arXiv subjects

Rui Li

Publications and source records attributed to Rui Li.

At least 253 records · Page 14Linked to original sources

arXiv2025

Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data.

astro-ph.GA↗

arXiv2025

Discovery of 19 strongly-lensed quasars, dual and projected quasars in DESI-LS

We report the follow-up spectroscopic confirmation of two lensed quasars, six dual quasars, and eleven projected quasars that were previously identified as lensed-quasar candidates in \cite{He2023}. The spectroscopic data were obtained from two different sources: the P200/DBSP in California and publicly available datasets, including SDSS and DESI-EDR. The two lensed quasars (both pairs) have the following properties: $θ_E$ = 1.208'', $z_s$ = 3.105; $θ_E$ = 0.749, $z_s$ = 2.395. The six dual quasars have redshifts ranging from 0.58 to 3.28 and projected separations ranging from 15.44 to 22.54 kpc, with a mean separation of 17.95 kpc. The eleven projected quasars have projected separations ranging from 10.96 to 39.07 kpc, with a mean separation of 22.64 kpc. Additionally, there are three likely lensed quasars that cannot be definitively confirmed, contributed by two reasons. Firstly, their image separations (0.83'', 0.98'', and 0.93'') are small compared to the seeing conditions during our observations (around 1.2''). Secondly, no high SNR lensing galaxy can be detected in the Legacy Survey Imaging. Better spectroscopy and (or) imaging are needed to confirm their lensing nature.

astro-ph.GA↗

arXiv2025

Structural and Physical Properties of the Heavy Fermion Metal Ce$_2$NiAl$_6$Si$_5$

Strongly correlated electrons at the verge of quantum criticality give rise to unconventional phases of matter and behaviors, with the discovery of new quantum critical materials driving synergistic advances in both experiments and theory. In this work, we report the structural and physical properties of a new quaternary Ce-based heavy fermion compound, Ce$_2$NiAl$_6$Si$_5$, synthesized using the self-flux method. This compound forms a layered tetragonal structure (space group $P4/nmm$), with square nets of Ce atoms separated by Si-Al or Ni-Si-Ge layers. Specific heat measurements show a low temperature Sommerfeld coefficient of 1.4 J/mol-Ce K$^{2}$, with a reduced entropy indicative of significant Kondo interactions. Below 0.6 K, an upturn in resistivity and a deviation in magnetic susceptibility suggest the appearance of magnetic ordering or the development of dynamic magnetic correlations, which is further supported by a bulge in specific heat around 0.4 K. These results suggest that Ce$_2$NiAl$_6$Si$_5$ is a layered heavy fermion metal, naturally located in proximity to a spin-density-wave quantum critical point.

cond-mat.str-el↗

arXiv2025

Diffusion-Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians

Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DMs) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. These results highlight the effectiveness of our proposed method in improving optimization efficiency for general Hamiltonians.

quant-ph↗

arXiv2025

Shelving it rather than Ditching it: Dynamically Debloating DEX and Native Methods of Android Applications without APK Modification

Today's Android developers tend to include numerous features to accommodate diverse user requirements, which inevitably leads to bloated apps. Yet more often than not, only a fraction of these features are frequently utilized by users, thus a bloated app costs dearly in potential vulnerabilities, expanded attack surfaces, and additional resource consumption. Especially in the event of severe security incidents, users have the need to block vulnerable functionalities immediately. Existing works have proposed various code debloating approaches for identifying and removing features of executable components. However, they typically involve static modification of files (and, for Android apps, repackaging of APKs, too), which lacks user convenience let alone undermining the security model of Android due to the compromising of public key verification and code integrity checks. This paper introduces 3DNDroid, a Dynamic Debloating approach targeting both DEX and Native methods in AnDroid apps. Using an unprivileged management app in tandem with a customized Android OS, 3DNDroid dynamically reduces unnecessary code loading during app execution based on a pre-generated debloating schema from static or dynamic analyses. It intercepts invocations of debloated bytecode methods to prevent their interpretation, compilation, and execution, while zero-filling memory spaces of debloated native methods during code loading. Evaluation demonstrates 3DNDroid's ability to debloat 187 DEX methods and 30 native methods across 55 real-world apps, removing over 10K Return-Oriented Programming (ROP) gadgets. Case studies confirm its effectiveness in mitigating vulnerabilities, and performance assessments highlight its resource-saving advantages over non-debloated apps.

