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| :Added; thanks! [[Special:Contributions/172.71.158.230|172.71.158.230]] 00:14, 22 November 2022 (UTC) | | :Added; thanks! [[Special:Contributions/172.71.158.230|172.71.158.230]] 00:14, 22 November 2022 (UTC) |
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− | : If you don't mind sharing: which program did you use? Did you tweak things like relative distance / camera FOV, to effectively select a specific point in the continuum that makes up the {{w|Dolly Zoom}} effect, and at the limit on one end results in {{w|orthographic projection}}? (Edit 10 minutes later: a better article to look at is {{w|Perspective distortion (photography)}}) Or did you just leave it at whatever the default is? Can you recreate the image with the two extremes, and share them? And lastly - can you upload the image (and potentially the new images) to the wiki directly, so they can be embedded in the page? Thanks! --[[User:NeatNit|NeatNit]] ([[User talk:NeatNit|talk]]) 17:21, 22 November 2022 (UTC) | + | : If you don't mind sharing: which program did you use? Did you tweak things like relative distance / camera FOV, to effectively select a specific point in the continuum that makes up the {{w|Dolly Zoom}} effect, and at the limit on one end results in {{w|orthographic projection}}? Or did you just leave it at whatever the default is? Can you recreate the image with the two extremes, and share them? And lastly - can you upload the image (and potentially the new images) to the wiki directly, so they can be embedded in the page? Thanks! --[[User:NeatNit|NeatNit]] ([[User talk:NeatNit|talk]]) 17:21, 22 November 2022 (UTC) |
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| This one shows the beauty of Explainxkcd: people reading the explanation are likely to learn accessible methods of substantial practical utility. [[Special:Contributions/162.158.166.173|162.158.166.173]] 00:38, 22 November 2022 (UTC) | | This one shows the beauty of Explainxkcd: people reading the explanation are likely to learn accessible methods of substantial practical utility. [[Special:Contributions/162.158.166.173|162.158.166.173]] 00:38, 22 November 2022 (UTC) |
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| Whoever wrote the 1st explanation needs to go touch grass and learn how real people talk, pissed me off so much I just effectively rewrote the whole thing from scratch [[Special:Contributions/172.71.202.46|172.71.202.46]] 06:34, 22 November 2022 (UTC) | | Whoever wrote the 1st explanation needs to go touch grass and learn how real people talk, pissed me off so much I just effectively rewrote the whole thing from scratch [[Special:Contributions/172.71.202.46|172.71.202.46]] 06:34, 22 November 2022 (UTC) |
− | :Intrigued, looking at the first explanation (give or take that person's initial small errors/omissions) I personally find it more to the point than what it has become. Not to say the complete rewrite was wrong, but it got it not that much closer to the mythical perfection. IMO. [[Special:Contributions/141.101.76.169|141.101.76.169]] 20:29, 22 November 2022 (UTC)
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| Going in a different direction than "this is silly" - if we ignore the "viewing point/parallax" issue, doing a change of basis like this is similar to linear methods like [SVD https://hadrienj.github.io/assets/images/ch12_svd/ch11_SVD_geometry.png] & [https://jakevdp.github.io/PythonDataScienceHandbook/figures/05.09-PCA-rotation.png PCA], and considering the graph as a mappingg in a "higher dimension" is similar to the "kernel trick" popularized by [https://i.ytimg.com/vi/wqSTBCguVyU/maxresdefault.jpg Support] [https://miro.medium.com/max/4800/1*gtF6KeL7b9zNHd7pXtC1Nw.png Vector] [https://dinhanhthi.com/img/post/ML/support-vector-machine/svm-3.jpg Machines] 11:31, 22 November 2022 (UTC) | | Going in a different direction than "this is silly" - if we ignore the "viewing point/parallax" issue, doing a change of basis like this is similar to linear methods like [SVD https://hadrienj.github.io/assets/images/ch12_svd/ch11_SVD_geometry.png] & [https://jakevdp.github.io/PythonDataScienceHandbook/figures/05.09-PCA-rotation.png PCA], and considering the graph as a mappingg in a "higher dimension" is similar to the "kernel trick" popularized by [https://i.ytimg.com/vi/wqSTBCguVyU/maxresdefault.jpg Support] [https://miro.medium.com/max/4800/1*gtF6KeL7b9zNHd7pXtC1Nw.png Vector] [https://dinhanhthi.com/img/post/ML/support-vector-machine/svm-3.jpg Machines] 11:31, 22 November 2022 (UTC) |
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| 1. Apply the affine transformation which generates the image on the right with the tilted paper. | | 1. Apply the affine transformation which generates the image on the right with the tilted paper. |
| 2. Apply the statistical tests which Randall Munroe is alluding to. | | 2. Apply the statistical tests which Randall Munroe is alluding to. |
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− | :Knock yourself out:
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− | {{cot|Digitized data courtesy https://apps.automeris.io/wpd/}}
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− | <pre>
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− | 0.000000, 0.015366
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− | 0.001887, 0.000000
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− | 0.002830, 0.041488
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− | 0.024528, 0.060695
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− | 0.033019, 0.014597
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− | 0.038679, 0.009988
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− | 0.044340, 0.072220
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− | 0.047170, 0.055317
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− | 0.050000, 0.072220
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− | 0.064858, 0.092964
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− | 0.070215, 0.117001
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− | 0.088207, 0.088354
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− | 0.091037, 0.122928
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− | 0.091037, 0.109099
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− | 0.100943, 0.140215
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− | 0.103773, 0.165338
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− | 0.106603, 0.178246
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− | 0.128891, 0.171331
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− | 0.147641, 0.196685
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− | 0.146226, 0.187465
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− | 0.162264, 0.215124
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− | 0.180188, 0.264910
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− | 0.182452, 0.218812
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− | 0.202830, 0.275052
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− | 0.204245, 0.261222
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− | 0.208490, 0.272747
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− | 0.217923, 0.293491
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− | 0.227358, 0.267369
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− | 0.230322, 0.234880
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− | 0.241744, 0.311930
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− | 0.256603, 0.344199
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− | 0.262263, 0.338930
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− | 0.299056, 0.376467
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− | 0.308254, 0.420261
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− | 0.313206, 0.417956
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− | 0.336791, 0.456371
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− | 0.344338, 0.433322
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− | 0.355659, 0.456371
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− | 0.367923, 0.496323
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− | 0.374055, 0.503237
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− | 0.388206, 0.503237
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− | 0.389621, 0.514762
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− | 0.409433, 0.533201
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− | 0.412263, 0.525518
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− | 0.415093, 0.540884
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− | 0.432074, 0.555328
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− | 0.446225, 0.599275
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− | 0.443395, 0.588519
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− | 0.449526, 0.537811
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− | 0.449055, 0.588519
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− | 0.468866, 0.609263
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− | 0.487263, 0.627702
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− | 0.490093, 0.636922
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− | 0.516979, 0.670727
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− | 0.523448, 0.697179
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− | 0.519809, 0.662276
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− | 0.548111, 0.697618
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− | 0.551413, 0.740642
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− | 0.550941, 0.689935
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− | 0.565092, 0.726813
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− | 0.572168, 0.724508
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− | 0.576413, 0.772911
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− | 0.582073, 0.772911
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− | 0.582073, 0.763691
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− | 0.601177, 0.785588
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− | 0.604714, 0.791350
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− | 0.625335, 0.775545
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− | 0.643394, 0.817473
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− | 0.664620, 0.855119
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− | 0.688812, 0.871693
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− | 0.688003, 0.821643
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− | 0.710374, 0.925035
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− | 0.707544, 0.806716
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− | 0.715091, 0.888156
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− | 0.717921, 0.880473
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− | 0.724148, 0.976665
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− | 0.749054, 0.927010
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− | 0.757544, 0.961913
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− | 0.763204, 0.959608
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− | 0.783016, 0.983426
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− | 0.781601, 0.971133
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− | 0.797166, 1.028756
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− | 0.802827, 1.031060
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− | 0.805657, 0.999560
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− | 0.821223, 0.966523
