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Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Overview of the CL-SciSumm 2016 Shared Task

Overview of the CL-SciSumm 2016 Shared Task

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Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries, BIRNDL 2016

作者： Jaidka, Kokil Chandrasekaran, Muthu Kumar Rustagi, Sajal Kan, Min-Yen Big Data Experience Lab Adobe Research India India School of Computing National University of Singapore Singapore Singapore Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee India Interactive and Digital Media Institute National University of Singapore Singapore Singapore

The CL-SciSumm 2016 Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. The task built off of the experience and training data set created in its namesake pilot task, which was conducted in 2014 by the same organizing committee. The track included three tasks involving: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 30 annotated sets of citing and reference papers from the open access research papers in the CL domain. This overview paper describes the participation and the official results of the second CL-SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016), held in New Jersey,USA in June, 2016. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https://***/WING-NUS/scisumm-corpus.

关键词： Digital libraries

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PEDESTRIAN DETECTION VIA A LEG-DRIVEN PHYSIOLOGY FRAMEWORK

PEDESTRIAN DETECTION VIA A LEG-DRIVEN PHYSIOLOGY FRAMEWORK

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IEEE International Conference on Image Processing

作者： Gongbo Liang Qi Li Xiangui Kang Western Kentucky University Department of Computer Science Ogden College of Science & Engineering Guangdong Key Lab of Information Security Sun Yat-Sen University School of Data and Computer Science

ISBN: (纸本)9781467399623

In this paper, we propose a leg-driven physiology framework for pedestrian detection. The framework is introduced to reduce the search space of candidate regions of pedestrians. Given a set of vertical line segments, we can generate a space of rectangular candidate regions, based on a model of body proportions. The proposed framework can be either integrated with or without learning-based pedestrian detection methods to validate the candidate regions. A symmetry constraint is then applied to validate each candidate region to decrease the false positive rate. The experiment demonstrates the promising results of the proposed method by comparing it with Dalal & Triggs method. For example, rectangular regions detected by the proposed method has much similar area to the ground truth than regions detected by Dalal & Triggs method.

关键词： Pedestrian detection Leg Line segment Bounding box bounding boxes Line sections Leg Pedestrians Physiology candidate species vertical line ground truth inspection methods

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Mechanics of jumping on water

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Physical Review Fluids 2017年第10期2卷 100505-100505页

作者： Ho-Young Kim Juliette Amauger Han-Bi Jeong Duck-Gyu Lee Eunjin Yang Piotr G. Jablonski Department of Mechanical and Aerospace Engineering Seoul National University Seoul 08826 Korea Urban Data Science Lab SNU BDI Seoul 06324 Korea Département de Physique Ecole Normale Supérieure PSL Research University 24 rue Lhomond 75005 Paris France Department of Nature-Inspired Nanoconvergence Systems Korea Institute of Machinery and Materials Daejeon 34103 Korea Korea Institute of Science and Technology Evaluation and Planning Seoul 06775 Korea School of Biological Sciences Seoul National University Seoul 08826 Korea Institute and Museum of Zoology Polish Academy of Sciences Wilcza 64 00-679 Warsaw Poland

Some species of semiaquatic arthropods including water striders and springtails can jump from the water surface to avoid sudden dangers like predator attacks. It was reported recently that the jump of medium-sized water striders is a result of surface-tension-dominated interaction of thin cylindrical legs and water, with the leg movement speed nearly optimized to achieve the maximum takeoff velocity. Here we describe the mathematical theories to analyze this exquisite feat of nature by combining the review of existing models for floating and jumping and the introduction of the hitherto neglected capillary forces at the cylinder tips. The theoretically predicted dependence of body height on time is shown to match the observations of the jumps of the water striders and springtails regardless of the length of locomotory appendages. The theoretical framework can be used to understand the design principle of small jumping animals living on water and to develop biomimetic locomotion technology in semiaquatic environments.

关键词： Biological fluid dynamics Locomotion Surface tension effects

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Thalamo-cortical structural co-variation networks are related to familial risk for schizophrenia in the context of lower nuclei volume estimates in patients: an ENIGMA study

