Search results for "Gauss"
showing 10 items of 701 documents
Conjugacy problem for braid groups and Garside groups
2003
We present a new algorithm to solve the conjugacy problem in Artin braid groups, which is faster than the one presented by Birman, Ko and Lee. This algorithm can be applied not only to braid groups, but to all Garside groups (which include finite type Artin groups and torus knot groups among others).
Dynamic factorial graphical models for dynamic networks
2014
Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molec- ular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. H…
PCA Gaussianization for image processing
2009
The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transforms are often quite restrictive to describe the PDF of natural images. In fact, additional non-linear processing is needed to overcome the limitations of the model. On the contrary, the class of techniques collectively known as projection pursuit, which solve the high-dimensional problem by sequential univariate solutions, may be applied to very general PDFs (e.g. iterative Gaussianization procedures). However, the associated computational cost has prevented their extensive use in image processing. In this work, w…
Representation of Strongly Stationary Stochastic Processes
1993
A generalization of the orthogonality conditions for a stochastic process to represent strongly stationary processes up to a fixed order is presented. The particular case of non-normal delta correlated processes, and the probabilistic characterization of linear systems subjected to strongly stationary stochastic processes are also discussed.
Experiments with an adaptive Bayesian restoration method
1989
Abstract This paper describes a Bayesian restoration method applied to two-dimensional measured images, whose detector response function is not completely known. The response function is assumed Gaussian with standard deviation depending on the estimate of the local density of the image. The convex hull of the K -nearest neighbours ( K NN) of each ‘on’ pixel is used to compute the local density. The method has been tested on ‘sparse’ images, with and without noise background.
Syntheses, Structures, Magnetic Properties, and Density Functional Theory Magneto-Structural Correlations of Bis(μ-phenoxo) and Bis(μ-phenoxo)-μ-acet…
2013
The bis(mu-phenoxo) (FeNiIII)-Ni-II compound [Fe-III(N-3)(2)LNiII(H2O)(CH3CN)](ClO4) (1) and the bis(mu-phenoxo)-mu-acetate/bis(mu-phenoxo)-bis(mu-acetate) (FeNiII)-Ni-III compound {[Fe-III(OAc)LNiII(H2O)(mu-OAc)](0.6)center dot[(FeLNiII)-L-III(mu-OAc)(2)](0.4)}(ClO4)center dot 1.1H(2)O (2) have been synthesized from the Robson type tetraiminodiphenol macrocyclic ligand H2L, which is the [2 + 2] condensation product of 4-methyl-2,6-diformylphenol and 2,2'-dimethy1-1,3-diaminopropane. Single-crystal X-ray structures of both compounds have been determined. The cationic part of the dinuclear compound 2 is a cocrystal of the two species [Fe-III(OAc)LNiII(H2O)(mu-OAc)](+) (2A) and [(FeLNiII)-L-I…
Poor-Contrast Particle Image Processing in Microscale Mixing
2010
Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is…
Linear-response theory for Mukherjee's multireference coupled-cluster method: Excitation energies
2012
The recently presented linear-response function for Mukherjee's multireference coupled-cluster method (Mk-MRCC) [T.-C. Jagau and J. Gauss, J. Chem. Phys. 137, 044115 (2012)] is employed to determine vertical excitation energies within the singles and doubles approximation (Mk-MRCCSD-LR) for ozone as well as for o-benzyne, m-benzyne, and p-benzyne, which display increasing multireference character in their ground states. In order to assess the impact of a multireference ground-state wavefunction on excitation energies, we compare all our results to those obtained at the single-reference coupled-cluster level of theory within the singles and doubles as well as within the singles, doubles, and…
Sparse model-based network inference using Gaussian graphical models
2010
We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood of structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The model is defined on the basis of partial correlations, which results in a specific class precision matrices. A priori L1 penalized maximum likelihood estimation in this class is extremely difficult, because of the above mentioned constraints, the computational complexity of the L1 constraint on the side of the usual positive-definite constraint. The implementation is non-trivial, but we sh…
Vector anisotropic filter for multispectral image denoising
2015
In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.