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第18届全国计算机新科技与计算机教育学术大会

作者： ZHANG Dongsong 张冬松 WU Tong 吴彤 JIN Shiyao 金士尧 WANG Bing 王兵 National Laboratory of Parallel and Distributed Processing National University of Defense Technology 国防科学技术大学并行与分布处理国家重点实验室长沙410073 Modemized Education Technology Center University of Shanghai for Science and TechnologyShanghai 200 上海理工大学现代化教学中心上海200093

近年来在能量敏感实时系统领域出现了许多节能调度技术，但大多忽视了实际处理器所具有的时间和能量转变开销以及电压/频率调节的离散性等限制。对于许多硬实时应用来说，忽视这些细节可能会得到次最优甚至是无效的结果。本文对有关静... 详细信息

近年来在能量敏感实时系统领域出现了许多节能调度技术，但大多忽视了实际处理器所具有的时间和能量转变开销以及电压/频率调节的离散性等限制。对于许多硬实时应用来说，忽视这些细节可能会得到次最优甚至是无效的结果。本文对有关静态任务调度和电压分配技术进行了分析和探讨，针对以上实际限制提出了空闲时间选择法，通过理论证明优于忙周期选择法，并给出改进后的统一的离线节能调度优化技术。

关键词：能量敏感实时系统离线节能调度静态任务调度电压分配技术空闲时间选择法

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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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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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The Hi-GAL compact source catalogue. I : The physical properties of the clumps in the inner Galaxy (−71.0◦ 67.0◦)

arXiv

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arXiv 2017年

作者： Elia, Davide Molinari, S. Schisano, E. Pestalozzi, M. Pezzuto, S. Merello, M. Noriega-Crespo, A. Moore, T.J.T. Russeil, D. Mottram, J.C. Paladini, R. Strafella, F. Benedettini, M. Bernard, J.P. Di Giorgio, A. Eden, D.J. Fukui, Y. Plume, R. Bally, J. Martin, P.G. Ragan, S.E. Jaffa, S.E. Motte, F. Olmi, L. Schneider, N. Testi, L. Wyrowski, F. Zavagno, A. Calzoletti, L. Faustini, F. Natoli, P. Palmerim, P. Piacentini, F. Piazzo, L. Pilbratt, G.L. Polychroni, D. Baldeschi, A. Beltrán, M.T. Billot, N. Cambrésy, L. Cesaroni, R. García-Lario, P. Hoare, M.G. Huang, M. Joncas, G. Liu, S.J. Maiolo, B.M.T. Marsh, K.A. Maruccia, Y. Mège, P. Peretto, N. Rygl, K.L.J. Schilke, P. Thompson, M.A. Traficante, A. Umana, G. Veneziani, M. Ward-Thompson, D. Whitworth, A.P. Arab, H. Bandieramonte, M. Becciani, U. Brescia, M. Buemi, C. Bufano, F. Butora, R. Cavuoti, S. Costa, A. Fiorellino, E. Hajnal, A. Hayakawa, T. Kacsuk, P. Leto, P. Li Causi, G. Marchili, N. Martinavarro-Armengol, S. Mercurio, A. Molinaro, M. Riccio, G. Sano, H. Sciacca, E. Tachihara, K. Torii, K. Trigilio, C. Vitello, F. Yamamoto, H. INAF-IAPS via del Fosso del Cavaliere 100 Roma00133 Italy Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park Ic2 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Aix Marseille Univ. CNRS LAM Laboratoire d’Astrophysique de Marseille Marseille France Max-Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany Infrared Processing Analysis Center California Institute of Technology 770 South Wilson Ave. PasadenaCA91125 United States Dipartimento di Matematica e Fisica Università del Salento Lecce73100 Italy CNRS IRAP 9 Av. colonel Roche BP 44346 Toulouse cedex 4F-31028 France Université de Toulouse UPS-OMP IRAP Toulouse cedex 4F-31028 France Department of Physics Nagoya University Chikusa-ku Nagoya Aichi464-8601 Japan Department of Physics & Astronomy University of Calgary ABT2N 1N4 Canada Center for Astrophysics and Space Astronomy University of Colorado BoulderCO80309 United States Canadian Institute for Theoretical Astrophysics University of Toronto McLennan Physical Laboratories 60 St. George Street TorontoON Canada School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom School of Physics and Astronomy Cardiff University Cardiff WalesCF24 3AA United Kingdom IPAG University Grenoble Alpes Grenoble38000 France AIM Paris-Saclay CEA/IRFU - CNRS INSU - Univ. Paris Diderot Service d’Astrophysique CEA-Saclay Gif-surYvette Cedex91191 France INAF Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy I. Physik. Institut University of Cologne Zülpicher Strasse Köln50937 Germany European Southern Observatory Karl Schwarzschild str. 2 Garching85748 Germany Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany ASI Science Data centre 00044 Frascati Roma Italy ESA/ESAC PO Box 78 Villanueva de la Cañada Madrid28691 Spain

Hi-GAL is a large-scale survey of the Galactic plane, performed with Herschel in five infrared continuum bands between 70 and 500 µm. We present a band-merged catalogue of spatially matched sources and their properties derived from fits to the spectral energy distributions (SEDs) and heliocentric distances, based on the photometric catalogs presented in Molinari et al. (2016a), covering the portion of Galactic plane −71.0◦ 67.0◦. The band-merged catalogue contains 100922 sources with a regular SED, 24584 of which show a 70 µm counterpart and are thus considered proto-stellar, while the remainder are considered starless. Thanks to this huge number of sources, we are able to carry out a preliminary analysis of early stages of star formation, identifying the conditions that characterise different evolutionary phases on a statistically significant basis. We calculate surface densities to investigate the gravitational stability of clumps and their potential to form massive stars. We also explore evolutionary status metrics such as the dust temperature, luminosity and bolometric temperature, finding that these are higher in proto-stellar sources compared to pre-stellar ones. The surface density of sources follows an increasing trend as they evolve from pre-stellar to proto-stellar, but then it is found to decrease again in the majority of the most evolved clumps. Finally, we study the physical parameters of sources with respect to Galactic longitude and the association with spiral arms, finding only minor or no differences between the average evolutionary status of sources in the fourth and first Galactic quadrants, or between "on-arm" and "inter-arm" positions. Copyright © 2017, The Authors. All rights reserved.

关键词： Galaxies

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