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Enhanced Deuteron Coalescence Probability in Jets

Physical Review Letters 2023年第4期131卷 042301-042301页

作者： S. Acharya D. Adamová A. Adler G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt I. Altsybeev M. N. Anaam C. Andrei A. Andronic V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera F. Barile L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. G. Chavez T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. S. Chizzali J. Cho S. Cho P. Chochula P. Chri Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna Bologna Italy Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy University of Houston Houston Texas United States Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Westfälische Wilhelms-Universität Münster Institut für Kernphysik Munster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National Laboratory Berkeley California United States SUBATECH IMT Atlantique Nantes Université CNRS-IN2P3 Nantes France Laboratoire de Physique Subatomique et de Cosmologie Université Grenoble-Alpes CNRS-IN2P3 Grenoble France Universidade de São Pa

The transverse-momentum (pT) spectra and coalescence parameters B2 of (anti)deuterons are measured in p-p collisions at s=13 TeV for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest pT in the event (pTlead>5 GeV/c) is used as an approximation for the jet axis. The event is consequently divided into three azimuthal regions, and the jet signal is obtained as the difference between the toward region, that contains jet fragmentation products in addition to the underlying event (UE), and the transverse region, which is dominated by the UE. The coalescence parameter in the jet is found to be approximately a factor of 10 larger than that in the underlying event. This experimental observation is consistent with the coalescence picture and can be attributed to the smaller average phase-space distance between nucleons in the jet cone as compared with the underlying event. The results presented in this Letter are compared to predictions from a simple nucleon coalescence model, where the phase-space distributions of nucleons are generated using pythia8 with the Monash 2013 tuning, and to predictions from a deuteron production model based on ordinary nuclear reactions with parametrized energy-dependent cross sections tuned on data. The latter model is implemented in pythia8.3. Both models reproduce the observed large difference between in-jet and out-of-jet coalescence parameters, although the almost flat trend of the B2Jet is not reproduced by the models, which instead give a decreasing trend.

关键词： Hadron-hadron interactions

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Measurement of the Lifetime and Λ Separation Energy of HΛ3

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Physical Review Letters 2023年第10期131卷 102302-102302页

作者： S. Acharya D. Adamová A. Adler G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali Y. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt I. Altsybeev M. N. Anaam C. Andrei A. Andronic V. Anguelov F. Antinori P. Antonioli C. Anuj N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi A. Badalà Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera F. Barile L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch F. Baruffaldi N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi J. B. Butt S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo M. Carabas A. A. Carballo F. Carnesecchi R. Caron J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. G. Chavez T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. S. Chizzali J. Cho S. Cho P. Chochula P. Chri Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna Bologna Italy Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico COMSATS University Islamabad Islamabad Pakistan Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy University of Houston Houston Texas United States Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Westfälische Wilhelms-Universität Münster Institut für Kernphysik Munster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National Laboratory California United States SUBATECH IMT Atlantique Nantes Université CNRS-IN2P3 Nantes France Laboratoire de Physique Subatomique et de Cosmologie Université Grenoble-Alpes CNRS-IN2P3

The most precise measurements to date of the HΛ3 lifetime τ and Λ separation energy BΛ are obtained using the data sample of Pb-Pb collisions at sNN=5.02 TeV collected by ALICE at the LHC. The HΛ3 is reconstructed via its charged two-body mesonic decay channel (HΛ3→He3+π− and the charge-conjugate process). The measured values τ=[253±11(stat)±6(syst)] ps and BΛ=[102±63(stat)±67(syst)] keV are compatible with predictions from effective field theories and confirm that the HΛ3 structure is consistent with a weakly bound system.

关键词： Meson & hyperon induced nuclear reactions Relativistic heavy-ion collisions Hyperons

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Scan matching and KNN classification for mobile robot localisation algorithm 3

Scan matching and KNN classification for mobile robot locali...

