只有 发表于 2025-3-25 03:54:13
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Cezara Drǎgoi,Patricio Inzaghi Pronestiotential of machine learning techniques, especially deep learning. This paper presents our proposed solution for CCKS-2022 task 8, a chemical domain knowledge-aware framework for multi-view molecular property prediction. As a generative self-supervised approach to molecular graph representation learExpurgate 发表于 2025-3-25 14:40:22
Philipp Körner,Michael Leuscheln multiple-access (DS-CDMA) systems. The standard version (postcombining) of the LMMSE receiver minimizes the mean squared error (MSE) between the filter output and the actual transmitted data sequence. Since the detector depends on the channel coefficients of all users, it cannot be implemented adaCLAM 发表于 2025-3-25 16:30:14
http://reply.papertrans.cn/99/9818/981743/981743_24.png无目标 发表于 2025-3-25 20:22:27
Chukri Soueidi,Yliès Falconetion technique (BCT) are adopted to improve the efficiency of codes. Meanwhile, the accuracy of the codes is evaluated by comparing the present prediction with the analytical solutions to the fluid flow problem, LGK analytical results and the experimental measured columnar dendritic morphology and s多山 发表于 2025-3-26 02:58:11
stem. Focusing on the case of weakly interacting electrons, we investigate thoroughly the dependence of the conductance on the strength and the shape of the double barrier for arbitrary temperature ., down to zero .. We systematically analyze the contributions to renormalized scattering amplitudes fLipoprotein(A) 发表于 2025-3-26 07:55:28
http://reply.papertrans.cn/99/9818/981743/981743_27.pngStable-Angina 发表于 2025-3-26 10:36:50
https://doi.org/10.1007/978-3-031-25803-9computer programming; artificial intelligence; software architecture; parallel processing systems; compuVisual-Field 发表于 2025-3-26 15:45:19
978-3-031-25802-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerlascend 发表于 2025-3-26 20:03:46
Verified Software. Theories, Tools and Experiments.978-3-031-25803-9Series ISSN 0302-9743 Series E-ISSN 1611-3349