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arXiv 2025年

作者： Adriani, O. Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Argüelles, C. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Berti, E. Bertin, V. Betti, P. Biagi, S. Boettcher, M. Bonanno, D. Bottai, S. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Janik, O. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kamp, N. Keegans, J. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. INFN Sezione di Firenze via Sansone 1 Sesto Fiorentino50019 Italy Università di Firenze Dipartimento di Fisica e Astronomia via Sansone 1 Sesto Fiorentino50019 Italy Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace rue des Frères Lumière Mulhouse68093 Cedex France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Harvard University Department of Physics Laboratory for Particle Physics and Cosmology Lyman Laboratory 17 Oxford St. CambridgeMA02138 United States Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Università di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Czech Technical University in Prague Institute of Experimen

On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic E−2 flux, the best-fit single-flavour flux normalisation is E2Φ1fν+ν¯ = 7.5 × 10−10 GeVcm−2s−1sr−1 in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the "High-Energy Starting Events" sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of 2.5σ − 3σ, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

arXiv

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arXiv 2024年

作者： Shi, Suwen Huang, Ziwei Gu, Xingxin Lin, Xu Zhong, Chaoying Hang, Junjie Lin, Jianli Zhong, Claire Chenwen Zhang, Lin Li, Yu Huang, Junjie Department of Chemistry Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States College of Professional Studies Northeastern University BostonMA02215 United States Department of Thoracic Surgery The First Affiliated Hospital School of Medicine Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis. © 2024, CC BY.

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021年 12511 LNCS卷 v-vi页

作者： Pang, Chaoyi Chen, Guanliang Chen, Lu Zhang, Bailing Li, Qing Gao, Yunjun Popescu, Elvira Hao, Tianyong Navarro, Silvia Margarita Baldiris Klamma, Ralf Ningbo Institute of Industrial Technology Ningbo China Monash University ClaytonVIC Australia Department of Physics Zhejiang University Hangzhou China Zhejiang University Ningbo China Department of Computing HongKong Polytechnic University Kowloon Hong Kong College of Computer Science Zhejiang University Hangzhou China Faculty of Automation Computers and Electronics University of Craiova Craiova Romania School of Computer Science South China Normal University Guangzhou China Universidad Internacional De La Rioja Logroño Spain RWTH Aachen University Germany

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Ensuring the quality of fuel equipment joints in series production conditions by graded kitting

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AIP Conference Proceedings 2023年第1期2889卷

作者： Oleksandr Kupriyanov Hanna Hrinchenko Roman Strelchuk Mykhailo Kupriianov Ukrainian Engineering Pedagogics Academy Innovative Technologies Faculty Department of Information Computer Technologies and Mathematics 16 Universitetska st. Kharkiv 61003 Ukraine Ukrainian Engineering Pedagogics Academy Power Engineering and Automation Faculty Department of Automatization Metrology and Energy-Saving Technologies 16 Universitetska st. Kharkiv 61003 Ukraine National Technical University «Kharkiv Polytechnic Institute» Department of Integrated Technologies of Mechanical Engineering named after M.F. Semko 2 Kirpicheva st. Kharkiv 61002 Ukraine Kharkiv National University of Radio Electronics Faculty of Computer Science 14 Nauky Ave. 61166 Kharkiv Ukraine

A graded kitting method is proposed, which provides the required accuracy for a complete assemblage of a part batch. The new kitting method was tested on the example of fuel equipment produced by the Kharkiv Tractor Plant. The connection of the body and slide valve was studied under workshop conditions. Kitting accuracy and unfinished production were compared at full interchangeability, selective kitting, and graded kitting. The method proposes measuring the actual parameters of the parts and using them to improve and optimize the assembly process. The use of data on the part dimensions makes it possible to increase the efficiency of the assembly process at the expense of information support. These data must be linked to specific parts, which can be done by address buffer stations or special marking based on optical or magnetic tags. The graded kitting with full assemblage allows achieving the precision, which significantly exceeds the technological requirements. Therefore, by reducing the manufacturing requirements from the actual 5th-6th grade of accuracy for the body-slide valve connection to the 9th grade of accuracy, the total accuracy that is sufficient to ensure a structurally required fitting is achieved. The use of graded kitting makes it possible to achieve the required kitting accuracy for batches of 5 to 15 parts with easily achievable manufacturing precision requirements.

