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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Morphological geodesic active contour based automatic aorta segmentation in thoracic CT images

Morphological geodesic active contour based automatic aorta ...

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International Conference on computer Vision and Image processing, CVIP 2016

作者： Dasgupta, Avijit Mukhopadhyay, Sudipta Mehre, Shrikant A. Bhattacharyya, Parthasarathi Computer Vision and Image Processing Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Institute of Pulmocare & Research KolkataWest Bengal700156 India

ISBN: (纸本)9789811021060

Automatic aorta segmentation and quantification in thoracic computed tomography (CT) images is important for detection and prevention of aortic diseases. This paper proposes an automatic aorta segmentation algorithm in both contrast and non-contrast CT images of thorax. The proposed algorithm first detects the slice containing the carina region. Circular Hough Transform (CHT) is applied on the detected slice to localize ascending and descending aorta (circles with lowest variances) followed by a morphological geodesic active contour to segment the aorta from CT stack. The dice similarity coefficients (DSC) between the ground truth and the segmented output were found to be 0.8845 ± 0.0584 on LIDC-IDRI dataset. © Springer Science+Business Media Singapore 2017.

关键词： Image segmentation

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Cyber-Attack Reconstruction via Sliding Mode Differentiation and Sparse Recovery Algorithm: electrical Power Networks Application

Cyber-Attack Reconstruction via Sliding Mode Differentiation...

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International Workshop on Variable Structure Systems, VSS

作者： Shamila Nateghi Y. Shtessel J-P Barbot Gang Zheng Lei Yu Department of Electrical and Computer Engineering University of Alabama in Huntsville Huntsville AL USA EA7393 Non-A INRIA Cergy-Pontoise France INRIA-Lille Nord Europe Villeneuve d'Ascq Cedex France Signal Processing Laboratory Wuhan University Wuhan China

In this work, the unknown cyber-attacks on cyber-physical systems are reconstructed using sliding mode differentiation techniques in concert with the sparse recovery algorithm, when only several unknown attacks out of a long list of possible attacks are considered non-zero. The approach is applied to a model of the electric power system, and finally, the efficacy of the proposed techniques is illustrated via simulations of a real electric power system.

关键词： Security Electronic mail Power systems Sensor systems Generators Actuators

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Dense matter with eXTP

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Science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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Stability and Performance Analysis of Saturated Systems Using an Enhanced Max Quadratic Lyapunov Function

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IFAC-PapersOnLine 2017年第1期50卷 11847-11852页

作者： Li Y. Lin Z. Li N. Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 United States

This paper revisits the problem of estimating the domain of attraction and the nonlinear L2 gain of a saturated system with an algebraic loop. A max quadratic Lyapunov function, the maximum function of a group of quadratic Lyapunov functions, is proposed for use in the stability and performance analysis for a saturated system. The max quadratic Lyapunov function involves a partitioning of the state space. Exploiting the special properties of regions of the state space, we propose in this paper an enhanced max quadratic Lyapunov function, which results from adding a term that characterizes the regions of the state space to the max quadratic Lyapunov function. The matrix associated with the enhanced max quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability and performance conditions are established, from which a larger estimate of the domain of attraction and a tighter estimate of the nonlinear L2 gain can be obtained. Simulation results indicate the effectiveness and superiority of the proposed method. © 2017

关键词： Domain of attraction enhanced max quadratic Lyapunov functions input saturation L2 gain

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Higher dimensional quantum communication in a curved spacetime: An efficient simulation of the propagation of the wavefront of a photon

arXiv

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arXiv 2018年

作者： Miller, Warner A. Alsing, Paul M. Ahn, Doyeol Department of Physics Florida Atlantic University Boca RatonFL33431 United States Air Force Research Laboratory Information Directorate RomeNY13441 United States Center Quantum Information Processing Department of Electrical and Computer Engineering University of Seoul Seoul130-743 Korea Republic of

A photon with a modulated wavefront can produce a quantum communication channel in a larger Hilbert space. For example, higher dimensional quantum key distribution (HD-QKD) can encode information in the transverse linear momentum (LM) or orbital angular momentum (OAM) modes of a photon. This is markedly different than using the intrinsic polarization of a photon. HD-QKD has advantages for free space QKD since it can increase the communication channels tolerance to bit error rate (BER) while maintaining or increasing the channels bandwidth. We describe an efficient numerical simulation of the propagation photon with an arbitrary complex wavefront in a material with an isotropic but inhomogeneous index of refraction. We simulate the waveform propagation of an optical vortex in a volume holographic element in the paraxial approximation using an operator splitting method. We use this code to analyze an OAM volume-holographic sorter. Furthermore, there are analogue models of the evolution of a wavefront in the curved spacetime environs of the Earth that can be constructed using an optical medium with a given index of refraction. This can lead to a work-bench realization of a satellite HD-QKD system. Copyright © 2018, The Authors. All rights reserved.

