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Titlebook: Schwarz Methods and Multilevel Preconditioners for Boundary Element Methods; Ernst P. Stephan,Thanh Tran Book 2021 The Editor(s) (if appli

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發(fā)表于 2025-3-23 11:24:07 | 只看該作者
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發(fā)表于 2025-3-23 16:17:33 | 只看該作者
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發(fā)表于 2025-3-23 21:11:21 | 只看該作者
Ernst P. Stephan,Thanh Tranoven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning
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發(fā)表于 2025-3-24 00:04:23 | 只看該作者
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發(fā)表于 2025-3-24 04:49:53 | 只看該作者
Ernst P. Stephan,Thanh Trannumber of top ranking documents from each algorithm form a pool. Traditionally, expensive experts judge the pool of documents for relevance. We propose and test two hybrid algorithms as alternatives that reduce assessment costs and are effective. The machine part selects documents to judge from the
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發(fā)表于 2025-3-24 07:08:08 | 只看該作者
Ernst P. Stephan,Thanh Trane manner. In order to produce a tweet timeline that constitutes a summary of a given topic, a TTG system typically retrieves a list of potentially-relevant tweets over which the timeline is eventually generated. In such design, dependency of the performance of the timeline generation step on that of
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發(fā)表于 2025-3-24 13:10:27 | 只看該作者
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發(fā)表于 2025-3-24 22:44:30 | 只看該作者
han continually and holistically. This type of data is ideal for compression-based archiving, provided that random-access to small fragments of the original data can be achieved without needing to decompress everything. The recent RLZ (relative Lempel Ziv) compression approach uses a semi-static mod
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發(fā)表于 2025-3-25 02:30:18 | 只看該作者
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