Partially coherent optical beams present phenomena that may not appear in perfectly beams. In this regard, spontaneous parametric downconversion is an intriguing physical process, since the properties of pump laser beam are transferred to quantum down-converted photon pairs. Here, authors study transfer twist phase, a novel property so-called twisted Gaussian Schell model beams, from photons. T...

Some quality characteristics are well defined when treated as response variables and are related to some independent variables. This relationship is called a profile. Parametric models, such as linear models, may be used to model profiles. However, in practical applications due to the complexity of many processes it is not usually possible to model a process using parametric models.In these cas...

The importance of considering statistical power in marine pollution studies is unequivocal. However, the vast majority of ecological literature on power analysis focuses on parametric rather than non-parametric tests. This note describes a Monte Carlo simulation method for estimating the power of non-parametric tests. The method is illustrated using ordinal data.

In this paper, we propose a non-parametric method for state estimation of high-dimensional nonlinear stochastic dynamical systems. We combine diffusion maps, a manifold learning technique, with a linear Kalman filter and with concepts from Koopman operator theory. More concretely, using diffusion maps, we construct data-driven virtual state coordinates, which linearize the system model. Based o...

We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model.We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model.We develop efficient and practical estimatio...

In this paper we have proposed a new test for pixel randomness using non-parametric method in statistics. In order to validate this new non-parametric test we have designed an encryption scheme based on 2D cellular automata. The strength of the designed encryption scheme is first assessed by standard methods for security analysis and the pixel randomness is then determined by the newly proposed...

The classical quadratic loss for the partially linear model (PLM) and the likelihood function for the generalized PLM are not resistant to outliers. This inspires us to propose a class of “robust-Bregman divergence (BD)” estimators of both the parametric and nonparametric components in the general partially linear model (GPLM), which allows the distribution of the response variable to be partia...

We consider the problem of multi-task reinforcement learning (MTRL) in multiple partially observable stochastic environments. We introduce the regionalized policy representation (RPR) to characterize the agent’s behavior in each environment. The RPR is a parametric model of the conditional distribution over current actions given the history of past actions and observations; the agent’s choice o...

This report present a non parametric matching method based on non parametric invariants to geometry and intensity transforms of images. The use of invariant makes it possible to match images under large geometric transforms. The use of non parametric measures makes it robust to partial occlusion. An algorithm to obtain sub-pixel precision is also shown. Results on real images validate the appro...