涂掉
发表于 2025-3-27 00:42:29
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Frenetic
发表于 2025-3-27 03:12:22
Death of Distance in Science? A Gravity Approach to Research Collaborationeasingly depends on a region’s position in global networks rather than its specific local characteristics such as institutions, endowments and amenities (‘space of flows’ versus the ‘space of places’).
TIA742
发表于 2025-3-27 07:46:44
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怎样才咆哮
发表于 2025-3-27 10:05:56
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gout109
发表于 2025-3-27 15:39:37
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不出名
发表于 2025-3-27 20:26:53
learns user community by using community detection method and message clustering by using short texts clustering algorithm from social contextual information on the basis of homophily assumption, respectively. Then CBRMF incorporates the impacts of homophily characteristics on users and messages as
Jingoism
发表于 2025-3-27 22:23:31
Pier Paolo Saviotti, to alleviate the staleness problem, SGC updates model weight with the accumulation of delayed gradients at local, called local update technique. The experiments over the sparse high-dimensional models and deep neural networks indicate that SGC can compress 99.99% gradients for every iteration with
nutrients
发表于 2025-3-28 04:44:39
Koen Frenken,Jarno Hoekman,Suzanne Kok,Roderik Ponds,Frank van Oort,Joep van Vlietrtional to the their degrees, depending on how repeatedly the area has been visited. Experimental results show that our solver significantly outperforms state-of-the-art MinDS solvers. Also we conducted several experiments to show the individual impacts of our novelties.
真实的你
发表于 2025-3-28 07:59:33
Frank Beckenbach,Ramón Briegel,Maria Daskalakis logging. We conduct trace-driven experiments on the TPC-C benchmark to measure the performance of our proposal. The results show that LFLogging achieves up to 4~5X improvement in system throughput than existing approaches including WAL and PCMLogging.
寡头政治
发表于 2025-3-28 11:00:59
Andreas Pyka,Nigel Gilbert,Petra Ahrweiler represented as vector space model (VSM) using TF-IDF after words segmentation. To improve performance, we construct a set of judicial stop words. Besides, as TF-IDF generates a high dimensional feature vector, which leads to an extremely high time complexity, we utilize three dimensional reduction