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arXiv 2022年

作者： Ren, Pu Rao, Chengping Chen, Su Wang, Jian-Xun Sun, Hao Liu, Yang Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Key Laboratory of Urban Security and Disaster Engineering The Ministry of Education Beijing University of Technology Beijing100124 China Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Recently, there has been an increasing interest in leveraging physics-informed neural networks (PINNs) for modeling dynamical systems. However, very limited studies have been conducted along this horizon on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a new PINN model for seismic wave modeling in semi-infinite domain without the need of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. To scale up, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy to account for parametric loading, which estimates the wave propagation in semi-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Wave propagation

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Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross C...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Generalized quantum teleportation of shared quantum secret with quantum walks

arXiv

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arXiv 2020年

作者： Li, Hengji Li, Jian Chen, Xiubo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts Telecommunications Beijing100876 China Center for Quantum Information Research ZaoZhuang University ZaoZhuang Shandong277160 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data GuiyangGuizhou550025 China

Very recently, Lee et al. proposed the first secure quantum teleporation protocol, where quantum information shared by an arbitrary number of senders can be transferred to another arbitrary number of receivers. Here, by introducing quantum walks, a novel secure (n, m) quantum teleportation of shared quantum secret between n senders and m receivers is presented. Firstly, two kinds of (n, 2) teleportation schemes are proposed by n-walker quantum walks on the line, the first walker of which is driven by three coins, respectively, based on two kinds of coin operators: the homogeneous coins and the position-dependent coins. Secondly, by increasing the amount of the coins of the first walker to m + 1, the previous (n, 2) scheme can be generalized to (n, m) teleportation scheme. Then, we give the proof of the information security of our proposed scheme, in which neither any single nor subparties of senders and receivers can fully access the secret quantum information. Moreover, the projective measurements are needed, instead of the joint Bell measurements that are necessary in Lee et al.’s protocol. Our work can also be extended further to QWs on the cycle. This work provides an additional relevant instance of the richness of quantum walks for quantum information processing tasks and thus opens the wider application purpose of quantum walks. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Recombination of repeat elements generates somatic complexity in human genomes

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四川生理科学杂志 2022年第7期44卷 1291-1291页

作者： Giovanni Pascarella RIKEN Center for Integrative Medical Sciences (IMS) Yokohama 230-0045 Japan Institute for Protein Research Osaka University Osaka 565-0871 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8562 Chiba Japan Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Cell and Molecular Biology Karolinska Institutet Stockholm 171 77 Sweden Stockholm University Frescativägen 114 19 Stockholm Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm 171 77 Sweden Science for Life Laboratory Tomtebodavägen 23 171 65 Solna Sweden Department of Neuropathology Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (TMGHIG) Tokyo 173-0015 Japan Central RNA Laboratory and Department of Neuroscience and Brain Technologies Istituto Italiano di Tecnologia (IIT) Genova 16163 Italy Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8562 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) AIST Tokyo 135-0064 Japan Human Technopole Milan 20157 Italy

Non-allelic recombination between homologous repetitive elements contributes to evolution and human genetic ***,we combine short-and long-DNA read sequencing of repeat elements with a new bioinformatics pipeline to show that somatic recombination of Alu and L1 elements is widespread in the human *** analysis uncovers tissue-specific non-allelic homologous recombination hallmarks;moreover,we find that centromeres and cancer-associated genes are enriched for retroelements that may act as recombination *** compare recombination profiles in human-induced pluripotent stem cells and differentiated neurons and find that the neuron-specific recombination of repeat elements accompanies chromatin changes during cell-fate determination.

关键词： determination. elements companies

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Neural networks for protein structure and function prediction and dynamic analysis

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Biophysical reviews 2020年第2期12卷 569-573页

作者： Yuko Tsuchiya Kentaro Tomii Artificial Intelligence Research Center (AIRC) Tokyo Japan. Biotechnology Research Institute for Drug Discovery Tokyo Japan. Artificial Intelligence Research Center (AIRC) Tokyo Japan. k-tomii@aist.go.jp. Biotechnology Research Institute for Drug Discovery Tokyo Japan. k-tomii@aist.go.jp. Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL) National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan. k-tomii@aist.go.jp.

Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

关键词： Contact prediction Deep learning Neural networks Protein allostery Protein-compound interaction

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Erratum to: Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins

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Briefings in bioinformatics 2021年第6期22卷 bbab287页

作者： Hideki Yamaguchi Yutaka Saito Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba 277-8561 Japan. Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Koto-ku Tokyo 135-0064 Japan. AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) Shinjuku-ku Tokyo 169-8555 Japan.

