检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Zhang, Xingjian Zeng, Pei Ye, Tian lo, Hoi-Kwong Ma, Xiongfeng Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Department of Physics University of Hong Kong Pokfulam Hong Kong Dept. of Electrical & Computer Engineering Department of Physics University of Toronto TorontoONM5S 3G4 Canada Quantum Bridge Technologies Inc. 100 College Street TorontoONM5G 1L5 Canada

Complementarity is an essential feature of quantum mechanics. The preparation of an eigenstate of one observable implies complete randomness in its complementary observable. In quantum cryptography, complementarity allows us to formulate security analyses in terms of phase-error correction. However, in the device-independent regime that offers security without device characterization, the concept becomes much subtler. Security proofs of device-independent quantum cryptography tasks are often complex and quite different from those of their more standard device-dependent cousins. The existing proofs pose huge challenges to experiments, among which large data-size requirement is a crux. Here, we show the complementarity security origin of the device-independent tasks. By linking complementarity with quantum nonlocality, we recast the device-independent scheme into a quantum error correction protocol. Going beyond the identical-and-independent-distribution case, we consider the most general attack. We generalize the sample entropy in classical Shannon theory for the finite-size analysis. Our method exhibits good finite-size performance and brings the device-independent scheme to a more practical regime. Applying it to the data in a recent ion-trap-based device-independent quantum key distribution experiment, one could reduce the requirement on data size to less than a third. Furthermore, the complementarity approach can be naturally extended to advantage key distillation to ease experiments by tolerating higher loss and lower transmittance. © 2021, CC0.

关键词： Quantum cryptography

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Where is VAldO? VAscular lesions detection and segmentatiOn challenge at MICCAI 2021

arXiv

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arXiv 2022年

作者： Sudre, Carole H. Van Wijnen, Kimberlin dubost, Florian Adams, Hieab Atkinson, david Barkhof, Frederik Birhanu, Mahlet A. Bron, Esther E. Camarasa, Robin Chaturvedi, Nish Chen, Yuan Chen, Zihao Chen, Shuai dou, Qi Evans, Tavia Ezhov, Ivan Gao, Haojun Sanguesa, Marta Girones Gispert, Juan domingo Anson, Beatriz Gomez Hughes, Alun d. Ikram, M. Arfan Ingala, Silvia Jaeger, H. Rolf Kofler, Florian Kuijf, Hugo J. Kutnar, denis li, Bo lorenzini, luigi Menze, Bjoern Molinuevo, Jose luis Pan, Yiwei Puybareau, Elodie Rehwald, Rafael Su, Ruisheng Shi, Pengcheng Tillin, Therese Tochon, Guillaume Urien, Hélène van der Velden, Bas H.M. van der Velpen, Isabelle F. Wiestler, Benedikt Wolters, Frank J. Yilmaz, Pinar de Groot, Marius Vernooij, Meike W. de Bruijne, Marleen MRC Unit for Lifelong Health and Ageing at UCL University College London London United Kingdom Centre for Medical Image Computing University College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Clinical Genetics and Radiology Erasmus MC Rotterdam Netherlands Centre for Medical Imaging University College London London United Kingdom Department of Radiology and Nuclear Medicine Amsterdam University Medical Centre Amsterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Radiology University of Massachusetts Medical School Worcester United States School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Chinese University of Hong Kong China Department of Informatics Technische Universitat Munchen Munich Germany Department of Radiology Zhejiang University Hangzhou China Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands Pasqual Maragall Foundation Barcelona Spain Department of Radiology Hospital San Pau i santa Creu Barcelona Spain Department of Epidemiology Erasmus MC Rotterdam Netherlands Institute of Neurology University College London London United Kingdom Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China LRDE EPITA Paris France ISEP-Institut Supérieur d’Électronique de Paris Issy-les-Moulineaux France Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Departme

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular lesions detectiOn and Segmentation (Where is VAldO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level. © 2022, CC BY-SA.

关键词： Brain

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TACKlING GRAPHICAl NATURAl lANGUAGE PROCESSING’S PROBlEMS WITH RECURRENT NEURAl NETWORKS

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Xinan Jiaotong daxue Xuebao/Journal of Southwest Jiaotong University 2019年第5期54卷

作者： Sosa, Ali Sami Mohammed, Saja Majeed Abbas, Haider Hadi Al Barazanchi, Israa Computer Science Department College of Science University of Baghdad Karrada Al-Jadriya Baghdad Iraq Information and Communication Engineering Department Al Khwarizmi Engineering College University of Baghdad Karrada Al-Jadriya Baghdad Iraq Computer Technology Engineering Department Al-Mansour University College Al-Andalus Sq. Baghdad Iraq M/625 g/21 d/32 Baghdad Iraq

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data;thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network. © 2019 Science Press. All rights reserved.

关键词： Recurrent neural networks

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Towards Robust Waveform-Based Acoustic Models

arXiv

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arXiv 2021年

作者： Oglic, dino Cvetkovic, Zoran Sollich, Peter Renals, Steve Yu, Bin The Applied Analytics and Ai Data Sciences and Ai BioPharmaceuticals R&d AstraZeneca CambridgeCB2 8PA United Kingdom The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Institute for Theoretical Physics University of Göttingen Göttingen37073 Germany The Center for Speech Technology Research University of Edinburgh EdinburghEH8 9AB United Kingdom The Departments of Statistics and Electrical Engineering and Computer Sciences Uc Berkeley BerkeleyCA94720 United States

We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of paramount importance for the deployment of speech recognition systems that need to perform well in unseen environments. First, we characterize data augmentation theoretically as an instance of vicinal risk minimization, which aims at improving risk estimates during training by replacing the delta functions that define the empirical density over the input space with an approximation of the marginal population density in the vicinity of the training samples. More specifically, we assume that local neighborhoods centered at training samples can be approximated using a mixture of Gaussians, and demonstrate theoretically that this can incorporate robust inductive bias into the learning process. We then specify the individual mixture components implicitly via data augmentation schemes, designed to address common sources of spurious correlations in acoustic models. To avoid potential confounding effects on robustness due to information loss, which has been associated with standard feature extraction techniques (e.g., FBANK and MFCC features), we focus on the waveform-based setting. Our empirical results show that the approach can generalize to unseen noise conditions, with 150% relative improvement in out-of-distribution generalization compared to training using the standard risk minimization principle. Moreover, the results demonstrate competitive performance relative to models learned using a training sample designed to match the acoustic conditions characteristic of test utterances. Copyright © 2021, The Authors. All rights reserved.

关键词： delta functions

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Multipartite Intrinsic Non-locality and device-Independent Conference Key Agreement

arXiv

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arXiv 2021年

作者： Philip, Aby Kaur, Eneet Bierhorst, Peter Wilde, Mark M. Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Mathematics University of New Orleans Louisiana70148 United States Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States School of Applied and Engineering Physics Cornell University IthacaNY14850 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

In this work, we introduce multipartite intrinsic non-locality as a method for quantifying resources in the multipartite scenario of device-independent (dI) conference key agreement. We prove that multipartite intrinsic non-locality is additive, convex, and monotone under a class of free operations called local operations and common randomness. As one of our technical contributions, we establish a chain rule for two variants of multipartite mutual information, which we then use to prove that multipartite intrinsic non-locality is additive. This chain rule may be of independent interest in other contexts. All of these properties of multipartite intrinsic non-locality are helpful in establishing the main result of our paper: multipartite intrinsic non-locality is an upper bound on secret key rate in the general multipartite scenario of dI conference key agreement. We discuss various examples of dI conference key protocols and compare our upper bounds for these protocols with known lower bounds. Finally, we calculate upper bounds on recent experimental realizations of dI quantum key distribution. © 2021, CC BY.

关键词： Additives

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Author Correction: Aerial additive manufacturing with multiple autonomous robots

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Nature 2024年第7998期626卷 E5页

作者： Ketao Zhang Pisak Chermprayong Feng Xiao dimos Tzoumanikas Barrie dams Sebastian Kay Basaran Bahadir Kocer Alec Burns lachlan Orr Talib Alhinai Christopher Choi durgesh dattatray darekar Wenbin li Steven Hirschmann Valentina Soana Shamsiah Awang Ngah Clément Grillot Sina Sareh Ashutosh Choubey laura Margheri Vijay M Pawar Richard J Ball Chris Williams Paul Shepherd Stefan leutenegger Robert Stuart-Smith Mirko Kovac Department of Aeronautics Imperial College London London UK. School of Engineering and Materials Science Queen Mary University of London London UK. Department of Computing Imperial College London London UK. Department of Architecture and Civil Engineering University of Bath Bath UK. Department of Computer Science University College London London UK. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. Department of Informatics Technical University of Munich Garching Germany. Stuart Weitzman School of Design University of Pennsylvania Philadelphia PA USA. Department of Aeronautics Imperial College London London UK. m.kovac@imperial.ac.uk. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. m.kovac@imperial.ac.uk.

