270,184 research outputs found

    Power Spectra of X-ray Binaries

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    The interpretation of Fourier spectra in the time domain is critically examined. Power density spectra defined and calculated in the time domain are compared with Fourier spectra in the frequency domain for three different types of variability: periodic signals, Markov processes and random shots. The power density spectra for a sample of neutron stars and black hole binaries are analyzed in both the time and the frequency domains. For broadband noise, the two kinds of power spectrum in accreting neutron stars are usually consistent with each other, but the time domain power spectra for black hole candidates are significantly higher than corresponding Fourier spectra in the high frequency range (10--1000 Hz). Comparing the two kinds of power density spectra may help to probe the intrinsic nature of timing phenomena in compact objects.Comment: 21 pages, 10 figures, to appear in Astrophysical Journa

    The Hahn Quantum System

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    Using a formulation of quantum mechanics based on the theory of orthogonal polynomials, we introduce a four-parameter system associated with the Hahn and continuous Hahn polynomials. The continuum energy scattering states are written in terms of the continuous Hahn polynomial whose asymptotics give the scattering amplitude and phase shift. On the other hand, the finite number of discrete bound states are associated with the Hahn polynomial.Comment: 18 pages, 7 figure

    Least-Squares Approximation by Elements from Matrix Orbits Achieved by Gradient Flows on Compact Lie Groups

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    Let S(A)S(A) denote the orbit of a complex or real matrix AA under a certain equivalence relation such as unitary similarity, unitary equivalence, unitary congruences etc. Efficient gradient-flow algorithms are constructed to determine the best approximation of a given matrix A0A_0 by the sum of matrices in S(A1),...,S(AN)S(A_1), ..., S(A_N) in the sense of finding the Euclidean least-squares distance min{X1+...+XNA0:XjS(Aj),j=1,>...,N}.\min \{\|X_1+ ... + X_N - A_0\|: X_j \in S(A_j), j = 1, >..., N\}. Connections of the results to different pure and applied areas are discussed

    Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

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    This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance system utilizing face detectors. We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image. This is because the adversarial perturbation specifically generated for one face may disrupt the adversarial perturbation for another face. In this paper, we call this problem the Instance Perturbation Interference (IPI) problem. This IPI problem is addressed by studying the relationship between the deep neural network receptive field and the adversarial perturbation. As such, we propose the Localized Instance Perturbation (LIP) that uses adversarial perturbation constrained to the Effective Receptive Field (ERF) of a target to perform the attack. Experiment results show the LIP method massively outperforms existing adversarial perturbation generation methods -- often by a factor of 2 to 10.Comment: to appear ECCV 2018 (accepted version
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