Derives decoupling conditions for fluctuating-growth size-structured populations and connects them to Feynman-Kac formula via random time changes and exponential tilting.
springer Berlin
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U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.
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Size-structured populations with growth fluctuations: Feynman--Kac formula and decoupling
Derives decoupling conditions for fluctuating-growth size-structured populations and connects them to Feynman-Kac formula via random time changes and exponential tilting.
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Learning Unified Representations of Normalcy for Time Series Anomaly Detection
U²AD learns unified normal data representations via score-based generative modeling and a novel time-dependent score network to outperform prior methods in accuracy and early anomaly detection for multivariate time series.