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2016 International Conference on Innovations in science, engineering and Technology, ICISET 2016

作者： Uddin, Zia Torresen, Jim Jabid, Taskeed Department of Computer Education Sungkyunkwan University Seoul Korea Republic of Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Engineering Sogang University Seoul Korea Republic of

ISBN: (纸本)9781509061228

This paper proposes a novel approach for human activity recognition based on body part histograms and Hidden Markov Models. From a dept. video frame, body parts are segmented first using a trained random forest. Then, a histogram for each body part is combined to represent histogram features for a dept. image. The dept. video activity features are then applied on hidden Markov models for training and recognition. The proposed method was superior when compared with other conventional approaches. © 2016 IEEE.

关键词： Hidden Markov models

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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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Scalable exploration of relevance prospects to support decision making

Scalable exploration of relevance prospects to support decis...

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2016 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2016

作者： Verbert, Katrien Seipp, Karsten He, Chen Parra, Denis Wongchokprasitti, Chirayu Brusilovsky, Peter Department of Computer Science KU Leuven Leuven Belgium Dept. of Computer Science Pontificia Universidad Católica de Chile Santiago Chile Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States School of Information Sciences University of Pittsburgh PittsburghPA United States

Recent efforts in recommender systems research focus increasingly on human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency, and user control. In this paper, we present a scalable visualisation to interleave the output of several recommender engines with human-generated data, such as user bookmarks and tags. Such a visualisation enables users to explore which recommendations have been bookmarked by like-minded members of the community or marked with a specific relevant tag. Results of a preliminary user study (N =20) indicate that effectiveness and probability of item selection increase when users can explore relations between multiple recommendations and human feedback. In addition, perceived effectiveness and actual effectiveness of the recommendations as well as user trust into the recommendations are higher than a traditional list representation of recommendations. © 2016, CEUR-WS. All rights reserved.

关键词： Recommender systems

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Erratum to: Measurement of differential cross sections for single diffractive dissociation in s = 8 TeV pp collisions using the ATLAS ALFA spectrometer

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Journal of High Energy Physics 2020年第10期2020卷 1-19页

作者： Aad, G. Abbott, B. Abbott, D. C. Abdinov, O. Abed Abud, A. Abeling, K. Abhayasinghe, D. K. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B. S. Achkar, B. Adachi, S. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Adelman, J. Adersberger, M. Adiguzel, A. Adorni, S. Adye, T. Affolder, A. A. Afik, Y. Agapopoulou, C. Agaras, M. N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahmadov, F. Ahmed, W. S. Ai, X. Aielli, G. Akatsuka, S. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Al Khoury, K. Alberghi, G. L. Albert, J. Alconada Verzini, M. J. Alderweireldt, S. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexandre, D. Alexopoulos, T. Alfonsi, A. Alhroob, M. Ali, B. Alimonti, G. Alison, J. Alkire, S. P. Allaire, C. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alvarez Estevez, M. Álvarez Piqueras, D. Alviggi, M. G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amrouche, C. S. An, F. Anastopoulos, C. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Andreazza, A. Andrei, V. Anelli, C. R. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Anthony, M. T. Antonelli, M. Antrim, D. J. A. Anulli, F. Aoki, M. Aparisi Pozo, J. A. Aperio Bella, L. Arabidze, G. Araque, J. P. Araujo Ferraz, V. Araujo Pereira, R. Arcangeletti, C. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Armbruster, A. J. Armstrong, A. Arnaez, O. Arnold, H. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Asimakopoulou, E. M. Asquith, L. Assamagan, K. Astalos, R. Atkin, R. J. Atkinson, M. Atlay, N. B. Atmani, H. Augsten, K. Avolio, G. Avramidou, R. Ayoub, M. K. Azoulay, A. M. Azuelos, G. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Backman, F. Bagnaia, P. Bahmani, M. Bahrasemani, H. Bailey, A. J. Bailey, V. R. Baines, J. T. Bajic, M. Bakalis, C. Baker, O. K. Bakker, P. J. Bakshi Gupta, D. Balaji, S. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Department of Physics University of Massachusetts Amherst United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan INFN Sezione di Pavia Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Department of Physics University of Toronto Toronto Canada Niels Bohr Institute University of Copenhagen Copenhagen Denmark 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 High Energy Physics Division Argonne National Laboratory Argonne United States of America 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 University of Tokyo Tokyo Japan Institut für Physik Universität Mainz Mainz Germany LAL Université Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France 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 Department of Physics Bogazici University Istanbul Turkey Istanbul University Dept. of Physics Istanbul Turkey Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cr

Figure 5b of the paper [1] contained a misinterpretation in the comparison between the reported new ATLAS measurement of the process pp → Xp and previously published CMS data [2]. The ATLAS measurement corresponds to...

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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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Interim design report

arXiv

引用

arXiv 2020年

作者： Abramowicz, H. Jovin, T. Agatonovic Alonso, O. Amjad, M.S. An, F. Andricek, L. Anduze, M. Anguiano, J. Antonov, E. Aoki, Y. Arteche, F. Attié, D. Büscher, V. Bach, O. Balagura, V. Baudot, J. Begunov, V. Behera, S. Behnke, T. Bellerive, A. Belver, D. Benhammou, Y. Berggren, M. Berriaud, C. Bertolone, G. Besançon, M. Besson, A. Beyer, J. Bezshyyko, O. Bhattacharya, D.S. Bhattacharya, P. Blazey, G. Bocharnikov, V. Boronat, M. Borysov, O. Bosley, R. Boudry, V. Boumediene, D. Bourgeois, C. Jelisavcic, I. Bozovic Breton, D. Brianne, E. Brient, J.-C. Briggl, K. Buesser, K. Callier, S. Alamillo, E. Calvo Carrillo, C. Catalán, A. Chadeeva, M. Chau, P. Chera, M. Chetverushkin, B. Chrzaszcz, M. Claus, G. Colas, P. Colledani, C. Combaret, C. Cornat, R. Corriveau, F. Danilov, M. de la Taille, C. Deguchi, Y. Desch, K. Dieguez, A. Diener, R. Dixit, M. Dong, M. Dorokhov, A. Dozière, G. Drutskoy, A. Dulucq, F. Dyshkant, A. Edy, E. Eigen, G. Einhaus, U. El Bitar, Z. Elkhalii, A. Emberger, L. Esperante, D. Eté, R. Fang, Y. Fedorchuk, O. Firlej, M. Fiutowski, T. Fleck, I. Fourches, N. Fouz, M.C. Francis, K. Fujii, K. Fujii, K. Fullana, E. Fusayasu, T. Fuster, J. Göttlicher, P. Gadow, K. Gaede, F. Gallas, A. Ganjour, S. García, I. Cabrera, H. García Garillot, G. Garutti, E. Gastaldi, F. Ghislain, P. Goffe, M. Gomis, P. Gong, W. Gonnin, A. Goswami, D. Goto, K. Graf, C. Gregor, I.-M. Grenier, G. Guillaumat, R. Habermehl, M. Hagge, L. Hartbrich, O. Hartjes, F. Henschel, H. Heuchel, D. Hidalgo, S. Himmi, A. Hu, T. Hu-Guo, C. Ianigro, J.-C.T. Idzik, M. Irles, A. Ishihara, H. Ishikawa, A. Jönsson, L. Jaaskelainen, K. Jeans, D. Jeglot, J. Kacarevic, G. Kachel, M. Kajiwara, S. Kalinowski, J. Kaminski, J. Kamiya, Y. Karl, R. Kato, Y. Kato, Y. Kawada, S.-I. Kawagoe, K. Khan, S.A. Kleinwort, C. Kluit, P. Kobayashi, M. Koffmane, C. Komamiya, S. Korpachev, S. Kotera, K. Krämer, U. Krüger, K. Kunath, J. Kurata, M. Kurca, T. AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland University of Bergen Institute for Physics and Technology Bergen Norway University of Birmingham School of Physics and Astronomy Edgbaston United Kingdom Budker Institute of Nuclear Physics Siberian Branch Russian Academy of Sciences Novosibirsk State University Novosibirsk Russia University of Bonn Physikalisches Institut Bonn Germany Bergische Universität Wuppertal Fakultät 4/ Physik Wuppertal Germany Carleton University Ottawa Canada Irfu CEA Université Paris Saclay Gif sur Yvette France CIEMAT - Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid Spain Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France CSIC University of Cantabria Instituto de Física de Cantabria Santander Spain Deutsches Elektronen-Synchrotron Hamburg Germany Dhofar University Salalah Oman University of Hamburg Faculty of Mathematics Informatics and Natural Sciences Hamburg Germany Institute of High Energy Physics Austrian Academy of Science Wien Austria Halbeiterlabor der Max-Planck-Gesellschaft München Germany Keldysh Institute of Applied Mathematics Russian Academy of Sciences Moscow Russia University of Tokyo Tokyo Japan CSIC-University of Valencia Instituto de Física Corpuscular Paterna Spain Kraków Poland Institute of High Energy Physics Chinese Academy of Science Beijing China Indian Institute of Technology GuwahatiAssam India Barcelona Spain Institute of Physics of the Czech Academy of Sciences Prague 8 Czech Republic Institut Pluridisciplinaire Hubert Curien Strasbourg France Institut de Physique des deux infinis de Lyon Villeurbanne France Instituto Tecnológico de Aragòn Zaragoza Spain Iwate University Morioka Japan University of Kansas Department of Physics and Astronomy LawrenceKS United States High Energy Accelerator Research Organisation KEK TsukubaIbaraki Japan Kindai University Department of Physics Higashi Os

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Erratum to: Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at = 13 TeV with the ATLAS detector

