Additive
发表于 2025-3-28 16:22:48
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占卜者
发表于 2025-3-28 19:04:13
https://doi.org/10.1007/978-3-031-22665-6ff up to the knee by a streetcar by which Musya – his home nickname – got hit) and was wearing an extremely uncomfortable and heavy artificial leg (at the time these were the only kind available). The artificial leg would often rub against the remaining flesh of the limb, and E. M. would feel and be
STYX
发表于 2025-3-29 02:17:20
https://doi.org/10.1007/978-3-031-22665-6t many other important things as well. Braverman not only created a new direction in economic theory – an original theory of disequilibrium economic systems, but also developed its fundamental provisions.
Meager
发表于 2025-3-29 04:05:32
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Conquest
发表于 2025-3-29 08:04:13
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牵索
发表于 2025-3-29 11:43:57
Deep Learning in the Natural Sciences: Applications to Physicsfor the natural sciences. Here we describe applications of deep learning to four areas of experimental sub-atomic physics — high-energy physics, antimatter physics, neutrino physics, and dark matter physics.
Strength
发表于 2025-3-29 15:53:59
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Mhc-Molecule
发表于 2025-3-29 22:14:32
Braverman and His Theory of Disequilibrium Economicst many other important things as well. Braverman not only created a new direction in economic theory – an original theory of disequilibrium economic systems, but also developed its fundamental provisions.
DEI
发表于 2025-3-30 02:18:52
Potential Functions for Signals and Symbolic Sequencesasis is placed on a generalized probabilistic approach to construction of potential functions. This approach covers both vector signals and symbolic sequences at once and leads to a large family of potential functions based on the notion of a random transformation of signals and sequences, which can
AMPLE
发表于 2025-3-30 07:14:16
Braverman’s Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clusteringgin by recalling their Spectrum clustering method and Matrix diagonalization criterion. These two include a number of user-specified parameters such as the number of clusters and similarity threshold, which corresponds to the state of affairs as it was at early stages of data science developments; i