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arXiv 2019年

作者： Ma, Jingying Du, Jinming Zheng, Yuanshi School of Mathematics and Statistics Ningxia University Yinchuan750021 China Institute of Industrial and Systems Engineering College of Information Science and Engineering Northeastern University Shenyang110819 China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Ministry of Education Shenyang110819 China Center for Complex Systems School of Mechano-electronic Engineering Xidian University Xi'an710071 China

Coalescence, as a kind of ubiquitous group behavior in the nature and society, means that agents, companies or other substances keep consensus in states and act as a whole. This paper considers coalescence for n rational agents with distinct initial states. Considering the rationality and intellectuality of the population, the coalescing process is described by a bimatrix game which has the unique mixed strategy Nash equilibrium solution. Since the process is not an independent stochastic process, it is difficult to analyze the coalescing process. By using the first Borel-Cantelli Lemma, we prove that all agents will coalesce into one group with probability one. Moreover, the expected coalescence time is also evaluated. For the scenario where payoff functions are power functions, we obtain the distribution and expected value of coalescence time. Finally, simulation examples are provided to validate the effectiveness of the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Coalescence

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Author Correction: CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

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Nature communications 2025年第1期16卷 1569页

作者： Chunge Zhong Wen-Jie Jiang Yingjia Yao Zexu Li You Li Shengnan Wang Xiaofeng Wang Wenjuan Zhu Siqi Wu Jing Wang Shuangshuang Fan Shixin Ma Yeshu Liu Han Zhang Wenchang Zhao Lu Zhao Yi Feng Zihan Li Ruifang Guo Li Yu Fengyun Pei Jun Hu Xingzhi Feng Zihuan Yang Zhengjia Yang Xueying Yang Yue Hou Danni Zhang Dake Xu Ren Sheng Yihao Li Lijun Liu Hua-Jun Wu Jun Huang Teng Fei Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health Sciences Northeastern University Shenyang 110819 China. National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 China. Foshan Graduate School of Innovation Northeastern University Foshan 528311 China. Peking University Third Hospital Beijing 100191 China. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Clinical Research Center the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Institute of Gastroenterology Guangzhou China. Department of Cardiothoracic Surgery Jinqiu Hospital of Liaoning Province Shenyang China. Department of Thoracic Surgery The Fourth Affiliated Hospital of China Medical University Shenyang China. Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education) School of Materials Science and Engineering Northeastern University Shenyang China. BeiGene Institute BeiGene (Shanghai) Research & Development Co. Ltd 200131 Shanghai China. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Peking University Cancer Hospital & Institute Beijing 100142 China. hjwu@***. Department of Biomedical Informatics School of Basic Medical Sciences Peking University Health Science Center Beijing 100191 China. hjwu@***. Center for Precision Medicine Multi-Omics Research Institute of Advanced Clinical Medicine Peking University Beijing 100191 China. hjwu@***. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. huan

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optimization of the raw material field layout for iron-steel enterprise

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IOP Conference Series: Materials Science and engineering 2019年第2期631卷

作者： Wenbo Liu Yuehong Kong Department of Logistics Management Liaoning Provincial College of Communications Shenyang Liaoning 110122 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Shenyang Liaoning 110819 China.

This paper studies a short-term layout optimization problem with the raw material field for iron-steel enterprises. The raw material field is a centralized storage place and distribution unit of various raw materials such as iron ore, which plays a significant role in the iron front logistics for iron and steel enterprises. Whether the stacking of raw materials is reasonable or not directly affects the logistics efficiency and logistics cost of raw materials for iron-steel enterprises. Based on the status and basic information of the raw materials, storage space of the field shall be made full use of to allocate the optimal position for each type of raw material. To achieve this goal, a mixed integer programming (MIP) model is developed, which is purposed to minimize the transportation cost and the loss of mixing. The solution compares favorably with the current practice in the iron-steel enterprises.

