1,721,947 research outputs found
FuNeL
No full text<p>FuNeL is a protocol to infer functional networks from machine learning models. It is a general approach that uses BioHEL, a rule-based evolutionary classifier, to describe the expression samples as a set of rules and then infers interactions between genes that act together within rules to predict the samples class. FuNeL generates co-prediction networks, that capture biological knowledge complementary to that contained in popular similarity-based co-expression networks.</p>
Exposé du colonel Funel secrétaire général
No full textFunel , CLUVIREIP. Exposé du colonel Funel secrétaire général . In: Études Normandes, livraison 70, n°218, 1er trimestre 1969. Activités du Cluvireip. pp. 1-2
Analysis of the Web Graph Aggregated by Host and Pay-Level Domain
No full textIn this paper the web is analyzed as a graph aggregated by host and pay-level domain (PLD). The web graph datasets, publicly available, have been released by the Common Crawl Foundation(http://commoncrawl.org ) and are based on a web crawl performed during the period May-June-July 2017. The host graph has ~ 1.3 billion nodes and ~ 5.3 billion arcs. The PLD graph has ~ 91 million nodes and ~ 1.1 billion arcs. We study the distributions of degree and sizes of strongly/weakly connected components (SCC/WCC) focusing on power laws detection using statistical methods. The statistical plausibility of the power law model is compared with that of several alternative distributions. While there is no evidence of power law tails on host level, they emerge on PLD aggregation for indegree, SCC and WCC size distributions. Finally, we analyze distance-related features by studying the cumulative distributions of the shortest path lengths, and give an estimation of the diameters of the graphs. © Springer Nature Switzerland AG 2019
High performance computing on CRESCO infrastructure: research activities and results 2013
No full textIl volume, in inglese, contiene una raccolta di articoli che illustrano i principali risultati ottenuti nel 2013 utilizzando le infrastrutture di calcolo scientifico CRESCO in campi come la scienza dei materiali, la fluidodinamica computazionale, la ricerca sul clima, la fisica nucleare e la biofisica.
I sistemi CRESCO fanno parte di ENEA-GRID, l'infrastruttura di calcolo ENEA distribuita sui Centri di Portici, Frascati, Casaccia, Brindisi, Bologna e Trisaia. Le risorse di calcolo più importanti sono
ospitate nel Centro di Portici. I servizi di calcolo sono stati forniti a più di un centinaio di utenti, che svolgono attività di ricerca e sviluppo ENEA in molti settori scientifici e tecnologici, e a gruppi di ricerca universitari e industriali. Il rapporto mostra la varietà delle applicazioni del calcolo ad alte prestazioni, una tecnologia importante per la scienza e l'ingegneria
The Graph Structure of the Internet at the Autonomous Systems Level during Ten Years
No full textWe study how the graph structure of the Internet at the Autonomous Systems(AS) level evolved during a decade. For each year of the period 2008-2017 weconsider a snapshot of the AS graph and examine how many features related tostructure, connectivity and centrality changed over time. The analysis of thesemetrics provides topological and data traffic information and allows to clarifysome assumptions about the models concerning the evolution of the Internetgraph structure. We find that the size of the Internet roughly doubled. Theoverall trend of the average connectivity is an increase over time, while thatof the shortest path length is a decrease over time. The internal core of theInternet is composed of a small fraction of big AS and is more stable andconnected that external cores. A hierarchical organization emerges where asmall fraction of big hubs are connected to many regions with high internalcohesiveness, poorly connected among them and containing AS with low and mediumnumber of links. Centrality measurements indicate that the average number ofshortest paths crossing an AS or containing a link between two of themdecreased over time
Causal Paths in Temporal Networks of Face-to-Face Human Interactions
No full textIn a temporal network, causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this paper we study the causal paths structure in temporal networks of face-to-face human interactions in different social contexts
A method to compute the communicability of nodes through causal paths in temporal networks
No full textWe present a method aimed to compute the communicability (broadcast and receive) of nodes through causal paths in temporal networks. The method considers all possible combinations of chronologically ordered products of adjacency matrices of the network snapshots and by means of a damping procedure favors the paths that have high communication efficiency. We apply the method to four real-world networks of face-to-face human contacts and identify the nodes with high communicability. The accuracy of the method is proved by studying the spread of an epidemic in the networks using the susceptible–infected–recovered model. We show that if a node with high broadcast is chosen as the origin of the outbreak of infection then the epidemic spreads early while it is delayed and inhibited if the origin of infection is a node with low broadcast. Receiving nodes can be treated as broadcasters if the arrow of time is reversed
Recherche-développement : Haïti : conclusion
No full textBoutillier J.L., Brossier J., Funel J.M. Recherche-Développement. Haïti. Conclusion. In: Bulletin de l'Association française des anthropologues, n°20, Juin 1985. Recherche et/ou développement, sous la direction de Jean Chiappino, Bertrand Gérard et Josiane Massard. pp. 50-58
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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