arXiv subjects

Zhichao Zhang

Publications and source records attributed to Zhichao Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Optimal Sampling and Reconstruction of Graph Signals in the Fractional Fourier Domain

Graph signal sampling and reconstruction are commonly formulated in the graph Fourier transform (GFT) domain. However, the reconstruction performance may be limited when practical graph signals are not sufficiently concentrated in the GFT spectrum. To address this issue, this paper proposes a graph signal sampling and reconstruction framework based on the graph fractional Fourier transform (GFRFT) domain. The fractional order is introduced as an adjustable spectral domain parameter, and the optimal order is selected to provide a more suitable representation domain for a given graph signal and sampling model. Under a unified sampling reconstruction formulation, subspace, smoothness, and stochastic priors are incorporated, and both unconstrained and predefined reconstruction mechanisms are considered, leading to several fractional domain sampling and reconstruction methods. Furthermore, the theoretical analysis shows that the optimal GFRFT domain can provide a more suitable low-dimensional spectral representation by improving energy concentration and reducing projection residual. The effects of residual leakage and noise amplification are further considered to explain how this representation advantage is translated into reconstruction error reduction. Experimental results show that, GFRFT domain sampling and reconstruction generally achieve better recovery performance than GFT domain methods.

eess.SP↗

arXiv2026

A cryogenic gas target for high-intensity radioactive ion beam production at HIRFL-RIBLL

A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82--86 K during beam irradiation and operating pressures up to 1000 mbar. The system was used to produce $^{7}\mathrm{Be}$, $^{16}\mathrm{N}$, and $^{15}\mathrm{O}$ RIBs via the $^{1}\mathrm{H}(^{7}\mathrm{Li}, ^{7}\mathrm{Be})n$, $^{2}\mathrm{H}(^{15}\mathrm{N}, ^{16}\mathrm{N})p$, and $^{1}\mathrm{H}(^{15}\mathrm{N}, ^{15}\mathrm{O})n$ inverse kinematics reactions, yielding purities of 85\%, 99\%, and 95\%, with intensities of $1.02\times10^{6}$, $2.7\times10^{5}$, and $1.0\times10^{5}$ pps, respectively. A $^{93m}\mathrm{Mo}$ isomer beam was also produced via the $\mathrm{^4He(^{94}Zr,} 5n)^{93m}\mathrm{Mo}$ reaction, achieving an intensity of $5.38\times10^{3}$ pps and a purity of 20\% (which can be further improved to $\sim$50\% with offline time-of-flight gating). By delivering a broader range of high-intensity secondary RIBs, this setup establishes a robust platform at RIBLL for low- and medium-energy nuclear astrophysics and reaction studies.

physics.ins-det↗

arXiv2026

DPC-VQA: Decoupling Quality Perception and Residual Calibration for Video Quality Assessment

Recent multimodal large language models (MLLMs) have shown promising performance on video quality assessment (VQA) tasks. However, adapting them to new scenarios remains expensive due to large-scale retraining and costly mean opinion score (MOS) annotations. In this paper, we argue that a pretrained MLLM already provides a useful perceptual prior for VQA, and that the main challenge is to efficiently calibrate this prior to the target MOS space. Based on this insight, we propose DPC-VQA, a decoupling perception and calibration framework for video quality assessment. Specifically, DPC-VQA uses a frozen MLLM to provide a base quality estimate and perceptual prior, and employs a lightweight calibration branch to predict a residual correction for target-scenario adaptation. This design avoids costly end-to-end retraining while maintaining reliable performance with lower training and data costs. Extensive experiments on both user-generated content (UGC) and AI-generated content (AIGC) benchmarks show that DPC-VQA achieves competitive performance against representative baselines, while using less than 2% of the trainable parameters of conventional MLLM-based VQA methods and remaining effective with only 20% of MOS labels. The code will be released upon publication.

cs.CV↗

arXiv2026

Generalized Linear Graph Representation: A Compact Operator Space for Graph Signal Processing and Graph Neural Networks

