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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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A Comparative Study of Two Complex Ontologies in Air Traffic Management

A Comparative Study of Two Complex Ontologies in Air Traffic...

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Conference on Digital Avionics Systems (DASC)

作者： Eduard Gringinger Richard M. Keller Audun Vennesland Christoph G. Schuetz Bernd Neumayr Corporate Research Frequentis AG Vienna Austria Intelligent Systems Division NASA Ames Research Center Moffet Field CA USA Software Engineering Safety and Security SINTEF Trondheim Norway Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

关键词： Information Exchange Models ATM Information Reference Model NASA ATM Ontology Ontology Matching Ontology Air traffic management Automatic Teller Machines classification scheme MISMATCH aviation data exchange data models Information management

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Improving the efficiency of application development based on the Macchinetta framework

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NTT Technical Review 2017年第2期15卷 1-7页

作者： Suzuki, Gengo Kanamaru, Akira Iwatsuka, Takuya Katada, Junya Okada, Satoshi Mochida, Seiichiro Natsukawa, Katsuyuki Motohashi, Kenji Hishiki, Takanori Kaneko, Takehiko Tanabe, Kenji Izumoto, Hiroshi Sakai, Miyotaka Yamashita, Katsuyuki Iwaki, Yosuke Software Engineering Project NTT Software Innovation Center Japan Center for Applied Software Engineering Production Engineering Department System Engineering Headquarters NTT DATA Corporation Japan SE Department in Core Technology Quality Management and Engineering Division NTT Comware Corporation Japan

At the NTT software Innovation Center, we are working to improve the stability and productivity of software development throughout the entire NTT Group by conducting research and development on new software development techniques and making these techniques accessible to developers. This article introduces our work on the Macchinetta framework that we developed in order to produce web-based enterprise applications more efficiently on the Macchinetta tool suite that commonizes development tools, and on the Macchinetta talent pool that provides talented developers to support efficient development.

关键词： software design

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Mixed-Valence CuI/CuIIIMetal–Organic Frameworks with Non-innocent Ligand for Multielectron Transfer

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Angewandte Chemie 2023年第50期135卷

作者： Shang-Yuan Fu Cheng-Han Chang Dr. Alexander S. Ivanov Dr. Ilja Popovs Dr. Jeng-Lung Chen Dr. Yen-Fa Liao Dr. Hsin-Kuan Liu Dr. Suman Chirra Prof. Dr. Yun-Wei Chiang Jui-Chin Lee Wei-Ling Liu Prof. Dr. Watchareeya Kaveevivitchai Prof. Dr. Teng-Hao Chen Department of Chemical Engineering Hierarchical Green-Energy Materials (Hi-GEM) Research Center National Cheng Kung University Tainan City 70101 Taiwan Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Department of Chemistry Tamkang University New Taipei City 25137 Taiwan Chemical Sciences Division Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge TN 37831 USA Contribution: Data curation (supporting) Software (lead) Validation (supporting) Contribution: Conceptualization (supporting) Formal analysis (supporting) Funding acquisition (supporting) Validation (supporting) National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Core Facility Center National Cheng Kung University Tainan City 70101 Taiwan Contribution: Data curation (supporting) Investigation (supporting) Department of Chemistry National Tsing Hua University Hsinchu 300-044 Taiwan Contribution: Data curation (supporting) Methodology (supporting) Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Resources (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) School of Pharmacy College of Medicine National Cheng Kung University Tainan City 70101 Taiwan Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

We report two novel three-dimensional copper-benzoquinoid metal–organic frameworks (MOFs), [Cu 4 L 3 ] n and [Cu 4 L 3 ⋅ Cu(iq) 3 ] n (LH 4 =1,4-dicyano-2,3,5,6-tetrahydroxybenzene, iq=isoquinoline). Spectroscopic techniques and computational studies reveal the unprecedented mixed valency in MOFs, formal Cu(I)/Cu(III). This is the first time that formally Cu(III) species are witnessed in metal–organic extended solids. The coordination between the mixed-valence metal and redox-non-innocent ligand L, which promotes through-bond charge transfer between Cu metal sites, allows better metal-ligand orbital overlap of the d-π conjugation, leading to strong long-range delocalization and semiconducting behavior. Our findings highlight the significance of the unique mixed valency between formal Cu(I) and highly-covalent Cu(III), non-innocent ligand, and pore environments of these bench stable Cu(III)-containing frameworks on multielectron transfer and electrochemical properties.

