1,720,960 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
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
Analyse expérimentale et théorique des algorithmes d'apprentissage par renforcement
No full textIn Reinforcement Learning (RL), an agent learns how to act in an unknown environment in order to maximize its reward in the long run. In recent years, the use of neural networks has led to breakthroughs, e.g., in scalability. However, there are still gaps in our understanding of how to best employ neural networks in RL. In this thesis, we improve the usability of neural networks in RL in two ways, presented in two separate parts. First, we present a theoretical analysis of the influence of the number of parameters on learning performance. Second, we propose a simple feature preprocessing based on the Fourier series, which empirically improves performance in several ways.In the first part of this thesis, we study how the number of parameters influences performance. While in supervised learning, the regime of over-parameterization and its benefits are well understood, the situation in RL is much less clear. We present a theoretical analysis of the influence of network size and L2 regularization on performance. We identify the ratio between the number of parameters and the number of visited states as a crucial factor and define over-parameterization as the regime when this ratio is larger than one. Furthermore, we observe a double descent phenomenon, i.e., a sudden drop in performance around the parameter/state ratio of one. Our analysis is based on the regularized Least-Squared Temporal Difference (LSTD) algorithm with random features in an asymptotic regime, as both the number of parameters and states go to infinity while maintaining a constant ratio. We derive deterministic limits of the empirical, the true Mean-Squared Bellman Error (MSBE), and the true Mean-Squared Value Error (MSVE) that feature correction terms responsible for the double descent. We show that those correction terms vanish when the L2 regularization increases or the number of unvisited states goes to zero.In the second part of this thesis, we study the preprocessing of features through a Fourier series. In addition to the number of parameters, the amount of optimization that can be achieved in practice remains limited. Neural networks behave thus as under-parameterized models that are also regularized through early stopping. This regularization induces a spectral bias since fitting high-frequency components of the value function requires exponentially more gradient update steps than the low-frequency ones. We propose a simple Fourier mapping for preprocessing, which improves the learning of high-frequency components and thus helps to overcome the spectral bias in RL. We present experiments indicating that this can lead to significant performance gains in terms of rewards and sample efficiency. Furthermore, we observe that this preprocessing increases the robustness with respect to hyperparameters, leads to smoother policies, and benefits the training process by reducing learning interference, encouraging sparsity, and increasing the expressiveness of the learned features.En apprentissage par renforcement (RL), un agent apprend comment agir dans un environnement inconnu de façon à maximiser sa récompense sur le long terme.Ces dernières années, l'utilisation de réseaux de neurones artificiels a conduit à de nombreuses avancées, notamment en termes de scalabilité.Cependant, de nombreuses lacunes subsistent dans notre compréhension de la meilleure manière d'employer les réseaux de neurones en RL.Dans cette thèse, nous proposons d'améliorer l'utilisation des réseaux de neurones en RL de deux manières, présentées dans deux parties distinctes.La première partie présente une analyse théorique de l'impact du nombre de paramètres sur la performance d'apprentissage.La seconde partie propose un prétraitement simple des données, basé sur la série de Fourier, qui améliore empiriquement les performances des réseaux de neurones de plusieurs façons.Dans la première partie de cette thèse, nous étudions l'influence du nombre de paramètres sur la performance.Alors que dans l'apprentissage supervisé, le régime de surparamétrisation et ses avantages sont bien compris, la situation en RL est beaucoup moins claire.Nous présentons donc une analyse théorique de l'influence du nombre de paramètres et de la régularisation L2 sur la performance.Nous identifions le rapport entre le nombre de paramètres et le nombre d'états visités comme un facteur crucial et définissons la surparamétrisation comme le régime où ce rapport est supérieur à un.De plus, nous observons un phénomène de double descente, caractérisé par une chute soudaine de performance au-delà d'un rapport paramètres/états visités de un.Notre analyse est basée sur l'algorithme de Least-Squares Temporal Difference learning (LSTD) régularisé avec des caractéristiques aléatoires dans un régime asymptotique, où le nombre de paramètres et d'états tendent vers l'infini tout en maintenant un rapport constant.Nous dérivons des limites déterministes de l'erreur quadratique moyenne de Bellman (MSBE) basée sur des échantillons collectés, de la vraie MSBE, et de l'erreur quadratique moyenne de la fonction de valeur (MSVE) qui comportent des termes de correction responsables du phénomène de double descente.Nous démontrons que l'influence des termes de correction diminue avec l'augmentation de la régularisation L2, ou avec le nombre d'états non visités.Dans la seconde partie de cette thèse, nous proposons l'étude d'un prétraitement des données basé sur la série de Fourier.En effet, outre le nombre de paramètres, le nombre d'optimisations réalisé en pratique reste souvent limité.Par conséquent, les réseaux de neurones tendent souvent à se comporter comme des modèles sous-paramétrisés régularisés par un arrêt prématuré.Cette forme de régularisation induit notamment un biais spectral, puisque l'apprentissage des composantes à haute fréquence de la fonction cible requiert exponentiellement plus d'itérations dans la descente de gradient stochastique que pour les composantes à basse fréquence.Pour pallier à ce problème, nous proposons un prétraitement des données basé sur la série de Fourier afin d'améliorer l'apprentissage des composantes à haute fréquence et surmonter le biais spectral en RL.Nous présentons des expériences indiquant que ce prétraitement peut conduire à des améliorations significatives des performances, en termes de récompenses obtenues et de données utilisées.De plus, nous observons que ce prétraitement favorise une plus grande robustesse face aux hyperparamètres, conduit à l'élaboration de politiques plus régulières, et bénéficie au processus d'entraînement en réduisant l'interférence d'apprentissage, en encourageant l'apprentissage de caractéristiques distinctes et sparses (ou creuses), et en augmentant l'expressivité des caractéristiques apprises
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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