A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.
A universal algorithm for sequential data compression
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.
citing papers explorer
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Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level
A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.
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MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
MesonGS++ achieves over 34x compression of 3D Gaussian Splatting models post-training while preserving or exceeding original rendering quality through size-aware hyperparameter optimization.