arXiv subjects

Ye Li

Publications and source records attributed to Ye Li.

At least 91 records · Page 5Linked to original sources

arXiv2025

The magnetar model's energy crisis for a prolific repeating fast radio burst source

Fast radio bursts (FRBs) are widely considered to originate from magnetars that power the explosion through releasing magnetic energy. Active repeating FRBs have been seen to produce hundreds of bursts per hour and can stay active for months, thus may provide stringent constraints on the energy budget of FRBs' central engine. Within a time span of 214 days, we detected 11,553 bursts from the hyper-active FRB 20240114A that reached a peak burst rate of 729 hr$^{-1}$. This is the largest burst sample from any single FRB source, exceeding the cumulative total of all published bursts from all known FRBs to date. Assuming typical values of radio efficiency and beaming factor, the estimated total isotropic burst energy of this source exceeds 86% of the dipolar magnetic energy of a typical magnetar. The total released energy from this source exceeds that of other known repeaters by about one and a half orders of magnitude, yielding the most stringent lower limit of $4.7\times10^{32}$ G cm$^3$ for the magnetar's magnetic moment. The source remained active at the end of this observation campaign. Our findings thus require either the FRB's central magnetar engine's possessing exceptionally high emission efficiency or a more powerful compact object than a typical magnetar.

astro-ph.HE↗

arXiv2025

ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation

Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cues. Prevailing methods, often limited by shallow feature representation, inadequate reasoning mechanisms, and weak cross-modal integration, struggle to achieve this depth of cognition, resulting in prevalent issues like incomplete target separation and imprecise segmentation. Inspired by the perceptual strategy of the Hundred-eyed Giant-emphasizing holistic observation, omnidirectional focus, and intensive scrutiny-we introduce ArgusCogito, a novel zero-shot, chain-of-thought framework underpinned by cross-modal synergy and omnidirectional reasoning within Vision-Language Models (VLMs). ArgusCogito orchestrates three cognitively-inspired stages: (1) Conjecture: Constructs a strong cognitive prior through global reasoning with cross-modal fusion (RGB, depth, semantic maps), enabling holistic scene understanding and enhanced target-background disambiguation. (2) Focus: Performs omnidirectional, attention-driven scanning and focused reasoning, guided by semantic priors from Conjecture, enabling precise target localization and region-of-interest refinement. (3) Sculpting: Progressively sculpts high-fidelity segmentation masks by integrating cross-modal information and iteratively generating dense positive/negative point prompts within focused regions, emulating Argus' intensive scrutiny. Extensive evaluations on four challenging COS benchmarks and three Medical Image Segmentation (MIS) benchmarks demonstrate that ArgusCogito achieves state-of-the-art (SOTA) performance, validating the framework's exceptional efficacy, superior generalization capability, and robustness.

cs.CV↗

arXiv2025

Physics-informed deep operator network for traffic state estimation

Traffic state estimation (TSE) fundamentally involves solving high-dimensional spatiotemporal partial differential equations (PDEs) governing traffic flow dynamics from limited, noisy measurements. While Physics-Informed Neural Networks (PINNs) enforce PDE constraints point-wise, this paper adopts a physics-informed deep operator network (PI-DeepONet) framework that reformulates TSE as an operator learning problem. Our approach trains a parameterized neural operator that maps sparse input data to the full spatiotemporal traffic state field, governed by the traffic flow conservation law. Crucially, unlike PINNs that enforce PDE constraints point-wise, PI-DeepONet integrates traffic flow conservation model and the fundamental diagram directly into the operator learning process, ensuring physical consistency while capturing congestion propagation, spatial correlations, and temporal evolution. Experiments on the NGSIM dataset demonstrate superior performance over state-of-the-art baselines. Further analysis reveals insights into optimal function generation strategies and branch network complexity. Additionally, the impact of input function generation methods and the number of functions on model performance is explored, highlighting the robustness and efficacy of proposed framework.

cs.LG↗

arXiv2025

Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation

Generative artificial intelligence (AI) is rapidly transforming medical imaging by enabling capabilities such as data synthesis, image enhancement, modality translation, and spatiotemporal modeling. This review presents a comprehensive and forward-looking synthesis of recent advances in generative modeling including generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and emerging multimodal foundation architectures and evaluates their expanding roles across the clinical imaging continuum. We systematically examine how generative AI contributes to key stages of the imaging workflow, from acquisition and reconstruction to cross-modality synthesis, diagnostic support, and treatment planning. Emphasis is placed on both retrospective and prospective clinical scenarios, where generative models help address longstanding challenges such as data scarcity, standardization, and integration across modalities. To promote rigorous benchmarking and translational readiness, we propose a three-tiered evaluation framework encompassing pixel-level fidelity, feature-level realism, and task-level clinical relevance. We also identify critical obstacles to real-world deployment, including generalization under domain shift, hallucination risk, data privacy concerns, and regulatory hurdles. Finally, we explore the convergence of generative AI with large-scale foundation models, highlighting how this synergy may enable the next generation of scalable, reliable, and clinically integrated imaging systems. By charting technical progress and translational pathways, this review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.

