arXiv subjects

Ping Zhang

Publications and source records attributed to Ping Zhang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Nonlinear asymptotic stability and optimal decay rate around the three-dimensional Oseen vortex filament

In the high-Reynolds-number regime, this work investigates the long-time dynamics of the three-dimensional incompressible Navier-Stokes equations near the Oseen vortex filament. The flow exhibits a strong interplay between vortex stretching, shearing, and mixing, which generates ever-smaller spatial scales and thereby significantly amplifies viscous effects. By adopting an anisotropic self-similar coordinate system adapted to the filament geometry, we establish the nonlinear asymptotic stability of the Oseen vortex filament. All non-axisymmetric perturbations are shown to decay at the optimal rate $t^{-κ|α|^{1/2}}$. At the linear level, this decay mechanism corresponds to a sharp spectral lower bound $Σ(α) \sim |α|^{1/2}$ for the nonlocal Oseen operator $L_\perp - αΛ_\perp$, and we identify an explicit spectral point attaining this optimal bound. Combined with the spectral estimates obtained in \cite{LWZ}, our analysis fully resolves the conjecture proposed in \cite{GM} concerning the asymptotic scaling laws for the spectral and pseudospectral bounds $Σ(α)$ and $Ψ(α)$. These results provide a rigorous mathematical explanation for the shear-mixing mechanism in the vicinity of the 3D Oseen vortex filament.

math.AP↗

arXiv2025

Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication

Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming 6G era. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of signal-to-noise ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to low Earth orbit (LEO) satellite channels. In response to these constraints, we propose an importance-aware robust semantic transmission (IRST) framework, specifically designed for scenarios characterized by bandwidth scarcity and channel variability. The IRST scheme begins by applying a segmentation model enhancement algorithm to improve the granularity and accuracy of semantic segmentation. Subsequently, a task-driven semantic selection method is employed to prioritize the transmission of semantically vital content based on real-time channel state information. Furthermore, the framework incorporates a stack-based, SNR-aware channel codec capable of executing adaptive channel coding in alignment with SNR variations. Comparative evaluations across diverse operating conditions demonstrate the superior performance and resilience of the IRST model relative to existing benchmarks.

eess.SP↗

arXiv2025

Patterns of X-ray and $γ$-ray Flares: from Blazar to Maganetar and Sun

Using Gaussian process methods, we analyzed the light curves of three extreme solar X-ray flares observed by the RHESSI satellite. Their variability characteristics were then compared with those of HXMT-HE X-ray burst (XRB; in SGR 1935+2154) associated with fast radio burst (FRB) 200428 and blazar $γ$-ray giant flares, to investigate the origins of these extreme flaring events. The variability patterns of the solar X-ray flares follow the stochastically driven damped simple harmonic oscillator (SHO) model. The derived timescales $t_{\rm B\underline{} steep}$ and $t_{\rm B\underline{~} flat}$ (corresponding to PSD breaks) are in the range of 4-7 s and 16-53 s, respectively. The FRB-associated HXMT-HE burst has a $Q$ value near 0.3, matching those of the solar flares occurred on 23 July 2002 (flare 1) and 3 November 2003 (flare 2). By contrast, blazar $γ$-ray giant flares show $Q >$ 0.3, similar to the solar flare occurred on 25 February 2014 (flare 3). We proposed that the critically damped state of the system may be the condition triggering the association between the XRB in SGR 1935+2154 and the FRB. In this scenario, the critical damping $Q$ value of the system is around 0.3, not the theoretical 0.5. The similarity in $Q$ values might imply that the FRB-associated HXMT-HE XRB and solar X-ray flares 1 $\&$ 2 share comparable dynamic behavior, while blazar $γ$-ray flares and solar X-ray flare 3 exhibit another distinct but similar dynamic behavior. Like solar X-ray flares, these extreme flares may all be related to the magnetic reconnection process.

astro-ph.HE↗

arXiv2025

A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties

Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API access, or protected health data -- barriers that limit community participation in this critical research area. We propose a practical, fully reproducible framework for evaluating medical AI security under realistic resource constraints. Our framework design covers multiple medical specialties stratified by clinical risk -- from high-risk domains such as emergency medicine and psychiatry to general practice -- addressing jailbreaking attacks (role-playing, authority impersonation, multi-turn manipulation) and privacy extraction attacks. All evaluation utilizes synthetic patient records requiring no IRB approval. The framework is designed to run entirely on consumer CPU hardware using freely available models, eliminating cost barriers. We present the framework specification including threat models, data generation methodology, evaluation protocols, and scoring rubrics. This proposal establishes a foundation for comparative security assessment of medical-specialist models and defense mechanisms, advancing the broader goal of ensuring safe and trustworthy medical AI systems.

cs.CR↗

arXiv2025

Error-Resilient Semantic Communication for Speech Transmission over Packet-Loss Networks

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source-channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. In this case, the robustness of speech communication is consequently evaluated primarily by the error-resilience to packet loss over wireless networks. To address these challenges, we propose \emph{Glaris}, a generative latent-prior-based resilient speech semantic communication framework that performs resilient speech coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that \emph{Glaris} consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and speech reconstruction quality.

cs.SD↗

arXiv2025

Global Gevrey solution of 3D anisotropic Navier-Stokes system in a strip domain

We investigate the three-dimensional (3D) incompressible anisotropic Navier-Stokes system with dissipation only in the horizontal variables, posed in a strip domain. To overcome the difficulties arising from the boundary terms and the absence of vertical dissipation, we impose a Gevrey-class regularity condition in the vertical direction. For the remaining directions, we prove that the solution exhibits space-time analytic or Gevrey-class regularization. Furthermore, the solution is shown to possess an enhanced Gevrey regularity in the direction of strong diffusion, which is unconstrained by boundaries.

math.AP↗

arXiv2025

Foundation Model in Biomedicine

Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive unlabeled datasets through unsupervised methods, enabling them to excel in diverse downstream tasks. These models, like GPT, can be adapted to various applications such as question answering and visual understanding, outperforming task-specific AI models and earning their name due to broad applicability across fields. The development of biomedical foundation models marks a significant milestone in the use of artificial intelligence (AI) to understand complex biological phenomena and advance medical research and practice. This survey explores the potential of foundation models in diverse domains within biomedical fields, including computational biology, drug discovery and development, clinical informatics, medical imaging, and public health. The purpose of this survey is to inspire ongoing research in the application of foundation models to health science.

cs.LG↗

arXiv2025

Achieving Fairness Without Harm via Selective Demographic Experts

As machine learning systems become increasingly integrated into human-centered domains such as healthcare, ensuring fairness while maintaining high predictive performance is critical. Existing bias mitigation techniques often impose a trade-off between fairness and accuracy, inadvertently degrading performance for certain demographic groups. In high-stakes domains like clinical diagnosis, such trade-offs are ethically and practically unacceptable. In this study, we propose a fairness-without-harm approach by learning distinct representations for different demographic groups and selectively applying demographic experts consisting of group-specific representations and personalized classifiers through a no-harm constrained selection. We evaluate our approach on three real-world medical datasets -- covering eye disease, skin cancer, and X-ray diagnosis -- as well as two face datasets. Extensive empirical results demonstrate the effectiveness of our approach in achieving fairness without harm.

cs.LG↗

arXiv2025

Overview of CHIP 2025 Shared Task 2: Discharge Medication Recommendation for Metabolic Diseases Based on Chinese Electronic Health Records

Discharge medication recommendation plays a critical role in ensuring treatment continuity, preventing readmission, and improving long-term management for patients with chronic metabolic diseases. This paper present an overview of the CHIP 2025 Shared Task 2 competition, which aimed to develop state-of-the-art approaches for automatically recommending appro-priate discharge medications using real-world Chinese EHR data. For this task, we constructed CDrugRed, a high-quality dataset consisting of 5,894 de-identified hospitalization records from 3,190 patients in China. This task is challenging due to multi-label nature of medication recommendation, het-erogeneous clinical text, and patient-specific variability in treatment plans. A total of 526 teams registered, with 167 and 95 teams submitting valid results to the Phase A and Phase B leaderboards, respectively. The top-performing team achieved the highest overall performance on the final test set, with a Jaccard score of 0.5102, F1 score of 0.6267, demonstrating the potential of advanced large language model (LLM)-based ensemble systems. These re-sults highlight both the promise and remaining challenges of applying LLMs to medication recommendation in Chinese EHRs. The post-evaluation phase remains open at https://tianchi.aliyun.com/competition/entrance/532411/.

cs.CL↗

arXiv2025

An Unusual Dresselhaus Spin-Orbit Contribution of Even Order in Momentum

The spin-orbit (SO) coupling is conventionally known to manifest as \emph{odd} functions of momentum. Here, through both model calculations and symmetry analysis along with the method of invariants, we reveal that, in ordinary semiconductor heterostructures, a \emph{quadratic} Dresselhaus SO term -- inheriting from its bulk crystal form -- emerges via the interband effect, while complying with time-reversal and spatial symmetries. Furthermore, we observe that this unusual SO term gives rise to a range of striking quantum phenomena, including hybridized swirling texture, anisotropic energy dispersion, avoided band crossing, longitudinal \emph{Zitterbewegung}, and opposite spin evolution between different bands in quantum dynamics. These stand in stark contrast to those associated with the usual \emph{linear} SO terms. Our findings uncover a previously overlooked route for exploiting interband effects and open new avenues for spintronic functionalities that leverage unusual SO terms of \emph{even} orders in momentum.