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Biological psychiatry 2025年

作者： Annalisa Lella Linda A Antonucci Roberta Passiatore Loredana Bellantuono Pierluigi Selvaggi Teresa Popolizio Guido Di Sciascio Alessandro Saponaro Patrizia Ricci Mario Altamura Giuseppe Blasi Antonio Rampino Chris Vriend Vince D Calhoun Kelly Rootes-Murdy Aaron L Goldman Inmaculada Baeza Josefina Castro-Fornieles Gisela Sugranyes Elena De la Serna Edith Pomarol-Clotet Mar Fatjó-Vilas Raymond Salvador Andriana Karuk Paola Fuentes-Claramonte David C Glahn Amanda L Rodrigue John Blangero Lei Wang Taeyoung Lee Karolin E Einenkel Saskia Hamers Oliver Gruber Adrian Preda Young-Chul Chung Soyolsaikhan Odkhuu Corentin Vallée Paola Dazzan Machteld Marcelis Stijn Michielse Katharina Brosch Frederike Stein Igor Nenadić Benjamin Straube Florian Thomas-Odenthal Tilo Kircher Sean Carruthers Susan L Rossell Phillip J Sumner Tamsyn E Van Rheenen Caroline Demro Ian S Ramsay Scott R Sponheim Rebekka Lencer Susanne Meinert Tim Hahn Udo Dannlowski Dominik Grotegerd Mariateresa Ciccarelli Felice Iasevoli Giuseppe Pontillo Godfrey D Pearlson Derin Cobia Fabrizio Piras Nerisa Banaj Daniela Vecchio Marjolein E A Barendse Neeltje E M van Haren Hang Joon Jo Kang Sim Yann Quidé Melissa J Green Rachael Slate Giacomo Cecere Wolfgang Omlor Stephanie Homan Philipp Homan Sophia I Thomopoulos Jessica A Turner Theo G M van Erp Paul M Thompson Alessandro Bertolino Giulio Pergola Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy. Department of Translational Biomedicine and Neuroscience (DiBraiN)-University of Bari Aldo Moro Bari Italy Lieber Institute for Brain Development Johns Hopkins Medical Campus Baltimore MD USA. Istituto Nazionale di Fisica Nucleare Bari Italy. Azienda Ospedaliero-Universitaria Consorziale Policlinico Bari Italy. Neuroradiology Unit Scientific Institute for Research Hospitalization and Health Care Casa Sollievo della Sofferenza 71013 San Giovanni Rotondo Foggia Italy. Department of Mental Health ASL Bari Bari Italy. Department of Mental Health ASL Brindisi Brindisi Italy. Department of Mental Health ASL Barletta Andria Trani BAT Italy. Department of Clinical and Experimental Medicine University of Foggia Italy. Amsterdam UMC Vrije Universiteit Amsterdam Department of Psychiatry and Department of Anatomy and Neuroscience de Boelelaan 1117 Amsterdam the Netherlands Compulsivity Impulsivity and Attention Amsterdam Neuroscience de Boelelaan 1117 Amsterdam the Netherlands. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University Atlanta GA USA. Department of Child and Adolescent Psychiatry and Psychology 2021 SGR 01319 Institute of Neuroscience Hospital Clínic Barcelona Barcelona University of Barcelona Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) CIBERSAM Spain. FIDMAG Germanes Hospitalàries Research Foundation CIBERSAM ISCIII Barcelona Spain. Department of Psychiatry and Behavioral Sciences Boston Children's Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Olin Neuropsychiatry Research Center Institute of Living Hartford CT USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Human Genetics and South Texas Diabetes and Obesity Institute School of

BACKGROUND:Structural brain differences in the thalamus and the cortex have been widely reported in schizophrenia (SCZ) relative to neurotypical controls (NC). Most prior studies examined the thalamus as a whole, single region-of-interest. Additionally, findings in individuals at familial high-risk for schizophrenia (FHR) remain inconclusive. Here, we investigated whether local and network-wide thalamic-related structural alterations vary as a function of familial risk for schizophrenia. METHODS:Structural MRI scans were obtained from 5,197 participants (3,409 NC, 257 FHR, 1,531 SCZ) across 32 cross-sectional samples within the ENIGMA Consortium. Random-effects meta-analyses, and network analyses were conducted on (i) local thalamic alterations (volume estimates of seven thalamic subdivisions), and (ii) network-wide thalamic alterations (thickness and surface-related thalamo-cortical/cortico-cortical co-variation patterns) across groups (NC, FHR, SCZ). RESULTS:Individuals with SCZ showed significantly lower gray matter volume estimates in the anterior, pulvinar, medial, posterior, and ventral thalamic subdivisions compared to NC (q<0.05). FHR did not differ from NC. At the network-wide level, thalamo-cortical co-variations discriminated FHR from NC (q<0.05), with FHR showing intermediate co-variation between SCZ and NC. Cortico-cortical co-variation patterns revealed that SCZ and FHR shared similarly disconnected clustering configurations, distinct from NC (q<0.05). CONCLUSIONS:Results revealed lower thalamic volume estimates in SCZ but not in FHR, hence yielding no evidence of a familial risk trait, whereas thalamo-cortical and cortico-cortical co-variation estimates were associated with familial risk for SCZ These findings suggest that, once the thalamus is parsed into subdivisions, network-wide thalamo-cortical features may identify trait-dependent, neurobiological correlates of genetic risk for SCZ.

关键词： Familial high risk for schizophrenia meta-analyses morphometric measures structural neuroimaging thalamic subdivisions thalamo-cortical networks

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Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

arXiv

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arXiv 2017年

作者： Cao, Xiangyong Zhou, Feng Xu, Lin Meng, Deyu Xu, Zongben Paisley, John IEEE IEEE School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China National Laboratory of Radar Signal Processing Xidian University Xi’an China NYU Multimedia and Visual Computing Lab New York University Abu Dhabi United Arab Emirates Department of Electrical Engineering & Data Science Institute Columbia University New YorkNY United States Columbia University China Scholarship Council

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using α-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Spectroscopy

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Fully Integrated Solid-State LiDAR Transmitter on a MultiLayer Silicon-Nitride-on-Silicon Photonic Platform

Fully Integrated Solid-State LiDAR Transmitter on a MultiLay...

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Optical Fiber Communication Conference, OFC 2016

作者： Xu, Weihan Guo, Yuyao Li, Xinhang Liu, Chuxin Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We demonstrated a LiDAR transmitter incorporating both a hybrid-integrated tunable external cavity laser and a high-resolution 2-D optical phased array beam-steerer on a tri-layer silicon-nitride-on-silicon photonic p...

ISBN: (纸本)9781943580071

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