arXiv subjects

Lei Li

Publications and source records attributed to Lei Li.

At least 199 records · Page 11Linked to original sources

arXiv2025

The influence of rotation and metallicity on the explodability of massive stars

During the late stages of massive stellar evolution, failed supernovae (FSN) may form through core-collapse processes. The traditional evaluation criterion $ξ_{2.5}$ $=$ 0.45, primarily established using non-rotating progenitor models, suffers from significant inaccuracies when applied to rotating pre-supernova systems. The effects of metallicity and rotation on the explodability landscapes of massive stars lack robust quantification. We aim to investigate how rotation and metallicity influence the explodability of massive stars. We investigate how rotation and metallicity affect stellar explodability using MESA simulations with initial rotational velocities of $0$, $300$, and $600~\mathrm{km,s^{-1}}$ at three metallicities ($Z_{\odot}$, $1/10,Z_{\odot}$, $1/50,Z_{\odot}$). Core-collapse phases are simulated with GR1D to determine critical heating efficiencies. Our results yield revised $ξ_{2.5}$ criteria: 0.45 for non-rotating models; 0.48 for $300~\mathrm{km,s^{-1}}$; 0.47 for $600~\mathrm{km,s^{-1}}$ at solar metallicity; and 0.59 for low-metallicity models. Chemically homogeneous evolution in rapidly rotating low-metallicity stars significantly raises the compactness limit for successful explosions and narrows the zero-age main sequence mass range for failed supernovae. Rotation substantially affects the explodability of low-metallicity massive stars, underscoring the importance of incorporating rotational effects in models of core-collapse supernova progenitors.

astro-ph.SR↗

arXiv2025

Random ordinate method for mitigating the ray effect in radiative transport equation simulations

The Discrete Ordinates Method (DOM) is the most widely used velocity discretization method for simulating the radiative transport equation. However, the ray effect is a long-standing drawback of DOM. In benchmark tests that exhibit the ray effect, we observe low regularity in the velocity variable of the solution. To address this issue, we propose a Random Ordinate Method (ROM) to mitigate the ray effect. Compared to other strategies proposed in the literature for mitigating the ray effect, ROM offers several advantages: 1) For benchmark tests that exhibit ray effect, the computational cost is lower than that of the DOM; 2) it is simple and requires minimal changes to existing DOM-based code; 3) it is easily parallelizable and independent of the problem setup. A formal analysis is presented for the convergence orders of the error and bias. Numerical tests demonstrate the reduction in computational cost compared to DOM, as well as its effectiveness in mitigating the ray effect.

math.DS↗

arXiv2025

Convergence Analysis of the Random Ordinate Method for Mitigating the Ray Effect

The Discrete Ordinates Method (DOM) is widely used for velocity discretization in radiative transport simulations. However, DOM tends to exhibit the ray effect when the velocity discretization is not sufficiently refined, a limitation that is well documented. To counter this, we have developed the Random Ordinates Method (ROM) by integrating randomness into the velocity discretization, which mitigates the ray effect without incurring additional computational costs. ROM partitions the velocity space into n cells, selects a random ordinate from each cell, and solves a DOM system with these ordinates. It leverages the average of multiple samples to achieve a higher convergence order, especially for solutions with low regularity in the velocity variable. In this work, we provide a detailed convergence analysis for ROM, focusing on bias and single-run errors. This analysis is crucial for determining the necessary mesh size and the optimal number of samples required to attain a specified level of accuracy.

math.NA↗

arXiv2025

Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports

The manual creation of the "Impression" section in radiology reports is a primary driver of radiologist burnout. To address this challenge, we propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings. The system first produces a draft impression and then refines it using machine learning and reinforcement learning from human feedback (RLHF) to align with individual radiologists' styles while ensuring factual accuracy. We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine. Our approach is designed to significantly reduce administrative workload and improve reporting efficiency while maintaining high standards of clinical precision.

