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REPRESENTATION: 作品数：925被引量：1104H指数：13; 导出分析报告; 相关作者：徐亮孙正兴张福炎熊洪允郑运平更多>>; 相关机构：中国科学院大学浙江大学西安外国语大学华中师范大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金国家重点基础研究发展计划国家教育部博士点基金国家高技术研究发展计划更多>>

Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning: 《Tsinghua Science and Technology》2025年第1期215-233,共19页Xin Xi Jinhui Yuan Shan Lu Jieyue He; supported by National Key R&D Program of China(No.2019YFC1711000);Collaborative Innovation Center of Novel Software Technology and Industrialization.; The combination of multiple drugs is a significant therapeutic strategy that can enhance treatment effectiveness and reduce medication side effects.However,identifying effective synergistic drug combinations in a vast...; 关键词：western medicine synergistic drug combination heterogeneous network contrastive learning

Improving the representation of traffic states: A novel method for link selection of urban road networks: 《Journal of Intelligent and Connected Vehicles》2024年第4期266-278,共13页Syed Muzammil Abbas Rizvi Bernhard Friedrich; The macroscopic fundamental diagram(MFD)represents the aggregated traffic states of a road network.However,the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of link selection.Insta...; 关键词：macroscopic fundamental diagram network traffic dynamics link selection entropy weights HETEROGENEITY

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning: 《Journal of Electronic Research and Application》2024年第5期32-37,共6页Yan Zhao Weifeng Rao Zihui Hu Qi Zheng; Science and Technology Research Project of Jiangxi Provincial Department of Education(Project No.GJJ211348,GJJ211347 and GJJ2201056)。; A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and oth...; 关键词：Heterogeneous information network Link prediction Presentation learning Deep learning Node embedding

Localization in modified polar representation: hybrid measurements and closed-form solution: 《Journal of Systems Engineering and Electronics》2024年第3期575-588,共14页CONG Xunchao SUN Yimao YANG Yanbing ZHANG Lei CHEN Liangyin; supported by the National Natural Science Foundation of China (62101359);Sichuan University and Yibin Municipal People’s Government University and City Strategic Cooperation Special Fund Project (2020CDYB-29);the Science and Technology Plan Transfer Payment Project of Sichuan Province (2021ZYSF007);the Key Research and Development Program of Science and Technology Department of Sichuan Province (2020YFS0575,2021KJT0012-2 021YFS-0067)。; Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) u...; 关键词：LOCALIZATION modified polar representation time difference of arrival(TDOA) angle of arrival(AOA) closed-form solution

A malware propagation prediction model based on representation learning and graph convolutional networks: 《Digital Communications and Networks》2023年第5期1090-1100,共11页Tun Li Yanbing Liu Qilie Liu Wei Xu Yunpeng Xiao Hong Liu; This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098);Chongqing Technology Innovation and Application Development Project(Grant No.cstc2020jscxmsxmX0150);Chongqing Science and Technology Innovation Leading Talent Support Program(CSTCCXLJRC201908);Basic and Advanced Research Projects of CSTC(No.cstc2019jcyj-zdxmX0008);Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900605).; The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of netw...; 关键词：MALWARE Representation learning Graph convolutional networks(GCN) Tensor decomposition Propagation prediction

Application of the Point-Descriptor-Precedence representation for micro-scale traffic analysis at a non-signalized T-junction: 《Geo-Spatial Information Science》2023年第3期406-430,共25页Amna Qayyum Bernard De Baets Laure De Cock Frank Witlox Guy De Tré Nico Van de Weghe; supported by the Higher Education Commission(HEC),Pakistan[grant number 50040696];Bernard De Baets and Guy De Tréreceived funding from the Flemish Government under the“Onderzoeksprogramma Artificiële Intelligentie(AI)Vlaanderen”program.; An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also pla...; 关键词：Vehicle interaction traffic research pattern identification traffic safety spatiotemporal representation movement pattern

A recursive least squares algorithm with l_(1) regularization for sparse representation被引量：1: 《Science China(Information Sciences)》2023年第2期277-278,共2页Di LIU Simone BALDI Quan LIU Wenwu YU; supported by National Natural Science Foundation of China (Grant No. 62073074);Key Intergovernmental Special Fund of National Key Research and Development Program (Grant No. 2021YFE0198700);Research Fund for International Scientists (Grant No. 62150610499)。; Sparse representation aims to identify a few basic elements in a signal, so as to use a combination of such elements to reconstruct the original signal. The l_(1)-norm has been widely applied in sparse representation,...; 关键词：REPRESENTATION SIGNAL SPARSE

An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference: 《Journal of Computer and Communications》2022年第6期44-62,共19页Lijuan Yan Xiaotao Wu Jiaqing Xiao; Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segm...; 关键词：Time Series REPRESENTATION SAX Feature Selection CLASSIFICATION

Sparse signal reconstruction via generalized two-stage thresholding: 《Science China(Information Sciences)》2022年第3期284-285,共2页Heping SONG Zehong AI Yuping LAI Hongying MENG Qirong MAO; supported by Beijing Municipal Natural Science Foundation(Grant No.4194076);National Natural Science Foundation of China(Grant Nos.U1836220,61672267);Jiangsu Province Natural Science Foundation(Grant No.BK20170558);China Scholarship Council(Grant No.202008320094)。; Dear editor,The sparse representation model has received a great amount of attention in various signal and image processing applications.Compressed sensing(CS)[1]has consistently focused on devising sparse representat...; 关键词：REPRESENTATION nonzero ENTRIES

Modeling Temporal Dynamics of Users' Purchase Behaviors for Next Basket Prediction被引量：1: 《Journal of Computer Science & Technology》2019年第6期1230-1240,共11页Pengfei Wang Yongfeng Zhang Shuzi Niu Jiafeng Guo; This research work was supported by the National Natural Science Foundation of China under Grant Nos.61802029,and 61602451;the Fundamental Research for the Central Universities of China under Grant No.500419741.; Next basket prediction attempts to provide sequential recommendations to users based on a sequence of the user's previous purchases. Ideally, a good prediction model should be able to explore the personalized preferen...; 关键词：SEQUENTIAL RECOMMENDATION dynamic REPRESENTATION NEXT basket RECOMMENDATION

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