arXiv subjects

Rong Wang

Publications and source records attributed to Rong Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Fully-Passive Twin-Field Quantum Key Distribution

We propose a fully passive twin-field quantum key distribution (QKD) setup where basis choice, decoy-state preparation and encoding are all implemented entirely by post-processing without any active modulation. Our protocol can remove the potential side-channels from both source modulators and detectors, and additionally retain the high key rate advantage offered by twin-field QKD, thus offering great implementation security and good performance. Importantly, we also propose a post-processing strategy that uses mismatched phase slices and minimizes the effect of sifting. We show with numerical simulation that the new protocol can still beat the repeaterless bound and provide satisfactory key rate.

quant-ph↗

arXiv2026

FSMC-Pose: Frequency and Spatial Fusion with Multiscale Self-calibration for Cattle Mounting Pose Estimation

Mounting posture is an important visual indicator of estrus in dairy cattle. However, achieving reliable mounting pose estimation in real-world environments remains challenging due to cluttered backgrounds and frequent inter-animal occlusion. We present FSMC-Pose, a top-down framework that integrates a lightweight frequency-spatial fusion backbone, CattleMountNet, and a multiscale self-calibration head, SC2Head. Specifically, we design two algorithmic components for CattleMountNet: the Spatial Frequency Enhancement Block (SFEBlock) and the Receptive Aggregation Block (RABlock). SFEBlock separates cattle from cluttered backgrounds, while RABlock captures multiscale contextual information. The Spatial-Channel Self-Calibration Head (SC2Head) attends to spatial and channel dependencies and introduces a self-calibration branch to mitigate structural misalignment under inter-animal overlap. We construct a mounting dataset, MOUNT-Cattle, covering 1176 mounting instances, which follows the COCO format and supports drop-in training across pose estimation models. Using a comprehensive dataset that combines MOUNT-Cattle with the public NWAFU-Cattle dataset, FSMC-Pose achieves higher accuracy than strong baselines, with markedly lower computational and parameter costs, while maintaining real-time inference on commodity GPUs. Extensive experiments and qualitative analyses show that FSMC-Pose effectively captures and estimates cattle mounting pose in complex and cluttered environments. Dataset and code are available at https://github.com/elianafang/FSMC-Pose.

cs.CV↗

arXiv2026

AI assisted optimization of integrated waveguide polarizers containing 2D reduced graphene oxide

Reduced graphene oxide (rGO) exhibits strong anisotropic light absorption and high compatibility with photonic integrated chips, making it a promising material for implementing high performance onchip polarization selective devices. The performance of rGO integrated waveguide polarizers is highly dependent on the waveguide geometry, and achieving optimal performance requires exploring a large parameter space, making conventional mode simulation methods computationally demanding. Here, we propose and demonstrate a machine learning framework based on fully connected neural networks (FCNNs) to map the dependence of the polarizer figure of merit (FOM) on the waveguide geometry. Once trained by using a small dataset of low resolution mode simulation results, the FCNN framework can rapidly and accurately predict FOM values across a large structural parameter space with high resolution. Results show that this method can reduce overall computing time by more than 4 orders of magnitude as compared to the mode simulation methods, and achieve high prediction accuracy with an average deviation (AD) below 0.05. These results highlight the FCNN based machine learning framework as an efficient tool for the design and optimization of rGO integrated waveguide polarizers.

physics.optics↗

arXiv2026

AI based design of 2D material integrated optical polarizers

On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency.

physics.optics↗

arXiv2026

Dynamics of a nonlocal epidemic model with a new free boundary condition, part 1: Spreading-vanishing dichotomy

This paper investigates the long-time dynamics of a nonlocal epidemic model with free boundaries, where a pathogen with density $u(t,x)$ and the infected humans with density $v(t,x)$ evolve according to a reaction-diffusion system with nonlocal diffusion over a one dimensional interval $[g(t), h(t)]$, which represents the epidemic region expanding through its boundaries $x=g(t)$ and $x=h(t)$, known as free boundaries. Such a model with free boundary conditions based on those of Cao et al. \cite{fb27} was considered by several works. Inspired by recent works of Feng et al. \cite{fb20} and Long et al. \cite{fb5}, we propose a new free boundary condition, where the expansion rate of the epidemic region, determined by $h'(t)$ and $g'(t)$, is proportional to a linear combination of the outward flux of the pathogen $u$ through the range boundary (as in \cite{fb27}) and the weighted total population of infected individuals $v$ within the region (as in \cite{fb5}). We prove that the system under this new free boundary condition is well-posed, and its long-time dynamical behavior is characterized by a spreading-vanishing dichotomy. Moreover, we obtain sharp criteria for this dichotomy, including a sharp threshold in terms of the initial data $(u_0,v_0)$; and by studying a related eigenvalue problem, we also find a sharp threshold in terms of the diffusion rate, which complements related results in Nguyen and Vo \cite{fb7}. This is Part $1$ of a two part series. In Part $2$, we will determine the spreading speed of the model when spreading occurs, and for some typical classes of kernel functions, we will obtain the precise rates of accelerated spreading.