cs.CL↗

arXiv2025

Electronic Reporting Using SM2-Based Ring Signcryption

Electronic whistleblowing systems are widely used due to their efficiency and convenience. The key to designing such systems lies in protecting the identity privacy of whistleblowers, preventing malicious whistleblowing, and ensuring the confidentiality of whistleblowing information. To address these issues, a SM2 traceable ring signcryption scheme for electronic voting is proposed. This scheme combines the SM2 elliptic curve public key cryptography algorithm with the ring signature algorithm, enhancing the overall efficiency of the scheme while ensuring the autonomy and controllability of the core cryptographic algorithms. Security analysis demonstrates that the proposed scheme satisfies confidentiality, unforgeability, traceability, linkability, and deniability. Efficiency analysis shows that, compared to existing ring signature schemes, the proposed scheme exhibits significant efficiency advantages during the signature phase. The electronic whistleblowing system designed using the proposed scheme can track malicious whistleblowers while protecting user identity privacy, and ensures that the content of whistleblowing remains unknown to third parties.

cs.CR↗

arXiv2025

Attention Consistency for LLMs Explanation

Understanding the decision-making processes of large language models (LLMs) is essential for their trustworthy development and deployment. However, current interpretability methods often face challenges such as low resolution and high computational cost. To address these limitations, we propose the \textbf{Multi-Layer Attention Consistency Score (MACS)}, a novel, lightweight, and easily deployable heuristic for estimating the importance of input tokens in decoder-based models. MACS measures contributions of input tokens based on the consistency of maximal attention. Empirical evaluations demonstrate that MACS achieves a favorable trade-off between interpretability quality and computational efficiency, showing faithfulness comparable to complex techniques with a 22\% decrease in VRAM usage and 30\% reduction in latency.

cs.CL↗

arXiv2025

Causal Reasoning Elicits Controllable 3D Scene Generation

Existing 3D scene generation methods often struggle to model the complex logical dependencies and physical constraints between objects, limiting their ability to adapt to dynamic and realistic environments. We propose CausalStruct, a novel framework that embeds causal reasoning into 3D scene generation. Utilizing large language models (LLMs), We construct causal graphs where nodes represent objects and attributes, while edges encode causal dependencies and physical constraints. CausalStruct iteratively refines the scene layout by enforcing causal order to determine the placement order of objects and applies causal intervention to adjust the spatial configuration according to physics-driven constraints, ensuring consistency with textual descriptions and real-world dynamics. The refined scene causal graph informs subsequent optimization steps, employing a Proportional-Integral-Derivative(PID) controller to iteratively tune object scales and positions. Our method uses text or images to guide object placement and layout in 3D scenes, with 3D Gaussian Splatting and Score Distillation Sampling improving shape accuracy and rendering stability. Extensive experiments show that CausalStruct generates 3D scenes with enhanced logical coherence, realistic spatial interactions, and robust adaptability.

cs.GR↗

arXiv2025

DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation

Medical image segmentation grapples with challenges including multi-scale lesion variability, ill-defined tissue boundaries, and computationally intensive processing demands. This paper proposes the DyGLNet, which achieves efficient and accurate segmentation by fusing global and local features with a dynamic upsampling mechanism. The model innovatively designs a hybrid feature extraction module (SHDCBlock), combining single-head self-attention and multi-scale dilated convolutions to model local details and global context collaboratively. We further introduce a dynamic adaptive upsampling module (DyFusionUp) to realize high-fidelity reconstruction of feature maps based on learnable offsets. Then, a lightweight design is adopted to reduce computational overhead. Experiments on seven public datasets demonstrate that DyGLNet outperforms existing methods, particularly excelling in boundary accuracy and small-object segmentation. Meanwhile, it exhibits lower computation complexity, enabling an efficient and reliable solution for clinical medical image analysis. The code will be made available soon.

