From f909ef48d221f626c88b7d71c089fbfc113be2b4 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Fri, 27 May 2022 17:28:03 +0100
Subject: [PATCH 01/27] First cleanup step

---
 .../constrainedzoom/methods/__init__.py       | 35 ++++++++++++-------
 .../constrainedzoom/plots.py                  |  5 +++
 2 files changed, 28 insertions(+), 12 deletions(-)

diff --git a/tools/toy_implementation/constrainedzoom/methods/__init__.py b/tools/toy_implementation/constrainedzoom/methods/__init__.py
index d6e86439..79a23821 100644
--- a/tools/toy_implementation/constrainedzoom/methods/__init__.py
+++ b/tools/toy_implementation/constrainedzoom/methods/__init__.py
@@ -1,5 +1,6 @@
 import abc
 import math
+from typing import Tuple
 import numpy as np
 import scipy.fftpack
 import scipy.interpolate
@@ -27,7 +28,7 @@ def get_default_plot_padding(self):
         """The default plot padding (in coarse pixels) to hide from the high-res region"""
         return 0
 
-    def realization(self, no_random=False, noiseP: FFTArray=None, noiseW: FFTArray=None, seed=None) -> [FFTArray, FFTArray] :
+    def realization(self, no_random=False, noiseP: FFTArray=None, noiseW: FFTArray=None, seed=None) -> Tuple[FFTArray, FFTArray] :
         """Generate a realization of the Gaussian random field with constraints, if present.
         
         This implementation calls a generic sequence of operations which are overriden in base class implementations to
@@ -39,7 +40,6 @@ def realization(self, no_random=False, noiseP: FFTArray=None, noiseW: FFTArray=N
         :param seed: if present, the seed for the numpy random number generator
         :return: delta_low, delta_high: the fields for the low-frequency (nP) box and high-frequency (nW) sub-box respectively
         """
-
         k_high, k_low = self._get_ks()
 
         if no_random:
@@ -139,27 +139,28 @@ def X(input: FFTArray):
 
 
         alpha.in_fourier_space()
-        alpha*=cov**0.5
+        alpha[:] *= cov**0.5
         alpha.in_real_space()
 
 
         # assemble the result; this is going to be in the delta basis (not the noise basis)
         delta2 = noise2.copy()
         delta2.in_fourier_space()
-        delta2*=cov**0.5
+        delta2[:] *= cov**0.5
         delta2.in_real_space()
         result = -(mask*delta_diff) + complementary_mask*alpha + delta2
 
         # deconvolve the power spectrum to get back to the noise basis (necessary if there are multiple zoom levels)
         assert not result.fourier
         result.in_fourier_space()
-        result*=inv_cov**0.5
+        result[:] *= inv_cov**0.5
         result.in_real_space()
         return result
 
 
     def splice_realizations(self, noiseP_1, noiseW_1, noiseP_2, noiseW_2, mask_range):
-        """From two realizations (1) and (2), generate a third realization which =(1) in a masked region, ~=(2) outside.
+        """
+        From two realizations (1) and (2), generate a third realization which =(1) in a masked region, ~=(2) outside.
 
         This method operates on the noise vectors, but the mask is understood to apply to the delta vectors, i.e.
 
@@ -189,15 +190,25 @@ def splice_realizations(self, noiseP_1, noiseW_1, noiseP_2, noiseW_2, mask_range
         low_end = self._hires_to_lores_pixel(mask_range[1]+boundary_safety)
         mask_low[low_start:low_end+1] = True
 
-        noiseP = self._splice_realizations_one_level(noiseP_1, noiseP_2, self.C_low,
-                                                     mask_low)
-        noiseW = self._splice_realizations_one_level(noiseW_1, noiseW_2,
-                                                     self.C_high, mask_high)
+        noiseP = self._splice_realizations_one_level(
+            noiseP_1,
+            noiseP_2,
+            self.C_low * self.filter_low(self.k_low)**2,
+            mask_low
+        )
+        noiseW = self._splice_realizations_one_level(
+            noiseW_1,
+            noiseW_2,
+            self.C_high * self.filter_high(self.k_high)**2,
+            mask_high
+        )
+
         return noiseP, noiseW
 
     @in_fourier_space
     def _get_whitenoise(self, noiseP: FFTArray=None, noiseW: FFTArray=None):
-        """Generate white noise fields nP and nW, with unit variance per mode.
+        """
+        Generate white noise fields nP and nW, with unit variance per mode.
 
         :param noiseP: set to provide a specified pixelized noise vector
         :param noiseW: set to provide a specified windowed noise vector
@@ -219,7 +230,7 @@ def _get_whitenoise(self, noiseP: FFTArray=None, noiseW: FFTArray=None):
         return noiseP, noiseW
     
     @abc.abstractmethod
-    def _modify_whitenoise(self, noiseP: FFTArray, noiseW: FFTArray) -> [FFTArray, FFTArray]:
+    def _modify_whitenoise(self, noiseP: FFTArray, noiseW: FFTArray) -> Tuple[FFTArray, FFTArray]:
         """Give subclass an opportunity to modify the whitenoise fields before the transfer function is applied
  
         :param noiseP: the low-frequency pixelised whitenoise
diff --git a/tools/toy_implementation/constrainedzoom/plots.py b/tools/toy_implementation/constrainedzoom/plots.py
index 990591d8..00dd1e2a 100644
--- a/tools/toy_implementation/constrainedzoom/plots.py
+++ b/tools/toy_implementation/constrainedzoom/plots.py
@@ -354,6 +354,7 @@ def zoom_demo(nP=256, nW=256, hires_window_scale=4, hires_window_offset=8,plaw=-
               show_covec=False,errors=False,linewidth=None,
               constrain_potential=False,seed=None,
               splice_seed=None,diff_seed=None,
+              diff_abs=False,
               splice_range=(100,200),plot_kwargs={}):
 
     if errors and not no_random:
@@ -386,6 +387,10 @@ def zoom_demo(nP=256, nW=256, hires_window_scale=4, hires_window_offset=8,plaw=-
         delta_P-=delta_P_diff
         delta_W-=delta_W_diff
 
+        if diff_abs:
+            delta_P = np.abs(delta_P)
+            delta_W = np.abs(delta_W)
+
 
     if errors:
         delta_Ps, delta_Ws = Y.realization(no_random=no_random)

From 32da3ced1d2c3dcbf94bd0713709dd66f9162dca Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Fri, 27 May 2022 17:56:16 +0100
Subject: [PATCH 02/27] Update toy model implementation

---
 .../constrainedzoom/methods/__init__.py       |   35 +-
 .../methods/detail/geometry.py                |   36 +-
 tools/toy_implementation/gene_splicing.ipynb  | 7347 +----------------
 3 files changed, 296 insertions(+), 7122 deletions(-)

diff --git a/tools/toy_implementation/constrainedzoom/methods/__init__.py b/tools/toy_implementation/constrainedzoom/methods/__init__.py
index 79a23821..4f544848 100644
--- a/tools/toy_implementation/constrainedzoom/methods/__init__.py
+++ b/tools/toy_implementation/constrainedzoom/methods/__init__.py
@@ -87,50 +87,51 @@ def get_spliced_realization(self, seed_in_mask, seed_out_mask, mask_range):
 
     @classmethod
     def _splice_realizations_one_level(cls, noise1: FFTArray, noise2: FFTArray, cov: FFTArray, mask: np.ndarray):
-        """Implementation of splice_realizations for single level of the zoom hierarchy.
+        """
+        Implementation of splice_realizations for single level of the zoom hierarchy.
 
         :param noise1: noise for inside the mask
         :param noise2: noise for outside the mask
         :param cov: power spectrum / covariance in Fourier space
-        :param mask: masking array (in real space)"""
-
+        :param mask: masking array (in real space)
+        """
         complementary_mask=~mask
-        inv_cov=1./cov
+        inv_cov = 1./cov
         inv_cov[cov==0] = 0
 
         assert noise1.fourier == noise2.fourier
         # Cast into form X.alpha = z
 
-        delta_diff = (noise2-noise1)
+        delta_diff: FFTArray = noise2 - noise1
         delta_diff.in_fourier_space()
-        delta_diff*=cov**0.5
+        delta_diff[:] *= cov**0.5
         delta_diff.in_real_space()
 
         z = delta_diff.copy()
-        z*=mask
+        z[:] *= mask
         z.in_fourier_space()
-        z*=inv_cov
+        z[:] *= inv_cov
         z.in_real_space()
-        z*=complementary_mask
+        z[:] *= complementary_mask
         z.in_fourier_space()
-        z*=cov**0.5
+        z[:] *= cov**0.5
         z.in_real_space()
 
 
-        def X(input: FFTArray):
+        def X(input: FFTArray) -> FFTArray:
             nonlocal cov, inv_cov, mask, complementary_mask
             v = input.copy()
             assert not v.fourier
             v.in_fourier_space()
-            v*=cov**0.5
+            v[:] *= cov**0.5
             v.in_real_space()
-            v*=complementary_mask
+            v[:] *= complementary_mask
             v.in_fourier_space()
-            v*=inv_cov
+            v[:] *= inv_cov
             v.in_real_space()
-            v*=complementary_mask
+            v[:] *= complementary_mask
             v.in_fourier_space()
-            v*=cov**0.5
+            v[:] *= cov**0.5
             v.in_real_space()
             return v
 
@@ -178,7 +179,7 @@ def splice_realizations(self, noiseP_1, noiseW_1, noiseP_2, noiseW_2, mask_range
         :returns noiseP, noiseW: the low-res and high-res noise vectors for the spliced realisation
         """
 
-        boundary_safety = 5
+        boundary_safety = self.pixel_size_ratio
 
         mask_high = np.zeros(self.nW, bool)
         mask_high[:] = False
diff --git a/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py b/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
index 084fe1c6..cfb7a69b 100644
--- a/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
+++ b/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
@@ -90,15 +90,49 @@ def upsample_linear(self, delta_low) -> FFTArray:
         result = delta_highres[self.offset * self.pixel_size_ratio:self.offset * self.pixel_size_ratio + self.nW]
         return result.view(type=FFTArray)
 
+    @staticmethod
+    def interp1d(x_vals_low, delta, x_vals_high):
+        delta_high = np.zeros_like(x_vals_high)
+        for ih, xh in enumerate(x_vals_high):
+            i = np.argmin(np.abs(x_vals_low - xh))
+            if x_vals_low[i] > xh:
+                i -= 1
+
+            # Now do a trilinear interpolation
+            p0 = delta[i]
+            p1 = delta[i+1]
+            m0 = (delta[i+1] - delta[i-1])/(x_vals_low[i+1] - x_vals_low[i-1])
+            m1 = (delta[i+2] - delta[i  ])/(x_vals_low[i+2] - x_vals_low[i  ])
+
+            t = xh - x_vals_low[i]
+            t2 = t * t
+            t3 = t2 * t
+
+            delta_high[ih] = (
+                (2 * t3 - 3 * t2 + 1) * p0 +
+                (t3 - 2 * t2 + t) * m0 +
+                (-2 * t3 + 3 * t2) * p1 +
+                (t3 - t2) * m1
+            )
+        return delta_high
+
     @in_real_space
     def upsample_cubic(self, delta_low) -> FFTArray:
         "Take a low-res vector and interpolate it into the high-res region - cubic interpolation"
 
         x_vals_low, x_vals_high = self.xs()
-        delta_highres = scipy.interpolate.interp1d(x_vals_low, delta_low, kind='cubic')(x_vals_high)
+
+        # Note: scipy's `interp1d` uses information from non-local pixels. This is slightly
+        # more accurate, but is not equivalent to what is done in the C++ code. Instead
+        # we use here a simple spline cubic interpolation that only uses information from
+        # pixels in the [-1, 2] range, where [0, 1] would be the left and right pixel location
+        # in the low-resolution region.
+        # > delta_highres = scipy.interpolate.interp1d(x_vals_low, delta_low, kind='cubic')(x_vals_high)
+        delta_highres = self.interp1d(x_vals_low, delta_low, x_vals_high)
 
         return delta_highres.view(type=FFTArray)
 
