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CENTRAL LIMIT THEOREM FOR TEMPORAL AVERAGE OF BACKWARD EULER-MARUYAMA METHOD
《Journal of Computational Mathematics》2025年第3期588-614,共27页Diancong Jin 
supported by the National Natural Science Foundation of China(Grant Nos.12201228,12171047);by the Fundamental Research Funds for the Central Universities(Grant No.3004011142).
This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem...
关键词：Central limit theorem Temporal average ERGODICITY Backward Euler-Maruyama method 
Temporal-spectral correlation dynamics of Raman random fiber laser
《Science China(Information Sciences)》2025年第4期162-172,共11页Longqun NI Yifei QI Xingyu BAO Jing ZHANG Pan WANG Han WU Zinan WANG 
supported by National Natural Science Foundation of China(Grant Nos.62435002,62075030);Ministry of Science and Technology of China(Grant No.DL2023167001L);Sichuan Science and Technology Program(Grant No.2023YFSY0058);111 Project(Grant No.B14039)。
Raman random fiber laser(RRFL)possesses rich physical properties of spectral,temporal,and spatial domains due to its unique feedback mechanism and complex nonlinear effects.Characterizing and controlling the microscop...
关键词：random fiber laser microscopic characteristics temporal-spectral correlation random spikes nonlinear optics 
Study of Driver’s Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement
《Tsinghua Science and Technology》2025年第2期796-812,共17页Bilu Li Xin Pei Dan Zhang Xinmiao Zhang Zhuoran Li Duanrui Yu Shifei Shen 
supported by the National Key R&D Program of China(No.2021YFC3001500).
Understanding how drivers perceive and respond to external stimuli in driving tasks is important for the development of advanced driving technologies and human-computer interaction.In this paper,we conducted a tempora...
关键词：naturalistic driving experiment driving safety PERCEPTION response temporal response function FNIRS 
STDNet:Improved lip reading via short-term temporal dependency modeling
《虚拟现实与智能硬件(中英文)》2025年第2期173-187,共15页Xiaoer WU Zhenhua TAN Ziwei CHENG Yuran RU 
Supported by the National Key Research and Development Program of China(2023YFC3306201);the National Natural Science Foundation of China(61772125);the Fundamental Research Funds for the Central Universities(N2317004).
Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of shor...
关键词：Lip reading Spatio-temporal feature fusion Short-term temporal dependency modeling 
Layered Temporal Spatial Graph Attention Reinforcement Learning for Multiplex Networked Industrial Chains Energy Management
《Tsinghua Science and Technology》2025年第2期528-542,共15页Yuanshuang Jiang Kai Di Xingyu Wu Zhongjian Hu Fulin Chen Pan Li Yichuan Jiang 
supported by the National Key Research and Development Program of China(No.2022YFB3304400);the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007);the Key Research and Development Program of Jiangsu Province of China(No.BE2022157);the Defense Industrial Technology Development Program(No.JCKY2021214B002);the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
Demand response has recently become an essential means for businesses to reduce production costs in industrial chains.Meanwhile,the current industrial chain structure has also become increasingly complex,forming new c...
关键词：demand response multiplex networked industrial chains MULTIAGENT reinforcement learning 
Labeling-based centrality approaches for identifying critical edges on temporal graphs
《Frontiers of Computer Science》2025年第2期89-104,共16页Tianming ZHANG Jie ZHAO Cibo YU Lu CHEN Yunjun GAO Bin CAO Jing FAN Ge YU 
supported by the National Natural Science Foundation of China(Grant Nos.62302451 and 62276233);the Natural Science Foundation of Zhejiang Province of China(No.LQ22F020018);the Key Research Project of Zhejiang Province of China(No.2023C01048).
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in networks.In general graphs,edge closeness centrality indicates the importance of edges by th...
关键词：temporal graph closeness centrality between-ness centrality temporal path 
Learning Temporal User Features for Repost Prediction with Large Language Models
《Computers, Materials & Continua》2025年第3期4117-4136,共20页Wu-Jiu Sun Xiao Fan Liu 
Predicting information dissemination on social media,specifcally users’reposting behavior,is crucial for applications such as advertising campaigns.Conventional methods use deep neural networks to make predictions ba...
关键词：Reposting prediction large language model semantic adaptation temporal adaptation 
Temporal Quasi-Phase Matching Assists Robust Acoustic Adiabatic Passage
《Research》2025年第1期89-91,共3页Klaas Bergmann 
Recent work demonstrated stimulated Raman adiabatic passage-type transfer of energy along 3 acoustic cavities. After brief comments on the stimulated Raman adiabatic passage method, remarks on the scientific and techn...
关键词：acoustic adiabatic passage recent applications scientific relevance stimulated raman adiabatic passage method energy transfer technological relevance temporal quasi phase matching stimulated raman adiabatic passage 
From Detection to Explanation:Integrating Temporal and Spatial Features for Rumor Detection and Explaining Results Using LLMs
《Computers, Materials & Continua》2025年第3期4741-4757,共17页Nanjiang Zhong Xinchen Jiang Yuan Yao 
supported by General Scientific Research Project of Zhejiang Provincial Department of Education(Y202353247).
The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key...
关键词：Rumor detection graph convolutional neural networks recurrent neural networks large language models 
A Reinforcement Learning Based Approach to Partition Testing
《Journal of Computer Science & Technology》2025年第1期99-118,共20页Chang-Ai Sun Ming-Jun Xiao He-Peng Dai Huai Liu 
supported by the National Natural Science Foundation of China under Grant Nos.62272037 and 61872039;the Beijing Natural Science Foundation under Grant No.4162040;the Aeronautical Science Foundation of China under Grant No.2016ZD74004;the Fundamental Research Funds for the Central Universities of China under Grant No.FRF-GF-19-B19;the Australian Research Council Discovery Project under Grant No.DP210102447.
Partition testing is one of the most fundamental and popularly used software testing techniques.It first divides the input domain of the program under test into a set of disjoint partitions,and then creates test cases...
关键词：partition testing reinforcement learning temporal-difference learning intelligent software engineering 
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