检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Huang, Anqi Barz, Stefanie Andersson, Erika Makarov, Vadim Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Functional Matter and Quantum Technologies University of Stuttgart Stuttgart70569 Germany Center for Integrated Quantum Science and Technology University of Stuttgart Iqst Stuttgart70569 Germany Institute of Photonics and Quantum Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Russian Quantum Center and Misis University Moscow Russia Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada

Quantum cryptography is information-theoretically secure owing to its solid basis in quantum mechanics. However, generally, initial implementations with practical imperfections might open loopholes, allowing an eavesdropper to compromise the security of a quantum cryptographic system. This has been shown to happen for quantum key distribution (QKD). Here we apply experience from implementation security of QKD to several other quantum cryptographic primitives. We survey quantum digital signatures, quantum secret sharing, source-independent quantum random number generation, quantum secure direct communication, and blind quantum computing. We propose how the eavesdropper could in principle exploit the loopholes to violate assumptions in these protocols, breaking their security properties. Applicable countermeasures are also discussed. It is important to consider potential implementation security issues early in protocol design, to shorten the path to future applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Publisher Correction: Extreme risk induced by communities in interdependent networks

引用

Communications Physics 2019年第1期2卷 1页

作者： Jiachen Sun Rui Zhang Ling Feng Christopher Monterola Xiao Ma Céline Rozenblat H. Eugene Stanley Boris Podobnik Yanqing Hu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Institute for Molecular Engineering University of Chicago Chicago USA Institute of High Performance Computing A*STAR Singapore Singapore Department of Physics National University of Singapore Singapore Singapore Asian Institute of Management Metro Manila Philippines School of Data and Computer Science Sun Yat-sen University Guangzhou China Institute of Geography University of Lausanne Lausanne Switzerland Department of Physics Boston University Boston USA Faculty of Civil Engineering University of Rijeka Rijeka Croatia Faculty of Information Studies Novo Mesto Slovenia Zagreb School of Economics and Management Zagreb Croatia Luxembourg School of Business Luxembourg Luxembourg

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Anomalous negative magnetoresistance in quantum-dot Josephson junctions with Kondo correlations

arXiv

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arXiv 2018年

作者： Deng, M.-T. Yu, C.-L. Huang, G.-Y. López, R. Caroff, P. Ghalamestani, S. Platero, G. Xu, H.Q. Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology NUDT Changsha410073 China NanoLund and Division of Solid State Physics Lund University LundS-22100 Sweden Hefei National Laboratory Hefei230088 China China Greatwall Quantum Laboratory Changsha410006 China Institut de Fisica Interdisciplinària i de Sistemes Complexos IFISC Palma de MallorcaE-07122 Spain Instituto de Ciencia de Materiales de Madrid Madrid28049 Spain NanoLund and Division of Solid State Physics Lund University Box 118 LundS-221 00 Sweden Beijing Key Laboratory of Quantum Devices Key Laboratory for the Physics and Chemistry of Nanodevices Department of Electronics Peking University Beijing100871 China Beijing Academy of Quantum Information Sciences Beijing100193 China

The interplay between superconductivity and the Kondo effect has stimulated significant interest in condensed matter physics. They compete when their critical temperatures are close and can give rise to a quantum phase transition that can mimic Majorana zero modes. Here, we have fabricated and measured Al-InSb nanowire quantum dot-Al devices. In the Kondo regime, a supercurrentinduced zero-bias conductance peak emerges. This zero-bias peak shows an anomalous negative magnetoresistance (NMR) at weak magnetic fields. We attribute this anomalous NMR to quasiparticle trapping at vortices in the superconductor leads as a weak magnetic field is applied. The trapping effect lowers the quasiparticle-caused dissipation and thus enhances the Josephson current. This work connects the vortex physics and the supercurrent tunneling in Kondo regimes and can help further understand the physics of Josephson quantum dot system. Copyright © 2018, The Authors. All rights reserved.

关键词： Vortex flow

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A Graphene‐Supported Single‐Atom FeN5Catalytic Site for Efficient Electrochemical CO2Reduction

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Angewandte Chemie 2019年第42期131卷

作者： Huinian Zhang Jing Li Shibo Xi Yonghua Du Xiao Hai Junying Wang Haomin Xu Gang Wu Jia Zhang Jiong Lu Junzhong Wang Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education Anhui University Hefei 230601 P. R. China CAS Key Laboratory of Carbon Materials Institute of Coal Chemistry Chinese Academy of Sciences Taiyuan 030001 China Department of Chemistry National University of Singapore 3 Science Drive 3 Singapore 117543 Singapore Institute of Chemical and Engineering Sciences Pesek Road Jurong Island 627833 Singapore Singapore Dalian National Laboratory for Clean Energy Dalian 116023 China Institute of High Performance Computing A*STAR (Agency for Science Technology and Research) 1 Fusionopolis Way #16-16 Connexis S138632 Singapore

