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arXiv 2017年

作者： Akutsu, T. Ando, M. Araki, S. Araya, A. Arima, T. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Baiotti, L. Barton, M.A. Chen, D. Cho, K. Craig, K. DeSalvo, R. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujibayashi, S. Fujii, Y. Fujimoto, M.-K. Fukushima, M. Furuhata, T. Hagiwara, A. Haino, S. Harita, S. Hasegawa, K. Hasegawa, M. Hashino, K. Hayama, K. Hirata, N. Hirose, E. Ikenoue, B. Inoue, Y. Ioka, K. Ishizaki, H. Itoh, Y. Jia, D. Kagawa, T. Kaji, T. Kajita, T. Kakizaki, M. Kakuhata, H. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kasuya, J. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawamura, S. Kawazoe, F. Kim, C. Kim, J. Kim, J.C. Kim, W. Kimura, N. Kitaoka, Y. Kobayashi, K. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kubo, K. Kumar, R. Kume, T. Kuroda, K. Kuwahara, Y. Lee, H.-K. Lee, H.-W. Lin, C.-Y. Liu, Y. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsumoto, N. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyake, K. Miyamoto, A. Miyamoto, T. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Muraki, Y. Murakoshi, M. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakano, M. Nakao, H. Nakao, K. Narikawa, T. Ni, W.-T. Nonomura, T. Obuchi, Y. Oh, J.J. Oh, S.-H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohmae, N. Okino, K. Okutomi, K. Ono, K. Ono, Y. Oohara, K. Ota, S. Park, J. Peña Arellano, F.E. Pinto, I.M. Principe, M. Sago, N. Saijo, M. Saito, T. Saito, Y. Saitou, S. Sakai, K. Sakakibara, Y. Sasaki, Y. Sato, S. Sato, T. Sato, Y. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shiga, K. Shikano, Y. Shimoda, T. Shinkai, H. Shoda, A. Someya, N. Somiya, K. Son, E.J. Starecki, T. Suemasa, A. Sugimoto, Y. Susa, Y. Suwabe, H. Suzuki, T. Tachibana, Y. Tagoshi, H. Takada, S. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. National Astronomical Observatory of Japan Tokyo181-8588 Japan Graduate School of Science University of Tokyo Tokyo113-0033 Japan Department of Physics Graduate School of Science University of Tokyo Tokyo113-0033 Japan Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Tokyo113-0032 Japan Department of Physics Graduate School of Science Osaka City University Osaka558-8585 Japan Graduate School of Science and Technology Hirosaki University Aomori036-8561 Japan Department of Physics Graduate School of Science and Engineering Tokyo Institute of Technology Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Chiba277-8582 Japan Institute for Cosmic Ray Research University of Tokyo Gifu506-1205 Japan Graduate School of Science Osaka University Osaka560-0043 Japan Department of Physics Sogang University Seoul121-742 Korea Republic of University of Sannio at Benevento BeneventoI-82100 Italy Sezione di Napoli NapoliI-80126 Italy University of Toyama Toyama930-8555 Japan Department of Physics Graduate School of Science Kyoto University Kyoto606-8502 Japan Institute of Physics Academia Sinica Taipei11529 Taiwan University of Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik HannoverD-30167 Germany Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of Daejeon34055 Korea Republic of Department of Physics Myongji University Yongin449-728 Korea Republic of Department of Computer Simulation Inje University Gimhae-shi50834 Korea Republic of Daejeon34047 Korea Republic of Department of Physical Science Graduate School of Science Hiroshima University Hiroshima903-0213 Japan Department of Applied Physics Graduate School of Science Fukuoka University Fukuoka814-0180 Japan Graduate School of Science and Engineer

Major construction and initial-phase operation of a second-generation gravitational-wave detector KAGRA has been completed. The entire 3-km detector is installed underground in a mine in order to be isolated from background seismic vibrations on the surface. This allows us to achieve a good sensitivity at low frequencies and high stability of the detector. Bare-bones equipment for the interferometer operation has been installed and the first test run was accomplished in March and April of 2016 with a rather simple configuration. The initial configuration of KAGRA is named iKAGRA. In this paper, we summarize the construction of KAGRA, including the study of the advantages and challenges of building an underground detector and the operation of the iKAGRA interferometer together with the geophysics interferometer that has been constructed in the same tunnel. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

