检索结果-内蒙古大学图书馆

Quantum state and process tomography via adaptive measurements

science China(physics,Mechanics & Astronomy) 2016年第10期59卷 25-32页

作者： HengYan Wang WenQiang Zheng NengKun Yu KeRen Li DaWei Lu Tao Xin Carson Li ZhengFeng Ji David Kribs Bei Zeng XinHua Peng JiangFeng Du Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Department of Applied Physics Zhejiang University of TechnologyHangzhou 310023China Institute for Quantum Computing University of WaterlooWaterloo N2L 3G1Canada 4Department of Mathematics&Statistics University of GuelphGuelph N1G 2W1Canada Centre for Quantum Computation&Intelligent Systems Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydney NSW 2007Australia State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Department of Physics University of GuelphGuelph N1G 2W1Canada State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Canadian Institute for Advanced Research Toronto M5G 1Z8Canada

We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.

关键词： quantum tomography state reconstruction nuclear magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

Modelling the generic TiO2 memristor with the parasitic components

Modelling the generic TiO2 memristor with the parasitic comp...

引用

International Conference on Signals and Electronic Systems, ICSES

作者： Bartłomiej Garda Maciej J. Ogorzałek Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland Department of Information Technologies Faculty of Physics Astronomy and Applied Computer Science J agiellonian University Kraków Poland

In this paper the problem of existence of parasitic components in a real memristor has been analysed. These unwanted components have some impacts on behaviour of the memristors causing discrepancies in the observed dynamic characteristics. To model this kind of behaviour additional L and C elements have to be introduced in the memristor model. Simulation tests have been carried out to uncover the changes of behaviour caused by the introduced parasitics. Several graphs show comparisons between characteristics of an ideal element and the model of real one with parasitics.

关键词： Memristors Integrated circuit modeling Hysteresis Mathematical model Analytical models Resistance

来源：评论

学校读者我要写书评

暂无评论

Memory efficient sign language recognition system based on WiSARD weightless neural network technique

Memory efficient sign language recognition system based on W...

引用

International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Faisal Ghazanfar Najmi Ghani Haider Applied physics Computer and instrumentation center PCSIR Labs complex Karachi Karachi Pakistan Department of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan

ISBN: (纸本)9781509040605

A novel single hand Sign Language Recognition (SLR) method utilizing Weightless Neural Network (WNN) known as a RAMnet or n-tuple network is evaluated as a memory efficient technique. In contrast with standard multilayer perceptron neural network (MLPNN), the RAMnet does not require long iteration of presentation of training data in the training phase i.e. long training time, weights adjustments and activation function calculations. This alternative technique is based on WiSARD Neural Network. Two algorithms are proposed and implemented: one for hand tracking through image sequences, and the second for SLR using WiSARD. The pixels containing the hand region are used to train the Sign Language (SL) alphabetical symbol discriminators. Each sign is assigned a discriminator, thus there are as many discriminator as signs to be recognized. The system performance is checked by evaluating for single handed SL English alphabets and produced result of accuracy 75%. The conventional Random Access Memory (RAM) is used to save and process binary image sequences in WiSARD that are addressed by Boolean input and produced Boolean output. The system can implement signs for any other sign language, thus it is language independent.

关键词： Random access memory Training Assistive technology Gesture recognition Radiation detectors Testing Cameras