eess.IV↗

arXiv2025

BDFirewall: Towards Effective and Expeditiously Black-Box Backdoor Defense in MLaaS

In this paper, we endeavor to address the challenges of backdoor attacks countermeasures in black-box scenarios, thereby fortifying the security of inference under MLaaS. We first categorize backdoor triggers from a new perspective, i.e., their impact on the patched area, and divide them into: high-visibility triggers (HVT), semi-visibility triggers (SVT), and low-visibility triggers (LVT). Based on this classification, we propose a progressive defense framework, BDFirewall, that removes these triggers from the most conspicuous to the most subtle, without requiring model access. First, for HVTs, which create the most significant local semantic distortions, we identify and eliminate them by detecting these salient differences. We then restore the patched area to mitigate the adverse impact of such removal process. The localized purification designed for HVTs is, however, ineffective against SVTs, which globally perturb benign features. We therefore model an SVT-poisoned input as a mixture of a trigger and benign features, where we unconventionally treat the benign features as "noise". This formulation allows us to reconstruct SVTs by applying a denoising process that removes these benign "noise" features. The SVT-free input is then obtained by subtracting the reconstructed trigger. Finally, to neutralize the nearly imperceptible but fragile LVTs, we introduce lightweight noise to disrupt the trigger pattern and then apply DDPM to restore any collateral impact on clean features. Comprehensive experiments demonstrate that our method outperforms state-of-the-art defenses. Compared with baselines, BDFirewall reduces the Attack Success Rate (ASR) by an average of 33.25%, improving poisoned sample accuracy (PA) by 29.64%, and achieving up to a 111x speedup in inference time. Code will be made publicly available upon acceptance.

cs.CR↗

arXiv2025

Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks

The optimization algorithms are crucial in training physics-informed neural networks (PINNs), as unsuitable methods may lead to poor solutions. Compared to the common gradient descent (GD) algorithm, implicit gradient descent (IGD) outperforms it in handling certain multi-scale problems. In this paper, we provide convergence analysis for the IGD in training over-parameterized two-layer PINNs. We first derive the training dynamics of IGD in training two-layer PINNs. Then, over-parameterization allows us to prove that the randomly initialized IGD converges to a globally optimal solution at a linear convergence rate. Moreover, due to the distinct training dynamics of IGD compared to GD, the learning rate can be selected independently of the sample size and the least eigenvalue of the Gram matrix. Additionally, the novel approach used in our convergence analysis imposes a milder requirement on the network width. Finally, empirical results validate our theoretical findings.

cs.LG↗

arXiv2025

Multi-Masked Querying Network for Robust Emotion Recognition from Incomplete Multi-Modal Physiological Signals

Emotion recognition from physiological data is crucial for mental health assessment, yet it faces two significant challenges: incomplete multi-modal signals and interference from body movements and artifacts. This paper presents a novel Multi-Masked Querying Network (MMQ-Net) to address these issues by integrating multiple querying mechanisms into a unified framework. Specifically, it uses modality queries to reconstruct missing data from incomplete signals, category queries to focus on emotional state features, and interference queries to separate relevant information from noise. Extensive experiment results demonstrate the superior emotion recognition performance of MMQ-Net compared to existing approaches, particularly under high levels of data incompleteness.

