Parallel Sparse Matrix - Vector Product
DEFF Research Database (Denmark)
Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd
This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...
Sparse matrix test collections
Energy Technology Data Exchange (ETDEWEB)
Duff, I.
1996-12-31
This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.
Sparse matrix decompositions for clustering
Blumensath, Thomas
2014-01-01
Clustering can be understood as a matrix decomposition problem, where a feature vector matrix is represented as a product of two matrices, a matrix of cluster centres and a matrix with sparse columns, where each column assigns individual features to one of the cluster centres. This matrix factorisation is the basis of classical clustering methods, such as those based on non-negative matrix factorisation but can also be derived for other methods, such as k-means clustering. In this paper we de...
Finding Nonoverlapping Substructures of a Sparse Matrix
Energy Technology Data Exchange (ETDEWEB)
Pinar, Ali; Vassilevska, Virginia
2005-08-11
Many applications of scientific computing rely on computations on sparse matrices. The design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of nonoverlapping dense blocks in a sparse matrix, which is previously not studied in the sparse matrix community. We show that the maximum nonoverlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm that runs in linear time in the number of nonzeros in the matrix. This extended abstract focuses on our results for 2x2 dense blocks. However we show that our results can be generalized to arbitrary sized dense blocks, and many other oriented substructures, which can be exploited to improve the memory performance of sparse matrix operations.
Massive Asynchronous Parallelization of Sparse Matrix Factorizations
Energy Technology Data Exchange (ETDEWEB)
Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)
2018-01-08
Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.
Better Size Estimation for Sparse Matrix Products
DEFF Research Database (Denmark)
Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus
2010-01-01
We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected t...
Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.
Energy Technology Data Exchange (ETDEWEB)
Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2018-01-01
Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.
A direct parallel sparse matrix solver
International Nuclear Information System (INIS)
Tran, T.M.; Gruber, R.; Appert, K.; Wuthrich, S.
1995-08-01
The direct sparse matrix solver is based on a domain decomposition technique to achieve data and work parallelization. Geometries that have long and thin structures are specially efficiently tractable with this solver, provided that they can be decomposed mainly in one direction. Due to the separation of the algorithm into a factorization stage and a solution stage, time-dependent problems with a constant coefficient matrix are particularly well suited for this solver. The parallelization performances obtained on a Cray T3D show that the method scales up to at least 256 processors. (author) 5 figs., 2 tabs., 9 refs
Eigensolver for a Sparse, Large Hermitian Matrix
Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris
2003-01-01
A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).
Better Size Estimation for Sparse Matrix Products
DEFF Research Database (Denmark)
Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus
2010-01-01
We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected...... time O(n) for any ε > 4*(n^(-1/4)). The previously best estimation algorithm, due to Cohen (JCSS 1997), uses time O(n/ε^2). We also present a variant using O(sort(n)) I/Os in expectation in the cache-oblivious model. We also describe how sampling can be used to maintain (independent) sketches...... of matrices that allow estimation to be performed in time o(n) if z is sufficiently large. This gives a simpler alternative to the sketching technique of Ganguly et al. (PODS 2005), and matches a space lower bound shown in that paper....
Sparse Nonnegative Matrix Factorization Strategy for Cochlear Implants
Directory of Open Access Journals (Sweden)
Hongmei Hu
2015-12-01
Full Text Available Current cochlear implant (CI strategies carry speech information via the waveform envelope in frequency subbands. CIs require efficient speech processing to maximize information transfer to the brain, especially in background noise, where the speech envelope is not robust to noise interference. In such conditions, the envelope, after decomposition into frequency bands, may be enhanced by sparse transformations, such as nonnegative matrix factorization (NMF. Here, a novel CI processing algorithm is described, which works by applying NMF to the envelope matrix (envelopogram of 22 frequency channels in order to improve performance in noisy environments. It is evaluated for speech in eight-talker babble noise. The critical sparsity constraint parameter was first tuned using objective measures and then evaluated with subjective speech perception experiments for both normal hearing and CI subjects. Results from vocoder simulations with 10 normal hearing subjects showed that the algorithm significantly enhances speech intelligibility with the selected sparsity constraints. Results from eight CI subjects showed no significant overall improvement compared with the standard advanced combination encoder algorithm, but a trend toward improvement of word identification of about 10 percentage points at +15 dB signal-to-noise ratio (SNR was observed in the eight CI subjects. Additionally, a considerable reduction of the spread of speech perception performance from 40% to 93% for advanced combination encoder to 80% to 100% for the suggested NMF coding strategy was observed.
Sparse Matrix for ECG Identification with Two-Lead Features
Directory of Open Access Journals (Sweden)
Kuo-Kun Tseng
2015-01-01
Full Text Available Electrocardiograph (ECG human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.
Massively parallel sparse matrix function calculations with NTPoly
Dawson, William; Nakajima, Takahito
2018-04-01
We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.
A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors
DEFF Research Database (Denmark)
Liu, Weifeng; Vinter, Brian
2015-01-01
General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle...
Enforced Sparse Non-Negative Matrix Factorization
2016-01-23
mixture of topics constitutes a document . Other common methods for topic modeling include the following: latent semantic analysis (LSA) [1...probabilistic latent semantic analysis (PLSA) [2], and term frequency- inverse document frequency (TF-IDF) [3] analysis. More recently, non-negative matrix...factorization (NMF) [4]–[7] is used as a technique for document classification and topic modeling. The NMF has also been used for graph cluster
Vector sparse representation of color image using quaternion matrix analysis.
Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong
2015-04-01
Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.
Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.
Energy Technology Data Exchange (ETDEWEB)
Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-12-01
Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scienti c computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.
Ab initio nuclear structure - the large sparse matrix eigenvalue problem
International Nuclear Information System (INIS)
Vary, James P; Maris, Pieter; Ng, Esmond; Yang, Chao; Sosonkina, Masha
2009-01-01
The structure and reactions of light nuclei represent fundamental and formidable challenges for microscopic theory based on realistic strong interaction potentials. Several ab initio methods have now emerged that provide nearly exact solutions for some nuclear properties. The ab initio no core shell model (NCSM) and the no core full configuration (NCFC) method, frame this quantum many-particle problem as a large sparse matrix eigenvalue problem where one evaluates the Hamiltonian matrix in a basis space consisting of many-fermion Slater determinants and then solves for a set of the lowest eigenvalues and their associated eigenvectors. The resulting eigenvectors are employed to evaluate a set of experimental quantities to test the underlying potential. For fundamental problems of interest, the matrix dimension often exceeds 10 10 and the number of nonzero matrix elements may saturate available storage on present-day leadership class facilities. We survey recent results and advances in solving this large sparse matrix eigenvalue problem. We also outline the challenges that lie ahead for achieving further breakthroughs in fundamental nuclear theory using these ab initio approaches.
Graph Regularized Nonnegative Matrix Factorization with Sparse Coding
Directory of Open Access Journals (Sweden)
Chuang Lin
2015-01-01
Full Text Available In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC. By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local features of original data. The target function of our method is easy to propose, while the solving procedures are really nontrivial; in the paper we gave the detailed derivation of solving the target function and also a strict proof of its convergence, which is a key contribution of the paper. Compared with sparseness constrained NMF and GNMF algorithms, GRNMF_SC can learn much sparser representation of the data and can also preserve the geometrical structure of the data, which endow it with powerful discriminating ability. Furthermore, the GRNMF_SC is generalized as supervised and unsupervised models to meet different demands. Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods.
Algorithms for sparse, symmetric, definite quadratic lambda-matrix eigenproblems
International Nuclear Information System (INIS)
Scott, D.S.; Ward, R.C.
1981-01-01
Methods are presented for computing eigenpairs of the quadratic lambda-matrix, M lambda 2 + C lambda + K, where M, C, and K are large and sparse, and have special symmetry-type properties. These properties are sufficient to insure that all the eigenvalues are real and that theory analogous to the standard symmetric eigenproblem exists. The methods employ some standard techniques such as partial tri-diagonalization via the Lanczos Method and subsequent eigenpair calculation, shift-and- invert strategy and subspace iteration. The methods also employ some new techniques such as Rayleigh-Ritz quadratic roots and the inertia of symmetric, definite, quadratic lambda-matrices
Maximizing sparse matrix vector product performance in MIMD computers
Energy Technology Data Exchange (ETDEWEB)
McLay, R.T.; Kohli, H.S.; Swift, S.L.; Carey, G.F.
1994-12-31
A considerable component of the computational effort involved in conjugate gradient solution of structured sparse matrix systems is expended during the Matrix-Vector Product (MVP), and hence it is the focus of most efforts at improving performance. Such efforts are hindered on MIMD machines due to constraints on memory, cache and speed of memory-cpu data transfer. This paper describes a strategy for maximizing the performance of the local computations associated with the MVP. The method focuses on single stride memory access, and the efficient use of cache by pre-loading it with data that is re-used while bypassing it for other data. The algorithm is designed to behave optimally for varying grid sizes and number of unknowns per gridpoint. Results from an assembly language implementation of the strategy on the iPSC/860 show a significant improvement over the performance using FORTRAN.
Optimizing Tpetra%3CU%2B2019%3Es sparse matrix-matrix multiplication routine.
Energy Technology Data Exchange (ETDEWEB)
Nusbaum, Kurtis Lee
2011-08-01
Over the course of the last year, a sparse matrix-matrix multiplication routine has been developed for the Tpetra package. This routine is based on the same algorithm that is used in EpetraExt with heavy modifications. Since it achieved a working state, several major optimizations have been made in an effort to speed up the routine. This report will discuss the optimizations made to the routine, its current state, and where future work needs to be done.
Dense and Sparse Matrix Operations on the Cell Processor
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Husbands,Parry; Yelick, Katherine
2005-05-01
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. Therefore, the high performance computing community is examining alternative architectures that address the limitations of modern superscalar designs. In this work, we examine STI's forthcoming Cell processor: a novel, low-power architecture that combines a PowerPC core with eight independent SIMD processing units coupled with a software-controlled memory to offer high FLOP/s/Watt. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop an analytic framework to predict Cell performance on dense and sparse matrix operations, using a variety of algorithmic approaches. Results demonstrate Cell's potential to deliver more than an order of magnitude better GFLOP/s per watt performance, when compared with the Intel Itanium2 and Cray X1 processors.
Sparse Matrix-Vector Multiplication on Multicore and Accelerators
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel W. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bell, Nathan [NVIDIA Research, Santa Clara, CA (United States); Choi, Jee Whan [Georgia Inst. of Technology, Atlanta, GA (United States); Garland, Michael [NVIDIA Research, Santa Clara, CA (United States); Oliker, Leonid [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Vuduc, Richard [Georgia Inst. of Technology, Atlanta, GA (United States)
2010-12-07
This chapter consolidates recent work on the development of high performance multicore and accelerator-based implementations of sparse matrix-vector multiplication (SpMV). As an object of study, SpMV is an interesting computation for two key reasons. First, it appears widely in applications in scientific and engineering computing, financial and economic modeling, and information retrieval, among others, and is therefore of great practical interest. Secondly, it is both simple to describe but challenging to implement well, since its performance is limited by a variety of factors, including low computational intensity, potentially highly irregular memory access behavior, and a strong input dependence that be known only at run time. Thus, we believe SpMV is both practically important and provides important insights for understanding the algorithmic and implementation principles necessary to making effective use of state-of-the-art systems.
A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs
Directory of Open Access Journals (Sweden)
Guixia He
2016-01-01
Full Text Available Sparse matrix-vector multiplication (SpMV is an important operation in scientific computations. Compressed sparse row (CSR is the most frequently used format to store sparse matrices. However, CSR-based SpMVs on graphic processing units (GPUs, for example, CSR-scalar and CSR-vector, usually have poor performance due to irregular memory access patterns. This motivates us to propose a perfect CSR-based SpMV on the GPU that is called PCSR. PCSR involves two kernels and accesses CSR arrays in a fully coalesced manner by introducing a middle array, which greatly alleviates the deficiencies of CSR-scalar (rare coalescing and CSR-vector (partial coalescing. Test results on a single C2050 GPU show that PCSR fully outperforms CSR-scalar, CSR-vector, and CSRMV and HYBMV in the vendor-tuned CUSPARSE library and is comparable with a most recently proposed CSR-based algorithm, CSR-Adaptive. Furthermore, we extend PCSR on a single GPU to multiple GPUs. Experimental results on four C2050 GPUs show that no matter whether the communication between GPUs is considered or not PCSR on multiple GPUs achieves good performance and has high parallel efficiency.
Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction
Directory of Open Access Journals (Sweden)
Dong Zhang
2017-01-01
Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.
Fast convolutional sparse coding using matrix inversion lemma
Czech Academy of Sciences Publication Activity Database
Šorel, Michal; Šroubek, Filip
2016-01-01
Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf
National Research Council Canada - National Science Library
Grimes, Roger
1999-01-01
This final report describes the status of work performed during the months of Sept 1995 through Jan 1999 on the Algorithmic Research And Software Development For An Industrial Strength Sparse Matrix...
Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model
DEFF Research Database (Denmark)
Bender, Michael A.; Brodal, Gerth Stølting; Fagerberg, Rolf
2010-01-01
We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse Nx N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number...
DEFF Research Database (Denmark)
Garrido, Pablo; Sørensen, Chres Wiant; Roetter, Daniel Enrique Lucani
2016-01-01
Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages...... we propose two novel tuning functions with a lower computational cost, which do not increase the overhead in terms of the transmission of linear dependent packets compared with Random Linear Network Coding (RLNC) and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques....... The results show a reduction of 3.5× in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder...
Directory of Open Access Journals (Sweden)
G. Akinci
2014-12-01
Full Text Available Sparse matrices are occasionally encountered during solution of various problems by means of numerical methods, particularly the finite element method. ELLPACK sparse matrix storage scheme, one of the most widely used methods due to its implementation ease, is investigated in this study. The scheme uses excessive memory due to its definition. For the conventional finite element method, where the node elements are used, the excessive memory caused by redundant entries in the ELLPACK sparse matrix storage scheme becomes negligible for large scale problems. On the other hand, our analyses show that the redundancy is still considerable for the occasions where facet or edge elements have to be used.
Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms
Directory of Open Access Journals (Sweden)
Valeria Cardellini
2014-01-01
Full Text Available We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that obtained by a CPU-only PSBLAS implementation. Our experiments exhibit encouraging results regarding the comparison between CPU and GPU executions in double precision, obtaining a speedup of up to 35.35 on NVIDIA GTX 285 with respect to AMD Athlon 7750, and up to 10.15 on NVIDIA Tesla C2050 with respect to Intel Xeon X5650.
Directory of Open Access Journals (Sweden)
Vibha Tiwari
2015-12-01
Full Text Available Compressive sensing theory enables faithful reconstruction of signals, sparse in domain $ \\Psi $, at sampling rate lesser than Nyquist criterion, while using sampling or sensing matrix $ \\Phi $ which satisfies restricted isometric property. The role played by sensing matrix $ \\Phi $ and sparsity matrix $ \\Psi $ is vital in faithful reconstruction. If the sensing matrix is dense then it takes large storage space and leads to high computational cost. In this paper, effort is made to design sparse sensing matrix with least incurred computational cost while maintaining quality of reconstructed image. The design approach followed is based on sparse block circulant matrix (SBCM with few modifications. The other used sparse sensing matrix consists of 15 ones in each column. The medical images used are acquired from US, MRI and CT modalities. The image quality measurement parameters are used to compare the performance of reconstructed medical images using various sensing matrices. It is observed that, since Gram matrix of dictionary matrix ($ \\Phi \\Psi \\mathrm{} $ is closed to identity matrix in case of proposed modified SBCM, therefore, it helps to reconstruct the medical images of very good quality.
Energy Technology Data Exchange (ETDEWEB)
Cho, Paul S. [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195-6043 (United States)]. E-mail: cho@radonc.washington.edu; Phillips, Mark H. [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195-6043 (United States)
2001-05-01
For dynamic multileaf collimator-based intensity modulated radiotherapy in which small beam elements are used to generate continuous modulation, the sheer size of the dose calculation matrix could pose serious computational challenges. In order to circumvent this problem, the dose calculation matrix was reduced to a sparse matrix by truncating the weakly contributing entries below a certain cutoff to zero. Subsequently, the sparse matrix was compressed and matrix indexing vectors were generated to facilitate matrix-vector and matrix-matrix operations used in inverse planning. The application of sparsity permitted the reduction of overall memory requirement by an order of magnitude. In addition, the effect of disregarding the small scatter components on the quality of optimization was investigated by repeating the inverse planning using the dense dose calculation matrix. Comparison of dense and sparse matrix-based plans revealed an insignificant difference in optimization outcome, thus demonstrating the feasibility and usefulness of the sparse method in inverse planning. Furthermore, two additional methods of memory minimization are suggested, namely hexagonal dose sampling and limited normal tissue sampling. (author)
Cho, P S; Phillips, M H
2001-05-01
For dynamic multileaf collimator-based intensity modulated radiotherapy in which small beam elements are used to generate continuous modulation, the sheer size of the dose calculation matrix could pose serious computational challenges. In order to circumvent this problem, the dose calculation matrix was reduced to a sparse matrix by truncating the weakly contributing entries below a certain cutoff to zero. Subsequently, the sparse matrix was compressed and matrix indexing vectors were generated to facilitate matrix-vector and matrix-matrix operations used in inverse planning. The application of sparsity permitted the reduction of overall memory requirement by an order of magnitude. In addition, the effect of disregarding the small scatter components on the quality of optimization was investigated by repeating the inverse planning using the dense dose calculation matrix. Comparison of dense and sparse matrix-based plans revealed an insignificant difference in optimization outcome, thus demonstrating the feasibility and usefulness of the sparse method in inverse planning. Furthermore, two additional methods of memory minimization are suggested, namely hexagonal dose sampling and limited normal tissue sampling.
Cho, Paul S.; Phillips, Mark H.
2001-05-01
For dynamic multileaf collimator-based intensity modulated radiotherapy in which small beam elements are used to generate continuous modulation, the sheer size of the dose calculation matrix could pose serious computational challenges. In order to circumvent this problem, the dose calculation matrix was reduced to a sparse matrix by truncating the weakly contributing entries below a certain cutoff to zero. Subsequently, the sparse matrix was compressed and matrix indexing vectors were generated to facilitate matrix-vector and matrix-matrix operations used in inverse planning. The application of sparsity permitted the reduction of overall memory requirement by an order of magnitude. In addition, the effect of disregarding the small scatter components on the quality of optimization was investigated by repeating the inverse planning using the dense dose calculation matrix. Comparison of dense and sparse matrix-based plans revealed an insignificant difference in optimization outcome, thus demonstrating the feasibility and usefulness of the sparse method in inverse planning. Furthermore, two additional methods of memory minimization are suggested, namely hexagonal dose sampling and limited normal tissue sampling.
Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.
Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver
2018-02-15
Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R
Sparse Non-negative Matrix Factor 2-D Deconvolution
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N.
2006-01-01
We introduce the non-negative matrix factor 2-D deconvolution (NMF2D) model, which decomposes a matrix into a 2-dimensional convolution of two factor matrices. This model is an extension of the non-negative matrix factor deconvolution (NMFD) recently introduced by Smaragdis (2004). We derive and ...... this form of factorization. The developed algorithms have been used for source separation and music transcription....
Large Enforced Sparse Non-Negative Matrix Factorization
Gavin, Brendan; Gadepally, Vijay; Kepner, Jeremy
2015-01-01
Non-negative matrix factorization (NMF) is a common method for generating topic models from text data. NMF is widely accepted for producing good results despite its relative simplicity of implementation and ease of computation. One challenge with applying NMF to large datasets is that intermediate matrix products often become dense, stressing the memory and compute elements of a system. In this article, we investigate a simple but powerful modification of a common NMF algorithm that enforces ...
Graph Transformation and Designing Parallel Sparse Matrix Algorithms beyond Data Dependence Analysis
H.X. Lin
2004-01-01
Algorithms are often parallelized based on data dependence analysis manually or by means of parallel compilers. Some vector/matrix computations such as the matrix-vector products with simple data dependence structures (data parallelism) can be easily parallelized. For problems with more complicated data dependence structures, parallelization is less straightforward. The data dependence graph is a powerful means for designing and analyzing parallel algorithms. However, for sparse matrix comput...
Energy Technology Data Exchange (ETDEWEB)
Aktulga, Hasan Metin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Buluc, Aydin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Williams, Samuel [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Yang, Chao [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2014-08-14
Obtaining highly accurate predictions on the properties of light atomic nuclei using the configuration interaction (CI) approach requires computing a few extremal Eigen pairs of the many-body nuclear Hamiltonian matrix. In the Many-body Fermion Dynamics for nuclei (MFDn) code, a block Eigen solver is used for this purpose. Due to the large size of the sparse matrices involved, a significant fraction of the time spent on the Eigen value computations is associated with the multiplication of a sparse matrix (and the transpose of that matrix) with multiple vectors (SpMM and SpMM-T). Existing implementations of SpMM and SpMM-T significantly underperform expectations. Thus, in this paper, we present and analyze optimized implementations of SpMM and SpMM-T. We base our implementation on the compressed sparse blocks (CSB) matrix format and target systems with multi-core architectures. We develop a performance model that allows us to understand and estimate the performance characteristics of our SpMM kernel implementations, and demonstrate the efficiency of our implementation on a series of real-world matrices extracted from MFDn. In particular, we obtain 3-4 speedup on the requisite operations over good implementations based on the commonly used compressed sparse row (CSR) matrix format. The improvements in the SpMM kernel suggest we may attain roughly a 40% speed up in the overall execution time of the block Eigen solver used in MFDn.
Energy Technology Data Exchange (ETDEWEB)
1978-01-01
The program and abstracts of the SIAM 1978 fall meeting in Knoxville, Tennessee, are given, along with those of the associated symposium on sparse matrix computations. The papers dealt with both pure mathematics and mathematics applied to many different subject areas. (RWR)
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
Xu, S.; Xue, W.; Lin, H.X.
2011-01-01
In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication (SpMV) on NVIDIA GPUs using CUDA. SpMV has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of SpMV based on ELLPACK from
Pulse-Width-Modulation of Neutral-Point-Clamped Sparse Matrix Converter
DEFF Research Database (Denmark)
Loh, P.C.; Blaabjerg, Frede; Gao, F.
2007-01-01
literature. Addressing the afore-described issues, this paper focuses on the operational mode analysis of a three-level sparse matrix converter implemented using a neutral-point-clamped inversion stage, and the design of a number of PWM and modulation ratio compensation schemes for controlling the converter...
Sparse subspace clustering for data with missing entries and high-rank matrix completion.
Fan, Jicong; Chow, Tommy W S
2017-09-01
Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Carver, M.B.; Baudouin, A.P.
1976-01-01
The algorithm due to Gear is recognized as one of the best available integration routines, permitting large time steps while maintaining acceptable error tolerance and stability. The method is implicit, requiring a matrix solution involving the Jacobian. This has hitherto required excessive storage for large equation systems (>100 equations). The algorithm has been used for the solution of small systems involving neutron kinetics, and encouraging results motivated an investigation of the application of sparse matrix techniques to the matrix manipulation within the algorithm. The resulting routines handle large systems of equations very efficiently. Part 1 of this paper describes these new routines, and Part 2 discusses their application to neutron kinetics. (author)
International Nuclear Information System (INIS)
Dubois, J.; Calvin, Ch.; Dubois, J.; Petiton, S.
2011-01-01
This paper presents a parallelized hybrid single-vector Arnoldi algorithm for computing approximations to Eigen-pairs of a nonsymmetric matrix. We are interested in the use of accelerators and multi-core units to speed up the Arnoldi process. The main goal is to propose a parallel version of the Arnoldi solver, which can efficiently use multiple multi-core processors or multiple graphics processing units (GPUs) in a mixed coarse and fine grain fashion. In the proposed algorithms, this is achieved by an auto-tuning of the matrix vector product before starting the Arnoldi Eigen-solver as well as the reorganization of the data and global communications so that communication time is reduced. The execution time, performance, and scalability are assessed with well-known dense and sparse test matrices on multiple Nehalems, GT200 NVidia Tesla, and next generation Fermi Tesla. With one processor, we see a performance speedup of 2 to 3x when using all the physical cores, and a total speedup of 2 to 8x when adding a GPU to this multi-core unit, and hence a speedup of 4 to 24x compared to the sequential solver. (authors)
New sparse matrix solver in the KIKO3D 3-dimensional reactor dynamics code
International Nuclear Information System (INIS)
Panka, I.; Kereszturi, A.; Hegedus, C.
2005-01-01
The goal of this paper is to present a more effective method Bi-CGSTAB for accelerating the large sparse matrix equation solution in the KIKO3D code. This equation system is obtained by using the factorization of the improved quasi static (IQS) method for the time dependent nodal kinetic equations. In the old methodology standard large sparse matrix techniques were considered, where Gauss-Seidel preconditioning and a GMRES-type solver were applied. The validation of KIKO3D using Bi-CGSTAB has been performed by solving of a VVER-1000 kinetic benchmark problem. Additionally, the convergence characteristics were investigated in given macro time steps of Control Rod Ejection transients. The results have been obtained by the old GMRES and new Bi-CGSTAB methods are compared. (author)
Sparse and smooth canonical correlation analysis through rank-1 matrix approximation
Aïssa-El-Bey, Abdeldjalil; Seghouane, Abd-Krim
2017-12-01
Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far, the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper, two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank-1 matrix approximation and the orthogonal projectors onto the space spanned by the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithms are tested on simulated experiments. On these results, it can be observed that they outperform the state of the art sparse CCA algorithms.
Directory of Open Access Journals (Sweden)
Anil S Thakur
2007-10-01
Full Text Available Crystallization is a major bottleneck in the process of macromolecular structure determination by X-ray crystallography. Successful crystallization requires the formation of nuclei and their subsequent growth to crystals of suitable size. Crystal growth generally occurs spontaneously in a supersaturated solution as a result of homogenous nucleation. However, in a typical sparse matrix screening experiment, precipitant and protein concentration are not sampled extensively, and supersaturation conditions suitable for nucleation are often missed.We tested the effect of nine potential heterogenous nucleating agents on crystallization of ten test proteins in a sparse matrix screen. Several nucleating agents induced crystal formation under conditions where no crystallization occurred in the absence of the nucleating agent. Four nucleating agents: dried seaweed; horse hair; cellulose and hydroxyapatite, had a considerable overall positive effect on crystallization success. This effect was further enhanced when these nucleating agents were used in combination with each other.Our results suggest that the addition of heterogeneous nucleating agents increases the chances of crystal formation when using sparse matrix screens.
Xu, Jun; Xiang, Lei; Wang, Guanhao; Ganesan, Shridar; Feldman, Michael; Shih, Natalie Nc; Gilmore, Hannah; Madabhushi, Anant
2015-12-01
Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images. Copyright © 2015 Elsevier Ltd. All rights reserved.
Library designs for generic C++ sparse matrix computations of iterative methods
Energy Technology Data Exchange (ETDEWEB)
Pozo, R.
1996-12-31
A new library design is presented for generic sparse matrix C++ objects for use in iterative algorithms and preconditioners. This design extends previous work on C++ numerical libraries by providing a framework in which efficient algorithms can be written *independent* of the matrix layout or format. That is, rather than supporting different codes for each (element type) / (matrix format) combination, only one version of the algorithm need be maintained. This not only reduces the effort for library developers, but also simplifies the calling interface seen by library users. Furthermore, the underlying matrix library can be naturally extended to support user-defined objects, such as hierarchical block-structured matrices, or application-specific preconditioners. Utilizing optimized kernels whenever possible, the resulting performance of such framework can be shown to be competitive with optimized Fortran programs.
Directory of Open Access Journals (Sweden)
Wang Wen-qin
2015-02-01
Full Text Available The waveforms used in Multiple-Input Multiple-Output (MIMO Synthetic Aperture Radar (SAR should have a large time-bandwidth product and good ambiguity function performance. A scheme to design multiple orthogonal MIMO SAR Orthogonal Frequency Division Multiplexing (OFDM chirp waveforms by combinational sparse matrix and correlation optimization is proposed. First, the problem of MIMO SAR waveform design amounts to the associated design of hopping frequency and amplitudes. Then a iterative exhaustive search algorithm is adopted to optimally design the code matrix with the constraints minimizing the block correlation coefficient of sparse matrix and the sum of cross-correlation peaks. And the amplitudes matrix are adaptively designed by minimizing the cross-correlation peaks with the genetic algorithm. Additionally, the impacts of waveform number, hopping frequency interval and selectable frequency index are also analyzed. The simulation results verify the proposed scheme can design multiple orthogonal large time-bandwidth product OFDM chirp waveforms with low cross-correlation peak and sidelobes and it improves ambiguity performance.
Single-Channel Speech Separation using Sparse Non-Negative Matrix Factorization
DEFF Research Database (Denmark)
Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard
2007-01-01
We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording. The method of choice is a sparse non-negative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied....... The performance of the unsupervised and supervised adaptation schemes result in significant improvements in terms of the target-to-masker ratio....... to the learning of personalized dictionaries from a speech corpus, which in turn are used to separate the audio stream into its components. We show that computational savings can be achieved by segmenting the training data on a phoneme level. To split the data, a conventional speech recognizer is used...
A hierarchic sparse matrix data structure for large-scale Hartree-Fock/Kohn-Sham calculations.
Rubensson, Emanuel H; Rudberg, Elias; Sałek, Paweł
2007-12-01
A hierarchic sparse matrix data structure for Hartree-Fock/Kohn-Sham calculations is presented. The data structure makes the implementation of matrix manipulations needed for large systems faster, easier, and more maintainable without loss of performance. Algorithms for symmetric matrix square and inverse Cholesky decomposition within the hierarchic framework are also described. The presented data structure is general; in addition to its use in Hartree-Fock/Kohn-Sham calculations, it may also be used in other research areas where matrices with similar properties are encountered. The applicability of the data structure to ab initio calculations is shown with help of benchmarks on water droplets and graphene nanoribbons. (c) 2007 Wiley Periodicals, Inc.
Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction
Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua; Wang, Jing
2018-02-01
Spectral computed tomography (CT) has been a promising technique in research and clinics because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of the different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifact suppression and resolution preservation.
Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications
Papalexakis, Evangelos E.; Faloutsos, Christos; Mitchell, Tom M.; Talukdar, Partha Pratim; Sidiropoulos, Nicholas D.; Murphy, Brian
2016-01-01
How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like ’edible’, ’fits in hand’)? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we enhance any CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, produces sparse and interpretable solutions, and parallelizes any CMTF algorithm, producing sparse and interpretable solutions (up to 65 fold). Additionally, we improve upon ALS, the work-horse algorithm for CMTF, with respect to efficiency and robustness to missing values. We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. Finally, we demonstrate the generality of Turbo-SMT, by applying it on a Facebook dataset (users, ’friends’, wall-postings); there, Turbo-SMT spots spammer-like anomalies. PMID:27672406
Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications.
