arXiv subjects

Xue Zhang

Publications and source records attributed to Xue Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Semi-Blind Channel Estimation for Dynamic NTN Systems via Spiked Random Matrix Theory

Semi-blind channel estimation offers an attractive tradeoff between pilot overhead and estimation accuracy in large-scale wireless systems. However, reliable channel acquisition becomes particularly challenging in highly dynamic environments such as non-terrestrial networks (NTNs), where rapidly varying channels and high system dimensionality significantly degrade the performance of conventional covariance-based estimators due to sampling noise. In this paper, we propose a robust semi-blind channel estimation framework for multi-user uplink systems operating in NTN systems. The proposed approach introduces an optimally regularized least-squares formulation that balances training-based information and blind subspace structure. By exploiting the spiked covariance model within a random matrix theory (RMT) framework, we derive a closed-form characterization of the resulting channel mean-squared error and obtain an analytically tractable design of the optimal regularization parameter. The resulting estimator is computationally efficient and particularly well suited to high-dimensional regimes. Simulation results under realistic Third Generation Partnership Project (3GPP) NTN channel models demonstrate substantial performance improvements over conventional semi-blind and training-based estimators.

eess.SP↗

arXiv2026

Joint Beamforming, Energy Management, and Trajectory Optimization for Figure-Eight Loitering in Solar-Powered HAPS-Enabled ISAC Systems

Solar-powered high-altitude platform stations (HAPSs) provide a promising platform for integrated sensing and communication (ISAC) owing to their wide-area coverage and long-endurance operation. This paper proposes a solar-powered HAPS-enabled ISAC framework for sustainable day-night operation, where a figure-eight loitering architecture is adopted to provide persistent ISAC services over geographically separated regions while harvesting solar energy. A unified communication-sensing-energy model is developed by jointly characterizing solar energy harvesting, battery dynamics, propulsion power consumption, communication transmission, and synthetic aperture radar (SAR) imaging. Based on this model, coupled optimization problems are formulated for daytime operation (DTO) and nighttime operation (NTO), where the battery state bridges the two operational phases through a long-term energy budget. The proposed framework jointly optimizes communication, sensing, mobility, and energy management to maximize daytime communication performance while minimizing nighttime propulsion energy consumption. Efficient iterative algorithms are developed to solve the resulting non-convex optimization problems. Simulation results verify the effectiveness of the proposed communication-sensing-energy co-design and demonstrate that the proposed framework effectively supports sustainable day-night ISAC operation.

eess.SP↗

arXiv2026

Penalty-scaling effects in nonsymmetric interior-penalty DG discretizations of viscous rotating shallow-water equations

We investigate how the scaling of the interior-penalty parameter affects nonsymmetric interior-penalty Galerkin (NIPG) discretizations of the viscous rotating shallow-water equations in geopotential variables. The hyperbolic terms are approximated by a local Lax--Friedrichs flux, while viscosity acts on the momentum variables through a penalty law $\mu_e=\sigma h_e^{-\beta}$. The standard choice $\beta=1$ and the super-penalized choice $\beta=3$ are compared with a symmetric interior-penalty Galerkin reference. For the diffusion form, we establish consistency, continuity for $\beta\ge 1$, and an exact coercivity identity in the momentum DG seminorm. Manufactured-solution tests show that super-penalization can recover the expected momentum $L^2$ accuracy, whereas the coupled geopotential variable need not exhibit the same improvement. Rotating and topography-aware tests further show that the standard scaling generally gives the better accuracy-cost compromise for the explicit implementation considered here.

math.NA↗

arXiv2026

Beyond-Third-Order Quantum Coherence in Two-Dimensional Spectroscopy via Order-Selective Isolation

