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J. M. Miotto

Publications and source records attributed to J. M. Miotto.

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Length scales in Brownian yet non-Gaussian dynamics

According to the classical theory of Brownian motion, the mean squared displacement of diffusing particles evolves linearly with time whereas the distribution of their displacements is Gaussian. However, recent experiments on mesoscopic particle systems have discovered Brownian yet non-Gaussian regimes where diffusion coexists with an exponential tail in the distribution of displacements. Here we show that, contrary to the present theoretical understanding, the length scale $λ$ associated to this exponential distribution does not necessarily scale in a diffusive way. Simulations of Lennard- Jones systems reveal a behavior $λ\sim t^{1/3}$ in three dimensions and $λ\sim t^{1/2}$ in two dimensions. We propose a scaling theory based on the idea of hopping motion to explain this result. In contrast, simulations of a tetrahedral gelling system, where particles interact by a non-isotropic potential, yield a temperature-dependent scaling of $λ$. We interpret this behavior in terms of an intermittent hopping motion. Our findings link the Brownian yet non-Gaussian phenomenon with generic features of glassy dynamics and open new experimental perspectives on the class of molecular and supramolecular systems whose dynamics is ruled by rare events.

cond-mat.soft

Is this scaling nonlinear?

One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g., patents) scale nonlinearly with the population~x of the cities in which they appear, i.e., $y\sim x^β, β\neq 1$. More recently, the generality of this finding has been questioned in studies using new databases and different definitions of city boundaries. In this paper we investigate the existence of nonlinear scaling using a probabilistic framework in which fluctuations are accounted explicitly. In particular, we show that this allows not only to (a) estimate $β$ and confidence intervals, but also to (b) quantify the evidence in favor of $β\neq 1$ and (c) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare $5$ different models to $15$ different datasets and we find that the answers to points (a)-(c) crucially depend on the fluctuations contained in the data, on how they are modeled, and on the fact that the city sizes are heavy-tailed distributed.

physics.soc-ph