Papalexakis, Evangelos E; Faloutsos, Christos; Mitchell, Tom M; Talukdar, Partha Pratim; Sidiropoulos, Nicholas D; Murphy, Brian
2016-08-01
How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we enhance any CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, produces sparse and interpretable solutions, and parallelizes any CMTF algorithm, producing sparse and interpretable solutions (up to 65 fold ). Additionally, we improve upon ALS, the work-horse algorithm for CMTF, with respect to efficiency and robustness to missing values. We apply Turbo-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Turbo-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. Finally, we demonstrate the generality of Turbo-SMT, by applying it on a Facebook dataset (users, 'friends', wall-postings); there, Turbo-SMT spots spammer-like anomalies.
Graph Transformation and Designing Parallel Sparse Matrix Algorithms beyond Data Dependence Analysis
Directory of Open Access Journals (Sweden)
H.X. Lin
2004-01-01
Full Text Available Algorithms are often parallelized based on data dependence analysis manually or by means of parallel compilers. Some vector/matrix computations such as the matrix-vector products with simple data dependence structures (data parallelism can be easily parallelized. For problems with more complicated data dependence structures, parallelization is less straightforward. The data dependence graph is a powerful means for designing and analyzing parallel algorithms. However, for sparse matrix computations, parallelization based on solely exploiting the existing parallelism in an algorithm does not always give satisfactory results. For example, the conventional Gaussian elimination algorithm for the solution of a tri-diagonal system is inherently sequential, so algorithms specially for parallel computation has to be designed. After briefly reviewing different parallelization approaches, a powerful graph formalism for designing parallel algorithms is introduced. This formalism will be discussed using a tri-diagonal system as an example. Its application to general matrix computations is also discussed. Its power in designing parallel algorithms beyond the ability of data dependence analysis is shown by means of a new algorithm called ACER (Alternating Cyclic Elimination and Reduction algorithm.
An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data
DEFF Research Database (Denmark)
Liu, Weifeng; Vinter, Brian
2014-01-01
matrices. Recent work on GPU SpGEMM has demonstrated rather good both time and space complexity, but works best for fairly regular matrices. In this work we present a GPU SpGEMM algorithm that particularly focuses on the above three problems. Memory pre-allocation for the result matrix is organized...... by a hybrid method that saves a large amount of global memory space and efficiently utilizes the very limited on-chip scratchpad memory. Parallel insert operations of the nonzero entries are implemented through the GPU merge path algorithm that is experimentally found to be the fastest GPU merge approach....... Load balancing builds on the number of the necessary arithmetic operations on the nonzero entries and is guaranteed in all stages. Compared with the state-of-the-art GPU SpGEMM methods in the CUSPARSE library and the CUSP library and the latest CPU SpGEMM method in the Intel Math Kernel Library, our...
Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization
Energy Technology Data Exchange (ETDEWEB)
Shiga, Motoki, E-mail: shiga_m@gifu-u.ac.jp [Department of Electrical, Electronic and Computer Engineering, Gifu University, 1-1, Yanagido, Gifu 501-1193 (Japan); Tatsumi, Kazuyoshi; Muto, Shunsuke [Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603 (Japan); Tsuda, Koji [Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8561 (Japan); Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047 (Japan); Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi Koto-ku, Tokyo 135-0064 (Japan); Yamamoto, Yuta [High-Voltage Electron Microscope Laboratory, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603 (Japan); Mori, Toshiyuki [Environment and Energy Materials Division, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044 (Japan); Tanji, Takayoshi [Division of Materials Research, Institute of Materials and Systems for Sustainability, Nagoya University, Chikusa-ku, Nagoya 464-8603 (Japan)
2016-11-15
Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI and determining the chemical state of each spectral component from the SI data stored in a huge three-dimensional matrix, it is more effective and efficient to use a statistical approach for the automatic resolution and extraction of the underlying chemical components. Among many different statistical approaches, we adopt a non-negative matrix factorization (NMF) technique, mainly because of the natural assumption of non-negative values in the spectra and cardinalities of chemical components, which are always positive in actual data. This paper proposes a new NMF model with two penalty terms: (i) an automatic relevance determination (ARD) prior, which optimizes the number of components, and (ii) a soft orthogonal constraint, which clearly resolves each spectrum component. For the factorization, we further propose a fast optimization algorithm based on hierarchical alternating least-squares. Numerical experiments using both phantom and real STEM-EDX/EELS SI datasets demonstrate that the ARD prior successfully identifies the correct number of physically meaningful components. The soft orthogonal constraint is also shown to be effective, particularly for STEM-EELS SI data, where neither the spatial nor spectral entries in the matrices are sparse. - Highlights: • Automatic resolution of chemical components from spectral imaging is considered. • We propose a new non-negative matrix factorization with two new penalties. • The first penalty is sparseness to choose the number of components from data. • Experimental results with real data demonstrate effectiveness of our method.
ON-LINE NONLINEAR CHROMATICITY CORRECTION USING OFF-MOMENTUM TUNE RESPONSE MATRIX
International Nuclear Information System (INIS)
LUO, Y.; FISCHER, W.; MALISKY, N.; TEPIKIAN, S.; TROBJEVIC, D.
2007-01-01
In this article, we propose a method for the online nonlinear chromaticity correction at store in the Relativistic Heavy Ion Collider (RHIC). With 8 arc sextupole families in each RHIC ring, the nonlinear chromaticities can be minimized online by matching the off-momentum tunes onto the wanted tunes given by the linear chromaticities. The Newton method is used for this multi-dimensional nonlinear optimization, where the off-momentum tune response matrix with respect to sextupole strength changes is adopted. The off-momentum tune response matrix can be calculated with the online accelerator optics model or directly measured with the real beam. In this article, the correction algorithm for the RHIC is presented. Simulations are also carried out to verify the method. The preliminary results from the beam experiments taken place in the RHIC 2007 Au run are reviewed
ON-LINE NONLINEAR CHROMATICITY CORRECTION USING OFF-MOMENTUM TUNE RESPONSE MATRIX
Energy Technology Data Exchange (ETDEWEB)
LUO,Y.; FISCHER, W.; MALISKY, N.; TEPIKIAN, S.; TROBJEVIC, D.
2007-06-25
In this article, we propose a method for the online nonlinear chromaticity correction at store in the Relativistic Heavy Ion Collider (RHIC). With 8 arc sextupole families in each RHIC ring, the nonlinear chromaticities can be minimized online by matching the off-momentum tunes onto the wanted tunes given by the linear chromaticities. The Newton method is used for this multi-dimensional nonlinear optimization, where the off-momentum tune response matrix with respect to sextupole strength changes is adopted. The off-momentum tune response matrix can be calculated with the online accelerator optics model or directly measured with the real beam. In this article, the correction algorithm for the RHIC is presented. Simulations are also carried out to verify the method. The preliminary results from the beam experiments taken place in the RHIC 2007 Au run are reviewed.
Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery
Sun, Weiwei; Liu, Chun; Li, Jialin; Lai, Yenming Mark; Li, Weiyue
2014-01-01
A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to detect anomalies in hyperspectral imagery (HSI). The detector assumes background images are low-rank while anomalies are gross errors that are sparsely distributed throughout the image scene. By solving a constrained convex optimization problem, the LRaSMD detector separates the anomalies from the background. This protects the background model from corruption. An anomaly value for each pixel is calculated using the Euclidean distance, and anomalies are determined by thresholding the anomaly value. Four groups of experiments on three widely used HSI datasets are designed to completely analyze the performances of the new detector. Experimental results show that the LRaSMD detector outperforms the global Reed-Xiaoli (GRX), the orthogonal subspace projection-GRX, and the cluster-based detectors. Moreover, the results show that LRaSMD achieves equal or better detection performance than the local support vector data description detector within a shorter computational time.
Hard constrained sparse bundle adjustment of multi-camera with block matrix
Shi, ZhongChen; Sun, JunFeng; Shang, Yang; Zhang, XiaoHu
2017-10-01
Because of the ability to optimize the 3D points and viewing parameters jointly and simultaneously, Sparse Bundle Adjustment (SBA) is an essential procedure and usually used as the last step of Structure from Motion (SFM). Recent development of SBA is incline to research on combination of the numeric method with matrix compression technique for more efficient and less memory consuming, and of prior information with SBA for the high accuracy. In this paper, a new hard constrained SBA method for multi-camera is presented. This method takes the prior information of 3D model or multi-camera into account as a hard constraint, and its solution is accomplished by the Lagrange multiplier method and Schur complement combined and with block matrix. The contribution of this work is that it provides a solution integrate constraint and multi-camera SBA, which is desired in the SFM problem and photogrammetry area. Another noticeable aspect is that obvious less time consuming with block matrix based than without, and the accuracy is maintained.
Auto-tuning Dense Vector and Matrix-vector Operations for Fermi GPUs
DEFF Research Database (Denmark)
Sørensen, Hans Henrik Brandenborg
2012-01-01
In this paper, we consider the automatic performance tuning of dense vector and matrix-vector operations on GPUs. Such operations form the backbone of level 1 and level 2 routines in the Basic Linear Algebra Subroutines (BLAS) library and are therefore of great importance in many scientific...
Abdelfattah, Ahmad
2016-05-23
Simulations of many multi-component PDE-based applications, such as petroleum reservoirs or reacting flows, are dominated by the solution, on each time step and within each Newton step, of large sparse linear systems. The standard solver is a preconditioned Krylov method. Along with application of the preconditioner, memory-bound Sparse Matrix-Vector Multiplication (SpMV) is the most time-consuming operation in such solvers. Multi-species models produce Jacobians with a dense block structure, where the block size can be as large as a few dozen. Failing to exploit this dense block structure vastly underutilizes hardware capable of delivering high performance on dense BLAS operations. This paper presents a GPU-accelerated SpMV kernel for block-sparse matrices. Dense matrix-vector multiplications within the sparse-block structure leverage optimization techniques from the KBLAS library, a high performance library for dense BLAS kernels. The design ideas of KBLAS can be applied to block-sparse matrices. Furthermore, a technique is proposed to balance the workload among thread blocks when there are large variations in the lengths of nonzero rows. Multi-GPU performance is highlighted. The proposed SpMV kernel outperforms existing state-of-the-art implementations using matrices with real structures from different applications. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Archer, A.W.; Maples, C.G.
1989-01-01
Numerous departures from ideal relationships are revealed by Monte Carlo simulations of widely accepted binomial coefficients. For example, simulations incorporating varying levels of matrix sparseness (presence of zeros indicating lack of data) and computation of expected values reveal that not only are all common coefficients influenced by zero data, but also that some coefficients do not discriminate between sparse or dense matrices (few zero data). Such coefficients computationally merge mutually shared and mutually absent information and do not exploit all the information incorporated within the standard 2 ?? 2 contingency table; therefore, the commonly used formulae for such coefficients are more complicated than the actual range of values produced. Other coefficients do differentiate between mutual presences and absences; however, a number of these coefficients do not demonstrate a linear relationship to matrix sparseness. Finally, simulations using nonrandom matrices with known degrees of row-by-row similarities signify that several coefficients either do not display a reasonable range of values or are nonlinear with respect to known relationships within the data. Analyses with nonrandom matrices yield clues as to the utility of certain coefficients for specific applications. For example, coefficients such as Jaccard, Dice, and Baroni-Urbani and Buser are useful if correction of sparseness is desired, whereas the Russell-Rao coefficient is useful when sparseness correction is not desired. ?? 1989 International Association for Mathematical Geology.
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Oliker, Leonid [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Vuduc, Richard [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Shalf, John [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Yelick, Katherine [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Demmel, James [Univ. of California, Berkeley, CA (United States)
2007-01-01
We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientific study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.
Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms
Energy Technology Data Exchange (ETDEWEB)
Williams, Samuel; Oliker, Leonid; Vuduc, Richard; Shalf, John; Yelick, Katherine; Demmel, James
2008-10-16
We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific-optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD quad-core, AMD dual-core, and Intel quad-core designs, the heterogeneous STI Cell, as well as one of the first scientific studies of the highly multithreaded Sun Victoria Falls (a Niagara2 SMP). We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural trade-offs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.
Directory of Open Access Journals (Sweden)
G. Franchetti
2008-09-01
Full Text Available In this paper we present a method for measuring the nonlinear errors in a circular accelerator by taking advantage of the feed-down effect of high order multipoles when the closed orbit is globally deformed. We devise a nonlinear tune response matrix in which the response to a closed orbit deformation is obtained in terms of change of machine tune and correlated with the strength of the local multipoles. A numerical example and a proof of principle experiment to validate the theoretical methods are presented and discussed.
Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario
2018-04-01
This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.
Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario
2018-04-01
Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.
Nano-fillers to tune Young’s modulus of silicone matrix
International Nuclear Information System (INIS)
Xia Lijin; Xu Zhonghua; Sun Leming; Caveney, Patrick M.; Zhang Mingjun
2013-01-01
In this study, we investigated nanoparticles, nanofibers, and nanoclays for their filler effects on tuning the Young’s modulus of silicone matrix, a material with broad in vivo applications. Nano-fillers with different shapes, sizes, and surface properties were added into silicone matrix, and then their filler effects were evaluated through experimental studies. It was found that spherical nanoparticles could clearly improve Young’s modulus of the silicone matrix, while nanoclays and carbon nanofibers had limited effects. Smaller spherical nanoparticles were better in performance compared to larger nanoparticles. In addition, enhanced distribution of the nanoparticles in the matrix has been observed to improve the filler effect. In order to minimize toxicity of the nanoparticles for in vivo applications, spherical nanoparticles coated with amine, acid, or hydroxide groups were also investigated, but they were found only to diminish the filler effect of nanoparticles. This study demonstrated that spherical nanoparticles could serve as fillers to tune Young’s modulus of silicone matrix for potential applications in medicine.
Bustamam, A.; Ulul, E. D.; Hura, H. F. A.; Siswantining, T.
2017-07-01
Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geographical factor.
Zhang, Haicang; Gao, Yujuan; Deng, Minghua; Wang, Chao; Zhu, Jianwei; Li, Shuai Cheng; Zheng, Wei-Mou; Bu, Dongbo
2016-03-25
Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates
Speculative segmented sum for sparse matrix-vector multiplication on heterogeneous processors
DEFF Research Database (Denmark)
Liu, Weifeng; Vinter, Brian
2015-01-01
. In this paper, we propose a compressed sparse row (CSR) format based SpMV algorithm utilizing both types of cores in a CPU-GPU heterogeneous processor. We first speculatively execute segmented sum operations on the GPU part of a heterogeneous processor and generate a possibly incorrect result. Then the CPU part...... of the same chip is triggered to re-arrange the predicted partial sums for a correct resulting vector. On three heterogeneous processors from Intel, AMD and nVidia, using 20 sparse matrices as a benchmark suite, the experimental results show that our method obtains significant performance improvement over...
The application of sparse estimation of covariance matrix to quadratic discriminant analysis.
Sun, Jiehuan; Zhao, Hongyu
2015-02-18
Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.
Mohr, Stephan; Dawson, William; Wagner, Michael; Caliste, Damien; Nakajima, Takahito; Genovese, Luigi
2017-10-10
We present CheSS, the "Chebyshev Sparse Solvers" library, which has been designed to solve typical problems arising in large-scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of nonzero entries, making it well-suited for large-scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT, we show that such a favorable setup is indeed possible in practice. In addition, the approach based on Chebyshev polynomials can be massively parallelized, and CheSS exhibits excellent scaling up to thousands of cores even for relatively small matrix sizes.
Sparse Non-negative Matrix Factor 2-D Deconvolution for Automatic Transcription of Polyphonic Music
DEFF Research Database (Denmark)
Schmidt, Mikkel N.; Mørup, Morten
2006-01-01
We present a novel method for automatic transcription of polyphonic music based on a recently published algorithm for non-negative matrix factor 2-D deconvolution. The method works by simultaneously estimating a time-frequency model for an instrument and a pattern corresponding to the notes which...
Lu, Na; Yin, Tao
2015-07-15
Brain activities could be measured by devices like EEG, MEG, MRI etc. in terms of electric or magnetic signal, which could provide information from three domains, i.e., time, frequency and space. Combinatory analysis of these features could definitely help to improve the classification performance on brain activities. NMF (nonnegative matrix factorization) has been widely applied in pattern extraction tasks (e.g., face recognition, gene data analysis) which could provide physically meaningful explanation of the data. However, brain signals also take negative values, so only spectral feature has been employed in existing NMF studies for brain computer interface. In addition, sparsity is an intrinsic characteristic of electric signals. To incorporate sparsity constraint and enable analysis of time domain feature using NMF, a new solution for motor imagery classification is developed, which combinatorially analyzes the ERP (event related potential, time domain) and ERSP (event related spectral perturbation, frequency domain) features via a modified mixed alternating least square based NMF method (MALS-NMF for short). Extensive experiments have verified the effectivity the proposed method. The results also showed that imposing sparsity constraint on the coefficient matrix in ERP factorization and basis matrix in ERSP factorization could better improve the algorithm performance. Comparisons with other eight representative methods have further verified the superiority of the proposed method. The MALS-NMF method is an effective solution for motor imagery classification and has shed some new light into the field of brain dynamics pattern analysis. Copyright © 2015 Elsevier B.V. All rights reserved.
Wu, Qingyao; Wang, Zhenyu; Li, Chunshan; Ye, Yunming; Li, Yueping; Sun, Ning
2015-01-01
Predicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance. We investigate a latent graph approach for finding an integration latent graph by exploiting various latent linkages and judiciously integrate the investigated linkages to link (separate) the proteins with similar (different) functions. We develop a regularized non-negative matrix factorization (RNMF) algorithm for CC to make protein functional properties prediction by utilizing various data sources that are available in this problem setting, including attribute features, latent graph, and unlabeled data information. In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction. Experimental results on KDD Cup tasks predicting the localization and functions of proteins to yeast genes demonstrate the effectiveness of the proposed RNMF method for predicting the protein
Energy Technology Data Exchange (ETDEWEB)
Pinto, S. [University of Minho, Portugal; Roldan Gutierrez, Manuel A [ORNL; Ramos, M. M.D. [University of Minho, Portugal; Gomes, M.J.M. [University of Minho, Portugal; Molina, S. I. [Universidad de Cadiz, Spain; Pennycook, Stephen J [ORNL; Varela del Arco, Maria [ORNL; Buljan, M. [R. Boskovic Institute, Zagreb, Croatia; Barradas, N. [Instituto Tecnologico e Nuclear (ITN), Lisbon, Portugal; Alves, E. [Instituto Tecnologico e Nuclear (ITN), Lisbon, Portugal; Chahboun, A. [FST Tanger, Morocco; Bernstorff, S. [Sincrotrone Trieste, Basovizza, Italy
2012-01-01
In this work, we investigate the structural properties of Ge quantum dot lattices in amorphous silica matrix, prepared by low-temperature magnetron sputtering deposition of (Ge+SiO{sub 2})/SiO{sub 2} multilayers. The dependence of quantum dot shape, size, separation, and arrangement type on the Ge-rich (Ge + SiO{sub 2}) layer thickness is studied. We show that the quantum dots are elongated along the growth direction, perpendicular to the multilayer surface. The size of the quantum dots and their separation along the growth direction can be tuned by changing the Ge-rich layer thickness. The average value of the quantum dots size along the lateral (in-plane) direction along with their lateral separation is not affected by the thickness of the Ge-rich layer. However, the thickness of the Ge-rich layer significantly affects the quantum dot ordering. In addition, we investigate the dependence of the multilayer average atomic composition and also the quantum dot crystalline quality on the deposition parameters.
Energy Technology Data Exchange (ETDEWEB)
Pinto, S. R. C.; Ramos, M. M. D.; Gomes, M. J. M. [University of Minho, Centre of Physics and Physics Department, Braga 4710-057 (Portugal); Buljan, M. [Ruder Boskovic Institute, Bijenicka cesta 54, Zagreb 10000 (Croatia); Chahboun, A. [University of Minho, Centre of Physics and Physics Department, Braga 4710-057 (Portugal); Physics Department, FST Tanger, Tanger BP 416 (Morocco); Roldan, M. A.; Molina, S. I. [Departamento de Ciencia de los Materiales e Ing. Metalurgica y Q. I., Universidad de Cadiz, Cadiz (Spain); Bernstorff, S. [Sincrotrone Trieste, SS 14 km163, 5, Basovizza 34012 (Italy); Varela, M.; Pennycook, S. J. [Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Barradas, N. P.; Alves, E. [Instituto Superior Tecnico e Instituto Tecnologico e Nuclear-, EN10, Sacavem 2686-953 (Portugal)
2012-04-01
In this work, we investigate the structural properties of Ge quantum dot lattices in amorphous silica matrix, prepared by low-temperature magnetron sputtering deposition of (Ge+SiO{sub 2})/SiO{sub 2} multilayers. The dependence of quantum dot shape, size, separation, and arrangement type on the Ge-rich (Ge + SiO{sub 2}) layer thickness is studied. We show that the quantum dots are elongated along the growth direction, perpendicular to the multilayer surface. The size of the quantum dots and their separation along the growth direction can be tuned by changing the Ge-rich layer thickness. The average value of the quantum dots size along the lateral (in-plane) direction along with their lateral separation is not affected by the thickness of the Ge-rich layer. However, the thickness of the Ge-rich layer significantly affects the quantum dot ordering. In addition, we investigate the dependence of the multilayer average atomic composition and also the quantum dot crystalline quality on the deposition parameters.
International Nuclear Information System (INIS)
Pinto, S. R. C.; Ramos, M. M. D.; Gomes, M. J. M.; Buljan, M.; Chahboun, A.; Roldan, M. A.; Molina, S. I.; Bernstorff, S.; Varela, M.; Pennycook, S. J.; Barradas, N. P.; Alves, E.
2012-01-01
In this work, we investigate the structural properties of Ge quantum dot lattices in amorphous silica matrix, prepared by low-temperature magnetron sputtering deposition of (Ge+SiO 2 )/SiO 2 multilayers. The dependence of quantum dot shape, size, separation, and arrangement type on the Ge-rich (Ge + SiO 2 ) layer thickness is studied. We show that the quantum dots are elongated along the growth direction, perpendicular to the multilayer surface. The size of the quantum dots and their separation along the growth direction can be tuned by changing the Ge-rich layer thickness. The average value of the quantum dots size along the lateral (in-plane) direction along with their lateral separation is not affected by the thickness of the Ge-rich layer. However, the thickness of the Ge-rich layer significantly affects the quantum dot ordering. In addition, we investigate the dependence of the multilayer average atomic composition and also the quantum dot crystalline quality on the deposition parameters.
Chen, Mengxiao; Pan, Caofeng; Zhang, Taiping; Li, Xiaoyi; Liang, Renrong; Wang, Zhong Lin
2016-06-28
Based on white light emission at silicon (Si)/ZnO hetrerojunction, a pressure-sensitive Si/ZnO nanowires heterostructure matrix light emitting diode (LED) array is developed. The light emission intensity of a single heterostructure LED is tuned by external strain: when the applied stress keeps increasing, the emission intensity first increases and then decreases with a maximum value at a compressive strain of 0.15-0.2%. This result is attributed to the piezo-phototronic effect, which can efficiently modulate the LED emission intensity by utilizing the strain-induced piezo-polarization charges. It could tune the energy band diagrams at the junction area and regulate the optoelectronic processes such as charge carriers generation, separation, recombination, and transport. This study achieves tuning silicon based devices through piezo-phototronic effect.
Directory of Open Access Journals (Sweden)
Shengmao Lin
2015-08-01
Full Text Available Scaffold mechanical properties are essential in regulating the microenvironment of three-dimensional cell culture. A coupled fiber-matrix numerical model was developed in this work for predicting the mechanical response of collagen scaffolds subjected to various levels of non-enzymatic glycation and collagen concentrations. The scaffold was simulated by a Voronoi network embedded in a matrix. The computational model was validated using published experimental data. Results indicate that both non-enzymatic glycation-induced matrix stiffening and fiber network density, as regulated by collagen concentration, influence scaffold behavior. The heterogeneous stress patterns of the scaffold were induced by the interfacial mechanics between the collagen fiber network and the matrix. The knowledge obtained in this work could help to fine-tune the mechanical properties of collagen scaffolds for improved tissue regeneration applications.
Hine, N D M; Haynes, P D; Mostofi, A A; Payne, M C
2010-09-21
We present calculations of formation energies of defects in an ionic solid (Al(2)O(3)) extrapolated to the dilute limit, corresponding to a simulation cell of infinite size. The large-scale calculations required for this extrapolation are enabled by developments in the approach to parallel sparse matrix algebra operations, which are central to linear-scaling density-functional theory calculations. The computational cost of manipulating sparse matrices, whose sizes are determined by the large number of basis functions present, is greatly improved with this new approach. We present details of the sparse algebra scheme implemented in the ONETEP code using hierarchical sparsity patterns, and demonstrate its use in calculations on a wide range of systems, involving thousands of atoms on hundreds to thousands of parallel processes.
Li, Xuan; Gan, John Q; Wang, Haixian
2018-02-01
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the organization of the brain functional network and found several consistent communities consisting of functionally connected but spatially separated brain regions across subjects. However, increasing evidence in many disciplines has shown that most realistic complex networks have overlapping community structure. Only recently has the overlapping community structure drawn increasing interest in the domain of brain network studies. Another issue is that the inter-subject variability is often not directly reflected in the process of community detection at the group level. In this paper, we propose a novel method called collective sparse symmetric non-negative matrix factorization (cssNMF) to address these issues. The cssNMF approach identifies the group-level overlapping communities across subjects and in the meantime preserves the information of individual variation in brain functional network organization. To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance comparison in the experiment. Experimental results show that the proposed cssNMF method accurately and stably identifies group-level overlapping communities across subjects as well as individual differences in network organization with neurophysiologically meaningful interpretations. This research extends our understanding of the common underlying community structures and individual differences in community strengths in brain functional network organization. Copyright © 2017 Elsevier Inc. All rights reserved.
Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E
2017-04-15
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pnegative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pNegative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.
Bogiatzis, Petros; Ishii, Miaki; Davis, Timothy A.
2016-05-01
For more than two decades, the number of data and model parameters in seismic tomography problems has exceeded the available computational resources required for application of direct computational methods, leaving iterative solvers the only option. One disadvantage of the iterative techniques is that the inverse of the matrix that defines the system is not explicitly formed, and as a consequence, the model resolution and covariance matrices cannot be computed. Despite the significant effort in finding computationally affordable approximations of these matrices, challenges remain, and methods such as the checkerboard resolution tests continue to be used. Based upon recent developments in sparse algorithms and high-performance computing resources, we show that direct methods are becoming feasible for large seismic tomography problems. We demonstrate the application of QR factorization in solving the regional P-wave structure and computing the full resolution matrix with 267 520 model parameters.
DEFF Research Database (Denmark)
Montoya-Martinez, Jair; Artes-Rodriguez, Antonio; Pontil, Massimiliano
2014-01-01
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured...... matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios...
Sol, Jeroen A H P; Dehm, Volker; Hecht, Reinhard; Würthner, Frank; Schenning, Albertus P H J; Debije, Michael G
2018-01-22
Temperature-responsive luminescent solar concentrators (LSCs) have been fabricated in which the Förster resonance energy transfer (FRET) between a donor-acceptor pair in a liquid crystalline solvent can be tuned. At room temperatures, the perylene bisimide (PBI) acceptor is aggregated and FRET is inactive; while after heating to a temperature above the isotropic phase of the liquid crystal solvent, the acceptor PBI completely dissolves and FRET is activated. This unusual temperature control over FRET was used to design a color-tunable LSC. The device has been shown to be highly stable towards consecutive heating and cooling cycles, making it an appealing device for harvesting otherwise unused solar energy. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
Energy Technology Data Exchange (ETDEWEB)
Shan, Hongzhan; Austin, Brian M.; De Jong, Wibe A.; Oliker, Leonid; Wright, Nicholas J.; Apra, Edoardo
2014-10-01
Attaining performance in the evaluation of two-electron repulsion integrals and constructing the Fock matrix is of considerable importance to the computational chemistry community. Due to its numerical complexity improving the performance behavior across a variety of leading supercomputing platforms is an increasing challenge due to the significant diversity in high-performance computing architectures. In this paper, we present our successful tuning methodology for these important numerical methods on the Cray XE6, the Cray XC30, the IBM BG/Q, as well as the Intel Xeon Phi. Our optimization schemes leverage key architectural features including vectorization and simultaneous multithreading, and results in speedups of up to 2.5x compared with the original implementation.
Directory of Open Access Journals (Sweden)
Seyed Sina Sebtahmadi
2016-11-01
Full Text Available A rotational d-q current control scheme based on a Particle Swarm Optimization- Proportional-Integral (PSO-PI controller, is used to drive an induction motor (IM through an Ultra Sparse Z-source Matrix Converter (USZSMC. To minimize the overall size of the system, the lowest feasible values of Z-source elements are calculated by considering the both timing and aspects of the circuit. A meta-heuristic method is integrated to the control system in order to find optimal coefficient values in a single multimodal problem. Henceforth, the effect of all coefficients in minimizing the total harmonic distortion (THD and balancing the stator current are considered simultaneously. Through changing the reference point of magnitude or frequency, the modulation index can be automatically adjusted and respond to changes without heavy computational cost. The focus of this research is on a reliable and lightweight system with low computational resources. The proposed scheme is validated through both simulation and experimental results.
Second SIAM conference on sparse matrices: Abstracts. Final technical report
Energy Technology Data Exchange (ETDEWEB)
NONE
1996-12-31
This report contains abstracts on the following topics: invited and long presentations (IP1 & LP1); sparse matrix reordering & graph theory I; sparse matrix tools & environments I; eigenvalue computations I; iterative methods & acceleration techniques I; applications I; parallel algorithms I; sparse matrix reordering & graphy theory II; sparse matrix tool & environments II; least squares & optimization I; iterative methods & acceleration techniques II; applications II; eigenvalue computations II; least squares & optimization II; parallel algorithms II; sparse direct methods; iterative methods & acceleration techniques III; eigenvalue computations III; and sparse matrix reordering & graph theory III.
Technique detection software for Sparse Matrices
Directory of Open Access Journals (Sweden)
KHAN Muhammad Taimoor
2009-12-01
Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.
Ahmed, M; Guleria, A; Rath, M C; Singh, A K; Adhikari, S; Sarkar, S K
2014-08-01
Herein, we have demonstrated a facile and green approach for the synthesis of Cadmium selenide (CdSe) quantum dots (QDs). The process was mediated by bovine serum albumin (BSA) and it was found that BSA plays the dual role of reducing agent as well as a stabilizing agent. The QDs exhibited sharp excitonic absorption features at ~500 nm and subsequently showed reasonably good photoluminescence (PL) at room temperature. The PL is seen to be strongly dependent on the concentration of the precursors and hence, the luminescence of these QDs could be conveniently tuned across the visible spectrum simply by varying molar ratio of the precursors. It can be envisaged from the fact that a red-shift of about 100 nm in the PL peak position was observed when the molar ratio of the precursors ([Cd2+]:[Se2-], in mM) was varied from 10:5 to 10:40. Subsequently, the charge carrier relaxation dynamics associated with the different molar ratio of precursors has been investigated and very interesting information regarding the energy level structures of these QDs were revealed. Most importantly, in conjunction with the optical tuning, the nanomorphology of these nanoparticles was found to vary with the change in molar ratios of Se and Cd precursors. This aspect can provide a new direction of controlling the shape of CdSe nanoparticles. The possible mechanism of the formation as well as for the shape variation of these nanoparticles with the molar ratios of precursors has been proposed, taking into account the role of amino acid residues (present in BSA). Moreover, the QDs were water soluble and possessed fairly good colloidal stability therefore, can have potential applications in catalysis and bio-labeling. On the whole, the present methodology of protein assisted synthesis is relatively new especially for semiconducting nanomaterials and may provide some unique and interesting aspects to control and fine tune the morphology vis-à-vis, their optical properties.
