e-6 -2.9011209107967457e-5 … 3.153671328003313e-5 -1.9818920640399425e-5; -0.0002910340279704332 0.0003305351165404875 … -1.9818920640399425e-5 0.00044171492533078975], * Status: success (objective increased between iterations)\n", "\n", " * Candidate solution\n", " Final objective value: 1.709262e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 4.96e-11 ≰ 0.0e+00\n", " |x - x'|/|x'| = 2.16e-11 ≰ 0.0e+00\n", " |f(x) - f(x')| = 6.82e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 3.99e-16 ≰ 0.0e+00\n", " |g(x)| = 7.99e-10 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 3 (vs limit Inf)\n", " Iterations: 117\n", " f(x) calls: 301\n", " ∇f(x) calls: 301\n", ", LinearAMR{Float64}(0.1003007979961189, -1.8706256011936246, 13.7, (true, true)), GaussianDispersion{Float64}(0.11460351512571251, (true,))), StarFormationHistories.BFGSResult{Vector{Float64}, Vector{Float64}, LinearAlgebra.Hermitian{Float64, Matrix{Float64}}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}, LinearAMR{Float64}, GaussianDispersion{Float64}}([9.269941883906982, 4.2372988287656573e-23, 2.651453310773277e-24, 8.947523119884961e-21, 1.1048041691660221e-52, 5.531117155117084e-64, 6.740859665462692, 2.270776185449281e-45, 7.174837374713279, 1.3322062347660846 … 1.7319149058394643, 0.12823736200491312, 0.9186067580144406, 0.6967376137093034, 0.8018552101653253, 0.6381326055550991, 0.8524338158730563, 0.10025736163467495, -1.870423274970492, 0.11462347088972359], [2.150691663094505, 4.446845712534452e-14, 2.9303267070862746e-15, 8.623425316522823e-12, 3.058345306923918e-43, 1.8826831183726932e-54, 4.516070763937952, 5.271153074523374e-36, 7.3070939782021025, 4.741044366817262 … 0.47737164061033205, 0.2841652047454499, 0.15361948062286632, 0.145466101526483, 0.09206217422434648, 0.05480388445110038, 0.04553539212524457, 0.0005277080120701197, 0.005809005182286097, 0.00244648136583862], [0.05382726088504485 5.467097519834217e6 … 3.7188488452456195e-7 -0.00012398627375233507; 5.467097519834217e6 1.1013514727930616e18 … -66770.89073190159 309199.85569248395; … ; 3.7188488452456195e-7 -66770.89073190159 … 3.374454120782673e-5 -2.052570505161021e-5; -0.00012398627375233507 309199.85569248395 … -2.052570505161021e-5 0.00045570935843758847], * Status: success\n", "\n", " * Candidate solution\n", " Final objective value: 1.681799e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 1.21e+00 ≰ 0.0e+00\n", " |x - x'|/|x'| = 8.31e-03 ≰ 0.0e+00\n", " |f(x) - f(x')| = 9.09e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 5.41e-16 ≰ 0.0e+00\n", " |g(x)| = 9.53e-09 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 5 (vs limit Inf)\n", " Iterations: 159\n", " f(x) calls: 451\n", " ∇f(x) calls: 451\n", ", LinearAMR{Float64}(0.10025736163467495, -1.870423274970492, 13.7, (true, true)), GaussianDispersion{Float64}(0.11462347088972359, (true,))))" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mdf_sfr_result = fit_sfh(LinearAMR(mdf_α, mdf_β, T_max),\n", " GaussianDispersion(0.2),\n", " sfr_templates,\n", " data,\n", " template_logAge, template_MH;\n", " x0=fill(1,length(unique_template_logAge)))" ] }, { "cell_type": "code", "execution_count": 75, "id": "17c20d21-c9d7-42ec-82e5-ae1d05d82b94", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "71×10000 Matrix{Float64}:\n", " 0.00994213 0.00828003 0.0018737 … 0.00671931 0.00752873\n", " 0.000303725 0.00203215 0.00627689 0.00101142 0.00061142\n", " 0.00931286 0.000606987 0.00192891 0.00302538 0.0029088\n", " 0.00103286 0.00154624 0.00194906 0.00141109 0.000367741\n", " 0.00215436 0.00202194 0.00534193 0.00122134 0.000962152\n", " 0.000790138 0.0147861 0.00320347 … 0.000485645 0.00322632\n", " 0.00581511 0.00106585 0.00265586 0.00207506 0.000786997\n", " 0.000638616 0.00155904 0.000914119 0.000868078 0.00246849\n", " 0.00159201 0.00189683 0.00959484 0.00595235 0.000312999\n", " 0.00261004 0.00192042 0.000887527 0.000693042 0.0130447\n", " 0.000268943 0.00124094 0.000517893 … 0.00176515 0.00178163\n", " 0.00029769 0.000257781 0.000705224 0.00325438 0.00212995\n", " 0.000469178 0.000795544 0.000166066 0.000580718 0.000257101\n", " ⋮ ⋱ \n", " 0.000479874 0.000504521 0.000581791 0.000844804 0.000475806\n", " 0.00064185 0.00115554 0.0015424 … 0.000502193 0.00111465\n", " 0.00168184 0.000634539 0.000660696 0.000912893 0.000809152\n", " 0.000245639 0.00109551 0.000866754 0.000965202 0.000660862\n", " 0.000986042 0.000523756 0.000439433 0.000343495 0.000739705\n", " 0.00103433 0.00126093 0.00136642 0.00121989 0.00105701\n", " 0.000472906 0.000309534 0.000324472 … 0.00042934 0.000680778\n", " 0.00108779 0.00101846 0.000732825 0.00093351 0.00073658\n", " 0.000623093 0.000558592 0.00087738 0.000701988 0.000924636\n", " 0.000803428 0.000830851 0.000741301 0.000821514 0.000625736\n", " 0.000655879 0.000728378 0.000649059 0.000651461 0.000729245\n", " 0.000837101 0.000859248 0.000855221 … 0.000790216 0.000778657" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mdf_sfr_sample = rand(mdf_sfr_result.map, 10000)\n", "@views mdf_sfr_sample[begin:end-3,:] *= sfr_norm # Correct the SFR coefficients for the template normalization" ] }, { "cell_type": "code", "execution_count": 76, "id": "a16529bd-f6ef-456f-93fb-5333a3d530b5", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Now calculate quantiles\n", "mdf_sfr_norm_lower = quantile.(eachrow(@view mdf_sfr_sample[begin:end-3,:]), 0.16)\n", "mdf_sfr_norm_med = median.(eachrow(@view mdf_sfr_sample[begin:end-3,:]))\n", "mdf_sfr_norm_upper = quantile.