cs.CR↗

arXiv2025

Explainable Diagnosis Prediction through Neuro-Symbolic Integration

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable thresholds. Our models, particularly $M_{\text{multi-pathway}}$ and $M_{\text{comprehensive}}$, demonstrate superior performance over traditional models such as Logistic Regression, SVM, and Random Forest, achieving higher accuracy (up to 80.52\%) and AUROC scores (up to 0.8457) in the case study of diabetes prediction. The learned weights and thresholds within the LNN models provide direct insights into feature contributions, enhancing interpretability without compromising predictive power. These findings highlight the potential of neuro-symbolic approaches in bridging the gap between accuracy and explainability in healthcare AI applications. By offering transparent and adaptable diagnostic models, our work contributes to the advancement of precision medicine and supports the development of equitable healthcare solutions. Future research will focus on extending these methods to larger and more diverse datasets to further validate their applicability across different medical conditions and populations.

cs.LG↗

arXiv2025

An Image-based Typology for Visualization

We present and discuss the results of a qualitative analysis of visualization images to derive an image-based typology of visualizations. For each image, we seek to identify its main focus or the essential stimuli. As a result, we derived 10 image-based visualization types. We describe coding decisions we made in the derivation process. The resulting image typology can serve a number of purposes: enabling researchers and practitioners to identify visual design styles, facilitating the categorization of visualization images for the purpose of research and teaching, enabling researchers to study the evolution of the community and its research output over time, and facilitating a discussion of standardization in visualization. In addition, the tool and dataset enable scholars to closely examine the images and how they are published and communicated in our community. osf.io/dxjwt presents a pre-registration and all supplemental materials.

cs.HC↗

arXiv2025

Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN.

astro-ph.GA↗

arXiv2024

Plug-and-Play Training Framework for Preference Optimization

Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, current methods fail to account for the varying difficulty levels of training samples during preference optimization, leading to mediocre performance in tasks with high accuracy requirements, particularly in mathematical reasoning. To address this limitation, we propose a novel training framework, which employs multiple sampling to analyze output distributions, assign different weights to samples, and incorporate these weights into the preference optimization process. This plug-and-play approach enables LLMs to prioritize challenging examples during training, improving learning efficiency. Experimental results demonstrate that our framework integrates seamlessly with various preference optimization methods and achieves consistent improvements in mathematical reasoning tasks.

cs.CL↗

arXiv2024

A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1 405B face limitations due to large parameter sizes and computational demands, often requiring cloud API use which raises privacy concerns, limits real-time applications on edge devices, and increases fine-tuning costs. Additionally, LLMs often underperform in specialized domains such as healthcare and law due to insufficient domain-specific knowledge, necessitating specialized models. Therefore, Small Language Models (SLMs) are increasingly favored for their low inference latency, cost-effectiveness, efficient development, and easy customization and adaptability. These models are particularly well-suited for resource-limited environments and domain knowledge acquisition, addressing LLMs' challenges and proving ideal for applications that require localized data handling for privacy, minimal inference latency for efficiency, and domain knowledge acquisition through lightweight fine-tuning. The rising demand for SLMs has spurred extensive research and development. However, a comprehensive survey investigating issues related to the definition, acquisition, application, enhancement, and reliability of SLM remains lacking, prompting us to conduct a detailed survey on these topics. The definition of SLMs varies widely, thus to standardize, we propose defining SLMs by their capability to perform specialized tasks and suitability for resource-constrained settings, setting boundaries based on the minimal size for emergent abilities and the maximum size sustainable under resource constraints. For other aspects, we provide a taxonomy of relevant models/methods and develop general frameworks for each category to enhance and utilize SLMs effectively.