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3rd IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2017

作者： Markom, M.A. Adom, A.H. Shukor, S. A. Abdul Rahim, N. Abdul Tan, E. S. Mohd Muslim Irawan, A. School of Mechatronic Engineering Universiti Malaysia Perlis Ulu Pauh Perlis02700 Malaysia Program Diploma Studies Center Universiti Malasyia Perlis Kampus Uniciti Alam Sungai Chucuh Padang Besar Perlis02100 Malaysia Group Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang Pahang Malaysia

ISBN: (纸本)9781538625392

Mobile robots have made tremendous impact in our modern lives today, and its development is set to continue further. One of the most important domains to allow the interaction of mobile robots with human is its ability to know where it is in its environment, and how to navigate through it. This ability, however, needs algorithm has become more complex and requires high computational ability due to the demand for high accuracy, real time implementations and multi-tasking requirements. These are partly due to the need of multi-sensory system. This paper presents the use of single laser range finder for the mobile robot mapping and localisation system. The localisation algorithm is developed using scan matching method which is incorporated with K-nearest neighbours (KNN) classification. The mobile robot and the developed algorithm are tested in static environment. The results of the location estimation are able to achieve 80% of accuracy for each scan location with the distance range of ±2cm compared to the real location. As conclusion, the simple flow of the algorithm is suitable to replace the complex and high computational algorithm and system. © 2017 IEEE.

关键词： Mobile robots

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Monitoring electrical energy in electronic energy audits through internet of things technology

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Journal of Physics: Conference Series 2019年第7期1402卷

作者： Sulistyanto, M P T Pranata, K B Afandi, A N Sendari, S Sulistiyowati, I Informatics Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Jl. S. Supriadi No. 48 Malang Jawa Timur Indonesia Physics Education Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Jl. S. Supriadi No. 48 Malang Jawa Timur Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Negeri Malang Teknik Elektro Universitas Muhammadiyah Sidoarjo Jl. Raya Gelam 250 Candi Sidoarjo Jawa Timur Indonesia

Energy conservation, especially electricity, can be done by conducting an energy audit. Energy audit activities can analyse and find energy-saving opportunities from energy use. This study developed a prototype of a wireless electrical energy monitoring system on a laboratory scale to monitor the use of electrical energy from both electrical equipment using a microcontroller as a sensor node. These nodes have installed several sensors, namely the current sensor, humidity sensor, temperature sensor and light sensor. The protocol used in communication between nodes and servers is the HTTP protocol in the Internet of Things design that can communicate using internet network intermediaries. Data on the server can be monitored in real time using the application on the client side.

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Proton acceleration in thermonuclear nova explosions revealed by gamma rays (Apr, 10.1038/s41550-022-01640-z, 2022)

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NATURE ASTRONOMY 2022年第6期6卷 760-760页

