Titlebook: Smelter Grade Alumina from Bauxite; History, Best Practi Benny E. Raahauge,Fred S. Williams Book 2022 The Editor(s) (if applicable) and The

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粗鲁的人

Benny E. Raahauge,Fred S. Williamspproach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not scale well beyond a few variables. In this work, we bring together the ideas of graphical m

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Agility

青少年

György (George) Bánvölgyi,Brady Hanemanrrent . approach towards assessing the statistical significance of a motif . relies on counting its occurrences across the network, and comparing that count to its expected count under some null generative model. This approach can be misleading due to .. That is, there may be a large count for a mot

rheumatism

Manfred Bach,Fred Schoenbrunnrowth size of the information cascade is widely used in the fields of preventing rumor spread, viral marketing, recommendation system and so on. However, most of the existing methods either cannot fully capture the structural representation of the cascade graph, or cannot effectively utilize the dyn

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Quentin D. Avery,Manfred Bach,Stephan Beaulieu,David Cooling,Ken Evans,Fred Schoenbrunn, PE. Research has shown that many natural graphs can be organized in hierarchical communities, leading to approaches that use these communities to improve the quality of node representations. However, these approaches do not take advantage of the learned representations to also improve the quality of t

refraction

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Steven J. Healyior estimates of the number of patterns is useful, as these can be used to appropriately set the threshold and avoid waiting endlessly for an unmanageable number of patterns. Moreover, we argue that the number of patterns for different thresholds is an interesting summary statistic of the data: the

climax

Robert LaMacchia,Raphael Costavious methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the same set of parameters for different stocks. On the other hand, learning different models for each stock would face data sparsity