arXiv subjects

Qing Liu

Publications and source records attributed to Qing Liu.

At least 73 records · Page 4Linked to original sources

arXiv2025

LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements

Health, Safety, and Environment (HSE) compliance assessment demands dynamic real-time decision-making under complicated regulations and complex human-machine-environment interactions. While large language models (LLMs) hold significant potential for decision intelligence and contextual dialogue, their capacity for domain-specific knowledge in HSE and structured legal reasoning remains underexplored. We introduce HSE-Bench, the first benchmark dataset designed to evaluate the HSE compliance assessment capabilities of LLM. HSE-Bench comprises over 1,000 manually curated questions drawn from regulations, court cases, safety exams, and fieldwork videos, and integrates a reasoning flow based on Issue spotting, rule Recall, rule Application, and rule Conclusion (IRAC) to assess the holistic reasoning pipeline. We conduct extensive evaluations on different prompting strategies and more than 10 LLMs, including foundation models, reasoning models and multimodal vision models. The results show that, although current LLMs achieve good performance, their capabilities largely rely on semantic matching rather than principled reasoning grounded in the underlying HSE compliance context. Moreover, their native reasoning trace lacks the systematic legal reasoning required for rigorous HSE compliance assessment. To alleviate these, we propose a new prompting technique, Reasoning of Expert (RoE), which guides LLMs to simulate the reasoning process of different experts for compliance assessment and reach a more accurate unified decision. We hope our study highlights reasoning gaps in LLMs for HSE compliance and inspires further research on related tasks.

cs.CL↗

arXiv2025

TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning

The selection of base station sites is a critical challenge in 5G network planning, which requires efficient optimization of coverage, cost, user satisfaction, and practical constraints. Traditional manual methods, reliant on human expertise, suffer from inefficiencies and are limited to an unsatisfied planning-construction consistency. Existing AI tools, despite improving efficiency in certain aspects, still struggle to meet the dynamic network conditions and multi-objective needs of telecom operators' networks. To address these challenges, we propose TelePlanNet, an AI-driven framework tailored for the selection of base station sites, integrating a three-layer architecture for efficient planning and large-scale automation. By leveraging large language models (LLMs) for real-time user input processing and intent alignment with base station planning, combined with training the planning model using the improved group relative policy optimization (GRPO) reinforcement learning, the proposed TelePlanNet can effectively address multi-objective optimization, evaluates candidate sites, and delivers practical solutions. Experiments results show that the proposed TelePlanNet can improve the consistency to 78%, which is superior to the manual methods, providing telecom operators with an efficient and scalable tool that significantly advances cellular network planning.

cs.AI↗

arXiv2025

Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning

Gliomas are the most common primary tumors of the central nervous system. Multimodal MRI is widely used for the preliminary screening of gliomas and plays a crucial role in auxiliary diagnosis, therapeutic efficacy, and prognostic evaluation. Currently, the computer-aided diagnostic studies of gliomas using MRI have focused on independent analysis events such as tumor segmentation, grading, and radiogenomic classification, without studying inter-dependencies among these events. In this study, we propose a Glioma Multimodal MRI Analysis System (GMMAS) that utilizes a deep learning network for processing multiple events simultaneously, leveraging their inter-dependencies through an uncertainty-based multi-task learning architecture and synchronously outputting tumor region segmentation, glioma histological subtype, IDH mutation genotype, and 1p/19q chromosome disorder status. Compared with the reported single-task analysis models, GMMAS improves the precision across tumor layered diagnostic tasks. Additionally, we have employed a two-stage semi-supervised learning method, enhancing model performance by fully exploiting both labeled and unlabeled MRI samples. Further, by utilizing an adaptation module based on knowledge self-distillation and contrastive learning for cross-modal feature extraction, GMMAS exhibited robustness in situations of modality absence and revealed the differing significance of each MRI modal. Finally, based on the analysis outputs of the GMMAS, we created a visual and user-friendly platform for doctors and patients, introducing GMMAS-GPT to generate personalized prognosis evaluations and suggestions.

eess.IV↗

arXiv2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today's heterogeneous computing systems with GPUs. In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are three-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers up to 6.6x throughput in data refactoring and progressive retrieval tasks. It also leads to 10.4x throughput for recomposing required data representations under Quantity-of-Interest error control and 4.2x performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

cs.DC↗

arXiv2025

Bias correction in treatment effect estimates following data-driven biomarker cutoff selection

