A Model for Staircase Formation in Fingering Convection

Fingering convection is a convective instability that occurs in fluids where two buoyancy-changing scalars with different diffusivities have a competing effect on density. The peculiarity of this form of convection is that, although the transport of each individual scalar occurs down-gradient, the net density transport is up-gradient. In a suitable range of non-dimensional parameters, solutions characterized by constant vertical gradients of the horizontally averaged fields may undergo a further instability, which results in the alternation of layers where density is roughly homogeneous with layers where there are steep vertical density gradients, a pattern known as “doubly-diffusive staircases”. This instability has been interpreted in terms of an effective negative diffusivity, but simplistic parameterizations based on this idea, obviously, lead to ill-posed equations. Here we propose a mathematical model that describes the dynamics of the horizontally-averaged scalar fields and the staircase-forming instability. The model allows for unstable constant-gradient solutions, but it is free from the ultraviolet catastrophe that characterizes diffusive processes with a negative diffusivity.     

Ollivier’s Ricci Curvature,Local Clustering and Curvature-Dimension Inequalities on Graphs

In this paper, we explore the relationship between one of the most elementary and important properties of graphs, the presence and relative frequency of triangles, and a combinatorial notion of Ricci curvature. We employ a definition of generalized Ricci curvature proposed by Ollivier in a general framework of Markov processes and metric spaces and applied in graph theory by Lin–Yau. In analogy with curvature notions in Riemannian geometry, we interpret this Ricci curvature as a control on the amount of overlap between neighborhoods of two neighboring vertices. It is therefore naturally related to the presence of triangles containing those vertices, or more precisely, the local clustering coefficient, that is, the relative proportion of connected neighbors among all the neighbors of a vertex. This suggests to derive lower Ricci curvature bounds on graphs in terms of such local clustering coefficients. We also study curvature-dimension inequalities on graphs, building upon previous work of several authors.     

The dual Cheeger constant and spectra of infinite graphs

In this article we study the top of the spectrum of the normalized Laplace operator on infinite graphs. We introduce the dual Cheeger constant and show that it controls the top of the spectrum from above and below in a similar way as the Cheeger constant controls the bottom of the spectrum. Moreover, we show that the dual Cheeger constant at infinity can be used to characterize that the essential spectrum of the normalized Laplace operator shrinks to one point.     

Quantifying structure in networks

We investigate exponential families of random graph distributions as a framework for systematic quantification of structure in networks. In this paper we restrict ourselves to undirected unlabeled graphs. For these graphs, the counts of subgraphs with no more than k links are a sufficient statistics for the exponential families of graphs with interactions between at most k links. In this framework we investigate the dependencies between several observables commonly used to quantify structure in networks, such as the degree distribution, cluster and assortativity coefficients.     

Coherent neural oscillations induced by weak synaptic noise

Nonlinear Dynamics - We analyze the effect of synaptic noise on the dynamics of the FitzHugh–Nagumo (FHN) neuron model. In our deterministic parameter regime, a limit cycle solution cannot...