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A General Framework for Clustering and Distribution Matching with Bandit Feedback

IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering data

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Operations Research Forum 2025年第1期6卷 1-28页

作者： Mohanty, Sonaxy Shimamura, Airi Nicholson, Charles D. González, Andrés D. Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma 202 W. Boyd Norman 73019-1022 OK United States Data Science and Analytics Institute University of Oklahoma 865 Asp Ave. Norman 73019-1022 OK United States Faculty Fellow Data Institute for Societal Challenges (DISC) Five Partners Place 201 Stephenson Pkwy Ste 4600 Norman 73019 OK United States

In light of the far-reaching impact of the COVID-19 pandemic, the accurate estimation of infected cases, fatalities, and recoveries has become crucial. While much research has focused on national-level predictions, it has become evident that an exclusive focus on broader statistics may lead to inadequate preparedness in managing hospital resources in rural and smaller regions. Given the critical role local areas play in the spread of COVID-19 and the hierarchical structure of available data, this study proposes a novel modeling framework using hierarchical time series forecasting (HTSF) tailored to county-level clusters within the USA. This approach aims to provide short-term daily forecasts for every county in the USA, employing bottom-up, top-down, and minimum trace optimal reconciliation techniques. A major barrier to accurate short-term forecasting is the scarcity of COVID-19 hospitalization data in both rural and urban regions across the USA. To address this limitation, the study employs county-level clustering, a method that enables the generation of forecasts even for areas with limited or no available data. The primary aim is to improve the accuracy of COVID-19 case forecasts by combining autoregressive integrated moving average (ARIMA) models as base forecasts with the HTSF approach. The findings reveal that the bottom-up HTSF method offers comparable performance at the county level but significantly outperforms other approaches at the cluster and national levels for 3-week-ahead forecasting. This highlights the vital role of local regions in achieving more precise and effective pandemic prediction strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clustering COVID-19 Hierarchical time series Time series forecasting

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A quinolinyl analog of resveratrol improves neuronal damage after ischemic stroke by promoting Parkin-mediated mitophagy

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Chinese Journal of Natural Medicines 2025年第2期23卷 214-224页

作者： Qingqi Meng Yan Mi Libin Xu Yeshu Liu Dong Liang Yongping Wang Yan Wang Yueyang Liu Guoliang Chen Yue Hou Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110167China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Shenyang Key Laboratory of Vascular Biology Science and Research CenterDepartment of PharmacologyShenyang Medical CollegeShenyang 110034China Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical UniversityShenyang 110016China

Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or ***,extracted from Polygonum cuspidatum *** Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective ***,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately *** study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in *** research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic *** findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy ***,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in *** results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.

关键词： (E)-4-(3 5-dimethoxystyryl)quinoline Resveratrol Ischemic stroke Mitophagy Parkin

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Effect of high silicon content in enhancing the creep properties of a PM near-α Ti–6Al–2Sn–4Zr–2Mo–0.5Y–0.5Si alloy

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Journal of Materials Research and Technology 2025年 36卷 4864-4881页

作者： Xuemei Yu Fuyang Yu Bowen Zhang Xiaogang Wu Hongzhi Niu Deliang Zhang School of Material Science and Engineering 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 (Ministry of Education) Northeastern University Shenyang 110819 China

This study investigates the effect of high Si content on the creep properties and deformation mechanisms of a powder metallurgy (PM) near-α titanium alloy with a composition of Ti–6Al–2Sn–4Zr–2Mo–0.5Y–0.5Si (wt.%) fabricated using high-energy ball milling (HEBM) and thermomechanical powder consolidation (TMC). Under 600 °C/250 MPa, the alloy exhibits a 263 % increase in creep life, a 48 % reduction in steady-state creep rate, and 100 % improvement in creep fracture strain compared with the corresponding titanium alloy containing 0.1 wt% Si under the same fabrication and heat treatment conditions. The superior creep properties are attributed to the stabilized α/β and α/β t interfaces, the uniform dispersion of (Ti, Zr) 6 Si 3 nanoparticles, and the increased Si solubility in both α and β phases, all of which collectively hinder dislocation movement. Additionally, Si addition suppresses the formation of continuous α GB plates, while the presence of higher Si solubility and dispersed nanoscale silicides alleviates stress concentration, delays void and microcrack formation, and ultimately enhances creep fracture strain. These findings underscore the critical role of interface stability, nanoscale silicide dispersion, and enhancing Si solubility in optimizing high-temperature titanium alloys for extreme service environments.