+
     @in_real_space
     def downsample(self, hires_vector: FFTArray, input_unpadded=True, output_padded=True) -> FFTArray:
         """Take a high-res region vector and downsample it onto the low-res grid"""
diff --git a/tools/toy_implementation/gene_splicing.ipynb b/tools/toy_implementation/gene_splicing.ipynb
index 056405e9..3a6e9953 100644
--- a/tools/toy_implementation/gene_splicing.ipynb
+++ b/tools/toy_implementation/gene_splicing.ipynb
@@ -391,2520 +391,34 @@
     },
     {
      "data": {
+      "text/html": [
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+       "</pre>\n"
+      ],
       "text/plain": [
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       ]
      },
-     "execution_count": 7,
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-     "output_type": "execute_result"
+     "output_type": "display_data"
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XAAAAAKD0yv0wZwAAAKCIXT4Gq07Vqk6fR507l+f4x197TV8sX67QSpVsXn179NCEF190K5fH3ntPHy5aJIvFYhO/rX9/XTNypFtzAQAAAABKLwonAAAAuXjnnXcUHBysmjVrqmbNmqpRo4Zq1qypSpUqeWyNtLQ0bdmyRSdOnHB4/fTTT6pdu7bH1iqJ8i2c5LPjJC4+XokpKUpMSdGp8+et8ao1aridS1BwsEPRRMq/eAMAAAAAKFsonAAAADhhsVj01FNPKcvJHRrff/+9brjhBo+sk5iYqD59+jh99u2336pu3bqKjIxUZGSkTpw4oQYNGujVV1/1yNolweX7Q65q2lT/GjJEdapWtb5qZ79kmpJhOB0fn5DgNB4SEuJ2LrVr1XIaP5OjIAMAAAAAKPsonAAAADgRExPjtGgiSTUKsJshN+Hh4apYsaKSk5Mdnv3zn/90iHXo0KHsFE5MUz7Zd5R0atlSnXK5JP5iZqbMXC5uz7VwUrmy2+nUyGV3T9SFCzItFhk+XA8IAAAAAOUB//UHAADgxNmzZ3N95snCiWEYatCggcv9IyMjPba211ksMpwcjWXv8nFezsQlJjqNF2THSa06dZzGU9LSlBAb6/Z8AAAAAIDSicIJAACAE8VVOJHkVuHkwoULTnenlEZ5FURyMvLoF59b4SQ01O18atgVTvx8fVWvenV1ueIKJV286PZ8AAAAAIDSiaO6AAAAnDiXy4XgVapUUUBAgEfXcqdwIl3addIyl2OtShNXCyd59Zv79NOKiY1VXFKSzevKNm3czqdm/fr64bXXVDs8XHWqVlXVkBD5ZB/PlVC9ujLdnhEAAAAAUBpROAEAAHCiYsWK6tSpk86dO6ezZ88qLS1NklSzZk2Pr9WgQQNVqVJFDRo0sL6qV6+uKVOmOO1fVgonee0kySnXwolp6prWreXs2viEZs3cLnT4BwTo+m7d5OPkbhtXizwAAAAAgNKPwgkAACjxTNPU/Pnz9fXXX8s0Tf3jH//QjTfeWKRrDh06VEOHDrWuHx8fr7Nnzyo1NdXjaz3zzDN6/vnnHeIffvihLly4oLp166p+/fqqX7++GjRooDq53MVR2pjZxaj8+KSnO39gsTgtmkiS6etboJwsFSrIJyXFIe5qkQcAAAAAUPpROAEAACXeSy+9pMmTJ1vby5cv17x58zRmzJhiWd8wDIWGhiq0APdmuMI3lw/5d+/erapVq8rPr2z+le2ld97Rx/Pnq1ZYmGpXrapaYWGqFR6urldcoTuvu87aL7eiheFkZ8hlBS2cmP7+kpPCCTtOAAAAAKD8KJv/FQ4AAMqM6OhovfHGGw7xBx98UEOHDi2yYkZJUBTHgpUkZ8+dU2JKig6npOjw6dP/i/fpY1M4ya1oURSFE4u/v9M4hRMAAAAAKD98vJ0AAABAXpKSkjR8+HBVr17dJp6QkKAZM2Z4KSt4wrnz553Ga4eH27RLQuGEo7oAAAAAoPxgxwkAACjRGjVqpBtuuEFNmjTRTz/9pE2bNlmf/d///Z/+/e9/yz+XD7tRsp3JpXDy9/Hj+vLnn3X6wgWdvnBBUTExeu/LLxUQFGTTL7fCiSlJPgX7/SCTHScAAAAAUO5ROAEAACXe/v379fLLLzvEIyMjtXjxYo0ePdoLWaGwzl644DT+07Zt+mnbNpvYAzt2qF337jaxXAsnvr6Skdu18XnL86gu0yzwvAAAAACA0oOjugAAQIn34IMPKiAgwOmz6dOnF3M28IS0lBSdjY11uf+uv/5yiOVZOCmgXI/qsljyPBoMAAAAAFB2sOMEAACUeDVr1tSYMWNs7jQJCwvTv/71Lz300ENezAwFFWCaOjx3ro6cPq2IqCgdiYpSRFSUlmzY4LT/zp07dZddzCcz02lf06/gf8V1dlRXekaG9h4/ruCwMFVr0qTAcwMAAAAASgcKJwAAoFR47LHHNGPGDDVp0kSPPfaYxo8fr0qVKnk7LRSQX0aGmtSurSa1a9vEH5o+Xf9dscKh/7Y9exxiS1au1NG//1at8HDVrFJFtcLDVSs8XGENGxY4L0uFCtp17Ji2/v23/jp0SH8dOqSdERFKz8jQ1Gee0b1PPFHguQEAAAAApQOFEwAAUCq0atVK69evV48ePeRbiKOYXGGapgzusihSvmlpTuNXN2/uNL7nyBGlpaTYXBC/dPVqffPzzw59x99yi96ZNatgiRmG/v3RR/rV7o4VSdqxc2fB5gQAAAAAlCoUTgAAQKlxzTXXuNXfYrFo1qxZmjZtmiTphhtu0GuvvabAwEBJ0qlTp/THH3/ozz//VNu2bTVy5EhJ0kcffaRJkyapZs2aqlGjhmrWrKmaNWuqdevW+ve//+3ZL8oNpmnq/PnzioyMVFBQkFq1auW1XArLJ5fCyVVXXGH952Z16qhTixa6ukULXd28ufzT0qQchZOz5887naNG9eqFyq1D27bOCyd//12oeSVJWVnyycyUpUIFLpoHAAAAgBKKwgkAACizZs6cqQkTJljb+/fv17vvvqvrr79eu3fvVlRUlPXZzTffbC2cnD17VomJiUpMTNSRI0esfbp3717shZOPP/5YixcvVmRkpCIjI5WamipJuvPOO/XFF18Uay6elFvhpHm7dvr57bfVsWlTVQkOtnmWlJGh9Bzt3AonNWvWLFRu7a+6Spo3zyG+7/hxJcXHq1JIiPuTmqaCIiMVcP68DNOUxd9fiU2aKMvuawQAAABQzpmmdOqUFB0t+flJtWtL1ap5O6tyx8fbCQAAABQFi8WiF154wemz1atX2xRNJOnPP/+0/vPZs2edjivsB/IFERERoV9++UWHDh2yFk0kKTIysthz8aTcjuryCQ5W7x49HIomkuSXnGzTPhMT43SOGrVqFSq3dp06OY1bLBb9/ddfBZoz4Nw5BUZHyzBNSZJPRoaCDx+WLJYC5wkAAACgDDp4UNq/X7pwQTp7VtqxQzpzxttZlTsUTgAAQImzcuVK3Xvvvfrxxx+VkZFRoDnWrVunM2785fLUqVPWYsr58+edXjzvjcJJ/fr1ncZPnDhRzJl4kGnmuuMkKyBAmRUrOn3mm6NwkhQfr8SUFKf9qheycNLoiitU2UkOAf7+On30aIHmrBAb6xDzycpyGgcAAABQTmVkSCdPOsaPHSv2VMo7juoCAAAlzty5c/X1119r1qxZCg8P16233qrbb79d1157rfz9/Z2OSU1N1W+//aaffvpJP/30k7Y5uaMiP3/88YeGDh2qBx54QHfccYdGjBhh87xGjRoF+noKo0GDBk7jkZGRyszMlJ9f6fvrnJGZad15Yc8SEKCsXHZh+KakXNqh4eOjjKQkDenWTWdiY3UmJkZnL15UenaRrbCFEx9fX/Vo106xFy/q6ubNra82jRrJUqOGkvOfwoFfUpLTeODp00qvWrVQ+QIAAAAoI86fv3RUl73ERCkrS/L1Lf6cyqnS91/aAACgTEtKStJ3331nbcfExGjmzJmaOXOmHnnkEb377rtOx3366ad6+OGH853f399fNWrU0KlTpxyeXS6cXHfddUpOTlalSpWUlOMD74YNGxbgKyqcJk2aOI1nZWUpMjJSjRs3LuaMCi+33SbSpcJJbpemG6Yp35QUZVWqpKqVK2vFyy9bn5mmqYuJiToTE6Oqubxn7lj6yScKOnfOIZ6VXJCyiS2LxaKIqCjtjIhQQnKybr7yykLPCQAAAKAMyOUXriRJ6elSUFDx5VLOUTgBAAAelZWVJV+734LJyMjQggULdPz4cfXs2VP9+vWTkcuH4999951ScjmCaejQobmuO2DAAJfyy8jIcCiaNG7cWJ07d1aHDh2ssYoVK+rmm2/W/PnzJUlBQUEaNGiQS2t4Um6FkeDgYJ09e7ZUFk5yu9/E9PGR6eurrKAgmYbhdFeKb3KysipVko/dEW6GYSiscmVVCQ3VxcDAQueY5eSoNknySU11/ze9TFM7jxzRh99+q10REdp99KiSsu+rqRYaqqH/+Y8MH07QBQAAAJAHCifFisIJAADwiNmzZ+uNN97Q8ePH1bt3b3322Wdq2LCh4uPj1b9/f/3xxx/WvnfffbdmzZrldJ558+Y5jdeoUUO9e/fOdf0rrrhCderU0enTpx2eVaxYUclOdgpMnTpV48ePV7Vq1ZzOOXLkSM2fP1/h4eGaMWOG6tatm+v6RSU4OFiPPPKIatasqcaNG6tJkyZq3LixqlatmmvxqaRbuHix/vjtNzWpXVtN69S59L+1a6titWrW3SZZFSs6Pd7KLzFR6dWrOxROLrN46OiyrFzuWTF06ciwLCeX1+fGyMrShfh4fZpjJ9Vl5+PidO70adWsV6+gqQIAAAAoK3I5tljSpftPUGwonAAAgEKbOXOm7rvvPmv7p59+0jXXXKNDhw7ps88+symaSJeKLLfddptuvPHG/2fvvMPjqM79/53tq9WuepdsSZZtuReMjW16cAyEUBJa4IZOIOESktzcmwD5AbkQQhJCckPnhgDhElKooZleArhh494lWZLVpVXbvjszvz9GWm2ZMzuzWrXV+3mefew9087uzq7OOd/3+75R7S6XK1ygPZZvfvObcU6WSDiOw7p16/DMM8/EbSsrK8Ndd92F7373uxgYGEBxcTF+9atf4YorrlB8XevXr8dTTz2FCy64AFlZWYr7jiWs9GRTDb3LBWtLCz56/3383/vvx23/t699DX949lkAQMhmkxdOhtq4UEj2GiKjBo5WBLMZgl4PHc/H98Hj0SycLFZIH7Z/+3YSTgiCIAiCIAiCkFwlyWwjUg7lBCAIgiAIYlQcPXpUtrZIc3Mz3n33Xbz00kuyxz322GNxbZmZmdi6dSueeuopFMcU+I4t1C6HXLquyspKnHrqqbjwwgvR3d2NtrY2NDQ0JBRNAMBkMuGqq66aUNEkXTAMDMB+6BCMLhfqGeJYQUFB+P8hhjCh9/vBBYNsx0mKhBNwHARGyi+WaMM8Fc8jPysLpYwi8Ht379bcPYIgCIIgCIIg0hASTiYN5DiZxnAcZwSwFsAMACUAXABaAXwpiuLRCewaQRAEMUUQRRHXXXddVAH1SPbt24fPP/9cdtvrr7+O3t5e5OTkRLXrdDpcddVV+OY3v4lf/epXuP/++7FgwQKccsopCftzxhlnICcnB6eddhrWrVuHdevWobq6OiqlVawgQ4w9Or8ftvr6cM2SOoZwUhlRryUUUWPE4/Phi0OHsHHfPmzcvx/X3ngj1tfWyp4jZcIJAJHhcOKU7PNy+w8JLUuqq9Ha0xO3fe++fdo7RxAEQRAEQRBE+sGoBwmAUnWNMyScTDAcx90F4M5RnOIZURSv0njNAgA/B3AJgFzGPp8DeEAUxRdH0TeCIAgizamvr8eXX34pu239+vWYN28e89g///nPcaJJJHa7Hffccw+uu+46DAwMqKrnUVRUhO7ubuio0PbkQRBgq6sLp7xye73o6O2N2sVkNCIQDGJmRDor0WTC3c8/j39++il21NUhFJEya/b8+Thr1izZy6UqVRegIJzIpO9Swud2g3e5sLCqCm9FpK3jOA6zy8pQSo4mgiAIgiAIgiAAcpxMImhVYZrBcdxZAPYA+C4YoskQawC8wHHc/3EcZ1PYjyAIgpjGzJo1Cw8//LDstjPPPBNVVVVYsGCB7Pazzz5b1TUqKyuxePFi1X0i0WRyYWlvh8HrDT+vb28HABTn5uK6s87C3372M5x1/PEAgMrZs6OO3dXcjC8OHYoSTQBg6/btY14cHgBE1r2k0XHy0muvIeeCC/Cbv/89qn39ihU4+NRTuP/aazWfkyAIgiAIgiCINEMQlF0lJJyMK+Q4mUZwHHcqgFcAmCKaRQDbAdQDyAawDEB+xPbLATg4jjtfFEWa0RMEQRBxXHLJJXj++efxz3/+M9y2du1a3HzzzdDr9dizZw+CwSDq6+uxd+9e7Nu3D01NTchj1HtIR0QR8Hgk13VWFqBQ4z69EEWYu7qimnIyM7HloYdw3OzZ6HO5cMFdd+GT3buh1+lQUlkZte/xxx2HF959N+60W/ftg+DzQSfzRqbUccIQTrSm6urv65Ntbx56bzgAep8PfEaGpvMSBEEQBEEQBJFGJBJGKFXXuELCyeTjWwA2adjfpWYnjuPKAbyEaNHkMwDXi6K4P2I/M4AbANwPYHjl4esA7gFwm4Z+EQRBENMEjuPwyCOP4KOPPsLAwAAsFgv+9Kc/QR+xqG00GjF37lzMnTsX3/jGNyawt+NPKATs2QN0d0vP9Xpg8WJgOuhGukAAuphC6uUFBSgvKEBDWxvOvv12HGhuBgAsqqmB0WSK2nfFmjWy5/X4fNi8fz/WLlwYty2VNU7qW1txdPt2uHw+uL1euHw+uLxeFJSW4ps/+pHq8wwMDMi2Z0fUcdF7vSScEARBEARBEMR0JpFwQo6TcYWEk8lH+xgVZv85gMhE8p8DOEMURV/kTqIo+gH8geO4JgAvR2z6Ecdxj4ui2DgGfSMIgiCmOGVlZfjtb3+L66+/Hg899BDmzJkz0V2aNBw9OiKaAADPA7t2ASefnP7OEx1jYP/lkSM467bbomqd3HD11XH7LVixAmajEX6ZyKoTf/hD2DMyUJSdjeLcXBTn5KA4Nxf/9dvfItOWmiyjf3vrLfz3gw/Gta9asCAlwklWpHDi8UwPNY0gCIIgCIIgCHlIOJlUkHAyDeA4bjaAKyOaAgCuihVNIhFF8RWO456JOM4MqYj9NWPWUYIgCGJKc+2116K6uhqnn376RHdlUtHWFt/G80BPD1BYOP79GU9ajh7F4MGDqCgoQGF2drj+zIzCQpx2wgn4aPNm5DocuOHqq3HJDTfEHW+yWLC8thYbd+8GIAkNJ8ybB18ggI937cKgx4NBjwdHWlvDx9z+yCMp67+NIcC4fcwhlCz9KoQTS2cnDIOD0IVC4K1WeGbMgGA2a7oOQRAEQRAEQRBTGL9febsgSJPJdI/AmySQcDI9uAxA5DfqJVEUD6s47leIFlwu5jjue0qCC0EQBDF94ThOk2giiiI4jhvDHk08fj977Ot0ahNOOjo6sGfPHtTX16OhoSH8b21tLZ555pnUdDjF/P3ll3Hn738PADAZjagoKMCMwkKsWboUjzz3nKpz3HTllbi2tRWr589HbUUFdDod/vrhh/h41664fbMzM2G2WlPW/wyWcOLxaDrPwOCgbHt2ZmbUc4PXCwDQBYOwHziA/kWLAFaBeoIgCIIgCIIg0gs1jpJAAEjhnIdgQ8LJ9OCCmOdPqTlIFMX9HMdtBrBqqMkG4KsA/sk+iiAIgiDU8dhjj+HPf/4zrr76alx88cXIzs6e6C6lHMZ6OQApUEgLTz75JG6//fa4drfbrbFX40dLS0v4/4FgEHWtrahrbYVFQyqtr19yCRwHDkS1tUek+IqkOMWprmwxwsYwWh0nv/vhD/GzCy9En8uFfrcbfW43+t1uLKqqYh6jC4VgcjoRyM/XdC2CIAiCIAiCIKYoiRwnAAkn4wgJJ2kOx3HFAJZENIUgFYVXy0cYEU4A4CyQcEIQBEGkgKeffhpbtmzBpk2bcMstt+CCCy7AVVddha985StRheWnMkrCiShqO1cVY5G9oaEBos8Hrq8PMBiA7Gzp30lAc0QKrUjKS0tVn4O32eAtLoa1vT3c1u50yu5bNE7CiWvIGaKW8rw8VCYxuTG43SScEARBEARBEMR0Qa3jhBgXJsesmhhLFsY83yWKopbQ1M9jni8YZX8IgiAIAvv27cOWLVvCz30+H55//nk8//zzuOeee2SdFVMRJeFETTBRJNXV1bLtHo8HnW+8gaKsLKnBagWWLQMyMrRdYAxojhA7IikvK9N0Hl9ZGQK5uTC43chobGQLJykWGWyZmbCYTLBZLMi0WsP/ZlosEAUBnMo0WpxWe9EQJqcTnpkzkzqWIAiCIAiCIIgphhpRJBgc+34QAEg4mYzcwHHczwDMA5AHIAigB0AjgE8BbBBF8V8azjc/5vkRjf2pS3A+giAIgggjCAL6+/vR09MTfixduhSlMQ6Dp59+mnmOiy++eIx7OX6kUjhhOU4AoKGlZUQ48XqBI0eAxYu1XWAMYAknZeXlms8lWK0IWK0QdTrcc/XVuPass9DR24t2pxPtQ/8uP/740XY5ipNOPBHeN96Q3darwTKUSDgRRRGHW1rw0c6duHLdOphNpsiNQJrXAiIIgiAIgiAIAuQ4mWSQcDL5uDTmuRlAJoCZAE4GcBvHcV8AuFUUxfdUnK8m5nmTxv40xjzP4zguRxRF+eTiBEEQRFoTCoXQ29uLnJwcGGLSQc2ZMwd1dXUQBCGq/fnnn8ell478eauvr8d778n/CVu1ahVmz56d+o5PAKGQpGGw0CqcFBQUwGKxwCdTX6Ozry+6oasLEIQJLSzuHhiAjzGoL5sxI+nzBrOzUVpcjPKCgrhtnvJyaHxbFREV3j9OECCqSSkninHCiSiKONjcjI937cJHO3fio127wi6a+TNn4sSFC8PX4IJBiJFCCkEQBEEQBEEQ6YnaGifEuEDCydRkBYB3OI77JYCfiaJiyGN2zPNOLRcSRdHFcZwPgCWiOQsACScEQRDTkEOHDmHBAilro8PhQF5eHnJzc5Gbm4ve3t440QQAvvWtb2Hz5s2YP38+5s+fj6KiInz55Zey57/uuuvGtP/jiZLbBJCKw/M8oLacC8dxWLp0KQKBQPg9z9XpkMtxqC4pid5ZFKUBtcUif7JxwG40wvvGG+jq60NTZyeau7rQ1NmJps5OVM+dm/yJdTr4CwthbWuL2xTMzk7+vIxrseAEAao8J6IILmaoxgsCVt58MwY9nrjd//3BB3H+2rXYc/Qo9hw9il/efTdOPfdcjR0nCIIgCIIgCGJKMTxBTESyqbr8fsBonNDguqkGCSeThxYAbwLYAmA/ACcAAVK6ruUAzgGwPmJ/DsBtAHQAblU4b2xVU23VTEeOiVx5sSdxjjg4jisEEB8uqsysVFybIAiCSA5nRG2JgYEBDAwMoKGhAQCwZMkSdHd3yx73+9//Pvz/5uZmnHXWWXjrrbei9iksLJw2abqG8fu1lSLZuHFjdMP+/UBLi/zOXu+ECie6QAAcx6EwJweFOTlYMSSWiAD6kkjVFYmvpAQGlwvGoTdZBOAtK4NgNo+y19EoOUrU1i2R28+g1+PEBQvw1tatcdt21tdjZ319+Pm+3btJOCEIgiAIgiCIdEdtSgKtjhO3G9i7FxgYkJ7PmAHU1JCAogISTiaeLZAEkXcVnCOfA3iI47gVAP4CIDKHyU85jtskiuKrjGNjhZP4/B6J8QLIUThnsnwPwJ0pOhdBEAQxDvT09Mi2W61WlJWVYefOnYrHm81mlJaW4sEHH8RXv/pV1A8tEGdmZuLpp5+Gw+FIeZ8niuFxqRJahRMAUhRSb6/0r5I6I5PSazzRMQb0otE4+podHAfX7NkwuFzggkHwVisEq3V055RBKVUXZNxVcrAEllOXLJEVTmI5tGcPTD09CGZlQTTQ0J0gCIIgCIIg0hK1gogW4YTngZ07gUine1MTYDIBlZWaujcdodnXBCOK4psa9v2C47gTAGwEMCdi030cx70uiqKa0Ef1lUxHdwxBEASRhkQ6TiLJy8tDXl5ewuMrKyuh0+kwa9Ys7Nu3D2+++SY8Hg/OPPNMVcdPJdQ6TjTh8wHbt0cPfFkoFVgZB1jCiZCqeh0ch5A9JSZYxWuIkGy+cZtSIJyoYW99PWxHj0LkOAzOmQM+M1XxKwRBEARBEARBTBrGQjhpbZWfO7a0kHCiAvLkTDFEUXQC+BaixYxaAKcxDnHFPE8mHDP2mNhzEgRBENMElnCSm5urSvioqqoK/99sNuOCCy7A5ZdfnnaiCc+r0zY0CyeHDqk7MTBpHScpE07GA45jpusaTaouAFg+ezZqysoSHr+vqQk8z4MTRWQeOQIuFFJ1XYIgCIIgCIIgphBqBZFgUKppmQhRlOaPcni9AM0rEkKOkymIKIrbOY57B9E1T84E8J7M7pNZOHkEwD80HjMLACstGUEQBDHGKDlOrr322rBz5PLLL8chmUFapHCSzrjd6saymoQTUQQYNWRkIeEkNeh0skUa1TpOduzYgTeefx5ZNhuyMzORZbMhy2ZDXk4O7rvnHlzx3e/Co/BZef1+NLS3o6asDDqeh7mjAz4VggtBEARBEARBEFMItZNDQZDmJ4nS+HZ3K09K3W4gK0t9/6YhJJxMXTYgWjhZzNivP+a5pmLsHMdlIl446dNyDhaiKHYC6NTYn1RcmiAIgkiSH/3oR7j44ovhdDrR09MDp9MJp9OJ0tJSLFy4EAsXLgQA/OMf/8D+/fvR0NCA+vp6NDQ0oKGhAXPmzElwhfRAbZYsTcJJKKS6roamTowR6SKcMOucqPwsdu7ejV/97W9x7VUlJdi2dy82LViA1/7+d7jdbtz76KPQ6XSYU1aGBZWVWDj0KMzOBgC0dHfjV488glvuvRe5RUXJviSCIAiCIAiCICYbWlJwBQKJhZPGRuXtJJwkhISTqcvRmOcsQeRwzPOZGq8Tu79TFMVejecgCIIgJhGhUAj33HMPnn76aVgsFlx44YW46667YFBReFptLZPFixdj8WKWpp/+qDV7BFwBoHsAyMkBGCmhwmjN6+XzSRFGExF0IIrQBYOym9JFOFGbqqu/r0+23TFUq6S8pgbfve02AMClq1djdl4eLDHvUbvTiTueeQaPvf46/MEg3ti8GR98+ikcQ4IKQRAEQRAEQRBTHC2psxhzrTADAwBjHhLG7VZ/vWkKCSdTl9gwUlYKrv0xz2s0Xqc65vk+jccTBEEQk4zbb78dv/71r8PPf/GLX6Cvrw8PPfTQBPYqvVArnPjdQWDHDiAjA1i4EHA42DtriUACJNEkEADMZm3HpQAuFALHsIVPNeHkp489hq07d8Ll88Hl9cLl9cLt8+GWa67BD37+84THDwwMyLZnyRR5n1tbC1P/iFm4q68Pv/773/HwP/8Jb4RwVt/aisrqavzHNdfgp/fdB70K0ZMgCIIgCIIgiElMIjEkkkRzw46OxOdQWztzGkPF4acu+THPWUnP98Q8X8xxXIaG66xNcD6CIAhiCtHQ0ID7778/rv2pp57C4ODgBPQoPVEtnGBI1PB4gC+/VI760SqcANL56uqATz8FNm4Ejh5VV3xllHB+P0SWcGI0jvn1U8meujp8sns3th8+jEPHjqG1pwf9bjdTEIllgPG9yrLb49p4a3QczDPvvov7//GPKNEkkt/+6U94+Be/UNUPgiAIgiAIgiAmMVocJ4nmIi4V5anJcZIQEk6mLqtinrfK7SSKYhuAXRFNBgAnarjOqTHP39JwLEEQBDHJuP/++yHI1GbweDw4cODABPQoPVErnPAwIIShFF3BoCSesFJyxQgnPM+jZ2AAR1pasOXAAfjlhJXdu4GGBqlDbjdw5IgkpIwxjz36KE764Q9x2b334if/+794+NVX8drGjdh99CjEKeaOsGXIx5u4VU40WMKJQ044sdminn/3nHPC9U1Y/OWll1T1gyAIgiAIgiCISYwW4aSrS3m7GveK1yvVbRRFSYjxeMYlyG4qMbVmrgQAgOM4C4BvxDR/pHDIy4guHn81gHdUXKcW0QKNW81xBEEQxOQkGAzihRdeYG4/evQojj/++HHsUfqiVjgBJNeJAZ6RA3fuBFasAGJrawQCaOvpwYk//CGcg4Poi4kiOvz006gpK4s+Rm7A3NwMVFUlrqkSweb33kPDkSOonjMHK08/XXHfwf5+/M8TT6Ctpwef7d0btW1ORQU2ffObqq87GchgCSfe2Kyp8iyorMQZy5ejz+VCv9uNfrcbfW43HDJp2YJZWeDNZuiHxDOb1YrbL7sMtzzySHifXLsduXY78hwO5NrtKMrJmbhaNgRBEARBEARBpAYtqbpcLkn4sDIqN6g5lyhKAszhwyMT2OxsYPFiYIqlVx4rSDiZmvwEQOTKCA/gDYX9nwPwM2A4pBXf4DhutiiKsYXj5a4Tyd9FUdSwFEQQBEFMJoxGI/bu3YuCggLZ7WpTDxHK8Ly2Ma8fZtgQkV92YADo6QFiPye/HzaLBfVtbbLncapNtcbz0kA7KyvhrgLP4z+uuw7PvPpquO2idevw2PPPg2MUTX/gzjvR1tMju+2UVbGG2cnPaB0n//lv/4bbzj8/rt1VUIC424TjMDhnDmyNjTC4XBCMRlx98834vK4Of3v7bZy7ejVevusu6GLe+15B0CSEEQRBEARBEAQxiRBFbY4TAOjuBioq5LepTfO8e3f0874+SUhZsEBbX9IUStU1gXAc922O44o0HnM9gDtjmp8WRbGRdcyQQPJMRJMJwNNDzhXWdc4DcFVEUwBA4gqoBEEQxKQmPz8fDz/8MH75y1/iggsuCLd7PB5ce+21E9iz9EGL2wQAApCJ5pETsQIB2DMyoGcIFqqFE0BdzlsAn27YECWaAMA/3n0Xb/31r7L7H9q5Ew8/95zsNoNej2tvukl9HycJTOFEZTFFjudl23Vms2y7aDLBNXs2+pYuxcCCBQgVFeHR55/H2889h/L8fPzf++/Hn0vrJIsgCIIgCIIgiMnDcMosLbDSdQmCFCyXLO3tozs+jSDHycRyLYDHOY77B4C/A/hIFEXZ8EWO41YAuA3ABTGbWiC5SRJx59CxOUPP1wB4j+O460RRDCe15zjODOA7AH4bc/xvlcQZgiAIYurwve99L/x/URTR0tICK8viS2hGq3ASLhAfiZybIRAAx3HIsdvR3d8ft7lXi3Cict/XXnkFgCR6rJ4/H1897jisX7EC85csgVwM01OPPYYQY5B90+WXY86SJer7OEk4bc0a2EMh2CwWZFqt0r8WC4pY0V0xsIQTMZFDJCb11sp16/DVwkKIMhMqLhQCGEIMQRAEQRAEQRCTHKWUBYWFQGdnfHtvr+RSia0hqSX9gRyiKNU7kanJON0g4WTisQK4YughcBx3GMBRAP2QUnDlAVgCQM6Z4gRwpiiK7YkuIoriMY7jvgHgbSAc2roWwD6O47YBqAeQBWA5gNgcLq8D+H/aXhZBEAQxFeA4DuXl5Sk735tvvok9e/agp6cHPT09cDqd6OnpwYUXXoibb745ZdeZzGgVTo6gBjPQhKhlcoZwAkg1LuSEE02OE5X7HjlyBDl2OxZWVsIfDOLJDRtw31//imvPPBP/+cAD6HU6UVZZGU7bJfp8mFVaisaOjigBpSQvDz/6+dQ0rp5+4ok4p7Iyrj1ktULNu5i0cCKzvwjpOxt3DXKcEARBEARBEMTURWk8X1oqL5yIopSuq7g4un20wgkgZSgg4YSEk0mGDsDcoUci3gdwlSiKx9SeXBTFjziOuwDA0xgRRzgAK4YecjwP4HpRFMmjRRAEMc3Zvn07nnvuOeTm5iIvLw+5ubnIzc1FQUEBlgw5Ce6++25s2rQp7th58+aNd3cnjKG63qoRocMuLMYi7IYOQ24Cjye64LcoRgkncmhO1aWioHjv4CB6Bwfxr5jct3945RU8/sYb8AeDmFVWhj8/9RTmHXccHvv+98EJAkI8j5bubtS3taGhvR2WykrYVdRUmYyIjNRonCCoOFhMmXACjoNoMMiKJKxrEARBEARBEAQxBVASTnJyJHe53ESzvT1eOFFb30QJlamd0x0STiaW/4GUamstgJkq9ncDeAfAw6Ioxie4VoEoim9yHLcQUr2SSzCSuiuWTQDuF0XxxWSuQxAEQaQfO3bswAMPPBDXXl5ejubmZgBAG6Nw+eOPP44HH3wQRqNxTPs4GdDqOAGALhTiIOZiHoayZ4oi4PUCw/U1gsFwztv/d/nlcPt8yLXbketwICczE7l2O+yMWhyy8Hz0+Rm0Moq8A4B/KJKprqUF5110Efbu3h0WEwx6PWYWFWFmURFOA9A/Zw5UyAyTEpbAoUqsEASwpCnNwsnwMTKTKqpxkhqkrAR6BAI62GwhmEwa80wTBEEQBEEQRDKwxvM6HaDXAwUFwDGZ2PnubmleF5l6O1WOE4KEk4lEFMWXAbwMABzHZQNYAKACUlquDEgOlD4AvQD2A9iVCueHKIqdAL7LcdwtGBFtiiEJMy0AvhRFsWG01yEIgiDSix7GInpeXl74/2aFOgvTQTQB2MKJwyFf832YdhRjLg6OuE7c7hFhIyJq6OxVq1LT0cFBReHE7/XKpgSTo7u/HxvffBPnzZolu120WJLq4qRgFI4TJXElKeHEYJCNNKNUXaOH54H6+kwMDAz/TomYOdOD/PwUROwRBEEQBEEQhBIssWO4fklRkbxwAkjts2cnPpcWSDgBQMLJpEEUxT4An43zNQMAPhzPaxIEQRBTF6fTKduem5sb/v8tt9yCm266KW6fk08+ecz6NZEIgjROdTqlQKCqKrZwUlQkInewEUfFStntPAzwwYIMeKUGj2dko5LdeuFCKcLo4EFlZSYWlwtbGhvx3HPPwefz4cwzz8Spp54a3tzZ0qL+XACa6+oAGeFE1OmkBf8pCitVFwQhYbqzVAsnAuN9JOFk9HR2WiJEEwDg0NiYAYcjSM4TgiAIgiAIYmxhjeeHx//Z2YDNJl8Ls6UFqK6WJqRAaoQTv186zzQJfmTBmAkSBEEQBEFEwxJOIh0nX//612WLV1966aVj1q+JZM8e4NAhySHd0QFs2hStd0Ri4fyoEY9gDg4yzxeAaeRJ5KCYJZwYDFJO26wsqWigBl5/4w2sWbMGf/jDH/DEE0/gG9/4Bp588snwdpvBgJ9fcQVu+NrX8PUTTsCS6mrF8w0w7g/BZEpYS2Uyw0zVBYTTp7HQKQheSTtO5PpCNU5GTV+f3KSQQ1+fSaadIAiCIAiCIFIIS+wYFi44DqiokN8nFAIiU2anQjgByHUCEk4IgiAIglAJK1VXpOOkoqICzz77LAwRC7xXXHEFrrnmmjHv33gzOAh0dqrf3xKUBp4VaGbuwxROWBXnI1OjlZUBNTXAsFBhtwNz5wIz48uoBYJBXPfzn4OPWXC/7777EBha7C/KysId3/42HvvBD/DPu+/Gjscfh/juu7CY5BeSa0pKZNt5hfRtUwGm4wSJBYtDe/Zg07596I+JDBOMRmYKMMW+yIgt/kAA/QzRilCPxyMvSrW3T+E0cwRBEARBEMTUIJHjBABKSqKfRxKZxisVxeEBEk5AqboIgiAIIm3ZvXs3Lr30UtTW1kY95s6dC4fDofl8y5Ytg8vlgtPpDD/6+vqiHCcAcPnll+Occ87B5s2bUVNTg6qqKlkXylRHoW66LJaglEaLA2CBFz5Y4/aJE06GU0GxBr+RIgbHAZWVklAiiiML893dQGNj1GF6nQ5//s//xJPbtuHvL7wQ8Zp6sGfPHixfvhw6RqSS5/XXAQAvfPIJfvrkk7Cazdj52GPQMxwUwhQXTpQEDk4QoOQ5efTpp/Hkiy8CAEpyczFvxgzUVlTg9JNOwumLF2vuyisffYQ/Pv00egYG4BwcRM/AANw+H1bMm4d3PhvXjK9phZL+xfPp99tFEARBEARBTDLUCCd6vRQsFzO3AyCJHIGAND9kOU5MJm2iCgknJJwQBEEQRLqyb9++8CMSk8mEhoYG7NixA42NjTh69CgaGxvR2NiIrKwsbNiwQfZ8t99+e1wbz/MIygzMsrKy8NWvfjU1L2SSEumGTgTHASbPSKF1M/yywokfESJDKCQNepUGuHLuD46LTo2VmRm3i06nw9fvuAMZGRmw2+0YHBwMb2sbemGsNFPDIthFp5yCc1evRktPD1M0Aaa+cKKYUitBgfiDdXXh/7c5nWhzOvHBjh3gMzJw+nXXae5LR28v3v/yy7h2Z3+/zN6EWoJBtjiW4CMmCIIgCIIgiNGTqDj8MCzhBJCKbSoJJ7m5QHu7+j6RcELCCUEQBEGkKwcOHJBtr6mpwTvvvIOrr746bltk2i016PV6xUXzdEZLWQmLBeA8I+maTJAXJaIcJ4DkOtEqnMRiNku5cSMG0BzHoTQvD0dlBs7DwgmnIjeu2WRCNSNF1zCCmj5OYgSOw676erh9Pri83qh/T8nKQlFNDfPYAw0Nsu1z58xJqi85OTmy7c6BgaTOR0gEAkpp07iw8YsgCIIgCIIgxgSW4yS2OLvVKg1M5WotDs8ZWfO4rCwp17TayCCXC9N9IEzCCUEQBEGkKSzhpLa2FpWVlbLbnE4nBgcHYbfbx7BnU59AQAroUYvFLAB9IwdoEk5yctg1TtSIEhwnDZK7u6OaS3NzZYWT9qE2VqouOYJDA32jTM7dqV7jBByHZd/9LgSZCcYrK1YwhRNnRwe6GU6Q2fPmJdWVnPx82fY+lwuhYBCG2IkVg7bGRrQ3N6Nm0SLYs7KS6ks6oSycSNvNZrKeEARBEARBEGOEmlRdwFAqA5P8/HC4TSlVV2YmoDboiuelSa81PlPCdIGEE4IgCIJIU5SEk5kyBcOHaWxsxMKFC8eqW2mB1gB/iyF6IKxaODlwQLKrsBwnakWJ0tI44aSMsQgfTtWlQTj5aOdOnHnbbagoKEB1SQmqiotRXVyM6pISrF+6FFM5RonT6WCzWDDo8cRt88QUfY/k8J49zG01SX6/WMIJAPT39CCvuFjx+FAwiF/+5Cf43dNPS+ez2/HrO+7AN6+9Nqn+pAuJhBO/n4QTgiAIgiAIYgxRK5wA0hyQJZwIgrJ7RYtwAkiuExJOCIIgCIJIJwRBwMGDB2W31dbWory8HDqdTjaKnoSTxGgVTrKN0YvuqoUTANixI67pxX/9SyoQ/vHHcPr9cDqd6O3txc9+9jMsW7Ys/hwFBUB2NtDXF24qy89Hjt2Osrw8FJWVoWjmTJSUlGDFihUA2DVOAllZMMU4KRra2yEIAho7OtDY0YEPh9qLcnKw/z//U/Y8U4lMq1VeOFHI++tsb0em1QqX1xvVnmWzobC0NKl+ZOXlMbf1dXcnFE7eeP75sGgCAL2Dg7j59ttx3Jo1qEzSBZMOqBFOCIIgCIIgCGLMYAWtyTnKWcFzfj/7PMPnkql/qUhfnzSXnKaQcEIQBEEQaYggCHjqqadw4MCB8OPgwYPwer2ora2F0WhEWVkZmpub4449evTo+Hd4iqFFONHpgEJ9T1SbGfKpt/wwQwQSOjSuuf9+DMgs5F922WXywgnHAXPmAFu2hJt+e8MN+N13vwsACNlsGKytHdmf58Exct8KFgsQI5zUD7lUYqkqK0vwSqYGNotFtt3lcmHrhx+i7uBBzF24EMtOPDG87Rsnn4xvvfoqjnV1YX9TU/gBkwmcLrmF+LzCQvzXxRcjz+FArt2OPIcj/P+iBLVmAOBvf/tbXJsvEMAzjz+OO3//+6T6lA4oFYcHAL9/etVxCoU4dHeb4PEYYLHwKCryYZqWsiIIgiAIghh7RFG740SORMKJyQRoTcntdLK3iSIwOCil9MrKkia+aQYJJwRBEASRhhgMBlxyySVRbYIgoLm5GcVDUemVlZVwOp2ora2NeqxevXoiujxlEEVtwklBAWD0DUa1sRwnAvTgoYcBypXncx0OWeHEqTSwdTiklF2trQAAXcTAVu/xSLbuoTalNF28jFW7QaZWCgBUVlSw+zOFsGVkyLb/4K67op7fcMkl+OWjjwIA9D4fOI5DRWEhKgoL8dUhJ48/Lw/xn5w6rDYb7vvOd8DJFIN06fVIlFztRxdfjJrCQmw5eBBbIlL5/c+f/4w7HnggaUFnqkOOkxF4Hjh0KBNe78g00ek0Yf78gXScCxMEQRAEQUw8vMLcT044YdW5DARS7zgZHJTOG3tNnw/YvXskoM5qBRYv1i7MTHJIOCEIgiCIaYJOp4uqbfLaa6/B4XCA46ZyBYrxx+9nlxyRo7QUwIHoWhgs4QSQ0nUZ4GVuB4Bcu122sLuicAIAFRVh4SQSThSh93rB22wAgO7WVuzYuROleXkozcuDLUIs4WXcFyzHycx0EU6GXj/Hcci0WpFpsaBN5r1+/G9/w7kXXIDVX/0qdD6f7Lnk3j/VcBxEgwGczISIU5pwAYAoormlBQ+9+qrs5gM7dmDe8uXJ922KIooknETidJqiRBNActx0dppRXCzvlCMIgiAIgiBGQSKxIxaW42RwEOjslN+m10tBcslEwvT2AkVF0W2HD0dnIfB6JSHlhBPSynlCwglBEARBTFOysrImugtTkogyIQnR6YDcLF4aSEaQSDjJUCGcyJFQOMnMlAbNMovsBrc7LJx88q9/4Zof/zi8LctmQ2leHubOmIGnXngh7liWcFJZVaXcnynCq7/7HWxeLywmEziOQzAUQvUVV+BYV1fUfvaMDOzatAmrv/IV6BnqmjAa4QSAqNfLTq4s7e0QjEaE7HYpNVsMXCiEr69axaxttOGVV8ZeOBEEGAYHwYkiQjYbRLmJ4DjD8xwEQVk89vv1EEXZtzXtcLnkp4ctLRkknBAEQRAEQYwFrDRdgLZUXaIIyKTiBhAtwFitcfNTRWKFk1AI6OiI38/jAbq7gcJC9eee5JBwQhAEQRCEIsFgEO+88w5mz56NqqoqGCfBYudEEQgAe/ao33/GDIDzuOPaDeChRwi8zFDMX1ULNGxWPG8Ow2Ld09Mj2w4A9913HwwGA1Y6HFheUoLMmJRbevdIP9tjhJB+txv9bjc4nQ5+nscZ//Vf6HO50Od2o8/lgnNQSkV22emnoyArCw3t7ahva8Os2bMVX8dUweFwwBQhNrz4r39FiSanLV2KC9auxelLl6KqqgohP3uBeVSOEwCCwQC5chN6nw/2w4cRyM6Gu7o6bpVfFwigIDsbJy1ciI937Yo7/rV33sEP//u/R9U3JbhAAPaDB8OCkqDXwz1rliT0TCDBYGI1RBA4hEIcjMb4FGnphtPJmIhDqn1iMKT/e0AQBEEQBDGupEo4USJyDl9eLjlG1BIbnKeUt7q5mYQTgiAIgiCmD/X19TjnnHMAAHq9HjNnzsTs2bNRU1ODu+++Gzk5ORPcw/HB5wO2b9d2TGkpgP544QQATAjCKzMUCxht0gBZYQBdkJUFW0YGKmbMQHl5efixZs0a2f1FUcRvf/tbdHd3A5DSti2YORMr587Fv593HpbW1MAQIZy0yaTzAoCSggIYzWZ8tHMneBnXwhVnnIH1xx8fft6/cCHkS8xPLcQIu7koivhtjOvmwx078OGOHTAbjWh67jnkHzokfx6Og5DMRCfyHHKTpwhMfX0IOp0I5OVFteuGBIsL1q6NEk70Oh1OWbwYF6xdC87ngzhKYUe+0yIy6+ujXDg6noetoQH9ixZNqJUjUZquYfr6jMjPD6S968RgEBAKyb8nAwMG5OYmqqRDEARBEARBaIKVqms4vVYsycwnImuUlJYCjY3R+aeNRmDmTODIkfhjPR5pMjw8T1ASTvr6pNzWo5zzTBZIOCEIgiAIQpEjEYMnnudRX1+P+vp6vPPOO7j//vvHpQ+iKLl+29ulNdayMmA89RqvF9i2TRovqqWqCsjIANDKEE50IXhlVIVAUAfk5cnbn4f4/Q9+gIdefhmcXs57EM/Ro0fDogkACIKA3Q0N2N3QgMtOPx0AoPf7wYVCEA0GtDOuXVJUBE6nQ3ZmJnpkBsx9EeKLCEBgFS6cYogR7/Nne/fiC4Yw8u0zzkBhTg6zwCNvsYxaJEgknACAqacnXjgZmpCdt2YNbnvqKaw/7jicv3Ytzlm1CrkOBwDA098P/xgIJ8b+/ihhLrJPeo8nnCJuIlArnDQ12dDVZUZNjQsmU/q6LgwGkanZDgwYSTghCIIgCIJINazBF2vcbzRKcwpRw5g00nFiNAIrVwJ1dYDLJaVzrqqShBo54QSQ0nWVlEj/j6xtEosoSpP2iNqqUxkSTgiCIAiCUOQww8ZbUVEBy1hEp8vQ2Bg9hmtvBxYtiq9RNxaoEU1MJmDhQqlfPC/1q6BgaKPLJX+MUQBkMjoFApAOVhBOjJWV0sBWJVu2bJFt5zgOx82ZE36ud7sRyspCW0ztjmGKi4sBANl2u6xw0h8pnBgM6VMUIiLS61hXF3Lt9nB6skh+8I1vKJ5mtPVNgGgRh4VxcBCxRTmGHSeVxcXoefFFWGRELWNfH/wp/lIJPI8Nzz+PC447DnqZvuuCQSQoaz+mqBVOAMDrNaCuLhO1tYNpc2vHwvPsFzYwYJw2tV4IgiAIgiDGDa3CCcdJE1CF9MBxxKbbtliABQvi97PZAJmAJ3R3jwgnSo4TAGhtHcpZPfUHjelT5p4gCIIgiDGBJZzMHqf6FYEAUF8f337gACCTLSql8DywY4eyaGI2A8uWAbm5wPz5kqBTWBgxTpQbeAIwm+UHkn4/gPx85YFmebmq/g/T1taGW265Ja69tqICWRHR/sOuABejzyVDwkkWo8ZKX4RIJKRRLZzIVF2XnnYamv/yFzz6/e9jbkVFuH1JdTW++4c/4Om334aHccMEUmCTEhQcJ4FgEBv37cN9f/0rDn/5ZdQ2XYQVX040AQCDywWOlSogSd76299w0e23Y9F3voO/f/xxXGF6juHOGS+0CCcA4PEYMDCQvrFnoRD7dycY1MHno+kjQRAEQRBESmGNv5XmU1pTYamdm7HmK52d0kR1+KGE2z0irgiCJKRMUdJ31E8QBEEQREo4wrDr1tTUjMv1u7rkBZJgUNo2lq6Tnh6m7gFACshZtmwk3WscgiBZVmQwWXWATLBOIAApuig3V+pALCUl0TlqVfCd73wHLpcLjzzyCIIRA/OVc+eOXDcYRMuRI2g4cgS/vf56rKqtRZvTidaenvBjzerVACTHiRyRwomYTsJJjFMiw2LBjV//Or7zta/hra1b8bsXXwQvCPho5078a/du3PLII7j8K1/Bd84+G0uHvie+wkIEs7NH35cY4eSzPXvw/pdf4uNdu7Bx/354hyYyd2dnY/by5dj3xRc4uGcPKgIBnLF8OTgFQY6DlFYrkJ8/6n4Cktvk3t/8BgCwv6kJl9xzDxZXV+O/r7wS565eDY7jwCkVwxwHgkHtQkBHhwVZWfJOsolAFKWHXApsLQgCIIrKkYH9/UZYrRqiGwmCIAiCIAhltDpOgLETTvLzgWPH4ttFUWpnzAPjaG+X9t2xQ0ofMUUh4YQgCIIg0oxnnnkGPM+jtrYWtbW1yM3NHdX5vF4vOI6DGJNDdbwcJ52d7G1tbWMrnDCybAEAHA5JNFEcg3o8zE2mDPkDw8aAmTPjhROdDqisVLigPBkZGcjIyMCFF16I559/Ptze53Zj2Y03orWnB519feH2mUVFOPp//4easjLUlJWF2/sXLIAA4Ipzz8VXlyxBdmam9LDZkJ2ZiRmFheF906W+CcCuK6LT6fC1Vatw1vHHo+bKK8PtAx4PHn3tNTz62mu45NRT8ccnn0QwRUV5XIEADhw6hJ6BAfQMDOCyX/5Sdr/PN21C8M47cc/DD4ddHqctXYoN994Lk8JNa+zrS5lwsnPjRuyPmSjtqq/H+XfeiZ9ccgnuu+66CRdOtDpOAGBw0AiPR4+MjIl1y4gi0NFhRmenBaEQh8zMECor3UnXYFFK0zXMwIARxcUknBAEQRAEQaSM8RBO1M7N8vIAq1U++O/YsZF0XYno6gKyswGnU3UXJyMknBAEQRBEmnHffffhwIED4ecFBQWora3Ff/3Xf+Gcc85hHtfd3Y0DBw6EHz/+8Y9RXFyMTz75BD6fD/X19Th8+DAOHz6MI0eOYPWQ+2AsCQaVx1rd3ZJTWOu4US0R2Y2iyMhQIZoAbOGE42DKZAsnoghwublAbS1w+LCUM8xqBebMkWwuSXLjjTeGhZNcux0XnXwy/u2+++L2a+3pgSiKUe6EUEYGhKE3+tJzzoFZzg0TQTql6go6HBAhOTLkeP/LL9HQ3i67bfHy5SkTTQDgy337cNZNNyXc763PPsNbn30W1fbhjh1YcsMN+P13v4sZhYWoKCxEptUatY9xYEC63zTU0GGxe/t25rZvn3EGAEA3gcKJKCYnnABAZ6cZlZVsYXQ86O42oaUlI/x8cNCIQ4fsWLBgIKmU0mqEE7fbQHVOiKTgecDv18Ni4UftjiIIgiCItIKVqktJONEapKZ2bsZxQEUFcOhQ/LZgEGhqUncenw84elR19yYrJJwQBEEQxCgRRRHNzc3Ytm0btm/fjm3btiEzMxN///vfx70vwWAwLrVWV1cXurq6cPPNN8ftHwgEcNppp+HAgQNwxigU69evDxcDt1gsmD9/PubPnz92nZehu1ta3FSirS0pE4YqWOlbc3JUjj1Zeb6sVpjM8itHgiAtMBkMkGqZlJZKUUgGw6hz8Zx00klYsGABQqEQnv/BD8Az6ksEQyF09/ejICK1lK+kJLxaqqZAeToJJ6LRCHdVFWwNDWHxhDeb4S0vh7W5Gf/75puyxxkNBlx0zTUp7Uv2KN0gB5qbceZtt4Wf737iCSysqgo/50QRxoGBlIg9Toa4Nqe8HAuGvrQTWeOE5zlmaiq7PQhRBFwu+fvY6TShrMwLozE5d0cq6O6OV4z9fj1cLgPsdu2ClFJ9k2EEgSPhhNCEKEpC47FjVgAcdDoR5eUeFBQwIhMIgiAIYrrBCiRKZY0TZm5pGUpLgbo6aVI6GgYHR3f8JICEE4IgCIIYBS+//DK+853voLu7O6rd4XBAEAToxjmssr6+HiHGwKu2tjauzWQyob6+Pk40AYADBw5g3bp1qq8tipLBwu8HsrJSErCumKZrmLY2KavVWCzksRwnqgN8WI6TjAzFsa7fHxFgpNNpjyhiwHEc3njjDZSXl0O/ezfaDh5k7tvS3R0WTkJWK4JZWeFtrNRVkaRTjRMACObmot9uh8HlgmA2g7daAY6DvqsLNosF65YvR1d/Pzr7+tDZ14cQz+Prp5yC/NLSlPYjp6Agpecrlzmfsa8vJcJJD8MuNjsi/dtEpury+9m/z1VVbhiNInp7jaivz4zbLoocnE4TioomJm2V9Hsr/z3s7TUmJZyocZwM76fTTZxgREwtXC4Djh0bcUYJAoempgxYLEJS9ylBEARBpB1jnarL4ZBSJqjFYJBScsnVOplmkHBCEARBEKOgoKAgTjQBgIGBAdTV1Y1bHZBhIlN0RcJxHLMvtbW1aJdJM3RQYVE9Fp4H9u0DOjqk5zodsHAhEFHyQjOhkHxt9Fjcbmm/FJVliGLUwgnLcZKRoXiOQEBbRq6XX34Zr776KubOnYva2lrMnTsXNTU1MMlcZObMmcP/QWFHB/Q6HfihGhiRtPT0jBQ2j3CbACOOkwG3G//z8ssoyc1FaV4eSvPyUJKXh4KsrLRynAwjGo1xgoLocODX11+PLQcPYsuBA9h84AC2HDyI3sFBXHHVVSnvQ3ZeXsJ9bBYL3D5fwv3sGRnIkrnRTH198AjCqB1O8yorcc4JJ6C7vx9fHDqE0FDU2hubN+OHjz6KnoEBOLKzcfcTT4zqOsnCEk44ToTBIAkD2dlBmEw8AoF4JdjtNgCYGOEkGGSLHMmmH9MinEyk04aYWjidcn/sOLS2WjB3rkIhMYIgCIKYLiSTqkutcGKzSRNzrVRUkHACEk4IgiAIYlQsXbpUtnA6AGzfvn3SCCdVVVWwMOy5tbW1+Oijj+La9+3bp+qaogjs3i2l1RpGEKS2k04aERmCQUkQsFjUuVF6eqTzqOHAAeCEE5THlloRRXaqLlXj1GELjhw2G/R66X2Qc0CzBBsW7777Lp555pmoNr1ej4svvhh/+ctf5A/KzoY+NxfFubloiRH/MiwWDA71nbdYEIxI2QWMCCeNnZ24I+a6AGDQ67Fr2zYUj6Iey1RhX2cnjr/oorj2qpISnHjmmSm/nsliQZbNhn4ZUc6RkYH3fv1rLKupQfHFF6NnYEDxXBUFBVF1bAY9Hnx55Ai2HzmCk/V6VC1ePKq+fufcc3HTqacCAJ586y1c98AD4W2/f+klqQ+Fhbh7VFdJHr9f/ofIbBbCOiHHAbm5AbS3W+P283hSYKtLElbfgeTdd6GQOsFFrcBCEADg88nfVy6XEYODyaWVIwiCIIi0YixSda1YIU3ELZbkgqEyMqRBZaK82QB7UpsGkHBCEARBEKMgMzMTtbW12L9/f9y2bdu24ZJLLhnX/uTm5uL444/HgQMHMBiRU1QuTVeibRs3bsTg4CDsdjvzWJ4Hdu2Sd4aIoiSmlJQADQ3SQxSl8dvcuUBRkfJraW1V3h6JzwfU10u101MFz7OFG1WOk2CQPQgeskqbTIDXG7+ZJdhE8v777+PWW2/FsWPH0NbWFred53lkZsanGArDccDMmbj7yivBcRzK8vPDD0dGhiQIAvDMmBG3EjssnLTKuK0AgBcE5A3Vx0l3Zi1ahPNPOw2vfPhhVPv9v/wldKnIVyfDKccfj3/GiJ0moxF7//hHlBcUQBRFfPCb3+BwSwsOt7Tg1ieflD1PRUkJHnn9dXy6axe2HzmCQ8eOhUXgB/LzRy2cRKbhymX8jjgTiDtjCctxYjZHT/wyMuQngn6/HqEQF3anjCdKacYEITlhQ60gksz5BQFob7dgYMAIo1FAQYEfDgctmE8HXC72kkNbmwV2O7lOCIIgiGkMz7NFB6WowETu/pjAN81wnCS6yE1WY8nJkSawaVDTJBYSTgiCIAgiSd544w34fD74GClxtm3bNs49Aq6//npcf/31EEURbW1tOHDgAA4cOICSkhLmMccff3zU89mzZ+Pqq6/Gt7/9baZo4vVKQoXMen0Ufr8kntTXj7QFAsDevYDdzk616vOpS9MVSVOTJMZElOIYFUquD1XCCStNFxDOw2U2Jy+c8DyPrVu3Ku5TXFyMt99+G0eOHMFNN90Uv0NBAa76xjfAyThjRACeykqEZO6BsHDC+JAKs7NhTFFdlqnAY889h4W/+AU++fxzmE0m3HDjjTjt3HPH7Ho//X//D59t3x7lKLnr298O1yvhOA6Lq6uxuLoagPR5vLllC5q7utDU2Yn2odojZaWleP7jj/Hpjh1x19ixc+eo+xkpnOQ5HLL7uH0++L1emK3xjo6xhi2cRCumNht7gd/j0U+IAKCUjkspjZcSWlJ1aaW+3ob+/pHfhL4+I2pqXMjKIvEknRFF5WDVwUEj3G49bLb0jFIlCIIgiIQoTTqVXCVjUeBT7vpqhBOLRaqjQsIJQRAEQRDD3HnnnUxx5L777sPq1avHuUcjcByH0tJSlJaW4vTTT1fc94QTTsDixYuxcuVKXH311Vi9enVU+p5YgkFg2zZJ3EhEMAi0tMS3C4KUMpXlEFFym9jt7DHZwYPA8cenZhypJF6oStXFStNlMIQjhFjnUSOclJeXJ9zn7rulJEg6nQ5XXHFFvBDGcRCrq8Ht2RN3rGfGDAQY9TSEoeinNkbx7+KxKDgziTFZLPjR3XfjR+N0vdply/DRhx/i5eeeg9vlwsmnn471JSXMidc1Z56JayLShvkDAbT09CBUUoI/PP20vHCiMlWfEjoVjhMA6OvpQZGK+znVKKXqisRoFGEwCLKprNxuw4QIJ0qOk2AwuRonodDYCCc+ny5KNJHg0NFhQVYWuQ3SGZ7nIIrK90t7uwWzZikEGhATy+AgUFcH9PcDVqs0cBttFDNBEAQxwmgmnVlZ0u9zLJWVo+pSGEaqb9n9cnOjoyXTBBJOCIIgCCJJOoYrocfwxz/+Eddee+049yZ5dDodduzYoSiWRNLRoU40ASThhJHNCU1NwOzZ8SKHKLKdLDYbcNxxwMaN8mPMgQFJqEnFGiwr+Ge4NklCFArDD79o1lg0VcLJMIIgYMuWLfjKV74Sv7GwEN7SUlja28EJAgS9Ht6KCqZoAiR2nJQOOR+IsaOsqgr//rOfhZ/zdXXQqyyOYzaZUF1SAvfMmVi6dCnw3HNx++xraIDP44GFZQtLhCCAi8h1x3KcAEBvV9e4CyeCwBYYYlN1cZzkOolf/J+4OidKNU5CIV040l8LY+U46e2Vd58NDiZIMUFMedS4nwYGjEndr8Q4EAwCO3aMDEqGI2dWrpSiWAiCIIjRwxq/63SJJ535+fLCSapSJqstQG+xSH8XjEZ2ofspCgknBEEQBJEEoiiis7NTdlthYeE492b0yIkmfr8fZpnBkhYHLst0EXmu2PXU3l62I7i0VDJszJ0r1VaR48ABKc2qUl3yvj5JADIYpHPKZQkaVWF4QLEwfKJzqRFOHA4H7HZ7VC0bJc444wz09fUhSyaXma+kBL7CQuiCQQhGY8JBOs9x+OLgQRxobpbdXpyogA2RcnirVbqxNSCYTFi0YoXsthDP48CXX2Lp2rVJ9YeLqe+Ta7cjx25Hnt2OXIcDeXY78hwO5NrtcKiNZkshSo6NWMcJINU5kZuXut0TM51S6j8gLVibTNpqr4yVcKIkLvG8SiGamJIopZQbRhA4+P06mM0COjrM6OuThLaCAj9ycwMkqEwkTU3xAxJRlCzD8+ZNTJ8IgiDSDaVJZ6I/ghUVUn7ryDlAVRWgVOdSC1qEE46TJuGMNZKpCgknBEEQBJEEfX19CDCiQ4rSZNH48ssvx6effoqFCxdGPYAFANRFGsotNEbS2RktnIiiVEReDo6TCs0DQGEhUFAAdHXJ77txo+Q6qamJr6nX2AgcPjzyvKkJWLYsPvMEK/hHdekOJcfJEKyxqM8HVRG45eXl2L9/v8oOQVY0CaPXQ1C7gqnTYdX3vw9BiF9g/tV11+HM889X3SciNfBJOEMEsxmzFixAhsUCT4SNLDszE8traiAq1elJgC5GODGbTHC+9JLsvq7iYox3bBrbsSHCZIq/r1l1ToJBHYJBDkbjiEgx1tHzggDZtGGx/TKZtNWNGKtUXTodW8AJBnXQ6+PfbyI9UJs2zuPRo7PTjK6uERHV7Tagv9+Iyko3dMllnyNGg9KALKK+FkEQBDFKRjPpNBiA5culyEOPR0rdpeDy1oyWVF2AlK6LhBOCIAiCIFhpuoD0EU727NmDjo4OdHR04P333w+333DDXbjwwjtTco2ODmDWrJFFxoYGadwnR0FB9PhxzhwpwEZm7R6AFBDZ1ydllPB4gFBIOv7Ikej9eF5Kx7p8eXQ7K/hHlXAiCOx8ZhGOE9ZYVFoYDZdCYfLTn/4UgUAA5eXl4ceTTz6JH//4x3H7XnbZZSo6rg6dXo8smw29Mm6XquJizJo9G+qSRhGpgtdYXD1ktUIwmaDnOFx33nnI4Dgsr6nBcXPmoKq4GBzHwZ+fjwSmMSaxjhPFffnxLwzNcmyYTILsIm1GBruPHo8eWVkh+P06HDtmRV+fCRwnoqjIh9JSX8pFlERuE2B4wTq+zzwPeDwGCAJgt4eiXqtaQYT1m8tC6fUHAjpYLCScpCtqHCeAlK7L6Yz/49rba0IoxGHWLBc5k8YbpciXNCz+SxAEMWGMatIJKaVXXp70SDVqHSfDfc3JSX0fJhgSTgiCIAgiCbxeL6qrq9HR0QF3TFR2OggnPp8PhyNtGRHMnLkwZdfxegGXS0qJ2turXE+utDT6udUq1b1TOsblAj74IHE/nE5JqIh0p7CCf1QXhhcZUdYqHCeANIZOJJxcccUVcW3XX3897rvvPnRHFJfhOA4/+clPlE+mkezMTFnhpM/thpio40TKEUwmCHo9dCpECEGvh7e8PLyi/atbb4VFxr6lT5RrTwFNwomGfVMFS3yQS9MFSAXijUZBNoJ+uED80aMZcLmke18UObS3W6HTASUlKotCqUTNYrTcPj6fDkeOZIbdNnq9gNJSHwoK/OC4xC6WYbQ6TgSBvX+yhezTnXSp+aH28+3pYf8xHBw0oq4uE7Nnu9LiPZkysIrNDdPUJA2csrOlhTL6cAiCIJJjVJPOMUaN48RsRjgSJyNDeq4m7/QUgYQTgiAIgkiCZcuWoa6uDgDgdrvDzozu7m5YJiBff6o5cOCAbBomAJgxI3XCCSC5ec1mYM8e9j5ZWfJBNDNnSnN7Vk0ULbhc0em6RpWqSylSM0I4UTqXz5dcelqHw4H33nsP3//+97F7924UFxfj/vvvx+LFi7WfTIFsRuf6XC6pTgoxvnAceKsVOpcrqrnf7UZXXx9qysrgLS6GYDIhmJUFMeLmY6X50nu9kr0giTw53oEB6P1+WFVM+mLTeo0HrFRdLOEEkNJ1DddfiMTj0cPt1odFk0i6uswpF06UCsMPI1eUu7k5I+pYntehuTkDTqcJVVVuRYEjEq3CiVIKsOF+Dg5KaZl0OhG5uYFp6UIRRaClxYqeHhN4nkNubgDl5V4YDNpq1Uwm1DpOEjE4aITLZYDdPv6/FdMSQZAswUocOjTy/4oKqfgcQRAEoZ1R54ceQ0wmSRhnBQQC0eLKcJ2T9vax79s4QcIJQRAEQYwSm82G6upqVFdXT3RXUsYehophMplQWDgrpdfq7JTSZbMCU/R6YMEC+WBGvR5YtAjYsYM95lRLrHDCdE3zHsCvV44CYuUby8yMWoTW6dhBOaMJ1FmyZAk+/vjj5E+gAhJOJh9tXi+2f/YZdtTVYWd9PXbW1aGhvR0ra2ux6eGH4Sstlf0isYQTThSh9/mSqp/yuyeewC8fewwZFgvyHQ7kZ2Uh3+HA2gULcMe3vx19nUnlOGE7djIy+Kjam8N4PAZ0d8v/HgSDOoRCXEoXv9Wn6hohFOIwMCD/vXS7Ddi7V30+7FQKJ4GADp2dZjQ3j9xjHR0W1NS4pt0ieUuLFR0dI4sPPT1m8DyH6mr3lA3mlxPwksXt1k+7e2LC6O6W3CRqaW4GyspSV4yYIAhiOqFUHH6i4TipH6wU1EC8KyU3l4QTgiAIgiAmF9/73vewfPlynHPOOSguLh71+Y4dO4YTTjgBc+fOxdy5c+FyufDcc88hMzMTHJfa4YPbza6jDgDz50eZNOJwOIDVqyXxJFExeiUis06JIhBkVKs2Hz0IHO2RbDCLF8cPakWRLZzI5H1lCSdK49PJQFZGBjiOgyMjA9mZmci22ZCdmYkZhYWUqmuCeH3jRtx0111x7Tvr6hDU65mpVHirFSLHgZOJJtO73UkJJ06nEwDg8fnQ5POhaahQpJwDZbxrnIgiW3xQcjpkZLAKxHMYHGT/LgYCOhgMqXuNyQgniY4RRfUL3NqFE/a1/X59XJomQeDQ2mrB3LkuxlHphygCPT3xkaV9fSa43T5kZo5/HaBUkMpUbD4fFTkZN5JZ8OruJuGEIAhCK6I4uR0ngHbhJM3qnJBwQhAEQRBTnCNHjuDRRx8NP1+1ahXOPfdcnHvuuViwYAG4JEJVf/rTn+KnP/0pdu3ahTvuuAOvvvoqAODSSy9PWb/VUF4OqCkZYzQCS5cCozFZRAonwSDbkWwaLnve3y/lFzvuuOgdPB525FBublwTK5hosqeG/cutt8JsNEInk8apd6qGR09x5i9ZItvuDwZx7k9+guffekv+wKE0XwaZmiYGlwuBggLNfelhiIf5jnhnw3g7TqT0QfL3qJLjxGRiiSocAgH2om4goFMsLq8VNemPYiP91Ygtakml44TlgnG5jOB5TJuC4MEgxxSY2tstqKlRiC6YpAiC+ro5aiDhZByRs9YlorVVKjxHEARBqEdp0jkZHCeAJIwoRSfGCidWK2CzKUdGTiGoGh9BEARBjAFerxcff/wx7r33Xpx99tk4cODAqM/55z//GaeeeioWL16Mu+++G8EhS8Rrr70Wtd/mzZtx++23Y9GiRehIlKOaQWtrKy655BIsWbIkLJoAwFlnnZv8C9BIZiYwe7b6/Y1GaYyWLC7XyLhVSbQwI2Jjb298BA7LbQJE5wIbglUSZ7ILJ2abTVY0ISaOOQzhBABMCSZfIcaXxzgwoJzXmAFTOMnKimsb7xonSiICWxxR3pbs9bQiuWXU1DiJdZykbtFZbS0UaV9tbpZIUlUfYyqg5Mzo7zeBUXJsUpNKtwmQ2u8RoUAolFzuUwqYIAiC0I7S7+1kcpwoITeZ1TKJn+SQ44QgCIIgUshtt92GDz/8ENu2bQsLGwBw/vnno7a2NunzPvLII7jpppvCz3fv3o2mpiY88cQT+Otf/yp7zMqVKxXTdgUCUo0Ng8xoIDc3F+0xqRrWrVuHM8/8Bnbtkj+fXg+kKuPOcO0SrdHGOTnJB7cIgmQWsdmUxrAijIjJ4eVyRQ8YWcKJ3S6pOzGwxqKTPVWXr6QEGc3Nce2BNLNnTyWsNhsWzZqF3XV1cdvOP1dZ9Aw5HEBXV1y7LhSC3uvVnK6rm/E9yJNznIxzqi7WAqzRKCj+5uj1gF4vgOe1LeCmUgDgeU6VcBEK6SAIIyWVJspxouQ2SYTfr4fVOgUVgyRIJDL09pqQlzfKQl7jTKqFr1Ao9fWCCBm83onuAUEQxPRBKVJusggnrCg/pe35+cCyZcDRo1LUT3n5mHRtPKCwDYIgCIJIIe+99x42bdoUJZoAwGeffZb0OUVRxP333x/VtmrVKpx33nl4++23sWXLFtnjzmUslIZCUj2QTz4BPvoI+PLL+HoeFosFGzZswPPPP4+f/vSn+L//+z+89dZbEEX5mAu9PrVju7lzk3OPyBg6NOEaSqnPLAyPAHSIWbSJTG+kVN9EJk0XMHVTdQWysyHKRJgGGK+TGB++ffHFcW2ZVivO+OY3FY8L2u2xd3YYw8CA5n70MCz9kam6/IEA2np6sO/IEYjjGFLPSvmjlKZrZB/t/UzlArIWASRStEit64VT7YAYTaqm6eU4URaY5OqfTHZSWRh+GHKdjAMyKRtV4fMl5U4kCIKY1rCi9YzGkeiXiSYZxwkA5OVJKa1XrAAqKlLfr3GCHCcEQRAEMQqCwSBefPFFXHDBBTCbzTjxxBOxdevWuP0+//zzpK/R2dmJhoYGAMBpp52G22+/HbW1tdi/fz9eeeUV5nHnn39+XJsoSmU5urtH2np6gL17pRohkVitVlx66aW49NJLw22sgulGo/RIRaBiRgZQUpLcsaMVTvbvl4Sltjb57eH6JpFELjK43ewBMMOJwRprhkLS51JSwtRcJhTRZIKrpga2hgboQiGIOh28paUIjvZDIEbFlbfcgiNHjuCJf/wDAFCYk4M/P/EE7DIpsqLQ6xHKzITRNVKQe9DjwQc7duBQTw+ukyk6z0QU8cH996O7txfdAwPo6utD98AAPt61C9f89re469ln0TMwAHeErar5jDNgk3GjjAWsxVc1oojJJGheV0zlYq/Ho96G19pqRVmZF0ajmNJUXYDkOtHpEi+Sjs5xMkkWDMaBRI6TwUED/H5dUsLdeMLzUio3g0FkCl86nagp3VskPp8eNtv4OtSmHckO5HheGrjIOGsJgiAIBsxovUkUMJHIcSKXviKNSO9XRxAEQRBjRG9vL/73f/8Xf/jDH9DS0oKnn34aV155JdasWYPf/e53cfsfPXoUoigmVai9vr4eADBnzhy43W584xvfwMBQBHhnZyeuueYa3HnnnXj99dfDx6xfvx4LFiyQ6Xe0aDJMd7e03p9ojMYqRTAsnKjBalWel8+YkXyqbItFeg1K6WI5jh0UGQpJ4gmLhMIJy23CcUxVRymIp61NehQXA7W1k29cGnI40L94MXSBAASTiXKcTwKMJhPue/xx3HbvvWhtasLsRYugV3njBBwO7N2xAxu++AIbtm7FZ3v3IsTzMOj1uOSHP0wsvgwjCJhbVoa5ZWVRzeevWYMNW7eiqbMz7pC+np5xFE5YjhN1wolWUumccLvV/wj09JjR12dEdbU75dH/0sI3CSepInE9EA59fUYUFU1eK2J7uxltbVYIAjeU9k7+/rDZQvB49JpT3gGAzzd97okJI1nHCSAN7kg4IQiCUA9r0jpZCsMDifuS5vM/GnkQBEEQhAbq6urw/e9/HxUVFfjJT36ClpYWAMADDzwAURQxY8YM2eNCoRD6+vqSuuaw2+TQoUPYsmVLWDTJzMxEfn4+li9fjtdeew2bNm3C+vXrAQD//d//HXceUQSOHGFfR81cOZHjRA1KKU71+uTdJsPY7extK1YAp58O1NQkd+6owvDDRL5xjPREsNuZqoeacXF7O7B5MzA4qKKT4w3HQTCb037QPNVw5OWhdtky1aIJAOxoacGy734Xtz75JD7etQuhodojIZ7H5++8o/o8rGLvcvVNhumTU3THAKm4OstxkjiSPRnhhOd1Kav/pMVxMnzturpMAPLfT4sluY6prXOipR5KLKl2yUxm1AhbXu/kfT+cTiNaWjLCTpJgUMdMiWcyCcjPT65ey3S6JyaM0Qgnk704G0EQxGSDJZxMJsfJZBJxJgASTgiCIAhCAw888AAefPBBuGMqkO/atQsXXHAB8vPzo9qNRiNKS0uxZMkSeJKcjA47TmKpqqqKcrCsWrUKGzZswJ49e7By5cq4/Z1OQKlUgZr5rlIaVjXCidkMFBayt1dUaC8IH0tBgXy7wQBkZUnr+0riihKyjhOfD+FVUdYbrFAwXa9X9955vVI9GiU3DUGMhnnLl6OYkRfuw/feU30ejiGc5Ch88XrHSTgJBjmIovwitRrHSbKpklLhOhGE5BbP2WmRROTnJ+dgUCuIjLbGyXQpmZDYcaJun4lAFKW0cGoxGgUUFflgMmkX7chxMg6MJucqCScEQRDaYKXqmkxixTQPjqORB0EQBEFo4Ac/+AFz26uvvory8nJ89tlnOHToEHp7e+H3+9HS0oIdO3agLCZtjVpYwkl1dbVsu1yKLlEEGKcJo2a+q+Q4URPYbrVKD7mMPzpdaurGFRXJ96WsbGTcl5mZ3LllHSeAtNAQCrEjNROkIFI7Ng4EgI4OdfsShFY4nQ5nrF0ru+3DjRtVnaOvqwtPP/ggnnzrLTTHpOTS6/XIstnkj3M6tXU2SZQi1scqVVei66pFcpukbvJqNIrIymL8qCdAvXCSfH8FgRvV8VMJdcLJ5Hwv+vqMmu5vk0mA0SiipsbFrJPDcfLtPp9+2ohpEwLPsxfx1JCKQncEQRDTiangOAHYc9mqqvHtxwRAwglBEARBaGD27Nk4+eSTZbfV1NTAZDJhzZo1mD17NrKzs5OqaRILz/OwyBRlYwkncvT0sLNIDTNa4USNa8I6FJQ6Z068uFFbm5rgGqMRmDcv+vzZ2dHjOrM5ufFoLhiLu243EFFUO44EFhctr1vpMgQxWk4//XTZ9oa2NoiCsmiw/ZNPsGjJEtzy61/jugcewOIbbsCbmzeHt4sGA/IYdVKcPT3Jd1oDrDRder0AgyHximyywkkqHCceT2qLHJnNPMxmAUZjMunHxl44AaZHnRNRVPc+TVbHSUdHgqKxMRiN0vfMahVQXe2CXK2c8nL5BXhR5CatgJQWjFb4IMcJQRCENqaC4wRgp4xQSiWRJkyyEqMEQRAEMfn5xS9+gZNOOimu/eyzzx6T6z3zzDN4+umn0dHRgfr6etTX16OhoQGrV69WdbwoAocPJ95PzXx3tMXhh4WTrCxg9Wqp8DnPSy6RZF0gchQVSWKJyyWlwhpO0RVJaSlw9Kj6c1aiAZlwy2/0eNgDX4Nh5IUz0DI2Hk0wKEEkonLWLNn2EM9jsL8fDkbaOT4Uwk3f/z7cET8kfS4XLrz7bnz++99jaU0NRIMBt199NQSPB3kOB3LtduQ5HMhzOJAxdy6SkyQSI4pAd7cJ/f1G9PfLK6ZqU3Dp9SJ0OlEh/ZU8qRBO3O7U1ncwm4Wh1IVBOJ3aJujjkaoLGH7fUlQgZpLC8+z0cZGEQhxEcXJlzHC59HC7tS0pRNYSysoKYfZsFxobMxAI6GE0Cigv9yArK4jm5gzZ430+PUwmxmCEGB2jqW8CkHBCEAShBUFgT64nm+OkrAzo6oqOxJw5M/n811MIEk4IgiAIQiMnnngibr31Vvzyl78Mt1VXVyum8RotHMehuLgYxcXFWLNmjaZj29okQ0QixsNxkhGxDmI2A5WViY9JFrNZWZCorJSEleHSCno9UF09ki7M5fRjYEc9eOiRjT5kQaFAjNJig92ecKVLxlDEhIQTYiyZWV2NX1x9NfKzspDncCB/SNjIz8qCWeFG3fLBBzjY1BTX7vX7cf5dd+GLhx9GVmYmvn3uuTDK1ALy6XQYqyQvTU0Z6O5WFgbUCiccJy38er3aplGpcE6wHCeFhT64XAbNjpTh12y3hzQLJwnMR2FGUxweGE5xllw6samCegeFlLps2LExGdDqNjEYBFgs0TePwxHCokUD4HkpZefwn0uDQZAV3nw+PRwOEk7GBBJOCIIgxg+lwpWTzXFiNALLl0tFUz0eKSoxO3uiezUukHBCEARBEAn4+OOPcccdd2DRokXhx09+8hOsX78eb775JmbOnIlLLrkEeXl5E93VOHgeqKtTt2+i+a4opi5V12TAYACWLBkxi2RlRRemd1iCcKBF3ck8HnbEkIpInAQlUKIg4YQYS3KKinDbZZfJbhsQBGbs/6svvsg8Z2NHB5544w38+Hvfg6hjpMoao9z4/f2GhKIJEB0FnwiTSdCc0Wa0jhOeZxfGttlCKCvzoqPDgp4ek+p6E8Ov2W7XvghNqbpSh5YUXMGgDkbj5HDg8LxU30QLOTkBZhyBPua2tVh4uFzx7810uCcmDNYPW1YWMDiYWDENBqWxkJqidwRBENMdpUndZHOcANIf6oKCie7FuEN/0QiCIAhiiP7+fhw8eBAejwennnpquH3r1q345JNP8Mknn0Ttv3r1anz++efj3EttHDumfqE9FFKe7/I8mEVZp6JwAkiRrTab9IiDpRLJobSgoEIVyc2V+qDGGRQISJdirD8TxOjQ6SBCvgQ5xxIHRRE/OOccBAcG8NTbb8dtvveaa/DTSy9FwGAAz4ig03s8SHUeIkEAM91PLGodJ0BydU5YwkkwyKG/3wifTw+/Xwe9XkROTgBZWdHvteQmkX9vbDYeOh1QUuJDUZEPO3Zkq0r9NPyazWYBVmtIxkUjIjMzBJcr/sedhJPUoU04mTx5urxePVj3JIvcXIXo2hjMZkG2pldPjwlFRT6YTJPHeZM2sBwndrsknsi4CuPw+VKbe5UgCCJdYU3SdToSoCcR9EkQBEEQ05aNGzfiySefxKFDh3Dw4EF0dnYCAObNm4d9+/aF99u9e7fs8Q4tNoEJoqND2/5K810lN7Ea4USvVyeuTBq0CCdKUZgqHCc6HbBihVSLpq9PEpjy8oBDh+T3DwS0pfciCNVwHESDQVYk0TGEE8PgIOaVluLJ//gPuH0+/P3jj8Pbrli3Drd+61sAAMFgQIjxA6Pjeeh8PggpVFe7uswa3BdjK5yEQro4wbO724Smpow4kaOnx4zycg+KikYm1Kz6Jnq9ENUfnQ6wWnlVabsiX3N5uReHD2ciciG8qMiPUIiTXbxWI5yI4uhTdaWiNsxkR4sYEiuyDAtver0Iuz0Eg2H8xARJOFGP0SjAZlPvlrFa5ffleR3q6zMxd+7gpKr3khawhJOMDCmPqdkMdHZKAndfn/y+JJwQBEGog+XyM5snV0GzaQ4JJwRBEMS0pbm5GU8++WRc+5EjR8DzPPRDeSN27dole/yiRYvGtH+jRRQhu+ClhNJ8lxVsDkiCSKLAmIyMKTYGVHrBatHrowu7KGA0AvPnjzwPBtnCic9HwgkxdogGg+z9z3KcmHp7pe0chz/9x3/gQHMzAsEgXvn5zzG7rCy8X8jhAJ+RwXS0GNxuBFIknASDHFpb1Z9LS6ouLSJLJH6/DlardKzHo0djYwZYEfttbRbk5/vD6YtYi9Q2Gx/3u2qzhRIKJzqdCL1+ZJHd4QihtnYQ3d1m8DyH7OwAcnKCaG6Wfw/VCCKS22T0wkm6O+y0OE6GhSRRBNrbLWhrs4SFN5OJR02NK3yPjTU+nzbhRClNF2v/Y8eskLuH3G4D2tstKCmhmhopg+fZ0c/DA7iZM6UHAPzrX/L7U50TgiAIdSiJ1cSkIY2HoARBEAShzJw5c2Tbg8EgGhsbAQChUAj79++X3W+yCyc+n/oCvsMo5e1XMmAYDNKcWslRMtnSdCVEi+OEhYrC8CwMBvZiIdU5IcYSgaGCygonogjjkHACADarFa/ffTc2Pfgg5lZUQDd0EweysyW3iU4HnjEhNKjJVaeSnh4TBEHdd4/jRE0Ft5UcJzZbCID8ufr7R34gW1rkF4SH4XldlPjBEk4yMuI/EzVR/RaLnODCY+ZMD6qr3cjNDYLjECWuRKLmvR2t20SCS3vXidYaJ8Egh8OHM9Haao1yKwUCehw7Nn6LLVodJ1rSdAGAySQqCiPt7Rbwk6PcS3qgNACUG8CxojfGqF4VQRBE2kHCyZQgvUehBEEQBKHA7NmzmdsOHjwIANi/fz/8jFXqyS6cKK1BslwlSoGCSoXhhxfglFwnU044SYXjREWaLhYcJzm15SDhhBhLRMYXWS5Vl7G3F7qY1cuKwkJk2WwI2Wzw5+fDXVkJd3V1+IciJFtUCNBH/mixCiqpxOVSb6w3mwVN+qaScJKZGWJub2uzIhDg4HLpMTCQOG+hxyMtTIsiO7pfLp2RJN4o43CoE4ZZwol6x8noSfc6J1pSdfn9Ohw6ZMfgoPz9MzBgHDcxQYtwYjLxyMjQ3rGSEh8yM+XvVUHgNH3PiQQoDSzkBnCsQV0KHCduN3D0qFRShbWuSBAEMekRRaC7G2htlephxsL6gZtyk+b0hkYaBEEQxLTFZrOhvLwcx44di9t26NAhnHXWWSgvL8df/vIXbN26FVu3bsW2bdvg9XphNBoxb9482fN2dHTgoYceQldXF7q6utDZ2Rn+f319PbKyssb6pQFgp+myWKRC5HLblea7rBonkS4To5EdbDjlxoCpcpyMArNZ/v0k4YQYS1jCCRe7IsvzyJD5/QQAUafD4Jw5srYp3mYDurri2vVeLwyDg7C2tEDv8UAwm+GpqEAoiXpSWlwKWtJ0AYDBIMJgEBAKxV8jNzcAUQQ6O+MXlQWBw7FjGaoFhWHhxOfTMYu9WyzxIo3ZLECvF8Dz8u+BxcKjuFjd4ubohJPUCB5SnZoUCNmTFC2OE5ZgEonHY4DdPrbvVyjEafp8S0p8SZkvOQ6oqnJj794sWZfT4KARWVnpe2+MK0qDPDn7K8txMkrhpKsL2LVrRDuvqwOWLAFyc0d1WoIgiPGF54Ht24H+/pG26mrpAUgBeqwJHSPAiJgYSDghCIIgpjVz5syJE06sVitcQ6pCTk4OvvWtb+FbQ8WNQ6EQ9u3bh7q6OliGJo08DzidUlqs7GzA5XLhnnvukb1eV1fXuAknLMeJzZbcfFfJcTKMUh76Kec6ZjlO9HqoDukd5WdNjhNiIlCbqsva2god44chkJ3N/EGQc5yIooimzk5Yenthz8kBAOh9PmQePozBefOY6b3kEEWoLgoPQHNBbY4D8vP9aG+PVoMzM4OwWiVRorvbLLvQ29trUn2d4Yh+dmS/CIsl/reI4yTnS39//LXMZh5z5gyGa6ckYjI4TrSmhJpKiKI24UQNXq9+zIUTpc+kpmYQra1WeDwG6HQiiop8yMvTlqYrEpNJRFZWUPa7MzBAyxkpQ80gLxLWQHIUA5QbbvgerNaFOPvsG8NpHnleqve2atUUq5NHEGMIzwMDA9L/s7PpuzEpaWyMFk0AoL4eKC6WJsVa0yMSEwaNNAiCIFJAX18fsrKywNGoZcrxzW9+E4sWLcKcOXMwd+5czJkzB2VlZeEJWywGgwGLFy/G4sWLAUgO2+3bRwQHgwGorCxgXq+rqws1NTWq+vbll1/ia1/7GqqqqlBdXR1+VFVVYe3ateHi9SyUhJNkMiywdITIObWSSWPKjQFZL6akBGhvT5zKy+EYdcTQGKxLEERCmI6TiHte7/HA3NkpfzwAX3Ex8/yC2YyBQADb9+/Hpv37sfnAAWzavx/tTifuuuIK3Pntb49cE4C5sxOeykrV/Q+FONX1TQB1qa1iKS31QRC4cC0VhyOImTM9Q7WeRJSVedHcPDq12OvVQxDYi9QWi8AUq0tKfHHCidEoYPZsl6Z6LmMhnFitIVitPJxOhjIcg8+Xvqm6BEFdvRgtjIfQxPpM9HoBDkcIDsdg2DGgFFChFrtdXjjxeg0IBjkYDCIGB6XfrYwMXrMYSoA95jExxF5WO8u5koA333wTTzzxKADggw/+ih/96I8oL5dqEbpc0vh0yo0jCWIMGBgAdu8eWXe324FFi6ZggFo6I4pAS4v8to0bpckz67eS4+jHbpJBwglBEESSOJ1OvPrqq/jHP/6B9957D5s3b8ayZcsmuluERr73ve+N6viDB6PFhlAIaG62w2w2y9ZG6ZJJT8Oirq4ObW1taGtrw+effx5uN5lM8CYovimKysKJkpNBEOQXOtQEIyppCSwRYNLCejEmE1BYKOWrVaK8fNRdYK1LkHBCjCU9g4PY9OWX6B4YQM/AALr7+9Hd348QgHv+9CcAgLmjg1na3F9YCEFp0sdxuPQXv8BbEb9rw9z15z/jmvXrUVFYGG7TWjRea00MNcXUY+E4oKLCi/Jyb/h5JAUFfnR3m+D1jma6xcHr1WuqbzKMzSY5S1pbLQiFdLDZQigr82oSTQD2orcocsy/FcOwxBWDQRKWBgeNqtwWXq8eopieEbWpdpsAIynexhKWOGO18uHPKZWfl8PBHlz09prQ2WkOu8wMBgGzZrmQmUmV4zWh1XHCGqAIgjR+Uip6F4Pb7cb1198Qfr57979www1LcMUVP8eFF/4Ier0Bg4O0lkgQoggcOBBtVhgcBA4fllLaEZMEt5s9WRNFZYHZak3PAc8UhoQTgiAIjTzzzDN4/vnn8f777yMUsbD6wgsvkHAyBfjoo49w6NAhLFiwAAsWLEB2dnbS5xpO0RWL388hL68Ara3xuf87GRHacrQyFuZnzpzJdMSM9IGdTcpmU57P+v3yk1M1c+rsbKCjQ36/KTcGVHrBOTnKwonRCBQVjboLSo6TdF1IJCaeTTt34uL/+q+4drPRiLsFAaIoYvfWrVhWWRnnfBOMRnhLSxNe47hly+KEkzyHA7PLyvDse+/hp5deGv6d0/t8bEVXBi31Tez2YFJFq4dhfQc5TnKl1NVlJn1uQFqgVlqkVsJuD2HuXEaxK5WwHCeA5JTQ6djbWTUw9HoRJpOI2bMH0d5ugdttgNXKIysriMbGeJcez+sQCnGaRZ+pwFgIJ+MhNCm5oMYCs1mAycQjEIi/bqyzKxTSoaHBhoULB+hvpBZSJZwA0qKgBuHkmWeejRszBwI+/PGPP0Fd3Q7cdttf4HJJMSsEMZ1xu0dSdEXS1SV97ZS+lsQ4oiFQMg6yDk06SDghCILQyLPPPov3338/rv0f//gH7rnnHkrXNcl59tln8aehiGkAKC0txfz583HhhRfihhtuUDgynkBgpHhlLFlZ8sKJFscJS2QpUrEgrxSgbbMpL6iw0iGomVMXF8sLJwpZeyYvrBdsMEgKkdnMjiYqLYXqIgIKsJxBPC89NKxLEIRqcvLzZdv9wSDcg4NoPnwYa2+8EY6MDKxdsACnLF6MkxcvxnGzZyNQVaXq3l9xwgnAww+Hnx83ezZeuOMOVDJ+LPQeD/hMdSIEq76JxcKjtNSLjg4LBEESFsrKlN17o8FuD0JKXKY8LtDpRJjNvKw7xeUyMB00iYSTVKAknPA8p5gSidXv4WOsVgFVVZ5wezDIfp98Pj2MxvQqAi6KQFeX/I+8TidCEIBE9478eTn4/boxEzEAJOWCGi0ORwjd3er+rgYCkuA4GlF02qFUHF6ORMKJhsW/5577u2w7x3E499ybAEhR9QQx3RAEac42PG+TE02G6esjcXHS0N2d/LFUGH7SQdNtgiAIjVx00UWywsnhw4exa9cuLCGf7KRm7969Uc9bW1vR2tqKhQsXaj6Xkst2xYrTMHfuTBQWFqKgoCD8OO6441SfnyWyFKoYFbOEE5NpZA5sMMhno2LVOVEjnOTlSeO9yOtLKW0SdnlyIYrKRV04Tqp1cvSo/D4pSNMFsIUTQPqcVK4jE4QmWMIJAPR1dWHTJ58AAAY8Hry1dSve2roVAJCdmYmDhw+DscwWxep161BRWIjmIYF42+HDWHD99bjj8svxowsvhDFGFTRoEk7kF+zNZh45OUHk5CgUY0oher2UMsvtVp5yZWaGYDIJssKJ02kCa/FcrjB8qkkknLAIhbhwzYlYTCb5BX2jUYTBIMg6Vcaj4Pl409lplq3bAUjvUSjEMevEJMLpNKGoyJcK/T6OYJBjuonG8p6024Po7lZXFwcYEeL0Hg8s7e2AKCJkt8NfUEB2TTm0Ok70eskFKMh8nzXUOenq6sKmTR/LbjvnnBuxcOFaAFKdE4KYLgQCwL59UmYDgwEoKwOqq5Xriff3k3AyKlwu4Ngx6e9DYaGUXSAZAoH4ovBaoJyEkw4STgiCIDRywQUX4Hvf+x4EmYnCP/7xDxJOJjGiKGLfvn2y2xYsWKD5fErzwu9977c44QTNp4yC5TgZjXASue5oschPROUG5aLINldEzql1OuC444AjR6TIJ6sVqKoCsrISdnlywfNsO9HwCy4tBRob4/crKEjZoFdJOPH7STghxoZchd8YZ1cXPtu4UXbbvKoqGJVu2giMJhOe+/OfceG3voXO3l4AgMfnw0+ffBLPvv8+Hv3+93HSokXh/fUJ6jpFwhZOxi4Cn4XdHkwonDgcwaGUV/HvnSjKL/BynDgur0cpFZeScNLXZwRL8JGcOPJYLDxcrvjPL90KxHd3m3DsGPvvhMMRxOCggSlQJKKtzYr2dguKinwoLfWlVCdguU2AsXWcaBXOQiEd9C4XHAcPhttMfX3Qu93wVFWluntTH63F4TlO2iYXbaNBOHE6B3HyyRdh8+Y34PVGD0q/9rURJ7jXq7l0CkFMSUQR2LFjxF0SCAANDZJWqZRRYDRr9dOe7m7pTR+muRmorU0uEK6nZ3R9oVRdkw76s0MQBKGRwsJCnHrqqfjggw/itm3atGkCekSopbm5GYMMr38ywolSgW63W1NKflmuuuoqLF++HJ2dnejq6kJnZyc6OztRXV2d8FhWZF6k+1eLcDJcYkCOWI3AZALmz0/YxcmNUpX74Vl7RgZQUyNVZBzGbJYG2ilCp5N0Grn1DCoQT4wVjtxc6HQ62QABZ1cXPt2+Xfa4tatWabrOwpUr8dGHH+K6K67A57t2hdv3Hj2K7/7hD9jx2GMw6PXo6O3F/sOHsWTmTFXnZdU4mQjhxOEIob1deR+7PcTUaVlEFuEeS3Q6SaSRE3CUhBPJKROPycTDZmMvrlutPFyu+Ah3Vk2NqYYoAu3tFrS2skUTk4lHSYkPPp9NMbo48bU4tLdbYbEIyMtTv5CdCNZnYTAIiqnbRovRKMJqDck6s+QIhThkdMWnTDU5nfCVlkJQKfJOC0RRu+MEGJ1wEgwCTifsXAFuv/2v8Pu92LbtXXz66YvYuPGfcDjyUF29OOoQl0vKlEoQ6YzLJZ+Sq7lZWTgcGBj93HNaIorAgQPx7XV1UnYBrdbN0aTpAkg4mYSQcEIQBJEEF110UVg4mTlzJi666CJcdNFFOP744ye4Z4QSsWm6Ipkfs9J/5MgRzJw5E0aFCaPSvFAUpYGvw6G5m2HOO+88nHfeeZqPE0V2RFKkcMIal3k86toSnWdKw1pAAKIXEWbOBHJzgd5eaWBdXJyS2iaRmM0knBDji06vR57DgWAohDyHA/kOh/RvVhYGOjvDDpFYVp94ouZrFc+YgZffeQeP3HsvHnv2WXQMnXtVbS3Ov/NOfHnkCFp7emA0GNB8/vkwWSyK5xMEJeFk/Osd2GwhpvAASIvNVis/JJwkrocyzHjUNxlGrxdlU0axhJNgkJ2mKycnqCj4sOpyKLkcpgo8DzQ22pjpuQBJpJo1yw2DQYTRmBqhr7fXmFLhxONh1xAaazEvNzeAlhZ1Sxh8QIRBZjDEATC4XAiQcDKCILCjYxIJJ3IkEk66u4G9e4FgEF1YDMAOs9mKNWvOxZo15yIYDKCjozGubiQJJ8R0wOmUb/f7lcf+giDVAppyLv+Jpq9PXgAOBiUbT26u+nMJwugcJzqdcroBYkIg4YQgCAJAT08P3n77bWRlZeH000+HNUGanQsuuAD19fW46KKLsGLFCioIP0XQ6XRYvXo19u7di4GIUJ7y8nJkRYwy29vbcdlll2Hr1q0477zz8Pvf/x4zZ86M+5wTzQtHK5wkSyDANkykWjgxmdI0bYKScBL7gu126TFGmM3yziASToix5NjLL8Mk80Pyz88/h9lohD/mO6LT6bDilFOSupbRZMItd92FW+66C/u/+AJrv/pV/GnDhqh9gqEQDu/ejQUJAhQk0UT+bzKrtsZYotNJNUwGB+UXH+32ULjwq8UiqBYIxlM4MRhE2b8prGLukjAgvy03V/kPJ+t1hUI6BIMcjMaxczSMJT6fDkeOZMLvV/58Z870hAuap0o4SXRNrbCEk/EoxF5Y6EdPj1nV90T0Sr9Roiii3+3Gsa4uHOvuxrGuLlQuXozjtASmiKJkx03XQY/SgDbVwgnPA/v3A8EgeOjQgzyZS5pQWDgDR47sQEPDLtTXS4/S0gK89tpz7HMTRBrAqjUpiiKuv34hCgoqMGPGPMyYMQ8zZ87HjBnz4HDkIRDwo6NDQFYW1cjQBCM1NgDJxqNFOHG7lbMWJEIQqAbXJCQN/+oTBEFo4+2338Z5550H/9AqZG1tLd58801UxeQ/FkURPM/DYDCgqKgIv/71ryeiu8QoWL9+PdavXw9RFNHS0oK9e/di7969UYJIMBgMiyYA8Oqrr+LVV19FQUEBjj/+eFx55ZW4+OKLASQWThhZwcYcpfy3aoSTYFB6bZHzYZZwkpZuE4A96B0uhjqOsALsSTghxhKd2Sz7PTh3zRr0vfIKth48iE927cLHu3bh8337UFtZCXsKwhxrFi+WFWYAYPe2bQmFE1Z9E2BiUnUBysKJwxGM2m8yCicmk7ygEwzKv9e9vfKv1WzmE/Zbqbi4z6eH0Tj1CsSHQhwOHbIz369hSkq8Uc6QVIlELAdWMggC2/0zHsKJTgfMmuXCgQN28Lzy6+L9Am78/e/x3AcfwBWT8+w7F12kXjjp7wd27x5ZzczKkmqcFRWlj4ii1mUbSzLCSU9PeADTgzwIiL+fNmz4E373u+9AEKLvqcbGYvZ5CSJNYJm/urqOobFxHxob9+GLL96O2qbT6aHX6/Gznz2KO+64Zhx6mSaIItDVxd4ulzNNCVaubLUUFIzueGJMSJO/9ARBEMkRDAbxrW99KyyaAMCBAwfw4x//GC+++GLUvseOHcOMGTNgt9uRm5uLnJwc5ObmIjc3Fw899BCKiorGu/tEknAch/LycpSXl2P9+vVR23ieR05OTtwxXV1dePPNN/H9738/3DbVhBOjMXqOGymixDIcWDnMtBNOWIsIE7BIwlqXIOGEGEsEhXvdYjLhpEWLcNKiRbj98ssRDIXQmKIUdUaTCQtnzcI2mXzTu3btwqUJjmctEhuNwoTl/XY4gmhrk48AjSx4XVDgR3c3260xjMEgIDNz/AQElvNB7r3mecDlYqXpCiQMpDQaRRgMgmxRdK9Xr7lA+GSgv9+YQDQRUVHhRWFh9I+6GseJ0ShAEKAoIggCB55PTRZJn0/PTDuXkTE+n43FIqCqyo0jRzKh9F3hh/6Mx4omANCSqPBQ+CS8VDA4ckzQ3y89GhqAhQvTI3fUeAonEe99JwpldykoqIgTTQCgp6cdHR2dKCqSP44g0gGW46SpaT/zGEHgIQg8tm7dCICEE9W4XOw3HBh/4WQiUlUQCaGyQQRBTGu2bNmCXplc7S+99BKcMQlGh58PDg6isbERO3bswAcffIAXXngBOqrClhYMDg7ia1/7Gl566SXZ7VdffTW++tWvhp8nWrgeHITmgr+pQE19E0Ca77IWUmLPofacaQPLcaK0gDBGsBwnSuN8ghgtogaR0GgwoLimJmXXXhJTc2qYnQp1qoZhpSWaiPomw9hsPKzW+N8Uuz0Y5YLJyOBRUJBYES0t9Y6rCMRKcSYnBkiFw5NL0zUMy3Xi803NsRYrtRUg1TSprnbHiSbAcBo35UGE1crD4UgsWCRyu6iF9Vo4TmTWpxkLsrJCqKpyQ6djvz8hUY8KRvRuS0eHugt1dbFFBZ8P+PJL7QtrkxGlwvBKaidLOPF4gE2bgH37pEicSIaiikQATsinwFm4cAnzkl98sZvdH4JIA5IRTobZvftzmh9oQSlNFyB9GIkiJSMZjXDCcZKbkZh0TM3RJ0EQRIqoq6tjbnvvvfeinscKKZHIORSIqceLL76IDz74QHbb8ccfj0ceeSQqrVeicRTPx88XxwO1IgfHqatzwvPsQfy0c5xMgHDCqhEYCLDt/AQxWrQIJwAQysxM2bUXL14s277r8GEIvLIAwkrVNRH1TYbhOGDGDE+Ug8Bs5jFjRryVr7TUB4OB3desrADy81NX6FsNWhwnknASj06nfmGdlc6Lle5ssqMkWtTWDiInR/7vjcEgJhTSLBYexcU+6PXK7+1wH7xeHTo7zXA6jUkFdijVNxnvtOy5uUEsXNiP7Gz570MQRpSzhJNEi2XD9PUpbx92pEzEYC+VsAa0icY8LOEEkBYQW1uB7dul9wmQBpND75UXVgQgP8A57rhC5OTIO/m3bNmu3CdCM6EQ0NICNDYCHR3SHGAiAr8I6X1/++0XsGXLW2hs3Aevd2QhvqlpX8LjGxv3oampbwx7mGYopekaRksKiWSFE4sFWLaMCsNPUihVF0EQ0xolwWPDhg3hWhYAZJ0pAGC322FIlxzH05yrrroK+/btw29+85uo9sLCQrz44ouwRIT+8/zIPFCJwcF4cSEYDMI4hgvwWtwhGRny48FI4URpPSBthROW42QCvussxwkgrUGk7WdATChKqbpi8efmahZalDj59NNxx5EjyLRakWm1wj70b6bVCjEYVMw5xBJOJqq+yTCZmTwWLOgPp7Gy20OyrhGDQURFhQcNDfFClNEooLLSM+4L1GzHCQdRjA5IZwknVqv6hXVWrQyfTw+fTzeuzoZUwEofV1DgS1gXpKzMi4EBI7OuiNXKIyODR23tILq7zejokP+DEQjo0N5uRkuLFcOOoPb2EGbPdmmqpeLxyH/PxytNVyxGo4icnAD6+uIX8EMwoDw/X/a4rr4++DweWBL9Ae3vT9yJQEAST1auTE0+tIkg2WARNYt8Xi9w8KB0rmPHws39YNfEyssDZs9egi1b3onbtnnz5sTXJFTj8UjaVmyAlM0G1NRIw96mJukWycoCqqun7m0+FQgGgd/97kYMDPSE23Q6HQQNkVIbN27CnDlnjkX30guPR53QMTAg/SglIhjUnkf5xBOl30b6Uk1qaKWPIIhpzde//nXccMMNePzxx+O29fT0RD1nOU5yc+Vt5sTU5Fe/+hUqKipw7733oqurCyeddBKefPJJVFRURO2n1rU7OChFRX722Wd466238NZbb2HJkiV49tlnx6D3Ur9YfZMLCFfjOGHVN+E4wCqftn/qM4kcJyScEBOBWiEkmJkJb1lZSq9dXVODn195pey2fo4Da/lAFNmL1BMtnADSvDgrK/ECc05OEAMDfvT0jCxK6nQiqqrcMBjGPwyYvbDOIRjkYDKNbFcSTtTicLDrLfT1GVFcPLUKPAWD8oqRGgFIpwOqqtw4cMAuW1vEZguFz1Ve7kV/v7zI4nIZ0NMTXT/H6zWgs9OMsjJ1eV1Eke040fL5pprY74QoiuA4TtZxwnEcSnJzUV5QAJfTqSychELqI43dbsldETNWnDIkO+ZRcpxE0toa19SHbNldbTYRRiOHJUtWYsuWd6DT6VBZuRC1tatQW7sKa9eeqO6ahCqOHJF3lbvdwM6d0W19fZKWeNxxyhnciOTp6XFHiSYA4kST1avPRV9fJ/bv3yR7jrq6QwBIOEkIIyg2DrXpGFmRiywsFuVJHjFpIOGEIIhpz5lnnonHH38chYWFOPPMM3HmmWdi3bp1yI+JUiPhZHrAcRxuvvlm3HzzzYrOEDXCyd69n+Pee3+Lbdvew0DEoKutrQ2CIIxJbRyWyAGwHSes8wxHErPGgVYrJqzY8pgziYQTg0F6yJlgKI8xMVYMCycHmpowu6wM+phoOFGnw+DcueCt1pSvoIhKkXcKUZc8zzGLZE9kqi6tcBwwc6YHmZkhDAwYYTIJyMvzw2qdmNeg9N4FgzqYTNKiuSimRjgxmUTYbCG43fFT1b4+05QSTkSRnapL7T2ZkcGjvNyL5uboP9gORzDunjCZBFnhpLtb3hngdJpUCyc+n06hMPzECSd6fbRwsm/fRvzqV9/G8uXrcNGiPDx2yy1YVFWF8oIClOTmwjj02zaYlQVFGVNLehZAWlEeD+GkuxtobpYGorm5qbEAsMY8iYQRg0H6wdKY16lnYAAbDm5B9Xw7bLZo50m2NQDAjMsvvxJz556BOXOOg9U6Evmj1yPO6UYkB8+ry1QUybB4kp09Fj0i6uqaEu7zzW/+CEuWnIJf//pK7Nu3EdnZhSgurkRGRhZstizMm3fcOPQ0DVCbYlGtcKL1b0aRfDpCYvJBwglBENOedevWYfv27ViyZIniQva//du/YeXKlXA6nXA6nejt7YXT6UQBI38yMfVRSqelxonrcvXiww/jC813d3fjiy++wMqVK5nHbt26FW63G4WFhSgsLERubq4qoYXlODYY5Oe/LOFEEKTXaLGwxZi0dTpIYbXy2yYoLZ/FIv/ZknBCjBWCwQCPz4elN94Iq9mM05cuxbrly7HuuOMwq7QU3pIS8GP0IyAq/NZxCsIJy20CTGxx+GTgOCA/f/zrmcih14vgOFF20TwQ0CEjg4fLZYDbrWcKV1odCVlZQVnhxO3WIxjkNKWXmkhCIY4pNrBqx8hRWOiHwSCgq8uMUEgHhyOIsrL4RR8t5wSAQEAPnle37s5K0wWIk8pxcvToHrS11eONNx7HG29IaW5WzJmD89eswa3f+lZ4P12iCBg1aboi0ZqiJRm6uqJtAIOD0qOoSCpOAQBlZUBhoTZlIdlgEY6TBpcaX/uG7Tvwo1/cDY7jUFW1GAsWrMXChSdi4cITMb/ICsCMJUtqEAzWxB3L81JATwrLak1b+vuTq2XidJJwMlbU1zcm3Ofo0T1YsuQU/Nd/PSO7vaR46gSKTChqJ1F+v/RIlJqQNQnPzgZycoCGhpE2mw2orFR3fWLCIeGEIIhpj81mw7JlyxLuV1paitLS0rj2/v5+bNu2De3t7Whvb0dHRwfa29thMplw//33j0WXiUmAGsfJkiWnwWg0IxiMn1C++eabisLJz3/+c7zxxhvh5zqdDvn5+bjllltw2223MY9Tqm8iN4dWWvf0eKapcNLTwx5MT5ClmoQTYrwR9Xr8a/du+INB+INBvPTpp3jp008BAOUFBXj79ddRUlw8NhfX6SAiMqnQCJxCcSlWfROOE6fMQvtkhOOkBflAIH513e/X4fDhzISF27UurGdnB9DaKpcLkkN/v3FSCEpqUCoMr9UFlZsbRG4uO40ZoF04AQC/X6/KMaKUpmsi3aexjpOmpv1RzwVBwJYDB6DX6aKEE44lFgxv1yqc+Hw4ePAgPv74Y7S1vXNahgABAABJREFUtUU9SktL8corr2g7nxyNMouqTqf0iHxeXAzMn6/eFpxscXhAtXAS4nn4AwHYrFZ8vPcQACmtWn39TtTX78Rrrz2C8vI5OPjPfwLIh93ONrP095Nwkgr6+tjbBgd7cccd52Lduitw2mnfinL9qA3AJ7TT2JjYcdLYuFdxe8gbBEBFxhOiZRI1OJi8cJKZCcyaBeTnSz9eBoP0G522aRvSDxJOCIIgkmR4IP/qq6/iSplc7IWFhSScTBLcbjd6e3tRVlYGLkXefjXCicWSgSVLTsUXX7wdt+2tt97CXXfdxTy2s7Mz6rkgCOjs7ExYHFBLYXhAmhMbjfLBhh6PlAVi2gknEcVLo+A46Q2ZAFh6DQknxFgx6PfjTIZIe6yrC4VVVWN3cY6TJpQyv3fJOE5MJoHSuowSk0leOGlpSfyHwGTiNddmsVgEmM08/P74a/b1TR3hhO2CEsekXk0yAqHXOzrhZCLTdAHDwsmI1BornAxTG5NGS9FxIors1WG7XT4li9+PDz/4AN/93vfiNs2YMYN9LbWIovJKdyTt7dJAdfFidU7Z0aQnTZDOK8Tz+M8nnsCTGzbA4/Ph1CVLsKcpvuYJACxeuBpWdzeAudDrpbda7mMYGJCMNcToeOqpP+Jvf3sUixadjIULT8KiRSchO1vKpPDee89iz55PsWfPp3jssR/h9NMvxznn3ICammWqMxwR2vH7BeTllcLpbIPIsAMdPbpH8Rx8gBwnqtByIw8MSMIHC1FkT8KHVd6sLOlBTDlIOCEIgtCIxwPs3SvNmUwmQKeTz0/Z3d0Nnufj8sIToycQCOAPf/gDtm3bhpqaGlx33XWYOXMmc/8PP/wQX//615GVlYX58+djwYIFWLBgARYuXIgzzjhD8/VdLtk6l7Icf/xZssLJ1q1b4XQ6mTVyuhhJhxOlhtMqnACSACIXWOl2S/Nu1nw6LYUTr1fKHy5HYWHiaKMxgoQTYrwxK/xo5Dkc0I9x2jpRr5cVSZQcJ0rCCTE6RuPYSSaNE8cB2dlBdHTEj6H6+41wufTIzJz86ddYheGNRnFMxLxk7nWfT13Uq1ztFGDihROOk8QTnlcWTubFiBc6BceJzu9nO1KKiuSFE0FACWOM1t7eHi5anzRa/+A7ncCuXcCyZYnTdo2hcPLzZ5/F718aSVv7/pdfMvdduWApOJ9X6o/RCIdDXjjRagYi4hEEYOPGD3H48HYcPrwdL730ewDAjBnzsH791Xj33ZE0UF6vayj13eNYs+Y8/Pd/vwJBoID5seDcc2/EqafeiGAwgD/96Ta88MJvw9syMhyorFyI2tpViucI8RQpkpDhnNRqSfT76/fLF6MEyB6XBpBwQhAEoQKe57F7925kZ+eiqWlG2G3g8wFut3yqEkEQ0NXVheKxSmUyTeF5HmeeeSY+/PDDcNsDDzyABx98EFdffbXspHTvXsnS3N/fj40bN2Ljxo0ApAjARrm0BwwEAdi/H2hrU9/flSvPwqOP/gAAkJmZiTPOOANnnXUWzjrrLKZoAsQ7ToYpLCxkHhMKsceAyQgnHo/2YvNTnpYW9rby8vHrRwxKwgkVSSXGAoPRiMKcHHT29sZtu+373x/z6zPrnCg4TlipusxmEk5Gy2jEp2TrX2RnB9DRIffjx+HoURvmzx+Y9At3oy0Mr5VkUnWxBJFIBAEIheRfi8Uy8QKWwSCC56UF3s5O+VQ3ccKJguPEwEq5AkhBFEeOyG4qycuTbQ8EAnA6nchjbFdFMmH+TifQ26vsluV59u9qouLwCfYRRRF/evudxOcY4uSF86T/DAwAeXnIypI3Abtc0ph3gsrOpQUDAyJ27vw4rr2paT8+++wVHD0qnw6qunoJRBFoapJKN9jt6mokEeoYXp83Gk0466zrkJtbjMrKhaisXIj8/OjMCfnoQjfixdqQQJOChGitSZVIOFH6m0HCyZRnkg81CYIgJoZgMIjNmzfjN7/5Dc455xzk5eVh2bJleOihJ+NSNOXmsoWR9vb2Me7p9OPjjz+OEk0AwOPx4MYbb0RdXZ3sMcPCSSwLFizQdO22Nm2iCQCUlc3GVVfdg7ff/gA9PT14+eWX8Z3vfAcVMSkjhnnllVdw/vnnw8NQLJSEE5bbBFAes7EEkIEB+aBKQJokqZlPTykEgW0lstkmtBImSzgRBHagKEGMljPWrIlry7RacW5EnYCxgiWckONkYkhmQX6YZIUTm42H2Sx/rN+vZ9RAmVyw7snRvJ9KjJVwwhIlgckhTA6nPQsG/Tj//JuxfPkZyM+PzuWkSThRSrlitTJD7UsUxgltWgeQsShFsiiRqCCF0iBilI6Tfrcbrd3yDupYMjLsWFqWIz0ZSkmmlNWG6myMjt27j6K7Wz5YqKlpn2y7TqfDWWddC0DSDr/4Ati4Mflbk4iG56NTQc+YUYuLLvoxjj/+TBQUlEeJJmU4hiJ0yJ4nJNAyb0K0OvgSCS0s4cRiIYU3DaBPkCAIQobLLrsML7zwQlz7p59+gjPPjG5zOPKh0+lka090dMgPaIjk+fjj+OgoALjkkktQU1Mjuy2VwolWOI7D5ZffjiVLEgsNDz74IL6fIJI7GeFEr1fOMMUSVYJBoLmZfUzauRxcLnbxmvLyCX3BVoX1QZ8vDUUsYlJw+z33YPOOHagbcmKZjEb85o47kFdSMubXFhkhrKwaJ6II2XoYAAknqWAiHCccB1RUeHDkiF12e0eHGYWFPphMqa8VkipYjpOxE06i632owefTJXQusr5bgDgpvl/DBeIdjjzcdNMfwu257j0INX+E/U1NqCyKTq2rC4XAyjekYy2qZWVJb5TZLOsAKbLL36uAJJwsXLhQxathkOzqdKKifErbRymcNDLc0wDwwx8+AY7ToaenFd3dLdDpOGRxQ9E6DQ3AsWOw6vQw6lYjKMil7JuwsnNpwYcffsLcdtllt8NoNEMQBDQ07MKGDX8CIKUfLiyMFiB9PmD7dmD1anKejBa1a/k2uDAHh+CE/BcgJPN9IWLQKpwk2j+ZXNnElIGEE4IgCBlWr14tK5zs2LEJgYAfJtPIKrRer0dWVgEGB3tQVFSEoqIiFBcXo7i4OGE9CkI7vTJpYwCgj1EwUxAE7N8vn+9ai3CipSanHP39gNLtEAqF8Mtf/lJ229tvvx1O/VamUA2TlfPZZlNeEMnJYdZhZs7T09J1zIoW0umAcVgoVsJkkj5DuTqRPh/gcIx/n4j0p2TmTHy8cSM+ePVVuAYHsfKkk1CtUXBOlqPt7ehpasKg1wvX0GPQ68WsxYux8pvfjNuf5zkIjPQULNcCoZ5kF/o5ToTFkvzCelZWCLm5fjidcuo/h95eE4qKNKbcGEfG2wXFcZJ4wqqtwjgKfr9O8XNSeh0TFVPABYOwtrbC2N8PizAfAzIpa8y2XCyvrcXK2lrZc+iCQQgykSXM+ifDi2AWi6xwYhIELFmyBGazGSUlJVGPefPmqX9xciQrnCSKlB5Dx0l9p1O2XafTY/36q6HXjyxHWeCFDp9F9YtDEA70ogfxRZmpzknyiCKwefO/ZLfNnbsI3/zm9Xjzzb9iw4YncfDg1vC2s8/+juwxPp+kddXUAF1dknlbEKR5T1lZGgZajRFKa/NmMxAKicjjO1GLA9BDgAHyNTUE6KgGTSK0Cic8r5wfMFFheGJKQ8IJQRDTloMHD+Lkk09Gbm4ucnNzkZOTE/7/eeedJ3uM3+/DoUNbsXDhiVHtTz11ECUldqxcSSOUsWaA4c3PZqRHaGxshJeRF1qLcKI2tbTJJB+8l2iCd/jwYWYah02bNuGOO+5QPF4QAFZgX6JgF71emtxoMUil5TiQlZfMZptwmzXHMddpqEA8MaZkZGbinMsvH/fr3vbII3jh3Xfj2q/5xjdkhRPWwi5AjpNUkOx7aLXyo140q6jwYmDAKFtjQ02aqYlEqTj8WGE0CkynCwuvV68onEy6+kGiiMy6unBKLTPkB2lBsBf+RVGE6PfHW3JFMSqNF8/z+Pmzz2LVvHlYXV4uxXgrFB7bsWOHhheigWRqnACJHSdKheHVfHmVhBOX/AAlP78sSjQBADPkBZ4s9IeFk4GBHhw4sAUHDmzGoUNb8M47f4PDwXb5TDR+vzS0zMiQHpMFtxu45JJbMXfuCdiz51/YvfsTtLcfBQAsXboMF19cif7+vqhjCgoqsGrV2cxzNjZKgkxk6cieHknvmzNnDF5EGsIazxuNwEknAWJnN7hdu8PtLOEEkNb4yY2uAOvNzsxkB9L5fPITYFFkC9uT6YtPJA0JJwRBTFt6enrQ2dkpW4T77rvvht1ux6DMQuquXZ/ECSc2WxYGBqQBslJKpKlOIBCAXq+HfgK92P0MBSKLkQi5qqoKTqcT+/btw969e8OPffv2aYr+U6r5FklhIbuQpRLNrJxYAD744IOEwklvL3vumx8fqBdHUZE24UQhG8XUhfUhTRKViJEZhIQTIi3JZCi+bkZUH2thl+PEMV2kni4kkwIKAPLzR+8GMRhE5OQE0NUVv1jt803egBVBAHh+/OvuJF/nhO08YH2/JkqUNDmdUXVIWAuIw8LJM++8gyOtrTjW1YVj3d3hf/9w7704/8oro47heB5chL1zb2Mj7n7uOenJz36G+fPnY83ChVg7YwYuPuUUZESKKGP1B1lpYS4RoxFO1KCwOnu0V37MHpvuCWALJ3pvG+77nzuxf/9mtLYeidq2ceM2rF9/qrp+jjF+v+S2GA5GamsD3n13Hw4d2o7y8jk45ZQVWLhQNyncF243UFo6C6Wls3D22dcBADo7m7Fnz79w3nlzsGLFIvznf/5neH+O4/Af//FknNgVSaxoMkxzM1BZSYv4amD9fAyn6+V80ZOA4d89URQRCPjg8QzA7e6H2z2A449fApNJ5Xd4OqKUjpE1H/T7R+aEPA8cOiR96XleeshBqbrSAhJOCIJIS44cOYIHHngAs2bNCj+qq6thi/jj5XTK28fNZjMyMzOxdu1abNiwIW77668/ijlzVsBuz4XdnoOSkupwsbauLqkUQrohCAJuvfVWPPHEE/B4PDjttNPwzDPPoCgmX/R48Oijj+LnP/85BgYGcPjwYVgsFvT392P+/PnMY3JycrB27VqsXbtWdjvPSwvSSvXbWGaEWHJz5YWTYJCZShtAYuEEkI73eKQoouE028Ow3CY6nTrhJC9Pmuwp1F2OYpJoCalDFNkD5UmiEikEuBJE2mFjTDZdMYuHPA/09prQ2Cgf1TeRqYTSiWRSQBUU+JCXl2DRViUsNwS79sbEo+SCGqsaJ9K5tQuFXV1m5Of7mcey3ueJcpxYW6KLWhsZok9oaLnjV3/7G/Y3NcVtb5UZsHExQsJnMXXy9u3bh3379uGPAF7+7DMcPHYMP7vsMjR3dWHTwYMoW7wYZWVlKC8vR1lZGebOnYuKigotLy8en08+V6cakq1xolY4MZulxcFYUdtoxHnfvBYZ2cvR2dmErq4mdHZKj4qK+NRpLOGkxBLEli1vYXAwft72r39tnhTCSWcnsGdPdMrb//u/u/HnP98JcehzW736XDz33D9QVTXxCkJIRmcsLKzA2WdfhlWrgJUrj8OSJUvw97//HXq9CcuX34Dq6sXhfb1eFxob92H27OWKYgog3bZOJ1BcnOpXkX6wsj2FgzJjoqea24/hwn8vgNvdj1Ao+nfrlFOOIitr5hj0Mk1QUqlY6SOGjxFFYOdO6cZOBDlO0gISTgiCSEt27NiBRx99NK69srIS9fX14DiOKZzk5uaC4zicfPLJ+OSTT7BmzRq43W5s3LgRANDVdQy33roeAGAwGPHmmyMD/c7O8RFOXC4X/ud//gevvfYacnJycOutt+Lkk08es+v94he/wK9//evw87fffhtf+9rXsHXr1rBoNF4M148BgBNPPDHB3olpaQEOH5YmERwHzJ4NVFTEZydQ6zhRKuIdDLIdSbW1tbj44ovx97//Pap9xYoV0Ol08PuB3btH6qxYLMDSpZKAoZSmq6BAXbHG4Qi59vbE+1qtE565KvX4/eyoy0miErHuLRJOiHTExphsuiJWFkIhDocPZ8LjYf8gUZqu1KEmBVRlpRsWCw+TSUip08dikVf1g0EdeF5bUWJBkP7Gj/XwRem9GkvhJJl7PhjUYdeuLJSVeVFcHL2ALYpsEWgi6gfpvN64GiQs4WTYcVJWWCgvnMikSI09d6xwEsk/h+YG248cwcHmZry5ZQvw2WdR+9x000146KGHmOdQRbJuEyBx5M5oHSeANHjeuTNa3KmpQYWrDOvWLVR1CpZwYuJ4LKg9Dpu2xqdu3Lx5k/o+jhGhEHDgQLRoUle3E888E+0U37jxn3j88Sdw333/Ps49jIf1kQ+P7TmOw7p167Bu3Tr4fH788pev4sMP/4qmpn1oaNiNtrZ6AMAf/7gXM2eyA9eG6esj4SQRwwKTHOHhUIxw4jAb0N/fLXtMf28fABJOZBFF9uTJYpEeSsJJe7s60cRoJKtVmjB5vc0EQRCjoK6uTrbdZDKB4zjwPI9du3bJ7pOTkwMAuPnmm9HX14d3330XN910k+y+dntulHDQ25s4sCsVXHnllfjZz36GzZs3Y8OGDTjllFPw9ttvj8m1enp6ZIuWb9u2bezyOI8T/f3SZGc48koUJdetXDYwNcLJcJAKC6V7Y+3atfjb3/4GnuelvNuiCK/Xi9dffx0AcORIdHF6nw/YtUuKtu7rY0+CCgsT93sYtQaiSaIjpBalD5gcJwQx7mQyvneRjpPubpOiaAJMYA2GNCTxYr+IrKwgbDY+5enRlD5HtXVOQiEOR47YsGNHNnbsyEZzszXpIH41sMQGvV7QJPRoJXlRhkNLSwb6+qIXzEMhDoIgrzJNhDBp7uqKa1MSTgQAZWVlsttbWlvj2nQxgzUl4WSYHXV12Hb4sOw21rU1kWx9k2GUBqCjdZwAkrV55Upg5kzpsWIFUFamaXzCEk4AYPm8xbLtO3ZsDjs6Joru7vi38O23n5Ld96OP4jMZTARyjhNA/iPnOOAXv7gMf/3rL/H556+GRRMAaGjYHX+ADDROTczAALBhw/+hszNe4M3NHfpPzO9Ajo3tZnD29Kaye+lFKMROsWCxsKMc/X7pWMZvfRyUpittSLd4UYIgCABs4aStrQ3//u//jhdeeAEdjIIOZnMugkEgM2J1mOVOyczMiXouilK6rlTMkVh88skneOmll+Laf/Ob32D9+vUpv15PTw+zuPquXbuwbNmylF9zvDh6VD7zQUsLEFlrPhRSN2fNzVWeZ0aKGzzPo6mpCQcPHsShQ4dw8OBBHDx4EJdddhmuueYaAIDFYoHFYoEgyNcf8XikdlbhebVpuiL7ryZdV1oKJ6xcbGaztsWDMYQlnAQCysGkBDEVGa5xotPpkGmxINNqhT0jAzMjFN6BgcTfTXKcpI5E72VubgAGw9gsYkop10SIYvwCvt+vg82W2PlQV2eDyyXdM6IIdHZawHFAefkoF6UZTERheOn8o7vnjx2zwuEIhv+msOqbACkWJtX8IRMEmGTG5KwaJyJ0CJmsKC0pkd3enMBx0trdjaMqrLgfKgQSlZamYFIwGscJIA0UWIMIP0Ow0Fq00W6PCzRhnVr2cgrCyQm1c/CITHt3dxs2bDiGuXMrMGPGxLih29qkMf3Ro3uwb99G7N+/Ee++++eofTIyHCgrq8GKFWeOfwdlSOQ4iURKYZ2F/n65VGkv4tRTL0l4vYEBrT2cfmzbdgS/+tW3AQAVFbVYsWI9jjvuq1i27FRkZ2dIf7RiJqNmkwlGownBYLz42efsG4deT1GUJvXDjhM5fD5p8UBtlCyl6UobSDghCCItYQkng4ODePjhh8PPn376GXz5ZT96e50YGHBicNCJ8vI5qKsDaiPS77KEE7s9N66tvX1shZNnn30WALB06VKceOKJWLFiBVasWIHayA6nkDlz5mDp0qWy7pKGhoYxueZ44PNJIpccsTlm1bhNzGZgxgxpzm8wyEdzRY6zzjnnHNkaOjU1NWHhZBivNzoFQCT79rHTjeTna0tfotcDJSXyNVoimSQGjNQyyeubAOxxPCDdzzQ+J9KJKy65BDesXAmr2Rzl7BSMRgxrxUoLusOQcJI6bDae+XfT4QhixoxRLu4qwHFSui6vN376mqiwOQB4vbqwaBJJd7cJZWXeMUnbxUrVNdb3pNL58/P96O5WXhD3+/Xo6jKjqEhayGY5Z3Q6MSVCmWFwEBlNTdD7fBCMRngqKhDMyZHd95/PPoutH36IG845B4uqqgBIi9YtnY3Y1tqHlpbDKCqqxKpVZ4eP8ZkzMWOmfMqaJhlRJLLGSV1bGyoKCnCsuztpZ0MoVDb64IZUCCcsWOqG0qBDJalynJw8N/rzy8srRW3tKtTWroLLZUZ9vZSyduXK8Q0iEQSgpWUAl1xSDo+HXQzR4xnA4cPbsW7dlePXOQW0OE4A4OSTv4LXXvtHXPsnn/wDr7zyEEwmC44e3YOjR/fAYDDh3nvfjNovEJBuM61a3HRiw4aRzBHNzQfQ3HwAL7/8P7BYrOjs7IDdZJKdDNpsDvT1xafr6uvtG8vuTm1YP0wcJ92krBu1vz86/UMiyHGSNpBwQhBEWsISTmJZvHgNyspq4tpbW6V0vcOLzr298nZXuz1+YtfbO3aDQ1EU8c9//hOAVMdlz549cDgccDgcuPTSS2VTaqWCqqoqWeGksbFxTK43HsTUFY1iYEAK7BleSFEqDF9WJs0tS0tHPnOTSX5SEhnhVV1dLXu+Q4cOxbUlcruw5vJqU29FMmNGYuEkLR0nLOFkEr1YEk6I6YTZakWGzE3PRVjiAoHEyvBE1GBIV3JyAmhpscYIAiJKSnwoKfGNec0Qs1mQ/XuoJlUXK6Ubz+sQDHIwmVLvAmEJDmNZ3wSQ3ie9XgDPx15fREmJF8Egh/5+5bzrbW0W5OVJDiJWYXjJBTS6vur8fmQePgxuaCCjCwaRWV+Pgdpa8DKLTi+9+ir++dFHeHhoLCzHCSd8PUo4CZhsqK6pwdJZs1BZXIyZhYWYWVSEmYWFqCwuRmyRnEjHyUmLFqHpL3+BPxBAE8dhh8uFiy++WNNr5LgydHRIgSlJM9pUXSxxRBRT5ziJQRDi9Rqv142enlb09LSgu7sF1dWLUVW1SLqcgnBS4TDgumt/gdKyuaitXYWCgviCki6X5P4Yy+C1WHp7AYvFgezsQkXhZJi5c4/XXJNpLGAJJyzHzurVy2WFEwB4+OGbo56bzVYIggBdjII1OKjulhJF6XMcHJTmUxUVaVhXMYZgEPj0U/mU23PmLIDdbpduNhkyM+SFk/5+svkwUapvIkVpyG9PlJIhFhJO0oY0/wkiCGI6IooizjnnHBw6dAh1dXWor69n7rtr1z5Z4UQQpICCvDzpeXl5OVasWInWVidcrl64XL0QBEHWcQJI6ZNmzEjFq4nmiy++QGdEFfBQKASn0wmn04lFixal/oJDVFZWyrYfPXp0zK6ZDKIoYvPmzVi5cmXcgD0SKUJM+VzB4Ei9EtaaenY2MG9efDsrYityAjl37lzZfQ4ePBjXlsx82WTSlqZrmIwM6bhu+VqDANhFyqcsPB9vMxpmEjlO9Hp2KjXWJJggpioia2VJEABRBM+ouRALOU5Sh04HzJs3gGPHrPB4DDCbeRQV+WG3j88PEKtAvBrnkdfLXqn0+fQwmVL/GliOk7EWTnQ6oKDAj/b26D/WJSU+mEwiysq88Hr1isIjz+vQ1mZBRYWX+f6mQpQ0OZ1h0STq3J2d8Aw5SiI5oCIwatOm13DDDUtQU7Mcs2cfh6JLT8WqE0/El489Jrt/fyAAIWJgE1vjBJBS4syursbs6mpUVVWhoaEBZYWFCAQC6EoQgZyfX4bOzlEIJ6I4do6TQIAdfTNK4SRybfLuuy/Gtm3vwO2Ozi27fPkZuPnmR1BUVAqDWQQYXw0dRPx/9t47TpK6Wv9/V+c00z05552dzQkWdmEBF1EyIkYURUDlChdB0atfRQw/r9d0RcyoV1TQi1wUERAkiIKkhc05zU7YyXl6Zjp3/f6o6ZkO9anununZndnt5/Wa125XVVdXd1dXnXOe8zzn397/bwyhnndF0Nt7YomTSKy8bNk5dHZqn5t6vYGGhjUEAiefOBFZdYnyl+uv/zBf/vLdBERPjILP5+Ezn9nMhg1XUFW1hMWLz2R0tB+ns5TCwiLN58qyMr8xWtl4/Dhs2DBvXHPnBN3dfnbseEF13ZQNtyAZ/Ny//xcjYQd2uzPqL5clVVn/Xnw+xQrE71f8qCOFHRFxErnmZUBtB2Q72k4hZImTLLLI4pSDJEn86Ec/mnocCAQwCSZ2Hziwl4qKq1TXDQxM318/85nPcNNNn2HrVuVxOBzG43ETEnQe9PRAcbGXnp4enE4nruiBGbPAo48+qrrcaDRy+eWXZ+Q11LBy5Uo2btxIbW3t1F9dXR2LFiWSTicLnZ2dLFu2jJHJgR+VlZUcO3YMg0qbUl9fcnvSiYlp4kSkOBGJEUTBfXS+ISJOOjs7cbvdSndR1LGki2jFVLqorhYTJ06n2BpswUJEmsC8UpyAcm6pXXZSyGWzyGJBQRaQ3xKALBMMJr/ASZI85/MkTjcYjTJ1dXNnyaUFi0W9qur16mNUompIRpzk5maeOBEpTk4EmVde7sVgkBkaMhEOQ0GBn+JipaPfag2zbNkoIyNGfD49nZ3q3RCDgyYqK7WIk9m/D6NgSJt5cDCBOAkFgxxTmUmihubmXTQ37+KZZ37Ne67eRqXRiMzk9SMOunjiRHRDnSyqPfroo5SXl1PU38/E0aPkvOMdhFUsdL7whf9l3bqLGBzs4vDhbezf38XatWuF8Z8QXq+Y3IiHyaQe4IqCXq0hJBkkTvx+bwJpArBt23PccMNizj33Si79y68V/1lBUTOX0aTESUtLP3V1ZlyuuW96icy1BFi2bCPPPfeA5vZ1dSsxm60EApmry84U6SpOysvLeeCBB/j4xz/O6OgoBoOBj3zkI/zP//yPqoXd7t0vsnv3izHL7rrrPtav/7jmcQ0NJdoo+3zQ3Azp/mwWEv7+91fweNS79K64Qps4OX/9BfSSaDEQDqfgM30qY3wctm6dvva1tkJtLSxaJCZOIveBTNiGSNIp2Gl4+iJLnGSRRRanPIxGI1dccQVPPPFEwrqDB/fx1reqP29gIPZxdB6l0+mw250x63/wg1tpadnD8HAPg4PdUwnCL3/5S2666aZZvYcI1IbCA1x44YU4nU7VdZnADTfcwA033DBn+58NmpubueCCCzge5y91/PhxvvCFL/Ctb30rxh8fFCu2ZJiYUBQlspz++AsBTxeTty5evFj42ocPH2bdunVTj9NVnDidUFqa3nOikZen8AVq73tOeYRgUBm619ursD41NbN7I6lCxIzpdPOuW0iU1GYVJ1mcahAqTlDsugKB5O2nZvPsrYSymD8QKRzCYSmp3ZYWcZKKYiVdhMPi4fAngjiRJCgp8U3NKYmHXg/5+QEis2HUyJNgUDepTJk74uSPTz6JJElUFRVRWVhIWUEBhsnfvuT3I0cFVJ0tLQTSvNnl5OThl9cwODyO02iMmV8SQYzCRJZVtwGmimmrV69WHo+NYbNYWFJVxb4o69rCwgr6+zv4xjeuTdjFPffckz5xohUEOhxKwGqzKR0z3d2Kz1E8RASJYLmMRGefiWMtSnxRUKDsPp2Cf3RtsrBQWwZSUlKudISfe67SHdLdDQcOxGzjJJF4CYWCHDz4Blu2PMWbbz7NoUNvcu+9v+K22z6S+oHOEGNj0+9x2bJzhNutXv0WABYtUuL6+dDo0tXVicFgx2rNiVHoa6k63ve+93HJJZfQ0tJCUVER5eXl/O1vL9Denpo99u7d25JuI5qh1d0Nixefgo1bk+jr81Bfv4rm5l0xy+32HDZu3KA8EDR5GVC/Jp72eUFzcyJh3NIClZXaVl2QGeLEZjt1T9jTEFniJIssspi/kGUlUoowGDU1M7bNWb58+RRxUlRUxPLly1m2bBlLlrxF+JzxceW+GrmHJgt0Dxx4ncOHtyYs71YZPDkTyLLMww8/zKOPPsqjjz7Kzp07p9Zdc801GXmNhYiKigrhZ/yd73yHV199lR//+MesWrUKUE4rgU1sDCIqj4kJ8WB2EYkgIk6iz6Hq6mrMZjM+n4+KigoWL15MU1MTixcvpjSOLEiXOGlqml2sJklQVwe7dyeuKy+f+X6TYt8+hTSJYM8e5cOf0xdFLOlxOOZd0CsiTuZDIp5FFhmFht2iFA4LbZCiUVCg0U2dxYKDSHECykBzkd1WMChpni+pzEhJFwrZcPKIk3RQXOylq8uCLCce7+ioYU6VM//vV7+KUZHodTrKCgr475tv5rK6OgL50wqDFpUZcMmwaNE6JEni2DEHZlMFlbQkbKOPIg+kYFDwrZFYTJtMEG658krGvV7WLlrESMONuFxFfOYzm9m58x8Ju+hKUTETA9EN3mBQPIyi5VaiADcNxYkMHNQt5fiB6U+iuXmEJ554lsOHn+J73/vvlJT06RAnZWWT6yVJeV8qzWDxxMkvfvE5nnrqF7jdse/5qaeenjPixO9XCBO9PrbBrrZ2BVarg2DQz6JF61i27ByWLdvIsmUbE977yY7XZBluvnk9AwOdU42ADkceDoeLr3/9G7z3vRcLn+t0OqeJQ2DJkhUpEycHDmzD7xfnSKDO+YHymY2MKA1tpxrCYViz5lLuu+9SBga62Lr1Gd58829s2/YsGzeeizHCZgmIEz3qDQXB0PzKX04oZFmx/1DD4GBy4kSnE6v3UsVcDLvN4qQhS5xkkUUW8xeHD0Nb2/Tj7m5Yu3baPysN3HDDDVx22WUsXbqUoqJpf9X9+7VnXfzrX8q8h4aG5J0beXnqk7jvu+8+CgoK6Onpobu7m+7ubhwOBw88oC3pjockSaxatYpVq1bx5S9/mebmZh599FH+/Oc/c9VV6nZjpxq2b9/Ogw8+iNPpJDc3d+rfoMaX869//Yunn356ijjxeMRESDQitXSBiwSSlL5VV3T8pdfr2bZtG9XV1Tg0ZByynB5xUlUFubmpby9CSYkixGhtnc7JGxpUc9nMwO2OJU0iaG2de+JEFEDPM7UJiM+t076zLItTDskVJ9rESWGhb8qaKItTAwaDjMEQJhhM/O69Xp2wt8br1T5X5kJxIiIbIDNKjUxCrwe7PcjYWOINZmDAjIgAmu2Mk3AoREecL2goHOZ4Xx8mgwGj2x1DnBw7ciTt12hsPGPq/8dDZarESbTiRG2+yRTiC2GTMcKt73gHoBAOz6PkGPn56gNNMkqcRAKC6AYPLcmzLMPoqFKA1emU54/GDpCWgUMspj1URnPzTt544ym2bHmKvXtfJhxWvu9LL72E97znPUkP+7e/vY+JCQvFxdX4fNqBbHl8nGe3K+8rygbKjJ98BhhEyQNDoUACaQLwr389QygUQp/hQSJ9fUp/j9rXodfr+cEPXqO8vAGTSVuWc7KJk1AIxsaUzy0cDuN2D019jsGgIB4WYOXKFTz77GMpbdvcvIvBwQClpeqBbDisHcv29Z2axEn0+VBQUMbb3349b3/79YTDYZYsGVZWhMPCJi+R4iR0OhMnWhbMWn7d0ZI6i2V2xEnWpuuUQpY4ySKLLOYn/H5ob09cfuTIjIiTpqYmVWl8KsFrf7/SnJBs0HZ+fqxK4LLLPsBf//p72tvb+cQnPhGzrri4OPkLJ0F9fT133nknd95556z3tVCwa9cuvve976W8vSRJ3HXXXXz2s5+dWiay3YpHMuLE4RDPEEllxgnAsmXLANi9ezd6vZ6qqqqY2SagNAOmQvTodMrg0cbG5NumikWLFCJmfFypD8ypJ7NImTU+rmRSIqlFJpCs82geIas4yeJ0gWjGCQDhsNAGSa8Ps2LFKAZDdrbJqQizWUSciIukWjZdoBAn4bCmyCltiMgYgyGc0dfJFHJy1IkTrc91tgTQQHc3fsHNq7KwEENcwHbDFVdwWU0NRzo7lb+ODn7w5z+j1+lwWK2MqBTLGhunbU/HQjbCSOiIvTbEECeim6kkJQZ3OTlKcWyyuyXE9GeVUeIknYEUIuLE41FkxGoNKlFoo5p2qgGZL33pSvr6EnOxv/71rykRJ/fc8/8YHU1B4g1UVsYpUnQ6JdCOs1Jt4iA79GfiCZk488xL+OMf70nYl9s9xIsvbmHz5o0pvXYqCAbFpEkEtbXLU9rXbGqxmcD4uE9IZBUU5KW1r3//91tYu/ajPPDAV3nmmV9rbhsI+Ni+fT+XXrpKcFzar9XXl9kcZ75A9PPW6XQUF08SxxMTwjlHBrMeVHpEguHTmDgRJe+g/SOOzvtmqxiJIv2zWPjIEidZZJHF/MTQkHqA4HYrleQMyR9T7dIOh5PmGuTlTRMnFoudu+/+LXv2/Iu2aNXMJPr7++ekG+pUx2hcZ1wE5eXl1NTU8Oqrr04t27RpE9///vc544wzYrZNlTjxeJRTUBR7aSkvtKy61AbY3nbbbfzzn/+c3K+TqqoqqqqquOmmm7jwwncJX2fRIiXGs1qV5ry54BbM5hOkNo4fKhQNj2fGNn0pYQERJ1nFSRanDSRJOMxZS3GSkxPMkianMCyWEOPjiTe72RAnIOH36zStwNLFXM4FmQvk5ASFNjlqyAQB1Bk1FyQeVcXF6L1epGAQeTK4sYTDNFZW0lhZObXdvbfeOvX/9lAxz3aYOHx4K4cPb+XQoa00Na2P2quEHxOWuCpjNHGiOd9Ekqbm3skyOBw6dKtWKQOIg0F8TAdLBQUC4qSjg7RZumSKk/jjVIMsJ01k/Bhpph5QGo/OOutSnnzy5wnbPf3004TD4ZjZGPFwu92apElpaS1jY8OMjQ0DUF2tYuWVm5tAnNiZYIN9N8MNZ1BXdz5f+YpVlQR47LGnM0qc9PfHfg29ve28/PKjnHXWZVRULEprXye70aW/f1i4rqgoPeKktraclha4+ebvcsklN9LWtp/m5l385S8/TtjWYDCyb1+zkDgRpHhTmJhQyBW7Pa1DzCgiVm0Oh7blWDrQOh+m8jpR8ipJGGxmAXFyGtcYtE4mrXXxipOZwmRK3nGbxYJCljjJIoss5ie0OgVGRiBFxUak8O31KnXX+GArk8FrtFWXzZZDKKTnscceY+3atQnbhsNh+vr6EmZZZAKyLCcMQ08H3/jGNzhw4AA33ngj559/vmZidKIxIjgvqqur+cc//sGTTz7J0aNH2bRpE2effbbq55AqcRIKKfV60fZadliiYFqWlXMufn17lLpqZGSEkZER9uzZw6WXXiq06TIYlLE/82wEx8yQzI9sLomTcFg8NHUeEidZxUkWpw0kCVmvRwol2gFpzTgxGudnYTqLzMBiUbeHcrsNBAISRmMiaZacOFGIlxNBnMy3+SYR2O1BJElWnXOihky8jy41ZTlgMhopmuxOMYyNEZj059GL7tWTKNf3s6p6LdXVH+Stb/2g6jY+zAnEiRQITJEZQqsusxmfD/buVVTooChxly/PwbluHWzbhi84TVpEN1NFkGvPIQfgxRdh5crUFfRaM07iMcNq7t2//jUv7DvKyg3v46yzLqeyspH169WJk+7ubnbu3Kma3wAMD8Pzz6t/twC//W0zZWV1AHi9EwwMdLJsWXXihoK4Tz8+SkG+TEGBhXXr3sKrrz6VsM3f//4M8FXhMaSL4eHYx6+88hg/+cnt/OQnt1NZuZizzrqMs866jJUrz8dkUs6DAwe20Nq6j9HRAUZHB3C7BxgZ6WfTprfwX/91W8aOLV3094sJrYICV1r7kiRFfZ+bW8DKleexcuV5ABw7totgMMCiRWtpbDyDxsZ11NQsx2w2EQqpK/Z37TrMrl0tLF58Jjk56gROX9/JIU5kGY4dU+aNR9DYqORhQnR3K3bDXq/iMbZkSSKx6fUSPNQBNCQ8XZJk9PrJ67FIjmOzYTCqX7OD4fmTv59waNWRRDYORmPsiZlq12BlpdK9FnFOMJth+XKxLUUWCxJZ4iSLLLI4qRgaGuKSSy7hqquu4pprrmHp0qXKCq326RSJk3BYSXCiZ4PV1SlzGlpbW3nhhRfo7s7HbM4jJycfhyOP3Nz8pN608bBYlJiopGQ6erJac/D5oKFBveMMlMRjtsSJLMt0dHTw5ptvTv1t376dw4cPkzvDQRcvvfQSTz/9NA888AD19fXccMMNXH/99VRVVc3qWDMBEXHidDoxmUy8853vTLqPZFLwaIjco5TXFK8TqQIgkTgJh8McP35cdduqqiohn2C1zpI08fuV7F+WFTnxyRxiNz6uMFUipDPkJV1oeSbMQ+IkqzjJIhUMDxvp6rIQCknY7UEqKz2qBeX5jk//5Ce0dHYy5vEw5vHgnvz3q5//PI1nqReeFuL7zCJ12O3q9wpZlujvN1NW5o1bnhpxkuk5Jz6f+mvOV+JEcUYK4nZrBDBRyIRyRhcK0VRVRXtfHxNRys+KggKCoRDbDhxgrL2dM66+GmQZXRLiRE+YdWyjm1L2oW6b5MWCk9iOYwlFdRK2WGKsumIakcxmmpunSRNQOuD37YMNG3KRqqrwHZuOVVatuoC77nqYgoIy8vOVv6WWbhpoVm7YO3fC+eenJhUW3eDVAoIZEid/evll9ra08K/tr/HTn36KiopGVq26QLj9X//6V1XiZGQEtm2Djo5RPvrRb/H44z+hp2daWSRJUsywdIvFRkXFInWORNQwEwopH77dzkUXXcKrrz6lfL5Lz2bJEuVv6dIzVRXeM0U8cbJly1+n/n/8+CGOHz/En/70fVas2MQ997wEwF/+8mOeffa3Km/LAsxX4iQ9xQkop2F8KP3f//1P1ea1cFgxlVBrxn/kkQf41a/+PwAqKhppalpPU9N6Vqw4j8WLz6C/v5OxMS+1tfVpH+NsMTgYS5qAMorV5RLkg319sGdP7OOxMdi4cVptFg7Dtm0EJ9TPc4McRMIASOIuPrsdg0lAnKDP6G9gwSAUSi/ZjyA+50s1B3Q4FPKkvl75Ti2WubWXzuKkIPuNZpFFFicVjz/+OFu2bGHLli3cddddLFmyhGuuuYZrlixhXWmpunIiPnoVoLs7ljQBpVukoABef/11brjhhoTn2O1O/vzn1PY//RyFOFm79iIsFjte7zhWq4NQCPLyCtHpdIRVuhu6taryKaC3t5dVq1bRE/8mUWaBbNq0aUb77ejomPp/c3MzX/rSl7j77ru5//77uf7662d8vJmAyKorVZJIY7aeKkRfkcGgPTdcK2+Nbxzs7e3FLyjeV1VVCY93VjPnRkdh+/bpgzEYYM2akzd1cSiJB3Y6X1q6ENl0wcklkwQQxeJZ4iSLCNxuA0ePOqYe+3x6PB49S5e6F1wC/cwbb7Bfxc5ncHAwqzg5TeFwBDGZQvj9icREX5+Z0lJvzHkeCEiEQslJES2rr5lAbNU1u4Hqc4mcnNSJk0wQQO84/3ze19SELMu09/Xxf//8J79/4QW2HT6M653vxOPzsXHlSp68+mqkQABJ4PEfDR0y5XRxjDo8JAZqPsy4JyZo7emhtbeX1p4eWnp6uObGG2k666wY4uSi//gP2vv6qC8ro7ZhEeHctZSW1rNkydkUFyvNROPjSlHVYCxgkOlYpbi4amqb6NeeQsQDOH4ouhrSseoyGJTCbCrD8SbR0t3N3paWmGUdHYfp6Disur3JZKY5voqM8pL79in/Llu2ge7u5hjSBKCoqAqjMTFIVg23HI6EAfFT6O8Hg4GbbvowS5deQ2FhRULOOD6u7GK2iK/Der0T7Nz5guq2b3/7Zior4fhxyM1Vt+oZGtKwpj0BWLZsHb/+9WHGxoYm7dKU4fBe7zD2Gcg5qqrgwIHYZVrOBwMDicRJKAS7d78x9Thy/v39778HoKlpPQcPvsHb3vZhLrvsNxmzyUoVonywq0tAnETl0lPweJTzNtL82d8PExMEUJ+FYcQPoxPKC4iIALsdA6L7m0QodBrW8N1u4TwYTcQTJak2oEZ+M1qFgSwWPE63n1EWWWQxz/CnP/0p5vGBAwf4xje+we9KSzn228QuHUAp+gr8gScmJgiFQuTk5CSQJhEcP64UXdSQm5v+4Hm7XQkCrVY7d9/9CF/72ruwWpXukVBIT3W1Ij8vKSmhtLR06q+uri7t14pGUVERY4IOFDUyJVV0qAR7sizPi3ksK1as4NJLL2V0dJSRkZGpf/PyUuuQGh9PL5YS1eudTu0OHr1enLfGcyTtAqsKUIiTuFx2CrOKz/bvj03Eg0El69mwYRY7nQWSkaFzqTgRESd6/bzMNkSKE9H8nCxOLQQCEl1dFoaHTej1MiUlXgoK/DHfe0dHIqvq8RgYHTXgdC4shs0uuNCNjY0TDKqf7Fni5NSGJEFRkY+OjsRzIxDQMTJixOWavr+lojaBzCpOwmGExN58VZwA5OQEgNS6MjLxPiK2WJIk8firr/KZn0/bQnkm1SVv7t+Pz+PBnmZ3gBmfKnHylhsv51j70YTldWvW0HTWWTEzTg60t9M5MMDhjg54803gIQBuu+3HXHXVLVPbbd8O4Jr8EyOGOIHUY5t0rLokSene0WoKicOTr78uXPeVrzxKOBwmL68Yl6uEvLxibLZczj8/8frb1jZd3w2FQvzud19P2GbTpmtUX0fVFVivV5IstVzn8GE4fJjKHCfFxWeqWsyNjGSGOGlvH+HAgcM0NKzGYDCyc+cL+P3qn++73nU5S5YoDgd//at6Tjk8fHKJE53OrDqXxW6fWQxZVAQHD6aeX6ml4G63zMGDbySumERk3WuvPUF3d5Dq6hMbn4vmPx0/rjhwJaC/X/0Jx45NEyd9fQAEUA/sDQSVcz8nR3ytcDgY6R3hqaceY3x8hImJUcbHR6b+f+aZD+JyzT/1/JxCy6ZLC/HEic2mXECSeXyfzKE7WZwwzL+KQBZZZHHaYGxsjL/97W+q667euJHOgQGOdXfT3NVFe28vX/zgpF+xLCvkiUp3/EMPPcRNN92E3W7H6SwjP7+U/PwyqquXcv31itdtdzcMDqp3uOfkqHd9aCH6frl+/SU8/HAPx48fApQi+bFjx9LeZypQ5O6FjKt0oYiIoWTweDzC55aXlyPLMn19fbS0tMT83XXXXZSn0jU3S9x2223cdtvM5e2pzjdJhlSaUER5azxx0tvbiyRJyHEZh8lkoqioiP371fc/Y8WJz5cwbBNQPpyTMXVRlpMrTk4GcWKxzEsWQsTlyLJSrJsH/GYWcwSPR8fhwzlTBdlAAFpbld9rYaFyYQkGJdXB2QBut3HBESc5guvRqHsC9bHxWauu0wGFhX46O62qxdLeXnMMcZKqkiSTihOR2gTm73B4UGzQdDqZcDj5vS+TxAnAeStXqm4TCAY5sH076xsbVdeH9XpkgyFh/olZbVoyygxCNbS3tcUck9fvp3NAvcBdVjYzq6AZEyfpWHVB2sTJ6/FygUksWrSWc8+9WnVdfz9URM1z93hirYy83nGWLDmb48cPEQ4rKqvly8/luuvuTvm4AKVorBG8690j5OgnGA0l3itGRmKPcaZ48sln+fd/fw9ms5WmprPYteufqtu5XIWcffaZgKKgKS9XV5wMDwuK6icI6Z5OyWA2K+TBgQPT5InLpRAqh1VES+PjyukZXafes6eF0dHkhJLbPcizz77MTTeJbeQyjWSEUFpNS5H8S5anyJWgoCRrJKD8sCYmxAdhtzMwdIzvfe+jqqsH+u7F5crAj2AhQWv4uxbUEuuSEu3igcEw8x9OFgsKWeIkiyyyOGl4+eWX8QoC+3sffZR7H300ZtktV11FXsTvdmRElTjpmmwJGR8fZ3z8CJ2dRwAl+I8QJwAdHYM4nU7OOec9PPXUL6eWR4iThgY4mtiQpgqrNVZdYLU6aGxcB4hnTmcK+fn5tApsTGaCzs5OzddyOBxMqMgw3vOe95wQ4mS2mInlqRq05ptEYDSq563xjYOXX345Xq+Xzs5O2tvbaWtro729fVI9JQkbDWdMnGgl02NjJ5448Xi054yAcswCldmssYAGw0Py+TlZ4uTUhGK/ZVe1HOrutkwRJ263OLQfHDRRWTmHJOQcQKQ4GR0X2/dlFSenPgwGmbw8P4ODif4+brcxZkh8qoqTQEAnHFqcLrTUK/NZcSJJYLWGhORrNDJhORat7lhWU4PVbJ5SmkRj19atbKhWGR4OhC0WfAUF2CeJjwgMFglUwp2Skmr2HtyRsLy9owNCIXST89ZaNZTbpaUzU4wnECeiAC8e6Vh1QfI5JyZTTNzVLGinX716s3AXg4OxpMSRI7Eqa7s9l89+9n4++tFvsX//a+Tnl9LYuA69Ps3yU06OuN1/ErmhQUZRJ04ygVdffQUAn8+TQJrcc89L7N//Gs8//ztWrVoRo84vLFRXnIyODsxZSJsK0hEwpYqKCmVc4siIQqQ4ncrrqBEnoDg1RJ8/r722JeXXevrpv/CRj1xwwuLdZBzk5LidaSSzyZNl5YOa/CI0FSfj4+LCvSSBzUZegUv4UkMDQ9B4mhEnmVKcgEKcaBWEZirTymLBIUucZJFFFicNx1UK/lo41t09TZwMD0NNTcI2orkh+fmxQ9rXrHk7d975OW688daY5Tk5iuVTUVHqxInRmLq6YKYIh8NIkpTgGZufr66QmSlxombTFUFDQwNWq1WVOGkR+UnNM2jFnulYeKWqOFGD2jlhMpmora2ltrY2ZrlW08yMrbq03mgantgZQzK1CSjH7PXOjX+sKCOah/NNQDuxzc45OTXh9eo4csQh7AL3+fT4/RImk8zoqJhZC4cXnp2bkDgZExFAMgZDVnFyOqC42KdKnABMTOin1FWpEieg/JZsttkTAiLFidEYPmnF0lRhswVTIk5mTQDJcozixKDXs6ahgVf37UvYdOfOneguu0x1NyGzGX9REbJej7m/HykcJuB0IkkOUAlpi0pqVffT3tGBKco2VEQmSJJESUli/pEKApgII6Fj8hqVSndVOKwMgFCDKCDQIk5MJli5ErZunVq0Yfkqxo0ldHc309vbPqUQKS9vENrORodOoZAyrkUNeXnFnHPOVeLjQZmRIUQKAbeTEY6TuJPxcSUumg0hIMuwbdsrwvXf+taH6e5WnAXuvvtzMevKy0vIzy8jN7cAp7OQ3NwCcnMLcToL8ftlLJaTczPOtOIkAqs1tqnLbBY7HcUTJ4ODY7hcxQwPC06kKLzyymPs3PldKislCgrmvmEo2ZjFhJ6zZMG4xzNl0wXwl78/xq5jbTgceeTk5OFwKH9ry8ystOvFXX82G+h05BWK3TKGBoa1j+VUg9+fltouBmrESTK7ruxck9MGWeIkiwWNr33ta7jdblpbW3n22WcXRMd7FihRaHMzwX37sJhMeFNkF5q7ulgXkeqPjKhWgLoEiU48cdLY+DYOH36Df/7z4Zjl9fWrkSQlADKbU8tpTgRx8ve//51LL70Ul8tFXl4eLpcLl8vF888/j8VioaCggPz8fPLz88nLy2Pp0qUzep1zzz2X48eP09HREfPX399PTk4OtbW1DKhYFyx04qSkRDz4Lx42W/JmPtCeRZEqRC4OkjSLur7WAZyMynsqxAkoH8aJJE4WqOIki1MP/f3mpNY54+MGTKYAo6Pi0D4U0uH36+a1VVA83nbOORRbLDgsFhxWKzk2Gw6LBdfic1S3NxrlBUUMZTFz2GwhjMaw6iwRr1chTmQ5PeLE69VlhDjx+dRfcz6rTSKwWpO/f70+POtipRQIJJjtndHYmECc6HQ6RoeG0AkC8vBkMBTIzycQ1UxkHFQnUIuK1dUibV1dmKLi22OCoLCgoByTaebxQceIh+/9/n5e3bePkoICvvi973HWWWeJn6AVl4kCAq0AsbZWkQNEMSKf/tgXuYTlAIRCQXp72+nuPkZFRSOVlcrsknhE5zda8wMbG6GgQCEvfD54Q2WMRUmJ+HBTGVLiRNxhPjqqKCFmioEBD4cPbxOuj5AmRUWlXHPNFTHrLrhgE3/4g7qS/2Q2uohe22Bg2g7b7VY++2RDHZOgoEA994oebSjL8La33cTmzTfS29vGwYNvcPDgGzz88LdV92k0mmlpGWRwsACHA9aundtep3jewu/30da2j6NHd3L06A4+9KHreOc7z5zeIFkw7nbHECd/f/lvPPfiYwmbve+y9/O2Oz8mTl4n2Zqc/DxVy2eA4cFh7WM51TAbT25R3qdl1zVj+4csFhqyxEkWCxoPPzxd9G5tbc0SJwsFvb1w7Bg3X3EFN19xBcFQiHGvl/EVKxgLhWhqalJ9Wkz3l9+vWkgVEyelMY8DAZkvfek/YpbZ7U42b74Wo1GJEV0uhAPmo2EwpKcumAmGh4cJBoP09/fTHzVwzuFw4FabVzFD6PV6KioqqBCYAtfW1rI1qkstgqOpynPmAmNjyhel0ylSIUGSFQyKa+QlJcrplIq6t1DdsjgBmTgntFykZpzHaGVrc+0tFw9ZVp8SqYa5mnOywIgTnQ5hB2hWcXJqIpXC7/i4AZsthN+vve3EhH5BEScfvPpqbtq4MWH5EXMTLSqXq6xN1+mDiK2UGnES+c34fDrVOSgiKITH7BlokeLkVCFOMjLfRKW4ePaSJbywcydnNDZO/a1paMBqsfCOz3yG6qIiFlVUsKi8nEXl5dSVlRES3KtFxxhNnOh0OioKCqgpKWFReTnGqHj6yg0bKMnLo7mri4MjHnYd66erq5niYnXLsFTQ0rKX679wE5190131T593Hlu3bmXFihXqT9IqwoqIExHZYDYrbf46naLkmKxejzDtP6vXGygrq6OsrA6bTWxNG0+cqEGng+rq6XjVYoGlS5VB4hGrqsWLVZ2Xp2EwKLmeRtu/FQ9GXYhAOPH+NzIyO+LkX//aSjCofU3Iy8vn17/+Hxxxn/t8bXQROr8ZZGVQSbT7QEkJLF8+Y1+xvDxQM5jw+6fPgUBAUS1F1FwlJTWcf/67ee97P8t736uwaitWnMfGjVexceNVMYPtx8agpQUEpYOMIHJ+//Snn2L79udpa9tPKDQdcFdXV6RHnPT0xJzPI2Pq9gIuh135kASzliK/c53BgN2Wy9h4YhI7NDSsfSynGmaqNtHpxD/Y4mKxDUkKxG4WpwayxEkWpwxaW1vZqJJcZzEPEdfFZdDrcdrtOHU6aGjgknPO4elXEmXRCbL5/n4lIo/ZdWpWXQCXXfZJZNnMkSPbaWxcx403/helpbVT9828vOTEiSQpEmFRp0um6tDD0a05UShMtYqfITQ0NKgu37Vr1wk9jin09sLu3dOtbseOKRYERUUJm2rNN3E4lOTtzTe1naz0elWHOFVkQnEiIllm1VmldQCZYvpSxfh46q+ZTCs/E4RC4s9jnlp1gXJuqV1bsoqTUxOBQPLC79iYIaWZAxMTevLyFs6JIguKNf6wwBM8a9N1WsFiCana00WIk3TUJqAoTjIBEXGSibkgc41UiJNMkK86lXv/dRddxHUXXZSwvLO/nydeey1huSRJ/OPJJ1m5YUPCusiMm3isWLGJv373hywpdlJZVIRR4ONUVVxMVXExAMdrN3GgRSFo1Lq6U8Heva/wpS9dgdsdq7L1+/185jOf4emnn1Z/olZHhMiDqrBQCRTig4LFi6d9jZzOKeJkAnU1b36+uAkoFGJqJtCnP/1vmM0V1NQso7p6GRUVizAYjNhsiU0+FRVQWqqEdDZbijZLubmaMaAE5Jq8DHgT55zMdE50BIcOtWIyWfD7YwuyNpuDP//5TwQCAc466yzVfEynU96fmtPayYzXhIoT/0QsaQJKIlxUpHxpM4BWKB0MKueXqC/K6Szk/vsfx+E4m9xc9XkxoJQVFi+eOxvSSP7Y1rafY8d2J6zft29n7IJkeU2cr92oe1h1szzHpD25yEY54g8mSdTWNDHu8WC3O7HbndhsudjtTspKKrWP5VTDbGy6RCeQ3a5cL+O7K/X62bGyWSwoZImTLE4ZqA3IzmKeIkqeGoOWFqiro34yUYnAabdTX1ZGRXxQ2tOTQJw899xzdHZ28uqr3bS0dDE01M3gYBcNDWtitpMkiU2b3smmTe9MOIxIHqLZATWJiDrlRChO1OBK5SAziFWrVk3932w2s3z5clatWsWZZ56p8aw5gizDoUOxTEc4rCwrKEjojhKpbPV6JV6yWmHJEti/X307nQ7WrEm9np6Jc0K0bSpWYULMJ8WJqItKDXOhONF6v/NUcQLTlhfxyCpOTk0Eg8mLuRMTegyG5AblqcwumE+QBVW1QEj9fWQVJ6cXREV+r1c/ORorPeJEZLGVLkTD4ReC4kSvB5NJW72WEcVJGsHQgfZ21eWyLFMlaOgRXQtycwtYs3YTheF+1fUJyMnBHZiOB+JnDaaKnJx83vWuT/PGv/6XvUdi7ciee+45+vr6KFJp+hFW2COdW2rQ6+HMM2HfPoU5MJsVi65oT6yo2SFe1OMdq1U73lTGCQzx0EP3xb28gcrKxXz3u78HVqseXmRkZUrIyUnqp+s0jDEgGBA/m9le55//Qf785/fQ3LyTvXtfYd++V9i792Wampbztre9Lenzjcb5R5wIh8NPCGT3vb0zJk5GRwd47LGHGB3tZ3i4j5GRPkZG+hkZ6eOVV16htNQhrHXrdPCBD1zGiy9qj2AMBFQGtGcQ3/rWp+jv7+bNN/+muv7w4R34/VG/lTS/3NEx9c8935HkDUWpHe7/4TMMhxPlYXUFs2QOFxoyOd8kGkuXwpYtsSdiff3sBihlsaCQ/aazOGXQpmbAmsX8g1an1mT70vVvfStvWbGC+rIy6svKpgfCx2NkRCmmRvlL1tXVUVdXN1XkngkiSgG7XbmPat2DI/fL04U42bx5M7///e9ZtWoVTU1NGE5mwODxqH85Ho+SKMZ9NiLixOGYTqgqKpTTqjPOkliSYNUqRYWUKsSKExlZllJK4k44cXKiFSep2nTB3BAnWj/ueU6cqCGrODn1IMupKU5kWWJkJPmFYWJCv6AGxAsVJ7L6BTZLnJxeEBEn4bCE36/TUJzIkDBhIzOKk3BYTHYuFJs8q3XuiRMpjUaN/YIcrzgvD5ca2UCk2z9MKJT4XUzY88GdInFSXs6o+piKlCDLMrt2/ZM9e17m1Vf/wsE40uQrn/0sH/7EJ9RJE9DwVTJqX8jtdli/XjkhJSlx20kPLpnZESf7VbqNQqEgra37KC/PkCq+pASOHNHMIZ2MAiUEAn6OHdvNgQOvT/09+eTjrFzZmPbLhkKK2sBoNNHUtJ6mpvVcc83tANTWasjYo2A0zu0MzFQgy9O2WJKkMRx+SDCYvVewPAWMjw/xox/9u+q6rq4+SksdwvDeYlHi3fx8xWRCC0NDc0Oc+P3wyitP0Nl5RLhNW9sB+vq8VFRM/o7S6GIKIzE2pj7rMT9H4w0pXpVTDw26MKha+J5mKtyZNgAmy/kcDjj33GlFVl5eekWBLBY8ssRJFqcMsoqTBYJkNzSPh7OWLOGsJUtS219Pj9JFFQVlGOjMDg+mC96SpMTqWqdWZNuTRZxYLC76+5Wgcob2s2mhvLyca6+9du5fKBVonUsDAykTJ/GB9pIlyncfiY1MJsXet0CsEleF6JwIhyVCQRmDMXnlUpQvz4o4mS9WXeFw6oPhQflRZ7riKyJOjMYU/SNODkSkXFZxcuohGJRQK/DOFAttQLxIceKX1S+CInueLE5NWCxiWymPRy8kThyOIGNjiRfSUEhHMCjNyvJNpDaBhaE4AYU40Zr5lon3ccPnPse/tm6lsrCQqqIiqoqLqSwsZNOKFQk5gEhx0pTEO9VkCuPxqBAn5lxIYTygrNMhF5cydij5tiJIksS3v309vb2J5M/mNWtYt2QJdXXqA+sBDXlAimUcUXJgsYDdjnc8hIz6Nlar8jKSpM5Z+P2wZ8++xBWAzZZDfX2GZo9aLMoQi4MHheSJPdDPpz51IwcPvkEgEJsf7N59aEbEidst5mrKylKr0mfCtjdd+P3KcZvNSjPYwYNKP5lOp6TMP/rRHZjNVhyOPBwOFw5HHjk5eSxqlChKpgTy+5WdWa0pMRUlJQJCEOju7gPqhKF4hBeoq0tOnAwPQ+UcuFINDfno7m7W3CYcDrF9+14qKs5QFqTx5QYxsG7d2xgZ6WdsbIixsSHc7iHGx0cocKhb6AGKz13Ub9ugUz9RlRjyNMJcKU5A+UHV189s/1kseGSJkywWNM477zxWrFhBdXU1q1cnSoGzmIfQYDR8mOjePkCIOgrpJzeVrKa7O4E48fm0Jb3JEJ2LzJY4ifYABqXzbGxsjO7ubnp6eqivr6e8PHlicfvtt3P55ZczPDzM8PAw7e3DHD06RHX1MnbsUBT3q1fP67EMs0YwGESWZYxGI4EAtLcbGWENFrzU0oKVqGBJxdRYNOMkfq6bTqcochctUvIDq3VmpJTJqN7RChDoH8FQ5kq6j5OiODlR7ejDw+n9UEMh5fgyeZKLAux5/kPKKk5OH6gNvp4tFtSAeJWLr4wSL6ghqzg5vaBlKzUxoRcqSPLyAqrECSiqE4dj5rNIRPNNQF5QxIkWMnH9aO3ooKO/n47+fl4/cGBq+Reuuy6BOBEpTpoENl0RGI2yatrhkyyETCb0yZpFiooY8xk1xfKp4MILP8BDD30zYbnNbOat69drP1koD0huzZgU9fV4d6uTUqDEv5IEx45tY9eu1xga6mFoqIfh4V6GhnpYv34tOTnq1+Lq6mU4HBmMJSsrlQ6mQ4dULZ8tYT+dnUcSSBOA/v40bGGjIJqPotcrdetUIIrX5yJe8/th166p0TUJCIfh8OEQjz56r+r65d/+AbVrl6o/ORSCrq5Y8qqsTEmYNJKk3NxcDAYjwWDiG+7tVb5HUWkgQpw4nbB2rTKfW/SdDAyEkWVdxtOXPXsOE9bIVRobz6ChYQ2h0LT6I13i5K67/pCwPBQKcS7/AgT7ikteDXr1YwzO/7FamYPizzmz585jl4Es5geyxEkWCxo//elPWb58+ck+jCzSgSA6GiWHbawj6FMSgWYaWMo+Kkiijx8bU/6iAojZOvpE5yI5OUrgJtpnpICpVWf1+eCDH7yGHTt20Nvbw0TUgMOf//znfOxjH0t6TI2NjTQ2Nk7t75VX4MILp9ePjiojYpqaku5qVhgfV2Jnu/3EN+Q/++yzXHbZZVitVqzWXGw2Zfid01nIN7/xBKvYRSGTyVGkKD8ZzPv9YhIinjiJwGicYV4aCoHPh9EXBHJVN/H3DWOdBXEyq3xZizhRfIFmycykiHRsuiLweDJLaohUS/M8gM4qTk4fzEW34EIaEK9m1RVCTxj1G1CWODn9ILKVGh42ImpeyMkJoNPJhMOJ630+/ayIE9GcFKNRPiHK4EwgGXGSCQKoQ2D/U6qiIrnpkktYWVfH/rY2DrS309rTAzAVF4sguh4EAjr8BQVYu7qEz5UlCbmujrEMjAh473v/gy1bnqK5eXqI9L9dcQU/uPVWjMlOCi2rrtmipATPiB1UeCmDYTrHefXVP/OrX/1/CdtYLOByqce5NTXLUiYXUobVClVV6rMyAwGczgIGBxNnofT1pWjLFgdRkT43N/X+ohOpONmzR+a++77Diy/+H7Ic5pxzruaaa+7AZpuWkYyPDwufX5Cj8YUNDEAUwQkoRIrLpfgcCyBJEi5XIf39ib+1nh5t4iQ6FC8oUP76+mDnThgfH2XfvlfYtetFdu9+EZ1Oz+uv/zPavSoj2Lv3gHDdE09MYDYrLxiTS6ah3g+gfoLo9Xosat5bEcSpfQyCnDwYXiA3nUwgGFQfKJQKMn3iZHHKIUucZLGgMTSkXCOzc5kWEATR0UGaCMYFDwdYgpMRHIwrAYJIMtDTExOxRPESM0J0kBux62ppUd820nSjVWduaYEDB47T0nIsYV13kmGHaujuVo8Lurpg8WLlmMPhMP/5n//J5s2b2bBhw6xnkYRCsGPHtLOS2azM/HAmzqGbM4xM+kZ4PB48Hg+Dg0rinJtbQBg9O1nNUvZTTpdywFFzTkQ2XSAmTtJCKKQMRuntVUgbWcYASFyoaoHg7R/DmUTZEVErqWHOrLqA2AmHcwgRcVJUpKxTe/Mz7SQSQbS/eU6ciH7OWeLk1MPcKE4WTtCkZtUlKjRA1qrrdITFEla1lfJ41M9zSZKxWMJYLCHV38Js55yIFCcLRW0CymeqBb1+dr8zORymR2DVWVJbiyxJSFEyj2svvJBro7qFxj0eDnV0YF+7VvN1RJ95IKDDX5GvSZxMVFVhtdnwzny8wxRycvL48Y/f4KWX/sjocBcfXWVjdUQtk8zCeLZWXUng0asHwdF1xKKiYtVt+vt7ufjia/D5LLS27qOz88hUd/6iRcvmhijUiE/z8go5lphqMTCQWcVJrjpXpIoTRZwEAvCtb32Ohx/+ztSyw4e38fjjP+Xmm7/LhRd+AICxsWHhPoocGsVjgV0eHR2axAlAXl6RKnHS39+nKRJQq2Xv3/8Kt9zySY4e3R6jBNHrDXR0jLNoUWYHnWzbljjDB6CqaskUaQJKiWIqrUsjGA8Ky7EyejRIgHjFiWA3EwHTgpprNyvMJkec53lfFicfCydzyiILFRw4oNwI1qyZm4FgWcwBVFiNcWyM4EpYLqNjH8tYzxtIBQVK9KmmP+7vhyi5fiYVJ6BNnESCOr1eETeoqXk7O8HlKlF9fs9k11w6EB1LMKi8d5sNdu7cyd133w2A0+nkbW97G5deeimXXHJJStZg8di/P3Ychc8Hu3crc9JOVDA2Kshg7PbIgEsd+1iOhEwZ3coBJyFOjMYMcATj4wqrFHfiSYAFLx4SO7gGwy5KRkc1mSethqU5s+oC5cvNCJukgXBYMY9WQ36+cp1QI0pnOvRPhAVKnJwMz+wsTg5SGQwvgsjCaDb7PNFQU5xoESezLehmsfCQTB0RD4slhCQpdlNqjTZe7+zktCLixGxeOJ4pyeK62cZ94243fsENK7+wkJDNhkHULAXYrVbWLlrESE2NVk+2UHHi9epo7ctjiPMxEqCMLmppmdIn+V0udg8McNaiRZq1OJNJidVEM0CiYTAY2bz5/QAs4x/AZCyWLK6ZQ6sut9vN0aMDyHIlen1sWSg6DCopUc9hBgZ6+MAH7uTCC+8EwO/30dFxiLa2/Zx55hxZaGsEwPmufNXl/ckGZKggEBA34s2UOAmHw4yPjzA6OkB3t4/16zPnmjE05OWJJ+5LWD442MXeva9MESdut3i2YIHWMHLRTMLR0VhPahUsXboGg8GG01mE01mI01mEy1XEunVvwe8Xu/aqheIFBU4OH96asDwUCvLCC6+yaNFF4veQBgIBpc60Zs1mAoEv0ta2n7a2/XR0HCYUClJdHWsnKMvKT9liIa1gXBTPGAloT7eLK3zZBPO+fLIJtzu9c3bBYXQU2tqURtqZwGqd93lfFicfWeIkiwUPjweOHFHmO2SxAKDCavSi3skEMIqT41RRVVam3NjUiBO3O6ZLPpOKE1BqyDab+n4jcYskKS8vSrDy8kpVl6erOPF6teMxtxusVpmHH354atnIyAiPPPIIjzzyCCaTiaGhIWwq+vmnnnqK//3f/6WioiLmr6Cgiu7uMtVjGR6GvLy03sKMMSKYVGqzxUaDh2mklG6koSFloiCpzzdJCbKskHV+v+LlduyYkK3LY0idOCEf+jpPDnGSLKDPNDkhOgZRlcHpVJQ7al9apofXi97rAp1xklWcnHqYqeLEZgtSVOSjtTWxEDIXKpa5QkiS6OjvZ8zjwT0xwZjXS5dHz+6JZs4661IcDlfM9lni5PRDusRJZHvRYHmt4e6pQPT8haQ4AcjNDTA6mljUczpnfx8e1lAAuAoKCOTmJhAngWCQNw4eZH1TE0aDAVmnI5wkGBIp0EIhHb29SqEsgImjLCKMjgaa6ZqY4NYf/YhHH3uM//iP/+Cqq74MKnFcVZWi8g6HlRzg5ZdTD5+GyKOYSbupZHHNHCpOnnnmGd797nej1xsoLq6mtLSO0tI6qqqWcOedd05tJyJOxsfdDAx4AKWLzGQyU1e3krq6lfHjJzMHo1HIVOW5lIREp9ORk5NPbm4BubmFlJenP9BZpDaB9IrQzzzzJ7761S/idg8wOjpIOKxcdyoqFnHddYfTPi4RDh06wsSE+kG/732fm/r/2Jg6ASJJErkztSsaHdVMBr/73d+ozgvNz4fBwQluvfWCyXOvlpKSWkpL6ygpqWXjxkUQRywsXboUl6uA4eHEa8g///kib33rRdTUzM5OOhCArVuVprsVKzaxYsWmqXXBYICuruap7zEaXm/6xIlIcWJAI6CXpAQ5Tn4eSN1hVZeD/r4wubkLJ+5LC/39SvPibFBdfZpIcrKYDbLESRZIklQHrAHKAQfQBbQCr8iyvCD6V/v7kzY7ZDFfEFVgvuwLXwDAb61Ab3FhtTqwWh287W3Xx3RyHNE1UmzSYS7Q+IIHBpQhdcxecRKfi0iSUn/fuzd2uU6nuApFYDaLiZP8/MwQJ2qW0N/5zg0cP34Qt3uQ8fFBRkeHCAoqqJs2bVIlTQC2bNnCAw88kLB8+fIz+f7331B9zsRE5ogTWZaRNAKXZIqTCPyYGceOI2rOiUhxkjZxEu9ZlgT5DNJJooTdgw1P9wjWBrF+WpRLS9Is8mVZTu7/mmlyIt3XMJvFxEUmSZ1QSMw0zHPiJKs4OX0gmnEims8QWVdTMyFUlgSD0oKxbhgdG6Pu2mtV1/3kJ1tpbFw39VinkxfEe8ois1AIEBnRPJN4RIgT0YBzr1c/q9+HWHGysIiTggKfKnGSmzt7ht5ls/GrO+9kwO1mYHSUgdFRBif/X1BSQtBiQe7sZH9bG89t28Zz27bxj127cE9M8PL3v885y5cTMpuTfknpkFXH5QpeeeMhPvzFL04t+/GPf0xV1aUsWfKWhO0tFuXlI7lnZaUyvDoVxBAngYB2EjuHipNjk75WoVCQrq5murqaAaioaOSuu6aJk9JScYNbZ2cvRUWJc2kyPt8kgkinmko8+Nl//xLv/+B3cDhc6KLUijPJU0TEidGYXnO6LAdob0+ckzE6OpDR+3BPj1hVk5c3TXw5HHmcd967cLuHGBuL/A2j1+liPrO0kKSLTnSq+v1w+HArhw69yaFDbyasv+CCIyxa1BCzTKfTsXHjeTz11J8Ttt+9+0Wam5U5KGeeObO6UCTNE+WNBoORqir1gaJeL9OzIlOEluJECLudeB88g8tBHkMMUpCweV9PiPqGU5Q4OXJkds8vLFQu3llkkQRZ4uQ0hiRJ7wY+DWwUbDIoSdIfgLtlWZ7ZVLUThIg/Ztaua54jGJwKJmRZ5pmtWwmFw0BsUX716s0xxEkorKO7G2pqbGLpxyRxIsuZV5wAlJYqDfBtbUot3mRSZnxEF7C1Gt9EihNVMsDvV+SmXq/SfV9UNBVZqxEnR45so7l5l9ZbmsKll14qXNfR0aG63OkUe9fOlqSKwO12c/7553PHHXfw4Q9/WJVAESlO4okTADc5OMLjMDKC7MrLHHHS0ZEyaQIKcSLCgNdG5fi48CBE3ILJNItEKxVJwskmTrT80zJ5bMnIm3kMEXEWDk9xhVmcIhCpQ4qKfDgcQfr6zHg8+imrC7s9RHm5B5stxPi4qGogEQpJGAzzX51h02jt9XhiL+xZtcnpCZ1OISVEQ9njEZnfIVKcyLJEICBhMqV/Pil8/KmhOMnLC9DXF2BsbDootlqD5OfP/j7sstm44ZJLEpbLwHBhIYFwmKYbbuCwSlz63LZtCnGSQne8yKorHgMDXfzgB5/glVcei1k+Pj7ObbdtprS0lhtv/K8pqy1ILJ7X1Chhe+SQbTalcKvmSjpEXJHZ7xcPJ57D4fDNzSoDQYCysvqYw6moKKW8vAGXq4S8vOLJf0twuYqxWNSv0XOak5vNqsRJsTOHkVCiXddMmkr6+31AYiyYzmB4gKKixEI2KLNGvN4QVmtmuj57e9VLNRaLHaNxOqZuajqTu+9+JGG7nPAgsG1mL67mBhEFUUgfCMCRI+rnoCRJVFdXqa678MILVImT/ftfw+/34nZbOHpUUYSlA1lWbKgF6WZSeDxMB+IpYkaKE7Ufl81GodTJoKycbwMDXWzZ8ldee+1xJEni+ecfne+pTfrw+bSHmCaD1QrLly+MLqIsTjqyxMlpCEmSHMAvgPcn2TQf+ARwjSRJ18uy/Lc5P7hZIEucLABEVdn9gcAkaZIIqzWxkDwyonjDtoyPUx4IYImPwgYGuOP227E7nLjdpeTnl5Gfr/xbWFiBwZB6gqGWi0gSLFqkJEYR4iT+PqtFnKxe/RZuu+3H5OWVkp9fSklJCVdcUYIjvmju88Gbb8YyEmVlsHw5Pp96bOpwpN5KdYlKohpBZ2en6vKCAjFxElHY9PX1sXPnTtrb27nhhhtSPp4IvvjFL7Jjxw4+8pGP8OCDD3LfffdRXx8rrb/zzjt597vfzdatI/T2jjI+PsLExChlZYkS/DEmP9eREbyWPKHIIu1rRpoKIRMBHLgZIydh3SD5VPb1zYg4mTFSyR5PhFWX6M0ZjZEqmPr6TB7bAiZOtOolgcC8P/yTjoWitgAxcWI0hnG5Arhc4t+0FjESDC4M4sRoMmE2GvGpXLs8ntiKZJY4OX1hsYRSJk5sNqUgpaUA8Xr1mEzpKytEapNkrzcfIUnQ2DjGwICJiQkDVmuIggJfRtT9kqCJQ9brQZKQ9HoaqqtViZPnt2/n7g99iHAKbf/KNS65GumNN55KIE2i0d3dgsUSGzDGv7xOB0uXKsVav19Z39mpzAiMxxg5/ItzcTBGDa3k+XzqxIlW93oGiJMjR5pVl5eW1sW8v6IiF7/5TXqd3XOakwsCYaOsHtfNpOfmxhsvpL+/iyVLzmLx4vUsWXIWixatIzc3vTdWVlaoulyWZTo7B2hoEKt50kFVVSPvf//n6epq5p//fDhqubo6Ih4m3SyuTyMjmoGVluKkRTC4s7i4ApPge77wwvOn/u9yFbNq1QWsXHk+q1adj8GgPKetTWl6TMdWraNDcTHRQk2NUqtXcxv0ekmbpZux4iQekoR3vIUH/vh7XnvtiRgFj8FgpLXVzeLFibnogkaqDFdlpdL0GG3/aDDAypUZuY5mcXogS5ycZpAkSQ/8AbgsblUfsB0YARqAtUxHmSXAY5IkXSTL8r9O1LGmC63hfVnME0SRAWMaX5jFYicUCvHyy49GDWPbT3v7QTweDy/dcw+bVqyIeY5/YoJ7f/AD1f1961vPsm7dRTidqd1jtWyQtO6vWgXt2trl1NZODwGUJEFS0daWKOPo6oLKSnpH1edh5OSoD0OMR2VlJcuXiwcRihQnhYWJxEl3dwuPP/5T2tp20tKyc8pyzGAwcO2112JJQ8d+5MgRfvzjH089fu6551ixYgVf+9rXuOOOOzBMfiE1NTXU1NRgNovnikfgjhAVHo9wvgmkqTiRZW3TYwEKGBASJ/JwlzCdnxPiZL4rTiJv7kQoTkQkjE43730fta5RwWCWOFGDLENPj5m+PjPBoA6nM0B19cS8Jw9EdlupdFJrbSOyAJuPyLHZ8KncvLOKkywisNtDKcV3RmN4SkliMMgYDGFVhchM55yIyRs5ZfXDfIJiSesHMhsX6ETESdTN7YLzz+fpV19N2ObV/fsZ83hS8ktSRgGE8Hi0Sx4XX3wDBw++wRNP/Ex1vcFgZPnyc2KWiV5er5/mQPI1wnMvVrxYGSSfJR3HObxzJ1u2bGHLli188pOf5LzzzlM6tUTz4DIw4yRi1RWP0tK6GB4n3bjTbM7I4Wm/gAqMIXUZfGSsXqoNEx5PkEOHtuH3e+nuPsY//vEHQLGJ+ulPH6ShQd0+Ug2VlerzYUCxqcoUcdLQsJqbblIGvt511x8YHOwmP78UIwGtEvwUNAv1yRAMKkXp+KRKlmFkBNOwH1TmmcqyWPVUWVkrfLnVq1fzgx/8gry88ygpWSy0ed6/H9avT12FrebqEIHVCitWKEYQBxKd14DJ9D1N4mRGihNB8uoOuvntb7+S+BrBAE888Syf/vQ10wsnJhSJpMOxcDqJ4pFqTm6zQX09tLQohSCbTfFgnzM/wSxORWSJk9MP3ySWNAmg2HX9XJan2zQkSVoG/JJpGy8z8GdJklbKstx1og42HWSJkwWAaOJEw+PJanWg0+n47/++SXXQ3f62tgTipEfDPilik5WXl5w4MRhmHj+kU6yUZSXOTCBiBgXWTsePMxxOjzi5+uqrufXWW3nqqad4+umnOffcczVniKRDnPh8Ezz88LcTlgeDQXbt2sVZZ50lfJ14/OhHPyIcpz7yeDx89rOfJRQK8bnPfS5mXSq1fzc5So+hxyNUkFssaSZ2M7zI5DNIK7UJy4MYcY9JiJqhssSJCgKBzHlRaQ2Gn+dJRDLFSRaJ6O0109ExnSQNDZnw+3U0Nbnn7dcdDitDjNWQCuGj04lnoShKlvSGap8sOKxW+lVu3hMTsQy6wbDwCtNZZAYOR2rqELs9djuLJcTYWOJvzOudGXkuUpwYjXLWQjEKkkAGHE2cnP/Wt8K3vpWwTSAY5KXduznnjDNSeq28vEBS4kSSJD75yZ+wYcNK7rnnbgbi2skvueQmcnOnLZd0utRiMatViTVF4eMvf/l5Xn/9Sdra9sXEwY888gibNm2isqyMClmmorCQhrIyrjoniryZZae0LMv09/eprqusrIvpH0k37pzpjPGUITggU0j9g46M90s15j90qA2/P3Ff4XCY5csbUz5MgLKyUsxmKz5fbN7rdBbS0yO2800X8bFfZLZmPgP0EGsXPTLSz9GjO1m6dANWq9LFNyviBBRLhOiCfjisDAft6cGIFTXiBKCtrUV1eU1NnfCl9Ho9t932UXw+pRbe3q6+ndutqEiq1B2/YiDL4qY8sxnWrZs+r0Xnt5biZJQchnFhwUsh/ehQYji3L0jYGE6YL5O2VRewbsMGigtK6B3oSVj37LOPc8cd16ALBWDPnmnJjM0GTU1QoG4pN6+RquLEYlGuGel6t2WRRRSyxMlpBEmS6oHb4xa/R5blBG2yLMv7JEl6K/A80+RJAfBl4N/m9EDThM/noa+vnWPHWnnhhVY+9KEPYc62285PRJEl4xpFaKvVMeltupQDB15PWL+/JzEg6NYgTgoKyjAaIScFheps8pB0EwW/X+X1VKK2Ix0d/OWRRxguPgtJcuFwuMjJycPhyKO0tJbcXHXiZHx8nIsuuoiLLrqI//7v/yagUVGVZZnrr7+e9vZ2Ojo66Ozs5PjxDvx+nypxUlnZhMVix+tNlHO8+eabKRMnbrebX/3qV6rrqqqquOWWWxKWp1L7D2DChxn3mIUWQV6S9nwTLemKBlwMoyNEmMRCTL/PQa5gKKjo6zqlrboib07rGh7xwJjrY5jH0OkUbketETWV38dMoJC9ir3TfCUaRJBlhTiJx/i4AY9Hj802PwkELVVIqt3rBkMYvz/x+rKQFCe5URdrnU5HcUEphSX1uFxFMdtlFSenLxT7reSWTPG/dbM5rGqRPjxspLLSk/a1TjwYfn5eY04WNK26JtG0di3FLhe9cd0vdouF9r4+winmesXFXvr6zELbw6ljkiSuvvoGPvCBS7n33nv5y1/+AhjZsOHdfPjDX4nZNjIYPhXk5SnCcTW0tx+gpWWP6rp//SvW5GFZTU1GiRNJknj99T5ef72VtrZ9tLbuo61tP62t+1i0KNbeyWgUxxxqmHP3G5HiJCAechkIpE6cHDhwVLhu1aolwnVqkCSJj3zkPwiH7ZSV1VNW1kBZWT12ey7l5WntShPCkDY0xqv/+h0Hmvdz9OgOjh7dQX+/0ij3ve+9yMqV5wGQS/qK+hgMD8cO2e7pUf4Ak4Zi7W1v+zDV1Uvo7m6hp0f56+/voKamNulLms1KzT8nB/btU9/m+PHUiBOfT5wiLV0am+OrpSChUIjOzj7keinhLnSEBo7JtfT2tpGfX0ehcYK1bMdAiM985Xq2bHsBu92Jw6Hk9jk5eXzwqqv59LkqNms6nbDgIDmdXHL2Rn771z8nrHvttb8yPh4mp+1QrM/YxARs3w61tYoqY6Ew/Om4QGQiZ8zitEeWODm98GWIMVL8tRppEoEsyx5Jkj4C7AYilaSbJEn6tizL6qaoJxif/vT5uN2xVdG3vOUtLFq06CQd0WkIj0eZ++DzKd0KRUXa204iPyeHT33oFto9VrzecTyeMbzeMTyecaxWheGoqVmmTpyoKCO61MxGUeT1OTn5mM2p+e3OJtjPy1Nqrqk27Pt8qR3TtiNHuPO++4D7Ypbn5OTzpz8NcMYZb8disZOTk09OTj4VuUbWb66mLC4iN2q8OUmS+M53vjP1eHAQtm6VcbsHE3ydQen2aWxcx+7dLyWse/PNNxOWifCb3/wGt6DF58tf/jI5cWxXRKmTCjopp8VfK1yfCpEWg3QG0DU0KG1QoRB6wrikUQblxFk0HVRQ+8I/0Fktiv578eKpwn3adf1AQGmtCoeVkzFPZfZNKh9eMAh9fUrCEwwqv+ny8syqMGaqOAHlh5OJIFhEEC0A4kSSlGuV2sc4F8TJ8LCRtjYbgYAOgyFMdfUEeXkLR9oSCEiq5AGA222Yt8SJVqHPaEytgmUwyILzZIEkx8B377wTq9dLdXEx5QUF7NWvpU+lezVLnJy+0OsVUmRiQju1jVecWK3qv32/X09/v2nSpip1iCy+Ftpg+LmGiDgJR1W2dXo9m9ev55Hnn+esJUu4aO1a3nbGGZy9ZAl6h4PRFAt8ej1UVHhoaUkecAcCOlwuF1/+8pf58pe/TE+Pi+PHE2OfdEIQLeKkqekszdkq0djX2sodP/kJR7u6ONrZybd/+EOuuOqq1A9EBT6fjrKyOsrK6jj77MunlpfEuUtpxRxqmPMwSjTjJAlxkmqD2+HD6sRJfn4JOTnpdl3Bpz71FVVVxIT4cNOGqOhv0QX5zvdvxT2W2J1/9OgOVq48DyN+itHwqUoF8d3/UcNC9ISQCCOT+JvduPFKNm68MmaZ3+9jzZrUY8yyMiVt6VMRUI2PK3FxMtKsp2ecH/7wc9TXr6K+fjW1tSum1DjOOMOHzs4j/Pa3D9LT0zpF9vT1Hcdmy+XKXbuJpvXGsPPYzja+/e230NvbhsFg5Lrr7uYL176fRl0Lo2OjhMNh3O4h3O7pJtC3bj5PuXjFq/NKSsTkhs3GOzeuVyVOhod7ObzvAOvcgotRS4simVkow9LHxhI/GxGyxEkWGUCWODlNIEmSFXh33OJE/XMcZFk+JEnSn4H3Ti4yAB8Avp7RA5wh4kkTgNbW1ixxcqLgdsO2bdPR2vHjSseC6POPIk5K8/O5/cO3cxixbLK6eqnq8td37uS5bdvoGRpi0O2moqCAQwKbqby8UiRJwmRS1KjJOqZm48mr0ylS3t27UxMnJCQgggCgW2Df5XC4AFi37iLWrbtoarkRP2c1jkF+HqOjo/T29tLT00Nvb+/U/6+77rqE4evROH5cIVOirQnisXjxmbMmTj70oQ8RDAb54Q9/SHPzNB9bUFDABz7wgYTtteye49FMg3CdThfbGJUS0lGcVFcrLU7Dw2AwUDCcy6DKXE0fFroppdzTpfw+3G7YsAEkSXN+egImJmDLltiqeUOD4uEajVSr6jt3Tv+/v1/Zf2N69gSa0LLJAu32xkxZiYn2s0AUiwaD+lvItFWXx6Pj6FE7kU7uYFBHc7OdpUvd85ZwiIdWMdXjmb/zbETEiSTJKZMEImWKaHbKfMR5GzdiiaqIiDzBs8TJ6Q27PZiUOIm/ZrlcAY4fV1eqdHVZKSjwp9WAK1acZImTaKQy4wTgK1/6EvfdeivOuC4jX5qS4fx8P7295qTnR/x1USSOTydM0JpzsmRJ6ra2APc++ujU/w8cOsQVaT07EaL3p0YwxDeGybJMOBxCr0/8TOecOBF8AQbZj4SMrPJ7njp2v1954w6HsAB99Kg6cVJVJc4rtCAapaDhWp02hCp1KciyhiW8vjOxEfHo0R0AVHIcPbO8Rnk8Smwf+W6i3CEkFNWJj9QK2CaTmcLC1H9kkqSUHtSIE1Bq7C6X9j62bdvDX/4yPW9TkiTKyxexaNEq1q//H5xR7MnAQAcPPPDVhH243YP09rmJFrgcHNbxxS9eOmXVFgwG+PWvv0RdZQ13X1CGe2xY9XjyCgpg1SolH4vY+DkcSm4ngiSxedO0Ks1oNOF0Fk39eXsGQWusR3e3csHKpBRqrpCqTRdkB8BnkRFkiZPTBxcTe6l8VZZlwWirBNzPNHECcA3zhDhRQ1tb28k+hNMHzc2JkVpLi1Iwjg9qZTkhQgypWBdFQ0ScDA0P87a4uRcAb127loC9lMNHD9LdfQxZlikoUG7+ZrMSHxcUxDTBJGC291aHQ6l7T0wodXaPBw4fVt82oegZ91mGw2F+9/zz3P6Tnwhey6W6PIAJ39AE5vw8SkpK8KpkRqtXrxYSJz6fOPiMxuLFZyYsq6mpYenSpciyrDlPJQKn08kdd9zBbbfdxlNPPcW9997Lc889x80334zVaiUUCtHa2sqBAwdwuVycccY5SfeZChYvnkF9PFXFic02bb1VWAhAmQ2OHgkTVum2aqWGMpQh8fL4OC37PHSP2tJTnBw6lEiKHDsGFRWxT5hpVb2tTSFFMxV8JmOFJEk5bjWCJR0rsUBA+d70esiNmyaTjLyZ5xB9FZkmTvr7zSQWFSX6+01UV2cw659DjI+L7zXz2bJKRG6kY5cmmoWykBQn4biLXpY4yUINDkdQM3Yxm0MJvwezOUxhoX/yOheLQEBHf7+Z4uLU7zlZxUlqSGXGCUDJ8uXYDxyIac2XJQlvvCQi2etJUFU1wcGDOWjZucWT1aIwIR1rXotF2V6tSK4WR6cKUXE/HYgK92oN2mZzbBg8Pj7CNdfk43Dk4XIV4XIVTxVo7777SzQ0JNr8ZgwCZkZiUhmjEgcF/GHYs08pDjO54erVqhX1t7zlvVitFXR0HKGr6yidnUfp6WmhtjwFzycViM4Xn0/pmVNx69VGZCCH0QgWCzKSMPYzEmBVfYOAOFGapKoQDAlJF2632EaNAD4seDzjjIz0MTLSz8hIH1VVSygri23yMhjSTzciqZfapWV8PDlxsmPHzpjHsizT0XGYoaGuBPeDhoYa4X6OHOugKoos/dMrrybMtwH4w59+ymcv+LaQOMnPz1OKFuedp9hAGI2K9CXJyZJbVsYffrMbs7Mamy0nJhevZj+g3mg6hbY2RcIz31Un6RAn8/29ZLEgkCVOTh9cEvf4H2k89yUgyPT5slaSpBJZlhMHTcwD9GtVxbPIHGRZXF0fGEjsVvD5ErrHkxEnNTXLqKxcTHX1Uqqrl2Eyefjtb78v3D4YCvHf3/wTwx4LPp+Hjo7DU8FKJI5bvFiJ60TJUNr2TSqQJMWCK9Ig19eH6oByEXEiyzJ/e/NNPv/LX7KzWeyKZ7e7hOvGR0NYJIni4mJVMrFHZU5MBN3dqak6liw5m3PPvZqzG+t52+JCzvjQhyhKW8ahQK/Xc8UVV3DFFVfwla98hb///e+sWrWKQ4cO4Zv8st7znvdw//2zJ05qa2egNpHl1BUnKp2QJhOUWYfo8CQqeMZxMEABhQxwmEbaurTagVTyxUBAnQ0Mh5XfYlnZ9LKZ+jjJsrKv0tLk26ayL1GGF51wmc3qP9RUFSe9vbB///RrFRTAypXTsrIFPOMExOq4TFt19faqdwj29VkWEHEiDnd9vvmrOBGRG6nONwEt4mThJJJZ4iSLVJBsQHy8TVcEZWUeBgZMyHLib6K720JRkS+luksoBKFQVnGSClKZcaJsKDHW2IiluxvD2BhhoxFPeTnhGUwfdzhC1NWN095uE15b44kTkSIjXeeX8nJQ4zkcDhdVVU20tx8EwG53Mj6eWkFwtsRJMCgOg9Q+3vhC9vBwH7KsWPq63YNT7wHg7rsTm9syCo0GF6M+jD8Qex7JsoynoxdGuqcXBgLKbIfzz08oRtfXn0lFxfqYZaFQiCrvdiUXSMVnOQpap6vXm+buxsdhx45p1stmI1hVhyyXqW5uws+ZDTX8YvJx9HzKlpY9FAePYTJkqONmdHSqYSwe//6ZS9lzYFsCiXDrrT/g6qtvi1nmcKRf65Yk5Xlq9fRU+t727t2lurypaWXC4Paqqkr0ej0hFZbmaFsHm/Onv4utO19W3e++fa9ybMIaY88VjcLCSbtlozHRO08DUm4uteU2RslNWOcnhfxmbEz5HuP9yeYbNGbbxiBtVnJhIlK3yXJEc4cscXL6YEXc41dTfaIsy+OSJO0G1kYtXg7MS+JkQDDrIosMQ6vru69PnTiJQzLipKysnvvvnw7E77nnQwnbOO121jQ0sHbRItY0NOCdfBmz2Up9/aqp7SJ1F5sNzj5badDv7o7dl9GYmbpwPETxfcJHMlngHRkf571f/zruJOa3IsUJQMCjJKUlJSWqxElvr9jLVo3kUUN5eQNf+cqjlNHJcgRT+WYAWZZ56aVEC7ADBw7MuihcUqKtchbC45mWSieDwEKipmiCjrZ81Lodj9KAi2E6SN6hl1DX17rmjYxkhjiB9Ga8aCEQEDNz0W9ORGCkojgJBGJJE1A+p9ZW5QSQ5VPCqksNmVScpGqLN58hyzAxIb7X+Hw6ZHl+JhsixUl6xIn6tguZOAmg3ooqIomyOD1gMskYjWGhxZ3drq5yMJlkCgt99PUlVsMDAR1erw6rNflvTouEzSpOYvHws8/islgoyM2d+nPa7TEzTiKQDQY8M2zKiUd+fgCXa4S+PjPHjyc2qQSD0tT9IBQSX4PTJU6qqpRwTK3H5eabv4dOp6eubgUFBeUMDDzBG2+00tnZQX9/B88994DqPmdLnGj1AqlZS8WHRiMjYnlXaanGvMtMQKdTgiCVmPb//u8etu7YwuhoP6OjA4yM9DM62s/H3nczP74+biZMKKTEhsXFMYt8vsTvXa/XU2zXQ3s7LElvQLwWceLxpEGchEKKdVO0VGhiAv/BY4A6cWIkwPqG6qnHq1ZdwCWX3ERxcTW9vS2UBo6AIUPxQGRYt0q+FA76VZUXw8OJ51GavFTM80ZGwOudoLV1Hy0te2hp2cPq1Wu5664PJmwfDCqE5tAQHDy4U2WPsGLFqoRlBoOB4uJKurpaE9a1dHTAmunvwucTsK/AX97YRSiknpcVFKjMqUwFubmYUK8fiGKnBBw/Pr+Jk+5uMasdj7ko7MwjhMOwa9cE//jHa4DERRedw7Jl5rQsRrNIDVni5PRBvOeRitu+Jo4SS5wsA/4+qyOaA+h0OlVboizmAFoRt9rVWqXYKeoaVYPbPcRzzz0y9fij73oX/+8d76Bu8oYYkaK+IKsHBdEBv8kEK1Yo3M7x40qc53AoIxzmYn6YqP4rUpy4HA4+9973ctevf625X03ixBsEWaY4KhmIhpbiJN2fkIfJjKCvbwZSjkQ0NTWpLlfUJyFIQriJYDYruc6MCqTpzDcRECc2l5nitl56SewccpPLHlYQSuE3kRZxEl9Fn01VPVXiKBm0FCPRb05EYKSiOOntVX+vx49DfX3q5M08RsrXlVlA5NcfQTAozbtitcejY3jYRCgkkZfnx2CQhV3gALIs4fPpsFjmX2FTVABOdTC81rZag+fnG6KJE5mZK06kYBDd5A0uZLfPT7Ysi1nBYBATJzabuHGgrMxLX5+aLaEyI8lqTX5hFV8v5SxxEoVQMMgHv/515Lh7sF6n4y+/+x0bL754Tl9fpxOrk2RZIhRS7mta9790cwWDQXGFcveM491zBDvjvM7ZhNFz9tmXxWx79tlXxjTtb9p0DV/5yjsT9tna2kogEMA4QwtVUVir06m/v+iYw+udYNeuf6o+32Sy4HLNsPKdDsxmVeJk165/8eKLjyUs7xd1qHd0xBAnWnNHbExA31jaxEnkM1XLr9IaEN/drfoErYK4CT/La2qwW22MeybYsuWvbNnyV5YsOZtPvvc6Cs25aNnXpYXRUcXWQaUOUOBSH/ajRsDZA8MwZhDmUyI8+OC3+c1vfk5XV3PM9aWv790JxEk4DG++qfSEybJMc7O64mTdutWqyysra1WJk7bODmDagu+rX36Yd7y3luHhxIbFN/bt4kc/2oLbPcT4+DBu9xBjY8qQ+Pp6sR2YJmw2jPoxUOkTSElxAsp8msWL59dsEK9XyeH6+9Nr5CtTJxRPFTz++F4++tGL6e9XLNiKi6v57W+f5+KLs/OeM40scXIaQJKkfCD+bpXuIJD47TM4JXjmuO22H7No0TpycwvIzS1gzRoX9fULpxiwoKFVSFbrak9DcWLCh5/Younzzz+I3z8dcf7yj3+k1GJh49KlFLlcrG9qIoSOkCzYp0qskJ+vPbQxU4h+7b17X6Grq5murmb6+5txu5tpbm7mN7/5DW+NIgxuueoqvvLAAwRVZMDvf//nee97/yMh6YxGQDaAz0eJQN6rpTgR2pgxiltF+uuNDPsbHFS+e1ErfIpYIkhIfD4ft9/+McrLz6WgoJz8/DKKiipxOtVl4fFYunQWMWA6xInI781up4a9qsQJQD/JO/QMhrh8JGKhJUL8cc9GcZIpUjpV4mQ2ihMRMRgIKJ+JlpRigShOUlayzQLJBqd7vTocjvkzIH501MDRow7CYaUI0NNjwWpNfs4vPOIkM4qT+aq0iYdsNBIZ363EDeoHrUWcGNxu7M3NU0Opg3Y7Y4sWJcxUyGJho6DAz/Hjat+pnDAYPhpGo4zVGsLjSXzu+LiegkSXzQRozTdZCL+zE4XRwUHV+DUUDuNINoggQ9C6hgYCmSdOQLnW5uYZyEUpFOcyyjCJXeXx4Xl5eQOSJFFYWElZWT3l5Q1Uldfy1rcv1swDkkFUsBdxyuGwl9tvv5CurmaGhsTNV05nEWbzCTjhTSbV2Dzfqd6pPzgqIE7i4mIxcSJjwQs+WfnwRBPfBbBa1cPolAfEy7KimlZB35iXA8e3TM6YKcRqdSBJEjpC6AmjNxq5+bJL+d4f/zj1nAMHXudPTwT4t03fTOt9aMLvh9cTZ6kA5OeqKxhUiZPeZugdVIr31dUqz1JHMDhOZ2eiEqu5eQ9+f2xaMTAwXX/3+Txs3nwtx47torl515SVGcAZZyQqTgDOPXczZnMBJSU1lJTUTv3bUFYC7AEgjIRfsrBmzYX84x8PKe/N7mTVqgtYs+ZC1q+/hKoq9WbBqpmN0wFJwpSfAyqCsJQVJ+GwQijW1s7wIDKMoSHFVi+VBj5Jms7x6uvnt3JmlggG4XOf+8gUaQLQ29vGZz97Axdd9NLp4lJ2wpDNFk4PuOIeT8iynEYVEID4Kuu8uAqtWnUBtbXLpx5nstM2iyTQKiSrRYZpECcOo4/BQGxVcMWK87jkkpt46aWHGJ987a//7ncAeJ58UnkJxAXPk1kLjQ7UvvrVa1QTjqNHj7KhtBRzKIRBrycvJ4fNq1fz7LZtMduVlNSQm1tITk5iYtDRcYSiokpMJovSkTs2FqM4sVqtlJSUUFxcTIPAryocFv+OnIyoEic+zISR0EWK+CUlyk6MxhlV5RYvXixc99BD9wP3Tz1et+5tfOtbzyTdZ0WF0HY3NaTT3SLKqK1WnJKbQrkvJZJEDQlcgtutfeGbmCCmOjob4iSS3Q0MKMMDZVmZtlhbq64yE0F0vPGs0GwUJ263eN3goLYPwGmmOBkdhY4OiUDAitMZIDd3+hxJTpzo5xVx0tlpnSJNIlArhMbD69XjdGZ4OEwGILLTSkflI952urN63kOSCJtM6P1+TaWqkDiRZezHjk2RJgCG8XGsx48zMV8KA1lkBE5ngOPHIzTbNHJzg0lvU3a7OnEyMZFauiwqtGfVJrEYEs1HBPJSYagyAEWJl3iegEJYW61hje8zvZAn7oWn/utiWJU4iQ9fqquX8cQTE5hMsbHl5s2Kff/WrVv5/ve/zy9+8QssaTA6ojROFB7l5lpoa9vPmGCYdQQuV/GJaVQXvNeC3MQ8BWBwZFh9P3F5iohQsuBFx+Q9prc37aKy1ao+liFl4qSvT3hwr+7dy213fWLqsdFoxukspKiglL0//C8kSeI/b7yRYCjEL59+Gn8gwDvOOYf77rgjZnj4XKJIUMAeGUn0r3MwmXMdOqSogVI8r9eujXemV9DRcZj+fi/l5dP7ibaltlhs3HHHzwAIh8N0dx/j6NGddHbuYs0adeLk85//Mrt3Jy7XEZq6sigKD4m3v/16Fi1ay5o1F7Jo0Vr0SSraBsPsmlqMTpsqcZKgOLFalcRfrePq6FFlfRrzVeYMBw6kRppUVUFNjfKjsli0PfJOAezb187Bg28mLN+z52Xa2weorT0x99PTBVni5PRAvM5xJtNc458z6xHakiQVQ9rVQ83pBFmXrhOIZMRJfCtrOsRJvonBOG5h0aI13HnnL7nvvnv4298e4hc/+xlvbNvGlz74QSyTFUQtCerJrIVG13/LyupViZPm5mbu/Pvf+fnDD1PkdFKWnx8zGP5TH/wYizd9goaGNcIg9847L2BoqIfq6qWsXNTE5g1LWbVqFTt37qS+vh673Z40QNbqVncxzHESW2BkdPgwY8WrBLmHDyvngNGodHuk2TbjcDioqqqivb096bYFBckluIX0sbghn5lafAGpK06KisTRrk4HdjtLx/bzGk4CqUqmo2Dyj8Ggf1oqpWaYHY1IQBxJOmZj1eXxKN9v9MycwUEly1+tLmVXRapD2bUUJ1qt8l6v9vscGhKrokymhdGCjzavFA4nL+yEw3DkSOTrlAALvb1mqqo8FBcrFwKt2SCgEA7zBaGQ9hB4Lcyn9xGNTChOtLadj1ZrIsyGODGMjaFTuSaYBgbwVFZmVSenECyWMEVFsfNKJEmmvDx56qNYeSVeWCcm9CldU0W/1yxxEoshjbjFWTTHszEmIUkKqaxGTke+RxFxMitLX51OiY0DAZykNgRer9erFlo9Hjh+/ACXXHIJ/f39dHR08Nhjj5EjUj3HQRTWioQUJhOUltZx5Mj2hHU5OXlceeUtjIz0UVxceWL89QUHWih4/8MixUmcakdEZNii50b09MyIOFFDSlZdGmoTgL7IbJFJBAI++vs7kMPBqbzPYjJx76238v1bbsEXCEzlzicKxa5E4kSn0yPLsddHAwFMROUJ/f0p20CvWaNOnITDIXbuPEB5+ZqpZaLvWafTUV7eMPl3jdBEQHQdCKMngBETgamGzvXrL2H9+ktSeg8we4cs0VeboDhZvFjJ4aLqDVOQZdizh4Csx1BSePJSI78/tRzc6VRmWBoMc+O7Pg+xZYu6ukuWZY4ePZIlTjKMbKZweiCeOJkJvRB/e0nPdFIdtwBfzsB+ppAlTk4gtCK9UEjpbI++86dBnJhzLRgG1JvjDYYcPvaxj/Gx669n5/33Uxwl6xcpTvT6WbtHzQrRAUxZWT379r2asE1zczO+/n5kWaZ3eJjeqFaYf7viCu78yB3sZ5nwNYaGehkY6ASYGob3+HPKur/+9a+sWqXeMRNBb28vOTk5+Hzi7gwXw8J1XiwKcRL9PQcCcPCgki2kKff4+Mc/TiAQoKmpiSVLlvCxj32MbXHqG4CCgnIAnAyTyyjtVAESEmFK6KGCDlwMIwU2gmmGnsuynFrQJknJEym7HfPYGEvZzy7SIBsmoQ96YdsOWLdOIU+0bLoiGB+fDiJnozgJBmNJkwj6+pTXSHWaY6rEiYgZkOXE60s0RD7WEQwPg6AjcaGoTUBbRef3a+cNfj/s2hXbcadAoqvLQlGRD0lKzaprviAYnPmxiCx2TibCYRLUMxGkZ9WlYekYmJ8WZWqIzDmJJk7C4TAezxh2u/J7FhEnfceP03rsGCV5eRTk5qKbrOpJgHFkBP8J6nLP4sSgqsqDwxFkdNSITidTUuLDbE5+nouGx8uyhMejF66PQDRIPEucxEJEnBgNBhwpFv0zAaMxrHrfiHyPc0KcgBJnpEGciNDTM8qll15K/+Tn+cILL3DhhRfy1FNPUZgk5r7qqnfQ2jpCTc0yqquXUV29lJqaZeTnl2K3q5/HFouYONmw4UpuuOHrQNoOVjOH4IUK7OrLh0cH1fcTR6qLCurW6FKI261smEZHu9c7wJtvbp20az5KZ+dRurqOYjSa2b9/i3ZhemRE+ROgb3hUdbkrN9GPWpKk1EiTvLzk8XQauOzsjcilj+J0FuFyFdHT00pz8y5qa1fQ29tOUVElkiRhZzxWB5ayJAcaGxsxGk0EAol5xq5de7j00jVTj7/5zU/z5psvUlZWT2lpHWVl9ZSV1VNTs4zCwgpAe8SK1nVgAhsmRjSdMLQw22uMKD3yY0a2WJGMBiVfLSpS7KVbWhIUHYPkcUBewsQeO8aDMosXSydnXEgq339NjUKanGYT0b1escXAxETWhifTyBInpydm0l64IFoSs8TJCYA86e2azA8mojiIII44OdjezsFADjprERaLA4vFjsViQ5IkDAYlFlVz25m6fxqNrF68WCFpIi8hCFBO9siC6Pi0tLRedZvm5mb0ArlHWX6+ZpctwMsvPypct3bt2qTHuGHDBo4dO4bVasPhKMDpLCQ3t4D3ve9zrFt3EXqCmPFhIEBQxSPVg5U8EbHS2Zk2cXLXXXfFPB4dVU8KIsSJgSBNHKKOY3ixYGMCQ/RkvG3blGGOM+lm9HjEEuHCQmWdXq+oa5Il/ZOJXjF95DPAIOkV7PSR93TsmEJUaCRSU5iYgIIC5beSqQHv8RgZSZ04EcmaUiVOYNoKTg2JbEAsAgEx4XSyLxZpQOtQo0VG8QiHlZ+DyH0uGNQxMaHHYgklJRTmk1IjFJp5O9x8eh8RiGy6IPkQ9GjodKDTyaokjNZrzDe8cfgwzz35JAd7RjjQO0Zvbxt9fe00Np7Bvfe+jCTJwpz5oUcf5Yvf/S6gDKAudrkozc9nw9KlfP/uu7PEySkGSYL8/AD5+ekpLK3WEJIkI8tqA+JTIU5ECjHx7/Wv//u/fOFrX6N7cJANK1bw9f/6L1acdVZax73QEAoEKHK5GBwdJRQVkxTk5iKdwMKX0Sir1uQi36PPp35fmLX7y+RsDhMBbIwzwcyaeh566Pe0tLTELHvzzTc577zzeOaZZ6gSqL1lWeall15keHg4YcD7pz71CzZu/Kjq84xGqKhQz2G6uppjtjshEHwRy2tquPTSj5KbG8llCnE6C3E5C5Dji/KQEJOmRJyA0jSUxvyNLVv+zv/7f+9NWG40mvF4wthsGud+EnV5/6h6LiAayJ4ASYJzzlEsyAIBJWfIzYV//jNjecOSyjKGKzdOPX7hhYf47W+n+2dtthyqq5dxwRkbWP+RK6afmJIkR4HBYKChYSkHDuwEwGy2UlOznNraFRiN1fT3K29NkuDAgR0cPryVw4e3xuzjne+8nVtu+T6gndJFLPvUPp4OKnCdROJExIvJSIQ2nBvbTGqxKKTD4cNTi7yY2cEawpMNroGAxN69yk/uBI2hmoZWcc/hgMZGUhpCdgrC6xXHONXV2eHwmUaWODk9EF8emUnIF/+cNAz/TxyCwYzMps5ChJ4exWcyFbsfrzc24ogLTO/46U95+o03YpZJksT73vd57rnnG8mJE0lS7uBR1T+RVdfJbiKPJU7qVLdpbm7GITjQ0hSIk3vv/TfhutLS0qTHODBZSPZ4JvB4JujrU2yyLr/8ZgDM+JBQEge3CnEyNSBeDXGTLp955hn6+vo499xzqampSWofJssynZ2dquuiiRMAk1mHyady4vh8sHMnLFqU/rA7rfkmK1eS1vS1KHLBwVjaxMmU7+/wsOI/mwoiScds1CbJkA5rPVurLlC+TxFRk4w40drmZF8s0kBkJIxa0qZludffn3xkz3QBMJm1ny7BwiYUUjq0T7QF1GxIgEBARyiU3k95rqFFBKX72RoMYfz+xDe3kIiTrQcO8NUHHkhY3tur2JdokUm9UfegUDhM1+AgXYODFOTmYhwZSc3bLotTHpIENltI1fJvfNxAUZF2w1C61nqvPvMM191669TjF7dv59rrruOn997L7x98kHAoxNXXXMPF7373CSUU5hpXbd7M+//v/5BlmdGJCQZGRxkYHWXiBHvBiL6XQEA32SOmfkOYtaIiKs5wMTxj4mTLlldUlx84cIDvfOc7/OAHP1Bd393dzbAgBqquXqL5/qqqYnMYSZIoKqqaisXhBIZRggM9q6mRTzd9UHVdiBdim6pg2iXBYECWU7TqgtQtfCexZIk66RQI+Ghu7mTFCg07qiTkwcCwujIkP9eV2sHZbEpOXVMTuzwvT9xoJApABYix3wLa2vbFPJ6YcHPgwOvUFOQCUcRJGooTgDvv/DIdHRK1tSsoLa2LsbnbsUPpd1u6FDo7VeypUJwhQHl7WsSJJCncktpPqYtybEzQQUVaxx7BbMlZEXkZCgXxenU4HHH3k+pqpbYzScT2UDJFmkSjvX0eESeSBGefvWDslecCV155PatXfxi3e4ihoR6GhroZHOxGkiTy80+GPOjURra8fHpgvhInPwH+L83nNACPaW3g9WpLK7OYIUZHUZ2CJkL0jS4SlEZhTCUQkmUZk8mMXi8OGmKeZrUy0NnJ37dvZ2l1NYGKBtS4k5PdRB5laUxZWT02Ww6lpYokeMWKelatqqeuro6rrrxS9fll+fmJvqRxePvbP8Izz/w6YXlRCgoLv98vVHQ4nYpSxIxSibXgVR0Q74m7RITDYZq7umgoL1eIkaiZFJ/97GfZtWsXAOXl5Zxzzjmce+65XHLJJSxZsiRh38FgkOuuu47Ozk6am7vo7u5keLiHcDg8FRgYmLRuslq1q8bHjileuemwq6LkyGpNv9IaNRwxIQlLAUWRaX+yrCh5UkF7u1Itn8toN53EJlXiRKdTvic1wke0j1S9cEU42ReLNCBJykemllMcOKCIgPLzExuxko3FAaW7LBBI5dyW8PmUQbrBoMSxY3ZGR5XfltMZoK5u/ISREbNRnIDSXWyzzZ9B91rvJx3FCShEi9pPZjb2ZicaFYLO3oGBToLBAGaz+L30Ck76krw8JFnGODJCIC9xSHMWpx/s9qAqcZJs3pMi6EzPWu9Xv/xlwrKO/n6u+uB00ffhZ57h7r17ueOrX9V8/YUEafKeLkkSTrsdp91OfVkZAYfjhHbliYkTCa9Xp6o8guTC4qSIinWcjNA5w+Lqvn07VJcXFhbx7W9/V/i8vXv3Ctc1NS3V5JA3bbqQQOBnU/ZGxcXVGI2xsdsJI04MBuXF4m5uRsTNfQGMU8SJHyNucrDgxe7zgcFAX5+bP/3p/sn5FosoKanFZFLiwgTFiVauoYKmJvGY1gMHjmoTJ0lea9itrjgpdLpSOTQxG1hQICZOXC5lzmGKUL6XyNh0aGlRPw+X18Td6z0e7bmGcXj3u9/Jm4nzsqfQ3w9HjgSmGgTjESFOKiqSp4oVFeI+rEOhOkKhwIx+D7MlZ93uAb73vc8zOtrP8HAfIyPKn9s9xK5dLaxcGUeQSZKiOpnsrOoTjCDu6VH6BU8oRPllXt5pTZqAcumTJInc3Hxyc/OpqVk6tW42I02zUEeWODk9EH83tUmSZJdlOZ3qUnHc4+HZHRLIstwL9CbdMArJOtMhS5zMGbq60ts+upqnEvCpEScAFos9LeLkjYMHee/XFV9dnU5HaWk91dVLqa5eyk03/Rc6nW5eNJFPWhqzcuV5/PnPI1PncmOj0uDT399PMKRetNOy6jLiJ4CJVasuUCVOvvSpTyU9tkGNwDc3V6m6RoiThMQB8Pt9PL97C3879jR7WlrYfewY+9ramPB6aXnwQWpKSpQ3bzLxP//zP1OkCUBnZyePPPIIjzzyCGNjYwkWXQBGo5H77rsPUCyGBgchFAoxPNxLTo4iQzcQVD5kq1VbcRAKKRXldGS9okJ8qtZU0bBaFbuwvr60iZMq2siZaVnB40m7aystpJNAikgPNdLCbFYnTkSvl4raRAsLiDgB5XDViBO/X5kj2tqqXF8aG5XlspzaWJxAQJeyGsHr1WO1hidJk2mCd2TERGsr1NfPgshKA7NVT3i9ugVBnGhZUomgVSBcKKgUKAVlWaa/vwOnU1x06u3rU11eOkmWGIeHs8RJFgDCa4DHo9dUpYnUJiBWiA0Izst4fOfnP+e6m2+msLw8+cYLADqB+lU+wXYBIgu1QEAnnO8lSTML/WIQpzhRgyzL9PW1c+zYHurqVlBcHFtM9vu9HDu2X/W511//TbZuNXHGGeqWP1u3bk1cCLhcxZSXa8fGy5YtwW5PbHCKxgmz6gKlwpwmcWLBy1EaaKN6qqu+8oiXxSthz55D/OQnt09tH1HUlJcv4rmv3AH2qOQ0TeLE5XKRm5vPqMqslSNHjgIXiJ+cxB77//vyQ/SPBxgZ6WdkpI/h4T5GR/s5pzLFBFiLOBEhJ0dRwqSoOJdQckkfFkKhIMePH1TdblVNHJEYCk3lkKkglRrQ1q1thAVqmbq6OhoaUjMnKClR5qpH0qvx8RH+7/++y4svPkJ7+wF0Oj2XX/5xPvrRb2Gzpc64ztaqy2CQeOqpRGIeoLu7L5E4AeXiZjbD2Bh/e/1fdHW1kpdXjNNZNDmXppjc3AJ8Pv2JTZVE59esPRMXPrQuQXNpMnG6IkucnAaQZXlAkqQhIDorrAbUIy51xF9hD6tuNQ+QnXMyR0jm7RKP6Ku5ypV93Kt+tbdYHJrEid8fZcdmtbI/alh1OByms/MInZ1HOHDgNT72sW8B86MWOmlpPDWUNoJILOzMyWHnfffRPWkh0jU4SNfAAN1DQ1QXF9MpuFxb8RDAxHnnvYvHH/8JBw9O258tr63lPRe/i717FTW10wlVVYkNGv0aLei5ubGKE7V+wEDAy+2ff6fq8/e0tCjEidfLn554go9//OPC1zr33HOF6yKIBAJ6vZ6CgmkZagxxkgxjY+kRJ6Jzf6bZ87JlcOgQtt4R4h0D1FBHM3kMkU/mhjRmHKmSMrKcuuIkskyNuBLtY7aDLOcDy5oGUrm2tbUpIiurVclxU8n1AwFdyoPfvV49gUBwSmkSjeFh4wmzwBIRDVZrkNJSLyMjRgwGmd5eM2oWZMqck/nToiVSg6SrNgFx4XYhKU7KNKoYvb1tLF4s7truETQHlESIk5GRtDpak0KW0Y+PIxsMhM3m074rciHBbhdVGyQmJgzk5Kiv1yJOVIlLWeb8lSv5x86dSY/J4/Px4tNPc82NNybddiFAEjQJnXjiRGzVNTGhfiw2WwbuZ1Fxho2JqQaoP/3pXn760zsSNv/kJ3/ClVd+ImaZwWDi5z/fjcWyg23bdvDii9s5fHg7w8N9LF26EY8H9u2DdesSX/61115TPazGxnVJw9pUQuwTGkbZbAkNMwaCSMjIKvd5D1aaqac/rqP+eJ8FVy8cPBhrgSvLMr29bQwP9ZBn/VzsztIkTgAqK+vZty/xfnT8uLr6YfIgkr5WQDLjcNhxOFxUVEzPNljFTiAFglZEnEQsvNRifKdTicXTaKwsppd2qunoOEIwqB5vra5RIYg9npRPLINBSc+0xOfRM3ni8c531qWs+NDpoK5O+a0B2O1OLrzwg4yPj9DT04Lf7+Xxx3+K2Wzj5pvFKrB4zJYTyMtzodfrCalca3t6NM6Hyc/4+b/9ihde+nPC6ssu+xhr1/6c4vh26rmEqLA3W3bpFIAWn5pVnGQeWeLk9MF+4Jyox4tIjziJN+ZM57knFNkLxRwhlSHU0Zih4sRqVYgTrfhoYkLxFR0NBvn0z36muo0sTxeK5kMtVHQMkY/GKEmsqq9nVb26B26rwKrLiodRnNhsOXzrW8/yzDO/4fDhrdTULOeuqzaxd7CRyCfR0wMjbSOsrBxS9MWTbWEDGi3o8YoTNeLEbndSXFxNb29bwro9x45x+dlnM9rXx8033yzs8KmpqeGcc85RXRcNUQeFgaASaeYm2oglQG14jgiK0bX6uplK24xGWL4c8zLQ/T1MWBYXXPQEaUAc4M8beL2pFR6DQWU7Naj9SETMgCiJTJfgTfX15ilSOVxZVtT3VVWpqU0A/H5x4SgeXq8Ot9uAGhkhyxJ+v2LlNdcQkQAGgxwzKNrv1zE8nHiujY3Nr5BYRATNZHaMmDhZOAX9XJcLl8PBsMpvvKenFb1+g/C5vQLiJKI40YVCGMbGCM7agwf04+M4jhyZ6qoP5OQwXl9/wovCWcwMZnMYvT5MKJR4PfF49BrEifpvSaeTVQvtOr+fC9es4WsPPpjScT373HOnDnEiUpyc4CFTIuIkHJaE94MMXCJiYh0JyGWUAQp58EF1O7Yf/OAWzjnn6phmIZ1OR3X1Es4+ewmNje/nkkuUvGdwsBuXS6lqDg2pz/384Q9/xOrV17F//+vs3/8ahw69ic/nYenSDUkLxqkUdE+44iQOEorqRG3u5W5WCXfV3Q2HDx9RXVdeWpPQ+Ibfn/Z8rKamlXi9AfLySsjPLyUvrwSXq4QNG84TPykYFM8SsVgIef2EhK4EKRZFRF+8JCnSi/1xZR+jURkWYrMpyWWKs04aOMoQeQSDftatexttbfvo7++YWq/T6VhSVZX4RI8nxuo4GaqrEw85GjU1y/nMZ+6nq6uZ7u5jk/82o9OBLU2frNJSxQU6UtYoK6unrm4VVqsDv1+phTzyyH/T2XmEysomCgsrOOOMt6PT6bjvvjspLa2bsr4rK6unvLwOk2l21ik6nY7c3AKGhhKNXXp7Nfx6J3+8Q8Pq5IrTWcTICFniZJ4gS5ycWGSj+NMHe4glTjYCj6fyREmS7JAQaezJ0HHNCjt2vMDWrc8wMtLP6OgAo6MD+P0D3H77x7j22mtP9uGdOgiH0xoAByQnTrzaxInFIp47Nz6u1MetGtYadXXTJpzzoRYqOoapm16SO5zIqivaOstud/LOd35y6nEL/oSOqx6vk/oje7EfP660otlsnH322ezfv5++vn7+8Y9+hoeV39L4+MiUd7EF5fu0M060R20EtbUrVImT3ZOD5rZu2UJOTo5Q3fLd734XcwpflDZxYleGOoi6oyJIp7g+MSE+92fp1yBJYDOHGdPo7I987inBZks6QHLOEFGSJPsOtaK8dIgT0fucQQdg0mOYx0j1cI8cUc63Q4dS2z7acisZvF7t2SAKoTH3xEmqRIPVGlJ1dHO7Dfj9EibTiR1qL4Lo/cxEcSIqEC4k4gTgkg0b6POacRY3UlJSQ1FRNcXF1VRWLhZ/LrJM2+9+R9/wMD1DQ3QPDSn/Dg5y5uLFU5vpklihpARZxnH0aIwVkdHtxtrRwUT80N0s5iUkSblGjI0l3pe1rO3SHQyv8/vZsHQpFpMJbwrn3j+3bEEOh0+JIfEi4iQ8T6y6ANU5N5B54gQgBzcDFOJw5OF2q6tmI01MMG3jtX//6/zP/7xOf38vn/vcb5EkKYZciYRl8R9rQUEFmza9i02b3gVAMBigpWUPubkFC09xIjggI35V4kQLExNw5Ih6kFRbIbh++3xpyQO+851fcexY4nJNTkArrs3LI9AltlueNXECUF6uJN7Hjyv5kNWqDLrQ6ZQGsrPOUvyqepO7rxsIsYYd+OvP4lvfegaAsbFh2tr209q6j/DgASxqJ1CaVsPl5cr5f+CA+vrCwnIuvvgjCcuLitLPISKqk507/Tz11C954IGvMjyc+Fm88sr0iN7PfOZ+7HYnr732RMJ2er0Br9eDYZbXw/z8IlXipE/LInLysx8aUe+ycrmKEIxEnRMEPEGOBBcxjAsLXmppIS9ib3iaEycq44NjkLXqyjyyxMnpg6eBaI+ct6Tx3POIPVe2y7Lck4mDmi2efvpXHD26PWH5gQManRtZpI+Z+J95vYpHzOio0sYThyMPP8lL3hV4PGNTf17vOHV1K9HrJ4vKNvUad0R+a8zN5ewlS3hdJTLavPkDU/+fD8SJKJHIJHESj4AgaRgiD7u3A/+WLZg2bsRisbBkyRLq68XxeURxoieMjQkmiM2u6upWsmXLXxOet2cyQ9i8bh3Nzc0cP36cl156aepvz5493HPPPbz73e8Wvo9oBIOJpA1EKU50OjjjDKXVSNRePz6eepeYltZ71kbXk+e4xs/LwuTQJi0CJ4JFi+Dw4bmdZaIFrzfzxIkomRsfT1S4yPLsvRrnw8UiDaR6uKGQOIGcLbxePX6/+Ld0oorzoteJL6i7XAG6utQKHRJDQyZKSmZJvmUImSRORIoTLXuh+YjffOMb7BmpZ5BEq0W9Xv23LwWDmI1GKouKqCwq4uk33mDM42HC5+P7jz5K3/AwfSMjfOB97+P9t946q+PTj4+jU7mXmwYHmaiuzlp2LRCIfi+i3yTMgDjx+TCbTJy7fDnPb0/MY+LxvgsuwDc4iKWwMOm28x3zZcaJwZA+oT9XxAnAW996HQ8++DWVzS0YjSZaW/dz//1fYP/+1xkcnLZHkiSJf//3H2G3Jyqu1VKL+LDWYDCyaNFaIHlYm0qt8mQrTiANwiAKXi+0tKg7kTdUqKggIG3iJGkuKHoNEVwuAl1iFb2JFBoCktk8SBIsXqywA7KsfMHR9zKHA1atgo4ObZnHJCz4WM1OtnIGYfQ4HC6WLdvI8mVnczavAyp519GjirKlpERRwCS5l0qSYlFbVAQvvZT0kKbgdM4sBzh27BVuuulauroSGwjV8N3v3iBcV1ZWPWvSBBTi5OjRxOWaxInRSBhJqDhxuYoZHc2ss6kIsgxvbpMYR5lfN46DQfI5g624GDntiZNk/RZZxUnmkSVOTh/8DfAAkbv7RkmSlsiynEop5SNxjx/N5IHNBg6HeouGlvVQFjPATAuxGu3NJrMdp7kQpzMxCYzEC3a7NnGCTsdNV16ZQJyYzVbOO+9d0681D5rI54o4MRJAT1Ao0wZ4/fUneeWVxxga6mF4uBf3UAdDw32U5udz+I9/RDrzTDAYNGPzCHECil1XPHFSW7tC9XkHjx8nFAqhnyxoV1ZWcu21104pwl566SXOOy81olMRPkn4/V50Oj0Gw3R2pic0nbxYLLB2rWLJ9frriTuK2G+lYrUlUqdYrRkZ3GBz6EDcLKYQJ2VliteS1vwOh0PJEDo70/+9lpcrz5stvN7kUnpRpKfXq3+eou8oFFLeZ3TSrGVnsGaNoqXXshw0m0/MMI4MYj7wPFq2JnDiivNixUnsOWG1hrBYQpMzTWIxMDB/iJNUiaBUICoQBoPSCUmAM4WwySS8F4o+l3gi47v/93+qher1Z5456+MzCmwgpXAYnddLODvMdEFAdC5pzQQSqVFEqgbdZMB1zaZNFLtcXLhmDReuXUuR00nTDTdwwapVvP2MM3jbGWdQWaTMY/D4/XjTtAaaj5gvM070esVKLRxO/QI4F8RJxAL3wgs/oEqcnHPOOwAwGk28/PKfE9bLssw///kHdDoDbvfA5IDwftzuASoqcvjDH34bs72oY9xsTlSnxEOnU0Ls7dtfZ8eOv08NjXa5pgdIm0wpWOZmChkkTsJhKCioprS0k56e1hjL58WVghlaaaqcM0qcmExgt2soa+TUPgebLbUgIBkjlkYh28ko63mDgzThJgcHYyziCA410iSCsTHlLxBQiJwUYDCE6eg4TEeHYskV/feFLzxEZWVjzPYzvUUvXboEmy0zjGFVVV1G9rN58xWUlCyZ/F0WTQ55L2TFCo3PzmjEE5IYHVWvozmdRYRCSh1mpm7VqaK/H8Y9sXG6jI52qnBJo/MjATqJyBInJx5Z4uQ0gSzLE5IkPQJ8KGrx5wAx5Q1IkrQYiJ76HAR+n/kjnBkcDpfqcq1h11nMAHPQwR5CvUApSdPxm6jzKbpb6oZrruHgsWPc86c/EQ6HcbmK+OIX/0BOTt7U/k5o95MAWlZd4XBicScaYSRN/1ojAU3ipLPzKH/96y8Slh/t7OQf//oXm/PyoKlJI/6XY7qWHIzRS0nMFnV1KynIL2VdbTkr6upYWVvLiro6llVXo9frVc8hWZbZtGmT8LhlWWbnzp3s3buXffv2sXv3XrZt20dX11G++c1nWbv2wqltpxQn0bDblRNAbaaG2w2Dg9DaqqzPz4empsSTRaQ4yYDaBCaJEw1Y8EJJpUIKaBEnkQ6snBwl2kwVVivU1yudXIJiRspI5TqRzmB40P6cx8Zik2atBNZuV6zp9uwBUbdVucpAynmO+ZI3aM1DmW+KE0mC/Hw/nZ2JGbLHY8DjOTEzWZIhVSIoFYjnokiEQtKM5qacDGgRJ6LPRYq7txYJyN3+TDTcaKgCDRMT+LPEyYKAiDjJpOJEP3m/uuWqq7jlqqti1nU89BCSSiHT2tWFpacHX2EhnsrKhcN4RiMcRhL8Tk70jBMAkymsSqKrwWLJUD4RaRSZjLlsTKAjRFVVE5df/nGefPLnU5vm5OTxvvd9HlBmJzidhYyMJMZ4jzzyPdrbE3shi4pKEpaJiJNUSSGrFXbs+Du/+tUXEtY1Nq7jyiu3prajTECvVwKhuPgvXmnR2rqPxx77EXV1K6mrW0lt7QrVGsIXv/i/APj9Prq6mieL7ofZvFJAnKSpchbFbBHbHVXiSos4sdkICGZgGgmo6PNVkOZMDyHSVADkMMaZRJ0rJhNU1iu2X1ro6FDylhSI1mAwwA03LI0hwSJQZo5khjjJz8/nueee45uf/jT3Par0F+t0OuFcTy3U1qrPOk0XN998p+pHqTkK1GRiyBtm2bJzGBnpY3i4l/Hx6YYzp1Mh8UdG5p44EfXz9VDKSvORBd9AMFskI04iI0UXYpgwX5ElTk4vfAV4P0zdYT8iSdKjsiz/RW1jSZIswP0Q08rwP7Isqwj/Tg7sdpfq8qziJMM4gcRJxKYLxDXTiQl4883JGoVvFdff/EOu+dC32N/no6qqKWaAn8k0P24aWgVOrxdscXfAIHomsOFgTFgoAoUwMKBtZLlx41X89Kd3qAaOP/jzn9m8di00NODzqb+OGR86pp+rNiC+vn4VD/2hi7fwAgZUCvAqyYVaYSB+/cUXX0yvim9ua+veWOJEF07MaJUpf+rkx+HDsVFHd7dyjPFdxyLiJEMRY7J8xWKdbO/Ly0PVGBmUk6tkMjkuK1PfbtEi5f1NTCg/CL1eiZ7LypTnm0yz/52nkkCmS5wYDOKZNWNjsRMKNSVTZuV8WL1aIaB6ehTibGJC+SwqKpREbIFhPqjpkuHkK04Sr3si4gRgcNBERcUsLd8ygBMx4wTA59NhMMySND1BCJvNwiKRXifo7I+zBSpyuVS36xMMkE8HIgsiAP3JslDMIm2IiEQtEngmM05E0IqNpHAYS28vssGAt6xMuN18hWi+CZz4GScAdnswZeIkI2qTCKJiLgklrh7Fye23/4wVK85j+/bnKSgo5+1vv57KSqVDXJIkliw5m9dffzJhd11d6qn50NAAsizj8/l4/fXXGR11EwyeQU5O4rmjWVCNgtUKIyMiK5+iE1/PtNkS4j/7pHLB7R7iyJHt/OhH/05bW6yNVFFRJUuWbODuu/8PUJq1wuEQer0Bk8lMTc1SamqWArCWF0Att8mQ4gTU59FovsZk7O7XWVTHyKWsuslUB046xInLBcuXK81rExPKSVVbq22PHEEopMT/gnt5NMxmM0VF5fT2diSs6+pKZBVm09tQW1vLzz79aX52yy28xtk8+c+n+PrX35f2furrM6M4EZ1rmkoEoxGrPZ977315apHf72N0tJ/h4T6qqpoAhXytEHCJ6SBS3DcYEms1Wo5istmSGil4CiP6shAKBRkbG8btHmR0dJCxsSHc7kHOO+9ajMbTm2DKJLLEyWkEWZabJUm6F/hM1OJHJEn6NPBzWZanonhJkpYCvyR2oPwA8NUTcrApQqQ4yRInGYZWwr9smTJ5OM3BqmK7jen/azWbTw/3tTGKDWygNnt1vnRkawVjHg/YJiMZGThMI20ob8aInzoEBXMmFSf6sGo8H0FpaS0bNlzJq68mcqR/efVVWrq6qB0exuspQG1+SLRNF6gTJ5FEfxw7TlTa2YJBjXYqMZYtW6ZKnLS07I15bLAJGLKcHPVAXO18HR5WlCiR7DgcnnvFSTLipGByA6dTrJ6pr59+7zabQg7s26dExwaDQppUVmq/UCbYxbkgTkAhqUTESSqvbzLFdibl5Sl/oHyesrxgO5ciVtNqp0WmYTaH0OtlTXWJGk6c4kT9O1QjGszmMHZ7UHUA8NDQqUicyEJLGo9Hj92+MIiTkMksjh1k9WpAvOKkWFBs6c8EcaIRB+knJma9/yxODGamOJmZVddMYRoYWJDEyeGDB7nxYx+jIDd36i8/J4eC3Fw+dNddnOiQvbjYx8BAaq+a0Q7ruGaVCHEiSRIXXXQdF110Xezm+PBjFhInwaD69S8YDLJlyxZuvPFG9u3bB8D/+3+/58ILr03YNpnTagRWK6rDrwFyc/NS20kmYbMlKLK72nbzoS9eQnd3i/BpfX3H6et7hP/4j4vo7W2jr6+dW2/9IZdd9tGY7Uz41BvCIH3iRBdEVH7z+2RsNpXriBZxAgQMNtRGmaRMnGRK6RWZlSK6D1osClliNE47AixZErtNqgqN8fGUiBNQrK/UiJPu7tjc2mDIgKJsMt7IY4jly8/lrrseZmCgg/7+6b/du1/U3EVjY2aauETvRbNcYzQmNKeYTGYKCysoLJxmSgYGxOdxKpBlxc29o0P5yh0O5dRIlZz2mXI4vSecwMMPP8j9999Hf39HwrkcwR13XEFJSYoX9iySIkucnH74PLAcuHTysRH4IfAlSZK2AW6gHlhHbAXVD7xTluUu5hHy88uprV1Obm4hubkFU3+bNy+8zuF5DRFxUlen2Nv09IgHcQugpTiJIBPqYZslBILXOpEQxZOhUIgtW3byn1/8KIZQiP5xP9/43pap9x7AxCGahPs1EFSCoyR1r6uv/qQqcRIOh/nd88/zxbPPxjeRCypeufHEiRUver2sWkRwk6NOnIByHqXZsrd48WL+8Y9/JCxva9sX89hgFUSI6Wa60cSJxyOuSGcog05qGVw6mYTq9cpvrSMu+LfbFdVINIqK4Pzzp4e1p0IKpEOcRFlMxCAV4kTLckAEh0O99SieOEmSXKoi2htwAUKSlLeXplvEFEwm7dEw0cjNDRAM6ki3/nsiFCeyjNCnXtQ9XlDgUyVOfD49gYAkLHieKGRyxokkgcUSUiW9Uu22ng8ImszIqJ9PprAPVMqu8TaYQqsuLSvEFPHQE0/gMhqpLi6murgYs9HI7mPH2H7kCPuOH+crP/850gIlaU8niGzfRMRJOAyhUBqKk1BIU52UCnQ+34L04ejr6mJPS4vqug9/9cT35tlsIZxOPyMjyeWbGVecREGtISkaBQzQRTlLl26IWV5R0cjSpRtwOov44x+/p/rc66+/noMHD049bmpar7pdOoqT55//neq6ffteTW0nmYRKZ9riQis9Pa0pPX379uen/t/bmzjc24ZG0JMmcaLvaENHDWGVvNR/pA3WVSYSGaJK92Rs69OrJ8vxuZsQhYmzRmcMi0V8vFbrdNOS1vNTQSrKlEnUVNexdeu/EpbHF5tn7aSp3AgAhTgpLFzNBRe8J2Gzn/3s0/zxj/fELCsoKOecc97Be97zWS6/PH+WB6JAlFaFQsqfKl9mNCbMzAmFgrS07OXAgden/iwWO3v2vD5jzu3YMWhvn348Nga7dsHGjUrKGg7DT3/6KXJzC6iuXkp19VLKyxdhNCrH5tHbT3vi5NCh/ezZk3heR6OvbzBLnGQQWeLkNIMsyyFJkt6LoiaJ1g8WA5cIntYLXC/L8ktzfXzp4uyzL+N97/tswvLzzz8JBzNfMTEBR48qd6XIPINUo+MIRMRJ9DDuNJEKcaLTiV16UoW99xhsHYGVK0+6r43VOh1Put1DfOUr7+TQoTfxemMDwMOHt7F69QUp7FFGTwijSQdJCqdr115ITc0yWlunCYeLzzyTT159NZesXw9DQ/hCVarPjQ++pYJ8HEFJdc72GBqEgtebUuY5PKzwA8Eg+H3qzMLu3S8xPj6C3e5ERwidTRDxpktwRAfjosHwkDFP4GQ1D3NuVCGwoUFJ0iIzTBwOWLVKnRiRpPSygNJS5TqRDGazQpgeSPTSnlPFiRo8ntjoX5TAzuD6tJAwU+LE5VKa/fbs0T7VI8jNDTIxkX6WdCIUJ1qvISIanE5xN6bHo8donF1hc7ZIx3osFYiIE49n4RAnWrO8jEF14iTeGqiqqIimqiqKnE6KXS6KnE6KXC4qJgdwzxRyOMy/fec7TGj8GD/2hS9QuQAtAU83iIfDS6pchdb1R404CY2PEwgGMc7CmkpCGbJ+ogeqzxZDgiarHJsN00m6V5eVeVMiTtJNnTSRJnHiYpgBCliy5Cw+9KEvs3TpBpqaziI3Vymyjo+PCImTaNLE6cyjvLwhYRurNfVue63Q8vzzr05tJ5mEigI8z2amsmIR7ccPp7UrNeLEikYSmiZxIvV0Y6YUD4k5hH9kQqkm19am9hqTxMlPfvsDXt/2KgUF5TF/Z1U7WRWd1rlc0XYNCiyWzJ7YFot4iE4qObhgZk0CUiFOOjqgvZ06u/oJm3HiJKpJw8WwcLPNmy/Das2hqqqJysomKisXY7NN58b5meFNNH/PgYCAOJGkGOu31157gv/8z/fh9caSh3q9gbExP05n+nUVrxfUuHOPR+mRKymBwUEvf/7zD2JmxOj1BsrLF/HZz/6a5WWNiTs4zdDT0510m76+QSAz1m9ZZImT0xKyLI8B758cFn8nsEGw6SDwB+DLsixrOA3OPwQCJ71GPj/g98PWrdMByPi4Imc+66zUrYYCAaWCrYZIlOF0JnbCJ0EqxAkohzkr4oTJ97xnjzIc+iRC8QVW/u9wuDh2bNcUafLhyy/n1R07ONzRwe9//5/k5haQn19Kbm6B0O86MvjPaDMiEnlEIEkS73jHbfziF5/l4os/wn9dtY4lVVERtduNT1AsiCFOdDqoq8PRRfrESQpfZG8v7N49LfRYsfI9wI8TttPp9BiNSpKtOhg+gnSJk+jCvqi13mbLnLQ9CWI4EZNJseHy+RTCwGbLXLdpcbE6cZKXpyQxHo/yY6yvF3+PqdixJemcU4XoO5Rl5ZoWSfpEBcv54tc3R0j3Xme1wjnnTJ86qTxfkmRycgKaVjUinAjFidZxibrHjUYZgyGsavHl8ejJzT15xIksZ9aqC8BqVZcler0LRwGhWaAOepBJLADFK04uO/tsLjv7bNV9DIXDM7btG+7v1yRNAPZt25YlThYAxOSkRDicePvXusapKdeef+YZPvBv/0Z1cTF1paXUl5ZSX1ZGY0UF77lAaZqRJQlJlpFlmf6REeVvdDTm3/eUlFCQzIZznmFQQJwUZJSVSA92e4jc3ACjo+JKY0FBhnsw0iROzPhwMoLfXsyHP/yVhPU2Wy4lJTXY7S5ycwtwOhUnhvb23ezYMd2VvGTJmUiSxIsvPsL69ZditSq5YKo2XaDEEPFD7CPYvDmxw37OITj45YuWpU2c9PW1JyxLqjhJVfnl98PEBCb8qsSJDzN0dcUSJ7KclDjZvW8bb775t4TV117+Pi69Y9J2TK9XOmW2b5/en14PS5dmVrWm9SNJlZkzmWZPnHR2wn5lpk1DqTILsaCgnNLSuqm/mpplMU/JJHFiIoADN2MkNgteffVFNDVdNMsXS45k83REX1UgijgpLq5OIE1AUaG0t3fjdFandCzhsDJKdGwM2hK5ySkMDCjEye7dh2JIk+nXPIDd7sRz2utNoK8vOXHS3z97JXUW08gSJ6cxZFl+BGXGSR2KNVc5YAe6gVbg5ei5JwsJmoOvTie0tiYGH6GQMhh7zZrU9qFV7I5EGcXFymtFBzIGA6xYATt2qD41HeIk0mA/E0QGBDI4qHwWJ7GIGh2USZJEU9NZU8Hub5+c9izetu1ZPv7xleTk5PGHP3RPSVPjYSAIkoTBnlrl9O1vv54LL/wAdnsu9fwTov1vZRlfUP07segDUFCsBL3V1WC343Crv8YYDmSmff5iuiqTFJU8Hti7V0aWp4P45cvPobCwgv7+WGLuzDMvxmRSvktN4sRsVo471YtC9PkuOt4MqU0iqKtTn+eumgsqnjsZfX1A+aFVVMQSoEYjNDUlEhdaAzW8Xm2iYyaKE6t1Wrsdj7GxaeJkJlZdpwDSfXsFBbF5cirPt9uD6PWKaiFdhMNKsXEuHYpmojhRRFkh3O7EA5uJsiaTUE71E0Oc+P16sW3DPIMWQWYOeFSFl/HEiRZ0gQDhGV4vOgT2Q9HYvWMHb3/3u2e0/yxOHLR+Y8GgDr0+9l4kIk4kSVbdV2tzM+FwmJbublq6u3lhcnldaSmX3HQTsl5PyG7HNDhIaP9+it+jXow+66qrFhxxMiSYJZSXTuV+DlBe7mF01IDadTcyxzqjiIt5TEnmUVjw4mKYPopV10uSxIMPtiQsf+qpH8cQJ6WljXzzm9fx5pt/45FHpnsi0+GtjEZ45ztv58UXH8Htnv4+zzzzYpYu3Zj6jjIFk0l5A3FKh9UNjTz9j/R2lbZVVzicerfmpNrDNDmQJBgMMDzcy+BgN0NDPYwU6GhYZIhVUgcC4ph78l7V26/uol5RUDD9wGRSYvMNG5R8OBxWGqMynU9o7