Sparse graphs using exchangeable random measures.
Caron, François; Fox, Emily B
2017-11-01
Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this array-which can aid in modelling, computations and theoretical analysis-the Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We explore using completely random measures (CRMs) to define the exchangeable random measure, and we show how our CRM construction enables us to achieve sparse graphs while maintaining the attractive properties of exchangeability. We relate the sparsity of the graph to the Lévy measure defining the CRM. For a specific choice of CRM, our graphs can be tuned from dense to sparse on the basis of a single parameter. We present a scalable Hamiltonian Monte Carlo algorithm for posterior inference, which we use to analyse network properties in a range of real data sets, including networks with hundreds of thousands of nodes and millions of edges.
Parallel and Scalable Sparse Basic Linear Algebra Subprograms
DEFF Research Database (Denmark)
Liu, Weifeng
proposes new fundamental algorithms and data structures that accelerate Sparse BLAS routines on modern massively parallel processors: (1) a new heap data structure named ad-heap, for faster heap operations on heterogeneous processors, (2) a new sparse matrix representation named CSR5, for faster sparse......Sparse basic linear algebra subprograms (BLAS) are fundamental building blocks for numerous scientific computations and graph applications. Compared with Dense BLAS, parallelization of Sparse BLAS routines entails extra challenges due to the irregularity of sparse data structures. This thesis...... matrix-vector multiplication (SpMV) on homogeneous processors such as CPUs, GPUs and Xeon Phi, (3) a new CSR-based SpMV algorithm for a variety of tightly coupled CPU-GPU heterogeneous processors, and (4) a new framework and associated algorithms for sparse matrix-matrix multiplication (SpGEMM) on GPUs...
DiMaggio, Peter A; Subramani, Ashwin; Judson, Richard S; Floudas, Christodoulos A
2010-11-01
In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2008). Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9, 458-474.; DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2010b). A network flow model for biclustering via optimal re-ordering of data matrices. J. Global. Optim. 47, 343-354.) to identify biclusters corresponding to subsets of chemicals that have similar responses over distinct subsets of the in vitro assays. The biclustering of the in vitro assays is shown to result in significant clustering based on assay target (e.g., cytochrome P450 [CYP] and nuclear receptors) and type (e.g., downregulated BioMAP and biochemical high-throughput screening protein kinase activity assays). An optimal method based on mixed-integer linear optimization for reordering sparse data matrices (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Li, G. Y., Rabinowitz, J. D., and Rabitz, H. A. (2010a). Enhancing molecular discovery using descriptor-free rearrangement clustering techniques for sparse data sets. AIChE J. 56, 405-418.; McAllister, S. R., DiMaggio, P. A., and Floudas, C. A. (2009). Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem. J. Global. Optim. 45, 111-129) is then applied to the in vivo data set (21.7% sparse) in order to cluster end points that have similar lowest effect level (LEL) values, where it is observed that the end points are effectively clustered according to (1) animal species (i.e., the
Tunable Sparse Network Coding for Multicast Networks
DEFF Research Database (Denmark)
Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant
2014-01-01
This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity....... Coding density tuning can be performed by designing time-dependent coding matrices. In multicast networks, this tuning can be performed within the network by designing time-dependent pre- coding and network coding matrices with mild conditions on the network structure for specific densities. We present...
Directory of Open Access Journals (Sweden)
Philip Wong
Full Text Available Accurate reconstruction of 3D photoacoustic (PA images requires detection of photoacoustic signals from many angles. Several groups have adopted staring ultrasound arrays, but assessment of array performance has been limited. We previously reported on a method to calibrate a 3D PA tomography (PAT staring array system and analyze system performance using singular value decomposition (SVD. The developed SVD metric, however, was impractical for large system matrices, which are typical of 3D PAT problems. The present study consisted of two main objectives. The first objective aimed to introduce the crosstalk matrix concept to the field of PAT for system design. Figures-of-merit utilized in this study were root mean square error, peak signal-to-noise ratio, mean absolute error, and a three dimensional structural similarity index, which were derived between the normalized spatial crosstalk matrix and the identity matrix. The applicability of this approach for 3D PAT was validated by observing the response of the figures-of-merit in relation to well-understood PAT sampling characteristics (i.e. spatial and temporal sampling rate. The second objective aimed to utilize the figures-of-merit to characterize and improve the performance of a near-spherical staring array design. Transducer arrangement, array radius, and array angular coverage were the design parameters examined. We observed that the performance of a 129-element staring transducer array for 3D PAT could be improved by selection of optimal values of the design parameters. The results suggested that this formulation could be used to objectively characterize 3D PAT system performance and would enable the development of efficient strategies for system design optimization.
Parallel transposition of sparse data structures
DEFF Research Database (Denmark)
Wang, Hao; Liu, Weifeng; Hou, Kaixi
2016-01-01
transposition for sparse matrices and graphs, have not received the attention they deserve. In this paper, we first identify that the transposition operation can be a bottleneck of some fundamental sparse matrix and graph algorithms. Then, we revisit the performance and scalability of parallel transposition...... approaches on x86-based multi-core and many-core processors. Based on the insights obtained, we propose two new parallel transposition algorithms: ScanTrans and MergeTrans. The experimental results show that our ScanTrans method achieves an average of 2.8-fold (up to 6.2-fold) speedup over the parallel......Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel...
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin
2015-04-03
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Sparse Exploratory Factor Analysis.
Trendafilov, Nickolay T; Fontanella, Sara; Adachi, Kohei
2017-07-13
Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods.
Biclustering via Sparse Singular Value Decomposition
Lee, Mihee
2010-02-16
Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.
Fast wavelet based sparse approximate inverse preconditioner
Energy Technology Data Exchange (ETDEWEB)
Wan, W.L. [Univ. of California, Los Angeles, CA (United States)
1996-12-31
Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.
Biclustering sparse binary genomic data.
van Uitert, Miranda; Meuleman, Wouter; Wessels, Lodewyk
2008-12-01
Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.
Rotational image deblurring with sparse matrices
DEFF Research Database (Denmark)
Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos
2014-01-01
We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...
Zhang, Tianzhu
2015-06-01
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.
A medium-grain method for fast 2D bipartitioning of sparse matrices
D.M. Pelt (Daniël); R.H. Bisseling (Rob)
2014-01-01
htmlabstractWe present a new hypergraph-based method, the medium-grain method, for solving the sparse matrix partitioning problem. This problem arises when distributing data for parallel sparse matrix-vector multiplication. In the medium-grain method, each matrix nonzero is assigned to either a row
PERI - Auto-tuning Memory Intensive Kernels for Multicore
Energy Technology Data Exchange (ETDEWEB)
Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H
2008-06-24
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.
PERI - auto-tuning memory-intensive kernels for multicore
International Nuclear Information System (INIS)
Williams, S; Carter, J; Oliker, L; Shalf, J; Yelick, K; Bailey, D; Datta, K
2008-01-01
We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to sparse matrix vector multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the high-performance computing literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4x improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications
Parallelism in matrix computations
Gallopoulos, Efstratios; Sameh, Ahmed H
2016-01-01
This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations. It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms. The book consists of four parts: (I) Basics; (II) Dense and Special Matrix Computations; (III) Sparse Matrix Computations; and (IV) Matrix functions and characteristics. Part I deals with parallel programming paradigms and fundamental kernels, including reordering schemes for sparse matrices. Part II is devoted to dense matrix computations such as parallel algorithms for solving linear systems, linear least squares, the symmetric algebraic eigenvalue problem, and the singular-value decomposition. It also deals with the development of parallel algorithms for special linear systems such as banded ,Vandermonde ,Toeplitz ,and block Toeplitz systems. Part III addresses sparse matrix computations: (a) the development of pa...
Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul
2012-12-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
Sparse structure regularized ranking
Wang, Jim Jing-Yan
2014-04-17
Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.
Parallel sparse direct solver for integrated circuit simulation
Chen, Xiaoming; Yang, Huazhong
2017-01-01
This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...
DEFF Research Database (Denmark)
Mortensen, Christian Hviid; Vestergaard, Vitus
2014-01-01
and explore the physical gallery space. We implemented a simple low-cost prototype system called Exaudimus allowing users to search for the audio streams using their own bodies as a metaphorical radio tuning dial. We tested the concept in a public exhibition at the Media Museum in Denmark. A small qualitative...
Quantifying Registration Uncertainty With Sparse Bayesian Modelling.
Le Folgoc, Loic; Delingette, Herve; Criminisi, Antonio; Ayache, Nicholas
2017-02-01
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients.
Sparse approximation with bases
2015-01-01
This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications. The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...
Efficient convolutional sparse coding
Energy Technology Data Exchange (ETDEWEB)
Wohlberg, Brendt
2017-06-20
Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.
Multiple Sparse Representations Classification
Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik
2015-01-01
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and
Supervised Convolutional Sparse Coding
Affara, Lama Ahmed
2018-04-08
Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.
Sparse time series chain graphical models for reconstructing genetic networks
Abegaz, Fentaw; Wit, Ernst
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of
Supervised Transfer Sparse Coding
Al-Shedivat, Maruan
2014-07-27
A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2015-01-01
, and it is formulated as a generalized eigenvalue problem. Thus RGED can be applied to effectively extract sparse features and calculate sparse discriminant directions for all variants of Fisher discriminant criterion based models. Particularly, RGED can be applied to matrix-based and even tensor-based discriminant...... techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest...
DEFF Research Database (Denmark)
Mortensen, Christian Hviid
web accessible archives or through listening kiosks in the exhibition. Neither one takes advantage of the unique affordances of the physicality of an exhibition. Instead they most often rely on users clicking through menus, playlists and folders. We therefore propose an alternative way of exhibiting...... bodies as a metaphorical radio tuning dial. We tested the concept in a public exhibition at the Media Museum in Denmark. A small qualitative user study conducted in the exhibition shows promise for this type of immersive experience. The users however tend to perceive it as a unique and finite experience...... radio heritage where users move around and explore the physical room. We implemented a simple low-cost prototype system called Exaudimus that uses webcams to track visitors in a gallery. Using Exaudimus we position audio around the gallery allowing users to search for the audio streams using their own...
Pansharpening via sparse regression
Tang, Songze; Xiao, Liang; Liu, Pengfei; Huang, Lili; Zhou, Nan; Xu, Yang
2017-09-01
Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high- and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.
Fast methods for resumming matrix polynomials and Chebyshev matrix polynomials
International Nuclear Information System (INIS)
Liang Wanzen; Baer, Roi; Saravanan, Chandra; Shao Yihan; Bell, Alexis T.; Head-Gordon, Martin
2004-01-01
Fast and effective algorithms are discussed for resumming matrix polynomials and Chebyshev matrix polynomials. These algorithms lead to a significant speed-up in computer time by reducing the number of matrix multiplications required to roughly twice the square root of the degree of the polynomial. A few numerical tests are presented, showing that evaluation of matrix functions via polynomial expansions can be preferable when the matrix is sparse and these fast resummation algorithms are employed
Compressed sensing & sparse filtering
Carmi, Avishy Y; Godsill, Simon J
2013-01-01
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.Â Apart from compressed sensing this book contains other related app
Wang, Jim Jing-Yan
2014-07-06
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.
Visual tracking based on extreme learning machine and sparse representation.
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-10-22
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.
A flexible framework for sparse simultaneous component based data integration
Directory of Open Access Journals (Sweden)
Van Deun Katrijn
2011-11-01
Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such
A flexible framework for sparse simultaneous component based data integration.
Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven
2011-11-15
High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform
A flexible framework for sparse simultaneous component based data integration
2011-01-01
1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are
Wu, Hongyue; Yang, Junfeng; Wang, Xiaoxue; Gan, Shucai; Li, Linlin
2018-03-01
A series of LiGd(MO4)2: Sm3+, Tb3+ (M = Mo, W) phosphors was prepared by a conventional solid state reaction method. Powder X-Ray diffraction (XRD) analysis reveals that the compounds are of the same structure type. Their luminescent properties have been studied. The optimal doping concentrations are 8% for Sm3+ and 18% for Tb3+ in the LiGd(MoO4)2 host. Sm3+ and Tb3+ have different sensitivity to the Mo/W ratio. For LiGd(MoO4)2-X(WO4)X: Sm3+ (X = 0, 0.4, 0.8, 1.2, 1.6, 2.0), the strongest emission intensity is 1.766 times than that of the weakest, while 171 times for LiGd(MoO4)2-X(WO4)X: Tb3+. The experimental results show that Mo/W ratio strong influences on the properties of LiGd(MoO4)2-X(WO4)X: Tb3+. With the increasing of WO42- groups concentration, the shape of characteristic excitation peaks of Tb3+ is almost the same and the excitation intensity gradually increase. Moreover, the energy transfer from Tb3+ to Sm3+ has been realized in the co-doped phosphors. The experimental analysis and theoretical calculations reveal that the quadrupole-quadrupole interaction is the dominant mechanism for the Tb3+→Sm3+ energy transfer. Therefore, luminous intensity can be adjusted by different sensitivities to matrix composition and energy transfer from Tb3+→Sm3+. By this tuning color method, white-light-emitting phosphor has been prepared. The excitation wavelength is 378 nm, and this indicates that the white-light-emitting phosphor could be pumped by near-UV light.
Partitioning sparse rectangular matrices for parallel processing
Energy Technology Data Exchange (ETDEWEB)
Kolda, T.G.
1998-05-01
The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.
Sparse decompositions in 'incoherent' dictionaries
DEFF Research Database (Denmark)
Gribonval, R.; Nielsen, Morten
2003-01-01
a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...
Balanced and sparse Tamo-Barg codes
Halbawi, Wael
2017-08-29
We construct balanced and sparse generator matrices for Tamo and Barg\\'s Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-04-11
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-12-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Combining sparse coding and time-domain features for heart sound classification.
Whitaker, Bradley M; Suresha, Pradyumna B; Liu, Chengyu; Clifford, Gari D; Anderson, David V
2017-07-31
This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification. In sparse coding, preprocessed data is decomposed into a dictionary matrix and a sparse coefficient matrix. The dictionary matrix represents statistically important features of the audio segments. The sparse coefficient matrix is a mapping that represents which features are used by each segment. Working in the sparse domain, we train support vector machines (SVMs) for each audio segment (S1, systole, S2, diastole) and the full cardiac cycle. We train a sixth SVM to combine the results from the preliminary SVMs into a single binary label for the entire PCG recording. In addition to classifying heart sounds using sparse coding, this paper presents two novel modifications. The first uses a matrix norm in the dictionary update step of sparse coding to encourage the dictionary to learn discriminating features from the abnormal heart recordings. The second combines the sparse coding features with time-domain features in the final SVM stage. The original algorithm submitted to the challenge achieved a cross-validated mean accuracy (MAcc) score of 0.8652 (Se = 0.8669 and Sp = 0.8634). After incorporating the modifications new to this paper, we report an improved cross-validated MAcc of 0.8926 (Se = 0.9007 and Sp = 0.8845). Our results show that sparse coding is an effective way to define spectral features of the cardiac cycle and its sub-cycles for the purpose of classification. In addition, we demonstrate that sparse coding can be combined with additional feature extraction methods to improve classification accuracy.
In Defense of Sparse Tracking: Circulant Sparse Tracker
Zhang, Tianzhu
2016-12-13
Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...... Bayesian hierarchy for sparse models using slab and spike priors (two-component δ-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated...... computational complexity. We attribute this mainly to the stochastic search strategy used, and to parsimony (sparsity and identifiability), which is an explicit part of the model. We propose two extensions to the basic i.i.d. linear framework: non-linear dependence on observed variables, called SNIM (Sparse Non-linear...
International Nuclear Information System (INIS)
Kwon, Sun Yong
2005-02-01
This book deals with tuning for oracle application, which consists of twenty two chapters. These are the contents of this book : what is tuning?, procedure of tuning, collection of performance data using stats pack, collection of performance data in real time, disk IO dispersion, architecture on Index, partition and IOT, optimization of cluster Factor, optimizer, analysis on plan of operation, selection of Index, tuning of Index, parallel processing architecture, DML, analytic function join method, join type, analysis of application, Lock architecture, SGA architecture and wait event and segment tuning.
Algorithms for Sparse Non-negative Tucker Decompositions
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai
2008-01-01
for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Non-negative matrix factorization (NMF) in conjunction with sparse coding has lately been given much attention due to its...... part based and easy interpretable representation. While NMF has been extended to the PARAFAC model no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed is non-negative it may well be relevant to consider purely additive (i.e., non-negative Tucker...... decompositions). To reduce ambiguities of this type of decomposition we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse non-negative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms...
Sparse-view Reconstruction of Dynamic Processes by Neutron Tomography
Wang, Hu; Kaestner, Anders; Zou, Yubin; Lu, Yuanrong; Guo, Zhiyu
As for neutron tomography, hundreds of projections over the range of 0-180 degrees are required to reconstruct the attenuation matrix with the traditional filtered back projection (FBP) algorithm, and the total acquisition time can reach several hours. This poor temporal resolution constrains that neutron tomography is only feasible to investigate static or quasi-static process. Reducing the number of projections is a possible way to improve the temporal resolution, which however highly relies on sparse-view reconstruction algorithms. To assess the feasibility of sparse-view reconstruction for neutron tomography, both simulation and an experiment of water uptake from a piece of wood composite were studied, and the results indicated that temporal resolution of neutron tomography can be improved when combining the Golden Ratio scan strategy with a prior image-constrained sparse-view reconstruction algorithm-PICCS.
P-SPARSLIB: A parallel sparse iterative solution package
Energy Technology Data Exchange (ETDEWEB)
Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)
1994-12-31
Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.
Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
Cho, Myung
2016-06-24
We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... and computational complexity. We also analyze the impact of transceiver filters on the sparseness of the channel response, and propose a dictionary design that permits the deployment of sparse inference methods in conditions of low bandwidth....
Sparse random matrices: The eigenvalue spectrum revisited
International Nuclear Information System (INIS)
Semerjian, Guilhem; Cugliandolo, Leticia F.
2003-08-01
We revisit the derivation of the density of states of sparse random matrices. We derive a recursion relation that allows one to compute the spectrum of the matrix of incidence for finite trees that determines completely the low concentration limit. Using the iterative scheme introduced by Biroli and Monasson [J. Phys. A 32, L255 (1999)] we find an approximate expression for the density of states expected to hold exactly in the opposite limit of large but finite concentration. The combination of the two methods yields a very simple geometric interpretation of the tails of the spectrum. We test the analytic results with numerical simulations and we suggest an indirect numerical method to explore the tails of the spectrum. (author)
Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.
She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie
2014-02-01
To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.
Sparse Representations of Hyperspectral Images
Swanson, Robin J.
2015-11-23
Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.
Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images
Thompson, David R.; Castano, Rebecca; Gilmore, Martha S.
2009-01-01
Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.
Mammalian odorant receptor tuning breadth persists across distinct odorant panels.
Directory of Open Access Journals (Sweden)
Devin Kepchia
Full Text Available The molecular receptive range (MRR of a mammalian odorant receptor (OR is the set of odorant structures that activate the OR, while the distribution of these odorant structures across odor space is the tuning breadth of the OR. Variation in tuning breadth is thought to be an important property of ORs, with the MRRs of these receptors varying from narrowly to broadly tuned. However, defining the tuning breadth of an OR is a technical challenge. For practical reasons, a screening panel that broadly covers odor space must be limited to sparse coverage of the many potential structures in that space. When screened with such a panel, ORs with different odorant specificities, but equal tuning breadths, might appear to have different tuning breadths due to chance. We hypothesized that ORs would maintain their tuning breadths across distinct odorant panels. We constructed a new screening panel that was broadly distributed across an estimated odor space and contained compounds distinct from previous panels. We used this new screening panel to test several murine ORs that were previously characterized as having different tuning breadths. ORs were expressed in Xenopus laevis oocytes and assayed by two-electrode voltage clamp electrophysiology. MOR256-17, an OR previously characterized as broadly tuned, responded to nine novel compounds from our new screening panel that were structurally diverse and broadly dispersed across an estimated odor space. MOR256-22, an OR previously characterized as narrowly tuned, responded to a single novel compound that was structurally similar to a previously known ligand for this receptor. MOR174-9, a well-characterized receptor with a narrowly tuned MRR, did not respond to any novel compounds in our new panel. These results support the idea that variation in tuning breadth among these three ORs is not an artifact of the screening protocol, but is an intrinsic property of the receptors.
Image understanding using sparse representations
Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas
2014-01-01
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin
International Nuclear Information System (INIS)
Bailey, D H; Williams, S; Chame, J; Chen, C; Hall, M; Dongarra, J; Moore, S; Seymour, K; You, H; Hollingsworth, J K; Tiwari, A; Hovland, P; Shin, J
2008-01-01
The enormous and growing complexity of today's high-end systems has increased the already significant challenges of obtaining high performance on equally complex scientific applications. Application scientists are faced with a daunting challenge in tuning their codes to exploit performance-enhancing architectural features. The Performance Engineering Research Institute (PERI) is working toward the goal of automating portions of the performance tuning process. This paper describes PERI's overall strategy for auto-tuning tools and recent progress in both building auto-tuning tools and demonstrating their success on kernels, some taken from large-scale applications
A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array
Directory of Open Access Journals (Sweden)
Wenhao Zeng
2016-01-01
Full Text Available To improve the performance of two-dimensional direction-of-arrival (2D DOA estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP. Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimation can be acquired through solving the polynomial roots. The proposed algorithm can achieve high accuracy of 2D DOA estimation in sparse array, without solving autocorrelation matrix of received signals and scanning of two-dimensional spectral peak. Besides, it decreases the number of antennas and lowers computational complexity and meanwhile avoids the angle ambiguity problem. Computer simulations demonstrate that the proposed FPC-ROOT algorithm can obtain the 2D DOA estimation precisely in sparse array.
High-SNR spectrum measurement based on Hadamard encoding and sparse reconstruction
Wang, Zhaoxin; Yue, Jiang; Han, Jing; Li, Long; Jin, Yong; Gao, Yuan; Li, Baoming
2017-12-01
The denoising capabilities of the H-matrix and cyclic S-matrix based on the sparse reconstruction, employed in the Pixel of Focal Plane Coded Visible Spectrometer for spectrum measurement are investigated, where the spectrum is sparse in a known basis. In the measurement process, the digital micromirror device plays an important role, which implements the Hadamard coding. In contrast with Hadamard transform spectrometry, based on the shift invariability, this spectrometer may have the advantage of a high efficiency. Simulations and experiments show that the nonlinear solution with a sparse reconstruction has a better signal-to-noise ratio than the linear solution and the H-matrix outperforms the cyclic S-matrix whether the reconstruction method is nonlinear or linear.
Abdelfattah, Ahmad
2015-01-15
High performance computing (HPC) platforms are evolving to more heterogeneous configurations to support the workloads of various applications. The current hardware landscape is composed of traditional multicore CPUs equipped with hardware accelerators that can handle high levels of parallelism. Graphical Processing Units (GPUs) are popular high performance hardware accelerators in modern supercomputers. GPU programming has a different model than that for CPUs, which means that many numerical kernels have to be redesigned and optimized specifically for this architecture. GPUs usually outperform multicore CPUs in some compute intensive and massively parallel applications that have regular processing patterns. However, most scientific applications rely on crucial memory-bound kernels and may witness bottlenecks due to the overhead of the memory bus latency. They can still take advantage of the GPU compute power capabilities, provided that an efficient architecture-aware design is achieved. This dissertation presents a uniform design strategy for optimizing critical memory-bound kernels on GPUs. Based on hierarchical register blocking, double buffering and latency hiding techniques, this strategy leverages the performance of a wide range of standard numerical kernels found in dense and sparse linear algebra libraries. The work presented here focuses on matrix-vector multiplication kernels (MVM) as repre- sentative and most important memory-bound operations in this context. Each kernel inherits the benefits of the proposed strategies. By exposing a proper set of tuning parameters, the strategy is flexible enough to suit different types of matrices, ranging from large dense matrices, to sparse matrices with dense block structures, while high performance is maintained. Furthermore, the tuning parameters are used to maintain the relative performance across different GPU architectures. Multi-GPU acceleration is proposed to scale the performance on several devices. The
Biclustering Sparse Binary Genomic Data
Van Uitert, M.; Meuleman, W.; Wessels, L.F.A.
2008-01-01
Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two
The application of sparse linear prediction dictionary to compressive sensing in speech signals
Directory of Open Access Journals (Sweden)
YOU Hanxu
2016-04-01
Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.
Tamellini, Lorenzo
2016-01-05
In this talk we discuss possible strategies to minimize the impact of the curse of dimensionality effect when building sparse-grid approximations of a multivariate function u = u(y1, ..., yN ). More precisely, we present a knapsack approach , in which we estimate the cost and the error reduction contribution of each possible component of the sparse grid, and then we choose the components with the highest error reduction /cost ratio. The estimates of the error reduction are obtained by either a mixed a-priori / a-posteriori approach, in which we first derive a theoretical bound and then tune it with some inexpensive auxiliary computations (resulting in the so-called quasi-optimal sparse grids ), or by a fully a-posteriori approach (obtaining the so-called adaptive sparse grids ). This framework is very general and can be used to build quasi-optimal/adaptive sparse grids on bounded and unbounded domains (e.g. u depending on uniform and normal random distributions for yn), using both nested and non-nested families of univariate collocation points. We present some theoretical convergence results as well as numerical results showing the efficiency of the proposed approach for the approximation of the solution of elliptic PDEs with random diffusion coefficients. In this context, to treat the case of rough permeability fields in which a sparse grid approach may not be suitable, we propose to use the sparse grids as a control variate in a Monte Carlo simulation.
Penerapan Sparse Matrix pada Rekomendasi Berita Personal untuk Pengguna Anonim
Mulki, Rizqi
2015-01-01
Online news links are being spread through the social media by news agencies in order to encourage people to read news from their site. After users have logged in to their site, users will keep on reading news that is relevant to their personalized news recommendation. But, nowadays personalized recommendation could be provided to users only if the site has recorded much of users browsing history and it‟s mandatory that users have to log in to the site. This could be problematic if the news r...
A Computing Platform for Parallel Sparse Matrix Computations
2016-01-05
4]![4]![3]![2]![2]![1]! α! β! 23! A NASTRAN benchmark Car&body&dynamics&–&& – A&x&=&λ&B&x&(order:&1.5&M,&and&7.2&M)& – A&=&AT,&B&:=&s.p.d...seconds)& Speed& Improve.& Nastran &1& 1.5&M& 1000& 59& 121& 2.2& Nastran &2& 7.2&M& 1000& 418& Failure& All&other&eigensolvers&failed&on&this& NASTRAN ...benchmark&All&Trilinos&eigensolvers&failed&on&this& NASTRAN &benchmark& 26! TraceMIN w/ multisectioning vs. Intel’s Math Kernel Library’s FEAST… using 129
Sparse Matrix Motivated Reconstruction of Far-Field Radiation Patterns
2015-03-01
Millennium Drive Suite 101 Belcamp, MD 21017 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY...Fig. 13. Note the lack of back-lobe measurement due to the presence of the supporting tower . 14 Fig. 13 2-D radiation pattern of the
Color normalization of histology slides using graph regularized sparse NMF
Sha, Lingdao; Schonfeld, Dan; Sethi, Amit
2017-03-01
Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The
Programming for Sparse Minimax Optimization
DEFF Research Database (Denmark)
Jonasson, K.; Madsen, Kaj
1994-01-01
We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed by the dete......We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...
Shearlets and Optimally Sparse Approximations
DEFF Research Database (Denmark)
Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q
2012-01-01
of such functions. Recently, cartoon-like images were introduced in 2D and 3D as a suitable model class, and approximation properties were measured by considering the decay rate of the $L^2$ error of the best $N$-term approximation. Shearlet systems are to date the only representation system, which provide...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....
Language Recognition via Sparse Coding
2016-09-08
exploiting variation of the nonzero locations and magnitude, we can build a discrimi- native pipeline for language recognition. Figure 1 describes a...classify each language as a target within six predefined language clusters. The language clusters are Ara- bic, Chinese, English , French, Slavic, and... Language Recognition via Sparse Coding† Youngjune L. Gwon1, William M. Campbell1, Douglas Sturim1, H. T. Kung2 1MIT Lincoln Laboratory 2Harvard
Multiple Descriptions Using Sparse Decompositions
DEFF Research Database (Denmark)
Jensen, Tobias Lindstrøm; Østergaard, Jan; Dahl, Joachim
2010-01-01
In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source...... ﬁrst-order method to the proposed convex problem such that we can solve large-scale instances for image sequences....
Katz, Bob
2013-01-01
Apple's exciting new Mastered for iTunes (MFiT) initiative, introduced in early 2012, introduces new possibilities for delivering high-quality audio. For the first time, record labels and program producers are encouraged to deliver audio materials to iTunes in a high resolution format, which can produce better-sounding masters. In iTunes Music, author and world-class mastering engineer Bob Katz starts out with the basics, surveys the recent past, and brings you quickly up to the present-where the current state of digital audio is bleak. Katz explains the evolution of
Shirazi, Jack
2003-01-01
Performance has been an important issue for Java developers ever since the first version hit the streets. Over the years, Java performance has improved dramatically, but tuning is essential to get the best results, especially for J2EE applications. You can never have code that runs too fast. Java Peformance Tuning, 2nd edition provides a comprehensive and indispensable guide to eliminating all types of performance problems. Using many real-life examples to work through the tuning process in detail, JPT shows how tricks such as minimizing object creation and replacing strings with arrays can
Robust Self Tuning Controllers
DEFF Research Database (Denmark)
Poulsen, Niels Kjølstad
1985-01-01
The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...... has several operation modes and a detector for controlling the mode. A special self tuning controller has been developed to regulate plant with changing time delay.......The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...
Low-rank sparse learning for robust visual tracking
Zhang, Tianzhu
2012-01-01
In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.
TUNE: Compiler-Directed Automatic Performance Tuning
Energy Technology Data Exchange (ETDEWEB)
Hall, Mary [University of Utah
2014-09-18
This project has developed compiler-directed performance tuning technology targeting the Cray XT4 Jaguar system at Oak Ridge, which has multi-core Opteron nodes with SSE-3 SIMD extensions, and the Cray XE6 Hopper system at NERSC. To achieve this goal, we combined compiler technology for model-guided empirical optimization for memory hierarchies with SIMD code generation, which have been developed by the PIs over the past several years. We examined DOE Office of Science applications to identify performance bottlenecks and apply our system to computational kernels that operate on dense arrays. Our goal for this performance-tuning technology has been to yield hand-tuned levels of performance on DOE Office of Science computational kernels, while allowing application programmers to specify their computations at a high level without requiring manual optimization. Overall, we aim to make our technology for SIMD code generation and memory hierarchy optimization a crucial component of high-productivity Petaflops computing through a close collaboration with the scientists in national laboratories.
