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"/>mimetext/htmlrootassigneelast_run_timestampAٚpersist_js_state·has_pluto_hook_features§cell_id$75824976-a2f9-4c5e-80b3-de5bae5ddaa3depends_on_disabled_cells§runtime   >[`published_object_keysdepends_on_skipped_cells§errored$867c780f-beed-4b10-a5dc-eb1385ef3941queued¤logsrunning¦outputbody+<div class="markdown"><h2>Setup</h2>
</div>mimetext/htmlrootassigneelast_run_timestampAٚslapersist_js_state·has_pluto_hook_features§cell_id$867c780f-beed-4b10-a5dc-eb1385ef3941depends_on_disabled_cells§runtime pڵpublished_object_keysdepends_on_skipped_cells§errored$6b5deada-4836-498b-aeb2-6817274733f9queued¤logsrunning¦outputbody.<div class="markdown"><h3>Jacobian</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚtmYpersist_js_state·has_pluto_hook_features§cell_id$6b5deada-4836-498b-aeb2-6817274733f9depends_on_disabled_cells§runtime _jpublished_object_keysdepends_on_skipped_cells§errored$18375b3b-a8b9-4d6d-8605-10525e5e2614queued¤logsrunning¦outputbody;colpack_symmetric_coloring (generic function with 1 method)mimetext/plainrootassigneelast_run_timestampAٚspersist_js_state·has_pluto_hook_features§cell_id$18375b3b-a8b9-4d6d-8605-10525e5e2614depends_on_disabled_cells§runtime Dpublished_object_keysdepends_on_skipped_cells§errored$4f6561fc-15e0-4a28-9ae2-9c4979244222queued¤logsrunning¦outputbody/d_values_small (generic function with 1 method)mimetext/plainrootassigneelast_run_timestampAٚs°persist_js_state·has_pluto_hook_features§cell_id$4f6561fc-15e0-4a28-9ae2-9c4979244222depends_on_disabled_cells§runtime y*published_object_keysdepends_on_skipped_cells§errored$86948d71-6933-422d-9345-d9e5f51f60abqueued¤logslinemsgn=10 - d=10: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#86948d71-6933-422d-9345-d9e5f51f60ab:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=100 - d=5: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#86948d71-6933-422d-9345-d9e5f51f60ab:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

