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Fast deformation of shocked quartz and implications for planar deformation features observed in shocked quartz

AIP Conference Proceedings 2020年第1期2272卷

作者： Toshimori Sekine Tomoko Sato Norimasa Ozaki Kohei Miyanishi Ryosuke Kodama Yusuke Seto Yoshinori Tange Subodh C. Tiwari Aiichiro Nakano Priya Vashishta Center for High Pressure Science & Technology Advanced Research Shanghai 201203 China Graduate School of Engineering Osaka University Suita 565-0871 Japan Department of Earth & Planetary Systems Science Hiroshima University Higashi-Hiroshima 739-8526 Japan Graduate School of Science Kobe University Kobe 657-8501 Japan Japan Synchrotron Radiation Research Institute JASRI Sayo-cho Hyogo 679-5198 Japan Collaboratory for Advanced Computing & Simulations Department of Physics & Astronomy Department of Computer Science Department of Chemical Engineering and Materials Science and Department of Biological Science University of Southern California Los Angeles CA 90089–0242 USA

Fast deformations in the quartz crystals during shock compression process have been investigated with two time-resolved methods of in-situ diffractions by x-ray free electron laser (XFEL) and large-scale molecular dynamic (MD) simulations in order to get insights in planar deformation feature (PDF) mechanism. PDFs in quartz provide strong evidence for impact and are used to estimate the impact conditions. The present experimental results on X-cut and Y-cut quartz single crystals at pressures below phase transition indicate ideal uniaxial compression and rotation of fractured grains to a direction to heat up locally due to dispersed particle velocity distribution in the dynamic movement. This could explain the observed disordering at given planes, as supported by the MD simulations.

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Ultrafast quantum-path interferometry revealing the generation process of coherent phonons

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Physical Review B 2019年第18期99卷 180301(R)-180301(R)页

作者： Kazutaka G. Nakamura Kensuke Yokota Yuki Okuda Rintaro Kase Takashi Kitashima Yu Mishima Yutaka Shikano Yosuke Kayanuma Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology 4259 Nagatsuta Yokohama 226-8503 Japan Quantum Computing Center Keio University 3-14-1 Gakuen-cho Yokohama 223-8522 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo 4-6-1 Komaba Meguro Tokyo 153-8904 Japan Research Center of Integrative Molecular Systems (CIMoS) Institute for Molecular Science National Institutes of Natural Sciences 38 Nishigo-Naka Myodaiji Okazaki Aichi 444-8585 Japan Institute for Quantum Studies Chapman University 1 University Dr. Orange California 92866 USA Graduate School of Sciences Osaka Prefecture University 1-1 Gakuen-cho Sakai Osaka 599-8531 Japan

Optical dual-pulse pumping actively creates quantum-mechanical superposition of the electronic and phononic states in a bulk solid. We here made transient reflectivity measurements in an n-GaAs using a pair of relative-phase-locked femtosecond pulses and found characteristic interference fringes. This is a result of quantum-path interference peculiar to the dual-pulse excitation as indicated by theoretical calculation. Our observation reveals that the pathway of coherent phonon generation in the n-GaAs is impulsive stimulated Raman scattering at the displaced potential due to the surface-charge field, even though the photon energy lies in the opaque region.

关键词： Optical phonons Quantum interference effects

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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Publisher Correction: A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images

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Scientific reports 2020年第1期10卷 8591页

作者： Zichao Guo Hong Liu Haomiao Ni Xiangdong Wang Mingming Su Wei Guo Kuansong Wang Taijiao Jiang Yueliang Qian Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China. Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China. hliu@***. Research Center for Big Data of Biomedical Sciences Institute of Basic Medical Sciences Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100005 China. Suzhou Institute of Systems Medicine Suzhou 215123 China. Graduate School of Peking Union Medical College Beijing 100005 China. Department of Pathology Xiangya Hospital Central South University Changsha Hunan 410013 China. Department of Pathology School of Basic Medical Sciences Central South University Changsha Hunan 410013 China. Department of Pathology Xiangya Hospital Central South University Changsha Hunan 410013 China. ***@***. Department of Pathology School of Basic Medical Sciences Central South University Changsha Hunan 410013 China. ***@***. Research Center for Big Data of Biomedical Sciences Institute of Basic Medical Sciences Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100005 China. taijiao@***. Suzhou Institute of Systems Medicine Suzhou 215123 China. taijiao@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Distinct molecular profiles of skull bone marrow in health and neurological disorders