cs.CV↗

arXiv2025

UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection

Underwater object detection faces the problem of underwater image degradation, which affects the performance of the detector. Underwater object detection methods based on noise reduction and image enhancement usually do not provide images preferred by the detector or require additional datasets. In this paper, we propose a plug-and-play \textbf{U}nderwater joi\textbf{n}t \textbf{i}mage enhancemen\textbf{t} \textbf{Module} (UnitModule) that provides the input image preferred by the detector. We design an unsupervised learning loss for the joint training of UnitModule with the detector without additional datasets to improve the interaction between UnitModule and the detector. Furthermore, a color cast predictor with the assisting color cast loss and a data augmentation called Underwater Color Random Transfer (UCRT) are designed to improve the performance of UnitModule on underwater images with different color casts. Extensive experiments are conducted on DUO for different object detection models, where UnitModule achieves the highest performance improvement of 2.6 AP for YOLOv5-S and gains the improvement of 3.3 AP on the brand-new test set ($\text{URPC}_{test}$). And UnitModule significantly improves the performance of all object detection models we test, especially for models with a small number of parameters. In addition, UnitModule with a small number of parameters of 31K has little effect on the inference speed of the original object detection model. Our quantitative and visual analysis also demonstrates the effectiveness of UnitModule in enhancing the input image and improving the perception ability of the detector for object features. The code is available at https://github.com/LEFTeyex/UnitModule.

cs.CV↗

arXiv2025

ShareCMP: Polarization-Aware RGB-P Semantic Segmentation

Multimodal semantic segmentation is developing rapidly, but the modality of RGB-\textbf{P}olarization remains underexplored. To delve into this problem, we construct a UPLight RGB-P segmentation benchmark with 12 typical underwater semantic classes. In this work, we design the ShareCMP, an RGB-P semantic segmentation framework with a shared dual-branch architecture (ShareCMP Encoder), which reduces the parameters and memory space by about 33.8\% compared to previous dual-branch models. It encompasses a Polarization Generate Attention (PGA) module designed to generate polarization modal images with richer polarization properties for the encoder. In addition, we introduce the Class Polarization-Aware Loss (CPALoss) with Class Polarization-Aware Auxiliary Head (CPAAHead) to improve the learning and understanding of the encoder for polarization modal information and to optimize the PGA module. With extensive experiments on a total of three RGB-P benchmarks, our ShareCMP achieves the best performance in mIoU with fewer parameters on the UPLight (92.45{\small (+0.32)}\%), ZJU (92.7{\small (+0.1)}\%), and MCubeS (50.99{\small (+1.51)}\%) datasets. And our ShareCMP (w/o PGA) achieves competitive or even higher performance on other RGB-X datasets compared to the corresponding state-of-the-art RGB-X methods. The code and datasets are available at https://github.com/LEFTeyex/ShareCMP.

cs.CV↗

arXiv2025

Multi-year Polarimetric Monitoring of Four CHIME-Discovered Repeating Fast Radio Bursts with FAST

In this study, we report multi-year polarization measurements of four repeating FRBs initially discovered by CHIME: FRBs~20190117A, 20190208A, 20190303A, and 20190417A. We observed the four repeating FRBs with FAST, detecting a total of 66 bursts. Two bursts from FRB~20190417A exhibit a circular polarization signal-to-noise ratio greater than 7, with the highest circular polarization fraction recorded at 35.7%. While the bursts from FRBs 20190208A and 20190303A are highly linearly polarized, those from FRBs~20190117A and 20190417A show depolarization due to multi-path propagation, with σ_{\mathrm{RM}} = 2.78 \pm 0.05 rad m$^{-2}$ and 5.19 \pm 0.09 rad m$^{-2}$, respectively. The linear polarization distributions among five repeating FRB--FRBs~20190208A, 20190303A, 20201124A, 20220912A, and 20240114A--are nearly identical but show distinct differences from those of non-repeating FRBs. FRBs~20190117A, 20190303A, and 20190417A exhibit substantial rotation measure (RM) variations between bursts, joining other repeating FRBs in this behavior. Combining these findings with published results, 64% of repeating FRBs show RM variations greater than 50 rad m$^{-2}$, and 21\% exhibit RM reversals. A significant proportion of repeating FRBs reside in a dynamic magneto-ionic environment. The structure function of RM variations shows a power-law index of $γ\sim (0-0.8)$, corresponding to a shallow power spectrum $α= -(γ+ 2) \sim -(2.0-2.8)$ of turbulence, if the RM variations are attributed to turbulence. This suggests that the variations are dominated by small-scale RM density fluctuations. We perform K-S tests comparing the RMs of repeating and non-repeating FRBs, which reveal a marginal dichotomy in the distribution of their RMs.We caution that the observed dichotomy may be due to the small sample size and selection biases.

