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arXiv 2019年

作者： Zhang, Si-Ping Zhang, Ji-Qiang Chen, Li Liu, Xu-Dong Key Laboratory of Biomedical Information Engineering Ministry of Education Key Laboratory of Neuro-informatics & Rehabilitation Engineering Ministry of Civil Affairs Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an710049 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Physics and Information Technology Shaanxi Normal University Xi'an710062 China

Large-scale cooperation underpins the evolution of ecosystems and the human society, and the collective behaviors by self-organization of multi-agent systems are the key for understanding. As artificial intelligence (AI) prevails in almost all branches of science, it would be of great interest to see what new insights of collective behavior could be obtained from a multi-agent AI system. Here, we introduce a typical reinforcement learning (RL) algorithm - Q learning into evolutionary game dynamics, where agents pursue optimal action on the basis of the introspectiveness rather than the birth-death or imitation processes in the traditional evolutionary game (EG). We investigate the cooperation prevalence numerically for a general 2 × 2 game setting. We find that the cooperation prevalence in the multi-agent AI is amazingly of equal level as in the traditional EG in most cases. However, in the snowdrift games with RL we also reveal that explosive cooperation appears in the form of periodic oscillation, and we study the impact of the payoff structure on its emergence. Finally, we show that the periodic oscillation can also be observed in some other EGs with the RL algorithm, such as the rock-paper-scissors game. Our results offer a reference point to understand emergence of cooperation and oscillatory behaviors in nature and society from AI's perspective. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture

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Procedia Computer science 2018年 137卷 127-138页

作者： Mohamed Ali Said Fathalla Shimaa Ibrahim Mohamed Kholief Yasser Hassan Faculty of Science University of Alexandria Egypt Smart Data Analytics (SDA) University of Bonn Germany Institute of Graduate Studies and Research University of Alexandria Egypt College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text ( Simultaneous Ontology Enrichment ). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.

关键词： cross-lingual ontology enrichment multi-agent knowledge management ontology learning.

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Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging

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European heart journal. Imaging methods and practice 2024年第1期2卷 qyae042页

作者： Xiang Chen Yan Xia Erica Dall'Armellina Nishant Ravikumar Alejandro F Frangi School of Computing University of Leeds Woodhouse LS2 9JT Leeds UK. School of Medicine University of Leeds Woodhouse LS2 9JT Leeds UK. Christabel Pankhurst Institute The University of Manchester Oxford Rd M13 9PL Manchester UK. Department of Computer Science School of Engineering The University of Manchester Oxford Rd M13 9PL Manchester UK. Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Oxford Rd M13 9PL Manchester UK. NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Oxford Rd M13 9PL Manchester UK. Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg UZ Herestraat 49 - bus 7003 3000 Leuven Belgium. Department of Cardiovascular Sciences KU Leuven UZ Herestraat 49 - box 911 3000 Leuven Belgium. Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 postbus 2440 3001 Leuven Belgium. Alan Turing Institute British Library 96 Euston Rd. NW1 2DB London UK.

Aims:Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. Methods and results:We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Conclusion:Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

关键词： CVD diagnosis CVD prediction cardiac biomarker mesh reconstruction multi-channel VAE

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Online Educational Resources Classification Using Visual Features

Online Educational Resources Classification Using Visual Fea...

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International Conference on Digital Home (ICDH)

作者： Xiangping Chen Yancheng Chen Yonghao Long Yongsheng Rao Hao Guan Mouguang Lin School of Data Science and Computer Sun Yat-sen University Guangzhou China University of Chinese Academy of Sciences Beijing China Institute of Computing Science and Technology Guangzhou University Guangzhou China

With the promotion of the Internet, people can easily retrieve various kinds of education resources on the web. However, current education resources sharing platforms do not support the resources retrieval through the visual information. Therefore, we need to classify the resources which are related to visual characteristics into several categories. In this paper, we propose a novel classification method for resources on Netpad[ http://***/]. We extract the important visual features including graphics features and text features. Then, we use the random forest algorithm to train a valuable model. The results of the experiments indicate that, using graphics features and text features, most of the data are classified correctly, which means that our proposed method can solve the classification problem of Netpad effectively.