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四川生理科学杂志 2023年第9期45卷 1704-1704页

作者： Zeynep Ilgin Kolabas Institute for Tissue Engineering and Regenerative Medicine (iTERM) Helmholtz Center Neuherberg Munich Germany Institute for Stroke and Dementia Research LMU University Hospital Ludwig-Maximilians University Munich Munich Germany Graduate School of Systemic Neurosciences (GSN) Munich Germany Institute of Computational Biology Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany Division of Mental Health in Older Adults and Alzheimer Therapy and Research Center Department of Psychiatry and Psychotherapy University Hospital Ludwig Maximilian University Munich 80336 Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Munich Germany Ageing Epidemiology (AGE) Research Unit School of Public Health Imperial College London London UK Munich Cluster for Systems Neurology (SyNergy) Munich Germany Sheffield Institute for Translational Neuroscience University of Sheffield Sheffield UK Department of Nuclear Medicine University Hospital Ludwig-Maximilians-Universität München Munich Germany Institute for Diabetes and Obesity Helmholtz Center Munich and German Center for Diabetes Research (DZD) 85764 Neuherberg Germany Munich Medical Research School (MMRS) 80336 Munich Germany Helmholtz Pioneer Campus (HPC) Helmholtz Munich Neuherberg Germany Wellcome Sanger Institute Cambridge UK School of Computation Information and Technology (CIT) TUM Boltzmannstr. 3 85748 Garching Germany Department of Radiology University Hospital LMU Munich Munich Germany Charité - Universitätsmedizin Berlin Department of Rheumatology and Clinical Immunology Berlin Germany Immune Dynamics Deutsches Rheuma-Forschungszentrum (DRFZ) a Leibniz Institute Berlin Germany Biomedical Center (BMC) Core Facility Flow Cytometry Faculty of Medicine LMU Munich Munich Germany Institute for Diabetes and Cancer Helmholtz Munich Munich Germany Institute of Neuroradiology University Hospital LMU Munich Germany Institute of Diabetes and Regenerat

The bone marrow in the skull is important for shaping immune responses in the brain and meninges, but its molecular makeup among bones and relevance in human diseases remain unclear. Here, we show that the mouse skull has the most distinct transcriptomic profile compared with other bones in states of health and injury, characterized by a late-stage neutrophil phenotype. In humans, proteome analysis reveals that the skull marrow is the most distinct, with differentially expressed neutrophil-related pathways and a unique synaptic protein signature. 3D imaging demonstrates the structural and cellular details of human skull-meninges connections (SMCs) compared with veins. Last, using translocator protein positronemission tomography (TSPO-PET) imaging, we show that the skull bone marrow reflects inflammatory brain responses with a disease-specific spatial distribution in patients with various neurological disorders. The unique molecular profile and anatomical and functional connections of the skull show its potential as a site for diagnosing, monitoring, and treating brain diseases.

关键词： neurological disorders diseases

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Problem-based learning methodology applied within a data network infrastructure design course a real case implementation

Problem-based learning methodology applied within a data net...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Valéria Farinazzo Martins Paulo N. M. Sampaio Antônio José A. Cordeiro Bruno Ferreira Viana Mackenzie Prebisterian University Computing and Systems Graduate Program Salvador University (UNIFACS) Computational Modelling and Industrial Technology Senai - Cimatec Management and Technology Graduate Professional Program (GESTEC) Bahia State Estácio University (Estácio FIB)

Education professionals are always searching for new ways of promoting learning, either by modifying the pedagogical project of courses or by the adoption of new learning strategies. In particular, this paper presents the results of an effort in order to bridge the knowledge acquired by students within classroom in face-to-face classes with technical skills required in real life situations. The contribution presented in this paper is the result of an assignment applied within an undergraduate Data Network Infrastructure Design course in a Brazilian university using an adapted Problem-Based Learning methodology (PBL), as a motivational tool. This paper also presents and discusses the resulting data related to the learning assessment.

