arXiv subjects

Zhiyong Zhang

Publications and source records attributed to Zhiyong Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Tensor-Engineered Van der Waals NbOCl2 Resonant Metasurface for Polarization-entangled Bell State Generation

Polarization-entangled photon pairs are essential resources for quantum information technologies, yet realizing compact sources with intrinsically controllable entanglement remains challenging. Van der Waals (vdW) nonlinear materials such as NbOCl2 provide atomically thin platforms for quantum light generation, yet their native crystalline anisotropies as natural materials impose limitations on accessible quantum states. Here, we develop a resonant vdW nonlinear metasurface based on NbOCl2 that exploits its intrinsic optical anisotropy to engineer polarization-dependent nonlinear responses. The anisotropic optical dispersion enables selective manipulation of resonant modes, resulting in a three-order-of-magnitude enhancement of the nonlinear response along the c axis. By further tailoring these resonant modes, we redistribute the effective second-order nonlinear susceptibility tensor between orthogonal polarization channels, balancing the spontaneous parametric down-conversion pathways along the b and c axes. This enables polarization-entangled photon generation with a measured fidelity of up to 92%. Our work establishes metasurface-enabled nonlinear optical engineering as a strategy for enhancing and controlling quantum light generation in vdW materials, providing a pathway toward scalable quantum photonic platforms.

physics.optics↗

arXiv2026

Securing High-Concurrency Ticket Sales: A Framework Based on Microservice

The railway ticketing system is one of the most important public service infrastructure. In peak periods such as holidays, it is often faced with the challenge of high concurrency scenarios because of a large number of users accessing at the same time. The traditional aggregation architecture can not meet the peak user requirements because of its insufficient fault tolerance and low ability. Therefore, the system needs to use microservice architecture for development, and add multiple security methods to ensure that the system can have good stability and data consistency under high concurrency scenarios, and can respond quickly to user requests. This paper introduces the use of B/S architecture and Spring Cloud to design and develop a railway ticket purchase system that can maintain stability and reliability under high concurrency scenarios, and formulate multiple security design methods for the system. This system integrates a range of functions, such as real-time train inquiries, dynamic seat updates, online seat selection, and ticket purchasing, effectively addressing common problems associated with offline ticket purchasing, such as long queues and delayed information. It enables a complete online process from inquiry and booking to payment and refunds. Furthermore, the "add passenger" function allows users to purchase tickets for others, extending the convenience of online ticketing to people with limited internet access. The system design prioritizes security and stability, while also focusing on high performance, and achieves these goals through a carefully designed architecture and the integration of multiple middleware components. After the completion of the system development, the core interface of the system is tested, and then the results are analyzed. The test data proves that the system has good ability and stability under high concurrency.

cs.SE↗

arXiv2026

Mixture-of-Finite-Mixtures Wishart Model for Clustering Covariance Matrices with an Application to Brain Functional Connectivity

Data represented as covariance-type matrices arise in many fields, including brain functional connectivity and diffusion tensor imaging. We develop the MFM-Wishart, a Bayesian model-based clustering approach for such data that combines Wishart mixture components with a mixture-of-finite-mixtures (MFM) prior, allowing joint posterior inference on both the number of clusters and clustering assignments. Theoretically, we study the properties of Wishart kernels in the context of mixture models and then establish results for posterior consistency for the number of clusters and posterior contraction of the mixing measure under standard regularity conditions. Computationally, we develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior inference. Simulation studies show competitive clustering performance and accurate recovery of the number of clusters, even under model misspecification. We apply MFM-Wishart to cluster infants based on functional connectivity during sleep, estimated from functional near-infrared spectroscopy (fNIRS) data, illustrating the practical utility of the model and revealing interpretable heterogeneity.

stat.ME↗

arXiv2026

Discriminative-Generative Synergy for Occlusion Robust 3D Human Mesh Recovery

3D human mesh recovery from monocular RGB images aims to estimate anatomically plausible 3D human models for downstream applications, but remains challenging under partial or severe occlusions. Regression-based methods are efficient yet often produce implausible or inaccurate results in unconstrained scenarios, while diffusion-based methods provide strong generative priors for occluded regions but may weaken fidelity to rare poses due to over-reliance on generation. To address these limitations, we propose a brain-inspired synergistic framework that integrates the discriminative power of vision transformers with the generative capability of conditional diffusion models. Specifically, the ViT-based pathway extracts deterministic visual cues from visible regions, while the diffusion-based pathway synthesizes structurally coherent human body representations. To effectively bridge the two pathways, we design a diverse-consistent feature learning module to align discriminative features with generative priors, and a cross-attention multi-level fusion mechanism to enable bidirectional interaction across semantic levels. Experiments on standard benchmarks demonstrate that our method achieves superior performance on key metrics and shows strong robustness in complex real-world scenarios.