Graph Signal Processing (GSP) and Graph Neural Networks (GNNs) rely fundamentally on the matrix representation of the underlying graph topology. This representation defines key operators such as the graph Fourier transform, spectral filtering, and convolution. Existing parameterized operator families interpolate only partial subsets of classical graph matrices, while broader formulations become non-compact when representing transition-type operators, limiting both theoretical analysis and stable learning. To address this issue, we propose the Generalized Linear Graph Representation (GLGR), denoted by $\mathbf{Q}_{α,l}$, as a compact two-parameter operator family defined on a bounded linear domain. GLGR unifies major classical operators together with transition-type operators without requiring asymptotic parameters. Theoretically, we show that $\mathbf{Q}_{α,l}$ admits a variational decomposition balancing local smoothness and global degree-weighted energy, derive spectral perturbation bounds, and establish graph-aware sufficient conditions for positive semi-definiteness. Building on this formulation, we develop Adaptive GLGR Convolution (AG-Conv), which makes the propagation operator itself learnable within end-to-end GNNs. Experiments on graph classification and node classification benchmarks show that GLGR improves both fixed-operator representation search and adaptive graph learning across multiple backbones.

eess.SP↗

arXiv2026

Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering

Graph spectral representations are fundamental in graph signal processing, providing a rigorous frameworkforanalyzing graph-structured data. The graph fractional Fourier transform (GFRFT) extends the graph Fourier transform (GFT) through a fractional-order parameter, enabling flexible spectral analysis with mathematical consistency. The angular graph Fourier transform (AGFT) further introduces angular control by rotating GFT eigenvectors; however, existing constructions may fail to reduce exactly to the GFT at zero angle, weakening theoretical consistency and interpretability. To address these complementary limitations, namely the lack of rotation-based basis control in GFRFT and the defective zero-angle degeneracy of AGFT, this paper proposes the rotation-parameterized graph fractional Fourier transform (RP-GFRFT), which unifies fractional order and rotation-parameterized spectral analysis. A degeneracy preserving rotation matrix family is constructed to guarantee exact GFT reduction at zero angle. TwoRP-GFRFTvariants,I-RP-GFRFTandII-RP-GFRFT,arethenformulated, with theoretical analyses confirming their unitarity, invertibility, reduction behavior, and smooth parameter dependence. The fractional order and rotation angle are jointly optimized for adaptive graph spectral filtering. Experiments on real-world signals, images, and point clouds demonstrate that RP-GFRFT improves denoising accuracy, reconstruction quality, and feature preservation over GFRFT, AGFT, and representative filtering baselines.

stat.ML↗

arXiv2026

Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising

Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising, with a particular focus on non-stationary signal processing in practical applications}. We integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error denoising in the WVD domain. {Numerical experiments on typical simulated radar signals and real-world electrocardiogram data comprehensively demonstrate that the proposed adaptive CCTFD outperforms several state-of-the-art methods in noise suppression.

eess.SP↗

arXiv2026

MPFSR-Enhanced GNNs: Spectral Graph Neural Networks Enhancement Through Learnable Multiple-Parameter Graph Fractional Fourier Transforms

Graph neural networks (GNNs) excel in processing non-Euclidean data, but traditional spectral GNNs rely on static bases and fundamentally lack active spectral regulation. Although the graph fractional Fourier transform (GFRFT) introduces cross-domain modulation, it applies a uniform fractional parameter across all frequencies. This ignores frequency heterogeneity and restricts the models' adaptive capacity in graph node classification tasks. In the paper, we propose two novel types of multiple-parameter GFRFTs (MPGFRFTs) and establish their corresponding theoretical frameworks, including essential properties, computational complexity, and parameters differentiability. By assigning independent, learnable fractional parameters to distinct frequency bands, MPGFRFTs enable fine-grained spectral regulation. Then, we operationalize this mathematical framework by designing the adaptive multiple-parameter fractional spectral regulation (MPFSR) module, a plug-and-play component for mainstream spectral models. We also establish rigorous theoretical bounds on the spectral stability of this module, guaranteeing a stable and reliable convergence during the end-to-end parameters optimization. Experiments demonstrate that integrating the proposed MPFSR module alleviates the constraints of static bases and yields performance gains in node classification on complex graphs, advancing a novel paradigm for active spectral modulation in graph representation learning.

