1,721,024 research outputs found
Towards matching the peripheral visual appearance of arbitrary scenes using deep convolutional neural networks
No full textDistortions of image structure can go unnoticed in the visual periphery, and objects can be harder to identify (crowding). Is it possible to create equivalence classes of images that discard and distort image structure but appear the same as the original images? Here we use deep convolutional neural networks (CNNs) to study peripheral representations that are texture-like, in that summary statistics within some pooling region are preserved but local position is lost. Building on our previous work generating textures by matching CNN responses, we first show that while CNN textures are difficult to discriminate from many natural textures, they fail to match the appearance of scenes at a range of eccentricities and sizes. Because texturising scenes discards long range correlations over too large an area, we next generate images that match CNN features within overlapping pooling regions (see also Freeman and Simoncelli, 2011). These images are more difficult to discriminate from the original scenes, indicating that constraining features by their neighbouring pooling regions provides greater perceptual fidelity. Our ultimate goal is to determine the minimal set of deep CNN features that produce metameric stimuli by varying the feature complexity and pooling regions used to represent the image
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
The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.
Full text linkIn laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so
Machine Learning for Psychophysical Scaling with Ordinal Comparisons
Full text linkDie objektive Bestimmung der subjektiven Reizintensität beschäftigt die Wissenschaft seit mehr als 100 Jahren. Die neueste Generation dieser sogenannten psychophysischen Skalierungsverfahren kombiniert experimentelle Aufgaben, in denen Versuchspersonen die Ähnlichkeit von Reizen vergleichen, mit ordinalen Einbettungsalgorithmen, wie sie im Bereich des maschinellen Lernens entwickelt wurden, um eine robuste und mehrdimensionale Koordinatendarstellung der Reizwahrnehmung zu erhalten. Die korrekte Anwendung dieser Skalierungsverfahren erweist sich selbst für Expertinnen und Experten als schwierig, da viele Lücken in der Anwendung der Algorithmen und der Interpretation der Ergebnisse bestehen. In dieser Dissertation beschreibe ich eine Pipeline, um psychophysische Skalierung mittels ordinaler Vergleiche und maschineller Lernverfahren zugänglich zu machen. Dazu stelle ich zunächst eine Open Source Python Toolbox vor, die die wichtigsten Algorithmen und Methoden in einfach zu bedienenden und effizienten Implementierungen zur Verfügung stellt. Dann schlage ich ein Verfahren vor, um die wichtige Wahl der Dimensionalität der Skala auf der Grundlage statistischer Überlegungen zu vereinfachen, sowie Analysemethoden, um die Stabilität einer Skala zu schätzen und wissenschaftliche Schlussfolgerungen daraus zu ziehen. Ich schließe die Arbeit mit einer neuartigen Anwendung der vergleichsbasierten Skalierungsmethoden in einem Virtual-Reality-Experiment ab. Dabei messen wir die wahrgenommene Stärke der optischen Verzerrung von Gleitsichtbrillen, die zu schwerwiegenden Nebenwirkungen wie Schwindel führen kann. Die Pipeline, die ich in dieser Thesis vorstelle, ermöglicht es Forscherinnen und Forschern, ihre eigenen Wahrnehmungsfragen zu beantworten, indem sie selbst Skalen berechnen, die Dimensionalität auswählen und unter Berücksichtigung von Unsicherheit interpretieren. Fragen, die bisher mit aufwändigeren experimentellen Paradigmen oder eingeschränkten, z.B. eindimensionalen Analysemethoden untersucht wurden, können nun in einem neuen, umfassenderen Licht betrachtet werden.Objective measurement methods of subjective stimulus intensity have occupied scientists for over 100 years. The latest generation of these so-called psychophysical scaling methods combines experimental tasks in which subjects compare the similarity of stimuli with ordinal embedding algorithms developed in machine learning to obtain robust and multidimensional point representations of stimulus perception. However, even for experts, the correct application of ordinal embedding-based scaling methods is technically and methodologically challenging. In this dissertation, I describe a pipeline to make psychophysical scaling with ordinal comparisons more accessible using machine learning techniques. First, I introduce an open-source Python toolbox. This toolbox provides the most important algorithms and methods as userfriendly and efficient implementations, making ordinal embedding methods more accessible to psychophysicists. I then develop a procedure to simplify the essential choice of scale dimensionality based on statistical considerations and propose analysis methods to estimate the quality and variability of a scale to draw scientific conclusions. At last, I present a novel application of comparison-based scaling methods in a virtual reality experiment to measure distortions of varifocal glasses that lead to serious side effects such as dizziness. The pipeline I present in this thesis empowers researchers to answer their perceptual questions by computing the scales themselves, choosing the dimensionality, and interpreting them with uncertainty in mind. They can reconsider questions previously investigated using cumbersome experimental paradigms or limited, for example one-dimensional, analysis methods and use ordinal embedding methods to view these questions from a new perspective
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
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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