arXiv subjects

Siyang Zheng

Publications and source records attributed to Siyang Zheng.

4 recordsLinked to original sources

arXiv2026

Femtosecond Engineering of PolyMethylPentene(PMP) Nonlinearity via NA, Spin/orbital angular momentum (OAM), and In-Situ SC Diagnostics

While femtosecond vortex beams are widely studied for photonic fabrication, how OAM affects third-order susceptibility, energy deposition, and structural symmetry in transparent polymers remains unclear. To address this gap, we investigate femtosecond vortex beam interactions with polymethylpentene (PMP) to establish OAM control over beam shaping and energy deposition in laser micromachining. A 775 nm femtosecond laser is frequency-doubled to 387.5 nm and phase-shaped by a reflective liquid crystal-spatial light modulator, with OAM imprinted via computer-generated holograms and independently controlled spin via a quarter-wave plate. Theoretical modeling of the tensorial nonlinear polarization, combined with systematic experiments on low-NA filamentation, supercontinuum spectroscopy using an advanced spectrometer, and high-NA inscription with NA = 0.4 and 0.7, reveals that the same $χ^{(3)}$ tensor components govern all regimes. The transition from perturbative to dissipative behavior is determined solely by whether the local intensity exceeds the plasma formation threshold. Through an analytical framework based on the isotropic material model, the key innovation lies in identifying that $χ_{1122}$ governs the energy deposition and plasma threshold, whereas $χ_{1221}$ mediates the transverse nonlinear current and helicity-dependent asymmetry, thereby converting phase topology into permanent chiral structures. This enables OAM-controlled structuring with sub-micrometer feature sizes (approximately 0.43 $μ$m diameter and approximately 200 nm$^2$ area) and localized intensities reaching $10^{14}$--$10^{15}$ W/cm$^2$, providing an engineering framework for deterministic fabrication of chiral waveguides and photonic devices through precise control of topological charge and focusing conditions.

physics.optics↗

arXiv2025

Improving Contactless Fingerprint Recognition with Robust 3D Feature Extraction and Graph Embedding

Contactless fingerprint has gained lots of attention in recent fingerprint studies. However, most existing contactless fingerprint algorithms treat contactless fingerprints as 2D plain fingerprints, and still utilize traditional contact-based 2D fingerprints recognition methods. This recognition approach lacks consideration of the modality difference between contactless and contact fingerprints, especially the intrinsic 3D features in contactless fingerprints. This paper proposes a novel contactless fingerprint recognition algorithm that captures the revealed 3D feature of contactless fingerprints rather than the plain 2D feature. The proposed method first recovers 3D features from the input contactless fingerprint, including the 3D shape model and 3D fingerprint feature (minutiae, orientation, etc.). Then, a novel 3D graph matching method is proposed according to the extracted 3D feature. Additionally, the proposed method is able to perform robust 3D feature extractions on various contactless fingerprints across multiple finger poses. The results of the experiments on contactless fingerprint databases show that the proposed method successfully improves the matching accuracy of contactless fingerprints. Exceptionally, our method performs stably across multiple poses of contactless fingerprints due to 3D embeddings, which is a great advantage compared to 2D-based previous contactless fingerprint recognition algorithms.

cs.CV↗

arXiv2025

OLMA: One Loss for More Accurate Time Series Forecasting

Time series forecasting faces two important but often overlooked challenges. Firstly, the inherent random noise in the time series labels sets a theoretical lower bound for the forecasting error, which is positively correlated with the entropy of the labels. Secondly, neural networks exhibit a frequency bias when modeling the state-space of time series, that is, the model performs well in learning certain frequency bands but poorly in others, thus restricting the overall forecasting performance. To address the first challenge, we prove a theorem that there exists a unitary transformation that can reduce the marginal entropy of multiple correlated Gaussian processes, thereby providing guidance for reducing the lower bound of forecasting error. Furthermore, experiments confirm that Discrete Fourier Transform (DFT) can reduce the entropy in the majority of scenarios. Correspondingly, to alleviate the frequency bias, we jointly introduce supervision in the frequency domain along the temporal dimension through DFT and Discrete Wavelet Transform (DWT). This supervision-side strategy is highly general and can be seamlessly integrated into any supervised learning method. Moreover, we propose a novel loss function named OLMA, which utilizes the frequency domain transformation across both channel and temporal dimensions to enhance forecasting. Finally, the experimental results on multiple datasets demonstrate the effectiveness of OLMA in addressing the above two challenges and the resulting improvement in forecasting accuracy. The results also indicate that the perspectives of entropy and frequency bias provide a new and feasible research direction for time series forecasting. The code is available at: https://github.com/Yuyun1011/OLMA-One-Loss-for-More-Accurate-Time-Series-Forecasting.

cs.LG↗

arXiv2023

DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation

Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis. However, existing methodologies grapple with the challenge of data scarcity during the training phase, leading to over-fitting. To mitigate this issue, we introduce a novel Unsupervised Dense Few-shot Medical Image Segmentation Model Training Pipeline (DenseMP) that capitalizes on unsupervised dense pre-training. DenseMP is composed of two distinct stages: (1) segmentation-aware dense contrastive pre-training, and (2) few-shot-aware superpixel guided dense pre-training. These stages collaboratively yield a pre-trained initial model specifically designed for few-shot medical image segmentation, which can subsequently be fine-tuned on the target dataset. Our proposed pipeline significantly enhances the performance of the widely recognized few-shot segmentation model, PA-Net, achieving state-of-the-art results on the Abd-CT and Abd-MRI datasets. Code will be released after acceptance.

cs.CV↗