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Aspiration dynamics generate robust predictions in structured populations

arXiv

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arXiv 2018年

作者： Zhou, Lei Wu, Bin Du, Jinming Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China School of Sciences Beijing University of Posts and Telecommunications Beijing100876 China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Institute of Industrial and Systems Engineering Northeastern University Shenyang110819 China

Evolutionary game dynamics in structured populations are strongly affected by updating rules. Previous studies usually focus on imitation-based rules, which rely on payoff information of social peers. Recent behavioral experiments suggest that whether individuals use such social information for strategy updating may be crucial to the outcomes of social interactions. This hints at the importance of considering updating rules without dependence on social peers' payoff information, which, however, is rarely investigated. Here, we study aspiration-based self-evaluation rules, with which individuals self-assess the performance of strategies by comparing own payoffs with an imaginary value they aspire, called the aspiration level. We explore the fate of strategies on population structures represented by graphs or networks. Under weak selection, we analytically derive the condition for strategy dominance, which is found to coincide with the classical condition of risk-dominance. This condition holds for all networks and all distributions of aspiration levels, and for individualized ways of self-evaluation. Our condition can be intuitively interpreted: one strategy prevails over the other if the strategy brings more satisfaction to individuals than the other does. Our work thus sheds light on the intrinsic difference between evolutionary dynamics induced by aspiration-based and imitation-based rules. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamics

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The tianQin project: Current progress on science and technology

arXiv

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arXiv 2020年

作者： Mei, Jianwei Bai, Yan-Zheng Bao, Jiahui Barausse, Enrico Cai, Lin Canuto, Enrico Cao, Bin Chen, Wei-Ming Chen, Yu Ding, Yan-Wei Duan, Hui-Zong Fan, Huimin Feng, Wen-Fan Fu, Honglin Gao, Qing Gao, TianQuan Gong, Yungui Gou, Xingyu Gu, Chao-Zheng Gu, De-Feng He, Zi-Qi Hendry, Martin Hong, Wei Hu, Xin-Chun Hu, Yi-Ming Hu, Yuexin Huang, Shun-Jia Huang, Xiang-Qing Jiang, Qinghua Jiang, Yuan-Ze Jiang, Yun Jiang, Zhen Jin, Hong-Ming Korol, Valeriya Li, Hong-Yin Li, Ming Li, Ming Li, Pengcheng Li, Rongwang Li, Yuqiang Li, Zhu Li, Zhulian Li, Zhu-Xi Liang, Yu-Rong Liang, Zheng-Cheng Liao, Fang-Jie Liu, Shuai Liu, Yan-Chong Liu, Li Liu, Pei-Bo Liu, Xuhui Liu, Yuan Lu, Xiong-Fei Lu, Yang Lu, Ze-Huang Luo, Yan Luo, Zhi-Cai Milyukov, Vadim Ming, Min Pi, Xiaoyu Qin, Chenggang Qu, Shao-Bo Sesana, Alberto Shao, Chenggang Shi, Changfu Su, Wei Tan, Ding-Yin Tan, Yujie Tan, Zhuangbin Tu, Liang-Cheng Wang, Bin Wang, Cheng-Rui Wang, Fengbin Wang, Guan-Fang Wang, Haitian Wang, Jian Wang, Lijiao Wang, Panpan Wang, Xudong Wang, Yan Wang, Yi-Fan Wei, Ran Wu, Shu-Chao Xiao, Chun-Yu Xu, Xiao-Shi Xue, Chao Yang, Fang-Chao Yang, Liang Yang, Ming-Lin Yang, Shan-Qing Ye, Bobing Yeh, Hsien-Chi Yu, Shenghua Zhai, Dongsheng Zhang, Caishi Zhang, Haitao Zhang, Jian-Dong Zhang, Jie Zhang, Lihua Zhang, Xin Zhang, Xuefeng Zhou, Hao Zhou, Ming-Yue Zhou, Ze-Bing Zhu, Dong-Dong Zi, Tie-Guang Luo, Jun Zhuhai519082 China Centre for Gravitational Experiments School of Physics MOE Key Laboratory of Fundamental Physical Quantities Measurement Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Huazhong University of Science and Technology Wuhan430074 China DFH Satellite Co. Ltd. Beijing100094 China Yunnan Observatories Chinese Academy of Sciences Kunming650011 China Beijing Institute of Control Engineering Beijing100094 China Beijing Institute of Spacecraft System Engineering Beijing100094 China SISSA Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Former Faculty Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy School of Physical Science and Technology Southwest University Chongqing400715 China SUPA School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China School of Physics and Astronomy University of Birmingham BirminghamB15 2TT United Kingdom Center for High Energy Physics Department of Physics State Key Laboratory of Nuclear Physics and Technology Peking University No.5 Yiheyuan Rd Beijing100871 China Lomonosov Moscow State University Sternberg Astronomical Institute Moscow119992 Russia Dipartimento di Fisica "G. Occhialini" Universitá degli Studi Milano Bicocca Piazza della Scienza 3 MilanoI-20126 Italy School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai200240 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China HefeiAnhui230026 China HannoverD-30167 Germany Leibniz Universität Hannover HannoverD-30167 Germany Department of Physics College of Sci

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about 105 km. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between 10−4 Hz and 1 Hz that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation 17 cm single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018;a new laser ranging station equipped with a 1.2 m telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon;and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements. Copyright © 2020, The Authors. All rights reserved.