S/VLh+LRbFN1oLXq92oFeUD9cG3bKJmsweTSTm2iM1cSUkNg4Pd5OWVIElSRokTgGra2EfshcNgUIiBSU5nTpFMcSJCtFVXUZG6EwXA8ePdrFiRnDgJhWDLFj9PPvkozc27qKxczLnnvhO7PfGiMzCgnO67d6t/QAaDkfLyBrwnVxA+L5AKcTIwMPvZfVlMI0ucZIEsy8dAY/r0PIaoSSJLnKAEFMePq6/r71fkAqkkKiLiJGKuD0okcMYZCnkyMqIUluvrwWLhn4cPkwOsrq9HH1WZCRksoHLji4+BZjODWyIcK7EeGzupRdT4eHLZso2qXUIRXHzxjULSBCYJg5wcxaorBZjNVsxmJTL0Y8JEgOGxMe66/37ynS4Gc/fHzAuqq1uJ0WjCXFkMjQUx+xLVxoMY8RZWYe1vp2doiHWf+AS3XHkld1xzDXYN4kSe8LB3S5BQKLY7x2AwcscdP+e//vM9jHuU5KWoqIpPfGLaH1aTOJEk5WBT9a9PhTjJcKJRXKxOnMzSOSZ9LFmiJFJDQ8rvpKxM/XPV+g1pESehkHiYhlZCpdMp1xQ1P6losvY0tupKB/E2AKnksg5HcPK1UhzcGYdAQDfj56YCUUFdkmRNwsZmC+F2J2aXJ9u+SjToHmZOnFgs4s/f610YA+Ij37Pf76O7+xhdXUfp7DxKV+cR/mLs5qv33ZfwnPd98YvYzWZK8vIozcub+nddYyN5cdaRUjA4/YOSZQxuN4axMcJmM36XS5Nd6tRqo5zErj17Un+zKWDHyy/zwt/+htPp5OJrrqGiLmvJkAlo/cbUrjXiwfDhhDxFPzHB8SjrpGjUVlYSjIrL/QUFyOecg8loxK+S2Ayqzf6a5xgUECcFJ5k4sdtDVFdP0NZmI0KeWCwhFi2SKCnRZX6UjMqN14ifgMqcQVCK7Vr2PyI0N8fOA3zssZ8AsHJlrKd1OsSJJMGiRcv44Q9f5/HHf0Jn51FWrbqAq666ldrak6RgzM9PIE7WN9ZO/d/pLGTRonUsWrQWl6uY1tZ9PP30/yTspq+vHVmWY5T+msQJKLFnKoHUZB7ynR9/nsf//hdGR2PVV++54nredft1Sqwb+T34fAyQTxvVyEjkM0g1beiQwWwmHIb+/k7Vl6ssiAr2IlV0o1Gp3M8VMqE4yc1Vn2sYj4kJ9RNXlmPyhVyzHht6goDP5+Hee/8Nj2d6vdlspbS0jv/8z6/xwQ++K7VjVEPcNbqMLrxYOEYdMjpsNqWf1GCYTrXikclbuCSJ+wa1BsRHEycOhwuj0UQgkPiErq6elI6jtdXHLbdcxo4df588Lok1azarEic+n/K17hcwSxUVjRgMRrzBkzuDcD5gYEBMnOj1BnJy8vH7539cv5CQJU6yWNAQNRpkiROUznG1Ac4RNDfD2rXJ96M13yQ6kzCZoDHRc/LTP/sZ23btwmm3c/7Klbxl9WpGxsd5o9uDzlyAxeLAYrFjtTooLa3l2mvfG/P82RAnNiaU4DKCWQ7jnC3ia9AXX3wDv/vd1wmFYo9Lrzfw7v+fvfMOc6O62vg7o97LStu71713G5veMQYChBIgIXyEhB5CCSSEXkJIIaEmJKGF0HsJCc1gbNx7x/auvb1rV6te5vtjVlqVe0cjrXZXu57f8+ixd2Y0Go2kmXvPe857zr8ZP/7xg4L7UyAAmEyix6Kx8AMjF5o6O/Hk+5Gm8S/GbfPKKw2w2UqgtCZ/CEKtSpysCRrU44GXX0ZTZyfufP55PP7ee/jNFVfgJw89BGXiBIPjULe+DY5gBXF/CxeegU/+8T+s3bYevcpyzJlzUtyAS44goBY4oLw88cKJz4doevwwCScGA1BRweuOEcxmYNgTSRmG73VSWCi8HcvSPYiFKtSERuqpJp16PVk4iSwLhegX/jFecSJUAEQiUTgRV3HC30vkcg4yWZjaCJlGMMgM6cdAExpkMk4w4EW3r5KNaN9lYeuxzCaMSmUYLMshHE7et8czeoSTFStew0MPXQwu4Ytv1uuR2Fo64Pfjra+/Ju7r00cewUkJ9p2x/VA09fVQxwRvVBoN+saPB0e54TaKEU727Uu5jVhefvJJ3HDXXdHz8PDjj+Pt117DdIoNmYR4hH5jZOGE1hg+fj/qxkZoWlpQ20zOEK8sS87uZVgWNpMJTYTSa5rtVS5z1rHHokytRpfTic7eXv7hdGLW5MkjfWiw2/0wmwPo65NDpQpDownBbDYNzX2AMObRwEMVThQIQI4gWIZDmBN/QLW1u4jLY22KGCb9xvelpUAgUIOf/Sy+r0peHuUJQ01eXlLjhOMmVeK++95HTc1s2GwlcWLIgQNbUVu7Dfn55cjPL4fdXhb9f0bCiZgT2F9xEg54k0QTAGjr7v+NO51R4aSjPYxPWqz48MPfIxDwYd68U7F8/kxMw05AqYTbFUJXFzmAWpYXI0ZmMlHMBKH5kdjBKs06OJFYq95YIvZpMWjggRMKrFnzYZxowm/uwaFDu5CfP0i7wIQ5CAOgGrWoVDYhsPDouDFwQQF5WppPLijLGJpwIhQnC3ADwTWGYWA2FxAt7FpaxAkn//rXK1HRBABmzz4R+fnJlSouVy+2bv0SRmYCvtu7k7iv+vo9eOutP0GjUWDO3OtEvf5YJBgELrnkTnR0NKKrqwVr136I3t5OhEJB/OY3b+Doo88DwzBJ7ZIkBocknEiMamjjgCNeOAmHhU0kAb4e0uHgI7RCpGoML4DD4cDm7dsBAD0uFz5YswYfrFlD3X7q1CW49NJ44WQwrkhJg12hwO0wkHjK8vPLcc897+DRRy9Hb28n1EoVjj3+B7jkkt+gqCh12okcQcBkEmwpQSOSUdJJa+IHwGjkZ0BKQ/JAWC7n3w/p69Eb0qGvuRl/jbEfa+3uxnV/+AP+8Oab+P0f/4hzzz03us7b3I2DAXo5MACY7ONwxokVaESymiBnOWEfoNJS/vcg9vP3evk3N0zCCcBrjvn5/E9Sq+WD2zltnaNWk4UTITu2wQonJCLCiZAf8hgXTtJJ1DWbkxMOxJyeSMUJwFcuuFzpCSd8YHHoAvOZ9gOhCSccx8DrZaHRDF2VjBBCwkmmFSe8y9/obhAfCjHIyytOEk0AwNHXB0d7O8wxpXodlAA1ABRaLEnL2H7hROZ2x4kmACD3eKBqa4O3pIS4P1k4jIqCAjS0tyOUUFmnU6sxs7oas2tqEPb5wA7ymuRxuXD7gw/GnYfO3l5cfsUVWLl2LbTp9vaSiINhQBUZSbaAdOEkprGtywVNCx/gPEgTTirIySN5FOGkczA+tiPE0unTcUJ5ctDMa7cLteAeNhQKDhbLMEwiCWOechzGDkxPWq5DHxgATFEhDG6G2F+QRnn5FDidXaiv3xOXNR4rnOj16Y83y8r4aWTsFKKqanDJboPCZOLfREzCYJ5OheMXL4EXyfPVceNm4okn1kX/9nj6oFCoIJfHBxZU8EKGMD9wksnSH/dGCAaj9lMFFjNxk/buLhxANWwdAZhK+dj/h1/ux1VXHxX97N5++zHsvuxuPP1/Z0LHMGhsbEc4TB7HlMcKJ8PV+FVofiT2yxHpp3jwoPB2tD4nhM9IDS+cMOLLL/9NfIrFbMVxxx0n7vhoUIJPrFKRNM4uKuJ/P7HDjOrq9AXMVPh8DjQ0tMHhaEdvbwccjnb09LTjpJOOQXn5EuJz/OH4MaLFUoC+vm5YLIWwWApgtRbCbC5AWdnElK/PccCnn74Xt+zUU68AwPcs2bt3PTZu/B82bvwUu3evQTgcguvahzB5ylJ4fAGsXh3/3HA4jGee+QWKiqrxhz9cN2LJTSON3w8sX351yu1GOF94zCEJJxKjGqnihEJra+rGagCfnZOq18kghJOVK1cSAxw0NBp90uBdoaAnt6ciqdliJjvJImo1PyGPPSWLFp2JN95og7nlY+TbirFfKb6BvRxBwGiBIoN4ZEQ46RCYgfF+sBzkcvLIxGgkfz2cfhXe/+wzBAh37NpDh3Bo61YgRjg5fBjgIByIdUNLzfpKKRzJ5fz3fONG4SqsCF4v/8WjbTtE1k8mU3pB8BFFowFx9k6qColAE05YNvWsnRYI9Pn4kaHQb3uMW3WZTPQWMIkQEppTzqdVqlBcBrZKFYLLld7wUagHSTag7T9VdYZaHQLDxPdViuB2y6HR8N9ZjgOcTjl8PhZ6fXDIBRWacMKywhU0qdBoyMKJ1zs6hBO/n0VREb25et2+fZgVEU7CYXQLZK0WEIQTpn/wqKTYCSl6e6nCydXnnosbjz8eoVAIWw4cwE3PPAOb0Yil06bhxu99L2pV6vT5EBykcLJl1Sq4CMG62uZmPHbfffjV7343qP1LAHJ5GH5/8u8i9rcZDATw9G9/iw++3ACdqQhnnvkzzJp1fHR9rHCi7K8O4TgOtS3kDPEKik+LjfBdBYBusZW0OQRLieRwmWQAjWZUqqQJgR3tRLuuApUDKB0HVFTAfIA89KJxww1PAuCDlM3NtTh8eBcOHdqFmTMHvqfp2HRFUCp5h+bOTn4eYDaP8PiVZXn/owQx0QAnUThJ5LXXfodXXnkIhYWVKCkZj5KSCSgpGY85E8bj6Cky/vOiCSdi5pb91SYAUEgRTnbt+hZ/evcjzJl9Ak7OA7RaDg//7mdJNkmvvvowTj/9ShR8AfT0qHDllY+gs7Mp5tGIzs4mlOXFvM5wVZwoFOQKcZksdaJmLNXVvF9xXx+wfz/5HNOEE8K9UQMPnM5urFv3MfEp5592OhTpnqNwmD8GhYKfZ9CCT4RBtkwGzJjBC48uF//bGQrR8aabzsDWrd8S1vwKF1+cLJyEw0AgHG9N+Nhjq4i24WLspH0+YPfuAYFSpzNhyZJzsGPHKtx001Lic77euBr33/8Bzj7nRqxe/R7uvvucpG3a2g7D7Q5BpxsdY9dsIzYP9IiPh2aZI2yUIjHWkIQTCrENnoWINIgTytYfhHCyYsUKcccR3WWycAIAxcXJPSAUCj5eKqTLRBvDRxjhipNIX+/EU8qyLAqKJ4CjNAOmIWfDgEYDRQZ6kJ9VA2Hg8kcfFX4NOUP9ehgMvEaXyPufvIs/vPkm8TlldjuuXriQNzHVahHwc2jsSz3bckMbbaiYiEwh4rwZjcD06cDWranLxT0e4UnGGA/Ei4L2++/q4kfKpMCgUGP4VNFgodIzn4+e8RfJEhzDyOX8HHP//oFlLMtPatrb+Us8w/CFVyQbgFQx3NhqE0C4VwYNWkZ2tsi04oRlefHE46FXYYRCwIED+rheKEVFHhQXi8gyzRCaEJRptUkEWoXNaKk46euTw2otglKpht+ffP4PHTiAWUuWgPX5oDtwAD179hD3I2NZ5BGihZGgrpp0YwMgd9MtW9j+65tMJsPcCRPw9R//SNxO5nYjmEmkMoYugUqDr1auxMPr1gFGIzwlJQimmcIqdzqhamsDEwohYDbDZ7ePnGfdCEL7rcX+Nq+59FK8+emn0b+/+eZt3Hff+1iw4HQAMVZdHAdFTLR7/RNP4GBzM2pbWnCwuTn6/yqC3S0A5MUIJzq1GjaTCTajEfmjJtNiAIaSkHLECSdyOX9DjrnWyBDGDEMttnsnwN/fN6ewEKicUopIblGmH7lMJkdp6XiUlo7HUUedHbcu0356Mln2rYUGhdWaJJzQ5g2J7N+/CeFwCE1NfN+s9es/AQCcesyZuGLKjXxUmzZ3SFM4KaYIJwDw5JM3QKlU45Zb/omqqonYt29D0jaBgB/33/99qFRaaDR6nHXWtbjwwtvitlFzLuRj9cCC4RJOGIYfkG7fHn++KivTH4sbDPzD4yFXn6QpnGzZspHYqwMAfnjqSekdW28vP5+MfPZ5efSgFOXcM8zQJ8yVlpZi69bk5bt3byPa0bpcASxfboLRaIPZbIfZnA+Tif/3nHOuR0HBQFWkmJDKnj28iBdh/Pg5ePjhH2DVqncxceJ87N27Puk5W7euQCDgh0KhREFBJXG/oVAQtbVNmDZN2K1irCI2D/iIj4dmmSNslCIx1pCEEwLhsPh0pHCYv/PRAsEcRw9IihBOvvzyS3HH0Y9aTRZOKir4pJNISatCwTdYq69PGiPHkVShMMIVJwDd3soDDRikFxRTqGQAw2TW40RlADzAAz/+MW548smk9TNnHgdAOBudFvux28fBSQkw3fvDH0KtVPIfZkUFGuoCCFE8nWMJQQ4XyOk4cqXIwbjNxosnu3cLXyQ8HvobZ5jhK3nPZWw2ckd7juMDAQQrDkHhJBVC0X2fj/7bHuM2XREqK/n5ZXs7f18sLOST/iKaklJJv2SndkmLF05UqvRL3Eau4iS1yKPR0IUTjgPq6nRJDeSbmzWwWPxDVnmSqRCUCrWa/NkFAiyCQSbj/inDAZ/cKQfLMigqqsahQ8ne/XX9wRVNYyPkHg9aKRn5+WYzWEJGACPG14DS/IYVmZgh7+vDYEcivQJjvNV//jPkMhngckG/bx96p0xBWMR4DQAU3d3QxwSoFE4nZB4P3BQLqbEM7bcW+W3u3749TjTh1wXx2muPxAgn/PWB9Xoh6/9+MAyDqZWVmJpgPs4B6Jk5kzgK/O0tt+BPP/4x8oxGaGLuaX6TKTE9KOeh/ca4MZ7gQGTiRH4sGqlwM5thmVqOpSoGLhd/b04cwqSTtC8Gu30E+5JkG8IbsaOdaPELAGaFC46ADu3tDdi48X/EbcaV9D/XbOYTvkiImVvG3IuKLcJidnHxOFRVzaBWRwDA7t0Dttfffvs+br/9XzjxxEuiy9SMH0xsMt5wzlvy8/lypJYWPsMxP39wChut4tztJid/UoSTo48+F88//x2+/PIVfPHFy6iv3wsAKMkvwlEU0ZpIOAxs2xb/uQv1mxpBUXjWrFn46KM3kpZ/993mqCt1LK2tnfD5PGhvr0/qa3LSSZelLZysWrUu7u8tWwbiQr/4xa/w059+L+k5Hk8fdu9egxkzjol7vUT27687YoUTsXnAklVXdpGEE4lRTWzAmOM4uN296OnpQGNjJwA1ZsyYMWLHNmK4XOl1CxYSTkIh+r5SBCSDwSDMZjNUKhV8IgULtVpHFE7kcr6k1e3mxykRK9vubiHhhMu5ihOAHrz0QMNbb6WBXMUPFllWvFVPBL/aCPhYXHbSSbjnxRfR1e+9G+HYYy8EIJykREtkramZjVkz5mDLtk1xy6dXVeGyk0/m/3C7EQoB9Y3iJ8u0cnu5Jo1bWX4+PwFyOoGdO8nfCa+X/v1WqYQrtI4UjEa+CoQ0kWxuzr5wIpPxFwLSKNDvp09cj6DqoLy85LiBSpVaO0o1p9Ppxm7FCQBotSGQnJlcLhna21VwOMjfz54eBTSa9EPgEduvQICFwRCAUslFlwN8TJ72fsQIQULQKk4AwOtlodfnboN4Xsjiz0tR0TgcOrQLDMPAbi/DxCIbJhTlY9a4cXx2f3927+Tyctx03nlo6epCa3c3Wrq70drdjSJKpDAS1N1eW4vqwkLoCDdsJhAAl3jN4riozVcq5JEx2iCqOCqLi7Fs4UJ8tHbtwHExDJZMncqLJpFlANQtLXBTLKDi4DhoCNXKqo4O+C2WQVfJjDZoImIwyF/LPqRU1W7b9hXC4TBYlo0KJwoRyUwBi4VadVFcUgINSegTYz2aS3AcVTgJH2kVJwA/9pk9mxdPgsFoZS0L+vhaqeSLH2jJ9ukwbRrfpHrMFJRptUnezhZ0Qwkf/IgfCNmMPqiNOjgagFdf/S2CQfL1e+nESv4/ZjN9kpWqx0koFNcMZnp5MW6//V+wWArgcLTh4YcHBI8zzvgJrr76MajVWlRWTsWmTZ9RRZ1YXnrpXpxwwg+iTe1VifL8cFWcRDCbs6fyCXlYud3JwgphPsBbd3MoKanBpZf+BpdccicOHNiCL754GXMtLrBeb2oHjggOh7i+NhGG+9zHsGgR2f67o6MRBw60Ytq0grjlzc3txO0BwGyOF7/EhFQ2bFhHXF5WNg4//vEy3HSTHm53ssXzxo3/w4wZx0CvN0OvN6Ovz5G0zcGDdQCOTn0QYxDJqmtkOAJHKRJjCbkceOWVh/H224/B6exCKDQwIF+2bBk+/PDDETy6ESIhAJ4SoauqkFSdYpIjl8vxxRdfwOv1Ys2aNVixYgVWrFiBNWvW4Nprr8WePQ64XH3wel3wePrg8fShqKiaWsnLMPzYKXb8JORAoYSfb+gXS45UnJDwQCO6pDyC3MhPshiG/zjS0YUCYTkwfz7Mhw7hnd//Hhff/QCaWuqhVKrxve/diDPP/CkA4fGeQkGuoGFZFvc/+E/8/MZzcaA/e7WysBDv3XvvQFDH7UZLC+APDT7LMHIeRKNU8hHmoiLg0KHk9R4PPdp8BAXiBWEYvqyBVD7vdPIlYiImMwDEV4WoVORrkpBV1xFScTKUJAoluVhxQhcaUgsnNDEhFGJRX0+/tvj96YtBwSCDAwd06OuLXFg5lJe74fXK0N2tBMcBVqufmq8w2IoThYKjNr32eGQ5LZz09Q2MOa688hFcddWjKCiohFopx3FYARYcAno93H4/mP4TuGjKFCyaMiVpX6H+gDPHceh1u9HW3Y32nh545XJUmUyYcdVVAIASmw3jS0qijx+feiqUgQBCCcJJR20t2hsaML6kJBq4osEGg2D9foQHcW06fv58nFFaindXrQIAnLOE3OQV4KtGxAg1Mo8HMso1WtPQAOfkydF9tDU24v7bb8eWXbswvrISN9x0E2YtJfuVj1ZSVZx8uXIl9bldXS2w2YqjVl3KGJueRDgAAbMZLoGqHpqgQusXkrOEw1RD2iPOqitCpIo5jYqAiBNSLEYjH/cVajMXS3k5P4QbU0S8j9raootkCGMC9mEXpiAMfr6hghfjqoEOJ5/d/tlnLxF3l28rwvfmT+cTd/R6ulrlctEtagF+ThFzU8836nDKicsQghwvv/wAAECrNeDnP/8bZs48Drt2rcbhw3tw1lnXYNmyq0QJJ42N36G9vQH5+WX97zHhWj6aK+U1muQGoRFcruS5BmE+oIYPdq0b7W4+iMAwDGpqZuPkGhPG4SC/b49HXKOR5ub0jn8EhZP582dT161btxnTpp0Wt6y5uY2yNWAy2eL+DoX4KRnt0s1xwJw5y+H3y7Bnzzrs27c+KoDMnr0ACoUCS5acgE8/fT/ueXK5Aj09A1mx+fkV0efJ5Qrk51egoKACKpWZeqxjHcmqa2Q4QkcpEmMFuZwvjXc4ki/0LZTmi2OedIUToWi7UDabyEmOWq3Gcccdh+OOOw4A4PV6oVKp8OWXDDF5J525k5BwQuwX4vcPOtNzsNCEEy/Uafc4UZgy7yTn94M/gdOm4Zhp0/Dxwiuxa1cTdDoTtNqBE5tqvBexn02ktHQmtm3fjo0ff4xgXR2Onj49LhMWHg/a2zggzfdMQqXOMJud+mF46YF4STgZgCacAHyJfk1N/DLatUbspEKpJE9cJauuQaNScfD5SL/F5GbkMhlvQZNOFclQV5xEssATESM0CFVhCJHJe2ptVcWIJgDA4PDh+Ot4Wxv9GjNY4YRh+PfrctF7uuQqscdcUTE5+n8TusD2GxzJfD6wImaU76xahRufegrtPT0IxASfKwsL8Y+YarnGjg40dnRgRb9J+A9OOAHqQACRb0xvZyeuveIKfNQfRC/Pz8cffvpTnH/MMYKvL3O5BiWcRCoNhASTCGwgIEqoEaqKYF0uNG/bhqKZM+FxuXDyiSeivj84ubO2Fp+vXYv/fvABJs2mB2lGGzTRNSKczJ88GSu3bCFu09pa1y+chMEEg2CdTmIms6ewEN6iopRZzrRqDFHWcjmEkNBzxAonGZCfD0yZwrulhsN8T/QJE4AdOwa2CYfD6OvrRk9PB3p7OzFhwry4Bs9D0Yg6J0gQTgCgEK0woQcdsIFFGAVMO+R5x6AvwPfW/Mc/duHVV3+Ljz/+W1wPjFsvvoy3FjaZ+N+o0KSzpYX3lSZBmE8o4YcHcqxe/R4AQKs14oknrkNvL2/5NHPmcTjnnOswbdrROPbYCzBt2tGYPv1o3HPPOWhpqSO+zMGDW+nCyWj+fbEsX01EGv87nXzZVCyU+dvUEgf2OrXoaAkgDBalaEAVYiyH+/rE/TDSqTYBRlQ4KSgoQH5+CdraGsGyLMrKJqGmZjZqauagsHBS0vZtbeSKE4PBArk8+X34/fSvlssFTJ58FCZPPgoAf01qajqAvXvXYcmSEgDAiSeeiN2792DixAWYNGkBJk1aiOrqmVAqB8Yr11zzZ8hkchQWVsJiKYSsP5ZgtaZ1KsYUYoWTSC/gMVNVOMKM4quohAR/sbbZSojrGsU2SB9rZFM4GUTFCQ21Wg2Oo1c8p2NzLGTbbYaDvMLvH9FgKu2Yw5DBg/QqJ+SqgZOVrgtZ4vahEAu7PdkHOFWSksGQNEcBwFela7VaHH3sscRybc7rhSOQJeEk04+TJoL4fGQ1SOg5RyJaLf/ZkrJpGxv5SWTshGEwVl0A/YOWrLoGjd0ONDQkLy8pIf8OTKYAOjqSPw+9PpAgDPAMRcVJKMRPBlh2cBUnCgUHuTxMFV9oZFJx0tU1uKzPwQonwOgUTjguvuIkFhMGAv5MIACZiKBG2GBAE8GTvN3hQO2+fcTnaFQqqJVK9HR2Qtt/T3voV7+KiiYAcLitLT5BgILc5UJgELP+dC2a5H198GconHy+aRNuffZZdPT2Ys3GjXj7+eejokkEp9uNV557DveOIeEkVXP4R376U/zj/fehVmkQgBw+nwd+vwcLF56JwsIqABwCvj7cce01mFVejuvOPjupGslvtYqyhqGJCswoi4oICT1HZI+TDGEYoLiYf8TS3d2EK644Ab29nXA6uxCOmWi9+OIBFBVVR/8es8IJxR5KAy/K0D/I0RsAhokOD222Elx33eO48MJf4pVXHsYnn/wdVrMd1592bPw+dTphi9o0hZP6ju5o8/eOjviYxeLFZwEALJZ83HnnawCAzs5mtLUdJr5EaekEjBs3K/p3nHDCsqL6kuY0tIqfGAs0AAO9WwnIdSpMrWCA3g3g3O7k2adYQSTdNP4RrvZ56KFn4HLloapqBjSagR++Usm3xN27l5/yarXAoUNk4cRkshOX+/1Rh8EkEj8almVRWjoeZWXj0Z9Li5tvvh7z5t0g6DA/c+axxOW0afqRQDraXTA4otrdmEISTiRGNbxwQm761traCr/fD+VoLk9NF948Pb3nZGLVFYlWZYjQnD+duRPD8JlWpP6vBWglP2mEhZNsxnEHs69QiH9Ezjfta5DqZkuzPY/EslWUEZULOgRD5O9QHtOFTk58UCnjj1NoIpE44osgBeLjKSwkCyeBAHDgADApJqOJJpykY9VFwuuVKk4GSVERh8ZGRHtIAIBMFobNRv7Mioo8cDrl8PkGLtjFxR5otUHs308WTrIV3wuF+IbtPT3865jNAWr1h1ihwWwmC0FCpFtxEgwy8PsHFxzMRvN2rTYIIPm9ut3ynIrBchzfd8XrlaGvT04937FJEgwAOe3a3U9Qp4MpoTF3BJfXi52xadsxeHw+2M47D3f87Ge49aGH4HW78cK778ZtM7O6GmcfdZTg6wP9fU4GQUbCiUAHaCYQSDqmHbW1uO3ZZ/Gf9eujyz565RXc8+ijxH189MUXuDetoxKmYf9+tLe0YNLs2dCMQJSX1k8oFGKAUAjyQAAdb72FVlkZdiPZDq7+8Bb89EfLcLCpCQDwu9dfx7IFC3DmokU4YdYsqIxGhEWOJ6jCCfjvwmip1qA2hocknGQDo1EbbXidSE9PR5xwQgt2jnoMBrqtU4T+60niZcVuL8UNNzyJiy66HaaWj6GKxA8iwgnD8Ba/Bw4k77Ovj59/k6pSKMLJmjV0O/GIcBJLXl4RXn21Cc8+exu2b/8ara2HwPW/z4ce+iQu+S1OOCkoSG9ynYsYjUArYV7f2xsvHgul4keutyoVGJL4JUYQ4TiycCbECF+fzznnTGzenLzc7wdibu/o6QFmzToL995bDoejHQ5HG3p62tHT057U3yR2HzRoRax6/cDXUS5nYDDQp9xCeL2jKm8AAP/13LOHnzZrNLzWmlgwJYa0rNkDknCSLUbHSEtCgoJcDmKWPMB7Vzc3N6NCwDd4zOHx0FWJhIZ5UTKpOJHJUt6pNm3ahG+++QY33HBD0rpsCScAUFaWLJwo4IcdlAZnPp9wufUQo1Dw7zGduIfRmDyoMJniY/jFxUD//Fw0fv+AdpCpi5LQqeztBexWcmDAATNxOYMwiowudPaIE04YZhADgkxEEEk4iaegANi3j1xC1tDATzJNpgGljoRYcZu2nYB/vCSciMNgAGpq+tDUpIHXy0KrDaGszE0N1CuVHCZP7oXDoUQgwMBgCEKnC8Htpl3AGQSDTNTzfzDU1eniGrZ3d9O/P2KbqZOEoFQEAumJQdmo6MhWxQmJcJiB389CpRpcA/ps4HAocPiwVpQ4FVtxAgBt+/fDpNPBQIgMusvK4LPZYBUIkKzZuFHw9Vr7qy02f/MNfAn7uevSS9HR0wO9RgONwLVH5naLb0RLIBPhRIjYahOO43DNX/6Cv338cVzGOgB8vXIlOikRjoPpDkAoeN1uXPvDH+KdL74AAFiNRvzzySdxzLJlWdm/WIQqTiJVTXKZDH4kX39CoRDuu/ci1MWck4b2dvz1o4/w148+glqpxO9uvRWXTp8u6liEGqczweCoEU4O19aibu1a5BkMyDMakWc0wqzX8/f20RT9ylGsVhNYVoZwOPn6ELGAAvgx85jNKWRZftIkYD0YUUyUSsBmAzo64ldPzVdhZv64gQUm08D/acIJwFediBROZAihqKgaRx11NjZu/B98voH0+crKqSgrrgDpKm+xFOC2214AAHR3t+Gbb95Gbe12FBVVxW2nkoUAyPjkpokTycc7mqBl6QWDvJARUcGEUvEj8zfal19MNNrjoVtm0BjhqHU6IY+CAr5/iFiEThlNDIn9OQHkGIcYOI4P6YyWaXk4DGzYMFApEwjwvapkMv46JJZgkMOnn74Oq7UINlsJ8vKKwXFctN9zX183nM4u9Pby/3/jjTcxd+6EoXlTRxijY6QlIUFBoSBXnDAMg4KCQnR3dx9ZwolQtYnVSm5olknFCWWS5vP58MYbb+DJJ5/EmjVrwDAMzjrrLFQmZHcKOYClK5zk5/NJ7bV7/fBxCpjQgynYldwYPkK6nlZZJtLkPp1BwvTpwM6dA/FhvR6YNi1+G4uFLJzU1AD795P3GyucZFpxolTSNTmXC7DbZcQNumEh7s8AJwxmFhCY88SiUg1ivi2T8W8gne/EaBmhDRcKBd9ltK6OvH7DBmD2bOHqnsFaddFgmDGcVpl9jMYgjEbxFYsyGZCXF//bERIqsiGcBIMMHA7xk1CxQoNSyWHSJCeamtRob1dBnIVgemJQbgknZJtEt1s24sKJ18vi4EFdXPUTDR36oED8gOLihx7CNzt2wGowoKqwEJWFhagsKMAZy5dj9ty5AACbQFfkdTt3Cr5mc3/W6zcrVsQfi1qNTzdtwnVPPIH6f/8bABAwGPjm7P2Ew2Hsa2jAmt27MUupREXijVwktMz9oEYDeYJ/BcdxOFRbC1VJSdRiLJG6tWtRbbFAo1KBYRg0dnQkiSYAUHeYbBOTTZ548MGoaAIAXb29uPzqq7Ft+3boEyMuQ4hQjxPWMxCc8xGqt7Zv/xp1h8mZ/wDg9ftRncZnLySMjKY+J598/jluefDBuGUsy2L+5Mn4T4zlnURmKJUMjMa8uL6fFksBtFpjXLPlMWvTFcFkEiWcAMDUqcCOrSF0Olgw4GBDB6Yi5h7AsvETU7WabnUQ6e2XKIgTgvlyBDF37smYO/dkeL1ubN78Ob799n2sWfMBzl00D0djJdphhws6sAjjIMYl7cNiycfy5T8jvkXl0gWAjBuUO0ROYTTSK4l6egY+U1rFiUw2ELugTWzFzAVTJCEQGWHhRGiengkcx6Gvz4Hu7lbodCaUlhYRtqGfqkQNLPbvN9/8I2y2EkybtpRqxR9LV1eyZWGu0tFBthdraEhPOGltdeDBBy+KW6ZSaeLE11haWtoASMJJNpCEE4lRjVwO6HRGXH31n/qV11LY7aXIyyvG/PkKCDgTjE1owolORw/4ZlJxkjCJ8/l8uOeee/D3v/8dHTGpOxzH4ZlnnsFvf/vbuO2zWXECAKWlQCnaEN6zN9oklorLxSsM3d38+7Pb+QyiYcx2M6q86IW4ALxMxsec583jByEsy3+UiWPh/Hy+rURs8r3BwFfkHDhAHmtGPvpQiJ5AIyambTCQB2TRQZNWG7cBB3rFiQXd0OjUKSvtIwy6oECjkYSTwVJVxU8YSZleHAds2sRPNGkMtuKEhtE4+u0JRhlCIkIgwEKjGVxQ3utlkU5fpHSsreRyDuXlHtjtPrS2qtHXJ4dKFYZOF0RzM1n4CwRYKBTisv/p1TjiyYZwIpMBKlWYWF3j8chgsaTp351luruVokQTILnaBADq+oWNLqcTXU4nNn73HQCgdMoURDpwGK1WyFgWoYQbn1qpxPv33YfqoiL8+Pe/x8rt25P239zOV7N+s2ZN3HKX14tnPvwQZy1eHG1e6ikpwVevvopvtm3Dmt27sXbPHjj6b4y/UyhwZabCCWUQFTCZwLpc2FFXh5Xbt2Pljh1YuX07mjo78e9nnsFpF1wQt31HUxNuv/FGvP3553js6quxfPFiVBcV4ZgZM/BBwvsDgO6eHux97jm8/tVXONDUhAPNzTjQ1ISmzk5seeaZjN5LIm999FHSMkdfH/731ls494orsvIaYqD/1hhwnoExA0k4Wb/+P4L7PnXxYiw++WTxB8Oy4BgGDGFQxAaDxMz0XKSrqytpWTgcBjtWgrsjjEIBmM32OOGkp6cDDz74MfLyBqKLR4RwIkTMCVAogNlzWYS++RZhnz9JiEdZWfLzi4rIwonfz8/DE1+fMDY2YiB7Tq3WYvHi5Vi8eDkUYTfm+j6HHCEUoWVgF1CjCfGBZIejHfv3b0ZeXjFsthLo9WYwDMNrBAoG2eghmTPIZPznRorG9/YORM9pFSexc7fBVJyMQuEEoM/TxfK3v92Kbdu+QldXCxyOVgQC/Lm67rp7sXjxXUnbR9pvkUi8/kR+Ln6/F//85x3RfRcWVmH69KMxbdpSLFnyPZhMyepCZ+fQCCeRoWE2b0319eTliRVvqairS+7hTBNNAKCzk3CtksgISTiRGNVEbHrOPffnSevS7d01JqAJJwYD/cYtdKJoCkeCcKJUKvHBBx/EiSYR/v73v+Oee+5BOKyG05k69plxnNNuB7t3L1LpJjh0KP7v9nZ+IDRh+NR4o7sVgLhKqFhhQK+nbyeT8Yn9LS3810Cn4wcTkaIKIZc2oa+AmPGeTke+8UfHlxpN3CTDCzV8FOHIDAdYTQU0GnE2soO2G1CrhTPTYonNWJIYQCbjrQC2bqVvQ5pkAvxFXOw5TVclo2RXSwwdDMP3RgkR+hel2xOEBK0JPI1MhAaNJozKyoGLD8cBzc1qkIIQgYD448lGxUk2epwAgFYbIgon2RB3Bks65ym2vwkA+AMBNFJmoaUxla+sTIbn77gDZrUa+WYz7CYT7GYzdGp1tIH3j085hSqc+L1ebNi9m/g6x82cibBMBnd5OUI6HR545RV8tWlT0nYbN2/GlSLfZyI04SSk0WDOtddiK6HMdM2qVXHCSVtjI8487TTsb+Qn4T9/+mn8/OmnMaWiAr2Um29LVxcmlJbizksuiVse8dl3DNZwnOOwYMIEtHZ1RQWmCH9//vkcEU4AzjMQXCVZdc2ffzpYVz1Wrfsc9e3xtrG/+PGPcesDD4BJMyLDyeVgSIO1UTTZ6aKMA6zSvTorKJXAzJnHo65uoGIiHA7hiSeux5/+NFDRc8QLJ4kV0AwDWaEdssT5IcBnpZGW7dlDzjhLFE7CYeIEzI52sAyHcEKSQDnbBD2hQrsEjUnCyY4d3+Dee8+N/q1UqpGXV4ySkmqsX/9p8rGNdkwmunASYTDCiZhrabr9yeTynLAhNBrTD9DH0tSwB3v3rk9a3traQthaWINKPP1aLSBDEPv2bYiKJgDQ0lKLlpZafPrpi1g6oRQwnZ60r87OQbmeJhEOA3v38rEUjuOnkeXlQF7e4D9G2jQYSK9XS319erao7e2dqTcaRsQkxeYqUgRIYtSjUJDvdZFlXq8X//rXv/DJJ5+gsLAQN998M6qqqpKfMNoRagxvMGSWYSHU4yQGhmFw7bXX4pprrknatLOzE0899TpmzvxhyovloHrOq1S8f9XOnen7j9bXA5WVw2P6y3EwupuRiXCSCpkMKCFUttKEk8hvROgrIEY4oQk6bnf/YCBhEkCrNgH6M4hVKmi14oSTQReACFlIZf3FxjB2O9ksOhXpeJunK5wIVblIDBkKBUfU3Fta1LBY/IOa4ASD4p/MslxWJlN8ggZHFEn8fnEvwHG5Y9UF8HZdpEmc2z3y0wKx51QhD6EgGN8str69PRrET6QsYdx38WmnRXtVkCimlCy3ORxQut1ofu01rNy+HSu2bsWKrVuxaf9+cByHBWecgZ6ZM6PXtbkzZxKFk0MNDYLvjwrHUYUTTibDlAkTiMLJ6g0b4v5+9e9/j4omsewiBQ/78fr9CASDUCSI3RGxiQkGwQ0iu5b1+6GUy5NEEwAYbklPSKQMe4WFk1mzjseFs4pQyl2BXYcO4T/r1+NPb72Fn112Ga67Kzk7Vwx/fOstbN+zB529vejo6UFH/783//SnuO7OOzPa53DTLQknQ4pCAZxzzvVYufItdHUNWDN3dTWho6MR+fl89cSYF05SjdVJA4OqKj6RLnbiUVpKFmHkcn5eTUq6Sgys+/3ESKECQUwd58WuWk10vGRHGypAvv6a0IuaEjcONGmju+vsjA+g+v1eNDcfBMuO4sikEEYjb62QiNPJJ3rKZHThJHb+kG48JBzm/ZSamtKvOMmBahNg8K1diyzkHjMdHa3E5ekkZDJcGCb0YMeOb4jb67R6LK2yYRthXTDI62bZuoXs2xf/Fevq4h86HR9iGsx59HrdUKvJ9tHBoPivSn094TcgwOrV6/CLX1yeC/odAGG7/lxn5GdIEhKDRKiQguM4XHHFFXjllVeiy1944QWsXr0a00U2ZRw1+P30m77QlT4YpMv1afQ4ufTSS3Hbbbehr39QsXjxYpjNZmg0Bhw40I0ZM1K9gSwk8xcU8MHbnh5+gNNCzoRIguP459jtgzwAEXg80HF9kCGIkIhLcDa0nFRjRKEBjpjPhDYJC4f7e/Yl9JloB/k869AHJQJR4UQMg7bqSqcHhiScCDNlCrBxY3oZWel8wWUy/jolVhiVgjEjgkYTgtebHOb0emVobNSgrIxeUp6KYFD8yD9bIgMAKBRhYsWM2Coar5cVtJ+yWPywWPw4eFCgrBDZe09aLfneHgiwCAaZrFW2ZIKwcMIfl04XQkV5H9i9QKSdmcfnwytffkl9ZkmCcBJWKASFkwmlpbjitNNQnJeHIqs17l9NTw/UOh3OXLQIZy5aBIC3k/pq505MXLQoTgwup/TZa2glBxxSEQoE0NbdDZNOB3XC9ZOTybB40SK88vHHSc/bvHcvvG431P33vK9XrRJ8HaVCgRdvuw1FVisKrVYUWa0wpLhfMqHQoIWTuRMm4B+ffJJ8/Pv3IxQMQjZMVZ9Cv7VQgF/HgWzVBQAq+MAwDKZWVmJqZSVu+f734Zg+PWVRNI1P1qzB5+uTM367OnMrm1SIrlgv2RisUpJDVlAogNLSCXj88TX473+fQ1PTAcyceTyOO+7CuIDdmBdOAL4qpK2NvJyEXA4sWsTPHQMBPkgv5Pet15OFk8TAusA9pqBEDksx4O4JQLZ1IwwQDspXjleisIrfpcORLJxEsNtHSdOHdKFVEkUaaphMdD8qMRUnodCAABPB4wF27BDvTJBIjggniQmObW31+Pjjv6GkZAJKS/mHwUDrPdqLEgv53NOEE1o4KrFlEAAgEIABTmzfTu5zNWfKbOTJnGARQpiQQtHZmZ3pXihE7hUL8NPadev4MNPhw53wen2YNKkINTWMqGlsIMDhkkvKoVJpUVk5DVVV01BZyT8qKqbC71eJ/qo0ksRDCtOmLcW8eWehu5tvdZwLSMKJhMQIIiScfPTRR3GiCQD09fXhgQcewGuvvTYMRzeMCGVBGAyCgzcEAuQIdBrCicFgwI9+9CM8+eSTAICDBw9i2bJlmD9/OUpKxHk5Z6UdgUzG3x1cLvHCCSCuvCEbuFxgwPvbdiP1XWzQwgBS98GjCSdyubgKIKFJmMsF6GKqOnpgRBsKiNua4eAPViYTPbEbtLCUzgxSEk6EUSqB+fOB9evFiyfpfIAMw/8gSN31EtHrJVu1ESIvz4fubvLn2tamhtkcgMGQ2cg5HeFEqFF9uiiVYeItQqxwQq824TB7tiN6ndXrA+jro8+esllxQsPjkWX8+QyWcJhuf1ZT44TBEATDDOgSH27dig8//xzr9+7F9tpaBCmVGHazGbqEjqRCDbcBoKqoCP+4+WbiOo6QOW/W63H6KafAlbDfYpJHPoCmzk4EAwHI0wystLe0YEq/5ZZSoYBJp4NZp4NJp8Nz//oX5vYLOYmEwmF0tLSgtLoawECvFhJnHHUUXv3lL1MKJYkMtlE56/NhHsU21e31Yv+OHZg4a9agXkMsQraDQSj6/5UTAzkAL5zEElKrwQ1iwJJHERdo9lc5CcdBLpMl/U4tknCSFSKXkvz8clx22d3EbfgeV8N4UCNFYSFZOCkgzz8A8BOe0lJx+6fNHRLHvrS5d7/1r5IBlHYFoA4CAtN06HSAXA61nJ+KyOV04cRmG6PCiU7HnzfSfX7vXr4ZqBirLqF7rt8/4ETQ1cVbEAs1Zk1FjggnanX8qdu/fzNefvmBuG1MJhvKyibhD3/4Kq7vlB3tKKQIJ52dLUSbqa6uXrS398JkskOpHLjgKBQEk4FAAPpQF3buJCdzHDNtGmQIw4JudILvcxIKBbF79xqsW/cflJaW4ne/u1rEWRCmr084L6+npxMPP3wjvvzyFYTDYUyYMA8PPfQCzj13SspqjgMHWtDb2wmgE+3t9XF90H7/+xVYtOjYpEuK18sfj0YTf858viC0WgPcbrLLDMuyWLLkXJx//s2YMoUfDx46JAkn2UCKKkiMeoSEk8cee4y47pNPPhl7DQlpgX+Vij9JQneDLAgnAHDNNddEhZPW1lb885//xD//+U+YzXa8+moTZDLhS05W+zinO0EVE4zNBv0C13AKJ7RT0d0dCVSl97xEIg3sSaewrw/It/CD0C5YsAWzqPsxwxF9w2L1DKniJMeQy3nxZMUKcdun+zsVK5xIgZgRw2gMwmz2w+Egf7YtLWoYDBk02ER6Vl1abfZaJisU5Pun2B4nNAsstTocJ05brf5hEU4UCg5yeZh4Pt3u7Akn4TDQ06OA1yuDTheMCh8RPB4WHo8MGk0IGk2kqod8TpXKcJKQ/+W2bfgboZk4ADx3yy2oa21FXUsLlITrfHgQwiqpSTcABAnVvSUU4SQcDqO1oSGpEiYVvTGBcn8ggHaHA+39mfwypRL5JL/OyHO7uoB+4eR7S5diRlUVmjo7ow+Pz4fzTz4Zf33lFei3bEnbjJpmISYW1u/HjKoqKORyBGLGnzKWxbTKSvSRAqFDiEyWbDvY29uFTtaNAj3ZpiuCEvEpt4FUfRdSYKVEPV58/33sWLwYDz3wABaceOKgXmOo+c+jj4L1+dDn8aCztzf6KJw7d6QPbUwgk9HjyhF0upxouTD02O38I1YgttnoFSfpQvMojrg/RMa2QoH82A8iVYJjgvCv1dKFk9LSMSqcMAx/HkhicW8v8O239AmtGKsuYEA4CQYHL5oA6c0xhxCG4b9ikaK/ffs2JG3T09MBjaYxGhvjOA4Mw8CGDhRRhJPu7laEQslhoXfffQN33MF3cdNqDTAabTCb7aipmYqjj/5n/MZ+PyxMD554/Fvs3rMBu3d/i507V+PgQb5v5onT+5Mpuvfiv+s+xPr1/8HGjf9DXx//ZqqrZ+L++68edDyA5nYPALW1O3DXXWehpaU2umzfvg24+ebvY/HiTSguFn7xTZt2UNdVVk6Nq9Bxu3kdMFJMqtEAM2YMmMf83//dg/POuwdutxOdnbwNY0dHI0pLNVi7djVOOeVaFBePi3uNzk4+HiPUK3e4GEVt2ZKQhBOJUQ9NOPH56MJIb28v9u3bh0mTJg3hkQ0ztGBiJHMiVYYFCdqAgaJwTJkyBddddx2eeOKJuOUXXHBbStFEYLeZke4ddLiEk/5sJCN6U2zIkw2rLtqN0ufjW1LQKpvTSZTR68mn0OXidxSQa7AtOIOanQkAFnQDKv5gh004USjojZISSacfypGMvD8lTmgSGCHdD1Ds9pJN14jBMEBFhRsul5xYkeF0ypPcEMSSTjP27Aon5ACy2H4ctIqTRMssiyWAw4fp+8lWwIth+KoTp5MsnGSDYJDB/v16uFwD936bzYfycj7J4/BhLTo6Bn7P+flemM3067BSmSxezZ47F3j+eeL25yxZAnP/zc9vsSCxBi40BNdzknBSOm4crjz9dJTZ7fwjPz/6/6DdjnS/pU6K3REAGMxmKNVqLJk6FRaDAVaDARa9nv/XYEBhJPgeDuPByy+Pe244HIajrw/BadPAsCxvZSbUAI3AoIUTnw8qpRLnLl0KrUqFuePHY96ECZhRXQ2NSgWv3Y5hGqkBIAuV7733BM578W4UWq2oKa+GtXwhyssnY/z4uSgoqEBrax1aWurgMxzCsvnzos8LGMke8WIRsrPatHcvzrzoIqz7+mtUTp48qNcZSthgEAzDwKDVwqDVorKwEADQV12NURxLySkUCuFYb7Z0g5yHYfhoY2cnH4nUaHjhJFs3UaFJSl/fQHq3mAoIgI+KClQBJlpusyxQWVkNlepsqFRa9PR0oKnpO7S01KGgYAwnDtGEE0B4Hh97viMN20mJAZF7Xmfn4EUTIKfmIh9//Df8978f4+DBbXECQCwtLbV45JEfYseOlfjNb97E9AlTYYATc8ZV4Uc/ug9WayHM5gJYrYWwWApgsRTA708WTjo6Br7LbrcTbrcTLS214DhyU2KPy4Grrz4DXl/870Wv1WPRxPEAgJXfvIvf/+X3SU8/eHArdu5swpw5gxMMacYtwWAAa9Z8gOnTj0ZRUTU2b/48uu7QoV146aW38ctfXiy47+3bycKJ1VoIk8kGn4//OjY386JJ7FfP4wG2bQOOOor/2kZiNlqtAVrtRJSVTQQATJwIXHrp+di4kXwM9fVALgwPpIoTCYkRhBZYDgRYvPPOO9BTosbr1q07soQTluXvbKQrFi1onGbFCQD8/ve/R0lJCf71r39BJlPhhBN+itNPv1LgwAcY0YqT4bLqiqk4EUM2Kk7sdnpriG2kbmv9pCOc6HTkMX9kINImL0YwSN9hIZqhhg9Q26KvrVQKN64HsmQ5oNWK868dbHe9Iwm9Xpxwkm42ltjfdQ5NVo5E5HIOFRUu7N+f/JvhOAY9PQpYremHytKpONHpsjc6p1ecDE44SbTMkss5KJUh+P3J2yuV2ROCAF5YcjqTr8nZaGIPAB0dyjjRhF+mQl6eDx6PLE40AXgbt1CIHNSSycLE8cHMhQupr//Zpk04/5hjAABhwnUjOMhAdiJhmYwoxhjMZvztllvAEG7AfX5/2sJJL+VeJZfJoNXrwbAsVv7lL+TXs1gQAMASxnwsy8JqNKLHYEAY/VZmaQon7GCtuvpf79Vf/5q4Xj5c47TI6xF6/dTWbgcAtHR1oaWrC9iSnLkLAKfOmxcVTjiWRXCQqZ55Qv0WAARDITz1xz/id88+O6jXGTI4jvidBFLb5kmIR6ulD72sVqC8fHiPZ0RhGF4sGQqUSnrSlcuVmXAiBKFibebM8fjuu93Ytu2raPY9ACxaNEt4X6MZi4X3HUqX2PPNMPznR8oajNzz0unVKEQOzUV27lyD1avfS7ndZ5+9BADYvv1rnDAhHwyAKSV2XHrphcTt/f7kqVw7RQTMyyP0OA0EYDEY8JPTT8fj774Tt+pXF18ITf9E/+wFs3Aj5Zg//vgTzJlzBfU9iYFWcSKXK3DxxXcAAL788lXs2rUaPt9AzO3f//4rfv7ziwXjEbt2kYWTysppAPhzWFcHHDhAfr7Hw1cLmc30ZFeVil9vNPIFWIl0dIBoqzbcSMKJhMQIQrtQ+XyATqfDzJkzsXXr1rh1s2fPhjIbqfy5RCrhBOAHeaQrFm1ynEI4ueGGG2Cz2VBZWYmKigpUVFSgtLQUt99+O26//Xbs38/fCMQyohUnETPJobRv47ioQKOGFwr4ERCwegCy1+OkoIDPZEj3eWKhxQTcbv60doOeAZWHDkzGbv6PmDes0wnHbSJa4KARI5zIZDlTcj0q0Ov5UVoq0rXUEvOD0GqPEBPv3MZoDFKbqmcunIgb8TMMJ9jHI11I1Q6R40k1EQkEGKrAQjrG0lIPsUm8Tpdd4YR2frxeWVYmVz095BtIZ6cKDgd9HQmVinz+S6urYTOZ0JFw/S6wWGCKyQgOEa4HYZUKIZUKMtosNE1CiUbUERgGYaWS2IieTVOYAAAn5V5l0unA9I9fwnI5sVokUhEi9Lrh/ht/OANv9sFWnKT6LGRu97DO/BMrTjiOw86d34h67qHWgaa5AYNh0GPLyZMm4QcnnACb0QiFXA6byQR/IIDXvvoKB5ub4fX7sWLNmkG9xlAi1P+Gy+rg/8imoIBvzZBIWRkwfvzQTnGOKBiGn6SQKgBjg+7ZEk4IkyyLRYlt276KW5aXl4fTTjtVeF+jmbw8/lwI9XVNRKlMDjDQhJOIEJYNkV6jyam5yOzZM/H66+K3f+aZX2CGsQ+TTl4MGcKQIYgQIXRMOo2dnekJJwDwh59ehTK7DS9++ikYhsE1y5fjJ2ecEd2soqAA1dWTcfDg7qRdbNiwBkDmwgnHAevWrUFBwTiYzcnHGAoFsWyZBqFQ/H3Mbi/FrFknoKEhjHHj6BfX774jCyebNn2G3/72UsybtxgnnXSt4DH29fGXHJrzvkrFX5bKy4EdhJfz+fhQ4UiHMiThREJiBKHdkwIBvtTt7LPPxtSpUzF//nwsWLAAs2bNgnakrxrZhuPowknse1UqydtlIJy4XC48/vjjSatkMhlWrFiBpUuXEgfvQmR17pTuzjiOH+Cq1fxAOBDgg7rZFNgiKgJ4F3cTetABwiCiH5bNXluN0tL0hZN03jqtaj2iFXUHyMqKEj7MxFaw6A9QJAgnQn1PI4OEQSPGF8xoHPk0jdGEmOxatTp9+zMxX8oUmbkSwwPDAGazH+3tyRexnh5FRvFPscKJVhvKaoCIVnECMAgEGCiV9F4Q3d307yzJTsxsDsBoDKC3dyBwzTAc7PbsBPgjqNXkIDfHMfD7WapYIRZar5bEShMx0IQrhmWxcMYMfLRyZXTZD044AX+59lrkxVSUkCpOAL7vhKytDaFQCH/96COcfdRRKMkwQzksECDJpnBCqzgxxVxzaYHoSPCaoVQZcywbHTtxmQgng5kRh8PU44run+PAer0ID9JmzdnTg28//RRqjQZHnXIK5JT3mlhx0tR0AF1dLaJeo661NeoR789CV9alS5fi9P7G1q9/9RUufOABqBQK5JvNKLRaUdfSgv2NjWg5fBiFOVhWIPTdGEy/IYl4iov5aV4kaU2tBsaNA4qKRvSwxiZ6PVk4iQ3qixVOUgXYCdf0n/3sZ6ivr8ef/vQnBAIBjB8/Hq+//jrUY7kfI8MAc+YAu3cLW5vFQkrQos0lIvfkbNh351C1CQDMmzcj7efkawbGXkr44SGEjknDmK4ucuJcXh5hfNW/A4VcjlsvuAC3XnAB+WAYBkcffTxRONmzZ/Ogcl97egK46aZjEQj4YbOVYNy4WdHH+PFz8NhjP0sSTQwGC1544QAUCiVaWvj2caQ5jc8H3Hnnm6it3Y4771yWtP7zz19GIOBPKZy43cLJpJFLiNAUuLt75IUTqceJhMQIEhkfbN++EvX1e9De3oCOjga0tzfA7W7A9ddfh3vvvXdkD3Ko8fvpXpyJFSckSFcxjqPvUy7HIUqpbCgUQlFREYJB4UZbJEY86czhABobB6oPGAaYNo1P4coGCaW/RvQKCif5+dk7J0Zj+kk66Vp10Sxj29sBX4h8u5mEPQOiCZAknAiRtUQeMaOILNu6jHnE2Jpl0sBdzIfe75suMfKYTAGicBIKsejrk6fVhJy/JYmbFSX2DhkstB4nAG/XJWSj1dlJnqArlSHifhkGqKpyob2dr8xQKsMoLPRmveKEJpwAvF3XYIQTWkZcptCEEwD4wcUX46OVK2E3m/H0DTfgvKOPTj4eAeFE3daGb3bswLWPP45rH38cNpMJBo0GT91wA06bP1/0MQoKJ5SbKckyKxXnn3oqji8sRI/LBYfLhZ7+hzzmPkazPopWnFBeN/Y4Y/+/Ye9eLL/rLkwpL8eUigoEQyEwDIMiqxVymQzdfX3odjoxfeZMXHbbbWm/J4AXkcTIonKXC/5BCCer//tf/Pjqq9HeH/CcNm4cXn7lFZTV1CRtm1hxsn3716Jfx+v3o83hQF5JCQKZ3OsSiP1Mj5k+HQDgCwRQnxA8XPPllzjnRz8a9OtlG6FqJMmqK3swDFBTA1RW8jH7I6YZ/K/otf8AAK+ISURBVEhAm6S4XANzaJpgmChuMAxv70XKOCwm926Qy+V45JFHcN9996GtrQ2lpaVgjoQPW6kEZs6EaFsLUmMf2gQ3lXAycSL/WXk8Ax23aeSYcDJr1vS0tlcqlDh17pyBv+GHB8nzZVLFSXc3WdSy2+kVJylRKLBgwTy88MLAIpZlUVY2CVVVM+F2Z978fMuWPQgE+M8+0mx97dqPAAAlJaVobGyI237JknNw220vQqHgx5YeDx/rIH3VXC4gP78M+flluOiiO/Dqqw8nbdPdnTohw+USdsCODHMVCn4KTorBdXcDJSUpX2pIkYQTCYkRJDJXfemle+MaNkX47rvvhvmIRgChzIjYCSa9IUzyMiHLBQHhhGEYlJWVweEgB9GFyLpwYjaTs4Fo7NoV/zfHATt38vJ9NiZ2CQOsYjShFlXgkBwMtNn48Vm2YBi+6mTPHvHPSUc4YVn+q0aqbm5oSF7Gw/EN4WOJmUxIwskoRqOhN9aJkMmkItWHrtVKn1UOYTAEwbIcwuHkybzDoUhLOBFbbQJk39ZKJuPAMBw4LvkYeBsu8ut5PCzcbvK9w2qlp47J5RyKirwoKhLRJyhDZDJQ+6l4vYMr1xHb+0UsQsLJaRdcgAd27MBVxx8PO+WaQhM1gno9XH4/Lv/9QMPRjp4edPT04PRf/QqnzpuH/zz0kKhgFMkOLPr6lLFXJhUnZq0WRVVVScsDBgMiIwxaxUmkBwlVOIk5zljhZNfhw9GeHl9s2UI9trM8HlyW4vhp7Nq6Fe+/8AKqCgtRWVCAqsJClOfnQ5Vw7mSDsFDxuFy47qaboqIJAOw4cAD3/epXeJbgYSKXx3/vXK4e6HQmuFx0a8/JkxdhelUlDu7fhMaODmgXLsxK5Dq2KqPQasWE0lLsIwyuVn/zTU4KJ7T+NxzDSP5RQ4BcnnkQUUIktBMcCPABeKEIIakqpLCQLJykSAZSqVQoKysT3GZMUlHB9zsRCjawLDkFX6jiJBikp/ZrtQP7M5uBBCv4OHJMOLHZbLDbi9He3hRdptUaUVExBbt3J9s8XnXOOdDHxJBUIFc9k4SThx76BF1drXA42tHT046eng709LTjmGOOS95YbCSdZXH88Ufh9NP/DzU1c1BTMwfV1TOgVvNzeKcz82vehg1biMsVCiU2b96Em2++GV9//TXUag1OP/0mnH76lWAT7lu7d/OtiBKHgrGhn7w8cumfmEpWt5ve30SpjL+NWix04SQY5ENjHg8/DygoGN7EZcmqS0JiBJHJ+ACvzVZKXN9Aj9qOHWjCSeTkREiVYRGL0JVNJkMdJcujqKgYHKdM26YLGAIr0HSFExLhMJ9Vko2qk4SKEzV8mIUt2MVOgy+sBMvy2QqlpfzNN9uJQ4WFfIKO2JtWuk4dej1ZOKHd6A1wQoGEg8nVihOpMXx6sCz/AQqVnWWShZvKqquwUEqvzCFYFjAaA3A4kj83h0OB0lKP4Mfl87GQyTjI5Vyawkl2R+YMw9t1kUQGv58e9KP17AAAmy39oHm2UavJ78nrHdwsSuicZIKQcMKwLK696y6YduwgBlDCcjk9MMuyuOTRR1HXQp606jWaONGktbsb9//rXxhXXIya4mKMKy5GdVER1EplSqsu4stnIJzQMvdjxZJUFSc0S6xYsSTWqmuXyGa8jlS9wgRYv24dfvvqq3HLGIbBnJoabHjqqegykuWZWL766CPiZ/3255/j9x0dMCXYtCV+78477yacc84NqK3djh07VmLbtq+xffvXcDjaotvs3r0GjL8HT159FabNnw9XlsYOiZ/psTNmEIWTbzaQm9WPNDSrLqnaRGLUIjRJcbmEE4dI94vCQqC1lZ9zRigpGWg0LxGPQsHPI4QCDlYrOfFRSDgRSkaNnS+KSeTKMX74w+vQ2RlGdfUMVFfPQH5+ORiGwd69G/D731+Kurq9AIDly5fjwRuujytxoAknicOYUAgwGPJgMOShoiJ+3bx5hB2IFU44DpMnT8Rdd/2dWOjT25u5JeHWrVuIy8eNmwq73Y4XX3wxumz7dv5nmkggwOfazp4dPw2NPVaLhSyCdnQk38s5jkN7ewMaGvaivn4v2toO4cEHf0d8fuJX0WIBDh9O3s7nA1asiF928CD/uQyXw58knEhIjDAqFd+gicQRLZwkNitN5ekZi9CVTaDixGyuwDffpF9tAgxBa4KSEt56K/amrNfzD0qghMggJupxEO70eejC0rLD8JXVZK9nBwW5HJg0idw0jES67V3EtAqJxQxH/IKEbu9KJT8upo2psiacRMplaL8jhSL9XhwS/O+MJpyoVJmd01RqnmTTlXOYzWThxO+XUXtp+HwsDhzQwePhrwc6XTAtMWSw/TlIKBQc8VYZCCRftHm9XYnWVvJMRK8PDMkxpotaHYrrpRJhsMKJz5dd4STVueKUSrjLy6EjjEsCKSrQTjj+eLyXOJPsZ/mZZ6KupQXNXV1o6uzEv7/4Am9/E98cnGEYlNhsWLt+PWhXNKpwEgwiXWNuUcJJih4ntIqTWLHEGQjgi9Wr0dLdjUdee03UsXX39orajkQ9YZbPcRyUCdf8wTSgf+ett4jLOY7Duq++wsnnnRe33GQKJFWayWQy1NTMQk3NLJxzzvXgOA4PPHAhvv76jeg2uw7sxml33IHtGzcis445BFgWYZkMbP/7P3bGDDz78ccAgCkVFTh2xgwcM306jp0xA8qODgRMpoz61AwVNOFE6m8iMWpRKvkHaWDQ10e/rqtU5HUsC8yaxQsnPh8/qcqxqoWcw24XFk5I3kmAsAMHraqRYeKjy0KRZqUyJ5O4br31Dmzfnrx84sR5+OCDbZDJDkAul2P8+PG8ChDTHFUJcqJHYnKkUD4I8ZaUhnAC8HmMJOEkXXv4WHbt2kJcPn36rKRl5eVk4QTgv4oNDUBsAVhsziyt4sTn82DZMg3KyyfD53Ojvn4vcbsrr7wNDJM8qkiMhyReNvx+Lw4f3o2DB7dh0qSFKC+fFF3n9QLffQdMT8/JLWP279+VeqMcRRqtSIwJVCrhipP2dn4colTy2fzZ7PedEwgJJ7Gk0+MkhXAyZcoUnHPOOairq8OhQ4fQ3d/Fu7CwUlA0qajgd93ePnBzZRhg/PghSOrXaICFC4EDB/g7g8nE3/HSFdMyyApNIhymDsYYg37YlP7CQn4s3tqa2ho23Tm3xQLU1qaxPcmmK2agyTB8wg4tiTWrFUpaLf13ZDDk5AA45xGqmbZYMjunkYkLTczMwQyvIx2TiT4pIgknHIc40QQAXC45XC7xQ9ah+LkqleHEokEAybZUgQCDvXsN8Pno4kNe3shXmwD0Picejwwcl/l5HM6Kk+hr5uVB7nJB1THQlDQsl8ObQkw94cwzwd57L8IJ2cFatRpzTzoJVXPnCj6f4zh4fD5oBAQamnAC8CKGULVKImKEE1owOp0eJ519fTj77rtFHxcAOAYRuThESo8EUJlQ7ZtpA/pwKISvBaoxVIRxnkwG1NT04eBBHbW/EsMwuOqqR3H48C7U1e0EAMhlMrzw5JOwlZLnJZkSMJmg6g8SnjJ3Lt686y4cPW0a8hOrNw8dAseycJeWwk/ylB8BqFZdknAiMZrR6Wjdsek3UKEJH8PwXs0S4rDbgb3kIHN0PYlMKk4Sk1EVCrolcaalD0OMUJzFYFCiomLywIKEIIDYihMhHYQYV0gzxmI0xuk5UZxOZDRu9fmAzk5yMu3s2bOSlplMAy5xJA4f5mONkeOI/TpFKk4MBius1kJYrUWwWgvx+ecvw+/3Yv/+zYLHunPnXkybllo4USiAvr5DWL16Bb744t/YuPF/0XWXX34/Lrnkzrjt29r42NxQ3445DnjoodyzEhWLNFqRGBOo1fSKk6amJmzaFIKsf1LZ0MCXpI2p+BrtJp/4JoUGColZj7TJab8f8Q9/+EP88Ic/BMCPWfbs6UVb2yHIZMLR9rIy/vOaNIm/yfn9/I086zZdEdRqYOrU+GXpfvg0r6l08HjoZTjplmoMEoOBf6jVwj1P0hVOzObUbS0GIPQ3IXwJNJphFE5iy+NjkXpmZIaQcDKYc2owkIWTbDYFksgacjkHmSxMDDySAuxeLxsnmqRLYaGAzcIgUCjIF7ZE4aS+XisomrAsB4slN4QTjYYchA+HGQQCDJTKDEpHkV3hhGW5pCbdRBgG7vJyBAwGKJxOhJVK+K3WlKJESVUVfvL97+OvCVUVd95wA0qqqlBZWEi18oowLkWAnNYcHuDturItnKSqONmxezeKLRbkGY1xPt2xx2nPIPDTPQjhpK6xkbi8KkH4yrTiJNDdjavOOAP3v/xy0rq3774by2bNAunojcYgZszoQfsePxo8ZMvWgoIK/OUva7B9+0p0d7fisosWoKw6+4Ezb1ER5H19kPn9sJvNOO/oo6nbMuEwdIcPgw0E4C0qGvHkjwefegpfrVqFPKMReQYD8oxGWA0GzJo7FwsmTBjRY5OQyBi9nm8ckEiMgJ+EZP2bPdRqfj5Bqna0WukTWdpyjiPvC0hORmUYfuJLqnjJUeFEo+GD46QQT5IjXELMSEg4iQ0h0XQQ3vI2YWE4LNxTN5Z+3y/azycU4kMt6YZ4+vqAv/99J7q6WnDgwFYcOLAl+li4cDbxOePG8T970lfF4+HPgUrFH1Ps+SgqqsZHH3mhVA6M+cLhML7++k0EAqljTTfdtBQTJsxDXl4xbLYS5OUVIy+vBKeeuhiTJ0+K2/axx67BV199nLSPrVtXJAknHMd/jWMLtHw+XqDy+Xgt12od/DDC6fTA7x+aOdpwIAknEmMC3qqrDHl5xbDbS2G3l8FmK4XdXgqbrRQcFwbATyT9fj4rPjGWPqqhlZWKrTgBgC++4Ms+ysv5KyNNOJHLk66c3d2ATmdEVZVwnV8kWA/wuxixeHS6FkHZEE5oAQWGGXbhJEIq29x0Mw9Ylt+n0Hwhgh59gv1NxBxDVoUToTI0STjJDKHJ4WB8+UpK+JK1WHQ6oLg4831KDCkKBUecG5FsroREh9Rwgk3XBwNNOEkUCXp7hS+cZrN/WBsxCqFW01Vur1cGpTKz7P5sWnUplWHxkzWGQcBqRSBNT/h7/vhHFBcX44333gPLsrji0ktxyTXXAAAWz5mDuo+TJ5+xVJWXC7+ATIba9nYcbmpCfXt73OPyK67ACeefL/pY0+lx4vH50OV0otvpRHdfHxxeL2aVl2PWVVcBABRyOYqsVhRZrSjOy8PdDz+M8v5zp1SrkWc0ojOF/da8CRNg0ethMRhgNRjAhcNgMmj2XdfURFz+7y+/hNfvR3dfH/9wOvHmZ59BluYAxeTz4b7LL8d9l1+O+rY2fLh2LT749lus3rULJ8+dyzedD4WIXVJZFigONaIB9F53Go0eCxacDgAoriAEUrNAWK2Gc/JkKHp6wAQCUPT0QEHyLIk9ruZmMKEQPLEpsCPAjr17sZLgEXOlw4EFF100AkckIZEF7Hagvj6959DsoyQyo6iIHMEWEi+E5ny0bD1S7KCykg+CxCZGFhZm3qV8iGEYfgqVWC1hMBAOOSFmFPD0YPv+lejqakZnZzO6uiKPFnz11cfQ6fh7J63iRKEg3ILE2nQB0d+N0Kl1OtMXTiKV5HwFSCHmzz8VAB9joOUmsCwwbRqwejX9OFSq5LxmlmXjRBMA6OlpFyWaRNi3L7lyVqv9PU4+OV44OeaYY4nCyebNnyMQ8EOhiP8NdHYOXJpcLmDTpoHwV309bxQxbdrgYi+trZQE1VGCJJxIjAnUamDcuJl49VVyxloiHR2RexyH5557Dm63G9ddd92QHuOQkShnx5J4k0/lUfbdd/zJLCigZwAkTFb9frLXJIkccQwYGeGEdpK02rT8zbNJqsFFJoeVlydOOEmqNgGId2OLhe6sllXLPSFxRBJOMkOp5K8liWaweXmDEwttNmDGjAELvrw8oKaGGPCSyA2UyjCxb0ZitQa/LNPgHofCQi80mqHpHUKzi/J62Wi2HZ88R79wMgyHoqIs9czKAnI5B7k8jGCQVPkjg9GYmXCSzYoTMTZdg0Wl0eD63/wG1//mN0nrFi5YgFdSCSeVlSlf4+zf/AbbDxxIWj5v4cKsCyffbNiAcy69FD5CUGLdkiXR/weCQRxua8PhNr65+W8SrqFFNhtROPndT36CH518MvKMxmg1dwRHKAQuzcFDyO/HyXPm4GBzM+paW9ESk8Fb19KCPyb0JnF2d8Oc5oBSERMMK8vPx9XLl+Pq5cvhDwSifVTkfX0ImkzJTw6Hofd3g0EYHITfG8tyQzqk4+Ry+PsTDzi5PKVwAgDqtjYEdbq0BcVs4qAIcOZEmzEJidGExcKPZ0k+niRUKv45EtmjpISfZzgcA8tsNuGeh0ITSNqcnzRptlqBuXMH+qlarfENLnKQceP43NimJj4WZjbz/S2SRI0E4aS+8QB+8YufEPfZ1NSO8eP5800LSQ2qv0lNTTR2I5fzHwUpZ9jp5Ked6UA7hFShIq2W/zmTvi59ffxXkGYIE0trK8XzKw0mTqxMWnbaacfh/vuTt5XJ5Ni9ew1mzDgmbnln54DV2eHDye+ruxtYu5YXTzIdSrS0iAgQ5TCScCIxJkhX/QwEgNraFtx440/w4YcfQqlU4rTTTkNNTc3QHOBQInRVTlc4Afi6vIICesVJ4iTZkXqXEXJGOEnXgyobPU5oFScjXLJtNpM/w0yt7MQWEthAuHkSfsh5efxXLjFWZDJl2YvTZCK/kE43hD5yRwDjx/MX3EggzGzmffoGS36+lLU3ihBrc0VbJoRMFkZJiQdabQg6XeaNo1Oh1dL2zcDjkUGnCyEUoos+DMNh4kSnYJXHSKBWh9DXR7ZMywSOG33CiRALYoQGGtXV1Sm3KS0sJAonjRSLKhpihBOt0UgUTQDgu507qfu2l5TE/V1os2HHwYNJ27V0dSX31Yg5vnSbkitCIbx8xx3Rvz0+H1Zs3Yozfv1r4vY9XV1pCSeM3w85Zawc23yeJpywPh9YcNDAAzeERX+1OjRshR0BoxEcADEvp+zuHlHhpIuSxW2Rml9LjGYYhndq2L1b3PYFBSNumzfmYFlevGht5WMiOh0fcBA6zzIZec4nBC2SbjYnd+POYVgWmDyZdzcOhQRCIgkxo7I8QlJBP/X1zVHhhCZEEENQQvGVWbP45DizOanMxGAgCycpCmSJpCX0JGAwAC5XAPX1e3DgwFYcPMhbfR133Kn4wx9uESWctLWR+7vFYjbnw+FoI66TyWQ49dRjk5YvWDAHVms+urrinxcKBfHUUzfi6ac3gYn5jXi9vP6r19MTYP1+YPNm3rUnRftAIm1tUsWJhMSIk25j7a++eh0XXHA1urv5YJ7f78df/vIX/OUvfxmCoxtihK7KiSdGJkvdhCJyJxKy6oqBZO1KO5ScqVxlGH4AJOaOBvDngmLhIBqacDLCJ6WyEtiyJXl5pkUWWm3qU2tAL6wgeMISRilyOZ8ds2/fwDKW5ZdlFbkcqKoC9u8fWMYwfOBfmuRkjloNzJ7Nj8g4Lrm5osQRgVibKyB94cRm88NuH/qeISpVGAzDgeOSv78R4SQYpH+3p0/vgUKRWc+QoUStDhMLIkkVQmLgK4ay9xtXqYZODBPDhFmzYNLp0ONywWYy4agpU7Bk6lTMnzgRNpMJDe3tqDruuJT7KaXYhjSQupzS4DhRwolJIEC+lxLgM+l00CXc+EuLilBmt6Ow386ryGpFodWKo6dNo+4/kx4kic3qNSoVFk+ZQt3e0dmJijT2rxSZ4aNwOkGqB5P1p15q4U4pnJhMaViPDBJOqYTfZoMqJsqxef9+fLR2bdSiravfpq3AZsPfEip3hhMHZQwsVZxIjHoKC/m5g5js+XTT4SXEwTDpR3KVSvFxACB9t4och2VTOEskzMnzTSbIWBlC4eR7fENDMwC+HwhViOD8ABLUE9pvRibjSzYoGI3JZgaA+MIvMYcgJtf4qad+ieeffwyBQPyb1mg0AG7BJ598jK+/XoPCwioUFVWhqKgaeXklcZW6s2adgEcf/QJtbYfR2noIbW2HsW7dR+jqasFf/rIGZWUTodOZcPnl49HUlJx8s2jRCbARzpVcLsfdd9+NG2+8Nm75vHmLcMstb8eJJhE6O/k4jpDRCscBO3bwYbGEXJuUtLVJFScSEiNOOgnhjz32M3z00V+Tlr/00kt45JFH+i92owjaTV+tJt8RFQrhK2JkXZaFk1TJH8OO0ZjegMnny7wMw++njyRGuOIkL4+vGo/9HGUyPoFqMPuk2WsBwETsJYfVKOe3vJzXl9rb+WMbMvvYysrkF5JsugZPRKiUOGKhCQYkW650rbpooky24b/GIbjdyUNnj4efBJEsryKIanA+AtAaxEfeU7qk06OGZhMWy0hXnLAyGV5+6CHUGAyYUFqaNNmcXlUFR0UFUn26JZQZZkOKxvOxuJxO3PvUUzDr9TDpdNGHWafDlPJyRFJlzAIBhz179xKXFxGe8/T990NNik4IwNDGjkLPIYgtRq0WDMOA45LPbI/YgWc/CpHCicztju9y2w/bPy7WwYUOCFe6DKdwAgDu8nKEVCooHQ5wLIsN+/bhN88/n7RdZSbpoVmkS7LqkhiryGR8BLGuTng7jUaaU+QSR7hwkpIE5YBlWVgt+WjvTE72aGwc6FFGFSIcrUBdiJ9rp9xYWLWghWP8/gG7KbEI9WRJhc1mShJNAOC777YiFAI+++xDvPHG03Hr5HIFTjzxUtxyyz8BAEajFbNmHS/4Oh5PHyZMmIeurhZ4vfHq0IUXfp/6vOuvvxoFBTY8/fQzcLncuOyyS3DVVT/B9u1qYiufzk7x2u7u3fz5rqwUf75PP/0CPP54Fa6/fqG4J+QYknAiMSaQy/mHmPnauHGziMsdDgfefvttXHLJJdk9uKGGdtOn3VX0emHhJBTiHyKEk1HZ3ySCzUZOV6Dh92cunNCqTYARrzhhGL4a9tAhvh+eUsnbsw5mbC8knBQUcDB7OCBxDq1QCIpIVmvmnpppYbMJZrlISEikDy34HQiwSZOcdCtO5PLhEyRowonbHRFOyLOHoe57MBjUarJwEgyyCAaZtM9vOjZdxcUeNDRoEQ7TZl0cDIbM+qxkkxNOOgkaSvNyjmWjzdiFKCktJS5vaGsTPdPv7ujAn995h7huw1FHobp/oKU3GsGyLMKE6uJdBLswACgiDNLCGfhhZlJxQhJbWJaFRa9HF2H85OgiVKwK7FsuNAaL3ZbjeLuuhAEQ25/4ooNwOqtcHhaw9BsiGAa+wkL4+oURLcVymHQehwsuHMY9l12Grv4KmNhqmMLi4hE7LgmJrFFayk+kCEJvlMLCHMsgPMJJx1JSpTry+igS7v92K1k4aY6pnH3uuT+irq4WJpMdZrMdRqMNZrMdmhIlwHr530HEESVDn6zGxgP461+fRk9PB3p7O9DT046eng643b1ob2+DQiH+dzYYq665c2cSl7e1HUZDQzcOH062Og0GA1Aq07PK0Wj0+PWvX4XP58H69Z9g5co38e2378Pv9+DCC8+hPo9hGFx44QW48MIL4pbn5SFJOGls3I///nclLr/8NLz55iuw28tgt5chP78MFkthUj87ANi4sR379vXi5JPHiZrjBAIM1OpB9DgdYSThRGLMoFKJE05OOeVH+Oc/70BfnyNp3d///vfRJ5zQRJAYm669e/diz549qKqqwoySEl5SFsLvFyWciE36Uypz0P6zoIBv5habiWix8H+TBr6DaRBPm7AqlTnRP0MmA0RYtIvGYiELmSwLjB/PAN4JvD9YZAOG4fte5GpkUUJCYlDQqkI4jkEoFB+cT184Gb6KBK02RLx9ejwycBxdOBnOY0wXmnAC8IJQug3i0xFOzOYAADcOH9aCZO9VXOzNCXuzoFCCg0jRo7i/YaxZr0eZ3R73UNXXwyeizNMpUDlhiBlksTIZLHp9UmN3rVqNu37wAyyZMgXNXV1o6uyMPoqqqlK+vhjYLFWcAMDc8ePR63bDYjDAotfDotfDajRiYmzGagoUDkdaxnFylytJOLn27rtRW1eH0sJyyAoXoKioGkVF1Sgrmwi93hzdzmQKjHhc1ESp4Oh1uRAMBCBPt8dfFmBDIdz8fXJWbO/kyRhZMz4JiSygVvPWvrG+wrGwLECxa5QYIcR4MUU40qpNAH5co1DElWTk59lR12iA1VoUfeTlFWHKlEXRbb784m1s274qaXc3XnI1llx+Lt9AI5JIkqFq0dPTgTff/ANxXVeXEwUF4rM/B1NxsmABWTgBgPXrt6GxsZa4rqgos6CLSqXB0qXfw9Kl34Pf74XTuQH5+elnJttswO7dHmzdugLr1/8H69b9B01NvFW5Vvs0/vrXm+O2l8nksNtL8fzz+6MCitfrxm9+sxzNzQfwzDMf48IL56cc/4hxM8xlJOFEYsygVovzNlSpNDjxxEvx3ntPxC1XKBTIz89HIBCAYgQmFhmToszx7rvvxgMPPBDNPPzRj36E5x58EMy+ffSmaH4/fV2M4ixWOKmpycGYOMsCc+YALS38F8dg4Mti1qwhV/EMRjihleWMsE3XUBHpS5LoCDJxYr+epzYDCxfyllihEH/ec6YBjoSERLYRstMKBAaEE45L36pruCtOSIRCLAIBhmo7NZzHmC4KBQeZLIxQKPnYnU552sKJzyfuZq9ShaBQcLDb/dDpQujpUSAYZBAOM5DLwzAagzlRbQIAQYFqU07k4GbekiXoff99GEgBmPZ2hI1GBFJkmAgKJwkB81fuvRdaAFaDISo8qGICRcU2G+bGbO8tKEDiyIcmGHEMg5BaTWy4nlGPE4rY8r9HHiEu9xQXE3uRkBBr0xVBRug4u3brVuysrQWwBcD70eU//vGD+MEPfhX9mxcCRxZzXh51XW9XF6wj0GNB6DuRSVWThEROUl7Oz+sOH+aDw5EkPIYBJkzI3LVAYmhIRzg5Uj87pTIuzvTM3Y+jXj4paTOdbiCHpLu7nbgru7m/uXxn54BwImQ3L0BBAd0ZoqWlQ7Rwws85yOvEfD1KS0tgNFrR25tcBbtu3Sa0ttYRn1dYmDpRRaUSDj0plWqcccbS1AdJgON6cf75RfB6k8c7//3ve0nLQqEg/H5vVDQJhUL47W8vxZ49awEAV1+9DPPn78a4cfTxByAJJxISOUM6ifunn35lVDiZPHkyrrzySlx22WWw55yflAgEKkNWrlyJ++67L27xCy+8gNNOOw0XXXgh8MUX5OqKFBUnu3fvRklJCRwO8o1JJuOrDliWvzcOi8VSJrAskGgToFKRb+S0rAgx5Ghj+KGkrIxP0Glt5b9iRUV8aWgUjWZwjVQkJCRGDXzVAAdSVYHfz0Kj4YUVvmJj9AknAF91Qq84yV3hhGEAgyEIhyN5ltjbq0BJidgQNY/YipPCwoH9arWh4bc4SgeZDEGtFnJCUN0v0tpRodNBVVQUX+Uag/bQIfQYjYJZJj2U56oUCqgTAjvHL1hAPF4aYULCUEinQ1guTxI2AmYzHwzPknCS7nNEbx8KQUHprUGi1+XClt27MX3cuOgyLhxGbXOyNQmQHPwwGEY+KkCrOAGAns7OkRFOBKqQuCPN/kZibGOx8I9AgM8uDAT4SfCRWLGQ66QTODpSP7+EcYGeUj3tcgFffgnYzEE4esgNwPON/TGPrq6BXmJeyvgyhXBSWEgfd7W2dgAQV9ERCtHd9cTkUDMMg8mTZ2Lt2i8hlytQXj4F48bNRHX1TNhsU4n9T4D4sUMoFITD0Y62tsMoLh4Hk4l/b2VlwP799NfW6zMPI5lMRtjtJaiv/y5p3ZYtXxGfY7eXRf//t7/dglWrBmxjHY52vPLKW7jzzqsEX1cSTiQkcoR07n/jxs3EZZfdjdNOOwUXX7w4qdnnqIJ2FZLL8de//jX6J8MwWL58OX7+85/juOOO46MlSiVZzhYQTgIch2nTpiEcDsNgsKCgoBIFBRXIz6/A9753I4qKqjBxYrIeMWqgpRhkWnESCgG04MUYrTiJILULkZCQAPjbjVzOEYWFWGsumvAgxHDaYMnlHJTKEPz+5GCf2y0flVZdAGA0BojCidstS7vPSSrhhGE4FBZ6kZc3iGSEEcBvtZKFkzSaW7vLyiB3OsESAv9sMAhldzf8AhUDTlI3TwAmwuxZTN+VWEjCCRgGrqoq6A8cANNftRxSq+EpKYGmsZG4n2w1h8/G9h+//jre+ve/cd7RR+PMRYtg0g14a/fm5WHnypVYv3cv1u/diw379mFvQwM4jsPe44+HvaQEANDR3Aw3JbgTa7dhMARywgLfbLdj4aRJsBgMsPY/IhZnlhEac9I+L45hcrAcXUIiCygUQH7+SB+FhBC6NHotpLPtWCJhXKAEfdwWDgPN7YDTSe5BVmDuv/+EQnwCicVCF05SCFUmkxEymRyhUPJ4o62NLNyQ6Ox04umn74LRmAej0QaTiX8YjTbMmzcBQOqykzvv/B2ampQoK5sEhWJg+5aWOhx99PloaalFS8tBOJ0DNi2PP34NfD4vurtb4HC0getXby699C786Ef3AgBKSvjcFNJQi2V5F4/BhC/Ly8cRhROfj1wFVFZWhmnTgLvuehRvv/1Y0vpNm9YDEBZOBpODnAtIwonEmCGFOJ3ED394DwyGMdCnjTJJ9XMc3nuPL7c755xzcPnll6OgoABOpxP79u3DxIkT6XWAPh91vw3t7VHbL6ezG05nN/bv3wyAr+QBRnkhBU2By/Rq73LR0xnGuHAiISEhEUGpDBOtrGKFk3T7mwDD369ToyELJx6PDKHQ6Ks4ASBgicXA6ZTDYhGXJsZxdOGkuroPJhO/n9EYK/XZ7VB2d0Me4wnrtdsRSiOgwimVcFVVwUBJI5S5XAmlmfHkm81YtnAhelwuOPr6+H9dLmJAPN1M/jAlaSRoNKJn+nTInU5wcjmCOh3AskkWS6FQCA6XC+5QCPo0epAA6YstYrd/55138M6qVXhn1Soo5HKcNHs2zl26FMuPPRY9hYU46sYbic/bumYNTjrvPADAYYGUz1jhJFeEQL3RiG8ff5xYt+c0GjES5nc0KzZOJhsDkzAJCYlRSTpiyKgObAyChHGBCsJJpL299B66RaaYc9jZycdAaEkQKYJ6DMPAbLahs7MlaV06wklDQzNRBACAE06og9lckXIfS5fOw4YNycsLCytx111vRP92uXrQ3FwLh6MOf/rTtWhra0p6zttvP4bvfe9GWK1WKBR8+9fSUr54ze/nc6WVSt7FY7DucdXV47AquRUNldLSMuTnh/Hss7cR1+/atRV79vC3dJuNPJSVKk4kJHKETHps9/XxP+LR1NIkDr4bLXHVl2vWoK+/t8a7776Ld999N7ruP//5Dy+c0KorBCpODlMsCwCgoIC/waQrYuUUtC9SphUntEYwLHvkeqZKSEgccdD6nMT2NMlEOBluNJoQSIn/brcMMhlZIMl14USlCkOhCBPPfzrCicslA8eRA6FKZXhUCiZRWBbOiROhcDjABgIIaTQIZpD8EDSZ4LNaoepKzspMZa110qJFWE6wuPSr1Uhs8Zdu7wihChVOLkcgobLmuffew99efBHdfX3odjrR23/sx86Zg3c++yyt16ZWJIBs3Cem4oQLh/HlunXRvwPBIP6zfj3+s349ftrYiIefeQZl+fmob2tLeu7mjRujwkn9wYPE/Ws0+qilhl4fgNWaG8IJGAacXE4UlzKxUcsG1M9X6m8iISExUiiVSc3PibCsZNXVTyrhBABOOeVy9PZ2wOFoR08P/3C7nSg0xQhVHR2AkG2kiEDSsceejZ6evmilSOTfefMWpHxuBN7Wi4xQH5VYxGpqOp0JNTWzkJc3C11dG/Doow8mbeN29+L113+HG274LYCIle7Q5NmOi7EkTaS0dALa2+vjqk/KysrAsiweffRp3Hrr1UnPqavbgUOHQpDJZKiv59s6xQ5XhfrJjBakEYvEmEHoGpufDxDmRuA4YN8+YOrUoTuuIUUg6+7tjz8mLjcajTjhhBP4P4RsqcLkIFcnxSpCqzVAqzWAZUexEAVk16qrs5NuUKnXS5l2EhISRwx8n5NkYisU0m0MPxLQenH4fCxVIMl1qy6G4e26OjuTEwd6exVAUttwMh0dtAwWDmp1DvcwEQvDJAkImRDU64nCicztHuiwSnp5SgCaIQy60q44SXPg1u10YsuBA8nL0+gpEmHRFVegu7cXZr0elsjDYMDlZ52FowiTezEVJz2dneiiHMtpZ54JAJg7dSpRONmybVv0/3MnTcKfr7kGB5ubUdvSgoPNzTjY0oKyohLk5/ug0YRgs/lzajjHyWTE+QGt8mOooX1eUn8TCQmJEYNh+KoTSu+wKFrtkTtfTxgXKBAAPaUBsFgKcOutzyUtD/v7UCJfPbDA5QIxAwngz7WIbOi7736GGNtLp10xrTpFqVRDpxOX3CqX87oarc99IhoNcMcdN+OZZ56A05l8DlaseBXXX38ngKGtcpo2bQqqq2egqGgciovHRf8tLq5BUVEVOI6D09mFtrZ6tLfX47TTJgMAFi+eR9xfIOBHW9uhaCVubS1vNxa5zY/Q8COrSMKJxJhBqyUnDjAM7wMY6dGWSHMzL3qPyl4MFOk2FArh3Y8+Iq4788wzoYyIAzSRQCDr0dFfxZKIXs8HEzSaUT6+oN2sA4GBZmZicDqBrVslmy4JCQkJCFWcZG7VZbEMf5Y3vUE8Myqbw0cwGIJE4cTnk8HvZ6BUCr+HUAjo7iaPKUym3Oj/kCuEKNWmDMeB9XoRpmS3MpQxHykAnU42P8eyaXvemSkCksPpTGs/AHCwqQndhOcdd9RRZOFEROWEkMXWhOnTAQCzZ83Cu19+mbR+w65d4MJhMCyLiUVFmPG978Wt5zgOnXI5ZOUiIyXDDCeXE5N9cq3iJN2qKAkJCYmsIkY4OVJtuoCkOBELDkr44Ud6Ni96JQMWCeP7hgbyxmq1qEASLdcjHWf19naycGI229Lqf2wypSecWCwW3Hzzbbjnnl8DACorp2LRouVYtGg5Jk6cj7y8oc9AXr78VJhMp1LXMwzT3/slDzU1szBlCr989uypsFjyUVExFdXVM1FQUAmfz42zzroGOp0p+vxAgA+Fmc3836O9vwmAxG+whMTohWWBsrLk5ePG8bHw/j6PRHbvHqVKKOWge91unHrKKTBHrlYxnHvuuQN/ZCKcUCbFkYvlqLbpAoSzHNK56u/bR63aAcAbVEpISEgcISiVmQsnRmMADJMcuB8JexyVKgw+4048o0E4MRrpNfR81YkwXV1KhMPkiabNNgZmTFkkpNFQv0FUu65wGApKhiapWiSdbP50q00AwEQRTkgCiOBrh0LUhBwTpd+LmMqJwxSLLZVCgYLSUgDArLlziduEQiF099vSsiQBgmGgF+hFM9LQPvt0e8lkC6niREJCIicR0+fkSG0MDxDVCSX86O5uxS9+cQzeffdxdHbSLdwjaEhVy65Eg9F+RAaSaMOWdOyghISTdEinEDmSF3PzzTcgLy8fAFBXtxOvvvpb/PznS7BsmRr/+tfDab1+JigU6eXLRMJjWq0G//tfCx599AtcffWfYLEU4J///BUuuqgYd911Nj766G9ob+dFMa934Pl79nyHe+75Hl544e4svovhRUr1kBhTVFXxP+zWVj77sbiYf0TWtbaSFWGfjxe+0+xnOfJQJiMWgwEvvvQS/IEAVqxYgXfeeQfvvvsuHA4HTjvttIENaSIBrUoCwMUXX4y5ixdj0yYHmpsd6OtzwOVywGCwAhgDNqA0MQngvyhibujhsHAGS3X1gAQvISEhcQQg1OMk4k5Es+rSaoMoKPCitlaHYJCFTBZGUZEXZvPwG+YyDG87lo6tWK5bdQH8e1KrQ/B6k2dSPT2KlOIHzaZLLg9Hm8JL9MOyCGk0kMcMSN1eL7YdPAhPUxNmLV+e9BSFwwGWkrkfJGTEplVxkkHmP63ixOl2I+D3QyE0loqhz+EARxlz0oQTJhwWtDQDAL/LheK8PDR1xjerrSwqAtsfLZi5cCEMWi3m1NRg/sSJ/GPCBFQWFsKl1SIAgKUkzIQzaaw4TNAqOYaj4sTrduONf/wDrS0tmDJ9Ok6/4AJs3rEDTE8PrAYDLAYDzHo95DKZ1ONEQkJiZJGEE2EI93EVfPj66zexfftKbN++Ek89dSNmzDgWxx57IZYuPRcWS37Sc4jCCQ2RwgltiJGOcJKfX44FC85Ab28Henr4h9vdC6t16IUTvV6Pu+66CzfeeF3c+nA4jPPOOyOt188EhuGPhZK3Eodczj8iGI1MNMy1di3vcOP1uvHtt+/j22/fBwAsWnQmXnvtg+hzamsPYdWqd7N09CODNGKRGFMwDF9ZQqoukcmAKVOAjRvJz21qAioqRpnNFO3uIJcDDAOlUolTTjkFp5xyCp588kns27cPutgBgMiJbSxFZWUoqqiAUklOFhj1FScKBf8lIE3kxfY58fvp4lNZGa/iSUhISBxB0HqcRCyueDGCXHGiUHAwGoOYMaMHgQC/7Ujeq2mN1GmMhooTgK86oQknoRA9O83tlsHtJk8pbDbf6BpXDRM7W1rwxWefYfP+/di0fz92Hz6McDjMN1cnCCeqBAEgQlgmQ4CQiEHK5v/LO+9g8/79KLRaUWixoMBiQaHVirKJE2FN8/hNVvozejo6YItkLaXAQej1EsFos/FZUOCbu3c7nehyOtHd14fqSZOgFAhoXXrqqfi/uXPh8flwqLU12qOEjRGZjHl56Pz4YygIY2m5y4WAyUQVTkI5LJyMVMVJX08PTjz6aHwXY8Fy4Xvv4cDhw9iwe3fctkatFndefz2u/OUvh/SYJCQkJKiIseE6kq26CPc5Dgy++ur1gb85Dlu3rsDWrSvw/vtP4NlndyQ9ZyiEk2xUnJx88kWYNeuihOf7kZdHd14hodHwpypVmCgiVkS4/vprsGfPbjz99JMAAJZl8cgjv8Ps2TPTev1MESucJH4kkZ9EKBTC+vX/IT7Hbi+LOx+06p7RhCScSBxRWCx83Lq+Pnmd2833qRpVhQC0SRAhi4tlWUyaNCl+YbrCCcMALAuOo3s5jnrhJNKULLa+MIJYqy7ScyNUV48ydU5CQkJi8NAqTgC+QbxCEaKKEZGKDYZByl4bw4HQeyEhk438MYvBbA6grS35Js5xDHp6FLBayTPSnh661ZNk00Xms82bccszzyQt37x3L8KhULQqAgAYvx9ySqNzv9VK7L3W7Xbjn2+/HRUaupxOvPz558R9/PSCC/DwkiVpHX9xWRluv+giFPaLL9F/rVZwZnOcFZnP48E7L7yAw4cPY9LkyVh+ySVg+o+5R0A48XAcyn7wA3Q7nXAljKs2zp2LqojpNoGI4KFRqTCpvByTyssBAF67PS6EEzYYAMIxyDwesH4/pQUuEM4g8Wi4oFVyDHXFyV8eeCBONAGA1/77X+K2vW43GMmqS0JCYiRRKvmYCS2ewrJjwEpjECiV/DmIsR7/699/je3bvyZuvnTpecTlwy2cpChIjds2eb9K5OWld39nGD7G2NIivJ3VGh+iYxgGTz31BG6//Tbs2LEDM2fORIlQb4EsQ2m3l0SifhZp07t37zr09pKTehYuXJYgnJC3G01IwonEEUdlZbxw4nL1oqOjEZ2djVi7tgFAI3w+H+67776ROkTxCFWciCHdiV//fiN90kmMifGFUkkWP8RWnNBUJZmMfqeXkJCQGMPI5RwYhgPHJc9mAgEWoVCI2iODXq0yMqRzPCzLkeLaOYleH6RW03R3K2GxBOBy8dUlanUIBkMQDMNXnJD3F+jvCSORyPQ5c4jLe10u1O/fj4qJE6PLVJ2d1AC+n2Jn5fL58POnnxZ1LHa7XdR2sVgKCvDQ//0f8bicwSAiYSiPy4XTjz8e22KatZ/51lt44e23wbAsKoqK8Nqdd6K7X+CJ/Te/tBQN7e3E1xcSXABybxIg2WIrRBm0MoEAtdqEtJ9cglZxIqY3TMavGQ7jtQ8+SL1hDBaBqiUJCQmJIYdheCsuSv8waLVHdrIjw/BCRkzvNVmYnhx67LEXwO/3oq3tMEpKxkcbrGsMckBs+zORgSRaCIvjeB1MTLiFdovPJFQTEU76+hzw+73YtetbbN26Ai0tB3H//fy9sb+9WhLl5eUo70/uGE7ExuwStSydjv9qrFnzIXF7pVKNmTOPjwuldXRIFScSEqMOlQqw2YCODuCbb97Bvfeem7SNVqvFvffeG73g5yy0SZDAFd/v96Ourg779+/H/u++g7K2Fj8780xxr9d/haXpAsAYqDgB6L1fBltxMiZOjoSEhET68L1BwvD7k4N6gQAjaH2VboXHUJNOz5LR0N8kAsMAZrMf7e3J96qeHgXq6rTo6hq4P5pMflRXu+DxkAO1BsPINKMeDUyeMwcMwxD7e2zfsCFOOFFSRIKQWo0QJWWQ1h+ERH5BgehtozAMOLmcaP/ExiT1/Pm+++JEEwD48Ouv8dk77+Dk885DnsGAC449NmkfYZkM3VYrWJZFmJCpI2TxBY6j9yZJiLZwlPEyGwjQxReFgljlkytEKk78gQBfbdTbi+6+PjiDQSyYOnVIXnPv5s2ob2tL6zlCdm8SEhISw4KQcHIk23RFSBBOFlQVgpaSceutJ8Dh4O8Db73VCaOxv/9tSR6wJ3UT+ejriUBI3PD7xYkftPzjTApKPZ5GPPnk7/Duu39JWtfe3oCyslLY0mudMuSIFU4Sw2KRQiyWZWEwWOB0dsetnzXrBKjV2riwWUcHOQlmNJG7oz4JiSEkYr1stRYR17vdbjiEmnvnCmlUnLz66quorq6GRqPBxIkTsWzZMtz485/jsXfeEf96/VmJNF2AZTO72eQcNOFEbMUJ7QSNiXIcCQkJicygVWr4/SyCQXqiQq4JJ+lUnIyW/iYRLBbyuILjmDjRBAB6epSoq9PB5yMLJ1rt0DejHq3oTSZMKCsjrtu2dWv0/x6XC5fddRfufO45PPfJJ/h62zY0tLcjHA7DZ7NRs2E1Oh3UIgdk+YWF6b8B9AsIBJj+sanf68Vzb7xB3Ob9d9/lt6UkAHEyGViZDGZK4KpHYIzOBAJgKH3mEitFqI3Ug0HIKGO5XLbpAoDPV6+GfvlyqM44A4UXXIApV16JJT//Oc751a+G7DXZvj6cu3RpWs8xp9NRV0JCQmIoEGr+fiQ3ho+QELs4f+kS6CnxjIhoAgBtbYcAAAr4IS+yiy/jyIJwIqbPCcfRt8uk4sRgUBBFEwB44YW7UFIysr0ZSYi16iJ9JEolcPnl9+ONN9rwxz9+jQsv/CUqK6cB4G26AD5sFhmKLV68DBdddAcWLUru4TdakCpOJI5IbDb+B2+z0X0EGxsbYcn1Qb3IipNgEAgG5aitrU3a9GBTE0KhEGSpvIZVKr5BDIT7m+TaTSEjaJNisR3HpIoTCQkJiSSUyjBcruTlTqeCGmRnWY7alHykSEfIGW3CiZBdF4nubnoQWaORhBMhZk2ejL2HDyct37ZjoLnq4e++wytffpm0jVqpxPpvvkGJQLWIxWBAM6WpfCyZCie0XhqRipNP334b7RSB453PP8fjoPfdiNhNWQ0GdBH6u3QLVJzIBJJcEpu60ypOGAAyN7lBbC43hgcAtV6f1BMGALx+PzwuFzRDEAycU1iIt+6+Gx98+y3OuuuuuHUylkWIUDVkTqMqSkJCQmJIELoeSgmPSedAr9Hgs0cewQUPPojDra3Up7W1HUZNzWxo4AVkRj75tqlJ+LUiPVVEIJMB4XAADkcneno60NvbgZ4e/pGXdwZmzxa2vhIK6WQinBQU5KOwsAwtLcmNlP/73+ewdu0ZqKo6P/0dDyEqFR+34zigo6MRb7/9GGpqZuOEE34Qtx0pfGWzAQ4HIJPJMX360Zg+/WhceeVv0dp6CFqtEQAQCvEPuRyYO/d0TJ58OurqdmLNmvRsPXMFSTiROCJhWaCwEHC7C6lWCQ0NDZg2bdoIHF0aiGgO39QE7NsH9PXVEDcNBIOob29HZaqJ8/jx0f2O+YIK2h1TsuqSkJCQyBidLkgMtLtcMni9ZHUk16pNgHSFk9w7fiGE7LrSQSYLQ6kcXe99uDlv+XKUGY0osdlQarOhpP+RX1QUbaV6KMHmKkIgGER+Ck9sq9FIFU4mlpWhpasLPS4X7EXk6utUUCtO+semL/3rX9Tnvnz77YDPRxdO+sebZqMRaGxMWt9Ls1aBQH8TmQyJKiztPQDAa++9B6NGg+qiIlQVFkLXP8jN5f4mgLAFVk9nZ9aFEyYYhLyvDwCwfPFi3HXppdhRV4fnbrkFMpaFVq2G0+3mbcOczqh1WDGl4kpCQkJi2BCy4zIah+84chVC7GLh5Mmoe/lldM2ciZrxk+FwJPevuPvuczB//mlYMns+Fpx0H5Cfn1o4SSNOwjDA2Web4fUmJzjMnPnuoISTTItKTz31TLzwQrKRmcViwznniLTFH0ZYlj/lnZ1O1NZuxxtv/B4A8PDDl0CnM+G00/4P+fllmDz5RwDik8nz8wHS8LSgoCLub5+PH3aJNW3JZSThROKIJT8fOHxYAYulAF1dLUnrGwkTtZwjRY1hXx+wezevJBcXj6PuZn9Tk7BwYrEAMVmNY14XGEzFCccdASdIQkJCIn2MRto1lEFXF/m6m5vCydi16gIAszkwaOFEqw2NjQrUIeT0007D92N6mUTgOA4ejgMYBnUHDhCfW15QAEWK2X1pYSFqm5sxtaICUysqMK2yEtMqKzFn/HjYzWYAfBWCq6JCcD80UvUH+et11+FRux1PJTQNv/L003HOkiXoc7moDcsjFSf3/Oxn8Hd1wWIwwKLXw2owwGIwQFZRAVqLWrGN4QFeoOEAYpP76594Ao5+QQAACiwWVBUW4p4778SCiOdvDmIWMFJ3dHaicJBNaIOBADZ/8w0aDx/GouOPR5leH3f+7r7sMjAME9cn0qjTwajToSJmLtEtBSUlJCRGGrUaMJv59PlYjEbxXkZjGUpWLMNxyDMaUVMzFRs2fEXcZv36T5Cn7U9WsFr5c02LkQi8Fg2TKY8onLS3p25Enu2KEwC48spLiMLJNddcA3WOxoBuvvlkrF37WdJyl6sHb731RwDAr399UdJ6rRaoqAAOHeLHrC5XDzo6GtHZ2RT9t7OzCXr99Zg3byIoOTKjCkk4kThiiVy/8vJKosKJ2WxHXl4Jxo8vRWGG1gXDSoqKk/r6AW9BrdYAi6UA3d3JZZX7Gxtx0pw59NeZMCHOg0vIqmtMQLtjBoNAOCxcRhoMgnp3GDMnSEJCQiJ91Oow5PIwgsHkayi94iT3hIexbNUFACrV4MUqqb9Jami9MphwmB9ryGSorasjblNVQreajfD8o48i3+UCKzBmUWo08FAst1JBq9ZgAwEoOzpQWVCAJ2+4AX+59lp8smEDnv34Y3y4Zg2uWrYMHMdh98qV8Pb1YW5lJbQJ46NIxcnJS5ZA2d2d9Bo+0jiL4yDv64OmJTkZCqBUilCa3Hc7nXGiCQC0dnejtbubKhjlCqkqTgZDT0cHfnzxxVixcSMAvjnsP37xC1x+6qnRbYS+bxE4lh0j3r4SEhKjnsmTgc2bB4L6KhW/TEI4duHxoKysAhs20DepKu2vLGRZYOJEIKaHW1qvRcBstqG1Ndkaq6MjtXBCMxFh2aTCVNEsXboE1157HZ588onossWLl+Cuu36d2Q6HAavAeAEAjEYLioryievGjwfy8njNcdKkQgQCyUkrZ511IqZNS04QGo1IwonEEUtkvvrLX74EpVKNvLxiKJX8pGr2bP5CkPMQ5PKf/PGP6FOpUFRVDZ+vGCZTMWy2ElRUTEFxcU2ccFJWVoaa0lLkCWV9mc2AwQAA6OrqwpNPPoX2djO0WjP0ev6h05lRVjYRGk1uTyZFI5TFGQjQm8cDdFUJkIQTCQmJIxqG4atOEpuMC5GLdk/8xCqMUCh1gHC0WXUBEWGIlocvDq2WktghEUUoAG/euhWcXI5DBw8S11eJqBIxFBSApQgv0WPIUDQB6MKJzOuFqr194G+ZDMsWLsSyhQvR2t2NUCiEU2+/HZ9u2gQAqCgowIu33YZjZswYOK7+yAVHiWAkWXxxHHQHDkDZ0wOO46I2vMFQCIr+90gTqlr6+tDa0IDW7m60dHWhtbsbH69bR33fZTVk69tcQaXRQKNSwZNQeaNUKOBOEIPS5a9//GNUNAGAcDiMK/7wB0woLcVRU6eK3k94EN87CQkJiayi0wGLFwMRC0iTKfPo+Vgj0neE0KcKXi+WLj0OfX3Ap5++SHx6TUWMJaPdzj9ixgdxpBknsVrJ1ZWJwonXyz+MxoHcV6fTC7+ficb+Igw2L+KJJx7HhRdegO3bt6OsrAzLli0TlUwwUixZshD/+c/r1PWXXXa54PFbrYDVysBuL0ZTU3Iv5YaGpjFh0wVIwonEEQzL8veCiorkjAKhKsKcgeOIFScfrl2LFkLTzJtv/gcuueROBAI+FBfXYObMasyfrwFaW4Ht2+mvE5PVWF9fj7vu+g1xszfeaINabU//feQiQnfNVMIJ7cvDMMLPk5CQkDgCMBqDaQknanVuVi4oFJyo0vPRWHHCsvxxB4ODEU5y83PLJYT6azAcByYQwClz58Ko1eJAUxMONDdHqyAqKytT7j8kIgghdAypEBJ+WMqPo8BiwTl33x0VTQDgUGsrrvjDH7D7H/+IihwRwYQWYE+sEFG1t0PZH/Qa98MfQq1UYvfhwwCAh//v/zCprAyzTz0VpDShH91/Pz4TEEpiUSoUKBwFvTlev/deGORyWI3GqMWZRqWCu7ISIrv1EXnzg+SmrhzH4Sd/+hM2PfUUVCLN4WmCmISEhMSIIJPxUWCJeBiGFzTcyZZY8Hhw+eU/xoQJF1KFkwlVpfELJk4EurrI7hxp2jfShJOuLl44CYWAHTsGdBq5HJgxg/+YX3/9JfzqV1dBo9HDZLLBaLTBZLKhpmYKjj76D2kdRyJHH300jj766EHtY7i48spL8Oc/P4r29vhKXblcjiuv/Cn+8IeHRe0nP58snDQ1NY6OuKoIJOFE4ohGpSKX6o0KZTQUGvDh6icYCqGVYGkA8JZk8+cPlNL39vLvU3CSw7J8M5h+HIn+nzHodKax3xweSN0gXqi/iWRLICEhcYRjMIjoFRVDNmyjhgKFIky1F4tlNAonAF/pQ7JUEwPLcjn7ueUULIuwXE7t8wEAN513XtzfXb29ONjcDP3MmSl3H1KrU9YNDcZ2ysdx2L1nD1q6utDSX60R+ffJ669HEaV0+28//zmau7qwbs+e6LIDTU34bNMmnL5gAYABwURUxQnHRStc3F4vahOsuu74xz8AAO+OG4djCD1l7AI9QRKpLCwEOwqC/qctXQo5oQI6qVInDVy9vThAae6769AhPPLaa7jrsstE7WswlU4SEhISEsOIRkMWTrxeqPOB7u4W6HQmuFw9SZtMGJfQU0ut5sWTXbvilxuNaQsneXnx926t1giTyQadjm9kXlcXX9wSDAJbtgALFw5UpXg8ffB4+tDSUgcAcLlS23yNJQoKCrBq1de45577sHHjJlRWVuK8887Bueeei7w07HcKC8n2sc3NUsWJhMSYQKUCnM7k5aPiB06YaLd0dYHjyEEamy2+kSXHAQ0NwLhCAeGkoCCuVLWnJ/mGCABqtRZKpVLQ4WpUETG4JE0wUzWIpwknUrWJhISEBJRKDmp1SJToAORyxYk4YWA0WnUBvHBCmieLQa2WGsOLJaxQCAoniViNRliNRvRWVSHlL0MmQ1ilgkxgUDuYipM+vx8Lr7+euO72iy6iCicfrV2L3115JX7/5pv4cM2a6PKnPvgAE0pLYdHrIe//AtEC7LECgMzjgax/7JUomsRSPm4ccXmBnV4tzbIswjEWJVWlpdRtcwmq4JTGdy0R1uvFHRddhIdeeYW4/q1vvsEdF18MhVyOgNEIRW9v2scnISEhIZFj0DJjPR6oVEBRUTXefdcBp7Mbzc0H0dJSi+bmg2huPojKSoKtaHExH2upr+eDblZrUj9dMfz0p7/A0qU/gdFog9GYB4WCD0Rptfx60nAgHI5UoZDtwiwW8YkUY4Xx48fj5ZdfGtQ+CguLictbWxtHR1xVBJJwInFEQ3MxGBU/cEIAv0mg6WNeXvIFrakJqK5Qg6GJBAlWELSKE53ODIVijBVUKJXkfiWZCidjphxHQkJCYnAYjQFRwgnLcjlbsSG2aX2uHn8qBtNbRqeT+puIJUwba6QgJDIZI6RWCwong8n8N1qtUCoU8BPGRSTL2Ajvr1mDd1etgj5hXPThmjVRIeXt55/HcWedRQ2wx4pNypix75/feYe8PcuiuKqKuM5OEU6mVVZi09NP43BbGw42N6O2pQXm8nLitrmGGMEpXUwsiwevuAJ1ra349xdfxK2746KLcNdll0EhlyOkUsFVVQWzQBNgqeJEQkJCYpRAC5h5PHGtYAwGCwyGuZgwYW50mVLXRn5uYSH/GATV1ZXEBB+/nxdIaEMrpxNobiZXliRWsUiIoyTG2t9szofNVoK8vGJMmTJfsuqSkBgL0Oado+IHTsgaowknCoUKBkOyb6fPB/hDMqiKivjyk1hKSvhmaTHQhBO93owxNwdSKMh33MFYdUlISEhIwGAIoo0yl4ollysXxFecjE7hROz7I6HT5WaVUC6SiVVWWC4X3bg2pNEMNLwl7WsQFScMy6LQasXh1takdc0xwgkHIGCxQNlvJbtg4kS8u2oV+gQEI3O/1zxVAAiH+YQfloWy/7Xaurvx5sqVxO2nVFVBSRn05xcUEJe3OhxQyOUYV1yMccV88pG7tBSjIbdKjOCULrL+z+uxq6/GfzdsQGdvLxZMmoS//+IXmB4jSrnLy8HJ5QgplZDFjJk5jsPhtjb4AwGUUc65hISEhESOQUv+9Hr7bePJA3UtXBhKOxLaroPB1PkoPT2ScJJNLrvsx5gy5UJYLAXRyh+AT6pevXoFOE4Lm60EoUEkb4w0Yy3UKSGRFjThZLRWnIwrLsbtF1+MrQ4FWlub0NHRiM7OJhgMVjCU6JPbDagmTOAn4a2t/ES0sBCoqUnaNi8vD7NmLURnZw9cLgf6+hzw+73Q6caocEIi04oTSTiRkJCQAADo9eKCd7ncJ0OMsCCThXNW+ElFphUncnkYFstg2k8fWYRFBhW6nU44+vpQVVSEcBrWn6EU1a6DEU4AoCAvjyictMT02wtYLHCXlkLhcIDhOCwg9BlJxBQRTigCQG1zM55/6y3cfNttUTEg32LBF7/7HU765S/RmWATdaVA7w17f9Yry7LIN5tRYDaj0GpFkdUKjuPixs9iP6+RZigqTiJ2aHazGU9cdx1au7tx3dlnQxbzGfmsVgT7feq3Nzdj9apV2F5bi20HD2JHXR163W58/5hj8PzChRkfh4SEhITEMEKLYYTDgN+PkkIZGluS7znlOAwohq5KU2j4IuAUCQAoL5+EPXvWwumMr4612cT39ZAYoKgoD/n5yeeO44D7778cra2HAAAMk1nvxFxgrIU6JSTSgjb3DAT4ewGby7/tmKyxzt5e/PbVV7Gnvh7zJk/BFVc8DLN5oKm7309XgjwewGJhgfHj+QfHUT23LrnkEixYcAkOHRpY5vd74fd7BW9eoxLa5FhIOAmF6BUpknAiISEhAYCvwlCpQvD5hLPmc7W/CSCukkSrzd3jT4UY4aSgwAuG4dDWpkY4zEClCqGqypXbY6ccwxkIYNe+fWjs6EBjZyf/b0cH6lpbcd3ZZ+Orbdvw9bZt2FZbi7MWL8a7996bVvA+pNHA5/fju8ZGFFqtsBoMYGM+oME0hweAQkpj9VirrqBeD06phLeoCJqmJswTI5z090chvdeP167FpY88gm6nE0VKJX62fHl03ayaGqx9/HHc9uyzeG/1apj1evzy2mtx6bXXUl9r8eLFaH39deQZjXEiAIl0RKuRZCh6nMhiUngvOv74pPVBnQ6esrLo36+tWIHfPvNM0nZvfP01ftPYiNJB2rRISEhISAwDQgkYHg9K8uRoatGCw8DYQgE/CtAKKJMTcbPFYIST88+/BXPmnAyrtRCbN3+BLVu+wPbtX2Py5OrsHuQRAm1oFA6H0dHRGP2b43I3IS4VknAicUQjFMv2+XK8LUV/AL+upQXH3nwzDvf7nny4Zg2qVqzHn/70DXQ6PutLqaRP9JK8IVOkxybqBkqlGkqlWqo4AYRLlSThREJCQiKKXh9MKZyM9oqT0Vx5IUY4USjCKCjwobDQC45jIJNxo7bCZqRYtWULzqUE9Vck9IhYuX07wuGw6P4mAB/o39fYiBlXXZW0Tq1UYs+OHTBGOqlmQIHdTuxzsuXAAazasQOza2oQ7Ld99RYVIWAwQNnbi9fvvBMt3d244cknifs1xlh1+fttvkKhEO596SXc//LL0e1ueuYZLFu4EGX5A8lC44qL8e8//xmuigowIlQ8pcEAs8Ui6v2mc+5HknCWK06YYBAsZfwbMBjgy89HwGiMyzibMnUqdX9rNm/G+XPnUtdLSEhISOQICgV/bQ8TxoXffQdjeTlmYy8OYBzc0MKIXkzEXiiYEIYyQCQknCS6y3MchwMHtmLNmg/w7bfvY9++DVCrtXjrrU7U1MzG979/M2SyABYvzt15Ry6jVPIhRC4hp8zhaEMoNDb6Hko5YRJHNELzn/b2XrQS7AdyhmAQHp8P5957b1Q0iVBbux3//veDonYT6wG5fv16eFM0eKElq4054YSW0SnU40TIUFMSTiQkJCSiiGkgnssVJ6mbw3OwWFJYO+Yw/PsTfo8RcUUm4ytwJNEkfQpjMvRT0eV0YuehQ+lVPbAslv3618RVXr8fmn5bpUx59Ne/hvejj/D8rbfGLV+zezeW3nQT8s47D56Y6oeQXg9vcTHO+t73MKW8HFWFhTDr9XF2WAatFvKYiIi7vBytbjfO+PWv40STyHv4aO3apOPyW62iRBOAF2fEdCIKy2Sie8uMNNmuOJEJjG9dlZUImM1JZfqTZsygPker12d0HBISEhISwwzD0LOJe3qA7dthRTfmYwOOxdeYjS3QwsMrG0M4MJTJ6OKJ0znw/6amA7jkkgpcffVsvPDCXdi3bwMAwOt1Y/PmL6Lb5ecroFaPjuSIXINhyKGz2GqT0c5YC3VKSKRFpL/m6tUf49tv30NbWz3a2/mHy9WD5cuX4/333x/pwyQTDOIv77yDzfv3E1e///6TuOCCW2EyCTe5crk4fPzxf/C73/0OX331FZ599llceeWVQi9LZMwJJ5lUnHSQG41BoRg1k20JCQmJ4UBMA/Fcrjjhqys4cBx5Umg0BkdtY3iAnwQpFBwCAfqkdzAN5CV4itIQTgDg623bUHnqqWk9p769nbpOMcieHQqdDkxnJzbs20dcP6myEkpC4khIp8OJc+bg4Esv8X+HQuh1u9Hd14fehHEWJ5djj1yO/23cSHyNxOQhAAilU0XDMODk8pSiwmix6QJS9DgRsOSlQRNOwjIZ1e6tavJkWAwGdMdGsPqZtWhRWq8vISEhITGCWCyAy5Xec4bBx91sBkhDnD/96SrU1e1EUVEV7PZytLfXE5//7bfvY+HCMwAA/YWuEhmiUiWbr3R0NIzMwQwBYy3UKSGRNioV8N13G/HRR39LWldfT77I5gSBAH5x/vnYU1+P5//3v6TVXq8Lb775R/zf/z1EebofX375Ct5441HU1e2MLn/llVck4QRIXzgJhwFahZLJlJ1jkpCQkBgjaDQhsCyHcJgcwJPJwjktPPAl6fTgo9U6em26IiiVYQQC9Kz9TBvISwxgtFqhUangEbL6jOHr7dtxSZoB/Ku+/3387Y03kpZPqaxMaz8kIkFzmnAyd/p04vKIfVcEmUwGi8EAi8EAAOhO2H7G4sWYNXEituzdm7Svj9atw1XLlsFuMkHXnxVLEw5ohBWKaJN56jajSDjp7O3Fi++9hy6nE919fejq7eX/dTrxr3ffjVqhiYUmnIQ0GqoII5PLceVFF+HRZ5+NW37ywoUozsJ3T0JCQkJimKiu5hUKkWMVACMqnOzdux4HDmzBrl2rBZ+/Zs0HCIefAsuyEOnYKUGBNERyuXqhVKrh9ws72owGxlqoU0IibdRqwG4nZ/zltHASDEIhl+OJ664jCidz556CxYuXE57I89JL9+KVV5JFlTVr1iAQCEBBudnR5pVHTHP4UIh/JFaQdHfTbbwKCrJ7bBISEhKjHIbh7bqcTvLNQ63O/aC8TBZGKEQWFszm0S+cCFeUcCLsyiRSwbAsSu12fNeQOitvZnU17CZT2g3dL/7Rj4jCyQlLl6a1HxJhuRyBYBBbDhwgrp81axZxeSiDJoILZ84kCifbDh5E1WWXAQA0KhUmlJbiy/Xr09o3p1AI261i9PQ3AYAetxvXPfEEcV1rQ0PawglLsfFN9Tne+sAD4MJhvPj22+jzeLDs6KPx6F//mtZrS0hISEiMMEolMHcusHGjePFkkBWtYkgUO8LhMOrqduDAgS2int/Z2YTvvtuE2bPnSa7qg8RgSBaxTj75Mpx00qVwOrvQ3t6AnTtX4fHHyX39ch2px4nEEY9KBeTnlxPXdXZ2wp3UPT1H6K980Gk0eCShQuS++97Hb3/7X0yZsji6LNHG+qijzibu1u12Y9u2baleNokjpuIEIJ+ElhbytiwL2O3ZOSYJCQmJMYRQnxOVKnf7m0QwGsnHbzb7x4Q7o1BFidTTJHsUxzQ2F2LrwYPY0dCQts3SzKOOwg+WLYtbVlFYiKtuuCGt/ZDgFArsrKuDl5I4MnP+fPITBfqPhCjBFpttwHo2j9KbxePzwSPUi45CpJn6e6tX45KHH8bX27aBS+hymonYM1IUlJWBpZzjuu++S2tfXDiMPz33HD5cswZ1LS0IxzQITnVO5AoFfvXoo9j93XeoO3QIf33tNRjN5rReX0JCQkIiB9BqefFErMIwDFm1ej2HurqtePvtP+Oee87F+efb8dOfzhT9/MrKqejr65ZsurIArWKHYRgYjXkYN24mZsw4dngPKouMtVCnhETaqFT0ihMAaGhowIQJE4bxiEQSU/pxzVlnYcuBA7j8lFPQW3oSzIXzkjY3GIC+Pt5RCgDGjZtFLZ1btWoV5s6dm7Sc4ySrLgB8ZUnsoCEcJteJArxoMuZOjoSEhMTg0evpwolMlvvVDBaLH93dyUFeuz0NK4McRkg4kWy6ssebjz2Grd9+i9v+/nd819gIp0DCztU/+1lGr/H4Cy/glJdfxppvv0VFRQW+d+mlyC8pyfSQo4QVCswcNw4HXnwRG/btiz427tsHXyCACTPpAQxvfj7UhP4kAcrs+6c/+hGuO/ZY5BmNUMjl+M3zz+OBhGbxAGDPIAISqeJ58r338OmmTfj3F19gUlkZrlq2DD88+WRYzWYERpHtqkqjQXl+PuoIST0HKb0RaTQfPoxf/m3Azliv0WBaZSWmVVbi1vvug11EchDDspALiGUSEhISEqOAiHiycSNAqUSMMizJBhxuvfUkOByUPrMEZs06AYsXn4XFi5ejqKgaAD3oLyEeozFiY8z/7fd7sWfPOmzf/jVmzjwe06YtGdkDHCRSNE9idMNxQG8vXzJoNJLN9VLACyel1PU5K5zEVD3oNRr8+1e/AgCsVc+Ak3AfU6v5e11fH/+3QqHExIkLsH3710nbrl69GjcQMhHD4YGLYSJjThtIp+Kko4OuKEk2XRISEhJEhBrEC4kquYLZHIDN5kNHR2TswaGgwEetRBltCIkjUmP47KE3m3HinDnY+NRT4DgO7Q4HvmtsxNo9e/Dr555DeUEBzEYjbrvlFpxwNrlaOBUMy+Ksyy7DWf2WVlmDZcEwDKqLilBdVIQLjuWzCcPhMOrb26EUGJf77Hao2toQWz/DAfBTIhgWmw363t7o3+0OB3E7WwYRkLBCgYPNzfh006bosj319fjFM8/g9n/8A4/dfjsumj077f2OJOPKysjCCcVWjcbB3bvj/u7zeLBm926s2b0bv/7znwd1jBISEhISowyNRpx4IrKadjCwLItFi47BJ5+8LWr7t97qhNGYnFwhCSeDRyYD6upW4csvP8G2bV9jz561CAT4RLJzz/25JJxISIwou3cPWCSxLDBlClBYmNYuVCpApdLghBN+AJ3OjPz8MtjtZcjPL8PZZ5ehrIwuqowolEC9L0j+WatU/H0uIpwAwNSpR0WFE4PBgEWLFmHJkiU46aST0nlJAGNQOGEYXjwh2XIlLiNkTALgT0qMtYSEhISExAByOQedLgiXK/4GwjAcDIbcFx8YBqiocMNm88HnY6HVhkZFbxaxCIkjUvJ49gjHWFMxDIN8iwX5FguWTJuGa6+6Cu6qqhE8utRwABLNw1iWRUlVFZwCzwur1eirqYHu0CGwgQBCSiU8paUIJTSOj75OwkCzvaeHuJ0tL0/8wcfs+9mPPyau8wcCmLRgQdr7HGnGVVbic0Kvl4OHDqW1n1pKhUq+xQKTNMaVkJCQOPLQaIB584AdO4DEJIZITE6rHZZDOeGE46jCycKFy9DV1YKWllpYrXbU1FiTwjYlJcPSjuWI4IMPnsF77/0raTkpUXu0MdZCnRJHGj09AxJxOAzs2gVYrWld/SKOS3fckVzuX1qao03PQ6EBz60YwmDgD5K9r1Wq5PvXMcd8H/n55Viw4ChcdNE0yARM2Xfu3IlFixZBrTZBrzdDpzNDr+cfN9zwNBQKw6DeUk5CE05i/bPDYb7ihER+vhRdkpCQkBCgtNSNvXsNiA295uf7RlXjcZ0uJFg9M1qR7LiGh7DAQDM8ChqS+61WqLq6kpZ7RVTcBk0m9MyYASYYBCeTCfZvSTxPoXAYcpkMwVD8by8vA6uuvfX1+O2rrxLXTauuxszFi4nrcpnq6mqY9XqMLylBTXExxpeUYHxJCSaPH5/Wfupqa4nLx5XmaGKZhISEhMTQo1bzlSceD+/+wnF83MNgwHA2+jv11ONw223kdeeeexPmzDkR+P/27jtezqOwF/5vjqSjLsu2JHdbslzBNuCCKcaYXgMJgRdCEuIkkBBCAinAfcnNDWmkviEhpBCSUG4w9yaQQEjoPbTYmGLTcS9yt9W7NO8f+0herXbP2T1Fp+j7/XyeD5pnZ54ZyWc45+xvZybJ0Udvy1lntX7MuOuu1uvHH5+cdtohGuhh4LLLHt81OLn++q9ny5buH3aZKQQnzC77zpoYYN/mkX4n3b59mibQPZZ+7MxwDv7cX8u+FSftTj/9/Jx++vmZN2/072/r16/P5s2bs3nz5tx77+0HvParv/r3s2/FSdL6j99tr/H2MGX9+t5LcQ7BElWAmWzJkj156EM35q67Wp9iWLZsV448sktgzSE3Ung1f/7sC4qmykjBSecqi+lo54oVGX7ggZS2vVz3zJ/f86ySbvr5e3bWef9v/3ZqrVm/eXPu2bAh96xfn7vXr8+JXc7oG82OXvvQJnnJC1+YMgM/BPPyl740r33Sk1I6wqiaZP3evX1/sOeGm27qev/Uk08e5wgBmNFKaX0y9xCtLunmnHMemiOOOCobNjz4AY65c+flzDMfmdZ3vJYjj1yY4eHk3HNbC2JK8fnWifa0p13a9f7evXvzrW99MatWzdyfG6b/T+MwqBtvHCg4GR4+8CCjdjum6/muvbbpSu8UqNuKk3127Wo9cqTfW9f32Et63rz5mT9/wez8xtPrzYz24KTXofBz5rRWPwEwogUL9uaUU3ofiM3UaP0+vDtbtx78w4Fwa+LsHeETOnsOyeGq47N76dJsPu20LLjzzgzt3Jndixdn24knjrh6ZCzqnDkHbQtWSsmRS5fmyKVLc0azAmLzmjUZ9KvztHPOyTFHHpm7HnjggPtLFi7M//Oyl41r3FNm8eKDQpOk9e83tGNH9vb5tbVw3rwctXRp7t904MZrp07zLeQAmP2Ghoby+Mc/JTfccHse9rDLct55j8/ZZz86CxceuO1n+7e8Q7gg5rBy1lmn5+ijj8199x18vtq1134uT3nKT0zBqCaG4ITZZ8C0o5RWeNKt2bQNTrptH5XewcnQUCsUGenDAFu3JsuW9X59Q4+9pJcsWZ65cyf89+PpYbTgpNbewcnRR/sYAwAz2sqVO3LzzQf+urB48e4sXGjFyUSp8+Zlz/z5mdPxQ+feuXOzu8d5H9PN7mXLsnmkHyInQimp8+al9PgZeJ+xrNKZO29efv0XfiGveeMbD7j/pt/5nSxbvnzg500He4eHu54/kyRzduzInO3bM7/5GXbXEUdkx6pVB/8wX2uueN3rUmrN/Rs35vo77sh1t9+e69aty2Mu7f7JUgA4lN73vvfkyitL141C9pkBn0OZ8UopeeQjL82HP/zP++8tWrQs55xzSdasOXfQo6inFcEJs0+trWuAd/IXLOgekmxfd3+yYtGDB6FMoWuvvTZ///d/n3PPPTfnnXxyHrpjRxZ3fAfYke7jnD+/9c8xf37rvfwux6Nk06aRg5NeK072BSezUq9Pge4742Tz5tZ+bt2sXDk5YwKAQ2TFitb3u7vvnp/du4eybNmunHTS1tn5YYmpUkq2nXBCFt9ww/43uWuSbccf7wMYHfbOnZuhSQhOkuRnf/3Xc9Lq1fnPD34w27Zvz4/9+I/nCc95zpieNS0MDWXv8HDmtJ/L11iwbl3mbtu2vzxv06YM7dyZbSeddEC9smvX/i3Yjlq2LEctW5aLzjwzSbLxrLMiPgVgqs2dW3LOOcmVV/auIzg5NJ71rGdl48ZdOe+8S3PuuZfm1FMfljlz5mRoKDnyyG9N9fDGbLa+3cnhbvv2gf7fccGC1jnz+9x99y1Zv/6eHHvG8clXvp1cdNHIh6EcAp///Ofz5je/eX+5lJK1xx2XS845J3/+mt/N93JmNuaIrm33Db2U1j/Lli0H17ntttYBWb3eDOkVnCxefETPhRkz3mgrTnqtNiklWbFicsYEAIfQihU79wcoTI5dRx6ZTWeemeEHHkhqza7ly7N7sldwzED9hCIjnRkzmqc+//l56vOfP+b2083eBQu6Biftock+8++5J9uPO+6Af+POVVAHPHuKfy8CgH2WLUvWrk2uv/7g1xYu7P22DhPrFa94Sc4//yUHvd948snTeDefPghOmJ02bRooOOn82f+d7/ytfOxj78ilj35G/vwlz8kjTjwxWb16Ysc4oGuvvfaAcq01161blxVHrcpXcmFqen8qsf3vd9RR3YOTTZuSBx7ofSzHC1/4wixf/pDceOP6bNmyPlu2bMjmzeuzYsUJs3fFyViDk+XLfXcGAPq2Z8mSbFuyZKqHMa11C0X27t2b7Tt3ZlGzOnysK05moz3z56ffn0ZLrZmzZUt2H/Hgh7CGerzLsXfOHP/OAEwrp5yS3H9/6z2tdh2LKZlEpSTnnpt861ut9xeHhlofzl67Nvn2t6d6dGPnJx5mp82bk1Wr+q6+YP6DuwDffvt1+cQn/neS5HNf+nDO/9KH88NPeELe8KY35WEPe9hkjLYv11xzTdf7q9ZcPGJokhwYnJx4YnLrrd3r3XJL7+Bk7dq1ecIT1uaMMw5+bdb+7jTSVl27drW+G3Rjmy4AgAn1gc99Lh/44Adz9/r1uWf9+tb/btiQJ59/fj78xjdm75w5s/TQvbEZdFXInB07sjvZv+Vxz+DEahMAppmhoeThD0+uuy65997We1THH996/4tDZ8mS5JGPTPY0+3nOhvcKZ8FfAbrYvHmg6gu2PZCklRi8+92/l717D9y19/2f/nTe//CH5xOf+ESe9KQnTdQo+1ZrPWjFyT6r14we5rT/frN4cWsXqXvvPbjevfe2VqP0Oot09+7u92fD/xl21WvVSK0H7u3WyTZdAAAT6rs335wrPvWpg+7f3WwnaxXEgfZ0nNH4teuuyxe/9a389NOetn+FTrtFt96ahbfdljo0lB3HHJM5Pc7xE5wAMB3NmZOceWbrYuqUMrveI3TiILNTr5UAPcy/7/Ykyb333p6Pf/ydXeucdtppefzjH5+NGzfmW9/6Vj7ykY9kS7c9rybBLbfcko0bN3Z9bc2a80Zt3/n7zckn96576601P/jBD3LFFVfkV37lV3L//ffvf61XcDJrd6Ua6S/WuQZ0n7lznT4GADDBVvRY0Ss46a4z4PibD34wr3zLW7L4h34ov/mOd+Tmu+46qE2pNUN79mThunUZ7nG+4R7BCQBwmPDTJbPTtm2td/n7+QVq9+5mxUny13/9qp7VrrvuuqxYsSIb2lYaXH311Tn//PPHPdzR1Frz0pe+NNdee22uvfbabN26df9ra9acM2r7zt9vjjyytYRu38Kc3bt35Z3v/F/53veuynXXXZ1Nm9bvr/v0pz89T3va05I8eLRHpzlzBvrrzBy9tupKWhtodrN4sW0iAAAm2Ioe2/DevX59aq3jOhh+Nto7PJyaJLXm9nvvzc133ZW/eMUr8qq//uv83rvfnd+/4oo846KL8vof+7E89pzRf5/Y/9wuq1UAAGYjwQmz15YtSdsBhyPVG87OlOzNiSeOvKZvQ8f2TLfeeushCU5Wr16dt73tbUmS7dv35l//5fv5/vXfyZ133pjFi0f/O3YGJ6W0Vp3sO6Bpzpy5+djH3pH777/zoLZXXXXV/uDksFtxMmdOa7PMvXsPfq3XqiYHuwIATLgVxxzT9f7OXbvyxiuuyBOe/OScvXbtIR7VNDY0lB/93d/Np66+OhuaVfIfu/rq/S/XWvOhK6/MOatXZ2hoKOeuWZMlfayatuIEADhcCE6YvTZt6js4KUkWZHue+MQX5z3veWPfXdza65T1SfT97w9l1XFnZdVxZ/XdptvvN+2HwJdScsYZF+XLX/7gQfW+8pWv7P/zYXfGSSmtVKjH4ZhdCU4AACbc0T2CkyT5n+94R147b17OfuYzD+GIZoC5c/eHJt0csXhxPvX1r+eP//mfkyQrly/P4889N+euWZOnXnBBHvWQhxzUxhknAMDhwhknzF79HhDf/DIxPzuyevVD89rXvjPz5rV+IZg/f2F+7Wdfm8ed2/0ckVtuuWVChtqvLVuSu+8erM2CBd230po//8BA5cwzL+ra/qqrrkrS+lTad7/79bz//W85qM6sDU6Skbfr6kZwAgAw4VYce2xe8pSn9H59xYpDOJqZ4SWXXz7i6xu2bMlXvv/9/eV71q/Pe//rv/Jb73pXHv2qV+WCV7wiL/y938vr/+Ef8o8f+Ui+8J3vOEsGADhs+KmH2WvA4GRBtidJnvKUl+TSS1+QW2/9Xk444bQcs3BPrr3p5iTXHNT0UK84ue++3q+dkpty7/wTs2XHgdP6uOO61y+ltSBnXxDTKzhZt25dfuInfiJf+tKXcsMNNyRJLr74WTnuuDX768zarboSwQkAwDSwaMmSvON1r8t/fPnLub/Llqm9zkA5nD3+2c/O2/7kT/IHb3pTbli3buD2X/3BD/LVH/xgf/m8007LZ37iJyZyiAAA05YVJ8xe21tBSGpNbr01+eY3k+9+N2k7WD3JQcFJ0lppctppD8/ChUuyMUfkqJWnHvT4UsoBh7QfCr2yoEXZkrW5Pg8/bXOWL2/dKyU58cRkzZrubZLsr5skZ5xxYc967373u/eHJkny+c//6wGvz+oPng2SCs2fP8tTJACAKVJKdiVdQ5MkOXrlykM7nhniR3/2Z3PVNdfke9demy9+8pP52L/+a45cunRMzzrtlFMmeHQAANPXbH67k8Pd9u2tQ72/+c0D97daty551KOSRYuSPXuSbduStLbq6uWii56exYuXZeXKk7Jq1clZufKkPPaxJ2TNmkP7Jnmv4OTo3Jeh1CxcPJQLL0x27myFGUOjRKPtR8AcccSKPPe5r8yxx67JmWdelBe+8BF5znMuy9Vth0ju88lP/lNe8IJf21+e1cHJICtOrDYBAJg09/YITZLkaCtOeipDQ1l5wglZecIJSZLHXXBB/v0znxn4OU976lMneGQAANPXbH67E5J77jn4UJC9e5PvfS95xCP2rzZJDlxx0um88y7NeeddesC9PXsmdKSjqrV3cLIkzQvNaod+3+tfurQVruzd2yq/8pV/uf+1PXuSH/3RH+0anFx//dfzL//yp3nBC349ieBkP8EJAMCkWTA8nDf9wi/knvXrc8+GDblnw4bc/cADuWfDhqw49tipHt6MsfqkkwZuc9oJJ+S5P/mTkzAaAIDpaTa/3QnJ7bd3v3/ffcmOHX0HJ91sH6z6uG3d+mDA0Wlpmk/fDbhN1NBQsmxZsn79wa+tX58873nPy+tf//qubY8//rT9f+52+PysMci/6eLFkzcOAIDD3BFLluTVz3te19ceOP74QzyamWvNCHv5Pv688/LZa67J8Lx52blr1/77v/Obv5nhBQsOxfAAAKYFwQmz2/33937t7rtb4UljpK26ujnUwUnvs+5rFqcJgMaQYBxxRPfgZMOG5JJLzsyjH/3ofOlLXzrgtWXLjs7DH/7EJK1coZSBu505rDgBAJj+Rtujlv0ee8kl+bOXvzynHndc1h53XNYce2wWL1x4QJ0b7rgjr33b2/K+//qvXHbBBXna858/RaMFAJgaghMOX3fddcAeU/OyK0PZk73pL3zYMVjOMmZbtmzJwoULs3lz918GF2Vr5mRv6+8yhgSj/ZyTdtu3t/6Ob33rW3PJJZdk48aNSZK5c+fl537uT7N48bKmPHCXM8sgwYkVJwAAk2b30qWZc999Uz2MGe+Mc87JRaP83nDqccflvf/rf+Uz3/hGFj7iESmCKQDgMDPb3/KE3tavP+CTaSWtVSfbsqiv5tu3t84dmezVFpdffnk+8IEPZOXKE3LUUSdmxYoTs3LlibnggqfmgguectD5JoNavrz3axs3Jueee25uvPHGvOlN78zdd+/JRRc9I6tXP3R/HcFJY9GiWb5nGQDA1NqxcmXmdwlOdhx99BSMZubaO8AHgx757Gdnx6pVkzgaAIDpaba/5Qkj6zg0ZF52ZVufTWtNdu5M5s+f+GG1u+2227Jr166sW3dT1q27af/94eEFBwYnY0wwhoeTBQu6bz22cWOycmVy1FFH5ad+6ldy000H15n1wUm/gZTVJgAAk2rPokXZtXRp5m3atP9eHRryxv6gRlk9suXkkzO0e3d2HXFE9izq70NlAACzzWx/yxMGsjhbsjE99q7qYvv2QxOcdLNixYlJMu4VJ0nrgPhewck+u3d3bzvrg5M5c1q/XHaEbAfxSyUAwOQqJZtPOy3z77478zZtyp7587Nj5crs7Tifg9HtnTcvQ22Hv7ff37ly5RSMCABgepntb3nCQJZnfe7I8X3X37699xkhE2H37t254447ur62cuVJSZKlaT5xN44EY9my5O67D75/333Jpz/dWpWyrcdSnFkfnJTS+gfoliy1E5wAAEy+oaHsOPbY7Dj22KkeyYy286ijsuCuuw66v93qHQCAJIkT3pgdJmibpGNyVxZ0bNY1nB0Zyp6u9bdvTzZs2JCdO3dOSP+d7rrrruzZ073vlStPzJzszoI0b+iPc8VJL3v29A5NksMkL+jn39ZWXQAAzBA7VqxI7diya++cOdm5YsUUjQgAYHqZ7Z8V53Bx5JHJli3jfszc7Mkjy1dyfV2TTVmapdmUk3NLvpuz8kCOysaN9+e6676WH/zg6lx33Vdz441X5+abr8tnP/vZXHrppRPwFzlQr226ktZWXUuyOfvPph/H0o+lS8fcdMTD5WeNfg7QPCwSJAAAZoO9CxZk05lnZsG6dZmzbVv2LFyYbSeemDrrl5MDAPTHT0XMDosXt97cnoCVH8PHr8jZt3/3gHsLsj211lx++enZtOn+g9p89atfzaWXXpq77rorf/zHf5w//MM/zLxxrADZ57zzzsvVV1+dz3zmttx00225555bc889t+WBB+7K0qVHZkluf7DyOPqbN6/1vv/WrYO1K2Xk1SqzxmjBydy54/r3BwCAQ23PokXZctppUz0MAIBpSXDC7LBwYesab3CycGErCbj99gNuL8j2lFJy2mmPyNe+9smDml199dX52te+lmc/+9lZt25ddu/enb/4i78Y31iSLFy4MOeff362bXtEzjuvHPT64rStshnnp8OWLh08OFm2rHV2+qw3WnCyaFErRQIAAAAAZjxnnDA7LFjQusZr5cquAcS+c0ROP/38rs0+97nP5RnPeEbWrVuXJHnzm9+cK664YvzjSVJrsqPHueTzs+PBwjiDk7GsHDnyyHF1OXOMtprENl0AAAAAMGsITpgd9q04Ga8VK7oGEPsCitNPv6Brs1tuuSV33XXXAfde9rKX5dprrx33kHbtSvbW7qsZ9h8Mn4x7q6ixBCeHxfkmyegrThwMDwAAAACzhuCEmW94uLVfVD8rTo49duTXly8fccXJaad1X3HSzdatW/O85z0v69ev77tNNzu2156vTeSKk7EcEH/EEePqcuboZ6suAAAAAGBWcMYJM9++wKSfFSfHHZds3550CzOOPTYZGhoxODn++LVZtGhphocX5PTTL8hpp52fiy9ek1e/+mVdu7vpppuyefPmLB/H0oztG3ckOTgUKtl7YHAyzhUnc+e2Fk5s2TJ63SRZsuQwOg/dVl0AAAAAcNgQnDDz7QtMRgtOVq5Mjjoq2b27e3By4omt/+0SnMzNnszJ7mRobt71rhuybNnRKc1h4N/73rt7dvmpT30qJ+577hjt2Lgz3YKT4ezMARt4jXPFSZIcfXT/wclhs9okseIEAAAAAA4jtupi5mtfcbJkSfc6J5yQnHtuUkqyalVy0kkPvlZKcuqpDx7Y0SOA2Lfq5IgjVuwPTZLkmc98ca688sq85jWvyerVq/ffX716dS655JKx/q32275lz4jj2W8CgpOTT+5/FclhczB8MnpwMmfOoRkHAAAAADDprDhh5tu30qSU5PTTk2uvba0qSVpbb51xxoOrSfbVO/PM1r0dOw4+WH5oqFWnHni2yIJsz5YcHMzs2FFy0UUX5aKLLsof/dEf5eqrr86//Mu/ZNWqVQcELGPV64yTg4KTofHnoAsWJI96VHLjja0dzZYvT269tfXP1G7OnNbincOGYAQAAAAADhuCE2a+9tDj6KOTiy9O7rsv2bOndW7J/Pnd2y1e3Lo6ldJ6o3xf+NI4KKhobG+7XUrJhRdemAsvvHDQv0VP23d2D0QOON9kwcFbeY3V/PnJWWc9WF6+PLn66gNzpDVrRl+EcdiYgJU+AAAAAMD04R0/Zr7O0GDhwgNXmIzF3Ll9Byf9ngmyc+fOfO9738s111yT888/P2efffaI9d/3vvfls5/+dLL7xBx5zOk55pjVOfbY1Vm69KiUUg4czyQu/1i+PHn0o5PbbmuFJ/uOijnsLF/e/WyctWsP9UgAAAAAgEkkOGHmm8DVFvt1WUWwON0Tkm3bkl27up8N8nd/93f57Gc/m2uuuSbf/e53s7sJY974xjeOGpx8/OMfz1vf+taD7j/2sT+SN7zhXx9ccTJvXutwkkm0aFFrx7PD2qpVBwcnpSTHHDMlwwEAAAAAJofghJlt3rzJOX+iS3CyLBt7Vt+06cBVGFs378kdN27P/3n3+/Ppz334oPrXXnvtqEO4+eabu94/6qjjkiQLjl6SLFucHH/8gduVMTlOPDHZvDlZt65VHhpKzjnHnmUAAAAAMMsITpjZep1fMl5dgpP52ZF5Q7uza+/Br+0LTmpNbr5uV66/eU5qFueY4x+eZOTgpNaabdu2ZdGiRQfUuemmm7oO7ZhjTmmN5+xTk0lYbEMPQ0PJQx7S2ppr27Zk6VKHxgMAAADALNT91GmYKSZrpUWXN8RLkqVzu59zsmlTsnNn8vWvJ9fdPC+1mVpr1pzbtf43v/nNXHHFFXnpS1+ak046Ka95zWsOeL3W2nPFybHHrk5JnbTMiFHMn98670RoAgAAAACzkhUnh7FSyqIkj01yYpJjkqxPcnuSq2q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+     },
      "metadata": {
       "needs_background": "light"
      },
@@ -2934,11 +448,13 @@
     "replaced in that region.\n",
     "\n",
     "This isn't precisely true in detail because of the filtering operations, which 'smear' information a little.\n",
-    "Thus in the toy implementation, we expand the masked region by a few (specifically 5) extra pixels to\n",
-    "ensure the new field $\\vec{f}$ continues to match the reference field $\\vec{a}$ within the region, on the basis that artefacts don't really matter when the external field is random, but you might want to match the internal field very accurately.\n",
-    "\n",
-    "Probably a better solution could be obtained by going through the geometry of the fields, as we already\n",
-    "did for constraints/modifications in the Appendix of Stopyra 2020. As yet I haven't done that."
+    "In practice, the zoom region requires us to splice the high-resolution whitenoise, $\\mathbf{n}_H$, and the low-resolution whitenoise $\\mathbf{n}_L$ separately.\n",
+    "Because the final high-resolution density will be given by (see [Stopyra+2020](https://arxiv.org/abs/2006.01841), Eq. A.18)\n",
+    "$$\n",
+    "\\mathbf{\\delta}_H = F_{WH} C_H^{1/2}\\mathbf{n}_H + m^{-1/2}P_W^+ W_P F_{PL} C_L^{1/2}\\mathbf{n}_L,\n",
+    "$$\n",
+    "so that we need to splice $\\mathbf{n}_H$ with covariance matrix $F_{WH}^2 C_H$ and $\\mathbf{n}_L$ with covariance matrix $F_{PL}^2 C_L$, i.e. we filter out the low-$k$ modes when splicing the zoomed region, and we filter out the high-$k$ modes when splicing the coarse region.\n",
+    "Note that because the coarse region will then be upsampled to the high-resolution, we need to add a buffer region of size $m$, where $m$ is the resolution ratio between the high-res and the low-res."
    ]
   },
   {
@@ -2996,2027 +512,34 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "</pre>\n"
+      ],
       "text/plain": [
-       "(-156.00513494576094, 156.00513494576094)"
+       "\u001b[1m(\u001b[0m\u001b[1;36m-156.00513494576091\u001b[0m, \u001b[1;36m156.00513494576091\u001b[0m\u001b[1m)\u001b[0m\n"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/svg+xml": [
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     },
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hogqyZw9w333ipW3RIuD97wfq6kq9VUREREREVAhWihARUfXRdSCRMB+XrXiwa59VITNFtJ4e02G9rQ2ZyZPzPj+9sdF8/sPD4vYlIqoAzz6by3o3bgT+93+BgYHSbhMRERERERWGoQgREVWfZNK6cB+Pi3+DHrSeyYQqJNCkHjB6U1NBuz3rDQ3SEbqYQ0JEVAGOHzcf7uwEXnqpNNtCRERERETBYChCRETVR64SAXKVInYzRQqp+AjRXBFN2sVZb2yELs0F8UNvarJeBltoEVGFkDstAtaghIiIiIiIygtDESIiqjy6DgwN2QcZqkqGbKVI0O2zgFC10LKEIgVWiqCuDohIHycYihBRhVC9fIfoJZ2IiIiIiPLAQetERFRZkkng178Gtm8HJk0CPvxh8a+RXCkSjeYqRAoctJ5evBjRDRvU5xEGcjP8xkbohYQY0Sj0ujpTWy5teBjhaRhGRJS/bC5uFKaXdCIiIiIi8o+VIkREVFm2bhWBCAAcOwa88or1NHKlSLZKBCi4UiS9dKniyPCsoFkqRRoaCpspEolY/56VIhRCqRTw6KPA974HPPAAR9+Qu0xG/fIdoo6IRERERESUB4YiRERUWR57zHz4zTetp5ErRYwzNQoYtK63tiIzbZr1FyFaQVPOFDGGQn5Fo9BrasyXodq1mqjENmwAVq8GenuBd94B1qwp9RZR2Nnl2SHKuYmIiIiIKA8MRYiIqLLI8y1UnCpFChm03tpqrTQBoIVlBU3XlaFIQTNFolHrdQ7L9SUyePhh8+EnnyzNdlD5sMt3+RJHRERERFTeGIoQEVFlUYQSFk6VIoW0z2ppUYYyNStWiFknpTY8bFnN05uaoBuvv1+qUISVIhRCISrYojLBUISIiIiIqDIxFCEiosoSdKWIn0Hrra3i8qVtiOzcidjLL7v/fZEZh6GPKXCmiGlIfRZDESKqAHYvZQzYiIiIiIjKG0MRIiIqnpERMfT8+PHxu0wvoYifmSKpFKDr3lbBWlvFv4pqlZhq4Ps4s7TOqqsDYrGCKkX0SAS6dJtpXDEkogrAShEiIiIiosoUcz8JERFRHoaGgP/5HxGIxGLAbbcBJ55Y/MsttFJE1X4rnfZeKQJAj0ahlbJdlq4DfX1AfT1gHILe328+XWOjOHmhlSLybc5KEQoZXS/1FlA5YihCRERERFSZWClCRETFsXp1rkIklQIef3x8LjefShGn9lmA2H4vIUe2UsTLNhRLMon4r3+Nuv/6L9T+6EfQjhwZ+5VyyDpgrpTxi+2zqAzIOSiRFwxFiIiIiIgqE0MRIiIqjtWrzYc7O8fnclWBhNzOSV4hNYYCxsqKrFRKVF64aWmx34ZxEtmxA5HduwEAWm+vaZaJPFNEb2gQ/zGGQn6pQhGuGFLIyEVSWaUs6KLw40wRIiIiIqLKxPZZRERUHJpWmstVtb9KJs3Bh1OliCoUSSbtV1WN6uqARMJSkTG2aW+9hdgbb0BvbUXy6quhZytLAlTz3HPmy9y8Gdl1X6231/S7sUqRQu6raBS6dJtrDEUoZOyevsPD6qc8EcBKESIiIiKiSsVKESIiKo5ShSKqKg05BHGqFFG1zxoZAWyCDpPsdbYZYFDz5JPQuroQ2bULsWefdT+/fKi2f1TE0EoLAPSJEwu+OD0SsQZRbJ9FIeMUihDZYShCRERERFSZGIoQEVFxhCkUkXvkOFWKaJp11/GensD7pUQ3bw70/LJ0u1AkmYQmtTDLTJuWO5Dv7vKRCNtnUegxFKF8MBQhIiIiIqpMDEWIiKg4ShWKqC5XDkWcKkUAa0CQHRhvvIw5c8zHZeeJ+FGMFVlV+zAA2uHD5goWTYNuCEUS116b3yyUaJSVIhR6DEUoH04zRWwKAomIiIiIqAxwpggRERVHqUIRVUWHXBniVCkCWEORri7z4cZG4JJLgJ/9LHfcDTf42UoAgNbVBX3GDN9/58gmFIkcPmw6rE+ebLqemYULMTJ5MrTeXugtLai95x7PlydXp3CmCIUNQxHKh1O+m8nYvtwSEREREVHIMRQhIqLiKFUoolqQD7pSpKkJmDcP+PCHgV27xP/nzRsLZFLnnIPY66+7bqrW3R18KKJqn5VKIdLRYTrK1DprlD5lCvQpU/ytFKsqRRiKqKXTiOzaBb2xEfr06aXemqrCUITy4RSKpNMMRYiIiIiIyhVDESIiKo6whiKZjDUkcasUkUOR5mbx78knix9J6oILoCUSiL79tuOmRrq6EOykEkBXrdIlEqJ9lkGmvd3+TOSQKHvezc3Q+vqsl8f2We50HfFf/QqRAwcAAMmrrgIuvbTEG1U9GIpUh+zLezwezFuQWyhCRERERETliTNFiIioOMIaisitswD3ShG5fVZTk/M21NYiefXV0BsaHE+myWFLEBSVIloigcixY6bjdEWlSO4P1PddZuZM65GqQesMRSwiu3aNBSIAEHv55RJuTfWxC0WGhsZ3O6h4enqAH/4Q+Kd/An7+c2tBYD7c2mcREREREVF5YihCRETFEaZQxBiEqEIRt0oRefXLLRSxOx+JJoctQVANSx8astwuboGNit7YaD1SMVOEq4VW0c2bTYc1u1V6Kgq7BXKvlSJHjohOeXxoh9frrwPZgridO4GNGws/T1aKEBFRudqzB9i2DTh0SBS99/TwcwwRkRHbZxERUXGoQhFdL35Y4lYpoloddasUkWXbZ7mRwxaJ1t3t7Xz8UHzb0VS7w6tmjzixuy6K9llaEJUiySQie/dCnzAB+sSJhZ9fqanuA10f/+2oQqqOeVleQpE33wQee0z8/6STgI98pHSZL9l76SXz4YcfBpYtK+w8GYoQEVG5WrEC2L/ffNwXvgC0tJRme4iIwoaVIkREVByqVcPx2D3Jb/usWMxaXeEWinisFLFUUEi0oSFgcNDTeXmmuP6qUERXVZQYz0ZaTUxcfbX6hMWYKZJMovbHP0b8/vtRe889iGzfXtj5hYA2MGA9km3GxoWqOCzLSyjyyiu5/+/YISpGqDowFCEionKl2g+Ms9SIiHIYihARUXGoQpHxWEVya58lf0NQDRUfp0oRANB6erydl1eq4EkVvKgGshukli+HPmUKACC9aBEyCxeqT6iaKVLg/Rxdvz43byWdRmz16oLOLwyU1TpOq/UUmEJDEWkcD9auLWx7qHxwpggREZUr1f44nKVGRJTD9llERFQcYQpFjJUi8ipoEUMR3UsokkohyCZKmsdKEbf2WXpbG0Y+8QlxezoFKJpmrYgp8H6Ovfqq6XCkEnbNV90HIyPeAzbKm9PA7Xz2mMzmdVT5WClCRETlSNeB1lbrflGsFCEiymEoQkRExaFqzxSGUERenK6vt57eKRRpbBTfMrxwC1fkbQuC11DEpVLE1+mk+7rQmSJapX1jSybV14mVIuOikEoR1dgXhiLVg6EIERGVI00DPvUpYN8+cVjXgYkTgYaG0m4XEVGYMBQhIqLxU+xVJF13D0XkXaZU3w6cwowTTvA+ZbkUoYiX9lmapg6t8qWqFNH1/KdRq3btT6X8D4cPCa23V/0LhiLjopBKEVUo0tdX2PZQ+WAoQkRE5Wz27FJvARFReJXn6gIREYVfKdpn2TV5Ny4+y1UT+YQiHukeQpFCqyosVJUicigSjeYXWNj9jRxW6Lq4L7xWoxjZPUYGB4GWFv/nFwK2c2MYiox54YUX8Itf/AKnn346/vRP/xSRAEM7p1AkkxE/dhdn9/RMJt0zz2efBVatEg/bW24BZszwtr0UHpwpQkRERERUmRiKEBFRcagW0IMOAGR2C+pOlSJ+22fNnet9ezzMFBmPShG5fZZlBohHenu7+heq8COVyisU0bq71ccPDEAv11DE5jo5rtZXke3bt+OSSy6BPlqWkUgk8IUvfCGw83fLnlIp+6eq3UtKZycwfbr9eR49Cjz/vPj/8ePAihXARz/qvq0ULqwUISIiIiKqTAH2ziAiIjJQ9Z0p9iqS027dWYW0z6qvB6ZO9b49JWifpRq0brnOee6Fn164ELphMHjqvPMAALoq/MjzvtaOHVMfPzCQ1/mFAStFnH3zm98cC0QA4Itf/GKg5++WPeWz8N3Z6Xyea9eaD+/apX5JpHBjKEJEREREVJlYKUJERMWh6i1S7FUku/MPqn3W9Om+2k6Fpn2WfJ3znc0RjWLkox9FdP16oKkJ6cWL7c8vz+uldXWpj5eDnTKi9ferf8FQBADwyksvFfX8vVSK2Mk3FJGfcgDQ2wu0tjr/HYULQxEiIiIiosrEUISIiIojTKGI3/ZZdqFBU5O/7SlF+yzVbSDvop7PrI+spiakzz3X9fy0dBr57BgfsQlFLPdbObFblWcoIrhNOy9QIZUidr/r7fV/nseOMRQpNwxFiIioHO3cKd6nmpqA5maxD1g6Lb52qPYHIyKqRgxFiIioOFQrRmEMRfxUijQ2+tocT7M7xiMUkRUSiqiorme+7bMcZoqUK83uPmYoIgT9HJAUo1LErRBK9TDu7ATmzXP+OwoXDlonIqJy9MwzwP791uOnTAE+/enx3x4iojDiTBEiIiqOMFaKZDLWvdL9DFr3GYp4qRQJvH2Wh8EF+Q5at6Vp1qAl3+tls4JdzqGI7ao8B62Pi2LMFMknFLEZl0MhxkoRIiIqR3adW1XtPYmIqhVDESIiKo4whSLZRenhYWto4KdSpAzaZykHrcvyHLTuKKBQxG77y3mmiO1twUqRcTHelSLptLq9FkOR4inWEHunl2eGIkREFEa6DvT1qX9X5I6lRERlhaEIEREVhyoUCboqQua0gpnJqOdSVGP7rKArRWC9rlq+vWXsbo8yDkU0zhRxpCtWtFXH5asYM0Wc/qanR71Iz1CkeIrx1pLJOLfIYihCRERhlEwCU6eq9+VKpYretZSIqGxwpggRERVHmCpFAPENQK4Zr6lRBwTl3D6rFDNFVOeZ7/Wy+TvNrg9AOWAo4tvIyAjq6uoCOi/n3wddKWIzFgfd3eLvipBJVr1ivLW4vYRxpggREYVRPA586lPi/4kEsGkTMGmS2A+sro6fQ4iIsvhySERExRG2UCSR8DZkHQiuUkQRiuh1ddCMtetB7q6VyZRmpghQ/FBkaEhcv2K0/ioy20HrnClia6C/P7BQZLzbZ9mFIrouhq23tztvD/lXilCElSJERBR28TiwdGmpt4KIKJzKb2WBiIjKA0MR5a5Y+tSp5iOCDEW83r7FCBbk65rnfW1bOaPr5TkdMpOxv49ZKWJrUDWUI0/jXSly/Lj97zZtct4Wyg9DESIiIiIi8oOhCBERFUfYQpGREeuien29+rR27aX8VljU1UE3Bi81NUifeqrpJIG2z/J6+47HTJF87mtdd1yJ1Hp6/J/neJMrdZxCLzZ1BgCoapsG7Mot8jDelSJdXfa/W7eueEPBq5lTlhr0eWYxFKFKo+s6fvvb3+L222/HvffeG+hsJyIiIqKwYfssIiIqjrCFIn4qRTQtmO2JRJC84grEf/97AEDy0kuhNzebTxN0+ywvijFTRK4+ySfscbkttO5u6DNm+D/f8aDriD39NGLvvIPMtGlIvP/9QHOz83VipQgAIKF4rAz29QVy3rpurRSJx803vdPLRj6D1vfvt//d8ePi97Nn25+G/HMKr+wK/9xwpghVm+eeew633HILAODee+9FQ0MDbr311hJvFREREVFxsFKEiIiKI2yhyMiI91AkQJlFizD8hS9g+LOfRfrMM60rdKVon1WMUCSI9lkuq5ARp75EJRZdvx6x1auBRAKRffsQe/VVAA7zRACGIqMGFLdDUO2z0mnrS5HcBS/ISpGeHvHjZMsW59+Td4mECJrsnmaFvOWwUoSqzRe+8AXT4a9//esl2hIiIiKi4mOlCBERFYcqFAmyVZSKW6WI3JLHTyhSSJAQiYxVUljaTAV4m2ged13WxyMUyed6ufyNFtZQRNdR8/jjpqOie/ciBTgGH1oyWbbD44M0qLiNBgIKRVTzRBoazHM/gghF+vtFGNLZaT6+rg5Ytgx45ZXcceXQBa4cdHQAP/85MDBgXw1SyMurW2bJUIQqTTweNx3ewgSXqCxt2yb+bWoSP42N4qNmtkutrouqWSKiasdQhIiIiiOMlSKHD5uPk4eeO6mtzW+bZKpKEV0PpmVXCStF5KAln5kibgFRWEORyN69luO0o0fFf9wqgRIJsXJepVKpFBKKx8pgf38g569a2A6qUiT7tN27F/jlL9UBzOzZooua0cCA8zaTNytW5G7LYlSKyIWFQZ43URjdddddeP3118cOL126tIRbQ0T5euopIPsxFADa2sTnlqEh8d517rnA1VeXbPOIiEKDoQgREQVn82bRG+aEE9QrRqUMRbq6rKuR06Z5P++gdqmSQ5FMRvwEEVSUcNC6vP2xF15AZsoUZBYs8H4ebjNFQhqKRNeutRyXGZ19orntbl7lociATUIQVCgiBxWaBtTXm49zCkWcBnhnn7bPPqsORAARisgFaW6L7eQukwF27HA/XSGVIm73E2eKUKW5/PLL8cgjj2Dq1KmYOnUqpkyZUupNIqI8yGPZ6uuBQ4dyh4eGxnd7iIjCiqEIEREFY88e4P/+T/z/rbfUpyllKCLvzV9TA0ycaH/65mbzt4olSwrbtlFy+ywAIgwYz1BkPNpnAaj5wx8wctJJ3ttDuVWKDA4Cw8OhCxG0ri7rkdkVUy+VIlXMLvwYCGjQumrIup9Ob25D2KNRYNcu+9PMmWN9CDAUKZxxD1gnhbzluFX0sFKEKs3s2bMxe/bsUm8GERUglRIflY2mTDGHIvLviYiqVXU3sSYiouDYBSFGpQxF9u83H542zbll1VVX5f5fXw+cf35h25alan4f1LB1j7dvUWaKKM5TGxjwtTuapX1Wba3lPgpjtYiyVdhoKOI4aB2o+lBkwGbAxmBAPabkp31jY7ChiJuJE9WVIrru/rdkT9GxTonts4iIqJqMjADTp4t9u7L7JE2ebD4NK0WIiARWihARUTC89DIpZSgic2udtWSJ+EZx9Chw6qnWnjv5UoQiWiqFQNZIva60FmOmiE1LLi2RgC4PcbAjrTLrtbVAfT207u7c+R0/Dn369Hw3szgUfXTGht67hR52fZeqhF0oMhBQ+6zNm82H580b31Ckrs56HqmUeFgENaaoGu3b5+10xWyfxVCEiIjCprER+NSnxP8zGRGAJJPAzJniM0l9fXBfaYiIyh1DESIiCsa0adYmtrIwhSLt7e6nmTtX/AQpGhXVD8YAI6BKEc/DzcdhpsgYP5UQ8u0Qi0FvbTWFIpGeHoSulb8qjPIaihSyalsBBnt71ccHUCnS12etFDn1VOuCej4zRbK/c5orEYmIDFSuFAHEgjtDkfyNR6WI/BBsbDQfx5kiREQUZpGIeO8CxLB1IiIyYyhCRETBaG11P02xF4CDrBQpFk0TK6XGxfJxbp9VjEoRzaYWX0skPFfByO2z9Joa6PLubPm0m0omEdmzB3pzM/Ri3O+q1dHR+8K1fVaVhyIDdqFIAL0dtm4151W1tSLjNPbVBsRdMDICPPUU0NEBLF0KnH22eKq6VYo49eXOdn+Lx8VTznheg4PAhAl5Xa2qNzgIGHJSR0FWirS0mEMRVopQJfn2t7+NHTt2YN68eZg3bx6WL1+OuUHvFEJEREQUIgxFiIgoGF5aN4WpUmTSpOJthws9FoNmWNwPrH1WKWeK2K0O+wkx5BXMaNRa1eI3QEqnEf/5zxE5cgTQNCTf+16kTzvN33m4UYUirBTxxC4UGQigUuTAAfPh+fPFw0nVPuvll4HVq3N/N3Om+HF6SiWTzn256+rEv5om9tQ0XlUOW8+fnwGxft4Ssm9h2TFG8n3U3GwO1BiKUCV59NFH8fLLL48d/v73v4+Pf/zjAICWlpZSbRYRERFR0XDQOhERBcPLClFYQpFoVN3TZrzIc0UqoFIkc8op6l8U0j6rpga6dFt5bhE2KrJ9uwhEAEDXUfP4477+3hOnShG30KPKQ5FBm9khQVSKyA+9bA6qCkWef9583IoV4l+3ShGnzTS2x5JfbgKaI1+V/LxcplLA7t3AT38K/PrX6gqT3buB//gP4O/+DvjHfwQeflj8nXwfNTebDzMUoUqyc+dO0+HPf/7zaG1txQ9+8IPSbBARERFRkbFShIiIglFOoUhzc2534FIoVijitcl9EUKR9Pz5iE6fjojUm0jzE4pI959eU2PdVp+3VXTbNvMRXofR+6G63bOXw0oRR3YD1YMIRVSFR4C3QetdXeJft1DE6e7LVooA1lCElSL58/MSMDwMPPKIaI8GiKfjH/1R7ve6Djz4INDTkzvvt94CZs2y3vdyKMKZIlQpBgcH0dHRofzdkexOBUREREQVhpUiREQUDC8rRGEKRUpIl1ZlXasJPCrpoPW6OiQ+8hHo8m3rIxSxzN9Qtc/ye1sV47rKFEHL2H3hdv2DCsTK1EBfn/p4Pz2SbMhPh+xDwctDKnuXug1a99I+C2AoEiQ/T5n163OBCADs2GH+/eBgLhAx2rXLehwrRahSpdNpfPvb38Ydt91m+R1DEaLysmmTmKl28KBo2yl/PdN18T7K9zAiIlaKEBFRUMqtUqSUKrBSJHu+mfZ2RA0L3b4qReQV6Joa623l8zGknJ+STgd6G2gOM0U4aN3ZoE0fqSAqReSHip9KkWwoElT7rMZG8+8YiuTPz8ulKnNLpXKPAbs2ZsePmw/HYuaQC+CCElWO5uZmfOlLXwJ+9jMs6uzE5558cux3DEWIyssTT5hnmH34w8DJJ4s2kkePis8t6XTueCKiasZQhIiInKVSwP79wIQJQGur/elYKeJdiWeKFGXQelY8bj5cSCgSi1m2dSxkSCYR3bIFel0dMiedZN8OTXVdh4etq9SF4KD1vNkNVA+iUsRr+yzV0yZ79xUSijhVinCmSP78zhSRjYz4D0UaG60vJQxFqOLs3In3nXwyTpo4EVMbGzGloQFT//EfS71VROSRrgNyV9KmJvHvwID5dwF8zCIiKnsMRYiIyF4iAfzP/wCdnWIh/8MfBk48UX1aLytExV4ALpNQpFjts0o5aD1LN+4eD3+VIvLtoMdi1gAplQJ0HfFf/hKR0R7oqfPOQ+rii9VnqmifpSUS0McjFNF1dUsw4/3EUER5/KCx51Ge7NpnyQ//8agUYfus4Ph5yqjuv0Qil4nahSLy/dPQwFCEqsA11+CkJ57ASRMnisNz5wa7AwERFdXgoPUjaTYUqa83H89QhIiIoQgRETlZvVoEIoDYPXfVKvtQhJUi3pW4UqSoczbk6+anUkS+HWIxZa+jyP79Y4EIAMReecU+FFEJ+pug3WM/k7FcJ72hAZqxp0+VhyKDNunAoJ/HjQ2vlSKqp5/XmSJODyXOFCkOPy+XqoeRMW/zWrGjCkU4aJ0qzvLl4oeIylIyCcyYIVpHDgyIzzLZXFMORQLoUkpEVPYYihARkb1Vq8yH5Sm1RuU0U6Slpbjb4UYKDrQA9ooHbGZbqBSzfZZUKVJQKFJTY22flUpBO3DA8qc1v/0tMrNnI3322UAkYn+eELe3dTR6nnRdOWgdgHg8Stdfr683hyJvvin2xl240L4FWAUbsEkHBopYKSKHIqqLCqJShKFIcfgJRVT3rfE4uc2IHbbPIiKisGtrA+64Q/xf18Vnjex710UXAWefLT6b1NezCIyICGAoQkRETvzsUV9OoUip22dJoYzW1RXMGXudKWIMDQKmy4GPn/ZZ0vbr0ai6fZZCdPt2RLdvB+JxpJcty/1CdZsEWSniFERlMtb2WfKuepkM8JvfAOecA1x7bXDbVSbsQpEgKkW8DlpXKXb7rOFhcdcX8alYsQotrDM+tLxWijQ1We8rhiJERBRWmmYOPmbOLN22EBGFFUMRIiKy52fVh+2zPMtMmGA6PN6hSFHbZxUyaF1RKaLqdSSHJ6Y/efppUyiimtcSVGUOAOfHfSKhbJ+l9PrrwDXXVF21yKBNQDWcSiGTySBSQGog3/V2lSIqQQ9al7MwXRcVC/Lx5K7QUCSf9lnTp7NShCrT0aNH8aEPfQjz5s0b+7nxxhtRI++QQERERFRhGIoQEVEwyqVSJB63tngaZ/qkSabDWl+fWECXAwW/QtA+S5eug59KEcsqtioUSaedew/JjwFVZUmQlSJ2rbMAaL291pO3ttqfVzpd3MAqhAYcUoXBgQE0FRBglrpSxBiKGP+fNTTEUCQfpQhFZs60Xm4mIx4nVZZjUoXZvn07Vq5ciZUrVwIA4vH42KwnXdfR29uLaDSKpuy0ZiIiIqIKwaJ9IiIKhtdKEa+rUPnwMrS61EPWAegTJlhW0gKpFinzShG5qkOPxaBL26qlUtC8DgIA1DNFAmjNNMbhcW8JRWIx6E5NnAtd7S1DdpUiAHCso6Og8/Y6aF0lnRZ3bSGD1o3Za02NNYvkkNP8BBmKeHkpqasDJkyw3n9O44SIysXOnTtNh+fOnYu77roLs2fPRl1dHdra2nDPPfeUaOuIiIiIioehCBERBcPrgvy//zvwxhvAW28Fvwjs5fymTw/2MvNRU1OUuSKeB60Xc6aIXIXj5z5W9TuSV7BTKWg+gjVlq61xmiliGqgOiEDEqSVJkGFNmRhyuM4PPvBAQeftddC6nUTC+WVtaMg5NDFWh2iatSokyIdhNRnvmSIzZoj7T/WyyRZaVO7kUGReJoPul17C/v37kRh9shw5cqQUm0ZERERUVNXVo4GIiLzzu0DrdUF+eBh4/HHx/zVrgE98wt/lOJFXKBctAjZuzB2eNAm49NLgLq8A+sSJ0Hp6xg5Hjh+Hx1vQnpcVulisuP1e5EHrIyPee8zIq52xmDJEULWlslXCmSLydupNTc4r8tVYKeJwX/z83nvxuS9/Oa/z1fXC2mcBoqLA6SklZV4Wcj5YX2+uTGClSH6CqhRRjPxRmjFD/Kt63KTTzjknUdhdddVViMVi2Pnww9h15AiWz5yJTqlFJUMRovKwfr0o2G5qyv2oAn1dFx+P+f5FRNWOoQgREamp+oo4VRjks8vsvn3A4cPAtGn+/1ZFXgA/7zxg3jzgyBFg1ixg8eKiztPwIzNxIiK7do0d1o4dK/xMvdwHxb7+cvssP9+85O2vqYGu2N5CQxGUKhRpbFRenzFVGIo4VYqs2bIFG9avx+LTTvN9vqqnQnZROxIRP245rlsoYsg0leRFdLlShKFIfoIKRfzMEwHUL52sFKFyt3z5ciw/80zT++LfPf+86TQMRYjKwyOPmPdpu/124IQTxP+7u4Ff/ELsmzY0JN6/vva10HwtIiIqCYYiRESkploxcvrknO/q0PHjwYQi2cV3o5oa4KyzCj/vItAnTjQdHq+ZIo6L8gGQB60DEIstHkIRTVrt1KPRwuefKG4TLcC+RZrToHW5fZZbpUg1ts9yWeFe+cADgYUixod+LOZ+c7uFFm6hiFwcJQ9bZyhib/du4JVXxAioK64w33ZBtc/yGopMmSL+ZShCFau723Tw1sWLceb06Zja2Iipy5Zh6sc+VpLNIiLvEgnr55qmptz/YzGgs9P8++FhwGnUHRFRpWMoQkRULo4eBdatAxoagIULgba24l6eqlLEaddqr+2zZHKPmXxlMtapt8UcKF4gSyjitsLqhZf7YLwrRSDCDtd5xHahVj61/en02PWUgxYAJasUAdtnmei6jkHpG/zctjbsNizQ9R44kNd5qwqE/IYia9bkddG2OFPEm6EhsTdr9j5MJoEbb8z9PqhKES9D1gERzAAMRaiCTZkCfOlLYieVP/wBpwI4ddEisVo6d6743ElEoaZ6TzOGIvKOGQBDESKi8K4WERFRTn8/8MMf5laJVqwA3vMeYPnyYC9neFisGLW2qj9dp9P28yHyXR0KalVJtQoa4ma5uvTtRLl471cYQpFYzNqbaHT1ObJpE2Kvvw69uRnJK64AjMPmVfdfLJbf9iYSuRVo1UyR8Rq0LvVl1xsbGYoASKfT0HUdmUwGGSnIbGtoMO21nMgzwHJqnyX/387atXldtK1qb5+1aZN466qtBa67LjerQ/bGG+an7dq15lDEabi9F9mHlPT0VKqtzeX2qpeiQreFKDTq68XPxz9e6i0hojyk0+J9tb9f/MRi5v3OYjHxY3zfqrbPIUREMoYiRETlYPt286fYTAZ4+mlg2TLlnvl52bED+M1vxIrR6acDUiWD6bJVq0P5VooEtapkt6geVvK2pVLeB5Lb0LwOWi8mTYMej5uCB+3YMeiNjYg/+qh4nBw6BESjSF5/fe7vFIGAnh0KL3+Lc5NMOoYigbap8vG4d50pUgXts1555RXccsstOHDgAD71qU9Zft8q7ZGcyPM28dI+q5guvth6XDWHIskk8OCDuYf4Y48Bd9yhPq3beKWgKkW8vKRkq0QAkfVqmrkgMZ9MP5EQ29DUVNDLPRER0ZgpU3Lvq7qu/oxx881if7G6OvGZxLhvEhFRNXKYmEtERKGh2ls6mQSkmQUFee653OWsWwds2aI+nWoVSNW6ykhVs50V1N7x5RaKqKpYCg2IvMwUCapdmRMpqIs/8gjiv/udKUCIbtpk/huHSh/dZ8WPZni+qIIibWTE+fHqh59QpEjts3Sb69LV1YUbbrgBU6dOxR133IFkCCpRvvKVr+DAaEusH/7wh5bft0l9HPINRby0zwrajBnA0qXA5ZcD73639ffVPFNk61Zz5nfwoP1T0O0uD2qmiJenrrxgJGeafkORAweAf/s34LvfBf7v//Lfl4CIiMiOpqm73p16KnDSScDMmWLftzB/TSIiGg8MRYiIyoHdYnlQC7sAsG+f+fChQ+rT2YUiTpx2RQqqUkS1UhbiT/vKioFCV/s8rNBl5swp7DK8UFy3yMGDjn+iOYVafltoGVdVVbeprgc3V8RnpUiQocjGjRuxZMkSNDY24qtf/arl9//5n/+Jhx9+GEePHsWPfvQj3H///b7Ovxiee+45x9+3GhtgA0jk+ZyQnwqRiPjJKkZnvUWLRJuniy5SP2SreaaIn7eNYoci2ae+l0DDWCkCWJ++fkORF1/MdabcsgXYtUucx9tvA2++WRXFYhQi3/nOd3DVVVfhzjvvxHe+8x28+uqrpd4kIiIionHDUISIqBzYrbyUYjdT1ba4rQw59QkpVqWIpplXQcOmRJUimRNPLOwyPJDnpdif0BDqqe6/7Mqy30qR7GMqk7F9jsRefz2YUNHrc1DTgMZG0RLMjs8V0a985StYv349hoaG8A//8A944YUXTL//+te/bjp81113+Tr/oHmp+miRQ5GAKkXkkCKoroNGbsNKq7l9lurl3+7lTnWXZ59muj6+oUjQlSKbN5sPP/888PDDwEMPiZZiv/iFv/MjKsSrr76KFStW4Ic//CG+9KUv4eGHH7acRtd1dHd3Y9DLEB4iIiKiMhLi1SIiIhpjt3pULqFINGq/YlismSLZeRRhpVgcV1ZL+OHh8ZCZObOwy/BA99qk2Pi4kVc6jfef34qf7Kqqw+0Ze/llRLZv93e+Kh6DFb2hQYR0TlUvPld75QWsz3/+846n7+3t9XX+QTt69Kjj7+tiMdRJyUFQlSLyQ6gYoYiqVYVRkKHI9u3Af/4n8MMfAvv3538+40X1Umz3tqEq4sqe1iHn9CydFi8NXs5HrhQpNBSRJZPmosy9e4Hjxws7TyKvdu3aZTo8b968sf9/+MMfxqxZs1BbW4sJEybgN7/5zXhvHhEREVFRMRQhIioHYQ9F3LYjEhHVIirFDEXCLBq1rhQWeFt4ClX8tqLKQ/qss7yd0LBLuDz7wzhHxHE4uYLmIRQBgIi0IJQPzWsokg0FnapeHAKAl18Gvv1tsRDe0QFkFM+5NWvWKI8PiyNHjjj+vj4WQ1yqMgoqFJEfQsUYreNWKSIXUCWT+T3l02ngd78Djh4VC+oPPxxsJ8Vi8BOKqCpFsrdTUIWFiUR+7bOCDkU0DejuNh9X4uySqoSu69i5c6fpuHlvvw3cdx/Q2Ylj+/bhwIEDY7Oo3F6/iYiIiMoNQxEionIQ9lDEbWUoErFfMSx0laurS+wqLa+kFWNoQJA0zRrcBDxTRK7YSF57bWHn71Fm9mwkbrzR9XSa0+wP4+qj3/vSYyiiBdEOxOtzMLsKn8dMke5uYMUKMYPi6FHx/+M2u5O//vrrjpuRbzuqILiGIjU1iEtpRVDts4KqFHEKPvy2zwLymyuybx9gfOgePRrciJxiKTQUyZ42qFBkZCSY9ll+Qi278Uby46aaZs1Q6ei6jh/+8If4hz/7M3xi2TJcOncuFsTj4jPVo49iqpTOMRQhCrd33gG2bgUOHBCfG93e43S98GCfiKjchXw3WiIiAlD8Qet+ziffShG7vf0LqY546y3gkUfU2x/2ShGIagjNsFKmpdMo6B6VbsvUeech9sYb0Lq6kF6wAOnTTivk3H3RJ01yP5Fx9VN+HBiDEIf7cvhP/xTxJ59EZMcOy/m6Vc5oQQx18BqKZFfhsxO/VX9nEwC89pr5Ib5jB3DI2HPH4MEHH8S55547tnevbM+ePViwYIG3bQ7YkYMHHX/fEI8jLqUViTxfH9wqRfINRf7oj4C771b/zm+lCCBaaNkV0dlRvQSXY6WIn5kiQVeKjIyMf/ssVdiRyYi2a8ZqEY5uoPEQiUTw//7f/wPa2wHj+/WECUA0iqlSP0CGIkThpevAgw+a39fuuAOYMcN8ug0bgGefFe9HQ0PA7NnAxz42rptKRBQq4V8xIiKi4g9a97Oyk+9MEbs9vvMNRTIZ4A9/sF8NLINQxLLCVuiKn/R40CdPxsgnPylu+2L0C3Kge1l1NrbPkh4HxoHktsPJYzGgudnUagswDFp3e2yNYyhiuj1iMfXzweb+V21mR0eH8rTr168HABw7dkz5+x07dtiGIr29QE8PMH16cZ4+hw8ccPx9fU2NNRTJ8zlRjEHr73qXWD+8/XbgJz+x/t7tPKNR8TQ0VnUENWw9xF3TbNmFO6qnbanaZ8mBlfy88PPWqbqvMxngkkvE9WpsFAHJhAnez5OoYHK/tgkTgHQaHzvjDFw+bx6mNjZi6rnnYsqHPlSa7SMiV8PD1s8Bqh01Uimgs9P8d0RE1awMVoyIiKjo7bOKHYpEIvbhRb6rXHv3OveMKYdQRG4LVeCKnyVYyM4tGedABICnlldaMpmrjFENWlf930BvbRXXT16Nzj4uwlQpYrgP9GgUih3nbe9/1dWwC0Wyw9Q7jd96DbbbDJfftQv41a/EJkybBnziE8EPI3etFKmtDSwUcRu07vcpccklwIUXiv/PnJnXJgEQ1SLFCEXC3gJD9TRJp8Xj7fnngcOHgdNPB+bPV/99MSpFvNxmEanRcNCVIsPDwMKF3s+DKHDXXQdceSVw/Lj4aWsDtm/HknnzsKSxUaysnnyyuv8fEYVCf7/1OFUoIlesBvUZhIioXJXBihEREYU+FPHSPuv00wHVgmy+lSKbNzv/vgxCET0WMy2Oy8PGfXPbPX48eZkD4tQ+y0MokhndpdquUsTt9tSGhkRYp+rt41U+lSJ2AaFNNZVqIdiufZZbKLLD2GbM4Mknc5dz+DCwZQuwZIl6M/N1xCbIyaqPx1ErzxTJ8/UhyEqRlhYRimQV8tIiX24h3QONwh6KqLYvlQJeeQVYtUoc3rYNuOUW578PslJEfuouWQKsX597ei5dav27QkIR1eLT4GDhL0FEBdE0EXjU1+d67cycCVx8cWm3i4g8y2REle/AgPipqVF/VpGzTVaKEFG1C/+KERERhSsUSaVEA/S6utwuR14qRRYtAp57TgzxlM/PL12viFAk0EHrmYx1sb2Ut0E0aj87Y5RxnorTTBE59Bg7vrVV/Efe7d/joHWkUuI2L6Qkwu9MEQCa3d/Y3P/qUCS/ShG7UETOK159tQihiNug9XjcOmg9j+fEzp3AY4+Zjytk0LpqFsiyZWKkUZbXvf0LWVR3EvZQxK5S5JlnzMc995z677NP5aBCpEzGepu1tQG33iqCmrY2sfO8LMhKkQ0bxONw3TpgwQLROouIiMivadOAT31K/F/X7TsWT5kC3HyzCEfq6lgARkRUBitGRERU9EHrflZ2fvUrcbl1dcAHPwiceKK3SpFYDLjzTuBnPwP278/9Lp8goLPTPJlWpRxCEXmxv5AVP9XflrJSRNOgx+PQnHZD8zhTxO566NlKEXmFO/uYkh5bekMDNGmKsTY05G3+iR2Pz0HTZfgMRVR3rV0o0tfXBwA4evSo8vd27bNkxei4dsQmqMlqqKuzhiI+nxO9vcDPf249vpBKEdWiwSWXiKqCZFLsaH3GGd7OK4hQRPWQK9dQRGbzsA28fVY6bb38SESEW04Bl3z/+Xl4Gl8Kk8ncdf3Vr8Rj7FOfEnv6EhER5cupa259PXDaaeO7PUREYVYGK0ZERBSqQevZFbnhYbFb74kneqsUAcRK5NKl5lAknyDALRABvLVvKjU5uCkkFFHcB3opQxFA3AceQ5G8Zoq0teUux0CzmynS0GCdRjk0BGQrTvJgW/UhM35DLWH7rIOKuR6qh10xQpHDNsPfs+pVM0V8Pidee01988pPBT/XT1Up0toKfPrTos3YjBnA7NneziuIUCSfsU6lpto+VQ90t78PMhSRn7peXi6Dap9lfAtLJkWLtqlTvZ8XUSEymQwWL16MOXPmYN68eZg3bx7+5E/+BBNGdzQgIiIiqgYMRUpM07S/BfCNAs7ip7qufyyYrSGi0ApT+yyjPXu8bYexYXoQQYCXvfPLoFJEl7ZRrpbwJWyVIoDr7vhe22fZBVz6lCnqy7GZKaLHYkBdnalaRBsaQkH1VvnMFAmgfdbhw+pKkWQyiZGREdtQpKenB0NDQ6g3lD8MDFhPF3Qoous6jriEmfW1tQVXisjd+bKCbp8FiBZLy5d7Px+gekMR1UPepZuayXhUihQ7FDHmw3IoMmdO7rw5Y4SKrb+/H5s3b8ZmQxvSW2+91TYU0XUd3d3dqKurM713EBEREZWz8K8YERFReEMRr39v3M4g5mh4ud5lEIoEOVNEWbFQ4ttAr6mB49qeU/ssw+qjquJFb26G3tIi/i+tcGt2M0ViMVEtYmyhpZp+7EceM0VsQ71kUrkiqnpYdDgMLe/t7cXWrVttf3/48GHMnTt37LBqj/2gHzq9vb2uAUdDfT3iUgIx4jMUsdvuQtpn2YUi+cgWzWUxFPFmvNpnuZEfX263++AgsGKFaOt2+HDu+PZ28Rjs7hbXbccO4D//U5x+aAj4m78pfaZNlev48eOW41SByHXXXYc1a9bg6NGjSCaT+N3vfocbb7xxPDaRiIiIqOg8fPwnIqKSq6RQJIg5GpUSihR7pkipbwO3ShFjuyinShHF9cjMmpULD+TLyZ6voiWXLu3lqo1TKKJ7Kb1QTX+G9aZJpYbR3W1d1Mp673vfiz/84Q+2v5cDFVUoEtRA6yy3IesAUF9XZwlF/FaK2HXNK0alSD7khe58XsLLMRTxMz/E6e/LrX3WH/4AvPWWCD2Mz7PmZuCEE0Q3yZtvFscdPSqqtjKZwrNaIifdUtVeRNPQ9OMfA2+/nTvyyBEc3bcPBw8eRHL0iefldZyIiIioXJTBilHV+RCAV32c3kdHZiIqW2EatK7itrLnVClSze2zpBW2gtpnyfehpnnb9bmIXAeYZ8MLXbfO0zDef4qV7oxhiIMuzxQZPS9l+yy5qqQUlSJOkknLY1deCO7vPwwnb7zxhuPv5XkkqlAkqMXnLLvB70b19fXW9lk+X5u8VooUOlMkX8VqnxVUPl4squ3r6fH+99mXxqDCuvFqn7Vunft5ZovDjG9rAwNAU5P73xLlo729HT/4wQ/Q/dRT6D52DCPpNCJye8MHHsBU6f2RoQhROK1fLz4qNzUBjY0iePfynpZ932HLRiKqVuFfMao+Hbqu7y71RhBRyIRp0LpqG9g+Kz/FrBQJw/V3G3afTELr6ED8wQehSSukpnkrxnZXozKzZuUOqGaK6Lr6NpFXw8crFPG6Cp9MAoZqFl23PkUGBgpbmJIrRVQzRYKuFBnwMFVb1T7Lb6WI3SKAfLzbQ9OoHEKRsFeKFPpWNR6hiJcMOYj7TzY8LB5jxpcixUseUWCmTZuGz/75nwN9feYnlbGFVmMjpjY2mv7OS7hNROPvkUfM+xZ9/ONiVpXKU08BW7eK956hIeCSS4CLLhqXzSQiCp0QrJgQEZGrMLfPSib9VYqwfVZOkDNF5KqIMDSkd1l51hIJxFatsgQiAEy3jd7WZvn12JB1wBo4ZJMERShiaZ9V6Oqjx2otuZrFllQxMzKSu4ju7t146qm/wKZND/jZQgsv7bOCrhQZ9FAWEESliN3LifxUi0TEw1O+nrW14jY3b5evTXBUraFIUB0avbxdqO5D1fkF0T7LaXu8FnIODgJXXy0ekw0N4mfSJG9/S5S3wUHxgHYIRe466yzctHAhpjY2Yuoll2DqTTeN/3YSkaNk0lpsLeWZJv39QGdn7vDwcHG2i4ioHJTBihERUZVT7fGeFZZQpJBKkVRKOVzaUaW0z5K2UQ42fMmnF0yRuQYBiQSie/eqf2f428zcuaIaZPRbX+rss02PF+XlJBLeQpFCvw16Xfn0UyliYNy8xx//U2zf/oTHDbNXilBkSG7NotDQ2GipFEllMshkMoh4bAVn91KpejrE49brOWMGsGuX+9/mq1pDkfGsFPFyf41H+yx5kcrO4KCYLUI0rhobgS9/Wewqfvw40NUleu5kNTXhXSefLE7X2AicfHKwZXNEFAhVta9TKCI/jTnDioiqWfhXjIiIql0mY7/wGpZQxG07jOcvhxW6Ln7vJ8Twcr399McplSAqRZJJdVVECEIR10HrDtfXFHTU1WHkIx9BdO1a6C0tSJ95puvlaImEZUaLHo1ad/sv8Nug5rVqyXB/pJctQ/Stt9SnlW6T7OYlk0OBBCIA0OFhpkjQ7bMGPYQi9Q0NllAEAJIjI6j1WK5h95BSvbzU1loXE6ZPt4YiQearDEXyMx6hSNDts1TVKum0uBzjPgBslUUlo2m58qSZM82/e897xA8RhVoqJXbo6O8Xn2k0zXk/HPnjFCtFiKiaMRQhIgq7IPpzuClkxcpvpYgqrEil/K08VkilSKGtxCK7d6Pm4YehDQ1Bl3cLC8P19zpoXUVafdSnTkXqyivtTxuJmB9nXitFCl2R9PDckQfOp5YvR2TLFvXfSrdJ9svq4cNr89q8j59xBs6ZORN3Pv742HGH9u83nWZcKkVUuzJK6hsaEFeEH4mhoYJDEbtKEdnEiWJx4eBBcbihwb4vdz6CCEVUD5uwhyLFap916qnA5s25w2edBWzf7u388mmfJb+s+g1Fdu0COjqAtjbg7LOB008P9vFFRETVZfJk4I47xP91XXyMdCq+X7QImDJFhCN1deYCMSKiahOCFRMiohLYswdYs0asikydCpx5pnOtcSk5rbqEpVKkkPZZgP/d0it1pojP2yG2ahW00VICTV50DkGliFv7LMfWVX4qfTQNem3t2G0BjFahyLdnTU3w7bO8PBal1Xe9rQ0jH/846vr7oT3wgHk7bdpnHTy4Oq/N++gZZ6Bbuo4lmSniIRRRtc8CRCjildeZIoA6FGlqAm68EXjySXEbXHZZsE8luRqBlSLe2FWKzJwJzJ4NrF4tFoYuvhjYudP9/EpVKdLdLa5DZ6fYo7exEVi82P1yiYiI3LhViQDAtGnih4iIGIqE0ac0TfsqgIUAJgFIAjgGYA+AVQD+oOv6iyXcPqLy19sL/Oxn5tWMd94B7rrL31yL8eK0UB6WUMTPoHXV6qTfFdgKCUUsM0V83g4RaY9/03mHIBRxrRRxur5+77+aGnMrLFX7rFjMuk3JpP+ZNkZeKkVU31CbmsSK7rRpwIEDueN37QIWLhw7mM0zDh16M6/Nm9HcjDrptuw4ehS6rkPTNCQS6oKdUoQi9XahiI/gqtBKkaYmsQflRz7i+SJ9kbcjn5fwcgxFCt0+u1CkpgY491zgggtyx4VlpogciiSTuQAyFhMhzNy57pdJFLSvfvWr2LhxI9ra2tDW1oYbb7wRF110Uak3i4iIiGhchX/FqPr8P+lwLYAmACcAeDeAr2ia9iaAv9Z1/enx3jiiirBnj3Ul48gR4NgxsatpmGzaBPz61/a/D0so4vb3J5yQ+38QlSKV0j6rwEoRX+ddAq6D1p3+1u/2yyvcIyPWVcl43Ho6XRePYbcAx04elSIm8+aZQ5G33wYuvxyorUUikcCvfnUP1q4dwM6d+b3lT29qQlxayU2mUjh+/DgmTpyoHNAJBD9TZMhDtUd9Y6Nt+yyv/AxaV911TU2eLyovnCmSH7v2WaqXCa+hSD7tswoJRXp7c/+Px0WgI49xyCokpyVy88ILL+DFF3P72M2bN881FMlkMuju7kZjYyNq3XZFJyIiIioDpV8xoXycBeApTdP+CcBXdT2ooQKApmlTAUzx+WcnBXX5ROPCbo5B2CbNpVLAI484nyYMoUgq5bwddXWieXpWJCJWloyXWa3ts7zMFMneTn4rP8qhUsSJz/tPr6mBcQ1Ryw6gl7ZHGdQkEqULRc46C1i1Khf0JRLA2rXAOefg9ttvx69+9av8tgtAS20tGuNxxBR9gTo6OjBx4kTbIc+FFtDIBj3MbmloblaHIgFUiqgeTqq3AoYixVGs9lmq+9VLG6yg2mc5vXXJocikSaKqpadHtDdZvDh3fgcPipZtg4Pip6YG+Nzn3LeHKB/Hjx83HZ4wYYLtaS+99FJs2bIFR48eRSqVwjPPPINLL7202JtIREREVHRlsGJUNQ4A+D2A1wFsAtAFIAPRQutMAO8D8B7D6TUAXwEQAfDXAW7HnwL4RoDnRxQ+dqsYQfeLKVRHh7kdkEpQmWjQlSJLl4oWQMeOAaedJoIRo1jM/DePPQb88R+7N8LNqpBQxNI+S3psRjdsQM0f/gDoOpJXXon00qW5X7rdBmG4/oWEIn6rTOTHTiIBTVr11mtqlNukJZPI+5nk4TkoD1o3aW0FTjnFPC36nXeQOessz4HItGnTcPjwYcvx00dX+GtjMUyqr8cxw+vJ3r17sWjRItuXGF0XT9GgHkZeK0VqxrF9luq0xX7aVHIocuSIKHSaOFGM6TKGDMVqn1VIpch4t88CxFthXZ0YsH7DDbnjMxlRxJoVi7FahIqnWwpF2p58Erj+emsq3NGBjr17cejQobGjjhw5Mh6bSERERFR0IVgxqXqvQ4QdKxwqPl4G8B+app0F4FcAFhh+92VN017Vdf3hIm8nUeWwWzULWyjiZRUpDJUiqpkikQhw6qn2f1NTY14xOnAAePxx4KabvF2mlzCogNZN40Ze0TPunp/JILZy5djjMvbss0gvXpz7G5f7TPey23OR+W6BZfxbn5UucgWIlkxaSwFqa8Xtp2nmx5Bd9ZgHmpfnoFvYd+aZ5lBk/34kurs9b8OsWbOUociM5uax/584YYIpFNmxYwcA2FaKAGIBOqiQwEulSH1zM7RoFDWRCJKG29VPKOJn0HoBd3veKjUUGRwEfvSj3NvowIAYep41npUixWyfJV+e31Aky/DUBCAGrhulUoV19SNy8jef/zwOP/oojg8Po3t4GPOam9XvU/fdh6npNAzvTgxFiEJmwwbx3tTYKH5aWvwVi2c/DjOEJ6JqxFCkxHRd/72P076padq5AF4BcLLhV9/WNO0xXddDsJ8gURmwWzULuol+obyENGEJRfzucqtayVq3znsoUiGVIsrgZnT3fK2vD5phIVkbHoZ25Aj0GTPEEW6P1zBc//GsFJEvy65SRNOg19ZCMy60FxKIehm07nY7zJsnrm92O3QdCWNI4mLWrFlYvXq15fjphpXX+RMn4s2DB8cO79i2DYBzKJJMWou88jXkIdhoGN3e2lgMScN9lxgeRmJ4GCvuuw9TZs7EOVddZXsefipFJk8GpB2mi65SQ5E1a8y3/bPPBhuKBD1TRPU4yad9ltPt7hS6yTvkNzRYTzM4yFCEiuPOD30I6OvLHRGNqp9MjY2YIj04GYoQhcvjj5s/y/3xH4uPlU7uvRfo7xedo4eGgE98Ash+vSAiqiYhWDEhP3Rd79I07UMA3gTG2qefCuBSAEEMXv8vAPf7/JuTALBShcpHuVSKeNk7OqyhiNvqktuCfWenaLx+wgnq01bIoHVlNUQymeud4sQtFAnBTJFCBq0XGopoIyPKmSJj5214fmkjI/m3zyp0pggg7u9584AtW8aO8hOKzJ49W3n8DMPK60lSz/jto+fv1NUqyJfEQen17ORJk7D12LGxw/WxGOpHQxF5MPzI8DAuW7oUL23dCgD4/he+gM9997u+tlm1B+S73w2MZkMAgMsuc7sWhStWKBLUW0G+DHmb0ni2z/ISbqgCi/Fon5UlV4rE48A114hwpKFB7O1b7Pk2VMXkz5d1deoXycZG/MX55+MTy5ZhamMjpr7nPZj6vveNzzYSkatMxvo5Tq48VDl6VFR0ZoVtrCYR0XgJ/4oRWei6vkbTtKdgnjFyNQIIRXRdPwLA1y5AGmstqdyUy0yRcgpF/PYhsVvwTqWArVuB3/5WnOe0acAdd1jPr5IrRbKPT9V1NLzeyvNHLMJw/QvZzdlv+yz5svr77bdHUVWSNy+VIl5m5Zx8sikUGTGu1ruYNWuW8vgZUqWI0Y7t2wEAjz/+Szz22G8wc+ZyXHDBXyESyT1ugnxJlCtF/uzss/HQli14ZtcuAMAfnX46YqND1uPSY/fV554bC0QA4PPf+54yFMlk7O8O1V08axbw3vcC69cD7e1iCHaxyQv2lVIponopM87ECFv7LNVjezxDETnw0DRg+XL3yycKxLx5wFe/Kj5nDg/bfy5ubsa5p56a68szf7732W9EVHSDg9Z9qLyEIvX15lDEQ4dTIqKKFIIVE8rTH2AORU4v1YYQlR27lb6wtc/yEoqEYdB6Z6d1pS+f9lmAWEV64oncCtrhw2KheNEi8+kqJRRRbKOWSomqBdXj1Hi9XR6vfmdyFEU8Ln7yCR38Bu5ypYjx296obHCi19TAeO5aIav/Xgate9nl+6STTAcTPr6h2oUilxv6J8iVIjv37cOqVavw3e9+BACwZcsjiMebsHz5n4+dJsiXRLlSpCkexyP/7//hwc2bEY9GcfOiRWMr63KlyB+eecZyfp379mGyVCHjdDeqXg40DTj7bPEzXvLJd2XlEooMDeXaQoWtfZbqJSno9ll+KkWIxl0sJtI5p/en979//LaHiHxLpUTbq4EBsS9QJqNuxyiTT+NUNUxEVMnKYMWIbOyWDk8pxUYQlSVWilgVsqK2caP1OLfVJbv7YHjY3OcaANautYYiFdI+S7mNdrtEQwz11uXT2QlDKBKJILVsGWKvvQYASJ9yCrTeXkQOHQr8oiyD1sNUKeJlBVT6hprw8ZxUtc+65bzzcPq0aWOHT5IqRYYTCVx//fWm41566Z9NoUig7bOkFeKGWbPQGI/jI6eP7tPR0jL2fJArRRpHK0iMNr32Gi6SrrfdU6K+Hpg7N7/tDlqlzhRRhSIDA7mHdRDts1TD0UvdPiuVMlfEGPmpFCEiIvKrrU0U1APivSiR8PYeeOGF4j0q27Kxra2YW0lEFF4eXjIppOQ837piQERqnCliFfSKmtvqkmGWgIlqFUm12ublenv5VlBqmmZd1Rt9HGqq+8S46ut2n4UkFEpdcgkSt92GxAc/iOQNN/ifFeKVW6VINDr2uJRbbRVUKeLlsehlBVS6v/yEIqeddhqmT58+dri5uRn/etttptNMP+ss1EuX0dXVZTrc12ceDBFo+yzpuV0/Zw5w5ZXiQE0NcMUVYyvLcijS2d1tOb+Na9ZYjlNt78yZwIc+FI6MEAgmFFE95Eodiqgu3/gUDKJ9lir0yrdSRHVe+YQigP11cwpF2IGIiIiCpGne31tOPhlYskQUKU+fLnYeISKqRuFYMaF8TJYOd5ZkK4jKkd3uxOXYPiusoYhbIGG3Z75cJQJ4W4VavBjYsSN3m82Z47/9UonoNTWm+SBaOm3fPst4unKoFAEATUPGuEd/kUIRy0wR6TFt+n2QlSJBtc+KRMR9NrrdqlDk5JPfh61bH7Mc39bWhp/+9Kf4/Oc/j2g0irvvvhvTt2wRkzRHaXPn4qRJk7D+8GH3bRkVaPssuVKksRG44ALRuyqTQbqmDp2HRVshORQ53NNjOb+NGzZYjlM9ZT7xiXDlo5VaKaK67e+9F7jhBmDp0mDaZwUZiqh4eZyoLi+dVl+mUyhCVCqPP/447rnnHrS1taGtrQ1LlizB7bffXurNIoM33wS2bxcFlC0twOzZwAknlHqriIiIKg9DkfIlj2M8qDxVperuBlauzPWrr60FLr64bBZBqcRYKWI13pUikyapq0WkPdcBqFeh5IXo+nrg5puB554TrwfXXut5U0tOvq2yj0PVCqDxuHKYKaKgF6uCxW2ouzGMCTAU0Vyeg3p9vfeqnZqasefiiPScjMeb8aEPPYpvftP6PheJRHDllVdi/fr1uSPXrTOfqL4e82fNcgxFjEPWgYArRaTbuD47CTQeRzIJ3Ptj4MAB8VSOSB9PD/f2Ws5v044dluPkp0Q0Gq5ABKiuUAQAHnpIBF1BtM/yGorke5/nWylid90KDUXs2nIRFWLDhg148MEHxw6/5z3v8RyKDA8PY+vWrZgzZw7a2G+naPbtAzZvzh1evpyhCBERUTEwFClDmqbVAbhJOvq5EmxK6fT3A++8kzscjwOXXFKyzaEyU0kzRcIwaF3FbVXq/POBRx+1Hn/8uLfzlxeiNQ2YP1/8lBt5VS/7+FTcJ6aWWuVSKSIbp/ZZFoaeApb5I8WcKeJneEBNzdjzXq4UiUZdrp9Mvk7xOObMmgWsXm37J3V1babDgc4UkbanwXC7rF8vAhFADPtMJc3PiUHFhmzcv99ynHyykHSQM6nUUMTpKfTqq/7z+4kTzRl50O2zZJGItwAiiFCkvV19/LZtwKpVwOCg+JkyBfjYx9y3iciPbqkd4YQJE1z/5hOf+AReeOEF7Ny5E5lMBr/97W/xgQ98oEhbSHLRdEtLabaDiIio0oVs/zny6EsAZhoOpwE8XqJtKQ35m6bbYhiREStFrMa7UmTZMlHdJVNViqhW2+QwKGy7g/shr+qN3hfKORdSmy1f5xsWxWqf5XK+uodKkciePYiuWSOCd6/cKkX8hiLZTQo6FKmtRYvLwPeREfNKTFDts/r6+pCSbqd6w+2yapX59Hra/TFyoKcHPR0dpuPkp0yx8rdCVGoo4vT2uXu3/7cqY8c9IDdAVqZ6q8k3FPHCayiSHQwv0zTgqqvU5z0yAuzZI7reDQyYZ7IQBeW4tPNJ286dwNq19n9w6BB2b9qE7du3IzP6RN60aVMxN7HqycWRDEWIiIiKI6QrJtVB07Q/AvCUruueG3xrmvZJAN+Qjr5X1/U9gW5c2MnfjPv7gf37gVmzSrM9VF44U8RqvEORSAS49FKgowPYsiV3vNdQRL7eZRyK6LEYjDsoj4UdBbbPCmulSNHaZ7lNlzQEIZZKkWQS0XXrUPP73wMAYqtWYeSOO4C6OvfLHadQJBbzOZlZsfNAU7ZllY10egTpdGIsgAkiJ37kkUdw/fXXW45vcLhdYhFvacbGl1/GeTflCmflpwRDkfHjVCnS1ua/ldSsWdZ1WvktMRZTV3fk89Ln9W+8hiKq2+Pd7wZOPRWYMUN93g0N5sODg962yU0yKUZuxWLlWUxJwXr/+9+P9j17cLyrC93Dwzh/xgznnSjuvReLUik8Yzhq48aNRd/OanbhhaJwurdX/EyaVOotorDatEm8DzY1AY2Nol1lPh+z7do1Dg0B99wjzjv7c+WV3B+ViCoHQ5HS+gSAH2qadj+A3wB4Ttd15X5hmqadBeArAG6UfnUAwFeLupVhpPp0uGsXQxHyhpUiVuPdPitLXshWtc9Sraap2meVK/m2yq7suoUiLo/XooUPhfKwUp0+5RTfZ1topUjshRfGDmqDg4iuW4f0Oee4X7BbKOISRJgEVSkyPKysFHELRQBRLdLQIN5jg3hJ/NKXvqQ8vsGhaiUa9fbYfWnFClMoUg7ts+Snu66Lh5CfXDeMoYjTY6WlBXAYZaMkV4oA1rcCu/s3n4y8kFBE9VKtetu66CLnlz9VKFLIXJF9+4CXXhKBSDIJzJnDUISAa665Btds2GCuiHTaAaCxEQunTDEdJVebULDOOKPUW0Dl4oknzJVFH/4wcPLJ7n/X2wv89rci9BgcFP9+5SvW99X+fqCzU/xkXX11MNtORBQGIfy6WHXqAfzx6E9G07RtAHYD6IFoizUJwFIA0xR/2wXgal3XOxS/q2xTpwKnn24eJDs0VLrtofJSDjNF7Bqoy8IainhdYZJDEdWMlGptn6W4/33NFAnrbeIWXtTXI3Xuuf7P18dMEcvjLpGAJrXMim7cGEwoUl/vfh5ZhsdCQaGIPDdE04CWFjR5qFoZGekNLBTJZDLYbJwWa1DvEIp4rRR57uWX8ReGw+VYKQJURijiVCmSDX78aG21HifvJ2B3/6pu43jceRu93v6aJk5rvD6q214ORSIR95CurQ249loRjmR//Nq0SVSitLaKj8XGp9++faIll5+cliqU/GRyCUWuOukk/PSGG7BoyhSc+pGPoOnd7y7u9hE2bhT7CWVb6S1fbl9lRtVJ161tFr2+vsdiwN695uOGhkSliZF8/nV1/qoxCwn2iYjGA0ORcIkAOGX0x81KAB/Tdd06abRayAtNXvaqJwLsV/rC1D7L6+NZFSIcPy6mNM6a5X2lJyyVIipeKkXCGgB4IX+7cKoUMdxP5TpTxKmCJXnZZUgvWuRvOHmWSyiiO7XP8vIYsz1jxXPQKM9KkRHp/s+GIuee+wW8+ur3xo7/kz/5E/N5pNPAa6+Zj1u0CKir8xyKZBX6ktgnT4s1cApFolFvacaLW7YgNTiI2OjqcTlUiti1X/KzrWEMRZwCtETC3/adfbb69lC1z1LJJxTxs8gTi5nPy0soEo+7LwzV1QFeclg73d3AAw+Iy7noInFexuut68DWrWKkF1W5L39ZPKGyP1IliElzM+afcALmL1wo3s+cTkuBef55c4XdvHkMRchsZMT6/uP1I2ddnXivMH6EHRx0D0W8nn8iAaxYIZZrLrvM298QEZVCCL8uVpV/hWh/dQGAEzycfgDAUwD+U9f1lcXcsLLQ0iLaaNXViR9+SCcvdL08KkW8Vj7JC7cbNohVkUwGOOEE4GMf87aLTqkqRbzMbPAyU6SMd0OSQwIte90KnSkSxhVhwDG8yMybl18gAojHgNPKp0P7LK2nx3p2XkORICtFPLTPOvfcz2LDhl+jr+8Apk6dii9+8Yvm89i50zql9fzzAcB3KFLoS2KP4nYFgNpoFBGHx4HXSpHekRG8/eSTOOtG0Vm0HAetA/5ffssxFHF7Op18slibnTRJjJtS3U6FtM9yKyTzE4p4mQvjZ0f8oKxYkXtbeOYZ4O23xceAbdvEy+MJJ+RXfUIVKBYT77Ve3m9vvbX421MBRkZG0NHRgTlz5kAL4DOpvPgsFbMSIZkEZs4Uj42BAfH67zW0iETE+5Lx66ZqjtWsWcDNN+cqlrLvpYmE/fvq3r3AQw+JMZGaJt7f2eGciMIqpCsm1UHX9QcBPAgAmqa1AVgMYDZEq6wGiMqRbgDHAWwCsE7X9RJ/7Q2RCy4QP0R+pNP2e3aHKRTxWikirzS9+GLuuD17xGqIl+ay5VYpUknts2xmiqjaZ8FP+6ywDlp3WKnWC5zcqNfUQLMLRRwqRZQ8lkm4hSd6W5un8wHgEoqI50pr6xz86Z+ux8jIevzN35yKyZMnm89D7vU+fbr41gyg0WcosnEj8N735p+v2YUiDTU1jomFn1Zhz/z+ibFQpFzbZ/l5+c1k1G9hQXVSzIeuFx6KnHmmGEJuJLepKrRSxImft5AwhiK7d4t9IozmzxdFYqedBixYwECEKGjHjh3DxRdfjEOHDqGrqwsAcPToUev7ch7kt2t5j32i5mbgk58U/9d18V7r53PP1VeL97OGBlHRoRrZ2toqfhIJMb51xw7gP/5DvDf/+Z9bTz8wAPzsZ7nPY7oOPPggcOed4fxMRkTEUCQkdF3vBvBSqbeDqOI5LXSWY/sseaWpQxoxtGZNaUKRCRO8nc5LKJJMWpvSVlL7LJuZIqrHoykocbnP9JCGIo4r7IV+Y4rHbVcOTIGLh/BF6+/31gzZoX1WZvp06KpvmXY8Dlqvq2vD9OkXQrnuIleZGYYzNLW0uG5CIpFreTUyIgZxfvCD+RVj9dqEIvVuoYjHShEAeHXN1rH/l3P7LK/sTlvKSpFUyrmLnJf2War7Sm5TVcxQpNwrRaZOBc46S4wT0nWxwHXppdZOs0QUnJaWFmzcuBG64QXw0KFDgYQi7e2iAqCpSez9P3duwWdJFUzTvH2lMlq61Ptpjx0D7rvPfFx3t5iFZdTYCLz73aJaMau3Fzh4UFQrEhGFTQi/LhIRFZHT7qyVUCmS7/k4rVhNnw4cOuTtfADREN5rOzsvn+CzuyEbV7XkFbgybp/la6aI4ThlJYnT+YaFU/Dh9xudRI/HYftIMFaKeLmcVEr0EnDrRSA9B5PXXguts1OcxfLl7pdj5GPQuu3dL4cihlVRL6GIsVIEEIOavRacyXqOHVMe31BT45hYxHxUinQcyb1uF1wp0tEBHDkimrfn28bNRRCVIiqlDEWcZnUA3t6GVPeVHIrID20/7bPcHgtBhSLDw8BzzwGvvmo+TbFDkYYG4H3vE8HIE0+I6hAGIiTr7OzEHXfcgba2NkyYMAFtbW34y7/8S9SNR3+3CjA8PIybbroJX/ziF3H55ZejpqYGkydPxtGjR8dOc+jQISxZsiTvy3jqKdF2qKUFOPFEUeXV3h7E1hPlr71dvM8YW2zt3CmqPGUXXijmV+3fD8yeDdx4IzBx4vhtKxGRHwxFqDy99JL4xFhbKxa6FiwYaw9C5MhpIVlVkVAqhQxaN/I6m0ReUbvuOtGPIxYTE/Ief1ysjgLiE+6+ferzWbQIuOYab5cJeF8EHxkxhyIVVCliGTxe4TNFbFtXxWKF348Ou4ObLtfjarnW2wvdZyiiNzQgne9USeOgdZdQxHYRPOBQBBBd+RYs8P/S6BiKONzXEY+D1gEgnUlhaEhczYJmimzdCvzf/4n7s6kJuOsu7825fVBdbT+Bhl1f+VKGIm77E3hp7aV6uaqvNy/A9PWZf++nUiT7kLPblkLaZxlfih96KPdWaTRea87t7WKUGJHKkSNH8OCDD5qO+/KXv+zrPJLJJHbs2IFNmzbhhhtuCGR+RjnQdR133HEHnnjiCTz11FP43ve+h8985jOYPn26JRQpxK5d5v2QGhsZilDpaZrYX2T9+txxe/eKUETXRSXIjBnidJEIcMMNwJYtwHnnlfVXNCKqAuFcMSFys2WLeCfOamxkKELeOK3e6LpYWQrDYrIcirS1Ae95j6hBfuKJ3PHGFR7Vak++lSLt7cC73pU7fPPNYmqrrot663/8R/PpJ08GLrkEWLzY38qp15UieVfkChq0Lq+waRU+U8Tu+eVrILkN3enx5LN9FjAaikyf7nyiIAM6j+2zgDxDEUMrLTsrVvwldF3H+ef/xdhi17594mfOHNc/N7ELRWpd0go/lSIZPY2dO4Fp04A335TOx8dL+cjvfod/WLkSaw8fxkeWLMEt55wjekAELLtg4PbSrbJ9O/CLX6h/lx2XVYqXwiCKLFX3VUODaNmR1SvldX5CkWhU/Njd1n5eLu06HgLqQATw/lb39tvAW2+JMGhgADjlFOD6671vG1Deb4dUXN3d3abDjY2NqPGYHh87dgzvfve7sW3bNiRHn/T79+/HzCr5/vXb3/4WP//5zwEA6XQan/3sZ9HZ2Ynvfe97AID29nZMnz4dE7y2j7Uhv8552JeBaFwsWCDel+bNA046KRfWHT4M/OhH4rG6aJH4mT2bo1+JqDyEYOWPKA/yAmmBw3mpirgtJKdS4QhF5FWmmTOBhQuBd94xH29c4VGtTOVbKSKvEMVioi9H1pQpQHbPuEhE7JqaT7sZP5UiRpU0aN1H+ywtlRL3aSoFrVxnitgswOhBTAF2ClaM7xPZFVKX21CTVydUxikUicXMz5V8QpGG5mZPm/H003+FuXMvwcyZZ48dt2NHHqHI6OBZWZ9Lv6XsUHkvMpkUtm8X1SwyP5Ui31uxAt964QUAwCNbtuDtk07C0iKEIoB1cd5rlcfzzzv/PpMpTRbq1j7LC7tQxKiQSpHsU94uwAmifZZT4abXUKSvD9izJ3fYrjKoUGEpiqXxJYciE6JRUd50ww32f6TrwOAgJvT0YM+uXWOBCABs2rSpakKRX/7yl6bD9fX1+OAHP4jFixcHdhnptHUsmse3baKiW7pUPYdkwwbxb2+vaB25eTPw2c+O77YREeUrBCt/RHmQv4Fv2CC+OZ55JpsokzO3XVqTyfHrc+G2HUbZ1T15wdUtFPGyWqXr1oVdtxWiq64CHnhAnP/ll+fff99rKOJWKVLOoYjdbseK+zOyezfq/vM/3cM9ILSVIrbts4pcKaJL4bkej0NzCQ09hSJBBnQOoUgkYp0polzYdAhFIvE4GmtqMOBh1/59+35kCkXkBWkveo4fVx5/3OV2l6tinKQzKaxdq64A8JNvf8U4FRTA369Ygfu//W3vZ+CDvDjvNRSx61poPJ9SPO3lh1NtrTXHduMlFPHyN4D6KZgNRewU0j4re/853Y9eP1bI19nYPqxQg4NiPtDmzaIC5667GIxUm5NOOgnf+MhH0L1tG7qHh9Ecj7u3YQWAf/93RIaHcWpbG1YbXr83bdqEK664oohbHB4nn3wyzj//fHR0dODQoUP49Kc/HWggAoi74rrrxOJyb6943/VQ4ElVassW8ZhpbBRfw5qbC9uvT/5MmU4D//3f4vyzP5dcYu4squvAxo3m81m0iO8tRFQ+GIpQeVq2THxafOMNcXjrVvFz0kkMRciZ22JyWIat200Mlj9lGr/M2gUgiYRzNZVqJcdtZW3BAuCv/kqsRBbyCTzfSpEKbp+VvT9sK0G8BCJAOCqeVOzaZ41npQggnlNBhCLyTJFCQpFYDP2JBL72zDP419deM/1KFRQoKwMcQhHEYmiKxz2FIoODR02HvdwUsl5pr+Ssbpfb3ddMET1t2xKpkB2YNx0+nP8fu3Aa1G3Hy1tTqeaKyG89dXXiZcrP9gQZijhVitgJolLE6aXZaygij7GxC0UyGWDFCrEv0JQp7ufb1QX8x3+YX66OHBFt56h6nHLKKfjbW24B1qzJHen24NQ00SJ1/34smjIFq0cHXrS3tyPl9fNIBfjOd74z9n9d16F7CZN8isXMnWuJnDz5pHhtz7r1VhFIeLV/P/DUU+J9ZnBQfFz8zGdyvx8cBDo7xU/WJZeYz2NoyLpTQcBZIRFRUYV0xYTIxUUXiX/XrzcvAHmdn0DVy21lKSxf8OTtzK7++K0UAcRq5uTJ9peVTyiS3ZZCKzSiUbE47Xa/yKtuFdQ+S25zpXlZYfMipJUitu2zil0pIgdwHgI5zcNu2lqQAV1NDb778sv4gRSIAOpQxFIZoOtj74N9fWKv8A31dTj/ZrHPAGpq0BSP47Dcn0Nh1ixzr6y8KkV6epTHj0iPbfklKJm2r3Brr29Bx1Auoclk1Cvv554LnHiixw1VvAY2FrFiMJ9QxMvtX6pQRFXYGI97794IqAMOtzn3YQtFgqgUaW8Hrr1WBEINDfZFmBs2AK+8IlqVLFwoxt84DWOeMEH0ezfmlJs3MxSpSvJ3JS8PztFQ5IvnnYc7zzoLC6+4AhM+9KHibF8Z0DRtXAbMr18P9PSIlloDA8D55/M5SznyRzm390xZOm0e0Sq/h8nnr2nWfY8aGoBPf1qE7Bs3iorWGTPMpxkcFHNHstVPsZj4jEZEFAYMRai81dUxFCF/yqVSJKj2WYD4BFqMUCQotbX+Q5FKap9lN1OkgMeiPmlSeEMRu5VMv9/mFGxDkUjEstu5Ho/DdUnDSzAV8EyRv7UZHKEKRVIpqQAmmQTSaei6WPAcGAAOdNXjgQeAL34RiI5WinhRX2++n/IKRTyWl8g38+CgekB7e30rrl52De59+f/Gjkvr1teviROBq6/2vp26IvxqKmLVaaWFIqoxb8lk4aFIubTPchgDNcZrUWRbG3DOOc6n0XVgdPzNWOuS7m7gk5+0z2Q1DTj1VBGiZG3ZAlx8sbftogpy000ieRseFj9e2p9OmQLEYli6dKn4//z5xd9OwjPPmCsBTjmFoQgJqZS1iN7vx2j5PXZkxLyzjRyKNDTYv1dOnSp+VHbvBn7zm9zhCRMYihBReDAUofI2YYJ4d66ry/0Q2RkaErXGTsISiti1z8q3UsRJqUOR5mb3abJug9bLuX2W3UwRnyuc6YULEdm3D4jHkfSzIjzeNE1ZHRREpYhd+yy9ocH6GPHyfuHlPijSoHWZPGgdUGze6Cq0cVhrqqYeqUGxN+CJc6OeQ5FUyvxteHBQvCz56crWY7OS/w/XXCNdlvn3A0PWAe1fee/daJ5/MY4e+I3p+LSiUsTvQ2nwmDWEYaWId3aVIl5FIuq3nGpsn+XFpk3AUXN3O1x0kfvb4CmnAKtXi6qxU08FTj45uG2iMhKLiSDEzyy4c84BzjuvvHdAGQfDw8Po6OjAzJkzUePwfu5VY6M5FHH7qEzVI5EQLUIHBsTjIpUqPBQBxMfI7EvDtGnAzTfnKpVUX0EPHRLb4lSZ29xsPtzXZzMTj4ioBBiKUHn74z8u9RZQObnvPveVpbCEIkG3z3JS6lCkrU18qnZSTZUi2ZkiPttnpZcuRfL664PaqqLSa2qgyaFIADNF7CpFdMXijzx4XUXeRuuZ6IE+FhMOPcrt2meZGEIRANC1CFLR2txxmoZGj7usJ5PWFlt9fWJfBK96FCs4c9va8BGp4bR8PU6bcRZe3PW06biasz6FYQDJvebbIaOoFPH7UOrq6LAc5/V2ygdDETO7cCPIUMQueHH6G6+XO96hyNy5osLjtdfEjv5Tp4qQw80JJ4hRYAGs1VK14YPGVjKZxBlnnIFDhw7h+PHjAIDNmzfjlFNOKfi85Y8uHjpfUpVoaBDVgYD4KOo2PlKlvt7crrGhwbxTSVMTcNpp1r/bvx946SVg1y7xHjRjBnDHHfaXI4ciqZT4uBrEKEEiokIxFCGi6jA0ZG6caiesM0UKGbTutpoWhlDETQUPWlfOFNF1349FPayD1VXy6ZXjhV2liGqPWC+L3m73gSrEKCAU2S/v/m3gJxQZa+cTqxt7bmQ3q8njYv/w8ICloMdvKNIrreB84+KL8cXzzkOzYRtUD/UZZ38as9b+N/b3i5kkf/KuT439rr/f/NhJBVApckwRitQVcRFQfnm1GxRvFOZQRNU+q9Ah60DwoYjTU7OQ9llBzhTxoqEBuPRSseP+66+LvXm9vAUGMQaMiMxqampw4MAB0wytQ4cOBRKKTJ8uXl8bG8XP7NkFnyVVIE3z3qLRKBJxb9eokkyKisWsQ4fEx0+7z17NzeJ3TU1itlVzs7fPPURE46GMVlCIiArgtQIkLJUiQbbPyicUGc+VEy+hSAUPWres7KVS4j5xqBpQCusMERXF/VXMShFVTwEvlSKuK7uqb3UFPBb3OFRM2c0UMZEqRZKx3DfU7HFNHldnBwYG0Nxsbt3hd65IjxSKXDJ3rikQAcRNKD/UU62zcO83HsDjP/sJJrTNRez8v0Q2Fo1EzGGFqlIkiFAkYRcyB0B+iARVKVKqRQZVhl/KUMTuKVjK9lnFKDyqqxMD1om8+ou/+At0dXVhwoQJaGtrw80334yFCxeWerNCb9WqVXj77bfR3t6O9vZ2zJ07F7NmzRr7/fTp0y2hSD7uu0/829IifpYu5XOcwmf2bHMXXF0XVSOLFqlPH40CX/rS+G0fEZEfDEWo/Bw9KnoGxOPiW2ZjI3DWWaXeKgo7rys0YQlFgmyfJVdZyOTbJhod38qL1lb301RT+6xUKr+KpXKqFFE8VgOZKRKPi9tTekzrqkbLHlYpPbXPkhXwWNyrWJzPyqtSpCZ3m2avyniFIrquo0eatN2avc0NuybaPdSHF16OlpsuRxqA8WpGpNshrUgCfLfP6uy0Xn4RQxH5Kf/228AZZzg/dCq5UsQu/62tVT6dx/ipFNG00oYi5fwWRZXjwQcfxM6dO8cOL1myJK9QJJPJYPfu3di4cSMGBwdx6623BrmZofPQQw/hu9/97tjh2267Db/4xS/GDre3t2Pz5s1jhzsc3svt6Dqwfbv59e7EE719RCYaT7GYqGLq6RGzqWbO9FdFTEQUJmW0gkI0qqsLePPN3OEJExiKkDuvi8xhb5+VTyjitrgnLyqOd8VBlbfPsqzsZTIVH4oow4YgQhFNg15XB02qUFCFIp4qRVIp52mQigV5vYDH4p4DB2x/F43WWtpZuYUixkqRsVDE4+08MDCAlhbzcW7jiYwGBwctgUVrXZ3Yvd1LKDKsPl6LmB/n6SAqRRShyEgRA3L5JXbvXuChh4CbbrL/mzCHIoVWithdN00TAZfd7+06nNm9hTm9tQXRPiuojw+rVgFbtwKDg+Jn+XIxQ4SoUNmZF1ltXj5/SZ588knccMMNGB59kZ41a1bFhyKHDx82HW5vbzcd/ta3voWR4WFMnzQJ0+fOzet2HRy0vm7K78FEYbF0KfDoo8AbbwAbNwLXXy+CEiKicsP9lqj8yIujfqeKUXUq90oRu5kiQVSKyCs5471Lq5cvj/I2VlD7LF3adi2V8j1kHbDOJgk11WM1qGBLsSKeb6UIdN35tUPVr6iA67HXIRSpq4tb3u4sT20vlSKK2+cbi27GKc0zTMdlK0WM/FSKGFuJZLUsXw7cdRcwefLYcX5DkUjUvBIeRCjSZSyHGTUyjpUiALBunfPbT1hDkcFBYO1a83HxeHAfzZyqfuz2TB3v9lnZx3BQt//x4yIo6+wUt2+xhyv77dRI5SmTyVhel9see8z7AyCZBPbtw4zu7rFABAD279+PXj+JeRmSKz/GQpH+fuCee3Dh6tW4/NVXsejhhzGhsRFaHp8D5JtQ06yD1onCYtky8VNfD8yZA8ybV+otIiLKT/nsVkqUJS9UHD4sdlEYHhafIJctK812Ubh5XWQOSygib69d+ywvg9b9hiJFHDCs5KWdj7yNldQ+S67wSKcrvlKkmKtwel0d5OUI1aB1T5UigLgv7G7boGeK7Ntn+7uGhjhaWswLpJbcYXShamzP9VjuueUUikyJt+Djcy/Fl9755dhxg4ODhYUi3d2W41ovuMDSC8TuoS513hoTiZrDrLReePusY4pQZDzbZ2UlEuqX35ER94I/QNzvhw8DHR2i7cp47GX8f/9nPS4eD26+idN9OWmS+vh8KkWK3T7LD/k6Dw6K+zWTAdrbC8+PV68GDh4Ejh0Twcv73y9aoFBlS6fT+NpnPoPuV19F9/Awjg8PY1o87v0B1dUF3HMPTk6lENE0ZAzv45s3b8Y5+UxsLhNLlizB0NAQOjo60NHRkQtFamoA+X27vx+YONH3ZTQ3A9ddJ8KR3l7xelLOH22puLZuFe89TU2ik3hLSzBfA4zF0XffLd7PGxvFz0UX5T7CRSKiOuT97y/vYn0iojJaQSEaNWUKcPbZYoVgwwbxqfE3vxG/a2lhKEJq5VQpouv+2mdlP8Hm2z7LLoAJE/n+kxfVy/kTuWqFLZ/H4XiHWWGlWt3Mt1IEcF7pDDgU2bt3r+3vGhriaG0FjPNbLbmDh/ZZDYpQpCFWCz1ivi6qSpGuLuduYka9UpuW+lgMNfIZIoBKkUwA7bMUAc5IEVsp2i3A212k1zBq2zax4J3JiNvgzjuL249+cFBUNMjmzgUMYwsKYheKtLTYV6PkM1PEz9NWlWMDxQ1FnnsO2LQJmDpVtC05/XRYnp9erV1rvt86O62hSDoNHDkiPnKH8SMB+VdTU4O/vfNOc3Wun0EAkyYBmobaWAwnTZiAbV1dmNDWhkWLFyMVltazRfK9731P/EfXgb17kZk9WxyurRUvRMbP2X19eYUiTU3Au94VwMZSVVi5UoTlWTfeKN4b/Nq4EXj11Vy7xtmzgQ99SDyk5dE4559v/fty/vpFRAQwFKFydMIJ4gcQs0TuuSf3O7tVFCKvX9jsdk8eT+m0ddHfLhQB3EORkRHnlcxyCEWqqVIEgJbPXurl1D6rmBQBaN4zRSDamdnWtQQ4aF3XdddQRO40Z6kUGS0jcWqfpdq+hmgtEDXfbgMDA5g2zXy63l4RxHhZR/uVYQgtYJgnIrHLq+1nisjtswIYtK5o/TKeM0Wy7N6mBge9ne8bb+T+PzQkFr/f/W5/2+aHqq3TjTcCM2YA+/d7P59zz7X/nd19aVclAox/+6ygQ5ETTwSuvVZc94YG8fZ/773id0eOACtWiKdSvguokyaZQ5Fjx6yn6ewEfvhDcVtOniwqVG64obzfagnWF1YvlbpZsZh48e/qwgO33oqpjY2Y+pGPQDvjjEA3MdRWrgRWrULk8suBCy/M9bgyVhv29wd6kevWiZylv1+85r773aYOlFSl5IeZat8fL+SdG7Lv66rPHX4/WxklEuJzQV+f+BkeBq64Iv/zIyIKSghXvoh8kD/MJxLi2ykXBwu3d6/YNbGuDrjqKm9zH8LMbuWtvd28K0wYQhHVyopd+yxABASRiH0okq08sVsEDkMoMnu2tQWBkXz/VdCgdeUsEL8BbzRaVqtVqfPPR+zll8cOp4NcVFE911WP/QAqRbQAK0WOHTtm6tMua2ystez1bylwGF2Yyd4Ew7W5P8i+PMgzbACgIRoHIubXj2QyidbWJBoba0yL37t3u4ciK1euxL/dfbfpuEn19coFOP+VIub7UhWK+K4UUZRiDJcgFLG7yHw35fXXixuKyIsmsVhuT1WnzHHKFDGQ9Z13xL/nnWd/WjmYywpjKGL3MSO7H49X7e3iJ+uNN8znHYsBixf7O08jeUG1s9N6muzHokxGBDEjI2X1FkN2liwB5s8XL7DZtsN+TJsGRKNYsnCheCJLA8cr2vbtwKpV4v8rV4o08brrciu7zc0iIAm4b+HTT5vnjSxZwlCk2mUy1vfffOfPqCoTAetOD9Go94/NKsePAz/7mfm4Sy4J5354RFRd+DJE5a2+Xuwakd0Dta5OrLAwFPFvxw7g5ZfFh/nLLgN++cvcLIqREeCP/qi021co1crbokXArFnhC0VUK2B2g9aBXEDgtHI2MhLuUOTKK4Ef/zh3+IwzgLffzh2u4EHrqtcrzW0OjKzMvlWkly5FdP16aL290JuakHLaVdwvj8MMfM0U8XNZeT4Wh1xee5qaXCpFEomxPkvZTR5qyK0cZ18eMorXkIZYLbSo9fVjcHAAc+e2YcOG3HG7d7t3qfyx8bk86sbFi5W3jd3Na3e8JrXPSkmD1iMR/0O+uxQlD8Vsn2X3EPF7W7jJd89Rr+RFGeNaoNN9EI0CN90kqkrc1mTnz1cf7xSKqLi1z/LzEuq3UuSyy7yft93ltbXlQtBTTvG3g79s1izxFjt5srgdp061nkZum1JNa98VLRoVq6f5rqDeeqvtkzaRSCDu98W3nKxZYz68bp3oWLBokeno4eFhHNq7F9OnT0ddIU/UUY2N5lAk4EIUKkPJJDBzZq56KJnM//3eLhSZMAG45RZx/gMD4n2ukP3PVFlhf3/573NZ7nRdx5o1a/DEE0+gpqYGn/70p9GU7/sDUZkqr1UUIllTE/CXf1nqrSh/g4PAr36V+2a/dq15sW/HDvu/7eoSTU1nz87/S9Z4kHfhnDJFfLl75x3z8WEPRezaZwHOs0OcfidfXikW2OfMAT78YdEU/4QTxIqPMRRxqxQp51BEdXv7rBTRy2yeiN7aipGPfxxaVxf0iRMLW+GTZObPR8TYaNnuW1wQM0VUu4bn+VhMuLRMU4Uivb2G4khD+46xUKQuV9KRfZovVOy2HtUiaFQ8hgYGrKHIrl3uc0WOSf14pjQ04BvXXqs8rd8F/4iifZaeyUAbvd0bGnx+cU+lcEwVioSofVa+m1Lst2Q5FDEurriFIoC3+6mtTSzey9UMfveUjkadQxE/8zn8hCJ//Mf+K0VkZ54pgsg9e8THs9NOK+z8jF1o7cj3LUMRAmD7pP3973+PP//zP8cPfvADvO997xvnjRoncmnmGWeIhHHU2Wefje3bt6N79HQvvfQSzlcNYfBJXuxWtS2k6lJbC/zJn+QOJxL5jxWcPBl473vF+3d9fe59vKGhsIpEWV2d+LpjfJ/s62MoUmpf//rX8fd///djh5955hk8+eSTJdwiovHHUISIgNdeMy/uqfZ+Vq2C7dsH/PSn4hNOYyPwqU8FXjYeGHm1IrtiI/dZ8dq8vZjyaZ8FuFeKeL28Ui2wn3xybtrr7t3m31Vw+6wgKkX0cuylUFcHfcaMwM82tWwZYm+8MRYEJq++Wn3CeFw8blRzQQwcZ4rI91P2PPMw4nKfNzXFLe2zdF0EIxMmwBSKpNOidVbGUFWRfXm4ZNkyLJoyBRuPHgUAXDddDCZoqrWuYotQxHxcb69og+A0R3ZIGnbyjYsvRky1Ozr8hyJazBpm6ZkktIg43m/rLH1wEF2KMHy4iJUidtm73Uu4alMaGtzfrvzeFn7Ji3NeQxG/ueHMmdZQxK1SZO7c3NtINCrWLw0d+yz8fHSRX7Kz94/8NrVkCTBvnvfzdaJp4jrJz8diufFGMdfk8GHg4EFxnVetEtfx4ovHZxso/Hbt2oXPfvazePTRRwEAn/3sZ3HFFVcEUiHhha7reOaZZ3D8+HG8//3vL26lyhVXiDe//n7xI5VMdnV1jQUiAHDo0KFALnbmTPFe39gogu6ZMwM5W6oghTzsm5qAs88OblvsaJqoSkylxE4ILS2FteOiYFxyySWmUOSpp55CV1cXJjp9yCeqMAxFiEjdUFqWyVhXAlasyK0GDAyIcOXKK4PfviDIq0rZ6yKvGg0Nue8GXWyqyo3s9oxHKBKGVkx2q05ZFd4+y2+lSMZmwbkqNTVh5PbbEd28GZnJk5FZsEB9Ok0Tjxu7QQBZTjNFpPtJL+AbnlulSEtLLerrxZdf40l7eqyhSCoFDNWbv9Bk/0arqcGq22/H//fMW0j01uHq9jMAALWxKGKRCFKGwHFgYAALFojFGOMC+MGDLqGIcegsgPqaGmD5cuVpfYciEeu3fz2dAEbDEr+DQHuPHUNaEYyNpFK5eU0BkzKjMV4rRebMAT7+cWD1amB0LdLX+QUl30oRvzfp4sWiQiIrFnPfu/Tqq4GHHxbbeOml4q3eqVLETygiv0XaVYqE4a20ELW14ro980zu9aOuDrjoovJ+y61Wv/71r/Hkk09iwoQJaGtrw1lnnYVrrrmmoPNsbGzE0qVLsWrVKhw/fhw7d+7Ev/zLv+BrX/taQFvt7Jvf/Ca++c1vAgAuvPBCvPDCC9AC/vz+/PPP45133kF7ezumTZuGE086CbMMFSJZ06dPx86dO8cO+wlFEgng3ntzi8UtLcA554jn4KWXBnEtiErvjjtKvQUku/DCCy3HHT58mKEIVZUy/7hOVWnVKrFwHY+LT4sLFvhvLk1mXhZgjQPsdV2Uke/daz7Nyy+HNxSRFz6zqxXyClo67TyUfDzIK2DGyg3VSkQqJSYxHjlif55OC65hXMmxW3XKqqT2Wdlm94br6LtSxG4acZXSJ0xAyml689gJrYvhekMDNONqr0PYKN9PegG75ruFIs3NcWga0NoKjBZ5ADB09DC0rEqlgKE28xeasasRi2FCfT0+ufh8U0FWNAo01tSgx3CdBgYGoGnAjBmis13Wb38rXu6vv149CHtQWi2vnzEDOPVU5fXyHYpErZVsejp32/ltxdBjs1PAcCoFPZWCVoT3Ar+hiF0xn9t31iJ2AANQePssr+bPF4+zbFe80093f8lvbxfFq3bbZ1RT46+Dn9f2WWF4Ky3UpEnmjw/Dw+J+mD69dNtE+Xn55Zfxk5/8ZOzwxz/+8YJCEV3XkUgkcNFFF6Gurg7f+c530Nvbi+eeew5f/epXAw8nVO69914sXLgQc+fOxYknnoiuri5MCvg74QMPPIB///d/Hzt8++23K+dmTZeeFB3yYB4Hvb1iZwOjIEetERGp1Bp25jr//PNx6623Bv4aShR2FfBxnarOW2+ZFn/Q0sJQpFBeFmCz3/rTaTF/RDVnJMwD7r1WigC50K1UnNpZqb5krl0rwkIn5V4pkk7nKnhU7Y7KuX0WIG5zY/DjMxTJTJkS8AZVrkxGRyKRQl1djToUaWoyhSKaUyWJ3AepgJYhXkIRQCz6K0MRuVKkwfy+aAxFAPPDLRmrRyQyhAYpFMmGG3IoAogFnMceAz7xCeu2DkmP3waHHkK+Z4pEFe2zUrnLmz3b3/n12yQUOoDU8DBqivBesHixupWTXYhhN/bJ7aOPy0OqYONVKRKJAB/7mBgzVVsrWmHlw+72amnx9xbiNRQp5CPRk08CBw6ICq3BQVH5snRp/ueXr5YWEb4Zi7/27GEoUo66pZkYEyZMUJ/Qo//93//FHYZdv6dOnYp/+7d/wx/90R+NBSK6riOZTBalrdXg4CD27NkDANi0aRMA0b4r6AU9Odxotxmw86UvfQl33Xknpk+ahOknnIBWHwl9X5/5cF1d6TrZElF1GRoaQjweR6ScdzAkKgAf+VR+5G/5pVy8rhReK0UAYOdO+8HrYX4ztasUqa21roaUeti60+Bz1W380kvu51luoYhqG7L3oWrmTZgfex7o0uqZ70oRBsOedHb24txzr8eMGdNw3nk34XCPYiiDvBIxTu2z3GaKtLbmQhGj3t7R/4zuLJDJiKxHbp8lhyLGp9G2k9+LkRknolF6Px0Y7ZllN/pl3z718UPS+3S9Q0+rQCpFMrnLmzPH3/n1ycNzDUaKNGPqzDPVL3Fe22dlH6LNzc4LZ2GtFMnn5bq+HjjvPHHb5ftyb/cy6WfIOmAfith9zMjHoUOiGPfYMfGRpNjDlZ2eh9k5JlOmiN7zRRgFRePg+PHjpsNtGzaIhMuv7m5g7Vp8dPZs/M0NN+CCxYvxN3/zN9iyZQs++tGP4kc/+hGuuOIKnHLKKWhsbMRf//VfB3MFJEcU1dHTilA1ezhbojbKEookEsA99+CsVatw2SuvYOFDD6EtEvFVKTP2Pj4qrOMZiajy1NXVMRChqhaClS8in+RQZHBQLNQPD4ufSZOAE04ozbaVKz+VIi+8YH+acqwU0TSx2mJc3QlbKOLWPsvL/ee0y7BTCFMqqsdSKiW2TRWKlHuliHx9fYYioX7uhcjXvvbf2L5dVFVt2fIcPti5G09/7GbEoobnlfz4d1pZlgPlIrbPamkRq8xyMUoyCfH87u8HkHupG6z3XimSjsaRnjwNjdIKu1soAqhHMFlCkcZG2793G+ki06LW1fZMOncf+S2a6ncIRYYHBtDk7+w8mTwZ+OQngf/+b/PxpofaSy+Jn9ZWaG03AchdsexDVNPEXvzSmp31/NJpYPt2kVr4LaVx4BSKOIU1pXq5am0V2yU/pf0uQI5H+yxpLI/lti5UX5/4OHfsmBgrNzQEfOUr6rfSiy8GLr9czBai8nXjjTdi3rFjOD4wgO7hYSxqackvYdy6Ffj97xEH8PdLl4oyJkOvup07d2LlypVjh/fZpecFksOK2tpatBQhTVi2bBkymQw6OjrQ0dFhDUVqakTppPHNrL9fvOB4NHs2cN11Ihzp6+NzjZzt2SM+ftbXi/fd5ubgh5ZnO8Q1Noqf885jY45ylkwmcdddd2H+/PlYsGAB5s+fj8WLFyMWhu/8RCXGZwGVn9NPFwuGiYT4WbvWXLlw1lkMRfzyE4o4LT6HeS8Dp104wxaKOLXPyvc29lMpEoaafadKEVX7rDA/9ryQK0V8DFrXm4qxbFuZVq36g+nw68d24+9Wvom/u+qcseN0+fHvsGpvqRRxaJ+VTmcQjdo/Tt1CkaYm8Y1X3rxkEtj3TjeGt4k1mOzDYbh+guV0xjMwZoupaC0itTW2lSJOe9KnUtZtGpJWnZ1CEd/ts2KK2zidux/85qN9dgM+ULxKEUDMyDj1VGDz5txxY7fFsWPQn3oKO44fR1tnJ9r0p4HGD42dznh7q0IRLZNGW88eRCMtgD4J+NnP0Lt1K2oiEdS/973A+ecHch2cQhFNUwcQQOleru1CpDCGIosXA6+8kjtsbJkXBE0D3njDfFxPj1j82rpVPD4nThT3lY+1XQqx2z/6UWtlSD4tH+XkubPTlI7PloLX/fv3+78MDxoaGnDzzTfj8OHDOHz4MOLxeFHmmPzgBz8wHdblz6CaJt54je8lcj8sFxMnus+IIsp66SXxOp112WXAu9+d//m98gqwcaN4Tx8aApYtA/bvN39OPPPM/M+fSm/37t245557TMd1dXUV3EaRqBIwFKHy8973mg+vXGkORXwsJtIoL43Hs9/6nXbxDPPe6k7NvuW9uw8eBBYuDLb64PBhcX5Tp7qf1qlyI99tKrf2WXaVIkBFts+y3OY+KkWSBQxKrSb79x/DgQNrLcf/YvNbplDEElD5qRQxLDAdPtyDHTsOYefOg/ja1z6HkZEB3Hjjnfj+9z+PeNz6HHMLRbI92eUAYt8+4JHXerD4gJhBMGsWkIg3IRMxX8bY1Ri9fqZKkVgtInVx20oRQOxroOq0kkyatymTyWBEek2pdwju/LfPsq8UOeUUf+cFAP0Oi1cjRQ7I7Tq16atX4wO/+Q0e3LwZTfE4vnfuLcCluVDE+HIh77mp6Rmc+dY9aO47iGhNBHjiLPz1Pffg2y+9hLa6Oty3Zw+uDiAUSaWsHx3kLmnxeLhCEUBU6RQjFNH1YGeKnHYa8Prr4rw1DbjggvzPS6WxUbxcGV/COjvFDu733y8O19SIgfW3317+b7EE9fejfEKRyZPNh5NJEQiM9naUQ5FiVYosWbIE92cfrMX24ovApk1AUxO0pibgpJNEcpnV3GwORUYrN4Py9tviLAcGxM9ll1lbaVL1cNohIR89PeZ2qMeOWb9qBVG9lMkAu3eLzDBbFXXZZQWN4yOPtm/fbjo8ceJEBiJEo0Kw8kVUIPmdtNR7+Zcbr71Lyj0UcasUMVq1SnxCvOmmYIKRp57KTdW9+GLg0kudT+/UPkvT7IeNOym3UMTvTJEyb5+lR6MwXgMvlSJ6QwNSl1yCjMMQa8p56KFVyuMPD/cgmc6gJlvFIT/2/MwUGX0/euaZd/DHf/wBDA52mn5/333/hNWrX8JTT92Hlhbz647bTJGa0dcBeSF9YABoHcgtxuzfDww3W3ftTiZHd+YdPWx8GqWjcUTr7CtFADHk2S4UMRpSvAcHWSmiCkX0dBKaBlxxhb/zApxDkeEiD3Kw69T2xqZNeHC0hKQ/kcD/t/ZpfPiSDHRNPEaNjwG5k8uE4zvx5Kbf4bf7X0V7XRu+/+sd+Pbo3Knu4WF86emncXUA264qolGFIqqbsJQfFVTtP/zOFLF7ewpypsjMmcCf/Amwaxdw4onBDzbXNHFbHDiQO+7YMfN9k0yKdXQGIhWivh74i7/ItRseGspvNbWpSTwgm5tF1cjkyabePcuWLcM///M/Y/bs2Zg9ezbm+B30FEZHj4odprJqa82hyMUXizezpiZxuwRcwfvkk+avt2eeyVCkmskfswro3ArA+jKgqkwsNHgBxGfQn/3MfNy73sVQZDxs27bNdHjBggUl2hKi8AnByhdRgbLNLuvqxI+8BxM587ro4yUUUS1Wh4XTLpyqT3rvvANcdJG3yg4nQ0O5QAQAnn8euPBCf9Nx5dNGIv4b8TvthR7GUMSpUqQK2md5kbjlFuhBr5RVsGeffV55vA7gUO8g5kxoQvrUUwsbtD76ze7v/u6fLIFI1tatL+C7370P3/zmx03Hu1WKZNuCqF46aofNLaBGatW7vqdSQM3o67TxJSQVrUWk3rlS5IwzgCNHgFdfNZ+np1AkyEqRiPUGmDp5BFff5H+eCAD0OVWKFLF9FmD/UPuJoR8/AGztOYya5CAS8SbL3516quhHn93LM7Pt9/jPHU8CAA6P9OAyaQVind0AEp/km0bTrAsbdsPWS7kAogpFCq0UAcTzKei30unTgw9DjJYtE9VVkyaJj84TJ4p9OIyKMLeaSiXb5qnQBXtNM80Qkc2ZMwd/9Vd/Vdhl+PDSSy/hjTfewMaNG7Fx40Z89rOfxS233BLshciVH/JtKC0wDg0N4dDevZg6dSqaAghIGhvNC+EBF6JQmWlpEe85g4Pi612hgYUcqkQiwC235CqThoeD6awcjYrHsnHpoa+P7zPjYfHixfjkJz+J7du3Y9u2bTj55JPHfrd582b86le/wtGjR3H06FG0trZaWm0RVbIQrHwRFeiMM8QP5cdvKOLUSsbpd6Xmp1Ik69ChwkMR1e42Q0POny7dVlbyCUXKrVJE08SnZ1OPH4dKkXIPRfK5zcMw+6WMrF37gu3vDvYMYvaMKUiddx6i69aZfqc5rdrLAcDoau+6dX9QnDhn585dluPcQpEs1d1eNyKFInXqIQDJJFAz+jyS22fF6mocQ5FIRMzTff11aR6JdPMMKYKEBodd8f2HIhFEEEEGuY246soklizxdz5Z/Q6rSyNFbsdpV5TUoHhPigweA0ZDEePfxWKivVFXl8iLP3bfSsvfWmQyBb9myndzfb31LMMYiqgCkLCGIsV21lnW42Ixsd6bfVrIlUhGhjESRCXzne98B4888sjY4QsvvDD4UET+rmQTdFx22WVYs2YNekZbaT3yyCO47rrrCr74xkbR3s5uc6i6fPSjuf+nUoV/BZo7V3Qnb2gQ7+XZIrBiaG42P357e4tzOWR2+eWX4/LLLx87nDF8kN+5cye+9a1vjR2ezh3uqMqE/OM6ERWd31DEaXHd46JeSfiZKZIVxEK73/AC8FYp4pfT/eY0w6SU5FDEqVKk3Fdm8uknw1DEl+5u+77mW087D2fcfq1YefBTKSI9r/S6OmQy7q3tRkasi+2FhCJypchwrX0okl2pNj61MloUsYY4GhxCEePlG6+2pVJEETLUBxiKAEA0EjF9oUsVEMg7hSLDRa4UsWuf1aRIDUZ69gJtJwBQvyVMniwyut6E+4DfTDKJiKHdTT689DQPYygya5bY1uz2T5jgf8d5r6FImDuK2nnPe8RPf7+YvWIc/tzfL1ro7d4tfs45Bzj77FJtKZGwcOFCUyiycePG4C/kqqtEW93+fvFjs2t7f3//WCACAAeNLbcKMGuWeN3PNkdwCiupugTxlW3KlOKFIKrLymTEzgjNzeI9mMZfxLCWMEW68zs7O6Hr+liFOlGlC8nKFxHlrbsbWLFCBBIXXyw+OfvhNxRx2nM2lRKnC+NKQD6VIj6GXdtSVTW4rQK6hSJePqTMnStWLbL8tM8Ky2J7LGbe7gquFNHz+FaTz99Uq+HhJDIZ+4XzfSPITZH0OlMkmbT+rr4e+/ap22aZt8faYsptpkiW8ekZSw3jhD0voK3HPOzDrlIkkQBwyinQa+JjIczxthMBTUNds/NMEePlO4YiUjuqiKahxqHhdT6hSEyLwHixyQJeq/sc3gNLNWi9VvF6NtSzF9kswe6pX1MDxDX318LE4CDqShiKFNr/vBA1NcCNN4oe/dEocO21/jN1u1AkyJkipabqsrRiBbB2be7wrl0MRcrF9u3b8U//9E9oa2tDW1sbpkyZgjvvvLPUmxWIRYsWmQ4HGYqsXLkSGzduxLRp09De3o558+ZhlsP3LHkP60OHDrlexqFDwKOPigXilhaxSHz++ebTXHllXptPFDof+ECpt4BkciiSTCbR29uL1lb1dwmiSlPGH9epKh08CGzcKL5px+NAayuwcGGpt6q0Hngg10z80CHgc5/z903ca2NaL6EIIFbdSrniYcdpF067ECeIyhfVip9b9YiX9llOPvMZMTHVGIqUW/sswHq/ZLezAkORvLbfbsWRLPr7nRe3OzqOjP1fDpts22cpFsz12lps2LDDdXsSCevzMZ9KkRN2P4/Z+1+xnMatUmTkhg+i+7VnkYrVYdv8awAAjROsM0UGFZUS8kK+W6VIfSwGzeGxmk8xXVRa+C+oUsShGmS822dlr4ZqLstQ3/6xUMQut45GAc0h/MsaGRxEXYG7Z8otL1TVFmGsFAFE+/9CZoxWSvssv+bONYcie/awhVa52LVrF3784x+PHZ46dWpwoUhfH7ByJTBz5rimZN/61rfQ2to6Vo1x880345xzzsFi4wD0At1///344Q9/OHb4U5/6FO6++27b08uhyFFVC13J8ePmGe6qUISIqFimTp2KG2+8EVOmTBn7iYZxB1cqCq/fPytZhX1cp4rX0QGsWpU7PHNmdYciqVQuEAFEwLF9u5i86lWQ7bOA8IYiTrtw2u0JEcSbhGqxzm0VsJD2WZMmiR857CrHUETejuztVonts/K5zcNyP5WB3l7nUMS0cOGxUkRunQUAqKvD1q17rMdLRkas25NPKDK5c7P6/B1migBA39STsObMk0y/a2zzXimiOs+sQalSpL6mxvGxmlf7LM38Za2QUKTPIRQZLnKliN1DbUCxTf19h5CNMexuTk0D+keOu15uEAPku7vNh9varKcJY6VIEFRvwalUZbTPcjJ3rvnwwICYczBebVcof93SE7ZN9YTNR0cHcO+9YoeprVuB006zPMF1Xcfg4CAas9WYAUgmk/j6179uOu4b3/gGTjvttMAuAwAOHz5sOtzu0rfqz/7sz/DBW2/F9IkTMf2EE9Di4XaWA2a/M46IiArR0NCA3/3ud6XeDCqRnTt3lnoTSo4rKlRe5EWjAts/lD3VXqzHjvk7Dz+hiOpbvyysabPTwv+cOeKxJC9yBtE+S3UehbbPcgpFsl9G5efGyIj9Lp1hDUWqqVIkn9Wzcg+CPNq0aT+SyTROP/2EvM+jv995j/9jx7yHItF16xBbtQqatJKh19UBkQh27drruj2qSpF82mfVD6sXwBM1jYjHrS/H2ZcWOTOtrwdiDXE0SavYvT3mWSWAfXVD1pD0nlIfixU9FEkW8L7T7xB8FLtSxK59ljIU6c8tzjl1OOwbdp9aGkRbMDkUUe1bENZKkUJpmnhIGx+7lVApkkqJjwp2a9dtbcDUqeK+njsXOOEE88wRCi85FJkwPAw8/7xou5uvHTuAvXvFQKP9+0VPvRdeAN7zHjz33HP4+7//e+zbtw/79u3D4sWL8cYbbxR2JQxUFRjTbOZ8ZO3duxff//73MTQ0hC9+8YtY4KFcrKOjw3TYNhTRdeAnP8Fpvb3iDTaVAu6809PnNDkUcRjBRUREFKgtW7aUehNKrsw+rlPVkxeNst+4OzrEh/HhYfEzf3517GqjWtjwuweo1z1s02lvIUFYQxG5OsO4CB2PA7ffDsgl8UFcF9V5FLN9VraxuxyK6Lq4r1WrVGFdybGrFFGFImUeEHA+iNpXvvI/uPvuvwag46abvoD//d+v5nU+fX3Or4vHj3sMRQYGUPOHP6gfg6MrvXv37nbdnkSi8EoRLWP/OtLQqOGWW0Sf8q6u3PFjlSLSLO6mJnHGbdJqdY8iFPHbPqshHnd8fubVPisSYPssh+DDa1CVL7uAaUDx3t43mHuM2r5c6Dp6PAxaD6JSRH5oVFOlCCA+QsihSDnOFNm0CVi9WlR89PQAixcDN9+sPq2mAXfdVfZvt1Vp0aJF+Oy11+L4gQPoHh7GyRMn5pdIG61dC6xbZz3ukkswODiIlStXjh29z1jVHgC5giMSiWDSpEm2p9d1HR/84Afx6quvAgBee+01vPnmm64tYs466yzU1NSgo6MDHR0d9sGLpok3W+P7n8f2xKefLnKl3l7x43c0JFWXI0fEQ62hQbyXNjUF/576y1+K5ZTGRvFzzjmAS+ZIIfYf//EfWLduHRYsWID58+fjXe96F+bMmVPqzaKQ2Lp1a6k3oeTK4OM6kcHUqcCSJWKhOZHIvUPfd5/5G/qHP1y9oYi8+6Ybr1+K0mn3eSJAeEMRt4X/9nbgoouAF1/MHVesUKSYg9btKkUAcf/Jq1SZjHWBNywrOfKXVbv2WeVeJQJUxnUIWCKRwt13f3ns8O9+9z387d/ehVmz7Bc+7LjNFOnu9jZTJLphgzoQwWilCICDB3e7bk8iYX0tdQpFPv/5z4/9P/tyUJO0Lmy3twPpWSfgzjvFW6BdgCGv1TQ1AYjHMUEKReS9i42XnyW/nFkqRZzKGpDvTJEA22c5vK+VrH2W4nJ7BkerQHUdDRveBJ7dKO6MWbPEikVdHTAygu6Ee/VnoZUiqZQ1WPMTipR7pQhgfXtKJKxvTWF5K3WS7bqatX49cNVVYm911UcNBiLl6YILLsAFH/qQ+c4u9Il4zjnAO+/kqpCXLQMuuwxYsQKzn3nGdNLDhw9jZGQEtQFV+NfV1eHmm2/GkSNHxgKSiMPnqLfffnssEMke3rhxI5YsWeJ4Of/+7/9uOqyr2rdmNTWZ31zlF0kb7e3ih8iLd94xf1VdvBi45ZbCz/fJJ3MFX3LTiQDH9FAJPPbYY3jyySfHDn/zm9+0tB+k6jV//vxSb0LJlcHHdSKDhQvVM0Tq682hSJFbXoSGamHDb/ssrytS5R6KOFWKZMkrOMVqn1XMmSLZUET1ZXd42BoWqhYTw7KS47V9ViUECi63eWbmTEQOHBinjQmH48eti7vPP78Ot912qe/zGhhwfu3q68uFIo6VIjaBCICx59yRI7tdtyeZdA9F2uvaMKd+MpaePAt/93d/Z9m8eNJ6+5x8MhC55lRg9Gkuv6TZVYo0N0NZKdLd0wNd16EZVkJdK0WkKoR6hyHrQDChSEHtsxxe54tdKWLbPkvxXtsz2iptQvcu1D3zGPamelEbjWLqli3Q9u4FPvIRjBw/joGU+zYXWimiKCDy3D4rFnNu/1Uu5Lcn1UOlHGaKqHau/973xEeFz3+eIUhFkV9XCg1FZs0C/uzPRAutOXNyD6ZUCrMVT/7/+q8DWLBgHlpbgfPOK+yj5sKFC3H/ffcBzz0nwojGRmDXLmD2bOUZbzeGQaPWrVvnGooAEJexdSvQ1AStqQlYsED9XbS5WXQuyPJYKeJFJiOKcAYGxNkODOTCS6ou8lf/bIOAQh05Yh5TahTgOCAAwO7dYh/Ovj5RHXXOOZxNVUzbtm0zHfbSOpAqw969e3Ho0CFMmDABTU1N2LhxI15++WV87WtfG/tud+ONN+JrX/taibe0tEKy8kVUIPmDfZH37gwNu1DEbnaEip9KkXJun+WlRZT8Ja5U7bPkL67ynnVeQpFIxDonRRVqqe7/sKxYeR20XgGrNrpL7XvqoosQ/81vxhblU+9613hsVkml09YAIpnMr92HW6VIf/9RpNMZRKMR56EZDqs4em0thoYS6O11D69UlSLyAvzFkxfizpOuwumXTkJTU9PY8dGo+JErRTQN0C65GDjrrLHjfFWKaBraDJcDAKlUCgMDA6bLDzoUccqZ7ATWPiuTQZ/D+9pwkXewsG2fpbjc4yO9iGRSiB9Zj8uevwdvHBKPszmtrfjRddfhqg9+EJ3793u63EJnpcihSF2den1VdddXQpUI4C0UCcv+BU4mT1Yf39hYEW+tZPShD4nvDUND4vPg1KmFn+ekSdZkraUFrbW1+P+uvBIzmpvResLpWKHfjuPHZ+LNN8VJJk9W5wq+9PcDq1aZj/vyl5VPvFtuuQV33XUXHn74YRw8eBCXXnoprrnmGm+Xc/QocPBg7nBDg3rjzz8fOPNMkVQ0NY2+sQZD04DHHzd/ZF++nKFINZL3aQiqdZZTuBJ0KPLww8Bxw0i8uXMZihRLIpHA7t27TcexMqC86LqOwcFBNDQ0mHZS8+KnP/2psiropptuwmmnnRbUJpa9Mvi4TuRBtull9pt5JTSs9kIViiSTYtcLr+3DqqVSRF78V+3CKYcPQVwXv4PWdd36iVf+pOplpggw1k5ljNdQJCwrOVVUKZKZOdPx93pbGxI334zomjXQ29qQeve7x2nLSmd42Pr8S+XZA31gwPycqqlpRNJQaaHraRw6dByzZk2yts8yvkY6BYZ1dThypAe67r7Kn0y6zxSpiYjtiMD6Gl1TYw1FEi2ToV12qeV05ssV/8qhSHZhpU2xwtLd3e0rFBmU22e5tEuR34ImTjTPQVGJyJUieYYi+sgI+h1e50tVKTKouNyuRD9qEgN4c+fKsUAEAPb29ODjDz+MfUNDOOqxmmy4wEoRL0PWAfXTpVI+nqnaZ8nC8lbqpLkZuPRSMR/b+FxkO58KlP2uVGytrdA0DV88/3wAwIHYHLyeMvevf/bZAEKRAeN7uI6716zBhi9+ERs3bcLGjRvx9NNPmxZ9/uu//gv/9V//5f9ylHsRKJx4oung4OAgDu3Zg4kTJ2LChAn+L9dA08RdZwykB9w7JVIFamgQ7SoHB8X7TlCVIqrzueQS8TgL+n27pcUcinjsNEd5SCaT+Id/+Ads374d27Ztw7Zt25SVIqtXr8bDDz+Mo0eP4ujRozjppJPwz//8zyXYYjI6fvw4rr/+erz44otYvnw5fv/732PixIm+/l7l97//PUMRgzL4uE7kQRDNNMuRXUXMsWPeQ5FqqRTxMgG1GO2z/FaKJBLW3/sJRYyfXOvq3NvKhTkU8TpovQJCEb29Xayy2Tw29GgU+rx5yMybN85bVjqJhHWhO51PryUAg4Pmx/7EiXNw+PAm03H793eKeSX5VorE4xgZ8bY4n1K0OJJDkdjown9EEbLU1AA10uwIvd76jVZelM6+BCgHrQNoUbxvdHd3Y5Zh8muxK0UWLzb3y1aJRYKZKTLc24uMQ4/48R60nkqJXHxA8b4xnElC7z+Mo73W/hYH+vrQe/Qojhr3ZnYQdKWIap4IUNmVIvJ9V66VIpoGXHyx2NYtW4DDh8V1YShCeZs/H7jtNpGWtrRgz5o64CnzSQJZ0DeEFZqm4R9efBEHDC9OmzZtCmbRR95YlwqQG2+8Ec888wx6e3sBAD/+8Y9x++23F7wZDEUIAN73vtz/89xPSGnRIlGtUV+fC14KzPJsyfvfMBQpnsbGRnz5y192Pd1bb72Fb33rW2OHzzJUnVPp3HvvvXhx9EvRa6+9hh/84Aemlspu7EKRJ554An/1V38VyDZWgjL4uE5EtuwWNo4ds+yxZIuVIjlBts/q7xe17lu2WH/ndJur9uCVQxEvg9YB6+qTl1BE08ITMthVilRg+yzEYsi0t9vPDQlLS7NxNDxs/baXTOYXisiVIo2Nbaira8PwcPfYcfv3HwVwinUlM5MRP5EIdKfHWm0tRka8fUP1VimSDUW8VYroDdY9gOW1m+wiit2Or9HaWrTU1qLXsMIrD1t3yowAYEgK6xtcVsHll8MTT1SHIrFY7iVArhTJt4Koz2UG1/A4hyKAuI4DNpd7whv/gdd69ip/133kCI4cOuTpcgsdtC5XivgJRSq1UqRcZ4pkXXCB+NF1cf86ZZmvvSbCk95esZh1+eVinhERABGGGMrHTjlFPFYMc84xNOSv069SQwNw2mljQzYWzZplCkU2btxYwJkbXHONSCP6+8WPS9ux4eHhsUAEAA55fF12M3u2uMrZgp8gup9ReQsyeD/hBPEzHiZPFo/flhYRkLgUy1OxJZOYIu2hdPTo0RJtDBn9y7/8i+nwmjVrfJ9HbW2taSevZcuW4eKLLy542yoJQxGicma3sOHWe8TIT6WIl1CkyItIeclkrAvpqk+SQbbPevppYNMm9e+cbnN5169YzLo64RRaGK9DPqFILBaekKGKKkUAIDNrln0oUg67HAcsyPZZg4Pm18ra2no0N08zhSIHDowOW1cFUKmU4yph99AIkiNpZXWLSio1jEzG/JokVyX4DUW0RmuliByK9PWJEEN+KRjbay8ex4S6OsdQxK7lU5YcitT7DEXicXEZcthSU5O7rGgkmPZZ/fLqvmSkyCG/6qH2+8cy6FM89gHg6cPr8Fb3buXvukdbHngRdKWIXfss1VPGpZta2XALRaLR8LyV+qFp7nsHb9ggZmtn2eyISCHyuc99DtFoFG1tbZgwYQI+8IEPYPr06eNy2ZMmiXEbr74qPpZmF/WTSefwzdXs2eLn8GHg6FEseuYZrNiwYezXhYYiTz31FDZv3oz29nZMmzYN8+fPx0wPq7fy7eoUinR1AY8+Km6X2loRGl91lfq1w+sIFKKwu+QS8UMhsGkT8NBDmLJ9u+lohiLhsHDhQtN7yHnnnefr73/yk5/gJz/5CTo7O/E///M/uOGGG7Bo0aKgN7PsVd8qC5W3VavE4nY8Ln7mz6/uKXN2oYhhDyVXfipF5NMuWyYWa994I3dcGCtFVAupXitF8t2V7e237X/np1KkocF6+Q4hwEAyjprE6FWRd8lVLYTJi4lhWnz3OlOkHFeeFDKzZoldcGWaVl67HAdEtdA9MpLf64u8UF9bW48JE6bh6NFcJdfBg4cBiFZlio0B4nFoiqngX3vyNXxv7fOo+e//wfU3/pnHLdIxNJQwLT7at89ShyLxpBSgKhpCy6FIdkdX29PV1KCtrg57DKvebqGIpVJEep1xmymiyjjf/37ggQdyx82bJ2bdZkUj5tepfNtn9bmFIiWoFFm3OonBlPr63Lvnedvz6j52bNxCEfnP7brJqBY8K+WlzC0UCdNbadDkLnt+PnJSadx9992m17Ply5ePWygCiK9qX/tacM//f/zHf0RjYyOmTp2KaatW4eyWFrw7FsOR007DoosvxqLLLsMZZ5xR0GX8+te/xo9//OOxw5/5zGfwb//2b65/J9+unZ2dtqft7wd27codrq0F3vMe/9tKRJSXyZOBkRHMbmnBTQsXYkpjI6ZceSWmtLcjk8kgUiE7HparPqm33NQ8SwQnT56Mr3zlK6bjkskkbrvttrzPs5JU8Ed2qkirVpm/jd9+O0MRFXk3Tid+KkXkhae6OusCfRhDEVUI4aVSRNcD2JVNwek2dxuyDtiGIjt2AL+9O4ZkM/CBDwAL5T20VY8XeVvC1KbJrlJErvqpkA9smWnT1L8IU/WOD7/+9QtYvXo9PvKR9+L00/3X5A8PWxeG5YoPr+S/q6trQF2dud3U4cMiFFE9B7RUCjpgWcE/0jeEf133InQAidQQ7r//Xyx/a6e/37yybNc+S/NYKRJpdm+fpQpFamoML33xONqk1w2/ocigPFPEZ6VINCo6oqxdC2zfLi7vggtEN8KsoNpn9bu8Vw4X+f1M9TakJ/qRVsyRcdPd1YWjHnfZLzQUUd1nKgxFKhP7wZeXoaEhS8DbZtfzrmh0dHd3YdKkSQWfUyaTwde//nXTjLE1d9yBmxYuxE0LFwIXXSR6ugH43//9X3R3d+O9730vTj31VGiGz1K6rmNgYABNNqluR0eH6fA0u89oko997GO48oorMH3CBEyfMwfNDqVX8ktxpcxcIqIyMVrqO7u1FQ/ceqs47s//HPAxzJuK5+6778a+fftw5MgRHDlyBMuXLw/svDdv3oz7778/sPMrZxX8kZ0qjq5bF9yDXqwuN0GEIn4qReRv/apP72EMRfKtFAHEdQ76cea3UkSmCAH6+4F9+4D0rBqkUsDKlcDC0/JsnxUWXitFKiQUsWu2r4fpPvHo+99/AN/61icBAD//+b/guedW4ZRT/DUNVrWiyjcUGRoyP6/q6urR1GTe3fnoUftQZOx1TXrsvbjrEFJ5LGADwMCAcyiSrRSJImOpWPPaPktevBwctL49NDUZznq0UsRIHtLnWikivU/UOwyRUHU2jETE9tx2G9DZKdqsNDSYLzcSVPssl/fKYrfPUj210yM+3r8Nuru6cMzj6nShFTCFhCKV8nLtpX1WpZo7V7wdZ/vBcyfDcJODbQBoe/JJYMGCol5uR0cHbrvtNuzbtw/79u3D8PAwBgYG0KD6XOvDsWPHTIEIAExtNOwUYKjM+P73v4+NGzfiL//yLzFv3jx88pOfREdHB9atW4d33nkH8+fPxyuvvGK7/Ubt7e3uG/eLX2BBZycW9PWJF8qPftRxcZGhCBGVlPxBbepU72tDVHRnnnkmzjzzzKKc97p164pyvuWo/FZaqHql09bF0OwLeTIpemsMD4ufRAIosGy6LNiFItkP416+lRcyU6S21rqiFcZQxGuliGoFJ5/r43abFhqKKKoGdu8W/2aiYuWwsxPeZorI1y9MC/BeK0XKsIpCqaZGPUyhDFfXHnrot2P/HxnpwZ/92VexYsVPfJ3HyIh1oXs4zz3ch4fl9ll1lr0+jx0bXQCJRKz3g00oEkH+j72BAfMqqrxQHR9tEaVpEI99w/OhpgaoSZjbZ0Wb3dtn6bpov257mgAqRSztsxxWeRTdyMYe7poGTJmivtyoJrXPynfQulsokmfY4pWmmQfIA0BmOM9Q5PhxDHvc3vGqFFEdX4YvZ0rVXClyyinih8pDTU0NPnvbbehevx7dw8M4PjyMtnG43ObmZjzzzDOm4/bt24dTCnzwHJbfxABMnTEDaG8XbxqzZwMAdu/ebZorsnPnToyMjOBf//Vfx44bGhqybRGzfPlyNDU1oaOjAx0dHd5CkZ4ewPie6RJUz5gBXH21+Hg+MqL+yE/l7cCB3PLE8DCwcGH+93Nfnzi/hgaxH1V2p5Eg3X+/aImYnf2zbBkwa1awl0HjY+fOnbjjjjuwYMECzJ8/HwsWLMB1111nqpgDAHzuc+K7j9wbkyoaQ5GcCv7IThUnkwGWLBGfGhMJ8W92saWrC/if/zGf/vTTK2eXRBVdtx98ruvik5Nbebyu+6sU+f/Z++4wuanz66PpdXvxene9tte994IbHUzHdDAdQyCFTghJCIS0H6F9SSAQSsD0GpoxYGMwYGNs3Htva29v0/t8f9zVjHQljTRlZ5vO8+jZlUaj0WikW97znvPSRcAtFmFErDuSIkqVIjod2c69Jql8H7ks3E6wz2Jje2FNPHIY1pvA+5b0/bJnD/DBB/xt3SmS09eUIgCiZjMYsQrTPQxbt37BW1+//iNEIlFoNMpJhGBQ+JxkihQxmy3o358f4Ghriwdaono973dggkFR+6z2FGucAMqVIhoNhKSILgp9iP+dxOyzLBbyfu5p0zVfeWoSEaVI0qQI1f4lygoW636kguY8UoRSiqRsnyVTDKGz7bMAISkS8qdWoKGtvR1BhdchXQWMUlJEjK/uLaQI3VX2JVJERc9CUVERnvrVr4DPPotvtAr7i4zip59g/fpr5JtMaOX025kgRQwGAy699FLU19ejvr4ewWAQ+nvuEey3hOu5CHIdbrzxRjz00EOxbW63G4cOHcLgwYMF73/mmWd461E6IUcMdju/AJZYES/eOZFFRe/Fyy/zx0klJakTGUeOENKCRVER8AulpewU4N13ge3b+duqqzN3fBXZxc6dO/HVV1/hq6++AkDaQNHac1m3U1SRLqLRqJDcShKnnHIKfD4ffvjhB6zj1gfug1CH7Cp6DgwGUihBDGL2HH6/pB1Nr4DfL8yY58LhkO/kkpFHipEiVqswiN8dSRH6ezKMdCDdYOArcFKxGZF7T4bts4JBskQZDaJM/LWgVoYUWbZMeOzuFMmRUor0ZlLEYgFDB2q702+iGAwAfvu0atVOzJkzSvERxIqq0zZYSuHzUXUuzGZUVPA9XxwOjlWG0ch/FiWUIm3e1G2IPB5lNUUACNoMY9QHhrLtElOKMAxpprnJqpQjiEApki9DitC3Ix2HFyhFEvTDYk2h1OPcKfZZMgGrzlaKAOR7cS9ZKFX7rGRIkSwpRQCgvJxktrLoJBeArIP+zvTQp7eQPyp6CbLt06TRAG43yiy5PFJk1aqjOPXU9A49bNgwvP3GG8DXX8fT2Q8cAAYM4HVQkyZNwqJFi/DZZ5/h2LFjmD9/PsrLy1FYWIjm5ubYflu2bBElRQAQL9p9+wCbDYzNRiRSI0ZInxwtz8xgwZ1wmNTacrvjy/z5qrqku8Ns5pMi6XS/9BQx02GOmhrhts7iT2tqiKuBw0Eek5EjAanHUEVq2Lt3L299SFERsGYNsfRjl140h+7NWL16Nb7//nv8+OOP+PHHH/HCCy/gzDPPTPgel8uF7du3Iz8/HwUFBcjLy4NOp0NbWxuWLVuGzz77DMuXL8c777yDE044IUvfpHuiJ0ZaVKgQQmxw7/P1blJEyjqLBdcWxO8nI6m8PH7qZjKkSDAoHI3ZbMKIWHckRehz1GqlLZdoUqQzlCIZJkVYy/+Ihl+QO6Axgfdk0CNxsWyR7hSAl1KK9Fb7LEC0zYr2wOhaRcUE1NRs5G17/fWP0yZFUlWK+P20UsSMykq+fZbP1waHw4ucHDOiBgPPGIuRIkV8qZMibjf/nASkCKON1deg2wxTmCKoAehzxSMjdjs/LkPzABlXilDfw5wgYpPIPosGt2nSaij7rBTJC6dMwCpbShEuQv7UgmhtDgcCCkkRX5ZqigDAySeT7FOfD5gxA8hAneVugb5sn6WiB2LaNGDUKDK+9fk6nxTpsGH5+di5qGuIoMBagsOz70dubn8cPEiGuiZTGlnobjfw/ff8bffey3vwZs6ciQkTJuC5557D5s2bodfrwfh8+MWiRdCYzRg3bhzGjh2LQYMGSX9OYyNfXpmbm5gUmTqVXGebjXSuGYwoMwzw6af8fnPKFKCqKmMfoaITYDKRwD+LdEgReuqfaUJMTJjeWaTIt98SwwIWNptKimQa+/bt460PNRiAzz+Pb7j33s5XDarICO69916sXr06tr5mzRpZUmTbtm2YOXNmbN1isaC1tRUVFRVwcxKdpepq9SWoQ3YVvQOsF7/BQEYfJlNiFUVvgFJS5NAh4M03yYx92DDg8svjQfVkSBGnU3hNrVb+SA8Qj3J1NejvmShaYTTy11MJismNeJOxzxIbrFDRGDZmybXOAoCAlgqw+3zxgs1Sz0d3iuT0RaWIGJHbA+2zIhFh27Ju3bcA7ld8jEBA+Jz4/ZkhRSwWCwYNKhXsd/hwI8aOHSC85h3tAEM9N+0png8AeDz8toWuKaLTaON8n4hShPvusEYPvUX8PqETVxO+nkJNEb+fJOzOm0ceRQ9daL0T7LM0NCmitC+LRMiHdhzMTbW3DMPwLFLo79IZEJBMgRRJEadTuVIki6RIdTVw992k2+tNeSpSnD2L7tSVqlARmxtlC7m5AICTSkehHkBIa4TD3h9tbTq88grZZfDgNEkRLjQawGJBXV0d/v73v2PHjh3YsWMHGhsb4XQ6MWHCBGDtWuDRR/GQyQScdBIwZ47859BZBHIBxI56Jiy8Xi8ajx+H3W5Hfn6+/OclgEZDrJe4as+jR1VSpLvDaiXjLPYRpKeZyUCvB/LzyVSxMwwxrNaOmpQc8BJnMgj6uBkUVanowKWXXor+/ftj79692Lt1KyZzvfqMRgGrtnLlSixfvhyNjY1obGzE9OnTcd9992X5rFWIYcaMGTxS5Mcff5R9TyubNduBvLw8GAwGzJ49G198Ebe5/u677zJ3oj0U6pBdRe8AwwAPPNC7MsbloJQUWbIknsK4Zw9/BJ2MDztNfjAM6UzpyEAyREu2IKYUkQJdbF0ueHT0KNEAV1eT2YqS9yS6RmJ1W2TOkX0LW2SdRUBDTYCjURLcNRqlyavuFMmRurfoc+9Fz70oKdKdfhOFCIeFmftNTYcF21wuH55/fgkKC3OxcOEpvJojgYBYofXU7LMCAX57abWaUVycA53OhFAoTmwcOVJPSBG6HZBSiqQRXPZ4lClFAAjaDD0CfFJEa5CcaCdFiogoRehBtRhHt3IlaaqmTxdRiiQIIqVqn0UXWldkn3XoEJEsuN3A7NnAqafyMqUAoMRuRz2nr/P0JKWI242gwv43nZoiYqXI5MRsbN5Kb4Lcd+6BzbYKFZlDfj5w1VXYvDQX+xpyENIJCRk6Bygp0GSFxQIwDBiGwRNPPMF76dChQ6guLwe+/DKeEPTNN8DkyfKp9vTnyHWoHbj88svxySefwNPxJZ966incfvvtit6bCJWVfFJEzO5IRffCtddm7lgzZ5IFIP1wpqfcM2aQ8q1mM3k0iorSI3ESgSZFZEq8qUgBc+fOxdy5c8nKunUkJsSioEAwf165ciX+9Kc/xdZTrdenIo57770Xq1atQltbG9ra2vDHP/4RN910k6L3Pvnkk3jqqadQUlKCn376iffa2rVrZeuK0PM3lpg/66yzVFKEgjpkV9F70IsCo4qghBRxOIQWSceOxUmRZEZTYppdjUYYGejpShGpYKgYDhwAXn2VTLK0WuCWWwgxkmqh9UhEqDKRIUWi0TgpQitF/IxIVqDXS0a4Ur89lRXepZAqXkCrXHqRUkTUPqsHRtfElCJOZx18viBMJn3HPlHMnXshDh0i2S7ffXc3nn/+t7H9xQLdqStF+NEXi8UMjYaB3d4Pra2HYttrakix9SjVDsSKrlPtmyOQjlJEptA6VylCfa4hGgA3nB/WGgRNFwu5GA5vYmoyCUiRgwcP8talgts//CBBimTIPounFNFSShEFE7fD77yDX7z4ImqdTjywcycWTJoEFxWVK8nN5ZEi7kAgrq7rJAiVIonrnEihze1GQGH/6xOxplOKZH6z3gy579zbr8nWrcDBg3E/+AkT4sE6Fd0Hx48fh8/nk66Z0VnQ6YChQzHbDIxoIlOSn37iZ4PTOUBJwWIBxowhpIXbHVNwlJSUoKCgAC0tLbFdd+7ciWqvlz/2DoeB5mZ5UuTcc8lN7nKRpbhY0emFw+EYIQIATXT6fYqorAT27wcqKsj/qkqke+PwYTKts9vJWMxqzVzfoNVmvp8ZOTKzx0uEwkKgtJRcm5wcoH//7H12n0RBATB+PNDSQhaul2k4DDidKKYGpKJF2VUkhZ07d/LsqZK5pkePHsWRI0dw5MiR2Laf/exnmD59OmbMmCH7fr/fD5PJFLOeZkmR+fPn47777sNJJ52E+fPnY8iQITj77LMVn1dvRM+LtKhQoYKAWzNEDA4HQBXYAsCXz6eTAcBm/9IB6Z6gFMmUfdbXX8cD9OEwyUJbuFDePkvqGnm9woC/DCkSCMS/XoSylAnAILTKYs9N6hzq6xOdeXahVCnSi0gRUaVID4yuhcNibUsU+/fXYfRoYi+xdOlPMUIEAN5//3EeKUKTBGSbDBksATGlCADk5pbySJFjxxrIP1KKMQEpkrpSxMepRxKNRgXf16DRSSpFDODvm4gUkbM+4JEmIqQIAJx44on44osvYDQaJUkRlk9NRynCMNL8QyKliBL7rNtfegmfdhhYX/7ee6i95BK4KbK/pKCAqP864A4EyEl2IjFJ/26hYGqRwjaPB3qFcgyPN3VSJBnLs96Mvq4UOXQI2LAhvs6pW62iG+HJJ5/EY489hpNPPhk33HADFixYAHMWfewqKsgCAEOGAM8/H89C7yg7khrKy4GLL46vB4PAzp1gGhsxqqQE33NIkR07duAceh5QXS2wuuJi6dKl2LNnD0pLS9GvXz8MHToU5eXlik+viGtTA+lA2EcfkWG50UimZxMnkkCxFMaOBcaNU3waKroY33/Pn4qfcooy17a+gLFjyaIiS6iu5vsVcgdze/cCb72F4h07eG9RSZH00NzcjAMHDvC20ZbEidDQ0MBbv/fee/Hoo48qfv/111+P66+/Hj6fD62trbEEsqFDh6KlpQWWjhjT9u3bFR+zt6KXD9lV9CocPUpSLgwGsuTn9+0UGbpRzcnha08dDn4FMxbcAHk6BAYbSesJ9lnJeH0kY5/FCZ4BANiCZqnaZ4l5CciQItxMO4F9VpAhsyxu4E+OFOlOqZ59sNB676kpIk647tt3LEaKrF69SfB6MBiGXk9+d/GaIqmRIsEg/9myWslzVVBQikOH4tuXLfsUf/zjDYhS17wzlCJeznMZCoV4tSwAQMdwlCJU26CLCEmRVOyzWBfEGEwm5IvcgytXrsTixYuxaNEiydtRoyHZsQGqbbEkOAExflPqceYGmTUaftugxD7ro9274/tHInju00+FpAhVBdwbCiHi90OTRVIkEEiRFPH5kKewLfT4VFIkXcjdEr2dFKED2qr1SfdDMBjE4sWLAQArVqzAihUrcP/99+Ovf/1rl5xPWRnw+9+n31787W9/g9FoRGlpKUpLSzF16lTk6HTA228DABZWV+OkkhKMWrgQo6ZPx7Bhw8gDOWYMmRft2ZO4WDqAN954A6+99lps/e6778Zjjz2m+BxpUqRZgjXcv5//7FRXJyZFetFwt08gRfc1FSo6H9yGuGMiMLSgABeNHIliux3FZ5yBygTEsQp5fPHFF9i5cydvWzqkSLFCpSINk8mEsrIy3jaLnEqyj6GXD9lV9CocOgR89VV8fdgwlRThorycP7L2eMhomwY3opEJpUhPsM9KRimSjH2WFFK1z6IVJgaD+OyRE/3kkiKCQusBJE+KjB4tcdJdgD5YaF2UBOuB0TUpUuTgwWOcfYRtRUuLC6WlpEBrZypFbDaihigu5kcfdu/+Bn/4w0v48ylD6AOwJ83b7AymrhTxeuPvFfuueg2npsjixcBppwGzZvHPpwNhnVFSKZLID9pmox4fCaUIAKxZswaLFi2SDGiVlCAm0ebCnCAKkAxfzS+0zm/rFBda56ChtRUumhQRmXB4HQ5Y5YrrpgH6d0vVPqvd54NZYVvhVZUiaaOvK0XUIrndH5999pkgqHLNNdd00dkADBNFS0szrFZrymqVaDSKhx56CH7OOHvNmjWYPn06mZe43bhlyhTywpw5fPJjyBCyzJ/PS65pa2tDa2srBg0aFNtWxy3cAaBfv35Jnef111+Ps+bPR5HNhuKyMuRSJAkLusvsrPoNKroGKimiokegY4w7vl8/vHfppWTbb3/bI5PyuhPElDb33HOP4vc/9thjOHToEBoaGtDQ0IB58+Zl8vRUcNCLIkkqej3ooFFfHznSpIiYGadY8J0b1EtH1SFlnxWNdj9iJJnIWzL2WVJI1T6L/iypKKdSpQhLinDBBgHFzuGWW0gqX3eBlFKkF5MiYkqRaA/8fmKF1gHg6NE4KUIrIwBg+vTZWLjwt2hv9yAYFD57wWDyyoxIJCpQiths5DoPGzZcsP8LL/wVYapmBSSUIq4UzoeFzxcPyIuRIjylCACsWBF7fpmgcqWIVDMCiEzQTSbkGI0YJUIO7Nq1i3x2Ansrh0i6uD0vT/Lz6WYo0a3OJ0WSrCki0icFAgG4qba6RCRF193JdZYEXU4oterDUQDNcrXGOuD1KyhMLwGVFCHo6zVFysqAadOIHcyFF5I4s4ruhcbGxpiHOADMrKzEyKVLgWeeIZ7yWcKll16KYcOGwWKxoLi4GN98803Kx3I4HDxCBCB1RAAI631IWb8wDL76+mucc845GDBgAPLz87Fo0SLeLmmRIu++i8EffYSZX32FoR98gLzGRtFiuJGIcNgvkZOgogciGgVyc4mqjh3bqKSIim4JNiFPoyEZD6WlqcU/VPBAkyIXX3wxhg4dqvj948aNw3nnnYebbroJDzzwAKZNm5bpU1TRgV6ex6SiV0EuYOzxEKLA5yOL2Qxwsn56FaJRZaSIGDKlFJGyz2I/ozsFctNRisipPsSQqn2WUuJPghShlSLBIMjgprY2vpG9b8TOIclMuE4H/Tt5vWSS28fss5juRjIqgFihdQA4dqwm9r9YINvhOIbPPvs3HnmkDMGg8PVAIPmAsd8fRDTKPx+WFPnlLy/Gyy8/Dqcz/ox4vc1wBCPghlcY9pmm7j1XKB2lSDwgL6sUAcgze+AAUXNRbUyimiKJEr0E9UY6IjLvXnIJRj/zDO+lnTt3IhqNigZ32NNziNS6sufmSn4+fWsrV4rw24agTF8WFmmTA8EgXApIEU8np8ALxInB1NRQAATWZVLwBVRSJF30dfussrLulUOhQoibbroJCxcuxEfvvouXHn4Yl40eTRTlDkdWH9r9+/ZhL6ewwlHaejYJ1IvUvStl2+3KSjIeLC4mSwI3gfb2dixZsiS2vmXLFt7rJ5xwAgoLC1FXV4e6urr4ZyiB08mv+yjRh0SjwJlnxqetfr8aNO9NYBjgppvI79rUBLS2kr/NzcTJLVkEg8QAgq3JYzaT/MReNAVSkUFcf/31OH78OIYOHRorpC0ZkDeZgPvvJ3EH9YbKGHw+H7RaLcIdA+dU7a9UdD56+ZBdRa9CcTGxzPL7SfCYk/0EgFR8XL48vj50aO8lRdzueOYyi6IiEl2RY/a5EY1MKEXEJlaZCuLW1pLiXxUVwODBqR+HDpglU1Mk0fXUaoXXMBoVkiKlpfwC5lIBvBSUItzEYEGh9QCAggL+e1lfY7EU7e42EBL7nZ57Dpg0ib+tOxFw6ULMUqIHZutI2WfV18eVIi7aV4CDl156EJdeep9geypKEYdDGGRm7bOKinKwbNkyzJjBn6G6Q1EeKcK2t1yCKhAKwxdJPbjMzXalM18BQM9ohbd2x3NrNwqVIlLNWlJKEZ0O0OkwqrgY+375Swz55z9jL7W2tqKpqUlyUB8OAw6KrDdqtTB2gn2WVpucfZafyx53IBAMkkLqHBSzGcccuDu5WIJAKRJMTSmSDKRIkXCYdE8Gg3R3oJIiROW2bNlbWLFiB8aNW4iiIqHirLeTIip6BkwmEy4791xcRtvpZkOSUFMDvPEGKj0ebOBsTocU0el0uPTSS9HQ0ID6+nq43e64N/oppyg+zjiqWnljYyPq6+tj5Mdzzz3He11M2SoJOttAYqyj1QIzZig/rBSOH29AYWFJnzdQ6K44fhx45ZX4usWSGinS3g689RZ/2+9+p/Y1KsSxcuVKHDx4EF9++SUAonaTJEUYRpWpdQIee+wxPProo2hra0NjY2PKtpFScLvdaG9vR3+lidFSUKgy781Qm1EVPQdTppBFCnRjLmdh1JNBq0S0WjIIt1iSI0UyUVNELCCdiWLrDQ3ACy/Ej3XllYQUSwX0+SQaQSZjn2Uy8aUaAKl7Q9sS5ObySRGp60MHRhWQIlz+SdQ+q5hfOFiSFOmOUS2x3ykUAjZv5m/rbmROOhBL61dQRLq7QYoUaWyMK0WczsTBZjElCW2DpQROp/A9dnu8dkthIS2XAJxBPrHLiNQUafGkrhIBFNhnabTCW7vj80vyAjhsiddfrxpqSGhrJQXRIIrZDDidGJiXB4NWy1Mf7N69G8XFxRgyBNi3j/+2cBhop9q+XJMpYXubjH0Wv9B6ckoRn0hQKhAICEgRe14eTDodfJzjuUXUL5mEQJyYhvpIKfwibUprK/D66ySTdehQ4NJLxe+dnsCndzb+9a9/4Te/+RUA4IcfHscddxyC1con1NRAlYpuA3o+xDCJ2fIM4dPlJpSu9MAczOFtT4cUGTx4MN5+801iJ2m1kuXAAaISScL/fnBlJSxmMzwdwSC9Xo99+/bxFSFffgkcPAjYbGBsNlKfZLiQABWATgToBLXhoUOkXvzBg0HcffdYlJX1x623Xo0rr7wy6fonKjoXYuGJaDT5fpOOW+r1Pb+f2bePhDS8XrKMGkXyIFWkh1AohMOHD/O2DW1oAH78kSRL9usnIhVX0RnQaDQoKChAAZ2kmiJWrVqFV199FT/++CO2bt2Kiy66CG+//bbovhs3boTVakV+fj7y8vKgl+oj2dhQH0YPb0pVqOCgL5Eira389dxcEpmwWISECY1M1RSRs89KFytW8I/z4YfAfcLMcUXoLPsso1FIinz/vXA/ukhvBpUi3OQ10ULrhRQp0tJC3tQTSBGpc6J/k96kFBEB04tIkdbWuFJEjhQRIwpCIR8ikSg0GuWzSadT2Bfk5MSzdex2YeaOM8DPCv147S7c9MhwBLxt+M2kebj/5Elo8aSn4OEWJRe1zxJTinTsxwQDmDgROHaMNGdl86QDXIliX6KkiMkEOJ3QajQYVliIbZxCvbt27cLs2bNx8snipIiDGljnGI0Js89Stc9i6JoiMn2OT0Qp4g8E4KLaEltuLqxGI48U8SRQNGUCgkLr4SyQIqEgwmH+9V61ihAiABFo7tlDnNpo9ISuo7Pxq1/9KvZ/KOTFypWP4Kyz/snbpy9el65EIEACxR4PWbxe4OST+x5hJ4qiIuDnP4/7NAUCWbkwx925sLmBubnjMXTkAEwemoORv/kVLBWjcfAgGT7n5AADBiR5YI9HONa++25xUmT7dhIAZMfCmzYB330HTUsL/u/EE1FQXY1xt9yCYcOGwUA3xo2NfPvZggJlpMiECcSpwG4nc6VO8MTatQtYswbYs+dLuN0N2LevAXffvQn33XcfDh8+jPLy8ox/porUQCeHRyIk1ylZXtJD5fdYLOL79SR8+y1w5Eh8PT9fJUUygdraWkSoAXZVTU08aXPuXNJBquhx2LVrF0/J+OOPP0ruO3fuXJ4rw+rVqzFz5kzhjiopopIiKnoRTCYSHDWbyf8Jirv2eNDEB/tdlYyQsqEUyYR9VkdR3xjo0WAySIcUSaQUUSqnp0mRDBVaj0b5pyCqFKEzE/x+MhPtCZEtpSlQvZwUiYhY+nR3SBVa93pb0NrqRn6+FS5X4gz8UEj8GC6Xj0dqyMHlotsOBhZLnA0wGHTQaPSIcKywXH5+m/HAl0vgdpOCeX9ZvwI3ThuFFk96xHsg4Of8z3/2SZF1Rhi3YtvBQAB6PTBwYMd2s/TsOlHyrGgTwyExRhQVCUgRgJSwWrgQeO21+NvCYcBBK0WMRsl2LBQigkAuFNtnUQSwXKF1MVLE6/PBS73P2kGKNHP2z7Z9VjgNSzalCEVCcDj4LqQ//cTfZ9UqlRRRigMHlgm29fQM3mzi8GEydE+nq3O7gTfe4G+bPVu6NFufgk4nLEKeBbS69AjqzBiZU46ROeUYO9SIbfsK8fXn8fZ73LgUSBG6PWcY4TgbIOPd998nc5LCQqI2t9liAaBfTJtGxshSXkY0Ia6U3KAK7ng8HjQ1NaGgoAC2DBEklZWEFNmy5VXe9ilTpqiESDeDyUT6STY8YTLFbSqTRX4+GQb6/eJuuz0N9HdQXXwyA7vdjueffx41NTWoqalB3Zo1KORebDphkoPPPvsMq1evRmNjIxobG3H22WfjxhtvzMJZq1CC6dOn89YPHz6Muro6gUIwGAwKbKpzpWo8dvI8pydAHbKr6LFYsmQJbr31VkQiETz99NM4/7zzgN//vm+khWWKFElH0cF2rp2lFMkkkiFFkrHPUvo96d8lg6QIF6I1RXJzyfflXoPm5p4R2bJYyO8hV7i+lz3zoblzofv2W7Ki0SA0eXLXnlCSCIcjAKQJw4MH65GfPxhud+JBWFBCIeN0epMiRZxO/ixLr7cIlCZ6vQV+f5ykcQXiz0swHMFBV2NsPRSN4OPtB1FsTS9Nj2ufRdcU0XU8ywK+j50x0m1FgsgfwxBCQexyypIi1MTpiy++wIwZMzBt2jRotfxIVjgMtDv4KsYcs1n0+fR4gMWLgbo6/vZE/GbCQutyNUVEZtptIgoQW14eLNRFcWdZKRIKp5GsoBDBSBBtbXFSRIzfV1pTpDt2HdlGwC9sy1RSRBn27AHeeYcMKW+4QVguUCnEhr8eT98jRcLhMDQaDZguHhcFAqS72jb2CoR0JviMuRhzhxHaXQA4KsOU8p1oUsRsFu889u+PJ2k1NwNr1wJXX83fp6UFCAQQ1euxePFiXH755TCyNw3d9osRLwkwa9YsbNy4Ed6O/uejjz7Ceeedl9QxpFBZSRS5tbXreduvpr+fii6H0UjCE+li+PC4UCkc7pHOugKopEjnIC8vDzfddBNZiUSAP/+ZP3hLYOX0ySef4Nlnn42tl5SUqKRIGnC73bjkkkvQ1tYWW9avX48yDnEuhhdffBEPP/wwSkpKUFJSghNOOAG/+93vMHLkSNhsthjhodVqsX37dgEp0ibiHJMvNcCaMyel79aboA7ZVfRIhMNhLFq0CLUdsuZFixbhrLPOkvbK622gPc5ZUkTJgJ2r4khHKcJGQjqr0LpGk7mC7enaZ0mZvyq9fpm2z+qYsNGBLFH7LIYhgx9uSnZzs/CcumNkS6cD5s0jvs6J0MuUIqEZMxDV66FpaUFozBjiL9GDEAgkfi7cbjLr8Xjk7LPEyTCXK7lZk4dSdOj1QkJFrzfxSBE3p6ZIi1t4HjqNBq3e9JQi3KLxAqVIR9Bf0OxIkSIyKYcGg/gEWtI+qwPDi4p4L23btg2XXHIJ7HY7PvjgBwBxKYFYofUciVTGTZuEhAigXCmiSbLQuphSpF4kM8qalwcrZfeVbVIkmAWlSDAS4sX7xL6iVLOjkiJCBH1tgm194brs3w9s20ZKJjgcJFB77rnK379lC3FGjUTIMRYvJsRIMlbnrA2cwUD+cu9Pjyd1kqWn4rXXXsOjjz6KG264AQsXLuTXyMgitFrguuuAtrYBaG8n05bcXOHQU6RplofJRNQdbjdZpFLmd+/mrw8cCJSXk46VHdcXFABOJ5556y384he/wLPPPov333+fFK49/3xyY7pcZKH6QzkEAoEYIQKQYu6ZQk4OMG6cDm+/vQvHj3+DL79cjM8++wSXXXZZxj5DRXo4eJAM1VgHNZstc/2CVts7+piiItJvmEzkMZaJE6tIBeEwCXq3tJD5f0uLuFIkFAJcLhRTE4NMtlt9EUajEUuXLuVta21tlSVFjh07hqNHj8ZqcLH2jlqtFnfffTesVitmzJiByZMnwyKSFeJ0OmEymXhWzfn//jeJmVRUANdck5XaYj0FKimiokfi0KFDMUIEIA329u3bMWHChK47qWyCnkWwM8hsKkXYQLQYWZAJpUgms9zSUYpEo+T9YoRbqqRIJCJOtCjN/tZqAY0GEaoYtKh9FkAGPzQpQvv8d9fR9QknkMnvE09I79PLSBFoNAhPnYpuprdSDDlSxNNRi8PjSWyf5XS2iW53i5AUiUCTKAaDsJ2ktzn98cC0WEF1i16P487kojnFxcPR2BgP0vj98qSIpFKEVk/JDGyl8gXklCL5EvVAnE4nHn30NzjhhI9j28JhwEER9rkSRL0Uz6m40HqypIhI+mGtSPFbq90uIEU8KUXtlINu5kMS9XgyiWAkzMvQplzPAEjHGVVSRAhfSHh/9QWlSGMjsHFjfD0ZO5doFFi/np/70tpKLIFOO035cZYuBerrgWnTSICNLa9nsXSP+X4kAuzcGS8k7PWS+FRnWd+8+uqr2LFjB+655x7cf//9eOihh/Db3/62cz4sAbRajr0jB+w0xWwmQ+OUSKv+/YGLLxZ/rbUV2LGDFEjavJn/2rBhpDO8+GKSTFZSAuj1+Pbbb3HHHXcAANasWYPJkyfj/fffxwknnJDCycVRRJEoTWzRpg5s3UpO1WQi/UB5OTB2rPLjk0ugBXAKrrnmFAQCAeh0Ohw9ehRarZYQOyq6DN9+S4gRFmecAYjZ+fdlzJ5NFhWdCL0eOPFE+f02bgSWLEExRSarpEhqcLlc2L17N4qLi6HVahHmDJ7FVBw0Gih/4WKXiwyaJk3CQw89JPv+wYMHw+v1wrd0KVqXL0erzwcTO8mqqSGNk5IaWX0EfWDIrqLXYPVq0rAbDBgoMgsPdzfLps4EHeBhZ1fZqimi1cYD+gwjTM/LxG+h0aR/nGgUOHCAPyoFEkdxxGbRfr94VFHp+YkFBsNhYdREafY3wwAGA6I+fqa6qFIEEMpkm5tJ4UkuunNkKyeHzK4PHRJ/vZfZZ/V0BIOJ2xXWKsrnS6wUcThEIrUANm7cg0mTBis+H1aZwkJMKWIw8Le5vIlJEY0GaPPJK0VOOuk6jB8/HsOHD8Lu3Qfx1FN3xl4LBLKrFFG8nUMKGBK0C8uWfQJuzCgUAtopUiQnSbsRxUoRqq0LytUUEfFo8VPtt4ZhYDKZYKGVIp1Migjss7KkFPG4owDIDSZWY1HqkqqkiDL0BVKEVnTI2VJ/8QVRrI0eDVRVAVdcAbz8clw1NmECcMopyj/f5yNqk0AAOHqUZGIvWAAMVt49ZAXvvstfHz++80iRrVu3xv4PhUKoqqrqnA9KEUOHAg8+mHouy6OPPgq9Xo+SkhKUlpZiypQpyOPWkGxuBpZxavwYjSQjds8eYMQIso1TLCkYDOK6667j1aWqq6vjJd6lCjlSpLaWEGYsxo1LjhTh4tFHH8XLL7+MAwcOwO/345577sHf//731A6mIiNItSSNChVdgo7x+piSElw8ahSK8/NRfMopGK4GzlPCxo0bMXfuXNHXUiFFSvx+4JNPyBxNrOCfBEyRCMrsdpTRA7a9e1VShIM+MGRX0SsQDvNSS7UACgsK0MxJb3SKZH32WtABHpYMyZZShI6C0KRIpuyz0sV77wHbtwu3J2OfBYjXFYlGlV8/sdlvKJQ6KdLxWjTKD8pKKkW4E0ag5xRa56K4WJoU6W1KkR4Ovz9xgNrrDSASicLnS6wUcTiaRLffe++V8PtfwW23KfNp8XhopYhQ/UCTIl5/IGbh1yJik+UPRdAmV+sGQGlpPzz44LUAgL//nR9k4dpnCWuKkGdZVCkSjSZNiqSqFElEitCIRoH2dn5UNCfJKIBSUoShaoqEZPocMVKEhtVgAMMwsFL9qDsl03vlSFUpYrfbcc3pp+Pp999P+jOD0TA8jiAAcgOIKUWk/Mp7WtfRVegLpAhtseZ0JnYb3bCB5Jj89BMJEF58MbBwIfDSS2RufvrpyeU4bN7Mbwo9noT1Y7sEGg1pUrkcemf65tPBlhH19cB33xHGKRlfsk5CMOjHd999B41Gg5NPPjnp9z/yyCO84rHffvst5nD90OmC8n4/+d4nnSR6PL1ejw8//BAXXHABDnYkUP32t7/FRRddlPS50bj//vtx2623othuR1FJCXJLSgSnxoWEMFMR2trasJPDsOzbty/B3iqyAZUUUdGj0DH2PWnQIJw0aBBpkO6/v4tPqudCTGHz9ttvIy8vD5MmTZJ9/0MPPYSrr74aDZ98gob9+zFrQEcdx6VLkyJFJAcce/dKD9j6IPrAkF1Fr4BIUHrZhx/CUlKCvLw85ObmwpTOaLInIRQSXo9kSJFM1BShoyB05K472Gc1NooTIkDiaIVOJ6xnIkaKKP2OdrtylUkyljgGg7JC62LHCQZ7XmSLmkzyoJIi3Qpy9llerx/t7W5Eo4mfIbdbJH29Ay+88IxiUsTrpUkReaWI2+NDtNAIxutFi0dIigTCYfhC8hn93DpXZjO/j0qoFNEmUIqEw0LiOUWliFxNkWRIEQBod/CTE3IlgnCspTuNRI8y91S0Wv4XSsU+i4a142JYKRLb08mkiFApwn9+yotG4VjTjti63W7HDTfcgJ/97Gf437/+lfLnOtu8YEkRVSmSefSF65KfT2yr7HZCkCSKue/fzx/iuN2EwLDZgJtvJm1RssO+Xbv468OHk7oV3Q1mc3ZIkXA4jCuvvBJtbW1ob29HW3Mzio4eJWzV118T+6izz+4ScuTbb7/F3//+d6xYsQIejwfz5s1LmhTxeDw8QgQgRYB5yMkhjSq3T21sTFibbdy4cVi3bh2uuOIKGAwGPPzww0mdlyg+/BCjDx4kkfFwGDjvPICq70KLTaUcc5Wgurqat75///7UD6YibUQiRKTPlqMJh5WV/VShosvA3qBaLYknWa3kRlbn2CmBVgZOnz4dl156qeL3jxo1CqNGjSI+i1wrRAWECg/z5hF56uHDwPffk20nnEBkmypiUEkRFT0DIkHpiVOnCtNq2tvJTMvnI0u/fkLroJ4OsdlUtpUidAdJz/4zZZ+VDqRUBUBiUqTDmoo3WxHLCFdKKFVUiEdHxN6fpFKEjovS9lnBYEcSAE3KhEI9L7I1YgRRi4mlL6tZDt0KSuyzGhtlfFYAeL3i9lkAcOjQj4rPhy60bjQKSRF6m9frJWSm14s2n7D/8YfD8IXk2zkuKWKx8PurxIXWEyhFxNqjTCpFOKRAsqSIgwpY5UgEoqRIkUQfx33MaaVIUKbP8SuwOrN1jCeyrRTRaEiXxHYJQYoUmTX+Wny16SU0N+/G8OHD8dVXX6G8vBwAkEOrAJOAs80NgESQVaVI5tEXlCI2G3DWWcr2pXNUBgyIx+ZTzWlauJAQI2vXkvn+tGmpHaezUVZG4ksWC2leO4uT0Gq1+O9//xvfsHx5PAgSiZCL1Fm+XTJob27Gp59+GltftWoVHA6HZB8hBtpOBICwkDzDkDp0DEOKzBQXk2IdMigsLMRnn30Gn88HbSYaNbebzElZiPQjY8aQ02OnrApOUxI0KVJXV4doNApGHR93CTQaYNEi8n80Skrc+HyEHPb5CEdXWZncMffuJWM2th2xWNR4tQohIpEIZs6cidLSUlRUVKCiogK33HILCuVklIWFRBmSSoaCCgHcbjevlghtp6gYZWWk72aRLLtaXEyWIUOAqVOFmSP79pHEiT6OPjBkV9ErwDCE0fT7SeA4EBCP5rz/PnDkSHz97LN7HylCD6wZJj6j7Er7LC4yYZ8l1iEnI/OjdctcyEUraFIkHaVIRYX454m9P2n7LP4m2j4rGiWBLQP9+T1RKWK3A9ddBzz/vPA1dVaQEiKRKF577SvU1NTjllvOQ2FhZiI1wWDiZ8Pr9aOlJXsDMJ+PJkWEEThRUsSQB0C8dkggFEZAQRuQSCkSDMYJbilSRNDcRaPig1eZFNPOqCkiBoeb3+7mSqRtSzXjSj+O0XSeUsRCkyKd6XXTAYMhToqEIvzvkp83ELfcshGBwDE88kgFTxWbiBTRgEEEIsxTB1ztZCwRjaqkSCZAByD7AimSDE48kQg+t28ntRSScX+QglZLjjN6NBEDpBpz6Ez897/k+e7XjziZjh+fJaFGOAxs2sTfNn589m/McBh44gmc3NoKg1Yb6zdDoRCWL1+OBQsWKD6URqPB5Zdfjvr6etTX16O1tVW8jznvvJROVafTwZYpjyN6PiZCiowcSZZk8ac//Qk+nw9XX311zO9/3LjxePrpl1FUVI25c4egtLRUJUS6CRiGWAf+8EN824QJyZEi0Sjw9tv8fLpFi9Ij0lT0TjQ2NmLt2rW8bddee638G1mvRxUZwZ133onbb78d7e3taGpqSr09njmTqDzdbrJQiQDRaBTt7e2IRqPIz8+XPg7DCAkRjwd47TVAJOGgr0EdsqvoGcjJAa66Sn4/ujFXkB3a40APrE2meFBYSQZYT7HPEgt0B4OyGdExpEOK0AFGMVIkXaVIuqSIXi8gRWilCHtIQ29QigBk9P+LXwC0ZUxra9ecTw/HnXc+hVdffQQAsHjx09i69Tvo9enfB3KkiN8fQHOzvFIkU1Bin2Uy8QMYXq8XUX0xGEC0dog/FEYgLN8GGAxcpQi/XQmF4s87XVNEq5VQigCAWIE+KSmIzMuZJkXaqaLkORKkiEYj3gTK8Zv5+eRx11AEsGxNESWkCKsUobKwPFkgRbjfm1aKaPUm6PVmFBYOEQxxchJMgLQaLSIJ6pO4HWQs0d4uToCopEhyCIW80Ovj7YhKivBRUADMnk2W5mZlOTzJgC4l0R0QCvETPAGS35UVUkSjAS67jERjt20jD3SythspIhIBHnuMKIlyc7WYuBOoLjVgblUVlh84AIZhMHXqVF7SgBIMGDAAb77xBlHAWK3kAw4cILKjJI8FgDRmzc1AfT2ZZ3KL0i9dSn48m40so0cnZzdC3+BU35gqAoEAnnzySbS0tODPf/4zJk2ahgUL/gmjcRpcrmvR1kZq9ah8SPpwOp045ZRTcNttt+Hqq69OS0GUbngiGBROO7tI9JVRRCLAzp1EBM0us2dnvn/oS6ipqeGtazUa9Nu0CTh2jKhBBg9WG4gsQaPRID8/PzFZIYcRI0Q3P/LII3jttddQU1MDj8eDX//61/jb3/4We/3gwYMIBoOxz9eJDUpVMiQGdciuokfC6/UiGo3CbDbzmVd61JGFYEbWIVVkHRCPKFVWAkePxtezoRTpLFIkFFJOiiSagChRinCRjn1W//5k8EHXKaHfL1Y8OVH2t0YjEOTQShGg45C9QSnCQkz+q5IiKYElRACgoWEXFi9ehhtvPDPt4wYCiWtt+P3+rJIiypQi/G0+nzf2/Dn8IkqRSBh+Be2c0RhvS0wmfrsSDsfbFa+XrimSgBThWnIApL2SmeCINZt6vcTx0yBFnFSfK6VkSFUpMn8+8N57QlJEzj5LCSli6+hLaVLEnYXkCvZ6MNEIglStHY2W/B5iMb9EpIie0SKIBKSI04tolGTYi0GtKSINDcMgQmUl+HxtPFKkL14XpVBSDN3lEk2KTAi3m8R9PB6yMAxJslQCpzPzZAXdVANELZIVMAwZ/1dWAmecQciDRLXZMgi3O/4bNDQAOQ05GNLPjbtmzMB148djzg23wTj1VLjdhK8pLCQOIYrg9QKrVvG33Xln8sVkvvmGFKBnG7Tx4/mkSGMjUFcXXy8pSY4UmTiRBB9Zb/4MRXmXLl2KFo60b8OGtZg9uwhsVxAIEI5H8fVUIYmnnnoK69atw/XXX49HH30UjzzyCC666KKUjpUuKZLIObun4733+Haq48f3nu/WFaBJkTKbDdp168iK2Qz8+tddcFYqMo22tjbs2bMntk7/7nfffTf+97//xdb/9Kc/4be//S3/INw+ro9DJUVU9Ej83//9Hx5++GFotVrYbDZceuml+M9//kOCWDod+Wsw9E4ZID0yolNFpk8Hfuzw28/JAcaNkyZF6KiHyaRspNZV9llSqatiEJuNspCLVtARRKX2WXRxRyAeydLp+K/R7w+FhCb7iQgghhHsrjPpBF79wSCE0TSxlKOeEsFhJ/rce3rChC47nd6ENWs2ZIQUkasp4vX60daWHinCDT7Kwe/nt5kmkzC9zmwW2mdFO/qPdimlSIIs/Ph5xp89mhQJheJt7ZEj/HPUdRQSFyUPxEgR2fMQbpN8G6ff1CfRLkSjUTh8/O+RK2FfmSopMmwYiX8tf02PZz+Kb5e1z1LQr7EF1i2UfUo2SRGEQyJKEXJeoqRIguiyVqMFElyWkN+DQEA6UUxVikjDZjDAQbULXk8L7PZ4MUxVKZI8jh0D1qwh3XtbGxGHsr78SlBbC7zxRnzdZpMnRWprSYx9xw7i0DlgQCpnLg5a1Gc2p1dMO2WYTMCoUVn7OLqLCppzoNfXYv7QoYDRiBVb7fj2p/jrs2YlEcQXS3hKpYK12cxvzOrr+a/TavNkbbVKSngklNvtRlNtLcrKymBQmtwlgsWLF/PWZ8+ejerqwTwLxKNHVVKEi1AIOHiQ1B/S64EzFQxzm5ub8dhjj8XWd+7ciVdeeSVlUsRsJn2l2Uwex2Q5vGCQKGU9HpKnp9F0UVuSYbDfgzvM6o35rNnEhAkT8Pzzz6OmpgY1GzfCxm3bFFrKv/POO9i8eTMaGxvR2NiIa6+9FhdccEHnnLCKlFBRUcFbp0mRViphVNQaMhTqnbHSFKAO2VX0SLg6BqvhcBjt7e2oqanB8ePH0f+ss0gdkd6MREoRgGSEFReTAf3kySQ7jItEShGbLTVSJJv2WXJoawO++ILMdKXQGfZZWi0wdy6R9bPgzsbpa0a/X+wzEk2cNBoBKcIY9DAwfGFLIADAQkXT2GIj9Pn3FJx7LvDCC+TL5eRkxpxcBXS6zNwDcvZZgUD6pEg4rJwgVaIUoYkSn8+LaEkJsGsXHAERpUhYWU0Rrn2W2cxvV6LRCAKBEEwmA7Zt40eRzHoS5FGsFJE9D+E2yQm1yURSpp1OWaVIJBKCpqPoeSDgQpRqlKSUDFKkiJLyQGYzkJdHEUxy9llKCq2zShFq4uARUwpmGLHvHQkIqoAwKZIiOibxbxcOeWOZ3GJQSZHkEPA289ZVUiR5+P3A1q3x9dpach8qdUYSK+MgVopu5UryGps/xGL16sySIiUlwIIFRMza1tZ3yp/RXVTzxFPBLDqVjNeMRgQ+B7Am/npSzlL0ziZTag8bLUFqbCQJXeyPlC4pAlLwePDgwWhoaIjZeG7evBnjxo1L/nw78Pvf/x5VVVV4/fXX0dDQgKuvvhrFxfG6UFVVWRME9Qjs2we88058imUyAaedJt9vvfXWW3A44uNUhmHwpz/9KeXzGDuW5CimiqIi4Pbbyf/hMJmq9xYHJLNZJUUyiaqqKtx0001kZelSfkenkBR5/fXX8fHHH8fWJ06cqJIiaeCbb77Bo48+ira2NrS1tWHgwIH47LPPJPd/7733cNddd6G4uBglJSWYOHEi/vKXv5AXfT7g+HFUUOP/Y8eO8dZpUkTUwmvWLGD4cNJQ/vvfqX25XgJ1yK6iR8JJFZpdunQprrjiCqxcubKLziiLkCNFNBpgyhT+OheJLJzsdqCpSf4c6GN2hn2WWJBLCSmyfDkxKE2ETNhn0d9RpyNy+S1bSJQpP59PitCfSb8/WVJERCkS1elh0PJP1+8HkCPyfekgYU+KbJWUkNlBXR1Jh1N11klDjLhIxy+Zf+zECgqfzw8ggZJLASKRICKRKDQa+Vmhn7K/UqIU8fu9iPTrBwBoFyFF/OEw/ApqivALrQtZCLfbD5PJAJ+PIkUMhBTJlFJEbBfJt2k0wKmnAv/7nywpEg67oNHkAQD8fiHRlSsRtJcKDiq9BXX6JO2zFBAbVglSxJ0FUoT9nSNBYTQgkX1WboLK0lqNFlqNHuGIeL8ZCaqkSKoIi4xPAm5+pnlfIUVqa4G1awGHg1hQmc3A9dendqzycvDUrpEIOT6XqDh4kMTFq6uFQml6KBCJkDEQnQi5fbv4fb97NykxocTeSwns9vQCocni66+/xnfffYe8vDzk5uZi2LBhmKnUPyyDGDwYuPZa0lW1twN6fTHAqflCCzuSIkWMRhJhZv3VUk2X5zIHOTmEJPH54jfRggXkM9glhZtCo9HA5XLx6po1KZljJcCECRMwYcIEPProo1i2bBlmzpyJ2lrSp06bpipEaJSU8KdXPh8pFTN4cOL33XbbbaiursYDDzyAjRs34oorrkiKzNq/n0yx2ZI0Nlvm+kqtNjVxVHdF//7k+pjNZMlKzaW+ghEjSAfY3EyY0455jSSCQcDlQjHVmTZK+ayqUISWlhYsXbo0th6UiWcdP34cR48exdEOVwyv1wusWEHiW01NQDSKE6ZNw5tvvony8nJUVFSgf//+vGPQ9TTz168n/eWll5LBzr59wN69pJM++eQMfdOeiz4yZFfR20CTIgDx1usToFMo5ALCiQgLMaWIEmTDPkssyKWEFNm7V36fZEkRpUoRqxW4+WYSHcjJ4X+OHHFEB94YJvF5MozgMmv1GhiNJDDBor4eGNJfJJpGf15Pi2xZrSQqogIAsHLlNnz66UrMmzcV55wzTXZ/l0sY6M8WKRIIBBAQe6aSRCgUhsEgP4zx+fhEstksVIqIkiIdmaTOoDAoHgiHEYgoqSnCJUWEz6HH40dhoR1+P5/osOrI+YiSB3QNnUzbZwHE1Hn4cBhefz3hcUMhJ/T6PADipIg9SfsspZnUOkNyShG6kL0Y2FoiFmpG7s7AvSoH9ntHQx7BawntsxIUKNAxOlxw4at4//3LRV8PB71wuaTzIMJhfuI0dzvv/HpY15EJhOiMBAABD/9C9pXr4vEAGzfG17lD0kAgHoBUcj2MRhLEZN0+DAYynOJizRoyn2cYoKKCJDqydUgtFmJLY7HEF7GmgRYBsNDrSa5FpkgRFh4PIXPYQsKRCDBvXmY/AwCWL18ezyYFcNlll3UJKWI2A4MGSb9usZDfz2wm/+fkJHHwsjIgRQsjwUnccANR1otVrB4yJP3PAFBUVITm5riKjA0uOp3AZ5+ReKXJRO79uXOT6AN1OsyfPx8AqVMzcmT8tUgkgtraWhw4cABz5szJyPfoqcjJIUH348fj23bvlidFGIbBmWeeidNPPx233347bDYbHnnkEdTW1uLyyy/H3LlzE77/m2/4Lr9nnw1MnZr69+jNuOSSrj6DXoxBgxI3xjS++w749lsUc29eqKRIsggGg9iyZQuKiopQXFyMPGqsLhezbKCyNoqLi4nclPM7lAcCuPxy8fE9AOzevRu+3bvR+p//oNXnQ4XFAhw6RF78/HO+v+fhw/JfqpdDJUVU9AwcOUJmSR21Qp7+wx8wbdo03HnnnbFd+gwpQitFxAbzXCQKxospRZQgG/ZZYlVe5UiRaFRc1UEjE/ZZYkoR9q9YIDBZ+yy54ski9llaLcmo5Pale/YAs6b2MqWICh5WrdqJiy8+FeFwAC++yODppz/AFVckjri43UJSJHP2WYlJEb/fTwqZpwmfL6iIFAkEaKWIGCnC3xYIeAGrFdGcHLiCEvZZCkgROaWIx0PaKylSRLQJoNNqM22fxcJkgkGmXk84HI8u+n18ssai10Mnkc6Yak0RFvokSRElShHWb9dKReg8gYC4B08GkUgpwuikSRGj0Qi9ViuqlNFqtBg9+hK43Q04dOhr7Nr1P97rkZAHx48n7lZDIeG9o5IiEkoRTzzwqdH0Hasketjo8ZD7Rqcjwcf33yfD1BEjgDFj5HMZZs4k76+sJPFq7nUMhwm5AJBH8uhR/tDJYCA1hxIhHBa3ZznlFCKylhtSp4KWFuDdd+Pren3nkCL0PCg32cIFWcKECURYnewz8vjjj4NhGJSWlqK0tBSTJ08WtwRRikx6pUmguLgYu3fvjq2zShGXiy9q12jSuCe2bQMGDEC914tTTz0V+/fvj2UJNzc3o0ChZU5vxYgRRHFWWUmcYlgSVQk0Gg127NiBFStWxLZVV1fLkiIZcF9ToSL76BizTyorw8WjRqG4tBTFc+Zg0qRJXXxiPQvHjx/HFK5rC4XW1lZEo1EwEvMKmhQpcbmIxJULqoaIGEyRCMrsdpSxAzWTiUw4hgwBfuIU9jpyRPZYvR0qKaKiZ2DHDpIe1oH8iRMFjU2fJUWSVYpwJ/OdpRTJBCmSilJEaSH2TNhnJVuoPFn7LLlAp4h9lkYDDB3K7+eOHgW8IT0E83yVFOk1+Otf/4lwmL1/onjggV/hiis2J3yPyyVi06PJllLEh5aW9LOOAoEgILyzBaALrVsswvdYrfx2lH1PpF8/uEISShEF9llGY/w5tljE7LPIc+j1tvHPR0f2VRQ0UmAfkrRSpAPanBwwgKDOBYtQyIVoNAKAQcjbwnst12iUPLd0SRHaPisUiSAaiYCRuGCK7LMkSBF3IEDa6070Q2JPOyxmn2Ug96bYb8gwDHJMJjSL+M/oNFowjAbTp/8S06f/Es/9vQR1nvhzFw64YkljUggGVVJEDGERpYifU1Okr1hnAeJZ/k4nUWywjrZeL1GTtLfLkyKJeNgjR/hDJYZJXjAaiQCzZ8fdl5qbidVTUmqFJEETLcFgnDjKJNopa8U8n4+MX7tZRWatlpADX3zxBZYuXYrKykr89a9/lX3fX/7yF7RwKoovX74cp5xySmeeatp48sknEQ6FUJSTg6J+/WJ1tughOBurShpOJ2EeARQMGICdO3cizGmk9+3bh2nT5NXDvQ1Hj5LpcWEhITsnT07dcqqM8iSrTVSzEoSwVUkRFT0SHTGlS0aPxiWjR5PMhJ//vItPqudBTFnzt1NOQb7ZjLxp05A3fXpCUuSee+7Beeedh4ZvvkHDpk2YWFjIH3xrtaTTCIcTD8LpjoYdjNDBIin5bB9CHxq2q+jREAkYjx8/Hj/88APy8/Nj/rl9AsnaZyVScdCB/Z5un6WkSDwgH8VJxT5LbnYrRxzRnyE3iS0pEdpnaYkkXKeLn14kAuw/wGAMdyPQ8+2zVMSwevVbvPX29qMSe8bhdguDxJmzz5IrtB5AW1v6pIjfL09KkM/jtwtipIioUgSAJ68QPpGaDIGIUvuseLtgtQqfaa+X/A4eDz+YZdOR81EUJMl0TREOGJ0OBq0Wfgmi+/nnz4TX246KiumYOGIB77WcBKSIVBehNHPYJBLd8LvdMEmoHRWRIqx9FhUd9YZCiPj90HRipJv9naMhEVs7nXRNEQDIMZtFSRGthn++Zj3/vg/42nmWImIQ63L7OikSiUQQFSVF4sHavnRNjEZgxgwyfLTbCblgswGbNwut2SZPTu+ztFoylz94kAxnysqSD3Tq9UQVkk2IqU+83sx750+ZMgXuDRvQ5vWi3efDwKYmYrdRUZHZD0oTixcvxnXXXRd7jiorK/GXv/xFMkAEEDsSLiECAKV0sfR0EAqRjklJx6gUL7+MKc3NJJktHAauuSamJKe7pJR5q61bY0V49IcPY2BuLvZzrtP+/fv7JCmyZAmxwispIcqQiROzR4qEw+Rz2XI04bBKiqjoIWAfEq2WxJfUGzcl0KRImc2GX8+eTVZmzZKt4TFs2DAMGzaM9En0b3DTTaQ2jJI5yZAhwFVXkQGHzxcvsDZoEMkoqa4m+zQ0AH/7m9Kv1yuhkiIqegZERo92ux0zZszgbw+FiEdeIEDeEwwCw4Zl7TSzgkzaZ9HRDaWj8s62z4pGUyNFxDwRxNCZ9llSSMU+KxGmTEHkzW8AkGtSUz4dGg2Z8A8axC+tsn8/hKSIqhTpNTAYbAgEksvyEFOKZApKaoo4HOkVGwUAv1+ZMixI2V+J1RSxWqmAcQcp0gDxSHQgHEYwIk/KGAx6zv86MIymQ1lBwNpn0aSIVUuef0UkgYLKquLWSwqOrdVCn4AU8XhI8OXo0dVorN/Key2XNUoXgVQXobQZookLAPC0t0uTIgrqgtg6jmkVSbDwtLXB1omVTYuKiENoWIwU6bgXJEkRicQInRwp4nekJL7s66RIWOLm9XLs4/qSUoRhgDPPFG4fMoSQIBs3knl9WRkwalR6nzVgAJnfB4PEAluEm8oYMumYZzKRZ5wtJGw2d44b3x2//CXuoFXzInaRnYlolAwvRacmwSDgcGByaSmPWDx69Ch27NiB0aNHSx5XLPM2Y6RINAp8/DEhkK64InOyIZeLX+SPM38rKCB2WX5/guslh2gU2LSJt6l6wIAYKWI2m3H0aFsKB+7ZaG0lhAhAYn0NDST2l6rT2rBhwzB58mT069cPZWVlmDVrVsL9dTpg0SLyP/s8GI3kvDwesu7zETsvpbfa0aOk77VY4rV4+lrfq0Ieu3btwpVXXomKigqUl5ejsrISv/nNbxISzjxUVQG/+Y28hbeKhHA6ndDpdAh1xF2KufMHpQm8gNAu+aSTkktysNvFsy8MBuDqq+Pras0YlRRR0UNQVERmQyzZIcVct7QAzzzD3/aHP/Sehj0cFjam6RRaT9YCSmq/TNtnSb0/U0qR7mCflS4pYjSi8fybcLx5LfymXBytPAH9O06huppPitTVgUTUuNdHVYr0GhiN9qRJEbGaIkGl9nMyCInVA+LA7/fB5cqUfZaS/Wj7LDFShN+OBoMkeNHsECePggqVIlxSBAC0WiNCofgxfT7yHHq9fFIkVDgMcB5X1nUpMMgWa06kguw8aLUwKGwbfAEnb91qNEr2vemSImaRQb7H6QTPOf3oUTS98QaCPh98lK2MGKwdxxQlRRwO2MrLlZ1cCjjpJGIXHAnykx60jAYMQ5ixZEkRgVLEyCd1gtTvJQaxR1klRcRvXp8vfo/1JVJECnY7cO65pEbI118DkyZlbiiu12esDrYkli4lipTKSrIMHkwswZTi0CGSrZ6XR5Zp08jSqRAbA2eZFFm+nDgeX301VV7v1VdJhg6AUdEoKktLcbS+Pvby0qVLE5Ii0WgUl19+ORoaGlBfX4+mpiYUFhZm5qS/+w7YsoX8//zzwJVXKko2kAXdNnNIkZISsijFhx9+iKeffhqTJk3CxIkTMXXqVFQPGEACZO3tsTH9Q7//PX7GFKGlpRrHjpWhqKiPFDfigFPCBQD5GSor5d/3v//9DzabDWVlZSgrK0NBQQEYhsGiRYuwiGU5kgTDxAmv114jdn0sLr1UOVH8+efAsWPx9XPPTV95p6L34dChQ9i4cSM2btwIACgpKcEDDzyg/ABabd8b1HUCLr30UlxyySVo//prNP7vfwhwx41FRcoPNGUKiX+63aT/6N9fdLdoNIq2tjZ4PB6Ud+JcpTdDHbar6Bk46SSyyEEs8hMIdJ6fbjhMRjiNjcQnYM6czq2uKTbhSdY+K1FNEaUzefqYmbbPSpUUyZRSRIl9VrLXLtM1RQD480qxZ/i5sXX2Z6GT5xobgYhNB96v1g0iWx1Jg8jNVYNI6cBszoXTyZfzh8MRaLXSbZHH03mkiNxxvF4XfL423rb33iPm8xdfrLzSqM+nzD4rGKRJEbGaIvw+giVSGtuEtkQAW2hdSU0RfiRbp+OTIsuX/4Df//4R+P0O3n6essnw+53QaDoC16ecQgz66Si1wlRDsYC6okc+CVKEhiVBvyvFmyntPi0i0Umv0wl4vdj74ovYuXUrDh85gnuWLeNPRhKAVYpYRAgXtwJSJR0UFQGXXAJ8+hyfrNYx8WsvSYpIKFhoUsRipOrmKCBSVaWIEGGJm9frV0kRMbD3dk/DkSNk7NTYCGzYAJx6KqlFohTs1IBNwCwrywIpYjLBvfAWWDQ+MIF0JAipYdUqsgDASy8RYiQ2HuW02QzDYP6oUXjN6cQpp5yC+fPn45xzzom9vmXLFvz73//GkSNHsGTJEgBAeXk53nzjDeCrr8i8x2oFDhwgAaN0LK8iEf7cyukEXn8duP12hZkDCZCAFEkW33//PZYvX47ly5cDAM455xx88sknwHnnAfPnA7t2AXv3onjshfji9Tj7eOQIKTKeCY6npyAaJbmTrE3+8OHKxhZXX3013JzM7G+//RZz5szJ2HnR/GQyCePJlhPtSXC7CQHt85FpfDQKyNSxVyGBY1zmDECFxQJ88QUprjN4MMVUZxaHDhFV1vDhySUQ9FYwDIM8rRZ5XPJ+xIjkBgJDh5JFAm+88QYefvhh1NTUwOPx4PTTT8cXX3yB5uZmNDc3x0oM6NPty/oA1GG7ih6HaDQKv98Po9EolAOKBWE6q8igxwM89hifABg7tlM7HNGgfzr2WZlSimTaPksqYiaTga5ohMkw8qNjJfZZmVaK7NjBX1cwyROrKQIIs89CIcAT1COhM2iWI1ttbcArrxA5eUkJsVpWrUtTg9ksDIrX1bWhvFy6LRKzzwoosBhSArmaIs3NxwTbhg3rj9LSPNjt/eB01in6HKVKEdo+S6y2h83Gn2GyxEWLQzyIQWqKpEKK8J/rf/zjLtH36a0l+GnKzzDlpN3A0AJg4EAyU1yxgr/jyJGy5wCkETNKgxQxS2QoRyLStjdKP8pgMkHDMIhwDuRxOrHimWdw1q9/LWn3lQisQsQiEm1wOxyCbZnG6NFA8wwf8M/4Nj2H2JAkRSQaTgEpYuHvp5IiqSEsQfp6Odezr12TzkIoRAK6NTUkYVLJvL69nahjPR6y2GzA+PHJfa7fT+zsuFCSac4F7WKVl5fc+1NBc5sW//2wDGPHAqefnroyJxXrsK1bgWXL4usuFxnj3X57x5B6yBDCLnXgD5Om44H/fIywxga3m0xlvv/+ezz88MOxwL/BYOAXow0EgO+/53/w7benR4p89BEpgMPFeeelT4gAhEWbMoVEsFkiJ0Vs4Fw7AJg0aVJ8Ra8nHvMABh39FhMPt+KYqRoNpWMBAD/+CFxwQcof3eMwcyapc1RTQ7giMVVZOEyecTbx2ul08ggRQFhLJF2kQ4rQ0/8scp2djqYm4L334utGo0qKpIqamhreernBAPzwA1lZsKDTYlRbtwLvv0/+X7EC+NWvehdxlzI6mc0Mh8PYs2dPbJ0lxd555x3cdtttse2nnHJKrF9VIQ6VFFHR4+ByuZCTkwOdTge73Q673Y7Vq1cTuRgbzNZqyf8GQ/oBeimYzeQzuCOVxsbskiJ6vfzsm349GiVRKY0mdaVIT7bP0unkZ3ti9ln0LDFdpQhLirjdZCZJV7xVQORJBaesVn6WFAA4PN2LFPnuO0KIACSzZNs2MolRkTzoYCcAHDvWnJAUEVOKyNleKYXccVpb+YN2htGgrCwfWq0GL7zwBi67LHEBOhZKa4pwlRmAuFLEbudvi0RC8PmCaHWIK0V8oSAikDe0N5n4bYlOp8zKxGTKRdBgRXTiJIBVWs+aRR6UhgayrtcDY8YoOp5YfEdR0CsdUkSiDUt0eyj9KEajgVmvh5tD5HmcTvzuqadSIkSAOCmi6Ti2l9PfeLi+8J2IANWHKSJFJOqo0KSIlWonfAHxe5sLlRQRIiRJisSvp6oUSQ+RCPDyy8Quhr3fysuJKEAOu3cDn30WX6+qSp4UOXaMT9xqtZKuFZLobFIkFOLfZ+3twOLFZNz3ww+EPzj77OTF69Eo8OKL5LqdcILyOP6gQSQuX8fJaTjjDM5QdvBgcjKRCFBYiB3OIfjxvxoE9fF9d+7cyQvcBAIBeL3eOFHtEiFy0631NHIknxQ597g1DgABAABJREFU44zM1aLkeL9Ho1G43W40HTuGqqoq5R7/He9l7XBY8EgRAFi9Gti4EVoAEwC427RoKB0bc8PJZI2cngCGiVvfsQgGgZ07SRuxbx95hu67j9yjYsXTO4MU0WpJ+MBkUs7lRaPxWCo7ROhNAWea4PH742EKFclhwYIFKC8vx7GaGtR88gkmcK0jkrQbfPnll7F37140NjaisbERd911l6Ry6vPP4//7fMD69cQ8pc/jzDOBE0+M219lmM2soOqLsKRYKxtg6YApyzaaPRHqsF1Fj4OzIzgRCoXQ2tqK1tZWaDQaNDY2wmg0Iuf3v8/OLJ1hgOJiok2ORMiMZO1aIr32ekmqSqZnxnTQX0njKnYtIhFy/qlGN+RIke5sn6XkN6GvayRCRmncTiUTSpFQCHjhhTg7wMXgwbKnSV9m7gCypIQmRXTon+h2yXJka/16/vqyZSopkkkcP94EQFpy6/UKSZFMKUVCocRB6QBde8JaFLP6Ou20CTjxxGvwzTeLZT9HrqA72SeMcJj/vWw2sZoiwgC+y+WFNyD+Ga6QsmtlNPKfe51OiWqRgcFg69ifs1mrJX4kn31GBtizZyuu0tklpIjEIDwRZ5HMJNhCkSJelws/UFlyycDG0ftbDAYeKZINpQgA+Kk+XqeEFJG4BwSkiJVSigRVUiQVSCpFOPVgVFIkPWg0ZMjFvddqapSRInKORStXkubTZiPx9EGDhLlMVVWkUPLRo/EhfrLCgVmzSIZ6WxtZlJx7MnjpJVJGMTeXfJeOch0xrF9PruPZZyd33B07yLWuqSFcwfXXK4ul2WzAddcBb74JHD5MYkE8MspkAi6+mDAnBQVoew8Ibou/7HYT+6Lf/e53aGCJf5DgTowUoYvOGgzpqUQA4vVy8cXE+6W6WrH6UimOHTuGGTNmoLGxEf6Ouh8OhwN2CTJbDJFIBC+++CI2bNiAjRs3Yv369Zg4cSJ/J85NXFYGFB5uxcknk7oT6fJGvQXhMBEGcduV/ftJXQ+/34+xY8eitrYWTU1NsNvtsGb4wl10UWqBfoYhmfcAaYu83qyXCupUiIUyfL7eRfxkC2PHjsXYsWOJNzWNJJn5Z555BuvWrYutn3XWWZKkCN00b92qkiIA4kWFzGY88cQTWL58Odra2tDW1oZbbrkFt99+u+Aty5Ytww033ICSkhKUlJRg+PDheOqpp/g7RSJAQwPKqfhRe3s7XC6XgBTJz8/P9DfrdVCH7Sp6HFwimUL9O1K4Hn/8cdx1l7gdSUbR3s4nRaxW0gMEAvGZybhxmTdVTEU/KzYCC4fFiQu9HjjnHODTTxMfU84+a+NGkoZjMhEZerIpdp2pFFESwREbCLvd8eLkubmZUYocOCBOiMyZo6j6XqLgVEkJOTyLNo8e6EakCI1057V9GcGgX7CNkCLS8IoQiMFgpkiR5BQndnsxb72wUFn1USVKETGbMKtVTCkinH05HF74JQget8g1FwNdaF2vl7/RjcacWHFtQfNttwOXXabos7lIOeMunZoiEv1TJpQiACEuuDMxj1gWcRKwciaMVqMRzZxju9M8dswsW2pi4nIBDgcC1HOZDimi0/DvNauVv58vKG4Nx4VaaF0IqZoinqAnZvXT10gRh4PYnxw5QtatVuCee9LLTK+o4FtYffkludeGDCHxX6ljy5Ei27fHxXaAuKOIVkuUKeXl8skaBw6Q54QWF/TrF3M06hS0t5Nmxecj12ngwGYsXvwMjMZcmEx5MBpzYbGcizlzNKLcudtNriH3ekWjhDRi4XIRcuX005Wdk8kELFxIsvHHjRPZgTOuFfudTCYTfv7zn+MPf/gDiouLccMNN/DJA6OR2BS73eTkMmFxxTBEcalQdZkscnJyBJY2jY2NSZEiWq0WCxYswIIFC2LborQHJecmNhiA06e0gOmBFkTRKMktbGwkRGImrfBNJuJEyiUQd+8mt+XYsWOxZcsWACRBqJlbET1J7N1L+kmbjSx2O2lTMqF80Gh6H8llNpPaUx2xY5jN0harKhRCrwfOOot0zg4HaS+VskzBIOByoZhqoxrZAlkUxH4rTokoFR3YsmULli5dGls/evSo6H61tbWoqamJ9RvHaSeRzZtJnC4YxIBQCG8tWoTya65BRUUF+vfvD4PBEEsgZ1FQV0cyFk49lcQuVQjQx4btKnoD6AedizZar95ZWLWKqEIAos2dNo3MTLg9g9fb+aSIklQRsYiFFCliNJKZzMGDhNTwSwT+lNhnsb/F558DN9wgf55cSEXNaFKE1oPT14eV6nOhJFqh15Nrwf3+n39ORroMQ4oe0+eYCinC9Rlgce65JLVLARIpRehi621OHZAo2y+LkS2xW09hwrsKEQQCwuf0P//5J1599b8YOnQM/vnP+5Gbyx8Md6Z9ltJaHyxycmhSRNz2i2E0iEbjN4+SwvBOp/B7iilFxLa5XD4EpAKgYWUEktA+S14pYjLF+41M2gfQ1iaiQSsanWCflTGlCHV8D52qliSsnP7aSh87HVJk1y7g7bdJfzVlinC2ePgw8MYbgN+PwLZtvJf02nhESIo4zpHI/hMoRWz8RtYfkldWqkoRIaSUIqFoGKGQD3q9uc9dEyBOiAAkZn3kCFFcpIrKShKQZ5j40JqNJyQqem61kqE3W8aBLrlDP8rpBBidTuKjHo1yamdIoKWF1EfxeslitwMTJqT2ucGgMDN3wICj+PrrB2PrWq0Wb74ZlCSPVq0ijktsQHLsWEICcQkjQGgDJge9XlnfYrXGk2it1vjvcNttt6GsrAwLFy6EmSbWS0tJun0Pgs1mg9FojKlEAKCpqQkHDgyG202mcSYTEajQ9QAl4fOBoed/BQVkyc+P/V9XV4d9+/YhLy8PYzqJ9MkUolFSg6auLp7fdtNNPAeyjGD4cD4pQpOmAKllQ1tn/fDDD9i2bRtqa2tRW1uLc845B2dLyLBWrCDPOovzzgNotzMVceh0wC9+0dVn0ctgNidX0JuLzz4DNm5Efyppcz8tR+wAwwAPPUTyglkOpjMTAnoCNmzYgIKCAhQXF8cUZ3nUWF0qZtlAdcIldMdgt8cG5yadDpdVVpLsDU6c6dlnn8VTTz6J1gcfRKvHA7vBQBjgefPS+2K9GCopoqL7IxQCNm2K1QgZY7Viz44dcHq9uPzyy7F3797Yru3t7dk5J+5o5+hRMsoym/mjK7GRVrrIlH1WOCxePNxgII3qJZeQ9RUrgG+/lT9mokjWMWFRZVnIKUU8HuDDD4m+f+xYotNnGOH1sdtJOh0XSlM4rVY+KcLeZ2wqHa3kSMU+i559jhmjmBAB5JUiXDi8+sQerVlMbRV7TFVSJHWEQkJS5MiRnwAAu3d/g7y8PEFBb5+vM5UiydV0yM/nkyIlJUWi++n1Vp71VkDC2ooLt1tIitjtSkkRL3wSn+EWueZiMJn4z5VeL0+KGI0kOF9aKgzopYOTTyYBPL+feMUrUtJrtdCnyMwIAlodSESKJBNMNlMsgTddUoRzsWnCJWWlSCAA/O9/qHM60eh2Y+xPPxFfHa5iZOVKbD58GLuamlBPfQdFShGJH1JDkyI5/CQNb1BeWamSIkJI1RQBAL+/HXq9uc8pRWw2YQ7Ktm3pkSJDhhC3wNxc4F//4r+WqOh5aSlw553ir4XDwqF5qm1sMEjUMewje/AgMGKE9P579vB91wcNSp0UERtDRSL8jTk5OQnrVrCJ8F4vmcZUVZEY2ty5hCypqiLB+mQLzCvFrFlEFE13L0VFRVi0aFHnfGgXgGEYFBUVxQrgAiTj+sCB+G8AEGJKMSnyyivk7/jxZA5ktRLPrA6PpQcffBBPPPFErHD4LbfcgmeffTYTX6dTsGsXsdClxRkNDZ1DiuzbR/4OH678+X/yySfx7rvvxtbz8/MlSRF6uJDJcZwKFZ2OjiD+1PJy7GpuxpjhwzH6vPMwc+bMhG8zGEg7ViQ+heszCIVCmMyJ5ZjNZmz43/+QR8XdFJMiPh8ZPJSWAhMnEvcVbrZIOEzYZKqxNGm1KLNaUcbN/MhwTZPehD42bFfRI+H18uycjACG3ncfYLHg/PPPx2OPPRZ7LStKkUhEmOFfViYkRZTUt0gWmbLPYmtkcKHVCgPjUjN7+piJoiLhMFmSiZzIkSKrVpEZJgD8+CNJSZg4UXh9cnLSI0VaWqTPg5aRJqsUCQaFxx84UNm5dYC+TNyfhVbmhzUypEgWI1tijmGqfVbqkCMzXnvtTwJSRKymiBLlhRIkqzgpKOCPoEtKxJUiBoOZR4oos88SU4oI202tVgOdzswryu50ehEIiX9GMKqM+BEqReRvdJMpF+PHAyedpOgjFGPYMODuu0m7oXhcnI5SpLPtsyjiwplG/2/U6aDjtNFWKgs3ZVJk82a8+dNPuOqDDxAFcPW4cVh89dU8UuR/S5bgknffRVjEg0CniTMhyZIiOi3/DVY7n3n2hlRSJBVI2WcBgM/XDputX58jRTQakum+fTtZz8khgtp0wFrPbNrE3240ph4ojURIIJ51X3K5hGMlpfj4YyLyYnHgQGJShG4O05kiFBSQ+Hd7O1mcTqClpY23T55WS9iNE04QPQYdgGYDWSefTJbORl96Rt544w0YDQYU5eSguH9/2HNz8fjj/H0U14ior48n5tXWEl+5RYvIPDR2LFOMEAGAffv2pfkNOhe1tcL7ERDmjSVCOEy4ooEDyXNYViZusZebC1x5ZfLnSCtHBJY2HYhGhSoulRRR0aPQYbN18+TJuHnyZFLj9Jpruvikeg5o6z2v14u8Zctwms8H8ymnIN9sRt4FF2Awr+hWHLfccgvmzZuHhlWr0LB2LUYUFQFr1pCaVxMnks6iqIjEofLzicRTrEMVG2SopIgk+tCQREWPhZiFU0cw5Gc/+xkuuugi5OfnIy8vTyBN6xQ4HMLIcr9+pBPhNoTZIEXSsc+ir6uY7l9q1iJnn0UjGEwuciJnn7V5M3/7Rx+RjkJMKUIjGVIkEWgbN7nvR0e0/H6giar7QHteyYC2oeKeAv01IxqdqG2V6Js7GWKkSKLscRWJIaYUkYNPpP5OSIIASBY0KUKTDTSKivhKkdJScVJEp+O3d0psumilCMNoYTKJR5f1ev55ut1e+BUUcyfH1YBhtIhE+OdkNPI/y2CQV4oUFubhwgsVfWzSSJp81GgyTop0ln1WA7cAQZKwUhfGSp27JxXlZzQKxzff4NoPPwRLd7y6ZQse3LsXQ6qrY7vd9eWXooQIoIwUyZWogqyhSBFbbh5v3RuWJ0XUmiJCJCJFAl4yBuxLAV8W555L5umBADB9euYKAY8YQcTL+/aRpbIy9XtOr0+frGFRWkpKCbKQcBaJgW4O0xGTazRxtyQWq1YV4YJp09DW0IA2nw8VOTnClPUORCLCfKFUsntpB9uUkbEDdTMEg8Dzz2Oux0N+8EiE+KwxjGC6ovh5oec/Nptg7jBkyBDeupTtTXeBWNdtMIhb7UqhqYlY9h05QgwOcnOB225LbGmXDGhSpJbrGMFBIEASuVnSNRRKnXhVoaJLwMY/tFryvxpITwpitVcK7Xb0s9kwa8AAsuHMMwmZIYLq6mpUV1eTAQt3bsKNS110EWlYEsWqLBbgqqtI7JCta5ipBrEXog8O21X0ONA2T1ptbEYUazi48HrJ4NPvJ++12TKr5cvLA+6/n0R22fQWsU6ju9tnKSFFpGaeydhnAeR3SGaGnEgpEomIT/SOH+9aUiRZpUhdnXDEr1g7T5AoOEV/HKsUkUQXkyIZKmfRJxFSkPFNo3Pts/jEgMFgTUiK0HZZ/fuLB3m1VJBXGSnC/1y9XrrN1OvNPN7Z45GuKUJDpzMhEhG2WxoNP9CjxD7LZstwLap0wDAwpBjh7WyliJnqU2olihYqgY06Fl0k3p2KNVdtLV5esQJBquE9cPAguCGrQwkULlqtAvssuko0+14N/w35xfwi756QH6N2vAeTtxW1/SejtmwSDAb+kEtVigghVVMEAIIeov7sa9cEIEO8TKvb2OOOHk2WaFS61F22MXgw+ZuTQxI4q6sTx/bZqQhbSDjTgdJZs2Zh1h13EN9wFhLjbo0G+M1vyFisuZkElFOpvfrEE6Q9Z7/T2WeTgLAsgkHg0CHCcu3dS+ossRe0F6G5XQfsaUZ+Tjg+TfJ4EM3Lx6xZ5F72+chfKTVBJBKBhn1zNMpn4gBSwIWag3FJEaPRCIvFhp07w2hs1GLOnO7HP9HmCyedRGzckjlPmuQLhzMQ/4tGiQ/gyJEYOnQoJk+ejLKyMpSVlWHixImibzEaSS0U9u1+f/w82HKbPl98GT1a/jQaGkhIwWIhz5nF0jf7GBWJ8f777+PPf/4zysvLUVFRgUmTJqVmRThqFMlGMBi6X2PRA9De3g6dThdLEMwzmaCnE59EEhMFoOcd3LiUkqItej0wdKj8fioAqKSIip4ArZakhgUCZHQhNxJYtgzYsCG+Pn06MH9+Zs+JYYRpWhZ+IeNuY5/FMEKzZ7GaIp2pFBGrX5IIiUgRKUurDRuE10ds1ql0JJls9c1ka4rQ55qXl/QIPlGhdVUp0ncQCiVPZvj9nakU4f+YRqMVHk+TxN5AaSlNikgEeSlSJKhAxUEXlKfVJlwYDHQg3KOYFNFqjYhG5Qe5SpQidns3IkWAlEkRi0QbmqmaIhYq4PfCd98pfzMFurA6rRRxp5DkEG5uxj9+/FGw3U0T6gmg18azxCRJEQmlSG5O/A1GIzB6Av+5CkbDeO37v+G4txXn9p8My1lPQ59brJIiMkhUUyTY0c71RaVINsAwmVOgpIuyMlIcuLBQGDf65BOSsZ6XR5ZRo0gNkU4vJkwzRgkulkZDzr2wkFgrJotolARqw+H4cFZC8CbEq6+SC8Ri375eR4rs3Am8+y6D6TssKNA7MXlyxxjd4wHDKCcQ//znP+Oll17CpEmTMHHCBJw6ZQpmlJeTpLzaWlJXhMLIkSPx9ddfo7y8Gh99VA6XS4O33yavjR5NfvPuhEsuIWqRujryd+TI5GOxDgd/Pdk6hW+88QaKiorQr18/lJWVobCwEJo9e0ghtrw8XHTyybho3bqkToxur9ragH/+k7/P8OHy/cWaNfywxrRpwFlnKT4NFX0Ee/bswcaNG7Fx40YAwBlnnJEaKSI12FSISIT0DWZz3xsfAiRBIRAIwLF+PRoXL4aT2y9XVpJGQUm8Z/x4ogJ0u8ki4xsajUbR2tqK5uZmDFXJkKShDttVdH+UlgI33qh8f7qh6ey0skiERHlZ7W4kQkxNO0Mpkop9FiAkRcRqioj5qmTSPisZJLLPkrJIOXhQ+D6xUXGmlCLJHlfu9SRVIkDi4JRGw//ZI9quUYr88MMPePPNNzFu3DjceOONYBhGJUUyjEzZZ4XDyp/T48dbcccdf0FLSyPuvvuXmD8/XlSOts8yGCz023koLs7jrefniz97Ol3ypAhtn5VIKSIkRXwIKGy7dDoDwmF5ckqvl/ev0nWziGrKShE6UaADGbPPymB01EaRIFaq/fekkOTw0+bN2C/S2Dm50RuZhk+rpKaIRISraqABQ4eShLQTTwRMGiHZ+EntegDA+rYD+OOYK5FTfQEv41YlRYRIZJ8V9JGkjW72CKvoBDCMtAi9sTG+AGS/QYOycFJnn02U1H4/ol4fHPZyNOwlQ+Ek3VllEQwK2wLFLiuDBglJkdNPl3/fV1+ReY/NRsboAwZ024J027YRkihosMLtcqK+vqPsR5Jzww0bNuDQoUM4dOgQPvjgAzTdfjtmPPUUUYhIwGw248QTTxR97fjx7keKlJdLOskoRk4OIRgcDrLkJpFb4vf7cdVVV/G2bduyBaO//ZastLUBH3xAmK7LLkv5HMWGLD6ffM2RVPIhexqamwnHxzr95OUBY8d29Vn1LNTU1PDWywMB4IsvyMMxfrwweTdDOH6cNMNvvRU3a4lGgZtvVqgc7IVgGAa5Wi1yucnTZWXJxTMHD1aULLB27VosXLgQNTU18Hq9yM/Px5o1a2KlBfRpklx9BeqwXUWPA09KLAZ6gJysSiFZHDgAvPYaqbh48CCZDVdVdY5SJBX7LIBELbgT+a6wz0oGUoGiUEi68h6tnQY61z4r2ePKvZ6Cd0EipQj7keyl7wr7rH379mHOnDkId/yefr8fP//5z1X7rAxDiX2Wy+WDzRafkfn96dlnXX/9vVi37oOO/1dg586dMTIjHOb/mEZj4mfJZlM2UBcqReQJC1opQhMfXFgsfBK1sbFZsVJErzchEJAPdihRirhcypUE2UDKpIhEG5ox+6wMRgbowupWavLoTqE/rzl2THS7i2v/KNM36jhKEaluNic/X3S7zaYHN87jaBdXYLHYd+xHDDBfwNumkiJCJK4popIi3QGsJRRbyqGkJLsuErQjXjbKHQIg48jiYixbBqxbF29eZs4Ezjgjsx+VVg3XIUOAlSvh95M2JtDSgNrV7SgflSt9rYJBgFYD/uIXmbVHziD8fhIY3F99OphoBJoTLCg705J01e0NXIkAgEmTJknvHAgAe/YQRX1rK9DWhv79rsaeffHO4/jx3hlsHjaMr3hSqlry+YCtW4XJdmXNzXFWk8WoUWmcYeqkCC0u7Y2F23fsIJwni2HDeud92pmgSZGKcBj44Qeykua9yyIajYKh1FLvvSdu4iFR0qrvgCbAO4mUslgs2Lt3b2y9tbUVw4cPj63bbDY0NDRkdM7UG6EO21X0OPzsZz/Dyy+/DLvdDrvdjptvvhkPPPBAfAc2uK/VEoKks2enbDCbZWJDITIwzYZSJBlShIt0C63T0Zls2Wd5PMDKleKviQUq0jFt7mz7LBopdJZywSkuKRLR6BInJSuIbEWjwE8/kWye0aOJh3Yi/OEPf4gRIgDwi1/8Aj/72c9FJ9OqUiQ1BINhRKPyF6+52ckjRQKB9OyzWEKEHMuFp556Gw8/fEPHcfjnYzIlfpYsFmUZ/6nUFPF46Joi0p9VXFyBffvi6zU1NYqIFwDQ6YxgGHmZg0FBVqvb7ZDdJ5swpJhlpJdQCGXMPiuTpAh1LEsGSBGXhE0WVynipz0/KHCVIlKuHRaLBRqGQYSKAtG/m1UmkhIIuARDCrpbjUSEZLxKisTh9xLGv6oqW2ejQgxr1wJc57pJk7JHioRCwiBmJkkRtk5BIqGcVssfdovUfU2I3buJWwdbm3XSJIDmXm024NZbyevsoli8V14OWK34fl8RDuuHoKVgCFxf5OAie4JrJRZh68bR4cJCIoBpzSeZvo16IJobhcvlQnt7OypkrFAAoLm5GUe4ihooIEXee4+3qXKKE3uQC4OBJCpLcOi9DnIuV59/DmzaRO7xmhp+0XSDwYD86mqS9Mhe/7IyYMyYtM5JqyWhgkiETN9NJmXF5E0mMkVkwwq9sXA7PfbojLzS3o77778f5513Hmp27sSxr7/GzMpK8gLDpNxWtra24pVXXsH27duxbds2HDhwAMePH4e2Y+AXjQqt61ikUoqvV+GEE0jn6fGQi9FJ8Ui5viQQCMDUXXxHuzFUUkRFj4PL5UIwGERLSwtaWlrg7Jh9BAIBtLe3o2DyZGinTcteql5OjpB88Xgy36MHg8IIhdJGjo5aiNlndaeaIpmQDRgM4t9JafpQtpUiKVgAKCFFWGTCPmv9emDJEvL/hg1kQpzIkmHNmjWCbVK1xVRSJDV4PMqss5qbnaiqiquRxJQiydhn0aipiWfF00SC2Zz4WbJalbVjtK1UMBhEJBJFNBqFVitOSNCkiNEoTT6WlfH9G+rqjsFoVHpuBoWkiLxSZMGCixR9ZragT7EvZbTJK0WSss/KYNaVjToWTSC4lRRFpOCWSJNzcWaKzubmhMfQ6eT7BYZhkGMyoY0ac+ipPkWr1cKi18MjQfQFAi7BkILeVawP6WukSKKaIqFQO84/XyVFuhp008AGE7/5hvxvtZIY0aBB/NKAmYBGA1x3HVGLsEsmSRGXC3j8cTL8z80ly0UX8Ye7tBtrsqTIp5/yiZ0BA4TBdK02DUsujQa4804cfU2HI4fimxMG0egXtdoMVNLuPHAtqo4dW4f/9/8uxFVXNcHv96OgoADNMm0/AOTl5WHz5s3YsGEDNm7ciK1bt2LEiBHSb7BayVyCM+eaMKAFI6bnorAwuf61tyMajc9HotEwqqrGwuOpQ2NjI/r16wemshK4/nqivPnyS2DqVCKzcbnIA5Yiu/TrXycfnli4kPwNh8nH98aE775Mihw8COzdS5o4l4uUnZBwv0uImTNnYubMmYTV5o5hbbaUB2qhUAh33nknb9v+/fsxrEOW5fVKj+n7PCnCMOTGNpuBwkJEo1FcvXAhWlpa0NbWhra2Nrz//vsYOXJk7C1r167FhRdeiJKSEpSUlGDAgAF4/vnnxY/vcgGtrchtb4fFZIJHYp6Sn58vUPeoEEIlRVT0ODipFKy//e1v+Mc//gFPx6xn//79GNxZBfsOHyYjqX794oQEwxC1iMNBZj4WC0kFyXSPLtbYKR0Z0SPhbNtnJVtTJBMRcoNB3IS9s0iRdJUiKUzu5OyzuF8/E4XWP/2Uv/7ll8DVV0vvL9YJd0dSJBKJwOFwwG63x7Jfego8HmWEY0sLv90MBITtUyoF2+PHi7+Xts+SJ0WE975Go0ckwm83aKXI4sVP4d///j0slgI88cRzWLDgBMFxvF7aPkua5Kio4JMijY01KC0dkPDcWej1JkWkiFHmOS8sHIzbbrtQ0WdmC6kqRaAVz0zLlFIko/ZZMqSI1GQDAJkRbtxIGuAJE2Jfwi0xI+SSIg4xzwEOtFplZHmO2SxLigCA3WSSJEWCQbdgSEHvKvbb9bAmM20kUoqUlbVh4sQsnowKUUiRIjt28B1YFyzIPCkSDBJSrLOIMdYp1ucjS1MTGe6+8MIL8Hq9yM3NBZAHp3Mq7PYyGI0kdyscJs9qMEiuQWGhdF6V2cwnRTolQKnTCX8ndxTYf4C0p3Pm8FkXg4HU0WAjh1pt8tW4s4jSUkImFRYCgwaZ8MIL8cSR1tZWhEIh2fphWq0W48aNw7hx43DdddfJfyjDkGA9p/aiPdgCe3E2CtpkFh4PIfOam0mydabBJSorK0/Agw9uwQ03kGSbVtbjl2FIoZLvvgM+/jj+hrPPJiSJCHbvJtNMmy2+cH/mdPI1tdrkaqX0JNhsJJRiMpH2p68omo4fJ0mGW7fGt6VNXpaUAGedFS+wkwp5HAgAbjeK/X6UFBaigUPibtu2LUaKsAXVuX3ELbcQNVMnuUX1WDAMgyVLlqCN46/Z2NjII0Xq6upw/PhxHD9+HAAwYECCOejXXwPr14MB8PaFFyJ/wgSUX3opVq1ahYUskwogX6MB3nyT9J+jR2f6a/UaqKSIih4HmhQBECNEAPAam4xj5UoipwXITOrkk4mc9tpriaHis8/G9/V6ycgoU4N2sVlJqkqRcFio3ujMQuuZss9KBqxOmYZSUsRsJr+d0v27uVIkrNEjkkiAk0Jki7IvFUCs9o9UbLGraoo0NTXh3HPPxZo1azB58mQsXboUxSnUd+kq0IXEpXDPPffiscf+jlNPHQ9AihRRRl6Gw0J2jUuoJE+KCNsxkykXHk8TbxtdaL29ndyADscx/O5392HBgu8Fx6ELyidSflRV8UmR1tYaFBQoS4XV6w2KMnESkSL9+o3E5s0/8GzOugNSJUUijLjHQ8aUIskS1wlAkyL0sd10EgEXb74J7N9P/t+3L1aIVYoUcXJJETmliJbcLxMmJNwNORaLwNRZrLii3WxGvYStlz8gJEXorlslRRKTIm5396oH1Fdhs5HgocVCFja2Tou3MtGEhMMkX2r/fjI9aGwE7rtPfEhXW0uCvKzdVGVl8gXY6fJ5ublkqPp///d/2Mfxf3z66fexcOEC2O38aUh9PfDCC+R/m43Ez66+mr8P3Q6kIJRTBKs1nkw7uPFHDPpoNVDQHj+5M8+M71xSQlisHoKqKuAG4iiK2lp+3ZNoNIo33mhFaWkxjEZyf3LiYuLw+UgHKTdXGDCAXNiCAkTz89FgNGL/6tXYv38/rrzyym6V+MP2J9xTcjqB557jP6vDh2fmWeWCG3RnmPi56PV6lNBSqw6/qnXHjmFjXR1qDx1CndmMefPm4fLLL+ftunw5X5m1YAGJRapIjKoq4Oc/7+qzyD6WLSNKES7SrsWRnw9Mm5beMd55B6yf8JicHKygSJEFHW1xURFRPwUChH9xu4nTXF/G9u3bYbVaUVRUBKvVypsb5uXl8eKUdMyygaqbK2iLuMiJ18E8Z9gwoKICGDgQAwcOxEUXXYTWF15A686dCEUihK1l7dRUiEIlRVR0f3z3HRkxdRQQfOXll9HU3Ayn0wmn04nzzjuPt3unkSLRKJnRsGhpiY/kDAYhJc5qczOVzUrPSoxG5RGkTNcU6QmkiE4nfn2UGLgC5L1Wq/LRSbpKkRRIESWF1mP7avWIJPoZUpgoyfFFyZAiXaUUefXVV2M2X+vXr8e///1vPPjgg11zMinA71dGZBw58hMuv/w0LFv2IyZOHCRaU0SpfZbbLQwQc+t70DVFjEZSbyMaFX/2xEgAkylHhBSRfobq6nagpqYZFRWFvO1eikxOVGh9yBA+KeJ01sPrVab/NhhMikiRRDVFLrzwOpSUdL80wFRJkTDEoxgZqymSSfssShlipUy7JUmR1la0btuG//fjj9AyDO7w+2G/4ALAaOQpQrhwcZI4nDLjFbvdgLFjgdNOS3z+OSLXQuxesydIpAgE3QLbabq9VkmRxKRIuwThpCK7GDlSGGQOh4Wl/jJRkiIUAl57jT8eO3xYvIbJ6tX8jODZs5MnRegmg80cp+c+I0bkcWMmMTRxulWXiwz96a6ruJgoSljnj87K2j79dGD+/I6x61cu4DsO47N5M3DqqdmzQu5EFIkUg//xx8ZYAo7Y/SryBuL/VlREoo7Dh4tn/Z59NgCgpaUFVVVVcHHmMCeddJKiWibZwq5dwAcfEL6LVdZMmCDsdxoakn9O5DBgACED8/PJM5SwH+toKF7auBHPrl8f2xwOhwWkCD1l7I31P7o7Nm0i5LPdTpZ+/bqn8oQOK7HoFgXKOSzkOUOHomroUIw+/XSMGTMGE0XksAYDaZpEmro+h9NPPz2m9DCZTFiyZAlOnjYNqKtDHjUGlyVFIhFSAKmwUKhOozt4TnEXk8mEMpMJZdwkT7WuSEL0/JGGit6NaBRYtYo3Qqq66ipUTZ4cWx86dCj27t0bW+80UqS9XajW4NLhYuQHqyvMBFItsg4Io+VKa4pkyj6rM2qKnHOO0M+JCylpvVLlB0AGwkpHJ+kqRVKQtyZVUyQD9lnJQowUkbJh6CpS5N577+Wt/+EPf+hRpIjSmiIAEImE8H//92+89dajCAbFaoooe069XuF+waC0UkSr1UGrNSIUEvvxGZjNwuDttGmn4fPPn6OOkzg4/91323DFFfN425JRitCkCBBFY+PhhJ/JgihF0rPPyqQdVCaRKikSisajAZEI8NVXJJO6rk76PUnZZ2VSKUIdiyZFPFJ9WGsrznrjDazpkM19e+QIlv3mN4DRCDcdge2Ak7PdITNeGTXKgIsUlJjJEbkWovZZCe4xf8grCOCopIgQ4QSdVXu3iGaoEEMkAsydS4Z0rANTJgKWRiNJ0OTWw96/X5wUyYRv/vTpJIDe3k4IErOZKA/aKQlJ7g8/EDU7BVqcJhbEOuec5M8rFfC6lokTSRIcC6+XZLf2QLsPn4+oBUwmcn+YTHosXboUBfn5KMrJwcYd5diyJX7zKYpV1daS+UtjI1ksloTXJj8/HyFqLnXo0KFuRYrU15M+pbaWLE4nscoqKuKPExobE5MidXXAzp0kRpibS6yx5IKzFgtQXa3wRDsaijIqCFlLRbRDIeEznQniVUVy2LGDlIJhcdJJwLx50vt3FdrahGOsadNIf9Ll4CTa3DlzJjB+PHBh97L27Y6IRqNo4mQe+Hw+2O12kinx7ru4dcgQtJSXI7+oCHkXXojp06fz3n/VVVdhwoQJaFi7Fg2rVmFAbi6wZg1QXi4kRex2EujJySEL7XKRTtywD0IlRVR0b7hcwh6DeuhffvllaDQa5OfnIy8vDwWZNghm4fUSEqShgYzizGa+uadeTxon7iA0k0a86TRuXa0U6YyaInIWR5mI1FRXJ47gcdEN7LMSKUXCmgSF1hkmJZs3OX4pmZoiXWWflSjA1RPg8yknRQBgzZqlAB5FMCimFFFGiogRMVzrLXoirtfrodOZREkRnc4EjUZ4n/z1r3dh+fLFsffcdtujWL9+bcLz+umnLQJShC4obzJJt5ulpbkwGGwIBOKBzZaWg5L7c6HXGxWSItLPucXSPQesqZIi/kg86LNuHclvkENS9lkZjDbYqOioRUwpImKHuXv37hghAgDLDxxAa2Mj8nNy4Jbo/12c7XKkiFwNGhY5ItdCjBQxJ+gX/UGvIIATCpGF7UvExC99jRRJVGhdJUW6L/R6UY4gI6iu5pMi3P+5yAQpotMR917uVCcUCuOsuXPRtm8f2v1+tPt8KJA4OOvCxI4HCwtFd5NFJt2BAZAvNHAgcOgQIiX9sCE6EZXWwUi1lntX4vhxYPFi8r/J24rJe97EmbM8JFEuGsXWcb/njY8VNfN0SrmMRw3DMBg4cCB27doV23bo0CHMnj1b6dfodNDTq379yN+SEvKaVkvIDbkhSE0NcbdmUV4OLFqUwROdOhWOYVNQWtaf+Ph3gCZFAgES0Ha5yBIKqaRIV4AWbHZXtY7LRQg8dhhosRDlXLcolcQm2uh05MRSqUvSB+F0Onk1NoEOpWBHh3szm9Rts8WsdrmoqqpCVVWVsOEQSwKrrgZ++1vpG+bss+NxVK+XX6NLhQAqKaKie4NrzAmQkRFVZeyEE6jiutEoaQT8fjJC8fvJKCVdCXZZGakeFQqR83I6hQ2RxcKTr2WUFKGjycnI4JSQIsnUFKEjV11hn1VQwJ/Z0ZA6J6X2WQAwc6aySF6iz2ORBfuslJUiKRaszLR9VsYn2QpQXFyMRqqdCYfD3cp3ORGSUYoAcfsoMYKCVnhIwedLXimi04nf3zqdeDtWVVWMpUu/xQsvvIdRo4bj5z8/H+efvzHhee3cuU3kXJUrRTQaBnl5FWho2CW5jxSIfZZ8RN9kkp5Y9CZSRAMG3mA802zpUvn3lJUl101nkhShC6tbqYxQdzBI+n7qWhw9elRwLHd7O/IBSVLEybknHXSBAAqJ7Na4ECNFDCKT2JYE9k6+kJAUAUibbbORr0+LM83mDBQFTRO7d+/Gnj17MHfu3I4i052LhPZZEuogFb0bQ4aQzOTqarJIZfrm5pIgL2tL1b9/Zj5fp9PhwyeeIF5ELCQCIGefTUp1tLQQ1Qi34HQyeP99oohhv8vUqfK1j2Rx6qmARoP9rjJ8+gYDvEwEJOedl/zYsK0NOHSI2CR1Vq6cFLjTq4hGhxxfA8DhS4dU+GAwWeD3k/aVJQO42LhxI/Lz8zFw4ECyE912KzDuZ0kRvV6PysoBaGoiSce1tSRzPtvXhQanHjyA+C07dy5Z2GmeHOhuVMw2ThH+8Q/Sx9tsJJI+bx6iefl452Mzdu4Ejh0bgvHjp2DAgDKUlZVhxIgRvLdbLMBNN5H/o1FyH9DdcCBAfkqfjywGQ2Kr/5oachz2lNhylyqkQT8qH39MHpfuVuuishK44w7CldbVkb+p/rYPPfQQPvroI1RUVKCiogLz588X2MsnhWnTSKNuMKg3XBJobW2FXq9HkJM8U/zf/wobMrlCXXQGkhgpIve7DB6c+HUVPKikiIruDbOZjIibmggRkZ8v3wgEg8Bjj/G3/epXmRv96XTSvavZzCdFMjlBpifiyQSq6ABvJCIkKnqSfZZOR0aIer2Q3OHuI4Zk7bNmzCCzCDl0d/ssbQKlSIoEQCZJEYDcltnmIsrKygSkyNGjR8lEtAdAjKBIBL3ehEgkilAodaWImH0Wt9A6rRTR6bTQ68XJCKntADB5cjUmT/415ziJn6H9+7cItvn9/O8pZ1FVWFieIiliwF13/REPPXRjbNsJJ1wu2C+RUsRq7aakSAqErUGjgz9M+ig68CGGqirgrLOS+4yMkiJUKqGVCq77QiFE/H5oqH7XI0JqeDrGAC6Jxo673cEdL4jAqDD5IUckCiSmFKlLQIoEQj7RXAuWFNm6VZisPH58186XP//8c5x//vkIBAKoqqrCpk2bkJdqlFchVFJEBQ02Mz0UAv7+97iFT14ecOKJcSeSSZPI0ilIInGKU6YxZXg88YLxADBmTOrHiqGDTdr4TnzTxo2knVE6JAuHgW+/JU5ckQhpn264Ibs1Zrk/RUhvFgz/Rw90Y/TUxDWx7rzzTqxcuRJjxozBueeei+sXLsTQnBzSCNfVKTLvf/rpp2E0GlFWVob/9/80aGsj9vQAiZd1NSnys5+R8UF9PflK5R0OpsnWJaC70WRIkUgkgldeeQVlxcUo27EDZXY7iiwWaBgGmD0b+/YRay4AKC8/GVOnrsPNN8sfl2HEH8H164EvvoivV1eT2iZSWL2a2EGxmDu38xRvvQHRKGmL6Gl7e3v3I0VYWCzpx6937NiBTZs2YdOmTQBIQe+0SJEUxv1cRCJxB/kekmOYEVRVVcHv98O5bx8a//lPNHo8sLKDZKs1nkVgMpGLJBU/GzWKDCTcbrJUVSn6/Gg0itbWVhw7dgyjRo3qMQme3QEqKaKi69DWRkYa/fpJm4WWlQHnn0/+j0aVBdf1ejIa4UZskw3KpwKXi6QnHD1KegKLJbNKEbkIeCLQjW4olP1C6w4H+V2U6FjllCK5ueRYiUgR9pwKCkhKHItkZ6Tz5pF0MzkbLTlSo4sLrSe0z+qkTjNZUiQczv7gSSxIvnfv3l5Nirhc4j+CclJErNB6fButFNHpdNDpxJ+PRKQIDZ0uMRHc2LgHra1u5OfHM2ra2vjF2uVIkdLSitgEOBkYDAbccMN8vP32Kdi58yvk5Q3AAw/8SrBfT1SK6FNQipi0egRB3rdNKODhYcqU1DzszSmSIoNLSnCAKmZoo6IoFpGoiqe9XVCQvZ3bt3TA3RGhcUs0dk6/n8zQc3PhkCnMrZSQElNIiJEiTQnGJL6wH1ot6Yq4Qyb2LQcO8PcvLu764MwtixbFrAoOHz6M5//zH9x7332d+pmJSBFfMIhAIJASkaii58PhIEPShgayAFl8RkaOJA8lKz8wmRCNknhKQwPJKxs2LHMFh+mmJFM1XD0efhAYAPbtU0aK+HzASy/Frz1ApoI//JBdUiQSibejEY0OrR4jNm/2w+8nydeMDHna0tKC77//HgCwbds2bNu2DTNmzMDQ885LquDAYE6ktayMr6g4fpyQTV0Js5n8rukOt/v3J7d9ezt5BpMRDDY2NuKGG27gbTt2113ob7cDNhv2Uq6tx4+TJVWVF/2cyIUJ6CFCb7bjOnYMaG2Nk62DBydfX4NhiBLu6FFyPBZi1p+9CTUcG1cAqGhoIAxoTg5xvUgheyUYBDZvjpcxamwEfvnLeLgiGgUOHoyXtNDpgGefJeEwr5e8ftttxA6vL4FhGORotcgpKEA1yzxbLABVwzQhqqoUEyEAqac8depUHDt2DN6ORuXQoUPEikuFIqikiIqugcsF/Pvf8YD2hRfKj84YRlk2PbsfNyAhFTjPJDZsIMbpbNZ5Xl5mlSLpkCL0vmLBms4kRXbvJotGQ0Yr06Yl3l+OFGHPNVGwjj2n+fOBt98mRFD//mTimAzMZuDGG4laadUq6Qif3L2p0Ujbfel0KXmQJKMUCeuMCGmNAESehRTrasgpRYJB4SAs0cA0FEo7OSVpuEVOaN++fTjttNOyeyIpwuORkeBSCAZ9CUiRICKRqGiNDy7ElCJeb/w6RiJ0TRFp+6zkSBG5IUsUq1fvwNlnk2J0wWAYBw+u5+0xfPiQhEfo358utq4MBoMJNpsJ3377No4caUJxcQ5sNuF3M5sTkSIZiiplGKkpRfQIRkiDRAe4aKRKhNJ1P2jcc8steOy55wTbh1dVCUgR2i7LKqI2cLe1wVbOvz+aaItPAO6OKIYUKeIKBIAnnwTOOw9OmRoUipUiIlEgMfusP//xj7jr178WbAcAX4j0DSYTnxTx+Ugm7969/P2nT89+e03jCBUI+G8WSBFaCUejvb0dxemk4KvICOrqSO6V10uG4gMHxjPROwt0iSCTKXNkgSzsdkHS0QvP8wODRmPmSBG6ectkDVfuUNliUR7PM5kIL8Q27/37A2PHZr9w8eTJZGlrA556Ctg18kLs0OgQ1Fsw4iYrcvonjmwvXbqUV+/OZDLh1FNPVX4CR46QpaWFRJlHjEBZ2XRwyosIVH89GdOnk4VFMoYAdF0QBkCJ1Urml0ajqLogHZdGuj2Qc9HpKfUxMoFvvuGPMzSa1J9d2m2ot5Mix7gNPYAKt5vIZSwWgLaZV4DDh4nabt8+/vbGRqB//yicTif0+pxY7SSA9AE0ydfbr7sklNhfZRA5OTk4fPgwz7Zr4MCBsNlseO+993DGGWd06uf3BqikiIquwZYtfKJi3TpFKSt79uzBuHHjYLfbYbPZYLfbsX79emEmq8EQryZoNCZXRyJVFBfzg/Qej9DoNB1kkhQRI2vEgvpSgfpk7bNYRCLAsmXEEk2M0KipIaoMqSqVNJSQIkOHktQGh4PMkFKJwOn1JM1KypaDYZRFh3Q6cdVSigXM5JQi3MsTZTRoGH0Shrs/Fx6ok0jDUEh4X+zYERUtwA6kzM2kBZdIUHIvHf3rxki20Lrb3QqXSyo1LYpQKAyDIfHQQOwzvd64fwEdNNRqdSnZZ9GQU4oAwOHDcUXX6tU74ffzfRXOOCMxIVtVlVo6KVsQW6vVYNAg6bSoRIWzbbbuqRRJhRQxavUIRxhEInyhnhg6ixS55oor8PzLL6Odat+GV1dj6bp1vG20XRZtpwUAHhFVR2NTk+R+bol21RUIYHNdHcZ/+aW8UkRpoXWRvkkv8t7LFi7Ek//6l2gtFF84AESjMJkYnh3J66+Lf2Z3tKLQyUWYMoCwTEflcrlUUqQbYPlyfkDn1FOzT4pk2sktECDjPqVEC92MUVywJPx+ogxj67P6fELFyxVX8C20MtUeWCzAzTeTzx88mNSZSCbJ+ayzSPYy69N/0knZVYlwkZtLxuFNRfHaE03BKBiPC263G/3EiomA2CoWFhaiubkZAHDqqafCwnqwKcH27cCPP8bX7Xb0H0vmCMXFZCrUVdckG1B6vxw9CvzjH3wXgJLCQuhOP51kaTEMJk4k0zt2Gsgw6cU32WdXqyVBZLmf1WolGftsjLU3kyI0sZqO2cawYaT9tVrJkm1iNNt48cUXceTIEdSsXYuadeswkh2DpFhg5/hxfv/Z1LQba9f+E59+ug2HD29HXl4evv+eP1f2+Qjp3toa39ZnSZExY0ixMY+HLAqYWp/Ph0gkklxb3wGNRoP+/fvj8OHDvO0ul0vWIUEFgUqKqOga0EamNTVkACKTCex0OuH3++H3+9HU1AS9Xs8jRAKBALxeL3JvvZWMYFIsIJ0Siov5o5tAgJ+ilS7kqmonAh0tFxtpiAW+pK5dskoRLoJBQnpUV/O3HzkC/Pe/ylJ82PskESnCvZdyc9NL7WEhFaBSWohMrxcnRVJMt01GKQIAjdUzgDPGEtNrLpI18O2AfE0R4X0RjYbBMOLPeXchRfbRqTHdGH5/cvZZXm8r3G5pIsXrDSggRYSf6fPFr6OYfZZeL36PGwzKSRElNk5ud5zwXbGC73uQnz8QQ4aIByFYDB06QPH5cKGUODCbpb9D76opYkAoFJfQJ0IyxdW5MMtEB4w5OXCIEBNDRcybbVT/IDaJcC9eDPz0E7FUHDsWANDInf2x+7lcQDRKFCESmPDcc3jstNPgECFVuEiLFBH53fr374/NmzfjxRc3Y+ePjXjpvUtjr3nDfkRDIZhM8s+ZVitZx7lLoc3CeC+RfRYA+DNpm6oiZdBxheXLSZ6SzUaCZJ1RU2HkSKCwkJAjbW2ZV4ls2QJ8+ik5bm4u+Q6JEkBLSsBTB4gI20ThchGBNRdz5/Lb6s60ROnXT7z4uBJYrYQY+f574IILUj9OJsAw5H6oqwN27vwfli79Jf761yYEAn5MmjQJ69evF33frbfeiptvvhlr1qzBJ598gmly6noatByopQWDBwO/+U1y5Sh7O8xmIBjUoLR0PFyuWrjdjSgtrwBmz+btN3o0yATF5SJLjZP8sBzy+/hxcq/J5QgOGAD89rfkWVLSXS1aRP6yH9/JCeddimStxRJhypT0ziXbOHCAkBAuFyESKitJPSqliCnJqqv5jV6KpAg9NA0EnFi37unYenNzM+rrPQDiHa3VSj5OJUVAHm62fkhhYWzzli1b8Le//Q1tbW1oa2uD1WrFsmXLAACffvopLrvsMgwZMgRjx47FnDlzcPvttyf+HK+X/FgOBypyc3FYZJf8TMlDezlUUkRF10DMQLS+XjaNy0llVdrtdnz66ae45ZZb0NbWBo/Hg7Fjx2LLFmHB3bTw009kpFNWRmYCYiRAQQEJLjc0kNmY1Qo0N5OIUCYm6p2pFNHp0iNZkk31PXRISIp88YVyzfOsWeSvEqVIJiEVoFKq9JCK/mWJFAmFQO7LW28Fnn8+XtCe/i0UQv7nEs4OQiG/ZNBdJtbUKRCzz+pJSpFkSZFg0IOmJmkFm98v/yOIkSJ+vxwpIv6MJEeKyA9ZXK7477luHZ8UGTlyhuz7R4xILX0yUa0QLvqKUsSgNSAcVjYhSlkpkoDoLs/PR/W4cRBronJEogq0XZZGo4FZr4eXI0Wvc7lQevgwij/8kEQjrVZxUsTpBMJhuGVqmT20ciUmykTsjAozvHJEJj1iShGATJCmTj0Rx/du5G0PR6MIuFwwmeQnUCUlqZNZnQltCjaUyUJOKeKXUf+oyA7Eki25ArEFCzJPipjNSVuBJwVWfO7zkYWNtaxevRo//fQT8vLykJeXh0GDBmHs2LGxmK1eT6YnLhexTSosTDzsFGt2vN6ek6U+ejSpU5uF5kAWw4YRAtlo1OKdd+KJck0yhLhWq8WsWbMwi53vJAP6xm5thVbb8woe+/3xWgZ+PzBDfgiXFPLygOrq01FdfToAYiF75ZUS7ffbbwN79sTXTz45Roo4HMCLLxKi8oQTiPGF1PQ01d9Bq81Mbl93Rl4euaRsHerumHiRKWzdSsZQZWXkd62pAVavjr+e8rM6fDi5gE4nuTFTlPA1NQHakB+GoBv6oAf5Bv4JRaNR7N69C2bzpBh5lZMDnHIKmcuzCp0URA+9Gm1tbXjzzTdj63mcuceWLVsQiUSwZ88e7NmzBw6HQ54U2bYNWLIEAPDolClonjsX5/3rX7xd8leuJPHIZFi2PohuOKVR0SdgMpERutdLiJCKCkXpD3RWt91uh0ajwfHjx2Pb2mj9eroIBoEvv4xn+Gu1wGWXkZEuFxoNScN58sn4tmg0+apvUqAn4smM9unelU6/SNa+KVX7LBaHDgm3JVLVjBlDUih8PjLbHDqUbO8tpEgn2WeJkiIAGWkuXEjIvpwckvncCWAY4X1BinmLP+vZVoqEw+FYQTIuDh48iGhU2uarO8HrTd4u5vBhaTNpJYXbxeyzAgFnrB4JTYro9VoYDOL3uNGYDCkiH5znkiK7dq3hvaYk27K6ugwajR6RSFB2Xy6kvh8Ni0X6O3RbUiSF9smgI0oRJWW1Mq0UGTdkCJ565hloJfqAqAibK1ZDxGow8EiR0159FQDwy2nT8I+zzgImT0aTSADc7XYjGgjIkiKuQADfyVhFKlaKiJAihgQX1mgEdCLkh6u1VREp0h2tswBAl4WoXyiYuG0I9NnUyO6FnBwSZLNYyEILQLuqYPGhQ4QsZq2nxoxRTs7Q0xt2avHpp5/ir3/9a2z75ZdfjjfffBNDhwK3306uA8MAjz8OsGWWcnKAiy4SJ3DEFC49iRRhmOyZBMiBtR374Qe+pV59fRNWriRt8ZQpCfrBxkYylywqUj7XKioCBg0CCgoQzctDQzSKQ2vW4PCRIxgzZgxGjRqV+hfKAPx+cv+z96UYDh4EXnklvm40krohmfxddTpyTzud5Lj5+XpYLBIPY0eDsbmuDj8eO4bavXtR+8YbmDRpEgYNuhnhMLEK/fRTolL65S97HgnV1TjhhJTKX/RIrFgRV1SYzcJcYZlyc9JIR2bHwbRpwIyNLyF8vB4eD8md/EdFBQ5w6rh5vdvw619PQiBAQl3hcO8mspRg79690Ol0KC4uhtVqFcQR8qi5Rnt7OyKRCDQaDbZu3cp7bWyHIj0hOEqgEyorgZwc+Hw+tC5bhtalS9Hq86G0vl5WMSQzrO0TUEkRFV2HG28kI2+pEc5PPxH1SFERSR0oLcWcOXOwbt06OJ1OOJ1OaDQaQQOTcVJk3z6+5VEkQgxZxZCTQ0ZuXMuOxsbkSZHGRuDjj0mvOGcOMGlS5ypFkg16pasUOXaMXFOlGciDBgFnn02uR1FR/J7pLaRINpUiLAYOFFdsSSCZwoVxCJ9tQoqII9ukiJhKBAD8fj8cDgdye0BaVrJKEQA4ejRdUkS4TyQSgsfjh81mEmRS63R6SaVIMjVFlNhneTratn376tDWxg84n3SSPCmi12uRnz8Azc37FZ8XoFwpkmg/u72bkiIptU8ahMPKSJFUm2qNyBuHFhdjs5zSS2TCaBOZMFiMRlGpyz/XrsWvDhzAkIkT0ShBigRcLoRTazR5UKoUEauBoklQS81gALTmPMF2V2urooLJ3ZUU6RZKEdU+q1uAG2ALh4FHHuG/3lU2NB99xLcXKS5WTorQZQrZIQo972HHLtxC734/v2izwyFdHF2jIcEttu6BydQ9lWGRSPdQgyhBEWVT6/d78OWXHuj1lsRWP99/D2zeTH6Afv1ItHLcuMQfVlAAXHstAOCcs8/GZ599FnvpL3/5S5eTInv3Au+9R6ZOpaVkinfSSfx96GfC789cjiEXl18et6NLeI93kCLv7diBP333XWzz+ecvwPTpN/N2HTJEJUS6CqtXxwlcu53k3aboINVp8Hr5fYDXSx7tnTvj27o6t2L0aABTrMCB+LZbzz8f/rIyjBkzBmPGjMHAjhiCwZCyC3evwzXXXIM1a0hCntFoxH/+8x9cc8015MWmJuRRfXU0GoXD4UBeXh62bdvGe22c3w8sXUrY45kzxT+QvrldLhj1evSzWNCPW9tOxstTtVVUSREVXQm5mffu3WTkxGLuXOSefDKmUKPHHTt28NadTidCoRB0mRrBU40UBg6UTjFjGDLD4TDpaGwkI6Rk8MUXpAIcAHzyCVFGpEOK0LMGemaVLClCE1nJzkoiEeDw4bjiQw46XdybkQulNUVEEI1GceTIEZSUlCgvQiUVHOwiUiRlpUgGPksZpJQi4sg2KSJWT4RFXV2dJCkSDoexe/dulJSUCCa62UYgkFyhdYB8NykEg/I/ghQR09Li6iBF+Ckner0OJpO4hjoZpYiSNp0lRVau3MTbbjDYMGPGcEWfU1JS1WmkiMXSA0kRkfatv92OWqcTUQClFhvqPfxnKcKQ9kbJxC6TwTajgpH9EJGAkElkwmBN0K5//P33uOuss9Ao8gXdLhdcGUrOMCgsSmAWKeztS5AKbzQCeoOQSHE7HLJ1EKyeBoQ/fxV7f7Jg6MKFXZY+Lqb4yQopotYU6XGIREhNDLc7Xhagq1QPZrMwIKYU11xDAsPt7UQ1whYPbqfG9HlHj5JCFhwCuKNmdwwMk5iMufVW5efVFWhqAl56CRgxglhlDRqUeFrENhfZUJDU1ZFzMRpJLEqvByorK7FkyRJEwoVY8UUujPZy6HRm6HQyfWBtRxJLKETmlnKECIVyypb6kJhSP8tgh6B+PyknKdbViuUYNjQISZEdO0j+ZE4OWYqLSQxRKWRcu+PoaDDKqBM4erQW8+fHfyaG6Ttqh+6IzZvJ/cDigguACRO66mzEQU/BtFrSjjU3E7LeZusmRA6VOXDP2WcD8+crfvvOnTsxcuTITJ9Vt0Yjp3CX3++HlXsNV65E0YYN+PWsWcgzmZA3ejTyTzghZqu8adMmbN++HVs//xxblizBDAD48UfCOCUiRXS6eAOYk0NkHz7KRUItti4LlRRR0X1BVwQUmfQDwMCBA7F06VLk5eUhPz8feXl5krYZKWH2bDIK276dzERGj068vxgpkiy4Ov9olJhPZlIpQiPdiompXO/9++OkiFxGrdTxU1SKhEIhXHDBBViyZAmsVisefPBB3HXXXfJB125knxWJCC+bnFIkHXlkaoRFcqRItmuKSClFAKC+vh7DhwuD6JFIBOeeey6WLl0Ko9GI9957D+ecc05nnmZCiFlZyaG+XpoUUaIU8YsUrwaAtjY3BgwoQiQirClit4uP8JOzz1KiFCG/6dq1/HoJAwZMgF6vrJ0qK6viZWwpQaJaIVyYzdIEaE5O9xy0ihXs/tnkyThn2DAcczoxvbwcJY89xns9CtI+KbEAyGR3TZMif7jvPjz86KOx9X8+/jhmzJiB8ePHY/PmzQCAa6+9VtQqz5qAHdBFIgg5HGilJx4APF4v3HTiQYpQqhQpFBkf5SUgbI1GQKPRwqjRw8+xinO1t8OUwP4gxxbBhvfPx33b1oAB8MR33+GON95QdI6Zhk8kmqwqRVSIQa+P2xh1NehHOpnbRacjRAZNZowYMQLzBg5Eu8+HNp8PZeGwYNDm95NgcXs7aZvz8rqn+oNGOEzE5QcOkPNla2Dv2EGUiBs2kKW4GLjttjjpEY0CGzeSAGldHfl7003ZyWh+/XW+Kue6k49g4LZPcZbbjZZjXuj25WDNjDsAyCTwBoPCOaSUU4EEqih/tO5AinCD1oC45Q6bY3jsGLnfS0rEp0q7dgHcMqIzZgBnnpn+OYZCwP/+F3cjKh04DvZ7R8P+wVLikdWB2tpa3HwzsftatYqcY6ZrFalQDlq8m0ny2+Fw4JNPPsGuXbuwe/duHD58GGvWrEnaaplhCIlbV0fa/9JSoKQkigsvRPeybWaLVul05GFM4mJ+/PHHuOCCC3D33XfjL3/5i6L5W28AXSuqmFJrWPR6/O3UU8n6lCkAJ35gtVoxbdo0TAP4maiJCrNYLMBvfytk+ydNIlkTPh+5ybqrvLsboQcMh1T0KoRCZEReUJA4XScQEJrnSpAiFosFZ9IjoHCYNAR+PzmWTpf6SLisjCynnUbIDrnj0K8nS4qIRa4Dgc4lReSIHjmkEojgEj8igSUeMkyKfP/991jSUZjK7Xbj17/+NX766Se88847ic+jG9lnicVlOlMpkhop0r3tsxIpRerpWVsH1q1bh6VLlwIg5MDVV1+NlpaWLhvIBmTqFoihpaVB8rVU7bMAoLWVzEaEhda1kqSIWIa+FJQUWvd6iVJkx45NvO2jRk1S/DkDBgxQvC8Lk0nZM2w2i7cVWq0BWm339AERU4oYtFpMLCvDxLIyhEVkZJEOxtbhkD9+ZypFbrnjDny6fDnWb9iAE+fNwzU33QSGYbBy5Ur85z//gd1ux4033ih6LEuCezPs96NZog6W2+PJGCliUFj4ID8/H2eccQa++OILAMCUKVMwJIFCle1yjFqDgBTJlygSPXo0MGvAOtx9L7EGiAK48803cfvixWC6ILrqFSG1M5oQI4GQqhRRkQYKCsg0iBU/J5PVLoXf/+Y3+D09gKLar0GDgDvuIFOMlhb5YXcirFtHPPHZ71BVBZx+eurHk8Lu3cD778cdjHNygFmzyPSRMgnAwIH8aSXDkHPkDvNYR+bOBn1tDQYQmQMAox4Y1M8D73gyReV2rzfddBMqKipw3nnnYeLEiWAcDvKl2f6EYZI27WctbvR6PQYMGIDCwv7YvBk4fpwsU6cmLT5JG/TwWqoEwqWXkphfou6FHmOklGEfiQBPPBFP07fb0Tj6VGzfbsP27WQXhjHi/vuB+vpB6N9/Kuz2MthsZSgrGwgAGDyYLErmMV4vIfR8PrIUFoq3A4cOkXkbawVlNnefWjndEWJ17L7+miTbu91ENSIRSlIEp9OJhQsX8rYdO3YMFaxkTyFY9+polDzamzfvwMiRC7B7927ccsst+Pe//53UnPK0005DXV0dBg4ciKqqKtx0002YkAl5zOTJwNixQH5+UnGeQ4cO4dprr0U0GsVjjz2GNWvW4K233hKo1nobgsGgoE5p0fvvA+vXA1dfLWTApTphemybyOtT6j7JUG2ZvgSVFFGRXdTUAC+/TEY5lZVkJC2mM41EgFNOIYRCUxNZWMZaCVavBr76Kr4+bBhw5ZXpnTvDkHOWA93j0pp1OYhlr+v1nUeKmM2kglY6SGWU1tREPATy8+U9VqTIjxRJEbFMqXfffRdHjhxJHBCVIj+UBnbTJEV+/PFHvPrqqxg5ciSuvfZmAPzvn1RNkSSRCmERDguDpT2dFNm1axdvva2tDatXr8asWbMyem5KIaXaSISWFmmliN8vf5NIfWZ7O7mekQj/hzQYdMiRmKlmWini9boRiURx4MAG3vYpUyYq/pxBgySiwgmgtHyEVKF1vb57qkQAcVJEz2lsxLLzo0mQIpmMYw8bPJi3XlZWhrXr1sHr9cJiscQmmrm5ubj33nsTHsuaQKXR3NKCRilSxOuFW8kXVwCjSAF1Kbz33nt46qmnEAwGcccddyQ+bsdPataZ4AjG+1+3w4EyiUeyshJYu369YLunrQ3WLrAR9IgQT9kotC6rFEkn2qyi1+PsszvhoGL3nMS4VK9PvxguWySejf90VtH6wkJ+SUeHg0yp9HqhBY1Yble/fvz8q7q69HPA5BAOC3PbDHnxTF+rFRhmCWD4uSHeIL2hoQEvvfQSotEoHn74YZSXl+PLL7/EqDvvJHOkujqSLJhkxvW5556Lmpoa9OvXD1qtFu+8QxQQ3PPJNimyaFH8HvJ4SChADEoIjnRIEY/Hg7fffhtlubko27cPZXY7ijvGCfXFp/H2zc8n/eall06Cy7WW99rBg4QQAZSNZ95+mxAeLObPJ0XkaaxYQezFWJx1Fikpo0Ic4TBRCjmdZDlyhG/c4XAoJ0X27weWLCHHPOMMYMSICJYtWybYb9euXUmTIiwYhpBhTz/9EHbv3g0AeO6557Bw4ULMZiVxCrB161bU19fHalLMnz8/M6SIhDIkFJIOZwSDQVxyySW8Olfff/89Pv30U9xyyy3pn1M3hl6vh8/ng6uxEY1//jOaPB4MttsJ86XXx0lXk4nE3qQkZUOHkn3cbrIkqfKIRqNoaWnBsWPHMHDgQMn5two+VFJERXbB1snweEgKkNMpToqYTKTAOItoNLnAOx3ESSFwmDLodA+Ph/SqSifrUoFaOhs3nZoiXIwc2XUV4fbtI2lKch4rUtLBFGuK0Ew+i/r6+tRIkSzYZx09ehRz5sxBsGO2tXTpl5g48T1otfFrQP+M9OXJPiki/MDuZJ+VCikipmx44403uowUCQYTt206nRl6vRleb0tsm8ORiBSRV4pIqVPa2khwVagU0SE3N32liMEgHwzw+dzYvv0ovF4+GT1vnnJSZNiw5JUiY8cOlt8J0jVFdDrl1yHbECNF5HrjKEh/JVKHXIB0up9fXXQR/vH++7H13z/+uGAfjUbD9/VVCGuCFO7m1lY0sibiFNweT0ZIEa1Wi5IkrC1tNht+97vfKdqX/UlN1H3nSlBTpLISaNshfNHR1NQlpIhX5OZS7bNU9EmYTKSwNpt67vfzSBGvl+SYNTYSwcIpp6Rcyg5A9uzKCwtJXI77qB84QALDd9xB1CI7dhCuQGzoXlrKJ0UkhnUZRSBAxt5cYuRYqwWte+MkQFERMMTj4UXwlyxZwquT1N7ejmo2Yc1qTTl5LScnhxcUGzKEr7LZ23Fe2bScZ5UPmcCIEUT5xNbaSaYQ+5EjR3DDDTfwtnkeeABmoxG17fx5J5t0XVlJrLw6hD+orEzeLIG+1lI8Ot3FdRb52J2wf3+ccPV6iQuQ0u9tNPKt0555Jv47AcqLl4fDhDhkp4gffwzk5zfj+uuvF+y7e/dunMraIaWId999l7f+7LPPKiZFfD6fYL5atXMnGViXlQHjx6d1bgBpX9etI9eysZGE5H72M1JHhy1jwfYn0agO1dU3YOPGLbH5/gUXXIqbb7457fPoCWAYBnaNBvb8fAzmJjVZrYSxmzFD/iAVFfGCYUlixIgRMYINIP3KWWedldKx+hpUUkRFdsGSIizKykgPeOwYofObm4Ff/EJIgCSrRKBH+ylYzKQMsd7b7VaeviLWa3emfVaHtDptFBcnbxV24EDnkSIJvrOH1tfKbOd9HsMI08KzYJ+1fPnyGCECAEuWfIxjx27B+ee/FNvWFfZZkYj0hEDMZqQ7KUXkaoqIQSwg9vbbb+Opp57qEs9UOaXIiBFzUVZWgWXLXoxtc7mk7bOCCgrPSH2mw+HCBx+sRkvLAd52vV6HvDwpUkR5PR0l19fn8+DbbzfxtpnNhRg9WoHKrwMjRyZHikyffjFmzRIW7xaDXq8Fw2gRjdJqmm6sFEmCuGKRjH1WIrtcOTzywgsIRKPYs38/bv3FLzB4zJjUD0ahbPBgkqopghaHA00SbYTb54MrRVLkkT/+EQ/+4Q+IRqO44447kJcJbx0RsF2OgSZFXC7R4JhOR4JCe0RedDQ3oyvcij1KCtZ0AmQLrWczCUdFQtTUkOGlx0OWUaN6qd+/Tkf8sUQQCgGPPsofto4fn3RpCh5o3q+zAuoMQzLwN28mBMfgwfFYUV4eyak74QTCAYmNQ6uqiCC9tDR7biJmM7F4D4fJefn9wKaNZqzOWYBAoRVBvQWVwy0YQs0VP/nkE9766aefrrhWmSj8fuCnnwhj0NpKlp//HKNG6WIZ8AAZwx85AoiU0OsRSMe2rZZKbMg1GuFu14Mpt6F6CIMo4vVo2HuHYUjN4+PHSUmAVFRX9JBKjEePRju3PkZ3xdtv80M2AwemTgbRuTBKhwz79vH39fmAgweF471//etfOO200wTbk8X555+Pjz76KLY+bNgwxe89wpUSdaCquRlYs4YwoEmSIuEw8PzzhJAuKiJ/CwpIvRwu9u2L4pVXGuDztaGwcBiKihj88peAXs9gzJhbkZMzFe++ewm0WgP++tcXuletlM4GHUsyGLJWwItLiADA9ddfLxnPAEg78/332c0d765QSREV2QVdJ6SgAHj1Vf625mZJ09dLLrkEK1euhN1uh91ux1133YVrrrlGuCN3IJmFzEEezGbymVxlh8ulnBQR67X9fmHEOJnvlWhgnSlZ3SmnEPPfYJCMGLdtk08TZu0v5EYqUjOuFEkRKaWI1PYYGIZcSzqtJwukiFPkWm7a9F+cdtqjsFjI89IV9lnhsPStmKxSpDvZZ9XR3gwdECMNmpubsXXrVkyapLxuRaYgV1MkGo0gL095FMjvlydFpD5z7dr1+PTTfwm263Q65OdLkSLKoylKlCJ+vxsHD/InCZWVY6DRKB+Ql5VJWxZZtAZ4wgHYdCZ8eMf9MF+4AMOHlyd1fJ3OhGCQT8h1a1JErH0rKCAS7/Z20t4//DC1A4nAKXESSqcLysnLw785SpFMYvapp+KfL7wg+lqzw4FGiTbC4/fDnWLA/v7f/AZXX3MNgsFgPEu4ExBXivDvO7fLJaoUKS7u6F9E6sc4WlqEb8gCxJQiwSx0ImpNkZ6D998nsWAWbICnJ8LpJEPFZOPkOh2x/uE+po2N8qRIezsJ/Hq9pB23WADWkWXePPI/m82djk+/HE46iQS+E4n9pK7JsGFk6QpoteSaWSxAUTGD+tK4R1WtJwqHy4lgMIjCDmvou+66CwMHDsTHH3+M/fv347zzzkvvBKJRgLb7aW+HqbAQw4aR6fi4ccROrK+6q9CkiMncHx97TsW8Sg3v3olG+fOTiRPJkirYPlarJdNbsalhKERCIU5nPFeyL5AiZjOfFEmnOx0+nLRNVitZlJYLpFySMWAA4PXyA8sFBQX4+c9/nvrJcVBI2cM3J2G7Xl5ejq+++gqHDx/G4eXLUb97N+xsg5jCDdPSQohA7vD23nvjYa39+7/EDz88gX/9axuam4mF7H33NcNgIB0rw5Br3b//FNx88wb88MPjePXV/8JsdsDhcOCOO+5A/3QY+Z6Aykrg/vvJg+vxyCZmu91uHD58GMOGDYMuTfIkJycHDk5S1sknn5xw/7o6Um2gQTpXss9AJUVUZBe33kpGYjU1RDUyZgxhs7mT22PHJEmRxsbG2AKA51nIIhgMIlxRAdM995CRsk6XvNLE5yM1L8rLk38vw5C0Bm6maDIBErHsdTFSJBmlSCI9cSY1zPfcQ0ZyVivRZMuRIiw1nej6aLXSpEG2SRGgc0gRBe+Xyj71eltjpIgSpUiyTnQspGJNoZD0zxAKCQPsPYUUkcqskAqIibVF2UAgkDi9IxIJIz+JugRKSBGpe1GMEAGIOsJqFQ/6SxUeF4PBID9k8fs9aKGCtAUFyi2IAHK+BoMNgQD//tCAwfrrb8TaI404aUh/5I+sRHBk8hJnnc7Ys0gRkSg5Y7UCV10l+R49o2x4yXaX3RHzTjxR8rUWlwv1ErMIt88HtxLfMAoMw0Cr1aJKymA9g2C7RyNVy8aVgBQBgIDIs99lpIhI++1XoHRLF7L2WWrKXbeBxcInRZYuJUPswYO7Lzmyfv16PPbYYygpKcGDDz4YC5h9/DEZVptMZEg/Zw6ZQilBcbGQFJHDvn0AV7xQUREnRfLzyZINdJJYLqtgY55r1z6N77//C9zuRtx5J2mrWMus2bNnY/bs2Xj88cexa9eu9AOHJhNZuPOV9nagsBAXXZS1xOVuDZPJhPHjx+PgwVo4HI0w5w3EkQGzcbwK4KYkMEzH9QqHyXzV6SR/8/JSkh+ddBJw8smJwxN6PbEoQsfHut3dd6yUSZjN8XxJQFlijRSUOBWJwe/nm0KMGgV4PAzGjx+P+vp6NDQ0oDTdwkwcnHvuuSgvL0c0Wgi/vxD9+o3F4sVE6TZvXuL3Wq3WeODbbCbJSixSYDubmvjrNhsJ6RQUkNf8fgf27/9C8D5umMlmI7+h2ZyPNWuewnffxec7CxYs6P2kCMPE21+Resivv/46lixZgra2NrS1tcFkMuHrr7+GwWDAqFGjMHPmTDzzzDPKPy8QIOyG04n/3HMPLn/wwY7TYPDrX/864VsPHEj4cp+C2iWqyC4YJj6aHjuWbCsv59PyNTWScj86iGnvCOjffffdeOutt9DW1gaPx4Pf/e53eOSRR1I/z127gA8/JAOe0aPJzCOZQkc0KZJMgESpUiRbpIiYXZQUjMZ4gD83V9i70mCDB4mMPi0W6VFjIoVFCjVFFJMiSrYlc04KlCI+iZFhJBIP0sspRaJRkumRiod/IlJECtxzix+nZ9cUkSJFFN07nYBgMPGMoaCgFIWFyqMXgYB8UFGujgkNg0GPggLxwbk5Cd8NJfZZfr8LLS38dic/XzgolYPJlCMgRXQaLaoK7KgqIG1mOEUVosWSz6vxAvQ8UoRmYK+bMAEvb9oUW79qxPlQcpfYbF1X0koOpaWlqCwpwVER8qPZ7cZxKfssvz+hNZ8U9BpN1i0GDHq+d5nb7RbtzliXTVFSpIsIYTH7rADVPq9atQpNTU0466yzMmZvqBZa7zmgrfkcDuDTT4EFC7onKeJ0OnHaaaehtYPJqa2txTvvvAMgLrRny4awt2Fraytee+015OXlITc3F3l5eZg9ezY0nDa6uJiQHIWF5H8lcVwlFj8qlKGwkKgympvDWLr0eMJ9GYbByJEjM/PBeXn8lO+Om6inESIOB7H4OnyYfIUE+RhJYcGCBViwYAE++QRYty4UG/NJdmkff0y83FjMmMEvYqEQydbz0Wr7jpqnoIC0bWYzWVIoB5c2Lr2UhCV27SL1d0aOBHJzT8amjjFuJBIRdW9Qgq++Im1rWRlZzGbgggsuwAUXXIBvvgG++Ybc7w5H4rxPUcycSWoPsQcoL0/6/GiRSlERAL8f0/s3IKptwKlGH6gSKAD49+dpp5FYg80GPPdcDurq4uNhRwbq7WUTTqczFmvMFDZu3Ig333xTsD0QCGDTpk1J1doEQPz9XiJW6pdGo9BdcgnWtLTgvKlTMWHnTuDQIeDss0VvqP37U/kGvRM9rFtU0StRVUWIgPJykoqUQN9Id0JsQ+V0OnH8eHygmXbW9rZt7IGIkWJTE3DFFcrfT6dzdLVSRGo0ZTLJj86SIUW4UJJGxgYPEl2fRIbzGVaKyNYUAcSvVxeSItzaBHQ8TUqSnUlSJFF8SNw+Kx5Us9n4P313qykSjUYFQUqpmhuK7p1OgJx91oMP3ol163YqPp5YofXHH38Xr732IioqqvHss4/IfiYNnU6LoiLxQWUyNUWU2GcFAh60tfFJkcLC5ItAm825cDj4wQs9Qz04KZIiEybMw1df8UeiRmP3JUX0Yu0b9Vw8MHs2NtbWYntDI84qm4SR+dXYpODY3X2iX1VdLU6KeDw4JqUU8fsFhOup48dj9Ikn4ssvv8TOneLPo74L2CEjRYq43O6Yjz+bQWa1xjPSuxMp4hVpvwOc9vnRRx+NZcnNmTMHK1euzAjppCpFeg5yc0lcmL5FuzLjOhQiwV1uMeETTiDjtY8++ihGiACkAG8kEgHDaHjZ00A81+nQoUP41a9+Fduu1WoF45Q5c0h2ejJNjNJi0CrkYTQSIo5hKvEvjqD2/PMXobFRwn7sm29I4ztsWHLVw7kYORKoqEA0JwcNkQgOtbTg8DvvgGEYXHLJJakdM02EQmTopHT41NgIPP00f5vDkdmxQ14eoNHoYDbnwW5PMLXsaDh2NDbiu8OHUbtjB45/8AGGDRuGe+65J3Mn1Idx6aVdfQYEVisweTJZaGg0GuSm8EyGw8Dq1fy57g03xMNedL+UdG5NVRVZ0sCIEaTtb2oiy4CcNuCvT2EqZ58H7rkHf3388ZjKbdEivsELt1RtTo4ddXVxm7qeRIo8+uijePbZZ7FixQoMlKm/Gw6H8c9//hNTpkzBpEmTYEkQu5KrFTiWTRpXCk5jyDAMLho1ChexG9gaIxJ2jOXl5D5T7bNUUkRFd8DMmWRhsXcvycYoKiIjxbKyGNv99ttvo6WlBU7n/2fvuuOjqNP3M9uT7GY3vZJACIHQO9IRERGxoKjonYpiwXYneGc5xXrWs/3snt2zgRUVu4A06b3XJEBCSM9mN7vZ9vvjm9mdvrMt2eA8n89+kpmdnbI78y3v8z7Pa4XVasXIkSMB8BuYiEgRu52vJwu1eGskpEgslCIJCWSUxw3oxtKgVI7m3e0OyJHFIJVJHuVC67Ky/WNBisj4fDCliFotjxRxuUL3pCbHEV4vpe4IVlMkK6tzSREppYjD4YDVakUyZ+YlphTpLFKEq9q4/PK7ceTIQVRWHsbll1+HESOKsXOn/FQQrlJk48ZDeOyxmwAA5eUb8I9/GMMgRTRISxOewSYmys+IkWOf5XLZ0NTETnVKTw89JTgxkT/h0aqiQ4rMnDkDv/32Dmud1do5FkRyIEiKcK69V1oats2bhzVrSBtTntaL/xkBxDspMvfGG7H6jz94620uF44yfXmY77W18QjX1ORkvPDCCwCAmaNG4esNG3if03UCKaLTsccqLe3t2MyZwK+/kqHHuHGBbi+eSBG7ECnCaJ+ZtgGrVq3CihUrcOaZZ0Z83KA1RRRSJG4wYwYZV3CF452RfUyjrY1fSnHIEDIc37lzJ2/7Y8eOISurkGdLTsfkmjhsidls5pF/4Yz5aMsUg6HzMra5OHSI/J49e3Y9tQMATJkyBZmZvXDq1EEAQFNTN5w4IUCKuFyk+q3bDSxdSubBF10UelXvdu+dD//3P1b9zdLS0k4jRX7/HVi1itxXiYnEjOGss8S3T0/nu4CVlwfMJqKBIUOIRZLZHOS+ap8z/3z4MOb/FLAQmjhxokKKxAF+/ZX8NZnIq7AwPtotGjU1/HluJsPdN9zC8NFEejrHwd5nBnaw40eP3Xwz7rr/fr+VfY8eZtGQy8iRI5GbmwuTyYTk5OQuY5318ssvs5JqfvvtN5RIFKjau3cv5s+fD4AkJvTv3x9r167lkyMnTsASxOZ1gN0OfP45uSGMRpLVIAWjUTqB2WAQna+edRZ5bdwIvPaa9GFOd3TBIYWC0x6VlUTPRWu6ioqA9sHcYNrQloOokiKNjUTlQGsINZrQK/XFQinCjUqHEkChKGE7KzmkSLiZlXINhx2OuFGKyCJFhL6PSEgRugpmEIgFWrxeMsISulwxpUg4CNU+ixQmlK4pwlWIxpN9FkDUIlxSJN6UIi4XO1LSrVseXnuN7SGakCCfeHC52D/C44+/xFr+6ac3MXz4zJDOUavVwGxOBEWpWcomIPqkiM/nRX39cda6jIzQ7bOSkvjR+uiRImNw223sdRUVW8LaV0dALUQEc6/9/POBb7+FSgW41Xocz5dn5hzvpMjll1+Ohx9+GGVlZbz39onYQ9pdLh4pksTox7QiUZeOVooMHQps/po9C7e1t2MmEyFGuBAkRTop809QKSLRifz4/fdRIUUUpUjXgtcLTJhAhtYtLeRvZxYsFnLGsNvJOf3973/HkiVL4HQ64fV6MX36dHTr1g0qFXD//SRDvqmJTFPotpM737Go1STCMWIE7zihICsLYAhQ4gK//07KUer1QHExMHZs8ILxXNjtkjGiiGG1kmmNwUDOU6sNTBtMJhMWLNiEDRu+Q2P9IXQvOkt4+nD4MHtAXF0dUWfJrVNVVlYmqIQOFxs3bsRrr72GXr16obi4GKWlpegvkkxID5VpG7hgCiSKIpn0Bw4E1lVUEFJk9WpCMprN5OvJzQ0vCG40ylSPtW+Uw/ktuAXbFXQONm1i30/XXAP06NF558MF9zZJSQGef/4JVFVVoaCgABZLAXJyzkCPHgVISorN+NjrJeTRvn3kuznvvCBtIUUR1pbhxoJTp2AuKpKllvkfNwMgRmhqasKOHTuQn5+P3Nxc6DkNq9vtRlNTE6+ovRAWLVqE22+/3b98/PhxTJw4EXv37hVVeWxgJDp5PB40NjYKq0Xefx8jTpzA3WPHwmIwwDJ5MnoMG4YRI0Zg586d2LlsGabU1QUca+SQImp1II6UnExuNOY4VIYdl1So7c8ChRRREH/gVgAU1BWzcemll2Lo0KGwWCywWCxIFynULgu5ucBtt5FB6O7d4aXWx5tSBBAmReT0uOEOmuVWR3Q6A6NkIYRLisSqpogQ5N4fQiOPYcNkGYcGU4oI7bojSBGx9V5v8Joi3Fs4mkoRr9eL2tpamM1m3uCIRjBS5OTJk+jVi53xHn9KEfZ9IXStCQnyDYy5KpDDh3cJHDO0oJ9Wq4ZKRcFgMPNqaSQmym9b5dYEsFrZM4+cnND7A0FSJEr2WQkJOmRm9sGpU4FaWuecc2NY++oQCPU13H5h2DDAZMLxxhocNvSDSytvhB3vpEhCQgI2btyIDz74AHfeeaesz9ja2tDCsfqMR1Jk4kTgqwx2dLglSDsWV6SIQH9NkyI+gYy5egHzZJ/Ph+eeew5vvvkm+vXrh9deew2ZzNRNAXjEZJPtUEiR+IJWS4oaxwtUKn7mO30r5+bmYh+zxiIDGg1RbnBroSQnJ2Niv35orK1Fk9OJgqQkiaIIXRdVVYQQAci0YffuQOF3Kfh8wLJlpLRGey1a3Hxz6KILuVi3jrgu0xg4kFhnYfduYMUKjFppx0h7KxrNhdjebYxwvIq2PKFRUCCtmg8CrvVLa2sramtrkdE+v3a7Celw+DBRV4U67du4cSPeffdd1ronnngCt956K8+Pn9tsy7msggJChHTrRrL/e7ZXQd+yBahnDCkvvZQoTyJBayvwwQek7k52NrlPCgrah3u9ewN33YWcjRvBLKygkCKdD5eLT7BFQn63tpJwjMNB2pukpEBp2a1bt+Kbb77B/v37sW/fPgwZMgRvv/120H2mpZGh8smTJMSUkwM88cRif60SAHjzzTcxder1ss4xHGJz925i4QWQZ8doJNaKksjMJCdNF6WSm/Tagdi0aROmTJniXy4pKcF+Rjv61ltv4ZFHHsEff/zBI4m5OPPMMzFw4EDs2LHDv+7uu++WtL3awFF/jxw+nHQ+3N/HYMCo/HyMys8ny5deCrTHGSZOnIiJKSnAl18GtpfLVjAzGJqbSR0Rh4PcyFGqp3e6QyFFFHQMysrIqCs/PzhjyQ3cyyA4evXqxQtewu0mPVlbG/mbkiI/eE1RgRFROOD2xHJJEbdbOG3G6eQ3rKEG5ISiT3JGDIMGAZs3B5blkh1inaZKxVa92GzS0fB4U4oIyRPlVs0TSkWaMEHWR+XYZ3GhVvO/7lgrRerr67F161b06jVAkhQxGvmkTbRIEZvNhgsuuADLli1Dz549sXTpUvTu3VtwOyns2bMH4zkZGvFGirjdbBJDqEZHQoJ84oFbU0SokLvDEdq16vXa9nNL5pEioahY6P2Eiuzs0JUiRmNwpYgvglTTl19+DZdfPqVdOUNh9uxLgn6m06BWY0h2Nra2F2xVURSumDqVv11JCU71KoGjhv+WGOKdFAGA9PR0LFiwAI/cfz+aZPQRPgB1nKBkEqOvFSP3dB3sB2M2A6PGpeG9XwPr6oIQHELWec2d4fEAEfus9k5EiJhoEDBN3rp1q9/2ZP/+/cjPz8f//d//SR43qFJExF6wqqoKf/zxB4YPH44Cidp5Ck5/pKeTYT1dTDjUwstMTJ48GZPvu48dSA/HL0smTp0C3n2XnDdtgfSXv4SfPyUXzPrWNIqKgn+OooAdO8CqyXLyZOxIEW7T4/8pPB6gpgb5KYDLCJiMdjR1F1EolJaS7Q8cIHNCgfFrKMjNzYVarQZFUSgoKEBhYSFsNhsslgx8/z0pJk2f9+DBhHwIBYcOHeKtu/feezFu3DiMGzeOtZ47VJYT8xs1itTdYQ65fD4S+2MirPGE3Q68/DL5IUwmWO1GnDo2HVVV5IdTq4F//at9W60W0GrRraAAI0eORE5Ojv/l9Xqhkjkm9PlIrJKOWTochOxhDgEOHiT3rslETi0xMfbPWFeG0DDEaCSqCJst8Jo1S15M//ffCcFJY8gQ4MILyf/r16/HQw895H/PGyRRgkZBQaB+iMdDnrmbb67gbCN/bFBRUYGBAweisLDQ/3r22WdFEwEBtuLKYJBZrmjqVMKWxniM2tRE7vvsbBIuDAXHj7MdAnSMTrW5uRkPPPAAampq8MILL+D555/nff7KK69EaWkpJkyYgFGjRmH58uU455xzsGnTJjz22GO44447JI9fUFCAQYMGYefOnfB6vRjZ3Aw89hhh02bODBRaMRjYDRc3tsMd14YjfUtOJmy8gpCgkCIKOgarVxMzWFqGN3GieDrHpElkxFpbS1Qj4Y5cX3iB3UtefbW8EXQ0IKQUEWKMuRAL0jqd/M4oHKUIF4xATUNDAz7++GPk5eXhwgsvDGQfTJhAUgscDjIinTFD3vHE0n8MBvaoOFiWabwVWheC3OBocTG5Hvo4550nu8MLVmhd7BQ0GrC8qGOpFDl27BjGjBmD48ePIzk5GXY7/7elSZFzzyVcqZxjhIpvvvkGy5YtAwAcPnwYTz/9tGAWTzClyNKlS3HTTTex1sWbfZbbzZ6BGwz86IrQOjFw7bO4pAsA1NdX8tZJQavVtJ8Hf/ZvNMaeFMnPD50USU7mt5e8mg8RzFCnTBmETz/9BT/+uApTp07E7NkTw95XzKFW46kpU3DFF1+gweHAo2eeiSwRBWeoc6auQIrQSDMaZZEiAFBZzyb/khn9b7woRQAgj5NBfKKhgbDoIh2KoFKEo4rpKEgpRYSsteq5laoBLFy4kLX84osvBiVFwqkpUlZWhpEjR6KmpgY6nQ5r1qzB8OHDJfej4PTF9fISgeWDOz6UYZcRLuz2QIF4+lAdEawdPBjYsCGQ5DN6tPxpUHY2mxSpro766fkhSoq0z2WKi8miN8mOoVeLNLUlJeTl8RCJRCTuByB13SoqKpCVlQU140vz+Yj6hnnOO3aEToocPHhQcP22bdt4pMill5KpLn0fBRHmARCe6tnt/PlMKLWvGxoa8NVXXyEnIQHZhw8jx2RCVlIS2k5Q8OYGihJnZvLvsx49emD9+vXyD8aBywU8/TR73d//zg7W//BD9FUwpzPUauCMM4gSjLaw0+tJm8GcAzc3C5Mi5eXAH3+Q2jIlJewm1Ofz4scfP4BWm4ns7Gye+unAgQMhkWL0+Xq9LajnjBWvvvpqZGRk4LPPPkOfPn0k91FeXo7m5mZiu7RzJxISEvDSSy9JfobJXzoc4NWqEoRAHMbtJs9vsNxaj4c8q3TZCzHYbMBbb5HfDiDkVSjlfLmkSH47q1JTU4PnnnvOX//k66+/xnPPPcdS2JSXl+OTTz7xL+t0Ouzbtw+//vorPvvsM1wvo8P+17/+hX/961+w2WzYescdKDAayZfU1MRuQIzGgIejwcDPiOjeHTjnnACLF2Hbr0A+FFJEQezh8wF0Y+XzkTQjKfTpQxoFtToyyRe3oelISwMuKeJykZ4nWPaWVOY6d/QXRVKkra0NQ4cO9fumP/bYY5g0aRIefvhhWCwWPPPAA+jW1kYIKrkGvmK9n14fGikSg0LrUbfPkgu9Hrj1VkIypaeHRNKJ1xQRV4oAkZMiPp8Pd955J956612kpg7ExRd/jOTkPMH9LV682D8wEbNU8XjaMGAASYSjrRDCPTcx3MYp2PDOO+/IIkUmT57sJ1MA4Ndff0VraysSGPegWEAs5veOCNra2G2GkPIilLod3Exwp5NPHDU0HOOtkwJdC0Sr5bd/odUUCb0/oCgVcnJCl3pza8kAgIZrnxVJei+As88ejFmzJoY0keoUqNU4u2dPnPzHP+Dz+UjwXqR9D7Vb6kqkSGpyMo5w7T1FcILT/qUwfIx1IveNGFkSS+RzFLZVLS1wNzZCw/XoaYegUiSI4i5WECKine3ts0OA8K4XIG+OCdSKCYZwlCKff/65f1Le1taGm266CZuZClwFCiLB1KlkLE17vQSxB4kEXP4lAlenkJCdTWyotmwhUxGp4txCn2UKaYJNQyMBd4hIB1d9CYk4fChABtiddpT+1YfsHIlIoVodtaIIQgWOKYokFDOGvdi9G5g2LbS+/JJLLkFeXh7eeOMN1vqtW7fytk1Kil7x65EjyW3f3EyCqbLqgrRj3759mDt3rn9Zr1aj9b770OhKhI+hCo6Fokir5av4mc+VzxcIDtPozDpIHQmnk4SMaOLV5SIqoWBITib3LRdGI5tcEhuu7N5N6mzs20cTFoH3Wlsb8Prr1+L114U/29raimPHjgW1ZeLC4/Fg4cKFqKiowPvvvw+A1LKkX3JIESYKzWZQn31GvoyePf22TDR8Pr7ZRSgx96NHCcm0bx/ZT3IycZvnDmlbW4F33iH5wPTU+B//kH4+jx9nJ0Zu3BgaKeJ0OpGQkOCfi9OkSGVlJZ588kn/dmVlZdi5cycGMpQUK1euZO3LZDKhsLAQKpVKFiHCRJJOh3F5eeyVTFKpvUayKHJyAj5tCjoUCimiIPaoqeGPooXSULZvJy3uiRPkM7NnE4IkXHAJiM4kRQDSO4iRInV1wG+/EQ2zXEQj+tS+7qOPPmIVkr3vvvuQnJzsD2w3Njbip59+Cu14YuBmsEWiFJEKIkm8J5bVLyuwHWkAMymJjORDhLh9lnihdYD/NYRKPCxfvtwvNbVaV2LNmqdw7rkvCu7v4YcfDrq/8ePbcEm7U1CsaopoZAYXuaTIpZdeihUrVvil0K2trVi2bBnOO+88/zbxZJ/l9fpgt7OzjDIz+QRAKHU7mEqY1tY2tLTwowdud2gEkEajav/LDwaHcm7hKEUSElKhVof+zCYn82egPPusjooGdTbaH1QNs+2LEinSlSb6aSGkoR7n9GsWBikiRn50tH0WAOTTacvt8Pp8OHn4MPJFSBEhpVxzJ6nkBJUi7Z1IqwAp0mC14ocffkBZWRkuu+wypKWlwROGyiUcUuSVV15hLW/ZsiWqhY4VCMPrJcEWuz3wGj48pkKKzkFeHnl1AMKpCREt9O8fWqCMRs+eZHyZlUUIEhm1dsPG5ZcHrHGczsC0j0pLxdqcS9DgTIRLlwSXNhFZ9UB2rONfW7aQeXVjI3lNmkSqlLejf39CilAU4dIGDhR2CZbCnDlzMGfOHPTp0wfz589HYmIiBg4ciJKSkmheCQtJScD06eF/nlsHJMdkgsdDwZxrRI8exKwiKSk2jxVFkeeGGaBnPldtbYQMYKIrjZUiQUMDwKzNTVFEFRZuV5mUFJwU8fnY4RePh6hJGhpIwN9m40vLzjnnHJSUlKBPnz7o06dP0FpkQjCbzXjkkUcAAKtXr8ZhRt2zWq6VvAB4pEhSUuBCNBoeKQIAl1xC7u26OvKXKfpevJhcc3o6eZWWspVcdjuwd29gubkZePxxomC69NLAer2e7JvZjrS0SJMiFMXOl+VcWlA88sgjePjhh9HY2Ijjx4/7kxlpezH6u0pKSsK+fftYpMiqVatY+xo/fnz4yWpC4+FoscBRgs9HLDDT00nfGCXevctDIUUUxB4uFyFBqqpIBNVsFg7Qb9tGSBEaJ06wSJHVq1fjkksugclkgslkQn5+Pr799lvx4zKpa4qKXhq6HGi1pFdgEjFWq/hI/Lvv2NcuB6FGn4TIhfaG+ueff+a9xcz0F3pfFoxGvtEnlxiKhBShKPJdC1kaxUopMn48W39aWhr8M1FAODVFgMhJkVtvvZW1vGHDSyxShBkf6tatG/YEIfZcrkDAKFY1ReSSItyaIt26dcPYsWNZA6QffviBRYrEk31Wfb2VV7clJ4cfzAyl0Drz+g4erAKpkBAZaIWHECmSlBSKUiR0eyGTSdjmKRgsFhn2WX8yUoQFkaiJ1KOXnx8QjQKk6e4EHiBspMmtpwX+U2NhpOOJ1RTpDKVIekYGdGq1n0wAgOOHDiF/xAjB7QWVIp1Figj0iW0eD3w+nyApUlFfj+ntUbTHH38cBw8ehDuMZBkuKaKiKHgZz4NToI8oLi5mJZ4AxG5SqS0Se7zzDnuZa40SL3C73byxi8/nw/LldaioSMTZZyd2FO/hx8mTJEhG1z3IyyPf35w5gUzuGJYviRqYXv4dAbWaTF1Y0xeDAb7+A9DAmOrV1fM+Gn3s28cuJFBXx3o7NZVY3vfoEbly84orrvAHi9UdbAdZXV2NtWvXYtCgQejevXvQoCaXFDGnF+CDyilwq/WoPErul+uui935GgwkQK9Wk/+ZygSnk5B3LS1kG58vNBVMVwZ3WO3zke8j3Da7tJQk3huNJOQh1A5UVPBDFVdcQYLGKhWwfHk1nnkm8J7FYsGPP/4Y3gmJID09nUWK1HGeUyHccMMNGDNmDMrLy1H+1VfowbznBR5miiI8iQBXAgCorCS8Kf1opKWxSREx3ocbplGpyO9otwMNDUdQXr4Kr73WDL3eirS0NJ4tNSBsadbWFpogn6IopKSkIIWxM4qiMHfuXBw7dgwXXnghzjrrLBg4N9Pll1+OpKQkrFy5Etu2bePVEg0JycnAPfcEsjBsNtFO0maz4bfffsOAAQP8ypRDh4AlS0is5pxziG1ktFFXR+75igrCmVMUcPbZ0T9OV0MXmo4q6LLIywPmziVP+MmTwiwqvR2TGOD4AzY2NuLUqVM41a575vowMuFyuaCaORNqjYa0qFpt8DSDLVtIb1BcTKI30VAEMCfcYprNtrbQCREgdFIkK4sQUrSxbmqq31KrScBrm4uwshrPOw9YtCiwfOaZgd6WRrBjB6vCZzBEjRSRFdguKCCpVDt2kM4vks4zBASrKSIWT4uUFOEGcrhg7q+4uDgoKcIMqnF/omjxlnJJESsnSzgpKQlnnnkmixThZuLEk1KksrKBty43lz+yDIV4YJIiBw6EZpMlBlqpodPxB4YmU2zts0wm4Yz3YLBYFKWIHyH0NVKb5uaSJnPPHtK9RpLl2RlIDYEU4UIWKdIJNUUoikJ+airLFuy4xHhEkBTpJOtAu8BxfT4fPB6PoH0WE8ePH8cXX3zBIjOY+5Aa67g5pEiSVgsr43thKkVaW1vx9ddf4wAzvbIda9euVUiRGENoGH/smLw6Bh2NSy+9FOvXr0fv3r2xYsUKAEBKSgoaGhowe/Y3KCs7H0VFwJQp8l1sI8Xq1cCuXYHlceOImiDOEl+7DNLS2NM9GXHPyMHttxobeZsMGhSdQ2VlZSErVhXsOfB6SSIV3Z0uX74cV1xxBQBifzN16lR8/vnnop83mUwYPHgwTp48iVOnTiGzRzEqCgL1TwS+JgK3m0TQW1oCnl2hFmEBCYvQiSHc7iY5GZg3j/xP12SIxE28K0FoWN3aGj4pIsd6KysLuOgiMjY9fJiQIcw+gqIoDBo0CNXV1aipqYnJPX711Vdj6tSp8HrT4HKlw+U6Ax98QNzkJ0wQO2/G89bYyI6nhMhwulz8e55rrZWaSsb43ARGoUPRpTMqKtZgyZI5WLKErB88eLAgKcJtppKTSdgsQpdiAPzacVycddZZOKvdj7GpqQm+IFI5l8sFm80Gi9CcgKIC9UJEFNcAcM8992DRokWsGMv48eNx4YUrYLWSgcvSpSQ3XNa97/GQZHLaS7C5mcTcBL5ABvcGgPxWXcnKOFZQSBEFHQeNhpANYuC+V13NKk7ODWByC10NGzYMJ0+eRGNjI+x2OzZu3BhaIcstWwgRs3IlaYGmTYuMouVqNsWCpjIICUGEGkBRqUjlql9+Id/p1Kn+77ZRdPQXgMfjkR1s9qN3bzIi2bOHkF6jRpHqcUxEUlMEIL+VkAVGrJQiFEWMjadPJ6PUDgpkBVOKyCVFRIQOIR+XBpMjkCMfliJFoqUUEQs6MuHz+XhkoMVi4dWS4CpD4okUqapiE8NqtQ5mMz9aEYpShPn7HD16IvyTY8BiIeek0fB/l1jbZ5nN4RWpS0vjjxB1XFIkHtONY4EQ2jipLkKrJRL7qqqg84W4hOAESCZSGJNosfZJ10lRj/yMDDYpUlEhum08kSKtIiqPNodDsNA6Fz/98AM8zPTcdjgcDlYdKS64n0nkkiLtfYbP58OECROwadMmwf2sWbMGs2fPDnqeCqKLMBzTOgT79u1DVVUVK4O9oYEkPtTVkUz/I0f4U4lFixahra0NZrMZFosF/fr1Q1qU/KG4j0GQ4aCCICgpIVPDtDTS//F+Jrs9eCJYqODaPoY754wDnDxJhPrl5YTcHD0amDiRvLd9+3b/dlarNeiY/Oqrr8bV7f7+brcbZWWt+PDDwPtWK5nf8MY0v/4KrFsXWB40yE+KhJI8KPdnVqv/PNZZABknJieT5PqEBPKKtcukwUDCPYMHkzaO+4hMmjQJ27ZtAwB4vV5ePCoYfD5iCZaSEigZkZ3NHlrfcsstAIDly4Hffycu8jU1ISjxzjuPnDhdZCeEYiE+H6kVwgWrfXK7oa6pwYxup7BzWQ1UXjcOFZMiLkIB9XPPJX9//92Er78OrBerN6rVAlddRZori6XzVORmEZvcmpoafP/991i6dCl+/vlnzJkzBy+88ELYx/n88895SaenTtX5CRGAxGtOniTEWFB4vXxZ7IEDpG0aNYpVp+TIEfZmPXvG/hnrClBIEQXxg/x84nOan08C6NnZrKc0GCly/Phxv4oEkBfo98NuJwwrDYcj8lEId8QjNkkPd4AajpKlWzdBPbCc76qtrS10UkSlIuTL1KmBdaHWFAk2IhALTEqca1QKrXdwQFS80Lp0TRHu1xft+BWTyBCzlmKCeR2daZ/V0tLirx1Cw2Kx8Iogc7/3eLLPqq5mkyKJiWlQqfgjm6Qk+cSD2x24vvLyyJUi55xzA5KTSWRFqNC60PmKQa8PfciSkhIeKZKSIlBondvmRjt4Ea+IklKEFmx2VJZztJEcQk0RLuLVPgsA8rOyWIbaxysrRbdtE6op4nSyElg6CnaRCG2bwwGHjPZY19LCU30AZKwpSYpwPpPI+T1ppcjy5ctFCRGAKEUUxB6jRwN//EH+V6sBEWe4DoXdTshh2oLK63XhENOWlQOaFMnNJQEMJu6//37WZ7/88kvMnDkzKufJfQw6if88bVBSQl6CsNmAZ58lwavevUmKcEZG5O1qfj4wbBh8ZjNOeb0ot9tRtngxysrKMG/ePF4iUCzh8ZDLCdeIYds2Nh/B5O+ZpAgADApB/qLRaJCfH5jzm0wkgO1wCNhWMWIDa48dw+p9+7Dho4+wYcMGvPjii7joootkH1cBHxQFLFjQecenk/zFoFKpRAPnYmhq4gei77iDr44A+Cq8IKLXAMKo3+N2E9OLdesAOnw2aRJp53nqpIoK4IMPMATAkCIAWi1c/zwHzVZKcCpE16k4epTdvoiRIgC/b4snvP7663jggQf8y0uXLsULL7yAdevW4X//+x9GjhyJkSNHonfv3rLqkaQI+IV17z6Qt05286zVkg6b2UnX1ZEXo4YUQNSmPXoQxUhZWXx/7x0JhRRRED8wGuGvwCyA888/H0VFRWhpaYHVauV1ShaLJXxS5MgRtke6Vks04pGA27OJTdJDOU8m2qNPTqcTb7zxBmw2G2666SakhpF+K5cUSRTo+aqqqrBv3z4MHTpU3kAhFAPinJzgEwKx0YtIdM7lcokWSg2JFOlghKsU4XJ70c6SZAon5JAioShFKitJrC47mxRykzs3lKMUEbrnhUgRbma0mFKkM+6d2lq2fZbRKPzsq9UqqNU6eDz8LG8umL9hZeVxiS2lce21D2H06KG4+OKx/nVabWQ66HDss1JTw8uYTU/nj0SdHvZvryhF+AimFOnKMAuZHsuEhfHZuCNFOIUKjp88KbqtkFKkzeOB02qFvoP196JKkdZWwZoiXGgdDkH7rJamJknVI48U4RLp7X3EOmb0TgDbt2+Hx+PpcO/9PxvOOosMOxsaCCESD9ZPx48DH38cWHY4joiOLQCguZmQHuPH88dB3LFMqAE7KZhMRNFgMJB4Swc5I/05cfAgyfY9cYK8Vq0C7r478pTpwkKgsBBulwu5BgMrGWjKlCkYOnRohCcuH3v3Ap9/HrifcnPZBZqDoaCATYocO0a+MpWKjN/T0tL8tRhCIUUAck633SYjS53Bkjy4YgV+ZUS7N2zYoJAinYRvviF5SiYTeRUVxU/tKO6QKiGBL+CiwSXhZIhew0ZrK7FoYg5pKCqg8mCBOyZyuaC1NSItTXpcnJGRgaFDh8JkMiE5OTlqKsaOxnnnncciRQ4dOoQDBw7gl19+wauvvopXX30VALHi+vXXX8V35HQCtbWwCNhajemTh1s8L6CWykSlJwuq/FykpoZQtzY5WThzgfMgZGSQ1xlnBGI4+/fLP8zpCoUUUdBlkJOTgxyG/IsLrrVFSKRIVhYxyz10KKBVi3QgGmulSPtEes6cOfj0008BAJ9++im2bdsWcu0POTVFhIIhmzdvxllnnYWmpiYUFhZi3bp1yM7Olt6RnFEKRZHvj6kwEYMQySKRiiSV0d8Z2f5yEW6hdS4pEkyYw0QwX02APZgSuke4kFtTpL4eePvtwP5dLmDIkKC7ByCsFOEWMeW2DxRFITk5GXrO/SSXFOmoe+fLL9diwYKb4HRakZzMbg9NJvHBplqtl0WKtLQEWLNTp8QzxoPhqqsuwODB3VnruIRTqAjFBoxGeggSciaE7LOc3JlDvMy4Yo0Q+pNgSpGujGQJ+yy9Xi+q5tNrNKzCjlqR56DT7LM4dS2O19aKbiukFAGA5tpaZMQLKeJwoFVGe9zU2uonMJiw1tWJVyKFgH0Wp8+g7bOCZQt6PB60tLRENYitgA+NhmS/xhO404PExCLs3r0b+z/7DPtWrsR3Bw/i8n79UDJoEEpuvBE5OQU4cICIB5jw+Xy8sYxl61Zg8uSonOeIEfGhrIk22tpIZnR2dnRtWtxuQr4ZDGRqIlnO0ucjA1z6BPbtY79fXBzVk9NqtcjPz0cFQ15RVlYWMSkyZ84caLVa9OrVC8XFxZg4caJo4JOO1zkcIiqMIOCWYGprI47MJSXA++9/DI3Gh8rKSmzfvj00++x2yBou0pMqjQYjiopYpMj69etDPqaCyOF0Egd0Jm6/vWOH6D6fD16vVzDJgVtKNTsb8Pm8mDXrUuTl5aGgoAAFBQWYPn060tONGDSIkPdGozh5Eg2YTED//gBTZLVxIzB2rEDTk5TEVyKcOiVcIZ2BAQMGYPPmzaLvf/QRId779iXPdzjCuLKyMhw7dgz5+fnIzc3lzeOjgSFDhiAnJ4dlb7lu3Tps4PiODeCoMniorATefx+XpqZi2NixMJtMsA8fjuTkZMzs3h3pu3cjHY3oozoAaPIAhECKpKWR34cb6JFQPneWTVk8QvkqFJw2eOqpp+BwOGCxWGCxWJDHyX6UREYG0ZNNmUJS6aNhnBuJUiQ5mXQ0nCLPLKjV8Hg8fkIEAHbs2IHVq1djfIjFv+UEdJnB4ba2NmzZsgU333yzn1ApLy/Hm2++GbSgVVClyODBwAUXkP/laKyFRj1h1BMJ9l5nI9xC69w4VTClyMqVK3H8+HFceOGFokE+JiJRikjZZ23YwF5eskQ+KSKUiW2321k2AdxAQnJyMlQqVVClSGfbZy1ceA+am4nVX00Ne+AjVVRco9GjrS24TGjTpm+wf/9C9O6dB5stBAaNg4wMfoD02muvwIoVH/iXs7P7hrRPrVYNgAIQnKyj0a1bEJJWBGlp/Nm6wxP47X0dYXQcLwjhOqUG2NEomNiZSJZQYc6YPh1ffPWV4HspnLGAGDnYaUoRjmHx8YYGUTssMVLEWl+PjKKiWJyeKOwi/ZPTbpdln1XT3IwmgX7V2tAgsHUAXMstnlKEJkUE6pVwoZAif05wYxRerxYlJX3Rt3dvwOfDvfQYfuxYkvIM4WLYLpcLY4cMQWNFBRodDjQ5nbAEuX//rPB6iUqhupok3fh8JHAazcTl+nqgPWEYAJmK3HcfYyqzbh2weTOZD9rtZFB7wQUBhQgTvXtH78Ta0b17dx4pQqOhgXw3dL3mrKzgZTU9Hg8++eQT1jh59erVGDRoELZv345t27Zh69atWLhwIQoLC3lJzKE6kBqN5GtRqwOOjxs3kteCBUByMoW8vLzQ5v+hoqCAKHgMBozs35/UGAF9Lhvh9Xpl2ecoiB6EhKHRqMNCh4McDkK85OWx2+7//ve/WLNmDfbv3499+/bhpZdewlVXXcXbT2kpGf+ePEkIktxc4NSpU/jyyy9Z2504cQK5uUbIcT8M9T6rqQHee4+0d+np5DV6NHnRpEh6OlEPCIKiSKPQ1ERUI5mZQQmRYKirIwI5AFi/nvxm114ber3BRYsW4Z577vEvz5o1C5999pno9ocPH8aSJUtgs9mCx6zaQVEULrvsMpSXl+O8887D9OnTkZOTg3/+85+s7UYOHEjYWjFGvD1mdeOwYWRZpQIWLiTbMouvAHx1TjBcdhkJyuzZQ27a1lbyNx7ksV0ACimiILbYtIlESTIzCfEQw0zISUKpYB4P6cloz2s5LS2tvYwU4SpF+vcHZs4kWlAxUkSlAijKX4SRia1bt4ZEisjJ7mdu53A4MHbsWGzhpmUAeOihhyInRfT60AxnhUiRMOqJBHuvsyFeUyQ0pYgUKfLSSy/hb3/7GwCgb9+++OSTT3jb6PXsYHd9PZFd5uZG1z5Lwl47KISUIjabTZIUoZVm4dpndQQp4vX6UFW1S/T9lBTx2b1GIy9zxu124N57/4Mvv3wBDkf4XmtCpMh5543E0KEXYMuWb6DVJuIf/5A3GGVCrdbKUrzQ6NUrPBtEtZrfBrHss/4sKhExiCgCT2eliFli/PDMs8+itr4ev//+O+89C2dCIqYU6TRSpLiYtXyiuRnelhaoBMZBokqR+nrB9bFEq8jYRa5SpLy6Gi4BK82WIMpZnn0WVynS3keoqquDnkOLbMNwBacTEhJInCIxMVBM2O0GNNOmAUOHArW15BWkwqpOp8OKN98EmIRsMLV2hPi//yN/6fOeNo1M7+IdKhUZVzKbDZstuqQId5iuVnOmM04niU7SaLd6gkpFigyUlZEB9cGDYdUICIbu3btj7dq16NatG7p3785S9m/eDKxeHdi2b9/gpMjx48d5Y+Ti4mIUFxejmtH+TZs2DYWFhbzcQIkEZlFccUXAWpeGSkUIk23btuH777/HiRMnUFlZiZ49e+KZZ54R31l9PfDmm2SiZDSSvxdeKD0H1Wj8c8yRI0di6NCh/noCI0eODNmpwe0m9+XOnWS3M2cCu3eTIDodjkhM/PPk4IQD7rxWrw8k4Xg8pHi5zUZeLS3kHjKZAq7pYt/ta6+x81mvuQbokev0t8/fvPceltIFqwDsF/Egys5mN8s+H7BxYwVrG61WG9xpg4HnnnsOjz76KAoLC1FYWIizzz7bP38XQm1t4DuoqCC3+5gx5LzGjSNdTdCC21dfHX5BIAHs3s1e9vmE66wEw/HjbKtnbs1hGhs3bsR1112HXbsC8+h33nkHZ511FiZMmIDJkycjPz9f9Djcwuoulwvz58/Hhg0bsGHDBpw4cQIjq6uBxx8nD3NSEnD22SSuR4M7d/R6iRWGThco7EIjHL9KjQYYyK9NoiA4FFJEQWyxYkWAwqco0hPFYKAniN27ASZTnJEB3HprxxwbCF8p0r8/GUlLkQftkad6gUCE0DopnOI2wiKgB75LliwRJEQAebUcggYTQ00n/hMoRbxeryh5RRdaD0UpIlYTlzmg2rNnD1588cWg57Z7N3klJAA2W/Tss4QmS3Jr+YopRZiQS4pwySgpUmTRIpJtN2CACn37Bs8SBoDvvtuA//zneZjNafi//3sAPXqIZ4Y4ndKkk1DhNhparfhzl5VViurqvf7lNWs+h8v1LFpbw1OKaLVJ0On4N6RKReHnn9/F5s2HkZlpRmFh6JEUlSo0UqS0tFvIxxADkxTxdXXZQ6gYNy4QMcnJEa25dTqTIskS0bOs7GwsWrRIcFIrlxSJ1F4uXHBJEbfXi5pjx5DVl6/kiidSxB4hKXKQ62nRDmsQ61WefRZnDOJXiojULmMiGAGj4PREYiLASTAlMFhIVCiUeRJX7RRDwt7rJVMWn4+Mdeh1XQVJSXxSJJoI+lMwk+W0WjbroFaTqGTPnvIHuyHi5ZdfxjvvvCNo8cMVrMlxoD7EyV4yGo3IzMzEwIED8csvv/jXb926FbNmzcLEiYTzs9tJEnO4+YdcdxijkcRqN23ahPvuu8+/fhidjc1BVVUVvv/+e+So1cg5cgS5JhOyjEYy/5STpt+O3NxcSWugYCgrAz79NHDfaDTA9OlEGc+8T6+9NvIyp10ZPh95VsXs1pKSiOLBaiUv5nxYpQLWrmW3U1Yruff27we++44MaXNziQiIWXRarwdsNh+2b38fSUmZ2LZCD+3OpcgxGqFWqdAHwFLGeezjWuCJgKLAUmwBZN467y9/QZ3Tibr6etTV1WHbtm2iNcfKy8vR3NyMnTt3YufOnUHryXJdUZlWcVOmyDrt0AgRp5MoH6ZMgceSBpuNfOfMZo1JbAJEUaNSkXu/oYG8uncP3qVxSRExYiMvL49FiABELff222/j7bffxsKFC/HII4/IvEDymzEVKpWVlcj54gvyQLvdwvb4BgO5QQ2GwMvtJm3PRRcROVF1NXnFUvGmgAeFFFEQO9CUPA25So1ogRtgkKmIiBq4ShG7nT/Q9Xj4KQ70yFSqF5AgRU5wJdhBcFKiqCoTdDD7+eefF91GVlBHjlIkFESRFHE4HHEpfZaysQq10LrbTSYjcmTr77zzDm+dWEC6tRWor4+eUkTo/Fpa5E2ihH4/G2f2yyVFaEIhXPssm82OPXt8ACiUlemQn+/gEVJcNDXZcf31s9DWRtrJuXObsWzZB6Lb2+3SdmYpKVL2WeLP5u2334P777/Gv+xy2bF373E4neFlMSckiNvBqFQURowoFn0/GPT6JLhc8iIZer0ZOTmWsI/FhdPLIMT+bKTIWWcB+fmkH+vfXzRgc1oXWhdJh9ap1TAYDNDr9dCq1Tz1gYXTaMVbofU0gWy0hlOnhEkREVK4uRMse1pF2uK21lZRu0k5CJUUSeKSIu3fkZyM4ZZQat8pOK3hdBKBgMtFrLJkD0NLSkj2S7iFGkI8R26puXCy/TsLRmOAzAGEbXciAXeayZvOMOfAKSninrAxkgWIZVAD/AxtOXxtjx498OSTT+LQoUM4dOgQEhISQFEUhgwZwiNFgIC6KFKYzcDIkYQcaW4O3PK5ubms7Sorhevibd68Gddff71/OcdoROWdd0bHJSIEZGay7xm3G9ixg38fdfBpxQ1OnSIKmp07ybN0883C22VkAOecI/weRRHShBlqoaeDVVWkDTh4kLx69OCTIg5HI5YsuRYA8PHHZH3Z3/+OQosFfThFaMSUIkLo3bs3Fi5ciIqKClTs2wdfdTXeZNihA0BDQ4NoXcRyjpNIYV0dsHgxeTgGD+apDGhRGo0wyy3Kg88Hx6Il2PHpXtjfPYLdPS9ETUZf3Hsvu02cNYskVe7ZQziAfv2AF15gE7Jz5gQVTEKtViMxMdGf/ChGiuTm5mLEiBHYuHGj4PsTJkyQfYli++clQHODGQkJwP33C++AtiWLEerqyO2h1BERhvK1KIgduAoEjaZzSREZ9RGiCq5SxOslExbmiLC5mT/DoEemUqRI+2ypjtvLgZ+5EwzVMiwegEBwWMiaiIYsUiQY5R8qKSK0fZikCECIkcRQjW5jDKkAD11TROySjUYyKGTeZs3N8kgRoULrUln6jY2hkSLcn455mUJxw6YmeZMDIeKCqxThWs/RSpFwC637fF588sn5aGmpwtCht2Hv3kswapR0pvBrr33tJ0QAYNu27yS3t9mk27CMDPFMdimlSJ8+3WEwWOBwNDLO5RCczvCUIlKkSKRISEhBS4s8dVtaWkHwjUIASykSg0J+cQ2K4lf5FcBprRQRUWJZEhNBURQoikJOSgoqOCl5KZwUXFH7rE76gnQ6HRK1WtgZ7WaTwNgCEFeKNHUwKeLz+cRJEadTllJEDC3cNGQOuH0AVynS5vHA5/PJqnykkCIKAGDvXlLvwuMhwSq1mnDPKhXafbUkpuspKRF7u8uF0PC5K5EikyeTr5MuYhxtq/V+/Ug36XSS8SxPRdO9OyGxDhwIzycmXBw7BmzbRqKNjY3kJrviCtYmFgvh1iwWEjizWIILVoqKinD33Xfz1g8ZMgRqtRqlpaUYPHgwJk6cGL1rAcnsz8nhr+eSItXV1XC73bx5KzegXED30TEkFIWQmAgUF5PbgQbDjcmPDj6tuMDx48Bbb7HXVVeH5ygkRopwOTPuPWUwADYbP0aS1f6DjMzLw7xLL0WfsWPRu3dvlJbKL4o9YMCAQFHuV1+Fs7IShsceY21TV1cnnxRRqQLSi6Ii3hc1aRJRYtDOjD16yD7V0LFuHbQH97TzA070270YB4vPRUvLKNa8Pz0dmDiRvGprSYiQOzym1SJS+Pzzz+Hz+dDY2Ijjx48jS+ImufDCCwVJEY1Gg9GjR8u+REHQcT4mZHYyDQ2BLmH/fhLvoF9TpkSni//wQ0IC0lZp/fv/OdsWMSikiILYQaslLXB1NXnaMzIi8iI899xzcejQIZhMJphMJixcuBBTpDR/3KCVy8Uf4blcZLIRi6wcoaizzUaK6e3cSTosrtWGThcgDcJUisSaFJEK3HADyiIbRfY+FyHWFAlW+6G1tTWuSJHly5fjr3/9q+j7wZQiajXpk5kZcVZr+LbTPp8XXq8HKhU/+unxhEaKcL9ml4u8tFryl4vGRpKsHgxCyppgSpFIa4oAwMGDREi9dOlcXHzxaADSo/eaGuHAoxjsduns54wM8VGTTifeniQm6pCV1Qvl5YGB4pYtO8EtaD579j349NMng55nYmLsSJGkJAvLklsKmZmR+Q0YjZksAub87ozM+T+bUkQmTmeliFiWrZnRkOVnZvJIEYtMUkTXiV+QOSGBRYo0ipEiIu2fzWpF7datqNixAwOmT4c2xkUG2tra4BUg7YF2+6wIrDCtQUgRnn2WQFS4ra3Nb6MlBcU+SwGpd0bB4yHtSMNJJ3Y+9RuqqDoMyK1DblITGq6/HpWNjejXr1+Hn195ecDuyGoFLr6YtOV0HdeulHUa00BgO9RqMrYVnEao1cCVV5KUXRn2elFDYyMpGiKBjAxSrDwauOiii2C1WpHQAYxZWVkZFi9ejEGDBqFbt24499xzkZubi7y8POTm5sLj8QQlRQr79SPe/50QIezfn5AihYXAgAGkxs3PP5NnzWYjQ80/43AzL4+QdMzumA6dhIp+/cj3m5REXt26kXAQlxThcGq48kqgoKAar7wSWJes18PQfj8NHDUKr40dC/TqFfpJMXHqFPQaDYw6HVoYc87a2lr07t1b8CNfffUVjh49ivIjR1D+4Yc4gzk55nrhgQTcLRZAZHfRg9cL7NgBtZo0dx4PsKH5BDad3IqTr26CRtOM6dOnY9CgQayP0dxPSgo7p1puvg1FUUhJSZG0kQaAa665BpMmTcKIESOwa9curFy5EqtWrYLT6URSpCw5RQH33EMeXLudvGSwGS0tpFZXYiK57+ni8zSGDo2cFKmvD3yXTGWUQooE0IWGMgq6HPLzgcsvJ/+3tUVs3nrkyBFWwJ8b1OTCbbFAc/vtZDSh15NRPJf8+PlnQskWF5NXUVH0/Hi1WnJsZlC1ooIUUKfBDbJYLIFzDLOmyPHjx9Ha2ip7MBp3SpFOrCki5/2OhNvtxpVXXilpcUbXFJHK0DaZ2KRIkJhPUHi9LhFSJLSaIkK3aGsreXSE3O7kJtUK1V8Jt6aIXPssNnxYv/4rzJw5T3IrIa9Yl8sDrVb4x7Tbpb/frKzw7LMMBj3y84tZpMjOnVt529133/WySJGkpNiRIkajhbdOpdIiKSkdViu7RkBubmRKkX//+2XMnz8bPp8XRo0B/xzP8Kf+M85SZeB0Voqo1WoY9Xq0cEhXC2NWkZeVxTNKtnAyguNNKQKQuidVjI5BlBQRaf9+++kn3Pt//4f61lYMys7G6i1bYBRK5Y0SWiV8b9ocDjgiUAZbuZamHMghRZxOJxwy+gopq65Tp04hPT097uw8FYijpaUFN998M3766SdMnToVb731FgxBxryffPIJbrjhBqSmFiAlpQ96dD8T4zxtKLNVYeFvS3G0uQ61Dz2E1NRUQXV4rPHZZ+zx41VXsS1mFISBaFZ3lwMhb6wY1S0BEPSejyZWrVrFUqsMHz4c33//veRncnNzMWrUKJSXl+PkyZMoHDQIGDtW/kHdbvJQWK3kr8EQNuNWWkrIKKbd7k03kb9er7A6688AiiKE0dq1gXUiZcCCYvx4/jqfD7juOkKMVFWRv9wSDjodoNFQGDx4MKorK3GqpgZZdOBcqyXeTkEg6zHr2xfYswd3jBoFFUVBXTAAttwx2L+/Fw4dIqEp7jUUFRWhqKiIeC1ybdM7UoXGhUpFvtjvvoN23XZ4PMBrVZtRtvUtfNZegCU1NZVHitBgBv9NpqjWdQdArLVoe62hQ4di6NChuOOOO8LaV1NTE3755RfodDpccMEF5IfW68krBGcc+uez2/mECDlOCCd1/DgJltDegsOGARkZOHyYvZnRGFOnri4JhRRR0DGIQqoDd6LKzdpct24drr/+ejQ0NKCxsREpKSm84kss+Hyk9WluBrZsIa+zzw5tYBQMiYns6C53oMadfHdjFAUOUykCAEePHkVfAT9wIUSTFJEV1Am2TTSUIiIjkObmZlYRPiHEEymyZcuWoDVfgilFADLYZg4mg8R8gsLjaYNGw//evd7QlCIGA9/ay24n5ytEisgdGAiRIuEqReQWWg92PCEIBbtaWhxISRHOVrHZpJUiUqSIlFIkIUGLoqJirFkTWHfkCJ8USU01IiEhDa2t0oEZozGWpAh/32q1DgUF/bF7N3vGVBhhZcqrr56CrKxfsGn9dlzddAA90wOz1j9doXWZOJ2VIgCQnJDAI0XMDFJEyMvYwpkc6UT6uM4kRcycDDkxOywxpcjirYH2YvvJk1jy1lv4y8KF0TtBDlolOrE2pzOifrwlSKEBXk0RgZRwp8MBp4w6dlJWXUOHDkVdXR2Ki4vRu3dvPPzww36lwOLFi3HkyBHcddddCmkSR/jwww/x4YcfAiBjhcbGRmQHkeXSPvT19RWor69AaqoJPSdNQra1FtdtPObfrr69+G4aI6C+ZcsW7N69GxaLBRaLBfn5+egRZTlEQgKbFImjIbICueAGSemgfrSKVZw6RYJxaWkkGKjRkHUdUB18+/btrOUcGWT8HXfc4Q+EOhwOwTmDJNauBZYtCyyXlPhJkdbWVjQ3N0ta+DCh1YqPjVSq6Fu8dSUMGADs2kXIkQEDwnc4EAJFkds1LY3sWwwTJkwgNXH27oX3009hpcd/MlLsHQ7gxReJuoW2fCstFRgnt48hHp08GTAYsFU7Ekusk0HXYpfMcTWZgL/8hQTCm5oC8qJYwWolCb6nTgG//07WcYuFaLXARRchT1UATWsLevx3HcpOMnchPn474wwSx7dY4nfOsGLFCjzyyCNYtWoV3G43Ro4cSUiRMCEVqgRCJEW4nnONjcDs2WhoYMdbiopixol3WSikiIIuA+5ElUuK+Hw+7N69278ctMhlfT0/9bw4/ALAgkhKYh8jWECVmX4lgxQRyxo7dOiQbFKkRqYXDT1olKpv4ZEjB6eogKZSCNEgRUS+5wsvvJB1jwghmL1WR0JOYEcOKcKd9wjFYWT9dv5thScQoZIiKhX5+ZiXSf8fbaVIuKSI1+uFx+PxqzrkkiI6XfDJpnBBeHFSJFih9dxccVJEq5VSiuhQUsJO/WxqYo/SNBoDEhJ0SE7ODEqKSBXzjBRmAVm4Wq1Dr179sHv3L6z1RUXdeNuGinPOGYJzzh4Ew9NPs9/4s9UUkYnTWSkCEKusSm7bwbjf8wr46iQLR/cuap/ViUSbhTPBF1PiipEiXBw5ejTSU5KEXYJMcDocaI2k0HqQrAE3p68Ustt0trbKUqu0iByrpaUFJ9rTB3ft2oVdu3bhwQcfBADs27cPc+fORUtLC6qrq/H8888HPY6CjsGGDRvQrVs3zJo1Cz6fD1999RVuFqsO3I59+/axls/OcqIgsRZugwValQouBgl34MABlu/5Z599hiefDKg3r7jiCnxMVwOOErhDbIUU6YIwGoHhwwGzGXU+H440NaFs6VKUHTuG4cOH48wzz4xs/wcPAr/8wl//j3/4g8c+H3lFm8PlkiKDBw8O6fMGgyF0ZQunv1y5ZQs++v13bNiwATt37sQVV1yB//3vf6HtUwEP2dnA/PlxErzt0QOquXNhttkIoSjjRj55kiT6HT1KXmo1IUV4OPNMUvQjIQFQq9G2HsAPgbclc+z0+sjtu0LBgQPAt9+y1wnFDygK3S4cBrz3HtIb2XUgDx4UH791pshFLlwuF5YvX+5f3rBhA/bu3RtSTRkmak95ofJ64VUFAjnduhEXNLM5Qm65fRw5dSowbhy5Dw8fJjyuAjYUUkRBl8GXX36J5uZmWK1WWK1WFHMIDK5Fhc1mg8vlEs++LCtjL5tM0deShVKbQqUi1C2NCJQiodQVkZtRSQeamyQo62AZln5otdEjRYS2F7CtaGhowIoVK4LuLp6UInLOJVihdYAtywaElSJCdTjEIE6KBM+24h4nMZE9yaY5qWgrReTaZwnVxWlra/Pb0cmzzwJaWsJTikipQRwO8d9IpdIgPV2cjNDrpZQievTvXyT6PgDodGQSaLFkobp6r+S2yckWyfcjgdC+NRotSkr4hHZJSZQKrQvMyBSliDBOe6WIQJ/OrBmSL5ClzSu0HodKEQuHyBTr59tkkucumeRJuAimFInIPiuIyo9nnyVEitjtsvpUMVLkIMdDgaIoFBcXo6WlBZdccol/rPXaa6/hqaee6lRCTUEANTU1OHbsmJ+oooksKRxgVlkG0Ke9EdUMG4aehYXY104wqtVqP1FGg/ucCiUNRAqLhQTlEhLItETxIO+CoChgxgwAwB1XXeVXMwHALbfcEjkpIjIXxcmT/mTDujrg5ZfJPZSQQMb+110nPXeRg2HDhsFqtWLnzp2w2+2itjxRBae/3HXkCP771Vf+ZaFCzgpChxwyxOkEli4lPwn96t07BrWODAZAIOmFC6/X65/bcY0eMjJEzosjB+K2sXJDKx0CIQWUFEFUXY3ipCQMyMiG2q1Hm6Un6uuL0djYNQgQIUyYMIG3bvHixbL6ewAkCNPc7C/MdVn3Fjh2/owGKg2nkInsITnInjUuvJMbOxYs2wdG7eXERFJfpxNKk3UJKKSIgi4DyaLq4JMiAAl8ZogV/Bw6lNCvhw6RV0pK9NMRQtG95uWxiZAISJEyLuEjAbnBcDrQLFXLRTYpIjVaCZUUEQokCQRk5CpA4okUaZBRYUyOUoR7Gwol0YYiHxcjRUIttA7wZcGbN5Nid2JKETn+rNFUitD7o0kR+fZZwT3K1GohUkT8eXQ4xH+jxMQ0qFTiX4xOJ/5cJSbqMHiwNCliMBBmLTU1uCVAcnLslCJC7bxarcPIkf156/v2jVwpAkDwhvMK2CQpEA9w0EUXuzrMAhFBM4N1zhMw2zdz7DxEa4p0YmDbzGHOG0WUGHKVIiHbkYQIu1RNEaeTpxR5aOJE9EhJwfyffkJ9kD6+JVRSRGCc57TbI1KKcEmRwm7dkKDX45333sMeRs0ap9OJuro6WZYxCmIPrpI6mZuRIoCtW7fi4IoV2PfOO9hfV0cK5mo0wPnn40kQy9qSkhJ0796dR5zyxjFVVUBNDYm+RQmXXBK1XcUVfD6SkJOQEL2pX1NTQAGt0cRJhjsHffr0YS3v3Sud5CKGTz/9FG+++SZ69eqF4sZGjDAYMLF7d/ZGVVV+UoRudh0O8mpu9mL3blLouKSkBFOnTg3rPGillMfjweHDh4Pa1UUF9DhAowGMRozs3x9gkCL79+9HY2Oj4HhVQXTR3Azs2MFed//9nXMuAPDjjz8iPz8fAwcO5NVAobvpDRs24PHHH0dBQQEKCgpQUlLCsl9KTyelQoxGMn+P9DZyu6NIEuXmkgRiZjV0qYZOr8d9Z5yFqeqzAAA7B1yJurQSfPUVcM01kSvHtmzZApvNhvz8fOTm5gomNkYEgaCDXq/HvHnz8PrrrwMgCU1XXnml/H2uXg2sX+9fpAAk6IAE1CAXNUD1KQBhkiKjRhE5SGUlkYPIdI1RoJAiCk4jpKWl4eOPP/Z761osFqRwbCtYoCjS86SnExPDWCAUUoQbTInAPkuylgoH0SRF7HY7y2pIFFIZsaEGhoQ6Y4FsfrnX2fVIkeBKEe5gSCiu1VmkCDfJ9sgR4LXXhEmRtjaSFRRM6R7NQuvc/clVitjt4aX2SClF7Hbx97Kzpa3/pEiRhAQd1GoVzOZ8nm1WYBtCdFx77V/wxx+LJI8Vi2xVGqmpFt46tVqLSZMGoKBgGCoqNgMABg8+D2lp0SNn3OPHQ7NqFQDAU1wMX0dMvLsgxNqh00ElAgDJAqQI0x4rT0jnzgmMiilFOtU+i/PMRkqKSNlsRgOShdadTrRy+vuUhARcPWgQdlRX49k//pDctzVIAgWXFNEZDFBTFDyM4ljO1lZ5ShERAubSSy/FxAMHsP/wYeyvrYUPAHbuxFVXXYW5c+eytm1oaFBIkThBM+e5kdMXJiYmYlBmJgb1ZxD7aWmASoULL7xQ8rPd8vMxMCsLTQ4HmpxOpNhsguNfBQROJ/DOO0T5YreTQtbcIteR4JNPAtnhKhUwc6Z0vYLOANfihUmKOBzERKGpiSQhtbUB558vvJ/Nmzdj2bJlWNZeW2PWkCFsUkStxq59+7D5yBFs3boVa9duw8CBTyE/fxTWrXsBK1c+ggceIHOc2bNnh02KBA6nRklHecJkZgL33EOS+CgKA9vaoH/6aX+br1KpsHv3bowNs0ap0wns30+ECQqvIg1uXkFCQvQIAI+HtBMOB/lNvF6+WKS5uRnz58/H/v37sW/fPtTV1cFkMuHzzz/H5MlT0acPaROqqgD68di1axeWLFni38fAgQNZpEhWFmk7xOBwOEKyfKPn1OnppGsZNSoC3lylIgXmt28nEhavN2iia1ISuabqakDtJs9IdTVRj0XK3z/xxBP4/PPP/cuPPvoo7o8WK3bsGPDTT8Ds2Tz5zn/+8x+o1Wrs3bsXt9xyC3oVFxN2LjeXfMlSRFGw3y4S15rkZOCGGwJkTjwy83EKhRRREBusXk16kKws8mof4McSOp0OV1xxBf8Nj4cdTQ3VOzQSMAoiBgWnGCvUahJJEprgtH+XYkqRWJEiPp9P0j4LIMHnoDUFxCJkFBWd6JlA4Ob0JUWCK0XimRQRKiAndSptbeGRIkyliNfr5d3HUqQI896RrxQJToq4XEKKJvFgYmur+D1cWNhb8lhipIhKpfErVoYOnYLly98T3C4hgQwIL754LF5+eRp27PgRKpUWeXkDcOzYFta2KSmxI0UsFn70Qq3WQaWi8NNPn+PJJ9+DTqfDfffNiepx3WPHwlNYCKqtDV5uNqQCP8TaodOFFOEqKgB2IfXc3Fze+704fs+iSpHOtM/iJJA0iZAOcu2zQrFjDAeSpEhbGxycPsAwcCBwww3ofeQIECEpwq0potbpoNdoYGeM1WQrRUSug/J6keX1IquwEBNooi09HVqtFm+88QZMWi1SampgGTcO3ZX2KG7wxx9/oKWlBU1NTWhubpaftT5kCFGu19aSl8xs16ceewxPcTNLYjjH+fJLEidKSCCv4cNFPPLjFDodEdIweU2bLXqkCPOR93rjs9+jSZFu3bqhtLQUpaWl/mS2hgbg008D21IUMH26cLIDV83Wa9o0YO5ckqXcoweQm4srhwzBzp07/dukpW1Afv4o6HRGtLYG5jcrV66Ez+cLXgtUBg4dOoRFixahsrISJ06cQEJCAj755BP+hidOAB9+SIKdJhNhH+QWS+ZIX3U6HRYsWIDU1FSMHDkSQ4cOhTEMn7lDh8gp0Rg1Cpg2TYlrSoFLigiFH9ragJUrSQzfZiNN7DXXBCecjhwBPvoosJycTEhUuFwkol9bC1NNDfZs3Ih1jPvcarXikksuQVlZGUpL03htZAVdQb0dBRoN8OOPpIBEr16EvZDAxIkTsX//fhQWFqKgoAB33XUXxo8fL7itxwM0NJD2yGolj+eQIdLXHRSJiQCjtpUkzjwTcLlQBD3WfqZHgzsd3bsT0ica+XPceFdaKHE3KVRWkofR6QTefZfcMIyOwmg04uWXXw5sX19POkiA9MG5ucDllwv35cH6aCGLslCgkCFhQSFFFMQGW7eSDoPGBRcQu6qOxrvvAuXlgeXzzweGDeu444eSvSc0gDIYhEmRIPZZx44dk33YUEiRlpYWeDlZkly0tLQEJ0XEImftWTcRQyBwI/c646nQupQqhwZdUyQ+SJHg+wimFAkGOUmQwZQiQvexXKWIXFKktTU4KSJ0nlLEh1RNkeJi6UJ7YpJitTqw/qmn/olJk76D3V7L2y4xkTzTKhWFZcs+wooVu5Cbm4JPPvkZL73EJUWiFGEQQHq6hbdOoyGRh6wsM55//u8xO7YvPx++4Jv9qXHaK0UE+jYzg1DQ6/W47bbb/JOlGTNmoAenzohoTZHOtM/iRAcaRRQMckmRUGt6lJeX4/Dhwxg1ahSSZChs7RIWV21OJ1p55HsCkJeHy2bOxH9/+w2bKitFP98SROXCVYqodTro1Wo2KdLaCqeMPlW0fgkdxWCifbJ/4zXXAO+/T1JYGxtD70QVxAwqlQrJyck826y9e/fi0UcfxSuvvCKsYFerScpsqGmzQs9ZtO1DGGhqIrcmna/TWzoXI+5AUeRxYXKRQdzyQgL354jhTxE2SkpKYLVaBYP23CClz0fsiYRuWS4pUlxcTKoDdwvYlg7hkCI9ex7DtdcCo0ZNYNVqrqysxOHDh3m1QsNBWVkZK1M8lZNw+OOPP+Lw4cMoVKlQWFaGHikpMOp0EUdoH3/88Yg+DwCMrwoAcdgZOTK0/MrTGT4fcPw4sG4d4Q0mTSJ/R48mAX+rVfheValIri4TL7xAPpuTQxyGhMhdnc6HrVvfQ1JSJozGLLS2ZsHjyYV6/Xrg118BEOuj7264ARcuWoQ1jFoO77zzjmiAnkeKqFTAunXYfvIkXqmtRa3Xi7q6OpjNZnzzzTe8z5eXl6OpqQk7duzAjh07MG/ePNHvTGIo0TFot+vTA5iSTLjI0aODh3pcrkBfo9MRnlUI3HhXvtUK7NpFCAyurMdmk+fgUl0N/O9/gQa9ro7EE6+7Tph1A/wFzQEQaVF1tbjzCe2vSCdqZ2YCY8aQz1RXByRFEcLhIHOv08G6uCOgkCIKog+Xi190LVLWM1xwo8Exzl7kITOTtEZyAglipIiQ57RaDY/HIxo0r66uRltbmyw7jlBIkWAqEUBmXRGxCFkMZxCnu1KkK9lnMTPChJQiUgjGSXi9XkHigqkUEXpughVapyHXPqu1NXhNEaF9SRdaF3+vX79wSZFAG1FcnIOrrroTb7xxL2+7xMRAkEelojB5MvGEMBr5A8zU1FiSIvyJq1qtDGXiBae7UkSoRoCFM8N88cUXce6556K1tVXQ/kbUPqsTI2gWTuCoSSRS6IoBKfL7b79h+nnnwe50orhbN2zeuRPJQQJUrVKkSFsbzz4roX0ibC4owB9z5+JoQwOeXrMGb23dyvu81eHA3r17cdNNN6G+vh4PPfQQZs2a5X+fS4po2pUiTDhbW3lqFSG0iCVhUBQwcKA/G9U/gXa7gUWLSBYjQIpwnXOOMuuNU9TV1eGhhx7Ca6+9Bo/Hg5ycHDz77LPRO4BeT7JR6UINDkdMlSLc4XGo47d4QFISmxSJVhFjn69rkCJqtZoQIrTdHyM6mZBA4njMpqupSTjQ/Nhjj2Hv3r04ePAgDh48iP5M+7d2DB06FB988IF/efz4YSgsBAoKeiErKwvV1dXo0aMHJk6cGDThLigcDsDt5qk16+vrWXZDH374IT5ipP/fMWoUnp82TTzY2YEYNIi4EtGgqLg4rbjAtm3AihUkD4BGdTVxNsrLk/4s3X1yp1C0MM9sFiZFXK5mfPPNdax1N954BD05So40pxO//vor5syZg0WLFuHpp5/GpZdeKno+l156KfLz81FRUYGKlSsxuF1RWG2z4c0vvvBvlylgo+RwOFBdXc1aV7BhAwn4m82ksDZjPFDLyXEzGjvWMIWJwkLyCoa1a4Gffw4sFxcLkyI+nw8WiwUNDQ3+xMf8w4eBzz8n7PdddwU2PnQI+OIL4Prrg7NCBgNpDJkdXkaGdAIKN9EmL0+c+Rk8WFiuE+ValcuXA3v3kgoBQ4d23u/eVaBEEhREH/X1gcEWQBqFSPzxIgGXFIhx8U8e1GpCCElkJfohRoqI7FcqYO7z+VBVVYVCGb1PKKSIHOWCLFJELHIWw0xZuR7nXY8UiQ+liM/ng9cbnDDw+XzweDzQtJ9UtJUiYqQFUynC/V4pivKrm9RqNVQqFWuCFo5SxOEIVykSnn3WkCHhkSIaDfuZmzBhON54g79dUpKwFYBOx492CxEX0UJmpoW3zuXqYLJbgShOd6WIWUgpwpkgUxSF6dOni+5DJ9Kvd6pShEOKNAr0gz6PR75SJIT+5P7582FvH4ccOnYMbz3/PBY89JDkZyQLrbe1wcHpBwx09LZfP2g2b0YvlQpvXnABbh4xAh/t2IHn1q3zb2t1ODBv3jysaq8hdM0112DSpElIb/+deUoRtZpPijgcspQioqRIWhpw8cXkf2a09fBh4ulBw+MhE/cwrFoUxB4LFixgBYVffPFF3HTTTaHXPvB4yD3AHTBptR3qX3U6kCJTp5JHKikp8IoGKAr417/Iz0TXIAjigtOxaGsj/viNjYGiIfPns74AiiJxOZeLWAuZzeJB+YsuuggXXXSR5CGvuOIKPPTQQ/654x9//IHLL78cFEXhs88+Q/fu3dGNoSyJCHv2AN98g1yBQUhVVZVfsVnOdI8AUEirJOOgDe3RgwSN6VMsLY3ptLhLwedjEyIAXwgghaQkPilCQ8D1FADQ1FTNW5f+3feAmqMZb2mBAcDHH3+M2bNnY8YM6VpQ5557Ls4991yi9nz6af/6NE6DWl9fz7OV46pMAKCwrY3c/zodaeAYKC4GbrmFkCMiZWjjDtw2R8QUBRRFYdeuXcTafeVKHF+0CMX0WJaZWOP1kravtZXIjM47j7+z998njR7N3Fx7LfDBB8RvsWdP4LLLgmefmkyBRGYppq7992xsJL9Jbi6/L62oIHVompqIWq9PH0CAdxZFayuwZQtpy3/+Gfj9dzKk7Grqzo6EQoooiD4MBmDs2IAMTKeLOCKyZMkS3HXXXTAajTCZTBgwYABeeuml4B/kBgLpiaXTSc6rIzz3cnODkyIqlfDsQoIUEbPOonH8+PH4JUXE7odwR3/p6ex0CAEJtqIUCUCIN4icFJEXLAOA7du3Y1i7jV2ok+pgpIjYdUgpRcxmM1SMmkc6nY5FosWKFBEicKRqikjdw336SKdKiZMi7PW9egnPDoxG4Zmxx8PP7ktNjV1qW2Ymn3Bpa4sfy7s/O053pYhJgMW1hGh5I0Z+dKpShHMNja2tZCLJaBfdIfQRcggBGqs5niHPvfhiUFKkVcLm0ilkn0X/bgYDyRSsrwe+/RZDARh1OhYp4vJ4sHLlSv+y3W7H+++/jzvvvBMA4OaSIhpN+EoROeMNigqMBYXsZRRSJG7x4IMP4tNPP/WPIbKysvwJIT6fD3v27EHPnj2Fi+YeOABs3EiiJo2NaM7Lw8HBg2E2m6NiMyQXbW3EGcThILGhhgaS6NraGnmR3M5Az56x27daTXiruHS002iItTWz/Wps5LFCV18dvUNmZmZix44d+Pbbb9GvXz9MmDDB/55YDQS52Lt3L1544QUMGjQIgwYNwoCqKiQDMLvduLB3b2Tk5SF33Djk5eXBzAiQlpWVsfZTeM45xKNKSA7TwaAo4MorSb1mlYo8ZwoI+vcndUHoaTFFhRYkHjCAtFk+H7BhA/s9MafzlhY2KWLU6WBuqAmssFgC1cs9HqhUKhQVXYRXXpF2WvJDrQZmzCCR71WrkM5pONxuN5qbm1n3b1FREQ4ePIjy8nKUr16Nqt9/RzI9djSbeXEtjYbkJXdWbjJA5twrVqyA1WpFc3MznE4nFixYILo991FsbOQNR1mgKAoWnw8W5kXSqu7mZmDfPkJuAMD+/aRQEvN7amoixVYA0kYChEmaM4fILc45RzrzFADOOou8mptJhymjc9y7l3A1ALmF+vcnJVgAIgJmqsaMxtDu902b2PESlwuQW+LszwqFFFEQfZjNxBuPhky7GSlUV1fjwIED/mXZRTzPPBMYN46QIzpdIOj+1ltEM52eThquUaNiZ/Elp66I0ShM0ERAisitKxJt+yybHIPeaCtFZswgLL/PR3rNyZN5m3TFmiKxLLTu87FvudBIEf4zLaeeCI3hw4fjyJEj6NGjR8iTx2CchNh1MH9X7rPD9fjmkiLMe0eufZbTaZUcxImdq5RSROoepouli8FgkKcU6dEjCyqVlqf6EasTJEQSCREX0UJKCj/453RG0RRcQUQ43ZUiKoELtIQ429TGoVKES4q0tLXB/sILaHW5kHbhhUCfPmgLIWEgFKUIF3KIZ6nkhba2Njg4+0hgkgYqFRn7zZkD7NkD0/LlQY+3Z/du//88pYgQKeJwwCmjrxCqX1JZWQmv14u8vDx+4WG1mngh6HQBP2qFEIlbFBUV4Y477sDTTz8NiqLwwQcfoKioCABw6tQp9O/fHyqVCt27d0efPn3w0Ucf+a08YbcDBw/iweXL8cbmzahuH1svWLAguhZcQdDcTIbXTFx9teLY1uWgUpFAITMpqKkpuPdQhOjWrRtuueWWqO/3jz/+wH//+1//cq+sLByYNw8UReHr2bPJPJBBwgCEiBw/fjyOHDmC8vJynDx5EoUTJ4Zf89TrBU6dIkHQykoSS4gw+qzXAyNGRLSL0xJaLXDjjaTOys6dpNsTcDMVxaRJgf+nTiU5u1VV5OcTK7iuVlMYOnQoTp44gVM1NciiCUSNBrjnHtbk2uMBfvmRCBEAYPFiUptbMpau1wPDh5P/9+5FhsuFW0eMQHpiIqg+o2E3D8B33+ngdhMyd9w4QKPRoLi4mBDjGg3bmSVY5fhOQnl5OWbOnOlfVqlUmD9/Pn980w7mlNxoJMsORxCyOSuL+M81N5MXrRhxOoHvvw9s19xMfnimPIijHkNCAokNUhSJL4WC5GTZNyazRnxdHdstn+sgKyP8xkJVFXu5X7/oFLY/naGQIgpijyhEQ7jqA7EA3eOPP47//e9/sFqtsFqtuPbaa/HCCy+wN/J6SZagxwMcO0ZesUzHENNlMiE2qZUgReqC6CCPM1tbCZwWSpHu3UlaRnk5UFQk+J2LXWf37t1ZmUOySJ0OghxShFZohKIUAcjtz1wfqVJETj0RJr7++mvMnz8/6koRsd+Z+bty/Vi5vq1cVUU4SpG2tha0tUl7eLpcoZEici3ghCCXFFGrVTCb89DQUMZaL1SUEwAGDeJrcY3G2BmXqlT8QbTLFT9E5p8dp7tSxCsQ8DCHmGEaj6QI1wIMAJLuvBMqisLCn3/GQz/8EBop4nJhz++/Y8OyZRg/YwZ6hhDlkVO3RCp5oa2tDa1c+yyh2TRFAf36EfXP7bdLHu/YoUP+/3k1RTQa6Dk3uGyliECb/uyzz+K5555DUlISevXqhWuuuQZ33HFHYINp04LuV0HHo7W1FTU1NUhOTobJZIK6fVB233334cMPP8Rf//pXTGYk7Ozbtw8AqYN25MgRVFZWsuc27b7nXp/PT4gA8CeINTY24ttvv4XFYoHFYoHZbMaAAQNEA03hQqi5UsRJXRQWC5sUkTGnC4rVq8nNkJpK7tnERNK2ulwk4nzyJJljR5lF285MowYwkNs3C6RFUxSFjz/+2L/scDj8yq2w8N//kutjHjMzE16vFwcPHkRFRQXOPvvs8PevgIWEBEJuTJwoboUlBxoN4QKD8YHjxo3D5s2bgb174f30U7TQfbrRyBvsLl8eIEQAElr68cdATN3lcmHJkiW4+OKLWc4EfiQmIlGrxcvTpwMGA9alnIsfqwbh4EHyNs3HHD1KHiubDXCcGoK+Y3PR3dJIouYdWkFdPri1+LxeL+x2O5JEvAsTE4lQw2IJISxUWipsJZmeTtomOhmSosgzK0WKFBZ2iJMMszY7wC4pEikpctllhHRZu5YoUkaPDu8c/0xQSBEFXQJWTrFxMVKkrq7OP9EAhAsq+wkRJmKpAafZZiabz0WopIhKhZqaGuH32hELUiTmhdYjCQp160ZeIhC6zqeffhrHjx/Hiy++6F8nh/jpKERLKSL0dbvd0SVF5NQTYaKy3VIu2jVF5ChFTjInMSCWFkzoOPdhOKSIy9UCp9MHg0F8YBUtpcgttzwtuJ4JcVKEvz4tjU+KmM3Cbe7ZZw9GYeFIlJcTPfr06TcLEhexhEKKxA9i0bTHE0wCk06x8YgY4rLQukh2q9fnw7+XL8e8nTtBhZCF+Mfhwxh81llweTwwPvkkNi5bhj5jx8r6rFsGKSJln9XqdPKIlQSJwgFJKSlI1uvRLDEWOsZInPBwxnKCShGnE04ZfYW9rQ0ej8cfQAcCQW+bzYZt27bhPI7/9UcffYRXX30VjY2NaGhowJlnnskqHKygc7B+/XqcSXtfACjo1g3lZWVITk7GsmXL/DUNaOzfv5+1XJKSAvXHH5PM4dJSf4CrhNPm0PfHkSNHcDXD60itVstWsoYCocQVh0MhRbokBg0iiWMWC/bX1WFPRQX2rliBvXv34v7770fvUA3nXS7g11/Z6264AViyhFga0wRyfj68GVmSyulQwSVFBs2aBdx6Kwl4VlfLSkgUtK0LBZmZLFJkz7p1uO3uu7F582Y0NzfDZDKhsbFROAiuIGxQVAfXM+rRA6rrr0dySwthIwQC5mPGALt2sXlGlQqw21vx7rvv4Omnn0ZFRQWWLFmCCy64gH+MWbMIcZiQQD64DgAj258mgTZsIEFugmQkTExG93hWFh08CNPSpbzVzc3NoqRIVEsRUxTxTqupIQU1evXiBx7OOIMcsKyMECShFKsJEy4XYEz0wlNdjxZdKnyUikXSpaWRkJbZTF7hhCnz8wk50twcmqrqzwqFFFHQJTB79mz069cPVqsVLS0t6N69u+B23OAEl0wBEPAVpGE0xrZ31WgISy2l7AhDKXKU9j8UAW2f1dTUhAceeACHDx/GzTffzJtg/1kKrXOvc+TIkfjnP/+Jhx9+mLU+mC1ZR8Hj8aC5uTnodrRSJBT7LIBvQxUKKdLcfAzr178Ai6UIvXtf0H6+odmlPPPMM3j//ffxxBNPA5gj+3Ph2mdJKUWyORllYqSIz+fjkSIJCWloba2DyZQLqzVQO8jn86KxsRVmszjrI6QUkXoenU4+YTJgwDm4884rRD9DQ68Xfra4ShEAyMzsBkZiNAAgOVm4jVKpKKxY8SVee+1rmM0m3HRTiFJjBacVtFqSLMnhHU8bpch5552HpKQkf3syZsyYkAMeYooQMbKkI5CYlASNSsWrlwEQEuDg/v3oPnBgSPukiYmWtjY8+9BDePOXX+R9TuAcuLBLqFaaBcYgCRIRXEqvR6/UVGzm+g0wcOzUKX/BUy5po45AKQIQwp45duUFyzlFuWtqarB27Vr/cpXEeSvoOHCThlRWK7BmDTB+vGCw+QQnTbR3Sgpw+HAg2zUhAZg8GSXFxcDXXwOAXwXi9Xp5x7NYLFFXiQAkTmc2B+J1BkPHlGNUEAMMGeL/95zu3VlFx6dPnx46KSI0X8rIIIVomO34yZN44i1CiiQmkvvo4osjK0R/+eWXIz8/H9u3b8e+ffswaPBgsvOUFDLp2bePpNcLZY9HC3l5pABIO8zNzVjOsGO0Wq04ePBg6N+rgviCwSCZdAmQW2/2bODtt8mtP2MGedwmTz6PdU889thjOP/88/ltNSdqzVzUagFacMTlEeSEXDoVTieS6+pQ1J58kmw0wtTRzwMjWUEQGRkBK32fj584HQNorfW4fkQ5vMeXwNYCNHhMyFjTE7joAkClQvfuwNy50TmWQojIg0KKKOgS6N27t6xBhSxSpHdv4O9/J+QIM5MllsjIiDopcuTIEdYqjUbDCtjSSpE777wTb7/9NgDghx9+QFlZGbq1d+4ejycEO6A4V4oEAdd6iLZISqV9J9shR53REZCrWAmn0DoQGSny8893+v+fOvU5jB49P2T7LIAEd2677Wbccccl0OvlZVtHo9A6lxQJphT56qs2mM3AGWfwB0rXXbcGZnMB2tqseOYZ9n4aGlpQWChOirjd/IuR8srnEibTpt2Ejz9+QnR7JhISRCx7tPxnLieHrykXU4qQ9xJxzz1XyjoPBac/hg5lW/gCxNb3dIDRaMTHH3+M+++/H8nJyXjttddC3odWpP/rTPssiqJgTkhAnYh9ZENtLXIj8Kt469df8abAep+AglaOfVarxLk0CYxBDBJKEej16JWeLkmK2JxOnDhxAvn5+cJKES4pIlMpApBxKj129Xg8OMVhFHszPRXAr4EVT+rWPzO442OzXk8840Xw8MMP4x8LFuDAPfdgX3U1sul5AB0ppihgwgT0a2nB119/jZKSEhQVFfnHromJiXjv7rsx56mnyPE0GlKZddCgqF/b/PlR32VcwOcjVmAJCZETPU4nGZ/q9WTMHe/EUWlpKYsU2RtIP5eE1+tF//79kZ+fj17p6Siuq8M1gwcjNSGBVJbW6UhmBKNdch+rgstF7kunkxTLpr+f+vp6rF69GitXrsTKlSvx7bff8sbkQrj55ptx8803t+/TSYLMGzawlSvFxbEnRQBCxOTlIS8/HzmvvMIiqjdt2qSQIn8SZGcTsi85OXBrXHvttSxSZMOGDTCbzZgzZw4KCgowbtw4nHHGGbx99exJHierlbQrO3YQYoQbLpJy/HY4SGygU5OS9Hro1Goc/tvfyHJyMiBRZD0U/PzzzzAYDMjPz0deXh7P+josUJR4lml9PXDwIHnl57ML1YSKDz4AGhuhUpHf2QQrUHZEuhCpgphCIUUUnFaYMWMGCgoKYDKZYDKZhAdWKhUZwKSkAJwMvJghI4NkrYghCqTIxIkT8dtvv/mXT5w4gZaWFj8hApDB7MqVK/GXv/wFQAgF6yGsFJkxYwYqKyuxZcsW/7quohShO09ugCFelCLySZHoKEVCuReY+PnnBRg9en7I9lk0HA4HrNZV0Ouny9o+GvZZoZIiTU1OLFsGVFfzg1wqlQZarbDSrKlJuj5NqEqRtjb2ewaDfIWbOCnCH0R268YnRSwWxStDgTwMHMgnRUK1yYtnXHDBBcL2BzJBUZSgKkMXqZVHhLDo9ZKkSFsEpIhWhLUX2qeQWoWLUEkRKaUIVCr0yssj1VslsGfPHkKKcGuKaLVI5tzgJ2tq4JBpZcQcM6nVajR8/TVOfPcdDtTVYX9tLUoPHyZFg9th4diYKaRIfICr7DUbDIEx/PHjhOzgPOMmAMOyszGMOQbhpM8bjUZceOGFvOONGjUKfQ8exLUAfAAGpaTwlfAKeLDZSDzKZiMvnw+4++7IDQO2bAF++on8r1KR/LvLL4/8fGOF0tJS/Pjjj/5lJily4AAhLhobiZf9xImkjjFA5pZ79+7F3r17QWv/Zk+cSLKr6SSz7Gwy56UoIDUVDor/5SYmEhK4R48erGdn1apVmDVrVkjX4g+Gcq0sZajtI0JODnDXXawBzvDhw/Htt98CIHOLeKpTqSB0uFyEXHA6A7aBUk6iXA5u9uzZeOCBB1i1S61WK1566SUAwD333CNIiuj1pLD8vn3keMXFZH7Y2noc+fk9kJysgtFIbkExrFpFxIpmM+lW+vdnicU6BlyiIooZUjfeeCOL2BW1JosG1q4Ffv45sGy1ElKkupr4meXlEcs+qQQcJoTG+zLIYAWxg0KKKIg+fvqJqCKyskgj0YHGs6KKEq83kMbTGTqyYGaAYo1oCKTI6NGjWaRIbW0tvvvuO95HmbVIQiVFuFnsgwYNQmZmZuikSCcoRcRIEa5SJF5IEbmKFTlKEZWKvJixnEiUIkIIRylCY+hQPSorg1tjAeHbZ7W2tsLr9UKlUgUlRbhEAW0NtmUL/xrVanIvazQGUJTab2cGAI2NAko1BtxuIVJEXCnCJUVCyYpJSBDJThd4FvPz+aPs1NTQ6ibEEhkZJaipOeBfLiyMZ0PdPx8MBmDYMGDz5sC6DrDo7VLQqtW84H9n2mcBQIWEErS+vj4iUiRZ5NpsYQb0Je2zBOqNJAQZ9/USsWRlYu+uXZg6dSpfKaJWozcnMrHnyBG0ybRgaOEE7qj6euQnJyM/ORmTe/Tg2XYMGTIEr776KlJSUmCxWJARy5p4CmTjlltuwVVOJ5pPnECTwwE1RZGo++LFwJ49wLhxwJQp7A+ZzcC//kUyUGtryV+5QRUAJpUK/TMz4XC78cDEiZLKlEixcSOJCyUkkFdhITBhQswOFzPo9SSOxYTNFjkpwpxidIQBQaQoLS1FVlYWSktLUVpaigmMH/OHHwgpQqNv30C87hDHWzUxMRHZ995L2CX6Sxg0iKS6Z2UBOh1aTgLYGPgMRdE2bGqMHj0aP9FsEoCVK1eGTIr40U6KVLe04M0tW1DZ2orKn39GQ0MDfv/998B2Bw8CX3xBtjcaST2Bc88N/XhqNS/jY/78+Zg7dy6GDx+O3NzcmFjaKQgdDgewaBH5m5hIFGJz5gQPOyxZQmqF0Bg/HjjrrPaF2lpS9by2lsS9hg3jDXa1Wi3uvvtufPXVV/jXv/6Fu+66Cxs2bPC/X3DqFKnIbjYTmTWjDTeZgBGM6c2WLVswffoYJCYmol+/fhg8eDDeeOMNAML3WG0t+dvURF4yyuxEH2lpxEuMVk5GqY/yer08C0quFXZUwVHsorqafKmHDwMrVgTWl5QAV8pwTlBIkbiDQoooiD4OHQpkZ0ybRgoYdRaqqojBIx1J1WiA++/v+POIMilid7l4xaJHjGAHBp1OJ9577z3eZ5kWY6GSIlzCw2Kx8CzKujopEi/2WXLPw+fz+EkPKWg0xOaXRvRJEenP5+TkiHqfFxYSL9ang9cLD1spAhC1iNFoDEqKcOts0NdGE1BMqFSkG6UoCnp9MhyOwO8WXCnCvxiuzRt7e2ELODkQU4oIFVpPTzfz1iUldWRVQ2k88cRzuP56unYJhYULH+zU81HAx9lnk/nCiRNAv34dJ8rsKlALBEo60z4LAIb07IkNBw4IvtcQISliFok42kSIGLp+hxhaJcYuTRxShAKgDRLxLJZxgx7eswder5dn+aXWalHKmTBvP3gw6P5otHCJITqSQYOjHChwuXBzfj6J7pw6JZ0RoaDDoFKpYL7zTph9PnRzOoF16wLSAYAsjxhBgl9MaLUkGBJOQMThwLdXXIFck4mosWKoNrNaSaCcHpp2srAtbGg0JC7HbEJstsjqWwCBQsg0OpnjDoq5c+fihhtuEHzPYmGTIsxmmrZmptGjRw/SVjOrX9NuDO1ITwduuYUEou12Mhehm/cJEyawSBEWeREqTCZApYJVp8NChmURQLLzX3nlFZhMJhQ6neheUYFeqanQcydHEeLMYPULFHQauKVYt24lZSSE4PP58N577+Ho0QxUVWXBaMyCyZQHh4PR337zDVBREVjOyhLMALrpppswb948AIF6rzQKWltJ3wAAgwfj559/xmeffYba2lrU1dVhxIgRePbZZwEAu9rZGbvdjo0bN6KlpUVynMQdSqSliW4aOyQlAcOHh/VRl4uo1eh+Z8iQQJjo1KlTPPv3/KoqwmClpUlLaMJBfj5p35gJOQcPkkkOE3ITrw0G0hl5veSVlQX06RPRKS5dSk6xf/8oFqr/E0EhRRTEFp3Nemo07Oiv200an4727As22hYbPXMnT+04wq1iCz4pAoA10KTBDMSGSorUcnrYlJQUXvC+q9tn2e12OJ3O6HhTRoBQlCJS1lk0Yk2KBLPPys3NFSVFHA4HEhPJ7R6sbE24ShGA1BVRqVS8ezQapAgA6PUmFinS3Cz9LAgrReTbZ4VyjxoMws+WTsffx/jxA6DVJsHlsrUfJxlFRfGTwXLxxWPg9X6DX35ZhbPPHo+LLx7T2aekgAODAfjrXzv7LLoWOts+a+yZZ4qTIk1NaIvA9oBrL0VDTCnSarMhUUJlbJcgaBo5pEiCVgsqiKl2r/79Jd8HgJa6OngE1B9qjQalhYWsddUhKE55pEj//mTMWFdHXtxIRk0NW4YlUJdFQSeCToMfMABYuTIgG3C7SUapgBVW2Jg0CYVDhpCIvMMRMLKPAbjirEiVFZ2JpCQ+KRIpuM1jvJNGKpWKtB1eL49Y5doDMZuo0aNH44033kB1dTVOnjyJTBnRN41GPEg3YcIEGAwGjB49GhMmTMDEiRNDu5DmZnKCWVmk3Vy4EDl2O/AEu97e8ePH8cADD7DmwOvmzsWo/HxpPyQFpwVCndK3tLTguuuuY627/fZDcDp7BlakpbFJES4L0Q4mcfHoo4+ivLwcFXv2oGLzZvSkEzN1OkCnw+7Nm/HWW2/5t9cwJvg7ORaf/bVa4M03yeS5qIhFPni9bGITiJz47Uh4POQRZqruCgoCXEdzczP69OmDY8eOwWazQU1RyNq8mTBdvXoB7TbxgrDbCaFRX0/kjnISS1Qq4mF25AjZf69eRA23ejV7O7l98GWXRTUWabeTYaHXS4YdGRnApZcq5EgoUEgRBbFFZz+NQr2g09nxo3mpCXlSkvj3ZDQSb1YOCXKEE1zOzs5GdnY2r9i6EJhB41BJEa46JTs7G6dOnWKt6ypKEUP7jIWrFAEIIRFTGaYMhEKKyOnPucRJR9tn5ebmYjMzkMMAbcsmZ9AaiVLEarWyaovQ4NtniZEi/IOrVIF7WadjW0xZrcFIEf7+pO2z2IFAg0H+KD8pSaymCP9ZTElJwvXXP4A33rgPPp8P11+/EElJ8ZX2OGvWOMyaNa6zT0OBgqhB28lRtL/fey9WbdqELVu3wsvxf6lvbIxMKSJGioh4vjedOiVJikgpRVo47yVotUEnn2k9esBiMKBR4hptjY2ipEhJaSkokNoOoYJHioxjtGtChAf3Pongd1EQQ6SlETuVje2+QQkJkSWKeb0k+JuUFBgsFRVFfJpywSVF4j3oL4VzzyXcVVISmWZFo+bVjBnEHY2uPRC3pNHatcTypamJ3E9TpvAcHbKyiGufxULirUzOt7i4GMXFxVE7ndGjR6OxsVF2kk9jYyPMZnMg0Lx3L/H7Aog6pbQUSVOnwmw2o4mRZbV582aeOruQJkNEEhAVnD6gKBLHpkWcBgOpfycGrqMAAEw4+gO6adKAcy4gjUd6OqqqSABfpwNU++uQ1Cr97M+dO5f8s3078NVXgTdcLuDRR5G2fTtr+7q6Ov//zPoZADAgNZUoFU6cICfAIEUoCrjjDqLwa24mf7sSKaJWE+EXM1GyoSFAipSUlGDv3r3w+XxoXrwYVatWQU2P88TUGqdOETlFRUVgbLVqFWngCgtJISgphcl555G+l257vF7iLXjiBFBZSX5DGaSIwwGUl6tgsZA2Vqzpq6sj5ju0/ZnRKO7yt3cvm0BqaFCatVChkCIKoo8BA0hvY7eH5I0rhYkTJ8Jut/sLqP/73//GgAEDgn9QKMje3Nw5o9WxY0nFKyZMJtLISkW1e/UKSooUFRWBoiikpaUJduRMhKsUcTgcPAIkKysLRk7goqspRbhFSwHin97ZpEgohdblKkWYiCYp4vG4gipFzGYzEhMTBUkJ2jJKzpxo61YyMJgwQZhbC0aKcO2p9Ho9kjkDKK5SxO0m904wpYjBwCZFwlGKuFzizyT3PTFLLCEkJspXigDAY4/dgFtuuQQqlQo5ORbZx1GgQEFwCAXPO9s+q7CwEOs3bIDX68Uzf/877n31Vf97DVZrREoRrUgnJUaKNNbUIEci4NsaQn9lkNFBUmlp6JWaio2VlaLb2G02eASSTjRaLRLy8tAjJQVHwrDfZBZg5Z+YgDUGd/wqUV9FQSdj4kRST2TIEEJ2hcMkLFkCHDtGohweD/EajdBmI1S0tJAguUoVyD7u1atDTyGqiMW5q9VkyhulaW/scPIkIUVoMIKuNM44o+Ocr9VqNdQhWABef/31WL9+PS644AJcdNFFmNjcDH/P2dDgl/3Mnj0bXq8Xubm5yM3NhY7Tv+r1emT+9a8kJsCp26Tg9MQFFwC//ELCU+PGSYeCuLGURK0Ws4bVAqgNxGzS03G4Jhk13jTYE9NhPZSLMQelyRY/8vMJk0pHvFUqYNs2pHFOikmKfP3116iursbOnTux64MPMJHJ5nJiGRRFuIHk5JgKCGVjz549OHToEJqbm9Hc3Ix+/foFVYWlpvJJES4oioLZ44E5PR0+H9m+pTkZeT6B4VNiIpsQAQiTcPQoefl80qQIt/9WqYCpUwP7qamRxTxVVwOffBJYTk4G5s/nn29lZYDvBaR3vXs3e7mkJP4tHOMNCimiIPqYMIGYiEcRW7duZdWuuPvuu+V9UK8HbriBBNv1euLv+9prJKqakUEmKrJ6ryhg4kTSgNbWkuwxuZk2JSWEyWbgCGcSXdQePEhPTw9KioSrFKmuruZl2WRnZ4dHinSCUkQoGE5ORQuTycS6v+Khrois7xHxoRRxu1uD1hTRarXIzMwUDADRShG5P/+qVWT8cfbZ/PeCkSL1HFuT7Oxsnicrt86GXPushAQ2KRLsNwy10Dq3pkgoSpGEBOFtuRNFJvLy+CoqBQoUxAa6OEgtVqlUUKlUSOFYNjU0NKAtzKLoAOAUaZdbRPwSG2tqJPdnD6G/MsqZGZrN6JmWJkmK2Fpb4RY4rlqjAVJSUJqeHhYpsmb9egCkH9y4cSNKSkqQlZUl7hWelgaMHEkm6AYDGc8qiE8YjSRdN4h9myToIuw0BILYsQYzGR8gMWSOY5yCrgJuZE3E8qdD4ROKYPLR2tqKH3/8ETabDa+++ipeffVVvHvNNZjTvXtgo/aEttdff5312a1bt+LSSy9FeXk5ysvLYbFYoBo8ODrnb7US4rKykmSNFxeTREgFcQWTCbj4YnnbUhSFYcOGobqyEtXV1cii2U66KBEAlJRgzajerLwE2d1xWhrbGtPrBbZtQ0laGm4bMQJpiYlImzoV2QwGl6IovzPI2QcPEuUDjTiXBbz44ovtReEJbr311qCkSEoK4SqMRvK/qKKvuBhISMCOVc04sb8Z5c4U5CQBF13E2c5oJAwRpy6SH5F0aiqVbCUodyjNFJ8wwf1Jm5rEm8qzziKH372b8LwyHGEVcKCQIgriHj6fjxdcZBYLZ8LpdGLChAmwWq3+19atW9GDpsnprMSWFvLqyFQnnY60WqFCgOI/ygnsMkmRYAhXKVLB9M1sR2ZmZpdXigCkrgiTFOEGzjsDrZzsz0GDBuGee+7BkSNHcN999/nX+3ydrxRxuexB7bM0Gg0yMjIESZFQlCI01qwJjxQJVmSdnKuwfZbQNarVgWBHUhK7XbLZQrfP4tYNYcLlYl+bGNEhBDH7K5NJZlE4BQoUxBSdbZ/FRGpGBmt53fHjePGVV8Len0PE99DG6HeZaAoS+A1FKZIuJ0qhUiErSBVSu8MhqBRRazRARgZK09OxVEaB9YzERNQwFJNrN2+Gz+fDzp07/UGC5ORk9O3bF2vWrCHe/6wLSgemTw9+TQo6FCdPnkRCQgJMJhP7NxMjROx2EkxNSxMfEwPkfaZtSieQIly+VnFs68LgtO0IQkCLYv9+QgKkpgYCvNwbxeslpF5VFVGo0On5TU3knqZfY8YAQ4cGPeRvv/0GG6MAjEqlwnkDB5L5PO0dI6LyHzJkCBYvXuxf5ib5RYQ//iC2ZDTUamDsWJw6dQqbNm3Cpk2bcO+99wra1SqIT4wZMwabNm0C9u2D75NP0EKPOZKS/FFpt4fiCTVFwlPBoVIBBgN6paXhpenTA4X58vN5mx45AjT0vAzOpAa4axtRlNaE/HiQg0iAG7drFlEJMzFlCjBtmozQ0MiR8HqB4zpgdzbpWk9tJzECnnKvTx9hUkSlEvyuYwEuKSJW0ohLirhcRBgsRA7l5ZHX1KmEn412nfk/AxRSREHcw2azwcfxVeYG4mnodDps2rSJ5Yftb3h9Pn5GTFcwWFSpgPHjA2oRjQbVHOIhr70zTAsyqQfCJ0W4tkdpaWnQ6XT+2hw0uIoMQYgNDGM4YJQiRVJTU1mkTzyQItzve8yYMZg9ezbeeecd1vp4UIq4XPag9lkajUa0IGM4pIgYpO7pSEkRIaUIRQW+fD4pIhzwoyFsnyVfKRJKoXUxAuXii8+RvQ8FChTEDp1tn8VEisDY6NcjR8Len1OMFBFJomgMkr0cilIkTWaUIitIDTybwwGPwHWotVogIQGlMifUY7p1w5L9+/3LtQ0NOHDgAA4witw3NzejtraWT4i0Y8GCBdiwYQMaGhrQ0NCAp556CldddZWs4yuIDUaNHImKY8cAkADQV19+ibOmTBH/wP79xBqLxogRxE6XC+64XoRIjCUUx7bTCLm5hJxIT4ctKQkrdu/G3meewd69e3H06FH89ttv4io1JvbvB7ZsCSwPHUr8iWj4fMAzzwB2eyC7uWdPUgfnl1+AXbsC25aXyyJFVnOKGo8bNw4ZCxaQCU1NDfGlkRkNjCpBwQlIt5aVoU9hIWteecEFF2BwtJQpCjoO3buDuv56mGw2Qr4xng2Xixij0Hm2VmuEws0bbyQT4YQEyTpoGzYA+/alAyDjNO8wID+eC2r/8AOSDx1irbLK6MdCqfVEUaTQOD2Mpq20eKTIgAGEGC4qIj9gRQVpfxyOmCbmcs/VZAp05WKkiMlEissnJxOCxGwOXpudoshnFIQOhRRREPfQ6XRYvHgxS/2Rwc10aQdFUTAajSwG2t/wtraSCnhMiOwn7jBxImnZ6+uBwYNRywmM09+HHKVIuPZZXNDBZK79jqzsmzgotM5VijARD/ZZXKVIYvvogOu9K1RTZOnSpSgvL8fs2bP9heRjS4oEt8/SaDSiCq9Q7bNoeL38AUI0lCJqtTxSRKVSsyaP3OsLrhThP39SNUXcbjYpEkpNkYQEHVQqDesa+vU7G2efPVj2PhQoUBA76OLIADglCEEQKkRJEZGJcWOQxITWELJ808VmnBwI9QVM2J1OQVJE0z6eyZVJihSazcg1mVDJuPY1a9bg6NGjrO1KSkpE97Fz506sYdSoqwk321tB1NDESP+0Wq3QL11KUl3FwP3NxFTWJSUk0kZn5EejKniISEhgvxTHti4Ms9l/XzYcP44ZM2ey3j5+/Di6yamxwW2jueQdRRHPG7sdW7eSUh9VjSfR2LcI09IKUQAOKcLA+vXrsWfPHlx77bWs9U888QSuueYafP311/j6668xkz53jYaQITk5ZJ5/4gSJOFqt5L0hQ4JfTyTgkCIJbje0nLnapk2bFFKkK8JgEK05k5AAXHppFI+VKs+ymBvol+m23XmorkaW240eFguS9XqYsrNRWloa1UNQFOmXmAIUweElzS4AJBbVp09kNbqsVhJfDGHMPGECebndhLgRS2xVqYDrrgv/1BSEBoUUiVNQFNUDwGAAuQCMAKoAlANY6/P5oqj5jH/odDpcGkKvYzKZhEmRxETgvvtIpaaaGqIakTlZ7nRoNKyqd7WcLEqaDImlfRYXdCFybraNLFIkDuyzmAqXVM5AZP78+Rg3bhyGDx8es/MJBq5SJKE9VU/D+e68Xjfr63zmmWfwz3/+EwDw5JNP4uDBg9Dr9Z1un6VSqUTvjXCVIq2t/MFhMFJkPyNDFwjcx0zwSRG60Dr7/FUq9r1vNrNJEbtdeqQqRCS1tQmnYLa1uWGzsYMoYsXThaBSURgwYCq2b/8eAGA0ZuKjj8K3w1GgQEF0EUqx2VgjVcSCJFw4BWynAHGlSJNE/RKXywU3Qw0cDGkyx3mZQa7Z5nSK22cBsOTmyjqOD8DYbt3w2Z49/nVr1qyBJTkZeq3WTyD1zskhhbUF7gsL55riIZHjzwyv14tmzr2cnBzEmpL7u4qN3zMyhBO43niD2BJlZpLPJicTr5EYIDcXkFvKsavB6xUeS4YCn48E/fV6Mr2RI7SIB+Tl5fFqKu7du1eUFKmqqsIFF1xA6hrU1CBbp8P9EyZAr9EIB3Szs4ETJ9CiseBg95FAsw+VlUBrKce7v7ERaGxERXMz7rnnHnzyySdISEjAlClTWOdCURRKS0tRWlqKe++9l+ciAQA4cAD44ovAcnp67EkRs5kcx2wmD0teHobv2YPDDKJ706ZNuP7662N7Hgr+FOCS0kLDKIdDvFZFh0Onw43DhuHGYcPI8llnEQeUMOHz+fDJJ58gPz8f+fn5yMvLg16vh8kkgxSJBurqgB07SFtTVQX06AFcc03Iu9Fo+Fyygs6DQorEGSiKmgVgAYDRIpvUUxS1CMADPp8vDqqjxR9eeOEFeDwemEwmmEwm9GdWG1KrycAlSrZZJ06cwGeffYY+ffpgWowmI1y0tbXxZIc0GSLHPitaShExUkRWcD3O7LO4ShEAmDRpEqqqqkTVDbGGXKWIz+dhza1pQgQAjh07hg8++AA33HADjxTh8hORFloPZp/l8XhESRH6WkMlRWy20EiR+vp6rGX6/gIYKiDZl1tonVlkHQBSUtgGoDabcBHhwH7534eQfdbWrUdx+eWX89aHohQBgEWLXsU997wAu70FCxfejoKCLmAfqEDBaQjBYE4cISXKhsSipAjDH56JRglSpFXgMzq1Gm0ej+D26TKzL7OCkBr2tja4xeyzAJhlKkV8Wi2PFFm9ejX2r16N/yQno6KpCQfq6pCr05GOWoAU4Y5ZpL4vBbFHS0sL75k2S5FxPh/Ara/Wu3doB6WVJqdOkVdKSsxIkdMNTU3ARx+RMaTdTn6O+++XLu0ihdZW4hQFkMdVrwduvTUyoqUjQFEU+vTpg40bN/rX7d27F1OnTkVlJfGmb2wk31dBAaDVVpI6C+1Qq1R4+JJLyAZC7WxeHrB5M1TNjUjSncK+3hcCAHR5GSQamJFBihsXFqLe7Ua/fv38dSlbW1tx77334sMPP5Q8fx64ZCQ3MupyRZ+5oijgtttYq4aNGIFFn32GxMREDB06FL1Dfb4VxAV8PpJE6HAQEZLDQbg+0baivp60xVG8v7xeL3755Rf0798fubm5SE+nUFhIyJGkJMLDcfHWWyQH2Ggkj8TZZ3eirRJ3ch9B7AkA6urq8Je//IW17tixYxg5Mh+trcR2ymSKoUN+TQ3w+++B5aNHhQMSCroUFFIkTkBRlBHAmwBmB9k0FcDNAC6mKOoan8/3U8xProth1qxZwm94vUBbG+moomBT0dDQgIEDB/rrT7z22muYN29exPsNhjqBIounjVIkhikN3Fon3JoiXNhsNqxbtw5nC1Xz7gBwlSI0KRJMKcLF999/L0iKdLRSxO12Y9y4cfjmm29479G/jZBQKCmJjDWEILRe6jqWL1/OI5vGC2SryLfPYn+paWnsQJXd3ih6Lsz9MiFkqfX002+gtvYQb31iYmjtWGZmMt5554GQPqNAgYI/H5ItFlAUFTXyJmRSpEmcUG4VSP/rYbFgv0gB6jSZM+OsIKSGva1NWikis9CpxmzGWM62FRUVaCorg5mi0N1iQXeLhUQzDMLE9xVXXIEhQ4YgJSUFFosFPXv2lHVsBbGByWRC49KlaPrlFzQ5HGh2OpEjZcdGUSSzdNMmYvXTr1/oRV4HDyafpyGQ3KNAGDod4ZGYsNn4hW3lgjm98HgI0RJHJaIkccYZZ0Cr1aK0tBR9+/bF5MmTAQB79gCcEh4wGjn2s9nZUP3972R+LTR/698f3lWr4XbXI6t6B47njUKLKQeJSe0kAuMzqQCuuuoqvPbaa/51H330Ee64447QVPvtiWx2lwvPrl2LSqsVlWvXovLkSfz0009IXbEC2LuX/NgWC1GR9Osnf/8y8de//hXTp09Hnz594koFqiA0eL3AY4+x1916K0O853KRyue1teR1/DhR782cKR7rOHwY+O23gJVTZqZkTZ2jR4/6E29TUlIwYMAAfP/990iSCMJbraQtamoirxDEtdHH4MGEkdHrySsCtqKsDNi3j10oXaVSISsrq6PqpJN6JFysWtXhSQktLeS3zc2NE0VQF4dCisQBKFKpdxGA6Zy3agBsBdAEoCeAIQDo2z4LwBKKoqb4fD7OsEUBC0uXAtu2BVLjx42T9vmViXfffZdVkLujSBGudRZFUf6swY4kRWJSUySGCFUpAgAnT56M6TlJQcw+i19TRLrQOm0lF4wUieRekFNTxO12Y+7cuXjooYd41yZln5WdTcaPQgiVFOGqRPr37y/4zIiRIlzih2uflZnJJtfs9oZAgUkBCJMifKXITz/9V/DziYmhKUUUKFCgQA5UKhUsBgMaolRRWZQU4fQFNJqYHggc2AXe656eLk6KyKwdlxnER9/j9aKqqoq1TqNSwdCesGCWeZxbL74Y3RsacGnfvhiZl4ex3bph6PXXQ8/tuySUv2cmJeHM/HwSja2pIfYNCjoNFEXBPH06zOeeS7Jgnc7gUQqOLW7IYN4fBgOpPahAFgwG4tfODBRGQopwh88qVfiqk47Giy++KLieK3RqbARaWthzIn8dJrHqvzodqHnzMGD0MdgS0pGjNaO1tX3fAs/Hww8/jI8++gjNzc0wm81YuHAhBgwYENL1wGgE1GpokpLwwIoVrLfuu+8+zNDp0MPtRl+XiwSxY0Qo5+TkICfKiksFHYd3330X6enpyMrKQktLFhIT8/yJcKzn3ecDPv+cbb9QU0MalL/+VbghqKsDKivJCyCEOIMU+d///offf/8ddXV1qKur8ydFAiQhd/fu3ax1XNBdEBOdZHpB0LNnxM9ZQwPwyy+ErK2uZpMi2Skp0G7YQCQxOTkxlIi0Q6cD+vYlJ0Nj4MDYHlMA27YBv/5K8iH69SM15IOUxlMggS7SZZ/2eBJsQsQFYqH1X5/P558lURTVF8BbCFhr6QF8TVHUAJ/Px56pKQjA52N3VhHK9mgcOsTO3N6xYwe8Xi9UYoPDKIFLiqSkpPjVA/Fgn+VyueDz+YRlzTQ6gdKWIkWElCIAsXzqLIjZZ/GVIoFC60JZvU3tGbdcHiqaShG32x7UPsvtdiM1NRUbN25EP05WlpR9VlZW9EgRLiZMmCC4XqUKTymSlcUm1xyORrhcwhmDHo+Xtz/6GG1tbuh0wbvnhISOJxcVKFAQOeLbPIvAE8XUwlBJkUYJM2ghpUj37GyAUy+KRrrMApiGtDSY9Xo0SYyLDnE6o2yjEar2rIQEsxkalUq03omaonDvuHEozskBzGYsZtbKq64Ghg0jPtt1deQlNbvdv5/dMf6JlSJOpxNr167FmWee2dmnQsa2BoOowieqKC4mlX5TU0kgqBOSjboqKIqokJlNiZgiWQ44QvT48fKXA6+XRBwtFpZVH5cgamwEZs4cjzfffBMnT57EyZMnZQX9Kb0OaSN7Qo51fkZGBh566CEcPnwYDz30kHSiX00NUVllZ5O0ffrcdTrg/vuhoyhkPvMMTjEkQa+//jpeB1BoNqPsjjvIyq5SW1RBh6G8vBzXcSpc3377QaSmFgPgPO86HXDRRaSODbPvz8kRZ0a5SljmPeh24/dff8XbH3wgen4D0tJAPfMM+ZzZDIwezSoGLzR8ClbiKp5x4gTw7ruBuEVjow/du/dBTc0x2Gw2WNwGHHvnF2RnA9opE4GOGAuce24gKeWMM4Q9zGKItjbgjz/I/w0NRNXX2gqcf36HnsZpBYUU6WRQFFUE4O+c1Zf6fL4l3G19Pt8eiqLOAvAbAsRIGoAHAcReotBVwY1IRhD8ZeK+++5jyXwBUmNErEBdtCBWZJ37vxhibZ8FkAC40PrOhBQpYhCZwLaIFIHtCISiFKHHXUKEAE2KxNI+y+eTZ58FAH379sWjjz6KhQsX+t+jlSJC40epeFakpMhEkcxKPikiXGhdrWbf4zk5bHLN4WhAa6sPhw+fwIMP/h/cbhceffRO9OvXDQ6H+Pdlszmg0xlF36eRlKQoRRQoUBAbNMsYHwwpKcHWAweQajSiXqK/dHu98Lhc/vobNGwiSpRGiX1xa4ro1WrkSBRJT5NbND4pCSkJCZKkyOEjR1jLOUajP0uaSkiAxWBArQDRM76gACuvvTawols3YNeuwPKxYySwInf8yB2zcKOyfwI4nU688847ePzxx1FVVYUDBw6gSMjW4nSFWAF2BbJw/vkkjp6UFHiFi8JC4J//DNQc6MR8KnnwekkQt6aGELAeDzBvHiEY2pGaSpojOu5qsQDFxb3Qq1evmJ7a/PnzeeuefvppDBs2DBMmTAjMLQ8cIKnjAGmD+/cHLr6YLLczUrm5uSxShEZ3ZhBaIUUUcPDFF1+wlpO1iTjz8K/wnNiII/3Oh9tN5mfr1pH3jcZ+SJlkQOaKRdB628i9OHWq+AG4pAjNQO7dCyxahDRO0i0AJCUl+e1G+6enkwmwzUYYgyFDWNumpgILFhByxGolNktxFpIJCTk5RBhZ3e7e17v3+Rgy5Hz06+eD9cX/g6qpHIcPAxUVwBkXJqNDzOpMJuDqq0P+2PHj5GezWMgrmJu/0wls3x6wQWtqIs6bx48Tm0YmmCWUFYQOhRTpfDwIgNlUvSdEiNDw+XytFEXNAbATAB25m0tR1NM+n++I2Oe6Mt5//3288sor/sLpY8aMwV133SV/B1Eu8EQjNzcXd999N3r27Ik+ffqgd+/eyOiACYoUKRIPShGAEC9diRQR8+PuTFJErlKEWWhdyJ+9Y0iR4IXW3YwD0gQPDe61MiGVLBspKXKGiHWFWh1eofXcXLZSxOfzoKGhBVdeORfl5aSQ5cyZa7Fv33o4HOLnabM5kZIihxSJvDaSAgUKFISDGy+8EC8tXoy9e/ciPz8f04YNw6byctHtnXY7Ejnpx2KkSItEn2DnpEEmaLUBGxcBpMus9QGDAUlBCgEc4qhRcpKTA52rwQCzXi9IihiYHXBKCr/iaUsLGZvKrXf3JydF3G43Bg0ahP2M3+Oxxx7D22+/3YlnpaAroaQkevtSqSInVjoUKhWJqjGDs7W1LFIkLQ2YO7cTzo2DEydO4O677wYAWCwWzJgxA8899xwymPbGXq+gJPvCCy/EiBEj8Oabb7LWFw4fTgiUxkbhAvHRgtvddXzUFPjx+eefs5ZnlpZgxuCTUKtPQnXvRUD7FHb1atJ1E/TE1Wdfi6KqNSTBQUoqNmoU0L17IMpNF8No79fTONZYU6dOxQ8//IDy8nLs2roV+T9xyglziD2ViihD4kkd4vF4sHr1ajQ3N/tfV155JcwyPAtVKiLMeO89spyYCEyYAKxcSWGw1wWdgewjIwNQp8TRRQtg0yZie0Vj7FhAqnStzwd8/z17ndVKyprMmAF8+y1Zl5ZGyHkF4UNpqTsRFEUlAOBWBX8q2Od8Pt8BiqK+BnBZ+yoNgCsB/DuqJxgnKCsrw8aNG/3LIdtTDR4MNDcDmzeT5fJy0opEqDGjKApPPvlkRPsIB1KkiJzOJdY1RbjHiBdwr5WpDjnjjDPQv39/7GJmbgKwSlh4xBrhKEW4nwHk1xSJXCkSvKYIDa4yh1aKdOtGslno2yczU3pQFwkpolarkScSLAvHPkutBtLT+bVpfvttk58QAYDa2kPYvr0MmZniz2pLCzsgSFFq+Hz81ENFKaJAgYKOhF6rhdPlgkatxhW33gqdTodBgwYBAAxBCAWnzcYnRUSC+XaJsUkrJ1khQatFloSNS4pcX3eKQmIQUoJrn5WbmhoIfuh0sIioTvV0B6zVEu9wrRYYNAjIywO6dcPP27fDuHkzSkpKZCl+0aMH6XRoq6YOtm7oaJw6dQopKSn+ZBuNRoMZM2awSJH3338f9913X8RqkUOHDsFqtWLAgAG8JBQFCk4bpKezSZGams47F4AQGzU1vEyob775xv9/Y2Mjvv32W7zzzjuBtHEaAsT4Qw89BAAYO3Ysvv/+e5SVlaG8vBw9+vePTQ2AxkaiAKTrRRgMRIGjoEvh3//+Nz777DN8+fnnOFVbi8v69yVKC7rPBblduXPQxJ45wFhuWE8A3boJq0LbyZCReXm4bcQIpCclIe3ii1HSuzdUKhV69OiBHkYjsGMH+3PhFkPqYEyaNIm1PH78eFlxK4BwSP37k5JBEycSq6gtm32oyh0GvaMJemczhvZrZn0XtKN4PFkZNjSwl41B8h/p2vTMIXFTE8mtGTaMhDc3bgRmzRIv7aRAHpTRXufiHABMOvgPn8+3T+Zn30WAFAGAi3EakyJMBJswfvzxx3jxxRdhtVphtVoxYcIEfMiUuLW28jVnUYDP50NzczPq6+tRV1eHwYMHx2RCJUWKUBSFHj164OjRo/5106ZNw48//uhfjhYpktwerRZTisQbHJzgC1MpolarsWbNGmRnZ7NUC/FknyVVU0RKKeJ0OuFyuaDRsH+naJIiXm8rPB5p/3k5ShHamvXXX8n/558vnWQVCSmSk5PDI5hoUFTohdY1GvJMqFRqeL0BAuPHH3/h7b+2tgkmUwJvPQ27nX0NiYmpsNn4E1Y5dUcUKFCgIBw8/q9/4V+PP+5ffunpp3HuxRfjxx9/xOjRozGUURgUIISJFJwC6g8xRYhdoh23c/rlRJ0OWSKkQIrBAE0IFUaDkiLHjrGWc5jjUZUK5gThdt2g0RASZNSogBpk5kz/+/PGjvWP21JTU/HJJ59gqpT9Rv/+fxqvhIaGBkyePBkFBQVYvHgxjO1RhH/+85949dVX/eOHcePGCSaGhIK33noL8+bNg8fjwSWXXIJFixaJjhOYaGlpgdfrhdFojHldQQUKooKMDHZdovYEqqBoawO++44oLNLSAjVtwrnvnU5g/XqSrHjsGMmIuvtulhJuyZKAeYZWq8V5550HrUZDSB2XKxBllLBJvOaaa3DNNdf4l4XqL0YFTU1kAkNDpYK9qQnb9+zBpk2bsHHjRixYsACDBw+OzfEVRAWTJ0/G5MmT8fLtt2P100/jDFrJYTT6I+w2WyDoTiPiYubt8/xJ3btjUvfupGbUDTewlaGpqcAtt6BiZxOayhvR1mDD8R906NsXiLGzXURQq9UsCzAg9MTTSy4JEBwJCcDNt1ComDEZGzYA1lYg+Wrgyy8JN9nSQhQV114bXzkjjY3s5WDufRRFeB6mCyCTy540CRg+PAr3ngKFFOlkTOMsrwjhs6sAuBH4DYdQFJXl8/mqJT7TJcHN3u/bt6/k9jU1NVi/fr1/ubKykp8BE2Wbq9raWmRnZ7MKc1dVVfktpqJ9LCa4JNHf//533NFeQG7MmDGYPn06ixSJln0WTYYIkSKRBNhjBSn7LIAEtOfNm4fnn3/ev66zSBGfzxdCofWAUkSIFAGA6upqaDT5rHXRJEU8Hju8XunAgRylCAD060deNKRq/R47BixbRgYF9HxM7nXk5+eLvheuUoSiKCQkWGCz1fnXb9r0A2//tbVNyM0Vt7qz2di/vccjXKRYgQIFCmKFGxcswG9r12LFqlWYPm0arr3lFiQlJeHWW28V3F6OUoQLUaWIRDvOrSmSoNMhS6QWR1pioqCtihiCkSINnEl8Dic72Szin6M3m1kkCBMOh4OV/FNfXx80+cdqteKKK65AY2MjGhoa0NjYiK1btyJTZlH5roQ5c+Zg9+7d2L17N84880wsXboUmZmZyMrKwi233ILNmzfjwQcf5GWhhoPHH3/cP47/4osv8L///Q9z5swJ+rknn3wSjz32GCiKgslkwpVXXIHXXn894vNRoCBm6NfPX5fmUFMTli5bhr3z5vntED/66CPhz9XX8zPV77tPlBTx+SQytdVqYOVK9oSkooLlbXbvvffi2WefxcqVK/HEE0/gItqa6LL2vFCHg6hGQphvU7FKHc/OJudGR8u9XowZMwbb9+zxbzJ8+HCFFOkiUBcVYeK//00i7DYb60b2+UheAl2zw2bzcxriCGanlpQE3HYbifgnJAg/U2o1kJmJNacysb+ifd1WEjiPW1Kkvh744QckazRgjt6aOUTsN998g7fffhsDBgzAgw8+yIsvcR9biiKWUYWFgVhBWRmb3+1Eww8efD5Cgni9gfOSU9KouJg4Z5jN5MU0uaAohRCJFhRSpHPBTfP6Q+4HfT6fjaKonQCY1ZX6ATjtSJEvvvgCu3btwq5du7B7926MHj1acnsTp3WwWq2kI2IOVKJMilgsFhYhAgB1dXWdRoqMHj0a1dXVmDZtGpEZMxANpQhFUf7sua5gn+Xz+XjBci4pAsCfgUijs+yzuKoWQNw+i1lTRCxLsqqqKuakiMcjHUySS4pwESz5bOVKkqRWWkqW5V5HN04Qze0mNe4MBkCl0nPeowuti5Mi9Dg3KSmVRYo0NfE99uvqGuF0ij8jLS3s78PtFvfXV6BAQddDzDJVo4i0tDT8unw5fD6frCCSIQihIKQUsYm0160SY4hWropSp0O2iJlyGiOzUw4SReyvxJDDseayiJAiUoTRwYMHefdDSZCCB3q9HkuXLmWtq6+vP+1IkbKyMpZ9zqZNmzB37lx8226k/cQTT0S1ft0ll1yCt99+Gw3t2efXXnstZs+ezRuzcEEHd2jFuHvjRmKfE08pqgoUMMGw8Nn06af+ZDryljDJDIDv/5KcLFnFeelSYOdOEuNNTAwI5gCQgXNeHlGK0CgvZ5EiOp0O1113HTZs2ACz2Yxp0zj5pAYD30y/rY0kQ9KVpl0uYMwY8WuKFvR6omBhJGIO6dGDRYps2rQp9uehIDowGAL1PjhITiaWRbLQ1ASsXUus1W69VZw9UanI/SMDXNslkZzI+IDHAxw8iAKjEToAyXo9knv18id7AmQcNHPmTHi9XnzzzTew2+147rnnWLuRGovSsQKTiU2KdKLhBw8URZQrAGmSmprkkSJSomEF0YNCinQuSjnLh0L8/GGwSZG+AJZFdEZxiIKCAhQUFGD69OmythckRa65hkQ96+rIYIVb5DIE1NXV4ZZbbsHIkSMxYsQIDB06FEajEcnJySzWu76+PuxjSCEYKQIAI0eO9P/PnTBGQymi0+n8HVPU7LNiaDkgFCiXQ4p0llJEqPC4lFKE/urElCJVVVXo0YO9LrqkSCs8Hunfj1m/I5RC63JQVhYgReTe00yliM8HvP8+UZ4AwKlTYkoR9n2tVrPtswDAaExhyVyF0NjYhNZWCc98xnterw9u95+riK4CBQriB3KzasMiRUTa6zaPB26XCxqB8QXXPitBr0dSVhb6ZWRgN0cVnB5ipdGzBwzAF3T9ORnI4QRMzCIpe3oJUsRqtaJvYSEOHj8Ol8eD/KwsGIOMoXQ6HRITE1mJEI1cX4bTAAcPHuSte/nll/3/R5MQAYhtyjPPPMNa98EHH+DGG2+U/FwTJ1Bs1utDUigp6Fx4vURwEDTTWwROJ0ng7qolaEpL2eGIY8eOweFwsMjAY8eOYebMmcg2GJBlsyHbaMRDkyZBG6QmgN1Ovh+nk1jHFBdzNigsZJMiTG8YAE1NTaiurka3bt3w4osv8ub4gqiuBt5+O7Cs0QCjR3dMcYH+/UkkNjcXyM3F8M8+w3sMAjsYKdLW1oZDhw5hz5492LNnDy644AJFWdJV4fMRq7mtWwNShvXrgTPPjHjX3PwL5rCIVq3ETS2N9rHhuuuvD6y7+27ClLbD6/Xi0UcfxapVq/Djjz/i+eefx7333osMRhLzG2+8gddffx3nn38+LrjgAgwbNoxnV8kli+JJKcKEViub/1LQQeii3XfXB0VRqQBSOasrhLaVAHf7eBXOdShGjBiB//73vzCZTDCZTAHSQKMhhdgEirGFgo0bN2Lx4sVYvHgxACApKQlNTU1ITU2NG1KECa6SIxpKEeY+wyZFUlLYGUftxVpjAaHrFCJFuIPtziJFhBQfUoXWg5EilZWVPFltNEmRX399N+g29913n///UJQicsCcQ4Vjn3XqVIAQAQC1OnT7LHoybDJZgh67oUFaKWK3B74PrmpEgQIFCuIRUoF/IDSlCEAKqptSUuyeNXYAAQAASURBVHjrD3DqzCXodEBiIm4YORJ3cNQToRoPXjNpEh7+/HNUyez7czgJNmKkSIpEIG/MmDHYfc89cFdW4mhDA2rtduL1P3y45LEtFgtrrNDAzeA+DZCXl4d//OMfqKqqQlVVFYxGIwq5WeH19SQzPArK7GnTpmHChAlYuXIlcnJy8OCDD+JaOrVTAk0cQsqs17N94BXEHerqgEWLSADRbicBxIULwwskfvYZcOgQIUb0emDy5KCPb1yhqKiIt+7o0aPIzy/Fnj2EzNi48QQ2MwhjvVaLf195ZVDnBW6zzyu7VFJCnt/CQpKwyIn2Tps2DWVlZbIViwD4fjJuN2G9fD5yArGMFk+cyFocMXIkVCoV+vbti+HDh2PkyJGS13LmmWdi7dq1/mWj0aiQIp0Mny9wC9HhBFkBbYoikgCmD/SGDUS1FCSJhMZ7772HoqIi9O/fH6mpgbBhRgYpPp6URIgAWpTo9QLPPEPyTI1Gomi5+GIScuk0CF2r08lqDLKysvDcc8+hri7gtPDaa6/hgQce8C9/88032L59O7Zv345///vfuOmmm/A6x6Zy8GCgqIg0AUYjKXukQIEcKKRI58HCWbb7fL5QxW/cfGTpdA0ZoCgqE0Co3lI9Iz1uNNG9e3fccMMNMdv/xo0bWcs2mw0jRoxARQWbo2I27NFEqKRILJQizH2q1WpQFMWyf5AVmJ4+Hfj4YzLa0OmACRPCOhc5ELpOITuEWNlnLVmyBLfffjvUajVef/11nHPOOZLbh6YU8chSinAz2KJJighhyJDroVZr4fUewH333YIeDKlKtJUiTA4pHPusqir2e+GQIjRXlZzM5br5aGxsgsMhfp4OR+B+VUgRBQpOQ5yGxZiDKkU4ZH+b0wm3RNEoe1MTjxQ5fPgw3vjqK9a63nl5AEXh6tmzeaRIdmrw9pgJQ1oats2bhw937ECh2YxXN23CsvYC6FxQADI5pIhFRJlSIlHDCj4fUFcHjUqFXmlp6JWWJivi8uCDD8Lj8cBisSAlJQXDu1IUVib69u2L//znP8Jv1tUR/8ydO4kFz3XXRRzspCgKTz/9NJYvX46//e1vLHsPKfzvlVdQ37Mnmp1ONDmdyE9Olh30UtA50GrZxWt9PhLAD0ctQk8xPB72eLSrwGQy4cILL0RaWhp69uyJnj17IicnB1Yr8EN7Wbx9+06yPpOdmwvq5puD7pv7ffC+3/x8UYsiJkKqA8KYyz2xahXKm5pQ+ccfqNy1C+9efDEG9O5NfGtmzJDnXxMBhg4diubmZiSJWCvu3LkTy5cvx+233w6KotC7d28WKbKHYb2lIHZobGxEVVUVTzUFANu2AUuWBJZzc4Eg4sEAxo1j1+BpbQWeeILYG5jNhBQUICUBYsvIJOVzc3OxatUqFBUVYcAAYMAA/mdaWkhb5vGQhMGmJkl3u46BTgdMmUL6RFpFyWkILBYLzj//fLz33nv+da+88gruvvtu6PV6tLS0YNkythmOUB0xgZ9PgQJZUEiRzgNH4IVwooLcz0Sj1M4tAB6Mwn5OW3BJEQDYunUrb10slCJ2u52nIgiVFJFSiqhUKnilKlu3g6s+0el0rH3JUor06gXMnUsi0j17xjSNQa5SJBb2WS6XCzfccANq2m09brjhBpSVlfEkn0xwf2OVSuX/HblKEcAHivICUInWFKmsrOxwUiQhIRVnn/0UzjoLGD+e/V60lSJM7iocpQj3pxAjRTwe9n2tUvHts5KTg9/HTU2NaGsTf0aYhElLi3DX8Je/3Ce4XoECBV0Af0ZShNPO24LYPR0rK8Oib75Bbm4uLrnkElAUhSceeQRtjPGFRqXCLRddBABImTgR1w8ZgrcY47Fz+vQJ7SJGj0bmpk1Y0F677v3t20U3zUxKgoYzbjGLWMn0kgr6NTWRbFImgqU3uly4saiIBFgcDmILG673T1dEWRnxvKSTcY4dA44cIWNJkESQNWvWYJZs0/cARo0ahVH+ogfyYCoogOm550jGO51O3FW9lP4kEHpcZBVLFgB3CNsV+bCvv/6at445fWxpYZcslVsz87LLAmqc1lZ/GZPwIVm5vR0aDUkVV6vxL04gddWRIxiQlgbU1nZItFij0fCS2WhYrVbMmjULBw4cwMqVK/H222+jX79+rG12794d83NUAHz22We48cYb0bdvX1xyySW47LLL0L8/Kf3LfZ5DmrJmZgJ9+hD159ChwNGjhI3du5e8n5goSIo8++yzvJqw1dXVyDOZJJ8Bbi6nWs232upwUBQhh4Jg/vz5eO+996BSqXDxxRdjwYIF/ljNr7/+yorlaDQaTDMYCGubnExe3bsrVccVhA1lxNZ54JIi4UQFudEy7j7jGitWrMCGDRuwceNGbNq0CR999BHGdEQhtAhx5ZVXIjs7Gxs2bMCuXbtgsVgEVSGxIEWE9pkWZPIcin1WdnY2Kisrg54Hd59arTZ0UgSQnSEUKYSC7nLss6KhFNm6daufEAGIL29lZSUrKM8Fl9xITEz0Z0kJD649AFSiSpGampoOJ0VoYkHodLmkiMfjgcvlCtsfnM6MoajwSBEul6TRsO8Nj0d+ofWUlOCZyc3N0vZZTmfgGux2/vX87W/P4oEH5gQ9jgIFCuITt956K5599ln/8pAhQyS27hoQ6lOZcHAa2iaO6pWLEQzf7YcWLsSDjzyCVb/9xtrmthEjUHL++WQhPx/PnnMODjc0YM2xY5jVty9mXXppCFcAQkZMnUqKoprNSOR5vQSQQ/szMGARSe7oJVXHzmAALrqIBOlqakiVUK4xNhcqFbB6NXud3f7nIUa6dSNZtgxizbdiBaoMBjz19NN44403kJqaGhYpEjZUKvJbKrZZXQIaDfmpmNMDmy2oG5QguHlXXe4W8PkIOWu1sliLxETyPbndQPfuk3D++W+id+9qOBwnpYuxM5CWFmULm3ffJc9+nz6kQIlYv7NgAZkUzJ/PWv3cH3/glhEjCCHSie2lz+fDjTfeiKp2qfoXX3yBbdu2YcGCBQCIKqBv374hE7QKwsMXX3wBAP5aLvv378eiTz8FKIr3PIdssnHOOUQdkZQEvPoq+z2hRAq3G599+ilPJVTSowf0r7xCbKcyM4GcHGDaNNY2zCLjABlKxE1tkSAYOHAgXn75ZUyfPp3lLgEAM2bMwMqVK/HNN9/g22+/RV5eHiyHD7NZoCuuAHr37uCzVnC6QCFF4ge+4JtE5TNxg7/97W/YuXOnf3njxo0sUsTj8aChoSGoEqKjMXv2bMyePRsACV6//vrruPPOO3nbxcI+i0uKUBQFSxDpbyj2WTk5OWGTIkyEVWg9hhBSinCvAYiNUkTIGiqYDJz7GaaFA18pQgfrtaKkiNVqDUqKhGulJga6CLnA6fLsswBCXIVLirhcZJBqMMgjRVQqFXJycvzL3J8okpoiKTIUTy0tjSzigwvmb2Gz8e+fBx6YA5Wqi4xyFShQwMOdd96Jjz/+GFVVVUhISMCLL77Y2acUMYKRIlylyClmIacgeOjRR/HgI4+gnlOE97xzzyXWSQBAUUi++mos0+vh8XpJXxmOF/uYMeQFIOnDD0U3yzGZeCmYZoH2P8VgQFowUiTU81SrSVCPOdaK0IYybuHxkGtjjs/UaiJB/fZbsqzToUyrRWnPnv7+s6qqCk1NTaLqHQUKLrqIPEa0L3+4MfKbbgoIhByOqJS36RhUVwNffUWs6FwuQjDcc48/ikpRhHugKMBs7g2zuTd69yZJ2Z2CujqAtqreuZO0A/PmCTNZ7ddw44034r///a9/9RNnnUX+MZs7NVrs8Xjw8ssvY9y4cbjtttsAkPnDX//6V1x55ZVB5/YKoof6+nr8xkm4uOSii4DHHgOSkpDSloQBO43Y3/sCtOmM8HiIc6NWG6hdkZMjoRCjxwU+H4vIB8AnRVauBJYtQzpnrAMAA7p3J/+0tgLl5YKSld69gTvvJOSI1couZ9IVcOuttwqu12g0GD9+PMaPH4///Oc/aGlqAl54gb1RpzVM8rBlC7lXLBbyClKGT0EHQyFFOg/caKt4Opo4uJ+JRlXoVwF8FuJnegJYEnSrdixbtgxOpxO9evVikSKbNm1ibXfw4EGUlpYiMzMT/fv3R//+/fHss8+KylA7A4mJiTjvvPOQkJCANWvW4KOPPvK/1xFKEYvFImnDBISuFJEDbvBaingJF16vFy+88AK++uorjB49Go888ohgHRA54F6nVqsV/N64pIjNZoPX6w36HYdybCA4acRVijBJBKH7n6I8gp+jIUSKeDxksERfmtRvlpSUBZutWvR9IdDWUnKUIgAhRbhKHRppaWQuRGPUKGD9evY2LS0ktiSH3MnJyWF9j9yvjUuKeL1u+HxeeL0uznbM2jrkb2qqJejxg5EibPss9qBXozEohIgCBV0cOTk52LFjB9auXYt+/fqhZ8+4Ks0WFhxBgvJcUqTmxImQ9u92u9HAaaxTuGRCaSnwl79AfewY0LevzGqo4kiUGHP0b69lwoQQKdIrLQ1ULJJ7EhLYpEiENpRxi2+/JQGga65h+/8PHkwGAiUlwJgxyFWr4b7uOtZH9+/fj5EjR8o6zO23347t27cj22hEdkoKZl17LSZMmcLaJhJFq4L4Q6juemJISooDi5pwoNcDJxm1QpxOMphmjMU7UmwVFPv3s5cTEoLKUP75z39i1apV+H/2zjy+iTp/48/kaNIrbXqXtpTS0lLKfd+CiKByqIjIgqAi6nrtut6rrrteu567rrvrz3PXWxEVVDxQAQEBuc9CoQdQoIXebXq3ye+Pb5PMTCbJJM3Zft6vV17tTCYz36RpMvN9Ps/zOXr0KK684gpc+de/spN+XwkiHR3MBSi6vlapVCgsLMQ9XU6W6OhorFq1CroAn9jtiZw4cQIJCQmWolCtVovLp05l77e6OkQZ63B5FnDZvUpootl181NPsetoMytXWuszHLJwobXZR10dIO571qWsxIoU2tmzZ+PxuXOFjZASEmx2r1Cwf99ATJEqLy9HQUEBGhoaUF9fj+joaFx++eVu7SsCsMZnmgng/52ODnYqwx/ybbcFkYDeCwic2eXeR0CKIiaT6QJsG7g7xKXmZwD+9re/4YcffrBZLxZFzDmaFy5cwIYNG5Cfn4+XX37ZpWP5gpycHOTk5KC1tVUgivjCKRIjo4moK04RuaKIVE8RPp5wivzwww8WB87WrVvRt29fSzWNq4ifp72KVqlJ+cbGRruT9e4c2946Pu45Rew3WpcSRQB2QqdQsIolcS+Zzz//HJ988gkaG0ORkHAL3n7btWg7s2AgdVwpp4ijZuuXXgp8/DE7mQgLA6ZOBQ4cEM4BNTSw+S85/UkGDBggWHYmigCsn4gcp0hsrPP/yaYmZ/FZ1vdHc7NYFHHnq4IgiEAjLi4O8+bN8/cwPEa9k7jJVtFn/AUZrlQ+xSdOoFP0PRWTmGi74YAB7OYBwh2Uji/s6jvCJ1pC/EjV6exO3O3YsQPHjx/HwIEDkZOT45qrYcgQJoqYI5s8mlETINTVsS63AIvNWb7cOomkVAK//a1lclMDoH///jhx4oTl4a6IInv27MH27dstywMBTL3oIkCtRnt7O95880089dRT+OabbzBs2DAPPDmC8DNRUbaOs4oK/86oGgxMBNVoWDwWn2PHhMs5OU77c2VlZeHQoUNobW1FaGioy3MWbtHUBPz4I3DuHJvENpmYA4d37dna2opFixZZhNZ3333XJjKI8A3jxo1DaWkpduzYgc8++wytra2CPHqFAgiNUAIxWoBjf16+IALI/JfhOOfnJl3nHJf2748ojQaxcXGIu/xyjBo1CoNOnBCKIlLnPwHM559/LnCCTJkyBZdffjlefvllfPPNN8jOzkZOTg4mTpyIkSNHOt6ZUskmA+rr2c1gkLT6mUUIf0eI1dfbajhkBgssSBTxH2JfXBjHceEmk0l6VlMasURc270heZ+Ojg5s27ZNsC4xMRHPPPMMxowZI1h/+PBhwbK54ZUcFi5ciDNnzqChoQENDQ346KOPvN6vRCxQeMMpUlNT4/CYUrjSaJ0fKeQIX8RniS2Ud911l8dEEXuOE7FTBGARWt0RRaQm+52JIq46RVhPEfuiiMFgkBQn2tvZ9ZCUS2TYsGG46qqrsHEjsG5due2DnWAWFqTis+w5ReyRk8OqcC5cYNdI4eHsBJT/EHPSmSNxxYz4vWUbn2UrmnV0tMgSReLinMdnNTfXOWy0fu7cOcyceSPOnDmOhARh7IpaHWxh1QRB9AbqncRNir/3LpS79r1yRGwPBKD3cpldmB1RpL9ej9ES56RREsJEQni43avfDz74AP/6178sy7fddhteffVVeYObOVPedkFKY2Mjbl66FMmVlUiOiEByZCQWVFYi9MknrROhopmO6667DjU1NRaRyenECo9y0fsxqaMD2L4da6qrcf/996OwsBAA8Nprr+E/vFz4zs5ONDY2IiIioluuYoLwORzHqom6+lpAq3Ucw1dfD/zyC8uAMd+ysiRnHDs7WbFSbS1Lt3LqpDl0CNi0yWoLj4gA7r5bmDGzcCGr3j92jDWslmn1USqVdj/LvYJazcRcvohfXg6kp1sWNRoNvvzyS2zcuBFTp07tEX3FghmFQoGJEyda54qOHxduEB5ueZ+L6z84zoNOsa736ZKhQ7Fk2DBW7GC+Zh03jhUKXLgAnD+Pk8pMlO9g178GAzB4sK2OGEiIXVD1XQ1Qtm3bhvXr12P9+vUAWMN1p9/dERHAxRdL3rVuHUsGbGhgt2uvZYZSfyJOTaP2Y4EHiSJ+wmQyVXEcVwOAP4PWF8BRF3aTLlo+IblVALFv3z6bidtDhw4hXiIP9OhR4Uvhiiiya9cunDp1yrLsDYFCjC9EEXecIt6Iz/KFKFJUVNTtfZjpjlOku31FpJq1O4sXc9UpYjK55xQx9xWRGo/5b1xdLe2ccIaj+Cy1Wg2O42DilU04EzP69GE3MxERrKDNjPllFosrY8eOxc6dOy3L99xzj03zVdtG67ZnKh0dLejsFL6vzc+RPYb9jI93/j/Z0lLrUARat+7flt/Pnxd+DqrV5BQhCCLw4JxEm9r0FLngkikZh/fsER4PgM6drsguYM8pcl1eHjiJrIwoCadIYlyc3WrmY6LK5z78LzknbN68Gc899xxqampQW1uL9PR0fPPNN7IfH+icO3cOH3/9tWDdVePHO6wMf+KJJ9w6lslksjQ8NpMcEQFs2IAtdXUWQQSwvTYpLi5GdnY2OI5DZGQkoqKiUFhYKNm3jugZFBWxW3U1u+XmAtOn+3tUbjJzJpvVjY/Hq+++i0P//jeKiopQVFSEN998E9OmTbNuW10tzK7VapkDQoTBALz4IquMHjUKmDNHxjh0OmFOrsHABBj+CxsZCYwezW4tLUx8CETUahZtxBdaz50TiCIAayw9dOhQHw+OkEV6OnDzzex9KLq2VqmYUdM86W4ySRcAukVaGnDnnUwc0WqF33ccZ21IkZ2NbR8KtRu9PoBFkd27Ebl3r2CVWRQ5LhKgckTN0o8fP46XX34Zf/rTn5Aowx1z9iz7dzPjxMTsEzgOSE1l4ojBQC6RQIREEf9yFADfvpAF10SR/hL7C2haW1sxZcoU/Prrr2hra0Nubq6kIAKwCronnngChw8fxuHDhzF58mTZxxFPbktNTHuaWFGFoC/is+Q0dZYSLEwmEziOc9sp4oueIp5EPAFtTxQJCQmBuisqwUx33ztSj/e0U8TsYHDcU8QENo1kRa4ootXqodOlor7+jMNx83HUaJ3jOISGhgrGKyf2io9Yv7LnFPnzn/+MN954AwcOHMCyZcvw6KOP2uxL/LJJCQ/OnCLm55mY6Px/0mQyoqrKPdE0JIRKSwiCCDzufvhhvLdhg937xd97FS6eI5kjVc1Ea7VQeLn611518YJBg1hZpgh1ZCQiQkJg4H2nzh43zu7+C0QZ+QNdaHJQVVWFdevWWZZd/Q4NdMQiRWRICMK9FBdiMpnw7v/+h7I9e1C+fTvKDQb0i44GVq7EuF9+ETR0PXNGeB5U19UQ12Qyob6+Hi2NjQjZtInlfhI9kuJigB96ENTJdf2tUwmvvf46Dhw4YFk+fvy4UBQRF4lJuOsBVjXPonmZAaSoiM3xpqcDs2bZGUd6OosW4sXf4cABFpEjbTl3/Lw6OpgoYZ65bmhgleW+ytHp08cqinAcc9kQwYNGw2axJYiNBRYs8NJxQ0Jk90IT//vZqYsMDCoqEF1dDb1WC51GA110NPoOGgSj0WgjimR32To2b96MhQsXWgpo4uPj8ec//9npocTzA4EgimRkMI0NYCkdPex0rUdAooh/OQyhKDIBwFdyHshxXDgAcXnBYaltA4nJkydj8+bNaGlpwc6dOx1OOCuVSmRnZyM7OxtXX321S8fxhiiyatUqPPfccxg7dizGjBmDCRMmCC5gxa6NpqYmtLS0uN0cXAp34rOkqtU6OjqgVqttJimiZUrXvugpolQq0SkO7XQTuU4RgEVo8V9nbzhFXBVF+BMz7sRnGY1GtLU1g+PCBJmW5j+TPVHEZGKFWxzH4cor38G6dbcjPLwD99xzp6U5oD3M7hJ7xcNarbZbooj4ZLChwfw8hc+lb9+++Pzzzx3uS2xSsecUkROflZTkXBQBgAsXKpxvJEFICDlFCIIIPEbNmIHfLV6Mf33yCbKSktDR3o4inp3PJj7LRVHk8AmhGVofFub1Ca4wiUyMuLAwDB8xQnomVKvFg5Mm4bGNGwEAl/Tvj3F2+k90dnZiRP/+0La0oLiqCp1GIwZK9Nuyh7goplaczxDk2Dg30tOBq65y/kCTic3ChocDMgt9FAoFFi5aBCxaBPz8M5CZaZkQS0lJAcdxSExMREpKCrJEpbhmUcRMlEbjwbJhwlcYjWyiSo7OKr70El2aBS2ZmZkCUcTs2C8pKcE111yDJK0WSU1NSIqIwJMXXwyFHVGE41i7kupqoK2N3WprBfqLNDNnAoWF7P9nwgRg8mT3/5eam4E33xSuGz/egzlHThg8mOWG9enDuik7uO4UYzKZUFxcjPz8fBw5cgT5+fl4/vnnZVXJE70H8VvZ3FqDl/QVOGg0uKhfP1Q/+CBbHjwYuOYadHR04O2338bx48dRUFCAgoICy9zaO++8I3AU/+Uvf8HVV1/t1F0l/ljq5jSOx1GrA9fk1pshUcS/fAfgFt7yNBceOwXCv98+k8l03hOD8gVarRZTp0712v7/8Ic/oLq6GpGRkYiMjPRIU8Rt27Zhz5492LNnD1599VXMnTsXX375peV+KYGipqZGtvtCDp5otA6wSXApUcSRWMDHF/FZ/hJFIiMjPSqK1EtUB3kjPstkMtmIZnwMhgao1WHgH9qZKNLcbK1myMi4GHfeeQx33w00N591Koo4is8CbPuKzJ8/H0ePHpX9/yJVCSIlrDgTJdvbhT0mASZ2cJwSJpP1/dfR0Qyj0Xl8VnR0KJRKDTo7HQtfVVXuiSJqtfwLK4IgCF/BcRz+8eGH+MeHH8JkMmHRxImORREXJ/GPiCr09T6Y3AqXmPS7OCMDCnvnlBoNHp06FdP69UNtSwtmZ2WBszNxqFQqsfbhh4Ht29HW2Ymi6mpkulBNLC5iqa2ttbiAewLZ2dl44IEHUFZWhrKyMqSmprKIHXuYTGxC9eefgTNnWDOyxYtdP/BFFwkWx40bh9bWVslzacD2HE+n0VBYeJBw/jzw2Wds0qy5mRVpP/wwa7ERH2///FVs0q+pYW+/YP/Xy8zMFCwXFRXBZALWrz+Dvbzom0itFk8vXcpeJDuYRZGQEPZ7ZibAN51IkpAAzJ/PyqqjorrxTGCdGeZXgtXX+04U6d9fhgokjclkwtChQwWFY8uXLydRxI+YTMx81NpqFU992aZGivh4oF8/9paOiGD/ay+8wFxakZHsduONAaLRiwt0u84HVSoVFi1aJPmQe+65B2+//bZgXVFRkVNRJC+PfZSYXwOKqiLkQKKIf/keQDMAc2nYBI7jBppMpmMOHmPmBtHyF54cWLAj7hngCXbt2iVY5jeGP3PmDK6SqGCrqqoKSFHELFoEuijiKVx1ivAJhvismpoKTJt2I/bt2+dwHGp1okAUMf9uTxQR98FVKNjJBcc5PxN0FJ8FCJ8TwKotb7/9dnzxhbyPMqlKEClRRHwcMXYSx6BWh6KtzSqIyXWKKJUcQkOjYTA41qirq93TsMkpQhBEoMNxHDSi8wTx5/MFUYV9ZEgIGlyI3vSFKBIqcYxLMjJYEwEpus6FJvfta13nqO9JZSUAIESpRG58vGxnA8BckE8//TT0ej30ej2io6N7lCgyYsQI15oPHzwI8M8fTp3yyEy1tDvXymWXXYaTzz6LupIS1Jnf4y5UhRP+Q6lkPYvNtLayLPr//Q9ITGTGISlNMy6OTbzFxDCBRMalWFAwZcoUVFRUIDMzE5mZmRgyZAg4Dti6VXgxEJOYCtx6q8N9XXkl+zgMDXXxX3D4cJfHLYlCwf54/Guw115jn8dpacC8eZ45jhdQKBTIzc3FHl4frSNHjmDGjBl+HFXP5PDhw4iMjERiYqLDIrr33mOxeWYuu4z1PfcnQ4eym5niYmDLFuZ6q6tj1/gBIYgATL255BImjmg0spSKwYMHY8mSJfjggw8AsF4js2fPdvq4buiRRC+GRBE/YjKZmjiOWw3get7qBwHc6OhxHMdlA+DPwHcA+NDzI/Q9LS0taGtrg85RNZgfaG9vt5lwHjt2rOX32NhYwcmLGU83W/dEo3XA2lfEXVHEFz1FAkUU6Y5TpLi4WLLxqTNRxFWnyMcfv4HNmzc73CcTRYTr7DlFOI6DUqmE+O2r17PrDGdCA+C6UwQA1qxZ43S/ZsQVOi0t0s3anTlF7PV3V6m0AlGkvb1ZVk8RNrYYp6JIXZ1rTYbNaDRUgUoQROAjFkX433smkwkVou/WfrGxOCSKTHKEXmwX9AJSzeNnjBhh/4Ke41hJdFfsDEJDgUGD7B+gQuQYlJklDgAx7e3445gx7MuvpYUF+DtoQt7j4QtRANDSApPBgLaQENnntu6g1WqRft99bAaqpYXNrNtxBxGBhdSf6c032aRiaSnwxhvMbJSUJNxGpwMWLvTNGH3J3LlzMXfuXJv1HR1CUSQ6OslmGzHdNXpY6OwECgpYB2mJ61mHREezk3O+K7GiIij+PwcNGiSYV8jPz/fjaHous2bNwrmurtzR0dH44osvhH10uhC/9XzaE8JoBE6fZvYHB/YUsdHUB6dI8klNtdujxRFvvvkmxo4di6qqKqxYsYLNPxiNwOrV7AnqdOyWnU3FCES3IFHE//wZwHUAzNOVN3Ac94XJZPpSamOO47QA/guA//H8lslkKvLqKL3Il19+ia+//hq7d+/GoUOH8MQTT+Dhhx/297AEcByHdevWYdeuXdi5cyd27dqF0aNHW+4PDQ1Feno6Tp48KXicp0URcTySO43WATYJ7k5VvRlf9RTxFK7GZ/Fx1ynyxRdfYNGiRZKvhTPRyJFTRCEx6fHxx285HY8rokhISAg4jrMRRcwanJwJBpWKbSPXKeIq4ufS0eFZp4hKJXwcc4oI/5ZmNwzb3ro+PDza4TEBoKHBvfgsjYacIgRBBD5a0fdEK+97pr66Gm2ieMx+iYmuiSI+KJ4ZM3w4FBwHY1cEy+CEBPTnFcRIMn8+8MMPbNZk6lTHE3lXX80m6Sor2c2VeJSaGuCXX6zLMs4HezTR0YBKhW+PHsUnR47gWGUljr30EhYuWoQ33njDM8cwGqWFJ4WCRWZRbFZQYW7/wv8oMhqtv9fVATt3BrSpwHOYTMxVUVnJTmh5IuPYsTPR1PQWDIZyGAzlGDGin+Ch9v4tPMLJk8CqVWxM/fszl97w4fLsJytWsJ/r1gH8tIcgyNMZNGgQYmNjkZeXh0GDBmHmzJn+HlKPw2g04vx5awFbbW2ttShX5DIUX/Y6qW3sPh0d7AK1uZlFIbz3HlsfGcnEkcWLbaoOxdMVAVZf7BZarRZ33323cGVjIyAWCe+7j0QRoluQKOJnTCZTMcdxLwO4j7d6NcdxfwDwuslkslxFchyXC+BNCJuzVwH4i08G6yXWr18vuGDZtWsX9u7di6ysrIBxjKhUKkyfPh3Tp0+3u01OTo6NKFLlYiNRZ3jSKSI12S/39Q62+CzxZLmjiXJPOUWefPJJu69Ddxqtcxxn0+9CDg0NDTZzM+bhicdj/vvaE0U4jsOQIUNw6NAhyWMplSFISmKxF644RcxjkSe6sJ81NcX46quVaGw8j6amq222c7Yv+6KIcHysp4jz+CwAiIiIdnhMdtxKp9tIQU4RgiCCAfFnb2tbGzo6OqBUKnHh1Cmb7dOTk4H9+2XvX++Dia342Fj8c/ZsPL5pE2JCQ/H6nDnOcxl0OmDBAnkH6NvX1uEgF/F3qE9LVwMQjgMmT8bh8nK8w2sWfeyYnERisJlxe+edVVXAxo1s9vfaaz0wWCIQ4Dj2r6rVslx+gwFYu9ZacZ2SAlx+uX/H6BN27gR++sk605udDfzmN5a7J00aiL59ByIqijlAxAXf77zDBKT4eDZfO3Soa/quQ44eZT87OoDjx9lJuyuxegAbHJ8gEEXuu+8+PPjggw7jEHfv3o2///3viIuLw1/+8hebPlOEY6qqqmx6lyYlJTEh4sUXmSsjIgIID0e4dh4Aa9Hktm0s7TIykn1OeLRx9mefAfzr69hY6+/muRuJi+tJk5heWF/PNuuxGoHYEqNQ+K5XkBtUVrJos+ho681V0xvhfUgUCQweApAH4LKuZTWAVwA8xnHcXgANAPoDGAmA/+3YBuAqk8kkv7TOT5SUlOCPf/wjpk6diqlTpyI3N9dS8c7vzQEAP//8M9asWQOTyYS+ffti8ODBePPNNz3am8Mb/PGPf0RFRYWgGZ0nnSLt7e02DR2702i9o6PDZr1Op8OcOXPw9ddfO9ynM1HEW/FZnZ2dboklYlHEUaSS2CnirijiqL+Hq/FZfBHHZGKT8a42oXfkFBGPx/z6iPu2899u//jHP7B06VLU1dWhf//+KCurRnNzGxIShmDSpAcREcGuiOyJIkZ+OR6P+vp6xDvKYO9CpQLa2hrx3nszUVPDgl6ffPKIYBuNRiPprOFjLz5LrbZ1irS1NYrGoOX9bl0fGRntZPTu4ywOjCAIIhAQiyL/27AB/1OrMXHgQNz3+98L7gsPCUGcjM99Pr4QRRAaijvGjsUdY8da+3VkZHj/uHIQF3e0tPSMbs9mWlqAkhI2KRUZyX466e+BadMwsKEB+Pe/LavkiCIrVqzA8c2bkaTVIikmBosnT8bEm29mM7s//QTs22e1EJw9y2bBiB4BP90uMZG1yvjkE5a4dN11zt9yPQKtVlj6Lor1GzuW3aQwmVhfluZm9pqdOAGkp3tQFOE45oIzX5AMHOj6Pq67js0S19aym68blptMTFg9d44JTjLO4531MqqsrMRll12Gyq6+VKWlpfj88889MtzeQlVVFUJCQixzFhzHsevP/HwmwtXXs5tKhQm3azF6Ons7vvwye/xnn7Gfv/udh42aYjVDXGCbkCD5MH6D9UAlPz8fVVVVqK+vR319PSZOnIj09HTXdiIWRXS6gD7vOXkS4CepJyc7bclE+IHe8FUf8JhMpk6O464Fc4Es4t2VAMBeR6ELAJabTKYt3h6fJ9i0aRM+/vhjfPzxxwCAvLw8HD58GAAEMVSAUEg4ffo0SktLEeWxcFLvMXXqVOTk5AhEEfPJiieo5WeidtGdRuviiXC1Wg2NRoNnn30WGzduRGNjo83j7O3TV06RlpYWhLtRDeCKKOLpRutSuBqfxXeKsNhwJVzURNDQ0GATRWr+M4lFGPPrI9Zu+C/9xRdfbMlhBYCff2aFlGLsaVj28nFdEUU2bHjEIohIIUdAkOsUaW9vRnOzUOQMDbX+//GfZ1RUtNPjugvFZxEEEQzYc+ltO3YMTz/7rGBdQkSE4HtODnpfdDdOSWHiQ3MzE0Ryc23FCDcoLy/Hq6++ioEDByInJwc5OTmun9tERABDhrDxmKObjMYA6qzaTS5cYLPTZkJCgIcfdjr5MVA0aVpZWYmqqirE8qttRezevRsHCwsty6P1ekxsb2ezTPn5MLS0oKi6Gmfq63H2mWcQOn48rr/+erv7I4KX8HBg2TI27xbIk4seRdzLqLbWsXOKh8FgW1zkor5tn7Y2NiHMr9ByRxRRKGCxubg6AdsdTCbgww9ZPwjzBdX117O+U93k6aefFswx/PDDD93eZ29j4MCBaGlpQV1dHcrLy1FRUcHmM8TXp5mZiIxh8xxS0zoeb1EjdY6hUjGhBrArigQD1157LY4cERYwXnbZZcjJyUF2djZuu+02h+4oAKxCc+pUq2jl5NzJZDIXlHZ39O4hnr7r7UmngQqJIgGCyWQyALiuq/H6vQDG29m0GsAnAB43mUzuhdL7AXET6EG80pyBAwdi6dKlGD58OEaPHo2DBw8K8gMzMzNdvlj++eef8de//hUNDQ1oaGhAeno6vvrqq+49CRmI3SwlJSUe27e4nwggr6cIx3FQqVQCZ4iU60Sn04HjOAwaNAiHDh3Ctm3b8NFHH2HdunU2+/RFTxGpCv+mpia3RBFfN1q354KwNx4xjpwiLLvX9Y/uhoYGm8aH5j+TvXgx8TAd2T3t6Q/2ip2kRD4ANu9Le9TWVmDnzlccbiOnb4k97U+qp0hzs7BaJzQ0lre9dX10tPfOeMgpQhBEMODoe3aP6NwoQadDmIvf7XoHk9weQ6UCbrwR2L6dTVRMnuyR3e7fvx9PPPGEZVmv16Oqqsr5ZACf8HD5MV1BRllZGe657TYkV1cjOTISyRER+M1FF0Ep4/XJyMjAbbfdhpycHIvo5Oxcubxc2Eg6KSKCzXRptcCoUVj9r3/hxrVrLfcP2bLFIoo0NzfLcqUSwYNKJXRG93j4T1alYrN2TU1AZKTVIVdbyz4Hzf8bERHAgAG4cEG4K7Xag+lUarVwxnnIEFsBJ5DhOKYY8S+mzp3rtihiMpnwj3/8Q7DOYDDAaDTS55CLcByH6OhoREdHM0HdZGLvO74IkZdn2V48JaDVejg6CxA2U+c4ICeHxTZWVwPnzwv+XwsLmbHLYGDXs4MHA1lZHh6PB5GKaf/222/x7bffIikpCb/97W+d7yQx0anb68cfmRbZ0MBul18OjBzp7qi7h3i6g1LuAhMSRQIMk8m0GqynSAZYXFYfAOEAygGcAvALv89IsCAWRaZOnWr5XalU4j1zAykA27dvh1KptMQDDR482OXjVVVV4fvvv7csiyeZvUVOTo5gWXaWsQzEUVxhYWGyei8AzMnBF0Xa2tpsJp/5sVEZGRnIyMjAoUOHZIki3nCKSFmHxQ4Kufg6PsuZu6Q7ThGjEeA41ytBHcVn2XOKiIfpSBSxpz/YOz+/9tprsWrVKslxAsB3332HwsJCXHPNNSzjVcShQ3thMjkWn+QICPbMXFI9RZqahKJIWJh1Uo5fUOfNXN/uNqgnCILwBXLPTwCgT2ys604RX4giAKvKnD/fo7sUnxtmZ2e7Joh08c9//hPr169HbW0tampqcNNNN+Hee+/11DD9xqlTp/DJt99aljVKJZbOmSPrsSqVCq+++qrsY3V0dKBCFBdkEUUAICUFKaIJmLNnz1p+v3j6dPy6cyd0Oh2ioqLwwgsvYOHChbKPTxB+R6sFbroJ0Oux78QJvPX22yhcuBBFRUVITU3Fxo0bmWPj11+tjwkPB+6/H+npwG23MWNXRQWbR/ZYkg3HAVdeycQQoxEYMMBDO/YhffoAZ85Yl3kOe6c4KHHfunUr3njjDaxatQrNzc34+eefSRDxBBwHXHMNE7JOnGA9bbKzLXdrNOztaDB4sW/H0KHsvR4Wxv43zX/XuDgbUfDXX9kwzcTEBKgoYjQCX3wBnYMUEvEcWne4cIGJIma8EPohm9hY1oeptpa9b0gUCUxIFAlQTCZTCQDP2Qz8iNFoxMMPP4zNmzfj559/xunTpwWiiJiHHnoI99xzDwoKCnD48GFZUTpixBPb7kYg1dTU4K233sKyZcuQIMOuKP5AP3HihMcqN9xpsm4mJCREMPFtzykixt6khi96iki9Zq6KW21tbfj0008tsW1mPBWfVVlZicWLF2Pr1q2YN28e/ve//yE0NBR14qZ+Ipw5RcSP54+JxWe55xSxJ4rYc4qI/4yOTv6kXlKVyv7F0Q033CApitTX1+M///kP7rjjDgCsYX1RUZHN38Vkci68yREQ7Iki4p4i7e1NaGkRurX48Vn8lzAmJtrpcfkoFGoYjfKERFcmGgmCIPyFxgVX2yUTJyLUVaeIxzJafM8xc/PgLsSRT3IRF66ckmhgH4yUlQlbJSZFRIDzUpaRyWTCqo8/RllREcrPnEF5WRnSrrrKGsmRm4vU3/8eeP11y2Oqq6vR3NzMzveqqmAymVBXV4e6ujoYv/oKIFGECDb69gUAnDl7Fv/m9eSxXAOJC8S6/j9UKiApid28gkLhvhhiMrEeQOZy8YYGYMIE2GQJe5M+fay/R0Q4P/avvzLhpKKCXaBcdx3Qv79gE47jMGnSJEyaNAkvv/wy1qxZgylTpnhh8L0YjYbZLkSFucnJPjBohofLbhwuju5yoDn4F44DjhyB3mhEtFYLnUaD06K5jmye+NRdxKcL/hRFpk9nN4DNu5hM/hsLYR8SRQivo1AocPPNN+Pmm28GwC7a0tLSHD5Go9Fg6NChGDp0qFvH9JQo8uGHH+L+++/Hww8/jHnz5uHWW2/FpZdeand7sSjS0tKC06dPo1+/fm4dn093RBEp0UKOKGJPPPCFU0SqEbyrTpErrrgCP/74o816V0QRR06R119/3bL/VatW4bLLLsMNN9zQLVHEZDLhgsiPzhfkuhOfJRZFzKKHlFOkowM2fUtcdYo46hN42WWX4fvvv8esWbME6+vr6y2CCABcuHAB77//Pm677TbBdkaj7ftDjDOnSGurbb82M2KniMFQbuNM4cdn8a97YmOjnY6Nz9Spv8GmTe/I2jY0lOKzCIIIfFwRcOcvW4Y9O3a4tP8YXzfL9SBDs7JwWXY2jlVX42RVFQaGhJi/3F3aj9iVaC+WMtgQiyLJOTks/8JVzNUKDs4F1Go1rrn2Woe7SUlJAcdxSExMRGpqKlJSUtDY2IjQ0FDUi64vdFS4QAQxWaIy8/PnzyMnJweZMTFIbGlBUng4npkxA5zHGyl4if/+V3gxM3Cgb0WRzExg8WJ2kSBH2D1wwOImaWxrw9avvsKRzk7k5+fj7Nmz+JbnoAOAqKgoLF++3BsjJ4IAsXbiz8l/h3AcoNHgI56ilD9pEvZduICCggIcP36cFUx3dDiePJCJ+OPJjdAPr+DxqDXCY5AoQvicdB80Oevfvz+ef/55REZGWm7u8OabbwJgE/Sff/45lEqlQ1EkMTERUVFRgknxgoICj4gi4p4icvqJmJESLbrjFBHvzxs9RaSEA1dEkT179kgKIoDn4rMeeeQRwfKNN94oSxRx5KRpbGy0cW7w3VJs3sT1+CyDweCSU0RqiK72FHFmob/00ksxceJEbNu2zbJOqqfI+vXrbUQRk8l5p3lnTpEqYRoWOM5awSEWRRoazkJMeDgTRQYPFkYyx8dH2WzriJycgdi0Sd62YWEUn0UQROCjlRn1Nz49HSmjRuHo4cMu7V/vtdJk73P7vHm4vauks7m9HR38iAwXEIsiUr3ngpERI0bggQceQFlZGcrLy1mMrtxJ2O3bgWPHWJV1YyNwySXd7gWj0+nQ2tpqc+4LAHWiWagoiXNpgggWMjIywHEcTLxy5uPHj+N41+86bQQu73c9EvTx8FzYjZfgn9Sb8fWscWQk6wlhD4OBzWybL5ji4y2iyLmGBsz+618Fm1dUVLiVoEF0H5OJzdm3trJbTIwHo+LcJCEB6NcPKC1l2l9+PvDSS0BKCrBokX/HZoNGw3rsdDEoPR2DZs5kCyYT8OWXwNNPs4iwpUth0wjVBbKz2b9VRAT7F+zGroheAokiRI8kISEB9913X7f2sXfvXuzfv1+wbsWKFQ4f87///c9mEv3YsWM21fDuIBYExI4GR0iJFt6Mz6qqqsK//vUvxMbGYtGiRW7Fh0kJB67EZ23ZssXufa40WnfHZdQdp4jYJQIIRRE58Vk6nQ5Dhw7F1q1bLetc7SniqigiNf8lRxsTv++kKl3DJWzEnWIbiwTOnCLi6Cy9HkhPB/bts220Xl8vFEU0Gi3uvTcMbW3spJRPfLxrjdYTE+U3jiSnCEEQwYDc+KyrZswAOA5hLhSvcAB0wTwxxPvyCVWr2QyGG0yfPh1PP/009Ho9oqOj0V8UsxKsTJgwARMmTHDvwZWVAD9GzF5GpotICSIAcOijj1D3zTeob21FXWsrBnmgCIog/IVWq8Vdd92F2NhYZGZm4tdff8Urr7xiuV8T2Rc/Zd2K0VkIfFEEYJVkfI4cYW6RQKC1lTlZYmOBq65iF1K877X+ej00KhVaeckJ+fn5uOiii/wx2h7HwYMHERUVhcTERKfXiw0NwD/+ITQdPfig/X6avmLoUHb79VfAbCKqrw9QEWD8eDYxoNGwG9/te/Iku/gGWHTcF18AN9wgfLzBwP5nZPSTS0lx+7SK6KWQKEIEHEajEYWFhWhpaXE7PssThIWFYdmyZfj000/R3NyMvn374pJLLnH4mIiICJvJ2oKCAo+MR+y+EAsTjnA3PstdUWTt2rVYu3YtAGDXrl146aWXZI/VTHedIuL4BT6eis+yh7MIC0eiiLjhp0ajEbhXnDVaHzhwIA4ePIhXX33VbVEkNDQUUkN01SkikYBmg/h9V1Ji20pJShSRilcT48wpIp4riYsDpk1j10zOnCI6XQzsmbUSEqTORjkA0kGiycnyJ/fCwkgUIQgi8JErigwaNQoAXBJFIkJCoPBlBIqnEX3Pw02BZ2JMDCaOGcNiolpaHMZE9RpEjWhRVYXq6mpotVqEeeE902/OHGDmTDZZ09LiluOHIAKJl19+2fL7+fPnBfeFh7OJTCe1X4HDhAnMPWZm2DD/jYWPyQSsXcss61VVwGuvAddey3q7jB4NxMdDGR+PgWvW4MChQ5aHkSjiOWbMmIHKrgvBqKgorFu3DpMmTZLcNiTENlK6tdUHosiBA0wESEhweBEurt8MSMPi+PH270tNZb24CgtZ5/iTJ4GyMtbExcy+fcBPP7Hv+JwcYNAgUj4Ij0GiCBEwfPPNN3jhhRewZ88e1NfXY9asWfjuu+/8Np6BAwfinXfewT//+U989NFHCAkJgVLpOLZIqlnmkSNHPDIesXOiO6JIe3u7jQPCkz1F+Lz33nsuiyImk6nbosi5LvuxFK7EZ3nDKeIoPkssisTHx4Pj+XOd9RTp168f1Gq15PMQv2XsxWdJOUVCQhzbhN2NABWP88SJEzbbSIkbnnCKiOel4uJYdc0ddwCFhaHgR9wbDOWCbaOj7VeqJCZG26xTqbTo6LB1OqlUWuh08hsMU3wWQRDBgFbmBHRUV9WfK6JIuEbj/9yK7jBzJpucq6xkX0QZGe7t58QJYM8e63J4eOBM+vmLLlHkwR9+wLYzZ1BQVYWKm2/G559/jquuusrzx1Mo2MyYv0uGCaK7mEysGrumBqiuBgYOxGWXXYbo6Gjs21eOnTvLERPDeo7U1QFNTezawAMtALzHpEnsc7asjJXUZ2b6e0QMkwkYORIoKGCz7bW1wHffATfeaGl6DwDjJkyANiwMgwYNwpEjRxDLq5I/deoUzp8/j/r6etTV1WHw4ME2vU0Jadrb2y2CCMCu23UbN7I3dG6uTVyj+RqYn8YmunT2PPX1zDFhRq8HbrpJsjeNeKrCzdR4/6FQMKsLvxB1xw7moDJjLjKurGS39nYSRQiPEchfY0Qvo7GxERs3brQs7969GyaTSTAh7A+ioqJs+hnYIysrSzKL1RN0RxQRb9tdp4izniJ8KisrXf47dnZ2Cl5DM3Ljs7Zv347333/f7v2uOEVaW1vR3t7uUPgR48n4rARRNpOzniLmE2YpUcQVp4iUKOINxO87KVFELNoA8kQRjcZ1pwjAhJGMDMeCiiNRJDTU9r1iTxTRanUIDZXfmDU8nJq4EgQR+ETIzG9wRxQJC/ZuleHhTAhxVwwxIz6X8fosTRCQnAxccgl+/eYbbD192rLaoWu7sxNwVPRUWwucP88mqRoaWJj88OEeGzJB+B2TCXj+eaZ0mLnxRuTm5iI3NxdlZcC2bez8OCqK/Qv88AOwfz/7PSGB6bGBkkxlISICWLLE36OwxWAA1qyx2g9CQ4Grr7YR+1977bWuzQ3o06cPFi1ahL///e9YuXIl3nnnHWzevNmy7bPPPosHHnjAV88gqJGKqk6qqwPWrQO++QaYOJEVL3TBcewrgh9SUFDATJ5O6mXd47XXmJDHp6HBtrN6F/Pns/ZZDQ3sa0rUbizwUSqBsWOBDRus644fB9ra2ASEwQCcFfX2DAIBsLiYacyRkewWHU31E4EKiSKEV3n88cexY8cODBs2DMOHD8ekSZPsNlofM2aMYLmqqgrr16/3SD8OXxEaGoqPPvoI1113nWVdRUUFjEajW301+IhFEVcm6eU0WpdqRu9ufJaY5uZml2IL7IkGcpwir7zyCu6++26H27jiFAGYYCduaAqw10c81o6Ojm6JIlJOET7Oeoq4I4rIabTuoA1LtxCLImfOnLHZRirCTE58Vnm5Fvv3sws1KU1OXFnDb5buLHpLr3eeacpHpZL+H9Fqo6DVyv9fJqcIQRDBQJSM3GcA0HWp0S47RYieK4oYDKxzrLlLakSE/HL0iAhg8mQMHDkSP+/aZVl97Ngxyc0XL16M0p07kRQaiuTYWCy76CKMWbkSSEuzbnTwoHCyZsAAEkWIngXHsQlX/nVWTQ1rtAemNS5YIHzIxo1MSzEnQPEMDoQzTp0CGhvZ7yoVE0QczGR/+umnluSCHTt2YOfOnRgviiMSX9cT9qmurhZcwysVCsSa5ylMJuEFYRcLFwIffWRd3rgRGDfOS6KI1LxKfLzdeEaFwjrx3qePF8bjIRobG1FYWIi6ujo0NDSgo6MD8+fPZ3eOHg1s3sw+h8aNY04q83xTdTVbb54P0Ggsn02BzIED7GZm/Hhg9mz/jYewD4kihFfZuHEjtmzZgvXr1wMAnnrqKTzyyCOS26anpyM9PR2nuhokKhQK5OXl+WysnmLy5MmC5c7OTlRVVdlMbjvi7NmzuPfee1FWVoYHH3wQl19+uUedIt5utC6msbHRZ6LIf/7zH6fbuOIUAZigICWKREVF2VSbVFZWejw+i4+zniJxXRNM3XGKaLVam54ivnKKSCElishxijQ0aLFmDXvNRo60vV/cCJ7/dncWvRUdbXvC7Ijw8FgYDLaVSWFhOmi18if4IiIoM54giMBHJ1MUiTKLIi50Bg0LYlHk/vvvh06nw8CBAzFw4EDk5OS4dD4nICkJGDKElR5qtaxcuydw9izwySfWZZ0O+MMfXNqFOM62sLBQcrvdu3ejsLjYsjw1IQFjxOcXkZFo6ejAscpKnK2vx5kTJ1B/7hzuv/9+l8ZEEAGNXi/Mla2utrupyeSx1ki9kyFD2Ox1SQlTk5x8dr/xxhuC5dmzZyMlJQXbtm2zrHN27UlYGTJkCJqbm1FXV4fyX35B1WefQWGunuM4ScuTeBpDpfJewaDNwYAe8f2+d+9eTJ06VbDuvvvuw4oVK9h39k03sfMasfjTty9w773AuXPMomM0upTbZzKxh3hFwHJA0Mea9SJIFCG8hslkwgG+PApgmIOsY47j8Morr2DZsmWor6/HE088gdTUVLeP/+CDD+LgwYNoaGhAQ0MD/vznP3snT1iEOO4IYI3qXBFFfve73+Gzzz4DwCpCysrK/NJo3RM9RQAmirjy/O2JInLis+xVA/KxJ/YA0k297TVbl9r2tddes2lMKKb78VmuO0UMBgOUSiMA64mGWZuR4xTxligi5cwR464oolYzV8V339mKIkajbdM8/tvYmVMkPt7xhN+kSYvxyy/WkqIHHngc9957nc12YWE6ybgte4SHk1OEIIjARyfzO1/X9R2niYhAZEgIGhwUDZgJD9KG4u3t7Xj55ZcF53O//PILJk6c6N4OBwxgtx7E8ePH8diddyK5oQHJERFI1emw5LLLXN7PxRdfjOeee84iPmXYiSorLxf2C0uKiLDJk4dOhyMXLmA0b2JS+ckn+N3vfge1Wu33mF+C8Ajm6niVirkWuq6VOisqYNqxA6qoKIt7qyFxgE3xVA+Ys/UtsbHs5oTOzk5MmzYNJSUlls+rlStXYuvWrdBqtdDpdIiKioJer/f2iHsUHMchOjoa0ZdeCvTvD+Tnswn3lBTJmCqpCW6vffSLr0EzM1l/HAkKC5lAaTAw89GQIYHTOkeM1FzTCy+8gCuuuIKJIo5sLhzH/jYy+4hs2cISuBoa2GszdSq7+RISRYIHEkUIr3Hy5EmbiXdHoggAzJ07F2VlZTAajS45C6TYtm0btm7dall21HjbTHt7O06cOIFBgwa5fVy1Wo3Y2FhUVVVZ1pWXl2Pw4MGy92EWRAAmYHz44Yceb7Tuq54igH1RwR72nBTOnCJi4cgejlwAKpUKoaGhAgHG3vilIpz+/Oc/Oz1+9+OznDtFpP6ebW0GANb1jpwi/uopIoW78VkqFfs7S72dpB7OLzpx5hRJT3d8IfPYY7/H9dfvRUVFAaZOXYqlS2fg3ns5AMJeOeHh8nuKKJUhSE11LbaLIAjCH0TExMD2E0+0TUgIlF1fLpxKhalZWViXn+9038HqFCkpKbE5T+luY9yVK1eirKwMtbW1qK2txXvvvYcRI0Z0a5/+pLCwEKt+/NGynBwRgSULF7q8n6FDh2Lo0KEOtzEYDDbnF/ZEkRTRZE1nZyeeeuopPP3009DpdNDpdJg2bRreeecdl8dKEAHBpEnAhAmATodHH3sM+555BkVFRSgpLsbaa6/F7CzWZB2RkdDdey8efphNxlZUsPgsmvDzAiYTlM3NeOaZZ/CXv/wF69atw6effoorrrgC8+bNwwsvvODvEQY/ajVrrp6byy6MzbFmIiIimOBgMLDJbq/27bjoImDyZKsL1EEE+44dTBgxExsboKJIcTEif/lF8q7suDjnvb1cpKaGpXCaEQsUviA5mb29zMIMfUYGLiSKEF4jNjYWq1atwoEDB7B//36cPn1alvPD2WSkXKTig5yxYcMGzJ49GxMmTMCKFSuwaNEiyTglZyQlJQlEEWfOAT5STaUrKyu71VPE043W3XGKuIK7TpGamhpZ+3f2HouIiBAcy957x5G44QhH8Vlip4hUfJYcp0iURBRJc3Md+KKI2S0h5RQRPzVf9RSRwl2niEpl31UhpZ+54hSJi3MsTowdOwD5+ezkT6lkJ7MhIRFoaxO+lyIidAgLc6Y4sanFa665B3q9dJM9giCIQEKhVCJSo0G9g+/JKNHn7MwJE2SJIuFB2qlS7GSNj4+3fGe7y7p161DGa8gq1UA2mCgTNZdNNgelewGVSoXVq1ahvKQEZaWlKD93Dn0WLLA94YmPR8Kf/gTV008LCjLy8/NhNBotglSFeXbKPHlMEMEE7/9s/fr12MXryVPEj9Lqui7WaIDUVHYjPEhLC+tib1acmpuBBx6AOiwMV155Ja688kp/j7DnolbbVTv69vVh3xwXFBfxNJWLUy6+o6YGupMnJe9K/vRTJojExAAjRth1xbiC+LTBH6LI1VdbfzcafX98Qj4kihBeQ6fTYeHChVjoRoWXJ3BHFPnyyy8BANu3b8f27dvx/vvvY+PGjS4fOzExEUeOHLEsi+35jpC6oA0PD/eoU6S5udnGdREMoogzp4gnRRG+Y8OeU0SuKDJ//nysXbtW1uPEThGp+CxHPUXMf0edTgeO42AyWet0GxtrAaQJtm9v95xTZMECgGdywiWXOH+ML5wiUjgTRZy9R2IkmvCJMYshZjSacElRxJFTJDV1BFatYpWnAwfSlSdBEMGDTqt1KIroRJ+zM+fMAd56y+l+g1UUSU1NxZ2TJuHY+fM4VlWFjPh4oLIS6HJ4ukN0dLRASKitrfXASP2HjSgyfDhw8cXu77CtjZWxJybaVNtqtVoskHmNolAokJaWhpaWFqSkpCA1NdWmoCQqJMT3oeUE4QUyMzMFosid336LMoMBT118se0sLOFZQkKA/fuFGb8VFUHRWJrwPWITiYvhHL5Do0FsaCgaHn4Yh86fx/G2NhSkpaH9wAEWQWk0svMhqcJRk8nlrDKxKOLv18WB2YcIAEgUIXosixcvxqhRoxAZGYnIyEin0V0mk8kiipiZNWuWW8dOTEwULLviFJESUBQKRbd6ioi35btYzHSnp4izsbgqirgbn1XtoCkgH2cT3lL9OKSQcvVIIRY27IkiJpNJZnyW/Y9uc58ThUKByMhIgSOoqcm2CV97u+d6iuTmst4dBQWsmmbUKOeP8UVPESm6G5/lTnWvRhOJhgbh/7dOp0NoqP0XV63WkBhCEERQogsNBRw0f40SxaTmzpgha79hQSqKjBw0CCN51QLtnZ3dLh8U58jLLQ4JVCZNmoQHH3wQZWVlKCsrw5hx46SbzjqiowP44AMmhpjPge66S1Z+vyMKCwuh4M1s3HH77YL7ozQaFnVCEEFOpkT+zhv7D2LxyKWoOhqPgjeYNrJ4sR8G19NRKNhnFb9IkkQRwg7ir7WA1eU1GnAch4iQEExIS8MEnQ74wx+Al19mWVdm+EUinZ3AkSPA9u3A/PmsCbtM0tOByy9n4khkJCCjBpPoxZAoQvRYXLWXlpSU2ExIz5s3z61jJ4k+tF1xikht29DQ4FGniJR40J2eIs6cIq72FPF2fJajRusAbCLTpFxGJpNJtlNELIrYE30MBoONQOFqfBa/F09UVJRAFGFOESGedIqoVICr/zLejc+S7xRRKoVVHM7is9wRRbRa2+grnU4HhYKDUhmCzk7b94VKJT8mjyAIIpCIkmhWKrhf9F3LRUbi+qFD8d7Bgw4fF97NnnN+o7JSsKhWqazNjd1kxYoVuOKKK6DX6xEdHY2xY8d2a3/+Zvr06Zg+fXr3dqJSsQlFfkFOZWW3RRGFqNTz8UcewS1GI+paW1Hf2oqUyEjvZY0ShA+59NJL8cEHH+AkL+4mJqUfPo2+hS2cpXx8rxIfLxRFRN8dRPfYt28foqOjkZSU5PR6L9AZMYJpBgYDu5Z10krLf8TGslgsjYbdwsLYpIb4gtz8Pb1rF7BunXX99u3AVVfJPlx8PLsRhBxIFCGILvr374+qqiqsX78eX375JY4dO4bc3Fy39uVpp4inRREpp4hU75TeHJ/FR2pSXm5Td0C+U0Qsykk9loki9stA+KJIdHQ0Snldxhoa6sBxzIVqxp5TRHz+7c+eIi0tLejo6ICKZ+WQF5/FTnSlHLfiP5/4LezoPRIVFYX+/fs7Pb4YKVEkOpo9f3uiiFpNEywEQQQnOifihZRo8peHHsKPt9yCMgfFFGE9RBRBTIzQougGN+XmMmtmczPLoqdySEZsrEAUqTt5EiXNzRg+fLjHDpHQpw8S/v53oLWVvfatrfT6Ez2CqVOn4rbbbsNDDz1kWScu+DMYPN4bmTAzdCjQpw+b1U1IACT6RBLuM23aNEvRoE6nw/r16zFu3Dg/j8o9wsKA228HTp5kb5VupHF6l5gYYOZM2/X33cfOX6qq2M38BMRzPgcOADk5wKBB3h8r0esgUYQgeISHh+Oqq67CVS4o0VKITxy7K4rU19d7tNF6pejCPCIiwqYCDvCfKBJo8VlSThFXmqzLFUXEf5eQkBAbgcaZU4Rf8SJutl5XVwu1WhjX6UmniDvIic8CmDAVzWs854pTROrPLdZUxPNSjiqH3nvvPaduIym0WlvhMTqaPX+lUgPAdhLQlf9zgiCIQELnzCki8fmfsWABioxGFB84gJLqasyV6DES7mS/AcuwYUBGBotCqaz0zGzitm3CDqIjRlB5JADExeHM4cNY9sUXOFZZiTKDARzHoampyek5oGw4DggNZTeC6AmYTExMrKnB3AEDkPD00yjnOJw/fx79+mWD37LIZGLpdKIEP8IT5OSwmwM6WltRtHEj6js6UKfVor6+HvPmzRMUkBG2NDc3C1IU6uvrEb1nD1P5Ro4Myjd0WFiQawWhoUBqKruZGT0a2LJFeMFO/YwIL0GfmoRXMBqNkpPsvQWxU6S78VlSoognnSL2qvU91VPE0/FZ9fX1+O1vf4vt27djyZIleOKJJ8BxnE+dInL7iQC2EVj2RB+x+BIVFcWaj/Ho7HTcaJ0/mc8XEQCgrq5Olijibk8Rd1Cr1dBqtU5fT3dEEXNPEU86RaZPn465c+c6PbYUYWFSoggTrlQq6RdYpSKnCEEQwYmU6MFHJ3V/SAhClyxB3pIlqFm1SrLxeliwiiIcxyp+o6KArCzP7FOrFYoiTmJGew0jRyI6ORkb//53yyqTyYQTJ05gyJAh1u06OuS5dQwG4OxZNgvc0MBOGqZM8cLACcKPbNsG/PADAGAQgEEZGcDy5QCYCLJ2LesBbubdd4Hhw4GLLvL5SHs3RiMq/+//MPD3vxesvnDhgs01JyFEqlA16fRpFlc2cGBQiiI9kvBw5iz59lu2nJsLpKX5d0wucOQIM5Cae5rExFC6ZiBDogjhFW655RZs2LABw4cPx7BhwzBnzhyMktN1uYcgdopUVFSgs7MTShlVgfbiszzZaF3sSLAnitirNnG1p4in47Oee+45fPjhhwCAp556CsOGDcM111wjyymiUCicVtHIabQu1ymiUCgQI8oMt/dY8XGkIs2MRvsT5SEhIYLnJnaK1NbWok8f4WNaW002goRWq4V4iN4SRQD2/pMjivCRF5+l7drW9j5noog9p0h37NWhobaRLzEx7L1m72+qVnvxhScIgvAikqIHD/F3lJhQO+JH0DpFvIH4u8qFgo2ApLYWOHeOzSJERLCf7lQ+p6YiIjUVqampOHPmjGX1jh07LKLIlVdeiYoDB5AUHo7kmBjcPGMGht9yC5CcbLu/0lLgk0+sy9HRJIoQPQ9RMRW/ATLHAVdeya5DzG2famqEmizhI44cQRS/50gX9fX1JIo4obq6GhqNxnItrlWpoDPPVpMTIbAYNw7IzGTFHqmp0lWOAcqvvwKnT1uXL7uMPR0iMCFRhPAK+/btQ0lJCUpKSvDFF19Ap9P1KlFE7BQxGo2orKy0WS+FVAWDp50i4gnmODsBlGKXgr1j+1oUeeWVVwTr77nnHlxzzTWynCJardbu8zIjp9G6XFEkJyfHxnXQ2dkpKZKJjyMVLWU0Amq19EmbOGddyimSni58THNzO4xGo2CdlFPEm9UNycnJuCBxcs9H/J6VF58V2rWt7X3O4rPsOUVS+dZeF5Fqmh4bq+u6T15UHUEQRLDgrGeUU1HEzgRFGE1cWOnfnzlPtFomkEhN6AcTJ08Ca9ZYl5OSgNtuc3t348ePx+rVqwEAWVlZgu/UXbt24dy5c5bly1JTMdzejiIj0Wk04uD58zjb0ICzBgPOVFbigQcflB0DShABj7hKvq7OpnGIuP0hzcH7gU2boFWpoFIo0MG7hqurq/PjoIKDkSNHsgit4mKU//3vqGlpsc4LUMFF4OGhJikmE/so81W6nHjqiE4TAhsSRQiP097ejiNHjgjWDRs2zOfjKCgowJ/+9Cc0NDSgoaEBSqUSmzZt8smx4+PjwXEcTLyO1ufPn5clinijp4izbS+99FLZ+wL811OkubkZbW1tgixQADhz5gxqampsRJGQkBCbfcnpBSEnPktKFFEqNejsFK6fPn265DHb2tps3AhyRJHOTiAkRJ4oIuUUEf+pDAbbqlJf9hQBmNBw4MABh9u4J4pou7ZlJ0N8Lcxdp0h3RBGpMcfGRnYdX/oFJqcIQRDBijPRI0pclSzCrlOEri6tTJvm7xF4jD179uDZ++9HcmMjkiMj0V+vx7XdjBlbsWIFcnNzsWDBAgwdOtQy+dXZ2WlThJRsdqdIodOhw2jEyNdft6776itct3gx8vLyujVGgggYzM52pZIJJEYj8MUXrINzRARMkTq0tAj/J0VtEwlvU18PVFeD4zjoNBo0trUhKioKupgYmyI3QhqO4xCVnIyom25i0YiNjcxlSf1YehS7d7P+7A0N7M+cmwssWOD945pMtqKIkxohws/Qfz7hcU6ePGkzYewPUaShoQGrVq2yLNuruN6yZQsOHTqEuXPnIs1DWYUqlQpxcXGo4JXTlJeXY+jQoQ4fZzKZ7MZnielOfBYfpVKJm266Sfa+pPbnq54iTU1NNoKbmbVr19rEZ6WkpKCkpESwTk6DTTnxWeK4J4VCjcjIPqitFR5v+vTpkq9Pa2urzcS73PgsuaKIvZ4iwmPa5o9rNKE2ooE3RRE5/3fuxGeZe4oAttUh4ucnPg+2J/R1RxQxGm1Fkbg4x04RarROEESwonMmioiiJcWE2hE/SBSxcuLECTzxxBOCwpBffvnFz6Nyj/z8fHy6caNleXBCAq5durRb+5w9ezZmz55ts76qqsqmUCEpIsJ+pXBEBDSxsYiLiEAl73xk8ODBSE9PR1RUFL799lv0EWeUEkQwodUC99zDypoVCuDoUUFsHBcdjd/9/vdoamKOkQsXmJmL8DLNzWyWNSGB/W1uvBHYswdlDzyAEI0GuPde716o9UTCwlhjdcJ3bNoEVFcDra1MpcjIAEaNYm5XL/UiLi21/n7qlG2RpDfo6GBt48wtyAwGcooEOiSKEB6nX79+2LdvH/bv34/9+/fj7NmziI2N9fk4xBPbbW1taGtrs5mgfv311/H+++/jjjvuwIgRI3DvvfdiyZIl3T5+QkKCQBQR9/GQwmAwWCKi+NTX19tMoHcnPovPnDlzkJKSIntfUvvzVXyWwWDA22+/LXnf559/buMUcVcUcSc+S6XSoLHRNvrsIjvdB6XcMHLjs+yJIuL3iBynSFOTrVNEqbR1SXgzPssdUcQVpwhg20tVrKmIXxd7EWuedoqEh2u6ji/9/xwSQl3ZCIIITnROnCBRThqa2o3PoqtLCwaDAe+//75lWalUwmQyOY0JDUTKysoEy0kREV7LeI+IiMDqVatQXlKC8jNnUHb2LBIWLrQ/MaNQAPfcg9R330Ulv9M0gFOnTgEAQjZuBDxw/UAQfoV/7SAuCuv6fwwLA9LTYRPJS3iQsjJg/XqmPhkM7O9yzz1sRrdvX6BvX4TMng2cP0+CCBEcFBSw97WZs2eBrVvZBXpcHHDrrR5VLMSfT/X1rG2Zk1PPbqNWA9ddZ102GoOqHUqvhEQRwuOo1Wrk5eVh+PDhfh2H1IRyQ0ODQKDp6OjAunXrLMv79u1DbW2tR44v7tNRIQ5hFXHmzBm7EUL19fVQiC7UXBFFxI4BPldddZXs/ZgRX2z7Kj4LAP71r39Jrj9+/LhNlqqU2OOOKCInPkup1CAkJBx1dacF6+Pj423ivqQeD3g+PkvKKSJ+20g5RRQK29fI2/FZznCv0brQKcLHWXyWPbrTwJAfpydGpaL4LIIgehbORA+dk4IZrZ0J8XDKIbAg/p7v7OxEY2OjpNM00BGLIsnjxwOTJnV/x+3trDq1rQ3oKsIICwvDgoULXd5V3759cf78eRgMBptzNh1NTBI9DTuiCOEDFAqAX9hXV8cq7PlVaqGhQL9+Ph8aQbiFvQrLjg72Pe1h5SAujgm45nrjsDDfiCJivGSCITwIiSJEj0Wv1+ORRx5BZGSk5SaupP/ll19s3AXz5s3zyPHFoogjp8hrr72G22+/3W4WaEdHh83EuiuiiKNJ53QPlPk4E0U2btyICRMmICQkBC+99BJGjRrlcHu5Tcz5NDY2SjpFxLgTnyXPKaLF2LF344cf7rOsu/feewFI/62knqOn47OknCLi8xFxTxGVSoW2NtuvhkCLz3LHKcLHWXyWPcTipCuMHz8B27Z9LHmfWi19oqjR0CQLQRDBiTPRI8rJ/SF2oozIKWJFL3F1X1NT41FRxGQyYc+ePWhqasLUqVM9tl8xs2bNglarRVlZGcrKyjBq7FgW5+MuhYXA11+zyUSTiXWEvuOObo1xzZo14DgOO3fuxLhx4yzrtSoVQuh9SfQ0kpNZxJDBwG7UVd13xMaySWJ+QVVlJeBiugNBBAyOJhM81FCdD8cBU6awwsf0dHYIcmwQUpAoQvRYQkND8dRTTzncJioqCkuXLsW6detQU1ODESNGeKyviLii3J4oYjKZ8NhjjzltjtadRuuORBFXo7OkkCPQ7NixAwCwZMkS5OfnO5xcdkcUqaqqsnmc1HPzVKN1cU8RlUqD4cNvwJEjH+Pcud1ITh6EBx54AID069O9+Cw7E0UynCK2oojQKaLVaiHSlhAZyXouegtfiCLizZ3FZ3mDu+++Bq+//jQMhgsAgBtv/DPv+Pbis6inCEEQwYnOSc+QKCcTbJydL55wJ71KehM6nQ73338/9Ho9oqOjLTdPUFRUhDfeeAOrVq1CSUkJxo4di19//dUj+5bCXv8PtwkJYWWhZqqr2UlUN4obzE7pwYMHY9+zz6L+6FHUtbSgpaPDuzmjBOEPBg5kN8L3qFSs8X1VFVtWKln+T4CIImfOnMHatWtRVVWFqqoqKJVKvPTSS/4ellP27NmDmJgYJCYm2lw3E14mN5cJqxoNsGWLsELRS1H7EyZ4ZbdED4NEEaJXM3z4cLz33nvo6OjA1q1b3ZqMt4fc+KwLFy44jdaSwlNOEU+IIq4INAUFBTh69Cjy8vLsbuMoPssezc22MVCeis9qaGiwyeiWis8KC4vFihU70NRUgczMeCQksAkdhUIBtVqNdt6Xv7vxWd1xijQ3N0OhaANgfe+Ie4qEhobaiCJO5rW6jZz3oPi1Ecdn9ekzGufO7RasUyiUvO2F+3MnPqu7Ge06XSg2bfoZr732Ofr3T8PKlVdY7rPXO4R6ihAEEaxEOan8i0pIcLwDO5+5YSSKWFA0NuK5efNYI96WFnaS4CHHQmFhIZ599lnL8s6dO3Hy5En0C5a4FtH7r7O9Hdu+/Rar16/Hd999h7179yLcXmN1J4SFhWH4737HcjlaW9lrT7FuBEF4kosvZiJufDy7GOMLuvn5TOhtaWGfQXl5PovSqqurQ05OjqAPalhYWMCLIiaTCZMnT7YUNkZGRmLTpk0YSQ3XfcOIEdbfp05lLqimJib8kUBF+BESRQgCLDJo2rRpHt2nXKeIuBG4XFwRReLi4hASEmIjNsTExHikSsIVUQQAtm/f7lAU8ZQ4lZycbLNOjigSI1IBOjs7UVdXJ6i+lGq0DrCJ+IiIJIiNPyEhIU5FETnxWY56ijhrtA4AbW11AKzvzcZGW6dIdbXwMd7O3hSPWwpnTpH+/WcC4HDu3C4AwJgxd4i2F+7PnfgssdDpDv37J+LZZ39rs96+U4TiswiCCE50Dj4z++v1CHHz/COUJp+tNDYCP/5oXeY4YN48j2REXHzxxYiJiUE176Rg9erVuO+++xw8KoAIC2OZ+83NaOU4ZP373zjz5JOWu7/99ltcc8017u9foyF3CNHzME9U1tSwSfeaGmDMGJq09Afma2WTCdi0iS2biwl27UL9sWMoNxhQ19KC+uJi6MaPx5gxY7w+rOLiYoEgAgBNTU3o6OiASm4esR9oaGgQJD00NDRAv3s3e4+PGgV4yGVJyITjgPBwdiMIPxK4n1pE0OJOlX9PRK5T5OTJk27t35XJUoVCgZSUFBsBxhMuEcB1UWTbtm24+eab7d4vRxRZuXIl3njjDbv363Q6SaeFHFFEqpl2RUWFE1FEuF/xJLxGoxE0nO9efJZ7jdbZcWthFkXq68/in/+8UnC/P5wicnAmiigUaixfvhGHDn0AjSYKeXnCBqpip4ic+CyVSiVwpHiq35AU9hwhWi2JIgRBBCfhdhT1iJAQPPe737k9cc/5Iu8wWBCf05hMrKG4Bybr1SoVrrrkEry1ahUAIDMzU/K8JKCZMAGIi4MmKwsDt27FGZ6AtHrVKtdEkdZW1vi4oYHF2DQ3A3PmeGHQBOFn/v534Ylyv34slJ/wD0VFwM8/s1taGpvA12jw75078ccNGyybXXHFFfj666+9Ppzi4mLJ9TU1NZLX0IFCeXm5zbrEU6eAc+esAhRBdJPdXcEVOh0z7sbGerc3K9F93A9VJQg7jBkzBkOHDsX111+PH/nVa70MuY3W3XWKuCpESEVoOYrV8uZYfvnlF4f3yxFFli5d6vD+8PBwSReMUkZzjLCwMJvHikUtcU8RpVI4ASGedBf3MpHjFOmuKKLVam3+Nswpwli79gZ0drbbPMbXThE5iF8bcXyWQqFESEg4Ro26BYMHLwLHCb/enDlFpN7CL774Iu9+Nf7yl7+4PnCZ2BM5ySlCEESwwkn0brh35kycP3QICx5/vBs7pk6ZFqQKPSTiRF2hsLAQZ8+eBbZvxw3h4Xho0iTsefBBnMjPx6233tqtfTukspJFwpw+zSp3xV/U7jB1KjBoEBASYiOAfPLpp7g8Jwd3TJmCI089BTiLsm1oAD7+GFi3juWh797tmTESRCDBcbYn/uJqKcK37N1r/b20FNixA9BooBNdW9bV1cEXSM1d3H///bKusf1JbW2toDgyMiQEYeYLQIl0BoJwh02bgK+/Bj78EHjtNeDUKX+PiHAGOUUIj9PZ2YlDhw7h0KFDmDx5Mi655BJ/D8kviCslqqqqYDQabRqM+8IpAnhXFHHUNF2K48ePo7KyUiAcdXZ24uWXX8aGDRuwbt06h4/nOA65ubkOtwkJCZGMZTKZTLLGGB8fj1O8bzGxKGIvPsuM+DpZ/PeS01PE1fgssSjCcRyio6MFY29pqbX8XlxsK1pqtaEQn1P7winSr18/h/8LfJcNYOsU4TjHJ+LuxGfdddddiIqKwuHDh7F06VKPOauksN9ThCqiCYIIXqZkZWFLYaFl+a7nnkNYdrYfR9TD0GiAAQOYOBIayn5200nzpz/9CR999BHyMjMxKycHS4cPxwitFjh6FBg2zEMDl+D4cWD9eutyRgawfLnHdn/llVfi9ttvR15eHg4dOgQA+Pb4ceD4cVybmem8AXtXocrB8+dxuq4OZ+rrcfbhh7F05Urk5OR4bJwE4XdiYoQiobhaivAdBgNw7Jhw3ciRgNGIKJF7p76+3idDmjJlCv7yl7+guLgYJSUlGDduHJ577jmfHLs7jB07Fk1NTWgoLET5P/+JWnOBI8dRPFwvoL0dOHsWOHkSGDvWO39yo5GlmvIJNoNtb4REEcKrDB8+3K/H//e//40tW7bAYDCgoaEBy5cvx0033eSTY4udIp2dnaitrbXpVyFVbaHT6aDX6wWT8mJ8JYpwHCdbSHCFbdu2CeKIPv30U9x7772yHtunTx/JaCg+Go1G0iliFDf7sINYFPn888/xZFcW9T/+8Q/JRut8xOlYYqeIL+KzANZXRCiKMMXD3t+0rU1r0w/FF06RF198EQsWLLAsDxo0CPn5+ZZl8ettG5/l+OvMnfgsjuOw3IMTMo7QaKT/n+2tJwiCCAaeffppLLrlFpQ1NODxJUuQ7ufzwh4HxwFLlghWmUwmuOulMRqN+OGHHwAAR4qKcKSoCDmLFmFEXBywf7/XRJENGzbg1ccfR3JbG/pERmJgXByuHDrUo8dITEzEyZMnERcXZ3O+lBQR4bxSuKuHyMJPP8Xxqiq2bt065I0eTaII0bMQn/iXlACFhUBWln/G05vhOGDSJGDfPiaQqNXA0KGAVouoigrg//4PGo3GMnfgC8aNG4dx48b55FiehuM46Pr0ge6mm9jraTCw2XIXCzyJ4OL999nHmHn6IDERcFJf6xYGA0sx5UOiSOBDogjhNTiOw+DBg/06hm3btuGTTz6xLE+YMAEAsHfvXrz88ssYPHiw5ZaWlubRY0s1Za6srLQRRcTV8ddccw1ee+01LFmyxOuiiJzKd4VCYTMB7Qk2b94sEEVuvPFG2Y+NjIyEWq226fnAR6PRSDpFXBFF+Lz77ruW35cuXWoT3yXVU6SzEzA7iZ05RYxGo40bwp4oolZLlzZIiSJi8chgYNVeJpP039RgEL5m5uJTbzN//nw89dRT+PrrrzF16lSkpqbi7rvvttwvfr2k4rMc4U58li+Ji5PO4CVRhCCIYGbCtdfi9Pz5TImmZppeoaCgAG+++SYKCgpQUFCA8PBw7OXHrfAwmUxYu3Ytjh49iquuugoDBw4U3L9v3z6buNdLk5LYL6WlrOLDC7GOBw8exOqtWy3LU9PTcaUXipjS0tIkXalJer2859WnD1Li4qyiCIDFixejqanJZ0VXBOF1Lr4YSEkBPvuMLZeWAt99B9x5p3/H1RsJDwdmzACmTWNuuro6S2zi7Nmz0dLSYlN4RzghPBwYMcLfo+h9VFez6LeDB9mERnw8cPPNPolENZmEcwGnTjkXRUwm14dmMgEDB7K0zYYGlmZKJqTAh0QRwuO8++67qK+vx9mzZxHu5wtg8aSyuRJ/x44dgknuYcOGYf/+/R49dmhoKMLDwwUT3ZWVlcjmxUYYjUYb4eMPf/gDYmJioNPpHO7fV04RuaLII488gqeffhoAMGrUKOzbt8+hALFp0ybBsrhHhyNUXVlHYWFhdq3C9uKz3BVF+Jw6dUrgYmBjsj0hbWuzCgrOeoqIBRHAfnwWxymgVoehvb1JcJ/U801OThYsl5efRkQE0Nlp61Rhz0Mo7uj1volvVyqVeOSRR/DII48AAN555x3B/WJnTXedInLis3xJv37SAmVoKF3oEAQR5HRV2BPe4fz583jhhRcsyxqNBp2dnZL57q+99hp++9vfAgCef/557NixQ3BeevLkSURFRVmy6bNjY9HPXFyh0bC+H336ePw5lJWVIT4qChVdx02OiPBaeWVsbCw+W7UK5SdPovzMGVwoK4Pu6qvlnewsX47Un34CCgoEq2u//BK4/nr/V1gQhCcICbHNgCFR278olTazuK729CQIv9LUBOzcaV0+exZ45hng2mtZDKgXSU8Hioqsy3L6fHz9NXuMXs9ugwY5N8tFRQHXXWdddkdYIXwPiSKExxk5ciTy8vL8PQwA9kURc5awGW85WuLi4gST3eXl5Vi2bBnee+89DBs2DK+88orNZG+/fv0AwKko4uqJkJRzpY+MC1tO5if5k08+ifHjx6OqqgrXXnstkpKSHGab7tu3D3V1dYiKipK1fz7mJmmORBGNRiPZ68SVniKOKC8vFyyL47MANvFuTxQR/93F0VmAfacIwCK0xKKIlFOkf//+guUzZ0owcKB9UaShoUywLPG28QnOnDXd7SkiJz7Ll2RmSguUGg1d8BAEQRD2EUc3tba2orS01HI+yee///2v5feamhr89re/xU8//WRZt2DBAsyfPx+7du3C999/D/2ZM8D48WwyLi3NEjFiMplknx+aOXr0KL799lvcfffdluIWM3fccQcUCgX+9re/AQD6zJjBcvM9SX09cPQoIo8exdWJicD997u1m3RRjj8A6IxG/1dXEIQnMRiEy9SImiCI7iBVHNPe7pPPFvPXNscByclAv37OBYuqKqC2lt1KSoDYWNcTBEkQCQ7o7I3o0cyYMQMhISGIjIxEZGSkRaw5fPiwYDtviSLivhTvv/8+vvjiCwDAgQMHcPvttwu212q1SOqKKXAkiqhUKpcvRrMlGpv27dvX6ePkHofjOMyZM8eyHB4e7lAUMRqN2Lp1K6644gpZ++fzxz/+EYC0CGDGnpNGrlMkISHB4f1ih4+UKMLXPcSiiNgZYxBffMC5KNLYeEFwn9TrkZGRIRp3sUNR5MIFoWDor8hYZ84aV+OznDlF/C2KZGdLO0UoPosgCIJwREJCAnQ6neCcq6CgQFIU+ec//4nbb7/dEq+1YcMG/Pjjj7jkkkss26hUKkyYMMESOWvGaDRix7ZtWLVqFT777DP8/PPPNoUX9jAajbj11luxZcsWfPDBB3jjjTcwkid69O3bFzt27LAsz5k/37Puon37gLVrrctVVcDs2W7NWDz55JP49MMPUVBcbFkXFRlJsx9EzyIlhQmTBgNzjTgpFiMIgnCIve/02FivHzolhbVf69tX/qlFTY1w+ccf2dzO9OmeHx/hX0gUIXo0s2fPxuzZs23W33DDDRg0aBAOHz6Mw4cPe9UpwscsiJgRizPp6ekWEcKRKOJqdBYAJCUlYcmSJfjggw8AADfffLMsl8asWbPw5Zdfuny8yMhIlJWVOdxm06ZNuOKKK9AunqEWccMNN+Czzz5DQ0MDxo8fbxFfHIki9vJVPRGfBQDnzp0TLItjpwChKGJ2t5gRT/KLnSIhISGSf2e+KCJGjlOkpKQYKpV9USQhYYjl94ULARkJa17BmbPG1fgs/uYmU+DFZ/XpEwOVKhQdHc2C9SSKEATRm1EpFOiQ+b3dW+E4DrfddhuUSiVycnKQnZ2NoXaalOfm5uLs2bOWZYVCgT179ghEEXvlk2PGjBH0Kvn000/x4IMPyhrjW2+9hS1btgBgff3GjBmDtWvXCoppJk+ejLS0NMyZMwcXX3yxrP3KRtw3sKEBOHPGdr1MPnv5ZVSuXo261lbUt7ZiHDWgJnoaubne6URM9BgOHTqEn3/+GVVVVaiqqkJGRgbuuecefw/LLrt27UJsbCwSExP9HvHeKwkNBZKSAH7aRmioV/qUiVGpXEvo6uxk5lI+JhOL0yJRpOdBogjRK1mxYgVWrFgBgEUAyJ0odxVnE+ti+FV54gbZfNwRRQDW7+W6666DUqmUFIuk+Otf/4p169ZZJqHff/99WY9LSUnB8ePHHW6ze/duACwP2xGzZs3Cs88+i3PnzmHIkCGWnGxvOkVc/dtJ9RThT7w7c4qIRRGpfiKAdXLfXVHk/Pnz4LhGdHS02mwLAGPHWpsoOjHLeBVn8Vlip4g4PiskRChK8TeXapHjb6eIQsEhOjoFlZWFgvVhYSSKEATRe3l34UL85pNPLMuv8SbRiS4MBjy7eDFzP1RVAadPAyKXhxmdToennnoKK1euxCWXXIKXXnoJQ4YMEW70n/8AOh2QmcluCQkAx9mIIqtWrZIlihgMBjz00EOCdenp6Zgumll48sknZT5hN4iLY5XuFRXWdUePui2K5M2eDUyaBLS2Ai70xCMIgvAILS3AoUPsM8h8mz3bEnHoLf7v//4PX3/9NTIyMrBu3TqUlJRY7psyZUrAiiJGoxETJ060XD9GRETgl19+sVtAQHgBtRq47Tb2u8lkdaEFIBwHrFwJ7NrFjKZmqqqoT0hPhEQRotfDcZxkM0pPINXHwxHz58+3/O5IFHG3sZpCoRBU5clh0KBB+OWXX7BmzRqMGjUK11xzjazHScU2iKnp8iU6c5RoNBokJCTYRFq54xSJiYlxOi7AdVHEWXyWM6eIOD5LKjoLcOwUkWq0Lo7PAoCGhpPo7LQ9aZ448X7k5S2yLPvTPeEsPsvWKeJYFOFvLmVM8rcoAgDR0Yk2ogg5RQiC6M0seOgh3F9aiu8KC3FRejqu74rPJHhUVwNr1liXlUp2siA1OVZXhxsVCvRbuRIzrr0WnNgpXV3NhIOKCmtX0ttuA5KSsHDhQrz22muWTffu3YuTJ086Pd+LiIjAl19+iVtuuQX5+fkAgFdffdX3lbqDBgE7dgA5Oez3zEz396VSsc6rBEEQ/qC1FVi3DpVNTahpbmauNYUCg0eOdBoB3R127NiBdevWSd5XVVXlteN2l+rqakFBncFgQMzOncCFC8CYMaxDNuE7OA6IjGS3AEShAPr0AaZOFYoizc2sXzwZjXoWJIoQhBeRmpC2R2hoKC699FLLsqNoK3edIu4ybtw4jHOxuYRUI0oxdXV1AGyblouRmux3tB6wTqrfcssteP311y3rH3vsTzh2DDh3jrnCk5OlH+8Jp4gjUcSZU8SZKKJW2wpCUiJRWFgYkpKSBK9xfX0xjEZhPxmOU2LmzOcE6wJJFHE1Pkv8L8J3ikiJIv6OzwKA6GjbixitNgDUGoIgCD8RMnQonvvrX/FcYSHrlDl2rL+HFHiI87g7O4G6OulJ+40boTx9Gpf06QNs3crcE8OGWe83CyFmIiKAxEQAwEUXXYT4+HjExcXh2muvxcKFC2UVwADApEmTsG/fPjz//PMoLi7GrFmzXHiCHmLiRGDKFPaF39bmXjWE0QgcP87it+rr2c9LLwUcFOkQBEF4nK7rpIlvvYUT1dVs3RtvYPXq1ViwYIHXDst3hogJZFFEKpUi4dQpFqPI/w4kCB5RUew0Sa9np1pxcazuxB6bN7PTAbPek5AQGHMMhGPoT0QQXmT48OGyt7300ksFk9reiM/yJXJEEXNTUGdOEXuOGznxWY8//jjOnTuH48eP4/bbb0d7+yB8+inbZts24PbbASnziOuiiG1PEf7ku6uiiLP4LI6zrQC193pkZGQIRJHa2mKEhycJtlEqbd9T/nRPdDc+S2wU4mso4qbrQGA4ReLipESRwP9fJwiC8BoKBSvVmzrV3yMJXMLCAK1WGONUVSUtivCagwMAvvgCGDwYX33zDQ4dOoRZAEaYTFCYsyH697fkRKiUShz66SckVlayq/y8PJeGGRISgkceeQQmk8n2zgMH2FgGDQIuuYTNJnj6i9l8YmA0An/9K9t/RAS7XXWVfOfHqlXWChWACXUkihAE4Uu6Ps+iRNeX5oJDbyElisyfPx9xcXFISkqSeERgUFNTg9DQUDQ3s96NsaGhCDHPbtu55iYIhQK44w5527a3Axs2CNfdeScTUojAhkQRgvAiruRUzpgxQ7AcSE4Rd5DrFDGZTE6dIvYECjnxWX369MFXX31lWf/nP1u36egACgqko7cjIiKg0WhsJuPt4Wp8llgUkROfZTKxokTANVGkf//+2L59u2W5pqYEWu1o0fht31OB5BTp6OiA0WiEoisOxJNOEY5zXPXhKxISEm3WkShCEARBOITjWBRURwcrZYyNtWkK1t7ejn379mHQ5MmI+OYb4eNLSvD2229jzZo1eARAnF6PvyxahNtzcgBzA/GSEuCrr5BorkiOiGA9NdzIr+ekwri/+IL9zM9nt7w8YOFCl/cti6YmdkLV1sbiwqqr5Z/wKBTsufM7sNbXs5wNgiAIX8FxQP/+0EVHs/iDLurF3aE9zDPPPIOioiIUFxejpKQE//nPfzBYHMMYgEyePBmNjY0wHD+O8n/9C/Xm63ulkhUVEL0CkwmorQVOnWK3zk7g6qs9s29RfSuAgE0HI0SQKEL0aKqrq/Hwww+joaHBcvv2228dxi55kqioKGRkZDi0mppJTU0VLPcGp0hnZyeam5u96hThI3WeePSotCjCcRzi4+Nx5swZh2Mz42581ieffIKXXnoJO3fuFNwvFkXa24FPP2U9ydj4XBNF+FRUFCEhQRhHJRZF/C0USPWEaWtrs7yOznqKOHKKiEURlSowGqYlJNiKf9RonSAIgnCKSEDo7OxEW3Oz5Xy3oKDAEoPaLy4Og2Ni8Pkf/wj1rFloj43FTz/9ZHlsZU0NIidOBJYuZTMIAGu8bhZEAHYycuYM0FcYxemQ+nq2Hzl4s3JXVIQCjnMtIFynE55QSs2EEARBeJtlyxC1Zg2Qnw+1Wo2oqChL8Zi3WLp0qVf37004jkNkaioib7rJ2uS7szMwLgIJn1BUBLz/vnVZpQLmz/fMnId4nkmjsZ2PIAITEkWIHk1HR4egnwQADBkyBDNmzMDgwYMxePBgTJs2TbpqzUMMGzZMliiSLGpu4Y1G675ELPLYo66uzqEootVq7U72u9po/fhx2+0cpSWkpqbKFkXccYqcOnUKS5YssZngB2zjs7ZsEY5fHBcF2O+xkpKSIlhubKxCZ6fQASMWdfwtFEj9/VpbWy2vo7P4LEdOEXF8VqD8O82ePQEvvCBcFxZGZ1MEQRCEc/773//iq6++wvHjx1FYWIhHHnkEjz32GADgyJEjlu1OVlaiXaWCevlyAEBVeTmWLVuG1157zfLdOnPmTHYSYD4RMLtPLlywHjA/X74oUlMD/Oc/rJnbrFlCEaKiwnb7AQPkP3FXEYsi4eGuOV6SkthEml5vDQ0nCILwA++88w4+/PBDm+tMwg7h4cCIEf4eBeEnRFMi6OgAKistrdMsGI2uG2FDQtgpjrndGP1LBg8kihA9GqkIoqKiIhR1NZKMiYlBZWWlV8cwfPhwrFmzxul2YlFE56CaLhicInLHWF9f7zA+Ky4uzq5o5QlRRJRiJeDyyy/Hjh077G/Aw52eIj/++KOkIAIwMc1MZSVr3MVHyimishP/IH6d2tub0dnp2Cni76ZgUu8ffpSZq/FZ/M1FPdv9/lzNjBzZH+PHX4sdO1YBABYuvA9qdQDkehEEQRABz549e/CFOYYKzB1i5vDhw4JtB/POMU6fPo23337bIoiMHj1aOhs+N5eJItHR7PchQxyOp6amBnq9nrlN1q1jJ0UHDwInTgCzZ1ub28bEAMuWMetufT2Qnc3iwLxFv37A3XczccRgEJ4gyGHOHODtt4Fjx1gZaEEB64MSBBEyBEH0LKTmOsQ8/fTTeOmll5CRkYEPPvgAOTk5PhgZQQQeoaGseTq/9c7580JRpLkZeP55tp1ez24zZzoXOfr0ARYtsi5LtU8jApMAmQoiCO+g1WqhVCrtTjwPHjzYqy4RQDi57YhEkUStVCoRGRlp04AbCA5RxB5arVbQT8OZU8RRw3NX4rOMRhaJLaapyf5Yb7jhBvzpT3+yvwEPqfgsfjsSKVFk7969kvuaOXMmbrjhBgBs3FKampQoYg+xg0SOKOJv94Q9p4gZV+Oz+O4Qsb01kPrrff31q/j886UIDdXgiivG+Hs4BEEQRJAgnug6zqsEqa6uhkKhgLGrQXger0n6yJEjMW/ePHzyySeIiYnBSy+9JH2AUaOAgQOZU8LJuXNTUxNiYmKg1+uRlZKCLAAvz56N+PBwNuNw5oxVFFEqWUN3UdSn11CpmBATE+P+Plpb2QlaczO78RuvEwRBBAiHDh3Co48+CoB9DzzyyCNYvXq1n0dFEP4jOZmddiQlMTFEXANSW8u+0mtq2I3jgMsvd/04lMoWPJAoQvRoOI7DPffcA7VajYiICDzyyCOC+33RGEyOKKLX6yVtr1FRUT1OFNHpdAJRpL6+Hhf4cQwi7PUTAVxzijQ32/aSAByLImlpadDr9aipqbG/URdS8Vn8P11Li/Dv29TUgj179gjWjRw5Eh999BEGDBhgEeu2bGFzB2K6I4q0tQW+U8ReTxEzrsZn8TUUfnUIwCpBAgWFgsM110z29zAIgiCIIEMsihQUFMBkMoHjOPz73//Giy++iGPHjuHw4cMYOHCgZTuVSoWPPvoIL7/8MiIiIhBur7+GTgfodPjxxx/x/fffW5zXc+fOxVNPPSXY1OzIrqmpwa6aGuwC8Oa8eezOyEhgxgyPPW+/0CqMIKWcDIIgAhFxjPhnn33mnQO5kzdEEH7g2msdv1XF0z5RUUBhIVBWxtI7qqqAiy/2bson4VtIFCF6PM8//7zl96lTp2L79u04fPgwDh8+jJEjR3r9+Onp6TbuCDHi6Cwz0dHRkj0tgqGnCABcfPHF2LBhg2U5KSkJkZGRAhGksrLS4WvjSBSx10MDsBWO7KUjNDfb3QUA4OOPP8asWbMcbwRpp0htrfX3U6eEF8xnzxpw4UKBYN3f/vY3ZGdnW5bb2oBffpE+XmrqBBw48I7TcQG2r1Nra+CLIt2Nz3LkFAlkUYQgCIIg3GHIkCG47777kJOTY7lZaG+HtrERw4cOxfDhw20ey5WWIjE6Wlbew6ZNm/ACrwFWv379bLYpLCwULKf06YOw4cOBI0dYyWWwiwji81bqpkoQRAAi7lE6f/58z+z41Cngu++w9cgR7C8pQVVnJyozM3HRRRfhmmuu8cwxPMh7772HQYMGIS8vj/qv9HKcaXdiUUSvB7ZvB06etK47f55EkZ4EiSJEr2Ly5MmYPNm3VdgKhQI5OTk4cOCA3W0ks5thv9l6sDhFnn/+eYwaNcqy/Le//Q2vvPKKYJuzZ8863IennCL2kg3MqQf2viAvvfRSvP766/jggw/w888/2z2elFOktpZFTh85Aly4IDwBO33a9v0gFukuXLDtf2Fm+PDl2LTpcTQ2ngcA/OUvf7E7NndEEX/rbgqFAiqVSuAIcSU+y5FThC9WASwenSAIgiCCmeSmJjx/+eWsjDE/n30RmpuAnzsH/Pe/rOIhPp7lR5idGyYT65FhJjubCRd2vhwzRf0+zK4QPidOnBAsZw0YACxcCEyYAKSmuv0cA4Zbb2XCSGsr+0nN1gmC8DVlZcDZs9bPofh4YOhQwSalpaWC5bS0NLcPN2nSJMTExCCjXz9khIVhidGId3/5BW/w4qCNRmPAiSLl5eVYtmwZABZPnpubix9//NEmupwgAJYU2q8fE0dqa5m59fRpoSji5ZbEhI8hUYQgfMDAgQMdiiL2nCJRdkrYg0UUGTlyJNavX4/Vq1dj/PjxWLZsGd59913BNuKTNTHu9hQRiyL2nCImEzuPdLArrFy5EitXrsSSJUvw4YcfSm5jr9H6xx/bv59Peno6YmNjBescfeGqVFrceus+hIS8gyFD+mLx4sV2tw1GpwjA/oZ8UURufJZSyW58yClCEARB9Gjy8wF+LCe/4Mbs0O3oYBNp/EoRXnN2AMDx48Cll9o9jFgUKS4utsR0mbnnnnuwYMECFBYWorCw0Hp+0xMEEYCVjhIEQfiTggJ0bNiA6uZm1Le2or5PH7QYDJg4caJlk6uvvhrJyckoLS1FaWmp7F6nYurr67Ft2zbBusvvuAOxomvMqqoqt/bvTfhzMJ2dnSg+fhxxW7eyC8Bx49isN0F0odUCKSnsZkYctx6Ab3OiGwTAtBdB9Hz42c1SOIrPkiJYRBGANQ2fOXOmZVmn0wnul4oH4+OuU0RufBbAvugciSJmIh2cNKnV9qO8AOeiiFSUm7MqhMjIZNx220M2DcLEiEWRzs5OtLU1CtaJnS6BIIqEhISgsdE6TrnxWUql7fjNm3d22jZaJ6cIQRAEEfSICisEV+0VFcL7+M4GcWVAdLTtvnjk5ORg4cKFyMzMtNyMRiOUvGoEtVptuU9OBClBEAThIlotdp49i0k8p19ERISgH+m8efMwz+wK7AYlJSWCZQ5AenQ04kQX0IEoiuzfv1+wPDQhAcpDh9iCD6LUieAnORnIzATi4thNbA5tbWWR5zod09giI1ldCrXZCQ4CYNqLIHo+zkQRe/FZ9pwiwdJTRArxc3LmFPF2fBbguNk6H7GgIzyeY7uBM1EkLy/PZp1YFImJAaqrhevkfNlK9V5paakVLAdafBZg+zfkiyJipwg/PkuhsHWKnD8P7N4NZGXZRqaTU4QgCIIIehyJImKLJP+KftIkoKDA6iaZOhXguT7EJDY1YdV997ETkpoaICPD9kuXIAiC8C4aDXSiayWDwYDOzk6BSO0JxKJIn8hIaFUqZMXEYHLfvojLy0NscjKGSfSs8jcajQZZWVmWXlfD+bFZERF+GhURTPTrx272qK0FNm8WrnvsMS8OiPAoJIoQhA/ozU4RMa46RdyNz3LVKSIHR04Rjca+YAI4F0Wk/tZiUWTAAODXX4XrHIk9ZqREkdZW4QRJoMZn8eHHZzlyiphM0uP/+mvbRBC1GpB4eQiCIAgiuIiPZycKsbHWm8mEz7/4All5eciZNQua+nomfvTta32cVgvceCNQXMxiofr0cXycjRtZwLYZZ7MFBEEQhOeJikJURobN6oaGBrtzCO4yatQo/Pe//0VJSQlKtm5FZJftfv7AgZh/7bXA1Vd79Hie5Pe//z1+//vfoyE/HwdfeAHR5kbrISG2TSiJXkFzMzvlKS9nt44OYPly9/cnTqEID6dakWAiAKa9CMK7rFmzBt999x0aGhrQ0NCA6dOn45577vHpGLKzsx3e31MbrUshdoqcO3fO4faecop4UxRRqdRORQ9n94vFos5OW1dITo6tKBIe7nC3AACt1vbYwSCKiN/njuKz+D1FOjvtn4hs3Spcjo52WBBLEARBEMFBTAywZIll0Wg04mh+PhYsWACANZgdMGAAvvvuO6SLzztDQwEJx6oker1QFKmpsd2msRHYt4+NKTaW/QwECypBEERPoX9/6G65BXjgAQCASqVCVFQUGhsbBXMIn3/+OU6ePImamhrU1NRg2bJlGDt2rEuHSktLww033MAWzFnE5k7UQZJDHNm3LybdfTdgMLCbODqA6DVUVwOffmpd5jjWC9bd05TiYuGyg3ARIgAJgGkvgvAuO3bswGuvvWZZ/uqrrxAREYHFixcjwkeWSUeT9wDsjiPYG61L4SiCSgpP9RRx5KhobpY3FntjDw3VCRqMSuFMFBELLjU1tmPu0wcYPZrFQAFAdra83nDS8VmORZFAmLuwF59lNBphEp3I8uOzOjvtizpiAYyiswiCIIieQnt7O5YuXYqCggIcP34czbwTnM7OThw/fhyJ/OgQdxA3GRdXcAAss/LHH63LGg3w0ENUhUAQBOFBdDodysvLodPpoNVqJa9H//a3v2HXrl2W5WHDhrksighQKtn3gPi7INCJiAACMN6L8D0JCex0xDydYDIxE21CAnt7u9oLZPp0Vv+xcyfbT2am58dMeA8SRYgej1R1/y233ILf/OY3Ph3HzTffjDfffNNmfWRkJIYMGSL5mJ7oFHFVFIl10OxTarLfTHecIidOsFvfvsDgwdb19pwiYWHOZ9ZdFUXEPVEjIljCxRVXsHSMjg7ASSob79gqqFQqQR+OYHCK2IvPErtEANv4LLmW1SApbiIIgiAIp6jVamzatAkXzP1BRAwYMEDSPeoSiYlASgpzf0jEbV24cAEhp04hmr8yJoYEEYIgCA/DcZxToVsvEi9qa2u9OCKCCHzUaiZi8KPKy8uBY8dYw/ToaHZ6k5vLClKdERLCths1ihlpg00v7O0EwLQXQXgXqYnsjIwMhMvJHfIgTzzxBKqqqnD8+HGEhIQgPz8fJpMJzz//vN3J/d7QaN0Zjp6rN+KzTp8GPviA/b5zJ5tcz81ly74URcT9RMyGGY5jMVquEhoaioaGBsuyM6dIIIoiZqeIlCjCj88C5I+/uwWzBEEQBBFIZGdn2xVF8uRGZDkiN9d6YtSFyWSyVCg//vjj+L//+z/EhoYiKyYGK0aMwMply7p/XIIgCEI2//73v7Fx40asX79esL5GKvKQIHoZ/fsz8SMpid369gXWr2dJHdXV7JaQ4No+OQ5IT/fKcAkvEgDTXgThXUaMGGGzbjC//N9HJCcn4/PPP7csNzU1wWQyORRnertT5MEHH3R4vyOniCvxWXxR5PvvhfetXWu99rc3dq3W+XPylCjiLmJRxNYpIhQgAkF3s9dThO94McOPzwLkOUXi44GhQ90fH0EQBEEEGjk5OdjKa6B122234dlnn0V+fr7Himrq6+vx9NNPo6ioyHIrLS1FVFQUCgsLAQBVzc2oOnsW83JymFOEIAiC8Blbt27FZ599Jli3cuVKjBkzxjsHNJnIEUgEDZdfbrtOrBfyHR/NzcCuXUBVFZunqakB7r2XGqr3BEgUIXo8U6ZMwalTp7B7924cOXIEhw8fxqRJk/w9LKd9RgAmpEgRzKKIM6fIihUrMHbsWMTFxeGqq65yuK1arYZarUZ7e7vNfe46Rc6eFd7X0mL93Z9Okfh4p4dwiFhA6mlOEX58FiDvBOXmm1nMOUEQBEH0FJYsWYIxY8YgOzsbOdnZSG5qAmcyYfy4cR6bsNJoNHj++ecF/b2KioowcuRIiyhiJmvFCiAAzrsJgiB6E6dPnxYsv/zyy7j77rs9d4D9+4Fff8W63btRXF6OKq0WVUlJWLp0KcaNG+e547hJZ2cn/vSnP2Ho0KEYPnw4srKyoKQZbMIBjkQRANiwwXb77hauEv4nAKa9CML79O3bF3379sXVV1/t76G4RFpaGvr164eTJ08K1gezKOLMKZKSkoJbbrnFpf1VVVXZrBe/Ro5EEbmN1u2JIqGhzkURnc7x34y/b5PJO04RPj2tp4g4PktOZHoguGEIgiAIwmN0dGC6QoHp8fFAYSHw88/W+zQaVmGxdKm8L0kHaDQapKWlCSbdiouLMWTIEJwVVZdkDRzY7eMRBEEQEhw9CjQ2Aq2t7DZsGGuWAKC0tFSwaVpamluH+Oyzz/D444+jf//+yEhNxehRo3D9TTexysGyMjz13XfYceaMZfu8vLyAEEUKCwvxzDPPWJbDwsJw5swZmx4rBGHmjjuY0FFby37yY7ZDQ4HwcPbvZqaykkSRnkAATHsRBGEPjuNwySWX2DRo78k9RVxtxB4VFSUpiogn1B3FZ3XNtTvF3tg0GudjjojgoNVq0cK3nvDgiyIGAzuv5eNpUUSMWBQJhLeYa/FZwq+zsDAgK4vNCUmhVAIKhWfGSRAEQRABgVIJbNkCSHxPorUVqKjwmEUyMzNTIIoUFRVBrVbDYDDg1KlTKCwsxIkTJ5Cdne2R4xEEQRAivv8ebVVVqGtpQX1rK+pqa6EfMQIZGRl47LHHUFJSgtLSUpSWliIrK8utQxw7dgxHjhzBkSNHAACzMjNxfUWFJU4hVnSNKXVd7g8OHDggWNapVNB//TUQEcFuEyawnwTRRXg4u6WmSt8fFycURdavZ71JgrhemQCJIgQR8EiJIsGMM9HDHVFEClfis+rrgd27gYwMx8eS6xTRaGxFjfBwQKORFkUUCoVAtBC7RNRqwMWXRWKMrokigegUcRyfZWuHvu464PXXAal+s4Eg+hAEQRCER+E4ViV8/rz0/QkJHovQuvbaazFmzBhkZmaif//+GDJkCABW0DBgwAAMGDAAl112mUeORRAEQUig0eCOdevw5r59bPmVV3DXXXfhn//8J1auXOmRQxQXFwuWM6KjBfnSsaJI8EARRfbv3y9YHp6QABQVWVeMHevbARFBz8CB7DRKr2eCSHU18OKLwPDhwMSJgJPaXyJACYBpL4IgHHHxxRfbrDtv72LXRxw7Buzdy1IYLrrINXXcUWN5wL7wYA97oogr8VkA8PXXzoUAtVoNjUZjmZw3I3aKxMcDPBcxALMoIl2dqdPpwPEmKaSis7o7hxGMThF78VlSThFxfBbA/p7jxgFffWW770B4fgRBEAThcRyJIt1tUGamrg63jRzJqkmqq21zJgiCIAjvo9FAJ7peqq+v9+ghSkpKBMsZ/PipAQMw5NJLMUWjQVx8PGKTkzF+/HiPHt9dcnJycNlll2H//v0oKyvDcPF3lJM5CaL30dnpuC/phAns59Gj1nWtrcCvvzJhhESR4IREEYIIcOLj45GSkiLIaJ4yZYrfxlNVBXz8Mfv9+HH2xSGh29hFrVZDqVRKVvsDrjtFoqOjJde74hQxI5U2IUan06GiokKwTqsVfgMmJNiKImFhgNZOprZYCBLt3iNZlcHoFLEXnyWn0boZe+IH2VwJgiCIHkl2NhAZiXd27EBkYiIGjx+PzH79oKyq8twkUGUl8M031mWtljVE85ALhSAIgpBBYiKiRJP9nhZF/vrXvyI/Px8lxcUo+eorjOnTx3rn9Om4b8kS3OfRI3qG5cuXY/ny5QCA8zt3AqtWWe8MDQ2Mi13Cr9TXA/v3szqS8nI2X/T73zt/3K5dwuXUVCA52RsjJHwBfRIQRBDw1ltv4corr0RLSwsmTZqEiy66yG9j2blTuLx5s2uiCMAm6A0Gg+R9norPEvddcdRTxBUiIyOdiiJShZjt7fJFEU83WQeciyIqlUa03P1jdpfuxmcB9p8HOUUIgiCIHsnw4TANG4Z7r79eEGPy008/SbqP3SImRrjc0gI0N7MKkJ07WZVJTIz1FggnFQRBED2NK66A7sQJYO1aAIBKpYLJZBJs0tTUhI8//hg1NTWora1FTU0NnnvuOYSJYq/sMW7cOGvj9D//mc0k19SwW1dT90AnMTcXWLqUNe5sbCQBnwDATl02bBCua25mmpkjJk5kpzUnTrB6kDFjvDdGwvvQGSpBBAGzZs1CSUkJysrKkJeXZ1NB70tqa7u/D2+LIiEhIYI4KkCeU8Qe/OJHqfGFhAjXSfVsU6vliyLiAh/x3IM79Kb4rEmTrOvtPY9AeH4EQRAE4Q0+/vhjm1z3vn37eu4AUVGAQiGsOKmpsYoi/OqOq64Chg3z3LEJgiAICzfddBOuu+46REVFQavV2lwDt7S0YMWKFYJ1Dz30kGxRRIBSyRoq8CO0goHISJZvRBA8YmPZW5o/T3T+PNCvn+PHZWWxW00NsG8fkJfn1WESXoZEEYIIEpKSkpCUlOTvYUgmLzjLXxTjaILeU6KImO6IIh0d1kl0qZ4nYqeIQsHSK44ft64bPhwIDZUniojn/O20InGJYIzPcsUpkpSkREUFO7nhV2uQKEIQBEH0NuIlLKsZGRmeO4BCAeTksJO/mBhAr0dHeDgO7t6NrLIy6Phfsp6o7CAIgiAk0el0Dq+fpa6Va2pqkJqa6s1hEUTAo1Sy2POyMuu6//0PyMwE5sxxrv3p9a4nphCBRwBMexEEEUxIiSKNjYArWoa3RRGphubdic9qa7NOoks1ig8JsRVFZsxg/VdqaoDx41nOpFyniHjO3xXByR7BKIrI7SnCcRxuvVWBhgbm0uHPxZAoQhAEQfQ2pk2bhgEDBuDEiRMAgNtvvx1KT5xM8Fm0SLBYfPw4RnVVJSSEhyMrJgY/LVsGLYkiBEEQPuWGG26AwWBAWlwc0iSuXWs9Ef1AED2AvDwgOlrYPL2oKDDmQgjfQH9qgiBcQmpev6HBc6KIlBPDEXJFke44Rdrbrb8rFAqb+8XxWUolkJgI3HmnMLI0mESRQBAN5MZnqVQqqFTS1Rz2Tmio0TpBEATRU1GpVNi+fTveeustxMXFYdmyZV45zieffILPPvsMRUVF2Lt3r2X9hcZGdJpM0EZEsEgtgiAIwmd8++23uHDhgmVZpVBgWr9+0MfEIHrECMR6qxcIP3OaIIKAyZOBU6eEoohKJR2HTvRMSBQhCMIlRL3bADBRxBXsTdBrtVqbBunO8EV8VtdcPADYNK8DAI0mSiScsJ/ic8JgEkUCoTpCbnyWo+pXcooQBEEQvZHYs2fxwMKFLBvCSxw6dAiffvqp5H1ZQ4YA995LE2QEQRA+pKWlRSCIAMC+W2/F4IQEYOhQ4OqrZe3n22+/RXJyMoYNG2bTp8TCxo0wFRTgo61bUVVbi6q4OFRFRuKhhx5CSkpKd5+KWzQ1NWH58uUYPny45danTx/7z4Ho9YiNU3o9nbr0JgJg2osgiGBCKobKU6KIq9FZgO/is6z7sd2RWh0pKYqIIVHENeTGZ5EoQhAEQRA8Tp4EPv9cuO622wAP96bLzMy0e19WVhZ92RIEQXgTg4F93re0AK2tAMfhjIQQnma+xpZ5rW0ymXDrrbeitLQUaWlpmDNnDv5w/fXIYk0yrRvW14MrL8ctq1ahkXcxvHjxYr+JIocPH8bq1auxevVqACxmuaGuDuEuplEQvYehQ62N02tqSBDpbQTAtBdBEMGEN0URV6OzACA6Otpmna+dIiaTcFLe3hx9YIsiQiEpEOYxXInPsgeJIgRBEESvo7zcdp0bhSfOEIsiCoUChw8fRlFREeLi4jx+PIIgCIJHRQWwejXaOjtR39qKOo7DyZkz8eKLL6KyshKnd+xAZXk5KyJsbQUkigmlOHDgAEpLSwEApaWlePXVV3EHAHz/PcvS1uuBxYst8YixYWForKuzPL6qqsrTz1Q2+/fvFyznDBiA8H/+k2VbJyWx2/DhgVEBSAQEHMf65oaHA6mp/h4N4Wvok4AgCJcIBqeIlD3WUz1FpEQR8WviqlOE/7yNRtuIMn84RQKhQoLiswiCIAjCDbKzge++sy7HxHilt0dubi7uu+8+ZGZmWm4ZGRnIzc31+LEIgiAIEVotTCYTwp95Bh3mC9LnnsPevXsxYsQI4bYtLfYvUkV89dVXguX+6ekYFB9v3U9ZGXOMdF1fxoaG4nSAiiLDBgxgF/NnzrCbWg2MHOmfwREEEXCQKEIQhEt4UxSJcKOjlZQo0tzcbLPOU/FZ4eHhTvfdnfgsKfHGE6KIvWNbjxF4ncc9EZ+lUDCBRyw0UaN1giAIoscSEwNMnAhs28a+8C691CuHiS8rw/MzZwLV1UBhIZCRERhVFQRBEL0BjQYcx0Gn0aCad/1bxxMoLDi5FuSj0+mQkZGBkpISAMDciy4SFh1GRjJxYcAAIDwco/PzEXn6NGLj4xGblOQwWtHbXHHFFVCpVNi/fz/279+P4eLS/8RE2eIQQRA9HxJFCIJwiWAQRVpaWmzWeSo+649//CPWrFljWV68eLHNvgNRFHHFKTJkSPeP5wnsOUVcic/iOHbOzv8bAuQUIQiCIHo4l14KTJrEIkIkeq15hF27gK6IFQBAZSUL5iYIgiC8j1YLxMdDFx4uEEXq6+ut2+zdyzpJt7Sw2/jxQJ8+Dnf7u9/9DnfffTfy8/Px1VdfYUZKClBcbK0y0+vZz8REIDERr69a5eEn5j5XXHEFrrjiCgCsF2jbJ58ABQXWDTzcW4sgiOCGRBGCIFwi0EQRKaFByikiFhuk3AP24MdnjR49Gn/4wx/w73//GwMGDMBjj/0Jn3wi3L47PUX8LYpMngxMmdL943kCez1FXHGKACSKEARBEL0UCXerR4mJEYoi333HTq5iYoC4OCA21rvHJwiC6M2EhQF33IGoN95gojTYdZHgWnjHDhQXFGDTyZOobWlBzfffI2bgQNxzzz0Od81xHPLy8pCXl8dWdHYCdXWsE3WQOC0UCgW08+axPlvmW0aGv4dFEEQAQaIIQRAuISUkNDUBHR3y+5V5UhSR6h8i5RQRizmhoWzccuBPqHMchxdffBEvvvii5H6B4HWKJCQAl1zS/WN5CnvxWWKniBxRRM46giAIgiBcwFwtzOf779nPlBRg5UrfjocgCKIX8tVXX0GtVkOn0yE0NFR4fazVYseZM1jx5ZeWVXl5eU5FERuUSiZ4x8R4aNQ+IjwcyMxkN4IgCBEkihAE4RL2enMYDEB0tLx9eFIUkUKOU8RdUcRMSwuwdav0PrrTaN2fokigFf3IbbTuKD6L3W+7jkQRgiAIgugmqalAXh4O1NQABw8iU6dDhLmgIdgmzgiCIIKUtLQ0+3eGhkIvugatra317oAIgiCCBBJFCIJwCXuiiKh43yH2Juj5jonuIJ40Z+uEy2FhQFWVvP3x47PMfPYZcOKE9PauxGfFx6cgOzvb7jgd7c8VnIkiKpUm4EWR7sRnyVlHEARBEIQLZGUBWVl4ZM4crFu3DgCgVanwymWX4eZp0/w7NoIgiF5GfX09LrnkEqSlpaGvToe0mBjcmpsLfUqKYLuamho/jZAgCCKwIFGEIAiXsCeKuNLI3NtOESmk4rPkInaKdHbaF0QA+06RWIls7bvvfkngdJDqfSKREOYycpwinhBfPImUKGIymVxqtA5ICyCiZC6CIAiCINygurraIogAQEtHB5oVCnKKEARB+JjS0lLs2rULu3btsqz77R//iITWVozKzoa+b19ER0dDr9fDaDRC4Y2KOJPJMxevBEEQPoBEEYIgXMITTpGwsDDJ9d4URaTis+QiFkUk0rkE2Du/nDVrFqKjk1FbWwYAmD79KUybdq3DcSqVvhFFFAp1wDlFxD1FAGDz5s0uO0Wk7ianCEEQBEF0n3CJZu7Zv/0tMGSIH0ZDEATReyktLRUsx4eFIVStRn+9Hrs//hgYMcLuY00mEy666CKMGjUKc+fOxZQpU6CWc8H0xReoO3UKX+/ejaraWlT17YsalQovv/yyZO9Pb7Jw4ULExsYiKysLWVlZmD59OqKionw6BoIgggsSRQiCcIlAdIro9XqBDXjUqFE223hSFHHWi8TeHL1Op8Ozzx7AunVfITY2G337TrYZl5Qo4gkciSIKhRocxwWcKCJ2igDAtGnT8OijjwrWORNFpCBRhCAIgiC6j0ajwbx58/BlVxPftLQ0XHzxxVQpTBAE4QvKylgmdGsrTm/cKLgrjS8I8HpYSrF3715s2bIFW7ZswT/+8Q9ERUWhoKAAiaGhQGMjoNcDUv0xKypQXVKCpR98IFj99NNPeywaWw4GgwGrV68WrDu8Yweixo6l7yOCIOxCoghBEC4RiKLIO++8g3nz5lmWX3jhBZttuhOfJe4p4q5TBAD0+niMGHGTZdlXokhUVBQiIyPR0NBgc59SGeLRY3kKeyfSn3/+uWDZWXyWFCSKEARBEIRneOedd/C3v/0NdXV1uO++++RVFxMEQRDdZ8cO4MABtHd2Iq+iAvddcw1OAdCUlSE9MhLQaIDWVsCJY2Lr1q2C5YSEBCQmJgJbtgA//cRWhoYCgwcDV1xh3TAsDLESKRBVVVU+FUWKi4tt1vX/8kvg55+BpCR2mzEj8C54CYLwKySKEAThEoEoisydOxerV6/Gpk2bMGvWLEyTaO4p1WhdLp6KzwJsz8PEr6e3RBGlUolnnnkGd911l8R9TBQJNKeIXq/HlClTsGXLFsH6E6KGLuQUIQiCIAj/ER0djb/97W/+HgZBEETvQ6PBBwcPYukXX1hWDRs2DPv377du09rq9OLn1KlTguXRo0ezX/hN2ZubJSsNI0NCoFIo0MG7r6qqCv369XPlmXSLwsJCwXJqYiJC1WoW8VBcDFy4AFx6qc/GQxBEcECiCEEQLmEySa/3hCjSnWqSBQsWYMGCBXbv92V8liuiiK+cIgBw5513oqKiAk888YRgfUsLO9kNNFEEAL788kukpqaisbHRsq5dZN1xJopIvWdJFCEIgiAID1BRAezfD1RXs8kzrRa44QZ/j4ogCKJ3oNEgQtSHsb6+3mYbZ4wbNw7Lly/H6dOncfr0aQwYMIDdwRdFABajxWfkSHCZmZj07beAUonYhATEJiZC5ySuy9Pk5ubi6aefRmFhIYqKipAo3iApyafjIQgiOCBRhCAIlwhEp4gcfBWfxXGBK4oAwMMPP2wjinjrWJ4gOjoar732GpYuXWp3G3fisyhaliAIgiA8QH098Msv1mWNhlUj0BctQRCE94mKQlSiUAKoq6uzLjQ3M+G6pYX93tICXHmlzQXrokWLsGjRItv9d3ayz3NzlZlYFMnIAABs2rmzm0+ke+Tm5iI3N9e64uOPgWPHrMvJyb4fFEEQAQ+JIgRBuESwiiJSYkP//sxN6wxXnCLO3Bbi+30timi1WmRmZqKoqMjp2AIFZ9Zrd+KzCIIgCILwADExwuXWVqC21nbijCAIgvA8o0dDp1AA//ynZVVTUxNMJhM4jmMXst9/jx1nziC/ogI1zc2o3bkTk6dPx6xZs5zv/6abgI4OoK6OfbYn2ngwApNFi9h4y8vZrX9/f4+IIIgAhEQRgiBcoieJIldfzfrGtbWxHiO7dkk/1pWeIs6EBX87RQDg/fffx4QJEyzLublXAwhcUSSjqwLJHs6cIvYi3wiCIAiC6CZSzXsbGkgUIQiC8BGDBg3CsWPHoNPpEBUVhdDQUCaIAJZ4hP/s2oX3Dh5k6374Afd2dMgTRQBApQJiY9ktWOA49j2k1wN8BwlBEASPAJ0CIwgiUOlJokhEBDB/PrBwITBokOPH8iO0HIkizkQMfzVa5zN+/HjcddeLiIzsg7S0SZg583kAgSuKJCUlQeMgC5ecIgRBEAThJxQKYMQI63JCApCW5r/xEARB9DK0Wi1ycnKQnJyMsLAwqyACsEaKCgWitVrBY2pra307SIIgiACEnCIEQbiEPVGko0P+PuyJImFhYW6MSB7icYsFAGeFL01N1mLI7sRnBYJTBACWLv0DYmL+4JNjdReFQoF+/fqhoKBA8n5nooiP+/wRBEEQRO9izhwgNZVFZ40YQf1ECIIgfMz69evx6KOPom/fvkhTqzE4OxsrbriBXcAOGAB9WhrA6/tRI26gThAE0QshUYQgCJfwplNE4SWrgslkO27xPHpkpON98EWRYI/PAqTHGahOEQAORRFn8VkXXQQcOmRdHj/ekyMjCIIgiF6OUgmMGuXvURAEQfRaCgoKsGvXLuzqyoOemJaGFWaB+sYb0b+tDaNOnoRer4der8d4uiAiCIIgUYQgCNew15/BFVHEURSSN5Aam1hs4DgWoZWfL72Pxkbr746cImq147EEiigitd9AFkUc9RVx5hSJiwOuugrYvZv9ftFFnh4dQRAEQRAEQRCEfygtLRUsp/Gt8lFRWL58OZYvXy752NWrV2Pz5s1IS0tD3759MWTIEAxylC3Np7MT+OADnDl7Fj8fOYLKmhpUDRqEsNhYPPTQQ+4+HZd455138OGHHyIrKwuZmZkYO3YsJk+e7JNjEwQR3JAoQhCES3jCKcL5OFZBasxSAsBllzFRw2AAioqE95mFEJPJsVPEWVST+LiBJIoEanwW0D1RBACGDWM3giAIgiAIgiCIHoHRCBQVofTwYcHqvuaIA45zGonwww8/4PXXX7csr7z5Zrw+eTKQkgLk5LCf9qrnFArg9GnsOXwYSz/5hK375hukp6f7TBTZtWsX1q9fj/Xr1wMAll19NSaPGmVpMk8QBGEPEkUIgnAJT4givkaOUwRg54tXXcV+/9//gJMnrfeZRZG2NsfP1ZkoEgiN1u3tN5CdIkOHDrV7n7P4LIIgCIIgCIIgiB7JBx/gruRkXHTFFThdV4fS5maMz8sDNBp2c3JBKXaZ9NXrgZISdtu6FQgLA26/HYiIsH0wxwGhoYgV9Qatqqrq9tOSS2FhoWA5s7oaePZZIDoa6NMHWLiQel0RBCEJzSQRBOESPVkU4SPu+W4WRRy5RADXRZFAcooEsigyc+ZMDBs2DAcOHLC5T45ThCAIgiAIgiAIokehUAAhIZiYloaJaWls3c03A6mp7Pf2dqe7OH36tGC5r/jaSqsFwsPt7yAsDLEiV4bBYEBbWxtCQkKcHr+7iEWRrJgY9kttLYuBIEGEIAg7kChCEIRLBKMoIjc+i09PF0Wknn8gawtKpRKffPIJhg8fjpaWFsF95BQhCIIgCIIgCKJXotGwOAMz/GslZw0vASxZsgQFBQU4ffo0Tp8+jYz2drZPMzk5joWFqVMRP3Aghv70E2JiYhCTkICY+HifiSJ//etfUVBQgMLCQhTu3IncuDjrnUlJXj8+QRDBC80kEQThEsEoinjSKeKoyToQPKJIsDlFACAnJwejRo3CL7/8IlhPThGCIAiCIAiCIHolsbHMzWGOy+ILGgBw/jxQXMzEkuZmICoKmDTJcvfDDz8s3P7MGaCggN0uXGCiiCPy8hCXl4cDx4556Am5xsKFC60La9YAhw5ZL6pJFCEIwgEkihAE4RK9XRSprHT8uGButB7ooggADBkyhEQRgiAIgiAIgiAIALjhBsf3nzkDfP89viwoQGVTE2o1GtR89x1WrlyJvn372m6fmspuM2YANTVMRAkWrrwSmDuXXbSXl7Mm8QRBEHYgUYQgCJcIRlFEaszOokWlRBGjEfj1V8ePC+ZG68GgLQwePNhmHcVnEQRBEARBEARBSKDVAgBWfPklKnmxB9OmTZMWRfjo9d4cmXdQKoHERHYjCIJwQBDUBRMEEUiYTNLrOzq6t19vVvtLCQ3ORBFxL7mmJuDYMaC62rXHiaH4rO4hJYqY7L0pCYIgCIIgCIIgejNdTdD1XeKImZqaGn+MhiAIImAIgikwgiACCU85RWbPni1YfvTRR90ckXPcERqknCJHjjh/nDNhIVBEEalxBqsoUlxc7IeREARBEARBEARBBAhNTcDmzcD+/UBJCVBVxSoaIyOB/v2hj4kRbF5bW+uXYRIEQQQKlDlCEIRLeEoUeeKJJ7B9+3bU1dUhIyMDd9xxR/cHZwfxmOVM/otFEaORnVd2l0ARRaT268w9EwjExsbarDvmp6Z+BEEQBEEQBEEQAUFVFbBhg3VZqQQefRSIjweWLUPepk3ojIqCXq+HXq93Hp1FEATRwyFRhCAIl/CUKDJmzBjk5+fjxIkTGDlyJCIjI7s/ODt4wikCAHV13R9LIDdaD1YyMzP9PQSCIAiCIAiCIAj/UV8vXNbpBFVvb7/9ts1DHnnkETQ0NKBvWhrSIiIwee5cpKSmunbcqirg669xqLAQ+4uKUG0woGbkSOTl5WHhwoXuPBNZGI1GDB06FGlpacjKykJWVhaWLVsGfTD2QSEIwi+QKEIQhEt4stF6nz590KdPn+4NSAbuCA1qNbu1t1vXNTc7fkxCgvP9BnKj9WDhH//4B37/+99blu+66y7/DYYgCIIgCIIgCMJfNDQA588D27cL10dFOX3ohx9+iJMnT1qWV+/YgQXXXAPk5AADBsiPEigpwQcbN+LZX35hy199heuuu86rosjZs2dx5MgRHOFlXC+6+GLW5DMkxGvHJQii50CiCEEQLuFJUcRXuBOfBTC3iCN3SG4ucPSodfnyy53vUyo+y2Synm/6UxQJln7lt956K06fPo2tW7di7ty5mDdvnr+HRBAEQRAEQRAE4XtOnAC+/FK4TqNxKop0dnbi7NmzgnVpISHAnj3AmTNAdra843c1co/p+mnG243cCwsLBcvhajUSV69mF9aRkcCKFUB0tFfHQBBEcEOiCEEQLhGMooi7QkNoqGNRZORIoH9/4PRpVkyTnu58n+Jjm0z+EUWCoam6PbRaLV588UV/D4MgCIIgCIIgCMK/aDTC5bg44M47nV6gnz9/Hu38WAQAfc1CSnKy/ONrtQDH2Ygi1dXV8vfhBmJRJCsmBpz5otpgACIivHp8giCCHxJFCIKQjSMnQU8URdRq5/ePGcNucpE6dmenVaQgpwhBEARBEARBEAQhC7Eo0trKfjq5kFSr1Xj00UdRWlqK07t2ofzCBSSEh7M7dTr5x1cogMsuQ2pcHIadOAG9Xo+YxETk5uW58CRc59JLL8X777+PwsJCFO3ZgzS+M0WnA1Q03UkQhGPoU4IgCNnYc4kAgS2KuBuf5ew8yp3zLKljd3ZaBRjqKUIQBEEQBEEQBEHIIjSUxUVpNMy1IeWQKCwEKiqAlhbWKDM7G/FZWXjyySfZ/XV1QHU1+1lfLy8Cgc/YsZg9dixm33FH95+PTNLT05FuHufBg8CPP1qbzcfG+mwcBEEELySKEAQhm2AVRdwVGrwhithzikj9bm97T0BOEYIgCIIgCIIgiCAnJQW4917H2+zZg5q9e7Hp5EnUtLSgNiUFrWlpePjhh9n9UVGyGrMHLEOHslt7O1BT43jigiAIogsSRQiCkI2jc4uODt+Nw1W8GZ/lKlLH5r+uvhJFzHGrBEEQBEEQBEEQRA9Gq0VxTQ2uXrXKskqtVuOhhx6y9uHoCajVQEKCv0dBEESQEMStdgmC8DU9xSniz/isQHGKSOFKdCxBEARBEARBEAQRBISGIlqrFaxqb29HU1OTnwZEEAThf8gpQhCEbIJVFBGP21NOEU+JInyXjS9FkcGDgcOH2e+RkcCAAd47FkEQBEEQBEEQBOEFSkuB4mJrDJZeD0RHW++Pj4d+4ECbh9XU1CDc3FydIAiil0GiCEEQsglWUcRbPUXcjc/SalmPOzN1dUBcHPvdl6LI/PnsuC0twPjx8h00BEEQBEEQBEEQRIBQUgJs3GhdzsoCli61Lo8YgaihQ5H50kuIjo6GXq+HXq/vWdFZBEEQLkKiCEEQsnEkihiNrFF3IJ5XBVKjdYAV7pSVWZdra62/+1IUUauBadO8t3+CIAiCIAiCIAjCy9TVCZclmqYrlUoUFhYCAJqbm7F48WI8++yz6JuairT0dMybNw+hoaHuHb+4GNiyBZsPHsSpsjJUcxyqs7Iwb948jBo1yr19OuDJJ5/EBx98gKysLGRmZmLmzJmYM2eOx49DEETPhkQRgiBkYzI5vr+z032hwJt4o9G6QuG+syI6WiiK1NSwnyaTf3uKEARBEARBEARBEEHGiRPC5fZ2h5ufOXMGa9euFayre+EFhCYlAUOHAkOGuHb8lhagpAR//PRT/FJaalmdlJTkFVEkPz8fBQUFKCgoAAAoamsxZ+xYVn3oTpwDQRC9kgCcviQIIlBx5BQBep4o4ui5dOdcS68XLpudIlKvL4kiBEEQBEEQBEEQhF0SEoD6euty//4ONz99+rRgOVqrha6hAWhoANLTXT9+l8NEL3KaVFdXu74vGZwQiUBZFRXAf/7DFsaPB2bP9spxCYLoWVCCPEEQspEjigQi3hBFuiP+8HveAVZRROr1I1GEIAiCIAiCIAiCsMuECdaqvaQkYPBgh5uX8twcAJCm01kXJKK3nBIWBgCIEYkiNeZIBA/S2dmJ/Px8wbrs2FjrAjWOJwhCJgFY000QRKAiRxTp7ASam1mxSKBM6HsjPqs7oojYKVJTAxw7BlRW2m4bKK8hQRAEQRAEQRAEEYBkZgJ33sl6i6SkOL2IHDp0KB599FGUlpbi9ObNGMC/QHVHFImKAmbNQuaFCxjW2oqYmBjoExMx2Ik44w4cx2HPnj04cOAADh48iANr1mBYUpJ1g5gYjx+TIIieCYkiBEHIxpkoUlcHfPgh65eRlAQsXQpERPhmbI4ItPgssVOksRH4+GPpbUkUIQiCIAiCIAiCIBwSFWVf0DCZgP37We+P5maMbGnByAceYBfrNTXsQr6ujkVw8V0XctFqgQkT8KcJE/Cnbj0J5ygUCuTm5iI3NxfXLVoEZGez6kKDgW1AoghBEDIhUYQgCNk4E0V27rQ2EC8vZ+ddkyd7fVhOEY/b304RsSjiiEDs0UIQBEEQBEEQBEEECRwHrFuHwgsXcOj8edS0tKBm+3b0Gz4cCxYsCF4hgeOAG25gv7e1MYHHHVGHIIheCU23EQQhG2eiyMGDwuUffwwMUSTQeoqo1awox1zM4ghyihAEQRAEQRAEQRDdIjQUnxw+jEc3bmTLX36JuXPnMlGkJxASAiQm+nsUBEEEEdRonSAI2TgTRQIVsSiikPnJ5634LEC+W0TuWAmCIAiCIAiCIAhCEq0W0VqtYJU3GqETBEEECzTdRhCEbEwmf4/APQKt0TogTxRRKpkjmCAIgiAIgiAIgiDcJjUV+vR0wSoSRQiC6M1QfBZBELLpKU4Rf8dnAUB4uPNtIiO7dwyCIAiCIAiCIAiCwPz5SIyIQOYPPyA6Ohp6vR5ZWVn+HpVLNDQ0QKFQIFzOxTRBEIQTyClCEIRsepso4sgp0t34rLAw59uMGNG9YxAEQRAEQRAEQRAEAMyYMQNvv/02UlNTkZubi8x+/fD11197Zuc7d6Ljrbfw9R134N3f/Ab/uP12PP744x51o7zzzjuIjIxEdnY2Fi5ciLfeestj+yYIovdBThGCIGTT20QRbzpFnIkiGRnAlCndOwZBEARBEARBEARBmDl06BDWrl1rWR6floY5p08DUVHAjBlAUpJ7O66tBXfqFOb+5z+C1QsXLoRer+/OkC0cOHAAJpMJJ06cwIkTJxDW2IgVkycDMTGAXt/9i3SCIHoV9IlBEIRsXBVF5IoP3iYQRZHQUPv3DRgAXHUVNVknCIIgCIIgCIIgPMfp06cFy311OuDCBXa7+GL3d6zVQqlQIEqjQV1rq2V1dXW1+/sUceDAAcHy0I4O4KOP2ALHAQ884PhCmyAIggeJIgRByMZVUUSucNDYCGzcCLS0MHdEYqLrY3NEIDZat+cUycoClizp3r4JgiAIgiAIgiAIQoxYFEnT6awLUVHu77hLjIgJDRWIIp6KzzIajTh69Khg3TC+q0WjAbRajxyLIIjeAYkiBBEkVFYCdXVAerr/XKHeEkW+/BIoKGC/FxUB993nWZeJN5wi3uopQoUtBEEQBEEQBEEQhDf4zW9+gwEDBqD02DGc3r0bw8wViWp19y5G09OB2bMxaMMG6CoqEBMbi5ikJMTFxXlk3AqFAhcuXMCRI0dw8OBBHFi/HsP5okhMDHOLEARByIREEYIIAo4eBT79lIkSiYnALbf4J5rKZHJtezmiiMlkFUQAoLkZOHECGDjQtWM5wl1RRKlkEVZSYpC3nCJU3EIQBEEQBEEQBEF4lIYGoLAQc+PiMHf6dOCii4D/+z9WeVlXB7S1dU9USEgAEhLw9ebNnhuziNDQUIwePRqjR48GxowBdu4EqqtZ9ERMjNeOSxBEz4REEYIIAr7+2joxf/48cOgQMHy478fhqlNETk+M9nbbdS0trh3HGe6KIgATP9rapNd3B3tFOOQUIQiCIAiCIAiCIDxKVRXAa7CO0FDWWD1YxYQhQ9gNAFpbpScWCIIgHEBtfAkiCGhsFC4fOuSfcbgqiojFCCmkBBCpaKqqKqCwUFqgcIZ43K6KIlJ0Nz5LrQZCQmzXk1OEIAiCIAiCIAiC8CjiC82WFtejIAIVjQaIiPD3KAiCCDLIKUIQQYir4oS/jutIFDlzhu1Po7G9Tyw4FBQAq1ax/cXFAbfe6poo0R2niL3jeKKvS2iorchDThGCIAiCIAiCIAjCo4gvNE0m5rCgqjyCIHopJIoQRBDir4IOT4kiP/4IbN3Kfo+Ntb1f/Py++ca6r8pKID8fGDbM/XHIifUyY0/88IQoEhbG4lv50DkpQRAEQRAEQRAE4VFCQ4E+fdgFp1ZL1XgEQfR6SBQhiCAkmJ0iHR1WQQRgsVjOHicWDvbv754oEgjxWYB0s3U6NyUIgiAIgiAIgiA8SkgIcMst/h6FW5w/fx4XLlzAwIEDofbEhThBEARIFCGIoCRYRJGODtt1zc3OH+esF4lULw57mEyBHZ8lhpwiBEEQBEEQBEEQhNf4//buPEyy864P/fed0ez7SDMayZIsWbKwdluWLNuyjbwb4g1sAlwItoFAIOCwOXnuJbkIHnwTrm+AkOAQh2AIBAcMGJvF2GDhfceyLQl5kSxL1jZaZqQZaTSjWc7943S7q09XV1d3V3VV9/l8nqee7nPqnFO/bk2/6j7f+r3viRP1PM6D/uPzr/86D37ta/n4TTdl/8MPZ9/ZZ6fatSs//dM/vajL/vEf/3He+MY3Zu3atbnwwgvzqle9Ktdee+1gagZaSygCy9ByCUWqqj6nc7qqbkFJU2eI0W1h9fmGIs3puAbRKTKIUKRbHTpFAAAAGJoHH0x+8zfrBT63basf3/u985tnupu77spN//APeeXv/u43d23fvn3RocgXv/jFJMnjjz+ez3/+87n0iU9MPvWpZOfO+rF9+/z+yAeIUASWpVGFIgtZy+T48em/W3ULObqdM+nRR2c+v5hF1pPBdIoMomu3lJn7hCIAAAAMzeT81EeOJPfdV//RvdhAJEk2bMiORvfJQw89lOPHj2f1IkKLL3zhC9O2L9u6NXnve6d2XH118uIXL/j6QDsJRRi4v/7r5JOfrN9J/7rXjbqalWm5dIokdWdIZ4Bw9Ojc53QGGY88MvP5bmFCP9eaNC6dIt1+7zRFKgAAAEPTXLRz27bBXHfDhuzs8i6/hx9+ODt37lzQJauqysGDB6ftu3THjukHnXzygq4NtJtQhIG77bb6jQbr1o26kpVrXEKRtWvn7vxohhKD6BTpJ1iZ7fWT8QlFugUg8wl8AAAAYF6GFYpcckl2nnJKznzHO7Jz+/bsPOWU7Dz11Byfa9HQHkopufnmm7N///7ccMMN+cL11+fpe/dOP0goAiyAUIShOXKk7hIYxM1jphuXUGTTptGEIv1cY7bXT8Zn+qwrrqinQp10zjmLvyYAAADMcO+9yT331H/MXnJJ/U7WM85INm8ezPXPPz8bzj8/d9xzz2Cu12HHjh153vOel+c94xnJddfVX8vevfWNp1NPHfjrASuf29UM1WOPJVu2jLqK5a3bOh7jFIrs39/7nObC6oOYPmspQ5FuoV4pSWOq1AXZtSt5znOSj32s/jl54QsXf00AAACY4cYbk49+dGr7kkuSpz51ZOUsyPr1ybd/e/15VSUHDgzmj3OgdYQiDNxVVyUXXVQvGG0KrcXrFoCMUygyl+U+fVa3jpBzzhnc2h8velHyghfUQYupswAAABiKZnhw+PBo6hiUUgY39RfQOkIRBu6KK+pQhMEY51Bkw4Z6sfBe9Sw2FFlsp0iztvmGD90CmEsu6f/8fnRbcB0AAAAGphmKPPbYaOoAGANCERhz3QKHblNqLUUdN944fd+qVfXvVYcOzX5eMxSZ7/RZg15TZPXq+YUi3QKLCy7o/3wAAAAYuW3bktNPr/+IX7++ns8ZoKWEIjDmxqVT5I//ODl4cPq+VavqbpFeoUhzTZFRT581n6mzkuTss5NPfnJq++KLTVkKAADAMvPkJ9ePZeK2227L5z//+Vx22WU5++yzs8oUC8AACUVgzHVbE2OpQ5EDB5IvfWnm/slQpJdRT5/VfP35/h513nn1dFk33ZTs3p285CXzOx8AAADGwpEj9dQTw3in35EjyZ//eb52++256bbbsv/hh7PvqU/NWeedl+/8zu+c9+X+6q/+Kj/5kz+ZJNmyZUte85rX5O1vf/ugqwZaSigCY24cOkX27eu+f9WqZOPG3ucuZPqsya/v2LHua78dPVr/HtfPNFiL7RQ56aTkNa9JXv3q+uu1GDoAAADL0uc+l7zvfcm6dfV0Wuefn7zoRYO59urVyc035w8//OH8u7//+3rfO96RV7ziFQsKRW644YZvfn7w4MEcu//+5N3vTk49tX7s2TP3uzQBZiEUgTE3DqHIbOuv9dMpspjps7pNnZXUgcixY8maNf1fa9J8Q5HFngcAAABj4eGH649HjiT33VcHC4Ny0knJmjXZ2bhJsH///gVdrjMUSZJLduxIrr9+ascVVyQvf/mCrg0gFGHgPvCB5AtfqNeZOPXU5BWvGHVFy9s4hCLNtUQmDXv6rG5TZ3VeZylDEQAAAFjWJkORSdu2Dfb6GzbMCEX2zTb1xBxOOeWU7Ny585vnX7J16/QDTj11QdcFSIQiDMGXvlSvvcBgdFtTpKr6nz5qEGYLRUqZfyjSz/RZk+ccODD7Mf0uti4UAQAAgMz8I3vQochzn5tT16/PWR//eHZu354du3bl3PPPX9Cl3vOe96Sqqtxzzz254fOfzzM+8pHpBwhFgEUQijBUhw6NuoLlb7aukOPH6+7UpTBbx8aqVTOnx2pazPRZs4Ux/V6n81qThCIAAAC00hveUAcjBw7UXSNnnDHY6195ZZ5/5ZW5/cd/fCCXK6Xk9NNPz+mnnJLs3Jnce2+yd29y//1CEWBRhCIMlVBk8WYLRY4dW7pQpNf0WVXV+9zFTJ8lFAEAAIABOemkOlzYuXPUlczP2rXJM585tX3iRH1DAmCBhCIM3GWXJRdckGzcWD9YnF6hyFLpFYpccEHy4Q/Pfq7pswAAAICBEYgAiyQUYeCe85zkootGXcXK0W1NkWQ8QpHjx+v1Y571rOQTn0i2bq1DnM7ptnSKAAAAAADjQigCY262TpF+OyUW6/jx2adBm+zkeOlLkxe9qF54/U//NLnppqljRr2mSPP7JxQBAAAAgPYSisCYG/X0WbMtsp5Mn95qMmxohg6dnRrHj8/e+dLtnF6hiOmzAAAAYGW544478s53vjOXXHJJLr744px22mkppYy6LGCFEYrAmBt1KNIrmDj55Jn7eoUi8wkyHn88OXx49mNMnwUAAABj5K67ko9+NF/48pdzxz33ZN/Ro9l/8cV53vOel8svv7yvS3z0ox/Nz/3cz31z+9JLL80XvvCFYVUMtJRQBMbcOIciV145c99JjVGls875BBm9XjdZeKeI9dgAAABgCA4fTm6+Of/6D/4g77/11m/ufstb3tJ3KHLjjTdO237Cpk3J29+enHpq/TjrrGTXroGWDbSPUATG3KgXWu8WTlx0UXLZZckpp8x8rlenyCBDEZ0iAAAAMEY2bEiS7Jz4OGnfvn19X+KGG26Ytn3J7t3J7bfXjyS54ork5S9fXJ1A6wlFGLivfCX56lfrxbkPHUouuCC5+upRV7V8jbpT5NFHp29feGHyXd81+/GDmj6rc72SboQiAAAAMEYmQ5H166ftnk8ocumll+a+++7LTTfdlEcffTSXbNs2/YA9exZdJoBQhIHbuze5996pbV2NizNuocjGjb2Pb4YOC5k+68SJ4U2fJRQBAACAIdi0Kbnmmpx+77154t13Z8e2bdm5Z0/OP//8vi/x5je/OW9+85tz4sSJfP2WW7Lzd35n+gGnnjrgooE2EoowcI03BOTQodHUsVLMFor86Z8m112XXHppcs01SSnDef3mf7+5QpHmmiILnT6rGcY06RQBAACAMbJ2bXLNNfn5a67Jz//Wby3qUqtWrcqTzj03+cEfrN95u3dv/RCKAAMgFGHghCKDNduaIkmyf3/yoQ8l55yTnH32cF5/vqHIoKbPmuvfjVAEAAAAVrDVq5Pzz68fAAMkFGHgdu1Kduyop5LcuDHZvn3UFS1vs3WKdPrwh5cuFNm0qffx811o/fnPrxdsf+c7p/ZVVfdpuzpraQYsX/lKctttyXnnJeee2/31u9UHAAAAALSHUISB27MnueiiUVexcvQTigyzG2ex02d1rinSDDLOOiv51m+tO16aHnlk+vbWrbOHIl/9avKHf1h//olPJG94Q/LEJ9bbQhEAAAAAYNKqURcA9NZPKLJu3XBeu6oGO31W82uZPLZbUNFcaH3r1unbnaHIu989/bkPfaj768/2WgAAAABAO+gUgTHXa02RSc11XAblyJGZQcZiQpHm17JqVfdzkpmdItu2Td/u7EBpHvu1r019PlsQAwAAAIyHvXv35tprr80ll1zyzcd287EDQyIUgTE3yk6R5roeyeKmz5pPp0hTr06RplUdPXA6RQAAAGCJfPGLqW66KR+74Ybs378/+7Zsyf7TT8/3fd/3ZdeuXbOe9vnPfz6/9Vu/9c3tzZs35+GHH86qVSa5AQZPKAJjrp9QZFi/IzSnzlqzpn70MqhOkab5hCKdnTNCEQAAAFgiDz6Y8uUv5yVveUse63iX5LOe9ayeocgNN9wwbfuis87Kql/7teTUU5Pdu5MnPSk577yhlQ20i7h1hEopv1tKqRbxuHbUXwPD108o0tmNMUjzXU8kmd+aIoMIRR5/fOaxGzZMfd783niTCQAAAAzJxB/kOzv/ME+yb9++nqc1Q5FLTjutXmz0lluSj388+dKXBlsn0Go6RRiKT3wiuffe5LHH6hvrV1+dXHDBqKtanvpZU6RX18RiDCIU6Wf6rFLqR1XNfs1Nm2Zet6pmLsieTA9FHn549ucAAACAAZr4o3vHhg25q+MP9rlCkRe+8IU5duxYbrzxxtx88825ZMeO6QeceurASwXaSyjCUHz1q9MXu96/f3S1LHfj1CnSDCa6aa4p0s/0WaXUn88WAG3c2H3armPHuocipdQfDx2auS5Kj25dAAAAYDFOOy255pqc9bd/m4MnnZQd27dn55492blzZ8/TfuAHfiA/8AM/kCQ5evhwjv7yL08/YPfuYVUMtJBQZLw8N8md8zj+oSHVsWjNjoLmzXX6N06hSD+dIs3pqTrrn61TZPLz2UKRDRtmhi1J3SFz4ED3/UnywAMzX6/5ZhMAAABgQHbvTnbvzl997GM9D3vwwQezY8eOrgupr1m3Lmve+MbkvvuSvXvrj0IRYICEIuPlzqqqvj7qIgZBKDI4/YQi4zx9Vmf9s3WKdDuv04YN3TtFjh7t3ikyGRLdf//0/SefbE0RAAAAGLXnPve5ueeee/KMZzwjz3jGM/KTP/mT2T0ZfJTyzXAlF1882kKBFUkowlCcc059k3vjxvqGtqkfF66fNUWG1SnSnHpqIZ0ix4/Xa3+UMvtC60nvUGS+02dNhkTNUMTUWQAAADBaX/7yl3PzzTcnSd7//vfn/e9/f974xjeOuCqgTYQiDMUFF1hYfVCW+/RZyeyhSHP6rNls2FBfd9Wq6deYb6eIUAQAAABG613vete07Sc96UnZ5Q92YAmZSAbG3Cinz2qu17HQUGTya1jo9FkbN9ahSnNdkbnWFGmGIqecMvtrAAAAAMP30Y9+dNr2M57xjBFVArSVThEYc6PqFDlyZGbgcPLJc5/XKxRZTKdIUk+h9fjjU/t7dYp0q98bTwAAAGC0/uIv/iJ33nlnPvWpT+XTn/50rrrqqlGXBLSMUATG3KjWFGl2WaxatfhQZDGdIsnMdUVmC0VOnEj275+5v5/6AQAAgEX44AeTe+9NDh9OHnssueaaaXOsl1Jy5pln5swzz8xrX/va6edOvhNy7dolKxdoH6HIePn5UsoFSc5NsjPJY0n2JbklyUeS/GVVVdePsD5GoNldsWXLzCDg2LGpdTsG5b77pm/v3Dlz+qpuuoUbk2HIQhdan+wUab7+vn2zh0bNReLXru2vfgAAAGARvv71+jGp27zXs/niF5O/+qtkx47k1FOTJz0pufLKQVcItJw1RcbLDye5OsmeJGuTbEtyTpIXJ/mlJJ8rpXyglPL00ZVILydOJF/5SvKNbwz2mp2uuir5kR+Zvq+q+usomY+FLlI+jOmzZusUaQY3nZqhyLp1sx8LAAAADMjkOxsnPfZY/+fu3Vvf5Ni3L7n55uT22wdbG0B0iixHL0jy8VLKz1RV9ZuDvngpZXeS+a68cG63nX/3d3VHw6FD9f//Xv7yZM+exdc4jh5+OPnc55Lrr6/fAHHeecn3f/9grt2tu2Lz5pnHHTs22E6IYYQiC50+q3NNkV41dnrkkenbOm8BAABgCSwmFGm++/HUUxdfD0CDUGQ83JLkr5P8Q5IvJ3k4SUkdTlyZ5DuTPLvj+LVJ/ksp5VhVVf9twLX8eJJfGMSFvvCF6dM8HTy4ckORr389+dCHprZvvTV56KFk+/bFX7sZJKxe3T38GPS6Is3fQ3bv7u+8YS+03qvGTjpFAAAAYATOPz/ZurX+Y37Dhv5vKFTVzHc/9nsuwDwIRUbrfUl+q6qqT87y/M1JPpzkP5ZSXprk95J0RuS/WUr5TFVVnxtynQuyceP0UOTQodHVMmwXXpi89731GmJJ/f/xz30uecELZj/n8ceTD3wgueee5OKL6ykyu60J0q1TpFsocvTowutvOnJk5pSf49Ap0vy6J9df60YoAgAAACPwlKfUj/kqJfnpn66Dkb1763dCnn764OsDWq+Va4qUUv5LKaVagse1veqoquodPQKR5rHvS/KsJJ3vjV+d5D8s+BsxZJPrQExayaHImjXJpZdO33f99TMDjU6f/GTyqU8ld9yR/PVfz74OSbdQpNkxkSyuU+Qb30g++tHk3nvr7X37pj9fSnLyyf1dq1sospiF1tetm3qu29c9G6EIAAAALDNr1tRByNOelrz0pcmWLaOuCFiBdIosI1VV3VZK+RdJ/qxj94tLKedVVXXLgF7mrUneOc9zzk3y7ubOCy6op8vauLF+nHnmQOobW5dfnnz601PbBw8mt9xSd412c91107c/9rHkrLNmHtctSCilDgo6Oy8WGorcemvy+79ff/6hDyWve13dKdJp06b+1ysppa6xs+7FTJ/VGa7NJxSxpggAAAAA0CQUWWaqqnpXKeVLSTr7EF+W5L8M6Pr3ZXo3ypxKtzmfkjzjGYOoaPnYsyd5whOSu+6a2nf77bOHIk3793ffP1uQcNJJ00ORhU6f9alPTb/Ge9+bPPOZ049prpE2l9lCkV7TZ3XrMGm+tk4RAAAAAGAx2hqKvDvJnUvwOh8d0nXfl+mhyKWzHcjSOu206aHIbEFFVc3c15xubNJsQcKaNdM7OhbSKVJVyVe+Mn3fXXcln/1sf7XNphlwDKpTpN9ulUQoAgAAAADM1MpQpKqqv03yt6OuYxG+3tjucwlshq15Y78ZaEx67LGZ+2YLHmZbh6MZECwkFLlvlp6g22+fvr2QTpFOs4Uincdt2tT9WgvtFGl+74UiAAAAAEArQ5EVoHlLfZ63rBmWfkORgwdn7psteBhmKHLHHf0dN6hQpNf0WTt2dL/WQtcUabKmCAAAACyB48eTv/zL+h2hjz2WHD6cfN/3JVu3zn7OY4/VN0tOPnn2qSQABkQosjyd0th+YCRVMMNsYUDTgQMz93WbUqvbNTqnz+q0kDVF+g1F5jt91mzhUK/ps2YLRRbaKdKkUwQAAACWwKpVyRe+MP0mwKOP9g5Fbr01+ZM/qW8UnHJKcs45ycteNvxagVaaZWljxtxVje27R1IFMyymU2S2Y5v7Oxda77RSO0U6X3s+a4o0CUUAAABgCZSSrF8/fV+3ecQ77d1bfzx+vP78Ae//BYZHp8gyU0rZneSFjd0fHEEpczp8OPngB+v/7x06VD9e97qVPY1Rv6FIt06R2UKNYU2fdexY8vDD/R07rDVFOr9fW7fW5zWP6Xf6rNWrZ/9+J0IRAAAAWDIbNtQ3giYdPtz7+Oaip7t3D74mgAlCkeXnV5J0TmZ0IGMaiiTJJz85ffvQIaFIMphQZLHTZ83n+PlOn7WQhdZXrUq2b0/27Zt+TL/TZ+3Zk9x11+zPr+R/dwAAADBWrrgiefzxumNkw4bkCU/ofXyzk+TUU4dXG9B6QpERKaX8SJI/qqqqr/fql1JKkl9M8vrGU2+pqurQzDNGb926me/8P3SovvG9Us21pkhVJZ/7XP1omi1AGVanyOOP93/sUkyflXQPRToDmdmmz1q1qn4TSa9QRKcIAAAALJFnPWt+x//gD9bdJPfdV0+f9cQnDqcugAhFRun/SvIrpZR3JHlnko9XVXWkedBEGHJNkv974mOnG5L86nDLXLhS6hvajzwyte/QWMY3gzNXp8g99yR/8Rfdz50t1BjWmiLDDEUWstB6kmza1Pu1Z+sU2blz7tBDKAIAAABj4NOfTr7xjTr4OPPMZNeu+t2O69cnZ51VPwCGSCgyWtuT/NjE41gp5eYkdyZ5OElJckqSy5N0W4L6tiQvG9cukUlPe1p9Q3zDhjog2bVr1BUNztGj9bpfBw7Ua3OcODHzpn0z0Lj99tmvt9hOkXGfPuvEibpTptdxzXXYmq89WyhyyilzL8Ju+iwAAAAYA9dfX79r9IYb6u1XvCJ5+tNHWxPQKkKR8XFSkksmHnP5oyQ/VlXV/uGWtHgvbC4JvwCPPz6eN7TvuCP5/d+f2t64MXnxi6cf0ww0jszoBZrSrdOjqvpfU2ScOkVmC0Wamp0i3cKXzv/2vUKRXuuNNK8DAAAAjMADD9SBSKczzxxNLUBrrZr7EIbkl5L8WZJ75jpwwsNJ/meSZ1RV9T3LIRBZrGPHkg98IPnP/zl59NFRVzPTtm3Ttw8dmnnjv9n90SuImC0UaRrUmiL9doqsWTN3F0ZTv6FI87inPrWedm3Snj3Tt2er46yzete4du3M1wIAAACW2E03Td9et25lTSsCLAs6RUakqqrfSfI7SVJK2Z3kgiRnJNmVZGOSKslDSfalXjvk5qrqdot8ZbrzzuTd707uv7/efu97k9e+dvoxJ07UYcmBA8nBg8k55wxv3YjDh2dO7bR168zjHnts+vZ8QpFu02d12zfbmiLznT6r306R+U6dlXQPRbp9Lc3jduxInvvc5CMfqb/fL3rR9Oe7dYNs2pQ8+cnJQw/NXo/1RAAAAGAMXH55/Yf8HXfUj1NOmf5uSIAlIBQZA1VV3ZfkvlHXMU4+8YmpQCRJbrwxueii5IIL6u2qSv79v58eBPzwDydnnDH4Wqoq+fVfr2/g79pVP57znGT79vr/46tX110j27bNvdB6r+CiW6dHr+6KpZo+a75TZyUzw47jx/ubPitJXvCC+vu7evXM59etq39X6owHn//8et9cnSIAAADAiG3ZklxxRf1I5v8OT4ABEIowlr7t25KvfW2q82LVqnox80ml1IFEZ3fAwYPDqeWRR+pOkaReKP322+ub9knysz87PQD4ylemn9sMAubbKdIrFFmq6bMWEoo0w4x+O0UmzRZirF1bd4VMfp/POKN+k0nSe00RnSIAAAAwhuZaIBRgCIQijKXNm+tg5M/+LDnttOTVr05OPXX6MVu2TA9FDhwYTi2dHStJ/f/ryfVEmjf15+oUme+aIsMMRZZ6+qx+O0Xm8l3flXzmM3W3yNOfPvv3o5NQBAAAAABIhCIM2YMP1jewDx2quz5WrUq+93v7O/eSS+rjL7ywe0fB5Joeq1bVAcmwpqBshiK9prtcTCgy3zVFmm+maK5nMpdhdoosdKH1fqxZkzz72d33z0YoAgAAAAAkQhGG7NCh5JOfnNqeT1dkKcnFF8/+/EtfWneTbNo03DW5rryynrLp/vvrR3PB9U7dwoBOc3WKVNX0r6VXkLBjx/T9Dz5YX6NXx0S/tXQaVCgyn+mzFsKaIgAAAADAXIQiDFVz6qWjR+vHIKaMnOwUGbZVq5KdO+vHt3xL72MX0ykyeXznzf1eoUhzOrETJ5IHHkj27On9Gv3WMqlXCDSbfhZaL2XpQpF+gyIAAAAAYGUb4C1JmKnbehTzneZpORlEKNKpVyiybt3MbpF77527xkmjXmh9kIFIIhQBAAAAAObmViFDtX598tSn1uHIxo31DfZuUxkdPZrcdlty+un1IuvLxYkTyd699SLvDz+c3H779CmsTpyYPiXWXKHIsWPT17+Ya8qpU09N9u+f2t67t//al7JTpNuaIgtZZL0XoQgAAAAAMBe3ChmqUpJXv3ru4+69N/nDP6w/37o1ecITkn/6T4e7VsigvO1tdfCR1GuoHD9eL/w+6cSJOgA4cWLu7oxmCHLs2PTt5s39PXuSL31pansxnSKrV3cPYYa10LpOEQAAAABgqZk+i7Fw111Tnx84kOzbtzwCkVWrpq9tsmpVcuTI9GMmg4Z+pqtqhiDN7eZaLM11RfbunQpoZnPkSN0l0uwU2bSp+/GD6hQxfRYAAAAAMGpuFTIW7r57+vbpp/d33okTySOPJAcP1mHK6acn27YNrq6HH66ns+oVDGzdWh+X1EHO4cPTn58MA/qZrmquUKRbp0inQ4fq70W3Rei/8IXkIx+pF2M/6aSZ1964sf4eNg2rU2TQ02c1A6NOQhEAAAAAIBGKMCZWrarXGpkMDvoNRd72tulTRr361fUaJoPynvckt95aT4e1a1fy7Gcn5503/ZgdO+pQZNu2OkD51KemPz8ZBvQTisx3+qzt2+vApjOIufPO5MILpx93993Ju941+3WT2TtFBrHQ+vHjOkUAAAAAgNFzq5Cx8OpXJ696Vd3FcPfdyVln9Xfeli3TQ5FunQ6Lcf/99ceDB+vH058+85jv+I6pqb4OH05uuWX687N1iqxaVd+s79w/306RUur1V269dWpft1CkWVM3GzfO3DcZVs3XKDpFVq2qvx/dpg8TigAAAAAAiTVFGIHHHuu+IHgpdTfGZZfV3Rf96FzQPKmDi0E5cmRmyLJr18zjOtc+6Xajf7ZQZO3amTfr59spkiRnnjl9+xvfmHnMoUMz9zV16xRZv35ha7uMYqH1UmYPP4QiAAAAAECiU4Qlsn9/8rnPJV/7Wt0JcvLJyU/8xOKvO7l2xqpVdUCykK6G2ezfX1938mb+qlXJzp29z+l2o79XKNLsamiGIM3F2butm9EMRe6+u75OZxDw2GOz1zxptlBkIUax0HpSf83dFrQXigAAAAAAiVCEJXLoUL3I96QHHqi7MLotCD4fz3hGcsUV9Q39hXQ09LJnT/LzP588+GA9jdYjj8x9c73bjf7JUKV5s37t2rk7Q/rpFHnCE6ZPG3X8eHLPPdPDkn5CkW7TZy1kPZFkNNNnJTpFAAAAAIDeTJ/FkjjttJk32DvXwViojRuTzZsHH4hMWr062b07ueii5Kqr5j6+lO6LjCfdO0VmO3ZSP6HI+vUzp/W6887p26PuFDl+fPjTZyVCEQAAAACgN6EIS2LVquScc6a2TzopefTR0dUzTPMJRZo36xfSKZLUXS2dmmurLGZNkYVofg+6TZ+lUwQAAAAAWGpuFbJkLr20XpPjSU9KzjqrvlG9d29y223J6afXN/YHuSbIUrr//nqarQMH6g6YrVuTbdvq53qFIs3QYyGdIsnMtUaa11no9Fnd1jDpxygWWk+EIgAAAABAb24VsmSe8pT60ekrX0k+8IH681KSiy9OXvOapa9tsf7mb6amA7vjjuSMM6ZCkckwoFsocuTI9H3veU9y3XXJk5+cfPu39x+K9Oo4qar+QpF162buW2g3xygXWu9moeEOAAAAALCyCEUYqbvvnvq8qha+sPeoTQYgSX2zvzPsmM/0WUm9oPv11ydnnz1zcfbZbu73CkWOHJlahL2XbtdeaIeFhdYBAAAAgHFkTRFGqjMUSepptObrxIl62qo770xuvjm5997F17V/f3LPPTNDidl0hiKl9B+K9AoG3v/+/jtFei3Y3s96IrNde1ChiIXWAQAAAIBx4FYhI3P8eHLuucldd9Vrcpw4sbBQ5F3vSm64YWr76qtnLjw+X9dfn3z4w3XAsX17ctllyTXXzH789u1T64jcccf0G/69ps/qdbP+0KHBTJ/VnDpr9er662peu1tAs9BuDgutAwAAAADjyK1CRmb16uSVr6w/P3q07vA45ZT5X2fLlunbBw4svrb7768/VlXdNdIMNJouu6x+JHWXyH33TT230E6RjRuHE4ps3JhccEHy6U9P7du5s/t1d+yYvb5eLLQOAAAAAIwj02cxElVVBw+f/GTy+c/X61mceebCbpQ3Q5GDBxdf32QoMmnXrv7P7TZ1VDL/TpFhhSIbNiTf+q3160+68sr640UXTe1bv75e+H4hxm2h9WF0pQAAAAAAy4/3T7Pkbr45ee97pzo69uxJnvrUhV9v69b64+rVdUCyefPi6quq+lHK1ALl8wlFZlvfoxmKrFkzuFCk15oi3UKRTZuSH/3R5ItfrLtzJsOPV7yi/n4++mjy7GfPvrD7XMZpofXVq4cTwAAAAAAAy49QhCW3ceP0Ka7uvbe+Cb9p08Ku9+QnJ296U33dUhZfXynJT/5kPaXXAw/UXSO7d/d/frf1NJLpi68nybp1vYOBDRtmTgU2W0jRq1OkudD6hg31x5NPTp7//OnPrV+fvPSls9fUr3FaaN3UWQAAAADAJO+fZsmdccb0qZuS5GtfW/j11q6tA5VBBCKd1qxJTjstufTSmfX2MlvXRrdQpNcN+1WrFj591j33JG95S/KZz3TvFBm2fqbPWqpOEaEIAAAAADBJKMKSW706Ofvs+vOtW5OnPW3hC3qPo9nWFGmGIuvX9w4Gjh9feCiS1N03733v9EXfk7qjZti6dcvoFAEAAAAARs3tQkbiBS9IXvKSegqnQXd4jMrDD9cdGrfdltx6ax16POEJU10SR49OP36uTpETJxa+pkjnNW67bfq+cekUEYoAAAAAAEvN7UJGYs+eUVcweP/4j8n73pfceGO9Fsn27XUocvz4zEXWk7lDkcV2isxmVKHIqBZaF4oAAAAAAJNMnwUDsnVr/XGy8+Xw4frj8eNTn3eaa6H1YYUi27b1f+xC9ROK6BQBAAAAAJaa24WsCCdOJI88khw4kBw8WE9ddc4587/Oo48mX/96smtXsnPnwsKGyZv9R44kVVWHG831REqpF3Kfa/qs5pRba9Z0P7bfOk8/fWo9l2Hqtq6KhdYBAAAAgFFzu5AV4SMfSf7+76e2zz9/YaHI3Xcn73xn/fmqVfX0Vz/0Q/2du21b3S2ya1cdhqxbV388cWJmKLJuXR2M9AoGjh6tz+803zVFml73uqUJCZr1TIZDnXSKAAAAAABLze1CVoQtW6ZvHziwsOscPDj1ebeFzueq4Wd+JvnLv0w++9mp/d06Rdatqz/u2jX79ZrnJIubPuuss6Zed9i6BR7N76VQBAAAAABYatYUYUWYXM9jUme4MR/N85phSz+6TR3VDDjWr68/nn56ctVV3a/TbXH2xYQiC/laFqpb4NGcCsz0WQAAAADAUhOKsCJM3vBfvTrZvj05+eSZC3v3Y3Laq0mbN8//Gs2b/bNNnzXpZS9Lrr565nUG3SnSDI6GSacIAAAAADCO3C5kRTjllORNb0o2bqzX6lioa66pH48/XneNLKSboXnO8ePJ4cPT93WGIqUkL35xHcC8731T+7tN3bWYNUVG3SnS7HzRKQIAAAAALDW3C1kRVq9ONm0a3PXWrq27TRain+mzuq3t0Wt9kcnrzhYkLIdOkWYoMoywQigCAAAAAPRi+iwYsG6dIv2EInN1TvS6ub9q1dzTUS1lp0i3r6X5PVizZvCv2+17tJjOIQAAAABgZfEeahigw4eTO++sH0eO1FNgnX/+8EORyee7Lc4+adSdIs2F1ofRwTGMoAUAAAAAWDmEIjBABw4k112X3Hrr1L7HH58ZEnQLRebq9JgrRJgrVBn1miJNS9UpAgAAAAAwyS1EGKCtW2dO1/TIIzP3rV8/89xBdIos5vlB6icUWao1RQAAAAAAJrmFyIpx4kRy8GD9OHCg3nfhhf2ff/Bg8uCDdUfF5s3duznmsn59PV3W4cP15+vW1R0R/UyftdhOkXEKBPpZx0MoAgAAAAAsNbcQWTG+9KXkj/94anvHjvmFIrfckrz73VPbZ56Z/NAPzb+OF784OXRoanvVquSxx6Yfs5A1ReaabmqcAoFS6q/n+PHZjxnG9Fn9dKgAAAAAAO3lFiIrRnPNjIMHk6rq//yDB6dvd5viqh/NG/PHjy9Np0ivUOXyy3ufOwyL/XoAAAAAAAZNKMKKsXXr9O1jx2Z2aPTyyCPTtxe6MHkznOg3FBnWmiIbNybPelbvc4dBKAIAAAAAjBu3JVkxNm+up21ataoOSLZsSY4end811q2bCjAGFYocPVoHNM3Xmeu8poWEImeemXzP9ySbNvU+dxjmCkWGMX1WNzt3Ls3rAAAAAADjTyjCirF6dfKmNyU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+     },
      "metadata": {
       "needs_background": "light"
      },
@@ -5917,1010 +612,65 @@
     }
    ],
    "source": [
+    "%autoreload 2\n",
     "import constrainedzoom.plots\n",
     "constrainedzoom.methods.use_filters=True\n",
-    "constrainedzoom.plots.zoom_demo(nP=128,nW=128,seed=1,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'b'})\n",
-    "constrainedzoom.plots.zoom_demo(nP=128,nW=128,seed=2,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'r'})\n",
-    "constrainedzoom.plots.zoom_demo(nP=128,nW=128,seed=1,splice_seed=2,splice_range=(38,94),plaw=-1.5,plot_kwargs={'color':'k'})\n",
-    "p.xlim(0.00,0.5)\n",
-    "\n",
+    "N = 512\n",
+    "splice_range = (256-50, 256+50)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'b'}, linewidth=1)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=2,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'r'}, linewidth=1)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, linewidth=1)\n",
+    "p.xlim(0.00, 0.5)\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Converged after 4 iterations\n",
-      "Converged after 3 iterations\n"
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n"
      ]
     },
     {
      "data": {
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\n"
+     },
      "metadata": {
       "needs_background": "light"
      },
@@ -6928,790 +678,155 @@
     }
    ],
    "source": [
-    "constrainedzoom.plots.zoom_demo(nP=512,nW=128,seed=1,splice_seed=2,diff_seed=2,splice_range=(38,94),plaw=-1.5,plot_kwargs={'color':'k'})\n",
-    "p.xlim(0.00,1.0)\n",
+    "%autoreload 2\n",
+    "import constrainedzoom.plots\n",
+    "constrainedzoom.methods.use_filters=True\n",
+    "N = 512\n",
+    "splice_range = (256-100, 256+100)\n",
+    "offset = 50\n",
+    "fig, axes = p.subplots(ncols=3, figsize=(10, 3), sharex=True, sharey=True, constrained_layout=True)\n",
+    "fig.sca(axes[0])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'b'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=2,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'r'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, linewidth=1, hires_window_offset=offset)\n",
+    "p.xlim(0.00, 0.5)\n",
+    "\n",
+    "fig.sca(axes[1])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, hires_window_offset=offset)\n",
+    "p.xlim(0.00, .5)\n",
     "p.title(\"Difference with outer realization\")\n",
-    "\n"
+    "\n",
+    "fig.sca(axes[2])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=1,splice_range=splice_range,plaw=-1.5, plot_kwargs={\"color\": \"k\"}, hires_window_offset=offset)\n",
+    "p.xlim(0.0,0.5)\n",
+    "p.ylim(-10,10)\n",
+    "p.title(\"Difference with inner realization\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Converged after 4 iterations\n",
-      "Converged after 3 iterations\n"
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 3 iterations\n",
+      "Converged after 6 iterations\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Text</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Difference with inner realization'</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;35mText\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m0.5\u001b[0m, \u001b[1;36m1.0\u001b[0m, \u001b[32m'Difference with inner realization'\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 300</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x900</span><span style=\"color: #000000; text-decoration-color: #000000\"> with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 300\u001b[0m\u001b[1;36m0x900\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m3\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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gSdeYWanH0YngGSr843kxx0m6wcwOq1F/Gs7MJitUp/+4Cgfb83mxpKtrEUQ1s89L+qZCAKdUiyT9w8wq+bwAxi3OkwPOk3fhPLm0dXGeXNkyOE9uI2Y2W9LfJZV7XHujQsXoatf/Ukl/UXnn+n2Sfm1mL6xgfS2xX5vZgQr77aHVLKeWMgH5z6jCc8wmfd9whEo8pucxU9J5ZvbhKpYx3hyl8s6/YntL+quZvaFG/WmKVvkestHHSDReTa5YAwAAAAAAACr0V4WqmknHmFmHu4/WYPnrJd2aGO5RbgWwhzLTpXk08++9yv7xbr5yq5LcqvxWpzWaWb/CDxsHpoy+XaHq2x0Kt8CWQvW3YxWCUdMS006V9Csze7q731igH/nMV6golPxy/jqFYMNKSZslLVCoavfKPMv4qUKFutijCu/zzZLWSdqhENg8QiHkGAcWPmFmj7n7Nyt4HpL0Q2X/kL9MIVBwt8L7PCOz7n9W7nv4LEnvU6iUVBIze4ek7+YZ/YjCa3ibpMcUbk07U6FK1NMU3suSfxAxs+7M8tKqDd2nUJlwqcLzHJY0R9KRCtvL3MS0XZL+z8xWu3taiKdW/qJwO90xx5jZFHffWmS+QkGDEyX9rdDMZrafciv6/qXIOku1Rdn7ehzQXKM8+3vCo0XGJ71IIbg4FibdqLANXKVQtapDIaj4UklHR/NOkfQjMzvS3YfLWGfFMj9ufTpl1LDC9vkXhf2iSyFo9GKFKnzJsGyPpJ+Y2bC7/7K+Pa5Y8jOh3M+DMfk+d8Z+9L42ZblSODZfqXBsG1B4vRYohIZfJClZzXIPhQqGT3H3lSX0KXaoQmW7saJNI5L+oXBMf1jh1vF7KRxTj6hg+ZX4nsKPyWMeVahsv1Rhn5iuEKhIrQiWCdv9WrlhgiGFY8u1Cn8XbFT4bF0k6bnKPe4eJel3ZvYsd99Z5nMYUaiGeIfCZ9PjkjYp7AfTJS1W2C+eruyCWVMVgnZHuPtDZa6z3lartO0+n2mqsEJwxqDC7eHv1O59Y7PCsWaGwt9Zz1CobJe0h8KP2k9x900Flp98bgcoO/C5VeGYUMxQCdOUzMw6FC6CSfvM3KwQ0r1O4b0Z265OUeh/0p6SrjCzp7l7Kc8jrS/vULjjxZjHFP72uj7z/97Mek9RbjXqeZL+TxMsQAyUgPNkzpM5Ty59XZwnc57c0ufJNdKlcIFlMux9s6RLFKq9b1A4hhwt6Z+UW+H/dWb2O3c/r8L1H65wV4+x86jtCsewKxTe22GFc/znS3peSt9/YGaHuPvGUlbWQvv1ZEkXKDvof7vC8fM+hfOOeQrV2fN9DtTa2xQC8mO2SPqzwufmGoXtf2+FY358h5xW+r5hmcJx4y6Fu79tVjhnmqbwGXiEwvbUl+y+pM+b2W3uflEF66yn+DhYLpNUzQXQrvC5epvCa/qYwvcMI9p9vn2kwnaR/D6kW9L3zewOd7+pwPLr+j1YpVroe8jD1cBjJJrE3Xnw4MGDBw8ePHjw4MGDBw8ePHjwyHko/Cjl0eOyGq/jqSnrcElLisx3WSX9yvOcTqug32fEy6nw+f82pT9XSjq6yHz9kr4kaTSa935J00pYb7zO4cT/b5V0bIF5e1Pa3peyzIcUfmTqLLCsLoWKepujeQclPbWE5xFvB9sT/18l6RUF5p0q6ccp/R6Q1Ffi+3dspq/xMu6TdKokKzL/NEmvUQiuXlrC+r6Wsq47FL6kz7suhR+lPqzwg1X8XPepZNst8fU5OqW/Ly5hvkcS08fb+PUlzP+2lPXuWWSeB6LpzyrxOcbrOaOK1+u0lOWNbdOjCsGW/gLzvzLaB8Yer67Xexyt/+nKPpaMPf6uAsf0zH50V8p8GyTtW8J6z4jnrbD/i1L6cFqj1p9YXo/Cj9txXy6UdFCReedLOidl3utU4Fhc4Pkn38/LJB1cYP6cz4YabFNnFejPNknvldRdap8UgkUD0TJ3KtxafG6RvhyuEOSOX6MvlfhclisE/E+RNKPEefaVdG7KOn9fxmtY0fZZ6+26yLr6FP72iZ/nL4rMtzWzjbxApX9uH6IQ2InX9c0y+ntZNO9lVT7/B6LlnVXifB9OeR6ucDFK3m1M0ptT9oOx40Te/anQtqHdnz1DmX6lHg8UwgzvUQh7xMs4pl7bGA8etX6I8+RCy6nJ54c4T+Y8mfPktPk4T87dptvhPPmsaL0PlDhf/Nonn8MySc8tMO9cSX9Mec53lbju4wu85i7pbEkLCsz/bIWLieNlfLiM160p+3XK6578HLhf0ksKzNslqavG20/a9p/s03ckzS4wf3xO3JTvGzLz/0jhYpi3Sdq7xOc/VdInlfuZslalfyalbc/H12u+Kt7rL6es7z5JUwvM8zeFi21epyLfaSTmmSPp68r9HLmtjL6eEfe1yud+VrS8B0qcr1nfQ6ZtGw09RvJoziNZCQMAAAAAAABotFsVQm6xaqqatgUze7tyqxR+W9Iz3P3aQvO6+wZ3/w9Jb4lGLZL0zgq6M3Yb+CslPdPd41uZJ9e9IzlsZodK+u9osmskHeruv3L3nOpJiWUNu/sPFKrMJivJ9kj6bBn9HzNW9W6FQmArb6Uud9+i8IPVJdGofoUqfgWZWY/CbWl7olGXS3qau//CM9+eF+jDZnf/mbs/QyH4Vmh9L5D0b1Hz+ZKOcPc/FVqXu2939/+S9BJl72/9yq5AW2s3aHeVyTGFqvDJzA5WqK475npJDyaGn2Jms4qsN17HXe5eTnW8VtKr8GPLm9z9P919Q74J3f1Xyj0mSCGkU1dmZpLO1O5jyZg/SHqeuy/PN2/mePN0hapbSTMUqhRONGcoVBlL+rC7v8zd7yo0o7uvdvc3SPpUNOpISa+ooC9j7+dvJJ3o7ncWWPeOfONqaKw/WyW90N2/5gWqyaf06afKrrq7TdIL3P2D7v5YoRW7+y0KVfz+HI16j5ktLKHvR7r7P7v7b73ECmXuvtLdX6uwTSS92MzSKvy2HTPrlPQzhdc26SqFz+hC9nT309z9EnffXsr63P0OSS9UqOqb9GYzm1nKMlqBmS1SetW+j7n72wttY+7+I4XPybiC/5HK/TujVL0K4ZcXuft/5TseePANSR9PGV33zyqgzXCenI3z5IDz5Nz1cZ4ccJ6cRzPPk2tsbD++XuGin7x3MMic27xcofJ60oFm9owq1/9xd3+ju68qsP7LFe4wFu9/ae9Djhbbr8c+B5ZJeroXqKKeOXY34i4JY336D3f/V3d/vECf4r/Lz1Dzvm/4d3c/zt2/7+4PlzC93H2Lu39K4f1MvrZzJb2hlGW0AzP7T4WL85IeU/jeY0uBWU9x9xe4+0+Lfacxxt3Xufu/Kffz9VAze37pvW6uFvwesmHHSDQPQXsAAAAAAAA0TeYHiLTbhu7d6L40kpl1Sfpo1PxHd39XsR+dkzJhqR9Eze8zs0lp0xexUdKp7h4Hr4r5iLJvObtKoRpbybc6dfdblRt8eKGZHV5mX6TwQ9qrvITbFmde6/enjHpBCes5TeFWxknLFapbDZQwf9yXB4pM8oloeKmkV7r7UBnr+LNyfxB7s5ml3S66apnwyGVR84lFZot//P+zwq12x3RIem6+mTM/tMTj/1Jkna3uK+5+TikTuvu5CtXEkp5tZr1p09fQSyQ9MWp7UGFfLLqNuvt6hSBAHJZ9iZnFyx23MkHf90TN33X3OKRVkLufodxA+Icr7NZKhQBL3kB7E3zY3a8oZwYzO1GhalnS6YUCKrHMtvxKSesSzd1K/xyJ563m9t+fVgjUjDFJp1exvFbybYV9P+luSS8rFp6v9DV191FJ71KoKDxmskLl3HbxbmX/7SVJv3H3z5Uys7vfKOntKaP+LXPxQyU+4O5/LT6ZJOl/FaryJpXytxcwYXCenIXz5GycJ2fjPDngPLmAJp4n19oGhTtSpH0+ZHH3QUkfTBlVzd9cv3H3ki72cfcrJf0qaj7AzPYvYfZW26+HJb2mxS5M+bW7f7mcGZr9fUM158Tu/hdJX42a2+1imVRm9lqFu+wlbVX43Lyn0LxVvqY/lhRfeNdOr2krfg/ZqGMkmoSgPQAAAAAAAJptQ0rb9EZ3osFeLWnfxLAr98eOUn1a2RVQ9lBumLAUX3b3OPRUUKai6qui5o9V8gO6pHMlxT8gnFzJcjIBspJ4qNB8U9T81BJmTfvB8k3uvrXUdZcqU3Esrvb7vgqDr1+WtDkxPEnSiyrtWwni0N2hRX7YjAMEf1FuAKBQtb/DFW6DXKgP7WSzcn8cLuYn0XCXpCfXpjt5vTul7T/L2R/c/X7lVv00hUDsRPEuhVujj9ki6UMVLiuudn145phdrk8UqaDWaPdJ+lYF88Wv49/d/RflLiTzQ/bXouZTKuhPOet0SXGIqNJKlC3DzD6p3LD3owp3GSgaHqpGprJj/KN2W7ymmUBYXOluSGVWo89s/5dFzfso98KHUqxQGftl5u+XeP/b28zmVbBuYDzbkNLGeXLpOE+OlsN5ckGcJ7eXdjlPrrWvufuDxSfb5U8KVbGTStmP04wqff8uJH7Ni66/Rffrc9z95grmq5dRSf9ZwXyt+H1DOc6Oho8ws8l1XmddmdkJks5S+O5rzLDChSXXp85UW/Fr2hbnxBmt9j1kQ46RaC6C9gAAAAAAAGi2tB+b+xrei8aKb6l7mbvfW8mC3P0h5d7q9NnlLkbSDytY/ckKP06O2apwm/iyZYKEF0fN5T4PSfp+BfPElc2WFJrYzA6RFFeY+bu7X1PBuksRby/3l1OFOSlTIfjSqLmS17lUaVXyUgMAmQqWyb5sk3RVZhnJkEyhAEE8Lq1aYDv5RQXVM+PtWcqt8lQzZtaj3G1otaTfVrC4/1P27cCl4tUdx5N4X/9VBe//mKuUG9Ard1/fpNwwcrP9qJyKtpJkZrOUW8EzrnJbjoui4X3NbN/UKWsnDtg9xcziiuZtw8zeJumMqHmjpBeVGR6qRvyaHtOg9VbrSEn9UdsF5YZAM76d0lbJMfeHmTsFlKOhn1VAm+I8mfNkzpML4zyZ8+RyjIe/PcrajzN3T4gvsKn0Of/N3e8rc55rU9qKrb8V9+szK1l/Hf2thLt9pGm17xvKFZ+/dUl6Wp3XWTdm9hRJv1Huncre6u7x5369xK/pAjOL70rTclr0e8hGHSPRRF3FJwEAAAAAAADqKq0YRFkhunaSuWX3M6Pmq6pc7P3KrsR1RJnz3+vuD1ew3vhL7VvcfVsFyxlzfzRc7vPYrvQfT4uJvwjvNLOpBao4H5/SVtItyysUv8612F6Syn2dS+bud5vZw5L2TjSfIOmnKZMfpewqnVdkbve71sxu0+5tfH8z2y9TeSj2vGj4+mpupdwCLq9gnrQfdmZU25ECniKpN2r7nbvHP1QV5e6rzewfyt7Hnmhms9398Sr62PIyt3F/UtRc8b7u7qNmtlLZgdwjJP24jMVcmwkntJI4KFGKZyq7QpxU3XE07dhzhKSVpS7AzKZKepbCce1gSbMVjn9TlP530dRoeJJCZd5KPrubysxOkvSdqHlQ0snuvrSK5c5WeK+fJOkgSTMlTVN4TeP3X5JmRcMLK113gz09pe28Cpd1vsJrP6nI8otph88qoB1xnsx58hjOk9Nxnsx5cjna/W+P+yq8sDJ+3pU+57Jfc3dfa2ZbFf4eL3X9rbZfb5dUr4uFKlX2OXErft9gZp0Kd5o5PNO3BQrnb9NUep615UPhaczsCZL+oPBckz7q7uV8ZxMvd5JCVfrDJB0qaa7C58dUSZ0ps/SktO0jqVEXv1eqFb+HbNQxEk1E0B4AAAAAAADN1p/S1mrBvlo6SLnhrjeZ2UurWGb8w0J8S/Bi4lvClyoOYx1iZrdUuCwp93WZYWbdZdwiemWFt5NO+4F5hsJtlNMcm9J2ZQXrLcrMpin3duYnVvk6z4+Gy91eyvVXSW9KDOertBe3/yX6/5OjabOquWUqGsXhnLRKge0kri5VirSKZPUO2sduqGJ51yv7By5T+MG23d/LYo5VbqDuI2aWdjvsUh0QDTfqs6FeXNItFcyXFhz+tZnVMqxY0mtrZk+V9AFJJ6n6qsT9arOgvZkdq1DRN/kj/6ikN7j7ZRUu83mS/l3SC5RbDbAcXUXCg62iZsdcdx8ys6UKVfLHHGJmk9x9sIxFVfJZle9vLwC79ae0cZ5cHs6TA86TS8N5cvtoh/PkWqvkOUu5+3Glz7ma9ZcUIm3R/Xpp5s4AraSSz6aW+b7BzOZL+rCkVytcPF6N/irnbzgzmyvpj8p97t909y9UuMwDFF7TV6j641p/lfM3Qit+D1n3YySaj6A9AAAAAAAAmm1mSlult65tB3vnaUtrr9TsMqdfW+4KMj/Wzo2a+1X7L+RnSVpT4rTrK1xHWuigUFgv/jFkRNJdFa67mAXK/TFsXuZRK+VuL+X6i7IDBHub2RPdfVk0XbEAwfsTwycq97bpx0maXGAZ7ajsbToTnIybqwmfFpP2Y2o1+8OdJa5jvEn7DHhCjddR98+GOttSYTXYtNc2DnBUq+Bra2bdkr4i6V+VXqG4Em31A6yZHSjp98q9wOB97v6rCpY3XeFz4FU16N6YQuHBVhEfD4cquEV90p3KDtp3KPxtvrqMZVTy91e5f3sBExHnyZwnF8J5MufJY//nPLkETThPrrVa7ceV5gRrtf5Cr3kr7tetdk4sVdanlvi+wczeLul/lX2Xjmq02znxFEkXSVocjTpP0nsrXOYnJH1U2Xcpq0Y7vKat+D1kI46RaLJafZkHAAAAAAAAlC3zI3RagOChRvelger9Y61UfpXcSgIbjXgeUnnPpZIqfZWIKwpucPdaVkZOasTrHN9ut9bSfsQ/MTmQ+bHpmETTWklLE8NXSBpKDD/Xcn8ljwMI2yRdXV5XW06jtulqpB3DN1SxvIGUtnifG4/Gy2dDPVXan6a+tpmQ/a8kvUu1/V2ubX6ANbM9JV2i3H35v9396xUsb3pmebUM2Uvt8ZrGx9y0asPlqPqYW2GVZAAFcJ5cN5wn1xfnyeXhPLlyE/Fvj2Y/50asvxX361Y7J5Za97Op4OeSmX1Q0v+pdiF7qT3O3yRJZtal8L3AkdGoKyS93t1HK1jmtyR9SrUL2Uvt8Zq24veQzT5GowEI2gMAAAAAAKCZjlB6NaUVje5IA6V9GdxswxXM04rPo1HiH4XqWf227V9nd18t6Y6oOf6x/9nK/jHnr8lQhrtvlXRNYvxsheNHoWVe4e5DQr1NS2nbWsXy0uZNW8d404r7eiWfDfVUaX+a/dp+SNLLU9ofkfRtSa+XdKykhQrVbnvd3ZIPSc9pVGdrzcxmSLpY0j7RqHMkfaTCxX5Z2aGzMfco3DngVZKOkrSnQkW+SSmv6ZsrXHezxcfDao63+eafCMdcoNVxntwaOE8uD+fJZeA8GcjRivt1q50TS2342WRmz5D03ymjtko6V9I7JR0v6QCFvk6W1JFyDtfOfiDpRVHb7ZJe7u6D5S7MzF6v8LrF1ks6U9Lpkp4paZHCa9qX8nruV+56WwTfQ6IpKr0lDAAAAAAAAFALx6a0bZJ0X6M70kDbU9pOdvfzG96T6qQ9j6+6+/sa3pPGi6tHTa3jutJe539396/VcZ318FdJhySGjzezTncfyQzHP/6nVff7s6RnJYZPlHSTtKu68dNS1on625zSNqWK5aXNm7aO8SZtXz/c3W9teE/Gn/i13eDuDQkamNk85YbJhyV9QNI33b3UkES5FXhbgplNkvQ7SU+ORl0i6S2VVLk1sycpNyS/RdK/SDq3jGW25Wuq3ONhNcfbfPNPhGMu0Oo4Tw44T24vnCeXj/NkYLfxsl+3omZ/3/CVlLazJL3P3TeUsgAzq/ddRurGzP5L0pui5ockvbDU5x8tr1vS/6SM+i9Jn3b3tPc7zXg5J5b4HhINQEV7AAAAAAAANFP8o6EkXVXH24u3gnUpbe1YQWa8PI9KPB4N96fcnr1WxsvrHAcCZij7dsmlBAjituQ8z5HUWcIyUHtpt1jur2J5afOur2J5pWr27bHHy77eiuLXtt/M+hu07pMUqvElfcjdv1pGyF4q/7blTWdmHQpV64+PRt0g6RXuXumt1U9V7u+bb3L3n5b592PbvaYZ8TF3RpXL609pa8QxF0BhnCcH7fi30Hh5HpXgPLl8nCcDu42X/boVNe21NbMDlHvBz4Xu/uYyQ+aza9erxjGzdyvc5S5pQCFk/0iFi322pAVR2zfc/SNlhOyl8XNOLLXn95BoMwTtAQAAAAAA0BRmtli5t0yVpAsb3ZcGW5PSFld6bXnuvlm5FZHa7nlUaHU03Cnp4Dqta1xsL5IuU+7trU+QdlV8PjTRvtzdH0xZxvWSNiaGn5GoaBUHEB6TRCXwxngspe2gKpaXti+l/Sg8Jiesm6nuVa5m/2g7Xvb1VtTM1/bEaHhA0jcrWM4TatCXRvuqpFdGbfdJeom7b6liufFreoe7/6aC5bTjayrlHnN7zGz/KpYXH3NHRagAaCrOk7O03d9CnCdn4Ty5uMvEeTIwZrzs162olc6JJenTFSyn7c7fzOyVkuI7MuyQ9DJ3v7OKRcev6aikz1WwnLZ7TTOa/T0kJiiC9gAAAAAAAGiW9yn3+6lBSb9qQl8aaanCl+pJL2xGR2rgumh4PzN7YlN60lhXp7Q9vR4rcve1kh6I12Vm0+qxvnrJBE7i7WXsh6ETJCUrHaZW2HP3EYUgwpheSc9ILCPpb+O84mcruSmlLa5WVo4jo2HPs44xm1Laplew3gMqmKeWrk1pSwvZoXzxsUdq3Gu7MBq+1t2HKljOsbXoTKOY2UckvSdqXivpBZnPtWrEr+nfK1xOW72mCTU75ppZj3IDNrdXuI0CqB3Ok3fjPLm9cJ5cJs6Tgd3Gy37dopr5fUN8/rbD3W+oYDltdf5mZs9WuMNb8m+6EUmvcfcrq1x8/Joud/e0iymKaavXNKHZ30NigiJoDwAAAAAAgIYzsxMlvSNl1E/dPa0qybjh7jsk/SNqXmBmz2tGf6r055S21ze8F413WUrbG+q4vvgH9R5Jr6rj+uolfh7HmNkU5f74n7Zd5VvGCWa2l6QDi0xXLyPRcGeD1ttKblJuKOpkMyv7tTCzPSQ9M2pe5u6FqitvSGmrpCrXsyuYR8qtQKlKnru7r5R0b9R8lJktqbBf2C3tmHKqmXU1YN1zouGyK4Wb2RxJz6lNd+rPzN4o6fNR8xaFSvb31WAVtXhNn6TKK97F+3yjj/tXpbS9osJlvUwhjFds+QAahPNkzpPb3GUpbZwnF8d5MrDbeNmvW0qTv2+o+vwto222g8z55vmSJkWj3uXuv6vBKmpxTtwt6eQK11+T78Gq0OzvITFBEbQHAAAAAABAQ5nZEyT9TLnfTW2T9MnG96gpzk9pO6PRnaiBC1La/s3MZjW8Jw3k7ndJWhY1P8PM6lUJKG17+X+ZSrTtJO0H02dJSoZnRiRdWsYyTojmzzddvWyOhqc2aL0tw913Kvc9m6/KfrB7u6Q4/PynIvPE+6IkHVXOSs1shir/0TbeBqTKt4N4X++Q9IkKl4UMd39E0o1R836STmvA6rdGw/EP4qV4l3LD0C3JzF4o6cyoeaekV1RYtTBNLV7T91ex/mYf969T7gVGLzOzPStY1jtT2oodcwHUCefJkjhPbmucJ1eM82Rgt/GyX7eiZn3fEJ+/zTSzsvKqmerwT61dl+rHzPaR9EdJM6JRn3H3/6vRampxTvxaSQsqXH8tvwcrWwt8D4kJiqA9AAAAAAAAGsbMXibpekmzU0Z/yN0fbnCXmuVMSaujtmeY2Yea0ZlKufttyg0RTJd0Trk/mrSh/05pOytTea7WLpJ0S9S2n6Sv1WFd9XSNQlXjpHdK2icxfIO7b8y3AHe/W9JDiaYjJJ0aTXafuz9QRT/LMRANV1JJfTz4VkrbF81scqkLMLN9JX04avY8y066VSHIm/TaUteb8UmFY1cl4m1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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%autoreload 2\n",
+    "import constrainedzoom.plots\n",
+    "constrainedzoom.methods.use_filters=True\n",
+    "N = 512\n",
+    "splice_range = (256-10, 256+10)\n",
+    "offset = 50\n",
+    "fig, axes = p.subplots(ncols=3, figsize=(10, 3), sharex=True, sharey=True, constrained_layout=True)\n",
+    "fig.sca(axes[0])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'b'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=2,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'r'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, linewidth=1, hires_window_offset=offset)\n",
+    "\n",
+    "fig.sca(axes[1])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, hires_window_offset=offset)\n",
+    "p.title(\"Difference with outer realization\")\n",
+    "\n",
+    "fig.sca(axes[2])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=1,splice_range=splice_range,plaw=-1.5, plot_kwargs={\"color\": \"k\"}, hires_window_offset=offset)\n",
+    "p.xlim(0.15, 0.3)\n",
+    "p.ylim(-10,10)\n",
+    "p.title(\"Difference with inner realization\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converged after 2 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 2 iterations\n",
+      "Converged after 6 iterations\n",
+      "Converged after 2 iterations\n",
+      "Converged after 6 iterations\n"
      ]
     },
     {
      "data": {
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 300</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0x900</span><span style=\"color: #000000; text-decoration-color: #000000\"> with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
+       "</pre>\n"
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3MJmeylm1L+N6Km9nQe3bv+qYs2pfplPHvO0Ete/MqX2ZTF1zFqbSU3mr9iU9lrOzoO7tb3XMW7UvU6ljznZCHetejfZ0xddaxo+Y6YnjH3HR+nEQrdebk/GPnvhOy/SMY0vyyJbxDePXpD/UMm9hCj2Ts6WUs5L8S5ITm6ZvSHJpVVV3LtR9qaWeydtZ2t8yXt6VKFgI/Zqz9Ld+zdtNLeM6vGBTy8da7cs0apm3MIWeyVm1L016Jm9nSe3bv/o1Z+lv/Zq3at8ZUvsyhVrmLEyjZ/JW7cu4nsnZWVL39rd+zVv6V7/mbB3rXo32dMX/axk/bhbn/nCSwabxVzr8y/h8Yms99mPzioS6qXPeQjs9kbOllNPTeLLl5Kbpm9J4suX2hbgntdYTeTsHp7aM6xIX89evOUt/69e8fXjL+LauRDFRnR9rtS+TqXPeQjs9kbNqX1r0RN7Ogdq3f/VrztLf+jVv1b6zo/alnTrnLEymJ/JW7UuTnsjZOVD39rd+zVv6V7/mbB3rXo32dMXVmfgRD48opZw7w3OvaBl/qCMRTX69l5VSlk130vgxL53mWvS2OucttFP7nC2l3DeNJ1vu0zT93TSebLl1Ie5J7dU+b+foSS1jHzHcP2qXs1VVfbWqqjLbLcmbWy71ly3HrO9EfNRC7fJ2vkop5yc5v2X6U10IpVWdH2u1L5Opc95CO7XPWbUvbdQ+b+dI7du/apezal9moHZ5O19q3zlR+9JOnXMWJlP7vFX70qL2OTtH6t7+Vru8Vfsyjdrl7HzVuO7VaM/iq6pqd5K/b5n+xenOK6Wck+Q5TVMHkvx1B0NLks8mublpfGqOfSKlnZcmOaVp/J0k13QwLrqs5nkLx6h7zpZSTk7yz0lOa5r+QRpPtny30/ejN9Q9b+eilHJSkp9omb6qG7HQef2Ys/S/fsvb8Re//0fL9I1VVV3fjXia1fyxVvvSVs3zFo5R95xV+9JO3fN2LtS+/a0fc5b+1295q/adM7Uvx6h5zkJbdc9btS+t6p6zc6Hu7X/9mLf0t37L2TrXvYlGe7rnyiT7m8ZXlFKeOdnBpZQVSd6dZLhp+l1VVX1nqpuUUqqW7XFTHV9V1cEkv94y/fZSymlT3OO0HPuP/Ferqjo01b3oSVemhnkLU7gyNczZUsrxaTzZclbT9O1JHl9V1U1TncuScGVqlrellNWllDeUUlbO5BtoOm9zkv+bZG3T9D1J/mY216H2rkzNchZm4MrUMG9LKT8zfq8ZKaUMJ/mzJJe17GpdqaObrkwNH2u1L9O4MjXMW5jClalhzqp9mcaVqVneqn2ZxpWpWc7CDFyZGuat2lftSy1cmRrmLEzjytQwb9W+TOHK1Cxn1b3MwJWpWd7CNK5MDXO2H+tejfZ0xfgv1H/YMv33pZTXjf/DOaKU8oA0fjF/ZNP01izcP6T3J/li0/i4JJ8vpbR+BFBKKU9O8m9JNjRNfz7JBxYoNrqoznlbShkspZzWbkuyvuXwNZMdW0pZsxDx0R11zNlSyvokn0jS/HFFu5K8Ksn+KXJzsvymz9Qxb5MMJfmDJDeVUt5eSnlEaywtcZ1QSnljkm8kuahl93+vqmpLh+Oji2qaszClGuftHyW5uZTy+6WUh5dSBtsdNP6777PSqNte0bL7k2nUdLVQ48c6UfsyiTrnrdqXduqYs2pfplPHvI3alynUNGdhSjXOW7Wv2pcuq3POqnuZTB3zVu3LVOqYs1H3Mo2a5i1MqsY523d1b6mqqtsxsESVxsc9fCTJU1p23ZXky0lGk5yR5OIkpWn/viRPqKrqszO4R2uCP76qqk/N4LyTknwhyX1bdt2Q5Ovj8TwwE9+VmyS3JLmkqqo7p7sHvamueTteeN481TEz9Iqqqt7TgetQE3XL2dJ4V+O/ziD0Gamqqkx/FL2mhnm7Psm9LdP7klyfxqoc28bjWJfknPHY2uXmr1RV9dbpYqP31C1n56KUcmUmrvD1l1VVXdGp61M/dczbNsfvTaP+uj3J9jSeBD8+yQ8lafdi4X+k8XHEo9PFtpjq+Fg3naf2pa265q3al8nULWfVvsxEDfN2fdS+TKFuOTsXat+lp455q/ZV+1IPdc1ZdS9TqVveqn2ZTg1zdn3UvUyjbnk7F2rfpaWOOduPdW/bdwrAYqiq6mAp5UeT/HmSFzTtOj7J5ZOcdleSl8/kH/g8Y7u9lPLEJH+bie9KPHt8a+fLSV7gyZb+Vue8hXbkLL2oR/J2OMmF49t0bk3y+qqqPrSQAdE9PZKzMEGP5O3yNJ70mU6V5I+T/GJVVWMLG9Ls1fmxVvsymTrnLbQjZ+lFPZK3al+O6JGchQl6JG/VvmpfuqDOOQuTkbf0mh7JWXUvE/RI3sIRPZKzPV/3DnQ7AJa2qqp2VlX1wiTPT2Mlgcnck+R/J3lQVVX/b5Fi+3aShyf5pSQ3TXHod8aPuaSqqhsXIza6q855C+3IWXpRzfJ2R5JnJvnTNFY0ODSDcw4k+WySn0jyAE+49L+a5SzMSA3z9r8n+ac0PqZwJu5O8r+SnFdV1c/W7QmXZjV8rJtjU/vSVp3zFtqRs/SimuWt2pdp1SxnYUZqmLdqX7UvNVHnnIXJyFt6Tc1yVt3LjNQsb2FaNczZvqt7S1W1rtIP3VNKOT2Nd6+cnGR1kjuSfDfJNVVV7etybD+UxkcDnTw+dVuSb1dV9Z/di4o6qHPeQjtyll5Up7wtpYwkOS/JaUlOHI8naTw5sz3Jt5JcW8df/lk8dcpZmKk65W0p5dQk909yapKNSVYmOZjGx7puSfLVqqq+s5gxdVKdHus2sal9aavOeQvtyFl6UZ3yVu3LTNQpZ2Gm6pS3at+uxqb25Rh1zlmYjLyl19QpZ9W9zFSd8hZmok452y91r0Z7AAAAAAAAAAAAAABoMtDtAAAAAAAAAAAAAAAAoE402gMAAAAAAAAAAAAAQBON9gAAAAAAAAAAAAAA0ESjPQAAAAAAAAAAAAAANNFoDwAAAAAAAAAAAAAATTTaAwAAAAAAAAAAAABAE432AAAAAAAAAAAAAADQRKM9AAAAAAAAAAAAAAA00WgPAAAAAAAAAAAAAABNNNoDAAAAAAAAAAAAAEATjfYAAAAAAAAAAAAAANBEoz0AAAAAAAAAAAAAADTRaA8AAAAAAAAAAAAAAE002gMAAAAAAAAAAAAAQBON9gAAAAAAAAAAAAAA0ESjPQAAAAAAAAAAAAAANNFoDwAAAAAAAAAAAAAATTTaAwAAAAAAAAAAAABAE432AAAAAAAAAAAAAADQRKM9AAAAAAAAAAAAAAA00WgPAAAAAAAAAAAAAABNNNoDAAAAAAAAAAAAAEATjfYAAAAAAAAAAAAAANBEoz0AAAAAAAAAAAAAADTRaA8AAAAAAAAAAAAAAE002gMAAAAAAAAAAAAAQBON9gAAAAAAAAAAAAAA0ESjPQAAAAAAAAAAAAAANNFoDwAAAAAAAAAAAAAATTTaAwAAAAAAAAAAAABAE432AAAAAAAAAAAAAADQRKM9AAAAAAAAAAAAAAA00WgPAAAAAAAAAAAAAABNNNoDAAAAAAAAAAAAAEATjfYAAAAAAAAAAAAAANBEoz0AAAAAAAAAAAAAADTRaA8AAAAAAAAAAAAAAE3+/61Qno7cK8gaAAAAAElFTkSuQmCC\n"
+     },
      "metadata": {
       "needs_background": "light"
      },
@@ -7719,9 +834,26 @@
     }
    ],
    "source": [
-    "constrainedzoom.plots.zoom_demo(nP=512,nW=128,seed=1,splice_seed=2,diff_seed=1,splice_range=(38,94),plaw=-1.5)\n",
-    "p.xlim(0.05,0.25)\n",
-    "p.ylim(-2,2)\n",
+    "%autoreload 2\n",
+    "import constrainedzoom.plots\n",
+    "constrainedzoom.methods.use_filters=True\n",
+    "N = 512\n",
+    "splice_range = (256-200, 256+200)\n",
+    "offset = 50\n",
+    "fig, axes = p.subplots(ncols=3, figsize=(10, 3), sharex=True, sharey=True, constrained_layout=True)\n",
+    "fig.sca(axes[0])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'b'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=2,plaw=-1.5,plot_kwargs={'alpha':0.5, 'color':'r'}, linewidth=1, hires_window_offset=offset)\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, linewidth=1, hires_window_offset=offset)\n",
+    "\n",
+    "fig.sca(axes[1])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=2,splice_range=splice_range,plaw=-1.5,plot_kwargs={'color':'k'}, hires_window_offset=offset)\n",
+    "p.title(\"Difference with outer realization\")\n",
+    "\n",
+    "fig.sca(axes[2])\n",
+    "constrainedzoom.plots.zoom_demo(nP=N,nW=N,seed=1,splice_seed=2,diff_seed=1,splice_range=splice_range,plaw=-1.5, plot_kwargs={\"color\": \"k\"}, hires_window_offset=offset)\n",
+    "p.xlim(0., 0.5)\n",
+    "p.ylim(-10,10)\n",
     "p.title(\"Difference with inner realization\")"
    ]
   },
@@ -7729,14 +861,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As can be seen above, the filtering leads to some oscillations around the edge. There is a padding built\n",
+    "As can be seen above, the filtering leads to some oscillations outside the edge of the zoomed region. When splicing a region, \n",
     "in to the toy implementation; otherwise this oscillation would be inside the mask region rather than just outside it."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -7750,7 +889,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,