Test Summary:   | Error  Broken  Total  Time
Column coloring |     2       6      8  0.6s
  n=10 - d=5    |             1      1  0.5s
  n=10 - d=10   |     1              1  0.1s
  n=100 - d=5   |     1              1  0.0s
  n=100 - d=10  |             1      1  0.0s
  n=100 - d=20  |             1      1  0.0s
  n=1000 - d=5  |             1      1  0.0s
  n=1000 - d=10 |             1      1  0.0s
  n=1000 - d=20 |             1      1  0.0s
text/plaincell_id$86948d71-6933-422d-9345-d9e5f51f60abkwargsidPlutoRunner_d1acb81efileR/home/runner/.julia/packages/Pluto/GVuR6/src/runner/PlutoRunner/src/PlutoRunner.jlgroupPlutoRunnerlevelLogLevel(-555)running¦outputbodymsgASome tests did not pass: 0 passed, 0 failed, 2 errored, 6 broken.stacktracecall4finish(ts::Test.DefaultTestSet; print_results::Bool)urlsfile:///cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlinlined¤fileTest.jllinelinfo_typeCore.MethodInstancepathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallfinishurlinlinedäfileTest.jllinelinfo_typeNothingpathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallmacro expansionurlinlinedäfile1Test.jl#@#==#86948d71-6933-422d-9345-d9e5f51f60abline9linfo_typeNothingpathٖ/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jl#@#==#86948d71-6933-422d-9345-d9e5f51f60abfrom_ccalltop-level scopeurlinlinedäfile0index.jl#==#86948d71-6933-422d-9345-d9e5f51f60ablinelinfo_typeNothingpatht/home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#86948d71-6933-422d-9345-d9e5f51f60abfrom_c¤mime'application/vnd.pluto.stacktrace+objectrootassigneelast_run_timestampAٚt*7persist_js_state·has_pluto_hook_features§cell_id$86948d71-6933-422d-9345-d9e5f51f60abdepends_on_disabled_cells§runtimepublished_object_keysdepends_on_skipped_cells§errored$ccace506-dcb4-4fa4-bc02-280c0c25f60bqueued¤logsrunning¦outputbody1<div class="markdown"><h2>Correctness</h2>
</div>mimetext/htmlrootassigneelast_run_timestampAٚs;persist_js_state·has_pluto_hook_features§cell_id$ccace506-dcb4-4fa4-bc02-280c0c25f60bdepends_on_disabled_cells§runtime YYpublished_object_keysdepends_on_skipped_cells§errored$6e1fed93-3b43-41eb-9993-ac8ced7244e7queued¤logsrunning¦outputbodyC<div class="markdown"><p>A comparison between matrix coloring packages in Julia.</p>
<p>At the moment we include:</p>
<ul>
<li><p><a href="https://github.com/gdalle/SparseMatrixColorings.jl">SparseMatrixColorings.jl</a></p>
</li>
<li><p><a href="https://github.com/exanauts/ColPack.jl">ColPack.jl</a></p>
</li>
</ul>
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fPPNN7m5uWfPnpXaLV8PAABNgkAIwOYEBwfPnTtXaufk5PTs2bNLly633367u7v7008/LW13cnJ69dVXpbZpQXmDwTB58uQ2bdrExcVFR0f7+Pj88MMP0i4XFxdpIYTRo0ebZjc5cOBAcHBwdHR0XFycv7//0KFDTdOEjhw5sp4K27Zta0plZWVlI0eO9PT0dHd3nz59ukajMXVr4KSjjeLp6fn222+bXi5ZssTf39/d3T0uLs607sKAAQNMSzWEhYW98sorpv7Lly9v166dq6trVFTUtm3bTNsXLFhww0lfJkyYMGLECKmt0+mef/55Hx8fNze3/v37m+Y+dXJy+vzzz2/5U96MF154wcfHR2pXVlZOmzYtICCga9euS5culaUeAACaBIEQgC2aPXv25MmTpbZer09KSjp+/Lj5hJm7du3q0KGD9PL99983X50iPz//3Llzqampprs6lUrlypUrg4ODhRChoaEbN2403Rqq0+lSU1PPnTtnPlTYr1+/69dvqOEf//iH+UuNRiOtOvjSSy8pFAppY0pKink+bCrTpk17+umnTWcRQpSVlZnaUVFRa9euNR/enD179qxZs8wXAJSmmZE4ODi8+eabpqtdv2XLlg0fPtz00mg0mn/AgICANWvWmD/O15I8PDxWrlzp6el5/a7u3bszyygAwEIRCAHYIicnp++++27dunU10oWfn9+cOXPOnDnTv39/00aVSrVp06bvv/++f//+NdY9d3R0nDp16rFjx+677z7TxiFDhiQnJ0sDXDXOGxsb+/nnn+/ateuGawM++uijS5YsMT23JoTo1q3btm3b3n33XdPK6fn5+eajc01FqVR+/vnnu3fv7tOnj/nMLkFBQe+//35SUpJ5VUIIBweHhQsX7tu3r1+/fubXx97e/s477zx16tT8+fMbeOqQkJBdu3Z98803kZGR5ts9PDymT59+4cKFsWPH3sInu1WjRo06duyYNBQs8fT0fOqpp3799ddagyIAAK2fwvQwDADYpsuXL6vVajs7u3bt2vn7+5uPjF1Pp9NlZWWp1WqVShUaGhoQEFAjItZQVFSkVqsLCgr8/f3btm1r/nBgQ+j1+szMzKysrLZt27b81JpCCIPBkJGRkZeXFxMTc32+vV5RUVFGRkZpaamnp2dkZKSbm9tNn1qtVqvV6qqqKj8/v+jo6BprwcsrKysrIyPD398/MjJS+oGJiYmR1jJp3759Wlqa3AUCANBQBEIAAG7s/Pnzpoc2Y2NjzW+aLS0t9fb2lu7pHTt2bEJCgjwlAgDQeNwyCgDAjU2ePLnLf7388sum7Xq9/tlnn5XSoBCiV69eMhUIAMDNYIQQAIAbW7BgwezZs6W2QqEYNmzYoEGDqqurN2/efObMGWl7dHT00aNHeZ4QAGBBCIQAADTIc8899+mnn9a1NyIiYsuWLZ06dWrJkgAAuEUEQgAAGurUqVMLFy7cu3fv1atXq6qqlEplcHBwZGTko48+Onny5FY18w0AAA1BIAQAoNGMRmNBQYGHh4f57DIAAFgcAiEAAAAA2ChmGQUAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbxYpJt0qhUMhdAgAAAAST5wM3gRFCAAAAALBRjBA2jRb7RkqtVuv1+pCQEJVK1TJnBFpYfn5+eXm5r6+vq6ur3LUAzaK8vDw/P9/V1dXX11fuWoBmodfr1Wq1SqUKCQlpmTNyxxZw0xghBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtFIAQAAAAAG0UgBAAAAAAbRSAEAAAAABtlJ3cBaJASjW7L+Zxf0/PTc4t0ekM7v/z4CJ97OgUEeTjJXRoAAAAAS0UgbO10BuM7u377eG96sUb759bM0uXHrv5NpXy0d9sPxnTycOK/IwAAAIBGI0i0aqVVutFLD+/PKKh1b7Xe8NWhSz+l5O1+sl+4j0sL1wYAAADA0tnWM4TV1dUajUbuKhrKaBTjlx+rKw2apOdXjFySWFala5mqAAAAAFgNmwiEycnJEyZMiI6OdnFxcXV1DQ8PHzdu3MmTJ+Wu6wZWnVTvvJjXkJ4peeXv7k5t7noAAAAAWBnrD4QLFy7s1q3bunXrUlNT9Xq9wWC4dOlSQkLC7bff/tprr8ldXX0+3pvW8M4L92dodIbmKwYAAACA9bHyQJicnPzCCy9UV1e3b99+zZo1Fy9eTEtLW716dfv27Q0Gw1tvvbVr1y65a6ydulhzQl3c8P5lVbq9afnNVw8AAAAA62Plk8q8/PLLWq3W09PzyJEjvr6+0sb27dsPGjSoU6dOxcXF77///h133CFvkbW6mFdmNDbuLcm5pSNj2zRPOQAAAACskJWPEB46dEgIMW3aNFMalAQHBz/wwANCiNOnT8tT2Y3klVU39i25pY1+CwAAAABbZs2BsLi4ODs7WwgRFxd3/d6wsDAhxLVr1yoqKlq6sgZwc1Q19i3urEYIAAAAoDGsOUIolcqNGzcKIeLj46/fm5GRIYRo06aNi0trXMEv2MOpBd4CAAAAwJZZcyB0d3e/7777at21ffv2VatWCSEee+yxli2qoboGebRxc2j4jaMKhRgW7desJQEAAACwMtYcCGtISkpaunRpfn7+4cOHU1NThRDjx49/8803b/jGJUuW3LBPWVlZE5T4vx7o7P/l4asN7Dww3NtLpWuOMoAWVl5eXl5e7uTkZGzsxEqAhaioqCgvLxdCODo6yl0L0Cz0en15eblKpeIvE6D1U9jOn1wJCQnjxo0zvRwyZMj333/v7+9/wzcqFIob9rl06dItFVeb/Ard0H8nF1fpb9hToRCbJ8Z08Xdu8hqAlldYWFhZWenl5dU6b+cGbl1FRUVRUZGzs7O3t7fctQDNQq/X5+TkKJXKwMDAljmjNDeE7fxZCzQhGxohjI2NfeWVV4qKilJTU3fv3r1nz56IiIh169bddddd9b/xiSeeqGevNH7o5ubWlLUKIYRwcxMrHuw0YfW5qhutOG80ioO/V/Vrz5oTsAbV1dUKhcLNzY1ACGulVCqrq6tdXFya498OoDXQ6/WlpaUqlYofcqD1s6ERQnOpqaljx449f/68k5NTZmZmQEDATR9KGj9svsu4P6NgysoTlwora91rr1Jq9X/ExU/u6/zswPbNVAbQYvLz88vLy319fV1dXeWuBWgW5eXl+fn5rq6uNZZEAqyGXq9Xq9UqlSokJKRlztjcf48BVsyal52oR1RU1LJly4QQGo3m3//+t9zl1GdAhM+FOUM/v79Lv3BvO+UfN68qFKJrkMcbI2Mvzxs+qsMfd70+/8P5NSfV8lUKAAAAwMJY8y2jaWlpWVlZTk5Ot99++/V7e/Xq5e7uXlpaKk0w05o526ue7h/+dP/wSq3+TOpljVbXPaqdp8sfUxGsf/j2O5ckHsgoMBiND60+5eFkP7rjjR+MBAAAAABrHiFcv379wIEDBwwYoNPprt+rUCiUSqUQwoKeU3K2V4V6OER4Obo5/pnkXRxUWx7v3S3YQwih1RvGLz+2L71AvhoBAAAAWAxrDoTSwGBVVdWZM2eu35uenl5cXCyE6N69e0tX1tS8nO23Te8T4eMihKjU6u/55sgpdYncRQEAAABo7aw5EPbs2dPJyUkIMW/evOv3zp07VwihUqni4+NburJmEOzh9NOTfQPdHYUQxRrtyCWJKXnlchcFAAAAoFWz5kDo6en52muvCSG2b98+atSo/fv35+fnFxQU7Nu3b9SoUWvXrhVCvPzyy9HR0XJX2jQifV13zujr7WwvhMgtq7praWJWiUbuogAAAAC0Xla+7IROpxs1atTu3btr3Tt+/PhVq1bZ29vfyilaeJpjtVqt1+tDQkJUKlWtHQ5lFo746lB5tV4IERfovndmfx+XW/qAQAtj2QlYPZadgNVj2QnAgljzCKEQws7ObteuXStWrOjatat0+6gQwtfXd/DgwTt37ly7du0tpsFWqF+495qpPe1VSiFEUnbp6KWHpXAIAAAAADVY+QihOaPRqFarHRwc/P2bclWG1jZCKFl1Qj111UmD0SiEuCOmzZbHezvaWXn4h9VghBBWjxFCWD1GCAELYkMhQaFQhIaGNm0abLUm9QhZOC5Oau9KyXt0zSkDvyIBAAAA/C8bCoS25un+4a/eGSO1V59Uz9qQJG89AAAAAFobAqE1e2Nk7N8HtZfaiw9mvvrjRXnrAQAAANCqEAit3IJ7Oz/Sq63UfuunlI/2pstbDwAAAIDWg0Bo5RQKsWRC17s7BkgvZ28+9+2Ry/KWBAAAAKCVIBBaP3uVcu3DPQe29xFCGI3iibVnNpzNkrsoAAAAAPIjENoEZ3vV5sd73xbiKYTQG4xTVp7cm5Yvd1EAAAAAZEYgtBWeTvY7nugb6+8mhKjU6u/99siJq8VyFwUAAABATgRCG9LGzeGnGX3bejkLIUo0ulFLE5Nzy+QuCgAAAIBsCIS2pa2X87bpfXxc7IUQeWXVd36VeLmwUu6iAAAAAMiDQGhz4gLdt03v4+ZoJ4S4UlQ54qtDuWVVchcFAAAAQAYEQlvUp513wqO9HO2UQoiUvPIxXx8pq9LJXRQAAACAlkYgtFHDo/1WT+mpUiqEEEevFI399miVziB3UQAAAABaFIHQdo3rEvj5/V2k9s+p1yauOK4zGOUtCQAAAEBLIhDatBn9wt6+q4PUTkjKnvaf00YiIQAAAGAzCIS27pU7omcPiZTay49eeWnbBXnrAQAAANBiCIQQH4zp9HifdlL7Hz+n/vOXNHnrAQAAANAyCIQQCoX4anzXB7oGSS/nbD3/9eHL8pYEAAAAoAUQCCGEECqlYtWUHiNi2gghjEbx5Loza0//LndRAAAAAJoXgRB/cFAp1z18e89QTyGE3mCcsvLkjot5chcFAAAAoBkRCPEnDye7H5/o2zHATQhRrTfc/6+jBzML5C4KAAAAQHMhEOJ/+Lk67JzRL8zbWQhRUa0f++3R8zmlchcFAAAAoFkQCFFTqKfTT0/2C3B3FEJcK6++86vEzIIKuYsCAAAA0PQIhKhFtJ/r5sd7uzvaCSHUxZoRXyXmlFbJXRQAAACAJkYgRO16tfX64bFeTnZKIUTqtfI7v0osqtTKXRQAAACApkQgRJ2GRvl9/1BPO6VCCHEmq2TcsqManUHuogAAAAA0GQIh6nNv58BvJ3ZXKIQQYk9a/oP/PqYzGOUuCgAAAEDTIBDiBqb2DP1kbJzU3nwu59E1pwxGMiEAAABgDQiEuLFnBkbMGRYltb87fvXZhHPy1gMAAACgSRAI0SDvje74RN8wqb1of8Y7u36Ttx4AAAAAt45AiAZRKMTi8V0e7B4svZy3PfnTfenylgQAAADgFhEI0VBKhWLFpNtGdfCXXj7/w/k1J9XylgQAAADgVhAI0QgOKuX6h2/vH+EjhDAYjQ+tPrU9OVfuogAAAADcJAIhGsfFQbXl8d7dgj2EEFq9YfzyY/szCuQuCgAAAMDNIBCi0byc7bdN7xPh4yKEqKjWj/n6yCl1idxFAQAAAGg0AiFuRrCH009P9g10dxRCFGu0I5ckpuSVy10UAAAAgMYhEOImRfq67pjR19vZXgiRW1Y1eunhrBKN3EUBAAAAaAQCIW5e1yCPrdP6uDqohBBp+eUjlxwurNTKXRQAAACAhiIQ4pb0C/deM7WnvUophDibVTJ66eHyar3cRQEAAABoEAIhbtWYTgHLJnZXKhRCiMRLheOWHa3SGeQuCgAAAMCNEQjRBCb3CPlsXJzU/ikl79E1pwxGo7wlAQAAALghAiGaxsz+4fNHxF6In7oAACAASURBVEjt1SfVszYkyVsPAAAAgBsiEKLJvDkq9rlB7aX24oOZr++4KG89AAAAAOpHIERT+ujezg/3aiu139iZ8tHedHnrAQAAAFAPAiGakkIhlk7oOrqjv/Ry9uZzy45ckbckAAAAAHUhEKKJ2auU6x6+fWB7HyGE0Simrz294WyW3EUBAAAAqAWBEE3P2V61+fHet4V4CiH0BuOUlSd/Tc+XuygAAAAANREI0Sw8nex/fKJPrL+bEKJSq7/nmyMnrhbLXRQAAACA/0EgRHPxd3PcNq1PkIeTEKJEoxu1NPFibpncRQEAAAD4E4EQzai9r8vOGX19XOyFEHll1SO+SrxcWGnam1dWfTar5GxWSW5ZlXw1AgAAALbLTu4CYOXiAt23Te9zx5eJZVW6K0WVo78+vOHh21edVH9/6vdkswHDaD/XB7sH/21ARIC7o4zVAgAAADZFYTQa5a7BsikUCiFEi11GtVqt1+tDQkJUKlXLnLFJ7ErJG/PNkSqdQQhhp1ToDLVfLk8n+68mdP1L9+CWrQ6tS35+fnl5ua+vr6urq9y1AM2ivLw8Pz/f1dXV19dX7lqAZqHX69VqtUqlCgkJaZkztvDfY4A14ZZRtIQ7YtqsntJTqVAIIepKg0KIYo32r98d/+bw5RYsDQAAALBdBEK0kM6B7krFjbsZjeLJdWfOZZc2f0UAAACArSMQooW8su1CPWOD5nQG40vbLjR3PQAAAAAIhGgJZVW6LedzGt5/+4Xc/PLq5qsHAAAAgCAQomUczCzU6AwN768zGA9kFjRfPQAAAAAEgRAt4/cSTaPfUszihAAAAEDzIhCiJVRq9Y19S3m1rjkqAQAAAGBCIERL8Hdr9HLzQR5OzVEJAAAAABMCIVpCxwC3xr6lg3+j3wIAAACgUQiEaAmdAtyj/Vwb3r+dt/NtIZ7NVw8AAAAAQSBEi3lxaGQjOg+JVDRgFXsAAAAAt4JAiBbyWO92gyN9G9IzPtznyfjwZi4HAAAAAIEQLUWlVCQ82qtfuHf93W5v67X58d52SsYHAQAAgGZHIETL8XK23/t0/FujOvi42NfaIdLX5cDf+te1FwAAAEDTspO7ANgWe5Vy3ojo5wZF7EzJ259ekFVaZTQa3Rztlh29YjAY0wsq0vMrmF8UAAAAaBkEQsjAzdHu/i5B93cJMm25Vl79Q1K20Si+OJD52bg4GWsDAAAAbAe3jKJVmNU/XGr86+iVEo1O1loAAAAAW0EgRKtwR0ybuEB3IURplW7F8atylwMAAADYBAIhWgvTUhOf/ppuNMpaCgAAAGAbCIRoLR6+PdTTyV4I8du18t2/5cldDgAAAGD9CIRoLdwc7R7uFSq1Fx3IlLUWAAAAwCYQCNGKzBoQoVQohBCbz+VkFFTIXQ4AAABg5QiEaEWi/VxHxPgJIQxG45cHL8ldDgAAAGDlCIRoXWYNiJAaSxMvVVTr5S0GAAAAsG4EQrQud3cMiPZzFUIUVmpXn1TLXQ4AAABgzQiEaF0UCjGjX5jUXnQgQ95iAAAAAOtGIESr83ifdq4OKiHEKXXJ/owCucsBAAAArBaBEK2Ol7P9pB4hUnvRfgYJAQAAgOZCIERr9OzA9gqFEEKsP5OlLtbIXQ4AAABgnQiEaI06B7oPjPAVQugMxiWJrD8BAAAANAsCIVqpWQPCpcaXBy9V6Qyy1gIAAABYJwIhWqlxXYJCPZ2EELllVevPZMldDgAAAGCFCIRopeyUihn9wqU2608AAAAAzYFAiNZrRr8wJzulEOJQZuHRK0VylwMAAABYGwIhWq82bg4TugVL7S8OZMpaCwAAAGCFCIRo1Z4d1F5qrD6pzi2rkrcYAAAAwMoQCNGq9Qz17N3OSwhRpTN8c/iy3OUAAAAAVoVAiNZu1oAIqfHloUs6g1HeYgAAAABrQiBEa/eX7sEB7o5CiMuFlZvOZctdDgAAAGA9CIRo7RxUyml92kntz/dnyloLAAAAYFUIhLAAT/cPt1cphRA/p147m1UidzkAAACAlSAQwgIEeziN7RwgtRcfvCRvMQAAAIDVIBDCMpimlll+7EphpVbeYgAAAADrQCCEZRgc6ds1yEMIUVGtX370itzlAAAAANaAQAiLMXNAuNT4/ECmwcj6EwAAAMCtIhDCYkzpEerjYi+ESL1WvuNintzlAAAAABaPQAiL4eKgeqRXW6m9aH+GvMUAAAAAVoBACEvytwERKqVCCLE9OTclr1zucgAAAADLRiCEJQn3cRkV6y+EMBrFlwcz5S4HAAAAsGwEQliYWf+dWmbZ0Svl1XpZawEAAAAsG4EQFmZkrH+sv5sQoqhS+93xq3KXAwAAAFgwAiEsjEIhnooPk9qf7ctg+QkAAADgphEIYXke693Ow8lOCHE+p3Rver7c5QAAAACWikAIy+PuaDelZ6jUZv0JAAAA4KYRCGGRZvWPUCiEECIhKTuzoELucgAAAACLRCCEReoY4DYsyk8IoTcYlyZelrscAAAAwCIRCGGpZg2IkBpLEi9pdAZ5iwEAAAAsEYEQluqeTgERPi5CiGvl1d+fVMtdDgAAAGB5CISwVCqlYka//64/wdQyAAAAQOMRCGHBnugX5uKgEkKcuFqceKlQ7nIAAAAAC0MghAXzdrb/S/dgqc36EwAAAEBjEQhh2Z4Z0F5qrD2dlV1aJW8xAAAAgGUhEMKydQ/xiA/3EUJU6w1LEy/JXQ4AAABgSQiEsHizBoRLjcUHL2n1rD8BAAAANBSBEBZvfNegEE8nIURWiWbj2Wy5ywEAAAAsBoEQFs9epZzWp53UXnSAqWUAAACAhiIQwhrM6BfmoFIKIfalF5y4Wix3OQAAAIBlIBDCGgR5OD3QNUhqLz6YKWstAAAAgMUgEMJKzOwfLjVWnlDnl1fLWgsAAABgGQiEsBL9I3xub+slhKjU6pcdvSJ3OQAAAIAFIBDCejwVHyY1Fu3P0BuM8hYDAAAAtH4EQliPyT1C/d0chRCXCiu3XciVuxwAAACgtSMQwno42ikf7d1WarP+BAAAAHBDBEJYlafjw1VKhRDip5S85NwyucsBAAAAWjUCIaxKO2/nezoFCCGMRvHFgUy5ywEAAABaNQIhrM2sARFS419Hr5RodPIWAwAAALRmBEJYm+HRfnGB7kKI0irdv4+x/gQAAABQJwIhrNBT8eFS47N9GUaWnwAAAADqQCCEFXro9lBPJ3shxG/Xynf9lid3OUBrpy/LMGqZhAkAAFtEIIQVcnO0e7hXqNRetD9T1loAC1CW9GFF6r/krgIAAMiAQAjr9LcBEUqFQgix5XxORkGF3OUArZehuqgy7d8VFz4VRoPctQAAgJZGIIR1ivJzHRHjJ4QwGI2LD2bKXQ7QelWkLDFqy3QlqZqrW+SuBQAAtDQCIayWaf2JrxMvV1Tr5S0GaKUMuooLn0vN8nOfyFsLAABoeQRCWK27OwbEtHEVQhRWalefVMtdDtAaVV5apy+/LLWrs3/RFpyStx4AANDC7OQuAGguCoV4om/Y7M3nhRAL92c83qed3BUB8vtRnZxSfM30svzcf/TGHqaX9ge/cm4/2fSyT5t2fdrwfxwAAKwZgRDW7PE+7V7bcbG8Wn/695L9GQUDInzkrgiQh1Fbqr12VF+h/jrp4voCjdme9kK0//NVnhB5CaZXL4d4dI2KUrmG2rfpq1A6tFy5AACgpXDLKKyZl7P95B6m9Scy5C0GkJHC3k1zdVvRvoe0Bacb/q4q9Y6iX6dU5x4iDQIAYK0IhLByzwyMUCiEEGL9mayrxZobdQeslcKj14cevRs5bYxC5TVopVuXOc1TEgAAkB+BEFauc6D7oPa+Qgidwbg08ZLc5QBycu30rEObPg3v79z+r87t/9p89QAAANkRCGH9TOtPfHnwUpWOpbdh01Ru4Y3o7B7ZbIUAAIBWgUAI63dfXGCop5MQIresat2ZLLnLAQAAAFoLZhmF9bNTKp6MD5+3PVkI8fmBjMk9QuSuCAAAWCqFNDkB0IKMRmPzHZwRQtiEJ/qGOdkphRCHMguPXimSuxwAAACgVWCE0NIYDUJXIgRjXI3Txs1hQrfgFcevCiG+OJC5bGJ3uSsCLIDRqDe1cypLC6oqOnoFyFgPALQezTpiA5i0wIg0I4SWJu8n1dXlchdhkZ4b9Mfq26tPqnPLquQtBrAImrTvjNoyqf1d2olOG/8Z/P2bi5MPylsVAABoQgRCC6O4vFRxZZlRz3p6jdYj1LN3Oy8hRJXO8M3hy3KXA8jDUNGIeZX0ZZkFu+4yVhcLIX7OShVCZFWUOCi5tQQAAOtBILQk2sIzisJEhTa/6tJauWuxSH+uP3Hoks7AnR6wRfryzEb1r87Zn79jmEGT5+fk6u3gLIQYFhRVV+czBVkFVRW3WCEAAGhJBEJLUp604I/GuQVCkGca7S/dgwPcHYUQlwsrN53LlrscoHmdK6rlh9y+Td9GHEKhEEJo80/kbx/0bY8B1ya9eWrs8xHuPnV1n7R3ZZvVr/Xc9PGLR7dsu3qhTMu92QAAtHYEQoth0ORqMv8jtfVFSdXZe+WtxxI5qJTT+rST2ov2Z8paC9CMfslKHbTt864JC84X5Vy3sxHPpju1HSsUKiGErjg5f/sgY1l6N5/gujpnV5aeL8oxGI0n8tUfJu25+6dvvFfN77910fwTP/6Slao3Gm7mkwAAgGZmGY+CXLp0SaFQtGvXTu5C5FR+YaH5o4Pl5z52CBwiXzmW6un+4R/8kqbVG35JvXY2q6RLkIfcFQFN76Nzv+7LyRBCvHnqpzVDppjv6uoT1PC7OmPDe3pH/afo10lGfZW+LKP0xDyvwavr6pxbWdanTbtj+Vd0hj+yn85gOJibeTA38/MLB/ImvXGznwYAADQjOQPh6dOnO3bs6ODgUE8fnU73ySefvP7661u2bLGhQGjQ6TU5hgq1vjLbUH5VX5ltqLiquZRg3kVzdUvRvqkqlxClS7DKOVjpEqRyCVE6BylUjnJVbRGCPZzGdg5YdyZLCPHFwczFD3SVuyKg6b1+251br1ywVyp9HF0MRqPSbMbql7sOf7nr8MYcrKfPiO0Fu++18+zkGb+knn5dfYIOjflbqbZqb3baz1mpv2SlninMMhiNQohBge1VCm5IAQCgNZIzEO7bt2/evHnr16+vKxMePnx4xowZp0+fbuHCZGbUF+2bWpmx5kbdDJVp39XYZu/X2+fOHUoHr+aqzSrMGhAhBcJ/H7v67uiO3s72clcENLGevqEL+943pm2nMDfvWz+aQ+BQ3zt3qTyiFfbuN+zsbu84pm2nMW07CSGuacqlcNg/IKKu/nuz0z47v39oUNSokNgoD79brxYAADSKzF/Zbtmy5YEHHqiurq6xvbi4eObMmfHx8VIa7N27d0xMjBwFykGh8hq8yi3u/xr7PsfgEb4jd5EGb2hwpG/XIA8hREW1fvnRK3KXAzSLmR37N0kalNi36aN0rHMumbr4Obk+EN718373T2p/W119tl9N3nDp7N8SNy66cODWagQAADdDzhHC7t27Ozg4bNmyZfz48evWrTONE65du/bZZ5/NysoSQnh4eLz99tszZ85UKm3qdiOF++3/ULoElxx9XjRsJgbnqIe94pcKJYNdDTJzQPiMtWeEEIv2Zz4zMML8hjrAIpwu+P2d07tndew/KLB9XX0M1UUVyV+4xf2faMUrB0rLG4p6V7NouLOFWb+VXGtg5wg3n9t8Q279pAAAWDQ5/0oYMGDAhg0b7r///s2bN0+YMGHdunVqtfrpp5/evn271OH+++9fuHBhcHCdk9pZN9dOz6pcQor2Tb3hMvRuXea493yvUZMH2rgpPUJf2nqhoEKbll++42LeXR385a4IaKij1668cWrn1isXhBD5VeW7Rz1Zez+jvujXKVVXt1Zn7/Ea8r3SoclGC5vW8oETd2el/pKVWk+ynXNsa5m2alhQ1JCgKF9Hl3qO9u1vRz45t6+Bp34itu9X8eMbVy4AAFZH5q+N7777bikTbtq0acCAAUlJSRUVFUKIdu3aLVq06J577pG3PNk5hY/3dvAq/OV+o7a09h4KlWffRS6xdfxFiDq4OKge7d1uwZ40IcSi/RkEQliE4/lXXz+5c8uV86Yte7PTLxbnxXq2ub5zydHZVVe3CiGqft9Vnf2rU7uxzVSVNi+xcWsb/q+OXgEdvQJmdexfVwejMC5PPZZTWfpF8kGlQtHVO2hYUPSwoKhBge3d7ZlDCwCAWyX/fZhSJnRwcDhy5EhFRYVKpfr73/9+/vx50qDEMfgOlVt4XXsVSjvnqEdbsBzrMat/uEqpEEJsT85NySuXuxygPifz1WN3L+u16VNTGlQplJMje5wb92KtabAy9V/l5z+R2m7d5jdfGiy/8Nm1rf1Kj89tpuMLIc4V5uRU/vGNmMFoPFXw+0fn9o7Z9Y3PqvnxWxeWaaua79QAANgC+QOhMMuEQogpU6Z89NFHrq6uchfVWhi1pbqi83Xu1VfpCk61ZD1WI9zHRRoYNBrFlwcz5S4HqM+j+7/fdPmcURiFEEqF4q/tbzt73wvfDZpUaxqszvm1+OAMqe0U9oD7ba83U1Wayz+UHH5OCFF29h8lR18QwtgcZ4n28Pt51JPzut0R7x9uZ/Ywuc5gyKksc2OQEACAW9MqAqEwy4TLly9/4w3WL/6T9tpRYdTX06E6L7HFirEyswb8MRX+sqNXyqp08hYD1OPV7iOEEEqF4i8R3c/eN3vV4MkdvQJq7akvyyz8ZYLRUC2EsPfp7jVwefM9XewYMsqp3b1Su/zcR0X7HhGGpv//kaPKbmhQ1Fs9Rh24e1bhpLe2jnj8hbjBt/mGKBWKJpmHBgAAG9dyzxAWFhYuX768/j7Dhw/fvn3766+/npOTU2OdifHjx4eGhjZnga1U9bUj//Pas6fB3k95bYdpgzYvUYhnW7osq3BnTJtYf7eLuWVFldrvjqufjA+TuyKgduPC4mbHDXk46vY478B6uhm1ZQW77zVocoUQSucA7+GbFHbNeLeFQuXoNeQ/Rb9O0WSuFUJUpv1bGKq8Bq5ovumO3ewdR4d2HB3aUQhRUFVRodM204kAALAdLRcIc3Jy/v73vzew8+LFi2ts6d69u20GQu1/BwCVzkHut71e6HKX3mD01h0q+XWSURiEEFVZu2Qt0IIpFOLp+PBnE5KEEAv3Z8zoF8byE2idFELxz15jbthNX6E2VhcJIRQqR+9hG1WubZu9MKWD9+DVxfZuFb8tE0JUZnxv0JZ5D12rUDk396l9HF18uF0UAIBb1nKB0N7ePiTk5ld8cnS00X/5q/OOCKW9a+xT7j3eUth7FKrVQugdw8a73fZb6cn5QgiD5pqhMkfpXPv9Y6jfo73bzv8xuUSjO59Tuift2tAoP7krgo26UJTz9uldb/e4K8K90eu/m9h5xvqNOVL4ywMusU86tOnXhOXVR6Hy7P+Nwt6j/PynQoiqq1sLfxnvc8fWFjo7AAC4NS0XCCMjI69evdpip7MO+rJL9t6dPfp8ZufZscYu17jZ2vxjzjHTqtU/aQtOOIbcJUuFls7d0W5Kz9AvDmQKIRbtzyQQQhYfnP3l5ePb9UaDk8r+mwEP3sqhlM6Bvnf9KhSqRr2rKr/UztVJ5XTTt3oqPHp/orD3LDv9pkLp4Npx1s0eBwAAtDSZ1yFE/VQuIT53/lTrLoXKyXtYghDCKXRMc0zkYDtm9Y9YfDDTaBQ/nMvOLKgI96lv2WugOfRtE6Y3GoQQK9KOv9PzrkBn91s6XCPToBDi911JTm08goZ1vpXTut/2htLBU+Xa1lK+nyrTVctdAgAA8msVs4yePHny448/3rBhg/nGmTNnvvzyy4mJtj2FprJhib2B3VCbjgFuw6L8hBB6g3FJ4mW5y4EtGhTYflhQ1B3B0ftHz7zVNNh4VfmlhWcv5+y/qNfcakBy7fy8U/iEJqmqBaxJP/ns4QRWMgSA1mzChAl33dW83zP269cvMDBw0aJFzXqW1kzmQFhWVvbQQw/16NHj+eefP3HihPmu48ePv/fee/Hx8c8//3x1Nd/johmZ1p9YmnhJozPIWwxs09YR034aOaN3m3Ytf+rfdyUZDUa9pjr34G8tf3YZGYzGz87v77zxn2szT8tdCwD8qVKrL6+ub8mxW2cwGIKDg/3qtn379mYtoIGKiop++OGH+Pj4Zj1LXl5eTk5OWVlZs56lNZN5ZOmBBx7YuXOnEMLDw6N9+/bmu+65556srKzLly9//PHHDg4O77//vkw1wvrd0ykgwsclo6DiWnn19yfVD/dq9rkZYYMyywp0BkOUR+3PqTqp5PltLA0PSu2c/Rf946NVTg6yVCKXy+VFD/6yYmjQoU/7jO3iHSR3OQBslN5gXH8ma+UJ9b70/MJKrRDC1UHVL9xnfNegh24PdbZv9LMA9bt8+XJWVlY9HaqqWsXdE1u2bNFqtePGjZO7ECsnZyDctm2blAZffPHFt99+28Hhf/4KeeWVV+bOnTt37twPP/zw448/njZtWlQUaxCjWaiUihn9wuZuvSCE+Gx/BoEQTetyedGCpD1fXUy8Mzhm0x2PNdVhy86+b+fZ0and2Fs5iDQ8KLWlQcJbfJKwTgZda7u5fUhQZHJRbnZlqRDil6zU7j98NLl9j4963+vn1IyLNzY5o6G6MnW5S8x0uQsBcPPOZpVM+u5EUnap+cbyav2ulLxdKXlv/ZTy70m3DWvSee9+++2PW0Lmzp2rUtWSNmNjY5vwdDctISEhMjIyLi5O7kKsnJz/PG/atEkIcccdd3zwwQe1dlCpVB988MFPP/10+vTp3bt3EwjRfJ7oF/bmTykV1foTV4sTLxX2DfOWuyJYg8vlRe+d2f1typFqg14IseXKhWPXrtzu1wTfOGgubSg9/rIQwq37q+7dXxPiZtbQNB8elDTTIKG+/HLBzlEefT5xDL6zaY9cQ6CzR0evhq7BMyQwMmHYo2+e+mnhhf1ag95gNK5IO24UYsWgvzZrkU1Lk7Gm9MQ858ipCpWT3LUAuBlHrxQNW3yorKrOCQLVxZoRXyaumdpjQrfgpjppamqqECIgIOC9995rqmM2ucrKyh9//PGpp56SuxDrJ2cglL6cuPfee+vpo1AoBg8efPr06fPnz7dUXRasOvsXzZXNHr0+krsQy+PtbD+xe8i3Ry4LIRbtzyAQ4hZdLS9+78zur1MOS1FQMiiwvZ2yCW770eYfL9o3VQijEEKbe0gYDTcxs6j43+FBSXMMEuorfs//cZi+NK1w971eg7+/xSHN+s3pMnROl6GNesuC3vdMj+3z7OGEneoUR5Xd67c1b2RtcuXnPjFocjUZa5yjHpG7FgCNVlSpHfP1kXrSoMRgND606mTXII9Yf7cmOa/0R3hMTMytHCQ7O/vatWs+Pj7BwcFCiMTExD179uTn57dr165v3769evW6vnM9RwsJCfH2/p+/vnbu3FleXn79/aJFRUVbt25NTk42GAzR0dF9+/bt0KFDrccsLS3duXNnUlJSSUmJn59f3759Bw4caGfXum5XaQ3kvCKlpaVCCHv7G6x8ZTQahRCFhYUtUZPlMhqKfp1UmfG9EMIx6A7H0NFyF2R5/jYgQgqEa09nfXhvVaC7o9wVwVK9f+bn10/trNL/+Q/8wICIN24bOTSoCW5zMFRmFf58n1FXIYSw84z1GvL9zaXB64cHJU0+SGjUVQijTghh1FcV7hnv1X+Zc+SUpjp4k+jg6b/jzicSLiddKiuMdPeVu5xGqM7eoy04KYQoO7fAOerhmxsoBiCjd3f/llvWoKf1NDrD/2258MNjvW7ctQGkEcJbvC/0ww8/XLBgweOPP/7Pf/7zr3/9644dO8z3jhs3btWqVU5OTuad6zna0qVLp02bZr5l48aNgYGBffv2Nd+4adOmhx9+uKioyLRFqVQ+8sgjH3744fV58qGHHsrJyTHf2KNHj9WrV99iErY+cs4yGhYWJoQ4ePBg/d0OHTokhAgNDW2JmiyXQqmw95CaJUeeNepbxaPAlqV7iEf/CB8hRLXesDTxktzlwIKFuHqa0mA//7BNdzz26+iZTZIGjXpNwc/368uvCiGUDt7ewzcpHbxu7lDXDw9Kmny6UTuPKL+7D9l5dRZCCIOuaN9D5ec/bcLjN5X72sU922mg3FU0Tvm5j6WGrjCpOnuPrLUAaDSD0bji2NWG9996ISevrGkm3pdGCDt06FBYWLh69eo33njjgw8++PHHH3Nzcxt7KJ1ON2bMmB07dgwbNuyll16aNWuWNGC4cePGF154wdTNx8cnrDY+Pj5SB4VCUeOwmzdvHjt2rFL5Z1p59913x44dW1RU5OXlNWrUqMmTJ0dFRRkMhm+//faRRx4xf/snn3wyatSonJwcJyen+Pj4qVOndu3aValUnjhxokePHr/88ktjP6Z1U0jjb7JYvnz5I488Ymdnt3nz5lGjRtXaZ/HixU8//bQQYu/evYMGDWrZAhtE+vFtscuoVqv1en1ISMj1TwAbqvLzNsQaqvKFEO63/8Mt7v9apiRrsvqketJ3J4QQQR5Ol+YNt1e1ioU6bU1+fn55ebmvr6+rqwxze1zTlMclfNjw/ikPzPGwr/nslt5o6Lzxnz6OLm/cNnJEcBN+DWks+nVqZfpKIYRQqHzu2HzTS8BX5Zee+3hbrYFQCKFycujyf2Oa9klCgya3YOdIbcEp6aV799fdur/WhMdvbsXVGk+HJntIr7y8PD8/39XV1df35gck9WUZueujhfGPe5Kd2t7jPXxTExUI3Cq9Xq9Wq1UqVUhISMucsSX/Hmvgua4Wa/R1/JqVnM8uG/114xbcXnR/lzGdbvCkdFsvJ6WivvsFDAaDs7NzdXX13XfffeDAAfPRNkdHx/nz58+ZM6ch91XOnj17wYIF4CTNQgAAIABJREFU0hjgihUrxo8fL20vLS2Nj49PSkry8vIqKChQ1F1MSUlJ3759L1y40KFDh8TERE9PT9Oun3/+efjw4T/++OPIkSOlLWlpaR07dtRqtXfeeee6devc3d2FEHq9ft68edJiBKtXr544caIQIjs7OzIysqKiolOnTps2bYqMjDQdc8KECQUFBd26dTt58qSpsKioqLS0tPfee2/u3Lk3/NQtrwV+tuW8ZXTq1KkrVqzYvXv3/fffP3369FmzZkVHR0u7jEZjcnLyggULli9fLoR48MEHW2cabFWUjr5u3V8tOfysEKLs9NvO7aeoXJrs4WMbMb5r0IueTupiTVaJZuPZ7Ae7cwFtjt5oyKksvXG//zLU9gtapVD+Onqmv1PTPOlhUnbmvT/SoBAevT+56TQo6h4elDTHk4RKJ3+fUb8U7rq7OvegEKL01OtGfaV7T8tYT8hgNI7Y8ZWfk+vHvcfGeraRuxxhqCowVGaVnf2HKQ0KITRXt1Zd3Wrn1VnpHKRQccc7IL/uC/bmlzfxStqzNpydteFs/X3y3hzp51rfN3qXL1+WlvjeunWrSqXq0qVLp06dMjIyzpw5o9Fo5s2bt2fPnp07d9YT5MxpNJq5c+ea0qAQwt3dfc6cOVOnTi0qKvr999/r+lJAr9dPnDjxwoUL3t7emzdvNk+DQoiNGzd6enoOHfrnY+Hz5s3TarWBgYGbNm1ydPzjt5xKpXr33Xd//PHHU6dObd++XQqEb7zxRkVFhb29/fbt29u1+3OB32HDhn3zzTfjxo07ffr0mjVr/vpXS5pCrFnJOQCiVCpXrVrVtm3bysrKzz77LCYmxtnZOTo6OjY21sXFpVOnTt98841Op4uMjFyyZImMdVoQ19in7bzjhBBGbWnp8Tlyl2N57FXKaX3++MWx6ECGvMXAojV5GhRCKB28pZUbXGJnuHacddPHqevpQXM5+y/qNU38d4zSwcvnzp2OwXdIL3Ulv5nnmdZs2W9Hj167sv1qcp8tn5ZqZbghvyJ5ceGeB/O3DchdH5m9wjlntW9eQlxl2or/6WQ0FOwak7suInuFU86aNnk/dCn4aVTR/kd0JSktXzCA1sy05kT//v0zMzPPnDmzZs2aw4cPp6SkDB8+XAixa9eupUuXNvyAzz//fI0tpof0tFptXe964YUXtm/fbmdnt3bt2uuXEvjhhx/uvvtu06J0Op1u3bp1QoiZM2ea0qBEoVDMmDGjf//+Ot0fD2ts3bpVCDFp0iTzNCgZO3as9OTkli1bGv4BrZ7Md8T5+/ufO3fu1VdfdXNzE0JoNJrU1NSUlBSNRiOEcHV1nT9//smTJ2t8Z4A6Ke08+3wmNSvTVlbnHpC3HEs0o1+Yg0ophNiXXnD8arHc5QB/cunwlO+IHU5hD3j2WXgrx6l/eFCi11Rnrj96w26NpbBz9R6+xandWMeQkV6DV9/cdDgtL7OsQKVQCiGe6zTI3V6GwTen9hMNlVnVuQf0pelGveaG/Q2aa7rCpCr1TnuvODsP5k4A5NHWy6mdt3M9//N3a/TvE28X+/qP2c7bWaW8wcheUFDQe++9Jz00aD5JR9u2bRMSEqQBvXnz5jWwJH9//zZtat46Yf7gX62+/vrrTz/9VAjx0UcfSSnU3NGjR69cuWI+v+ilS5ekvDdwYC0Pez/55JP79+9fuXKlEKKqqkqtVgshBgwYcH1PhUIRHx8vhEhPT7/RJ7Mh8s+76u7u/sYbbzzzzDNHjhxJS0tLTU3V6XRRUVHR0dG9e/f+f/bOO66J+//j77vLHqywN4iAoLjFhRO3uLqs/Tr7a2sdlQ5Hh3a62tqldtpW66hWW1xoVdQ6i4KiIEP23glkz7v7/XEWERIIGBLAe/7h48bn7vNODMm9Pu/V/BNG0zIs97Ec/2c0hYcBSNmNVc4xSXTduTbhYcd5KsLj95QyAPj+euFPz/a1tUU0nZoKtcyBxbXadCyPcSyPcY9zB3PcgxT16SXpX8T7zhxk19P9cWZsAoKxHcccIUk9glq422HH8fGAyU/7R2xJvbCmjT0tLAXKcnSamFB/5X+awiNmXoJgbPuRe7gBz3WoYTQ0NC2Q8sbolgfkiZVBmy606Z775g2Y2sv1MYwCAOjdu7epVu8CgSA2Nnb16tU1NTVlZWXmpIA298K1yqVLl6gSIS+99NLKlSubD4iLi+NwOI0rjFBlUQHAx6eVXr4FBQUEQQCAv7+/0QFUVUtaEDbG9oKQQiQSTZnS/nwYmsbYDf5cWxrPEAbZDd5Gq8F2sGKkPyUI998u2zKtl6jFQHyaJ5xajQq6VASDOe7BBrQSBdLaSnN7QBlIp/n1MZO+Tp6/j7FltwwEYzuOPijlrFRlfdfqYJTl4Dj+GMuNzr2noenU9BDxw9yEGVXmJq4L2IxxPZ071CQA6NOnD7WRlpZmjiBsXuawZfLz85966im9Xh8VFbVz506jY+Li4iZMmEDFD1JIJBJqw87OruX7K5VKaoPH4xkdQFWjkUrpKLCHdIEiimvXruVwOHSkr/lgfF/R5H+cY26x3FtZmqIxynB/p0E+DgCg1uO/3CyxtTk0nZo+jpb0nnU02lpz3YMUCIryvBxbH/fEg5OEnuj4fEgEsxv8Bcfv6ZZHoVwPp8n/0GqQhqZLsGKkv/mD/y/Sl8Po8Ef3hurHTVL1LIJMJouJiRGLxf7+/n/++afRbuSZmZlZWVlN+tE3uPtKS1tp1BEQEEBtFBcb/70rKipqPIwGOomHsK6uLj8/32g11ZqamiNHjmi12sLCQqvb1YVhOlumb+kTy7Lh/ksO3QGAndcK3hgd2Go4Pg1N54fEifyD1xEGxnUWghm140iCBIKU3q9w6utnBfO6ND/cT/w6/cpXkTOneId20BS4okCV9b0q52eqt1ALsNyiEIyOa6Ch6Rq8NNRvd1LJzeL6Vkf6OnI3TLRMSvCbb76pVCqjo6MblwZtIDv7QSUqU2Gl7YYqK5qRkSEQCI4dO2YqLywuLg7DsJiYmMYHG6rOZGdn9+vXr8kl9+7d27dvH4IgH3/8sZOTk4ODQ319fWZmptH7U8cbWhvQgM0FYX5+/nPPPZecnNzqyL596VQuGusxb4DXuvjMaoW2qE4dn1k1I7wreYFoaIxAkrl7r6jK6wCA7SQIfH64OZqwBXC1DuMwH/MmzSAVdz/hBi3G+N6tj+00SLSqDbf/FmtVU8/tmu3Xe9vgGQFCJ8vdntSWnVVl7dSUxgNJmHOBpvAPTXGcIPxNQd/1CMN4xBQNDU0ngYEiJ14cMvbbf1sOHHUTsk+/FOnINeJPawfl5eUHDx48d+7cnDlzmld/OXbsGAB4eHhYvJDH6tWrT58+jSDI3r17IyIiTA2Li4sbOXKks/MjwbEuLi7e3t6lpaU//PDDs88+2+SSX3755csvv+zVq9fmzZsBYMCAARcuXNi1a9c777zTxM+ZmZl5/vx5AOjfv7/FXljXx5Yho1qtNioqqlU1aG9v/+233xqtKURD00GwGejiIQ+ylndcLbSpLTRPIgZppvh0FK4otNgdEcQ+2IPaxDjMx29vW/jXzYxv/q5LKwbLtcqVJa+Rp2wQn44yyHItdU8rkCIp0xMPpFpc0b3wuM82pJxRGUxWWjcfQiup+StEcm6ypuREgxrEhIHQqgOQ0CvSttQcDdOU0K3qaWg6O64C9s3YqGUj/Kki501AEeTpCI+7b44OcxNaakaq/15+fv7bb7/d5NSBAwf++OMPANiwYYOlpqOgNBsAfPzxx7NmzTI1rLS0NDk5uUm8KAVl0oULF3bv3t34eH5+/s8//wwA0dHRjUeWl5evWbOGIB4updXX17/66qsEQTg4OLz22muP/6K6Dbb0EB44cKC8vBxF0S1btsyZM6euru6ZZ54pLCy8fPlyQEBAdXX18ePHN27c6Ofn9+KLL9rQTponk2XD/T//Jw8nyIScmqxqRair5dvK0XQ2ajSKdcnxtrYCCK2k7vwMgyy39uQQx3FHWa7DLXJb1+HBujolxmU9fsd5VUVdfUYpkJB/8HrYa1O4bhaoq2OQZqkytwMArigUnx4lmniW6qra+Rnv0TP7qbVv3zq1JzeZIEk1rv/4zrk9OUmfDY55NuCxYltQthPCFgHkAAAgKNtzIj90Ocrzqj0xoPEwBOM07kWBsOxJnRQAcEVR3fmZHJ8Yu8hvMIH/41hCQ0PTofBZ2M45fd4e3/Pw3fLLeeIKmVZPEB5CzjB/xzl9PHq5WfgJJCYmZubMmceOHfv000+TkpJeeOEFT0/PwsLCM2fOUO7BESNGvPLKKxacsbKycunSpQBgb28vl8vXrVvXfIyvr++yZcvi4uIAwKhiXLJkyVdffZWRkbF48eLz589HR0e7ubndvXt327ZtMpnM3d19/fr11MjRo0dPmzYtPj7+m2++SUtLmzNnTmBgYGpq6i+//EL1YHznnXcaUiVpwLaC8OLFiwAwZ86c1atXU0deffXVtWvXpqSkREVFeXt7DxgwIDQ09Pnnn3/33Xc/++wzG5pK8wTi68iNCXM7eq+SJGHntcLts7vGsylN+9Dghm8yrmxKPS/Vtd7krWMh9HX/PEO5yEhcjTD4Fry39zTLRMhoauQYi4lr9Y7hPhZRgwDAsA91HH+87sIc0qAk1BXiv8c4TTjdVdKh3bjCX0Y+tzRk2MobR2/WFANAsbL+uX/2ptdXfth/0uPcmd9rhUyaxe25mBeyjGEXBACq+z80nMX4PsIBn7B9YpRpW5XpX5KEDgCA0DuM+Fl2ax2hqQEATckJbcV5QcR7goh1dN1pGprOjLc95/VRga+PCuzoiaigzWnTpl25cuXixYvUA3kD8+fP//rrrxGLZgQoFAqqQ71UKt26davRMZGRkZQgHDBgANUZogkYhl24cGH+/Pnnzp3bt2/fvn37Gk55e3vv37+/cYzroUOHVq5c+euvvzZ5gWw2e/PmzbGxsRZ7bd0CWwpCqkzQ2LEP2zoNHDgQAO7evdtwZO7cuV988cUXX3yxZMmSXr16Wd/I7gSuKMIEdHGINrBiZMDRe5UAsCepZOOUUDtOpyjCRGNx0usrp5/7pVAhsbUhAADSm6t0FRcAABDUIeo3plP7XUwGpZbB75Au6k4RvnY93auvZjn2adp+itAZUCajfbqD7TnRaeKZuoTphK6e0IrFZ8Y7jT/Och/z+AZbhyEuvv9OW7knN/ntW6eq1HIuxlwUZJ6gJQlAjGdwcP2f5fjObpwKqK+9AQAIg8fvvVrQZx2CcQBAOHALN2ix7Obr2rLTpEHFEA1wmZ0lv/W2KmcXkARpUOlr6Z60NDQ0DxEKhZcvXz516tRvv/2Wn59fWVkZGBgYERERExMzYcIEM28yceJEgUDQuLV9A56enu+//z4AODg4AICTkxO12wLe3t5isfjy5csffPCBqTFubm5nzpzZtWvX4cOHc3NzMQwLDQ0dOnRobGwsn//I+imfz//ll19mzZr122+/paWlyeVyZ2fnoUOHrlixonn64muvvSaRSIw2sn9CQIzW9rQOgwYNunXr1v79++fNm0cdKSwsDAgIiIqKunz5csOw9evXf/LJJ5s3bzbqX7Y51AqK1d7GsrIyHMe9vLza1PUFV5XJb72tKfzDeWYaw46uqtQGIj6/lFYhA4Dts3uvGElXKLYGYrFYqVSKRKImX+4dhxrXh/65tVhZDwDBdi7Zshrzr6174WMLNqZXZe2UJq6gtoUDPhFEvNvuW1Vfzy5PuBfy8jiuu4OFrDOLoj9vKkvFHuN6O/b2aZ8A0UtSJGcnUd4tBGM7jD7I8TWZbdI5kek1H90558Divtc32ugApVIpFov5fL4dFKru/0hoqh3HxZl585q4MMyuh33kDqMLfJqSE7KbsYLea3ghrwCAXnxb+u+rhvp7LrPS6ahRGmuC43hZWRmGYeY0srMI1nwes/Kz35MDjuNyuZzP5xttR/HEYoXPmy2Lynh4eABARUVFwxEfHx8Wi5Wamtr4NVNeYyrkl6Z91F+er87bS+JaefJqW9vSxVg67MEj1zdXCuhv/u4KF2NuGjhVxOZ9FTnzwuSltjJDW3FeevNBEAs38HlBxDvtvlXZ33dLTt7GNbrcvVf0cusFwWolCvGdQnWVNP/3a8qyVrojmILp1F805TJVaJTEtdJ/l5IGpUXN7HDsmJzPB8eYUoMAQBpUaPnv+kuja08MUmX/qCk+ZpCZ9xtHEvYjdjmNP2Eq3IPjE+MyK50pGkjtMkUDnKddF025QqtBGhqazg+GYQ4ODrQatD62DIGjWkweOnTojTfeoLQvhmE9e/ZMT09PSUkZMOBB0nx1dTUAMBh0tF77sRu0tTZ+KJCEpviYtuxvttdkW1vUZVgwyPudU1lSjT6nVpmQUzMh2MIlmGk6CfN69J/hGy5ksvUEfnHKq+ZfKGBYJiYTl+fV//MsEAYAYDoPsR/x8+ME+DlG+NbcyMG1Bo6zEGW1IZrgMVGV1yEYSuKEsIcb37v9+foM+1DRlCviM9GEpspp3FHLJlLaFoMsV3X/O2X2z5he2miJidSWnmKErWr9egRttcgQgnGYzoMa7zNFA0wPp6GhoaF50rGlylq4cOGOHTuSkpJmzJjx2muvUSHLQ4cOTU9P/+ijj/78808MwxQKxf79+4FuH/l4MJ0H84IWq3J+BgDZzVjnmakISrctNgsBm7FosM/XV/IBYMfVQloQdlcQQIRMNgAwUWyMew/rG4ByPVge4zSFRzCel+O4OAR7rDBUnqdj4PMj6jJKfWMGIsbqmHcQjr19hP4ulVeyHMIft5EgJvB3nnrFIMtjugy1iG2dAVnyamX6FwRJPg3PjiBLViA3OQw2N/AFXq/lTEeTLbk6FIMsB+W4oCyrxhXT0NDQ0HQqbJlDCADPP//8wYMHAUAkEtXW1gLAjRs3hg4dCgC9e/cODw9PTEwsKipiMpkZGRlBQUE2NNUUXSKHEAAITXX1X8FULXK7QZ/xe7/VMQZ2Q3JrlSFbLhIkiSJIztvjAkV0o+eOpYNyCI8W39uceuHUhP8TsTvt/yCpuPsJ22vKI+4dmm6EOm9f/ZX5eyFiLTkBAMI5WMqctUy2BbvYtxESrz0xCFdX2A36nNvjBbrqDI0FoXMIaWgsRTfPIQSA/fv3f/jhh76+vg3FbSMjI19//XUAuHfv3qFDh4qKigBg/fr1nVMNdiFQjquw74P6TvK7HxHqipbH0zQQ5MyfGOICAARJfv9voY2toWkXL179Y/b53Tdrij+6c9bWtrQAIui7vturwbIzd6XZT+j3DyfgWZTrEc8aRu3O8B9hSzUIoMzcoZfcIdRV9Vfmi0+PMdSn29AYGhoaGhpbYWMPYQO1tbXOzs4NuwcPHty/f391dXVISMjcuXOnTp1qQ9tapqt4CAEACH3N8X6G+gwA4AYtdBi52+LmdVfiM6um77oJAI5cZumGCTwrJmU9gXSEh/B4cfrM878CgBtXmPf023wGHTJtG1RlksxvzwIJfB+Rx9gw+1AruQ6sCokDYvIrgtQrCAZve9rlX3OSTo9a6Oniak3TmqApOSFLXI4rSx7so0xB+JuCvusbd7mgoWkftIeQhsZSWOHz1lkEYdelKwlCAG15guTsBABgOkaIpv1L/+qbCUlC6NYL2TVKANj1bN8XI5v2XqOxIB0UMjrp7I9hDm7r+05w6rwho+1BK5azRUJbW2Euefuu1meUNuzyfZ09x/e26+ne1vsYpJmYsEdny4XGlaWq+9+rcn4WTblCNZE3hVKprBXXCvgCkaj9pXcsAqlXyO9+qMz4Ggg9dQQT+NlFfsPxmWFbw2i6OrQgpKGxFN0/ZJTGyrA9o3k9X7SL/MY55hatBs0HQeCVYf7U9varBTa1haadnJ7w0pdDZnYnNUgSZPHR5Ixv/laWtrPBg/XxmzXIfXQvlPlgPUtZXJvz6z9Z3ydIs8rMv4leclccP6LuwmwSV3eMmW1GV3mx7uLT1X8GKFI3EupK1f1vW70E6RwJewhTYDfoM5eY2yy3KOoIriiqOz9TX5tsW8NoaGhoaKyGbTyExcXFaWlpmZmZ9vb2ffv27devH4vVuRZ6zadreQhp2k29Wu/90TmlDgeAKytGjAywZeZP9+ZxPIRa3MDGukCLGpLQPaZ3C9ca8g9cleVUAgBTyO21fCLT7rEKk1oTvUxdeSmjJimPNBANB4MWjLIP9Wz1WkJTXXMsglBXAQDLfbTT+BMI02YOUlIvV+ftU2btbJJ9x3SMcJ55p4UaLQ2N6Y16CHdmXkuRlG0aONWVI7C80SYh1bl7ZMlrCE0Nx/8ZxzF/WHFqmm4I7SGkobEUVvi8WfvJSSaTrV279ocffmj8qoKDg3/66adRo0ZZ2RgaGvNx4DJfGOD9Y2IRAOy4WkALws5GZn3VmuR4Fw7/l5HP2dqW1iCJ+ovPoFxXu6E72ycL9XJ17p7LqvI6ateupzuDb5l2iNaBacf1iRnoMTa86tr96mvZhAFni4R2wR7mXItyXHkhSxV3PgQAXeUl8ZnxThNOo2wbBF7qa5PEZ6JJvazRMYTtMY7XaznHZ0a7K3ZKtKr3U86ItapDBXffDB/9Tt/xLNQ6y38IN2gR22eG4s77/N5rrTIjDQ0NDU2nwKoeQoIgBg8efPv2bSN2IMiZM2eoVoRdC9pD+OSQXinv8/k/JAkMFCl4L9rbnmNrizoRhLqS0EoYDmGPf6u2eggrVLINKWd+zUnCSQJFkFszXu/n1LqjyYbIb61TpG0FAJbbKKdJ59qqCdVV0tzdl3RSFQAAAp7jenuM790RdloHnVRV+U+GMMDVMaINqbnK9C9lSW8CkADAcOwtmngW5ZqlJy0IiWuqD/sSmhoAQJhCbo8F/F7LGfa9zLm2BQ/hd1nXl/37V8NumIPb15Gzoj3pTrw0XQzaQ0hDYym6m4fwhx9+oNSgh4fHmjVrIiIiqqqqjhw58tdff5Ek+dJLL6Wnp1u2jAQNjQUJdxeOChRdyhMbCPKnxKIPJ4XY2qJOhDJrJ6GqsB+xy5qTqgz6bff++fTePwq9ljqCIsi1qoLOLAjVeXspNQgALJehbVWD8ryqvP3XcI0OABAU8Z0xyHlID8tbaUVY9jzfmW3utMEPfx1hCqX/LgUSN9Tdqz0VJZqUgAn8O8BAkyAYhxf8f5qio/xey7k9FlgqcvXV0OE9hM6rbhzNklYDQEZ91YQzP8z2671t8IwKtez1m8fNvI83z/7PcQstYtKjkHS7QhoaGppuhlU9hH379k1NTXV3d09LS2vcZGLdunVbt24FgJMnT06bNs1q9lgE2kP4RHEkteKZPckA4CpgF6+PZjPoskwAACSurT7iR+rqXZ8uQrluj3k3czyEBEnuzbv17q3TZSppw8Gp3r0+Gzw9zOFxDeg49DWJ4r/HkrgGADg+MY7jjgLSho+QpkaW8c3fJE4AAMZmBr4wwi6ozfU5uxw1N3K5bvYCf5fmp9QFh+qvzKfKY2J8b+fpSSjXyBuCK4tJXMOwC26fAS0kfJK4BsHY7RBILecQAoCewLdnXv0w5ZxMr6GOcDFmbNiozWnnzZwiUCjKe/rtthrWMrrqa7KbsfZDdzKdh1j2zjTdD9pDSENjKbpVlVGSJHNycgBg3bp1jdUgAKxfv14oFALAnTt3rGYPDU07mNXbnYoUrVZoj6Q+oc21m6PO+41QV5G4VpX9o3Vm/DUnadGVgw1qsK+T57lJr8RPeLEzq0FcVV538WlKDTLsezlE7W2TGgQAjoudy9CeAMCy54W8Mv5JUIN6mbo0PuX+j+dzfrmIa/RNznIDnnMadxTBuADA9pxgajFCkbZDkbq9rVOTuFZTeFh8aqT81jumxiAYp4PcZUwUeyN8dPZTaxf3HIwiCACgCBJib0QVWw/CIEtcrq9Nrj05tP7KAkJTa0tjaGhoaGgsh/UEYVlZmVqtBoCBAwc2OcXn80NDQwGgsLCwg2Y/ffr0G2+8MX369IEDBz733HMff/xxbm5uB83V5cCVJdLEZaRBZWtDugAMFFk63J/a3kH3n/gPVeYOakOZuYMSPB3N/KCBPYQiAPDk2f0w/OlbM2I7eZIVaVDVJUzHVWUAgHKcnSbEIyz7dtzHZ2o/99G9QpZGc90dLG1jZ6TinwzCgAMArsMxDrP5ALb3VKcJp3jBL9sP32VUm5F6uTg5u/ZWOaE1tzkHriiU31pXfdi77p9nddXX1Dm/2Orr0Y0r/GXkczdjVg139V8XMc6ZY8uUCr34lkGaDQAApDpvb83RcHXeb1QaJw0NDQ1Nl8Z6OYQKhYLacHc3sqrt6ekJHeMMraioWLp06fHjD/Mubt++/ccff2zcuHHt2rXr169nMLpAkfqOQ5m5XX5rHWlQoRxXYb8PbG1OF+DloX6fnMvWGIjEorqkkvrBPk/Ec3kLaMvP6utSqW1CU60pOMQN6ojMpUdgoding6enSipW9xnDZ3SBpjXSxOV6SQoAICjLceyfmCCgnTdCEK9JfS1pWefGfVQoSRDi5HzPaJOFc1juY1juY0ydVd7fLaueSJJMZcYvwv6rW5yN1JafU2Xu1JTGA4k/PIqr9bVJLPfR7bDfIgwUeV+ZuhwnibNl2bayAQCYLpEus9OliSu1pfEAQGiq668sVOX8Yj90J8Mh3IaG0dDQ0NA8Jt0/A2rRokWUGoyKivr000/37t379ttvu7u7a7Xajz766KOPPrK1gTYGYfCoxW9l2qe4otDW5nQBXASsZ/o+qFmy81qhTW3pDJDK9C8b7yszvgKSMDXagszx6/NB/4ldQg0CgKD3akzYAwDshu5kudEtdsyF5cD3mzW491sxzeNjJXeKlKWtOf1IovpqIm5wInBh9bVUKtvQFOJTUZKzkzQlxxvUICYIEA7a6vpsqQ3VIAWKIEwrNZ9oCUwQ4BR90nFXfR/QAAAgAElEQVRcHMZ/UBJWV3mp5nh/ZcbXtjWMhoaGhuZx6ObOsSNHjpw9exYAXn/99W3btlFJmQCwdu3amJiYK1eubNq0aebMmc2jWJ8ceEGLVfd/0NcmkbhalvSm49g/bW1RFyB2VODeW6UAcDCl7NPpvVwFXakFXLshca2m8BCuKCY0VbiyBFdXEapSXF3V5CFbL7lT8RsL47qhPC+M647yvDCeB8rz4vo/gzDt2jYjkNnSWhvnTVkIhkOY8/QbmsLDvOD/s7UtXQ+WA6/JEYNaV3wsGdfq7UM8/Z+JZPCM/w2qC4/LykdQ27KqUcq8P/k955qcxS1KV30NAAAQttdEXuhyjve0tuZ5PglwfGexPSco7nykyPgSCD0Q+o7wENap9f/kijOq5HKtQchmhLkJR/cQOfGMhA3T0NDQ0Dwm3VwQfvfddwDg4eHxySefNKhBALC3t9+3b19gYCCO4ydPnnySBSEgqH3kN7XxwwFITdFf2vIEtme0rW3q7Azwth/i63CzuF5rIH6+Ufz2+E6dvWYpEIyNMASK1I2tZwmSOK4qx1Xl/ylFxG7Qp21Vg4m1xe9dTMiUVuU8tc6FI2iXyZ0LlC3ihSw1c7CiqJbr7oCxu/lXdLupvpqFa/UAoKtXMrgmvMQkXnUxATeMpPYIXFh18Upgj6cBNf6u8kJfVWX/xO0xnxe6jGH3RPxRtxuEwRcO2soNWiD9dxnG87Tsr0apVLPh76z9t8p0+COxBiwMfWGg10eTQ+kesF0FQq1HefSSCg1NF6CbP20kJiYCwJQpU3i8pgvMvr6+oaGh6enpt27dsoVpnQimy1Bu0AJ17h4AkN1c5TLjDqD0KmwrrBgZsOBACgB8d71o9dggBvpENObi+M1xmnBacmEWqZO2PhoAABCMbT9yDzfguTZNRJDkyuQTmbJqAPgg5ezOYXPabGtXRpxSWPTXTWEPt6AFo5An46PVVkSDeuiVWnFyvse43oAgJK5R5+0jVGW4qpxQV1DrEYRaUl/4ReOrpCX9yvfYYzx7jOeB8jwxrgfK88R4ntzA/yEMHsb3dX2uvK2dIbsEV6sKsqTV/UVevR3c2ZjFfvcZDuGiKf9YtuJOQnbN3H23xUpd81M6nPj1Zsmxe5UH5w+cENwdAge6PeqUMqaLAHy8bW0IDQ1NK9hAEB4+fNjFpelXeVFREQDcv39/1y7jja2nTZvm4eHRpomkUqlKpQKAsLAwowMolajXt5RV8oRgN3CLpiiO1MsM9RnKzB388NdtbVFn57l+nqtPZFTJtSX16uPplXP6tO3D2XVhuY9xnnpNcm4KrixpdTDKcnAcf6wdKXMognzSd8JTV/ZzMIYTu+lqTvdGXVFfeCQRSJBlV5SfS32iSsiYD9uR7zdrsHtUKNtJAAAIxkFZDrIbKxu7r5X1E3GDU+OrCFyorB8vQE4Q6goQ3wYAQDD7Yd8ijAefsW6pBgFgX97tH+7/CwBMFAtzcOvn5Nlf5NXfyaufyNOO+ZjeNgRhWKz2aVJJ/axfk5Q6vIUxEpV+9q9JF5cNpwt6dXL0co06o1LLY5Fj+9MLWzQ0nRwbCMJ33jHZ0+nq1atXr141eurixYttFYR2dnZyuRwAOBwjP3hVVVVpaWkA0KtXrzbdtluCct2FfdfLklcDgPzuh9weL6AcV1sb1alhYehLQ30/OZcDADuuFj45ghAot8C0xLqzk/T191oYhiAo0zWKUFeSOmk7WixM8gjeNHDqvMD+fgLHxzC268H1cPAYG15xIZ3r7kB1HaQxBVskbNjm+D/txHaSXJzzwH1NMhWS2c0vUUhi+A7nEEQDAAiD7zj6INtnurXstRkpkjJqQ0/gdyXldyXle3KTAQAB5NDY/z3j31GLDqrsHzk+M1CuWQ0zcYL8v0N3W1aDFEodvuj3O3ffGv2EhGZ0UWqu3icNBC7TSO4Uiga0t64yDQ2NVejOIaMIgggExlOPSJJcuXKlRqNBUfSll15q+T4JCQmtzqXRWKP3GgBotVocxzUaDYZZuOIcGvgylr2LUFdxeq/XETyw1ivquiwZ6LH1Qp4eJy7m1iYV1vRxF7Z+TbcAr7ujyfoK10pQnjehKjU1jCQJbekJbekJQJkM5+EsrykMjymYXXCr99dqtVqtVqPRvB48HKz4x2VBSE018hhLKk4jepJM1KGfL8FGu+LLtxmOw4XjEhSXZxGqMoUkBjcY8SARuFBZP0HgeAJhOQpG/UU6D7XJO6zRaLRaLYPBaNPsOp2RQEpTkCTZcPNnfPr4cO3u1lXkKyREo/ZOJJB+bDtTNoi1Kic2FzHW3dEcDDVX5deXylhrOL3f5/R8GZBWfrOOZVSnVsjMvHlGlfxwSsnscHrhspNiUGhrk/Ko7fLz93ih7rSTkIamM4N0ROs/o2g0mnv3WvIntExISIhQaJkH7tra2pdffjkuLg4AYmNjv/zyy5bHN65GYwoq5NUKVFZWEgTh5uZmcUEIAKDIBJYrsESWv3M3ZdmpovicegD4X4Ro49hunyZBopJLSNEPaN31BwdQLhDqNt2C8F+OB65p2E2VVbmw+B6P1oypq6tTq9UODg7NU3+7BEjNWUbGa3joVsJtpq1teRJBNKXY7SWV994mCeOlR1FM7trrU2LATyQv0Mq2NaBSqerr67lcrqNjGxzgF2oLF9893vw4CtC804sv1/7K8KYdQZW4PkNek66oSZfXpMtrCtXS1FEvM0xUUh33795qnTJc6PpV2ESPthZ2InHGzcmI8kHjRFLYBw/5hLTr18IVsWeK47LqzJ9hZojDN5P92mYVjbVQXi9UpZY37ArHBnFCOly9+/n5Qcd0tG4O9WTYgXOROKGXoawnK0CGxhQd/nmzpoeQw+EMGjTIatMZRa/Xf/fddx9++KFEIgGABQsWbNu2rdWroqNbqp9G+Q+NRqV2BGw2myAIDofTIYKQ09/y9+zWvDTYgxKEf2XWbRjrb8/ppi53Uk+UHiELvwd51iPHW1SDaO9PQVdLVJ0FWWpDZ0Km8zAmhwMAZWrZpvtX/irPmOMZtrPftMYXcjgc6kNutT8rC0LKM4jMWMDVWPoqBkqgPs+3eolerCRxguX6pHiYOxxOkMJ+F0mY9F0TuFAj/EnoZDy33DoQBMFms9v6IWcyjZT7ssexhRKPb1yavl4EQZrfnAOcKL4wCh4oYQNBMFDjalCF6wvU9QRJJkvLPewcOCZKs7YA2XsTmb6OVOQCACJPY9yajfguQILfRpjGc/8ya9vmqs2s1XbFr4gnAVypU2dUNj6ivl1m38eHdhKaj6boL4P0vqDve9ac9PDhw+7u7lFRUS0Pk8lkd+/eFYvFrq6u/fv353K51jHPTG7cuDFz5sz9+/ePHz++g6aoqKjo378/AJw6dWrAgAEdNIuV6abPr8Y4c+bMqlWr7t+/DwCOjo5fffXVggULzLnw3LlzLZylVLurq5UCV/R6PY7jrq6uHSIIadrITFfXiItlqRUylZ6IL9TEjrKZw6Hj0Jadqb+6mFRXPDyEMrj+zzDsQuR3PmjhQj7LIBi0FWAroa7SlMZrS0/pqq649JqjINAtqRe+TL+sxvUA8Fd5xrqBEwaKvHFlMcb3AUAwDFMqlSKRiM+3WLEK60BoqmsvLwaDEgAwYaBz7/+h7Fb87fL86vLDKSgLC10azXLoYq+3c0IaiKrUxJbHKFIlAZOcUFbTX8D6jFKMy2I7CVh2vPZGSpqFUqlEUZTP54tEbYjIcNCKmx/8X73HPKnbEYeacqa28XEMwx7nhymjvkrE5tdoFKH2rr7unkbHaHHD4BNfRzh5NFSpeaQElOtTZEiM8t5nirsbSVwNJEEW7UaqTgkHf8btMR+avb9irQE8cs23sEbW02q/vDRtojQ+hTQ84rQ2SNWMchWdSWg+yoyvDLJcfu/VCGalRseJiYnPPvvstGnTWhCEKpVq9erVv/76q1r9YDnYzs5uxYoVH3zwgdHlKptw5MgRhUIxfPjwjpsCx/GqqipoYxh/J+eJEIQymWzZsmX79+8HABaL9eqrr7733nvOzs62toumO7BipP/Lh1MBYMfVwteiAlAzAoy7FpgwgNBUUdsIU8gL/j9+r1WYwE+auKxhDMpxFQ7YiLLsZcmrccWD8Gl97Y0HZ7luvJ5LeD2X4IThp5zkDSlnqtTyhmtn+fZ2YvEAyNqTQwFIjvc0wmEUcAcDdLHQZZLQ1V18ClcUAgDCtHMaf7xVNUjo8YKD13GNDtdA/sF/Q5fSLUAtQG1ynk7aSiMEg1JXcyPXLSq0yfHCwzeo9oZ9353N4D/yHKarUzLtuAhmy6Zq/USee6LmNj6CafHgPwpRktzDGVk+/JHy3cLHKx8a5uBW/fwHpUpprVZpasy9+sq0uoq0uor9ebepI34CR6p4aaSL72SvUARlCSLe5QbOkyau1JbGAwChqVakbuEEzG1S0LVaodUTehCVmW8hQ02XXOqM6OWamptGhH3FhXSnfv60k9Ac9LXJuurrAKAp/IPbY751Jv3+++9bHqDVaseNG3fjxoNfdhcXl5qaGplMtmnTprS0tKNHj6Imwg2szNGjRydPntzZ/JadH+sJQo1Gc+fOnXZfHhYWZmfXtsbWFBKJZNSoUenp6QAwe/bsbdu2BQTQa1Q0FuOFAd5rT2bWqfV5YuXfWTVTe3W3FWuGXTDHZ6au5jovZCm/12so2ymzviqnOF1WmoOTPQDF2F5TuIEvUIX7yUFx2pIT6sI/wKBBKkocitN9BY79nDwBIKE85/Wbx+7VPYwjGuzss23IjCi3AADQ1yYR6goAUOX8DPAzE2UoHQeR/rM4vjMY9l2jDrAscaWu6ioAAII6jtrPcGg9IhFlYgHPD8/59R8Gj+03y8YR9d0D0kBUXso0Z2Tl5UyXyKDGTkKDQkOpQYzNbKIGSYK8t+0kALAc+OGvT7WVLPTi2S8IeuRzUnY2tVJPAoBTjnxUzCiWo4WdzN58e2++yRLBKeKm+q1IUVekqDtafG+4q/9krwd6GxMEOEWf1JSckN14DVcU2g/dQalBHU5cK5D8nVXzd1Z1aoUMsLa1gHITds8eIV2dqsuZhN5IqVitREGXGzUTZfqDbCblvc+tIAi1Wu327dv37NnT8rANGzZQanDp0qXvvfeel5dXbm7umjVr4uLiTpw4sWPHjtdee62jTW2Ve/fu5ebmvv/++7Y2pOthPUFYWFg4bNiwdl9+8eLFMWPGtPUqlUo1bdq09PR0Pp//yy+/PPvss+02gIbGKDwWtiTSd9s/eQCw42pB9xOEAGA/7DuE7diwov9b3q0tqRcA+gL0BQKgBKDk0KNXTAYA0AGc/3Vh0KDdUXMBIK2uokEN+vIdNg+a9nxgv4YChriqDOW4EprqBzcgDLg4US5OlN9ax/ac4DTxbMe/ysdCmf6FKvtHattu0KfmdzIQBrj2mDeS6+HAsu+SFXQ6G+a4BykMSm0TJyFJkE79/LViOcZuGvukq1OSBAkAJE6YUoOEAa+6ksV2ErBFAraTgMHr8EAvg0pb8+9/VVtwovJypu9Mqy4rzAsc0MfRI0VcliIpSxGXpdVVaHADdaq/yKvJYI5PDNtjnKb4+E0y+NezZ4orsKQcvVRtaPfsSi1xv1oR4trGajc0HYkp9yAF7SQ0B1xVpi76k9rW16XqKi+x3Ed3xET19fU7d+5MT08/f/58dXV1y4PFYvGOHTsAYOrUqTt37qScgUFBQfv374+Kirp169bWrVtfffVVmweO/vXXX0wmc/r07t9MyOJ085DRr7/+OjExkclknj17tkPjibsxhLrSzC5STywrRwZ8dTkfJ8i/71dn1yiDXbpYJhiJa9X5+zCeN9trktEBKNft8WdZ3mvEt1nXq9WKdRHjYsOjuNgjPxsc31kcnxn62iRNyQll4XFSdg/gQTUtTNjj8WfvUAzS+7LkB6VTuUGL+OFvtuly+1DjCVo0bcV89yBFEych044b8OxQoyMNai3LgaeTqtlOJuWHTqIsP5fWsItxWGzRA3HIdXdwivA13zAzqbp6H9c+FFS1yfnuo3pZ3EnYAjwGM9LFN9LlwUszEESWtJoShxM8H2kwYyDIxKK6kxlVCdletzUnSPc8AAAnDyh7MIyBIsFuwoy2zF4gUYV9+s9TER6bpoYGOXexb93uiin3IAXtJDQHVeZ2IB56y5UZX3aQIKysrHzvPXOL1pw8eVKlUgHA5s2bG4eGcrnc2NjY+fPnl5eXX716dezYsS3chCCIjIwMAAgNDWUwGPX19XFxcTk5OSwWKyQkZPr06Y27CTQMNgWTyQwJCWlyMC4ubsyYMQ4OTStXJScnX7x4saqqysPDIyQkZMKECWy28TW7tLS08+fPl5WVsVisgICAyZMne3t3+xryANYUhEFBQSUlJe2+3MXFpfVBj4Lj+HfffQcAy5Yto9VgOyBxjfLe54q0zQ6j9nF8jbR4pqHwc+ROCXU9mVFFkvDd9cIvZ4bb2iJzIbQS1f3vlZnbCXUl03mwKUFoEVgodnDM/3z4Dq6m6tcjKNMlkukSqfN7XSnJE2puQHWCtiKBY9rbRuoVCNP2/gGGfYjDiJ+l/77CFA2yH9ZKGgZNx2G+e5CiuZPQFHxvUZ81M0gDYdCYLCGglcgb7+IanapMoiqTUJebEoSU47EdPpPG7sEHt7KFk7AxDBTt7eje29F9fo+B1JF8sSohpyYhu/bM/WqZ5j/t6qN4sKERBIp40T1dooOdJ4W46vQ1LkfOtGlGgiQP3y0/eq9y8WCfDRODvezpoqO2pGX3IAXtJGwMiWsIdQWuKidUFbiqjFCV4+oKTckjrWU0JSfqL89Due4YzwflumF8b5TrjvG8qTSNx8HDw+PXX39t2N2yZQtVdtEoVHlFHx+fiIiIJqemTp2KYRiO4wkJCS0LQpVK1adPHwAoKSm5fv36iy++qFAoGs66urru2rUrJiamyWBTeHl5lZY+Ul25sLDwzp073377beODUql08eLFVKu5BgICAr7++uuGuSgUCsXy5ct/++23xgeZTObHH3+8Zs0ac1rQdWmsJwgZDIaVRXZSUhIlQTUaDeXpNkpwcPDEiROtaFeXQZG6UXH3EwCQ3XyT7TUZwegMXZOsGBlwMqMKAH69WfLx5BABu7P73nF5vjLjK1XOL6ThQcUIfW1Sx4WmUAwUmf0NwHZneS7h91lJ4hoEMflmym6u0pafY3tP43hPY3mMe/wfyHbDDVrIsA/BhIFWqwhH0xzRgACnvn4GWQ6uKmO7j2lhpK76KspyYjiEtSkbEGGgTIFJycFy4LuP6qWVKDRiuVasIHQPfXdskcllC3l+de7uf1gOfIcwb++pLbXpa0IT9yCF9Z2EzVHr8WsFkoSc2oTsmlul0uYDWGpHRyHTwJZ/PnPkovCHy2cF/64BaEMwwoxwt+N3JQCgx4kfE4t2J5UsGuzz4eQQdyH9N2hVCD0uzSxTV0sJraEF9yAF7SR8CKGXXl2sLjjYyjCSUOf/3uQYy3WEY/RJlGW8g4uZ2NvbL1q0qGF39+7dLQhCyllH9VpogpOTU0REREpKSlZWVvOzRomPj3/11Vc9PDyeeuopPz+/W7duxcfHV1dXz5s3LzU1lar0gaIo1ViyOSUlJQRBNFdocXFxCILMnPmwA3Bpaeno0aPz8/MRBAkLCxs4cGBVVdXFixcLCgrmzJlz9erVyMhIaqRYLB46dGhubi4A+Pr6RkZGKpXKGzduiMXidevWXbly5eTJk2a+ui5KZ39sfRyo/1cA+OGHH1oY9txzz9GC0Cj88DdU978nNLW4okB573NB3/W2tqjzMjHYJdRVkFWtkGr0+26VLR3eedsl6yUpyvQv1QW/A/HwaRLluvNCXmE4NV35szkIZnrJnyQ0pacIdaXq/veq+98jGIflNpLtPZ3jOwsTWP7915adadmDynQxHm1IIc+vRjBU4EcXN+5AqOBPjBsO0IqXnus3zuKzc90dvCY/fD7TKzRaiUIrVmjFcq67yec2rVhOEqRWojCotE1OybIrKi5lsJ0E9sEejn0ecTA2dw9S2NBJSDkDT6RXJWTXaB5tOUBBOQOnh7lNCHHhMJrqcF3lJU3RnwDLml9oip+e7btulP6901kXcmsBQIcTPyYW7b9dumJkwNpxQY7czlIEv3uDaw1pnx7H1ToEQcxcXnminIS4shRI4yIZ4/s4jD6A2Yco7nzYpntyfGc6jP69+Rp947kwvg8glqx9lZ+fDwCmFJqfn19KSkpeXp6Zd4uNjY2Ojj506JCjoyN1ZPfu3YsXL1YoFMeOHYuNjQUAHo9XWFjY/Nqffvrp5ZdfBoD165s+lMbFxUVGRnp6PszCWL9+fX5+PpfLPXDgwKxZs6iD2dnZ48aNKysrW7hwYYOI3bhxY25uLoIgH3/88bvvvksdVKvVS5YsOXjwYHx8/F9//TVnzhwzX2BXpLMIwps3byYmJubn5xcUFJAk2bNnz6CgoNGjR4eFtb93sPkfTRqjoCxHYf9PpP8uBQBF2hZu0EKMb/k0mO4BgsCrw/1XHb0HANuvFrwyzK/TBhfIk9dqyx+21mQ69eWHv8kNmAtoF3t+whWFQDyM3yNxjbY8QVueILsZy3DsI5p0AeVYTH3hqrK6f552nZPTvnxayZ3Cwj9vYmxmyNJojjPdgP6JgCngMAUcgW8rH0KDUgsIAGnEi6iukioKahQFNRib2UQQGnUPUljTSajS4dcLJScyqo6nVxVKjATr8ljYcH+n6WGuM8Pd/Z1MOvANslxtaTxgbGhLiRl13v4B9t5nXghOFIe8eyrrcr4YAJQ6fOuF3G+vFS4b4f/2+CB7Thf7WutyYGwGz9NRnldFkiRpaMU9SPFEOQlrj/cjjLUPBQC3uTUox1nY7wOU5ShLegNII8sozeEGznMYuQdQI0/vjeeibt5us5sjl8sBoHluHgV1XCo1EhFgFAaDceDAgQY1CACLFi365JNP8vLyqKYAprh06dLy5csBYOXKlZQsbKCmpubatWubN29uOJKZmbl3714A2LJlS4MaBIDg4ODt27fPmTPn/v37BQUFAQEBxcXFVKDpyy+/3KAGAYDL5f7+++9ZWVl37tx57733Zs2a1Ulaa3QEtheEd+/efffdd+Pj45ufQlF0wYIFH3/8cftiTd9//3268uxjwgt+SZXzs742iTSoZEmrHcccav2aJ5XFQ3zW/50l0xgyquT/5NWODeqkviB+77coQchyHcHvs5bjM715h+guASYMdJtbrZfc0ZSc0Jac1ItvN9ShIXVSy/4WqjJ3kHqF6v4Pgn5t/kqp/Cej7FwqkGBQaYv+uhny8ngLGkbT1fEYF+42KlQnUWLNpItG/CApsUklG1PuQQorOAnzxaoTGZUnM6qv5Iu1JpyB08PcYsLcogJF7GbOwOYgCKIpPgq4pk1myJLe4ApcnCb8PTLA6dLy4QnZNWvjM2+XSgFArjVsvZC760bx6jE9Vo4M4LGwNt2Zxih6habubpHA34Xn5dT4uKi/v65Oqat/UInXHJ4oJ2Gr8MNWYXzv+sv/I834E7Ab8rVRNdihaDQagiAAgMMxHrNDNf1TKk32LG3CwoULmzcDDw4OzsvL0+tNtp/Jy8t76qmn9Hp9dHT0F1980eTssWPHCIJoLPz++OMPHMft7e2XLFnSZPCUKVNGjRqF43