作者： Acciari, V. A. Ansoldi, S. Antonelli, L. A. Engels, A. Arbet Artero, M. Asano, K. Baack, D. Babic, A. Baquero, A. de Almeida, U. Barres Barrio, J. A. Batkovic, I. Gonzalez, J. Becerra Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bokenkamp, H. Bonnoli, G. Bosnjak, Z. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S. M. Colombo, E. Contreras, J. L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Mendez, C. Delgado Depaoli, D. Di Pierro, F. Di Venere, L. Espineira, E. Do Souto Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Elsaesser, D. Ramazani, V. Fallah Alonso, L. Farina Fattorini, A. Fonseca, M. V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. Lopez, R. J. Garcia Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, J. G. Green, D. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hutten, M. Inada, T. Ishio, K. Iwamura, Y. Martinez, I. Jimenez Jormanainen, J. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. Lopez-Coto, R. Lopez-Moya, M. Lopez-Oramas, A. Loporchio, S. de Oliveira Fraga, B. Machado Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martinez, M. Aguilar, A. Mas Mazin, D. Menchiari, S. Mender, S. Micanovic, S. Miceli, D. Miener, T. Miranda, J. M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Rosillo, M. Nievas Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J. M. Pavletic, L. Penil, P. Persic, M. Pihet, M. Moroni, P. G. Prada Prandini, E. Priyadarshi, C. Puljak, I. Rhode, W. Ribo, M. Rico, Universidad de La Laguna Instituto de Astrofísica de Canarias and Departamento de Astrofísica La Laguna Spain (GRID:grid.10041.34) (ISNI:***) Università di Udine and INFN Trieste Udine Italy (GRID:grid.5390.f) (ISNI:0000 0001 2113 062X) International Center for Relativistic Astrophysics (ICRA) Rome Italy (GRID:grid.5390.f) National Institute for Astrophysics (INAF) Rome Italy (GRID:grid.4293.c) (ISNI:0000 0004 1792 8585) Max-Planck-Institut für Physik Munich Germany (GRID:grid.435824.c) (ISNI:0000 0001 2375 0603) The Barcelona Institute of Science and Technology (BIST) Institut de Física d’Altes Energies (IFAE) Barcelona Spain (GRID:grid.473715.3) (ISNI:0000 0004 6475 7299) Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X) Technische Universität Dortmund Dortmund Germany (GRID:grid.5675.1) (ISNI:0000 0001 0416 9637) Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb Croatia (GRID:grid.4808.4) (ISNI:0000 0001 0657 4636) Universidad Complutense de Madrid IPARCOS Institute and EMFTEL Department Madrid Spain (GRID:grid.4795.f) (ISNI:0000 0001 2157 7667) Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134) Università di Padova and INFN Padua Italy (GRID:grid.5608.b) (ISNI:0000 0004 1757 3470) University of Lodz Department of Astrophysics Faculty of Physics and Applied Informatics Lodz Poland (GRID:grid.10789.37) (ISNI:0000 0000 9730 2769) Università di Siena and INFN Pisa Siena Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641) Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany (GRID:grid.7683.a) (ISNI:0000 0004 0492 0453) ETH Zürich Zurich Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780) Instituto de Astrofísica de Andalucía-CSIC Granada Spain (GRID:grid.450285.e) (ISNI:0000 0004 1793 7043) Università di Pisa and INFN Pisa Pisa Italy (GRID:grid.5395.

Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surface of the white dwarf3, brightening the white dwarf to ~105 solar luminosities and triggering ejection of the accumulated matter. Novae provide the extreme conditions required to accelerate particles, electrons or protons, to high energies. Here we present the detection of gamma rays by the MAGIC telescopes from the 2021 outburst of RS Ophiuchi, a recurrent nova with a red giant companion, which allowed us to accurately characterize the emission from a nova in the 60 GeV to 250 GeV energy range. The theoretical interpretation of the combined Fermi LAT and MAGIC data suggests that protons are accelerated to hundreds of gigaelectronvolts in the nova shock. Such protons should create bubbles of enhanced cosmic ray density, of the order of 10 pc, from the recurrent *** of the 2021 outburst of the nova RS Oph in very-high-energy gamma rays by the MAGIC telescopes is reported. Investigation of the gamma-ray emission provides evidence for acceleration of protons within the nova shock, which then propagate outwards to create bubbles of enhanced cosmic ray density.

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The Enhancement of PVDF Pyroelectricity (Pyroelectric Coefficient and Dipole Moment) by Inclusions

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Energy Procedia 2017年 119卷 545-555页

作者： K. Al Abdullah M. Anwar Batal Rawad Hamdan Toni Khalil Jean Zaraket Michel Aillerie Chafic Salame Laboratory of microelectronic-University of Aleppo Faculty of Electrical and electronic Engineering-Aleppo-Syria Laboratory of nanoscience-University of Aleppo Faculty of Electrical and electronic Engineering-Aleppo-Syria Postgraduate Student (PhD) University of Aleppo Faculty of Electrical and electronic Engineering-Aleppo-Syria CEER Faculty of Sciences II Lebanese University B.P 90656 Jdeidet El Mten Lebanon Université de Lorraine LMOPS EA 4423 57070 Metz France CentraleSupélec LMOPS 57070 Metz France