cs.CV↗

arXiv2025

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Meta-learning is a powerful paradigm for tackling few-shot tasks. However, recent studies indicate that models trained with the whole-class training strategy can achieve comparable performance to those trained with meta-learning in few-shot classification tasks. To demonstrate the value of meta-learning, we establish an entropy-limited supervised setting for fair comparisons. Through both theoretical analysis and experimental validation, we establish that meta-learning has a tighter generalization bound compared to whole-class training. We unravel that meta-learning is more efficient with limited entropy and is more robust to label noise and heterogeneous tasks, making it well-suited for unsupervised tasks. Based on these insights, We propose MINO, a meta-learning framework designed to enhance unsupervised performance. MINO utilizes the adaptive clustering algorithm DBSCAN with a dynamic head for unsupervised task construction and a stability-based meta-scaler for robustness against label noise. Extensive experiments confirm its effectiveness in multiple unsupervised few-shot and zero-shot tasks.

cs.LG↗

arXiv2025

Uniqueness of Hahn--Banach extensions and inner ideals in real C$^$-algebras and real JB$^$-triples

We show that every closed (resp., weak$^*$-closed) inner ideal $I$ of a real JB$^*$-triple (resp. a real JBW$^*$-triple) $E$ is Hahn--Banach smooth (resp., weak$^*$-Hahn--Banach smooth). Contrary to what is known for complex JB$^*$-triples, being (weak$^*$-)Hahn--Banach smooth does not characterise (weak$^*$-)closed inner ideals in real JB(W)$^*$-triples. We prove here that a closed (resp., weak$^*$-closed) subtriple of a real JB$^*$-triple (resp., a real JBW$^*$-triple) is Hahn-Banach smooth (resp., weak$^*$-Hahn-Banach smooth) if, and only if, it is a hereditary subtriple. If we assume that $E$ is a reduced and atomic JBW$^*$-triple, every weak$^*$-closed subtriple of $E$ which is also weak$^*$-Hahn-Banach smooth is an inner ideal.\smallskip In case that $C$ is the realification of a complex Cartan factor or a non-reduced real Cartan factor, we show that every weak$^*$-closed subtriple of $C$ which is weak$^*$-Hahn-Banach smooth and has rank $\geq 2$ is an inner ideal. The previous conclusions are finally combined to prove the following: Let $I$ be a closed subtriple of a real JB$^*$-triple $E$ satisfying the following hypotheses: $(a)$ $I^*$ is separable. $(b)$ $I$ is weak$^*$-Hahn-Banach smooth. $(c)$ The projection of $I^{**}$ onto each real or complex Cartan factor summand in the atomic part of $E^{**}$ is zero or has rank $\geq 2$. Then $I$ is an inner ideal of $E$.

math.OA↗

arXiv2025

Beyond Frequency: The Role of Redundancy in Large Language Model Memorization

Memorization in large language models poses critical risks for privacy and fairness as these systems scale to billions of parameters. While previous studies established correlations between memorization and factors like token frequency and repetition patterns, we revealed distinct response patterns: frequency increases minimally impact memorized samples (e.g. 0.09) while substantially affecting non-memorized samples (e.g., 0.25), with consistency observed across model scales. Through counterfactual analysis by perturbing sample prefixes and quantifying perturbation strength through token positional changes, we demonstrate that redundancy correlates with memorization patterns. Our findings establish that: about 79% of memorized samples are low-redundancy, these low-redundancy samples exhibit 2-fold higher vulnerability than high-redundancy ones, and consequently memorized samples drop by 0.6 under perturbation while non-memorized samples drop by only 0.01, indicating that more redundant content becomes both more memorable and more fragile. These findings suggest potential redundancy-guided approaches for data preprocessing, thereby reducing privacy risks and mitigating bias to ensure fairness in model deployments.

cs.LG↗

arXiv2025

TF-TransUNet1D: Time-Frequency Guided Transformer U-Net for Robust ECG Denoising in Digital Twin