h+fn5AQEBCQoJWq0UQZM2aNc3nXb169QsvvJCZmZmXl9ezZ7dtf2pjQXjr1q3hw4frdDoA4HK5/v7+vr6+AFBQUFBQUKDX63fv3h0fH5+amuruThe6tAUIajfkS/GpKABSU/iHrnIpy72ljOEnGSGbMX+g985rhQCw42phpxWEbM8Jgoi3OQFzmY6dLkC0zSAYUzSQKRoo7PcBrijUlp7SlJzUVV5sqesDSbQ1kIY0qFTZPwGAMutbfp+1LUWxNp2KLD6WXJv0IFSB6+4Q8Fz7W+/QdFdQBsZxNdJl13Ncb8fePlqxQhjwSDObFtyDFDVJeQI/Z8fevogZYsxMlDr8Qk7tyYyq01nVJfXq5gP4LGxskHNMuNuUUFcfh7YlnGPCHqIpl+vPzgRJG65iOISKJhzD+A/Wi6ODXZJ7upzMqFr/d9bdchkAiJW6dfGZ2y7lvTm6x6pRgc3jVGnMpyYxp+TkbZIgnQf38Jv9iCB06uevrqivumYy/aw5T46TEOP7IAwT7nr04ToFx+8pp2hHyYXZpF5mfDACCFOEMvmNr2ppLtPD2gGD8UAs4LhxJzCl4swvuxIebiSqv2Xnm0wmmzFjhlgsDgoK+uOPPxpMaiAuLi48PDw4+GF9YypxrH///gJB0/gLDodz6dKlhl0qotDT0zMwMLD51CNHjqQ28vPzaUHYISgUiueff16n04lEonfeeefll19u/H9WU1PzxRdffPnllzU1NYsWLTp9+nS3r/DTOWG5juD2eEGdtw8ApDdWucTctv7qVFdh+YiAb68XkiQcS68slKhaCJHqaHBVGcZr2gfsPxDhgE1WtcYqYAJ/XugyXugy0qAiDQpTwzTFR2VJb3F8prO9p7Hcx5hTA0adu4eKwyE01er839keYxGWQ6vZ/ITOkH/gmjS7gtoV9nDr8cLI5l4gGhpTMO24TDsuBD1ysGX34AMIsuCPREKHOw953JYt6ZXykxlVCTm1l/PEOtyIMzDMTRgT7hbd03lUDxGrLRV6moBy3Z3Gn4TDW82/xHHU7wj2SG96BIGYcLdpYa5/pla8eyorp1YJADUK3br4zJ3XCt+b0HPJEF9GJ/NKkbi6SxRs43k6Ug7AurQSn5gBKOOh3kBQxHtaf+9pTcuN4DheVlaGYZiXl6kfo+6P84wUM0eyPMZhPC+D1LggRFC223NlLf9gmT9XW2EwGCwWS6fTaTTGfZjU8ea6yxSU78d8cByfO3duRkaGndPdPvEAACAASURBVJ3d8ePHG8eaUsjl8vPnz69evbrxQUoQ+vj4tHp/ShD6+/sbPevl5cVgMAwGA5VI2V2x5ZP9rl27cnJy2Gz2iRMnmvesd3Fx2bx5c2ho6KJFi86cOXP+/Pno6Gib2EkjHLhVU3yM1MsJZalemtEdPEsdQy83wfieLgnZNThB/phYvGlq6+XsLY6+JlFx73NN8VHnaYlMZ5sVoLchCIPXQrlRbelJXFGgzNyuzNyOMPhszwls72ls76kYz1QzQFKZub1hR5n+mfLep0ASjtEnGHbBJi4BvVydu+eyqryO2hX19/ebM6RNBS1pHh+CJO+Wy64WSCplWgBwE7JHBDj197JDu/LaYqvuwQa4bvbtm0Ks1F3IrU3Iro3PrCqTGnn+E/FZ44Kco4Odp/Vys2CnB4TZtrxHXdXlqhsvIUw7hrAHZhf08F9hj2f6es/q7f57SvkHZ+4XSFQAUFKvfuVw6ubzuW+PD3pxiC/WaWShLHGFcMAmi/R67VD4Ps4cZyFDyBH17/5uPZtA6qQGmUkvK4lrDXV3mc5DrGlSY1xcXMrKyqqqqoyeraysBIDmUaCmwLC2OTDfeuut06dPoyj6+++/9+rVq/mA+Ph4rVY7e/YjDdIkEgkA2NkZib9oAhXsyuMZf3LAMIzL5crlcvOTJLsithSEFy9eBIAXXnihuRpsYOHChd99992NGzdoQWhDMJ6nsO8GgyxbOGAjymlzQ8gnihUj/BOyawDgp8SiDRODrRekRBKakuPK9G26qqvUAWX65w6jW6tn/eShq7rWsE0alJrio5riowAIU9TfbshXLLeoJuM1xccM0oe9zg31D7Ylf491eSrX1NK+5E5Rgxr0GBfuOb5P18zT7MLsvVW6KSEnq7qpr7inM/+d6J4LB/l0RVVolnvwP8R3CvkmqtpK7hTyfZwbl7EhSDKlTJaQXZOQU3spT6xv5gxEEaS/l110sEt0T+fRPUTMTrC6YZAXMAFIvUwvSVFI0j4lR7yM7HADBQDww1bZDflqwSDvuf09dyeVfHAmu0KmAYBCieqVw6lfXy74YFLw0xGeNv8M4Kpydd4+TOAn6LvBxqYA6OXqurQSl6E9jaf2IdBr5WSUSWdjdhS62hst15XR1STaUBCGhISUlZWZcpEVFBQAQGhohyyC79q166uvvgKArVu3Tp061eiYuLg4X1/fAQMGND7o7++fnZ3dpFehUahGF8XFxUbP1tXVUTV1qGHdFVsKwvLycgAYPHhwy8MiIyNv3LhRVlZmFaNojMPv/ZatTegaxIS7BTjxCiSqWqXuUErZwsGtxyo8JiSuVuf+pkz/wiBr/KSIEAYlkDgg9O/3I7jMuqeruqIpjdeWnGz0jpF68W1cUaiovk61BiZU5biqnFBXmMryJ0mD5OxElOuJ8TxQnifbbVTjthNuI0PVlfWS1GLfmYOcBxnJSaDpOHQ4Mf9Ayh93yo2ezalVLj5459i9yt/nD+xySWXmuwfBdLlRvUxdcDgRSOC42rksGX+pQJyQXXsio4qSTE1w5rPGBjlHBzvHhLl52HWytu8kjrDsSZ0UAH4m+38Hg3aTfdcg116BW5jwwR8dC0NfHur3vwHeP90o2pSQW63QzrC/EollnDvlce5Sj5lDh00dONSGvUNVWTtJQqfM3Mnvvcb85GTLQugM9Rml4pRCeV4VSZBskcA+xHi4BK0GOxR9zc3Guyy3USjXVVN4pNGAG2DEN2YlBg4ceOHCheTkZBzHm/j3amtrqZDLJnrMIly6dGnZsmUAsGDBgrfeMv4gqtVqT58+vXjx4ibHg4KCzp49m51tfBHtq6++qqysHDt27KRJk3r06AEARUVFarWaKpDTmMzMBwvB3TiBEGwrCKmUUJnMRAbtf1ADWCxWy8NoaDoDKIK8MsxvXXwmAHx9paCjBaFBel98OorQ1DQcQVAWJ3CeIPxNhmPvDpo0T268iHbXAGWyPMaxPMbB4G0GWa629KS2NF5XeRnlebJ9ptedn9HgYm0ZQl2tU1dT2yyXYbyeLz5yGgG/OUNcIoP4rXUdoLE4C0yrwQaO3quct+/WnwsH29xHZD5tcg+C6XKj9dkVVDneu1Lt/I/OEeTDypAIAAmAoUg/T7voYJfpYW7D/R2tFmHLwZivhAwDAG35OVxuMlcHEwSwvSYCgMuA6cLILYSmtr4ua0fCCTDgamAKBL6ooZghfCTzksfCVkUFvjjEd+e1QuG97bPt/uu7kwHlGSjO9uQ7BTOEPXg9X2S6RHbgK3wUElersn8EAEJTrSk4yA1aZLWpAYAkSHl+lSSlsC69lNA9XGWQ3CkyJQhpOhRdTSK1gfG8hAM3cXvMB0B0lZdkN1fpJXcBQFdzw4bmTZs27bPPPpPL5adPn54+/ZGybYcPH6Y2YmJiLDtpfn4+VVZ06NChP/74o6lhCQkJcrm8Sbwo/CdQMzMzr1y5EhX1SPhPXV3dW2+9heM4NYb6V6PR/PzzzytWrGhyn2+++QYAeDxe44o13Q9bCsLAwMDExMQLFy4YLfNKYTAYrly5Qg22omk0NO3n5WF+H53LVunwlDJpYlHdUL+m2c8WhGHXE2U5UIIQZTnwQl7h9XrNdDqcBThblh1XlNZx97cmDLsgRlgsPyyW1MsN8nyU5eg0MaH+yv8aL8o2gBtEGMOIEub4znIYfaB57CiCobQatD5HUisOtaYGKeLSKn9PKZs3oMuUuyB0uN+cyIu5td//W0S2Vt7fXcjeODXUjvdwIbVaob2UJz6RXlWeUTqFYAxBDX8rycZq0FXA/j9XTkydWNTLy2NAgCDA2tkBPAbz++FPAUD14VgcMRnlhRIebsN/eLjLcXbyGHkk2m1N0kmFXrdq9lamifqKAjZj7bigKrWaeLiABigQqLZUV1Gqq7jA9prEBOOCEFeWIAw+ynYyerZ9qHN/IzS11LYifRs3aKHVOgBJs8qKjibrZY9Wi0UQYYALrQZtBKmvvYFgXH7Ya4KI9xDmg3Bulvto55jb6vx9sqTVuDyP0FSjHNeWb9RBREVFhYeHp6enr1+/fsKECWz2A796fX39pk2bAGDy5MmWfVCXy+VUWVFvb++4uLiGGZsTFxcnEomaSD4AmD9//saNGwsKCt54443Tp083TnF87733cBxnMBijR4+mXt3YsWMvXry4cePGMWPG9O79cD39zz//pBTvm2++aX7VnK6ILQXhzJkzDxw4cObMma+//nrVqlVGx6xatSovLw9BkCYLEjQ0nRZHLnNuP69fbhYDwI6rBR0qCAFB+eFvKNK28MNieT1fRJgd2/T8TNn9Wed/NRAEALBQzE/Q+ktz53aBPuwIU8h06gsACMZ2HH1Qylmpyvqu8QCC4NWWvOfi9w6KPvIIxe+1wm7I123tY0HTcWxMaIMP7eNz2V1IELIceHUYtvjgPRluRuSe1MDJl33/dMStUumJ9MqTGVW3y6QN6u88sJnAZgBJOQOnh7nFhLsP8LIvOZZcU1Yuu5XPE3KsLwgpcFU5rnyoBpnOg/lhq5SZ2/X/uUcIdQWuLMb4j1QpHO/RMykmtlwlM6UGG3Dov8FQlyqX5BQV3wNVniezhgEPKukvO6NaPlU60NtIMR7ptf/Tlp9F2U6YsAfDLggT9mAIg6htlNu+nlikMuPrhh1D3T1dxUWWx7h23arNMO14jdUg183eqZ+/Uz8/lr3NKmM/4RhkeUyXofaR32CCZllqCMrtsYDtE6NM26qX3GF7TrSFgYCi6LZt26ZOnXrnzp1x48YtXbq0b9++169f37FjR2lpKZfL3bq1DSWCzeHdd9+lmtRHRERQOYTNWbJkSVBQ0PHjx2fMmNG8UA2Lxdq0adPzzz+fnJw8ePDgl156qX///lKp9Pfffz9+/DgAbNiwwc3tQT2nLVu2DBs2rLKyctiwYcuXL4+MjFQoFBcuXNizZw9Jkm5ubk1KmHY/bCkIn3322b179548eTI2NvbUqVOxsbF9+vTx9PQkCKK0tPTmzZufffZZcnIyALz11lsREXRlS5ouw8qRAZQgPHy34vMZWndhB+ao8Hq+yOv5f1boBXKuPHv2+d0a3AAAvnyHf6YsCxBacr28s4Bg9kO/xfh+8ltvN3S6V9ZNxfXuqvqJAqdjDeME/TYI+31gIystAE6Qf6VV/Jla8W9RXZVci6GIlx0nKlA0b4DX+J5d0rdZIFHdKWslB6ExWdWKzCpFL7cus+i77VKeTGNuDuHPN0oO3a2Qa4x0eXYXsicEu8SEu00McbFv1ApFUfzAYWUf7PH41rYP/X+BcyhbJOi7nt9rBSAYN3CepvCILOlNXFkCAPqaG00EIQCgCOLNb72wKttzAttzAh/AHaBUqvniYtapW0meWLk/q+JgHXf3V5en9XL7ZEpoX89HKhMa5LkAQGglhFair01qfMp52r+N84dNQeIaQl2Bq8qp/GRDfUbjalUAIEtezfGbg/G8Ua47xvNCeR4oW9TqbdsHz9OR62ZvUGmdIvyc+vvzPDty1ZLGDBjCAKfxJ1oYgLIchQO3AGm8DaB1mDRp0vbt22NjY69fv379+vWG4wKBYP/+/RZ/SqdqhALAqVOnTp06ZXTMmDFjKisra2pqmseLUsydO1ev1y9btqywsPDdd99tfGrhwoXvvPNOw+6QIUPOnj07f/78ioqKJuI2MjLywIEDQmEXWN1+HBCy1dCTjqS2tnbo0KFUNioFk8kkCKJx78uoqKjz588zmZ20fxfVHdFqb2NZWRmO415eXm0t2ktjZUbuuHatQAIAXnYcA0kycBmb6zjQ2yEm3O25fp7mN+wicbU6dw+hrhL0e78j7W2Fc+XZMxN+VeN66Hg1KBaLlUqlSCTi89tWht6yyO98oLjzIQAQBK+q4BsS56OYwjXgNcpJKBywSRDxtl6urriY4TO1vwU7gFuHpJL6+QdS7jcrwkkRFej02/P9bdhIs33EpVXO2Z3U+rhGPN/fs6/nAxXBZqA81sPvVS4Ta1x1hs9isLCHEX1CDoPRqI2yPYfRkGuHIODAffiDhSGIHafpeo1SqRSLxXw+XyRqw0N/z80XcmuV5o9vDIeBRgWKJoe6Tgl1NaWBSZxQFNfKcys9xvcxXmoSoPz8PYzFsAv2aHdbi5aRJ69VZHzJD3lV2P8jhPXIFKRBqbj3mTJtCy90hd3gz9t02/W3/74vrfls8PTmcQ15YuWHZ7L33y5rCKDFUGRef6/3JwX3EFFfQWTticEGaRZpMPLmu82tNlV8W5q4DEHZKFOgzN5FqCvbZDAAIBhH0He9IOKd1oc2gyRIeW6lMNDN1FeTrl7JtOOZ+l+2CNbvQ2jN5zErP/tZgd27dxcWFgYHB8+bN6+FYXfv3v3222+vX78ukfw/e+cd31T5/fFzs3eb7kH3opOWDTJaKEMoGxwoXxVRBBFRAUXAgaKAiqKgCLjwh6iIRSh7b8oshe69R0bb7HVzf39cSNM0aVNok7S97xcvXsm9z8190qa59/Occz5H7OnpmZSUtGTJEivtNzUaDZ5f+vzzz4eGhprs/eOPP/Lz8+Pj46dPnw4A//77b2ZmZtsv+Pzzz2/btm3nzp1CoZDBsGjLVFBQ8PXXX1++fBlPQI2MjJw/f37rFFMAEAqFmzdvPn36dHV1NZVKDQ4OnjZt2qJFi3DTEwMSiWTz5s0AsGDBgj59+ljz3h8TG3ze7CwIAUCtVn///ffr168XiUzrc4KCgtatW/fcc885ckv6XisIMVQlv/8lI3AOxSnCvjNxQKRqXcqu9AvFD9a3gmjVSz32vVX5IC/az5n509P9xoW3k5GlVwnkudsUud/rVQKETPeYXfqouUmPy4Xa4kknd8l1GgDwZzuffXJRMLerlq7BcQThnbWyu58CgFQ0WyqahW/kuf2JBwm5CZ9QvJcW/Hpe0yh3ifMPenp4N2oscSy3ftrPN8z2GTfgyqZdeH14lKejrImK5BqRQiuSa8RKjUiuFSk0IrlGpNAIZPgDrVihqZeqtXpHvz+jkkkcGhnDMAzDEARhUMntqNCHT1E9drZQ2NHThbqxJ/b1mNjXPSnEzfhEjwaG6u9++i/udEpzYvHCvZ3CvbmhnmR6p63Yyu5tYPjPML6sVJ3I9B3fHHzQSQpUpf9w4lZZ/5rVCkn4/g1ynYZFob4ROWJV3Fgnmum9Y3ad9MNjefvv1Rgu5lQy6cVBfdaOC/dzflAhrFfW6iSFqLRQJy1CJYU6aZFeJfCYXWLhtFjt72wMVVrY+/DtaD0o1Hqzuzix73IHbLD2TRpRdzG37lKeVqoMeW6Ec7Qt7lbNQghCAhsgl8v1en3PD9/1BkGIo1arCwsLi4qKioqKqFRqSEhISEhIcHCwiSh3QHqnINTUXWi89BIqLab7TnQZd9SOM3FAxArt6G2X79dKDVs+89n+nMuJwXk/1WkfLE6TEGTHnLiXh5hmPeHoJPnyrM3Kwt3GNxOcfh9wEz7u0pmbpVgqij3wpUKnBYAADv/ck4sCOV2bKeogglB8Ypy6+pQhPIhvNAQJ6X4pOuZXFWm3AQAhIRGvJbP7dKFI7kSKRYq4L8/JNe2nHgW6sO6vSGQ/topoAw2qF8k1YgUu8LQihUYgU+PCT6TQiB8+EMm1ese4VHUvEkPczi622Ob3EZAW1+fvOmOyESGTOAFuvHBvr5GRnb4sgp8xbH4SL/TRW7d/k3XxreuGZG9wZ3DWxicvjBhGa1VweKeq6YNjeWnZzd236RTSwmEBq8aGdTTzH5VX1u9r32VaWP4pz+NXGqOwxVaE7DR0KyvitQ6d0UD1qXs1Z7IAwDmqT8jzIx7tRR4fQhASEHQWNvi8OYrcotPp0dHR0dHR9p4IgVUgVB4qKwMAddUxVcVBht9Ue8/IgXhq901jNehEls3hn6Eiuv+5HPmi7jl8ox7DXvsns68H54mgFuJKK7wuy/xcVXHQuEEtmROIe8bYZv4mBHNdP4gf/97Nw35s59MTX+tqNegwYFrhTQCQN0wyqEEA0KMcvJJQK0j3fCZcLZKKbpcEz32iu6hBAHjvcI41ahAASsWKL84WfjThUVIA5Bq0OYKnaA7iGQJ9ArlGJNdIre6q10XMiPUKd3+QP6nW6RVGPxmlFlXpmv8MZWqdcfhRotKiRk8blTrsYcUphkGjsrlyT6fH7PU2nZmdfIlnejsHzh7SlF8jLajVKTX4RgzVS4vrdUqN16jO75JWc+Y+ANScuvc4gnBZ9MhYF693bxy+JaoEAIFKtvTagS/vnfuk/8R5oQMQIxWb4Ot06OXBV0sb1h7LPV0gBAC1Tv/txZJd6eVLnghamRTiyra2AxaJ5sRP+kcnKUSlRXhcEZVXGsqScdTy/hpViEw0y8W3uWYJobCcR//J8LPKwR9V6zQNMqaXs/FG14TAmjNZVB6T4cGzdCABAQGBMY4SITSgUqnw4IC9J2ItvTNCCABN1xbjToxkboj79Pv2aqrraOzPrJn9203jLa+771/t9RsAiHROg/J+Uumb7yf693G6uWyUcUK0/P6XkpvNTlZUt4Hs6OXMgFk28Ixpm2+yLk71j+rSTFEDjhAh1DXlCFKjTMKDOIYgocesIhI7SNMgo7t2m2QVsULr+eFxndV5lb5OjIq144w/ogZFh8s8sVJrkropkmvECo2xlOoUnBhUNzbNjU1zYVFd2TRXFs2VTXVl0VzZNHcO/pQGeizos9PWvzsSglR+kGzjfusaVC/XoAqFQiwWs1gsFtdJqW3+WSm0qNpEhT7M7NWgWMpP6R261Lw2POCHWV3ix4bpMXmlSJJX05Rfo6huAAzzGh3pO6Ff557FOCD5mEFCAMAA21ucseb20RKp2LDxy0FT3okZbXb82ULh2mN5eCk4Do9BeWtU8Fujg42deDowAVSla8xtSl+irb+MbxGWf6pRhQCAm/9aPEhIovM5savYMe37GWolysqjGY3ZlTQ+O3rZJJO9snIhu49rl5YItgsRISQg6Cx6bIQQRdFLly6lpqa++eabJqWoubm5CQkJfn5+KSkpCxcu7Nevky8wBJ0FN+FTVek+vUqISovkWZsfrfC957HzWpnxUwqgL7kexh+7UppmOp37o6HZM/p2ZdPtqhYu56yIhdLMTzGNhO4zlhW51MpFYhuwLNpM+XUPRlN/FVqFB3EMQUKN4BqTG9yN1CAAnC4QWK+XAKCqSTVm+xWNDhMruiR1k0JCXIykHS7zHgq/FqqPYt2t7ZgwtxN5gvbHAQDAiCAXG6tBAKCRSTQmiaanoHQym0Fx7cgE+vs63apssn78iKCuWsFBSAjH343j7+YzLlYnUzUV1LbhVNmQWd5UUOMU7s0L9SIzrY2wwcPw4IPHjxckBAAEkLnBCXMC434puLH29rF6lcyZxnwxbJCl8UmhbpeWuB3Jqf/gWC7+Y5eodB+fyP/uUsmKpNA3RgR1NJsaITOorvFOw74X/tcPHoYH8V2GICGZEyS5uVJZ9H+cfmsYgbPb6ExIZlAbc6r0WlRVL1FUiVm+LXI3OEQTVAICgo5gB0F46NChV155pa6uDgAWLFhgdkxFRcUPP/ywc+fO1atXr1mzxvErCXshJLoLN/7jpmuvA4As8zNmyP/IbLsVrzsIap3+XFELb6TJzld8qM0+EK+4HdzbOA7Dmq/xR3PqjQUhQuU6D99F4UdTnDo/+YrAerSCdKAFy6Uzze6VS5/heN3VCtKZwW25sTkgZQ3tWFy05lyhqd2XNTAoJD6LxmdS+Swqn0k1PPBxYnhzGQ82sqieHDq5U4MYGyZHnsoXWqNaEQTWT+rbiae2AU/F+1gvCFk08qRIW/SwpnAYrgmBbQwQ3ytvzKoU3Srxm5zg8YS1GcjS4nppcbPbiqxcKCmse0xNCABUEvnViKFPBfXbkHnGh+XkSm/HSndSpMekSI9T+YIVadl4UxOxQrvqcM5X54qWJ4YsHRnEpHZMFhr6akhFzV8vKnm8RhVKYxRqxXcBQNuQ2XDuKapLAif+A4b/NLOykESj8KP7iO6UMr2dUXunXhMQEHR3bC203n333S+++AIPeiII0rqZhJeX1+zZs8+cOSMWi3U63ccff3z8+PELFy44bNuJ3gwrYqEif6dWnIHp5NKbK5xH77X3jOxMrag+gFzmwRB7UcSeVLEXRTyOd914QASjfLvfpgaUV6bxKlb7CLR8sZgB+kAgNX+8GYGzbT5xAlNoniNl8oX6++a7nKNqAM9/qe7ZNp7V42Nc2/Zo4KmbJjE9/LGb0dPHN7R8NBJ8ndZP6rvqcE67Iz8cHzEiqJsVxC55Imjb5dJy61T9u0mhfKb9r5sYqpcWPvBo4bVqbyivFNFdOBSWGb8W4/Dggy2PHSQ04Exjbhg42frxyeHut94atfdO9cfH8wqEcgAQyjXvHc7Zeqlkzbjw+YP9qFZ3EtIK0qFleBDnQZAQ0yNkFoYqAEArviM69RyFH+fUfwUjYEZrWeiVGOU5sq9JASEBAQHBI2DTGsJ9+/Y99dRTAODs7LxmzZqnn37aUvsOvV5//PjxJUuWFBcXA8AXX3yxfPlym82zQ/TaGkIcTd1F0dHRABgA4vrkOZrnKHvPyG5Ib78vy/z8EQ4ks/1dJ54lc4M7fUrdFEeoIURV2ntfHEIf2ma0hsKix6yYQqZ3j+QFoVxzpkB4ukC4/16NSG7xTZnlg/ERyWFuHU3dtC+bzxevOpxjqa8GlUxaNzHivTGmXbBsyaP1IQSA80WiiTuutVuimRTqdvSVIXRHaI+JYfIqsSS/VlHTEPJcS8dLDO5tOqiVKlm+LiHPjaDymIY9Zu1MoTMqCa0ku7EulOfW2oZUp8d+u1Hxycl842B7sCvrw/ERz/X3tSbcLfgvVtdw31A9aAxeScgfc0Bde7nh1mV540C1fADb+QjP7S+q+xC3SVcAcYBfqNUQNYQEBJ1Fj2o7odFoIiMji4uLg4ODjx8/3rolZWuUSuW4ceMuX77M5XLz8/O9vOzThK1terkgBIDGC3OVxX8yQ57nDfqSxLBFhpLDUnHzS9L9lSTowIdBQAqOnnWWzDbff8LGXKkv/TzzzJ+Jz7MpHajz6XQcQRBWn75fc9o0QGGC7/g4r8Qo28znEVBo0Esl4lMFglP5wrvVkkeu/ct7b0y4uz37fzwaOXWyjWcL92fWyIyy6dg08vQYr/fGhsV42bny85EFIQCcLhA+v+d2rVRtacCsOO+fn47nMRx9tUJZ25j97TEAoDBpcatnGDug5O86Y5wvaoDj7xbxWnKXTwzVRuzfiACs7pe8IHwIqVUnZA2q33G17LPThTUSlWFjXw/OxxMi5vTzaaNxMqaV1v7hrJbFi6rM2MYw2Bkc139QxnvSKled/MHvl0wRegYtZce8zRv0ZSe8NxtCCEICgs6iR5nKnDlzBg/3bd682Ro1CABMJnPHjh3x8fFSqfTUqVPPP/98F8+R4FHgDvyCHfU21W2gvSdifzzi3372mHiz71d0xKogzC1FRG7o73EOowYnntgp1aqnn/7lYPJ8Jtn+yWb2AlVp66+YTxY1pu5SnvuwcIcKEqJ6LKNacipfcKpAeKlY9PhWn6Fu7O6oBgEg0pPz6zPxO+fE3amS4HftXjx6gq8TzerUPodlbJjbvRWJ31wo3pVeXmckC0kIMjyQvzwxZFqMIy6etkav0XEC3OQVIm6Yl7EaNKkeNKazKgnbZkvWxQp5IwB8lHHiuZD+rRfIaGTSkhFBrwwN+O1mxYfH8nBxnlsve/r3W+tO5n84Pnx2nHlZqBFeB0wvFT9l9rwqebxKHg8AAM2/VgqHjVFDODEOmiRFQEDQM7DdrUxBQQEA+Pr6Tps2zfqjoqKiEhISrl+/np/f/v0ZgV0gs3zJLBut/zk4dApJ4zV9Xgl7V+BnPJKi7cHHJEMXVyzvp9UkR0vife3fLepqfZlUqwaArMa61a1GJgAAIABJREFUGoXENh0mHJO6y3ltJIsa0CnUgqv5jhAkLBYp8EjgqXxBg7kqQTIJiffhJYe7J4e57bld9euNCitfedHwwM6cqM2hkkmD/XtghZUbm/bpk30/mdg3q05aKlYotagrmxbv4+TC6k7rOGx/t4iFyahSg6pbfGhbVw8aU7T7As2FTaJSPIaHWzKzEd4oarhXDgBug0L4sS1W3MQZZY05lQDg0i/QOcr8lSukDtaJQlSobqRnsDDtrsEWjEyjOEX6coMeJMLQKaRXhwY817/P1kslG88UBqtUMSQ9CMTrfr/xpZ/LmuTwKdEPhKu0uF4lkCAIgigzMO47GmVA2z8ZAKA5sVziA1ziA5meTpj+OYRkz6wNAgKCHo+tBaGVsUFjIiIirl+/npeX1wWTIiDoZF4d5j8tJ2564cY9wR97U4SWhv0imry2+lU9IOnl4gFfX5gV570pJTLQpR2/uy7lnZjRWj26JfvimYmv9WY1aGV4EMeOQcJ6mfp8kehUvvB4Xr0l79BgV1ZymHtyuNu4cHfnh/4iMd68tOw6oRWVhLHevCUjAjtxzgSdC4JAjBfX7umvjwmZSTPuRdFGeBBHr0NV9RIA0MlUlsaohFJJYR0A8MJMbWwUNQ0N9yoAgN3H4rfcMJ1LsJgPANAkFYLUeBeVyzQIQhw2jfzumNDFTwSe/PVSQFktAGzR0X4vb5z68/XhgS6fPhmRFOrWlFdddzEXAHzHJ0vq6gDEYBmncE/PkVHcYA94GGRsSw3qtcrSfczAp+zeq5aAgKBbY7tvELlcDgDu7u4dPZDP5wOAWNzWFygBgYMwNdprYl+PY7lwQ9Z3qvMlS8N+EqUAQiIjGKoHPYbtu1udll23dGTQe2NCne1nDPhe3JhXI4a6tOfD3rOxMjyIY+MgoVyDXi0VnyoQnsoX3K5qMltN4MWljwx2TQ53e7Kvh58z0+yAwwuGjPvxqkTVllV9AJ95eMHgHpBgSdC9aDs82IK2avUwi2Pa2NV6jNUn5dIpg/2c6spqAYBMJgEKAHClVDzmh6vJ4e6fOz9wCFWKVPLKdm5mZFoIDbE2J1ZRtLvp8gLpnbWcuPdZIf8zNqwmICAgsB7bCUJvb28AqK2t7eiBFRUVAOCYjjIEBNqGTFRWZtxB/s95/RO/v9Kf3VaUaRA7/+NZKQP9nD84lrfvbjUAKLXoxjOFO6+VrUwKXTYq2F72gL1cDXYoPIjT1UFC47LAC0Uis86ZHDplaAA/OcwtOdy9v69TGze6OIP9nW+/Perlv+6eLzLTY5CEIE/F+2ybGdu98g8JegDthgcN+E8d6BxjsfOt2+BQXrgPADDcTMOnrglBbD83AGB6W8wl5sf4MdzMpPGjGh0n0B0AGjXK/yu6/WrEUGMbUm6wJ0ImAcA6f3fP4qZvLhSrdXoAOJUvUJPQZ/jcIQHORfcq+e29NbSkLjuzMirOir6+eh1ubY1Ki5suL5Dd/ZQTt4oZ+iKRX0pAQNBRbOcyunfv3rlz59JotJqaGhcXa7s/aTQaDw+PpqamdevWrV27tktn+GgQLqO9E0wrVZb8qcjfpRVeJzG9POeUGy/NyqQ10v0+bRze6DM/cvxP+OOrpQ0r0rIvlzQvG/vzmauTwxYM8W/tbtcbcASXUUyPIXbtr2AoCzyRJ2hSmSkLpJCQfg/LAkeHuFrfBs2YyyXifzJr0ssbqppUVBLJ14kxKsTl2QTfKM/unYXo+DyOy2gPxpK5aGtsYzdqifduHt5476w/29mSDSkAlDco158q+Pl6uU7/4PZgNFn3FcWiN6wxmVTGvA+ntdviBUPV8uyv5Vlf6VXN5Qlktj8n9j1m2HyEbKa7oy0hXEYJCDqLHtV2QiaTeXh4KJXKFStWbNq0ycqjNm/e/M477wBAZmZmbGxsV07wESEEoVkwVAWYHqH0wHCTVnBNkb9LWfoXppUZNvLH/Mvwn2F4qqo42HC62TypWud1V913Avu8oSMF1XWA25SbhgEYBv9kVr9/JLdQKDdsHOjnvCklMinUrQvfjENid0GorG0s3H3BL2WAJc+JLqJWqr5YLDqVLzySU1fZZL4+ylAWOLGvB9eRDE4JOgQhCFtjqfegJWzWk9CEKkVT+P4NCt2DZZqBbn6bBk5O8jbvj1AiVmw4XfjT9XJUj+2mKaOQBxH+ejb3ql8LaxlvqXRwVZnhaf6o2GcnRlszH0wrU+T9ILv/pV7VrKXJbD/36VkI1W4rO/kC+ZUSUWG1gEImRfp5jgp28eYxuvqkhCAk6Kn0KEEIAG+++ea3335LJpN/+eWXefPmtTv+7NmzkydPViqVI0eOvHDhgg1m+AgQgrA1qoqDkutvMQOf4g54lEbtDoui4Gd59te6hhYlLgiZwQiYxY5ZTnWJN2w0NKlHKCx2zApO7HsImaEV3ZakL9XUXwYAIFG95jaaCGYtqv/lRsUHx/KMreSTw92/mhoV592ZNqT3G2qDuC72bTbYBvYVhJgey/3+hKK6AQD6PBnvObJvl55OptZdK2vAywJvVTaZHWMQgWNC3VzZDvpbI+gQhCBsjfXhQRx7BQk1evT7nCuf3j0pUjdbSU/2i9w4cHK0s/nalqxa6be/X3u1oTlDu5jv+k9UP+MxoQ3CmdmZhqe5TOZzaztgyY7pFIq87bL7X+iVtQDACJjFT/rH+sM7kT9uV204U3ivRmKyfWyY2wfjw0cFd+EHnhCEBD2VniYIZTJZbGxsaWkpgiCzZs167733BgwYYHZkdXX1+vXrd+3apdFoaDTajRs34uLibDbPDkEIQhPU1SfEJyYAAEKmu027R+GF2XtGnYbk+jJ59hbDUwo/lhW+gBkyj0QzrQoRHRujqT1L90txGrKVzDFeBsZUpf9IbixH5eWuky7RPJ5ofZYmlXbD6cItF0uUWhTfQiYhLw3y+3hihE9nrLDeFlUlH9se5+JzeNzLjqkJ7SsIa85mVZ+8BwAkKjlq6US6K1ei0hUK5WKFxpVNC3VjP35cTqXTXy4Rn8oXnC4Q3q5qQvVmvj3cObQxoW7J4e5jw9yC7Go/S9AVEILQBFStFVwr7OhR7kNCyAz7fIk1aVQb7p3ZknVRiT4IFZIR0mt9h20dOsPs+EMb0jiS5gSQcr7rkagWdzUBDaInjQQhAAx+fTzbt92SwxZgqFKR96P8/pf85MNUl37tH9B5fJN18VJdSXp5Q2WjRfdXBIG+HtxYb+4zQQkzAmI6fQ6EICToqfQ0QQgAxcXFY8aMKSt7kBeRlJQ0ePDgwMDAwMBAHo9XWlpaVFSUl5e3f/9+lUoFABQKZd++fdOnT7flJDsEIQhbgYmOjMTjYPQ+k12S0+w9n05D13Bf8F8sQuUyg55hhb1MdR9ifhymFx1L4vb/lOY50vx+nUJ2fxOZ4c7q+7qlc1U1qdadyMcTjfAtLBr5jRFBq8aGOjEe3erjtqhq3PEfxWoFAMwMiN0/5oVHfqmuw46CUFUvydl6XK9DAcDnyfhzHO62y6XpZQ2GKiAKCXkiyGXJiKCZsV4dqvDUY9idKsmpfMGlEvH5IpFUbcbhk0UjDw90wb1hEnx5vbOCtJdACMKeQaW86YM7x34rvKnHMABYEZu4aWCK2ZEzfrlx4H6zqZ6Xq/OofhHGA6qFjZcyW7TXGhXiyqNT+Ewqn0V9+D/N+KkHh26+zlCvs30Xitlnd+8vzWx/HAAAfNJ/4pp+nR/dJQQhQU+lBwpCAKivr1+2bNnevXvbHRkWFvbzzz+PGDHCBrN6ZAhB2Bqt6LYwbRBgegBwSU6j95ls7xl1GqryVLr3OITKaWsQhgIggLRn8qHXtmsRnlkjeTct51hucxqVO4f2wbjwhcMCHsFE5K64euyx7XiakzONeWriwgGuVhjZ2Rx7CUJMj+VtP4mbwlN9+EuBe77EjAknTnK4++9zE7y47dg2FArlp/KFpwsEZwqFYoV5b5hB/s5jw9ySw9yHBfKJNg+9BEIQ9iRyGus+vHPiVHV+4exVloyaJ+64djxPYHhqjSC0Bj6T6vzw38PHlJZPmx+zaWQAUBb+xgiY1c5VrOMM3L/9lsTaGO/CkMTto8wr58eBEIQEPZWeKQhxbt68uWPHjgMHDggEApNdFApl8ODBCxYsePbZZxmMLq9CfkwIQWiWpisLFfk7AIDMDXGfnmV3uzNrQBVVyqL/U5Xuc5102aEmfLpAuDIt+7ZRgVmYG/uzyZGzYr2tjyFliKuTH6pBVzrr1MTX4l3a8kG1I/YShLXnc6qO3wUAoJBWMvhnRBYTn3CiPLkXlzzRujdDvUx9ukB4ukB4Kl9gqWV8pCcnOcx9bJhbYqjr44R8CbophCDseVQrJD4si8XeL+y9szujBB76inm58EfFtMiZrBaLL93Pan6OUgDr5OUhKpk03uX+Tq/3pBjvtH5OOu15OsOFzzJRjxRnBhXfaP36FIYB54cvFUxr+4pFYDG58198xLdhGUIQEvRUerIgNFBcXJyXlycWi1UqlbOzs5eXV0JCAovVbWpmCEFoFr1aLPg3XK8WAQB3wAZO7Lv2npFFML1GXXVcWfS7quxfwFAA4Cf+zQicY+95tQC3IV11OLdI1FyFMsjP+YspUaND2r+hzBTXjD2+XaiSA4AzjXlywqsD3fy6cLqPh10EoUoozfnumF6LAsBpV9d3q9tRgzjPJvj+8Xx/AFBo0CsPW8bfqZLozX0heHLpo4Jdk8PdJkZ4+PPNtIwn6D0QgrBXUSrRbbhSlykrBdKDynAGieVO9Tceo9TLhNpKw1Oqij8p0NObplHpUKUWbVBoG5TaB/8/fCCQqXXmKpDbYH/w+8PYD3zRpChrt/jJ7wSzJaj5b1oGhcRn0ZhUEoNCfpCnaiF/tUSsmHLyV+CZru9bgiIMEr2xkMfo5LxWQhAS9FR6hSDs7hCC0BLynO8k6UsBAKFy3GfkkVkOF4/SNeUo8ncpi37Xq1pcxhiBT/ET/7LXrNpArdNvu1y6/lS+cfLhtBivzydFRnpazP+531CbdOwHXA3yacxTE1/r72rTbgodxS6CUHi9qPzgTUyP6d14w6pQ1LqjEAReGxaQVSu7VtZgtmU8j0FJDHFLDncbG+ZGNPcjMEAIwl7Fhjv371R7dPSoBA/ae4Oc2xiAYdCo0jYotI1KbYNS22j493Bjo1LX/FilVWp1TzufXurxdyCtOZQnQdk7hVN3iqZakoXW4p9tvSCE+sArTz83LLBjljntQghCgp4KIQi7AYQgtIheJzjUX9dwDxCS0/CdrLD59p5QC+Q530rS32yxCSHTfSeywhcw+kxut7rPjsjUui/PFW06W2SwISUhyHP9fTemRJpt9FSrlI45tj2nsY5PY5501LpBY+yVMqqoaShPvbnXib/uds3jvI5xy/hRIa5EWSBBawhB2HtQ6LRD/tsbxZjY0QPbFYSPgFKLNsiViqI9tMJNVGWxYbsK4XxL2VOo8jKEH8UKjVpnZpGrBSQ9kHRAQoGMglcRsButnUd94P5Js2bGej/q+zAPIQgJeiqEIOwGEIKwDTR1lyQ33nYa8i3Vfai952KKrilPkBqJV3SQWb7MkOdZEa+ROYH2npe1lDco1xzN3XO7ypCdyKaR30kMWZEYwmnVF6FOKZ155rctQ6Y5cqaoAXu2ncAg4evzGVWmHbTaBUEgzpuHd4kYFeyKmzcQEFiCEIS9hwPl2ZtuCv3oHe5o2hWCsBlMryrbL73zga4pFwCorv3dptwEQACgWiE5WJ7VpFU5UVhTfPq1iD0+DEXmyqqOqE88+tnrAw9Mnj0txnzbxkeGEIQEPRUbfN5sbUxM0KugeY5wS7lu71mYh+IUQfNOItFdWeEL6N7J7ZuCOhj+fObuuQlvjw5593D2iTwBAMg16LoT+T9eLftwfPiCIf7GNqSeTO7lyUvsN9nuAwLVTeoOHcGhk3fO6TcmzM2D40BGRAQEBLZHqcMqpLowZ6qx3dc0/6h/suu1VqahdxCNHlWjOi7V/JdPqUy8LeeKRKuSatVynUah0zSolQpUK9eqo/leR8YtYATMVJb8Jctcz4n/EFeDAFAma1h0dT8ADPMIeC1yiK+TmcST6wLukbTHEIQAnu1ZNBPYDL1eX1ZWlp+fz2AwYmJi2l2lkkgkd+/eFYlEHh4eCQkJTKZjlcSnp6dPmzZtz549Y8eO7aJT1NTUJCQkAMCRI0f69+/fRWexMYQgJOjJaOouUt0GIWTzXrWu4092Ox1oQrwv7/irQ0/mC1amZeNxrTqpevH+e1sulnw2qW+nJ+T0BtrPkmoJm0Z5JsGhazJ7MwtOCrUtXTd+neBOtHck6ArWXmkoaNBiAFsSXb3YzTkCCIAni1IpNdN6tG2KpaKfC/Lnhw02u/eWqHJY2ndaPTrIze/6lDfNjhGpFV/eP2d214P2GAiZGTyXGfQMGElYDpWGP5BpNZbmxqHSAYBBprAoNGcak4SRC5vEQLb2PVLJpFhvi46sBDZDrVZv2LBh06ZNCoXCsDEsLOzLL7+cOnVq6/EKhWLFihW//PKLUvnAQJvH4y1ZsuSjjz6iUh2l0Oaff/6RyWTDhw/vulOgKFpXVwcAGo3Fv5FuByEICXogelW9snC3ouAnXVOu86g9zOC55sd1czVoYFy4+623Ru25XbXmaG55gxIA8upls369OTzQ5YspkcMDXew9wW6ARKU7nld/MKtOpjbTLbAN2m1FSGBHVDrMRBACZgiEEBB0mFKJrrBRWybRpQSzPFktMsMZZAT/qJVJdcaCEACmBLHKpLrt+WeqlPUYpgcAZ4pHFLvFDatYV5srv2a8RdkguybBLAlCBpmi1aMAINNZlm0Ui99OLY5qeSn0YnJfjRjKpzF92U4AoCz6P7pPMonZIr2zr5MH+uIXpIcyEsOAt/1LGdnathPhbmwir95ArVJarbC2TsGFzgzkdM41XaFQDBo0KDs7GwAQBPH395dKpWKxuKCgYNq0aS+//PKuXbuMx6vV6jFjxqSnp+NP3d3dBQKBRCL57LPP7t27d+DAARLJIe6pDhw4MHHiREeLWzo+hCAk6EFgenXNGUX+DnX5f5j+wdVOUfCTRUHYgyAhyLwBfZ6O9/n1RsWao7kCmQYArpSKn/jucnK4+9fTomO8CItLM5Q3KI/l1R/KqjuZL+hobBCH+MESEHQFahRT6lqIeSYFoZNtquZRDExOuC9ffrNODQARfKqJIAzgUbJEGl8OpXWZT6IfAwA+zr5foRLjW3SYaTBNpZdXqvPxxwwyx5XiJdJW83QWb/1xsUdGSGTL7Wi9WdyNAyc705hsCo1FoXGpdCcag0Whssi0BxFCc7gzOD8On/1gnpLCxssvIUBihr7Iif+AzHqQDUFqeVIEgWgvbnqjtYIwKdTNypG9gR/zrn50x9r826eD4v9MfL5TzvvJJ5/ganDhwoUbN250cnICgIyMjEWLFl27du2nn34aPXr0vHnzDOM/+OADXA2+9tpra9as8fX1LSwsXLlyZWpq6qFDh7Zu3bp06dJOmdjjcP/+/cLCwg8//NDeE+l+EIKQwO50woq9XiVQ5P2gKPgZlZUZb0doThSnvoChgPSKxchimZDv1Zj33piNZwq3XChW6fQAcCpfkPDV+fmD/T+eGEGEs3CyaqWHs2sPZtdfKRU/ZpF2p/siEBBYyT2hRqhssYoR707jMxxikf7xOVyi+CtPbrxlTjh7dpgtjKbOV6rOVarKJNrnIzlj/FrEGQJ4FFwQlkl1I1oeNSOU/UwEm0J63MsZCSEP501zo/ryWNWhrhZ9O/3Yzqr/baCT27qL41EZK2OTHmcysrufgF6HASjydyiLdrPCF7Bj3iWzzVhV+zkz0602GSUKCO1OdXX15s2bAWDSpEnbt283bI+Pjz958mR8fHxRUdGqVasMglAkEm3duhUfv23bNjwYGBoaumfPnpEjR966dWvjxo2LFi2ye+Lov//+S6VSU1JS7DuN7ojtBGF1dfWvv/46c+bMvn077LVF0FPR1F3EUCXdZ/xjvo5e0yS98xFuGYpD8xzBClvACJyDUCyug/Yw8iWCsce21yllP494asPkga8/EfjpyYKfrpejekynx3ZcK9tzu3LJiKD3x4Z1ejvgboFOj10ra9h3tzr1Xq2uSf49VUXX0TCs+UcR5cmdEu05oa/7wr8zC4TyNl7KQKQnZzohCAnsxJESxe36FumCqwc78xk0e82n2yFS6cslOic6KdipxVeiWIVmizQAUCYxjeNFulCH+zACeZQYV9MbXza1c6KX0azhblRfAJArfeYExFgaRkKQttVgp8AMfkbXlKsVXgcADFXJc7Yq8ncyw17mxL5HZncDz2oCS5w5cwavf3v//fdNdnE4nKVLl7755ptVVVU1NTXe3t4AkJaWhtcZfv7558apoUwmc9myZfPmzauurr506VJSUlsLEHq9Ho9J9u3bl0KhNDY2pqamFhQU0Gi0iIiIlJQULpfberAlqFRqRESEycbU1NTExERnZ1N73ps3b549e7aurs7b2zsiImLcuHF0uvlViXv37p0+fbqqqopGowUFBU2cOLFPH0dv1tUp2O6+UKVSffjhh6tXr46IiJgxY8aMGTMGDRqEWE51IOipqCoOIgiZ3mcyAMizvu4UQUjhhdK8EjW1Z0kMd2bI/1jhCyhOvW7dYem1A3gdwpvp/6X4Rfk5s36cE7d0ZNC7aTmHc+oAQK5BN54p/Cm9fM24sNefCHr8lexugVihPV0gOJRVdzCrrkmlBQAE4Duqxg/BNlHVv2NYVqBPSpTHrDifPg/N9L6fFTtp13WtuUbzxtAppB1z+lGJNoMEBN2QY6XKX7KkADDGj7kwrkXidwDvgdirU5h6g8a60WLdulZy5ypuOFM8venBz0dyTJSq7aH7Pkn3fVJddVSasU4ruAYAGKpW5H6vzN/lMv44zSvRvtMjeGRyc3MBAEEQ3C3ThOjoaPxBXl4eLghPnjwJAH5+fnFxcSaDJ02aRCaTURQ9depU24JQoVDExsYCQEVFxZUrV15++WWZTGbY6+HhsWvXrilTppgMtoSvr29lZaXxltLS0oyMjO+//954Y1NT00svvZSammq8MSgoaMuWLYZz4chkstdff3337t3GG6lU6ieffLJy5coeL1hs910THBxcV1d38ODB1NTULVu2bNiwwdfXd/r06TNmzBg9ejSF0htDFr0NVFHVdHmBuuoYme3nPiNHrxKoKg4Cptc1ZlOcox7zxbnxa/WqRXT/aQiply6Q7018ftyxH3Ob6lPHvmioD4n24qYtGHwqX7DioQ2pUK5ZdiDrhytln0yMmB3n01O/4krEioNZtWnZ9eeLRCbSzgPBQkl6AAAEef/lER4hHibHJoe7/z434aU/M5SWreI5dMrvcxNGBBGGPQ7EgpNCtKWFjKmjDMD8EwKTLbvGu9u2MI3AdgiV6JVqdZlU58EkPx3RIt20D/dBHUFZK//PCD713UFO/lyKG9MOtQZaTHWp6d9tg16bFGT61WQvHsjC6hOyjHWa+ssAQGK4m7QXfjooPobfnC4hV2u1+VvJaBMAaBjB3NDnaZTmtbPRXiG2mjuBefr37//OO+/weDwWy0wWVVnZg+obQ3AMD9aZVY8uLi5xcXF37tzBRaY1HD58eNGiRd7e3rNmzQoICLh169bhw4fr6+vnzp2bmZkZFBQEACQSKSAgwOzhFRUVer2+tUJLTU1FEGTatGmGLZWVlaNHjy4uLkYQJCoqasCAAXV1dWfPni0pKZk5c+alS5eGDBmCjxSJREOHDi0sLAQAf3//IUOGyOXy9PR0kUj03nvvXbx4MS0tzcp3102xqQxzcXF58cUXX3zxRYVCcfTo0dTU1D179mzbto3P56ekpMyYMWPChAlmP5oEPQMSlasVZwAAKq+QZX6O6eSAoQAgz/nWadj2to/VNdxT5O+ieY5gBM4xO4Dm9ViVEj0APo15bPwrRVLREHd/k13J4e633hq1P7Pm3bScErECAPLqZU/tvjU0oHhTStTI4B6iavQYdqdKciirNi277lZlU+sBwa6slCjPKVGeI3x5dSczKQxaazWI83S8T6w3Fw+umhQZkhBkWoznxpSoMDdblDMRWI9Cq0fbqwhV6IhG0j0TFINGFeraUsKJVfo9uTIA8OdSTARhAJfCoiD+PEqYs5nkz/4eXVvk1qiru9p00HiLUi81PMZAH+jscHb2dJ/xdJ/x6prTsoyPGYGzTfo5zQ6Mmw3NsSNV6d8NOccePrnmHraa4hRpy9k6CJXyJhQzn2zix3YmPcaK7GO+8syZM2fOnGl2F4qi27ZtAwB/f39cmwFAcXExAFhSaAEBAXfu3CkqKrJy8suWLUtOTv7rr7/4fD6+5ddff33ppZdkMtl///23bNkyAGCxWKWlpa2P3blz56uvvgoAa9euNdmVmpo6ZMgQHx8fw5a1a9cWFxczmcw//vhj+vTp+Mb8/PwxY8ZUVVW98MILBhG7fv36wsJCBEE++eST1atX4xuVSuX8+fP//PPPw4cP//vvv5Z+Yj0D+8TlWCzWrFmzZs2apdVqz549m5qaevDgwd9//53JZE6YMGHGjBkpKSkuLj3kJpXAAELl8QZ83njpJQCQZ30FpAeXYWXhbm7CpySGGdsxTCtTlvypKNilFaQDgFZ825IgJAAANwbbjWFepZAQZE4/n2kxXj9cKf3oeH6jUgsA18oaRm27nBLluXladPeVN0oteipfmJZddyi7rkaiMtlLQpAEX15KlOeUaK8BfZwM2wOmD4I2pUGUJ/fQy4OLRYqT+YLceplEpeMxKNFe3HHh7gF8ws+65zD3SP2bCbzhPi3ubo+VKsskOgYFGd2HEchrcaGsV6BKHcagIHw6iUbEFjsJNYqpW6p5TavwvBrFJJoWd8B0MkInI0Il+sXNpkoZ6sYkbUkToi+LAAAgAElEQVRs0VPbj0tBEMAwqJLptHqMapQnz6WRfpng3slvw2pUenmF2tpwCk6GQHOmXLmoH49Jseenju49lu49FixIEQOyrK+NnmHy7G+dhv3QpRNzTOL/+0qkVpjdJXj2Y0vXazu+slarfeWVV27fvg0Aq1evJpMfrLBIpVIAaF2bh4Nvb2oysw5rFgqF8scffxjUIAC8+OKLn376aVFRUVZWVhsHnj9//vXXXweAN954A5eFBgQCweXLlz///HPDlpycnN9//x0ANmzYYFCDABAeHv7dd9/NnDkzLy+vpKQkKCiovLwcTzR99dVXDWoQAJhM5t69e3NzczMyMtasWTN9+nQHaa3RFdg5UZNKpY4fP378+PHff/99enr6v//+m5qaeuDAAQqFMnr06OnTp0+fPr2XVHP2EpihL8jzftQKrmGoCtAH9+4YqlTk7+DEtahs1grSFQW7lCV/YdrmdVNN3SVdUx7FybSMmMBKaGTSmyOD5w3w23S22YY0LbvueJ7gpUF+3cuGVCDTHM2tT8uuO5pbL1ObJn0xqeSxYW5Toj2nRHl68xhmX8Ead9tgV9bCYebXRAl6DNRW9bRZIs31WjUARLpQTQThnlzZtRo1ACzr7zTMu8Xfy95cWWGjjkZGZoSywvkt4k75DdpGtZ5JQQJ5FC7tUW4p7gs1YlWLu3CTpwBwT6hpVLfYGOvWPXxHDxUr9uW34+R0sEhxsKjFHfCsMPZT4WwnOqlCqkMxqFOgKh3GMNJLTAoyPYTtyiAF8CiPE42xOzVy9Ns7TXItViVrWDnIyaTjhR142LoQ0yl0DZmosg6VV+hVdai8Uie+q9dKUWmh8XBFwU9a4XUSy4fM9CaxfMgsHzLTh8TyofLjgERUDDkK6enpixYtunPnDgC89NJLr7zyCr5dpVLp9XoAYDDMX0zxpn9yuVVmbADwwgsvuLmZxgDCw8OLioq0WoutgIuKivBIUnJyMm6Rasx///2n1+uNhd/ff/+NoqiTk9P8+fNNBj/55JOjRo1CUbS4uDgoKOjUqVNqtRpBkJUrV7Y+74oVK5577rmcnJyioqKwsDAr32O3w1H+DhEEGTp06NChQzdt2nT//v3U1NTU1NQ33nhj6dKlAwcOxEPb4eHh9p4mweOBoXpVPbvv4kZhOrTMw1PkbqPwY8hMbxLLl0RzEh15Qiu+azwAITMYATNYYQsoTsTH4HFxYVE3TI5cPDxw/amCXenlegzTonqDDenq5DAu3VG+GVpTLFIcyq7dd7fmammDvlXLCHcObWKEx5Rozyf7enAc+F0QOBSMViEX1cNoVevGd6qHSaeMVrvKpbr7Ig0AjAswvW06UKS4VacGgBUDnQZ6tpCRWzMkuWItjYwsiOFEubYogb5Wo65XoiwK0s+ddqhYkSFoJ43wYLFpxGBVK99RDEChxRgUxGT6DSq9Ro8BgAuDZKKQq2QoHr7z5ZBNfiBFjVqFDkMxCHEyFbr3hZpaBQoAgzzpTvQWuy5UqRpUegAY5ct4fL1KJSE+HEqFVOfOJIvVeh9KC7H0TISj5D5kTntH33ZaghFsSovf2u16tVyLAYAKxVp/XO0IQqIpCn5S5O9qZ5xeqxXdBtFt40M58R9QXft35ezsjx/b2eRXaYD8eLGmzn3l8vLyVatW7d27F8MwMpm8fPny9evXG4r0DDYfKGq+rh5XcdbbrhhMa4xpO/gmkUimTp0qEolCQ0P//vvv1s4jqamp0dHRxkoBLwhMSEjgcDgmgxkMxvnz5w1P8WRXHx+f4ODg1qceMeJBi5ni4mJCENqUmJiYmJiYtWvXlpWV4cpw9erVf/75Z0ZGhr2nRtBhMFQlubZE25CpV1Shyjq8aLA1qKK64fTDOmCEDEjzJ5PCj2GFLWCGzCPRiSziByhRLZP8uN1+/PnMH+fEvTLU/920nDOFQnhoQ/r7zcoPJ4TPH+zvODakqB67WtaQll134H5tXr2s9QC8Y0RKlOfwQL6lOID4bhk/xg8hTEEJWrJnkkfrT8z0ENYQL7oaxfpwTEMxrkyyP5eiRrHWbQYMMrK1VmyWka3u5pvUeoESBYDW5Y1nKpR3BRoAeH+w+UytjrL2SkN+gxYAPhnON4lhbr7dhO9aN5wf0XLXj5mSPAu7fsuWNe9qKQj/zpfju/y4FBNBeLJMiZ8r0oXaKQHMtwc4OdNJLEdSSq3hUB89/2JyEMuJRvolS7Z8gJPTI0WYuwoSxWn4DhLLV5bxcceOGvo9K/yVLpuWo3Bn2tsO/so6ne7LL7/85JNP8K4SgwcP/u677wYPHmw8hkKh0Gg0jUajUpkWZeDg21vrLkv4+5s6HbQNiqLPPPNMdnY2j8c7ePCgca4pjlQqPX369IoVK4w34oLQz6/9/ii4IAwMDDS719fXl0Kh6HQ6vJCyp+KIgtBAQEDAsmXLli1bJhAIrl+/bu/pEDwKCJnB6bdafGIiqqi29hgMBQwFBGH4z+TELDexMiMolzcmHf1hRUzia32HPf6rDfRzPr1o2Kl8wfJD2XerJQBQLVEt3Jf59fnidRMj5vTzafcVug65Bj1TINx3t/pQdh1e9GgMmYQMDeBPifKcHuMV4dHOdUicUVby99Xa8zmBswazfImVhZ7JppEuu3NkL0RxDOaQL58QmhiN7p5oWjZmVkREu9KiXc1sB4BXY7nmdwAsiOGJVagaxfy4ptfWCD6VSUHUKNb6bv7RZOSjYVjkaWW/2ryrVdwdECt2tfGCHdr1aPiw7Z1C2fWM8GUM8KTbt4DQAgg3/iMS3U1y/c12awsBAKGw+Yl/0/tMssHMCNqmrq5u5syZV65cAYCwsLD169fPmWPepsHd3b2qqqqurs7s3traWgBonQVqCUNpopUsX7786NGjJBJp7969kZFm3IkOHz6sVqtnzJhhvFEsFgMAj8dr9/XxZFdLrpZkMpnJZEqlUuuLJLsjDi0IDbi7u0+ePNnesyB4RMicINfJVxpOTdEIrlp5CIUfzU/8pxf2EmyXUpk48egPZbKG16/9SydTXgob1CkvmxzufvvtUfsza1am5ZSKFQCQWy97avetYYHFm1KibNxcoaJJfSxXdKGy6nShWNOqGSCbRk4KdZvTz2dqtKcz06owqU6urki7DQDK2kbR7VJCEPZILlWrdmRK1Sj2S5Zs9RBnS3fNNFIXdpPy5ZB9W0UUcZ62nLj4/mBnqUavRjGPVoVhT/gywvhUlQ5z6aQ6QBICCACLauaH4Moge7L0AEBpdao+HIoGxQCgtYlOqDOVSkJICHBaxUtj3Gi+HAoAONNNX3GkLwOPNHaL+kbHobUa1GPQ0Mpb1S6wI5eQ2X0az8/FUGUbw0h0V37yIZp7J6xmEjwmEolk4sSJGRkZVCp11apVq1evptEsdu2KiIioqqqyFCIrKSkBgL59u+SebdeuXd988w0AbNy4cdIk8+sIqamp/v7+/fu3yEAODAzMz8836VVoFtxMtby83OzehoYG3FPH4LnaI+kegpCgu0Oiu7pMONlw7ml15eF2B9O8klzGpCI0p3ZH9jYq5I1jj/1YJmsAACqJ7MGwNj3DGnAb0smRnt9dKvn8dCHew/1qacPIrZdTojy/nhYdas6GFMMgq056sVhUI1FjgHly6MMDXeJ9eY/g35BVK913tzotu+52VVPrQEQAnzkhwiMlynNCX3daB9M+KSy67/i4iiN3KGy6z3jTproEPQMvFhkPBmaLNCVNOrt39LYeJgVhUszf0E8I6GQ/2w+HmqZaGViaYHEd/RXLQdF5kRa/hWaHWdTAyf6m74tFQUxEr1KHKVsm0TIpiIkicvAcURuwJ1d2vlL1ZgIv1s3+DXgZ/tNdxh8TnxiPoWqzA8gsH5eJ5ym8UBtPjMAszz77bEZGBp6EOXr06LYHDxgw4MyZMzdv3kRR1CS+JxQK8ZRLEz3WKZw/f37x4sUA8L///W/58uVmx6jV6qNHj7700ksm20NDQ0+cOJGfn2/2qG+++aa2tjYpKWnChAkhISEAUFZWplQqcYMcY3JycvAHPbiAEAhBSGAzEArbZex/TVcXKfJ3tjGMETDLedT/mTQ4IgCACnlj0tEfiqUiAKCTKfuTXpjs1/ltnVg08rtjQhcM8f/iXNE3F4rVLW1I102M8DSyId13t/qTkwX3aiQmLxLmxl41NuzFQX7tqkKVTn+pWHQou+7fzJrKJjPFCVGe3Dn9vKdEe/X3dXr0wA4CboNDuCGeOoWaTDjN9FBCnakpway7As3ifjwTX9CeQbwH3YXR4j7srlAtUrYIoce700zGuHaTENykINakoBb5Wv8Wyv/Ka2FamBLMakNk9kIuVanSihUA8Pn1xo+GmRaF2gWqSwKmN/V8bobMJNSgg3D37t0jR44AwK5du9pVgwAwefLkL774QiqVHj16NCUlxXjXvn378AdTpkzp3EkWFxfjtqJDhw7dsWOHpWGnTp2SSqUm+aLwUKDm5ORcvHhx5MiRxrsaGhqWL1+Ooig+Bv9fpVL99NNPS5YsMXmdb7/9FgBYLFbP9rbsgVdNAscFITsN/1FdfQqVlZjdT2J68RP/NvhZExjA1WCRVAQANBL5n6T/dYUaNODKpuE2pGuO5v7f7UoMA9yG9I87Va8/EbgmOYxBJS/4++5vNyrMHl4glM//K+PA/do/5/VnUs2EPkRyzZlC4aGsuv+yaiUq07sHOhkZ5MuZGuM1d2Cgr1OnLQ3QXTl0186MqRI4Gk+Fs5+J4PTU1oBPBpquW2+8gYqULXxHJwex4tztHykisA1ebLILgyRW6ft70sMcQA0CgEZ43ZJ1HACg0iK9qp7E8LDllBwfGonColj762OQO+e+/YcffgAAf39/S0WDJowcOTI6OjorK2vt2rXjxo2j0x8sDTc2Nn722WcAMHHiRLMWnY+MVCrFbUX79OmTmppqOGNrUlNTXV1dTSQfAMybN2/9+vUlJSVvv/320aNHjUsc16xZg6Io3uIOf3dJSUlnz55dv359YmJiTEyMYeT+/ftxxfvOO+9Y75rTHSEEIYFNwbQyVG4+SxsA9Mo6vVpstkN9b6ZS3mSsBvePeSHFL8oG5/XnM3fPTXhjZNDKQ9nnikQAIFPrNp4p/L9blQEuzCslDW0ffjCr9unfbx14aZAhfRTvGJGWXX+uUKhr5SbhyqZN6usxJdpzsAcF0apcXV3ZbCJQTGAGPQZpJYpGlf5/US0uz63bCRIQ9GBCnamfj3DZmyt7MZrrIB99rSC9nQHCG/Q+hCVEC1bFjVkVN8bGJ7116xYA1NTUtO0Ec+3atdDQUAAgkUhfffXVpEmTMjIyxowZ89prr/Xr1+/KlStbt26trKxkMpkbN27s3BmuXr0ab1IfFxeH1xC2Zv78+aGhoQcPHpw6dWproxoajfbZZ589++yzN2/eHDRo0CuvvJKQkNDU1LR3796DBw8CwAcffODp6YkP3rBhw7Bhw2pra4cNG/b6668PGTJEJpOdOXPmt99+wzDM09PTxMK050EIQgKbohXeaGP5EADTCtOJq4UJ1wRlpbIGAKCTKaljXnyyj029dgb5OZ9dPPxUvuCdg9mZNRIAqGpSVZlL72zNoay63TcqYn2cDmXV7rtbk10nbT0m2JWVEuU5JcozMdQNb3QhEonkFjvTEvR2lDrs0/TGwkYtAtDfkxbj2lZAbNc4N5OFh+7cnJyAwBRnOmlRv/ZNFG2GVthsCI9QWKj/a4imHqnaa7juawTEJd4hwO1htFqtSCRqY5hO15zCM2HChO+++27ZsmVXrlzBjUlxOBzOnj174uI6uT4f9wgFgCNHjuDZra1JTEysra0VCASt80VxnnnmGa1Wu3jx4tLS0tWrVxvveuGFF95//33D08GDB584cWLevHk1NTUm4nbIkCF//PEHl2uxlLpnQAhCApuiEbZoH4JQ2ICQMW1zERpxtWjN7MC4PaPnzr/015+J82ysBg0kh7vfeWfU/92qWnU4p1pilRrEWfB3JtrKIoZMQuJ9eClRnk/H+0Z69uQcDIJOh/nQfQQDOFGqbFsQOlQLbwIC2yBS6e/Uq1s799gAjeAa/oDul8IZ+E1tE5VMJrv3f0eS/qam7gIAaB8OILAjKIouXboUa+3e1gqT+OHixYufeOKJ77///sqVK2Kx2NPTMykpacmSJVbab9JotA8//BAA8KijCXPnzh04cGB8fDz+dObMmWaHGRMaGrpt2zY2mz1u3DhLY+bNmzd06NCvv/768uXLeAJqZGTk/PnzW6eYjh07NjMzc/PmzadPn66urqZSqcHBwdOmTVu0aBGF0kIu8Xg8/I306dPHivfdPUCs+UAQtAHu3m2zH2NVVRWKor6+vh3t4uIgNJyZrir/D39M90txGrIVoXJkGevkudvwFUS6z3iX8cftOkcHpVYp9WLaf4GqWCQP/ewsBo/ygWfRyGNC3aZEe06N9vLiWqwHEIlEcrnc1dWVzSYMJAjMINHoV14Uj/JlPN1tKwblcrlIJGKz2a6uFtodWsHGG42361vUEK4e7NxjagiPlCgOFCmMt0wNZqUEm28URmBAq8c+utpY2Kgd4ctYGMtt3Smk60BlpfX/BFFd4nlDvqV5jkRRtKqqikwm+/r6AoCq4pAkfale0+D1rLiLnAJseT9m43s/AkvI5XK9Xt/jw3c2+LzZM0IoFAqZTKbJPV9paelXX32VkJAwadIkLy8ve82NoIvQCNIBgOranzd4C81zBL6RN2QLM/QFSfpSTf1ljTAdMD3hK9MaR1CDAJAnkHdUDbowqU8n+E6N9kwKdaO37nHWBcjKhDQek8Yn9GTPhEcjfZvoass7Xcck1o3Ga9nirye19WvtO0pgDQeLFIWNWgC4Wq16MpAZ6mw7sxmdtMhp2HZW+AJAzCxYM/ym0L3Hyu5tROVlZE5P7udGYEuIhePOwg6CUKvV7ty5c9++fRcvXszIyDA28wGAxsbGrVu3AgCJRJo1a9a2bdvc3d1tP0mCrgCVlYJeyxv8DTtyickFg+ra33XSRVXpP5Iby3WSPIpTF1poEjwO9VLzDabaYEVS6HtjbWc1jqq0JX9e0SnU3mNivEb1JYrGujUVUt2u+9JXY7l4i3MDhBoEAEIvEbRmaghLqNSfqVC+GM21pRoEALr3WPAe28YAhMLiJnxss/kQEBBYj60FYUVFxdNPP3316lVLA5CHd296vX7fvn3nzp3bsWPH9OnTbTVBgi4EIdM9ZhVZ7jiPMALn0H0n6rWmfe0IHAdKB5vCAwDNJlFBAxWHb2uaFABQfyXffUgImdFDMuh6IecqVDvvS3V6bMc96UdD+YS0JyBoFyoJWRjHfcKHHuMAreoJCAi6Cza9UautrU1ISMDVoIuLy4IFC/DMcmP69et3//79r7/+esiQIQAgEAjmzJlz//59W86ToIsgMb0tq8EHIFQumWX6qeg91Cql9SqZvWfRFt48i7V/lmijXLDTkRTWiW4/6HLpP3UAoQa7NYFOFLxkorhJVyqx3O2agICgJWbVIFHuRkBAYAmbCsLVq1fj/raLFy+uqanZuXMnn89vPSw6OnrZsmVXr17dvn07k8nU6XRvvPGGLedJQGAX6lWy5GM/jj7yfY3CcWOkg/ycO1oHOCzQzJ95V4CqtWX/puN3PS79Apyje479V+8kkEeZEcoOc6ZuGOES5ER4YhMQPDo369QfXW1oUOvtPRECAgJHxHaCMD8//9dffwWAxYsXb9u2jUZrZ+UeQZCFCxd+/PHHAHDu3LmzZ8/aYJIEBPZCp9ePO/ZjVmNtblP9pJO7UMxBL9tcOmV8RAfKegf7Owe52KjSqfJIhqZRAQAUNt1vcoJtTkrQpcwMY68bzvfldEtTZQICB6FKhm7NkOSKte9fEpdLiWA7AQGBKbYThDdu3NDr9XQ6fe3atdYf9dZbb+FppZmZmV02NQIC+0MhkdbGj6OQSGSEtDI2iezAPqufT4rEO8hbw6dP2qhxorSoTnizCH/sP20ghcOwzXkJOgWdHvs7X/5jptRkOxkBqz9rBAQE5ilo1KpRDAAoJIRPd9yLCwEBgb2wXRJOQUEBAERERHSomQSFQomLi6uqqsrLy+uyqREQOASzA+P02HMIAnMC+9l7Lm0R7cXdmBL1zsGsdke+OyZ0XLgtXIL1Gl3pv9fxZFF+jB8/xs8GJyXoLJQ67IMrDXjgYrAXPcGDqPwkIOhMEvswXBmk7ZnSFQOcuDRCEBIQEJhiu++FsrIyAAgMDOzogaGhoQBQVFTU6VMiIHA0ngrq5+BqEOft0cFbpse0UUxIJiEfTYj4fJKN2oeQqBTv0VFkOoXCovtNHWCbkxJ0FkwK4s99sDp5sUpl38kQEPRIYt1oWxJd/HlELS4BAYEZbPfVwOPxAABF0Y4eKJfLAYDFIhouERA4EEtHBo2PcN94pvCfzBqZurkohUklT4n2XDUmLN6XZ7vZIOA2OIQX7qUWyahEsmg35MVoTkGjdlIQa0IA095zISDombRO9UcxqJbp/LiESiQg6O3Y7lvAz88PALKy2k8zM+Hu3bsA4OPj0/lzIiAgeAz6enB+eSZ+x5y421VNNRI1hoEnl9a/jzPDto0HDdCc2TRntl1OTdAhMACTO1MujfR1oivRbZ6AwJbszpaeqVAtjOWO8CXW0QgIejW2E4TJyckAUFpaevbs2aSkJCuPysrKunXrFgCMHj26CydHQNBJ6PR6Jaq1cjAZIbEo1C6djw2gkklD/G3UWIKgB1DQqN11T/pGAq8Pp8UFiFCDBAS25Hyl6lipEgC+y5A4M0gxrkTtLgFB78V2gjA+Pj42NvbevXuLFy9OT0/HM0jbRqPRLFq0CADYbPakSZO6fo4EBI9Lavm9p87+buXgGL7XvenLu3Q+BAQOxcky5c9ZUj0GP9yVfjKcTziIEhDYi3A+tQ+HUinTDfOmRxNqkICgd2PTzK4tW7YAQG5ubmJi4v3799seXFlZOWPGjIsXLwLA2rVrORyOLaZIQGBDtPoOl9QSEHRrIl2pZAQBgCqZjuiHRkBgR7zZ5E+f4KcEsxb14xErMwQEvRybCsKkpCS8CeGdO3fi4uKmTJly4cIFpVJpPAZF0cLCwjfffDM0NPTIkSP4UW+99ZYt50lAYBuUOmuTSwkIegZ9OJTZ4ew4d9qXo1wCCcNDAgK7wqQg8yI5dCJdm4Cg12Pr6/G6des4HM6aNWu0Wm1aWlpaWhoAeHp6BgYG8ni80tLS0tJSrbb5LvnJJ5/ct28fjUYkMxD0QHg0oo7/EVHWNCJUMsONa++JELQDhgHS8m5zWjBrWgiLuAMlIHBMBEr0fKVqVigbIf5KCQh6DXYwA1y5cuWdO3cmT55MIj04e11dXXp6+smTJwsKCgxqMCgoaNeuXUeOHGGzCdtAAgKCZjCdvuTvqznfHqs9n4PpMXtPh8A8GhT7I1e2NUNish1BTC1GCQgIHAQNim2+1bQvX77hRqNcS3y7EhD0FuyTsRMdHZ2WllZRUXHw4MH09PT/Z+++w5q63geAvyEJhL1kI0MQUQSrgqC4QKwKiBVX1bpb66itVlHrbN1bW9FaR10VxYU4qywRRBRRFBFQQBCCBAgrZEBI8vvj9ptfTABRIBfw/Tw+fcK5J/e+l6LcN+ec92RmZpaVlQkEAj09PVNTUzc3Ny8vr2HDhkkzRoQQkiqMfsFnVQJA0d00gy+sVXVxk9I2h18nWRlfVsQVAYC7mVo/UzWyI0IIfdidPH5OZR0ApLGF77h19nrtvg42QqgpyFzC0blz54ULFy5cuJDEGBBC7Y5BL+uq10U8ZpnFqC8wG2yb1GmU7gZ0IiFMLq7BhBChdsHPVoMrlIRlcWf31MJsEKHPB67pRwi1M+omuo7zfMqe5Rn2tiU7FtSgad21syrqArpoDLbEtbIItQ8UCkzqpulupoY1nxD6rOBfeIRQ+0Ohqhj2wWywDRFLQG5TQU06ZecgA6xLgVC7U282KJIAliNFqKNSXkJYUlKyffv2T377/Pnz7ezsWjAehBBCLSKjTHg4teqHL3S76L73OwWzQYQ6hqSimpCM6qWuupZaOJCAUAekvL/YbDZ79+7dn/x2f39/TAgRQqitufmGd+pltQTg0POqrQMNcAwBoQ4mn1MX/KxKUCdZe7/8l356Dvq4trD1SUAsEqnQqGTHgT4X5HzSo6+v/7FbC+JWhAh9zt5Fp/FZFVZjXGkaWJ6kbeljrHYuk1sjkpQJxExOnRUuPUKoY2HxRBIJAICOmooFjhAqRUUGs6aUYzLIkexA0OeCnL/YIpHoyy+/nDBhwogRIzDTQwg1jl9U8S4mTSISV+eVdps7TM1Ai+yI0P8z1aR+3U0ztVQ411lbn4F7BSHU0biaqG0coB+cUvVjbx1NOs4BUIaimLSacq6Ru72KapvLwKuqqp49e8Zms42NjXv37q2urk52RO95+PDhmDFjzpw5M2zYsFa6xLt373r37g0AN2/e7NOnTytdRcmU98u7a9euN2/enDVrlr6+flVV1enTpwMCAoyNjWfMmHHjxo3a2lqlRYIQal8qXjIlIjEAqOlpqulrkh3O506ksFv1KFuNFW66mA0i1FFZ69B2DDLorN3mkpMOqSKdyS0oq+PWlDzMar2riMVic3PzTg27deuW3Ft4PN7ChQtNTU0HDx48duxYT09PU1PT1atXC4XC1ovzY128eLG6unrAgAGtdwmRSMRisVgsVkdKXpT3+5tKpY4aNervv/9msVg3b96cOXOmvr5+ZWXlqVOn/P39TUxMZs2adevWrTb1U4UQagvMvJ3sZwxWM9CyDuyHhUpIxBVK/nxetSe5Uq4d/5cg1OEp/tNbJ5a8KsdntpZXFJP234t76eLaula6ytu3b9+9e8duWE1NjWz/mpoab2/vgwcP8vl8ADAyMgKAqqqqLVu2jBs3TiwWt5uubYMAACAASURBVFKcH+vKlSsjR45sa+OWbR8JH/bQ6fRRo0aNGjVKKBRGRkaeP38+PDy8vLz8xIkTJ06cMDAwGDt27MSJE729vWk0/CwKtTOjOzsVTFrbxM50FVwv3lS63cx1uppRVDD1IA2/ThIUx2bzxQAQzxQMtMDdBRH6rB1Pq47O50921ArookF2LB0HMTxIvCYGCVtpJeHr16+JFytXrqRS63ka6datm+yX69ate/jwIQDMmzdvzZo1FhYWWVlZy5cvDwsLu3btWnBw8I8//tgacX6UFy9eZGVlrV+/nuxA2h+KRKIw+0fphEJhREQEkRlWVFQQjZ06dSIyQy8vr3p/UtsICoUCAEr7NjKZTJFIZGFh0Za/Jwg1B5vN5nK5hoaGmpo4O7R18esk6x6Uy7Zo01XWeeg11P/vF5zbeXwA+NJafU5P7VaPr+PicrlsNltTU9PQ0JDsWBD6FHcLBH8+qyJeL3fV7Wvy/+W+OLXiBdFs+N+jEYVC0VdT+cOr1X/Ulfk81nrXyjh4R5oQAgBNU805aHRrrCT8888/FyxYYGJiUlRU9MHObDbbysqKx+P5+vpeu3ZNReW/CYZ8Pn/QoEHJycnm5ua5ubl0OskVaDds2LBp06bi4mI9vQZ/kTVfQUFB586dAeDBgwceHh6tdyEpJfxst4khODqd7uvr6+vrW1tbS2SGV69eLS0tPXLkyJEjR4yNjQMDA9evX29qakp2pAgh1HGIJfC26r35SLqqja0jmOKolVNZF2Cn0c8Ua70i9FnrbaTqaEDPKBMOtGDIZoOE2veWGktqFVceo/rIDg8SWm+QkBghdHBwaErn69ev83g8ANi6das0GwQAdXX1xYsXT5s2rbCwMD4+3svLq5GTiMXily9fAoCjoyONRquoqAgLC3v9+rWqqmq3bt38/f21tbUVOzeETqfLjWECQFhY2NChQxWzwcePH8fExLBYLDMzs27dug0fPlxNrf7fYqmpqVFRUUwmU1VV1dbWduTIkZaWlo2E0WG0iYRQSlVV1c/Pz8/Pj8gML1y4cOXKleLi4kOHDk2aNAkTQoQQUhqhWEJ/f44ug0bZ5KlPVjwIobZDV01lnYf+tRyerw0u1mox0tWD7zXeS2+NcqNZWVmgMC+0IREREQDQuXNnFxcXuUO+vr5UKlUkEkVGRjaeEPJ4PGdnZwDIz89PSEiYM2dOdXW19KixsfHRo0dHjx4t17khFhYWBQUFsi25ubkpKSkHDx6UbaysrJw1a1ZYWJhso62t7e+//y69FqG6unrhwoWnTp2SbaTT6Rs3bly+fDmlo9cvaFsJoZSKigqdTqdSqbKfQyCEEFKOJ8W1R19U/fiFrqMBbkKNEKoHlQJf2eHqwRZTmSE/PEhopUFCYoTQ0dGxvLz833//ffXqlbq6uouLS58+fYyNjeU6E4N1xF4LcgwMDFxcXJ4+fZqRkdHES9+4cWP+/PlmZmbjxo2ztrZOTk6+ceNGcXHxlClTnj9/bmtrCwAqKirW1tb1vj0/P18sFitmaGFhYRQKZcyYMdKWgoKCIUOG5OTkUCiUHj169O3bl8VixcTEvHnzJjAwMD4+3t3dnejJZrM9PDyIJNnKysrd3Z3L5T58+JDNZq9cuTIuLu769etNvLt2qm0lhGKxODY2NjQ09NKlS6WlpUSjlpZWQEBA165dyY0NIYQ+E1dzeGfSqwHgcCpn+yB9OtbyQQg1DYsnuvKaS3YUbUVtJQ8aWPelqqshV7n1XXQ9w4ME6SCh9ISKb5e9qKqOeuMVucVicU5ODgDExMRs2rRJWr8DANTU1NauXbtixQrZyo5E54YyNGtr66dPn2ZnZzdyRVmLFy/28fEJDQ3V1/9vysmJEydmzZpVXV0dHh6+ePFiANDQ0MjNzVV875EjR+bOnQsAa9fKF/ALCwtzd3c3NzeXtqxduzYnJ0ddXT0kJOSrr74iGl+9euXt7c1kMmfMmCFNYjdv3pyVlUWhUDZu3Lh69Wqikc/nz549+9y5czdu3Lh8+XJgYGATb7A9ahMJoUQiuX//fmho6MWLF6VrWzU0NPz8/CZNmuTr64vVYxH6fAgr+WVhKRq+vTW7Y1EZcvQ3U7v8msuvk/DqxEVcEW4+hhBqCkGdZOfjynxOa+2U0O6k779dx6up91Cv1WNpmv+/jK2h4UGCdJBQekK5t8td1GmJb0NHCW/fviX20Ltx4waVSnV2du7Ro8ebN2+eP38uEAjWrFlz9+7dO3fuSEfhOBwOADRUqYVor6yU35GoITQaLSQkRJoNAsDMmTM3bdqUnZ2dltZgVgwAsbGxCxcuBIBFixYRaaFUSUnJ/fv3t27dKm1JT08/ffo0AGzbtk2aDQKAg4PD/v37AwMDMzMz37x5Y2tr+/btW2Ki6dy5c6XZIACoq6ufPXs2IyMjJSVlzZo1X331VQeet0jyr/lHjx6FhoaeP39eOg+YwWCMGjVq0qRJ/v7+WGMQoc+OBNi300Rs3tszCWK/3sb9m7TeHTUFVyjZkPheTVHFfaOqheIVcWUAoEGjUCmgTVc5kVa9tuG6owihz1xVrXhRDJt4LZKAsL76MRU14hm3S2RbdFWVUXe0HWlkeJBADBK21OWke054enqeO3dOWjclPz9/1qxZUVFRkZGR0rE4gUBAbDPIYNS/2xAxbMPlNnVkeMaMGZ06dZJrdHBwyM7ObmQ38uzs7HHjxgmFQh8fnz179sgdDQ8PF4vFsonf+fPnRSKRrq7u7Nmz5TqPGjVq8ODBIpEoJyfH1tY2MjKypqaGQqEsX75c8bpBQUFTp05NT0/Pzs7uwNMVyUkInz59GhoaGhoaKh0OVlVV/fLLLydNmjRmzBjZKkMIoc9KycPXgvz/khYtK/lfGKg5xBJJbtUHPrkXSUC2T7WwTrvRuqMIoc+cRAKCug8UEZWAfB81lc+i7qiqroaKav2bhMluq9v48CCBGCSUnrCRXXlVdTU+uGevmZnZ1q1bqVTq/PnztbS0pO2dO3e+cuWKo6Mjk8lcs2YNkRBK546KRKJ6z0ZkcU0vu+Lk5KTY2PjgW1VVVUBAAJvNtre3P3/+vOJG5WFhYU5OTrJFU4kFgb1795a9QQKDwYiNjZV+SUx2NTc379Kli+KlBw4cSLzIycnBhLBlvHjx4vz586Ghoa9evfrv8jSaj4/PpEmTvvrqq1bdMwQh1PbVVnCZt58Rrw0HdNWwMCA3HoQQQuiTdV80oindPjg8SCi6l96UPQmbctGePXv27Nmz3kNaWlqLFy8OCgoqKSlhMpkWFhY0Gk1VVbW2tlYgENT7FqJdMe9qiJWVVRN7EkQi0ddff/3y5UsdHZ2rV6/KzjUlcDicqKiooKAg2UYiISQ2DGwckRDa2NjUe5T4DtTV1RELKTsq5SWEmZmZ0gKyVCp16NChkyZNCgwMxG15EUIAABLIC0sS1dQBAFVf3cCzxebGIIQQQm1TU4YHCa23J6Ec6eN6amqqhYUFABgZGTGZTBaLVW9/ovyH4izQhlCp9Y+aNmTZsmW3bt1SUVE5e/Zs9+7dFTvcuHGjpqZm7Nixso1lZWUAoKOj88HzE5NdNTTqL5lLpVLV1dU5HE7TF0m2R8pLCCUydZZMTU1LS0sPHDhw4MCBJr796NGjrq6urRMaQoh8pUnZVa+LAICiQtH26qpC+7hfGAghhFC708ThQUIr7UkoRzpUI929vVu3bkwms6Ehsjdv3gCAo2OrZKpHjx7dt28fAGzfvt3X17fePmFhYVZWVn369JFttLGxefXqldxehfUiNrp4+/ZtvUfLy8uJmjpEt46KnDWETCaTyWR+1FtkN69ECHUwwip+wb//TRbVcbWmGzd15glCCCHUTjV9eJDQUoOES5cu5XK5Pj4+48ePVzwqXdglnVbat2/f6Ojox48fi0QiufG90tJSYsqlXD7WImJjYxcsWAAA06dPX7ZsWb19ampqbt26NWvWLLl2e3v7O3fuSO9Fzr59+4qKiry8vEaMGGFnZwcAeXl5fD5fcV+D9PR04kUHXkAIykwIdXR0Jk2a9MlvV9wlEyHUYZSnFYhqagGAYaSjN8COJ+CTHVEHpElX2TX4vWWZ/DrJ2oT36o5qq6qsf7+mKBU3IUQINUxXTeWfUUayLdW1knlRpbIt+moq+73fWx+E/6wQ1E31ui/88qPeoqJGb/51CwsLz507FxERERgYqFjNJTw8HADMzMyMjP77P+vn57dz504Oh3Pr1i1/f3/ZzhcuXCBejB49uvmBycrJySHKinp4eBw+fLihbpGRkRwOR26+KPwvQU1PT4+Lixs0aJDsofLy8mXLlolEIqIP8V+BQHDs2LEffvhB7jx//PEHAGhoaMhWrOl4lJcQmpubnzt3TmmXQwi1I8b9uzI6ab+9kmQ91q2GimUtW4UKBeR2FOQK5Qv9qYB8H4QQahz9/Y+NaPVVEKXjR0v1UdXTVNUjYYu1yZMnnzt3Licn55dfftm+fbvsoZCQkPPnzwPAunXrpI2DBg1ycnJKS0tbu3bt8OHDpVNJKyoqtmzZAgAjR46st0TnJ+NwOERZUUtLy7CwMOkVFYWFhRkaGsqlfAAwbdq0zZs3v3nz5ueff75165bsEsc1a9aIRCIajTZkyBDi7ry8vGJiYjZv3jx06FDZcjuXLl0iMt6lS5c2vWpOe4S/+BFCbYJOV1Onn/0oVJUaNpvsWDqyaqFYjUrBhzOEEPpsjR49esyYMeHh4Tt27EhKSpo6daq5uXlubu7t27eJ4UFPT8/vv/9e2l9FRWX37t2+vr4pKSne3t7z5s3r1atXQkJCcHBwQUGBurq6XFbZfKtXryY2qXdxcSHWECqaPXu2vb391atXAwICFAvVqKqqbtmyZfLkyY8fP3Zzc/vuu+969+5dWVl59uzZq1evAsC6detMTEyIztu2bevfv39RUVH//v0XLlzo7u5eXV0dHR198uRJiURiYmIiV8K042mjCWFJSYmmpmZDBX8QQh0SBccGW1mNSLI9qZKmAkGueho0zAkRQuhzRKFQTp8+7efnFxcXFxMTExMTI3t02rRpv//+u9y+giNGjNi/f//ixYsTEhISEhKk7VpaWmfOnHFxcWnZCIkaoQBw8+bNmzdv1ttn6NChRUVFJSUlivNFCV9//bVQKFywYEFubu7q1atlD82YMWPVqlXSL/v163fnzp1p06a9e/dOLrl1d3cPCQnp8Hukk5MQFhcX379/X0tLa/jw4XLta9asuXDhQkVFBQD06tVr3bp1gYGBpASJEEIdiVgCu5MrX5ULAWBjYvkmT9zmESGEPlPa2tr37t27efPmqVOncnJyioqKunTp4uLiMnr0aLmHc6kFCxZ4enoePHgwISGhrKzMxMTEy8vrhx9+aGL5TVVV1fXr1wOAvX0920pNmTLF1dX1iy++IL4MDAyst5sse3v7AwcOaGpqNhQwAEybNs3Dw2Pv3r33798nJqB279599uzZilNMhw0b9vz58z179kRFRRUWFtLp9C5duowZM2b+/Pk02nvpko6ODnEjlpaWTbjv9oEiuxuEEtTW1q5YseKPP/4Qi8Xjxo27ePGi9FBRUVG/fv3y8/Pl3rJixYotW7YornltI4hPUJT2bWQymSKRyMLC4mN3cUGovWCz2Vwu19DQUFOThJUVHdvNN7xT6dUSCcxy0h5po84VSmbfKZHtoKuqcnh4U/eSQp+My+Wy2WxNTU3ciRd1PJxa8bcR8kVlDvm0+j8synweU/KzH2oIl8sVi8UdfvhOCT9vSh0h5HA4Xl5eycnJ9R6dPn06kQ1aW1sHBgZyudyIiIg3b95s3769Z8+e33zzjTJDRQihjsfXVkOTrsIWiEbaqAOAOo2yY9B744Q4aRch1EzaqiqnRhqJRKLCwkIqlWpubo5rllErwQ+OW4pSE8I9e/YQ2WBgYODChQtldyxJTU2NiIgAAGdn53v37unp6QEAUUY2Kipq48aNkydPxjExhBBqpiGWDOlrFQpY67TRleQIofZLjUoRAUVVBagqoEbFdBChtk55nwaz2ezdu3cDwIIFCy5duuTt7U1kfYSTJ08SL4KDg6Xt2trawcHBFArl1atXt27dUlqoCKFWVcetyfgzgpPNIjsQhBBCCKHPnfISwri4OA6Ho6mp+dtvvykeJYYH7e3tBw8eLNvu6Ojo5uYGAOnp6cqJEyHU2t5eTebms1/9HfMuJo3sWDq45yW177gisqNACCGEUNulvIQwOzsbADw8PGS3hiSwWKzU1FQAqLegKFG86M2bN60fI0Ko1dVW8qqyiojXmpZYUaMVZVUIdyVXrrlfTlQWRQghhBBSpLyEMCcnBwBsbGwUD0VGRhKVc3x8fBSPmpqaAiaECHUUqroaTkt89ZwsO7na6XQ1JTucDqtOLNn7pKpGJKkWio++4GAxPIQQQgjVS3nlBMRiMQCoqqoqHoqMjAQANTW1gQMHKh4tKSkBANykHqEOg67FsJs6UCISkx1IR0ZToSztq7v1UQUALOmjS8GyDgghhBCqj/ISwq5duwJAVlaWXLtEIrlz5w4AeHp6qqurK76ReEuXLl1aP0aEkPJQcIuDVtZFl/bbAP1akcRME0s0I4QQQqh+ynsgIxLC+Pj4d+/eybbfv3+/sLAQGpgvWlxc/OLFCwCws7NTSpgIIdRxmGtSbXBjCYQQQgg1THkJ4eDBgw0NDfl8/qJFi2pra6Xt69evJ174+fkpvuu3337j8XhUKnXYsGFKChQh1AqqsooAl7EhhBBCCLUxyksIdXV1165dCwCXLl0aMGDArl27Tpw4MXLkyOjoaABwc3NzcXGR7S8UCleuXHno0CEAmDFjBjHAiBBqjyozC1//fTfzcKSglEN2LB2WBOBIKic6n092IAghhBBqT5Q6lWjBggXR0dFXr15NTk5OTk6Wtquqqu7Zs0f65cOHDzdu3PjkyRNicqm2tva6deuUGSdCqAWJBMK3Vx4DQHVeaVHsS5tx7mRH1DGFZFRHvuVHvQVOrWSMHVbhQgghhFCTKLWoA51ODw8P37t3r5mZmbTRzs4uLCxMtr5oenr6jRs3iGzQysoqMjLS2tpamXEihFqQCp1q5NGVQlWhaapZjvyC7HA6phqR5FlJLQBIAFg8EU7ORQghhFATkVBsYPHixYsXL3737l1GRoa5ubm9vT2V+l4FPE1NzR49etjZ2fn7+0+fPp3BYCg/SIRQS6FQVUyHdNfpalrHq6FpqpEdTsekRqVs6K+/O7mSTqXM6amNe0wghBBCqIkoEtyuuHkoFAoAKO3byGQyRSKRhYWFXBaNUIfBZrO5XK6hoaGmpibZsbQzQrFEIgFVKuaDbR2Xy2Wz2ZqamoaGhmTHglCrEIlETCaTSqVaWFgo54rKfB5T8rMf+swp4ecNy5EjhFAHQVfBVBAhhBBCHwc3hkYItYDaCh6vsJzsKD4vFTVi/HQaIYQQQs2ECSFCqAUU3X35LuoF2VF8RsoF4jX3y4OfVmEBGYQQQgg1B04ZRQg1V20FrzQ5RyIScwvKNC0NyA6n4+PXSbYkVZTwRSV8kVAs+bmvLtkRIYQQQqi9whFChFBzFd19KRGJAaAoJg0A+EUVddwasoPqyBg0iksnVQCgUsDbSp3scBBCCCHUjuEIIUKoWYjhQeJ1RTqTk1uad+lhHa/GckSvTv3syI2to6IATOuupUGjmGhSvzBSJTschBBCCLVjmBAihJpFOjxIyLuYWFNWDQAF/z7T62lJ08CNB1vLuK64LQdCCCGEmgunjCKEPp3s8CCByAYBoLN/b8wGEUIIIYTaOEwIEUKfTm54UErH3sSwt63y4+nAEgoFlbX1fKsRQgghhJqjTUwZlUgk4eHhcXFxOTk5b968EYlEXbt2tbe3HzZs2IgRI8iODiFUP8XhQSmj/t0A90hvOUlFNftTqozUqav66ZlqUskOByGEUEdz4cIFU1PTQYMGNd6tqqrq2bNnbDbb2Ni4d+/e6uofKGyWnp6ek5NDpVIdHBy6dOnScvG2jL179+7Zs+fly5fa2tqtdImlS5eeOXPG29s7JCSklS7RfOQnhDdu3Fi9evWzZ89kG1+8eAEAO3fuHDx48M6dO/v160dSdAihBjU0PAgA7MfZet3NlRxPR1VZI96fUiWWAIsnOpNRvRQ3mUAIoQ7q5MtqnvC9X6yze2qrUVv9E9bExMSJEyf6+fk1khDyeLygoKDjx4/z+XyiRUdH54cffvj111/pdLpi/+jo6EWLFr18+VLa0q9fvz///LNPnz4tHv8nCwkJcXBwaL1sEAAqKytZLFZZWVnrXaL5SE4Ib926FRAQIBaLAcDIyKhnz562trYAkJ2d/ezZs4qKinv37nl5eT169MjJyYncUBFCshoZHgSAinQm7knYUnTVVH7qrbPvaZWxOvV7l1b8pYUQQohc9wsFlTXvJYTTe2irtf68kEOHDjXeoaamxtvb++HDh8SXRkZGJSUlVVVVW7ZsSU1NvXLliorKe8vQLl++PGHCBOIJX1tbWygUCgSCR48eDRgwICoqytPTs5Vu5KPk5+c/fvx4//79ZAdCPjLXEObl5U2dOlUsFnfq1OnPP/8sKCiIjo4+duzYsWPH7t69m5+fv2XLFgaDwePxxo8fz+PxSAwVISSnkeHB/zrEpCktmA6vr4naGne9Vf10tei48BshhFCLqamp2bVr18mTJxvvtm7dOiIbnDdvXkFBQXFx8evXr8eOHQsA165dCw4Olu1cXFw8Y8YM4gk/KiqKzWaXlZVdvHhRXV29pqZm4sSJAoGg9e6o6cLDwwFgzJgxZAdCPjKfLQ4dOlReXq6mphYeHj5v3jxV1fd209LS0vrll18OHz4MABkZGWFhYSSFiRCS1/jwIIEYJFROPJ+Dbvp0Q3VcPYgQQqgFVFRUbN68ecqUKVZWVkFBQY13ZrPZRMrn6+t74MABCwsLALC3tz9z5kzfvn0BYPv27UKhUNp/z5491dXVNBrtypUr3t7edDpdXV193LhxRNpZWFh47NixVry3Jrt8+bKrq2vnzp3JDoR8ZE4ZJT5pmDhx4oABAxrqM23atN9//z05OfnBgwdTp05VYnQIoQZ9cHjwv24xaXbTPrA8HSGEEEJKVlRUtGbNmiZ2vn79OjFTb+vWrbJTQ9XV1RcvXjxt2rTCwsL4+HgvLy+iPTQ0FABGjx4tNzV0woQJdnZ22dnZ58+fX7hw4QcjLC0tNTAwMDc3B4DExMS7d++y2WwrKysPDw83NzfZzhwOJy8vr5Gz6enpWVpayraw2ex79+5t2LBBrqdQKIyKikpKSuJwOLa2ts7OzgMHDqz3nHV1dXFxcYmJiaWlpTo6Ok5OTl9++aWOjk7j99U2kZkQVlZWAoC7u3vj3Tw8PJKTkysqKpQSFELoA5oyPEjAlYSfpk4s+f1plY+Vei8j1Q/3RgghhD6SmZnZ8ePHpV9u27YtMzOzoc4REREA0LlzZxcXF7lDvr6+VCpVJBJFRkYSCWFWVlZubi4A+Pv7K55q9OjR+/btS0hI4PP5jVco3bVr1+7du+fMmbNz587Jkyffvn1b9ujYsWNDQkIYDAbxZWxs7OjRoxs529SpU//55x/ZlmvXrolEImLWq1R6evr48eNlC+EAwJAhQw4ePNijRw/Zxuzs7MmTJyclJck2GhkZnThxwtfXt5FI2iYyE0Jzc/MnT54UFxc33o3NZgOAiYmJUoJCCH1AE4cH/+uMg4QfSSKB/SlVj4pqnhTXLuylPcCcQXZECCGEWsXJl9WCOolsC//9LwHgRBqHpvJeldEZPbQYtObWHdXV1Z05c+b/X+XEiUYSQiJB6t27t+IhAwMDFxeXp0+fZmRkyHZuqL+Xl9e+ffvq6uqysrKcnZ0/GGddXZ2/v39CQoK3t7e7uzuHw7l8+XJhYWFYWNjSpUsPHDhAdNPQ0LC2tlZ8u1AoLCwsBAAKRf47FhYW1q1bt+7du0tbIiIixo4dy+Vy1dTU+vbt27Vr14yMjIcPH8bGxo4aNSolJUVfX5/oGR8f7+vry+FwKBSKk5PTF198kZubm5ycXFJS4u/vv23btuXLl3/w1toUMtcQEp8cXL16VXbasRwOhxMbGwsAX375pfIiQwg1oOnDgwRcSfixymrEryuEAFAnlpTycSd6hBBqr9gCcQlfVMIXSeSzvP8OxRYIovP5sn9qRfJd7zHl+xTx6ho6Yb3Xar6cnBwAqDfjkrZnZ2fLdm6ov7RR2r9xoaGhT548uXDhQlRU1JYtW/bv35+RkdGzZ08ACAkJkfzvbr29vXPrQwxaamho/Pzzz7Kn5XK5RPonbamrq1u4cCGXy7W1tU1JSbl///6JEycSExPDw8OpVOrbt29/+eUXoqdEIvnpp584HI6uru7NmzdTU1NPnz4dFxeXnp7u4uIikUjWr1/PZDKbcndtB5kJ4fTp03v27JmSkjJjxox6yw3xeLzJkye/e/du6NChmBAi1BZw35bq9bDQd+7c9D88JiaEH8GQobJ5gIGVDm2EtXqAnQbZ4SCEEPpEK+LKfohm/xDNrhbKf7pHHOIqtDfttOUNnbDeazUfh8MBAD09vXqPEu3EQjAAqKqqkm2vt7Ns/8YJBILFixePHz9e2qKtrb1ixQoAqKioIEb/GrJly5YzZ85QKJSTJ0/KDVf++++/fD5fNiE8duzY69evASAsLMzR0VHaHhAQQCx3vHXrFtFy/vz5J0+eAMChQ4dGjhwp7WltbX379m01NTWBQKC4NLGNI3PKqLq6+vXr1wMCAs6ePRsVFTV//nxXV1cbGxsKhUIMvAYHB5eUlFhbW+/du1cx1TYwMNDQwKclhJRK38VK38WK7Cg6OH2Gyob++s2fEYQQQgg1k0AgILYTlC7Yk0MsBeRyucSXRPkZOp0utzOhbGfZ/h8kN7gHAA4ODsSLRuYYne0PTgAAIABJREFUhoWFEVVz1q9fL5tPSo9aWFjIVqY5d+4cAAwbNqxXr15ynadMmZKcnAwAZWVlBgYGN27cAIAuXbpMmDBBrqepqen06dOPHDly/fr1Jt5dG0FmQnj8+PFffvmltrYWAIqLi3/77bd6u+Xl5dU7C/n06dPffPNN64aIEEJkUMdsECGE2jlDhooalQIAKgoL2IhDZQKx+OOneBoyVBo6Yb3XaiYa7b9kQSQS1duByMqki/SI/o13hvoW9dXL2NjYyMhIrrHeVFNWSkrKtGnTJBLJuHHj1q1bpxjDjRs3pk6dKhtDVlYWAAwaVE/VA3d39/j4eOmXxGTX/v37U6n17AU1cODAI0eOvHv3TiAQNJRCt0FkJoRcLpfFYpEYAEIItQXlArE+A3ecRwihDmX7oAaLbBOHvo0o5dR+dEK4fZCBJl0+m2rkWs1Eo9FUVVVra2sb2k2eaNfS0iK+1NTUBACxWCwUCul0er2dZfs3zsrqoyclsVisgIAALpf7xRdfnDx5UjHzjImJqaiokJ0vKhAIiKmITdmTkEgIbWxs6j1KLJKUSCRv3ryRrVjTxpGZEE6YMMHV1fWT325vb9+CwSCEECnyOXXrH5QPtmDMcNLGYUGEEPp8zOmpLXy/iszxNA7v/UKj3/bUJob+pJS/oMDIyIjJZDY0ilNUVAQAnTp1knYmXrBYLLmt/6SdZfs3rt5RuEbU1NSMHTs2Pz/f2Ng4PDycyE7lXL58WV9ff8iQIdKW8vJyoj5NU3YRJCa7NrRsTVtbm3jRxEWSbQSZCaGJiQluJoEQ+pyV8kWbH1VwhZJbuXw6lTLVsUmfmCKEEOoA+pupybX8k1EN7yeEA8wZiuOBStatWzcmkyktHyrnzZs3ACAtxNKtWzfiRU5OjmJCSHSW7d+yvv322wcPHqiqql6+fLne0UWJRBIeHj569GjpVFgAMDU1ZTAYAoGgoKDgg5ewtbVNTU19+/ZtvUfz8vKk3T7pDsiBk5QQQog0umoqDnp0ANCgUTxxy0GEEEJtT9++fQHg8ePHiisDS0tLiSmUffr0IVpcXFyIXCsxMVHxVA8fPgQAQ0PDhjaxaI6tW7cSu8//+eefnp6e9fZ58OBBUVHRV199JdtIoVC6dOkCAK9evVJ8C4/HW7ly5cqVK4kypHZ2dgCQnp5e7/mJdm1t7fY16EXmCOG7d++auAkJADg7O+vq6rZqPAghpGR0FcpPfXSPp3EGmjNsdMj8BxkhhBCql5+f386dOzkczq1bt4hdxKUuXLhAvBg9ejTxQldXd+DAgXfv3j1//rzc/uwikejy5cvECT9YGOZjXblyZfXq1QCwePHi2bNnN9QtLCxMXV19xIgRcu19+vR5+fLl2bNnt2/fLp32SYiMjNy+fTsALFmyhOh55cqVuLi41NRUZ2dn2Z5CofCvv/4CgHrLYbZlZD5/XLp0adGiRU3sHBMTM3To0NYMByGESEClwLc9tT/cDyGEECLDoEGDnJyc0tLS1q5dO3z4cDW1/2a6VlRUbNmyBQBGjhxJjLAR5s+ff/fu3eTk5NDQ0EmTJknb9+7dS+wcOH/+/JaN8Pnz5998841EIhkxYsSuXbsa6XnlypURI0YorgBcvXr12bNnKyoqlixZ8tdff0nXLtbW1v76668A4OzsTAz6LVq0aO/eveXl5QsWLLh27Zp0Z0WxWLxixQpiKqliadM2rh18IG1ra6uioiLdtwQhhBBCCCGkHCoqKrt37/b19U1JSfH29p43b16vXr0SEhKCg4MLCgrU1dWJATSpCRMm7N+/Pz4+fsaMGc+fP/f396+urr569erBgwcBYOrUqR4eHi0b4TfffEPUerGxsSHGCRVt2rQpPT09Kytr7dq1ikcdHR3nzJlz+PDhY8eOvXr1auLEiQ4ODnl5efv3709NTVVRUdm/fz/RU09Pb9WqVUFBQfHx8W5ubrNnz+7Vq1dubu6lS5eio6MBwNfXd9iwYS17g62NzITw22+//frrrxXbJRJJfn5+ZmZmcHBwQkKCs7NzSEhIvWWCEEKo3YnO53uaM+SqxiGEEEIze2jVvr9MT+3jqmy2lhEjRuzfv3/x4sUJCQkJCQnSdi0trTNnzri4uMh2plAoly5dGjly5NOnT7ds2UKMIkrPc+TIkRYPr6ysjHhBzNis16+//hoWFkaj0eRmvUoFBwdra2vv2bMnLi4uLi5O2q6urr5jxw7ZqqTLli2j0WgrV67MyspatWqV7ElmzpwpTR3bETITQgaD0dCOjUZGRn369Jk8efKWLVtWr149e/bs0NBQJYeHEEIt7koW72xmddRbwUo3XW1VLOuFEELo/w0gqbrYzJkzhw4d6uDg0EifBQsWeHp6Hjx4MCEhoayszMTExMvL64cffqi3nKaxsXFiYuLRo0cvXryYk5NDpVIdHR2nTp06efLkJm5J/+WXX2ppaSnWKQUAc3Pz9evXA4B0uubPP/9cVVXV+AlpNFpYWNiQIUMMDOrfs5FOp+/atWvUqFFHjhxJSUnh8Xhdu3Z1dnZesmSJYgmcxYsXe3t7BwcHJyYmlpaWamtrOzs7z5gxQ7qWUiogIMDS0rKN75ZHIbbdaMuGDBly7969+Pj4huoFkYv4sVbat5HJZIpEIgsLi4/dmAWh9oLNZnO5XENDw443LyC3qm5lXBnxj0VAF42p3XGTic8Ul8tls9mampqGhoZkx4JQqxCJREwmk0qlWlhYKOeKynweU/KzH2oplZWVqqqq7W4ZmhJ+3trB59M+Pj4AcO3aNbIDQQihZrHRoc3uqU2hgJOh6sRuHS3dRQghhNoyXV3ddpcNKkc7KCpDDAczmUyyA0EIoeb60lrdWIPaTZ9OV8E1hAghhBAiXztICKOiogAA59UghDqGL4xUyQ4BIYQQQug/bToh5PP5e/fuDQ8PBwAnJyeyw0EIIYQQQgihDoXMhPDEiRNr1qxp6KhEImGxWCKRCABMTEymTJmixNAQQqgFcGrF+55UTe+hZa3Tpj99QwghhNBni8xnlOrq6qasDDQzMwsNDe149QYRQh1brUiy83FlZrnwt8Ty5a56jgZ0siNCCCGEEJJHZkI4aNCgvXv3NtKBSqU6ODj0799fR0dHaVEhhFCLYFaL8jl1AMCrk1TUiMkOByGEEEKoHmQmhL169erVqxeJASCEUOux1aVtGKC/5VFFgJ2mh5ka2eEghBBCCNUDl7UghFBr6axN2zXYUJOOO0wghBBCqI1SXkJYV1dXVFT0yW83MjJSU8OP2BFC7QxmgwghhBBqy5SXEGZlZXXv3v2T3x4TEzN06NCWCwchhFpemUBswFAhOwqEEEIIoaZqBw8uqqqqampqKirtIFSE0OfsRWntT3fZEXl8sgNBCCGEEGoq5Y0Q2tjYPHjwQK6xqKho7ty5JSUl6urqU6dO7du3r62trVgsfvPmTWJi4rlz54RC4ahRo86cOYPbTiCE2rKcyrodjytrRZKjLzgUCvhYqZMdEUIIIYTQhykvIWQwGB4eHrIttbW1/fr1KykpGT58+D///GNsbCx7dMGCBZs2bRozZkx4ePj06dMvXbqktFARQuhjGWuoWOvQXpULjTWofU1wwTNCCCGE2gcy52GePn362bNnXbt2vXTpklw2SLCysrp27Zquru7ly5djY2OVHyFCCDWRFl1ldT+9geaMVf309NVwijtCCCGE2gcyn1quX78OAF999ZW2tnZDfSwtLYcMGQIAN27cUF5kCCH08Rg0yqLeOmaaVLIDQQghhBBqKjITwvz8fACws7NrvJutrS0AFBYWKiMmhBBCCCGEEPpskJkQEnVinjx50ni3x48fA4COjo4yYkIIoaYRSyAijy+WkB0HQgghhFAzKK+ojKLevXvfu3fvwoULy5Yt69q1a719oqKiEhMTAaBPnz7KjQ4hhOBuviCm4L1tJHys1AdZMCQAR1I50fn8lJLan3rrqFJx93mEEEIItUtkjhB+9913DAajvLzcz8/v7t27ckfFYvGFCxcmTpwoEonMzc3HjRtHRowIoc9aCV+UUSaU/cPmiwHg4bua6Hw+ADxm1dxjCsgOEyGEEELoE5E5Qujk5PT7779///33r1+/9vLycnNzI/YhFAqFOTk59+/fz8zMBAAajXb+/Hl9fX0SQ0UIIVnuZmpj7DTCs3mDLBjDcMtBhBBC7RaPx7t69Wp4ePibN29YLJaJiYmDg4OTk9PcuXOb//j9+vXrqKgoGo327bffShufPXumuDk5AFAolE6dOjk4ONjb26urK+l3a1VVVUhICABMmDDB0NBQORdta8hMCAFg7ty5fD5/w4YNZWVlSUlJSUlJch3s7e137tzp6elJSngIIVQvCsAURy07PbqriRrOFkUIIdROXbhwYdGiRSwWS9qSm5v78OFDANixY8e6det+/PFHCuXTf9E9fPhw/vz5ampqsglhREREUFBQI+9iMBhBQUErV67U0ND45Es3UXFx8fz58wHAw8MDE0LS/PTTTzNnzvzjjz8SExNfv36dm5urqalpa2vbpUsXX1/f6dOn02jkB4kQQorcTXEDeoQQQu3V2rVrN23aBAA0Gm348OH9+/fX09PLz89/9uzZnTt3ysrKFi9ezOVyV61a1UoBmJqaUqn/v1eTSCRisVgSiUQgEGzcuDE8PDwpKUlVVbWVro6k2kSupauru3btWuK1RCJpzucQCCGEEEIIocYdOnSIyAZ79+595syZ7t27yx59/PjxpEmTcnJy1q1b5+npSewK3uLi4uLs7e1lWwQCQVRU1NKlSzMzM58/f75ly5Zff/21NS6NZLWJhFBKKBRWVlbS6XRdXV2yY0EIfXai8/lx71eIKeGJ5fpE5fOfldbItnh1Vh9swWj14BBCCKEWwmQyly9fDgDdu3ePjo7W09OT6+Dq6hoWFta/f38ej7du3brY2FjlBMZgMPz8/FxcXJydnSsrK4ODgzEhVAIyq4xKPXr0aObMmXZ2dqqqqkZGRitWrACArKysFStWpKenkx0dQuhzUcITv2QLZf+U8EVyfYp5Irk+pQp9EEIIobZs9+7dHA4HALZt26aYDRJcXFy+/vprAIiPjy8uLm7oVFwuVygUtmx4nTt3HjNmDACw2exGLl1dXd30c35U588NyQmhRCJZtGiRh4fHyZMnc3JyZA9VV1fv2LHDyclpz549ZIWHEEIIIYRQR1JbW3vq1CkAcHJyGj16dCM9p06damhoqK+vHxkZKXcoLS1t3Lhxenp6WlpaqqqqlpaW8+fPZ7PZLRVkly5diBdyCcLbt2+XLFlib2/PYDC0tbU1NTUdHR3Xrl1bVFRU73mSkpL8/f01NTW1tbV1dHRcXV2PHDkiFstP/1HE5/MXLFgwfvz46dOnl5SUNP+O2jKSp4yuXLkyODgYAMzNzceMGSMUCo8ePUoc6tSpk42NTW5u7tKlS2k02o8//khqpAghhBBCCDUVi8VqSuLR4kxMTFRUGhvyefLkCZG5LVy4sPHKHd7e3qWlpYrtf/3115IlS/h8vrSFyWQeOnTo2rVr//zzz9ChQz8xdBlZWVnEC2tra9nIBw4cKHtdHo+XmZm5adOmY8eOPXr0yNLSUvYkwcHBS5cura2tJb7kcDjJyclz5849fvz43bt3GylXw+fzx4wZExERoa6ufvXqVSMjo+bfUVtG5ghhdnb2rl27ACAwMPDly5cHDx708/OTHrW0tHz16tWsWbMAYMOGDVVVVaQFihBCCCGEUIfw6NEj4oVcIZkmunnz5rx58/h8fufOnY8dO5aWlvbo0aMdO3ZoaGgwmUw/P7937941M8KcnJwrV64AQKdOnczMzIhGkUgUGBjI5/P19fV///331NTUgoKCpKSkZcuWUSiUd+/eEYvOpEJCQhYtWlRbW9u/f/8LFy7k5OQ8ePBgxowZAPDgwQO5zrJks8Fr1675+Pg083baPjJHCA8fPiwWi+3s7E6dOqWpqanYgU6nHz58ODIyMj8/PywsjPhfiBBCCCGEEPo0ubm5xAsbG5uPfa9YLP7ll18AwMHB4d69eyYmJkS7m5vbsGHDBg4cyOPxNm7cePDgwU+Lrby8PCIiYsWKFcSSv6VLl0oPvXjxIi8vDwBOnDgREBBANFpYWLi6ugoEguDg4MTERGlnoVBIbGHg4+Nz7do1BoMBALa2th4eHqqqqkeOHDl48ODWrVuJdll8Pj8gICAyMpLIBocNG/ZpN9K+kJkQvnjxAgAmTZpUbzZIoNFofn5+hw4dysjIUGJoCKHPkY8Vo7fxexNIovP5Mfnv1R0dbi1fU9RQnQoIIYRQO1FeXg4AVCpVboJlU9y+ffv58+cAsG7dOmk2SOjTp8+sWbMOHjx49OjRzZs36+vrf/Bsffr0kZ3dKhKJZEu/uLu7L1u2TPplZWXluHHjqFSqr6+v3Hnc3NwAgMViSVtCQkKIxYdbtmyRy/qWL19+5MiR2traxMREudmtxNhgZGSkhobGtWvXvL29P3gLHQOZCSGx+rNr166NdyN+nvLz85URE0LoM2aoTpXL7p6V1Mr16cSgOujTlRgUQggh1JIkEsknvzctLQ0ANDU1J02apHj022+/PXjwoFAofP36db9+/T54NqLSqSJVVdWgoKB169bRaP+fqgwePHjw4MH19n/y5Em9LT179iRyRVn29vbR0dEikUguB5GODQJAUFDQ55MNArkJoaGhIQBkZmY23o0YG2zKxwwIIYQQQgihRhAP1SKRKC8vz87O7qPem52dDQA2NjayqZqU9Gw5OTlNSQh37dolN8xIp9O7devWo0ePRiq+SCSS/Pz8zMzMnJyczMzM+/fvS1dFShE1aaSlSuV4eXkpNs6cOfPVq1fE64MHD/74448GBgYfvIWOgcyEcMCAARERESEhIb/88ouOjk69ffLz86OiogDA1dVVudEhhBBCCCHU0Ujrdubk5HwwITx37lxRUZGGhsbcuXMBgFjF19BcUx0dHR0dnaqqKukyxcaNGTPG3t6+6ZGzWKwNGzacP39ervapiYmJ7HxR+N9mFR81J/bVq1fW1tYrV66cP39+SUlJUFDQsWPHmv72do3MKqNz5szR09N7+/btpEmT6t108u3bt19//XVVVRWxKYXyI0QIIYQQQqgj8fDwIF4ozrSUU1tbO2fOnCVLlvz9999ECzG/j1iFqKiuro7L5QKArq5ui4X7P69evXJ2dj548GBpaamtre2UKVM2b9584cKFN2/eENsWyNLS0gIAIpgmsrW1jY2NnTdv3uTJkwHg+PHjcXFxLRh/W0ZmQkgUqwWAf//919HRcebMmdevXweAnJycvXv3zpkzp3v37gkJCRQK5dSpU3p6eiSGihBCCCGEUAfQt29fY2NjANi3bx+Px2uk5/3794kO0pV4xIgiMU6oKD8/XyQSQRNKhHyCxYsXl5SU6Orq3rp1Kycn58yZM6tWrRo/fny9tVKJgceG4qytrRUIBHV1dbKNp0+fJsZO9+7dq6+vL5FIvv/+e+kehh0bmQkhAAQGBh46dMjQ0LC8vPzkyZNEfhgREfHzzz///fffPB6vU6dOR48e/UxKviKE2hqvzozf+uvL/hlkKV+iGiGEEGpH6HT6nDlzAKCoqCg4OLiRngcOHCBeBAYGEi+IhJDFYqWkpCj2//fff4kXHzURtCnEYjFR7uXnn38eOXKk3FHp2j8pIiNNTk6urKyUOySRSKytrdXV1ffs2SPbLt31wMTEZNu2bQCQnp6+ffv2lruJtovkhBAAvv/+++zs7FWrVnl4eBDD0ACgr6/fr1+/VatWZWVlzZ49m9wIEUKfrU7qVEcDuuwfQwb5/2wihBBCzbF06VKimstvv/0WExNTb5+wsLBLly4BgIuLy5AhQ4hGPz8/Yjroxo0b5foLBAIifXJ3d/+EHQ4bJxQKxWIx1Fdmsrq6+sKFC3KNEyZMUFFR4XA4+/fvlzsUHR1N7HQgt+eErO+++27gwIEAsGXLFqI+TcfWJp5sdHV1N2/e/ODBg9LS0rL/efjw4ebNm1tjCjJCCCGEEEKfLUNDw+PHj9NoNB6PN2LEiKCgINkyMKWlpUFBQRMmTAAAGo32xx9/SHcLNDAwWL58OQBcvnx51qxZ0m0D8/LyhgwZQkzR3LlzZ4sHrKam5uDgAAAnT56UXRn48uVLPz8/YksCHo/39u1bot3Z2Xn69OkAsH79+t27d0tnhyYnJxNDTS4uLn379m3ochQK5a+//qLT6QKBYP78+S1+O21Nm0gIZenr6+MOEwghhBBCCLWeUaNGXbhwQUNDQygU7tq1y9bWtnPnzr179zYyMjIyMtq1a5dIJNLS0vr777+lw4OExYsXE9s2nDhxQk9Pr1evXnZ2djY2NsTeD8uWLRs0aFBrBEwkosnJydbW1mPHjp08ebKrq6uzs3NSUtKaNWtUVVUlEknPnj2XLFlC9N+8eXPPnj3FYvGyZct0dXXd3d0dHR3d3Nzevn2rrq4eEhJCpVIbuVyPHj2IK0ZGRp45c6Y17qjtaEMJYX5+fmRk5MWLF5OSksiOBSGEEEIIoY7sq6++Sk9PnzJlCoPBAICCgoKUlBTpjg5ff/11RkbGtGnT5N6loaERGRm5adMmVVVVkUj0/PlzYo8HY2Pjq1evtsbwIGHmzJm7d+/W1dVls9lXrlw5d+5ccnKym5vbkydPNm7cuHnzZmKOqHSHc3Nz80ePHs2dO5dCofB4vEePHmVmZkokEh8fn+TkZCcnpw9ecc2aNcRiyJ9//rmhwqodA0UikZAbgVAo/P3334ODg6WFgL7//vtDhw49e/Zs7ty5s2fP/u6776Tj1G0QhUIBAKV9G5lMpkgksrCwaPxTDYTaLzabzeVyDQ0Npcu7EepguFwum83W1NSUrpxHqIMRiURMJpNKpVpYWCjnisp8HmvitVgsFrHsTclMTEw+9sm5urr67t27eXl55eXllpaWDg4ODg4OnTp1avxdHA4nPj6+sLCQTqd36dLFw8Oj3t3qW1ZlZWVKSkpOTg6Dwfjiiy+6d+8uPVRcXFxSUmJvb6+mpib7FhaLlZCQwGazLS0tu3fvLt2Gsb1Qws82mRvTA0BlZaW3t3e9u6BIJJJHjx49evTo33//DQkJUVdXV354CCGEEEIIdWxaWlr+/v4f+y5tbe1Ro0a1RjyN0NXVHTJkiNwsVoKxsTGxnYYcExOTsWPHtn5o7RjJI28TJ04kskE/P7/Q0NB9+/ZJD9nY2EyZMoVCoVy5cmXp0qXkxYgQQgghhBBCHROZCeG9e/fu3LkDANu2bbt+/frEiRNlx3D19PTOnDlz+PBhADh69CgxOxkhhBBCCCGEUEshMyEMCQkBgIEDBxI1fOr17bffurq6CoVC6U6XCCGEEEIIIYRaBJkJ4evXrwHAz8+PWCvZEE9PTwB49eqVksJCCCGEEEIIoc8DmQlhVVUVAHywhBFRTpPNZisjJoQQQgghhBD6bJCZEBKViJ8+fdp4t9TUVAAwNTVVRkwIIYQQQggh9NkgMyH08fEBgBMnTjQyHTQ+Pj4qKgoA6i0vixBCCCGEEELok5GZEM6ePbtHjx48Hm/EiBERERGKHS5evDhx4kSxWOzh4TFy5EjlR4gQQgghhBBCHRiZG9NraGiEhob269cvNzf3yy+/tLa2JuaFxsfHjxkz5sWLF8RWEzo6OiEhITQamaEihBBCCCGEUMdD8sb0PXv2TEhIGDZsGADk5eU9fPgQANLS0q5evUpkgz4+PnFxcba2tuTGiRBCCCGEEEIdD8kJIQB88cUXkZGRd+7cWbly5bhx45ydnZ2dnceOHbt8+fI7d+5ERES4uLi04OU2b97s6OiYlpbWgudECCGEEEKoHUlJSRk1apSRkZGRkdGzZ8/IDqdV8Hg8U1NTU1PTmJgYsmNp09rKPMzhw4cPHz68ta8ikUgOHTpUUFDA5/Nb+1oIIYQQQgi1QSwWy9vbu7y8nPiytR+Ma2trnz9/DgA9evTQ0NBo1WvJEovFLBYLAGpqapR20faI/BFCORUVFa235eD27dsLCgpa6eQIIYQQQgi1fWFhYUQ2ePTo0aKiIldX11a9XGFhoZubm5ub28uXL1v1QujTtIkRwsLCwh07dsTFxeXk5FRUVACAvr6+vb39sGHDgoKCDAwMmnn+ysrKx48fHz169Pz58y0RL0IIIYQQQu3VmzdvAKB79+5z5swhOxZEPpITQolEsnLlyj/++EMgEMi2l5eXJyUlJSUl/fnnn5s2bfrhhx8++RJz5sz5+++/mx0pQgghhBBCHYFIJAIAHR0dsgNBbQLJCeHWrVt37NgBACoqKv7+/n379iUKimZnZxNb0ldWVi5atMjc3DwwMPDTLqGhoWFtbU28FggExExihBBCCCGEPjfFxcXFxcXE+iw+n//ixQsAsLOzU1dXl/ZhsVgRERH5+flsNtvY2NjKymrkyJF6enr1npDH40VGRmZlZRUVFVlYWHTr1m348OFUKpU4WldXl5GRUVhYSHyZnZ3NYDAMDAzMzc1lT1JUVHTz5s3s7GwKhdKlSxcfHx8rKyu5C3E4nLy8PAaDYW9vDwCxsbF37tzp0qVL0wc5WSxWeHh4Tk4Og8FwcHDw9/dvKCXmcrm3b99OTU3lcrk2Njbu7u59+/aV6yMWi4kZsN27d6dSqVlZWaGhoRwOZ9u2bR97a+STkCchIUFFRQUA+vbt+/Lly3o7dO7cGQB0dXWZTGbzrygtMZSUlNT8sxGU/G0sKCjIy8urq6tT2hURUrLS0tK8vLzq6mqyA0GotVRXV+fl5ZWWlpIdCEKtpa6uLi8vr6CgQGlXVObzWBOvVVRUVEgGkUjUSFTr169XTAcePHhAHBUKhQsXLlRTU5ProKWl9euvvyqe7fDhw8Qu4rIcHR3v3btHdMjPz1e83Jw5c2RPsm/fPlWumjpjAAAgAElEQVRVVdkONBpt+vTpHA5Httu1a9cAwMnJSSwWSycPDhs2rJGb5XA4RLdbt24dOnRINukFACMjo/DwcMV3RUZGKt7UoEGDMjMz6z15SUnJmTNn6HQ6AFCp1E+4tcYp4WebzBHC48ePi8ViU1PTa9eumZmZKXbo37//lStXBgwYUFlZeeHChZ9++kn5QTYRsWuiErBYLJFIVFNTI/3oBaEOpqKigsfjVVVVKbMQGULKxOPxKioqNDQ0KisryY4FoVYhEolYLBaVSsXqjm1Nly5dhg0b9vr167dv3+ro6Li5uQGArq4ucTQoKOjAgQMAYGpq6unpqa+v/+7du+jo6Orq6l9//dXMzGzu3LnSU509e5b4UldX18vLy9DQMDs7OzY2NiMjY9y4cc+ePTMzM2MwGMOGDRMIBPfv3weAfv36aWtr9+jRgziDRCIZP3785cuXAcDMzKxfv340Gi0xMZHJZJ46dSotLS0qKkoam9T27duDg4OJ19ra2k2562PHjl28eNHS0nL48OGdO3dOSkq6detWSUnJN998k5qaKp1LCAD79u1bunSpWCzW1tbu16+fmZnZ06dP09LS4uLiPD09IyMje/XqJXfy+Pj46dOnE7NwpfF82q2RplXTzcZ5enoCwMKFCxvv5u3tDQDffvtt86/4aSOE2Y0i838eQgghhBD6n+Y/KzZFE6/VNkcICUuXLgUAd3d32cbS0lLi1r7++muBQCBtLykp6dmzJwAMGjRItr+lpSUADBgwoKqqStp4+/ZtYgLg5s2bpY1EDRtQeAI/e/Ys0b5s2bKamhqiUSgUrl27lmhfsmSJtDMxQqinp0en03v06HH79u2ysrLGb1M6iAcAvr6+lZWV0kNHjhwh2g8cOCBtzM3NJUZHhw8f/u7dO2n7+fPnicmlffv2lX57pSc3NjbW19c/evSo7JD4R91a45Tws01mQtivXz8A+OuvvxrvtmTJEgCYNm1a86/4aQlhy/wThRBCCCGEWlPznxWb/mT4wW7tLiG8fv06cWuKC7X++OMPANDT05O2FP5vWeDFixflOnt7exsaGsrOC603IaypqSFKh4wdO1YxwpkzZwIAnU5ns9lEC5EQAkDXrl1l89VGSHM2HR0dxeyRWMv3ww8/SFumTJkCAFZWVopTOk+cOEGc6urVq3Inp9For169ku38sbfWOCX8bJO5D6GdnR0ApKWlNd4tMzMTAIhvKym6NIqsqBBCCCGEEGop/fv3T01NffnypVzFFwAgxs2EQqG0hUKhEC8Un+SjoqJKS0uPHj3a+OUSExOJRHHdunWKR9esWUNcMT4+Xu7QypUrFVc5Nm7GjBn6+vpyjd26dQOZmxKJRBcuXACAJUuWaGlpKZ6BGBGVDi9JTZ48uWvXrrItn3xrZCFzDeGUKVPOnj178eLFlStX1ruGEAAyMzNjY2MpFMq4ceOUHJ5U4/NCib8PSssM6+rqRCIRjdYmNpBEqDUQH21SqVTpLxuEOhiJRCISiSgUCq4GRx1YXV0dlUpV2hOL0qo5dGAGBgaKu39XVVXdv39/z549cu2mpqbdunXLzMz87bffcnNzZ8yYMWDAAKKwShO9fv0aADQ1Na2srIh9yGUZGhpqaWlVV1enpKQEBATIHiLmGH4U6apFWcTUVqnc3FwiOXR2dlaMBwCcnJwKCgpSUlLk2hXj+eRbIwuZeYW/v//48eMvXrw4YsSIEydO9OnTR65DUlLS1KlTuVzuokWLXFxcSAmyiZS2mJDJZLJYLGNjY3yMQB1VeXk5j8fT19fHojKoo+LxeOXl5RoaGoqfWCPUMYhEouLiYhMTEwsLC+VcET9DbClMJjMsLOzJkyeZmZk5OTlFRUUN9Tx9+nRAQEBRUdHx48ePHz+uoaHh7u7u4+MTGBjo6Oj4wQsRWROXyzU0NGykm3Rlo5TiAOYHNWWnByIeAPDx8WlmPJ98a2QhMyGsqKhYs2ZNaWnp3bt3+/btO2TIEFdXVxsbGwqFkpubm5ycTIzJWltbu7q6/vPPP3Jv9/T0JHEeKUIIIYQQQh3J+vXrt23bVltbS3zJYDCcnJxcXV01NDT+/PNPuc5ubm4ZGRn79u27ePHiixcveDxeTExMTEzM6tWr/f39Dx061PjHAcTQmZqamo2NTSPdFD84+4TPi+UGAxuJBwC6du3aSH/Fm1KM55NvjSxkJoT//PPPokWLpF/GxsbGxsYqdsvLy5sxY4Zi++nTpzEhRAghhBBCbZCJiQnZIXyc3bt3b9iwAQD69+///fffDxgwwN7enhh6/eeffxQTQgD4v/buOyCqY9Hj+ACCgKioKIgKCihqFCuCsaDRWJBojL1rNLmWG9MsaT5NNCa2JGK/9p5YImgUFTW2KLaIQcSCiCBSIohgoe/7Y17O27sLCAqscL6fv9Y5w9k5Zdfz2zlnpnLlyjNnzpw5c+adO3eOHz9+4sSJgICAhISE33777Z133jlz5kw+d7TJy/gqVapcv3692LapEJRYsW/fPvl44Uuu6tXZtOcyZCAsV65cYR8J1cY9kwAAAECR+PHHH4UQPXr0OHDggM4tuE+fPs3/b+vVq1evXr0xY8ZkZmZ+8sknS5cuPX/+/JUrV/SfCFPIgVji4uKSk5Otra2LYgteijIwTFhY2EsGwldt057LkKOMjh8/Pu0lDBkyxICNBwAAAMqGZ8+excTECCF69+6t/0DmyZMndUoWLlxobm5uZWX16NEj7XJTU1PlBsD4+Ph83rF58+byzsxNmzbpLz169Ki1tXXVqlWjo6MLuSkvqGrVqvIOz1zbk5SUVKtWLWtr659//vm5q3rVNu25DBkIAQAAABichYWFfKQtPDxcZ5Gfn59MQXKse1nYtGnT9PT0J0+ebNy4Uaf+wYMH5Qv97sGsrCzltZOT08iRI4UQM2fO1Jm74uHDhx9//PGjR4+aNm1ap06dl9y0gps1a5YQYs+ePVu3btUu12g0H3300f3797Oysnx8fJ67nldw0/L3is5eIEdizcjIaNOmjf5MIAAAAACK0BtvvLF79+4lS5ZUr169Z8+e5ubmt27d2rJly86dO62srFJSUtLT06dNmzZ06NBWrVq1a9fO0dHx7t27n376aXh4eJcuXSpVqpSQkBAQECBHgvT29laeolSGXfnmm2/69+9fr169zp07CyFmz569Y8eO5OTkVq1aTZw40dPTs3LlylevXl22bNmdO3csLS0XLlxYkntgxIgRixcvvnz58vDhw/39/Xv27FmnTp2oqKh169b98ccfQoj58+cXMJi8apuWP8MHwosXL/76669VqlSZOnWqLDl58uSgQYPkKLcmJibTp0+fM2cOowkDAAAAxWTp0qVBQUExMTGff/75559/rpSPHj36hx9+eOONN4KDg3/44YfQ0NCDBw9aWVlt3769R48eKSkpS5YsWbJkifaqPD09t2/frvyzRo0aLVq0uHz5ckBAQEBAwNixY2UgrF279tmzZ4cMGXLt2jX5BKPCxsZm48aN7u7uxbzR/8XY2DgwMHDs2LH+/v47d+6U89RLZmZmM2bMmDhxYgFX9aptWv4MHAiXLFnyySefZGVlKfPOp6SkDBkyRJnzJDs7e+7cuTk5Od99993Lv12nTp00Gs3LrwcAAAAopbp162ZlZVW7dm3tQjs7u7CwsB9++OH48eMRERHm5ubNmzcfPXp0z549hRD+/v4LFiyIi4uT/xRCtG3bNjIycsmSJWfOnImOjk5JSalTp46Tk9OwYcOUOopDhw4tXbr02rVrFStW1F7q5uZ28eJFX1/fAwcO3L1718zMrH79+u3atfvggw8qVqyovYYGDRrMnDlTCFGuXEHzi5mZmfwTFxcX/aXDhw/39PRs3bq1dmG1atX8/Py2bdv2888/h4WFZWRkODs7u7m5ffjhhzqzG+S/8kJtmsEZGTAg3blzx9XVNTMz09LS8qOPPvr222+FEMuXL580aZKxsfHatWvbtGmzaNGidevWVahQISoqqmrVqoZqaj5k12WJ7UYmpkeZx8T0KPOYmB5lnqEmpi+Z67ESvvaDypXA+WbIQWVWrFiRmZlpb29/7do1mQaFEHv27BFC9OnTZ/To0Y0bN169enXdunWfPHkiywEAAAAARcWQgfDGjRtCiH/961+Ojo6yJCMj4/Tp00KIPn36yBJjY+Pu3bsLISIiIgzUTAAAAAAomwwZCO/fvy+EcHZ2VkouXLiQlpYmhPDy8lIKa9SoIYR4dWbqAAAAAICywZCB0M7OTgiRlJSklAQGBgohHB0d5byQkpzvslKlSiXdPgAAAAAo0wwZCOWYPH5+fvKfWVlZchbI3r17a1c7evSoEMLBwaHEGwgAAAAAZZkhA+GwYcOEEMeOHXv33Xf37t07YsSI8PBwIUT//v1lhejo6HHjxoWGhgohXn/9dQM2FQAAAADKHkPOQ9i6deshQ4Zs3759/fr169evl4Xe3t4dO3YUQly8eFGZsbFjx47t27c3WEMBAAAAoCwyZA+hEGLz5s1Tp061traW//T29l63bp18nZWVJV907tx5+/bthmkfAAAAAJRdhuwhFEKYmJjMnz9/3rx5d+7csbGx0R45xt7e3tfXt1WrVp6ensbGBg6uAAAAAFD2GDgQSkZGRk5OTjqFDg4OH3zwgUHaAwAAAABqQM8bAAAAAKgUgRAAAAAAVIpACAAAAAAqRSAEAAAAAJUiEAIAAACAShEIAQAAAEClXolpJwAAAIBSxMjIyNBNAIoGPYQAAAAAoFL0EAIAAAAFpdFoDN0EoCjRQwgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUCkCIQAAAACoFIEQAAAAAFSKQAgAAAAAKkUgBAAAAACVIhACAAAAgEoRCAEAAABApQiEAAAAAKBSBEIAAAAAUKlyhm5ACXn69Km/v39oaOjTp08dHBx8fHxcXFwM3SgAAAAAMCRVBEI/P7+xY8cmJSUpJR9//PF7773n6+trbm5uwIYBAAAAgAGV/VtGDx061L9//6SkJGNj4zZt2vTu3btSpUpCiNWrV48aNcrQrQMAAAAAgynjgTA9PX3s2LHZ2dnW1tZXr149d+6cv7//gwcPfHx8hBA7duzw9/c3dBsBAAAAwDDKeCDcunVrTEyMfNGoUSNZaGpqun379nr16gkhFixYYMj2AQAAAIDhlPFA6OfnJ4Ro2LCht7e3drmVldWAAQOEEGfPnv37778N0zgAAAAAMKgyHghPnTolhOjSpYv+ot69ewshcnJyTp8+XdLNAgAAAIBXQFkOhPHx8cnJyUKIJk2a6C91d3c3MjISQty8ebOkWwYAAAAAr4CyHAijo6Pli9q1a+svNTMzq169uhAiKiqqRJsFAAAAAK+GsjwP4ePHj+ULKyurXCtYWVklJCSkpqbmvx7ZkfjydQAAAADglVKWewifPXsmX5QvXz7XCrL86dOnJdcmAAAAAHhllOUeQnNzc/kiIyMj1wrp6elCCDMzs/zXo9FoirZhL8POzi4+Pj4uLs7W1tbQbQGKxciRIzdv3rxp06YRI0YYui1Asdi8efPIkSNHjBixadMmQ7cFKBbx8fF2dna2trZxcXGGbguA5yjLPYQVKlSQL548eZJrBdk3mNcNpQAAAABQtpXlQFirVi354v79+/pLs7KyEhIShBD29vYl2iwAAAAAeDWU8UAoe/9u3LihvzQ8PDwnJ0cI0ahRo5JuGQAAAAC8AspyIBRCvP766+Kf6el1KIWyDgAAAACoTRkPhH369BFCnDt3LiwsTGfRxo0bhRCtW7euU6eOAVoGAAAAAIZWxgPhqFGj5Ozz48ePT0tLU8pXrlz5xx9/CCGmTp1qsMYBAAAAgEGV5WknhBAVKlRYunTpoEGDTp482bRp07fffrt69epHjhw5cuSIEOKtt94aMGCAodsIAAAAAIZRxgOhEGLgwIGpqamTJ08ODw9fuHChUj5o0KA1a9YYGRkZsG0AAAAAYEBlPxAKIcaOHevj4/PLL7+EhoY+e/bMwcGhT58+7u7uhm4XAAAAABiSkUajMXQbAAAAAAAGUMYHlQEAAAAA5IVACAAAAAAqRSAEAAAAAJUiEAIAAACAShEIAQAAAEClCIQAAAAAoFIEQgAAAABQKQIhAAAAAKhUOUM3AMXu2rVrGzdunDdvnqEbAhS9Z8+e7dq16/r16+XKlWvRokXv3r2NjfmdC2VKRkbGrl27QkNDjY2NmzRp0q9fv3Ll+L8bZda+fftOnz7NRQtQkow0Go2h24DiNXHixF9//TUuLs7QDQGK2I0bN7y9vSMiIuzs7NLT0x8+fNiyZcv9+/fb2dkZumlA0bh3796bb755/fp1Ozu7nJychISExo0bnzhxwsbGxtBNA4re7du3W7RoYWlpyUULUJL4Kb3MyszMvHr16owZM1auXGnotgDFYvz48TExMYGBgbGxsUlJSevXr798+fK4ceMM3S6gyEyZMiU8PPzw4cOxsbHx8fGbNm26du3a9OnTDd0uoOhlZWUNHTo0NTXV0A0BVIcewjJrzJgxGzZskK9tbW35sQ1lTHx8vJ2d3b/+9S/tnzwGDhy4e/fulJSUChUqGLBtQJHQaDQVK1bs2bPnzp07lcJWrVo9evQoPDzcgA0DisPnn3++atWq2rVrJyQkcNEClCR6CF8VT58+jYuLy8rKKkjlR48ePbfmnDlzQkJCQkJC2rVrVxQNBF5WVlZWXFzc06dPC1L5yZMnaWlp+VS4efNmhQoVOnTooF0ob6vjSgIGlJCQkJycXJCaGRkZKSkp+VRITU19++23BwwYoF1obm7O7x0wrOTk5ISEhILUzMnJSU5OLkjfw7Fjx+bPn79q1Sp7e/uXbiCAwiEQvirGjx9fs2bN4ODgfOpcunSpX79+FSpUsLa2Ll++fPPmzZcvX57X92ytWrWaNGnSpEkTKyur4mkyUDibNm2qWbPm8uXL86kTHx8/adIkW1tbKysrCwsLBweH6dOn53rR3KFDh8ePHw8bNkwpSU9P/+2332xsbBwdHYu+9UABRERE2NraDh8+PJ86mZmZ33//vaurq7m5eeXKla2trUeMGHHr1i39mpUqVdqyZcvAgQPlP5OTk1euXBkUFDRkyJBiaT1QABqNpnnz5k5OTvlX27lzZ7t27cqXL1+lShVzc/Nu3bodPnw4r8qJiYkjRowYNWqUzs8fAEqIBq+A1NTUihUrCiEuXLiQV51t27aZmprqH0EfH5/MzMx8Vt69e3dbW9tiaDVQOJ06dRJCLFiwIK8KoaGhtra2+ie5k5NTTExM/itPTEzs2LGjEGLlypVF3XCgoGbNmiWE6NWrV14VHj9+7OnpqX+SV6hQ4ciRI/msWRkqacqUKcXQcKCgjh8/Ls/YfOp88MEH+ie5kZHR3Llzc63fp08fFxeX1NRUDRctgCHQQ2h4WVlZY8eOzf8p6tDQ0HfffTczM7NmzZq+vr4hISG7d+/u0aOHEOK3336bMWNGSTUWeEG+vr7yMiIv6enpffr0iY+PNzExmTFjRlBQ0LFjx/79738LISIiIvr375/XH2ZnZy9dutTV1fXcuXOLFi3617/+VeSNBwri/Pnz33//ff51JkyYEBQUJIQYOnTogQMH/vzzz3nz5llZWT158mTAgAH53O38/fffz5kzp3379osWLZo5c2YRNx0omMTExPfeey//OuvWrVuyZIkQwsPDY8uWLaGhoatXr3Z1ddVoNF9++eXBgwd16q9YsWL//v3btm3jhibAYAydSNUrMjLSz8/vq6++ql27tnI48uohlFfDFhYW2hXS09PlA1QWFhZxcXF5vRE/tsFQkpKSAgMDFy5c2Lp1a+Ukz6uHcOnSpbLCsmXLtMunTZsmy/39/fX/KjQ01N3dXQjRu3fvsLCwYtkMIG/p6emnT59etWpVnz59lDkw8+ohDAkJMTIyEkIMGDAgJydHKd+3b5/8248//vi579inTx8jI6Pw8PAi2wbgeYKDg7du3Tp+/Hgls+XVQ5iWllarVi0hRIMGDZKSkpTyO3fu1KhRQwjRsmVL7fqRkZEWFhYff/xx7D86d+5cvXr12NjYv//+u3i3CsA/CIQGM2jQIP18nmsgfPjwoZyG+LPPPtNZpDxzuHjx4rzeiEAIQ1mxYoX+SZ5XIGzZsqUQwtPTU6c8KyurWrVqQoi+ffvqLLp48WLFihWdnZ2PHTtWLBsAPE9YWJj+SZ5XIPzkk0/kT3gPHz7UWfT2228LIWxsbLKzs5XCq1evLliwIDo6WrvmunXr8vp9BCgm+jfz5xUI9+7dKyscPHhQZ9FPP/0kF125ckUpPHXqlP4nSJKdigBKQLm8Pocobl5eXubm5vJ1XFzcoUOH8qoZGBgoxxTVf9i6WbNmDg4OUVFRBw8enDx5cvG1FngBrq6uo0aNUv65cePGvGomJCRcvnxZ5HaSm5iYeHt7b968+ciRI1lZWfLHESFEdnZ2v379atasGRQUVKVKlWJoPvB8lStX1j7JDx48GB8fn1dlebNc165dra2tdRa99dZbfn5+Dx48uHjxYps2bWRhcnLy1KlTq1SpMnbsWKWmXH+dOnWKcCuA/A0aNOjRo0fydXBw8JUrV/KqKU9ya2vrN998U2fRW2+99dFHH8k6bm5ustDV1XXz5s3a1RYsWHD37t2lS5dWrly5CDcBQD4IhAYzYcKECRMmyNfHjx/PJxDKb15zc/NmzZrpL+3evfvq1av/+uuvYmon8MI6d+7cuXNn5Z/5BMK//vpLo9EIITw8PPSX9ujRY/PmzampqZGRkS4uLrJw//79d+/enTdvXnp6us6TVzVq1FBu3gOKVc2aNZUZX4UQnTp1yisQpqen37hxQ+R9kssXf/31lxIIW7ZsaWNjs3Dhwj59+tjY2AghoqKili5dWqdOnddee61ItwPIz+LFi5XXs2bNyicQykWtW7fW/xJ2cnKqX7/+rVu3tK9YqlevrjMq75YtW+Lj4/MfqhdA0eKaqRSQw5HXqVPHxMREf6mzs7MQ4v79+wWc3g14BSlj7terV09/qTzJtasJIWSP4vTp02vqKeAEWUBJioiIyM7OFnmc5Pb29hYWFuK/T3ILC4tly5bdunWrfv36PXr06Nq1a6NGjVJSUs2LkHYAACAASURBVDZt2mRmZlZiLQcKTp7AuZ7k4p8v81wnWQFgQPQQlgJJSUlCCPk0tj5ZrtFokpKSLC0t9SsMHz68a9euxdpC4CXJk1zkcZ4rhYmJiUphq1at8hprkaHq8ArK/yQXQlSvXj0qKkr7JBdCDBw4sFGjRqtWrbp165aVldUnn3wyadIkZQoK4FVTkCsWnZNcBxctQMkjEJYCT548EUIoDxzqkD8qK9X0cd8FXn3y7DUxMVEeEdSW60nu4+Pj4+NTMs0DXp5y9ub/Za7/Td60aVNlDF7gVZaRkSGHPCjsSa6Nixag5HHLaCkgn62Sg5XntVQIIb+FgdKIkxxlnnIa53+ec5Kj9OIkB0opAmEpUKFCBSFEWlparkvT09O1qwGlkTx7s7Ky5ENWOjjJUQYoZ2/+X+ac5Ci9ypcvLwc74CQHShcCYSkgByhXnj/R8eDBA+1qQGmknL25nuec5CgD8j/JxT8PVnGSo1QryBULJznwqiEQlgL169cXQkRFRSk3Y2iLjo4WQtjY2PANi9JLnuRCiLt37+ovlSe5djWg1HF2dpb30eV6kj98+PDx48eCkxylnDyBcz3JxT9f5pzkwKuGQFgKNG3aVAjx+PHjmzdv6i+9dOmSEKJJkyYl3Syg6CgnsDyfdcjC8uXLcxmB0svCwkLOopnPSS74MkcpJ69Ycj3Jnz17FhYWJjjJgVcPgbAUePPNN+UEr7/99pvOor///vv8+fNCCG9vbwO0DCgitWrVkpcI+ie5UtilSxfmXkOp1r17dyFEYGBgRkaGziJ5kleuXPn11183QMuAIiJP8ri4OP1MeOjQoczMTMEVC/DqIRCWAtWrV+/Ro4cQwtfXV2ew5oULF2ZmZpqZmQ0ePNhArQOKxsiRI4UQAQEBwcHB2uUBAQFXrlwRQowaNcowLQOKiDzJExMT//Of/2iXx8bGbtiwQQgxdOhQU1NTg7QNKBI9e/asXr26EOK7777TLs/JyZk3b54QonHjxq1btzZM4wDkgUBYOnzzzTflypWLiop655135OVyfHz8/PnzFy5cKISYOHFinTp1DN1G4KVMnDixVq1a2dnZffv2DQwMzMzMfPLkye7du0eMGCGEaNmy5YABAwzdRuCluLu7v/3220KIqVOnrl279tGjR9nZ2WfOnPHx8Xn06FHFihW/+OILQ7cReCmWlpZffvmlEGL37t1TpkyRDw1ev3591KhRQUFBQoi5c+fmNSkFAENhYvrSoVWrVr6+vpMmTTp8+PDhw4erVKny8OFDuahz587yVzegVKtQocKePXu6dOkSGRnZrVs3Kyur9PR0eX+Rvb39r7/+yjUEyoB169bduHEjLCxs3Lhx48ePt7CwSE1NFUKYmZlt27atdu3ahm4g8LImT5587ty57du3L1q0aNGiRdpXLF988UWfPn0M2zwA+ughfCVYW1t7eXl5eXlVrFgxrzoTJkwIDAxs3769kZGR/G6tU6fOt99+e/jwYR6sQqkgT/J8erPd3d2Dg4MHDBhQvnz5x48fZ2ZmVqxY8b333rty5Yqjo2NJNhV4Mc2bN/fy8pLjauSqSpUq58+f/+STT6pVq5aVlZWamlquXDlvb+9z5875+PiUZFOBF1O3bl0vL68OHTrkVcHIyGjr1q3Lli1r0KCBEEJesbRs2XLnzp3ffvttyTUUQIEZ5TqTAV5lT548iY2NrVSpUo0aNQzdFqBYZGRk3L9/38TExN7eXk5zDJQxOTk58fHxz549s7e3Nzc3N3RzgGKRmJiYlJRka2tbqVIlQ7cFQJ4IhAAAAACgUtwyCgAAAAAqRSAEAAAAAJUiEAIAAACAShEIAQAAAEClCIQAAAAAoFIEQgAAAABQKQIhAAAAAKgUgRAAAAAAVIpACAAAAAAqRSAEAAAAAJUiEAIAAACAShEIAQAAAEClCIQAAAAAoFIEQgAAAABQKQIhAAAAAKhUOUM3AChr0tLSIiMjhRAuLi7lyvERM5h79+5lZmbWq1fP0A0pqOvXrwshatWqVbFiRUO35cXl5OTcvXs3OTnZ2dm5UqVKhm4O/k9KSsr9+/eNjY0bNGhg6LYUvUePHsXGxjo7O5uamhq6LQBQ+tBDCBSx4ODgRo0aNWrUKC4uTpbs3bv3s88+++yzz65evWrYthWTn376SW7g06dPDd2W/xMVFdWwYcMVK1YYuiGFIE+bQ4cOGbohLy4oKMjV1dXJyally5bLly83dHMM4NSpUwsXLpw+ffrGjRv//vtvQzfn/+3du7dRo0Zubm6GbkixSE5Obtas2ezZsw3dEAAolei+AIrd4cOHly1bJoRo3bp1kyZNDN2cordmzZrQ0FAhxJQpUywtLQ3dHCGEmDhxoomJyWeffWbohqhIenr6wIEDo6OjhRAODg41atQwdItKVEJCwpgxYw4cOKCUVK1a9T//+U+/fv0M2CqVcHR0fP/99+fNmzd48ODGjRsbujkAUMrQQwigrNmxY8f+/funTp1atWpVQ7dFRa5evSrT4Lp16+7evfvuu+8aukUlR6PRDB8+/MCBA3Z2dnPmzPH19W3btm1SUtKIESNu3rxp6NapwldffWVqavr+++8buiEAUPrQQwgUuxEjRrRu3VoI4e7ubui2FItvv/324cOHQohX5OG3WbNmmZmZTZw40dANURflHukBAwYYtiUl7+TJk4GBgaampidOnJAP6U2aNKldu3ZBQUErVqz48ccfDd3Ass/W1nbw4MFr164NDAx88803Dd0cAChNCIRAsfPw8PDw8DB0K4pRnz59DN2E/3f48OGwsLC+ffvSPVjCNBqNfKHCsZTOnz8vhGjYsKEyZIuxsXH//v2DgoKCg4MN2jQVGTFixNq1axcvXkwgBIBCUd1/20ARunbt2po1a/78888nT57UqVPHx8dnxIgR+tUiIyN/++03IcSECRNMTEyEEBqNRj5VOGbMmPLly69fv37v3r2xsbHW1tZdunT54IMPrKyshBAnT55cv379jRs3TExMXF1dJ0yY0KpVq1xbcuHChc2bN4eEhDx58qRu3boeHh7jxo2rXLmydp3MzMxVq1bJN61QocLRo0c3bdp069YtMzMzV1fXd999N9fU+ueff27ZsiU8PDwuLq5WrVqurq7jx4+vW7eudp0//vjj8uXLdnZ2/fv31/nzc+fObdmy5erVqykpKTY2Np6enmPGjNH5cyHEjh07EhISunXr1qBBg+vXr69atSo4ODgtLc3FxaVXr16DBg0yMjLK+zj8l8WLFwshRo4c+TLbfuDAgYiICFdXV/0ry9u3bwcEBBgbGys9kEV+NIUQu3fv3rFjR2RkpJmZWaNGjYYPH96xY8dcaz59+nTDhg2BgYExMTGVKlWqX7/+sGHD2rdvr1NNHiM3N7eOHTvevXt37ty5ly9fnjRp0qhRo567S597EGNiYvbs2SOfIxVCrFy5sly5cq+//nrLli3zWqf8UFSrVm3IkCFPnjxZuHDh4cOHW7Vq5evrq71pa9euPXr0aExMjJmZWb169d56662BAwcqJ0NWVtaqVas0Gk3Lli1ff/11nR0YGxsrhBg8eLCNjY1S/vjx4w0bNgghPDw8lB77Z8+erVu37sKFC7dv3zYxMXFycnrjjTeGDh1qbFzQpyrk5zo6OjonJ0f5K9kACwuLAq5EW3Jy8po1a37//ff4+PiaNWu6urqOHTu2UaNG+jWfu5fy8uDBg59//lkIMWLECJ3vCiHE9u3bExMTPT095d0NQohz585duHDhtdde69y5c2ho6IoVK65cuZKRkdGgQYP333+/Q4cOQojU1NQVK1YcOXIkKSmpdu3a7du3/+CDD8qXL6+sVh53a2vr4cOHZ2Zmbtu2zc/PLyYmxsbGpkmTJpMnT65du7Z+UwvyLSSE6Nixo6Oj44EDB8LDw11cXPLffADA/9MAeCGrV682NzfX+UA1b97cz89Pvo6OjpY19+3bJ0uePXsmSzIzM2VJSEhI27ZtdVbStGnTlJSU6dOn65QbGxtv27ZNpxlZWVkzZ87Uv3K1srLauXOnds3U1FS5KCoqKtfbKWfMmKGz5mHDhulXMzMz8/X11a756aefCiE8PDy0CzMzM7/88kv9hlWqVGnr1q06WyGj0ebNmzdu3Kh9+Sh16dIlMzOzIAclPj7eyMjI3Nw8PT39ZbZd9nkOGzZM/y127twphDAxMdHeUrmSlzyasnzr1q36Pa5GRkbTpk3T3wlXr17NdQiN7t27P3r0SLumPEaTJk0KCQmpXr26rDZ79uz892cBD+Lvv/+u34bvvvsunzXLD8Vrr7326NEjJY136dJFqRAcHNywYUP91fbs2TM+Pl6p9tprrwkhunXrpr3y7Oxsa2trWX/jxo3ai/bu3SvLDxw4oDTezs5O/42aNGny4MGD/PeP4saNGzKAKW93+fLlChUqCCF++umnAq5EcfHiRf3pUsqVKzdt2jSdmgXcSxqNZvPmzUKI8uXLKyWXL1+WlW/duqXfBrljtc+QL7/8UggxduxY/Q+psbHx2rVrr1+/7ujoqNOSZs2aaX8Y5XF3dnZ+8OCB/s8WFhYW+/fv125Gwb+FpI8++kjofZwBAPkjEAIv4qeffpLXJdbW1uPGjVuxYsWUKVPkb9LK1XZBAqGbm5uZmdlnn3126NChX375RemycHZ2FkK0bt163bp1R48e/eqrr8zMzOTKs7KytFsyePBg+SceHh6zZ89es2bNpEmT5LN8xsbGO3bsUGoqoWjChAlCCG9v7/Xr1x87dmzBggXVqlUTQhgZGZ05c0apP23aNFm/c+fOP/300+bNm2fNmmVvby+vTS9evKjUzDUQKp2lTZo0+eKLL9auXTt58uSaNWvKwuXLl2tXloHw/fffNzExady48eLFi3///ff169fLq1LxvHSh2LZtmxCiffv2OuWF3fYXC4QveTRlZScnJyFE/fr1p02btmrVqvfff185o8aNG6fdksuXL8veJwsLi/Hjx//nP/+ZO3duu3btZOWWLVtmZGToHKPRo0fXr19fCFGlShVPT8/du3fnvz8LeBAjIyNnz549fPhwWT5z5szZs2f/8ccf+axZCYRDhgyRm+Dm5jZz5ky5NDQ0VEYOU1PTESNGLFu2bP78+d7e3nL9jo6Oykdp6tSpQghLS0vt1KFEHbnJ2u/78ccfy7eTa4iKipInQNWqVadMmbJhw4YVK1b07dtX/m3fvn3z3z/axo0bJ4SwsrIKDg7etm2bnIPx9ddfT0tLK/hKNBpNUFCQ3HYbG5tJkyatW7fuu+++k0dNCLFu3TqlZsH3kqboAmHjxo3LlSvn7u6+fv36o0ePzpgxQ7bBwsLC3t7e1NR0+vTp8vxXOsAXL16srEQedycnp+7du8tu9t27dwcEBHz44YfyTuNq1ao9fvxYqV/wbyHpl19+EUK0bdu2UPscAFSOQAgUWnJysnw+zcXF5caNG0p5SkpK165dlSvRggRCY2Pj33//XVnD48ePlavtHj16aF/jfv/997L85s2bSuHp06dl4Zdffpmdna2U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   Installed StaticArrays ────────── v1.9.5
   Installed IntervalArithmetic ──── v0.22.14
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  [4e9b3aee] + CRlibm_jll v1.0.1+0
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⌅ [e9f186c6] + Libffi_jll v3.2.2+1
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  [f50d1b31] + Rmath_jll v0.4.2+0
  [02c8fc9c] + XML2_jll v2.12.7+0
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  [4f6342f7] + Xorg_libX11_jll v1.8.6+0
  [0c0b7dd1] + Xorg_libXau_jll v1.0.11+0
  [a3789734] + Xorg_libXdmcp_jll v1.1.4+0
  [1082639a] + Xorg_libXext_jll v1.3.6+0
  [ea2f1a96] + Xorg_libXrender_jll v0.9.11+0
  [14d82f49] + Xorg_libpthread_stubs_jll v0.1.1+0
  [c7cfdc94] + Xorg_libxcb_jll v1.15.0+0
  [c5fb5394] + Xorg_xtrans_jll v1.5.0+0
  [9a68df92] + isoband_jll v0.2.3+0
  [a4ae2306] + libaom_jll v3.9.0+0
  [0ac62f75] + libass_jll v0.15.1+0
  [f638f0a6] + libfdk_aac_jll v2.0.2+0
  [b53b4c65] + libpng_jll v1.6.43+1
  [075b6546] + libsixel_jll v1.10.3+0
  [f27f6e37] + libvorbis_jll v1.3.7+1
  [1317d2d5] + oneTBB_jll v2021.12.0+0
  [1270edf5] + x264_jll v2021.5.5+0
  [dfaa095f] + x265_jll v3.5.0+0
  [0dad84c5] + ArgTools v1.1.1
  [56f22d72] + Artifacts
  [2a0f44e3] + Base64
  [8bf52ea8] + CRC32c
  [ade2ca70] + Dates
  [8ba89e20] + Distributed
  [f43a241f] + Downloads v1.6.0
  [7b1f6079] + FileWatching
  [9fa8497b] + Future
  [b77e0a4c] + InteractiveUtils
  [4af54fe1] + LazyArtifacts
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2
  [8f399da3] + Libdl
  [37e2e46d] + LinearAlgebra
  [56ddb016] + Logging
  [d6f4376e] + Markdown
  [a63ad114] + Mmap
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.10.0
  [de0858da] + Printf
  [3fa0cd96] + REPL
  [9a3f8284] + Random
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization
  [1a1011a3] + SharedArrays
  [6462fe0b] + Sockets
  [2f01184e] + SparseArrays v1.10.0
  [10745b16] + Statistics v1.10.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test
  [cf7118a7] + UUIDs
  [4ec0a83e] + Unicode
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.4.0+0
  [e37daf67] + LibGit2_jll v1.6.4+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.2+1
  [14a3606d] + MozillaCACerts_jll v2023.1.10
  [4536629a] + OpenBLAS_jll v0.3.23+4
  [05823500] + OpenLibm_jll v0.8.1+2
  [efcefdf7] + PCRE2_jll v10.42.0+1
  [bea87d4a] + SuiteSparse_jll v7.2.1+1
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.8.0+1
  [8e850ede] + nghttp2_jll v1.52.0+1
  [3f19e933] + p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
Precompiling project...
  ✓ Chain
  ✓ RangeArrays
  ✓ IOCapture
  ✓ Calculus
  ✓ LaTeXStrings
  ✓ IndirectArrays
  ✓ AbstractFFTs
  ✓ PolygonOps
  ✓ StatsAPI
  ✓ SentinelArrays
  ✓ TensorCore
  ✓ ADTypes
  ✓ Contour
  ✓ CEnum
  ✓ TriplotBase
  ✓ OffsetArrays
  ✓ Chairmarks
  ✓ Format
  ✓ FillArrays
  ✓ IterTools
  ✓ Compat
  ✓ PkgVersion
  ✓ Grisu
  ✓ Observables
  ✓ EnumX
  ✓ StableRNGs
  ✓ Reexport
  ✓ DocStringExtensions
  ✓ RoundingEmulator
  ✓ IntervalSets
  ✓ Hyperscript
  ✓ Extents
  ✓ IrrationalConstants
  ✓ ProgressMeter
  ✓ MacroTools
  ✓ PtrArrays
  ✓ NaNMath
  ✓ LazyModules
  ✓ Ratios
  ✓ InvertedIndices
  ✓ Inflate
  ✓ Adapt
  ✓ UnicodeFun
  ✓ ConstructionBase
  ✓ StaticArraysCore
  ✓ MappedArrays
  ✓ Missings
  ✓ Crayons
  ✓ SuiteSparse
  ✓ PooledArrays
  ✓ WoodburyMatrices
  ✓ Statistics
  ✓ StringManipulation
  ✓ FileIO
  ✓ Graphite2_jll
  ✓ Libmount_jll
  ✓ LLVMOpenMP_jll
  ✓ Bzip2_jll
  ✓ ColPack_jll
  ✓ Rmath_jll
  ✓ Xorg_libXau_jll
  ✓ libpng_jll
  ✓ libfdk_aac_jll
  ✓ IntelOpenMP_jll
  ✓ Imath_jll
  ✓ LAME_jll
  ✓ EarCut_jll
  ✓ CRlibm_jll
  ✓ JpegTurbo_jll
  ✓ Ogg_jll
  ✓ oneTBB_jll
  ✓ Xorg_libXdmcp_jll
  ✓ x265_jll
  ✓ x264_jll
  ✓ libaom_jll
  ✓ LZO_jll
  ✓ Opus_jll
  ✓ Xorg_xtrans_jll
  ✓ Libffi_jll
  ✓ Libgpg_error_jll
  ✓ isoband_jll
  ✓ SIMD
  ✓ FFTW_jll
  ✓ OpenSpecFun_jll
  ✓ Xorg_libpthread_stubs_jll
  ✓ FriBidi_jll
  ✓ Libuuid_jll
  ✓ XML2_jll
  ✓ Compat → CompatLinearAlgebraExt
  ✓ InlineStrings
  ✓ Showoff
  ✓ IntervalSets → IntervalSetsRandomExt
  ✓ GeoInterface
  ✓ LogExpFunctions
  ✓ SimpleTraits
  ✓ OffsetArrays → OffsetArraysAdaptExt
  ✓ AliasTables
  ✓ Automa
  ✓ AxisAlgorithms
  ✓ SignedDistanceFields
  ✓ PDMats
  ✓ Chairmarks → StatisticsChairmarksExt
  ✓ Pixman_jll
  ✓ FreeType2_jll
  ✓ ColPack
  ✓ FixedPointNumbers
  ✓ OpenEXR_jll
  ✓ Rmath
  ✓ libsixel_jll
  ✓ libvorbis_jll
  ✓ MKL_jll
  ✓ Libgcrypt_jll
  ✓ IntervalArithmetic
  ✓ Isoband
  ✓ Gettext_jll
  ✓ StaticArrays
  ✓ FilePathsBase
  ✓ Unitful
  ✓ ChainRulesCore
  ✓ IntervalSets → IntervalSetsStatisticsExt
  ✓ StackViews
  ✓ PaddedViews
  ✓ FreeType
  ✓ DataStructures
  ✓ Fontconfig_jll
  ✓ FillArrays → FillArraysPDMatsExt
  ✓ Ratios → RatiosFixedPointNumbersExt
  ✓ XSLT_jll
  ✓ Glib_jll
  ✓ Adapt → AdaptStaticArraysExt
  ✓ StaticArrays → StaticArraysStatisticsExt
  ✓ ConstructionBase → ConstructionBaseStaticArraysExt
  ✓ FilePaths
  ✓ Unitful → ConstructionBaseUnitfulExt
  ✓ ChainRulesCore → ChainRulesCoreSparseArraysExt
  ✓ AbstractFFTs → AbstractFFTsChainRulesCoreExt
  ✓ ADTypes → ADTypesChainRulesCoreExt
  ✓ ColorTypes
  ✓ MosaicViews
  ✓ SortingAlgorithms
  ✓ AxisArrays
  ✓ LogExpFunctions → LogExpFunctionsChainRulesCoreExt
  ✓ FillArrays → FillArraysSparseArraysExt
  ✓ StaticArrays → StaticArraysChainRulesCoreExt
  ✓ QuadGK
  ✓ ConstructionBase → ConstructionBaseIntervalSetsExt
  ✓ Xorg_libxcb_jll
  ✓ TableMetadataTools
  ✓ SparseMatrixColorings
  ✓ AbstractFFTs → AbstractFFTsTestExt
  ✓ QOI
  ✓ ColorVectorSpace
  ✓ PlutoUI
  ✓ Colors
  ✓ FillArrays → FillArraysStatisticsExt
  ✓ StatsBase
  ✓ SpecialFunctions
  ✓ StructArrays
  ✓ Interpolations
  ✓ PrettyTables
  ✓ Xorg_libX11_jll
  ✓ Graphics
  ✓ Animations
  ✓ ColorBrewer
  ✓ OpenEXR
  ✓ ExactPredicates
  ✓ ColorVectorSpace → SpecialFunctionsExt
  ✓ StructArrays → StructArraysStaticArraysExt
  ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt
  ✓ Xorg_libXrender_jll
  ✓ Interpolations → InterpolationsUnitfulExt
  ✓ Xorg_libXext_jll
  ✓ DelaunayTriangulation
  ✓ ColorSchemes
  ✓ StructArrays → StructArraysSparseArraysExt
  ✓ DualNumbers
  ✓ Cairo_jll
  ✓ FFTW
  ✓ StructArrays → StructArraysAdaptExt
  ✓ HypergeometricFunctions
  ✓ HarfBuzz_jll
  ✓ GeometryBasics
  ✓ StatsFuns
  ✓ libass_jll
  ✓ Pango_jll
  ✓ PlotUtils
  ✓ Packing
  ✓ ShaderAbstractions
  ✓ MakieCore
  ✓ GridLayoutBase
  ✓ ImageCore
  ✓ FFMPEG_jll
  ✓ StatsFuns → StatsFunsChainRulesCoreExt
  ✓ Cairo
  ✓ FreeTypeAbstraction
  ✓ ImageBase
  ✓ JpegTurbo
  ✓ Sixel
  ✓ PNGFiles
  ✓ ImageAxes
  ✓ ImageMetadata
  ✓ MathTeXEngine
  ✓ Distributions
  ✓ Netpbm
  ✓ Distributions → DistributionsTestExt
  ✓ Distributions → DistributionsChainRulesCoreExt
  ✓ KernelDensity
  ✓ DataFrames
  ✓ TiffImages
  ✓ ImageIO
  ✓ DataFramesMeta
  ✓ Makie
  ✓ CairoMakie
  213 dependencies successfully precompiled in 259 seconds. 36 already precompiled.
text/plaincell_id$da55551a-228e-11ef-3448-ef1ce3658ba7kwargsidPlutoRunner_d1acb81efileR/home/runner/.julia/packages/Pluto/GVuR6/src/runner/PlutoRunner/src/PlutoRunner.jlgroupPlutoRunnerlevelLogLevel(-555)running¦outputbodymimetext/plainrootassigneelast_run_timestampAٚqFpersist_js_state·has_pluto_hook_features§cell_id$da55551a-228e-11ef-3448-ef1ce3658ba7depends_on_disabled_cells§runtime   >µpublished_object_keysdepends_on_skipped_cells§errored$185bba7c-bde5-4787-a99f-7a6cce6130a1queued¤logsrunning¦outputbody*<div class="markdown"><h3>Misc</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚsJpersist_js_state·has_pluto_hook_features§cell_id$185bba7c-bde5-4787-a99f-7a6cce6130a1depends_on_disabled_cells§runtime ^published_object_keysdepends_on_skipped_cells§errored$a36b9ca9-a9e4-44df-8eca-991c2f3aa2e0queued¤logsrunning¦outputbody@<div class="markdown"><h1>Matrix coloring comparison</h1>
</div>mimetext/htmlrootassigneelast_run_timestampAٚs)persist_js_state·has_pluto_hook_features§cell_id$a36b9ca9-a9e4-44df-8eca-991c2f3aa2e0depends_on_disabled_cells§runtime 뽵published_object_keysdepends_on_skipped_cells§errored$75e1f722-dfdd-486d-8fd1-bb62909cd6e5queued¤logsrunning¦outputbody8colpack_column_coloring (generic function with 1 method)mimetext/plainrootassigneelast_run_timestampAٚspersist_js_state·has_pluto_hook_features§cell_id$75e1f722-dfdd-486d-8fd1-bb62909cd6e5depends_on_disabled_cells§runtime lpublished_object_keysdepends_on_skipped_cells§errored$1854bc5c-70a9-4543-86e2-f81dc232959equeued¤logsrunning¦outputbody-<div class="markdown"><h3>Hessian</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚt:]persist_js_state·has_pluto_hook_features§cell_id$1854bc5c-70a9-4543-86e2-f81dc232959edepends_on_disabled_cells§runtime Vpublished_object_keysdepends_on_skipped_cells§errored$54524afe-643d-4c54-bb3c-95de77c2c7bfqueued¤logsrunning¦outputbody;<div class="markdown"><h3>SparseMatrixColorings</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚsmpersist_js_state·has_pluto_hook_features§cell_id$54524afe-643d-4c54-bb3c-95de77c2c7bfdepends_on_disabled_cells§runtime lI͵published_object_keysdepends_on_skipped_cells§errored$45220b76-353f-4c23-b2d9-aacff0e9a3c0queued¤logslinemsgn=10 - d=5: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#45220b76-353f-4c23-b2d9-aacff0e9a3c0:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