(eachrow(@view mdf_sfr_sample[begin:end-3,:]), 0.84);" ] }, { "cell_type": "code", "execution_count": 77, "id": "ec8f4fe0-41e6-47a9-9ce8-94add90d5096", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject Text(0.5, 29.371800193718016, 'log(Age [yr])')" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs = plt.subplots(nrows=1,ncols=2,sharex=true,sharey=true,figsize=(14,7))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.1)\n", "\n", "axs[1].bar(unique_template_logAge, mdf_sfr_norm_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", "# axs[1].bar(unique_template_logAge, mdf_sfr_result.map.μ[begin:end-3] .* 1e3 .* sfr_norm; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", "# axs[1].bar(unique_template_logAge, mdf_sfr_result.mle.μ[begin:end-3] .* 1e3 .* sfr_norm; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(mdf_sfr_norm_med .- mdf_sfr_norm_lower) .* 1e3, \n", " (mdf_sfr_norm_upper .- mdf_sfr_norm_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"Result, SFR Normalized\")\n", "axs[1].bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "\n", "axs[1].set_xlabel(\"log(Age [yr])\")\n", "axs[1].set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "axs[1].set_ylim([0.0, axs[1].get_ylim()[2]])\n", "axs[1].legend()\n", "\n", "axs[2].bar(unique_template_logAge, mdf_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(mdf_sfr_med .- mdf_sfr_lower) .* 1e3, \n", " (mdf_sfr_upper .- mdf_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"Result, Stellar Mass Normalized\")\n", "axs[2].bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "axs[2].legend()\n", "axs[2].set_xlabel(\"log(Age [yr])\")" ] }, { "cell_type": "markdown", "id": "1b70bc27-3f04-4f40-af32-7233c9be6625", "metadata": {}, "source": [ "## SFH Fitting: Unconstrained Fitting\n", "\n", "We will now explore the methods offered to fit SFHs without constraints on the metallicity evolution. These methods are most useful when dealing with simpler stellar populations (e.g., globular clusters / ultra-faint dwarf galaxies)." ] }, { "cell_type": "markdown", "id": "2bd1845d-9992-4826-a98f-ce4d16ee58be", "metadata": {}, "source": [ "### Simple Optimization" ] }, { "cell_type": "markdown", "id": "354c6e34-3b6a-4749-b6dc-619f4d0f677c", "metadata": {}, "source": [ "Again, the best way to fit this SFH would be to use `fit_sfh` with the correct `LinearAMR` and `GaussianDispersion` models as above, such that we are fitting the same type of hierarchical model as we used to generate the SFH. For example purposes we will show how to use some of the simpler fitting methods that fit per-template stellar masses here. These methods are generally most useful when you have a smaller set of templates that you want to use to fit the SFH. For this purpose, we will create a smaller grid of templates centered around the real age-metallicity relation; we will accept any isochrone within ± 0.2 dex of this metallicity evolution guess." ] }, { "cell_type": "code", "execution_count": 68, "id": "16f9e079-2c7d-4a09-9188-9d0882359f21", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# new_α, new_β, new_σ = 0.15, -2.25, 0.2 # -0.15, -0.2, 0.2\n", "new_α, new_β, new_σ = α, β, σ\n", "met_func(logAge) = (T_max - exp10(logAge)/1e9) * new_α + new_β\n", "met_func_true(logAge) = (T_max - exp10(logAge)/1e9) * α + β\n", "laxplot = 6.6:0.01:10.2\n", "fig,ax1=plt.subplots()\n", "ax1.plot(exp10.(laxplot)./1e9, met_func.(laxplot),label=\"Guess\",zorder=0)\n", "ax1.plot(exp10.(laxplot)./1e9, met_func_true.(laxplot),label=\"Input\",ls=\"--\",zorder=1)\n", "ax1.set_xlim([13.0,-0.1])\n", "ax1.set_xlabel(\"Lookback Time [Gyr]\")\n", "ax1.set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")\n", "ax1.legend(loc=\"upper left\")" ] }, { "cell_type": "markdown", "id": "7eebd0bc-793b-46f4-b4a3-df03af309e4f", "metadata": {}, "source": [ "Now create the limited template set." ] }, { "cell_type": "code", "execution_count": 69, "id": "f1ff4a94-e9fd-4656-aa31-3ea8ab1f2841", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Single-threaded with push!, easier with this example\n", "free_templates = Vector{Matrix{Float64}}(undef,0)\n", "free_template_logAge = Vector{Float64}(undef,0)\n", "free_template_MH = Vector{Float64}(undef,0)\n", "for logage in unique_logAge\n", " mean_met = met_func(logage)\n", " for mh in unique_MH\n", " # If the current mh is more than `new_σ` dex away from mean, skip template\n", " abs(mh - mean_met) > new_σ && continue\n", " local good = findall( (table.MH .== mh) .& (table.logAge .== logage) )\n", " # Chop off the last star because its the 30 mag weird thing that parsec does.\n", " local m_ini = table.Mini[good][begin:end-1]\n", " local iso_mags = [table.F090W[good][begin:end-1], table.F150W[good][begin:end-1]]\n", " push!(free_templates, partial_cmd_smooth( m_ini, iso_mags, [F090W_error, F150W_error], 2, [1,2], imf, [F090W_complete, F150W_complete]; \n", " dmod=distmod, normalize_value=template_norm, edges=edges).weights )\n", " push!(free_template_logAge, logage)\n", " push!(free_template_MH, mh)\n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 70, "id": "49588f69-bfe4-46da-a16c-3498f480791d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "142" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `templates` has 1846 entries, free_templates has ~10% of that.\n", "length(free_templates)" ] }, { "cell_type": "markdown", "id": "5c2b55bd-9db8-40dd-a306-2a8b2c0ddefa", "metadata": {}, "source": [ "Let's start off by just obtaining the maximum likelihood estimate (MLE) via `fit_templates_lbfgsb`. We'll construct the initial guess vector `x0` with `construct_x0`." ] }, { "cell_type": "code", "execution_count": 71, "id": "d20b6999-e88f-464e-91f9-360efad5c064", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RUNNING THE L-BFGS-B CODE\n", "\n", " * * *\n", "\n", "Machine precision = 2.220D-16\n", " N = 142 M = 10\n", "\n", " * * *\n", "\n", "Tit = total number of iterations\n", "Tnf = total number of function evaluations\n", "Tnint = total number of segments explored during Cauchy searches\n", "Skip = number of BFGS updates skipped\n", "Nact = number of active bounds at final generalized Cauchy point\n", "Projg = norm of the final projected gradient\n", "F = final function value\n", "\n", " * * *\n", "\n", " N Tit Tnf Tnint Skip Nact Projg F\n", " 142 4850 4976 5355 0 43 9.263D-06 1.738D+03\n", "\n", "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n", "\n", " Total User time 1.978E+00 seconds.