International Nuclear Information System (INIS)
Dinev, D.
2001-01-01
On the basis of the comparative review of the methods for the betatron tune measurement in cyclic accelerators of synchrotrons type, the research of these methods is carried out from the point of view of their applicability to Nuclotron. Both methods using measurement of the statistical fluctuations of the beam current (Schottky noise) and methods using coherent beam excitation have been discussed. The emphasis is on the final results of importance for the tune measurement practice. Signal processing is briefly discussed too
A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion.
Lee, Kangjoo; Tak, Sungho; Ye, Jong Chul
2011-05-01
We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, a minimum description length (MDL)-based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.
The Real-Valued Sparse Direction of Arrival (DOA Estimation Based on the Khatri-Rao Product
Directory of Open Access Journals (Sweden)
Tao Chen
2016-05-01
Full Text Available There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV. The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.
Dynamic Representations of Sparse Graphs
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Fagerberg, Rolf
1999-01-01
We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....
Sparse Matrices in Frame Theory
DEFF Research Database (Denmark)
Lemvig, Jakob; Krahmer, Felix; Kutyniok, Gitta
2014-01-01
Frame theory is closely intertwined with signal processing through a canon of methodologies for the analysis of signals using (redundant) linear measurements. The canonical dual frame associated with a frame provides a means for reconstruction by a least squares approach, but other dual frames...... yield alternative reconstruction procedures. The novel paradigm of sparsity has recently entered the area of frame theory in various ways. Of those different sparsity perspectives, we will focus on the situations where frames and (not necessarily canonical) dual frames can be written as sparse matrices...
Link Prediction via Sparse Gaussian Graphical Model
Directory of Open Access Journals (Sweden)
Liangliang Zhang
2016-01-01
Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.
SAR moving target imaging using sparse and low-rank decomposition
Ni, Kang-Yu; Rao, Shankar
2014-05-01
We propose a method to image a complex scene with spotlight synthetic aperture radar (SAR) despite the presence of multiple moving targets. Many recent methods use sparsity-based reconstruction coupled with phase error corrections of moving targets to reconstruct stationary scenes. However, these methods rely on the assumption that the scene itself is sparse and thus unfortunately cannot handle realistic SAR scenarios with complex backgrounds consisting of more than just a few point targets. Our method makes use of sparse and low-rank (SLR) matrix decomposition, an efficient method for decomposing a low-rank matrix and sparse matrix from their sum. For detecting the moving targets and reconstructing the stationary background, SLR uses a convex optimization model that penalizes the nuclear norm of the low rank background structure and the L1 norm of the sparse moving targets. We propose an L1-norm regularization reconstruction method to form the input data matrix, which is grossly corrupted by the moving targets. Each column of the input matrix is a reconstructed SAR image with measurements from a small number of azimuth angles. The use of the L1-norm regularization and a sparse transform permits us to reconstruct the scene with significantly fewer measurements so that moving targets are approximately stationary. We demonstrate our SLR-based approach using simulations adapted from the GOTCHA Volumetric SAR data set. These simulations show that SLR can accurately image multiple moving targets with different individual motions in complex scenes where methods that assume a sparse scene would fail.
Permuting sparse rectangular matrices into block-diagonal form
Energy Technology Data Exchange (ETDEWEB)
Aykanat, Cevdet; Pinar, Ali; Catalyurek, Umit V.
2002-12-09
This work investigates the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for the solution of the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. We propose graph and hypergraph models to represent the nonzero structure of a matrix, which reduce the permutation problem to those of graph partitioning by vertex separator and hypergraph partitioning, respectively. Besides proposing the models to represent sparse matrices and investigating related combinatorial problems, we provide a detailed survey of relevant literature to bridge the gap between different societies, investigate existing techniques for partitioning and propose new ones, and finally present a thorough empirical study of these techniques. Our experiments on a wide range of matrices, using state-of-the-art graph and hypergraph partitioning tools MeTiS and PaT oH, revealed that the proposed methods yield very effective solutions both in terms of solution quality and run time.
A preconditioned inexact newton method for nonlinear sparse electromagnetic imaging
Desmal, Abdulla
2015-03-01
A nonlinear inversion scheme for the electromagnetic microwave imaging of domains with sparse content is proposed. Scattering equations are constructed using a contrast-source (CS) formulation. The proposed method uses an inexact Newton (IN) scheme to tackle the nonlinearity of these equations. At every IN iteration, a system of equations, which involves the Frechet derivative (FD) matrix of the CS operator, is solved for the IN step. A sparsity constraint is enforced on the solution via thresholded Landweber iterations, and the convergence is significantly increased using a preconditioner that levels the FD matrix\\'s singular values associated with contrast and equivalent currents. To increase the accuracy, the weight of the regularization\\'s penalty term is reduced during the IN iterations consistently with the scheme\\'s quadratic convergence. At the end of each IN iteration, an additional thresholding, which removes small \\'ripples\\' that are produced by the IN step, is applied to maintain the solution\\'s sparsity. Numerical results demonstrate the applicability of the proposed method in recovering sparse and discontinuous dielectric profiles with high contrast values.
Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Badiu, Mihai Alin
2015-01-01
-valued models, this paper proposes a GSM model - the Bessel K model - that induces concave penalty functions for the estimation of complex sparse signals. The properties of the Bessel K model are analyzed when it is applied to Type I and Type II estimation. This analysis reveals that, by tuning the parameters...... of the mixing pdf different penalty functions are invoked depending on the estimation type used, the value of the noise variance, and whether real or complex signals are estimated. Using the Bessel K model, we derive a sparse estimator based on a modification of the expectation-maximization algorithm formulated......In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex...
Sparse High Dimensional Models in Economics.
Fan, Jianqing; Lv, Jinchi; Qi, Lei
2011-09-01
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed.
Covariance matrix estimation for stationary time series
Xiao, Han; Wu, Wei Biao
2011-01-01
We obtain a sharp convergence rate for banded covariance matrix estimates of stationary processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices. We also consider a thresholded covariance matrix estimator that can better characterize sparsity if the true covariance matrix is sparse. As our main tool, we implement Toeplitz [Math. Ann. 70 (1911) 351–376] idea and relate eigenvalues of covariance matrices to the spectral densities or Fourier transforms...
Image fusion using sparse overcomplete feature dictionaries
Energy Technology Data Exchange (ETDEWEB)
Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt
2015-10-06
Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.
Combinatorial Algorithms for Computing Column Space Bases ThatHave Sparse Inverses
Energy Technology Data Exchange (ETDEWEB)
Pinar, Ali; Chow, Edmond; Pothen, Alex
2005-03-18
This paper presents a combinatorial study on the problem ofconstructing a sparse basis forthe null-space of a sparse, underdetermined, full rank matrix, A. Such a null-space is suitable forsolving solving many saddle point problems. Our approach is to form acolumn space basis of A that has a sparse inverse, by selecting suitablecolumns of A. This basis is then used to form a sparse null-space basisin fundamental form. We investigate three different algorithms forcomputing the column space basis: Two greedy approaches that rely onmatching, and a third employing a divide and conquer strategy implementedwith hypergraph partitioning followed by the greedy approach. We alsodiscuss the complexity of selecting a column basis when it is known thata block diagonal basis exists with a small given block size.
Sparse Channel Estimation Including the Impact of the Transceiver Filters with Application to OFDM
DEFF Research Database (Denmark)
Barbu, Oana-Elena; Pedersen, Niels Lovmand; Manchón, Carles Navarro
2014-01-01
Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one......) and receive (demodulation) filters. Hence, the assumption of the CFR being sparse in the canonical Fourier dictionary may no longer hold. In this work, we derive a signal model and subsequently a novel dictionary matrix for sparse estimation that account for the impact of transceiver filters. Numerical...... results obtained in an OFDM transmission scenario demonstrate the superior accuracy of a sparse estimator that uses our proposed dictionary rather than the classical Fourier dictionary, and its robustness against a mismatch in the assumed transmit filter characteristics....
Sparse inverse covariance estimation with the graphical lasso.
Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert
2008-07-01
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging
Desmal, Abdulla
2016-03-01
Electromagnetic imaging is the problem of determining material properties from scattered fields measured away from the domain under investigation. Solving this inverse problem is a challenging task because (i) it is ill-posed due to the presence of (smoothing) integral operators used in the representation of scattered fields in terms of material properties, and scattered fields are obtained at a finite set of points through noisy measurements; and (ii) it is nonlinear simply due the fact that scattered fields are nonlinear functions of the material properties. The work described in this thesis tackles the ill-posedness of the electromagnetic imaging problem using sparsity-based regularization techniques, which assume that the scatterer(s) occupy only a small fraction of the investigation domain. More specifically, four novel imaging methods are formulated and implemented. (i) Sparsity-regularized Born iterative method iteratively linearizes the nonlinear inverse scattering problem and each linear problem is regularized using an improved iterative shrinkage algorithm enforcing the sparsity constraint. (ii) Sparsity-regularized nonlinear inexact Newton method calls for the solution of a linear system involving the Frechet derivative matrix of the forward scattering operator at every iteration step. For faster convergence, the solution of this matrix system is regularized under the sparsity constraint and preconditioned by leveling the matrix singular values. (iii) Sparsity-regularized nonlinear Tikhonov method directly solves the nonlinear minimization problem using Landweber iterations, where a thresholding function is applied at every iteration step to enforce the sparsity constraint. (iv) This last scheme is accelerated using a projected steepest descent method when it is applied to three-dimensional investigation domains. Projection replaces the thresholding operation and enforces the sparsity constraint. Numerical experiments, which are carried out using
Diffusion Indexes with Sparse Loadings
DEFF Research Database (Denmark)
Kristensen, Johannes Tang
to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared......The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the traditional principal components (PC) approach.We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find...
Diffusion Indexes With Sparse Loadings
DEFF Research Database (Denmark)
Kristensen, Johannes Tang
2017-01-01
The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model...... that is better suited for forecasting compared to the traditional principal components (PC) approach. We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on U.S. macroeconomic data. Overall we find that compared to PC we obtain improvements...
SPARSE FARADAY ROTATION MEASURE SYNTHESIS
International Nuclear Information System (INIS)
Andrecut, M.; Stil, J. M.; Taylor, A. R.
2012-01-01
Faraday rotation measure synthesis is a method for analyzing multichannel polarized radio emissions, and it has emerged as an important tool in the study of Galactic and extragalactic magnetic fields. The method requires the recovery of the Faraday dispersion function from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we discuss a recovery method that assumes a sparse approximation of the Faraday dispersion function in an overcomplete dictionary of functions. We discuss the general case when both thin and thick components are included in the model, and we present the implementation of a greedy deconvolution algorithm. We illustrate the method with several numerical simulations that emphasize the effect of the covered range and sampling resolution in the Faraday depth space, and the effect of noise on the observed data.
Schaffhauser, Dian
2012-01-01
"Tuning the blend" is a phrase that educators hear a lot these days. It refers to finding the correct balance of online activities and face-to-face instruction in hybrid--or blended--courses. Finding a mix that meets the needs of both faculty and students requires experimentation, experience, and constant tweaking. And, as with coffee, the same…
The tuning fork used to modulate the radiofrequency system of the synchro cyclotron (SC) from 1957 to 1973. This piece is an unused spare part. The SC was the 1st accelerator built at CERN. It operated from August 1957 until it was closed down at the end of 1990. In the SC the magnetic field did not change with time, and the particles were accelerated in successive pulses by a radiofrequency voltage of some 20kV which varied in frequency as they spiraled outwards towards the extraction radius. The frequency varied from 30MHz to about 17Mz in each pulse. The tuning fork vibrated at 55MHz in vacuum in an enclosure which formed a variable capacitor in the tuning circuit of the RF system, allowing the RF to vary over the appropriate range to accelerate protons from the centre of the macine up to 600Mev at extraction radius. In operation the tips of the tuning fork blade had an amplitude of movement of over 1 cm. The SC accelerator underwent extensive improvements from 1973 to 1975, including the installation of a...
Efficient MATLAB computations with sparse and factored tensors.
Energy Technology Data Exchange (ETDEWEB)
Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)
2006-12-01
In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.
Low-Rank Sparse Coding for Image Classification
Zhang, Tianzhu
2013-12-01
In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
Ma, Anna; Flenner, Arjuna; Needell, Deanna; Percus, Allon G.
2014-01-01
We propose a method to improve image clustering using sparse text and the wisdom of the crowds. In particular, we present a method to fuse two different kinds of document features, image and text features, and use a common dictionary or "wisdom of the crowds" as the connection between the two different kinds of documents. With the proposed fusion matrix, we use topic modeling via non-negative matrix factorization to cluster documents.
Continuous speech recognition with sparse coding
CSIR Research Space (South Africa)
Smit, WJ
2009-04-01
Full Text Available , we show how sparse codes can be used to do continuous speech recognition. We use the TIDIGITS dataset to illustrate the process. First a waveform is transformed into a spectrogram, and a sparse code for the spectrogram is found by means of a linear...
Numerical solution of large sparse linear systems
International Nuclear Information System (INIS)
Meurant, Gerard; Golub, Gene.
1982-02-01
This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr
Sparse seismic imaging using variable projection
Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan
2013-01-01
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Yang, Jian; Zhang, David
2016-04-01
Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.
Social biases determine spatiotemporal sparseness of ciliate mating heuristics.
Clark, Kevin B
2012-01-01
findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts.
M3: Matrix Multiplication on MapReduce
DEFF Research Database (Denmark)
Silvestri, Francesco; Ceccarello, Matteo
2015-01-01
M3 is an Hadoop library for performing dense and sparse matrix multiplication in MapReduce. The library is based on multi-round algorithms exploiting the 3D decomposition of the problem.......M3 is an Hadoop library for performing dense and sparse matrix multiplication in MapReduce. The library is based on multi-round algorithms exploiting the 3D decomposition of the problem....
Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction
Directory of Open Access Journals (Sweden)
Gangquan Si
2017-01-01
Full Text Available The sparse strategy plays a significant role in the application of the least square support vector machine (LSSVM, to alleviate the condition that the solution of LSSVM is lacking sparseness and robustness. In this paper, a sparse method using reconstructed support vectors is proposed, which has also been successfully applied to mill load prediction. Different from other sparse algorithms, it no longer selects the support vectors from training data set according to the ranked contributions for optimization of LSSVM. Instead, the reconstructed data is obtained first based on the initial model with all training data. Then, select support vectors from reconstructed data set according to the location information of density clustering in training data set, and the process of selecting is terminated after traversing the total training data set. Finally, the training model could be built based on the optimal reconstructed support vectors and the hyperparameter tuned subsequently. What is more, the paper puts forward a supplemental algorithm to subtract the redundancy support vectors of previous model. Lots of experiments on synthetic data sets, benchmark data sets, and mill load data sets are carried out, and the results illustrate the effectiveness of the proposed sparse method for LSSVM.
Threshold partitioning of sparse matrices and applications to Markov chains
Energy Technology Data Exchange (ETDEWEB)
Choi, Hwajeong; Szyld, D.B. [Temple Univ., Philadelphia, PA (United States)
1996-12-31
It is well known that the order of the variables and equations of a large, sparse linear system influences the performance of classical iterative methods. In particular if, after a symmetric permutation, the blocks in the diagonal have more nonzeros, classical block methods have a faster asymptotic rate of convergence. In this paper, different ordering and partitioning algorithms for sparse matrices are presented. They are modifications of PABLO. In the new algorithms, in addition to the location of the nonzeros, the values of the entries are taken into account. The matrix resulting after the symmetric permutation has dense blocks along the diagonal, and small entries in the off-diagonal blocks. Parameters can be easily adjusted to obtain, for example, denser blocks, or blocks with elements of larger magnitude. In particular, when the matrices represent Markov chains, the permuted matrices are well suited for block iterative methods that find the corresponding probability distribution. Applications to three types of methods are explored: (1) Classical block methods, such as Block Gauss Seidel. (2) Preconditioned GMRES, where a block diagonal preconditioner is used. (3) Iterative aggregation method (also called aggregation/disaggregation) where the partition obtained from the ordering algorithm with certain parameters is used as an aggregation scheme. In all three cases, experiments are presented which illustrate the performance of the methods with the new orderings. The complexity of the new algorithms is linear in the number of nonzeros and the order of the matrix, and thus adding little computational effort to the overall solution.
Matrix-Free Preconditioning using Partial Matrix Estimation
Czech Academy of Sciences Publication Activity Database
Cullum, J. K.; Tůma, Miroslav
2006-01-01
Roč. 46, č. 4 (2006), s. 711-729 ISSN 0006-3835 R&D Projects: GA AV ČR 1ET400300415; GA AV ČR IAA1030405 Institutional research plan: CEZ:AV0Z10300504 Keywords : matrix-free algorithms * linear algebraic equations * large sparse matrices * preconditioned iterative methods Subject RIV: BA - General Mathematics Impact factor: 0.841, year: 2006
Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul
2016-01-15
Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.
Ren, Yudan; Lv, Jinglei; Guo, Lei; Fang, Jun; Guo, Christine Cong
2017-01-01
Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.
Directory of Open Access Journals (Sweden)
Yudan Ren
Full Text Available Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA, sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.
Discriminative sparse coding on multi-manifolds
Wang, J.J.-Y.
2013-09-26
Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.
Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin
2015-01-01
Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the
Directory of Open Access Journals (Sweden)
Xin Tang
Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our
Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Chunhui Zhao
2015-09-01
Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.
Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding
Huang, Hong; Luo, Fulin; Liu, Jiamin; Yang, Yaqiong
2015-08-01
Sparse manifold clustering and embedding (SMCE) adaptively selects neighbor points from the same manifold and approximately spans a low-dimensional affine subspace, but it does not explicitly give a projection matrix and encounters the out-of-sample problem. To overcome this drawback, we propose a new dimensionality reduction method, called sparse manifold embedding (SME), based on graph embedding and sparse representation for hyperspectral image (HSI). It utilizes the sparse coefficients of affine subspace to construct a similarity graph and preserves this sparse similarity in embedding space. Furthermore, we try to make full use of the prior label information to design a novel supervised learning method termed sparse discriminant manifold embedding (SDME). SDME not only inherits the merits of the sparsity property of affine subspace but also boosts the compactness of intra-manifold, which achieves discriminating features and further improves the classification performance of HSI. Experiments on two real hyperspectral data sets (Indian Pines and PaviaU) show the benefits of the proposed SME and SDME methods.
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Sparse modeling theory, algorithms, and applications
Rish, Irina
2014-01-01
""A comprehensive, clear, and well-articulated book on sparse modeling. This book will stand as a prime reference to the research community for many years to come.""-Ricardo Vilalta, Department of Computer Science, University of Houston""This book provides a modern introduction to sparse methods for machine learning and signal processing, with a comprehensive treatment of both theory and algorithms. Sparse Modeling is an ideal book for a first-year graduate course.""-Francis Bach, INRIA - École Normale Supřieure, Paris
Sparse adaptive filters for echo cancellation
Paleologu, Constantin
2011-01-01
Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati
Sparse Image Reconstruction in Computed Tomography
DEFF Research Database (Denmark)
Jørgensen, Jakob Sauer
In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... and limitations of sparse reconstruction methods in CT, in particular in a quantitative sense. For example, relations between image properties such as contrast, structure and sparsity, tolerable noise levels, suficient sampling levels, the choice of sparse reconstruction formulation and the achievable image...
Particle spin tune in a partially excited snake
International Nuclear Information System (INIS)
Lee, S.Y.; Tepikian, S.; Courant, E.D.
1985-01-01
In this paper, we address the question on the effect of the particle spin when a snake is turned on adiabatically near a depolarization resonance while not accelerating. The spinor equation and its solution are reviewed briefly and the spin transfer matrix method in the presence of a snake are used to evaluate the spin tune and the precession axis
Codesign of Beam Pattern and Sparse Frequency Waveforms for MIMO Radar
Directory of Open Access Journals (Sweden)
Chaoyun Mai
2015-01-01
Full Text Available Multiple-input multiple-output (MIMO radar takes the advantages of high degrees of freedom for beam pattern design and waveform optimization, because each antenna in centralized MIMO radar system can transmit different signal waveforms. When continuous band is divided into several pieces, sparse frequency radar waveforms play an important role due to the special pattern of the sparse spectrum. In this paper, we start from the covariance matrix of the transmitted waveform and extend the concept of sparse frequency design to the study of MIMO radar beam pattern. With this idea in mind, we first solve the problem of semidefinite constraint by optimization tools and get the desired covariance matrix of the ideal beam pattern. Then, we use the acquired covariance matrix and generalize the objective function by adding the constraint of both constant modulus of the signals and corresponding spectrum. Finally, we solve the objective function by the cyclic algorithm and obtain the sparse frequency MIMO radar waveforms with desired beam pattern. The simulation results verify the effectiveness of this method.
Global Convergence of Schubert’s Method for Solving Sparse Nonlinear Equations
Directory of Open Access Journals (Sweden)
Huiping Cao
2014-01-01
Full Text Available Schubert’s method is an extension of Broyden’s method for solving sparse nonlinear equations, which can preserve the zero-nonzero structure defined by the sparse Jacobian matrix and can retain many good properties of Broyden’s method. In particular, Schubert’s method has been proved to be locally and q-superlinearly convergent. In this paper, we globalize Schubert’s method by using a nonmonotone line search. Under appropriate conditions, we show that the proposed algorithm converges globally and superlinearly. Some preliminary numerical experiments are presented, which demonstrate that our algorithm is effective for large-scale problems.
Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition
Directory of Open Access Journals (Sweden)
yuan Shuai
2017-01-01
Full Text Available In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.
Second Workshop on Sparse Grids and Applications
Pflüger, Dirk
2014-01-01
Sparse grids have gained increasing interest in recent years for the numerical treatment of high-dimensional problems. Whereas classical numerical discretization schemes fail in more than three or four dimensions, sparse grids make it possible to overcome the “curse” of dimensionality to some degree, extending the number of dimensions that can be dealt with. This volume of LNCSE collects the papers from the proceedings of the second workshop on sparse grids and applications, demonstrating once again the importance of this numerical discretization scheme. The selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures, and the range of applications extends to uncertainty quantification settings and clustering, to name but a few examples.
SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos
Energy Technology Data Exchange (ETDEWEB)
Ahlfeld, R., E-mail: r.ahlfeld14@imperial.ac.uk; Belkouchi, B.; Montomoli, F.
2016-09-01
A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5
SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos
Ahlfeld, R.; Belkouchi, B.; Montomoli, F.
2016-09-01
A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10
LCLS Undulator Tuning And Fiducialization
International Nuclear Information System (INIS)
Wolf, Zachary; Kaplounenko, Vsevolod; Levashov, Yury; Weidemann, Achim
2007-01-01
The LCLS project at SLAC requires 40 undulators: 33 in the beam line, 6 spares, and one reference undulator. A new facility was constructed at SLAC for tuning and fiducializing the undulators. The throughput of the facility must be approximately one undulator per week. The undulator tuning has been partially automated. Fiducialization techniques have been devised. The new facility, the tuning techniques, and the fiducialization techniques will be discussed
On affine non-negative matrix factorization
DEFF Research Database (Denmark)
Laurberg, Hans; Hansen, Lars Kai
2007-01-01
We generalize the non-negative matrix factorization (NMF) generative model to incorporate an explicit offset. Multiplicative estimation algorithms are provided for the resulting sparse affine NMF model. We show that the affine model has improved uniqueness properties and leads to more accurate...
Gao, Quanxue; Wang, Qianqian; Huang, Yunfang; Gao, Xinbo; Hong, Xin; Zhang, Hailin
2015-12-01
Sparse representation shows impressive results for image classification, however, it cannot well characterize the discriminant structure of data, which is important for classification. This paper aims to seek a projection matrix such that the low-dimensional representations well characterize the discriminant structure embedded in high-dimensional data and simultaneously well fit sparse representation-based classifier (SRC). To be specific, Fisher discriminant criterion (FDC) is used to extract the discriminant structure, and sparse representation is simultaneously considered to guarantee that the projected data well satisfy the SRC. Thus, our method, called SRC-FDC, characterizes both the spatial Euclidean distribution and local reconstruction relationship, which enable SRC to achieve better performance. Extensive experiments are done on the AR, CMU-PIE, Extended Yale B face image databases, the USPS digit database, and COIL20 database, and results illustrate that the proposed method is more efficient than other feature extraction methods based on SRC.
Image super-resolution via sparse representation.
Yang, Jianchao; Wright, John; Huang, Thomas S; Ma, Yi
2010-11-01
This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.
Franklin, Joel N
2003-01-01
Mathematically rigorous introduction covers vector and matrix norms, the condition-number of a matrix, positive and irreducible matrices, much more. Only elementary algebra and calculus required. Includes problem-solving exercises. 1968 edition.
Non-Negative Spectral Learning and Sparse Regression-Based Dual-Graph Regularized Feature Selection.
Shang, Ronghua; Wang, Wenbing; Stolkin, Rustam; Jiao, Licheng
2018-02-01
Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed, but most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and do not make full use of the characteristics of the data. To overcome this problem, this paper proposes a new unsupervised feature selection algorithm, called non-negative spectral learning and sparse regression-based dual-graph regularized feature selection (NSSRD). NSSRD is based on the feature selection framework of joint embedding learning and sparse regression, but extends this framework by introducing the feature graph. By using low dimensional embedding learning in both data space and feature space, NSSRD simultaneously exploits the geometric information of both spaces. Second, the algorithm uses non-negative constraints to constrain the low-dimensional embedding matrix of both feature space and data space, ensuring that the elements in the matrix are non-negative. Third, NSSRD unifies the embedding matrix of the feature space and the sparse transformation matrix. To ensure the sparsity of the feature array, the sparse transformation matrix is constrained using the -norm. Thus feature selection can obtain accurate discriminative information from these matrices. Finally, NSSRD uses an iterative and alternative updating rule to optimize the objective function, enabling it to select the representative features more quickly and efficiently. This paper explains the objective function, the iterative updating rules and a proof of convergence. Experimental results show that NSSRD is significantly more effective than several other feature selection algorithms from the literature, on a variety of test data.
Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.
Directory of Open Access Journals (Sweden)
Yunsong Liu
Full Text Available Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B converges faster than previously proposed algorithms accelerated proximal algorithm (APG and alternating directional method of multipliers for balanced model (ADMM-B.
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, Mark; Schniter, Philip; Rangan, Sundeep
2017-08-01
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a "learned AMP" network that significantly improves upon Gregor and LeCun's "learned ISTA." Second, inspired by the recently proposed "vector AMP" (VAMP) algorithm, we propose a "learned VAMP" network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.
Highly sparse representations from dictionaries are unique and independent of the sparseness measure
DEFF Research Database (Denmark)
Gribonval, Rémi; Nielsen, Morten
2007-01-01
The purpose of this paper is to study sparse representations of signals from a general dictionary in a Banach space. For so-called localized frames in Hilbert spaces, the canonical frame coefficients are shown to provide a near sparsest expansion for several sparseness measures. However, for frames...
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
Wang, Jim Jing-Yan
2017-06-28
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.
Low-Complexity Bayesian Estimation of Cluster-Sparse Channels
Ballal, Tarig
2015-09-18
This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over clustersparse channels.
Sparse Signal Recovery via ECME Thresholding Pursuits
Directory of Open Access Journals (Sweden)
Heping Song
2012-01-01
Full Text Available The emerging theory of compressive sensing (CS provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery. However, in some certain circumstances, greedy algorithms exhibit superior performance than convex methods. This paper is a followup to the recent paper of Wang and Yin (2010, who refine BP reconstructions via iterative support detection (ISD. The heuristic idea of ISD was applied to greedy algorithms. We developed two approaches for accelerating the ECME iteration. The described algorithms, named ECME thresholding pursuits (EMTP, introduced two greedy strategies that each iteration detects a support set I by thresholding the result of the ECME iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support set I. Two effective support detection strategies are devised for the sparse signals with components having a fast decaying distribution of nonzero components. The experimental studies are presented to demonstrate that EMTP offers an appealing alternative to state-of-the-art algorithms for sparse signal recovery.
Sparse Bayesian Learning for DOA Estimation with Mutual Coupling
Directory of Open Access Journals (Sweden)
Jisheng Dai
2015-10-01
Full Text Available Sparse Bayesian learning (SBL has given renewed interest to the problem of direction-of-arrival (DOA estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs. Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.
Sparse Parallel MRI Based on Accelerated Operator Splitting Schemes.
Cai, Nian; Xie, Weisi; Su, Zhenghang; Wang, Shanshan; Liang, Dong
2016-01-01
Recently, the sparsity which is implicit in MR images has been successfully exploited for fast MR imaging with incomplete acquisitions. In this paper, two novel algorithms are proposed to solve the sparse parallel MR imaging problem, which consists of l 1 regularization and fidelity terms. The two algorithms combine forward-backward operator splitting and Barzilai-Borwein schemes. Theoretically, the presented algorithms overcome the nondifferentiable property in l 1 regularization term. Meanwhile, they are able to treat a general matrix operator that may not be diagonalized by fast Fourier transform and to ensure that a well-conditioned optimization system of equations is simply solved. In addition, we build connections between the proposed algorithms and the state-of-the-art existing methods and prove their convergence with a constant stepsize in Appendix. Numerical results and comparisons with the advanced methods demonstrate the efficiency of proposed algorithms.
An efficient optical architecture for sparsely connected neural networks
Hine, Butler P., III; Downie, John D.; Reid, Max B.
1990-01-01
An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.
Sparse Pseudo Spectral Projection Methods with Directional Adaptation for Uncertainty Quantification
Winokur, J.
2015-12-19
We investigate two methods to build a polynomial approximation of a model output depending on some parameters. The two approaches are based on pseudo-spectral projection (PSP) methods on adaptively constructed sparse grids, and aim at providing a finer control of the resolution along two distinct subsets of model parameters. The control of the error along different subsets of parameters may be needed for instance in the case of a model depending on uncertain parameters and deterministic design variables. We first consider a nested approach where an independent adaptive sparse grid PSP is performed along the first set of directions only, and at each point a sparse grid is constructed adaptively in the second set of directions. We then consider the application of aPSP in the space of all parameters, and introduce directional refinement criteria to provide a tighter control of the projection error along individual dimensions. Specifically, we use a Sobol decomposition of the projection surpluses to tune the sparse grid adaptation. The behavior and performance of the two approaches are compared for a simple two-dimensional test problem and for a shock-tube ignition model involving 22 uncertain parameters and 3 design parameters. The numerical experiments indicate that whereas both methods provide effective means for tuning the quality of the representation along distinct subsets of parameters, PSP in the global parameter space generally requires fewer model evaluations than the nested approach to achieve similar projection error. In addition, the global approach is better suited for generalization to more than two subsets of directions.
