1,720,983 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
Apprentissage de représentations par noyaux pour des séries temporelles
Get PDFLes séquences temporelles jouent un rôle essentiel dans des domaines tels que la finance, la santé et les sciences de l’environnement, où la compréhension des dépendances temporelles et la capacité à effectuer des prédictions précises sont cruciales pour soutenir la prise de décision. Par exemple, en finance, l’analyse des séries temporelles permet de modéliser les prix des actions et les tendances du marché, souvent influencés par de nombreux facteurs interdépendants. En santé, les données temporelles servent à suivre l’évolution des indicateurs cliniques chez les patients, facilitant ainsi le diagnostic et les décisions thérapeutiques. De même, dans les sciences environnementales, elles sont essentielles à l’analyse des données climatiques et à la prévision des phénomènes météorologiques. Toutefois, les séries temporelles présentent souvent des complexités inhérentes, telles que la non-linéarité, le bruit et la dérive de concept. Ces éléments rendent difficile l’apprentissage des dynamiques sous-jacentes, en particulier lorsque les relations entre variables évoluent de manière imprévisible. Pour relever ces défis, l’apprentissage de représentations pour séries temporelles s’est imposé comme un paradigme central. Plutôt que de s’appuyer exclusivement sur des modèles orientés tâche, cette approche vise à extraire des représentations compactes, informatives et transférables à partir de données brutes. Ces représentations peuvent ensuite être exploitées pour différentes tâches en aval, telles que la prévision, la détection d’anomalies, la segmentation de régimes ou le regroupement. Cependant, apprendre des représentations à la fois robustes et adaptatives demeure un défi majeur, notamment dans le contexte de séries temporelles co-évolutives, non linéaires ou de haute dimension. Cette thèse propose une série de méthodologies d’apprentissage par représentation à noyau (Kernel Representation Learning, KRL) pour aborder ces problématiques. Les contributions principales incluent : (1) le développement d’un modèle non linéaire, piloté par les données, capable d’apprendre de manière adaptative des représentations à noyau pour capturer les structures complexes de séries temporelles de haute dimension ; (2) la proposition d’un cadre d’apprentissage à double volet (twin learning) permettant d’identifier conjointement des structures temporelles et des segmentations adaptatives dans des séquences non linéaires et indépendantes du domaine ; (3) l’extension de ce cadre pour traiter la dérive de concept dans des séries temporelles co-évolutives, établissant ainsi un nouveau paradigme pour le suivi de concepts latents et l’analyse dynamique ; (4) l’application des techniques KRL à des scénarios réels, notamment la modélisation écosystémique de données financières et le regroupement de séquences catégorielles, avec validation empirique à l’appui. Les résultats montrent l’efficacité de ces approches pour identifier des interactions non linéaires, détecter des changements de régime et améliorer significativement la précision des prédictions ainsi que l’interprétabilité des modèles. Des expériences approfondies sur des jeux de données réels confirment la robustesse et la généricité des modèles proposés. Nos approches surpassent systématiquement les méthodes existantes selon différents critères d’évaluation, démontrant leur capacité à généraliser et à s’adapter à des environnements dynamiques et complexes.Time sequences are essential in fields such as finance, healthcare, network security, and environmental domains, where understanding temporal dependencies and making accurate predictions is crucial for informed decision-making. In finance, for example, time series analysis is used to model stock prices and market trends, which are often influenced by a multitude of interdependent factors. In healthcare, time sequences help track patient health metrics over time, supporting diagnosis and treatment decisions. Similarly, in environmental science, they are critical for analyzing climate data and predicting weather patterns. However, time sequences often exhibit inherent complexities, including nonlinearity, noise, and concept drift. These challenges make it difficult for traditional models to capture the intricate dynamics of multivariate and co-evolving sequences, especially in situations where relationships between variables shift unpredictably. As a result, there is a need for more advanced methodologies that can adapt to the evolving nature of time series data, better capture hidden dependencies, and maintain robustness in the face of noisy or changing environments. To tackle these challenges, time series representation learning has emerged as a core research paradigm. Rather than relying solely on task-specific models, representation learning focuses on extracting compact, meaningful, and transferable representations from raw sequences. These representations can be leveraged across multiple downstream tasks, including forecasting, clustering, anomaly detection, and regime identification. Nonetheless, learning representations that are both robust and adaptive remains a central challenge—particularly in settings involving high-dimensional, nonlinear, or co-evolving time series. This thesis introduces a suite of Kernel Representation Learning (KRL) methodologies aimed at addressing these issues. The key contributions include: (1) the development of a data-driven nonlinear learning model that adaptively learns kernel representations to capture diverse structures within high-dimensional time series; (2) the proposal of a twin-learning framework that jointly identifies temporal structures and perform adaptive segmentations for genenral sequences; (3) the extension of this framework to tackle the challenge of concept drift in complex co-evolving time series, establishing a novel paradigm for tracking latent concepts and dynamic transitions; and (4) the practical applicability of KRL methods to real-world scenarios, such as ecosystem-based modeling in financial markets and clustering of categorical sequences. These case studies highlight how kernel-based representations can be effectively integrated into diverse domains, showcasing the versatility and adaptability of the proposed methodologies in addressing real-world sequence data challenges. We demonstrate the effectiveness of the proposed models through extensive experiments on real-world datasets. The results consistently show substantial improvements in both predictive accuracy and model interpretability, outperforming existing approaches across multiple evaluation metrics. These models have proven to be robust in handling complex, high-dimensional data and demonstrate strong generalization capabilities. Their adaptability to evolving data environments further proves their practical applicability in a wide range of dynamic and challenging real-world scenarios
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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