1,720,956 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
A research on medical image segmentation with deep learning
No full textMedical image segmentation plays a critical role in computer-aided diagnosis, image quantification, and surgical planning, which identifies the pixels of homogenous regions including in organs and lesions and provides important information about the shapes and volumes of the organs and lesions. However, it could be one of the most difficult and tedious tasks to be performed by humans consistently. Therefore, there has been a good amount of research to propose various semi- or automatic segmentation methods, which depend mainly on conventional image processing and machine learning methods. However, these methods may be vulnerable to variations in image acquisition, anatomy, and disease. Due to the above problems faced in conventional image segmentation methods, many scholars continue to seek more robust medical image segmentation methods.
In recent years, the deep learning model has been widely applied and popularized in computer vision. This success has been rapidly applied in the area of medical imaging. In particular, deep learning has achieved a leap in precision and robustness with regard to variations of anatomy and disease. Several deep convolutional neural network (CNN) models have been proposed such as Residual Net, Visual Geometry Group (VGG), fully convolutional network (FCN) and U-Net. These models provide not only state-of-the-art performance for image classification, segmentation, object detection and tracking tasks but also a new perspective on image processing. Therefore, at present, deep learning can assist radiologists and surgeons to segment various anatomic structures as a reference and multiple abnormalities in computed tomography (CT) or magnetic resonance imaging (MRI) images.
In this research, we conducted various experiments to find and evaluated adequate deep learning based semantic segmentation models in medical images from the viewpoint of their accuracy in the clinical context. The aim of this study was two-fold as follows: 1) identifying and/or developing a deep learning based semantic segmentation model and the properties of an imaging modality that are adequate for the clinical context. 2) Solving specific tasks including smart labeling with humans in the loop, fine-tuning the models with different label levels in imbalanced datasets, and comparing deep learning and human segmentation where these models are developed and applied. For achieving these tasks or meeting these objectives, we proposed a fully automatic segmentation network with various kinds of CNN models considering organ-, image modality-, and image reconstruction-specific variations. Toward this, segmentation of a glioblastoma and acute stroke infarct in brain MRI, mandible and maxillary sinus in cone-beam computed tomography (CBCT), breast and other tissues in MRI, and pancreas cancer in contrast-enhanced CT were all performed in actual clinical settings. Basically, in case of slices with more thickness, 2D semantic segmentation shows better performances. Additionally, pre-processing is sensitive to developing robust segmentation that needs image normalization and various augmentations. However, because the modern graphics processing unit (GPU) lacks memory for 3D semantic segmentation, cascaded semantic segmentation or patch-based semantic segmentation gives better results. An anatomic variation could be easily trained by semantic segmentation, but disease variation of cancer is hard to be trained. Further, size-invariant semantic segmentation could be one of the important issues in medical image segmentation. Variation of contrast agent uptake may be vulnerable to the overall performance of semantic segmentation. For multi-center evaluation, subtle variation including variations in vendors’ image protocols and high noise levels at different centers may cause problems to train robust semantic segmentation. Furthermore, as labeling of semantic segmentation is very tedious and time-consuming, deep learning based smart labeling is needed.
Based on theses issues, we have developed and evaluated various applications with semantic segmentation in medical images including smart labeling, robust radiomics analysis and disease pattern segmentation, and automated segmentation.
We concluded that adequate semantic segmentation with deep learning in medical images can improve the segmentation quality, which can be helpful in computer-aided diagnosis (CAD), image quantification, and surgical planning in actual clinical settings. Medical image segmentation and its application may be sufficient to provide practical utility to many physicians and patients who do not need to learn sectional anatomy.Docto
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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