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The authors universal (meta-)logical reasoning approach is demonstrated and discussed with a challenge puzzle in epistemic reasoning: the wise men puzzle. The presented solution puts a particular emphasis on the adequate modeling of common knowledge.
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Contains the sorption and moisture transport data used in paper.
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The replication data for "Globalization and Top Income Shares"
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This dataset aimed to be published in Data in Brief. The dataset presents the validation process of a survey of factors affecting Indonesian K-12 school teachers’ Teachers’ Information and Communication Technology Access (TICTA). The instrument was established from previous studies. It was piloted to 120 K-12 school teachers and tested for its reliability. For the main data collection, the instrument was distributed online and responded by 2775 Indonesian K-12 school teachers. The main data analysis was conducted for the measurement model using four assessments; the reflective indicator loadings, internal consistency reliability, convergent and discriminant validity. PLS-SEM was used for the analysis.
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Hourly values of ambient dust concentration (TSP) were measured on the roof of the USDA-ARS Plant Stress and Water Conservation (PSWC) Laboratory located at 3810 4th Street in Lubbock, Texas (33.593508 -101.897688). Measurements were obtained continuously from 1 January 2003 to 8 June 2008, a period of more than five years and four months. The sampling system consisted of a model 1400a TEOM (tapered element oscillating microbalance) manufactured by Rupprecht & Patashnick. No cyclone pre-separators or impactors were installed in the sampler inlet so that the system measured total suspended particulates (TSP). The system was mounted on a raised platform such that the sampling inlet was 2.2 m above the flat roof of the building and 7.0 m above the surrounding ground level. Dust is primarily generated in the highly erodible cropland outside of the city limits of Lubbock and transported across the city by winds blowing from various directions. A portion of this dust is blown across the PSWC Lab where ambient dust concentration was measured and recorded. An attempt was made to obtain a continuous record, however, mechanical failures led to occasional gaps in the data record. All TSP concentrations are reported in units of micrograms of dust per cubic meter of air.
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Source data for “Dynamics of oligomer and amyloid fibril formation by yeast prion Sup35 observed by high-speed atomic force microscopy" (Konno et al.)
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Here you can find raw data to generate figures of the paper: "Keratin 8 Attenuates Necrotic Cell Death by Facilitating Mitochondrial Fission-mediated Mitophagy".
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This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the ``goodness-of-fit'' of their data to the algorithm. This is important for Visual Analytics applications, which combine information visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work. The CSVs show the qualitative and quantitative data collected in the study, separately for each algorithm visualisation. In particular, they show users' preference and ability to differentiate various levels of uncertainty using the visual means opacity, blur, texture, variable grid, and contour lines.
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This dataset is the research data for: Accurately Wildlife Censusing by Using Deep Learning for UAV-Based Thermal Imageries The dataset (00_UAV-derived Waterfowl Thermal Imagery Dataset) was captured in a playa wetland area (Straightwater WMA) of Nebraska, the United States, during the spring season at 200 feet AGL flight height. The dataset contains 355 thermal images with more than one wildlife presented (512x640 px, 7.5cm/pixel GSD) and 187 images without wildlife presented. The corresponding label was given in a CSV file with five columns where the first column indicates the image name, and the last four columns indicate the position of each wildlife in the corresponding image (every row indicates a single wildlife). Every wildlife was represented by a bounding box defined by the last four columns (the position of the upper left corner the bounding box was expressed as (x,y) which is equal to the (column, row) of the image, the width and height indicate the pixel additions based on the upper left corner (x, y) in the column and row direction respectively). The dataset can be used as training data for automated detection with machine learning or deep learning algorithm. The ccorresponding RGB images were also given in the '01 RGB Images' file. The dataset can be used as visual references to label the thermal training dataset. The orthomosaic of Smith WPA, which contains 2915 wildlife, was given in '02 Test Orthomosaic'. The orthomosiac can be used as test data. The corresponding labels were also given in the same format as above. l
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This is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components.
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