Variational models for image segmentation, e.g. Mumford-Shah variational model [47] and Chan-Vese model [21,59], generally involve a regularization term that penalizes the length of the boundaries of the segmentation. In practice often the length term is replaced by a weighted length, i.e., some portions of the set of boundaries are penalized more than other portions, thus unbalancing the geometric term of the segmentation functional. In the present paper we consider a class of variational models in the framework of ?-convergence theory.

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We study the competition between domain coarsening in a symmetric binary mixture below critical temperature and turbulent fluctuations. We find that the coarsening process is arrested in the presence of turbulence. The physics of the process shares remarkable similarities with the behavior of diluted turbulent emulsions and the arrest length scale can be estimated with an argument similar to the one proposed by Kolmogorov and Hinze for the maximal stability diameter of droplets in turbulence.

Intermittency effects are numerically studied in turbulent bubbling Rayleigh-Benard (RB) flow and compared to the standard RB case. The vapour bubbles are modelled with a Euler-Lagrangian scheme and are two-way coupled to the flow and temperature fields, both mechanically and thermally. To quantify the degree of intermittency we use probability density functions, structure functions, extended self-similarity (ESS) and generalized extended self-similarity (GESS) for both temperature and velocity differences.

The problem of the interplay between normal and anomalous scaling in turbulent systems stirred by a random forcing with a power-law spectrum is addressed. We consider both linear and nonlinear systems. As for the linear case, we study passive scalars advected by a 2d velocity field in the inverse cascade regime. For the nonlinear case, we review a recent investigation of 3d Navier–Stokes turbulence, and we present new quantitative results for shell models of turbulence.

We report recent results from a high-resolution numerical study of fluid particles transported by a fully developed turbulent flow. Single-particle trajectories were followed for a time range spanning more than three decades, from less than a tenth of the Kolmogorov timescale up to one large-eddy turnover time. We present some results concerning acceleration statistics and the statistics of trapping by vortex filaments.

Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom.

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data.