Scientists who engage with society perform better academically

Most scientific institutions acknowledge the importance of opening the so-called 'ivory tower' of academic research through popularization, industrial collaboration or teaching. However, little is known about the actual openness of scientific institutions and how their proclaimed priorities translate into concrete measures. This paper gives an idea of some actual practices by studying three key points: the proportion of researchers who are active in wider dissemination, the academic productivity of these scientists, and the institutional recognition of their wider dissemination activities in terms of their careers. We analyze extensive data about the academic production, career recognition and teaching or public/industrial outreach of several thousand of scientists, from many disciplines, from France's Centre National de la Recherche Scientifique. We find that, contrary to what is often suggested, scientists active in wider dissemination are also more active academically. However, their dissemination activities have almost no impact (positive or negative) on their careers

Active Learning for Hidden Attributes in Networks

In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attributes of the other vertices. We assume the network is generated by a stochastic block model, but we make no assumptions about its assortativity or disassortativity. We choose which vertex to query using two methods: 1) maximizing the mutual information between its attributes and those of the others (a well-known approach in active learning) and 2) maximizing the average agreement between two independent samples of the conditional Gibbs distribution. Experimental results show that both these methods do much better than simple heuristics. They also consistently identify certain vertices as important by querying them early on

Coalescing Cellular Automata

We say that a Cellular Automata (CA) is coalescing when its execution on two distinct (random) initial configurations in the same asynchronous mode (the same cells are updated in each configuration at each time step) makes both configurations become identical after a reasonable time. We prove coalescence for two elementary rules and show that there exists infinitely many coalescing CA. We then conduct an experimental study on all elementary CA and show that some rules exhibit a phase transition, which belongs to the universality class of directed percolation

Stochastic Minority on Graphs

Cellular automata have been mainly studied on very regular graphs carrying the cells (like lines or grids) and under synchronous dynamics (all cells update simultaneously). In this paper we study how the asynchronism and the topology of cells act upon the dynamics of the classical Minority rule. Beyond its apparent simplicity, this rule yields complex behaviors which are clearly linked to the structure of the graph carrying the cells

Studying Lyon's Vélo'V: A Statistical Cyclic Model

International audienceLyon's community bicycle program called Vélo'v is a major initiative in shared public transportation, in activity since May 2005. It is studied here at a global level, to assess the evolution with time of the number of hired bikes. Based on the entire Vélo'v data set, up to December 2007, a statistical model is proposed to describe the daily and weekly patterns in a cyclostationary manner, jointly with the non-stationary evolutions over larger time-scales larger. Combining this model with linear statistical regression, a procedure is developed for the prediction of the number of bikes hired per hour. This prediction method involves several explanation factors such as the number of subscribed users, the time in the week, the occurrence of holidays or strikes, and weather parameters (temperature, volume of rain). The conclusion is that, for most days, the observation of the number of actually hired bicyles is satisfyingly explained and predicted by the model proposed here

Cartographie des pratiques du Vélo’v : le regard de physiciens et d’informaticiens

L'étude des données de location du système de vélos en libre service Vélo'v, installé dans Lyon et Villeurbanne, amène à poser des questions de méthodes quand il s'agit, en tant qu'informaticien ou physicien et non de géographe ou de cartographe, de trouver comment faire des cartes représentant ces données. Partant d'une discussion sur les masses de données numérisées accessibles aux scientifiques, tout particulièrement pour des études en sciences sociales, des outils utiles pour manipuler sont évoqués. L'exemple de l'analyse des déplacements en Vélo'v permet d'illustrer pourquoi et comment réaliser des cartes qui décrivent des résultats sur les types de déplacements effectués ou sur la disponibilité des vélos ou des places aux stations. Une conclusion traite des pratiques, en partie nouvelles, liées aux masses de données.From the study of the data of Vélo'v, the Bicycle Sharing System of Lyon and Villeurbanne, several issues are discussed about how researchers from computer science and physics, and not from geography or cartography, were lead to draw maps representing these data. First, the diversity of available digital data is shown, especially for studies related to social or human studies. Some tools to manipulate them are discussed. The example of the Vélo'v data of trips illustrates why and how to propose maps describing features about the movements made with these bikes, or about the availability of bikes or free stands at stations. This practice gives way to a conclusion that suggests a whole new meaning related to the mass of data

A kd-tree algorithm to discover the boundary of a black box hypervolume or how to peel potatoes by recursively cutting them in halves

11 pagesInternational audienceGiven a subset of \R^\ndim of non-zero measure, defined through a blackbox function (an oracle), and assuming some regularity properties on this set, we build an efficient data structure representing this set. The naive approach would consists in sampling every point on a regular grid. As compared to it, our data structure has a complexity close to gaining one dimension both in terms of space and in number of calls to the oracle. This data structure produces a characteristic function (i.e. a function that can be used in lieu of the oracle), allows to measure the volume of the set, and allows to compute the distance to the boundary of the set for any point

Characterizing the speed and paths of shared bicycles in Lyon

Thanks to numerical data gathered by Lyon's shared bicycling system V\'elo'v, we are able to analyze 11.6 millions bicycle trips, leading to the first robust characterization of urban bikers' behaviors. We show that bicycles outstrip cars in downtown Lyon, by combining high speed and short paths.These data also allows us to calculate V\'elo'v fluxes on all streets, pointing to interesting locations for bike paths

Shared Bicycles in a City: A Signal Processing and Data Analysis Perspective

International audienceCommunity shared bicycle systems, such as the Vélo'v program launched in Lyon in May 2005, are public transportation programs that can be studied as a complex system composed of interconnected stations that exchange bicycles. They generate digital footprints that reveal the activity in the city over time and space, making possible a quantitative analysis of movements using bicycles in the city. A careful study relying on nonstationary statistical modeling and data mining allows us to first model the time evolution of the dynamics of movements with Vélo'v, that is mostly cyclostationary over the week with nonstationary evolutions over larger time-scales, and second to disentangle the spatial patterns to understand and visualize the flows of Vélo'v bicycles in the city. This study gives insights on the social behaviors of the users of this intermodal transportation system, the objective being to help in designing and planning policy in urban transportation

Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems

Most current methods for identifying coherent structures in spatially-extended systems rely on prior information about the form which those structures take. Here we present two new approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures. One, local sensitivity filtering, is a modification of the local Lyapunov exponent approach suitable to cellular automata and other discrete spatial systems. The other, local statistical complexity filtering, calculates the amount of information needed for optimal prediction of the system's behavior in the vicinity of a given point. By examining the changing spatiotemporal distributions of these quantities, we can find the coherent structures in a variety of pattern-forming cellular automata, without needing to guess or postulate the form of that structure. We apply both filters to elementary and cyclical cellular automata (ECA and CCA) and find that they readily identify particles, domains and other more complicated structures. We compare the results from ECA with earlier ones based upon the theory of formal languages, and the results from CCA with a more traditional approach based on an order parameter and free energy. While sensitivity and statistical complexity are equally adept at uncovering structure, they are based on different system properties (dynamical and probabilistic, respectively), and provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv requirements; write first author for higher-resolution version