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arXiv 2022年

作者： Ghiringhelli, Luca M. Baldauf, Carsten Bereau, Tristan Brockhauser, Sandor Carbogno, Christian Chamanara, Javad Cozzini, Stefano Curtarolo, Stefano Draxl, Claudia Dwaraknath, Shyam Fekete, Ádám Kermode, James Koch, Christoph T. Kühbach, Markus Ladines, Alvin Noe Lambrix, Patrick Lenz-Himmer, Maja-Olivia Levchenko, Sergey Oliveira, Micael Michalchuk, Adam Miller, Ron Onat, Berk Pavone, Pasquale Pizzi, Giovanni Regler, Benjamin Rignanese, Gian-Marco Schaarschmidt, Jörg Scheidgen, Markus Schneidewind, Astrid Sheveleva, Tatyana Su, Chuanxun Usvyat, Denis Valsson, Omar Wöll, Christof Scheffler, Matthias Physics Department IRIS Adlershof Humboldt-Universität zu Berlin Germany The NOMAD Lab The Fritz Haber Institute The Max Planck Society Humboldt University Berlin Germany Fritz Haber Institute of the Max Planck Society Berlin Germany Van’t Hoff Institute for Molecular Sciences Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands TIB Leibniz Information Centre for Science and Technology University Library Welfengarten 1B Hanover30167 Germany AREA Science Park località Padriciano Trieste34149 Italy Center for Autonomous Materials Design Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Lawrence Berkeley National Laboratory BerkeleyCA United States Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Computer and Information Science Linköping University Sweden The Swedish e-Science Research Centre Linköping University Sweden Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany Berlin12489 Germany Department of Mechanical and Aerospace Engineering Carleton University OttawaONK1S 5B6 Canada École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland UCLouvain Chemin des Étoiles 8 Louvain-la-NeuveB-1348 Belgium Eggenstein-Leopoldshafen76344 Germany Jülich Center for Neutron Science MLZ Forschungszentrum Jülich GmbH Lichtenbergstraße 1 Garching85748 Germany CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Chemistry Department Humboldt-Universität zu Berlin Germany Department of Chemistry University of North Texas DentonTX76201 United States Eggenstein-Leopoldshafen76344 Germany

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. © 2022, CC BY.

关键词： Metadata

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High-dimensional classically entangled light from a laser

arXiv

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arXiv 2020年

作者： Shen, Yijie Nape, Isaac Yang, Xilin Fu, Xing Gong, Mali Naidoo, Darryl Forbes, Andrew School of Physics University of the Witwatersrand Private Bag 3 Wits2050 South Africa State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Ministry of Education Beijing100084 China CSIR National Laser Centre PO Box 395 Pretoria0001 South Africa

Vectorially structured light has emerged as an enabling tool in many diverse applications, from communication to imaging, exploiting quantum-like correlations courtesy of a non-separable spatially varying polarization structure. Creating these states at the source remains challenging and is presently limited to two-dimensional vectorial states by customized lasers. Here we invoke ray-wave duality in a simple laser cavity to produce polarization marked multi-path modes that are non-separable in three degrees of freedom and in eight dimensions. As a topical example, we use our laser to produce the complete set of Greenberger-Horne-Zeilinger (GHZ) basis states, mimicking high-dimensional multi-partite entanglement with classical light, which we confirm by a new projection approach. We offer a complete theoretical framework for our laser based on SU(2) symmetry groups, revealing a rich parameter space for further exploitation. Our approach requires only a conventional laser with no special optical elements, is easily scaleable to higher dimensions, and offers a simple but elegant solution for at-the-source creation of classically entangled states of structured light, opening new applications in simulating and enhancing high-dimensional quantum systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Vaccinations or Non-Pharmaceutical Interventions: Safe Reopening of Schools in England

Research Square

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research Square 2021年

作者： Cuesta-Lazaro, Carolina Quera-Bofarull, Arnau Aylett-Bullock, Joseph Lawrence, Bryan N. Fong, Kevin Icaza-Lizaola, Miguel Sedgewick, Aidan Truong, Henry Vernon, Ian Williams, Julian Pagel, Christina Krauss, Frank Institute for Data Science Durham University Durham United Kingdom Institute for Computational Cosmology Durham University Durham United Kingdom Institute for Particle Physics Phenomenology Durham University Durham United Kingdom National Centre for Atmospheric Science University of Reading Reading United Kingdom Departments of Meteorology and Computer Science University of Reading Reading United Kingdom Department of Science Technology Engineering and Public Policy University College London London United Kingdom Department of Anaesthesia University College London Hospital London United Kingdom Centre for Extragalactic Astronomy Durham University Durham United Kingdom Department of Mathematical Sciences Durham University DurhamDH1 3LE United Kingdom Institute for Hazard Risk & Resilience Durham University DurhamDH1 3LE United Kingdom Clinical Operational Research Unit University College London United Kingdom

