Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning

Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the stochastic behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.Comment: 11 pages, 8 figures, submitted for publicatio

Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

International audienceMixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images

A robust test for nonlinear mixture detection in hyperspectral images

International audienceThis paper studies a pixel by pixel nonlinearity detector for hyperspectral image analysis. The reflectances of linearly mixed pixels are assumed to be a linear combination of known pure spectral components (endmembers) contaminated by additive white Gaussian noise. Nonlinear mixing, however, is not restricted to any prescribed nonlinear mixing model. The mixing coefficients (abundances) satisfy the physically motivated sum-to-one and positivity constraints. The proposed detection strategy considers the distance between an observed pixel and the hyperplane spanned by the endmembers to decide whether that pixel satisfies the linear mixing model (null hypothesis) or results from a more general nonlinear mixture (alternative hypothesis). The distribution of this distance is derived under the two hypotheses. Closed-form expressions are then obtained for the probabilities of false alarm and detection as functions of the test threshold. The proposed detector is compared to another nonlinearity detector recently investigated in the literature through simulations using synthetic data. It is also applied to a real hyperspectral image

Flux Balance Analysis with Objective Function Defined by Proteomics Data-Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

We present a study of the metabolism of the Mycobacterium tuberculosis after exposure to antibiotics using proteomics data and flux balance analysis (FBA). The use of FBA to study prokaryotic organisms is well-established and allows insights into the metabolic pathways chosen by the organisms under different environmental conditions. To apply FBA a specific objective function must be selected that represents the metabolic goal of the organism. FBA estimates the metabolism of the cell by linear programming constrained by the stoichiometry of the reactions in an in silico metabolic model of the organism. It is assumed that the metabolism of the organism works towards the specified objective function. A common objective is the maximization of biomass. However, this goal is not suitable for situations when the bacterium is exposed to antibiotics, as the goal of organisms in these cases is survival and not necessarily optimal growth. In this paper we propose a new approach for defining the FBA objective function in studies when the bacterium is under stress. The function is defined based on protein expression data. The proposed methodology is applied to the case when the bacterium is exposed to the drug mefloquine, but can be easily extended to other organisms, conditions or drugs. We compare our method with an alternative method that uses experimental data for adjusting flux constraints. We perform comparisons in terms of essential enzymes and agreement using enzyme abundances. Results indicate that using proteomics data to define FBA objective functions yields less essential reactions with zero flux and lower error rates in prediction accuracy. With flux variability analysis we observe that overall variability due to alternate optima is reduced with the incorporation of proteomics data. We believe that incorporating proteomics data in the objective function used in FBA may help obtain metabolic flux representations that better support experimentally observed features

Methyl 2-(4,6-dichloro-1,3,5-triazin-2-yl­amino)acetate

The title compound, C6H6Cl2N4O2, was prepared by the nucleophilic substitution of 2,4,6-trichloro-1,3,5-triazine by glycine methyl ester hydro­chloride, and was isolated from the reaction by using flash chromatography. The crystal structure at 150 K reveals the presence two crystallographically independent mol­ecules in the asymmetric unit which differ in the orientation of the pendant methoxy­carbonyl group. Each mol­ecular unit is engaged in strong and highly directional N—H⋯N hydrogen-bonding inter­actions with a symmetry-related mol­ecule, forming supra­molecular dimers which act as the synthons in the crystal packing

Glycine methyl ester hydro­chloride

The title compound [systematic name: (methoxy­carbonyl­meth­yl)ammonium chloride], crystallizes as a salt, C3H8NO2 +·Cl−, with the charged species inter­acting mutually via strong and highly directional N+—H⋯Cl− hydrogen bonds which lead to the formation of a supra­molecular tape running parallel to the c axis. Tapes close pack in the solid state mediated by multipoint recognition synthons based on weak C—H⋯O inter­actions and van der Waals contacts between adjacent methyl groups

El nicho ecológico de los hominidos del Pleistoceno Medio de Atapuerca.

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Peritoneal Dialysis Is an Independent Factor Associated to Lower Intima Media Thickness in Dialysis Patients Free From Previous Cardiovascular Disease

Carotid intima media thickness (cIMT) displays prognostic value as a marker of cardiovascular risk in dialysis patients. However, few data are available regarding the impact of dialysis modality on cIMT. The aim of this study is to determine whether the modality of dialysis influences cIMT values. We compared 237 peritoneal dialysis (PD) and 451 hemodialysis (HD) patients without previous cardiovascular disease included in NEFRONA, a prospective, observational and multicenter study. This cross sectional study included the determination of cIMT in 6 carotid territories by arterial ultrasound. cIMT was determined in territories without atheroma plaque and averaged. A second analysis was performed using all territories, giving a truncated cIMT value of 1,5 mm to territories presenting with atheroma plaque. Age and plaque presence at baseline were the clinical variables more closely associated to cIMT in dialysis patients. The evaluation of the impact of the modality of dialysis on cIMT showed that PD patients had lower cIMT than HD patients, both in territories with no plaques and when using truncated cIMT values. No differences were found between right and left sides, neither in cIMT or truncated cIMT values. Lineal multivariate analysis adjusted by several clinical variables showed a statistically significant association of PD with a lower cIMT (slope -0.036; SD 0.010). These results were also confirmed when truncated cIMT values were used. We conclude that the modality of dialysis has an impact on cITM. HD patients have greater global cIMT than PD patients, and PD is and independent factor associated with a lower cIMT

Spatial extremes of wildfire sizes: Bayesian hieralquical models for extremes

In Portugal, due to the combination of climatological and ecological factors, large wildfires are a constant threat and due to their economic impact, a big policy issue. In order to organize efficient fire fighting capacity and resource management, correct quantification of the risk of large wildfires are needed. In this paper, we quantify the regional risk of large wildfire sizes, by fitting a Generalized Pareto distribution to excesses over a suitably chosen high threshold. Spatio-temporal variations are introduced into the model through model parameters with suitably chosen link functions. The inference on these models are carried using Bayesian Hierarchical Models and Markov chain Monte Carlo methods