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Corrigendum to “Realistic acceleration of neural networks with fine-grained tensor decomposition” [Neurocomputing 512 (2022) 52–68]

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Neurocomputing 2023年 527卷 47-47页

作者： Rui Lv Dingheng Wang Jiangbin Zheng Yefan Xie Zhao-Xu Yang School of Computer Science Northwestern Polytechnical University Xi’an Shaanxi China Northwest Institute of Mechanical & Electrical Engineering Xianyang Shaanxi China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Key Laboratory of Environment and Control for Flight Vehicle and School of Aerospace Engineering Xi’an Jiaotong University Xi’an Shaanxi China

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 764 LNNS卷 v-vi页

作者： Milrad, Marcelo Otero, Nuno Sánchez-Gómez, María Cruz Mena, Juan José Durães, Dalila Sciarrone, Filippo Alvarez-Gómez, Claudio Rodrigues, Manuel Vittorini, Pierpaolo Gennari, Rosella Di Mascio, Tania Temperini, Marco De la Prieta, Fernando Linnaeus University Växjö Sweden University of Greenwich London United Kingdom Department of Didactics Organization and Research Method University of Salamanca Salamanca Spain University of Minho Braga Portugal Mercatorum University Rome Italy Facultad de Ingeniería y Ciencias Aplicadas Universidad de los Andes Santiago Chile Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Dipartimento di Ingegneria Informatica Sapienza Universita’ di Roma Rome Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain

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Complex Dynamical Behavior of a Two-Stage Colpitts Oscillator with Magnetically Coupled Inductors

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Journal of Chaos 2014年第1期2014卷

作者： V. Kamdoum Tamba H. B. Fotsin J. Kengne F. Kapche Tagne P. K. Talla Department of Physics Laboratory of Electronics and Signal Processing (LETS) Faculty of Science University of Dschang P.O. Box 67 Dschang *** Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang P.O. Box 134 Bandjoun *** Department of Electrical Engineering Laboratory of Automation and Applied Computer (LAIA) IUT-Fotso Victor of Bandjoun University of Dschang P.O. Box 134 Bandjoun *** Department of Physics Laboratory of Mechanics and Modelling of Physical Systems (L2MPS) Faculty of Science University of Dschang P.O. Box 67 Dschang ***

A five-dimensional (5D) controlled two-stage Colpitts oscillator is introduced and analyzed. This new electronic oscillator is constructed by considering the well-known two-stage Colpitts oscillator with two further elements (coupled inductors and variable resistor). In contrast to current approaches based on piecewise linear (PWL) model, we propose a smooth mathematical model (with exponential nonlinearity) to investigate the dynamics of the oscillator. Several issues, such as the basic dynamical behaviour, bifurcation diagrams, Lyapunov exponents, and frequency spectra of the oscillator, are investigated theoretically and numerically by varying a single control resistor. It is found that the oscillator moves from the state of fixed point motion to chaos via the usual paths of period-doubling and interior crisis routes as the single control resistor is monitored. Furthermore, an experimental study of controlled Colpitts oscillator is carried out. An appropriate electronic circuit is proposed for the investigations of the complex dynamics behaviour of the system. A very good qualitative agreement is obtained between the theoretical/numerical and experimental results.

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On the Potential Galactic Origin of the Ultra-High-Energy Event KM3-230213A

arXiv

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arXiv 2025年

作者： Adriani, O. Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Berti, E. Bertin, V. Betti, P. Biagi, S. Boettcher, M. Bonanno, D. Bottai, S. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. Bojaddaini, I.El. Hedri, S.El. Mentawi, S.El. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Janik, O. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Keegans, J. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. INFN Sezione di Firenze via Sansone 1 Sesto Fiorentino50019 Italy Università di Firenze Dipartimento di Fisica e Astronomia via Sansone 1 Sesto Fiorentino50019 Italy Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace rue des Frères Lumière Mulhouse68093Cedex France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3 CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Università di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2

The KM3NeT observatory detected the most energetic neutrino candidate ever observed, with an energy between 72 PeV and 2.6 EeV at the 90 % confidence level. The observed neutrino is likely of cosmic origin. In this article, it is investigated if the neutrino could have been produced within the Milky Way. Considering the low fluxes of the Galactic diffuse emission at these energies, the lack of a nearby potential Galactic particle accelerator in the direction of the event and the difficulty to accelerate particles to such high energies in Galactic systems, we conclude that if the event is indeed cosmic, it is most likely of extragalactic origin. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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The efficiency improving methods analyzing for the gasoil pump unit

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AIP Conference Proceedings 2019年第1期2141卷

作者： O. A. Lysenko A. V. Simakov M. Y. Chernev 1Department of Electrical Engineering Energy Institute Omsk State Technical University Mira Ave 11 Omsk 644050 Russia 2Department of Electrical Machines and General Electrical Engineering Electromechanical Faculty Omsk State Transport University Marx Ave 35 Omsk 644046 Russia 3Department of Theoretical Electrical Engineering and Electrification of Oil and Gas Industry Faculty of Automation and Computer Science Gubkin Russian State University of Oil and Gas (National Research University) Leninsky Prospekt Moscow 119991 Russia

In this article, the issues of improving the efficiency are considered. The aim of the work is to determine the benefits of using the PMSM-drive for the gasoil pump unit. Firstly, the object of study is selected. Secondly, the pump power consumption is analyzed. Then, energy optimization options are calculated. The method for determining power consumption parameters is a mathematical calculation. As a result, the object power characteristics are built. The pump energy consumption is calculated, the low efficiency of the pumping unit is determined. The efficiency may be increased by using PMSM-drive.

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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