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Ping Zhang

Publications and source records attributed to Ping Zhang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Towards Semantic Internet of Everything in the Age of Agentic AI

Semantic communication improves task effectiveness by transmitting task-relevant information. However, most existing schemes remain organized as task-specific, end-to-end pipelines, which are difficult to reuse across models, applications, and deployment environments. Against this background, we propose the Semantic Internet of Everything (SIoE), a composable service architecture that represents heterogeneous communication and artificial intelligence (AI) functions as capability-profiled services and coordinates them according to application objectives. SIoE comprises three planes: a task and service plane, an agentic orchestration plane, and a semantic capability plane. In this framework, task requirements are captured via a semantic service-level agreement (SLA), while an agentic planner discovers and composes candidate capabilities under deterministic compatibility, resource, privacy, and policy validation. Feedback from the communication, semantic, and task levels enables continuous adaptation and replanning. A lightweight vehicle-to-everything case study illustrates profile-grounded capability planning under explicit service constraints. The results demonstrate the feasibility of decoupling service objectives from fixed communication implementations and also highlight key open challenges, including semantic SLA design, capability interoperability, scalable planning, and trustworthy execution.

eess.SP↗
arXiv2026

Quantitative bounds for bounded solutions to the Navier-Stokes equations in endpoint critical Besov spaces

Building on Tao's quantitative regularity theory and triple-logarithmic blow-up estimate in $L^3$ in \cite{Tao_20}, we consider classical solutions $(u,P)$ of the three-dimensional incompressible Navier--Stokes equations on $[0,T]\times\mathbb{R}^3$. For $3<p<\infty$, under simultaneous uniform control of the two scaling-critical quantities $\|u\|_{L_T^\infty(\dot B_{p,\infty}^{-1+\frac{3}{p}})}$ and $\||D|^{-1+\frac{3}{p}}u\|_{L_T^\infty(L^p)}$, we obtain explicit quantitative estimates for all spatial derivatives of $u$. As a consequence, we derive a mixed blow-up criterion coupling a double exponential of the critical Besov norm with the $L^p$ norm of $|D|^{-1+\frac{3}{p}}u$, which forces quantified growth of at least one of these two critical quantities near any finite blow-up time. The low regularity and lack of dyadic summability in the endpoint Besov space are handled through a finite iterative decomposition that successively improves spatial integrability and produces an energy-class remainder, together with refined nonlinear energy estimates. The nonlocal signed quantity $|D|^{-1+\frac{3}{p}}u$ is treated by localized mean-zero vector tests and almost orthogonality across geometrically separated concentration scales.

math.AP↗
arXiv2026

Loss-Resilient Semantic Communication over Packet-Loss Networks at Extreme-Low Bandwidth

In extreme-low bandwidth network scenarios, generative semantic codecs have emerged as promising solutions to reduce bandwidth cost for visual communications. However, these learned codecs are usually optimized solely for compression efficiency and thus not robust against transmission errors. Corruptions due to packet-loss among these highly compact generative latent representations often cause more critical degradation in fidelity and realism, intensified by the severe error propagation across the latent contexts and multi-step decoding process. In this paper, we propose ResiGLC, a novel loss-resilient generative latent coding framework designed for robust semantic communication over extreme-low bandwidth packet-loss networks. Motivated by the inherent goal-consistency between generation and compression, we sufficiently exploit the impressive in-context predictive capabilities of language models. Integrated with the masked learning strategy, our model supports arbitrary context modeling of latent codes, which could mitigate the error propagation and handle unpredictable packet loss patterns. At the receiver, a progressive resilient decoding pipeline is presented, which leverages both the contextual relationship of the latent codes and the multi-modal semantic prior in the generative latent space, separately. By jointly optimizing toward both compression efficiency and packet-loss resilience, our proposed progressive decoding mechanism offers graceful performance when dealing with dynamic packet losses. Through extensive experimental evaluations, we establish that under packet-loss network conditions, ResiGLC can effectively improve the loss-resilience in terms of perceptual fidelity and realism qualities with extreme-low bandwidth cost.

eess.IV↗
arXiv2026

The Radioactive Background of the JUNO Calibration System

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is a reactor antineutrino detector employing 20 kton of ultra-pure liquid scintillator to determine the neutrino mass ordering and to precisely measure oscillation parameters. The total singles background rate from radioactivity is required to be below 10 Hz in the energy range of 0.7-12 MeV within the fiducial volume for reactor neutrino analysis. The calibration system is designed to characterize the detector energy and position responses, while several of its components are located close to the target and may contribute to the background budget. Therefore, extensive material screening and selection are required to construct a low-background calibration system and to ensure that its contribution remains within the design requirements. In this work, a comprehensive study of the radioactive background induced by the calibration system is presented, including material radioactivity measurements using high-purity germanium detectors and neutron activation analysis techniques, detailed Monte Carlo simulations to evaluate the background, and comparisons with in-situ detector data to validate the predictions. In this data analysis, dedicated spatial selection methods are developed to isolate calibration-related contributions and to suppress the liquid scintillator background. The total radioactivity contribution from the calibration system is estimated to be less than 76 mHz, which satisfies the requirement of 200 mHz (2% of the total background budget). The results from in-situ data are found to be consistent with the expectations based on material assay and simulation within uncertainties. These results demonstrate that the calibration-induced background is well understood, in agreement between data and simulation, and negligible for reactor antineutrino measurements in JUNO.

physics.ins-det↗
arXiv2026

MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematical solutions must be expressed visually through diagrams, plots, geometric constructions, and structured symbolic layouts, where correctness depends on precise visual composition. This naturally raises the question of whether generative models can still do so when the answer must be rendered visually rather than written in text? To study this problem, we introduce MathGen, a rigorous benchmark of 420 problems spanning seven core domains, including 350 Clean-Scene problems and 70 paired Open-Scene problems. Each problem is evaluated under a Script-as-a-Judge protocol with problem-specific verification criteria implemented through reusable executable scripts for deterministic and reproducible evaluation. Experiments on representative open-source and proprietary text-to-image models show that mathematical fidelity remains a major bottleneck: even the best closed-source model reaches only 53.7% overall accuracy, while open-source models achieve just 1--11%, often near 0% on structured tasks, particularly those requiring precise geometric and functional rendering. Overall, current T2I models remain far from reliable at even elementary mathematical visual generation.

cs.CV↗
arXiv2026

Adaptive Source-Channel Coding for Bi-static Integrated Sensing and Semantic Communications

Semantic communication (SemCom) has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G, due to its potential to enhance transmission efficiency by transmitting task-relevant semantic features rather than raw bits. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes a sensing-aware adaptive source-channel coding (SA-ASCC) and beamforming design framework for bi-static integrated sensing and SemCom (ISSC) systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is derived for target position under imperfect time synchronization due to the transceiver deployed at different places in our considered bi-static ISSC system. To characterize the achievable region between SemCom and sensing performance, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget, which is non-convex due to the coupled design variables and the mixed-integer program. Subsequently, an alternating optimization (AO) algorithm is proposed to decompose this problem into the model selection and joint rate and beamforming optimization subproblems, which are solved by the exhaustive search method and the combination of successive convex approximation and fractional programming, respectively. Finally, simulation results demonstrate that the proposed scheme outperforms the DJSCC-WF-ZF and BPG-WF-ZF benchmarks.

eess.SP↗
arXiv2026

A Unified Symmetry Framework For In-plane Anomalous Hall effect

The in-plane anomalous Hall effect (IPAHE), driven by an in-plane net magnetization or an applied magnetic field, challenges the conventional anomalous Hall paradigm. Despite growing interest, a unified symmetry principle governing these phenomena has remained elusive. Here, we establish a comprehensive symmetry framework that bridges the spin space group, which dictates the magnetic geometry, with the magnetic space group, which governs the anomalous Hall response. We show that spontaneous IPAHE can emerge in ferromagnets when spin-orbit-coupling-induced spin-group symmetry breaking permits additional net magnetization directions. For field-induced IPAHE, we analyze how an applied magnetic field reduces the symmetries of all 122 magnetic point groups and identify 54 groups that support IPAHE. Our framework naturally predicts IPAHE in a broad class of unconventional magnets, including altermagnets and odd-parity magnets. In particular, symmetry analysis reveals characteristic one-, two-, or three-fold angular harmonics of the Hall conductance under an in-plane rotating field, providing a symmetry-resolved fingerprint for unconventional magnetism. Using this framework, we screen the MAGNDATA database and identify candidate materials supporting spontaneous or field-induced IPAHE, encompassing ferromagnets, antiferromagnets, and unconventional magnets. Finally, we validate the symmetry predictions through first-principles calculations for two representative materials.