Electrocardiogram (ECG) signals serve as a foundational data source for cardiac digital twins, yet their diagnostic utility is frequently compromised by noise and artifacts. To address this issue, we propose TF-TransUNet1D, a novel one-dimensional deep neural network that integrates a U-Net-based encoder-decoder architecture with a Transformer encoder, guided by a hybrid time-frequency domain loss. The model is designed to simultaneously capture local morphological features and long-range temporal dependencies, which are critical for preserving the diagnostic integrity of ECG signals. To enhance denoising robustness, we introduce a dual-domain loss function that jointly optimizes waveform reconstruction in the time domain and spectral fidelity in the frequency domain. In particular, the frequency-domain component effectively suppresses high-frequency noise while maintaining the spectral structure of the signal, enabling recovery of subtle but clinically significant waveform components. We evaluate TF-TransUNet1D using synthetically corrupted signals from the MIT-BIH Arrhythmia Database and the Noise Stress Test Database (NSTDB). Comparative experiments against state-of-the-art baselines demonstrate consistent superiority of our model in terms of SNR improvement and error metrics, achieving a mean absolute error of 0.1285 and Pearson correlation coefficient of 0.9540. By delivering high-precision denoising, this work bridges a critical gap in pre-processing pipelines for cardiac digital twins, enabling more reliable real-time monitoring and personalized modeling.

cs.LG↗

arXiv2025

Controlling the $\mathcal{PT}$ Symmetry Breaking Threshold in Bipartite Lattice Systems with Floquet Topological Edge States

We investigate the control of the parity-time ($\mathcal{PT}$)-symmetry breaking threshold in a periodically driven one-dimensional dimerized lattice with spatially symmetric gain and loss defects. We elucidate the contrasting roles played by Floquet topological edge states in determining the $\mathcal{PT}$ symmetry breaking threshold within the high- and low-frequency driving regimes. In the high-frequency regime, the participation of topological edge states in $\mathcal{PT}$ symmetry breaking is contingent upon the position of the $\mathcal{PT}$-symmetric defect pairs, whereas in the low-frequency regime, their participation is unconditional and independent of the defect pairs placement, resulting in a universal zero threshold. We establish a direct link between the symmetry-breaking threshold and how the spatial profile of the Floquet topological edge states evolves over one driving period. We further demonstrate that lattices with an odd number of sites exhibit unique threshold patterns, in contrast to even-sized systems. Moreover, applying co-frequency periodic driving to the defect pairs, which preserves time-reversal symmetry, can significantly enhance the $\mathcal{PT}$ symmetry-breaking threshold.

quant-ph↗

arXiv2025

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration

Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are limited by excessive radiation exposure or incomplete spatial information from 2D techniques. We propose an integrated 4D joint analysis platform that combines: (1) a dual robotic arm cone-beam CT (CBCT) system with a programmable, gantry-free trajectory optimized for upright scanning; (2) a hybrid imaging pipeline that fuses static 3D CBCT with dynamic 2D X-rays using deep learning-based preprocessing, 3D-2D projection, and iterative optimization; and (3) a clinically validated framework for quantitative kinematic assessment. In simulation studies, the method achieved sub-voxel accuracy (0.235 mm) with a 99.18 percent success rate, outperforming conventional and state-of-the-art registration approaches. Clinical evaluation further demonstrated accurate quantification of tibial plateau motion and medial-lateral variance in post-total knee arthroplasty (TKA) patients. This 4D CBCT platform enables fast, accurate, and low-dose dynamic joint imaging, offering new opportunities for biomechanical research, precision diagnostics, and personalized orthopedic care.

cs.CV↗

arXiv2025

Towards Non-Invasive Sediment Monitoring Using Muography: A Pilot Run at the Shanghai Outer Ring Tunnel

