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Wei Quan

Publications and source records attributed to Wei Quan.

At least 19 recordsLinked to original sources
arXiv2026

Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability

In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package. We here extend this previous work in two key ways, focusing on data from Japan as a large dataset is necessary in order to allow for the training of a deep learning (autoencoder) model. First, we move from whole-region prediction (considering, for each candidate event, the likelihood of an event M $\geq$ 5.0 anywhere in the region in the next 15 days) to localised predictions in which both the region of feature computation and the region of prediction are restricted to a circle of radius 24 km around the candidate event, and we show that performance remains excellent, similar to our previous whole-region study for the same area. Second, we here couple this proven set of SSFs, based on one-dimensional (catalogue) data, with a novel feature based on magnitude-stratified two-dimensional seismic maps, obtained by training a VQ-VAE model to reproduce such maps as output and identifying a measure of its error in doing so with anomalies in magnitude distribution reflecting a localised build-up of crustal stress. We show that while localised prediction based only on a set of SSFs can be effective, with test AUC values as high as those obtained in the case of Japan in our previous whole-region study, the inclusion of the new natively-spatial VQ-VAE-derived feature, top-ranked by SHAP analysis, both improves prediction and points toward a new approach that might allow computationally time-demanding traditional seismic features to be entirely set aside.

cs.LG↗
arXiv2025

Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks

The increase of bandwidth-intensive applications in sixth-generation (6G) wireless networks, such as real-time volumetric streaming and multi-sensory extended reality, demands intelligent multicast routing solutions capable of delivering differentiated quality-of-service (QoS) at scale. Traditional shortest-path and multicast routing algorithms are either computationally prohibitive or structurally rigid, and they often fail to support heterogeneous user demands, leading to suboptimal resource utilization. Neural network-based approaches, while offering improved inference speed, typically lack topological generalization and scalability. To address these limitations, this paper presents a graph neural network (GNN)-based multicast routing framework that jointly minimizes total transmission cost and supports user-specific video quality requirements. The routing problem is formulated as a constrained minimum-flow optimization task, and a reinforcement learning algorithm is developed to sequentially construct efficient multicast trees by reusing paths and adapting to network dynamics. A graph attention network (GAT) is employed as the encoder to extract context-aware node embeddings, while a long short-term memory (LSTM) module models the sequential dependencies in routing decisions. Extensive simulations demonstrate that the proposed method closely approximates optimal dynamic programming-based solutions while significantly reducing computational complexity. The results also confirm strong generalization to large-scale and dynamic network topologies, highlighting the method's potential for real-time deployment in 6G multimedia delivery scenarios. Code is available at https://github.com/UNIC-Lab/GNN-Routing.

cs.NI↗
arXiv2025

On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach

The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming must be delivered on demand while minimizing network operational costs. Traditional routing approaches, such as shortest-path algorithms, fail to optimize flow multiplexing across multiple destinations, while conventional Steiner tree methods cannot accommodate heterogeneous quality-of-service (QoS) requirements-a critical need for 6G's personalized services. In this paper, we address a fundamental but unsolved challenge: the minimum flow problem (MFP) with multi-destination, heterogeneous outflow demands, which is pivotal for efficient multimedia distribution such as adaptive-resolution video streaming. To overcome the limitations of existing methods, we propose a two-stage dynamic programming-enhanced On-demand Steiner Tree (OST) algorithm, the first approach that jointly optimizes flow aggregation and QoS-aware path selection for arbitrary outflow requirements. We rigorously prove the optimality of OST using mathematical induction, demonstrating that it guarantees the minimum-cost multicast flow under differentiated service constraints. Extensive experiments in 6G-like multimedia transmission scenarios show that OST reduces total network flow by over 10% compared to state-of-the-art methods while ensuring on-demand QoS fulfillment. The complete code is available at https://github.com/UNIC-Lab/OST.

