arXiv subjects

Xiaoxuan Li

Publications and source records attributed to Xiaoxuan Li.

10 recordsLinked to original sources

arXiv2026

A 0.35 mm Silicon Carbide Diffractive Waveguide with Dual Parameter Apodization for Full Color Augmented Reality

Augmented reality eyewear offers a transformative interface poised to reshape human information interaction. In this context, silicon carbide (SiC) offers unique advantages for diffractive waveguides with its high refractive index and excellent thermal conductivity. However, in single-layer full-color displays, existing SiC waveguides generally remain thicker than 0.5 mm to reduce the bounce count of total internal reflection, thereby avoiding severe spatial variations in luminance and color. Here, we demonstrate a 0.35 mm SiC diffractive waveguide with an ultra-lightweight of only 1.98 g. The challenge of spatial non-uniformity is addressed by dual-parameter apodized gratings with continuously varying depth and duty cycle, enabling fine spatial control over local diffraction efficiency. To realize high-throughput production, a parallel gradient transfer method compatible with nanoimprint lithography is introduced, enabling wafer-scale patterning of four lens pairs per 8-inch SiC wafer. Furthermore, magnesium fluoride planarization suppresses grating visibility and achieves a high see-through transmittance of 92%. Optical simulations confirm that this architecture achieves balanced full-color transmission across a 30-degree field of view. This strategy provides a scalable route toward ultra-light and visually unobtrusive glasses, opening new opportunities for practical consumer-grade wearable displays.

physics.optics↗

arXiv2026

Monolithic Multifocal Diamond Metalens for High-Power Laser Systems

High-power laser systems increasingly rely on multi-beam processing to enhance manufacturing throughput. However, conventional multifocal systems remain constrained by bulky architectures, stringent alignment requirements, and susceptibility to laser-induced degradation under intense irradiation. Here, we demonstrate a monolithic multifocal diamond metalens with a 7.2 mm aperture that maintains exceptional thermal stability and power tolerance. The device employs high-aspect-ratio truncated-cone diamond nanopillars to generate two focal spots separated by 200 μm at a focal length of 4 mm. Under sustained 25 W pulsed-laser irradiation for 1 h, the diamond metalens exhibits a focal shift of only 25.5 μm, resulting in a maximum processing-depth variation of 33.2 μm during 4H silicon carbide (SiC) laser scribing, far below the 319.1 μm deviation observed for a commercial objective lens combined with a beam-splitting diffractive optical element (DOE). Even under extreme optical loading, the metalens withstands continuous-wave laser irradiation up to 8.25 kW for 30 s without structural degradation, while complementary pulsed testing yields a laser-induced damage threshold (LIDT) of 2.45 J/(cm^2) for diamond. This work broadens the operating envelope of transmissive meta-optics to extreme optical loads, opening new opportunities across high-power photonic systems.

physics.optics↗

arXiv2026

Coupling model of metallic target ablation-plasma evolution-radiation under nanosecond laser irradiation

The interaction of nanosecond laser pulses with metallic materials involves multiple complex physical processes. It is challenging to construct a self-consistent model capable of uniformly describing all stages. This work establishes a multi-physics coupling model for pure iron, encompassing laser energy deposition, solid-liquid phase transition, gas-liquid interfacial kinetic transport, plasma expansion and ionization, and spectral radiation. The numerical solution adopts a partition method, utilizing an implicit compact difference scheme for the target and a Mac-Cormack explicit scheme for the plasma. The simulations elucidate the emergence of plasma shielding and its inhibitory effect on the evaporation process, thereby confirming that 81.6% of the early-stage ablation products are transported through a supersonic expansion mode. The model successfully captures the complete evolution of the plasma plume from a high-temperature, highly ionized state to a low-temperature, neutral atomic state. Based on this, spectral calculations demonstrate the dynamic evolution of radiative characteristics from an early stage featuring a strong continuum background dominated by ion lines to a later stage where the continuum attenuates, atomic lines become prominent, and self-absorption appears. The emergence of self-absorption proves the ability of the model to effectively capture the optical thickness effects arising from spatial inhomogeneity within the plasma. Through systematic comparison between experimentally measured spectra and calculated results from the PrismSPECT and NIST LIBS spectral programs, the model presented here achieves the highest comprehensive scores in quantitative evaluations of multiple channels. This validates the necessity and superiority of the full-chain self-consistent modeling approach, especially in describing plasma inhomogeneity and radiation transport.

physics.plasm-ph↗

arXiv2026

How Many Reflections Make a Dihedral Set Large?