astro-ph.HE↗

arXiv2025

SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning

Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenges, notably the threat of backdoor attacks. Existing backdoor strategies predominantly rely on end-to-end label supervision, which, despite their efficacy, often results in detectable feature disentanglement and limited persistence. In this work, we propose a novel and stealthy backdoor attack framework, named SPA, which fundamentally departs from traditional approaches by leveraging feature-space alignment rather than direct trigger-label association. Specifically, SPA reduces representational distances between backdoor trigger features and target class features, enabling the global model to misclassify trigger-embedded inputs with high stealth and persistence. We further introduce an adaptive, adversarial trigger optimization mechanism, utilizing boundary-search in the feature space to enhance attack longevity and effectiveness, even against defensive FL scenarios and non-IID data distributions. Extensive experiments on various FL benchmarks demonstrate that SPA consistently achieves high attack success rates with minimal impact on model utility, maintains robustness under challenging participation and data heterogeneity conditions, and exhibits persistent backdoor effects far exceeding those of conventional techniques. Our results call urgent attention to the evolving sophistication of backdoor threats in FL and emphasize the pressing need for advanced, feature-level defense techniques.

cs.CR↗

arXiv2025

Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion

The main problem in RGB-T tracking is the correct and optimal merging of the cross-modal features of visible and thermal images. Some previous methods either do not fully exploit the potential of RGB and TIR information for channel and spatial feature fusion or lack a direct interaction between the template and the search area, which limits the model's ability to fully utilize the original semantic information of both modalities. To address these limitations, we investigate how to achieve a direct fusion of cross-modal channels and spatial features in RGB-T tracking and propose CSTNet. It uses the Vision Transformer (ViT) as the backbone and adds a Joint Spatial and Channel Fusion Module (JSCFM) and Spatial Fusion Module (SFM) integrated between the transformer blocks to facilitate cross-modal feature interaction. The JSCFM module achieves joint modeling of channel and multi-level spatial features. The SFM module includes a cross-attention-like architecture for cross modeling and joint learning of RGB and TIR features. Comprehensive experiments show that CSTNet achieves state-of-the-art performance. To enhance practicality, we retrain the model without JSCFM and SFM modules and use CSNet as the pretraining weight, and propose CSTNet-small, which achieves 50% speedup with an average decrease of 1-2% in SR and PR performance. CSTNet and CSTNet-small achieve real-time speeds of 21 fps and 33 fps on the Nvidia Jetson Xavier, meeting actual deployment requirements. Code is available at https://github.com/LiYunfengLYF/CSTNet.

cs.CV↗

arXiv2025

UniDrive: Towards Universal Driving Perception Across Camera Configurations

Vision-centric autonomous driving has demonstrated excellent performance with economical sensors. As the fundamental step, 3D perception aims to infer 3D information from 2D images based on 3D-2D projection. This makes driving perception models susceptible to sensor configuration (e.g., camera intrinsics and extrinsics) variations. However, generalizing across camera configurations is important for deploying autonomous driving models on different car models. In this paper, we present UniDrive, a novel framework for vision-centric autonomous driving to achieve universal perception across camera configurations. We deploy a set of unified virtual cameras and propose a ground-aware projection method to effectively transform the original images into these unified virtual views. We further propose a virtual configuration optimization method by minimizing the expected projection error between original and virtual cameras. The proposed virtual camera projection can be applied to existing 3D perception methods as a plug-and-play module to mitigate the challenges posed by camera parameter variability, resulting in more adaptable and reliable driving perception models. To evaluate the effectiveness of our framework, we collect a dataset on CARLA by driving the same routes while only modifying the camera configurations. Experimental results demonstrate that our method trained on one specific camera configuration can generalize to varying configurations with minor performance degradation.

cs.CV↗

arXiv2025

Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks

In the context of over-parameterization, there is a line of work demonstrating that randomly initialized (stochastic) gradient descent (GD) converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. However, the learning rate of GD for training two-layer neural networks exhibits poor dependence on the sample size and the Gram matrix, leading to a slow training process. In this paper, we show that for training two-layer $\text{ReLU}^3$ Physics-Informed Neural Networks (PINNs), the learning rate can be improved from $\mathcal{O}(λ_0)$ to $\mathcal{O}(1/\|\bm{H}^{\infty}\|_2)$, implying that GD actually enjoys a faster convergence rate. Despite such improvements, the convergence rate is still tied to the least eigenvalue of the Gram matrix, leading to slow convergence. We then develop the positive definiteness of Gram matrices with general smooth activation functions and provide the convergence analysis of natural gradient descent (NGD) in training two-layer PINNs, demonstrating that the learning rate can be $\mathcal{O}(1)$ and at this rate, the convergence rate is independent of the Gram matrix. In particular, for smooth activation functions, the convergence rate of NGD is quadratic. Numerical experiments are conducted to verify our theoretical results.