引用

BMC NEUROSCIENCE 2016年第Sup1期17卷 54-54页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego CA USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories 2-2 Hikaridai Seika-cho Soraku-gun Kyoto 619-0288 Japan Krembil Research Institute University Health Network Toronto ON M5T 2S8 Canada Department of Physiology University of Toronto Toronto ON M5S 1A8 Canada Department of Medicine (Neurology) University of Toronto Toronto ON M5T 2S8 Canada Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest 1121 Hungary Department of Mathematical Sciences KAIST Daejoen 34141 Republic of Korea Department of Mathematics University of Houston Houston TX 77004 USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston TX 77005 USA Department of Biology and Biochemistry University of Houston Houston TX 77004 USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles CA USA Center for Neuroscience Korea Institute of Science and Technology Hwarang-ro 14-gil 5 Seongbuk-gu Seoul 02792 South Korea Department of Neuroscience University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejon 34113 South Korea Department of Psychology Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong 892 Dongnam-ro Gangdong-gu Seoul 05278 South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton MA 02301 USA Center for Neural Science New York University New York NY 10003 USA Courant Institute

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

关键词： NEUROSCIENCES PATHOLOGICAL psychology DENDRITES

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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DESHIMA on ASTE: On-sky responsivity calibration of the integrated superconducting spectrometer

arXiv

引用

arXiv 2020年

作者： Takekoshi, Tatsuya Karatsu, Kenichi Suzuki, Junya Tamura, Yoichi Oshima, Tai Taniguchi, Akio 'IchiroAsayama, Shin Bakx, Tom J. L. C. Baselmans, Jochem J. A. Bosma, Sjoerd Bueno, Juan Chin, Kah Wuy Fujii, Yasunori Fujita, Kazuyuki Huiting, Robert Ikarashi, Soh Ishida, Tsuyoshi Ishii, Shun Kawabe, Ryohei Klapwijk, Teun M. Kohno, Kotaro Kouchi, Akira Llombart, Nuria Maekawa, Jun Murugesan, Vignesh Nakatsubo, Shunichi Naruse1, Masato Ohtawara, Kazushige Laguna, Alejandro Pascual Suzuki, Koyo Thoen, David J. Tsukagoshi, Takashi Ueda, Tetsutaro Visser, Pieter J. De Werf, Paul P. Vander Yates, Stephen J. C. Yoshimura, Yuki Yurduseven, Ozan Endo, Akira Institute of Astronomy Graduate School of Science University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Graduate School of Informatics and Engineering University of Electro-Communications Cho-fu Tokyo182-8585 Japan SRON-Netherlands Institute for Space Research Sorbonnelaan 2 Utrecht3584 Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 Netherlands Department of Physics Kyoto University Kyoto606-8502 Japan High Energy Accelerator Research Organization 1-1 Oho Tsukuba Ibaraki305-0801 Japan Division of Particle and Astrophysical Science Graduate School of Science Nagoya University Aichi464-8602 National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Graduate University for Advanced Studies 2-21-1 Osawa Mitaka Tokyo181-0015 School of Physics and Astronomy Cardiff University Parade CardiffCF243AA United Kingdom Department of Astronomy School of Science University of Tokyo Bunkyo Tokyo113-0033 Institute of Low Temperature Science Hokkaido University Sapporo060-0819 Japan Joint ALMA Observatory Alonso de Cordova 3107 Vitacura Santiago Kavli Institute of NanoScience Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft2628 Netherlands Physics Department Moscow State Pedagogical University Moscow119991 Russia Research Center for the Early Universe Graduate School of Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara252-5210 Japan Graduate School of Science and Engineering Saitama University 255 Shimo-okubo Sakura Saitama338-8570 Japan

We are developing an ultra-wideband spectroscopic instrument, DESHIMA (DEep Spectroscopic HIgh-redshift MApper), based on the technologies of an on-chip filter-bank and Microwave Kinetic Inductance Detector (MKID) to investigate dusty star-burst galaxies in the distant universe at millimeter and submillimeter wavelength. An on-site experiment of DESHIMA was performed using the ASTE 10-m telescope. We established a responsivity model that converts frequency responses of the MKIDs to line-of-sight brightness temperature. We estimated two parameters of the responsivity model using a set of skydip data taken under various precipitable water vapor (PWV, 0.4-3.0 mm) conditions for each MKID. The line-of-sight brightness temperature of sky is estimated using an atmospheric transmission model and the PWVs. As a result, we obtain an average temperature calibration uncertainty of 1δ =4%, which is smaller than other photometric biases. In addition, the average forward efficiency of 0.88 in our responsivity model is consistent with the value expected from the geometrical support structure of the *** also estimate line-of-sight PWVs of each skydip observation using the frequency response of MKIDs, and confirm the consistency with PWVs reported by the Atacama Large Millimeter/submillimeter Array. Copyright © 2020, The Authors. All rights reserved.

