1,721,047 research outputs found
Processus Gaussien sous contraintes et de rang faible sur certaines variétés
No full textLa thèse est divisée en trois parties principales, nous résumerons les principales contributions de la thèse comme suit. Processus gaussiens à faible complexité : la régression par processus gaussien s'échelonne généralement en "" en termes de calcul et en "" en termes d'exigences de mémoire, où "" représente le nombre d'observations. Cette limitation devient inapplicable pour de nombreux problèmes lorsque "" est grand. Dans cette thèse, nous étudions l'expansion de Karhunen-Loève des processus gaussiens, qui présente plusieurs avantages par rapport aux techniques de compression à faible rang. En tronquant l'expansion de Karhunen-Loève, nous obtenons une approximation explicite à faible rang de la matrice de covariance, simplifiant considérablement l'inférence statistique lorsque le nombre de troncatures est faible par rapport à "".Ensuite, nous fournissons des solutions explicites pour les processus gaussiens à faible complexité. Tout d'abord, nous cherchons des expansions de Karhunen-Loève en résolvant les paires propres d'un opérateur différentiel où la fonction de covariance sert de fonction de Green. Nous offrons des solutions explicites pour l'opérateur différentiel de Matérn et pour les opérateurs différentiels dont les fonctions propres sont représentées par des polynômes classiques. Dans la section expérimentale, nous comparons nos méthodes proposées à des approches alternatives, révélant ainsi leur capacité améliorée à capturer des motifs complexes.Processus gaussiens contraints:Cette thèse introduit une approche novatrice utilisant des processus gaussiens contraints pour approximer une fonction de densité basée sur des observations. Pour traiter ces contraintes, notre approche consiste à modéliser la racine carrée de la fonction de densité inconnue réalisée comme un processus gaussien. Dans ce travail, nous adoptons une version tronquée de l'expansion de Karhunen-Loève comme méthode d'approximation. Un avantage notable de cette approche est que les coefficients sont gaussiens et indépendants, les contraintes sur les fonctions réalisées étant entièrement dictées par les contraintes sur les coefficients aléatoires. Après conditionnement sur les données disponibles et les contraintes, la distribution postérieure des coefficients est une distribution normale contrainte à la sphère unité. Cette distribution pose des difficultés analytiques, nécessitant des méthodes numériques d'approximation. À cette fin, cette thèse utilise l'échantillonnage Hamiltonien Monte Carlo sphérique (HMC). L'efficacité du cadre proposé est validée au moyen d'une série d'expériences, avec des comparaisons de performances par rapport à des méthodes alternatives.Enfin, nous introduisons des modèles d'apprentissage par transfert dans l'espace des mesures de probabilité finies, désigné sous le nom de "". Dans notre étude, nous dotons l'espace "" de la métrique de Fisher-Rao, le transformant en une variété riemannienne. Cette variété riemannienne, "", occupe une place significative en géométrie de l'information et possède de nombreuses applications. Au sein de cette thèse, nous fournissons des formules détaillées pour les géodésiques, la fonction exponentielle, la fonction logarithmique et le transport parallèle sur "".Notre exploration s'étend aux modèles statistiques situés au sein de "", généralement réalisés dans l'espace tangent de cette variété. Avec un ensemble complet d'outils géométriques, nous introduisons des modèles d'apprentissage par transfert facilitant le transfert de connaissances entre ces espaces tangents. Des algorithmes détaillés pour l'apprentissage par transfert, comprenant l'Analyse en Composantes Principales (PCA) et les modèles de régression linéaire, sont présentés. Pour étayer ces concepts, nous menons une série d'expériences, fournissant des preuves empiriques de leur efficacité.The thesis is divided into three main parts, we will summarize the major contributions of the thesis as follows.Low complexity Gaussian processes:Gaussian process regression usually scales as "" for computation and "" for memory requirements, where represents the number of observations. This limitation becomes unfeasible for many problems when "" is large. In this thesis, we investigate the Karhunen-Loève expansion of Gaussian processes, which offers several advantages over low-rank compression techniques. By truncating the Karhunen-Loève expansion, we obtain an explicit low-rank approximation of the covariance matrix (Gram matrix), greatly simplifying statistical inference when the number of truncations is small relative to .We then provide explicit solutions for low complexity Gaussian processes. We seek Karhunen-Loève expansions, by solving for eigenpaires of a differential operator where the covariance function serves as the Green function. We offer explicit solutions for the Matérn differential operator and for differential operators with eigenfunctions represented by classical polynomials. In the experimental section, we compare our proposed methods with alternative approaches, revealing their enhanced capability in capturing intricate patterns.Constrained Gaussian processes:This thesis introduces a novel approach used constrained Gaussian processes to approximate a density function based on observations. To address these constraints, our approach involves modeling square root of unknown density function realized as a Gaussian process. In this work, we adopt a truncated version of the Karhunen-Loève expansion as the approximation method. A notable advantage of this approach is that the coefficients are Gaussian and independent, with the constraints on the realized functions entirely dictated by the constraints on the random coefficients. After conditioning on both available data and constraints, the posterior distribution of the coefficients is a normal distribution constrained to the unit sphere. This distribution poses analytical intractability, necessitating numerical methods for approximation. To this end, this thesis employs spherical Hamiltonian Monte Carlo (HMC). The efficacy of the proposed framework is validated through a series of experiments, with performance comparisons against alternative methods.Transfer learning on the manifold of finite probability measures:Finally, we introduce transfer learning models in the space of finite probability measures, denoted as "". In our investigation, we endow the space "" with the Fisher-Rao metric, transforming it into a Riemannian manifold. This Riemannian manifold, "", holds a significant place in Information Geometry and has numerous applications. Within this thesis, we provide detailed formulas for geodesics, the exponential map, the log map, and parallel transport on "".Our exploration extends to statistical models situated within "", typically conducted within the tangent space of this manifold. With a comprehensive set of geometric tools, we introduce transfer learning models facilitating knowledge transfer between these tangent spaces. Detailed algorithms for transfer learning encompassing Principal Component Analysis (PCA) and linear regression models are presented. To substantiate these concepts, we conduct a series of experiments, offering empirical evidence of their efficacy
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
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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