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation.
Xu, Yong; Fang, Xiaozhao; Wu, Jian; Li, Xuelong; Zhang, David
2016-02-01
In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
A tight and explicit representation of Q in sparse QR factorization
Energy Technology Data Exchange (ETDEWEB)
Ng, E.G.; Peyton, B.W.
1992-05-01
In QR factorization of a sparse m{times}n matrix A (m {ge} n) the orthogonal factor Q is often stored implicitly as a lower trapezoidal matrix H known as the Householder matrix. This paper presents a simple characterization of the row structure of Q, which could be used as the basis for a sparse data structure that can store Q explicitly. The new characterization is a simple extension of a well known row-oriented characterization of the structure of H. Hare, Johnson, Olesky, and van den Driessche have recently provided a complete sparsity analysis of the QR factorization. Let U be the matrix consisting of the first n columns of Q. Using results from, we show that the data structures for H and U resulting from our characterizations are tight when A is a strong Hall matrix. We also show that H and the lower trapezoidal part of U have the same sparsity characterization when A is strong Hall. We then show that this characterization can be extended to any weak Hall matrix that has been permuted into block upper triangular form. Finally, we show that permuting to block triangular form never increases the fill incurred during the factorization.
Incomplete Sparse Approximate Inverses for Parallel Preconditioning
International Nuclear Information System (INIS)
Anzt, Hartwig; University of Tennessee, Knoxville, TN; Huckle, Thomas K.; Bräckle, Jürgen; Dongarra, Jack
2017-01-01
In this study, we propose a new preconditioning method that can be seen as a generalization of block-Jacobi methods, or as a simplification of the sparse approximate inverse (SAI) preconditioners. The “Incomplete Sparse Approximate Inverses” (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations. Those arise, for example, in the context of incomplete factorization preconditioning. ISAI preconditioners can be generated via an algorithm providing fine-grained parallelism, which makes them attractive for hardware with a high concurrency level. Finally, in a study covering a large number of matrices, we identify the ISAI preconditioner as an attractive alternative to exact triangular solves in the context of incomplete factorization preconditioning.
SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS
Desmal, Abdulla
2015-07-29
A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.
Sparse regularization for force identification using dictionaries
Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng
2016-04-01
The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.
Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation
Muraraşu, Alin
2012-01-01
Multi-core parallelism and accelerators are becoming common features of today’s computer systems, as they allow for computational power without sacrificing energy efficiency. Due to heterogeneity, tuning for each type of compute unit and adequate load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression. Its performance critical part is the interpolation routine used for decompression. Results show that our load balancing scheme allows for an efficient acceleration of interpolation on heterogeneous systems containing multi-core CPUs and GPUs.
Fluctuations in percolation of sparse complex networks
Bianconi, Ginestra
2017-07-01
We study the role of fluctuations in percolation of sparse complex networks. To this end we consider two random correlated realizations of the initial damage of the nodes and we evaluate the fraction of nodes that are expected to remain in the giant component of the network in both cases or just in one case. Our framework includes a message-passing algorithm able to predict the fluctuations in a single network, and an analytic prediction of the expected fluctuations in ensembles of sparse networks. This approach is applied to real ecological and infrastructure networks and it is shown to characterize the expected fluctuations in their response to external damage.
Multisnapshot Sparse Bayesian Learning for DOA
DEFF Research Database (Denmark)
Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki
2016-01-01
The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...... powers). For a complex Gaussian likelihood with hyperparameter, the unknown noise variance, the corresponding Gaussian posterior distribution is derived. The hyperparameters are automatically selected by maximizing the evidence and promoting sparse DOA estimates. The SBL scheme for DOA estimation...
A characterization of sparse nonstationary Gabor expansions
DEFF Research Database (Denmark)
Ottosen, Emil Solsbæk; Nielsen, Morten
and prove that the nonstationary Gabor frame forms a Banach frame for the decomposition space. Furthermore, we show that the decomposition space norm can be completely characterized by a sparseness condition on the frame coefficients and we prove an upper bound on the approximation error that occurs when......We investigate the problem of constructing sparse time-frequency representations with flexible frequency resolution, studying the theory of nonstationary Gabor frames in the framework of decomposition spaces. Given a painless nonstationary Gabor frame, we construct a compatible decomposition space...... thresholding the frame coefficients for signals belonging to the decomposition space....
Scalable group level probabilistic sparse factor analysis
DEFF Research Database (Denmark)
Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard
2017-01-01
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...
Binary Sparse Phase Retrieval via Simulated Annealing
Directory of Open Access Journals (Sweden)
Wei Peng
2016-01-01
Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.
A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary
Gillis, Nicolas; Luce, Robert
2018-01-01
A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.
Compressed sensing reconstruction of sparse spectrum based on digital micro-mirror device platform
Liu, Li-xing; Yang, Chun-yong; Wang, Run-yu; Ni, Wen-jun; Qin, Xian-zan; Deng, Yang; Chen, Kao-ming; Hou, Jin; Chen, Shao-ping
2018-01-01
A new method which employs compressive sensing (CS) to reconstruct the sparse spectrum is designed and experimentally demonstrated. On the basis of CS theory, the simulation results indicate that the probability of reconstruction is high when the step of the sparsity adaptive matching pursuit algorithm is confirmed as 1. Contrastive analysis for four kinds of commonly used measurement matrices: part Hadamard, Bernoulli, Toeplitz and Circular matrix, has been conducted. The results illustrate that the part Hadamard matrix has better performance of reconstruction than the other matrices. The experimental system of the spectral compression reconstruction is mainly based on the digital micro-mirror device (DMD). The experimental results prove that CS can reconstruct sparse spectrum well under the condition of 50% sampling rate. The system error 0.078 1 is obtained, which is defined by the average value of the 2-norm. Furthermore, the proposed method shows a dominant ability to discard redundancy.
International Nuclear Information System (INIS)
Svensson, Urban
2001-04-01
A particle tracking algorithm, PARTRACK, that simulates transport and dispersion in a sparsely fractured rock is described. The main novel feature of the algorithm is the introduction of multiple particle states. It is demonstrated that the introduction of this feature allows for the simultaneous simulation of Taylor dispersion, sorption and matrix diffusion. A number of test cases are used to verify and demonstrate the features of PARTRACK. It is shown that PARTRACK can simulate the following processes, believed to be important for the problem addressed: the split up of a tracer cloud at a fracture intersection, channeling in a fracture plane, Taylor dispersion and matrix diffusion and sorption. From the results of the test cases, it is concluded that PARTRACK is an adequate framework for simulation of transport and dispersion of a solute in a sparsely fractured rock
Selectivity, hyperselectivity, and the tuning of V1 neurons.
Vilankar, Kedarnath P; Field, David J
2017-08-01
In this article, we explore two forms of selectivity in sensory neurons. The first we call classic selectivity, referring to the stimulus that optimally stimulates a neuron. If a neuron is linear, then this stimulus can be determined by measuring the response to an orthonormal basis set (the receptive field). The second type of selectivity we call hyperselectivity; it is either implicitly or explicitly a component of several models including sparse coding, gain control, and some linear-nonlinear models. Hyperselectivity is unrelated to the stimulus that maximizes the response. Rather, it refers to the drop-off in response around that optimal stimulus. We contrast various models that produce hyperselectivity by comparing the way each model curves the iso-response surfaces of each neuron. We demonstrate that traditional sparse coding produces such curvature and increases with increasing degrees of overcompleteness. We demonstrate that this curvature produces a systematic misestimation of the optimal stimulus when the neuron's receptive field is measured with spots or gratings. We also show that this curvature allows for two apparently paradoxical results. First, it allows a neuron to be very narrowly tuned (hyperselective) to a broadband stimulus. Second, it allows neurons to break the Gabor-Heisenberg limit in their localization in space and frequency. Finally, we argue that although gain-control models, some linear-nonlinear models, and sparse coding have much in common, we believe that this approach to hyperselectivity provides a deeper understanding of why these nonlinearities are present in the early visual system.
Stress-tuned conductor-polymer composite for use in sensors
Martin, James E; Read, Douglas H
2013-10-22
A method for making a composite polymeric material with electrical conductivity determined by stress-tuning of the conductor-polymer composite, and sensors made with the stress-tuned conductor-polymer composite made by this method. Stress tuning is achieved by mixing a miscible liquid into the polymer precursor solution or by absorbing into the precursor solution a soluble compound from vapor in contact with the polymer precursor solution. The conductor may or may not be ordered by application of a magnetic field. The composite is formed by polymerization with the stress-tuning agent in the polymer matrix. The stress-tuning agent is removed following polymerization to produce a conductor-polymer composite with a stress field that depends on the amount of stress-tuning agent employed.
Random matrix improved subspace clustering
Couillet, Romain
2017-03-06
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.
Runcie, Daniel E; Mukherjee, Sayan
2013-07-01
Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.
Power-Constrained Sparse Gaussian Linear Dimensionality Reduction Over Noisy Channels
Shirazinia, Amirpasha; Dey, Subhrakanti
2015-11-01
In this paper, we investigate power-constrained sensing matrix design in a sparse Gaussian linear dimensionality reduction framework. Our study is carried out in a single--terminal setup as well as in a multi--terminal setup consisting of orthogonal or coherent multiple access channels (MAC). We adopt the mean square error (MSE) performance criterion for sparse source reconstruction in a system where source-to-sensor channel(s) and sensor-to-decoder communication channel(s) are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE in single-- and multiple--terminal setups. We propose a three-stage sensing matrix optimization scheme that combines semi-definite relaxation (SDR) programming, a low-rank approximation problem and power-rescaling. Under certain conditions, we derive closed-form solutions to the proposed optimization procedure. Through numerical experiments, by applying practical sparse reconstruction algorithms, we show the superiority of the proposed scheme by comparing it with other relevant methods. This performance improvement is achieved at the price of higher computational complexity. Hence, in order to address the complexity burden, we present an equivalent stochastic optimization method to the problem of interest that can be solved approximately, while still providing a superior performance over the popular methods.
Subspace Based Blind Sparse Channel Estimation
DEFF Research Database (Denmark)
Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki
2012-01-01
The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...
Multilevel sparse functional principal component analysis.
Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S
2014-01-29
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.
Comparison of sparse point distribution models
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Vester-Christensen, Martin; Larsen, Rasmus
2010-01-01
This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior m...
SAR Image Despeckling Via Structural Sparse Representation
Lu, Ting; Li, Shutao; Fang, Leyuan; Benediktsson, Jón Atli
2016-12-01
A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a homogeneous class (with a constant terrain reflectivity). In the proposed method, different sparse representation based despeckling schemes are designed by combining the different region characteristics in SAR images. For heterogeneous regions with rich structure and texture information, structural dictionaries are learned to appropriately represent varied structural characteristics. Specifically, each patch in these regions is sparsely coded with the best fitted structural dictionary, thus good structure preservation can be obtained. For homogenous regions without rich structure and texture information, the highly redundant photometric self-similarity is exploited to suppress speckle noise without introducing artifacts. That is achieved by firstly learning the sub-dictionary, then simultaneously sparsely coding for each group of photometrically similar image patches. Visual and objective experimental results demonstrate the superiority of the proposed method over the-state-of-the-art methods.
Sparse DOA estimation with polynomial rooting
DEFF Research Database (Denmark)
Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren
2015-01-01
Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...
Sparse acoustic imaging with a spherical array
DEFF Research Database (Denmark)
Fernandez Grande, Efren; Xenaki, Angeliki
2015-01-01
proposes a plane wave expansion method based on measurements with a spherical microphone array, and solved in the framework provided by Compressed Sensing. The proposed methodology results in a sparse solution, i.e. few non-zero coefficients, and it is suitable for both source localization and sound field...
Sparse Text Indexing in Small Space
DEFF Research Database (Denmark)
Bille, Philip; Fischer, Johannes; Gørtz, Inge Li
2016-01-01
other applications in which space usage is a concern. Our first solution is Monte Carlo, and outputs the correct tree with high probability. We then give a Las Vegas algorithm, which also uses O(b) space and runs in the same time bounds with high probability when b = O(&sqrt; n). Additional trade......In this work, we present efficient algorithms for constructing sparse suffix trees, sparse suffix arrays, and sparse position heaps for b arbitrary positions of a text T of length n while using only O(b) words of space during the construction. Attempts at breaking the naïve bound of Ω(nb) time...... contribution is to show that the sparse suffix tree (and array) can be constructed in O(nlog 2b) time. To achieve this, we develop a technique that allows one to efficiently answer b longest common prefix queries on suffixes of T, using only O(b) space. We expect that this technique will prove useful in many...
A sparse-grid isogeometric solver
Beck, Joakim
2018-02-28
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.
A sparse version of IGA solvers
Beck, Joakim
2017-07-30
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.
Mini-lecture course: Introduction into hierarchical matrix technique
Litvinenko, Alexander
2017-12-14
The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations (mesh points). The H-matrix technique allows us to work with general class of matrices (not only structured or Toeplits or sparse). H-matrices can keep the H-matrix data format during linear algebra operations (inverse, update, Schur complement).
Svensson, Urban; Löfgren, Martin; Trinchero, Paolo; Selroos, Jan-Olof
2018-04-01
In sparsely fractured rock, the ubiquitous heterogeneity of the matrix, which has been observed in different laboratory and in situ experiments, has been shown to have a significant influence on retardation mechanisms that are of importance for the safety of deep geological repositories for nuclear waste. Here, we propose a conceptualisation of a typical heterogeneous granitic rock matrix based on micro-Discrete Fracture Networks (micro-DFN). Different sets of fractures are used to represent grain-boundary pores as well as micro fractures that transect different mineral grains. The micro-DFN model offers a great flexibility in the way inter- and intra-granular space is represented as the different parameters that characterise each fracture set can be fine tuned to represent samples of different characteristics. Here, the parameters of the model have been calibrated against experimental observations from granitic rock samples taken at Forsmark (Sweden) and different variant cases have been used to illustrate how the model can be tied to rock samples with different attributes. Numerical through-diffusion simulations have been carried out to infer the bulk properties of the model as well as to compare the computed mass flux with the experimental data from an analogous laboratory experiment. The general good agreement between the model results and the experimental observations shows that the model presented here is a reliable tool for the understanding of retardation mechanisms occurring at the mm-scale in the matrix.
Oracle SQL Tuning pocket Reference
Gurry, Mark
2002-01-01
One of the most important challenges faced by Oracle database administrators and Oracle developers is the need to tune SQL statements so that they execute efficiently. Poorly tuned SQL statements are one of the leading causes of substandard database performance and poor response time. SQL statements that perform poorly result in frustration for users, and can even prevent a company from serving its customers in a timely manner
Varactor-tuned Substrate Integrated Evanescent Filter
DEFF Research Database (Denmark)
Zhurbenko, Vitaliy; Acar, Öncel; Dong, Yunfeng
is considered. In contrast to other methods described in the literature, it avoids etching split ring resonators in the metal layer of the SIW. The filters presented here use varactors as tuning elements. The varactors (as well as DC decoupling circuits) are mounted on the surface of PCB bringing the lower...... metal layer of the waveguide to the top layer with metalized via holes. The present filters are analyzed using models based on impedance matrix representation. The developed models allow computationally efficient and relatively accurate prediction of the filter behavior in a wide frequency range (at...... least up to frequencies below the cut-off of the second propagating mode). This work investigates the applicability of the evanescent SIW approach to tunable filter realization. The advantages and disadvantages of the approach are analyzed. As an example, a second order microwave filter is designed...
Improved Sparse Channel Estimation for Cooperative Communication Systems
Directory of Open Access Journals (Sweden)
Guan Gui
2012-01-01
Full Text Available Accurate channel state information (CSI is necessary at receiver for coherent detection in amplify-and-forward (AF cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS and least absolute shrinkage and selection operator (LASSO, are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.
Synaptic learning rules and sparse coding in a model sensory system.
Directory of Open Access Journals (Sweden)
Luca A Finelli
2008-04-01
Full Text Available Neural circuits exploit numerous strategies for encoding information. Although the functional significance of individual coding mechanisms has been investigated, ways in which multiple mechanisms interact and integrate are not well understood. The locust olfactory system, in which dense, transiently synchronized spike trains across ensembles of antenna lobe (AL neurons are transformed into a sparse representation in the mushroom body (MB; a region associated with memory, provides a well-studied preparation for investigating the interaction of multiple coding mechanisms. Recordings made in vivo from the insect MB demonstrated highly specific responses to odors in Kenyon cells (KCs. Typically, only a few KCs from the recorded population of neurons responded reliably when a specific odor was presented. Different odors induced responses in different KCs. Here, we explored with a biologically plausible model the possibility that a form of plasticity may control and tune synaptic weights of inputs to the mushroom body to ensure the specificity of KCs' responses to familiar or meaningful odors. We found that plasticity at the synapses between the AL and the MB efficiently regulated the delicate tuning necessary to selectively filter the intense AL oscillatory output and condense it to a sparse representation in the MB. Activity-dependent plasticity drove the observed specificity, reliability, and expected persistence of odor representations, suggesting a role for plasticity in information processing and making a testable prediction about synaptic plasticity at AL-MB synapses.
Bodewig, E
1959-01-01
Matrix Calculus, Second Revised and Enlarged Edition focuses on systematic calculation with the building blocks of a matrix and rows and columns, shunning the use of individual elements. The publication first offers information on vectors, matrices, further applications, measures of the magnitude of a matrix, and forms. The text then examines eigenvalues and exact solutions, including the characteristic equation, eigenrows, extremum properties of the eigenvalues, bounds for the eigenvalues, elementary divisors, and bounds for the determinant. The text ponders on approximate solutions, as well
Fingerprint Compression Based on Sparse Representation.
Shao, Guangqi; Wu, Yanping; A, Yong; Liu, Xiao; Guo, Tiande
2014-02-01
A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.
Sparse brain network using penalized linear regression
Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.
2011-03-01
Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Sparse model selection via integral terms
Schaeffer, Hayden; McCalla, Scott G.
2017-08-01
Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.
Greedy vs. L1 convex optimization in sparse coding
DEFF Research Database (Denmark)
Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor
2015-01-01
Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...
Yielding physically-interpretable emulators - A Sparse PCA approach
Galelli, S.; Alsahaf, A.; Giuliani, M.; Castelletti, A.
2015-12-01
Projection-based techniques, such as Principal Orthogonal Decomposition (POD), are a common approach to surrogate high-fidelity process-based models by lower order dynamic emulators. With POD, the dimensionality reduction is achieved by using observations, or 'snapshots' - generated with the high-fidelity model -, to project the entire set of input and state variables of this model onto a smaller set of basis functions that account for most of the variability in the data. While reduction efficiency and variance control of POD techniques are usually very high, the resulting emulators are structurally highly complex and can hardly be given a physically meaningful interpretation as each basis is a projection of the entire set of inputs and states. In this work, we propose a novel approach based on Sparse Principal Component Analysis (SPCA) that combines the several assets of POD methods with the potential for ex-post interpretation of the emulator structure. SPCA reduces the number of non-zero coefficients in the basis functions by identifying a sparse matrix of coefficients. While the resulting set of basis functions may retain less variance of the snapshots, the presence of a few non-zero coefficients assists in the interpretation of the underlying physical processes. The SPCA approach is tested on the reduction of a 1D hydro-ecological model (DYRESM-CAEDYM) used to describe the main ecological and hydrodynamic processes in Tono Dam, Japan. An experimental comparison against a standard POD approach shows that SPCA achieves the same accuracy in emulating a given output variable - for the same level of dimensionality reduction - while yielding better insights of the main process dynamics.
Sparse district-heating in Sweden
Energy Technology Data Exchange (ETDEWEB)
Nilsson, Stefan Forsaeus [SP Technical Research Institute of Sweden, Building Technology and Mechanics, P.O. Box 24036, SE-400 22 Goeteborg (Sweden); Reidhav, Charlotte [Chalmers University of Technology, Department of Civil and Environmental Engineering, SE-412 96 Goeteborg (Sweden); Lygnerud, Kristina [Goeteborg University, School of Business, Economics and Law, Department of Business Administration, P.O. Box 610, SE-405 30 Goeteborg (Sweden); Werner, Sven [Chalmers University of Technology, Department of Energy and Environment, SE-412 96 Goeteborg (Sweden)
2008-07-15
This paper presents a review of the sparse district-heating research programme undertaken in Sweden between 2002 and 2006. The goal of the programme was to increase the future competitiveness for district heat in low heat density areas, e.g., suburban single-family houses and small villages. Such areas are unfavourable, since revenues from heat sold are low compared with the investment cost for the local distribution network. In Sweden, district heat has a dominant position in the heat market for residential and service-sector buildings. In order for the business to grow, it is necessary to increase the rate of expansion in the detached-house segment. This is why the programme was initiated. The extent of the programme was set at EUR 3.6 million with equal financing from the Swedish District-Heating Association and the Swedish Energy-Agency. The research was carried out in three phases: a state of the art survey; a development phase focused on productivity gains where new research on both technology and customer interaction was performed; and finally a demonstration phase where new methods were tested in full-scale field operation. The programme has shown that the Swedish district-heating industry needs to adjust in order to reach a higher profitability for sparse district-heating investments. Tradition from large-scale high-density district heating is hard to scale to fit sparse district-heating systems. For example, the construction becomes very labour intensive and the industry is weak when it comes to market-oriented business logic, sales and private customer interaction. Innovation seems to be a way forward and active management of innovations is a way to create increased value of the investments. Other keys to improving the profitability of sparse district-heating investments are more efficient working routines (resulting in higher productivity) and revised ways of customer communications. These seem more important than increasing efficiency in district
Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.
2003-01-01
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or wo...
SAR Image despeckling via sparse representation
Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang
2014-11-01
SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.
Decentralized Sparse Multitask RLS Over Networks
Cao, Xuanyu; Liu, K. J. Ray
2017-12-01
Distributed adaptive signal processing has attracted much attention in the recent decade owing to its effectiveness in many decentralized real-time applications in networked systems. Because many natural signals are highly sparse with most entries equal to zero, several decentralized sparse adaptive algorithms have been proposed recently. Most of them is focused on the single task estimation problems, in which all nodes receive data associated with the same unknown vector and collaborate to estimate it. However, many applications are inherently multitask oriented and each node has its own unknown vector different from others'. The related multitask estimation problem benefits from collaborations among the nodes as neighbor nodes usually share analogous properties and thus similar unknown vectors. In this work, we study the distributed sparse multitask recursive least squares (RLS) problem over networks. We first propose a decentralized online alternating direction method of multipliers (ADMM) algorithm for the formulated RLS problem. The algorithm is simplified for easy implementation with closed-form computations in each iteration and low storage requirements. Moreover, to further reduce the complexity, we present a decentralized online subgradient method with low computational overhead. We theoretically analyze the convergence behavior of the proposed subgradient method and derive an error bound related to the network topology and algorithm parameters. The effectiveness of the proposed algorithms is corroborated by numerical simulations and an accuracy-complexity tradeoff between the proposed two algorithms is highlighted.
Robust Fringe Projection Profilometry via Sparse Representation.
Budianto; Lun, Daniel P K
2016-04-01
In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.
Nikitin, Anatoly G.; Karadzhov, Yuri
2011-07-01
We present a collection of matrix-valued shape invariant potentials which give rise to new exactly solvable problems of SUSY quantum mechanics. It includes all irreducible matrix superpotentials of the generic form W=kQ+\\frac{1}{k} R+P, where k is a variable parameter, Q is the unit matrix multiplied by a real-valued function of independent variable x, and P and R are the Hermitian matrices depending on x. In particular, we recover the Pron'ko-Stroganov 'matrix Coulomb potential' and all known scalar shape invariant potentials of SUSY quantum mechanics. In addition, five new shape invariant potentials are presented. Three of them admit a dual shape invariance, i.e. the related Hamiltonians can be factorized using two non-equivalent superpotentials. We find discrete spectrum and eigenvectors for the corresponding Schrödinger equations and prove that these eigenvectors are normalizable.
A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations.
Chen, Tao; Wu, Huanxin; Guo, Limin; Liu, Lutao
2015-11-24
In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity.
Signal sampling for efficient sparse representation of resting state FMRI data.
Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming
2016-12-01
As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain's signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain's signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.
Directory of Open Access Journals (Sweden)
Elham Kordi Ghasrodashti
2017-05-01
Full Text Available In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently.
Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data
Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming
2015-01-01
As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924
Phase tuning by length contraction.
Jin, Warren; Polcawich, Ronald G; Morton, Paul A; Bowers, John E
2018-02-05
Typical integrated optical phase tuners alter the effective index. In this paper, we explore tuning by geometric deformation. We show that tuning efficiency, Vπ L, improves as the device size shrinks down to the optimal bend radius, contrary to conventional index-shift based approaches where Vπ L remains constant. We demonstrate that this approach is capable of ultra-low power tuning across a full FSR in a low-confinement silicon nitride based ring resonator of 580 μm radius. We demonstrate record performance with V FSR = 16 V, Vπ L = 3.6 V dB, Vπ Lα = 1.1 V dB, tuning current below 10 nA, and unattenuated tuning response up to 1 MHz. We also present optimized designs for high confinement silicon nitride and silicon based platforms with radius down to 80 μm and 45 μm, respectively, with performance well beyond current state-of-the-art. Applications include narrow-linewidth tunable diode lasers for spectroscopy and non-linear optics, optical phased array beamforming networks for RF antennas and LIDAR, and optical filters for WDM telecommunication links.
Integrated unaligned resonant modulator tuning
Zortman, William A.; Lentine, Anthony L.
2017-10-03
Methods and systems for tuning a resonant modulator are disclosed. One method includes receiving a carrier signal modulated by the resonant modulator with a stream of data having an approximately equal number of high and low bits, determining an average power of the modulated carrier signal, comparing the average power to a predetermined threshold, and operating a tuning device coupled to the resonant modulator based on the comparison of the average power and the predetermined threshold. One system includes an input structure, a plurality of processing elements, and a digital control element. The input structure is configured to receive, from the resonant modulator, a modulated carrier signal. The plurality of processing elements are configured to determine an average power of the modulated carrier signal. The digital control element is configured to operate a tuning device coupled to the resonant modulator based on the average power of the modulated carrier signal.
Xu, Li; Shan, Lin; Adachi, Fumiyuki
2014-01-01
In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods. PMID:24983012
Exact recovery of sparse multiple measurement vectors by [Formula: see text]-minimization.
Wang, Changlong; Peng, Jigen
2018-01-01
The joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed sensing. It aims to recover a set of jointly sparse vectors, i.e., those that have nonzero entries concentrated at a common location. Meanwhile [Formula: see text]-minimization subject to matrixes is widely used in a large number of algorithms designed for this problem, i.e., [Formula: see text]-minimization [Formula: see text] Therefore the main contribution in this paper is two theoretical results about this technique. The first one is proving that in every multiple system of linear equations there exists a constant [Formula: see text] such that the original unique sparse solution also can be recovered from a minimization in [Formula: see text] quasi-norm subject to matrixes whenever [Formula: see text]. The other one is showing an analytic expression of such [Formula: see text]. Finally, we display the results of one example to confirm the validity of our conclusions, and we use some numerical experiments to show that we increase the efficiency of these algorithms designed for [Formula: see text]-minimization by using our results.
On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.
Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi
2018-02-01
On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and -sparse random binary matrix [-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and -SRBM encoders with reduced area and total power consumption.
Microglial activation - tuning and pruning adult neurogenesis
Directory of Open Access Journals (Sweden)
Christine T eEkdahl
2012-03-01
Full Text Available Adult born neurons are encountering numerous choices during their development from neural stem cells to mature functionally integrated neurons in the brain. Microglia are part of the microenvironment within the neurogenic niches and possibly involved during the entire decision process. Mounting evidence suggest that microglia act as local equalizers capable of amplifying as well as filtering homeostatic signals. Depending on their state of activation, they may induce or facilitate different fundamental decisions in neurogenesis, such as proliferation or quiescence, cell survival or death, migration or establishment, growth or retraction of dendrites and axons, synaptic assembly or pruning, or tuning of synaptic transmission. Microglia are activated as a first line of defence against infections and participate in transforming the innate immunity into an adaptive immune response by recruiting systemic immune cells. So far, most studies have reported an acute decrease in the survival of new neurons following this classically activated microglial reaction. However, the long-term effects are more complex. In several neurodegenerative diseases the microglial activation is also evident, including a heterogeneous population of microglial phenotypes and a plethora of immune mediators, where the initiating agent may be protein deposits or cell debris. The transformation from a pro- to an anti-inflammatory cytokine profile and the de-activation of microglia is not clearly defined, or even dysregulated, and the adaptive response is often sparse. The diverse role of microglial activation in neurodegenerative diseases is reflected by the numerous studies reporting both beneficial and detrimental effects on the different steps of neurogenesis. This review will highlight the most recent findings on how microglial activation modulates adult neurogenesis, and specifically discuss the role of microglia in synaptic integration, currently a fast expanding research
Incoherent and coherent tune shifts
Energy Technology Data Exchange (ETDEWEB)
Zhang, S.Y. [Brookhaven National Lab., Upton, NY (United States). AGS Dept.
1995-12-31
The longitudinal and transverse microwave instabilities are the two important limiting factors for the performance of an accelerator. Comparing with the fairly unified approach for the longitudinal microwave instabilities, different approaches have been used to define the transverse microwave instabilities. One reason of this is related to the role played by the space charge incoherent and coherent tune shifts. In this article, the transverse microwave instabilities will be discussed by defining separately the roles of the space charge incoherent and coherent tunes, which are represented by the space charge transverse impedances. Preliminary results for the AGS as proton driver are presented by using this approach.
Remote tuning of NMR probe circuits.
Kodibagkar, V D; Conradi, M S
2000-05-01
There are many circumstances in which the probe tuning adjustments cannot be located near the rf NMR coil. These may occur in high-temperature NMR, low-temperature NMR, and in the use of magnets with small diameter access bores. We address here circuitry for connecting a fixed-tuned probe circuit by a transmission line to a remotely located tuning network. In particular, the bandwidth over which the probe may be remotely tuned while keeping the losses in the transmission line acceptably low is considered. The results show that for all resonant circuit geometries (series, parallel, series-parallel), overcoupling of the line to the tuned circuit is key to obtaining a large tuning bandwidth. At equivalent extents of overcoupling, all resonant circuit geometries have nearly equal remote tuning bandwidths. Particularly for the case of low-loss transmission line, the tuning bandwidth can be many times the tuned circuit's bandwidth, f(o)/Q. Copyright 2000 Academic Press.
HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION
Directory of Open Access Journals (Sweden)
H. Zhang
2016-06-01
Full Text Available In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.
Iterative solution of general sparse linear systems on clusters of workstations
Energy Technology Data Exchange (ETDEWEB)
Lo, Gen-Ching; Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)
1996-12-31
Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.
An NoC Traffic Compiler for Efficient FPGA Implementation of Sparse Graph-Oriented Workloads
Directory of Open Access Journals (Sweden)
Nachiket Kapre
2011-01-01
synchronization to optimize our workloads for large networks up to 2025 parallel elements for BSP model and 25 parallel elements for Token Dataflow. This allows us to demonstrate speedups between 1.2× and 22× (3.5× mean, area reductions (number of Processing Elements between 3× and 15× (9× mean and dynamic energy savings between 2× and 3.5× (2.7× mean over a range of real-world graph applications in the BSP compute model. We deliver speedups of 0.5–13× (geomean 3.6× for Sparse Direct Matrix Solve (Token Dataflow compute model applied to a range of sparse matrices when using a high-quality placement algorithm. We expect such traffic optimization tools and techniques to become an essential part of the NoC application-mapping flow.
The Design of Data-Structure-Neutral Libraries for the Iterative Solution of Sparse Linear Systems
Directory of Open Access Journals (Sweden)
Barry F. Smith
1996-01-01
Full Text Available Over the past few years several proposals have been made for the standardization of sparse matrix storage formats in order to allow for the development of portable matrix libraries for the iterative solution of linear systems. We believe that this is the wrong approach. Rather than define one standard (or a small number of standards for matrix storage, the community should define an interface (i.e., the calling sequences for the functions that act on the data. In addition, we cannot ignore the interface to the vector operations because, in many applications, vectors may not be stored as consecutive elements in memory. With the acceptance of shared memory, distributed memory, and cluster memory parallel machines, the flexibility of the distribution of the elements of vectors is also extremely important. This issue is ignored in most proposed standards. In this article we demonstrate how such libraries may be written using data encapsulation techniques.
Energy Technology Data Exchange (ETDEWEB)
Craps, Ben [Theoretische Natuurkunde, Vrije Universiteit Brussel (VUB), and International Solvay Institutes, Pleinlaan 2, B-1050 Brussels (Belgium); Evnin, Oleg [Department of Physics, Faculty of Science, Chulalongkorn University, Thanon Phayathai, Pathumwan, Bangkok 10330 (Thailand); Theoretische Natuurkunde, Vrije Universiteit Brussel (VUB), and International Solvay Institutes, Pleinlaan 2, B-1050 Brussels (Belgium); Nguyen, Kévin [Theoretische Natuurkunde, Vrije Universiteit Brussel (VUB), and International Solvay Institutes, Pleinlaan 2, B-1050 Brussels (Belgium)
2017-02-08
Matrix quantum mechanics offers an attractive environment for discussing gravitational holography, in which both sides of the holographic duality are well-defined. Similarly to higher-dimensional implementations of holography, collapsing shell solutions in the gravitational bulk correspond in this setting to thermalization processes in the dual quantum mechanical theory. We construct an explicit, fully nonlinear supergravity solution describing a generic collapsing dilaton shell, specify the holographic renormalization prescriptions necessary for computing the relevant boundary observables, and apply them to evaluating thermalizing two-point correlation functions in the dual matrix theory.
Stochastic convex sparse principal component analysis.
Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu
2016-12-01
Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ 1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.
Political Tunings of the Piano
DEFF Research Database (Denmark)
Højlund, Marie Koldkjær; Riis, Morten S.
According to Timothy Morton the creation of ambient art posses an inherent critical potential similar to the ambiguity of the dialectical image found in the writings of Walter Benjamin. Subscribing to an object-oriented ontological understanding of how objects are riven between essence and appear......) as unfolding the ambiguity of various political tunings of the piano....
Tuning History in Latin America
Velázquez Albo, Marco
2017-01-01
This article analyses the development and achievements of the area of History in the Tuning-Latin America Project from its launch in 2004 to its completion in 2013. Through two phases and nine general meetings, academics from Argentina, Brazil, Chile, Colombia, Costa Rica, Cuba, Guatemala, Mexico and Peru, along with academics from Spain, Portugal…
Functional fixedness in a technologically sparse culture.
German, Tim P; Barrett, H Clark
2005-01-01
Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.
Wavelets for Sparse Representation of Music
DEFF Research Database (Denmark)
Endelt, Line Ørtoft; Harbo, Anders La-Cour
2004-01-01
We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...
Parallel preconditioning techniques for sparse CG solvers
Energy Technology Data Exchange (ETDEWEB)
Basermann, A.; Reichel, B.; Schelthoff, C. [Central Institute for Applied Mathematics, Juelich (Germany)
1996-12-31
Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.
A view of Kanerva's sparse distributed memory
Denning, P. J.
1986-01-01
Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.
Sparse Dataflow Analysis with Pointers and Reachability
DEFF Research Database (Denmark)
Madsen, Magnus; Møller, Anders
2014-01-01
Many static analyzers exploit sparseness techniques to reduce the amount of information being propagated and stored during analysis. Although several variations are described in the literature, no existing technique is suitable for analyzing JavaScript code. In this paper, we point out the need...... quadtrees. The framework is presented as a systematic modification of a traditional dataflow analysis algorithm. Our experimental results demonstrate the effectiveness of the technique for a suite of JavaScript programs. By also comparing the performance with an idealized staged approach that computes...
Abnormal Event Detection Using Local Sparse Representation
DEFF Research Database (Denmark)
Ren, Huamin; Moeslund, Thomas B.
2014-01-01
We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality...... measurement based on the difference between the normal space and local space. Specifically, we provide a reasonable normal bases through repeated K spectral clustering. Then for each testing feature we first use temporal neighbors to form a local space. An abnormal event is found if any abnormal feature...
Diagnosis of Compound Fault Using Sparsity Promoted-Based Sparse Component Analysis.
Hao, Yansong; Song, Liuyang; Ke, Yanliang; Wang, Huaqing; Chen, Peng
2017-06-06
Compound faults often occur in rotating machinery, which increases the difficulty of fault diagnosis. In this case, blind source separation, which usually includes independent component analysis (ICA) and sparse component analysis (SCA), was proposed to separate mixed signals. SCA, which is based on the sparsity of target signals, was developed to sever the compound faults and effectively diagnose the fault due to its advantage over ICA in underdetermined conditions. However, there is an issue regarding the vibration signals, which are inadequately sparse, and it is difficult to represent them in a sparse way. Accordingly, to overcome the above-mentioned problem, a sparsity-promoted approach named wavelet modulus maxima is applied to obtain the sparse observation signal. Then, the potential function is utilized to estimate the number of source signals and the mixed matrix based on the sparse signal. Finally, the separation of the source signals can be achieved according to the shortest path method. To validate the effectiveness of the proposed method, the simulated signals and vibration signals measured from faulty roller bearings are used. The faults that occur in a roller bearing are the outer-race flaw, the inner-race flaw and the rolling element flaw. The results show that the fault features acquired using the proposed approach are evidently close to the theoretical values. For instance, the inner-race feature frequency 101.3 Hz is very similar to the theoretical calculation 101 Hz. Therefore, it is effective to achieve the separation of compound faults utilizing the suggest method, even in underdetermined cases. In addition, a comparison is applied to prove that the proposed method outperforms the traditional SCA method when the vibration signals are inadequate.
Fast multipole preconditioners for sparse matrices arising from elliptic equations
Ibeid, Huda
2017-11-09
Among optimal hierarchical algorithms for the computational solution of elliptic problems, the fast multipole method (FMM) stands out for its adaptability to emerging architectures, having high arithmetic intensity, tunable accuracy, and relaxable global synchronization requirements. We demonstrate that, beyond its traditional use as a solver in problems for which explicit free-space kernel representations are available, the FMM has applicability as a preconditioner in finite domain elliptic boundary value problems, by equipping it with boundary integral capability for satisfying conditions at finite boundaries and by wrapping it in a Krylov method for extensibility to more general operators. Here, we do not discuss the well developed applications of FMM to implement matrix-vector multiplications within Krylov solvers of boundary element methods. Instead, we propose using FMM for the volume-to-volume contribution of inhomogeneous Poisson-like problems, where the boundary integral is a small part of the overall computation. Our method may be used to precondition sparse matrices arising from finite difference/element discretizations, and can handle a broader range of scientific applications. It is capable of algebraic convergence rates down to the truncation error of the discretized PDE comparable to those of multigrid methods, and it offers potentially superior multicore and distributed memory scalability properties on commodity architecture supercomputers. Compared with other methods exploiting the low-rank character of off-diagonal blocks of the dense resolvent operator, FMM-preconditioned Krylov iteration may reduce the amount of communication because it is matrix-free and exploits the tree structure of FMM. We describe our tests in reproducible detail with freely available codes and outline directions for further extensibility.
Ordering schemes for sparse matrices using modern programming paradigms
International Nuclear Information System (INIS)
Oliker, Leonid; Li, Xiaoye; Husbands, Parry; Biswas, Rupak
2000-01-01
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. In previous work, we investigated the effects of various ordering and partitioning strategies on the performance of CG using different programming paradigms and architectures. This paper makes several extensions to our prior research. First, we present a hybrid(MPI+OpenMP) implementation of the CG algorithm on the IBM SP and show that the hybrid paradigm increases programming complexity with little performance gains compared to a pure MPI implementation. For ill-conditioned linear systems, it is often necessary to use a preconditioning technique. We present MPI results for ILU(0) preconditioned CG (PCG) using the BlockSolve95 library, and show that the initial ordering of the input matrix dramatically affect PCG's performance. Finally, a multithreaded version of the PCG is developed on the Cray (Tera) MTA. Unlike the message-passing version, this implementation did not require the complexities of special orderings or graph dependency analysis. However, only limited scalability was achieved due to the lack of available thread level parallelism
Sparse adaptive finite elements for radiative transfer
International Nuclear Information System (INIS)
Widmer, G.; Hiptmair, R.; Schwab, Ch.
2008-01-01
The linear radiative transfer equation, a partial differential equation for the radiation intensity u(x,s), with independent variables x element of D is contained in R n in the physical domain D of dimension n=2,3, and angular variable s element of S 2 :={y element of R 3 :|y|=1}, is solved in the n+2-dimensional computational domain DxS 2 . We propose an adaptive multilevel Galerkin finite element method (FEM) for its numerical solution. Our approach is based on (a) a stabilized variational formulation of the transport operator, (b) on so-called sparse tensor products of two hierarchic families of finite element spaces in H 1 (D) and in L 2 (S 2 ), respectively, and (c) on wavelet thresholding techniques to adapt the discretization to the underlying problem. An a priori error analysis shows, under strong regularity assumptions on the solution, that the sparse tensor product method is clearly superior to a discrete ordinates method, as it converges with essentially optimal asymptotic rates while its complexity grows essentially only as that for a linear transport problem in R n . Numerical experiments for n=2 on a set of example problems agree with the convergence and complexity analysis of the method and show that introducing adaptivity can improve performance in terms of accuracy vs. number of degrees even further
Determining biosonar images using sparse representations.
Fontaine, Bertrand; Peremans, Herbert
2009-05-01
Echolocating bats are thought to be able to create an image of their environment by emitting pulses and analyzing the reflected echoes. In this paper, the theory of sparse representations and its more recent further development into compressed sensing are applied to this biosonar image formation task. Considering the target image representation as sparse allows formulation of this inverse problem as a convex optimization problem for which well defined and efficient solution methods have been established. The resulting technique, referred to as L1-minimization, is applied to simulated data to analyze its performance relative to delay accuracy and delay resolution experiments. This method performs comparably to the coherent receiver for the delay accuracy experiments, is quite robust to noise, and can reconstruct complex target impulse responses as generated by many closely spaced reflectors with different reflection strengths. This same technique, in addition to reconstructing biosonar target images, can be used to simultaneously localize these complex targets by interpreting location cues induced by the bat's head related transfer function. Finally, a tentative explanation is proposed for specific bat behavioral experiments in terms of the properties of target images as reconstructed by the L1-minimization method.
Interferometric interpolation of sparse marine data
Hanafy, Sherif M.
2013-10-11
We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.
Topological sparse learning of dynamic form patterns.
Guthier, T; Willert, V; Eggert, J
2015-01-01
Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches.
Atmospheric inverse modeling via sparse reconstruction
Directory of Open Access Journals (Sweden)
N. Hase
2017-10-01
Full Text Available Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4 emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
Atmospheric inverse modeling via sparse reconstruction
Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten
2017-10-01
Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
An efficient algorithm to compute row and column counts for sparse Cholesky factorization
Energy Technology Data Exchange (ETDEWEB)
Gilbert, J.R. (Xerox Palo Alto Research Center, CA (United States)); Ng, E.G.; Peyton, B.W. (Oak Ridge National Lab., TN (United States))
1992-09-01
Let an undirected graph G be given, along with a specified depth- first spanning tree T. We give almost-linear-time algorithms to solve the following two problems: First, for every vertex v, compute the number of descendants w of v for which some descendant of w is adjacent (in G) to v. Second, for every vertx v, compute the number of ancestors of v that are adjacent (in G) to at least one descendant of v. These problems arise in Cholesky and QR factorizations of sparse matrices. Our algorithms can be used to determine the number of nonzero entries in each row and column of the triangular factor of a matrix from the zero/nonzero structure of the matrix. Such a prediction makes storage allocation for sparse matrix factorizations more efficient. Our algorithms run in time linear in the size of the input times a slowly-growing inverse of Ackermann's function. The best previously known algorithms for these problems ran in time linear in the sum of the nonzero counts, which is usually much larger. We give experimental results demonstrating the practical efficiency of the new algorithms.
An efficient algorithm to compute row and column counts for sparse Cholesky factorization
Energy Technology Data Exchange (ETDEWEB)
Gilbert, J.R. [Xerox Palo Alto Research Center, CA (United States); Ng, E.G.; Peyton, B.W. [Oak Ridge National Lab., TN (United States)
1992-09-01
Let an undirected graph G be given, along with a specified depth- first spanning tree T. We give almost-linear-time algorithms to solve the following two problems: First, for every vertex v, compute the number of descendants w of v for which some descendant of w is adjacent (in G) to v. Second, for every vertx v, compute the number of ancestors of v that are adjacent (in G) to at least one descendant of v. These problems arise in Cholesky and QR factorizations of sparse matrices. Our algorithms can be used to determine the number of nonzero entries in each row and column of the triangular factor of a matrix from the zero/nonzero structure of the matrix. Such a prediction makes storage allocation for sparse matrix factorizations more efficient. Our algorithms run in time linear in the size of the input times a slowly-growing inverse of Ackermann`s function. The best previously known algorithms for these problems ran in time linear in the sum of the nonzero counts, which is usually much larger. We give experimental results demonstrating the practical efficiency of the new algorithms.
Richardson, Thomas M.
2014-01-01
The reciprocal Pascal matrix is the Hadamard inverse of the symmetric Pascal matrix. We show that the ordinary matrix inverse of the reciprocal Pascal matrix has integer elements. The proof uses two factorizations of the matrix of super Catalan numbers.
Data Driven Tuning of Inventory Controllers
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Santacoloma, Paloma Andrade; Poulsen, Niels Kjølstad
2007-01-01
A systematic method for criterion based tuning of inventory controllers based on data-driven iterative feedback tuning is presented. This tuning method circumvent problems with modeling bias. The process model used for the design of the inventory control is utilized in the tuning...... as an approximation to reduce time required on experiments. The method is illustrated in an application with a multivariable inventory control implementation on a four tank system....
Tuning for temporal interval in human apparent motion detection.
Bours, Roger J E; Stuur, Sanne; Lankheet, Martin J M
2007-01-08
Detection of apparent motion in random dot patterns requires correlation across time and space. It has been difficult to study the temporal requirements for the correlation step because motion detection also depends on temporal filtering preceding correlation and on integration at the next levels. To specifically study tuning for temporal interval in the correlation step, we performed an experiment in which prefiltering and postintegration were held constant and in which we used a motion stimulus containing coherent motion for a single interval value only. The stimulus consisted of a sparse random dot pattern in which each dot was presented in two frames only, separated by a specified interval. On each frame, half of the dots were refreshed and the other half was a displaced reincarnation of the pattern generated one or several frames earlier. Motion energy statistics in such a stimulus do not vary from frame to frame, and the directional bias in spatiotemporal correlations is similar for different interval settings. We measured coherence thresholds for left-right direction discrimination by varying motion coherence levels in a Quest staircase procedure, as a function of both step size and interval. Results show that highest sensitivity was found for an interval of 17-42 ms, irrespective of viewing distance. The falloff at longer intervals was much sharper than previously described. Tuning for temporal interval was largely, but not completely, independent of step size. The optimal temporal interval slightly decreased with increasing step size. Similarly, the optimal step size decreased with increasing temporal interval.
Dose-shaping using targeted sparse optimization.
Sayre, George A; Ruan, Dan
2013-07-01
Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method. In designing the energy minimization objective (E tot (sparse)), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot (sparse) improves tradeoff between
Dose-shaping using targeted sparse optimization
International Nuclear Information System (INIS)
Sayre, George A.; Ruan, Dan
2013-01-01
Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E tot sparse ), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L 1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot sparse improves
Distributed Tuning of Boundary Resources
DEFF Research Database (Denmark)
Eaton, Ben; Elaluf-Calderwood, Silvia; Sørensen, Carsten
2015-01-01
The digital age has seen the rise of service systems involving highly distributed, heterogeneous, and resource-integrating actors whose relationships are governed by shared institutional logics, standards, and digital technology. The cocreation of service within these service systems takes place...... in the context of a paradoxical tension between the logic of generative and democratic innovations and the logic of infrastructural control. Boundary resources play a critical role in managing the tension as a firm that owns the infrastructure can secure its control over the service system while independent...... firms can participate in the service system. In this study, we explore the evolution of boundary resources. Drawing on Pickering’s (1993) and Barrett et al.’s (2012) conceptualizations of tuning, the paper seeks to forward our understanding of how heterogeneous actors engage in the tuning of boundary...
Computation of undulator tuning curves
International Nuclear Information System (INIS)
Dejus, Roger J.
1997-01-01
Computer codes for fast computation of on-axis brilliance tuning curves and flux tuning curves have been developed. They are valid for an ideal device (regular planar device or a helical device) using the Bessel function formalism. The effects of the particle beam emittance and the beam energy spread on the spectrum are taken into account. The applicability of the codes and the importance of magnetic field errors of real insertion devices are addressed. The validity of the codes has been experimentally verified at the APS and observed discrepancies are in agreement with predicted reduction of intensities due to magnetic field errors. The codes are distributed as part of the graphical user interface XOP (X-ray OPtics utilities), which simplifies execution and viewing of the results
Tuning Properties in Silver Clusters
Joshi, Chakra Prasad
2015-07-09
The properties of Ag nanoclusters are not as well understood as those of their more precious Au cousins. However, a recent surge in the exploration of strategies to tune the physicochemical characteristics of Ag clusters addresses this imbalance, leading to new insights into their optical, luminescence, crystal habit, metal-core, ligand-shell and environmental properties. In this Perspective, we provide an overview of the latest strategies along with a brief introduction of the theoretical framework necessary to understand the properties of silver nanoclusters and the basis for their tuning. The advances in cluster research and the future prospects presented in this Perspective will eventually guide the next large systematic study of nanoclusters, resulting in a single collection of data similar to the periodic table of elements.
Zhan, Xingzhi
2002-01-01
The main purpose of this monograph is to report on recent developments in the field of matrix inequalities, with emphasis on useful techniques and ingenious ideas. Among other results this book contains the affirmative solutions of eight conjectures. Many theorems unify or sharpen previous inequalities. The author's aim is to streamline the ideas in the literature. The book can be read by research workers, graduate students and advanced undergraduates.
MOS voltage automatic tuning circuit
李, 田茂; 中田, 辰則; 松本, 寛樹
2004-01-01
Abstract ###Automatic tuning circuit adjusts frequency performance to compensate for the process variation. Phase locked ###loop (PLL) is a suitable oscillator for the integrated circuit. It is a feedback system that compares the input ###phase with the output phase. It can make the output frequency equal to the input frequency. In this paper, PLL ###fomed of MOSFET's is presented.The presented circuit consists of XOR circuit, Low-pass filter and Relaxation ###Oscillator. On PSPICE simulation...
Sparse principal component analysis in hyperspectral change detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Larsen, Rasmus; Vestergaard, Jacob Schack
2011-01-01
This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very...... similar although the loadings are very different from their usual non-sparse counterparts. The choice of three wavelength regions as being most important for change detection demonstrates the feature selection capability of sparse PCA....
Hand posture recognition via joint feature sparse representation
Cao, Chuqing; Sun, Ying; Li, Ruifeng; Chen, Lin
2011-12-01
In this study, we cast hand posture recognition as a sparse representation problem, and propose a novel approach called joint feature sparse representation classifier for efficient and accurate sparse representation based on multiple features. By integrating different features for sparse representation, including gray-level, texture, and shape feature, the proposed method can fuse benefits of each feature and hence is robust to partial occlusion and varying illumination. Additionally, a new database optimization method is introduced to improve computational speed. Experimental results, based on public and self-build databases, show that our method performs well compared to the state-of-the-art methods for hand posture recognition.
Tuning oxygen sensitivity of ruthenium complex exploiting silver nanoparticles
Energy Technology Data Exchange (ETDEWEB)
Ozturk, Osman [Department of Chemistry, Faculty of Science, University of Dicle, Diyarbakır (Turkey); Oter, Ozlem [Department of Chemistry, Faculty of Science, University of Dokuz Eylul, 35397 Buca, Izmir (Turkey); Center for Fabrication and Application of Electronic Materials (EMUM), University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Department of Nanoscience and Nanoengineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Yildirim, Serdar [Center for Fabrication and Application of Electronic Materials (EMUM), University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Department of Metallurgical and Materials Engineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Subasi, Elif [Department of Chemistry, Faculty of Science, University of Dokuz Eylul, 35397 Buca, Izmir (Turkey); Department of Nanoscience and Nanoengineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Ertekin, Kadriye, E-mail: kadriye.ertekin@deu.edu.tr [Department of Chemistry, Faculty of Science, University of Dokuz Eylul, 35397 Buca, Izmir (Turkey); Center for Fabrication and Application of Electronic Materials (EMUM), University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Department of Nanoscience and Nanoengineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Celik, Erdal [Center for Fabrication and Application of Electronic Materials (EMUM), University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Department of Nanoscience and Nanoengineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Department of Metallurgical and Materials Engineering, University of Dokuz Eylul, 35397 Buca, Tinaztepe, Izmir (Turkey); Temel, Hamdi [Department of Chemistry, Faculty of Science, University of Dicle, Diyarbakır (Turkey)
2014-11-15
In this study, we utilized silver nanoparticles (Ag NPs) along with ionic liquids as additives for fabrication of polymeric oxygen sensitive fibers. Plasticized polymethyl methacrylate and ethyl cellulose (EC) were used as matrix materials. Fibers and porous films were produced by electrospinning technique. Oxygen induced spectral response of the fluorescent tris(2,2′-bipyridyl) ruthenium(II) chloride (Ru(bipy){sub 3}{sup 2+}) was followed as the analytical signal. Utilization of silver nanoparticles in electrospun polymeric fibers for oxygen sensing purposes resulted with many advantages such as tuned sensitivity, linear calibration plot for larger concentration ranges, increased surface area and enhancement in all sensor dynamics. Linearity of the calibration plot for the offered composition was superior with respect to the previously reported ones. When stored at the ambient air of the laboratory there was no significant drift in intensity after 12 months. Our sensitivity and stability tests are still in progress. - Highlights: • Ag nanoparticles and Ru(II) complex doped together into polymers for the first time for O{sub 2} sensing. • The ionic liquid EMIMBF{sub 4} was used for matrix modification. • Fabricated electrospun nanofibers offered enhanced linearity for a large concentration range. • Exploited polymeric matrix materials and additives provided long-term stability. • Silver nanoparticles tuned oxygen sensitivity and facilitated fabrication process.
Hopkins, Michael T.
2015-01-01
The purpose of this study was to explore the relationship between eighth-grade violinists' pitch perception and instrument tuning skill. The perceptual task was a researcher-developed computer-based Violin Tuning Perception Test. The instrument tuning task involved tuning two violins, one mistuned flat and the other mistuned sharp. Participants (N…
Linearithmic time sparse and convex maximum margin clustering.
Zhang, Xiao-Lei; Wu, Ji
2012-12-01
Recently, a new clustering method called maximum margin clustering (MMC) was proposed and has shown promising performances. It was originally formulated as a difficult nonconvex integer problem. To make the MMC problem practical, the researchers either relaxed the original MMC problem to inefficient convex optimization problems or reformulated it to nonconvex optimization problems, which sacrifice the convexity for efficiency. However, no approaches can both hold the convexity and be efficient. In this paper, a new linearithmic time sparse and convex MMC algorithm, called support-vector-regression-based MMC (SVR-MMC), is proposed. Generally, it first uses the SVR as the core of the MMC. Then, it is relaxed as a convex optimization problem, which is iteratively solved by the cutting-plane algorithm. Each cutting-plane subproblem is further decomposed to a serial supervised SVR problem by a new global extended-level method (GELM). Finally, each supervised SVR problem is solved in a linear time complexity by a new sparse-kernel SVR (SKSVR) algorithm. We further extend the SVR-MMC algorithm to the multiple-kernel clustering (MKC) problem and the multiclass MMC (M3C) problem, which are denoted as SVR-MKC and SVR-M3C, respectively. One key point of the algorithms is the utilization of the SVR. It can prevent the MMC and its extensions meeting an integer matrix programming problem. Another key point is the new SKSVR. It provides a linear time interface to the nonlinear kernel scenarios, so that the SVR-MMC and its extensions can keep a linearthmic time complexity in nonlinear kernel scenarios. Our experimental results on various real-world data sets demonstrate the effectiveness and the efficiency of the SVR-MMC and its two extensions. Moreover, the unsupervised application of the SVR-MKC to the voice activity detection (VAD) shows that the SVR-MKC can achieve good performances that are close to its supervised counterpart, meet the real-time demand of the VAD, and need no
Narrowband interference parameterization for sparse Bayesian recovery
Ali, Anum
2015-09-11
This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.
Sparse suffix tree construction in small space
DEFF Research Database (Denmark)
Bille, Philip; Fischer, Johannes; Gørtz, Inge Li
2013-01-01
in 1968. First results were only obtained in 1996, but only for the case where the suffixes were evenly spaced in T. In this paper there is no constraint on the locations of the suffixes. We show that the sparse suffix tree can be constructed in O(nlog2 b) time. To achieve this we develop a technique...... the correct tree with high probability. We then give a Las-Vegas algorithm which also uses O(b) space and runs in the same time bounds with high probability when b = O(√n). Furthermore, additional tradeoffs between the space usage and the construction time for the Monte-Carlo algorithm are given....
Fast Generation of Sparse Random Kernel Graphs.
Hagberg, Aric; Lemons, Nathan
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most (n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Multiplication method for sparse interferometric fringes.
Liu, Cong; Zhang, Xingyi; Zhou, Youhe
2016-04-04
Fringe analysis in the interferometry has been of long-standing interest to the academic community. However, the process of sparse fringe is always a headache in the measurement, especially when the specimen is very small. Through theoretical derivation and experimental measurements, our work demonstrates a new method for fringe multiplication. Theoretically, arbitrary integral-multiple fringe multiplication can be acquired by using the interferogram phase as the parameter. We simulate digital images accordingly and find that not only the skeleton lines of the multiplied fringe are very convenient to extract, but also the main frequency of which can be easily separated from the DC component. Meanwhile, the experimental results have a good agreement with the theoretic ones in a validation using the classical photoelasticity.
Bayesian learning of sparse multiscale image representations.
Hughes, James Michael; Rockmore, Daniel N; Wang, Yang
2013-12-01
Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared--and learned--across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images.
Feng, Li; Coppo, Simone; Piccini, Davide; Yerly, Jerome; Lim, Ruth P; Masci, Pier Giorgio; Stuber, Matthias; Sodickson, Daniel K; Otazo, Ricardo
2018-02-01
A 5D whole-heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high-resolution cardiac and respiratory motion-resolved whole-heart anatomy in a single continuous noncontrast MR scan. A non-electrocardiograph (ECG)-triggered 3D golden-angle radial balanced steady-state free precession sequence was used for data acquisition. The acquired 3D k-space data were sorted into a 5D dataset containing separated cardiac and respiratory dimensions using a self-extracted respiratory motion signal and a recorded ECG signal. Images were then reconstructed using XD-GRASP, a multidimensional compressed sensing technique exploiting correlations/sparsity along cardiac and respiratory dimensions. 5D whole-heart imaging was compared with respiratory motion-corrected 3D and 4D whole-heart imaging in nine volunteers for evaluation of the myocardium, great vessels, and coronary arteries. It was also compared with breath-held, ECG-gated 2D cardiac cine imaging for validation of cardiac function quantification. 5D whole-heart images received systematic higher quality scores in the myocardium, great vessels and coronary arteries. Quantitative coronary sharpness and length were always better for the 5D images. Good agreement was obtained for quantification of cardiac function compared with 2D cine imaging. 5D whole-heart sparse imaging represents a robust and promising framework for simplified comprehensive cardiac MRI without the need for breath-hold and motion correction. Magn Reson Med 79:826-838, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Oracle SQL tuning with Oracle SQLTXPLAIN
Charalambides, Stelios
2013-01-01
Oracle SQL Tuning with SQLTXPLAIN is a practical guide to SQL tuning the way Oracle's own experts do it, using a freely downloadable tool called SQLTXPLAIN. Using this simple tool you'll learn how to tune even the most complex SQL, and you'll learn to do it quickly, without the huge learning curve usually associated with tuning as a whole. Firmly based in real world problems, this book helps you reclaim system resources and avoid the most common bottleneck in overall performance, badly tuned SQL. You'll learn how the optimizer works, how to take advantage of its latest features, and when it'
Sparse Non-negative Tensor 2D Deconvolution (SNTF2D) for multi channel time-frequency analysis
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N.
2006-01-01
We recently introduced two algorithms for sparse non-negative matrix factor 2-D deconvolution (SNMF2D) that are useful for single channel source separation and music transcription. We here extend this approach to the analysis of the log-frequency spectrograms of a multichannel recording. The model....... The algorithms are demonstrated to successfully identify the components of both artificially generated as well as real stereo music....
Zhang, Di; He, Jiazhong; Zhao, Yun; Du, Minghui
2015-03-01
In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. Copyright © 2015 Elsevier Ltd. All rights reserved.
New tuning method for PID controller.
Shen, Jing-Chung
2002-10-01
In this paper, a tuning method for proportional-integral-derivative (PID) controller and the performance assessment formulas for this method are proposed. This tuning method is based on a genetic algorithm based PID controller design method. For deriving the tuning formula, the genetic algorithm based design method is applied to design PID controllers for a variety of processes. The relationship between the controller parameters and the parameters that characterize the process dynamics are determined and the tuning formula is then derived. Using simulation studies, the rules for assessing the performance of a PID controller tuned by the proposed method are also given. This makes it possible to incorporate the capability to determine if the PID controller is well tuned or not into an autotuner. An autotuner based on this new tuning method and the corresponding performance assessment rules is also established. Simulations and real-time experimental results are given to demonstrate the effectiveness and usefulness of these formulas.
