Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

La haie vive, une technique durable et peu onéreuse pour protéger les cultures et terroirs ruraux

No Abstrac

From training to artisanal practice : rethinking choreographic relationships in modern dance

In the first part of the twentieth century early modern dancers created both a new art form and the forms of group social organisation that were its condition of possibility. This paper critically examines the balletic and disciplinary ‘training’ model of dancer formation and proposes that the assumption of training in dance can obscure other ways of understanding dance-making relationships and other values in early modern dance. An ‘artisanal’ mode of production and knowledge transmission based on a non-binary relationship between ‘master’ and apprentice and occurring in a quasi-domestic and personalised space of some intimacy is proposed as a more pertinent way to think the enabling conditions of modern dance creation

The Human Ecology and Geography of Burning in an Unstable Savanna Environment

According to new ecological theories, many savannas are inherently in disequilibrium and can flip from tree-dominated to grass-dominated landscapes depending upon the disturbance regime. In particular, a shift in a fire regime to a more frequent and intensive one can radically alter the tree-to-grass ratio in a given savanna. Drawing upon the ecological buffering model we argue that savanna persistence requires a relatively stable fire regime. We hypothesize that anthropogenic burning practices perform this function by producing a regular annual spatiotemporal pattern of fire that is linked to vegetation type. We test this hypothesis using a study of two areas, one in Mali and the other Burkina Faso. We use two sources of satellite data to produce an 11-year time series of the spatiotemporal pattern of fires and an example of the annual burned area pattern these fires produce. We combine the analysis of satellite imagery with interviews of rural inhabitants who set fires to understand the logic underlying the patterns of fire. Analysis of a time series of imagery reveals a strikingly regular annual spatiotemporal pattern of burning for both study areas, which cannot be explained by the regional climatic pattern alone. We conclude that the regularity of the annual fire regime in West Africa is a human-ecological phenomenon closely linked to vegetation type and controlled by people\u27s burning practices. We argue that the anthropogenic burning regime serves to buffer the savanna and maintain its ecological stability

Deep generative models for fast photon shower simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques

Deep generative models for fast photon shower simulation in ATLAS

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques