With the exponential increase in data dimension and complexity, conducting state-of-the-art brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. Here we describe NeuroPype, a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on NiPype and MNE-Python and benefits from standard Python packages such as NumPy and SciPy. The pipeline also incorporates several existing wrappers, such as a Freesurfer Pyhton-wrapper for multi-subject MRI segmentation.

In the present study, we revisit the MEG inverse problem, regularization and depth weighting from a Bayesian hierarchical point of view: the primary unknown is the discretized current density and each dipole has a preferred direction extracted from the MRI of the subject and encoded in the prior distribution.

The establishment of thermal diffusion in an Ar-Kr Lennard-Jones mixture is investigated via dynamical non equilibrium molecular dynamics [G. Ciccotti, G. Jacucci, Phys. Rev. Lett. 35, 789 (1975)]. We observe, in particular, the evolution of the density and temperature fields of the system following the onset of the thermal gradient. In stationary conditions, we also compute the Soret coefficient of the mixture.

The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. We present a methodology able to clearly detect the truly significance of the communities identified by some technique, permitting us to discard those that could be merely the consequence of edge positions in the network. Given a community detection method and a network of interest, our procedure examines the stability of the partition recovered against random perturbations of the original graph structure.

Unlike for systems in equilibrium, a straightforward definition of a metastable set in the non-stationary, non-equilibrium case may only be given case-by-case-and therefore it is not directly useful any more, in particular in cases where the slowest relaxation time scales are comparable to the time scales at which the external field driving the system varies. We generalize the concept of metastability by relying on the theory of coherent sets.

Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method.

We derive and test a formal explicit approximated rule for the reconstruction of a damaged inaccessible portion of the boundary of a thin conductor from thermal data collected on the opposite accessible face.

We raise the analytical knowledge of the eccentricity expansion of the Detweiler-Barack-Sago redshift invariant in a Schwarzschild spacetime up to the 9.5th post-Newtonian order (included) for the e(2) and e(4) contributions, and up to the 4th post-Newtonian order for the higher eccentricity contributions through e(20). We convert this information into an analytical knowledge of the effective-one-body radial potentials (d) over bar (u), p(u) and q(u) through the 9.5th post-Newtonian order. We find that our analytical results are compatible with current corresponding numerical self-force data.

An algorithm for coupling a classical Finite Volume (FV) approach, that discretize the Navier-Stokes equations on a block structured Eulerian grid, with the weakly-compressible SPH is presented. The coupling procedure aims at applying each solver in the region where its intrinsic characteristics can be exploited in the most efficient and accurate way: the FV solver is used to resolve the bulk flow and the wall regions, whereas the SPH solver is implemented in the free surface region to capture details of the front evolution.

Bruno de Finetti è senza dubbio una delle figure più importanti per la storia della Statistica e del Calcolo delle Probabilità in Italia. Però i suoi interessi furono molto più ampi e compresero molti settori della cosiddetta matematica applicata. In particolare giocò un ruolo importante nella nascita del calcolo numerico e dell'informatica in Italia, settori che peraltro costituiscono importanti strumenti per l'applicazione dei suoi studi principali.