Test Summary:   | Error  Broken  Total  Time
Row coloring    |     1       7      8  0.1s
  n=10 - d=5    |     1              1  0.1s
  n=10 - d=10   |             1      1  0.0s
  n=100 - d=5   |             1      1  0.0s
  n=100 - d=10  |             1      1  0.0s
  n=100 - d=20  |             1      1  0.0s
  n=1000 - d=5  |             1      1  0.0s
  n=1000 - d=10 |             1      1  0.0s
  n=1000 - d=20 |             1      1  0.0s
text/plaincell_id$45220b76-353f-4c23-b2d9-aacff0e9a3c0kwargsidPlutoRunner_d1acb81efileR/home/runner/.julia/packages/Pluto/GVuR6/src/runner/PlutoRunner/src/PlutoRunner.jlgroupPlutoRunnerlevelLogLevel(-555)running¦outputbodymsgASome tests did not pass: 0 passed, 0 failed, 1 errored, 7 broken.stacktracecall4finish(ts::Test.DefaultTestSet; print_results::Bool)urlsfile:///cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlinlined¤fileTest.jllinelinfo_typeCore.MethodInstancepathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallfinishurlinlinedäfileTest.jllinelinfo_typeNothingpathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallmacro expansionurlinlinedäfile1Test.jl#@#==#45220b76-353f-4c23-b2d9-aacff0e9a3c0line9linfo_typeNothingpathٖ/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jl#@#==#45220b76-353f-4c23-b2d9-aacff0e9a3c0from_ccalltop-level scopeurlinlinedäfile0index.jl#==#45220b76-353f-4c23-b2d9-aacff0e9a3c0linelinfo_typeNothingpatht/home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#45220b76-353f-4c23-b2d9-aacff0e9a3c0from_c¤mime'application/vnd.pluto.stacktrace+objectrootassigneelast_run_timestampAٚt:persist_js_state·has_pluto_hook_features§cell_id$45220b76-353f-4c23-b2d9-aacff0e9a3c0depends_on_disabled_cells§runtimepublished_object_keysdepends_on_skipped_cells§errored$5b93fc63-435b-4d98-8b01-bea6ad2a84f5queued¤logslinemsg	n=10 - d=5: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=10 - d=10: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=100 - d=5: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=100 - d=10: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=100 - d=20: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=1000 - d=5: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=1000 - d=10: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

