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Topology analysis system for vehicular Ad Hoc network 17

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Topology analysis system for vehicular Ad Hoc network

17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016

作者： Dong, Baihong Deng, Jian Wu, Weigang Meng, Tianyu 1. School of Data and Computer Science Sun Yat-Sen Univ. China Guangdong Province Key Lab. of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509050819

With the development of the technology, Vehicular Ad-hoc Network is developing rapidly and continuously. And many new algorithms in VANET were put forward. For example, many people apply the Named data Networks or Software Defined Networking6 to the VANET. However, few researches are looking into the analysis of the topology of the VANET, among most of which are based on simple situations and calculation of the track of vehicles. And therefore, these analysis are inadequate for analyzing the topology under complex situations. This paper is going to put forward a new method for analyzing the change of the topology of VANET, which is capable to quantitatively analyze the change under different and complex situations. In the real experiment, when compared with traditional analysis methods, this new method can analyze more precisely the impact of some possible wireless communication problem on the change of the topology, for example, the impact of hidden node on the vehicular wireless communication and whether the number or the density of vehicles makes the topology of VANET more stable. And this new method can provide a numerical result instead of purely qualitative analysis. © 2016 IEEE.

关键词： Topology

Gaia data Release 3: Apsis II - Stellar Parameters

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arXiv 2022年

作者： Fouesneau, Morgan Frémat, Y. Andrae, R. Korn, A.J. Soubiran, C. Kordopatis, G. Vallenari, A. Heiter, U. Creevey, O.L. Sarro, L.M. de Laverny, P. Lanzafame, A.C. Lobel, A. Sordo, R. Rybizki, J. Slezak, I. Álvarez, M.A. Drimmel, R. Garabato, D. Delchambre, L. Bailer-Jones, C.A.L. Hatzidimitriou, D. Lorca, A. Le Fustec, Y. Pailler, F. Mary, N. Robin, C. Utrilla, E. Aramburu, A. Abreu Bakker, J. Bellas-Velidis, I. Bijaoui, A. Blomme, R. Bouret, J.-C. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Demouchy, C. Dharmawardena, T.E. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Piccolo, A. Jean-Antoine Kontizas, M. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Romeo, A. Magdaleno Manteiga, M. Marocco, F. Martayan, C. Marshall, D.J. Nicolas, C. Ordenovic, C. Palicio, P.A. Pallas-Quintela, L. Pichon, B. Poggio, E. Recio-Blanco, A. Riclet, F. Santoveña, R. Schultheis, M.S. Segol, M. Silvelo, A. Smart, R.L. Süveges, M. Thévenin, F. Elipe, G. Torralba Ulla, A. van Dillen, E. Zhao, H. Zorec, J. Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Nice Cedex 406304 France INAF - Osservatorio Astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Dipartimento di Fisica e Astronomia ""Ettore Majorana"" Università di Catania Via S. Sofia 64 Catania95123 Italy CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Department of Astrophysics Astronomy and Mechanics National and Kapodistrian University of Athens Panepistimiopolis Zografos Athens15783 Greece National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Thales Services for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid

Context. The third Gaia data release (Gaia DR3) contains, beyond the astrometry and photometry, dispersed light for hundreds of millions of sources from the Gaia prism spectra (BP and RP) and the spectrograph (RVS). This data release opens a new window on the chemo-dynamical properties of stars in our Galaxy, essential knowledge for understanding the structure, formation, and evolution of the Milky Way. Aims. To provide insight into the physical properties of Milky Way stars, we used these data to produce a uniformly-derived, all-sky catalog of stellar astrophysical parameters (APs): atmospheric properties (Teff, log g, [M/H], [α/Fe], activity index, emission lines, rotation), 13 chemical abundance estimates, evolution characteristics (radius, age, mass, bolometric luminosity), distance, and dust extinction. Methods. We developed the astrophysical parameters inference system (Apsis) pipeline to infer APs of Gaia objects by analyzing their astrometry, photometry, BP/RP, and RVS spectra. We validate our results against other works in the literature, including benchmark stars, interferometry, and asteroseismology. Here we assessed the stellar analysis performance from Apsis statistically. Results. We describe the quantities we obtained, including the underlying assumptions and the limitations of our results. We provide guidance and identify regimes in which our parameters should and should not be used. Conclusions. Despite some limitations, this is the most extensive catalog of uniformly-inferred stellar parameters to date. These comprise Teff, log g, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (5 million), diffuse interstellar band analysis (1/2 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). More precise and detailed astrophysical parameters based on

关键词： Stars

Application of Structural Equation Modelling for Oil Accumulation System Control in Oleaginous yeast

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Journal of Physics: Conference Series 2019年第1期1391卷

