1,721,176 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
New iterative approaches with theoretical guarantees for unsupervised domain adaptation
No full textCes dernières années, l’intérêt pour l’apprentissage automatique n’a cessé d’augmenter dans des domaines aussi variés que la reconnaissance d’images ou l’analyse de données médicales. Cependant, une limitation du cadre classique PAC a récemment été mise en avant. Elle a entraîné l’émergence d’un nouvel axe de recherche : l’Adaptation de Domaine, dans lequel on considère que les données d’apprentissage proviennent d’une distribution (dite source) différente de celle (dite cible) dont sont issues les données de test. Les premiers travaux théoriques effectués ont débouché sur la conclusion selon laquelle une bonne performance sur le test peut s’obtenir en minimisant à la fois l’erreur sur le domaine source et un terme de divergence entre les deux distributions. Trois grandes catégories d’approches s’en inspirent : par repondération, par reprojection et par auto-étiquetage. Dans ce travail de thèse, nous proposons deux contributions. La première est une approche de reprojection basée sur la théorie du boosting et s’appliquant aux données numériques. Celle-ci offre des garanties théoriques intéressantes et semble également en mesure d’obtenir de bonnes performances en généralisation. Notre seconde contribution consiste d’une part en la proposition d’un cadre permettant de combler le manque de résultats théoriques pour les méthodes d’auto-étiquetage en donnant des conditions nécessaires à la réussite de ce type d’algorithme. D’autre part, nous proposons dans ce cadre une nouvelle approche utilisant la théorie des (epsilon, gamma, tau)-bonnes fonctions de similarité afin de contourner les limitations imposées par la théorie des noyaux dans le contexte des données structuréesDuring the past few years, an increasing interest for Machine Learning has been encountered, in various domains like image recognition or medical data analysis. However, a limitation of the classical PAC framework has recently been highlighted. It led to the emergence of a new research axis: Domain Adaptation (DA), in which learning data are considered as coming from a distribution (the source one) different from the one (the target one) from which are generated test data. The first theoretical works concluded that a good performance on the target domain can be obtained by minimizing in the same time the source error and a divergence term between the two distributions. Three main categories of approaches are derived from this idea : by reweighting, by reprojection and by self-labeling. In this thesis work, we propose two contributions. The first one is a reprojection approach based on boosting theory and designed for numerical data. It offers interesting theoretical guarantees and also seems able to obtain good generalization performances. Our second contribution consists first in a framework filling the gap of the lack of theoretical results for self-labeling methods by introducing necessary conditions ensuring the good behavior of this kind of algorithm. On the other hand, we propose in this framework a new approach, using the theory of (epsilon, gamma, tau)- good similarity functions to go around the limitations due to the use of kernel theory in the specific context of structured dat
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
Super-résolution pour l'imagerie de la microstructure osseuse : Super-résolution par guidage morphométrique
No full textL'ostéoporose est une maladie du squelette, caractérisée par une diminution de la masse osseuse, une dégradation de la microstructure osseuse dont une augmentation de la porosité et un affinement de l'os cortical. L'ostéoporose représente un problème de santé publique majeur touchant 39% des femmes de 65 ans et montant jusqu'à 70% de celles agées de plus de 80 ans. La tomodensitométrie quantitative périphérique à haute résolution (HR-pQCT) a été développée dans le but d'imager et d'étudier la microstructure de l'os chez le patient. Cependant, sa résolution de 82µm par voxel, ne permet pas de visualiser les plus petites travées. Avec les régulations sur la dose de radiation maximale, il n'existe pas de technologie permettant d'obtenir une image à plus haute résolution en clinique. Pourtant, cela permettrait d'obtenir une meilleur description de la microstructure de l'os et ainsi de mieux prédire et comprendre l'ostéoporose. Dans cette thèse, nous développons une nouvelle famille de méthodes de Super-Résolution (SR) par Deep Learning afin d'augmenter artificiellement la résolution spatiale d'une image basse résolution. Dans un premier temps, nous analysons la capacité des méthodes de l'état de l'art appliquées à l'imagerie osseuse. Après avoir démontré l'incapacité de ces dernières à reconstruire fidèlement la microarchitecture osseuse malgré une qualité visuelle accrûe, nous proposons de guider les méthodes de SR par de l'information intrinsèque à l'application : la morphometrie de l'os. Cette méthode a été baptisé MiCT-SR. Ce guidage est rendu possible par l'élaboration d'un réseau neuronal convolutif capable d'estimer les paramètres morphométriques d'une image.Osteoporosis is a skeletal disease characterized by a decrease in bone mass, degradation of bone microstructure including increased porosity and cortical bone thinning. Osteoporosis represents a major public health issue, affecting 39% of women aged 65 and increasing to 70% in those over 80 years old. Peripheral high-resolution quantitative computed tomography (HR-pQCT) has been developed to image and study bone microstructure in patients. However, its resolution of 82µm per voxel does not allow visualization of the smallest trabeculae. With regulations on maximum radiation dose, there is currently no technology available to obtain higher resolution images in clinical settings. Yet, this would provide a better description of bone microstructure and improve prediction and understanding of osteoporosis. In this thesis, we develop a new family of Super-Resolution (SR) methods using Deep Learning to artificially increase the spatial resolution of low-resolution images. Initially, we analyze the capabilities of state-of-the-art methods applied to bone imaging. After demonstrating their inability to faithfully reconstruct bone microarchitecture despite improved visual quality, we propose guiding SR methods with intrinsic application-specific information: bone morphometry. This method is named MiCT-SR. This guidance is made possible by developing a convolutional neural network capable of estimating the morphometric parameters of an image
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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