arXiv subjects

Teng Yin

Publications and source records attributed to Teng Yin.

3 recordsLinked to original sources

arXiv2025

Theory of Remaining Exceptional Points from Nongeneric Splitting in Non-Hermitian Systems

In non-Hermitian physics, high-order exceptional points(HOEPs) with eigenvalues and eigenvectors coalesce are known for their enhanced sensitivity to perturbations. Typically, they exhibit eigenvalue splitting that scales as {\epsilon}^(1/n), which is referred to as the generic response. However, under certain conditions, a nongeneric response of HOEPs occurs where the splitting follows a lower order {\epsilon}^(1/m) (m<n). A nongeneric response of HOEPs with a lower order splitting lead to the remaining EPs. While the presence of these remaining EPs is acknowledged, a thorough elucidation of their fundamental properties has yet to be achieved. In this work, we demonstrate those unsplit eigenvalue points must constitute remaining EPs in a perturbed n-orders HOEPs system. Combining graph theory and topological analysis, the number and splitting order of the remaining EPs is studied. This framework not only resolves a fundamental challenge in HOEPs but also paves the way for exploiting remaining EPs in applications such as anisotropic sensing and the design of Dirac exceptional points.

physics.optics↗

arXiv2025

PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis

Missing modalities pose a major issue in Alzheimer's Disease (AD) diagnosis, as many subjects lack full imaging data due to cost and clinical constraints. While multi-modal learning leverages complementary information, most existing methods train only on complete data, ignoring the large proportion of incomplete samples in real-world datasets like ADNI. This reduces the effective training set and limits the full use of valuable medical data. While some methods incorporate incomplete samples, they fail to effectively address inter-modal feature alignment and knowledge transfer challenges under high missing rates. To address this, we propose a Prototype-Guided Adaptive Distillation (PGAD) framework that directly incorporates incomplete multi-modal data into training. PGAD enhances missing modality representations through prototype matching and balances learning with a dynamic sampling strategy. We validate PGAD on the ADNI dataset with varying missing rates (20%, 50%, and 70%) and demonstrate that it significantly outperforms state-of-the-art approaches. Ablation studies confirm the effectiveness of prototype matching and adaptive sampling, highlighting the potential of our framework for robust and scalable AD diagnosis in real-world clinical settings.

eess.IV↗

arXiv2022

A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection

Empirical likelihood-based methods have been used under the Bayesian framework (BayesEL) in recent times. For statistical inference, these methods require efficient Markov chain Monte Carlo (MCMC) samplers for drawing observations from the parameter posterior distributions. However, the complex, especially non-convex, nature of the empirical likelihood support makes such MCMC algorithms harder to design. Such difficulties have restricted the use of BayesEL methods in many applications. In this article, we propose a two-step Metropolis-Hastings algorithm to sample from the BayesEL posteriors. Our proposal uses the current values of suitable subsets of the parameters and the estimating equations determining the underlying empirical likelihood to propose values of the remaining parameters. The proposed method is thus suitable for sampling from BayesEL posteriors in many complex problems, especially those with discontinuous estimating equations, e.g., simultaneous quantile regression. Furthermore, the proposed method easily extends to BayesEL model selection through a reversible jump Markov chain Monte Carlo procedure. Several illustrative, real-life applications of our proposed methods are presented.

stat.ME↗