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IROS 2019 lifelong robotic vision challenge lifelong object recognition report

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arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria D'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén David Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn Rochester Claudia Mazzà Silvia Del Din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

Segregated interactions in urban and online spaces

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arXiv 2019年

作者： Dong, Xiaowen Morales, Alfredo J. Jahani, Eaman Moro, Esteban Lepri, Bruno Bozkaya, Burcin Sarraute, Carlos Bar-Yam, Yaneer Pentland, Alex Department of Engineering Science University of Oxford Oxford United Kingdom MIT Media Lab CambridgeMA United States New England Complex Systems Institute CambridgeMA United States MIT Institute for Data Systems and Society CambridgeMA United States Department of Mathematics and GISC Universidad Carlos III de Madrid Leganés Spain Mobile and Social Computing Lab Fondazione Bruno Kessler Trento Italy Behavioral Analytics & Visualization Lab Sabancı University Istanbul Turkey New College of Florida SarasotaFL United States Grandata Labs Buenos Aires Argentina

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both shopping activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric, i.e., the amount of interaction from poorer to wealthier neighborhoods is considerably larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications. Copyright © 2019, The Authors. All rights reserved.

关键词： Geographical distribution

297Seeing is believing: the feasibility and acceptability of using wearable cameras to enhance self-management of heart failure

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European Heart Journal 2019年第SUPPLEMENT_1期40卷

作者： Cartledge, S (*) Rogerson, M Singh, T K R Huynh Huu, V Phung, D Gurrin, C Neil, C Ball, K Maddison, R Deakin University Institute for Physical Activity and Nutrition Melbourne Australia Deakin University Department of Pattern Recogntion & Data Analytics Geelong Australia Dublin City University (DCU) School of Computing Dublin Ireland Western Health Melbourne Australia

database of parliamentary speeches in Ireland, 1919–2013

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Database of parliamentary speeches in Ireland, 1919–2013

International Conference on the Frontiers and Advances in data Science (FADS)

作者： Alexander Herzog Slava J. Mikhaylov School of Computing Clemson University Institute for Analytics and Data Science Department of Government University of Essex

ISBN: (纸本)9781538631492

We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dail Eireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dala, members of parliament), such as their party affiliations, constituencies and office positions. The current version of the database includes close to 4.5 million speeches from 1,178 TDs. In addition, we also present three applications in which we make use of the new data.

关键词： Speech databases Economic indicators Unemployment Government Finance Estimation

Understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care

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arXiv 2019年

作者： Komorowski, Matthieu Celi, Leo A. Badawi, Omar Gordon, Anthony C. Aldo Faisal, A. Department of Surgery and Cancer Imperial College London London United Kingdom Department of Bioengineering Imperial College London London United Kingdom Laboratory of Computational Physiology Harvard-MIT Division of Health Sciences & Technology CambridgeMA United States Beth Israel Deaconess Medical Center BostonMA United States Patient Care Analytic Philips Healthcare BaltimoreMD United States Department of Pharmacy Practice and Science University of Maryland School of Pharmacy BaltimoreMD United States Centre in AI for Healthcare Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom MRC London Institute of Medical Sciences London United Kingdom Behaviour Analytics Lab Data Science Institute London United Kingdom

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018;Bos, Azoulay, & Martin-Loeches, 2019;Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication. We acknowledge the online comments by Jeter et al (https://***/abs/1902.03271). The sections of the present document are structured so as to address some of their questions. For clarity, we label figures from our main Nature Medicine publication as "M", figures from Jeter et al.'s arXiv paper as "J" and figures from our response here as "R". Copyright © 2019, The Authors. All rights reserved.

关键词： Publishing

Discovery of an Exceptionally Strong β-Decay Transition of F20 and Implications for the Fate of Intermediate-Mass Stars

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Physical Review Letters 2019年第26期123卷 262701-262701页

作者： O. S. Kirsebom S. Jones D. F. Strömberg G. Martínez-Pinedo K. Langanke F. K. Röpke B. A. Brown T. Eronen H. O. U. Fynbo M. Hukkanen A. Idini A. Jokinen A. Kankainen J. Kostensalo I. Moore H. Möller S. T. Ohlmann H. Penttilä K. Riisager S. Rinta-Antila P. C. Srivastava J. Suhonen W. H. Trzaska J. Äystö Department of Physics and Astronomy Aarhus University DK-8000 Aarhus C Denmark Institute for Big Data Analytics Dalhousie University Halifax Nova Scotia B3H 4R2 Canada Computational Physics (XCP) Division Los Alamos National Laboratory New Mexico 87545 USA Heidelberger Institut für Theoretische Studien D-69118 Heidelberg Germany Institut für Kernphysik (Theoriezentrum) Technische Universität Darmstadt D-64289 Darmstadt Germany GSI Helmholtzzentrum für Schwerionenforschung D-64291 Darmstadt Germany Zentrum für Astronomie der Universität Heidelberg Institut für Theoretische Astrophysik D-69120 Heidelberg Germany National Superconducting Cyclotron Laboratory Michigan State University East Lansing Michigan 48824 USA Department of Physics University of Jyväskylä P.O. Box 35 FI-40014 University of Jyväskylä Finland Division of Mathematical Physics Department of Physics LTH Lund University P.O. Box 118 S-22100 Lund Sweden Max Planck Computing and Data Facility D-85748 Garching Germany Department of Physics Indian Institute of Technology Roorkee 247667 India

A significant fraction of stars between 7 and 11 solar masses are thought to become supernovae, but the explosion mechanism is unclear. The answer depends critically on the rate of electron capture on Ne20 in the degenerate oxygen-neon stellar core. However, because of the unknown strength of the transition between the ground states of Ne20 and F20, it has not previously been possible to fully constrain the rate. By measuring the transition, we establish that its strength is exceptionally large and that it enhances the capture rate by several orders of magnitude. This has a decisive impact on the evolution of the core, increasing the likelihood that the star is (partially) disrupted by a thermonuclear explosion rather than collapsing to form a neutron star. Importantly, our measurement resolves the last remaining nuclear physics uncertainty in the final evolution of degenerate oxygen-neon stellar cores, allowing future studies to address the critical role of convection, which at present is poorly understood.

关键词： Beta decay Electron & muon capture Lifetimes & widths Novae & supernovae Nuclear astrophysics Nuclear physics of explosive environments Nucleosynthesis in explosive environments 20 ≤ A ≤ 38 Astrophysical & cosmological simulations Hydrodynamic models Nuclear structure & decays Radioactive beams Shell model Spectrometers & spectroscopic techniques

Special Issue on Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷

作者： Jorge Munoz-Gama Niels Martin Carlos Fernandez-Llatas Owen Johnson Marcos Sepúlveda Human & Process Research Lab School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Department of Clinical Sciences Intervention and Technology (CLINTEC) Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom