Searcharxiv⌕ Search

arXiv subjects

Yuting Bai

Publications and source records attributed to Yuting Bai.

6 recordsLinked to original sources
arXiv2026

Local Current Algebra for the HK Universality Class

We show that a Hamiltonian in terms of the local real-space currents obeying an $\mathfrak{su}_1(2)$ affine Lie algebra eliminates the non-locality in the Hatsugai-Kohmoto model for a doped Mott insulator. We establish this local correspondence through the Bjorken-Johnson-Low prescription for anomalous commutators. With this result, we show that the charge susceptibility computed from the current Hamiltonian is identical to that with the elemental Fermionic fields. Consequently, the HK model is local in real space, though not in terms of the Fermionic fields, thereby eliminating the key criticism of this model and reinforcing the utility of current algebras for strong interactions.

cond-mat.str-el↗
arXiv2025

Proof that Momentum Mixing Hatsugai Kohmoto equals the Twisted Hubbard Model

We prove formally that the momentum-mixing Hatsugai-Kohmoto model (MMHK) is the Hubbard model with a twist. With this result in tow, we rely on the proof of Watanabe's that two models which differ by a twist must have the same bulk physics. Consequently, we have proven that MMHK=Hubbard in the charge sector.

cond-mat.str-el↗
arXiv2025

Wrist2Finger: Sensing Fingertip Force for Force-Aware Hand Interaction with a Ring-Watch Wearable

Hand pose tracking is essential for advancing applications in human-computer interaction. Current approaches, such as vision-based systems and wearable devices, face limitations in portability, usability, and practicality. We present a novel wearable system that reconstructs 3D hand pose and estimates per-finger forces using a minimal ring-watch sensor setup. A ring worn on the finger integrates an inertial measurement unit (IMU) to capture finger motion, while a smartwatch-based single-channel electromyography (EMG) sensor on the wrist detects muscle activations. By leveraging the complementary strengths of motion sensing and muscle signals, our approach achieves accurate hand pose tracking and grip force estimation in a compact wearable form factor. We develop a dual-branch transformer network that fuses IMU and EMG data with cross-modal attention to predict finger joint positions and forces simultaneously. A custom loss function imposes kinematic constraints for smooth force variation and realistic force saturation. Evaluation with 20 participants performing daily object interaction gestures demonstrates an average Mean Per Joint Position Error (MPJPE) of 0.57 cm and a fingertip force estimation (RMSE: 0.213, r=0.76). We showcase our system in a real-time Unity application, enabling virtual hand interactions that respond to user-applied forces. This minimal, force-aware tracking system has broad implications for VR/AR, assistive prosthetics, and ergonomic monitoring.

cs.HC↗
arXiv2025

TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis

Traditional Chinese medicine (TCM) tongue diagnosis, while clinically valuable, faces standardization challenges due to subjective interpretation and inconsistent imaging protocols, compounded by the lack of large-scale, annotated datasets for AI development. To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis, comprising 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories (averaging 2.54 clinically validated labels per image, all verified by licensed TCM practitioners). The dataset supports multiple annotation formats (COCO, TXT, XML) for broad usability and has been benchmarked using nine deep learning models (YOLOv5/v7/v8 variants, SSD, and MobileNetV2) to demonstrate its utility for AI development. This resource provides a critical foundation for advancing reliable computational tools in TCM, bridging the data shortage that has hindered progress in the field, and facilitating the integration of AI into both research and clinical practice through standardized, high-quality diagnostic data.

eess.IV↗
arXiv2024

Geometry Dynamics in Chiral Superfluids

We investigate the geometric response of chiral superfluids when coupled to a dynamic background geometry. We find that geometry fluctuations, represented by the flexural mode, interact with the superfluid phase fluctuations (the Goldstone mode). Starting from a minimally coupled theory, we derive the equilibrium conditions for a static background defined by supercurrent, curvature, and tension, and then obtain linearized equations for the propagation of the Goldstone and flexural modes. The equations reveal distinctive chirality-dependent effects in the propagation of the flexural mode. Specifically, a background supercurrent induces a chiral drag effect, localizing flexural waves at the superfluid boundary, while background curvature introduces anisotropic corrections to the superfluid phase and group velocities, as well as a tension in the flexural mode dispersion. Furthermore, curvature couples flexural and phase modes into dressed excitations, with tilted Dirac cones along the principal curvature directions. These effects provide dynamical signatures of the formation of a chiral condensate, and can be tuned by manipulating the background geometry.

cond-mat.quant-gas↗