eess.SP↗

arXiv2026

SVD-Based UGRM-GFT on Directed Product Graphs

Traditional directed graph signal processing generally depends on fixed representation matrices, whose rigid structures limit the model's ability to adapt to complex graph topologies. To address this issue, this study employed the unified graph representation matrix (UGRM) to propose a generalized graph Fourier transform (UGRM-GFT) method based on singular value decomposition (SVD) for signal analysis on directed graphs and Cartesian product graphs. We defined UGRM-GFT for general directed graphs by introducing a parameterized UGRM that incorporates traditional representations such as the Laplacian matrix and adjacency matrix. The SVD is used to construct spectral transform pairs with both left and right singular vectors. We extended this approach to two types of UGRM-GFTs applied to directed Cartesian product graphs. UGRM-GFT-I performs SVD directly on the composite UGRM matrix of the two-dimensional graph structure, suitable for globally coupled graph signals. UGRM-GFT-II separately applies SVD to the UGRMs of the two-factor graphs and then combines the results, significantly reducing computational complexity while preserving spectral expressiveness. Theoretical analysis confirmed the monotonicity of the proposed method with respect to the parameters alpha and k embedded in the UGRM. Experimental results on real-world datasets demonstrated that the proposed method significantly outperforms traditional fixed-matrix approaches in denoising tasks, with a particular emphasis on signal-to-noise ratio and bandwidth efficiency.

eess.SP↗

arXiv2026

Node-Oriented Proactive Spectral Modulation: A Unified Fractional Framework for Graph Signal Denoising

Graph signal denoising is a fundamental task in graph signal processing. While the node-oriented filtering approach enhances spatial adaptability, it suffers from spectral rigidity due to its reliance on the graph Fourier transform. Conversely, emerging fractional-domain transforms provide crucial spectral flexibility but are fundamentally limited by their globally shared filtering paradigm, failing to accommodate localized topological variations. To bridge this gap, this paper proposes a generalized node-oriented fractional filtering (NOFF) framework that seamlessly integrates localized spatial adaptability with proactive spectral modulation across various fractional transforms. However, straightforwardly assigning independent full-rank filters to all vertices incurs a prohibitive parameter space, leading to severe overfitting on random noise. To mitigate this, we introduce the low-rank NOFF (LRNOFF) architecture. By imposing a strict low-rank constraint, LRNOFF inherently acts as a powerful implicit regularizer, preventing noise memorization and ensuring the extraction of robust spectral bases. Furthermore, we develop an efficient computational implementation termed LRNOFF-Fast, which drastically reduces computational and memory overhead while preserving theoretical optimality. Experiments on real-world datasets demonstrate that the proposed framework achieves state-of-the-art performance.

eess.SP↗

arXiv2026

Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

In modern nuclear physics experiments, identifying events of interest is challenging for nuclear reaction studies with the active target Time Projection Chamber (TPC). In this work, machine learning techniques are employed to analyze the complex data of the 12C + 12C fusion reaction from a TPC named MATE (multi-purpose active-target time projection chamber for nuclear experiments). Specifically, we successfully applied Residual Neural Network (ResNet-50, ResNet-34 and ResNet-18) and Visual Geometry Group (VGG-19) to classify elastic scattering and fusion reaction events from the 12C + 12C reaction. The classification results of the four models are nearly identical, with accuracies of approximately 97% for the simulated data and 90% for the experimental data. Moreover, these approaches successfully identify some events that are misclassified by traditional methods. These models are also applied to classify events from different fusion reaction channels, with classification accuracies of approximately 95% on simulated data. In addition, a Convolutional Neural Network (CNN) model is developed to reconstruct the reaction vertex, providing an alternative strategy for vertex reconstruction. These results indicate that machine learning techniques can effectively classify reaction events from different channels and reconstruct the reaction vertex, thereby paving the way for future analyses of complex nuclear reaction data.

cs.LG↗

arXiv2026

Graph Embedding in the Graph Fractional Fourier Transform Domain

Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spectral information. However, the embedding space of traditional spectral embedding methods often exhibit limited expressiveness, failing to exhaustively capture latent structural features across alternative transform domains. To address this issue, we use the graph fractional Fourier transform to extend the existing state-of-the-art generalized frequency filtering embedding (GEFFE) into fractional domains, giving birth to the generalized fractional filtering embedding (GEFRFE), which enhances embedding informativeness via the graph fractional domain.The GEFRFE leverages graph fractional domain filtering and a nonlinear composition of eigenvector components derived from a fractionalized graph Laplacian. To dynamically determine the fractional order, two parallel strategies are introduced: search-based optimization and a ResNet18-based adaptive learning. Extensive experiments on five benchmark datasets demonstrate that the GEFRFE captures richer structural features and significantly enhance classification performance. The GEFRFE provides a new paradigm for the development of graph embedding from the "fixed domain" to the "generalized domain". The results indicate that introducing the GFRFT into the graph embedding domain is a correct and effective research path. Notably, the proposed method retains computational complexity comparable to GEFFE approaches.

