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Paper#Dynamical Systems, Koopman Operator, Spectral Analysis, Data-Driven Methods🔬 ResearchAnalyzed: Jan 3, 2026 06:39

Data-Driven Spectral Analysis with Pseudo-Resolvent Koopman Operator

Published:Dec 31, 2025 16:33
1 min read
ArXiv

Analysis

This paper introduces a data-driven method to analyze the spectrum of the Koopman operator, a crucial tool in dynamical systems analysis. The method addresses the problem of spectral pollution, a common issue in finite-dimensional approximations of the Koopman operator, by constructing a pseudo-resolvent operator. The paper's significance lies in its ability to provide accurate spectral analysis from time-series data, suppressing spectral pollution and resolving closely spaced spectral components, which is validated through numerical experiments on various dynamical systems.
Reference

“The method effectively suppresses spectral pollution and resolves closely spaced spectral components.”

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Research#speech recognition📝 BlogAnalyzed: Dec 29, 2025 08:21

Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191

Published:Oct 16, 2018 16:47
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Herman Kamper, a lecturer at Stellenbosch University, discussing his research on low-resource speech processing. The focus is on speech recognition in scenarios with limited or no training data. The discussion covers the differences between low-resource and standard speech recognition, the interplay between linguistic and statistical approaches, and the specific methods used in Kamper's lab. The article highlights the importance of this research area, particularly in languages with limited resources, and the challenges involved in developing effective speech recognition systems in such contexts.
Reference

“The article doesn't contain a direct quote, but it discusses the work on limited- and zero-resource speech recognition.”

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