\n", "\n" ] }, { "data": { "text/plain": [ "(1737.561767858499, [0.0, 6.3092243392336025, 0.3774299718970162, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 392.5961395092347, 1172.9423464017354, 139.94575680533316, 648.3585828080876, 300.2553224886903, 372.28817543771146, 514.5852180676904, 465.4206345967334, 325.870356070181])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: fit_templates_lbfgsb, construct_x0\n", "lbfgsb_result = fit_templates_lbfgsb(free_templates, data; x0=construct_x0(free_template_logAge, T_max; normalize_value=template_norm))" ] }, { "cell_type": "code", "execution_count": 72, "id": "8e50d8b0-aafd-404e-adbe-0959e068cd64", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "([6.6, 6.65, 6.7, 6.75, 6.8, 6.85, 6.9, 6.95, 7.0, 7.05 … 9.65001, 9.70001, 9.75001, 9.80001, 9.85001, 9.90001, 9.95001, 10.00001, 10.05001, 10.10001], [1.0, 0.999373967714971, 0.9993365172559225, 0.9993365172559225, 0.9993365172559225, 0.9993365172559225, 0.9990821204493999, 0.9990821204493999, 0.9990821204493999, 0.9986779855091602 … 0.645604860384021, 0.645604860384021, 0.6161085840251482, 0.5212806946260483, 0.5017263175071199, 0.4298684064067988, 0.39091308557882415, 0.26064186758978575, 0.16651572801819506, 0.07851578583591874], [0.012988244361669194, 0.0006924857893413836, 0.0, 0.0, 0.0, 0.002968010106175201, 0.0, 0.0, 0.0033379522516951317, 0.00013650350443439693 … 0.0, 0.0004860835386089413, 0.0013927712861641693, 0.0002559691974138455, 0.0008383363709931671, 0.00040505174323767916, 0.0012072360447614766, 0.0007774168820056895, 0.0006477778697086845, 0.0007125820296876785], [-0.5000166309399301, -0.6000162328319801, -0.6000162328319801, -0.6000162328319801, -0.6000162328319801, -0.5000166309399301, -0.5000166309399301, -0.5000166309399301, -0.5000166309399301, -0.5000166309399301 … -0.8000106239270766, -1.0389866712368319, -1.0428091179564716, -1.2000131046997948, -1.291358278452335, -1.2000131046997948, -1.3893550269052133, -1.5683590517025818, -1.64198738317488, -1.7588322857582623])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the cumulative SFH, SFRs, <[M/H]>(logAge) from the above result\n", "free_coeffs = lbfgsb_result[2] .* template_norm\n", "unique_free_template_logAge, free_cum_sfr_arr, free_sfr_arr, free_mean_mh_arr = calculate_cum_sfr(free_coeffs, free_template_logAge, free_template_MH, T_max)" ] }, { "cell_type": "code", "execution_count": 73, "id": "504fb970-2d7a-4d8e-a773-e1e83521b876", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject Text(457.3650849792141, 0.5, '$\\\\langle$[M/H]$\\\\rangle$')" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=false,figsize=(10,5))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.35)\n", "\n", "axs[1].plot( exp10.(unique_free_template_logAge)./1e9, free_cum_sfr_arr, label=\"LBFGSB Fit\" )\n", "axs[1].plot( exp10.(unique_template_logAge)./1e9, cum_sfr_arr, label=\"Input SFH\", ls=\"--\" )\n", "\n", "axs[1].set_xlim([13.0,-0.1])\n", "axs[1].set_ylim([0.0,1.1])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "axs[2].plot( exp10.(unique_free_template_logAge)./1e9, free_mean_mh_arr, label=\"LBFGSB Fit\" )\n", "axs[2].plot( exp10.(unique_template_logAge)./1e9, mean_mh_arr, label=\"Input SFH\", ls=\"--\" )\n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")" ] }, { "cell_type": "markdown", "id": "caab9ea0-6093-41c0-9739-35664c2592e9", "metadata": {}, "source": [ "We can also use `fit_templates`, which uses BFGS to build a dense estimate of the inverse Hessian. This function is slower than `fit_templates_lbfgsb` above, but allows for estimation of random uncertainties." ] }, { "cell_type": "code", "execution_count": 74, "id": "f139e522-565a-4055-a316-346b75da2e9d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(map = StarFormationHistories.LogTransformFTResult{Vector{Float64}, Vector{Float64}, Matrix{Float64}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}}([0.6711597287808827, 2.786979899683696, 0.6617579206662607, 0.8963766468970037, 0.49741326176615464, 0.6892953485437623, 0.4681920517844792, 0.5467529941473369, 0.5016994229652576, 0.49781429244012415 … 139.9339354464, 203.88157215510986, 1077.1169421323534, 239.99636639376698, 632.9659868671882, 333.8928576448296, 374.43089134518607, 516.1480680692041, 465.7771353998209, 328.5100565799611], [0.6393845424282453, 1.1272124579304348, 0.6311136743868222, 0.8422151672507794, 0.486052783833231, 0.6653206733640258, 0.46081892665111956, 0.5347183079884412, 0.49223495790745797, 0.4905934761438525 … 105.57212462718002, 129.27332813735748, 138.76135186373827, 142.32709266297593, 68.88387604504705, 86.57834760160917, 39.61706911928787, 44.13880761680111, 38.88437346966368, 36.823920335366324], [0.9075540216681596 -0.11357936162431746 … 0.0003583789943635559 -0.0002822366689825427; -0.11357936162431725 0.16358515389074654 … -0.0005445993331393253 -0.000937549734993933; … ; 0.000358378994376122 -0.0005445993332548694 … 0.0069693757559757975 -0.004006472672467619; -0.0002822366689761744 -0.0009375497349792803 … -0.00400647267234645 0.012565006182566108], * Status: success\n", "\n", " * Candidate solution\n", " Final objective value: 1.424759e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 5.39e-10 ≰ 0.0e+00\n", " |x - x'|/|x'| = 