1,720,978 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
Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models
Full text linkNon-negative matrix factorization (NMF) has become a popular dimensionality reductiontechnique, and has found applications in many different fields, such as audio signal processing,hyperspectral imaging, or recommender systems. In its simplest form, NMF aims at finding anapproximation of a non-negative data matrix (i.e., with non-negative entries) as the product of twonon-negative matrices, called the factors. One of these two matrices can be interpreted as adictionary of characteristic patterns of the data, and the other one as activation coefficients ofthese patterns. This low-rank approximation is traditionally retrieved by optimizing a measure of fitbetween the data matrix and its approximation. As it turns out, for many choices of measures of fit,the problem can be shown to be equivalent to the joint maximum likelihood estimation of thefactors under a certain statistical model describing the data. This leads us to an alternativeparadigm for NMF, where the learning task revolves around probabilistic models whoseobservation density is parametrized by the product of non-negative factors. This general framework, coined probabilistic NMF, encompasses many well-known latent variable models ofthe literature, such as models for count data. In this thesis, we consider specific probabilistic NMFmodels in which a prior distribution is assumed on the activation coefficients, but the dictionary remains a deterministic variable. The objective is then to maximize the marginal likelihood in thesesemi-Bayesian NMF models, i.e., the integrated joint likelihood over the activation coefficients.This amounts to learning the dictionary only; the activation coefficients may be inferred in asecond step if necessary. We proceed to study in greater depth the properties of this estimation process. In particular, two scenarios are considered. In the first one, we assume the independence of the activation coefficients sample-wise. Previous experimental work showed that dictionarieslearned with this approach exhibited a tendency to automatically regularize the number of components, a favorable property which was left unexplained. In the second one, we lift thisstandard assumption, and consider instead Markov structures to add statistical correlation to themodel, in order to better analyze temporal data
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
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
Contributions à la factorisation en matrices non-négatives probabiliste — Estimation par maximum de vraisemblance marginale et modèles markoviens temporels
No full textLa factorisation en matrices non-négatives (NMF, de l’anglais non-negative matrix factorization) est aujourd’hui l’une des techniques de réduction de la dimensionnalité les plus répandues, dont les domaines d’application recouvrent le traitement du signal audio, l’imagerie hyperspectrale, ou encore les systèmes de recommandation. Sous sa forme la plus simple, la NMF a pour but de trouver une approximation d’une matrice des données non-négative (c’est-à-dire à coefficients positifs ou nuls) par le produit de deux matrices non-négatives, appelées les facteurs. L’une de ces matrices peut être interprétée comme un dictionnaire de motifs caractéristiques des données, et l’autre comme les coefficients d’activation de ces motifs. La recherche de cette approximation de rang faible s’effectue généralement en optimisant une mesure de similarité entre la matrice des données et son approximation. Il s’avère que pour de nombreux choix de mesures de similarité, ce problème est équivalent à l’estimation jointe des facteurs au sens du maximum de vraisemblance sous un certain modèle probabiliste décrivant les données. Cela nous amène à considérer un paradigme alternatif pour la NMF, dans lequel les taches d’apprentissage se portent sur des modèles probabilistes dont la densité d’observation est paramétrisée par le produit des facteurs non-négatifs. Ce cadre général, que nous appelons NMF probabiliste, inclut de nombreux modèles à variables latentes bien connus de la littérature, tels que certains modèles pour des données de compte. Dans cette thèse, nous nous intéressons à des modèles de NMF probabilistes particuliers pour lesquels on suppose une distribution a priori pour les coefficients d’activation, mais pas pour le dictionnaire, qui reste un paramètre déterministe. L'objectif est alors de maximiser la vraisemblance marginale de ces modèles semi-bayésiens, c’est-à-dire la vraisemblance jointe intégrée par rapport aux coefficients d’activation. Cela revient à n’apprendre que le dictionnaire, les coefficients d’activation pouvant être inférés dans un second temps si nécessaire. Nous entreprenons d’approfondir l’étude de ce processus d’estimation. En particulier, deux scénarios sont envisagées. Dans le premier, nous supposons l’indépendance des coefficients d’activation par échantillon. Des résultats expérimentaux antérieurs ont montré que les dictionnaires appris via cette approche avaient tendance à régulariser de manière automatique le nombre de composantes ; une propriété avantageuse qui n’avait pas été expliquée alors. Dans le second, nous levons cette hypothèse habituelle, et considérons des structures de Markov, introduisant ainsi de la corrélation au sein du modèle, en vue d’analyser des séries temporellesNon-negative matrix factorization (NMF) has become a popular dimensionality reductiontechnique, and has found applications in many different fields, such as audio signal processing,hyperspectral imaging, or recommender systems. In its simplest form, NMF aims at finding anapproximation of a non-negative data matrix (i.e., with non-negative entries) as the product of twonon-negative matrices, called the factors. One of these two matrices can be interpreted as adictionary of characteristic patterns of the data, and the other one as activation coefficients ofthese patterns. This low-rank approximation is traditionally retrieved by optimizing a measure of fitbetween the data matrix and its approximation. As it turns out, for many choices of measures of fit,the problem can be shown to be equivalent to the joint maximum likelihood estimation of thefactors under a certain statistical model describing the data. This leads us to an alternativeparadigm for NMF, where the learning task revolves around probabilistic models whoseobservation density is parametrized by the product of non-negative factors. This general framework, coined probabilistic NMF, encompasses many well-known latent variable models ofthe literature, such as models for count data. In this thesis, we consider specific probabilistic NMFmodels in which a prior distribution is assumed on the activation coefficients, but the dictionary remains a deterministic variable. The objective is then to maximize the marginal likelihood in thesesemi-Bayesian NMF models, i.e., the integrated joint likelihood over the activation coefficients.This amounts to learning the dictionary only; the activation coefficients may be inferred in asecond step if necessary. We proceed to study in greater depth the properties of this estimation process. In particular, two scenarios are considered. In the first one, we assume the independence of the activation coefficients sample-wise. Previous experimental work showed that dictionarieslearned with this approach exhibited a tendency to automatically regularize the number of components, a favorable property which was left unexplained. In the second one, we lift thisstandard assumption, and consider instead Markov structures to add statistical correlation to themodel, in order to better analyze temporal data
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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