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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset

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arXiv 2024年

作者： Rudie, Jeffrey D. Lin, Hui-Ming Ball, Robyn L. Jalal, Sabeena Prevedello, Luciano M. Nicolaou, Savvas Marinelli, Brett S. Flanders, Adam E. Magudia, Kirti Shih, George Davis, Melissa A. Mongan, John Chang, Peter D. Berger, Ferco H. Hermans, Sebastiaan Law, Meng Richards, Tyler Grunz, Jan-Peter Kunz, Andreas Steven Mathur, Shobhit Galea-Soler, Sandro Chung, Andrew D. Afat, Saif Kuo, Chin-Chi Aweidah, Layal Campos, Ana Villanueva Somasundaram, Arjuna Tijmes, Felipe Antonio Sanchez Jantarangkoon, Attaporn Bittencourt, Leonardo Kayat Brassil, Michael Hajjami, Ayoub El Dogan, Hakan Becircic, Muris Bharatkumar, Agrahara G. de Mattos Farina, Eduardo Moreno Júdice Colak, Errol Department of Radiology Scripps Clinic Medical Group University of California San Diego United States Department of Medical Imaging St. Michael's Hospital Unity Health Toronto Canada The Jackson Laboratory Bar HarborME United States Department of Radiology Vancouver General Hospital Vancouver Canada Department of Radiology Department of Radiology The Ohio State University ColumbusOH United States Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Radiology Thomas Jefferson University PhiladelphiaPA United States Duke University School of Medicine DurhamNC United States Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Radiology and Biomedical Imaging Yale University School of Medicine New HavenCT United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA United States Departments of Radiological Sciences and Computer Science University of California IrvineCA United States Sunnybrook Health Sciences Centre University of Toronto Canada Alfred Health Monash University Melbourne Australia University of Utah Salt Lake CityUT United States Department of Diagnostic and Interventional Radiology University Hospital of Würzburg Würzburg Germany Medical Imaging Department Mater Dei Hospital Msida Malta Department of Diagnostic Radiology Queen's University Kingston Canada Department of Diagnostic and Interventional Radiology Eberhard-Karls-University Tübingen Tübingen Germany Division of Nephrology Department of Internal Medicine China Medical University Hospital College of Medicine China Medical University Taichung Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Imaging Liverpool Hospital Sydney Australia Hospital Universitario Ramón y Cajal Madrid Spain Department of Radiology Gold Coast Uni

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for noncommercial use via Kaggle at https://***/competitions/rsna-2023-abdominaltrauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes. © 2024, CC BY.

关键词： Grading

The XMM Cluster Survey: Exploring scaling relations and completeness of the Dark Energy Survey Year 3 redMaPPer cluster catalogue

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arXiv 2023年

作者： Upsdell, E.W. Giles, P.A. Romer, A.K. Wilkinson, R. Turner, D.J. Hilton, M. Rykoff, E. Farahi, A. Bhargava, S. Jeltema, T. Klein, M. Bermeo, A. Collins, C.A. Ebrahimpour, L. Hollowood, D. Mann, R.G. Manolopoulou, M. Miller, C.J. Rooney, P.J. Sahlén, Martin Stott, J.P. Viana, P.T.P. Allam, S. Alves, O. Bacon, D. Bertin, E. Bocquet, S. Brooks, D. Burke, D.L. Kind, M. Carrasco Carretero, J. Costanzi, M. da Costa, L.N. Pereira, M.E.S. De Vicente, J. Desai, S. Diehl, H.T. Dietrich, J.P. Everett, S. Ferrero, I. Frieman, J. García-Bellido, J. Gerdes, D.W. Gutierrez, G. Hinton, S.R. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Lima, M. Marshall, J.L. Mena-Fernández, J. Menanteau, F. Miquel, R. Mohr, J.J. Ogando, R.L.C. Pieres, A. Raveri, M. Rodriguez-Monroy, M. Sanchez, E. Scarpine, V. Sevilla-Noarbe, I. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Weaverdyck, N. Weller, J. Wiseman, P. Department of Physics and Astronomy University of Sussex BrightonBN1 9QH United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Kavli Institute for Particle Astrophysics & Cosmology Stanford University P. O. Box 2450 Stanford CA94305 United States Department of Statistics and Data Sciences The University of Texas at Austin AustinTX78712 United States AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Santa Cruz Institute for Particle Physics University of California Santa Cruz 1156 High St Santa CruzCA95064 United States Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Instituto de Astrofísica e Ciências do Espaço Universidade do Porto CAUP Rua das Estrelas Porto4150-762 Portugal Departamento de Física e Astronomia Faculdade de Ciências Universidade do Porto Rua do Campo Alegre 687 Porto4169-007 Portugal Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Astronomy Department University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy Uppsala University UppsalaSE-751 20 Sweden Department of Physics Lancaster University LancasterLA1 4YB United Kingdom Department of Physics University of Michigan Ann ArborMI48109 United States Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France University Observatory Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich

We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of three years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg2, bounded by the area of 4 contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray selected sample is fully matched with entries in the redMaPPer catalogue, above λ >20 and within 0.1X − TX), luminosity-richness (LX − λ) and temperature-richness (TX − λ) scaling relations. We find that the fitted forms of the LX − TX relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e., LX − λ and TX − λ) between the optical and X-ray selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray selected samples compared to optically selected samples. © 2023, CC BY.

