Jean-Baptiste Rouquier

Characterizing the speed and paths of shared bicycle use in Lyon

Data gathered relating to the Lyon’s shared bicycling system, Velo’v, is used to analyze 11.6 millions bicycle trips in the city. The data show that bicycles now compete with the car in terms of speed in downtown Lyon. It also provides information on cycle flows that can be of use in the planning of dedicated bicycle lanes and other facilities.

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 Frances 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. Copyright , Beech Tree Publishing.

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 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 systems 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.

Active learning for node classification in assortative and disassortative networks

Active learning for networked data that focuses on predicting the labels of other nodes accurately by knowing the labels of a small subset of nodes is attracting more and more researchers because it is very useful especially in cases, where labeled data are expensive to obtain. However, most existing research either only apply to networks with assortative community structure or focus on node attribute data with links or are designed for working in single mode that will work at a higher learning and query cost than batch active learning in general. In view of this, in this paper, we propose a batch mode active learning method which uses information-theoretic techniques and random walk to select which nodes to label. The proposed method requires only network topology as its input, does not need to know the number of blocks in advance, and makes no initial assumptions about how the blocks connect. We test our method on two different types of networks: assortative structure and diassortative structure, and then compare our method with a single mode active learning method that is similar to our method except for working in single mode and several simple batch mode active learning methods using information-theoretic techniques and simple heuristics, such as employing degree or betweenness centrality. The experimental results show that the proposed method in this paper significantly outperforms them.

Complex systems science: Dreams of universality, interdisciplinarity reality

Using a large database (∼215,000 records) of relevant articles, we empirically study the complex systems field and its claims to find universal principles applying to systems in general. The study of references shared by the articles allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory, but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific “trading zones,” i.e., subcommunities that create an interface around specific tools (a DNA microchip) or concepts (a network).

A Dynamical Network View of Lyon’s Vélo’v Shared Bicycle System

Community shared bicycle systems are an instance of public transportation systems that provide digital footprints of all the movements made using this system. The completeness of such dataset allows for their study using a complex system point of view. This chapter discusses how Lyon’s shared bicycle system, called Velo’v, can be seen as a dynamical complex network, and how using community detection methods gives interesting results thanks to the aggregation in space and/or time that communities propose.

Topological phase transition in a network model with preferential attachment and node removal

Abstract Preferential attachment is a popular model of growing networks. We consider a generalized model with random node removal, and a combination of preferential and random attachment. Using a high-degree expansion of the master equation, we identify a topological phase transition depending on the rate of node removal and the relative strength of preferential vs. random attachment, where the degree distribution goes from a power law to one with an exponential tail.

Stochastic minority on graphs

Cellular automata have been mainly studied on very regular graphs carrying the vertices (like lines or grids) and under synchronous dynamics (all vertices update simultaneously). In this paper, we study how the asynchronism and the graph act upon the dynamics of the classical Minority rule. Minority has been well-studied for synchronous updates and is thus a reasonable choice to begin with. Yet, beyond its apparent simplicity, this rule yields complex behaviors when asynchronism is introduced. We investigate the transitory part as well as the asymptotic behavior of the dynamics under full asynchronism (also called sequential: only one random vertex updates at each time step) for several types of graphs. Such a comparative study is a first step in understanding how the asynchronous dynamics is linked to the topology (the graph). Previous analyses on the grid [1,2] have observed that Minority seems to induce fast stabilization. We investigate here this property on arbitrary graphs using tools such as energy, particles and random walks. We show that the worst case convergence time is, in fact, strongly dependent on the topology. In particular, we observe that the case of trees is non trivial.

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.