关键词： Near-α titanium alloy Thermomechanical powder consolidation Effect of high silicon content Microstructure Creep property

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Evaluation of Cuffless Blood Pressure Estimation Accuracy at Each Heartbeat During Exercise 6th

Evaluation of Cuffless Blood Pressure Estimation Accuracy a...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Ono, Kouki Maeda, Yuka Yoshimura, Takumi Huang, Ming Tamura, Toshiyo Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba305-8577 Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba305-8577 Japan Medical and Welfare Engineering Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japan School of Data Science Nagoya City University Nagoya467-8501 Japan Future Robotics Organization Waseda University Tokyo169-8050 Japan

ISBN: (纸本)9783031863226

Continuous blood pressure monitoring is crucial because of its dynamic nature, influenced by factors such as physical activity and mental stress. Although cuffless blood pressure estimation is a promising technique for continuous monitoring, its accuracy during drastic fluctuations, especially during exercise, remains insufficiently validated. In this study, we evaluated a simple Pulse Arrival Time based blood pressure estimation method and several improved methods for sharp fluctuations induced by exercise. Sixteen subjects in the experiment, with data from 13 valid for analysis. The proposed method achieved an average correlation of 0.527 in systolic blood pressure, suggesting that cuffless blood pressure estimation could be applied to estimate continuous blood pressure variability during exercise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blood pressure

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TRDM: A Two-stage Real-time Discrimination Method for Spiral Weld Defects under Dynamic Distorted Imaging

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IEEE Transactions on Automation science and engineering 2025年 22卷 15420-15434页

作者： Cui, Wenqi Song, Kechen Zhang, Yu Jia, Xiujian Liu, Xiaozheng Yan, Yunhui Northeastern University School of Mechanical Engineering and Automation Liaoning Shenyang 110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang 110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry China

Non-destructive testing of weld based on radio-graphic images is crucial for quality control of spiral steel pipes. However, accurately discriminating defect images from high-resolution and low-contrast weld images remains a challenging task. Moreover, the real-time imaging of radiography in industrial settings introduces dynamic distorted interferences to weld images, which has received limited attention in existing research. To address these issues, we propose a two-stage real-time discrimination method (TRDM) consisting of two carefully designed networks: an extremely lightweight weld region segmentation network and a semantic reconstruction-based discrimination network. Our method aims to adaptively reduce interference from invalid regions in the weld images to better exploit defect features from a semantic perspective and reconstruct them as defect-free regions as much as possible. To overcome the scarcity of weld defect image data, we construct two publicly available industrial weld datasets, including an image dataset and a video dataset. TRDM achieves competitive performance with state-of-the-art techniques, demonstrating its effectiveness in discriminating weld images. Furthermore, we integrate the TRDM into developed detection software for defect assessment, validating its efficacy in real welding scenarios. © 2004-2012 IEEE.

关键词： Non-destructive testing penetration state recognition quality monitoring weld defect detection

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Secure decentralized decisions to enhance coordination in consolidated hospital systems

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IISE Transactions on Healthcare systems engineering 2020年第2期10卷 99-112页

作者： Badré, Adrien Mohebbi, Shima Soltanisehat, Leili Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering and Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering University of Oklahoma Norman OK United States

Shared decision making has become a crucial solution to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of shared decision making, its efficiency has not been addressed quantitatively. In this paper, we propose a Decentralized Patients Assignment System (DPAS) as a universal decentralized decision making architecture. It utilizes the blockchain technology, machine learning, and integer programing to enhance coordination among healthcare providers and patients in consolidated hospital systems. To test the efficiency of the proposed DPAS, a prototype system is developed using an Agent-based model and Ethereum and is compared to the current practice of central referral systems in consolidated hospital systems. The agent-based model consists of four agents including patients, physicians, hospitals, and miners interacting within a decentralized system. The proposed system highlights the importance of interoperability and consensus among healthcare agents in the decision making process. The results demonstrate the DPAS efficiency in decreasing computational time and rejection rates for patients transfer. © 2019, © 2019 “IISE”.

关键词： Blockchain architecture intelligent agents optimization patients referral predictive analytics smart contracts

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Bounded-Degree Connected Approximations of Stochastic Networks

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2017年第2期3卷 79-88页

作者： Quinn, Christopher J. Pinar, Ali Kiyavash, Negar School of Industrial Engineering Purdue University West LafayetteIN47907 United States Data Science and Cyber Analytics Department Sandia National Laboratories LivermoreCA94551 United States Department of Industrial and Enterprise Systems Engineering University of Illinois UrbanaIL61801 United States

We propose a method to find optimal sparse connected approximations for large complex networks of nodes interacting over time. Optimality is measured by Kullback-Leibler divergence. The sparsity is controlled by the user through specifying the in-degrees. The approximations have spanning tree subgraphs, enabling them to depict flow through a network. They can also depict feedback. The approximations can capture both significant statistical dependencies in, and preserve salient topological features of, the full (unknown) network. The proposed method can be applied to networks without restrictions on the dynamics beyond stationarity. We analyze computational and sample complexity. We demonstrate their efficacy using a large-scale simulation of the turtle visual cortex and experimental data from the primate motor cortex. © 2015 IEEE.