关键词： Calibration

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Vibration isolation system with a compact damping system for power recycling mirrors of KAGRA

arXiv

引用

arXiv 2019年

作者： Akiyama, Y. Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Baiotti, L. Barton, M.A. Cannon, K. Capocasa, E. Chen, C.-S. Chiu, T.-W. Cho, K. Chu, Y.-K. Craig, K. Dattilo, V. Doi, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Fukunaga, M. Fukushima, M. Furuhata, T. Haino, S. Hasegawa, K. Hashimoto, Y. Hashino, K. Hayama, K. Hirayama, T. Hirose, E. Hsieh, B.H. Huang, C.-Z. Ikenoue, B. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kang, G. Kasuya, J. Kawai, N. Kawasaki, T. Kim, C. Kim, W.S. Kim, J. Kim, J.C. Kimura, N. Kirii, S. Kitaoka, Y. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. Kotake, K. Kozu, R. Kumar, R. Kuo, H.-S. Kuroki, S. Kuroyanagi, S. Lee, H.K. Lee, H.M. Lee, H.W. Leonardi, M. Lin, C.-Y. Lin, F.-L. Liu, G.C. Marchio, M. Matsui, T. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Narikawa, T. Quynh, L. Nguyen Ni, W.-T. Nishizawa, A. Obuchi, Y. Oh, J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Ono, K. Oohara, K. Ooi, C.P. Pan, S.-S. Paoletti, F. Park, J. Passaquieti, R. Peña Arellano, F.E. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sasai, M. Sato, S. Sato, T. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shimoda, T. Shinkai, H. Shishido, T. Shoda, A. Someya, N. Somiya, K. Son, E.J. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Tahara, H. Takahashi, H. Takahashi, R. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tanioka, S. San Martin, E.N. Tapia Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Uraguchi, F. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wakamatsu, T. Watanabe, Y. Xu, W.-R. Yamada, T. Yamamoto, K. Yamamoto, K. Yamamoto, S. Yamamoto, T. Yokogawa, K. Yokoyama, J. Yokozawa, T. Yoshioka, T. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. Graduate School of Science and Engineering Hosei University TokyoKoganei184-8584 Japan National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan University of Tokyo KashiwaChiba277-8582 Japan High Energy Accelerator Research Organization TsukubaIbaraki305-0801 Japan Earthquake Research Institute University of Tokyo TokyoBunkyo113-0032 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan Department of Mathematics and Physics Hirosaki University AomoriHirosaki036-8561 Japan National Astronomical Observatory of Japan GifuHida506-1205 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Department of Physics Osaka University ToyonakaOsaka560-0043 Japan University of Tokyo TokyoBunkyo113-0033 Japan Department of Physics National Taiwan Normal University Taipei116 Taiwan Department of Physics Sogang University Seoul121-742 Korea Republic of PisaCascinaI-56021 Italy Department of Physics University of Toyama ToyamaToyama930-8555 Japan Department of Astronomy University of Tokyo TokyoBunkyo113-0032 Japan Advanced Technology Center National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Institute of Physics Academia Sinica TaipeiNankang11529 Taiwan Department of Applied Physics Fukuoka University FukuokaJonan814-0180 Japan Yukawa Institute for Theoretical Physics Kyoto University KyotoSakyo606-8502 Japan Graduate School of Science Osaka City University OsakaSumiyosi558-8585 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency SagamiharaKanagawa252-5210 Japan University of Tokyo GifuHida506-1205 Japan Graduate Schoool of Science and Technology Tokyo Institute of Technology MeguroTokyo152-8551 Japan Department of Physics Ewha Womans University SeoulSeodaemun-gu03760 Korea Republic of National Institute for