cs.LG↗

arXiv2026

FGFRFT: Fast Graph Fractional Fourier Transform via Exact Spectral Splitting and Fourier-Series Approximation

The graph fractional Fourier transform (GFRFT) for unitary graph Fourier transform (GFT) matrices can be interpreted through the scalar function $e^{jαθ}$ on the unit circle. Under the principal branch, its Fourier-series representation encounters an intrinsic obstruction at the spectral point $λ=-1$ for non-integer orders. To address this issue, we propose a fast graph fractional Fourier transform (FGFRFT) based on exact spectral splitting: the $λ=-1$ component is treated exactly, and the complementary component is approximated by a truncated Fourier series in integer powers of the GFT matrix. This construction yields an offline--online implementation that reduces the online complexity of repeated operator updates from $O(N^3)$ to $O(2LN^2)$ for truncation order $L$, while preserving differentiability with respect to the transform order. We further derive truncation-error bounds, approximate unitarity and additivity, and reconstruction-error bounds. Experiments on approximation accuracy, transform-order learning, image denoising, and point-cloud denoising show that FGFRFT provides substantial online acceleration while remaining close to the exact GFRFT under the tested settings.

eess.SP↗

arXiv2026

Field-theoretical approach to estimate mean gap and gap distribution in randomly rough surface contact mechanics

We extend the statistical field-theoretical framework of rough surface contact mechanics to characterize the interfacial gap between an elastic half-space and a randomly rough surface incorporating exponential repulsion. Building upon a cumulant expansion to second-order, we derive an explicit analytical relation between the mean gap and the applied normal pressure. This result provides a closed-form expression for the drift and diffusion coefficients in a convection-diffusion equation governing the scale-dependent evolution of the gap distribution. Solving this equation with appropriate initial and boundary conditions yields the gap distribution under varying external pressures. Both the mean gap and the gap distribution are found to be in good agreement with Green's function molecular dynamics (GFMD) simulations. Our results demonstrate that the field-theoretical approach enables quantitative predictions not only for contact stresses but also for interfacial gap in rough surface contacts.

cond-mat.soft↗

arXiv2026

A Unified Framework for 2D Nonseparable Fractional Fourier Transform: From Geometric Completeness to Applications

The one-dimensional (1D) fractional Fourier transform (FRFT) generalizes the Fourier transform, offering significant advantages in the time-frequency analysis of non-stationary signals. While various 2D extensions exist, such as the 2D separable FRFT (SFRFT), gyrator transform (GT), coupled FRFT (CFRFT), and earlier nonseparable definitions, they suffer from fragmented theoretical frameworks and a fundamental lack of geometric consistency with the 2D Wigner distribution (WD). Addressing these limitations, we propose a unified 2D nonseparable FRFT (NSFRFT) framework. Theoretically derived from the intersection of the symplectic and special orthogonal groups (isomorphic to the unitary group $\mathrm{U}(2)$), this transform inherently possesses four degrees of freedom and mathematically incorporates the 2D SFRFT, GT, and CFRFT as special cases. Unlike prior algebraic generalizations, it strictly preserves the rigid 4D rotational geometry of the 2D WD, ensuring geometric consistency and numerical stability. We derive its essential properties and develop efficient discrete algorithms with a computational complexity of $O(N^{2}\log N)$. Numerical simulations validate the superiority of the 2D NSFRFT in analyzing coupled chirp signals and demonstrate its robustness in filtering and image encryption and decryption applications.

eess.SP↗

arXiv2026

A Unified Fractional Spectral Framework for Spatiotemporal Graph Signals: Bi-Fractional Transform and Geodesic Coupling

Graph signal processing extends spectral analysis to data supported on irregular domains. Existing fractional transforms for two-dimensional graph signals, including the two-dimensional graph fractional Fourier transform (GFRFT), typically impose a shared fractional order across dimensions, which limits adaptivity to heterogeneous spatiotemporal spectra. To address this limitation, we propose the two-dimensional graph bi-fractional Fourier transform, which assigns independent fractional orders to the factor graphs of a Cartesian product, enabling decoupled spectral control while preserving separability, unitarity, and invertibility. To further resolve the basis ambiguity in temporal fractional analysis, we develop a geodesic-coupled GFRFT by constructing a coupling path along the principal geodesic on the unitary manifold, thereby unifying graph-induced and discrete temporal bases with guaranteed unitarity and a closed-form inverse. Building on these transforms, we derive a differentiable Wiener-type filtering framework with a hybrid optimization strategy: the fractional orders are learned end-to-end from data, while the coupling parameter is fixed as a structural regularizer. Experiments on real-world time-varying graph datasets and dynamic image restoration tasks demonstrate consistent gains over state-of-the-art fractional transforms and competitive learning-based baselines.

