Publications

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.

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.

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, non coalescence for two other, 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.

We study a way of coupling two configurations of the same cellular automaton rule for all elementary cellular automata (ECA). We experimentally show that there are only two possible behaviors: either synchronization for all coupling strength, or a phase transition. This transition is shown to belong to the directed percolation universality class, even for a non chaotic rule and for rules with particles.

The aim of this paper is to experimentally study the combined effect of the introduction of two kinds of structural perturbations to the behavior of cellular automata. We present the results obtained by simultaneously perturbing synchronism and topology of elementary cellular automata. We show that very interesting and different behaviors appear, including phase transitions and non monotonicity (i.e. introduction of both perturbations is less effective than the introduction of only one of them). These results lead us to think that this study is worth to be now developed more accurately.

We have developed a method to obtain robust quantitative bibliometric indicators for several thousand scientists. This allows us to study the dependence of bibliometric indicators (such as number of publications, number of citations, Hirsch index...) on the age, position, etc. of CNRS scientists. Our data suggests that the normalized h index (h divided by the career length) is not constant for scientists with the same productivity but differents ages.

We also compare the predictions of several bibliometric indicators on the promotions of about 600 CNRS researchers. Contrary to previous publications, our study encompasses most disciplines, and shows that no single indicator is the best predictor for all disciplines. Overall, however, the Hirsch index h provides the least bad correlations, followed by the number of papers published. It is important to realize however that even h is able to recover only half of the actual promotions. The number of citations or the mean number of citations per paper are definitely not good predictors of promotion.

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.

• Interview de P. Jensen, RFI, chronique « Microméga », 24/08/2008. Extraits : Télécharger.
• Le progrès, 21/08/2008 : « Quand les cherheurs cherchent sur eux-mêmes ».
• La Croix, 26/08/2008 « La vulgarisation, une affaire de « pro » ».
• Interview de P. Jensen, France-Inter, chronique « La Tête au carré », 25/09/2008. Extraits : Télécharger.
• Intervention de J.B. Rouquier, café des sciences le 16 novembre 2008, pendant la Ville européenne des sciences en ouverture à la fête de la science.

Lyon'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.

Keywords: Complex System; Community bicycle program ; Vélo'v; Cyclostationarity; Auto-Regressive Process.

Community shared bicycle systems, like 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 people’s movements. A careful study relying on nonstationary statistical modeling and data mining is done 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 about social behaviors of the users of this intermodal transportation system, the objective being to help in designing and planning policy in urban transportation.

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.

In many networks, vertices have hidden attributes that are correlated with the network’s topology. For instance, in social networks, people are more likely to be friends if they are demographically similar. In food webs, predators typically eat prey of lower body mass.

We explore a setting in which the network’s topology is known, but these attributes are not. If each vertex can be queried, learning the value of its hidden attributes— but only at some cost—then we need an algorithm which chooses which vertex to query next, in order to learn as much as possible about the attributes of the remaining vertices. We assume that the network is generated by a probabilistic model, but we make no assumptions about the assortativity or disassortativity of the network. We then query the vertex with the largest mutual information between its type and that of the others (a well-known approach in active learning) or with the largest average agreement between two independent samples of the Gibbs distribution which agree on its type.

We test these approaches on two networks with known attributes, the Karate Club network and a food web of species in the Weddell Sea. We find that the average agreement algorithm performs better than mutual information, and that both perform better than simpler heuristics. The algorithms appear to explore the network intelligently, first querying vertices at the centers of communities, and then vertices along the boundaries between communities.

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