With high levels of the Delta variant of COVID-19 circulating in England during September 2021, schools are set to reopen with few school-based non-pharmaceutical interventions (NPIs). In this paper, we present simulation results obtained from the individual-based model, JUNE, for English school opening after a prior vaccination campaign using an optimistic set of assumptions about vaccine efficacy and the likelihood of prior-reinfection. We take a scenario-based approach to modelling potential interventions to assess relative changes rather than real-world forecasts. Specifically, we assess the effects of vaccinating those aged 16-17, those aged 12-17, and not vaccinating children at all relative to only vaccinating the adult population, addressing what might have happened had the UK began teenage vaccinations earlier. Vaccinating children in the 12-15 age group would have had a significant impact on the course of the epidemic, saving thousands of lives overall in these simulations. In the absence of such a vaccination campaign our simulations show there could still be a significant positive impact on the epidemic (fewer cases, fewer deaths) by continuing NPI strategies in schools. Our analysis suggests that the best results in terms of lives saved are likely derived from a combination of the now planned vaccination campaign and NPIs in schools. © 2021, CC BY.

关键词： COVID-19

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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REBELS-25: Discovery of a dynamically cold disc galaxy at z = 7.31

arXiv

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arXiv 2024年

作者： Rowland, Lucie E. Hodge, Jacqueline Bouwens, Rychard Piña, Pavel Mancera Hygate, Alexander Algera, Hiddo Aravena, Manuel Bowler, Rebecca da Cunha, Elisabete Dayal, Pratika Ferrara, Andrea Herard-Demanche, Thomas Inami, Hanae van Leeuwen, Ivana de Looze, Ilse Oesch, Pascal Pallottini, Andrea Phillips, Siân Rybak, Matus Schouws, Sander Smit, Renske Sommovigo, Laura Stefanon, Mauro van der Werf, Paul Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands Hiroshima Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo Japan Núcleo de Astronomía Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército Libertador 441 Santiago Chile Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy School of Natural Sciences The University of Manchester ManchesterM13 9PL United Kingdom International Centre for Radio Astronomy Research University of Western Australia 35 Stirling Hwy CrawleyWA6009 Australia Australia Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa56126 Italy Sterrenkundig Observatorium Ghent University Krijgslaan281-S9 GhentB-9000 Belgium Department of Astronomy University of Geneva Chemin Pegasi 51 Versoix1290 Switzerland Copenhagen Denmark Astrophysics Research Institute Liverpool John Moores University 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands SRON - Netherlands Institute for Space Research Niels Bohrweg 4 CA Leiden2333 Netherlands Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Unidad Asociada CSIC "Grupo de Astrofísica Extragaláctica y Cosmología" Instituto de Física de Cantabria Universitat de València Spain

We present high resolution (∼ 0.14" = 710 pc) ALMA [CII] 158μm and dust continuum follow-up observations of REBELS-25, a [CII]-luminous (L[CII] = (1.7 ± 0.2) × 109L☉) galaxy at redshift z = 7.3065 ± 0.0001. These high resolution, high signal-to-noise observations allow us to study the sub-kpc morphology and kinematics of this massive (M∗ = 8+−42 × 109M☉) star-forming (SFRUV+IR = 199+−10163 M☉yr−1) galaxy in the Epoch of Reionisation. By modelling the kinematics with 3DBAROLO, we find it has a low velocity dispersion (σ¯ = 33 ± 9 km s−1) and a high ratio of ordered-to-random motion (Vrot, max/σ¯ = 11+−84), indicating that REBELS-25 is a dynamically cold disc. Additionally, we find that the [CII] distribution is well fit by a near-exponential disc model, with a Sérsic index, n, of 1.3 ± 0.2, and we see tentative evidence of more complex non-axisymmetric structures suggestive of a bar in the [CII] and dust continuum emission. By comparing to other high spatial resolution cold gas kinematic studies, we find that dynamically cold discs seem to be more common in the high redshift Universe than expected based on prevailing galaxy formation theories, which typically predict more turbulent and dispersion-dominated galaxies in the early Universe as an outcome of merger activity, gas accretion and more intense feedback. This higher degree of rotational support seems instead to be consistent with recent cosmological simulations that have highlighted the contrast between cold and warm ionised gas tracers, particularly for massive galaxies. We therefore show that dynamically settled disc galaxies can form as early as 700 Myr after the Big Bang. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

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Search for the lepton-flavor violating decay of the Higgs boson and additional Higgs bosons in the eμ final state in proton-proton collisions at s=13 TeV

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Physical Review D 2023年第7期108卷 072004-072004页

