Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning ...

Given two conformal field theories related to each other by a marginal perturbation, and string field theories constructed around such backgrounds, we show how to construct explicit redefinition of string fields which relate these two string field theories. The analysis is carried out completely for quadratic and cubic terms in the action. Although a general proof of existence of field redefini...

Data protection authorities (DPAs) represent one of the key actors, not only when it comes to the execution of privacy and data protection policies, but also in terms of awareness raising, consultancy and networking. Since they comprise the spearhead of regulators in the field of privacy and data protection regulation, their independence from the private as well as political domain becomes of t...

Data protection authorities (DPAs) represent one of the key actors, not only when it comes to the execution of privacy and data protection policies, but also in terms of awareness raising, consultancy and networking. Since they comprise the spearhead of regulators in the field of privacy and data protection regulation, their independence from the private as well as political domain becomes of t...

We use the mean-field approach to analyze the collective dynamics in macroscopic networks of stochastic Fitzhugh-Nagumo units with delayed couplings. The conditions for validity of the two main approximations behind the model, called the Gaussian approximation and the Quasi-independence approximation, are examined. It is shown that the dynamics of the mean-field model may indicate in a self-con...