In the last decades companies worldwide are facing a new kind of risk, namely cyber risk, that has emerged as one of the top challenges in risk management. Insurance was only recently applied to cyber world and it is increasingly becoming part of the risk management process, posing many challenges to actuaries. One of the main issues is the lack of data, in particular nancial ones.

In the wide scenario of the optimization techniques, a large number of algorithms are inspired by natural processes, in many different ways. One of the latest is the Imperialist Competitive Algorithm (ICA) Atashpaz-Gargari and Lucas (2007), judged by their authors as very efficient and competitive with other popular optimization algorithms. However, its diffusion is still limited, so that it has not yet been adequately studied.

Particle Swarm Optimization is an evolutionary optimization algorithm, largely studied during the years: analysis of convergence, determination of the optimal coefficients, hybridization of the original algorithm and also the determination of the best relationship structure between the swarm elements (topology) have been investigated largely. Unfortunately, all these studies have been produced separately, and the same coefficients, derived for the original topology of the algorithm, have been always applied.

In this contribution we deal with the problem of learning an undi- rected graph which encodes the conditional dependence relationship be- tween variables of a complex system, given a set of observations of this system.

Il Progetto Finalizzato "Sistemi Informatici e Calcolo Parallelo" è stato negli ultimi anni l'unica iniziativa a livello nazionale che ha avuto come obiettivo fondamentale lo sviluppo di ricerche avanzate e di prodotti innovativi nel campo dell'informatica, con particolare riferimento anche al calcolo parallelo. Per innalzare il livello tecnico-scientifico della comunità nazionale nel settore, il Progetto si è posto come obiettivo essenziale di favorire lo sviluppo di teorie e tecnologie avanzate, in grado di migliorare le conoscenze e le competenze sia metodologiche sia implementative.

A technique for the restoration of low resonance component and high res- onance component of K independently measured signals is presented. The definition of low and high resonance component is given by the Rational Dilatation Wavelet Transform (RADWT), a particular kind of finite frame that provides sparse repre- sentation of functions with different oscillations persistence.

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers.

We examine the constraints on the Yukawa regime from the nonminimally coupled curvature-matter gravity theory arising from deep underwater ocean experiments. We consider the geophysical experiment of Zumberge et al. [Phys. Rev. Lett. 67, 3051 (1991)] for searching deviations of Newton's inverse square law in ocean. In the context of nonminimally coupled curvature-matter theory of gravity the results of Zumberge et al. can be used to obtain an upper bound both on the strength a and range lambda of the Yukawa potential arising from the nonrelativistic limit of the nonminimally coupled theory.

We consider a variational model analyzed in March and Riey (Inverse Probl Imag 11(6): 997-1025, 2017) for simultaneous video inpainting and motion estimation. The model has applications in the field of recovery of missing data in archive film materials. A gray-value video content is reconstructed in a spatiotemporal region where the video data is lost. A variational method for motion compensated video inpainting is used, which is based on the simultaneous estimation of apparent motion in the video data.