The paper proposes an attention-guided hybrid framework combining 1D and 2D CNNs to robustly enhance ECG-based biometric recognition, achieving high accuracy across multiple datasets and demonstrating…

The paper introduces CardioLens, a rigorous evaluation testbed for multi-sequence Cardiac MRI, which reveals that current Multimodal Large Language Models (MLLMs) exhibit a significant 'clinical reali…

The paper introduces the Vector Network (VN), a novel recurrent architecture that replaces fixed weight matrices with reusable weight atoms, enabling superior compositional generalization by making st…

The paper introduces ArrythML, a highly efficient autoencoder-based TinyML model that enables accurate, low-power arrhythmia detection directly on resource-constrained embedded wearable devices.

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

VITAL introduces a novel latent-space reasoning framework for medical MLLMs, utilizing visual-semantic dual supervision to enhance reasoning capabilities and provide crucial interpretability without s…

The paper introduces Score-Guided Classification (SGC), a novel framework that uses an unsupervised anomaly score as a 'Pathological Prior' to guide EEG-based depression detection, overcoming the limi…

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

The paper proposes a multi-dimensional evaluation framework to assess EEG foundation models under realistic low-resource conditions, finding that while these models excel in long-context tasks, their…

VISReg introduces a novel regularization technique that combines variance control with a Sliced-Wasserstein-based sketching objective to stabilize self-supervised learning, achieving state-of-the-art…

The paper proposes a novel, highly secure real-time ECG monitoring framework that uses a patient's own ECG signal characteristics to generate unique, dynamic encryption keys, ensuring confidential dat…

The paper proposes a novel convolutional block and optimization algorithm to implement resource-efficient 1D-CNNs for atrial fibrillation detection on tiny smart sensor systems, achieving high accurac…

This paper identifies prediction bias, a failure mode of entropy minimization in test-time adaptation, and proposes Distribution Shift Bias Reduction (DSBR) to stabilize adaptation and prevent model c…

This paper benchmarks five positional encoding strategies for transformer-based EEG foundation models, concluding that the optimal encoding is task-dependent and no single strategy is universally supe…

The paper demonstrates that the valence structure learned by modern LLMs aligns with human EEG emotional representations, but finds that further supervised alignment is ineffective due to a phenomenon…

This paper provides the first non-vacuous generalization analysis for the Stochastic Variance Reduced Gradient (SVRG) method by establishing sharp, data-dependent algorithmic stability bounds, thereby…

The paper advocates for a paradigm shift toward joint Spatial Representation Learning (SRL) that unifies raster imagery and structured vector data into a single embedding space for developing more sem…

The paper theoretically analyzes the properties that optimal sparse autoencoder (SAE) dictionaries must satisfy, deriving constraints that explain observed SAE behaviors like hierarchical splitting an…