cs.NI↗
arXiv2025

Dynamically Polarized SERF Atomic Comagnetometer

Atomic spin sensors are essential for beyond-the-standard-model exploration, biomagnetic measurement, and quantum navigation. While the traditional DC mode spin-exchange relaxation-free (SERF) comagnetometer achieves ultrahigh sensitivity, further improvements require suppressing technical noise and surpassing standard quantum limit. In this work, we develop a K-Rb-$^{21}$Ne SERF atomic comagnetometer that dynamically polarizes the electron and nuclear spins, shielding signals from direct interference by pump light. We establish a three-phase evolutionary model for hybrid spin ensemble dynamics, yielding a complete analytical solution, and analyze the responses to various spin perturbations. Additionally, we achieve an averaged 38.5 $\%$ suppression of the polarization noise and identify the key factors that limit sensitivity improvements. The dynamically polarized comagnetometer exhibits effective suppression of technical noise and holds the potential to overcome quantum noise limit, while offering promising applications in exploring new physics and precise magnetic field measurements.

physics.atom-ph↗
arXiv2024

CVAM-Pose: Conditional Variational Autoencoder for Multi-Object Monocular Pose Estimation

Estimating rigid objects' poses is one of the fundamental problems in computer vision, with a range of applications across automation and augmented reality. Most existing approaches adopt one network per object class strategy, depend heavily on objects' 3D models, depth data, and employ a time-consuming iterative refinement, which could be impractical for some applications. This paper presents a novel approach, CVAM-Pose, for multi-object monocular pose estimation that addresses these limitations. The CVAM-Pose method employs a label-embedded conditional variational autoencoder network, to implicitly abstract regularised representations of multiple objects in a single low-dimensional latent space. This autoencoding process uses only images captured by a projective camera and is robust to objects' occlusion and scene clutter. The classes of objects are one-hot encoded and embedded throughout the network. The proposed label-embedded pose regression strategy interprets the learnt latent space representations utilising continuous pose representations. Ablation tests and systematic evaluations demonstrate the scalability and efficiency of the CVAM-Pose method for multi-object scenarios. The proposed CVAM-Pose outperforms competing latent space approaches. For example, it is respectively 25% and 20% better than AAE and Multi-Path methods, when evaluated using the $\mathrm{AR_{VSD}}$ metric on the Linemod-Occluded dataset. It also achieves results somewhat comparable to methods reliant on 3D models reported in BOP challenges. Code available: https://github.com/JZhao12/CVAM-Pose

cs.CV↗
arXiv2024

Towards Memory-Efficient Traffic Policing in Time-Sensitive Networking

Time-Sensitive Networking (TSN) is an emerging real-time Ethernet technology that provides deterministic communication for time-critical traffic. At its core, TSN relies on Time-Aware Shaper (TAS) for pre-allocating frames in specific time intervals and Per-Stream Filtering and Policing (PSFP) for mitigating the fatal disturbance of unavoidable frame drift. However, as first identified in this work, PSFP incurs heavy memory consumption during policing, hindering normal switching functionalities. This work proposes a lightweight policing design called FooDog, which could facilitate sub-microsecond jitter with ultra-low memory consumption. FooDog employs a period-wise and stream-wise structure to realize the memory-efficient PSFP without loss of determinism. Results using commercial FPGAs in typical aerospace scenarios show that FooDog could keep end-to-end time-sensitive traffic jitter <150 nanoseconds in the presence of abnormal traffic, comparable to typical TSN performance without anomalies. Meanwhile, it consumes merely hundreds of kilobits of memory, reducing >90% of on-chip memory overheads than unoptimized PSFP design.

cs.NI↗
arXiv2023

Axion-Like Particle Detection in Alkali-Noble-Gas Haloscopes

Revealing the essence of dark matter (DM) and dark energy is essential for understanding our universe. Ultralight (rest energy $<$10 eV) bosonic particles, including pseudoscalar axions and axion-like particles (ALPs) have emerged among leading candidates to explain the composition of DM and searching for them has become an important part of precision-measurement science. Ultrahigh-sensitivity alkali-noble-gas based comagnetometers and magnetometers are being used as powerful haloscopes, i.e., devices designed to search for DM present in the galactic halo. A broad variety of such devices include clock-comparison comagnetometers, self-compensating comagnetometers, hybrid-spin-resonance magnetometer, spin-exchange-relaxation-free magnetometers, nuclear magnetic-resonance magnetometers, Floquet magnetometers, masers, as well as devices like the cosmic axion spin-precession experiment (CASPEr) using liquid $^{129}$Xe, prepolarized via spin-exchange optical pumping with rubidium atoms. The combination of alkali metal and noble gas allows one to take the best advantage of the complementary properties of the two spin systems. This review summarizes the operational principles, experimental setups and the successful explorations of new physics using these haloscopes. Additionally, some limiting factors are pointed out for further improvement.