Predictive biomarkers are playing an essential role in precision medicine. Identifying an optimal cutoff to select patient subsets with greater benefit from treatment is critical and more challenging for predictive biomarkers measured with a continuous scale. It is a common practice to perform exploratory subset analysis in early-stage studies to select the cutoff. However, data-driven cutoff selection will often cause bias in treatment effect estimates and lead to over-optimistic expectations in the future phase III trial. In this study, we first conducted extensive simulations to investigate factors influencing the bias, including the cutoff selection rule, the number of candidates cutoffs, the magnitude of the predictive effect, and sample sizes. Our insights emphasize the importance of accounting for bias and uncertainties caused by small sample sizes and data-driven selection procedures in Go/No Go decision-making, and population and sample size determination for phase III studies. Secondly, we evaluated the performance of Bootstrap Bias Correction and the Approximate Bayesian Computation (ABC) method for bias correction through extensive simulations. We conclude by providing a recommendation for the application of the two approaches in clinical practice.

stat.AP↗

arXiv2025

CompleteMe: Reference-based Human Image Completion

Recent methods for human image completion can reconstruct plausible body shapes but often fail to preserve unique details, such as specific clothing patterns or distinctive accessories, without explicit reference images. Even state-of-the-art reference-based inpainting approaches struggle to accurately capture and integrate fine-grained details from reference images. To address this limitation, we propose CompleteMe, a novel reference-based human image completion framework. CompleteMe employs a dual U-Net architecture combined with a Region-focused Attention (RFA) Block, which explicitly guides the model's attention toward relevant regions in reference images. This approach effectively captures fine details and ensures accurate semantic correspondence, significantly improving the fidelity and consistency of completed images. Additionally, we introduce a challenging benchmark specifically designed for evaluating reference-based human image completion tasks. Extensive experiments demonstrate that our proposed method achieves superior visual quality and semantic consistency compared to existing techniques. Project page: https://liagm.github.io/CompleteMe/

cs.CV↗

arXiv2025

HRDecoder: High-Resolution Decoder Network for Fundus Image Lesion Segmentation

High resolution is crucial for precise segmentation in fundus images, yet handling high-resolution inputs incurs considerable GPU memory costs, with diminishing performance gains as overhead increases. To address this issue while tackling the challenge of segmenting tiny objects, recent studies have explored local-global fusion methods. These methods preserve fine details using local regions and capture long-range context information from downscaled global images. However, the necessity of multiple forward passes inevitably incurs significant computational overhead, adversely affecting inference speed. In this paper, we propose HRDecoder, a simple High-Resolution Decoder network for fundus lesion segmentation. It integrates a high-resolution representation learning module to capture fine-grained local features and a high-resolution fusion module to fuse multi-scale predictions. Our method effectively improves the overall segmentation accuracy of fundus lesions while consuming reasonable memory and computational overhead, and maintaining satisfying inference speed. Experimental results on the IDRiD and DDR datasets demonstrate the effectiveness of our method. Code is available at https://github.com/CVIU-CSU/HRDecoder.

cs.CV↗

arXiv2025

Enhancing Facial Privacy Protection via Weakening Diffusion Purification

The rapid growth of social media has led to the widespread sharing of individual portrait images, which pose serious privacy risks due to the capabilities of automatic face recognition (AFR) systems for mass surveillance. Hence, protecting facial privacy against unauthorized AFR systems is essential. Inspired by the generation capability of the emerging diffusion models, recent methods employ diffusion models to generate adversarial face images for privacy protection. However, they suffer from the diffusion purification effect, leading to a low protection success rate (PSR). In this paper, we first propose learning unconditional embeddings to increase the learning capacity for adversarial modifications and then use them to guide the modification of the adversarial latent code to weaken the diffusion purification effect. Moreover, we integrate an identity-preserving structure to maintain structural consistency between the original and generated images, allowing human observers to recognize the generated image as having the same identity as the original. Extensive experiments conducted on two public datasets, i.e., CelebA-HQ and LADN, demonstrate the superiority of our approach. The protected faces generated by our method outperform those produced by existing facial privacy protection approaches in terms of transferability and natural appearance.

cs.CV↗

arXiv2025

Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning

Large Language Models (LLMs) have achieved impressive results in various tasks but struggle with hallucination problems and lack of relevant knowledge, especially in deep complex reasoning and knowledge-intensive tasks. Knowledge Graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning. However, existing KG-based LLM reasoning methods face challenges like handling multi-hop reasoning, multi-entity questions, and effectively utilizing graph structures. To address these issues, we propose Paths-over-Graph (PoG), a novel method that enhances LLM reasoning by integrating knowledge reasoning paths from KGs, improving the interpretability and faithfulness of LLM outputs. PoG tackles multi-hop and multi-entity questions through a three-phase dynamic multi-hop path exploration, which combines the inherent knowledge of LLMs with factual knowledge from KGs. In order to improve the efficiency, PoG prunes irrelevant information from the graph exploration first and introduces efficient three-step pruning techniques that incorporate graph structures, LLM prompting, and a pre-trained language model (e.g., SBERT) to effectively narrow down the explored candidate paths. This ensures all reasoning paths contain highly relevant information captured from KGs, making the reasoning faithful and interpretable in problem-solving. PoG innovatively utilizes graph structure to prune the irrelevant noise and represents the first method to implement multi-entity deep path detection on KGs for LLM reasoning tasks. Comprehensive experiments on five benchmark KGQA datasets demonstrate PoG outperforms the state-of-the-art method ToG across GPT-3.5-Turbo and GPT-4, achieving an average accuracy improvement of 18.9%. Notably, PoG with GPT-3.5-Turbo surpasses ToG with GPT-4 by up to 23.9%.

cs.CL↗

arXiv2025

ObjectMover: Generative Object Movement with Video Prior

Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose based on perspective, accurately filling occluded regions, and ensuring coherent synchronization of shadows and reflections while maintaining the object identity. In this paper, we present ObjectMover, a generative model that can perform object movement in highly challenging scenes. Our key insight is that we model this task as a sequence-to-sequence problem and fine-tune a video generation model to leverage its knowledge of consistent object generation across video frames. We show that with this approach, our model is able to adjust to complex real-world scenarios, handling extreme lighting harmonization and object effect movement. As large-scale data for object movement are unavailable, we construct a data generation pipeline using a modern game engine to synthesize high-quality data pairs. We further propose a multi-task learning strategy that enables training on real-world video data to improve the model generalization. Through extensive experiments, we demonstrate that ObjectMover achieves outstanding results and adapts well to real-world scenarios.

cs.GR↗

arXiv2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth of scientific data is surpassing advancements in computing, creating challenges in storage, transfer, and analysis, particularly at the exascale. While data reduction techniques such as lossless and lossy compression help mitigate these issues, their computational overhead introduces new bottlenecks. GPU-accelerated approaches improve performance but face challenges in portability, memory transfer, and scalability on multi-GPU systems. To address these, we propose HPDR, a high-performance, portable data reduction framework. HPDR supports diverse processor architectures, reducing memory transfer overhead to 2.3% and achieving up to 3.5x faster throughput than existing solutions. It attains 96% of the theoretical speedup in multi-GPU settings. Evaluations on the Frontier supercomputer demonstrate 103 TB/s throughput and up to 4x acceleration in parallel I/O performance at scale. HPDR offers a scalable, efficient solution for managing massive data volumes in exascale computing environments.

cs.DC↗

arXiv2025

Fusion of Various Optimization Based Feature Smoothing Methods for Wearable and Non-invasive Blood Glucose Estimation

Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. To address this issue, this paper proposes a polynomial fitting approach to smooth the obtained features or the reference blood glucose values. First, the blood glucose values are estimated based on the individual optimization approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimization approach are computed. Third, these absolute difference values for each optimization approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimization method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimization method is computed. If the accumulate probability of any selected optimization method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimization methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimization methods in these regions are determined. The computer numerical simulation results show that our proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

physics.med-ph↗

arXiv2025

Multi-models with averaging in feature domain for non-invasive blood glucose estimation

Diabetes is a serious chronic metabolic disease. In the recent years, more and more consumer technology enterprises focusing on human health are committed to implementing accurate and non-invasive blood glucose algorithm in their products. However, due to the interference from the external environment, these wearable non-invasive methods yield the low estimation accuracy. To address this issue, this paper employs different models based on different ranges of the blood glucose values for performing the blood glucose estimation. First the photoplethysmograms (PPGs) are acquired and they are denoised via the bit plane singular spectrum analysis (SSA) method. Second, the features are extracted. For the data in the training set, first the features are averaged across the measurements in the feature domain via the optimization approach. Second, the random forest is employed to sort the importance of each feature. Third, the training set is divided into three subsets according to the reference blood glucose values. Fourth, the feature vectors and the corresponding blood glucose values in the same group are employed to build an individual model. Fifth, for each feature, the average of the feature values for all the measurements in the same subset is computed. For the data in the test set, first, the sum of the weighted distances between the test feature values and the average values obtained in the above is computed for each model. Here, the weights are defined based on the importance sorted by the random forest obtained in the above. The model corresponding to the smallest sum is assigned. Finally, the blood glucose value is estimated based on the corresponding model. Compared to the state of arts methods, our proposed method can effectively improve the estimation accuracy.

