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Titlebook: Index; Formula Index. C22–C Rainer Bohrer,Paul K?mpf,Hans-Jürgen Richter-Ditte Book 1994Latest edition Springer-Verlag Berlin Heidelberg 19

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樓主: 強烈的愿望
21#
發(fā)表于 2025-3-25 06:36:01 | 只看該作者
22#
發(fā)表于 2025-3-25 10:19:54 | 只看該作者
,A Brief Derivation of the Asymptotic Distribution of Pearson’s Statistic and an Accurate Approximattribution is introduced as an accurate approximation to be utilized when the chi-squared distribution proves to be inadequate. It is also explained that the exact probability mass function of this test statistic can be readily determined from its moment-generating function via symbolic computations.
23#
發(fā)表于 2025-3-25 14:17:06 | 只看該作者
,TransFGU: A Top-Down Approach to?Fine-Grained Unsupervised Semantic Segmentation,sting methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and
24#
發(fā)表于 2025-3-25 18:11:24 | 只看該作者
25#
發(fā)表于 2025-3-25 23:56:19 | 只看該作者
,Gründung und Untergrundentw?sserung,hon die verschiedenen Betonpfahlgründungen eine solche Mannigfaltigkeit der Verfahren aufweisen, da? es nicht m?glich und auch nicht zweckm??ig ist, sie alle zu behandeln. Erw?hnt seien z. B. nur: Simplex-Pf?hle, Strau?-Pf?hle, Pre?luftPf ?hle System Wolfsholz, Mast-Pf?hle, Franki-Pf?hle usw. Für di
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發(fā)表于 2025-3-26 01:53:08 | 只看該作者
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發(fā)表于 2025-3-26 05:20:45 | 只看該作者
28#
發(fā)表于 2025-3-26 11:29:50 | 只看該作者
29#
發(fā)表于 2025-3-26 16:24:39 | 只看該作者
Revisiting the Cubic UOV Signature Schemehemes called CSSv and SVSv, which are secure against Hashimoto’s attack and all other known attacks on multivariate schemes. Especially our second construction SVSv is very efficient and outperforms current multivariate signature schemes such as UOV and Rainbow in terms of key and signature size.
30#
發(fā)表于 2025-3-26 17:51:19 | 只看該作者
Clinical Ethics for Consultation Practice in the target user’s personal social network. Combining these three scores, the recommendation list containing unknown but interesting items can be generated. Experimental results show that UBI outperforms traditional methods in terms of the percentages of unknown and interesting items in the recom
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