MinicoursesAmine Asselah’s minicourse
Title: Folding a transient random walk.
Abstract: The motivation for the material presented in this course comes from the phenomenon of polymer folding, which we simply think of as (the many ways of) forcing a random walk, say on the threedimensional lattice, to localize in a small region. Thus, we aim at presenting simple and nonetheless useful tools to study the simple random walk. Our plan is
(i) to estimate the probability a random walk visits uniformly some domains, using simple, though original arguments.
(ii) to explain how the Newtonian capacity enters the game and highlight its central role, and properties.
(iii) to analyze some folding occurrences.
We should assume no prerequisite, and should provide all interesting proofs.
Jean Bertoin's minicourse
Title: Noise reinforcement for Lévy processes
Abstract: In a step reinforced random walk, at each integer time and with a fixed probability p in (0,1), the walker repeats one of his previous steps chosen uniformly at random, and with complementary probability 1p, the walker makes an independent new step with a given distribution.
Examples in the literature include the socalled elephant random walk and the shark random swim. We consider here a continuous time analog, when the random walk is replaced by a Lévy process. For subcritical (or admissible) memory parameters p < p_c, where p_c is related to the BlumenthalGetoor index of the Lévy process, we construct a noise reinforced Lévy process. Our main result shows that the stepreinforced random walks corresponding to discrete time skeletons of the Lévy process, converge weakly to the noise reinforced Lévy process as the timemesh goes to 0.