7.72e-11 ≰ 0.0e+00\n", " |f(x) - f(x')| = 2.27e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 1.60e-16 ≰ 0.0e+00\n", " |g(x)| = 9.89e-09 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 0 (vs limit Inf)\n", " Iterations: 379\n", " f(x) calls: 1094\n", " ∇f(x) calls: 1094\n", "), mle = StarFormationHistories.LogTransformFTResult{Vector{Float64}, Vector{Float64}, Matrix{Float64}, Optim.MultivariateOptimizationResults{Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}, Vector{Float64}, Float64, Float64, Vector{Optim.OptimizationState{Float64, Optim.BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Nothing, Optim.Flat}}}, Bool, @NamedTuple{f_limit_reached::Bool, g_limit_reached::Bool, h_limit_reached::Bool, time_limit::Bool, callback::Bool, f_increased::Bool}}}([2.2689104129681393e-43, 6.3144518028884695, 0.3748471248839392, 1.9170131225444348e-16, 1.7228712459554862e-46, 2.4194262809988427e-9, 3.2606961626166346e-54, 6.441836482749052e-36, 2.777997927216779e-38, 7.095239061504328e-51 … 1.7755211290463648e-11, 393.5085266029836, 1173.57649156109, 138.46812787866813, 648.3035374785626, 300.95383010324525, 372.25703996538925, 514.670446278075, 465.3230471713023, 325.8090482148636], [2.010476254618132e-37, 1.6481127191094649, 2.3575652405299214, 6.245448412461968e-11, 1.6283123095701895e-40, 0.0004171180370206213, 3.607917834321567e-48, 4.67656938756421e-30, 2.150573834267625e-32, 7.363138172526142e-45 … 4.846091977638514e-6, 261.52170864825933, 164.35093655853072, 230.541916712022, 72.43704689521083, 94.60348374276387, 40.87838865487651, 45.274137934053925, 38.934427739984635, 36.4494844457256], [7.85169099719256e11 551.8997959927075 … 47.0597060555785 45.27247230681136; 551.8997959929737 0.06812437275669841 … -0.0001648690872719994 -0.00043782811760489503; … ; 47.05970605558101 -0.0001648690872720217 … 0.0070009739998353445 -0.003862092522811753; 45.272472306794675 -0.00043782811760490273 … -0.0038620925228116157 0.012515738986832428], * Status: success (objective increased between iterations)\n", "\n", " * Candidate solution\n", " Final objective value: 1.737581e+03\n", "\n", " * Found with\n", " Algorithm: BFGS\n", "\n", " * Convergence measures\n", " |x - x'| = 2.69e-02 ≰ 0.0e+00\n", " |x - x'|/|x'| = 1.79e-04 ≰ 0.0e+00\n", " |f(x) - f(x')| = 6.82e-13 ≰ 0.0e+00\n", " |f(x) - f(x')|/|f(x')| = 3.93e-16 ≰ 0.0e+00\n", " |g(x)| = 2.79e-09 ≤ 1.0e-08\n", "\n", " * Work counters\n", " Seconds run: 0 (vs limit Inf)\n", " Iterations: 219\n", " f(x) calls: 683\n", " ∇f(x) calls: 683\n", "))" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import StarFormationHistories: fit_templates\n", "bfgs_result = fit_templates(free_templates, data; x0=construct_x0(free_template_logAge, T_max; normalize_value=template_norm))" ] }, { "cell_type": "markdown", "id": "e089877a-2d13-4d0f-a278-63a9ea8c8e89", "metadata": {}, "source": [ "`bfgs_result.mle` contains information for the maximum likelihood estimate; this is most similar to what is returned by `fit_templates_lbfgsb` but not exactly equal due to differences in the internal convergence criteria of the fitting methods." ] }, { "cell_type": "code", "execution_count": 75, "id": "7c03b156-c251-4917-b0d6-37a80f035908", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "142-element Vector{Float64}:\n", " 2.2689104129681393e-43\n", " 0.005227463654867037\n", " -0.0025828470130770387\n", " 1.9170131225444348e-16\n", " 1.7228712459554862e-46\n", " 2.4194262809988427e-9\n", " 3.2606961626166346e-54\n", " 6.441836482749052e-36\n", " 2.777997927216779e-38\n", " 7.095239061504328e-51\n", " 5.39587522916262e-41\n", " 0.0012824543353979756\n", " 1.1177621591696948e-63\n", " ⋮\n", " 0.5487546188081751\n", " -1.3181811035663173\n", " 1.7755211290463648e-11\n", " 0.9123870937488618\n", " 0.6341451593546026\n", " -1.4776289266650338\n", " -0.05504532952500085\n", " 0.6985076145549556\n", " -0.03113547232220526\n", " 0.08522821038457096\n", " -0.0975874254311293\n", " -0.06130785531740912" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bfgs_result.mle.μ .- lbfgsb_result[2]" ] }, { "cell_type": "markdown", "id": "0c9c68c1-e6da-4a2c-a0a4-288b9f05803b", "metadata": {}, "source": [ "Of additional note are the uncertainty estimates, available as `bfgs_result.map.σ` and `bfgs_result.mle.σ`. These are essentially standard errors derived from the diagonal of the covariance matrix of the fitting parameters. The `bfgs_result.map` result contains the maximum a posteriori result and is often very comparable to the mode of posterior samples obtained via Hamiltonian Monte Carlo (provided by `hmc_sample`) or MCMC (provided by `mcmc_sample`). We don't generally recommend the use of `mdf_result.mle.σ` because the estimate of the covariance matrix obtained during the BFGS fit can be poorly conditioned when best-fit SFRs are 0, but we provide it for completeness." ] }, { "cell_type": "code", "execution_count": 76, "id": "7e569063-49cc-49b3-9d1e-27f8ab11012e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "142-element Vector{Float64}:\n", " 0.6711597287808827\n", " 2.786979899683696\n", " 0.6617579206662607\n", " 0.8963766468970037\n", " 0.49741326176615464\n", " 0.6892953485437623\n", " 0.4681920517844792\n", " 0.5467529941473369\n", " 0.5016994229652576\n", " 0.49781429244012415\n", " 0.4975510460296729\n", " 0.6378922134625628\n", " 0.4594830634417243\n", " ⋮\n", " 462.68268391812825\n", " 172.96276963693958\n", " 139.9339354464\n", " 203.88157215510986\n", " 1077.1169421323534\n", " 239.99636639376698\n", " 632.9659868671882\n", " 333.8928576448296\n", " 374.43089134518607\n", " 516.1480680692041\n", " 465.7771353998209\n", " 328.5100565799611" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bfgs_result.map.