math.AP↗

arXiv2026

Multi-class Support Vector Machine with Maximizing Minimum Margin

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance between instances and the decision boundary. Although many efforts have been dedicated to expanding SVM for multi-class case through strategies such as one versus one and one versus the rest, satisfactory solutions remain to be developed. In this paper, we propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Adhering to this concept, we embrace a new formulation that imparts heightened flexibility to multi-class SVM. Furthermore, the correlations between the proposed method and multiple forms of multi-class SVM are analyzed. The proposed regularizer, akin to the concept of "margin", can serve as a seamless enhancement over the softmax in deep learning, providing guidance for network parameter learning. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods.

cs.LG↗

arXiv2026

Data-driven inference of brain dynamical states from the r-spectrum of correlation matrices

We present a data-driven framework to characterize large-scale brain dynamical states directly from correlation matrices at the single-subject level. By treating correlation thresholding as a percolation-like probe of connectivity, the approach tracks multiple cluster- and network-level observables and identifies a characteristic percolation threshold, rc, at which these signatures converge. We use $r_c$ as an operational and physically interpretable descriptor of large-scale brain dynamical state. Applied to resting-state fMRI data from a large cohort of healthy individuals (N = 996), the method yields stable, subject-specific estimates that covary systematically with established dynamical indicators such as temporal autocorrelations. Numerical simulations of a whole-brain model with a known critical regime further show that $r_c$ tracks changes in collective dynamics under controlled variations of excitability. By replacing arbitrary threshold selection with a criterion intrinsic to correlation structure, the r-spectra provides a physically grounded approach for comparing brain dynamical states across individuals.

q-bio.NC↗

arXiv2025

Neutron structure function via a maximum entropy analysis

We employ the maximum entropy method to extract the valence quark distributions of the neutron at a low scale, $ Q_0^2 $. At this initial scale, the neutron is defined to contain only three valence quarks, with no contributions from sea quarks or gluons. The distributions of these initial valence quarks are constrained by principles from quark models, quark-hadron duality, and quark confinement. Employing the DGLAP equations supplemented by parton-parton recombination corrections, we derive the neutron structure function $ F_2^{\rm n} $ at higher scales $ Q^2 $. The resulting ratio of the neutron to proton structure functions, $F_2^{\rm n}$/$F_{2}^{\rm p}$, aligns well with the world deep inelastic scattering data at Bjoken variable $x<0.7$, particularly when accounting for uncertainties from model-dependent corrections. Notably, this ratio is in agreement with the JLab MARATHON data after considering the quark-hadron duality assumption, especially in the region of $x \gtrsim 0.7$. Additionally, our findings for $F_2^{\rm n}$/$F_{2}^{\rm p}$ correspond well with the JLab BONuS experimental results after considering the impact of nucleon resonance contamination in the region $x \gtrsim 0.4, 0.5, 0.6$. We further compare our predictions for $F_2^{\rm n}$/$F_{2}^{\rm p}$ and the $ u/d $ ratios in the limit as $ x \rightarrow 1 $ with existing theoretical calculations. Finally, we observe a minor violation of isospin symmetry between the proton and neutron, evidenced by the differences in valence quark distributions and the first-order moments of these distributions.

hep-ph↗

arXiv2025

Systematic Framework of Application Methods for Large Language Models in Language Sciences

Large Language Models (LLMs) are transforming language sciences. However, their widespread deployment currently suffers from methodological fragmentation and a lack of systematic soundness. This study proposes two comprehensive methodological frameworks designed to guide the strategic and responsible application of LLMs in language sciences. The first method-selection framework defines and systematizes three distinct, complementary approaches, each linked to a specific research goal: (1) prompt-based interaction with general-use models for exploratory analysis and hypothesis generation; (2) fine-tuning of open-source models for confirmatory, theory-driven investigation and high-quality data generation; and (3) extraction of contextualized embeddings for further quantitative analysis and probing of model internal mechanisms. We detail the technical implementation and inherent trade-offs of each method, supported by empirical case studies. Based on the method-selection framework, the second systematic framework proposed provides constructed configurations that guide the practical implementation of multi-stage research pipelines based on these approaches. We then conducted a series of empirical experiments to validate our proposed framework, employing retrospective analysis, prospective application, and an expert evaluation survey. By enforcing the strategic alignment of research questions with the appropriate LLM methodology, the frameworks enable a critical paradigm shift in language science research. We believe that this system is fundamental for ensuring reproducibility, facilitating the critical evaluation of LLM mechanisms, and providing the structure necessary to move traditional linguistics from ad-hoc utility to verifiable, robust science.