Electrochemical conversion of CO 2 into valued products is one of the most important issues but remains a great challenge in chemistry. Herein, we report a novel synthetic approach involving prolonged thermal pyrolysis of hemin and melamine molecules on graphene for the fabrication of a robust and efficient single‐iron‐atom electrocatalyst for electrochemical CO 2 reduction. The single‐atom catalyst exhibits high Faradaic efficiency (ca. 97.0 %) for CO production at a low overpotential of 0.35 V, outperforming all Fe‐N‐C‐based catalysts. The remarkable performance for CO 2 ‐to‐CO conversion can be attributed to the presence of highly efficient singly dispersed FeN 5 active sites supported on N‐doped graphene with an additional axial ligand coordinated to FeN 4 . DFT calculations revealed that the axial pyrrolic nitrogen ligand of the FeN 5 site further depletes the electron density of Fe 3d orbitals and thus reduces the Fe–CO π back‐donation, thus enabling the rapid desorption of CO and high selectivity for CO production.

关键词： CO-Entwicklung CO2-Reduktion Einzelatomkatalysatoren Eisen Elektrokatalyse

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Joint product numerical range and geometry of reduced density matrices

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science China(Physics,Mechanics & Astronomy) 2017年第2期60卷 9-17页

作者： Jianxin Chen Cheng Guo Zhengfeng Ji Yiu-Tung Poon Nengkun YU Bei Zeng Jie Zhou Joint Center for Quantum Information and Computer Science University of Maryland College Park 20740 USA Institute for Advanced Study Tsinghua University Beijing 100084 China Centre for Quantum Computation ＆ Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia State Key Laboratory of Computer Science.Institute of Software Chinese Academy of Sciences Beijing 100084 China Department of Mathematics Iowa State University Ames 50011-2140 USA Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Canada Department of Mathematics ＆ Statistics University of Guelph Guelph N1G 2 W1 Canada Perimeter Institute for Theoretical Physics Waterloo N2L 2Y5 Canada

The reduced density matrices of a many-body quantum system form a convex set, whose three-dimensional projection is convex in R3. The boundary of may exhibit nontrivial geometry, in particular ruled surfaces. Two physical mechanisms are known for the origins of ruled surfaces： symmetry breaking and gapless. In this work, we study the emergence of ruled surfaces for systems with local Hamiltonians in infinite spatial dimension, where the reduced density matrices are known to be separable as a consequence of the quantum de Finetti＇s theorem. This allows us to identify the reduced density matrix geometry with joint product numerical range II of the Hamiltonian interaction terms. We focus on the case where the interaction terms have certain structures, such that a ruled surface emerges naturally when taking a convex hull of ∏. We show that, a ruled surface on sitting in ∏ has a gapless origin, otherwise it has a symmetry breaking origin. As an example, we demonstrate that a famous ruled surface, known as the oloid, is a possible shape of , with two boundary pieces of symmetry breaking origin separated by two gapless lines.

关键词： reduced density matrices quantum tomography numerical range quantum information oloid ruled surface

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Conditional gradient method for stochastic submodular maximization: Closing the gap

arXiv

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arXiv 2017年

作者： Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Laboratory for Information and Decision Systems MIT Simons Institute for the Theory of Computing University of California Berkeley Department of Electrical and Systems Engineering University of Pennsylvania Department of Electrical Engineering and Computer Science Yale University

In this paper, we study the problem of constrained and stochastic continuous submodular maximization. Even though the objective function is not concave (nor convex) and is defined in terms of an expectation, we develop a variant of the conditional gradient method, called Stochastic Continuous Greedy, which achieves a tight approximation guarantee. More precisely, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that Stochastic Continuous Greedy achieves a [(1 - 1/e)OPT - ϵ] guarantee (in expectation) with O(1/ϵ3) stochastic gradient computations. This guarantee matches the known hardness results and closes the gap between deterministic and stochastic continuous submodular maximization. By using stochastic continuous optimization as an interface, we also provide the first (1-1/e) tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a general matroid constraint. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Constraints on Cosmic Strings Using Data from the Third Advanced LIGO–Virgo Observing Run

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Physical Review Letters 2021年第24期126卷 241102-241102页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Auclair P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. D LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S.Angelo I-80126 Napoli Italy OzGrav School of Physics and Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illi

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO–Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10−15. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

关键词： Cosmic strings & domain walls Gravitational waves

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Measurement of Azimuthal Anisotropy of Muons from Charm and Bottom Hadrons in pp Collisions at s=13 TeV with the ATLAS Detector