cond-mat.mtrl-sci↗
arXiv2026

Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking

Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.

cs.CR↗
arXiv2026

Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications

In multiple-input multiple-output (MIMO) semantic communication, imperfect channel state information (CSI) and equalization mismatch can seriously degrade semantic reconstruction quality. To address this issue, we propose a unified restoration flow matching (RFM)-based framework for channel refinement and equalization correction. Specifically, the channel RFM (CRFM) module is developed to refine the coarse channel, thereby improving channel estimation accuracy. Based on the refined channel, the developed semantic RFM (SRFM) module is employed to correct the residual distortions in the post-equalization latent space. The key idea is to formulate the two cascaded inverse problems of channel estimation and equalization as the unified conditional restoration task, in which the learned conditional velocity field guides the perturbed distribution towards the target distribution. To enhance the robustness of these two modules under various distortion conditions, we develop a dual-anchor perturbation training strategy that jointly learns near-manifold refinement and large-error correction, and implement inference through a few-step deterministic ordinary differential equation (ODE) solver. Extensive experiments on MIMO channels and visual semantic transmission tasks demonstrate that the proposed scheme improves key metrics for channel estimation and semantic reconstruction quality. Moreover, compared with representative diffusion-based generative baselines, the proposed method requires fewer sampling steps.

cs.LG↗
arXiv2026

Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems

Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks. However, hybrid-field channel estimation for XL-RIS-assisted systems is challenging due to the high-dimensional cascaded channel and the coexistence of far-field and near-field propagation. In this case, traditional full-dimensional sparse recovery methods require a large cascaded dictionary and suffer from severe computational and storage burdens. To address these challenges, we develop a double-timescale channel estimation framework that decouples sparse dictionary representation and recovery. Then, by exploiting the quasi-static property of the channel at the base station (BS) and RIS side, we propose a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) scheme for sparse representation, which reduces the dimension of the corresponding dictionary as well as mitigates BS-side angular off-grid error. Furthermore, for the dynamic channel at the user equipment (UE) and RIS side, we propose a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm, which enables incremental learning of sparse channels by exploiting the identified low-dimensional subspace and pruning threshold. Analysis and simulation results confirm that the proposed framework avoids full-dimensional Bayesian recovery and achieves a favorable tradeoff among estimation accuracy, computational complexity, and storage overhead.

eess.SP↗
arXiv2026

Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

Camera traps are vital for large-scale biodiversity monitoring, yet accurate automated analysis remains challenging due to diverse deployment environments. While the computer vision community has mostly framed this challenge as cross-domain generalization, this perspective overlooks a primary challenge faced by ecological practitioners: maintaining reliable recognition at the fixed site over time, where the dynamic nature of ecosystems introduces profound temporal shifts in both background and animal distributions. To bridge this gap, we present the first unified study of camera-trap species recognition over time. We introduce a realistic benchmark comprising 546 camera traps with a streaming protocol that evaluates models over chronologically ordered intervals. Our end-user-centric study yields four key findings. (1) Biological foundation models (e.g., BioCLIP 2) underperform at numerous sites even in initial intervals, underscoring the necessity of site-specific adaptation. (2) Adaptation is challenging under realistic evaluation: when models are updated using past data and evaluated on future intervals (mirrors real deployment lifecycles), naive adaptation can even degrade below zero-shot performance. (3) We identify two drivers of this difficulty: severe class imbalance and pronounced temporal shift in both species distribution and backgrounds between consecutive intervals. (4) We find that effective integration of model-update and post-processing techniques can largely improve accuracy, though a gap from the upper bounds remains. Finally, we highlight critical open questions, such as predicting when zero-shot models will succeed at a new site and determining whether/when model updates are necessary. Our benchmark and analysis provide actionable deployment guidelines for ecological practitioners while establishing new directions for future research in vision and machine learning.

