Qian Wang

BioMoTouch is a multi-modal touch authentication framework that jointly models physiological contact structures (from capacitive screens) and behavioral motion dynamics (from inertial sensors) to achieve high accuracy and robustness in touch-based authentication.

The paper introduces MIRA, a bilingual benchmark that reveals that LLMs tend to dilute or omit critical medical information when responding to prompts from users with low health literacy, a pattern termed Differential Information Dilution (DID).

The paper proposes replacing expensive, always-on LLM calls for proactive agent triggering with a specialized Temporal-Graph-Learning (TGL) model, significantly improving efficiency and performance.

The paper proposes a unified, contrast-agnostic framework that uses parameter-informed disentanglement and adaptive experts to robustly correct motion artifacts in MRI across various modalities and severities.