1,720,953 research outputs found
Deep learning approach for data imputation of multivariate time series
No full textDiese Arbeit untersucht Deep-Learning-Ansätze zur Imputation fehlender Werte in multivariaten Zeitreihen. Die Bewertung umfasst 16 Imputationsmethoden - darunter 10 Deep-Learning-Architekturen (SAITS, BRITS, ImputeFormer) und 6 traditionelle Ansätze (KNN, XGBoost, MICE) - in 12 Vorverarbeitungskonfigurationen. Unter Verwendung der Org/Ref-Methodik werden authentische fehlende Muster aus Daten der Zackenberg-Forschungsstation im Nordosten Grönlands auf Referenzdaten von Geosphere Austria übertragen, was eine strenge Bewertung unter Beibehaltung der Grundwahrheit ermöglicht. Die Ergebnisse zeigen variablenabhängige Leistung: SAITS erzielt die beste Gesamtleistung (MAE 7,56) mit opti-maler Vorverarbeitung, während traditionelle Methoden wettbewerbsfähige Genauigkeit mitminimalen Rechenanforderungen bieten (XGBoost MAE 8,15, KNN MAE 8,58). Deep-Learning-Methoden (BRITS, SAITS) sind gut bei volatilen Variablen (Windgeschwindigkeit, Luft-feuchtigkeit) mit stündlicher Auflösung, während traditionelle Methoden bei glatten Variablen (Temperatur, Luftdruck) dominieren. Die Strategie der Datenaufteilung erweist sich als der kritischste Faktor: Die merkmalsbezogene Organisation (gleiche Variablen für alle Stationen) ermöglicht effektives Lernen mit durchschnittlich MAE 16,15 über alle Methoden, während die stationsbezogene Aufteilung (alle Variablen pro Station) mit einem MAE von 327,94 katastrophal scheitert - eine 20-fache Leistungsverschlechterung. Grundlagenmodelle (MOMENT)bleiben hinter den Erwartungen zurück, was auf eine begrenzte Wirksamkeit des Transferlernens für arktische Klimadaten hindeutet. Die Arbeit liefert praktische Empfehlungen für die operative Klimaüberwachung unter Berücksichtigung von Genauigkeit, Rechenressourcen und Variablencharakteristiken.This thesis investigates deep learning approaches for imputing missing values in multivariatetime series. The evaluation encompasses 16 imputation methods - including 10 deep learning architectures (SAITS, BRITS, ImputeFormer) and 6 traditional approaches (KNN, XGBoost, MICE) - across 12 preprocessing configurations. Using the Org/Ref methodology, authentic missing patterns from data from Zackenberg Research Station in Northeast Greenland are transferred to Geosphere Austria reference data, enabling rigorous evaluation while pre-serving ground truth. Results reveal variable-dependent performance: SAITS achieves best overall performance (MAE 7.56) with optimal preprocessing, while traditional methods provide competitive accuracy with minimal computational requirements (XGBoost MAE 8.15, KNN MAE 8.58). Deep learning methods (BRITS, SAITS) excel for volatile variables (windspeed, humidity) at hourly resolution, while traditional methods dominate for smooth variables (temperature, pressure). Data partitioning strategy emerges as the most critical factor: feature-wise organization (same variables across stations) enables effective learning with MAE16.15 average across methods, while station-wise partitioning (all variables per station) fails catastrophically with MAE 327.94 - a 20-fold performance degradation. Foundation models (MOMENT) underperform expectations, suggesting limited transfer learning effectiveness for Arctic climate data. The work provides practical recommendations for operational climate monitoring balancing accuracy, computational constraints, and variable characteristics.Sebastian Peter ScheiklMasterarbeit FH JOANNEUM 202
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
- …
