Titlebook: Soviet Economic Facts, 1917–81; Roger A. Clarke,Dubravko J. I. Matko Book 1983Latest edition Roger A. Clarke and Dubravko J.I. Matko 1983

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書目名稱Soviet Economic Facts, 1917–81影響因子(影響力)




書目名稱Soviet Economic Facts, 1917–81影響因子(影響力)學(xué)科排名




書目名稱Soviet Economic Facts, 1917–81網(wǎng)絡(luò)公開度




書目名稱Soviet Economic Facts, 1917–81網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Soviet Economic Facts, 1917–81被引頻次




書目名稱Soviet Economic Facts, 1917–81被引頻次學(xué)科排名




書目名稱Soviet Economic Facts, 1917–81年度引用




書目名稱Soviet Economic Facts, 1917–81年度引用學(xué)科排名




書目名稱Soviet Economic Facts, 1917–81讀者反饋




書目名稱Soviet Economic Facts, 1917–81讀者反饋學(xué)科排名

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Overview: 978-1-349-06368-0978-1-349-06366-6

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https://doi.org/10.1007/978-1-349-06366-6Eastern Europe; economics; history; Union of Soviet Socialist Republics (USSR)

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James P. J. Chong,J. Julian Blow be solved. Therefore, it is of great significance to study effective methods to recover missing blocks in time series for better analysis and mining. In this paper, we focus on the situation of continuous missing blocks in multivariate time series. Aiming at the blackout missing block pattern, we p

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,Schr?dinger’s Zebra: Applying Mutual Information Maximization to Graphical Halftoning, to be represented recognizably by elements that are only black or white. With this in mind, we ask what a halftoning algorithm would look like that maximizes the mutual information between images and their halftoned renditions. Here, we find such an algorithm and explore its properties. The algorit

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: Second-Class Citizenship and its Discontentsonal breadwinner function by way of women’s participation in paid labor. Paradoxically, the working-class men who had disproportionately . unification later developed the strongest feelings of relative deprivation, leading a significant number to ally themselves with rightwing populist forces after

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https://doi.org/10.1007/0-387-27925-3eed for XAI applications to improve the explainabilities of some ML models. To this end, XAI techniques and tools for interpreting and enhancing artificial intelligence applications, especially in the field of ambient intelligence (AmI), are discussed. Subsequently, the requirements for trustable AI