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arXiv 2018年

作者： Zhao, Zilong Zhao, Jichang Sano, Yukie Levy, Orr Takayasu, Hideki Takayasu, Misako Li, Daqing Wu, Junjie Havlin, Shlomo School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100191 China School of Economics and Management Beihang University Beijing100191 China Faculty of Engineering Information and Systems University of Tsukuba Tennodai Tsukuba Ibaraki305-8573 Japan Department of Physics Bar-Ilan University Ramat Gan5290002 Israel Sony Computer Science Laboratories 3-14-13 Higashi-Gotanda Tokyo141-0022 Japan Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8502 Japan Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

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Federated and Distributed Learning Applications for Electronic Health Records and Structured Medical data: A Scoping Review

arXiv

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arXiv 2023年

作者： Li, Siqi Liu, Pinyan Nascimento, Gustavo G. Wang, Xinru Leite, Fabio Renato Manzolli Chakraborty, Bibhas Hong, Chuan Ning, Yilin Xie, Feng Teo, Zhen Ling Ting, Daniel Shu Wei Haddadi, Hamed Ong, Marcus Eng Hock Peres, Marco Aurélio Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore National Dental Research Institute Singapore National Dental Centre Singapore Singapore Oral Health Academic Clinical Programme Duke-NUS Medical School Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Singapore National Eye Centre Singapore Singapore Eye Research Institute Singapore Department of Computing Imperial College London London United Kingdom Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

Objectives Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. Materials and Methods We searched five databases, SCOPUS, MEDLINE, Web of science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Results Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. Conclusions The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research. © 2023, CC BY.

关键词： Clinical research

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Retraction Note: Deep learning techniques for prediction of pneumonia from lung CT images

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Soft computing 2024年第1期28卷 293-293页

作者： Meena, K. Veeramakali, T. Singh, Ngangbam Herojit Jayakumar, L. Muthuvel, P. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur India

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Retraction Note: Radar optical communication for analysing aerial targets with frequency bandwidth and clutter suppression by boundary element mmwave signal model

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Optical and Quantum Electronics 2024年第12期56卷 1-1页

作者： Kavitha, V. P. Prabakar, D. Ranjith subramanian, S Balambigai, S. Department of ECE SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Insitute of Science and Technology Kattankulathur India Department of Electronic and Communication Engineering Jeppiaar Engineering College Chennai India Department of ECE Kongu Engineering College Perundurai India

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Expanding the horizon of automated metamaterials discovery via quantum annealing

arXiv

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arXiv 2019年

作者： Kitai, Koki Guo, Jiang Ju, Shenghong Tanaka, Shu Tsuda, Koji Shiomi, Junichiro Tamura, Ryo Graduate School of Frontier Sciences University of Tokyo Chiba277-8568 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Green Computing Systems Research Organization Waseda University Tokyo162-0042 Japan JST PRESTO Saitama332-0012 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Ibaraki305-0044 Japan

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

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THE ENERGY PREDICTION SMART-METER dataSET: ANALYSIS OF PREVIOUS COMPETITIONS AND BEYOND

arXiv

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arXiv 2023年

作者： Pekaslan, Direnc Alonso-Moral, Jose Maria Bandara, Kasun Bergmeir, Christoph Bernabé-Moreno, Juan Eigenmann, Robert Einecke, Nils Ergen, Selvi Godahewa, Rakshitha Hewamalage, Hansika Lago, Jesus Limmer, Steffen Rebhan, Sven Rabinovich, Boris Rajapasksha, Dilini Song, Heda Wagner, Christian Wu, Wenlong Magdalena, Luis Triguero, Isaac School of Computer Science University of Nottingham United Kingdom Centro Singular de Investigación en Tecnoloxías Intelixentes Universidade de Santiago de Compostela Spain School of Computing and Information Systems University of Melbourne Australia Department of Computer Science and Artificial Intelligence University of Granada Spain IBM Research Europe Dublin Ireland E.ON Digital Technology GmbH Munich Germany Honda Research Institute Europe GmbH Offenbach Germany National Grid ESO United Kingdom Coles Group Hawthorn EastVIC Australia Amazon United States Amdocs Israel University of Missouri Colombia CIS VP TA Universidad Politécnica de Madrid Spain Department of Data Science and Artificial Intelligence Monash University Australia

This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Technical Challenge on Energy Prediction from Smart Meter data in 2020 (named EP) and its follow-up challenge at the IEEE International Conference on Fuzzy systems (FUZZ-IEEE) in 2021 (named as XEP). These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors. The challenge aims to predict monthly and yearly estimated consumption for households, addressing the accurate billing problem with limited historical smart meter data. The dataset comprises 3,248 smart meters, with varying data availability ranging from a minimum of one month to a year. This paper delves into the challenges, solutions and analysing issues related to the provided real-world smart meter data, developing accurate predictions at the household level, and introducing evaluation criteria for assessing interpretability. Additionally, this paper discusses aspects beyond the competitions: opportunities for energy disaggregation and pattern detection applications at the household level, significance of communicating energy-driven factors for optimised billing, and emphasising the importance of responsible AI and data privacy considerations. These aspects provide insights into the broader implications and potential advancements in energy consumption prediction. Overall, these competitions provide a dataset for residential energy research and serve as a catalyst for exploring accurate forecasting, enhancing interpretability, and driving progress towards the discussion of various aspects such as energy disaggregation, demand response programs or behavioural interventions. © 2023, CC BY-NC-ND.

关键词： Smart meters

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Cosmology with fast radio bursts in the era of SKA

arXiv

引用

arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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Advancing bioinformatics with large language models: components, applications and perspectives

arXiv

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arXiv 2024年

作者： Liu, Jiajia Yang, Mengyuan Yu, Yankai Xu, Haixia Wang, Tiangang Li, Kang Zhou, Xiaobo Center for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Cell Biology and Genetics School of Basic Medical Sciences Xi’an Jiaotong University Health Science Center Xi’an China School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China McGovern Medical School The University of Texas Health Science Center at Houston HoustonTX77030 United States School of Dentistry The University of Texas Health Science Center at Houston HoustonTX77030 United States

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field. © 2024, CC BY.

关键词： Self-supervised learning

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