1,720,975 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
Less Biased Noise Scale Estimation for Threshold-Robust RANSAC
No full textThe gold-standard for robustly estimating relative pose through image matching is RANSAC. While RANSAC is powerful, it requires setting the inlier threshold that determines whether the error of a correspondence under an estimated model is sufficiently small to be included in its consensus set. Setting this threshold is typically done by hand, and is difficult to tune without an access to ground truth data. Thus, a method capable of automatically determining the optimal threshold would be desirable. In this paper we revisit inlier noise scale estimation, which is an attractive approach as the inlier noise scale is linear to the optimal threshold. We revisit the noise scale estimation method SIMFIT and find bias in the estimate of the noise scale. In particular, we fix underestimates from using the same data for fitting the model as estimating the inlier noise, and from not taking the threshold itself into account. Secondly, since the optimal threshold within a scene is approximately constant we propose a multi-pair extension of SIMFIT++, by filtering of estimates, which improves results. Our approach yields robust performance across a range of thresholds, shown in Figure 1. Code is available at https://github.com/Parskatt/simfitp
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
Towards the Next Generation of 3D Reconstruction
No full textHumans perceive our visual surroundings through the projection of light rays through our pupils and onto the retina. Aided by motion, we gain an understanding of our environment, as well as our location within it. The goal of image-based 3D reconstruction is to imbue machines with similar capabilities. The most prominent paradigm for image-based 3D reconstruction is called Structure-from-Motion (SfM). Traditionally, SfM has been approached through handcrafted algorithms, which are brittle when assumptions do not hold. Humans, on the other hand, understand their environment intuitively and show remarkable robustness in their ability to localize themselves in, and map the world. The main purpose of this thesis is the development of a set of methods which strives toward the next generation of SfM, imbued with intelligence and robustness. In particular, we propose a set of methods dealing with 2D: learning of keypoint detectors, features, and dense feature matching, and 3D: threshold-robust relative pose estimation, and registration of SfM maps. First, we develop models to detect keypoints, producing a set of 2D image coordinates, and models to describe the image, producing features. One of our key contributions is decoupling these tasks, which have typically been learned jointly, into distinct objectives, resulting in major gains in performance, as well as increased modularity. Paper A introduces this decoupled framework, and Paper B further develops the keypoint objective. In Paper C we revisit the keypoint objective from an entirely self-supervised reinforcement learning perspective, yielding several insights, and further gains in performance. We further develop methods for dense feature matching, i.e., matching every pixel between two images. In Paper D we propose the first dense feature matcher capable of outperforming sparse matching for relative pose estimation. This is significant, as previous work had generally indicated that the sparse or semi-dense paradigm was preferable. In Paper E we greatly improve on almost all components of the method of Paper D, resulting in an extremely robust dense matcher, capable of matching almost any pair of images. We lift our eyes from the 2D image plane into 3D, and investigate relative pose estimation and 3D registration of SfM maps. Relative pose estimation is a difficult task, as non-robust estimation fails in the presence of outliers. Random Sample Consensus (RANSAC), which is the goldstandard robust estimation method, requires setting an outlier threshold, which is non-trivial to set, and poor choices result in significantly worse performance. In Paper F, we develop an algorithm to automatically estimate this threshold from an initial guess that is less biased than previous approaches, leading to robust performance. Finally, we investigate registering SfM maps together. This is particularly interesting in distributed settings where, e.g., robots need to localize with respect to each other’s reference frames in order to collaborate. However, in this setting, using image-based localization approaches comes with downsides. In particular, computational complexity, compatibility issues, and privacy concerns severely limit the potential of such systems to be deployed. In Paper G we propose a new paradigm for registering SfM maps through point cloud registration, circumventing the above limitations. Finding that existing registration models trained on 3D scan data fail on this task, we develop a dataset for SfM registration. Training on our proposed dataset greatly improves performance on the task, showing the potential of the proposed paradigm
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
Towards Understanding Capsule Networks
No full textIn this thesis capsule networks are investigated, both theoretically and empirically. The properties of the dynamic routing [42] algorithm proposed for capsule networks, as well as a routing algorithm in a follow-up paper by Wang et al. [50] are thoroughly investigated. It is conjectured that there are three key attributes that are needed for a good routing algorithm, and these attributes are then related to previous algorithms. A novel routing algorithm EntMin is proposed based on the observations from the investigation of previous algorithms. A thorough evaluation of the performance of different aspects of capsule networks is conducted, and it is shown that EntMin outperforms both dynamic routing and Wang routing. Finally, a capsule network using EntMin routing is compared to a very deep Convolutional Neural Network and it is shown that it achieves comparable performance
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