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Correction: An Artificial Therapist (Manage Your Life Online) to Support the Mental Health of Youth: Co-Design and Case Series

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JMIR human factors 2024年 11卷 e69628页

作者： Aimee-Rose Wrightson-Hester Georgia Anderson Joel Dunstan Peter M McEvoy Christopher J Sutton Bronwyn Myers Sarah Egan Sara Tai Melanie Johnston-Hollitt Wai Chen Tom Gedeon Warren Mansell Curtin enAble Institute Faculty of Health Sciences Curtin University Perth Australia. Discipline of Psychology School of Population Health Curtin University Perth Australia. School of Arts and Humanities Edith Cowan University Perth Australia. Mental Health Commission of Western Australia Perth Australia. Curtin Institute for Data Science Curtin University Perth Australia. Centre for Clinical Interventions North Metropolitan Health Service Nedlands Australia. Centre for Biostatistics School of Health Sciences The University of Manchester Manchester United Kingdom. Alcohol Tobacco and Other Drug Research Unit South African Medical Research Council Parow South Africa. Division of Addiction Psychiatry Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Department of Clinical Psychology School of Health Sciences The University of Manchester Manchester United Kingdom. Mental Health Service Fiona Stanley Hospital Perth Australia. Curtin Medical School Curtin University Perth Australia. Centre of Excellence in Medical Biotechnology Faculty of Medical Science Naresuan University Phitsanulok Thailand. Optus-Curtin Centre of Excellence in AI School of Electronic Engineering Computing and Mathematical Sciences Curtin University Perth Australia.

[This corrects the article DOI: 10.2196/46849.].

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2020年 603卷 v-vi页

作者： Alfred, Rayner Haviluddin, Haviluddin Lim, Yuto On, Chin Kim Knowledge Technology Research Unit Faculty of Computing and Informatics Universiti Malaysia Sabah Kota KinabaluSabah Malaysia Department of Computer Science Universitas Mulawarman Samarinda Indonesia School of Information Science Security and Networks Area Japan Advanced Institute of Science and Technology Nomi Ishikawa Japan Center of Data and Information Management Universiti Malaysia Sabah Kota KinabaluSabah Malaysia

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Dynamic-K recommendation with personalized decision boundary 23rd

Dynamic-K recommendation with personalized decision boundary

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23rd China conference on Information Retrieval, CCIR 2017

作者： Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319686981

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users’ preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user’s preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users’ interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods. © Springer International Publishing AG 2017.

关键词： Websites

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NANOGrav 15-year gravitational-wave background methods

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Physical Review D 2024年第10期109卷 103012-103012页

