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Towards a unified multiresolution vision model for autonomous ground robots

ROBOTICS AND AUTONOMOUS SYSTEMS 2016年 75卷 221-232页

作者： Sustersic, John Wyble, Brad Advani, Siddharth Narayanan, Vijaykrishnan Penn State Univ Appl Res Lab University Pk PA 16804 USA Penn State Univ Dept Psychol University Pk PA 16804 USA Penn State Univ Dept Comp Sci & Engn University Pk PA 16804 USA

While remotely operated unmanned vehicles are increasingly a part of everyday life, truly autonomous robots capable of independent operation in dynamic environments have yet to be realized - particularly in the case of ground robots required to interact with humans and their environment. We present a unified multiresolution vision model for this application designed to provide the wide field of view required to maintain situational awareness and sufficient visual acuity to recognize elements of the environment while permitting feasible implementations in real-time vision applications. The model features a kind of color-constant processing through single-opponent color channels and contrast invariant oriented edge detection using a novel implementation of the Combination of Receptive Fields model. The model provides color and edge-based salience assessment, as well as a compressed color image representation suitable for subsequent object identification. We show that bottom-up visual saliency computed using this model is competitive with the current state-of-the-art while allowing computation in a compressed domain and mimicking the human visual system with nearly half (45%) of computational effort focused within the fovea. This method reduces storage requirement of the image pyramid to less than 5% of the full image, and computation in this domain reduces model complexity in terms of both computational costs and memory requirements accordingly. We also quantitatively evaluate the model for its application domain by using it with a camera/lens system with a 185 field of view capturing 3.5M pixel color images by using a tuned salience model to predict human fixations. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Computer vision Gaussian pyramid Visual salience parallel algorithms

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MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression

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BMC BIOINFORMATICS 2016年第1期17卷 1-13页

作者： Kim, Minji Zhang, Xiejia Ligo, Jonathan G. Farnoud, Farzad Veeravalli, Venugopal V. Milenkovic, Olgica Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA CALTECH Dept Elect Engn Pasadena CA 91125 USA

Background: Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and hence tend to result in large file sizes, typically ranging between 1-10 GB. This leads to challenges in analyzing, transferring and storing metagenomic data. In order to overcome these data processing issues, we introduce MetaCRAM, the first de novo, parallelized software suite specialized for FASTA and FASTQ format metagenomic read processing and lossless compression. Results: MetaCRAM integrates algorithms for taxonomy identification and assembly, and introduces parallel execution methods;furthermore, it enables genome reference selection and CRAM based compression. MetaCRAM also uses novel reference-based compression methods designed through extensive studies of integer compression techniques and through fitting of empirical distributions of metagenomic read-reference positions. MetaCRAM is a lossless method compatible with standard CRAM formats, and it allows for fast selection of relevant files in the compressed domain via maintenance of taxonomy information. The performance of MetaCRAM as a stand-alone compression platform was evaluated on various metagenomic samples from the NCBI Sequence Read Archive, suggesting 2- to 4-fold compression ratio improvements compared to gzip. On average, the compressed file sizes were 2-13 percent of the original raw metagenomic file sizes. Conclusions: We described the first architecture for reference-based, lossless compression of metagenomic data. The compression scheme proposed offers significantly improved compression ratios as compared to off-the-shelf methods such as zip programs. Furthermore, it enables running different components in parallel and it provides the user with taxonomic and assembly information generated during execution of the

关键词： Metagenomics Genomic compression parallel algorithms

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On the parameterized parallel complexity and the vertex cover problem 10th

On the parameterized parallel complexity and the vertex cove...

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10th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2016

作者： Abu-Khzam, Faisal N. Li, Shouwei Markarian, Christine Meyer auf der Heide, Friedhelm Podlipyan, Pavel Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Heinz Nixdorf Institute & Department of Computer Science Paderborn University Fürstenallee 11 Paderborn33102 Germany Department of Mathematical Sciences Haigazian University Beirut Lebanon School of Engineering and Information Technology Charles Darwin University Darwin Australia

ISBN: (纸本)9783319487489

Efficiently parallelizable parameterized problems have been classified as being either in the class FPP (fixed-parameter parallelizable) or the class PNC (parameterized analog of NC), which contains FPP as a subclass. In this paper, we propose a more restrictive class of parallelizable parameterized problems called fixed-parameter parallel-tractable (FPPT). For a problem to be in FPPT, it should possess an efficient parallel algorithm not only from a theoretical standpoint but in practice as well. The primary distinction between FPPT and FPP is the parallel processor utilization, which is bounded by a polynomial function in the case of FPPT. We initiate the study of FPPT with the well-known kvertex cover problem. In particular, we present a parallel algorithm that outperforms the best known parallel algorithm for this problem: using O(m) instead of O(n2) parallel processors, the running time improves from 4 log n+O(kk) to O(k · log3 n), where m is the number of edges, n is the number of vertices of the input graph, and k is an upper bound of the size of the sought vertex cover. We also note that a few P-complete problems fall into FPPT including the monotone circuit value problem (MCV) when the underlying graphs are bounded by a constant Euler genus. © Springer International Publishing AG 2016.