cs.LG↗

arXiv2025

Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework

Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. To semi-automatically generate high-quality evaluation samples, we propose CHARt-based document question-answering GEneration (CHARGE), a framework that produces evaluation data through structured keypoint extraction, crossmodal verification, and keypoint-based generation. By combining CHARGE with expert validation, we construct Chart-MRAG Bench, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents. Our evaluation reveals three critical limitations in current approaches: (1) unified multimodal embedding retrieval methods struggles in chart-based scenarios, (2) even with ground-truth retrieval, state-of-the-art MLLMs achieve only 58.19% Correctness and 73.87% Coverage scores, and (3) MLLMs demonstrate consistent text-over-visual modality bias during Chart-based MRAG reasoning. The CHARGE and Chart-MRAG Bench are released at https://github.com/Nomothings/CHARGE.git.

cs.AI↗

arXiv2025

The Bullseye: HST, Keck/KCWI, and Dragonfly Characterization of a Giant Nine-Ringed Galaxy

We report the discovery and multiwavelength followup of LEDA 1313424 ("Bullseye"), a collisional ring galaxy (CRG) with nine readily-identified rings -- the most so far reported for a CRG. These data shed new light on the rapid, multi-ring phase of CRG evolution. Using Hubble Space Telescope (HST) imaging, we identify and measure nine ring structures, several of which are "piled up" near the center of the galaxy, while others extend to tens of kpc scales. We also identify faint patches of emission at large radii ($\sim$70 kpc) in the HST imaging, and confirm the association of this emission with the galaxy via spectroscopy. Deep ground based imaging using the Dragonfly Telephoto Array finds evidence that this patch of emission is part of an older, fading ring from the collision. We find that the locations of the detected rings are an excellent match to predictions from analytic theory, if the galaxy was a 10-ring system whose outermost ring has faded away. We identify the likely impacting galaxy via Keck/KCWI spectroscopy, finding evidence for gas extending between it and the Bullseye. The overall size of this galaxy rivals that of known Giant Low Surface Brightness Galaxies (GLSBs) such as Malin I, lending credence to the hypothesis that CRGs can evolve into GLSBs as their rings expand and fade. Analysis of the HI content in this galaxy from ALFALFA finds significantly elevated neutral hydrogen with respect to the galaxy's stellar mass, another feature in alignment with GLSB systems.

astro-ph.GA↗

arXiv2025

Monge solutions of time-dependent Hamilton-Jacobi equations in metric spaces

As a classical notion equivalent to viscosity solutions, Monge solutions are well understood for stationary Hamilton-Jacobi equations in Euclidean spaces and have been recently studied in general metric spaces. In this paper, we introduce a notion of Monge solutions for time-dependent Hamilton-Jacobi equations in metric spaces. The key idea is to reformulate the equation as a stationary problem under the assumption of Lipschitz regularity for the initial data. We establish the uniqueness and existence of bounded Lipschitz Monge solutions to the initial value problem and discuss their equivalence with existing notions of metric viscosity solutions.

math.AP↗

arXiv2025

Fuzzy Galaxies or Cirrus? Decomposition of Galactic Cirrus in Deep Wide-Field Images

Diffuse Galactic cirrus, or Diffuse Galactic Light (DGL), can be a prominent component in the background of deep wide-field imaging surveys. The DGL provides unique insights into the physical and radiative properties of dust grains in our Milky Way, and it also serves as a contaminant on deep images, obscuring the detection of background sources such as low surface brightness galaxies. However, it is challenging to disentangle the DGL from other components of the night sky. In this paper, we present a technique for the photometric characterization of Galactic cirrus, based on (1) extraction of its filamentary or patchy morphology and (2) incorporation of color constraints obtained from Planck thermal dust models. Our decomposition method is illustrated using a $\sim$10 deg$^2$ imaging dataset obtained by the Dragonfly Telephoto Array, and its performance is explored using various metrics which characterize the flatness of the sky background. As a concrete application of the technique, we show how removal of cirrus allows low surface brightness galaxies to be identified on cirrus-rich images. We also show how modeling the cirrus in this way allows optical DGL intensities to be determined with high radiometric precision.

astro-ph.GA↗

arXiv2025

Liouville-type theorems for fully nonlinear elliptic and parabolic equations with boundary degeneracy

We study a class of fully nonlinear boundary-degenerate elliptic equations, for which we prove that u \equiv 0 is the only solution. Although no boundary conditions are posed together with the equations, we show that the operator degeneracy actually generates an implicit boundary condition. Under appropriate assumptions on the degeneracy rate and regularity of the operator, we then prove that there exist no bounded solutions other than the trivial one. Our method is based on the arguments for uniqueness of viscosity solutions to state constraint problems for Hamilton-Jacobi equations. We obtain similar results for fully nonlinear degenerate parabolic equations. Several concrete examples of equations that satisfy the assumptions are also given.

math.AP↗