关键词： Orbits

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Corrigendum to: The TianQin project: current progress on science and technology

引用

Progress of Theoretical and Experimental Physics 2021年第5期2021卷

作者： Mei, Jianwei Bai, Yan-Zheng Bao, Jiahui Barausse, Enrico Cai, Lin Canuto, Enrico Cao, Bin Chen, Wei-Ming Chen, Yu Ding, Yan-Wei Duan, Hui-Zong Fan, Huimin Feng, Wen-Fan Fu, Honglin Gao, Qing Gao, TianQuan Gong, Yungui Gou, Xingyu Gu, Chao-Zheng Gu, De-Feng He, Zi-Qi Hendry, Martin Hong, Wei Hu, Xin-Chun Hu, Yi-Ming Hu, Yuexin Huang, Shun-Jia Huang, Xiang-Qing Jiang, Qinghua Jiang, Yuan-Ze Jiang, Yun Jiang, Zhen Jin, Hong-Ming Korol, Valeriya Li, Hong-Yin Li, Ming Li, Pengcheng Li, Rongwang Li, Yuqiang Li, Zhu Li, Zhulian Li, Zhu-Xi Liang, Yu-Rong Liang, Zheng-Cheng Liao, Fang-Jie Liu, Qi Liu, Shuai Liu, Yan-Chong Liu, Li Liu, Pei-Bo Liu, Xuhui Liu, Yuan Lu, Xiong-Fei Lu, Yang Lu, Ze-Huang Luo, Yan Luo, Zhi-Cai Milyukov, Vadim Ming, Min Pi, Xiaoyu Qin, Chenggang Qu, Shao-Bo Sesana, Alberto Shao, Chenggang Shi, Changfu Su, Wei Tan, Ding-Yin Tan, Yujie Tan, Zhuangbin Tu, Liang-Cheng Wang, Bin Wang, Cheng-Rui Wang, Fengbin Wang, Guan-Fang Wang, Haitian Wang, Jian Wang, Lijiao Wang, Panpan Wang, Xudong Wang, Yan Wang, Yi-Fan Wei, Ran Wu, Shu-Chao Xiao, Chun-Yu Xu, Xiao-Shi Xue, Chao Yang, Fang-Chao Yang, Liang Yang, Ming-Lin Yang, Shan-Qing Ye, Bobing Yeh, Hsien-Chi Yu, Shenghua Zhai, Dongsheng Zhang, Caishi Zhang, Haitao Zhang, Jian-dong Zhang, Jie Zhang, Lihua Zhang, Xin Zhang, Xuefeng Zhou, Hao Zhou, Ming-Yue Zhou, Ze-Bing Zhu, Dong-Dong Zi, Tie-Guang Luo, Jun TianQin Research Center for Gravitational Physics & School of Physics and Astronomy Sun Yat-sen University (Zhuhai Campus) Zhuhai 519082 P.R. China Centre for Gravitational Experiments School of Physics MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Huazhong University of Science and Technology Wuhan 430074 P.R. China SISSA Via Bonomea 265 34136 Trieste Italy and INFN Sezione di Trieste & IFPU – Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy Former Faculty Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Yunnan Observatories Chinese Academy of Sciences Kunming 650011 China School of Physical Science and Technology Southwest University Chongqing 400715 China Beijing Institute of Control Engineering Beijing 100094 P.R. China SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ UK DFH Satellite Co. Ltd. Beijing 100094 P.R. China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing 100049 China School of Physics and Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Center for High Energy Physics & Department of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University No. 5 Yiheyuan Rd Beijing 100871 P.R. China Lomonosov Moscow State University Sternberg Astronomical Institute Moscow 119992 Russia Dipartimento di Fisica “G. Occhialini” Universitá degli Studi Milano Bicocca Piazza della Scienza 3 I-20126 Milano Italy School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai 200240 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 & School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui 230026 P.R. China Max-Planck-Institut für Gravitationsphysik (Al

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Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation

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ACM Transactions on Recommender Systems 1000年

作者： Qingbo Zhang Xiaochun Yang Hao Chen Bin Wang Xiangmin Zhou Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China School of Computing Technologies RMIT University Melbourne Australia

Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.

关键词： session-based recommender systems personalized next-item recommendation user intention intention-specific preference

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