关键词： Photons

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Compressively Sensed Image Recognition

arXiv

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arXiv 2018年

作者： Deǧerli, Ayşen Aslan, Sinem Yamaç, Mehmet Sankur, Bülent Gabbouj, Moncef Tampere University of Technology Laboratory of Signal Processing Tampere Finland Ca' Foscari University of Venice European Centre for Living Technology Venice Italy Ege University International Computer Institute Izmir Turkey Boǧaziçi University Electrical and Electronics Engineering Department Istanbul Turkey

—Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

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The effect of non-Markovian recovery on reversible failure propagation on networks

arXiv

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arXiv 2019年

作者： Lin, Zhaohua Feng, Mi Tang, Ming Liu, Zonghua Hui, Pak Ming Lai, Ying-Cheng Department of Physics East China Normal University Shanghai200062 China School of Information Science Technology East China Normal University Shanghai200241 China Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Physics Chinese University of Hong Kong Shatin Hong Kong School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Compared with the classical irreversible failure propagation, the spontaneous recovery model can better depict the failure propagation on network systems such as financial and transportation networks. In this paper, we study how the non-Markovian recovery process influences cascading failure dynamics in the spontaneous recovery systems. To this end, we compare two kinds of failure-recovery models: the non-Markovian recovery (NMR) model and the corresponding Markovian recovery (MR) model. We first develop a Pairwise approximation theory for the MR and NMR models, which can predict the time evolution, steady state, and hysteresis behaviour of failure-recovery systems more accurately than the classical mean-field (MF) method. We find that although the non-Markovian recovery mechanism does not essentially affect the steady state and hysteresis behaviour of failure-recovery systems, the two models exhibit distinct evolution processes. When investigating the effect of initial conditions on the phase transition and hysteresis behavior, we find in the hysteresis region, the NMR model exhibits a non-monotonic growth characteristic: with the increase of initial failed nodes, the steady state staying in relative low-failure phase first turns into relative high-failure phase, and then into relative low-failure phase again. We then use the MF theory to explain the phenomenon qualitatively and point out when considering non-Markovian dynamics, we should study the steady state not only from the perspective of the variation of parameters, but also from the initial conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Hysteresis

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Combing context and commonsense knowledge through neural networks for solving winograd schema problems

Combing context and commonsense knowledge through neural net...

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2017 AAAI Spring Symposium

作者： Liu, Quan Jiang, Hui Ling, Zhen-Hua Zhu, Xiaodan Wei, Si Hu, Yu National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui China Department of Electrical Engineering and Computer Science York University Canada National Research Council Canada Ottawa Canada IFLYTEK Research Hefei China

ISBN: (纸本)9781577357797

This paper proposes a general framework to combine context and commonsense knowledge for solving the Winograd Schema (WS) and Pronoun Disambiguation Problems (PDP). In the proposed framework, commonsense knowledge bases (e.g. cause-effect word pairs) are quantized as knowledge constraints. The constraints guide us to learn knowledge enhanced embeddings (KEE) from large text corpus. Based on the pre-trained KEE models, this paper proposes two methods to solve the WS and PDP problems. The first method is an unsupervised method, which represents all the pronouns and candidate mentions in continuous vector spaces based on their contexts and calculates the semantic similarities between all the possible word pairs. The pronoun disambiguation procedure could then be implemented by comparing the semantic similarities between the pronoun (to be resolved) and all the candidate mentions. The second method is a supervised method, which extracts features for all the pronouns and candidate mentions and solves the WS problems by training a typical mention pair classification model. Similar to the first method, the features used in the second method are also extracted based on the KEE models. Experiments conducted on the available PDP and WS test sets show that, these two methods both achieve consistent improvements over the baseline systems. The best performance reaches 62% in accuracy on the PDP test set of the first Winograd Schema Challenge. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vector spaces

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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