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

引用

NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

来源：评论

学校读者我要写书评

暂无评论

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

引用

Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

来源：评论

学校读者我要写书评

暂无评论

SciPy 1.0—Fundamental algorithms for scientific computing in python

arXiv

引用

arXiv 2019年

作者： Virtanen, Pauli Gommers, Ralf Oliphant, Travis E. Haberland, Matt Reddy, Tyler Cournapeau, David Burovski, Evgeni Peterson, Pearu Weckesser, Warren Bright, Jonathan van der Walt, Stéfan J. Brett, Matthew Wilson, Joshua Millman, K. Jarrod Mayorov, Nikolay Nelson, Andrew R.J. Jones, Eric Kern, Robert Larson, Eric Carey, C.J. Polat, Ilhan Feng, Yu Moore, Eric W. VanderPlas, Jake Laxalde, Denis Perktold, Josef Cimrman, Robert Henriksen, Ian Quintero, E.A. Harris, Charles R. Archibald, Anne M. Ribeiro, Antônio H. Pedregosa, Fabian van Mulbregt, Paul University of Jyväskylä University of Jyväskylä FI-40014 Finland Quansight LLC AustinTX United States Ultrasound Imaging Mayo Clinic RochesterMN55902 United States Electrical Engineering Brigham Young University ProvoUT84604 United States Enthought Inc. AustinTX78701 United States Anaconda Inc AustinTX78701 United States BioResource and Agricultural Engineering Department California Polytechnic State University San Luis ObispoCA93407 United States Department of Mathematics University of California Los AngelesCA90025 United States Los Alamos National Laboratory Los AlamosNM87544 United States Independent Researcher Higher School of Economics Myasnitskaya 20 Moscow101000 Russia Independent Researcher Saue Estonia Department of Mechanics and Applied Mathematics Institute of Cybernetics Tallinn Technical University Akadeemia Rd 21 Tallinn Estonia Independent Researcher NY United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States School of Psychology University of Birmingham Edgbaston BirmighamB15 2TT United Kingdom Division of Biostatistics University of California BerkeleyCA94720 United States WayRay LLC Skolkovo Innovation Center Moscow143026 Russia Australian Nuclear Science and Technology Organisation Locked Bag 2001 Kirrawee DCNSW2232 Australia Institute for Learning and Brain Sciences University of Washington SeattleWA98195 United States College of Information and Computing Sciences University of Massachusetts Amherst AmherstMA01002 United States Berkeley Center for Cosmological Physics University of California BerkeleyCA94720 United States Bruker Biospin Corp. 15 Fortune Drive BillericaMA01821 United States University of Washington SeattleWA98195 United States New Technologies - Research Centre University of West Bohemia Univerzitní 8 Plzeň306 14 Czech Republic Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX7

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Two-Qubit Pure Entanglement as Optimal Social Welfare Resource in Bayesian Game

arXiv

引用

arXiv 2017年

作者： Banik, Manik Bhattacharya, Some Sankar Ganguly, Nirman Guha, Tamal Mukherjee, Amit Rai, Ashutosh Roy, Arup S.N. Bose National Center for Basic Sciences Block JD Sector III Salt Lake Kolkata700098 India Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong Department of Mathematics Heritage Institute of Technology Kolkata700107 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India Optics and Quantum Information Group Institute of Mathematical Sciences HBNI CIT Campus Taramani Chennai600113 India International Institute of Physics Federal University of Rio Grande do Norte Natal59070-405 Brazil 7Center for Quantum Computer Science University of Latvia Raina Bulv. 19 RigaLV-1586 Latvia Centre for Astroparticle Physics and Space Sciences Bose Institute Block EN Sector V Salt LakeKolkata700091 India

Entanglement is of paramount importance in quantum information theory. Its supremacy over classical correlations has been demonstrated in a numerous information theoretic protocols. Here we study possible adequacy of quantum entanglement in Bayesian game theory, particularly in social welfare solution (SWS), a strategy which the players follow to maximize sum of their payoffs. Given a multi-partite quantum state as an advice, players can come up with several correlated strategies by performing local measurements on their parts of the quantum state. A quantum strategy is called quantum-SWS if it is advantageous over a classical equilibrium (CE) strategy in the sense that none of the players has to sacrifice their CE-payoff rather some have incentive and at the same time it maximizes sum of all players’ payoffs over all possible quantum advantageous strategies. Quantum state yielding such a quantum-SWS is called a quantum social welfare advice (SWA). We show that any two-qubit pure entangled states, even if it is arbitrarily close to a product state, can serve as quantum-SWA in some Bayesian game. Our result, thus, gives cognizance to the fact that every two-qubit pure entanglement is the best resource for some operational task. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

来源：评论

学校读者我要写书评

暂无评论

Surface plasmon polaritons at lossy metamaterial-air interfaces

Surface plasmon polaritons at lossy metamaterial-air interfa...

引用

Photonic Networks and Devices, Networks 2016

作者： Sang-Nourpour, Nafiseh Lavoie, Benjamin R. Kheradmand, Reza Sanders, Barry C. Institute for Quantum Science and Technology University of Calgary AB Canada Photonics group Research Institute for Applied Physics and Astronomy University of Tabriz Iran Photonics group Aras International Campus University of Tabriz Iran Department of Electrical and Computer Engineering University of Calgary AB Canada Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Hefei Anhui China Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information Quantum Physics University of Science and Technology of China Shanghai China Canadian Institute for Advanced Research TorontoON Canada

We derive rigorous criteria for the existence of surface-plasmon polaritons (SPPs) at lossy interfaces by considering properties of SPPs and employing the requirement of causality. These criteria impose limits on susc... 详细信息