关键词： Cu(I)/Cu(III) Metal–Organic Frameworks Mixed-Valence Non-innocent Ligand Redox Chemistry

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Elastic Organic Crystals as Bioinspired Hair-Like Sensors

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Angewandte Chemie 2022年第9期135卷

作者： Dr. Soha Yousuf Dr. Jad Mahmoud Halabi Ibrahim Tahir Dr. Ejaz Ahmed Dr. Rachid Rezgui Asst. Prof. Liang Li Dr. Praveen Laws Prof. Mohammed Daqaq Prof. Panče Naumov Smart Materials Lab New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Contribution: Conceptualization (supporting) Data curation (lead) Formal analysis (lead) Methodology (lead) Software (equal) Writing - original draft (lead) Contribution: Data curation (supporting) Supervision (supporting) Contribution: Data curation (supporting) New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Science and Engineering Department Sorbonne University Abu Dhabi PO Box 38044 Abu Dhabi United Arab Emirates Contribution: Supervision (equal) Laboratory of Applied Nonlinear Dynamics Division of Engineering New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Contribution: Data curation (supporting) Formal analysis (supporting) Center for Smart Engineering Materials New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Contribution: Supervision (supporting) Research Center for Environment and Materials Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 MK-1000 Skopje Macedonia Molecular Design Institute Department of Chemistry New York University 100 Washington Square East New York NY 10003 USA

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Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior

arXiv

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arXiv 2021年

作者： Natale, Joseph L. Viswanath, Varun Acevedo, Oscar Trujillo Giottonini, Sophia Perez Hernández, Sandy Ihuiyan Romero Cruz Millán, Diana G. Montserrat Palacios-Puga, A. Mandvi, Ammar Khan, Brian M. Lilik, Martin Park, Jay Smarr, Benjamin L. Halicioǧlu Data Science Institute University of California La Jolla San DiegoCA90293 United States Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA90293 United States Division de Ingeniera Bioqumica Instituto Tecnologico Superior of Atlixco C. Heliotropo No.1201 Unidad 8 Norte Nueva Xalpatlaco Atlixco74210 Mexico Colegio de Bachilleres del Estado de Sonora plantel Villa de Seris SON Hermosillo Mexico Instituto Tecnológico Superior de Atlixco C. Heliotropo No.1201 Unidad 8 Norte Nueva Xalpatlaco Atlixco Mexico Department of Health Sciences Universidad de Sonora campus Cajeme Cd. Obregon Son. Mexico Department of Biochemical and Chemical Engineering Instituto Tecnológico de Tijuana Tecnológico Nacional de Mexico B.C Tijuana Mexico Department of Emergency Medicine Department of Family Medicine University of California San DiegoCA United States Department of Emergency Medicine Department of Internal Medicine University of California San DiegoCA United States Department of Software Engineering Park Media Semarang Indonesia Department of Emergency Medicine University of California La Jolla San DiegoCA92037 United States Department of Bioengineering Halicioǧlu Data Science Institute University of California La Jolla San DiegoCA90293 United States

The SARS-CoV-2 pandemic has so far unfolded diversely across the fifty United States of America, reflected both in different time progressions of infection "waves" and in magnitudes of local infection rates. Despite a marked diversity of presentations, most U.S. states experienced their single greatest surge in daily new cases during the transition from Fall 2020 to Winter 2021. Popular media also cite additional similarities between states - often despite disparities in governmental policies, reported mask-wearing compliance rates, and vaccination percentages. Here, we identify a set of robust, low-dimensional clusters that 1) summarize the timings and relative heights of four historical COVID-19 "wave opportunities" accessible to all 50 U.S. states, 2) correlate with geographical and intervention patterns associated with those groups of states they encompass, and 3) predict aspects of the "fifth wave" of new infections in the late Summer of 2021. In particular, we argue that clustering elucidates a negative relationship between vaccination rates and subsequent case-load variabilities within state groups. We advance the hypothesis that vaccination acts as a "seat belt," in effect constraining the likely range of new-case upticks, even in the context of the Summer 2021, variant-driven surge. © 2021, CC BY.

