检索结果-内蒙古大学图书馆

On an approach to measure the level of polarization of individuals' opinions 12

学校读者我要写书评

暂无评论

On an approach to measure the level of polarization of indiv...

12th International Conference "Management of Large-Scale System Development", MLSD 2019

作者： Chartishvili, Alexander G. Kozitsin, Ivan V. Goiko, Vyacheslav L. Saifulin, Eduard R. Institute of Control Sciences RAS Laboratory of Complex Networks Moscow Russia Tomsk State University Laboratory for Big Data Science and Social Studies Tomsk Russia Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Dolgoprudny Russia

ISBN: (纸本)9781728117300

In this paper, we propose a new metric to evaluate the level of polarization of individuals' opinions. Our approach is suitable in the case where opinions are evaluated by scalar values representing personal attitudes towards a fixed issue. We demonstrate how the metric works, applying it to a time series of VKontakte users' opinions that are devoted to a political topic. We report an increase in the level of polarization. © 2019 IEEE.

关键词： Big data

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Multi-Scale Generative Transformer-Based Primal-Dual PPO Framework for AAV-Aided Intelligent Transportation Networks

学校读者我要写书评

暂无评论

IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Adam, Abuzar B. M. Kamal, Tahir Elhassan, Mohammed A. M. Alshahrani, Abdullah Alsamhi, Saeed Hamood Aziz, Ahmed Luxembourg City1855 Luxembourg University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Zhejiang Normal University School of Mathematics and Computer Science Jinhua321017 China University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21493 Saudi Arabia University of Galway Insight Centre for Data Analytics GalwayH91 TK33 Ireland Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Bahrain Polytechnic School of ICT Isa Town00973 Bahrain IBB University Faculty of Engineering Electrical Engineering Department Ibb Yemen Benha University Faculty of Computer and Artificial Intelligence Department of Computer Science Banha13511 Egypt Central Asian University Engineering School Tashkent111221 Uzbekistan

Intelligent transportation networks are increasingly integrating autonomous aerial vehicles (AAVs) to enable services essential for modernizing industries like transportation, logistics, and search and rescue. A significant beneficiary is the Internet of Connected Vehicles (IoCVs), where AAVs play a transformative role in supporting next-generation cellular networks. This paper addresses the challenge of minimizing the cost of delivering content to vehicles on road segments with congested traffic or insufficient infrastructure. Vehicles entering these segments request content from a AAV-hosted library that updates dynamically based on item popularity. Each vehicle submits a request, requiring the AAV to compute an optimal trajectory to maximize operational utility. Given the AAV’s limited energy, we aim to develop an energy-efficient solution. The problem is framed as a joint optimization of caching decisions, AAV trajectory planning, and radio resource allocation, formulated as a mixed integer non-linear programming (MINLP) problem. The environment’s complexity, with random vehicle arrivals and fluctuating content, makes traditional optimization techniques inadequate. To address this, we reformulate the problem as a constrained Markov decision process (CMDP) and employ a primal-dual proximal policy optimization (PPO) algorithm, enhanced by a multi-scale generative transformer (MGFormer) for improved speed and accuracy over traditional deep neural networks (DNNs). Simulation results demonstrate that the proposed framework reduces service costs by up to 30% and achieves robust performance even under high-density traffic and Doppler shifts, validating its superiority over traditional Primal-dual PPO. © 2000-2011 IEEE.

关键词： Markov processes

Glass transition of amorphous polymeric materials informed by machine learning

学校读者我要写书评

暂无评论

APL Machine Learning

APL Machine Learning 2023年第2期1卷

作者： Anwen Hu Yongdi Huang Qionghai Chen Wanhui Huang Xiaohui Wu Lihong Cui Yining Dong Jun Liu College of Mathematics and Physics Beijing University of Chemical Technology Beijing 100029 People’s Republic of China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing 100029 People’s Republic of China Interdisciplinary Research Center for Artificial Intelligence Beijing University of Chemical Technology Beijing 100029 People’s Republic of China School of Data Science and Hong Kong Institute for Data Science Centre for Systems Informatics Engineering City University of Hong Kong Tat Chee Avenue Kowloon 999077 Hong Kong

The glass transition temperature (Tg) is used to determine thermophysical properties of polymer materials and is often considered one of the most important descriptors. Methods for predicting various physical properties of materials based on machine learning algorithms and key molecular descriptors are efficient and accurate. However, it still needs improvements because an overly complex model is less practical and difficult to generalize. In addition, obtaining a large number of samples to achieve accurate predictions remains a challenge due to the complex and lengthy experimental process. In this work, based on Tg of 100 polymers, we use a feature selection algorithm combining FeatureWiz and the least absolute shrinkage and selection operator to quickly select molecular descriptors that are minimally redundant and maximally relevant to Tg. The processed dataset is interpolated from the original dataset using the nearest neighbor interpolation algorithm to solve the data deficiency problem. Finally, the synthetic minority oversampling technique algorithm is used to solve the data imbalance problem. The augmented dataset is used to construct the extreme gradient boosting prediction model to achieve good prediction accuracy. The experimental results demonstrate the robustness of the proposed model and the accuracy of its prediction results.

