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European Signal Processing Conference

作者： Tomoyuki Obuchi Yoshiyuki Kabashima Department of Mathematical and Computing Science Tokyo Institute of Technology

ISBN: (纸本)9781509018918

The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In this paper, a sampling approach based on the Monte Carlo method is presented as an efficient solver for such problems. Especially, the use of simulated annealing (SA), a metaheuristic optimization algorithm, for determining degrees of freedom (the number of used columns) by cross validation is focused on and tested. Test on a synthetic model indicates that our SA-based approach can find a nearly optimal solution for the approximation problem and, when combined with the CV framework, it can optimize the generalization ability. Its utility is also confirmed by application to a real-world supernova data set.

关键词： Degree of freedom simulated annealing approximation Monte Carlo technique optimization algorithms cross-validation optimal solution column vector

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Performance of Uniaxial Multilayer Cylinders and Spheres Used for Invisible Cloak Realization

Performance of Uniaxial Multilayer Cylinders and Spheres Use...

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European Conference on Antennas and Propagation

作者： Branimir Ivsic Tin Komljenovic Zvonimir Sipus University of Zagreb Faculty of Electrical Engineering and Computing Department of Wireless Communications

ISBN: (纸本)9781457702501

The paper presents the analysis of cylindrical and spherical structures used for invisible cloak realization. Different cylindrical and spherical cloak realizations were analyzed in terms of bistatic scattering cross section. In addition, optimization algorithm was connected with the analysis routine to find the constitutive parameters of the cloak that reduce both backscattering and forward scattering. The results show that it is possible to obtain adequate invisibility performance by covering the PEC sphere with only 2 thin anisotropic layers.

关键词： Spheres invisibility performance Spheroidal structure optimization algorithms Cylindrical backscattering Cylinders Cylinders Cross section bistatic scattering forward scattering multilayers swine enteric calicivirus

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ForeSee: A Cross-Layer Vulnerability Detection Framework for the Internet of Things

ForeSee: A Cross-Layer Vulnerability Detection Framework for...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zheng Fang Hao Fu Tianbo Gu Zhiyun Qian Trent Jaeger Prasant Mohapatra University of California Davis Davis CA USA DiDi Labs Mountain View CA USA University of California Riverside Riverside CA USA Pennsylvania State University State College PA USA

ISBN: (数字)9781728146010

ISBN: (纸本)9781728146027

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

关键词： Internet of Things (IoT) IoT security Multiple layer Vulnerability detection Intrusion detection Cyber security IoT privacy Model checking Attack graph Network security Data Security Model checking Internet of things optimization algorithms Network protocols Internets Exponential Growth physical environment Vulnerability

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Elliptical Wishart distributions: Information geometry, maximum likelihood estimator, performance analysis and statistical learning

arXiv

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arXiv 2024年

作者： Ayadi, Imen Bouchard, Florent Pascal, Frédéric Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 rue Joliot-Curie Gif-sur-Yvette91190 France

This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum likelihood estimator (MLE) are proposed: a fixed point algorithm and a Riemannian optimization method based on the derived information geometry of Elliptical Wishart distributions. The existence and uniqueness of the MLE are characterized as well as the convergence of both estimation algorithms. Statistical properties of the MLE are also investigated such as consistency, asymptotic normality and an intrinsic version of Fisher efficiency. On the statistical learning side, novel classification and clustering methods are designed. For the t-Wishart distribution, the performance of the MLE and statistical learning algorithms are evaluated on both simulated and real EEG and hyperspectral data, showcasing the interest of our proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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FAST COMPUTATION OF THE L_1-PRINCIPAL COMPONENT OF REAL-VALUED DATA

FAST COMPUTATION OF THE L_1-PRINCIPAL COMPONENT OF REAL-VALU...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sandipan Kundu Panos P. Markopoulos Dimitris A. Pados Department of Electrical Engineering State University of New York at Buffalo NY 14260

ISBN: (纸本)9781479928941

Recently, Markopoulos et al. [1], [2] presented an optimal algorithm that computes the L_1 maximum-projection principal component of any set of N real-valued data vectors of dimension D with complexity polynomial in N, O(N~D). Still, moderate to high values of the data dimension D and/or data record size N may render the optimal algorithm unsuitable for practical implementation due to its exponential in D complexity. In this paper, we present for the first time in the literature a fast greedy single-bit-flipping conditionally optimal iterative algorithm for the computation of the L_1 principal component with complexity O(N~3). Detailed numerical studies are carried out demonstrating the effectiveness of the developed algorithm with applications to the general field of data dimensionality reduction and direction-of-arrival estimation.

关键词： optimization algorithms dimensionality reduction deuterium ABSORBED DOSE

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Stochastic Gradient Descent with Only One Projection 12

Stochastic Gradient Descent with Only One Projection

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Annual Conference on Neural Information Processing Systems

作者： Mehrdad Mahdavi Tianbao Yang Rong Jin Shenghuo Zhu Jinfeng Yi Dept. of Computer Science and Engineering Michigan State University MI USA Machine Learning Lab GE Global Research CA USA NEC Laboratories America. CA. USA

ISBN: (纸本)9781627480031

Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of diem require projecting the solution at each iteration to ensure that the obtained solution stays within the feasible domain. For complex domains (e.g., positive semidefinite cone), the projection step can be computationally expensive, making stochastic gradient descent unattractive for large-scale optimization problems. We address this limitation by developing novel stochastic optimization algorithms that do not need intermediate projections. Instead, only one projection at the last iteration is needed to obtain a feasible solution in the given domain. Our theoretical analysis shows diat with a high probability, the proposed algorithms achieve an O(1/T~(1/2)) convergence rate for general convex optimization, and an O (In T/T) rate for strongly convex optimization under mild conditions about the domain and the objective function.