关键词： Problem-solving Education Computer networks Electronic mail Laboratories Bridges Tools

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Feedback techniques in computer-based simulation training: A survey

arXiv

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arXiv 2017年

作者： Wijewickrema, Sudanthi Ma, Xingjun Bailey, James Kennedy, Gregor O'Leary, Stephen Department of Otolaryngology University of Melbourne VIC3010 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Graduate School of Education University of Melbourne VIC3010 Australia

Replication or simulation of a real-world scenario for the purpose of training novices in a given profession is not new. For example, the use of cadavers in surgical education has been common practice for centuries. With the advent of computer-based technologies such as virtual reality, simulation has become even more useful as a training platform. Computer-based simulation training (CBST) is gaining popularity in a vast range of applications such as surgery, rehabilitation therapy, military applications, and driver/pilot training, as it offers a low-cost, easily-accessible and effective training environment. Typically, CBST systems comprise of two essential components: 1) a simulation environment that provides an immersive and interactive learning experience, and 2) a feedback intervention system that supports knowledge/skill acquisition and decision making. The simulation environment is created using technologies such as virtual or augmented reality, and this is an area which has gained much interest in recent years. The provision of automated feedback in CBST however, has not been investigated as much, and thus, is the focus of this paper. Feedback is an essential component in learning, and should be provided to the trainee during the training process in order to improve skills, to correct mistakes, and most importantly, to inspire reasoning and critical thinking. In CBST, feedback should be provided in a useful and timely manner, ideally in a way that mimics the advice of an experienced tutor. Here, we explore the provision of feedback in CBST from three perspectives: 1) types of feedback to be provided, 2) presentation modalities of feedback, and 3) methods for feedback extraction/learning. This review is aimed at providing insight into how feedback is extracted, organized, and delivered in current applications, to be used as a guide to the development of future feedback intervention systems in CBST applications. Copyright © 2017, The Authors. All rights reserv

关键词： Virtual reality

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Adaptive decision making via entropy minimization

arXiv

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arXiv 2018年

作者： Allahverdyan, Armen E. Galstyan, Aram Abbas, Ali E. Struzik, Zbigniew R. Yerevan Physics Institute Alikhanian Brothers Street 2 Yerevan375036 Armenia USC Information Sciences Institute 4676 Admiralty Way Marina del ReyCA90292 United States Industrial and Systems Engineering University of Southern California Los Angeles United States Advanced Center for Computing and Communication RIKEN Institute 2-1 Hirosawa Wako-shi351-0198 Japan Graduate School of Education University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan

An agent choosing between various actions tends to take the one with the lowest cost. But this choice is arguably too rigid (not adaptive) to be useful in complex situations, e.g., where exploration-exploitation trade-off is relevant in creative task solving or when stated preferences differ from revealed ones. Here we study an agent who is willing to sacrifice a fixed amount of expected utility for adaptation. How can/ought our agent choose an optimal (in a technical sense) mixed action? We explore consequences of making this choice via entropy minimization, which is argued to be a specific example of risk-aversion. This recovers the ǫ-greedy probabilities known in reinforcement learning. We show that the entropy minimization leads to rudimentary forms of intelligent behavior: (i) the agent assigns a non-negligible probability to costly events;but (ii) chooses with a sizable probability the action related to less cost (lesser of two evils) when confronted with two actions with comparable costs;(iii) the agent is subject to effects similar to cognitive dissonance and frustration. Neither of these features are shown by entropy maximization. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy

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