cs.CV↗

arXiv2026

High-Fidelity 3D Gaussian Human Reconstruction via Region-Aware Initialization and Geometric Priors

Real-time, high-fidelity 3D human reconstruction from RGB images is essential for interactive applications such as virtual reality and gaming, yet remains challenging due to the complex non-rigid deformations of dynamic human bodies. Although 3D Gaussian Splatting enables efficient rendering, existing methods struggle to capture fine geometric details and often produce artifacts such as fused fingers and over-smoothed faces. Moreover, conventional spatial-field-based dynamic modeling faces a trade-off between reconstruction fidelity and GPU memory consumption. To address these issues, we propose a novel 3D Gaussian human reconstruction framework that combines region-aware initialization with rich geometric priors. Specifically, we leverage the expressive SMPL-X model to initialize both 3D Gaussians and skinning weights, providing a robust geometric foundation for precise reconstruction. We further introduce a region-aware density initialization strategy and a geometry-aware multi-scale hash encoding module to improve local detail recovery while maintaining computational efficiency.Experiments on PeopleSnapshot and GalaBasketball show that our method achieves superior reconstruction quality and finer detail preservation under complex motions, while maintaining real-time rendering speed.

cs.MM↗

arXiv2026

A Methanol-mediated Room-Temperature Synthesis of Tellurium-Loaded Liquid Scintillators for Neutrinoless Double Beta Decay Search

This study establishes a methanol-mediated room-temperature synthesis approach for tellurium-diol (Te-diol) compounds for use in tellurium-loaded liquid scintillator (Te-LS). The synthesis involves the direct reaction of telluric acid with diols (e.g., 1,2-hexanediol) in methanol (MeOH) under ambient conditions (25$\pm$5\textdegree C), with the key features of lower energy consumption and enhanced safety compared with high-temperature azeotropic distillation method. Mechanistic studies reveal that MeOH serves not merely as a solvent but also exhibits a catalytic effect, playing a dual role in this water-free, heterogeneous room-temperature synthesis. The Te-diol compounds enable fabrication of high-performance Te samples exhibiting exceptional optical transparency (attenuation length = 20.1$\pm$1.1 m at $λ$=430 nm for 1\% Te mass loading), which is reported here for the first time. Furthermore, the Te-LS achieves long-term spectral stability approaching or exceeding one year for both 1\% and 3\% Te mass loadings, and demonstrates a light yield comparable those of both the azeotropic distillation method and the SNO+ collaboration's Type I loading method, albeit modestly lower than that of their Type II method. The developed protocol offers the potential for a more energy efficient alternative for large-scale Te-LS production, particularly valuable for next-generation neutrinoless double-beta decay experiments.

physics.ins-det↗

arXiv2026

From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification

With the increasing demand for robust person Re-ID in unconstrained environments, learning from datasets with noisy labels and sparse per-identity samples remains a critical challenge. Existing noise-robust person Re-ID methods primarily rely on loss-correction or sample-selection strategies using softmax outputs. However, these methods suffer from two key limitations: 1) Softmax exhibits translation invariance, leading to over-confident and unreliable predictions on corrupted labels. 2) Conventional sample selection based on small-loss criteria often discards valuable hard positives that are crucial for learning discriminative features. To overcome these issues, we propose the CAlibration-to-REfinement (CARE) method, a two-stage framework that seeks certainty through probabilistic evidence propagation from calibration to refinement. In the calibration stage, we propose the probabilistic evidence calibration (PEC) that dismantles softmax translation invariance by injecting adaptive learnable parameters into the similarity function, and employs an evidential calibration loss to mitigate overconfidence on mislabeled samples. In the refinement stage, we design the evidence propagation refinement (EPR) that can more accurately distinguish between clean and noisy samples. Specifically, the EPR contains two steps: Firstly, the composite angular margin (CAM) metric is proposed to precisely distinguish clean but hard-to-learn positive samples from mislabeled ones in a hyperspherical space; Secondly, the certainty-oriented sphere weighting (COSW) is developed to dynamically allocate the importance of samples according to CAM, ensuring clean instances drive model updates. Extensive experimental results on Market1501, DukeMTMC-ReID, and CUHK03 datasets under both random and patterned noises show that CARE achieves competitive performance.

cs.CV↗

arXiv2025

A Low Background Beta Detection System using a Time Projection Chamber

In this paper, we present a Time Projection Chamber (TPC) system for low-background beta radiation measurements. The system consists of a TPC with two-dimensional-strip readout Micromegas and an anti-coincidence detector with readout pads for cosmic ray veto. The detector system utilize an AGET-based waveform sampling system for data acquisition. The beta detection capability of the system was verified through experimental test using $^{90}$Sr beta source. Additionally, a dedicated simulation program based on Geant4 was developed to model the entire detection process, including responses to both the beta source and background radiation. Simulation results were compared with experimental data for both beta and background samples, showing good agreements. The simulation samples were utilized to optimize and train classification models for beta and background discrimination. By applying the selected model into test data, the system achieved a background rate of 0.49 $\rm cpm/cm^2$ while retaining more than 55% of $^{90}$Sr beta signals within a 7 cm diameter detection region. Further analysis revealed that approximately 70% of the background originates from environmental gamma radiation, while the remaining contribution mainly comes from intrinsic radioactivity of detector materials, particularly the FR-4 based field cage and readout plane. Based on the knowledge gained from the experiments and simulations, an optimization of the TPC system has been proposed, with simulation predicting a potential reduction of the background rate to 0.0012 $\rm cpm/cm^2$.

physics.ins-det↗

arXiv2025

Ultrafast Exciton-Polariton Transport and Relaxation in Halide Perovskite

Halide perovskites offer a great platform for room-temperature exciton-polaritons (EPs) due to their strong oscillator strength and large exciton binding energy, promising applications in next-generation photonic and polaritonic devices. Efficient manipulation of EP transport and relaxation is critical for device performance, yet their spatiotemporal dynamics across different in-plane momenta (k//) remain poorly understood due to limitations in experimental access. In this work, we employ energy-resolved transient reflectance microscopy (TRM) combined with the dispersion relation of EPs to achieve high-resolution imaging of EP transport at specific k//. This approach directly reveals the quasi-ballistic transport and ultrafast relaxation of EPs in different k// regions, showcasing diffusion as fast as ~490 cm2/s and a relaxation time of ~95.1 fs. Furthermore, by tuning the detuning parameter, we manipulate the ballistic transport group velocity and relaxation time of EPs across varying k//. Our results reveal key insights into the dynamics of EP transport and relaxation, providing valuable guidance for the design and optimization of polaritonic devices.

cond-mat.mtrl-sci↗

arXiv2025

Design, simulation and performance of the resistive-anode PICOSEC Micromegas detector

The PICOSEC Micromegas detector is a Micro-Pattern Gaseous Detector concept developed to achieve tens of picosecond timing resolution for charged particle detection by combining a Cherenkov radiator with a two-stage Micromegas amplification structure. To improve operational robustness, a resistive anode has been implemented using a DLC layer deposited on a Kapton substrate. While this design enhances detector stability, the resistive layer may influence rate capability, signal formation, and detector capacitance, altering timing performance. This work presents a comprehensive study of a resistive design, including an analytical model and finite-element simulations to quantify rate-dependent gain reduction due to ohmic voltage drop on the resistive layer. An analytical solution for the voltage across a finite-size resistive layer is derived, and a numerical model is developed to evaluate gain suppression under intense particle fluxes. The impact of the resistive layer on signal formation is investigated using time-dependent weighting fields and the Garfield++ simulation framework. The contribution of signal components induced by the resistive layer is quantified, and preservation of the signal leading edge is found for surface resistivities above 100 kohm per square. Single-channel resistive-anode prototypes were designed, constructed, and experimentally characterized. Laboratory measurements using single photoelectrons and power spectral density analysis show the predicted reduction in signal amplitude while preserving the leading edge. Muon beam tests with CsI and DLC photocathodes demonstrate a time resolution of 11.5 ps for CsI, comparable to 11.9 ps for the metallic-anode device, showing the suitability of the resistive design for precision timing applications.

physics.ins-det↗

arXiv2025

Optimization of gain uniformity in thermal bonding Micromegas for the PandaX-III experiment