eess.SP↗

arXiv2026

DWAFM: Dynamic Weighted Graph Structure Embedding Integrated with Attention and Frequency-Domain MLPs for Traffic Forecasting

Accurate traffic prediction is a key task for intelligent transportation systems. The core difficulty lies in accurately modeling the complex spatial-temporal dependencies in traffic data. In recent years, improvements in network architecture have failed to bring significant performance enhancements, while embedding technology has shown great potential. However, existing embedding methods often ignore graph structure information or rely solely on static graph structures, making it difficult to effectively capture the dynamic associations between nodes that evolve over time. To address this issue, this letter proposes a novel dynamic weighted graph structure (DWGS) embedding method, which relies on a graph structure that can truly reflect the changes in the strength of dynamic associations between nodes over time. By first combining the DWGS embedding with the spatial-temporal adaptive embedding, as well as the temporal embedding and feature embedding, and then integrating attention and frequency-domain multi-layer perceptrons (MLPs), we design a novel traffic prediction model, termed the DWGS embedding integrated with attention and frequency-domain MLPs (DWAFM). Experiments on five real-world traffic datasets show that the DWAFM achieves better prediction performance than some state-of-the-arts.

cs.LG↗

arXiv2026

Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework

Recent multimodal large language models (MLLMs) have demonstrated strong capabilities in image quality assessment (IQA) tasks. However, adapting such large-scale models is computationally expensive and still relies on substantial Mean Opinion Score (MOS) annotations. We argue that for MLLM-based IQA, the core bottleneck lies not in the quality perception capacity of MLLMs, but in MOS scale calibration. Therefore, we propose LEAF, a Label-Efficient Image Quality Assessment Framework that distills perceptual quality priors from an MLLM teacher into a lightweight student regressor, enabling MOS calibration with minimal human supervision. Specifically, the teacher conducts dense supervision through point-wise judgments and pair-wise preferences, with an estimate of decision reliability. Guided by these signals, the student learns the teacher's quality perception patterns through joint distillation and is calibrated on a small MOS subset to align with human annotations. Experiments on both user-generated and AI-generated IQA benchmarks demonstrate that our method significantly reduces the need for human annotations while maintaining strong MOS-aligned correlations, making lightweight IQA practical under limited annotation budgets.

cs.CV↗

arXiv2026

Enhancing Blind Video Quality Assessment with Rich Quality-aware Features

Blind video quality assessment (BVQA) is a highly challenging task due to the intrinsic complexity of video content and visual distortions, especially given the high popularity of social media videos, which originate from a wide range of sources, and are often processed by various compression and enhancement algorithms. While recent BVQA and blind image quality assessment (BIQA) studies have made remarkable progress, their models typically perform well on the datasets they were trained on but generalize poorly to unseen videos, making them less effective for accurately evaluating the perceptual quality of diverse social media videos. In this paper, we propose Rich Quality-aware features enabled Video Quality Assessment (RQ-VQA), a simple yet effective method to enhance BVQA by leveraging rich quality-aware features extracted from off-the-shelf BIQA and BVQA models. Our approach exploits the expertise of existing quality assessment models within their trained domains to improve generalization. Specifically, we design a multi-source feature framework that integrates:(1) Learnable spatial features} from a base model fine-tuned on the target VQA dataset to capture domain-specific quality cues; (2) Temporal motion features from the fast pathway of SlowFast pre-trained on action recognition datasets to model motion-related distortions; (3) Spatial quality-aware features from BIQA models trained on diverse IQA datasets to enhance frame-level distortion representation; and (4) Spatiotemporal quality-aware features from a BVQA model trained on large-scale VQA datasets to jointly encode spatial structure and temporal dynamics. These features are concatenated and fed into a multi-layer perceptron (MLP) to regress them into quality scores. Experimental results demonstrate that our model achieves state-of-the-art performance on three public social media VQA datasets.

eess.IV↗