We present an efficient variance-reduced particle simulation technique for solving the linearized Boltzmann transport equation in the relaxation-time approximation used for phonon, electron, and radiative transport, as well as for kinetic gas flows. The variance reduction is achieved by simulating only the deviation from equilibrium. We show that in the limit of small deviation from equilibrium...

We propose a fast stochastic Riemannian gradient eigensolver for a real and symmetric matrix, and prove its local, eigengap-dependent and linear convergence. The fast convergence is brought by deploying the variance reduction technique which was originally developed for the Euclidean strongly convex problems. In this paper, this technique is generalized to Riemannian manifolds for solving the g...

We present and describe two new speaker adaptation methods which apply principal component analysis to maximum likelihood linear regression (MLLR). If we apply MLLR after transforming the baseline mean vectors by their eigenvectors, the contributions of these eigenvalues to the variance of the estimates for the MLLR matrix are inversely proportional to their corresponding eigenvalues. In the fi...

McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of Monte Carlo simulations. More recently Owen (1992a) and Tang (1993) generalized LHS using orthogonal arrays. In the Owen's class of generalized LHS, we de ne extended Latin hypercube sampling of strengthm (henceforth denoted as ELHS(m)), such that ELHS(1) reduces to LHS. We rst derive explicit f...

We developed a new scheme for computing ”Greeks” of derivatives by an asymptotic expansion approach. In particular, we derived analytical approximation formulae for Deltas and Vegas of plain vanilla and average European call options under general Markovian processes of underlying asset prices. Moreover, we introduced a new variance reduction method of Monte Carlo simulations based on the asympt...

the specific vulnerability of children and by extension, the need to promote disaster awareness among children as an integral part of disaster risk-reduction strategies is an emergent theme in the disaster management fraternity. the challenge however, is in the design of awarenesspromotion tools that are relevant to and appropriate for the specific learning needs of children. the disaster aware...

The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD. To this end, we propose noise reduction method for Stochastic Variance Reduction (SVRG), called N-SVRG, which uses small batches samples instead of all the average calculation, while performing an incremental update gradient. In each round iteration, batc...

Medical images like ultrasound images are generally corrupted by speckle noise during their acquisition and transmission. The impact of speckle noise reduces the diagnostic value of the medical image modality. Thus, a speckle noise reduction technique is necessary for the suppression of noise and to retain the fine details from the corrupted image. In this article, a new speckle noise reduction...

the aim of this research work is to study the impact of teaching the verbal communication pattern based on the holy quranic verses on the reduction of mother-daughter conflicts. three experimental groups (mothers, daughters and mothers-daughters) and one control group were selected from isfahan, iran using the cluster sampling method. the murray a. straus conflict tactics scale was utilized to ...

A new technique for estimating interframe displacement of small blocks with minimum mean square error is presented. An efficient algorithm for searching the direction of displacement has been described. The results of applying the technique to two sets of images are presented which show 8-10 dB improvement in interframe variance reduction due to motion compensation. The motion compensation is a...