A stochastic algorithm for probabilistic independent component analysis

The decomposition of a sample of images on a relevant subspace is a recurrent problem in many different fields from Computer Vision to medical image analysis. We propose in this paper a new learning principle and implementation of the generative decomposition model generally known as noisy ICA (for independent component analysis) based on the SAEM algorithm, which is a versatile stochastic approximation of the standard EM algorithm. We demonstrate the applicability of the method on a large range of decomposition models and illustrate the developments with experimental results on various data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS499 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization

Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images.Comment: First published in the Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015) in 2015, published by EURASI

Distributed archive and single access system for accelerometric event data : a NERIES initiative

We developed a common access facility to homogeneously formatted accelerometric event data and to the corresponding sheet of ground motion parameters. This paper is focused on the description of the technical development of the accelerometric data server and the link with the accelerometric data explorer. The server is the third node of the 3-tier architecture of the distributed archive system for accelerometric data. The server is the link between the data users and the accelero- metric data portal. The server follows three main steps: (1) Reading and analysis of the end-user request; (2) Processing and converting data; and (3) Archiving and updating the accelerometric data explorer. This paper presents the description of the data server and the data explorer for accessing data

A Stochastic Algorithm for Probabilistic Independent Component Analysis

The decomposition of a sample of images on a relevant subspace is a recurrent problem in many different fields from Computer Vision to medical image analysis. We propose in this paper a new learning principle and implementation of the generative decomposition model generally known as noisy ICA (for independent component analysis) based on the SAEM algorithm, which is a versatile stochastic approximation of the standard EM algorithm. We demonstrate the applicability of the method on a large range of decomposition models and illustrate the developments with experimental results on various data sets

ERP correlates of word production before and after stroke in an aphasic patient

No abstract available

Control of embryonic Xenopus morphogenesis by a Ral-GDS/Xral branch of the Ras signalling pathway.

Ras proteins mediate biological responses through various effectors and play a key role in relaying the Fibroblast Growth Factor (FGF) mesoderm induction signal during embryogenesis of the frog, Xenopus laevis. One Ras effector pathway involves the activation of the small G protein Ral. In the present study, we have investigated the role of key components in the Ral branch of FGF and Ras signalling during early Xenopus development. Treatment of animal caps with bFGF, which converts prospective ectoderm to mesoderm, activates Xral. The Ras mutant 12V37G, which can bind to Ral-GDS but not Raf, also activates Xral as well as causing developmental defects and cortical F-actin disassembly. A similar phenotype is induced by Ral-GDS itself. FGF-induced expression of several signature mesodermal genes, by contrast, is independent of Xral signalling. This and other data suggest that the RalB branch of Ras and FGF signalling regulates the actin cytoskeleton and morphogenesis in a transcriptionally independent manner. We also find Xral to be specifically activated in the marginal zone of Xenopus embryos, and find that disruption of the Ral pathway in this region prevents closure of the blastopore during gastrulation. We conclude that Ral signalling is autonomously required by mesodermal cells to effect essential morphogenetic changes during Xenopus gastrulation