1,720,964 research outputs found
IchiruTake/AIP-BDET: AIPBDET
No full textAIP-BDET is a low-cost deep-learning tool that can predict Bond Dissociation Energy with impressive accuracy along with its extendibility and interpretability on ordinary atoms (C, H, O, N
IchiruTake/Bit2Edge: AIPBDET
No full textChange relatively to version 1.2.1
This release has fixed some several issues as well as gaining some better performance received.
[Hidden Changed]: Learning.py: changing some attribute name, unchanged result
[API Changed]: Learning.py: "getModelRatio" now has been converted to "prepare_model". Add hyper-parameter "DataCleaning" and "strict_cleaning" which performs exactly in "DataGenerator.createData()". Only works if 'retrainModel' is False. Note that using 'retrainModel' is typically dangerous as it did not pass any validation. This supplementation is applied after we have finished all of the value, and only be used if you want to apply Data Cleaning in the "training set" only (NOT on validation set or testing set).
[Bug Fixed]: Creator.py: "DataGenerator.createData()": "DataCleaning" don't work with strict_cleaning=True. Now it can be applied
[Behaviour Changed]: Predict.py: "visualize" method now has removed "axis-number" as hyper-parameter.
[Behaviour Changed]: Predict.py: "visualize" method now accept some extra "chosen_type".
With "chosen_type.lower() = "last" --> Remain the same.
With "chosen_type.lower() = "fingerprint" ("fingerprints", "multi-fingerprints") --> Remain the same but can select the coloring representation.
--> Compute all fingerprint and merge as three alone feature
With "chosen_type.lower() = "single"
--> Compute each 'selected' environment fingerprint and return as one value only per each
With "chosen_type.lower() = "meaning" ("nature", "attribute")
--> Compute large (Full Structural) and smaller (Radicals) environments into two values for representation
[Method Changed]: Predict.py: "visualize": hyper-parameter "decomposeMethod" now use "umap" as default with n_neighbors is equal to the number of unique bond recorded in the input dataset multiplied by 2.
Note that with chosen_type != "last", this function can only be used to verify the relevant of fingerprints.
Change versus 1.2.2-beta.2
[Minor Performance Boost]: Now it is faster to validate the data type by 15% - 25% by changing syntax (Syntax: type(a) is B or type(a) is C --> isinstance(a, (B, C))) but it is not significant. Test case show that for 50M loop, OLD syntax: 6.5515s NEW syntax: 5.4085s with 10% memory cache lower. Apply for all Python file.
[API Changed]: Hyper-parameter is varied between "FileName" and "filePath" is now merged as "FileName".
[Performance Boost]: Predict.py: class "PredictModel" introduce new method ".generate()" is directly attached into ".createData()" to help build up all configuration used in some other class methods. Reduced 0.1 - 0.3 seconds when need to find special position to locate instead of searching at everytime called. Extra 4 KB needed only ---> Better Design Pattern --> For Debugging.
Note: Next batch will introduce documentation to give better understanding for scaling and reproducing
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
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
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
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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