02. Linear Algebra.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Algebra\n",
    "A lot of the Data Science methods we will see in this tutorial require some understanding of linear algebra, and in this notebook we will focus on how Julia handles matrices, the types that exist, and how to call basic linear algebra tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling MAT [23992714-dd62-5051-b70f-ba57cb901cac]\n",
      "└ @ Base loading.jl:1317\n"
     ]
    }
   ],
   "source": [
    "# some packages we will use\n",
    "using LinearAlgebra\n",
    "using SparseArrays\n",
    "using Images\n",
    "using MAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](data/matrix_storage.png)\n",
    "### 🟢Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will get started with creating a random matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = rand(10,10); # created a random matrix of size 10-by-10\n",
    "Atranspose = A' # matrix transpose\n",
    "A = A*Atranspose; # matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A[11] == A[1, 2] = true\n"
     ]
    }
   ],
   "source": [
    "@show A[11] == A[1,2];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "norm(A * x - b) = 3.689263419778939e-12\n"
     ]
    }
   ],
   "source": [
    "b = rand(10); #created a random vector of size 10\n",
    "x = A\\b; #x is the solutions to the linear system Ax=b\n",
    "@show norm(A*x-b)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few things that are noteworthy: \n",
    "- `A` is a `Matrix` type, and `b` is a `Vector` type.\n",
    "- The transpose function creates a matrix of type `Adjoint`.\n",
    "- `\\` is always the recommended way to solve a linear system. You almost never want to call the `inv` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typeof(A) = Matrix{Float64}\n",
      "typeof(b) = Vector{Float64}\n",
      "typeof(rand(1, 10)) = Matrix{Float64}\n",
      "typeof(Atranspose) = Adjoint{Float64, Matrix{Float64}}\n"
     ]
    }
   ],
   "source": [
    "@show typeof(A)\n",
    "@show typeof(b)\n",
    "@show typeof(rand(1,10))\n",
    "@show typeof(Atranspose)\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix{Float64} == Array{Float64,2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vector{Float64} == Array{Float64,1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 adjoint(::Matrix{Float64}) with eltype Float64:\n",
       " 0.249899  0.332817   0.376245   …  0.196724   0.533504   0.545816\n",
       " 0.51139   0.291047   0.0906729     0.0955851  0.145757   0.328184\n",
       " 0.709529  0.27097    0.0946509     0.661237   0.785675   0.723368\n",
       " 0.93995   0.491115   0.303725      0.705366   0.52196    0.999402\n",
       " 0.10152   0.819124   0.997739      0.616617   0.985117   0.588296\n",
       " 0.556655  0.0694413  0.229682   …  0.402893   0.0629227  0.145643\n",
       " 0.701979  0.743977   0.275638      0.258079   0.389031   0.737054\n",
       " 0.903376  0.746583   0.096613      0.411208   0.0589579  0.463674\n",
       " 0.409278  0.890039   0.789442      0.361242   0.456934   0.839767\n",
       " 0.381081  0.110646   0.654256      0.0364732  0.277699   0.366428"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Atranspose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`adjoint` in julia is a lazy adjoint -- often, we can easily perform Linear Algebra operations such as `A*A'` without actually transposing the matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mj\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mt\u001b[22m \u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mj\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mt\u001b[22m! \u001b[0m\u001b[1mA\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mj\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "A'\n",
       "adjoint(A)\n",
       "\\end{verbatim}\n",
       "Lazy adjoint (conjugate transposition). Note that \\texttt{adjoint} is applied recursively to elements.\n",
       "\n",
       "For number types, \\texttt{adjoint} returns the complex conjugate, and therefore it is equivalent to the identity function for real numbers.\n",
       "\n",
       "This operation is intended for linear algebra usage - for general data manipulation see \\href{@ref Base.permutedims}{\\texttt{permutedims}}.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> A = [3+2im 9+2im; 8+7im  4+6im]\n",
       "2×2 Matrix{Complex{Int64}}:\n",
       " 3+2im  9+2im\n",
       " 8+7im  4+6im\n",
       "\n",
       "julia> adjoint(A)\n",
       "2×2 adjoint(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:\n",
       " 3-2im  8-7im\n",
       " 9-2im  4-6im\n",
       "\n",
       "julia> x = [3, 4im]\n",
       "2-element Vector{Complex{Int64}}:\n",
       " 3 + 0im\n",
       " 0 + 4im\n",
       "\n",
       "julia> x'x\n",
       "25 + 0im\n",
       "\\end{verbatim}\n"
      ],
      "text/markdown": [
       "```\n",
       "A'\n",
       "adjoint(A)\n",
       "```\n",
       "\n",
       "Lazy adjoint (conjugate transposition). Note that `adjoint` is applied recursively to elements.\n",
       "\n",
       "For number types, `adjoint` returns the complex conjugate, and therefore it is equivalent to the identity function for real numbers.\n",
       "\n",
       "This operation is intended for linear algebra usage - for general data manipulation see [`permutedims`](@ref Base.permutedims).\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> A = [3+2im 9+2im; 8+7im  4+6im]\n",
       "2×2 Matrix{Complex{Int64}}:\n",
       " 3+2im  9+2im\n",
       " 8+7im  4+6im\n",
       "\n",
       "julia> adjoint(A)\n",
       "2×2 adjoint(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:\n",
       " 3-2im  8-7im\n",
       " 9-2im  4-6im\n",
       "\n",
       "julia> x = [3, 4im]\n",
       "2-element Vector{Complex{Int64}}:\n",
       " 3 + 0im\n",
       " 0 + 4im\n",
       "\n",
       "julia> x'x\n",
       "25 + 0im\n",
       "```\n"
      ],
      "text/plain": [
       "\u001b[36m  A'\u001b[39m\n",
       "\u001b[36m  adjoint(A)\u001b[39m\n",
       "\n",
       "  Lazy adjoint (conjugate transposition). Note that \u001b[36madjoint\u001b[39m is applied\n",
       "  recursively to elements.\n",
       "\n",
       "  For number types, \u001b[36madjoint\u001b[39m returns the complex conjugate, and therefore it is\n",
       "  equivalent to the identity function for real numbers.\n",
       "\n",
       "  This operation is intended for linear algebra usage - for general data\n",
       "  manipulation see \u001b[36mpermutedims\u001b[39m.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> A = [3+2im 9+2im; 8+7im  4+6im]\u001b[39m\n",
       "\u001b[36m  2×2 Matrix{Complex{Int64}}:\u001b[39m\n",
       "\u001b[36m   3+2im  9+2im\u001b[39m\n",
       "\u001b[36m   8+7im  4+6im\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> adjoint(A)\u001b[39m\n",
       "\u001b[36m  2×2 adjoint(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:\u001b[39m\n",
       "\u001b[36m   3-2im  8-7im\u001b[39m\n",
       "\u001b[36m   9-2im  4-6im\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> x = [3, 4im]\u001b[39m\n",
       "\u001b[36m  2-element Vector{Complex{Int64}}:\u001b[39m\n",
       "\u001b[36m   3 + 0im\u001b[39m\n",
       "\u001b[36m   0 + 4im\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> x'x\u001b[39m\n",
       "\u001b[36m  25 + 0im\u001b[39m"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?adjoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 Matrix{Float64}:\n",
       " 0.249899   0.51139     0.709529   …  0.903376   0.409278  0.381081\n",
       " 0.332817   0.291047    0.27097       0.746583   0.890039  0.110646\n",
       " 0.376245   0.0906729   0.0946509     0.096613   0.789442  0.654256\n",
       " 0.866039   0.00363302  0.0391991     0.283632   0.524616  0.182196\n",
       " 0.54766    0.403914    0.0984212     0.805436   0.219447  0.384202\n",
       " 0.798657   0.602425    0.630361   …  0.0758388  0.791843  0.0837277\n",
       " 0.0147225  0.0898888   0.39432       0.8799     0.369629  0.30327\n",
       " 0.196724   0.0955851   0.661237      0.411208   0.361242  0.0364732\n",
       " 0.533504   0.145757    0.785675      0.0589579  0.456934  0.277699\n",
       " 0.545816   0.328184    0.723368      0.463674   0.839767  0.366428"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Atranspose.parent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "800"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizeof(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's because it's an array of Float64's, each is of size 8 bytes, and there are 10*10 numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 Matrix{Float64}:\n",
       " 0.249899  0.332817   0.376245   …  0.196724   0.533504   0.545816\n",
       " 0.51139   0.291047   0.0906729     0.0955851  0.145757   0.328184\n",
       " 0.709529  0.27097    0.0946509     0.661237   0.785675   0.723368\n",
       " 0.93995   0.491115   0.303725      0.705366   0.52196    0.999402\n",
       " 0.10152   0.819124   0.997739      0.616617   0.985117   0.588296\n",
       " 0.556655  0.0694413  0.229682   …  0.402893   0.0629227  0.145643\n",
       " 0.701979  0.743977   0.275638      0.258079   0.389031   0.737054\n",
       " 0.903376  0.746583   0.096613      0.411208   0.0589579  0.463674\n",
       " 0.409278  0.890039   0.789442      0.361242   0.456934   0.839767\n",
       " 0.381081  0.110646   0.654256      0.0364732  0.277699   0.366428"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To actually copy the matrix:\n",
    "B = copy(Atranspose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "800"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizeof(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `\\` operator allows you to solve a system of linear equations, and often uses a suitable matrix factorization to solve the problem. We will cover factorizations next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[0m\u001b[1m\\\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "\\(x, y)\n",
       "\\end{verbatim}\n",
       "Left division operator: multiplication of \\texttt{y} by the inverse of \\texttt{x} on the left. Gives floating-point results for integer arguments.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> 3 \\ 6\n",
       "2.0\n",
       "\n",
       "julia> inv(3) * 6\n",
       "2.0\n",
       "\n",
       "julia> A = [4 3; 2 1]; x = [5, 6];\n",
       "\n",
       "julia> A \\ x\n",
       "2-element Vector{Float64}:\n",
       "  6.5\n",
       " -7.0\n",
       "\n",
       "julia> inv(A) * x\n",
       "2-element Vector{Float64}:\n",
       "  6.5\n",
       " -7.0\n",
       "\\end{verbatim}\n",
       "\\rule{\\textwidth}{1pt}\n",
       "\\begin{verbatim}\n",
       "\\(A, B)\n",
       "\\end{verbatim}\n",
       "Matrix division using a polyalgorithm. For input matrices \\texttt{A} and \\texttt{B}, the result \\texttt{X} is such that \\texttt{A*X == B} when \\texttt{A} is square. The solver that is used depends upon the structure of \\texttt{A}.  If \\texttt{A} is upper or lower triangular (or diagonal), no factorization of \\texttt{A} is required and the system is solved with either forward or backward substitution. For non-triangular square matrices, an LU factorization is used.\n",
       "\n",
       "For rectangular \\texttt{A} the result is the minimum-norm least squares solution computed by a pivoted QR factorization of \\texttt{A} and a rank estimate of \\texttt{A} based on the R factor.\n",
       "\n",
       "When \\texttt{A} is sparse, a similar polyalgorithm is used. For indefinite matrices, the \\texttt{LDLt} factorization does not use pivoting during the numerical factorization and therefore the procedure can fail even for invertible matrices.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> A = [1 0; 1 -2]; B = [32; -4];\n",
       "\n",
       "julia> X = A \\ B\n",
       "2-element Vector{Float64}:\n",
       " 32.0\n",
       " 18.0\n",
       "\n",
       "julia> A * X == B\n",
       "true\n",
       "\\end{verbatim}\n",
       "\\rule{\\textwidth}{1pt}\n",
       "\\begin{verbatim}\n",
       "(\\)(F::QRSparse, B::StridedVecOrMat)\n",
       "\\end{verbatim}\n",
       "Solve the least squares problem $\\min\\|Ax - b\\|^2$ or the linear system of equations $Ax=b$ when \\texttt{F} is the sparse QR factorization of $A$. A basic solution is returned when the problem is underdetermined.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> A = sparse([1,2,4], [1,1,1], [1.0,1.0,1.0], 4, 2)\n",
       "4×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:\n",
       " 1.0   ⋅\n",
       " 1.0   ⋅\n",
       "  ⋅    ⋅\n",
       " 1.0   ⋅\n",
       "\n",
       "julia> qr(A)\\fill(1.0, 4)\n",
       "2-element Vector{Float64}:\n",
       " 1.0\n",
       " 0.0\n",
       "\\end{verbatim}\n"
      ],
      "text/markdown": [
       "```\n",
       "\\(x, y)\n",
       "```\n",
       "\n",
       "Left division operator: multiplication of `y` by the inverse of `x` on the left. Gives floating-point results for integer arguments.\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> 3 \\ 6\n",
       "2.0\n",
       "\n",
       "julia> inv(3) * 6\n",
       "2.0\n",
       "\n",
       "julia> A = [4 3; 2 1]; x = [5, 6];\n",
       "\n",
       "julia> A \\ x\n",
       "2-element Vector{Float64}:\n",
       "  6.5\n",
       " -7.0\n",
       "\n",
       "julia> inv(A) * x\n",
       "2-element Vector{Float64}:\n",
       "  6.5\n",
       " -7.0\n",
       "```\n",
       "\n",
       "---\n",
       "\n",
       "```\n",
       "\\(A, B)\n",
       "```\n",
       "\n",
       "Matrix division using a polyalgorithm. For input matrices `A` and `B`, the result `X` is such that `A*X == B` when `A` is square. The solver that is used depends upon the structure of `A`.  If `A` is upper or lower triangular (or diagonal), no factorization of `A` is required and the system is solved with either forward or backward substitution. For non-triangular square matrices, an LU factorization is used.\n",
       "\n",
       "For rectangular `A` the result is the minimum-norm least squares solution computed by a pivoted QR factorization of `A` and a rank estimate of `A` based on the R factor.\n",
       "\n",
       "When `A` is sparse, a similar polyalgorithm is used. For indefinite matrices, the `LDLt` factorization does not use pivoting during the numerical factorization and therefore the procedure can fail even for invertible matrices.\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> A = [1 0; 1 -2]; B = [32; -4];\n",
       "\n",
       "julia> X = A \\ B\n",
       "2-element Vector{Float64}:\n",
       " 32.0\n",
       " 18.0\n",
       "\n",
       "julia> A * X == B\n",
       "true\n",
       "```\n",
       "\n",
       "---\n",
       "\n",
       "```\n",
       "(\\)(F::QRSparse, B::StridedVecOrMat)\n",
       "```\n",
       "\n",
       "Solve the least squares problem $\\min\\|Ax - b\\|^2$ or the linear system of equations $Ax=b$ when `F` is the sparse QR factorization of $A$. A basic solution is returned when the problem is underdetermined.\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> A = sparse([1,2,4], [1,1,1], [1.0,1.0,1.0], 4, 2)\n",
       "4×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:\n",
       " 1.0   ⋅\n",
       " 1.0   ⋅\n",
       "  ⋅    ⋅\n",
       " 1.0   ⋅\n",
       "\n",
       "julia> qr(A)\\fill(1.0, 4)\n",
       "2-element Vector{Float64}:\n",
       " 1.0\n",
       " 0.0\n",
       "```\n"
      ],
      "text/plain": [
       "\u001b[36m  \\(x, y)\u001b[39m\n",
       "\n",
       "  Left division operator: multiplication of \u001b[36my\u001b[39m by the inverse of \u001b[36mx\u001b[39m on the left.\n",
       "  Gives floating-point results for integer arguments.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> 3 \\ 6\u001b[39m\n",
       "\u001b[36m  2.0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> inv(3) * 6\u001b[39m\n",
       "\u001b[36m  2.0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> A = [4 3; 2 1]; x = [5, 6];\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> A \\ x\u001b[39m\n",
       "\u001b[36m  2-element Vector{Float64}:\u001b[39m\n",
       "\u001b[36m    6.5\u001b[39m\n",
       "\u001b[36m   -7.0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> inv(A) * x\u001b[39m\n",
       "\u001b[36m  2-element Vector{Float64}:\u001b[39m\n",
       "\u001b[36m    6.5\u001b[39m\n",
       "\u001b[36m   -7.0\u001b[39m\n",
       "\n",
       "  ────────────────────────────────────────────────────────────────────────────\n",
       "\n",
       "\u001b[36m  \\(A, B)\u001b[39m\n",
       "\n",
       "  Matrix division using a polyalgorithm. For input matrices \u001b[36mA\u001b[39m and \u001b[36mB\u001b[39m, the\n",
       "  result \u001b[36mX\u001b[39m is such that \u001b[36mA*X == B\u001b[39m when \u001b[36mA\u001b[39m is square. The solver that is used\n",
       "  depends upon the structure of \u001b[36mA\u001b[39m. If \u001b[36mA\u001b[39m is upper or lower triangular (or\n",
       "  diagonal), no factorization of \u001b[36mA\u001b[39m is required and the system is solved with\n",
       "  either forward or backward substitution. For non-triangular square matrices,\n",
       "  an LU factorization is used.\n",
       "\n",
       "  For rectangular \u001b[36mA\u001b[39m the result is the minimum-norm least squares solution\n",
       "  computed by a pivoted QR factorization of \u001b[36mA\u001b[39m and a rank estimate of \u001b[36mA\u001b[39m based\n",
       "  on the R factor.\n",
       "\n",
       "  When \u001b[36mA\u001b[39m is sparse, a similar polyalgorithm is used. For indefinite matrices,\n",
       "  the \u001b[36mLDLt\u001b[39m factorization does not use pivoting during the numerical\n",
       "  factorization and therefore the procedure can fail even for invertible\n",
       "  matrices.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> A = [1 0; 1 -2]; B = [32; -4];\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> X = A \\ B\u001b[39m\n",
       "\u001b[36m  2-element Vector{Float64}:\u001b[39m\n",
       "\u001b[36m   32.0\u001b[39m\n",
       "\u001b[36m   18.0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> A * X == B\u001b[39m\n",
       "\u001b[36m  true\u001b[39m\n",
       "\n",
       "  ────────────────────────────────────────────────────────────────────────────\n",
       "\n",
       "\u001b[36m  (\\)(F::QRSparse, B::StridedVecOrMat)\u001b[39m\n",
       "\n",
       "  Solve the least squares problem \u001b[35m\\min\\|Ax - b\\|^2\u001b[39m or the linear system of\n",
       "  equations \u001b[35mAx=b\u001b[39m when \u001b[36mF\u001b[39m is the sparse QR factorization of \u001b[35mA\u001b[39m. A basic solution\n",
       "  is returned when the problem is underdetermined.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> A = sparse([1,2,4], [1,1,1], [1.0,1.0,1.0], 4, 2)\u001b[39m\n",
       "\u001b[36m  4×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:\u001b[39m\n",
       "\u001b[36m   1.0   ⋅\u001b[39m\n",
       "\u001b[36m   1.0   ⋅\u001b[39m\n",
       "\u001b[36m    ⋅    ⋅\u001b[39m\n",
       "\u001b[36m   1.0   ⋅\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> qr(A)\\fill(1.0, 4)\u001b[39m\n",
       "\u001b[36m  2-element Vector{Float64}:\u001b[39m\n",
       "\u001b[36m   1.0\u001b[39m\n",
       "\u001b[36m   0.0\u001b[39m"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?\\"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🟢Factorizations\n",
    "A common tool used in Linear Algebra is matrix factorizations. These factorizations are often used to solve linear systems like `Ax=b`, and as we will see later in this tutorial... `Ax=b` comes up in a lot of Data Science problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LU factorization\n",
    "L\\*U = P\\*A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LU{Float64, Matrix{Float64}}\n",
       "L factor:\n",
       "10×10 Matrix{Float64}:\n",
       " 1.0       0.0        0.0        0.0        …   0.0        0.0       0.0\n",
       " 0.714777  1.0        0.0        0.0            0.0        0.0       0.0\n",
       " 0.431294  0.879205   1.0        0.0            0.0        0.0       0.0\n",
       " 0.402595  0.494058   0.42929    1.0            0.0        0.0       0.0\n",
       " 0.55784   0.0226054  0.41306    0.372797       0.0        0.0       0.0\n",
       " 0.63672   0.756747   0.509309   0.628019   …   0.0        0.0       0.0\n",
       " 0.610917  0.308904   0.236169   0.0853205      0.0        0.0       0.0\n",
       " 0.550327  0.675756   0.622177   0.13992        1.0        0.0       0.0\n",
       " 0.908177  0.621365   0.340249   0.274056       0.267463   1.0       0.0\n",
       " 0.717008  0.291136   0.291335  -0.268112      -0.146209  -0.292422  1.0\n",
       "U factor:\n",
       "10×10 Matrix{Float64}:\n",
       " 3.65267  2.61085  1.57538   1.47055   …   2.23148     2.01016     3.31727\n",
       " 0.0      1.23501  1.08582   0.610166      0.381498    0.834564    0.76739\n",
       " 0.0      0.0      0.801694  0.344159      0.189336    0.498796    0.272776\n",
       " 0.0      0.0      0.0       0.572375      0.0488353   0.0800869   0.156863\n",
       " 0.0      0.0      0.0       0.0          -0.120383   -0.353458   -0.455433\n",
       " 0.0      0.0      0.0       0.0       …   0.185715    0.362473    0.131452\n",
       " 0.0      0.0      0.0       0.0           0.292697    0.176947    0.00272506\n",
       " 0.0      0.0      0.0       0.0           0.023562    0.185526    0.0335832\n",
       " 0.0      0.0      0.0       0.0           0.0        -0.0124478   0.010775\n",
       " 0.0      0.0      0.0       0.0           0.0         0.0         0.00230443"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "luA = lu(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.280369834735101e-16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(luA.L*luA.U - luA.P*A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### QR factorization\n",
    "Q\\*R = A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearAlgebra.QRCompactWY{Float64, Matrix{Float64}}\n",
       "Q factor:\n",
       "10×10 LinearAlgebra.QRCompactWYQ{Float64, Matrix{Float64}}:\n",
       " -0.466731   0.556721     0.116392   …  -0.0400424   0.508724   -0.134302\n",
       " -0.333609  -0.420934     0.685112       0.108506    0.0656284  -0.184771\n",
       " -0.201298  -0.47984     -0.535905      -0.287502    0.108374   -0.211555\n",
       " -0.187904  -0.180434    -0.134551       0.0339839   0.242066    0.0759608\n",
       " -0.260361   0.29205     -0.382156       0.216634   -0.283205   -0.204056\n",
       " -0.297177  -0.265198    -0.0384313  …  -0.414573    0.0930191   0.259962\n",
       " -0.33465    0.160772    -0.0662353     -0.154548    0.049564    0.628225\n",
       " -0.285134   0.0871597   -0.0063624     -0.258897   -0.158312   -0.598196\n",
       " -0.256855  -0.246975    -0.22312        0.767019    0.198405    0.0427175\n",
       " -0.423874  -0.00321333   0.100231       0.0691344  -0.71471     0.183707\n",
       "R factor:\n",
       "10×10 Matrix{Float64}:\n",
       " -7.82607  -7.39288  -5.76651   -4.70661   …  -6.45377     -8.68162\n",
       "  0.0      -1.50819  -1.85438   -1.14894      -1.7525      -1.48307\n",
       "  0.0       0.0      -0.718806  -0.473242     -0.457496    -0.145766\n",
       "  0.0       0.0       0.0       -0.631469     -0.157768    -0.266435\n",
       "  0.0       0.0       0.0        0.0           0.677052     0.616209\n",
       "  0.0       0.0       0.0        0.0       …  -0.361714    -0.107839\n",
       "  0.0       0.0       0.0        0.0          -0.217956    -0.00306423\n",
       "  0.0       0.0       0.0        0.0           0.148502     0.0280145\n",
       "  0.0       0.0       0.0        0.0           0.00907696  -0.00774295\n",
       "  0.0       0.0       0.0        0.0           0.0          0.0014477"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qrA = qr(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1605946732881715e-14"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(qrA.Q*qrA.R - A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cholesky factorization, note that A needs to be symmetric positive definite\n",
    "L\\*L' = A "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isposdef(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cholesky{Float64, Matrix{Float64}}\n",
       "U factor:\n",
       "10×10 UpperTriangular{Float64, Matrix{Float64}}:\n",
       " 1.9112  1.36608  0.824288  0.769438  …   1.16758     1.05178    1.7357\n",
       "  ⋅      1.11131  0.977068  0.549051      0.343287    0.750974   0.690528\n",
       "  ⋅       ⋅       0.895374  0.384375      0.21146     0.557081   0.30465\n",
       "  ⋅       ⋅        ⋅        0.756554      0.0645496   0.105857   0.207339\n",
       "  ⋅       ⋅        ⋅         ⋅           -0.124073   -0.364293  -0.469395\n",
       "  ⋅       ⋅        ⋅         ⋅        …   0.233683    0.456096   0.165405\n",
       "  ⋅       ⋅        ⋅         ⋅            0.537544    0.281694  -0.00656903\n",
       "  ⋅       ⋅        ⋅         ⋅            0.061188    0.41714    0.102246\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅          0.157784  -0.0366897\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅           ⋅         0.0887722"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cholA = cholesky(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4043333874306805e-15"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(cholA.L*cholA.U - A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 LowerTriangular{Float64, Matrix{Float64}}:\n",
       " 1.9112     ⋅          ⋅        …   ⋅          ⋅          ⋅ \n",
       " 1.36608   1.11131     ⋅            ⋅          ⋅          ⋅ \n",
       " 0.824288  0.977068   0.895374      ⋅          ⋅          ⋅ \n",
       " 0.769438  0.549051   0.384375      ⋅          ⋅          ⋅ \n",
       " 1.06614   0.0251216  0.369843      ⋅          ⋅          ⋅ \n",
       " 1.2169    0.840979   0.456022  …   ⋅          ⋅          ⋅ \n",
       " 1.37034   0.323543   0.260853      ⋅          ⋅          ⋅ \n",
       " 1.16758   0.343287   0.21146      0.061188    ⋅          ⋅ \n",
       " 1.05178   0.750974   0.557081     0.41714    0.157784    ⋅ \n",
       " 1.7357    0.690528   0.30465      0.102246  -0.0366897  0.0887722"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cholA.L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10×10 UpperTriangular{Float64, Matrix{Float64}}:\n",
       " 1.9112  1.36608  0.824288  0.769438  …   1.16758     1.05178    1.7357\n",
       "  ⋅      1.11131  0.977068  0.549051      0.343287    0.750974   0.690528\n",
       "  ⋅       ⋅       0.895374  0.384375      0.21146     0.557081   0.30465\n",
       "  ⋅       ⋅        ⋅        0.756554      0.0645496   0.105857   0.207339\n",
       "  ⋅       ⋅        ⋅         ⋅           -0.124073   -0.364293  -0.469395\n",
       "  ⋅       ⋅        ⋅         ⋅        …   0.233683    0.456096   0.165405\n",
       "  ⋅       ⋅        ⋅         ⋅            0.537544    0.281694  -0.00656903\n",
       "  ⋅       ⋅        ⋅         ⋅            0.061188    0.41714    0.102246\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅          0.157784  -0.0366897\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅           ⋅         0.0887722"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cholA.U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cholesky{Float64, Matrix{Float64}}\n",
       "U factor:\n",
       "10×10 UpperTriangular{Float64, Matrix{Float64}}:\n",
       " 1.9112  1.36608  0.824288  0.769438  …   1.16758     1.05178    1.7357\n",
       "  ⋅      1.11131  0.977068  0.549051      0.343287    0.750974   0.690528\n",
       "  ⋅       ⋅       0.895374  0.384375      0.21146     0.557081   0.30465\n",
       "  ⋅       ⋅        ⋅        0.756554      0.0645496   0.105857   0.207339\n",
       "  ⋅       ⋅        ⋅         ⋅           -0.124073   -0.364293  -0.469395\n",
       "  ⋅       ⋅        ⋅         ⋅        …   0.233683    0.456096   0.165405\n",
       "  ⋅       ⋅        ⋅         ⋅            0.537544    0.281694  -0.00656903\n",
       "  ⋅       ⋅        ⋅         ⋅            0.061188    0.41714    0.102246\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅          0.157784  -0.0366897\n",
       "  ⋅       ⋅        ⋅         ⋅             ⋅           ⋅         0.0887722"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factorize(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[0m\u001b[1mf\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mz\u001b[22m\u001b[0m\u001b[1me\u001b[22m \u001b[0m\u001b[1mF\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mz\u001b[22mation \u001b[0m\u001b[1mf\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22mal\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "factorize(A)\n",
       "\\end{verbatim}\n",
       "Compute a convenient factorization of \\texttt{A}, based upon the type of the input matrix. \\texttt{factorize} checks \\texttt{A} to see if it is symmetric/triangular/etc. if \\texttt{A} is passed as a generic matrix. \\texttt{factorize} checks every element of \\texttt{A} to verify/rule out each property. It will short-circuit as soon as it can rule out symmetry/triangular structure. The return value can be reused for efficient solving of multiple systems. For example: \\texttt{A=factorize(A); x=A{\\textbackslash}b; y=A{\\textbackslash}C}.\n",
       "\n",
       "\\begin{tabular}\n",
       "{l | l}\n",
       "Properties of \\texttt{A} & type of factorization \\\\\n",
       "\\hline\n",
       "Positive-definite & Cholesky (see \\href{@ref}{\\texttt{cholesky}}) \\\\\n",
       "Dense Symmetric/Hermitian & Bunch-Kaufman (see \\href{@ref}{\\texttt{bunchkaufman}}) \\\\\n",
       "Sparse Symmetric/Hermitian & LDLt (see \\href{@ref}{\\texttt{ldlt}}) \\\\\n",
       "Triangular & Triangular \\\\\n",
       "Diagonal & Diagonal \\\\\n",
       "Bidiagonal & Bidiagonal \\\\\n",
       "Tridiagonal & LU (see \\href{@ref}{\\texttt{lu}}) \\\\\n",
       "Symmetric real tridiagonal & LDLt (see \\href{@ref}{\\texttt{ldlt}}) \\\\\n",
       "General square & LU (see \\href{@ref}{\\texttt{lu}}) \\\\\n",
       "General non-square & QR (see \\href{@ref}{\\texttt{qr}}) \\\\\n",
       "\\end{tabular}\n",
       "If \\texttt{factorize} is called on a Hermitian positive-definite matrix, for instance, then \\texttt{factorize} will return a Cholesky factorization.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U))\n",
       "5×5 Matrix{Float64}:\n",
       " 1.0  1.0  0.0  0.0  0.0\n",
       " 0.0  1.0  1.0  0.0  0.0\n",
       " 0.0  0.0  1.0  1.0  0.0\n",
       " 0.0  0.0  0.0  1.0  1.0\n",
       " 0.0  0.0  0.0  0.0  1.0\n",
       "\n",
       "julia> factorize(A) # factorize will check to see that A is already factorized\n",
       "5×5 Bidiagonal{Float64, Vector{Float64}}:\n",
       " 1.0  1.0   ⋅    ⋅    ⋅\n",
       "  ⋅   1.0  1.0   ⋅    ⋅\n",
       "  ⋅    ⋅   1.0  1.0   ⋅\n",
       "  ⋅    ⋅    ⋅   1.0  1.0\n",
       "  ⋅    ⋅    ⋅    ⋅   1.0\n",
       "\\end{verbatim}\n",
       "This returns a \\texttt{5×5 Bidiagonal\\{Float64\\}}, which can now be passed to other linear algebra functions (e.g. eigensolvers) which will use specialized methods for \\texttt{Bidiagonal} types.\n",
       "\n"
      ],
      "text/markdown": [
       "```\n",
       "factorize(A)\n",
       "```\n",
       "\n",
       "Compute a convenient factorization of `A`, based upon the type of the input matrix. `factorize` checks `A` to see if it is symmetric/triangular/etc. if `A` is passed as a generic matrix. `factorize` checks every element of `A` to verify/rule out each property. It will short-circuit as soon as it can rule out symmetry/triangular structure. The return value can be reused for efficient solving of multiple systems. For example: `A=factorize(A); x=A\\b; y=A\\C`.\n",
       "\n",
       "| Properties of `A`          | type of factorization                      |\n",
       "|:-------------------------- |:------------------------------------------ |\n",
       "| Positive-definite          | Cholesky (see [`cholesky`](@ref))          |\n",
       "| Dense Symmetric/Hermitian  | Bunch-Kaufman (see [`bunchkaufman`](@ref)) |\n",
       "| Sparse Symmetric/Hermitian | LDLt (see [`ldlt`](@ref))                  |\n",
       "| Triangular                 | Triangular                                 |\n",
       "| Diagonal                   | Diagonal                                   |\n",
       "| Bidiagonal                 | Bidiagonal                                 |\n",
       "| Tridiagonal                | LU (see [`lu`](@ref))                      |\n",
       "| Symmetric real tridiagonal | LDLt (see [`ldlt`](@ref))                  |\n",
       "| General square             | LU (see [`lu`](@ref))                      |\n",
       "| General non-square         | QR (see [`qr`](@ref))                      |\n",
       "\n",
       "If `factorize` is called on a Hermitian positive-definite matrix, for instance, then `factorize` will return a Cholesky factorization.\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U))\n",
       "5×5 Matrix{Float64}:\n",
       " 1.0  1.0  0.0  0.0  0.0\n",
       " 0.0  1.0  1.0  0.0  0.0\n",
       " 0.0  0.0  1.0  1.0  0.0\n",
       " 0.0  0.0  0.0  1.0  1.0\n",
       " 0.0  0.0  0.0  0.0  1.0\n",
       "\n",
       "julia> factorize(A) # factorize will check to see that A is already factorized\n",
       "5×5 Bidiagonal{Float64, Vector{Float64}}:\n",
       " 1.0  1.0   ⋅    ⋅    ⋅\n",
       "  ⋅   1.0  1.0   ⋅    ⋅\n",
       "  ⋅    ⋅   1.0  1.0   ⋅\n",
       "  ⋅    ⋅    ⋅   1.0  1.0\n",
       "  ⋅    ⋅    ⋅    ⋅   1.0\n",
       "```\n",
       "\n",
       "This returns a `5×5 Bidiagonal{Float64}`, which can now be passed to other linear algebra functions (e.g. eigensolvers) which will use specialized methods for `Bidiagonal` types.\n"
      ],
      "text/plain": [
       "\u001b[36m  factorize(A)\u001b[39m\n",
       "\n",
       "  Compute a convenient factorization of \u001b[36mA\u001b[39m, based upon the type of the input\n",
       "  matrix. \u001b[36mfactorize\u001b[39m checks \u001b[36mA\u001b[39m to see if it is symmetric/triangular/etc. if \u001b[36mA\u001b[39m is\n",
       "  passed as a generic matrix. \u001b[36mfactorize\u001b[39m checks every element of \u001b[36mA\u001b[39m to\n",
       "  verify/rule out each property. It will short-circuit as soon as it can rule\n",
       "  out symmetry/triangular structure. The return value can be reused for\n",
       "  efficient solving of multiple systems. For example: \u001b[36mA=factorize(A); x=A\\b;\n",
       "  y=A\\C\u001b[39m.\n",
       "\n",
       "  Properties of \u001b[36mA\u001b[39m            type of factorization           \n",
       "  –––––––––––––––––––––––––– ––––––––––––––––––––––––––––––––\n",
       "  Positive-definite          Cholesky (see \u001b[36mcholesky\u001b[39m)         \n",
       "  Dense Symmetric/Hermitian  Bunch-Kaufman (see \u001b[36mbunchkaufman\u001b[39m)\n",
       "  Sparse Symmetric/Hermitian LDLt (see \u001b[36mldlt\u001b[39m)                 \n",
       "  Triangular                 Triangular                      \n",
       "  Diagonal                   Diagonal                        \n",
       "  Bidiagonal                 Bidiagonal                      \n",
       "  Tridiagonal                LU (see \u001b[36mlu\u001b[39m)                     \n",
       "  Symmetric real tridiagonal LDLt (see \u001b[36mldlt\u001b[39m)                 \n",
       "  General square             LU (see \u001b[36mlu\u001b[39m)                     \n",
       "  General non-square         QR (see \u001b[36mqr\u001b[39m)                     \n",
       "\n",
       "  If \u001b[36mfactorize\u001b[39m is called on a Hermitian positive-definite matrix, for\n",
       "  instance, then \u001b[36mfactorize\u001b[39m will return a Cholesky factorization.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U))\u001b[39m\n",
       "\u001b[36m  5×5 Matrix{Float64}:\u001b[39m\n",
       "\u001b[36m   1.0  1.0  0.0  0.0  0.0\u001b[39m\n",
       "\u001b[36m   0.0  1.0  1.0  0.0  0.0\u001b[39m\n",
       "\u001b[36m   0.0  0.0  1.0  1.0  0.0\u001b[39m\n",
       "\u001b[36m   0.0  0.0  0.0  1.0  1.0\u001b[39m\n",