The pyroelectricity of Poly vinylidene fluoride (PVDF) is improved by incorporation inclusions in it. The best inclusions properties (volume fraction, dielectric constant, pyroelectric coefficient, molecular weight and density) were determined, which increase the pyroelectrical coefficient and dipole moment of PVDF. For very small volume fraction (<0.035) and dielectric constant of nonferroelectric inclusion is slightly smaller than PVDF, the pyroelectric coefficient of PVDF composite increases. For pyroelectrical inclusion, the pyroelectric coefficient of PVDF composite can increase by three states. It increases for very small volume fraction (<0.035), very low pyroelectric coefficient and dielectric constant of pyroelectrical inclusion is slightly smaller than PVDF. In addition, it increases for very small volume fraction (<0.035), high pyroelectric coefficient and dielectric constant of pyroelectrical inclusion is slightly higher than PVDF. Beside it increase for high pyroelectric coefficient and very small dielectric constant of pyroelectrical inclusion. The maximum value of the dipole moment is obtained under the following conditions: very small volume fraction (<0.035), both inclusion density and dielectric constant is smaller than PVDF, and inclusion molecular weight is higher than PVDF, or also under those following conditions: high volume fraction, inclusion density is smaller than PVDF and both molecular weight and dielectric constant of inclusion are higher than PVDF.

关键词： Pyroelectricity pyroelectrical coefficient dipole moment Debye PVDF displacement Maxwell model Clausius Mossotti model Lichtenecker model Vo Shi model

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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

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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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Modeling preference time in middle distance triathlons 5

Modeling preference time in middle distance triathlons

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5th International Symposium on Computational and Business Intelligence, ISCBI 2017

作者： Fister, Iztok Iglesias, Andres Deb, Suash Fister, Dusan Faculty of Electrical Engineering and Computer Science University of Maribor Smetanova 17 Maribor2000 Slovenia Faculty of Mechanical Engineering University of Maribor Smetanova 17 Maribor2000 Slovenia IT and Educational Consultant Ranchi Jharkhand India Decision Sciences and Modelling Program Victoria University Melbourne Australia University of Cantabria Avda. de los Castros s/n Santander39005 Spain

ISBN: (纸本)9781538617717

Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athlete's abilities, health, mental preparations and even their current sports form. So far, this process was calculated manually without any specific software tools or using the artificial intelligence. This paper presents the new solution for modeling preference time in middle distance triathlons based on particle swarm optimization algorithm and archive of existing sports results. Initial results are presented, which suggest the usefulness of proposed approach, while remarks for future improvements and use are also emphasized. © 2017 IEEE.

关键词： Sports

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

引用

arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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The Influence of the Model Fractal Parameters on the Electromagnetic Response in Environment with Cylindrical Layers

引用

International Journal of Geosciences 2017年第3期8卷 349-363页

作者： Valcir Joã o da Cunha Farias Brígida Ramati Pereira da Rocha Alice Nabiç a Mores Faculty of Statistics Federal University of Pará Belém Brazil Pos-Graduate Program in Electrical Engineering Federal University of Pará Belém Brazil

The induced polarization response of an environment presenting cylindrical layers was obtained. The fractal model for complex resistivity was employed as an intrinsic property of the polarizable layers. The influence of the model fractal parameters on the electromagnetic response was investigated. The results demonstrated that the fractal parameters dominate the apparent resistivity phase response;measurements of the induced polarization data allow for the determination of the fractal properties of the environment without noticeable electromagnetic coupling effects at frequencies below 104 Hz.

关键词： Fractal Model Induced Polarization Complex Resistivity Well Log

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