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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

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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

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

arXiv

引用

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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Unsupervised feature learning from time series 25

Unsupervised feature learning from time series

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Qin, Zhang Wu, Jia Hong, Yang Tian, Yingjie Chengqi, Zhang Quantum Computation and Intelligent Systems Centre University of Technology Sydney Australia Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China MathWorks Beijing China

In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms searchbased algorithms on real-world time series data.

关键词： Time series

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An Integrated Tone Mapping for High Dynamic Range Image Visualization

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Journal of Physics: Conference Series 2018年第1期960卷

作者： Lei Liang Jeng-Shyang Pan Yongjun Zhuang Department of Computer Science and Technology Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Qihan Research Qihan Technology Co. LTD Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Techology Fujian China

There are two type tone mapping operators for high dynamic range (HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

关键词：

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Extortion provides alternative routes to the evolution of cooperation in structured populations

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Physical Review E 2017年第5期95卷 052302-052302页

作者： Xiongrui Xu Zhihai Rong Zhi-Xi Wu Tao Zhou Chi Kong Tse CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 People's Republic of China

In this paper, we study the evolution of cooperation in structured populations (individuals are located on either a regular lattice or a scale-free network) in the context of repeated games by involving three types of strategies, namely, unconditional cooperation, unconditional defection, and extortion. The strategy updating of the players is ruled by the replicator-like dynamics. We find that extortion strategies can act as catalysts to promote the emergence of cooperation in structured populations via different mechanisms. Specifically, on regular lattice, extortioners behave as both a shield, which can enwrap cooperators inside and keep them away from defectors, and a spear, which can defeat those surrounding defectors with the help of the neighboring cooperators. Particularly, the enhancement of cooperation displays a resonance-like behavior, suggesting the existence of optimal extortion strength mostly favoring the evolution of cooperation, which is in good agreement with the predictions from the generalized mean-field approximation theory. On scale-free network, the hubs, who are likely occupied by extortioners or defectors at the very beginning, are then prone to be conquered by cooperators on small-degree nodes as time elapses, thus establishing a bottom-up mechanism for the emergence and maintenance of cooperation.

关键词： Population dynamics Self-organized systems Social systems

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A New Evaluation System for Scholars and Majors Based on big-data Techniques

A New Evaluation System for Scholars and Majors Based on Big...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Wenhua Yu Lei Zhao Xiangyu He Jiacheng Zhou Tong Cheng Chengzhao Xue Fan Yang Jiangsu Big-Data Key Lab for Education Science and Engineering Xuzhou China

ISBN: (纸本)9781509036837

A good scholar evaluation system is very important for students to select advisors and majors and for government to get a good policy of the educational resource. The factor of scholars should be the most important parameter in the various college rankings, however, it seems not appear in the college ranking since it is difficult to evaluate it. In this paper, we propose a new evaluation system for the evaluation of scholar co-author network and citation chain based on big-data technique. The evaluation results are very close to the survey in the professional area.

关键词： Google Electromagnetic fields Microwave theory and techniques Education Couplings data mining Companies

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A Novel Practical Method To Classify Scholars Based On Research Subjects

A Novel Practical Method To Classify Scholars Based On Resea...

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IEEE International Conference on Ubiquitous Intelligence and Computing

作者： Wenhua Yu Xiangyu He Tongfei Gao Lei Zhao Xiaoling Yang Jiangsu Big-Data Key Lab for Education Science and Technology Jiangsu Normal University

ISBN: (纸本)9781509027729

we develop a practical technique in this paper to classify the scholars in different disciplines and organizations according to their research interests. The scholar classification is important to the scholars, research organizations, and government for research, evaluation and education and research resource allocation. It becomes really difficult because name abbreviation, interdisciplinary, especially tautonym for Chinese scholars. The scholar classification can be achieved by the publications and journals that they published with and keywords in their publications using big-data techniques.

关键词： big data Scholar classification data mining Machine learning

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Gaia data Release 3: Astrophysical parameters inference system (Apsis) I - methods and content overview

arXiv

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arXiv 2022年

作者： Creevey, Orlagh L. Sordo, R. Pailler, F. Frémat, Y. Heiter, U. Thévenin, F. Andrae, R. Fouesneau, M. Lobel, A. Bailer-Jones, C.A.L. Garabato, D. Bellas-Velidis, I. Brugaletta, E. Lorca, A. Ordenovic, C. Palicio, P.A. Sarro, L.M. Delchambre, L. Drimmel, R. Rybizki, J. Torralba Elipe, G. Korn, A.J. Recio-Blanco, A. Schultheis, M.S. De Angeli, F. Montegriffo, P. Abreu Aramburu, A. Accart, S. Álvarez, M.A. Bakker, J. Brouillet, N. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Dapergolas, A. de Laverny, P. Dharmawardena, T.E. Edvardsson, B. Le Fustec, Y. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Jean-Antoine Piccolo, A. Kontizas, M. Kordopatis, G. Lanzafame, A.C. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Magdaleno Romeo, A. Manteiga, M. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Poggio, E. Riclet, F. Robin, C. Santoveña, R. Silvelo, A. Slezak, I. Smart, R.L. Soubiran, C. Süveges, M. Ulla, A. Utrilla, E. Vallenari, A. Zhao, H. Zorec, J. Barrado, D. Bijaoui, A. Bouret, J.-C. Blomme, R. Brott, I. Cassisi, S. Kochukhov, O. Martayan, C. Shulyak, D. Silvester, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Cedex 4 Nice06304 France INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Cedex 9 Toulouse31401 France 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 Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Piero Gobetti 93/3 Bologna40129 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Cedex 9 Toulouse31401 France Dpto. de Matemática Aplicada y C

Gaia data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters inference system (Apsis) within the Gaia data Processing and Analysis Consortium. The aim of this paper is to describe the overall content of the astrophysical parameters in Gaia DR3 and how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra along with astrometry and photometry, and we derive the following parameters: source classification and probabilities for 1.6 billion objects, interstellar medium characterisation and distances for up to 470 million sources, including a 2D total Galactic extinction map, 6 million redshifts of quasar candidates and 1.4 million redshifts of galaxy candidates, along with an analysis of 50 million outlier sources through an unsupervised classification. The astrophysical parameters also include many stellar spectroscopic and evolutionary parameters for up to 470 million sources. 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 (up to 5 million), diffuse interstellar band analysis (0.5 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). This paper is part of a series of three papers: this Paper I focusses on describing the global content of the parameters in Gaia DR3. The accompanying papers II and III focus on the validation and use of the stellar and non-stellar products, respectively. This catalogue is the most extensive homogeneous database of astrophysical parameters to date, and it is based uniquely on Gaia data. It will only be superseded in Gaia data Release 4. This catalogue will therefore remain a key reference over the next four years, providing astrophysical parameters independ

关键词： Stars

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

引用

NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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