       "\u001b[36m   0.0  0.0  0.0  0.0  1.0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> factorize(A) # factorize will check to see that A is already factorized\u001b[39m\n",
       "\u001b[36m  5×5 Bidiagonal{Float64, Vector{Float64}}:\u001b[39m\n",
       "\u001b[36m   1.0  1.0   ⋅    ⋅    ⋅\u001b[39m\n",
       "\u001b[36m    ⋅   1.0  1.0   ⋅    ⋅\u001b[39m\n",
       "\u001b[36m    ⋅    ⋅   1.0  1.0   ⋅\u001b[39m\n",
       "\u001b[36m    ⋅    ⋅    ⋅   1.0  1.0\u001b[39m\n",
       "\u001b[36m    ⋅    ⋅    ⋅    ⋅   1.0\u001b[39m\n",
       "\n",
       "  This returns a \u001b[36m5×5 Bidiagonal{Float64}\u001b[39m, which can now be passed to other\n",
       "  linear algebra functions (e.g. eigensolvers) which will use specialized\n",
       "  methods for \u001b[36mBidiagonal\u001b[39m types."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?factorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mm\u001b[22m sp\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mm\u001b[22m \u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22m \u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22mind \u001b[0m\u001b[1mD\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22monal is\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22m Bi\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22monal Tri\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mg\u001b[22monal\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "diagm(kv::Pair{<:Integer,<:AbstractVector}...)\n",
       "diagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)\n",
       "\\end{verbatim}\n",
       "Construct a matrix from \\texttt{Pair}s of diagonals and vectors. Vector \\texttt{kv.second} will be placed on the \\texttt{kv.first} diagonal. By default the matrix is square and its size is inferred from \\texttt{kv}, but a non-square size \\texttt{m}×\\texttt{n} (padded with zeros as needed) can be specified by passing \\texttt{m,n} as the first arguments.\n",
       "\n",
       "\\texttt{diagm} constructs a full matrix; if you want storage-efficient versions with fast arithmetic, see \\href{@ref}{\\texttt{Diagonal}}, \\href{@ref}{\\texttt{Bidiagonal}} \\href{@ref}{\\texttt{Tridiagonal}} and \\href{@ref}{\\texttt{SymTridiagonal}}.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> diagm(1 => [1,2,3])\n",
       "4×4 Matrix{Int64}:\n",
       " 0  1  0  0\n",
       " 0  0  2  0\n",
       " 0  0  0  3\n",
       " 0  0  0  0\n",
       "\n",
       "julia> diagm(1 => [1,2,3], -1 => [4,5])\n",
       "4×4 Matrix{Int64}:\n",
       " 0  1  0  0\n",
       " 4  0  2  0\n",
       " 0  5  0  3\n",
       " 0  0  0  0\n",
       "\\end{verbatim}\n",
       "\\rule{\\textwidth}{1pt}\n",
       "\\begin{verbatim}\n",
       "diagm(v::AbstractVector)\n",
       "diagm(m::Integer, n::Integer, v::AbstractVector)\n",
       "\\end{verbatim}\n",
       "Construct a matrix with elements of the vector as diagonal elements. By default, the matrix is square and its size is given by \\texttt{length(v)}, but a non-square size \\texttt{m}×\\texttt{n} can be specified by passing \\texttt{m,n} as the first arguments.\n",
       "\n",
       "\\section{Examples}\n",
       "\\begin{verbatim}\n",
       "julia> diagm([1,2,3])\n",
       "3×3 Matrix{Int64}:\n",
       " 1  0  0\n",
       " 0  2  0\n",
       " 0  0  3\n",
       "\\end{verbatim}\n"
      ],
      "text/markdown": [
       "```\n",
       "diagm(kv::Pair{<:Integer,<:AbstractVector}...)\n",
       "diagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)\n",
       "```\n",
       "\n",
       "Construct a matrix from `Pair`s of diagonals and vectors. Vector `kv.second` will be placed on the `kv.first` diagonal. By default the matrix is square and its size is inferred from `kv`, but a non-square size `m`×`n` (padded with zeros as needed) can be specified by passing `m,n` as the first arguments.\n",
       "\n",
       "`diagm` constructs a full matrix; if you want storage-efficient versions with fast arithmetic, see [`Diagonal`](@ref), [`Bidiagonal`](@ref) [`Tridiagonal`](@ref) and [`SymTridiagonal`](@ref).\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> diagm(1 => [1,2,3])\n",
       "4×4 Matrix{Int64}:\n",
       " 0  1  0  0\n",
       " 0  0  2  0\n",
       " 0  0  0  3\n",
       " 0  0  0  0\n",
       "\n",
       "julia> diagm(1 => [1,2,3], -1 => [4,5])\n",
       "4×4 Matrix{Int64}:\n",
       " 0  1  0  0\n",
       " 4  0  2  0\n",
       " 0  5  0  3\n",
       " 0  0  0  0\n",
       "```\n",
       "\n",
       "---\n",
       "\n",
       "```\n",
       "diagm(v::AbstractVector)\n",
       "diagm(m::Integer, n::Integer, v::AbstractVector)\n",
       "```\n",
       "\n",
       "Construct a matrix with elements of the vector as diagonal elements. By default, the matrix is square and its size is given by `length(v)`, but a non-square size `m`×`n` can be specified by passing `m,n` as the first arguments.\n",
       "\n",
       "# Examples\n",
       "\n",
       "```jldoctest\n",
       "julia> diagm([1,2,3])\n",
       "3×3 Matrix{Int64}:\n",
       " 1  0  0\n",
       " 0  2  0\n",
       " 0  0  3\n",
       "```\n"
      ],
      "text/plain": [
       "\u001b[36m  diagm(kv::Pair{<:Integer,<:AbstractVector}...)\u001b[39m\n",
       "\u001b[36m  diagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)\u001b[39m\n",
       "\n",
       "  Construct a matrix from \u001b[36mPair\u001b[39ms of diagonals and vectors. Vector \u001b[36mkv.second\u001b[39m\n",
       "  will be placed on the \u001b[36mkv.first\u001b[39m diagonal. By default the matrix is square and\n",
       "  its size is inferred from \u001b[36mkv\u001b[39m, but a non-square size \u001b[36mm\u001b[39m×\u001b[36mn\u001b[39m (padded with zeros\n",
       "  as needed) can be specified by passing \u001b[36mm,n\u001b[39m as the first arguments.\n",
       "\n",
       "  \u001b[36mdiagm\u001b[39m constructs a full matrix; if you want storage-efficient versions with\n",
       "  fast arithmetic, see \u001b[36mDiagonal\u001b[39m, \u001b[36mBidiagonal\u001b[39m \u001b[36mTridiagonal\u001b[39m and \u001b[36mSymTridiagonal\u001b[39m.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> diagm(1 => [1,2,3])\u001b[39m\n",
       "\u001b[36m  4×4 Matrix{Int64}:\u001b[39m\n",
       "\u001b[36m   0  1  0  0\u001b[39m\n",
       "\u001b[36m   0  0  2  0\u001b[39m\n",
       "\u001b[36m   0  0  0  3\u001b[39m\n",
       "\u001b[36m   0  0  0  0\u001b[39m\n",
       "\u001b[36m  \u001b[39m\n",
       "\u001b[36m  julia> diagm(1 => [1,2,3], -1 => [4,5])\u001b[39m\n",
       "\u001b[36m  4×4 Matrix{Int64}:\u001b[39m\n",
       "\u001b[36m   0  1  0  0\u001b[39m\n",
       "\u001b[36m   4  0  2  0\u001b[39m\n",
       "\u001b[36m   0  5  0  3\u001b[39m\n",
       "\u001b[36m   0  0  0  0\u001b[39m\n",
       "\n",
       "  ────────────────────────────────────────────────────────────────────────────\n",
       "\n",
       "\u001b[36m  diagm(v::AbstractVector)\u001b[39m\n",
       "\u001b[36m  diagm(m::Integer, n::Integer, v::AbstractVector)\u001b[39m\n",
       "\n",
       "  Construct a matrix with elements of the vector as diagonal elements. By\n",
       "  default, the matrix is square and its size is given by \u001b[36mlength(v)\u001b[39m, but a\n",
       "  non-square size \u001b[36mm\u001b[39m×\u001b[36mn\u001b[39m can be specified by passing \u001b[36mm,n\u001b[39m as the first arguments.\n",
       "\n",
       "\u001b[1m  Examples\u001b[22m\n",
       "\u001b[1m  ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
       "\n",
       "\u001b[36m  julia> diagm([1,2,3])\u001b[39m\n",
       "\u001b[36m  3×3 Matrix{Int64}:\u001b[39m\n",
       "\u001b[36m   1  0  0\u001b[39m\n",
       "\u001b[36m   0  2  0\u001b[39m\n",
       "\u001b[36m   0  0  3\u001b[39m"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?diagm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3×3 Diagonal{Int64, Vector{Int64}}:\n",
       " 1  ⋅  ⋅\n",
       " ⋅  2  ⋅\n",
       " ⋅  ⋅  3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert(Diagonal{Int64,Array{Int64,1}},diagm([1,2,3]))\n",
    "Diagonal([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`I` is a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3×3 Diagonal{Bool, Vector{Bool}}:\n",
       " 1  ⋅  ⋅\n",
       " ⋅  1  ⋅\n",
       " ⋅  ⋅  1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🟢Sparse Linear Algebra\n",
    "Sparse matrices are stored in Compressed Sparse Column (CSC) form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5×5 SparseMatrixCSC{Float64, Int64} with 12 stored entries:\n",
       " 0.27572    0.905187  0.102799   ⋅         ⋅ \n",
       " 0.789701   0.140221  0.763729   ⋅         ⋅ \n",
       "  ⋅          ⋅        0.209574   ⋅        0.856283\n",
       " 0.0953587   ⋅        0.577905   ⋅         ⋅ \n",
       "  ⋅          ⋅         ⋅        0.251486  0.238713"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using SparseArrays\n",
    "S = sprand(5,5,2/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12-element Vector{Int64}:\n",
       " 1\n",
       " 2\n",
       " 4\n",
       " 1\n",
       " 2\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       " 3\n",
       " 5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.rowval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5×5 Matrix{Float64}:\n",
       " 0.27572    0.905187  0.102799  0.0       0.0\n",
       " 0.789701   0.140221  0.763729  0.0       0.0\n",
       " 0.0        0.0       0.209574  0.0       0.856283\n",
       " 0.0953587  0.0       0.577905  0.0       0.0\n",
       " 0.0        0.0       0.0       0.251486  0.238713"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-element Vector{Int64}:\n",
       "  1\n",
       "  4\n",
       "  6\n",
       " 10\n",
       " 11\n",
       " 13"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.colptr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🟢Images as matrices\n",
    "Let's get to the more \"data science-y\" side. We will do so by working with images (which can be viewed as matrices), and we will use the `SVD` decomposition.\n",
    "\n",
    "First let's load an image. I chose this image as it has a lot of details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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amWGfJPeSUpI0TVPf92YmacxTMCEYSQTzUkMIAKZaYtt83/d93yte8Yrbbrvt9ttvX+4utOYSHdVrgHWzuWrJOe/s7HRN23bJ3buuizHW1SrGWN2HYei6zqdpOeW2bZfLZdu2ZZzG5SKGQDMDH33kkX/+4//sxuf9pb/6vd8zn20iGJwMgWtmxjWn+OdBACRhH0kAknABSbwAAEnYJ8loWOM+d4c7zGapn6aJgpllVkmpbTyXw0cu/sf/6//ycz/7r8+cPl1z2V0sSI7jGGPEmhmapmEtkg4dOrS7uztNJefsU3X3kFJ1TykNiyXJpmlyznIvpaau/fSn/uPDDz/8ile84sglR7e3t9u2bZpmZ2cnpUQS+7gGIIQgCReQhDWSALgHMDNST8JfRJKZNU2zXC5jTHuWy+XGxsZ1T3/G6dOnNzY2LrvsskceeeTiiy++/vrrb7n5w0cvvfyFL3rJgw8+KCnnfO211+Zcv/jFL9511907u8vtxTLGJkh62tOfds0zrtnZWdx9990nvnoi50xiGFZd14Oec+77XkLTNO5YDGNKyUKIKUmapolWp5JDCKRZCPIy1QlAaoKjEmEcJzMDjUbPJcZYKyCXbOfM4swT96SULr306PHjx1bT2LZt37RN0yAFAIVsLUJQ9eIwk1ml27eAthZCIIMZSZCUHICZpZRqrVgzM6HKHYBUSUoKYEjmrgoxhuc9/6YXvOiFX7vv/t///d8/efIkaTEkyF2wFKtqjNFCLLkM22MInHLuuy6E4O6l1hijNUnjWGsVtDXfWC6XZtY2TRknULV6aFpU3L72l573/O973Wt3F8PmgS2XYowphGGaQghd103TVL2mtgFQawUQLQiQy4x7QAogYRbkEr6FJEiQMAHQGoAQAgB3rxBJXEASAIqlFLhXYk8IIXtF9VJz17T95sbf+4f/4Cff8xOrxdINNReXQgirYRFj7LqulCLU+UYPoLaJLtRSnTFGrwUCmZqmKSV7KQRIkFZrTam99U9u297eft3rXnfkyJE8Fk8IIVHwWmOMXmuMkYAkkqouCWtmBoCgJBgBkAqBFAAB4Br2EZDk7jhPkksuubskbJ85V6fy+OMnP/ShD11xxeV931/3zGfec889OeeDBw8++uijjz/++NmzZ5umWe0uDh88dOrUGUmr1erkyZMxNtHdY4yC79na2rrpppue++znSvrc5z53+vTpYRgdmM1mIYRpmkJIkpq2BzBNeRgWTdN0Xbdcrtq2kTRNk5kFgyR3lypMDBaighkqCFZ5rRVArb4n7gndNI4Pnnj4G994ZOvQ5vHjlx656IjZmNIwn8+blJbLQY6mTV1MZiylsNau61TdSaxJMoO7cy2EgDWt4UkOEeKfAYBksJRLCWZYe/p1z7zyyit3d3d/7dc++dijJ/tZH2jb2+f6rgtm0zSZMXXtnjysTp8513XduBq6We8+NiEA6Pt+tczL5RKAgcmCmxF+Xi61FHeKuPVPznvZS7/rRS958Xw+B5Bzns1mOeednZ2Ukrurett3Z8+e7bqu1so1SWbGNUn4/yIB0BpJSQC0B5BEEheQRIECBUoAnNAeguQwDPN+FmP8+//w3R/60IceuP++vu9q3VmuVqkJVarDME1TO2u2t7f7vj906JAO8OTJkyDdvQlx8upeKDdAZApBJACHyjhBevCBEx/+8Idf97rXHTt2TEQIgRbGcUwp1Vq7rlsul/P5vJQiCWtmRhL7hPMkYY9AEmuSsE/7AEgeQiiltG27u7tIKV166aW33nrrF77whZ2dncVieeDAgcuOH/+jP/qj7e3t02fONt1GztnMJKWUAFx00UVt0z3veTe95yd+MsYUEazIjYDRUWGyZO7+ku96SQjhm9987Pbbbz9z5kxxVVU5gMhg4zCl1Gx1/TAMq3F0YCzZzAAHERgAhMCUUvFspEioSjCzFM0YSLq7BIoAoiWHIN8+c+7ME6e7+eyyyy47evTokHOkdV03n29W1/bObkqxSYFkKQWAmQGQZGZSNTOumUV3AE4S+0iCNED4NpECSJ/y1KbUtM04jsVzN58h2H/7gz+4WCx+7dd+7dyZM1sHDyx2di2g7bsyTiGmxXKAPKQ2pNZyHXNl4Orcua6fgUaWYJZznnV9KUXVzUDSDKQigxOoJdfyB7/7e5///Odf85rX3PCcZ6eUdnZ2QggpJUpt2y6WO6VO81mXc4aMa2YmycxIYh9JACQlGQDJIZJY0wVcAsA1rEnCHkHnUdjjFEyQYLGZfFoOq1prv7nxjh961x1fvP0T/+Fj/dbGtFhN0wR5CNbPZ5JmfZvHqaQK4NCBg7vb2zHGkj3GmIUULBAOGQRCksu91EBOq+mxbz7+wQ986M1vfvM111zjqmjCbLaxWo2z2WwYhpSSu+ecY4wASOI8x7cIgCTsk4Q1d8c+AloDYGalFJJm1nUdgO1z2wcOHHj2s2984onTOzuLc+e2P/7xX12tVrXWQ4cOd26HDh06c/qJzc3Niw5uHT58+Otff1ipPXv27A//zR967y/9u2hmpRSYNTHVWiWRVmt195zzkSNHXv3qV1f3+++//8477xyGccw1pQA0tVbQur71tWmaAEjKOcPc9lDubmAwczOIldUMJjplksVI0l21VtKCkUSt7Lp2Gv2eu7/61fsf7OezWutyuey67nk3Pufo0aOzrjG2ZigjXCU2ja1JMiMAMwNQSjEzCWbGC4AEYIDwbSTdPcZYawXQtu00Te6eUqrFNzc33/Wud50+ffo3P/sbJFerxXJYdV03loJgIbR0wUI721wulzlPIabtnUU7m1sMwzA0TZNzTkwWEEOUZCGETJe81Bhjy9S1bc7l07/+qd/+7d9+1ate9Zzn3gjXNE0AGve+aWXc3t7e2toah0zSzNydJNa4hjWSkkhKAqA1AFxzdwA6DxeSBEASBa1BguFJJGutMcZcpiof80TyGTc86x8+7Wk/855/uXHowBNPPBFjnIZVCCGlVErpuu706dObm5sppQMHDuzu7qaUzu3uxq63kEIIpRR3RxUAA7qmqZKZ5VpWy+Utt9zyute97qlPfWrtBPqBAwdyzik1gI/jmFIqpZA0M/fCNa0xGNYkQZCENUnYJwmAJADjOLl713Wr1SqEmHN+7LHHrr322jvuuLNt26Zp+n5+/fXP/tznPnf48OGdxYKpefChB7a2ttq2HcfxwYce2NlenDp16nk3Pf+662/4xX/7SzHXAgLGqZZaawjB4FMZY2yargVQSgbwtGuecuVTrgwh/emdd91zzz21OolpGpqmARzwvm8leVapRXCaSRyGIaUQzZqQzKyUEmPIXkueYkpSlYswM0oopeacQ7BVHmUhxiaXsjh1lkRKaTbf3F4uw6lTeWNWch8CN/pZjLGUQjKEYOfBzoskQyDOM0lmImlm2FMlgmtOmpkkkpK6rhvHcWexCCGQdNRSS9N00zSJ2tjaeMsP/IDXesstt5w++cT29nbTNBY4jFMIoZRyYPOgLMQ6lVJms3YxDBZS13Wr1WrW9e4OyQwVDgQzAy0CfdeN47jaXcgY+3axc+6jH7n5Yx/76Fvf+tanP/MZMcblcnc2m4UQ+qZd7S4sNgAkYc3dAXAf1gIJCSQAd8eamWFNEgAJJLWGNUkA5PoWAhLXAJAchqHv+2DR3VerVUopzMI//if/5OZf+RWauftq2dQplzJFC6q1TSmPY9+2q+V48MBFi2FFopTJ3Em6O0mLATJCANydZK2VDKvl8uabP/zqV7/qhhtuOHjw4Pa53ZgsxphzSSnUWvEtDhCAqwIQBBGAnuQCIAl/FgFJWOtSk8tYxqmNKYQYwFz1xS99+fTps1Pxy6+8umma57/ghXfceefucvju737pg19/aHNzE65vfvPhoxdf/JSnPOX3fvcPtrYOllLOnDkDIAJwdzNLTQo11FolhRBISgJgZgBIisx5uv6GZz7zumsZ0l133fWnf3p3zrnUqWm6cRwlNjE2TVNylmRmZPDshQVAtGBgm5powWx0d6jKAYZgJgtmFoLFmHLOxWsFaCk09Ioh57M7O6sT42WXHJUunoaxa5JPOaXUzVqYSQohAAZAqiTNzN0lkZRAUhJJM4MAAoEGSCIJwN3HcTSzlFKtFYAkALXWpmlqrX3fr1arruve+ra37e7ufvG22z7/h18AlPoZqgP2xJmzbWrcMZtv7q4GkIxhtVp1TWvwUpzwArm7REjRDMHG5appmil4CKFO2YJV9ybGj9x8c9v3b37zm6+8+qphuYpNIhlCACAJ+ySRlEQSANfAbwEgag/WSErCGkFJJLEmCYDWsIf4fyHZ931dI3nw4MFSiqpPGH/wHe84derUL/3iv227zlNa7dZaqmo1sxjjuXPn+m6+GFZmNt/cGIcpe6lVAFJKBoKUa5qm1DZVaGIqXrNXiJ/97G9N0/TCF74wxtj3m7u7u03TABjHsWka7BNcEtYkAdAadB7+/2iasqsGSwB2d3ebpjl06FCt9eqrn3r99dd/4QtfOHbs2C233CLx1a9+9Yte9KIxT13XPfjA10opd99990033RRCmM26s2fPPv744wcObsaY0jiOpRSSAIpXCqlrVaq7SyKJYJIIhcgQRBNRrr/u2ufd+OyTJ0/e+ie3nT59pt/aOnv2rFc2KSl7rdVoZlClKtwddDOLwVIMhBaLBciUIoxy1FocIi3SkNogjV5yyS7AAqHqvlwuH9pz4oEjhw5efeUV09iFEDbrhsWYUmqaJiWEEMxgZjlnkmZG0gwkQwgkA+XYQ0hAICCcF5s0jmNKyRgASJpKCSEMeehjX1Gz59CEIQ8hpG4+e/FLvvPFL/nOj3zko488/OiUS55q0zSL1ZASd3Z2QtPWWj1nANM0MYUUIkESkkqZileTABAgvORxNjsApO3FLsmc8ziOUykf+MAHLj1+7E1vetPW1lbTxFpDSLwQAJLYZwBJkWbGNafvASCJpCSuVXcAXMM+SU7sIQnAFCRRkOSuWmvf96uyotkwTAgmV9vNpnF18dEj7/5H/+MD9913y0c+Mptt5HHyXGII05RjaBbDCkCtte271ERk0ask1FLgZpHGlNI4jm0/NzMVFheNeZx+53d+9+TJJ172speSjMkkLZdDSsHdSUncAwoAKTK4uyQAkij8V5LwFxBJuUB3Z9u27l5r/Y7v+I6rr37qr/7qr1555dWLxeK6Z93wpS996eChw2BIKS2Xy8OHD0/TtNjZuf3227H2+OOPA3jkkUfisBxjjJLGIZtZjIlCzjnSALh7CMGE7A6g67phWMYUUEEK8IsuuuilL/uurp3df/9Xv/zlL59+4oxULVkIjDGWUhwwgyQYYABZVXKd2r6pRe4OGUAzCEZScJUqC3FPrSWbu0syMjASnPJ05tz26du/bIZnXveMLKQUuvO8bZlSihFmxjUzC4GShUDJSE3F+SR3mMyMZAWCFGgUhuUqxtg0zTRNtdaUUs5ZUq3VzEopZCDpVNu2b3nLWx555JGPfPijqcFqNYaA2DY5Z8D7WUuEZcmxCSUXowxkDNxjZu5GSjJwWK5SSiWfF2ld1y3HYd7PFqulpfTwgw/9ws//m0suueSH/sY7vXgIqQIkzYxrWHN37QG4JsnMRNLk7lgjKYmkmXmtvADWnNhDEgBJSAC0RhJAKSWlxGCSxpJjjFPOTdvKPbhffe3T/rd/+n/86Z1f+eSvfuLcmbPLaTQzySOtQm3beqkAQggQ65pKia11TVeonLOXMkyThI35bLVaNV23u7t76623PvTgiR9461svv/zyYRiappmmEgLNwDUANPA81x5QEgCC2CcJfwF5rSm2tVb36q6z27sAnv/8FwzD8IxnPOPEiYeuvvrqcRzbJv7WZ3/j1OmTN9xww6WXXvroo48C2DxwaLG7atv24Ycf/st/+Tt+67d+6+DGLJbJ5R5CIoLca4ERkNG4xyijVB3uMTYlu7FxJ1AZYhVFWIxTnZ76tKuOX3ZJjPHs2bN3fOnObz7ySIUYEWllmmYbs3Ecxmk6t7Pt7iQlkSagaTp3r1UBqFOW1a5ts7Aqk1ABpxSYOpdfAAAgAElEQVRDDA5KJNrYFgcsVOOddz+w0X/jkiNHDx8+3PdT1w1t23Z7+nZPzmOM0SxM0xQiWxIwSSE1LnmtKZA0SGakk6CKzCJg01QAA+SlmFkwo6RamxhdFQBptRZGHr/i+Lv/p79/7ty5O9aQa87eNM00TWUc523jtVrX1DxZjBWsIKpCSKWel0IMIaSYhPOMrKU0IUbavOkWOwtLUbk88tDX//n//s9e9JIX/5WXvZzB2radpinGGEJwiSTMQgguyd3MJOWcJYUQYDQzgQBBAL7HYgAgQNhTAXBNIl04Ty6RhJEg4E1MACTVmgEkI4mUggCQ1sYUrVR/2rOe+Q+u+5//zb/+udNPnCrDmMcpBGqcjI0BBWKgO1KIKiylmNkwDLTYti0s2DSFGBY7O23bGdA1jQM7Z7c/+P4PvvGNb7zyqiva1MFrBVJqypRDhLsHmAXz6g6RNFKSu/ZwrZTCPThPcgCSAIQQx1JIVqe7lsMI4JOf+nVJJ06cCIGLxc6JE1+bdc3GxsZydwcALa6G6b6vfu3wRUe++fhjq93Fa77vVYF28cHN5bk+GuM4ZvecUooxujupGGxnsdyYdXIW97ZtqViyV4ghkHTtASEANJEseeq6huTW1sZ3ffd3ppS+/uA3vvjFL547u93NZqtpKLXEtplqAdC13TRmuZfiVSvAcs4xxq2NjWG1AJ1kCMHcgSqJrhSiuXytAg5Vw54w2v33f+3uu+89cODAlVdeefToxX2fF4tFP+uaJpKsKnA5onwoXtu2Nx/jGsGcM4CUkqNiD0nRAZKSANAoqdaKNTMTaWakASDh7gzhyCVHXvbfvOwlL3nxb//mZ++9557lcunubdtQcNGMMpM0jmOtNYTg7iLatqVQSpmmycy4JsndS86llK7rzMxzkTRO05/80R//4Rf++Lte/tIXvOAFW1tbwzCYWdM0JI2cpolkSqmUklKqtTZNU+UUJAGQxPOwRxK+TdhnIvaRxD5J2EcS+8wMgMzpRtKN5ibpb/3oj/xfP/0zZ4ahm/XL7XN92y12dtu+y15DiiGEWivJrutq9WEYQmzCeWyaxswkoXpRhgtQSGlcrX7lAx/4K9/93S968Qtns1mMdu7czrzv5DSznItKzV7NDEYz0x6HuwMgKQmSAJIAJAEOWM4ZMhJN0+TqbdtC9sjDj4J+5syZra2tzc35m9/4/bfdduutf/wnz3zm00MIp06fHFfDsWPHUmzPnH6i6buvfe1rZcrnts/EGOKZc9vNntS5OOVqZjSUUvp+YyoFCqkJYy5T8ZQSYYAASJRkJqyRTCmZWc4ZgBkAP3bZpUePfm+KzdnTpz//+c+fPn06xlhrJWy5WMUYzSwEjuMYY7OnFp3d3m1TqG4y0sgAhkrJzCgBMIgUYdUYSQDL5TJZaLvZOE333Hvv/V+778iRI8eOXXL48OGmSeNUuvmsS02uVfKmad291lpKMbNoIcZoZjnnEAJJAUYziaBwnmS4gLszBEm1FpJmFmN0aBgGSQRe9rKXvfKVr/z3v/zLjz32WK3VzJqurbXO2vlyuWy6tmma1Wo1jmPTNNwDhBDMjGTxWkrhmq81MQIIZpLatj1z5kw73/jd3/6dz/3n//La1772uuuuA0gh51xKmc1mZrZaLruuo1RrdXcCBOACwT2AASS9OgCSOI8ASJEQBIAkLiAJgCSSAEhKwprWsEcUBSMAOlJKf/tHf+Tn/tX/vVws5publNp5L+nw4cOrcXB3MtWqYRhKKV3XTFNxk2dvmoZE08RxzIDFGNx9XK1EbGxs/MkffWE1LF/5yldK1cBSiqSYLMY4jmNKqZRsMncBcNeT+CRhTYCMBEGwSWkcR5cNw5BSOw0jjNM0hRAOHTq0u31uc3P+2GOPPe/G5z7nhmfffe89u9vnLrr48O7OuWjc2pz3fV9rnc82P/Gbn3ji5GMk40c/+tHNrYNXXXXVU6+9dnNzc1qtTOpn7e5iNMgC6NFlNNKC1yo5SazpSXAAJZcYI4AYYwjB3SWlNrL4RRdd9IpXvKJpmgceOnHbbbcNq5Fr7p5SIwlgrdUd7r5yh1U3ClZctQoSSTMLEmmAOeSEAKd1s3nOudYaQgBsWI3f+Pojjz/+uLs/9Zprjh071u7sdGtN00zFkwULaJrGzEoptdamaUIItVYADKY1MziMZK2VpJmRBEBSaym1dU0Sg+0BYGAu1d1/+Id/+OzZs3/4nz9311131VoBDMPQNE3OdXd3l2TTNCSnaYoWzCzGaGaodHcAJCWZWa21lDKfz6dpaprmoqaphu3d5TSM/+GWjx48ePB1b3j9VVdcaTHEGMdx7Pve1larVdv3OecQAv8sM+yRRBJ/jiSSkkhiTRIASSRxAUkA3B1/nrG6d33/t3/k7/zy+/5dHYfd3d22781sOazMTJK7m8W+70Oqq9UKe1y1FjSNuwczg2xNIUiKMe6c27YYvnz7lx5//PHv//7Xz+dzM+wZh+qNA1itVilFSSY4YUIFIo2kJFxAEglJpRQzq1Vt287nm5IWu4vDhw8vl8u2bZuLLgIw77uLL7749JlT0zjAy5fvuP3pT3/6e3/xfRsbG23bLpfDiRMnplqYurZto4W0Wq3uuefeP73nfjLASDlVn/WsZz37+utnTbdYrpqmgdlqnGKMqgqRAHgetObuMsqYzEop0zSQjLExC16q5DEGEldeeeVVV101juNDDz305TvuHIZhzFOppe/m4zjSQtf1q9UgsVY5VeUAQggpxiYmg0wuVXfPtZRaq1AZSIsxkZxKkQWQxSHxwQe/fuLEQ7ON/ilPecqxY8fa4oF2YHM2jmUaS9d1ND2paRr3YmasNDMPZgYygJRFAJIAkNQegORqtQprJFVdEgAHNjc3t7e3m6Y5eOjQi77zxS97xcs/8uFbvvHQQ7nkqWSSTdeU7Hu6tg3ueRpqRVVJKcUYQ2hLcUk55xCCr0nKObt7iFFGgxsQDWdOnfzl9/3Swa2td7zznRdffDSkuNjZbZpmmiYAAWHKkwECeIFKcA0EjVgjCZGgywGQxJ8jCfskubskM8M+0iTBABdMoQnh0MEf+3t/9zc+8+kvf+mOJsSdnZ1SSghhHMcQgkQzm/edlzzWSXD36jVLMkbCKYcLRKBcZb7Rj9O0Wq0e/+ajH/vYx974xjfO53MzhBCWiyGlFGIwgdgjuAAEMtAYrNYKQHLskxMQyVprm1IeV1ded90f/uF/OXzk6J777rvP3cdhmKbpyuPHbrzxxv/0Hz+1u7vbde3hgwc3Z7M3/bU3HD126fv//QevfcbT3/Wud/3oj/zYYrmqsth13Wq18uqxbdwxrIYUbWNjYz7bzLUuF0OpWTQzOIy1wp0OkgAkYo8MVEpptVpN8rZtQwjuLsm9hGA5VzMD/EkhhGuuueaKK64g7L6v3n/HHXe6iqQY6e4wAwnXHgqBlowhUHJCpAKsBsJoZsFRHDAjCSCQFpsQTVLOeZqKVKez+bbbbje74/LLL3/WM6/b3T67sTFv2y7n3LZt1zeShmGIMQJuZpIgucsMQJAEoNbKfTDjGgB3l0TSzLg2DENKieQwDH3fp5Te9ra3pZTe+9737u7unjtzJtfapGYseY8kAGYGIOfs7iQl7pHk7mGt1to0Ta11HIbYhIu2NofVFCxUlFrq2dNnfuHnf/7IxZf89Xe8fWNjI+cMoO277e3tra2tXEbuw5rWSJqZu3NNEtYkYU0SSfxZkgBIwj5J+DbtASAoNmkaRiOL/PVveMN3v/Sl/+597+u8mtk0TSmlruuWy2G5XG5sbMxmM4gApP+HLTj/3vQsCwR/LffyPM/7vt+1tiy1ZC2qSCpC2AxNGokQQJg+raNj94FGW8Vxfpmx/4s59pzpH/zJZTwHRFB7UGFQ56hAwqJATAIJVZVKVVJ7av1u7/s+y33f13XNW1+Ihz49n8+QUgIAM3NIxAgA/ZDrup61cw7BVEfNqOu68+de+/3f//1PfvKTy8vLVUU/hmxSDBV2GQAiGhGqwS5EBADbBbtESgihlDJeXvr2t78dqnjw4MHz589fuHDhwIEDe9f3VFU1nc+cc1VV3X333Xv3rd+8db1t262tTV/FT/3KJ3/48qnPfOYzDz744NnXXlMBV1IOISC4YiqijpmIh2F4/vnnn/ved1ZXVx586P59+/bFuvIxppQJDIwBABEUEUDNjJFTSnxHEABQgTtUBEAN0YoVBHPOmaGZEUGsQ8n60EMPHjt2bDqdfu1rz9y4dUsVmD0Sq6kJLDAAIhFgztnAmICIEJmJmbwhdcNQdpFjZkJEU8spOWYjFDFQcOzM7OL5S1cuXd6ztnL48ME9e/Z5z6PRSES8WyBEVEVmNBNUJSIgQxRjBAAiAgBEJCK0OxgY1ACMiYAIEXUXMwJCO/TOuTIkNitayOg//vqvzefzr3/1ay/98GUkZKSSMjMjUvDBzNIu2EULYKBCTMQEanVVDcPg2ZHDPAxgagLRc0HLSbTIpcsXfud3/vd3vONdH/jZp0IIzrkqxG7eusgACoAACABmIACICKJmRkS4C/4FoZnBT0BEeJOZwU9ARDMDAEVYMDUAQFMAaPsu+pCH1IxGfUqxGf1vv/3b//XP//zShYtbtzeY2cxUS1UF1VKKhuiJKETftT0iqioTgYoi1E3MeVhdXZ7NZqNR3fctM4dQb9y89Xu/93uf+MQn1tfXx+PxdDpvmopNiXEB3lS0EJCZISL8BNsloiLSDZl9RuS+T1cuv1HEkFxd18MwqJY9aysv/fDlUFeIOJ1Ojx8/nhfKgKoO6eA9dx1fXv3Wt/7REfUlubW1tbZtu64XMQQCMETniZlABTY2Nn7wg5mqhhAefPih++8/4ohNFXYxM4AhIpEROgMppRCRI6daFph9sYJmauacUy2q6pxLqWcfnCcRyzktLy996EM/O+vab37j25ub28zA4JQUkXhX8C53rRqYKpqCKSIbgqF675EMgYmhZBUVNGBmAgRAIhYwEBVTMiCjzY3tra0t7+KBu/bdd999TVMF51dWl1UBEZmNCBCRdiEiOSMiAGBmAFBVABCR4NnMAMDMpBRVZWbvfc4DAIRQ5ZxjCAAQQiAiEQ0hfPRjP/f+D/zMs88+++KLL9ajxkS1FEQUETNjZkTMOQ/DEJwnIjNDRDNT1b7vJ5NR282rqtKiaBC8l1yqGPthcN6VnF947p9feumln//5nz967C1oQg5FFN9kZvAmRoJdRGRm8CYiBgAzg12ICG+yXYgIALjLzBARFhAWzAwA0GDBe28IyNTnhIRMrpf8kY9/7MzLJ7/6d3+/tbWlqs65EELf9977nCXGWFVVCAEN2rYtRdIwsHPsPbBDAEJENVQjhymlpmnmbfv5z3/+4x//+JEjR2KMqgqgoMjMiGg/ARHNDHfBLtvFzPP5fGVtz82bN7Ooj6EeNezd0aNHn3vuuaMPP7i6vHT9+s3RaDQMg/f+8qWrdx+81yHt3bOf7sDbt29NJhNHOqrcZNy4R9567NqN66+cftV5AiMoRdLAoWHmksB7b6JpGETy9194/uTLL4HaW97ylsOHDxOD976qKhE1M0RkRlNU0IxmBkScUiJA5xyKqBU1AwQgI4cAKqJEFKJLkkId1+r4kY9+KLAvpZx7/cJLL71UioqYlDzkIac0GtWoKCI5JwWq68ZUDSQwiWhgB6Leec1FAKwIABEhGgASIjLSgqqCaTvrXjv7+oXzl5aWx/fdd9++bs/S0hIzj+qmqmPOpa5rEQUwKxpCQICSMwB475lZRAol3FWkIKIjIjQtiYgAoJSECFkzIhI5AzMCAETHo6XJ0x/9yAc//PTJl17+f//mb5umZua2betQD0NCooABEU2tlIKIRAQgqqWJIfdDdN6KgAohaxFEJIIQHQITUTuf1tB84XN/Ml5e+q1P/6avYqi8C76U4r1n5pQSIjrniHkYBuccKi4wMyKKiFMEAEQEAFWFXYjoHMFPMDPYZWawgGhmAGBmaneg3YFMiGhmRdSAiP2xtx7fs2fPF77whZzz1taWgC0AgPc8nW737TzGOBqPiSD3OeeU8sCOvAug4r0fUgohZBVEBIDKV/Od+Ze+9KUnnnji3e9+9zDkKjhwjhiGYQghMHNKyTknmuFNRISIBlqkEDrv/fb29vLy8pmz5x566KF23m9Nd0D0yJEjAKCqa2tr8/ncVO+6++5rN29u70xzSkPf33vvvaZlfW1lFP2J428pfXvz5k23vDypqnDlypW+S/OuizGWlCUnMBrVVdfNR+Na8kBIIfi2bauqOn369MmTJ733hxeOHGzqsfPknEtJzKyqAhLllJxDFzyqLaiqd4wAeZeqAojd4QDEDAEUABAtl15Vjz50/wP3Hb558/b3v//927dv+xC8q3LOImIIznsVm3dtjLGkFB2riggSAYEpaGCPzotIKcXAGJiICUBUzQyB3C5Ane7MT58688orrxw+dN/d9xxIk+xnHGPt2OcszlGsYylFRLz3RJRzLqU450opRMTMsAsRYReaKfw3VBV3mZmqIqKIAMCJEyeOHz++s731pb/4S1UdhqFpmrZt0YCIVIWZc84A4L0HgKqqiKjt5sxMRI5dUUMDVQW1EP2Q0tJovDOfNc14urX9n3/ndx49ceJDP/d0PWq89ymlEAIAMHPZhYgAkHMmIjMDAO+9mcF/C3eVUgDAzOAnICLsUjUAsB9TAEB4EyEqGCEqECF7v3f/vt/8rf/5q1/96pnTr8xms3o8IoO2bUMIdax2dnbMbHl5ebO/PRnVbZ8QUVUjenAkImrAgMQEiADQNE3bts8880zbtu95z3sYUVVTSqNRMwyDmTGzgagqIgKA7UJE3YUshC442t7edo4uXrx4z90Hjz740MmTJw/ee/e3vvWtd73j8Rs3rh3Yvw8AZrNZVY9+9kMffOPK1a999e+7tvWEpe9K7tZXx3ftX5N+5tbWVy6cv0REOWdHVMe4Oe+oDgSFkOrKpb6rgi8qnsNoNJrPO3eHT6mcOXN2wfu4vDx54IEH1tZXYoyzWUtEIQRV2NzYrqqqjh6ZhlRUlYh4l4jAHQYAZIYGCwTKDswQLEvJe/esfuTDH8yp3Lhx4+TpUzdu3PDRAbkyFHSMYqpCBD5wQAcAIgKgCMoEqsIE5FlUTcxMCIkdIbKIZLkDEUExpSKST58+c/Lkyf379x87fnRlea3tO0K3tr7Sdb33biHnDABExMylFCSDXYhIgGqAiApGRIioAIhoZgCgUMCAyJFjBSUk772IZJXg/era2n/8jV93zv31X//1qVOnfBXIIKUEYIjoyQMAIuacHZKIeO8RMUkZSjYzJGBiJer71jknIkujcc6ZmDTnF55/7oenf3j8kbc+9dRTdV2bqHOum7ej0aioIOKQpa6bnHPbp6Zp+lQ8448AACLCm8wM/ju2C/6FGZghgC0Q/IiZocGCISiAqbJztXNPf/jDjz/++Gc+8xkA2NnarkaNiEzns9FknPrh5s2be9fXVZWm85RSN3RqQs4jGtMdSbSuqlyKiNQhtm37/Peeu3zh4i//u19CdIh48+atlZVlACCGvu8ZiQgQEcwMABARgMC0ZCDLWRhxtjOtmvr2rRv9aLS8NDYzz3Tz+htVDAy2cOXy5bsO3R9888QTT3znn749n8+XRxUTOFSH+NixBx87+oBr29bHAABVHYZhmM62n3zyiclk1LczJnzuO98lopwzAAxdL6YxRtkFAIhYinTdLKV0/fpNM1ldXT127NiBAwf6fnDOTSbLXdcNJLhgyOyJoBRVLc45ALBdAGg/JkWgSI4+xuhTKl2XnHNr68sfeOpfA8C51y/84AcvlQKOqcsDIxHDbDYl4hgjIoqIqgJoKcU5x7sEbYF2ZSkA5pxDNAASRENADKrqXNjY2PjGs9+aTCYnTpxYX997+dLVZhSWl5eqqlJVZnbOmVkphR2aGTMDAAEyMyICgAIgoiHCmwzu6Ps+xmhmpRTPfoGIBAzMhpRyzh/4wAfe//73/8Ef/EE3mzvnfKy891p0a2srxqiqm5ubIYTRuCmmIlJKISLnHBGhmTkHACLCzI6ZnSMf+mEA1ef+6TuvnDr9gQ984MSJEzFGZk4pmVk9anLu27YlImZOKYUQRDIA4E8AALzDEBEAbBfsQkQRAQBENDPYZWYAoKKwy8zQ4McMkHA6m4cQJOXJ0tJv/6f/9Bdf/OKrr75qovP53CENw1DFCABt2zrnVlZW5vN5KnkB1RaQUVQD+5xzKSWXAgBVVanqpUuXfvd3f/eTn/zk2traeDxOKQMYO0SDIskzAyL8CCIAoJknNNMhpy4N46YqJmj69p967Lvf/e65V8+8+52P11WUNBBazv2+9bXRaPTK2Vf/65//qZnlnJlHS6NRJFIZoiNH4JyPGxsXqqranu6w4yfe8863HnuLlDzb3tne2njooQcuXLjEBmJWijoOQxYA8N4hIgAgikpWBUR2Lmxtz//xO98FgNGoPnz48P79+1dXV7s+qyoTeu8RTUTYoQyZmQHAzACMiNAAFxx7T5LVTADAezZTAB2GbjQaHT588NChe/s+vfLKmYuvXxQRMC1qwdGQs5l575vxKIRQZK5mJoKIzjMiqqqZgAkTOfYKogpmlktR1RCqYRiKadM083n39a8/W1X10aMPHrhrT86paZoYYwhBRIjIOadFgAzUEBGIEJGZAUC1IKIRIiACA4DBHc45VXXOhRBMVKQAACKyYwALPgzDYAC/8qu/+q1vfvPUqVNlSLc3N/bt2TtZnuSUHDNBNLMuDYgIAIhoZqqaNeMCgJlF7/qujTGaliLqGE1lz9rqzs7sr7/05We+9rWPfvSjJ06cGLqemfu2iz5mKVLKaDRq21aJzBQRzQx3AQAiAgCiISIA2JsQEd5kZgCABmYGP2YLsMvgxwxAxEKsQwiDoXOu67qf+9jHTp48+ewzzyCiJ97c3ATUvABQUYOlxBibpun7Xgx+BBG7rgOmpmlijH3f4y4wCy5+4U8+/wu/8At7966ralXHPIgnBlADBLQfwV1gpgDMHD2287707cb21ijG2zevp77tZtulb29t3dqzvuqJmlG9ubUz3d65euPWKDZhPKLSz2c7kZHQCKCfbZfUu4WVtdUzZ8/FGFPqDx6858GHHtjZ2brFePdd+2+tre/ff9fzz79Q2sH7OKTiibNKSqmUQsjOOV4gFhFDMAMEUtWdndmpU6+cOvUKADzwwH1Hjhyp62gGzGQGpCiqiApAAIaIZgYGgFRUdEGkDlEJTLSU4gOLat+3ihBjDQAnHjn2jre9fTqdvvyDly5fvSK5AID3PuecUqpjREQAQAAzExEAwF1VVakWM5FS1IwcB+8B0QydcyplGAYGGo3Gfd+fPHn61bN27733HDx4cG1tbTQaOedCCLSAtqCqAGC7VBUAyCHcQYhISGYGhAAQgh+GQVWJCNSIiJlVVUTMjCuOMYqI9/6pp5563/ve98rJU88+++wwDKrqnDMzF8JQspoRomNW1ZyzqhKy9x4AEJGIqqrShSzL48m0nY/Go67tUJSI0rz7i//7i//Pl778yU/9hwMHDsQYtzY2J8tLPsRhGIhIVRFBVQEA32RmAIBouAsAbBcAIKKZwb8w+EmICP+dEGPbtg4hxNj3/dLS0nxn+9FHHz368MOf/exnb1y7zsw5ZwBARNGcW2maJoSQF1IWEUZa8LuYue97VWVmVUXE2WzGzH/8x3/ywQ8+dezY0aHtnHMY2dQMChKxASIxMwCYqXckkgnMMZmm1Lf33nXgzMmXQQRVqugdVSBioDs7U8uDcAnsVldXR024fun1pqqknc5nO5Wj6Dm62t3e3GL2W1tb46WlZtLU4xF7BwBmNp9PmRkAlpaWhv42oXMOhmFAphiqwqUUBSBmCiH0fY+7AACRTA2BzSyV/Oq5186efY2ZDx269/77jzR1LUKoZsYIAKiO2MAWgAzYGZMpCBGIkGPUXErx0SNiKUXLEL0ThJL7pg6Pv/Mdj/O7plvb3/ve97a2dogdAhQFkcLMDsnMSlEzc8TOI6ggGDGJGICaQSlqCKmomcVYI+JsOhczQBQwNH31tXNnzpxdX199+OGH77nnnrquu64bNRXvIiJVJSJENDOPDhfMkEhQiAgMELFtZ/6OIAsqiGhmqiqmMcZ26AEg98PS0lI7nzvnjh8//uijj154/fzf/u3fbm9tOefIezYldlmKqKkBEiGRmQJa5cMwDCZaxZgWimxu3G7GI0tFchnVlSGUompmRT732T+OdfXrn/70aDQZhkFEmNl737bzqolmBgCISETwJiJARCKCXYgIAIgIavD/Bw0QEQAQEQDMDADMrE9D1dQppZIyA7ZdZ8TOeTD79Kc//Vd/8ZenT5/WkhCRAHPOjivY5b0H4mEYcilmBsiqWhNVPjCgiJQiMQTj0HXzpmm+9vf/0M52nnjPexAQDRiJwUANEQiREO5QtVy0pCrWydPxhx8+//rrdWRCu3bj2nt/+p3Xr16tovfR5b4fuun21ubyPXubemlrayvw8v59+6AM127f2LsyHtqd6F1OvVueNNP5bDxu+qE9cOD+9fW9Q5+yaJ/K0rjZ2Zmt7d2zZ2d69dqtMp+TYx+rUoqIAAAzIkIpqZQEd5CBOecAoBQwA1X13osII5Wsr712/uzZ15oqHjx48PDhw1VwzIyI3hsRIYAByDAoAgOa2ZBTdJ7JI1nOWbV471Pq0SiEwCjkAhoMuUxWlp78mX89qprXXnvt5ZdfHoZBtZiJICOiYwcAZpaG4ticcyEEIspFzUwwIfKo4qxSSmHmZlQTUd/3dRVVS3TB2Lquf/75F55//oWDBw8+8sgjpYxDCDEE5713DhAJ0NBUAVERGRbIFsBgwTmHiJILADjnEFFEACCEkHNmco4p1FWXBs+60sMAACAASURBVOe9mdXjUd/3Dx19+ODhQ2dOv/KNb3yjlKJmAGhJFZGZQ/BM1Pd9Wuj6GKPkgiHmtl+aTEQk5YKeSilNrHZms7quU98753LqtaT/8p//j0dOPPrUB58OVSSirutijJKLmSAyEKoqEQGAmYko7UJEMwMAIlJVQFhAgwWEBUNEAFADM0FEAIZdZgYATC6nAoYxxpyzI14okg0W9OP/5mNPPfUzf/SHfzifTyeTla2trZwHQxWR2NTLse66bns667pOSoKFKpgBmcY6lsLDMKhhXdc5Z0L4zj/+k5X0oZ99v5S+DgxmqmqlALKiyYLmOjoredBh0tRW2pFDGTos/VLtT7/84v2Hjwz9TAuzyVJTvXD1Upzs8z6OKnfx/Otry+Pt2zdG0e3M51UI9WQMc3Df/vbXT5w48TPvf5/z1bwbVOjsa5crHwRoY3smyA4w1k1fBnZewPIwAIBzTlWZWbSwIwAQEUAz1VIyEQOAggGRGRCxAQAiASNZ18vpV86dfuXc2urqwYP37Nu3r6qC8+SJVQwZiEHEionjUEw8saiAYQgVqHkOZiKS2XEuPTvPzlQTMXR5dtfBfXff85SZnTr5yrlz5wBwGHI2cc6BUfDOTLpuGHIJIRA5M/Me1VBVHTofLOcMTABKBKqKZloMAAyAMKjqhfNXLl64OplM3vvk+0Z1HC8tpSQuBM8ITIYmkus6pCLMOAxDXdeiSkSh8ppLzjmEQIRShJlLFiBSwCKKiAAoBIiUVIypzR15euRtjx06cviFhX9+3gyaqs45930fRiMZ8qQeq+rQ93nIlQ/drI0+SpbgPShkMe/jdDptqiqw46oys1RUczbEH7z4/ZOnzzzxr9770+95LyINw+AIAYUYVQwQibyYmhki6i5aAAQAMwUmIzQzBCUFNABUA0A0UCXviFBURQQXiBHIijnypRQAwAXHpSR2rKDEOMyHldWl3/iNX/vTP/3C5u3NvXvXp/OZEc5nGcgUVEybqjZRSdm0DNMpIkbn1VRLIVVfhSH3jhkRHdDF189U/L5QEUPe3thEgxijFEupF8hVFXQwSSnGOk87Mfrpxx/tNm+MnW3ONvetr4wDeDFLs/GoQXS1Uyrz7RsX19bWPElKfTVqlpeXpfS+qedSMgbnGRgsxKAGDz/44LGHj/b9cP36tflsp56MWpq2beuia5qm7QcmZqZSSt/3PrhcCjMSYQgh56yqiCyqKSVEdBgAEcn+hRoCoBkCMBHcur1x+/ZtImqaat++Pffee+/K6hKixhjNLGliZiICIhFzzpVcSs7M5L0HUEQ0EzUyMwDDXQCAoKrw1keOvvWRY+dfv/jSSy+XoqLFFK1QE7x5Q8SURTUTOUNUlRDCrJ0Hz9570QwGjFZSH31A9vgmAEK0hbbtv/zlLzexevcTP72+shqbOBmNyFPOsLS0NAxDSqmu6xjjMAzMbEWm/eCc896LiJk55iJC3gMgggGAmsCCKAAUgOgDhxpUVXS8vPTkk0++973vvXHt+l/91V/hQEDAzG7EOYuBjsbjnAZUMFMRIMacMzMr4lDygqlqzojovY/epSJIwAzdfPrsP3ztma99/emnP/LYY48CGnkisqZpptNpUamqBtFKKWaGiLYAYGaISIRmYGagsMDMhISIAJosg2hRNTM0AjODgkaBGUCJAUCJDDU7FACoA4vkySQO7ayp3a996hNvXL36uc99zrkwbwctObXWrI1MFFVHVeSmrnyYz2fT6ZQIfQzMlKVkKczk2OU0gIPomEly3w6p69udpdFSGVoViNH1uY+B5tMZE6ZhHkJFaM4xk6Wuv3f/nr5v5zu3SxqaUTXd2VhbXvrIh57qYTRPevHyVfZuZ2dn//792WzIevjAPTeuXV7ff5czBUSWnF2oPWIMTtLQz3cs9xS5m826tk0p5ZyZGQxEC5J675lpaIdY1WUh9YSoAH4BIEFWQBUoMoTgzAwACBGJAICZzUy1OOcAbGHetRcvX7l24yaiPXri+Gg0qmMVY0QEVQVQUbAsRAjIgFiKqAozEzkRQQAEQACEOwwNEVTF+3jfkXsPHbzn+vWbL7744tAn57BPHRGZmiEjYyoJRBFxkAwmngIyI4CZsScAIANAUNWiCgBEDhEAIedhZbLUzufPfv2rTVUff+TYoXsP1qOGmSRl7/14PO6HltHATE2qqirFiMFAcs6I6JwTEQ9KRIiGiIawgIgAgIiSBy1IgGZoZmTAzOt7137jN3/91q2Nr3zly5ub26nvPYeU+5RSXceSpFkeabE8dJ6DggJgCIHBCFEWSjZC51xdu3nf1XFkpiJDE+pn/u6vv/3sP/z7T3xyeX2tSJ73mzFGIhKxLnVVVQEAgRERGuACAS6QMzMwNZVSBBFBVU2InZqYGe0CADRAkKDFtCCimSCTmRATAJAmwUJmiIVAZ9Pp/rXmf/1ffv2733v+hedfyt6nLNNbNyTLaLKcd82lxKZ2dZzuzPquY++SqZkhIJHzzry3BVUNIShYVSVDVgBfhVjHbrvb2Nze2d5eW93TdV2II1PIOQtq387BbDwe7WzvTEb1jTeu1XW9qQI+jdZXX7vw+mgyMcJIHlxkJnZFxFLSnMQRObnDYqj72fTmlUtEVBHkbn7l9s0Y4/bWxtLy6r968n2Xrlw9//pFx1BSAdC2ax979MThwwe///2X3njjDWYG1awLgECOWMyccyIGoLgLABARABDRzAAAkRDNDM00DQVQv/ud73nvlyaTQ4cO7du3L8aoagCGiMwMYCKKBgZGBGYKCD9iuxARAAw0RJ+GwVRjjAcP3rVv35oZnj9//syZM32fzMx7LFnMsK4iAOWcKx+6rhORpaWlqq6Gvh1SAocLBERFFQHRAMjMmFlKMhPPTrV8/4UXv//Ci4cPH378HW8XkRCCcw4QhmEQkRjjzs5OjFFEzIx2AUBwrCWrIhEgESEaIQIuGJiqEjl0ngHNTBUUzVcxlXzgngP/7j98wkS/8pWvnD17drK0NJ/Ph5xEJXeZDOqmNtEi2vcJEWmBGRANwAzENPdpfX399u3bKysrqpr7oY5xZzr9oz/6o3sPHfnY//Dxuq5NFJFEMplayQimiKhgAGbmiIzE8oDIBIoGZICIhAhozqMqACATIBoaoAlYZhNACSEgEjMPWXIeUj8YyKQZmUrWgQADK1pS1Xe//acePHLfl7/yN3NLjFRFLzkNXVtV1axtzaQZT6pxTbmQY+k056y7ENF7V9c1M3vPueSV5bVSDJCBMGmpJytdN9+6eHVtfX+fMiIDyM7OjhaZzWae2CR556bT7bWV1fHSKOccx5MbG5v3P/Bwl4Yhp1/51V/+/Oc/BwAhjl49+zqC3LjxqkPklEpT1aXvplK+9fXX0eTKlUshhKHkhx56aHV54mL05I4ePXrixImha7e3Nk6ePDmy6qGHHphMxu9+9zu/9KUvqRYzNJGcinPeGFMqRIBMuAsAVNXehIjMSEQAYGaqYgYIRBRE9NbtzY3NbSJqmua+w0fuvucAAFRVZaZEUIXg2CFiKYkYiQjUVNVMkAgRCSwPPSEvlGEICw6Z+eGj9z/48ANm+M///M+vvvp6U0Uzm7fT4CsiGo+bkn3XdXlIeUhg5jyrCRGxJ2IHu0xBVL3jkrInJgJTDc6lNJy/8PqFi+f37Nnz2GOPlb17EdF7v7w8aWdz9o6ZRYSZiaCUolrQoHIeQUFRQXHBUAkBwPmIaGY45ExGAIBIAOY8GmI39ORcM65+8X/6JSL6w9//gySFDEopjnkYhnbom1hJGao65lRUFQCYvYtEBsRY1zH17aiOqZ02sVLNJLjURHH+4vnXfve//J9PPvnk44+/rZUyGo2CJ009giEDIQKAmQEiMwKod8TsQRQBiIiZkUwkMQCZghmKgKpJARTnKKV+3loWQeQFIraSY4xpaHM/OALVrDI0TQMmVYwmk0/88i/98ee+sLkzRXKlz2TSTnfGo5GIdNMdUe3arpiy86rK5KWYlKGK1b59+0IIUjr2joAlJR88sDNJITqOkXwlRtN5n0tp6no2m4mVpgqO+Nixo7du3drYuHX44D3zrtMiRFSPxrN+WF1bS1J+7w/+r+Xx5OGjD3/rm8+sLy+tLq/Nd2auqioi0lyYILXJSS45YU4+8lDSSy++2GV56Ngj5qwOcWtrqwocgn/iiZ92zr3rXe+cz+cXLlxYW1u9ceOm976qIlEyQyIQxhBCloSIRISIZmi7AAARichMzAzuQERg5pQGHxyCRzIw6rrhzJmzp069ctcd+5dXJiF4SZ1ojnd4KQIO0FBVAdDMAMDMnHOIyIhD6lgREFLO7D0YZi1ve9tjb3/7T108f+HcudezD13XSYGt7YQGIVREJMWYSKwQOQATETMDALyDnHNd1zGzD5xSKqWMRqO6brquJec2Njb+4e+/Oho373zHu9bWV3POITjvfUpJRJqmUQVV9d6baJbCSMiICogAhKgIhDlnIAdgAITOEZGq5pxTX6oq1DFuT6c551KKc+5Tn/rUzs7ON5959tSpU9PpdDweE6AieO+D844QgHKRrMUUgTHEICX7wFoyGcy2t5k5EM2HPg+yurRUSvnON5797ree+djHPnr//Uc8xlHwxOAQiA0NzIwIPZuUwTETFAERNTQEIzVTEWYmMANlU1ABECu57TKyA4Dofc7iXBTRUkpdjdjh0PbO+eh927YpJec4pU4sGfD/+Iv/9vkXX3r55ZMlZTUlgtTN2raLIfoqjhwVsT5lIDQ0ACAiM1teXlZV74Om1HZdLmocQnSOqC+Z2D1w9Hhk4us3i5kP0bMr1iHYXfv2Xrrw+ng83ru+fu7cuXiH9+w0l6YZnTlzxjnnmf/u7/6OHR45fN/25q1ZO1dVVxSIqOTS1NVsujOuq51uippQwzDfIV+plhCcIM6ns2bUzHa2q6pSA1GrRqM+Z+fcY4899sYb15i5acY3b9w+f+ly1/aqWgoQLgAagBmCESIwIWJKxcxUFQCQCADNDEwBEIx0oYhqIUJzWIpcufLGtWvXEHHfvj2HDh0aTxpLkobCzoJnQmegAGCGiKALpaSUYvREAKAA4ByJqgHGKkAxALjn3rsOHjxoZhcvXn719KuqmlJSEFURNedjdFVKSQVUDXYhgmNg5hgCEamoIx/qSooKCLMzAXaEADub21//h69XdbjvvvsePnZ0e3u6trZGREPXIyIAILKqOvIFgcGIEBEQgRDNUEScA2YWNckZEZl9CAFVtZR5ysE5AnTEBIgA6yurH3766Z996mf+8ot/ce3atTqGlFKswtDOVYFcIEZQMJMs4MSZaSSfc15bWZ6W4pnn07mo1pOY2x1VrRi7+c43//5vtt567P3vfzL3O4hmjMyIAGZKRMhGLCCoQKbGwMxeFbQYETIKIaIKkzoGKTLkLCLeh5SKlryxsb20TCpQiqIriAAuFqau7yBU6FjMyBGKMHGbuqc/8sH3vOddn/3s525eu85MqR/GHpaaWEqZBL+0un/ap4u3N5OqYA7Oee/X19cNwXu/sbNdRIoAG6RsuYgyuuAV+q1Zx+zByMy8924yIbDXz59rqugISyloOgxd0zRd1znk5eXJNQe3N2/u2bMHLJc0bHdzJiqlIKIjIhWog8tDb1KGds5QPAJKiT6Y57wzpJR8PSKyvu/H46W2nTEzkbt86apods4z8913311V1f79d129dm1nPrulGzlnNWFiM1AV24ULTLsAkcwMEX0IZlZKMTVmNgMzY3bMYGZE7D2BGiLlnK9evXbt2g1mvOuuuw4dvjc41BC9RwAFAHYIAKrikKqqcs4Nw1CKqhbnQipZAQNASimEUFWx7wciuvfggYceuG9ra+vkD0/fuHGLmYVtPrSYmAEXmNk5BwAiYmYiYmbwJlVQ1RAqkSxg3oW+pOArVZBiP3z51NmzZ5dWVt717ncuL60AWtM03nPJiojACIgGpIYAimKqioiO2SQXyUBERogIlhTRoTg0ZgeokkqMXiQjIqM1te/n+d//8i8Off9nf/Zns5JSO5uMm/m8U01gXAUvBm3fzWczIggOguPZznQ0GlnRnHoiZyXnNCxPJgZSL49LN6s8zLdu1U10iI6IFO4wISAkABGxBTRDdhGAzbCoGKAKoJpIdiiMWPph2s4NuHKhH7IBb23PyY9N0ZAmrioiCDqf9/2QEWXIWlV+FKsKbHNjxwU/m+8UyR//uQ9/5Utfjs5PqgbMCJAMQojI/uL1m5WnUiQPCYGjHx86dKhpmr6fNaNxSumNzdsCvqGqz6Wumz6lS1euSp+8YyJXiprZaDQKjkvq89C3bTuZTK5fv76ytj6bzcZro+nOxva8PXLwrtWVUSnl2NEHNfeqxYjqKpRSXAgxpYTFXj35cgB9y4MPMLNIAQDn3MZ0XtcNA2ouoKAGBdW7WkT27Nk3Gk2GoTt38xw6V4Zhfd9e8rSysnLixCNFbWdn5/TJU7PZzCEVBABzjgFAVcFAVYlAtZgZEeACgPdeRACACM2MiBChFEU0AEADYmemgJSlnL946fLly4z2wAP3H9i/3+/KWRCxaaqUkqIpmBhqVuedCnjyWcVEPTstIqDROduV81DX8R3vfgcRnTv3+qtnX1PVUiQNJbBTESMCAEIMMaoqApdSEDkEn4pUVSgKzgWRnIfiyAMoEYJqjDWCbdzceOarX5dij7/jbQcPHzbQpi51U5mqc6SIfAdKylUVpAgCl5ydc5Wv+nYOiOycYyLTUlJVjcw0ONbceSIwwGwAMI7sHDnB3/q1T+Vh+OIXv3j58mVCyGLsXZFiSDH4nAeT3M113NRaJFRV3w7Lq+td1w2pY6T5dOocxIon49HQteury96hSPaOhqEDgKaqUkqBoxClnJ0L/VCWRpPpvGMXBwHnQjuUum44wPbmZk69QxqvHLh6+Uq9FIqkJHltz75528dqXFejIamvKiv51u3rIbqU+6XxBMm3fS9aXAyai2ohKLdvXdu7thwQSGzUNJDFRJeWRteu3XA6oGZC8t6BKAAgmhku3Lx1i9nHWG3tbMdmEqrIzMMgo9Ho1QuXHn3krYaAiCKyvr6+eftWCGFlZaXvewVoxhMVyDnBbCaprO9bsmE627y+d/+Bw3fv48BmxMxWpK4rd/369UnTlDJ0XRfqOAyDSQ5VnUruUyZyxUxE2IGqehdKKcwOER85/uiePWuz2c50tn3hwgXn3DAMVVWplVQGIre8PHnPE+/GAtvb21euvbGxsZFSUS1mYGaEC8BMOZdhGGiXCIoI7wIAMxMRM2N2ZgIAqioiiAIAZiAIZnDu3PlXX31tebJ08OA9a2tr7KgU9Z5FTEpW1eDIccw5p5TIs6oCAKoJqho64lj5PKSqjiVrUTl06N5DRw523XD27NnNG5taZBiG1BdEjDGiaR56M0QkESFyBIjIJqkvpQpRdpkZ3cGiRVWdc0OfReS57z3/3e88d/9D9z9y/HhJdd14IfTeA7MhEAEWlWHwVWi88w7mO7ebphEpkUBKrus6gaF2BhZCVDY0Eck+eEc8nU7J/GQcS+piwI9/7GlR+Oozz1y89Ma1W5vEAYFNtXKMBITQTndMUUQRfFE2IzCqm9D3XZISzOVSjh9/JITQzmel9KOmMkNVHXI2oO3pDAiBsKgQhq5NomyKFKqhoADuzNN8NmtiSFo2p7NbW1M2SykBwPLS6vXrt1ZW1tUwq0SAzc3N3A8ihoiEvLm9FeNeK6mUggqTyST66o2ty9PNjaVRRQqBcOvW7eXJ2FBuXLuiOXfTTQfACtF5NQMpwXu7A51ziDRaGlGoVRUVzUxzuXntuvPcdd3q8qSUsnf/vtu3by+Nx9PpFIgUqKqqeTdklfHyCjha4qi5zVnuvWvPdLa9tDRq572prq/t257uNLV3w9Bfu351EiMzq8Le/ftefeWMIRmw85S6GccKEaXk4HwpycCpCRKQgRUxkXHdeOIhtVbKhQvnJ5MJEjCj3KEOeGV1aWV1ycyGPt+6fePqlWu3tzZjjDknAKhiQEQzU9X/jyY4+bE9PQ/D/A7f8JvOWFW3+k7N7ia72aQoQ6EpyiIVx6FjxwshMARoFzibLIIYSf6MBDAMaBEYSGQERhAgm6xiC6ZJTbDkRJQlSyDEZjfZ0719h6q6NZ1zftM3vW+qO9Dz5Biqqso5p5yJyLmKkVJKJWXrHTMjJvgCIqgqKOSSCioAXN/srm923vuTe0enp6cnJ0cxRufsnVRk3o+I2rZtnEcRAVBrDDOCQoqzqBrmFGYRqOpKkbMU59qvf+1t+zV39eryzjyMu91unkctDkRQkS0uugUgX19fk8Gqqvq+BwBmJiL9a4zETCoKgs54UFGRTz785Nmnn927d/SdX/mlRVcVIVtVjgkBqKRN40UTlzz1Q1fXlPrKmBTGRdf14w0ZBuWc01zmVJK3joiGvq+cA43T2MfAjMjMbW2V7T/4B/+5KP3r7//usxfnL16cbVfLm+srZ5kJseS2XSiaApSkOFfVzqYSmnZ56G+L8qLpiG0oklSToIZ8xzmXExhjsxaDZNiAElufMhh2/WEaY3K+JWuQ7GZ777DbFzECrqu48bC/3bVtF+fZG3Nx9vL+w0e3l9fLus5hHg6HxbItKZWUK+8lBk0ZSkakxvnD7mCI33rjzd/9/vchpxgmFjk/K8zcNov97S2SeXz//icvL3KevfeGyTlXSjHWknXOVjkrKjCSs05KXq8WX/vqu88+e3p2dnZytCGmm+tb7+zF5VW7WApou1iGOymvt1vra2MopFhyXNT14XBbUuG6CsN+vd3sby9DiiVFE2M8OzurHz6MKS3r+uzi0lT1OAVFGsLsmjZlyQqN9/MwGufZ2BBCjvn3f+/fHB1t0p0cVcrRdrtcLh88ePD0+bO2bvpxQCCEz4kIAKiqsfTaFxDx5cuXZxcXUz8V0BxiVnHsTOVSjgjEzIhYSipFAcB7H3PCvyYiiKQqpZS6qud5FhHnHBGFXJ4/O3/x/Cyl9Nr9e6+//nrb1sYY56yK3t7uLSMRMJLcyQVQDIIlIiyIGFVyEAWKOVnrW2+16OnJ+sHpNuf8/Olnz549m6cQUhIEVhz7XdW061U7TPNht3fOpRSY2RgHICKgWhAJEWOMImKMyVkVlBCnabp8df57P/hXf/d7/3HtqzRiVzd17XPUfjowIKAYY+Zh762DzM7aqd9VdRNKHsc5pNhUNTsiBkRt2qrf3ZaizCg518vOoCmKY5jabm1s9Q//4X/hXPM7v/M7f/6nP/IMmKOvbF07azADhCkUtApUeSuFphA326OcYsylWSyKorJB0DnEJKVe1CEkVWRbKSihRaQSiwgZVMkljFOMeb05QoTLi1eb5WrS0lY1s+Y0nmyP+mkilI8++miz2TaVP713/OLlswd37p8+efIJgnRtM459vVklkW65PPRDmOYUYlVVb73+2BF9/1/9y5OTk8oayQURu265XK/6mLNftNe7cZqtIUIlgpxzVZn1en17s3/58iKGfHp631ccp9HU1cMHp5LmHGdUMd617fby4