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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

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Correction to: Geometric correction method for Tibetan woodcut document images

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Multimedia Tools and Applications 2022年第11期81卷 15633-15633页

作者： Hu, Jian Xiawu, Lijia Qiao, Shaojie Tan, Wenrong Yin, Feng Liu, Tao Han, Nan Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest MinZu University Chengdu China Minzu Language’s Information Processing Laboratory Southwest MinZu University Chengdu China School of Computer Science and Technology Southwest MinZu University Chengdu China Southwest Nationalities Research Academy Southwest MinZu University Chengdu China School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation and Intelligence Service Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China Library Southwest MinZu University Chengdu China School of Management Chengdu University of Information Technology Chengdu China

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Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

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Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

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Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

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ACM Transactions on Autonomous and Adaptive systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

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A fine-grained data set and analysis of tangling in bug fixing commits

arXiv

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arXiv 2020年

作者： Herbold, Steffen Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher Ahmed Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel G. Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James C. Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes Institute for Software and Systems Engineering TU Clausthal Clausthal-Zellefeld Germany Institute of Computer Science University of Goettingen Goettingen Germany Department of Computer Engineering Sharif University of Technology Tehran Iran School of Computing Queen's University Kingston Canada Department of Computer Science Guru Nanak Dev University Amritsar India Karlsruhe Germany Brunel University London Uxbridge United Kingdom Institute of Computer Science University of Goettingen Germany University of Tehran Tehran Iran Ericsson Hungary Ltd. Budapest Hungary Simula Research Laboratory Fornebu Norway Technical University of Košice Košice Slovakia LUT University Finland National University of Defense Technology Changsha China University of British Columbia Kelowna Canada Østfold University College Halden Norway Vrije Universiteit Amsterdam Amsterdam Netherlands University of Auckland Auckland New Zealand University of Saskatchewan Saskatoon Canada IBM Boulder United States University of Missouri-Kansas City Kansas City United States Università della Svizzera italiana Lugano Switzerland Trent University Peterborough Canada University of Maryland Baltimore County United States Imam Abdulrahman Bin Faisal University Saudi Arabia The Hebrew University Acumen Jerusalem Israel University of Oulu Oulu Finland Monash University Melbourne Australia University of Würzburg Würzburg Germany Technische Universität Darmstadt Darmstadt Germany University of Tennessee Knoxville United States University of Macedonia Thessaloniki Greece Department of Computer Engineering Bilkent University Ankara Turkey University of Melbourne Melbourne Australia Independent Researcher Tehran Iran University of Trento Trento Italy University of Stuttgart Stuttgart Germany Purdue University West LafayetteIN United States Eindhoven University of Technology Eindhoven Netherlands Softtech Inc. Research and Development Center Istanbul34947 Turkey Radboud Universi

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Scaled Background Swap: Video Augmentation for Action quality Assessment with Background Debiasing

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Xin Zhang Hongzhi Feng M. Shamim Hossain Yinzhuo Chen Hongbo Wang Yuyu Yin Hangzhou Dianzi University China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China and Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Hangzhou Dianzi University China Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia Hangzhou Dianzi University China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China and Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China

Action quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. Firstly, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Secondly, the video’s foreground region is up-scaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://***/Emy-cv/Scaled-Background Swap.

关键词： Background Swap Foreground Up-scale Data Augmentation Background Bias Action quality Assessment

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Preface

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2659卷 V-VI页

作者： Sloot, Peter M. A. Abramson, David Bogdanov, Alexander V. Dongarra, Jack J. Zomaya, Albert Y. Gorbachev, Yuriy E. University of Amsterdam Informatics Institute Section of Computational Science Kruislaan 403 Amsterdam Netherlands Monash University School of Computer Science and Software Engineering Wellington Road ClaytonVIC Australia Institute for High-Performance Computing and Information Systems Fontanka emb. 6 St. Petersburg Russia University of Tennessee and Oak Ridge National Laboratory Computer Science Dept. 1122 Volunteer Blvd. KnoxvilleTN United States The University of Sydney School of Information Technologies CISCO Systems Madsen Building F09 SydneyNSW Australia

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Preface

引用

CEUR Workshop Proceedings 2021年 2899卷

作者： Kovalev, Igor Semenkin, Eugene Gulyaev, Yuri Barakaev, Nusratilla Shaidurov, Vladimir Škraba, Andrej Tsarev, Roman Legalov, Alexander Saramud, Mikhail Grosspietsch, Karl-Erwin Stupina, Alena Department of Computer Science Siberian Federal University Krasnoyarsk Russia Department of system analysis and operations research of Reshetnev University Krasnoyarsk Russia International and Russian Union of Scientific and Engineering Associations Moscow Russia Rector of Bukhara Engineering Technological Institute Bukhara Uzbekistan Institute of Computational Modeling SB RAS Krasnoyarsk Russia University of Maribor UM Faculty of Organizational Sciences - Cybernetics and Decision Support Systems Laboratory Slovenia Department of Informatics Siberian Federal University Krasnoyarsk Russia Faculty of Computer Science School of Software Engineering HSE University Moscow Russia Department of Informatics Siberian Federal University Krasnoyarsk Russia European Organization for Information and Microelectronics Germany The Siberian Federal University Krasnoyarsk Russia

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The Jimmie Hamilton Award

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Naval Engineers Journal 1971年第3期83卷 36-38页

作者： NACHTSHEIM, JOHN J. BALLOU, L. DENNIS John J. Nachtsheim:is currently the Deputy Assistant Administrator for Research & Development for the Maritime Administration. His duties are the planning coordinating organizing evaluating and directing of the R&D activities of MarAd. His past experiences include: Naval Architect for the Naval Ship Engineering Center 1959 Deputy Chief Design Engineer for the Puget Sound Naval Shipyard 1958 to 1959 and Naval Architect the former Bureau of Ships 1948 to 1958. His education is comprised of a B.S. degree from the Webb Institute of Naval Architecture an L.L.B. degree from the George Washington University Law School completion of the Advanced Management Program at Harvard University and current study of Transportation at the American University. He is a Registered Professional Engineer in the District of Columbia and a Member of the Bar in the District of Columbia and the State of Maryland. In addition to ASNE his other professional memberships include the Society of Naval Architects and Marine Engineers the Society of Aeronautical Weight Engineers and the Association of Senior Engineers of the Naval Ships Systems Command (Honorary). USN Commander L. Dennis Ballou: USN is the Head of the Engineering Service Office Naval Ship Engineering Center. He is involved in computer hardware and software services to support engineering design automatic data processing systems design work study and quality assurance. Prior to NavSec duty Commander Ballou served in various billets afloat and ashore: tours on the USS Skagit and Tang supervision of the USS Skipjack's first overhul construction of the USS Nathanael Greene and helping to establish the Polaris overhaul program. He is a graduate of the U.S. Naval Academy Officers' Submarine School and the Webb Institute of Naval Architecture. He holds BS and MS degrees in marine engineering and naval architecture respectively. He has also completed many graduate

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