1,720,955 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
On Uncertainty Quantification in Neural Networks: Ensemble Distillation and Weak Supervision
No full textMachine learning models are employed in several aspects of society, ranging from autonomous cars to justice systems. They affect your everyday life, for instance through recommendations on your streaming service and by informing decisions in healthcare, and are expected to have even more influence in society in the future. Among these machine learning models, we find neural networks which have had a wave of success within a wide range of fields in recent years. The success of neural networks are partly attributed to the very flexible model structure and, what it seems, endless possibilities in terms of extensions. While neural networks come with great flexibility, they are so called black-box models and therefore offer little in terms of interpretability. In other words, it is seldom possible to explain or even understand why a neural network makes a certain decision. On top of this, these models are known to be overconfident, which means that they attribute low uncertainty to their predictions, even when uncertainty is, in reality, high. Previous work has demonstrated how this issue can be alleviated with the help of ensembles, i.e. by weighing the opinion of multiple models in prediction. In Paper I, we investigate this possibility further by creating a general framework for ensemble distribution distillation, developed for the purpose of preserving the performance benefits of ensembles while reducing computational costs. Specifically, we extend ensemble distribution distillation to make it applicable to tasks beyond classification and demonstrate the usefulness of the framework in, for example, out-of-distribution detection. Another obstacle in the use of neural networks, especially deep neural networks, is that supervised training of these models can require a large amount of labelled data. The process of annotating a large amount of data is costly, time-consuming and also prone to errors. Specifically, there is a risk of incorporating label noise in the data. In Paper II, we investigate the effect of label noise on model performance. In particular, under an input-dependent noise model, we analyse the properties of the asymptotic risk minimisers of strictly proper and a set of previously proposed, robust loss functions. The results demonstrate that reliability, in terms of a model’s uncertainty estimates, is an important aspect to consider also in weak supervision and, particularly, when developing noise-robust training algorithms. Related to annotation costs in supervised learning, is the use of active learning to optimise model performance under budget constraints. The goal of active learning, in this context, is to identify and annotate the observations that are most useful for the model’s performance. In Paper III, we propose an approach for taking advantage of intentionally weak annotations in active learning. What is proposed, more specifically, is to incorporate the possibility to collect cheaper, but noisy, annotations in the active learning algorithm. Thus, the same annotation budget is enough to annotate more data points for training. In turn, the model gets to explore a larger part of the input space. We demonstrate empirically how this can lead to gains in model performance.Maskininlärningsmodeller används i flera delar av samhället, från autonoma fordon till rättssystem. De påverkar redan nu din vardag, exempelvis via personliga rekommendationer i din direktuppspelningstjänst (”streaming service”) och genom att agera beslutsstöd i vården, och förväntas ha än mer påverkan i samhället i framtiden. Bland dessa maskininlärningsmodeller, finner vi neurala nätverk som har haft stor framgång inom flera fält under det senaste årtiondet. Framgången beror delvis på neurala nätverks flexibla modellstruktur och, vad det verkar, oändliga utvecklingsmöjligheter. Neurala nätverk erbjuder stor flexibilitet, men har en nackdel i att de är så kallade black-box-modeller. Detta innebär att det sällan går att förklara eller ens förstå varför ett neuralt nätverk tar ett visst beslut. Dessutom, så har den här typen av modeller en tendens att vara överdrivet självsäkra, vilket betyder att de rapporterar låg osäkerhet i sina beslut, även när osäkerheten i själva verket är hög. För ett självkörande fordon skulle detta till exempel kunna innebära att fordonet bedömer en vänstersväng som mycket säker, när sikten över det mötande körfältet är skymd och ett mötande fordon mycket väl kan finnas just bakom krönet. Tidigare forskning har demonstrerat hur denna typ av problem kan avhjälpas genom att använda flera neurala nätverk som samspelar för att prediktera eller ta ett beslut. På detta sätt fås en modell som är mer korrekt och som också är mer pålitlig när det kommer till att ge en uppskattning av den egna osäkerheten. I denna avhandling undersöker vi vidare hur vi kan lära ett enskilt neuralt nätverk att efterlikna flera samspelande modeller, för att minska de kostnader som kommer med att ha flera samspelande modeller i bruk. En annan begränsande faktor när det kommer till neurala nätverk är att de kan behöva en stor mängd insamlad data med tillhörande etiketter för att lära sig den uppgift som de är ämnade för. Att införskaffa etiketter för en stor mängd datapunkter är både kostsamt och tidskrävande och det finns en risk att det blir fel i annoteringsprocessen. Mer specifikt så kan felaktiga etiketter, så kallat etikettbrus, inkluderas i datan. Detta i sin tur kan skada modellens förmåga att ta korrekta beslut. Vi undersöker hur denna effekt tar sig form och finner att etikettbrus inte bara kan ha en negativ effekt på nämnda förmåga att ta korrekta beslut, utan även på förmågan att skatta den egna osäkerheten. Relaterat till annoteringskostnader, föreslår vi till sist ett tillvägagångssätt för att utnyttja brusiga etiketter i aktiv inlärning. Målet med aktiv inlärning, i denna kontext, är att identifiera och annotera de observationer som kommer att vara mest hjälpsamma i modellens inlärningprocess. Förslaget är att, i aktiv inlärning, ge möjligheten att samla in billigare, men brusiga, etiketter. På så sätt kan en begränsad annoteringsbudget räcka till att annotera fler datapunkter, vilket i sin tur kan leda till en bättre modell. Det senare är något som påvisas experimentellt.Funding agencies: This research was financially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.</p
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
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
- …
