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arXiv 2021年

作者： Knowles, Kenda Cotton, W.D. Rudnick, L. Camilo, F. Goedhart, S. Deane, R. Ramatsoku, M. Bietenholz, M.F. Brüggen, M. Button, C. Chen, H. Chibueze, J.O. Clarke, T.E. de Gasperin, F. Ianjamasimanana, R. Józsa, G.I.G. Hilton, M. Kesebonye, K.C. Kolokythas, K. Kraan-Korteweg, R.C. Lawrie, G. Lochner, M. Loubser, S.I. Marchegiani, P. Mhlahlo, N. Moodley, K. Murphy, E. Namumba, B. Oozeer, N. Parekh, V. Pillay, D.S. Passmoor, S.S. Ramaila, A.J.T. Ranchod, S. Retana-Montenegro, E. Sebokolodi, L. Sikhosana, S.P. Smirnov, O. Thorat, K. Venturi, T. Abbott, T.D. Adam, R.M. Adams, G. Aldera, M.A. Bauermeister, E.F. Bennett, T.G.H. Bode, W.A. Botha, D.H. Botha, A.G. Brederode, L.R.S. Buchner, S. Burger, J.P. Cheetham, T. de Villiers, D.I.L. Dikgale-Mahlakoana, M.A. du Toit, L.J. Esterhuyse, S.W.P. Fadana, G. Fanaroff, B.L. Fataar, S. Foley, A.R. Fourie, D.J. Frank, B.S. Gamatham, R.R.G. Gatsi, T.G. Geyer, M. Gouws, M. Gumede, S.C. Heywood, I. Hlakola, M.J. Hokwana, A. Hoosen, S.W. Horn, D.M. Horrell, J.M.G. Hugo, B.V. Isaacson, A.R. Jonas, J.L. Jordaan, J.D.B. Joubert, A.F. Julie, R.P.M. Kapp, F.B. Kasper, V.A. Kenyon, J.S. Kotzé, P.P.A. Kotze, A.G. Kriek, N. Kriel, H. Krishnan, V.K. Kusel, T.W. Legodi, L.S. Lehmensiek, R. Liebenberg, D. Lord, R.T. Lunsky, B.M. Madisa, K. Magnus, L.G. Main, J.P.L. Makhaba, A. Makhathini, S. Malan, J.A. Manley, J.R. Marais, S.J. Maree, M.D.J. Martens, A. Mauch, T. McAlpine, K. Merry, B.C. Millenaar, R.P. Mokone, O.J. Monama, T.E. Mphego, M.C. New, W.S. Ngcebetsha, B. Ngoasheng, K.J. Ockards, M.T. Otto, A.J. Patel, A.A. Peens-Hough, A. Perkins, S.J. Ramanujam, N.M. Ramudzuli, Z.R. Ratcliffe, S.M. Renil, R. Robyntjies, A. Rust, A.N. Salie, S. Sambu, N. Schollar, C.T.G. Schwardt, L.C. Schwartz, R.L. Serylak, M. Siebrits, R. Sirothia, S.K. Slabber, M. Sofeya, L. Taljaard, B. Tasse, C. Tiplady, A.J. Toruvanda, O. Twum, S.N. van Balla, T.J. van der Byl, A. van der Merwe, C. van Dyk, C.L. van Tonder, V. van Wyk, R. Venter, A.J. Venter, M. Welz, M.G. Williams, L.P. Xaia, B. Astrophysics Research Centre University of KwaZulu-Natal Durban4041 South Africa Centre for Radio Astronomy Techniques and Technologies Department of Physics and Electronics Rhodes University P.O. Box 94 Makhanda6140 South Africa South African Radio Astronomy Observatory 2 Fir Street Observatory7925 South Africa National Radio Astronomy Observatory CharlottesvilleVA22903 United States Minnesota Institute for Astrophysics University of Minnesota 116 Church St SE MinneapolisMN55455 United States Wits Centre for Astrophysics School of Physics University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg2050 South Africa Department of Physics University of Pretoria Hatfield0028 South Africa I-09047 Italy Hartebeesthoek Radio Astronomy Observatory SARAO Krugersdorp1740 South Africa York University TorontoON Canada Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany Department of Astronomy University of Cape Town Rondebosch7700 South Africa Centre for Space Research North-West University Potchefstroom2520 South Africa Department of Physics and Astronomy Faculty of Physical Sciences University of Nigeria Carver Building 1 University Road Nsukka Nigeria Naval Research Laboratory WashingtonDC20375 United States INAF – Istituto di Radioastronomia via Gobetti 101 BolognaI–40129 Italy Argelander-Institut für Astronomie Universität Bonn Auf dem Hügel 71 BonnD-53121 Germany School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville 3696 South Africa Department of Physics and Astronomy University of the Western Cape Bellville7535 South Africa Dipartimento di Fisica Universitá Sapienza P.le Aldo Moro 2 Roma00185 Italy African Institute for Mathematical Sciences 6-8 Melrose Road Muizenberg7945 South Africa Inter-University Institute for Data Intensive Astronomy University of Cape Town Rondebosch7700 South Africa Ltd. 64-74 White Road Retreat 7945 South Africa EMSS Ant

