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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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Linear operators in information retrieval

Linear operators in information retrieval

引用

6th Symposium on Languages, Applications and Technologies, SLATE 2017

作者： Hattab, Hawete Mbarek, Rabeb Umm Al-qura University Al-Jumum University College Department of Mathematics Makkah Saudi Arabia Sfax University Multimedia Information Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)9783959770569

In this paper, we propose a pseudo-relevance feedback approach based on linear operators: vector space basis change and cross product. The aim of pseudo-relevance feedback methods based on vector space basis change IBM (Ideal Basis Method) is to optimally separate relevant and irrelevant documents. Whereas the aim of pseudo-relevance feedback method based on cross product AI (Absorption of irrelevance) is to e ectively exploit irrelevant documents. We show how to combine IBM methods with AI methods. The combination methods IBM+AI are evaluated experimentally on two TREC collections (TREC-7 ad hoc and TREC-8 ad hoc). The experiments show that these methods improve previous works. © Hawete Hattab and Rabeb Mbarek

关键词： Vector spaces

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

引用

arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Discrete-Time Control and Simulation of Ship Course Tracking Using ZD Method and ZFD Formula 4NgSFD 3

Discrete-Time Control and Simulation of Ship Course Tracking...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Huinan Xiao Jinjin Wang Jian Li Pei Chen School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

ISBN: (纸本)9781509053643;9781509053636

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

关键词： Marine vehicles Trajectory Electronic mail Process control Convergence Mathematical model

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Type-z0g3 Controller of Output Tracking for Fractional Power System 3

Type-z0g3 Controller of Output Tracking for Fractional Power...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Fangzheng Lai Penghao He Jinjin Wang Dechao Chen School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

ISBN: (纸本)9781509053643;9781509053636

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

关键词： Trajectory Nonlinear systems Simulation Power system stability Sun

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Proposing,QP-Unification and Verification of DLSM Based MKE-IIWT Scheme for Redundant Robot Manipulators 3

Proposing,QP-Unification and Verification of DLSM Based MKE-...

引用

2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Min Yang Dechao Chen Weibing Li Dongsheng Guo Xiaogang Yan School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

To remedy the problem of joint-variable oscillation and instability as well as the non-zero final joint-velocity phenomenon arising in the conventional inertia-inverse weighted torque(IIWT) minimizati

ISBN: (纸本)9781509053643;9781509053636

关键词： Manipulators Torque Minimization Acceleration Kinetic energy Performance analysis

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ZG Controllers Handling VIP System with Nonzero Link Mass,Large Friction Coefficient and Different Initial Pendulum Angle 3

ZG Controllers Handling VIP System with Nonzero Link Mass,La...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Jinjin Wang Binbin Qiu Deyang Zhang Yang Shi School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

The vehicular inverted pendulum(VIP) system is one of the most important issues in dynamics and control theory,and has been widely used as a benchmark for testing control ***,the tracki

ISBN: (纸本)9781509053643;9781509053636

The vehicular inverted pendulum(VIP) system is one of the most important issues in dynamics and control theory,and has been widely used as a benchmark for testing control ***,the tracki

关键词： Trajectory Vehicle dynamics Mathematical model Friction Electronic mail Heuristic algorithms

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SELFIE: Self-Aware information Extraction from Digitized Biocollections

SELFIE: Self-Aware Information Extraction from Digitized Bio...

引用

IEEE International Conference on e-Science and Grid computing

作者： Icaro Alzuru Andréa Matsunaga Maurício Tsugawa José A.B. Fortes Advanced Computing and Information Systems (ACIS) Laboratory University of Florida Gainesville USA

Biological collections store information with broad societal and environmental impact. In the last 15 years, after worldwide investments and crowdsourcing efforts, 25% of the collected specimens have been digitized; a process that includes the imaging of text attached to specimens and subsequent extraction of information from the resulting image. This information extraction (IE) process is complex, thus slow and typically involving human tasks. We propose a hybrid (Human-Machine) information extraction model that efficiently uses resources of different cost (machines, volunteers and/or experts) and speeds up the biocollections' digitization process, while striving to maintain the same quality as human-only IE processes. In the proposed model, called SELFIE, self-aware IE processes determine whether their output quality is satisfactory. If the quality is unsatisfactory, additional or alternative processes that yield higher quality output at higher cost are triggered. The effectiveness of this model is demonstrated by three SELFIE workflows for the extraction of Darwin-core terms from specimens' images. Compared to the traditional human-driven IE approach, SELFIE workflows showed, on average, a reduction of 27% in the information-capture time and a decrease of 32% in the required number of humans and their associated cost, while the quality of the results was negligibly reduced by 0.27%.