From 1047a3150299275cd5233d29b9e1afe065de7200 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 6 Jul 2022 12:41:45 +0100
Subject: [PATCH 03/27] Interpolate using manually-computed interpolator

---
 .../constrainedzoom/methods/__init__.py            |  3 ++-
 .../constrainedzoom/methods/detail/geometry.py     | 14 +++++++-------
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/tools/toy_implementation/constrainedzoom/methods/__init__.py b/tools/toy_implementation/constrainedzoom/methods/__init__.py
index 4f544848..7db66cbb 100644
--- a/tools/toy_implementation/constrainedzoom/methods/__init__.py
+++ b/tools/toy_implementation/constrainedzoom/methods/__init__.py
@@ -1,6 +1,6 @@
 import abc
 import math
-from typing import Tuple
+from typing import Tuple, Callable
 import numpy as np
 import scipy.fftpack
 import scipy.interpolate
@@ -28,6 +28,7 @@ def get_default_plot_padding(self):
         """The default plot padding (in coarse pixels) to hide from the high-res region"""
         return 0
 
+    @in_fourier_space
     def realization(self, no_random=False, noiseP: FFTArray=None, noiseW: FFTArray=None, seed=None) -> Tuple[FFTArray, FFTArray] :
         """Generate a realization of the Gaussian random field with constraints, if present.
         