μ" ] }, { "cell_type": "code", "execution_count": 77, "id": "377f6315-c7e8-4319-bccd-7a44dda6a3d4", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "142-element Vector{Float64}:\n", " 0.6393845424282453\n", " 1.1272124579304348\n", " 0.6311136743868222\n", " 0.8422151672507794\n", " 0.486052783833231\n", " 0.6653206733640258\n", " 0.46081892665111956\n", " 0.5347183079884412\n", " 0.49223495790745797\n", " 0.4905934761438525\n", " 0.48934406308766926\n", " 0.6204659279637948\n", " 0.45360586719597845\n", " ⋮\n", " 175.86293344457067\n", " 128.37385116019712\n", " 105.57212462718002\n", " 129.27332813735748\n", " 138.76135186373827\n", " 142.32709266297593\n", " 68.88387604504705\n", " 86.57834760160917\n", " 39.61706911928787\n", " 44.13880761680111\n", " 38.88437346966368\n", " 36.823920335366324" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bfgs_result.map.σ" ] }, { "cell_type": "markdown", "id": "0a3eebe7-0b94-4ed1-9719-34a2d5c8d810", "metadata": {}, "source": [ "### Sampling from Inverse Hessian\n", "\n", "We'll now draw samples from the posterior using the same approximation techinque as we did in the previous section." ] }, { "cell_type": "code", "execution_count": 78, "id": "265e8cd2-1d28-4fe5-9242-1a62a541fe50", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "71-element Vector{Float64}:\n", " 0.011085321046883506\n", " 0.006912008817724217\n", " 0.0048872923222459995\n", " 0.0037854707328528314\n", " 0.0032763879831921453\n", " 0.003309822768685931\n", " 0.002543552364548502\n", " 0.002523793353354795\n", " 0.002741071311185307\n", " 0.0022329605704923465\n", " 0.001706048667020869\n", " 0.001810901125758526\n", " 0.0017060049595674222\n", " ⋮\n", " 0.0011677678815833795\n", " 0.0012725761337671066\n", " 0.0005254404031924141\n", " 0.0008430288658772412\n", " 0.0014851522877531952\n", " 0.001072563493437745\n", " 0.0010755570277097815\n", " 0.0006410180457440616\n", " 0.0013869231964129256\n", " 0.0008585908225286979\n", " 0.0006897226882523541\n", " 0.000755607836830087" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hessian_sample = rand(bfgs_result.map, 10000) .* template_norm\n", "# The above is effectively doing\n", "# hessian_dist = Distributions.MvNormal(Optim.minimizer(bfgs_result.map.result),\n", "# LinearAlgebra.Hermitian(bfgs_result.map.invH))\n", "# hessian_sample = exp.(rand(hessian_dist, 10000)) .* template_norm\n", "\n", "# Calculate the cumulative SFH for each sample\n", "# and find the 1-σ quantile range for both the cumulative SFH and the SFRs\n", "hessian_cum_sfr = Vector{Vector{Float64}}(undef,0)\n", "hessian_sfr = Vector{Vector{Float64}}(undef,0)\n", "for x in eachcol(hessian_sample)\n", " _, hessian_1, hessian_2, hessian_mh = calculate_cum_sfr(x, free_template_logAge, free_template_MH, T_max)\n", " push!(hessian_cum_sfr, hessian_1)\n", " push!(hessian_sfr, hessian_2)\n", "end\n", "hessian_cum_sfr = reduce(hcat, hessian_cum_sfr) \n", "hessian_sfr = reduce(hcat, hessian_sfr) \n", "# Now calculate quantiles\n", "hessian_cum_lower = quantile.(eachrow(hessian_cum_sfr), 0.16)\n", "hessian_cum_med = median.(eachrow(hessian_cum_sfr))\n", "hessian_cum_upper = quantile.(eachrow(hessian_cum_sfr), 0.84)\n", "hessian_sfr_lower = quantile.(eachrow(hessian_sfr), 0.16)\n", "hessian_sfr_med = median.(eachrow(hessian_sfr))\n", "hessian_sfr_upper = quantile.(eachrow(hessian_sfr), 0.84)" ] }, { "cell_type": "code", "execution_count": 79, "id": "95e1b669-12d9-449d-85fb-eae6e26734fd", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now plot cumulative SFH and MH evolution\n", "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=false,figsize=(10,6))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.35)\n", "\n", "# axs[1].plot( exp10.(unique_free_template_logAge)./1e9, cum_sfr_arr, label=\"Result\" )\n", "axs[1].plot( exp10.(unique_free_template_logAge)./1e9, hessian_cum_med, label=\"Result\" )\n", "axs[1].plot( exp10.(unique_template_logAge)./1e9, cum_sfr_arr, label=\"Input SFH\", ls=\"--\" )\n", "axs[1].fill_between( exp10.(unique_free_template_logAge)./1e9, hessian_cum_lower, hessian_cum_upper, alpha=0.3, fc=\"k\")\n", "axs[1].set_xlim([13.0,-0.1])\n", "axs[1].set_ylim([0.0,1.1])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "axs[2].plot( exp10.(unique_free_template_logAge)./1e9, free_mean_mh_arr, label=\"Result\" )\n", "axs[2].plot( exp10.(unique_template_logAge)./1e9, mean_mh_arr, label=\"Input\", ls=\"--\" )\n", "# axs[2].plot( exp10.(unique_template_logAge)./1e9, met_func.(unique_template_logAge), c=\"k\", label=\"Guess\",ls=\"--\")\n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].set_ylabel(L\"$\\langle$[M/H]$\\rangle$\")\n", "axs[2].legend()" ] }, { "cell_type": "markdown", "id": "41c2a74d-b711-4129-94fd-37a02ace5d12", "metadata": {}, "source": [ "We can alternatively use the `Measurements.jl` package to estimate the uncertainties on the SFRs assuming that the `bfgs_result.map.σ` are Gaussian and *uncorrelated*. In general we expect some correlation in the star formation rates between age bins (as shown above in the constrained metallicity case), but this approach can be useful for quick diagnostics. " ] }, { "cell_type": "code", "execution_count": 80, "id": "304717d6-cf60-4c67-add0-0a43c9c4f155", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "71-element Vector{Float64}:\n", " 2.6678057747113195\n", " 1.9309543383437673\n", " 1.3473394795444993\n", " 1.0287509021878751\n", " 0.9026882745090267\n", " 0.914783455031564\n", " 0.690504892868098\n", " 0.6889621372547996\n", " 0.758422952321689\n", " 0.6062329465844566\n", " 0.4814725180469458\n", " 0.4937245775362478\n", " 0.478365795267449\n", " ⋮\n", " 0.22239812889257207\n", " 0.1753681001335861\n", " 0.14463240601095143\n", " 0.2290343872639561\n", " 0.2998468184089493\n", " 0.2990620992952306\n", " 0.25205106899855917\n", " 0.17219961421711677\n", " 0.18277947099814565\n", " 0.09067119885338094\n", " 0.04332082820555777\n", " 0.048226737789552415" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Measurements: uncertainty, ±\n", "hessian_sfrerr = uncertainty.