ISBN: (纸本)9781943580149

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Measurement of the W boson polarisation in [Formula: see text] events from pp collisions at [Formula: see text] in the lepton + jets channel with ATLAS

引用

The European physical journal. C, Particles and fields 2019年第1期79卷 19页

作者： M Aaboud G Aad B Abbott J Abdallah O Abdinov B Abeloos O S AbouZeid N L Abraham H Abramowicz H Abreu R Abreu Y Abulaiti B S Acharya S Adachi L Adamczyk D L Adams J Adelman S Adomeit T Adye A A Affolder T Agatonovic-Jovin J A Aguilar-Saavedra S P Ahlen F Ahmadov G Aielli H Akerstedt T P A Åkesson A V Akimov G L Alberghi J Albert S Albrand M J Alconada Verzini M Aleksa I N Aleksandrov C Alexa G Alexander T Alexopoulos M Alhroob B Ali M Aliev G Alimonti J Alison S P Alkire B M M Allbrooke B W Allen P P Allport A Aloisio A Alonso F Alonso C Alpigiani A A Alshehri M Alstaty B Alvarez Gonzalez D Álvarez Piqueras M G Alviggi B T Amadio Y Amaral Coutinho C Amelung D Amidei S P Amor Dos Santos A Amorim S Amoroso G Amundsen C Anastopoulos L S Ancu N Andari T Andeen C F Anders J K Anders K J Anderson A Andreazza V Andrei S Angelidakis I Angelozzi A Angerami F Anghinolfi A V Anisenkov N Anjos A Annovi C Antel M Antonelli A Antonov D J Antrim F Anulli M Aoki L Aperio Bella G Arabidze Y Arai J P Araque V Araujo Ferraz A T H Arce F A Arduh J-F Arguin S Argyropoulos M Arik A J Armbruster L J Armitage O Arnaez H Arnold M Arratia O Arslan A Artamonov G Artoni S Artz S Asai N Asbah A Ashkenazi B Åsman L Asquith K Assamagan R Astalos M Atkinson N B Atlay K Augsten G Avolio B Axen M K Ayoub G Azuelos M A Baak A E Baas M J Baca H Bachacou K Bachas M Backes M Backhaus P Bagiacchi P Bagnaia Y Bai J T Baines M Bajic O K Baker E M Baldin P Balek T Balestri F Balli W K Balunas E Banas Sw Banerjee A A E Bannoura L Barak E L Barberio D Barberis M Barbero T Barillari M-S Barisits T Barklow N Barlow S L Barnes B M Barnett R M Barnett Z Barnovska-Blenessy A Baroncelli G Barone A J Barr L Barranco Navarro F Barreiro J Barreiro Guimarães da Costa R Bartoldus A E Barton P Bartos A Basalaev A Bassalat R L Bates S J Batista J R Batley M Battaglia M Bauce F Bauer H S Bawa J B Beacham M D Beattie T Beau P H Beauchemin P Bechtle H P Beck K Becker M Becker M Beckingham C Becot A J Beddall A Beddall V A Bednyakov M Bedognetti C P Bee L J Beemster T A Bee 180Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco. 115CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France. 144Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK USA. 10Department of Physics The University of Texas at Arlington Arlington TX USA. Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan. 148LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France. 183Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz CA USA. 199Department of Physics and Astronomy University of Sussex Brighton UK. 203Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel. 202Department of Physics Technion: Israel Institute of Technology Haifa Israel. 147Center for High Energy Physics University of Oregon Eugene OR USA. 195Department of Physics Stockholm University Stockholm Sweden. 196The Oskar Klein Centre Stockholm Sweden. 217INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy. 218ICTP Trieste Italy. 205International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan. 60Faculty of Physics and Applied Computer Science AGH University of Science and Technology Kraków Poland. 36Physics Department Brookhaven National Laboratory Upton NY USA. 139Department of Physics Northern Illinois University DeKalb IL USA. 130Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany. 170Particle Physics Department Rutherford Appleton Laboratory Didcot UK. 16Institute of Physics University of Belgrade Belgrade Serbia. 159Laboratório de Instrumentação e Física Experimental de Partículas-LIP Lisbon Portugal. 164Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain. 30Department of Physics Boston University Boston MA USA. 94Joint Institute for Nuclear Research JINR Dubna Dubna Russia. 173INFN Sezione d

[This corrects the article DOI: 10.1140/epjc/s10052-017-4819-4.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：