作者： A. Hayrapetyan A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic A. Escalante Del Valle P. S. Hussain M. Jeitler N. Krammer L. Lechner D. Liko I. Mikulec J. Schieck R. Schöfbeck D. Schwarz M. Sonawane S. Templ W. Waltenberger C.-E. Wulz M. R. Darwish T. Janssen T. Kello P. Van Mechelen E. S. Bols J. D’Hondt A. De Moor M. Delcourt H. El Faham S. Lowette I. Makarenko A. Morton D. Müller A. R. Sahasransu S. Tavernier S. Van Putte D. Vannerom B. Clerbaux S. Dansana G. De Lentdecker L. Favart D. Hohov J. Jaramillo K. Lee M. Mahdavikhorrami A. Malara S. Paredes L. Pétré N. Postiau L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer M. De Coen D. Dobur J. Knolle L. Lambrecht G. Mestdach C. Rendón A. Samalan K. Skovpen M. Tytgat N. Van Den Bossche B. Vermassen L. Wezenbeek A. Benecke G. Bruno F. Bury C. Caputo C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre J. Lidrych P. Mastrapasqua K. Mondal T. T. Tran S. Wertz G. A. Alves E. Coelho C. Hensel A. Moraes P. Rebello Teles W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao V. Dos Santos Sousa S. Fonseca De Souza J. Martins C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores P. G. Mercadante S. F. Novaes B. Orzari Sandra S. Padula A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Shopova G. Sultanov A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka S. Keshri S. Thakur T. Cheng Q. Guo T. Javaid M. Mittal L. Yuan G. Bauer Z. Hu K. Yi G. M. Chen H. S. Chen M. Chen F. Iemmi C. H. Jiang A. Kapoor H. Liao Z.-A. Liu F. Monti R. Sharma J. N. Song J. Tao C. Wang J. Wang H. Zhang A. Agapitos Y. Ban A. Levin C. Li Q. Li X. Lyu Y. Mao S. J. Qian X. Sun D. Wang H. Yang M. Lu Z. You N. Lu X. Gao D. Leggat H. Okawa Y. Zhang Z. Lin C Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Indian Institute of Technology Madras Madras India Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Korea University Seoul Korea Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China California Institute of Technology Pasadena California USA Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE)—Fudan University Shanghai China Rice University Houston Texas USA Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the

A search for the lepton-flavor violating decay of the Higgs boson and potential additional Higgs bosons with a mass in the range 110–160 GeV to an e±μ∓ pair is presented. The search is performed with a proton-proton collision dataset at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138 fb−1. No excess is observed for the Higgs boson. The observed (expected) upper limit on the e±μ∓ branching fraction for it is determined to be 4.4(4.7)×10−5 at 95% confidence level, the most stringent limit set thus far from direct searches. The largest excess of events over the expected background in the full mass range of the search is observed at an e±μ∓ invariant mass of approximately 146 GeV with a local (global) significance of 3.8 (2.8) standard deviations.

关键词： Higgs bosons Hadron colliders

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Dynamic mask stitching-guided region consistency for semi-supervised 3D medical image segmentation

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Expert Systems with Applications 2025年 292卷

作者： Jiahui Wang Dongsheng Ruan Yang Li Tao Tan Lianming Wu Guang Yang Mingfeng Jiang School of Artificial Intelligence Jiaxing University Jiaxing 314000 China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou 310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou 310018 China Faculty of Applied Sciences Macao Polytechnic University Macao 999078 China Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai 200001 China Bioengineering Department Imperial College London W12 7SL UK National Heart and Lung Institute Imperial College London SW7 2AZ UK Cardiovascular Research Centre Royal Brompton Hospital London SW3 6NP UK School of Biomedical Engineering and Imaging Sciences King’s College London WC2R 2LS UK

In semi-supervised medical image segmentation, consistency-based pseudo-label regularization may introduce cognitive bias, as it relies solely on models trained with limited labeled data to generate pseudo-labels. Moreover, the empirical mismatch between the limited labeled data and the abundant unlabeled data further exacerbates this issue, which is a common challenge in semi-supervised learning. To address these challenges, we propose a dynamic mask stitching-guided region consistency network that uses segmentation probability maps to generate dynamically changing masks for stitching during training. As training progresses, the mask converges to the correct segmentation target, directing the model’s focus to the target’s edges and mitigating cognitive bias from inaccurate predictions. Our experiments show varying prediction accuracy across different voxel source regions in the stitched image, highlighting the potential for improving segmentation through voxel-level region consistency supervision. This method uses the Ground Truth to supervise the voxel regions in the stitched image from labeled images, and the segmentation results of stitched and unlabeled images are used to construct low-entropy pseudo-labels for regional consistency regularization. We demonstrate the effectiveness of dynamic mask stitching between labeled and unlabeled data, achieving a 0.47–4.64 % improvement on the LA and Pancreas-CT datasets compared to state-of-the-art methods in semi-supervised segmentation. The code is accessible at https://***/Oubit1/DSRC .

关键词：

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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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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music technology for Health Care and Well-Being

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Music and Science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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