A central challenge in nonlinear spectroscopy is the order-selective readout of weak higher-order responses that spectrally overlap with dominant lower-order signals. This bottleneck is particularly severe in two-dimensional (2D) spectroscopy, where extending conventional phase-cycling schemes to higher orders rapidly increases measurement and analysis complexity. Here we introduce a computation-assisted strategy that combines rotating-frame acquisition with a frame-shift tracking algorithm to separate signals by their frame-dependent spectral shifts. In a rubidium vapor experiment, we use this approach to isolate a 7th-order nonlinear contribution from coexisting 3rd-order components, enabling direct access to higher-order quantum-coherence dynamics without sacrificing operation at comparatively high pulse intensities. The method is broadly compatible with multidimensional spectroscopy platforms and provides a practical route to probing many-body and collective ultrafast dynamics beyond third order.

quant-ph↗

arXiv2026

A physics-informed foundation model for quantitative diffusion MRI

Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring dense sampling and optimized acquisition protocols. To address this gap, we present a physics-informed generative microstructure network (PIGMENT) that learns a universal generative prior of human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-specific maps. Trained on 11375 scans spanning multiple sites, vendors, and field strengths, PIGMENT enabled reliable quantitative mapping for tensor, kurtosis, and NODDI models across external datasets from five independent centers. It remains effective where conventional fitting becomes unreliable, recovering meaningful maps from extremely sparse acquisitions while supporting downstream tractography and structural connectivity mapping. PIGMENT estimates demonstrated strong biological validity, preserving submillimeter cortical microarchitectural patterns and early-childhood white matter developmental trajectories from 10-fold accelerated scans. Furthermore, PIGMENT enables reliable quantitative tensor mapping on cost-efficient low-field systems and the extraction of tumor-related biomarkers using ultra-fast clinical protocols. Together, these results establish PIGMENT as a physics-informed foundation model that extends quantitative diffusion MRI into regimes traditionally too sparse, heterogeneous, or clinically constrained for reliable analysis.

eess.IV↗

arXiv2026

Toward Environment-Aware LAE: SAR as a Shared Sensing Infrastructure

The rapid growth of the low-altitude economy (LAE) is making aerial systems an important part of future digital infrastructure. Although major advances have been achieved in unmanned aerial vehicle (UAV) platforms, communications, and autonomous control, environmental perception remains a key bottleneck to reliable and scalable LAE operations. Existing sensing modalities, such as optical, LiDAR, and millimeter-wave radar, are limited by visibility, sensing range, and environmental conditions, resulting in fragmented situational awareness. This article argues that addressing these limitations requires a shift from platform-centric sensing to a shared, environment-aware sensing infrastructure. In this context, synthetic aperture radar (SAR) offers a distinct advantage by enabling all-weather, wide-area perception. We show that SAR can support UAV operations through global environmental awareness, enhance task-level sensing, and enable cooperative sensing across satellites, high-altitude platforms, UAVs, and ground systems. Building on this perspective, we present a system-level view of SAR-enabled LAE, highlighting key transformations from fragmented to infrastructure-centric sensing, from reactive to predictive operation, and from device-centric to environment-aware networking. We further discuss enabling architectures, including multi-platform sensing hierarchies, integration with integrated sensing and communication (ISAC), and the role of artificial intelligence and digital twins, along with the key challenges toward real-world deployment. By positioning SAR as a shared sensing foundation rather than a standalone modality, this article provides new insights into the design of scalable, reliable, and intelligent LAE systems.

eess.SP↗

arXiv2026

Spectral Hardness as the Primary Discriminator: Unveiling the Collapsar--Merger Boundary with a Gold-Standard Gamma-Ray Burst Sample

In this Letter, we establish a robust, physically motivated classification method using a Support Vector Machine (SVM) trained on a "gold-standard" sample of 24 GRBs with spectroscopically confirmed progenitors (associated SNe or KNe). By isolating the prompt main spike to excise contamination from extended emission, we derive a quantitative classification index, I_SVM = 5.01 log_10 E_p,i - 1.25 log_10 E_iso - 0.34 log_10 T_90,z - 12.90 (units: keV, 10^52 erg, s). Events with I_SVM > 0 are classified as mergers. Analysis of the standardized classification weights reveals that the discriminative power of E_p,i is approximately 5 times that of T_90,z, while E_iso contributes a weight comparable to E_p,i. This quantitatively demonstrates that spectral hardness and energetics, rather than duration, are the primary physical signatures distinguishing mergers from collapsars. The derived boundary implies a stringent hardness ceiling for collapsars, while mergers are identified as outliers with excessive hardness relative to their energy budget. The classifier successfully identifies the nature of historic test cases, including the ultra-long GRB 111209A (collapsar) and the short GRB 050709 (merger), independent of instrumental eras. This tool paves the way for cleaning archival and future high-redshift GRB samples for precision cosmology.

