Leopoldo Milano

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A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical(More)
Interplanetary turbulence, the best studied case of low frequency plasma turbulence, is the only directly quantified instance of astrophysical turbulence. Here, magnetic field correlation analysis, using for the first time only proper two-point, single time measurements, provides a key step in unraveling the space-time structure of interplanetary(More)
Landslide inventories show that the statistical distribution of the area of recorded events is well described by a power law over a range of decades. To understand these distributions, we consider a cellular automaton to model a time and position dependent factor of safety. The model is able to reproduce the complex structure of landslide distribution, as(More)
We study one-point statistical properties of the induced turbulent electric field for a magnetohydrodynamic (MHD) plasma under the quasinormal approximation. Assuming exact Gaussianity for both the velocity field and the magnetic field, and different degrees of correlations between their Cartesian components, we derive the probability distribution function(More)
This paper discusses the possibility that heating of the solar corona in open field-line regions emanating from coronal holes is due to a nonlinear cascade, driven by low-frequency or quasi-static magnetohydrodynamic fluctuations. Reflection from coronal inhomogeneities plays an important role in sustaining the cascade. Physical and observational(More)
In this paper, a novel information geometric-based variable selection criterion for multi-layer perceptron networks is described. It is based on projections of the Riemannian manifold defined by a multi-layer perceptron network on submanifolds defined by multi-layer perceptron networks with reduced input dimension. We show how the divergence between models(More)
A stochastic background of gravitational waves is expected to arise from a superposition of a large number of unresolved gravitational-wave sources of astrophysical and cosmological origin. It should carry unique signatures from the earliest epochs in the evolution of the Universe, inaccessible to standard astrophysical observations. Direct measurements of(More)
In this paper, a hierarchical Bayesian learning scheme for autoregressive neural network models is shown, which overcomes the problem of identifying the separate linear and nonlinear parts modeled by the network. We show how the identification can be carried out by defining suitable priors on the parameter space, which help the learning algorithms to avoid(More)