作者： S Aburatani Y Shida W Ogasawara H Yamazaki H Takaku Computational Bio Big Data Open Innovation Lab. (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) AIST Tokyo Waterfront Main Bldg. 2-3-26 Aomi Koto-ku Tokyo 135-0064 Japan Department of Bioengineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata 940-2188 Japan Department of Applied Life Sciences Niigata University of Pharmacy and Applied Life Sciences 265-1 Higashijima Akiha-ku Niigata city Niigata 956-8603 Japan

Recently, we developed a new statistical method for revealing the regulatory systems in living cells. Our method is based on Structural Equation Modelling combined with factor analysis. In generally, Structural Equation Modelling is utilized to detect the model adaptability with the measured data, such as large-scale questionnaire data. In this study, we improved our developed iteration algorithm and gene selection procedure to infer the causalities between variables as a regulatory network from limited numerical data. Our improved gene selection method is based on cross correlation to summarize the time preceding information from gene expression profiles, which were systematically measured at 8 time points. Cross correlation is usually utilized as a measure of similarity between two waves by a time-lag application, and we defined the values of lags ranging from −2 to +4. By this improved method, we selected 14 genes as regulatory factors for the specific system in oleaginous yeast. In the inferred model, only 6 genes among the selected 14 genes were considered to affect the volume of oil accumulation in a closed and specific system. Our method will be useful to artificially control cell systems in the bioproduction and biotechnology fields.

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Metric All-k-Nearest-Neighbor Search

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Metric All-k-Nearest-Neighbor Search

作者： Chen, Lu Gao, Yunjun Chen, Gang Zhang, Haida College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China

An all-k-nearest-neighbor (AkNN) query finds from a given object set O, k nearest neighbors for each object in a specified query set Q. This operation is common in many applications such as GIS, data mining, and image analysis. Although it has received much attention in the Euclidean space, there is little prior work on the metric space. In this paper, we study the problem of AkNN retrieval in metric spaces, termed metric AkNN (MAkNN) search, and propose efficient algorithms for supporting MAk NN queries with arbitrary k value. Our methods utilize dynamic disk-based metric indexes (e.g., M-tree), employ a series of pruning rules, take advantage of grouping, reuse, pre-processing, and progressive pruning techniques, require no detailed representations of objects, and can be applied as long as the distance metric satisfies the triangle inequality. In addition, we extend our approaches to tackle metric self-AkNN (MSAkNN) search, a natural variation of MAkNN queries, where the query set Q is identical to the object set O. Extensive experiments using both real and synthetic data sets demonstrate, compared with state-of-the-art euclidean Ak NN, MAkNN, and MSAkNN algorithms, the performance of our proposed algorithms and the effectiveness of our presented techniques. © 2015 IEEE.

关键词： Nearest neighbor search

Advancing image understanding in poor visibility environments: A collective benchmark study

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Orderness predicts academic performance: Behavioral analysis on campus lifestyle

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arXiv 2017年

作者： Cao, Yi Gao, Jian Lian, Defu Rong, Zhihai Shi, Jiatu Wang, Qing Wu, Yifan Yao, Huaxiu Zhou, Tao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China Chengdu611731 China

Quantitative understanding of relationships between students' behavioral patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that mainly based on questionnaire surveys, in this paper, we collect behavioral records from 18,960 undergraduate students' smart cards and propose a novel metric, called orderness, which measures the regularity of campus daily life (e.g., meals and showers) of each student. Empirical analysis demonstrates that academic performance (GPA) is strongly correlated with orderness. Furthermore, we show that orderness is an important feature to predict academic performance, which remarkably improves the prediction accuracy even at the presence of students' diligence. Based on these analyses, education administrators could better guide students' campus lives and implement effective interventions in an early stage when necessary. Copyright © 2017, The Authors. All rights reserved.

关键词： Students

data integration and visualisation for demanding marine operations

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Data integration and visualisation for demanding marine oper...

OCEANS 2016 - Shanghai

作者： Wang, Hao Zhuge, Xu Strazdins, Girts Wei, Zheng Li, Guoyuan Zhang, Houxiang Big Data Lab Faculty of Engineering and Natural Sciences Norwegian Unversity of Science and Technology in Aalesund Postboks 1517 Aalesund6025 Norway Mechatronics Lab Faculty of Maritime Technology and Operations Norwegian Unversity of Science and Technology in Aalesund Norway

ISBN: (纸本)9781467397247

Marine operations face the major challenge of increased complexity. Much more data are being collected for diagnostic and monitoring purposes with sensors on board a vessel. However, due to the lack of easy data presentation and interaction, the crew members are overwhelmed by the large volume of information and alert messages, causing serious information overload problems. In this paper, we explore data integration and visualisation techniques for maritime operations and present a proof-of-concept prototype using real offline data from our industrial collaborators. This work is an important part of our initial efforts towards a visual analytics framework for maritime operations. © 2016 IEEE.

关键词： data integration

The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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