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arXiv 2023年

作者： Maskoliūnas, M. Wyrzykowski, L. Howil, K. Rybicki, K.A. Zielinski, P. Kaczmarek, Z. Kruszynska, K. Jablonska, M. Zdanavicius, J. Pakstiene, E. Čepas, V. Mikolajczyk, P.J. Janulis, R. Gromadzki, M. Ihanec, N. Adomaviciene, R. Šiškauskaite, K. Bronikowski, M. Sivak, P. Stankevičiūte, A. Sitek, M. Ratajczak, M. Pylypenko, U. Gezer, I. Awiphan, S. Bachelet, E. Akowska, K.B. Boyle, R.P. Bozza, V. Brincat, S.M. Burgaz, U. Butterley, T. Carrasco, J.M. Cassan, A. Cusano, F. Damljanovic, G. Davidson, J.W. Dhillon, V.S. Dominik, M. Dubois, F. Esenoglu, H.H. Figuera Jaimes, R. Fukui, A. Galdies, C. Garofalo, A. Godunova, V. Güver, T. Heidt, J. Hundertmark, M. Izviekova, I. Joachimczyk, B. Kaminska, M.K. Kaminski, K. Kaptan, S. Kvernadze, T. Kvaratskhelia, O. Littlefair, S. Michniewicz, O. Nakhatutai, N. Ogloza, W. Ohsawa, R. Olszewska, J.M. Polinska, M. Popowicz, A. Qvam, J.K.T. Radziwonowicz, M. Reichart, D.E. Slowikowska, A. Simon, A. Sonbas, E. Stojanovic, M. Tsapras, Y. Vanaverbeke, S. Wambsganss, J. Wilson, R.W. Zejmo, M. Zola, S. Institute of Theoretical Physics and Astronomy Vilnius University Sauletekio Av. 3 Vilnius10257 Lithuania Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warszawa00-478 Poland Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudzi adzka 5 Toruń87-100 Poland Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Konkoly Observatory Research Centre for Astronomy and Earth Sciences Konkoly Thege út BudapestH-1121 Hungary Astronomical Observatory Volgina 7 Belgrade11060 Serbia Center for Astrophysics and Cosmology University of Nova Gorica Vipavska 11c Ajdovščina5270 Slovenia Astronomical Institute University of Wroclaw Kopernika 11 Wroclaw51-622 Poland 260 Moo 4 Donkaew Mae Rim Chiang Mai50180 Thailand Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States School of Physics Trinity College Dublin College Green Dublin 2 Ireland Vatican Observatory Research Group Steward Observatory TucsonAZ85721 United States Flarestar Obsevatory Fl.5 Ent.B Silver Jubilee Apt George Tayar Street San GwannSGN 3160 Malta Martí i Franqués 1 BarcelonaE-08028 Spain INAF-Osservatorio di Astrofisica e Scienza dello Spazio Via Gob-etti 93/3 BolognaI-40129 Italy Public observatory Astrolab IRIS Verbrandemolenstraat 5 Zillebeke8901 Belgium Istanbul University Faculty of Science Department of Astronomy and Spaces Sciences Istanbul34119 Turkey Istanbul University Observatory Research and Application Center Istanbul University Istanbul34119 Turkey Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Adiyaman University Department of Physics Adiyaman02040 Turkey Astrophysics Application and Research Center Adiyaman University Adiyaman02040 Turkey Pedagogical University Of Cracow Podchora zych 2 Kraków30-084 Poland Astronomical Observatory Institute Faculty of Ph

We present the results of our analysis of Gaia19dke, an extraordinary microlensing event in the Cygnus constellation that was first spotted by the Gaia satellite. This event featured a strong microlensing parallax effect, which resulted in multiple peaks in the light curve. We conducted extensive photometric, spectroscopic, and high-resolution imaging follow-up observations to determine the mass and the nature of the invisible lensing object. Using the Milky Way priors on density and velocity of lenses, we found that the dark lens is likely to be located at a distance of DL = (3.05+−42.1042) kpc, and has a mass of ML = (0.51+−30.0740)M☉. Based on its low luminosity and mass, we propose that the lens in Gaia19dke event is an isolated white dwarf. © 2023, CC BY.