(calculate_cum_sfr(bfgs_result.map.μ .± bfgs_result.map.σ,\n", " free_template_logAge, free_template_MH, T_max; normalize_value=template_norm)[3]) .* 1e3" ] }, { "cell_type": "code", "execution_count": 81, "id": "1988d302-c6ba-476f-990e-d468a04c72ad", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,ax1 = plt.subplots(figsize=(7,7))\n", "# ax1.bar(unique_free_template_logAge[begin:end-1], free_sfr_arr[begin:end-1] .* 1e3; width=diff(unique_free_template_logAge), align=\"edge\", yerr = hessian_sfrerr[begin:end-1], capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"LBFGS Fit\")\n", "ax1.bar(unique_free_template_logAge, hessian_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", yerr = hessian_sfrerr, capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"BFGS Fit\")\n", "ax1.bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "ax1.set_xlabel(\"log(Age [yr])\")\n", "ax1.set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "ax1.set_ylim([0.0, ax1.get_ylim()[2]])\n", "ax1.legend()" ] }, { "cell_type": "markdown", "id": "523954ac-a54d-4c7b-98b3-4a91f0e87b67", "metadata": { "tags": [] }, "source": [ "Overall the performance here is not bad, but inferior to the constrained AMR fit we performed previously.\n", "\n", "If we make the same plot with the `hessian_sfr_lower` and `hessian_sfr_upper` vectors, which utilize the samples from the full variance-covariance matrix, we see that the uncertainties look very similar across most of the plot, but are not identical due to the inclusion of covariance between parameters." ] }, { "cell_type": "code", "execution_count": 82, "id": "d0646193-5b0f-462e-83a0-24b3b45d3563", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,ax1 = plt.subplots(figsize=(7,7))\n", "ax1.bar(unique_free_template_logAge, hessian_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(hessian_sfr_med .- hessian_sfr_lower) .* 1e3, \n", " (hessian_sfr_upper .- hessian_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"BFGS Fit\")\n", "ax1.bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFH\", alpha=0.5)\n", "ax1.set_xlabel(\"log(Age [yr])\")\n", "ax1.set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "ax1.set_ylim([0.0, ax1.get_ylim()[2]])\n", "ax1.legend()" ] }, { "cell_type": "markdown", "id": "8d313856-e36f-40bc-a19e-7085d7f1fa39", "metadata": {}, "source": [ "### Sampling via MCMC\n", "The unconstrained fitting methods are not optimal for MCMC. Since so many of the fitting coefficients are 0, our transition acceptance probabilities are extremely low, giving us fairly poor convergence unless we run very long chains." ] }, { "cell_type": "code", "execution_count": 83, "id": "b9e0d11a-4229-4140-af95-cc052c38afbe", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mProgressMeter by default refresh meters with additional information in IJulia via `IJulia.clear_output`, which clears all outputs in the cell. \n", "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m - To prevent this behaviour, do `ProgressMeter.ijulia_behavior(:append)`. \n", "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m - To disable this warning message, do `ProgressMeter.ijulia_behavior(:clear)`.\n", "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ ProgressMeter ~/.julia/packages/ProgressMeter/kVZZH/src/ProgressMeter.jl:594\u001b[39m\n", "\u001b[32memcee, niter=2000000, nwalkers=1000: 100%|█████████████████████████| Time: 0:00:33\u001b[39m\n", "\u001b[34m accept_ratio_mean: 0.00872\u001b[39m\n", "\u001b[34m accept_ratio_std: 0.00423\u001b[39m\n", "\u001b[34m accept_ratio_outliers: 44\u001b[39m\n", "\u001b[34m burnin_phase: false\u001b[39m\n" ] } ], "source": [ "import StarFormationHistories: mcmc_sample\n", "import Distributions: MvNormal\n", "import LinearAlgebra: I\n", "\n", "nwalkers = 1000\n", "nsteps = 2000\n", "burn_idx = 200\n", "# Walker initial positions sampled from a multivariate normal distribution with means equal to the BFGS MLE\n", "# and identity covariance matrix. The `max` call makes sure none of the cofficients have initial conditions less than 0. \n", "mcmc_x0 = [max.(0.0, rand(MvNormal(bfgs_result.mle.μ, I))) for i in 1:nwalkers]\n", "mcmc_result = mcmc_sample(free_templates, data, mcmc_x0, nsteps; nthin=5); # nthin=5 to only save every 5 steps" ] }, { "cell_type": "markdown", "id": "02dbc5e5-4e24-4ca1-9449-29f167f39891", "metadata": {}, "source": [ "Note the very low transition acceptance ratio of <1%. If we plot the trace of the chains we can see that they have not reached their stationary distribution even after 2000 steps. MCMC can work well when considering fewer templates, or when fewer of the fitting coefficients are zero. However, this application is better suited to Hamiltonian Monte Carlo. " ] }, { "cell_type": "code", "execution_count": 84, "id": "9b879b91-8afd-4635-b318-72d9622ae690", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import StatsBase: fit, Histogram, normalize\n", "\n", "fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2,sharex=false,sharey=false,figsize=(10,6))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.2)\n", "\n", "ax0.axvline(burn_idx)\n", "for i in 1:size(mcmc_result,3)\n", " ax0.plot(first(axes(mcmc_result)), mcmc_result[:,end,i], alpha=0.1, rasterized=true) \n", "end\n", "\n", "ax1_hist = normalize(fit(Histogram, vec(mcmc_result[burn_idx:end,end,:]); nbins=100, closed=:left); mode=:probability) #:pdf, :density, :probability, :none\n", "ax1.bar(first(ax1_hist.edges)[2:end], ax1_hist.weights, width=step(first(ax1_hist.edges)), ec=\"k\"); # fc=\"k\"" ] }, { "cell_type": "markdown", "id": "a431f369-523a-40dd-af11-b09748cd581c", "metadata": {}, "source": [ "### Sampling via Hamiltonian Monte Carlo\n", "\n", "Let us now compare to `hmc_sample`, which utilizes the No-U-Turn sampler variant of Hamiltonian Monte Carlo implemented in `DynamicHMC.jl` to sample from the posterior. Note that the method implementing HMC sampling for hierarchical models like the `LinearAMR` age-metallicity relation used above is `sample_sfh`. This is a more rigorous way to obtain posterior samples than using the approximation of `fit_sfh` and can be more efficient than `sample_mcmc` for high-dimensional problems or when many of the coefficients are 0. However, we will show that the approximation of `fit_sfh` still compares favorably with this more accurate method." ] }, { "cell_type": "code", "execution_count": 85, "id": "782f432f-a2be-4af4-b7d6-090494c3cd98", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mWarmup: 100%|███████████████████████████████████████████| Time: 0:00:14\u001b[39m\n", "\u001b[32mWarmup: 100%|███████████████████████████████████████████| Time: 0:00:07\u001b[39m\n", "\u001b[32mWarmup: 100%|███████████████████████████████████████████| Time: 0:00:11\u001b[39m\n", "\u001b[32mWarmup: 100%|███████████████████████████████████████████| Time: 0:00:21\u001b[39m\n", "\u001b[32mWarmup: 100%|███████████████████████████████████████████| Time: 0:00:03\u001b[39m\n", "\u001b[32mMCMC: 100%|█████████████████████████████████████████████| Time: 0:00:47\u001b[39m\n" ] } ], "source": [ "import DynamicHMC\n", "import StarFormationHistories: hmc_sample\n", "mc_result = hmc_sample(free_templates, data, 1000; reporter=DynamicHMC.ProgressMeterReport());" ] }, { "cell_type": "markdown", "id": "eae06b46-3b74-4ce1-b8ba-aa453b1ec658", "metadata": {}, "source": [ "We can check the summary statistics to ensure the samples have a high acceptance rate and are converging properly (turning % should be ~100%)." ] }, { "cell_type": "code", "execution_count": 86, "id": "5667c03b-f0d0-4671-b991-b2cf60470b65", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Hamiltonian Monte Carlo sample of length 1000\n", " acceptance rate mean: 0.93, 5/25/50/75/95%: 0.75 0.92 0.97 0.99 1.0\n", " termination: divergence => 0%, max_depth => 0%, turning => 100%\n", " depth: 0 => 0%, 1 => 0%, 2 => 0%, 3 => 0%, 4 => 0%, 5 => 0%, 6 => 0%, 7 => 100%, 8 => 0%" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DynamicHMC.Diagnostics.summarize_tree_statistics(mc_result.tree_statistics)" ] }, { "cell_type": "markdown", "id": "448b93ae-c2a4-46f7-b221-e3e3f70c75ae", "metadata": {}, "source": [ "The parameters optimized by `fit_sfh` and `hmc_sample` are actually the logarithms of the coefficients, not the coefficients themselves, so we need to transform the samples here." ] }, { "cell_type": "code", "execution_count": 87, "id": "3b4a6890-33f5-4bfa-9603-8e3147bf4f19", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "142×1000 Matrix{Float64}:\n", " 906.517 1980.62 … 83.9435 1076.08\n", " 2202.19 263.16 2880.92 1826.49\n", " 1762.63 221.093 109.953 742.454\n", " 807.531 1737.32 2656.53 122.522\n", " 340.425 2394.54 6.87011 6.3403\n", " 3284.71 13.1083 … 1237.29 232.002\n", " 692.831 282.256 4.61349 1584.32\n", " 326.478 597.984 478.352 328.388\n", " 570.553 757.488 146.42 1043.92\n", " 849.908 60.8856 361.791 802.277\n", " 435.262 571.496 … 104.454 40.4165\n", " 745.516 216.969 68.2502 245.385\n", " 410.943 668.407 77.8323 1749.28\n", " ⋮ ⋱ \n", " 3.56652e5 360939.0 … 4.60975e5 5.75536e5\n", " 54155.6 2.3726e5 12536.7 5429.25\n", " 2.71025e5 46762.5 42911.6 2.79554e5\n", " 2.61656e5 3.61526e5 3.19463e5 1.75084e5\n", " 1.18254e6 1.28192e6 1.264e6 960510.0\n", " 44936.6 49717.5 … 156194.0 2.09432e5\n", " 7.30445e5 6.88155e5 655807.0 5.72012e5\n", " 3.12121e5 2.11318e5 2.42835e5 4.55439e5\n", " 3.44079e5 414398.0 3.6551e5 3.71711e5\n", " 4.54063e5 509523.0 5.56993e5 4.58974e5\n", " 5.23825e5 4.66227e5 … 4.56813e5 4.38726e5\n", " 2.78995e5 3.26058e5 2.93146e5 3.8095e5" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc_matrix = exp.(mc_result.posterior_matrix) .* template_norm" ] }, { "cell_type": "markdown", "id": "f1337585-e9e8-41fe-9c21-13de2dc62570", "metadata": {}, "source": [ "Calculate statistics from the MC samples." ] }, { "cell_type": "code", "execution_count": 88, "id": "ed98dc8a-b458-4f2e-9934-a8ba9b9204db", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "71-element Vector{Float64}:\n", " 0.00922978899941167\n", " 0.004978484769494773\n", " 0.0033437965297403735\n", " 0.0024190695981763225\n", " 0.00206856768732298\n", " 0.002168597926422008\n", " 0.0016077193922358784\n", " 0.0016490326729730357\n", " 0.0017536782563899087\n", " 0.0013729281884987813\n", " 0.0011435111915239985\n", " 0.0011630936630793879\n", " 0.001183042114353714\n", " ⋮\n", " 0.0011064295254224\n", " 0.0012316348039259147\n", " 0.0003686859327781162\n", " 0.0006490005604719685\n", " 0.0013668718329182946\n", " 0.0009411814630244037\n", " 0.0009207369240032512\n", " 0.0005004194403417012\n", " 0.0013375600793267242\n", " 0.0008388282737178627\n", " 0.0006879989025269941\n", " 0.0007555415152565343" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the cumulative SFH for each sample in the chain\n", "# and find the 1-σ range\n", "mc_cum_sfr = Vector{Vector{Float64}}(undef,0)\n", "mc_sfr = Vector{Vector{Float64}}(undef,0)\n", "for x in eachcol(mc_matrix)\n", " _, mc_1, mc_2, mc_mh = calculate_cum_sfr(x, free_template_logAge, free_template_MH, T_max)\n", " push!(mc_cum_sfr, mc_1)\n", " push!