引用

Physical Review Letters 2020年第8期124卷 082301-082301页

作者： G. Aad B. Abbott D. C. Abbott A. Abed Abud K. Abeling D. K. Abhayasinghe S. H. Abidi O. S. AbouZeid N. L. Abraham H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya B. Achkar S. Adachi L. Adam C. Adam Bourdarios L. Adamczyk L. Adamek J. Adelman M. Adersberger A. Adiguzel S. Adorni T. Adye A. A. Affolder Y. Afik C. Agapopoulou M. N. Agaras A. Aggarwal C. Agheorghiesei J. A. Aguilar-Saavedra F. Ahmadov W. S. Ahmed X. Ai G. Aielli S. Akatsuka T. P. A. Åkesson E. Akilli A. V. Akimov K. Al Khoury G. L. Alberghi J. Albert M. J. Alconada Verzini S. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa D. Alexandre T. Alexopoulos A. Alfonsi F. Alfonsi M. Alhroob B. Ali G. Alimonti J. Alison S. P. Alkire C. Allaire B. M. M. Allbrooke B. W. Allen P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. A. Alshehri M. Alvarez Estevez D. Álvarez Piqueras M. G. Alviggi Y. Amaral Coutinho A. Ambler L. Ambroz C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso C. S. Amrouche F. An C. Anastopoulos N. Andari T. Andeen C. F. Anders J. K. Anders A. Andreazza V. Andrei C. R. Anelli S. Angelidakis A. Angerami A. V. Anisenkov A. Annovi C. Antel M. T. Anthony E. Antipov M. Antonelli D. J. A. Antrim F. Anulli M. Aoki J. A. Aparisi Pozo L. Aperio Bella G. Arabidze J. P. Araque V. Araujo Ferraz R. Araujo Pereira C. Arcangeletti A. T. H. Arce F. A. Arduh J-F. Arguin S. Argyropoulos J.-H. Arling A. J. Armbruster A. Armstrong O. Arnaez H. Arnold Z. P. Arrubarrena Tame A. Artamonov G. Artoni S. Artz S. Asai N. Asbah E. M. Asimakopoulou L. Asquith J. Assahsah K. Assamagan R. Astalos R. J. Atkin M. Atkinson N. B. Atlay H. Atmani K. Augsten G. Avolio R. Avramidou M. K. Ayoub A. M. Azoulay G. Azuelos H. Bachacou K. Bachas M. Backes F. Backman P. Bagnaia M. Bahmani H. Bahrasemani A. J. Bailey V. R. Bailey J. T. Baines M. Bajic C. Bakalis O. K. Baker P. J. Bakker D. Bakshi Gupta S. Balaji E. M. Baldin P. Balek F. Balli W. K. Balunas J. Balz E. Banas A. Bandyopadhyay Sw. Banerjee A. A. E. Bannoura L. Barak W. M. Barbe E. L. Barberio D. Barberis M. CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Department of Physics University of Massachusetts Amherst Massachusetts USA INFN Sezione di 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 ON 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 Illinois USA INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy International Center for Elementary Particle Physics and Department of Physics University of Tokyo Tokyo Japan Institut für Physik Universität Mainz Mainz Germany LAPP Université Grenoble Alpes Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb Illinois USA Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Department of Physics Bogazici University Istanbul Turkey Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA LAL Université Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France LPC Université Clermont Auvergne CNRS/IN2P3 Cl

The elliptic flow of muons from the decay of charm and bottom hadrons is measured in pp collisions at s=13 TeV using a data sample with an integrated luminosity of 150 pb−1 recorded by the ATLAS detector at the LHC. The muons from heavy-flavor decay are separated from light-hadron decay muons using momentum imbalance between the tracking and muon spectrometers. The heavy-flavor decay muons are further separated into those from charm decay and those from bottom decay using the distance-of-closest-approach to the collision vertex. The measurement is performed for muons in the transverse momentum range 4–7 GeV and pseudorapidity range |η|<2.4. A significant nonzero elliptic anisotropy coefficient v2 is observed for muons from charm decays, while the v2 value for muons from bottom decays is consistent with zero within uncertainties.

关键词： Collective flow Jets & heavy flavor physics Particle correlations & fluctuations Relativistic heavy-ion collisions

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JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S DISEASE VIA MULTI-MODAL SPARSE LEARNING

JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S...

引用

International Symposium on Biomedical Imaging

作者： Haijun Lei Jian Zhang Zhang Yang Ee-leng Tan Baiying Lei Qiuming Luo College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers School of Information Engineering Shenzhen University School of Electric and Electronic Engineering Nanyang Technological University School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound

ISBN: (纸本)9781509011735

In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction and support vector classification model for class label identification. Our method is evaluated on a public dataset of 208 subjects including 56 normal controls (NC), 123 PD and 29 scans without evidence of dopamine deficit (SWEDD) via a 10-fold cross-validation method. The experimental results demonstrate that multimodal data can effectively improve the performance in disease status identification and clinical scores prediction compared to one single modality. Our proposed method also outperforms the related methods.

关键词： Parkinson's disease Feature selection Classification Prediction Multi-modal data

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