Meow-Omni 1 is a quad-modal MLLM that fuses video, audio, physiological time-series, and text to achieve 71.16% accuracy on feline intent recognition in the new MeowBench benchmark.
Inexact gmres iterations and relaxation strate- gies with fast-multipole boundary element method.Advances in Computational Mathematics, 48 (3):32, 2022a
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MUDRA extends FLDA to multivariate time series with missing data via an ECM algorithm and shows improved classification over prior methods on the Articulary Word Recognition dataset.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
citing papers explorer
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Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Meow-Omni 1 is a quad-modal MLLM that fuses video, audio, physiological time-series, and text to achieve 71.16% accuracy on feline intent recognition in the new MeowBench benchmark.
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Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
MUDRA extends FLDA to multivariate time series with missing data via an ECM algorithm and shows improved classification over prior methods on the Articulary Word Recognition dataset.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.