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In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms which require that the objects we want to cluster have a geometric interpretation. We show how the results of the clustering applied to financial returns could be used to construct defensive portfolios reducing the effect of a simultaneous financial crisis

This is the author's final version of the contribution published as: a b s t r a c t A growing interest of the scientific community towards multidisciplinary applications of laser-driven beams has led to the development of several projects aiming to demonstrate the possible use of these beams for therapeutic purposes. Nevertheless, laser-accelerated particles differ from the conventional beams typically used for multiscipilinary and medical applications, due to the wide energy spread, the angular divergence and the extremely intense pulses. The peculiarities of optically accelerated beams led to develop new strategies and advanced techniques for transport, diagnostics and dosimetry of the accelerated particles. In this framework, the realization of the ELIMED (ELI-Beamlines MEDical and multidisciplinary applications) beamline, developed by INFN-LNS (Catania, Italy) and that will be installed in 2017 as a part of the ELIMAIA beamline at the ELI-Beamlines (Extreme Light Infrastructure Beamlines) facility in Prague, has the aim to investigate the feasibility of using laser-driven ion beams for multidisciplinary applications. In this contribution, an overview of the beamline along with a detailed description of the main transport elements as well as the detectors composing the final section of the beamline will be presented.

In this paper we develop an analytical framework, based on the Che approximation [2], for the analysis of Least Recently Used (LRU) caches operating under the Shot Noise requests Model (SNM). The SNM was recently proposed in [10] to better capture the main characteristics of today Video on Demand (Vod) traffic. In this context, the Che approximation is derived as the application of a mean field principle to the cache eviction time. We investigate the validity of this approximation through an asymptotic analysis of the cache eviction time. Particularly, we provide a large deviation principle and a central limit theorem for the cache eviction time, as the cache size grows large. Furthermore, we obtain a non-asymptotic analytical upper bound on the error entailed by Che's approximation of the hit probability.