hUgzP1Q13UJEwDYumm71dHR0TD2082cYlTVMM8KdPbi+eboOMVwdna23m4Mg3njjdf7/b6oFNCma4lt1uCa7qMnT9n5FKOvamNMmoNh1BzmEACg9n443BoqMUZEPD4+VlXDiCBjv5+GOaVYVTUbk0MmImZUhZyLqiIiAD548Nrp6Qmj6cfh2dPPXl1dllTSHAsos0VEBVUFEUFEAEEVkIJ3FAiQABFZQYsoEhNSLlJiAgDLhpltZV5d7S6v/4oIttvtgwevLbvOWkZEVgVUFDXMzjBoEcklF2utkaJamLg2iFogZwMqKFC0tuYrX378+uPTy8vrjz/++LAfVEIJJbMWAWt4vW5CTKqSy+fYIJMlRhEpIlKSs9YwliygoEUMcY5ztfBtxSmNbbMEzYf9WDnXtm2YZmOIgIkQAOZ5Dikqch/3QlA5z2CRGYnZuDshBLIulSQija/qpjvs+jmGZrnulq2AMcaNQ/zOr3x7/+p82t10lWsq0zRdUghCP/v4s5t+7g8z4bKUDJKHYfCVU4BusUDWNOWqqr2v+2EYxjmE5JyzhirD8zyrwuEwEBp23jr/8P7JHNJ+92oMc9d1r86eMrNFtsYZ68Zx9FWVYnn0+ME0hmkeQKmpvLOY4iglVtbNY79eLr33Fy+eW8NVVU3TxNZokYuLi0evPz59cB9FD/tbBgwhDFM07KZpvr7qWbNjKGFKyWoRsigCRCQi29UagKSkod8zA2h68eyp5PT1d7+qUOZ5LrZsj48OhwOV4nyVc0ZEYpNFb/eHcRydq5jtHOKiW2VRMlFErLWISqDWGrPb7dbLLgw9It3sD7aqf/7JJ1//xi+NIY+H0VVNLIDEbW3Pnj9DROccAMx5rr2XPIdpsNaCpsO+b7t62B82q3WYLhzbME5EhplTCjkjAIgIIiCiqhJiySJYKmfffvvtr3/96+M4XlxcPHn6TBBEBIC+IDmVaZq89/AFRFRVAGBmRIwpEZExBhFzls+BlJQJsJTivUeki4vLFy9eNE1z797xm68/JBUmMqyOWSwaAoRCoClnRDTGgEjMhZGcc4DZkEqRHOZSCiLfO+oW9VeJeI75vfc/uLruyRKBzSkiILFVJf0cFEmoeIeI6rpOKcUYzRfGcTTGNFXtLREogXprYoy1t875cRy999baOYRFtxKRMY7jOJ0+fND302GYVit2zlXdarlcIiIzz+EVkAeGnPOcxdaLBXkbQ7NcTCl6Z13tb65unz99Ajmsu+bR6dHu5krCYJ23vk5xLDm2deuYL2+v2rpCwBTmv/ef/X1CiHNQkd3+RgvElIhoc3TMbHc319mZMA7GGCAyzvX9oRwOa9j247DarO/5oxjjyWa13+8JlEmdc4fDIY9hW5tvQgAAIABJREFU3x/qur3/8HHOZbffP3z08MmTJ5vt2hiTVbz3zjljTLvogLCoKIICsDWa081u76vmL//DX6Bq7SsRadt26HtX1W1TV21zebVThKP1ij4HiFiKqCoiWmucc4gAmtI0rJbd7e3tHMaf/OQnb7/9NiIW45vlKsaYVU3VqhZmF0XH3cF7/+Lli7qu2frD7a5quqZb7Pf7o6OjzWodplmdM8TQdV2aRmZu2u7Fy7Nxzn/10/eFDBna90PXobVWVYukeRgAAEmNMdvttmtXYVKDmFO0hv783/9ZSmWcpuPje3XboRJ8oZQCAIiAAIgEqiXHAmCMs8TlTo6CWnt7fLx98OhhCOHy8vL87FXf96UUYOO8LaWoKiICQClFRIhIVZ1zWUrMCRENOyRSEfgCEwJhyhmJqqaOOT1//vzTD59sVtWjh/cfvHZKllKKSFp5h1pUChTJMSCiNU5T7MdDyrOxQGicc6Q69rMgMtki5Iz5G7/4NWL7yadPn5+dO0tFNEtBMogknyuqaojveO/meayqChE1p9rZuq7n+fb09M2ikkpWhJAiEc1xZOY4hromWy+GXBbdKh5G4WoK8NmLK+eqr//Cl1erlbVMRMMwIFp2i2E3g/JyvUlzmCbNGacIpZ/XR9tpCofdoWmqnEIYBiLRUGOecs4VrwSpcTwEMKxx7o83y1LyxfnVf/vf/KM33nhMIKipxKBaHFtVurq6sZ/zyCTGFGfnORBwjPMseRiGbrOo26pA2fU3dV1PoScShYLGKhshts5vTloACDmP47g77M0ru1gtna98XQGAZVLEcZ7AsBgah6mua8tuHMd2scQs3/rlb5+fX/Q3O++cRNkuj7pKopRqUSeF8/NXaOxisRjHUTAjqkoWEVUZDn32PuW59iya4jR778dxfPftt2MpwzCs1tt9P4oAs9n3o/cWgIZh7OomZUFTZWUFEjQpl1dPnz18+HCe58ViMY7jPPaGFKZpWC6XhpjYNd16DXaYIhARq3egqiklNpRSqqpqnPoyZ/Rw/vLFatGhijXkCFMpBnRO0RpjiOdxtMbnXEpJpRQiYib9AoCoqjGmlKQ5IbKqxBiYuXI2zmNl7aPX7t0/ORLRvu9fnF1cXV0xsqgQMZEh0DsImkFSSgBAgMSMiDlnFbHWiogqqioAliKl6B1nTbfw+3548uSJpvm10+PKYEGRNGsRAvHWIKIkTSGWUnKOxpNjKyIg2bqq7VAKCOIwDHXVgoIBfvDa8euPH47j+LOff7IbpiIZBe9YYADUUnJKw2FvjFHVEOdlsxAtIYSmaUTE+9q5isg4Wy+W68ub61xUpGSIa9+8/sZbANDP883NDRrrfU3GN93K+GYOQSGPsXSuVq6sb+exH8YIAt7XzHmKwVb1/jCEEI+223k3vPnlN/79//Nvp/2+v77YLBcxRr0+iKs3bUNsx1iEPxem/tGD9XazNKxSwuXVK2ttP06q2raL7nPLYRjI8PnFRdd1wMZVVYnJ19UwjWTYGFotF8NA0zQ0dSeacyizyDglEYi5eDY553Hc121zc3v9xptf2t/uENQYU1KsqmaeZyTn6ibmRNbFIiFPiDDHkEI03nnvpWlINJMcbbfTNL26vBx76dZLx6SMy1VXNx7ZpTjnkBzrarUuKccYD31GkBfPn5ye3EMNbdOkItM0VV2XpOSc55hKKYgYh0REbM2UYwqJndsfDnoY20W3Wq1evHgxT9N2s76aRkZMuZgwj+vXH/38/Q/adjGO/fXNXgp86a0vx1SePn9GqJLTD3/4g7/7vb+jAKEURWZD8xzbtmbgNKeuxhximsY4jgzqjI1hqJo25bmAEhtVJaKUMqg45yQlUEVSKJJLUY2ISERFixbRPBN4IorjaL3bdG779pf0K68PU3j+/PnV9a2kSAhEBGSwgCgIYEoJEQWTYYwqQIoIzCQFVIHRAIioSMFQxBjLqIRCZSZmQspRrLUpBMdkrE1pVGRmO86TdQaA5nm+//BURAVCKiGntNls4jQf9rumaVZ1cxgOR8vFN3/x3SnJi5fn5y8vsBRnuJTSdcss5TDxlOLu5rBYV1OaiaCpG4XZ100qstlsYsxADtlvj+6HFLuuY+b9ft/3/e3t7f72RlMiyc65t955x3mvCN1ysdvtFstVCLN1jgxnFZ3m1aKN88SEBgAFvK+qqp1jwsp96d23/+t//I//99/+bZuidf703tpaO8xzUDo25i/e+yChd55cXRmDMY5IXSkJQOacXd2Uort+QsRhGEIIEOD03r15ilVd729vLRMAnBwdWzaGjTOeWj45OjbGAUApZRzHq6srEbm6uvTed0277FpG/OY3vjEfdhDn84uz1157reSkKbKKN3bfj0AMdxRSCsx46IfGWQFt2+pwc7WsKjW2xIOEybKUnEDQWI6SrSVEqbyb+93U76Dki93ldrOCnCpOCIR5dhCpZAnKxsxhrBYtEc0xiICqFlFmNtbVdd11XdM0lni3293e3l5cXJy9nBZdXdIsOUqO3tthOJjz86u339aUyvXV7fHxva997eH7P/vw1atXbddtV8vr3fXj1x87+lxR2O93htgwIxKzRWPbtt3f7rSIFiECYnvoh4Vxw3BwvmYgJgYALQIqqlpiAABiSDGKZPycgmDOCQAMgUVM08B3tEicU0qIrKoll/sn28cP708hnZ+f3+4OUJIhSgUrVxnCAlpyIcOMBAD4BVUQEQAQARG4g4iqWkrZLBeVtZJDSKVtWxE5Pj6+vbyeh7GqqlgyFFRBYkbE43uvTXNomi72IxtjnbN3iGKYb66vNhupjHGEmbRt3HbVOdUSk0QRgbqqwVAIAauKKOacVZO1dhgOq1W9Wq2MMX0/LpdLa3yMGZC6dlFUCalqGjImx1BVlal8zhlA7gBAzjml5Js6peSbWnMhIihChoyhmEKep30/nty/H0VjTsMwmDsE6+Pjqm2O6ntxf5hDPHt57urGtfXF2YUlTEIxqSFgts65UkpIOQtOMXXLloglyHazSSkSGVXd3R5KKQBgmENKcZ7efPPNEMJms4E7ot7X+/3euQoRVXG5XgHAcrms63oepziPOZeUgjMWvD85OXHOtW3LBmPIMcam6XxdAYC1dp7HeR6xZBStu2acp7atEbCp/Tj2Te2Fmk2zvrjdO+dyDOM4qurLly8tAyMUiY2zkCPkgFoMu9dOtuOwXy6XBLkUcM6EEMZxVFURtdYqoKoyc3tnufLGosrJyclisXj+/NmyW3388fPtcjGPAyoY4s1mY+6dHoUYv/q1r/34x3/16tWrxWJ556tf/eqnT5+gQSLa7/fH6/UwDETUNF2cwzzPy+XSV9U4jlNMnz55ioh1XRtjrOGmaXa7HRmbbg51XXvv0TApGEN3VDIRoSojsqGccwihxCwiRGQNQ8kxztZaRFTVlJIxLqVUtV1OJcYZkU5PT05O783zfLs79IdpnkdE9N5HUeusCkxhJjJEqKoAoAhAyMhEFEJoKwM5W2OYqPHNYZhKKSmlV69mLDqPQwhz1bYxBtGMSPt+dFW7XC6VuF0u+r53xszzbK3dbLdVXQOg3AFtmmY/ztM8MKPxXkmNcVK0advK7eM8WmYEFYESC7H56pffOTraNE0dpxDGCWqq61YB73Rtm1Iax7Gua2ttCCHG6Bpvrb25uTk+PmZjVBVALXOKaZ4mY0xVVSXHO4SgpSDiiyefHd+/X7G9nkO19H3fC7vf/M3f/L/+j/9zf3GBJRNROhzmkqq2a+ouRY5FFWwuBqkGqIitdSlBDlHJmJDwejctu+b2+mq56q6vbrplm1LSIsaYx196o24766tUxFV1SsX6qm6FkIdhcJV3YPf7/Xq91iIhRV81GlLXNfv93lR17X26UwSIs+id5bJKKVlrU5hBS9s0mmLrKwbsuuWL6/2yqceQvXVTBCSXiuRUcirjONd1nVJaLBbTsEdEVXV1JZKBEBUBqF2uzMAxJnIGGFIqZKGqqleX1wAEAJWv71jvAGCe52JL7WwM8YOff1BUTu/f/+z58ynEuggiTtMEAIbITFO4PNwAwPHpvZvd7vb2+uLyvG7by5vLL73xuGmaYbczxqVYEFFVfdXM85y7zjofb258Uw/D4EAlF1PBMAVkE1NR1d1uR0RN7QHAe+u9V8mIKFKgiHOOEAkECAlBVXOYmdE5x8yqiojM7FwV76TQtou6aWJM/TSqoG0ba+39U5dKubi4OAwTE6Y53KnbLme5U0pRVWMMEaECEXVdB2VWAGttVfkwHXLO1loikpJIYblchjCHEJyvxzmTNRZcVdfDPFlXNW1b1/XhcKjq+rDfl5JX3SKECGSyQBym4XAghcP+IKWkOaNgs1iO81TXdZA8zCHnXFWNc24cx/Pz869/7ZGIWGuvri6ran7wiw+A+NAPu+ubdrlo2xYARERVRQQVCNBbF6bZ1xURqSoTHaYh5+ydwa6ZByk5HeYxhVCyEvF8GMByiWl/c1s3/urqqjb0n/ynf+cPf/ADTbFrWyRD1iTRq8O0v8kqgoZTKYpM1lo1bOy0H9ni3O+89yIaUvF1E1Op6ta7mslWtTvabAEgp9ItOvhCUZmmSYrOcbzTtHXO0TkXY6yqql0snXOn3ZKI7t1/iIhhmg/9/uLiYuHaKUVv3e7m1lqO81Q1HgCn/oAAwzBU1h2d3Pv4w48ZaJ4mZt4sV7vDrSj38ywipQARIWKMIaXkmdWYlFKYBmZMUhwxE8QYF4sF2qqfIgDknEvWuq6bpss5g6KIzPM8jqMxk29qXnTzF5bLZdu2OWdjjIhYa0spOWfznV/72xcXFzc3H1nvYkqq+k/+6T957733phjf/xfvvXjxYpqmdbuY5/n0+OT6+tI4O47jZrXu+/7T+PTq4hURARIgCXJRFEDNWooQUc7ZO8MIMcagiQkYABClxMp5BCm5oIolKkVFlS1P02S8y1lKKcwcQnA5ISIROWes45QSiggAIBhWlVhZ8+7bX55D+vDDj2Iq1WIZc66djyVLLlJESPRzBTMQkdWy7Cr6Qs7Ze990bb8/1HUdx5mI1uv1FGPK4pyb53m5XgpC1dSEZgrzPE6Vv2NTSv3hcLPfG3ZzDF233O126/VaRSpjX51fGkuWbCmlqpuYS+18ESAizaVAsmTGcYwxWmNAQFVzzn3fs3W5pOPj42GeSikiklICAO+9SGaDzhlEZQRCiGHKYW5qz9zsrq/GcQQRBB2GIYXgrQvj9CpfgCFb+VJKmGa2Zhj7drlQBLImpkRU2rqap9l/DkKejGFEYUsxx1TyMI/WoauMIojKcrnY3944bxDw+PTebndzfHIMAMvV5nZ3PYZ5e3KsqnMIxvlURFSmGNjZEELOkRG89/M8C0DTLTKgty6XAgq2adaV81XtvFUkyLkxfNjdVk0d5skY66y9uLmBVKTufvVXv/sHv/sH0xhiCCh6dX3bNF3MguZzjx/fzylVzoVpqH01HW4XTcOIxpIxNAyDqu73AyqGmFmLKlrju+VSVW+vd6BsjGGmUgoROeeITInp5cV5KVlAv/a1d8dpJCIAIKKcMiJX1nzuwYMHTz755PHjx/tdX0r5oz/6o2bR3dzcfPe73/njP/53Jycnw+2eiIZh9L7u+72IsDUpCJKJJZdYmOztvm8Xy5AyG3c49FmklGIIjTHM7AwBgMHPqWTLZrVa9H0fxhkAFHGeZ1Bt2hYNI2Iphb8QQgCAuq4BIKWQ+zKOYwjJGGPvODdN06Lt6tqFMIEKghgCZWYCI1QABVG/IFIAwAC0XfvgwdY5N47jFIKrKISgqiUrAEzTVFW+bdur61tk9lXlqkoRiOjTJ0/u37+/3W77YV8KdV3X1PXZ2Zmr/Iuzl227WC6XJedHjx4drTc//ssf9/vDOM6SSy5puVyWQ18OAxMJIQAQ0TSOV1dXi+50mmZnLRt6/tlnxlVkmMmkkq213vvo3G63Y5Xleruqu2k4tA/vf/L0E4Mmlvjm61+qXXU4HOZpKDEgqCGs67qpXOVqFRJA63wScc7t9/vamnun93WaxjCTlKaqDRKApDDngt5SDL21lTPkK4Oo1vJi2e72/cXF2Xp71Pr6+vqCmavKxRhLSaoaQrDWhhC22+2HP/s5EYmItYaIS8lVVeecSymiEuew2ayGYVBCFTr042K9iUWapg0hsHNlGtvVWkq6f/8+SIn7W9BunKYkBWAOIawXSwDYLDZXF6/+1nd+7cd/+Zdtu3DGppREpF5UxjpyVhlQ1LI5Od5qiVjidru8vbkUyTGCqlaVZ6Lbq+thmNbHi8qYi8uzZrkupYQQRCSEAADWWmMMRCADzIgq15eXOefzl2cnJ0fzPKuqfEFVp5RNUcg5f/Nb3/rggw9SDo8ePQo5DK+Gly9f/kd/85t/8id/oirW2sPhoKWklKrWn95/eDgcnKHVZv38+fOYYte1zJxLicN0s9tvt9twGIgMGdIiOUaRAgA5ZwCQkhaLxXjoS0oAQESGuRiTcxYR6ysAYGLnnKqSscY4EaiqSgQkR1SoK4dMOacQQu1dmHspCUp0pNY5IIrzVIoCAhExgiKoKhAS4J15nkspzrmY57quFclaq4LWmRgmX3kRHfZ7a+08jQCAiCJyOBzeeuutaZrmMDZNA6JxnkrKiAgAiBhzbpft2Pf7/e3t1eXx8dFbb731wx/+HqGZwnx9c9t0y0Xb9vOUYqrrGpiLzGO/13JSSmE2ztgsUu6o7Pd77/2QQt/3jnC5XGqKRHTY79BYwzjsd5adcRzD9PMXT4mIAZumUSlasjGGEUvW5XJ5uz8gYooJiTabDUlJKS2apqpriwBFmLl2/jJnx36zbK9ujbNIWBgBQIjIGBqn/XqzmKfeGV50VYzx1avztm3DOC3b7vrV5Ze/8qZhnIbREpcUvfe2dvt93x92IGIIJYtzBrtGRJbL5RhmFVqv1wWgqttxmlUL5hxLNiIxzZVzzjA4d/nqooikkheLRbvoUkiay6HfdYvmS2887ve3WiSlZK09O7twzqecu3rx4uzlaw9OnaEYQ8kRtCBCCIFByx1JMc5ExtVVDHnoJ7QVGtM0zTCOIoIAIuK92243ddOWUkTAGApxjsvFfr8/f/liGnpVLVkRmA0Z6/eHG8PML168sEw//elPv/XNb3rvHz66/+Mf/9hVHhHX67X33iCnlOqqHabLlV8fhv6tt97a7/fvvf/+YRob33z13Xffe++9qu3217c55xgzAMwxHHeb2huQQgClJBFVFUJeLpevXr3yho0xOWdlrutaRJCMMCoiA7PBFIu11hg3z2PO2TnHzPIFzKgI3hqQ6KuqpFhZ8+brjy9vbi8ubq2xMWViYw1hwQKatagqEqmqSJmmIYRQcn7t9PQwjPkLUhIRAUApJcUSUmgWTSllv99XVa0I/f6ATMQY5+AMl1JyzsvlIoS4Xq9BRFWBwVpz7+HDq6srttwt2/4w1k0rhYiBFCwbdXpnnqa2ptVqhaTeW2ZrrfFkQE0huDOOI1l2zklMqpmQc85N07i6+eijj7q2pc/Bs2dPnXMGKeVECHdSCERUOcfM17c3ijzH8Nprr40xiIh1vq3r3dXVu7/w9Z//9L2bm+uT9XZ3ewup7PsbrFMOA1UL1WKZjTfEnHM+Obr3/MWL9Xo7z7Nzrq5rEbHW0heOT7bX19ej86rypTder2ufUlItItlam1LYbDbe2xSib9sQppJyiakoZRUAEhFkMswpROudxlxVFaoK6LOzl2R4uz5WkCnE/W7PzM5aKIqGHzy6/4d/+Pv9bg+q3vsY8vX19WKx+PjDi6pp7h0fMShKSXGuvWUCzQkMAYg3tuTCta18c+ivfGvbtn356gqRiQwApJRUNYRwe3ubc3HeM9tSytgPzz97drTZ1IuFlAIixhgAmOcZEZnZPH36dLVaffizD+7du3c47Ly3IYSjo6OfvPf+Rx991DTN1dUVCqzX2/OXF4v1IuesRa5uru9869u/8md/9meVq1fbo3a5AgDv/RzDYeiJqKoqa60UncPkjFVCJYwx184XAWJbQEMuRJwVJCZmRkIt4Gq/3x+qyjOSFEgxG+MQURUNGkScZW7bNsaoWkrKmaNBB6glp5PtZnc75CJtUw/j7JrGgO77XlQQGQBUlYjmeQYARPzkkydttww5ETEroGIR2PUHNg4LIbJzVSzRGAOEQz8659jbfCeGFGZnTMxyx3vbde0wj9Za7/3TF8/ncWS2r7/xxnvvvY+GrTVEhg1klTkGkWCtS3n23ltr5yki8jzPxLapvYCEEIx3wzDWdRWG0Xs79oNzrlus0zypqrUWQUvOIEVyiqUwMxHHGLOoIZxCynnqukUuaio/jOMUk3MmCwAhMv2t7/zqf/jTH1lrXl1eWCAizkmY/NFyXUpCAms8IiOCMW7VmXhUdruDr6rGd6UU5xSAmqaZxjGlQERdVd3eHtyjxyBq2czz6K3x1uScwzQaY1RLysl7P825lLI9PmZmIjPPIzKVoiVnNvaOaGaiFCZl0y5aMBagcIFus9Iit1fXy6YRkK5rEeH0tXvMrCUTmfPzC+9c3Z4Iyr3tRlLa3VyNw261WkLJde1jmFVKkcJsNBfn/Onp6b6fia0UYGYi2mw2KGKNYeZpmpgZEcCBYf70o49XbZdjWh+vzs/Pm6YppRCRrarLm5tutTRPnjz57nd/9Rvf+MY777zzL/63f344HAroer1erVZvvfXWH//bf7darYZ9P87T/fv3Qw7zPBnHjx49staGEE5PT6cx3Ds9feedd370ox9NcxAR1ZJSWS6XzEygnV0i4jQNoFRVNRszjmMpJeQ74j0jspKisczcz3MSYWYiHg69KtR1HUJw1hFjzpmZnXNEZK2NU7LOgGg/7xfdarFoY8zvvvPWx58+2+1763xJcUqprmsBHadgjNOSp2nmbYuIcIewWy1tCMZYSSlNY87RsCNmRE1ZYowiMo6z975tW2aexn7qB2dNzlGMGYYB2SGi914QRNVXVbvQtm2zKhnqlq0hM02hrkyOmUHryo1TAMndsrOWx3H03qdUci6Lug05GWcV9HA41HU9jpMz9vr69p2vvH12djZOAwez2qz74RBCqOs6xqh3iLuuu7y8DCFY4+YpLJfLxbJltr4x+3GwdUXMcwxttR7nGZmJ4b/7H/77f/o//k+WGYqu1+vTuj2/PVRNTdbcHG5yzIaZlLSAFA3jxMy1b3IWIG6bRUrp6uqGGZ1149jf7PZMNMdgHBOg9RUqzDGUomyZyBhTCmIRZWYAEhGQXFSbpimlIGkiQBU2xpKP8zzOEdmQrcCakpErU1GdY2qXKafMpZianDfDPNTOLpfLtm2Ns+M4StFu0S6XCyawTMaY2+vL05PVyfHRJ5980rZtCAEAdrudrxfkqm6xEpHj42NVjTECABEhYtu22vcMKCLjMKhKyTEHrqpKcwkhtHWjqtbarAoAMSVDBL//B7+7WiyLpHmec87Dex+cnp7+zV/+1p/+6Z+2i+7NN9989uSzYRjGsc+aq9r72pVSbm5uXjw/Syn9xm/8xuXl5YuXLx8+erTb7fq+V5GuXTx69HCapuGwZ2ZVYePYupRCTKHv+7quYymggGSQWKEUUUD13qecGc08xb4fmVkVVbWUYm2lDN57TZizGMPOOYTsnTXG5pyryjlnRODh/ZNxnoomUHbGEGLKuZQSQkCVVVupKjOLYe/99fW1dX5/GJ3lynk22FS1gIIpgMhs2VLJupsObA0RlBy9c0xoTCM5rlarfgyqutvt2Fkkszv0x8fH8zgNw3R8//TJk6fDMDBbVNWSrGGyZprnGOcwyTiOy+U2hghAi+WSmWMomkAU1uvtxatXTDDFwVjTjwNbs91ujTHOOcMUQkBUZ3gYhnZ7dHZx7qxfb9oQ0tHqczf7HRBUjXM5VVVVZI5pZmZCNFWVxoNHfvj4UWVcnsPRenMYx6++8+WL65u5JGeMsYwId6qq2l28QsS2bZxzc0xE5H2NyLvdrq7rQz8zW2JPBEAW2WmRLIURXdWIySGlUqK1rqndNE2phCzK1njvhynEMDEzARvilIIQqUrf9+M4LrfHbdcZb/f7/TyMXDnb8AKXcZhSDLf7/YNHDz/99OPTB/fnea679vj0taZpjrYn3aJZrVaXlxcpByIkAmPMze11lrJer8dxvL2+XXaLwg7ZrtabFy8vYozOuVKKc84QlFLeevNLivDxxx9fXl6kVCTl7WqNICnOhx6+8pWv/OxnP2vbVkQAoKoqX1UmzOPRZltK0Vy++c1v/uhHP2qqquu6i7Pzp0+fffvb3/7ss8+mabLWenK+su2y+7W//d1nz55973vf+/6//oG19ujo6PHjx8Mw/PSnP2XmcRw36zUSGMsmc1VVIjLPszFGVUWAmYkopQQAiKgKiMjMIkJEKecikiXFkK21zrlSivceAFRVREII4zwxI1HtnDNsh7733ivpbndzdHSy24+IClIEwFjLbFIWVPDO5ZyNMSEXRJzmOPYHY5gNzzEBQIrFM+dYImUk8q5KILf73lpLxEWLZwMAKRVSEGYmrFw9htl7v9v3tiZiu+8PDx48GIZhvV77uun3h6//4jf+7P/9keYyZ/kc5CmkFKPzhg3WdQ0Aquq9A4AsUNf1+eXl0b1TJLLWzvOsCkfLdeVrEQEA55wxBj+n4zjWdW2MmaaJyYhILLI9vtcultM8W1d5b0OY78jtLSK3dRNjNAwEAGQE9I233nr/r36iRRQhhnR2db0bxnrZKUoR8dbknPtxmFNsFh2RKSpsLRD305hSbharHJPxjWiJGZwzn3z67PT0hJlLScaYtm2NsUWyIFauDikJYFZCMkxWkRExpWStJSJmBBBmil+w3q+3x7vD3opa50Uh5dT3Q4rzsmoWi4Wm9L2///e0iEJpfDWOo6/bUgrI5w7DPoQUHVH9AAAgAElEQVTJWhviWHftR59+4o1dLpe+ri4vr5h5d+jrbkOkCmQrL9PMzCJSSkmSAeDy8vLBgwfnL89c5Q1Stequzs/quo4xNl37F3/xF6f3X1NVRCylVFVVN435G7/wde/906dPP3v66e72oF/44IOfHx8f/8ov/8onn3w6TdP5+fl2u51Sv4LV//07//Kf/bP/+fnz59/85jcfv/7w6Ojo/Z+/n3P+R//Vf/lbv/VbH/7s56f3jivrjDEoZX9zTWTu0BeKiHXOMJZSEOhOzjmWDACllBhjKWVOgZmJyFiqnSeiHBOAiTGmHJ1zZE0FjpnHcYSSraFSyjQf6rpGxGmafMVzSIClcXUWTSka9mpJUoa/9uDBgxjjYrHY7W7bquqHcVEvpnE0BCUXitl4h1Jud4cQAjLdSSUjoreGGJM1qOXoaBNLqevm+ubGeDcOsyvofQ1Avm7nmLWUcRxPT+698847P3vvp0WB0BCit7xsqgwquXjvq6pKsfR9v95sjTEistls5AulqIiAqq1qIC5Z6ro2hIZwf7sHAEYKIbRtO8dkrTW+Wq42U0hp3y9X6+urS+9tSdkwoygzOjYxRmDwdRVSDDm9+4u/8PMPP6idr33zlQcPLq6vP/r4U2Vgti9enZdLUAVFKEWnFOb5QMzHJ/dX221Vt65qLi+vt6u1iDhvtEgIk5ZiDMkd4NthvNpP1lpmttYKzavFMgn1Y49olDilVNd1KQVRS0mGyBmDiDGmorpsFiGk9Wo7juPZ+bn3drXspCgAsXFJSlU10z5WriolT1mpqpIW4wwjaZFDv8tSkFCBQsyARaJ0XffJx586VxGzNRhjtGz2+z0iNk0TQgDJOYbK+ZTSxdmLjz/+sJQ0DPH4+F4IgYwpqnXbZJHNZmOMsdZOMQCo984aNhcXF7/0S7/UNk0p5Qf/5ndPT16rm64/PNvvDz/5yU9eXV7ud7uj7bZpmsNufzjsf/u3/5f333+/rus///M/Z+Z79+7t+76U8sMf/vDXf/3X//n/+tt1XYdxeuerX3nnnXe+//3vH/Y9IiNiVTkiEs3znBHRWUZEIkp3RO/I54oxhoicc3hHFECZuUh23ooIEeUcSyl17YlqYwwDEuo0TTHG5XJ5fX1tfaWqJ0eb630/h2x9I6o5ZAAEpZTKYtXUbZvmw8K2i+Vqc3R8er+qqmZ/cw0pjv2hpIiloJIiEHu2pmkaEUkpIClIyaKV8ymVtm2ZeXN8cn2zM84C4Xq9RkQFyDlLzq9evTreHt07PXn26ROj6FyFyGjdOE9Xu9uqcm3bhjADgK+reZ4x6eboeA4xid6puzaWfLxZxxiP1pvdTrUIWRNCSCktFgsRQSZV7dqFrWrna2TLgsQG2bTdYp4H5xwUsJWXAiWJd84S9v3ovBvD1LZNs1xIzmjNzWGfi65PTsj5m8P+46fP6kW3WKxEldki8eZoszk+Xq2Pq6ar6qZuO0E/TVNJcr2/lVyIIYe4Wi3bdsma2beIysyllBjjEGV3di4px5jX65UoHvqxa1tE/f8JgDEGEUspTNb4CuH/4wjefjU/r8Mwr/Wef6fv+/bes/dwZjhDihJZi5J1dORAcZoIqd20vShSA3HhJA1q+N4BfNfCV0UL+LJA+xf4zgbqGrYD2FEMO6plWC1okZSZiCLF85z24Tv9Du9prdXxPI/e7Q5d192//9Jud5MLrTYnZZl3V1eb1RBzRetYaW0scxWRSoVK0YhGaaWtdr7WDFqlZW76xlt7GCc0VgS1scYjK03CGuHq6qrrVwBQStFa55yt1dM0tU2Tc+5Cw8yIyCBN18YYg3f3Ts8ePXkiOK3XawBIKR2PR3N267TtGoVYa33l5Zc/9/lX//2f/bnW5stf+srxeLy6utJaX19fj+P4n//DX3j99dd/8IMfhBCcsXdfuPPee+91XRdCeOedd3Y3N2dnZ7/0S//F9773PedN37d/+/Zbr33h1Tff+lGMWaEwBWEGYCq1af3xeBSRLgSnNTNrrbRyqFThgigipJRGJc45q908j7VWa60xChGVAut0aJxC0/rw9OlTY5wIGmOGYdBaF+Lzc59KLXnSCqgQMxvnRYQZlFJdN7SnQ41T1510XQfKzPMsIsSsjCZWAjAviwj6piGBQoCom35tjKp55loAVGXYHcbT01Mmun37jjY2hAAA9AyIc34ej0TEpRpUq37YXd9oNAA1zvMcl1rSyd0z68w8c6HqtLHGHsZZ8AaVjqUKKGXNnTt39jfboeuXZVFKHQ4HpVY5Z6UUER0Oh27oY4znF3cYFSKO48iiVt1KULf9kI8FpRz3h1Aa7xptjDdWqA7DsL15aqyNJX/r23//s08+VYAiamP7c4JuvalKVW2q8MnZ3WF9opQREe+Cb0JlNc50jBPsoogi0EvNrhs0qnke201fUY4pl1KY2XvrjVXON6F31lDJCgCYRGR/GGOaETE0rubUNA0gkkgtVEhc0xpjqMpmczqOI1HZbE6JSklZGXfr4s4yHp0L28vDZrMBBbkSImjrRLjUWkpmEOe9tso4zcOQSqwsyrj5ODfeGq0UGmVsLJmInHPWWuOcUso/Yw0R9W3rvVXGlFKaxivVtEO7LItrQmGaluX2nRdSKqlkYPHeB2eMc+7dd9812j548aV/9a//xydPnuwP4/vv//TjTz68f/++1to5e3Hn7s/93Dcvnz79t//2j3Kh+w/urdfr3WF/cnJire2f++ijj954442X7r8YQpjnuW37y8trEk6piAhqk0tBxLbxMcbjONZac0pt24YmxGlhZgCknAHZOoMiCsD7tmkaEeEJrfXW2pQWYxQiAoBSKi+zQdX3/TxNVisiAq6l1s3paWUZhvUHH3366PIGwHpniEkJaxSjWCmoJN16I0AMylpjK3PJ+3mqJRlUxhhOxVjLCNZYRJ1zJK7GKA0Q2m6Zxn41oMBhPCplTvrOWb897LW2xiiFyjl3rGyMQURC+No3vvb9v/xLJmqaRgvYYPSktEKrzaefPtxsNqCqoLHejdPcrdaVRGsYhgFZhmGoJT15Mp5u1oh8eXnpnGNmrXVoG0RtfZji0rR9KcW5UIlrrU3THPc7JVUDNI33IVhtUamSKkldUuyH9XZ7HZdpTmXzwp2aMqJVujscI6lwen6B3oPSoe2WXJQSRIwlw1ydb2xocqI5TaBQa61ctz3sqVTi0gWsNYcQnHfGaQLIrL21lfi4mzarPqVoja01u6ZzbRtLefjTz4ZVZ11vjCqlztMcY25bU2rV2jNz13XG2lITIlptbq6vN0NvQ3N1fdlvNoCqggAaEgIQBJ1roly0QuN8mnJKxViljW/bdp7n9eb8sDuS1giinOHCn3z62Re/+KWY67Isq82maRoFUGsOoZmm6ezsrJQCSiOKZlitT6Zpcs4dDofjcco5IyKwWGtrymZ9crrdbn0I0zJ/4bVXY07/5jf/za/92q/nbTxst1RK1/jV0H/6yUfn5+eo7/34J+//6J3/NAzDOI5f+cpX3vvph0QFEH/xF3/xu9/97n6/d6FhQFBmWtI455iLcw6UVtallPL+yMylpqZpVGViYIFUqOubaVycd0iFq1irQdQ0zQDYtr0y/mSzuby8XK02wrQsExIqA8YYlspSRcg5vYyjNVojKiao1Pfd7VtnTy+vtVEpl64f5umoOQdt22BTXqaUlYbCOaBCJU3f6f2emVMphsFYT0TOuVKr1Gqsds7UmnNJArSkODChAGp998V74zja51KugLZrm1oLM9daM1WFvB13oEE7DQ5WbXuz3TqPm02PiC+++OL19bYytt1GCoe2zUWUMqA0Zbq4c+vdH//HVdcqBcRVmEpagPnWxQWidi40oS3EaLQIioBCplqmQ6ppaTuHpHNOFy/cTpU2/WZ3PNSUj/MMyFfbXUoLajxmZq61kvXh4vSFb/3cq8L44Sef1kTDZk2gBUDQVqJMLFIPcT8QzCmyIAOo54h11w9aK+KinGcWbVoict5oY1IhJWqJpW2hiqrELCrFiohURNywnyk/3jOVrrFGq7bp1+vBWhszt223LDMzW2trrQrUar0GlO1u17S9dcFZe3n9tG8HoSKIlcn4fl6ue+tTTtZ3haQSD+tVSkX59SePnmita83n5+eFJQzh1VsvtKsVzHGeZ1CaAQGw7VdpWULbHafZOVdTnZdRKzuOc86ZiLQ1CCrlwsxtaG52W6uNefmVz623m+/9xf9z+/Zt49z7H3zwk/ffRxRjFUt13rz66qs5pm9/+9sffPD+Bx98gIht17/8yucePHjQ+PCHf/iHv/7rv/bjH//4jTfemOf5wYMHZ2dnf/qnf3rv3j0f2pwzalNZhCqgUqgTF2YBZWrh0DZEJIKolQ9tTMVol3MehqHWmnNpmg6UWVJxLqC2J2fny3QUoabpaq0gJQRXmeZ5zDF6t6olO9tarYWqYhZKJ5th3YUpVbE4H7ahcZzAaPDOLBmAyPqmbVtmtsoej0cAqCQCigWPx2O3Wiulaq3B+xhnIg3AJaV79+699967TFKpBB2UUsaYWqsCGYbBObfMs/d2mqaTk5NpOjbe3rlz+8GDF41SbdvWWq21iKg1TnGO89J1HYkCUKkmozUAdsO6lCQi77///u1b5znHtglt8FSe0W3baq2nJWqtldFE1IX20aNHFxcv7Pf7s7OznDMAGxAW3t1cCdHQb8ZxrKke52VZypJmUMqY9ma39b43Rp+vTgRNFHz/s0dMwALah8M0FRLnHGpBREZFTNo6dMahF0ZlDAkiSykFtUKtQRhEjDPWBwsgQkwAohgAtV+yGOtTzsuyjONIREPbiSitDFTFpOJuYcp3bp9VEmXROXez33ZdJ1RKqY0POS1913zw3vsi4qxNMYJI1w4iwoJM0Lad1yo4ezzsiIWpOheIZXeYpmk5OT1Vrmm7Dp4xXiox1zmmy+sPp2m6deuW1vo4jTkmay0KLMuCLIjovSci14XGORE4HK7Xm808z957a+08Tgiq7TuzWq0+/PDD137mP+v7/vvf//53vvOd3/u937t//37ftqvVahzHYRjGcXzjh3/zysufOzk5UcZ9/vOfDyEoVFdXT0Xo3p07/+HP/1w9F0JIKa2HzQ/fePP09HS73SrAWqqQQgGlFDNLJWs1EWnSbd8zw+npLRE5WZ/u91vngwCi0r4JyhhBpZRhKtv9znu/OxxWfRtCOB73iIoY5ikyg9Fut9t1TZNzFoVEdHZ2tqRsQ7NZ9+Pjp23bKS3MuQkmBMdcK+VhGJy3SkGMEbVZlmWeZ6UUM5dSrLWIOE1TP6xqycaYoe2UgtqGcRxzTCkutdZVP3AtOS7AtDk9y5XiMhmjUfjy6ZMQ3KrvmDnGGBrXNU3O6Rlrh3GctFZV6rIs2rol1dX6RGvNz4CgUOM9IoYQAMB7L0zPMHMphbkSkTHGew8AzHUcD33f7nY3fd8vy+ScM8bXWhqtV6HlJZMth8N8fbOblxi6VqElkRjLyeZitVqFtsmpjjFl4mk6Aij/jEJBBQpIQUrRWqutYeGu9doq5QKA0qDlGWKQ0LetsYrIcS2IaBQCQC41LbHWysy5QsqilNkfDgJkbRNab9tmv98LV8TatYGIu77Zpnrz8PHLD+63Tg2dtxaVszVziSOyxLhIycE5gzDOU5qXfr3iIloZ1IioayVE3Tb9VMtxnpVS1gURNNrtdgfUhgS6pj0cDoKAiCEERByG4fbt2zHGUsr19bUymiv1q2G/3TnrUkqr1Wpzdno4HDabzenp6XEcY4zr9fqTTz457g9EpBWa733veyKSUvmN3/iN/+N//z8fPXrkvb937950PN67d++9994zxjRNw8xE9Oqrr/7gr/+/m5ubX/iFX3j69OkXvvCK1voHP/jBCy+8kHO+ubkZxzGlNI5j0zQ/+clPzk5vwXMiAqAQEQSZGcACcCllmqa2bZdlCSF0XTfPs9aYakbQw9BobUXE2QAAoelijCKirJnnOaXkuzbn7JzTpA/TzlsnDAZVLXmzXsc4g6DV+KXXX1Mant5sLXIVskptVr211hvdNM0SY07JOXfYH64vr5w2iFhK6ft+mqY6TTlXQGWNBgBmNs+gsVYbq1erVYwx53h1dWWtFZHpeEilNk0Tl/nRzVWMc9c6FJrmcehbALi5uZmmKYRwc3PTNA1RneK02WxQm+Xyipn7tpljSUuOcSaSWjNXglU/DINCqCwpJTRaWaMM9q5DrVLM+u+YSmSMERHvfUrJGDMfD6TAanWY5u32cJiSoLJNw0pPy9y0/eZ0vd3ueUom0Z0798QnhwhKAWCtVQSNMV6ZyuSC0Vob75h5tVqJiLbGKK1EISIAGKe7brBWCzMR5ZyZGVEEFIMiQQHVNI2InZYIYILvlIJpSTFXAOXDAFwFFNhmrmXe7YTrx5989trLD16+fzfGXFJEqCgipTx69Mg7x0xUahua4zRLJREgYuM8Ihpj4hJXq9V03CulnHMxpbOzs93+WGtlwNVqZbXx3heqiKiUMsYopay1OecQwoMHDwDAGTuO46ofAEADOueWlBBxt9ut1+v43H6/BwDvfUoJEc3Tp0/btvW++f3f/31E/OCDD27durXb7eY5/uT9D6jwd/7xP/m//+D/CiFcXl+9/PLL3/jm17bPXF9arR8/fOit/au/+qsvf/nLjx8+olKHbnV9+eG6H3a73en6ZB5HZ4NVGp9hQRGjlAByJW3UM8BilHYugIAxLoQQ46yNy7mWQsH32hprLSgsOW6325wzEcVlUQqNMcwMoIjqer2ex4MLjUZom808j865tmuX8bC+dfql11/9GRZQSCQ5Jmt1WqbjYdd1nbMaiL3Rxdvzi7PrJ9dd1zFzKaVfr1IqSpnGBxYSkZwzonRtmKaj1YZKpZJBULiGkxOtYJqOKZU4TymloWud1YfDrvVus9mISK1lTssU5yXHZ0ThOB5rjk3ra5Rh6LVGRNEgTXA1F+dcnAsiKqWMMdN0bJvgnGu71a1bt1ApIhQERNRKL8uilAKR4K1zRrg2wZnNRnN+682/SRUqASvr2qFSEeaL+/cfPn4y3mxrZSM557Fd30JjABUqo7VFRVRFae19aLVGxMqslCKgnOhwOIBCY4wQW2uVUkucK2PbNcYYbZ3kKgC1UkwUM4mgCHAuOcaSyWlTcyIiQSDCrms0KuVcnBfn9fX17vHjz/76r/6y82b81s998P6HX/3ZL61XgzCi0KNHnzrnEESE5mXq2t57LwoRlQIE4FqrMRoAcs5EpLUWEWOMiHRdd3OzWw2r4+7Ydo21ViklIkSklNru991zx+Nx5T0AkPDusKdSQwgosDse5nm+e/cuszBz13Xb57TWiAgAzjkTQsg518o//OEPgTDG2Pf9yy+//N57P2XmEMIbb7zx7W9/++23395sNkqpX/mVX/nt3/7ttm13u903v/nNL37xi7/7u7/79ttv11wQcZqmw+HApTZNo5R+JsXinFNKoRIUVAqfYWarbNu23joAWK1WV1dX0zTVWo3zSgGR1FrnuKz9um3beR6ttV3XKJDWh4VIa6y1ighRNcZMhz0CzPN8cetkWZZpmogKoiCoNI1zLqCUIIggCjjboHDftFSzUZYZ5nm2xkxEzDxNk4horadp6rrhcBi11nFelIJaiTg5qx8+fLheDaUkQLY2lFK2261zToQA1MXtFx4/fHJ1+eT+/XvjOJZSlmWpJRNVRPTeb7db59yyLKUUhXg4HBD0vfsPlFG5FKqFiJlkqinlxTmXUhKRvu+HVc+1dF3Tdl1KZY6L975pmnEcN5uNUqrWOs+ztZaIaq273e7H7/zIORdzBuMETWF48PLLT2+2+3FG60nQdiHGtNqca98VEoVKK63QCCJo0UqjKKpijFYCyBhsYGYlClEbpaeUGOSZ/f6Qcmlja601xqQleu+JKOZCAtY6pRRlCkEDRVEkpTpvnXPLshBRivGNN9748Y9/nJb5K1/90rd+7mvf+OrX52lctc1/+LN/9+KLL3/6yeOf/dJrn332AWgDAAzom7AsqUq1waNSVUApFOJaUgVjrb28elJrRsRcilIm51xJ+r5n5ovb5wCgtU4ln52dbbdbRLx9+3bOeVkW5xwRLctSSnHOjTHlnLlSKaVtWyLy3m+3W1SqlCIi+/1+meZSysX5LcPM3vuPP/yk7/vN+jSEsF6fvPfe+865eZ6v5nl3PIhCF9qPPvlsvV7/zu/8jjHmS198/e2333702cPbX/val1//0jvvvJNLvXXr1uXjJ43zpO2yLOM41lqdc8Yqo7RGFAAFCM8gK8Dg/DRNTdNcXj4hov0+W2uVUgCAiEqpWlLJUbihWrWxXdOmacw5MVcNWEU90/f94XCY59lqbLy7vr4W5q7rvDNPnz713p/aM855SQm1QtTe+1o1ZOyHzoW/sywRAI67PZcMQs7aXAgRg3UlJqXU5eXlqu+0QlaggKfjePfOnd31dXNxy5im1sogBsFpRcwppt3NTRc8cPHeUXF933ddNx1H7/3xeFytVt774/HYNE0pJaXlvD27dXbLOUcC3nulzJIyIu53h7PNutZ6585tImrbLqW0WvXa2jmmWlmes9ZYa5dx8t4b7U5Xp4K4Xp289dZbl4+fKIR5iU3bG9e6brU+O98ds3aDNtZAQWUQ9Wl7iwpp0ystKNV7D4AVq9HOBo+IlckYAwAiorU+HA4IoIVrpq5tjDEiUktum65tW2utUkqjaZqm1up9k3MmIqVUkaWkPKUDIrZtW0p685233/zhW/vj+NL9B1/4wmt3bt/9r//LXyo1BW+mY8xLvV6O3ep8HCuT+uGb7wSv+2BIQCnR1q98IwgMmkHSHDUqr5Xz1jsb52meR66l8aFpQ4p5WZa2G2KMWluttbW2lOK9v3x6JSLr9fowjbVWpdTxOHrvr69v1v3grL84b7fb7ebktJQiTLVWVLoSdSGs1+urm2sAaJpGRKy1pmkaY8ydO3fGcTw7O6u1fvTRR6vVar/fhxBu37792Wefvfnmm8MwzPOcUrp35/aDBw/+4i/+4v79+9fX1z/4wQ8uLy/lOWbWWgNAjGPTNPM8e+8BQClUCrXWRATIgAzARJTSYq1lZmOMiDRNIyIxxqZpSinGqGFY15qPx31wdhzHxltE8M4YsKgEEZk5pcUYtVqtQnDH7c3mZK2YiMoyp83qRBCm45xzBtRtaApTKaXvexF2RtecL4/PjBcXF1rrcRyNMcuy7HaH+w8eKKXmeQZQArwsi/e2a4PWmOIcYz45XQ9Dh4g556bxRKSN0uCeMVqLyDAMu92uaRpmNsacn5+HELquW5bl7t274zgiorXWe5vyYoyZpqO1/jBOKZW2G1LOq/UAAE65WquIjON4994dZtba5pwBwBgTY0RQq9VqtzsopfJzAPD2O3/LzNp5q6x2INrcefGVy+3+ZjfbEOacGWq/GuY5aqUy5dPTW8EGREEgVMIEVmtlkGtmAGMMlWyMEWZUWHMyCrVWtVaoBRAUotMqOOutUQpFRAFzzVKl8RaFdtNRKcXM0zR+/Mknb7755jzPn//8519//fVf/Zf/QqOplQ+Hw3Q4Hg4H4QrclJRyTs5YBVhKQaraIpcyUQLOL738IFPu+35JcVoiKP2Ms9objUTLND55/GlwNnJBFK5krKZSmYpzbpomrZGIVquVsHLO5ZxjjCGEUsrNzQ0RAYC1VkS01tM4GWO890QECCml0Nhbt27VWtu25atLa21wvpQiIsYZq7WOMRLRsizW2tPT0/V6fXxOBFLKh8N+miYi+upXv3p5+fTuC3d+67d+6zd/8ze99w8fPqRcngGAZVlyrpvNpg2N1rprWma21nhncs7Om3E6LMvUtm3OUYQq5eBbrZXWiGiIirWWa5nHKkTCOsfl5HRdax0Pe0Tc7w7eGS5ZKWWNMsYQkXE2pTTul2neb/oenwENQACilAGAJUetbWg6IlpiGtY9ER2naVrm0DbM3Aa/vb6qqQbn52nKOd6/f69rw+6wL6UY49q+c8YucUIBFMhLNEZ3XVdKaZrGe8vM3ntAYWZvDSIWqs+cnp5aa5lZKQUKx3nSWndDb4xZ201KSWvNTFpZbQ0yI6Jzrmka7xulehbUWnvfzPO8Xq+dc4fd3jeBKj+jtTZKFyolVaNIo9pe33Td8Ojx44cPH6/XawJJqVSAYXPWdsNbb79zud2NMT++uibh1ckmLpmISizO2mlcgvXG6ETJOeOfm5e0LAuBWGu9b4hIazTG9H0vInGaWapxLsa42Wxyzl03hOcuLl4wxpyfnxttQ+M//fTTUspffv/7h/H4937+77/+5Z999Ytf/P73v/8P/8E/AABmFCEBzDn3fauUyoVqylarxFVqff/dv33tc3fH4/XZyaprDIKImPGw7ft+Hg+5MpWSayKGYHtgJkqH4w0giYizulL23i8xMwtREakKhak0ITBzZSq5Wmudcw+fPK61rlYrAJimiYjW63WMsV8NzHw4HJqmEeD98XB263yaJtTq9p0Xdof94XA4Ho8iAgBGKZVSeumll959992u647H4zQtzrlf/uVf/pM/+RMiXpaFiHLOTdP81v/0P/9v/+v/cn19/d3vfrfrOiJqmuaYMgDUWqdpapru5uZm1a+maRKRpglN60XEWltrbhqfkl2teuYWARBRhFkE0WmtmbmU0nVdqckra4xJeTkesXHeWJWWGIIzqBDRaGTmnBalNYBtmgbONnFZvPdGa+JijVfPIYCAqpVKKsrovhuCCzknY0zXtIAoKM9Mx9FZL8xd1202G621c2bVD0SEoImK71vr+nmchlV3584LAhSCY+bt9toY45zLOWujnHMAqtZCTFrbEAIR1Vqdc0opa60xBgBKKURUayWipmmAyuXl5TAMRElrzQhKAaI4Y6y1AjU4m9JirdbaxCg9mjUAACAASURBVCVZa4nIOSfEWusQQq21pNx13QcffHA4HFarVXqmFmP9S6+89vDJVefD61//unahkIzz9Nbf/ugff+c71loEsMoK8R/90R99/StfBwBlVa35jTd++Pd+/lvMDMogIgD8wR/8wT/75V9OS1RKtW0LAD/60Y9+5mdeY+au64homqauG/74j//4H/2j7xyPR4X6+upmWZaPPv7w8ePH//Sf/tK//h/+JTEUQQB4+PDJCxcXAGCUur65Oj8/51LTtAeKFrKxBDADiVPRIt+/GDpbN7dX1mAT0KBJuXqDjz796N79l13w3rglF6VtcI7yNB7343EHVCE47w2zJiopLcY4ZmIWYwwzz/McPPSr9ZPjpXNuu92uuj7VEmNsmub27duPHj06HA7OuVorEXnvEVEETk5OdrudUgoV7na7EMJ2u40xEhEAmHF/aJqmpiyVcozeWiWUl/hn/+7fj/vjNE0GFRdCbUvMv/qrv3rnhYtSyhtvvHF5edk0jVLKOcfM1tpSStM0+/0eBbqu22w2x+NhWRbm2nUdgDLGOGeISCllrRURIhKRmJNSCgCM0sTFaQMAXEpwrqSFc3LOdMEjYkqpUvbWKaVYEAXH8dA0jbW2loLGMMs4L8H9HRFRgC6EgEZElFKgVc6L0kYZxQxaaxQZxzHG2PjQuibGSFRLyQxMlYeuJRKtdZwn583JelivVyklFn7GGMPMtVZrLSJWFo6xKp1SKlSFoe97RIwlM2BgyTlbaxExpSQiVmnv/fXVDQsZY5jhcDgsy/LSSy8pEAQAqgxShVvfTnGpxQoUbZ0xBgW8dXFZQgjemVLEavyP7/6nFAuixDhrrb23L33+C9D0n73z7uqFewRIORvrwbnT8wvjrDaGK6FVVWo3tMarUooxDhEPx2mZk3rGaqVQALp+NS+pb/uccyVxzj29vP7Wz5/NcSnM1oSm1ajM5uSWD13TDkTknGuaJrTN177+jZdffomZ8zw3PpSUKUbDZCgrgVVAWXZcs6bdxcafdIQgpUZntUa7af3nLr6qVAEhg4AsJZc2BCjzydDunz4Z1qdG+03Tx5gNw7zMh+NWKVitVlTyMo2ISlvrvNnebNfrs/V6PU9LycU5B1ynaVKAH7z/01sX50qp+ThuNpvgA+XitOm67pNPPrl7927OWRuVc45pYeaTzSkRbfc7rXUIYbPZzONUa2Vm03UdIhpj+r4HgI8++mg1bACglKq1ZuZaq39ORIyxH3/86cnJervdnp+fT9OktY5lbpompZRzjjEOw7Df7qy1IhBjLDUhirW267qU0nq9XpZFa11Ksdaav+NKKSLCzyFwJWamWmvXdQLElUoxyEJEqAQRlxSVUkKcSl6v17VWrpRLcc4JAoNY75XWAJBy5VKVJDTaOQdMAui9B62WZYFa8Lmh66WS73oR0Vozs7aWFFnvjLZa6+PhoBSmJaZsWVhEcs7MbIwBACIShbXUZzRgSuni4kIplXN2TdDOP3r0mdbWWr1abQA452ytLcSHaby4uACANC8554uLi8PhkHME4BACM8ck1lp2xRuthMdlXm9uHY57b/zxsB+GoaZUtI4xvvXWmwCwWa3nFOcl3XvxpYs7L+znchgn3/WZOTijUccYK9PZ2ZlSZlmW9ao/7Pbe+7OzE1QyL6MGNMZBLc5qa72xVlmntT49Pb24dU5E3vvVMCCi0bptmvXJKufcNF1KSYF67bXXzs7ORKSUYowCABG21kzzcRzH4GzJUaEcn37yuc+9dNIqBWK73joN5M/Ci+tV74NGyUsswAxSNSVrIMXZOVdr1lo7q6gmZtDKaaUpRd/5tMzIcPP05nr/2FgZul4j51IAwFqDiCfrjVbWWm+Madt2miaqVQUFAjHG9XoNLMfjsW3bq6urpmlijHfu3JmmyXufUiIi7QMR9X2vlGLmUspmszHGPH36NKXUtm0ppdZqqP4dKrzqB6P0ZrU2xoLIqh+22y0RtW17c3MjIqenpzlnpdR+f5zn2DRdjLHrOhs8MRPRMAwi1LYhpQa1qrVYa0spuaRSqBRihhBCrayUgueIRCmolY0xiMjMQ9dP83HoVlqrnLOxloiOxyPXYq0NISita62lELKAqJyzUsoY03R92/VpiblyIfZNq7VWJtVaS8opL6BEa70s6ZnVySaE5jiOIoIAS4xD26UYndXW2nmejbJamxSXyNMwrIO11mmumajUWgFAKXV9fW2Dt9ZyyohIRCLShebk5AxRf/Tpp4g4DIOzYXNytiwppsXG3HVN01ilFCL6tosx+xBsaJjr06dPrbU1l75rakmIqFG3wdecQmiNVlZh0/im8TmWnDOVtF4NH3/88cOHD4+HXTf0yzIbZ19++UG/6qfp6MNwvN5vVhtkBAKlxKGlXO+/cMcKWhdU5db5FGPbhIuzWy+cnWsR9Qylu+cnLIBakSAz3bl1MgQbIzVNc9jfOOdoHinNpaD1rpYFgeOSL85P16sGEYkohCCV/vrpw1dfebFp3OnqXGpqhJHr/VP/0q32/u0hpcVbbbVaIjWoW0+5jMFb1DWXRTh754WqGDCKjTMAgEalWLQJtRbvXKlxmoFJpVSUBq2VtSrnzJRQRGvNzEphzjl4L6AOhwNXNsYI4zJF53Ho+yXG4/E4DIMz9sGDB9fX1977cRwRcb1e55icsUTU9/3N9pqINienUuD6+rpt21LKOI4aVQhBa22IqO/7jz/69Pbt2znn1WpVCj19+pQqG2OYeVkWY0zTNCmlEAJX0kadnJxM09T3/Wq1yjlfXV2FEJg553w4HABAax0av91uETGnqtAsy2KtJZKm6WKcjXEiws/N8+xsUEohIjNba51zxpiUEogqJWqtrVbW2vJcCAFR11pD0wZnWSoRMfMoY0pJKeVCmJYFkJ8JIRixS02xJKzKaJNSurq8cd6knJnZWWue0bqUIkLLsgCAShEUAop1mjjFFGNirbVzjpkR0VrrvbfeG2Nqrc57ADWO49XN7pXVyngHorwP8xR39XhxcaGUQtQA4Fzw1hGXZ0REG7dabZZpvr65tMYR5WNchlVXa3XOdX3T9c3NzQ1RQRQf3DQeUCkmakMApsPx5vHDT/fb6zY03ppKcdWthlVbqKDWTOl49TB065XdWF1LpsaYnI8tnJg6NU0T4zg4e3x61WK9efyR04YraQWnK7t7+rH3jWhdn2FwmPZXnwKoeKS27SktDksar5TRdQGl1DQtx+PxZL15+slPvPcpJWcsUUnHJ53KXmMbQi3E49gH+9XXX0GQxlSLYrDGGLFmA1UJGCCNyipBp4Jbcy6VhUpOaem6rjJLIe18KbXtupKrCy0DOm+Oy/64P7Z9S1RiWpSC4EyMSymlbTuFUIiM0QhKhLSyJqiYa0rJNY33XinVtq21NufcdV2M0Vp7OBxWq1W7afb7vXMupUREiDhNU855GAbv/dOnT2OMOSZrrYgYDagEqBRg9o1/9913h2GdUlKom6bJuXKV4Btvw5LisiygEFBN40EplVIhkmmahmFYdf2TJ0+01gCgNTLXyga1AkCtjXPeWQsACo11bl6WJWattTHGOg84MgARAYsauta5KsRUM1U0uhCdnZ/vt9tCHFN2zlkfsPASM3NqvTPKgVQQqkyVSVtTmbRVpdC8LCSkUYESZRAFQaAJXSxZGENoc44AwCy11hjj0DZSaXWyAoBcSx+8iOScmsYTFWYGYKUgxqXW6r2vzPUZEk3SNIFo75y73u7PlGGG7f5gre37XgQB1DCsAeDhw4dtaM7Pz42R42E6vbVaUiZhRDRGU84nJycA4I1G4cZZg9B6R1Qi5dB0IbjdbrcZVm0bjNZ//f3v55jbtkFUpaYHL71kfIhxbvsh1YIUv/EzLzrnqEbnmB3Umm/d6YxsVYW6rV7jcox3V17WgzGmlIJsnHP//L/9J2j0en1CCLUyoH79tTta61p4mo9929Wa/8V//98AsNVGRABgUeaVi7vI4r2fpkkHrREA9L/6Z7/IJa6HUNJBFH1w+SGvB63V+dnZeLzs+35ZFqUUACMiKOOCK5RjAQCTCdtmU4+ztaZrQqEMyNqYVGq3GgpRRo5pstYvMa7OVg8vH85xPj3dNE0Tp7EIe+s1aiJCqMb6UgoTD+0wT/F6v2+7Dq1LKYlIE8KyLKWUWuvp6WmMUWtttUEBIhqG4ebmxnsfQhCR4zitVitm3m63XdfVWu/fvz/Ps7XWLMusFBKVTNnW3DTN8bhXSqcUl8UpBdbqlBIAswARaW2Px523znnLJDFGaz1z/ejTT5xzNvhpt7NKe++Px70CTVS11szsvd/v9957ImKGpmlyzjFGRBQB670SRVyWFHVF51zXNfv9nohyrjVna21aojO2a1qNhrFYY2qtRGIM1udQCTMrxJRSjNFaTUQxRkQUEeeQmAG00sorr7VOaTHKhhAur540zl9dXxtzW4RijESESiJwjFFrPY7zOB5eeOEuACxLatsupeScS8ejbgKKjOPYPnd1deW9z7nmUvtusNaCAJMopUpJzKy1DiFYa5XyTbuyzhtjHj38zFoLwiEEa6333jkzzzNoVZi61XA8TmVZrKnH7fZss4l5yUW9/9PPCrAOLsbIpb7++hedtfMyKaMpz0PXpWUxksuxNmHgZbbPKNRKAwtXdkrKHHunLVZUqhIIsXHeaACg4E1jGBAjsgAsy5IBg3OmtVqXEmegHJx3hrTW2+1203U5Hvq+X5Zto1Ghcs5wrWWelAJM1itIKTldqczIWjiz5Fxma3CeZ2GstVrr27YtRTvnGGiZ5nkpaMM4paFxaDwzJRLbro4FjMZCEwIxs4g8vbw+u3X78slTRO2sqSYyFSJCRGZgZCAxxmqFIEohAss8Ta4FTrkdeiJCxJSSc+7Ro0frk00ppes6EUHE/X7fdV1KqdYqIsF5Iqq5aK2D89/8+jdCCB/89KfMbKpURj65dQogxKVyYiBhtt6kkpqmmZYptL6UjIKlFt90poZKhLkCgUENCEutXTdUot1hzyCZao+YUzLG6OdKyYDcdgGQl3m2RhmNYlQp8sxqszmMx2WKZ2cnrFRoArDEVJrQpbx44/bbQ9/3VlnU4rRRwsEa1YQYI1FRyj6Tc3bGzSk2TZNrAYW5kgtNrTmmZIwhxlrqcLKpuTBjKcX7ptZsrc05K2uGzXqMCwC3fWOMURqUUiG0otA4ZYMXwRizEKQlOudqrfo5ETlbbXJapmlyzlkfYsp9v7LWgkIARoNWmWVJ/aofx3Fa5oFWSKbruvG4WKdXw3o87qzV2oC1lojGMVvnSuUQnNLW2RCXjCJcUlnGmOfHlw/nWKpCJcb44fbdU2ccSjntzDjPAbXKykERTk4hSPHOs1RltLKKS1UoKOy0WMVp3nX9SqMuVEBUKaSsQYTW2XlJSFU513XNNC05JkTMVFwIjQ9SyWqtFKyHnog2q8E+oxWVYq3NOVtrAYC5Ho+TMW6ajhZh0wWNJh6nbrOqJYfQqCYoZR4+vtqcNLkwEhpj5pJ8E3JMDMq067maJnRLiaLN/lgfPd6+9cO/OV599s//u//qdB0A4GTzwl//4P999ZWXU4w1p5xS8FZrJQgaFaMh5hSpa3pvHeUiJStt8zyx0uv1ugqH0N7c3Ljgm66d5lEptZQ6z/NqtXLOLSkeDuPQ9QACgBrNpw8/vXVxvqQZEZd58t6ZZ5xzANA0jSCwSNd1iHEcZwZo20BEiCjCOee+6wTFOVdKIRaNaJy12qSUSkzaWZZqjEFEKtVYFZyttaKCWhiQRURrLSLGGK314XAIITjnAGBZlhgjCQuCsTal5I2d42JQMXMbmiXOGlCMMQqBhSsxMwI4Y0UEnwsh1FpLKcMwzHO2TjNzSRm1AsaUyq3T7vJ4iYhEJILe+9aHeQautfz/NMFrzK9rehD063Cfnuf5H97TOu299nlmOu30aGvoUItIGdpOAJW0AQMEISokUorxgxJtJJj4TeSDAY0hGPWDWMLBFrA0YBNabG07bafTPXumnZl9Wnuvtde73vf9H57Dfbiuy3evxt+vSK11yWW9WUlty1JOT7dlmik4ADVF713O2UWnqiEERGytJZdijCaqtdVaAUBaYfLMKE0BoJQSUpznOaZw6/Rsm3Pe765PT85DCLVoa22zXtdac8m3tFk/ROcckqnZdrt1MTnvTZQcn5+fH/c3hDge94f58Pjyo9ogxMGF/s7ZndP1ukn1JkDcxeiYnCMwqM04BkJqIi4wMMzzOAxDywqGzGqmLjAxELoukooiW6uFg4fWeueC58M8AaGzsh0219fPQueH4DS39cnqeDyINGwlECUfiCpCYdY8j323SikeZwVguoUOoV/33KWg2ZC9KQ6rk/3umplLrtuT86lYCN4RiTUfO5EqSLXa1WH+zS995c23vmrElzf7/+DP/9jDNz7z8JVPYb4aXAlOYoxPrmcX+k9/5tt+7Vd/0TOQD+z8kqfW2r0HLyKF6+td13XLreM474/Jh2VZaqP1yfZ42Dsf9rujj2EeJwUbVn3OGUC7LhLRPM25NkRU1VqriUptd+7cmafZeUop7fe7rutWq5UrVfeH65SSc66AlAbDejusT1trAOCJl2VJz9WyBO+P++uci+QCIaR1ZEIEc4xIoKrOOSLQVgGAmZdlIecBNcaIiNGHUgojOML43JJra83MUojq1LNzzMAoTbq+O97sY4wmxsw55xj9PE4nJyeXl5ebzWZa5pSStCr8seB9yTOYDH2SVmqtiEiAjl3arI7jGDwTWp4XNJNWxzyjNOddfE5E8rLcv3f3crxcD12tlZnnefYxMFkKbn+TPa/GZQ4hAECtJiLMnFslIhB1z9Va+tTd3OxPtuvlVl5SiCl4NE2hiz5MqyOY5Xlh9jnPZqYfkxTcauhidCEEQO36VQiJnMs5e8L1eig5o+P5MHrvr6+vJefN+oSc26yH07OTWtTQc0gVtdtstC2linOBHEuzpTQzI6EQY78aWjMKkZGIteUF0c/NVGpZaudd8mGqczkeP3x6g4hh6PbzgZlj9LvDMwKZDhMMCXI5fFCX1hSs5qKqRFRrFYOUUvDpGoDIuRDPz89BQJD66GpzS66dX6sBhX4/ZuAenQvEzeww5Y3rzOyrX/7qK594Ccm8X//iL/3y/Zc+9bkf+qM/+Ifd9W733/8P/9PDl18uwlrmeVYO3nsutfbrFRAb8Sc//Zkv/9YXHaMt7eLsbs4zGR32YwpJxazJMk41lzyPIQQyyMtS1M7Oz52n1opzbrtaGygjARMigqG7FaKZgaIZordhGG5ubpoKiF1dX4OZiF5dXbk7dx4gIjMfDofr3W5Zymq1EREiQsTgg6oej0cAyMu0Wq0IjcmQiBhi8GYCoF3X1dYYwW6JoZnUZiBooo0QMaUEAM65UoqqLstSSkFEbeaD7/teEZY5E6NIJYKujyYtRFfm2TEDKLMzs67rVHUYBkRMKTEzoSciRlIwUHPOgZpn54hba0SUl3xyummtmdnFxYVDF0IopRwOh1JKRBQnjBhjdM611rquW61Wh8PBM5lZzjmEUGs9HA6n25NxHAGAmWOMrTW4pRZSNDNE7GM6iKi1lIL37nisCHZx55wIzs9Pn109HQ+HYeiHYQWoABpD2u2umRm0ooH33gxqrV3XBeeJiAFFpAsJAKZpCiGki/6tr7x52O1OTratLB7odLsyk9043+zGs7MzYj01R4RdIEQ4jAu70PWrcRyj92BCBBydNEQmdi7nOfhQSlFVIkCDVjI0AW9gwuxMGomxg6vLZ2fDygdXGduyOG3QCklzDsmZiCIakLBhckDYkF0pi0glPkupL7UpqBF6NxwPS785/ehy12+2reUP3nv0m1/88tfffW9zevrSiw+/+bVX/5//99de//Qn0PE8tWc30/d83yfRpSlPS8OT8wtDdjEoyu54WHfDvoyHw8H89sFLL4duPZX6xjd95v23vz4vx1wtpI00I0NU3F/vlnnWXMGk5Dml0KRNxzZstof9frXeuD5M0/To0aPTs5NSinPOe19KmaapNCmlBBfNbJ7Hrus2m41z7ubmZpqmzXptZjFGB+REVcW2p+exG95++x0iDiECgIjsrq4AIHgWkb7vW2uI6AjUE5pKyWOdS2vDMCBo9L7WKtqCYzBxSF3XHcbMzI6wlSzeAaiZlVIIDECRgAlM28dKYVRw6IIDldYaIprZsiwOAZCrLERUWw4hqDUip00IAVS8d02ti5GZpVYCWPJsZuy9Zwzsjk2WcULEXKYaAiJ2MbB3ACBSY/Rm9vDhw+hcYDdN03a7HQ9HADITM5vHKc+Lc857j8+xQwP0ngGVCURUpCJansY+hlXXv/32u+d37qjq0yePL+6cffUrX16tVq21aZqSj9HFPI95ntHMRPq+D46j41qLJ8/sc67r7QkArPuhtXJzcwMmQHhz2D/b7bfDWmpLTC89fODIcitC8FM/88+WCvM0Rmrf/e2f/Nzv+z3rlY8xmiEAiMiQNuO0g1JDpEWKCrUGy3Q0CSIVjR2Tc1ClKKkZIAM5NOcwdhXIdSdjtTaX0K9rWVzFVptqc4itVFVNfcecllzzrTat1hv0Dtkpcm6tiDoX1KqU5rt+yu2f/uzP9duTT7z+ifM794fTq9e7k+/53u/tXDgf+vp//wvjIKKhO/3wox35FYc4pO7xs12/2jY0VCWi999//97J687TnXt3dwt58GNRc2lz3n/4K18436yGzfnh+kpqNbN3vvGN6AMZoDRQW/XDk8ePz87vaKvXV5enZxfX19enF+eIuFmvtcl0HJHp5OSEiJxzQJz61bg/3NzcOOd2T552/apJCSF03Z3tdnt1dZlLcUScUqq1qZr34ZaZPX78eL1emxkAhBCkldYaETEzmrD35FFqU1QRAQBEVFVHLFTR2EdPiOgwpT7Xa+cCM9dazSyEgIgAME2T1NbU+DnvHHXYrzqR2sVUSnFMLZcU/X4aOTgkY+BSinNORFJKTYwctlIYHRFF5uCciOSczazvexFprTFzrRUA1ut1ay0bEFG9JY0bo2MGbKbTNG2325TS06dPa8ve+2mZW2vD0NVay5Kdc9779XqNiKq6LEt4zp5jZkB0zt29d2fJ82pYpyE55+Z5NpCbm5uuG6ZpKqUwc6015xmRvef9fi8ifYrRh1aLqXrnpFTv3HQcY5daa9fX16VOjDCV+vjpzcn5BZZl3F3fe+Xh6Woz1rbqu0nbbpyaBaQoKkCeiJxzVeyjJ5f37r/Y9QOQ225OpeVlHh1HETFAAmg1A0At1TnXyqyqHFMzRdOSjSC89+TZ/pi9j4747Ozs6mbu06pPzGGMGmLEkcZ5ng0cMXlPCohaq6Bn73xsClMuqtB13rngU9rfzGm1+fCjZz/2p/8cGJH39YtfW5+ehrQlIo7p3v0Xxcj5cDyWpUFIqyKGAOOSKUTHoamxw6ury9X6W60cRWFe2sn5Rbc+qfs9efqFX/rCn/zRH93tZzDH3s3749nJ6XQ41lK3Q19yPtzcrPqh1irSTi/utJpT10upgCAiq9VQShnnabfbgaELkVzw3t/c3BDRCy+8sCyLmcXQCRUimqYpxm69Hhwi5DwDkHOOvUt9Wq9XiCAi+/1eSuVblJh9XWYp1Xny3jviSURMm8qtm/2OfhcwOud9qLUaMjL3fY/PEREAhBAAwDk3zzMRBYIuBuecmS2LlCV3XRRtq6Gfjvuuj9Px0PcJAVQVQL3nWksIq1IKEplZSsk5Z4jErFpV1ciqVhFgZkSUj7WUYq2l1jqsemY+HI9jma2VSIl96PtuvV7nnFvfAdOqP7nZH50LgaiUoqqttX41lJbJ4S2pCohIpKDkqLUWQ1RVH2LfnwLAlJeui8Qg1rp+FaPPOSO5u/fOm5Rlmsm7LnBrxTF65xmtlAxmjpiAmgg4vLnZrdVKzeM4IsmY52kpZrYsi4zTCw9e2G5Onz17Zj7UrFMhNDXRJhIcB3bjOHYJq+Anv+nTf/Nv/Z3zOxef//wfGpLvUpJSHCKbISN1qWr13k9QnHOM0lQ5+BCSRzcvbYbw87/y5n/0F368KAQX/8uf+K/+6n/91xwB1Pn49N3TvqwTzPM8jmMIidgDgE+dNA2pU8P9cXK+84lFdBmn7ckKDVLXN5VclEOHyC7Ey+vdy6+eF0UiGKcldGm/3w+rDbt+Xuq81AaYurBkuXv3bpUGQOM0fvjhh4fD4XyTiOPN9Qf3Xv5kERN2Qq4qf/Dk6sX7dwihlvz48RMtxRGuh+Fw2HcpDcMgYLnms4s7X/rNL33Lt32r1rZbbi7u3TXTUkpKqbWWawHDkLomUms9OTnZbreqWmsVkRDCbpxjjGpNRHLO7vrmqfd+vV5P8742FanOUQhBRIZheDY+7fu+ljJN02ZYLcvk2KcUvfdVWogxQMxLXfIETZmR2TvnAag1NUUUdc7pc4hoz4kIM4uI956ZVbXWimSqSqjLLM6ROe77fh6PyYdq4JyrNQ/DOue83W6naVmv1+M0heD4FlKtFRFba6ra972ZxRidc1rb06tnzLw5PVnGqeu62loTUYTUd7XWXIshdF0yhBjjNE1m1lojomG9evrkcUoJ0WKMJycnAMDMzjkjDOxUtbXmnFNVIhKREMKyLCGl1WoFhGZwdnY2jrOPMaVeRJi9iGy3p+v10HKR57bbrXMsrfWpa61dXl7Oy3J2dmFMy5Rj77uuu9ldtlZLXYC9VGlqeZFcxHk2AG0lUPJktWYoFRHPTrYnmxUA+JAePX72+Pr47tP9W2//7R/+3L/1nZ/5JDXZdqlJ1qZmWJo43xkIoCs5O+e0WcWGBIZ0dnZ3LjoKAadlqXF7YWnrPD1+76qLXS5LIKgNkIIPPRGVUloV72OrCsSeGdSaFDBEApUK5lQxdKFbDaUJOW5VpyKhH0LqTYqRnpycBJNOuwAAIABJREFU9H2v1mKMKhC7FDgY1Kvdzauvvt51XVkqBX/c35joYT8uZX99c/yH/+gff/Xtt0NKkd1S4fzug3mZE1FeytnZxXI4kOpht/eOzKzrOgFbSjaz7/zO73zrt796ducuBb+MEzCJCAC01kDNgESkiTEzER0Ohxij956Zl2Xp+97MVICIVNRtT/rD4fDOu09S6r0L61VSaznP2/Wa0aaUEIAMTzcny7IAEDAh+9gNoTQDUFUB7IZ1njKgGVCIHQCIzq01BQueV33vnKu1juNIRN77UsrJ6QZuiTKaSJMq0ftxmUIIqrDb7UpZog+BHTPWmqMPeV7MTJswY63ZO7olIuNyQMRcF2ZW1XlZUkqiygBGsN6uiV2uxQjnkpmx1ro9PXl2fZXWHQDQLcBac2uN0XVdd7Pf3blzxxDWp9tAzkBqreMyK6qZqaoLXlXpY7iUstlsRCQwG2JInYgcj9Nuf9hsTkqT2PVi1mozM4VmwKLw4eOPNuv1uu/6rislq6r3fppmBiR0iGyGIlJvHYqLab09ffrkQ2Ym4t04vfHGG//n3/8Hf+pP/HEjrnXxMZnJyeCgZuwoMnzy9VeklRiHisGv+2PlYXOxX8b/46f/xc/87M/+5//JX5qrptgzcx1HRRmLou9b0z5tpBU11qIQFBT3++M0LsfDlG0Kzg/r1X5/I51f5p1zy0nyIgszI6K0YkTeESBoy8jOMzcQ73ieMhD2gdFqabWJk5xLqyEEjv1S6vVut96ehBQta/C63W49w7RkpnJyuo4xjLl2fby6uvyWb/kUE8zj4Z//058G0eQTajk7Pdn/5nv/3p/40/taamvJ+f/mr/3VO/dfqtPh7bd+6/LDR4PnF+9d7K+unHPMqKpFjIhWw6bW2sReee3V3f4IasuST87Pbm6uh2Houg4RRZG9a1rnOTMjM88lMzPIx7z3IkLokNBF54ib88DOnAMfiJnNZLXuh6FTVe89IrZcRaQ+l4YeEZ0LangrxNjEQgjH42RmDFpF53n2MTWdizSVCirM7Jyrtc7zTETOuZIbEaFZa81Micg5XveDkaWUHEFrHYmJSKvNzFQ1pQQAIoIGRKSqOefUx3XchBDwlkGtdRzH1lrXdc45TqGDHhGZubW2LAuohBSrNJ+iqCIiIDZVRFiv157Dbre7f//+5mRba/XeExgTtNZEhJkAAJm6rvPeL8vCiKp6OByIqO97/VgtrQafXPCl1dxq8CmE2A0rEfHkx/HgYohRb25uRGofU/C+1YYAJ+vNNE15Kaenp2YQY1drzks5Pnt6OO5Mcq5VTF988aXVavUjP/KjSO6tr371k598g5m3sfv3/+SP/s5vv/vSSy9pyZGNEZZ5bo5/7c2vLFV3l9dE2Edan5y++96j7/nWb+ljnPOyXO1Xm9Mq5ik4MKlTdA4o1JZj9IHSBGG7PV1tNh26w2F3crLZroch8duHq9VJ2O8PKeAtM2NABjAA1ZZSWmomMEDJ84QEwzDUWgHBOUJzwnjnzh10LCIAcJzGvu/neV5FWqbjajUw47BK8zIb2dvvfP0b7z36jS998Wtf+20z+b7P/p5XX37tz/3ZP/Pf/rW30KCLMS8153I4jI0Z0I1z3h/npbbo470XXw5Mx6vL3fGQ+m73bITgSilDn5Ap9d1hGuOQRIF9ODs/b6Y55xAiAIQQWhMzZSRPvjsZ9vsbRHTOmVmt1cxCCF3X7fd7AFBVJ7Ws+siwFbHpeHAuaG2nJ2eISETM7Jwjg1va5FZeSkzpcByrKhHleWbnllyIHRGlEImc9/E4jfO83Lt3J49jSAnZlVIUsEsdgC7LMk85xthFT0RMyMxm4rybc0bEEKL3VqeplJJz1iYegcDHGI2Cc67cykofc6FLzJxztlZVVUxba6f9GQA45xBRtJFjRsCMotqa7A57F4MSdl2HiHmak/Nd183jBKCr1aqUklJaliUFz0iV8y01QETHTESHw0FVQ/DIhAZEBKi1NB+jKXrvBxoAqKqEEFabtXextYZGVDI57sPAjMPQaW2tCQFK02maiMh7fzgcHj58+fHlU2autaqad3E3Hb1nrXr34lxKDil89PTZSw9fQeTATvL09le/8vD+C+uglLxpnseSVusP33/n5/75zyS/Tb47TmNZyr/5fT/sHX3jG9/4xBuv98OqX6+dd2bFgX305PHp6dp1/ZIF2C2lxD7GODx9dvXz//JfoU8lz9j0+qPLHc6X77/7Ha/9a+N+cQ7neYZbTK01ZjazSDH1EdQI0IegYE2boY3HMfXrEEIlunPnXLWVWsUo53x2dtJKdY7FlEBNm6j4EC/unFxcnD189bU/8Lkf+Imf+C9+4Ad+4O7FGRGo6jAMIlWEAdxHTy5TSsuyIGNrzRDYeQBdn55Cy+Phhhxd767v3LnY728UrIkp6lhGDv6DD5/4GE4uLoBccs4FHsdRDcxgmmfnQmttmpZ763XtOhGptRJRCAEAVHUcx/Pz8/k5d7LZ7o8HE+26ntDVKrvdTlU361OR2loxMyZiZgADgK7rWpNulWACA3Del5y99zbbNE3npxf7/V7BQgj9alVr3W63pk1EvPeICACImFICtRBCDK61RkTMXEr1zASAiPSc0MccMQR2zqlqKQURGXBZFmkWUnTPtdbmea41IyIAOOfMDBFFBG6h3QIAImqGKaWzi3OXooABIRrIsELR8XBkxKHrCNCzU9W+761WAxORWquZAWKtuCxLCKHv+3meWql93wPAsiyOA6ghoj3HwXFhZAo+1SrMnpG2221d5tZq3/ciTUyjj2zQShUwR7fs1jvvvBOHHgBqlXnOALpeb1srDx48AFVm+sbXfuf8/A4RmRmh/coXfuWVl1/qIhwOT++cnQvY6uz062+/04f+L//H/+FP/sN//vajJysPfQyvvnhnvLlsFb/85luvffJTSxPJy3zYvfzg/roLBrJIbYhlyYFbW+Z5ufm27/j2P/C5H1pye/edr9+/c/fBgxd719778i+XZQnOqbVSKzPH6EUEmQhxKTkmLrUQOWRChJKrgQ7DAMSiGkLYbrfLsiD4+/fuSyu/8Au/8Eu/+K+2Kb52//SP/dHPlXI1rFdjtu/7vt+7PduC60TkeDxuNptaa+g8mVycn33w6BFau/vgZR94fzj4lEqrqhpuxa6VmQhdF3eH8fyVB+Nxx97PJfdDT8zjNKVh9ezqBgj71Xq1OTk5OzUzEalVnCMA8N4TMSIys6rWWlUVAPA5IjIzZv7www+JiJldXQoDn2w2QA6NUqA8l5ur6+i6dMsHEQHE1qTUxUyHYWDvxmWOMQLicRrXq5W0FkIgwBTcyOidW5althxjLKUwATvHzqmqqHrnEAEIfxcRMbNzpJUAgNAhspkxMxGFEEAaAHjP3ntQa60VyyBq0kpW55x5sSaoRkYqagbM3Frz3puZqjrnVCzn0lpjZqna932W5rxrKgBKaHmZtZX9/vDgwYM8j+M896uhtVbnxTkHAKpKRBzod5loF5PU6pH6lHLOy1woEQDGGJEsz3NyDAAiAgCISOSYGAAWOZqKMZgZIosYEzrnzGwumYjW6/U4zjFGVQUA772ZqKr3eHF2JrVcXl71yZ1uBzRa9d3XfuerF2fnqy6Z1S6iaPY+vv3226fbbQoxT9d//N/+3Nfefu8f/5Of/mM/9Ie9HO6d9MepNuBff/Ots4vzGODlhy/k62eJ5fo49o6k4bDu5+Mzq8tw/nBYb52Pq7RFeq8btvNUhcp8yNM4ey7eYfAeAFSB2SOxqq5X62YA7GI/TNPCzMZumaZsBaA6R4L1rbe++oU3f3vot5/7Qz8IAD/8gz/0R/7w552060dfrzkTU2stxuHFF8+8j/tx6fu+iTFzF6NDDp7Pzs4ePnwxelwEj9PknCNGLbosi6pWFWY2I47hmz7zzdPu0gV+cvXRvfv3b25ujuN8mMYNum6zevjSy+vt6VLyvJSUkgvUVKRq9I6I5nlGROZ4OBwCuwZFAVRqM2FgRGSHMThmJiInzbSqmGstO/YuptWqlFJ3ux0zO+e89yKSy6yq3lEKfinVEx9yRkTnXGvNO9dK7bpuHMc+JXKu1npzc/2t3/qZR19/ezV0RISIzjlmNtX9fi9SiYgVGQERzQwRa61mYLcUVbU1tedUlaIHAFVNKYmII86SiTDnzN611swMEUXEzABgHMcYo6q21mKMzNxaA8MhpavdzfF4HPMybNZEhKC1ViaqauthMJGa69B18zSnlKoaGjCzIKpqXSozouPA1HIxEWZelkVq8+w8u9Yk9ExE2oQMmJGRnHNmUmsFtrosJuqdW5Z5u90SOWsmKqbNRL1ziLgsU2v12bNnIYRlWUprotXMXnnloSP/9Orx4w/e+67v+E7P4L1//OGHUuqLDx8gKkcOwc/zAgApJQBAq13wtU2feuU+/sDvffX+1lku2ZaMmIa/94/+3tX++j/98b/w9OrZSjOhnm83x7zMWcBqipxFH737zsMXXui6XtF/dHn58sMXfUpQyu54mMbj8ebDofMAcDweVXUYBh+jiF1fXTkfW2sXd++JAQLXJkCOCbq0luaE+POf//zLn/rmXDTFAYyYGQxjjFfPbl576ay0JYTwtXfe+/S3fbbUGrtkSM75rhvWQ1enhRnOz8+9Z7PqYnd1c9WgecXgaJmOIlKlhBibUWk1t3x2cXE8XK269fV+t95uFI9n9+6EflitN7EbuqGP/XCcp9h34zjO8zwMQ611nmczbE2h5b5P3gdAvdVaMzNVFRFEDCEQkao6xrDUSmBgzhSffPCYmR8+eOH9Dx5fXV0t09T3fWutlAVAmd2yLOzDPC3MTERSS9eneZxSFxlBam0Ix911iNE5fu+991KXRKGUtl533nsRqZKRqAud915VpLW8LM5RdKyqRO4WEQFAa40Zf5eqSm3zPK9WqyXPIQQmjD7k1sBIn7PnQE1NjvOSfUE0ANAmfd8H5xFxnmcGRKM+DagmJkhABss0AwAaSG0OsJXap4SIDh0ImBkgmqjdAiSi0vI0jiLSb3qRZtZMdT6OPiWtTZlaq2qNnpNWapacK/hARIDWpNa8lCXEtOn7Ls9TqbXvOsc8z3Nrre/7LHp1dbXkSt55H1JKu93+6eP3TfL9e3c+/OBdEPAcrp7tVpvte+8+2pyt224O0avquJ9feuk1VBOp83xM6ex3vvplsIxlit0KUy8+fuW9y3cuDwr46OnO311TnTsHUrVLQwiuT6nWPZuuujVqWqYJY3ry0eNPf+oN0RIDLfVws/sIjjfjUb335XgsTaMZtYaI4+Gw2p7MpcKZBBcIbC41hoBEqqAC++l4cX63FjFFIjIz772ZtVaubq5Pt/78fKi1xdg55+YFFKxLXUy9ATUF8g6gbbdrF/w0jsju5HTjvRNp3vnD4ZBScs4pggHsj8fT0xOnZXOy1ZLv3r93PB7P71z0w9qldBjnbs3zUsYlk+PSaq6lWw1MzOidC103iMg85b7rDscjkoUQELHWamaMFEIAADMTEUfk+n7VWhsP+812670jonEczy9Ob673h8MBAEpZTJQZvffjcZ+6gRCwCRIGZm1SlxyIES0EP8+z1jK1RgZSqqimvifCUhaRGkLouk6ljuNIaAxIRE2qmTkXm4EaMJCZaYNSSnDeDG/luXRdREQRWZZlu90CQK21lNaGJs1yNTVswipVpMUYjZ0PgYjMTCkicakLokNv07z023UzYWJTWGoTpGkaL87Om0gVKbVaLqrax65VldYMbzGSIRiJ+uBVdRxH7310/hbCx3xKAKCqjMxISMhoOS9ITrWVopvt6ngU07pZb1NM+8Ph6urm/HS7OTkZD4dSSoxRVMXaPI7eezE10xj7EMJmlXZWGL0nYHQpdMfDdO/ePQUcNsM47dl5JJbSVpv19fV1YNclevTo0bCaN+uhi9ubm5v333//pTe+eQL/v/3kTxXz5NLf/l//7l/5sT+7Dkm0+OAJUEHzPAc28vHmJp/cWR/H/eHZ5aNHj87v3ok+OGueed0P3tt0cx19wjWX1kIIoIaOGCnnrE0ZqS45pNjHZIRNBFEF7GZ/89rLr4+tmtJxnrz3pRTnXGvt6uryu7/jE7VOovjs8uYTyky30LS1Wrt+HaPXPM/jbrtd13IjImWaFKCU4pAR+HAYY+xCSK0uCKCtMUcmOD/fXn30kaGenp/NuTYwqcI+3OwOaeg3m9XNfjeOdZ7Hvus+fP/R2dkZM+ecY4zO0+Fw6Ffd8pyIOOe89+2WimfHzIjoWmsioiBN6zQfQ3Tr9TovFQBLCnvi/c0uhICGXewRUVrdX1/6mEqt3WoAkTkvDkykrobOTEVqLQuxX683eRqRoEzj6fl5U/GOELSWGoMbd5UhEIKZMGAfU5EmUp1LfepKzcxMz6Vh2O2uz7cn8zynlGqtzHx9fU1Ezrntemg1i4UKlsUUuYhFF7NiE86LMnGMaSo0ZXHEtS0xhoJERZ0PpVZVnapJNehWz+ZZ/38mZoYu0CRq4F0IrbUheLXStLgmt2qtwzAcDgcQBVMAQO+b2GEah2HISxVT55qZIfuYXHBuyWMIbh6LgjP0IWApx+OUV8NGYXTelVZ9DJeXl3wLrZapv5Xcdrta8lzm5dVXXgzeWZPpODmO9++/4II3kgcv3G1a33zzS5/+1DdfXFy03HKeHVu7JfzGG58IzldpYvjrX/raP/m5/+swWfMRBVqVn/v5X/7Bz377aR+dTwLAwYM0qdpyY07b9bZFX4FyzqpQcksx5KmMY07eS1grO+KQ23HVrZZ51iIAMF7v4tAjk5WGRqXUpkqOS5lT71pbaq3O96VJqRXIFERUkodxvFbJaBZ8//RyctwlrXM+CAAjibnaKDCXVled2825T6ll6/s1u9gqaIbHH16uhpM8N22l97S/ud7GJpYD2enZUAWrNGGsCGTgvZ9LLqUcDocQ3ThOfZ8C0zB0d+9ePHr0CI1aKzH6ZVnmeRaQps3ACKlIMUAiqk1znpnZAYABMPlbMfpmuiyLmTmORJRSYmZtgkTeezQjUGJnoKZtPo5mhoh8iwARWyve83a7PR6PeZqHvndgBKatzMssElNKplpbYyZTJWLvg5CYGQDEGFuznLPzDABE5JxbllkV2DsuHII7HA6ttbPz05yzmYGaIhxzfny5n5sZAkhzBI6QiMwQycXYeUcewTmc89SkHI/H1poCOOdijIC66noAQAMDVTEDBYVbuY1qXFVcgdbKYZw6bBdnQ4wBQOx4LKUQOuUGTZkZEU9Otle7m9Yaw8dEBNGIoevjMk5EtCxL7FKMXQjpcLwuVVICRTg5PT+ON8xxt79WlaurZyEEH3zOy2b7EAAuLy9fuHcnpbQeVldXN03wpVdezzkTQpcSOfrSb/zWiy++FFMvCkstYvqNr30jxf71T3xCFCjERMOv/fpvCfpv+67veedn/2VgmpcxEXz05Mlms3EkQK6pnGw2Lc+QHUFc4faXf+3Xa1iZS4+ffBRD8syt7GuRV195PS/HzXq4efqsWb3z8mv78TikVQzBe19by61ORe6cXUzTNAzd9e4AjQBwWZbr6+tPOFcARGy33xERAKgqOZimo3PcSs25PvnoqlSRMsfgljyaGSJXUQewLAuJdDEZUjDq0lBKy0uNsTseRxH58ltvvfuNr3z9K785H57+Z3/pzyfft+UQghtzq1lPT06B/Hici+gtETkcDr0m5xwzA8Bqtfrggw8AQFUR0czmed6ebjh0+/1+WZau65xzLTcgQkMiQkSnBpvTk8PhsDk5SV14enm5OT0REefiuMz9ashLHY/HptZUpDZEDCGWJs6F2tSIABRV16tVznlZFnyu73sDV0oJKTKCPddaK6WYKgA4582stUYEzRQMEJGI1GScxzWvRMTMELHWSkTee0kx50yOCWxeMjMboAI2scM4vf3+o7m0psCoYOYIb5khABAyoKE0RGQXEKnWCs8RETOriupTACBAA0FFAAUgAEVQdq4oAhOqoLZ1hG/99GvDi3dW62Fdq5oZagjRp+S9V8TjcX96enp9ff3ghRdqrXNecs71eLRWGd3xOEYfwKDrYilVVV966aXdzdU8zw5ps9mMh2Nr7enVMwA47m66rnvw4IW8lKdPn56enXSrdS6t1N3773/wwv0XgUjMhhhLq1/58pfO71w8eOFhCNHMun74jd/4dfbhxdder4ree1H4whd+9XAs3/PZ7/8W7n/u539hXHYr1rsn3R/8N/71x4++8anXX20AMfWXVzebzQaCy5Ue3xy+9/t//2hs5H/yJ3/y8vLSpFneXdy7i45Xm/XTj55YE/Thaj+e3b3/9MlTxK4K9sOaTDmEac7OuVyW9dAh+yZwnJfD4bAsS9wMTfHpk8thWJshAM7zsn8uBUdMl5cfIdlccu9dkZZSAADvfUC4vLxa41LnZbXellzffPOtr/2Nv9Fau//CC5eXV6+++urLrzz89s98svzB7//pv/+/A9I4T+tuU0ohLF0XWsHQhWHtc85AWGt2zrXWuq5j9mi6Wq2ePn262WzUJASvqiG44/EIAETEgPvrm81m08V+WmbHzjsKITifYmvivc+1LLl670urzoVlWaZpWuaiqoBoqrVWM0MFQxARQFQE75yqiokL3sycc6qac0ZE57wSiAgxAUDyoapM02SqKSVEBKSmDasYIZFT1ZyrKuScmUhEWmtVmoIx8+XVDYCWZTFkZizSBu8VyJAVsDarTRWcoSI7NFFVBAQiBFYgMDEjU7MmxISYABSARLUIioj3ERHN4BYRmBkiAmitMxNmMRRgYjIxgJR6BVQzHwIwITCgMTskA0Nmzq30fS+tmRkB7na77XZrZkteVDXnfHJyUkoRkYuLi2WZU0qqurRFNHcpAEDXdWbmnEOmmNJHzy7FdHt6yo5bK+NhotBtTs/med6enQPqV377y6vN9sGLD6sZI+WSH733PqfVG2980sWIiPOcv/TWm83ouz/72dglKfKX/+Kf/+/++l8/P9382T/1I8Hy6coxQUrDYan9sK4GObdm6b1HH955dXcUQufzMp5sBse4HMQHPk7jpo/sXRy21YBipH47tesubgFAXTged/0QS16IoLVCbFrF0KHa5UfPogtlKWh8OIyrfp3n0vdJxDabEzNA5CytajVUH12TcjjsFexXf/WXv/jFL37jzV+vhw//yo/9mXkpx/myYfdd3/Xdn/v8vxOHdBz3f+d//l+++TOfrlqPsyam4/EogEihNolpEIi5iOMu58ruY8fjUVX5uZwzEYG0ruu2223OmQBFpNYaY5ymKfUdAaKBOQO1nDOoTXkSERN1J6fn8zy64GwG1davNwxsZl3fhxgX30A1cV+XXFsGAEfeDA2oqdbSABHMiKCUwo6hgZmFEHa73bwbt5uVqgKTiKhqa63eKqICQxfpFnhyJB+zJmpmRAQA8zyamYgsy2JAYkpgzvlhHXeHGxFJfVfFgLQseWlwGGcxU2JVbgBMpFpNzQzADEAI0XNAAAKvCGogYgpChEgI5IsaIpDBLTMDAEIAQHKxGQCQIAAYocu17vbH7aYrVzeK4FJHjuUWWMklxJ4Qk3dmotqQsO/7lBIzlyWbGQA479lhnkcFYheXklOIYopEzfRwPC45O+dyzobQd52IOOfOz+8YoiA92x216YuvvFYNOXZzrb/yq7+82awevvrGVMp22x+n5XAYX3r9m7quZxfMLAa3OaPzuw8MSYxKlc0mrtfdd3z6lX/3j3w+oJR5LFPeo/mCAvjkar85OWVwDeDx48dNShc71cK6HHcfoQHbcn5+SkRLLSfn9xDd9XH+Bz/9z37xV7/IFADIe//jP/YXiZ1wbOXggGKM++OBqavSTk7PW2sxdtPUhtXm8snldrVlYM9c1GouUqrFwMxLyX/zf/xbX/riF5wLJ+f3zfSzn/3e3//7vr8crn72p/5uqdL3K/bpcp9fuPcAAFprp2dn8zyfnJys12sHGklUFYlatVoyl1qyAroheRdDLsWFqAD9ajWPh34YWs3/H03wGbR7ehaG/Sp3+bfned5y+p49u2dXEqigLoqNTSREsZECchyKCcUxxp5hMsz4Q5o/ePKdmRBI4iTYwQELBEYCDHhEt1DXorKSVtJqV6s9u3va2576L3e7rrx7gN/POScJnHPXrl177rnnfO2naUJEIuq6jplBlfCv5ZxVcuVtSkAgJoSAyIjaNE0/9SLS1E1KKYQQYz4nIogITCUUEbGVUwDf1DpNipBSVilVVTFZAAEAEVFV72vRCADW2raqY8nTNMWSjTEAZRzHxaxVVRHBgjGkECMR1XWNiEQUwwjnmHLORBRTWW9283lXV9Xe/iEhxhy0SMpivGcsuSgZh2oAJKZEqN5aPYdWVFWkAKC+LJcgCMysqIioTKUUOIdAqgUeQABRBGRAIJICwoSIAIAIpaQhxJRyVbmmdqpaMhATWxNCUAgA4L0Pw0hEpRTrYD6fTw9UVVVKaZom5wwAOecpjLN5l0IUyZW3RHT3pfspJUK03mHOVx96qK6bA6a2mdVtE3J4zbXrBp2qNlW9Wq2Y9d3veU8puZTiKzuFcGn/0qWrmIUUuAg2XUUoRJAxh1jqZuEQDWMcNv/sn/z4bn1iBJWNc3XK6hCZeN41zrB3zcx1x8e391q77jeL2ezSwl9ceEs2R30uTWMIqrrehe0uPv3c81V3aJu97WYgIrDuV37jA2fHd/+Hf/EzVSl15Yd+Y33d70JVd9M0jbt+s9l0s0t939+/f//69YdTSs9//ejLX/jUww8/7L3POccMp6fH/9O/+l+G/r+qfPPxTzzxnz/8BBvc7Tat46Oje99wfe6rKuWyv3/wwtHY1U3UFGNGhdpXRBSnwXnOOTvnUFK36KSUnqeSoe/Xtqp95aYY27bNOTPzbrcICEHCAAAgAElEQVQ72F+ISIzRGMPMIYTKu3Ecq6oahqFy1TgM1lpmjjEiIjMjIhRBUTJslsuliMxmLRJoLoQ49kNVVVOZGKnxVQghxmiIrLXDMChASqmp6nEcmbmcEyGiEIKqGsYQwjBMiGjIEiAzGstTivlcycYYZgYARFaVnFMpJebEzIiQVbqqAZQiJueMZBREEZWk7mZk7enZara3cGw2fX+w2CMHqpzRxiKIXLIYa0pKiDjFDADEhMyicK6onGPHKgUIpBQERFIpoqqIqACIqKoAQgSAKIC5gAKBMgIiqKASYdM0zrnFYl7X9Xq7iTlBKBZREZqmqev6+Pj48uXLm35HRJW3hDrrOu/ccrls23bqh6ZpkKiqXDubb3Zbbx2iGmPu3btnrU0pOW+HYZjPFnt7+zkLEjnnBAHQJiVB8lU95nK0XD98/eoQiyXybZNSaGd76+3O2WazG/f2L7raJZFSepEsSiHB0Z2j7Xa7PLl74+rh1b1aSoqipmrP1n3btikV76mrPDJInpzz//THf7A/ebGyNBwd/Yt//mPT6s7tk3WOw0NXryhksD4ldW394U985nSMauvu4BKRufPiS+Q8sf2V977vn/34j2ynyMaHGHzdIOJmsxtC/NKXvvSpz7z/xTt3T0+Wt77+/Kxtbzxy/c1vfNOH//h3pjBaJle7czFGa3xK6eTk5NpDV1anJ7Ou6zdnq9UZMwNASknKcOFw3xBOodR1m0Oedx0LtLNZ3K2JcAqDJdhsV95apFIkzRZzMobYxpy99yGEqqp2u11VVSmlTBRjDCEQUSnFey8iOedJpqbuUkrTOFrHIYSUUtu2U5gAwBg2R/fuszV377x0eHhoDPf91lovKQPgNAxMVlVLztWs8W4mIogYQjA+DsMASABARMbaqqrGcez7vpTSdd0wDNvtdn9vXgqUUnLOAFBKSSmpoHOulIIPAKC1tq7rWPI4TvvzRZHEzCklRCJjEF5GRIjk6ibnzMyz2QLYMCKSKQR6DqGUYuuK2RChtSAigKyIikCAZAxqQUQAEFABJUQiKqh4DgAJAQBBQRBICQgRiVSREFAEFPWcMby/v4+I0zCklEIMzNyPwRF2XWetPTs7SymFEEopxhjvPQBM06SqIpJSaqt6GIbDw8OQ0zj2dV2nMKnqer0upVhrnHOqYpyb7S12Q19XLRkGwpxFiXdD+LX3vpeIQWV/Mf/+H/g+kDSfz7e7KaVg6262ONz10//2i//P4cWrF/b33vnOv3vp4kyBitBys+0nHCZ99eve0FU4c7I8O6lcu+0jVPu3T04fu34xxujZVnWVc0nT1oNlKBW7qq2GYZrGdeuw3r9w//iOa+tYzK+9/7eWJ6vvftd7fv0Dv92083FIIGH/8OKNhx/56Mf+Ynl2+u/f9/5/8O7v7WYe1ESRfrs7PLgYUrx58+a1G98Qi/y7X/6V97znPZcuX6gqZzRcvXyRmY1BAOjaVnIpJXVdd/v27W5+UHuTxp5J+u0GSXNOVe3vHp0eHNwgTZU1eeoRsicilN1q3dVGJRvDmkPtvbHkwFhrUhot4naYgNzx8bFzbhzHpmnGcfTeG2PGcdxut0TU933XdWdnZyklV1cxxpxzKQWSAAAi7nY7IiSFlJJZn63JkpZ0P0REaJpm6idrTE4ljME5IEQCTSFaNrO264cJEWOMTdOsN1vvfSlSSskPeO/DOI3jyMze+xDCpYNLUvI5VUXEUooUMMbEGBFRRQAglSIyNl1HRIKAzEUViJB4t9t541S1xGStyaUAGhFAVACQczkiGtVyzjkHAMxcSjbMRARIMSdEIMScg3MupcJsc86WXQyZrAURZi6lKAAhEjGooKq1HklFhIxR5b7vmQm0MFGaRtstcs7W+9lsdna22tvbs0g5y9nZagjTYrGIOasiInfdfLvdOueWy2Xt/KztnHOqGsIIbMq5cWIEKQIAqgoAOecxBmttXdf8QNO2dd0WJGU7BP3u73337dt3b954+MMf+fPZfE8lM3HVtmxwGKeQpyzmn/7z/+7O7aOuts56Y0zMyfp6mNZou9P18eEuHOxfnOLGzw6Xp7tVL7/8q+8refrWN77y3d/7d0KIMU3r1bauG8nFEEbGtaqput1YZnuX+2H8gz/+80985jPd4tKuDymUf//rvxHJnN2507ju8Zs3X3zxxeeff94Y07TdU1/52sHBp9753d+JSDL1bF0fgvV+mCZ0DhFF82ze1rUP45acHhzsMQIxDGG4dHhoCCSX7XoZh+HaK19ZwuANS445jcyYQiQoIHE2s85KHpOoSOhrD9v1el5ZhOQZ9rp2s55CnMY+iggaO42xbkDUagmH+4umaaZhVFXnzDRNKeYQUpzChQsX7t2/G2N0ziFizMk5l0oupewfXhiGoYiAKhDGmFitSSl5tufausklxRBSSuB8Shm0gBRUdc4BQMqBiJraj1OsrBljcsaGMBERgmw2GwSw5BBRREopANA0TYwRUOFvqCoAnjPGWGtzTNM0jeOIiECEpM65vt+KiHMuZ4kxWjLMXEoBwK6b5ZwQ0fu6lGSMESk555QSqogWiVpycpYlRz3HBhEBoEjSnISAyDK7FAsyW0uIpAo5F2ZGBSRQLaBKwHpOBEBSGJE9MxpDnEG1WDYpRUKN0yQTeOska9Iimrv5vFvMiWiz2VRVBQDDMKhqKQURl2fLpmlERFWbpgohlVxiTm3dAMrZ2RkiJoRzqjqfz8mwtZYM28oDk7EuZiRGJD+OZTNMxlXWV+OwsewROYsUBRGYQhRs+zGyCjPnnOFltFrvLl+7eny8uX7t8hCzFSpcv/f977t3tA5iSylPffW5d/39d7IpSDKfz1Wk5CgxGc9FcL1em2qx2m6fu303orXdfjF1ITy8NL92/cYXv/p00zRvfsMbH7l2vXL28Vfc/O3f/e2HH7351Gc/+7EnPpvZf9/ff6e3btpts9LhxUtAXFSYqe+3s1mbUui6VuNuf39RVS6XaNlcvXhBYnRMxrvV6VHrLZbIZEHj5cN9JuXKppTOlsevfPXrStxaRihau7I8enG7XvXWSJ5+7Ed+OMfAiLaqEuA5Y0zJcHJycnj5IQWDiDkmVT07O0NSZp76wVpbSrHWzufzlFJVVaUUBB6GARHruj4+Pj48POy6brfbFcnOORA1s64BAJEspaACSCFQUtBcCCCFoKrWWiJQNa6uQoyiWVUBpKp93ARCBADvfQzjNE0i0lQ1AISYmTnnbCwTIymeE1WFl6WUcs4gaoypaz9NUy5x3s6NMeM49uNUVRUosrHIdoqxqupcYtxuc87zWcfMpaRpmowxREwPoGAR0QcQUVUNMVvjvbeWHelstkCwscitW7dKKSCgRSwbAPDeE4AxTATWmKapzgHInXu3N5uBgRiQQQEEpFhDOaVZ64dpqttWBIwxYRjZYEqpaupSSi5qLAJAKQURh2EAgKptjHd93+ec67qezWYxS9/34zjGNK3Plk1TpQmnaeoW84sXLxrrnXXGOGu9CqKiKu52wwfe/zto7FNfeep1r31lCMFaa4yJIQJIU9Xk5N7ZkXPdOOVX3Lg26zrjcpYSc9lsduvts1/8/FOq40PX3p5U9i4/dLaLY+ag2Nazs83d1S4cLIzEVDkXp7BZ77xFQZOB1c22CX7uf/15087vrVamaamkv/3tf/dLT37hyc9/4crDD925c+epzz95dvfOO97xjl/7jfctFrOvPP0MIK934VOf+8L1Rx9982tflXHsmvls77AP0XjXT70iGEOAwAbDlKZx3FH23qY0XjiYO5ASEzNO/ebGlUtGg8Ywq7iyWvK03pzt7+9Xnm+/9LyvmgI6m7U/9ZM/5DC0XmqHbXMwTYMKI1KKaTdMe7OFNa6rbb9Lja924zSFYInHvveWt9utiOx2u6Zp5IG6asK0srU9lwtUTa2qyMTWKZJx9upD1+7duRtCIEbjvUdEKclao1pEMiMWScxExAAEAMyYc04piYitvCFUyZZYUZ1zAAJFnHeGGxBNKeUs0zQVga6tiZSIABGYsBCKlCJFJaXUtm1JeRxHIG2axlqbc1LV+XxunE8phSmWUvCBcRyNMSkl7721dpomaznGWEohtoioqgDAzKiac+4qH3MgEAJFVAQxbJxla7zNAiUDKBsDqEggIowAIMyGGJyjblbVziPimfdbRAAC0HMIgAqqiqIi0lS1lFLV7TAMtfeqOkyjgBpjmDlnIQJrbSllPp/fuXPHex9CaNvWOUdEpRRj7P7+/ma7Gs/6nONqNRljEDH/laJAaH2VUmLjSilo7Ha7/cl/8lN//Ed/8oY3vma9OkqSVBTzZK0FwFIKKqRYFot2tdpsNjtjL6sm630qsFwuv+s7v+MzT3z6Da/7pnY2325iH8Ijj7/iy1/+OqMZd8vWVCkXERNjLEUlZVd5Yt5Ng2ubPuOff/iTexcfavYPeqrGlIYpfvQjH9/rZu9617teuHt7s1mP6+3Xn//aL/2bZwQAnZm33XqcKt8OY/q/funfvf1vva129B3f/nf2LlwWhVQEmYdhp1AIMYSw2+329uYVq2pJIV67dACpr+sqx+Fw0exW9+9qmoZdZeBHfvAfhmlFkDSnRx+5zrYxxiRJknbXL+31212GXLlKZaorAyCImHPJubDzMRYVrOv69u3b4xi89ycnJ1VVlVJEJKVERDHGuqpEBBEBQFUJ2Rjc7XYA0DTNYrEIISBq3/fz+fz09JSZjIg0TTOfHezv7+Uc79y5M/a9qnpnVbVkKaXkrOeIUFXSNBnmUhIZm0JuK6+qTdMAiLUWRM+FMJ4rAqFpbGMAQB5ARABQLaqlaRuRnFJQLaQUY1SVRbcvIsgmht04jtZ672sBzKJNVU3TtF6v5/P5rGuZubJOVWPMRUqMMeesxAiAiAqQUipSmK0xxhnLRo1ha41ILFKKJGY2rERStIgkwxWAWgNI6gw6g9YgMgEAkSEyoAKgRGQM5JzBESLmnJuu3fU9MxdQNgiJYi5AbL1LIeasRaAI5JRL1v0r+6UU4x0yI6JzbhxHY8y87dZny1nXhRBSzt77vdkcAJgZEZ1zIYSKTAiRrOacSykpJSJixhymIgWNH3Y9YLZ1pcQvvnTLuovL1YadXa/X4IMVF7MD0eP7R+uzs5IjI6QUjTFvfvMbP/fZLzKyJ0EV55xKrr3zdZdSOnjooZOTEzff30xy72Q83SVhHwu5ppvX7d0XX1CRo3v3f+8//scLVy5fvnjpWErJ8cqVK29927f8yq+9119o3/SWx5988vNNvWAbP/6pz1y8sP+ZJ798/ZGb6YmnvuM7v9sRDmEqIt5X6+Xm/t17r3nkEuZhHGPJ0aDZre+XtYrIT/3kjzRNk6d82B0Q5mG3Yk6zebvdbi07KbGoeA/Wchg3BgUlMcp6tUbELAoAztfdbOGrJuhkHFdS1putlrw83eYYwPm9+eL4+JgA266LMdZ1S2REsqoiUM4ZiLuuizEaY7TIernCvbkx5u7du9baFLOJMaYUQMt8PnPOiUjOWVVXm3Vd14YtnyNUFVVNKeWcrbW5FFWdwlT5hoiMMSo5xlhSZubZbMbM45RyzqUgIiQpOWcF/CtElHOu69qy2W63IlJXno1B0bPV0nsvIqUUxDwMQ1VB0zS7zVpEFotFXdeqmlKaVNqmQUpjKjlnIAainARBiQgIUbGolnOSuJAaJZCqaXU3IqpILkURDREZQyJZz0FBQEBBVMRijAkhgBpQpJchg1SeAcA5N01TO1uMIbjKT9PQNFVIkQxba1UVkRExxhhCIKKcknMu5wwAiJhSAuIQgjFmuVzO5/NxHBGxbdt+GKqqqusaAKwxVVW1bRtyatrWpDzGtF6dXTh4JMZoHT/00FU2SIqENO8a6yhKKUwi2RjT9z0R7R8eTLp2VeWTX54e19ZADpqmFIemrayxl69c2pt5icmg3a7O7t1+cf/xS21VGWfGc9Nk64arVlt++lN/8dLRWZxiPFm94W1v+4uPfOQnfvQfvf833vfG17/uzp3bw9Abizdv3oxhfPzxx3/1V3/1+iOP3r1793u+87v29w8y4Ic+9KG6XWx3IYTxjVce+sRffuEP/+iPx+361a95zZ/+6Z9+yze/paR048aNvj+tDcxmCwBg5pJ7a1zdNTHGPO2aqjWoKqWZt5vtibcWREsJ45Dn+3sa8zAMzlpC0zV1CtP+Yr5abbw1qUjb1aAUwqiobEzRbA1vNuHw8HCz2RhjTk5OjDGlFBFh5hij994YQkRmLqUw8bndbgcAk6i1NsaYc2bmq1evPv/882a+t9hu18vVyjizv5iLZmMoxszGVHWdUh7DxExwjokNAhKSEmEuhZlTDsw8Tj0q5JxBlJkBwFqLZJkQoCAiPKDnEJjZOVfXdc7ZEC8WC9EyTVPJ2RjT7s0VcNcPZNh6xzGISIxxPt8LYUxhVFVENEggOk2TACoQIDOrImXNiHCOiFQNIsIDxhjvbVVVIaVpiqiEiCIAQCqoitb6UlJlHSIwMyKqKrMtWQGoFEVDKgCE3ntjHDABUNFsnc0l+7pKJRKBgBpjcs6qmkUVCQAQUUQWiwUoWWunaVLVhCgirhRm7nebGMYYIzMjaNc25gEiYmYRITQxJgWw1q7Xqy996Yu7zfqlW7fe8IZvYKbGOycuhIBovbdizHq97vseEacQxhi4ttvttmtqyWUxqw2W2rKlgkwhjtcu7RvMDz104fu+5517XZ3D2mGJ4waA2qoupRhbrbfD+373g0c9bvvp4atXrl69+uRTX9yfN3/xZ3/6bW9725Of/VyM8drNG89+/dmXbr1Q1/UrX/WNdde+6S1vvvcH/+mDH/xgW9V3j09I4ej47Nu+7dve+pY3feijH7t2/RFy/ktPfXFvMfvABz7wW//hffuLxU//xD+6cdAV1LEfCHBW+wxxHEbNGKaprRqSFGOsa5fC2DXVME1N04xjuHB4KKoxatvM5VzKIEBAkiSlVLcNiRBhKrmU4pyLZTSGmqbqeyclt02dUvbOSIGYcoSAiEGDqu7t7Xlfl1KYuaS822wr52tfLU/P/DlbxRgRQEoxzKaZNUo6bHe7vieCnESAYozGmKZp+n6YpokIs4qWbIxRoBiLAqjqYrEIIRhjEDGFmFICUVVUjTlnJCOo3jpmdmzkXBFUQCJnLSKmlASLdYaInHOqhYiMMUhcVVUKUUsGKcSsJUvGyrkw9oQARfpxcJb75cTOk28RQAUBlZkJpeScEwKqpb+GBKiaczZoVJWIAKAUVUFVBQARgSLnjGFUQYVzRIRMwFBSIrIZgESM88ZakSIiZE3K2VqfUkCDzGzZEREzE1EphZG896UURJzNZiLivV+tz6qqyjm3zSyFsFgsnv3qM1KgruucEwCI5FwSMwsUIkqSnK9zLrauQeFstXz7d3zr8cn9V3/jKw725iX3EcF5n7WEDCH0aPxuvUkpAeC9e0ePveJqGFZNU6/OTjeb1S/8ws+P0/T7v//7r3rtz6AWi7LXVT/9j3/08t58df/e4d6F5555oUZjDC37MyFu2tnYb8S2T3/la6tkv+vvvfuTH/nYs88+u3/xwm473Do+0ZRWq5WAPvf88877ytvNcvXEE0/kJKuz1WKxh2Bf9Y2ve+bWB+d7iwuXrjz73PNvfetbj49O75x+7eDihZzz0898tZ7Nt5tlO1v861/6t3/nra9/+9/61rpyhmmKJU4Te4xhvHiwKFlr5wfNTDCNE4Jx7JKUtpnFGJlt0zQpRUuMzKoaYwQAZjZsXWUQCVFqXxUtZycnxthpGOZdE1JZrdaXLl2KqYQ0+roCgFISIvV9P5vN6rrebDbWetW43W6JaLlcdk2bcx6GoapdjHDnzh1VNeTNot6PKS329krOWdEYjxwB8XS1quvKN1VKCRFVtYhmYEHMOauqS2l/f3+zWouqM96y4wf6vnfOlZIUCqpDAOe8b5qhn5xzRCRSEJEIVQVAVqsVM1d1OwxDPe9iHGKYpERE9I67ui6lENA49V3bzJpatQDmKU7GsuS0Hs5I0RoOISEToCCqMSbnrKqoxRKCFEOcY2JDJQYRUVVjXMipqVyMACDGEpOqRMbaGELEnGMuQUGIrYIkkMqZIYZUZG/WFDExlXOSp66p+n4L3jX1nAyjCkgh0GG3G9rGe59zTinVdV1KaZuZqh4c7udYKluNwzSNCcCEYSIuKpkNIioi+NoLFTIQZXK+UYQCtBl6pOysOKtM0s3nu92uILqmCbnPILV3KZWu6lrfXrl0ranaxqjzPIrOF+1P/uzP/Pp/+M3vfOe327obtyez2mHZUenTNi8av16uum7ezBrLxmY9XS7dwgviV569g2pfcePG7/zWb954+CYbd3Z2WtWuMrOj0yM/r4cw7R0cHB/fe/TRK8NueXZyetDO10dLDXQ07L7w9Ne56TbT1Pd9itMXv/QV42xO8VWPP/7VUnb95nR5fOnCwe37x5DLE0898/FPf+Ff/s//Y8UQBbJmS+dyKcWyTTEawnHYOW9KVkXKqTiPSGWcdgarMKV1ybvdpqoqtg5SYDKbfnDOISIgRompxAsHF6ZpMh0c3T85W26N9y++dG+xt2dcVoRSEqI2dbVer/u+Pzw8BMKqro0xs9ks53hwcLBZr6vaiQiAMlMIEwCYpqtTSuzNYrE4undMZES07ebbzQoRRQQRiUjPASqgKEZRBFKV/ID+De99CAlAmTnnzMyV82wohABIjtgYo6rMnLOGEKy1KsLMxhgRUVVgWp6eCSiBeu9BctJiGEGBFCrrmPEcs2m4ARAiGofk5GVFABUAVEvWcyKgqoJyTosqAQACI2KSQkQCqoQoWLKWUgwSYMFzinBOFR5QVUQkRgAVKUkkJBSRaZrqxm2G3lW+Mi5OgYmstaenx918MfZDVVWrs+Xe/ny5XBLD/t4hG6yqSjRLgVIKvEzYcJh6Zp5iCimH3XjhwsEUyny/2g2DIiBir31KpW7UuHrI+ZU3H92tzi4ezPK4IemGbajrJqUyxbHu3LiZVMtue3bt6n7jYX/WYsmusdO404yONJfx8Vc8KlpyiJUxod+BNft7s2m1EbZa8hRDKE3VVtonX7Vgfcjpt//gj7/xtW+cHe4/c+vF23fvLA72CUrO8e3veMenP/3Eers6aPeOj49ERFUvXbx459Ypiy0xLc9OgJsCuNxs3vTm15/cerE9PDg6OrLWMuCnPvnJKYTHHnsMlMZQFou9NI3t/oXVyfF//y//1eG8+6kf/2/mratSNEZ2/RjG6WBxsNuu2SixRbDMZopZEEUzETGxtUCWALqqqY0xioaIVBGUxnFs2zbKZNmVUiwxe9913ZQ1JkEiY5xDiGmyZEOcpjAyc4xxs9sadqDEzKUUERmGARFV1VoDAMaYqqoQ0Ugu/Xbn2HjvmdlaG6eQUkJEABARZk65KLxMVUEVRQlRifIDiCgiRBRjBoBpmpqmms1m9+/frb0dx7Gua1/VpagxDgitdzlnIhKRcRj29vbm870QAiDFmEIIrnIlRVU93N8PIcQYmdmwOadF4hSQlAgApAB576MUACilIBIgAgAiwt8oijkVVMhFSxGgMo6jiCiCijAzACBiKQVJziGqIogiAQGQFIWixhAAFGbNabsZt+tV5xeqPJ9307lhnM+7mKYcIgKcnRzPZjMAuf7wtZwjeSKCOA0n43YYBmOobWfGmJJjSXGI/YsvfHWMpQAKWdMejlJ76EKEWecuLC4QGUKPjq2pCdm6+PZv+WbM8E2PPcKkdV2dLldqxPuaiDTHedt4hz/7s/8t4viDP/jdRkVzf//2vaatKfG8deuTe7OKWCbMvTeA3hXFfopFmdnENGUo5HkXpwSYrNtk/cinPz+o+cTnvxglv+Vtb/3cZz53+fLFvXn7+c89OWz75ckSbUljrgxW1ax1zXpaN1V3eHDx+OTeozcfstXswqULy9XB9viMAIZd/9rXftOLt28fHh4uN+srl65YtrvNzlfu9GS5N+uO7h+z6qOPPnZ6cu9//ze/9MiNy//4J/5rVxkDaivPzrVmYQzlLKAM5ECmWIDQqsI5IkJC55wxBhEVoJTiXBVjHMexbVtVZeaUEjOnnFMR76u6sYAWDZeglLNiRjbDODGzgMYYK98QUUzlnKpO01RXFQAQUc5ZRFRVRAwA5JwvXrzIhN7ase9VdbvdzrpGRFQVAERE/wooPICoRKwliQgzl6LTFK21VVWJSM4ZEXPObD0jNXXL1k0xVo1dLpelpLqu+91GcxmHYTabqWqIsWla5+xsPu/7XklE8jAMpRQQHYaBgZ1zJeUYp6qqullFZEUEAIgIEQEUGQFREAhJVJEMMAEoACgCACpCUYw5AaEogoC1BlWMMWQNgYASEqhizlkVFSMBZy1aAEkNkwGkDEziLEtJxpK11jCg6jiObdcZb3xViUgucVr3dV2XnIxl76txSuPYxxhWq1VVVd25upm2IyNNYZcEE6giPP2ZpySHH/7Bdx26LobxYHEwn+2fnixrS4rFGoMKwMWhpDT0qx2DahoLYFv7cRoQE4ZcIaEJSNTYOuUJqHO1c+J+9If+YcimbezBYaMpFIXVctPsH4ppJhw3q56pWGOilJDi/v7V5fHSg/ujP//YkH0Qni1mr3/DG5988vNPP/10v938gx94z+Z085Y3veXLX/lcP/WP3XhsvnfwiY995NKV6/0Qj5anV69dSCXeufvCV599uqqa03539eKFxx9/fLk8fff3/5e//r7f/LZv/9sf/MM/euGlF/f29qy1zpqmbUCiY/q+7/+Bf/tL/7ew+/JzLyaxStW42zDBtFsdHBxMeZzGiGStJSBEBGOMoMYkqsrAZPicIhskESAiBDbGIKI1HklLKSKgxEUVENka4+oYs4iUoinHqnI5RCLqus4YI4AFS4yxqioAQev+sWEAACAASURBVMS6rgEg5wwPGGNU1QzbTRhGf9mp6jRNMUbDPJ/PCVVEUkpEVEoBAAFEAFVFeBkiKoCqIrJqcs5bazfrrXWGiGKMTdOM49h6BwDb7fZ0uWxnc19VCHB8fHx4sJdSyjkVkWEYRMQ5h4brujbOrpZn42asnAshpBDbtoUCRJRVU0rWWlUVkRhjEUgFARQR9ZzoOSSSIoioiopQAFmgZM1JQJKqGmNAQBUVoACCoAgAQ1FBMApUFFBRBRGZAFC0iJJmV/GF+ezq5YveUkrTNMl8voh9mKaprutxHMk6ZgYtlh0byDGYl1FMYbfbGOMAxDWdlrxer3ar1eZ4PfRxsxzq/b2U9KXbt4aprx3evXer8yUN/MxXPnvt6qNk+Pm7Y4z5woVL46ZvjJ3GLVIOEsk6NHWYpGma1vvKc1Wb/YMLztRoDWFJkhCxdvW4Get6FktczLzm4ByVmPYOLq0j/tz/+cslQwlh4fUVN6/9+E/8EMQAxs/3L94/27l6cePaqz71mc+JwC/+4v9x8fDSwdWL5iH6xMefeOd/8faPf/yj3/M9f++97/3V4/snd+8cN93+MMZmfz/k8PXbL83n7Ste9crbL93tuvnNRx795Kc++rDe+PyXnsK6vvH4zQ999CPVrBXGi5evnS1PltvNGIYLe4uT1frnfv4XvPeV3yNyn/7Lr77j2785T1NMYwkhTsfGYT+NgIyIoMqgllKM0dcLRSAieRkUVWNsytJ1c0WqmraoGGdjnFQ1pZSLGucLJBFRVSIAoOllo4iQYeMcGixQhqFn5nEcvbcqQkSqcm4YhrquVVVEFNAgIhHEGJumGce+lBKn4JxR1VJKzsl7r6r4V4BVFQBKKfg3VFREYozM3DQNEcUUSsmlFOecgI5hEsCqqYdxFBFn7TRFJkueSIGIVNV7j4ZDCLP5/HR5FmNEpmmaAMA5F2Mssbws5VJKSqmUQkQ5xymmoDarKJCIFAJSLSoACCpaipYyQo6gJYkxhoimKWYBINQCIqJyrhQVzZqhAAA+YEyZUspZEJiIGSFKVMW2qZumxjIgqWVMKeYSHbsQwziOHulcCGE2m23GIaVUSjo4OAAAa60xXArEOE2TGO88Oe/rzXrs2sU4xlR0t1vWDr3DpqpTKmqNKCbIOYQxx7ppm25B6hoCBFGKrGaMwVpAZIOyW57cun/nscdunt27n4lz0RyT75rNNF27du1wfmBEurr56lefftUrbjJKZow5WjdPytshLNp5SLvrDz++XO/qxo3DzlezvW7Gireefe7C3qLt6r1Fuzzbvvji7XnbvfobX/17f/iHr3/da9//u3/w8I1XPvf8S4vFfizlsZs3790/ns3ai5dmp8dHX/ziF17/mtc2Vf3MV75w6dLFD3/4w7P53gsvvDDf2++6rh/HnOTo6IQZrfcPXX/4peefJxUF0832L164cnz37h/94Z+97bWv3atb40x2ztc2SSJrlDjn7NgwitUSTHDtrAAiaikFCLUUNi5rHkNEIDZWFJENECMpEhgmkzXlYq0BTTnENI2ay7ztpjx5X1lrpmksL5PatWbeWWsRABFTijlnVZ3NZiKi5wANIjKziKSUrLWSSywZAYqIqgCAc87aCAACKAUYEAFzEWJgNoY4i6gqMvXD2LZtzImNqdvq9u0VEYGwtTZmOeecu3TpSilpt9sdHR1VtXNsVAGNQWOkgDVeREA0PdB2bYxx6EcA0FxAFBEBoOScQmTmlJKI5JJVlYi0ZAA8p6oIrAIimnMRUZUcpwIAiBhSBDIIpIpFBVQBYLvdogpqQUT4K0wIXDIZQwDAzKioUJhxnPraCGIRgRDHruk06/JofXDh0Dc1ItbO333pNhtS1XnXEeB2t56mCQC89007K6VAkfV2w8XFXOqm3Rzf2z+YvePb3zKN/dnJqUaQ7E+XJWO3HnG5HS5du3a86h997CpRmNarwjKM60IiprbWI5QiadZV48bFaQeIWRUADlqf4qZFPnvp1kl8lgCtd6ryZ1/5S+cck7/0yKtwcV1jVsUkAgmvXrsBAGkK/fbs8ABMMR2DULlzdNxWlydN3/rWN3/sY5/yrr728KN/8Zef7BG3Afp7m3UvP/nTP/afP/Qnb3rbN//6+95n8lhk7DfrWd3kYffmN7/+1tNfTLncvP7oZjfUtgq76ZFrDz/1pa8uZnul6Gq1euSRh8dtiKEYAsN+GuMLL34tjpuLrb9z95a7uHf98sWUIOaQJbnKC3EcFI0FFVQmBgEDSMCAKIiYIaOtUGIRICIpaK2NWYGsqLDxZE0qIn2PBOMwTGPIKbZNNZvN+nGDBIYAUUDUWFM5L0ZiCHVdlVJUNaXknBHJiJxzRmIDANb6uq63q3WOabfbMVHO2VpDRADgvXchikjKAFAQkQjOIaIxfK4URcScCxGllEopiCalpKpAmFJq5zOMpUxj34/L5ZII6qZpvAsh7MYdM89mM+ecFGBmVc05O+csG32AmTUXBTxnrSUC0JcBACISERRAREJCRCJi4FIKIgLCOXwZACIAiIiqIjMAwjlCVSVEIMKCQAqZABURVRWUFJAMA2FMQpaJyBi0lnOOScuUetfNuna+W+2aunvk5qMny7PWzEQkTMFav9ttDg73+36s67qpO2NMKcUYg4ilFEQMMXrrrDcppaauUELFTbtoLx0cjL18/JOfqeq2jxMZXvc7sl/e7Ybf+70/rayzCBcP9nMJymV2MFMtnuHi3vzqYk+RU0FyZK0TyTnF2vmSIAuMIc7mXYyBUC8f7k/TJEVvfe3Z++ElEUBkUGqa5sLh4f6Mx2FdQn/3pa8jNTrtLh9cyzmkuAslfu7JJzabM8Lh9/7T7zpPzzzzZWBi5CvXHnr/+39bKX3s4x+/+cqbx8f3ifnKlWsHbXP31tfH7eatb3zDvVX/0KOP/cmf/JlzZrXaAOrR8b1r128sl6fe+7sv3bbWppQOL14axp11/MM//EP/3//7r2Oanr/13NW91907up9zHMMkzlQ6R+vXQ4wODagDTAksiZJCRtGsqjHkycM4hrpqoeg0jW1LMU7GUr/ZOOdiCMYY0UKgoLmpnSNU5Bh6wzSO2yLUNY1giSHtBNq22263xnB6YBz7ruuGYWC24zgikmE0bW2H7e709DSEUFWVlNL3vaowMxGtVitiw8zW+RCCNSbnbJidMznnlJIqVFWVopRSpmlSVWuNcw4RU0qtdznnpmn7cUDEcRzbtmZmIqrretLinEspIaICASERIWKcAhFNw0gvAzTk62YcRwAQyUyUcwYAVUVEVTXGDFNGRFEtUugcEBIBofeOsDCqM15EEPFsszbGZCmIqH9NDHGIqWKbcjKGmFkAi6qAiohlo6qIKCJEZAypxtlstg2xaup+PbA1iry/d8hkVDIiN02zXJ6q6jiOqppzJjRZJEzJ+RaAUspd1236YRc2lh1SOlws0rRzbetMc2+3ESTXLVYnCTKKmtgnBk4UV9O28+0LJ8uc1HhzNg6iESXcr7fX3/445TSCvnTrtm+bytGe03FK6wGJDAOLoq8aZi45NrVfrbfW1buTQZRcVTPT/08WnEB9el6FYb/3Ptv7vv/t22fVSBpptFjyilcMGGxsKDHGBkwOcYEESEMpSUNz2hLKlpKkUIcSGtKEJRjSnIKxMTayHfCGbYz3VZJlbR5Zs30z3/Zf3+15nntvPw2B03P6+0leDgvTrWbOSrDqy6JL+qY3fc9/fOt7VvXRZDj+lpe9/H3v/4gJZIhe8pIXfPkrn+nq2diVsU9nTm3vXt/rJZ48cfeVa5c3NseHNw5L5z/9mc9+x6tf9eijT2zubO3uXr7r/ns2NkfXdp+OPX/dzgtvv/1cMajmi0PWfrw+WkxnoXR1v0Kjbd/85m//xrgsoW93Tp45OFrMgK0zdYzf+p2vy2hnTbsJYJE0xYEvVISI6rauqqqul2hIVUHJWsfM3hcxRlU1CDH1p06fXczmqe/7blVtbdzYvTYIvm2bQTVENKpegFXaYhBAJPX9ZLx+9cpu13UAEGPsui44E5zrmsYYJGNGo4G13jrnYozLetk0jYjEGFWkqirmHELIOYuIAosIJGYBQ6TKqsyMzJxzFgF5hjrnmLnr2q5vnDchBGttVVUIuFqtRKSqisFgtLY2Xi6XzXJRlL5vses6tEaPiZiAIpI5OeeYk3POIKWuZ5FqUqkqMwMQolprFVhVWYRZjyGiguJNRARJyBAiWUfeuqrwG6OJtZ5B68ebLiayxpDJwoaoCMVoUOZYDasCAKqqCiEQ2gTw0Jcfg2OExxRAUQGAmUE5Z6yqKqXkiiAIZRG6rkM0VVXM+14ERqNJ8EUInSoys4gQEQAtl0tQLMuy6btVXc/r1SB44QyyFmzV1NkXthxVi2vXsSmLgWfmNTeQXvYP99Bk56hNK2Hz4pd8w3Of83Vve8fbBlW1Wh2MJpt/9v6PvvJbvuXypcuPfOXi+bsvPOveOysny/kik2HmW8+eqkq/XNYCcPGp3dFocOLE2f1FKxitLzom54u1cbXs2FuzmB5y0o3hYGtz42je+RCY+emvXYxpWVpyBVgD7fTKHac3r15eLWZHQz+66/ZTh/tXzt9++6MPPfjs59//sU98bG24/rWnnrzvWXff2L12y87J//zu977iNa96+EufV4ncN4b8pz7+0cWyjf3ozKmdzc3NJx57fDQsU9efOrH59FNfO3v2tMowd80v//KbP/Te9xy2K8nd2ubadFF3ERthMIUIqCXnyq5PR4fTzc11X44j52Kwljgyq7W+aRoVNN6SM0RUrxYimKOi9YVxOfZdV1sDKikYTH09my2IqBpVy/mh6AAR+7Ytt3acMSkla13btn3fp167rsk5W2u9j94X4rK1PhxOZ7OjWWy7wpeqeKwoitR13ljmJCKWDAAws6rmKKJZhVUAQACEnmFBRVVUxXsPIKvVSkScMwCQUlrVLRASUdc1s5kcHR0ZUGORiFQFsmZDIgqtVGVpkIqymM2PlEUgAYC1dj6f5pxV1XsvmtEQ54zPAFUhIkQFVRFRZkQ0SIgAqCAMogDWWPTe2lAAADNb74wxIoIgiBhjp5JFvDMmOBeCt9ZRYkQFUEABssBwDBFFBAUQUVUBoOu6shz0KQpoFi5tWVSViKhy13XOOUQEIGb2hQcRSdw0nSqO19f29qebm9uzg/3N9Y3VKq9Wq42dE49fvHzhvru/8ez2c5//PIP20Ye+bJhL46azg+H6qEsxsr319rvOnbtH1PzU//CP/uPv/YdR2AjOXF7Ub33Hu7/+pa8Ybt526ux9f/D2B4LlW2655cGHL4rID//gGz/8wLu/87Xfxaw3VnDX857btPMPf+ovry80DNZSHbsMey382lveNh5S3y021zeuX79+4sRJhNAkvfe++1/wggsW0961ayS6Php9/JOfePHLX7Tu9WB/vmri3tXHYnNo9azJqV/MdsbD13zrN19++ms3rl59xTe98sHPfOnE5nYAuuPMWehzHnbLVfe8u+99+vK1K9d2X/Gylz300EMQ43httH5iezY7KgxMd3edseuj0Vt+4y1ntzchw87O6bqrQzksizEotil3/YoQY+wk5bppR+OJAyNojA3Bl3VdC1q0QbMuVi2RzTlWRZFzIqKcRXKqhqOuneecnQdjoKu59A4M5dyXlSu8yZm7vrl29epyvjhx8nQ5HLZt45yTHAGAUzYEbRMlMxSVnR7Nrl65lmIcDAbOOWYm1LZtSbWu6y51iEhEoKAsAkzGAAAiiggCMDMiAIgqAgARee8VuOsa723wHhEBwN9kfVAAa+14NCBUEXHOOuf6vnei3nsAYM6Ft4NBmblCha7rgCGlBArOOVX1wTZNx5wyJzJWlVhYARFJVQBBVVHhJiEFREsEhICqoJz7Tlm8sQQozICCSCDZGkdkDSLdZJAIUEQQURVUFVFBGQSICATzsUisOaUUYxRVYXbOGbTL5RJAfLCo1Wq1MNbGGEUkhNB2HQB4VxYFeO/3bhyIAAhaa1NiVljfPF0OJgOFLsXNtTXvLYpCTqVzHnhooVSxZMxoOK4Kb8QXRV+nV738xTk3AvqSF730cNaePH37o1974MLd93/xoYcvP/2kG2z/4N//jlD43//d/0s4/vpv/95ouF4v64NFc/3ak2ps5IBojcjaaCulFPt8GFuWuFxdt9ZfvnaQomS2WeFP/+xD3/uG16Z4bVyE22657cIdd0zr5a233TmZbCxW3Yc+8tHXve61X3rw4umTp/auXHnWHbd96iMfqrwtIH32L/98Y7j1ipe95J77L7z1rb8/GI5vP7m5e33vs3/xoZe87Ov7xcGDH/+o8WajJCvt/qU95+x6Qd5Yg/SSF9wvTZ/61XJ+0MfF1omdRd0cyxTGoyGAFN52qmrs5vaJxaorKg0hxCQp9UBGWND45Xw6Hq2llIaj9a6tjfFNVwcXUlZAOxgMFlPIsRMBEK2qQdt3bd8KcEwdKHnvNXMIJSKVZdm2jTGYYwYAliRCAJA5No3Yw8Np03SoEPtc5zqlNByU1loUadqVtzaEQGgFwXs4pplFWBCTsKqmyAqkqk2bQggAkDhbg6pKhG3bmLLKOYdQWGsB0RCl2DnnOMdj3jyDiHLODKCqBsY5phuL6yn1m2vrGByBOTysB9UIETNHuImIrHdFKOfLNmc5pkqISEhojHNOkgCAqloCa8hbMgSgYqynY/oMADZIlsAYBABEVGBQJFQkJULlZFCzKgiCKooaIG88KRkbJIsvfN+lEMq27spBBQCqGmP03jIzEeExg2TRgY0xnjlzZjab1atuMBh0MaeUrBIpTkZrqUuTtc2Tp89dvnJ1PNn0VChbjKYqKgeVBSuxJS02q+3D+SI1uQwBIffLI28Q0xJTPVlbP5zPZvPl2vYpF5yx8spXflNZftvly7urVYeI3//9f7tezd/3/o9+13e98aEvPXzixGQ+vcSSRLvCeVHIs8vBOtFkKJIjzhicWbZ9KIp+Lk2nSd0nPn/xqSevOISrh/nG3rW6XUTO3/btr13f3L7r2S/6v//T76+NT8xnR6MStW22Bv7Myc1bz546f8utD33xkc3N8toTX/wff/wHx+vre/uHidW6wvrw5UdPIpi2bfu+397ZXB9PQnDCGVG7rptOp5KLyXB444a94847u8y7+1PQTEK/9C/++eH04Bd+/mfLIiymcwL80oMPv+uBP/nFX/xF5yWlREQHBwfGhff8ybtf+tKXnjt3ThHXN7YWy1lZDZUlcXf96vVT24VzjruaiMoyJEkieTisFqvY9J1DJyKAdHR4WFYDvklVUkrMHGMkQhT11WBQFXZ3dzeEMBoMnXPK3Pd9jNEY6ruu7/uyLAEgxqiqaC0RqQoAIKIBJO+YWQFVsQRCNCKiytZ6ERVRMiQizNlKjlESizFmsZg558rCW2vKogwhWGtjjF1KFvH67lVjzHg8BBga1K7Po0G1s7NjyDFzHxFAiqIwxohIn5NzAaBTRTimZAgEAEmNRVQFAlUGQABRZVXyzgGLsFgia22SdMw7JDXGIIESARFYMoQqIgBACihqADMAKhgkFYFjhnIWb4lUVJWZ4ZjRqirIQNOsSIElI5ExxXA4nE6nIsLMIbiyLOt2ZonamAwSoJaFF+52r311Pp+TTW1HEjfMBdev4pNPfm1UFuuTgQX72KNfE8Kj3NzzghcygvXOqS7n8+DNcr60fm06v36bdW2KaCBxouSq0Vrbyfb29uGNKRpCsmsbOynrnRfuvvOO9WY1FTCktqTCkmtTzpJ8wOVyPhlvdFmZoO1h76Arh+sP/Ol7X/Etr5lN3/XqV33re979zsWsdo6cH37ko58UtKt2FcpRn/XFL37pxce/mHMOrihctbG+1fYxDIYCcNddd5KBdrUqC09RyJq+a++75+5HHnnUB/vCFzzHe7+ql9PDg8lktJjPnaVbTp28cuXa3v712y/c2aboyioMhsb6z33u4UcffVyVL168eO7cLar6R3/8zr/8+Kf7lH76p3/6zW9+s7W26zog++u//m9Tm77yyGPnz5//kR/9e23dOGeHo+GTjz/xW//+t7p6+ppXvnBtSOuVIxVEzTHysV7IADAQkbOhbRIiqqoIW2tjzPAMAVAAMMaIcNu21hVhUA3KciAiKTIAGGMAIOdsjLGWVBlQCY2ygIIwqzIQAopzhogUUAQUKKUkwqoKFGJOqY9lEdxwhIjMwsCq4JxfW1sLIbTNCkVBtW1WXexFhNAa7xEw59yuemutWHTOiWQiUGAFds6psi9CCM6JE0DOxpgVIgKAqgKgiOgxUUvGEHpngzPemjI4awoQPSYiqmosSsI+ZbiJbnIGnUEiAJAcIwgDGEQ0SARIAKgkWbPkYuDbrg9llXMelQNLBomccwBSr1YigobQojGm65rFYnby5Mn5fJ5ScqGYr5bj8bieLxyBxtZ7W3hSXBmDd94+PnHy9MMPX2yn3ooVAFFV65YZ2tVyrRygxWE5XIqGarSYL9suzhfZYvaVvb7av76/Gm+eskXlqnA0n+W82to+Xc9nqzZGzsPRpBiOV10cb2yv6m5QQjWwkhGzDAz3XRe8K70X7k5sjUDFEWeUKvhxtfWHb3/gX/7cP21i7tvuzJkzP/wjf//P3vvHr37FS6yj33nL237qf/mndd/+r//8X77x+9743gfeKQjf/ca/8x9+83eu7T312S8/+bKXvaxuWJeLndtu31/mGzd2P/Gpz5w5e9ut58+fOn02sXnyytFd997jxifmi8U7HvjA5UtPp9Svj8Yvft7znnXPfQ99+VJZVTu3OOPC+//8L5er9PwXfccfvPVd3o9Z+s99/ounz5zpWR5+5CuJc1EUKaXd3d2dnZ1BNfo//vW/WSwWyOicu3LlypNPPvmse+5Vlae+evHf/bvf6LuMYFerZnMyIcLcRyRyzimZVV/bwmmbGRQRmXV7+wQRMbO1tm35GKiiKLAIshoENXY8HuecY4wAkFIyxjjnmqbJHK21qppSIrLH+r4XYUQEJRXgZ2iMUUQyiwiQNQZM29UipfcFp5wy9zlba5l7QMMc+75HxNRHVeWcjbXK0ja9MWYwcKoKBKpqvUOimLNFatree48oooqIMSWjospkEI1FsqKoqgCIpPRX0CZOYNHchKgKklWMKiBYa5Oo3ISIxhjvvaoiIh1z1jlnLAqDIAMAAiCiMYYA4RhKZt0YT1xpUSWl5Jzr+z6UZds3qqzAOcfBoOpuGq5vMCci2t/f974YjQYulIjU9wlRc9+5oqjrmtxgMPHMyRUmc1cEIylC7skQaeq6OYs3FhU6QFe3eX1ru+36ajDM/bTv26bvBmiDH3VZYlJblDFGRO26bjgZP/Lok897zt2PPbJ/6sSOkh+OJuj8xvZOv6pJAxKEwno2pgpX9/c3drYHgyGKdk2/WjWTyQhdEFeMR4P5YloMJst6UQ0HObvValV4y0l+6If+nqpBMET25KlT/+gn//Gvvfnn59P9wodybfvc+ds/+PFP3nHnXU8+8ZVPfe7z2+vj2KWui9ePHv3E57+cRbuYfSg/9cVHn3Xfoxe/+pi3ejCbra9t7h7M/uyDH3/fBz7Z9dGXxee/8ujGzvZsNnvRi19mXFEOqrptmHk0mhDZrlvFxORcEkbAyWTive+6rl4siIhFu5TB2Mn6JqsQQFmWfd+LYOrTbLG66/yJLBHQWO/rtgOAyWTEmFWVM7QxKti278KwAEBjDIgoi4ICIAAwqwiogC1CtYzLrNF7nzkKJwAoy7LOkYhU0VqvaJKw8Y6IgBUAcs4OgzcOA7ZtS4hkgAjbvnfGKou3jkbjvu+BPFkf6x40AkDOGRUYcx9b5xwDNF1keAYDGEMbm+u7Vy8rWwPYxWTJAEAVCufMarUaDAuTjIh471XVh5IhoDsSVQBVZiFlAUESMgI2SwYARBRQJBKDoNql6JyPKRHaDNEFLyKGCAAEtI85CxggE1zd9iLgrBdRFhCRlHKf+8HOlhrbMQhZ533ft6o55Z5zXrS1MeScWywWILIx2bDklMVZ671HIud88L6p29K7ra2tS7MFI9iqUuu77LqYL5y9E5ROnS+AhXxSTvecP+EM5tiTAestuGJihtx0zlWQWbmZjCgb6dqDzuTVYn59f0+ZS2NiuxT1KfWAsY/toBofTVfrG9vzpjl99gyS++wnH1kbFkVREVFBlpkX00Vbw3A8OJxNX/TCl3zhoQ8B7Lty4MuNRZ1sVbWSisnIDauRHSmZrz11bT5bvOzV35nARJaiKsfra6me3bKzOaL8d7//9Vf355u33PnDP/7fx7b5iX/44//TT//Mn7zjbVvjCed8OF089tWLz3r2c77r9d/zv/3vv/JzP/uzovlX/9Uvro6ufusrv/HC3fe9448e+PZv+87nPvf5l69c+/e/9Zs/9/M//4dv+4M205333FdWvmmODPREsr22kZq4mM05sSga42OuB4NB7FtryBjjjWk4MSAbg877okp9U9dLgKzoyNm67kAtonb9yjkPpDGlAbmjg3lZlsxsyF072i9CuQZrqhrbTkQASJit9QBYVgPnHKKxseuUWQhBxBKKMSIMLKpqjHHOAYCwoiEASCmjYAihbVt5hjrnyrJU1RiTqhqDxhgiEhHOKgx9ZrSKiIBWJesxUSJCxBCCqpZlFULRti0gkjXL5VKA1tbW5vOlIde0bQjBujCdH7VtLSJk4FhVVc6GLApoYhYAwGOkAIDPMKpZn4GqrEqqCgCKkPoIAApARFnFGMNJjgEZIEQ0RASEqgoAzIyIqiqKSui9N8iZ43S5sGAUwBXBpmi9cc70fe+NFclVVXZdF0Igxb7rTUmqGHwZU2esdQ6MMWVVpJgzRxd8ygLoUq/Xrl7b2z/8zCOXvPd/+3u/hzT3LMo5sgBhMazqiuqFwQAAIABJREFUemmNzzkiaeHDqu3BwKpZbmyM7XqlttTh2S88/vT5W889+MXyqaeeHldr8xvTd/3xH125ckVid/6W0+PhJLK/5fSp3avXlPnk9k7frDixq7yxVlUv3HHnqqlTHyfj9bIa3XrufNc3xhZ1wqoaENq1ybDv+67rMmjpg7CGahDKkTh/Y39vc+OEKA2q0dpwxH3soanr+tte9NI26e7e/vraJrrwxu//O7/+5l/6gTf911duHL70Fd8yGI5WbddHjll98D/2D37irb/3b++5697BePKG7/7eW267e9nz1cNZNdm01eh1b/i+mLpRVdXtKgTTLntVHVRFU9fL2dwQlqFAQ5UdkYpRARbNibMUPtR95JRjjIvlTFMPkpVTUQ76LiGid6W0vfeFIiiiiLRtNxqMUuIiDFPuBqPhcDDqY/Ted3UjDN57ZgMi3vsQgjEGwVhU8daISM4ZAMwxQCCyZECUwACiQGZmAMw5c2Qi6rpYlqUSZBUSYmYF6OMzQgg5Z1U1xoUQSAFFiQgAWOCYAgPCX4kxMnPf96vVamtrqwxFzpGIlrPlxtr62traxYsXOeVrV6+G4DbW1iVlyTIejwtb5JxdCKCGmQ2RI9PnpKqIqKpwk/41AvwrImyMYWYAiDEWhQcAYwwAICIAEBEiAoCqSsqkgqQERlXlGAoiGmM21zYzsyDOl3ObTVWEZrUaVkUIbnp4OByMgvWx760xKSW8KfhSRGZHU2Uoy7Jt29WyaeuurIaHqxhRr+ynKJOgRWZ7sIDSh2V2nKQxw9bSURJbbqmQcw6dUxRyQNag8zVDQIMCnBtjJeV6a2Nzc/NM5SdH8/RjP/pjH/7wh775m77x8PrlnPP1K5c+9+lPfOTPP4Cpfd2rv8Gq9v1Sc0Jy42Gxc2Kw++BXFcUW5Uc//N7Eeu7MWSAfF/2p7e1hWSxmC015UlTtagkC1aC89ORF7/2iz30n5267MJ9GLM1kdOKRrzx96uwtn/v8Q3/r+1JRjS5dujSeDA1A8HY0Lp6+9GQT6cK99wP4BMCcB2Whqpx0Y+3MxuTkdLF0oWI0PehsVY83NufLuigrVT06OrJGX/2aV1366lP7+/vz+dRae2NvVyQbtH3dDodVQZoRVDUoF4Wv+7hW+MyyVpb1dHb25PbVrz6+NhzNmg4y902bYqxCQWhni7krwmi42aeYU69iUp9H1Wg6a8pyYJBUNXJ2RQjO9H1PBIhIRPmYZJv63jkHADn2woyiCZGIQght26bE3nsRUYAQilBW2QkzA4D3HgwcSykZY0QVAIgIEZlZVZGsqhIRM4sIIgKAMUZRrbUiwswhhJRSXdcA4L231tV1jWj6LpZlmbMY41DpGD9DDVEIgW8KIUSGtm9Xq1WMUcgxMygCPgP+C1FFVMBnGFQQEUQEFescM1uiLGqtBRa8iYgQEQBUNaUEACiKCsyMAMaYcNN0OvUhuKIwxhRFMR6PgisKb0XyaDQ6OpymlLxzMcaiKKqqnM/n3ltEZObFYkFkDNqcc1GVZK1KInvM101HWb13D/znD4xK/5739lVVLLtmWdfonEEohMvCsebnvOjruxTJDh57avexJ66MnZ2sb2wPTiTOiFgNB6GaLBcdmmCtXy7r4XB4I+fxeHzhwoXnf93zB5XdHpfQHQ1OTtq294XTFLc2NvduXD13bgcsAFkfhk3dtU3jrZ0Mhw9+6QsHiyN0oXDFjd3rm6PJ2ZOntrd2PvX5L86Wi4zu0Scev/22O4qiAOIQwqmtE7eev+MDH/sUEa1Wq+l0evrUWWbuurizs3X27OmHH71EaJBcKMqy8CIZlUBptWgff+ypg+nRy775lTEpGru3f+h9oaqcegK+4/w5Tf3Xv/gFz733DkuGyGbhna0XvuqVr1i27ebaOvftbLoX+3a5XPyTf/LfLVfNoukjA1m3u/v02rDcvd6LxltvPXsyyfVrV8ejCiTFPlkLxhjOKhqttcF5IprNFsyJCAjUOYdgrLWqJsWYc0Y0y+V8NBrFGFXRokoZPCLmlGKMue+ZWUQK50lJRZgZFFU1dr0iENmm68haASClYzm3xpicBdE4Z0Io9CYkIzflnDlncwzJW8cCROSNlcwo2tUNKUwma957ABiW1d5yj8jcuLFfuHJtPJlOpzHGqggoqiDO2pyzMHOStu/JFqpqrc1AjkxGBUQAwP8fAkQwfb8SESIqiiKlRETMrKqgigoEaJAQEW7KOSOiHFNBoaIogkFQqut2WJTWWkQcDAZA2rY9gYkxDyrfNLV3bjwapS4VzhfOr+YLUohtDIXb2thYzFd1XTsbYsxItm2b0biYLZtJIZpTTgvIuQwlc1Iwdc+EYTjwy7opB6VoXklCok7UhHHC4vJR/PSDVyh2w1GV8MGds+eSEjgfAd7/sb9YTpfPed4Lk+Cqi1Gw7mMxHEbmYjhqYzv0oU3dFx56+M67LgyCn64WZEExA2HKSQF9CGWxxuxKO/nJn/zJO++776tPP/2v3vx/9m13bba87dyto7X1rovvfNc7DubLq5evXLz4+P3Pefa9d547fWbz/rvveOrK9bZviMBa2t3dHY+HTd2DtGfO3OJc2N3drapqVSfI2RhDBjjlQVXddtvtt912fu/wCBRRKcXcte3J7S3IqWua2M4rG0lzYdUqE2BVVKpqhq6JS2ukzXPvdLLu9/cOz57btMZtnhiDsW0SJCMiRMQ52bBz9/3ft1o0s4M9bmbQr7zBGLuU+mIwBDQ5ZyXQrKNx2bT9xvqo7ZqU0ng8BsQYY0rRWusLmzgrUhYVVjuZTMbjMQJ0Xdcsl01KoAoAkbP3XkRYlZAQMcbYp5hYAWA8HqeUzE0iklLKOcNNIkJEqkpEzjlEVFUAwGPwjJwzIhbOA4Bzjogmk8nm5qYNfjAYHO7tEdH6+npdt33fL1dzEQGAnHMIru97731RFFVVpT47Mj0AIhpjWNAYoyqKQKgIagAJkBSOIRAiqqowkwIYAlUAUFW4CRWOqSreBAAEyMyqSoSGDB4jYOaUUi9xVFYpJcl5OBoxcN/3o8HYGez7DgBEZLVaFa4oy2HXNSEUXdsMqirmPkU2xhowqUspcdcmRBxWdlCa7a2Txtntk6eQ7Ac+9OHxZCMl9sGvVitJaaMKdbsEaxS4KlzqYxfT5vZ4Nptb68gokHXWpZSNcaPhkCVduXIpJgVnlm3XxPT//OHbjDF/6/XfvWj6Juax94ez2cUnvjwcrM2W3dVrN57/3Od0XfbeGkdoWKDMAmUobCj3ps2Z7dOZ8HC5vO3CHdunTniA61eYBZTwB37gB6ar+vd+93d+6qf/56qq+sXeE6un9g6vjdfHxtP+wY2TZ2+dLxe33367CJR+iFrEBPsHsz7laliGatDHKAwhBIjdzs5mSv1yWXtfZCQC4Jg21ifcN8JxNj2w0OV2wakzKN77nLMigLEpizqbIqe+WR+PQFO/PFrb2LA+VMO1zVEVMzRtTn2rqsHbejUzSFVpQjk6vD5vmhURDYfDJiaGHELIKRLqcln3MZfDEasMqjERtW3LzN57EWn75EKwRMwMStYQIigAIIKqiggAgqqCkrHMklK2loyz1gfrQ922imC9izEasjEnH0pE9IoikhKLgKrGmAyrP+YMKCNYIlLOqgosSMScmZkInbNFEVRFUs59ZGbnXIwRQBRgtVqNqoEPhTWkLEQEIG29KrxLKc7mq+yKum4zU8+gAIqgIESkNwHC31BVAGVmPEbUdZ2oWkfHVBVU4a+hwjFEVBZVRUSyqKoiwszwVxQJEdBYaw0ZyUkzx8R9u7LW9E27tb6RI7d1U1XFbDaLqQ8hcJK6Xxmyo9GkXrWGfFEMUkp9G4dlMS7dajUf2U3V/vXf/nIWNMYZYwBkfW2NVOo+RnXGlZbI9GloPXbzs5tlfWaYunaxWCkXZ7Z3hsZCzPXhgdHc9S2TCEoX+8wcOftqZHxlfHU4O3z329/5wuc9e7i2czSbobrffMtbsxAgNv3SOGfAAYAzNooIjX75V3+1Vr18eHDy/K2NxlXf/Npv/JuN8aSshqrcd03brzJ3ZAokadvVsBhHjqPRaLlcfOaBBx5//PGDvcOXv+Tlz7r7LsTK0Hi2aK0LfU7tYgpAzgVE9oW9cXDVuDxfzYEw5wiq89nB+vqLq6okpa89Md0YBRR1xobgACBnGQxGq6ZFVWiiEx6FMk7nVeHn0xvtwbQYDFJ+OgP2kbNAn3KM0VpKwqH046qw2scYB1UFAG0SawtVRXI5d1VVQNeVZamqy/libbLTdV2M0R5zno+JhqKQzGgYkWzTrIxB5wIR2eAdZ1XgYyLekKPC+mCM6W9CY8gaEUHEEEJVVdPpFABijMYYEQEAY4y1NucMAM45RAQAvEkBENE5570fj0e7u7uLxcJaq6rMXBSlCIsIETFzSkkZhsOqXi6tM96aza31ts1935ehMMb0fW+tBWu995gxSoZnKDPnnFEUAFD0GAACgD5Dur5XVWtMzlkBQAkAUmRPCDchIgCgwjFVBQBmFshkjPfeKVtrVSIiOueiaNM0xhtrLSKKaFVVqmKtbZomR3bOpdQjonNuNpsNh8OqqpaLxiA5F5i57VKf0mgQ+r5vDVeDgoQT58p79MScC2cNEsRVSlkyl8W6JVBV7Prx1qjr69tPb5zdepG3ZE04qvVLX75oyY3LMlj8pq9/6V985vPOmaZZOWeQ1JPj1B/zxu8tlsPBmMgeHM3qul5fX++TCpDxLjKVZWjb5L0X5lWX0PTkC7VyMD26cP4Ca14tF75wfYy3Xzitqt77xWIV/EAYDvcOVSmEQdPpxub25ubWy0+ced/73vcjP/qjqeeDw9nJ07etmiYUw3/xS7982/lb1zc3VbUoisXyaBjsvc+6MB6Puw/9ZR9jUmXFum3Ga6Mu9qmrr+8d3Hn+9nI4nE+PbtyYvu+DH5gtloqGjDVEp3e2vuGlLwqOqsEElPvUFFWp4jLLsm5WXTo4PJotVgK6vr5+5swp4bxY1dDVk8oT2mNJWMkbQCAdjn3qWmcHMfeDUFm7BMWyLE+ePNn3fYyxqqrBYCAibujKGI0xVkC72LtQCIsoAFLfRyISxZglZ7GWmq4zxgA9AwCdczmLMSYlDqG0z/Bd1wGQaE45IxGLcEohBEL03jMzAIwGVdu2xpimaUTYew8AzNx1Xc5ZRLa3t51zbVenyCISXEDEEAJLTpxjSmVZLpdL7/18uXDOMEgUiTFmJkSKMQoCEBxDRBFxweFN1lpWIERmFpGU0mAwaLtOVb33xhgENcaoKiJaa3POBgARyQCo0k2qurm5ub4xCSSEmHMm6waDwWw+Vc6j7ZOHB7PgsCgCMyNiVVUiUtdLRCSDzjlmTimFEJqm8WEgmsjkwiEV0DSxWcTuejtfpWE1uOO2bWRwBttF64JXb4EKluhYkDKB8871dTOovJG8Ws2Ha5Ou7yajE4jmYx/7+IMPfrGk7g3f/V0PfeXRT370o7lr2+X8H//Ef3t4OKVggXvum9isBsFz15XFwJGRlFETaAbuLfbc9aNiKBIzZ4uac3KIqLh3dfdFz35BQfaJpy97Mn3K2zsnRHEwGKlYwpLUXrs6lRze8pa37i3717z2DZxROB7z3isnY+n67m4ovQv+Z37uZ7qu+dqlpz/96c/+yr/+lb3rVybD8Pr/6pXpmIIgGe9QNYIMJmtVGVJbzOrUiHnwC4995KMfJkVnizZDKEsKg7ZbfXV3uvveD2yub7zuda/NKcagnRsnax9/6qkP/8XHGAwa63zRpURP78lnHrbaf8drXmGzboxG3oeUEiKyovOFqqJwEXyv7WC43nf5lrPnrbUIAqreuRACM1sfiAgRE0titnATM6eUcs6KhIaInA8WjlFWVQHUnFWVVRBRVUVEVQEgxphzTikhIhEZY1SVmVWVbhKRwWDQti0R1HU9GJakUBQFgMYYrbVElHMWkZzzYrHIHIfDYdv01lpvvar6ybhtW5Esx4BCCACgqmiNZBEWVQUlRLTWCgKQGmMYMyICACICAP61nDMiqioz61+Dm1QVAQFAVdEQIupNhCgiSmStK8syxogkwVFZlquui9Ftb283q+V8PlfVoigBdDAYqGqMMYRgrR0Oh3WzSinhTYQuC8fVipnLKjRdF2MkNGQdGk3i5qt06cp+jNEReu/DsOriShEQUXlauIKcvXpjT5EGg3K1PCTNse6ryab1cvuFC/c958V33XOhoBi7FSd+8de98PLXnk5dv2qj5PSHf/T7XeYT25v333Xhm17xDY8/8rDzBhrZ2t74sX/wo227DAUljl3XeVukxM6GrP7df/oxTIk81vPF1nhN+36+d/B33/RDDTP6IsZYDsZtk1Lk0vqrl6/dulaCGrLFiZNnVDUmBiVjjEL79NNPffj972377vyz7mcVX5R7e3vn77zjJ37ix2NT9/Vy/+pTCuZoNnvg3e89c+ttWzvbhoKo6XpVtbNl9/Z3vOfalSveuWDdoovleINZl3Usq2GKOq2XbOrf+J3/9KY3vUnC6GCV3/72P4hJE7MtfNclmyizGkPAMLDmT//0QxvD4rn33n16ZzOE0PVqXFgsZlVVNas5xz5423fRh8FyUXd9Y60NITBzTKmqKqPa9/1isai71pO3oaicc4DEoqyQRRSNIDGgiARfiGRATDEiSEophFJUY0rMbIzLOYcQWIAIjTGkCAAiAECIesw4t6rrnHpEtGTqujbG5JwK53PO1loRiTFaa5PwwfSIQIqiGA6HfEwkhACg5XCQc2ROSuiCJ7SZWUGQLDlrjFElVEIURGDhGCMiwk2qCoB/I8aMaFgk56x/DW4SESKDiKxgjEVEVsk5F6FAY1UVRXPqU3IhGEQkoqIoMqfFYsY5GjDeWzAEoL4svPccE5CiocTZe9/3fdf3GKP3pVWbohKa1FujQwR0jlJKHBnUsuqq08RYlgNbjjK5eXdQFMWJnRO7u7vWFzmmxC2Qffrq7untbUK/WEEHsVvunTx/Z6+cDa7a/quPX+yjFMVQsp45fe5w97Jz9IY3vCGK3nbu7N7VpweUGNiXPh2luqsReVgJYdfVU8w52LWN8SgmXtR1rJfctgjesFbWL+fLuGzWR+vStlSVIQTrjA9WNJPRo4Pdbb92NN1nv3bi5MkYI5Dp+75pGpG8WO5XA9P03a23n/Xed23c25/u7GzF2HnvL188/MO3vmtzc4vV/diP/8OyHF25elUS/fZv/m5dL0+fOglgp0f7ZVkVRYh977x99nPuvfvue+umefTRR7/0hc8Oh+Ploi2K4h3vfO9dd9/9mc98tmkFrSuHE1UsC0BjRXpQoyB97Ing7nvuPX/+vNGMiJmjda4cVEdHR5V3CZg59X3OjNPFdGNt1Pf9sm13Tp3aPzioqmHbxbrrfFGh8yBoQwhEBACIRgCYlQUUBBQzJ+89oHWIzGzIpJSatiUiY4y11jmvqiKSUjLGICIA6E2IqCoxxqqqmqaxhowxVRlSSiH4GE1sOxEBgJSSqq6trfmymM/nOXZVVfVdstamlJumccZ2fWuMsZZSZABwjmJm7qMtKhHJrFmYRQEBjwECgKoCAIP+DQBQhJQSGEKRzKz/H2BQBdHgMWa21iqCqnrvY4zWu1C4EMJ8Pt+Z7IxGo9WiFpFqPGLFrlsNBwNv7Hw6Wyx6a23bdpPJBERyztbaruucs4CYc0ZE7yExJ1FWICVhVc5l8MYarcqy8LNFPfAFKLIfsB8b7yO0qWU9qKOEXmxMLYEMSjtHsdam2AOB97bnNKjc0dFuFYrFauV9wczKUhQVM+/v7y+b+sTZc8vlarlcJuZeEjOnFAERidq+G5eh77vYxuFwyEmiiZxxa3MHwbR9JEu71/ecL8Zrk6OjI0Dp+nTL2XPGmPl8urW1TqSEyRqu6+kP/sD3P359aa0NwV27dr3wpbPWUX7yia9kbo2VC3feMZvNhoPJpa9deuWrvtE7h6K7u7vXd/f3DxdlMRABSTKpJpbcL/zcPyOFmJp/9gs/i2iMQWvwta9/7dra+NKlS1cvPeZ9UVj+8f/mRz74wQ9efOrprovWxM997ouZ0bkSyM5mi5RyORixdsKgyDm1awMPFBGNqAKnxWw2W6zImsn6JmleLfu2Xm2ub2Th67uXh+NRvVq1bb2+vnnpqaf6JGtrG8ZYZo6ZNzc3Y4yWgJRVUInIWt91WVUB0IWCO+EsmROpqCqRsdYaIAbNIiqKaBDR+2Ctq+sakRDhmKoCgAj0fTpmrQUU4+xsPh+Px6qKiMPJmJm999KCEtrgnXNEJCLXrl3zrjDGIprBYHBwcGAtldYBmpyFDKCxZVXknMkXsWdE1JtEBckAgKoSIgCoKoMqkgCKIjIwMxHpMThGqgoAqgqAAiqgQEZEAEBEEDGlFPzAhbBaLdYGznuztbXRrGaIEIILzvRJyiqE4DSzD3Y4HgIIEAIBsyqC884i9LF3Rfh/qYLTmM3S9DDI9/IsZ3mXb6u9qru6ep+exbPY47Edx068xEYIBUUIEWSEAckRSVAw+QHEThBgYiOZWPArgJQfSFYcx+B4mbGDBxw7sc2sds/WPb1WVde3v+t5zznPdt9UVzwiXBekZIyx3ueciXQcx6qujDcGZX9e1ZUBmtfzAz5fD6UIt2Mxu65Ma7T1lUnlvbNsA3GeNrBZnnCNz9w5IogoY1tXWNYTWx3VuiVNcZd329qao725Spy0fuy3/W5deWeYCGQ6nV6ePZrUfHB0ON/bX223IcUvv/oNiFLVdtr4IWqJaRy3MRfju9WQf+lXf+Ot43fNdPJz/+Pfe+7pmzQxX3/ztaH4Z158eQx9GHaz+aSqKcRuu7u4Od/PZVDNTetziZPJRFVFBEvZm7Xf/UPf8y/+8HPOubZtSykxxts3b5WYckxvfPObxhhUmR8eMHMcB00pjyF0ffOY9RozQ2GVH/2BHxy61Vsn71hrhsX5wa1bx8Py//o/P/3MU/c+/MoH/8lv/OZ6vfZ1Q0QAVLIQ4GwysdbGnFxVZZVEZRh611AqZduty9iF3YYAWu/Wi2PUkmOovD8+fqedTG5ev7Jeb2OOk7rertbeuywpxti0XgQODo4uzi8LqEFEIlJFNtYbHDkBKiLC+yiEoCCW2BiDCMycsiChMQYArLEpJVUtpQCAiAAoAOgTAKCq4zh2Xde01WNN07jHrEkpjeOYcyYiYwwixhhzzqUUY8xsutf3w2RSx5i7rmNmAEypDMOQUjLeZcG9vdrbakwZDbM1LJpUiyqryhNIBr5FVUUk54wAWYWQAJGJYpECysj6BADIE6WUnLOAAoAxJsbIVpxziLjdbruuM6qTyZSIYoxFM5DGGCyZ6XQiWlR1MpmoKiMTEQA0TQ2gVVXFGJnZGoeIxESWUolkECyqgYwF2I4pRcUuZteaQiYWLX1kBK809mM732s81OorK0cHbVvbWHIc+itH+yHFPsjl8TtjMUVwNqtWy83RQRWHlaVEOs4n9u3779x77rk33zydfuSVSe3mrbEA07pGhTiMKiIZu02onV9cbmaTvWGIorjaLV9+5UPf871/9rs8/ex//9/9F3/rv7SQ/qef/a+vXbvyB1/+5te+8drQrb/wpc+/d3r203/rb79475ZmvXJ41Vd13y8RKcd0eno6m82qqurXa89mPpl99MMf3Z/tlZRVaBz6aTM1ZNnAcnlZygDIT925pVoAyqPj+5Wj2WwSQnCNs5bPl5uXX3j27Owsh8EzTBp/HsN2uZg1jXf1cr28XG9efOkDX3v9jcVyvX94raiMZURmZE4lE9GY4mOIaq2PMex2w263K7u1NSA5LBe7UorzVmISVCyJRLrtFkFyDMt+VxQqnR4dHHZdN987qqoqxvjSSy/98VdeNVKKsRYARRUAjDEgmmIJYyqlSE6+coaNNZTCCADW2gJangCgXDKzAQBmRkQRFZFSCiISkX2s8sPismkrAKiqKsaIoH3fmyfwW0IIqWQtAkUQUQmL4hCSiKhCXVcxRlUUIGJLbI31AGSBN902pSRCAPg+JkYmIhVVUBHJWVPSGCNyUEkiAiAAQERQRFWRCABERFVFVERKKSmlXFQJS8zM3hiTJcUY27qOMTrPInkYgiL5xntvrSEGSjGRQWBitiJiDIhISTlnsdYiqqu8qgooMjFyXVfsnBIocmKj7IF90Apr29ZuiBDGEZDRFUEK5Po4bLd6hSeao2bJSS/HbTttkoKyYdGmhmnbcih9TM7r0dX68Ohe2h3/2U+90q0fXtuvJtWds0dvNFYevPG103fffvrbXv7A8/dCiZvLy6P9g4NJc7w43vVrB/P5fMYyskQF3J/uzezBzatX3jl9uD+dhX4wBlpf3b5xe/PPvvDKK69UlgjNtxv3wz/8wzJu/v4v/Nzp8ebu3cPlov/Mpz/7Xd/5yYuz89lsxoBtVXvrJdLp8eWHPtEwO6pMznk22wPAEEK3Xf3Hf+XHg+S3Hy1j2pWU1sNlNTMFAtfcp2E79uy8b6bONhatpliS0ewluTyki82KKn/lxm3pQyEbySz7QQmdrVRkyIWIJm27W20UyRgDmuM4np5c3L1+hciGYecMW2uHYbBAvmlF8mz/iIy9XPdN00BVUkpV0262u7feeuu5l17e7XbT6fT8YvH111+vqtp0275uGwCSArkURgSiCFFFCDCLMCCiGmNC0MdyTkoIAMxMRACQc04pWWtVVURKKapCRMYY7z0ATeqJYWeIN5vNpG1FBBFFxBiDiDEnRkImzUqGgbh2/nK5mbTEzCHmpmmGMeacm7ZCNsY45ypVzDkZ46QAAML7RFXL+0Q1E3ABRQIsGjOHVICiKIqAaCEiYIIEqviYIhUoqloEi4AUyEVDzqAMRMAGSgEphs2tm9ed5batwm6ctm0URUIR6YeEiqRQkhhjFrsVs2l8JVr92wcoAAAgAElEQVRqX6UnQtCqqVVANQNQTgkRRQTIFuTTTX+5Pu+GaGw7mR5cvXJTpDjnmrZVwhAGcMbzJAoWMtn4dx6eL1ZbY3HS5TGNu0gHBwcplYtucXqx2D88fHB6SkTXD45ykqxinbMkrWlq0+wdHBoyH/7Qh0DhwXvHm+0wn7R37tw4Otz/tldeYtIcO2NM3w0vv/jcxWozZrsrTddtSsqH+4eeqaT+xvUrb77x2na1RERrqgfvvfc93/vnNJc0Bmf8xz727Sen59vN8AM/9COLxeUffO6LDx++97/8/f/5+7/nkwf718/PVtvN4IwNKUiMpEhAJFhZk2OPVGI/vPDCCwDQ991ms2omkwLFEILEygA7rCyVlEsuDg0i9X3vXcXOpZSNqcKYV5sejU2lNN4VgYJE1mrOSqqEIYe6mqYYUylHs72u61OIEGPoBzuZbDcrw66QFIKYRPqAlGZtc35+3jSVtXYYBnaWciqltHXVj4P3/vDq1fv375t+DCGXqmqstQCQUlLV2rvddltVFRhGRGbudjtRUYS2bYdhUFUiklwq6xCxlExEpRRmEiFEfgwARAQyOudRadiNFo2knENB0iIYQmgnU1FQEQQgNiVlZ7wUqqqmFM1FkVkAjHNFddeHg4N91SIiKSUQ3fQbEUJgRC0lk2EpggpaQCQDkYiGULCPF6sNPKakRZ2r4H0kj+ljRZQs0w5G7/2661rf6hiHIYiyMhVRJDVYMI9WUmVMHkdjaBxHMgYKubruUwB2y+XGWRINltigSamoFjtxiDQMozGGgIx1Y0wla87ZkkmxgOJiHBObR6th24Wm4sMre0C+5N7XPKaYpRh2l4tNzlJNZsvN0EvsoX3za29qKUUVyIhiArGu+uEf/RHYaxe5sD/yVfXW6dYiiK4YQlOb/Xlbt865GskXtH5v/2z1+bpprl25IiTGYkqjYul3m8pb73m5PB4ziTGHVw6V4OzsYn++R5qhjDeuHewfzaWMlbNF4J37j/7ilSvO0mJ9djCvHjx4sFzvyFI3DtlVgfnf/w9+/Kmr12WIJw/OTdU8eO/+z/zMf3Pj9tP3XvjA0EVNmCR4kivz/ZJg24Wb9/YrW53vyupy1zQza70BTXH46//hX2aU09Ozd95+WDJEIsPpxs3DfhdjUCITx1SbyfL0oYRMAhKD921RAwrGWIUc49g0FRFKJuYq55Rzj6lHzdYQM7uq8b4OKVqypnF1PQlxOD8/LTmHzLPJ5Pi9h9eu3wyrVTOfjuPY9z2TffTggSUyxLYUGcdRRIjYGJMeC8E5G2NomgYRQwjMrKUgYggBAJxzzBxjFhEistaGEBDRWkNEqlpKUVVEstamZJkQUVUzITtvgVCRi+hqs26aScxJUyYia60q5pL39g6KiLWuaBhCCiEwc87x5OTEGDObtqWU3Xaz66NrD0pRACAGRsoEAKSqiIDvA1AWAAQSKArKRKpaQI0qIAKAqsacUxCRjIgAQGSYGcnEIs67oklEDCJqkZJKcc4yAQMpAJRSRCDmEoZhtdlO2wZUhCWEdHR0sF6vY4yqSkSllBjyLgUFapomxqSKCFaEAO3x2SKj7+KIpCenl6UgohYV570QDHFgNEjU1k1beyPN/KVJGPpH771bijrv1LrpdH777jNBLYB0m/7q0WE/FnQztkbTdhwkdCHE8ru//GuoxFR5X//4v/fjyy4en18aizdvHPVDuHnz9tnJ6bXDvWYyGfrhbLeN6hKGF+/uZdTzy4tbt24Nw8Ca6slEEISxIBByiHkya0Xj0cH0YH969ephM9+r3nx313fF1duub9sWEbPIxz/xiVe/+vnLzeLnfv5/SMJ9D7/yj3/953/+752dvPvi3etHB4evfeON+8dnn/q+Hx3HBEXfeuvt2/fu5qKzpjpbLR2DphFydBZXuxEArPPGGae8uVwBwKydphBTjI4NqqJCGEZfTQWolICkAOgrt1uPBIjEpZSPfuyVMPYVlzD0CjSZ7yHTW++8fffpe3VVL9fr6aQ+PNg7OTk5PDx8dHr29NPPJBXrq77vm3rCzOMwMtHzzz9vAEBVY4wiYq1DRFUtpahKXdc5Z+89IoYQJtNmt9vFmFQVEUspIaRSirWWiJgZAJgZAFJKpRQiMoYBBbAgMhFIVlElwwCQiogok990/Wy2NwwDkQ1jljSEEK5evdr3fYxJQBnREDCBcW67XhdjtKkll8o5X03WfVFVRAYlRAQFIhBRZiRCRAIAQUFEASqloAKAqIKqEpGIAICIWCIAsoxMNuSSUhLNxvlSij6RRUIqRQCACoA1zjEX0O3YmZSMMXU7mbfzbrtUKYhqnzDG1FWbc17HLRHt8tg0DZONMSKiMYbYrfvYx3B+uQzA1rUislgsnr13l5nHGIjBEAUpbWWt9cuTE9ibtxU6C3sTbu9eBaZdPxZkU+GsxvNH96uqBiCLbrXb1E0zhHFaz1Bh4nlxcdK6WQhh0jTXrt5YbtZH167evPGxfhy+8uof//MvfGOX/qiI1E213S6ZaAxpzHJ05Ub89X8mQuM4Hu5PX/v6V5575ub1KwdWi1g3gHhrji/PATGlcXV5vtuudv26ne6pjIxl2vix2zJi3dhxWPz+//MH7cS52tazhjKfrRbbMPzEf/LXuIyNyb/8D/835+v1evt3/9u/+8nv/N47d+50u2Fv76Cq6s1me3x8en3uMhBZ1+3CejfOZrNmtp/GsB27Zra/3PbWGUT9t/7SX+zG4ff+4A/feucdX1fj2DvniIgNGdIQAqFaa1DBIDLzfDobt4umadhb1YKid+7cUigiuar8MAx7TTWbTVeXi9l0ul4sfdts15tp0ypSCmOKEYi/9PnPGUQ0xpRS8vsKIuoTAICIxphhGFQLAGy3W2a21pZS8p8SYwwiigizAQBEFBFENMYQEQCoChGQQcOYBfWJVDTEVBTY+fNHp1K4aRrDrh8CIRtjnHO+in3fZxFVTSkRkbWGmY2lqqpijJWzBRigMHNWUNUkRUSYGQBUlYicc76ydV03k4lzTkS+/pWvIyKoAgAiqqqxBhWd4ZRASkLMIsUYN4ZkCULMxhhFzqWEFNlZ4xyqxCK184YIxwGAVEUKEKJzbhz6UvJsNlsu18zEzF3XTafTruuYTd/3TT2pquri4pKM26x7NdX6bMtsLDlR/fM/8Od/93c+C0UAlREIkJFaz44JpEwnbb9bz6v58aP7hsp3f993hTT248i+7oOwm3a7lBM6rlbn67fffQcYDg5mMG+mtb1x5aBfnpJmh1JZ3K4vjh+84w3P96YXb12enV+mrErOeFfN5yfrvqomfd4pwaPL7ZXDo369Ptqbo+o33/rma6+/ag3nImr93/xPf7Ju9xXpF3/pH338A88++MYXYy5obDeOV67dqOs6jmMY++s3jkoYcuqfvXtjfjj9v3//s4vFBZqpIMz39xWhENjGT/bmN27c+JOvvfaf/eR/7qp2u90dH5/+5m/+5u/8zj+9dXhwMDHP3tyfTWokG4s8dnJ26jzt7c37EI1xOWdGYCyr1YWy2XZrIopjaJtJUQgpFVF2dhzHaTsrKRpCzWU6aawpaqwwxVIMvK/2FRkbYyxZi6SuS6WU45PzZ196KcQ8mUwO9w/CMAJyjJEU+r6vfWWUkIgQoZScYkJEZkZEZoNsiuQkpZRUSlFVZjbGAKEoJVFFIGuAOKfEDEQEAKpqjHHOlVJyzt5bY4kZjWHNDChFUaQwWxDodkPM5e137t+6dWsymRnX9JuFd9SPg7VWNIMAExnvmXkcRykljpkUQHJTTbsxxZzhX6EIQKhZ2RCSEioAIKJ17CubcwYUAEJUAGGiAlBZh6TemumsbXy1t7eHwMDm9W++2YcRmf4lJQOamS0Zi5JUIBdhQFUEgFJKP2zSmG5cPyg5qRpr7TiOVTOx3g1hfOzGjRul6Hq9Hp5QwqKlmky7ULa7nohAFFR++9OfMcyz2SzEgYi6YZdSmkwmBkAVijKBnJ4eTxo3igIVxOI89XEsBankSdNs19EYZ7B6/tnnZnMfw8qSnD587e4VunOtvvWDnzRkAQ2b6s2379+4+dT68jSHzcsvP/Peo0epwPlinXq6cnh076WXvvilL8eYU+q0xINZYzRISQ6xIOacibgCU1mbYm5983uf/b2vf/6LYX38qe/46C/+yqd3KSWq7r5y/9l7LxgmY6jyzaufe/1gPkmbbl5VNVs1dnH+cD6fI4Oxthu6F1558fLscrndAFOW4pyxln/iJ35iPp1QHH771/4xPKbl6tH1aVM7NyHD00n1mPee2daNVyiTqWfSJHl5cVkKNLN5SCMSoWZLVrIymjD0xJhF5t4a4nHclZQAdRgismVmYmBlAEECz8YybrfbGzdu7HY7ZpNTMkTGmPWm267WRwdXckwpRpNzRsRSiogQkTGGiBCRmREx5+ycG4YkIpPJpO/7cRy998xsrQUAY4wKiIi1FhFV5TEAYGZEVC2qiog5Z0AtoFqUSlFAY70UAcoHR1cvL5fni6WimTaeiOq6zjk755hZJAGQtTbnLJq9t8YY6zhETCltt9sQcpICyEqIhChIRIrw/5GiWgyCJSyo8AQpPMbMiGosISq/jx6z1hp2VVUhSCnFWFdyToksMwCmkmOMhtASqyowAUBKJedMapxzOedSCiJuNl1VeUQMIYzjWFVVCMEYJyLT6ayu67fv348xR9Ux5sr5brNVNNbWhFBS/I5PfkJyqZpqt9v1fdc0DROJYt3OnDOTmhgS6Xh8cn/PmsVqrSbvlt3YDcaxqxtVYsOePYHMJpPKlKc+8RHN27oiEktoKz9ZrHYkulxc3Hn6KcP64Q88/5FXXkip9LH88j/5zHvnl76dWOtXq03b1KHffOgjr/zIn/vezWJx/+233n343nK17vsx7tbt7HAcd1mpcc16ueSsp2cLsu3BXvP1N979jV//zGb1Dy3x3/mpv/Pxj34Qxu18Pjfk57OrVTU/W+4uLhZXr141j5Fu1hsROTjYSzFKjkyuqaqxH2pfFcmWeddtrl99eb04PT8/By111U5nM9UAAFVVnZ+fpxAJVCVtu+UQFRGN4RCCogBoLpGIcine1btuNWlqFUGklKKktNlumqZumokI7Habw8PDvu9Ditb6JLlqaudckkJC1lDTNPH4zBizWCz0iWvXru26zpRS9AkiYmOYWVVFBAC6rjOWc86I3LaVqtR1HVMhNvyYMagIAEWLMUYAQBUVAFBESikAYIyJMSJpSbEIMZKqiiggJ1FRqKu2nbqmnl4uF1nKGAKBsjXMDKCHh/shBMnKzN7PVPc1FzZIgLWvAKBpmtP1QhUBFL4FEeFbEBURCZCIEBGKoIJAVkVSIIMAgIilZDAWRSUXLZI1i0hKiQFVVERyztaIqgJAyYIMKBmRnXPe+ywlxshIzDyOY1Fh4piTV4uIIZVmMgMAZNv1I7L1TZNKMcZ4zyA0Za8lTXy1HQJyKgm8r37hF37BGNO2jXPOWjubzeazia+aejpt2+bocL43rQnKwdWnFhfL67evFeXb91jVhZAvF5uT49OcekaUXKDGZuoM21yGBw8f1NY3zWQYDVNFWO1PD4dunFT1tK7H0M/n0+Vm5xievXMrDCOCVN7evn6tdMuwueTY3Zjx9VfufvuLd5BtEnWTg1/8lV9Nu7Le9NevXVuc7GIIYRizFKPqnauMBTe1hnIJX/nKG6vV4l986evWeOurX/2N3/32T/2Zk9P1tWs3JZcC6f5bbx3tzzabVRr6aVOXAqjiK2stj/1uNm1F5OTRe3tT3+86b3gI464DppgCDd3aIDRNVdXeV3bV7er2IMaxmewPKbrKpRwIEAFAaej7g729rltbY1LKvq4Rw9GVaymHLJhzaaezmMtsb3+9XqcsVVUj83Q+R/ab7S7GuO26q9evq6r3fjabXblyuFgsVIthZhEBACJCIBFJKeWcmdkY03UdM1trU0rM+Jj3vpSSUkJEAhKRUoqqdl3HzLX3xhgABQBEdM4Nw8AGRYAV2RhRRTJIJiUhY0NKrTFk6Omnnzo+PgYmLKhPdF03n8+ZGUSNceM4eu+TDk3TaBHnXN/3xAaIkaiAiggiqCr+K+hfYiBEBSmlAAgIiigAEBEAqGrOWaygeZ9zThQBoJRCDBkKE7DBx0jRe++ck5KttaoqIvq+QoBMhIgpRSICAO/9MAxN0wDA0dHRo0eP/BM5Z0S01oYQxpCU6vVm3TiTQl8xMlHEbJlTiqp6ebnw3iHiw4cPjTHAJAhEhCCS060b19brNSKnWNBYX9UhSl23bO3e3t7+fD6fTsjbR6cn8717grbgZLJ/e7fZXjxaTVrIBbZj3AdCQAB44/U3nLfT6RiyPnfv3qPFenO+Wq83bVuH7br0m2c+9HzYbSazdux3zloGdFXz+oO3T1cXPJkLxq9+9Qt/86/+tT/47D/NcRAoq9OlkxK3F61tiHDIuRQcRZp2psgh4x997ku//Tu/r0h93x0/fPNTn/jwyYN3c7xBwHUzLZkUWOB9qqiEKecY440bNxBykv4DH/jg5Wp47bXXplN79db1y4s1gMRhrKqqaarrt28NEeu6jjFaZ3OJItK2bYzFWuu8+cmf/BsAYpidhDe++PtNtYeSu64btrt2NssxEHOM0VhvKhKRDBhDrJvaerc3m64323rq2ZqqqkRksVwaY7pxNNvt2hhjrWdmVVHQ5XJJRN57Y0wYIyLylInI+8oYs95sjTFEnFJq22oYBmOMiBhjmHkIwT7GpKptW4/jiMbGEFPI+/uHRBRjTCmHGIzzfddVzSTmMJk2RHRwuFd7N/XcbdeI2LYtiBpiIhiGXV1VKQUt0m22Qx9yzt77CBhCUFVAeIyZY0r6BDxRSkFEIlJVIkopqSoRi2oBhZzhCSJS1QIKhFmKNd4Yo48BICgTVtakvrdWmqoex7Gpq77vva9FZDabnZ4eG0MIWnKeTJphGIjROdfnXBQYiYxl66yvdl2fiyDiOI6IWNf1kGQ2qUCm80nbD/HKjdvvPjzdjUGLAQOKOMYEAOYx52MKgiAI3ljD7tHpSnJBVADy5Dbrsagocr+86Hart99JIBlEVPLnvvRlJtBcjDFMkGOaTtvpbHL3zu11HOa2SQWbZvbYuw/fy6rP3H32tTc+O6/9sAUdds28Tca1zfSNN++f1xUTPHX7pndtNP4ffeaz0VeXj45/8Pu///O/+9na5leev7Hf+qMbh7sxbHbjo+PL4wdnJ2cLNQi+ch6QRYsymTBsnaMQY079g3ffWZw9qg2+/s133nt4jlh99c3Tp556inIAY3djQAABHKL0MZcwbLb97//RbxU1j127/sz+/uGNG+M79x/EGEMIe3sHimyfAMMC4J0fQ1BVa60kBdWf/dmfdRaZbcvywo35n/nkR4fd5sbe/jzmzbq7du2asUwKIadcJOcsJU+m++xsCzimyN7VbVNAq7YZxzGrQIEQo2maZrfbGeOYOYSAiHt7e+M4qmoIAQBUNcbIzOkJEck5D8NgjBnHEQDGcRSRqqpEpJTCzILwmKoCUMlinN/b2x/60Lbtbrdr2mm/2dVsi+p6u6mqqm3b3W5bShpDrqhKKeWcAYCcTymVFHLOqsrMpRQAcM6pas4ZrYsxqiogCqiI6BOISET4LaCUcw5jDCEgsiIoIfz/MTMiEpGqikhWKaWQYYL3aRHnXGXZWkssiNi2UwHISRDBOSciuYgqlKKIqKoA4Jyz1hpjvPdN0wBAKcUYM46jiAAAIpKKZ7qyPyfkfsyzvdnB/tGXXv1qUADQx4xxzAgApRRRIEQmJiBVJWBFAgBCE4bROUdAkqM3vF5e7u3NhyGqKiKDsUqcQYAYjBFMyz7MjiqqKjBMhkMYLMr56WiMmU+mKZd/80d/+OLi4u377za+enT88Ns++R23b1zfLJarywsp2fl1HM+j8fXk8OJytTe/+ujho/3JLPbdfOJDvwpbcAb3K7r60p2nr+7PDg63IZ6uNl/75v31Zjd2A6ORjAjoyVocGUyMwWErBe4+99xiPf6v/+AfjClK2BRJv/rrv/bhVz7w9M3rvm2HJIy87MfZwRGAA5WT84thGN5654G1/uadpw4PjqazvbPFomoaAEgpKZNkstZK0VISqf2vfuqnh34d4m673Y2bxfb4zRCzAO5CGkKZ7h8oZARShCIqQHU7dYZECgCSNRW0rm6UMJcymU1v3bl9cXYOAFVVGWbWJ6y1AJBSapom58zM1tq2bUsp4zgSsrUWEYcxCEJWcdaUosxMxlbOSU6qKiKICACICADMDAr7+/spBF9Xy+WyrlsRrarKGBNScc72ff/UU7dXq0XlLYrknIlIVXPO6oxCSU9MmpaIhBkeQ8acSlEEinFUKAAGEVUVv6WUoo+xISJjDDMjoogoAiqpFhEBZAHNIvqEiBhjmBmYHlNVKQAohlihEFEIfdd1s4ljaxAYSokxKhoytqQEQMZQjLGuK1f5uq77vheFXEQB9w8Ox3Fs27brOmLe9b0C5ZQM4ziG/clsudzOm0ltqJdSe6tJlAhFicg5m3MuWgxTirkyjgEVAAxmyKpqjBAgQBxjQGHVcjBpYxhRSt3U8bE0IjARg2FlLaJouZ7OduMwa/fHcXew187bJqVUt7NxjJbQG/P0/u0Xb+zlmPgjLyyXy+3ZMRt/eHg4DGHXp3FI1FSnDy8q31LQh9+8/6F7T5WkmPnatZtFRgIQyJZiyauS2THfuVLf3H/JGY+KcYjvHZ+++/C909U6cVdCIFuth64Ib4Zh3QU7mQvKZFox1++889bbb7+JKeV+8+brr969ffvOM8+u+1j5ahiGm9f2QgFbtaoYU3nw3qOnn3+2aiYKOMQEZEUAFVISAiTiHONv/danr109rL2d7s1v3779sD/vw4gCIUcFc7FYzif1MOwklywlpIKITVs9ZthhFgEFMmwYDY8xhsuL2WxeSjFTNqWUqqoAQFWbpum6zhgzm826rss513WtquM42secKSJoGBGNMaqac44xIiIzG2YAQERrLaoAACIaYxip7/va+8vLi9lkut5uJrN5zMlXE+b8mLV2uVzXdd1t121VMbOqIqK1lohSSohorUXEEEJKBZlySqUUY1xSLSqIqPCniAieKKoEICI5S86Si+aSttudqhYQABARQAElVSyKRQUKIKKIEKkIKBAhKrxPCihriLLt+tqzoYLWsXUAwJYrgyY5Jv+YSh6G3Wq1KqVYa5umCSEsl0vvPSLmnKuqSinVdX1xsQAAESHQ+Wy2XW4MgBFpbPXdn/pUNd9DW5UCyFx7n7XkHK21ZcxtVQNAVrHWIqKxVNd+Ujeq6r3dbDZ13QqCcw4ISymKyGxFJKUCiMwMAKpqSf7kC1+Yz/eH9XnXbe/evkFE3rcxxsqai5Pj3WqNUhxy6y3vTQtgLBhSsmxSKm1TAfOUwRg4mM8eLM6euXVnfbm9efXwm2+8nstw56nrk7YeRnGuybEULYRsFXLfk2htq1sHbVvd/mD9gpvOs8KX/+SrJ8cXuz7k4dITOTZK6tGiIkpo23a76p0zHtv1dntL0foajG0me/0Yp22tyCnEpq1F8mq9+6Vf+d+ta61zu5Ccr0NOqlo7l1P68Ic/eHp2/JUvf7FufFbR2P07/8YP5d2m5ARAimCMKaUwMqIYY7JAPw7M3LZtllJyeUwYMaOrK2YmohjjMAzeOgMAzrkQUvkW8jSZTEopIYSUUimFiJxzhNyH0ddtKtkaj4C+ccMwWOZSirUVACCiMUZLFhEAYEZgttaC6P7+XFWn02lMCQBWq5UgGOuJMcfkvZ21M2OIUMeQvDN1XQNIDhEVKl+VUkQkS0EVAGLnrfWAqKoAICKICABEVJ5gYxVZgbKULEUEVDWkIkD4PlYgACAiAEBEIhLVJGVM0QggMhEBEysBQBatnaHKsXMCNIQ4Bq3rFkDGnIhFBDzTmLIz0E4nzaRtmqbv+xBCLBkAJCYiaqez9Xp9uH+w2+289zHGMSQELDFVxpGCIZ7MZzefeUZ9ZZoJoVNV7wyA5Byt9ZC0aRoAKCrGELxPrONpOyHCYRgmbT2ZzLZd771fLFaHsyNVFc2PCWclROSS1RiDop/8+Kcuz4/VDVlFyapKUnFVDSJjKIweME/b2RAHa2vH3DCVUtyRTSGUlJHpJ/7yv54BH7778N/+C9+32WwX69Uu4Db5FPXNd7dxPBPJVeUO9verqpq1LWgy7GIYUgyurg4P2wC6HXbs/L27dz780svOEKIul5dvvfvWo5MzDVbATQ2ncWVLMKB57KOaD77yyv33zs7O12EcTaUAUHvv2IY4+ro+u1jnQuvlKgMKaJJSMlZVPY7D9WtXfuzH/t3KWY3Fe5fyEHfLh6//cdNUkkws0o9pHEK33s0mTRyHmBOQyVJSSuMQBXAYY9/3GXW32/m6btu2rmtDtF1vAMAAgIgAADN3XRdjJBxSSgCAiDHGnHN5AgBUte97IHTGqioiighZK08gIgAwc8pJnjDGxByNIQSQJ4hQQQAIEebzvdl83nWd99X5+enVw6OSomgmIueciEBOzGyIRcQ5V1WVdrsUi6ubIpJKBuNLVhEpAggACKpaSlFVEQGArIAFx3EkIiAqpRARPIaEiACAiPBEKUoEIgJKqhhzUkIVQUBABKWiaIyzrmLrELV2U19XpeRcIrPGkkVxt9seHsxUdbfb5ZxjjNbaJAURiUrbtjln730IwTmXUgJC4x0rEBEAtFV9tDdfxySSQRWkGIeqQCpEAAQEGQ0hKhEhEDMKgqjmnJbr5Xw6s9a2bdv3gZljzJVviGgYhhgGRPTeGmNCSGG387OZgi5Xq8PDQ2v1jW+cDFGb1gNzAslRrj/9/Pn5BaFZpdLM9n1VLRYXVw4OQNJmtTTsKuOJJQ3badMcvHQXQI67tfd+sQIdp5IAACAASURBVNmhaa/uXR2HnRQ7a9tut7k425Zw2TRV1VQpD/Pp7ODgoBCw92EcX3vrzcVy471/9s7Th9MpYz6cNUcfean9zo8tztfzg2snq83rb779zdffDv0wqc1sPn31S58fIs7mV55/5u4rLz19cvzeq3/y1cvtdra3F3O+XGxAzXpzOd3fB4lFgK1NpZBxJycnP/23fyqHWBsPoMQyb8yP/aV/jSVCkW0/pG0/35tJcp4hjDspGZEBwHnTNM1itZZcYoxZJYRQNY2qblarSdOWUnLORv6UMnNKiZlLKSJijPHehxBEBBEBwBhTVVVRdJU3xpRSwjAiIhE551JKzIyIzJzgfarKj0EhAi0FSRHUsKVcnHPjEOu6HnZDUzVxHBvfxDE5z2PfMzMRpZQMgDEGRFNKdV0zMyKWUnLORTSlkmMREQBAVGYWQABAYGsYVFMqAhkRl4uyXKzZcdd1xjhVJEJVLSoCmqVIzqMqM+66AZSM8xgRAFQxA1gkRBJFts5XDVtjLTtbs3VoGDKx0RIjAI8xb7fbtvJ93wNA1U6YuV+vEVEkDcMwbdphGOq6TikhYikaUvZk6rq1doOIpRRQdZbBGslFcASAjEqEopnYMtlYhpygqBjjqqoy1sNjoheLRdM0227wvgZmEUmSNPbA4iovuYxjDMOm67rd0C2WJ8v18uTkZDafdJvt8vLic69+7erRHkoQkZTNj/9Hf1WDv/fyh8lWY0wnJyemujq9ebWE4az/2qytrDXL8/cSoKlsRU4KPf30nYvV9pnnX/zi575Ycq6MtVWTU1JVRJ4006ZpsDYg7ny13RUCZ77we3/4x6+9kckYVw/d8I3XzjdnJ/tT+xd+8Htv3twzgIcVzW1sr0+fvv7x7/+OjzlXaRr7MX7j7UeIcPboQbe6fPDmH5cUu+3u9lPPfPQ7Prnq+t/755/vx2R9k6UAkTEmjOKcu3b16G/89b9iGBjUk99s1qX0x/ffKCmW1FdVFeMIqDHGvaYpeXRsyMEuhDHFMmlSDjHkUhQRvfPbXZdjzMw553GM1noGNgBARCklVSWiyWQiRauqijEWldR1OWf7hDGGApK1zByGkYhCCMYYRCQiVQUARGRmRAQARGRmKCQiYRzbth3HEQC6rlOByXQ/hLBZb6fT6aSpDw72l5cLIkKkGCMhWsveus1mFcfgnFPVkLIKKnHK2Vpnre3GJCIAYIjYGBFQVUQyxqQngMgA5JxDCl49M4sIAKmqiABgzjlFLlJyzoyYs/Rj8K5mZ1URgRCxKBAqEU8m9XRvnyQULevt1lWeGYmRgACI+H0IXFUN4jal4lKOMW6326qq2raNMYoIMw/DAAClFERu66bEkgGnh0c5yS6VW/eeNVwZX3fDmFJCUlQgQiRFhZAGBAZCAY0lxdhLKeM4pjHsdt2jh+8R0eXlZSkSYt5sNn3sVRUyIIAz3rEBkKxZNKUSn3/xhZh67+sr12+dXa6feubFp29fb5qmbq9wvV/tN6++9ejecy+t+vhwOc4mzaN1PNyb7t1+TuIw5pz9bJfChGpbtZdnCwXev3b98OqNO8/cK0PYrVfDrttu+oPDq8uLSza+aieDxAI8u3p9SOWLf/KVr7/1QJuZr1pkR7q73MVqcnC6Ovs/Pv3Zj3/8lU9+5KUpUkohpwJGKlPt+rW3XDfeMqXQMylobqqqz9E7t1hcfOa3fjMDbtcDowAIM+eUVLJ3FkAWF+c/8zM/U1tuaj+tZ0eHe1evzK/s1bbyyND1/RgSAJSS+l6a2h4cHMSS8+UCmLz3Y0je+8vlChHv3r27f3gQY7TW1lWrIpPJRERMP4S6rkGx2+4qX4cxttMZEI0xdX2PCMZZEUFE0WKdKSIpjm1dhRBQS1M1MWYRMcYgIhGpagEVhJRKCAmA2VSKOQvEGJ1ziNpM2rqxRDx6VsjAsFpfGEvW2mTMdLr3/9IEJ2CbleWBoJ93f8/2Lf/+185eQEEViwrKjguKEBElKhqXdKKTxnQmme6OmU5nJulJ0i5jOpo4Jn2ZRFFERQU0CiK4BQUV2Xcoaq/6t28957z70yXXNfftrUUkIQQhFKXcGONCTIDGOyAUEQHQOUspBUajTxQTeA8vQ0y1tfw4JlJKHhMAMC5SCEgIAKGcpJgQkSTCCLPWcc4SACHQWt9az5iJCRNSchwQwWj0PgFZ2rzJew8ppBQwJQ68bWxRFOijUplpTLCumpmxrSvzalrX9bSVUlLC28Y6GzqdTgwYfKrK6siRI5RypdRoOOGcy05VLXeeeXYvMXFbNeOQ0QRaqcFw+MQzT5CEw+GwyErT1MG1XNAQnMz0eDx23nMqnHPz8/PdXlXm6qWXXpRc7Dz11FyVEfA73/ve23/zBozQ63R+dv8De/fuve6665QS3tt7f3DfkYMH3v6230xAgLAdO0647Wu3v/qCi08/48zRagPKbd56wjaVPfzY44SQXPPV1WObdp1BKOvMLr70wvMxBJ7Pl9Wsmu1Op0223D9n9x7nnLV2557zHnrwge6mLXQ4KGbmc52V5WxT17U3ulDOhqzbHawOBi6lvDO7tIyMP/fcc9H5EzZvHqyubT39TDOd/ODBZ0wQF551ggUoex3nvQktFaT2vqiKjcnIR2KcEYIlIK11zgRFmMz5TJld8qrdNuDdP/7F0y/s01ICAIlOcv5f/uufj8dD9PbAS/tWV1fXV4+sHnn+9FNPiKZ76sknTdsDi8ubYoy2bdpmKnVW17UxVudlMi0XijHWmraxTafTW11dB0KyrAAAzgUAIJAQEz/OWssZF0KklBDRe2+tba2NMQJJBAgFSIDJBx88IEHANrUhBPGyEBLnPMbYNI3kLITgnJNSxhh9TEzItbVBkemUIAb03s/Pz0+nU9M2CASjZ0wxRgIh1rbWtgxYTL6qqoQBguecA8Di4iJQbpwtWbeuayFE3UwoEBMxJk8IxYQpeIyRckkIUsTofCIASAgFSikDREBATBARKQEEQimBBMAIJccBIEFCGBDCOWdAEAmldDqdKpEBJ0BSlmWMc0BSyKJtW2st59IY0ymrtjH1dIoIk9E0z9Ta6gZjDIBOp0POeW3qXOnofAQghBw9ehQRBVeAVAqmi8KEWBTliWfs+u5d9zzx/P6bbrrJW8c4qXrdbdu3P/rwY69+zcUkQPAWQ00ZPv7441u3bp2dmyuKIsZ41113bdm0eMaZO0Nwrzz/vC/dcvNzzzz1qlddaK19y7VvvO0rt1x99dVO8XN2nxFcfestn7/44oubplmYmTl4YP/Xv/YVH1HrfH1jVJS93XvOYzyvZrPBeDJ94UVVlCeeuGP/ocOKQNOo1tp9B441TZMpHRIKYFrooxuOINEyf/T5l7Zv3cYUe/7Fp+a3nbSxsYFZr8hzTgjNfDEPlPhKkcFw7eDa+tpwtP/g4XMuePUjTz0ZEKbT6c5TTt3/0n5nmqqqnPPV/PILB45tX1zg0ea5Na7tdLP+bI8KqXV3PDKECs65UJIwGhJSLvI8FwVTkjL0uZJZpiil3kUlOUZbdkuMdml+ATBsWlqIPmAyAuzKscO2bUyI06ZlUkTrer3e0SOHEHE0GnDOmeBt23rvY4IDhw91u31EXFtbiynledHtdrMsp5TGiCkBZ4zFGBljABBjBADvfUqJUiqlpAyOIwkZY+FllDDCqH8ZY8IYZ4xRSlFKhRDWGaCEAI0hVZ3KhVhUGglRgjlryrIEAIqglGJMhBgJIZxCCo5TAkKkEIwzudYhhISBpkQpZJny3ieIPnjTugTYmjqlhISUVVkqXVsffMwLbVuDyacEmcpSgoApRUBMJCEQBETAyBjFFClARCRIAJAgwZQYoTFGkggwigEiEDyOsCLTkGIIDpKSUoYQMUVCiNR5MpBnpbENpZyQ0NSGQrLexRil1HVdt61FxCa2RZWnEOtJo7XmnCuZHTlyhHNeFIWQ2oaYFWUEJJTe+O53f+UrX//a12977RVXqkxmVb79xJPm5pZ+8pOfnHHK6YInpXkM5owzznjk0ceOrazu3Hka4eSqN7/+jjvuOHjk4Omn79p/YHVp08lPPf3EoW99RzDSNFOCcNtXv1Jmpfe+LIp+r/OD+76/tLR00sknX3nZpbVpn3762de/9vL+zEJESggmEoxre7M9n2jdTJoj1riWU5ZX5bH1je7MTN7phBD6eU4pHW8MphOrpQjeTaft1h1qZW1NdeZ8SP3NPcZYjDF471pjve1INji6L5PV8mI2anyh+ROPPqRkftauM1gC3/q5/tx0MjKm5Yx0e5mIscxmMmCc89AObA1GUSpZtqnHaRETicEiEkp5CCG6OK5JQfOiKDjltYlraxshJM65t0FTOlzf+Mu//EvOOSWp36kyLWf6ne3Lc/Nz/cloKITYunVrTGFQt5TS/uyM1npSTwljABBjRAIppTwvORONaUNMMcamMXnZGY6nWZZRilVecEqpEIIx5pyjlBJCYox5nrsQYoxAUgiBEqCUphQZYzGhoJRzTggBoCklIQR/mXNOSmmtRUStNSJyzr33ADRGBEJ9ioqzlJJkHBGUlEUmyHGIVAhOaRtj27Z5kSklOFfB2+g857w2LWUKAHwMnHPvPaVUKTE/M3vhBX0EOh6NjDGT4YAxMZ7WxhgE7oIPIaQEiEgIUEoTYAQECICUIQJQigjHRYhwXEIACjQmllICoEipj6AzmUnhnIsxlmWXMzoeD6XUZdmRTDLGnPOMsfSyIi9TDIPBABGVUs45KWUKWBRVCA4RnXNlWVZVBQDOOSFEokxISSgHClzpt77thtu//o077vz2B373AwFip8g7Ve/Sy6/80ue/cM7ZZ1qzHr1tGzcej1966aXHHnuUslSWOSCuTptjh4/t3n1ut6re+Po3PvPMky/uff633/8BRCSEfe9739uyvOX0008PwcUYb7nllp2nn37KKae0bbtt87Z777nn3PNfWVb9Tdt7LnouZHQWCZdScCHG47HIuBAixjie1pRzIeS4abWUmzZvHeqNwdqqD04pobOq9StH1gYArCjLTVuXrPXDI0eZzGReWdtkM5s1w9VD+zdv2vHG1119749/uD4avvjE0yxiwoCI27dvX1s56pzdt2/fYlUJyjp5NZ3WczPzjZ10q97R1RVGJSDz3lsXMYFWeVaUUQYpNWMiBoiBaKmST4Kroii8M6ds3/yWt1wLXKWUBhsr6yvH1laPtc3kZz/76ZVXXJZSAoBnn31Wa704P7dv3z5CSIx1SkkIAZCEEIQQZ50QQkrZWtfpFFVVGWMYY1VVIZKUEgDwCBAQOSAAUKBCyIiJEAIAMcYEGH0iGAEAY2KMJRIRkVIKAD5iDIFz7mNML0PEtm0pEEppwEQIaYzjUiChiEgJCwgkJSGEMSbZRCk/DmNijEmpJBfbtmyeNHX8NZ+iJ5TY4DudTogEAHq9jnMuy2batu33u71eZ4ZnxvpeoY/DEAGgdZ5z3rTOuONCa5vjrGmj88a71hgXvHP+uBQTJYQxRhmklAihAIiYUoqMEEqOS5FgtA1SyLTKpHIuREbyogIglNEERAhl2jb6oIRo64YKDgBFWcUYKSEpISNUKxWcTwnzPJuZmdkYDrhU3nvjPBMyAq5uDIRLOq9CilmWvePGd33hC1/42m233XDjO1vjBOdVt/P+D/y7r992q5JpeXGu7PKtO7YFZ0xT733hucsvuzTXGaX08OHDv/rVw6997Wszrfvn7O7m6vOfv/k9v/W+mPDN17zlRz/6yb898PMLL7qQU/q+f/c7N998875Dh6+8/Eof4bIrXvuLB35x8s4zllKICThKa0IAqrOCIuRSaClSSlmWB0yEchs8l6rsdI1pZuf7Bw/tzfN8NBr8+Mc/VHmRUgohQl07F4RQUuoQAiaGPIdOMTFtZ9PJzXh45FePS5Q7FrZsjIaCEhscAp1MGy41pbTQ3Wa0MRwdo26YZ+V4Opibm9lYO2yNmU6GGD2lVErJuQTKnA2IhCVA4EXeTQm8jU1tMCVrbQyeMUYI4UJQCv0Td+zYtsm2LSVRYHz2mafm5ubatt2yZUue56apgRIpZF3X7GUxEcZlDEgIy7NiUjdl0VlYWCCcVYhCiBDCeDxFxJSAty+jVUcplVJijHEmjDE+RmMMlwIoiRHB++A8pZRLgYgppXhcAu89pdR7TwEYY84ZABBSIaJW2jrPORdcdapiONwQnNeTsZYCEE3bYkxCiNneMufcGMOZAADvvbXWmzbTUgkpJENE60JKgACU0jzPCSGUUkJY8B59lELqIosxIgNEyCVLCEWuskxLKYHREAIGjyl5bwNgSgmAxhiNcW3bmpfV9SSlFIIzxjgXAAARnbOUMClF8o6BFJLluY4xcs4BIIRACKaEUujBZC3LMmda51zbtsPhUGutpETEhGkwGGqtYoxKqbZtQwh1XSul+nOzg8FA5dXPHniwduiRJOBaZJ2yaq3du//AXd/93hve+HopZaYLWtG33/Cuf/6nz5544omnnLTDO4vRYgqC0HvvufeSSy7p9/tLS0uvfe2V373rrksvvbyquieceNq4jf/wuc+//nVX1W2TmNq3//BTX/hyURTBOp6Vh1c2/uc/f4FSniu9tLC0vjb4//7+M3vOPW9mbhGBzy9vpgjB14QSH2up1dbFRRO9sZ4KXjcGo5daHTx86JJLL11bW5vp95966qnxaMoEL8rKxXD40EpVVd3ObEqhnk59SJNEInCtSguTU049QwrhbaMZOGdaH5AAFTB1cfPWbaPRiGVqeXNvvpN7H4tet2lGNrqN0drKytFxPaFME8pTAgI0AaGEIoLgsiw7x46tjBtrjNWqbG1DIDz33HN/+7d/myhzzilJMyWKLNuyeXG+W830u1LK4XDobbu2trZ965ZebyZTEhFbg5xzBsA5R0RdFNgEzoIQgnOui9xaW9e1lLLb7Xrvx+Mxb9s2/RrEiCk4eRxTMUYAcM5RziilKUJKMXhPKUUCUsoQUgieck4I4VxSyhFj0zRlXhhjLNper5ciCqF8IuPppNvt5nm5sbYSveuUhbNt9IFCMo1tpxMhhDGuKjsEQUo5NzdnmxohNqYllpRlaUyLxwE1xugij84zRgaDQdM0jDFK6UyvL6VMKe7fvx8I6/R6ZadHkDBGKENICThQyoUgQoiQIgAFAAKMvwwhem9TSs7Ztm299SmlpmmGw2EC0rYtxjA7O6ulSDEgIqUkJaSUAsJxUkqtJcFIGaGctqZZWl5smsYao5SajidlniulCGNa68lkkhVaSu6TH69PAQkSOjc3t3N52+Lm7bPzy8F6a+2xY8dW1lf3Hzzw4/t/etlll6WUNOeLi0s33fT7f/epT2IMJ5243TnMVHHamWdv3nLCt7/97d5sP2IoO52kiy/f+Z0UWVZ0EePc4ranXzjQm51Z2n7qzt2vnJ2f6/U6VVkKIerJOFMKXVJCPvrwYy+88EJw8ZcP/DwmsrRp8/nn8Y0jK9u27ZifXVpZWel0OgdfeHFuYV4iOm87SrsYEOjc0qbGhZmFpeHG+rbtJzIqfAzWxICJUYmIa2vriNgpKyl01S0P7H/p6GBdcTG/aZPW1I1WT17uZwTKInPBj+qG6fKZffuPHRjP9Tu9+YxiSwEYF/2q6iRQnSyCY4ISygAJEsaVlkIRpCkm773W2nCeZZQQUte1VPy8c/a85zdvsLYVRTGZTEbDjel44/DhQ+PBxvPPP//6K6+o67bX61FIlNL9hw7meT4cjyhnIWLCUBSFEIpSTil3rkkpqTxjUkyn0xgjlyIBOtci4sbGBieElGXJOXfOBecBQKdICBGcxxittQDgjGWMCc4ypV3wSilEF2NMAG3bMib4/y+EgIje+7Zte71ea521PisK5xyh3LggKZtO60JLgrHIi7Ztk7Ott95FLMsQgtZyXE8YkJSAUq6UCiExLq21jWlDSI0ZVEXhnMOIEzfBGIqiUPNzglMT3Gg0IIQRRjnnPmLLGUAKzgvJyyznFFLwkBLnnAnJCKGcckIDJqkUQEpKdAoFiQDAYDBIwSKSQolOpyyKopmOsrLodDoxgTFNnufOOCFFir7b7Y42VmOMiCgUb50FgBhjXdecc+cc5xxDIITkZTYejb33VPCiyKwJQohX7j7v87d85R03npoiMiqqUs/PLUaIXEuh5cb6YGFuzlrfKfPWhN+76Q/+8bN/94tfPCQ41OMJZ5QiIOKBAwdOO+2U+bm57SecqHRx9z0/ePvbbthzziuAcSEVoZxwBoQBACEEIULC1vi1wXi+Nzea1s+9sL+etu997/t37jy1bd3szPzPHvz5Iw8/ZqZ1qVUu6Eyuj+3bd3TfXlXmp56+s61HSNm+Q0dOOXVnaL1HkFQQIIONjbIs52b7hPK6bgGgKnLbGsGp1Cqm5HzkSlvbSiWk0qO21SnkmcR6yjGVAEKJvs7n+72LLn4FENb4IFURIyIQG6NU2cZgTLkgVKYEKaUQgotBAI8x8l+jKSVEBEAuGCJa45umYYx4H8uyo5Xasrx05plnemOSmQzXV/v9/nA4nIwGc3MzM/1ZY9s8L7vdyjk3Ho+zLCOEUEoRMaTY7VWEkNFooFTW6XRc8KurqzMzM9774XCDC6kzXTBCiqIYDYbee+Y5Ivq21VpzKeC4hPw4Sq0PIUQptfUR6HE8z0vnnBCCEKZUhtEfVxQFYTylFGNkjBljUkqQEqWcMqqU8N5FH2xTYwiAkSCVUiIil8I5R4Aeh4iUkBRB59lodU0KLSVhDBFg2phcSaTJGNOpch/sdDrmnKeUYoxZJqXkjNFpMy1VhUiPY4xxTjEFRHj2macBgHE525/JipwCzYpMCpZSSClkWRZDYIxhDJJRygC0LHJZ5DKlVGUyOIOEKcmbekIpF0J79E1dhxB6vQ5CZIxASiE4gJRl2lmr8zwrsvF4rBmsra11u10kaTgcUiGLoiCESi7O3XPOo4888pqLLo0+MkK9dSqXnHGSQEs53BjQSEjCstMlFD74e3/4uX/4zKkn7ehVulMqJUBySincfvudW5fmTlycTYC/++5rvnHH1weDwaVXvDGElOUyImGcT5tWCEEo89Zymfdn9GA4koTFiGtrG7Ozc3XdFkUZUnzVK16589TTP/e5z6H3nPPBsRXKoFQsJTc4eggpObY+YEztf/rpfq8HkKqizPPcT8Yiz1jynNGhnUgpU0yMBUoIZ5xxNTM/d+DAAUa4zCvbNPPLJ2wc2jusfUcrSjB6S3w688wzL73qqqLKbHtssKE8spASYGKCmbZhoiRUhwhKKR9D27bWWiYhYWScJoghhcaYEB1XFSIePHjwox/9KCJahBijYExnstBqca6/eb4/N9NljIQQci1XV1dnev2UkoNw4PARYJwr7RMyqbjSxhgpOWKM0QvJANJwuJEAiyJDjAcP7q+qglNKQ4ouREppCEFKmWXZdDo1xmitASDG2Ol0jhw5oqXMsqwoihgjIqaUpGQpJURMKRljlFLWGa1127Zzc3OIqJRyjQspUUpDCG3bghSoOSGEc+6sqYpSS9k6RwgxxhTdbt0YxgSBhAghxpAcEsKEAsY5FQmdFEIwyTiJziulnQtZrggh3nshBBc0AepM2uApI9batm1SjDpIzQmltK0bLcVoMp0pCs45QQCCbTN1jFAK3U7FKKGU7t+3bzAYUEq73ZmUkhZCC5oiddZwIZVSiKi1tk0bk3fOVFXJKaYQfXRaa4wpRp8sHsc4J5ROp9Nurxe8z/N8NB6MJxMhmWQ0+VB186ZpzjnnnM9/6Wu7ztrTLXuMiU6nDCkyxnwMQgittZm0jLHEiPdeCP3+3/7Q1778peEGnrt7ZwLrQsi1vPKK1/38oV9OJpM95+6O0b/hDVf+9OfPPP/CgQ996H+rp+OmtYSJjeHw0ccfX1s5NhwOIcTRYIMheBt2nX4GIUQqhUAIF4xLjNDr9z/0oQ/dffd3MYb9B16cnZ1ljCCBQ4f2ZVlGmTr55NMwkcHRI4LTl5584uyzzy4YsmAyUgLakpPgaiWECY0UQoEICTOCKjlBKRgrKSNKn3zqrmcf/1XOlbftqKkrJX70r3dECle+/nW7du8Suh99YlS09aht28Y241FjbAQARCdEnmUZ5yzEiJAixLLK81xPjQGAlMKf//l/y5WUJDamZbIYjCcYrWmaw4cPrx45ePTo4U6ZLS8vppTaelIU1WQyAYAsU51OxznX6/XW19cppUqpqqqcD9ZaYPS4EB1lFBCm0zFjAjEWRcGVygghADGlxBhTSmmtp9NplmUxxmAtADjiOOedqmetRUm4lOsHD3e7XWOMlBIAUkrGGEJIAhpi4JynlBCJD945lwA554QQ9jJAGiNSTGWnU0+mRVHEGEO0y/OL09YQzjAG6yIiaq2I984G7yMgp5wppYXgEzN2PgjKCCGMsjwrhcqMMQETvowQkhARwJjWOaekJISklBBjXuiYvOSMEToej2dmZpw3McYUvFQ8EyLLFWfUO+Od6ff7SvIYI6ZgmppSbtpa51nwLiEY5zOl29opKVZWVspcF1VJKTRNY1szMzPjyuBa07bW+lAV5cLS8rNPP8UY27RpUzxwYDQa0JJzJlxrhBCj0eiiiy666667bOMo5YwRwuj80vzmrZsXFha3bt4y050ZDAa6yKtup60boeQ73nPjV2+5+f5fPHzunl2EwGTkpSxO2nXuzx544MEvfpUKSoAHA96lj/zHP/o///TPKKWEQq/Mc05ffPaZq69+4/YtW0lCyUWu8rvvuiul0AZX5lV3dmZlZS3PS4Jpfml+y/Zt6yvHpvW4qLInnnhicdPyps3L4/HYmvrhX/5ccUEp9c7s2LHjhacfr6rKTYYbhw8RzobDjaqqNtbWhGDj0ZBx3plZGA0nVaZn+l2OETM9tO3+/ftbJIeGYyBRZnr/oZeukCWbBgAAIABJREFUfsMlK0cP3fWNL9/+rWp+ece73/We4WRa5l3gestWRbfJs3a/5nvf+/54PFR5tr6xoZTy1mqlCSEJoOhUJ8/Nl798ajA1H/3YxwBjL9MXXnjhrj3nz83N2aZeWlratGULDedQP3ny8V+1bZtSEi8jBGOMw9HYWssocc7lWSGEACSj4ZgwmmUZISSEQPmvpZRm+j0AIgXlXHApZYyRK4WEcCmOCyFQSo0xWmvrHaXUOSeEMMYIIZRSdV2XZWmMEUIwxiiliKiUYowhIgBkWSalZIwlZwGAEGKMiTECIgDEGBmlUsqmniwtzEcERFxYXKjbBoCGEAglSik8LoWImIASSpH8Wt22zDOghCZuTGut6ZRZSgmAMsa894RyKSXl0lsvhFBKCSFicN62WORKSUAMISilEGNK1DmHgEKwadv4YI540+1WSsgsU6YWuZJN01CEEFyKsSgKHwIAuDCmXHa7PcpY00xjoFprRkXTNN77ssyVkEIo5xxWXUT03udZUdf1iSefApDauqnrOssKjGCDlXnFGCvLLO/M7N5zrqAKAJqm2Rit16Y5trLy0EMP/egHPxwPxoSQbSeedOONN3JBqWAUyQ3vuvGu79x557/ewygQgp1e15im6nbKTrX/0MEb331jaoPW+XhU/1//5SN//Kd/mtOKAbnwgvM3b1r6l3/63Ad/53eWl5ejS4wRa1suxdGjR7flxcZoXHY7KYKQcmrbrCziKk7remnzpv2H9l9++eVcCgQ4fPjwD+/78QvPPV9kWb/fX1tbOe+8844ePUwISQkQsW3bPNfeWmdMiG5xcdFsrIF1ra0noa3r2rlwbH2Ncr7v8LHnXnyuKtTMTHne2WcAJ1u2LvzWznffcud9L+0/8D8+9emmmV55xSU7d57a6fWMcZyJq6++OkZ/4OBLL+59etrU3aqbUpzWddu2ZVnakNq2/djHPq5ktr628tgvfv7II4/86/d/EFNiFDihF1100a7TT54vRdu2dT2hlCrB2rbNc01fJoTo97qHDh1yzhljnHNCCCa4MYZSKoRwMeR5DoBt2+Z5rpQSQnCgBAMKqYwxhJAE2LatUmrz1q0HDhwwxlBKGRNSZxgT4YwQlmWF9zGlpDI9nU6rsru+vp5pCQDT6VQI0bZtSqk2tY8BEKWQPv6aEDIRGkJQmfSIhJCUEhM8mDSup5RnQktMIcWIgCEEYwylFJFZ57xvezMzKSXvfZFpSNhMvRACABBJjIhIEAljglIOQGOMkFBrAQAxRsm5lJIzNh6PxXFcHJfLfDqddrolY8QFj8n3OmWe54xQxknCMB6PEQljDFMgCNG7FEJyPLrQRjPT7cXoBxsbiKiUahgbDwZllVMglLOCSgSaEKVUUmkptbeGS+VMSynlTNrWciF0VkaIhEHEVJRFSsl6m2UZYXRxedk4c9Kpp4QQlJCay/v/7WcPPvTIF774pfe+750RkRLgnF70mkvQ4wvPPf3W669N0RSlqutaCOFdvPWLN7/zN98FyQlJ3/f+G//+U3/zpmvectbZe6x3C3OzH/mTP/5/P/bxSy655NUXvBog6Tw799xzn3v2hcefeOq6t71dCIFAASAB9TEY5+YXFx578kmps9m5+fXBhnNu6/YT3/FbJx05cuTTf/uppR07cqUff/b5p558pizLlFBK2UxrLSWnlBHknFIiMj6dn5ubtPWBQwf3HT44nE6F1KfuPK07t9TdGHrXZln3wNG1e+6757zzzjrr7N2vveqKr339W9u2L5xxxmU//tF9373r9tnZ2d/6rQ8g8Bggz/Ot27csb5nDGB599PH1ldUIJAEx1gegeVF89GOfuPTiS1594asuvvjiSy+9tE0opB6PBkeOHHrwwQfv//G93E8vfNW5ITjGGOe0bdumkcdRCs65tbU155wQgnM+HU96vV47GkkpjbNlWRZFMdoYDMajfr8vRGCMRddwQggSQMQYo2AEACilWmtEdM7Nzc3VdR1CCiHkOvPeA4D3Ps9z51wIoaoq73yWZZjCcVprSmmupXOOIBwXgpNKCSHocUBi8D4myLiSEqMjBKfTadOYvDeTCDDOM5pZ20JCIURKSUrZGBtCkFkGALOzs61pJsMRYrS2pZTmqiOECiHEmIRQeZ6nCCQhIIkpppSij4KKTllyypqmAQDn3OzMPAC1zltrCalSSpxz61yn1+OcUYJ1XUsptdLGhBSQMy4YiS46ZzAg4axTdJy1CUFr3e12x+MxASazDBIZDseUsUyXhHNbGxdMnufD8VRLPhxPUvCT0ZBSKqUEwmKMNCVrbeLonKNcd6rCe9/rd1pneOKU0jzPMSbO+SWXX8bz/Ps/uO8f//Ef33rdtblWklOt80suu3Rxcf47/3rX5Ze/RgdZ6Ixz3gR3w3XXf/GLX3jd669a3LQJgbz1+rf863fvOXJ05aJLL8lybY37zx/5k89+9rNPPPHkO99x42mnn753717BGCR+59e/+Za3Xjczt2CMEUJY67XOjWkwxizL9u7bTymdm11IAIzRpaWl66+/7qWXXkJERsVxx46tvPf97zt955khpEzLlaNHHvzZz+6//98OHDj06vPPfeyJxzoz/Seefdoh/vGffASAWmtTSsG9/ss33zw72zN2snPXnkefef6Bh59cWFx61zvf/i//8oUtm5euf9tvIIYnn3z6M5/5FGX5BRdcetZZZ0nFIREm5CtfdaF37vDh/YyKotT7Dx/7q7/67//24EN33n7H9+7+Lo/h9F1nXvXmawlznU45P3/2mWeeqUjEZnDP3d+hlBJCYoxlWdaTiTFGSm6MCSEopSil4/GYERpjbJpGCAEAq6urBw8eXFxc7PV6hBAAQMSmrnlKiRASQkBEQigAZFkWQmjbtizLEGNd10VREUK89zHGEAKllDHmvddZ0TZtlmWI2NTWOZfn2ntfFP26rrMsI4RkWYaICREAQggpRmAkpUQIyfNcCEGsk1LnWemR+RRNMwVARikhAAS9923bEkKyLBtPx0yw4yilWmuGCX+NYAJMcJyUcm52IYQohBQBOWKR5cH55Jwzto5BSgkcsiwrO1Vdt957pRQCZHl29OhR502METmljB5HXqaEdM4ToISQtpkiYvQtF4JQwxhbWVuvur22sWXRGQ7HwSfn2rIshZTTaWOcQ0JTSuNprVXuvU8+EEjBR6UyRmjbttO2WZqfQ0qk1i4EhWitBYCUUkwJCXDFEUmKCJSG4PaccyaV+J1vffue79937ZuvoVy46KemPfHUk2fmZu+84/azzjrLOdO2NQVwrp1ZWLzr3ntSRONiXvR9TCs/+fHBo0evv/56nRWt87/9ux+8/fbb/8dnPnPT7/378+cX7v/BD4RkKsu/e+e3Tzj5lDPO3CVKnmXZZFIjxjLLF+aX2tr0+v3xeFoUWYrGtXWzsebGQ0Sybdu2x5r6L/+f/1Y3bdNOq25/UteJ0wsuuWjXubv/4TN/HyXT/Q5y6hH/8A//0DRtWeRcc06iZ2T3WSetrKzwvHjgl7+85jeuXV5evve+7/3jZ/+h0MWPfnBvv/vm+YX+aaectHv3nl/84vEHHvzpvffem3fy3/iNaxbnZilCTDC/uJQJRpOXQo/Hkz17zt2z51xn60N7X7zn+3d//OMf5VLN9LpvfNNVp512Gvhfm5+ZXd1YDSH0+/2maTq9XtM0Kfqmaebn51NKRVGoX8sQsW1t21rnXLfTC0V0Lkwm61VVdctKKQUx8egDIlofEBEAUkpSyvF4XAmhlGIpbt68ua5b7z0CUEoRMcYYQmCM1dOxzrPxZFgUhRAsBHDOMcZijEIIzrnWOiJpWisolVpFH2JwgkBCDDFkStY2IOEBwsZwmFd9IUVRFN57ZyznvMhLoMQ4T5jgnBdFwRizxmSZYozZ4DcvLTtrKaVCSUQklBVVFRGt9Uqp6XTctjR6zxlLCayP/dlu00xP27krhGBN7PXEtB6btu1UhZKcgGyaRrCCUtnt9NfcGgAlBCiQlJKL0bSuKLMYYwiJhiCF6na7KWFb18PhWAjR73abpmFCIpKQksryouy4EENIGGLr2l6n0zZTlRf1eEQYA8aY0sb6lNJgfT2vZlzbMCAARAgBCYQQk0mtlMqVbm2rtRYJzj///ELn3/j67cGla665WgqaFWWmpFb5DTe8+447v2lbc8KJ25J3SulNhd66dfPq+oaQ2aWXXAmMMa4PHTr06U/9zU0f/g9l1fMJ33TtdWfufemvP/6J337v+173+jc9+MBPg0+93sz+fQcefezxD3/4w+PxWGtdv2xtbW3Lli3ex6LQKSUlpKTENK0SMrrICBVCAEBeFokS650sMqAYQlAx37ZjO1BgggZAoURrGiEEYhQMXdOUVdEpq7W1tfXR6B3vfM+3v/Pdy6687Ko3vOltb73uR/f96KFf/eLWW2995avOPy7GeP755x08dOSFyd5dp+/56q1fjDGeevJpV73hTYIrJNA6e9Jpp40mk9RYALppaVGccPJNHz5jauzq6uojv3ro85//fEqpl6krLjx37cgBXeRZlrVtWxSZMSbPc8kpIm7atOnY2urBgwcXlpfW19djjFVVpRCrqsqK/OjRo4wxpVSMkVIKANZaToEBAZXL0WiESum8bIzhUrqXAdCUEiMUGHcuKKV8SDEFhMg5j9FH74Jvg6dcMgWaUkoIsd53yjKlMBgMdFlprRljMfmyyIxBSAEolbzrY/Teq6wqNGZFJaWkQFoXAAhQRhinVACjXObW2rYxcJzGEELyXkoJlCQCjBEkgAQCEu+8UBIoBc4wxpCi1noyHRMExjgTIiAIXRLKEPA4730KkVLwzpCEyYdD+w+xbduqSnCRARFZWfnGNNNaaeF94lJEQiNBITUiWmtDCM45qVWupXNOymo8Hlvv+7Mz1se2GW876TTnidb5wYMHVdFlHDWBibfI6aRtPMLUOU3peFIjEpQ20iZSNp00P378J9Y7qsSWrVuXlhfo/FxRFAkxRcIJ373rXMmy22677Utf+vKN73yHYNR7JEhmZmY++Du/+3ef+htTT1/1yvOVoE09oJy+6rxz/+2BB7/2lZvfev31hYQTtsy9821Xf/y//8Uf/fF/5apgQm/efsIf/MEf/c/P/sOlF1386osu5Zzffffdy5u3dPu9W2+9dXamn1JijCDC7ML8/NLi+vp6Y5uO6rTR51qPjaGMB4z11LbO18bIPAPKjwsxJkIZoRQAYzJtS2kKKXhvqWLAyLgZpxDnel3K1bQ2xqLU5c1fuuXi1772ru/f85ZrrtLZ8itefcHFV1z2F3/xf//y4SceeuTJlNLvfejfX3vtVbfccsvq6qH3veedralv/+adf/d3n3SBveud795xwpaNaQNcMYKCy6NHV01j5+ZmhJCnnHTy4vzc9dddB0h/9eD9B557BKPDKHxwnHPTTFWepZRaZxc3LbfW66xQWbExGI0G4+XlZcaY935jfVC5qmmmhLOqqoBBIkA468/N8hBCnueTyaiqKkJIjNHaNsZIKUVExigiEkK01pQG65zKNAKY1iBGyYVgJM8z/LXEORVCpZQQSUiJU641SRE8hOFwODc/MxqNEKNgxPukpVJ5zkIQQlHOYkAA8N4LIQAgBgSkwPhxnarLemwymQDBpmn6/a5t2uFoQylV15NOWSIBTIQwQoFRLhMgISgVz/Pcey+EkIwjYlaUQBkmAMrr6Wg4Hvc6HdXrjQbrkvGqKCfjERDaNO3M/HxeVb3Z2X0v7o3ea8l9+DUmOGUMgSIBQplPsd/vHzlyRAhR13VRVCsrKwCQEK21IcUsK7yLUpUJ2dz88sbGmgnNpk2bDh064J1vjU+EItcHDq8cWdngRJx11tl5XjrT9vqdpeWF7TtOnLZmbWP16aeeffjhh48dO2aMETzrdDoLc/MLCwsXXPjK6XR64MCh7du3SqWCtzF4a/0HP/g7X/zSF+6///6LX3NBVXUJQdu2rzhnz9LCwrdu//rrX3dVbYzS5TVvftNH/+qv3vLWt59y+lmCq15/9n//o//46U9/en04eu973/v2G97xne98OyQsy1JK6Zyz1sYYZ2ZmrG2rqmCMtW0tdWZcqOu2yPKYYH2w0e30GGMpAcbIBOdSkJha30JC15p8YSZC1EoSQsqyrOs6hjQ/O2/qBiBMm9DtzU1M86Hfu+mLX/1Ka90//dM/v+8975mfn4+N+eOP/OknP/nJ37jm2hf3Pv+JT3wiz/X111//rW/f8fijD+/Zs/uGt10nVP7Io8/cdvtXvfdbtm295OIrdpx4irMegSqd+5DqukGM/W43xii5PO+885b74pknHwkhEEIopSmF4XCIiFrrlJLSmgkulLTT6fziQlmW+196Mc9zSmkIoep1R6MRIjLGOOdCiKZpuJAyIQqpYwKdiYhJ54X3vq5rQggThDMBSLkQCDSmhIiMMUppSklKySXLhJhMJk3TEMJCJIjIKePcJyEY40uLy/sPHljb2ABKiixvpo0sK0RXdrpt2wLQ/fsPLi0vS6nb1obg8kIXRUGAhRAoAic0MEIZyYtMCBGss21b13Wn0xGMj0aDlJIQgnMegCCSEBIAKKVScNZanjGKhBCqlEQkiMT5wBijlPZ6Pdu2nJGy6LStrVuT5YXWeUpAkUIEpCRGdM5xRiGiFBoJIBIpRWsNZ6IoirquE6AxTuucEJJlWZFpH1xM0XlHhN5YX51bzASXQoiyLIGovOzYmIAKYLG1Ls9mxvWwPzO378W9TdN4b/O8nE7HW7Yu33vfPde+5a29mS5lgjFWVZX31hjDGDt4aP/KygqSpDKxtrEiFeFis+S07JXeGALpfR/4wLfuuOP7P/xhcN5bgxiNMYwxl/CWW7+m8ooJLaWuMn3f3XcdPXT0dW94U4xR6uym3//w44898tcf++uP/Kf//OZrr3nskUePHD64d99LZVnGGItCHNy3f9PSwmQ8znOd57kLHihNKWSZEpSllFrTmKYB7mYW513wo+Gk4EowKrSKMTIqbTtNGIJLtnaZyLOOlkKAB9e64WDUm+krmS/ML/+n/+Mjo9H67d/8ym3f/GaVF2984xvn5ube97733/z5L7z73e+++KLLHn30ka9+9TbE+KtHHtu0dcvmpWXv/dlnn3z+K8765je+te/55792ZMN5fOUFrzlOKWVNrTM5Go3G46GQfHFuHkgQQljjAYAyMMYIIQBpDMEa3zZrKcHi4mKn0wOgZdFJKS4sLHhriqo8dOgQ51xxIbhKEbTKKWHBJ04ICSF0Op3JZOK9Z4xZa9u2JYR476WUeZ4Hn6y1lFKtNeUMADCp8dh67pUWeZ47H53HEBEAGGOU8aa1bjShlE5q1zTNaaeePp4MhZAxwbhuFudmrfFKZm3TLC4upgRa6wMHDlRVNR6PKaXW+pQSIokxGu+EEJxzY5oyyxlHLmiWKdO0RZYJIRgVjXXO+6KoGGNN06SUGGBKQJECQEpJCEEpRcSiKCaTSV51gjWD9Y3FxYUYQvKOUQHgAKBt22PHji3MLyHFoigUgbaZeu+lVsF7oIQwurKyMr+41E8pYlJKcc4RkTFWVVU9GiBGqaSUJRGibevJaNCflQCk0+nojG8MVoExe1xMhOp9B47tOvvs6XSUV53nX9i7sLTY2kYXOaGsNtOVlaNzC4tKl8aY4DEEUEoxTk466cQTTthBKWWMEUiU0slkhJnCaZSCQ0qTSXPJFZc/+uijv/rlw1e/6c0x2EwKSqlLaCO59bZvXPbqi/acfY7ioq7rhx5+/NN/+4nf/6OPjMfDbrc6e/fuTrd703/4/T/7sz8786xdZ5999k/v/8kzzzyjlBwMhpzQr33lq71e7+yzd/HFxawojTHj8Xjblv9FE5yHW17WB4J/v+/+287vnHPP3amiAEFBQFkEBCkEUTbZomLiErN0MmZitp5OOk8n/cwfeWamnyQ9nbZNTyadxSTggqi4YYyMO2oQFUHFDYGqoqpu3XvP9lve/X2nZJ75fA548DFGjDHnPKsqq3TCkAlhlPbK5FzMplPnDjEqlDYr49VRPV62jdWWIDybLQZldezY8TPPOnv+/HFKeQxIyvLN97zV2f4b3/jGffd+0Ht/xx13vPZ1t/7TP77/t377N1/2sktf+cqrn3nmpw996hMfe/BTm5ubr7/15uStSd3tr7/13vs+cP0Nrzn6/Iknn3j0O49/PSuK3/zN3/LeAgDnouuanXCCQdzbm66urs5ms8ViUZal934+nxdF4ZzDGHsfMMZVVcUYKaEpIgJ41rbe++FwWFXVYrHgnCul2rbFGAMAxRgXRYEQyrJMay04b7ulECKEYK31LqKEEYohBACglDofYgxCCMYYAFDCjfVdp/Kiavq+NwZjLAXhlJZS1nUNiGVlrZSrBysohbbtRvXQhigZdy7M5kshRFVUzz3zHGMMEpJcQIo+WEIIQlFrE0LIOOMEo0DapimKjMqsWS4EY0VROBuKXLoYdNMh1COF27YdjuqIUlmW3noMNEWkla2zSmszm7fj8ZBgTClmgnddN6gqmsvpdLo6WccYd103Hk2yrJg3y0wWJoQYY0JYZFIrE2Ms8nI8WeVcpgSMCcbEsu1TSjKnGGNrbXC2pFgWUjlntD/RP09YNhytuGB9hJM7uyFipb3SfrK69tyJRUTU+3TOOec+9dST82ZR13VBUEz+lltf+/733f+Wt/6S6h3GxJuYFzIlb61ljIUQnHNlWQqReW/LqsaQMKWd7keDyifMKLnoksvHK+uf+sy/HL76Ss65Dz4ATpDuuedNn/jkp/b3Tl1/+DDj5MorLpqsj/7r//l/vOt3f48LEiG+6MUv+qM//uM/+/M/f/3r77ju2sNXXfMqxuVPfvzDlfGEMTocjq1WX/zCF44dO3bOeefedNNNWZYBQJZlR48eJQQow9E7KrnWuus6hjBl2HtLKRVC9H0fQprPl7PZTEppjPnmN771yJe/rJQajya7+7Nm2WltqQQhRLCIZPTwdTfecP1NTz/9k89+9rNHjhxx2rznL//v3/md3wkxTFY33v6Lv/y3f/u3J06e+uu/eS/H8MY3vrEe5DccPvzPn37wLW/7hVe84rzpYv6lL3/tf//f/iOn1Z13vmF7e7vIixBthLhYLLw2zoU8LzGmISQhMq3tysrKdDoVQlhrhcjm0wUAcE5N1yqliqIiQBHCQmS7u7tVVSEMhNH1yYR67zHGy+UyxsgYWywWMfk8zzHGMUYAiDESQqSUGOMQQpZlfd/HgGKMxrgEKCVICGvnABHOJMJAKU+QEhBMOQCbn5purE1CSH3bIYS7ThVFwQlLKQIAxhghlOd5jFFrDTZIyQEgpQQAVVWRFyiluKDBW+cMQqhpGi94jAkQcW7fuNBrRbkoskxrnVIKMQnOQRBKaUqJEGKtdc4xxtq2FZxr3RNC8rLUxpS0qKoKAFJKXdc999xRKXfXtza7pcIIyTwjjAJAikhrvexahFCe5yKTZrm03gshKKXOubb1RZY33mGMEYoAIKV0nkrJUwopJe8iQsg4C5ga13bavPyyy3/046dfdOjA88ePt2177NgxmdFlk4b12Ptw6NDBD33oA5PxxupkfWNjc21jbTgqKaUpIYwx5yKEoJSWUmhtq6pqm8VgMNzZ3VtZWdnb28slP/OsF73hzSsPvP8D5553zrnnngsAKQaZibvuvPNf/uVf7r///sOHr6OCZ0Jed/ia+/7p76+/8eatAwcxkGJQ/Nvf/4MHHnjg0Ucf/fe//weXveKKw4cPf+fxx5/+wQ/61kaU6sFocsmqjenv3vuPZZ77GKz3TIqiKDDGERByXkqpezXd31sZjgCAEJJS8t7Wo0EI7ps/8w2lzHnnveiCCy9UShFChsPh937w1J/+6X+6+NLLXvPaG6qy9N4bpYUgZ5197q/9+osWi8WDDz7406d//J//y7vvuuvOMw5sheDe8ou//IH77v2ffv3ffO3LX/zYxz4WA3rx+S9ZW1v71Cc+/tqbX1MV8ufuvq3t3E9/fOzee98reXb55ZfecOO1WneZLEwIMS7yPKeUtq1njJVlORgMdnd3hRCLxWIwGGKMKaXL2UxwmnExn8/zPI8olWW5VRTz5QIhZK1tmoaellLKssxaW5Zl13WAiFYWY0wJRy9gjFFKU0oxxhACIQQQKstSa50SpAje++Ai5SLFaKxDyVGGEU7OhZTiZDKhgnvvldF5URR5rq2hlDrnVldXF4uFjx5hBAlijM5ahBAhxHuvTY9AxkR65cqy7LoOIaS1jt4b1UfvxuMVlLDWlhDCOVdKOefato0pZFygFDLOKMP0BcZ5IRhgaoN33gMAxlhrPRwMGGNlPQwhNPNZSgljHEJYLpchBEyx8xZhEmLMyqIYVPP50vvogkcISZF3anbgwJYyerlcBhsyjFNKlFIfEwDWxhhnjh8/vra+WVY5EzTLMudcCCmEcOLEiWq4vljMmnYwm83GK8O6KqzSeZ5bp4ty8Jrrr1MmOO2ee/bYd5/4dvNI45Df2FgjhDln1tY2zj33nK2tM1JImci89YzK+ayR2SAhVg9XKcHBusGAv+Pf/Nbf/N3fffXR7xECKXpO8MrKilW+i+lDH3loa2trsVhwyurR6JuPPXbkyLFrr3t113WYiLe+7W2PPfrNd/3u7/3397wnAN06cPDQoUOcwvNHj3ztK480XY+5WNvYnIxXZnv7zoW6GozG4e/+/h+OHT9hnMUEKCYU8PpkdVCUKaVWtS46QoAyfPz4sZdc8JIsy9dWJ3u7+yH5LMvOv+j8y6++IqT4N+/9h3e/+92EsF/5lV+hmMi81NaWZSmy+PZ3/CqB9Mgjjzz4sU/G5K+55uorrrji4ktf8V/+23t+//feefkrXnb0yMnPfOZhIKLrmsVMr21WpjeDqnjp+Wc/++y1CUe7AAAgAElEQVRP9k/tf/Oxr373yW9srU8uv/Cli+WirAcbGxtNs6CCOueWyyWl1DnXNM3q6irGqOsaAFIWhdG9MW5je0tKubu/l1IqqrooqsFgiAlRSlFCiDEGY0wpnc/ng8FAaw0AWmvGGPz/vPcpJYRQSolS6q2lhDvbGe3ykhPOu0blPEMYp+QjIEw5SqCNm89mq6urzzzz/Etect7J5y1A0lqPx0NGmbV2Op2ORiPnHGOsNx1CKMuyEIL3nnNujGnbVgjBGJvNZsaYMs845xEDY0wIwRiVMlt2LSaMUhpidM5Za8PSB8Gl5PNpNxgMsizTWss8p5RGlJIPMssAWFUVKEZvbQghyzKtdV3XGxsbzgUAOLW/RymOCVIMZVF1Xdc0TVEUQgjOufWOUmp9nEwms8W8LEsAGNT1fG8/JMRkZqxnnCtjKGVGdft7pxjfJARhSBhBp1pOmXfuB99/ollOn3xytrY6kqyM0Y+HNaMEhWi1EiJfXamtdivj4WWXviylhDlPL1BGHzty9IknnvjmNx7rVI8iFFW5tbW1sbGR5/lkggEAIwghcpm1nfm1d/7Wfff902hQXPzSlzCcGKWMS+3TkeePPfHEE295y897rcp6aBN57NtPfvTDD9z6+tsJicaYl1xw/nA8ftfv/u4vvu3tF15wfkrRBLd18My73rRFCBw5dvx7339yvj9X3nPGEsXVaFSvrBw8+2yttZSSMjLfnUbvjdbDyer+bJplgklx9TXXzBZzzOhisSAEHzr7rK2tLYSQtp4waq35xV96h3Puk5/49H99939DCN19993nnfeSpu9tSIKQXuurD1932ZVXPHfkmQ984AOf/fznL7zwwo3NAx/56Cduv+3Wje0zfv2d79w5Nf3Wtx/73Oe+aEx/7atfdejQIYLo9dde85GPfPSeN92FYvrqV77sg0UIWWtPnToVgqvr2jlnrZ1Op1tbW8vl0hizt7c3HA6n0/lsNhvVQzRAxphO9SEE6/2AUauUdW5jZYWeFhEihPgQYowiy9q2zbLMGIMpdSFwzhHGCGMgJIbAhAAA771zHgCFEFJKnAtGBefBGNO2PQKwLiijR/VQKYUx7rqOMQYAxaDSupcy293fW12ZMMGyXCYMIbi2beEFEWEfEyFEKcU5J4QghJxzUsrZbCY545wTQra2tmb706Zp9venxgUuMEAEQkPwmEBKwVpdllLWNULJWgMAvepS7IqqhBSs1SGE6ENMIVgHAIOqIAwTIhIGWUgAWEGjxWIhhOCcG2Pq0TCE0HUdl8Jau7m6iRDKskwpRQhRSg1Ho/lsxvMipLjsdZbnMaKiKEKimHDBSCa4d4YzWJ0MIUVrY1VVNqayYoJTglA9yHXfHXnuuWowyPOSy8xx55zNRYYApWQFlwknBARjnOVydTSKgCCmgBIF4kLQWk+n03/92uMHDx58yQXnc17yjIUYskICJe/4lbc//JmHHv7cZ2563WsiQi7psqrOe/GhuhL33vu3t99yUzWQjJZXX/GKbz3+nb96z1/+9u/8HgJcFMWBMw+88zd+47577z1y5Llbbr1JstIHBwz1Vm9sbY0nK1mWnzq58+1vf9v/TGyaRoIMCHpjZaSD8Yruu6Kqdk6ciCgMxiuI0Lbt8nJwznlnH9g62HZLRkUiFGNMCbMxAWW61ymh17zutdccvu7JJ5/86Mc/0TYfvO7V1177quvaTlFKu14xxg6eec4f/Ps/mk73Hn744aMndjlC3/7OTy666PzedNUwu/GmV3/xC19ZXV390VNPf/Fzj6yvrt188+vGg8G3Hv364cOHX3PDYa9VSr5tW8C4Lmtr7WBQOmdC8CdPnszz3DmjVOe9L4qsLtcoJtNpSBEyWaTYpwQIIc45QmhnZwcRSmOMQogYoxAihEAIUUo55xhjKSUAYIxxzlNKIYQYo1Iqy7I8z733jDFCqLMhRgSIGGNWJqt93yulnEtdc9xpNaiqs88+JATb2dnBGJ3WtIujR4+ujEYYY6UUY4xyJqWMMcJpiJzWd9p5hxBijBFCEELGmOFwuFjMR6ORt7auB1mWzRZzJiROUWsdYyRcpJSEYARSCrHv+yKTMUYpJQCEEOq6poxnWYYQUkpRwSmluuuNMYyxEEI8LURrLSGEUprnMs9zAEAIYYwZYxhjITKtdQjBGMNERgjJijzGmBAa1DUBOp6shOAQispYQGE0WjE2IpROHj9KKGQZ31hdHQ0G1qR50waUbLCScxRDnokYoze2WbTNosWUAQCm5AXAKRMiK8qayzzPSy4ZxgwTTAhmBKMYAaAYDYajavuMjfvvf2A627/xta81xuV5HlKklEGiN9544yOCf+SjD/7c3XcLhFIKwdp6ULzh7jse/frXXsGltlPt0KgenHvO2X/0H/7wj//j/woAlNLVtckf/oc//MD73v+nf/af/92/+7eUEEaZ5BTFJLNsNpuNJis33nSzlFIpJYQwxlBM3GnWxhib+UL33fHjx0+cPJ4I2T7z7EvW1soyn8/nCJOImTK2s9a7uGyb6Ww+b5Yu+K7rtLLqZ0xZ1T6kz33+i1//2qMbGxvv+KW3CyF8is75LMuG48kdd/1cmb/tkS9++f/57Ge+9cR3rnnVlQfP3MaEvuKqK7/yha/cfNOtvWof+/pX33fvP3BGFnPULJeS8WXbbG1sIISUUovFIgZnraUUe++FEIQQ51zTNIPBIISw7FqnDedcCMEzqawhjIYQGGMhBM65j5ESQhhjSilKKQDked61imDkXUQI/j8hBGutMQZOIywi7IPtVR9jii+oqgoB6Y09efLkYDDAGGutpBBYCGutarsIqKoK5wIhRBt/5tlnEUadcwgAE9J0TXC+LMuu6waDgeBZr31dlSEE7z0Dmud5r1pjTAgphMA5n83mnDIAvLm5ubu/1ywVz6S1fjAY7O2cFJKVeZEJxilr2gXnlBDCGAWAlFIIAaXIGeWcY4whpizLrNUIIc45wdh7jzGmiCWICcNsPhuPVxBCjLIY4/7uqRgjpiyE4Jsuz3MgWAjhvM/zPEZ0GiYIEzTKC6Nd0zRS5pyz9fXVGL3zOt/aIIQ4iyhnNgYfXfSh77ronDE2OI8wUcYBACEEJeScCQHFYI3tm24JQACRhBPDjGeyzHKeSQp4vLradQ3jnBF4x9vf/MQTT/zgqe9dcOElCBjDCVzIchmdv/Kqa4ej1fff/3HBwFtTZKJv28lkEhH68pe+OhhNrAkRpcFw/JIXnf3nf/affvXXfm37wJneRhX83W+46/Of//yf/vmf/c/v/I2yLAFF06s8yxIm86YFRObPHdVaLxaLpmkgpb1Tu3t7e8aovutCCLrrXQze+4e/8CVCiDEmz3POKaaEU1FWFRMcAPJygBCqR+PxqODrtKxyghkhBAAYY0VRhBCscz4EIQQTvNdKSkkREln+6te89mWXXLJz8uhHPvqhGP2Lzz3n6quuyfPy249/49LLXnbt4SswueKp7/7wqad+/JH7P3HVVVdVA26cO+PgwaZZhhAW82kIYT6fA8GcCveC1fU1ay2lpFksKeUME57lIcZBPeq6LgYUKSCEt7bOOHr0KA0h6BeEEIqiIIQ4HjDGfd9TSjnnhBCEEGOMEIIxdiF5750NKSUppQ9p2fWMy5RgZTzhoscYN03DGHfOZZyfsb1plR6uDJfLpRAiy7IQXZZlGJOyFM1iaYzBQHXQXadSgul0XpYDSmlKYIyjFKeU2rYNITRNI6UkhBECSqnEY13Xzz77bAIcEJKUeh+PHz8uGedMQkyEEOcceoH3fjSsY4yEQoyRYDjNe59SgpTYz1QxesYEQvE0ACCEcM69M4PBwFrrvUdCWmvzPH/++edHKxOEUPLBWisyaa1lnFtrAUhKiZJkrSWQl2VJtUMYUkohBMaJ8wggMcYApYiQlBwBQzHVRSGFAIS9jz6kCNh575zrTde2S+9tQtFYS0lCCAMAxthh71vdt/OUkhDZc0eekXkeY8SYMsGrqlBWP/2THx08eIikwCjuvQIgmRTnn3/BwYMHv/T5zx46Y3NtZeSNxYzKokoRHn30sVyyV151TVYWnVKN0u/9m7++6upXXXf9q7GQMdAbrr9uVA//4i/+oixLrbWkBBCKKa2trbVtL6Ws61oIkULM8/yMgwcuvPCCsiwRQpxzQghjgrxAqU4wCTgRShljxhggNAEYY6qqNsbEGK03nJGQvGA8pBicTwinGDEh3jkfQgwh+EQAK6Vyme3u7uZCjlZGVZX/5rt+a7mYf+lLX3rf+++vq2o+PVXXg/POO9Mbc9HLLj77nBd/94kf/Otj//rz99wtpbTGcy5SSmJ9U/eqqiprbZ7LU6f2nDMxIkr53t5eWQw455TSXumqKpVSXAoXfCkqzvne3l5KiUopMcZFUTjnCGZGmxCCc04IQSnFQJ0NCQUAoJTGGEMIzrkQAkqYCQkAPqUQgjGGMA4AlNIiy5umGZSFlPy0jdW1rmtyIZU1TdMcPHiwaRogDBHsQkgpSSlZkMHHlBJgZH3wLjDKMRCEwCcUY1Bdh4EKwSmli9kcEOl6jQlTyjAuRSaFyPb35qPRSLXdysrK/s5J5w2kVBQFxSykSClHCGmlGWOY0JRSjFEIwRgzxmDAEXBICDAmlBNCKKWYEMjEbD7t2y7Pc0KwMZpiOplMMKbT6TTP88ViJjIeY6z5GCGEESjT2+SKqjC6Xy6XZTF0TtWD0WK2zyRFKFLKATxgloncg6OUYgAKGMV0GmNEZjwCRoApZ4iA9z5G77xRqgshWWuVUlprb6z3PkYfX6CU8t4SzmJAsYmU8oRA9WZv5wjydlhXeZ5zkbGsKOvBZFJdd/iV//zJj4qLLlhbW+u6zppEqbzqysuOHTv+yCOfu+7VNwhO+t796i+/7VMPffrUyWOvv/MuxiVj/PLLLrniFZeFkNBpwWMABLBcLotBxRjLeNa2LYYUY0QhEkKccyE4zjljzPuYUsIICIWEAgZI0ccEhIJ1lnCWUNC6BwBCIGeiXczLuoreOe8pxgkSQqnvW06o4BQicTE4HQTjVmnJWUyuaVRCgXI6WV294/Y7vfHfe/K7X/rSl77ylW9lcrC2OoouUkF96s960XZWZkBoCB5jCgjF4DClnBIhc4zR1vYB7y1CuK7r7z7xZJZlIUQfQz0YRRRkni/bNs9z47wxhhAyn88p5zylxDm3L+i6DiGEMc6yzHtvXxCTJ4QAgPFO8AKAAEAIwTlHKedCppSKouyUKrJ8dX2NwPZTP/j+ge0ta0zyQSmVUhqPxzZ4IYQxRkoZY+w7SwnHBMUYMSaYYu/95uZ2SuB9xBhLKbuuSygWRWGwQcgTwkIIKaW+7ymls+WiqmoEoK13bj4cDjHGdT08cWKHYxxDFJwRQgCgKiuIKWHI8xwAEEKEkJQgxmiM01oPBgMccTotIoxJSsk5772TgjjnGGMhBGutEGLv1F5Zlgih4DwATMZjSKgsS9X3eZ6fOrlTlnkKYe/UjpR5UZRG9/vThep6IDBZn1BKY4za2BRDRNR6QwjgmPJcQkIxIkII45gyHhBKGDBgISShlGAIyUNCAJBSCqc5571PKQBA3+u2bZ1z+/v73kdKqVI6y2RQTUw+E/zUyRN5nkeUECYIIaUUpfiiC1/aNEuMEcaYMRYDYoRub61TCo9946uXXnrZoGDG9XfdfvPXv/bo3/+Pv3rXu35bMuZUz7h01iOEKAEXozGGUhpCIIQ0TcM5Dd5jjFNMIThjFCEkhJBQjAgwBmcdxtC1y9W1lb7T1ikuMsrAWZVSBBwoYK0tY4QzrLoFxYwS7IyOCBihnFBCiFEaY4wQSikFY2OMIfqm3eecIQTWWgI4OC9EduZZh9528OBzR575/Be+vLm1dv755w6G2SteecX77rv34tnLASFAxAdbFEWMflBU1mnGmLWmrmuleu9D13XnnPMimWcoYeNddN4GP5/PJ5MJACCEGGM7Ozt5nlNljffe2eBDClpFQBhTzrlPyYaIUySE+JBc8FxQxlgIIcZICBFCIISttUBZ0zSYcGvteDzp2w6huL29XRR5PRg4rSQXITGlDMbYIy9ljhBKKWUZhgEAJIaJtVZrnWUZShgIopx1SgMk6x2KSWGFGW0WjZQSIuR52XWqqmobvHcxpEAIfQEfDMr5dEoIwYCKoojBMcY5F0VROueCj4RQjLG1lp0mpbU6JZTnJUIYIeScjTFSSgnBACTLKMG+LMvjx55fX18nmMxms5QSpVRKMRrVba8opU3XRZQQQoQUnGJnVIrOGTWqTxvMpsvBoEII5ou5LCRjDDClPEMoqflCCD4erVWZjMn/+Ic/Onr0KJdi64yDlAnKORMZlxnnHDxihBDOgnOEYEoppxjxEGNEISCEKJHbWweU0i+9QBrttNbGO923rp+nFBdNz4Rctl0mpNatYAS8TwGef75BCHXKlLk89uxzRVEAzbgsUwrra+Mf/fD7Zxw8kGclCvbKy14+HA7/x//133/+LW+rh5N2sRQiI4R1naYUI0BK65xC06gqr9q2pYRQQN65lJJ3zmhlnCUETu2fslZTBEopFNMjXzn5/ImTlJFe6QRIKVUWg67rKCGMCmNV9KHXHaeiU71gnItsuVjIrDDG5HnunLPWrq6u7uzsAIC1GoGjlNb1cGvrjGd/+sxstl+UubW6zOV8Pq/L8fMndubNcrHYX9+YXHDRJS6myWg8ny+4yJwNCNLebE4pDp1aLudto0J0RV4BRpSLFMGnAJjW41qZvqoHAJCCb9vWWlvXdYqeSpEbZLxTRVF476WUWtsYY0opxsgJJZQG64x3jADPimXbGe0IxlJyAPA+ZBgjhLRWUmZdsxiOxxih4bBeLBbBe9ub6Lw2Zm1tre97IoTTJqVECMwWi7qu80J6ZxglJJfOec4kSjjPypQS4ORcQATlRamUopQDJZzxtmkGowFllIFo2j4FJAXHlAnGmsUixoARuOgppXvzeT0a5mWBEAIAwbkxbliNfWqVtUAJAGAMnHPnHMaYUooQYozF6J2zkHBvGkopQsh7DzhJmaGIAMhi0VAucgQRoe3t7b3p/ubmpta6qgprDUo4y4Qz2up+Mh4eOXKMcpkLHq3zMa2sjRbLbn+2t7W1RTDWqvOmY4ylFAjDlFKMMeecMJZS0i8IIWCMsyyjFJ9GKT+NEYwBJwQ+2LW19ZQSIOpTHIzGwlmEECPY9UulujNZhjA2xjjncArL5Vz37XQxZxEZZ1FMi6Yr8tyFFKPvzJxz3vZKSvn0T54ZVJVzjmBaiOzyy15+4vmje3t7eV6q3uzv77fd0jnz3NEjCaG6rvdmU4hglLZGpZR03wohOGdSSkJgfX29VW1RFFb3ZZGtTyajkqK18qKLLg4oOe85l5TLBz/8YJbJm266GSOkdJfn8oEHHthY3bjlltvSaRGMMf903703Xn/1GWccEEKEEAghf/Inf3LxxRcdvu5qazVGwDm/8tILP/jBD952261ZJnwwIYTFYvntbz35cz/3Rm1VDKaZz7XWu7u7UmaU0vl01vXN5uamMQZj1DTdwe2DjzzyyIUXXkgpVf2cEFYMKsBgrFe9WSwWIbrTGKGEgORcSkGNcSkBxjSEBBEQQoKSlBImeGVtFRAy1o7WB888+yxIkec5ldWyaQXjSve669fWJn3f18Nh2zSUkJXRyDqHCbUmcJYtFzOGYDGdlWW5nM6AEkhI9e3u7q73djCont0/mWWZoEzwn+l6U483EsKnVdUgJj+i0loNGMs8Y4yG6BAjWZULyQiC2WxBCKnrGgCs1YwCLcVgsDqfz6WUTdPkVYkw9TEOy7JtG5SizAqXIiZECJZSSCEABm+jFCKEqKzOsiwFRzDkVWmNMinpTgnKCcLWOoRwVpQJ4cnq+olTOxFhTtj+/qysBt5HhFDEQIVI3jOKiyJbTKdWqIzBYr5HBZcUEo7RGtu3g1wsZrubG6vBxk73dV0bZxBKlGJCiFIq9YpQDgCUUp5JBNHZPpOD8XgMiAghPvnQp6uq2trY5JwavTMYDBDBQLD2jgqeELYhsGoyrFYDAu89KxAFlFAYrW0hiCkl6y3GyDmjVae1mu/PVOuti5EABXAhcsG9S+BTRMH7LmEycw5jjBCiCDiGWkbIxYH1i4ASzrkQAmJCCOWZMEY/+NEP/8IvvBlSCM4TimNIT37v+48//vib3ng3Rh6jRCheG+B7//ovXn/7netb28ouKfg33nnLgw9+4qEHP3b7bbdWDCXXvOUNt3384x9/39//5T333GOMAYC3vumWD3/4w5ub2zfccENECSH0v/zOr77/vg98+H1P333nbTG2KeJk0q3XX/WpD91/6+tv5Tlwhtcn5aWXvPiv/+rd7/iVX3amR8mh4A+edejkiVNEskFd5gV3zvR9Synd3t6ezWbnnXeelHJvb29nZ/fQoUN9p8tB5Zzf2dv33o9GI+q9ZJRzvpjPd/dP0rIc9H07GAwRiiEEjDEKsWkWGFPnHKciz3NrDGcMIWyMCwgJWXJGCOMEU+MDQtgY45zL8xwhlAlpjR8PR/uzed9pgdHm5vZisei6duuMMyjlwScpZdMYa+10Op2Mhx5oyvPJaLx7alrWYW19bbFYKGvQaZAw48dPHs9zOShzb6I2jmDwATyK5WBYlFgI0S6Xw+G46xoh2f5sKoSgMkNKW+tcQnlZLdsWxWicDxCpo5JxQmlwjguOMZrNZsbIhCHL8rquZ7NZLrMQAufUGqpiZIxxzrW26bQIdV0uu74oqhCCDwFwPA0TEh1wxpXqKCGAyXLZcs5PHD8mpTS6y7IV1XdlNWgWc4Ri9MZ61y0Xa5trQtSEUZlxnklMeUhxOp3neZ5higBSSsE6ynBdD1BKTmtESAhhMCjLPMcYW6VBiOlsbzieOOf3ZifyaiBFrqyRLCuKggBkeYEQWizm49EIIHVdRzASjMYYgBJZlBij9Y0DKFDvY8SEcqa6vp0vbNta1eveKKMTQ0DAWk8o4JTaTnEpko/eaesdiolSijFmFBdFJjm/5abX/PD7Tx7c3spyQRIhGL30xed9/4nHv/DwZ26+6QaMYgyWoPALb3rDhx988KKLX/7il17QOZ3JwRvuvOPRf338gfvvf+tb79baemtuu+Xmz33ucx98//tuv/12zrnM5J233/bQQw/d/4F777zzbpnny+XyzW9+00c+9NEPffD+u+56dQwBo3xlNLz5ta97+F8evu6Gq4aTgbV6Y2Pjogtf9omPfuraw1dxziURs9kME7RYLKTkhJBnn302z3NCyHQ6HQ1qQggAjMdjSnnf90xkznnOOcHUQ6SMY4wRxgAkL6osyygCkhCmTGjdx4gSQpyLrKys9T4hSdmiXWZc+JCcj9QFJnOKqbEqhAAAXddJxlOCPCtXxquUsq7ryrJkjFFM6ro+84zN6d5+AJwPapGXAOADhIjLakQIVINRq1yRSe3QUz96phqOIgo7+ydiRDQxAEAIdV3HRUY5VyZYGznn0YWUEGOCcG5MUCEhJhETWYVPi9ozWSrr6/EEkQUV2e50mWfCahNCQBgJIWKMwVtIqe97a23TtOWApJQG1Whn99R4PKaUBGNSTNaFPC/2dmdVPeIy7/seAYqAGGNKKQRQVVXXN/PFYjgaxRj3p82wroK3nHNnrU8IE9a0/cbGZghBSimEiMYVMlsu27quZ8tmZW21KDLrnHcxJVS/QIqCcYkQ9t5zTkMIzlnvvdKd84qJLITEKJYZLzOZBO26jhDczKYikyuD4VJ1plMiy3XoJpOVFGIIvu/7bz/+zUMHzxyPx4QQxggl0iOLEcEYhxAwpUJmGCjm4jRd6clozADrrk0+tVo5SB6S0S5465RuF/Ou61IKhJAEFGOUEPYxIA+ndmdSSqN7Avj5nV2tek5oVQ+Lon79HXd/59uPdb0ZjmrbtaOVta7vb7vjzk8+9NCp+fTSy69AWIVELr7kYiDkH+794D33vFEpZXy86OVXjibH/uHeD7397W93rRdyeNPNd/30pz99/DtPnf/SC4pi2LT9bXe++fHvPHrfA5980z1348SsRQ74eee/7Itf+caNN70mReqs3d46++iRU//61W9effXlSqng4mR1vLOzQynd398fjUZVVe3u7m5ubhpjptMZUDafz1eGo77vvffO2aLIKSPWIwCECcEAhADnNEVMu67TzosQXEyFzK3VPiTGJWVJcJ5SEiJbtm1elQCEcz4aj5uuXTYGIE1WVozVFHDbtsH56XQ6Go3PO++85aKVUtZ1NR4Pd3dObGxtzZZL4xOX5Xw58wgnwhIGl0IiEgMFKkaTNQK063vMKBAcvUcIee8ppVlWaK2bpUIIYYwpwX2vneszLgajYUSIA+MZi4Aoy2L0WV65BAhTZdxwPIkxAopdr1FKuci11tF5HxyKHgA550IIWZYBQAjBBW+tf/75E5PJmABWWnvvOWV5ngPAbDbLsowQcuTIkaKoMCGc87wsKCcIQEp58uRJjDFlYjgcatVpYzEgWZTr6+taW5EXCYEyDmMKCI1Go2XXDgYDwFRpgwjmWUZ6TbmYzZd5VjkXYnQYY84lQjEEnmUZ4EApppz43hijvZFadRiQFJwxYoxbzGanTu321p111ln7u6cGw1pIQoCllGRGrbVd1w0GA4yxMS4ErVRHKZVSxhg559r6wSA33iMHWZZZjHEMIzFBCE0o0Sm5GCilMSbb6+gdQrHrOtX3bdepvtdaL5fz3psYvQ86xQgA2jpCuE/41O4s7i6996OVtWeP7ZTTWZ7nTecJYYRld9z5hmePPjtfLvaefjZGrFXoWmVd+Md77+s7LSSzxofo1jY2733f+51zlFJrrZTSOff1R7/hQ8iyIgCz1lDO3vtPH0SBZGKQIkspuWQ+8tFPaZMwiJtuvG2+MDF0mPDJpEIxNrTAq70AACAASURBVE0zHo9jjJxzxljbtkKIrutCCAcOHFgsmslk4rQJKGGMASBhsO5nrLWUUpFlITiEUIyRDoYjP59ZH2JChHEKiBJGKO66bjpbjMdjpRulLaV4e3tLKdW2SwwwGY9ijIIxo3oXPGfEppBl8swzzgjWFmVmnaYMT6f72vrd/dn2gbOyIs+ygsosAZvO9lIKbbtkssiyjGFGeEExW6/He7NTVNDxeBxC2tvbk4BjSAkwlxkhZG9vb7FY1nU9HI91rzqlCCGYUd1rmfGmV5xzxri1NoQQUZIRWWsFI0VRYEhOm7IsY3A4IYyJMRoQKvPCBQ+EYEyn+/NOd6PRyAZvlfbOYiD7sznlQuZFNXDW2p0TO5PJJMZYlmXXddbaPC9bpW3wZT0QIrPazH3nrEaYck5V10fA2rpem8GwRiFQyoUQi2UbY1S9BsDeRwKEM8m5LIs6JtDaEk5lkSOEWtVbqwkhouAYY4RQSkkIASkwTgBQjN4b71QEIGWW9VJ2XUdSlAzyjHXtHCUMAJQQTgkn1BubizyhRClFPBJCOOEBBUo5xnQ2X/rosrIIwRGMGaVaaQyUERpT8j6EhAhhMi8oJt5bmVcU44gQpGStXS4XWusY/Ww6xRjv7Z0K1mndR4wxyyABSlh7zxibLVWvQog4xpgw+OgQhqbrJ2urnOeUCMHkbDajFC+XLeeUEBaCwxgTQrTWP/7xjw8dOrOu67Zty7J84oknnnn26OvvvMt4N6wZJenTD312NFy76MJLEkDCEQP75KcevuH6m4PHKxuHdnZ+aiNEhPq+k1L2SgXvl8u2rqvThBBKqRjRbLYghGCMmRRccaUU5QwApJRZlolMGquMVZmQmGOCgGJKGGN5nteMAUBQkTFmrS1fQBBMJpMY42g0KstSCBFQ8t4jhAiCPM85J0brEFzbotN++KOngk9MCkqp1noyWT1wcHuysuZC5FwihJh1ZVmGFDFGmDDnTTkYQML1eCXj2bFjR1bXt06dOhk8AqCMSs4yncxkZdIslqfFiIqqliJ3zlnvsiLnnBMEiNLog3NusZgNq2HTd1mWGWOaxXJzbTXGuFgsKAFOsdGeQArOA0oYY34aYwBAOVfGjUajMg5SCvt7s1xy7wKnJMZYlbXgmZS6LMssy0II1loXfEiRZzJhAIxd8IzL+WJZZLmPERNmfV9VlXMhRMjzEgjuuv7goXUgtO97SikTMiSEMO/UgobIRTEak5hAOx8BO2WDT3me1/UIoWiM8j4QmhAACoESHmP03mOCANOIYt+qKi9M11qjxoMyk0ItFCN4Pp8BYIwghMQpXl1b4Uym6GNA8TSfgnPeR04ZQSQhBDgNqjLLspQSJHTi+RNPPvHEwQOHZJ6JIgdCEMZlOZBZCQkxRhCKhBDvfQhBZGzEGEKob5uDBw+llJxzKURjlfden6Zs1zfT3ZPBG4y5D6HtVFGVfdeJTATkvfPNzqlhPW7mLQbKKHbOlkURAiqLnPGCM9KpfjRcHdblk09+Z1BlBw9saq0PX/tKIdnDn/30G+95U/AdxnDL6276yMc+tr29/aJzX4y5mM2by6+8Vln46dPPLjqNpUiUJcArKytKKUJk75rt7e22bQFguVzSn2GU0sVikVJijKUIRVViRrWxMcaiKjnnhBDKsDOOMSa4oISQuq4pJpTSXnUoRZkJxmnygXNujAkhbG5uppQIAcbkfD6PIWitYwj7e6di9MkHgJQS6BCKokAQvdWQGKSwe/LkdDp97tmjlFIuMyEyynld1xdfeBEQ1LYtBrDWooQB4MiRo+VgNJ+1q5PtclBlWRYiXiwWQkhr/OraRlGVxfK0eavajc314crI9AoAAYqCU280w0AgpegFJZzgclgTQqxRK+NR710hs2CNd4YyarSKzlJK+yZiRoXMaYyUsaZdjEYj72FtMumaZV3X3vvVtQ2ltLZ2tLKitcaUOucIYwhjGqLRtuvbsqrqwWjeLMvBUDDmvXdG18OVlckq50trdUReFjnlUinDODAqMIkIk7Yzs9mCc16VgxAi41lvrA8pRpQXRZZlGENMyTrjncuLynsXMcQAMQWRFShhziSGpIMrigKhGKJbWxnv7O3u75ysV8Zd1+RVSQkhAJ3uISGMwBpllQYgjLFcSoSQ8Y5Acla7FCnFVqu+W4xGI0ZF2y0xRoQCxihF76MDIKprTG+ccxQTzrnMeIwRAIQQjPCU0niy6pxjlLoQq7LCHTnN+0AIiTHEl5zbt41qu5QgRTJbLpxzi2ZubKd1LyTf3ZtJXqQYUnQYguoWCcCqplMql1JbWxWZ9/6sM8/wVjWLaZ7nONkrL3vZWWfOHvnCP7/qmqui9aPhyt2333LfBx8wTm+feWi60AjY93/04588/exNN904XTzHcrmcT3GRA4AxRmYFAkQZWzadlJJQdtpisRhUw+l0Opsd397eZowDEIxpUZR5XiCEKGUh+DwvMcYEE4oRlINKK0MZKVImBCtkFoJDCMfol3OdIDbL3hizmFOEYgiBENL3vZQ8BIcxJhSH6FACzqkxilLadd1otOKc4VxapbIss9oaqxSRQPB8uvfM01EWknOecbG+vo4xPW188UXNsu2NBgD7/1IFJ722pllikFfzdl+3u3POvTfiRleZldVIJYEHTLEM/gkMEMz4jQwQCA8wMHG5bNlIlilXRkZmNPeedu/9tW+3FieOXAOeJ5Vi67t37w+HwzzPOcdXXde1bdjvh21dcs7rOjdNI1LaptvmSUSen576vh3Hse26GKO1tpSyLIuzxhtzPp+9YZByna61VgJ8uZ5LKaebO2rUW6cIbWiWZUkpGWNUNaUEAIgUQpjnGQAQkZkRsd/tRAQArHcmO3Z2XJZapeuGtm0fP9+fbu/2w+6nP/1w3B9ezudaq+saItpSHHaH5+fn480p5rLfH+8fno43p9ONEy2740nOV0pl26K1VlVrFe+9hapVnA1snIjUWkHp7t0XIGqcW+fZ2VBylFKapjHGIKpI+umnP+1ubw+n25qzEDERIuZtTamoao6Lqg7DgIi5FgqNJvj47Xf/8Pu/dx6J4Mc//N4Yt60jgDSNb5qQSswphmZgxFoqI6WUREsusdZsjEFUAFCpUWSeZ2YOIazrMs/LMAylFBeCN01cp3cfvkhbrFUNNV8CvprWqcq2rrPWvCxb2vL56XGbr7lsr1Rk2VLTdPOytG17nRZjTDpfcs5t26a4WWsBYL87/Pbbr8fLS9uG6Xy2lv+H//6/+9t/++9NaATDv/33/2Ge5Z/+s/861/Tuw10/DE1wl8cHkVpKWdc1hKCqt7e3f/zjH7/99ttpmmqtf/zjH3e73e3tLTIDGSJ2zrVtz2RiWoNva62AJCJFwARnS9y01vE8lpyrluvzi2olMrXmEgswaKkgQkylJFCttVjCmqMhKiUVEWNYURTAMIHWtvHbOhOC1GiYcloAiIEJc62SawUmhDy+JGvt89O9MZbQAJD3/nC6abr2cHDWGkQ8n8/Fl9PpsK4rEeSYnLGLSClJRGKMfdMu65pyWtfV+WbdkirWqsMwbDkvy9KFFtDMa0wxkwpq2fXD9Xp9eXoAgKbvnp+fm74TkZhT1WitBdHr+XLcD7XWmCsz55y99y+XUVUBYLfbqaK1/t2HL2KMIbTO+VqU2FYVJNPt9vvd/g9/+AOonqf55u4uxuh8mMcJmZGp7bv0Kpe26/q+jzGmlBBZRIwxpcjL5RLa3iMSIYiigoikVJxvgHDYd9fr9d2H03y9XMalazzUus3Xvm1SjvO0PD49/PVf/zVuW9cNpGisr7Xuuz0pgCqhbnGrpYhI3Ph43M/Pl8bx08tZCXPc+q43FlrfqYDR49P9Q97WkpbhsCO0cZvZBWQmMs6ySHl+fnLeYggqebfbOTY553/3b/4OAIwxu93ueLyZppGIcklVChHVXymTZbYqWkSbdsem64c9gjBwjjl/FaWmUtI0TYj48vKSc16W5XK5GEspJUMeycaMhrt1jYha8kvOcV2WlHMIoQKEpvvum6+FbNPt//l/808/P5xBwQb63/6X//k3/9P/6MmkUowxbd88PdznnGOMXTfc3NzN82qMu1zuQ9s0XXu9jl5fIbOtRU/H21yLICAyAhMZVCElc//5FwABINWKokULCqrWChlAQBRAa8kiUlBrzc65qiK1qKiqACgiIKJqVQWFX6mCqgBAzYWZAYCUgEAlq4pKRcVUo6qKlpS3ytYYpwI5zvM8sjWqSkQhBDTcvmr87c1xmq4XqQBwezxV0PF8WeK2P92o1vFM1gVC3Lat1qqqFc3lemnbFowbpw1RrW+K1rrO16enxntrrSqmWOZ5VsVlWbICEf3www+/+c1vhmGQCjnncZxDCE3TEFHf9yGEUsq6rtu2IdPhcKi1srXztFrvEDmnOo6jisxbvLt7L1JijKVm4wIi5Vr7JsQYjTG11q7rQtMokoACISioqrW2Vh2GQURqBWZrHT99OgOISJ9SIWNFCYkJmW0IiNbzNo1VUUB9CN9//70P4R9+//vf/uVfz/NsbGhDQ8QpJWMMEYUQDHHOeV1nhSJavbPWmbu7EwBaawkEirjgUiqPD58NEZIaw1Dyy9PjYX/yjmKSabrmHLu+BdR1na3lxltGVchsVCHv+iG0nWXz/Px4PBwqYnyOOUfr2BC/MhyQtGRth15RRaBWsYZzKt411niQUlR8txeRm/cfBXQep5iTlFprOZ/P2zLNr8arDS6XmCQj+lKAKSxbzbmOc1aieSvGPk5T2ZJ89dVX/+f/8X+n+Zq3FbsdAKSUQgin083z89PNzc3T09P5fFbVb775pus6Irper+/ff/j86cH6LvgWALz316eRyCCgtV5EGOmVgVpEiiqKFFBVFFQCEABCVAQQqSAVVRmBrSEEVTBEqoqIoqIAWkWrIBIgvDJMiAgAIoJS4ZUKiCICq6oUQRCtzIwAJReVwiQiIEWUkCoRGSLKea4i52d+vP+l1hpC07btbrczoQHCIXQC6tqmlNI2wzhd8xYVaJwXH/xWxPi+2x3Pl2fNpetaExyUeDjdvNSCbIoAIiFR1w3GutB0v9w/7Ha7r7/5TgREMikE325JkDgXEUXQ/GocR0Qc9jsRmad1OOxjjNb6aZmZrKouy2qM+eXTp8N+T0RobON9rRVAXAhF9DpPzjlVbY2dpmsWJeZxHJkssLEuMPOHd+9SSpYNAszXcd8PP//8493pjtmwcVKByCCbV4ogUrp+9+OPf+q6xlr7X/yX/+Rf/d2//vPf/Q4RmVzTdKXWtCyOTdcNJctu11+3F2ttzoaZAcAYWpap63cVOU9ZCjlvUIGRYozWu1cEQKDrdHl/926bl1i5lLJtW5VikKx1JW0vT48iwgZTSoYxbcvhcKgpE+rzw/3Nu7tayrpM1rYE2rhQVFPaYqxbjqFxOa/LOh+G3bbFm4PbtsSWUhW2oeZMxrJhJ8aiECAbPLx7D1Jzztsyq2raIiJu8zKOY0ppnqZUYs451eJNKlUCI2D+4f/9f24Hz/t3XEot5XA4PTw81FqXbfNNa7wbn59SLc65x+eXu7u7HCMi5ZyttQaplNJ0bYzZWj9NU9sGAMipsjfGOFNrFhEVwV8pIRETkckxISCgogC+qVIAQEQAAAEAEQCICBHpHyGiiAAAIgIAKSARgigCAhCqgiIpqRATojCKojCKIRAQRa1UQACkFFBVICLmUIvUqsucl2l6eniAqojIzMY7cvZ0e+Osef/+vZY6zss3TWOcX5f49PLsbbDWto3PcSVCzLZu193hiKLrFtuhd84pUqm6bCUXeXq+dF1LRN6ZCjqtmw8hxtg0DQD8+KcfT6eTMWaaptA227Y1HfGytG1binjXqOo0zcxsjMm5TNOEiERUvWdARBj2h8vLMxHFGJn5Oo1MvoI6F0TEetm2BRFFgJm998+PT8PQ57iGYAxjjGveSr87MDMipjfeGiL3cn40zrnQ5FKeL09I5udfPq9b+fa3fznOa9M0Xb/LKX359Vetb9d1JrahcbkmVck5C+g8Te2wLxWr0LJFaxBRRQAIiQ2hmebL8fB+2zYAYOa0phD6nKMx5jD08zJez5dd3zWtjzF6Y9K6Ht69l5LWdev7/vD+g3Pu4eFBS3ZIWiuCaKklS875NNymmpy3fXua5/lf/O//6/ubD7fvPnBwbM3Q75uuXeNmwIHhksU62koxbIuq9aHtu5oLIqqyqgbraq1pi6WUZZnWtMQYr9fLdJ1i3AhkHq///L/9Z+s6//GPPxpjdrsdMw/D8NNPPynI3d3d8/Pz6XQar9PT05Mzpu97Y8zx6PTN+fnldHsDAG0bELFpmillfGMQBEEAlQhVoZSiVQCADSESAyACMYlISbmUknMhY5xzzAxvlEBEVUEVtIqqCgICMCIwAwgSquCvGBHQAqsSoIjUWkRFFLHkTQXJINakBGwsK9WqqpLTUqR614gAIgMiAUoVFc452cb+9MO1bZpSCjM3TZOWZX88GuYvPrzbD4ecI4Cs89QE93z/C5gdoxLgdV3bfmjafl6jCcGFzkyLiDy9XNu2LaV2XRu3qa4rEW1vbm7uaq2lFOdCycJkrfU515eXS8kVmYwxIQQiyjkT0bysIQSDlHJWEQDpuEEmYy0ilpJTSs6hANTKhi0zKdScsyrWLL/KuaTNGV7mOTiPAAg1rnOOqwBYaxERAGqtzGytRbLEeHP7Adg9PDx+/e03ikxsAdmGph123vB4mdBYMtX5pqlaSmLDKphyzRXZhlypVHS+nefRGHf77ov7hxdB8k0voIjIzKra9odpXkuRKtsWXClFRJjZGwu1SqnTdTzsDozknWtCmMdpY07rUuKW05ZSQhAFZvTW4Mv5fr8frMFlua7rbEn3u85wRajM5vHpU5f6uOWmadhZfQUeQNB7IiQFMqAAhIzka1U0Npct9DvHZnc4xhJLSYwiNW/b9G/+9l/3Q/vpp09t2x4OhyKViGKMXdfc3NwAgIp6F5z13mcRSTljjC3bIqVv+q5vLpfLNF1d8ESUUlKtIThEzTkaRIR/RESqCoCv5mUCABTNORMRM5dSAICIQCRtW5FfGWOsD0SAyKoVlVRFVWutlpkIERWJGElVEREAyDCCqGKtGUAAVLXmLABk0BChqkIpogCKgGiYmC1KRVAGKbWqQinFWm+MqTEaY9bL4pyTDJdlDL6druciYIz5mTgE74Ndp/GLD+8+fvHhej4fj4eff/zpq29/a629v7/vhkGRt5iLUhPaUnWcluRMEbk5HnKOl8slhEBEHz9+/PHHH/u+TymVUpxzMUYAeHk+O+eqSvhHxpgQwjiOzrkYY87QtU2M8enpxVqudQshbNvGzIhqyaBWEKolAUDcsioSGe+a3W6HpCUnazluqeaUsqSU2rYNTeO9F4S0res2n59fjsdDLdp1u2ma2n7/3jSH4w0Yu2wrW9N23bZtqaIwpq1upXLKSuya1jHlbS0VtpihVmCXS4xJgGxV6PpDmCsYb5gEcLc/GnZzrGiJ2BjrpcaUCrId9jsiSikxM4gCQNM0fd8/Pb3UWkuWr7/66uUhGUKsglqlJLYesGpN3ruY5nFe07aWlAASQkpR0zodze06jtbwftgXqet4TSUPwxBCWKZRX0lBxL5tFCGV0ja9qnhvCDCWzMxN6GLmEKjmGTg1QwMFFF2uFFMpNVUqHz58WNcZAGqtzjkiSimVUvb7PSKO4ygi+ZXJDBhCWJbFBa9avbe1FGNsSluMxazrmlIqpQCAqtZaQZSImqaBV0alZAIwxhCRMWa6TkjGWmsYs1RVXectxhWQkdQZby1bYkRUNGxNLMkCApKCggIiKqpBAlW2hghUFZERUSoQkaoSIgMiEiAoAoIgs5aachKFCrUNgVCZs0oySHXbHJu6RhEhMluKLoSyZdO18xoX1P1+D1J+/sP3vyCSC+frdNofQuOstfubu/NlPN7c/ulPf/rzv/irbV6IrfXLfuiXeRTAdY3DMABAjLHWaoxhZiJColJK55sY093deyLyTVDVlDZEMMbmnBVBEbYU+763zonqllbJVaVsOcW4tT7M89y2HQCEtkc2bTdQBURelu10akpM1polrwapcT6mDQAlFxGzrHWaJu+9sXQ8HhlpWZZgAwD1+0MpErwoURXxTZurXOa5axoQTblYH1KpW8yIaF3LzuRpBTbILlX88uOfreOnqvrx62/HcZpW/bPf/HbbtutlPh660HbIHJrw6fGyP95dzi/WUcm11MygAEQEtVYRZTYA9Pj4/OUXX10ul3WZn56eQDU4D7UwgtZivN/Sdn55evfhHSJ2rWlC+/y4VNlSngP3nW8cYR88lHyz34/zpLloLo+f7ruuM4TWWsPIzJhKqpvr/MvLL//yX/5fH959cTre3Ny874c9KZHheVtivG7bnKTUAui60HZN16jmKnleF2bTdL23Zl1X7721drfbPT09ffvtnyHybrd7enpSrcyoWnf7fl3nrm9y2gBg23JMW03FrOtaa1VVYwwzIyKIIuK2bdaZV0SkojnnlBIz932fUsol1leK7leGqFnjpgq1VgCJeU0pIaLzZn86SAUkfSUq+IoAgAAqv1FVBH4lLADAgoSgqgRQQWupAmqtEpEhICKslQhEFFFR1TIawFozEzGRamXDUhMT5HX21jCb+frEzAYBEXPcUgovTw/GmFprCAHZXq/Xw+GwH7qha4yltPXe22+/+er+/hNbh8Yg0BdffX0d51zEB2sC5ZwRIRVlH6qAb5tSAZEUTBUMTchF+u5Altblgcl67613Bz6N0wWIpumKANO6MiAjpVJzjDlXZ0NKWYFub99t83I63i7LcjjeScnrunbBxpSu16sgsTWGHQDM0zpPq2Fu3ozjuDueiCC0voICMxBNb+p+v+uHpmkQcdu2lGrXhMPhEEIYp8lYj4jW8ZbW0HbWQsoQC/jQiGJouq5r5+mZbWDr5yUDwOf7X5Z1wUUOx13X9XHdrsvaeKu1oiggh6arAj/99EsI4ebmZrqO/pUzVbOqiggjoNRtWxGRmRE1pW0cRykVtda8Ni7EZW2821I2SDVXEE2xHHa7UopW3eLovQ19G7dJUa/nq/XNOj73330d1+mnnzbnWkA0lqxn1UWkHg5H74a2aZB43XLXhziWUmDd1q7rlnVuu9aFRlWdDUhuXrau7+Z5RNSi8nK9kGFjDBuTU7HWq8hleqm5EIBRFSIspW7biojMrKq1VmuMSBGBrmtIwVo7jiMRXacrEQGAtcaxKaVM01RKMcboK4FSxRDvhg4AthSvLxcyxhABADM1bwxxzoJIznhVLVlAEIBUFRGIuNZqrSVQkeiNgVcChqyoMttShMggEhqsCviKjKIIABIJChCqVmYWTYzWOKw5qbWEYCRDLgYBSmGAsokS5rjE5fLw6UciMuy63TD0rWV8//6L/eHWOVeKMOPlclHyqRZi6zhsuQizb/vL5SpJSpGu65QBrY2VyXUi6qx/98VX67oKmGVeStpqFTbY9kNJ2VrLQj70ZZqc8aXq+HIW0aYbtnlp2l2tImC3bNg26sImKsD7m7ZWMc5ZZ6q8AmttLsV7m0UFaZ5nVfVecikVsG3bxto+BBEFUWd8rXW/31trkUAJa81ssGl9TEtojEGy1obGz6mmiiQVAJAUgWKu1rVsAmK5uz3+x7//T/v9vm3bdV1F2YbuOi27/Wnb1qrlr/7mn+Ra2Tb7MOz3exVJqYhmcn6bNu8cQF3X1ZjAzGRtLipSQ7Mv+SdE6ru2pgKg8zyGpsu5rCULMbnmi69Ol8uLMRC8OT9dvYP7zz/u9m2tRYk0p93QS81/+P57IvNn3/35sq37/R7JMxoi89X7jwBk0W45t7uh6QcbhlJKrVmhGNQxrvvhsCwbFrSuUygxJd94dPD48JJRT6fTmpNzriholfPTc9N0VZIqGHwTQsA3RMRvgreIqKoAoKVaawHAez8MWUTSq1oQGQmQGhFJMTOzZRaRUkrOGQC0yvF0t21bypuqAvCyLNN1rLUaY0Skbdumaax1RAygBMDMBAIAtVYltNYCQCnFWisiCIBo8BUgKAKgalUgIABgQFGAqqKlMjOAqEqtoCpVKyspIGlVBVACqAAIWBEJqwiQVKq1quCyjpeXR0TKtSK5btg1PuyPh9PpBIT0yprPnx9MrojYdL0AsnU5VWJ7PB63bUtpM8QhuK7rmNmHdl2mWhXZGsMGUV9ZZDalZhGxzrExSOKcizGnbfHei5QlbqHp0IYq6G0T03q8OdacjDHbtlnH67p2+yMz55jaNlyvVzZEwGTQWA/G1m0br5OAOueMcfO6SQUict4U0XmcrWUEtb4hs/GvcDd067aN03a4OXXDcdu2abpawGVZmmYopbxcRzZ2WafjcV+r5pyZmcgACDP70Jaqu50PX4bxcr3/9GmLScex9eF4ulEo63JNpTJjExwzb9tWSlmXKMSl1FojKAXfHg6Hx8/3zOw9NU1TKRLRtMzedaIYQpu2aVkW51yMG7EQqTE2V1Gp83hB0dY7a31J6zrPadss4+l0iDHudjtryPXhcp0+/tlv+/0ppTJPizU8zefn5zOzA+NvbvcINuc8j9fv//Cfbu92bJB9QKaYExExW1Vt214EpIJnW1MxTdMSkbXmlYiUUgjRGFOrWGuYARGrgqoCQK1VENCwJW/QExoRyTmLiHVJREDglXHOGYOIpZSX8xO9QcSUkqqGEA77XSlp27YY47ZtpRRE9D70feecQ1JmRsJXxhgRYWZV5VfwnyEivDHEqlpBX6ESvmJCRABBRAAgIlRCRQQC0FcAAqAAoECEhKCAAgAiAqBIJFJyVkRGxFrXeSrjub6cP2sVMuxcMNYa41rHZN3xeBi6ThFzrq9iXAmk8Y4AU4xTrQBw2O1LjqpVS5WqSgQACAbJbbWpfAAAIABJREFUCKRpnYiIhVXVkq2QBVCgLPEC1YMFpqrITdP7tp3mzXsvxpw+HGOMw+ldKUVVbKjMPJimSp6ntQ/NnKRtQ2u8c+4yjSKSFdZl3e+O26s5AWgbupeXJ2YuJSE6QJ7nte9q03TMdpm3mNYQQtu22zS2bVtLyTkD0rvj7ePj9wB0OuwRkYgQsdQUVREAQcZxdM4JoCKT89aFqvjy9FjSejz0Hz9+XKZLjKtvAlnTdN3j81O/OxAZAWr6virElBUNIvrGV5Wu390/PgLA4+PjN998k3M27GrOpVYmcr5zoVFVtpBiJtA2hNubm/NlfFVSUma2brxckXSZMTgLovv9kEv69MtP+92t5SAVcjR/8zf/1dPTvWiJuRz2xwA4HG++/O43f//3/7HWyAzLNO2HQ85ZrIQQxnFERFXd7/eIZI7Ho4gAgKrgG0IkIhFRVQAEAGYGgBACMGGtioAiqspkAICZEbHv+5xzzfWVqhpjCCDn7JxLKYmIMSYEDwCllMvlYgwhonceAMo/Wtc1pnVZFiIyxrRt2zSNqhpjEJGIEFHeqCoRIaJIRURmJiIAUFUBBQB8Q28YCd9oLQpAiACoqogIbxChahVRACBChVpqJSJmllJUsxYVzaQgGWrJAGDYKlJM5XJ5IXRt3w3Drg2hFhIpIqKqpUJM6zzP03wVESITurbWnHPdtqUUJQQfOpEaus57e71ey7yoatt3QDgus2tIlxlz9qHRSLvdwRb1TdOFbsubswEIm27IOYqVaZq893FOy7btDgfXWOPMMs9xW043d+fz2Vr/KucS2rYWBQDj/P54u21bN+zXeSYiw/U6jbza4/HovVdVZwMbpFabxr88PVvjrA+GXdsNMUbDr5CZU0oECCCq1Xvfdd22bap6++7ucrmkXO7u3h9Ox6fHz4R5XdcYs2FLaFIpy7IMx9vD4QSE8zj13c66JkVpu4GMDU2/ljJNU2iH3/z2d3/7t3/386dfvDWH/eCD+f73n29PO+udVK1SAKCUUquO41grbMtqvSLiOi/9XeO8ESnrPFnepbRZ7z7//NPzdTkd1t/+9i8N27vbd49Pn7tud76em34Yl6Vt+5ub95fL+Jvf/c3v/+E/3H/6xTtY1zWEICKXy4XZisg8rfM8p5RNKQIg8IbZGIM1l7gl5y39iuGVwVIKAGgpQAiEAAgKQKiqQKgACmq9a1ujqjlnVUVFthZVASDntG1bSomZvbd93xpjYoyllJQSo/He11pVdRynpml2u13OeZ7n6/VaSnFvQghdaPgVMb0BACUGAEFQBQBBRAaEN4RUpYIgoKAqIRZVRFRUAFFQBARAQQAEZ60IiCoRi0itVVUBEKASCllGEibKpYKkWmuJGzsPVco2lzwv0/ny9FihOiZiYOa2bfum84f90rVEpuu6tm2ttS8vL59+uS+lHI83Hz9+vE7L+fKMRKUgQGC2iKjgCpgKqmAAjbEB2ees07SgqOVOap7HqyFjnBFfiLANQWtJKRHou3d3w9B3Xff09IBEOW4xRjTMzuYqVTH44IIppVjr2BjngveW2RCR1tK2bdzStKxEtG3rNE1N63dtc71OtWrb9qnkl5eXvt8ZszWNb4NT1UvNAKBVrterMabWKiJITEQKZKxHMs8vDyLC1taSigqpiaUiUa76/HQe9jfH0ymuaeiab777TdO3tda2H8Zpafrhp8/3f/nx6x9++CHnvN/va8nruvZtYLa5akMmlWitSVt0bxCplBRCSKmoKqJ++vTpw/s7YwhEpuvl9vY2ros17v3dbYqFAI0xLy8vucj+cCBnnXO5wPF4c51m23Q5x6+//Y03KnnJOSNi2/ZEJsb4+PjYdzsRsdYYZkY0APIKEQFAREopCsLMzjlmpjfGmFyLiNAbfAUsIqoKb/QNEVlr8ZWiiIAIMzdNaNtWRACg1vwKAJhZRBARAFS11ioiNzc3ABBj3LaNmYdhKKWs6zrP87quZ3kWEWbu3jjnrLXwRlUB9BUAqKpzjohUFQBEBAAQkdmoFkBUQYT/n5yzqgIyvCFCREIEa4gIVFVqRmDQatgQUFEhKGxYpRICICIUA5LiZh3XAjFuL/JMhuOarAsA4FzY7/eHw+Gv/uqvrLUiYrwfdnI4vgNR1ZpSMpZSSuM8sXGDNzlVNt3QH8k4REwpPT180io5Z2OMGum6NsfI1szzLCKllHVd9/t9rfXTp8/Pz0/e235oX15ebPBaljWmr778sipKLmldSCXF2DRNzjmEwIC1mlSK8Q7rr5qmzTmVUq6XaYuLM3aNG6HJtWrVWsqyVEMgIqqKiN77Uoq1NsZIRNZZZjbGbNu2bLNvQtHqgykZY1zJWGYWglLKsNvlUoiYjd1SstYjO0OqQKHrP31+6Lvhcr3mXItIEfHeTctsLb//8OE6PlfRGDMaFkAB9K7pd8N4vReBZVn2+721/HJ+ur+//+qrL43h9Cqu/eGIrpmX/OWX35wvj4fjDRrtnJ/WaVrmVnq2zbxuCrRus7XmMp4R0VrrvVfVcRxrrcuyIOL5fDbGtG1raq0iknOOMZaUVRVREVFWISLrDCIaY1R12PdExGSJSN/Qr5Ad0JucswqoKiESESiKiHMW3hhjEBURAUBEaq0AoKrbttXyK0C21iKiqtZarbXGmFqrvGFARkKDAMDMqjqOY85ZRKy1rgmvvPfMjIgAkHO21jLgK0ACAEMsUAQQQBRRtQoCINIbRIRXyIgIAKoKb0SEEQkBQEi5VoFaShUkrDUTado2ImOcLbUSEZOAQK0ViBmJ0TACk9SqcV2eUny8/8xszStn+93x9ubLvtvvdjtEnMcpl0SGd9v2crlY769pmqfkfG06NsYQwvF40pqDMyKyH7pcorUeANYtGmNUAQBzLqowr3G/P8RtZjRdy8RcUm28m67XVErfds7yMo9D2xmEfhjmea615FweX55vb29d8GnJzGytBZQ4Lc45qRC3eDgNFnHZNqgcUxrnNecMACJChkspOedSirU2blspxTtz2N+JyHi5BufGcbRMxnnrXIwxuOa3v/uLn355SCmXIn3f55h86I33DJqq5Jy73dC0nQ/tP/zhh2EYuq7r23aZrLWOG3+ZLrFUYresuaRSysbWjvOaSk25hqbbHfbj5fzdd99dzxdrLYEOw6ACUvX6cjnevq8SrXMv509A2LbBBxOzGYYhV2TmGOf9rpvmF9BkmfbdaYmbquY3CBy3bJwFwnGezLIsRMTMIYSosG0bolpru65jZutMrTXnPC+LApRSYowhBO99CMEYAwBVRFVzzojonCOiWquqEjEROcO1VhFBRAAVESJyzpVSaq2I2Pc9IZZSYozOuWVZcs6llK7rAOB6vdY33lhVrbUiojHGWquqAOCcq7Vu2zZNk6oyszGGmUNwTdM4NtZaRFRVRFRFY62q6K8IERCV3qgqIiogvEFEeKVvhJBIRYAAEYnIqqKxtVZioCJs1LCWWqQCKQAwAQIoANSSctmMZa2iCEhEiERSJeY1AcDlPCMbQ9w03rnQdc3Hjx9Pp8PxeCRj55vt6eXchuZ0PKiAdWaeTK2Fmb338zzHGMmUruu2VA5tfxnn3eGWjC8iTbuzhvq2Q4Rpmt69uzHGFqkl5TJP27Z1XTcMAxE651JKzFxKcU0wsy1SZduMMW3bxnX13uctisiybMbZmDNb23QDBNGcQCUmYcaSY+O9qoqIqi7LQkQigsgAQsDMWEqJMfp+AM0IzMYVBcMu50xEl+uViZwzTdc7a9IWmbEKaElbzMt6OZ1OzgYi8t4bQ6D1/PwEQITmdHs4n59rETb41TffFdEvPn69bZsxdptnF5qh31trUSWXuq7j3c2tMeb9+5slFhEJrU9juo4v40rv7t43jd/i4mxfUvSWGcv1fF/zAlJyzn3fj+NYa+267vxy7brux59/ur299d6bm9MdviLNOd8cTwCAiCkla1lVmXlZlnlZm6b13gNERCLAmut1uwKqiHRd570P3qeUGBAVANBYC4ClFFVFRGttrTULdF2fUioKSojIwboYIxENw2CtNa+sFRFVrbWWnPf7fa0154yIOefGWO99znkcRxFh5lRL13V98OM4qqq1VlWNMSLw/HyuKRpjmhCstd77pmkITcwrADCzQSRCYFJVZhIRAnolIgCAiArK1iiIIrJjVHDOFNUi6gCICACc5yJFBKzhUgQAaxZjjKoiVEJyhkpJAICApa4AgG8IzRYrk1dlQJorrGTGKz3c/xGYQMmFcDjdfnj/LoRWJDoXmj4g7mutP3++D1mZXTO0bNzD4+Pdu/cxpW44WevVMAJYIFKpUiQnJL9u1Tm+zvO+706371B0HMct5n5o58vFe4eI47ocjyffNLnq7fE4z3OtNb8RkZyzgNaqAgCKhq3xpnUnAKHzeV3ndzd34+V8Ou0BpJayrnPN5eV6aUIrlhAkbSsBruvqnEu5LFvsdgMoGus+vP/i0+fP+33a7XaqOgz7UlK/8+u6DMEgr9O6AVA/9CVLCKGUFGPsu2Z32MccXWjYuNB0IbRS4auPw+9/+GMX2mlZfvzhT4f9MAy7VOqw26W4YkRjTGjbpuuvaywAZPj+4cFa671vh+7l5aVt++B7Kbnv+3W+zJfx9th/3q4A7vH53KyRmVVQKjRNo6rM7Jx7enoyy7IYY5w34zjez0vTNPv9ftu2da3GmKZp2r4LbYOvmGqtBijnzMzjOM7zfHNz/Pz5szGmC945txUNIVhrRYSIEFFVQwg5Z2ZWwlIKIhpjSlFmLrk45wixlNK2raoikYjgG1AFgFprjFFVSylQJaUUYySirutCCGuK8zynlBCx6zoA2LZNRABgGAZuQkop57yuq6paaxXAe9+2rQ/IzMZYICo1MzMAqICqwq8IAEUqMxEiAIkUEURSBWBmVRURIEVEAqmSAUUVvG1rVSISEUBQVQAgKEAGsAIi/GeooCBFoCKSKCqSFhYmFUJhUJrysm3zy/mRySCbJnTdsPvw5cdhOPT7AyJeLpecs6gOw3C9Xo/HI5FBxND1Oed5np0LQ9fef/7lw5cfHh8fwUA/HNYUj317f39vre+7JufMrp3jaoxR5s8PT23flZS3bUPEmGsqZei6bVkQeVmmLz7eDMebmAoQpyw5ryI1NAP74J1tmoYIr+dnVBCBeZ5Pp1Nc1prylnMbGhHZDQcmI5i2VGRavPdJ1Hv/7bffTtMiIiGE6zR66wDAOZ9rdS7ovKSc6+UyjcvXX3/NyF3Xreucc3bOgxIgl4pShZHmLeZUrmVxof2Lv/iLh4eHUuqnh/sPcDuOl5vTKcdNBa+XqT2cbNOtW5mm7bBvhn74008/fPjwYZ7X+8/Ph2Ff00KEyzh9+fHmiXmZtxDCtm3mzfV6TSlt29a2rapu22ZiWqc5v0opeeuWZYEqIYRUqioi54aNMU5UVdSw01LP52v9VW68//zzJyIkBa0QXLNt2zLNRWRd191wCI1DVUO81c1aS2RqrfjGGFdrdS7UWkUqIm7bZoxRVfhHCoCIzGyMAQDvPRGJyFAKAIhISqltW2YutQKAqsYY2Zjdfi81z/NoiV+p6n6/d85dLhdArlXXNW7btq5rypv3vuu6/X6PiEyWkEQFkRGQmVMt3lsRQFTnnGotpTAjMgtVIgBCEYRXDMxUchEBRAQARGUmRCbCogqA8EZVEUC1AoKCgKACKiAAIRAAg7BzTgU0a1qEyCCR5pjTcv/pF+M80K8A8N27d7d37/f7L7YtWWufXi5xi8fdMPgWSpRac67EDbvGNf3udBKRNI6fn69h2MUYuekp1Pv7++PxOM8z+36cHr84nD79/EsVEIHzy3PftEDkm/bl5WXYH67jMpzeAamyDa3PMVlmb900XZNgE3xcxrbtr+cX5xwiqgARee8Ncc7ZORe6tpQSfGvYI5OqLvOqqt57Q7xMMyi9MtaDatyW0LbTtBwOBzYuxhisc4bSFlEBFYLzAFJ+JX2/W+fFORdjKqKGdF626vXDxy9/+elP/bD//Pi069pl3qzjNeV26Peno6K7XJ5+99u/KaoPD/d9dzNP6fnxqWn86Xj4/vvvj/tDWtPT/Xm5JkR+uZ53ux0z/388wdW2ZdmRIFiDBRsOX3L3AEkpKXlUPTTk6NH/X/2aD10JUoZCQY4XDmxaYGZ93FXqOREhtk1VybWo6s8///zq1Su33W6HYQCA9Xp92O2fn5+H07nW+vWvvlVVZHLOGQCaAQAiA7q7u4cPHz6sVq3kEmNcr9eIYKLTNJ1OJxd836+appnn+fn5uYl+GIYite97F4NzzsxEBABExDetmYFZCGGeZyJC+IyIVLXWCgBMFL5QVREBgBCC9x4R8xfOuSpSSqm1eu8BoNaKYE3TROe7rmOiZVkul4uqEjMillJUa9M0N7d7VZ2m6dOnT7VWMPLex9j2fe+9N7MmhJST99H7mJfkve+6LqUEqogEV6qgCGQg8Bf4V4AGAKoCQMQEgGYCnxkAIsEVIyEaIsFnBqaGgAppXpDJMTIYIxia1FxrZvZ5TqrqnBOx92V6//OPqhpCw96ZQrxCvX24f3O3V8MpKbuby+UyzBNfPDHv9/vTcGFmZ5irNk3br/fjXA63r54fP663h3lOPrTON46J2Ze85FRFbL3ZXS5jaOPTy3G9PXCIVTHENsbonVtKnofLeBnGy8t2szoP4+uH+6+//vZ8Pkbvc86r9frp0+OynJumcc67EGxZVLWqtm0bQliWpW2b/X6fUpnm+f37j33XoErXQdu27z9+CL453N6M4zicL6vVCgGY+d27d8fzy6tXr4j4L8yslLLdbpc5T9O0Xq2madrv9zmnrutU1Te+azswI3LjOG523VdffYPg0rz8zW/+7t37H1MeN5vNq1f3tdbf//73//Fv/w6qpaT7+4dxHKdpJqJ5nruuJaJpmsZxdM51XScirus6Itput4jovb+/v//mzTeq+nI+5ZzZOQCY00JEHLwWZaScF2K+jINWWa26nHPTNIqKRPubG1WVaobsG991XQwu5+SCN7PT8eycSyWb2e3tbdv1S8loRsAlS4yx1gqI8AUROeeICBFBJOWMiM45dk5E1AwBiLntOkQUETNDMlXNOdda4S/UqqmpLHkWKavVOuWKiIRm5JjIRB3zuu9FFBEBKOc8DeNwvgCQWEW0zXbVd+tw5R2ApmVhZlV1ngCgqCIyEwKAiDjnzJAcq6qZmhn+hcGVIgMAIgAgfIHgEJAB4S8M0NgIAdCUBARLAUUgBKhmRr5450otwTXV1HJ1LpjUOqea0PuQZfrhu+f3P8e26QXd62//ZnO4vbt7/Y29EgV0nFLqm5aZd+tNqrIsGYDMMOfqXVuzdKst+6brurIkqWm92s3z5DwDKFBiF5cst82qGMzz1Ow2OWdTbZpG0qKIq9Vmnuda5Kdf3qKBmdwcDs/Hx7Zf39zen08vpZRc1F1xcF0YhsE5Pw3Ddrudp6SqiHhzOCypOOKap5eXF2TfNA0RTeOFEHfbtXMuTTMidl1HjmNsh8tUJbcxqsh+u1umD5v1evN3f3d+eT5fBu8oBF+l7na7WtI4XfbbwzAM4lypH5cFvv3mt13X1Vp3u93xVHe7DSKoyv/4H//P69ev2xibNqy7dpqmGOPlciEiESXiu7u7YRhCCGY2TYNbliV+UWtlJDMjIlXNOc/zDIgAMM6Tcy52fa2VAedxuru7Synt95vValVKakIw0yvvgwKY4VWtCqB9G9M8k+OU0mWciAgRmfnt27chBDAFgF2/7vveDLqum+bZviAi5xwRmVmtNcYoImaGXwAAESEifEFEzjlArbXGGPGLlBKoOefQxDlXc8k5d10zDIOKbDarpmmmaRyGCzMjUgjBex9CUAEzI3JmUnRhpstwnIbJzBC567qbmxvnHCgCKBkyMTGbGQBdqcJfICIRIQIzq6qZwWf4GbCZwWcEwGAI/z9EMmQf7AoBr8gQARABsOQFwUstUjGnDAUxtCLSNJ2A1aUaYWC3aEnzbMyn/3luujUisnPo/Fdff73Z7O72G+99UduSO54u51If3nzz/PyMiN7FnDT4bhpzF9vXX+1enh5rlTY0l2ncH+6qSnANkKtLYQqX87hZNd57Lea9T6rb7b6ULqXUBj8MA3Mg9vcPX1XBtx/ebfqOvXPARrikFAlVlZkB4OXlRaqJyJRy0zSXYe6aJnr07FIuVSoillL6tvv06VN0vuu6cRyH85m8syvV08uxdq2ZQK9930qWUsp2uw3RvTw9+dDstzfjOG7Wq3EcU0qr9fpyOf/u77++nDOSTsPYdofj6XJ7e5AvsuL//n/8y9PTp2mZ0KlcUtv3pRQACCGklOgLEem67ng8xhjc8/MjESGyqnZNm1LSoojonKu1IrkQQt+viYhdDL4Zx3F/cxDT3WFPaN2qz5lrLjE2tdbzMBhC2/aOnfMeQUspzjkg8j60beu9R3ZE9OrVq5RSSfl8PqvqPM/D+bg97Ju2RURmBgARAQAiCiGgY0UwMyRy6IjIzGqtzlG9koqATOxCcM4BgNa62+2kZBGJvlXVkrJz7nwe7m5va605L8MwqIpzjh2u+s08z+fzsdbK7INvVG2ah1KWnIbb2/v+dv/x46NUKYl++P5PTdMBABE1Xbter706ZvY+lFIUzIFDNAA0M1UDUCICMEQAIwQEIEQwQzAGQMXPCD4zREAUrWaGiICkCleICIRiuuQMAGKG5Mi7KiJgU1pc8IgEV+TUVLU6BMnnRRYRcSEiue/GZ8ehqvrYsws3N7dt0z8cbtsYX/3udy/H45TyarM+nU65ypTyMAze+RD7UmW73c9L3m526COSJyax1DhUKfOQaq3r1QoRc0rDcGmbXrQspb66u2cXJefz+eJ8M+fEaGmZfBNr1RBJVZ+fn7VmM9tvd1WsbdsYYxXIKTWhGYaLD02tNcYYQrAqq7Yz0ZIyALRtW1Tmefah6fs1aQbAkvI4TQT4/Pw8z/Pvf//7+Xy52e1//vEH56kE79mlNBMzh/Cf//kfu93Nn/78p29+9auPn8Z+3Z0uLwCE4OY5Pdzd28szB3734ZdvvvlqvIxNt5pTEjMFmOf5+fETESHi3d3dskyuj3Fc5rYJzkUGRh/M4el08t7HGH1o9jeHruuq6ZVzjskvy0IGsfElLT/+8POSJgDYb3chhNNlICIwl/MF7LP9tl+t+lKqC2G32xER+6CqpSQAOJ/PJtXMTueXsqQwjDln571zTmpNKRFR13UhBDRzRMSuWlUFRGPvyHFKybvA7InIzFQVgEpJ0cVlWZxz3tOcCjOGrtdS15uN916t5tyamaqUUlQVmbpV37ZdrqWkKqq1VhG5ud3H6NO8nKaFmb2nEMLt7e0wTMuypJTMTKuUUkTEx9BtenLctx1/QUQChohgAAhkhsBggMhmJgZ/gciIhgCIaIgA4L0TEQAgRkQwM0RkIt+2S8nOuWpaVVsKuWbvvZmJiNasYCrCzgFAzrWJvtY5hqaWGX3I0yLs2YWUswvxx+NTiO3PHESNidab3eH+wbMdNv39YTvP88vTM4B6F7330zS1sfnw4cPf/P5vlTAG5x2o5GG8bLdb8rpUm5fct/02xk8fPgJ6RZ8VGxe7ph9TPY8Da151oaiwaq3ZNLYhljQTURPCMAyIeP/w+qe372KMTRt++enn9ab3AOuub/pVLqWJUVUDu2E4r9fbYRjGl5fGcV6SaGk9q5S5zFKKER22W3dzU/P8+tWt1PLq9f3z83MIYRzH3W53mWbvfde2w/nUtX44P5/P59Nw+vrrb9mF9Xp3d3N7Op3u7m7+/MNxs989Pj6C0iG24zjuDvv5NL+8vCAxB5+rZKmv33zlViEEQiJXa1XU8+lYqjIzeaclj+OFHLroQhNFAMGuyFBV/+uPf2IiAD7sX51Op+enMxGpagjuchp2ux0jikhaitmwWe8ETMTYe0RkZlVW1Spl3fVX83hpQlStVq3rmpQWySX6CABlKWlKqrparVwAcoREZirViMhxJPYAKiJM6BxZlcABkZvYiUgFdU0LoEpcWRCpEDB6RjKzwNgB6helFIHC6J1zqRRV9eZqrcH5LnYOg4h4F9D5ZcnjtOBnXIqAzATYxbheb+aST5fx5eMzfdG27WazCyEQEQIQsQKZmWPHzEUqAHjvAFBEABEADMw5p6oAoGZqxgCqKloQG63imMAIEX0MVY2cV1VENKlkxo4ZrORFwZxz81wBQBWISHIBABXVWuBKFiaqy6KAgKzAj9PL08cfvI/A5JxbrVbb3e5wuN3tdkT0crqczxfv4I//+T//7m//oW1bpbhkZ0TnuXh35cNqv4g4doeHr3NZPp3OQi6bGYBv2zatZDkvc87LBFpjjHmZog9NCNM0ac2eOITw+On9btWu1+vT6fSrX3+TUjKg2PXH06VfrfvdoZSitUy1lnEgpn69UlV25DQQwTSM0XmHZCbeu9PpyQcOjomo1np3c7iMAzJOy2xmy7KogogUUZPqEH73698wc9v2jDBdnq3W00veb7aPjx+lwuGwez6+9OsNu1BEyXkzO9zc5ly95yVlZ2YxxnmeAWiaJlXYbDYc4vl8vn94WK1WyETeoSkjqBqS9pvVdBl+//vff/fdd0Q0juO333779PS0TGPf9w8PD5fLZRgGRtxu133fTvMgYCLigjezlFLTNCJyuVy8C03TPD4+juP45tVrQ021fPz4Mc8LM5thKSX6xgUfXPg0PWapTdcWEVW9ubuJsWnavhTxjr13NScECCGoqn2GAEDkEFEBkYkooKmZiRkyg5nCZ8iOUYjIQkBEAGhrzV2W0oMJI8xzGqbBuRC71lQv4xkJiTC0DSNpybUUrXkczv1+j0S1VjOTWudxmYZ3ZkZEZth13f7m0LZtLbkm8E0QUwBABOccEZkZgAEAIhKRI0KuvNUIAAAgAElEQVREVTUzREZEIjQ1lQpMZAiEZgYqDIhkagqlIhvjFZkZmAKAKiJ6tC/ICOQKlI0IAAyvWAFNEZjQMiIXszSeHj/+8j059j7nguTevPn64f5G1d6+/X6z3q0PhxhbQGyaBgFqrchMZtOyTMPgPf/9P/yTY57ncVoWAxAk9K3z2nIQKYZean26HGtJTdNcxjml9HB7t2q78/lsZkR0fDmzDyH4cUq72/thnInjMkzTPAA5Ud3uNn6evfeXyyWXxEivX7+Z5+l8PpvIrHW7W4sImqaUYtvkWgCo7fp5Tug4LSWl4pxDRALw3uclDcNE9Lzb7bz3zgUpWUT229u+b5dlWa/XP/70y41zzy+nr79+U6vWmkP0iJZzdWMpNae2bXMqHJv5MvXsQmxDLMuScz4yY1VRVeeolKJIh5u7vutSSb/5/W8AANVE5NWbB1C5ArL1drW/2amIav3xpz/XWg+399M0KZiqzstCRLUURMtLMql9210ZkruKzWq1EZFSyjQuLui6W/km9k0b2kZEqsr7jx9FxFQ/fPhwOg8hNLvterfbeSZVFQFQIwZA+AszU1O6AlRRMwNEvkKstaoqfkaAQKaICADhC0ADzVqLj6HtG0S+kmob3hBRrZWZVTVXpejarg8hfPjwjr1TVUTcrjd9v16m+eXlpWtbJHd1OR0/fXivYCE05Nz+5mBmROScAwBEJAUyVAM1RTQiQkRGBoCSChEZghoCAqKhAiESkpkxEoDVWgWEmdVUqzpHAKCqiBVA4UoAjFQEQU0RAAwIUQHADKyYVofARkhECmaKQBhjW6X+8Q//huS6bpVzHja75fv/+urrb2/uH7quIyJmBgAVcM69f/9+GM/zNNW03NzcvL5/eH5+RMRlnpnZlpSnsRIiqWZwrmHvj48v+816mNLz8YJkVTGVbMaB/HAa+u1uSfLqzdfTkmLXf/z0PjinqimleR5r9SY1BJdSmpa5adverGua0+n0cjkiIojudnvvvYiwU0UAlq5dIy21iqrWKkVsyUmGKYTGgIZxLuW8Wm0QURWQYE4ZkFKum82uVt3tDpfLeHf3MAxnItc0AQDc6rAfxzFXUyQX/de/+vWHDx/Gaam1ppQAwDnHBOv1uu/aj4+f9ne3wSORAlREAlEAEMlS63q9NuOaEzODWSnJzJxzZlZKGcdxTouI+BBU1Tt3PJ7//m//bhwvl/P5n/7hH0QUEVSVGEzRzADAEBkJCEGNHDszrpWZzQwAdpvtdrtHcrWkcRzRtKTEiG3brje9956cMwAzAyQCVNUQgpmBqqkKgJkRkXMO/kLFvoC/EjP03DRhhWtVLaWYYuu6lBIJg5pVY0+mmmpShIeHh6qiqt57Rjoen5cpee9FBEREi4gg8u1h53w8Ho9Pnx4VDBG99zHG5rPWe68iAICIZqaq9IWqiggzO2YDsCoGQETAiKAADACISIhwJSpanIsGAgaqeGVmiKhaAcwMABAAFBTNAMAMnXNmimCgYIaGgEBmMFyWfrUyyW0MebqY2eX8zMw//ekPf/7jv/vYhi9ub+8Oh4MofPVwezr5Dx8+kGcC3G3X03jZ728uIe33+8fHx/VOpOTL+Xhzvz8dXy7T4OPKt+vgXB2GeZ4321WF5TJMhZTIZYXWN8M8r/p+WWYfW5WSS64vuWvbWqv3HgBCCACAzKfLJTRNs1pdxpGIQhvIxSUXkeq9J0RCmedZ1Zh5tVqllAi5bducKxGpWSnFDFNKRKyqKRcAcM6pAhiqWdf2/apDoMtlDD42X7h+f+v7jRl+//33h22fxULTSS4+NN65WrOJSpU0zY13++0uL9MyDU3TpJQAYJlmZs45M9Lx5ZGIUI2IzAy+mKapZDldxhjjzc3NsiwhtgAwz/M33357PJ+0Sr/eVlVmn8vCzKJqZimlnLOPbRuiAUQXslREM8TXD/dmBgBi4EL0LooWRKw5zeOIZsz89qefN5tN0wTyjq8cE2itOVeIMRJxVTEzABARMyMiAMArIkIEAP2sGhIRqsFSKiK60CByFWla1zChmqrazmopVoWRikit1cycc2oqIsjQ9o19ISKI5j2bCRPc3h7GJQXfmNk4jpfT5XK6iGlKCYE3m83hcOi6DgxMDRg8ebXKiABoCKpgV1DFkBHUxEwIAUDNkIgaDmhgAAhApghoZgpXCKBICGCmiGCICJ9hWiZEdEjomIgQEUDArHE+zUNZUnHA7AEgTecYY87Vez9Nlwmg7brL08c/FCHnvI9E5ILf7/efPr53DF3TbLZtt5KUyzff/uZ0fJmm4ZvDzTxO/WZ7Ob70q268DP/xH/92d3d32Bw+Pj5udodPP388jx//5f/8v+a0kPNLUZ/rkrL3kZxnxJLG8/k8DIP3frtd393ejFfzSI7I8cvpOC25verX0zB2XQd5MYPo/LCcp2lSxKZpSsqp5PVqi7mcz5ftdkvkVGvf97VWUUDknBZmHsdLjB6RmXiy4mNzPg/9elOrGlKu4gSYfPPh3fu2WwE772mFhAaoSohpngg0+jCM53dvLzc3e/IUQhhOJyI6nU6vXr2qKStA433OiwuBmaNzzJxz/uXdh+1+v9uuPj0/bTabN19/VUoRBUSMMS7L0kSfc1ZV33Z//M8/1Foll4dXd+vV9tPTy/F4fPXqlbTy+PwEaofDYb/fiohzLsZYSkEkBaq1ApqJXk6nnHMb4+n8Mg6D9x7RvGoFUAQQndLivbdenXNmxl/QF2YGAGYGf4WIRI6dMzMQJTbCKzJAZldNmVhBkahx3hrTKs65mrOYaa2q6px76LuS8pITIylYOl8Qcb3uieh0egFkIz6fhrZtD4dDzuXx8REA7u7umtiJyMvLy7t378zMe9/3/Xq9bptQpRgIMDkXgAhMVBQcEQAY2BUokCE5ZqeqAGBfIMJfqZmpGiIaKBgBCACZaeOdmSAimIqImSGiqppWUegaP43ndbdWhOiQrARWzbkPLTmep6GaBt+AyLzMQAhA4+mIzG9//HO/XhnyZn+33t4EgofbmyW1aV7a2Dw/fVrv9iIS+/X/9i//d855HMfXv9p2V+vDebgAcdutTpfRex9uunkefWhMszd/s39TSnkAOL0cV6vOe+8cqer5fEYk76MBDeMc4+xDTCmVVNZ9O09jLYUQVURVzGyZ5jQvq/XWIXjvETFnRQQRIfbOOQCotTrnxnFerVbzPO92u5wrEXXdSkQQOefqAJCZX3/1lYk2IZ7P5+3D2kQlZxUZzqfz8VlEGh8kF8nFKliuh8261rrvXi/L0nofqNus1sMwIICZaMmn54uIRCbnHHknItM0PT2+rNdrRxRjzLUQkQEZ0IePHx+fXnKR1WpdfP7w/tMv9omZV6vNOC1zWnb7/TLPwzSO89CEuF2vS6laBRmACBGZWEzMzHvfNM35cmRmkSLVMZGZFKkEyGhS0jSIVFOEGKP3nr2LMYoIXaFDRPsCPkMwVlMiDMSmWkoBROccoSEQMyOiIaIZklYRFxo05dgyc/nCh8a3XZ4XFwOhm66WmZmRybtQDW5u90T0cny6nEdmbpum5jzkYmalSNe2m83GzKZpOh2Px1oUjYhj2/TdOnZtYAdMOS/sAyNVEzNDQFWttRIRGAEQAJkhAJIpAqgaIiAamAEYAiKAmamagaAZAKgIIrJzQACgaVlUPIKZ1isEcM6REgPM4xkAvA9mmqcLOTZDDj6nKpW6rqsln55HQr68PDN7EXHOMXO36h/uX8Xo1+v1vOTL5eJis93ffd21b9++J9P11q8265SKmimC977WOk1L24SUtZTy9HJKKa1WK0Nq2h4RlmUxw91267yHK8Km6QHZe//2/TswafztPA5dYGFYKsbg1qstgH3//feGsNlsfvrph4eHB6lFRABMpaSamdl7//Jyatu+6VZLrjnVGGO/akopbecBIJg5rcKemtiklEop++221vrxw/vL8VTy0ncNgA