检索结果-内蒙古大学图书馆

arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Deep Learning Algorithms-based Object Detection and Localization Revisited

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Journal of Physics: Conference Series 2021年第1期1892卷

作者： Safa Riyadh Waheed Norhaida Mohd Suaib Mohd Shafry Mohd Rahim Myasar Mundher Adnan A. A. Salim Big Data Center & Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq UTM-IRDA Digital Media Institute of Human-Centred Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Islamic University Najaf Iraq Laser Center & Physics Department Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV.

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Complete edge-colored permutation graphs

arXiv

引用

arXiv 2020年

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Institute for Theoretical Computer Science Universität zu Lübeck Ratzeburger Allee 160 LübeckD-23562 Germany Swarm Intelligence and Complex Systems Group Faculty of Mathematics and Computer Science University of Leipzig Augustusplatz 10 LeipzigD-04109 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

We introduce the concept of complete edge-colored permutation graphs as complete graphs that are the edge-disjoint union of "classical" permutation graphs. We show that a graph G = (V, E) is a complete edge-colored permutation graph if and only if each monochromatic subgraph of G is a "classical" permutation graph and G does not contain a triangle with 3 different colors. Using the modular decomposition as a framework we demonstrate that complete edge-colored permutation graphs are characterized in terms of their strong prime modules, which induce also complete edge-colored permutation graphs. This leads to an O(|V |2)-time recognition algorithm. We show, moreover, that complete edge-colored permutation graphs form a superclass of so-called symbolic ultrametrics and that the coloring of such graphs is always a Gallai coloring. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency

arXiv

引用

arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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Stochastic modified equations and dynamics of stochastic gradient algorithms I: mathematical foundations

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2019年第1期20卷

作者： Qianxiao Li Cheng Tai E. Weinan Institute of High Performance Computing Agency for Science Technology and Research Connexis North Singapore Beijing Institute of Big Data Research and Peking University Beijing China Princeton University Princeton NJ and Beijing Institute of Big Data Research Beijing China

We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is approximated by a class of stochastic differential equations with small noise parameters. We prove that this approximation can be understood mathematically as an weak approximation, which leads to a number of precise and useful results on the approximations of stochastic gradient descent (SGD), momentum SGD and stochastic Nesterov's accelerated gradient method in the general setting of stochastic objectives. We also demonstrate through explicit calculations that this continuous-time approach can uncover important analytical insights into the stochastic gradient algorithms under consideration that may not be easy to obtain in a purely discrete-time setting.

关键词： Nesterov's accelerated gradient modified equations momentum stochastic differential equations stochastic gradient algorithms

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2016 UU121: An Active Asteroid Discovery via AI-enhanced Citizen science

引用

Research Notes of the AAS 2024年第2期8卷 51-51页

作者： Nima Sedaghat Colin Orion Chandler William J. Oldroyd Chadwick A. Trujillo William A. Burris Henry H. Hsieh Jay K. Kueny Kennedy A. Farrell Jarod A. DeSpain Mark Jesus Mendoza Magbanua Scott S. Sheppard Michele T. Mazzucato Milton K. D. Bosch Tiffany Shaw-Diaz Virgilio Gonano Al Lamperti José A. da Silva Campos Brian L. Goodwin Ivan A. Terentev Charles J. A. Dukes Dept. of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave NE Seattle WA 98195 USA nimaseda@uw.edu The Raw Data Speaks Initiative LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue Tucson AZ 85721 USA Dept. of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei 10617 Taiwan University of Arizona Dept. of Astronomy and Steward Observatory 933 North Cherry Avenue Rm. N204 Tucson AZ 85721 USA Wyant College of Optical Sciences University of Arizona 1630 E. University Blvd. Tucson AZ 85721 USA Dept. of Laboratory Medicine University of California San Francisco 2340 Sutter Street San Francisco CA 94143 USA Dept. of Terrestrial Magnetism Carnegie Institution for Science 5241 Broad Branch Road. NW Washington DC 20015 USA Active Asteroids Citizen Scientist Royal Astronomical Society Burlington House Piccadilly London W1J 0BQ UK Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 53100 Siena Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive Royersford PA 19468 USA Astronomical Society of Southern Africa P.O. Box 9 Observatory 7935 Cape Town South Africa

We report the discovery of an active asteroid, 2016 UU121, for the first time via artificial intelligence-enhanced classification, informed by our NASA Partner program Active Asteroids, a Citizen science project hoste...

We report the discovery of an active asteroid, 2016 UU₁₂₁, for the first time via artificial intelligence-enhanced classification, informed by our NASA Partner program Active Asteroids, a Citizen science project hosted on the Zooniverse platform. The early version of our deep neural network, TailNet, identified potential activity associated with 2016 UU₁₂₁ in 40 Dark Energy Camera (DECam) images from UT 2021 September 10 to 11. The discovery was vetted and confirmed by our Active Asteroids core science team. In total, 66 DECam images of this object showed clear activity in the form of a tail. 2016 UU₁₂₁ has a Tisserand parameter with respect to Jupiter of 3.161, thus we classify the object as an active asteroid. Moreover, the activity occurred near perihelion, so 2016 UU₁₂₁ is also a candidate Main-belt comet.