关键词： White dwarfs

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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DELIGHT: Deep Learning Identification of Galaxy Hosts of Transients using Multi-resolution Images

arXiv

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arXiv 2022年

作者： Förster, Francisco Muñoz Arancibia, Alejandra M. Reyes, Ignacio Gagliano, Alexander Britt, Dylan Cuellar-Carrillo, Sara Figueroa-Tapia, Felipe Polzin, Ava Yousef, Yara Arredondo, Javier Rodríguez-Mancini, Diego Correa-Orellana, Javier Bayo, Amelia Bauer, Franz E. Catelan, Márcio Cabrera-Vives, Guillermo Dastidar, Raya Estévez, Pablo A. Pignata, Giuliano Hernandez-Garcia, Lorena Huijse, Pablo Reyes, Esteban Sánchez-Sáez, Paula Ramirez, Mauricio Grandón, Daniela Pineda-García, Jonathan Chabour-Barra, Francisca Silva-Farfán, Javier Faculty of Physical and Mathematical Sciences University of Chile Chile Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Universidad de Chile Beauchef 851 Santiago8320000 Chile Department of Astronomy University of Chile Las Condes Santiago Chile Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green St. IL61801 United States Center for Astrophysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States National Science Foundation Graduate Research United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States Kavli Institute for Particle Astrophysics & Cosmology Stanford University P.O. Box 2450 StanfordCA94305 United States Escuela de Ingeniería Eléctrica Pontificia Universidad Católica de Valparaíso Valparaíso Chile Instituto de Física y Astronomía Facultad de Ciencias Universidad de Valparaíso Gran Bretana 1111 Valparaíso Chile Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Barcelona School of Informatics Polytechnic University of Catalonia Barcelona08034 Spain Faculty of Physics University of Barcelona Barcelona08028 Spain Data Observatory Foundation Santiago Chile Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Santiago Chile European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Santiago Chile Department of Computer Science University of Concepción Concepción Chile Instituto de Astrofisica Facultad de Ciencias Exactas Universidad Andres Bello Las Condes Santiago Chile Department of Electrical Engineering University of Chile Santiago Chile Instituto de Informática Universidad Austral Valdivia Chile Fintual Administradora General de Fondos S.A. Santiago Chile Departamento de Física Facultad de Ciencias

We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real-time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input compact, multi-resolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multi-resolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the ALeRCE broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (1000 00) and small (r ≤ 1000) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination ( © 2022, CC BY-SA.

关键词： Galaxies

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Measurement of ω meson production in pp and p–Pb collisions at √sNN = 5.02 TeV

arXiv

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arXiv 2025年

作者： Acharya, S. Agarwal, A. Rinella, G. Aglieri Aglietta, L. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Akishina, V. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Molina, R. Alfaro Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altamura, A.R. Altsybeev, I. Alvarado, J.R. Anaam, M.N. Andrei, C. Andreou, N. Andronic, A. Andronov, E. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Appelshäuser, H. Arata, C. Arcelli, S. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Averyanov, D. Azmi, M.D. Baba, H. Badalà, A. Bae, J. Bae, Y. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Bala, R. Baldisseri, A. Balis, B. Bangalia, S. Banoo, Z. Barbasova, V. Barile, F. Barioglio, L. Barlou, M. Barman, B. Barnaföldi, G.G. Barnby, L.S. Barreau, E. Barret, V. Barreto, L. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Alba, J.L. Bazo Bearden, I.G. Becht, P. Behera, D. Belikov, I. Hechavarria, A.D.C. Bell Bellini, F. Bellwied, R. Belokurova, S. Beltran, L.G.E. Beltran, Y.A.V. Bencedi, G. Bensaoula, A. Beole, S. Berdnikov, Y. Berdnikova, A. Bergmann, L. Bernardinis, L. Betev, L. Bhaduri, P.P. Bhasin, A. Bhattacharjee, B. Bhattarai, S. Bianchi, L. Bielčík, J. Bielčíková, J. Bigot, A.P. Bilandzic, A. Binoy, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Bock, F. Bodova, T. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Carcamo, A.G. Borquez Botta, E. Bouziani, Y.E.M. Brandibur, D.C. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buchakchiev, V.D. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Burmasov, N. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Noris, J.C. Cabanillas Cabrera, M.F.T. Caines, H. Caliva, A. Villar, E. Calvo Camacho, J.M.M. Camerini, P. Camerlingo, M.T. Max-Planck-Institut fur Physik Munich Germany Bologna Italy Dipartimento DET del Politecnico di Torino Turin Italy Department of Applied Physics Aligarh Muslim University Aligarh India Institute of Theoretical Physics University of Wroclaw Poland Facultad de Ciencias Universidad Nacional Autónoma de México Mexico City Mexico Foundation Yerevan Armenia AGH University of Krakow Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Kolkata India California Polytechnic State University San Luis ObispoCA United States Central China Normal University Wuhan China Havana Cuba Mérida Mexico City Mexico Chicago State University ChicagoIL United States China Nuclear Data Center China Institute of Atomic Energy Beijing China China University of Geosciences Wuhan China Chungbuk National University Cheongju Korea Republic of Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia Creighton University OmahaNE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan Korea Republic of Department of Physics Sejong University Seoul Korea Republic of Department of Physics University of California BerkeleyCA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell’Università and Sezione INFN Cagliari Italy Dipartimento di Fisica dell’Università and Sezione INFN Trieste Italy Dipartimento di Fisica dell’Università and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Padova Italy Dipartimento di Fisica E.R.