关键词： parallel algorithms

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Ternary Matrix Factorization: problem definitions and algorithms

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第1期46卷 1-31页

作者： Maurus, Samuel Plant, Claudia Tech Univ Munich Helmholtz Zentrum Munchen D-85764 Neuherberg Germany

Can we learn from the unknown? Logical data sets of the ternary kind are often found in information systems. They contain unknown as well as true/false values. An unknown value may represent a missing entry (lost or indeterminable) or have meaning, like a Don't Know response in a questionnaire. In this paper, we introduce algorithms for reducing the dimensionality of logical data (categorical data in general) in the context of a new data mining challenge: Ternary Matrix Factorization (TMF). For a ternary data matrix, TMF exploits ternary logic to produce a basis matrix (which holds the major patterns in the data) and a usage matrix (which maps patterns to original observations). Both matrices are interpretable, and their ternary matrix product approximates the original matrix. TMF has applications in (1) finding targeted structure in ternary data, (2) imputing values through pattern discovery in highly incomplete categorical data sets, and (3) solving instances of its encapsulated Binary Matrix Factorization problem. Our elegant algorithm FasTer (FASt TERnary Matrix Factorization) has linear run-time complexity with respect to the dimensions of the data set and is parameter-robust. A variant of FasTer that exploits useful results from combinatorics provides accuracy bounds for a core part of the algorithm in certain situations. Experiments on synthetic and real-world data sets show that our algorithms are able to outperform state-of-the-art techniques in all three TMF applications with respect to run-time and effectiveness. Finally, convincing speedup and efficiency results on a parallel version of FasTer demonstrate its suitability for weak-and strong-scaling scenarios.

关键词： Three-valued logic Ternary data Matrix factorization Dimensionality reduction Missing values Imputation parallel algorithms

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UNLOCKING ORDERED parallelISM WITH THE SWARM ARCHITECTURE

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IEEE MICRO 2016年第3期36卷 105-117页

作者： Jeffrey, Mark C. Subramanian, Suvinay Yan, Cong Emer, Joel Sanchez, Daniel MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Nvidia Santa Clara CA USA

Swarm is a parallel architecture that exploits ordered parallelism. It executes tasks speculatively and out of order and can scale to large core counts and speculation windows. The authors evaluate swarm on graph analytics, simulation, and database benchmarks. At 64 cores, swarm outperforms sequential implementations of these algorithms by 43 to 117 times and state-of-the-art software-only parallel algorithms by 3 to 18 times.

关键词： Data Analysis parallel algorithms parallel Architectures Ordered parallelism Swarm parallel Architecture Programmer Specified Order Constraint Graph Analytics parallel Algorithm parallel Processing Schedules Multicore Processing Synchronization Benchmark Testing Analytical Models Multicore Ordered parallelism Fine Grained parallelism Synchronization Speculative Execution

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A batch, derivative-free algorithm for finding multiple local minima

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OPTIMIZATION AND ENGINEERING 2016年第1期17卷 205-228页

作者： Larson, Jeffrey Wild, Stefan M. Argonne Natl Lab 9700 S Cass AveBldg 240 Argonne IL 60439 USA

We propose a derivative-free algorithm for finding high-quality local minima for functions that require significant computational resources to evaluate. Our algorithm efficiently utilizes the computational resources allocated to it and also has strong theoretical results, almost surely starting a finite number of local optimization runs and identifying all local minima. We propose metrics for measuring how efficiently an algorithm finds local minima, and we benchmark our algorithm on synthetic problems (with known local minima) and two real-world applications.

关键词： Derivative-free optimization Multistart parallel algorithms Global optimization

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On Acceleration and Scalability of Number Theoretic Private Information Retrieval

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2016年第6期27卷 1727-1741页

作者： Unal, Ecem Savas, Erkay Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

We present scalable and parallel versions of Lipmaa's computationally-private information retrieval (CPIR) scheme [20], which provides log-squared communication complexity. In the proposed schemes, instead of binary decision diagrams utilized in the original CPIR, we employ an octal tree based approach, in which non-sink nodes have eight child nodes. Using octal trees offers two advantages: i) a serial implementation of the proposed scheme in software is faster than the original scheme and ii) its bandwidth usage becomes less than the original scheme when the number of items in the data set is moderately high (e.g., 4,096 for 80-bit security level using Damgard-Jurik cryptosystem). In addition, we present a highly-optimized parallel algorithm for shared-memory multi-core/processor architectures, which minimizes the number of synchronization points between the cores. We show that the parallel implementation is about 50 times faster than the serial implementation for a data set with 4,096 items on an eight-core machine. Finally, we propose a hybrid algorithm that scales the CPIR scheme to larger data sets with small overhead in bandwidth complexity. We demonstrate that the hybrid scheme based on octal trees can lead to more than two orders of magnitude faster parallel implementations than serial implementations based on binary trees. Comparison with the original as well as the other schemes in the literature reveals that our scheme is the best in terms of bandwidth requirement.

关键词： Number theoretic private information retrieval security privacy parallel algorithms

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Supercomputing-Enabled First-Principles Analysis of Wireless Channels in Real-World Environments

Supercomputing-Enabled First-Principles Analysis of Wireless...

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IEEE-Microwave-Theory-and-Techniques-Society International Microwave Symposium (IMS) / Session on Women in Microwaves (WIM)

作者： Shao, Yang Peng, Zhen Univ New Mexico Albuquerque NM 87131 USA

ISBN: (纸本)9781509063604

Wireless communications are expected to take place in increasingly complicated scenarios, such as dense urban, forest, tunnel and other significant cluttered environments. A key challenge emerging is to understand the physics and characteristics of wireless channels in complex environments, which are critical for the analysis, design, and application of future mobile and wireless communication systems. The objective of this work is to investigate high-resolution, high-performance computational algorithms for extreme-scale channel modeling in real-world environments. The system-level large scene analysis is enabled by the novel, ultra-parallel algorithms on the emerging exascale high-performance computing (HPC) platforms. The results lead to much greater channel model resolution than existing deterministic channel modeling technologies. All relevant propagation mechanisms are accounted for in first-principles. Such a modeling framework will be critical to gaining fundamental physics of wireless propagation channels in real-world scenarios.

关键词： parallel algorithms domain decomposition methods electromagnetics wireless channels

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Fast Coherent Particle Advection through Time-Varying Unstructured Flow Datasets

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2016年第8期22卷 1959-1972页

作者： Chen, Mingcheng Shadden, Shawn C. Hart, John C. Univ Illinois Dept Comp Sci Champaign IL 61820 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

Tracing the paths of collections of particles through a flow field is a key step for many flow visualization and analysis methods. When a flow field is interpolated from the nodes of an unstructured mesh, the process of advecting a particle must first find which cell in the unstructured mesh contains the particle. Since the paths of nearby particles often diverge, the parallelization of particle advection quickly leads to incoherent memory accesses of the unstructured mesh. We have developed a new block advection GPU approach that reorganizes particles into spatially coherent bundles as they follow their advection paths, which greatly improves memory coherence and thus shared-memory GPU performance. This approach works best for flows that meet the CFL criterion on unstructured meshes of uniformly sized elements, small enough to fit at least two timesteps in GPU memory.

关键词： Unsteady flow unstructured mesh GPU parallel algorithms

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parallel algorithms for large scale econometric models

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Procedia Computer Science 2011年 3卷 479-483页

作者： Bogdan Oancea Tudorel Andrei Ion Gh. Rosca Andreea Iluzia Iacob Nicolae Titulescu” University 040051 Bucharest Romania The Bucharest Academy of Economic Studies 010374 Bucharest Romania

In this paper we have developed algorithms to solve macroeconometric models with forward-looking variables based on Newton method for nonlinear systems of equations. The most difficult step for Newton methods represents the resolution of a large linear system for each iteration. Thus, we compare the performances resulted by solving this linear system using two iterative methods and the direct method. We’ve also described an implementation of the parallel versions of such algorithms using a software package. Our experiments confirm that the iterative methods have a low computational complexity and storage requirements, but the parallel versions of direct methods show a superior speedup.

关键词： parallel algorithms Macroeconometric models Rational expectations models Linear algebra Newton methods Krylov techniques Direct methods

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