1,720,974 research outputs found
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
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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Improving the Sensitivity and Data Analysis Techniques of the ARIANNA Detector with Deep Learning
Full text linkThe ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies, the ability to increase detector sensitivity and data analysis techniques is crucial to maximizing the number of neutrinos measured. In this work, deep learning techniques are explored to improve real-time data collection capabilities and offline neutrino searches. As an introduction, the broader field of multi-messenger astronomy is outlined, an overview of the ARIANNA experiment is provided, and deep learning techniques are detailed. Next, two projects utilizing deep learning to analyze ARIANNA data are presented. In the first project, the amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise fluctuations. Here, a real-time thermal noise rejection algorithm is created that enables the trigger thresholds to be lowered, increasing the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. This project demonstrated a CNN trained on Monte Carlo data can run on the current ARIANNA microcomputer; the CNN retained 95% of the neutrino signal at a thermal noise rejection factor of 100,000, compared to a template matching procedure which reached only 100 for the same signal efficiency. The results are verified by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. There are further studies of the CNN including deep learning network interpretability and hyperparameter optimization. Lastly, the CNN is used to classify cosmic rays events to confirm they are not rejected; the network properly classified 102 out of 104 cosmic ray events as signal. In the second project, deep learning is used in an offline analysis to classify experimental ARIANNA data collected between 2018-2021. This work compares a more traditional neutrino search technique using cuts on different variables to a new method using deep learning to classify experimental data in an offline analysis. In the second-to-last stage of data cuts, the traditional analysis is found to keep 99% neutrino signal efficiency while rejecting all except 53 experimental background events; the deep learning approach provided significantly better results with 99% signal efficiency while rejecting all except two experimental background events. Both groups of remaining background events were rejected in the final correlation cut stage of the analysis. Due to a limitation in simulating all background event types, the deep learning network was trained on a mixture of simulated and experimental data. A further study was done to check for potential artifacts between the two different types of data that could lead to inaccurate classification results. The study was conducted using the data from a cosmic ray configured ARIANNA station which contained experimentally detected cosmic ray. It is shown through a similar deep learning analysis on the cosmic ray station that there were no artifacts seen in the final model. This provides confirming evidence that artifacts are not affecting the efficiency and background rejection results of the neutrino analysis. This work concludes with a summary of the work done and final recommendations moving forward with deep learning techniques for the ARIANNA experiment
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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Performance and Simulation of the ARIANNA Pilot Array, with Implications for Future Ultra-high Energy Neutrino Astronomy
Full text linkThe ARIANNA pilot Hexagonal Radio Array (HRA), a seven station test-bed for the development of an ultra-high energy (UHE) neutrino telescope, using Antarctica's Ross Ice Shelf as a detector, was completed during the 2014-2015 Antarctic summer season. In the more than three years since, the HRA has demonstrated remarkable resilience and stability, operating with a typical 90% uptime during the summer months using solar energy, and surviving multiple Antarctic winters. Novel wind turbine design has, for the first time, enabled the operation of an autonomous station in Moore's Bay over winter. The ARIANNA data acquisition system, built on the Synchronous Sampling plus Triggering (SST) circuitry developed at UCI, has proven to be capable and cost/power efficient. Beyond the HRA, hardware research and development has been ongoing, with a new revision of the SST system seeing service in a variety of specialized stations. Dedicated cosmic ray stations have successfully self-triggered and measured the flux of UHE cosmic ray air showers in broadband radio frequencies, which serves as an important calibration source for the neutrino analysis. In an international collaboration with the National Taiwan University, the ARIANNA Horizontal Cosmic Ray station, designed to measure air showers resulting from the interaction of ν τ in the surrounding mountains, was deployed and deployed.The sensitivity of this fully autonomous array was simulated using the ShelfMC Monte Carlo, which has been developed to version 2.0, with significant improvements to accuracy and flexibility. A prototype analysis is presented which achieves 84.63% analysis efficiency while rejecting all measured non-cosmic ray backgrounds over a 1.5 year dataset for all HRA stations. The actualized livetime of 145 days per year per station is used to to construct a 5 year projection for an array of 300 ARIANNA stations. Results indicate that reasonable optimizations will allow such an array to probe or measure all but the most pessimistic models of the GZK neutrino flux. Lessons learned from the deployment and operation of the HRA will inform the design of the next generation of UHE radio neutrino detectors, which will provide insight into long standing questions on the nature and origin of the Universe's highest energy particles
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