1,720,973 research outputs found
Synthesising Dynamic Textures using Convolutional Neural Networks
No full textHere we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies
Synthesising Dynamic Textures using Convolutional Neural Networks
No full textHere we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies
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
A parametric texture model based on deep convolutional features closely matches texture appearance for humans
No full textMuch of our visual environment consists of texture—“stuff” like cloth, bark or gravel as distinct from “things” like dresses, trees or paths—and we humans are adept at perceiving textures and their subtle variation. How does our visual system achieve this feat? Here we psychophysically evaluate a new parameteric model of texture appearance (the CNN texture model; Gatys et al., 2015) that is based on the features encoded by a deep convolutional neural network (deep CNN) trained to recognise objects in images (the VGG-19; Simonyan and Zisserman, 2015). By cumulatively matching the correlations of deep features up to a given layer (using up to five convolutional layers) we were able to evaluate models of increasing complexity. We used a three-alternative spatial oddity task to test whether model-generated textures could be discriminated from original natural textures under two viewing conditions: when test patches were briefly presented to the parafovea (“single fixation”) and when observers were able to make eye movements to all three patches (“inspection”). For 9 of the 12 source textures we tested, the models using more than three layers produced images that were indiscriminable from the originals even under foveal inspection. The venerable parameteric texture model of Portilla and Simoncelli (Portilla and Simoncelli, 2000) was also able to match the appearance of these textures in the single fixation condition, but not under inspection. Of the three source textures our model could not match, two contain strong periodicities. In a second experiment, we found that matching the power spectrum in addition to the deep features used above (Liu et al., 2016) greatly improved matches for these two textures. These results suggest that the features learned by deep CNNs encode statistical regularities of natural scenes that capture important aspects of material perception in humans
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
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