(mc_sfr, mc_2)\n", "end\n", "mc_cum_sfr = reduce(hcat, mc_cum_sfr) # hcat( mc_cum_sfr... )\n", "mc_sfr = reduce(hcat, mc_sfr) # hcat( mc_sfr... )\n", "# Calculate quantiles\n", "mc_cum_lower = quantile.(eachrow(mc_cum_sfr), 0.16)\n", "mc_cum_med = median.(eachrow(mc_cum_sfr))\n", "mc_cum_upper = quantile.(eachrow(mc_cum_sfr), 0.84)\n", "mc_sfr_lower = quantile.(eachrow(mc_sfr), 0.16)\n", "mc_sfr_med = median.(eachrow(mc_sfr))\n", "mc_sfr_upper = quantile.(eachrow(mc_sfr), 0.84)" ] }, { "cell_type": "markdown", "id": "fd2c2fdf-41af-4250-bfde-b9609360cce7", "metadata": {}, "source": [ "And we'll plot the cumulative SFHs:" ] }, { "cell_type": "code", "execution_count": 89, "id": "c0d88496-3ef2-49f8-be69-4b19734c56d8", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs=plt.subplots(nrows=1,ncols=2,sharex=true,sharey=true,figsize=(10,6))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.1)\n", "\n", "axs[1].plot( exp10.(unique_free_template_logAge)./1e9, mc_cum_med, label=\"HMC Median\" )\n", "axs[1].plot( exp10.(unique_template_logAge)./1e9, cum_sfr_arr, label=\"Input SFH\", ls=\"--\" )\n", "axs[1].fill_between( exp10.(unique_free_template_logAge)./1e9, mc_cum_lower, mc_cum_upper, alpha=0.3, fc=\"k\") \n", "\n", "axs[1].set_xlim([13.0,-0.1])\n", "axs[1].set_ylim([0.0,1.1])\n", "axs[1].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[1].set_ylabel(\"Cumulative SF\")\n", "axs[1].legend()\n", "\n", "axs[2].plot( exp10.(unique_free_template_logAge)./1e9, hessian_cum_med, label=\"BFGS Result\" )\n", "axs[2].plot( exp10.(unique_template_logAge)./1e9, cum_sfr_arr, label=\"Input SFH\", ls=\"--\" )\n", "axs[2].fill_between( exp10.(unique_free_template_logAge)./1e9, hessian_cum_lower, hessian_cum_upper, alpha=0.3, fc=\"k\") \n", "axs[2].set_xlabel(\"Lookback Time [Gyr]\")\n", "axs[2].legend()" ] }, { "cell_type": "markdown", "id": "cff29c75-c2d5-441f-8a03-7388b5833d15", "metadata": {}, "source": [ "The differences are more obvious in the SFRs," ] }, { "cell_type": "code", "execution_count": 90, "id": "7cda8628-fe7a-4681-b099-ef65be8133df", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject Text(0.5, 29.371800193718016, 'log(Age [yr])')" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig,axs = plt.subplots(nrows=1,ncols=2,sharex=true,sharey=true,figsize=(14,7))\n", "fig.subplots_adjust(hspace=0.0,wspace=0.1)\n", "\n", "axs[1].bar(unique_free_template_logAge, mc_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(mc_sfr_med .- mc_sfr_lower) .* 1e3, \n", " (mc_sfr_upper .- mc_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"HMC Result\")\n", "axs[1].bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "\n", "axs[1].set_xlabel(\"log(Age [yr])\")\n", "axs[1].set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "axs[1].legend()\n", "\n", "axs[2].bar(unique_free_template_logAge, hessian_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(hessian_sfr_med .- hessian_sfr_lower) .* 1e3, \n", " (hessian_sfr_upper .- hessian_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"BFGS Fit\")\n", "axs[2].bar(unique_template_logAge, sfr_arr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"Input SFRs\", alpha=0.5)\n", "axs[2].legend()\n", "axs[2].set_xlabel(\"log(Age [yr])\")" ] }, { "cell_type": "code", "execution_count": 91, "id": "9e72c971-56d6-4070-9f74-215b094eb944", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# _, _, tmp_sfr, _ = calculate_cum_sfr(bfgs_result.map.μ .* template_norm, free_template_logAge, free_template_MH)\n", "\n", "fig,ax1 = plt.subplots(figsize=(7,7))\n", "\n", "ax1.bar(unique_free_template_logAge, mc_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"HMC Result\")\n", "# ax1.bar(unique_free_template_logAge, hessian_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"BFGS Fit\", alpha=0.5) \n", "ax1.bar(unique_free_template_logAge, hessian_sfr_med .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", \n", " yerr = [(hessian_sfr_med .- hessian_sfr_lower) .* 1e3, \n", " (hessian_sfr_upper .- hessian_sfr_med) .* 1e3], \n", " capsize=3, error_kw=Dict(\"elinewidth\"=>1,\"capthick\"=>1), label=\"BFGS Fit\", alpha=0.5)\n", "# ax1.bar(unique_free_template_logAge, tmp_sfr .* 1e3; width=diff(vcat(unique_template_logAge,max_logAge)), align=\"edge\", label=\"BFGS Fit\", alpha=0.5) \n", "\n", "ax1.set_xlabel(\"log(Age [yr])\")\n", "ax1.set_ylabel(L\"SFR [$10^{-3}$ M$_\\odot$ / yr]\")\n", "ax1.set_ylim([0.0, ax1.get_ylim()[2]])\n", "ax1.legend()" ] }, { "cell_type": "markdown", "id": "94a19e50-bf63-4348-bb94-4b4374eea672", "metadata": {}, "source": [ "Thus the approximations we made above when deriving the uncertainties from the BFGS results that \n", "1. the posterior is well-approximated by a multivariate Gaussian in the fitting variables `θ = log(coeffs)` in the vicinity of the maximum a posteriori and \n", "2. the inverse Hessian approximation produced by the BFGS optimization is a good estimate of the variance-covariance matrix of said Gaussian\n", "\n", "are not fully correct, but they are good enough in most cases that they can produce very similar posterior samples to the full HMC analysis at a fraction of the computational cost (a few seconds compared to minutes or hours)." ] }, { "cell_type": "code", "execution_count": 92, "id": "094e7dc8-3e97-47ec-9eb4-856801ab3c6c", "metadata": {}, "outputs": [], "source": [ "GC.gc() # Explicit garbage collection when finished" ] }, { "cell_type": "code", "execution_count": null, "id": "74e4af33-2efd-4116-aa2a-0b101b59f846", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia (8 threads) 1.10.8", "language": "julia", "name": "julia-_8-threads_-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.10.8" }, "toc-autonumbering": false, "toc-showmarkdowntxt": false }, "nbformat": 4, "nbformat_minor": 5 }