7jGaQSUd/3asLMVo0M0pzHYShFmiagQQzBzFIqgUNKKc+5WfWIOAxDCOHNmzfOx67rlmVJKYUQmHlZltVq9fXXX6vqR8Tz8aQiv/vNb6e0vH37tl+tdvvNx0+fHh8fReTh4WG96oLzJqKqMUYxq2ohRKm51mpmiCBfHA47IgKAnPOyTOM40hdLTkSkAt770DaqWqR673POfd9vN/u+75kZEeEzUgNH3sxKEUZqYgcARapnV00ZCQFrqUQUQ1RVs8qEhE5BDCi2DQKnPK/3hyo5hNBv1vM8Aygi55xLKUTkvTczAEDEEEKMUURzzsuylEIAwMz+M5ZCtdZcZThfzqfBzOAK9eHuntcUgidAQCRiBawiRGRmAHaFCF8QgAEofIYABgCICAaIWGsFVOfwM0UAMDM0qyYhOJHaNE3Oi/e+fqZkBACOyRCqJDAiRtMCgCVlKdqH/vz8qe/7WosCWDX0kYlK1mw2nj89vv8FmAAZyDG7r7/51TzP9+HV7e1hugynl0+//e1v3/7yvlv1qWQRWXf90Lam5So0XUnzZrdj8uOUHp+PnioCpSWt1rt5SqfLsOq3wBSbdknp9uEV1lpqAsKm75Zl0ZxA7Xh6Do5+9ze/rgppnr/9+g0i1quSzBDAUso+BABYr9fjPKfnZwBYr9dN1zrnjsejfYGIrgvRrkoNxAoCqp75/v7+4fbuu//6Q9M0Dpt5njfbDTMXrW3b5ZzNrJS67lbkmJBF67wsMbYi1fnofdO0vSnfv3qdqh3PL3/7t39bSokhlJwJTVVPx2cfg3MupxkBmujv729fPdwRMIgeL8fb28N2v/Hef/vNN2aCiGZWUv75/c9otl6vn1IyMyDHLpSa0AABpmkazhcAKEW8p7RMXdeF0DgX9H8BM9sdNl3XuRiYOeUqIp+en5ZcQ04/v3tbU76/vw+h6fvex6YWaUJA71QF7TPPDgACOwAEgOAcEYGiqgKgGDhHn4EZKJILTVcV2EeHpKohtoBqiqIl5wwAiLja9P1qpaq11jlNIlZKaft2s9k6pmmaSimqnNKiqg8Pr8zs/YdPzrm2CeM4Hp+ej89PZobM3WrVrVax7ZxzWg0AiZiRwUi1mhmAGggTq4iqMnszMDUkAwAErkURkb4oKleIWGsNIdRazUwEEMEMqqlzDgDFVEzNhCpcmZmAAdg8V+co5wEBUA3UZJkFwBAMGQitsiIYEBBX4j/98ULk/vzdfzI7R9g17enpw6p179/+5GJQsM26baKb57LdbsfxYsjsI5FbbXcMhlrmeWxX+5u7+x9//Bm4qUCN7zi23//40/3NgdFCDA93t58+/EJEIYRlmZxz5ELTtOj81cdPn7bbLTMOw7nv1z6EUgoTxuhTGpg5xmgIVeV4PIYQprSEEPAK0KVhAgDvPXkHZktepmkaxzHNY7fq1fT5fHLO+RiWZTGE5XQUkWXOztP5PAKoKsQ2mOh5Gom4lHwa52Jg3v3y7r1r+u12+/PPP8cYP378qFpDCETkvX95eRqG6etvv9pt9ssyhdCoKpqxo7u7O5HifUQGKQWZ0ICJzbnj8UgAfd9fLpdpWYpoEzt2mFKSkm8Ph1XXv33382G7OxwOhOa9L6XwFyklAIgxzssSQuDgc85d70opc06qGkLY7/dpmnPO07Skz3KMMc3zzX5/d3djogigWhGRyDE5MRURVXGEIQREKCqICFeoiA6JTIEdAoAAACEAIxoQstGh3xjI/1JNRJxzqsrsz+dzznmaxr7tvfdEhIgheOdcKeX5+JJSCiGcz0sphdvG1NbrNTl3Op0/fPrE7KvKerXtum6z2TVNg8gAgAhEVKuqAhEhoqoiIhhpNSBCBO+9mZVSRMQIzYzIIQoAmBkAmJmqmiEii4gi6mcVAFTtLwAUARAIgeDKTFRBDdUU4TNCAwIARSJ0hqhwRYJMREZOiebjp/PLB70iXq+25Pxhv7m92Xs6AMDxeByGAR2boQrMeemb2HTbm9v7aVoe3vxq/vOfU66WZX+3+s3v/n6/XT89vteazAdqe1TZtH0p6fRybjq+XMb97U0ppW1jrVUBmRkAEPFwOJjZ49Oziw2IhCYSkZmhYzWrtfZ9r6oi4rRKSulcs6jWL0TLFQAgIoGttxs0YOc6F5xzVQUAjCYDKVrarmPy6+2qbRqRQuSG4Xw8npv1ahPb4/FYcv30+Lxadel8NpOa8sL85qtXOefz8eW//bf/XrQ6AoxBamWiktNPb99O0+QcHw43m/0W1Vxwyzidz+enp6d13/XrzfF8HueZiDabTRXx3jdNY1LbvvdMxP7lfLq5vwO1XITZA6IprlfbeZ6XOcemJaJ5Ts45RASA/X7/+Pj4/PwcQri5uen7XkWqyOVyGYbh4eEW1f7w7/9v13X7w85fhYCmYAJmjhkRAdRMVdEMjICZPTtEVLAr5z0iEqCq1Coi6pwLPopWMzRkcsweiQgRAWBZ8prQqhCRqM5f1FpjDLe3t89Pzykl55yI7LYbVX1+/OScM5NuvdrttrxM87Q0TSh5WdBKziJiik3TrFarpg0xRq1VEK5MkQgBQKSSYxFThSsRY2ZPjoxUABVB4MoIVUHEVIUYDT5TVTAFADNRVTIAVAIEM1O1K9ErMwQAM4ErQviraghfmBkCGxEwAzk0KpqJHIc4XV6qyHB5gi+89+v1+vb2tl9tUhVC12tfSgFACs3T2093D69W20OP3HXd3au7Dx/eJbXQ79RyIeZ2S1pKXtD5zXbPkVv005xEJOfcdR4Rb24O05yWJcfYVanr9baIuR4BYFmW2PaocDodq1ouklLKObvnpyc1QwIDqLWWUgCAvHt1d//09FTNfv3qK0SUWmOMVQQAkOnVa6y1EhEzFxUzQ0SR4r03pjFlZTImjo1a2TTdsiwhuDwl7z2S/fLTT7vd7qvXbz5+eMvMPx6PXd8jYikFAOZ5dM47R8P5PI1nMGJmM2Nm731Kif3SNE2MsV+vUq7M7J0LIeRlXpbFxfCP//iPppWIUklaKiL2fT8v+V//9V/btmXmh9evROTT8xMRrdbrzWYTY/z222+JyMxAlIhc04jIat29/bm+PH1qY+MITy+PNc9tF31syTESORdcDI49IiMiu8YMzKzWiohEhIhEVGtFRL5yziOpMn5G7KNaNQGxCkaqCkAAEGNs25aZQSylFEJYr9eImNKylGV/e8ipLEuWXJ5entGgbVsA6PvOhzCO45Rmx945t+r6EBoAmOeUc04p5ZzVqkiJV23Ttm0TOzNDBGaPYOQdqIkpI5lZKQIAiAhAIgIAZETOISIRlSKqCnYlAIBoAGBmSACAYFcKBmampmYAgPAZAaiZAQCCXakqAJABwlUxRRMSoqLErlFm1LRk7fq+LpOIOOeW4TyeXo6PHw0pVwmh6fp1v956HwDg5ubGe354eAAj8m6eZyIXoieiy1CBQ+hWH9/+clj1gS1ZCTEIJM88TZOPzVxqE7uUNVdxHHORYZhTKYfbm2EYiByzN7Ou63KtwzB4HwAwxsYtKbF3gcMVIgooEbF3VdTHdp7nYZpXXe9Dg0CIgIgGUEpldqUUIpdSCSEYoAuxiqRSkV1VrWrdqg9rz8xSqkipMZhZ3zWg8vHjR88YozfT+5tDaJvvvvuu7/vYNq829wSc0jycBlA47G6894bAzMuyxL5vu26e5261bpqu6znGWEoBgK5raq1gSkQMvoqIQql6Pp/fvv/YdX2/3szj1Pfh7dv3m83m4f71OI6llMvlEtomhOCJr8g5RMw5H4/Hjx/fI+LNfiulXrWx8Y6IoNbcxo49l5wv0ygiTWzX661UdME7ZkVARAMwAzRomzbnLEVNgL8wM1U1QwUAAyRHQIgAgACgqqImWtGIfexDQ0QAKlqXPKelhCYgU3HUdnG8DMs8NU3w3iOiqgIAESHiNE2eOcaojktBA2XnPHnn2hBcrfV8fHkqjykVMFpfbfqu7wlRVKNvRK/Ee6+qjMTMCoZoWmrOGQC894aAamCGiISoZoDGyHYFYAZqBgAGCAQAaGaACMAICgB2BYBoiMiAZgZ2pSpVgaphKcXHpo19rWqar4ioLLP3nhnzfElFiD3UuozDy/NHQP7pB8fszbDpus1mG0LYH27jZhMbr7rqu7ZpmiI5zbn1bLXGHgBhu9vmnFPFlJKqUmiGJZuhDxE5tr17effu+N0P292GfWNmbYjMjtA93L+e0yIKIuLW241vYvMFMxepOWdVWa1Wr0OQImZaUzFTMQvB51q+++47IliW5Z/+6Z8up3OR+vjho2/ifr/3ntf9arvdIqKIgJFWa3xQKSLFAX7/pz9A480EVM6nl5ub/eVy2e/3w/nl2zevyXGutZSk1TarbrdaS6ne+5yzKnx4914MSilt2/V9//7Dh9Vq1XbdZrNxzpmZiDjnCGFZlh9+/CnGWEpp2/bm5vbx8fF8PpuZguWckemqbVsfQ0qz975d9QCAaogI9lkI4XA4PDw8zPP8h3//t1d3t2/evHn3y899v2uaxgU/p3R6ORtA068c+1p1HMfHx5/6bt11nY8hxkjeIZAhpJQQ0XtPRABgZogIgAYIYIAEoAZkoGgIoM4F+CsCNDNVrVqc46u7u908z4gUY3x5em7bxiOZyTiO03BxHFarFQKxd9u+r7WeTsd5Xgw5xgigOedxLCEE51wIoWmc90tKpUp+fJz96QSi5N12s2+aBgCUFb5QVREBUGQCAOecmaEpKAIqApohkqEi/JWZwWeECFcmigBmBqCGcGVfqAIRAiISodqVol2FEBRMtZZlliqIaiJMPtdMWoycmbEhG1iSUktoG2QqRSswIi+X5/n06Fz44bs/qmq/3vzqV7/a7DcA0Darf/7nf/7xu+9zXdq+f3l5aVe9Vr1/9VWp9ccff356Pq/XW0NYUvUtE7vD4T7nxTmKsfXemxmxb5ompRJCg8ilFPf6q2/IMQBUU0B0TOw9EZlZStlTSDk9Pz6XtCxTWm96T+QNx/PYdd2P3/1we3srqTzc3C05OUMGJiQyIiRiVAUfXc3lcj49Pz/Ol+GrN/cvT49aC6FF79Hg1cO9iDRth0bH0ymbLCkFFxC7pgmVsCwJRGquTDTPy+H2tmmayzjf3T5sdtu2jbXWUhMRiYGKeXaA9Ou/+W1K6eXl5fl4Skvdbvdvvlq9f/eLNbFr2ruH+2mafvzxR0NYrVab/QFASykMeGWqZsaAyPTyfL7abm+8bz99fKrVVGiZazoP6LwK5lrG+ViKqEDTNOuuD8GB1nnKyzwaQK1aTb0LzVXXhhAceUQEAEMwM0U0AwT8DIiRAbCK4RcAoGoAQOSa4EVyv17VIi54IlbVw+FgUj3SPI9t21bTaVyUEQzPwzmNl77vm6YBwHmel2lwzjEzqC7TGLu27RrHTNS1LZqZJ6411yy5lJenT0SEyFU1hBBj7LqOvfPeAYCaoIFoQTQ0AlBTQCYwQAM1BQADMjAAMEMAQAM0QQMFQwQFsM8AABXAFI0IgQHBCAEATVPJITgwK6UQExMQkpalcVyvSiYiz15rRXCdx5rPBuCYzRCRrVpOg/jIPhDQ8PThD8MLuwBMh8Ph5nB3e/eKyF0dbu5TSk27WtL/RxV8aFl2HAtiDZOZx15Xth1A8BlqZJdm6f9/Q1rz+Ag0mmhT7vpj0kSEbtWQMntPyNz2q+fn51TMeU9cAfo5JQVqmk4kPz2+NE1zfXvTdYuUyjSXeJ6YuQqNOxwO/XJRVZVDMEMxNTVDeH7ajqczAV7UIbiu3+12bqJTjCLiQxDVrq7JO0x8PB6993EeHXchBBHTIuwYQUGta1tJ0dZrTREA7u7ufnz7ikxV05pZyeKCN7WU8/rqaopxtXTMPJzO43nuug6InfNNX9X9Qh+fuq53zpVS1leb4LnExIzADgDmeYoxNk3Ttq2IVHW4f3d3c3PjyeecvffL5RpV+kWXSvFV9fMvf1JU55yV/PDwYKpNqBBRRAy5lAIAU4wlZS15PJ8Wff/+5hpE55zqZhnqCpCzSk5yHoecSxWquu0vqqoiImSa53m3283DuF510zTtdjtVbdt+tVrVdQ2EVd0WU1S7ADNEBDBAY2QAQHqFBiJiZqUIezcOU865aVoAUFUiFFEk8FVjZh6xafuilqXUbZPiHJgQmYjrusY3quoc55y7vvfe73a7eYp1CKYY/KvlqjfF3WEPAG3bEtE4z9M0DcMQYySGuq6rqlosFoiGBAZiioAIAngBqKqGgGgEbGZg/0AGAEaABgYAeAFgZkzeCNFIEVBRwACAAO2NiDqH3nvVgmpykWIIARlVBQQBQVWkiHOUSmIMjDwOQ9v24zCDKZRsSEgEyeI8AtNLnB+/fTVwRWGxWNZ1dXV1tdlsuraKOW1Wq3/7t3/79u0bIjtfIfL63dVu/7LfvoQQ2ratmnoYJiTnfNMuuJRCyBdus1qQdwhqUlTNec8+pFR++vDT+Xx+eXo+n4/zOK7Xy//5f/mfxnH8/PnXj59+vrm52e32P378eDkdifBPv/zUVkEkOwcpTogsCoF9SbnxqPNYMZ1z6toW1c7TGYjf3b/z3iNiSukwzNM0gVGb4HwemHmaJudc0zSnpx0AhBBUj2JqSA9Pj33bLfs2Ducyjgw4TZOrAns2k6autrvnr9+mDx8+MDMR+RBAlJlNoG1bRDNGQAA0Y0AAk4wG91ebv/31PzfvP5nZl4cH9B59WC77eDyklILn69tb7/3hNHZN8/O//qKqyJxzdiGUUu6ZVdU5J2LsQi4RnSsi6Kubdx/Ov/4GQE1oHLpSCgCM4xhjVLCUn7336+UqeC+SK+eJSFWQSVWJHZgVBUAwAOcCmjoKrgqMDApETgGQPTqa55mZiaiU4kK1aLuUUte0iCgiOWcRAYBSShaZpomIxtMxz76rq/WiB7OSVdUWbcOOTqchpeS9n+c5xggA0zQtFov721tVHYbh8D/W0JcAACAASURBVLw97vaKSkTe+1W/quuaLtjlnL33KRcAIBZPrCamys7ljEhA6IpmKcLMjrmoSMlgCEgIgACMcIHIimiGzjlVnaaJHXpiH1iTlVJQEQAyZESEC8JUsiHOMSEyezfNMxCaiRoSGqiUmAAAhXKaihlSUITjy/nMsH/4nYiM8CJUTdN2y81VqJrTMDuGqsb1uq/rkHOepmhAzH6YUrdYLppumKIZAoBjZgMoJWcpRJRylEm997FE7/n69uru7oYYUHUYTw9PP+7e3VOgOad+s/ofrjYhhGE418HlNE7DeR6Hw+kcQo0U2AUmkjibFOdJRFJK0zSYWdv3ShxFSykhVA178rVnzkm8a1ar1XKl4zgi2mKz8IynYUBmUt1sFimlp4fHl5eXtm0r55nZzLbbLTDe3F4ZSBqHu9tbAvNMFw7BmNB0juNu92JmoQ7r9ZICxzQzAQHFcUrDdH99JXE+nkdGAiJFOA2jq0Jdh1LKmOLCBfA8xLg9HjebazOrmpBSInIAWEoyQ+dCjqVqGlVNMd7c3IzT+f3794fDAUQRsW3bpu+KSsoZL6iggZTy9PIMZnd3N0YEYMwskgWUnCdEA1JVu1AgdMSAiGYGAGYmppLVhcrMFMBXtZnFGIkI2CEiAyEyvrKLUkpd12h6IZLNbJqHkiSl5F3V9/35fD6djlVVm2HO8ebmahxnZiwpvzw9dt1itegb76Y0KSEQapSXlxfJCgBt21dV1bStD8zea0m5zN4zEcY0O1/FkgEKIhGZvCEi773+g5gBgBERkBLBKzMAIFBVTEVU1SPDhcErhAtDQFAgRABFu0C7AFVBNFUlAjRSK/gGAMwMSIAQkExBAQ1fCWKaxvPp8PDjOxILUtMvVKVp2qvNan84LJdLBatCw6Ha7vY55xACs3fOu4fnp5hTVQXnKKU0xtk5d3f/XiTVoVJUk4LsgKBd9H/5y7+ZGSKKGABozlmVAbXkv/31P0Nw19fXC8Dj8axWAOMFgtaVJyHV0nVd217nnEVy2y+896WUum7NLEshwOfn7fW7VZzz7f2tmYlkF/w8Dlc3NymlL1++DMOw7Be3V9fjOAJAKWWYIznkOkjOu93uer1a1K1M4263dc6ZGSKCEXnnfcUONBbLMJ2OipAlheAL0nQeLBVUGOfkq/ru3X02myQT0aLvVItzQc2mYWrabrO6CiHM8+y9tyLBuRijiPz++XPbtvMw/2//6//+sn2Z4lxKMVXvuXsDhGamqkiuDh4ItQiU/P3rN/NutewfHx+/fP6taRry1LS9mfm68qbOOSLHBN5RzhnAzNDeEBEzI6KZEVHOWVWZGRHNjJlLVgEDBQBCJCBDAEZYVEsC1AsTfVWsWHlzHs91Xd/c3Z5Op3mKivD1x1ciZyYOXQiuroOBZM2+cgqATEjuQrIeDofjce+9P54POUciqirf1HXXNU2oQBScMaCIACIhgYEiIDIoEDARIzsAMlR7oyJiGS4ImZiI1MBAzBAAEMDMAM0MAFEByBDA0AxAXxmoKZohsJqBkVoBAyKCN6oZDQHYUAEQAOwCKEkJdV0UkBz7cNw+nfe7UDUi1nWdFL17/w76pQyn69UmxjxMs2MIxG73/III7c1GYprn6Wq97BZLRBGxeUx1VbEPaZrNxLPLJTlixw7UqsDCBAAIOo0TETVNE2PcHY6Ermrqcc6+rgh0nqcQwt3dfQhhu3sGgE+fPgFAVddeX5lR8DUzrzcIAIvlGgmYSEFijFVTi+off/zhnGuaxsyqtvF1JSkLWF/VWYpnMinbx6enx0cSCZUPIYAKEAPiMJ6mOIe6qbwPFDTrbjwbar9a1FXlDMPK5XF+enq6ubmtm+7lcOw2m/e3P6WStRTvyIBEZLlchRCIXCmlqqr/+I//yHMkxPV6fX9z+8tPP//2t18/vv95t90+/vjer1ebzcbXlfcspmYGAM57VU2ihCClPD8/7x4e7q42pmWYI4EpYgiOmYNnQBSz42FfRPmVb96ICCIyMwDYG0QEADPz3qtqKYWInHNE5IIXESMBAAIEADMjcvQKCLyZoAEQoKGamNk0TVZE0ZqmYe8RkV65/X6rWYidgaR4MbF3c0oKpkVW/aquXoVgi8XCEBB7VRXN0zQc9y9oIKbIbrW+6vueiACALgAAUUQAABEBEMBMwQzMNFReL8QM/oEuELUIvDFCADMEA0MAMwMAMwMwADRTMzAzJDUDs2JmAKBaEBEADNAMEAUB1cDeAIAjBlWTi4yIbKaimgCBjtuxXfRff/9NwaqqyaLL5fr9h09NwKoi1xgiYEse2U3H3f7HWMZxuVwiEJmdj6dxHOOrSUXqOgDYYrUEQCY/zwkRnXNznvu+3e/3QOycM0VmXi4bRDTTm5sbZiaCVDIgO+eKmCOOc0ZEEQmu0qKnw8F7j4g5plA5Ua18qEM1TGcAePfhPRqklEpKXdc1TVNKQeashoiPD98lxk8fPkKJh5fn9XIZggNQQwLHdR3mnKq2qVwFUbxzogoOQ1XlEvfnc46JFUQkxog+GML5fKYQ6rZxzhkAESEiEClYinGapnEYuq5LxO/fvdu/bD//+ltJebVc7vd7ZLoQkXmeH56fukVrAOv1Gi5EiMg5p6pEdH97d7tYvjw9EOBqtaq8H07HkjLVNAxD07YueOeImUMIYAgq5+MhpcTMXdeRY1UzowsAsjfMHEJARFXNObNjIzRjvABEtAsAFhEDQERVADMAQyMzA7DFqi9JiohfuGmep2ny3ptY2zVgmHJ82T07xy64EIKiNW0rpVjReR5Fcyllu4tzjHUT5nk2s0XXr9drRzzFOeYyDsNhvy+lMHPTtk3TeO9DqOHC4L8jJLwg1pLFihmaoZkiMAHgKwYABYF/sFegBAgABmpmhM5M4RWqqr2BfzBEBEBEQEQzAEQweGWmYChmaGxopponZlaFOU3sAiIet4/eV8RuzsnX1Xn//N+2jyHUznlX+VAkPX77Sg7bNpCnkufHHycCds7lXOZ5JoMyzzFGgtZ7jsM5xuxdQETyYYzTOE0GcnNzU9TmmOu63ayvgV95IkCNMRJRcLxZrRGRmXNK4zAjonOOPU5jfPj2UFUVkv3000+nw2G73Q7DiYP/059+csGHEOZ5brqWlwsAmHIqpQSuFBFE0MARmygZmOj2+eXqau0Do0Nm7Lqmgirm/Lx79gWDC03XOAoxTQCw7BfQw7e//+GCP55OXNWLxeJpt58eHvrl4v39fc45hEBEwzAiUxUaAGCipqpLTCXnpmkccR2q7XYb6qZfrIDhcDqyFPbU9b33XkTqus4pxXmuQoOEqlLVdXHu/v7+P//jr8zU1PXz48Pt7W3dNsBEzHNOp9MJgOq6VjEAiDHO80xEp9MJmcyMmZ1zy+W6rmszyznrGzOr6zqXpEhmBgCIhIgAdoGIAAgAiGwgCAQIgKhFpmkKvnaI4zQR0WazKaV459zIquqzg1qZeZ7H3XEnCsfTyTFfX1/XHOb5OecYQmjaKoRQSkkpDqdjmof1chWCz6nEkhHRe59zPuz3p9OJiFIqdV0vFou2bUMIAKCqAEqMDgiQDMmQ1BDULhAYUAEIQBUMzIDUzNQMAFQVEc0M/v/wjZmagZkhgmoBIEQDAAIEADMDAEXRTBeSRbFQU7ORgZDlIkpmKkBoxKzJLohYUpFI7t2H94fDbn/Y9qtuuerGeXCBG25LLC8vL4vF6n5zn+b4rmkQcb/dHo/brq27zaKu2pwlxiQqXVv3i9UY51L0559/DqGOWRApsJOSESA4f+GISymIqKrnw3mekpRiZkOoShGLRRQU5PNf/7a+Xn98/0GtxJTGcfQS2rZl74goliwiPgRknFJ8+PoQ57l2fL1aHY/H+XxAk7apfRWcc4JQxMTMmFyoqkpC4CZUVVcDokZLJY9xzin5upmnuNls+uVifzitVqv7jx9LKSZWh1qyCGnf96qacqmqqqnrkvKXL1+Y6P7m9u7jx8NuXzW1b5rtafevf/n3xfUmlei9B6am8jnnkuJ+u5+HsesWXdepwv54HoZTkvTp55+0lJeXF/bhefvSy9KH4OuKyHX90vtARNMUzaz3vm1bIjIzETEzZkbAaZr2+/08zyGEm5ubvu8BQEQAgMEAwcwAlIhBUVSJCAHBwNQACQDNRE3ZO0QPiCrK3jEzEXnvzcyH4JgRUbVccODFenUeB+/9PE6n0yFRWCz6vu+Ox6OqHI+HnHPb1mxABFXtAdk5dxzOALBcLlerBRENw7DdbrtuAaDHw2778mRmzrmm7qrabzZLUTFTZEeOydCQzEwVLghM0QwMwC4AwBQJEQ3AAAkQwMAADF4ZACAiEJmqmQGCmgEIAgKAmeIreGUGCMxcoTMzVEkpChiCghmTNytaFMCpAjPHVPAVu2JK3rH3iDhOk6h0y05EAODm/oaBc47kuKhILv2qX697lTycxvE8xJhzkiyyWC6JKISw2z8/Pb0s11dVVTnnTNV7BjVDcsTjOL68vMQYc4ym2DaNiMzDeNZT5UPlQ5aMZKIZVOc4ns9nUfW1v8g5AyG9QSJ80zTNn3/5GUW/f/02TdN6s5w8b5+flOg8x6Yh39YhhFRyUXNV2PhOY66Cm+KcUmr7frVYSM5mBmqfP38h5wEZyKnaYbfvus47Z6oEME3z7mVrCG3bMxE5h0x/+R//i0NSESBcrleLzSqKXr27y6WQwyo0xKCq5/P56eFRiyzbRX99PQ/z4WXL5LNKMjXDp5ed5MhI66urEELMCZh8qJumWSAh4jTFYZjMjAi7ruv7nohEhN6IyHZ/8N63bVtK+fbtGyJ2XYeIy9WGmQHAzJgZDQARUE3ByNAIyQgYGcBYzJxzpeRSxEyJmIhUtZQCb/SNewUi5j1vNhsz8+y6roNsquqcu76+zlJyziIZEU/HfQDHzNOcXrbPCtA07TxP+/2+aZrVavX+/fuXl5e6rvt+6ZwTkVKKmeWcv3z5UkoBwqZpm24Vqsa5gEhmCoAGiIaIZKggCACECP9k/x+qambwRsFU1cwQkYjh/4XwBhFFBNFESFWJnEpWlbqqshYiEs3MjEgmBcximpnZAACLOxwO8zySY2RiplA5K2IinjnN8/64N7MYMyLe3dw65yRFBFosFqUUzcdkxTknIqWU4+lc1629cc5pznBhUNd1KSVLccHf3t8hsokOw7B7fglV1fd9YPf4+Bhz/POf/0wOf/vy29fv34vmvu8/fnpPziGighGziKBZUVHE4GstyQOVnB0hI8eU9udTtVhcv79DRFEFqg7D/PT8rEh935dSQBRAAUA0R9GqqiQX732a5n6x2h+G7WFcLBZd02jW0/7EBkQUS2bnTCTUteT0++OP29vb5XKpRQAh1D7HZGjMvnI+qYTKGUDMGYoyYF1XV5t1YPfHlz/uru+Wy+Wvv/7qXQVM2dQFzik75n699p5jSoa02Vy54JlZBcys7RZt06sqoQVHOWcCIF8RUUophLrrysPDQ/VPdV0Ts6rGOMUY52kiohDC9eaKmQnMpDjnUEFMmdlAVQQRiEhECR17THMuUrz3iGRmiGgASFxEAcD5YGAIBgAhBADgCsxMiqmqWKkqb+ZyznVd5zk+PDw47+/ubtAxGBVJfd9XVZVz3u/3AJZzOp/PZkZEqsrMzrnr66uU0mkYzqfxcJrAqKoaEenq5urqChzCG1VFRBFBYlAlRDNLJSMiM5sYGBASEAIgAV6oqZoRuliyFWGHRAQGoAavEJEAgIjMTFWJIOdohCICQKoFsSAi4AWJFABAAzeOo3MUKu+9YwaRbGbeeynWd8sm9ABARPAmp+x8GE/H8TwxUTEF1Pv790hufx66vr+5uauaWoqBATITmpjGnBCRHKsqIozD9O3bNybaXG0WXU9Ev/3n39ZXqx9ff4x5WvXLX/79zwBABBcpJRFpmmYcx/12N6fYdV0IoYjMMqV5cmKmsl6uXl5ehunMwR/HgXbHvu/JO1Hwdffxp6WIpCxFUtvWzjEAnM9nVRUjQUDBul9VVeWcPx+H/X6fooa6MlETUS1GCAD37+9KKSnnrmnXy5WqmlkxkSjMRMiHwz7OGRHbRR9qH5iB2EStSGD35fPvIVRE8LR9QmYOXgmh5KLmq9oRpixVU1fs98fD9x+PHz58MARRq5w3QwBjZijxIvgaEeeciMh7n3Nu2/bTp0/Pz88xRiIys77vnXMxRlWN8wwAlQ+73W6/ff748SNeUI2IAECsRdTMgg+n40DMRpaTwitSVURUVQDAfzIzfUOGSEBEiMiApmqqCtY0DbwRkSqE3EaH7mKYp3ieYoyq6pzLpSZ0zKyqMUYiWq83iLjb7XLOAPbHH3/c3d0vl8vhPI0xmZlINrOU0rfvf8zzjGh1HZab9Wq1ADUGFgNAY+aKWf7Je29mICZgaACEjEQIqsqvnKqKZDNzxBdmBgAiAgBmCBf4Ss3glcEruwBEAEVkuEB1XdetVgtEi2kiAOd5nufT4dzUXSnCQIioRYdhEJHlaiEK3WpdVV1w/PDjR9M0RASO4RWJ2DTGXKIjRgMF2R8PseSmaTabjSEAwvJ61SxbUzWzwG4cxz//5V/M5ObdvWgWNkQkopRiU9VA8Pm3306n0+3Vdd/3lsvxeRtCDQClFDKdY2Km4/GITO8+fApdM4yjmGEIVV0HXwOQZE15kizsqt3+vFh06/V6sVrxBVLOGdSI6PPnLwBwd30Xqma/P6Y5m0l4A6g5529/fP/w4QOwiemXz78RUdu2TVMdh3Ecz3XdBnYqimpxGOdRSykAdkEEjnmz2ajaw/PTHHO3XPSLFTkfs1wgYuUZAMYpokHT9uv1WhHRkIhLUTBCuLAvv/3edQ2z32w2Vahyzk+7J+fcYrFgpPv7ezNTVbpgNrOmrZq2Wi37eZ63zy/r5fLTh4+/f/69qsJisXDOIWIIgb1j5kzQdZ0UK5JUhJGccwCQcw6+MjNVBQNDMAMAQkQABSMDMAMEQGRySMWIyAgvyMy5UGuNwOwQHddNY2YiEmNUVXK4XC9EZLoYx8NxZ2aiEkIwsxCCc44c17Vx8CJWVVXTNJIkzZNJEdOc8+Pj48vLi5lJyl3X9X1fVRU7RABGZOcQ/gGRmFABQFVUOVQMoKpmhsDMBACigEj2Bl4ZEcEbVbhANABQU0QEMwBARHilrlsts5mUooC55Ok0DcMgIvOsFwCUc/bsQuWMeC5FzOIpLrruZbdHcjHG7W5nxF2/NsSX7VZVq+AcsZYkkptF13Dz/Lw9nk8fPnwIIYzT2TlH7AD1PIzrzTLPUUQAzUyTFCJkxbar81wA4Oeff/5v/+f/hQY5pvF0vqhDDiEwMwABgJk55z7+6eeY09Pz84dPn4zQh1pVPQcQULKu6QHQTMGBkTkk1RJnQSzeudCE8/nM3jkO5J0VDSG4tvXeO0dmBgDjeK7r+unpqZQUgieiqnXTeD6fj1VVec+n00FzgQwIEKpKTVQ1BNde1K2Jtovu+/eHnPNPv/xJTHen82K5vnt3z+xAtZTCaPjKmLmYMnNK5fH7j9NxMNW6an75088fP/707dsfZtOHDx++f/8+TZOALZd9CGEYBhf8Rc4ZAFTVzJynlJIWOZ/PAKCqD0/PzJxjzCF47xExpeRUZyvj04wcQl0t+0XlQ4yxlOKcIyIAsH8CQrgwAhBmhwhoqCZgCABmJmCigob/HTARekQ0gLrpVIuZIWIpJaWUc1ZVIlosFm3TlFJU1XtvZtvt1ruaiHLO+8M25tw0jUg+7rbB++vra3b48vLivV8vl8xcTOdhJKL9fj/Ng5lVVdX3fdd1TdPknESUmY3ZVM2MEVUEEMFMVZkIEc1MRJjZzAAA3wCAmamqAV6YGV4AqCoAmBkiICKAOmuqItkMzKgkcD1tlisASHNW1SZUZqaqzlFJWcCMMPhKwNY3N2WKZvb4+OybcB5HF6pxmmOc68qzKaH1fQtSnh6fDejjx4+Ncykn75ypGMI8j8tu+fL4QIgxTst+hQwONISKAM/HAwGnaX56elouur7vnx8fz+dzXdcAOEyj9xUzVk2tquSdAviqJjo/P23vP7wnYDNAxSKFkL33VjSXQo4R2UQRqKlqZjSznIrz1Wp9NY7jy3ZL5LrFomsaQ1QwkRxj5FDV3i0WCyv58enh/v62rkMpxczQ8e4wMdq7j+9YHRh673NJOUfH6Jzz3ksueY6VD2BoRQQUALquA4BpmirvQwiSI7/CYRiYWVIOof706VOckmcnon///EWkqKiZ/f7blznFpqmCd7vd4XwevfdTHG9vb5fLZSmlquucs5iRC9N4AqB5Tm2lHz582O/34/k0DIOZhRAEpOW2qWsz9D6I4Xg6IxkRiUhJWNf1HCe8YM/MgKyqZqoKUTIRMDpEACBAEwMVY2ZANLjAV4x2AUqESB4BzMw77+tG3wBAKUVSDFWV/6mIqMxPT4/9cnF3dzvnVEpBNc+hDlVKk0Ps+3Ycx93+BREBIMeiqnUdbq6uRWS7fTkfj23bTtPEDhf96urqytU1miGA836YogvesyNSLaImiMhEhGgAaq8QEQDsDYEhIAApGIAhgJoBgL0BAGeMfb+qqpDSnGIkonmenx6ewRDN2q5a9b2qGohzLoQwxNk5N59Pp9121XbTNK021+McRaxids6VTJJLTHPDjE0dDO/WV88vu6ev3+8/vGfv4jAiYoxxmqaHr9+0ZFVtqjCdzqVkJAG1i6ZpEJEBl2233W4P20PTNO2ir+u66xa5FBFjZpGc5qFvu1RKzKntu7pqUY0IiJiRFDA4BtH9bpsuVO7ubuq6FhEtogWADN70q+VivfLEiCy5gJoLvqARc6/qzH7//GvjXd82q0V/PuxB+rqui2U0uN5cqSr7oEKlFDQBhoDOO6cl53FMqRBRnMc55u3L02JzteoXX//4w/nK+7BevvLel1LMoGkaVSWiz59/B7XgKlRbrzdd056HkyET4Xa7DXU1TfDp9v7q6kpVmRnImDnnbGYppZyzgIlI03fL5fL6+noaxu8/Hq+v1oHd8XSoqurq6ipJKqXknEvKpUjXdQKQczGz4/4gIre3t+SYiBhUAADhgoiY2YDsQg0ADcwEENl7AEJDQEQzQ0BARAU1TLl479BQpJgZIjKz915EnHMMyIx1XZdSRKRpGgIWEWQ6HvfH4RyC88wxxpzC+/fvp/N0Pu5cqFNKwzBe3Vxz7VKaz+fz4bBfLBZ3d3c555ftk/NkZuN0nr+PqmqKVVW1bbtcr7GoajEEvAAwNQUjIgAwMwAQEUQEACJSVXhDBopgZghgAIhoZgDgNqt1KSXNEQDr0AAA17752CCwZ6eqJuKYvW+QbJgnZJ+L7g8nE0tZ2m4xzS/jOPqqKVkAIITgwDIUy3n78Hja7pqmWVSvyvk85gxARiiSrZTzdusIRcQmdi6kNDfeEUFdVXF/qKrKgt9u94Zwe3vjq2Z3OJymsVmtNte36DwzoxozznMqJS3WG+89ADjmGONhtwcB5xwtFp5DznEch7btPfk85+PxOIynnPP6en13f5+luCqYWSkFwZwnUFOwKCV4zrn88f1bv+hrxznG3XZbOT7m4q83dVfbBYAgINruvI+pMJJjbIP3gS/MYNUFZl+SkFJKOQ7j9c3d7Ksx5dvbu65pYozBEQFKzsCMiIz04d37xWIxj/Hh2/fz8TSO42q5Xq2XX79+Tcn/6ec/s8P9fn84HMiTc+7dh3tVzaVUVZVLJB/MgH2YhhFCBYoxRkRUMAEzpJzzNE0KkHNRK6WoiMZpzjmbqXPOTIkwxhkiGAIAZhExQETnK+/9arEUKWDATABoJoBA5MQUkcBAwcCAkAANDELwQCZZAcl7BwCllFxy5UNV8TzPKWdEJB9C46qqQdBxHJFoc7VqF62BaM5NE87H85fPv27W13d3N/v9Ua00TT0NA7ObpgkRq6pKKR1P+8Vi8fHj+xITIopIjFFEAAhMhtNpOo/FlIjqtum7ZdU2fAGQS4F/MgAzIyIAQAAwMzC40AKEAIQAiAhvXCklhKDKUoqZaRYUrX0Tp4mdNwEmRrRpHD1T5b268Me3P3KWkgtaPAzjMMbF5irlErWknDyRr/3V5t4znPfH8/5Aam0I7DDGqaoCIH/78V1VQwj//q//cthtF4tFkTSOY8nDol8s2g4Rc85N0+5PRzK4fffBHH1/enI+/PKXvzRNk0WJOZcSnM9aXBXQMSJcqIgienbzMBKwcBnPg/deciaDNI0PP35YkfX1+tPHfzsej/vh9PDw4Kuac/TeEwECFlMCUrBQNQ+PP+ZhZNNxHLEK4/FY1XXJufGegxcRFWPPBKiiy+Uy5kSAJoWZicgMAGyc0/m8DaFmZo+8WS6kpJLjLz//DOTMzHtPgEhIRPpKzCwEdz6fHfFivRqG4d1PH92bpBra5vPfv/zrv//L/fv365trM6mqKuZZVKuqet5td88vHDyRExFPvAcw0c16TSt3Op7meQYTADgNY9M0vgqqjlic8ymlxWpJROfz2YXgnJtTWi0WhmhmKJpzVjMAMLO//ud/Y6SubpbLdV3X8EqVEBAuFIEULhDAABAxpeS8Z2YzUhVVBYCmacZxDBBUDJAAsYiKZkIgw36xYsYpRRcKM6c8k0HbdpILIqZU+lV/FW7SRSzjOLZto6pzHJl5ubxipm/fvknK3nt80/f9YrEQscPhkFMpRQUsxnw6DogITMx8e3vLzESETGamZqpAqGAEF2aGcIFqAGaEaACIAOAIwVRKzp645KIx77e7Usr11ZWVDGoGhkSMBKqSTaTcXW3sDQBdGBIillKcc4BW5unl8fvfH77++Zc/1X07HE+lFCIltqoGdDnm6d37TSliyFlzv1wYFkLoFtV684EJzodTjomBHx8fRTQWmee5Xi6vb27/eHz89vi0XC4X7SI4R47NMqCahyIBwwAAIABJREFUgffMwPM8H3b70+kUPI+n8831dSk5pTKOIxltNpvz+SzjoCLjmXYox+E4FwlNiwqLpo8yIQF7iuNcuQW7UFRvb27cNc7T8PT92zzP17e3oOXxxwNVfpxj29YuBNUCgGmcXN1UiJ5dAUDEx+dt5aq2bblxy7qtqmqc5v1+P8zT8qJrD9sXV4WuXWEVBBgB0zyfjodSUvDc973z3tioccvlLQCllA3tX/7LX0QKAJCnAmCIZpzJMkCoqt///veX591mve6b2iHst89A1ZwTofv+7cF7j4hFqamqxWJhJrloFaqq8r4KANAChBD2+70QhzfLu9aK+Loys3lO122rqj9+/Cjz+O7mejwPZZ72KYvInFNV1/1q2S16AvCBk2RG8uzG87mua658KUUAEBEAmBkAYoxV1RQRQzIDVSNyiGgAilDUihqSJ2Q1c64DVO/NN6BFwEUTKZoNlRyyJ1Swoi4wqG23267r3r27e3p4ADBDSDntvh2Wy+WHDx+ubm9eHvZE0C+XTdMUlQszI6LT/iRvUkqrq01VX7QqBdSIwVSRyAcWvTBRIUemCkZuOJ5yzldXV2iQ5/jtj6/eOSKqfSgp//jxY3c8eO+aplmvl8v1Cq0YEyADgCIBEgABKHunKjlFZhzH8fr6Kuc8x7EUGc7nAnPVeK6w6RpkJFImF+cyjkczyTnWjb9a96VkE6raynvP4Ah4GCdycDgcqG6mkryvjsejJGk+NCiY0uy8NnWtAvM8I4e+7YLzXds6pObTTyoyz/P3bw9926rAw8PD3d1d3/cxxmE8DcMBGJdXN9c3d3MxAGJmtSjJQnCgkOZUt7WAkFpg19YdSAFAMRSDxXJNRDFOzIho0zi+vLxsrq6yioipqnMul1IyRFE0MAQcCC48n4bzMI0hhK5dEOAwnmQgQoeIoErs+uCLpB8/vqWS373/6Ns6l0JETVOjAYCxd2CiVtDglVqShAZa5OdPP93d3P/+62/j4fDh9qr11XEY5zn5qqnr1jk3TKOphrrl4A+HEUBXN1d11Z7HgYiAaZ5Tv1q3i2UpxXtvZoqcswBAVVWai4g0odqdT6u26bo2eV9KCVwv/NrMYkrT8/Mco1pZdP27u/t5HDwzqEouyOS9R8ScSpHCSBcpzYCMiEQEF0yoKJKZyJDMDAwUAPAVkRMpiOgCkWM0RUTQVxw8EYFoTNM0jCml0+k0TXRzc5NFAMB7L2LIhEzznAHAFI/H83a/Q2QALaUw89V6U4dKVUfA8XTebrfsQlVVEtN6vQ5NAFW14pxDQ1RzSIqgCm44nRHx4ftDXddWhIimaVqtVn//9pWZu2V/8/4u52xmdVulkl3lwEzNVFVAAQCBLna73XG/O58OltLtzZUn+PH3r2kevbq+7+/urnzFc54RAJAI3TSn3W7fNF3TNF1TMVNJ0jRNzhLjHJwbjuNwOhfRuu1Xq1Xddd9+e9jcvvvw6WPlQ4mp5Lhe9DlHywKGXd0RupQSGt1srv72t78Np7OW1Lbt//Ff/+txf/j8+ct6ufn28OMvq7/cf3g/DP3xtE0lOefMjIFM1DPFbKLFBc/AVeVLEdV8Pp2n4eyRmq5LKX//4+/k3bcfj03T1E2Yp0gMTb9417TDOBJ7Na2C8973VQAxBQjOpVKIXAjBzEQkpTRNE47jnI9V3YaqHVI0M0c0z8NmuahqX/tgZm3doHOQrQ4V5sxIiJhLmsZxnIfgfNO1qOYIKx/KRUrzaZBpur7eMGDOeZqmxXJtyFMcbUY1q4I7nI7saX21Gobh67dvbduvVisiQkDnXJ4TIjY+pJgAQHImIiTyTKkkBLi9vmnq8PLyJCKq6r1vurauaxHBGM/ns5SyXq8906//8dc4Th8/vPfe14tGSi4qRMTI5B0DFtPgXS4CasyEiFmyKXrnRAQRiQgRCdH+H6KAaKgAgMiICIAI2PdLRLsIpa6rtkhCA0SMMU7TPI6jIYCR9/7q6qrrFqftSc1yEnK4Wq3J0/F4zHPcH7aqul6vr282zy8vREAEKc3/N1PwuSTHlSUI+oir3D08REYqAASLojjdPX9W2O77v8LarE2LqmKRBAGkiswM5eKqczZJszHb7zPI+/0+PadSChGEEJbL9Wq9TnPkN2TMcrk8n8/DMEoucZqNMTml3W43z/PN7e3xfLp7fDDGABMbtM5dX20BAJGJCBVVFQBQpfN+tuyJ1BlL3HhnmQswIojq/nBQBhe4XXSESEhd111cXI/jyMwGIZcYvAEAIgxtV+e0Wm0WTX9//3hxcem7xdNhv+qX37y7tWQgV1SwliUXgzTMU61orTiLIQRVTSXefnjv2DhnUoynYTDOBt8K6Me/fOvaJtbim3Dbv1MVAVQFb93xdDidnxGlCS5PWeJUAYiZLROCMz4O5+enXeN9t1yt12u2NE1TzNmHYIxJtQJw0y1rrXM61VpTqasQfBN840CwQbDWppyP+1MqsQ1d1y+G/bFxPueSMAbnxzgfDgcAfX5Oy9Vi0XU551/++c/Ver2+2J5fXlZdGP8UY0wpiYgxZj4PMUZruZRCbxTatv3m9mqapoeXnYg4NtaygLnarFQw1oiiteaY52E6l1IWi0Xf9/QGMKdsG0OIADCfh/v7exExxDc3N75xNWVJuZQCpTrrP3zzbYEiIkRkwACAiPgQ+r733r/snh++3r27vdZSv/7+eb1enYYjohrj2FnvfQhBAFKMiCwIKCqKhEwIAAogiAoAiAgA+v+DfwJgQFFReKNaVKyx9Q8CACEEohYAFCqz7fqFVFDVaZpO43Aah1jq5nKbUhqn6TyNj8+PiFhrNW+IGPF4PDpntttt0TLPqV10p8PZIFGi43FfqyLi3d3d58+frfHhD61hNjkXxybn7L1PKXWLhU2pWyyQKbhmfbl1wSrhm1KKtbbWKqIgooLwB0UiFP14+76sN//5H/+zxjSXvF4sa8zX19fGEoAASduFKnI8HovC1eXtMAzWeAAoJZ9P42433txce9/EGLvlqsxZGMm43cvzBfFq0cv5+PX3z7mWRdsZJGdtSuk4nnItzrZEVBUuLi7W27WA2DbUkqeam0U7DTMY/vHf/mUcx0oSGi8iCKIIRQQUAeB42D89PRkqV9cXw/l42J8auz6PEwfHTMaQ1ooKlrnpuqZphmlMQ7q4uLjdrEspiOhsUNVSU611sVwZa6VWHwIqGcc1iyLUWo0P26tm97ob57jxy8Vi8fKyz1Ur4Pk8AFPTNKVkqOX19fW0B2vMdrUCgeH11Rp6+PpkLSMQSE7zkGJWVSJCRGLMOXddV3OqJfbLDrUQICJtLq9M8M+HMyKGvtl2lyqS0oyoxphSCgCwsVIqEZecf/7yRUu9WG82q9X765vd49M8z5qLUH3e7c7TiIjW2tXlWqLYNhjjypuaiAiRBdBZn1Ptuq7WmnMtcXZNeNm/Ggt93zUhgNTpdJzOJ1WdUuz7VWgaYkw5A4P1BiqkktlYAFWtqioi8CcEUMA3AKBKiICIQogKUlUUQJHIIJEC1D+IDy0azLkCgA1NWPQAgAAoQAY3F6sVrOZ5zrVoFWauOaeUnLXO2SmOY5yG8/iyf2Y0tdYQwuXlBaqeTqdxGp0L4/mcYxyOg2Hm77777nw+P3y9A8IPHz5Ya4dh+Hp/V6bxNA6rdf/Nx48VNMbovcc3oigAjAZJVUVENVtD8zT8/Le/933vnAHVu7u7vu8Xy94YyjVVyeMUXw77aZoWXf/LL78x2VrFGFNrES3rfnE8Dzwla+35uGckFARCw+759UUUFqtlME4Jg3VSKwMiagihs6br+jnm4/GYagJCa02Ms/cWAfbn02a5ilMa44iOWBVQaq2ixRo0xiBijbJZ9Y7w692n4XBsGruvdX96dSFIziG0zpjX8xkVnHOLxQKZ8lD75brtehVk40RgmqK11tm2YOLWk0GtQAa1QikFkBDRWpOlTnGuVdFwETXIl5eXqUgBITSpFhHpbcsIbdMcX1+eHh8dG2+dCCTIPlAu0zzGaZ4tu+3lhXNOqobGv7y8IOpq2Vq7EilSU983cZxyBWac51m1eu+ZOZeIiNYbVHhDRKrKzESMCiGEq4vt4XW/f3mJw8hIUioq3H+9Y7ZVZXvxh9M4jOM0S7I5NU3jnENjAACRmLnmMk7j599/C12727/eXl0nrWOcck5pjtnOzCyi/MaYFnEYTtM0AJAxxnmfMxJRCCHmgkzMTH9SVRFRVQBQBQB9AwCqKiKqKqiISMzwRkFBEMgYV0VYSVUB0b5xjohyLs7Y0+lEzERoVSRqTDnG6K1l5vP5hAhXN9fG22maqrwp4zicTgdj+ericru5QIXTabi9vkFkADA+tLvnl2maLi6vx+msCMjU9Ysf2u8BIEsVLTFGskQEj/dfz68HrcLMoW2c8TnneZ5zziDatP7i4qLk+PLyggCLZW+sPQ1nZDaWnHPe2gtjSinMthcexymE0DQNAKQU4zwax1XxNM1daBwbQ/b6/YfD4Xg4Hod5Op1O4zwxMyq8MUjsLFuzajYEWksKwa3WnTEwjCfnXKmZAJvWj+NojHt6eLTWKtT1sve+QbSiuZTZkmWi0+vL6XTq26YN7vX1uaTYLi5S0aplf3jxxooUb10pKeds0Hrvd7tdKbK9urTIhtgGh4g1V0J2zuWaQEGKWHZShNmmnI1hz4YcLa4WseTD6zPU4tgslmvXhlorGn4zz/PL0+P5fL6+2Gz65cvueR6n68tLNDDmYwg+BO9mr0XJEtAbiDk3XdcsGiJMZUbU03kvAiKQ55JTsj5YU798+d0Gf3t7a5wlopoLG3Tsaq2qWqvUXH779VcGNMQhBCY+vO6hChFVUIAioLvdbhgGYEolZ8l5NlTR9ZYsp1oQ1RgDTM2i+7f//t+HYTifz4/7l7Ztr9+/L+dTjfPpNIQQrHeICADMvPDL+idjDKgOw1BKadoWyDKzMYaZgRD+hIjMDG9ERQX+hAAIgMiqKqAioqoAgsgIgMRI7DwTEQAIKNAfRKRpGmu9gDJb60LTdIyEWud5DsHP8/z582c0vFr1xFZEFsuFs1a1GjQ5pZKiN/z89FAV3xjbho253IjWHNfbFYgCwHQ+e+/nOCIAIu5fn/fHQ9d1jQ+31zfzNKWUUKHWKiKIyIxTnNJhtoSllKvLC++9lAxEMSdj2RunTFKztV4qPe9eiCwADOdpscjXN5eLxQLggh0rUK3VMM/D+PXT7zGndtFftU368kVAvffOuVKKM7aU4o1NKWqMxNwwJa2n55cyzYtVV1K0lrXC6XAaz+M4zo3zp9d50be7eay1smNnQERQCSp503hrxun0+/NdrZnYF5XN9sI0NqXZMKc0O3YpzUB4HM65lO12e3l9gwqlSHBWVUvOxhgtdZqH+/v7nHPTNLe3t96HlJKKxHlmZucc0h+am/colRgUqEgFJjYGEY217z9+8/m3T6+vh9Vy0ff9/de7z58/h2CrTa4xIbTdogVFEZhjiTEiojHknTOOIFU2ur7YoMJ0yDkfHu7u2dmrd98Ya8d5+uWXX6zl9+/fN61XVSmlSGVQESGmD998U3LWUj2b5aLfbi5+/vnnCkpEKRUXvHMu1YJAtdZF2/Z9H7oWEEXVG1tUUkoAwMxDnBerJTt7cXX595//sWybzWZ9eHnWIta74FuBqorA9PXr1365tMYM48hEpZThPL28Hq5ublUV/sTWWGu999banDMigugbAEBE+JP8SVUR0RhDRKoqIkQkUlQVEQUUEZksMIJUY0xVKaVY53wIKUZmBlFjDCJO02iMSbVUkcPhME0ToFg2tdZlt1h0nfd2OI0AigoKbIRUGZCAyeVcCJQAbWOlFiICAAK5utg+PT1VZ8GYCnr/5WsIAZjmeQaAeZ5DcEBIRP1ytdlsjsf948vzer3u+/6iaVJJi66LKYFW59w8p4vLm5SStR7/F5FCxLWKklrv0xzBGHKuAIS2GccxlVz1D8M0IXKKU601U/zp249fPv2emtB07XLRCVSZx6lMpZRsDAA441PJvTXjcK45H9JkvAkhpDlysIh6eD0SmLNYVR2nE7JsthehXag2u/3+Imz61aJtW2sIlWrNOVe/CF3XMfM8RffG8O7xPs3RWr9cLr0Lw2koqaSYgguGTJnSP//xD9+E/X5/8/7d+/fvS61NaKZ5NM5WlVISGgbVUpM1npnvHx7GmG7W2yqye3oUEZBiXVgse3YsAqpacjHGDcMwjuN6vX59ffXObbcra23KIyISUuPDmQcmyiCWTejaUoox5vb2mplJKKXERJa5pOpCU2sObSPFIcDd5y8///KLISLEH3/8EZn+4z/+gxy7zq+3F8Mw7Ha70+k0juP2+qppW0WooKUURNSSEcESj6czO386nZ0LCEzG+dDOh9M0JwUzxrFkqSrWhSnl83lUQoM0TRMRX11dI6L3vpRijIlvSh1O5+PxaIx7//59rdV7LyIA6Jwdx7GKhBBEpNZKaGqpRaq1FolAlRBqVQAgplpKrZWRiBgUjWUiEhEkA4qIYH2DoqY3RYRqrSrOuSJVoaY5Hl5fnl+eSo5933nvD/uzqlZAk0pWUCYyBq01UrOkXGuFKsaQlFpKfbx/WDTtsu2I6PnphZkBoMRk/8CqFf4UQliuV1VlfzyQ4fXlRQiBkYyzMWUiQjQpV2ucKvb9KudcSmFma60IpzQrAhHMOe33r8MwuOAX69X+fCql3Lx7N8VZSiXDAGSM8dZRztN5aDznFHeHl361ZscpJWRkZiXNOceYHXkAUkEFbZsmlbh/PrvGde26a0LftChcZogx5jLevn9P3hxP4zinH3/6KzsGEEDJtagqI1lvjKqIAIC1lhDncZiGcTieSimvu+eLy6uuaSbn0jzP4/j506da6+XFxfri4vu/fDfF+bQ/CEJOyYcwTjM5st4hYszJWVdy+eXTb1DlzRRnR+xcOI5TMPzy8nLbXeecUVCJSQmRV/3yanvJzH23UKhQS6rJWo8g0xQxU9/3Beg8DrXWMk5Sa9/33ticc60SrKsqUtQYV1JWFFU1zPM8f/j242Kx8NYxMzmrqtZ7Ab1/enRtY537+O23WMUYo4RZKjLP8/z8/DyO4yL4eZ4tGwDIRXwT2tAYhN3uGaSGph3mUYCX6808R0EtubKhaj0zhxAWOecYEXGappwzIoYQVDXGqKp933ddv9/vY4wAwMzb7VZEmBkJSynMTETzPDdvfDuOIzMDkEix1jJzSklVGx/GcVQEQsPM+L8QUc2CgEQgSIaUTDWqAFC1ipbGhzb4HCMjqGKWurlYVwURMA8P9/QG0RI7Yw0jKzBjKTXO6WX3LCL9ou2pe9k9j+NYUyYiAAEQRLLWrtdrAMm1NI0HUmtt1y+e9693jw+3t7cGiZGMMTlnVHDOQxVAiOOkqk3XllKmaXLOGeeJWRBqycv1xWK5Pu5fPn/9knO+vLx0vv1wdU2IgFirHo/H/X5/enn67nL7bnk7zfHuPgZnV5uVQiVLOWcBjTEe5bzabIiMdx0zV4mlpPM4NF0TvMk5G7J5zgw2xlhKqbVqxbnkc5SKoFIBxBhCw1IrEhljRISIVJUApnGYp7Fp/HLReteIwH/913+1oSml3NzcHI/H8XwuUud5GuYxpQSIRJylbjab66YJXatQc46IaIhLKcT0/bd/IaLD6/HlaVcNry8utpv1bz//g4iHYfSNt8yMbKxHMkLijZvnmRDZuFxkGGZEXXRtF/rQh1q1CBapp9NptVlv15vz+fz198/r9dp7j6KgiKA554eHB2RgxvVq1XVdSqlf9/RGoZSSpf71336qKm9yKcZYVGDvYkpS1bfNnKa7h69pjut+2Ti/bLrhfB6GIYQgMT49vzBz6+w4nonM1c2ls+H18ApAq83S9K5pPADVWkspqZxyVYG6WCzW6zURAUBNdd2vRSTnvN/vocpyuUwpvexfY04xRhEJIfR937atc67rupRSjDGEEOfIzIaNVqmi3jqRMs+jtZaYVaCUUgFVlQEBgMgAiIrCHxRAAVRRQEFVmahddBICqkoFY70UFQCpYLq2ZcZaVUuuJeU5SakGQUVqrev1+vHx8eHhYZ5nYwwzr65WqmqtBYBhGGrNzrmcxVorIi+7Z+Pser2+ur1BRGbWXI2zKZYQGkt8Po9pjvv93hiTSvz48WPXdYMqAGiVXCsY9tYVLMwcGnd1e9M4n3NmsgCAbwxLKpvLbbvq/zkNz8e9oRUQKMAUJzcba7moMrN7E4JvOueblCVLLrVoycbwcrkMwReNKc1Va57zdM7jOFrHh8NhcbFaLpeVyn/+/R/e86IN2+22bQMDCICqioiqGmN2j7u7u7vOBSbKc766uvKu6fsOka21nz59stY658KfVhcbAGBjnPMV1DmnhCkn3wQRARDvfSlFRKy1pUjXdatF//unTykVb3mzvRqn03p9oSiExhiXUnl93I3juN1eee/HcUxpFq1xmkQkpdI0zXM8IBkRnWMsQvH+wQW/WCwMOxXZv77WWhUoV2Xr2rYlAiQ9HA7//u///vHjx9Vq5a0TRGBqgxdQKCKqIYRaNcUYQvDeD2keptFa/umnn9Icv/z2KZ3Hq81FY9x+fK5z6vrFuuutd/f3X733znvjggAU0bb11gVrrQLVUkTEWrtcLhdtZ60FECLKOQOAFAEAROy6zlr7+Ph4PB43m82W6XQ6MXPbtiIyjuPr6+s8z941V1dXbdsOw+C9r7WCAjPnnEtMzpvG+5izKiGBQYOgqggFVJAIARiwVkGRWlUBRFWJidAgoIgUURA1xnljVQCARMQMxwOSllRBqjMGVf5AwEg5xjiN5/OR2V5cXBBRKWWcJ2tt49q+71ebpYg4Y0vNzJxSMmyRSUSI2LCrpcxz3O+el/16zBKcZzSvu3s2mGO82KwWodntHu8fnpSw1gqEf/3pJ2stMbwx6KZpmnPiN8TW2vM0a8whtNM8Pz2/Trnc3l7HNJeaI8o8HNTAerOUUkhqhgpI7NzL6RBTTUUcGwvqrXPBxpwAawiB1XrjGXKtqlrncUJruk1wNpyHU1h0jW+9DZadahWRIlAFrKPjcF5frLbbDWS1xpz2h93uZRzvkfnjNx+60Pztb3+b5/mnf/mrtXYYhr///HckU1Ssdz/+9a+NM6dxIGNijEzIbNI011qdc6UUEDTG/vPv/3DWuOBriofDgVhT1qK15DxNL0Rcqyrx4TScvt6HEJi51NyGbrvdOufGaWh7W0rJuTT9QpTHcRxO5zjNNUcAMMYhs/WhiMTz0LYtU1n1nUXw1nRNCM7mmo0xQCpQpyk657q2naZJVZ1lqZUQWx/ImiK5lOIMXV9uv376HUEcsUUCJEN8nIan58fLy+vTOMRSn1/2y+Vye3l9Pp9fDsf1et00zjGrCCIaYjFCRCKSc9WizjmxoqoxxlqriFhrxziP42i922635/M5pRTj5P8UQmiahhheX1/HcawlffjwwZErMS0WDYjGGBXJMhetIqpQBQnfGEb9A7xBICIAQiRBQETJiYmJqEoGBCQVhVqVmUEJEM3FZpXeYCQQb51lrLVKqSCKHna7Xdd1TdP5Jtx/vUOm9x8+2ODlD8U4iwoqlZkBoG1bS2ZOidl450+nYbfbjecjKhxezm0I11e3bdM0Iex2u1W/QIXHh7v94bDs+/VmAwA2eDI8nYdu0UwpIuJyuZQ/adVhGIB4mqZ//PzL/nQktt5ZNub15TQMwziO1lpEbHxQlFIyEIlCVZ3muQhWITH0xnpnnS+QQURVc41UKMaMoiXn9x/fQ+MqWZH63XffrbqOjRo0NVVVRYQ3RJRiWSwWqlpLdc6djqevD/fzPLfNYnt12fXt89Mu1fTh24//z//7P/71X/91vVn/b//H/14VFYGNqaDnaXTBx5heX19Ph4O3GJwHFAQbc0Iwb25vb+dxOh7Op/3OOqtQj+eh6RfLi87GhAqiKLVWgat37w+nY0l50awQcXcaLM3NoiEmNmRCGMf5fDy1bds1LTMf9y/jODrnBEBErA9F4/l8pjqvmwZEu6Z5vL93336LiLVWRBQRa62IlFJ+/+1TzllLbXy72WxsG6xzyBDHcT+eyxyvLraGuEDx3s8pHc8nJby+vjY+vFstFSDnLKAigoZzSnd3d1dXV6tFJyJaBREBoJRijCmlqGopBQBCCIhYa7XW9m/WK1UdprGUgojv3783hpg5xgwAzPz8/DyOY9/3hHp3dxesW69W+5eZAUMIKUXjrKoAkkJ9Q2SIqAKgCCECqCIAoYAqAiAUJQNQq4oAoUHLUCWXzMiAhACm884RijEMqKq1pJrLPI3MfDqditTNZpNSmeYZCEVVGIEQQGPKIuKMUVV6o0AKqGoIVWEahpry7eWVXGxaH2rFX//5yy//+LlfrHKJjQ9pmj8f9wq1Xy0Z9NNvv6RUrq6vxzjuj4cff/xxe3VJRCklEWFmQGzbtlY9ljOzBaCU8rurdyJirSVAb2wXWq3yunuuNS/6zrWN8w7Zv7/tlI0oEyLW0jgbSzZsDbcpzSQICfqwHsfp5eXZOUfWHWOuVYMNlow1BIKiYKwRqKkWZgBCQE4pgihBtcH/8OOPIYQ5JxFR0uvbK2SY5/lf/u2/9ZvleZyQyRgTmnAaJjIc+u7XX3993b1cXl7eXF4hFBWpNTO5xoeY6vPry/PzKyOUnNoQDOj17ftizd3T4ynGrl9erDeiaJiN9efjyUqN9XSYprZtNzc3jfMAYh0CaEnFhWa7vUrT/PXzl+Vyud1eaX2aU2RrFUByJmsWzpZz2T08CMBqvW77xd2XL8M0bTYbAe37fhrGWus8z6u+jzFSxda1RqCO43w+KZMiEOB6uSLRp4dHqAIM1rt21btFW0o5H4dpmlLOm82GiJ6enpSQmW9urqy1pRQEYGZ4U0UVUiqHwylNs4hGJAe8AAAbC0lEQVQAyOXlpTVGan16emqaplv21lpXXdu2IuK9V60xRmstEeVcr6+vt5vy9PSkou+ubw77ly9ffl/3S8O8f30JbcPekSUfWjZGQFWrKgKAKijgGxUVqUVFERCYyRJgllpVAABFVRSQqzIiMqBBEASxjLXWFGOtlQC9C09PT4gIAMMwLVerOaeKMKf4++9ffOtvLrdt22opQIhA8Cd5Q/z6/Pr8/BxCYMC787ltW2vtcJpzztbaOY6IWGtuGk+VXONvb2/75XK9vUwpVZHLm+sfnGXmeZ6996rqnJMKSBhjZrab5epyez3F8W8///Ph/uv644fN6oIqDsSX260x/MZaRkTrXVE4HM9dv0FhJisill2M5fevX5oubLcbRBuaUFCgknEODb+87Lc31w7NwtHr0+OZaLlcGGfhT+fz8Y1CHeehaRrrm6urm0XTKguhZikCal2otY45b66uETHnWgVcaJAopTSfhqZpFOF8Pm+32xLT6XDAtqyX/TgeD4dXIhNzRbKK1LYtIwzHKCLXtzexlLvHp9B377/5lplrrRKTkEkpWe823q3Xa0RkJETUN1CrVq3FWA+qOVU0rAjncWDm1fZC9/tcC1sDyERYcmTHhsz5NMQYvz7cb7fb64stELYhDKdzcI6cC8ZM47jb7aSU3rUlZb9ogakAWscAMAIuQri+vkrT/LDbha7tl8vjOO12u28+fGjbNoQwThMAXV5el1JUqzFUS0EFIprOw9PT0ziOzoWr25vlcqlt9/TwmFLufMg5f/7tEzCdTid4fPzLD987Y1NKIYQcE1tCRFUtRd6AEpJh406nk29rt1qP8/Tw8LDous1m3fhQsEoucznFkqc5EdFi0XvvAQgQkQhUEZRUBABRqwiQJQZAFn1TkYxjFAEkBCSTy0hMtSgwhLaptaY5T+PkfZNzZrZIlGqJOQmra4Kz7brvUQkRqwgbLFJQiKzLOTPyZnvRtm3jHarEGInd/Ifigu+75fPzc4xxsWhvv3kfY3w57H/+7RMgMtvNZvPuw0dFIOY5TqHrFGoFharGmJrUmgYBFMEQYC2mRirJItWi0xRr1W6xZGcBAJlUMKl+uXtIKRdpiUzOg76RlOcpF0Eorzgw83MZQICBY4zKVKp8/XrfdYu+7RXJGErz+XiaBdDboKX2wbZhUbRBMr98vt+/jt98/O7m6jrGMxEgmVzFWmd9yFXfGGurvKnsqAJYa1NK3nsGVdD3Hy5/+fmfMRLqwlsX51kI+/W2VBYBAiw5IhRnXJHqQ0Bjc9WaKllyxiEDESBiidkYI1WYuZZMzCKiIgoCiApQShWR8zDEWlBqA2LYrK8vY05TiswWiYrRNjRM4JcLxyZL3T+/OGuWiz7nbGuBkitKLvlweCWsN+9uuKoI9OvVnMvz696yX61WzjDUAjU7y86gihhAVnJk+7YTkZSSc6EU0QqGbEq1SjYEoKqldm1r37077E/n83jan854JkADnJL8+o9fRcQax9bc/uV9VZnGaRxH770F47yJeSYiACAiBVNExzk/nwYA+O3hfrtc+q4dx1FA9i+vssouWO9MjPMiuIULx+EYh3R6ycz24uIC2aoIE9WcF207paxKarTWqn9CRGOtiDCB1Fq0GmstEdU651RrTVqkFlXBeU7W2k2/Ms6OcQSmq+vb1WpFlaXWlEao1RhjrQEAFRRFJDPNyTD2q+Xr8/Pdly/MnKuIwE9//RdjTIy5vjyTs+wd+3C5WTfr5ePj4zynpm0X640yOhuU1RGO49kY45zDijVVa/08JRBAgvvPD7vne2/43V/+EmPcv+wFaLHejCnP5zNZU7IA/cHYFq0OUxaJzKam5Cz2681101jniChLfUNEqromjvN4PhxzSuN5mE+DEqZc1UBWQSZDHmoJTNC3obXjMCLIx2+/XW8uU0rBuSrR+ybnXEoBJUFgNrXUaZq6rqspM6AlriAgJRguJd09PThDm8UyzXH/skPEpmlOpxOapmYRJsNgrWXmoiJVrq+vHx53nz59Wi767XbrjRUtUKUNTZojAUqpUGsu1TIbY3OJhKbkTMzeOWZer9dk+OHhYXd4RdF+vbq6ujLOEqBIKTXN83TenwhwfbG53l7un1+0VOe9awwxppyNZaTVy+FoLIbW51SA1Aa73V644A2xSNEaa8qICCCnw8FY27bL7ebit99+2VxuF4ulqhJzyllVm+ClRgQhRiKa57zf74dzJMNd33trd3cPOSWDxMBMOIwTMv3973+33gEAEU3T9PXzlx9+/K7pPDAN48TkFWFOJbTtj//tp+NxP03D8eV5uWh/+umn8Xh8+HKHiHE3WMs+uAV0RNA6a6w/69k5l+axZDHGOBfSNDhia5gQi1bQapgJbX2TU865aTo2gMgmpuKcM9YTg1StpdRYU0rL1appGmPMaTjPc+qWfb9aigigGAR23hrMOcdpFkVrbRFl60JwAFCltsv1t64BFBfatuvnaTLs/ud//Y/NZiMiiuq7VhC7xfK7fglAAKDIijCVVOZ0PByOr7sU4+HwenFxqYLIpmRBxGXfe6ab7SVK/fLls1QNb7qu6bvVxcVCJUtNKc1T7PpFTgoAISxSSvMUcdEugi8lDeMs09w0jbFWgVKur/tnrbJYLG4+fEOIw/F03B/GcbQ+KKK1aJzNsZyHERhWy84whca5E8XhmENrDCOoZ5zHExnLSOfhuNu9nM+jiCCitTYE5723lpvgTqfTOJ6JyDf+ZnuVTvH56QVR52Gcctpev2v7i1rVERJKyQNIEUKpdZzn4Hzbtuv12hKDqlbx1qWYUNUYE2P85ed/DtPY+PDNtx+7rpEqBmmcpijKzgIAEa2Xq77vDbH1DgAqKIoCkGHXBF5/t3l9eXm8e7i+ulos+5eHR1ZZ9CGNsWk9IzaNv/GXxtpSpaJmFTSGAwpr1kJQATRrMWzW23XF4+75KZzHpl9c3d5MKf7+9cuUctMutttt27ZJkrMmlyilMHDUGrVmVEt4//D15vLq3Tfv9rvn/dMzM6aUjLNAqCLL5bJfr+ZhbNvWWzenKeZSYkGkNwLovAHQPMeL1XoPcvnDD8Nh/59/+9v765u/fP/9/uXF2H69Xrrga80iNdd0Ho4xJjINESthURiP5+eXp+40dV2H1jIzIpJDxIq1eue61seYa60AYHJVrCqiIqKqoKqERARE+CdjDBHFGF3M1rJWUamqNUaNcY5piqmowmpz1TQ+Vqm1euuaRfAh11qBMEpxTauq/+f//X+dz+f1eq2qSJpqAq0qSmSMtQg0xfnv//jHOJy+//jNzfW1M3YaTiqYqghSimWe52EYjnGGkr1BA9xfb9plb60fhtMvXz5ba65ubvq27deAwERmzuX4cjxPI1TNORlaL1eL7fUVIOobBCIyxlxcbkXKr7/88ref//Hjd9/7tqmvry40oMTGzCUO48kZv1wu6zx8/vz5m483i75/d3v9vD8dj2fDyBC9t4CcUkGk4Nu+CYvgc65vFotFjFPXBACpcTYg19t1jPE8TL/dP+mskkto3Lub2367eTmPz8/PZN2y6Rata1uHpCnXlIpVWi1b733rQ06JkKY5TqdzrbXvFjUXRrLEm8VyeLM/dt6N4/jrr7+KSNM0i2X//v37OaeubRWg1ppiUlUyDIhaK7ABwFqR2CtxqdpYx84O87Q/7Pp+wY5ziV2/yOMoBKnKaRjTcQ9KVaVpmlW/bINTgfV6Pc9znFMTXM651jyNZzVAbNlZquK9Nd6gwTjmIsAEZFgBnPdX19cAZIxBhXEYHh8fS0qhaz98+AAAP//yTzL2hx++B6a7u7vxdAaAkvJfvv/WNQ6VvG9qURFhopRm61yKU9+1cRpWq9XV1dXxfPLGLpbLx8eH+vx6eXVlnZ1TtNY7y8+vx3F+QjJataoQ4DgnlWFOiZmJqJSiqqBkrV2v123bBt9kyLVWk4sqVBEBAERUBVURhJoyAAioc269vphyOuxPJUedZ4OAqCLChoy1znkR+eWXX+aY33/89ru/fF9SOk2x8V6UyOAbMLbkbIjW2+08T9baUjKx8cGqaoqlqPAbY1br/rB/eXp6+v7DhxrT519+bxfLpHWMqSoCgAH0TCAKGb798K3fLJ8Or799/nWxWvzw3/4lNC6mQkSqWquytZ0P1vgLBENGpHomZpY3oM5aJcw5Tyk659JUSlV23rWd1KpsVHE4jTATGjImKOI4J4+82Wz2+/39/X1o+9AuuxCCN4ZdrdnZTgRyrjHGw3jOuZYsomX/+lRKMsaoynLROmcsFShytVlv+9Xuy1MUuLm6Llh3u91Y5PbbH7qud86hZqmzSLHGk9MFcbAu56wiAFBKOe73u8enEMKvh5/btv3pp59+/OGHT58+5ZSctQR83p88OxPser1erJY5FsMmTUkRichaj4j6J7JGgKTmrw+P8zDOMceSraFxnqmKAL0eD/26DyHM8+gtx5qNsaIl50zEAIAgjCqlOjYxxvF0ZjLeWFaoNWnFvluQM/vj2XlzfX0tIvMwLvpFSjOiSi0iYthaa2pVqUKAKc1THGsppFAkOxd8E5D5t99+U8J3797d3t6mlGrKNjRsGY3NpRAZrfL09CQi/bpvnC21IOJut9vvjxertfEBRRGMAt3fP8U4G8ehaw27m9tvYkxAJFmmFC2xa7u+7YyllJK3TkRUFYAQEVQP+/3x+MkY45wzPiyUEHIBlFprSklqLVJzLYhYSplzEUWyxjkXvFdjUKrWPI4jMBMiiE5jnKbprz/9y3JzMY6jtbZpGgBAUCKMJRvjmNk6F+NsrQVSb3wqcZ4rMxtLZBwRAch6vf7Cn1rvRERrDSGcTicw1vumW/aqenzdI4IPLYtY35zn+Hw8+kX7/puPVWEYZzIu5xpCAJKcKkBtFx0AzHM0xpZSAKoiIGIFlSqiimTmmL/cfQWmm+trICypbm+uDdkSK7OtKEUqARomA5U0jcPrMJ6HMQKMUHU4Tm1LADJBRORpjOM4Wh+W/YqIcs7L5dI68/j4eD4fLzYr50xOcblZjlMy5CyxacO6X57m81BKCKFpGiQTY6w1BsfGeSlAWgCglFJLQQAQBYDry6ubq+s0zc2Pf52n6T/+/d8BoJTShubr5y/3X++Y2Xp3e3ubc/77f/6XEjrnci3X19friwsCrCKqin8iZGNAFQQh17I/HBbNzc2H9y/3D3WOUvX+/n57uREpx/m4WC2JaNUv+wV67xEJFZwxWqWUYgj7rgeAWvT97Q0A7Q6v+5eX9eV2EZo0vcbh3DSN8y5Oo6oSAwMZZlDIcxQBY0yVvFj22+1WcskpLfp+Hubr29vXw76gns9n/hMA9OvVPM8xqTFGBBD17u5ut3t89+7daf/6FEdDPE3TxXpze3tLRKXqw909Ap6OQwjh5vYdO1ukzvP88PRirWXjnLXGwps0TQc5Oedqjn7jF4veGKOCOef5TYw+BEQkZgNkFbRCBcUiAsTBObdc1jk9P7/GGENonXExl2EYc4pOa56nEpNCvVksfGiGYXh92f3rTz/16yUisLPMXFJGRELMKbWtLymO41iy1Fqvt5e5ZCC0lgCg1gLAJHU8D7vn599+/eflxfpis4kx7p9eU5F+tVbEVGU6D7FkBGBridgbrqi+aUO72O12j7uXy8vLtg0AZIyTXHPOTdMh4jzOiEiIDAjMRKTwJyVEMIbJmlrr7ftvzofjMEzn84iixpjG03K9AiC2JAhpSuP5VCQHQ/1q/Wa324/DfLFaG3dpTEppZvYINIbYNI31rm0XQFRKsdaq6nq93G5X3hnDJEmPx+M0ZwY3z3Mt5XQ6uc4vnf3y9Pzw8LDeXC26xjnSmsZxLEJNaB0RVEEAVU0p3d/fl3kCAC3VOacixpiSspTqF77rOhF5fn7+/zqCs+U2jiwBoHfLzKpCASC4U9Riqd3R8+iI+f+/cEw4FO62tVDcSRCoNZd7h61zcBwB4PDw8OLi4nH77Jxb1qtXIpJSMgQRAcJSSi45hHB6erJarUqav335++b+btU2RyfHTMd31z9SnJyEccrLRctIVrCWahiGorGuF6WUnDMRxXF+6nZOwma9JgAz886dHh3fPD4/Xt+enp6ebY7G3e76y5ejo6Nm2fIrYEScp/n5JwZulu3h8UaEuq5bLVoz23edMJPg0dERCgNALqXr+6ZpYkqAWEpBcezDOI67fsfMi8UCrNSVn6cRoer/a0TknNQxY8nr9ZqdeF/Fkm9v7+u6/vTpVyTKakKsYI5lt9sOYxdjrJcrqWoQp0SGRuTaOtSrNufcdd1+v5fN0TEAlJJUS9fv9i/brAUz1nXVNBWzy6X0XS8izoU0x3EcS5wZUNgzOTQDVVB7uL9dNI0PYKkgBDbVogbgGf/6/PnH92tmVgUR+c8ff1xcnh9u1iQoxFnL/mX3su8QeLFY/Osfn6ZhfLx/GMeRgUPbZLXVcnngKwBQhNqHkmOaY1NVGUCzmiEUeFU579lP00QiTCRVBWopJ8+Sc56GoaoqcWxmiAiIgJhLVlUCY+YQ6va8IaIUIyIGcaaISMxshMU0VO3q4GDYb3dP90VzE6qLk4vv81W3HTYna2bHnJxzalDXdVXXzC6WrFlJ2BAQkL2rvZvGPprO80REbdv2u8kIDeDm5sYvwtG7t2dnZz8etk/PewQ93hxcvDlu6naccyllTokBXwEACdsrBFCT4N+9fWdm3rnff/891NU8z7vdrpRSVZX3vpSy2+18CJvNZnWwplciAOCCN7OYkyZ1zhHhPE9ENM1Dnqc3by+6l8XVty/10QkCte3q+TESuaZeiVDMqaiBle3TjoiqixqN+r5nwGmaxjGJGLIE54vpMEUWv1ouH5+e7q5vEOBwc3x+fOK9f354TCnFlHLOzrnFYnl2fEaAJHx3c9sN+1W7FhFEZO80F2AqxQQxxsjM3nsFMwRkAcScMxE553777beu6/7+958lx7eXbyrn99uXlLKQMyIA9d5rtGEaYYLFaum8JycSKnEuq4kgk9OUgHCxbNtlA4gAgIg5Z1OFnwzAAHwItaoBiAKp5qpexDS5EmCPhMjMc05AiERk5r0vpaQYASCEGsSh2jyPT4+Ph4cHy0X7j4+/PDw8/PnH/wETAAiyEKsCEYnQ6mD964f34zgyuboJlfOxRJ2GIUZGDHW9DKESybn0fffj9to5h84759YHh6FuimHXdcPLCyKKSBynnHNK6W67jTlVPhwcHn76319ccDkmzQaKjj0Z5Jwr75Pi7c3N8/Pz0cFm+/DonHv3y4eU0jiNu27fT2Nd12cX5wzIzpsZAARfwytEJBARVc2a2XkzUsMMgM4rAJLkbEKh308xRteorwlsbpqF81UBUwBiN6V52HZmxkKeCdFCXZWUxLMWIHbLtX+8exLhrJrnqZl6cJWZiciiqdq2JWAtRQABEZhUDcAIQEQ+fPhgVtDMin799q3rOkdc1fW7d+8WTfP58+dpmt6/f9+2bcr5r7/+euk759zV9Y/Ly8v1ZoNMpoCIwCROABFTZlQRLHkOFY/j0Pd7VcX/IlMqSl0/r1ZtynGecjaYpolIAODm5q5kyzkrmPehAIl4E2/Oee8IMOZEQIvFMs4jqO1323me45wXyxbE1aFJkr33ac5DNwrS5njTtu2+3/Vjx+5CFYqpgYmImqaUAAiIEcCIYsreC4Exc8rZzIZpdM59/Pjx73//efX128cPv7w5v/j69Zsw52JN05Sc2VHtQ9/3d3c3p+dnJydHzy+7r9+/vXnzNpbcVk0BK6BGiIYsCEax5AKGAM65GKMjLsVUCwqHphZmBoB5nvuhSymR80LEjCVlEu8hg3lnAIYKqDlBnPr9zlBDqEuO9/f3Itw21dnRYSkJhQFAjM0wTrOZ7fvu+fZuHEcAdCwvD8rMzknRJEJmRkQiUlUNi7AZAzii1WbdrDbbYfh+c+19dX5+vly0qIY/ZVNDUMJSigARoIiAgiBpLg+3d/M0pZRW7fL09Kytm0VVP8X7cd8R85vzixLT9dXVME+5FCBcVDWoWVFDeIWIAIA/AUBKCZlQGBGHcYox7rp+6ofjdZMLbO+exz4CoBm2y7UP6JyLOSUtLngt1PfdHBMHhwbMTIRIomBAiMYKWU1LscVikeaoqm3biogxV1W1bDertqm8Y0BTQgQgylqAANQUgQwAAQwUYJqnVPI8z1TXzbKV4Pt56ofh5OTk+9WViHz49PGf//OvVHIpxfAnpqyFmbPpMAzjOM7zfHSwBlDn3BzHcTvsX3aEeHBwgMhPj9ux7xFlHOLu5YYDHh8fB+S6WSESMyNiTClnVQRAhldMMcaX3b6UFEKoqoqMULj1a7ACaiJStO+GETkC8ZyScwGKFk2gVkgPTw6Xy+U4jl3XeV+FqkoaSylmgK+YU0rb/S6XEmMOIWwOVqUUA0BEVc2a8zyHEEwEzbQURyxIU5xUQUTMbD90xOyrMM6zC/5gs4kl/+fL3865s1NkQe8FkUAVEVPKjtkRp5TyHEGVBFCNmRAJiQUITWGcpm7oAcx7H5itFCJwThXRFMHglQGCYFhUbVObar/b73dbKEoAmkvfbVWVmQCACqRUhm4spYh3dV27qiKSEEIpZXy137Vtc3Zy7L1XVWJ2LuScn1PGKly8eVuY7p+fH/f7s8u3BwcbLw7NMCuA4SsiYGIRBqiAIBUzAwMztGLzOPdd3zTNPEx31zd2dFxx8OynfvbeX329KmAppcvLy+XBUs1UFZGsmCEgIiAAwStEBAAXBADmnIZheHzcp6KaE4iocTbb73sHTphzyd5X7DSVbK/Acs4K4kNg74uh5eI8oxkwIQKSgaoZ9F2nhdrVah7HaZqW65UBpJTQSqhc0zQEZpYBgIgUABHNDBHNDF4hGgIYhLr+9OuvoGo/FVMgPHv75vb6BhHXq6UhpJLFe1JVMFU1BCKKJT89Pe12u7pp2rbp+71pBrCUZ+/9arlg4BzzzY9rzebZoXC9WB4tKiAtprePT0zO/2RmMeasgEzTPCkCO6nb+uT4vfMMJSMAFgQzKjb0fYlzs2ypqvph2vejsHjxvq7IIKc4dP2+H6f04/z8tK7r+/v7lPXi7aVnr2YAhK+M5ik9P73EnOaUiXH7cG9mrgrOe2ACzXkcCWHVLglZTRdNU4UmpULil8vFog3j1Al7IBzjnHMWF8yslIJECgWNc84AilaICIqZwTzPd3d3KaW6rk9OTgiAjMAAEf8f4dFVndiL2lAAAAAASUVORK5CYII=",