关键词： Vaccines

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Toward Complete Transformation of Sodium Polysulfides by Regulating the Second-Shell Coordinating Environment of Atomically Dispersed Fe

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Angewandte Chemie 2023年第26期135卷

作者： Ruilin Bai Qiaosong Lin Xinyu Li Fangxin Ling Huijuan Wang Sha Tan Lingxiang Hu Mingze Ma Prof. Xiaojun Wu Yu Shao Xianhong Rui Prof. Enyuan Hu Dr. Yu Yao Prof. Yan Yu Hefei National Research Center for Physical Sciences at the Microscale Department of Materials Science and Engineering CAS Key Laboratory of Materials for Energy Conversion University of Science and Technology of China Hefei Anhui 230026 China Contribution: Conceptualization (lead) Writing - original draft (lead) State Key Laboratory of Molecular Reaction Dynamics Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 Liaoning China University of Chinese Academy of Sciences Beijing 100049 China Contribution: Data curation (lead) Contribution: Formal analysis (equal) Contribution: Investigation (lead) Experimental Center of Engineering and Material Science University of Science and Technology of China Hefei 230026 China Contribution: Methodology (equal) Chemistry Division Brookhaven National Laboratory Upton NY USA Contribution: Resources (equal) Department of Electrical Engineering and Computer Science Institut Polytechnique de Paris 91120 Palaiseau Île-de-France France Contribution: Software (equal) Contribution: Data curation (equal) Jiujiang DeFu Technology Co. Ltd. Jiujiang Jiangxi 332000 (P. R. China School of Materials and Energy Guangdong University of Technology Guangzhou 510006 China Contribution: Validation (lead) Contribution: Methodology (lead) Contribution: Writing - review & editing (lead) National Synchrotron Radiation Laboratory Hefei Anhui 230026 China Contribution: Project administration (lead) Writing - review & editing (lead)

Room temperature sodium-sulfur (RT Na-S) batteries are highly competitive as potential energy storage devices. Nevertheless, their actually achieved reversible capacities are far below the theoretical value due to incomplete transformation of polysulfides. Herein, atomically dispersed Fe-N/S active center by regulating the second-shell coordinating environment of Fe single atom is proposed. The Fe−N 4 S 2 coordination structure with enhanced local electronic concentration around the Fermi level is revealed via synchrotron radiation X-ray absorption spectroscopy (XAS) and theoretical calculations, which can not only significantly promote the transformation kinetics of polysulfides, but induce uniform Na deposition for dendrite-free Na anode. As a result, the obtained S cathode delivers a high initial reversible capacity of 1590 mAh g −1 , nearly the theoretical value. This work opens up a new avenue to facilitate the complete transformation of polysulfides for RT Na-S batteries.

关键词： Catalysis Coordinating Environment Fe Single Atom RT Na-S Batteries Transformation Kinetics

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Exomars trace gas orbiter instrument modelling approach to streamline science operations 15th

Exomars trace gas orbiter instrument modelling approach to s...

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15th International Conference on Space Operations, SpaceOps 2018

作者： Fernández, M. Muñoz Frew, D. Solaz, Muñíz Ashman, M. Beteta, J.J. García Nespoli, C.F. Aberasturi, M. Moinelo, A. Cardesin Geiger, J.B. Metcalfe, L. Crego, C. Muñoz ExoMars TGO Science Operations Centre Science Operations Development Division Directorate of Science European Space Astronomy Centre European Space Agency Madrid28691 Spain Mars Express Science Ground Segment Operations Department Directorate of Science European Space Astronomy Centre European Space Agency Madrid28691 Spain MAPPS Software Development Data and Engineering Division Operations Department Directorate of Science European Space Astronomy Centre European Space Agency Madrid28691 Spain

ISBN: (数字)9781624105623

ISBN: (纸本)9781624105623

ExoMars Trace Gas Orbiter (TGO) science operations activities are centralised at ESAC’s Science Operations Centre (SOC). The SOC receives the inputs from the principal investigators (PIs) in order to implement and deliver the spacecraft pointing requests and instrument timelines to the Mission Operations Centre (MOC). The high number of orbits per planning cycle has made it necessary to abstract the planning interactions between the SOC and the PI teams at the observation level. This paper describes the modelling approach we have conducted for TGO’s instruments to streamline science operations. We have created dynamic observation types that scale to adapt to the conditions specified by the PI teams including observation timing, and pointing block parameters calculated from observation geometry. This approach is considered and improvement with respect to previous missions where the generation of the observation pointing and commanding requests was performed manually by the instrument teams. Automation software assists us to effectively handle the high density of planned orbits with increasing volume of scientific data and to successfully meet opportunistic scientific goals and objectives. Our planning tool combines the instrument observation definition files provided by the PIs together with the flight dynamics products to generate the Pointing Requests and the instrument timeline (ITL). The ITL contains all the validated commands at the TC sequence level and computes the resource envelopes (data rate, power, data volume) within the constraints. At the SOC, our main goal is to maximise the science output while minimising the number of iterations among the teams, ensuring that the timeline does not violate the state transitions allowed in the Mission Operations Rules and Constraints Document. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Orbits

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

arXiv

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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

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