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The Ninth Visual Object Tracking VOT2021 Challenge Results

The Ninth Visual Object Tracking VOT2021 Challenge Results

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Matej Kristan Jiří Matas Aleš Leonardis Michael Felsberg Roman Pflugfelder Joni-Kristian Kämäräinen Hyung Jin Chang Martin Danelljan Luka Čehovin Zajc Alan Lukežič Ondrej Drbohlav Jani Käpylä Gustav Häger Song Yan Jinyu Yang Zhongqun Zhang Gustavo Fernández Mohamed Abdelpakey Goutam Bhat Llukman Cerkezi Hakan Cevikalp Shengyong Chen Xin Chen Miao Cheng Ziyi Cheng Yu-Chen Chiu Ozgun Cirakman Yutao Cui Kenan Dai Mohana Murali Dasari Qili Deng Xingping Dong Daniel K. Du Matteo Dunnhofer Zhen-Hua Feng Zhiyong Feng Zhihong Fu Shiming Ge Rama Krishna Gorthi Yuzhang Gu Bilge Gunsel Qing Guo Filiz Gurkan Wencheng Han Yanyan Huang Felix Järemo Lawin Shang-Jhih Jhang Rongrong Ji Cheng Jiang Yingjie Jiang Felix Juefei-Xu Yin Jun Xiao Ke Fahad Shahbaz Khan Byeong Hak Kim Josef Kittler Xiangyuan Lan Jun Ha Lee Bastian Leibe Hui Li Jianhua Li Xianxian Li Yuezhou Li Bo Liu Chang Liu Jingen Liu Li Liu Qingjie Liu Huchuan Lu Wei Lu Jonathon Luiten Jie Ma Ziang Ma Niki Martinel Christoph Mayer Alireza Memarmoghadam Christian Micheloni Yuzhen Niu Danda Paudel Houwen Peng Shoumeng Qiu Aravindh Rajiv Muhammad Rana Andreas Robinson Hasan Saribas Ling Shao Mohamed Shehata Furao Shen Jianbing Shen Kristian Simonato Xiaoning Song Zhangyong Tang Radu Timofte Philip Torr Chi-Yi Tsai Bedirhan Uzun Luc Van Gool Paul Voigtlaender Dong Wang Guangting Wang Liangliang Wang Lijun Wang Limin Wang Linyuan Wang Yong Wang Yunhong Wang Chenyan Wu Gangshan Wu Xiao-Jun Wu Fei Xie Tianyang Xu Xiang Xu Wanli Xue Bin Yan Wankou Yang Xiaoyun Yang Yu Ye Jun Yin Chengwei Zhang Chunhui Zhang Haitao Zhang Kaihua Zhang Kangkai Zhang Xiaohan Zhang Xiaolin Zhang Xinyu Zhang Zhibin Zhang Shaochuan Zhao Ming Zhen Bineng Zhong Jiawen Zhu Xue-Feng Zhu University of Ljubljana Slovenia Czech Technical University Czech Republic University of Birmingham United Kingdom Linköping University Sweden Austrian Institute of Technology Austria TU Wien Austria Tampere University Finland ETH Zurich Switzerland University of British Columbia Canada Istanbul Technical University Turkey Eskisehir Osmangazi University Turkey Tianjin University of Technology China Dalian University of Technology China Zhejiang Dahua Technology CO China Kyushu University Japan Tamkang University Taiwan Nanjing University China Indian Institute of Technology Tirupati India ByteDance China Inception Institute of Artificial Intelligence China University of Udine Italy University of Surrey United Kingdom Tianjin University China Beihang University China University of Chinese Academy of Science China SIMIT China Nanyang Technological University Singapore Beijing Institute of Technology China Fuzhou University China Xiamen University China Jiangnan University China Alibaba Group USA Mohamed Bin Zayed University of Artificial Intelligence UAE Korea Institute of Industrial Technology (KITECH) Korea Hong Kong Baptist University China RWTH Aachen University Germany Guangxi Normal University China JD Finance America Corporation USA Shenzhen Research Institute of Big Data China Peng Cheng Laboratory China Huaqiao University China University of Isfahan Iran Microsoft Research Asia China Eskisehir Technical University Turkey University of Oxford United Kingdom University of Science and Technology of China China Sun Yat-sen University China Penn State University USA Southeast University China Remark AI United Kingdom Dalian Maritime University China Nanjing University of Information Science and Technology China

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website 1 .

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Author Correction: A multipoint guidance mechanism for β-barrel folding on the SAM complex

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Nature structural & molecular biology 2023年第2期30卷 233页

作者： Hironori Takeda Jon V Busto Caroline Lindau Akihisa Tsutsumi Kentaro Tomii Kenichiro Imai Yu Yamamori Takatsugu Hirokawa Chie Motono Iniyan Ganesan Lena-Sophie Wenz Thomas Becker Masahide Kikkawa Nikolaus Pfanner Nils Wiedemann Toshiya Endo Faculty of Life Sciences Kyoto Sangyo University Kyoto Japan. Nara Institute of Science and Technology Ikoma Japan. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. Department of Cell Biology and Anatomy Graduate School of Medicine The University of Tokyo Tokyo Japan. Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan. Cellular and Molecular Biotechnology Research Institute AIST Tokyo Japan. Division of Biomedical Science Faculty of Medicine University of Tsukuba Tsukuba Japan. Transborder Medical Research Center University of Tsukuba Tsukuba Japan. Computational Bio Big-Data Open Innovation Laboratory AIST Waseda University Tokyo Japan. Sanofi-Aventis Deutschland GmbH Frankfurt Germany. Institute of Biochemistry and Molecular Biology Faculty of Medicine University of Bonn Bonn Germany. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. BIOSS Centre for Biological Signalling Studies University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. BIOSS Centre for Biological Signalling Studies University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. Faculty of Life Sciences Kyoto Sangyo University

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