cs.CV↗
arXiv2026

Self-similar blow-up solutions of $d$-dimensional incompressible Euler equations with $C^{1,\left(1-2/d\right)-}$ velocity

We investigate self-similar blow-up solutions to the $d$-dimensional axisymmetric incompressible Euler equations without swirl for $d\ge 3$. For any $α\in(0, α_d)$ with $α_d=1-2/d$, we construct a self-similar blow-up solution whose initial velocity field satisfies $u_0\in C^{1,α}_{\rm loc}(\mathbb R^d)\cap C^\infty(\mathbb R^d\setminus\{0\})$. Our construction relies on a fixed-point argument formulated for the self-similar profile equations, which form a coupled elliptic-transport system. Specifically, the transport equation recovers the vorticity profile from given data along characteristic curves, while the elliptic equation reconstructs the velocity field via Newtonian potentials defined in an auxiliary $(d+4)$-dimensional space. The main challenge consists in choosing appropriate function spaces that remain invariant under such nonlinear compositions and that simultaneously capture the exact singular behavior near the origin and the symmetry axis. Furthermore, we establish a finite-codimensional stability result for the self-similar profiles obtained above. As a consequence, after suitable truncation and correction of finitely many unstable modes, we obtain finite-energy blow-up solutions with initial velocity in $C^{1,α}(\mathbb R^d)\cap C^\infty(\mathbb R^d\setminus\{0\})\cap L^2(\mathbb R^d)$ and compactly supported initial vorticity. These solutions are asymptotically self-similar near the blow-up time.

math.AP↗
arXiv2026

High-accuracy ultrasonic positioning of calibration sources in the Jiangmen Underground Neutrino Observatory

Precise source positioning is essential for detector calibration in large liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with photon collection or contaminating the liquid scintillator. The method combines a sound-speed modeling based on dedicated laboratory measurements and in-detector temperature profiles, waveform-based arrival-time reconstruction, and an in-situ calibration of the effective receiver geometry using central-axis deployments. With six active receivers, central-axis positioning yields a mean error of 1.23 cm relative to the known deployment reference. For off-axis operation in the Cable Loop System calibration plane, a detector-realistic simulation that includes timing resolution, sound-speed variation, and receiver-coordinate smearing predicts a positioning uncertainty of 2.40 cm. These results demonstrate that ultrasonic positioning can provide centimetre-level source accuracy for large liquid scintillator detectors and can support off-axis calibration in JUNO-like experiments.

physics.ins-det↗
arXiv2026

SemDPLA: Semantic Communication-based Distributed Physical-Layer Authentication for 6G-enabled Dense IoT

With the rapid development of 6G, increasingly dense device connectivity imposes more strict requirements on multi-users Physical-Layer Authentication (PLA). Compared with cryptography-based methods, PLA enables lightweight authentication by using the uniqueness of wireless channels. However, existing PLA schemes in dense wireless scenarios often suffer from weak fingerprint discriminability and limited computation and communication resources. To address these challenges, we propose a Semantic Communication-based Distributed PLA (SemDPLA) framework. The framework constructs fused central semantic Channel State Information (CSI) fingerprints by fusing semantic information from a central node and multiple distributed nodes. Specifically, we introduce semantic communication to reduce the impact of low Signal Noise Ratio (SNR) and the consumption of communication resource during the transmission from distributed nodes to central node. Furthermore, we propose an ArcFace-based classification method and a semantic fingerprint-oriented distributed voting consistency mechanism to enhance device classification accuracy. Simulation results demonstrate that the proposed SemDPLA scheme performs better than single-node authentication, decision fusion, raw-CSI transmission, and feature fusion baselines. It achieves equal error rates (EERs) of 8.6% at 0 dB and 3.4% at 20 dB. It also achieves classification accuracies of 91.4% at 0 dB and above 95.8% from 5 to 20 dB. Moreover, SemDPLA is robust in low SNR environment and under attacks of abnormal nodes.

eess.SP↗
arXiv2026

Single-Shot High-Energy Muon and Particle Radiography with a Multi-GeV Laser-Wakefield-Accelerator-Driven Source