Qin, Yanhua; Liu, Yumin; Liu, Jianyi; Yu, Zhongyuan
2018-01-16
Sparse Bayesian learning (SBL) is applied to the coprime array for underdetermined wideband direction of arrival (DOA) estimation. Using the augmented covariance matrix, the coprime array can achieve a higher number of degrees of freedom (DOFs) to resolve more sources than the number of physical sensors. The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be off the search grid no matter how fine the grid is. This dictionary mismatch problem can be well resolved by the SBL using fixed point updates. The SBL can automatically choose sparsity and approximately resolve the non-convex optimizaton problem. Numerical simulations are conducted to validate the effectiveness of the underdetermined wideband DOA estimation via SBL based on coprime array. It is clear that SBL can obtain good performance in detection and estimation compared to least absolute shrinkage and selection operator (LASSO), simultaneous orthogonal matching pursuit least squares (SOMP-LS) , simultaneous orthogonal matching pursuit total least squares (SOMP-TLS) and off-grid sparse Bayesian inference (OGSBI).
Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.
Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin
2014-02-01
In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.
Sparse dictionary learning for resting-state fMRI analysis
Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul
2011-09-01
Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.
Spectrum recovery method based on sparse representation for segmented multi-Gaussian model
Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan
2016-09-01
Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.
Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution
Directory of Open Access Journals (Sweden)
Zhi-Kun Chen
2016-01-01
Full Text Available A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm.
The Roles of Sparse Direct Methods in Large-scale Simulations
Energy Technology Data Exchange (ETDEWEB)
Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.
2005-06-27
Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research.
Anonymising the Sparse Dataset: A New Privacy Preservation Approach while Predicting Diseases
Directory of Open Access Journals (Sweden)
V. Shyamala Susan
2016-09-01
Full Text Available Data mining techniques analyze the medical dataset with the intention of enhancing patient’s health and privacy. Most of the existing techniques are properly suited for low dimensional medical dataset. The proposed methodology designs a model for the representation of sparse high dimensional medical dataset with the attitude of protecting the patient’s privacy from an adversary and additionally to predict the disease’s threat degree. In a sparse data set many non-zero values are randomly spread in the entire data space. Hence, the challenge is to cluster the correlated patient’s record to predict the risk degree of the disease earlier than they occur in patients and to keep privacy. The first phase converts the sparse dataset right into a band matrix through the Genetic algorithm along with Cuckoo Search (GCS.This groups the correlated patient’s record together and arranges them close to the diagonal. The next segment dissociates the patient’s disease, which is a sensitive value (SA with the parameters that determine the disease normally Quasi Identifier (QI.Finally, density based clustering technique is used over the underlying data to create anonymized groups to maintain privacy and to predict the risk level of disease. Empirical assessments on actual health care data corresponding to V.A.Medical Centre heart disease dataset reveal the efficiency of this model pertaining to information loss, utility and privacy.
Energy Technology Data Exchange (ETDEWEB)
Moody, Daniela; Wohlberg, Brendt
2018-01-02
An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.
Local posterior concentration rate for multilevel sparse sequences
Belitser, E.N.; Nurushev, N.
2017-01-01
We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one
In-Storage Embedded Accelerator for Sparse Pattern Processing
2016-09-13
acceptable. For example, a facial recognition application would operate on features extracted from facial images. A feature vector corresponds to a...In-Storage Embedded Accelerator for Sparse Pattern Processing Sang-Woo Jun * , Huy T. Nguyen # , Vijay Gadepally #* , and Arvind * # MIT Lincoln...Laboratory, * MIT Computer Science & Artificial Intelligence Laboratory Abstract— We present a novel system architecture for sparse pattern
Joint Group Sparse PCA for Compressed Hyperspectral Imaging.
Khan, Zohaib; Shafait, Faisal; Mian, Ajmal
2015-12-01
A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.
Robust Face Recognition Via Gabor Feature and Sparse Representation
Directory of Open Access Journals (Sweden)
Hao Yu-Juan
2016-01-01
Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.
Comparison of Methods for Sparse Representation of Musical Signals
DEFF Research Database (Denmark)
Endelt, Line Ørtoft; la Cour-Harbo, Anders
2005-01-01
by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....
Convergence results for 3D sparse grid approaches
J. Noordmans; P.W. Hemker (Piet)
1997-01-01
textabstractThe convergence behaviour is investigated of solution algorithms for the anisotropic Poisson problem on partially ordered, sparse families of regular grids in 3D. In order to study multilevel techniques on sparse families of grids, first we consider the convergence of a two-level
Confidence of model based shape reconstruction from sparse data
DEFF Research Database (Denmark)
Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.
2010-01-01
Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...
Bhatia, Rajendra
1997-01-01
A good part of matrix theory is functional analytic in spirit. This statement can be turned around. There are many problems in operator theory, where most of the complexities and subtleties are present in the finite-dimensional case. My purpose in writing this book is to present a systematic treatment of methods that are useful in the study of such problems. This book is intended for use as a text for upper division and gradu ate courses. Courses based on parts of the material have been given by me at the Indian Statistical Institute and at the University of Toronto (in collaboration with Chandler Davis). The book should also be useful as a reference for research workers in linear algebra, operator theory, mathe matical physics and numerical analysis. A possible subtitle of this book could be Matrix Inequalities. A reader who works through the book should expect to become proficient in the art of deriving such inequalities. Other authors have compared this art to that of cutting diamonds. One first has to...
Tensor-GMRES method for large sparse systems of nonlinear equations
Feng, Dan; Pulliam, Thomas H.
1994-01-01
This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model formation and solution techniques for nonlinear equations with Krylov subspace projection techniques for unsymmetric systems of linear equations. Traditional tensor methods for nonlinear equations are based on a quadratic model of the nonlinear function, a standard linear model augmented by a simple second order term. These methods are shown to be significantly more efficient than standard methods both on nonsingular problems and on problems where the Jacobian matrix at the solution is singular. A major disadvantage of the traditional tensor methods is that the solution of the tensor model requires the factorization of the Jacobian matrix, which may not be suitable for problems where the Jacobian matrix is large and has a 'bad' sparsity structure for an efficient factorization. We overcome this difficulty by forming and solving the tensor model using an extension of a Newton-GMRES scheme. Like traditional tensor methods, we show that the new tensor method has significant computational advantages over the analogous Newton counterpart. Consistent with Krylov subspace based methods, the new tensor method does not depend on the factorization of the Jacobian matrix. As a matter of fact, the Jacobian matrix is never needed explicitly.
Sparse-sampling with time-encoded (TICO) stimulated Raman scattering for fast image acquisition
Hakert, Hubertus; Eibl, Matthias; Karpf, Sebastian; Huber, Robert
2017-07-01
Modern biomedical imaging modalities aim to provide researchers a multimodal contrast for a deeper insight into a specimen under investigation. A very promising technique is stimulated Raman scattering (SRS) microscopy, which can unveil the chemical composition of a sample with a very high specificity. Although the signal intensities are enhanced manifold to achieve a faster acquisition of images if compared to standard Raman microscopy, there is a trade-off between specificity and acquisition speed. Commonly used SRS concepts either probe only very few Raman transitions as the tuning of the applied laser sources is complicated or record whole spectra with a spectrometer based setup. While the first approach is fast, it reduces the specificity and the spectrometer approach records whole spectra -with energy differences where no Raman information is present-, which limits the acquisition speed. Therefore, we present a new approach based on the TICO-Raman concept, which we call sparse-sampling. The TICO-sparse-sampling setup is fully electronically controllable and allows probing of only the characteristic peaks of a Raman spectrum instead of always acquiring a whole spectrum. By reducing the spectral points to the relevant peaks, the acquisition time can be greatly reduced compared to a uniformly, equidistantly sampled Raman spectrum while the specificity and the signal to noise ratio (SNR) are maintained. Furthermore, all laser sources are completely fiber based. The synchronized detection enables a full resolution of the Raman signal, whereas the analogue and digital balancing allows shot noise limited detection. First imaging results with polystyrene (PS) and polymethylmethacrylate (PMMA) beads confirm the advantages of TICO sparse-sampling. We achieved a pixel dwell time as low as 35 μs for an image differentiating both species. The mechanical properties of the applied voice coil stage for scanning the sample currently limits even faster acquisition.
An Overview on Sparse Recovery-based STAP
Directory of Open Access Journals (Sweden)
Ma Ze-qiang
2014-04-01
Full Text Available This paper gives a brief review on the Sparse-Recovery (SR-based Space-Time Adaptive Processing (STAP technique. First, the motivation for introducing sparse recovery into STAP is presented. Next, the potential advantages and mathematical explanation of the sparse-recovery-based STAP are discussed. A major part of this paper presents the state-of-art research results in spatio-temporal spectrum-sparsity-based STAP, including the basic frame, off-grid problem, multiple measurement vector problem, and direct domain problem. The sparse-recovery-based STAP on conformal array problem is also introduced. Finally, a summary of sparse-recovery-based STAP is provided, and the problems that need to be solved and some potential research areas are discussed.
Sparse Frequency Waveform Design for Radar-Embedded Communication
Directory of Open Access Journals (Sweden)
Chaoyun Mai
2016-01-01
Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.
Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches
Bioucas-Dias, José M.; Plaza, Antonio
2010-10-01
Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmixing is, thus, a source separation problem where, under a linear mixing model, the sources are the fractional abundances and the endmember spectral signatures are the columns of the mixing matrix. As such, the independent component analysis (ICA) framework came naturally to mind to unmix spectral data. However, the ICA crux assumption of source statistical independence is not satisfied in spectral applications, since the sources are fractions and, thus, non-negative and sum to one. As a consequence, ICA-based algorithms have severe limitations in the area of spectral unmixing, and this has fostered new unmixing research directions taking into account geometric and statistical characteristics of hyperspectral sources. This paper presents an overview of the principal research directions in hyperspectral unmixing. The presentations is organized into four main topics: i) mixing models, ii) signal subspace identification, iii) geometrical-based spectral unmixing, (iv) statistical-based spectral unmixing, and (v) sparse regression-based unmixing. In each topic, we describe what physical or mathematical problems are involved and summarize state-of-the-art algorithms to
Belitsky, A. V.
2017-10-01
The Operator Product Expansion for null polygonal Wilson loop in planar maximally supersymmetric Yang-Mills theory runs systematically in terms of multi-particle pentagon transitions which encode the physics of excitations propagating on the color flux tube ending on the sides of the four-dimensional contour. Their dynamics was unraveled in the past several years and culminated in a complete description of pentagons as an exact function of the 't Hooft coupling. In this paper we provide a solution for the last building block in this program, the SU(4) matrix structure arising from internal symmetry indices of scalars and fermions. This is achieved by a recursive solution of the Mirror and Watson equations obeyed by the so-called singlet pentagons and fixing the form of the twisted component in their tensor decomposition. The non-singlet, or charged, pentagons are deduced from these by a limiting procedure.
Directory of Open Access Journals (Sweden)
A.V. Belitsky
2017-10-01
Full Text Available The Operator Product Expansion for null polygonal Wilson loop in planar maximally supersymmetric Yang–Mills theory runs systematically in terms of multi-particle pentagon transitions which encode the physics of excitations propagating on the color flux tube ending on the sides of the four-dimensional contour. Their dynamics was unraveled in the past several years and culminated in a complete description of pentagons as an exact function of the 't Hooft coupling. In this paper we provide a solution for the last building block in this program, the SU(4 matrix structure arising from internal symmetry indices of scalars and fermions. This is achieved by a recursive solution of the Mirror and Watson equations obeyed by the so-called singlet pentagons and fixing the form of the twisted component in their tensor decomposition. The non-singlet, or charged, pentagons are deduced from these by a limiting procedure.
Transformer fault diagnosis using continuous sparse autoencoder.
Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou
2016-01-01
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.
Sparse alignment for robust tensor learning.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming
2014-10-01
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.
Sparse Superpixel Unmixing for Hyperspectral Image Analysis
Castano, Rebecca; Thompson, David R.; Gilmore, Martha
2010-01-01
Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.
Joint Sparse Recovery With Semisupervised MUSIC
Wen, Zaidao; Hou, Biao; Jiao, Licheng
2017-05-01
Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.
Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M; Mao, Jian-Hua
2018-05-01
The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.
Robust Sparse Sensing Using Weather Radar
Mishra, K. V.; Kruger, A.; Krajewski, W. F.; Xu, W.
2014-12-01
The ability of a weather radar to detect weak echoes is limited by the presence of noise or unwanted echoes. Some of these unwanted signals originate externally to the radar system, such as cosmic noise, radome reflections, interference from co-located radars, and power transmission lines. The internal source of noise in microwave radar receiver is mainly thermal. The thermal noise from various microwave devices in the radar receiver tends to lower the signal-to-noise ratio, thereby masking the weaker signals. Recently, the compressed sensing (CS) technique has emerged as a novel signal sampling paradigm that allows perfect reconstruction of signals sampled at frequencies lower than the Nyquist rate. Many radar and remote sensing applications require efficient and rapid data acquisition. The application of CS to weather radars may allow for faster target update rates without compromising the accuracy of target information. In our previous work, we demonstrated recovery of an entire precipitation scene from its compressed-sensed version by using the matrix completion approach. In this study, we characterize the performance of such a CS-based weather radar in the presence of additive noise. We use a signal model where the precipitation signals form a low-rank matrix that is corrupted with (bounded) noise. Using recent advances in algorithms for matrix completion from few noisy observations, we reconstruct the precipitation scene with reasonable accuracy. We test and demonstrate our approach using the data collected by Iowa X-band Polarimetric (XPOL) weather radars.
Manifold regularization for sparse unmixing of hyperspectral images.
Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin
2016-01-01
Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.
Object tracking by occlusion detection via structured sparse learning
Zhang, Tianzhu
2013-06-01
Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.
Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays
Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.
2004-01-01
Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.
Sparse Principal Component Analysis in Medical Shape Modeling
DEFF Research Database (Denmark)
Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus
2006-01-01
Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...
Accelerating Dynamic Cardiac MR Imaging Using Structured Sparse Representation
Directory of Open Access Journals (Sweden)
Nian Cai
2013-01-01
Full Text Available Compressed sensing (CS has produced promising results on dynamic cardiac MR imaging by exploiting the sparsity in image series. In this paper, we propose a new method to improve the CS reconstruction for dynamic cardiac MRI based on the theory of structured sparse representation. The proposed method user the PCA subdictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. An accelerated iterative shrinkage algorithm is used to solve the optimization problem and achieve a fast convergence rate. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method.
A comprehensive study of sparse codes on abnormality detection
DEFF Research Database (Denmark)
Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor
2017-01-01
Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...
GPU-Accelerated Sparse Matrix Solvers for Large-Scale Simulations, Phase I
National Aeronautics and Space Administration — Many large-scale numerical simulations can be broken down into common mathematical routines. While the applications may differ, the need to perform functions such as...
GPU-Accelerated Sparse Matrix Solvers for Large-Scale Simulations Project
National Aeronautics and Space Administration — At the heart of scientific computing and numerical analysis are linear algebra solvers. In scientific computing, the focus is on the partial differential equations...
Studying the brain from adolescence to adulthood through sparse multi-view matrix factorisations
Wang, Zi; Karolis, Vyacheslav; Nosarti, Chiara; Montana, Giovanni
2016-01-01
Men and women differ in specific cognitive abilities and in the expression of several neuropsychiatric conditions. Such findings could be attributed to sex hormones, brain differences, as well as a number of environmental variables. Existing research on identifying sex-related differences in brain structure have predominantly used cross-sectional studies to investigate, for instance, differences in average gray matter volumes (GMVs). In this article we explore the potential of a recently prop...
GPU-Accelerated Sparse Matrix Solvers for Large-Scale Simulations, Phase II
National Aeronautics and Space Administration — At the heart of scientific computing and numerical analysis are linear algebra solvers. In scientific computing, the focus is on the partial differential equations...
Sparse Representations for Pattern Classification using Learned Dictionaries
Thiagarajan, Jayaraman J.; Ramamurthy, Karthikeyan N.; Spanias, Andreas
Sparse representations have been often used for inverse problems in signal and image processing. Furthermore, frameworks for signal classification using sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data representation domain, where data from different classes are sparsely represented using an overcomplete dictionary. We propose a source model to characterize the data in each class and present an algorithm to infer the dictionary from the training data of all the classes. We estimate statistical templates in the data representation domain for each class of data, and perform classification using a likelihood measure. Simulation results show that, in the case of highly sparse signals, the proposed classifier provides a consistently good performance even under noisy conditions.
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir
2013-11-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Sparse Machine Learning Methods for Understanding Large Text Corpora
National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...
Image classification by semisupervised sparse coding with confident unlabeled samples
Li, Xiao; Fang, Min; Wu, Jinqiao; He, Liang; Tian, Xian
2017-09-01
Sparse coding has achieved very excellent performance in image classification tasks, especially when the supervision information is incorporated into the dictionary learning process. However, there is a large amount of unlabeled samples that are expensive and boring to annotate. We propose an image classification algorithm by semisupervised sparse coding with confident unlabeled samples. In order to make the learnt sparse coding more discriminative, we select and annotate some confident unlabeled samples. A minimization model is developed in which the reconstruction error of the labeled, the selected unlabeled and the remaining unlabeled data and the classification error are integrated, which enhances the discriminant property of the dictionary and sparse representations. The experimental results on image classification tasks demonstrate that our algorithm can significantly improve the image classification performance.
Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising
Ma, Hongqiang; Ma, Shiping; Xu, Yuelei; Zhu, Mingming
2018-01-01
Stacked Sparse Denoising Auto-Encoder (SSDA) has been successfully applied to image denoising. As a deep network, the SSDA network with powerful data feature learning ability is superior to the traditional image denoising algorithms. However, the algorithm has high computational complexity and slow convergence rate in the training. To address this limitation, we present a method of image denoising based on Deep Marginalized Sparse Denoising Auto-Encoder (DMSDA). The loss function of Sparse Denoising Auto-Encoder is marginalized so that it satisfies both sparseness and marginality. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed and training time, but also has better denoising performance than the current excellent denoising algorithms, including both the subjective and objective evaluation of image denoising.
3rd Workshop on Sparse Grids and Applications
Pflüger, Dirk
2016-01-01
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.
Support agnostic Bayesian matching pursuit for block sparse signals
Masood, Mudassir
2013-05-01
A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Dipole localization in Moon rocks from sparse magnetic data
Chevillard , Sylvain; Leblond , Juliette; Mavreas , Konstantinos
2017-01-01
International audience; We consider dipole recovery issues from sparse magnetic data, with the use of best quadratic rational approximation techniques, together with geometrical and algebraic properties of the poles of the approximants.
Greedy vs. L1 Convex Optimization in Sparse Coding
DEFF Research Database (Denmark)
Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor
Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... and action recognition, a comparative study of codes in abnormal event detection is less studied and hence no conclusion is gained on the effect of codes in detecting abnormalities. We constrict our comparison in two types of the above L0-norm solutions: greedy algorithms and convex L1-norm solutions....... Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate their performance...
Detection of Pitting in Gears Using a Deep Sparse Autoencoder
Directory of Open Access Journals (Sweden)
Yongzhi Qu
2017-05-01
Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.
Sparse Vector Distributions and Recovery from Compressed Sensing
DEFF Research Database (Denmark)
Sturm, Bob L.
It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....
Beam Combination for Sparse Aperture Telescopes, Phase I
National Aeronautics and Space Administration — The Stellar Imager, an ultraviolet, sparse-aperture telescope, was one of the fifteen Vision Missions chosen for a study completed last year. Stellar Imager will...
Completing sparse and disconnected protein-protein network by deep learning.
Huang, Lei; Liao, Li; Wu, Cathy H
2018-03-22
Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network
Bayesian networks: a combined tuning heuristic
Bolt, J.H.
2016-01-01
One of the issues in tuning an output probability of a Bayesian network by changing multiple parameters is the relative amount of the individual parameter changes. In an existing heuristic parameters are tied such that their changes induce locally a maximal change of the tuned probability. This
Tuning and History: A Personal Overview
Isaacs, Ann Katherine
2017-01-01
The text places Tuning History in the context of the rapidly developing international collaboration among historians which began in Europe in 1989, with the ECTS Pilot project, and continued, from 2000 on, with the European History Networks (for research and for curriculum development) working in parallel and in collaboration with Tuning, in…
Efficient tuning in supervised machine learning
Koch, Patrick
2013-01-01
The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the
Distributed coding of multiview sparse sources with joint recovery
DEFF Research Database (Denmark)
Luong, Huynh Van; Deligiannis, Nikos; Forchhammer, Søren
2016-01-01
coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature...... transform (SIFT) descriptors extracted from multiview images shows that our method leads to bit-rate saving of up to 43% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources....
Efficient collaborative sparse channel estimation in massive MIMO
Masood, Mudassir
2015-08-12
We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.
Sparse encoding of automatic visual association in hippocampal networks
DEFF Research Database (Denmark)
Hulme, Oliver J; Skov, Martin; Chadwick, Martin J
2014-01-01
by these stimuli. Using multivariate Bayesian decoding, we show that human hippocampal and temporal neocortical structures host sparse associative representations that are automatically triggered by visual input. Furthermore, as predicted theoretically, there was a significant increase in sparsity in the Cornu...... for the sparse encoding of associative density. In the absence of reportability or attentional confounds, this charts a distribution of visual associative representations within hippocampal populations and their temporal lobe afferent fields, and demonstrates the viability of retrospective associative sampling...
Discussion of CoSA: Clustering of Sparse Approximations
Energy Technology Data Exchange (ETDEWEB)
Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-03-07
The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.
Detection of Contact Binaries Using Sparse High Phase Angle Lightcurves
Lacerda, Pedro
2007-01-01
We show that candidate contact binary asteroids can be efficiently identified from sparsely sampled photometry taken at phase angles >60deg. At high phase angle, close/contact binary systems produce distinctive lightcurves that spend most of the time at maximum or minimum (typically >1mag apart) brightness with relatively fast transitions between the two. This means that a few (~5) sparse observations will suffice to measure the large range of variation and identify candidate contact binary s...
Automatic tuning of free electron lasers
International Nuclear Information System (INIS)
Agapov, Ilya; Zagorodnov, Igor; Geloni, Gianluca; Tomin, Sergey
2017-01-01
Existing FEL facilities often suffer from stability issues: so electron orbit, transverse electron optics, electron bunch compression and other parameters have to be readjusted often to account for drifts in performance of various components. The tuning procedures typically employed in operation are often manual and lengthy. We have been developing a combination of model-free and model-based automatic tuning methods to meet the needs of present and upcoming XFEL facilities. Our approach has been implemented at FLASH to achieve automatic SASE tuning using empirical control of orbit, electron optics and bunch compression. In this paper we describe our approach to empirical tuning, the software which implements it, and the results of using it at FLASH.We also discuss the potential of using machine learning and model-based techniques in tuning methods.
Exhaustive Search for Sparse Variable Selection in Linear Regression
Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato
2018-04-01
We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.
A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization.
Li, Hui; Zhang, Qingfu; Deng, Jingda; Xu, Zong-Ben
2017-03-29
Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresholding methods is to find a so-called k-sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true k-sparse solution. In the noiseless case, it is easy to locate the exact position of the k-sparse solution from the distribution of the solutions found by our proposed method. Therefore, our method has the ability to detect the true sparsity. Moreover, any iterative thresholding methods can be used as a local optimizer in our proposed method, and no prior estimation of sparsity is required. The proposed method can also be extended to solve sparse optimization problems with noise. Extensive experiments have been conducted to study its performance on artificial signals and magnetic resonance imaging signals. Our experimental results have shown that our proposed method is very effective for detecting sparsity and can improve the reconstruction ability of existing iterative thresholding methods.
Saliency Detection Using Sparse and Nonlinear Feature Representation
Zhao, Qingjie; Manzoor, Muhammad Farhan; Ishaq Khan, Saqib
2014-01-01
An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection. PMID:24895644
Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle
Directory of Open Access Journals (Sweden)
Xiangwei Xing
2014-01-01
Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.
Structure-aware Local Sparse Coding for Visual Tracking
Qi, Yuankai
2018-01-24
Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.
Occlusion detection via structured sparse learning for robust object tracking
Zhang, Tianzhu
2014-01-01
Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.
Musical experience sharpens human cochlear tuning.
Bidelman, Gavin M; Nelms, Caitlin; Bhagat, Shaum P
2016-05-01
The mammalian cochlea functions as a filter bank that performs a spectral, Fourier-like decomposition on the acoustic signal. While tuning can be compromised (e.g., broadened with hearing impairment), whether or not human cochlear frequency resolution can be sharpened through experiential factors (e.g., training or learning) has not yet been established. Previous studies have demonstrated sharper psychophysical tuning curves in trained musicians compared to nonmusicians, implying superior peripheral tuning. However, these findings are based on perceptual masking paradigms, and reflect engagement of the entire auditory system rather than cochlear tuning, per se. Here, by directly mapping physiological tuning curves from stimulus frequency otoacoustic emissions (SFOAEs)-cochlear emitted sounds-we show that estimates of human cochlear tuning in a high-frequency cochlear region (4 kHz) is further sharpened (by a factor of 1.5×) in musicians and improves with the number of years of their auditory training. These findings were corroborated by measurements of psychophysical tuning curves (PTCs) derived via simultaneous masking, which similarly showed sharper tuning in musicians. Comparisons between SFOAE and PTCs revealed closer correspondence between physiological and behavioral curves in musicians, indicating that tuning is also more consistent between different levels of auditory processing in trained ears. Our findings demonstrate an experience-dependent enhancement in the resolving power of the cochlear sensory epithelium and the spectral resolution of human hearing and provide a peripheral account for the auditory perceptual benefits observed in musicians. Both local and feedback (e.g., medial olivocochlear efferent) mechanisms are discussed as potential mechanisms for experience-dependent tuning. Copyright © 2016 Elsevier B.V. All rights reserved.
An automatically tuning intrusion detection system.
Yu, Zhenwei; Tsai, Jeffrey J P; Weigert, Thomas
2007-04-01
An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Traditionally, intrusion detection relies on extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been deployed for intrusion detection. An IDS is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current systems depends on the system operators in working out the tuning solution and in integrating it into the detection model. In this paper, an automatically tuning IDS (ATIDS) is presented. The proposed system will automatically tune the detection model on-the-fly according to the feedback provided by the system operator when false predictions are encountered. The system is evaluated using the KDDCup'99 intrusion detection dataset. Experimental results show that the system achieves up to 35% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 10% false predictions are used to tune the model, the system still achieves about 30% improvement. Moreover, when tuning is not delayed too long, the system can achieve about 20% improvement, with only 1.3% of the false predictions used to tune the model. The results of the experiments show that a practical system can be built based on ATIDS: system operators can focus on verification of predictions with low confidence, as only those predictions determined to be false will be used to tune the detection model.
Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.
2018-01-01
Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.
Random matrix theory with an external source
Brézin, Edouard
2016-01-01
This is a first book to show that the theory of the Gaussian random matrix is essential to understand the universal correlations with random fluctuations and to demonstrate that it is useful to evaluate topological universal quantities. We consider Gaussian random matrix models in the presence of a deterministic matrix source. In such models the correlation functions are known exactly for an arbitrary source and for any size of the matrices. The freedom given by the external source allows for various tunings to different classes of universality. The main interest is to use this freedom to compute various topological invariants for surfaces such as the intersection numbers for curves drawn on a surface of given genus with marked points, Euler characteristics, and the Gromov–Witten invariants. A remarkable duality for the average of characteristic polynomials is essential for obtaining such topological invariants. The analysis is extended to nonorientable surfaces and to surfaces with boundaries.
Upgrades to PEP-II Tune Measurements
Energy Technology Data Exchange (ETDEWEB)
Fisher, Alan S.
2002-07-30
The tune monitors for the two-ring PEP-II collider convert signals from one set of four BPM-type pickup buttons per ring into horizontal and vertical differences, which are then downconverted from 952 MHz (twice the RF) to baseband. Two-channel 10-MHz FFT spectrum analyzers show spectra in X-window displays in the Control Room, to assist PEP operators. When operating with the original system near the beam-beam limit, collisions broadened and flattened the tune peaks, often bringing them near the noise floor. We recently installed new downconverters that increase the signal-to-noise ratio by about 5 dB. In addition, we went from one to two sets of pickups per ring, near focusing and defocusing quadrupoles, so that signals for both planes originate at locations with large amplitudes. We also have just installed a tune tracker, based on a digital lock-in amplifier (one per tune plane) that is controlled by an EPICS software feedback loop. The tracker monitors the phase of the beam's response to a sinusoidal excitation, and adjusts the drive frequency to track the middle of the 1 go-degree phase transition across the tune resonance. We plan next to test an outer loop controlling the tune quadrupoles based on this tune measurement.
Upgrades to PEP-II Tune Measurements
Energy Technology Data Exchange (ETDEWEB)
Fisher, Alan S.
2002-07-30
The tune monitors for the two-ring PEP-II collider convert signals from one set of four BPM-type pickup buttons per ring into horizontal and vertical differences, which are then downconverted from 952 MHz (twice the RF) to baseband. Two-channel l0-MHz FFT spectrum analyzers show spectra in X-window displays in the Control Room, to assist PEP operators. When operating with the original system near the beam-beam limit, collisions broadened and flattened the tune peaks, often bringing them near the noise floor. We recently installed new downconverters that increase the signal-to-noise ratio by about 5 dB. In addition, we went from one to two sets of pickups per ring, near focusing and defocusing quadrupoles, so that signals for both planes originate at locations with large amplitudes. We also have just installed a tune tracker, based on a digital lock-in amplifier (one per tune plane) that is controlled by an EPICS software feedback loop. The tracker monitors the phase of the beam's response to a sinusoidal excitation, and adjusts the drive frequency to track the middle of the 180-degree phase transition across the tune resonance. We plan next to test an outer loop controlling the tune quadrupoles based on this tune measurement.
Upgrades to PEP-II tune measurements
International Nuclear Information System (INIS)
Fisher, Alan S.; Petree, Mark; Wienands, Uli; Allison, Stephanie; Laznovsky, Michael; Seeman, Michael; Robin, Jolene
2002-01-01
The tune monitors for the two-ring PEP-II collider convert signals from one set of four BPM-type pickup buttons per ring into horizontal and vertical differences, which are then downconverted from 952 MHz (twice the RF) to baseband. Two-channel 10-MHz FFT spectrum analyzers show spectra in X-window displays in the Control Room, to assist PEP operators. When operating with the original system near the beam-beam limit, collisions broadened and flattened the tune peaks, often bringing them near the noise floor. We recently installed new downconverters that increase the signal-to-noise ratio by about 5 dB. In addition, we went from one to two sets of pickups per ring, near focusing and defocusing quadrupoles, so that signals for both planes originate at locations with large amplitudes. We also have just installed a tune tracker, based on a digital lock-in amplifier (one per tune plane) that is controlled by an EPICS software feedback loop. The tracker monitors the phase of the beam's response to a sinusoidal excitation, and adjusts the drive frequency to track the middle of the 180-degree phase transition across the tune resonance. We plan next to test an outer loop controlling the tune quadrupoles based on this tune measurement
Tune variations in the Large Hadron Collider
Aquilina, N.; Giovannozzi, M.; Lamont, M.; Sammut, N.; Steinhagen, R.; Todesco, E.; Wenninger, J.
