Citation Seeder generates realistic synthetic citation networks with communities using the Price-Pareto model and up to four orders of magnitude fewer parameters than baselines.
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Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
QGas is an interactive web toolkit integrating GIS geometry editing with topology-preserving graph operations to create and extend georeferenced gas infrastructure datasets.
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
The paper delivers an optimized Python implementation of Categorical Gini Correlation for computing dependence measures, confidence intervals, and independence tests.
citing papers explorer
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Generating Synthetic Citation Networks with Communities
Citation Seeder generates realistic synthetic citation networks with communities using the Price-Pareto model and up to four orders of magnitude fewer parameters than baselines.
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Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
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QGas: Interactive Gas Infrastructure Toolkit
QGas is an interactive web toolkit integrating GIS geometry editing with topology-preserving graph operations to create and extend georeferenced gas infrastructure datasets.
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Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
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gcor: A Python Implementation of Categorical Gini Correlation and Its Inference
The paper delivers an optimized Python implementation of Categorical Gini Correlation for computing dependence measures, confidence intervals, and independence tests.