      "text/plain": [
       "283×283 Array{RGB{N0f8},2} with eltype RGB{N0f8}:\n",
       " RGB{N0f8}(0.153,0.082,0.075)  …  RGB{N0f8}(0.678,0.659,0.675)\n",
       " RGB{N0f8}(0.118,0.047,0.039)     RGB{N0f8}(0.514,0.494,0.51)\n",
       " RGB{N0f8}(0.129,0.059,0.043)     RGB{N0f8}(0.624,0.604,0.62)\n",
       " RGB{N0f8}(0.137,0.067,0.051)     RGB{N0f8}(0.157,0.137,0.153)\n",
       " RGB{N0f8}(0.129,0.063,0.035)     RGB{N0f8}(0.243,0.224,0.239)\n",
       " RGB{N0f8}(0.125,0.059,0.027)  …  RGB{N0f8}(0.184,0.161,0.176)\n",
       " RGB{N0f8}(0.169,0.114,0.071)     RGB{N0f8}(0.208,0.184,0.2)\n",
       " RGB{N0f8}(0.482,0.427,0.384)     RGB{N0f8}(0.153,0.129,0.145)\n",
       " RGB{N0f8}(0.498,0.451,0.396)     RGB{N0f8}(0.141,0.106,0.125)\n",
       " RGB{N0f8}(0.49,0.443,0.388)      RGB{N0f8}(0.161,0.125,0.145)\n",
       " RGB{N0f8}(0.49,0.455,0.396)   …  RGB{N0f8}(0.149,0.114,0.125)\n",
       " RGB{N0f8}(0.498,0.463,0.404)     RGB{N0f8}(0.145,0.102,0.118)\n",
       " RGB{N0f8}(0.478,0.451,0.388)     RGB{N0f8}(0.145,0.102,0.118)\n",
       " ⋮                             ⋱  \n",
       " RGB{N0f8}(0.51,0.49,0.478)       RGB{N0f8}(0.459,0.431,0.459)\n",
       " RGB{N0f8}(0.49,0.475,0.439)      RGB{N0f8}(0.443,0.424,0.439)\n",
       " RGB{N0f8}(0.51,0.494,0.459)      RGB{N0f8}(0.467,0.443,0.459)\n",
       " RGB{N0f8}(0.482,0.471,0.443)     RGB{N0f8}(0.451,0.404,0.42)\n",
       " RGB{N0f8}(0.463,0.459,0.439)  …  RGB{N0f8}(0.463,0.404,0.424)\n",
       " RGB{N0f8}(0.443,0.443,0.435)     RGB{N0f8}(0.392,0.329,0.341)\n",
       " RGB{N0f8}(0.471,0.475,0.482)     RGB{N0f8}(0.4,0.341,0.353)\n",
       " RGB{N0f8}(0.471,0.482,0.502)     RGB{N0f8}(0.349,0.31,0.314)\n",
       " RGB{N0f8}(0.537,0.557,0.58)      RGB{N0f8}(0.396,0.361,0.365)\n",
       " RGB{N0f8}(0.529,0.557,0.588)  …  RGB{N0f8}(0.424,0.4,0.4)\n",
       " RGB{N0f8}(0.541,0.569,0.6)       RGB{N0f8}(0.443,0.408,0.412)\n",
       " RGB{N0f8}(0.541,0.576,0.604)     RGB{N0f8}(0.459,0.412,0.427)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = load(\"data/khiam-small.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typeof(X1) = Matrix{RGB{N0f8}}\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\"\n",
       "     width=\"25mm\" height=\"25mm\" viewBox=\"0 0 1 1\">\n",
       "    <rect width=\"1\" height=\"1\" fill=\"#271513\" stroke=\"none\"/>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "RGB{N0f8}(0.153,0.082,0.075)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@show typeof(X1)\n",
    "X1[1,1] # this is pixel [1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily convert this image to gray scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "283×283 Array{Gray{N0f8},2} with eltype Gray{N0f8}:\n",
       " Gray{N0f8}(0.102)  Gray{N0f8}(0.063)  …  Gray{N0f8}(0.667)\n",
       " Gray{N0f8}(0.067)  Gray{N0f8}(0.098)     Gray{N0f8}(0.502)\n",
       " Gray{N0f8}(0.078)  Gray{N0f8}(0.086)     Gray{N0f8}(0.612)\n",
       " Gray{N0f8}(0.086)  Gray{N0f8}(0.067)     Gray{N0f8}(0.145)\n",
       " Gray{N0f8}(0.078)  Gray{N0f8}(0.102)     Gray{N0f8}(0.231)\n",
       " Gray{N0f8}(0.075)  Gray{N0f8}(0.075)  …  Gray{N0f8}(0.169)\n",
       " Gray{N0f8}(0.125)  Gray{N0f8}(0.098)     Gray{N0f8}(0.192)\n",
       " Gray{N0f8}(0.439)  Gray{N0f8}(0.447)     Gray{N0f8}(0.137)\n",
       " Gray{N0f8}(0.459)  Gray{N0f8}(0.455)     Gray{N0f8}(0.118)\n",
       " Gray{N0f8}(0.451)  Gray{N0f8}(0.467)     Gray{N0f8}(0.137)\n",
       " Gray{N0f8}(0.459)  Gray{N0f8}(0.459)  …  Gray{N0f8}(0.125)\n",
       " Gray{N0f8}(0.467)  Gray{N0f8}(0.451)     Gray{N0f8}(0.118)\n",
       " Gray{N0f8}(0.451)  Gray{N0f8}(0.451)     Gray{N0f8}(0.118)\n",
       " ⋮                                     ⋱  \n",
       " Gray{N0f8}(0.494)  Gray{N0f8}(0.475)     Gray{N0f8}(0.443)\n",
       " Gray{N0f8}(0.475)  Gray{N0f8}(0.482)     Gray{N0f8}(0.431)\n",
       " Gray{N0f8}(0.494)  Gray{N0f8}(0.502)     Gray{N0f8}(0.451)\n",
       " Gray{N0f8}(0.471)  Gray{N0f8}(0.494)     Gray{N0f8}(0.42)\n",
       " Gray{N0f8}(0.459)  Gray{N0f8}(0.482)  …  Gray{N0f8}(0.424)\n",
       " Gray{N0f8}(0.443)  Gray{N0f8}(0.459)     Gray{N0f8}(0.349)\n",
       " Gray{N0f8}(0.475)  Gray{N0f8}(0.478)     Gray{N0f8}(0.361)\n",
       " Gray{N0f8}(0.482)  Gray{N0f8}(0.478)     Gray{N0f8}(0.322)\n",
       " Gray{N0f8}(0.553)  Gray{N0f8}(0.553)     Gray{N0f8}(0.373)\n",
       " Gray{N0f8}(0.553)  Gray{N0f8}(0.545)  …  Gray{N0f8}(0.408)\n",
       " Gray{N0f8}(0.565)  Gray{N0f8}(0.553)     Gray{N0f8}(0.42)\n",
       " Gray{N0f8}(0.569)  Gray{N0f8}(0.553)     Gray{N0f8}(0.427)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xgray = Gray.(X1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily extract the RGB layers from the image. We will make use of the `reshape` function below to reshape a vector to a matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = map(i->X1[i].r,1:length(X1))\n",
    "R = Float64.(reshape(R,size(X1)...))\n",
    "\n",
    "G = map(i->X1[i].g,1:length(X1))\n",
    "G = Float64.(reshape(G,size(X1)...))\n",
    "\n",
    "B = map(i->X1[i].b,1:length(X1))\n",
    "B = Float64.(reshape(B,size(X1)...))\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "283×283 Array{RGB{Float64},2} with eltype RGB{Float64}:\n",
       " RGB{Float64}(0.0,0.0823529,0.0)  …  RGB{Float64}(0.0,0.658824,0.0)\n",
       " RGB{Float64}(0.0,0.0470588,0.0)     RGB{Float64}(0.0,0.494118,0.0)\n",
       " RGB{Float64}(0.0,0.0588235,0.0)     RGB{Float64}(0.0,0.603922,0.0)\n",
       " RGB{Float64}(0.0,0.0666667,0.0)     RGB{Float64}(0.0,0.137255,0.0)\n",
       " RGB{Float64}(0.0,0.0627451,0.0)     RGB{Float64}(0.0,0.223529,0.0)\n",
       " RGB{Float64}(0.0,0.0588235,0.0)  …  RGB{Float64}(0.0,0.160784,0.0)\n",
       " RGB{Float64}(0.0,0.113725,0.0)      RGB{Float64}(0.0,0.184314,0.0)\n",
       " RGB{Float64}(0.0,0.427451,0.0)      RGB{Float64}(0.0,0.129412,0.0)\n",
       " RGB{Float64}(0.0,0.45098,0.0)       RGB{Float64}(0.0,0.105882,0.0)\n",
       " RGB{Float64}(0.0,0.443137,0.0)      RGB{Float64}(0.0,0.12549,0.0)\n",
       " RGB{Float64}(0.0,0.454902,0.0)   …  RGB{Float64}(0.0,0.113725,0.0)\n",
       " RGB{Float64}(0.0,0.462745,0.0)      RGB{Float64}(0.0,0.101961,0.0)\n",
       " RGB{Float64}(0.0,0.45098,0.0)       RGB{Float64}(0.0,0.101961,0.0)\n",
       " ⋮                                ⋱  \n",
       " RGB{Float64}(0.0,0.490196,0.0)      RGB{Float64}(0.0,0.431373,0.0)\n",
       " RGB{Float64}(0.0,0.47451,0.0)       RGB{Float64}(0.0,0.423529,0.0)\n",
       " RGB{Float64}(0.0,0.494118,0.0)      RGB{Float64}(0.0,0.443137,0.0)\n",
       " RGB{Float64}(0.0,0.470588,0.0)      RGB{Float64}(0.0,0.403922,0.0)\n",
       " RGB{Float64}(0.0,0.458824,0.0)   …  RGB{Float64}(0.0,0.403922,0.0)\n",
       " RGB{Float64}(0.0,0.443137,0.0)      RGB{Float64}(0.0,0.329412,0.0)\n",
       " RGB{Float64}(0.0,0.47451,0.0)       RGB{Float64}(0.0,0.341176,0.0)\n",
       " RGB{Float64}(0.0,0.482353,0.0)      RGB{Float64}(0.0,0.309804,0.0)\n",
       " RGB{Float64}(0.0,0.556863,0.0)      RGB{Float64}(0.0,0.360784,0.0)\n",
       " RGB{Float64}(0.0,0.556863,0.0)   …  RGB{Float64}(0.0,0.4,0.0)\n",
       " RGB{Float64}(0.0,0.568627,0.0)      RGB{Float64}(0.0,0.407843,0.0)\n",
       " RGB{Float64}(0.0,0.576471,0.0)      RGB{Float64}(0.0,0.411765,0.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = zeros(size(R)...) # just a matrix of all zeros of equal size as the image\n",
    "RGB.(Z,G,Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily obtain the `Float64` values of the grayscale image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283×283 Matrix{Float64}:\n",
       " 0.101961   0.0627451  0.0784314  0.0941176  …  0.509804  0.552941  0.666667\n",
       " 0.0666667  0.0980392  0.0745098  0.054902      0.505882  0.584314  0.501961\n",
       " 0.0784314  0.0862745  0.0784314  0.0862745     0.6       0.701961  0.611765\n",
       " 0.0862745  0.0666667  0.0745098  0.0941176     0.658824  0.705882  0.145098\n",
       " 0.0784314  0.101961   0.0901961  0.0745098     0.713725  0.682353  0.231373\n",
       " 0.0745098  0.0745098  0.0784314  0.0862745  …  0.729412  0.701961  0.168627\n",
       " 0.12549    0.0980392  0.0862745  0.0862745     0.627451  0.466667  0.192157\n",
       " 0.439216   0.447059   0.305882   0.137255      0.231373  0.184314  0.137255\n",
       " 0.458824   0.454902   0.45098    0.454902      0.196078  0.101961  0.117647\n",
       " 0.45098    0.466667   0.458824   0.45098       0.584314  0.121569  0.137255\n",
       " 0.458824   0.458824   0.458824   0.454902   …  0.521569  0.513725  0.12549\n",
       " 0.466667   0.45098    0.458824   0.47451       0.576471  0.741176  0.117647\n",
       " 0.45098    0.45098    0.462745   0.458824      0.560784  0.67451   0.117647\n",
       " ⋮                                           ⋱  ⋮                   \n",
       " 0.494118   0.47451    0.47451    0.462745      0.427451  0.435294  0.443137\n",
       " 0.47451    0.482353   0.470588   0.470588      0.439216  0.431373  0.431373\n",
       " 0.494118   0.501961   0.470588   0.45098       0.447059  0.447059  0.45098\n",
       " 0.470588   0.494118   0.490196   0.482353      0.431373  0.419608  0.419608\n",
       " 0.458824   0.482353   0.47451    0.466667   …  0.454902  0.435294  0.423529\n",
       " 0.443137   0.458824   0.45098    0.45098       0.309804  0.32549   0.34902\n",
       " 0.47451    0.478431   0.462745   0.462745      0.341176  0.345098  0.360784\n",
       " 0.482353   0.478431   0.458824   0.458824      0.419608  0.372549  0.321569\n",
       " 0.552941   0.552941   0.541176   0.533333      0.447059  0.411765  0.372549\n",
       " 0.552941   0.545098   0.576471   0.552941   …  0.435294  0.423529  0.407843\n",
       " 0.564706   0.552941   0.54902    0.505882      0.439216  0.431373  0.419608\n",
       " 0.568627   0.552941   0.517647   0.462745      0.439216  0.431373  0.427451"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xgrayvalues = Float64.(Xgray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will downsample this image using the SVD. First, let's obtain the SVD decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVD{Float64, Float64, Matrix{Float64}}\n",
       "U factor:\n",
       "283×283 Matrix{Float64}:\n",
       " -0.0635349  -0.131929    -0.116512   …   0.00868688  -0.0118621\n",
       " -0.0626293  -0.150522    -0.112161      -0.00150511   0.0575172\n",
       " -0.0616805  -0.165404    -0.105726      -0.0434962    0.0138229\n",
       " -0.0618268  -0.1749      -0.0962379      0.00219005   0.0054939\n",
       " -0.0626651  -0.178715    -0.0859759     -0.0395294   -0.0529901\n",
       " -0.0624273  -0.18066     -0.0777654  …   0.00598019   0.0735455\n",
       " -0.0617703  -0.1838      -0.0647771     -0.0149741   -0.015221\n",
       " -0.0612071  -0.189903    -0.0440992      0.0615694   -0.0389833\n",
       " -0.0600312  -0.193717    -0.026464      -0.0129492   -0.0310368\n",
       " -0.0595516  -0.193191    -0.0082394     -0.0041925    0.0186706\n",
       " -0.0592992  -0.189681     0.0127781  …   0.00350977  -0.00980804\n",
       " -0.0588581  -0.184106     0.0357618      0.0221717   -0.0327037\n",
       " -0.0588589  -0.173952     0.0602371     -0.0466892    0.0965736\n",
       "  ⋮                                   ⋱               \n",
       " -0.0569682  -0.0202558    0.0659664     -0.137251     0.0495428\n",
       " -0.0568661  -0.0124937    0.0650277     -0.0297994   -0.0666082\n",
       " -0.0570419  -0.00968205   0.0710139     -0.0263281   -0.107249\n",
       " -0.0569222  -0.0224364    0.080825       0.0962447   -0.0373149\n",
       " -0.0558972  -0.0287199    0.0772194  …   0.0333739   -0.0503132\n",
       " -0.0556624  -0.0315443    0.0755169      0.00224254   0.131499\n",
       " -0.0564903  -0.0249832    0.0791765     -0.0486639   -0.101717\n",
       " -0.0570535  -0.023077     0.0950146      0.0120903    0.166347\n",
       " -0.0569607  -0.0341167    0.103528      -0.0729744   -0.00130042\n",
       " -0.0564621  -0.0405538    0.0920885  …  -0.0511204    0.0274548\n",
       " -0.056417   -0.0347061    0.0899392      0.106521    -0.0937512\n",
       " -0.0567368  -0.0285247    0.0840819     -0.00502509   0.141953\n",
       "singular values:\n",
       "283-element Vector{Float64}:\n",
       " 118.02470275379117\n",
       "  16.329470293845436\n",
       "  14.475717736464638\n",
       "  12.775444083672541\n",
       "  11.69266465798729\n",
       "   9.21226889101219\n",
       "   8.392542050051626\n",
       "   7.46454576274542\n",
       "   6.548820440012604\n",
       "   5.435428419444214\n",
       "   5.289305141102475\n",
       "   4.827268476923795\n",
       "   4.5778154866519385\n",
       "   ⋮\n",
       "   0.03090885712575563\n",
       "   0.02799539349985343\n",
       "   0.02404889024142825\n",
       "   0.021245492946620915\n",
       "   0.019886446902621976\n",
       "   0.015105271901297816\n",
       "   0.01372268127428849\n",
       "   0.011652834020733208\n",
       "   0.0088554722463145\n",
       "   0.006128400462062909\n",
       "   0.003035083065666921\n",
       "   0.002022289284342762\n",
       "Vt factor:\n",
       "283×283 Matrix{Float64}:\n",
       " -0.0423701   -0.0427979   -0.0411351   …  -0.068855    -0.0668341\n",
       " -0.0171889   -0.00656414  -0.0199103       0.0209562    0.0628906\n",
       "  0.0466408    0.0331267    0.0305505      -0.0440781   -0.0465621\n",
       " -0.0198712   -0.0381761   -0.0419185      -0.0260347   -0.0132403\n",
       "  0.0444547    0.0248013    0.0172284       0.0521957    0.0696417\n",
       " -0.0524257   -0.0480213   -0.0647193   …   0.0136645   -0.035287\n",
       "  0.044339     0.0570753    0.0552864       0.01628      0.0301614\n",
       " -0.0278711   -0.020096    -0.0207029       0.0563081    0.0330192\n",
       "  0.0786039    0.0862486    0.132617        0.0382286    0.0364599\n",
       "  0.118329     0.134319     0.110004       -0.0535676   -0.0457819\n",
       " -0.0599732   -0.0444854   -0.0355576   …  -0.0484189   -0.0464691\n",
       "  0.116675     0.100495     0.0815002       0.0208476   -0.0066859\n",
       " -0.0150851   -0.0391767   -0.0298541      -0.0318803    0.0565046\n",
       "  ⋮                                     ⋱               \n",
       "  0.00952726  -0.0150849   -0.0535822       0.066615    -0.0853499\n",
       "  0.00522386   0.0304889    0.00156495     -0.0610843   -0.0156719\n",
       "  0.0634549    0.00279093   0.0878891      -0.0532867    0.0457019\n",
       "  0.0105359   -0.104826     0.0599095       0.0228524   -0.0766208\n",
       " -0.0634161    0.0557495    0.00316178  …  -0.00494265  -0.00759012\n",
       " -0.0390847    0.0359929   -0.0412283      -0.0133298   -0.0410064\n",
       " -0.0299082    0.0574482    0.0300026       0.0126923    0.00129676\n",
       " -0.0320997    0.0656215   -0.016768        0.0195575    0.00424165\n",
       " -0.00949598  -0.00387311   0.124957        0.0344757   -0.0255612\n",
       " -0.0480881    0.131576    -0.00266735  …   0.0501369   -0.0121825\n",
       " -0.0371619    0.0111629    0.0412245       0.104353    -0.0941486\n",
       " -0.00565901  -0.0243119    0.0488415      -0.00606318   0.045091"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVD_V = svd(Xgrayvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.985416478474782e-13"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(SVD_V.U*diagm(SVD_V.S)*SVD_V.V' - Xgrayvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283×283 Matrix{Float64}:\n",
       " 0.29657   0.318549  0.343035  0.342863  …  0.552066  0.572353  0.457859\n",
       " 0.301302  0.320177  0.348938  0.349233     0.537413  0.557303  0.429426\n",
       " 0.304972  0.31986   0.351791  0.353049     0.521999  0.540254  0.40225\n",
       " 0.313416  0.323556  0.356918  0.359548     0.515346  0.530358  0.386351\n",
       " 0.32363   0.32932   0.362593  0.366602     0.515531  0.526726  0.380811\n",
       " 0.326611  0.328579  0.361655  0.366891  …  0.508294  0.516375  0.370125\n",
       " 0.330997  0.328022  0.361055  0.367526     0.494981  0.499081  0.351644\n",
       " 0.341715  0.331637  0.364936  0.372786     0.477945  0.476419  0.325571\n",
       " 0.347785  0.33259   0.3661    0.374478     0.458421  0.45296   0.299809\n",
       " 0.355413  0.334757  0.367165  0.376502     0.44522   0.434822  0.282863\n",
       " 0.364931  0.338526  0.368979  0.379199  …  0.433394  0.417385  0.268694\n",
       " 0.3731    0.339849  0.367643  0.379321     0.419181  0.396356  0.253065\n",
       " 0.382235  0.341772  0.365584  0.379015     0.408898  0.378267  0.243974\n",
       " ⋮                                       ⋱  ⋮                   \n",
       " 0.354242  0.358328  0.352706  0.341149     0.446787  0.439007  0.396856\n",
       " 0.34905   0.35294   0.345329  0.334487     0.447845  0.438984  0.403409\n",
       " 0.351978  0.354083  0.345375  0.335005     0.446849  0.435978  0.402846\n",
       " 0.361234  0.35888   0.352543  0.343088     0.437318  0.423923  0.381958\n",
       " 0.361572  0.364431  0.360956  0.347908  …  0.431406  0.42378   0.373937\n",
       " 0.362478  0.367414  0.365122  0.350681     0.430516  0.425185  0.371956\n",
       " 0.371064  0.37998   0.376679  0.359154     0.4395    0.436824  0.385301\n",
       " 0.379477  0.381453  0.376175  0.361214     0.43536   0.425971  0.377987\n",
       " 0.385815  0.382321  0.378765  0.365761     0.426375  0.413328  0.358817\n",
       " 0.376774  0.373795  0.372047  0.360174  …  0.425234  0.413546  0.355563\n",
       " 0.3738    0.372571  0.369703  0.35723      0.427778  0.417003  0.362891\n",
       " 0.372627  0.376319  0.372809  0.358087     0.436367  0.429276  0.377653"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the top 4 singular vectors/values to form a new image\n",
    "u1 = SVD_V.U[:,1]\n",
    "v1 = SVD_V.V[:,1]\n",
    "img1 = SVD_V.S[1]*u1*v1'\n",
    "\n",
    "i = 2\n",
    "u1 = SVD_V.U[:,i]\n",
    "v1 = SVD_V.V[:,i]\n",
    "img1 += SVD_V.S[i]*u1*v1'\n",
    "\n",
    "i = 3\n",
    "u1 = SVD_V.U[:,i]\n",
    "v1 = SVD_V.V[:,i]\n",
    "img1 += SVD_V.S[i]*u1*v1'\n",
    "\n",
    "i = 4\n",
    "u1 = SVD_V.U[:,i]\n",
    "v1 = SVD_V.V[:,i]\n",
    "img1 += SVD_V.S[i]*u1*v1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "283×283 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.29657)   Gray{Float64}(0.318549)  …  Gray{Float64}(0.457859)\n",
       " Gray{Float64}(0.301302)  Gray{Float64}(0.320177)     Gray{Float64}(0.429426)\n",
       " Gray{Float64}(0.304972)  Gray{Float64}(0.31986)      Gray{Float64}(0.40225)\n",
       " Gray{Float64}(0.313416)  Gray{Float64}(0.323556)     Gray{Float64}(0.386351)\n",
       " Gray{Float64}(0.32363)   Gray{Float64}(0.32932)      Gray{Float64}(0.380811)\n",
       " Gray{Float64}(0.326611)  Gray{Float64}(0.328579)  …  Gray{Float64}(0.370125)\n",
       " Gray{Float64}(0.330997)  Gray{Float64}(0.328022)     Gray{Float64}(0.351644)\n",
       " Gray{Float64}(0.341715)  Gray{Float64}(0.331637)     Gray{Float64}(0.325571)\n",
       " Gray{Float64}(0.347785)  Gray{Float64}(0.33259)      Gray{Float64}(0.299809)\n",
       " Gray{Float64}(0.355413)  Gray{Float64}(0.334757)     Gray{Float64}(0.282863)\n",
       " Gray{Float64}(0.364931)  Gray{Float64}(0.338526)  …  Gray{Float64}(0.268694)\n",
       " Gray{Float64}(0.3731)    Gray{Float64}(0.339849)     Gray{Float64}(0.253065)\n",
       " Gray{Float64}(0.382235)  Gray{Float64}(0.341772)     Gray{Float64}(0.243974)\n",
       " ⋮                                                 ⋱  \n",
       " Gray{Float64}(0.354242)  Gray{Float64}(0.358328)     Gray{Float64}(0.396856)\n",
       " Gray{Float64}(0.34905)   Gray{Float64}(0.35294)      Gray{Float64}(0.403409)\n",
       " Gray{Float64}(0.351978)  Gray{Float64}(0.354083)     Gray{Float64}(0.402846)\n",
       " Gray{Float64}(0.361234)  Gray{Float64}(0.35888)      Gray{Float64}(0.381958)\n",
       " Gray{Float64}(0.361572)  Gray{Float64}(0.364431)  …  Gray{Float64}(0.373937)\n",
       " Gray{Float64}(0.362478)  Gray{Float64}(0.367414)     Gray{Float64}(0.371956)\n",
       " Gray{Float64}(0.371064)  Gray{Float64}(0.37998)      Gray{Float64}(0.385301)\n",
       " Gray{Float64}(0.379477)  Gray{Float64}(0.381453)     Gray{Float64}(0.377987)\n",
       " Gray{Float64}(0.385815)  Gray{Float64}(0.382321)     Gray{Float64}(0.358817)\n",
       " Gray{Float64}(0.376774)  Gray{Float64}(0.373795)  …  Gray{Float64}(0.355563)\n",
       " Gray{Float64}(0.3738)    Gray{Float64}(0.372571)     Gray{Float64}(0.362891)\n",
       " Gray{Float64}(0.372627)  Gray{Float64}(0.376319)     Gray{Float64}(0.377653)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gray.(img1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, it's still far away from the original image. Let's try using 100 singular vectors/values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "283×283 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.100856)   …  Gray{Float64}(0.630574)\n",
       " Gray{Float64}(0.0743689)     Gray{Float64}(0.562675)\n",
       " Gray{Float64}(0.0589517)     Gray{Float64}(0.533653)\n",
       " Gray{Float64}(0.0974717)     Gray{Float64}(0.222879)\n",
       " Gray{Float64}(0.104445)      Gray{Float64}(0.210656)\n",
       " Gray{Float64}(0.0676765)  …  Gray{Float64}(0.236768)\n",
       " Gray{Float64}(0.10611)       Gray{Float64}(0.154249)\n",
       " Gray{Float64}(0.437285)      Gray{Float64}(0.130097)\n",
       " Gray{Float64}(0.461987)      Gray{Float64}(0.103984)\n",
       " Gray{Float64}(0.444505)      Gray{Float64}(0.136438)\n",
       " Gray{Float64}(0.459768)   …  Gray{Float64}(0.116791)\n",
       " Gray{Float64}(0.471991)      Gray{Float64}(0.158639)\n",
       " Gray{Float64}(0.436686)      Gray{Float64}(0.128088)\n",
       " ⋮                         ⋱  \n",
       " Gray{Float64}(0.481918)      Gray{Float64}(0.453099)\n",
       " Gray{Float64}(0.47606)       Gray{Float64}(0.484488)\n",
       " Gray{Float64}(0.494872)      Gray{Float64}(0.481929)\n",
       " Gray{Float64}(0.467665)      Gray{Float64}(0.42415)\n",
       " Gray{Float64}(0.472108)   …  Gray{Float64}(0.414578)\n",
       " Gray{Float64}(0.432497)      Gray{Float64}(0.304487)\n",
       " Gray{Float64}(0.478971)      Gray{Float64}(0.341689)\n",
       " Gray{Float64}(0.492437)      Gray{Float64}(0.327358)\n",
       " Gray{Float64}(0.551738)      Gray{Float64}(0.350427)\n",
       " Gray{Float64}(0.524377)   …  Gray{Float64}(0.3988)\n",
       " Gray{Float64}(0.565698)      Gray{Float64}(0.393228)\n",
       " Gray{Float64}(0.586587)      Gray{Float64}(0.426679)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 1:100\n",
    "u1 = SVD_V.U[:,i]\n",
    "v1 = SVD_V.V[:,i]\n",
    "img1 = u1*spdiagm(0=>SVD_V.S[i])*v1'\n",
    "Gray.(img1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks almost identical to the original image, even though it's not identical to the original image (and we can see that from the norm difference)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.528187095958997"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(Xgrayvalues-img1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next problem will still be related to images, but this time we will solve a simple form of the face recognition problem. Let's get the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Any} with 1 entry:\n",
       "  \"V2\" => [0.08103 0.0729089 … 0.0529805 0.0594823; 0.089725 0.082329 … 0.05180…"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = matread(\"data/face_recog_qr.mat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each vector in `M[\"V2\"]` is a fase image. Let's reshape the first one and take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "192×168 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.08103)    …  Gray{Float64}(0.0990184)\n",
       " Gray{Float64}(0.089725)      Gray{Float64}(0.0999127)\n",
       " Gray{Float64}(0.0873804)     Gray{Float64}(0.0945833)\n",
       " Gray{Float64}(0.084184)      Gray{Float64}(0.0959911)\n",
       " Gray{Float64}(0.0892354)     Gray{Float64}(0.104112)\n",
       " Gray{Float64}(0.0872113)  …  Gray{Float64}(0.106626)\n",
       " Gray{Float64}(0.0906193)     Gray{Float64}(0.100638)\n",
       " Gray{Float64}(0.0987404)     Gray{Float64}(0.106904)\n",
       " Gray{Float64}(0.0893203)     Gray{Float64}(0.118179)\n",
       " Gray{Float64}(0.0858459)     Gray{Float64}(0.117007)\n",
       " Gray{Float64}(0.0931994)  …  Gray{Float64}(0.115279)\n",
       " Gray{Float64}(0.0892778)     Gray{Float64}(0.108608)\n",
       " Gray{Float64}(0.0994656)     Gray{Float64}(0.110505)\n",
       " ⋮                         ⋱  \n",
       " Gray{Float64}(0.0336083)  …  Gray{Float64}(0.103345)\n",
       " Gray{Float64}(0.0347806)     Gray{Float64}(0.0927523)\n",
       " Gray{Float64}(0.0287924)     Gray{Float64}(0.0846311)\n",
       " Gray{Float64}(0.0254871)     Gray{Float64}(0.0756157)\n",
       " Gray{Float64}(0.0296867)     Gray{Float64}(0.063851)\n",
       " Gray{Float64}(0.0269374)  …  Gray{Float64}(0.0611017)\n",
       " Gray{Float64}(0.0281097)     Gray{Float64}(0.0502313)\n",
       " Gray{Float64}(0.0269799)     Gray{Float64}(0.0347806)\n",
       " Gray{Float64}(0.0250824)     Gray{Float64}(0.0273421)\n",
       " Gray{Float64}(0.0273846)     Gray{Float64}(0.0289615)\n",
       " Gray{Float64}(0.0321579)  …  Gray{Float64}(0.0247619)\n",
       " Gray{Float64}(0.0360131)     Gray{Float64}(0.0270641)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = reshape(M[\"V2\"][:,1],192,168)\n",
    "Gray.(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will go back to the vectorized version of this image, and try to select the images that are most similar to it from the \"dictionary\" matrix. Let's use `b = q[:]` to be the query image. Note that the notation `[:]` vectorizes a matrix column wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32256-element Vector{Float64}:\n",
       " 0.0810300261581645\n",
       " 0.08972497847335578\n",
       " 0.0873803822279144\n",
       " 0.0841839709221837\n",
       " 0.08923537113064388\n",
       " 0.08721125794763387\n",
       " 0.09061926007535659\n",
       " 0.09874041385097843\n",
       " 0.08932030421406685\n",
       " 0.08584587638761629\n",
       " 0.09319940629980634\n",
       " 0.08927783767235538\n",
       " 0.0994655711726987\n",
       " ⋮\n",
       " 0.10334467325843819\n",
       " 0.09275226549880594\n",
       " 0.0846311117231841\n",
       " 0.07561567634556146\n",
       " 0.06385097046320852\n",
       " 0.06110169995847822\n",
       " 0.05023127567812609\n",
       " 0.03478055114733056\n",
       " 0.027342088151717514\n",
       " 0.028961527075438608\n",
       " 0.024761941927267744\n",
       " 0.027064071630997635"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = q[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will remove the first image from the dictionary. The goal is to find the solution of the linear system `Ax=b` where `A` is the dictionary of all images. In face recognition problem we really want to minimize the norm differece `norm(Ax-b)` but the `\\` actually solves a least squares problem when the matrix at hand is not invertible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "192×168 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.08103)    …  Gray{Float64}(0.0990184)\n",
       " Gray{Float64}(0.089725)      Gray{Float64}(0.0999127)\n",
       " Gray{Float64}(0.0873804)     Gray{Float64}(0.0945833)\n",
       " Gray{Float64}(0.084184)      Gray{Float64}(0.0959911)\n",
       " Gray{Float64}(0.0892354)     Gray{Float64}(0.104112)\n",
       " Gray{Float64}(0.0872113)  …  Gray{Float64}(0.106626)\n",
       " Gray{Float64}(0.0906193)     Gray{Float64}(0.100638)\n",
       " Gray{Float64}(0.0987404)     Gray{Float64}(0.106904)\n",
       " Gray{Float64}(0.0893203)     Gray{Float64}(0.118179)\n",
       " Gray{Float64}(0.0858459)     Gray{Float64}(0.117007)\n",
       " Gray{Float64}(0.0931994)  …  Gray{Float64}(0.115279)\n",
       " Gray{Float64}(0.0892778)     Gray{Float64}(0.108608)\n",
       " Gray{Float64}(0.0994656)     Gray{Float64}(0.110505)\n",
       " ⋮                         ⋱  \n",
       " Gray{Float64}(0.0336083)  …  Gray{Float64}(0.103345)\n",
       " Gray{Float64}(0.0347806)     Gray{Float64}(0.0927523)\n",
       " Gray{Float64}(0.0287924)     Gray{Float64}(0.0846311)\n",
       " Gray{Float64}(0.0254871)     Gray{Float64}(0.0756157)\n",
       " Gray{Float64}(0.0296867)     Gray{Float64}(0.063851)\n",
       " Gray{Float64}(0.0269374)  …  Gray{Float64}(0.0611017)\n",
       " Gray{Float64}(0.0281097)     Gray{Float64}(0.0502313)\n",
       " Gray{Float64}(0.0269799)     Gray{Float64}(0.0347806)\n",
       " Gray{Float64}(0.0250824)     Gray{Float64}(0.0273421)\n",
       " Gray{Float64}(0.0273846)     Gray{Float64}(0.0289615)\n",
       " Gray{Float64}(0.0321579)  …  Gray{Float64}(0.0247619)\n",
       " Gray{Float64}(0.0360131)     Gray{Float64}(0.0270641)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = M[\"V2\"][:,2:end]\n",
    "x = A\\b #Ax=b\n",
    "Gray.(reshape(A*x,192,168))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.32163450371629e-14"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(A*x-b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was an easy problem. Let's try to make the picture harder to recover. We will add some random error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "192×168 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.248022)   …  Gray{Float64}(0.0967667)\n",
       " Gray{Float64}(0.192978)      Gray{Float64}(0.236674)\n",
       " Gray{Float64}(0.252803)      Gray{Float64}(0.336422)\n",
       " Gray{Float64}(0.497706)      Gray{Float64}(0.40001)\n",
       " Gray{Float64}(0.106376)      Gray{Float64}(0.100361)\n",
       " Gray{Float64}(0.239785)   …  Gray{Float64}(0.223072)\n",
       " Gray{Float64}(0.154172)      Gray{Float64}(0.197963)\n",
       " Gray{Float64}(0.334381)      Gray{Float64}(0.266119)\n",
       " Gray{Float64}(0.203298)      Gray{Float64}(0.528924)\n",
       " Gray{Float64}(0.358539)      Gray{Float64}(0.190195)\n",
       " Gray{Float64}(0.428477)   …  Gray{Float64}(0.128021)\n",
       " Gray{Float64}(0.318602)      Gray{Float64}(0.470236)\n",
       " Gray{Float64}(0.375626)      Gray{Float64}(0.355348)\n",
       " ⋮                         ⋱  \n",
       " Gray{Float64}(0.263134)   …  Gray{Float64}(0.210367)\n",
       " Gray{Float64}(0.108467)      Gray{Float64}(0.154469)\n",
       " Gray{Float64}(0.186425)      Gray{Float64}(0.116572)\n",
       " Gray{Float64}(0.277233)      Gray{Float64}(0.398902)\n",
       " Gray{Float64}(0.40888)       Gray{Float64}(0.29144)\n",
       " Gray{Float64}(0.0388597)  …  Gray{Float64}(0.475542)\n",
       " Gray{Float64}(0.0798316)     Gray{Float64}(0.0973676)\n",
       " Gray{Float64}(0.270672)      Gray{Float64}(0.291312)\n",
       " Gray{Float64}(0.452664)      Gray{Float64}(0.452444)\n",
       " Gray{Float64}(0.347347)      Gray{Float64}(0.202306)\n",
       " Gray{Float64}(0.0776637)  …  Gray{Float64}(0.298246)\n",
       " Gray{Float64}(0.375351)      Gray{Float64}(0.178818)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qv = q+rand(size(q,1),size(q,2))*0.5\n",
    "qv = qv./maximum(qv)\n",
    "Gray.(qv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = qv[:];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.71317697959601"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = A\\b\n",
    "norm(A*x-b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error is so much bigger this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "192×168 Array{Gray{Float64},2} with eltype Gray{Float64}:\n",
       " Gray{Float64}(0.255817)  Gray{Float64}(0.248087)  …  Gray{Float64}(0.259212)\n",
       " Gray{Float64}(0.250522)  Gray{Float64}(0.270216)     Gray{Float64}(0.248469)\n",
       " Gray{Float64}(0.26298)   Gray{Float64}(0.270939)     Gray{Float64}(0.254083)\n",
       " Gray{Float64}(0.27818)   Gray{Float64}(0.27663)      Gray{Float64}(0.247897)\n",
       " Gray{Float64}(0.281201)  Gray{Float64}(0.272046)     Gray{Float64}(0.257239)\n",
       " Gray{Float64}(0.290408)  Gray{Float64}(0.258557)  …  Gray{Float64}(0.271501)\n",
       " Gray{Float64}(0.270041)  Gray{Float64}(0.247502)     Gray{Float64}(0.257466)\n",
       " Gray{Float64}(0.252734)  Gray{Float64}(0.258904)     Gray{Float64}(0.260903)\n",
       " Gray{Float64}(0.287141)  Gray{Float64}(0.277652)     Gray{Float64}(0.27691)\n",
       " Gray{Float64}(0.288537)  Gray{Float64}(0.26417)      Gray{Float64}(0.295811)\n",
       " Gray{Float64}(0.276403)  Gray{Float64}(0.269568)  …  Gray{Float64}(0.287917)\n",
       " Gray{Float64}(0.259451)  Gray{Float64}(0.267703)     Gray{Float64}(0.268673)\n",
       " Gray{Float64}(0.273444)  Gray{Float64}(0.271392)     Gray{Float64}(0.285126)\n",
       " ⋮                                                 ⋱  \n",
       " Gray{Float64}(0.288745)  Gray{Float64}(0.258137)  …  Gray{Float64}(0.309208)\n",
       " Gray{Float64}(0.291748)  Gray{Float64}(0.282995)     Gray{Float64}(0.290797)\n",
       " Gray{Float64}(0.277466)  Gray{Float64}(0.269845)     Gray{Float64}(0.301315)\n",
       " Gray{Float64}(0.259684)  Gray{Float64}(0.265649)     Gray{Float64}(0.311637)\n",
       " Gray{Float64}(0.275749)  Gray{Float64}(0.277168)     Gray{Float64}(0.29735)\n",
       " Gray{Float64}(0.296401)  Gray{Float64}(0.302365)  …  Gray{Float64}(0.304151)\n",
       " Gray{Float64}(0.294812)  Gray{Float64}(0.29531)      Gray{Float64}(0.309577)\n",
       " Gray{Float64}(0.30039)   Gray{Float64}(0.279027)     Gray{Float64}(0.311203)\n",
       " Gray{Float64}(0.299036)  Gray{Float64}(0.260642)     Gray{Float64}(0.308783)\n",
       " Gray{Float64}(0.301813)  Gray{Float64}(0.299396)     Gray{Float64}(0.293527)\n",
       " Gray{Float64}(0.3119)    Gray{Float64}(0.317293)  …  Gray{Float64}(0.288071)\n",
       " Gray{Float64}(0.335526)  Gray{Float64}(0.303628)     Gray{Float64}(0.271423)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gray.(reshape(A*x,192,168))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finally...\n",
    "After finishing this notebook, you should be able to:\n",
    "- [ ] reshape and vectorize a matrix\n",
    "- [ ] apply basic linear algebra operations such as transpose, matrix-matrix product, and solve a linear systerm\n",
    "- [ ] call a linear algebra factorization on your matrix\n",
    "- [ ] use SVD to created a compressed version of an image\n",
    "- [ ] solve the face recognition problem via a least square approach\n",
    "- [ ] create a sparse matrix, and call the components of the Compressed Sparse Column storage\n",
    "- [ ] list a few types of matrices Julia uses (diagonal, upper triangular,...)\n",
    "- [ ] (unrelated to linear algebra): load an image, convert it to grayscale, and extract the RGB layers"
   ]
  },
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   "source": [
    "# 🥳 One cool finding\n",
    "\n",
    "We can solve a simple form of the face recognition problem even when a face image has been distorted with wrong pixels. Example, one of our inputs was this image: <img src=\"data/0201.png\" width=\"100\">\n",
    "\n",
    "And we were able to detect this face to be closest to the input image: <img src=\"data/0202.png\" width=\"100\">"
   ]
  }
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