
Learning binary undirected graph in low dimensional regime
Given a random sample drawn from a Multivariate Bernoulli Variable (MBV), we consider the problem of estimating the structure of the undirected graph for which the distribution is pairwise Markov and the parameters' vector of its exponential form. We propose a simple method that provides a closed form estimator of the parameters' vector and through its support also provides an estimate of the undirected graph associated with the MBV distribution. The estimator is proved to be asymptotically consistent but it is feasible only in low-dimensional regimes.
Numerical simulation of a compressible gas flow in porous media bioremendiation
In a subsoil bioremediation intervention air or oxygen is injected in the polluted region and then a model for unsaturated porous media it is required, based on the theory
of the dynamics of multiphase fluids in porous media.
In order to optmize the costs of the intervention it is useful to consider the gas as compressible and this fact introduces nonlinearity in the mathematical model.
The physical problem is described by a system of equations and the unknowns are: pollutant; bacteria concentration; oxygen saturation and oxygen pressure.
The impact of ROI extraction method for MEG connectivity estimation: Practical recommendations for the study of resting state data.
Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis typically requires regions of interest (ROI)-based extraction of measures. M/EEG ROI-derived source activity can be treated in different ways. For instance, it is possible to average each ROI's time series prior to calculating connectivity measures. Alternatively one can compute connectivity maps for each element of the ROI, prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity estimation is still unclear.
Highly automated dipole estimation (HADES)
Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation.
Non-invasive investigation of three paintings attributed to Cavalier d'Arpino by means of ED-XRF, FORS and Multispectral Imaging
The aim of this work was to characterize the palette and painting technique used for the realization of three late sixteenth century paintings from "Galleria dell'Accademia Nazionale di San Luca" in Rome attributed to Cavalier d'Arpino (Giuseppe Cesari), namely "Cattura di Cristo" (Inv. 158), "Autoritratto" (Inv. 546) and "Perseo e Andromeda" (Inv. 221).
Cultural Heritage Preservation through Multispectral Imaging: Preliminary Results from the IMAGO Project
The IMAGO project aims to develop an innovative system that utilizes Multispectral Imaging and
Augmented Reality (AR) techniques for studying and preserving cultural heritage.
A Fast Retrieval Model for Synergistic Inversion of Nadir / Zenith Spectral Radiance Measurements
Starting from 2019, the Italian Space Agency (ASI) is supporting dedicated projects for the development of new methods, tools and competences for the interpretation and the exploitation of the future measurements of the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) experiment.
FORUM will be the ninth Earth Explorer mission of the European Space Agency, scheduled for launch on a polar orbiting satellite in 2027.
An all-densities pedestrian simulator based on a dynamic evaluation of the interpersonal distances
In this paper we deal with pedestrian modeling, aiming at simulating crowd behavior in normal and emergency scenarios, including highly congested mass events. We are specifically concerned with a new agent-based, continuous-in-space, discrete-in-time, nondifferential model, where pedestrians have finite size and are compressible to a certain extent. The model also takes into account the pushing behavior appearing at extremely high densities. The main novelty is that pedestrians are not assumed to generate any kind of "field" which governs the dynamics of the others in the space around them.





