1,720,964 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
Penalized regression methods for interaction and mixed-effects models with applications to genomic and brain imaging data
No full textIn high-dimensional (HD) data, where the number of covariates (??) greatly exceeds the number of observations (??), estimation can benefit from the bet-on-sparsity principle, i.e., only a small number of predictors are relevant in the response. This assumption can lead to more interpretable models, improved predictive accuracy, and algorithms that are computationally efficient. In genomic and brain imaging studies, where the sample sizes are particularly small due to high data collection costs, we must often assume a sparse model because there isn't enough information to estimate ?? parameters. For these reasons, penalized regression methods such as the lasso and group-lasso have generated substantial interest since they can set model coefficients exactly to zero. In the penalized regression framework, many approaches have been developed for main effects. However, there is a need for developing interaction and mixed-effects models. Indeed, accurate capture of interactions may hold the potential to better understand biological phenomena and improve prediction accuracy since they may reflect important modulation of a biological system by an external factor. Furthermore, penalized mixed-effects models that account for correlations due to groupings of observations can improve sensitivity and specificity. This thesis is composed primarily of three manuscripts. In the first manuscript, we propose a method called sail for detecting non-linear interactions that automatically enforces the strong heredity property using both the ℓ₁ and ℓ₂ penalty functions. We describe a blockwise coordinate descent procedure for solving the objective function and provide performance metrics on both simulated and real data. The second manuscript develops a general penalized mixed effects model framework to account for correlations in genetic data due to relatedness called ggmix. Our method can accommodate several sparsity-inducing penalties such as the lasso, elastic net and group lasso and also readily handles prior annotation information in the form of weights. Our algorithm has theoretical guarantees of convergence and we again assess its performance in both simulated and real data. The third manuscript describes a novel strategy called eclust for dimension reduction that leverages the effects of an exposure variable with broad impact on HD measures. With eclust, we found improved prediction and variable selection performance compared to methods that do not consider the exposure in the clustering step, or to methods that use the original data as features. We further illustrate this modeling framework through the analysis of three data sets from very different fields, each with HD data, a binary exposure, and a phenotype of interest. We provide efficient implementations of all our algorithms in freely available and open source software.Avec des données de grande dimension (GD), où le nombre de covariables (??) dépasse largement le nombre d'observations (??), l'estimation peut profiter du principe miser sur la sparsité, c'est à dire, seulement un petit nombre de prédicteurs de la variable réponse sont réellement pertinents. Cette hypothèse permet d'obtenir des modèles interprétables, améliore leur précision et facilite l'implémentation d'algorithmes efficaces sur le plan des calculs. Dans les études d'imagerie génomique et cérébrale, où la taille des échantillons est particulièrement faible en raison des coûts élevés associés à la collecte de données, nous devons souvent supposer un modèle sparse car l'information est insuffisante pour estimer les ?? paramètres. Pour ces raisons, les méthodes de régression pénalisées telles que lasso et group lasso ont suscité un intérêt considérable, car elles permettent d'obtenir des estimés des coefficients du modèle égaux à zéro. Dans le cadre de la régression pénalisée, de nombreuses approches ont été développées pour les effets principaux. Cependant, il y un besoin d'adapter ces approches pour les modèles d'intéraction et les modèles mixtes. En effet, une capture précise des interactions permettrait de mieux comprendre les phénomènes biologiques et d'améliorer la précision des prédictions, car elles reflètent souvent une modulation importante d'un système biologique par un facteur externe. De plus, les modèles mixtes pénalisés qui tiennent compte des corrélations dues aux regroupements d'observations peuvent améliorer la sensibilité et la spécificité. Cette thèse est composée principalement de trois manuscrits. Dans le premier manuscrit, nous proposons une méthode appelée sail pour détecter les interactions non linéaires qui applique automatiquement la propriété d'hérédité forte à l'aide des pénalités ℓ₁ et ℓ₂. Nous décrivons une procédure de descente de coordonnées par blocs pour optimiser la fonction objective, et nous démontrons la performance sur des données simulées et réelles. Le deuxième manuscrit développe un cadre général de modèles mixtes pénalisés pour tenir compte des corrélations dans les données génétiques issues de familles. Notre méthode, appelée ggmix, peut tirer profit de plusieurs pénalités, telles que lasso, elastic net et group lasso. Elle permet également d'intégrer des informations d'annotation antérieures sous forme de poids. Notre algorithme a des garanties théoriques de convergence et nous évaluons à nouveau ses performances à l'aide de données simulées et de données réelles. Le troisième manuscrit décrit une nouvelle stratégie appelée eclust pour la réduction de la dimension des variables qui exploite les effets d'une variable d'exposition ayant un impact important sur les mesures GD. Avec eclust, nous avons constaté une amélioration de la performance prédictive et de la sélection de variables par rapport aux méthodes qui ne tiennent pas compte de l'exposition lors de l'étape de clustering ou des méthodes utilisant les données d'origine comme des effets principaux. Nous illustrons ensuite ce cadre de modélisation en analysant trois ensembles de données provenant de domaines très différents, chacun avec des données GD, une exposition binaire et une variable réponse. Nous fournissons des implémentations efficaces de tous nos algorithmes dans les logiciels gratuits et à source ouverte
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
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
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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