arXiv subjects

Haoming Zhong

Publications and source records attributed to Haoming Zhong.

2 recordsLinked to original sources

arXiv2026

Magnetic Lifting of BIC Symmetry Protection Enables Nonreciprocal Beam Steering and Angular Information Encoding

Symmetry-protected bound states in the continuum (SP-BICs) offer exceptional optical confinement but remain nonradiative. Here, we demonstrate that magnetically lifting their protecting symmetry enables nonreciprocal beam steering with intrinsic angular information encoding. In a magneto-optical photonic crystal slab, an in-plane magnetic field converts an SP-BIC into a radiative quasi-BIC and, together with vertical dielectric asymmetry, continuously displaces the associated band extremum away from the Gamma point. The steered beam exhibits two independent locking relations: its wavelength is locked to the elevation angle, while its polarization orientation is locked to the azimuthal angle. These relations embed complementary spectral and polarization information into the scanning beam, providing additional sensing channels for multidimensional LiDAR. Moreover, the nonreciprocal dispersion gives different resonance frequencies at opposite wave vectors, supporting resonant emission at +k while back-reflected light at the same frequency is off resonance at -k, opening a route to intrinsic self-isolation and reduced optical feedback. These findings establish a unified framework connecting magnetic beam steering, angular information encoding, and nonreciprocal feedback suppression, with prospects for compact reconfigurable light sources and integrated optical engines.

physics.optics↗

arXiv2020

Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.

cs.CL↗