A vibration isolation system called Type-Bp system used for power recycling mirrors has been developed for KAGRA, the interferometric gravitational-wave observatory in Japan. A suspension of the Type-Bp system passively isolates an optic from seismic vibration using three main pendulum stages equipped with two vertical vibration isolation systems. A compact reaction mass around each of the main stages allows for achieving sufficient damping performance with a simple feedback as well as vibration isolation ratio. Three Type-Bp systems were installed in KAGRA, and were proved to satisfy the requirements on the damping performance, and also on estimated residual displacement of the optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Mirrors

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First light demonstration of the integrated superconducting spectrometer

arXiv

引用

arXiv 2019年

作者： Endo, Akira Karatsu, Kenichi Tamura, Yoichi Oshima, Tai Taniguchi, Akio Takekoshi, Tatsuya Asayama, Shinichiro Bakx, Tom J. L. C. Bosma, Sjoerd Bueno, Juan Chin, Kah Wuy Fujii, Yasunori Fujita, Kazuyuki Huiting, Robert Ikarashi, Soh Ishida, Tsuyoshi Ishii, Shun Kawabe, Ryohei Klapwijk, T.M. Kohno, Kotaro Kouchi, Akira Llombart, Nuria Maekawa, Jun Murugesan, Vignesh Nakatsubo, Shunichi Naruse, Masato Ohtawara, Kazushige Laguna, Alejandro Pascual Suzuki, Junya Suzuki, Koyo Thoen, David J. Tsukagoshi, Takashi Ueda, Tetsutaro Visser, Pieter J. De Werf, Paul P. Vander Yates, Stephen J. C. Yoshimura, Yuki Yurduseven, Ozan Baselmans, Jochem J. A. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 Netherlands Kavli Institute of NanoScience Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft2628 Netherlands SRON|Netherlands Institute for Space Research Sorbonnelaan 2 Utrecht3584 Netherlands Division of Particle and Astrophysical Science Graduate School of Science Nagoya University Aichi464-8602 Japan National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Graduate University for Advanced Studies 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Institute of Astronomy Graduate School of Science University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Graduate School of Informatics and Engineering University of Electro-Communications Cho-fu Tokyo182-8585 Japan School of Physics and Astronomy Cardi University Parade CardiCF243AA United Kingdom Department of Astronomy School of Science University of Tokyo Bunkyo Tokyo113-0033 Japan Institute of Low Temperature Science Hokkaido University Sapporo060-0819 Japan Joint ALMA Observatory Alonso de Cordova Vitacura Santiago3107 Physics Department Moscow State Pedagogical University Moscow119991 Russia Research Center for the Early Universe Graduate School of Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara252-5210 Japan Graduate School of Science and Engineering Saitama University 255 Shimo-okubo Sakura Saitama338-8570 Japan High Energy Accelerator Research Organization 1-1 Oho Tsukuba Ibaraki305-0801 Japan Leiden Observatory Leiden University PO Box 9513 LeidenNL-2300 Netherlands SRON Netherlands Institute for Space Research Landleven 12 Groningen9747 Netherlands

Ultra-wideband 3D imaging spectrometry in the millimeter-submillimeter (mm-submm) band is an essential tool for uncovering the dust-enshrouded portion of the cosmic history of star formation and galaxy evolution1{3. However, t is challenging to scale up conventional coherent heterodyne receivers4 or free-space diffraction techniques5 to suficient bandwidths (≥1 octave) and numbers of spatial pixels2,3 (>102). Here we present the design and first astronomical spectra of an intrinsically scalable, ntegrated superconducting spectrometer6, which covers 332{377 GHz with a spectral resolution of F/ΔF ∼ 380. It combines the multiplexing advantage of microwave kinetic inductance detectors (MKIDs7) with planar superconducting filters for dispersing the signal in a single, small superconducting integrated circuit. We demonstrate the two key applications for an instrument of this type: As an eficient redshift machine, and as a fast multi-line spectral mapper of extended areas. The line detection sensitivity is in excellent agreement with the instrument design and laboratory performance, reaching the atmospheric foreground photon noise limit on sky. The design can be scaled to bandwidths in excess of an octave, spectral resolution up to a few thousand and frequencies up to ∼1.1 THz. The miniature chip footprint of a few cm2 allows for compact multi-pixel spectral imagers, which would enable spectroscopic direct imaging and large volume spectroscopic surveys that are several orders of magnitude faster than what is currently possible1{3. Copyright © 2019, The Authors. All rights reserved.

关键词： Bandwidth

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

引用

LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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