n=1000 - d=20: Error During Test at /home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5:6
 Unexpected Pass
 Expression: color1 == color2
 Got correct result, please change to @test if no longer broken.

Test Summary:      | Error  Total  Time
Symmetric coloring |     8      8  1.1s
  n=10 - d=5       |     1      1  1.0s
  n=10 - d=10      |     1      1  0.0s
  n=100 - d=5      |     1      1  0.0s
  n=100 - d=10     |     1      1  0.0s
  n=100 - d=20     |     1      1  0.0s
  n=1000 - d=5     |     1      1  0.0s
  n=1000 - d=10    |     1      1  0.0s
  n=1000 - d=20    |     1      1  0.0s
text/plaincell_id$5b93fc63-435b-4d98-8b01-bea6ad2a84f5kwargsidPlutoRunner_d1acb81efileR/home/runner/.julia/packages/Pluto/GVuR6/src/runner/PlutoRunner/src/PlutoRunner.jlgroupPlutoRunnerlevelLogLevel(-555)running¦outputbodymsgASome tests did not pass: 0 passed, 0 failed, 8 errored, 0 broken.stacktracecall4finish(ts::Test.DefaultTestSet; print_results::Bool)urlsfile:///cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlinlined¤fileTest.jllinelinfo_typeCore.MethodInstancepathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallfinishurlinlinedäfileTest.jllinelinfo_typeNothingpathl/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jlfrom_ccallmacro expansionurlinlinedäfile1Test.jl#@#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5line9linfo_typeNothingpathٖ/cache/build/builder-amdci4-0/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jl#@#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5from_ccalltop-level scopeurlinlinedäfile0index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5linelinfo_typeNothingpatht/home/runner/work/MatrixColoringComparison/MatrixColoringComparison/index.jl#==#5b93fc63-435b-4d98-8b01-bea6ad2a84f5from_c¤mime'application/vnd.pluto.stacktrace+objectrootassigneelast_run_timestampAٚtrpersist_js_state·has_pluto_hook_features§cell_id$5b93fc63-435b-4d98-8b01-bea6ad2a84f5depends_on_disabled_cells§runtimepublished_object_keysdepends_on_skipped_cells§errored$b56392cc-3575-4c02-a672-c434263d803cqueued¤logsrunning¦outputbody0<div class="markdown"><h2>Benchmarks</h2>
</div>mimetext/htmlrootassigneelast_run_timestampAٚtfpersist_js_state·has_pluto_hook_features§cell_id$b56392cc-3575-4c02-a672-c434263d803cdepends_on_disabled_cells§runtime ]Wpublished_object_keysdepends_on_skipped_cells§errored$dd62ca86-8a27-4628-8b66-8626ac7ae006queued¤logsrunning¦outputbody-<div class="markdown"><h3>Hessian</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚTpersist_js_state·has_pluto_hook_features§cell_id$dd62ca86-8a27-4628-8b66-8626ac7ae006depends_on_disabled_cells§runtime vpublished_object_keysdepends_on_skipped_cells§errored$8e4a280d-9d38-447d-be1f-ed98ce856d83queued¤logsrunning¦outputbody0plot_comparison (generic function with 1 method)mimetext/plainrootassigneelast_run_timestampAٚtpersist_js_state·has_pluto_hook_features§cell_id$8e4a280d-9d38-447d-be1f-ed98ce856d83depends_on_disabled_cells§runtime!RTصpublished_object_keysdepends_on_skipped_cells§errored$1770b4f7-4021-4345-b191-2f92ce41deedqueued¤logsrunning¦outputbodyM<script>
	