cond-mat.mes-hall↗

arXiv2025

Revisiting semi-supervised learning in the era of foundation models

Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning. As vision foundation models (VFMs) increasingly serve as the backbone of vision applications, it remains unclear how SSL interacts with these pre-trained models. To address this gap, we develop new SSL benchmark datasets where frozen VFMs underperform and systematically evaluate representative SSL methods. We make a surprising observation: parameter-efficient fine-tuning (PEFT) using only labeled data often matches SSL performance, even without leveraging unlabeled data. This motivates us to revisit self-training, a conceptually simple SSL baseline, where we use the supervised PEFT model to pseudo-label unlabeled data for further training. To overcome the notorious issue of noisy pseudo-labels, we propose ensembling multiple PEFT approaches and VFM backbones to produce more robust pseudo-labels. Empirical results validate the effectiveness of this simple yet powerful approach, providing actionable insights into SSL with VFMs and paving the way for more scalable and practical semi-supervised learning in the era of foundation models.

cs.LG↗

arXiv2025

SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

cs.CV↗

arXiv2025

Neutron source-based event reconstruction algorithm in large liquid scintillator detectors

We developed an event reconstruction algorithm, applicable to large liquid scintillator detectors, built primarily upon neutron calibration data. We employ a likelihood method using photon detection time and charge information from individual photomultiplier tubes. Detector response tables in the likelihood function were derived from americium-carbon neutron source events, 2.2~MeV $γ$-ray events from cosmic-ray muon spallation neutrons, and laser calibration events. This algorithm can reconstruct the event position, energy, and also has capability to differentiate particle types for events within the energy range of reactor neutrinos. Using the detector simulation of the Jiangmen Underground Neutrino Observatory (JUNO) experiment as a large liquid scintillator detector example, we demonstrate that the presented reconstruction algorithm has a reconstructed position accuracy within $\pm$4~cm, and a reconstructed energy non-uniformity under 0.5\% throughout the central detector volume. The vertex resolution for positron events at 1~MeV is estimated to be around 9~cm, and the energy resolution is confirmed to be comparable to that in the JUNO official publication. Furthermore, the algorithm can eliminate 80\% (45\%) of $α$-particle (fast-neutron) events while maintaining a positron event selection efficiency of approximately 99\%.

physics.ins-det↗

arXiv2025

Multiwavelength spectroscopic observations of a quiescent prominence

In this paper we focus on the analysis of the multiwavelength spectroscopic observations of a quiescent prominence. The spectral and geometrical parameters in the prominence were derived and used to constrain the NLTE radiative transfer models. Applying this method with multiwavelength observations provides a good opportunity to reduce the large range of thermodynamic parameters in solar prominences. We used time-slice and optical flow methods in order to derive the plane-of-sky (POS) velocities, and used gravity center and peak position methods on Mg II h&k and H I Ly-alpha profiles to compute the line-of-sight (LOS) velocities. We used the integrated intensities and FWHM values of the H-alpha, Ca II H, and Mg II h&k lines to compare with the NLTE radiative transfer computations. Ionization degree and thickness of the prominence plasma could be further derived. Opposite flows are observed along two strands between prominence barbs. The POS velocity can reach 20 km/s and the largest LOS velocity is > 90 km/s. The derived electron densities range from 6.5e9 cm-3 to 2.7e10 cm-3, and the derived total hydrogen densities range from 7.4e9 cm-3 to 6.6e10 cm-3. The temperature ranges from 7 000 to 14 000 K. The ionization degree of hydrogen is in the range of 0.40 to 0.91. The comparison between averaged and modeled profiles of Mg II and Ly-alpha lines shows that macro-velocities of 15 km/s and 20 km/s are required, respectively. The bulk motions among prominence barbs indicate that the prominence plasma is not confined within magnetic dips but exhibits a large-scale behavior. The presence of high-speed cool plasma flows, along with a wide range of plasma densities and temperatures, suggests that the prominence plasma is far from thermodynamic equilibrium and is inherently dynamic in nature.