astro-ph.HE↗

arXiv2026

CRB-Based Resource Allocation in Multi-User Uplink Transmissions

In this work, we study the design of receivers for uplink multi-user systems, aiming to estimate both the channel and the transmitted symbols. We consider two estimation strategies: (i) a joint estimation approach, where the channel and symbols are estimated simultaneously, and (ii) a sequential estimation approach, where the channel is first estimated and then used for symbol detection. For both strategies, we derive the Cram\'er-Rao Bound (CRB) for symbol estimation to characterize fundamental performance limits. When efficient receivers achieving the CRB exist, these bounds provide accurate lower bounds on the mutual information. In general, however, such receivers may not be available, and we instead use these same CRB-based metrics as practical proxies for achievable throughput. Leveraging tools from random matrix theory (RMT), we analyze the asymptotic behavior of these lower bounds under various asymptotic regimes for both estimation strategies. This analysis enables the derivation of generic power allocation guidelines that asymptotically maximize the proxy metrics. Simulation results confirm the accuracy of the asymptotic expressions and their effectiveness in guiding resource allocation decisions.

eess.SP↗

arXiv2026

Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing

Complex dynamical systems-such as climate, ecosystems, and economics-can undergo catastrophic and potentially irreversible regime changes, often triggered by environmental parameter drift and stochastic disturbances. These critical thresholds, known as tipping points, pose a prediction problem of both theoretical and practical significance, yet remain largely unresolved. To address this, we articulate a model-free framework that integrates the measures characterizing the stability and sensitivity of dynamical systems with the reservoir computing (RC), a lightweight machine learning technique, using only observational time series data. The framework consists of two stages. The first stage involves using RC to robustly learn local complex dynamics from observational data segmented into windows. The second stage focuses on accurately detecting early warning signals of tipping points by analyzing the learned autonomous RC dynamics through dynamical measures, including the dominant eigenvalue of the Jacobian matrix, the maximum Floquet multiplier, and the maximum Lyapunov exponent. Furthermore, when these dynamical measures exhibit trend-like patterns, their extrapolation enables ultra-early prediction of tipping points significantly prior to the occurrence of critical transitions. We conduct a rigorous theoretical analysis of the proposed method and perform extensive numerical evaluations on a series of representative synthetic systems and eight real-world datasets, as well as quantitatively predict the tipping time of the Atlantic Meridional Overturning Circulation system. Experimental results demonstrate that our framework exhibits advantages over the baselines in comprehensive evaluations, particularly in terms of dynamical interpretability, prediction stability and robustness, and ultra-early prediction capability.

cs.LG↗

arXiv2026

Non-volatile Multistate Magnetic Switching via Spin-orbit Torque and Intrinsic Anisotropy

While current-induced bistate spin-orbit torque (SOT) switching has been well established, deterministic electrical control of multiple magnetic states remains a central challenge in spintronics. Here, we realize a conceptually new multistate SOT device in a SrIrO_3/SrRuO_3 bilayer, hosting four intrinsically stable yet electrically distinguishable magnetic states, including two in-plane canted (IP_c^$\pm$) and two out-of-plane canted (OP_c^$\pm$) states. Pulsed current excitations fully map all twelve deterministic transitions among the four states, establishing a robust switching protocol defined by two characteristic current densities. In-situ scanning nitrogen-vacancy (NV) center magnetometry provides direct real-space evidence for the previously unobserved IP_c^$\pm$ states, and spin dynamics simulations uncover a two-step switching pathway, driven by the concerted action of spin torques and the effective anisotropy field within the fourfold anisotropy landscape. Our demonstration of the intrinsic multistate SOT device directly addresses the density bottleneck of conventional bistate SOT technology, establishing a powerful paradigm for compact, high-speed, and energy-efficient multistate spintronics.

