1,721,158 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
Hierarchical Classification of Variable Stars Using Neural Networks
No full textVariable stars play a prominent role in our study of the universe and are essential to estimating cosmological parameters. They are considered '‘standard candles’' due to their intrinsic variability, which allows their distances to be calculated. With the proliferation of large-scale sky surveys that generate over 20 Terabytes of light-curve observations every day, automated methods are necessary to reduce manual efforts when classifying variable stars. To automate such classification, astronomers have developed various machine learning algorithms. Existing algorithms exploit star properties but fail to use the hierarchical structure known to exist in a specific family of stars. We believe embedding hierarchical information of stars into a learning algorithm can lead to more robust and efficient machine learning models. The goal of this thesis is to explore various approaches that exploit the hierarchical structure of stars within a neural network architecture. Results show the conditions under which adding information of the intrinsic hierarchical structure helps increase generalization performance.Computer Science, Department o
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
Novel Measures for Rare Transients
Get PDFData volumes in astronomy have been growing rapidly. Various projects and methodologies
are starting to deal with this. As we cross-match and correlate datasets, the
number of parameters per object—in other words dimensions we need to deal with—
is also growing. This leads to more interesting issues as many values are missing,
and many parameters are non-homogeneously redundant. One needs to tease apart
clusters in this space which represent different physical properties, and hence phenomena.
We describe measures that help to do that for transients from the Catalina
Realtime Transient Survey, and project it to near future surveys. The measures are
based partly on domain knowledge and are incorporated into statistical and machine
learning techniques. We also describe the discriminating role of appropriate followup
observations in near-real-time classification of transients. In particular such novel
measures will help us find relatively rare transients
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
Robust Domain Adaptation Using Active Learning
No full textTraditional machine learning algorithms assume training and test datasets are generated from the same underlying distribution, which is not true for most real-world datasets. As a result, a model trained on the training dataset fails to produce good classification accuracy on the test dataset. One way to mitigate this problem is to use domain adaptation techniques; these techniques build a new model on the unlabeled test dataset (target dataset) by transferring information from a related but labeled training dataset, (source dataset) even when their underlying distributions are different. One other important issue is that in domain adaptation, there is no allowance for obtaining class labels on the test dataset during the training phase. This issue can be handled by active learning techniques that assume the existence of a budget that can be used to label instances on the target domain. Active learning finds the most informative instances of the test dataset that can be labeled by the expert to get a better classification accuracy on the unlabeled test dataset.
The goal of this research is to build an optimal classifier on the target dataset by using information related to model complexity. We propose a novel domain adaptation technique using active learning to find the optimal value of a parameter of a class of models that yields the best classifier on the target dataset without assuming the equivalence of the class-conditional probabilities across the domains, unlike other domain adaptation methods. This research also proposes a novel data-alignment technique that allows the use of the source model directly on the target if the distributions differ due to a linear shift, thus avoiding building a complete new classifier on the target domain. Empirical results show that our methods yield better classification accuracy than the state-of-art methods.Computer Science, Department o
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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