2,862 research outputs found
Robust Discontinuity Indicators for High-Order Reconstruction of Piecewise Smooth Functions
In many applications, piecewise continuous functions are commonly
interpolated over meshes. However, accurate high-order manipulations of such
functions can be challenging due to potential spurious oscillations known as
the Gibbs phenomena. To address this challenge, we propose a novel approach,
Robust Discontinuity Indicators (RDI), which can efficiently and reliably
detect both C^{0} and C^{1} discontinuities for node-based and cell-averaged
values. We present a detailed analysis focusing on its derivation and the
dual-thresholding strategy. A key advantage of RDI is its ability to handle
potential inaccuracies associated with detecting discontinuities on non-uniform
meshes, thanks to its innovative discontinuity indicators. We also extend the
applicability of RDI to handle general surfaces with boundaries, features, and
ridge points, thereby enhancing its versatility and usefulness in various
scenarios. To demonstrate the robustness of RDI, we conduct a series of
experiments on non-uniform meshes and general surfaces, and compare its
performance with some alternative methods. By addressing the challenges posed
by the Gibbs phenomena and providing reliable detection of discontinuities, RDI
opens up possibilities for improved approximation and analysis of piecewise
continuous functions, such as in data remap.Comment: 37 pages, 37 figures, submitted to Computational and Applied
Mathematics (COAM
Data Mining Techniques in Analyzing Process Data: A Didactic
Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided
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