This article describes four software techniques to enhance the overall quality of multimodal processing software and to include concurrency and variance due to individual characteristics and cultural context. First, the processing steps are decentralized and distributed using the actor model. Second, functor objects decouple domainand applicationspecific operations from universal processing met...

In control systems theory, the Markov decision process (MDP) is a widely used optimization model involving selection of the optimal action in each state visited by a discrete-event system driven by Markov chains. The classical MDP model is suitable for an agent/decision-maker interested in maximizing expected revenues, but does not account for minimizing variability in the revenues. An MDP mode...

1. Introduction. The Monte Carlo method is widely believed to be the most accurate method for solving problems in radiation transport. Unfortunately, due to its very nature—following individual particle histories—certain classes of problems are particularly challenging for the method. One such class of problems consist of so-called deep penetration shielding problems. Because the purpose of a s...

Despite recent advances in Monte Carlo rendering techniques, dense, high-albedo participating media such as wax or skin still remain a difficult problem. In such media, random walks tend to become very long, but may still lead to a large contribution to the image. The Dwivedi sampling scheme, which is based on zero variance random walks, biases the sampling probability distributions to exit the...

In this paper, we examine the CE method in the broad context of Monte Carlo Optimization (MCO) [Ermoliev and Norkin, 1998, Robert and Casella, 2004] and Parametric Learning (PL), a type of machine learning. A well-known overarching principle used to improve the performance of many PL algorithms is the bias-variance tradeoff [Wolpert, 1997]. This tradeoff has been used to improve PL algorithms r...

With increasing signal frequencies inductive effects of metallic interconnections in all kinds of packages become more and more important for the electrical performance of a circuit. During the design process it is therefore of interest to have a stable, fast and accurate simulation tool which is capable of extracting the inductance of large and complicated geometries. This work presents a new ...

The problem of how to train variance parameters on scarce data is addressed in the context of text-dependent, HMM-based, automatic speaker verification. Three variations of variance flooring is explored as a means to prevent over-fitting. With the best performing one, the floor to a variance vector of a client model is proportional to the corresponding variance vector in a non-client multi-spea...

Computation of distance to fault on a transmission line is affected by: assumptions in modelling, factors reflecting system operating conditions, and measurement system characteristics. All these factors are subject to many sources of uncertainty including parameter setting errors, measurement errors, as well as absence of information and incomplete modelling of a system under fault conditions....

This paper proposes a combination of source-normalized weighted linear discriminant analysis (SN-WLDA) and short utterance variance (SUV) PLDA modelling to improve the short utterance PLDA speaker verification. As short-length utterance i-vectors vary with the speaker, session variations and phonetic content of the utterance (utterance variation), a combined approach of SN-WLDA projection and S...

In this paper we discuss the importance sampling Monte Carlo methods for pricing options. The classical importance sampling method is used to eliminate the variance caused by the linear part of the logarithmic function of payoff. The variance caused by the quadratic part is reduced by stratified sampling. We eliminate both kinds of variances just by importance sampling. The corresponding space ...