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Ordered magnetic fields around the 3C 84 central black hole

引用

Astronomy and Astrophysics 2024年 682卷

作者： Paraschos, G.F. Kim, J.-Y. Wielgus, M. Röder, J. Krichbaum, T.P. Ros, E. Agudo, I. Myserlis, I. Moscibrodzka, M. Traianou, E. Zensus, J.A. Blackburn, L. Chan, C.-K. Issaoun, S. Janssen, M. Johnson, M.D. Fish, V.L. Akiyama, K. Alberdi, A. Alef, W. Algaba, J.C. Anantua, R. Asada, K. Azulay, R. Bach, U. Baczko, A.-K. Ball, D. Baloković, M. Barrett, J. Bauböck, M. Benson, B.A. Bintley, D. Blundell, R. Bouman, K.L. Bower, G.C. Boyce, H. Bremer, M. Brinkerink, C.D. Brissenden, R. Britzen, S. Broderick, A.E. Broguiere, D. Bronzwaer, T. Bustamante, S. Byun, D.-Y. Carlstrom, J.E. Ceccobello, C. Chael, A. Chang, D.O. Chatterjee, K. Chatterjee, S. Chen, M.T. Chen, Y. Cheng, X. Cho, I. Christian, P. Conroy, N.S. Conway, J.E. Cordes, J.M. Crawford, T.M. Crew, G.B. Cruz-Osorio, A. Cui, Y. Dahale, R. Davelaar, J. De Laurentis, M. Deane, R. Dempsey, J. Desvignes, G. Dexter, J. Dhruv, V. Doeleman, S.S. Dougal, S. Dzib, S.A. Eatough, R.P. Emami, R. Falcke, H. Farah, J. Fomalont, E. Ford, H.A. Foschi, M. Fraga-Encinas, R. Freeman, W.T. Friberg, P. Fromm, C.M. Fuentes, A. Galison, P. Gammie, C.F. García, R. Gentaz, O. Georgiev, B. Goddi, C. Gold, R. Gómez-Ruiz, A.I. Gómez, J.L. Gu, M. Gurwell, M. Hada, K. Haggard, D. Haworth, K. Hecht, M.H. Hesper, R. Heumann, D. Ho, L.C. Ho, P. Honma, M. Huang, C.L. Huang, L. Hughes, D.H. Ikeda, S. Impellizzeri, C.M.V. Inoue, M. James, D.J. Jannuzi, B.T. Jeter, B. Jaing, W. Jiménez-Rosales, A. Jorstad, S. Joshi, A.V. Jung, T. Karami, M. Karuppusamy, R. Kawashima, T. Keating, G.K. Kettenis, M. Kim, D.-J. Kim, J. Kim, J. Kino, M. Koay, J.Y. Kocherlakota, P. Kofuji, Y. Koch, P.M. Koyama, S. Kramer, C. Kramer, J.A. Kramer, M. Kuo, C.-Y. Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Department of Astronomy and Atmospheric Sciences Kyungpook National University Daegu702-701 Korea Republic of Institute of Physics Silesian University in Opava Bezručovo nám. 13 Opava746 01 Czech Republic Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Avenida Divina Pastora 7 Local 20 Granada18012 Spain Radboud University PO Box 9010 Nijmegen6500 GL Netherlands Black Hole Initiative at Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard and Smithsonian 60 Garden Street CambridgeMA02138 United States Steward Observatory and Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Nasa Hubble Fellowship Program Einstein Fellow United States Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Department of Physics and Astronomy The University of Texas at San Antonio One Utsa Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot València46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 Paterna València46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory Onsala43992 Sweden Yale Center for

Context. 3C 84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of very-long-baseline interferometry (VLBI) above the hitherto available maximum frequency of 86 GHz. Aims. Using ultrahigh resolution VLBI observations at the currently highest available frequency of 228 GHz, we aim to perform a direct detection of compact structures and understand the physical conditions in the compact region of 3C 84. Methods. We used Event Horizon Telescope (EHT) 228 GHz observations and, given the limited (u, v)-coverage, applied geometric model fitting to the data. Furthermore, we employed quasi-simultaneously observed, ancillary multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. Results. We report the detection of a highly ordered, strong magnetic field around the central, supermassive black hole of 3C 84. The brightness temperature analysis suggests that the system is in equipartition. We also determined a turnover frequency of νm = (113 ± 4) GHz, a corresponding synchrotron self-absorbed magnetic field of BSSA = (2.9 ± 1.6) G, and an equipartition magnetic field of Beq = (5.2 ± 0.6) G. Three components are resolved with the highest fractional polarisation detected for this object (mnet = (17.0 ± 3.9)%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228 GHz. We used these findings to test existing models of jet formation, propagation, and Faraday rotation in 3C 84. Conclusions. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C 84. However,

关键词： Magnetic fields

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Coarse to fine: Diffusing categories in wikipedia 26

Coarse to fine: Diffusing categories in wikipedia

引用

26th International World Wide Web Conference, WWW 2017 Companion

作者： Cai, Pengshan Feng, Yansong Jia, Yantao Wang, Yuanzhuo Jin, Xiaolong Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781450349147

Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarse-grained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Taxonomies

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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