An update on lower bounds for the critical values of oriented percolation models

We obtain new lower bounds on the critical points for various models of oriented percolation. The method is to provide a stochastic domination of the percolation processes by multitype Galton-Watson trees. This can be apply to the classical bond and site oriented percolation on Z^2 , but also on other lattices such as inhomogeneous ones, and on dimension three

Construction of a short path in high dimensional First Passage Percolation

For First Passage Percolation in Z^d with large d, we construct a path connecting the origin to {x_1 =1}, whose passage time has optimal order \log d/d. Besides, an improved lower bound for the "diagonal" speed of the cluster combined with a result by Dhar (1988) shows that the limiting shape in FPP with exponential passage times (and thus that of Eden model) is not the euclidian ball in dimension larger than 35

A Branching-selection process related to censored Galton-Walton processes

We obtain the asymptotics for the speed of a particular case of a particle system with branching and selection introduced by B\'erard and Gou\'er\'e (2010). The proof is based on a connection with a supercritical Galton-Watson process censored at a certain level.Comment: 16 pages, 2 figure

Stéphane Beaud [dir.], Gérard Mauger [dir.], Une génération sacrifiée ? Jeunes des classes populaires dans la France désindustrialisée

Cet ouvrage collectif, réalisé sous la direction de Stéphane Beaud et Gérard Mauger, postfacé par Florence Weber, réunit une quinzaine de « jeunes sociologues » (p.15), partageant la même « boite à outils » méthodologique (l’ethnographie) et conceptuelle (la théorie bourdieusienne). Les contributeurs de cet ouvrage dressent un portrait très convainquant des recompositions sociales de la condition des jeunes des classes populaires d’aujourd’hui. Dès l’introduction générale, ils sont présentés ..

Random forest versus logistic regression: A large-scale benchmark experiment

BACKGROUND AND GOAL The Random Forest (RF) algorithm for regression and classification has considerably gained popularity since its introduction in 2001. Meanwhile, it has grown to a standard classification approach competing with logistic regression in many innovation-friendly scientific fields. RESULTS In this context, we present a large scale benchmarking experiment based on 243 real datasets comparing the prediction performance of the original version of RF with default parameters and LR as binary classification tools. Most importantly, the design of our benchmark experiment is inspired from clinical trial methodology, thus avoiding common pitfalls and major sources of biases. CONCLUSION RF performed better than LR according to the considered accuracy measured in approximately 69% of the datasets. The mean difference between RF and LR was 0.029 (95%-CI =0.022,0.038) for the accuracy, 0.041 (95{\%}-CI =0.031,0.053) for the Area Under the Curve, and - 0.027 (95{\%}-CI =-0.034,-0.021) for the Brier score, all measures thus suggesting a significantly better performance of RF. As a side-result of our benchmarking experiment, we observed that the results were noticeably dependent on the inclusion criteria used to select the example datasets, thus emphasizing the importance of clear statements regarding this dataset selection process. We also stress that neutral studies similar to ours, based on a high number of datasets and carefully designed, will be necessary in the future to evaluate further variants, implementations or parameters of random forests which may yield improved accuracy compared to the original version with default values

Random forest versus logistic regression: a large-scale benchmark experiment

Results on partial dependence. Additional file 3 includes a study on interesting extreme cases that allows to gain more insight into the behaviour of LR and RF using partial dependence plots defined in “Partial dependence plots” section. (PDF 256 kb

The anatomy of urban social networks and its implications in the searchability problem

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure