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arXiv 2021年

作者： Izawa, Syun Kitai, Koki Tanaka, Shu Tamura, Ryo Tsuda, Koji Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo Kashiwa277-8561 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama223-8522 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply quantum annealing (QA) to overcome the difficulty in the continuous black-box optimization. As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back. Our method has the following three parts: 1) Random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector. 2) A quadratic unconstrained binary optimization (QUBO) defined by acquisition function based on nonnegative-weighted linear regression model which is solved by QA. 3) A penalization scheme to ensure that the QA solution can be translated back. It is shown in benchmark tests that its performance using D-Wave AdvantageTM quantum annealer is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up a new possibility of quantum annealing and other QUBO solvers including quantum approximate optimization algorithm (QAOA) using a gated-quantum computers, and expand its range of application to continuous-valued problems. © 2021, CC BY.

关键词： Annealing

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Searching for gravitational-wave signals from precessing black hole binaries with the GstLAL pipeline

arXiv

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arXiv 2024年

作者： Schmidt, Stefano Caudill, Sarah Creighton, Jolien D.E. Magee, Ryan Tsukada, Leo Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Cousins, Bryce Ewing, Becca Fong, Heather George, Richard N. Godwin, Patrick Hanna, Chad Harada, Reiko Huang, Yun-Jing Huxford, Rachael Joshi, Prathamesh Kennington, James Kuwahara, Soichiro Li, Alvin K.Y. Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Niu, Wanting Pace, Alex Posnansky, Cort Ray, Anarya Sachdev, Surabhi Sakon, Shio Singh, Divya Tapia, Ron Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Nikhef Science Park 105 Amsterdam1098 XG Netherlands Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States RESCEU The University of Tokyo Tokyo113-0033 Japan Department of Physics University of Illinois UrbanaIL61801 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology MS 100-36 PasadenaCA91125 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States School of Physics Georgia Institute of Technology AtlantaGA30332 United States Concordia University

Precession in Binary Black Holes (BBH) is caused by the failure of the Black Hole spins to be aligned and its study can open up new perspectives in gravitational waves (GW) astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly non-trivial task, as standard matched filtering pipelines for GW searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio q 6, a volume increase of up to 100% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the ξ2 signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections. © 2024, CC BY.

关键词： Matched filters

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Beware of "Explanations" of AI

arXiv

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

引用

IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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Preventing Spread of Spam Transactions in Blockchain by Reputation

Preventing Spread of Spam Transactions in Blockchain by Repu...

引用

International Workshop on Quality of Service

作者： Jiarui Zhang Yukun Cheng Xiaotie Deng Bo Wang Jan Xie Yuanyuan Yang Mengqian Zhang Stony Brook University Stony Brook NY USA School of Business Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology Suzhou China Centers on Frontiers of Computing Studies Peking University Beijing China Hangzhou Cryptape Technology Co. Ltd. Hangzhou China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.

关键词： Peer-to-peer computing Computer crime Bitcoin Quality of service Earth Observing System

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(WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners 11

(WIP) Evaluation of a Cloud-Based System for Delivering Adap...

引用

11th IEEE International Conference on Cloud computing, CLOUD 2018

作者： Sun, Geng Cui, Tingru Dong, Fang Xu, Dongming Shen, Jun Chen, Shiping Lin, Jiayin School of Computing and Information Technology University of Wollongong Australia School of Computer Science and Engineering Southeast University China UQ Business School University of Queensland Australia CSIRO Data61 Australia

ISBN: (纸本)9781538672358

In this paper, we present an online computation approach implemented in a cloud-based system to assist open education resource (OER) providers and instructors dealing with the sparsity of data in micro OER recommendation. An algorithmic framework is provided to realize the novel micro OER recommendation system based on heuristic rules. These rules can also optimize the approaches to blending new-coming micro OERs into established learning paths. Comparing with different widely used recommender systems, our evaluation shows the proposed heuristic algorithms for online computation performs satisfactorily in terms of precision and recall values. © 2018 IEEE.

关键词： Recommender systems

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A data Analytic Elucidation on the Spatial Agglomeration of Singapore Maritime Industry

International Journal on Smart and Sustainable Cities

引用

International Journal on Smart and Sustainable Cities 2023年第1期1卷

作者： Lock Yue Chew Ning Ning Chung Wen Xuan Sia Hoai Nguyen Huynh Glenn Sim Alvin Chua Zhongwen Huang https://***/0000-0003-1366-8205 School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 637335 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore School of Global Affairs Faculty of Social Science & Public Policy King’s College London London WC2R 2LS UK Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

In this paper, we create three new statistical measures to characterize the spatial patterns and structure of firms and subsectors of industry. The new measures give a direct quantification of the tendency of subsectors to spatially agglomerate, the likelihood of cross-subsector spatial co-agglomeration, and the potential of leading and following behaviors between firms. Unlike the conventional measures which deduce possible agglomeration through deviations from the baseline distribution or distance density, our measures study agglomeration in terms of clustering and co-location behaviors to provide a clear physical picture on spatial agglomeration. With each measure quantifying one of the three different ways in which firms agglomerate spatially, their collective use provides a combined perspective on the agglomeration behavior and location choice of firms that is not available from the conventional measures. We employ this joint application of measures to show that Singapore maritime firms have a propensity to distribute themselves spatially in a manner that allows them to reap the benefits of localization externalities from the agglomeration economy.

关键词： Spatial morphology Agglomeration Co-location Co-occurrence Urban complexity Maritime industry

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Machine learning for modelling unstructured grid data in computational physics: A review

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Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： Reinforcement learning

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

引用

arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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