1,720,990 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Estimation de Paramètres et Modélisation des Données Radar à Synthèse d'Ouverture à Haute Résolution
No full textThe thesis is approaching this problematic by statistical modeling and Bayesian inference for complex SAR image analysis. The Tikhonov regularization method is applied for image restoration because it allows to reformulate the ill-posed image estimation problem into a well-posed problem by the selection of a convex function. It allows to use the required image and prior models and to find the Maximum A Posteriori (MAP) estimate solution, exploiting the connection to the Bayesian framework. Furthermore it allows the optimization to be performed on complex-valued data and to include the system impulse response which has to be included to correctly model the SAR image. The use of the Rate Distortion for model selection is possible because of the connection between the mutual information and the Occam factor which permits the model selection in the first level of Bayesian inference. The model selection is applied in order to optimize the parameters of the Model Based Despeckling (MBD) algorithm for image denoising and feature extraction : the optimal average analyzing window and the optimal average model order. The method is a global approach and suits in case of large data sets because of its simplicity and fastness. The Rate Distortion based model selection is appropriate for the design of image information mining systems. The Tikhonov regularization shows to be a powerfulmethod for the regularization of complex-valued images. It is recommended in applications where the phase is required, e.g. interferometry, target analysis, because it provides an estimation of the image reflectivity while preserving the phase of the signal.La thèse porte sur l'extraction d'informations et l'amélioration des données RSO de un mètre de résolution visant à fournir des meilleurs descripteurs de contenu pour la compréhension des scènes et la reconnaissance de cibles, pour des produits améliorés radiométriquement et spatialement. Pour atteindre cet objectif, la thèse approche le problème de la modélisation des images RSO et propose une nouvelle solution fondée sur l'estimation du problème inverse pour l'extraction d'information. Le problème de la sélection du modèle est géré par le taux de distorsion, en raison de sa correspondance avec le cadre de l'inférence bayésienne. Nous commençons l'analyse avec l'extension de la famille de champs aléatoires de Gauss-Markov linéaires a des données à valeurs complexes, qui s'applique aux variables aléatoires à valeurs complexes : la distribution normale à plusieurs variables complexes et le modèle paramétriques des champs aléatoires de Gauss-Markov en cas de variables aléatoires correctes et incorrectes. La méthode proposée est une régularisation de Tikhonov dans le domaine complexe. Le speckle est traité comme un processus aléatoire à valeurs réelles. L'approche dans le domaine complexe permet de gérer la formation de l'image cohérente comme information ou comme incertitude dans le cas de structures ou de textures de la scène. Dans le contexte de l'optimisation des paramètres pour l'extraction de caractéristiques, a fenêtre d'analyse optimale (moyenne) et l'ordre optimal (moyen) du processus d'auto-régression sont estimés à l'aide du taux de distorsion. Cela confirme que le taux de distorsion est une bonne méthode basée sur l'entropie pour la sélection de modèle
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
Estimation de Paramètres et Modélisation des Données Radar à Synthèse d'Ouverture à Haute Résolution
No full textThe thesis is approaching this problematic by statistical modeling and Bayesian inference for complex SAR image analysis. The Tikhonov regularization method is applied for image restoration because it allows to reformulate the ill-posed image estimation problem into a well-posed problem by the selection of a convex function. It allows to use the required image and prior models and to find the Maximum A Posteriori (MAP) estimate solution, exploiting the connection to the Bayesian framework. Furthermore it allows the optimization to be performed on complex-valued data and to include the system impulse response which has to be included to correctly model the SAR image. The use of the Rate Distortion for model selection is possible because of the connection between the mutual information and the Occam factor which permits the model selection in the first level of Bayesian inference. The model selection is applied in order to optimize the parameters of the Model Based Despeckling (MBD) algorithm for image denoising and feature extraction : the optimal average analyzing window and the optimal average model order. The method is a global approach and suits in case of large data sets because of its simplicity and fastness. The Rate Distortion based model selection is appropriate for the design of image information mining systems. The Tikhonov regularization shows to be a powerfulmethod for the regularization of complex-valued images. It is recommended in applications where the phase is required, e.g. interferometry, target analysis, because it provides an estimation of the image reflectivity while preserving the phase of the signal.La thèse porte sur l'extraction d'informations et l'amélioration des données RSO de un mètre de résolution visant à fournir des meilleurs descripteurs de contenu pour la compréhension des scènes et la reconnaissance de cibles, pour des produits améliorés radiométriquement et spatialement. Pour atteindre cet objectif, la thèse approche le problème de la modélisation des images RSO et propose une nouvelle solution fondée sur l'estimation du problème inverse pour l'extraction d'information. Le problème de la sélection du modèle est géré par le taux de distorsion, en raison de sa correspondance avec le cadre de l'inférence bayésienne. Nous commençons l'analyse avec l'extension de la famille de champs aléatoires de Gauss-Markov linéaires a des données à valeurs complexes, qui s'applique aux variables aléatoires à valeurs complexes : la distribution normale à plusieurs variables complexes et le modèle paramétriques des champs aléatoires de Gauss-Markov en cas de variables aléatoires correctes et incorrectes. La méthode proposée est une régularisation de Tikhonov dans le domaine complexe. Le speckle est traité comme un processus aléatoire à valeurs réelles. L'approche dans le domaine complexe permet de gérer la formation de l'image cohérente comme information ou comme incertitude dans le cas de structures ou de textures de la scène. Dans le contexte de l'optimisation des paramètres pour l'extraction de caractéristiques, a fenêtre d'analyse optimale (moyenne) et l'ordre optimal (moyen) du processus d'auto-régression sont estimés à l'aide du taux de distorsion. Cela confirme que le taux de distorsion est une bonne méthode basée sur l'entropie pour la sélection de modèle
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