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作者： Bajwa, Waheed U. Calderbank, Robert Jafarpour, Sina Department of Electrical Engineering Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 United States Department of Computer Science Princeton University Princeton NJ 08544 United States

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence- termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carr

关键词： Recovery

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AN EQUIVALENCY CONDITION OF NONSINGULARITY IN NONLINEAR SEMIDEFINITE programMING

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Journal of Systems science & Complexity 2010年第4期23卷 822-829页

作者： Chengjin LI Wenyu SUN Raimundo J. B. de SAMPAIO School of Mathematical Sciences Nanjing Normal and Computer Science Fujian Normal University Wenyu SUN University Nanjing 210046 China School of Mathematics Fuzhou 350007 China School of Mathematical Sciences Nanjing Normal University Nanjing 210046 China Pontifical Catholic University of Parana ( PUCPR) Graduate Program in Production and Systems Engineering (PPGEPS) CEP 81611-970 Curitiba Parana Brazil

In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,... 详细信息

关键词： Equivalency condition of nonsingularity nonlinear semidefinite programming semidefinite programming.

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Sci—Fri AM: Imaging — 05: Principal Component X-Ray Simulation Analysis of Breast Biopsy Phantoms

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Medical Physics 2010年第7PART3期37卷 3902-3903页

作者： R Tang JB Georgeoff RJ LeClair Biomolecular Sciences Program Laurentian University Department of Mathematics and Computer Science Laurentian University Department of Physics Laurentian University

Multivariate principal component analysis (PCA) was performed on x-ray simulations of breast biopsy phantoms. Semianalytic models were used to generate scatter (N s ) and photon transmission (N) signals from mixtures of fibrous (i), cancerous (j), and adipose tissue (k). A 5mm diameter 30 kV Mo beam is incident on a 5 mm thick biopsy. The incident exposure is 5.08 × 10 −4 C/kg. An energy discriminating photon counting detector is assumed (e.g. CZT). For simulation of scatter measurements, the detector is placed at a scatter angle θ=10°, whereas for transmission θ=0°. The distance from the biopsy center to a 5 mm diameter aperture above the detector is 15 cm. The variables used in the PCA are N s /N 0 (16 keV), N s /N 0 (25 keV) and N/N 0 (8 keV) where N 0 is the incident spectrum. These variables are calculated 10 times. Each trial consists of biopsy shuffling of tissue blocks for a chosen fractional composition (i,j,k). A statistical component was incorporated in No. As a preliminary test to the classification model, a total of 66 different biopsies have been simulated and are used as unknowns. The PCA classes generated are based on a biopsy composed of 5 layers and 5 radii. The 66 unknown compositions were tested against the PCA classes to determine if the biopsy tissue composition could be identified. Of the 66 biopsies, 46 were correctly identified. An example of a misclassified unknown was for the composition (0,0.2,0.8) which was classified as (0.3,0,0.7). Further work is required to increase accuracy of identification.

关键词： Image analysis Multivariate analysis Transmission measurement Cancer Biomedical modeling Molybdenum Medical imaging Photons Photon counting Photon scattering

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YES: A NSF S-STEM scholarship program experience at the University of Central Florida

YES: A NSF S-STEM scholarship program experience at the Univ...

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2010 ASEE Annual Conference and Exposition

作者： Massi, Lisa Georgiopoulos, Michael Young, Cynthia Ducharme, Alfred Ford, Cameron Small, Kim Lancey, Patrice Bhati, Divya College of Engineering and Computer Science University of Central Florida United States School of Electrical Engineering and Computer Science University of Central Florida United States Department of Mathematics College of Sciences University of Central Florida United States Management Department UCF College of Business UCF Center for Entrepreneurship and Innovation United States NSF-funded S-STEM Program University of Central Florida United States

According to a study conducted by J. D. Angrist (MIT) and colleagues1 involving 1,600 students at a large Canadian university (the equivalent of an American state university with heavily subsidized tuition), the combination of participation in (a) a scholarship program and (b) academic support services resulted in higher grade achievement and retention for females (but both males and females used support services and peer advising at higher rates), compared to groups of students who participated in either (a) or (b) but not both. A report by the Educational Policy Institute2 lists financial aid as one critical factor that affects the decision to pursue a college degree and success in degree attainment for minorities. This paper reports the progress of a persistence-to-graduation scholarship program funded under the National science Foundation Scholarships (NSF) in science, Technology, Engineering, and mathematics (S-STEM) program. We named our program YES (Young Entrepreneur and Scholar) scholarship program. The goal of YES is to enable academically talented, financially needy students to enter the workforce (Entrepreneurship Path) or to pursue a graduate degree (Research Path) following the completion of a baccalaureate degree in a targeted STEM discipline. This goal is facilitated through scholarships offered by the program to qualified student participants in the last two college years (maturing years - juniors and seniors), a mentorship program (faculty and industry mentors), and enhanced educational opportunities (Distinguished Speaker seminar series, Symposium, learning community). The program continues the efforts of and recruits from another NSF-funded program, science, Technology, Engineering, and mathematics Talent Expansion (STEP) program, which focuses on the first two student college years (early years- freshman and sophomore) and has been successful in retaining STEM students at significantly higher rates than academically similar STEM students at the

关键词： Students

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Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications

Hardware particle swarm optimization based on the attractive...