Micro-pattern gas detectors (MPGDs) are widely utilized in physics experiments owing to their excellent spatial resolution and high-rate capabilities. Within the PandaX-III experiment, which aims to investigate neutrinoless double beta decay, Micromegas detectors serve as charge readout devices. High energy resolution is a critical requirement for the readout plane in this context, and gain uniformity significantly impacts the achievable resolution, primarily because of the extended tracks of primary ionization electrons. However, scaling up MPGDs to larger active areas exacerbates the challenge of maintaining gain uniformity, and effectively controlling the uniformity of the avalanche gap is a key factor in the detector manufacturing process via the thermal bonding method. This study demonstrates that optimizing the thermal bonding films specifically at the detector edges can effectively improve the gain uniformity, achieving a gain uniformity of < 5% over the entire 200*200 mm2 active area in a 1 bar Ar/isobutane (96.5/3.5) gas mixture. Additionally, the gain uniformity of approximately 14% was characterized at high pressures of up to 10 bar, revealing promising potential for high resolution measurements in the PandaX-III experiment and other high-pressure applications.

physics.ins-det↗

arXiv2025

A mobile high spatial-resolution Muography instrument based on large-area Micromegas detectors

Muon radiography is an imaging technique based on muon absorption in matter that allows measurement of internal details in hidden objects or structures. This technique relies on measuring cosmic-ray muons tracks accurately, which reflects the incoming muon flux from both the target object and the open sky. In this paper, we report on the construction of a high spatial resolution muography instrument based on Micromegas detectors. Using four layers of 400 mm ${\times}$ 400 mm Micromegas detectors, channel multiplexing circuits, and the versatile readout system, a moveable muography instrument named $μ$STC-R400 was designed and constructed. Results show that the channel multiplexing circuits can resolve hit positions correctly, and the spatial resolution of the detector is approximately 190 $μ$m. Experiments were conducted at an under-construction subway tunnel and outdoors near a mountain, demonstrating the $μ$STC-R400's ability to maintain high spatial resolution outside the laboratory and its robustness in harsh environments.

physics.ins-det↗

arXiv2025

AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data

Multimodal medical analysis combining image and tabular data has gained increasing attention. However, effective fusion remains challenging due to cross-modal discrepancies in feature dimensions and modality contributions, as well as the noise from high-dimensional tabular inputs. To address these problems, we present AMF-MedIT, an efficient Align-Modulation-Fusion framework for medical image and tabular data integration, particularly under data-scarce conditions. Built upon a self-supervised learning strategy, we introduce the Adaptive Modulation and Fusion (AMF) module, a novel, streamlined fusion paradigm that harmonizes dimension discrepancies and dynamically balances modality contributions. It integrates prior knowledge to guide the allocation of modality contributions in the fusion and employs feature masks together with magnitude and leakage losses to adjust the dimensionality and magnitude of unimodal features. Additionally, we develop FT-Mamba, a powerful tabular encoder leveraging a selective mechanism to handle noisy medical tabular data efficiently. Extensive experiments, including simulations of clinical noise, demonstrate that AMF-MedIT achieves superior accuracy, robustness, and data efficiency across multimodal classification tasks. Interpretability analyses further reveal how FT-Mamba shapes multimodal pretraining and enhances the image encoder's attention, highlighting the practical value of our framework for reliable and efficient clinical artificial intelligence applications.

cs.CV↗

arXiv2025

Direct synthesis of single-crystal bilayer graphene on dielectric substrate

The growth of high-quality Bernal-stacked bilayer graphene (BLG) directly on dielectric substrates is crucial for electronic and optoelectronic applications, but there are still challenges such as poor quality, uncontrollable thickness and polycrystalline films. In this work, a novel method to grow high-quality and single-crystalline BLG directly on various dielectric substrates (SiO2/Si, sapphire, and quartz) was demonstrated. Single-crystalline monolayer graphene was applied as a seeding layer to facilitate the homo-epitaxial synthesis of single-crystalline BLG directly on insulating substrates. The Cu nano-powders (Cu NPs) with nanostructure and high surface-area were used as the remote catalysis to provide long-lasting catalytic activity during the graphene growth. The TEM results confirm the single-crystalline nature of the BLG domains, which validates the superiority of the homo-epitaxial growth technique. The as-grown BLG show comparable quality with the CVD-grown BLG on metal surface. Field-effect transistors directly fabricated on the as-grown BLG/SiO2/Si showed a room temperature carrier mobility as high as 2297cm2/Vs.