hep-ex↗
arXiv2023

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization.

cs.LG↗
arXiv2023

Yelp Reviews and Food Types: A Comparative Analysis of Ratings, Sentiments, and Topics

This study examines the relationship between Yelp reviews and food types, investigating how ratings, sentiments, and topics vary across different types of food. Specifically, we analyze how ratings and sentiments of reviews vary across food types, cluster food types based on ratings and sentiments, infer review topics using machine learning models, and compare topic distributions among different food types. Our analyses reveal that some food types have similar ratings, sentiments, and topics distributions, while others have distinct patterns. We identify four clusters of food types based on ratings and sentiments and find that reviewers tend to focus on different topics when reviewing certain food types. These findings have important implications for understanding user behavior and cultural influence on digital media platforms and promoting cross-cultural understanding and appreciation.

cs.CL↗
arXiv2023

Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, the aggregation and update functions in the GNN architecture are designed by utilizing the summation and power calculation components of the WMMSE algorithm, which leads to improved model generalization and interpretabiliy. Theoretical analysis of the proposed approach reveals its capability to simplify intricate end-to-end mappings and diminish the model exploration space, resulting in increased network expressiveness and enhanced optimization performance. Simulation results demonstrate the robustness, scalability, and strong performance of the proposed knowledge-driven resource allocation approach across diverse communication topologies without retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

eess.SY↗
arXiv2023

Exploring the Emotional and Mental Well-Being of Individuals with Long COVID Through Twitter Analysis

The COVID-19 pandemic has led to the emergence of Long COVID, a cluster of symptoms that persist after infection. Long COVID patients may also experience mental health challenges, making it essential to understand individuals' emotional and mental well-being. This study aims to gain a deeper understanding of Long COVID individuals' emotional and mental well-being, identify the topics that most concern them, and explore potential correlations between their emotions and social media activity. Specifically, we classify tweets into four categories based on the content, detect the presence of six basic emotions, and extract prevalent topics. Our analyses reveal that negative emotions dominated throughout the study period, with two peaks during critical periods, such as the outbreak of new COVID variants. The findings of this study have implications for policy and measures for addressing the mental health challenges of individuals with Long COVID and provide a foundation for future work.

cs.SI↗
arXiv2023

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at \url{https://github.com/UNIC-Lab/LOGNN}.

eess.SY↗
arXiv2023

Dark Matter Search with a Resonantly-Coupled Hybrid Spin System

Recent advances in tabletop quantum sensor technology have enabled searches for nongravitational interactions of dark matter (DM). Traditional axion DM experiments rely on sharp resonance, resulting in extensive scanning time to cover a wide mass range. In this work, we present a broadband approach in an alkali-${}^{21}$Ne spin system. We identify two distinct hybrid spin-coupled regimes: a self-compensation (SC) regime at low frequencies and a hybrid spin resonance (HSR) regime at higher frequencies. By utilizing these two distinct regimes, we significantly enhance the bandwidth of ${}^{21}$Ne nuclear spin compared to conventional nuclear magnetic resonance, while maintaining competitive sensitivity. We present a comprehensive broadband search for axion-like dark matter, covering 5 orders of magnitude of Compton frequencies range within $[10^{-2}, \, 10^3]$ Hz. We set new constraints on the axion dark matter interactions with neutrons and protons, accounting for the effects of DM stochasticity. For the axion-neutron coupling, our results reach a low value of $|g_{ann}|\le 3\times 10^{-10}$ in the frequency range $[2\times 10^{-2}, \, 4]$ Hz surpassing astrophysical limits and providing the strongest laboratory constraints in the $[10, \, 100]$ Hz range. For the axion-proton coupling, we offer the best terrestrial constraints for the frequency ranges $[2\times 10^{-2}, \, 5]$Hz and $[16, \, 7\times 10^{2}]$ Hz.

