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Molecular Contrastive Pretraining with Collaborative Featurizations 期刊论文
Journal of Chemical Information and Modeling (JCIM), 2024, 卷号: 64, 期号: 4, 页码: 1112–1122
作者:  Yanqiao Zhu;  Dingshuo Chen;  Yuanqi Du;  Yingze Wang;  Qiang Liu;  Shu Wu
Adobe PDF(1868Kb)  |  收藏  |  浏览/下载：25/9  |  提交时间：2024/06/21
Toward a Fast and Robust MI-BCI: Online Adaptation of Stimulus Paradigm and Classification Model 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 卷号: 73, 页码: 12
作者:  Wang, Jiaxing;  Wang, Weiqun;  Su, Jianqiang;  Wang, Yihan;  Hou, Zeng-Guang
Adobe PDF(1810Kb)  |  收藏  |  浏览/下载：74/10  |  提交时间：2024/05/30
Brain-computer interface (BCI)  classification model adaption  information transfer rate (ITR)  motor imagery (MI) duration  stimulus paradigm adjustment  
Covariant Peak Constraint for Accurate Keypoint Detection and Keypoint-Specific Descriptor Learning 期刊论文
IEEE Transactions on Multimedia, 2024, 卷号: 26, 页码: 5383 - 5397
作者:  Fu Yujie;  Zhang Pengju;  Tang Fulin;  Wu Yihong
Adobe PDF(15150Kb)  |  收藏  |  浏览/下载：48/11  |  提交时间：2024/05/28
Image Matching  Local Feature Extraction  Covariant Peak Constraint  Conditional Neural Reprojection Error  
Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2024, 页码: 14
作者:  Wei, Qinglai;  Han, Liyuan;  Zhang, Tielin
Adobe PDF(8060Kb)  |  收藏  |  浏览/下载：92/20  |  提交时间：2024/03/27
Direct dynamic programming (DDP)  Lorenz system  multiscale dynamics  point-to-point control  recursive least square (RLS)  spiking neural network (SNN)  
Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 1022-1038
作者:  Xiao Xue;  Deyu Zhou;  Xiangning Yu;  Gang Wang;  Juanjuan Li;  Xia Xie;  Lizhen Cui;  Fei-Yue Wang
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：69/17  |  提交时间：2024/03/18
Agent-based modeling  computational experiments  cyber-physical-social systems (CPSS)  generative deduction  generative experiments  meta model