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FlowLet is a conditional generative framework that synthesizes age-conditioned 3D brain MRIs using flow matching in an invertible wavelet domain, improving brain age prediction accuracy for underrepresented age groups with high efficiency.
WaveScope is an MCP server that applies wavelet transforms to codebases, providing LLMs with multi-resolution structural context to improve code understanding and editing, addressing context rot and structural awareness.
WaveFilter proposes a training-free, wavelet-guided KV cache filtering framework for diffusion large language models that enhances long-context capability by precisely identifying key tokens and constructing sparse caches, improving performance on complex long-context tasks.
This paper proposes DSFM, a novel generative framework that uses wavelet decomposition and spectral flow matching to synthesize realistic fMRI time series for brain disorder identification, addressing data scarcity and non-stationarity challenges.
Introduces QuChaTeR, a hybrid architecture combining wavelet-based preprocessing, chaotic maps, and variational quantum circuits with recurrent structures for earthquake prediction, demonstrating faster convergence and superior accuracy over classical and quantum baselines.