Cluster-Aided Mobility Predictions

Predicting the future location of users in wireless net- works has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user. These predictors rely on clustering techniques and extract from the training data similarities among the mobility patterns of the various users to improve the prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility Predictor), a cluster-aided predictor whose design is based on recent non-parametric bayesian statistical tools. CAMP is robust and adaptive in the sense that it exploits similarities in users' mobility only if such similarities are really present in the training data. We analytically prove the consistency of the predictions provided by CAMP, and investigate its performance using two large-scale datasets. CAMP significantly outperforms existing predictors, and in particular those that only exploit individual past trajectories

An {l1,l2,l∞}\{l_1,l_2,l_{\infty}\}-Regularization Approach to High-Dimensional Errors-in-variables Models

Several new estimation methods have been recently proposed for the linear regression model with observation error in the design. Different assumptions on the data generating process have motivated different estimators and analysis. In particular, the literature considered (1) observation errors in the design uniformly bounded by some $\bar \delta$, and (2) zero mean independent observation errors. Under the first assumption, the rates of convergence of the proposed estimators depend explicitly on $\bar \delta$, while the second assumption has been applied when an estimator for the second moment of the observational error is available. This work proposes and studies two new estimators which, compared to other procedures for regression models with errors in the design, exploit an additional $l_{\infty}$-norm regularization. The first estimator is applicable when both (1) and (2) hold but does not require an estimator for the second moment of the observational error. The second estimator is applicable under (2) and requires an estimator for the second moment of the observation error. Importantly, we impose no assumption on the accuracy of this pilot estimator, in contrast to the previously known procedures. As the recent proposals, we allow the number of covariates to be much larger than the sample size. We establish the rates of convergence of the estimators and compare them with the bounds obtained for related estimators in the literature. These comparisons show interesting insights on the interplay of the assumptions and the achievable rates of convergence

Modeling the biocontrol of an invasive plant

The giant bramble, Rubus alceifolius Poir. (Rosaceae) is one of the most invasive plants in la Réunion. In the last decades, mechanical and chemical control have been used to limit its spreading. However, la Réunion being one of the hot spots of endemicity in the world, the use of chemical products is limited. Moreover, parts of the island are completely inaccessible. Thus, control is real!y limited. That is why biological control agents have been considered, like Cibdela janthina Klug (Argidae). This sawfiy is native from Sumatra and can cause severe damages to Rubus alceifolius. After sorne preliminary studies between 2001 and 2007, Cibdela has been released in 2008 in the South-East part of la Réunion. Since then the Cibdela population has spread through the island. The first objective has been reached: Rubus alceifolius has. completely disappeared in sorne places (in particular at low altitudes), while in other places an equilibrium between Rubus and Cibdela has apparently been reached. Altogether, sorne unexpected situations appeared, and that is why new biological and ecological investigations have began two years ago. In this context, mathematical modeling can be a useful tool to formalize al! extensive knowledges about RubusCibdela interactions and to estimate the long term behavior of the bramble, when attacked by the sawfiy, at different altitudes. The aim of this talk is to present sorne preliminary studies about the modeling of these interactions. We will also present sorne theoretical results and illustrate our talk with various simulations. (Résumé d'auteur

Enhanced Recursive Reed-Muller Erasure Decoding

Recent work have shown that Reed-Muller (RM) codes achieve the erasure channel capacity. However, this performance is obtained with maximum-likelihood decoding which can be costly for practical applications. In this paper, we propose an encoding/decoding scheme for Reed-Muller codes on the packet erasure channel based on Plotkin construction. We present several improvements over the generic decoding. They allow, for a light cost, to compete with maximum-likelihood decoding performance, especially on high-rate codes, while significantly outperforming it in terms of speed

Low-rate coding using incremental redundancy for GLDPC codes

In this paper we propose a low-rate coding method, suited for application-layer forward error correction. Depending on channel conditions, the coding scheme we propose can switch from a fixed-rate LDPC code to various low-rate GLDPC codes. The source symbols are first encoded by using a staircase or triangular LDPC code. If additional symbols are needed, the encoder is then switched to the GLDPC mode and extra-repair symbols are produced, on demand. In order to ensure small overheads, we consider irregular distributions of extra-repair symbols optimized by density evolution techniques. We also show that increasing the number of extra-repair symbols improves the successful decoding probability, which becomes very close to 1 for sufficiently many extra-repair symbols