cond-mat.mtrl-sci↗

arXiv2025

Modelling Skewed and Heavy-Tailed Errors in Bayesian Mediation Analysis

Traditional mediation models in both the frequentist and Bayesian frameworks typically assume normality of the error terms. Violations of this assumption can impair the estimation and hypothesis testing of the mediation effect in conventional approaches. This study addresses the non-normality issue by explicitly modelling skewed and heavy-tailed error terms within the Bayesian mediation framework. Building on the work of Fernández and Steel (1998), this study introduces a novel family of distributions, termed the Centred Two-Piece Student $t$ Distribution (CTPT). The new distribution incorporates a skewness parameter into the Student t distribution and centres it to have a mean of zero, enabling flexible modelling of error terms in Bayesian regression and mediation analysis. A class of standard improper priors is employed, and conditions for the existence of the posterior distribution and posterior moments are established, while enabling inference on both skewness and tail parameters. Simulation studies are conducted to examine parameter recovery accuracy and statistical power in testing mediation effects. Compared to traditional Bayesian and frequentist methods, particularly bootstrap-based approaches, our method gives greater statistical power when correctly specified, while maintaining robustness against model misspecification. The application of the proposed approach is illustrated through real data analysis. Additionally, we have developed an R package FlexBayesMed to implement our methods in linear regression and mediation analysis, available at https://github.com/Zongyu-Li/FlexBayesMed.

stat.ME↗

arXiv2025

NeuFlow v2: Push High-Efficiency Optical Flow To the Limit

Real-time high-accuracy optical flow estimation is critical for a variety of real-world robotic applications. However, current learning-based methods often struggle to balance accuracy and computational efficiency: methods that achieve high accuracy typically demand substantial processing power, while faster approaches tend to sacrifice precision. These fast approaches specifically falter in their generalization capabilities and do not perform well across diverse real-world scenarios. In this work, we revisit the limitations of the SOTA methods and present NeuFlow-V2, a novel method that offers both - high accuracy in real-world datasets coupled with low computational overhead. In particular, we introduce a novel light-weight backbone and a fast refinement module to keep computational demands tractable while delivering accurate optical flow. Experimental results on synthetic and real-world datasets demonstrate that NeuFlow-V2 provides similar accuracy to SOTA methods while achieving 10x-70x speedups. It is capable of running at over 20 FPS on 512x384 resolution images on a Jetson Orin Nano. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow_v2.

cs.CV↗

arXiv2025

Predictive reinforcement learning based adaptive PID controller

Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support training. Findings: Experimental results show that the PRL-PID controller achieves superior stability and tracking accuracy in nonlinear, unstable, and strongly coupled systems, consistently outperforming existing RL-tuned PID methods while maintaining excellent robustness and adaptability across diverse operating conditions. Originality/Value: By adopting predictive learning, the proposed PRL-PID integrates system model priors into data-driven control, enhancing both the control framework's training efficiency and the controller's stability. As a result, PRL-PID provides a balanced blend of model-based and data-driven approaches, delivering robust, high-performance control.

eess.SY↗

arXiv2025

A High Compression Ratio Channel Multiplexing Method for Micro-pattern Gaseous Detectors

The demand for a large number of readout channels has been a limiting factor for the application of Micro-pattern Gaseous Detectors (MPGDs) in achieving higher spatial resolution and larger detection areas. This challenge is further compounded by issues related to system integration, power consumption, and cost efficiency. To address these challenges, this study proposes two novel multiplexing methods based on Eulerian circuits. Mathematical calculations indicate that with $n$ electronics channels, up to $n \times (n-1)/2 - (n - 2)/2 + 1$ detector channels can be read out, where $n$ is even. Three types of multiplexing circuits were designed, implemented, and tested in combination with Micromegas detectors. Experimental results demonstrate that, for a multiplexing circuit with a factor of 8, the spatial resolution remains comparable to the direct readout method, while achieving a detection efficiency exceeding 94\%. For a circuit with a multiplexing factor of 16, although the spatial resolution shows a slight degradation, the detection efficiency remains above 93.6\%. These results demonstrate that the proposed multiplexing methods can significantly reduce the number of readout channels while maintaining an acceptable level of spatial resolution and detection efficiency. These findings highlight the potential of the proposed multiplexing techniques for applications in fields requiring high-resolution and cost-effective detector systems, such as cosmic-ray muon imaging.

physics.ins-det↗