关键词： descent Stochastic iterative methods Convex function Feasible Solution optimization algorithms Projection Objective function Domain Theoretical analysis

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NUCLEAR NORM MINIMIZATION AND TENSOR COMPLETION IN EXPLORATION SEISMOLOGY

NUCLEAR NORM MINIMIZATION AND TENSOR COMPLETION IN EXPLORATI...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Nadia Kreimer Mauricio D. Sacchi Department of Physics University of Alberta Edmonton AB T6G 2E1 Canada

ISBN: (纸本)9781479903573

We consider the problem of multidimensional seismic data signal recovery and noise attenuation. These data are multidimensional signals that can be described via a low-rank fourth-order tensor in the frequency - space domain. Tensor completion strategies can be used to recover unrecorded observations and to improve the signal-to-noise ratio of seismic data volumes. Tensor completion is posed as an inverse problem and solved via a convex optimization algorithm where a misfit function is minimized in conjunction with the nuclear norm of the tensor. This formulation offers automatic rank determination. We illustrate the performance of the algorithm with a synthetic example and with a real data set obtained by an onshore seismic survey.

关键词： seismic data signal reconstruction tensor completion nuclear norm rank reduction seismic data Signal reconstruction Tensor seismology Atomic nuclei optimization algorithms Exploration noise attenuation signal to noise ratios

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Factory Planning Model of Aircraft Engine Transmission Parts based on the Genetic Annealing Algorithm

Factory Planning Model of Aircraft Engine Transmission Parts...

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International Conference on Computer Science and Network Technology

作者： Jie Jia Pei Qin China Aviation Planning and Design Institute(Group) Co. Ltd. Beijing 100120 China Aviation Planning and Design Institue (Group) Co. Ltd. Beijing 100120

ISBN: (纸本)9781467381741

According to the premature convergence and low searching efficiency of the standard simulated annealing algorithm in workshop planning applications, this paper proposes a factory planning model of aircraft engine transmission parts based on improved genetic algorithm optimized simulated annealing. First, the mechanism floatingpoint coding is referenced to arithmetic crossover operation for crossover operator of genetic algorithm, and then adaptive mutation operators are adopted to keep the diversity of population, and the global optimization of the algorithm is improved. Finally, transmission parts factory programming model is constructed according to the characteristic of aviation engine. The results showed that compared with simulated annealing algorithm, the genetic simulated annealing algorithm has good convergence in application to factory planning.

关键词： Aircraft engine transmission parts factory planning model simulated annealing algorithm improved genetic algorithm Aircraft engines simulated annealing algorithm Genetic Algorithm (GA) Industrial Plants optimization algorithms

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COHERENCE REGULARIZED DICTIONARY LEARNING

COHERENCE REGULARIZED DICTIONARY LEARNING

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mansour Nejati Shadrokh Samavi S. M. Reza Soroushmehr Kayvan Najarian ECE Department Isfahan University of Technology Iran Department of Emergency Medicine University of Michigan Ann Arbor USA

ISBN: (纸本)9781479999897

Sparse representations over redundant learned dictionaries have shown to produce high quality results in various image processing tasks. An important characteristic of a learned dictionary is the mutual coherence of dictionary that affects its generalization performance and the optimality of sparse codes generated from it. In this paper, we present a dictionary learning model equipped with coherence regularization. For this model, two novel dictionary optimization algorithms based on group-wise minimization of inter- and intra-coherence penalties are proposed. Experimental results demonstrate that the proposed algorithms improve the generalization properties and sparse approximation performance of the trained dictionary compared to several incoherent dictionary learning methods.

关键词： Dictionary learning mutual coherence sparse representation optimization sparse coding Dictionaries as Topic Sparse coding sparse representation coherence optimization algorithms

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Necessary and Sufficient Stabilizing Conditions for Networked Control Systems with Simultaneous Transmission Delay and Packet Dropout

Necessary and Sufficient Stabilizing Conditions for Networke...

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IEEE Annual Conference on Decision and Control

作者： Cheng Tan Huanshui Zhang School of Control Science and Engineering Shandong University Jinan 250061 P. R. China

ISBN: (纸本)9781479978878

This paper investigates the mean-square stabilization problem for discrete-time networked control systems (NCSs). Different from most previous studies, we assume that transmission delay and packet dropout may occur simultaneously. The stabilization problem for such NCSs remains challenging because of the fundamental difficulty of stochastic control. The contributions of this paper are twofold. First, we present a necessary and sufficient condition for stabilizing the NCSs in terms of the unique positive solution to a specified algebraic equation. It is also shown that the NCS is stabilizable iff the generalized Lyapunov equation has a positive solution, which is in accordance with the classical result for a delay-free system. Second, we propose an optimization algorithm for computing the maximum packet dropout rate. The key technique adopted in this paper involves the Riccati-ZXL equation established in our earlier work.

关键词： Networked control systems Transport delay Necessary Packet optimization algorithms simultaneous transmission Lyapunov equation control systems Stabilising Sufficient

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