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Voter model on adaptive networks

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Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of data analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

Group Buying of Competing Retailers with Fairness Concerns

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Journal of Systems Science and Systems Engineering 2020年第6期29卷 655-674页

作者： Na He Zhong-Zhong Jiang Minghe Sun Ying Sheng School of business Administration&Institute of Behavioral and Service Operations Management Northeastern UniversityShenyang 110167China bKey Laboratory of data analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Department of Management Science and Statistics College of BusinessThe University of Texas at San AntonioSan Antonio TX 78249USA College of Sciences Northeastern UniversityShenyang 110819China

The fairness concern behavior is particularly common among members in supply chain *** downstream retailers often compare their profits with those of the other members in the supply chain,especially that of the supplier,and do not like the unfavorable distribution of the total *** work considers a supply chain consisting of a supplier and two competing retailers with fairness *** supplier offers a wholesale contract with quantity discount,and the two competing retailers have the options of individual purchasing and group *** a Stackelberg game model,the optimal decisions for the players are obtained for the group buying and the individual purchasing *** results indicate that the supplier benefits more than the retailers from the group buying *** particular,the group buying strategy gives the supplier a higher profit if the operational cost of group buying is below a ***,the fairness concern behavior of the retailers does not always hurt the profit of the ***,the supplier will benefit from the fairness concern behavior of the retailers in individual purchasing when the retailers operates in a relatively poor ***,market competition between the two fairness concerned retailers does not always benefit the *** competition between the fairness concerned retailers reduces the supplier's profit if the group buying strategy is used when the retailers operates in a relatively poor environment.

关键词： Group buying individual purchasing stackelberg game fairness concerns supply chain

Reinforcement learning for wind-farm flow control:Current state and future actions

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Theoretical & Applied Mechanics Letters 2023年第6期13卷 455-464页

作者： Mahdi Abkar Navid Zehtabiyan-Rezaie Alexandros Iosifidis Department of Mechanical and Production Engineering Aarhus UniversityAarhus N8200Denmark Department of Electrical and Computer Engineering Aarhus UniversityAarhus N8200Denmark Centre for Digitalization Big Dataand Data AnalyticsAarhus UniversityAarhus N8200Denmark

Wind-farm flow control stands at the forefront of grand challenges in wind-energy *** central issue is that current algorithms are based on simplified models and,thus,fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere *** learning(RL),as a subset of machine learning,has demonstrated its effectiveness in solving high-dimensional problems in various domains,and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow *** review has two main ***,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL *** examining the latest research in this area,the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL ***,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on *** highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.

关键词： Wind-farm flow control Turbine wakes Power losses Reinforcement learning Machine learning

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

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Acta Mathematica Sinica,English Series 2024年第4期40卷 1127-1142页

作者： Yi WU Xue Jun WANG School of Big data and Artificial intelligence Chizhou UniversityChizhou 247000P.R.China School of Big data and Statistics Anhui UniversityHefei 230601P.R.China

Let{X_(ni),F_(ni);1≤i≤n,n≥1}be an array of R^(d)martingale difference random vectors and{A_(ni),1≤i≤n,n≥1}be an array of m×d matrices of real *** this paper,the Marcinkiewicz-Zygmund type weak law of large numb... 详细信息

关键词： Martingale difference random vectors weighted sums Marcinkiewicz–Zygmund type weak law of large numbers complete convergence strong law of large numbers multivariate simple linear regression model

Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation

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Tsinghua Science and Technology 2023年第4期28卷 769-785页

作者： Ying Jin Yi Zhang Yiwen Zhang School of Artificial intelligence and Big data Hefei UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China

Modern software development has moved toward agile growth and rapid delivery,where developers must meet the changing needs of users *** such a situation,plug-and-play Third-Party Libraries(TPLs)introduce a considerable amount of convenience to ***,selecting the exact candidate that meets the project requirements from the countless TPLs is challenging for *** works have considered setting up a personalized recommender system to suggest TPLs for ***,these approaches rarely consider the complex relationships between applications and TPLs,and are unsatisfactory in accuracy,training speed,and convergence *** this paper,we propose a new end-to-end recommendation model called Neighbor Library-Aware Graph Neural Network(NLA-GNN).Unlike previous works,we only initialize one type of node embedding,and construct and update all types of node representations using Graph Neural Networks(GNN).We use a simplified graph convolution operation to alternate the information propagation process to increase the training efficiency and eliminate the heterogeneity of the app-library bipartite graph,thus efficiently modeling the complex high-order relationships between the app and the *** experiments on large-scale real-world datasets demonstrate that NLA-GNN achieves consistent and remarkable improvements over state-of-the-art baselines for TPL recommendation tasks.

关键词： Third-Party Library(TPL) TPL recommendation Graph Neural Network(GNN) bipartite graph

Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big data analytics Xi'an Jiaotong University Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University Industrial Artificial Intelligent Center Pazhou Laboratory

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

What does Uber bring for consumers?

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data Science and Management 2021年第2期2卷 20-27页

作者： Jiehong Qiu Bayes data intelligence Technology Service Ltd. Guangzhou510670China

This paper estimates the consumer surplus that Uber brings for *** estimation uses three datasets:individual-level choice dataset—the National Household Travel Survey(NHTS)data of 2008–2009,origin-destination level dataset—Uber data,and Google data of ***,we use NHTS data to identify consumer's preferences in 2008 under a discrete-choice *** unchanged preferences of consumers,we use the coefficients of the discrete-choice model to reveal passengers'demand on different transportation modes in *** revealing the demand curve,this paper calculates the consumer surplus by differencing the consumer surplus in the circumstance where Uber is available with the consumer surplus of the scenario if Uber is not *** find that Uber brings at least$0.76 gains for each *** overall consumer surplus generated by Uber in San Francisco is around$100 million per year.

关键词： Uber Sharing economy Discrete choice Consumer surplus

Weighted Probability Density Estimator with Updated Bandwidths

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Journal of Systems Science & Complexity 2024年第2期37卷 886-906页

作者： WU Yi YU Wei WANG Xuejun PRAKASA RAO B L S School of Big data and Artificial intelligence Chizhou UniversityChizhou 247000China School of Big data and Statistics Anhui UniversityHefei 230601China CR Rao Advanced Institute of Mathematics Statistics and Computer ScienceHyderabad 500046India

In this paper,the authors study a class of weighted version of probability density *** is shown that the weighted estimator contains some existing estimators of probability density,no matter they are recursive or *** statistical results including weak consistency,strong consistency,rate of strong consistency,and asymptotic normality are established under some mild ***,the random weighted estimator is also *** numerical simulations and a real data analysis are presented to study the numerical performances of the estimators.

关键词： Asymptotic normality consistency numerical simulation probability density real data analysis updated bandwidths weighted estimator

Knowledge forest: a novel model to organize knowledge fragments

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Science China(Information Sciences) 2021年第7期64卷 236-238页

作者： Qinghua ZHENG Jun LIU Hongwei ZENG Zhaotong GUO Bei WU Bifan WEI School of Electronic and Information Engineering Xi'an Jiaotong University National Engineering Lab for Big data analytics Xi'an Jiaotong University School of Continuing Education Xi'an Jiaotong University

Dear editor,With the rapid growth of knowledge, there is a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a course is scattered in i... 详细信息

关键词： Information Systems and Communication Service