关键词： information retrieval Imaging Optical character recognition software Data mining Computational modeling Biological system modeling

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A multi-messenger search for exotic field emission with a global magnetometer network

arXiv

引用

arXiv 2024年

作者： Khamis, Sami S. Sulai, Ibrahim A. Hamilton, Paul Afach, S. Buchler, B.C. Budker, D. Figueroa, N.L. Folman, R. Gavilán-Martín, D. Givon, M. Grujić, Z.D. Guo, H. Hedges, M.P. Kimball, D.F. Jackson Kim, D. Klinger, E. Kornack, T. Kryemadhi, A. Kukowski, N. Lukasiewicz, G. Masia-Roig, H. Padniuk, M. Palm, C.A. Park, S.Y. Peng, X. Pospelov, M. Pustelny, S. Rosenzweig, Y. Ruimi, O.M. Segura, P.C. Scholtes, T. Semertzidis, Y.K. Shin, Y.C. Stalnaker, J.E. Tandon, D. Weis, A. Wickenbrock, A. Wilson, T. Wu, T. Zhang, J. Zhao, Y. Department of Physics and Astronomy University of California Los AngelesCA90095 United States Department of Physics and Astronomy Bucknell University LewisburgPA17837 United States Helmholtz Institute Mainz Mainz55099 Germany GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt64291 Germany Johannes Gutenberg-Universität Mainz Mainz55128 Germany Research School of Physics Australian National University CanberraACT2601 Australia Department of Physics University of California BerkeleyCA94720 United States Department of Physics Ben-Gurion University of the Negev Be’er Sheva84105 Israel Institute of Physic Belgrade University of Belgrade Belgrade11080 Serbia State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Centre for Quantum Computation and Communication Technology Research School of Physics The Australian National University Acton2601 Australia Department of Physics California State University – East Bay HaywardCA94542-3084 United States Center for Axion and Precision Physics Research IBS Daejeon34051 Korea Republic of Department of Physics KAIST Daejeon34141 Korea Republic of Institut FEMTO-ST UMR 6174 CNRS SupMicroTech-ENSMM Université de Franche-Comté Besançon25030 France Twinleaf LLC 300 Deer Creek Drive PlainsboroNJ08536 United States Department of Computing Math & Physics Messiah University MechanicsburgPA17055 United States Friedrich Schiller University Jena Institute of Geosciences Burgweg 11 JenaD-07749 Germany Institute of Physics Jagiellonian University in Krakow prof. Stanislawa Lojasiewicza 11 Kraków30-348 Poland Jagiellonian University Doctoral School of Exact and Natural Sciences Lojasiewicza 11 Kraków30-348 Poland Department of Physics and Astronomy Oberlin College OberlinOH44074 United States School of Physics and Astronomy University of Minnesota Minneapolis

We present an analysis method to search for exotic low-mass field (ELF) bursts generated during large energy astrophysical events such as supernovae, binary black hole or binary neutron star mergers, and fast radio bursts using the Global Network of Optical Magnetometers for Exotic physics searches (GNOME). In our model, the associated gravitational waves or electromagnetic signals herald the arrival of the ELF burst that interacts via coupling to the spin of fermions in the magnetometers. This enables GNOME to serve as a tool for multi-messenger astronomy. The algorithm employs a model-agnostic excess-power method to identify network-wide candidate events to be subjected to a model-dependent generalized likelihood-ratio test to determine their statistical significance. We perform the first search with this technique on GNOME data coincident with the binary black hole merger S200311bg detected by LIGO/Virgo on the 11th of March 2020 and find no significant events. We place the first lab-based limits on combinations of ELF production and coupling parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetometers

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GLOBAL CLIMATE

引用

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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