We present the measurement of the pT-differential production cross section of ω mesons in pp and p–Pb collisions at √sNN = 5.02 TeV at midrapidity by ALICE. In addition, the first measurement of the nuclear modification factor RpPb for ω mesons at LHC energies is presented, complementing the existing measurements of lighter neutral mesons such as the π0 and η. Within the measured pT-range, the RpPb of ω mesons shows no cold nuclear matter effects within the uncertainties, consistent with previous measurements at lower energies. The ω/π0 ratio is presented for both collision systems, showing no collision system dependence within the uncertainties. The comparison to previously published ω/π0 ratios at lower and higher collision energies in pp collisions suggests a decreasing trend of the ratio above pT = 4 GeV/c with increasing collision energy. The data in both collision systems are compared to predictions from PYTHIA 8, EPOS LHC and DPMJET event generators, revealing significant shortcomings in these models’ ability to describe the production of ω mesons. © 2025, CC BY.

关键词： Bosons

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Measurement of the 2νββ Decay Rate and Spectral Shape of Mo100 from the CUPID-Mo Experiment

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Physical Review Letters 2023年第16期131卷 162501-162501页

作者： C. Augier A. S. Barabash F. Bellini G. Benato M. Beretta L. Bergé J. Billard Yu. A. Borovlev L. Cardani N. Casali A. Cazes E. Celi M. Chapellier D. Chiesa I. Dafinei F. A. Danevich M. De Jesus T. Dixon L. Dumoulin K. Eitel F. Ferri B. K. Fujikawa J. Gascon L. Gironi A. Giuliani V. D. Grigorieva M. Gros D. L. Helis H. Z. Huang R. Huang L. Imbert J. Johnston A. Juillard H. Khalife M. Kleifges V. V. Kobychev Yu. G. Kolomensky S. I. Konovalov J. Kotila P. Loaiza L. Ma E. P. Makarov P. de Marcillac R. Mariam L. Marini S. Marnieros X.-F. Navick C. Nones E. B. Norman E. Olivieri J. L. Ouellet L. Pagnanini L. Pattavina B. Paul M. Pavan H. Peng G. Pessina S. Pirro D. V. Poda O. G. Polischuk S. Pozzi E. Previtali Th. Redon A. Rojas S. Rozov V. Sanglard J. A. Scarpaci B. Schmidt Y. Shen V. N. Shlegel F. Šimkovic V. Singh C. Tomei V. I. Tretyak V. I. Umatov L. Vagneron M. Velázquez B. Ware B. Welliver L. Winslow M. Xue E. Yakushev M. Zarytskyy A. S. Zolotarova Univ Lyon Université Lyon 1 CNRS/IN2P3 IP2I-Lyon F-69622 Villeurbanne France National Research Centre “Kurchatov Institute ” Kurchatov Complex of Theoretical and Experimental Physics 117218 Moscow Russia Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 2 I-00185 Rome Italy INFN Sezione di Roma P.le Aldo Moro 2 I-00185 Rome Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi (AQ) Italy INFN Gran Sasso Science Institute I-67100 L’Aquila Italy University of California Berkeley California 94720 USA Université Paris-Saclay CNRS/IN2P3 IJCLab 91405 Orsay France Nikolaev Institute of Inorganic Chemistry 630090 Novosibirsk Russia Dipartimento di Fisica Università di Milano-Bicocca I-20126 Milano Italy INFN Sezione di Milano-Bicocca I-20126 Milano Italy Institute for Nuclear Research of NASU 03028 Kyiv Ukraine INFN Sezione di Roma Tor Vergata Via della Ricerca Scientifica 1 I-00133 Rome Italy IRFU CEA Université Paris-Saclay F-91191 Gif-sur-Yvette France Karlsruhe Institute of Technology Institute for Astroparticle Physics 76021 Karlsruhe Germany Lawrence Berkeley National Laboratory Berkeley California 94720 USA Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai 200433 People’s Republic of China Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Karlsruhe Institute of Technology Institute for Data Processing and Electronics 76021 Karlsruhe Germany National Research Centre Kurchatov Institute Institute of Theoretical and Experimental Physics 117218 Moscow Russia Department of Physics University of Jyväskylä P.O. Box 35 FI-40014 Jyväskylä Finland Finnish Institute for Educational Research University of Jyväskylä P.O. Box 35 FI-40014 Jyvaäskylä Finland Center for Theoretical Physics Sloane Physics Laboratory Yale University New Haven Connecticut 06520-8120 USA Physik Department Technische Universität München Garching D-85748 Germany Department of Modern P