关键词： Luminance

Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences

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arXiv 2025年

作者： Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernández-Díaz, Raúl Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C.E. Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece 1Department of Biomedical Sciences University of Padova Padova35121 Italy Department of Biomedical Sciences University of Padova Via Ugo Bassi 58/B Padova35131 Italy Athena Research & Innovation Center Aigialias & Chalepa Marousi15125 Greece VIB Data Core VIB.AI Center for AI & Computational Biology Technologiepark-Zwijnaarde 75 Ghent9052 Belgium ELIXIR Europe Hub EMBL-EBI South Building Wellcome Genome HinxtonCB10 1SD United Kingdom Plaça Eusebi Güell 1-3 Barcelona08034 Spain Department of Pharmacy and Biotechnology University of Bologna Bologna40126 Italy Computational Genomics Platform IRCCS University Hospital of Bologna Bologna40138 Italy ZB MED Information Centre for Life Sciences Gleueler Straße 60 Cologne50931 Germany SequenceAnalysis.co.uk United Kingdom University of East Anglia Research Park NorwichNR4 7TJ United Kingdom Department of Computational Biology University of Lausanne Genopode 2024.3 Lausanne1015 Switzerland Swiss Institute of Bioinformatics Amphipôle Quartier UNIL-Sorge Lausanne1015 Switzerland Institute for Fundamental Biomedical Science Biomedical Sciences Research Center Alexander Fleming Vari Greece Department of Informatics & Telematics School of Digital Technology Harokopio University Athens Greece School of Medicine University College Dublin Belfield Dublin 4 Ireland Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin 4 Ireland IBM Research Dublin DublinD15 HN66 Ireland Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki57001 Greece Department of Biology National & Kapodistrian University of Athens Athens15772 Greece Department of Computer Science University of Manchester Oxford Road ManchesterM19 3PL United Kingdom Luxembourg National Data Service 6 avenue des Hauts-Fourneaux Esch-sur-AlzetteL-4362 Luxembourg Insti

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI *** Codes 92 © 2025, CC BY-NC-ND.

关键词： Reusability

Consensus and disagreement of heterogeneous belief systems in influence networks

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arXiv 2018年

作者： Ye, Mengbin Liu, Ji Wang, Lili Anderson, Brian D.O. Cao, Ming Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands Research School of Engineering Australian National University Canberra Australia Hangzhou Dianzi University China Data61-CSIRO formerly NICTA Ltd. Canberra Australia Department of Electrical and Computer Engineering Stony Brook University Department of Electrical Engineering Yale University

—Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals’ belief systems, allow a systematic determination of which topics will reach a consensus, and which topics will disagreement in arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals’ logic matrices, including especially differences in the signs of the off-diagonal entries, can be a key determining factor. This paper thus attributes, for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness. Copyright © 2018, The Authors. All rights reserved.

关键词： Multi agent systems

OTHR-04. DEVELOPMENT OF A FUNCTIONAL PLATFORM FOR REAL-TIME PERSONALIZED DRUG SENSITIVITY PROFILING OF PATIENT-DERIVED 3D FRESH TUMOR TISSUE CULTURES IN THE PEDIATRIC PRECISION ONCOLOGY PROGRAM INFORM

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Neuro-Oncology 2023年第SUPPLEMENT_1期25卷 i74–i75页

作者： Heike Peterziel Nora Jamaladdin Dina ElHarouni Xenia F Gerloff Sonja Herter Petra Fiesel Yannick Berker Mirjam Blattner-Johnson Kathrin Schramm Barbara C Jones David Reuss Laura Turunen Aileen Friedenauer Tim Holland-Letz Martin Sill Lena Weiser Christopher Previti Gnanaprakash Balasubramanian Nicolas U Gerber Johannes Gojo Caroline Hutter Ingrid Øra Olli Lohi Antonis Kattamis Bram de Wilde Frank Westermann Stephan Tippelt Norbert Graf Michaela Nathrath Monika Sparber-Sauer Astrid Sehested Christof M Kramm Uta Dirksen Olli Kallioniemi Stefan M Pfister Cornelis M van Tilburg David T W Jones Jani Saarela Vilja Pietiäinen Natalie Jäger Matthias Schlesner Annette Kopp-Schneider Sina Oppermann Till Milde Olaf Witt Ina Oehme Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Consortium (DKTK) Heidelberg Germany Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Cooperation Unit Neuropathology German Cancer Research Center (DKFZ) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Division of Pediatric Glioma Research German Cancer Research Center (DKFZ) Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital and German Cancer Consortium (DKTK) Heidelberg Germany Department Neuropathology at Institute of Pathology Heidelberg University Hospital Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Core Facility Omics IT and Data Management (ODCF) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Oncology University Children’s Hospital Zürich Zürich Switzerland Department of Pediatrics and Adolescent Medicine Comprehensive Cancer Center and Comprehensive Center for Pediatrics Medical University of Vienna Vienna Austria St. Anna Children’s Hospital Department of Pediatrics and Adolescent Medicine Medical University of Vienna and St. Anna Children’s Cancer Research Institute (CCRI) Vienna Austria Children’s Hospital Pediatric Oncology Skåne University Hospital Lund &amp Karolinska Univ