作者： Aaron D. Johnson Patrick M. Meyers Paul T. Baker Neil J. Cornish Jeffrey S. Hazboun Tyson B. Littenberg Joseph D. Romano Stephen R. Taylor Michele Vallisneri Sarah J. Vigeland Ken D. Olum Xavier Siemens Justin A. Ellis Rutger van Haasteren Sophie Hourihane Gabriella Agazie Akash Anumarlapudi Anne M. Archibald Zaven Arzoumanian Laura Blecha Adam Brazier Paul R. Brook Sarah Burke-Spolaor Bence Bécsy J. Andrew Casey-Clyde Maria Charisi Shami Chatterjee Katerina Chatziioannou Tyler Cohen James M. Cordes Fronefield Crawford H. Thankful Cromartie Kathryn Crowter Megan E. DeCesar Paul B. Demorest Timothy Dolch Brendan Drachler Elizabeth C. Ferrara William Fiore Emmanuel Fonseca Gabriel E. Freedman Nate Garver-Daniels Peter A. Gentile Joseph Glaser Deborah C. Good Kayhan Gültekin Ross J. Jennings Megan L. Jones Andrew R. Kaiser David L. Kaplan Luke Zoltan Kelley Matthew Kerr Joey S. Key Nima Laal Michael T. Lam William G. Lamb T. Joseph W. Lazio Natalia Lewandowska Tingting Liu Duncan R. Lorimer Jing Luo Ryan S. Lynch Chung-Pei Ma Dustin R. Madison Alexander McEwen James W. McKee Maura A. McLaughlin Natasha McMann Bradley W. Meyers Chiara M. F. Mingarelli Andrea Mitridate Cherry Ng David J. Nice Stella Koch Ocker Timothy T. Pennucci Benetge B. P. Perera Nihan S. Pol Henri A. Radovan Scott M. Ransom Paul S. Ray Shashwat C. Sardesai Carl Schmiedekamp Ann Schmiedekamp Kai Schmitz Brent J. Shapiro-Albert Joseph Simon Magdalena S. Siwek Ingrid H. Stairs Daniel R. Stinebring Kevin Stovall Abhimanyu Susobhanan Joseph K. Swiggum Jacob E. Turner Caner Unal Haley M. Wahl Caitlin A. Witt Olivia Young Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 Milwaukee Wisconsin 53201 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Department of Physics and Astronomy Widener University One University Place Chester Pennsylvania 19013 USA Department of Physics Montana State University Bozeman Montana 59717 USA Department of Physics Oregon State University Corvallis Oregon 97331 USA NASA Marshall Space Flight Center Huntsville Alabama 35812 USA Department of Physics Texas Tech University Box 41051 Lubbock Texas 79409 USA Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville Tennessee 37235 USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California 91109 USA Institute of Cosmology Department of Physics and Astronomy Tufts University Medford Massachusetts 02155 USA Department of Astronomy University of Maryland College Park Maryland 20742 Center for Research and Exploration in Space Science and Technology NASA/GSFC Greenbelt Maryland 20771 NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut) Callinstrasse 38 D-30167 Hannover Germany Newcastle University NE1 7RU Newcastle United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt Maryland 20771 USA Physics Department University of Florida Gainesville Florida 32611 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca New York 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca New York 14853 USA Institute for Gravitational Wave Astronomy and School of Physics and Astronomy University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom Department of Physics and Astronomy West

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms enterprise and ptmcmcsampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulation recovery, and Bayes factors. For the frequentist analysis, we test that the optimal statistic, when modified to account for a non-negligible gravitational-wave background, accurately recovers the amplitude of the background. We also summarize recent advances and tests performed on the optimal statistic in the literature from both gravitational wave background detection and parameter estimation perspectives. The tests presented here validate current analyses of PTA data.

关键词： Gravitational waves Gravitational wave detection

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Algebraic multigrid methods for saddle point systems arising from mortar contact formulations

arXiv

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arXiv 2019年

作者： Wiesner, Tobias A. Mayr, Matthias Popp, Alexander Gee, Michael W. Wall, Wolfgang A. Leica Geosystems AG Heinrich-Wild-Strasse 201 Heerbrugg9435 Switzerland Institute for Mathematics and Computer-Based Simulation University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 NeubibergD-85577 Germany Data Science & Computing Lab University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 NeubibergD-85577 Germany Mechanics & High Performance Computing Group Technical University of Munich Parkring 35 GarchingD-85748 Germany Institute for Computational Mechanics Technical University of Munich Boltzmannstr. 15 GarchingD-85748 Germany

In this paper, a fully aggregation-based algebraic multigrid strategy is developed for nonlinear contact problems of saddle point type using a mortar finite element approach. While the idea of extending multigrid methods to saddle point systems can already be found, e.g., in the context of Stokes and Oseen equations in literature, the main contributions of this work are (i) the development and open-source implementation of an interface aggregation strategy specifically suited for generating Lagrange multiplier aggregates that are required for coupling structural equilibrium equations with contact constraints and (ii) a review of saddle point smoothers in the context of constrained interface problems. The new interface aggregation strategy perfectly fits into an aggregation-based multigrid framework and can easily be combined with segregated transfer operators, which allow to preserve the saddle point structure on the coarse levels. Further analysis provides insight into saddle point smoothers applied to contact problems, while numerical experiments illustrate the robustness of the new method. We have implemented the proposed algorithm within the MueLu package of the open-source Trilinos project. Numerical examples demonstrate the robustness of the proposed method in complex dynamic contact problems as well as its scalability up to 23.9 million unknowns on 480 MPI ranks. Copyright © 2019, The Authors. All rights reserved.

关键词： Deformation

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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