cs.CL↗

arXiv2024

Customized calibration sources in the JUNO experiment

We customized a laser calibration system and four radioactive $γ$-ray calibration sources for the Jiangmen Underground Neutrino Observatory (JUNO), a 20-kton liquid scintillator-based neutrino detector. The laser source system was updated to realize the isotropic light emission timing within $\pm0.25$~nsec level and to allow the tuning of the laser intensity covering more than four orders of magnitude. In addition, methods to prepare four different radioactive sources ($^{18}{\rm F}$, $^{40}{\rm K}$, $^{226}{\rm Ra}$, and $^{241}{\rm Am}$), covering energies from O(10)~keV to O(1)~MeV, for the JUNO detector were established in this study. The radioactivity of each source and the risk of impurities leaking into the detector from the source were confirmed to meet the experimental requirements.

physics.ins-det↗

arXiv2024

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System

Trending topics have become a significant part of modern social media, attracting users to participate in discussions of breaking events. However, they also bring in a new channel for poisoning attacks, resulting in negative impacts on society. Therefore, it is urgent to study this critical problem and develop effective strategies for defense. In this paper, we propose TrendSim, an LLM-based multi-agent system to simulate trending topics in social media under poisoning attacks. Specifically, we create a simulation environment for trending topics that incorporates a time-aware interaction mechanism, centralized message dissemination, and an interactive system. Moreover, we develop LLM-based human-like agents to simulate users in social media, and propose prototype-based attackers to replicate poisoning attacks. Besides, we evaluate TrendSim from multiple aspects to validate its effectiveness. Based on TrendSim, we conduct simulation experiments to study four critical problems about poisoning attacks on trending topics for social benefit.

cs.SI↗

arXiv2024

Pressure induced superconducting dome in LaNiGa2

LaNiGa2 is a time-reversal symmetry breaking superconductor with symmetry protected band crossings, making it an ideal platform for investigating the interplay between unconventional superconductivity and electronic structure topology. Here we present a transport study of LaNiGa2 under pressure. The application of pressure to LaNiGa2 induces a significant enhancement of the superconducting transition temperature Tc at a pressure of 7 GPa. In contrast, powder X-ray diffraction (XRD) results show no evidence of structural phase transitions up to 26.3 GPa. Moreover, the ratio of band diffusivity shows a sudden increase at around 7 GPa, suggesting possible pressure-induced changes in the electronic structure that are closely linked to the evolution of superconductivity.

cond-mat.supr-con↗

arXiv2024

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems

Preconditioning techniques are crucial for enhancing the efficiency of solving large-scale linear equation systems that arise from partial differential equation (PDE) discretization. These techniques, such as Incomplete Cholesky factorization (IC) and data-driven neural network methods, accelerate the convergence of iterative solvers like Conjugate Gradient (CG) by approximating the original matrices. This paper introduces a novel approach that integrates Graph Neural Network (GNN) with traditional IC, addressing the shortcomings of direct generation methods based on GNN and achieving significant improvements in computational efficiency and scalability. Experimental results demonstrate an average reduction in iteration counts by 24.8% compared to IC and a two-order-of-magnitude increase in training scale compared to previous methods. A three-dimensional static structural analysis utilizing finite element methods was validated on training sparse matrices of up to 5 million dimensions and inference scales of up to 10 million. Furthermore, the approach demon-strates robust generalization capabilities across scales, facilitating the effective acceleration of CG solvers for large-scale linear equations using small-scale data on modest hardware. The method's robustness and scalability make it a practical solution for computational science.