diff --git a/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py b/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
index cfb7a69b..932da7a7 100644
--- a/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
+++ b/tools/toy_implementation/constrainedzoom/methods/detail/geometry.py
@@ -91,7 +91,7 @@ def upsample_linear(self, delta_low) -> FFTArray:
         return result.view(type=FFTArray)
 
     @staticmethod
-    def interp1d(x_vals_low, delta, x_vals_high):
+    def _interp1d_helper(x_vals_low, delta, x_vals_high):
         delta_high = np.zeros_like(x_vals_high)
         for ih, xh in enumerate(x_vals_high):
             i = np.argmin(np.abs(x_vals_low - xh))
@@ -119,7 +119,6 @@ def interp1d(x_vals_low, delta, x_vals_high):
     @in_real_space
     def upsample_cubic(self, delta_low) -> FFTArray:
         "Take a low-res vector and interpolate it into the high-res region - cubic interpolation"
-
         x_vals_low, x_vals_high = self.xs()
 
         # Note: scipy's `interp1d` uses information from non-local pixels. This is slightly
@@ -128,7 +127,7 @@ def upsample_cubic(self, delta_low) -> FFTArray:
         # pixels in the [-1, 2] range, where [0, 1] would be the left and right pixel location
         # in the low-resolution region.
         # > delta_highres = scipy.interpolate.interp1d(x_vals_low, delta_low, kind='cubic')(x_vals_high)
-        delta_highres = self.interp1d(x_vals_low, delta_low, x_vals_high)
+        delta_highres = np.dot(self._upsample_cubic_matrix, delta_low)
 
         return delta_highres.view(type=FFTArray)
 
@@ -152,14 +151,15 @@ def downsample(self, hires_vector: FFTArray, input_unpadded=True, output_padded=
     @lru_cache()
     def _upsample_cubic_matrix(self):
         matr = np.zeros((self.nW, self.nP))
+        x_vals_low, x_vals_high = self.xs()
         for i in range(self.nP):
             test = np.zeros(self.nP)
             test[i] = 1.0
-            matr[:, i] = self.upsample_cubic(test)
+            matr[:, i] = self._interp1d_helper(x_vals_low, test, x_vals_high)
 
-        test = np.random.uniform(0, 1, self.nP)
-        result = self.upsample_cubic(test)
-        np.testing.assert_allclose(np.dot(matr, test), result, atol=1e-5)
+        # test = np.random.uniform(0, 1, self.nP)
+        # result = self.upsample_cubic(test)
+        # np.testing.assert_allclose(np.dot(matr, test), result, atol=1e-5)
 
         return matr
 

From 82e68d5edadedbc74af51cb3245d38295751c718 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 12 Oct 2022 15:28:46 +0200
Subject: [PATCH 04/27] Compiling implementation

---
 genetIC/src/ic.hpp                            |  26 ++-
 .../src/simulation/field/multilevelfield.hpp  |  10 ++
 .../src/simulation/modifications/splice.hpp   | 156 ++++++++++++++++++
 genetIC/src/tools/numerics/cg.hpp             |  71 ++++++++
 4 files changed, 255 insertions(+), 8 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 9e7ac0f6..6d702d09 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -10,6 +10,7 @@
 #include <limits>
 #include <iostream>
 #include <list>
+#include <vector>
 
 
 #include "tools/numerics/vectormath.hpp"
@@ -1656,14 +1657,23 @@ class ICGenerator {
     newGenerator.draw();
     logging::entry() << "Finished constructing new random field. Beginning splice operation." << endl;
 
-    for(size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
-      auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
-      auto &newFieldThisLevel = newField.getFieldForLevel(level);
-      auto splicedFieldThisLevel = modifications::spliceOneLevel(newFieldThisLevel, originalFieldThisLevel,
-                                                             *multiLevelContext.getCovariance(level, particle::species::all));
-      splicedFieldThisLevel.toFourier();
-      originalFieldThisLevel = std::move(splicedFieldThisLevel);
-    }
+    // for(size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
+    //   auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
+    //   auto &newFieldThisLevel = newField.getFieldForLevel(level);
+    //   auto splicedFieldThisLevel = modifications::spliceOneLevel(newFieldThisLevel, originalFieldThisLevel,
+    //                                                          *multiLevelContext.getCovariance(level, particle::species::all));
+    //   splicedFieldThisLevel.toFourier();
+    //   originalFieldThisLevel = std::move(splicedFieldThisLevel);
+    // }
+    fields::OutputField<GridDataType> &a = *outputFields[0];
+    fields::OutputField<GridDataType> &b = newField;
+
+    fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
+    //   outputFields[0], std::make_shared<fields::OutputField<GridDataType>>(newField), multiLevelContext
+    // );
+    // auto vroum = modifications::spliceMultiLevel<GridDataType>(
+    //   outputFields[0], std::make_shared<fields::OutputField<GridDataType>>(newField), multiLevelContext
+    // );
   }
 
   //! Reverses the sign of the low-k modes.
diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index d4593f5a..e697753b 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -178,6 +178,16 @@ namespace fields {
       }
     }
 
+    //! Multiplied the field by the specified ratio.
+    void operator*=(DataType ratio) {
+      using namespace tools::numerics;
+
+      for (size_t level = 0; level < getNumLevels(); level++) {
+        auto &data = getFieldForLevel(level).getDataVector();
+        data *= ratio;
+      }
+    }
+
     //! Flips the sign of the field.
     void reverse() {
       for (size_t level = 0; level < getNumLevels(); ++level) {
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 2cda17f4..d657494e 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -116,6 +116,162 @@ namespace modifications {
 
       return alpha;
   }
+
+  template<typename DataType, typename T=tools::datatypes::strip_complex<DataType>>
+  fields::OutputField<DataType> spliceMultiLevel(
+    const fields::OutputField<DataType> &noiseB,
+    const fields::OutputField<DataType> &noiseA
+  ) {
+    auto multiLevelContext = noiseA.getContext();
+
+    // Prefetch covariances and masks
+    int Nlevel = noiseA.getNumLevels();
+    std::vector<fields::Field<char, T>> Mbars;
+    std::vector<fields::Field<char, T>> Ms;
+    std::vector<fields::Field<DataType, T>> covs;
+
+    auto transferType = noiseA.getTransferType();
+    auto filters = noiseA.getFilters();
+    
+    for(size_t level=0; level<Nlevel; ++level) {
+      Mbars.push_back(generateMaskComplementFromFlags(multiLevelContext.getGridForLevel(level)));
+      Ms.push_back(generateMaskFromFlags(multiLevelContext.getGridForLevel(level)));
+
+      fields::Field<DataType, T> cov(*multiLevelContext.getCovariance(level, particle::species::all));
+      cov.setFourierCoefficient(0, 0, 0, 1);
+      covs.push_back(cov);
+    }
+
+    // Compute the operator that applies T^+ ... T *at all levels*
+    auto Tplus_op_T = [&covs, &filters, &multiLevelContext, &transferType, Nlevel](
+        fields::OutputField<DataType> &inputs,
+        std::function<void(const int, fields::Field<DataType, T> &)> op
+    ) -> fields::OutputField<DataType> {
+      // Create output multifield
+      fields::OutputField<DataType> outputs(inputs);
+
+      // Compute operator using each possible pair
+      for (auto level=0; level<Nlevel; ++level) {
+        auto & cov_me = covs[level];
+        auto & f_me = filters.getFilterForLevel(level);
+        auto & out = outputs.getFieldForLevel(level);
+
+        fields::Field<DataType, T> tmp2(multiLevelContext.getGridForLevel(level), false);
+
+        out.toFourier();
+        out.applyTransferFunction(cov_me, 0.5);
+        out.applyFilter(f_me);
+        out.toReal();
+        // Apply splicing operator
+        op(level, out);
+        out.toFourier();
+        out.applyFilter(f_me);
+        out.applyTransferFunction(cov_me, 0.5);
+        out.toReal();
+
+        // Contribution from coarser levels
+        for(auto level_low=0; level_low<level; ++level_low) {
+          T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level_low) /
+                        multiLevelContext.getWeightForLevel(level);
+          auto & f_low = filters.getFilterForLevel(level_low);
+          auto & cov_low = covs[level_low];
+          auto tmp_from_low = inputs.getFieldForLevel(level_low);
+          tmp_from_low.toFourier();
+          tmp_from_low.applyTransferFunction(cov_low, 0.5);
+          tmp_from_low.applyFilter(f_low);
+          tmp_from_low.toReal();
+          // Reset to 0
+          tmp2 *= 0;
+          tmp2.addFieldFromDifferentGrid(tmp_from_low);
+          op(level, tmp2);
+          tmp2.toFourier();
+          tmp2.applyFilter(f_me);
+          tmp2.applyTransferFunction(cov_me, 0.5);
+
+          tmp2.toReal();
+          tmp2 *= 1. / pixel_volume_ratio;
+          out += tmp2;
+        }
+
+        // Contribution from finer levels
+        for(size_t level_high=level+1; level_high<Nlevel; ++level_high) {
+          T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level_high) /
+                        multiLevelContext.getWeightForLevel(level);
+          auto & f_high = filters.getFilterForLevel(level_high);
+          auto & cov_high = covs[level_high];
+          auto tmp_from_high = inputs.getFieldForLevel(level_high);
+          tmp_from_high.toFourier();
+          tmp_from_high.applyTransferFunction(cov_high, 0.5);
+          tmp_from_high.applyFilter(f_high);
+          tmp_from_high.toReal();
+          op(level_high, tmp_from_high);
+          // Reset to 0
+          tmp2 *= 0;
+          tmp2.addFieldFromDifferentGrid(tmp_from_high);
+          tmp2.toFourier();
+          tmp2.applyFilter(f_me);
+          tmp2.applyTransferFunction(cov_me, 0.5);
+
+          tmp2.toReal();
+          tmp2 *= 1./pixel_volume_ratio;
+          out += tmp2;
+        }
+      }
+
+      return outputs;
+    };
+
+    // Apply C^0.5 *at a single level*
+    auto Chalf_op = [&covs, &multiLevelContext](const int level, fields::Field<DataType,T> & input) {
+      input.toFourier();
+      input.applyTransferFunction(covs[level], 0.5);
+    };
+
+    // Apply Mbar C^-1 Mbar *at a single level*
+    auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &Mbars](const int level, fields::Field<DataType,T> & input) {
+      input.toReal();
+      input *= Mbars[level];
+      input.toFourier();
+      input.applyTransferFunction(covs[level], -1.0);
+      input.toReal();
+      input *= Mbars[level];
+    };
+
+    // Apply Mbar C^-1 M *at a single field*
+    auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &Mbars, &Ms](const int level, fields::Field<DataType,T> & input) {
+      input.toReal();
+      input *= Ms[level];
+      input.toFourier();
+      input.applyTransferFunction(covs[level], -1.0);
+      input.toReal();
+      input *= Mbars[level];
+    };
+
+    // Full splicing operator
+    // Q := T^+ Mbar C^-1 Mbar T (+ preconditioning)
+    auto Q = [&Tplus_op_T, &Mbar_Cm1_Mbar_op, &Chalf_op](
+      fields::OutputField<DataType> & inputs
+    ) -> fields::OutputField<DataType> {
+      // Precondition
+      auto outputs = Tplus_op_T(inputs, Chalf_op);
+      outputs      = Tplus_op_T(outputs, Mbar_Cm1_Mbar_op);
+      outputs      = Tplus_op_T(outputs, Chalf_op);
+      return outputs;
+    };
+
+    // Compute the rhs T^+ Mbar C^-1 M T (b - a)
+    fields::OutputField<T> rhs(noiseB);
+    rhs.addScaled(noiseA, -1);
+    Tplus_op_T(rhs, Mbar_Cm1_M_op);
+    Tplus_op_T(rhs, Chalf_op); // + preconditioning
+
+    // Solve the linear system [T^+ Mbar C^-1 Mbar T] nZ = rhs
+    auto nZ = tools::numerics::conjugateGradient2<DataType>(Q, rhs);
+    Tplus_op_T(nZ, Chalf_op); // + preconditioning
+
+    return nZ;
+    // Compute the solution
+  }
 }
 
 #endif //IC_SPLICE_HPP
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index bce1cf18..b863549a 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -57,6 +57,77 @@ namespace tools {
       return x;
 
     }
+
+    template<typename T>
+    fields::OutputField<T> conjugateGradient2(
+        std::function<fields::OutputField<T>(fields::OutputField<T> &)> Q,
+        fields::OutputField<T> &b,
+        double rtol = 1e-6,
+        double atol = 1e-12
+    ) {
+      fields::OutputField<T> residual(b);
+      fields::OutputField<T> direction(b);
+      fields::OutputField<T> x(b.getContext(), b.getTransferType());
+      
+      double scale = 0;
+      size_t dimension = 0;
+      size_t Nlevel = b.getNumLevels();
+
+      direction.reverse();
+  
+      for(auto ilevel = 0; ilevel<Nlevel; ++ilevel){
+        auto field = b.getFieldForLevel(ilevel);
+        scale += field.norm();
+        dimension += field.getGrid().size3;
+      }
+
+      if(scale==0.0) {
+        logging::entry(logging::warning) << "Conjugate gradient: result is zero!" << std::endl;
+        return x;
+      }
+
+      size_t iter = 0;
+      for(; iter<dimension+1; ++iter) {
+        auto Q_direction = Q(direction);
+        double alpha = 0;
+        for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
+          // distance to travel in specified direction
+          auto & res = residual.getFieldForLevel(ilevel);
+          auto & dir = direction.getFieldForLevel(ilevel);
+          auto & Q_dir = Q_direction.getFieldForLevel(ilevel);
+          alpha += -res.innerProduct(dir) / dir.innerProduct(Q_dir);
+        }
+        x.addScaled(direction, alpha);
+
+        residual = Q(x);
+
+        residual.addScaled(b, -1);
+        double norm = 0;
+        for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
+          norm += residual.getFieldForLevel(ilevel).norm();
+        }
+
+        if (norm < rtol * scale || norm < atol)
+          break;
+
+        logging::entry() << "Conjugate gradient iteration " << iter << " residual=" << norm << std::endl;
+
+        // update direction for next cycle; must be Q-orthogonal to all previous updates
+        double beta = 0;
+        for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
+          auto & res = residual.getFieldForLevel(ilevel);
+          auto & dir = direction.getFieldForLevel(ilevel);
+          auto & Q_dir = Q_direction.getFieldForLevel(ilevel);
+          beta += res.innerProduct(Q_dir) / dir.innerProduct(Q_dir);
+        }
+
+        direction *= beta;
+        direction.addScaled(residual, -1);
+      }
+      logging::entry() << "Conjugate gradient ended after " << iter << " iterations" << std::endl;
+
+      return x;
+    }
   }
 }
 

From d2d38dfae9ac68a69c8e7626b40ba024686eb3ba Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 12 Oct 2022 18:25:32 +0200
Subject: [PATCH 05/27] Allow to addScale multilevelfields in real space

---
 .../src/simulation/field/multilevelfield.hpp  | 32 ++++++++++++-------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index e697753b..baf5478d 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -203,22 +203,32 @@ namespace fields {
     //! Add a scaled multilevel field to the current one
     void addScaled(const MultiLevelField &other, DataType scale) {
       assertContextConsistent();
-      assert(other.isFourierOnAllLevels());
       assert (isCompatible(other));
-      toFourier();
-
-      for (size_t level = 0; level < getNumLevels(); level++) {
-        if (hasFieldForLevel(level) && other.hasFieldForLevel(level)) {
+      if (other.isFourierOnAllLevels()) {
+        toFourier();
+
+        for (size_t level = 0; level < getNumLevels(); level++) {
+          if (hasFieldForLevel(level) && other.hasFieldForLevel(level)) {
+            Field<DataType> &fieldThis = getFieldForLevel(level);
+            const Field<DataType> &fieldOther = other.getFieldForLevel(level);
+            T kMin = fieldThis.getGrid().getFourierKmin();
+            fieldThis.forEachFourierCellInt([&fieldOther, kMin, scale]
+                                              (ComplexType currentVal, int kx, int ky, int kz) {
+              return currentVal + scale * fieldOther.getFourierCoefficient(kx, ky, kz);
+            });
+          }
+        }
+      } else if (other.isRealOnAllLevels()) {
+        toReal();
+        for (size_t level = 0; level < getNumLevels(); level++) {
           Field<DataType> &fieldThis = getFieldForLevel(level);
           const Field<DataType> &fieldOther = other.getFieldForLevel(level);
-          T kMin = fieldThis.getGrid().getFourierKmin();
-          fieldThis.forEachFourierCellInt([&fieldOther, kMin, scale]
-                                            (ComplexType currentVal, int kx, int ky, int kz) {
-            return currentVal + scale * fieldOther.getFourierCoefficient(kx, ky, kz);
-          });
+
+          fieldThis.addScaled(fieldOther, scale);
         }
+      } else {
+        throw std::runtime_error("Expected the other field to be in real/fourier space, not a mix of these.");
       }
-
     }
 
     //! \brief Copy across data from another field.

