arXiv subjects

Qun Luo

Publications and source records attributed to Qun Luo.

5 recordsLinked to original sources

arXiv2025

Satellite Signal Detection via Rydberg-Atom Receiver

Rydberg-atom receivers aim for ultra-high sensitivity to microwave fields through various techniques, but receiving satellite signals has remained a significant challenge, due to the difficulty of capturing weak microwaves over long distances. In this work, we introduce a high-gain antenna to focus satellite signals, and then apply into an atomic cell via a microwave cavity. Using microwave-enhanced coupling, the minimum detectable power of incident microwave is down to -128 dBm, and the corresponding sensitivity is estimated as 21 nV/cm/Hz1/2 at frequency of 3.80 GHz. Furthermore, beacon signal from geostationary satellites is captured with Rydberg sensors for the first time, without the need for a low-noise amplifier. And C-band modulated signals are read out with a signal-to-noise ratio of 8 dB. Our results mark a significant breakthrough in facilitating satellite communications using Rydberg-atom receivers.

quant-ph↗

arXiv2024

Deep Learning-Driven Microstructure Characterization and Vickers Hardness Prediction of Mg-Gd Alloys

In the field of materials science, exploring the relationship between composition, microstructure, and properties has long been a critical research focus. The mechanical performance of solid-solution Mg-Gd alloys is significantly influenced by Gd content, dendritic structures, and the presence of secondary phases. To better analyze and predict the impact of these factors, this study proposes a multimodal fusion learning framework based on image processing and deep learning techniques. This framework integrates both elemental composition and microstructural features to accurately predict the Vickers hardness of solid-solution Mg-Gd alloys. Initially, deep learning methods were employed to extract microstructural information from a variety of solid-solution Mg-Gd alloy images obtained from literature and experiments. This provided precise grain size and secondary phase microstructural features for performance prediction tasks. Subsequently, these quantitative analysis results were combined with Gd content information to construct a performance prediction dataset. Finally, a regression model based on the Transformer architecture was used to predict the Vickers hardness of Mg-Gd alloys. The experimental results indicate that the Transformer model performs best in terms of prediction accuracy, achieving an R^2 value of 0.9. Additionally, SHAP analysis identified critical values for four key features affecting the Vickers hardness of Mg-Gd alloys, providing valuable guidance for alloy design. These findings not only enhance the understanding of alloy performance but also offer theoretical support for future material design and optimization.

cs.LG↗

arXiv2024

Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation

Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance. Researchers have identified that beyond behavioral signals, rich semantic information in item descriptions is crucial for capturing hidden user intent. While large language models (LLMs) offer new ways to leverage this semantic data, the challenges of session anonymity, short-sequence nature, and high LLM training costs have hindered the development of a lightweight, efficient LLM framework for SBR. To address the above challenges, we propose an LLM-enhanced SBR framework that integrates semantic and behavioral signals from multiple views. This two-stage framework leverages the strengths of both LLMs and traditional SBR models while minimizing training costs. In the first stage, we use multi-view prompts to infer latent user intentions at the session semantic level, supported by an intent localization module to alleviate LLM hallucinations. In the second stage, we align and unify these semantic inferences with behavioral representations, effectively merging insights from both large and small models. Extensive experiments on two real datasets demonstrate that the LLM4SBR framework can effectively improve model performance. We release our codes along with the baselines at https://github.com/tsinghua-fib-lab/LLM4SBR.

cs.IR↗

arXiv2013

Multiple routes transmitted epidemics on multiplex networks

This letter investigates the multiple routes transmitted epidemic process on multiplex networks. We propose detailed theoretical analysis that allows us to accurately calculate the epidemic threshold and outbreak size. It is found that the epidemic can spread across the multiplex network even if all the network layers are well below their respective epidemic thresholds. Strong positive degree-degree correlation of nodes in multiplex network could lead to a much lower epidemic threshold and a relatively smaller outbreak size. However, the average similarity of neighbors from different layers of nodes has no obvious effect on the epidemic threshold and outbreak size.

cs.SI↗

arXiv2013

Multiple Vectors Propagation of Epidemics in Complex Networks

This letter investigates the epidemic spreading in two-vectors propagation network (TPN). We propose detailed theoretical analysis that allows us to accurately calculate the epidemic threshold and outbreak size. It is found that the epidemics can spread across the TPN even if two sub-single-vector propagation networks (SPNs) of TPN are well below their respective epidemic thresholds. Strong positive degree-degree correlation of nodes in TPN could lead to a much lower epidemic threshold and a relatively smaller outbreak size. However, the average similarity between the neighbors from different SPNs of nodes has no effect on the epidemic threshold and outbreak size.

physics.soc-ph↗