cs.LG↗

arXiv2024

The potential impact of large-scale wind clusters on the local weather patterns

To decarbonise the electricity sector and achieve renewable energy targets, a rapidly growing number of wind farms have been authorised, constructed, and commissioned in the UK and EU in recent years. For instance, the UK Government aims to expand offshore wind capacity to 60 GW by 2030, while the EU has set a target of 120 GW of offshore renewable energy by the same year. Given these substantial projected capacities, it is crucial to thoroughly investigate the potential impacts of large-scale wind clusters on local weather patterns to prevent unintended consequences prior to deployment. In this paper, we use the WRF model to simulate four scenarios with varying wind energy capacities in the North Sea, assessing the potential effects of these wind clusters on the local weather patterns over mainland UK. Please note that the simulations of Case 3 and Case 4 are still ongoing, while all analyses in the current version of manuscript are all based on Case 1 and Case 2.

physics.ao-ph↗

arXiv2024

CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution

Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches struggle with the Out-of-distribution (OOD) problem, where the test workload distribution differs from the training workload, leading to performancedegradation. In this paper, we present CardOOD, a general learning framework designed to construct robust query-driven cardinality estimators that are resilient against the OOD problem. Our framework focuses on offline training algorithms that develop one-off models from a static workload, suitable for model initialization and periodic retraining. In CardOOD, we extend classical transfer/robust learning techniques to train query-driven cardinalityestimators, and the algorithms fall into three categories: representation learning, data manipulation, and new learning strategies. As these learning techniques are originally evaluated in computervision tasks, we also propose a new learning algorithm that exploits the property of cardinality estimation. This algorithm, lying in the category of new learning strategy, models the partial order constraint of cardinalities by a self-supervised learning task. Comprehensive experimental studies demonstrate the efficacy of the algorithms of CardOOD in mitigating the OOD problem to varying extents. We further integrate CardOOD into PostgreSQL, showcasing its practical utility in query optimization.

cs.DB↗

arXiv2024

Robust double Bragg diffraction via detuning control

We present a theoretical model and numerical optimization of double Bragg diffraction, a widely used technique in atom interferometry. We derive an effective two-level-system Hamiltonian based on the Magnus expansion in the so-called "quasi-Bragg regime", where most Bragg-pulse atom interferometers operate. Furthermore, we extend the theory to a five-level description to account for Doppler detuning. Using these derived effective Hamiltonians, we investigate the impacts of AC-Stark shift and polarization errors on the double Bragg beam-splitter, along with their mitigations through detuning control. Notably, we design a linear detuning sweep that demonstrates robust efficiency exceeding 99.5% against polarization errors up to 8.5%. Moreover, we develop an artificial intelligence-aided optimal detuning control protocol, showcasing enhanced robustness against both polarization errors and Doppler effects. This protocol achieves an average efficiency of 99.92% for samples with a finite momentum width of 0.05$\hbar k_L$ within an extended polarization error range of up to 10%.

quant-ph↗

arXiv2024

Merging high localization and TE-TM polarization degeneracy of guided waves in dielectric metasurfaces

The polarization degree of freedom is an inherent feature of plane waves propagating in an isotropic homogeneous medium. The miniaturization of optical systems leads to the high localization of electromagnetic waves, but also to the loss of polarization control, namely, breaking TE-TM polarization degeneracy. In this work, we discover the near-field polarization degree of freedom for highly localized guided waves propagating along a dielectric metasurface. We demonstrate the opportunity to create a metasurface with the degenerate TE-TM polarization spectrum for the required operating wavelength and different constitutive materials. In particular, we analyze several possible implementations including silicon nitride and ceramic metasurfaces consisting of disk-shaped resonators, and evaluate the impact of substrate. Finally, we experimentally implement one of the metasurface designs and verify its broadband degenerate TE-TM polarization spectrum. The obtained results form a fundamentally new platform for the planar polarization devices utilizing the polarization degree of freedom of localized light.

physics.optics↗