arXiv Machine Learning By Jaehyuk Lee, Yeajin Lee, Dayeon Shin, Donghun Lee

RIPPLE: Generating Multi-Channel Phase, Not Recovering It

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arXiv:2607. 27775v1 Announce Type: new Abstract: Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent decoder---applied independently to each channel.

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arXiv Machine Learning
Jun 10

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

arXiv:2606. 10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that pointwise metrics fail to detect.

By Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas
arXiv AI
Sep 15

MANAS-2: Constrained Reconstruction for EEG Foundation Models

MANAS-2 is a new EEG foundation model that integrates a Raw‑Band Hybrid masked autoencoder with a physics‑motivated Constrained Reconstruction (ConRec) regularizer. ConRec penalizes RMS energy differences in short temporal windows, guiding the encoder toward oscillatory‑envelope organization. Across seven held‑out EEG datasets, adding ConRec improves spectral‑power recovery (R² from 0.860 to 0.906) and band‑energy dynamics (R² from 0.283 to 0.354), while maintaining strong temporal waveform recoverability and outperforming leading EEG models on downstream tasks.

By Arvasu Kulkarni, Aditya Ray Mishra, Mahir Jain, Parshva Runwal, Lakshya Saini, Siddharth Panwar, Sandeep Singh
arXiv AI
4d ago

PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG

arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...

By Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury
arXiv Machine Learning
Jul 27

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

By Masashi Sode, Gianmarco Pinton