Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs

[EN] Recent works in signal processing on graphs have been driven to estimate the precision matrix and to use it as the graph Laplacian matrix. The normalized elements of the precision matrix are the partial correlation coefficients which measure the pairwise conditional linear dependencies of the graph. However, the non-linear dependencies inherent in any non-Gaussian model cannot be captured. We propose in this paper a generalized partial correlation coefficient which is derived by assuming an underlying multivariate Gaussian Mixture Model of the observations. Exact and approximate methods are proposed to estimate the generalized partial correlation coefficients from estimates of the Gaussian Mixture Model parameters. Thus it may find application in any non-Gaussian scenario where the Laplacian matrix is to be learned from training signals. (C) 2018 Elsevier B.V. All rights reserved.This work was supported by Spanish Administration (Ministerio de Economia y Competitividad) and European Union (FEDER) under grant TEC2014-58438-R, and Generalitat Valenciana under grant PROMETEO II/2014/032.Belda, J.; Vergara Domínguez, L.; Salazar Afanador, A.; Safont Armero, G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs. Signal Processing. 148:241-249. https://doi.org/10.1016/j.sigpro.2018.02.017S24124914

An experimental survey of the production of alpha decaying heavy elements in the reactions of 238^{238}U +232^{232}Th at 7.5-6.1 MeV/nucleon

The production of alpha particle decaying heavy nuclei in reactions of 7.5-6.1 MeV/nucleon $^{238}$U +$^{232}$Th has been explored using an in-beam detection array composed of YAP scintillators and gas ionization chamber-Si telescopes. Comparisons of alpha energies and half-lives for the observed products with those of the previously known isotopes and with theoretically predicted values indicate the observation of a number of previously unreported alpha emitters. Alpha particle decay energies reaching as high as 12 MeV are observed. Many of these are expected to be from decay of previously unseen relatively neutron rich products. While the contributions of isomeric states require further exploration and specific isotope identifications need to be made, the production of heavy isotopes with quite high atomic numbers is suggested by the data.Comment: 12 pages, 12 figure

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures