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Chinese Control and decision Conference, CCDC

作者： Xiangyu Xu Qifeng Zhou Qing Wang Langcai Cao Department of Automation Xiamen University Xiamen China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen China Department of Cognitive Service Foundations IBM T.J. Watson Research Center NY USA

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Bandit model is a general framework for solving the cold-start problem in recommender systems. In recent works, clustering strategies have been adopted in the bandit settings to further mitigate the lack of information for new users and items. Although these approaches prove to be effective, their performance may suffer from the clustering efficiency. On one hand, quickly identifying the underlying cluster to share the information has a significantly impact on retaining new users with low levels of interaction in the early stages of recommendation. On the other hand, problems like overfitting might result from repeatedly using the same clustering algorithm on the same set of users, which may decrease the recommended performance over an extended period in the later stages. To address these issues, we propose a Switch-Clustering of Bandit (SWCB), which utilizes the Bandit framework to enable online recommendation as well as new user clustering in the cold-start phase. In particular, the proposed model improves the efficiency of recommendations by switching the clustering algorithms automatically. SWCB can quickly identify the underlying clusters by splitting and merging operation. When clustering metric updated by Bandit reaches a certain threshold, SWCB alters the way of user clustering, thus speeding the information retrieval in the cold start stage and helping to retain new users of online recommendations. We evaluate the efficiency and effectiveness of the proposed model on one synthetic dataset and two real-world datasets. Compared with the baseline bandit models, SWCB gets higher rewards within the rounds of the cold-start phase.

关键词： Measurement Merging Clustering algorithms Process control Switches Information retrieval Control systems

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Evolution of Cumulative Reciprocity in Structured Populations 23rd

Evolution of Cumulative Reciprocity in Structured Population...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Luo, Shuangling Tian, Zhenjia Li, Juan Xia, Haoxiang School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China

ISBN: (纸本)9789819601776

Direct and network reciprocity are key mechanisms that promote cooperation in social dilemma games. Direct reciprocity focuses on exploring successful strategies in repeated interaction, while network reciprocity often simplifying players’ strategies to pure strategies or a limited set of classic strategies. Consequently, understanding the evolutionary dynamics of large-scale and diversity strategies in structured populations remains a challenge. Recent study reveals the cumulative reciprocity strategy’s extraordinary evolutionary advantages in well-mixed populations, but its performance in structured populations remains unclear. This study addresses this research gap by investigating the impact of the cumulative reciprocity strategy on the evolution of large-scale reactive strategy populations across different networks including lattice, small-world, and random networks. Through ecological evolutionary simulations, the results reveal that introducing cumulative reciprocity leads to noticeable changes in the evolutionary dynamics of populations employing reactive strategies. Cumulative reciprocity replaces the traditional generous tit-for-tat strategy as an evolutionarily dominant strategy, thereby fostering stable cooperation within the population. This conclusion holds true across various network densities, population size, mutation mechanisms, and network structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abiotic

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Exploring the space-time pattern of log-transformed infectious count of COVID-19: A clustering-segmented autoregressive sigmoid model

arXiv

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arXiv 2021年

作者： Shi, Xiaoping Chen, Meiqian Dong, Yucheng Department of Mathematics and Statistics Thompson Rivers University Canada Center for Network Big Data and Decision-Making Business School Sichuan University China

At the end of April 20, 2020, there were only a few new COVID-19 cases remaining in China, whereas the rest of the world had shown increases in the number of new cases. It is of extreme importance to develop an efficient statistical model of COVID-19 spread, which could help in the global fight against the virus. We propose a clustering-segmented autoregressive sigmoid (CSAS) model to explore the space-time pattern of the log-transformed infectious count. Four key characteristics are included in this CSAS model, including unknown clusters, change points, stretched S-curves, and autoregressive terms, in order to understand how this outbreak is spreading in time and in space, to understand how the spread is affected by epidemic control strategies, and to apply the model to updated data from an extended period of time. We propose a nonparametric graph-based clustering method for discovering dissimilarity of the curve time series in space, which is justified with theoretical support to demonstrate how the model works under mild and easily verified conditions. We propose a very strict purity score that penalizes overestimation of clusters. Simulations show that our nonparametric graph-based clustering method is faster and more accurate than the parametric clustering method regardless of the size of data sets. We provide a Bayesian information criterion (BIC) to identify multiple change points and calculate a confidence interval for a mean response. By applying the CSAS model to the collected data, we can explain the differences between prevention and control policies in China and selected *** Codes 62H30(Primary) 65D10 (Secondary) © 2021, CC BY-NC-ND.

关键词： Cluster analysis

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A Novel Mixed-Integer Linear Programming Formulation for Continuous-Time Inventory Routing

arXiv

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arXiv 2023年

作者： Wang, Akang Li, Xiandong Arbogast, Jeffrey E. Wilson, Zachary Gounaris, Chrysanthos E. Department of Chemical Engineering Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA15213 United States Shenzhen Research Institute of Big Data Guangdong Shenzhen China Air Liquide NewarkDE19702 United States

Inventory management, vehicle routing, and delivery scheduling decisions are simultaneously considered in the context of the inventory routing problem. This paper focuses on the continuous-time version of this problem where, unlike its more traditional discrete-time counterpart, the distributor is required to guarantee that inventory levels are maintained within the desired intervals at any moment of the planning horizon. In this work, we develop a compact mixed-integer linear programming formulation to model the continuous-time inventory routing problem. We further discuss means to expedite its solution process, including the adaptation of well-known rounded capacity inequalities to tighten the formulation in the context of a branch-and-cut algorithm. Through extensive computational studies on a suite of 90 benchmark instances from the literature, we show that our branch-and-cut algorithm outperforms the state-of-the-art approach. We also consider a new set of 63 instances adapted from a real-life dataset and show our algorithm’s practical value in solving instances with up to 20 customers to guaranteed optimality. © 2023, CC BY-NC-SA.

关键词： Mixed-integer linear programming

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Project duration-cost-quality prediction model based on Monte Carlo simulation 4

Project duration-cost-quality prediction model based on Mont...

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4th International Conference on Physics, Mathematics and Statistics, ICPMS 2021

作者： Chen, Xingguang Cheng, Luqiang Deng, Guohua Guan, Shuqi Hu, Lewei School of Business Jianghan University Wuhan430056 China Institute of Intelligent Decision-making Jianghan University Wuhan430056 China Big Data Research Center Manufacturing Industry Development Research Center on Wuhan City Circle Jianghan University Wuhan430056 China

Based on the earned value management theory, the project duration and cost forecast data are obtained through Monte Carlo simulation, combined with the knowledge of mathematical statistics to in-depth analysis of the data, and then the project quality calculation is completed by establishing the quality correlation function. On the basis of existing scholars' research, at the process level, the management focus is identified by calculating process influence and quality, and at the project level, the operation of the entire project is simulated and predicted by the calculation of the total construction period, cost, and quality. © Published under licence by IOP Publishing Ltd.

关键词： Monte Carlo methods

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Stream Concept-cognitive Computing System for Streaming data Learning

TechRxiv

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TechRxiv 2023年

作者： Mi, Yunlong Liu, Wenqi Shi, Yong The Big Data and Intelligent Decision Making Research Center Central South University Changsha410083 China The Data Science Research Center Kunming University of Science and Technology Kunming650500 China The Faculty of Science Kunming University of Science and Technology Kunming650500 China The School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming and most of stream learning methods are mainly designed for streaming data while ignoring other types of data. Hence, this paper proposes a novel dynamic concept learning (CL) algorithm by imitating human cognitive learning processes from the perspective of brain logical cognition, which is named stream concept-cognitive computing system (streamC3S). For streamC3S, it mainly consists of three aspects: the concept space, CL process, and model update process. Moreover, considering the concept drift frequently occurs in the streaming data over time, an extended version of streamC3S (namely, streamC3SE) is also proposed in this work. Specifically, we first show the related theories for streamC3S and streamC3SE on the basis of the concept space. Then an overall framework and its corresponding algorithm are shown. Finally, experimental results on various types of datasets, including the standard machine learning datasets, streaming datasets, image datasets, and two traffic data streams, validate the effectiveness of our streamC3S and streamC3SE compared to the state-of-the-art incremental learning and stream learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

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ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data

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Computers in Biology and Medicine 2024年 175卷 108485-108485页

作者： Li, Han Zhou, Yitao Zhao, Ningyuan Wang, Ying Lai, Yongxuan Zeng, Feng Yang, Fan Department of Automation Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making Xiamen university Xiamen361000 China School of Informatics Xiamen University Xiamen China State Key Laboratory of Cellular Stress Biology School of Life Sciences Xiamen University China Research Unit of Cellular Stress of CAMS Cancer Research Center School of Medicine Xiamen University China

Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide variations (SNV). Although single-cell RNA sequencing (scRNA-seq) technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, we introduce Interpretable Single-cell Multimodal data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one COVID-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability and can effectively identify disease-causing gene features. © 2024 Elsevier Ltd

关键词： Classification (of information)

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FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system

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Heliyon 2024年第22期10卷 e40350页

作者： He, Keke Zhu, Jun Li, Limiao Gou, Fangfang Wu, Jia School of Computer Science and Engineering Changsha University Changsha 410003 China Hunan University of Medicine General Hospital Huaihua 418000 China Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance Hunan University of Medicine Huaihua 418000 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang 550025 China Research Center for Artificial Intelligence Monash University Melbourne Clayton 3800 VIC Australia