Given a size-$k$ subset $S$ of a group $G$, how large can the product set $S^n$ be? We study this question, at several layers of refinement, for the infinite dihedral group. First, we give an explicit formula for the maximum size of $S^n$ among all size-$k$ subsets with a prescribed number of reflections. We then determine the optimal number of reflections that a size-$k$ set should contain in order to maximize $|S^n|$. When $k$ is fixed and $n\to\infty$, we obtain a clean asymptotic expression for the maximal size of $S^n$. Moreover, we compute this asymptotic separately for each fixed number of reflections in $S$. We show that the number of reflections influences the asymptotic size of $S^n$ only through a multiplicative coefficient, which admits a direct probabilistic interpretation. Finally, we compute the growth exponent of the maximum of $|S^n|$ when~$k=~n$.

math.GR↗

arXiv2024

Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process.

cs.LG↗

arXiv2024

Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs

Large Language Models (LLMs) have demonstrated remarkable efficiency in tackling various tasks based on human instructions, but studies reveal that they often struggle with tasks requiring reasoning, such as math or physics. This limitation raises questions about whether LLMs truly comprehend embedded knowledge or merely learn to replicate the token distribution without a true understanding of the content. In this paper, we delve into this problem and aim to enhance the reasoning capabilities of LLMs. First, we investigate if the model has genuine reasoning capabilities by visualizing the text generation process at the attention and representation level. Then, we formulate the reasoning process of LLMs into a causal framework, which provides a formal explanation of the problems observed in the visualization. Finally, building upon this causal framework, we propose Deconfounded Causal Adaptation (DCA), a novel parameter-efficient fine-tuning (PEFT) method to enhance the model's reasoning capabilities by encouraging the model to extract the general problem-solving skills and apply these skills to different questions. Experiments show that our method outperforms the baseline consistently across multiple benchmarks, and with only 1.2M tunable parameters, we achieve better or comparable results to other fine-tuning methods. This demonstrates the effectiveness and efficiency of our method in improving the overall accuracy and reliability of LLMs.

cs.CL↗

arXiv2024

Ultra-Thin, Ultra-Light, Rainbow-Free AR Glasses Based on Single-Layer Full-Color SiC Diffrcative Waveguide

As information interaction technology advances, the efficiency, dimensionality, and user experience of information transmission have significantly improved. Communication has evolved from letters to telegraphs, markedly increasing transmission speed; from telephones to video calls, enhancing communication dimensions; and from smartphones to augmented reality (AR) displays, which provide increasingly immersive user experiences. Surface relief grating (SRG) diffractive waveguides have attracted considerable attention for their optimal balance between weight, size, optical performance, and mass production capabilities, positioning them as a leading solution for AR displays. However, as consumer expectations for higher display quality and better device integration rise, traditional high-refractive-index glass-based diffractive waveguides face limitations, including bulkiness, heavy weight, and conspicuous rainbow artifacts in full-color displays. To overcome these challenges, a novel solution: ultra-thin, lightweight silicon carbide (SiC) AR prescription glasses was proposed. This solution achieves full-color displays without rainbow artifacts, with total weight of just 2.685 g and thickness of only 0.55 mm. Moreover, these glasses are compatible with prescription Fresnel lenses and are well-suited for scalable mass production. This innovation provides a robust platform for the seamless integration of augmented reality into daily life, offering significant potential to enhance user interaction.

physics.optics↗

arXiv2024

Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy

Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essential for personalizing the learning experience, is challenging due to the inherent noise in student-generated data. Moreover, while conventional graph-based methods can capture the complex network of student and question interactions, they often fall short under cold start conditions where limited student engagement with questions yields sparse data. To address both challenges, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness.

cs.LG↗

arXiv2021

DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions

Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question repositories. Existing methods for AQQR rely solely on explicitly-defined criteria such as readability and word count, while not fully utilising the power of state-of-the-art deep-learning techniques. We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform. Along with designing DeepQR, we investigate models based on explicitly-defined features, or semantic features, or both. We also introduce a self-attention mechanism to capture semantic correlations between MCQ components, and a contrastive-learning approach to acquire question representations using quality ratings. Extensive experiments on datasets collected from eight university-level courses illustrate that DeepQR has superior performance over six comparative models.

cs.CL↗

arXiv2014

On the Estimation of Directional Returns to Scale via DEA models

Data envelopment analysis (DEA) is one of the most commonly used methods to estimate the returns to scale (RTS) of the public sector (e.g., research institutions). Existing studies are all based on the traditional definition of RTS in economics and assume that multiple inputs and outputs change in the same proportion, which is the starting point to determine the qualitative and quantitative features of RTS of decision making units (DMUs). However, for more complex products, such as the scientific research in institutes, changes of various types of inputs or outputs are often not in proportion. Therefore, the existing definition of RTS in the framework of DEA method may not meet the need to estimate the RTS of research institutions with multiple inputs and outputs. This paper proposes a definition of directional RTS in the DEA framework and estimates the directional RTS of research institutions using DEA models. Further in-depth analysis is conducted for an illustrative example of 16 basic research institutes in Chinese Academy of Sciences (CAS) in 2010.

math.OC↗