1,721,083 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Détection d'événements et inférence de structure pour des vecteurs sur graphes
No full textCette thèse aborde différents problèmes autour de l'analyse et la modélisation de signaux sur graphes, autrement dit des données vectorielles observées sur des graphes. Nous nous intéressons en particulier à deux tâches spécifique. La première est le problème de détection d'événements, c'est-à-dire la détection d'anomalies ou de ruptures, dans un ensemble de vecteurs sur graphes. La seconde tâche consiste en l'inférence de la structure de graphe sous-jacente aux vecteurs contenus dans un ensemble de données. Dans un premier temps notre travail est orienté vers l'application. Nous proposons une méthode pour détecter des pannes ou des défaillances d'antenne dans un réseau de télécommunication.La méthodologie proposée est conçue pour être efficace pour des réseaux de communication au sens large et tient implicitement compte de la structure sous-jacente des données. Dans un deuxième temps, une nouvelle méthode d'inférence de graphes dans le cadre du Graph Signal Processing est étudiée. Dans ce problème, des notions de régularité local et global, par rapport au graphe sous-jacent, sont imposées aux vecteurs. Enfin, nous proposons de combiner la tâche d'apprentissage des graphes avec le problème de détection de ruptures. Cette fois, un cadre probabiliste est considéré pour modéliser les vecteurs, supposés ainsi être distribués selon un certain champ aléatoire de Markov. Dans notre modélisation, le graphe sous-jacent aux données peut changer dans le temps et un point de rupture est détecté chaque fois qu'il change de manière significative.This thesis addresses different problems around the analysis and the modeling of graph signals i.e. vector data that are observed over graphs. In particular, we are interested in two tasks. The rst one is the problem of event detection, i.e. anomaly or changepoint detection, in a set of graph vectors. The second task concerns the inference of the graph structure underlying the observed graph vectors contained in a data set. At first, our work takes an application oriented aspect in which we propose a method for detecting antenna failures or breakdowns in a telecommunication network. The proposed approach is designed to be eective for communication networks in a broad sense and it implicitly takes into account the underlying graph structure of the data. In a second time, a new method for graph structure inference within the framework of Graph Signal Processing is investigated. In this problem, notions of both local and globalsmoothness, with respect to the underlying graph, are imposed to the vectors.Finally, we propose to combine the graph learning task with the change-point detection problem. This time, a probabilistic framework is considered to model the vectors, assumed to be distributed from a specifc Markov Random Field. In the considered modeling, the graph underlying the data is allowed to evolve in time and a change-point is actually detected whenever this graph changes significantl
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Processus de rang et applications statistiques en grande dimension
No full textCe projet de recherche propose de développer des outils mathématiques et algorithmiques pour étudier et comparer deux jeux de données complexes en grande dimension: vecteurs, signaux multivariés, trajectoires, signaux sur graphes. Il répond à des enjeux fondamentaux liés à la quantification dans les sciences expérimentales, notamment les sciences de la vie et par-là même les neurosciences et ses applications cliniques.Pour se faire, nous proposons une généralisation des statistiques linéaires de rang à l’aide d’outils développés en apprentissage automatique. En effet, et grâce à des techniques d’ordonnancement biparti, nous articulons une étude avancée et non-paramétrique de ces statistiques à deux échantillons statistiques sous l’angle de la théorie de l’apprentissage statistique. Plus précisément, les méthodes d’ordonnancement permettent de pallier l’absence de relation d’orde dans les espaces de grande dimension grâce à l’apprentissage d’une fonction de score. Définie sur l’espace ambiant et à valeur réelle, cette dernière a pour but d’induire un ordre sur les observations multivariées en maximisant la statistique de rang généralisée.Nous proposons une première application dans le cadre des tests d’hypothèses statistiques, en associant décision (acceptation/rejet) de l'hypothèse nulle à l’apprentissage d'un modèle décrivant les données. Nous étudions, plus précisément, les tests d’homogénéité à deux échantillons. Ensuite, deux applications en analyse de données sont introduites et développées en utilisant les statistiques de rang comme critère de performance. Nous les appliquons aux problèmes d’ordonnancement bipartie et d’apprentissage des données extrêmes, ou anomalies, et précisons leurs relations à l’état de l’art. Enfin, dans la volonté de proposer des outils adaptés aux données issues des sciences expérimentales et dans le cadre de l’étude des données biomédicales, nous introduisons une méthode interprétable de comparaison statistique de deux populations cliniques ainsi que d’un modèle stochastique génératif de données longitudinales particulières.This research project aims at developing mathematical and algorithmic tools to study and evaluate the level of similarity between two complex datasets in high-dimension: vectors, multivariate signals, trajectories, signals on graphs. It answers fundamental questions related to quantification in experimental science, particularly in life sciences, neurosciences, and clinical applications.We propose a generalization of linear rank statistics using methods developed in machine learning. Indeed, thanks to bipartite ranking approaches, we articulate an in-depth and nonparametric study of those statistics based on two statistical samples, using statistical learning theory. More precisely, ranking methods circumvent the lack of relation order in high-dimensional spaces by learning a scoring function. The latter, defined on the ambient space and valued in the real line, aims at inducing an order on the multivariate observations by maximizing the generalized rank statistic.We propose the first application in statistical hypothesis testing by combining decision (acceptance/rejection) of the null hypothesis and learning a model describing the data. More specifically, we study two-sample homogeneity tests. Then, two applications in data analysis are introduced and developed using rank statistics as a performance criterion. They are applied to bipartite ranking and anomaly detection problems and specify their relation to state-of-the-art formulations. Finally, and motivated to propose tools adapted to experimental sciences and in the context of biomedical data studies, we introduce an interpretable method for the statistical comparison of two clinical populations and a stochastic generative model of specific longitudinal data
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