2015-04-01
The horizontal and vertical betatron tunes of the Large Hadron Collider (LHC) mainly depend on the strength of the quadrupole magnets, but are also affected by the quadrupole component in the main dipoles. In case of systematic misalignments, the sextupole component from the main dipoles and sextupole corrector magnets also affect the tunes due to the feed down effect. During the first years of operation of the LHC, the tunes have been routinely measured and corrected through either a feedback or a feed forward system. In this paper, the evolution of the tunes during injection, ramp and flat top are reconstructed from the beam measurements and the settings of the tune feedback loop and of the feed forward corrections. This gives the obtained precision of the magnetic model of the machine with respect to quadrupole and sextupole components. Measurements at the injection plateau show an unexpected large decay whose origin is not understood. This data is discussed together with the time constants and the dependence on previous cycles. We present results of dedicated experiments that show that this effect does not originate from the decay of the main dipole component. During the ramp, the tunes drift by about 0.022. It is shown that this is related to the precision of tracking the quadrupole field in the machine and this effect is reduced to about 0.01 tune units during flat top.
Selective enhancement of orientation tuning before saccades.
Ohl, Sven; Kuper, Clara; Rolfs, Martin
2017-11-01
Saccadic eye movements cause a rapid sweep of the visual image across the retina and bring the saccade's target into high-acuity foveal vision. Even before saccade onset, visual processing is selectively prioritized at the saccade target. To determine how this presaccadic attention shift exerts its influence on visual selection, we compare the dynamics of perceptual tuning curves before movement onset at the saccade target and in the opposite hemifield. Participants monitored a 30-Hz sequence of randomly oriented gratings for a target orientation. Combining a reverse correlation technique previously used to study orientation tuning in neurons and general additive mixed modeling, we found that perceptual reports were tuned to the target orientation. The gain of orientation tuning increased markedly within the last 100 ms before saccade onset. In addition, we observed finer orientation tuning right before saccade onset. This increase in gain and tuning occurred at the saccade target location and was not observed at the incongruent location in the opposite hemifield. The present findings suggest, therefore, that presaccadic attention exerts its influence on vision in a spatially and feature-selective manner, enhancing performance and sharpening feature tuning at the future gaze location before the eyes start moving.
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Brewster, Aaron S. [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Sawaya, Michael R. [University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); Rodriguez, Jose [University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); Hattne, Johan; Echols, Nathaniel [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); McFarlane, Heather T. [University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); Cascio, Duilio [University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); Adams, Paul D. [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); University of California, Berkeley, CA 94720 (United States); Eisenberg, David S. [University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); University of California, Los Angeles, CA 90095-1570 (United States); Sauter, Nicholas K., E-mail: nksauter@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)
2015-02-01
Special methods are required to interpret sparse diffraction patterns collected from peptide crystals at X-ray free-electron lasers. Bragg spots can be indexed from composite-image powder rings, with crystal orientations then deduced from a very limited number of spot positions. Still diffraction patterns from peptide nanocrystals with small unit cells are challenging to index using conventional methods owing to the limited number of spots and the lack of crystal orientation information for individual images. New indexing algorithms have been developed as part of the Computational Crystallography Toolbox (cctbx) to overcome these challenges. Accurate unit-cell information derived from an aggregate data set from thousands of diffraction patterns can be used to determine a crystal orientation matrix for individual images with as few as five reflections. These algorithms are potentially applicable not only to amyloid peptides but also to any set of diffraction patterns with sparse properties, such as low-resolution virus structures or high-throughput screening of still images captured by raster-scanning at synchrotron sources. As a proof of concept for this technique, successful integration of X-ray free-electron laser (XFEL) data to 2.5 Å resolution for the amyloid segment GNNQQNY from the Sup35 yeast prion is presented.
International Nuclear Information System (INIS)
Jin Zhao; Zhang Han-Ming; Yan Bin; Li Lei; Wang Lin-Yuan; Cai Ai-Long
2016-01-01
Sparse-view x-ray computed tomography (CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform (NUFFT) is presented in this work along with advanced total variation (TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively. (paper)
Bhowmik, Tanmoy; Liu, Hanli; Ye, Zhou; Oraintara, Soontorn
2016-03-04
Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.
International Nuclear Information System (INIS)
Bergamaschi, Luca; Pini, Giorgio; Sartoretto, Flavio
2003-01-01
The Jacobi-Davidson (JD) algorithm was recently proposed for evaluating a number of the eigenvalues of a matrix. JD goes beyond pure Krylov-space techniques; it cleverly expands its search space, by solving the so-called correction equation, thus in principle providing a more powerful method. Preconditioning the Jacobi-Davidson correction equation is mandatory when large, sparse matrices are analyzed. We considered several preconditioners: Classical block-Jacobi, and IC(0), together with approximate inverse (AINV or FSAI) preconditioners. The rationale for using approximate inverse preconditioners is their high parallelization potential, combined with their efficiency in accelerating the iterative solution of the correction equation. Analysis was carried on the sequential performance of preconditioned JD for the spectral decomposition of large, sparse matrices, which originate in the numerical integration of partial differential equations arising in physical and engineering problems. It was found that JD is highly sensitive to preconditioning, and it can display an irregular convergence behavior. We parallelized JD by data-splitting techniques, combining them with techniques to reduce the amount of communication data. Our own parallel, preconditioned code was executed on a dedicated parallel machine, and we present the results of our experiments. Our JD code provides an appreciable parallel degree of computation. Its performance was also compared with those of PARPACK and parallel DACG
Directory of Open Access Journals (Sweden)
Sapan eAgarwal
2016-01-01
Full Text Available The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational advantages of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an NxN crossbar, these two kernels are at a minimum O(N more energy efficient than a digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1. These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N reduction in energy for the entire algorithm. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.
Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment
Directory of Open Access Journals (Sweden)
Mohammed El-Kebir
2015-11-01
Full Text Available Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. We present a method for global network alignment that is fast and robust and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. We exploit that network alignment is a special case of the well-studied quadratic assignment problem (QAP. We focus on sparse network alignment, where each node can be mapped only to a typically small subset of nodes in the other network. This corresponds to a QAP instance with a symmetric and sparse weight matrix. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the open source software tool Natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative established and recent state-of-the-art methods.
Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.
Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang
2017-07-01
It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.
Image fusion via nonlocal sparse K-SVD dictionary learning.
Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang
2016-03-01
Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.
Low-count PET image restoration using sparse representation
Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli
2018-04-01
In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.
Sparse approximation problem: how rapid simulated annealing succeeds and fails
Obuchi, Tomoyuki; Kabashima, Yoshiyuki
2016-03-01
Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.
Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking
Directory of Open Access Journals (Sweden)
Honghong Yang
2016-01-01
Full Text Available Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
Sparse representation-based color visualization method for hyperspectral imaging
Wang, Li-Guo; Liu, Dan-Feng; Zhao, Liang
2013-06-01
In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.
Selectivity and sparseness in randomly connected balanced networks.
Directory of Open Access Journals (Sweden)
Cengiz Pehlevan
Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.
Domain Tuning of Bilingual Lexicons for MT
National Research Council Canada - National Science Library
Ayan, Necip F; Dorr, Bonnie; Kolak, Okan
2003-01-01
... (in this case, Chinese) to English. Using automatically induced domain-specific, comparable documents and language-independent clustering, we apply domain-tuning techniques to a bilingual lexicon for downstream translation of the input...
Dynamic Performance Tuning Supported by Program Specification
Directory of Open Access Journals (Sweden)
Eduardo César
2002-01-01
Full Text Available Performance analysis and tuning of parallel/distributed applications are very difficult tasks for non-expert programmers. It is necessary to provide tools that automatically carry out these tasks. These can be static tools that carry out the analysis on a post-mortem phase or can tune the application on the fly. Both kind of tools have their target applications. Static automatic analysis tools are suitable for stable application while dynamic tuning tools are more appropriate to applications with dynamic behaviour. In this paper, we describe KappaPi as an example of a static automatic performance analysis tool, and also a general environment based on parallel patterns for developing and dynamically tuning parallel/distributed applications.
Learning and Tuning of Fuzzy Rules
Berenji, Hamid R.
1997-01-01
In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.
Li, Yanming; Nan, Bin; Zhu, Ji
2015-06-01
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.
Speckle suppression via sparse representation for wide-field imaging through turbid media.
Jang, Hwanchol; Yoon, Changhyeong; Chung, Euiheon; Choi, Wonshik; Lee, Heung-No
2014-06-30
Speckle suppression is one of the most important tasks in the image transmission through turbid media. Insufficient speckle suppression requires an additional procedure such as temporal ensemble averaging over multiple exposures. In this paper, we consider the image recovery process based on the so-called transmission matrix (TM) of turbid media for the image transmission through the media. We show that the speckle left unremoved in the TM-based image recovery can be suppressed effectively via sparse representation (SR). SR is a relatively new signal reconstruction framework which works well even for ill-conditioned problems. This is the first study to show the benefit of using the SR as compared to the phase conjugation (PC) a de facto standard method to date for TM-based imaging through turbid media including a live cell through tissue slice.
Adaptive tuning of elasto-plastic damper
DEFF Research Database (Denmark)
Høgsberg, Jan Riess; Krenk, Steen
2007-01-01
Hysteretic dampers are frequency independent, and thereby otentially effective for several structural vibration modes, provided that the inherent amplitude dependence can be controlled. An adaptive tuning procedure is proposed, aiming at elimination of the amplitude dependence by adjusting...... technique, and maximization leads to an amplitude dependent expression for the optimal yield level. The amplitude is predicted from the most recent extremum of the damper response, and simultaneously used to adjust the yield level. Numerical examples demonstrate that the adaptive tuning procedure succeeds...
Possible explanations for fine-tuning of the universe
Kawana, Kiyoharu
2017-04-01
The Froggatt-Nielsen mechanism and the multilocal field theory are interesting and promising candidates for solving the naturalness problem in the universe. These theories are based on the different physical principles: The former assumes the microcanonical partition function ∫𝒟ϕ∏iδ(Si - Ii), and the latter assumes the partition function ∫𝒟ϕexp(iSM) where SM is the multilocal action ∑iciSi +∑i,jci,jSiSj + ⋯. Our main purpose is to show that they are equivalent in the sense that they predict the same fine-tuning mechanism. In order to clarify our argument, we first study (review) the similarity between the Froggatt-Nielsen mechanism and statistical mechanics in detail, and show that the dynamical fine-tuning in the former picture can be understood completely in the same way as the determination of the temperature in the latter picture. Afterward, we discuss the multilocal theory and the equivalence between it and the Froggatt-Nielsen mechanism. Because the multilocal field theory can be obtained from physics at the Planck/String scale, this equivalence indicates that the microcanonical picture can also originate in such physics. As a concrete example, we also review the IIB matrix model as an origin of the multilocal theory.
Behavior of greedy sparse representation algorithms on nested supports
DEFF Research Database (Denmark)
Mailhé, Boris; Sturm, Bob L.; Plumbley, Mark
2013-01-01
We study the links between recovery properties of Orthogonal Matching Pursuit (OMP) and the whole General MP class for sparse signals with nested supports, i.e., supports that share an inclusion relationship. In particular, we show that the support recovery optimality of those algorithms is not l......We study the links between recovery properties of Orthogonal Matching Pursuit (OMP) and the whole General MP class for sparse signals with nested supports, i.e., supports that share an inclusion relationship. In particular, we show that the support recovery optimality of those algorithms...... is not locally nested: there is a dictionary and supports Γ ⊃ Γ′ such that OMP can recover all signals with support Γ, but not all signals with support Γ′. We also show that the support recovery optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s...
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
Desmal, Abdulla
2014-05-04
Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
Desmal, Abdulla
2014-01-06
Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.
Identification of MIMO systems with sparse transfer function coefficients
Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios
2012-12-01
We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.
A General Sparse Tensor Framework for Electronic Structure Theory.
Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin
2017-03-14
Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.
Data analysis in high-dimensional sparse spaces
DEFF Research Database (Denmark)
Clemmensen, Line Katrine Harder
The present thesis considers data analysis of problems with many features in relation to the number of observations (large p, small n problems). The theoretical considerations for such problems are outlined including the curses and blessings of dimensionality, and the importance of dimension...... reduction. In this context the trade off between a rich solution which answers the questions at hand and a simple solution which generalizes to unseen data is described. For all of the given data examples labelled output exists and the analyses are therefore limited to supervised settings. Three novel...... classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...
MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS
Directory of Open Access Journals (Sweden)
Rami Zewail
2017-08-01
Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.
Data-driven initialization of SParSE
Roh, Min K.; Proctor, Joshua L.
2017-07-01
Despite the ever-increasing affordability and availability of high performance computing platforms, computational analysis of stochastic biochemical systems remains an open problem. A recently developed event-based parameter estimation method, the stochastic parameter search for events (SParSE), is able to efficiently sample reaction rate parameter values that confer a user-specified target event with a given probability and error tolerance. Despite the substantial computational savings, the efficiency of SParSE can be further improved by intelligently generating new initial parameter sets based on previously computed trajectories. In this article, we propose a principled method which combines the efficiencies of SParSE with these geometric machine-learning methods to generate new initial parameters based on the previously collected data.
An optimal tuning strategy for tidal turbines
2016-01-01
Tuning wind and tidal turbines is critical to maximizing their power output. Adopting a wind turbine tuning strategy of maximizing the output at any given time is shown to be an extremely poor strategy for large arrays of tidal turbines in channels. This ‘impatient-tuning strategy’ results in far lower power output, much higher structural loads and greater environmental impacts due to flow reduction than an existing ‘patient-tuning strategy’ which maximizes the power output averaged over the tidal cycle. This paper presents a ‘smart patient tuning strategy’, which can increase array output by up to 35% over the existing strategy. This smart strategy forgoes some power generation early in the half tidal cycle in order to allow stronger flows to develop later in the cycle. It extracts enough power from these stronger flows to produce more power from the cycle as a whole than the existing strategy. Surprisingly, the smart strategy can often extract more power without increasing maximum structural loads on the turbines, while also maintaining stronger flows along the channel. This paper also shows that, counterintuitively, for some tuning strategies imposing a cap on turbine power output to limit loads can increase a turbine’s average power output. PMID:27956870
An optimal tuning strategy for tidal turbines.
Vennell, Ross
2016-11-01
Tuning wind and tidal turbines is critical to maximizing their power output. Adopting a wind turbine tuning strategy of maximizing the output at any given time is shown to be an extremely poor strategy for large arrays of tidal turbines in channels. This 'impatient-tuning strategy' results in far lower power output, much higher structural loads and greater environmental impacts due to flow reduction than an existing 'patient-tuning strategy' which maximizes the power output averaged over the tidal cycle. This paper presents a 'smart patient tuning strategy', which can increase array output by up to 35% over the existing strategy. This smart strategy forgoes some power generation early in the half tidal cycle in order to allow stronger flows to develop later in the cycle. It extracts enough power from these stronger flows to produce more power from the cycle as a whole than the existing strategy. Surprisingly, the smart strategy can often extract more power without increasing maximum structural loads on the turbines, while also maintaining stronger flows along the channel. This paper also shows that, counterintuitively, for some tuning strategies imposing a cap on turbine power output to limit loads can increase a turbine's average power output.
A systematic approach for precision electrostatic mode tuning of a MEMS gyroscope
Hu, Z. X.; Gallacher, B. J.; Burdess, J. S.; Bowles, S. R.; Grigg, H. T. D.
2014-12-01
In this paper a systematic approach to precision electrostatic frequency tuning of the operational modes of a MEMS ring vibratory gyroscope is presented. In both rate and rate integrating gyroscopes the frequency split between the two modes of vibration which detect the Coriolis acceleration is one of the principal factors in determining the sensitivity and noise floor of the sensor. In high precision applications in the defence/aerospace sector a frequency split of the order of 10 mHz or less is highly desirable. In the ground-breaking Hemispherical Resonator Gyroscope (HRG) electrostatic tuning has been employed as a tuning mechanism. However, a description of the procedure is not available in the literature. The tuning scheme described here involves assessing mode mistuning by the ratio of the in-phase and quadrature components of the response to an external force that has similar properties to the gyroscopic force resulting from Coriolis action, and choosing the tuning voltages so that independent modification of the elements of the system stiffness matrix can be achieved. Experiments on a commercially available gyroscope with a natural frequency of 14 kHz show that the frequency split can be reduced from 1.5 Hz to 6 mHz. This represents a frequency precision of better than 1 part in a million.
BigSparse: High-performance external graph analytics
Jun, Sang-Woo; Wright, Andy; Zhang, Sizhuo; Xu, Shuotao; Arvind
2017-01-01
We present BigSparse, a fully external graph analytics system that picks up where semi-external systems like FlashGraph and X-Stream, which only store vertex data in memory, left off. BigSparse stores both edge and vertex data in an array of SSDs and avoids random updates to the vertex data, by first logging the vertex updates and then sorting the log to sequentialize accesses to the SSDs. This newly introduced sorting overhead is reduced significantly by interleaving sorting with vertex redu...
Sparse Source EEG Imaging with the Variational Garrote
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai
2013-01-01
EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutio...... as implemented by the Variational Garrote (Kappen, 2011) provides excellent estimates compared with other widely used schemes, is computationally attractive, and by its separation of ’where’ and ’what’ degrees of freedom paves the road for the introduction of genuine prior information....
Airborne LIDAR Points Classification Based on Tensor Sparse Representation
Li, N.; Pfeifer, N.; Liu, C.
2017-09-01
The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.
Adaptive identification of acoustic multichannel systems using sparse representations
Helwani, Karim
2014-01-01
This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals.The book discusses in detail the?derivation of general sparse representations of acoustic MIMO systems?in signal or system dependent transform domains.?Efficient a
A Projected Conjugate Gradient Method for Sparse Minimax Problems
DEFF Research Database (Denmark)
Madsen, Kaj; Jonasson, Kristjan
1993-01-01
as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... defined on a one dimensional path from the current iterate to the boundary of the trust region. Conjugate gradients are used to define this path. One iteration involves one LP subproblem and requires three function evaluations and one gradient evaluation. Promising numerical results obtained...
Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding
Desmal, Abdulla
2015-04-13
A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.
Low-rank and sparse modeling for visual analysis
Fu, Yun
2014-01-01
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic
Link prediction based on non-negative matrix factorization
Chen, Bolun; Li, Fenfen; Hu, Ronglin; Chen, Ling
2017-01-01
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity. PMID:28854195
Link prediction based on non-negative matrix factorization.
Chen, Bolun; Li, Fenfen; Chen, Senbo; Hu, Ronglin; Chen, Ling
2017-01-01
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.
A Matrix Splitting Method for Composite Function Minimization
Yuan, Ganzhao
2016-12-07
Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization and cardinality regularized optimization as special cases. This paper proposes and analyzes a new Matrix Splitting Method (MSM) for minimizing composite functions. It can be viewed as a generalization of the classical Gauss-Seidel method and the Successive Over-Relaxation method for solving linear systems in the literature. Incorporating a new Gaussian elimination procedure, the matrix splitting method achieves state-of-the-art performance. For convex problems, we establish the global convergence, convergence rate, and iteration complexity of MSM, while for non-convex problems, we prove its global convergence. Finally, we validate the performance of our matrix splitting method on two particular applications: nonnegative matrix factorization and cardinality regularized sparse coding. Extensive experiments show that our method outperforms existing composite function minimization techniques in term of both efficiency and efficacy.
Compressive sensing using optimized sensing matrix for face verification
Oey, Endra; Jeffry; Wongso, Kelvin; Tommy
2017-12-01
Biometric appears as one of the solutions which is capable in solving problems that occurred in the usage of password in terms of data access, for example there is possibility in forgetting password and hard to recall various different passwords. With biometrics, physical characteristics of a person can be captured and used in the identification process. In this research, facial biometric is used in the verification process to determine whether the user has the authority to access the data or not. Facial biometric is chosen as its low cost implementation and generate quite accurate result for user identification. Face verification system which is adopted in this research is Compressive Sensing (CS) technique, in which aims to reduce dimension size as well as encrypt data in form of facial test image where the image is represented in sparse signals. Encrypted data can be reconstructed using Sparse Coding algorithm. Two types of Sparse Coding namely Orthogonal Matching Pursuit (OMP) and Iteratively Reweighted Least Squares -ℓp (IRLS-ℓp) will be used for comparison face verification system research. Reconstruction results of sparse signals are then used to find Euclidean norm with the sparse signal of user that has been previously saved in system to determine the validity of the facial test image. Results of system accuracy obtained in this research are 99% in IRLS with time response of face verification for 4.917 seconds and 96.33% in OMP with time response of face verification for 0.4046 seconds with non-optimized sensing matrix, while 99% in IRLS with time response of face verification for 13.4791 seconds and 98.33% for OMP with time response of face verification for 3.1571 seconds with optimized sensing matrix.
Incremental Nonnegative Matrix Factorization for Face Recognition
Directory of Open Access Journals (Sweden)
Wen-Sheng Chen
2008-01-01
Full Text Available Nonnegative matrix factorization (NMF is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix factorization (INMF for face representation and recognition. The proposed INMF approach is based on a novel constraint criterion and our previous block strategy. It thus has some good properties, such as low computational complexity, sparse coefficient matrix. Also, the coefficient column vectors between different classes are orthogonal. In particular, it can be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our INMF approach gives the best performance.
Efficiency criterion for teleportation via channel matrix, measurement matrix and collapsed matrix
Directory of Open Access Journals (Sweden)
Xin-Wei Zha
Full Text Available In this paper, three kinds of coefficient matrixes (channel matrix, measurement matrix, collapsed matrix associated with the pure state for teleportation are presented, the general relation among channel matrix, measurement matrix and collapsed matrix is obtained. In addition, a criterion for judging whether a state can be teleported successfully is given, depending on the relation between the number of parameter of an unknown state and the rank of the collapsed matrix. Keywords: Channel matrix, Measurement matrix, Collapsed matrix, Teleportation
Wilson, Neil E; Burns, Brian L; Iqbal, Zohaib; Thomas, M Albert
2015-11-01
To implement a 5D (three spatial + two spectral) correlated spectroscopic imaging sequence for application to human calf. Nonuniform sampling was applied across the two phase encoded dimensions and the indirect spectral dimension of an echo planar-correlated spectroscopic imaging sequence. Reconstruction was applied that minimized the group sparse mixed ℓ2,1-norm of the data. Multichannel data were compressed using a sensitivity map-based approach with a spatially dependent transform matrix and utilized the self-sparsity of the individual coil images to simplify the reconstruction. Single channel data with 8× and 16× undersampling are shown in the calf of a diabetic patient. A 15-channel scan with 12× undersampling of a healthy volunteer was reconstructed using 5 virtual channels and compared to a fully sampled single slice scan. Group sparse reconstruction faithfully reconstructs the lipid cross peaks much better than ℓ1 minimization. COSY spectra can be acquired over a 3D spatial volume with scan time under 15 min using echo planar readout with highly undersampled data and group sparse reconstruction. © 2015 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Diego Javier Reinoso Chisaguano
2013-01-01
Full Text Available Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM with an electronically steerable passive array radiator (ESPAR antenna receiver can improve the bit error rate performance and obtains additional diversity gain without increasing the number of Radio Frequency (RF front-end circuits. However, due to the large size of the channel matrix, the computational cost required for the detection process using Vertical-Bell Laboratories Layered Space-Time (V-BLAST detection is too high to be implemented. Using the minimum mean square error sparse-sorted QR decomposition (MMSE sparse-SQRD algorithm for the detection process the average computational cost can be considerably reduced but is still higher compared with a conventional MIMOOFDM system without ESPAR antenna receiver. In this paper, we propose to use a low complexity submatrix divided MMSE sparse-SQRD algorithm for the detection process of MIMOOFDM with ESPAR antenna receiver. The computational cost analysis and simulation results show that on average the proposed scheme can further reduce the computational cost and achieve a complexity comparable to the conventional MIMO-OFDM detection schemes.
Tian, Shu; Zhang, Ye; Yan, Yiming; Su, Nan
2016-10-01
Segmentation of real-world remote sensing images is a challenge due to the complex texture information with high heterogeneity. Thus, graph-based image segmentation methods have been attracting great attention in the field of remote sensing. However, most of the traditional graph-based approaches fail to capture the intrinsic structure of the feature space and are sensitive to noises. A ℓ-norm regularization-based graph segmentation method is proposed to segment remote sensing images. First, we use the occlusion of the random texture model (ORTM) to extract the local histogram features. Then, a ℓ-norm regularized low-rank and sparse representation (LNNLRS) is implemented to construct a ℓ-regularized nonnegative low-rank and sparse graph (LNNLRS-graph), by the union of feature subspaces. Moreover, the LNNLRS-graph has a high ability to discriminate the manifold intrinsic structure of highly homogeneous texture information. Meanwhile, the LNNLRS representation takes advantage of the low-rank and sparse characteristics to remove the noises and corrupted data. Last, we introduce the LNNLRS-graph into the graph regularization nonnegative matrix factorization to enhance the segmentation accuracy. The experimental results using remote sensing images show that when compared to five state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.
Extended biorthogonal matrix polynomials
Directory of Open Access Journals (Sweden)
Ayman Shehata
2017-01-01
Full Text Available The pair of biorthogonal matrix polynomials for commutative matrices were first introduced by Varma and Tasdelen in [22]. The main aim of this paper is to extend the properties of the pair of biorthogonal matrix polynomials of Varma and Tasdelen and certain generating matrix functions, finite series, some matrix recurrence relations, several important properties of matrix differential recurrence relations, biorthogonality relations and matrix differential equation for the pair of biorthogonal matrix polynomials J(A,B n (x, k and K(A,B n (x, k are discussed. For the matrix polynomials J(A,B n (x, k, various families of bilinear and bilateral generating matrix functions are constructed in the sequel.
Energy Technology Data Exchange (ETDEWEB)
Grcar, Joseph F.
2002-02-04
A matrix lower bound is defined that generalizes ideas apparently due to S. Banach and J. von Neumann. The matrix lower bound has a natural interpretation in functional analysis, and it satisfies many of the properties that von Neumann stated for it in a restricted case. Applications for the matrix lower bound are demonstrated in several areas. In linear algebra, the matrix lower bound of a full rank matrix equals the distance to the set of rank-deficient matrices. In numerical analysis, the ratio of the matrix norm to the matrix lower bound is a condition number for all consistent systems of linear equations. In optimization theory, the matrix lower bound suggests an identity for a class of min-max problems. In real analysis, a recursive construction that depends on the matrix lower bound shows that the level sets of continuously differential functions lie asymptotically near those of their tangents.
Matrix completion by deep matrix factorization.
Fan, Jicong; Cheng, Jieyu
2018-02-01
Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.
Efficient coordinated recovery of sparse channels in massive MIMO
Masood, Mudassir
2015-01-01
This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.
Sparse Decomposition and Modeling of Anatomical Shape Variation
DEFF Research Database (Denmark)
Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte
2007-01-01
Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counter...
Sparse decomposition and modeling of anatomical shape variation
DEFF Research Database (Denmark)
Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte
2007-01-01
Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counter...
Sparse principal component analysis in medical shape modeling
Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus
2006-03-01
Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.
A Practical View on Tunable Sparse Network Coding
DEFF Research Database (Denmark)
Sørensen, Chres Wiant; Shahbaz Badr, Arash; Cabrera Guerrero, Juan Alberto
2015-01-01
Tunable sparse network coding (TSNC) constitutes a promising concept for trading off computational complexity and delay performance. This paper advocates for the use of judicious feedback as a key not only to make TSNC practical, but also to deliver a highly consistent and controlled delay......) can result in a radical improvement of the complexity-delay trade-off....
Image Super-Resolution via Adaptive Regularization and Sparse Representation.
Cao, Feilong; Cai, Miaomiao; Tan, Yuanpeng; Zhao, Jianwei
2016-07-01
Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation by solving an l0 or l1 norm optimization problem. The l0 optimization is a nonconvex and NP-hard problem, while the l1 optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the l1 regularization method. We also consider the best choice for lp regularization with all p in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter λ adaptively, and we discuss an alternate iteration method for selecting p and λ . We perform experiments, which demonstrates that the proposed regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.
Medical Image Fusion Based on Feature Extraction and Sparse Representation
Directory of Open Access Journals (Sweden)
Yin Fei
2017-01-01
Full Text Available As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM and energy information map (EM as well as structure and energy map (SEM to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.
Discriminative object tracking via sparse representation and online dictionary learning.
Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua
2014-04-01
We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.
The equivalent source method as a sparse signal reconstruction
DEFF Research Database (Denmark)
Fernandez Grande, Efren; Xenaki, Angeliki
2015-01-01
This study proposes an acoustic holography method for sound field reconstruction based on a point source model, which uses the Compressed Sensing (CS) framework to provide a sparse solution. Sparsity implies that the sound field can be represented by a minimal number of non-zero terms, point...
LOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY
Directory of Open Access Journals (Sweden)
YUNHUA XU
2013-01-01
Full Text Available Neuronal ensemble activity codes working memory. In this work, we developed a neuronal ensemble sparse coding method, which can effectively reduce the dimension of the neuronal activity and express neural coding. Multichannel spike trains were recorded in rat prefrontal cortex during a work memory task in Y-maze. As discrete signals, spikes were transferred into continuous signals by estimating entropy. Then the normalized continuous signals were decomposed via non-negative sparse method. The non-negative components were extracted to reconstruct a low-dimensional ensemble, while none of the feature components were missed. The results showed that, for well-trained rats, neuronal ensemble activities in the prefrontal cortex changed dynamically during the working memory task. And the neuronal ensemble is more explicit via using non-negative sparse coding. Our results indicate that the neuronal ensemble sparse coding method can effectively reduce the dimension of neuronal activity and it is a useful tool to express neural coding.
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
DEFF Research Database (Denmark)
Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan
2012-01-01
We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures asym...
Robust visual tracking of infrared object via sparse representation model
Ma, Junkai; Liu, Haibo; Chang, Zheng; Hui, Bin
2014-11-01
In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via l1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust.
Proportionate Minimum Error Entropy Algorithm for Sparse System Identification
Directory of Open Access Journals (Sweden)
Zongze Wu
2015-08-01
Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.
Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu
2014-06-19
Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.
New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging
2015-10-26
statistical analysis of one such method, the so-called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies...called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies the scaling of the phase transition...Demanet Department of Mathematics Massachusetts Institute of Technology. • Grant title: New Algorithms and Sparse Regularization for Synthetic Aperture
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
Fast Estimation of Optimal Sparseness of Music Signals
DEFF Research Database (Denmark)
la Cour-Harbo, Anders
2006-01-01
We want to use a variety of sparseness measured applied to ‘the minimal L1 norm representation' of a music signal in an overcomplete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal L1 norm representation is rather slow, and we...
Non-Cartesian MRI scan time reduction through sparse sampling
Wajer, F.T.A.W.
2001-01-01
Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI signal can not be taken at will, but lie along k-space trajectories determined by the magnetic field gradients. MRI