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

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

Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data

Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex’s signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online

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

Les relictes forestières de la falaise de Banfora : un peuplement original au voisinage de Bobo-Dioulasso, Burkina Faso

The Banfora cliff are home to a wealth of flora and fauna. Endemic species are found in its forests, as well as species of outstanding interest such as Albizia dinklagei, Acridocarpus chevalieri or Warneckea fascicularis, which are found nowhere else in Burkina-Faso. This is also the case with some insect species, such as Dicronorhina kouensis or Stephanorhina guttata. However, these forests are under threat, especially around Bobo-Dioulasso where the native fauna has already become partly extinct. Comparisons were made between the Banfora cliff sites and between these and other forest sites in the Bobo-Dioulasso region. Biological diversity is very high among these forests, and the dispersion of species suggests that they are remnants of much larger forest areas. The forest vegetation on the cliffs appears to be of three types: dense dry forest, characterised by Guibourtia copallifera, which may represent the original old-growth forest stock before it was invaded by savannah vegetation; dense humid forest, represented by most of the riparian species, which probably evolved along the river network from the humid forest mass; and relict old-growth mountain flora that depends on the sandstone cliff environment, with Warneckea fascicularis, to which rock species could be attached, as the sole representative. The rodent community in the Banfora cliff forest sites is a mixture of typically forest-dwelling species (particularly Praomys rostratus) in the least disturbed zones and highly anthropophilous species (like Rattus rattus) in the sites most severely degraded by human activities. Most insect species probably originated in the humid forests of Cote d'Ivoire, with some influence from Mali, like Coeliades aeschylus, or Togo, like Dicronorhina kouensis, both of which tend to be found in the Guinean savannah zone close to gallery forests. As these species are neither found in the South, nor respectively in the East and the West of Burkina Faso, the Banfora cliffs may be a meeting point for different types of fauna, hence its outstanding biodiversity interest