1,721,120 research outputs found
Training data for neural parameterization of the Lorenz 1980 model in a regime with energetic fast oscillations
No full textThe provided data is the solution of the Lorenz 1980 model in the parameter regime specified in Appendix A of the article "The High-Frequency and Rare Events Barriers to Neural Closures of Atmospheric Dynamics" by Mickael D. Chekroun, Honghu Liu, Kaushik Srinivasan, and James C. McWilliams, 2023. The first 1344000 time steps of the data (corresponding to a time window of length 700 days) are used as the training data to obtain the two neural parameterizations called NN1 and NN2 in this article
Metallalkenyl, Metallacyclopropene, or Metallallylcarbenoid? Ru-Catalyzed Annulation between Benzoic Acid and Alkyne
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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
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Probably Approximately Correct Learnable Fuzzy System
Full text linkThis dissertation develops the probably approximately correct (PAC) learnable fuzzy system to predict clinical outcomes from a small number of survey questions (short form). There are five layers in the system: input, fuzzification, inference, defuzzification, and production. The major product in this dissertation is to derive the PAC learnable knowledge-driven machine learning algorithm by growing sample using Bootstrap samples with Gaussian distributed noise. The input layer is the procedure for preparing data input. In the fuzzification layer, sample size is significantly increased using bootstrap re-sampling with replacement. The fuzzy set with proposed membership function is generated by introducing Gaussian distributed noise to survey responses of the bootstrap samples to reflect uncertainty. This is a natural language extension from the point option in survey questions to region input with probabilities from survey design space. The inference layer includes both classification and prediction. Here we use machine learning techniques to derive the algorithms in this layer, e.g. Naive Bayesian method and eXtreme Gradient Boosting (XGBoost). The final predicted values require a defuzzification process in the next layer to remove noise in prediction. There are four types of input after fuzzification, original input, fuzzy input, input required interpolation and input required extrapolation. The defuzzification process is based on weighted means of related information. The last step of the system is the output layer with algorithms, final prediction and validation internally and externally. Lastly, we apply this fuzzy system to derive PAC learnable algorithms to predict oral health clinical outcomes. The input predictors include short forms and demographic information. The short forms, developed from Graded Response Models in Item Response Theory, have two versions (children and their parents). The clinical outcomes are referral for treatment needs (categorical) and children’s oral health status index score (continuous). The prediction is evaluated internally and externally by sensitivity and specificity of a binary variable, correlation (between original value and predicted value) and root mean square error (RMSE) of a continuous variable. Both internal and external validation show the improvement of prediction when new information is added and generalizability as well as the stability of the algorithm. The best prediction (high sensitivity and relatively high specificity for categorical variables, low RMSE and high correlation) is reached when using child's self-reported short form, plus parent's proxy-reported short form, and demographic characteristics
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
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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