Transience and recurrence of random walks on percolation clusters in an ultrametric space

We study existence of percolation in the hierarchical group of order $N$, which is an ultrametric space, and transience and recurrence of random walks on the percolation clusters. The connection probability on the hierarchical group for two points separated by distance $k$ is of the form $c_k/N^{k(1+\delta)}, \delta>-1$, with $c_k=C_0+C_1\log k+C_2k^\alpha$, non-negative constants $C_0, C_1, C_2$, and $\alpha>0$. Percolation was proved in Dawson and Gorostiza (2013) for $\delta0$, with $\alpha>2$. In this paper we improve the result for the critical case by showing percolation for $\alpha>0$. We use a renormalization method of the type in the previous paper in a new way which is more intrinsic to the model. The proof involves ultrametric random graphs (described in the Introduction). The results for simple (nearest neighbour) random walks on the percolation clusters are: in the case $\delta<1$ the walk is transient, and in the critical case $\delta=1, C_2>0,\alpha>0$, there exists a critical $\alpha_c\in(0,\infty)$ such that the walk is recurrent for $\alpha<\alpha_c$ and transient for $\alpha>\alpha_c$. The proofs involve graph diameters, path lengths, and electric circuit theory. Some comparisons are made with behaviours of random walks on long-range percolation clusters in the one-dimensional Euclidean lattice.Comment: 27 page

Hierarchical equilibria of branching populations

The objective of this paper is the study of the equilibrium behavior of a population on the hierarchical group $\Omega_N$ consisting of families of individuals undergoing critical branching random walk and in addition these families also develop according to a critical branching process. Strong transience of the random walk guarantees existence of an equilibrium for this two-level branching system. In the limit $N\to\infty$ (called the hierarchical mean field limit), the equilibrium aggregated populations in a nested sequence of balls $B^{(N)}_\ell$ of hierarchical radius $\ell$ converge to a backward Markov chain on $\mathbb{R_+}$. This limiting Markov chain can be explicitly represented in terms of a cascade of subordinators which in turn makes possible a description of the genealogy of the population.Comment: 62 page

Cognitive-Behavioural Therapy

Cognitive-behavioural therapy (CBT) is a generic term, encompassing both: (1) approaches underpinned by an assumption that presenting emotional and behavioural difficulties are cognitively mediated or moderated; and (2) atheoretical bricolages of cognitive and behavioural techniques. This latter category may include effective therapeutic packages (perhaps acting through mechanisms articulated in the first category) but, when theory is tacit, it becomes harder to make analytical generalisations or to extrapolate principles that could guide idiographic formulation and intervention. In contrast, the first category of approaches posits that presenting difficulties may be formulated from an assessment of individual cognitive content (thought processes and underlying beliefs) and implies that we can bring about change in presenting difficulties through change in associated cognitions. Within this chapter, we formulate the case of ‘Molly’, using the theoretical model of CBT articulated by A. T. Beck, to understand the client’s presentation, current difficulties, and potential areas for intervention

The influence of altitude on the anaerobic and aerobic capacities of men in work Final scientific report

Altitude influence on anaerobic and aerobic capacities of working me

Percolation in a hierarchical random graph

We study asymptotic percolation as $N\to \infty$ in an infinite random graph ${\cal G}_N$ embedded in the hierarchical group of order $N$, with connection probabilities depending on an ultrametric distance between vertices. ${\cal G}_N$ is structured as a cascade of finite random subgraphs of (approximate) Erd\"os-Renyi type. We give a criterion for percolation, and show that percolation takes place along giant components of giant components at the previous level in the cascade of subgraphs for all consecutive hierarchical distances. The proof involves a hierarchy of random graphs with vertices having an internal structure and random connection probabilities.Comment: 19 pages and 1 figur