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

关键词：

MedShapeNet - A Large-Scale dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Evidence for the decays B0→ D̅(∗)0 and updated measurements of the branching fractions of the Bs0→ D̅(∗)0 decays

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arXiv 2023年

作者： Aaij, R. Abdelmotteleb, A.S.W. Abellán Beteta, C. Abudinen Gallego, F.J. Ackernley, T. Adeva, B. Adinolfi, M. Afsharnia, H. Agapopoulou, C. Aidala, C.A. Aiola, S. Ajaltouni, Z. Akar, S. Albrecht, J. Alessio, F. Alexander, M. Alfonso Albero, A. Aliouche, Z. Alkhazov, G. Alvarez Cartelle, P. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andreianov, A. Andreotti, M. Archilli, F. Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Bachmayer, M. Back, J.J. Baladron Rodriguez, P. Balagura, V. Baldini, W. Baptista Leite, J. Barbetti, M. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bashir, S. Bassi, G. Batsukh, B. Battig, A. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I. Beiter, A. Belavin, V. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Berninghoff, D. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bezshyiko, Ia. Bhasin, S. Bhom, J. Bian, L. Bieker, M.S. Bifani, S. Billoir, P. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Bjørn, M. Blago, M.P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Boldyrev, A. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bowcock, T.J.V. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Brea Rodriguez, A. Brodzicka, J. Brossa Gonzalo, A. Brundu, D. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Bursche, A. Butkevich, A. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calefice, L. Calero Diaz, L. Cali, S. Calladine, R. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Campana, P. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carli, I. Carniti, P. Carus, L. Carvalho Akiba, K. Casais Vidal, A. Casse, G. Cattaneo, M. Cavallero, G. Celani, S. Cerasoli, J. Cervenkov, D. Chadwick, A.J. Chapman, M.G. Charles, M. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Univ. Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Universita degli Studi di Padova Universita e INFN Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics Polish

Evidence for the decays B0→ D̅0 and B0→ D̅∗0 is reported with a significance of 3.6 σ and 4.3 σ, respectively. The analysis employs pp collision data at centreof-mass energies √s = 7, 8 and 13 TeV collected by the LHCb detector and corresponding to an integrated luminosity of 9 fb−1. The branching fractions are measured to be B(B0→ D̅0) = (7.7 ± 2.1 ± 0.7 ± 0.7) × 10−7, B(B0→ D̅∗0) = (2.2 ± 0.5 ± 0.2 ± 0.2) × 10−6. In these results, the first uncertainty is statistical, the second systematic, and the third is related to the branching fraction of the B0→ D̅0K+K−decay, used for normalisation. By combining the branching fractions of the decays B0→ D̅(∗)0 and B0→ D̅(∗)0ω, the ω- mixing angle δ is constrained to be tan2δ = (3.6 ± 0.7 ± 0.4) × 10−3, where the first uncertainty is statistical and the second systematic. An updated measurement of the branching fractions of the Bs0→ D̅(∗)0 decays, which can be used to determine the CKM angle γ, leads to B(Bs0→ D̅0) = (2.30 ± 0.10 ± 0.11 ± 0.20) × 10−5, B(Bs0→ D̅∗0) = (3.17 ± 0.16 ± 0.17 ± 0.27) × 10−5 © 2023, CC BY.

关键词： Tellurium compounds

Search for CP violation in D(s)+ → K−K+K+ decays

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arXiv 2023年

A search for direct CP violation in the Cabibbo-suppressed decay Ds+ → K−K+K+ and in the doubly Cabibbo-suppressed decay D+ → K−K+K+ is reported. The analysis is performed with data collected by the LHCb experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 5.6 fb−1. The search is conducted by comparing the D(s)+ and D(s)- Dalitz-plot distributions through a model-independent binned technique, based on fits to the K−K+K+ invariant-mass distributions, with a total of 0.97 (1.27) million Ds+ (D+) signal candidates. The results are given as p-values for the hypothesis of CP conservation and are found to be 13.3% for the Ds+ → K−K+K+ decay and 31.6% for the D+ → K−K+K+ decay. No evidence for CP violation is observed in these decays. © 2023, CC BY.

关键词： Tellurium compounds