Neutrinoless double beta decay (0νββ) is a yet unobserved nuclear process that would demonstrate Lepton number violation, a clear evidence of beyond standard model physics. The process two neutrino double beta decay (2νββ) is allowed by the standard model and has been measured in numerous experiments. In this Letter, we report a measurement of 2νββ decay half-life of Mo100 to the ground state of Ru100 of [7.07±0.02(stat)±0.11(syst)]×1018 yr by the CUPID-Mo experiment. With a relative precision of ±1.6% this is the most precise measurement to date of a 2νββ decay rate in Mo100. In addition, we constrain higher-order corrections to the spectral shape, which provides complementary nuclear structure information. We report a novel measurement of the shape factor ξ3,1=0.45±0.03(stat)±0.05(syst) based on a constraint on the ratio of higher-order terms from theory, which can be reliably calculated. This is compared to theoretical predictions for different nuclear models. We also extract the first value for the effective axial vector coupling constant obtained from a spectral shape study of 2νββ decay.

关键词： Double beta decay Neutrinoless double beta decay Nuclear structure & decays

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Operational Research: Methods and Applications

arXiv

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

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Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas

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Revista Española de Cardiología (English Edition) 2021年第9期74卷 790-799页

作者： Matthew J. Page Joanne E. McKenzie Patrick M. Bossuyt Isabelle Boutron Tammy C. Hoffmann Cynthia D. Mulrow Larissa Shamseer Jennifer M. Tetzlaff Elie A. Akl Sue E. Brennan Roger Chou Julie Glanville Jeremy M. Grimshaw Asbjørn Hróbjartsson Manoj M. Lalu Tianjing Li Elizabeth W. Loder Evan Mayo-Wilson David Moher School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Clinical Epidemiology Biostatistics and Bioinformatics Amsterdam University Medical Centres University of Amsterdam Amsterdam Países Bajos Université de Paris Centre of Epidemiology and Statistics (CRESS) Inserm F 75004 Paris Francia Institute for Evidence-Based Healthcare Faculty of Health Sciences and Medicine Bond University Gold Coast Australia University of Texas Health Science Center at San Antonio San Antonio Texas Estados Unidos Annals of Internal Medicine Knowledge Translation Program Li Ka Shing Knowledge Institute Toronto Canadá School of Epidemiology and Public Health Faculty of Medicine University of Ottawa Ottawa Canada Evidence Partners Ottawa Canadá Clinical Research Institute American University of Beirut Beirut Líbano Department of Health Research Methods Evidence and Impact McMaster University Hamilton Ontario Canadá Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland Oregón Estados Unidos York Health Economics Consortium (YHEC Ltd) University of York York Reino Unido Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa Canadá School of Epidemiology and Public Health University of Ottawa Ottawa Canadá Department of Medicine University of Ottawa Ottawa Canadá Centre for Evidence-Based Medicine Odense Odense University Hospital Odense Dinamarca Department of Clinical Research University of Southern Denmark Odense Dinamarca Open Patient data Explorative Network Odense University Hospital Odense Dinamarca Department of Anesthesiology and Pain Medicine The Ottawa Hospital Ottawa Canadá Clinical Epidemiology Program Blueprint Translational Research Group Ottawa Hospital Research Institute Ottawa Canadá Regenerative Medicine Program Ottawa Hospital Research Institute Ottawa Canadá Department of Ophthalmology School of Medicine University of Colorado Denver Den

La declaración PRISMA ( Preferred Reporting Items for Systematic reviews and Meta-Analyses ), publicada en 2009, se diseñó para ayudar a los autores de revisiones sistemáticas a documentar de manera transparente el porqué de la revisión, qué hicieron los autores y qué encontraron. Durante la última década, ha habido muchos avances en la metodología y terminología de las revisiones sistemáticas, lo que ha requerido una actualización de esta guía. La declaración prisma 2020 sustituye a la declaración de 2009 e incluye una nueva guía de presentación de las publicaciones que refleja los avances en los métodos para identificar, seleccionar, evaluar y sintetizar estudios. La estructura y la presentación de los ítems ha sido modificada para facilitar su implementación. En este artículo, presentamos la lista de verificación PRISMA 2020 con 27 ítems, y una lista de verificación ampliada que detalla las recomendaciones en la publicación de cada ítem, la lista de verificación del resumen estructurado PRISMA 2020 y el diagrama de flujo revisado para revisiones sistemáticas. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews. Full English text available from : ***

关键词： Guía de publicación Revisión sistemática Metanálisis Escritura médica Publishing guide Systematic review Meta-analysis Medical writing

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