cond-mat.mes-hall↗

arXiv2026

Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing

The escalating energy demands of artificial intelligence pose a critical challenge to conventional computing. Leveraging the efficiency of event-driven, in-memory neuromorphic architectures into the superconducting circuits with ultra-high speed and low power dissipation advantages offers a promising solution to energy-efficient computing. However, the potential of such a solution has yet to be realized, owning to the absence of a fundamental superconducting unit that unifies programmability, local memory, and multi-timescale plasticity. Here, we introduce a programmable Josephson-junction-based leaky integrate-and-fire (LIF) neuron that features intrinsic static memory and precise programmability by encoding somatic and synaptic parameters directly in the bias current. This neuron is also capable of dual-timescale plasticity: picosecond-scale short-term modulation of spike transmission and long-term weight retention exceeding 10,000 seconds, facilitating both rapid temporal adaptation and robust weight storage. It can operate up to 45 GHz with femtojoule-level energy dissipation per spike, and supports 10 somatic threshold levels and 20 synaptic states. Furthermore, we demonstrate a crossbar-based spiking neural network (SNN) utilizing this neuron, which achieves outstanding performance across multiple tasks. By fusing computation, memory and plasticity into a single superconducting unit, our work paves the way for the next generation of ultrafast, energy-efficient neuromorphic computing.

cs.ET↗

arXiv2026

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models

Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teacher inference with the same training-oriented backend as student optimization, leading to suboptimal efficiency. In this paper, we propose KDFlow, a novel framework for LLM distillation that features a decoupled architecture and employs SGLang for teacher inference. KDFlow combines SGLang for teacher inference with PyTorch FSDP2 for student optimization, allowing each model to run on a backend tailored to its workload. To enable efficient full-vocabulary distillation in this decoupled architecture, KDFlow transfers the teacher's final hidden states via Ray's object store and recomputes teacher logits on each student worker using a frozen copy of the teacher's output head. Furthermore, our framework supports both off-policy and on-policy distillation and incorporates cross-tokenizer algorithms through highly extensible and user-friendly APIs. Experiments show that KDFlow achieves a 1.44$\times$ to 6.36$\times$ speedup over MS-SWIFT in off-policy distillation and a 1.43$\times$ to 1.75$\times$ speedup over verl in on-policy distillation. KDFlow further scales to 64 GPUs, achieving 3.68$\times$ and 2.52$\times$ strong-scaling speedups in two representative model configurations. The code and documentation are publicly available.

cs.CL↗

arXiv2026

Some properties of G-SVIEs

In this paper, we investigated the solvability of G-SVIEs under two cases: time-varying Lipschitz coefficients and integral-Lipschitz coefficients. Using the Picard iteration method, we established the existence and uniqueness of solutions to G-SVIEs under these two conditions. Additionally, we prove the continuity of the solution with respect to parameters in parameter-dependent G-SVIEs with Lipschitz coefficients.

math.PR↗

arXiv2026

Exploring the central engines of gamma-ray bursts from prompt light curves

Hyperaccreting stellar-mass black hole systems are leading candidates for the central engines of gamma-ray bursts (GRBs). Their jets are thought to be powered by either the Blandford-Znajek (BZ) process or neutrino-dominated accretion flows (NDAFs), but discriminating between these mechanisms remains challenging. To address this, we propose using the luminosity decay slope (parameter d) of GRB light curves to distinguish between the BZ and NDAF mechanisms, thereby linking the light-curve morphology to the central engine physics. By analysing 85 single-peaked GRBs with fast-rise, exponential-decay (FRED) profiles observed by Swift/BAT using 64 ms background-subtracted light curves, we fit the decay slope (parameter d) with the empirical Kocevski-Ryde-Liang (KRL) function and compare the results with theoretical predictions for the BZ (d approximately 1.67) and the NDAF (d approximately 3.7 to 7.8) mechanisms. We find that the decay slope (parameter d) can differentiate central engine mechanisms, with 15 GRBs consistent with the BZ mechanism and 22 supporting the NDAF mechanism. However, most events exhibit slopes within the range between 2 and 4, suggesting a hybrid of mechanisms, with NDAF being dominant.