We report the first demonstration of single-shot particle radiography using a 1-10 GeV laser-wakefield-generated beam of muons, pions, and neutrons. The test objects were imaged ~15 m from the beam source, through dense lead shielding followed by the walls of a building and a truck. The muon content of the beam was directly confirmed using large volume scintillator-based detectors, which recorded particle decay events with timing delays consistent with the muon lifetime. Simulations confirm that the high energy component of the beam transmitted through the test object is nearly entirely composed of muons, directly showing their highly penetrative nature, with a single-shot fluence equivalent to >8 hours of integration of cosmic ray muons near the horizon. Our work establishes single-shot high-energy particle radiography with a laser-wakefield-accelerator-driven source.

physics.plasm-ph↗
arXiv2026

Resonant-impurity scanning tunneling spectroscopy in altermagnets: dual Fano resonance and Landau-quantization-induced nodal spin contrast

Using a Green's-function formalism, we study the spin-resolved local spectral function of a resonant impurity coupled to a two-dimensional $d$% -wave altermagnetic substrate. It is found that the interplay between direct tunneling from the impurity to the scanning tunneling microscopy (STM) tip and altermagnet-mediated tunneling gives rise to a dual Fano resonance in the absence of an external magnetic field. Moreover, the anisotropic spin-dependent oscillations of the local density of states and the corresponding Fano factors provide information on the altermagnetic splitting strength from complementary local and global perspectives. In addition, spin-selective tunneling can be achieved by tuning the Fermi energy and the tip position. In the presence of a strong magnetic field with Landau-level quantization, the dominant scanning tunneling spectroscopy (STS) signature appears as a spin-dependent nodal structure in real space: the nodal mismatch between opposite spin channels produces a large local spin contrast. These results establish resonant-impurity STM/STS as a phase-sensitive local probe of altermagnetic band anisotropy.

cond-mat.mes-hall↗
arXiv2026

-8 dB SNR + 90% Packet Loss: MamVSC -- CSI-Guided Semantic Mamba for Extreme-Robust Video Semantic Communication

Semantic communication, leveraging joint source-channel coding, is designed to mitigate semantic distortion introduced by the channel. However, most current studies focus solely on semantic deviation distortion caused by physical wireless channels, while overlooking semantic erasure distortion due to packet loss. A CSI-Guided Mamba-based video semantic wireless digital communication system (MamVSC) employing semantic grouping is proposed to simultaneously address both semantic deviation and erasure distortions. In this system, a semantic Mamba module, guided by channel state information (CSI) feedback, is utilized to dynamically adjust the granularity of extracted semantic information, adapting to channel conditions. Furthermore, a Semantic Channel Codec based on dynamic Semantic clustering centers is introduced, where the distance between semantic vectors within the same semantic class and their corresponding Semantic clustering center is dynamically adjusted according to channel conditions, enhancing robustness against channel noise. Additionally, a adaptive packet loss recovery module, dynamically adaptive to the CSI, is proposed. The system achieves an MS-SSIM greater than 0.6 and a PSNR exceeding 21 dB at an SNR of -8 dB and a packet loss rate of 90% in AWGN channel.

cs.ET↗
arXiv2026

Statistical inference for the probability of necessity for causal attribution

To answer questions of "causes of effects", the probability of necessity was previously introduced for assessing whether an observed outcome was caused by an earlier treatment. However, statistical inference for the probability of necessity is understudied due to several difficulties, which hinder its application in practice. The evaluation of the probability of necessity involves the joint distribution of potential outcomes, and thus it is generally not point identified and one can at best obtain lower and upper bounds even in randomized experiments, unless fairly stringent monotonicity assumptions on potential outcomes are made. Moreover, these bounds are non-smooth functionals of the observed data distribution and standard estimation and inference methods cannot be directly applied. In this paper, we investigate the statistical inference for the probability of necessity in general situations where it may not be point identified. We introduce a mild margin condition to tackle the non-smoothness, under which the bounds become pathwise differentiable. We establish the semiparametric efficiency theory and propose novel asymptotically efficient estimators of the lower and upper bounds, and further construct confidence intervals for the probability of necessity based on the proposed bound estimators. The resultant confidence intervals can effectively utilize the observed covariates to reduce lengths. The proposed approach has potential application in biomedical, epidemiological, and legal studies where understanding causal attribution beyond traditional causal effects is essential.

stat.ME↗