We describe a solution for fast indexing and searching within large heterogeneous data sets whose main purpose is to support investigators that need to analyze forensic disk images originated by seizures or created from bodies of evidence. Our approach is based on a combination of techniques aimed at improving efficiency and reliability of the indexing process.We do not rely on existing frameworks like Hadoop but borrow concepts from different contexts including High Performance Computing and Database management.

We present and compare the performances of two many-core architectures: the Nvidia Kepler and the Intel MIC both in a single system and in cluster configuration for the simulation of spin systems. As a benchmark we consider the time required to update a single spin of the 3D Heisenberg spin glass model by using the Over-relaxation algorithm. We present data also for a traditional high-end multi-core architecture: the Intel Sandy Bridge.

This paper investigates the problem of quantifying longevity risk in a quantile perspective. In this field, the idea of deepening the expected changes of future mortality rates over a single year is gaining. In the following the authors propose an approach which combines a stochastic model for the evolution of mortality rates and a quantile analysis of the mortality distribution in order to capture the trend component of longevity. An ex post analysis is proposed, relying on the past mortality experience of the Italian male population measured in the period of 1954-2008.

In this paper we propose a new modeling tech- nique for vehicular traffic flow, designed for capturing at a macroscopic level some effects, due to the microscopic granularity of the flow of cars, which would be lost with a purely continuous approach. The starting point is a multiscale method for pedestrian modeling, recently introduced in [1], in which measure-theoretic tools are used to manage the microscopic and the macroscopic scales under a unique framework.