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2010 International Conference on Reconfigurable Computing and FPGAs, ReConFig 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9780769543147

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. However, the PSO algorithm suffers from premature convergence and high elapsed time when solving multimodal and large scale engineering problems. This problem becomes an evident drawback for embedded applications in which the microcontrollers often operates at low computational capacity. This paper proposes a hardware implementation of a parallel self-adaptive PSO algorithm based on an attractive-repulsive scheme and using the efficient floating-point arithmetic which performs computations with large dynamic range and high precision. The parallel capabilities of the PSO are exploited by implementing parallel particles directly in hardware in order to decrease the running time. In addition, the attractive-repulsive technique avoids the premature convergence problem by self-adapting the swarm behavior according to its diversity. Synthesis and simulation results for benchmark test problems were performed, demonstrating the correctness of the proposed architectures. Finally, an elapsed time comparison between the hardware and software implementations shows the suitableness of the proposed architecture for embedded applications. © 2010 IEEE.

关键词： Global optimization

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACG University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide sub-optimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems. © 2010 IEEE.

关键词： Global optimization

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Multistate modeling and simulation for regulatory networks 10

Multistate modeling and simulation for regulatory networks

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Simulation Winter Conference

作者： Zhen Liu Umme Juka Mobassera Clifford A. Shaffer Layne T. Watson Yang Cao Department of Computer Science Virginia Technology Blacksburg VA USA Program in Genetics Bioinformatics and Computational Biology Virginia Technology Blacksburg VA USA Department of Computer Science Department of Mathematics Virginia Technology Blacksburg VA USA

ISBN: (纸本)9781424498642

Many protein regulatory models contain chemical species best represented as having multiple states. Such models stem from the potential for multiple levels of phosphorylation or from the formation of multiprotein complexes. We seek to support such models by augmenting an existing modeling and simulation system. Interactions between multistate species can lead to a combinatorial explosion in the potential state space. This creates a challenge when using Gillespie's stochastic simulation algorithm (SSA). Both the network-free algorithm (NFA) and various rules-based methods have been proposed to more efficiently simulate such models. We show how to further improve NFA to integrate population-based and particle-based features. We then present a population-based scheme for the stochastic simulation of rule-based models. A complexity analysis is presented comparing the proposed simulation methods. We present numerical experiments for two sample models that demonstrate the power of the proposed simulation methods.

关键词： Mathematical model Biological system modeling Numerical models Chemicals Stochastic processes Equations Proteins

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Comparison between two FPGA implementations of the particle swarm optimization algorithm for high-performance embedded applications

Comparison between two FPGA implementations of the particle ...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a high performance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set computer (RISC) microprocessors in which the operational frequencies are low in comparison with the high operational frequencies of traditional personal computers (PCs). This paper compares two hardware implementations of the parallel PSO algorithm using an efficient floating-point arithmetic which perform computations with large dynamic range and high precision. The full-parallel and the partially-parallel PSO architectures allow the parallel capabilities of the PSO to be exploited in order to decrease the running time. Three well-known benchmark test functions have been used to validate the hardware architectures and a comparison in terms of cost in logic area, quality of the solution and performance is reported. In addition, a comparison of the execution time between the hardware and two C-code software implementations, based on a Intel Core Duo at 1.6GHz and a embedded Microblaze microprocessor at 50MHz, are presented. © 2010 IEEE.

关键词： Global optimization

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An end-to-end simulator for the all-electric ship MVDC integrated power system

An end-to-end simulator for the all-electric ship MVDC integ...

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Grand Challenges in Modeling and Simulation Symposium, GCMS 2010, Part of the 2010 Summer Simulation Multiconference, SummerSim 2010

作者： Milošević Marden, M. Prempraneerach, P. Kirtley, J.L. Karniadakis, G. Chryssostomidis, C. Design Laboratory MIT Sea Grant College Program United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Mechanical Engineering Rajamangala University of Technology Thunyaburi Pathumthani Thailand Division of Applied Mathematics Brown University Providence RI United States

ISBN: (纸本)9781617387043

In this paper, a large scale Medium Voltage DC all-electric ship integrated power system is modeled from the prime mover (gas turbine) to the propulsion load. This system has a three-phase 21MW synchronous machine as a main generator and a three-phase 19MW induction motor as a main propulsion drive. The influence of propeller emergence on both electrical and mechanical components of the system is investigated.

关键词： Ship propulsion

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MADM methods in solving group decision support system on gene mutations detection simulation

MADM methods in solving group decision support system on gen...

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International Conference on (DFMA) Distributed Frameworks for Multimedia Applications

作者： Ermatita Sri Hartati Retantyo Wardoyo Agus Harjoko Department of Information System Computer Science Faculty of Sriwijaya University Indonesia (Doctoral Program Student Computer Science Gadjah Mada University) Jl. Palembang-Prabumulih Ogan Ilir Indonesia Department of Computer Science and Electronics Mathematics and Natural Sciences Faculty Gadjah Mada University (UGM) Indonesia

Detection of gene mutation is an activity that can provide contribution in the medical field. Detection of mutated gene is needed to avoid the diseases caused by them such as cancer. The detection of gene mutations can be performed by utilizing computer-based system. Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease. The ELECTRE method, which is a Multi-Attribute Decision Making, is a method in modeling multi-criteria GDSS. In this paper we propose a model for multi-criteria GDSS in which the simulation data is the mutated genes that can cause cancer.

关键词： Decision making Cancer Computational modeling Decision support systems Indexes Humans Data models

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