MeerKAT’s large number (64) of 13.5 m-diameter antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900–1670 MHz) observations of 115 galaxy clusters, observed for ∼ 6–10 hours each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at ∼ 800 resolution, and enhanced spectral and polarisation image cubes at ∼ 800 and 1500 resolutions. Typical sensitivities for the full-resolution MGCLS image products range over ∼ 3–5 µJy beam−1. The basic cubes are full-field and span 2◦ × 2◦. The enhanced products consist of the inner 1.2◦ × 1.2◦ field of view, corrected for the primary beam. The survey is fully sensitive to structures up to ∼ 100 scales and the wide bandwidth allows spectral and Faraday rotation mapping. Relatively narrow frequency channels (209 kHz) are also used to provide H i mapping in windows of 0 200. We find no dependence of the star formation rate on distance from the cluster centre, and observe a small excess of the radio-to-100 µm flux ratio towards the centre of Abell 209 that may reflect a ram pressure enhancement in the denser environment. We detect diffuse cluster radio emission in 62 of the surveyed systems and present a catalogue of the 99 diffuse cluster emission structures, of which 56 are new. These include mini-halos, halos, relics, and other diffuse structures for which no suitable characterization currently exists. We highlight some of the radio galaxies which challenge current paradigms such as trident-shaped structures, jets that remain well-collimated far beyond their bending radius, and filamentary features linked to radio galaxies that likely illuminate magnetic flux tubes in the intracluster medium. We also present early results from

关键词： Surveys

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IEC 61850 Substation Configuration Language as a basis for Automated Security and SDN Configuration

IEC 61850 Substation Configuration Language as a basis for A...

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IEEE Power and Energy Society General Meeting

作者： John O'Raw David M. Laverty D. John Morrow Department of Computing Letterkenny Institute of Technology Letterkenny Ireland School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Northern Ireland

ISBN: (纸本)9781538622131;9781538622124

IEC61850 has revolutionized the way substations are configured and maintained. Substation Configuration Language (SCL) defines the parameters needed to configure individual devices and combine them into a working system. Security is implemented by IEC62351 and there are potential vulnerabilities. Best practice recommendations are for defense in depth. SCL contains sufficient information to auto-configure network equipment, firewalls, IDS and SDN based networks.

关键词： Substations Security Protocols IEC Standards Switches Redundancy

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Approximate inference for constructing astronomical catalogs from images

arXiv

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arXiv 2018年

作者： Regier, Jeffrey Miller, Andrew C. Schlegel, David Adams, Ryan P. McAuliffe, Jon D. Prabhat Department of Electrical Engineering and Computer Sciences University of California Berkeley 465 Soda Hall BerkeleyCA94720 United States Data Science Institute Columbia University 3227 Broadway New YorkNY10027 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Princeton University Department of Computer Science 35 Olden Street PrincetonNJ08540 United States Department of Statistics University of California Berkeley 367 Evans Hall BerkeleyCA94720 United States Voleon Group

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical *** Codes 62P35 Copyright © 2018, The Authors. All rights reserved.

关键词： Supercomputers

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Hybrid metal-insulator-metal structures on Si nanowires array for surface enhanced Raman scattering

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光电工程 2017年第2期44卷 185-191页

作者： Kaichen Xu Chentao Zhang Tzu Hsiao Lu Puqun Wang Rui Zhou Rong Ji Minghui Hong Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117576 Singapore Data Storage Institute (A'STAR) Agency for Science Technology and Research 2 Fusionopolis Way 138634 Singapore Department of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China

Surface enhanced Raman scattering(SERS)is an efficient technique to detect low concentration *** this work,periodical silicon nanowires(Si NWs)integrated with metal-insulator-metal(MIM)layers are employed as SERS *** interference lithography(LIL)combined with reactive ion etching(RIE)is used to fabricate large-area periodic nanostructures,followed by decorating the MIM *** to MIM disks array on Si surface,the SERS enhancement factor(EF)of the MIM structures on the Si NWs array can be increased up to 5 times,which is attributed to the enhanced electric field at the boundary of the MIM ***,high density of nanoparticles and nanogaps serving as hot spots on sidewall surfaces also contribute to the enhanced SERS *** changing the thickness of the insulator layer,the plasmonic resonance can be tuned,which provides a new localized surface plasmon resonance(LSPR)characteristic for SERS applications.

关键词： surface-enhanced Raman scattering nanostructure fabrication plasmonics metal-insulator-metal

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Erratum to: Measurement of differential cross sections and W+/W− cross-section ratios for W boson production in association with jets at = 8 TeV with the ATLAS detector

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Journal of High Energy Physics 2020年第10期2020卷 1-20页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Bakker, P. J. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska-Blenessy, Z. Baroncelli, A. Bar Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene United States of America Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas — LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston United States of America Joint Institute for Nuclear Research JINR Dubna Dubna Russia IN

Two additions impacting tables 3 and 4 in ref. [1] are presented in the following. No significant impact is found for other results or figures in ref. [1].

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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Asynchronous parallel empirical variance guided algorithms for the thresholding bandit problem

arXiv

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arXiv 2017年

作者： Zhong, Jie Huang, Yijun Liu, Ji Department of Mathematics University of Rochester United States Department of Computer Science University of Rochester Singapore Department of Electrical Computer Engineering University of Rochester United States Goergen Institute for Data Science University of Rochester United States

This paper considers the multi-armed thresholding bandit problem – identifying all arms whose expected rewards are above a predefined threshold via as few pulls (or rounds) as possible – proposed by Locatelli et al. (2016) recently. Although the proposed algorithm in Locatelli et al. (2016) achieves the optimal round complexity1 in a certain sense, there still remain unsolved issues. This paper proposes an asynchronous parallel thresholding algorithm and its parameter-free version to improve the efficiency and the applicability. On one hand, the proposed two algorithms use the empirical variance to guide the pull decision at each round, and significantly improve the round complexity of the "optimal" algorithm when all arms have bounded high order moments. The proposed algorithms can be proven to be optimal. On the other hand, most bandit algorithms assume that the reward can be observed immediately after the pull or the next decision would not be made before all rewards are observed. Our proposed asynchronous parallel algorithms allow making the choice of the next pull with unobserved rewards from earlier pulls, which avoids such an unrealistic assumption and significantly improves the identification process. Our theoretical analysis justifies the effectiveness and the efficiency of proposed asynchronous parallel algorithms. The empirical study is also provided to validate the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Efficiency

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Efficient gravitational-wave glitch identification from environmental data through machine learning

arXiv

引用

arXiv 2019年

作者： Colgan, Robert E. Corley, K. Rainer Lau, Yenson Bartos, Imre Wright, John N. Márka, Zsuzsa Márka, Szabolcs Department of Computer Science Columbia University in the City of New York 500 W. 120th St. New YorkNY10027 United States Data Science Institute Columbia University in the City of New York 550 W. 120th St. New YorkNY10027 United States Department of Physics Columbia University in the City of New York 538 W. 120th St. New YorkNY10027 United States Columbia Astrophysics Laboratory Columbia University in the City of New York 538 W. 120th St. New YorkNY10027 United States Department of Electrical Engineering Columbia University in the City of New York 500 W. 120th St. New YorkNY10027 United States Department of Physics University of Florida PO Box 118440 GainesvilleFL32611-8440 United States

The LIGO observatories detect_ gravitational waves through monitoring changes in the detectors’ length down to below 10−19 m/√Hz variation—a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance in the gravitational wave signal of brief, loud noise artifacts called "glitches," which are environmental or instrumental in origin but can mimic true gravitational waves and therefore hinder sensitivity. Currently they are primarily identified by analysis of the gravitational wave data stream. Here we present a machine learning approach that can identify glitches by monitoring all environmental and detector data channels, a task that has not previously been pursued due to its scale and the number of degrees of freedom within gravitational-wave detectors. The presented method is capable of reducing the gravitational-wave detector network’s false alarm rate and improving the LIGO instruments, consequently enhancing detection confidence. Copyright © 2019, The Authors. All rights reserved.

关键词： Gravitational effects

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Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

arXiv

引用

arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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Large-Capacity Three-Party Quantum Digital Secret Sharing Using Three Particular Matrices Coding

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Communications in Theoretical Physics 2016年第11期65卷 501-508页

作者：赖红罗明星 Josef Pieprzyk 陶丽刘志明 Mehmet A.Orgun School of Computer and Information Science Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences Department of Computing Macquarie University Faculty of Information Technology Macao University of Science and Technology

In this paper, we develop a large-capacity quantum digital secret sharing(QDSS) scheme, combined the Fibonacci- and Lucas-valued orbital angular momentum(OAM) entanglement with the recursive Fibonacci and Lucas matrices. To be exact, Alice prepares pairs of photons in the Fibonacci- and Lucas-valued OAM entangled states, and then allocates them to two participants, say, Bob and Charlie, to establish the secret key. Moreover, the available Fibonacci and Lucas values from the matching entangled states are used as the seed for generating the Fibonacci and Lucas matrices. This is achieved because the entries of the Fibonacci and Lucas matrices are recursive. The secret key can only be obtained jointly by Bob and Charlie, who can further recover the secret. Its security is based on the facts that nonorthogonal states are indistinguishable, and Bob or Charlie detects a Fibonacci number, there is still a twofold uncertainty for Charlie'(Bob') detected value.

关键词： Fibonacci-and Lucas-valued orbital angular momentum(OAM) states the first kind of Chebyshev maps lower error rates longer destances

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