This study demonstrates the application of cosmic-ray muography as a non-invasive method for monitoring sediment accumulation and tidal influences in the Shanghai Outer Ring Tunnel, an immersed tube tunnel located beneath the Huangpu River in Shanghai, China. A portable, dual-layer plastic scintillator detector was deployed to conduct muon flux scans along the tunnel's length and to continuously monitor muon flux, allowing for the study of tidal effects. Geant4 simulations validated the correlation between muon attenuation and overburden thickness, incorporating sediment, water, and concrete layers. Key findings include a strong anti-correlation between the measured muon flux and the water levels observed at a nearby tide gauge. The results align with geotechnical data and simulations, especially in the region of interest, confirming muography's sensitivity to sediment dynamics. This work establishes muography as a robust tool for long-term, real-time monitoring of submerged infrastructure, offering significant advantages over conventional invasive techniques. The study underscores the potential for integrating muography into civil engineering practices to enhance safety and operational resilience in tidal environments.

physics.app-ph↗

arXiv2025

Radial oscillations of neutron stars within density-dependent relativistic-mean field model

The radial oscillations of neutron stars are studied using equations of state derived from density-dependent relativistic mean-field (DDRMF) models, which effectively describe the ground-state properties of finite nuclei. A novel numerical approach, the finite volume method (FVM), is employed to solve the eigenvalue problem associated with oscillation frequencies. Compared to conventional methods such as the finite difference method and shooting method, the FVM avoids the numerical instability encountered at high frequencies with an equation of state that includes a discontinuous adiabatic index and offers greater computational efficiency. The oscillation frequencies of high-order modes exhibit a similar trend of change. The radial displacements and pressure perturbations are largely influenced by the EOSs of crust region. {The frequency of the first excited state shows a strong linear relationship with both the slope and skewness parameters of the symmetry energy.} These findings suggest that the density dependence of the symmetry energy can be constrained through observations of neutron star radial oscillation frequencies.

nucl-th↗

arXiv2025

Bioinspired underwater soft robots: from biology to robotics and back

The ocean vast unexplored regions and diverse soft-bodied marine organisms have spurred interest in bio-inspired underwater soft robotics. Recent advances have enabled new capabilities in underwater movement, sensing, and interaction. However, these efforts are largely unidirectional, with biology guiding robotics while insights from robotics rarely feed back into biology. Here we propose a holistic, bidirectional framework that integrates biological principles, robotic implementation, and biological validation. We show that soft robots can serve as experimental tools to probe biological functions and even test evolutionary hypotheses. Their inherent compliance also allows them to outperform rigid systems in unstructured environments, supporting applications in marine exploration, manipulation, and medicine. Looking forward, we introduce bio-universal-inspired robotics, a paradigm that transcends species-specific mimicry by identifying convergent principles across species to inspire more adaptable designs. Despite rapid progress, challenges persist in material robustness, actuation efficiency, autonomy, and intelligence. By uniting biology and engineering, soft robots can advance ocean exploration and deepen scientific discovery.

cs.RO↗

arXiv2025

Estimates of the numerical density for stochastic differential equations with multiplicative noise

We investigate the estimates of the density for the traditional Euler-Maruyama discretization of stochastic differential equations (SDEs) with multiplicative noise. Our estimates focus on two key aspects: (1) the $L^p$-upper bounds for derivatives of the logarithmic numerical density, (2) the sharp error order of the Euler scheme under the relative entropy (or Kullback-Leibler divergence). For the first aspect, we present estimates for the first-order and second-order derivatives of the logarithmic numerical density. The key technique is to adopt the Malliavin calculus to derive expressions of the derivatives of the logarithmic Green's function and to obtain an estimate for the inverse Malliavin matrix. Moreover, for the relative entropy error, we obtain a bound that is second order in time step, which then naturally leads to first-order error bounds under the total variation distance and Wasserstein distances. Compared with the usual weak error estimate for SDEs, such estimate can give an error bound for the worst case of a family of test functions instead of one test function.

math.NA↗