LoadKAN combines feature-isolated temporal attention with KAN to produce competitive load forecasts on three U.S. markets and enables quantitative analysis of non-linear mobility-load relationships via learned activation functions.
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An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.
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Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
LoadKAN combines feature-isolated temporal attention with KAN to produce competitive load forecasts on three U.S. markets and enables quantitative analysis of non-linear mobility-load relationships via learned activation functions.
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Can I Check What I Designed? Mapping Security Design DSLs to Code Analyzers
An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.