Estimation of Regularization Parameters in Multiple-Image Deblurring

We consider the estimation of the regularization parameter for the simultaneous deblurring of multiple noisy images via Tikhonov regularization. We approach the problem in three ways. We first reduce the problem to a single-image deblurring for which the regularization parameter can be estimated through a classic generalized cross-validation (GCV) method. A modification of this function is used for correcting the undersmoothing typical of the original technique. With a second method, we minimize an average least-squares fit to the images and define a new GCV function. In the last approach, we use the classical $GCV$ on a single higher-dimensional image obtained by concatanating all the images into a single vector. With a reliable estimator of the regularization parameter, one can fully exploit the excellent computational characteristics typical of direct deblurring methods, which, especially for large images, makes them competitive with the more flexible but much slower iterative algorithms. The performance of the techniques is analyzed through numerical experiments. We find that under the independent homoscedastic and Gaussian assumptions made on the noise, the three approaches provide almost identical results with the first single image providing the practical advantage that no new software is required and the same image can be used with other deblurring algorithms.Comment: To appear in Astronomy & Astrophysic

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

Splines and Wavelets on Geophysically Relevant Manifolds

Analysis on the unit sphere $\mathbb{S}^{2}$ found many applications in seismology, weather prediction, astrophysics, signal analysis, crystallography, computer vision, computerized tomography, neuroscience, and statistics. In the last two decades, the importance of these and other applications triggered the development of various tools such as splines and wavelet bases suitable for the unit spheres $\mathbb{S}^{2}$, $\>\>\mathbb{S}^{3}$ and the rotation group $SO(3)$. Present paper is a summary of some of results of the author and his collaborators on generalized (average) variational splines and localized frames (wavelets) on compact Riemannian manifolds. The results are illustrated by applications to Radon-type transforms on $\mathbb{S}^{d}$ and $SO(3)$.Comment: The final publication is available at http://www.springerlink.co

Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach

A unified framework for analyzing generalized synchronization in coupled chaotic systems from data is proposed. The key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning. Several successful applications are presented, which show the capability of the kernel-based approach for detecting generalized synchronization. It is also shown that the dynamical change of the coupling coefficient between two chaotic systems can be captured by the proposed approach.Comment: 20 pages, 15 figures. massively revised as a full paper; issues on the choice of parameters by cross validation, tests by surrogated data, etc. are added as well as additional examples and figure

Smoothing spline primordial power spectrum reconstruction

We reconstruct the shape of the primordial power spectrum (PPS) using a smoothing spline. Our adapted smoothing spline technique provides a complementary method to existing efforts to search for smooth features in the PPS, such as a running spectral index. With this technique we find no significant indication with WMAP first-year data that the PPS deviates from Harrison-Zeldovich and no evidence for loss of power on large scales. We also examine the effect on the cosmological parameters of the additional PPS freedom. Smooth variations in the PPS are not significantly degenerate with other cosmological parameters, but the spline reconstruction greatly increases the errors on the optical depth and baryon fraction.Comment: 12 pages, 10 figures. Accepted to PR

Plant segmentation by supervised machine learning methods

High-throughput phenotyping systems provide abundant data for statistical analysis through plant imaging. Before usable data can be obtained, image processing must take place. In this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods. Because obtaining accurate training data is a major obstacle to using supervised learning methods for segmentation, a novel approach to producing accurate labels was developed. We demonstrated that, with careful selection of training data through such an approach, supervised learning methods, and neural networks in particular, can outperform thresholding methods at segmentation

Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation (r¯S) between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate (r¯S = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs

Eating Habits and Lifestyles among a Sample of Obese Working Egyptian Women

BACKGROUND: The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended.AIM: To figure out food habits and different lifestyle pattern among a sample of Egyptian females working at the National Research Centre.METHODS: A cross-sectional, descriptive study, including 138 overweight and obese Egyptian females (BMI ≥ 25 Kg/m2); working at the National Research Centre; was done. A specific questionnaire was used to gather information regarding lifestyle including dietary habits, physical activity and sedentary behaviour. RESULTS: The prevalence of overweight among the studied subjects was 27%, while that of obesity was 38%. Missing and or infrequent intake of breakfast at home, frequent consumption of snacks, low serving per day of fruits and vegetables with frequent consumption of sweets, fried food, eating while watching TV and sedentary behaviour were all predictors of obesity and overweight among the current sample.CONCLUSION: The present study identified several lifestyle factors and improper dietary habits associated with overweight and obesity among Egyptian females. There is a great need to change these habits to avoid the increasing risk of obesity. A national plan of action to overcome obesity is urgently needed to reduce its economic and health burden

Magnetic resonance imaging studies of spontaneous capillary water imbibition in aerated gypsum

International audienceIn this paper we investigate both capillary water imbibition and the sorptivity of aerated gypsum plaster, and how these sorption characteristics are related to the pore structure of the material. These characteristics are examined by monitoring mass change using the conventional gravimetric method and by obtaining water content profiles using nondestructive magnetic resonance imaging (MRI) techniques during capillary imbibition of water. Here, three different gypsum samples are investigated: one non-aerated reference gypsum sample and two aerated gypsum samples produced with different volumetric air fractions. The capillary water absorption into the reference sample follows t 1/2 kinetics (Fickian diffusion), where t is the time of ingress. However, in the aerated gypsum samples there are deviations from t 1/2 kinetics. The MRI results show unambiguously that two wetting fronts advance through the aerated structure ; an observation that cannot be made from the gravimetric data alone. The water content profiles of the aerated gypsum samples are therefore analysed by treating them as the sum of two separate absorption processes using Sharp Front (SF) analysis. The capillary water absorption properties of this material are well described as a parallel combination of fast absorption into fine matrix pores and slow absorption into a modified structure of matrix pores inter-connected to air voids introduced into the slurry by aeration. PACS 47.56.+r. flows through porous media PACS 47.55.nb. capillary and thermocapillary flow

<i>Trypanosoma brucei</i> DHRF-TS revisited:characterisation of a bifunctional and highly unstable recombinant dihydrofolate reductase-thymidylate synthase

<div><p>Bifunctional dihydrofolate reductase–thymidylate synthase (DHFR-TS) is a chemically and genetically validated target in African trypanosomes, causative agents of sleeping sickness in humans and nagana in cattle. Here we report the kinetic properties and sensitivity of recombinant enzyme to a range of lipophilic and classical antifolate drugs. The purified recombinant enzyme, expressed as a fusion protein with elongation factor Ts (Tsf) in ThyA^- <i>Escherichia coli</i>, retains DHFR activity, but lacks any TS activity. TS activity was found to be extremely unstable (half-life of 28 s) following desalting of clarified bacterial lysates to remove small molecules. Stability could be improved 700-fold by inclusion of dUMP, but not by other pyrimidine or purine (deoxy)-nucleosides or nucleotides. Inclusion of dUMP during purification proved insufficient to prevent inactivation during the purification procedure. Methotrexate and trimetrexate were the most potent inhibitors of DHFR (<i>K</i>_i 0.1 and 0.6 nM, respectively) and FdUMP and nolatrexed of TS (<i>K</i>_i 14 and 39 nM, respectively). All inhibitors showed a marked drop-off in potency of 100- to 1,000-fold against trypanosomes grown in low folate medium lacking thymidine. The most potent inhibitors possessed a terminal glutamate moiety suggesting that transport or subsequent retention by polyglutamylation was important for biological activity. Supplementation of culture medium with folate markedly antagonised the potency of these folate-like inhibitors, as did thymidine in the case of the TS inhibitors raltitrexed and pemetrexed.</p></div