1,721,122 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
Curvature-Sensitive Scheme for Tubular Anatomy Segmentation
Get PDFSegmenting tubular anatomies from medical images is a difficult task. In addition to all the obstacles typically encountered in the general task of medical image segmentation (obstacles like high inter-subject variability, noise, intra-model differences, etc.), tubular anatomies also have complications which stem from their intrinsic topology. Structures like the airway, aorta, colon, and spine curve in space in unpredictable ways. While this is a challenge in its own right, the problem is perpetuated because this characteristic greatly amplifies the effects of the other difficulties already mentioned (especially anatomical variability).The state-of-the-art in medical image segmentation over the last half decade has been convolutional neural networks. They have dramatically outperformed conventional approaches like multi-atlas segmentation. However, in the context of tubular anatomy segmentation, there is still room for improvement. Our experiments with synthetic data show that the popular convolutional neural network architecture, U-Net struggles to handle the segmentation of long, highly curved tubes, often predicting fragmented segmentations even with considerable training data.In this thesis, we present a new method for handling segmentation of tubular structures that uses both deep learning and conventional techniques in a curvature-sensitive way. Specifically, our method 1) uses a recurrent neural network to unravel tubular anatomies along their centerlines, thereby simplifying the segmentation task and 2) uses classical segmentation technique, region growing in combination with convolutional neural networks to carry out segmentation while maintaining connectivity. We show the effectiveness of our method on synthetic data and on a dataset of high resolution pediatric airway CT scans.Bachelor of Art
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
REPRESENTATION LEARNING WITH ADDITIONAL STRUCTURES
Get PDFThe ability to learn meaningful representations of complex, high-dimensional data like image and text for various downstream tasks has been the cornerstone of the modern deep learning success story. Most approaches that succeed in meaningful representation learning of the input data rely on prior knowledge of the underlying data structure to inject appropriate inductive biases into their frameworks. Prime examples of which range from the convolutional neural network (CNN) for images, to the recurrent neural network (RNN) for sequences, and to the recent trend of attention-based models (e.g. transformers) for incorporating relational information. However, most of the traditional approaches focus on a learning setup where there is a single input (and a single output if in a supervised setting). With the rapidly growing varieties of data being collected and the increasing complexity of the structures that underlie them, approaches that are able to take advantage of the additional data structures for better representation learning are needed. To this end, we introduce frameworks to learn better representations of complex data with additional structures in four arenas, where we gradually shift from supervised learning, to ``pseudo-supervised'' learning, and lastly to unsupervised learning. More specifically, we first propose a supervised approach that exploits relational-information among set elements for learning representations of set-structured data. We then propose a clustering approach that utilizes side-information, i.e. information that is related to the final clustering goal but not directly indicative of the clustering results (hence ``pseudo-supervised'' learning), for learning representations that are better for clustering. Next we introduce another clustering approach that leverages the structural assumption that data samples in each cluster form a trajectory. Lastly, we propose a general representation learning framework for learning interpretable representations of multimodal data.Doctor of Philosoph
Automatic Localized Analysis of Longitudinal Cartilage Changes
Get PDFOsteoarthritis (OA) is the most common form of arthritis; it is characterized by the loss of cartilage. Automatic quantitative methods are needed to screen large image databases to assess changes in cartilage morphology. This dissertation presents an automatic analysis method to quantitatively analyze longitudinal cartilage changes from knee magnetic resonance (MR) images. A novel robust automatic cartilage segmentation method is proposed to overcome the limitations of existing cartilage segmentation methods. The dissertation presents a new and general convex three-label segmentation approach to ensure the separation of touching objects, i.e., femoral and tibial cartilage. Anisotropic spatial regularization is introduced to avoid over-regularization by isotropic regularization on thin objects. Temporal regularization is further incorporated to encourage temporally-consistent segmentations across time points for longitudinal data. The state-of-the-art analysis of cartilage changes relies on the subdivision of car- tilage, which is coarse and purely geometric whereas cartilage loss is a local thinning process and exhibits spatial non-uniformity. A novel statistical analysis method is proposed to study localized longitudinal cartilage thickness changes by establishing spatial correspondences across time and between subjects. The method is general and can be applied to other nonuniform morphological changes in other diseases.Doctor of Philosoph
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
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