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions. However, deep learning models require a large amount of annotated data. Pathology images are expensive and difficult to obtain, and insufficient annotation data can easily lead to biased results. In addition, when current models are evaluated on an unknown target domain, there are errors in the predicted boundaries. Based on this, this study proposes a feature alignment-based detail recognition strategy for pathology image segmentation (FASNet). It consists of a preprocessing model and a segmentation network (UNW). The UNW network performs instance normalization and categorical whitening of feature images by inserting semantics-aware normalization and semantics-aware whitening modules into the encoder and decoder, which achieves the compactness of features of the same class and the separation of features of different classes. The FASNet method can identify the feature detail information more efficiently, and thus differentiate between different classes of tissues effectively. The experimental results show that the FASNet method has a Dice Similarity Coefficient (DSC) value of 0.844. It achieves good performance even when faced with test data that does not match the distribution of the training data. Code: https://***/zlf010928/***. © 2024

关键词： Assisted diagnosis Deep learning Digital pathology images Feature alignment Insufficient annotation sets

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A Universal data-Driven Spatial Interpolation Framework for the Measurements of Environmental Sites

SSRN

引用

SSRN 2023年

作者： Cheng, Zhen Guo, Yuzhe Xue, Wenbo Du, Qinyi Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making School of Environmental Science and Engineering Shanghai Jiao Tong University Shanghai China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning Beijing100043 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

The accurate spatial distribution of environmental pollutant metrics is crucial for evaluating human exposure and managing the environment. Spatial interpolation, which relies on the measurements of limited environmental sites, is often necessary. However, commonly used geostatistical methods often introduce significant prediction biases, leading to misleading assessments and decision-making processes. Reported machine learning studies usually required intensive spatial datasets and sites’ measurements, as well as special complex algorithm for target pollutant. In this study, we propose and validate a simple and universal data-driven spatial interpolation framework for environmental sites’ measurements. We employ a conditional generative adversarial network (cGAN) as the data-driven algorithm to leverage spatial dataset. We demonstrate the implementation and validation of the framework using two representative ambient pollutants, namely ozone and PM2.5. The results reveal a relative mean absolute error of only 9% for ozone and 13% for PM2.5. Compared to the traditional inverse distance weight method and ordinary kriging method, the proposed framework reduces errors by 70% and 79% respectively. Furthermore, the R-squared values between independent measurements from additional geographic locations and the corresponding predicted results using the proposed method reach 0.96 for ozone and 0.97 for PM2.5, showcasing its excellent and consistent performance. The unique input dataset and universal algorithm architecture illustrate the distinct advantages compared to previous machine learning studies. With the rapid increase of remote sensing and numerical modeling datasets in the environmental field, we anticipate that data-driven methods will replace traditional mathematical approaches. This study provides a simple yet universal interpolation framework and is expected to accelerate the adoption of these advancements. © 2023, The Authors. All rights reserved.

关键词： Spatial distribution

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Countering terrorist propaganda: Competitive resource allocation to communities

A I I E Transactions

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A I I E Transactions 2025年第1期57卷

作者： Xia Chen Yucheng Dong Jun Zhuang a School of Public Administration University of Electronic Science and Technology of China Chengdu China b Center for Network Big Data and Decision-Making Business School Sichuan University Chengdu China c Department of Industrial and Systems Engineering University at Buffalo NY USA

Increased terrorist propaganda has caused greater violence worldwide. Communities are efficient channels through which terrorist organizations can access target audiences; thus, communities are playing larger roles in terrorist propaganda distribution. However, no previous studies have examined the interactions between terrorists and governments regarding terrorist propaganda in communities from a game-theoretic lens. This article aims to fill this research gap. We first classify the relationships between the communities as independent or interactive. For both cases, we describe the process of terrorist propaganda distribution in communities, highlighting the dependence of the degree of terrorist ideology within a community on the terrorist-organization and government resource allocations; based on this, we build novel sequential game models of the terrorist and the government and obtain the equilibrium solutions. Furthermore, we conduct a comparative study to elucidate the differences in the expected government losses for independent and interactive communities. The outcomes are as follows: (i) the higher the community propagation centrality, the greater the resource expenditure of the government and terrorist organization, and (ii) introducing an interaction network among communities decreases the expected government loss when the network directly destroys the distribution uniformity of the community propagation centralities. Finally, we present a case study to illustrate the usability of the proposed models. This study provides novel insights for governments targeting optimal allocation of intervention resources to communities so as to counter terrorist propaganda distributed by a strategic terrorist.

关键词： Homeland security terrorist propaganda opinion dynamics resource allocation communities game theory

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