// Load the library for consistent smooth scrolling
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const indent = true
const aside = true
const title_text = "Table of Contents"
const include_definitions = false


const tocNode = html`<nav class="plutoui-toc">
	<header>
	 <span class="toc-toggle open-toc"></span>
	 <span class="toc-toggle closed-toc"></span>
	 ${title_text}
	</header>
	<section></section>
</nav>`

tocNode.classList.toggle("aside", aside)
tocNode.classList.toggle("indent", indent)


const getParentCell = el => el.closest("pluto-cell")

const getHeaders = () => {
	const depth = Math.max(1, Math.min(6, 3)) // should be in range 1:6
	const range = Array.from({length: depth}, (x, i) => i+1) // [1, ..., depth]
	
	const selector = [
		...(include_definitions ? [
			`pluto-notebook pluto-cell .pluto-docs-binding`, 
			`pluto-notebook pluto-cell assignee:not(:empty)`, 
		] : []),
		...range.map(i => `pluto-notebook pluto-cell h${i}`)
	].join(",")
	return Array.from(document.querySelectorAll(selector)).filter(el => 
		// exclude headers inside of a pluto-docs-binding block
		!(el.nodeName.startsWith("H") && el.closest(".pluto-docs-binding"))
	)
}


const document_click_handler = (event) => {
	const path = (event.path || event.composedPath())
	const toc = path.find(elem => elem?.classList?.contains?.("toc-toggle"))
	if (toc) {
		event.stopImmediatePropagation()
		toc.closest(".plutoui-toc").classList.toggle("hide")
	}
}

document.addEventListener("click", document_click_handler)


const header_to_index_entry_map = new Map()
const currently_highlighted_set = new Set()

const last_toc_element_click_time = { current: 0 }

const intersection_callback = (ixs) => {
	let on_top = ixs.filter(ix => ix.intersectionRatio > 0 && ix.intersectionRect.y < ix.rootBounds.height / 2)
	if(on_top.length > 0){
		currently_highlighted_set.forEach(a => a.classList.remove("in-view"))
		currently_highlighted_set.clear()
		on_top.slice(0,1).forEach(i => {
			let div = header_to_index_entry_map.get(i.target)
			div.classList.add("in-view")
			currently_highlighted_set.add(div)
			
			/// scroll into view
			/*
			const toc_height = tocNode.offsetHeight
			const div_pos = div.offsetTop
			const div_height = div.offsetHeight
			const current_scroll = tocNode.scrollTop
			const header_height = tocNode.querySelector("header").offsetHeight
			
			const scroll_to_top = div_pos - header_height
			const scroll_to_bottom = div_pos + div_height - toc_height
			
			// if we set a scrollTop, then the browser will stop any currently ongoing smoothscroll animation. So let's only do this if you are not currently in a smoothscroll.
			if(Date.now() - last_toc_element_click_time.current >= 2000)
				if(current_scroll < scroll_to_bottom){
					tocNode.scrollTop = scroll_to_bottom
				} else if(current_scroll > scroll_to_top){
					tocNode.scrollTop = scroll_to_top
				}
			*/
		})
	}
}
let intersection_observer_1 = new IntersectionObserver(intersection_callback, {
	root: null, // i.e. the viewport
  	threshold: 1,
	rootMargin: "-15px", // slightly smaller than the viewport
	// delay: 100,
})
let intersection_observer_2 = new IntersectionObserver(intersection_callback, {
	root: null, // i.e. the viewport
  	threshold: 1,
	rootMargin: "15px", // slightly larger than the viewport
	// delay: 100,
})

const render = (elements) => {
	header_to_index_entry_map.clear()
	currently_highlighted_set.clear()
	intersection_observer_1.disconnect()
	intersection_observer_2.disconnect()

		let last_level = `H1`
	return html`${elements.map(h => {
	const parent_cell = getParentCell(h)

		let [className, title_el] = h.matches(`.pluto-docs-binding`) ? ["pluto-docs-binding-el", h.firstElementChild] : [h.nodeName, h]

	const a = html`<a 
		class="${className}" 
		title="${title_el.innerText}"
		href="#${parent_cell.id}"
	>${title_el.innerHTML}</a>`
	/* a.onmouseover=()=>{
		parent_cell.firstElementChild.classList.add(
			'highlight-pluto-cell-shoulder'
		)
	}
	a.onmouseout=() => {
		parent_cell.firstElementChild.classList.remove(
			'highlight-pluto-cell-shoulder'
		)
	} */
		
		
	a.onclick=(e) => {
		e.preventDefault();
		last_toc_element_click_time.current = Date.now()
		scrollIntoView(h, {
			behavior: 'smooth', 
			block: 'start',
		}).then(() => 
			// sometimes it doesn't scroll to the right place
			// solution: try a second time!
			scrollIntoView(h, {
				behavior: 'smooth', 
				block: 'start',
			})
	   )
	}

	const row =  html`<div class="toc-row ${className} after-${last_level}">${a}</div>`
		intersection_observer_1.observe(title_el)
		intersection_observer_2.observe(title_el)
		header_to_index_entry_map.set(title_el, row)

	if(className.startsWith("H"))
		last_level = className
		
	return row
})}`
}

const invalidated = { current: false }

const updateCallback = () => {
	if (!invalidated.current) {
		tocNode.querySelector("section").replaceWith(
			html`<section>${render(getHeaders())}</section>`
		)
	}
}
updateCallback()
setTimeout(updateCallback, 100)
setTimeout(updateCallback, 1000)
setTimeout(updateCallback, 5000)

const notebook = document.querySelector("pluto-notebook")


// We have a mutationobserver for each cell:
const mut_observers = {
	current: [],
}

const createCellObservers = () => {
	mut_observers.current.forEach((o) => o.disconnect())
	mut_observers.current = Array.from(notebook.querySelectorAll("pluto-cell")).map(el => {
		const o = new MutationObserver(updateCallback)
		o.observe(el, {attributeFilter: ["class"]})
		return o
	})
}
createCellObservers()

// And one for the notebook's child list, which updates our cell observers:
const notebookObserver = new MutationObserver(() => {
	updateCallback()
	createCellObservers()
})
notebookObserver.observe(notebook, {childList: true})

// And finally, an observer for the document.body classList, to make sure that the toc also works when it is loaded during notebook initialization
const bodyClassObserver = new MutationObserver(updateCallback)
bodyClassObserver.observe(document.body, {attributeFilter: ["class"]})

// Hide/show the ToC when the screen gets small
let match_listener = () => 
	tocNode.classList.toggle("hide", (tocNode.closest("pluto-editor") ?? document.body).scrollWidth < 1000)
for(let s of [1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000]) {
	let m = matchMedia(`(max-width: ${s}px)`)
	m.addListener(match_listener)
	invalidation.then(() => m.removeListener(match_listener))
}
match_listener()

invalidation.then(() => {
	invalidated.current = true
	intersection_observer_1.disconnect()
	intersection_observer_2.disconnect()
	notebookObserver.disconnect()
	bodyClassObserver.disconnect()
	mut_observers.current.forEach((o) => o.disconnect())
	document.removeEventListener("click", document_click_handler)
})

return tocNode
</script>
<style>
@media not print {

.plutoui-toc {
	font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen-Sans, Cantarell, "Apple Color Emoji",
		"Segoe UI Emoji", "Segoe UI Symbol", system-ui, sans-serif;
	--main-bg-color: #fafafa;
	--pluto-output-color: hsl(0, 0%, 36%);
	--pluto-output-h-color: hsl(0, 0%, 21%);
	--sidebar-li-active-bg: rgb(235, 235, 235);
	--icon-filter: unset;
}

@media (prefers-color-scheme: dark) {
	.plutoui-toc {
		--main-bg-color: #303030;
		--pluto-output-color: hsl(0, 0%, 90%);
		--pluto-output-h-color: hsl(0, 0%, 97%);
		--sidebar-li-active-bg: rgb(82, 82, 82);
		--icon-filter: invert(1);
	}
}

.plutoui-toc.aside {
	color: var(--pluto-output-color);
	position: fixed;
	right: 1rem;
	top: 5rem;
	width: min(80vw, 300px);
	padding: 0.5rem;
	padding-top: 0em;
	/* border: 3px solid rgba(0, 0, 0, 0.15); */
	border-radius: 10px;
	/* box-shadow: 0 0 11px 0px #00000010; */
	max-height: calc(100vh - 5rem - 90px);
	overflow: auto;
	z-index: 40;
	background-color: var(--main-bg-color);
	transition: transform 300ms cubic-bezier(0.18, 0.89, 0.45, 1.12);
}

.plutoui-toc.aside.hide {
	transform: translateX(calc(100% - 28px));
}
.plutoui-toc.aside.hide section {
	display: none;
}
.plutoui-toc.aside.hide header {
	margin-bottom: 0em;
	padding-bottom: 0em;
	border-bottom: none;
}
}  /* End of Media print query */
.plutoui-toc.aside.hide .open-toc,
.plutoui-toc.aside:not(.hide) .closed-toc,
.plutoui-toc:not(.aside) .closed-toc {
	display: none;
}

@media (prefers-reduced-motion) {
  .plutoui-toc.aside {
	transition-duration: 0s;
  }
}

.toc-toggle {
	cursor: pointer;
    padding: 1em;
    margin: -1em;
    margin-right: -0.7em;
    line-height: 1em;
    display: flex;
}

.toc-toggle::before {
    content: "";
    display: inline-block;
    height: 1em;
    width: 1em;
    background-image: url("https://cdn.jsdelivr.net/gh/ionic-team/ionicons@5.5.1/src/svg/list-outline.svg");
	/* generated using https://dopiaza.org/tools/datauri/index.php */
    background-image: url("data:image/svg+xml;base64,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");
    background-size: 1em;
	filter: var(--icon-filter);
}

.aside .toc-toggle.open-toc:hover::before {
    background-image: url("https://cdn.jsdelivr.net/gh/ionic-team/ionicons@5.5.1/src/svg/arrow-forward-outline.svg");
	/* generated using https://dopiaza.org/tools/datauri/index.php */
    background-image: url("data:image/svg+xml;base64,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");
}
.aside .toc-toggle.closed-toc:hover::before {
    background-image: url("https://cdn.jsdelivr.net/gh/ionic-team/ionicons@5.5.1/src/svg/arrow-back-outline.svg");
	/* generated using https://dopiaza.org/tools/datauri/index.php */
    background-image: url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI1MTIiIGhlaWdodD0iNTEyIiB2aWV3Qm94PSIwIDAgNTEyIDUxMiI+PHRpdGxlPmlvbmljb25zLXY1LWE8L3RpdGxlPjxwb2x5bGluZSBwb2ludHM9IjI0NCA0MDAgMTAwIDI1NiAyNDQgMTEyIiBzdHlsZT0iZmlsbDpub25lO3N0cm9rZTojMDAwO3N0cm9rZS1saW5lY2FwOnJvdW5kO3N0cm9rZS1saW5lam9pbjpyb3VuZDtzdHJva2Utd2lkdGg6NDhweCIvPjxsaW5lIHgxPSIxMjAiIHkxPSIyNTYiIHgyPSI0MTIiIHkyPSIyNTYiIHN0eWxlPSJmaWxsOm5vbmU7c3Ryb2tlOiMwMDA7c3Ryb2tlLWxpbmVjYXA6cm91bmQ7c3Ryb2tlLWxpbmVqb2luOnJvdW5kO3N0cm9rZS13aWR0aDo0OHB4Ii8+PC9zdmc+");
}



.plutoui-toc header {
	display: flex;
	align-items: center;
	gap: .3em;
	font-size: 1.5em;
	/* margin-top: -0.1em; */
	margin-bottom: 0.4em;
	padding: 0.5rem;
	margin-left: 0;
	margin-right: 0;
	font-weight: bold;
	/* border-bottom: 2px solid rgba(0, 0, 0, 0.15); */
	position: sticky;
	top: 0px;
	background: var(--main-bg-color);
	z-index: 41;
}
.plutoui-toc.aside header {
	padding-left: 0;
	padding-right: 0;
}

.plutoui-toc section .toc-row {
	white-space: nowrap;
	overflow: hidden;
	text-overflow: ellipsis;
	padding: .1em;
	border-radius: .2em;
}

.plutoui-toc section .toc-row.H1 {
	margin-top: 1em;
}


.plutoui-toc.aside section .toc-row.in-view {
	background: var(--sidebar-li-active-bg);
}


	
.highlight-pluto-cell-shoulder {
	background: rgba(0, 0, 0, 0.05);
	background-clip: padding-box;
}

.plutoui-toc section a {
	text-decoration: none;
	font-weight: normal;
	color: var(--pluto-output-color);
}
.plutoui-toc section a:hover {
	color: var(--pluto-output-h-color);
}

.plutoui-toc.indent section a.H1 {
	font-weight: 700;
	line-height: 1em;
}

.plutoui-toc.indent section .after-H2 a { padding-left: 10px; }
.plutoui-toc.indent section .after-H3 a { padding-left: 20px; }
.plutoui-toc.indent section .after-H4 a { padding-left: 30px; }
.plutoui-toc.indent section .after-H5 a { padding-left: 40px; }
.plutoui-toc.indent section .after-H6 a { padding-left: 50px; }

.plutoui-toc.indent section a.H1 { padding-left: 0px; }
.plutoui-toc.indent section a.H2 { padding-left: 10px; }
.plutoui-toc.indent section a.H3 { padding-left: 20px; }
.plutoui-toc.indent section a.H4 { padding-left: 30px; }
.plutoui-toc.indent section a.H5 { padding-left: 40px; }
.plutoui-toc.indent section a.H6 { padding-left: 50px; }


.plutoui-toc.indent section a.pluto-docs-binding-el,
.plutoui-toc.indent section a.ASSIGNEE
	{
	font-family: JuliaMono, monospace;
	font-size: .8em;
	/* background: black; */
	font-weight: 700;
    font-style: italic;
	color: var(--cm-var-color); /* this is stealing a variable from Pluto, but it's fine if that doesnt work */
}
.plutoui-toc.indent section a.pluto-docs-binding-el::before,
.plutoui-toc.indent section a.ASSIGNEE::before
	{
	content: "> ";
	opacity: .3;
}
</style>
mimetext/htmlrootassigneelast_run_timestampAٚsjXpersist_js_state·has_pluto_hook_features§cell_id$1770b4f7-4021-4345-b191-2f92ce41deeddepends_on_disabled_cells§runtime>Ƶpublished_object_keysdepends_on_skipped_cells§errored$1399b39f-1311-47c8-b474-b33a8955017fqueued¤logsrunning¦outputbody-<div class="markdown"><h3>ColPack</h3>
</div>mimetext/htmlrootassigneelast_run_timestampAٚspersist_js_state·has_pluto_hook_features§cell_id$1399b39f-1311-47c8-b474-b33a8955017fdepends_on_disabled_cells§runtime ^ published_object_keysdepends_on_skipped_cells§errored±cell_dependencies !$1011abc1-0877-462b-9cff-5dee8be097e0precedence_heuristic	cell_id$1011abc1-0877-462b-9cff-5dee8be097e0downstream_cells_mapdata_hessian$8155f0dd-1843-487b-96ff-a5c4cbc6db7cupstream_cells_map:floorbenchmark_coloring$d1980626-0f1d-4af8-b7ba-1c798959b024^Int$434a9de0-45d5-4c42-8e23-5ea7ab0437a1precedence_heuristic	cell_id$434a9de0-45d5-4c42-8e23-5ea7ab0437a1downstream_cells_mapsmc_symmetric_coloring$5b93fc63-435b-4d98-8b01-bea6ad2a84f5$d1980626-0f1d-4af8-b7ba-1c798959b024upstream_cells_mapGreedyColoringAlgorithmsymmetric_coloringNaturalOrder$da55551a-228e-11ef-3448-ef1ce3658ba7$0f1bbfde-dede-4a8a-8914-9046a4673b88precedence_heuristic	cell_id$0f1bbfde-dede-4a8a-8914-9046a4673b88downstream_cells_mapcolpack_row_coloring$45220b76-353f-4c23-b2d9-aacff0e9a3c0upstream_cells_mapget_colorsColPackPartialColoring$b92ce3d8-0a67-4433-b722-031156b52cacprecedence_heuristic	cell_id$b92ce3d8-0a67-4433-b722-031156b52cacdownstream_cells_maplinestyles$8e4a280d-9d38-447d-be1f-ed98ce856d83upstream_cells_map$484036b2-8aba-476a-9336-2ddab9d1f23aprecedence_heuristic	cell_id$484036b2-8aba-476a-9336-2ddab9d1f23adownstream_cells_mapmarkers$8e4a280d-9d38-447d-be1f-ed98ce856d83upstream_cells_map$75824976-a2f9-4c5e-80b3-de5bae5ddaa3precedence_heuristic	cell_id$75824976-a2f9-4c5e-80b3-de5bae5ddaa3downstream_cells_mapupstream_cells_mapplot_comparison$8e4a280d-9d38-447d-be1f-ed98ce856d83data_jacobian$6f2a0f6e-068f-434e-af81-8a5b89b37e01$867c780f-beed-4b10-a5dc-eb1385ef3941precedence_heuristic	cell_id$867c780f-beed-4b10-a5dc-eb1385ef3941downstream_cells_mapupstream_cells_map@md_strgetindex$6b5deada-4836-498b-aeb2-6817274733f9precedence_heuristic	cell_id$6b5deada-4836-498b-aeb2-6817274733f9downstream_cells_mapupstream_cells_map@md_strgetindex$18375b3b-a8b9-4d6d-8605-10525e5e2614precedence_heuristic	cell_id$18375b3b-a8b9-4d6d-8605-10525e5e2614downstream_cells_mapcolpack_symmetric_coloring$5b93fc63-435b-4d98-8b01-bea6ad2a84f5$d1980626-0f1d-4af8-b7ba-1c798959b024upstream_cells_mapget_colorsColPackColoring$4f6561fc-15e0-4a28-9ae2-9c4979244222precedence_heuristic	cell_id$4f6561fc-15e0-4a28-9ae2-9c4979244222downstream_cells_mapd_values_small$86948d71-6933-422d-9345-d9e5f51f60ab$45220b76-353f-4c23-b2d9-aacff0e9a3c0$5b93fc63-435b-4d98-8b01-bea6ad2a84f5upstream_cells_map<=filter$86948d71-6933-422d-9345-d9e5f51f60abprecedence_heuristic	cell_id$86948d71-6933-422d-9345-d9e5f51f60abdownstream_cells_mapupstream_cells_map (Test.copyTest.rethrow@test_brokenTest.Random.seed!Test.ErrorTest.pop_testsetTest$da55551a-228e-11ef-3448-ef1ce3658ba7Test.Random.set_global_seed!Test.eval_testsmc_column_coloring$733a96e6-e5c9-435b-861b-725ce0a5a48dTest.Base.current_exceptionsTest.copy!Test.isacolpack_column_coloring$75e1f722-dfdd-486d-8fd1-bb62909cd6e5Test.recordTest._check_testsetTest.ThrewTest.Expr/Test.default_rng^Test.typeofTest.failfast_print==d_values_small$4f6561fc-15e0-4a28-9ae2-9c4979244222Test.push!Test.push_testsetTest.get_testset_depth:Test.finishTest.get_testsetTest.trigger_test_failure_breakTest.==Test.!Test.do_broken_testsprandTest.===+@testsetTest.>$ccace506-dcb4-4fa4-bc02-280c0c25f60bprecedence_heuristic	cell_id$ccace506-dcb4-4fa4-bc02-280c0c25f60bdownstream_cells_mapupstream_cells_map@md_strgetindex$6e1fed93-3b43-41eb-9993-ac8ced7244e7precedence_heuristic	cell_id$6e1fed93-3b43-41eb-9993-ac8ced7244e7downstream_cells_mapupstream_cells_map@md_strgetindex$8155f0dd-1843-487b-96ff-a5c4cbc6db7cprecedence_heuristic	cell_id$8155f0dd-1843-487b-96ff-a5c4cbc6db7cdownstream_cells_mapupstream_cells_mapplot_comparison$8e4a280d-9d38-447d-be1f-ed98ce856d83data_hessian$1011abc1-0877-462b-9cff-5dee8be097e0$d1980626-0f1d-4af8-b7ba-1c798959b024precedence_heuristic	cell_id$d1980626-0f1d-4af8-b7ba-1c798959b024downstream_cells_mapbenchmark_coloring$6f2a0f6e-068f-434e-af81-8a5b89b37e01$1011abc1-0877-462b-9cff-5dee8be097e0upstream_cells_map (stringSymmetric>islessquantilesmc_column_coloring$733a96e6-e5c9-435b-861b-725ce0a5a48dcolpack_symmetric_coloring$18375b3b-a8b9-4d6d-8605-10525e5e2614Base.CoreLogging.!length<nothing'Base.CoreLogging.Base.fixup_stdlib_pathcolpack_column_coloring$75e1f722-dfdd-486d-8fd1-bb62909cd6e5/Base.invokelatestDataFramesparse==Base.CoreLogging.convertminimumBase.CoreLogging.invokelatestBase.CoreLogging.===d_values$7d6c9868-b770-4b2e-a4c2-fd55b2a61bf5AbstractVectorcollect#___this_pluto_module_nameSymbol@bemediansmc_symmetric_coloring$434a9de0-45d5-4c42-8e23-5ea7ab0437a1Basepush!-Base.CoreLogging.isaInt##progress_id#225sortBase.CoreLogging.>=sprand@progress$0e776e0b-23c9-484f-8cd5-457c9f9dc088precedence_heuristic	cell_id$0e776e0b-23c9-484f-8cd5-457c9f9dc088downstream_cells_mapsmc_row_coloring$45220b76-353f-4c23-b2d9-aacff0e9a3c0upstream_cells_mapGreedyColoringAlgorithmrow_coloringNaturalOrder$da55551a-228e-11ef-3448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= benchmark_coloring(
	n_values=floor.(Int, 10 .^ (1:0.3:4)),
	matrix=:Hessian
)metadatashow_logsèdisabled®skip_as_script«code_folded$434a9de0-45d5-4c42-8e23-5ea7ab0437a1cell_id$434a9de0-45d5-4c42-8e23-5ea7ab0437a1codemfunction smc_symmetric_coloring(H)
	return symmetric_coloring(H, GreedyColoringAlgorithm(NaturalOrder()))
endmetadatashow_logsèdisabled®skip_as_script«code_folded$0f1bbfde-dede-4a8a-8914-9046a4673b88cell_id$0f1bbfde-dede-4a8a-8914-9046a4673b88codeٷfunction colpack_row_coloring(J)
    method = "ROW_PARTIAL_DISTANCE_TWO"
	order = "NATURAL"
    coloring = ColPackPartialColoring(J, method, order)
    return get_colors(coloring)
endmetadatashow_logsèdisabled®skip_as_script«code_folded$b92ce3d8-0a67-4433-b722-031156b52caccell_id$b92ce3d8-0a67-4433-b722-031156b52caccode9linestyles = [:solid, :dash, :dashdot, :dashdotdot, :dot]metadatashow_logsèdisabled®skip_as_script«code_folded$484036b2-8aba-476a-9336-2ddab9d1f23acell_id$484036b2-8aba-476a-9336-2ddab9d1f23acode6markers = [:circle, :star5, :rect, :utriangle, :cross]metadatashow_logsèdisabled®skip_as_script«code_folded$75824976-a2f9-4c5e-80b3-de5bae5ddaa3cell_id$75824976-a2f9-4c5e-80b3-de5bae5ddaa3code0plot_comparison(data_jacobian; matrix=:Jacobian)metadatashow_logsèdisabled®skip_as_script«code_folded$867c780f-beed-4b10-a5dc-eb1385ef3941cell_id$867c780f-beed-4b10-a5dc-eb1385ef3941codemd"""
## Setup
"""metadatashow_logsèdisabled®skip_as_script«code_folded$6b5deada-4836-498b-aeb2-6817274733f9cell_id$6b5deada-4836-498b-aeb2-6817274733f9codemd"""
### Jacobian
"""metadatashow_logsèdisabled®skip_as_script«code_folded$18375b3b-a8b9-4d6d-8605-10525e5e2614cell_id$18375b3b-a8b9-4d6d-8605-10525e5e2614code٢function colpack_symmetric_coloring(H)
    method = "STAR"
	order = "NATURAL"
    coloring = ColPackColoring(H, method, order)
    return get_colors(coloring)
endmetadatashow_logsèdisabled®skip_as_script«code_folded$4f6561fc-15e0-4a28-9ae2-9c4979244222cell_id$4f6561fc-15e0-4a28-9ae2-9c4979244222code,d_values_small(n) = filter(<=(n), [5,10,20])metadatashow_logsèdisabled®skip_as_script«code_folded$86948d71-6933-422d-9345-d9e5f51f60abcell_id$86948d71-6933-422d-9345-d9e5f51f60abcode@testset "Column coloring" begin
	@testset "n=$n - d=$d" for n in 10 .^ (1:3), d in d_values_small(n)
		J = sprand(n, n + 1, d/n)
		color1 = smc_column_coloring(J)
		color2 = colpack_column_coloring(J)
		@test_broken color1 == color2
	end
endmetadatashow_logsèdisabled®skip_as_script«code_folded$ccace506-dcb4-4fa4-bc02-280c0c25f60bcell_id$ccace506-dcb4-4fa4-bc02-280c0c25f60bcodemd"""
## Correctness
"""metadatashow_logsèdisabled®skip_as_script«code_folded$6e1fed93-3b43-41eb-9993-ac8ced7244e7cell_id$6e1fed93-3b43-41eb-9993-ac8ced7244e7codemd"""
A comparison between matrix coloring packages in Julia.

At the moment we include:

- [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl)
- [ColPack.jl](https://github.com/exanauts/ColPack.jl)
"""metadatashow_logsèdisabled®skip_as_script«code_folded$8155f0dd-1843-487b-96ff-a5c4cbc6db7ccell_id$8155f0dd-1843-487b-96ff-a5c4cbc6db7ccode.plot_comparison(data_hessian; matrix=:Hessian)metadatashow_logsèdisabled®skip_as_script«code_folded$d1980626-0f1d-4af8-b7ba-1c798959b024cell_id$d1980626-0f1d-4af8-b7ba-1c798959b024codefunction benchmark_coloring(; n_values::AbstractVector{Int}, matrix::Symbol)
	data = DataFrame()
	nd_pairs = collect(
		(n, d)
		for n in sort(n_values; rev=true)
		for d in sort(d_values(n); rev=true)
	)
	@progress for (n, d) in nd_pairs
		p = d/n
		for package in ("SparseMatrixColorings", "ColPack")
			bench = if package == "SparseMatrixColorings" && matrix == :Jacobian
				@be sprand(n, n, p) smc_column_coloring seconds=5 evals=1 samples=100
			elseif package == "SparseMatrixColorings" && matrix == :Hessian
				@be sparse(Symmetric(sprand(n, n, p))) smc_symmetric_coloring seconds=5 evals=1 samples=100
			elseif package == "ColPack" && matrix == :Jacobian
				@be sprand(n, n, p) colpack_column_coloring seconds=5 evals=1 samples=100
			elseif package == "ColPack" && matrix == :Hessian
				@be sparse(Symmetric(sprand(n, n, p))) colpack_symmetric_coloring seconds=5 evals=1 samples=100
			end
			row = (;
				package=string(package),
				matrix=string(matrix),
				n=n, d=d,
				evals=Int(minimum(bench).evals),
				samples=length(bench.samples),
				time_min=minimum(bench).time,
				time_median=median(bench).time,
				time_q25=quantile(bench, 0.25).time,
				time_q75=quantile(bench, 0.75).time,
			)
			push!(data, row)
		end
	end
	return data
endmetadatashow_logsèdisabled®skip_as_script«code_folded$0e776e0b-23c9-484f-8cd5-457c9f9dc088cell_id$0e776e0b-23c9-484f-8cd5-457c9f9dc088codeafunction smc_row_coloring(J)
	return row_coloring(J, GreedyColoringAlgorithm(NaturalOrder()))
endmetadatashow_logsèdisabled®skip_as_script«code_folded$6f2a0f6e-068f-434e-af81-8a5b89b37e01cell_id$6f2a0f6e-068f-434e-af81-8a5b89b37e01codebdata_jacobian = benchmark_coloring(
	n_values = floor.(Int, 10 .^ (1:0.3:4)),
	matrix=:Jacobian,
)metadatashow_logsèdisabled®skip_as_script«code_folded$c9eae8aa-2163-4290-b479-91cf194be563cell_id$c9eae8aa-2163-4290-b479-91cf194be563codemd"""
### Jacobian
"""metadatashow_logsèdisabled®skip_as_script«code_folded$733a96e6-e5c9-435b-861b-725ce0a5a48dcell_id$733a96e6-e5c9-435b-861b-725ce0a5a48dcodegfunction smc_column_coloring(J)
	return column_coloring(J, GreedyColoringAlgorithm(NaturalOrder()))
endmetadatashow_logsèdisabled®skip_as_script«code_folded$7d6c9868-b770-4b2e-a4c2-fd55b2a61bf5cell_id$7d6c9868-b770-4b2e-a4c2-fd55b2a61bf5code-d_values(n) = filter(<=(n), [5,10,20,50,100])metadatashow_logsèdisabled®skip_as_script«code_folded$da55551a-228e-11ef-3448-ef1ce3658ba7cell_id$da55551a-228e-11ef-3448-ef1ce3658ba7codekbegin
	using Pkg
    Pkg.activate(mktempdir())
	Pkg.add([
	    Pkg.PackageSpec("CairoMakie"),
		Pkg.PackageSpec("Chairmarks"),
		Pkg.PackageSpec(url="https://github.com/exanauts/ColPack.jl", rev="gd/super_upgrade_colpack"),
		Pkg.PackageSpec("DataFrames"),
		Pkg.PackageSpec("DataFramesMeta"),
		Pkg.PackageSpec("LinearAlgebra"),
		Pkg.PackageSpec("PlutoUI"),
		Pkg.PackageSpec("ProgressLogging"),
		Pkg.PackageSpec("SparseMatrixColorings"),
		Pkg.PackageSpec("SparseArrays"),
		Pkg.PackageSpec("StableRNGs"),
		Pkg.PackageSpec("Statistics"),
		Pkg.PackageSpec("Test"),
    ])
	
    using CairoMakie
	using Chairmarks
	using ColPack
	using DataFrames
	using DataFramesMeta
	using LinearAlgebra
	using PlutoUI
	using ProgressLogging
	using SparseMatrixColorings
	using SparseMatrixColorings: NaturalOrder
	using SparseArrays
	using StableRNGs
	using Statistics
	using Test
endmetadatashow_logsèdisabled®skip_as_script«code_folded$185bba7c-bde5-4787-a99f-7a6cce6130a1cell_id$185bba7c-bde5-4787-a99f-7a6cce6130a1codemd"""
### Misc
"""metadatashow_logsèdisabled®skip_as_script«code_folded$a36b9ca9-a9e4-44df-8eca-991c2f3aa2e0cell_id$a36b9ca9-a9e4-44df-8eca-991c2f3aa2e0code&md"""
# Matrix coloring comparison
"""metadatashow_logsèdisabled®skip_as_script«code_folded$75e1f722-dfdd-486d-8fd1-bb62909cd6e5cell_id$75e1f722-dfdd-486d-8fd1-bb62909cd6e5codeٺfunction colpack_column_coloring(J)
	method = "COLUMN_PARTIAL_DISTANCE_TWO"
	order = "NATURAL"
    coloring = ColPackPartialColoring(J, method, order)
    return get_colors(coloring)
endmetadatashow_logsèdisabled®skip_as_script«code_folded$1854bc5c-70a9-4543-86e2-f81dc232959ecell_id$1854bc5c-70a9-4543-86e2-f81dc232959ecodemd"""
### Hessian
"""metadatashow_logsèdisabled®skip_as_script«code_folded$54524afe-643d-4c54-bb3c-95de77c2c7bfcell_id$54524afe-643d-4c54-bb3c-95de77c2c7bfcode#md"""
### SparseMatrixColorings
"""metadatashow_logsèdisabled®skip_as_script«code_folded$45220b76-353f-4c23-b2d9-aacff0e9a3c0cell_id$45220b76-353f-4c23-b2d9-aacff0e9a3c0code@testset "Row coloring" begin
	@testset "n=$n - d=$d" for n in 10 .^ (1:3), d in d_values_small(n)
		J = sprand(n, n + 1, d/n)
		color1 = smc_row_coloring(J)
		color2 = colpack_row_coloring(J)
		@test_broken color1 == color2
	end
endmetadatashow_logsèdisabled®skip_as_script«code_folded$5b93fc63-435b-4d98-8b01-bea6ad2a84f5cell_id$5b93fc63-435b-4d98-8b01-bea6ad2a84f5code
@testset "Symmetric coloring" begin
	@testset "n=$n - d=$d" for n in 10 .^ (1:3), d in d_values_small(n)
		H = sparse(Symmetric(sprand(n, n, d/n)))
		color1 = smc_symmetric_coloring(H)
		color2 = colpack_symmetric_coloring(H)
		@test_broken color1 == color2
	end
endmetadatashow_logsèdisabled®skip_as_script«code_folded$b56392cc-3575-4c02-a672-c434263d803ccell_id$b56392cc-3575-4c02-a672-c434263d803ccodemd"""
## Benchmarks
"""metadatashow_logsèdisabled®skip_as_script«code_folded$dd62ca86-8a27-4628-8b66-8626ac7ae006cell_id$dd62ca86-8a27-4628-8b66-8626ac7ae006codemd"""
### Hessian
"""metadatashow_logsèdisabled®skip_as_script«code_folded$8e4a280d-9d38-447d-be1f-ed98ce856d83cell_id$8e4a280d-9d38-447d-be1f-ed98ce856d83codefunction plot_comparison(data; matrix::Symbol)
	
	data = @orderby(data, :package, :matrix, :n, :d)
	smc_data = @rsubset(data, :package=="SparseMatrixColorings")
	cp_data = @rsubset(data, :package=="ColPack")
	ymax = 1.05 * maximum(cp_data[!, :time_min] ./ smc_data[!, :time_min])
	
	fig = Figure()
	ax = Axis(
		fig[1, 1], title="$matrix coloring",
		xlabel="dimension (number of rows & columns)",
		ylabel="speedup SMC / ColPack",
		xscale=log10,
		limits=(nothing, (0, ymax)),
	)

	hl = hlines!(ax, [1.0], linewidth=2, color=:black)
	for (i, d) in enumerate(sort(unique(data[!, :d])))
		smc = @rsubset(data, :d==d && :package=="SparseMatrixColorings")
		cp = @rsubset(data, :d==d && :package=="ColPack")
		sl = scatterlines!(
			smc[!, :n], cp[!, :time_min] ./ smc[!, :time_min];
			label="$d nz/col", linestyle=linestyles[i], marker=markers[i],
			markersize=12,
		)
	end
	band!(
		ax, [minimum(data[!, :n]), maximum(data[!, :n])], [0, 0], [1, 1],
		color=(:black, 0.1), label="ColPack\nfaster here"
	)
	Legend(fig[1, 2], ax)
	return fig
endmetadatashow_logsèdisabled®skip_as_script«code_folded$1770b4f7-4021-4345-b191-2f92ce41deedcell_id$1770b4f7-4021-4345-b191-2f92ce41deedcodeTableOfContents()metadatashow_logsèdisabled®skip_as_script«code_folded$1399b39f-1311-47c8-b474-b33a8955017fcell_id$1399b39f-1311-47c8-b474-b33a8955017fcodemd"""
### ColPack
"""metadatashow_logsèdisabled®skip_as_script«code_foldedënotebook_id$e951f2da-27d4-11ef-35db-559d267a6df3in_temp_dir¨metadata