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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Design choices for productive, secure, data-intensive research at scale in the cloud

arXiv

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arXiv 2019年

作者： Arenas, Diego Atkins, Jon Austin, Claire Beavan, David Egea, Alvaro Cabrejas Carlysle-Davies, Steven Carter, Ian Clarke, Rob Cunningham, James Doel, Tom Forrest, Oliver Gabasova, Evelina Geddes, James Hetherington, James Jersakova, Radka Kiraly, Franz Lawrence, Catherine Manser, Jules O'Reilly, Martin T. Robinson, James Sherwood-Taylor, Helen Tierney, Serena Vallejos, Catalina A. Vollmer, Sebastian Whitaker, Kirstie University of St Andrews KY16 9SX United Kingdom Alan Turing Institute 96 Euston Rd Kings Cross LondonNW1 2DB United Kingdom MRC Human Genetics Unit Institute of Genetics & Molecular Medicine University of Edinburgh Western General Hospital Crewe Road EdinburghEH4 2XU United Kingdom Department of Psychiatry University of Cambridge Herchel Smith Buidling for Brain & Mind Sciences Forvie Site Robinson Way CambridgeCB2 0SZ United Kingdom VWV LLP 24 King William Street LondonEC4R 9AT United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Warwick Mathematics Institute Department of Statistics University of Warwick CV4 7AL United Kingdom MathSys Centre for Doctoral Training Warwick Mathematics Institute University of Warwick CV4 7AL United Kingdom British Library 96 Euston Road LondonNW1 2DB United Kingdom Edinburgh Parallel Computing Centre University of Edinburgh Bayes Centre 47 Potterrow EdinburghEH8 9BT United Kingdom

We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence. Copyright © 2019, The Authors. All rights reserved.

关键词： Risk assessment

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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parallel AND distributed TLM COMPUTATION WITH SIGNAL-PROCESSING FOR ELECTROMAGNETIC-FIELD MODELING

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 1995年第3-4期8卷 169-185页

作者： SO, PPM ESWARAPPA, C HOEFER, WJR NSERC/MPR Teltech Research Chair in RF Engineering Department of Electrical and Computer Engineering University of Victoria Victoria British Columbia V8W 3P6 Canada Poman So received his B.Sc. degree in computer science and physics from the University of Toronto Toronto Onatario Canada in 1985. He obtained his B.A. Sc. and M.A.Sc. degrees in electrical engineering (summa cum laude) from the University of Ottawa Ottawa Ontario Canada in 1987 and 1989 respectively. Mr So was a research engineer in the University of Ottawa from January 1989 to October 1991 his research interests included CAD techniques of microwave circuits and numerical methods for electromagnetic wave modelling. He specialized in the development of electromagnetic engineering CAD software and successfully implemented a number of electromagnetic wave simulators based on the two-dimensional and three-dimensional transmission-line matrix methods. From August 1990 to February 1991 he accompanied Professor W. J. R. Hoefer to Rome Italy and Sophia Antiopolas France. During that time he implemented a parallel version of the 3D-TLM simulator for the CM-2 Connection Machine using C-Star and X Windows Library. He is a research engineer and a Ph.D. student at the University of Victoria Victoria British Columbia Canada. He ported the 2D- and 3D-TLM simulators to DECmpp 1200 massively parallel computer using MPL C and C++. He also combined OSA90/hope a commercially available microwave CAD program with a 2D-TLM simulation module for geometry optimization using the Datapipe technique of OSA90/hope. Recently he has developed a distributed client-server computing technique for the TLM method this technique could accelerate the TLM simulation by more than an order of magnitude. Channabasappa Eswarappa received the M.Tech. degree in electrical engineering from the Indian Institute of Technology Kanpur India in 1983 and Ph. D. degree from the University of Ottawa Canada in 1990. He worked as an Assistant Executive Engineer and later as

This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributed computing in the UNIX environment, and the combination of TLM analysis with Prony's method as well as with autoregressive moving average (ARMA) digital signal processing for electromagnetic field modelling. By combining these advanced computation techniques, typical electromagnetic field modelling of microwave structures by TLM analysis can be accelerated by a few orders of magnitude.

关键词： Mathematical models

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