引用

Journal of High Energy Physics 2020年第6期2020卷 1-19页

作者： Aad, G. Abbott, B. Abbott, D. C. Abdinov, O. Abed Abud, A. Abeling, K. Abhayasinghe, D. K. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B. S. Achkar, B. Adachi, S. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Adelman, J. Adersberger, M. Adiguzel, A. Adorni, S. Adye, T. Affolder, A. A. Afik, Y. Agapopoulou, C. Agaras, M. N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahmadov, F. Ahmed, W. S. Ai, X. Aielli, G. Akatsuka, S. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Al Khoury, K. Alberghi, G. L. Albert, J. Alconada Verzini, M. J. Alderweireldt, S. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexandre, D. Alexopoulos, T. Alfonsi, A. Alhroob, M. Ali, B. Alimonti, G. Alison, J. Alkire, S. P. Allaire, C. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alvarez Estevez, M. Álvarez Piqueras, D. Alviggi, M. G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amrouche, C. S. An, F. Anastopoulos, C. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Andreazza, A. Andrei, V. Anelli, C. R. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Anthony, M. T. Antonelli, M. Antrim, D. J. A. Anulli, F. Aoki, M. Aparisi Pozo, J. A. Aperio Bella, L. Arabidze, G. Araque, J. P. Araujo Ferraz, V. Araujo Pereira, R. Arcangeletti, C. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Armbruster, A. J. Armitage, L. J. Armstrong, A. Arnaez, O. Arnold, H. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Asimakopoulou, E. M. Asquith, L. Assamagan, K. Astalos, R. Atkin, R. J. Atkinson, M. Atlay, N. B. Atmani, H. Augsten, K. Avolio, G. Avramidou, R. Ayoub, M. K. Azoulay, A. M. Azuelos, G. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Backman, F. Bagnaia, P. Bahmani, M. Bahrasemani, H. Bailey, A. J. Bailey, V. R. Baines, J. T. Bajic, M. Bakalis, C. Baker, O. K. Bakker, P. J. Bakshi Gupta, D. Balaji, S. Baldin, E. M. Balek, P. Balli, F. CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Department of Physics University of Massachusetts Amherst United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan INFN Sezione di Pavia Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Department of Physics University of Toronto Toronto Canada Niels Bohr Institute University of Copenhagen Copenhagen Denmark 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 High Energy Physics Division Argonne National Laboratory Argonne United States of America 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 University of Tokyo Tokyo Japan Institut für Physik Universität Mainz Mainz Germany LAL Université Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France 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 Department of Physics Bogazici University Istanbul Turkey Istanbul University Dept. of Physics Istanbul Turkey Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cr

A mistake was identified for the paper [1] in the treatment of the radion [2] cross-sections, which resulted in multiple changes.

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A new distributed learning automata based algorithm for maximum independent set problem

A new distributed learning automata based algorithm for maxi...

引用

2016 Artificial Intelligence and Robotics, IRANOPEN 2016

作者： Khomami, Mohammad Mehdi Daliri Bagherpour, Negin Sajedi, Hedieh Meybodi, Mohammad Reza Department of Electronic Computer and Biomedical Engineering Islamic Azad University Qazvin Branch Qazvin Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran Dept. of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Polytechnic Tehran Iran

ISBN: (纸本)9781509021697

Maximum independent set problem is an NP-Hard one with the aim of finding the set of independent vertices with maximum possible cardinality in a graph. In this paper, we investigate a learning automaton based algorithm that finds a maximum independent set in the graph. Initially, a learning automaton is assigned to each vertex of graph. In order to find candidate independent set, a set of distributed learning automata collaborate with each other. The proposed algorithm based on learning automata is guided iteratively to the maximum independent set by updating the action probability vector. In order to study the performance of the proposed algorithm, we conducted some experiments. The reported numerical results confirm the superiority of our proposed algorithm in terms of cardinality of the obtained solution. © 2016 IEEE.

关键词： Iterative methods

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Concept drift and anomaly detection in graph streams

arXiv

引用

arXiv 2017年

作者： Zambon, Daniele Alippi, Cesare Livi, Lorenzo Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom

Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Anomaly detection

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Rheological Study of a DNA Transient Network by Optophoresis

Rheological Study of a DNA Transient Network by Optophoresis

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Conference on Lasers and Electro-Optics Europe

作者： V. Vitali G. Nava T. Yang F. Bragheri R. Osellame T. Bellini I. Cristiani P. Minzioni Electrical Computer and Biomedical Engineering Dept. Università di Pavia via Ferrata 5A 27100 Pavia Italy Department of Biomedical Science and Translation Medicine Università di Milano Via F.lli Cervi 91 20090 Segrate Italy Institute for Photonics and Nanotechnologies CNR Piazza Leonardo da Vinci 32 Milano 20133 Italy

ISBN: (纸本)9781509067374

Micro-rheology recently emerged as a promising technique for investigating the viscoelastic properties of complex fluids at a molecular scale. The study of the rheological properties of polymer solutions and reversible gels is more complex than that of molecular fluids, because these fluids commonly exhibit nonlinear viscosity behaviour. The research activity that we report about has been dedicated to the analysis of the dynamic behaviour of a DNA-particles system. DNA particles shaped as nano-stars with sticky ending points are suspended in water, and by lowering the temperature, below a gelation threshold, the nano-stars stick one to each other to form an hydrogel [1,2].

关键词： CCD cameras Dynamic light scattering Femtosecond laser writing Laser beams Optical measurement Temperature

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