关键词：

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Multi-view graph learning by joint modeling of consistency and inconsistency

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. College of Mathematics and Informatics South China Agricultural University Guangzhou China Tencent Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

—Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

The featurecloud AI store for federated learning in biomedicine and beyond

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Matschinske, Julian Späth, Julian Nasirigerdeh, Reza Torkzadehmahani, Reihaneh Hartebrodt, Anne Orbán, Balázs Fejér, Sándor Zolotareva, Olga Bakhtiari, Mohammad Bihari, Béla Bloice, Marcus Donner, Nina C. Fdhila, Walid Frisch, Tobias Hauschild, Anne-Christin Heider, Dominik Holzinger, Andreas Hötzendorfer, Walter Hospes, Jan Kacprowski, Tim Kastelitz, Markus List, Markus Mayer, Rudolf Moga, Mónika Müller, Heimo Pustozerova, Anastasia Röttger, Richard Saranti, Anna Schmidt, Harald H.H.W. Tschohl, Christof Wenke, Nina K. Baumbach, Jan Chair of Computational Systems Biology University of Hamburg Hamburg Germany Institute of AI in Medicine and Healthcare Technical University of Munich Munich Germany Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark Gnome Design SRL Sfântu Gheorghe Romania Chair of Experimental Bioinformatics Technical University of Munich Freising Germany Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria concentris research management gmbh Fürstenfeldbruck Germany SBA Research Gemeinnützige GmbH Vienna Austria Department of Mathematics and Computer Science Philipps-University of Marburg Marburg Germany Research Institute AG & Co KG Vienna Austria Division Data Science in Biomedicine Peter L. Reichertz Institute for Medical Informatics TU Braunschweig and Hannover Medical School Brunswick Germany Brunswick Germany Department of Pharmacology and Personalised Medicine MeHNS FHML Maastricht University Maastricht Netherlands

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is often distributed across different institutions and cannot be shared due to privacy concerns. Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models without sharing sensitive data, but their implementation is time-consuming and requires advanced programming skills. Here, we present the FeatureCloud AI Store for FL as an all-in-one platform for biomedical research and other applications. It removes large parts of this complexity for developers and end-users by providing an extensible AI Store with a collection of ready-to-use apps. We show that the federated apps produce similar results to centralized ML, scale well for a typical number of collaborators and can be combined with Secure Multiparty Computation (SMPC), thereby making FL algorithms safely and easily applicable in biomedical and clinical environments. © 2021, CC BY.

关键词： Bioinformatics

Combining Multiple Ranking Systems on the Generalized Permutation Rank Space 5

学校读者我要写书评

暂无评论

Combining Multiple Ranking Systems on the Generalized Permut...

5th IEEE International Conference on Big data Intelligence and Computing, dataCom 2019

作者： Zhong, X. Hurley, L. Sirimulla, S. Schweikert, C. Hsu, D.F. Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Sciences New YorkNY United States University of Texas at El Paso School of Pharmacy Department of Pharmaceutical Sciences El PasoTX United States St John's University Division of Computer Science Mathematics and Science QueensNY United States

ISBN: (纸本)9781728141176

Given t ranking systems of n objects, consensus ranking (CR) aims to derive a ranking which best represents the consensus goal of these ranking systems. Since a ranking system of n objects is equivalent to a permutation of the n natural numbers [1, n] from 1 to n, the CR problem has been studied on the bubble-sort graph rank space Bn which consists of the set of all permutations of order n. However, it remains a challenging issue when a combination of ranking systems (consensus ranking) has ties. But Bn does not include permutation (or rank systems) with ties. In this paper, we propose a multi-layer combinatorial fusion algorithm for combining multiple ranking systems on the generalized permutation rank space where ties are allowed. Using two simulated data sets and an empirical data set in the molecular docking domain, we demonstrate the robustness of our approach. This study also provides a viable approach to data analytics, machine learning, and combinatorial fusion in the non-parametric rank space. © 2019 IEEE.

关键词： data Analytics

Three-Dimensional Reconstruction Method with Parameter Optimization for Point Cloud Based on Kinect v2

学校读者我要写书评

暂无评论

Three-Dimensional Reconstruction Method with Parameter Optim...

作者： Kai-zhang WANG Tong-kai LU Qi-hang YANG Xi-hao FU Ze-hong LU Bo-lun WANG Xin JIANG Key Laboratory of Mathematics Informatics and Behavioral Semantics and School of Mathematics and Systems Science Beihang University Peng Cheng Laboratory Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) School of Mathematical Science Peking University

Three-Dimensional(3D) reconstruction is a significant part in the field of computer vision. In this paper, we use Kinect v2 to obtain the 3D point cloud. As a consumer used 3D sensor, Kinect has numerous advantages such as low price, relative high dot per inch(DPI) and frames per second(FPS) and strong robustness. During the experiment, the object was placed on a turntable spinning and stop for every 12°, collecting 30 images of point cloud in total. Then we used Iterated Closest Points(ICP) algorithm to calculate the optimal rotation matrix and translation matrix to match all point cloud into the same coordinate. After that, three algorithms including Statistical Outlier Removal, Movement Least Squares(MLS) and Voxelgrid were used to reduce the noise. At last, we applied greedy projection algorithm to generate the triangulate mesh. During the procession, we observed the relationship between parameters and outcome and drew pictures to have the data more visible, which gave out the optimal parameters in return.

关键词： 3D Reconstruction Parameter optimization Kinect v2