关键词： Information theory

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Operations & Supply Chain Management: Principles and Practice

arXiv

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arXiv 2025年

作者： Petropoulos, Fotios Akkermans, Henk Aksin, O. Zeynep Ali, Imran Babai, Mohamed Zied Barbosa-Povoa, Ana Battaïa, Olga Besiou, Maria Boysen, Nils Brammer, Stephen Brandon-Jones, Alistair Briskorn, Dirk Browning, Tyson R. Buijs, Paul Centobelli, Piera Chiarini, Andrea Cousins, Paul Cudney, Elizabeth A. Davies, Andrew Day, Steven J. de Koster, René Dekker, Rommert Denicol, Juliano Despeisse, Mélanie Disney, Stephen M. Dolgui, Alexandre Duong, Linh El-Qallali, Malek Fahimnia, Behnam Fakhredin, Fatemeh Gershwin, Stanley B. Ghamat, Salar Giannikas, Vaggelis Glock, Christoph H. Godsell, Janet Govindan, Kannan Hannibal, Claire Haug, Anders Hernaus, Tomislav Hsuan, Juliana Ivanov, Dmitry Jahre, Marianne Johansson, Björn Kalidoss, Madan Shankar Kanellopoulos, Argyris Kannan, Devika Karul, Elif Katsikopoulos, Konstantinos V. Kilic-Ararat, Ayse Begüm Kolisch, Rainer Koppenberg, Maximilian Kumar, Maneesh Kuo, Yong-Hong Kusiak, Andrew Lewis, Michael A. Lim, Stanley Frederick W.T. Limère, Veronique Liu, Jiyin Maghazei, Omid Marić, Matija Meissner, Joern Meuwissen, Miranda Micheli, Pietro Nanayakkara, Samudaya Özdemir, Bengü Nur Papadopoulos, Thanos Pavelin, Stephen Perera, Srinath Phillips, Wendy Prak, Dennis Pun, Hubert Qazi, Sharfah Ahmad Ramanathan, Usha Reiner, Gerald Reitsma, Ewout Roehrich, Jens K. Sanders, Nada R. Sarkis, Joseph Schmid, Nico André Schmidt, Christoph G. Schroeder, Andreas Selviaridis, Kostas Seuring, Stefan Shi, Chuan Son, Byung-Gak Spring, Martin Squire, Brian van der Valk, Wendy van Donk, Dirk Pieter van Houtum, Geert-Jan Wilhelm, Miriam Wynstra, Finn Zheng, Ting School of Management University of Bath Bath United Kingdom Department of Information Systems & Operations Management Tilburg School of Economics and Management Tilburg University Netherlands College of Administrative Sciences and Economics Koc University Turkey School of Business and Law Central Queensland University Australia Kedge Business School France Center for Management Studies Instituto Superior Técnico University of Lisbon Portugal Kühne Logistics University Hamburg Germany Friedrich-Schiller-Universität Jena Lehrstuhl für Operations Management Germany Bergische Universität Wuppertal Germany Neeley School of Business Texas Christian University Fort WorthTX United States Department of Operations University of Groningen Netherlands Department of Industrial Engineering University of Naples "Federico II" Italy Department of Management University of Verona Italy Management School University of Liverpool United Kingdom John E. Simon School of Business Maryville University of Saint Louis Missouri United States Science Policy Research Unit University of Sussex Business School Brighton United Kingdom School of Management Zhejiang University China Rotterdam School of Management Erasmus University Netherlands Erasmus School of Economics Erasmus University Netherlands Bartlett School of Sustainable Construction The Bartlett Faculty of the Built Environment University College London United Kingdom Department of Industrial and Material Sciences Chalmers University of Technology Sweden Center for Simulation Analytics and Modelling University of Exeter Business School United Kingdom IMT Atlantique LS2N-CNRS Nantes France College of Business and Law University of the West of England United Kingdom Rabdan Academy United Arab Emirates Institute of Transport and Logistics Studies The University of Sydney Business School Australia Kühne Logistics University Germany Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts United S

Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. © 2025, CC BY.

关键词： Supply chain management

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