Partial integro-differential equation (PIDE) formulations are often preferable for pricing options under models with stochastic volatility and jumps. In this talk, we consider the numerical approximation of such models. On one hand, due to the non-local nature of the integral term, we propose to use Implicit-Explicit (IMEX) Runge-Kutta methods for the time integration to solve the integral term explicitly, giving higher order accuracy schemes under weak stability time-step restrictions.

The brain is a connected network, requiring complex-system measures to describe its organization principles. The normalized compression distance (NCD) [1] is a parameter -free, quasi universal similarity measure that estimates the information shared by two signals comparing the compression length of one signal given the other. Here, we aim at testing whether this new measure is a suitable quantifier of the functional connectivity between cortical regions.

Electrocorticography (ECoG) is a neurophysiological modality that measures the distribution of electrical potentials, associated with either spontaneous or evoked neural activity, by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data, however, requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals.

ElectroCOrticoGraphy (ECoG) is an invasive neuroimaging technique that measures electrical potentials produced by brain currents via an electrode grid implanted on the cortical surface. A full interpretation of ECoG data is difficult because it requires solving the inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible of the recorded ECoG signals, which is ill-posed. Only in the last few years novel computational methods to solve this inverse problem have been developed. This study describes a beamformer method for ECoG source modeling.

For clathrate-hydrate polymorphic structure-type (sI versus sII), geometric recognition criteria have been developed and validated. These are applied to the study of the rich interplay and development of both sI and sII motifs in a variety of hydrate-nucleation events for methane and H2S hydrate studied by direct and enhanced-sampling molecular dynamics (MD) simulations.

The nucleation mechanisms of methane hydrates are studied using well-tempered metadynamics and restrained molecular dynamics. The collective variables we used to follow the process are the methane-methane and methane-water coordination numbers, from which we computed the corresponding Landau free energy surface. This surface is characterized by two minima, corresponding to the two-phase methane bubble/water solution and clathrate crystal, and a transition state.

Source modeling of EEG data is an important tool for both neuroscience and clinical applications, such as epilepsy. Despite their simplicity, multiple dipole models remain highly desirable to explain neural sources. However, estimating dipole models from EEG time-series remains a difficult task, mainly due to the ill-posedness of the inverse problem and to the fact that the number of dipoles is usually not known a priori.

PDE models for network flows are used in a number of different applications, including modeling of water channel networks. While the theory and first-order numerics are well developed, there is a lack of high-order schemes. We propose a Runge-Kutta discontinu- ous Galerkin method as an efficient, effective and compact numerical approach for numerical simulations of water flow in open canals. Our numerical tests show the advantages of RKDG over first-order schemes.

It is shown that lattice kinetic theory based on short-lived quasiparticles proves very effective in simulating the complex dynamics of strongly interacting fluids (SIF). In particular, it is pointed out that the shear viscosity of lattice fluids is the sum of two contributions, one due to the usual interactions between particles (collision viscosity) and the other due to the interaction with the discrete lattice (propagation viscosity).