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Titlebook: Uncivil Engagement and Unruly Politics; Disruptive Intervent Femke Kaulingfreks Book 2015 Femke Kaulingfreks 2015 Unruly politics.informal

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發(fā)表于 2025-3-25 06:38:46 | 只看該作者
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發(fā)表于 2025-3-25 10:37:27 | 只看該作者
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發(fā)表于 2025-3-25 12:36:50 | 只看該作者
s in defect detection, and improves detection performance significantly. We performed extensive experiments on the MVTecAD dataset, and the results revealed that our approach attained advanced performance in both anomaly detection and segmentation localization, thereby confirming the efficacy of our
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發(fā)表于 2025-3-25 19:52:27 | 只看該作者
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發(fā)表于 2025-3-25 21:33:51 | 只看該作者
Femke Kaulingfrekse model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, su
26#
發(fā)表于 2025-3-26 03:29:31 | 只看該作者
Femke Kaulingfreksn resulting in unclear timestamps. Therefore, this article combines the conclusion dependency graph into a process dependency graph to determine the identification order of the timeliness of each process data; By constructing a weighted timeliness graph (WTG) and path single flux, a data timeliness
27#
發(fā)表于 2025-3-26 06:05:39 | 只看該作者
Femke Kaulingfreksney, Australia in February 2022.*.The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application..?. .* The conf
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發(fā)表于 2025-3-26 11:54:15 | 只看該作者
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發(fā)表于 2025-3-26 16:13:30 | 只看該作者
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發(fā)表于 2025-3-26 18:47:34 | 只看該作者
Femke Kaulingfreks previous studies, the BART model has often been used for multi-hop question generation(MQG) task, and it significantly improved the quality of generated questions compared to recurrent neural network-based models. However, due to the differences between downstream tasks and pre-training tasks, BART
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