astro-ph.SR↗

arXiv2025

The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective

As machine learning (ML) algorithms are increasingly used in medical image analysis, concerns have emerged about their potential biases against certain social groups. Although many approaches have been proposed to ensure the fairness of ML models, most existing works focus only on medical image diagnosis tasks, such as image classification and segmentation, and overlooked prognosis scenarios, which involve predicting the likely outcome or progression of a medical condition over time. To address this gap, we introduce FairTTE, the first comprehensive framework for assessing fairness in time-to-event (TTE) prediction in medical imaging. FairTTE encompasses a diverse range of imaging modalities and TTE outcomes, integrating cutting-edge TTE prediction and fairness algorithms to enable systematic and fine-grained analysis of fairness in medical image prognosis. Leveraging causal analysis techniques, FairTTE uncovers and quantifies distinct sources of bias embedded within medical imaging datasets. Our large-scale evaluation reveals that bias is pervasive across different imaging modalities and that current fairness methods offer limited mitigation. We further demonstrate a strong association between underlying bias sources and model disparities, emphasizing the need for holistic approaches that target all forms of bias. Notably, we find that fairness becomes increasingly difficult to maintain under distribution shifts, underscoring the limitations of existing solutions and the pressing need for more robust, equitable prognostic models.

cs.LG↗

arXiv2025

Long time evolution of a pair of 2D viscous point vortices

This paper studies the long-time evolution of two point vortices under the 2D Navier-Stokes tokes equations. Starting from initial data given by a pair of Dirac measures, we derive an asymptotic expansion for the vorticity over time scales significantly longer than the advection time, yet shorter than the diffusion time. Building on previous works \cite{GS24-1, DG24}, we construct suitable approximate solutions $Ω_a$ and employ Arnold's method to define a nonlinear energy functional $E_\ve[\om]$, with respect to which the linearized operator $Λ^{E,\star}$ around $Ω_a$ is nearly skew-adjoint. A key innovation in this work is the introduction of ``pseudo-momenta'': $\varrho^e_a, \varrho^o_a,\varrho^{te}_a, \varrho^{to}_a$, which correspond to eigenfunctions or other nontrivial elements in invariant subspaces of $Λ^E$, derived from the Lie structure of the 2D Euler equations.

math.AP↗

arXiv2025

Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study

Data augmentation as a technique can mitigate data scarcity in machine learning. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective solution to wireless data augmentation due to its excellent data generation capability. This article systematically explores the potential and effectiveness of generative data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of generative data augmentation in wireless networks from the physical, network, and application layers, providing a generative data augmentation architecture for each application. Subsequently, we propose a general generative data augmentation framework for Wi-Fi gesture recognition. Specifically, we leverage transformer-based diffusion models to generate high-quality channel state information data. To evaluate the effectiveness of the proposed framework, we conduct a case study using the Widar 3.0 dataset, which employs a residual network model for Wi-Fi gesture recognition. Simulation results demonstrate that the proposed framework can enhance the performance of Wi-Fi gesture recognition. Finally, we discuss research directions for generative data augmentation.

cs.NI↗

arXiv2025

6G Channel Modeling: Requirement, Measurement, Methodology and Simulator

Sixth-generation (6G) mobile communications have attracted substantial attention in the global research community of information and communication technologies (ICTs). 6G systems are expected to support not only extended 5G usage scenarios but also new usage scenarios, such as integrated sensing and communication (ISAC), integrated artificial intelligence (AI) and communication, and communication and ubiquitous connectivity. To achieve this goal, channel characteristics must be comprehensively studied and properly exploited to promote the design, standardization, and optimization of 6G systems. In this paper, we first summarize the requirements and challenges in 6G channel research. Our focus is on channels for six promising technologies enabling 6G, including ISAC, extremely large-scale MIMO (XL-MIMO), mid-band and terahertz (THz) technologies, reconfigurable intelligent surfaces (RISs), and space-air-ground integrated networks (SAGINs). A survey of the progress in 6G channel research regarding the above six promising technologies is presented in terms of the latest measurement campaigns, new characteristics, modeling methods, and research prospects. To support testing, optimization and evaluation, existing 6G channel simulators are summarized. Then, BUPTCMCCCMG-IMT2030 is introduced as an example of a simulator that was developed on the basis of the ITU/3GPP 3D geometry-based stochastic model (GBSM) methodology. We also address open issues covering standardization activities, AI-enabled methods, and system performance analysis in the context of 6G channel research. This paper offers in-depth, hands-on insights into the best practices of channel measurements, modeling, and simulations for the evaluation of 6G technologies, the development of 6G standards, and the implementation and optimization of 6G systems.

eess.SP↗