We present a new numerical procedure to assess the plausibility of a subglacial lake in case of relative small/moderate extension and surging temperate icefield. In addition to the flat signal from Ground Penetrating Radar remote survey of the area, early indication of a likely subglacial lake, required icefield data are: top surface elevation and bathymetry, top surface velocity at some points, in-depth temperature and density profiles of upper layer. The procedure is based on a mathematical model of the evolution of dynamics and thermo-dynamics of the icefield and of a subglacial lake.

A systems of self-propelled dumbbells interacting by a Weeks-Chandler-Anderson potential is considered. At sufficiently low temperatures the system phase separates into a dense phase and a gas-like phase. The kinetics of the cluster formation and the growth law for the average cluster size are analyzed.

Aiming at a quantitative understanding of basic aspects of pedestrian dynamics, extensive and high-accuracy measurements of real-life pedestrian trajectories have been performed. A measurement strategy based on Microsoft KinectTM has been used. Specifically, more than 100.000 pedestrians have been tracked while walking along a trafficked corridor at the Eindhoven University of Technology, The Netherlands.

Earth Observation (EO) mining systems aim at supporting efficient access and exploration of large volumes of image products. In this work, we address the problem of content-based image retrieval via example-based queries from Petabyte-scale EO data archives. To this end, we propose an interactive data mining system that relies on distributing unsupervised ingestion processes onto virtual machine instances in elastic, on-demand computing infrastructures that also support archive-scale content indexing via a "big data" analytics cluster-computing framework.

The purpose of this article is to present a methodology to identify the sources of activity in brain networks from functional magnetic resonance imaging (fMRI) data using the multiset canonical correlation analysis algorithm. The aim is to lay the foundations for a screening marker to be used as indicator of mental diseases. Group analysis blind source separation methods have proved reliable to extract the latent sources underlying the brain activities but currently there is no recognized biomarker for mental disorders.