We consider the Erdös-Rényi random graph G(n,p) and we analyze the simple irreversible epidemicprocess on the graph, known in the literature as bootstrap percolation. We give a quantitative version ofsome results by Janson et al. (2012), providing a fine asymptotic analysis of the final size A_n of activenodes, under a suitable super-critical regime. More specifically, we establish large deviation principles forthe sequence of random variables n-A_n/f (n) with explicit rate functions and allowing the scaling functionf to vary in the widest possible range.

In recent years, a number of functional inequalities have been derived for Poissonrandom measures, with a wide range of applications. In this paper, we prove thatsuch inequalities can be extended to the setting of marked temporal point processes,under mild assumptions on their Papangelou conditional intensity. First, we derive aPoincaré inequality. Second, we prove two transportation cost inequalities. The firstone refers to functionals of marked point processes with a Papangelou conditionalintensity and is new even in the setting of Poisson random measures. The second onerefers to the law of marked temporal point processes with a Papangelou conditionalintensity, and extends a related inequality which is known to hold on a general Poissonspace. Finally, we provide a variational representation of the Laplace transform offunctionals of marked point processes with a Papangelou conditional intensity. Theproofs make use of an extension of the Clark-Ocone formula to marked temporal pointprocesses. Our results are shown to apply to classes of renewal, nonlinear Hawkesand Cox point processes.

We give a general inequality for the total variation distance between a Poisson distributed random variable and a first order stochastic integral with respect to a point process with stochastic intensity, constructed by embedding in a bivariate Poisson process. We apply this general inequality to first order stochastic integrals with respect to a class of nonlinear Hawkes processes, which is of interest in queueing theory, providing explicit bounds for the Poisson approximation, a quantitative Poisson limit theorem, confidence intervals and asymptotic estimates of the moments.

We give general bounds in the Gaussian and Poisson approximations of innovations (or Skorohod integrals) defined on the space of point processes with Papangelou conditional intensity. We apply the general results to Gibbs point processes with pair potential and determinantal point processes. In particular, we provide explicit error bounds and quantitative limit theorems for stationary, inhibitory and finite range Gibbs point processes with pair potential and beta-Ginibre point processes.

In this paper we analyze least recently used (LRU) caches operating under the shot noise requests model (SNM). The SNM was recently proposed in [S. Traverso et al., ACM Comput. Comm. Rev., 43 (2013), pp. 5-12] to better capture the main characteristics of today's video on demand traffic. We investigate the validity of Che's approximation [H. Che, Y. Tung, and Z. Wang, IEEE J. Selected Areas Commun., 20 (2002), pp. 1305-1314] through an asymptotic analysis of the cache eviction time. In particular, we provide a law of large numbers, a large deviation principle, and a central limit theorem for the cache eviction time, as the cache size grows large. Finally, we derive upper and lower bounds for the "hit" probability in tandem networks of caches under Che's approximation.

We provide a Clark-Ocone formula for square-integrable functionals of a general temporal point process satisfying only a mild moment condition, generalizing known results on the Poisson space. Some classical applications are given, namely a deviation bound and the construction of a hedging portfolio in a pure-jump market model. As a more modern application, we provide a bound on the total variation distance between two temporal point processes, improving in some sense a recent result in this direction.

We give a general Gaussian bound for the first chaos (or innovation) of point processes with stochastic intensity constructed by embedding in a bivariate Poisson process. We apply the general result to nonlinear Hawkes processes, providing quantitative central limit theorems.

Bootstrap percolation is a well-known activation process in a graph, in which a node becomes active when it has at least r active neighbors. Such process, originally studied on regular structures, has been recently investigated also in the context of random graphs, where it can serve as a simple model for a wide variety of cascades, such as the spreading of ideas, trends, viral contents, etc. over large social networks. In particular, it has been shown that in G(n, p) the final active set can exhibit a phase transition for a sub-linear number of seeds. In this paper, we propose a unique framework to study similar sub-linear phase transitions for a much broader class of graph models and epidemic processes. Specifically, we consider i) a generalized version of bootstrap percolation in G(n, p) with random activation thresholds and random node-tonode influences; ii) different random graph models, including graphs with given degree sequence and graphs with community structure (block model). The common thread of our work is to show the surprising sensitivity of the critical seed set size to extreme values of distributions, which makes some systems dramatically vulnerable to large-scale outbreaks. We validate our results running simulation on both synthetic and real graphs.

We survey recent results on determinantal processes

Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian density bounds and tail estimates for the probability law of the solutions of several types of stochastic differential equations, including Stratonovich equations with boundary condition and irregular drifts, and equations driven by fractional Brownian motion. Our arguments are generally simpler than the existing ones in the literature as our approach avoids the use of the inverse of the Ornstein-Uhlenbeck operator.

Based on a new multiplication formula for discrete multiple stochastic integrals with respect to non-symmetric Bernoulli random walks, we extend the results of Nourdin et al. (2010) on the Gaussian approximation of symmetric Rademacher sequences to the setting of possibly non-identically distributed independent Bernoulli sequences. We also provide Poisson approximation results for these sequences, by following the method of Peccati (2011). Our arguments use covariance identities obtained from the Clark-Ocone representation formula in addition to those usually based on the inverse of the Ornstein-Uhlenbeck operator.

We analyze a peer-assisted Video-on-Demand (VoD) system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case, as well as in controlled nonsequential swarms. Our approach is able to capture several stochastic effects related to peer churn, upload bandwidth heterogeneity, and nonstationary traffic conditions, which have not been documented or analyzed before. Finally, we provide important hints for the design of efficient peer-assisted VoD systems under server capacity constraints.

We consider a communication system in which a given digital content has to be delivered sequentially at constant rate to a set of users who asynchronously request it according to a Poisson process. Users can retrieve data: 1) from one or more sources that statically store the entire content; and 2) from users who have previously requested the content, and contribute (for limited time) a random amount of upload bandwidth to the system. We propose a stochastic fluid framework that allows characterizing the aggregate streaming rate necessary at the sources to satisfy all active requests. In particular, we establish the conditions under which the system becomes asymptotically scalable as the number of users grows. Our theoretical results apply to increasingly popular video-on-demand systems exploiting users' cooperation.