cs.LG↗

arXiv2025

Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks

In this paper, we derive refined generalization bounds for the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). For the DRM, we focus on two prototype elliptic partial differential equations (PDEs): Poisson equation and static Schrödinger equation on the $d$-dimensional unit hypercube with the Neumann boundary condition. Furthermore, sharper generalization bounds are derived based on the localization techniques under the assumptions that the exact solutions of the PDEs lie in the Barron spaces or the general Sobolev spaces. For the PINNs, we investigate the general linear second order elliptic PDEs with Dirichlet boundary condition using the local Rademacher complexity in the multi-task learning setting. Finally, we discuss the generalization error in the setting of over-parameterization when solutions of PDEs belong to Barron space.

math.NA↗

arXiv2025

A Priori Estimation of the Approximation, Optimization and Generalization Errors of Random Neural Networks for Solving Partial Differential Equations

In recent years, neural networks have achieved remarkable progress in various fields and have also drawn much attention in applying them on scientific problems. A line of methods involving neural networks for solving partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs) and the Deep Ritz Method (DRM), has emerged. Although these methods outperform classical numerical methods in certain cases, the optimization problems involving neural networks are typically non-convex and non-smooth, which can result in unsatisfactory solutions for PDEs. In contrast to deterministic neural networks, the hidden weights of random neural networks are sampled from some prior distribution and only the output weights participate in training. This makes training much simpler, but it remains unclear how to select the prior distribution. In this paper, we focus on Barron type functions and approximate them under Sobolev norms by random neural networks with clear prior distribution. In addition to the approximation error, we also derive bounds for the optimization and generalization errors of random neural networks for solving PDEs when the solutions are Barron type functions.

math.NA↗

arXiv2025

SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking

Underwater observation systems typically integrate optical cameras and imaging sonar systems. When underwater visibility is insufficient, only sonar systems can provide stable data, which necessitates exploration of the underwater acoustic object tracking (UAOT) task. Previous studies have explored traditional methods and Siamese networks for UAOT. However, the absence of a unified evaluation benchmark has significantly constrained the value of these methods. To alleviate this limitation, we propose the first large-scale UAOT benchmark, SonarT165, comprising 165 square sequences, 165 fan sequences, and 205K high-quality annotations. Experimental results demonstrate that SonarT165 reveals limitations in current state-of-the-art SOT trackers. To address these limitations, we propose STFTrack, an efficient framework for acoustic object tracking. It includes two novel modules, a multi-view template fusion module (MTFM) and an optimal trajectory correction module (OTCM). The MTFM module integrates multi-view feature of both the original image and the binary image of the dynamic template, and introduces a cross-attention-like layer to fuse the spatio-temporal target representations. The OTCM module introduces the acoustic-response-equivalent pixel property and proposes normalized pixel brightness response scores, thereby suppressing suboptimal matches caused by inaccurate Kalman filter prediction boxes. To further improve the model feature, STFTrack introduces a acoustic image enhancement method and a Frequency Enhancement Module (FEM) into its tracking pipeline. Comprehensive experiments show the proposed STFTrack achieves state-of-the-art performance on the proposed benchmark. The code is available at https://github.com/LiYunfengLYF/SonarT165.

cs.CV↗

arXiv2025

Bright bursts with sub-millisecond structures of FRB 20230607A in a highly magnetized environment

We report the observations of a repeating FRB 20230607A for 15.6 hours spanning 16 months using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) with the detection of 565 bursts. We present three bright bursts with detailed temporal/spectral structures. We also report that one burst carries a narrow component with a width of only 0.3 ms, which is surrounded by broader components. This suggests that repeaters can make both narrow and broad components in one burst. With the narrow spike, we precisely measure the dispersion measure (DM) of $362.85 \pm 0.15 \;{\rm pc\,cm^{-3}}$ and the Faraday rotation measures (RMs) of and $-12249.0\pm 1.5 \; {\rm rad\,m^{-2}}$. We also analyze the statistical distribution of the burst parameters, including waiting times, temporal widths, central frequencies and frequency widths, fluences and energies, all showing typical distributions of known active repeaters. In particular, most bursts show narrow spectra with $Δν/ν_0 = 0.125\pm 0.001$. This fact, together with the narrow 0.3 ms spike, strongly suggests a magnetospheric origin of the FRB emission. Based on a predicted correlation between RM and the luminosity of a persistent radio source (PRS) by Yang et al., we predict that PRS should have a specific luminosity of the order of $10^{29} \ {\rm erg \ s^{-1} \ Hz^{-1}}$ and encourage a search for such a PRS.

astro-ph.HE↗