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1057 Vemurafenib acts as an aryl hydrocarbon receptor antagonist(Elsevier BV, 20180419)

665 Nail lesions in 30 old inbred mouse strains(Elsevier BV, 20180419)

An A Posteriori Error Estimate for Symplectic Euler Approximation of Optimal Control Problems(20150107)This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns Symplectic Euler solutions of the Hamiltonian system connected with the optimal control problem. The error representation has a leading order term consisting of an error density that is computable from Symplectic Euler solutions. Under an assumption of the pathwise convergence of the approximate dual function as the maximum time step goes to zero, we prove that the remainder is of higher order than the leading error density part in the error representation. With the error representation, it is possible to perform adaptive time stepping. We apply an adaptive algorithm originally developed for ordinary differential equations.

Analysis and Computation of Acoustic and Elastic Wave Equations in Random Media(20140106)We propose stochastic collocation methods for solving the second order acoustic and elastic wave equations in heterogeneous random media and subject to deterministic boundary and initial conditions [1, 4]. We assume that the medium consists of nonoverlapping subdomains with smooth interfaces. In each subdomain, the materials coefficients are smooth and given or approximated by a finite number of random variable. One important example is wave propagation in multilayered media with smooth interfaces. The numerical scheme consists of a finite difference or finite element method in the physical space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space. We provide a rigorous convergence analysis and demonstrate different types of convergence of the probability error with respect to the number of collocation points under some regularity assumptions on the data. In particular, we show that, unlike in elliptic and parabolic problems [2, 3], the solution to hyperbolic problems is not in general analytic with respect to the random variables. Therefore, the rate of convergence is only algebraic. A fast spectral rate of convergence is still possible for some quantities of interest and for the wave solutions with particular types of data. We also show that the semidiscrete solution is analytic with respect to the random variables with the radius of analyticity proportional to the grid/mesh size h. We therefore obtain an exponential rate of convergence which deteriorates as the quantity h p gets smaller, with p representing the polynomial degree in the stochastic space. We have shown that analytical results and numerical examples are consistent and that the stochastic collocation method may be a valid alternative to the more traditional Monte Carlo method. Here we focus on the stochastic acoustic wave equation. Similar results are obtained for stochastic elastic equations.

Applicability of Current Atmospheric Correction Techniques in the Red Sea(20161026)Much of the Red Sea is considered to be a typical oligotrophic sea having very low chlorophylla concentrations. Few existing studies describe the variability of phytoplankton biomass in the Red Sea. This study evaluates the resulting chlorophylla values computed with different chlorophyll algorithms (e.g., Chl_OCI, Chl_Carder, Chl_GSM, and Chl_GIOP) using radiances derived from two different atmospheric correction algorithms (NASA standard and Singh and Shanmugam (2014)). The resulting satellite derived chlorophylla concentrations are compared with in situ chlorophyll values measured using the HighPerformance Liquid Chromatography (HPLC). Statistical analyses are used to assess the performances of algorithms using the in situ measurements obtain in the Red Sea, to evaluate the approach to atmospheric correction and algorithm parameterization.

Basket call option pricing for CCVG using sparse grids(20160106)The use of processes with jumps to overcome the shortcomings of the classical Black and Scholes when modelling stock prices has became very popular. One of the bestknown models is the Common Clock Variance Gamma model (CCVG), introduced by Madan and Seneta in the 1990 [3]. We propose a method to price European basket call options modelled by the CCVG. The method could be extended to other model obtained by the subordination of a multidimensional Brownian motion and to more general options. To simplify the expositions we consider calls under the CCVG.

Batched Triangular DLA for Very Small Matrices on GPUs(20170313)In several scientific applications, like tensor contractions in deep learning computation or data compression in hierarchical low rank matrix approximation, the bulk of computation typically resides in performing thousands of independent dense linear algebra operations on very small matrix sizes (usually less than 100). Batched dense linear algebra kernels are becoming ubiquitous for such scientific computations. Within a single API call, these kernels are capable of simultaneously launching a large number of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the utilization of the underlying hardware.

Bayesian inference and model comparison for metallic fatigue data(20160106)In this work, we present a statistical treatment of stresslife (SN) data drawn from a collection of records of fatigue experiments that were performed on 75ST6 aluminum alloys. Our main objective is to predict the fatigue life of materials by providing a systematic approach to model calibration, model selection and model ranking with reference to SN data. To this purpose, we consider fatiguelimit models and random fatiguelimit models that are specially designed to allow the treatment of the runouts (rightcensored data). We first fit the models to the data by maximum likelihood methods and estimate the quantiles of the life distribution of the alloy specimen. We then compare and rank the models by classical measures of fit based on information criteria. We also consider a Bayesian approach that provides, under the prior distribution of the model parameters selected by the user, their simulationbased posterior distributions.

Bayesian Inference for Linear Parabolic PDEs with Noisy Boundary Conditions(20160106)In this work we develop a hierarchical Bayesian setting to infer unknown parameters in initialboundary value problems (IBVPs) for onedimensional linear parabolic partial differential equations. Noisy boundary data and known initial condition are assumed. We derive the likelihood function associated with the forward problem, given some measurements of the solution field subject to Gaussian noise. Such function is then analytically marginalized using the linearity of the equation. Gaussian priors have been assumed for the timedependent Dirichlet boundary values. Our approach is applied to synthetic data for the onedimensional heat equation model, where the thermal diffusivity is the unknown parameter. We show how to infer the thermal diffusivity parameter when its prior distribution is lognormal or modeled by means of a spacedependent stationary lognormal random field. We use the Laplace method to provide approximated Gaussian posterior distributions for the thermal diffusivity. Expected information gains and predictive posterior densities for observable quantities are numerically estimated for different experimental setups.