update jupyter-book
This commit is contained in:
Morten Hjorth-Jensen
@@ -353,6 +353,11 @@ const thebe_selector_output = ".output, .cell_output"
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Week 44, Convolutional Neural Networks (CNN)
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week45.html">
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Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -1335,10 +1340,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.03726977611299938
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4.262466605211622
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[[0.83354173 2.32104741]
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[2.32104741 7.32657772]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.07753675114613659
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3.7056640671387777
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[[ 1.04061312 3.10280114]
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[ 3.10280114 10.15295172]]
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</pre></div>
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</div>
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</div>
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@@ -1375,10 +1380,10 @@ a more brute force way. Here we scale the mean values for each column of the des
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07860645184491624
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1.2936571226638978
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[[1. 0.66747609]
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[0.66747609 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08394467351014487
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1.6207140122788155
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[[1. 0.62913273]
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[0.62913273 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -1408,30 +1413,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.26697306 0.29778864]
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[-2.19869916 -6.93548204]
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[ 0.7618873 2.50605353]
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[-0.42441412 -1.40921665]
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[ 0.4871032 0.64995019]
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[ 1.20793948 2.61639509]
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[ 0.97826564 2.87523613]
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[-1.00907717 -2.99547963]
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[ 0.78152283 3.2948209 ]
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[-0.31755493 -0.90006616]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-1.51530018 -5.52405562]
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[-0.12754161 -0.1185269 ]
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[ 1.52226126 3.76791825]
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[ 0.53838544 1.31599532]
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[ 0.62034064 2.82815583]
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[-0.57902694 -2.39362955]
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[-0.9992839 -3.26786103]
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[-1.07415889 -3.24735297]
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[ 0.61880799 2.07584629]
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[ 0.99551619 4.56351038]]
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0 1
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0 -0.266973 0.297789
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1 -2.198699 -6.935482
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2 0.761887 2.506054
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3 -0.424414 -1.409217
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4 0.487103 0.649950
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5 1.207939 2.616395
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6 0.978266 2.875236
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7 -1.009077 -2.995480
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8 0.781523 3.294821
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9 -0.317555 -0.900066
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0 -1.515300 -5.524056
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1 -0.127542 -0.118527
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2 1.522261 3.767918
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3 0.538385 1.315995
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4 0.620341 2.828156
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5 -0.579027 -2.393630
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6 -0.999284 -3.267861
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7 -1.074159 -3.247353
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8 0.618808 2.075846
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9 0.995516 4.563510
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0 1
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0 1.000000 0.978369
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1 0.978369 1.000000
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0 1.000000 0.977638
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1 0.977638 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1488,37 +1493,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
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0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.0 0.090448 0.082216 0.088200 0.084006 0.079901 0.077733 0.074768
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2 0.0 0.082216 0.075560 0.081246 0.077794 0.074400 0.072599 0.070081
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3 0.0 0.088200 0.081246 0.091136 0.087474 0.083840 0.083597 0.080857
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4 0.0 0.084006 0.077794 0.087474 0.084194 0.080928 0.080799 0.078308
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5 0.0 0.079901 0.074400 0.083840 0.080928 0.078015 0.077985 0.075733
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6 0.0 0.077733 0.072599 0.083597 0.080799 0.077985 0.079026 0.076796