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design of intelligent parking lot based on Arduino

Design of intelligent parking lot based on Arduino

引用

第二届材料科学应用与能源材料国际研讨会

作者： Yunqiang li Guobin lin College of Computer & Information Engineering Nanyang Institute of Technology National Maglev Transportation Engineering R&D Center Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University

With the single chip of Arduino series as the main control terminal,this article establishes the intelligent parking system by using various *** the connection of the single chip with Ethernet W5100 network port,this design transmits the collected information to the server and then uploads it to the Web interactive platform after *** achieves the query and booking functions through the WeChat public number platform so that the car park can be much more efficient and intelligent.

关键词： red design of intelligent parking lot based on Arduino

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low temperature plasma for biology, hygiene, and medicine: Perspective and roadmap

arXiv

引用

arXiv 2021年

作者： laroussi, Mounir Bekeschus, Sander Keidar, Michael Bogaerts, Annemie Fridman, Alexander lu, XinPei Ostrikov, Kostya Hori, Masaru Stapelmann, Katharina Miller, Vandana Reuter, Stephan laux, Christophe Mesbah, Ali Walsh, James Jiang, Chunqi Thagard, Selma Mededovic Tanaka, Hiromasa liu, daWei Yan, dayun Yusupov, Maksudbek Electrical & Computer Engineering Department Old Dominion University NorfolkVA23529 United States Greifswald17489 Germany Mechanical and Aerospace Engineering Department The George Washington University WashingtonDC20052 United States Research Group PLASMANT Department of Chemistry University of Antwerp Antwerp Belgium Nyheim Plasma Institute Drexel University PhiladelphiaPA United States School of Electrical and Electronics Engineering Huazhong University of Science and Technology Hubei Wuhan China School of Chemistry and Physics Queensland University of Technology BrisbaneQLD4000 Australia Center for Low-Temperature Plasma Sciences Nagoya University ES 425 Furo-Cho Chikusa-Ku Nagoya464-8693 Japan Department of Nuclear Engineering North Carolina State University RaleighNC United States Department of Microbiology and Immunology Institute of Molecular Medicine and Infectious Disease Drexel University College of Medicine PhiladelphiaPA United States Department of Engineering Physics Polytechnique Montreal MontrealQC Canada Laboratoire EM2C CNRS CentraleSupelec Universite Paris Saclay Paris France Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States Department of Electrical Engineering and Electronics University of Liverpool Brownlow Hill LiverpoolL69 3GJ United Kingdom Frank Reidy Research Center for Bioelectrics Old Dominion University NorfolkVA United States Department of Chemical and Biomolecular Engineering Clarkson University PotsdamNY13676 United States

Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This paper presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various biomedical applications, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come. © 2021, CC BY.

关键词： Temperature

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Minimal Solutions to Relative Pose Estimation From Two Views Sharing a Common direction With Unknown Focal length

Minimal Solutions to Relative Pose Estimation From Two Views...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yaqing ding Jian Yang Jean Ponce Hui Kong Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology INRIA Paris France Département d'informatique de l'ENS ENS CNRS PSL University Paris France

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images from a smart phone demonstrate the usefulness of the proposed algorithms.

关键词： Cameras Gravity Eigenvalues and eigenfunctions Smart phones computer vision Pose estimation Charge coupled devices

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learnability of the output distributions of local quantum circuits

arXiv

引用

arXiv 2021年

作者： Hinsche, Marcel Ioannou, Marios Nietner, Alexander Haferkamp, Jonas Quek, Yihui Hangleiter, dominik Seifert, Jean-Pierre Eisert, Jens Sweke, Ryan Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin14109 Germany Information Systems Laboratory Stanford University StanfordCA94305 United States University of Maryland NIST College ParkMD20742 United States Electrical Engineering and Computer Science TU Berlin BerlinD-10587 Germany FhG SIT DarmstadtD-64295 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany

There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two different oracle models, the probably approximately correct (PAC) learnability of quantum circuit Born machines, i.e., the output distributions of local quantum circuits. We first show a negative result, namely, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable in the statistical query model, i.e., when given query access to empirical expectation values of bounded functions over the sample space. This immediately implies the hardness, for both quantum and classical algorithms, of learning from statistical queries the output distributions of local quantum circuits using any gate set which includes the Clifford group. As many practical generative modelling algorithms use statistical queries – including those for training quantum circuit Born machines – our result is broadly applicable and strongly limits the possibility of a meaningful quantum advantage for learning the output distributions of local quantum circuits. As a positive result, we show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable by a classical learner. Our results are equally applicable to the problems of learning an algorithm for generating samples from the target distribution (generative modelling) and learning an algorithm for evaluating its probabilities (density modelling). They provide the first rigorous insights into the learnability of output distributions of local quantum circuits from the probabilistic modelling perspective. © 2021, CC BY-NC-SA.

关键词： Timing circuits

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