hep-ph↗
arXiv2023

Imaging molecular orbitals with laser-induced electron tunneling spectroscopy

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, mainly due to the fact that its phase information can be hardly extracted. In this work, we propose a method to retrieve the phase information of the ionizing molecular orbital with laser-induced electron tunneling spectroscopy. By analyzing the interference pattern in the photoelectron spectrum, the weighted coefficients and the relative phases of the constituent atomic orbitals for a molecular orbital can be extracted. With this information we reconstruct the highest occupied molecular orbital of N$_2$. Our work provides a reliable and general approach for imaging of molecular orbitals with the photoelectron spectroscopy.

physics.atom-ph↗
arXiv2022

A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization

This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature. Our proposed new boolean algorithm, MBOnvPSO, is notably simplified by the omission of a velocity update rule and has enhanced exploration ability due to the inclusion of a 'noise' term in the position update rule that prevents particles being trapped in local optima. Our algorithm additionally makes use of an external archive to store non-dominated solutions and implements crowding distance to encourage solution diversity. In benchmark tests, MBOnvPSO produced high quality Pareto fronts, when compared to benchmarked alternatives, for all of the multi-objective test functions considered, with competitive performance in search spaces with up to 600 discrete dimensions.

cs.NE↗
arXiv2021

A measurement method of transverse light-shift in atomic spin co-magnetometer

We disclose a method to obtain the transverse light-shift along the probe light of a single-axis alkali metal-noble gas co-magnetometer. The relationship between transverse compensating field and light-shift is deduced through the steady-state solution of Bloch equations. The variety of probe light intensity is used to obtain the residual magnetic field, and step modulation tests are applied to acquire the total spin-relaxation rate of electron spins and self-compensation point. Finally, the transverse light-shift is reduced from -0.115 nT to -0.039 nT by optimizing the probe light wavelength, and the value of the calibration coefficient can be increased simultaneously.

physics.app-ph↗
arXiv2021

Performance Prediction of InP/GaAsSb Double Heterojunction Bipolar Transistors for THz applications

The intrinsic performance of "type-II" InP/GaAsSb double heterojunction bipolar transistors (DHBTs) towards and beyond THz is predicted and analyzed based on a multi-scale technology computer aided design (TCAD) modeling platform calibrated against experimental measurements. Two-dimensional hydrodynamic simulations are combined with 1-D full-band, atomistic quantum transport calculations to shed light on future DHBT generations whose dimensions are decreased step-by-step, starting from the current device configuration. Simulations predict that a peak transit frequency $f_{T,peak}$ of around 1.6 THz could be reached in aggressively scaled type-II DHBTs with a total thickness of 256 nm and an emitter width $W_E$ of 37.5 nm. The corresponding breakdown voltage $BV_{CEO}$ is estimated to be 2.2 V. The investigations are put in perspective with two DHBT performance limiting factors, self-heating and breakdown characteristics.

physics.app-ph↗
arXiv2020

Polarization and fundamental sensitivity of $^{\text{39}}$K ($^{\text{133}}$Cs)-$^{\text{85}}$Rb-$^{\text{21}}$Ne co-magnetometers

The hybrid optical pumping spin exchange relaxation free (HOPSERF) atomic co-magnetometers make ultrahigh sensitivity measurement of inertia achievable. The wall relaxation rate has a big effect on the polarization and fundamental sensitivity for the co-magnetometer, but it is often neglected in the experiments. However, there is almost no work about the systematic analysis of the influence factors on the polarization and the fundamental sensitivity of the HOPSERF co-magnetometers. Here, we systematically study the polarization and the fundamental sensitivity of 39K-85Rb-21Ne and 133Cs-85Rb-21Ne HOPSERF co-magnetometers with low polarization limit and the wall relaxation rate. The 21Ne number density, the power density and wavelength of pump beam will affect the polarization greatly by affecting the pumping rate of pump beam. We obtain a general formula on the fundamental sensitivity of the HOPSERF co-magnetometers due to shot-noise and the fundamental sensitivity changes with multiple systemic parameters, where the suitable number density of buffer gas and quench gas make the fundamental sensitivity highest. The fundamental sensitivity $7.5355\times10^{-11}$ $\rm rad/s/Hz^{1/2}$ of 133Cs-85Rb-21Ne co-magnetometer is higher than the ultimate theoretical sensitivity $2\times10^{-10}$ $\rm rad/s/Hz^{1/2}$ of K-21Ne co-magnetometer.

physics.atom-ph↗