Fitting logistic multilevel models with crossed random effects via Bayesian Integrated Nested Laplace Approximations:a simulation study

Fitting cross-classified multilevel models with binary response is challenging. In this setting a promising method is Bayesian inference through Integrated Nested Laplace Approximations (INLA), which performs well in several latent variable models. We devise a systematic simulation study to assess the performance of INLA with cross-classified binary data under different scenarios defined by the magnitude of the variances of the random effects, the number of observations, the number of clusters, and the degree of cross-classification. In the simulations INLA is systematically compared with the popular method of Maximum Likelihood via Laplace Approximation. By an application to the classical salamander mating data, we compare INLA with the best performing methods. Given the computational speed and the generally good performance, INLA turns out to be a valuable method for fitting logistic cross-classified models

Efficient Management of Transient Station Failures in Linear Radio Communication Networks with Bases

Given a communication network undergoing a transient component failure, a swap algorithm provides the maintenance of the network functionality by means of a minimum number of adjustments. Swap algorithms have recently received growing attention for managing transient failures in classic wired networks, due to their practical usefulness. In this paper, we propose efficient swap algorithms to guarantee survivability in a linear radio communication network undergoing transient station failures. More precisely, given an optimal range assignment for a set of it stations with bases spread on a line, and a bounded number of hops h. we show that, as a consequence of a single non-base station failure, the network survivability can always be guaranteed through a minimum number of adjustments. i.e., by updating either of one station in O(h log n) time, or two stations in O(n) time, or three stations in O(hn) time, all using O(n) space, depending on the structure of the network around the failed station. Furthermore, our swap algorithms identifies the best network reconfiguration in terms of the additional required power