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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Direct evidence for shock-powered optical emission in a nova

arXiv

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arXiv 2020年

作者： Aydi, Elias Sokolovsky, Kirill V. Chomiuk, Laura Steinberg, Elad Li, Kwan Lok Vurm, Indrek Metzger, Brian D. Strader, Jay Mukai, Koji Pejcha, Ondřej Shen, Ken J. Wade, Gregg A. Kuschnig, Rainer Moffat, Anthony F.J. Pablo, Herbert Pigulski, Andrzej Popowicz, Adam Weiss, Werner Zwintz, Konstanze Izzo, Luca Pollard, Karen R. Handler, Gerald Ryder, Stuart D. Filipović, Miroslav D. Alsaberi, Rami Z.E. Manojlović, Perica de Oliveira, Raimundo Lopes Walter, Frederick M. Vallely, Patrick J. Buckley, David A.H. Brown, Michael J.I. Harvey, Eamonn J. Kawash, Adam Kniazev, Alexei Kochanek, Christopher S. Linford, Justin Mikolajewska, Joanna Molaro, Paolo Orio, Marina Page, Kim L. Shappee, Benjamin J. Sokoloski, Jennifer L. Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Sternberg Astronomical Institute Moscow State University Universitetskii pr. 13 Moscow119992 Russia Astro Space Center of Lebedev Physical Institute Profsoyuznaya St. 84/32 Moscow117997 Russia Columbia Astrophysics Laboratory Department of Physics Columbia University New YorkNY10027 United States Racah Institute of Physics Hebrew University Jerusalem91904 Israel Department of Physics UNIST Ulsan44919 Korea Republic of Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Tartu Observatory University of Tartu Tõravere Tartumaa61602 Estonia CRESST X-ray Astrophysics Laboratory NASA GSFC GreenbeltMD20771 United States Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute of Theoretical Physics Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Astronomy Theoretical Astrophysics Center University of California BerkeleyCA94720 United States Department of Physics and Space Science Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 7B4 Canada Institute of Communication Networks and Satellite Communications Graz University of Technology Infeldgasse 12 Graz8010 Austria Dépt. de Physique Univ. De Montréal C.P. 6128 Succ. Centre-Ville Centre de Recherche en Astrophysique du Quéebec MontréealQCH3C 3J7 Canada AAVSO 49 Bay State Rd CambridgeMA02138 United States Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Silesian University of Technology Institute of Electronics Akademicka 16 Gliwice44-100 Poland Institute for Astrophysics University of Vienna Tuerkenschanzstrasse 17 ViennaA-1180 Austria Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25 InnsbruckA-6020 Austria D

Classical novae are thermonuclear explosions that occur on the surfaces of white dwarf stars in interacting binary systems1. It has long been thought that the luminosity of classical novae is powered by continued nuclear burning on the surface of the white dwarf after the initial runaway2. However, recent observations of GeV γrays from classical novae have hinted that shocks internal to the nova ejecta may dominate the nova emission. Shocks have also been suggested to power the luminosity of events as diverse as stellar mergers3, supernovae4, and tidal disruption events5, but observational confirmation has been lacking. Here we report simultaneous space-based optical and γ-ray observations of the 2018 nova V906 Carinae (ASASSN-18fv), revealing a remarkable series of distinct correlated flares in both bands. The optical and γ-ray flares occur simultaneously, implying a common origin in shocks. During the flares, the nova luminosity doubles, implying that the bulk of the luminosity is shock-powered. Furthermore, we detect concurrent but weak X-ray emission from deeply embedded shocks, confirming that the shock power does not appear in the X-ray band and supporting its emergence at longer wavelengths. Our data, spanning the spectrum from radio to γ-ray, provide direct evidence that shocks can power substantial luminosity in classical novae and other optical transients. Copyright © 2020, The Authors. All rights reserved.

关键词： Luminance

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A Search for Cosmic-ray Proton Anisotropy with the Fermi Large Area Telescope

arXiv

引用

arXiv 2019年

作者： Ajello, M. Baldini, L. Barbiellini, G. Bastieri, D. Bechtol, K. Bellazzini, R. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Brandt, T.J. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Costantin, D. Cuoco, A. Cutini, S. D’Ammando, F. de la Torre Luque, P. de Palma, F. Desai, A. Digel, S.W. Lalla, N. Di Venere, L. Di Domínguez, A. Fegan, S.J. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guiriec, S. Hayashi, K. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kuss, M. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Meehan, M. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Morselli, A. Negro, M. Nuss, E. Omodei, N. Orienti, M. Orlando, E. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Piron, F. Porter, T.A. Principe, G. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thayer, J.B. Torres, D.F. Troja, E. Vandenbroucke, J. Yassine, M. Zimmer, S. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy NASA Goddard Space Flight Center GreenbeltMD20771 United States Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Institut für Theoretische Physik and Astrophysik Universität Würzburg WürzburgD-97074 Germany NASA Goddard Space Flight Center GreenbeltMD20771 United States Italian Space Agency Via del Politecnico snc Roma00133 Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy Istituto Nazionale di Fisica Nucleare Sezione di Roma "Tor Vergata" RomaI-00133 Italy Space Science Data Center - Agenzia Spaziale Italiana Via del Politecnico snc RomaI-00133 Italy Labo

The Fermi Large Area Telescope (LAT) has amassed a large data set of primary cosmic-ray protons throughout its mission. The LAT’s wide field of view and full-sky survey capabilities make it an excellent instrument for studying cosmic-ray anisotropy. As a space-based survey instrument, the LAT is sensitive to anisotropy in both right ascension and declination, while ground-based observations only measure the anisotropy in right ascension. We present the results of the first ever proton anisotropy search using Fermi LAT. The data set uses eight years of data and consists of approximately 179 million protons above 78 GeV, enabling it to probe dipole anisotropy below an amplitude of 10−3, resulting in the most stringent limits on the declination dependence of the dipole to date. We measure a dipole amplitude δ = 3.9 ±1.5 ×10−4 with a p-value of 0.01 (pre-trials) for protons with a minimum energy of 78 GeV. We discuss various systematic effects that could give rise to a dipole excess and calculate upper limits on the dipole amplitude as a function of minimum energy. The 95% CL upper limit on the dipole amplitude is δUL = 1.3 × 10−3 for protons with a minimum energy of 78 GeV and δUL = 1.2 × 10−3 for protons with a minimum energy of 251 GeV. Copyright © 2019, The Authors. All rights reserved.

关键词： Anisotropy

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A decade of Gamma-Ray Bursts observed by Fermi-LAT:

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Bhat, P.N. Bissaldi, E. Blandford, R.D. Bonino, R. Bonnell, J. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Cheung, C.C. Chiaro, G. Ciprini, S. Costantin, D. Crnogorcevic, M. Cutini, S. Dainotti, M. D’Ammando, F. de la Torre Luque, P. de Palma, F. Desai, A. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grove, J.E. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kocevski, D. Kuss, M. Latronico, L. Li, J. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ohno, M. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poolakkil, S. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Reposeur, T. Ryde, F. Serini, D. Sgrò, C. Siskind, E.J. Sonbas, E. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Takahashi, M. Tak, D. Thayer, J.B. Torres, D.F. Troja, E. Valverde, J. Veres, P. Vianello, G. von Kienlin, A. Wood, K. Yassine, M. Zhu, S. Zimmer, S. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Kanazawa Ishikawa920-1192 Department of Physics and Oskar Klein Center for Cosmoparticle Physics Stockholm University Stockholm106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy University of Alabama in Huntsville HuntsvilleAL35899 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Astronomy University of Maryland College ParkMD20742 United States Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France

The Large Area Telescope (LAT) aboard the Fermi spacecraft routinely observes high-energy emission from gamma-ray bursts (GRBs). Here we present the second catalog of LAT-detected GRBs, covering the first 10 years of operations, from 2008 August 4 to 2018 August 4. A total of 186 GRBs are found;of these, 91 show emission in the range 30–100 MeV (17 of which are seen only in this band) and 169 are detected above 100 MeV. Most of these sources were discovered by other instruments (Fermi/GBM, Swift/BAT, AGILE, INTEGRAL) or reported by the Interplanetary Network (IPN);the LAT has independently triggered on 4 GRBs. This catalog presents the results for all 186 GRBs. We study onset, duration and temporal properties of each GRB, as well as spectral characteristics in the 100 MeV–100 GeV energy range. Particular attention is given to the photons with highest energy. Compared with the first LAT GRB catalog, our rate of detection is significantly improved. The results generally confirm the main findings of the first catalog: the LAT primarily detects the brightest GBM bursts, and the high-energy emission shows delayed onset as well as longer duration. However, in this work we find delays exceeding 1 ks, and several GRBs with durations over 10 ks. Furthermore, the larger number of LAT detections shows that these GRBs cover not only the high-fluence range of GBM-detected GRBs, but also samples lower fluences. In addition, the greater number of detected GRBs with redshift estimates allows us to study their properties in both the observer and rest frames. Comparison of the observational results with theoretical predictions reveals that no model is currently able to explain all results, highlighting the role of LAT observations in driving theoretical models. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

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An Analysis of Linear Algebra Teaching Problems and Solutions

An Analysis of Linear Algebra Teaching Problems and Solution...

引用

2015年人文学科和社会科学研究国际学术会议

作者： Jianming Liu College of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou Normal University

The paper aims at examining real mathematics teaching situations existing in local universities and colleges and exploring relative practical solutions to these problems. Based on a thorough study on mathematics teaching situations and related experts' research reports, the author's in-depth discussion with seasoned mathematics teachers and his own teaching experiences, the paper displays his advice on improving linear algebra teaching quality in higher educational system from three aspects: classroom teaching, exercise selection and explanation and after-class studying communication and guidance.

关键词： Mathematics teaching situations,Practical solutions,Linear algebra teaching quality

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Linear Dynamic Sparse Modelling for functional MR imaging

引用

Brain Informatics 2014年第1-4期1卷 11-18页

作者： Yan, Shulin Nie, Lei Wu, Chao Guo, Yike Data Science Institute Imperial College London London United Kingdom Institute of Computing Technology Chinese Academy of Sciences Beijing China

The reconstruction quality of a functional MRI sequence is determined by reconstruction algorithms as well as the information obtained from measurements. In this paper, we propose a Linear Dynamic Sparse Modelling method which is composed of measurement design and reconstruction processes to improve the image quality from both aspects. This method models an fMRI sequence as a linear dynamic sparse model which is based on a key assumption that variations of functional MR images are sparse over time in the wavelet domain. The Hierarchical Bayesian Kalman filter which follows the model is employed to implement the reconstruction process. To accomplish the measurement design process, we propose an Informative Measurement Design (IMD) method. The IMD method addresses the measurement design problem of selecting k feasible measurements such that the mutual information between the unknown image and measurements is maximised, where k is a given budget and the mutual information is extracted from the linear dynamic sparse model. The experimental results demonstrated that our proposed method succeeded in boosting the quality of functional MR images. © 2014, The Author(s).

关键词： Kalman filters

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Correlation-based Top-k Recommendation for Web Services

Correlation-based Top-k Recommendation for Web Services

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IEEE International Conference on Ubiquitous computing and Communications

作者： Qi Xie Zibin Zheng Lu Liu Mengtian Cui College of Computer Science and Technology Southwest University for Nationalities Chengdu China School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computing and Mathematics University of Derby Derby DE22 1GB United Kingdom

ISBN: (纸本)9781509001552

The growth and popularity of Web services make personalized recommendation as one of the most important research issues in service computing, which can help service users find the optimal Web services from large functionally-equivalent service candidates according to the Quality-of-Service (QoS). Although significant works have been done on improving prediction accuracy by employing collaborative filtering (CF) approaches, some of the well-known approaches ignore relationships among Web services in neighbor selection of recommendation which have an important impact on QoS prediction accuracy. Different from these works which simply rank the neighbors according to their similarity values, we propose a correlation-based top-k recommendation approach for Web services that leverages the correlation graph and corresponding Web service ranking matrix. Our approach aims to improve the process of finding k neighbors by using correlation among Web services instead of the similarity. Experiments are conducted by employing real-world Web service QoS data to evaluate the prediction accuracy of our approach. The experimental results demonstrate that our approach achieves better QoS prediction results than other competing approaches.

关键词： Web services Service recommendation QoS Collaborative filtering

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Risk Management Using Big Real Time data

Risk Management Using Big Real Time Data

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IEEE International Conference on Cloud computing Technology and science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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bandicoot: a python toolbox for mobile phone metadata

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Yves-Alexandre De Montjoye Luc Rocher Alex Sandy Pentland Google MPI for Intelligent Systems MIT Media Lab. Cambridge MA and Imperial College London Dept. of Computing and Data Science Institute London UK Université catholique de Louvain ICTEAM Louvain-la-Neuve Belgium MIT Media Lab. Cambridge Cambridge MA

bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license.

关键词： CDR feature engineering mobile phone metadata python visualization

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