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7 0.0 0.074768 0.070081 0.080857 0.078308 0.075733 0.076796 0.074738
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8 0.0 0.071947 0.067679 0.078227 0.075911 0.073561 0.074636 0.072742
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9 0.0 0.069255 0.065380 0.075695 0.073598 0.071461 0.072539 0.070801
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10 0.0 0.068030 0.064276 0.075365 0.073257 0.071105 0.072914 0.071121
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11 0.0 0.065704 0.062251 0.073098 0.071167 0.069187 0.070966 0.069305
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12 0.0 0.063502 0.060329 0.070937 0.069172 0.067353 0.069098 0.067561
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13 0.0 0.061415 0.058504 0.068877 0.067267 0.065599 0.067306 0.065886
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14 0.0 0.059435 0.056769 0.066909 0.065446 0.063921 0.065586 0.064277
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1 0.0 0.064918 0.066625 0.070709 0.066738 0.062466 0.066234 0.061496
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2 0.0 0.066625 0.069854 0.074673 0.071414 0.067564 0.071285 0.066813
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3 0.0 0.070709 0.074673 0.081315 0.078121 0.074215 0.079025 0.074295
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4 0.0 0.066738 0.071414 0.078121 0.075719 0.072469 0.076899 0.072783
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5 0.0 0.062466 0.067564 0.074215 0.072469 0.069799 0.073864 0.070314
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6 0.0 0.066234 0.071285 0.079025 0.076899 0.073864 0.078892 0.074884
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7 0.0 0.061496 0.066813 0.074295 0.072783 0.070314 0.074884 0.071456
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8 0.0 0.057063 0.062501 0.069694 0.068679 0.066688 0.070846 0.067922
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9 0.0 0.052982 0.058442 0.065333 0.064718 0.063128 0.066918 0.064427
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10 0.0 0.060254 0.065730 0.073835 0.072549 0.070283 0.075233 0.071953
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11 0.0 0.055830 0.061349 0.069058 0.068223 0.066402 0.070895 0.068100
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12 0.0 0.051815 0.057304 0.064628 0.064155 0.062706 0.066794 0.064414
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13 0.0 0.048182 0.053593 0.060546 0.060365 0.059226 0.062955 0.060930
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14 0.0 0.044899 0.050198 0.056798 0.056851 0.055971 0.059383 0.057659
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8 9 10 11 12 13 14
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0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.071947 0.069255 0.068030 0.065704 0.063502 0.061415 0.059435
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2 0.067679 0.065380 0.064276 0.062251 0.060329 0.058504 0.056769
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3 0.078227 0.075695 0.075365 0.073098 0.070937 0.068877 0.066909
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4 0.075911 0.073598 0.073257 0.071167 0.069172 0.067267 0.065446
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5 0.073561 0.071461 0.071105 0.069187 0.067353 0.065599 0.063921
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6 0.074636 0.072539 0.072914 0.070966 0.069098 0.067306 0.065586
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7 0.072742 0.070801 0.071121 0.069305 0.067561 0.065886 0.064277
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8 0.070901 0.069107 0.069371 0.067680 0.066054 0.064491 0.062988
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9 0.069107 0.067454 0.067659 0.066088 0.064575 0.063119 0.061718
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10 0.069371 0.067659 0.068506 0.066860 0.065272 0.063740 0.062261
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11 0.067680 0.066088 0.066860 0.065318 0.063830 0.062393 0.061005
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12 0.066054 0.064575 0.065272 0.063830 0.062436 0.061089 0.059788
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13 0.064491 0.063119 0.063740 0.062393 0.061089 0.059829 0.058609
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14 0.062988 0.061718 0.062261 0.061005 0.059788 0.058609 0.057468
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1 0.057063 0.052982 0.060254 0.055830 0.051815 0.048182 0.044899
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2 0.062501 0.058442 0.065730 0.061349 0.057304 0.053593 0.050198
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3 0.069694 0.065333 0.073835 0.069058 0.064628 0.060546 0.056798
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4 0.068679 0.064718 0.072549 0.068223 0.064155 0.060365 0.056851
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5 0.066688 0.063128 0.070283 0.066402 0.062706 0.059226 0.055971
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6 0.070846 0.066918 0.075233 0.070895 0.066794 0.062955 0.059383
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7 0.067922 0.064427 0.071953 0.068100 0.064414 0.060930 0.057659
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8 0.064834 0.061731 0.068536 0.065121 0.061814 0.058658 0.055672
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9 0.061731 0.058976 0.065134 0.062107 0.059142 0.056285 0.053560
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10 0.068536 0.065134 0.072904 0.069119 0.065481 0.062027 0.058774
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11 0.065121 0.062107 0.069119 0.065772 0.062517 0.059399 0.056439
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12 0.061814 0.059142 0.065481 0.062517 0.059603 0.056786 0.054091
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13 0.058658 0.056285 0.062027 0.059399 0.056786 0.054238 0.051782
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14 0.055672 0.053560 0.058774 0.056439 0.054091 0.051782 0.049540
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</pre></div>
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</div>
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</div>
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