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− | 0.822638, 0.957304
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− | 0.842449, 1.038744
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− | 0.843864, 1.028756
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− | 0.859431, 1.049500
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− | 0.865091, 1.058719
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− | 0.876411, 1.077159
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− | 0.882072, 1.086378
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− | 0.889147, 1.077159
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− | 0.901883, 1.024914
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− | 0.904714, 1.017231
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− | 0.908605, 1.100208
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− | 0.913204, 1.107122
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− | 0.936553, 1.130171
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− | 0.937261, 1.116342
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− | 0.967447, 1.159370
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− | 0.969806, 1.205310
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− | 0.978301, 1.104817
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− | 0.983956, 1.101525
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− | 1.000000, 1.167820
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− | </pre>
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− | {{cob}}
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− | :104 points. [[Special:Contributions/172.71.154.39|172.71.154.39]] 19:17, 22 November 2022 (UTC)
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− | ::: I only count 69 distinct dots, although a handful look like they might be merged pairs. What's up with that? [[Special:Contributions/172.70.210.48|172.70.210.48]] 04:54, 26 November 2022 (UTC)
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− | :: Can someone please check my work https://colab.research.google.com/drive/1c_7Qj3S1VXtL-AckfSfHCd4ofGYYDYH5 and tell me if I'm doing it right? I'm pretty sure I don't really know what I'm doing. I kind of cargo cult-coded the Savitzky-Golay filter stuff linked from the explanation and have zero understanding of what's actually going on. [[Special:Contributions/172.70.211.126|172.70.211.126]] 21:58, 22 November 2022 (UTC)
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− | :: Here's how Randall seems to be suggesting to do it, based on the light gray figures: [superceded] -- Can someone please help fix the residuals on the second plot? [[Special:Contributions/172.71.154.158|172.71.154.158]] 01:18, 23 November 2022 (UTC)
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− | ::: I fixed the residuals and added an inset confidence interval comparisons for the two slopes, split by both their maximum difference and by the maximum sum of the r<sup>2</sup> values: https://colab.research.google.com/drive/1apKDIN5FE32mtGiQew5cE6wK6m6eM_Fr It's not clear from the gray text which method Randall is suggesting. [[Special:Contributions/172.70.211.126|172.70.211.126]] 22:07, 24 November 2022 (UTC)
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− | :::: I added this to the end of the Colab notebook:
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− | # Later in the Explainxkcd explanation, a "Significance of the Difference between Two Slopes Calculator"
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− | # at https://www.danielsoper.com/statcalc/calculator.aspx?id=103 is recommended, so ... we get:
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− | # split by maximum slope difference: (as shown in green and red)
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− | # t-Value: 5.52246856
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− | # Degrees of freedom: 100
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− | # Probability: 0.00000027 (significant as < 0.05)
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− | # split by maximum sum of r²s:
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− | # t-Value: 6.25478825
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− | # Degrees of freedom: 100
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− | # Probability: 0.00000001 (also very significant)
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− |
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− | # So, while the latter might technically be about 27 times more likely, both represent undoutably
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− | # different linear fits. Perhaps someone can ask Randall which he was suggesting, if indeed either?
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− | :::: What's the most reliable way to ask Randall this? Twitter? Email? Google Chat? [[Special:Contributions/172.71.158.91|172.71.158.91]] 23:08, 24 November 2022 (UTC)
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− | ::::: Why don't you generate a series of mildly noisy datasets of two slightly different but random lines each and see which method gets closest to the generating parameters? Also, please put more blank lines in your code, and consider right-aligning the comments. [[Special:Contributions/172.70.211.146|172.70.211.146]] 01:56, 26 November 2022 (UTC)
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− | What's funny is people are doing a lot of statistics and computer magic when you can just tilt your screen like the comic says and get the same effect :P {{unsigned ip|172.70.54.52|16:14, 25 November 2022}}
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− | :(Ɔ┴∩) ᄅᄅ0ᄅ ɹǝqɯǝʌoN ϛᄅ 'ㄣϛ:ㄥƖ ᄅᄅᄅ˙ᄅ9Ɩ˙0ㄥ˙ᄅㄥƖ ¡ƃuoɹʍ ʇᴉq ɐ ʇuǝʍ ƃuᴉɥʇǝɯos ʇnq 'ʇɐɥʇ pǝᴉɹʇ I
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