arXiv subjects

Hongwei Jiang

Publications and source records attributed to Hongwei Jiang.

3 recordsLinked to original sources

arXiv2025

Optical Pin Beams: Research Progresses and Emerging Applications

Optical pin beams (OPBs) represent a novel class of structured light fields engineered for resilient, long-distance propagation. Their exceptional stability and strong resistance to atmospheric turbulence make them a compelling alternative to conventional Gaussian and other structured beams for free-space optical systems. This review provides a comprehensive overview of the physical principles, generation strategies, experimental realizations, and emerging applications of OPBs. By precise spatial modulation of the optical wave vectors, OPBs achieve highly collimated, self-reconstructing propagation with distinctive pin-like features that confer remarkable robustness and self-healing capability. We further discuss several OPB derivatives--including vortex, inverted, and vortex-inverted OPBs--which expand the functional landscape by enabling flexible control over amplitude, phase, polarization, and orbital angular momentum. Experimentally, OPBs have demonstrated outstanding performance across diverse platforms, ranging from free-space and underwater optical communications to optical trapping and super-resolution imaging. With their unique combination of propagation stability, light-field tunability, and environmental adaptability, OPBs hold strong promise for next-generation optical communication, precision sensing, and advanced imaging technologies. This review summarizes recent research progresses in OPBs and highlights key opportunities and prospects for advancing their scientific discoveries and practical applications.

physics.optics↗

arXiv2024

A modified debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences about the effects of modifiable risk factors on diseases from observational data. One of the major challenges in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the risk factor of interest, a setting known as many weak instruments. Many existing methods, such as the popular inverse-variance weighted (IVW) method, could be biased when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator, which is shown to be robust to many weak instruments, was recently proposed. However, this estimator still has non-ignorable bias when the effective sample size is small. In this paper, we propose a modified debiased IVW (mdIVW) estimator by multiplying a modification factor to the original dIVW estimator. After this simple correction, we show that the bias of the mdIVW estimator converges to zero at a faster rate than that of the dIVW estimator under some regularity conditions. Moreover, the mdIVW estimator has smaller variance than the dIVW estimator.We further extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the dIVW estimator through extensive simulation studies and real data analysis.

stat.ME↗

arXiv2021

Distillation based Multi-task Learning: A Candidate Generation Model for Improving Reading Duration

In feeds recommendation, the first step is candidate generation. Most of the candidate generation models are based on CTR estimation, which do not consider user's satisfaction with the clicked item. Items with low quality but attractive title (i.e., click baits) may be recommended to the user, which worsens the user experience. One solution to this problem is to model the click and the reading duration simultaneously under the multi-task learning (MTL) framework. There are two challenges in the modeling. The first one is how to deal with the zero duration of the negative samples, which does not necessarily indicate dislikes. The second one is how to perform multi-task learning in the candidate generation model with double tower structure that can only model one single task. In this paper, we propose an distillation based multi-task learning (DMTL) approach to tackle these two challenges. We model duration by considering its dependency of click in the MTL, and then transfer the knowledge learned from the MTL teacher model to the student candidate generation model by distillation. Experiments conducted on dataset gathered from traffic logs of Tencent Kandian's recommender system show that the proposed approach outperforms the competitors significantly in modeling duration, which demonstrates the effectiveness of the proposed candidate generation model.

cs.IR↗