astro-ph.HE↗

arXiv2026

Exploring Memory Effects: Sparse Identification in Vector-Borne Diseases

Predicting the human burden of vector-borne diseases from limited surveillance data remains a major challenge, particularly in the presence of nonlinear transmission dynamics and delayed effects arising from vector ecology and human behavior. We develop a data-driven framework based on an extension of Sparse Identification of Nonlinear Dynamics (SINDy) to systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data. Using severe fever with thrombocytopenia syndrome (SFTS) as a case study, we show that this approach can uncover key features of tick-borne disease dynamics using only human incidence and local temperature data, without imposing predefined assumptions on human case reporting. We further demonstrate that predictive performance is substantially enhanced when the data-driven model is coupled with mechanistic representations of tick-host transmission pathways informed by empirical studies. The framework supports systematic sensitivity analysis of memory kernels and behavioral parameters, identifying those most influential for prediction accuracy. Although the approach prioritizes predictive accuracy over mechanistic transparency, it yields sparse, interpretable integral representations suitable for epidemiological forecasting. This hybrid methodology provides a scalable strategy for forecasting vector-borne disease risk and informing public health decision-making under data limitations.

math.DS↗

arXiv2026

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation

Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most existing studies employ a unitive process where queries of equivalent semantics across different languages are processed through a single-turn retrieval and subsequent optimization. Such a ``one-size-fits-all'' strategy is often suboptimal in multilingual settings, as the models occur to knowledge bias and conflict during the interaction with the search engine. To alleviate the issues, we propose LcRL, a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. We adopt the language-coupled group sampling in the rollout module to reduce knowledge bias, and regularize an auxiliary anti-consistency penalty in the reward models to mitigate the knowledge conflict. Experimental results demonstrate that LcRL not only achieves competitive performance but is also appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. Our code is available at https://github.com/Cherry-qwq/LcRL-Open.

cs.CL↗

arXiv2026

G-BSDEs with time-varying monotonicity condition

In this paper, we study backward stochastic differential equations driven by G-Brownian motion where the generator has time-varying monotonicity with respect to y and Lipsitz property with respect to z. Through the Yosida approximation, we have proved the existence and uniqueness of the solutions to these equations.

math.PR↗

arXiv2025

Reconstruction of dark energy using DESI DR2

Using a model-independent Gaussian process (GP) method to reconstruct the dimensionless luminosity distance $D$ and its derivatives, we derive the evolution of the dimensionless Hubble parameter $E$, the deceleration parameter $q$, and the state parameter $w$ of dark energy. We utilize the PantheonPlus, SH0ES, and Gamma Ray Burst (GRB) data to derive the dimensionless luminosity distance $D$. Additionally, we employ observational $H(z)$ data (OHD) and baryon acoustic oscillations (BAO) from Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) to obtain the first derivative of the dimensionless luminosity distance $D^{'}$. To obtain the reconstructed $D$ and $D^{'}$, we utilize the fiducial value from each dataset, with particular emphasis on the varying $H_0$. According to the reconstruction results obtained from PantheonPlus+SH0ES+GRB+OHD and PantheonPlus+SH0ES+GRB+OHD+DESI data, we find that $E$ are consistent with the predictions of the $\Lambda$CDM model at a $2\sigma$ confidence level within the redshift range of $z<2$. However, the reconstruction results for $q$ exhibit deviations from the $\Lambda$CDM model in the range of $z<0.3$. Furthermore, we observe that the mean value of $w$ exhibits evolving behavior, transiting from $w < -1$ to $w > -1$ around $z_{\rm wt}=0.464^{+0.235}_{-0.120}$. Combining data from DESI DR2 can slightly enhance the accuracy of our constraints.

astro-ph.CO↗