From 5f7da01ee7c6f96c9591b74d64b12ab3ee10ac53 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 12 Oct 2022 18:26:10 +0200
Subject: [PATCH 06/27] Version that runs but does not converge

---
 .../src/simulation/modifications/splice.hpp   |  2 ++
 genetIC/src/tools/numerics/cg.hpp             | 30 ++++++++++++-------
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index d657494e..36d2da6d 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -256,6 +256,7 @@ namespace modifications {
       auto outputs = Tplus_op_T(inputs, Chalf_op);
       outputs      = Tplus_op_T(outputs, Mbar_Cm1_Mbar_op);
       outputs      = Tplus_op_T(outputs, Chalf_op);
+      outputs.toReal();
       return outputs;
     };
 
@@ -266,6 +267,7 @@ namespace modifications {
     Tplus_op_T(rhs, Chalf_op); // + preconditioning
 
     // Solve the linear system [T^+ Mbar C^-1 Mbar T] nZ = rhs
+    rhs.toReal();
     auto nZ = tools::numerics::conjugateGradient2<DataType>(Q, rhs);
     Tplus_op_T(nZ, Chalf_op); // + preconditioning
 
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index b863549a..ea269fc0 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -67,16 +67,18 @@ namespace tools {
     ) {
       fields::OutputField<T> residual(b);
       fields::OutputField<T> direction(b);
-      fields::OutputField<T> x(b.getContext(), b.getTransferType());
+      fields::OutputField<T> x(b);
       
       double scale = 0;
       size_t dimension = 0;
       size_t Nlevel = b.getNumLevels();
 
-      direction.reverse();
+      direction *= -1;
+      x *= 0;
   
       for(auto ilevel = 0; ilevel<Nlevel; ++ilevel){
         auto field = b.getFieldForLevel(ilevel);
+        assert(field.isFourier() == false);
         scale += field.norm();
         dimension += field.getGrid().size3;
       }
@@ -92,16 +94,21 @@ namespace tools {
         double alpha = 0;
         for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
           // distance to travel in specified direction
-          auto & res = residual.getFieldForLevel(ilevel);
-          auto & dir = direction.getFieldForLevel(ilevel);
-          auto & Q_dir = Q_direction.getFieldForLevel(ilevel);
-          alpha += -res.innerProduct(dir) / dir.innerProduct(Q_dir);
+          auto & resField = residual.getFieldForLevel(ilevel);
+          auto & dirField = direction.getFieldForLevel(ilevel);
+          auto & Q_dirField = Q_direction.getFieldForLevel(ilevel);
+          assert(resField.isFourier() == false);
+          assert(dirField.isFourier() == false);
+          assert(Q_dirField.isFourier() == false);
+          alpha += -resField.innerProduct(dirField) / dirField.innerProduct(Q_dirField);
         }
+
         x.addScaled(direction, alpha);
 
         residual = Q(x);
 
         residual.addScaled(b, -1);
+        residual.toReal();
         double norm = 0;
         for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
           norm += residual.getFieldForLevel(ilevel).norm();
@@ -115,10 +122,13 @@ namespace tools {
         // update direction for next cycle; must be Q-orthogonal to all previous updates
         double beta = 0;
         for (auto ilevel = 0; ilevel<Nlevel; ++ilevel){
-          auto & res = residual.getFieldForLevel(ilevel);
-          auto & dir = direction.getFieldForLevel(ilevel);
-          auto & Q_dir = Q_direction.getFieldForLevel(ilevel);
-          beta += res.innerProduct(Q_dir) / dir.innerProduct(Q_dir);
+          auto & resField = residual.getFieldForLevel(ilevel);
+          auto & dirField = direction.getFieldForLevel(ilevel);
+          auto & Q_dirField = Q_direction.getFieldForLevel(ilevel);
+          assert(resField.isFourier() == false);
+          assert(dirField.isFourier() == false);
+          assert(Q_dirField.isFourier() == false);
+          beta += resField.innerProduct(Q_dirField) / dirField.innerProduct(Q_dirField);
         }
 
         direction *= beta;

From 3058317d20772e487d0a4b4844b5a0fa7fc41e5e Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 25 Oct 2022 16:28:32 +0100
Subject: [PATCH 07/27] Compiling + running version

---
 .../src/simulation/modifications/splice.hpp   | 82 +++++++------------
 1 file changed, 30 insertions(+), 52 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 36d2da6d..ca5965e9 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -145,10 +145,12 @@ namespace modifications {
     // Compute the operator that applies T^+ ... T *at all levels*
     auto Tplus_op_T = [&covs, &filters, &multiLevelContext, &transferType, Nlevel](
         fields::OutputField<DataType> &inputs,
-        std::function<void(const int, fields::Field<DataType, T> &)> op
+        const std::function<void(const int, fields::Field<DataType, T> &)> op
     ) -> fields::OutputField<DataType> {
-      // Create output multifield
       fields::OutputField<DataType> outputs(inputs);
+      
+      // TODO: optimize, as we don't need to compute the off-diagonal term twice (operation is symmetric)
+      // TODO: precompute F C^0.5 for each level (otherwise it is recomputed multiple times)
 
       // Compute operator using each possible pair
       for (auto level=0; level<Nlevel; ++level) {
@@ -158,63 +160,39 @@ namespace modifications {
 
         fields::Field<DataType, T> tmp2(multiLevelContext.getGridForLevel(level), false);
 
-        out.toFourier();
-        out.applyTransferFunction(cov_me, 0.5);
-        out.applyFilter(f_me);
-        out.toReal();
-        // Apply splicing operator
-        op(level, out);
-        out.toFourier();
-        out.applyFilter(f_me);
-        out.applyTransferFunction(cov_me, 0.5);
-        out.toReal();
-
-        // Contribution from coarser levels
-        for(auto level_low=0; level_low<level; ++level_low) {
-          T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level_low) /
-                        multiLevelContext.getWeightForLevel(level);
-          auto & f_low = filters.getFilterForLevel(level_low);
-          auto & cov_low = covs[level_low];
-          auto tmp_from_low = inputs.getFieldForLevel(level_low);
-          tmp_from_low.toFourier();
-          tmp_from_low.applyTransferFunction(cov_low, 0.5);
-          tmp_from_low.applyFilter(f_low);
-          tmp_from_low.toReal();
-          // Reset to 0
-          tmp2 *= 0;
-          tmp2.addFieldFromDifferentGrid(tmp_from_low);
-          op(level, tmp2);
-          tmp2.toFourier();
-          tmp2.applyFilter(f_me);
-          tmp2.applyTransferFunction(cov_me, 0.5);
+        // This loop computes contribution from off-diagonal terms (high-from-low and low-from-high)
+        // as well as the diagonal term (low-from-low).
+        for (auto level_other=0; level_other<Nlevel; ++level_other) {
+          T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level) /
+                                 multiLevelContext.getWeightForLevel(level_other);
+          auto & f_other = filters.getFilterForLevel(level_other);
+          auto & cov_other = covs[level_other];
+          auto tmp_from_other = inputs.getFieldForLevel(level_other).copy();
 
-          tmp2.toReal();
-          tmp2 *= 1. / pixel_volume_ratio;
-          out += tmp2;
-        }
+          tmp_from_other->toFourier();
+          tmp_from_other->applyFilter(f_other);
+          tmp_from_other->applyTransferFunction(cov_other, 0.5);
+          tmp_from_other->toReal();
 
-        // Contribution from finer levels
-        for(size_t level_high=level+1; level_high<Nlevel; ++level_high) {
-          T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level_high) /
-                        multiLevelContext.getWeightForLevel(level);
-          auto & f_high = filters.getFilterForLevel(level_high);
-          auto & cov_high = covs[level_high];
-          auto tmp_from_high = inputs.getFieldForLevel(level_high);
-          tmp_from_high.toFourier();
-          tmp_from_high.applyTransferFunction(cov_high, 0.5);
-          tmp_from_high.applyFilter(f_high);
-          tmp_from_high.toReal();
-          op(level_high, tmp_from_high);
-          // Reset to 0
           tmp2 *= 0;
-          tmp2.addFieldFromDifferentGrid(tmp_from_high);
+          if (level_other < level) {             // Contribution from coarser levels
+            tmp2.addFieldFromDifferentGrid(*tmp_from_other);  // upsample
+            op(level, tmp2);                                  // then apply operator
+          } else if (level_other > level) {      // Contribution from finer levels
+            op(level_other, *tmp_from_other);                 // apply operator
+            tmp_from_other->toReal();
+            tmp2.addFieldFromDifferentGrid(*tmp_from_other);  // then downsample
+          } else if (level_other == level) {     // Contribution from same level
+            tmp2 += *tmp_from_other;
+            op(level, tmp2);
+          }
+
           tmp2.toFourier();
-          tmp2.applyFilter(f_me);
           tmp2.applyTransferFunction(cov_me, 0.5);
+          tmp2.applyFilter(f_me);
 
           tmp2.toReal();
-          tmp2 *= 1./pixel_volume_ratio;
-          out += tmp2;
+          out.addScaled(tmp2, sqrt(pixel_volume_ratio));
         }
       }
 

From 5bcab31d94fac6a7189bb9607ec7f30bd637043a Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 25 Oct 2022 18:49:49 +0100
Subject: [PATCH 08/27] Switch from CG to MINRES

This allows for the matrix to be non definite-positive (but still symmetric)
---
 .../src/simulation/modifications/splice.hpp   |  2 +-
 genetIC/src/tools/numerics/cg.hpp             | 60 +++++++++++++++++++
 2 files changed, 61 insertions(+), 1 deletion(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index ca5965e9..eec0080e 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -246,7 +246,7 @@ namespace modifications {
 
     // Solve the linear system [T^+ Mbar C^-1 Mbar T] nZ = rhs
     rhs.toReal();
-    auto nZ = tools::numerics::conjugateGradient2<DataType>(Q, rhs);
+    auto nZ = tools::numerics::minres<DataType>(Q, rhs);
     Tplus_op_T(nZ, Chalf_op); // + preconditioning
 
     return nZ;
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index ea269fc0..63d6b89e 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -138,6 +138,66 @@ namespace tools {
 
       return x;
     }
+
+    template<typename T>
+    fields::OutputField<T> minres(
+        std::function<fields::OutputField<T>(fields::OutputField<T> &)> A,
+        fields::OutputField<T> &b,
+        double rtol = 1e-6,
+        double atol = 1e-12
+    ) {
+      fields::OutputField<T> x(b);
+      x *= 0;
+      fields::OutputField<T> r(b);
+      fields::OutputField<T> s(b);
+      s = A(r);
+
+      fields::OutputField<T> p(r);
+      fields::OutputField<T> q(s);
+
+      auto innerProduct = [](fields::OutputField<T> &a, fields::OutputField<T> &b) -> double {
+        double result = 0;
+        for (auto ilevel = 0; ilevel < a.getNumLevels(); ++ilevel) {
+          result += a.getFieldForLevel(ilevel).innerProduct(b.getFieldForLevel(ilevel));
+        }
+        return result;
+      };
+      double r2 = innerProduct(r, r);
+      double rho = innerProduct(r, s);
+
+      size_t dimension = 0;
+      for(auto ilevel = 0; ilevel<b.getNumLevels(); ++ilevel){
+        dimension += b.getFieldForLevel(ilevel).getGrid().size3;
+      }
+
+      // Start iteration
+      size_t iter = 0;
+      for(; iter<dimension; ++iter) {
+        // We have q = A(p), but no need to compute it again
+        double alpha = rho / innerProduct(q, q);
+        x.addScaled(p, alpha);
+        r.addScaled(q, -alpha);
+
+        double norm = innerProduct(r, r);
+        if (norm < rtol * r2 || norm < atol)
+          break;
+
+        logging::entry() << "Conjugate gradient iteration " << iter << " residual=" << norm << std::endl;
+        s = A(r);
+        double rhobar = rho;
+        rho = innerProduct(r, s);
+        double beta = rho / rhobar;
+        p *= beta;
+        p += r;
+
+        q *= beta;
+        q += s;
+      }
+
+      logging::entry() << "MINRES ended after " << iter << " iterations" << std::endl;
+
+      return x;
+    }
   }
 }
 

From 1806b254852dcdf7ea21718c8949fa4902591cdd Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Mon, 31 Oct 2022 12:25:24 +0000
Subject: [PATCH 09/27] Works for one level

---
 genetIC/src/ic.hpp                            |  43 ++--
 .../src/simulation/field/multilevelfield.hpp  |  52 +++++
 .../src/simulation/modifications/splice.hpp   | 201 ++++++++++++------
 .../particles/multilevelgenerator.hpp         |  41 ++--
 genetIC/src/tools/numerics/cg.hpp             |  59 ++++-
 5 files changed, 295 insertions(+), 101 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 6d702d09..5015569e 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -1636,8 +1636,11 @@ class ICGenerator {
   }
 
   //! Splicing: fixes the flagged region, while reinitialising the exterior from a new random field
-  virtual void splice(size_t newSeed) {
+  virtual void splice(size_t newSeed, int approach) {
+
+    logging::entry() << "ENTERING splicing" << std::endl;
     initialiseRandomComponentIfUninitialised();
+
     if(outputFields.size()>1)
       throw std::runtime_error("Splicing is not yet implemented for the case of multiple transfer functions");
 
@@ -1656,24 +1659,32 @@ class ICGenerator {
     newGenerator.seed(newSeed);
     newGenerator.draw();
     logging::entry() << "Finished constructing new random field. Beginning splice operation." << endl;
-
-    // for(size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
-    //   auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
-    //   auto &newFieldThisLevel = newField.getFieldForLevel(level);
-    //   auto splicedFieldThisLevel = modifications::spliceOneLevel(newFieldThisLevel, originalFieldThisLevel,
-    //                                                          *multiLevelContext.getCovariance(level, particle::species::all));
-    //   splicedFieldThisLevel.toFourier();
-    //   originalFieldThisLevel = std::move(splicedFieldThisLevel);
-    // }
+    
     fields::OutputField<GridDataType> &a = *outputFields[0];
     fields::OutputField<GridDataType> &b = newField;
 
-    fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
-    //   outputFields[0], std::make_shared<fields::OutputField<GridDataType>>(newField), multiLevelContext
-    // );
-    // auto vroum = modifications::spliceMultiLevel<GridDataType>(
-    //   outputFields[0], std::make_shared<fields::OutputField<GridDataType>>(newField), multiLevelContext
-    // );
+    if (multiLevelContext.getNumLevels() == 1 && approach == 1) {
+      for(size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
+        auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
+        auto &newFieldThisLevel = newField.getFieldForLevel(level);
+        auto splicedFieldThisLevel = modifications::spliceOneLevel(newFieldThisLevel, originalFieldThisLevel,
+                                                                  *multiLevelContext.getCovariance(level, particle::species::all));
+        splicedFieldThisLevel.toFourier();
+        originalFieldThisLevel = std::move(splicedFieldThisLevel);
+      }
+    } else {
+      fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
+        
+      for (size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
+        auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
+        auto &newFieldThisLevel = f.getFieldForLevel(level);
+        assert(originalFieldThisLevel.isFourier());
+        newFieldThisLevel.toFourier();
+        originalFieldThisLevel = std::move(newFieldThisLevel);
+      }
+      // At this point, we have already combined the fields ()
+      multiLevelContext.setLevelsAreCombined();
+    }
   }
 
   //! Reverses the sign of the low-k modes.
diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index baf5478d..8ef5f5be 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -588,6 +588,58 @@ namespace fields {
       return *(this->fieldsOnLevels[i]);
     }
 
+    /* Combine all grids together
+
+    This effectively converts from whitenoise basis into delta basis.
+    */
+
+   void combineGrids() {
+      auto filters = this->getFilters();
+      size_t nlevels = this->getContext().getNumLevels();
+
+      logging::entry() << "Combining information from different levels..." << std::endl;
+
+      for (size_t level = 1; level < nlevels; ++level) {
+
+        // remove the low-frequency information from this level
+        this->getFieldForLevel(level).applyFilter(
+          filters.getHighPassFilterForLevel(level));
+
+        // replace with the low-frequency information from the level below
+        this->getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
+          getFieldForLevel(level - 1),
+          filters.getLowPassFilterForLevel(level - 1));
+      }
+
+      this->getContext().setLevelsAreCombined();
+   }
+
+   void combineGridsLowerHigher() {
+      auto filters = this->getFilters();
+      auto copy = *this;
+      size_t nlevels = this->getContext().getNumLevels();
+
+      logging::entry() << "Combining information from different levels..." << std::endl;
+
+      for (size_t level = 1; level < nlevels; ++level) {
+        // remove the low-frequency information from this level
+        this->getFieldForLevel(level).applyFilter(
+          filters.getFilterForLevel(level));
+
+        // replace with the low-frequency information from the level below
+        this->getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
+          copy.getFieldForLevel(level-1),
+          filters.getFilterForLevel(level - 1));
+        
+        // replace with the high-frequency information from the level above
+        if (level < nlevels - 1) {
+          this->getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
+            copy.getFieldForLevel(level+1),
+            filters.getFilterForLevel(level + 1));
+        }
+      }
+   }
+
 
   };
 
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index eec0080e..f512c915 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -6,6 +6,11 @@
 #include <src/tools/numerics/cg.hpp>
 
 namespace modifications {
+
+  enum TOPERATOR {
+    TPLUS, TDAGGER
+  };
+   
   template<typename T>
   fields::Field<char, T> generateMaskFromFlags(const grids::Grid<T> &grid) {
     std::vector<size_t> flags;
@@ -61,7 +66,6 @@ namespace modifications {
       delta_diff.applyTransferFunction(preconditioner, 0.5);
       delta_diff.toReal();
 
-
       fields::Field<DataType,T> z(delta_diff);
       z*=mask;
       z.toFourier();
@@ -92,7 +96,8 @@ namespace modifications {
       };
 
 
-      fields::Field<DataType,T> alpha = tools::numerics::conjugateGradient<DataType>(X,z);
+      logging::entry() << "Solving splicing problem ------------------------" << std::endl;
+      fields::Field<DataType,T> alpha = tools::numerics::minresField<DataType>(X, z);
 
       alpha.toFourier();
       alpha.applyTransferFunction(preconditioner, 0.5);
@@ -126,28 +131,32 @@ namespace modifications {
 
     // Prefetch covariances and masks
     int Nlevel = noiseA.getNumLevels();
-    std::vector<fields::Field<char, T>> Mbars;
-    std::vector<fields::Field<char, T>> Ms;
+    std::vector<fields::Field<char, T>> masksCompl;
+    std::vector<fields::Field<char, T>> masks;
     std::vector<fields::Field<DataType, T>> covs;
 
-    auto transferType = noiseA.getTransferType();
     auto filters = noiseA.getFilters();
+
+    assert (noiseA.getTransferType() == particle::species::whitenoise);
+    assert (noiseB.getTransferType() == particle::species::whitenoise);
     
+
     for(size_t level=0; level<Nlevel; ++level) {
-      Mbars.push_back(generateMaskComplementFromFlags(multiLevelContext.getGridForLevel(level)));
-      Ms.push_back(generateMaskFromFlags(multiLevelContext.getGridForLevel(level)));
+      masksCompl.push_back(generateMaskComplementFromFlags(multiLevelContext.getGridForLevel(level)));
+      masks.push_back(generateMaskFromFlags(multiLevelContext.getGridForLevel(level)));
 
       fields::Field<DataType, T> cov(*multiLevelContext.getCovariance(level, particle::species::all));
-      cov.setFourierCoefficient(0, 0, 0, 1);
       covs.push_back(cov);
     }
 
     // Compute the operator that applies T^+ ... T *at all levels*
-    auto Tplus_op_T = [&covs, &filters, &multiLevelContext, &transferType, Nlevel](
-        fields::OutputField<DataType> &inputs,
-        const std::function<void(const int, fields::Field<DataType, T> &)> op
+    auto T_op_T = [&covs, &filters, &multiLevelContext, Nlevel](
+        const fields::OutputField<DataType> &inputs,
+        const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op,
+        const TOPERATOR Top
     ) -> fields::OutputField<DataType> {
-      fields::OutputField<DataType> outputs(inputs);
+      fields::OutputField<DataType> outputs(inputs.getContext(), inputs.getTransferType());
+      fields::OutputField<DataType> inputCopy(inputs);
       
       // TODO: optimize, as we don't need to compute the off-diagonal term twice (operation is symmetric)
       // TODO: precompute F C^0.5 for each level (otherwise it is recomputed multiple times)
@@ -157,8 +166,9 @@ namespace modifications {
         auto & cov_me = covs[level];
         auto & f_me = filters.getFilterForLevel(level);
         auto & out = outputs.getFieldForLevel(level);
+        out.toReal();
 
-        fields::Field<DataType, T> tmp2(multiLevelContext.getGridForLevel(level), false);
+        fields::Field<DataType, T> tmp(multiLevelContext.getGridForLevel(level), false);
 
         // This loop computes contribution from off-diagonal terms (high-from-low and low-from-high)
         // as well as the diagonal term (low-from-low).
@@ -167,90 +177,157 @@ namespace modifications {
                                  multiLevelContext.getWeightForLevel(level_other);
           auto & f_other = filters.getFilterForLevel(level_other);
           auto & cov_other = covs[level_other];
-          auto tmp_from_other = inputs.getFieldForLevel(level_other).copy();
+          auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
 
           tmp_from_other->toFourier();
+          std::cout << " " << f_other << " "<< " C^0.5";
           tmp_from_other->applyFilter(f_other);
           tmp_from_other->applyTransferFunction(cov_other, 0.5);
           tmp_from_other->toReal();
 
-          tmp2 *= 0;
-          if (level_other < level) {             // Contribution from coarser levels
-            tmp2.addFieldFromDifferentGrid(*tmp_from_other);  // upsample
-            op(level, tmp2);                                  // then apply operator
-          } else if (level_other > level) {      // Contribution from finer levels
-            op(level_other, *tmp_from_other);                 // apply operator
-            tmp_from_other->toReal();
-            tmp2.addFieldFromDifferentGrid(*tmp_from_other);  // then downsample
+          tmp *= 0;
+          if (level_other < level) {             // Contribution from coarser level
+            // Upsample then compute operation
+            std::cout << " P_" << level_other;
+            tmp.addFieldFromDifferentGrid(*tmp_from_other); 
+            tmp = op(level, tmp);
+          } else if (level_other > level) {      // Contribution from finer level
+            // Compute operation then downsample
+            tmp.addFieldFromDifferentGrid(op(level_other, *tmp_from_other));
+            std::cout << " P^+_" << level_other;
           } else if (level_other == level) {     // Contribution from same level
-            tmp2 += *tmp_from_other;
-            op(level, tmp2);
+            tmp = op(level, *tmp_from_other);
           }
 
-          tmp2.toFourier();
-          tmp2.applyTransferFunction(cov_me, 0.5);
-          tmp2.applyFilter(f_me);
+          tmp.toFourier();
+          if (Top == TDAGGER) {
+            std::cout << " C^0.5 ";
+            tmp.applyTransferFunction(cov_me, 0.5);
+          } else if (Top == TPLUS) {
+            std::cout << " C^-0.5 ";
+            tmp.applyTransferFunction(cov_me, -0.5);
+          }
+          std::cout << f_me;
+          tmp.applyFilter(f_me);
 
-          tmp2.toReal();
-          out.addScaled(tmp2, sqrt(pixel_volume_ratio));
+          tmp.toReal();
+          std::cout << " * " << sqrt(pixel_volume_ratio);
+          out.addScaled(tmp, sqrt(pixel_volume_ratio));
         }
       }
 
       return outputs;
     };
 
-    // Apply C^0.5 *at a single level*
-    auto Chalf_op = [&covs, &multiLevelContext](const int level, fields::Field<DataType,T> & input) {
-      input.toFourier();
-      input.applyTransferFunction(covs[level], 0.5);
+    auto Tplus_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
+                                const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op
+    ) -> fields::OutputField<DataType> {
+      return T_op_T(inputs, op, TPLUS);
     };
 
-    // Apply Mbar C^-1 Mbar *at a single level*
-    auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &Mbars](const int level, fields::Field<DataType,T> & input) {
-      input.toReal();
-      input *= Mbars[level];
-      input.toFourier();
-      input.applyTransferFunction(covs[level], -1.0);
-      input.toReal();
-      input *= Mbars[level];
+    auto Tdagger_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
+                                  const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op
+    ) -> fields::OutputField<DataType> {
+      return T_op_T(inputs, op, TDAGGER);
     };
 
-    // Apply Mbar C^-1 M *at a single field*
-    auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &Mbars, &Ms](const int level, fields::Field<DataType,T> & input) {
-      input.toReal();
-      input *= Ms[level];
-      input.toFourier();
-      input.applyTransferFunction(covs[level], -1.0);
-      input.toReal();
-      input *= Mbars[level];
+    // Compute Mbar C^-1 Mbar for a single level
+    auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &masksCompl](const int level, const fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
+      auto output = input;
+      output.toReal();
+      std::cout << " Mbar";
+      output *= masksCompl[level];
+      output.toFourier();
+      std::cout << " C^-1";
+      output.applyTransferFunction(covs[level], -1.0);
+      output.toReal();
+      std::cout << " Mbar";
+      output *= masksCompl[level];
+      return output;
+    };
+
+    // Compute Mbar C^-1 M for a single field
+    auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &masksCompl, &masks](const int level, const fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
+      auto output = input;
+      output.toReal();
+      std::cout << " M";
+      output *= masks[level];
+      output.toFourier();
+      std::cout << " C^-1";
+      output.applyTransferFunction(covs[level], -1.0);
+      output.toReal();
+      std::cout << " Mbar";
+      output *= masksCompl[level];
+      return output;
     };
 
     // Full splicing operator
-    // Q := T^+ Mbar C^-1 Mbar T (+ preconditioning)
-    auto Q = [&Tplus_op_T, &Mbar_Cm1_Mbar_op, &Chalf_op](
-      fields::OutputField<DataType> & inputs
+    // Q := T^+ Mbar C^-1 Mbar T
+    auto Q = [&Tdagger_op_T, &Mbar_Cm1_Mbar_op](
+      const fields::OutputField<DataType> & inputs
     ) -> fields::OutputField<DataType> {
       // Precondition
-      auto outputs = Tplus_op_T(inputs, Chalf_op);
-      outputs      = Tplus_op_T(outputs, Mbar_Cm1_Mbar_op);
-      outputs      = Tplus_op_T(outputs, Chalf_op);
+      std::cout << "                                               Q = ";
+      auto outputs = inputs;
+      outputs = Tdagger_op_T(outputs, Mbar_Cm1_Mbar_op);
       outputs.toReal();
+      std::cout << std::endl;
       return outputs;
     };
 
     // Compute the rhs T^+ Mbar C^-1 M T (b - a)
     fields::OutputField<T> rhs(noiseB);
     rhs.addScaled(noiseA, -1);
-    Tplus_op_T(rhs, Mbar_Cm1_M_op);
-    Tplus_op_T(rhs, Chalf_op); // + preconditioning
+    rhs.toReal();
 
-    // Solve the linear system [T^+ Mbar C^-1 Mbar T] nZ = rhs
+
+    std::cout << "                                               nB-nA";
+    rhs = Tdagger_op_T(rhs, Mbar_Cm1_M_op);
+    std::cout << std::endl;
+    // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
+    // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
     rhs.toReal();
-    auto nZ = tools::numerics::minres<DataType>(Q, rhs);
-    Tplus_op_T(nZ, Chalf_op); // + preconditioning
 
-    return nZ;
-    // Compute the solution
+    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs);
+
+    // T^+ M T (n_a-n_b)
+    fields::OutputField<DataType> Ma_minus_b(noiseA);
+    Ma_minus_b.addScaled(noiseB, -1);
+    Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
+    Ma_minus_b.toReal();
+    for (auto level = 0; level<Nlevel; ++level)
+      Ma_minus_b.getFieldForLevel(level) *= masks[level];
+
+    // T^+ Mbar T n_alpha
+    // Note: we use an alias here for the sake of readability
+    fields::OutputField<DataType> & Mbar_n_alpha = n_alpha;
+    Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
+    Mbar_n_alpha.toReal();
+    for (auto level = 0; level<Nlevel; ++level)
+      Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
+
+    // f = b + M (a-b) + alpha; 
+    fields::OutputField<DataType> outputs(noiseB);
+    outputs.applyPowerSpectrumFor(particle::species::all);
+    outputs.toReal();
+
+    outputs += Ma_minus_b;
+    outputs += Mbar_n_alpha;
+
+    // outputs.combineGrids();
+
+    Ma_minus_b.getFieldForLevel(0).dumpGridData("Ma_minus_b-0.npz");
+    Mbar_n_alpha.getFieldForLevel(0).dumpGridData("Mbar_n_alpha-0.npz");
+    outputs.getFieldForLevel(0).dumpGridData("output-0.npz");
+
+    // Ma_minus_b.getFieldForLevel(1).dumpGridData("Ma_minus_b-1.npz");
+    // Mbar_n_alpha.getFieldForLevel(1).dumpGridData("Mbar_n_alpha-1.npz");
+    // outputs.getFieldForLevel(1).dumpGridData("output-1.npz");
+
+    // Convert back to whitenoise
+    outputs.applyPowerSpectrumFor(particle::species::whitenoise);
+
+    return outputs;
   }
 }
 
diff --git a/genetIC/src/simulation/particles/multilevelgenerator.hpp b/genetIC/src/simulation/particles/multilevelgenerator.hpp
index 2e4cedd3..8d2a053e 100644
--- a/genetIC/src/simulation/particles/multilevelgenerator.hpp
+++ b/genetIC/src/simulation/particles/multilevelgenerator.hpp
@@ -91,39 +91,42 @@ namespace particle {
       generator.pGenerators.emplace_back(
         std::make_shared<ZPG>(generator.overdensityField.getFieldForLevel(0)));
     } else if (nlevels >= 2) {
-
+      
+      // Combine the fields on each grid
+      if (!generator.context.getLevelsAreCombined())
+        generator.overdensityField.combineGrids();
 
       for (size_t level = 0; level < nlevels; ++level)
         generator.pGenerators.emplace_back(
-          std::make_shared<ZPG>(generator.overdensityField.getFieldForLevel(level)));
+          std::make_shared<ZPG>(generator.overdensityField.getFieldForLevel(level))
+        );
 
-      logging::entry() << "Combining information from different levels..." << endl;
 
-      auto filters = generator.overdensityField.getFilters();
+      // auto filters = generator.overdensityField.getFilters();
 
-      for (size_t level = 1; level < nlevels; ++level) {
+      // for (size_t level = 1; level < nlevels; ++level) {
 
-        // remove the low-frequency information from this level
-        generator.overdensityField.getFieldForLevel(level).applyFilter(
-          filters.getHighPassFilterForLevel(level));
-        generator.pGenerators[level]->applyFilter(filters.getHighPassFilterForLevel(level));
+      //   // remove the low-frequency information from this level
+      //   // generator.overdensityField.getFieldForLevel(level).applyFilter(
+      //     // filters.getHighPassFilterForLevel(level));
+      //   generator.pGenerators[level]->applyFilter(filters.getHighPassFilterForLevel(level));
 
-        // replace with the low-frequency information from the level below
-        generator.overdensityField.getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
-          generator.overdensityField.getFieldForLevel(level - 1),
-          filters.getLowPassFilterForLevel(level - 1));
-        generator.pGenerators[level]->addFieldFromDifferentGridWithFilter(*generator.pGenerators[level - 1],
-                                                                          filters.getLowPassFilterForLevel(level - 1));
+      //   // replace with the low-frequency information from the level below
+      //   // generator.overdensityField.getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
+      //     // generator.overdensityField.getFieldForLevel(level - 1),
+      //     // filters.getLowPassFilterForLevel(level - 1));
+      //   generator.pGenerators[level]->addFieldFromDifferentGridWithFilter(*generator.pGenerators[level - 1],
+      //                                                                     filters.getLowPassFilterForLevel(level - 1));
 
 
-      }
+      // }
+
+      // generator.overdensityField.getContext().setLevelsAreCombined();
 
-      generator.overdensityField.getContext().setLevelsAreCombined();
+      logging::entry() << "Combined grids" << endl;
 
       for (size_t i = 0; i < nlevels; ++i)
         generator.pGenerators[i]->toReal();
-
-
     }
   }
 
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index 63d6b89e..532ffee9 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -139,6 +139,8 @@ namespace tools {
       return x;
     }
 
+
+    /* Adapted from https://stanford.edu/group/SOL/reports/SOL-2011-2R.pdf */
     template<typename T>
     fields::OutputField<T> minres(
         std::function<fields::OutputField<T>(fields::OutputField<T> &)> A,
@@ -162,7 +164,7 @@ namespace tools {
         }
         return result;
       };
-      double r2 = innerProduct(r, r);
+      double scale = sqrt(innerProduct(b, b));
       double rho = innerProduct(r, s);
 
       size_t dimension = 0;
@@ -178,11 +180,11 @@ namespace tools {
         x.addScaled(p, alpha);
         r.addScaled(q, -alpha);
 
-        double norm = innerProduct(r, r);
-        if (norm < rtol * r2 || norm < atol)
+        double norm = sqrt(innerProduct(r, r));
+        if (norm < rtol * scale || norm < atol)
           break;
 
-        logging::entry() << "Conjugate gradient iteration " << iter << " residual=" << norm << std::endl;
+        logging::entry() << "MINRES iteration " << iter << " residual=" << norm << std::endl;
         s = A(r);
         double rhobar = rho;
         rho = innerProduct(r, s);
@@ -196,6 +198,55 @@ namespace tools {
 
       logging::entry() << "MINRES ended after " << iter << " iterations" << std::endl;
 
+      return x;
+    }
+    template<typename T>
+    fields::Field<T> minresField(
+        std::function<fields::Field<T>(fields::Field<T> &)> A,
+        fields::Field<T> &b,
+        double rtol = 1e-6,
+        double atol = 1e-12
+    ) {
+      fields::Field<T> x(b);
+      x *= 0;
+      fields::Field<T> r(b);
+      fields::Field<T> s(b);
+      s = A(r);
+
+      fields::Field<T> p(r);
+      fields::Field<T> q(s);
+
+      double scale = b.norm();
+      double rho = r.innerProduct(s);
+
+      size_t dimension = b.getGrid().size3;
+
+      // Start iteration
+      size_t iter = 0;
+      for(; iter<dimension; ++iter) {
+        // We have q = A(p), but no need to compute it again
+        double alpha = rho / q.innerProduct(q);
+        x.addScaled(p, alpha);
+        r.addScaled(q, -alpha);
+
+        double norm = r.norm();
+        if (norm < rtol * scale || norm < atol)
+          break;
+
+        logging::entry() << "MINRES iteration " << iter << " residual=" << norm << std::endl;
+        s = A(r);
+        double rhobar = rho;
+        rho = r.innerProduct(s);
+        double beta = rho / rhobar;
+        p *= beta;
+        p += r;
+
+        q *= beta;
+        q += s;
+      }
+
+      logging::entry() << "MINRES ended after " << iter << " iterations" << std::endl;
+
       return x;
     }
   }

From 141572742956529136ee2632a221ad46b02e0a8a Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Mon, 31 Oct 2022 16:40:56 +0000
Subject: [PATCH 10/27] Very close-to-working version

---
 .../src/simulation/modifications/splice.hpp   | 209 ++++++++++--------
 1 file changed, 118 insertions(+), 91 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index f512c915..e1b57931 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -10,7 +10,7 @@ namespace modifications {
   enum TOPERATOR {
     TPLUS, TDAGGER
   };
-   
+
   template<typename T>
   fields::Field<char, T> generateMaskFromFlags(const grids::Grid<T> &grid) {
     std::vector<size_t> flags;
@@ -139,7 +139,7 @@ namespace modifications {
 
     assert (noiseA.getTransferType() == particle::species::whitenoise);
     assert (noiseB.getTransferType() == particle::species::whitenoise);
-    
+
 
     for(size_t level=0; level<Nlevel; ++level) {
       masksCompl.push_back(generateMaskComplementFromFlags(multiLevelContext.getGridForLevel(level)));
@@ -152,66 +152,64 @@ namespace modifications {
     // Compute the operator that applies T^+ ... T *at all levels*
     auto T_op_T = [&covs, &filters, &multiLevelContext, Nlevel](
         const fields::OutputField<DataType> &inputs,
-        const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op,
+        const std::function<void(const int, fields::Field<DataType, T> &)> op,
         const TOPERATOR Top
     ) -> fields::OutputField<DataType> {
       fields::OutputField<DataType> outputs(inputs.getContext(), inputs.getTransferType());
       fields::OutputField<DataType> inputCopy(inputs);
-      
-      // TODO: optimize, as we don't need to compute the off-diagonal term twice (operation is symmetric)
-      // TODO: precompute F C^0.5 for each level (otherwise it is recomputed multiple times)
+      const double power_out = (Top == TDAGGER) ? 0.5 : -0.5;
+
+      assert (inputs.isRealOnAllLevels());
 
       // Compute operator using each possible pair
       for (auto level=0; level<Nlevel; ++level) {
-        auto & cov_me = covs[level];
-        auto & f_me = filters.getFilterForLevel(level);
         auto & out = outputs.getFieldForLevel(level);
         out.toReal();
 
-        fields::Field<DataType, T> tmp(multiLevelContext.getGridForLevel(level), false);
-
-        // This loop computes contribution from off-diagonal terms (high-from-low and low-from-high)
-        // as well as the diagonal term (low-from-low).
-        for (auto level_other=0; level_other<Nlevel; ++level_other) {
+        for (auto level_other=std::max(0, level-1); level_other<std::min(Nlevel, level+2); ++level_other) {
           T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level) /
                                  multiLevelContext.getWeightForLevel(level_other);
-          auto & f_other = filters.getFilterForLevel(level_other);
-          auto & cov_other = covs[level_other];
-          auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
-
-          tmp_from_other->toFourier();
-          std::cout << " " << f_other << " "<< " C^0.5";
-          tmp_from_other->applyFilter(f_other);
-          tmp_from_other->applyTransferFunction(cov_other, 0.5);
-          tmp_from_other->toReal();
-
-          tmp *= 0;
-          if (level_other < level) {             // Contribution from coarser level
-            // Upsample then compute operation
-            std::cout << " P_" << level_other;
-            tmp.addFieldFromDifferentGrid(*tmp_from_other); 
-            tmp = op(level, tmp);
-          } else if (level_other > level) {      // Contribution from finer level
-            // Compute operation then downsample
-            tmp.addFieldFromDifferentGrid(op(level_other, *tmp_from_other));
-            std::cout << " P^+_" << level_other;
-          } else if (level_other == level) {     // Contribution from same level
-            tmp = op(level, *tmp_from_other);
-          }
 
-          tmp.toFourier();
-          if (Top == TDAGGER) {
-            std::cout << " C^0.5 ";
-            tmp.applyTransferFunction(cov_me, 0.5);
-          } else if (Top == TPLUS) {
-            std::cout << " C^-0.5 ";
-            tmp.applyTransferFunction(cov_me, -0.5);
+          // if (level_other != level) continue;
+
+          fields::Field<DataType, T> tmp(out.getGrid(), false);
+
+          if (level_other > level) {        // Contribution from finer level
+            tmp.addFieldFromDifferentGrid(inputCopy.getFieldForLevel(level_other));
+            tmp.toFourier();
+            tmp.applyFilter(filters.getHighPassFilterForLevel(level));
+            tmp.applyTransferFunction(covs[level], 0.5);
+            tmp.toReal();
+            op(level, tmp);
+            tmp.toFourier();
+            tmp.applyTransferFunction(covs[level], power_out);
+            tmp.applyFilter(filters.getLowPassFilterForLevel(level));
+            tmp.toReal();
+          } else if (level_other < level) { // Contribution from coarser level
+            auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
+            tmp_from_other->toFourier();
+            tmp_from_other->applyFilter(filters.getLowPassFilterForLevel(level_other));
+            tmp_from_other->applyTransferFunction(covs[level_other], 0.5);
+            tmp_from_other->toReal();
+            op(level_other, *tmp_from_other);
+            tmp_from_other->toFourier();
+            tmp_from_other->applyTransferFunction(covs[level_other], power_out);
+            tmp_from_other->applyFilter(filters.getHighPassFilterForLevel(level_other));
+            tmp_from_other->toReal();
+            tmp.addFieldFromDifferentGrid(*tmp_from_other);
+            tmp.toReal();
+          } else {
+            tmp += inputCopy.getFieldForLevel(level);
+            tmp.toFourier();
+            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.applyTransferFunction(covs[level], 0.5);
+            tmp.toReal();
+            op(level, tmp);
+            tmp.toFourier();
+            tmp.applyTransferFunction(covs[level], power_out);
+            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.toReal();
           }
-          std::cout << f_me;
-          tmp.applyFilter(f_me);
-
-          tmp.toReal();
-          std::cout << " * " << sqrt(pixel_volume_ratio);
           out.addScaled(tmp, sqrt(pixel_volume_ratio));
         }
       }
@@ -220,75 +218,97 @@ namespace modifications {
     };
 
     auto Tplus_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
-                                const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op
+                                const std::function<void(const int, fields::Field<DataType, T> &)> op
     ) -> fields::OutputField<DataType> {
       return T_op_T(inputs, op, TPLUS);
     };
 
     auto Tdagger_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
-                                  const std::function<fields::Field<DataType, T>(const int, const fields::Field<DataType, T> &)> op
+                                  const std::function<void(const int, fields::Field<DataType, T> &)> op
     ) -> fields::OutputField<DataType> {
       return T_op_T(inputs, op, TDAGGER);
     };
 
     // Compute Mbar C^-1 Mbar for a single level
-    auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &masksCompl](const int level, const fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
-      auto output = input;
-      output.toReal();
-      std::cout << " Mbar";
-      output *= masksCompl[level];
-      output.toFourier();
-      std::cout << " C^-1";
-      output.applyTransferFunction(covs[level], -1.0);
-      output.toReal();
-      std::cout << " Mbar";
-      output *= masksCompl[level];
-      return output;
+    auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &masksCompl](const int level, fields::Field<DataType,T> & input) {
+      input.toReal();
+      // std::cout << " Mbar";
+      input *= masksCompl[level];
+      input.toFourier();
+      // std::cout << " C^-1";
+      input.applyTransferFunction(covs[level], -1.0);
+      input.toReal();
+      // std::cout << " Mbar";
+      input *= masksCompl[level];
     };
 
     // Compute Mbar C^-1 M for a single field
-    auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &masksCompl, &masks](const int level, const fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
-      auto output = input;
-      output.toReal();
-      std::cout << " M";
-      output *= masks[level];
-      output.toFourier();
-      std::cout << " C^-1";
-      output.applyTransferFunction(covs[level], -1.0);
-      output.toReal();
-      std::cout << " Mbar";
-      output *= masksCompl[level];
-      return output;
+    auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &masksCompl, &masks](const int level, fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
+      input.toReal();
+      // std::cout << " M";
+      input *= masks[level];
+      input.toFourier();
+      // std::cout << " C^-1";
+      input.applyTransferFunction(covs[level], -1.0);
+      input.toReal();
+      // std::cout << " Mbar";
+      input *= masksCompl[level];
+      return input;
+    };
+
+    auto preconditioner = [&covs, &masksCompl, &filters, Nlevel](fields::OutputField<DataType> & inputs) {
+      const bool precondition = false;
+
+      if (precondition) {
+        for (auto level=0; level<Nlevel; ++level) {
+          auto & field = inputs.getFieldForLevel(level);
+          field.toFourier();
+          field.applyTransferFunction(covs[level], -0.5);
+          field.applyFilter(filters.getFilterForLevel(level));
+          field.toReal();
+          field *= masksCompl[level];
+          field.toFourier();
+          field.applyTransferFunction(covs[level], 1.0);
+          field.toReal();
+          field *= masksCompl[level];
+          field.toFourier();
+          field.applyTransferFunction(covs[level], -0.5);
+          field.applyFilter(filters.getFilterForLevel(level));
+          field.toReal();
+        }
+      }
     };
 
     // Full splicing operator
     // Q := T^+ Mbar C^-1 Mbar T
-    auto Q = [&Tdagger_op_T, &Mbar_Cm1_Mbar_op](
+    auto Q = [&Tdagger_op_T, &Mbar_Cm1_Mbar_op, &preconditioner](
       const fields::OutputField<DataType> & inputs
     ) -> fields::OutputField<DataType> {
-      // Precondition
-      std::cout << "                                               Q = ";
       auto outputs = inputs;
+      preconditioner(outputs);
       outputs = Tdagger_op_T(outputs, Mbar_Cm1_Mbar_op);
+      preconditioner(outputs);
       outputs.toReal();
-      std::cout << std::endl;
       return outputs;
     };
 
     // Compute the rhs T^+ Mbar C^-1 M T (b - a)
     fields::OutputField<T> rhs(noiseB);
     rhs.addScaled(noiseA, -1);
+    rhs.toFourier();
+    // rhs.applyPowerSpectrumFor(particle::species::all);
+    // rhs.combineGrids();
+    // rhs.applyPowerSpectrumFor(particle::species::whitenoise);
     rhs.toReal();
-
-
-    std::cout << "                                               nB-nA";
     rhs = Tdagger_op_T(rhs, Mbar_Cm1_M_op);
-    std::cout << std::endl;
+    preconditioner(rhs);
+    rhs.toReal();
+
     // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
-    rhs.toReal();
+    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-2, 1e-10);
 
-    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs);
+    preconditioner(n_alpha);
 
     // T^+ M T (n_a-n_b)
     fields::OutputField<DataType> Ma_minus_b(noiseA);
@@ -297,32 +317,39 @@ namespace modifications {
     Ma_minus_b.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Ma_minus_b.getFieldForLevel(level) *= masks[level];
+    // Ma_minus_b.combineGrids();
+    Ma_minus_b.toReal();
 
-    // T^+ Mbar T n_alpha
+    // T^+ Mbar T n_alpha 
     // Note: we use an alias here for the sake of readability
     fields::OutputField<DataType> & Mbar_n_alpha = n_alpha;
     Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
     Mbar_n_alpha.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
+    // Mbar_n_alpha.combineGrids();
+    Mbar_n_alpha.toReal();
 
-    // f = b + M (a-b) + alpha; 
+    // f = b + M (a-b) + alpha;
     fields::OutputField<DataType> outputs(noiseB);
     outputs.applyPowerSpectrumFor(particle::species::all);
+    // outputs.combineGrids();
     outputs.toReal();
 
     outputs += Ma_minus_b;
     outputs += Mbar_n_alpha;
 
-    // outputs.combineGrids();
-
     Ma_minus_b.getFieldForLevel(0).dumpGridData("Ma_minus_b-0.npz");
     Mbar_n_alpha.getFieldForLevel(0).dumpGridData("Mbar_n_alpha-0.npz");
     outputs.getFieldForLevel(0).dumpGridData("output-0.npz");
 
-    // Ma_minus_b.getFieldForLevel(1).dumpGridData("Ma_minus_b-1.npz");
-    // Mbar_n_alpha.getFieldForLevel(1).dumpGridData("Mbar_n_alpha-1.npz");
-    // outputs.getFieldForLevel(1).dumpGridData("output-1.npz");
+    if (Nlevel > 1) {
+      Ma_minus_b.getFieldForLevel(1).dumpGridData("Ma_minus_b-1.npz");
+      Mbar_n_alpha.getFieldForLevel(1).dumpGridData("Mbar_n_alpha-1.npz");
+      outputs.getFieldForLevel(1).dumpGridData("output-1.npz");
+    }
+    // Combine the grids
+    outputs.combineGrids();
 
     // Convert back to whitenoise
     outputs.applyPowerSpectrumFor(particle::species::whitenoise);

From e850c01dd96586f2885eed7d0651293e809f0018 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 13:09:55 +0000
Subject: [PATCH 11/27] Find OpenMP with cmakefile

---
 CMakeLists.txt | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bca192e9..f9d89a51 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -60,6 +60,7 @@ set(SOURCE_FILES
         genetIC/src/simulation/modifications/linearmodification.hpp
         genetIC/src/simulation/modifications/quadraticmodification.hpp
         genetIC/src/simulation/multilevelgrid/mask.hpp
+        genetIC/src/tools/numerics/cg.hpp
         genetIC/src/tools/memmap.hpp
         genetIC/src/tools/numerics/tricubic.hpp genetIC/src/tools/logging.hpp genetIC/src/tools/logging.cpp genetIC/src/simulation/modifications/splice.hpp genetIC/src/tools/lru_cache.hpp)
 
@@ -116,4 +117,9 @@ add_definitions(-DCUBIC_INTERPOLATION
         -DZELDOVICH_GRADIENT_FOURIER_SPACE
         )
 add_compile_options(-Wextra)
-add_executable(genetIC ${SOURCE_FILES})
\ No newline at end of file
+add_executable(genetIC ${SOURCE_FILES})
+find_package(OpenMP)
+if(OpenMP_CXX_FOUND)
+    target_link_libraries(genetIC PUBLIC OpenMP::OpenMP_CXX)
+    add_definitions(-DOPENMP)
+endif()

From 57a57bf8f7e2ff5e3aa2b301212473a516e430bf Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 13:10:51 +0000
Subject: [PATCH 12/27] Clear exception when dumping non-existing level

---
 genetIC/src/ic.hpp | 39 ++++++++++++++-------------------------
 1 file changed, 14 insertions(+), 25 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 5015569e..bb5e50b7 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -850,7 +850,9 @@ class ICGenerator {
   */
   template<typename TField>
   void dumpGridData(size_t level, const TField &data, std::string prefix = "grid") {
-
+    if (level >= multiLevelContext.getNumLevels() || level < 0) {
+      throw (std::runtime_error("Attempted to dump grid data for level that does not exist."));
+    }
     initialiseRandomComponentIfUninitialised();
 
     auto levelGrid = data.getGrid();
@@ -1636,9 +1638,7 @@ class ICGenerator {
   }
 
   //! Splicing: fixes the flagged region, while reinitialising the exterior from a new random field
-  virtual void splice(size_t newSeed, int approach) {
-
-    logging::entry() << "ENTERING splicing" << std::endl;
+  virtual void splice(size_t newSeed) {
     initialiseRandomComponentIfUninitialised();
 
     if(outputFields.size()>1)
@@ -1663,28 +1663,17 @@ class ICGenerator {
     fields::OutputField<GridDataType> &a = *outputFields[0];
     fields::OutputField<GridDataType> &b = newField;
 
-    if (multiLevelContext.getNumLevels() == 1 && approach == 1) {
-      for(size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
-        auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
-        auto &newFieldThisLevel = newField.getFieldForLevel(level);
-        auto splicedFieldThisLevel = modifications::spliceOneLevel(newFieldThisLevel, originalFieldThisLevel,
-                                                                  *multiLevelContext.getCovariance(level, particle::species::all));
-        splicedFieldThisLevel.toFourier();
-        originalFieldThisLevel = std::move(splicedFieldThisLevel);
-      }
-    } else {
-      fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
-        
-      for (size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
-        auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
-        auto &newFieldThisLevel = f.getFieldForLevel(level);
-        assert(originalFieldThisLevel.isFourier());
-        newFieldThisLevel.toFourier();
-        originalFieldThisLevel = std::move(newFieldThisLevel);
-      }
-      // At this point, we have already combined the fields ()
-      multiLevelContext.setLevelsAreCombined();
+    fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
+      
+    for (size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
+      auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
+      auto &newFieldThisLevel = f.getFieldForLevel(level);
+      assert(originalFieldThisLevel.isFourier());
+      newFieldThisLevel.toFourier();
+      originalFieldThisLevel = std::move(newFieldThisLevel);
     }
+    // At this point, we have already combined the fields ()
+    multiLevelContext.setLevelsAreCombined();
   }
 
   //! Reverses the sign of the low-k modes.

From 3bab23591fe0faedf2bc8e46e4f32467fd82c9f7 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 13:18:21 +0000
Subject: [PATCH 13/27] Cleanup splicing

---
 .../src/simulation/modifications/splice.hpp   | 40 ++++++++-----------
 1 file changed, 17 insertions(+), 23 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index e1b57931..4f102164 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -170,34 +170,36 @@ namespace modifications {
           T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level) /
                                  multiLevelContext.getWeightForLevel(level_other);
 
-          // if (level_other != level) continue;
-
           fields::Field<DataType, T> tmp(out.getGrid(), false);
 
           if (level_other > level) {        // Contribution from finer level
-            tmp.addFieldFromDifferentGrid(inputCopy.getFieldForLevel(level_other));
+            auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
+            tmp.addFieldFromDifferentGrid(*tmp_from_other);
             tmp.toFourier();
-            tmp.applyFilter(filters.getHighPassFilterForLevel(level));
+            // tmp_from_other->applyFilter(filters.getHighPassFilterForLevel(level_other));
+            tmp.applyFilter(filters.getFilterForLevel(level));
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            tmp.applyFilter(filters.getLowPassFilterForLevel(level));
+            // tmp.applyFilter(filters.getLowPassFilterForLevel(level));
+            tmp.applyFilter(filters.getFilterForLevel(level));
             tmp.toReal();
           } else if (level_other < level) { // Contribution from coarser level
             auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
             tmp_from_other->toFourier();
-            tmp_from_other->applyFilter(filters.getLowPassFilterForLevel(level_other));
+            // tmp_from_other->applyFilter(filters.getLowPassFilterForLevel(level_other));
+            tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
             tmp_from_other->applyTransferFunction(covs[level_other], 0.5);
             tmp_from_other->toReal();
             op(level_other, *tmp_from_other);
             tmp_from_other->toFourier();
             tmp_from_other->applyTransferFunction(covs[level_other], power_out);
-            tmp_from_other->applyFilter(filters.getHighPassFilterForLevel(level_other));
+            // tmp.applyFilter(filters.getHighPassFilterForLevel(level_other));
+            tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
             tmp_from_other->toReal();
             tmp.addFieldFromDifferentGrid(*tmp_from_other);
-            tmp.toReal();
           } else {
             tmp += inputCopy.getFieldForLevel(level);
             tmp.toFourier();
@@ -210,7 +212,7 @@ namespace modifications {
             tmp.applyFilter(filters.getFilterForLevel(level));
             tmp.toReal();
           }
-          out.addScaled(tmp, sqrt(pixel_volume_ratio));
+          out.addScaled(tmp, std::sqrt(pixel_volume_ratio));
         }
       }
 
@@ -232,30 +234,25 @@ namespace modifications {
     // Compute Mbar C^-1 Mbar for a single level
     auto Mbar_Cm1_Mbar_op = [&covs, &multiLevelContext, &masksCompl](const int level, fields::Field<DataType,T> & input) {
       input.toReal();
-      // std::cout << " Mbar";
       input *= masksCompl[level];
       input.toFourier();
-      // std::cout << " C^-1";
       input.applyTransferFunction(covs[level], -1.0);
       input.toReal();
-      // std::cout << " Mbar";
       input *= masksCompl[level];
     };
 
     // Compute Mbar C^-1 M for a single field
     auto Mbar_Cm1_M_op = [&covs, &multiLevelContext, &masksCompl, &masks](const int level, fields::Field<DataType,T> & input) -> fields::Field<DataType,T> {
       input.toReal();
-      // std::cout << " M";
       input *= masks[level];
       input.toFourier();
-      // std::cout << " C^-1";
       input.applyTransferFunction(covs[level], -1.0);
       input.toReal();
-      // std::cout << " Mbar";
       input *= masksCompl[level];
       return input;
     };
 
+    auto preconditioner = [&covs, &masksCompl, &filters, Nlevel, &Tdagger_op_T, &Tplus_op_T](fields::OutputField<DataType> & inputs) {
     auto preconditioner = [&covs, &masksCompl, &filters, Nlevel](fields::OutputField<DataType> & inputs) {
       const bool precondition = false;
 
@@ -281,7 +278,7 @@ namespace modifications {
 
     // Full splicing operator
     // Q := T^+ Mbar C^-1 Mbar T
-    auto Q = [&Tdagger_op_T, &Mbar_Cm1_Mbar_op, &preconditioner](
+    auto Q = [&Tdagger_op_T, &Tplus_op_T, &Mbar_Cm1_Mbar_op, &preconditioner](
       const fields::OutputField<DataType> & inputs
     ) -> fields::OutputField<DataType> {
       auto outputs = inputs;
@@ -296,9 +293,9 @@ namespace modifications {
     fields::OutputField<T> rhs(noiseB);
     rhs.addScaled(noiseA, -1);
     rhs.toFourier();
-    // rhs.applyPowerSpectrumFor(particle::species::all);
-    // rhs.combineGrids();
-    // rhs.applyPowerSpectrumFor(particle::species::whitenoise);
+    rhs.applyPowerSpectrumFor(particle::species::all);
+    rhs.combineGrids();
+    rhs.applyPowerSpectrumFor(particle::species::whitenoise);
     rhs.toReal();
     rhs = Tdagger_op_T(rhs, Mbar_Cm1_M_op);
     preconditioner(rhs);
@@ -306,8 +303,7 @@ namespace modifications {
 
     // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
-    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-2, 1e-10);
-
+    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-4, 1e-10);
     preconditioner(n_alpha);
 
     // T^+ M T (n_a-n_b)
@@ -317,7 +313,6 @@ namespace modifications {
     Ma_minus_b.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Ma_minus_b.getFieldForLevel(level) *= masks[level];
-    // Ma_minus_b.combineGrids();
     Ma_minus_b.toReal();
 
     // T^+ Mbar T n_alpha 
@@ -327,7 +322,6 @@ namespace modifications {
     Mbar_n_alpha.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
-    // Mbar_n_alpha.combineGrids();
     Mbar_n_alpha.toReal();
 
     // f = b + M (a-b) + alpha;

From 5536337009bb6f0a23a5d7a906e872ff6b8047f0 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 13:19:01 +0000
Subject: [PATCH 14/27] Add BiCGSTAB implementation

---
 .../src/simulation/modifications/splice.hpp   |  1 +
 genetIC/src/tools/numerics/cg.hpp             | 92 +++++++++++++++++++
 2 files changed, 93 insertions(+)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 4f102164..d53c9672 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -304,6 +304,7 @@ namespace modifications {
     // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
     auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-4, 1e-10);
+    // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs, 1e-4, 1e-10);
     preconditioner(n_alpha);
 
     // T^+ M T (n_a-n_b)
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index 532ffee9..5bf2b13c 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -249,6 +249,98 @@ namespace tools {
 
       return x;
     }
+
+
+    template<typename T>
+    fields::OutputField<T> bicgstab(
+        std::function<fields::OutputField<T>(fields::OutputField<T> &)> A,
+        fields::OutputField<T> &b,
+        double rtol = 1e-6,
+        double atol = 1e-12,
+        const fields::OutputField<T> *x0 = nullptr
+    ) {
+      fields::OutputField<T> x(b);
+      fields::OutputField<T> r(b);
+      fields::OutputField<T> rOld(b);
+      fields::OutputField<T> rhat(r);
+
+      double rho;
+      double omega;
+      double rhoOld = 1;
+      double alpha = 1;
+      double omegaOld = 1;
+
+      x *= 0;
+      fields::OutputField<T> v(x);
+      fields::OutputField<T> p(x);
+      if (x0 != nullptr) {
+        x = *x0;
+        r.addScaled(A(x), -1);
+      }
+
+      auto innerProduct = [](fields::OutputField<T> &a, fields::OutputField<T> &b) -> double {
+        double result = 0;
+        for (auto ilevel = 0; ilevel < a.getNumLevels(); ++ilevel) {
+          result += a.getFieldForLevel(ilevel).innerProduct(b.getFieldForLevel(ilevel));
+        }
+        return result;
+      };
+
+      assert (innerProduct(rhat, r) != 0);
+
+      size_t dimension = 0;
+      for(auto ilevel = 0; ilevel<b.getNumLevels(); ++ilevel){
+        dimension += b.getFieldForLevel(ilevel).getGrid().size;
+      }
+
+      double scale = sqrt(innerProduct(b, b));
+
+      // Start iteration
+      size_t iter = 0;
+      for(; iter<dimension; ++iter) {
+        // rho_i = rhat_0 . r_{i-1}
+        rho = innerProduct(rhat, rOld);
+        // beta = (rho_i / rho_{i-1}) (alpha / omega_{i-1})
+        double beta = (rho / rhoOld) * (alpha / omegaOld);
+        // p_i = r_{i-1} + beta * (p_{i-1} - omega_{i-1} * v_{i-1})
+        p *= beta;
+        p.addScaled(rOld, 1);
+        p.addScaled(v, -omegaOld * beta);
+        // v_i = A.p_i
+        v = A(p);
+        // alpha = rho_i / (rhat_0 . v_i)
+        alpha = rho / innerProduct(rhat, v);
+        // x_{i} = x_{i-1} + alpha p_i
+        x.addScaled(p, alpha);
+        // s = r_{i-1} - alpha v_i
+        auto s = rOld;
+        s.addScaled(v, -alpha);
+        double norm = std::sqrt(innerProduct(s, s));
+        if (norm < rtol * scale || norm < atol) {
+          break;
+        }
+        // t = A.s
+        auto t = A(s);
+        // omega_i = (t . s) / (t . t)
+        omega = innerProduct(t, s) / innerProduct(t, t);
+        // x_i = x_i + omega_i s
+        x.addScaled(s, omega);
+        // r_i = s - omega_i t
+        r = s;
+        r.addScaled(t, -omega);
+        norm = std::sqrt(innerProduct(r, r));
+        if (norm < rtol * scale || norm < atol)
+          break;
+
+        logging::entry() << "BiCGSTAB iteration " << iter << " residual=" << norm << std::endl;
+        // Save old values
+        rOld = r;
+        rhoOld = rho;
+        omegaOld = omega;
+      }
+      logging::entry() << "BiCGSTAB ended after " << iter << " iterations" << std::endl;
+      return x;
+    }
   }
 }
 

From 6d05fa7d5053fa7647a921fd63b38a0b17172288 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 13:21:59 +0000
Subject: [PATCH 15/27] No preconditioning

---
 .../src/simulation/modifications/splice.hpp   | 41 +++++++++----------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index d53c9672..f44760c4 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -253,27 +253,26 @@ namespace modifications {
     };
 
     auto preconditioner = [&covs, &masksCompl, &filters, Nlevel, &Tdagger_op_T, &Tplus_op_T](fields::OutputField<DataType> & inputs) {
-    auto preconditioner = [&covs, &masksCompl, &filters, Nlevel](fields::OutputField<DataType> & inputs) {
-      const bool precondition = false;
-
-      if (precondition) {
-        for (auto level=0; level<Nlevel; ++level) {
-          auto & field = inputs.getFieldForLevel(level);
-          field.toFourier();
-          field.applyTransferFunction(covs[level], -0.5);
-          field.applyFilter(filters.getFilterForLevel(level));
-          field.toReal();
-          field *= masksCompl[level];
-          field.toFourier();
-          field.applyTransferFunction(covs[level], 1.0);
-          field.toReal();
-          field *= masksCompl[level];
-          field.toFourier();
-          field.applyTransferFunction(covs[level], -0.5);
-          field.applyFilter(filters.getFilterForLevel(level));
-          field.toReal();
-        }
-      }
+      // const bool precondition = false;
+
+      // if (precondition) {
+      //   for (auto level=0; level<Nlevel; ++level) {
+      //     auto & field = inputs.getFieldForLevel(level);
+      //     field.toFourier();
+      //     field.applyTransferFunction(covs[level], -0.5);
+      //     field.applyFilter(filters.getFilterForLevel(level));
+      //     field.toReal();
+      //     field *= masksCompl[level];
+      //     field.toFourier();
+      //     field.applyTransferFunction(covs[level], 1.0);
+      //     field.toReal();
+      //     field *= masksCompl[level];
+      //     field.toFourier();
+      //     field.applyTransferFunction(covs[level], -0.5);
+      //     field.applyFilter(filters.getFilterForLevel(level));
+      //     field.toReal();
+      //   }
+      // }
     };
 
     // Full splicing operator

From ad04ef23c07fc87d7cbf069a833a50ae3607e9b3 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 14:05:23 +0000
Subject: [PATCH 16/27] Restore tests

---
 genetIC/src/ic.hpp                            |  8 ++--
 .../src/simulation/field/multilevelfield.hpp  | 37 +------------------
 .../src/simulation/modifications/splice.hpp   | 10 +----
 .../particles/multilevelgenerator.hpp         | 36 ++++++++----------
 genetIC/src/tools/numerics/cg.hpp             |  2 +-
 5 files changed, 25 insertions(+), 68 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index bb5e50b7..143a93bd 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -10,7 +10,6 @@
 #include <limits>
 #include <iostream>
 #include <list>
-#include <vector>
 
 
 #include "tools/numerics/vectormath.hpp"
@@ -850,9 +849,6 @@ class ICGenerator {
   */
   template<typename TField>
   void dumpGridData(size_t level, const TField &data, std::string prefix = "grid") {
-    if (level >= multiLevelContext.getNumLevels() || level < 0) {
-      throw (std::runtime_error("Attempted to dump grid data for level that does not exist."));
-    }
     initialiseRandomComponentIfUninitialised();
 
     auto levelGrid = data.getGrid();
@@ -906,6 +902,7 @@ class ICGenerator {
   * \param species - the field to dump
   */
   virtual void dumpGrid(size_t level, particle::species species ) {
+    checkLevelExists(level, species);
     auto & field = this->getOutputFieldForSpecies(species);
     field.toReal();
     dumpGridData(level, field.getFieldForLevel(level));
@@ -920,17 +917,20 @@ class ICGenerator {
   }
 
   virtual void dumpVelocityX(size_t level) {
+    checkLevelExists(level, particle::species::dm);
     dumpGridData(level, *(this->pParticleGenerator[particle::species::dm]->getGeneratorForLevel(
       level).getGeneratedFields()[0]), "vx");
   }
 
   //! For backwards compatibility. Dumpts baryons to field at requested level to file named grid-level.
   virtual void dumpGridFourier(size_t level = 0) {
+    checkLevelExists(level, particle::species::dm);
     this->dumpGridFourier(level, particle::species::dm);
   }
 
   //! Output the grid in Fourier space.
   virtual void dumpGridFourier(size_t level, particle::species species) {
+    checkLevelExists(level, species);
     auto & field = this->getOutputFieldForSpecies(species);
     field.toFourier();
     fields::Field<complex<T>, T> fieldToWrite = tools::numerics::fourier::getComplexFourierField(field.getFieldForLevel(level));
diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index 8ef5f5be..706f0f68 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -588,12 +588,8 @@ namespace fields {
       return *(this->fieldsOnLevels[i]);
     }
 
-    /* Combine all grids together
-
-    This effectively converts from whitenoise basis into delta basis.
-    */
-
-   void combineGrids() {
+    //! Combine all grids together
+    void combineGrids() {
       auto filters = this->getFilters();
       size_t nlevels = this->getContext().getNumLevels();
 
@@ -613,37 +609,8 @@ namespace fields {
 
       this->getContext().setLevelsAreCombined();
    }
-
-   void combineGridsLowerHigher() {
-      auto filters = this->getFilters();
-      auto copy = *this;
-      size_t nlevels = this->getContext().getNumLevels();
-
-      logging::entry() << "Combining information from different levels..." << std::endl;
-
-      for (size_t level = 1; level < nlevels; ++level) {
-        // remove the low-frequency information from this level
-        this->getFieldForLevel(level).applyFilter(
-          filters.getFilterForLevel(level));
-
-        // replace with the low-frequency information from the level below
-        this->getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
-          copy.getFieldForLevel(level-1),
-          filters.getFilterForLevel(level - 1));
-        
-        // replace with the high-frequency information from the level above
-        if (level < nlevels - 1) {
-          this->getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
-            copy.getFieldForLevel(level+1),
-            filters.getFilterForLevel(level + 1));
-        }
-      }
-   }
-
-
   };
 
-
   /*! \class ConstraintField
     \brief Fields used for defining constraints.
 
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index f44760c4..6f2662ba 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -95,8 +95,6 @@ namespace modifications {
         return v;
       };
 
-
-      logging::entry() << "Solving splicing problem ------------------------" << std::endl;
       fields::Field<DataType,T> alpha = tools::numerics::minresField<DataType>(X, z);
 
       alpha.toFourier();
@@ -176,27 +174,23 @@ namespace modifications {
             auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
             tmp.addFieldFromDifferentGrid(*tmp_from_other);
             tmp.toFourier();
-            // tmp_from_other->applyFilter(filters.getHighPassFilterForLevel(level_other));
             tmp.applyFilter(filters.getFilterForLevel(level));
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            // tmp.applyFilter(filters.getLowPassFilterForLevel(level));
             tmp.applyFilter(filters.getFilterForLevel(level));
             tmp.toReal();
           } else if (level_other < level) { // Contribution from coarser level
             auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
             tmp_from_other->toFourier();
-            // tmp_from_other->applyFilter(filters.getLowPassFilterForLevel(level_other));
             tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
             tmp_from_other->applyTransferFunction(covs[level_other], 0.5);
             tmp_from_other->toReal();
             op(level_other, *tmp_from_other);
             tmp_from_other->toFourier();
             tmp_from_other->applyTransferFunction(covs[level_other], power_out);
-            // tmp.applyFilter(filters.getHighPassFilterForLevel(level_other));
             tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
             tmp_from_other->toReal();
             tmp.addFieldFromDifferentGrid(*tmp_from_other);
@@ -302,8 +296,8 @@ namespace modifications {
 
     // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
-    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-4, 1e-10);
-    // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs, 1e-4, 1e-10);
+    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs);
+    // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs);
     preconditioner(n_alpha);
 
     // T^+ M T (n_a-n_b)
diff --git a/genetIC/src/simulation/particles/multilevelgenerator.hpp b/genetIC/src/simulation/particles/multilevelgenerator.hpp
index 8d2a053e..9408b29e 100644
--- a/genetIC/src/simulation/particles/multilevelgenerator.hpp
+++ b/genetIC/src/simulation/particles/multilevelgenerator.hpp
@@ -91,37 +91,33 @@ namespace particle {
       generator.pGenerators.emplace_back(
         std::make_shared<ZPG>(generator.overdensityField.getFieldForLevel(0)));
     } else if (nlevels >= 2) {
-      
-      // Combine the fields on each grid
-      if (!generator.context.getLevelsAreCombined())
-        generator.overdensityField.combineGrids();
 
       for (size_t level = 0; level < nlevels; ++level)
         generator.pGenerators.emplace_back(
           std::make_shared<ZPG>(generator.overdensityField.getFieldForLevel(level))
         );
 
+      if (!generator.context.getLevelsAreCombined()) {
+        auto filters = generator.overdensityField.getFilters();
 
-      // auto filters = generator.overdensityField.getFilters();
+        for (size_t level = 1; level < nlevels; ++level) {
 
-      // for (size_t level = 1; level < nlevels; ++level) {
+          // remove the low-frequency information from this level
+          generator.overdensityField.getFieldForLevel(level).applyFilter(
+            filters.getHighPassFilterForLevel(level));
+          generator.pGenerators[level]->applyFilter(filters.getHighPassFilterForLevel(level));
 
-      //   // remove the low-frequency information from this level
-      //   // generator.overdensityField.getFieldForLevel(level).applyFilter(
-      //     // filters.getHighPassFilterForLevel(level));
-      //   generator.pGenerators[level]->applyFilter(filters.getHighPassFilterForLevel(level));
+          // replace with the low-frequency information from the level below
+          generator.overdensityField.getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
+            generator.overdensityField.getFieldForLevel(level - 1),
+            filters.getLowPassFilterForLevel(level - 1));
+          generator.pGenerators[level]->addFieldFromDifferentGridWithFilter(*generator.pGenerators[level - 1],
+                                                                            filters.getLowPassFilterForLevel(level - 1));
 
-      //   // replace with the low-frequency information from the level below
-      //   // generator.overdensityField.getFieldForLevel(level).addFieldFromDifferentGridWithFilter(
-      //     // generator.overdensityField.getFieldForLevel(level - 1),
-      //     // filters.getLowPassFilterForLevel(level - 1));
-      //   generator.pGenerators[level]->addFieldFromDifferentGridWithFilter(*generator.pGenerators[level - 1],
-      //                                                                     filters.getLowPassFilterForLevel(level - 1));
-
-
-      // }
+        }
+      }
 
-      // generator.overdensityField.getContext().setLevelsAreCombined();
+      generator.overdensityField.getContext().setLevelsAreCombined();
 
       logging::entry() << "Combined grids" << endl;
 
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index 5bf2b13c..00e9435c 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -169,7 +169,7 @@ namespace tools {
 
       size_t dimension = 0;
       for(auto ilevel = 0; ilevel<b.getNumLevels(); ++ilevel){
-        dimension += b.getFieldForLevel(ilevel).getGrid().size3;
+        dimension += b.getFieldForLevel(ilevel).getGrid().size;
       }
 
       // Start iteration

From 0ed64fbcaa46030092ce4210f5dcd38a08f9ebfa Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 14:41:15 +0000
Subject: [PATCH 17/27] Update answer test + remove useless function

---
 .../src/simulation/modifications/splice.hpp   |  13 ++---
 genetIC/src/tools/numerics/cg.hpp             |  50 ------------------
 .../reference_grid/grid-0.npy                 | Bin 32848 -> 32848 bytes
 3 files changed, 3 insertions(+), 60 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 6f2662ba..f4ec1da7 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -95,7 +95,7 @@ namespace modifications {
         return v;
       };
 
-      fields::Field<DataType,T> alpha = tools::numerics::minresField<DataType>(X, z);
+      fields::Field<DataType,T> alpha = tools::numerics::conjugateGradient<DataType>(X, z);
 
       alpha.toFourier();
       alpha.applyTransferFunction(preconditioner, 0.5);
@@ -304,6 +304,7 @@ namespace modifications {
     fields::OutputField<DataType> Ma_minus_b(noiseA);
     Ma_minus_b.addScaled(noiseB, -1);
     Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
+    // Ma_minus_b.combineGrids();
     Ma_minus_b.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Ma_minus_b.getFieldForLevel(level) *= masks[level];
@@ -313,6 +314,7 @@ namespace modifications {
     // Note: we use an alias here for the sake of readability
     fields::OutputField<DataType> & Mbar_n_alpha = n_alpha;
     Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
+    // Mbar_n_alpha.combineGrids();
     Mbar_n_alpha.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
@@ -327,15 +329,6 @@ namespace modifications {
     outputs += Ma_minus_b;
     outputs += Mbar_n_alpha;
 
-    Ma_minus_b.getFieldForLevel(0).dumpGridData("Ma_minus_b-0.npz");
-    Mbar_n_alpha.getFieldForLevel(0).dumpGridData("Mbar_n_alpha-0.npz");
-    outputs.getFieldForLevel(0).dumpGridData("output-0.npz");
-
-    if (Nlevel > 1) {
-      Ma_minus_b.getFieldForLevel(1).dumpGridData("Ma_minus_b-1.npz");
-      Mbar_n_alpha.getFieldForLevel(1).dumpGridData("Mbar_n_alpha-1.npz");
-      outputs.getFieldForLevel(1).dumpGridData("output-1.npz");
-    }
     // Combine the grids
     outputs.combineGrids();
 
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index 00e9435c..a8a3c50b 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -200,56 +200,6 @@ namespace tools {
 
       return x;
     }
-    template<typename T>
-    fields::Field<T> minresField(
-        std::function<fields::Field<T>(fields::Field<T> &)> A,
-        fields::Field<T> &b,
-        double rtol = 1e-6,
-        double atol = 1e-12
-    ) {
-      fields::Field<T> x(b);
-      x *= 0;
-      fields::Field<T> r(b);
-      fields::Field<T> s(b);
-      s = A(r);
-
-      fields::Field<T> p(r);
-      fields::Field<T> q(s);
-
-      double scale = b.norm();
-      double rho = r.innerProduct(s);
-
-      size_t dimension = b.getGrid().size3;
-
-      // Start iteration
-      size_t iter = 0;
-      for(; iter<dimension; ++iter) {
-        // We have q = A(p), but no need to compute it again
-        double alpha = rho / q.innerProduct(q);
-        x.addScaled(p, alpha);
-        r.addScaled(q, -alpha);
-
-        double norm = r.norm();
-        if (norm < rtol * scale || norm < atol)
-          break;
-
-        logging::entry() << "MINRES iteration " << iter << " residual=" << norm << std::endl;
-        s = A(r);
-        double rhobar = rho;
-        rho = r.innerProduct(s);
-        double beta = rho / rhobar;
-        p *= beta;
-        p += r;
-
-        q *= beta;
-        q += s;
-      }
-
-      logging::entry() << "MINRES ended after " << iter << " iterations" << std::endl;
-
-      return x;
-    }
-
 
     template<typename T>
     fields::OutputField<T> bicgstab(
diff --git a/genetIC/tests/test_24_splicing/reference_grid/grid-0.npy b/genetIC/tests/test_24_splicing/reference_grid/grid-0.npy
index 87897734ba16a795989e863b7f9235a11d6cd78b..03414748cda379b99b23f691951208a7b3973dbd 100644
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From f7e6439d559832854474ddb0a9a7c3afab51917c Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 1 Nov 2022 14:51:29 +0000
Subject: [PATCH 18/27] Update answer test

(Tipsy file)
---
 .../tests/test_24_splicing/reference_output   | Bin 147488 -> 147488 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/genetIC/tests/test_24_splicing/reference_output b/genetIC/tests/test_24_splicing/reference_output
index ac80b8d66445a9daf9c716520fc0574f9d0621d3..d7dd07b1c02302ee90deb4052183272436f0db3c 100644
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From 613b1cf468f76207d63583ee1d2c47e3b667168a Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 2 Nov 2022 17:42:14 +0000
Subject: [PATCH 19/27] Splices correctly

The MINRES iterations are however still not converging
---
 genetIC/src/ic.hpp                            | 25 +++++-------
 .../src/simulation/field/multilevelfield.hpp  | 19 +++++----
 .../src/simulation/modifications/splice.hpp   | 40 +++++++++++--------
 3 files changed, 44 insertions(+), 40 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 143a93bd..064d73c8 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -1649,8 +1649,8 @@ class ICGenerator {
       throw std::runtime_error("It is too late in the IC generation process to perform splicing; try moving the splice command earlier");
     }
 
-    fields::OutputField<GridDataType> newField = fields::OutputField<GridDataType>(multiLevelContext, particle::species::whitenoise);
-    auto newGenerator = fields::RandomFieldGenerator<GridDataType>(newField);
+    fields::OutputField<GridDataType> a = fields::OutputField<GridDataType>(multiLevelContext, particle::species::whitenoise);
+    auto newGenerator = fields::RandomFieldGenerator<GridDataType>(a);
 
     if(multiLevelContext.getNumLevels()>1)
       logging::entry(logging::level::warning) << "Splicing operations on multiple levels work, but may have edge artefacts. Use experimentally/at your own risk!" << endl;
@@ -1660,19 +1660,14 @@ class ICGenerator {
     newGenerator.draw();
     logging::entry() << "Finished constructing new random field. Beginning splice operation." << endl;
     
-    fields::OutputField<GridDataType> &a = *outputFields[0];
-    fields::OutputField<GridDataType> &b = newField;
-
-    fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(a, b);
-      
-    for (size_t level=0; level<multiLevelContext.getNumLevels(); ++level) {
-      auto &originalFieldThisLevel = outputFields[0]->getFieldForLevel(level);
-      auto &newFieldThisLevel = f.getFieldForLevel(level);
-      assert(originalFieldThisLevel.isFourier());
-      newFieldThisLevel.toFourier();
-      originalFieldThisLevel = std::move(newFieldThisLevel);
-    }
-    // At this point, we have already combined the fields ()
+    auto & b = *outputFields[0];
+
+    fields::OutputField<GridDataType> f = modifications::spliceMultiLevel<GridDataType>(b, a);
+
+    // Copy the result back into the output field
+    outputFields[0]->copyData(f);
+
+    // Mark the field as already combined
     multiLevelContext.setLevelsAreCombined();
   }
 
diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index 706f0f68..b97fdca7 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -59,15 +59,15 @@ namespace fields {
     }
 
     //! Copy constructor
-    MultiLevelField(const MultiLevelField<DataType> &copy) :
-      std::enable_shared_from_this<MultiLevelField<DataType>>(), multiLevelContext(&(copy.getContext())) {
-
+    MultiLevelField(const MultiLevelField<DataType> &other) :
+      std::enable_shared_from_this<MultiLevelField<DataType>>(), multiLevelContext(&(other.getContext())) {
+      std::cout << "COPY CONSTRUCTOR" << std::endl;
       for (size_t level = 0; level < multiLevelContext->getNumLevels(); level++) {
-        fieldsOnLevels.push_back(std::make_shared<Field<DataType, T>>(copy.getFieldForLevel(level)));
+        fieldsOnLevels.push_back(other.getFieldForLevel(level).copy());
       }
-      transferType = copy.transferType;
+      transferType = other.transferType;
 
-      isCovector = copy.isCovector;
+      isCovector = other.isCovector;
     }
 
     virtual ~MultiLevelField() {}
@@ -168,6 +168,11 @@ namespace fields {
       addScaled(other, 1.0);
     }
 
+    //! Subtracts the specified multi-level field from this one.
+    void operator-=(const MultiLevelField<DataType> &other) {
+      addScaled(other, -1.0);
+    }
+
     //! Divides the field by the specified ratio.
     void operator/=(DataType ratio) {
       using namespace tools::numerics;
@@ -606,8 +611,6 @@ namespace fields {
           getFieldForLevel(level - 1),
           filters.getLowPassFilterForLevel(level - 1));
       }
-
-      this->getContext().setLevelsAreCombined();
    }
   };
 
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index f4ec1da7..f7feaa63 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -144,6 +144,7 @@ namespace modifications {
       masks.push_back(generateMaskFromFlags(multiLevelContext.getGridForLevel(level)));
 
       fields::Field<DataType, T> cov(*multiLevelContext.getCovariance(level, particle::species::all));
+      cov.setFourierCoefficient(0, 0, 0, 1);
       covs.push_back(cov);
     }
 
@@ -172,6 +173,8 @@ namespace modifications {
 
           if (level_other > level) {        // Contribution from finer level
             auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
+            tmp_from_other->toReal();
+            tmp.toReal();
             tmp.addFieldFromDifferentGrid(*tmp_from_other);
             tmp.toFourier();
             tmp.applyFilter(filters.getFilterForLevel(level));
@@ -193,6 +196,7 @@ namespace modifications {
             tmp_from_other->applyTransferFunction(covs[level_other], power_out);
             tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
             tmp_from_other->toReal();
+            tmp.toReal();
             tmp.addFieldFromDifferentGrid(*tmp_from_other);
           } else {
             tmp += inputCopy.getFieldForLevel(level);
@@ -283,12 +287,12 @@ namespace modifications {
     };
 
     // Compute the rhs T^+ Mbar C^-1 M T (b - a)
-    fields::OutputField<T> rhs(noiseB);
-    rhs.addScaled(noiseA, -1);
-    rhs.toFourier();
-    rhs.applyPowerSpectrumFor(particle::species::all);
-    rhs.combineGrids();
-    rhs.applyPowerSpectrumFor(particle::species::whitenoise);
+    auto rhs = noiseB;
+    rhs -= noiseA;
+    // rhs.toFourier();
+    // rhs.applyPowerSpectrumFor(particle::species::all);
+    // rhs.combineGrids();
+    // rhs.applyPowerSpectrumFor(particle::species::whitenoise);
     rhs.toReal();
     rhs = Tdagger_op_T(rhs, Mbar_Cm1_M_op);
     preconditioner(rhs);
@@ -300,41 +304,43 @@ namespace modifications {
     // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs);
     preconditioner(n_alpha);
 
+
     // T^+ M T (n_a-n_b)
-    fields::OutputField<DataType> Ma_minus_b(noiseA);
-    Ma_minus_b.addScaled(noiseB, -1);
+    auto Ma_minus_b = noiseA;
+    Ma_minus_b -= noiseB;
+    Ma_minus_b.toFourier();
     Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
-    // Ma_minus_b.combineGrids();
+    Ma_minus_b.combineGrids();
     Ma_minus_b.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Ma_minus_b.getFieldForLevel(level) *= masks[level];
     Ma_minus_b.toReal();
 
+
     // T^+ Mbar T n_alpha 
     // Note: we use an alias here for the sake of readability
-    fields::OutputField<DataType> & Mbar_n_alpha = n_alpha;
+    auto & Mbar_n_alpha = n_alpha;
+    Mbar_n_alpha.toFourier();
     Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
-    // Mbar_n_alpha.combineGrids();
+    Mbar_n_alpha.combineGrids();
     Mbar_n_alpha.toReal();
     for (auto level = 0; level<Nlevel; ++level)
       Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
     Mbar_n_alpha.toReal();
 
     // f = b + M (a-b) + alpha;
-    fields::OutputField<DataType> outputs(noiseB);
+    auto outputs = noiseB;
+    outputs.toFourier();
     outputs.applyPowerSpectrumFor(particle::species::all);
-    // outputs.combineGrids();
+    outputs.combineGrids();
     outputs.toReal();
 
     outputs += Ma_minus_b;
     outputs += Mbar_n_alpha;
 
-    // Combine the grids
-    outputs.combineGrids();
-
     // Convert back to whitenoise
+    outputs.toFourier();
     outputs.applyPowerSpectrumFor(particle::species::whitenoise);
-
     return outputs;
   }
 }

From 27570fb37c912cc80ef2f5633c38800b2f0b2fa2 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Wed, 2 Nov 2022 22:27:47 +0000
Subject: [PATCH 20/27] Move the actual splicing to the last moment

This allows the splicing to output a field directly in delta space
without needing to convert back into whitenoise.
---
 genetIC/src/ic.hpp                            | 22 ++++++++++++++++---
 .../src/simulation/field/multilevelfield.hpp  |  5 +++++
 .../src/simulation/modifications/splice.hpp   |  2 +-
 .../particles/multilevelgenerator.hpp         |  2 --
 4 files changed, 25 insertions(+), 6 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 064d73c8..91ca4bb3 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -174,13 +174,16 @@ class ICGenerator {
 
   tools::ClassDispatch<ICGenerator<GridDataType>, void> &interpreter; //!< Parser for parameter files
 
+  bool shouldSplice; //!< True if the code should perform the splicing operation.
+  size_t spliceSeed; //!< Seed for the random number generator used in the splicing operation.
 
 public:
   //! \brief Main constructor for this class.
   //! Requires a single interpreter to initialise, from which it will receive input commands.
   ICGenerator(tools::ClassDispatch<ICGenerator<GridDataType>, void> &interpreter) :
     modificationManager(multiLevelContext, cosmology, nullptr),
-    interpreter(interpreter) {
+    interpreter(interpreter),
+    shouldSplice(false) {
 
     // By default, we assume there is only one field - at first this will contain white noise:
     outputFields.push_back(
@@ -1268,7 +1271,10 @@ class ICGenerator {
     using namespace io;
 
     initialiseRandomComponentIfUninitialised();
-    applyPowerSpec();
+    if (shouldSplice)
+      spliceFields(spliceSeed);
+    else
+      applyPowerSpec();
     ensureParticleGeneratorInitialised();
 
     logging::entry() << "Writing output; number dm particles=" << pMapper->size_dm()
@@ -1637,8 +1643,10 @@ class ICGenerator {
     outputFields[0]->reverse();
   }
 
-  //! Splicing: fixes the flagged region, while reinitialising the exterior from a new random field
+  //! Save that splicing should happen in the generation of the output
   virtual void splice(size_t newSeed) {
+    this->spliceSeed = newSeed;
+    this->shouldSplice = true;
     initialiseRandomComponentIfUninitialised();
 
     if(outputFields.size()>1)
@@ -1648,6 +1656,10 @@ class ICGenerator {
     if(outputFields[0]->getTransferType() != particle::species::whitenoise) {
       throw std::runtime_error("It is too late in the IC generation process to perform splicing; try moving the splice command earlier");
     }
+  }
+
+  //! Splicing: fixes the flagged region, while reinitialising the exterior from a new random field
+  virtual void spliceFields(size_t newSeed) {
 
     fields::OutputField<GridDataType> a = fields::OutputField<GridDataType>(multiLevelContext, particle::species::whitenoise);
     auto newGenerator = fields::RandomFieldGenerator<GridDataType>(a);
@@ -1667,6 +1679,10 @@ class ICGenerator {
     // Copy the result back into the output field
     outputFields[0]->copyData(f);
 
+    // The field f is in delta space and we just copied it, so mark
+    // the outputFields accordingly. 
+    outputFields[0]->setForceTransferType(particle::species::all);
+
     // Mark the field as already combined
     multiLevelContext.setLevelsAreCombined();
   }
diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index b97fdca7..df5e3afb 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -77,6 +77,11 @@ namespace fields {
       return transferType;
     }
 
+    //! Manually set the transfer function
+    void setForceTransferType(particle::species transfer_type) {
+      transferType = transfer_type;
+    }
+
     //! Returns a reference to the multi-level context associated to this multi-level field.
     virtual multilevelgrid::MultiLevelGrid<DataType> &getContext() const {
       return const_cast<multilevelgrid::MultiLevelGrid<DataType> &>(*multiLevelContext);
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index f7feaa63..420dbce8 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -340,7 +340,7 @@ namespace modifications {
 
     // Convert back to whitenoise
     outputs.toFourier();
-    outputs.applyPowerSpectrumFor(particle::species::whitenoise);
+    // outputs.applyPowerSpectrumFor(particle::species::whitenoise);
     return outputs;
   }
 }
diff --git a/genetIC/src/simulation/particles/multilevelgenerator.hpp b/genetIC/src/simulation/particles/multilevelgenerator.hpp
index 9408b29e..56429d64 100644
--- a/genetIC/src/simulation/particles/multilevelgenerator.hpp
+++ b/genetIC/src/simulation/particles/multilevelgenerator.hpp
@@ -119,8 +119,6 @@ namespace particle {
 
       generator.overdensityField.getContext().setLevelsAreCombined();
 
-      logging::entry() << "Combined grids" << endl;
-
       for (size_t i = 0; i < nlevels; ++i)
         generator.pGenerators[i]->toReal();
     }

From 08ce96211df173e59f9973c11ce077cf27306e64 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Thu, 3 Nov 2022 16:41:28 +0000
Subject: [PATCH 21/27] WIP

---
 .../src/simulation/field/multilevelfield.hpp  |   1 -
 .../src/simulation/modifications/splice.hpp   |  68 ++++++++++++------
 genetIC/src/tools/numerics/cg.hpp             |   5 ++
 .../reference_grid/grid-0.npy                 | Bin 32848 -> 32848 bytes
 .../tests/test_24_splicing/reference_output   | Bin 147488 -> 147488 bytes
 5 files changed, 50 insertions(+), 24 deletions(-)

diff --git a/genetIC/src/simulation/field/multilevelfield.hpp b/genetIC/src/simulation/field/multilevelfield.hpp
index df5e3afb..de0e2e02 100644
--- a/genetIC/src/simulation/field/multilevelfield.hpp
+++ b/genetIC/src/simulation/field/multilevelfield.hpp
@@ -61,7 +61,6 @@ namespace fields {
     //! Copy constructor
     MultiLevelField(const MultiLevelField<DataType> &other) :
       std::enable_shared_from_this<MultiLevelField<DataType>>(), multiLevelContext(&(other.getContext())) {
-      std::cout << "COPY CONSTRUCTOR" << std::endl;
       for (size_t level = 0; level < multiLevelContext->getNumLevels(); level++) {
         fieldsOnLevels.push_back(other.getFieldForLevel(level).copy());
       }
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 420dbce8..d82b7b36 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -165,51 +165,53 @@ namespace modifications {
         auto & out = outputs.getFieldForLevel(level);
         out.toReal();
 
-        for (auto level_other=std::max(0, level-1); level_other<std::min(Nlevel, level+2); ++level_other) {
+        for (auto source_level=std::max(0, level-1); source_level<std::min(Nlevel, level+2); ++source_level) {
           T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level) /
-                                 multiLevelContext.getWeightForLevel(level_other);
+                                 multiLevelContext.getWeightForLevel(source_level);
 
           fields::Field<DataType, T> tmp(out.getGrid(), false);
 
-          if (level_other > level) {        // Contribution from finer level
-            auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
-            tmp_from_other->toReal();
-            tmp.toReal();
-            tmp.addFieldFromDifferentGrid(*tmp_from_other);
+          // if (source_level != level) continue;
+          auto & this_level_filter = filters.getFilterForLevel(level);
+          auto & source_level_filter = filters.getFilterForLevel(source_level);
+
+          if (source_level > level) {        // Contribution from finer level
+            tmp.addFieldFromDifferentGrid(inputCopy.getFieldForLevel(source_level));
             tmp.toFourier();
-            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.applyFilter(source_level_filter);
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.applyFilter(this_level_filter);
             tmp.toReal();
-          } else if (level_other < level) { // Contribution from coarser level
-            auto tmp_from_other = inputCopy.getFieldForLevel(level_other).copy();
-            tmp_from_other->toFourier();
-            tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
-            tmp_from_other->applyTransferFunction(covs[level_other], 0.5);
-            tmp_from_other->toReal();
-            op(level_other, *tmp_from_other);
-            tmp_from_other->toFourier();
-            tmp_from_other->applyTransferFunction(covs[level_other], power_out);
-            tmp_from_other->applyFilter(filters.getFilterForLevel(level_other));
-            tmp_from_other->toReal();
+          } else if (source_level < level) { // Contribution from coarser level
+            auto source_field = inputCopy.getFieldForLevel(source_level).copy();
+            source_field->toFourier();
+            source_field->applyFilter(source_level_filter);
+            source_field->applyTransferFunction(covs[source_level], 0.5);
+            source_field->toReal();
+            op(source_level, *source_field);
+            source_field->toFourier();
+            source_field->applyTransferFunction(covs[source_level], power_out);
+            source_field->applyFilter(this_level_filter);
+            source_field->toReal();
             tmp.toReal();
-            tmp.addFieldFromDifferentGrid(*tmp_from_other);
+            tmp.addFieldFromDifferentGrid(*source_field);
           } else {
             tmp += inputCopy.getFieldForLevel(level);
             tmp.toFourier();
-            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.applyFilter(this_level_filter);
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            tmp.applyFilter(filters.getFilterForLevel(level));
+            tmp.applyFilter(this_level_filter);
             tmp.toReal();
           }
+
           out.addScaled(tmp, std::sqrt(pixel_volume_ratio));
         }
       }
@@ -300,10 +302,30 @@ namespace modifications {
 
     // Solve the linear system [T^t Mbar C^-1 Mbar T] n_alpha = rhs
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
+    rhs.getFieldForLevel(0).dumpGridData("rhs-0.npz");
+    if (Nlevel > 1) rhs.getFieldForLevel(1).dumpGridData("rhs-1.npz");
     auto n_alpha = tools::numerics::minres<DataType>(Q, rhs);
     // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs);
     preconditioner(n_alpha);
 
+    // if (Nlevel > 1) {
+    //   auto Ntot = rhs.getFieldForLevel(0).getGrid().size3 + ((Nlevel > 1) ? rhs.getFieldForLevel(1).getGrid().size3 : 0);
+    //   for (auto i = 0; i < Ntot; ++i) {
+    //     if (i % 10 == 0) std::cout << i << "/" << Ntot << std::endl;
+    //     auto input = rhs;
+    //     input *= 0;
+    //     if (i < rhs.getFieldForLevel(0).getGrid().size3)
+    //       input.getFieldForLevel(0)[i] = 1;
+    //     else
+    //       input.getFieldForLevel(1)[i-rhs.getFieldForLevel(0).getGrid().size3] = 1;
+    //     auto output = Q(input);
+    //     std::ostringstream stream;
+    //     stream << "field" << i;
+    //     std::string filename = stream.str();
+    //     output.getFieldForLevel(0).dumpGridData(filename + "_0.npz");
+    //     output.getFieldForLevel(1).dumpGridData(filename + "_1.npz");
+    //   }
+    // }
 
     // T^+ M T (n_a-n_b)
     auto Ma_minus_b = noiseA;
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index a8a3c50b..605ecd1e 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -172,6 +172,8 @@ namespace tools {
         dimension += b.getFieldForLevel(ilevel).getGrid().size;
       }
 
+      dimension *= 10;
+
       // Start iteration
       size_t iter = 0;
       for(; iter<dimension; ++iter) {
@@ -238,11 +240,14 @@ namespace tools {
 
       assert (innerProduct(rhat, r) != 0);
 
+
       size_t dimension = 0;
       for(auto ilevel = 0; ilevel<b.getNumLevels(); ++ilevel){
         dimension += b.getFieldForLevel(ilevel).getGrid().size;
       }
 
+      dimension *= 10;
+
       double scale = sqrt(innerProduct(b, b));
 
       // Start iteration
diff --git a/genetIC/tests/test_24_splicing/reference_grid/grid-0.npy b/genetIC/tests/test_24_splicing/reference_grid/grid-0.npy
index 03414748cda379b99b23f691951208a7b3973dbd..87897734ba16a795989e863b7f9235a11d6cd78b 100644
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From 10c419da406e550314d1a5746767c29715062bf7 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Thu, 3 Nov 2022 17:48:50 +0000
Subject: [PATCH 22/27] Filter in window

---
 genetIC/src/simulation/modifications/splice.hpp | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index d82b7b36..28c24541 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -152,7 +152,8 @@ namespace modifications {
     auto T_op_T = [&covs, &filters, &multiLevelContext, Nlevel](
         const fields::OutputField<DataType> &inputs,
         const std::function<void(const int, fields::Field<DataType, T> &)> op,
-        const TOPERATOR Top
+        const TOPERATOR Top,
+        const bool filter_in_window=true
     ) -> fields::OutputField<DataType> {
       fields::OutputField<DataType> outputs(inputs.getContext(), inputs.getTransferType());
       fields::OutputField<DataType> inputCopy(inputs);
@@ -202,13 +203,23 @@ namespace modifications {
           } else {
             tmp += inputCopy.getFieldForLevel(level);
             tmp.toFourier();
-            tmp.applyFilter(this_level_filter);
+            if (filter_in_window && level < Nlevel-1) {
+              auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
+              tmp.applyFilterInWindow(this_level_filter, window, true);
+            } else {
+              tmp.applyFilter(this_level_filter);
+            }
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            tmp.applyFilter(this_level_filter);
+            if (filter_in_window && level < Nlevel-1) {
+              auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
+              tmp.applyFilterInWindow(this_level_filter, window, false);
+            } else {
+              tmp.applyFilter(this_level_filter);
+            }
             tmp.toReal();
           }
 

From 8f9fa10b6bf93b7167fd5e03a7912d4faf0b1ee6 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Fri, 4 Nov 2022 17:03:59 +0000
Subject: [PATCH 23/27] Almost working version in Fourier space

---
 genetIC/src/ic.hpp                            | 11 ++-
 .../src/simulation/modifications/splice.hpp   | 90 +++++++++++++------
 2 files changed, 68 insertions(+), 33 deletions(-)

diff --git a/genetIC/src/ic.hpp b/genetIC/src/ic.hpp
index 91ca4bb3..1c4c27b0 100644
--- a/genetIC/src/ic.hpp
+++ b/genetIC/src/ic.hpp
@@ -1271,10 +1271,7 @@ class ICGenerator {
     using namespace io;
 
     initialiseRandomComponentIfUninitialised();
-    if (shouldSplice)
-      spliceFields(spliceSeed);
-    else
-      applyPowerSpec();
+    applyPowerSpec();
     ensureParticleGeneratorInitialised();
 
     logging::entry() << "Writing output; number dm particles=" << pMapper->size_dm()
@@ -1656,6 +1653,8 @@ class ICGenerator {
     if(outputFields[0]->getTransferType() != particle::species::whitenoise) {
       throw std::runtime_error("It is too late in the IC generation process to perform splicing; try moving the splice command earlier");
     }
+
+    spliceFields(newSeed);
   }
 
   //! Splicing: fixes the flagged region, while reinitialising the exterior from a new random field
@@ -1681,10 +1680,10 @@ class ICGenerator {
 
     // The field f is in delta space and we just copied it, so mark
     // the outputFields accordingly. 
-    outputFields[0]->setForceTransferType(particle::species::all);
+    // outputFields[0]->setForceTransferType(particle::species::all);
 
     // Mark the field as already combined
-    multiLevelContext.setLevelsAreCombined();
+    // multiLevelContext.setLevelsAreCombined();
   }
 
   //! Reverses the sign of the low-k modes.
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 28c24541..d2ac3468 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -341,40 +341,76 @@ namespace modifications {
     // T^+ M T (n_a-n_b)
     auto Ma_minus_b = noiseA;
     Ma_minus_b -= noiseB;
-    Ma_minus_b.toFourier();
-    Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
-    Ma_minus_b.combineGrids();
-    Ma_minus_b.toReal();
-    for (auto level = 0; level<Nlevel; ++level)
-      Ma_minus_b.getFieldForLevel(level) *= masks[level];
-    Ma_minus_b.toReal();
+    Ma_minus_b = Tplus_op_T(Ma_minus_b, [masks](const int level, fields::Field<DataType, T> & input) {
+      input.toReal();
+      input *= masks[level];
+    });
+
+    // T^+ Mbar T n_alpha
+    auto Mbar_n_alpha = Tplus_op_T(n_alpha, [masksCompl](const int level, fields::Field<DataType, T> & input) {
+      input.toReal();
+      input *= masksCompl[level];
+    });
 
+    // Reconstruct solution
+    auto outputs = noiseB; // Tplus_op_T(noiseB, [](const int level, fields::Field<DataType, T> & input) {});
 
-    // T^+ Mbar T n_alpha 
-    // Note: we use an alias here for the sake of readability
-    auto & Mbar_n_alpha = n_alpha;
+    Ma_minus_b.toFourier();
     Mbar_n_alpha.toFourier();
-    Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
-    Mbar_n_alpha.combineGrids();
-    Mbar_n_alpha.toReal();
-    for (auto level = 0; level<Nlevel; ++level)
-      Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
-    Mbar_n_alpha.toReal();
-
-    // f = b + M (a-b) + alpha;
-    auto outputs = noiseB;
     outputs.toFourier();
-    outputs.applyPowerSpectrumFor(particle::species::all);
-    outputs.combineGrids();
-    outputs.toReal();
+    for (auto level = 0; level < Nlevel; ++level) {
+      auto & field = outputs.getFieldForLevel(level);
+      field += Ma_minus_b.getFieldForLevel(level);
+      field += Mbar_n_alpha.getFieldForLevel(level);
+    }
+
+    return outputs;
 
-    outputs += Ma_minus_b;
-    outputs += Mbar_n_alpha;
 
-    // Convert back to whitenoise
-    outputs.toFourier();
+    // // Tplus_op_T(Ma_minus_b, [&masks](const int level, fields::Field<DataType,T> & input) {
+    // //   input.toReal();
+    // //   input *= masks[level];
+    // // });
+    // Ma_minus_b.toFourier();
+    // Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
+    // Ma_minus_b.combineGrids();
+    // Ma_minus_b.toReal();
+    // for (auto level = 0; level<Nlevel; ++level)
+    //   Ma_minus_b.getFieldForLevel(level) *= masks[level];
+    // Ma_minus_b.toReal();
+
+
+    // // T^+ Mbar T n_alpha 
+    // // Note: we use an alias here for the sake of readability
+    // auto & Mbar_n_alpha = n_alpha;
+    // // Tplus_op_T(Mbar_n_alpha, [&masksCompl](const int level, fields::Field<DataType,T> & input) {
+    // //   input.toReal();
+    // //   input *= masksCompl[level];
+    // // });
+    // Mbar_n_alpha.toFourier();
+    // Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
+    // Mbar_n_alpha.combineGrids();
+    // Mbar_n_alpha.toReal();
+    // for (auto level = 0; level<Nlevel; ++level)
+    //   Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
+    // Mbar_n_alpha.toReal();
+
+    // // f = b + M (a-b) + alpha;
+    // auto outputs = noiseB;
+    // // Tplus_op_T(outputs, [](const int level, fields::Field<DataType,T> & input) {});
+    // outputs.toFourier();
+    // outputs.applyPowerSpectrumFor(particle::species::all);
+    // outputs.combineGrids();
+    // outputs.toReal();
+
+    // outputs += Ma_minus_b;
+    // outputs += Mbar_n_alpha;
+
+    // // Convert back to whitenoise
+    // outputs.toFourier();
     // outputs.applyPowerSpectrumFor(particle::species::whitenoise);
-    return outputs;
+    // outputs.combineGrids();
+    // return outputs;
   }
 }
 

From 379d7799375059bf1eb7b66bed9659e3b33621a4 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Mon, 7 Nov 2022 14:15:39 +0000
Subject: [PATCH 24/27] Splicing correctly now

The accuracy in the zoomed window is not perfect yet (order of 10^-3 only)
---
 genetIC/src/simulation/field/field.hpp        | 29 +++++++
 .../src/simulation/modifications/splice.hpp   | 76 +++++--------------
 2 files changed, 50 insertions(+), 55 deletions(-)

diff --git a/genetIC/src/simulation/field/field.hpp b/genetIC/src/simulation/field/field.hpp
index 08f8659b..28e4dcd1 100644
--- a/genetIC/src/simulation/field/field.hpp
+++ b/genetIC/src/simulation/field/field.hpp
@@ -479,6 +479,35 @@ namespace fields {
 
     }
 
+    void deInterpolate(const Field<DataType, CoordinateType> &hires) {
+      assert (!isFourier());
+      assert (!hires.isFourier());
+      int pixel_size_ratio = tools::getRatioAndAssertInteger(getGrid().cellSize,
+                                                             hires.getGrid().cellSize);
+      int pixel_volume_ratio = pow(pixel_size_ratio,3);
+
+      size_t hiresGridSize = hires.getGrid().size;
+      size_t hiresParallelisationSeparation = 4 * pixel_size_ratio;
+
+      Field<DataType, CoordinateType> *me = this;
+
+      for(int xoffset=0; xoffset < hiresParallelisationSeparation; xoffset++) {
+#pragma omp parallel for default(none) shared(hires, me, pixel_volume_ratio, xoffset, hiresGridSize, hiresParallelisationSeparation)
+        for(int x=xoffset; x<hiresGridSize; x+=hiresParallelisationSeparation) {
+          for(int y=0; y<hiresGridSize; y++) {
+            for(int z=0; z<hiresGridSize; z++) {
+              Coordinate<int> coord {x,y,z};
+              auto index = hires.getGrid().getIndexFromCoordinateNoWrap(coord);
+              auto location = hires.getGrid().getCentroidFromCoordinate(coord);
+              me->deInterpolate(location, hires[index]/pixel_volume_ratio);
+            }
+          }
+        }
+      }
+    }
+
+
+
     //! Evaluates the field at the specified co-ordinate using interpolation.
     DataType evaluateInterpolated(Coordinate<CoordinateType> location) const {
       int x_p_0, y_p_0, z_p_0;
diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index d2ac3468..42112382 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -137,6 +137,8 @@ namespace modifications {
 
     assert (noiseA.getTransferType() == particle::species::whitenoise);
     assert (noiseB.getTransferType() == particle::species::whitenoise);
+    assert (noiseA.isFourierOnAllLevels());
+    assert (noiseB.isFourierOnAllLevels());
 
 
     for(size_t level=0; level<Nlevel; ++level) {
@@ -177,7 +179,8 @@ namespace modifications {
           auto & source_level_filter = filters.getFilterForLevel(source_level);
 
           if (source_level > level) {        // Contribution from finer level
-            tmp.addFieldFromDifferentGrid(inputCopy.getFieldForLevel(source_level));
+            tmp.deInterpolate(inputCopy.getFieldForLevel(source_level));
+            // tmp.addFieldFromDifferentGrid(inputCopy.getFieldForLevel(source_level));
             tmp.toFourier();
             tmp.applyFilter(source_level_filter);
             tmp.applyTransferFunction(covs[level], 0.5);
@@ -264,9 +267,14 @@ namespace modifications {
     };
 
     auto preconditioner = [&covs, &masksCompl, &filters, Nlevel, &Tdagger_op_T, &Tplus_op_T](fields::OutputField<DataType> & inputs) {
-      // const bool precondition = false;
+      const bool precondition = false;
 
-      // if (precondition) {
+      if (precondition) {
+        Tdagger_op_T(inputs, [covs](const int level, fields::Field<DataType, T> & input) {
+          input.toFourier();
+          input.applyTransferFunction(covs[level], 0.5);
+        });
+      }
       //   for (auto level=0; level<Nlevel; ++level) {
       //     auto & field = inputs.getFieldForLevel(level);
       //     field.toFourier();
@@ -341,76 +349,34 @@ namespace modifications {
     // T^+ M T (n_a-n_b)
     auto Ma_minus_b = noiseA;
     Ma_minus_b -= noiseB;
-    Ma_minus_b = Tplus_op_T(Ma_minus_b, [masks](const int level, fields::Field<DataType, T> & input) {
+    Ma_minus_b.toReal();
+    Ma_minus_b = Tplus_op_T(Ma_minus_b, [&masks](const int level, fields::Field<DataType, T> & input) {
       input.toReal();
       input *= masks[level];
     });
 
     // T^+ Mbar T n_alpha
-    auto Mbar_n_alpha = Tplus_op_T(n_alpha, [masksCompl](const int level, fields::Field<DataType, T> & input) {
+    auto Mbar_n_alpha = Tplus_op_T(n_alpha, [&masksCompl](const int level, fields::Field<DataType, T> & input) {
       input.toReal();
       input *= masksCompl[level];
     });
 
     // Reconstruct solution
-    auto outputs = noiseB; // Tplus_op_T(noiseB, [](const int level, fields::Field<DataType, T> & input) {});
+    auto outputs = noiseB;
+    // outputs.toReal();
+    // outputs = Tplus_op_T(outputs, [](const int level, fields::Field<DataType, T> & input) {});
 
-    Ma_minus_b.toFourier();
-    Mbar_n_alpha.toFourier();
-    outputs.toFourier();
+    Ma_minus_b.toReal();
+    Mbar_n_alpha.toReal();
+    outputs.toReal();
     for (auto level = 0; level < Nlevel; ++level) {
       auto & field = outputs.getFieldForLevel(level);
       field += Ma_minus_b.getFieldForLevel(level);
       field += Mbar_n_alpha.getFieldForLevel(level);
     }
 
+    outputs.toFourier();
     return outputs;
-
-
-    // // Tplus_op_T(Ma_minus_b, [&masks](const int level, fields::Field<DataType,T> & input) {
-    // //   input.toReal();
-    // //   input *= masks[level];
-    // // });
-    // Ma_minus_b.toFourier();
-    // Ma_minus_b.applyPowerSpectrumFor(particle::species::all);
-    // Ma_minus_b.combineGrids();
-    // Ma_minus_b.toReal();
-    // for (auto level = 0; level<Nlevel; ++level)
-    //   Ma_minus_b.getFieldForLevel(level) *= masks[level];
-    // Ma_minus_b.toReal();
-
-
-    // // T^+ Mbar T n_alpha 
-    // // Note: we use an alias here for the sake of readability
-    // auto & Mbar_n_alpha = n_alpha;
-    // // Tplus_op_T(Mbar_n_alpha, [&masksCompl](const int level, fields::Field<DataType,T> & input) {
-    // //   input.toReal();
-    // //   input *= masksCompl[level];
-    // // });
-    // Mbar_n_alpha.toFourier();
-    // Mbar_n_alpha.applyPowerSpectrumFor(particle::species::all);
-    // Mbar_n_alpha.combineGrids();
-    // Mbar_n_alpha.toReal();
-    // for (auto level = 0; level<Nlevel; ++level)
-    //   Mbar_n_alpha.getFieldForLevel(level) *= masksCompl[level];
-    // Mbar_n_alpha.toReal();
-
-    // // f = b + M (a-b) + alpha;
-    // auto outputs = noiseB;
-    // // Tplus_op_T(outputs, [](const int level, fields::Field<DataType,T> & input) {});
-    // outputs.toFourier();
-    // outputs.applyPowerSpectrumFor(particle::species::all);
-    // outputs.combineGrids();
-    // outputs.toReal();
-
-    // outputs += Ma_minus_b;
-    // outputs += Mbar_n_alpha;
-
-    // // Convert back to whitenoise
-    // outputs.toFourier();
-    // outputs.applyPowerSpectrumFor(particle::species::whitenoise);
-    // outputs.combineGrids();
-    // return outputs;
   }
 }
 

From 7d04dfed42a5e33b2315c3de3c8308644e4d81d0 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Mon, 7 Nov 2022 18:11:17 +0000
Subject: [PATCH 25/27] Only use real grid when computing inner product

---
 genetIC/src/simulation/field/field.hpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/genetIC/src/simulation/field/field.hpp b/genetIC/src/simulation/field/field.hpp
index 28e4dcd1..b66d70e4 100644
--- a/genetIC/src/simulation/field/field.hpp
+++ b/genetIC/src/simulation/field/field.hpp
@@ -354,6 +354,9 @@ namespace fields {
     //! Add a multiple of the provided field to this one in-place
     void addScaled(const Field<DataType, CoordinateType> & other,
                    tools::datatypes::strip_complex<DataType> scale) {
+      assert(!other.isFourier());
+      assert(!isFourier());
+
       size_t N = data.size();
 #pragma omp parallel for
       for(size_t i=0; i<N; i++) {
@@ -367,7 +370,7 @@ namespace fields {
       assert(!isFourier());
 
       tools::datatypes::strip_complex<DataType> v=0;
-      size_t N = data.size();
+      size_t N = getGrid().size3;
 
 #pragma omp parallel for reduction(+:v)
       for(size_t i=0; i<N; i++) {

From d5e332202a4b6e1b37115001f5ac2712b4fbcb15 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Tue, 8 Nov 2022 00:14:41 +0000
Subject: [PATCH 26/27] First full working version

---
 .../src/simulation/modifications/splice.hpp   | 110 ++++---
 genetIC/src/tools/numerics/cg.hpp             | 290 +++++++++++++++++-
 2 files changed, 349 insertions(+), 51 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index 42112382..b63dd70d 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -155,10 +155,12 @@ namespace modifications {
         const fields::OutputField<DataType> &inputs,
         const std::function<void(const int, fields::Field<DataType, T> &)> op,
         const TOPERATOR Top,
-        const bool filter_in_window=true
+        const bool diagonal_only=false,
+        const bool no_filtering=false
     ) -> fields::OutputField<DataType> {
       fields::OutputField<DataType> outputs(inputs.getContext(), inputs.getTransferType());
       fields::OutputField<DataType> inputCopy(inputs);
+      const bool filter_in_window=true;
       const double power_out = (Top == TDAGGER) ? 0.5 : -0.5;
 
       assert (inputs.isRealOnAllLevels());
@@ -168,10 +170,15 @@ namespace modifications {
         auto & out = outputs.getFieldForLevel(level);
         out.toReal();
 
-        for (auto source_level=std::max(0, level-1); source_level<std::min(Nlevel, level+2); ++source_level) {
+        // Add a small value to the diagonal to avoid singularities
+        // out.addScaled(inputCopy.getFieldForLevel(level), 1e-6);
+
+        for (auto source_level=0; source_level<Nlevel; ++source_level) {
           T pixel_volume_ratio = multiLevelContext.getWeightForLevel(level) /
                                  multiLevelContext.getWeightForLevel(source_level);
 
+          if (diagonal_only && (source_level != level)) continue;
+
           fields::Field<DataType, T> tmp(out.getGrid(), false);
 
           // if (source_level != level) continue;
@@ -206,22 +213,26 @@ namespace modifications {
           } else {
             tmp += inputCopy.getFieldForLevel(level);
             tmp.toFourier();
-            if (filter_in_window && level < Nlevel-1) {
-              auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
-              tmp.applyFilterInWindow(this_level_filter, window, true);
-            } else {
-              tmp.applyFilter(this_level_filter);
+            if (!no_filtering) {
+              if (filter_in_window && level < Nlevel-1) {
+                auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
+                tmp.applyFilterInWindow(this_level_filter, window, true);
+              } else {
+                tmp.applyFilter(this_level_filter);
+              }
             }
             tmp.applyTransferFunction(covs[level], 0.5);
             tmp.toReal();
             op(level, tmp);
             tmp.toFourier();
             tmp.applyTransferFunction(covs[level], power_out);
-            if (filter_in_window && level < Nlevel-1) {
-              auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
-              tmp.applyFilterInWindow(this_level_filter, window, false);
-            } else {
-              tmp.applyFilter(this_level_filter);
+            if (!no_filtering) {
+              if (filter_in_window && level < Nlevel-1) {
+                auto window = multiLevelContext.getGridForLevel(level+1).getWindow();
+                tmp.applyFilterInWindow(this_level_filter, window, false);
+              } else {
+                tmp.applyFilter(this_level_filter);
+              }
             }
             tmp.toReal();
           }
@@ -234,15 +245,19 @@ namespace modifications {
     };
 
     auto Tplus_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
-                                const std::function<void(const int, fields::Field<DataType, T> &)> op
+                                const std::function<void(const int, fields::Field<DataType, T> &)> op,
+                                const bool diagonal_only=false,
+                                const bool no_filtering=false
     ) -> fields::OutputField<DataType> {
-      return T_op_T(inputs, op, TPLUS);
+      return T_op_T(inputs, op, TPLUS, diagonal_only, no_filtering);
     };
 
     auto Tdagger_op_T = [&T_op_T](const fields::OutputField<DataType> &inputs,
-                                  const std::function<void(const int, fields::Field<DataType, T> &)> op
+                                  const std::function<void(const int, fields::Field<DataType, T> &)> op,
+                                  const bool diagonal_only=false,
+                                  const bool no_filtering=false
     ) -> fields::OutputField<DataType> {
-      return T_op_T(inputs, op, TDAGGER);
+      return T_op_T(inputs, op, TDAGGER, diagonal_only, no_filtering);
     };
 
     // Compute Mbar C^-1 Mbar for a single level
@@ -267,41 +282,37 @@ namespace modifications {
     };
 
     auto preconditioner = [&covs, &masksCompl, &filters, Nlevel, &Tdagger_op_T, &Tplus_op_T](fields::OutputField<DataType> & inputs) {
-      const bool precondition = false;
-
+      const bool precondition = true;
+      // iterations to converge to 1e-5 with 64x64x64
+      // no precond:           520  chi² looks fishy
+      // T^+C^0.5T:             36
+      // T^+C T:                28
+      // T^+C^0.25 T:           44
+      // T^+Mbar C^0.5 Mbar T:  41
+      // T^+ T:                 47
+      // T^+Mbar C^0.5 T:      >80
+      // T^+ Mbar T:            51
+
+      // filter^2:             541 chi² looks fishy
+      // filter^2C^0.5:        310 chi² looks fishy
       if (precondition) {
-        Tdagger_op_T(inputs, [covs](const int level, fields::Field<DataType, T> & input) {
-          input.toFourier();
-          input.applyTransferFunction(covs[level], 0.5);
+        inputs = Tdagger_op_T(inputs, [&covs, &masksCompl](const int level, fields::Field<DataType, T> & input) {
+          // input.toReal();
+          // input *= masksCompl[level];
+          // input.toFourier();
+          // input.applyTransferFunction(covs[level], .25);
+          // input.toReal();
+          // input *= masksCompl[level];
         });
       }
-      //   for (auto level=0; level<Nlevel; ++level) {
-      //     auto & field = inputs.getFieldForLevel(level);
-      //     field.toFourier();
-      //     field.applyTransferFunction(covs[level], -0.5);
-      //     field.applyFilter(filters.getFilterForLevel(level));
-      //     field.toReal();
-      //     field *= masksCompl[level];
-      //     field.toFourier();
-      //     field.applyTransferFunction(covs[level], 1.0);
-      //     field.toReal();
-      //     field *= masksCompl[level];
-      //     field.toFourier();
-      //     field.applyTransferFunction(covs[level], -0.5);
-      //     field.applyFilter(filters.getFilterForLevel(level));
-      //     field.toReal();
-      //   }
-      // }
     };
 
     // Full splicing operator
     // Q := T^+ Mbar C^-1 Mbar T
-    auto Q = [&Tdagger_op_T, &Tplus_op_T, &Mbar_Cm1_Mbar_op, &preconditioner](
-      const fields::OutputField<DataType> & inputs
-    ) -> fields::OutputField<DataType> {
+    auto Q = [&Tdagger_op_T, &Tplus_op_T, &Mbar_Cm1_Mbar_op, &preconditioner](const fields::OutputField<DataType> & inputs) -> fields::OutputField<DataType> {
       auto outputs = inputs;
       preconditioner(outputs);
-      outputs = Tdagger_op_T(outputs, Mbar_Cm1_Mbar_op);
+      outputs = Tdagger_op_T(outputs, Mbar_Cm1_Mbar_op, false, false);
       preconditioner(outputs);
       outputs.toReal();
       return outputs;
@@ -310,10 +321,6 @@ namespace modifications {
     // Compute the rhs T^+ Mbar C^-1 M T (b - a)
     auto rhs = noiseB;
     rhs -= noiseA;
-    // rhs.toFourier();
-    // rhs.applyPowerSpectrumFor(particle::species::all);
-    // rhs.combineGrids();
-    // rhs.applyPowerSpectrumFor(particle::species::whitenoise);
     rhs.toReal();
     rhs = Tdagger_op_T(rhs, Mbar_Cm1_M_op);
     preconditioner(rhs);
@@ -323,21 +330,28 @@ namespace modifications {
     // that approximates [T^+ Mbar C^-1 Mbar T] and is symmetric
     rhs.getFieldForLevel(0).dumpGridData("rhs-0.npz");
     if (Nlevel > 1) rhs.getFieldForLevel(1).dumpGridData("rhs-1.npz");
-    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs);
+    std::cout << "rhs[0]=" << rhs.getFieldForLevel(0).getDataVector()[0] << std::endl;
+    auto n_alpha = tools::numerics::minresYolo<DataType>(Q, rhs, 1e-5);
     // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs);
     preconditioner(n_alpha);
 
+    n_alpha.getFieldForLevel(0).dumpGridData("n_alpha_0.npz");
+    if (Nlevel > 1) n_alpha.getFieldForLevel(1).dumpGridData("n_alpha_1.npz");
+
+
     // if (Nlevel > 1) {
     //   auto Ntot = rhs.getFieldForLevel(0).getGrid().size3 + ((Nlevel > 1) ? rhs.getFieldForLevel(1).getGrid().size3 : 0);
     //   for (auto i = 0; i < Ntot; ++i) {
     //     if (i % 10 == 0) std::cout << i << "/" << Ntot << std::endl;
     //     auto input = rhs;
+    //     input.toReal();
     //     input *= 0;
     //     if (i < rhs.getFieldForLevel(0).getGrid().size3)
     //       input.getFieldForLevel(0)[i] = 1;
     //     else
     //       input.getFieldForLevel(1)[i-rhs.getFieldForLevel(0).getGrid().size3] = 1;
-    //     auto output = Q(input);
+    //     auto output = Q(input, false, false);
+    //     output.toReal();
     //     std::ostringstream stream;
     //     stream << "field" << i;
     //     std::string filename = stream.str();
diff --git a/genetIC/src/tools/numerics/cg.hpp b/genetIC/src/tools/numerics/cg.hpp
index 605ecd1e..3b5134dc 100644
--- a/genetIC/src/tools/numerics/cg.hpp
+++ b/genetIC/src/tools/numerics/cg.hpp
@@ -160,10 +160,17 @@ namespace tools {
       auto innerProduct = [](fields::OutputField<T> &a, fields::OutputField<T> &b) -> double {
         double result = 0;
         for (auto ilevel = 0; ilevel < a.getNumLevels(); ++ilevel) {
-          result += a.getFieldForLevel(ilevel).innerProduct(b.getFieldForLevel(ilevel));
+          const auto & left = a.getFieldForLevel(ilevel);
+          const auto & right = b.getFieldForLevel(ilevel);
+          result += left.innerProduct(right);
         }
         return result;
       };
+
+      auto norm = [&innerProduct](fields::OutputField<T> &a) -> double {
+        return std::sqrt(innerProduct(a, a));
+      };
+
       double scale = sqrt(innerProduct(b, b));
       double rho = innerProduct(r, s);
 
@@ -173,7 +180,7 @@ namespace tools {
       }
 
       dimension *= 10;
-
+      double old_norm = 0;
       // Start iteration
       size_t iter = 0;
       for(; iter<dimension; ++iter) {
@@ -185,8 +192,13 @@ namespace tools {
         double norm = sqrt(innerProduct(r, r));
         if (norm < rtol * scale || norm < atol)
           break;
-
         logging::entry() << "MINRES iteration " << iter << " residual=" << norm << std::endl;
+
+        // if (std::abs(norm - old_norm) / norm < rtol) {
+        //   logging::entry(logging::warning) << "MINRES: stagnation detected" << std::endl;
+        //   break;
+        // }
+
         s = A(r);
         double rhobar = rho;
         rho = innerProduct(r, s);
@@ -196,11 +208,275 @@ namespace tools {
 
         q *= beta;
         q += s;
+
+        // Save old norm
+        old_norm = norm;
       }
 
       logging::entry() << "MINRES ended after " << iter << " iterations" << std::endl;
 
       return x;
+
+    }
+
+        /* Adapted from https://stanford.edu/group/SOL/reports/SOL-2011-2R.pdf */
+    template<typename T>
+    fields::OutputField<T> minresYolo(
+        std::function<fields::OutputField<T>(fields::OutputField<T> &)> A,
+        fields::OutputField<T> &b,
+        double rtol = 1e-6,
+        double atol = 1e-12,
+        fields::OutputField<T>* x0 = nullptr
+    ) {
+      assert (b.isRealOnAllLevels());
+
+      int Nlevel = b.getNumLevels();
+
+      auto innerProduct = [](fields::OutputField<T> &a, fields::OutputField<T> &b) -> double {
+        double result = 0;
+        for (auto ilevel = 0; ilevel < a.getNumLevels(); ++ilevel) {
+          const auto & left = a.getFieldForLevel(ilevel);
+          const auto & right = b.getFieldForLevel(ilevel);
+          result += left.innerProduct(right);
+        }
+        return result;
+      };
+
+      auto norm = [&innerProduct](fields::OutputField<T> &a) -> double {
+        return std::sqrt(innerProduct(a, a));
+      };
+
+      // my_minres(A, b, maxiter=None, x0=None, tol=1e-5):
+      fields::OutputField<T> x(b.getContext(), b.getTransferType());
+      x.getFieldForLevel(0);  // Trigger generation of grid
+      x.toReal();
+      if (x0 != nullptr) 
+          x += *x0;
+
+      int n = 0;
+
+      for(auto ilevel = 0; ilevel<Nlevel; ++ilevel){
+        const auto & field = b.getFieldForLevel(ilevel);
+        assert(field.isFourier() == false);
+        n += field.getGrid().size3;
+      }
+
+      auto maxiter = 5 * n;
+
+      int istop = 0;
+      int itn = 0;
+      double Anorm = 0;
+      double Acond = 0;
+      double rnorm = 0;
+      double ynorm = 0;
+
+      double eps = std::numeric_limits<double>::epsilon();
+
+      // Set up y and v for the first Lanczos vector v1.
+      // y  =  beta1 P' v1,  where  P = C**(-1).
+      // v is really P' v1.
+      fields::OutputField<T> r1(b);
+      if (x0 != nullptr)
+          r1 -= A(*x0);
+      fields::OutputField<T> y(r1);
+
+      double beta1 = innerProduct(r1, y);
+
+      if (beta1 < 0)
+          throw std::runtime_error("minres: indefinite preconditioner");
+      else if (beta1 == 0)
+          return x;
+
+      if (norm(b) == 0) {
+          return b;
+      }
+
+      beta1 = std::sqrt(beta1);
+
+      // Initialize other quantities
+      double oldb = 0;
+      double beta = beta1;
+      double dbar = 0;
+      double epsln = 0;
+      double qrnorm = beta1;
+      double phibar = beta1;
+      double rhs1 = beta1;
+      double rhs2 = 0;
+      double tnorm2 = 0;
+      double gmax = 0;
+      double gmin = std::numeric_limits<double>::max();
+      double cs = -1;
+      double sn = 0;
+      fields::OutputField<T> w(b.getContext(), b.getTransferType());
+      fields::OutputField<T> w2(b.getContext(), b.getTransferType());
+      w.getFieldForLevel(0);  // Trigger generation of grid
+      w2.getFieldForLevel(0);  // Trigger generation of grid
+      w.toReal();
+      w2.toReal();
+      fields::OutputField<T> r2(r1);
+      for (auto itn = 1; itn <= maxiter; ++itn) {
+          double s = 1.0/beta;
+          // std::cout << "s = " << s << std::endl;
+          auto v = y;
+          v *= s;
+          // std::cout << "v[0] = " << v.getFieldForLevel(0).getDataVector()[0] << std::endl;
+
+          y = A(v); // TODO: spare some memory by storing v in y
+          // std::cout << "y[0] = " << y.getFieldForLevel(0).getDataVector()[0] << std::endl;
+
+          if (itn >= 2)
+              y.addScaled(r1, -beta/oldb);
+
+          double alfa = innerProduct(v, y);
+          // std::cout << "alfa = " << alfa << std::endl;
+          y.addScaled(r2, -alfa/beta);
+          // std::cout << "y[0] = " << y.getFieldForLevel(0).getDataVector()[0] << std::endl;
+          r1 = r2;
+          // std::cout << "r1[0] = " << r1.getFieldForLevel(0).getDataVector()[0] << std::endl;
+          r2 = y;
+          // std::cout << "r2[0] = " << r2.getFieldForLevel(0).getDataVector()[0] << std::endl;
+          y = r2;
+          // std::cout << "y[0] = " << y.getFieldForLevel(0).getDataVector()[0] << std::endl;
+          oldb = beta;
+          // std::cout << "oldb = " << oldb << std::endl;
+          beta = innerProduct(r2,y);
+          // std::cout << "beta = " << beta << std::endl;
+          if (beta < 0)
+              throw std::runtime_error("non-symmetric matrix");
+          beta = std::sqrt(beta);
+          // std::cout << "beta = " << beta << std::endl;
+          tnorm2 += alfa*alfa + oldb*oldb + beta*beta;
+          // std::cout << "tnorm2 = " << tnorm2 << std::endl;
+
+          if (itn == 1) {
+              if (beta/beta1 <= 10*eps)
+                  istop = -1;  // Terminate later
+          }
+
+          // Apply previous rotation Qk-1 to get
+          //   [deltak epslnk+1] = [cs  sn][dbark    0   ]
+          //   [gbar k dbar k+1]   [sn -cs][alfak betak+1].
+
+          double oldeps = epsln;
+          double delta = cs * dbar + sn * alfa;   // delta1 = 0         deltak
+          double gbar = sn * dbar - cs * alfa;   // gbar 1 = alfa1     gbar k
+          epsln = sn * beta;     // epsln2 = 0         epslnk+1
+          dbar = - cs * beta;   // dbar 2 = beta2     dbar k+1
+          double root = std::sqrt(gbar*gbar + dbar*dbar);
+
+          // Compute the next plane rotation Qk
+
+          double gamma = std::sqrt(gbar*gbar + beta*beta);       // gammak
+          gamma = std::max(gamma, eps);
+          cs = gbar / gamma;             // ck
+          sn = beta / gamma;             // sk
+          double phi;
+          phi = cs * phibar;             // phik
+          phibar = sn * phibar;          // phibark+1
+
+          // Update  x.
+          double denom = 1.0/gamma;
+          auto w1 = w2;
+          w2 *= 0;
+          w2 += w;
+          w *= 0;
+          w.addScaled(v, denom);
+          w.addScaled(w1, -oldeps * denom);
+          w.addScaled(w2, -delta * denom);
+
+          // std::cout << "w[0] = " << w.getFieldForLevel(0).getDataVector()[0] << std::endl;
+
+          x.addScaled(w, phi);
+          // std::cout << "x[0] = " << x.getFieldForLevel(0).getDataVector()[0] << std::endl;
+
+          // Go round again.
+
+          gmax = std::max(gmax, gamma);
+          gmin = std::min(gmin, gamma);
+          double z = rhs1 / gamma;
+          rhs1 = rhs2 - delta*z;
+          rhs2 = - epsln*z;
+
+          // Estimate various norms and test for convergence.
+
+          Anorm = std::sqrt(tnorm2);
+          ynorm = norm(x);
+          double epsa = Anorm * eps;
+          double epsx = Anorm * ynorm * eps;
+          double epsr = Anorm * ynorm * rtol;
+          double diag = gbar;
+
+          if (diag == 0) diag = epsa;
+
+          qrnorm = phibar;
+          rnorm = qrnorm;
+          double test1, test2;
+          if (ynorm == 0 || Anorm == 0)
+              test1 = 1e99;
+          else
+              test1 = rnorm / (Anorm*ynorm);    // ||r||  / (||A|| ||x||)
+          if (Anorm == 0)
+              test2 = 1e99;
+          else
+              test2 = root / Anorm;            // ||Ar|| / (||A|| ||r||)
+
+          // Estimate  cond(A).
+          // In this version we look at the diagonals of  R  in the
+          // factorization of the lower Hessenberg matrix,  Q @ H = R,
+          // where H is the tridiagonal matrix from Lanczos with one
+          // extra row, beta(k+1) e_k^T.
+
+          Acond = gmax/gmin;
+
+          // See if any of the stopping criteria are satisfied.
+          // In rare cases, istop is already -1 from above (Abar = const*I).
+
+          if (istop == 0) {
+              double t1 = 1 + test1;      // These tests work if tol < eps
+              double t2 = 1 + test2;
+              if (t2 <= 1)
+                  istop = 2;
+              if (t1 <= 1)
+                  istop = 1;
+
+              if (itn >= maxiter)
+                  istop = 6;
+              if (Acond >= 0.1/eps)
+                  istop = 4;
+              if (epsx >= beta1)
+                  istop = 3;
+              // if rnorm <= epsx   : istop = 2
+              // if rnorm <= epsr   : istop = 1
+              if (test2 <= rtol)
+                  istop = 2;
+              if (test1 <= rtol)
+                  istop = 1;
+          }
+
+          // See if it is time to print something.
+          bool prnt = (n <= 10) || (itn <= 10) || (itn >= maxiter-10) || ((itn % 10) == 0) || (qrnorm <= 10*epsx) || (qrnorm <= 10*epsr) || (Acond <= 1e-2/eps) || (istop != 0);
+
+          if (prnt) {
+              // str1 = '%6g %12.5e %10.3e' % (itn, x[0], test1)
+              // str2 = ' %10.3e' % (test2,)
+              // str3 = ' %8.1e %8.1e %8.1e' % (Anorm, Acond, gbar/Anorm)
+            char buffer[100];
+            sprintf(buffer, "%6d %12.5e %10.3e %10.3e %8.1e %8.1e %8.1e", itn, x.getFieldForLevel(0).getDataVector() [0], test1, test2, Anorm, Acond, gbar/Anorm);
+            logging::entry() << "MINRES " << buffer << std::endl;
+
+          }
+
+          if (istop != 0)
+              break;  // TODO check this
+      }
+
+      int info;
+      if (istop == 6)
+          info = maxiter;
+      else
+          info = 0;
+
+      return x;
     }
 
     template<typename T>
@@ -249,6 +525,7 @@ namespace tools {
       dimension *= 10;
 
       double scale = sqrt(innerProduct(b, b));
+      double old_norm = 0;
 
       // Start iteration
       size_t iter = 0;
@@ -288,6 +565,13 @@ namespace tools {
           break;
 
         logging::entry() << "BiCGSTAB iteration " << iter << " residual=" << norm << std::endl;
+
+        if (std::abs(norm - old_norm) / norm < rtol) {
+          logging::entry(logging::warning) << "BiCGSTAB: stagnation detected" << std::endl;
+          break;
+        }
+        old_norm = norm;
+
         // Save old values
         rOld = r;
         rhoOld = rho;

From d6c50a37de8088625dfa96c502bbb3b02e7de062 Mon Sep 17 00:00:00 2001
From: Corentin Cadiou <corentin.cadiou@cphyc.me>
Date: Fri, 11 Nov 2022 15:04:27 +0000
Subject: [PATCH 27/27] Remove single level implementatioN

---
 .../src/simulation/modifications/splice.hpp   | 85 +------------------
 1 file changed, 2 insertions(+), 83 deletions(-)

diff --git a/genetIC/src/simulation/modifications/splice.hpp b/genetIC/src/simulation/modifications/splice.hpp
index b63dd70d..168284a9 100644
--- a/genetIC/src/simulation/modifications/splice.hpp
+++ b/genetIC/src/simulation/modifications/splice.hpp
@@ -38,88 +38,6 @@ namespace modifications {
     return mask;
   }
 
-  //! Return the field f which satisfies f = a in flagged region while minimising (f-b).C^-1.(f-b) elsewhere
-  template<typename DataType, typename T=tools::datatypes::strip_complex<DataType>>
-  fields::Field<DataType,T> spliceOneLevel(fields::Field<DataType,T> & a,
-                                           fields::Field<DataType,T> & b,
-                                           const fields::Field<DataType,T> & cov) {
-
-      // To understand the implementation below, first read Appendix A of Cadiou et al (2021),
-      // and/or look at the 1D toy implementation (in tools/toy_implementation/gene_splicing.ipynb) which
-      // contains a similar derivation and near-identical implementation.
-
-      assert (&a.getGrid() == &b.getGrid());
-      assert (&a.getGrid() == &cov.getGrid());
-      auto mask = generateMaskFromFlags(a.getGrid());
-      auto maskCompl = generateMaskComplementFromFlags(a.getGrid());
-
-      a.toFourier();
-      b.toFourier();
-
-      // The preconditioner should be almost equal to the covariance.
-      // We however set the fundamental of the power spectrum to a non-null value,
-      // otherwise, the mean value in the spliced region is unconstrained.
-      fields::Field<DataType,T> preconditioner(cov);
-      preconditioner.setFourierCoefficient(0, 0, 0, 1);
-
-      fields::Field<DataType,T> delta_diff = b-a;
-      delta_diff.applyTransferFunction(preconditioner, 0.5);
-      delta_diff.toReal();
-
-      fields::Field<DataType,T> z(delta_diff);
-      z*=mask;
-      z.toFourier();
-      z.applyTransferFunction(preconditioner, -1.0);
-      z.toReal();
-      z*=maskCompl;
-      z.toFourier();
-      z.applyTransferFunction(preconditioner, 0.5);
-      z.toReal();
-
-      auto X = [&preconditioner, &maskCompl](const fields::Field<DataType,T> & input) -> fields::Field<DataType,T>
-      {
-        fields::Field<DataType,T> v(input);
-        assert (!input.isFourier());
-        assert (!v.isFourier());
-        v.toFourier();
-        v.applyTransferFunction(preconditioner, 0.5);
-        v.toReal();
-        v*=maskCompl;
-        v.toFourier();
-        v.applyTransferFunction(preconditioner, -1.0);
-        v.toReal();
-        v*=maskCompl;
-        v.toFourier();
-        v.applyTransferFunction(preconditioner, 0.5);
-        v.toReal();
-        return v;
-      };
-
-      fields::Field<DataType,T> alpha = tools::numerics::conjugateGradient<DataType>(X, z);
-
-      alpha.toFourier();
-      alpha.applyTransferFunction(preconditioner, 0.5);
-      alpha.toReal();
-
-      fields::Field<DataType,T> bInDeltaBasis(b);
-      bInDeltaBasis.toFourier();
-      bInDeltaBasis.applyTransferFunction(preconditioner, 0.5);
-      bInDeltaBasis.toReal();
-
-      alpha*=maskCompl;
-      alpha+=bInDeltaBasis;
-
-      delta_diff*=mask;
-      alpha-=delta_diff;
-
-      assert(!alpha.isFourier());
-      alpha.toFourier();
-      alpha.applyTransferFunction(preconditioner, -0.5);
-      alpha.toReal();
-
-      return alpha;
-  }
-
   template<typename DataType, typename T=tools::datatypes::strip_complex<DataType>>
   fields::OutputField<DataType> spliceMultiLevel(
     const fields::OutputField<DataType> &noiseB,
@@ -331,7 +249,8 @@ namespace modifications {
     rhs.getFieldForLevel(0).dumpGridData("rhs-0.npz");
     if (Nlevel > 1) rhs.getFieldForLevel(1).dumpGridData("rhs-1.npz");
     std::cout << "rhs[0]=" << rhs.getFieldForLevel(0).getDataVector()[0] << std::endl;
-    auto n_alpha = tools::numerics::minresYolo<DataType>(Q, rhs, 1e-5);
+    // auto n_alpha = tools::numerics::minresYolo<DataType>(Q, rhs, 1e-6);
+    auto n_alpha = tools::numerics::minres<DataType>(Q, rhs, 1e-6);
     // auto n_alpha = tools::numerics::bicgstab<DataType>(Q, rhs);
     preconditioner(n_alpha);