cs.CL↗

arXiv2025

Learning High-Fidelity Cloth Animation via Skinning-Free Image Transfer

We present a novel method for generating 3D garment deformations from given body poses, which is key to a wide range of applications, including virtual try-on and extended reality. To simplify the cloth dynamics, existing methods mostly rely on linear blend skinning to obtain low-frequency posed garment shape and only regress high-frequency wrinkles. However, due to the lack of explicit skinning supervision, such skinning-based approach often produces misaligned shapes when posing the garment, consequently corrupts the high-frequency signals and fails to recover high-fidelity wrinkles. To tackle this issue, we propose a skinning-free approach by independently estimating posed (i) vertex position for low-frequency posed garment shape, and (ii) vertex normal for high-frequency local wrinkle details. In this way, each frequency modality can be effectively decoupled and directly supervised by the geometry of the deformed garment. To further improve the visual quality of animation, we propose to encode both vertex attributes as rendered texture images, so that 3D garment deformation can be equivalently achieved via 2D image transfer. This enables us to leverage powerful pretrained image models to recover fine-grained visual details in wrinkles, while maintaining superior scalability for garments of diverse topologies without relying on manual UV partition. Finally, we propose a multimodal fusion to incorporate constraints from both frequency modalities and robustly recover deformed 3D garments from transferred images. Extensive experiments show that our method significantly improves animation quality on various garment types and recovers finer wrinkles than state-of-the-art methods.

cs.CV↗

arXiv2025

Huizhou Hadron Spectrometer -- a Proposed High-rate Experimental Setup at the High Intensity Heavy-ion Accelerator Facility

The High-Intensity Heavy-Ion Accelerator Facility (HIAF), currently under construction in Huizhou, Guangdong Province, China, is projected to be completed by 2025. This facility will be capable of producing proton and heavy-ion beams with energies reaching several GeV, thereby offering a versatile platform for advanced fundamental physics research. Key scientific objectives include exploring physics beyond the Standard Model through the search for novel particles and interactions, testing fundamental symmetries, investigating exotic hadronic states such as di-baryons, pentaquark states and multi-strange hypernuclei, conducting precise measurements of hadron and hypernucleus properties, and probing the phase boundary and critical point of nuclear matter. To facilitate these investigations, we propose the development of a dedicated experimental apparatus at HIAF - the Huizhou Hadron Spectrometer (HHaS). This paper presents the conceptual design of HHaS, comprising a solenoid magnet, a five-dimensional silicon pixel tracker, a Low-Gain Avalanche Detector (LGAD) for time-of-flight measurements, and a Cherenkov-scintillation dual-readout electromagnetic calorimeter. The design anticipates an unprecedented event rate of 1-100 MHz, extensive particle acceptance, a track momentum resolution at 1% level, an electromagnetic energy resolution of ~3% @ 1 GeV and multi-particle identification capabilities. Such capabilities position HHaS as a powerful instrument for advancing experimental studies in particle and nuclear physics. The successful realization of HHaS is expected to significantly bolster the development of medium- and high-energy physics research within China.

hep-ex↗

arXiv2025

Conceptual Design of the Muonium-to-Antimuonium Conversion Experiment (MACE)

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. The Muonium-to-Antimuonium Conversion Experiment (MACE) is designed to utilize a high-intensity muon beam, a Michel electron magnetic spectrometer, a positron transport system, and a positron detection system, to either discover or constrain this rare process with a conversion probability of $\mathcal{O}(10^{-13})$. This article presents an overview of the theoretical framework as well as a detailed description of the experimental design for the search for muonium-to-antimuonium conversion.

hep-ex↗

arXiv2025

Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are forced to accept a compromise between performance and privacy: \textit{transmitting embedding representations risks sensitive data leakage, while sharing only abstract cluster prototypes leads to diminished model accuracy}. To resolve this dilemma, we propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing, thus moving beyond the limitations of conventional techniques. Our framework operates on a clear client-server logic; on the client-side, each participant constructs a private structural graph that captures intrinsic data relationships, which the server then securely aggregates and aligns to form a comprehensive global graph from which a unified clustering structure is derived. The framework offers two distinct modes to suit different needs. SPP-FGC is designed as an efficient one-shot method that completes its task in a single communication round, ideal for rapid analysis. For more complex, unstructured data like images, SPP-FGC+ employs an iterative process where clients and the server collaboratively refine feature representations to achieve superior downstream performance. Extensive experiments demonstrate that our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10\% (NMI) over federated baselines while maintaining provable privacy guarantees.

cs.LG↗

arXiv2025

First Extraction of the $ϕ$-$^{4}\mathrm{He}$ scattering length from near-threshold $ϕ$ photoproduction on helium-4

We present a model-independent extraction of the phi-4He scattering length from near-threshold phi photoproduction on helium-4, based on LEPS Collaboration data for the coherent process gamma + 4He -> phi + 4He and the Vector Meson Dominance framework. Assuming an energy-independent differential cross section, we extract the absolute value of the phi-4He scattering length |alpha_{phi4He}| = (3.33 +- 0.06) x 10^{-4} fm from a fit at threshold t_thr. This value is orders of magnitude smaller than those for phi-N and phi-d scattering lengths, indicating an inverse dependence of |alpha_VA| on the target nucleus mass. Our results provide new insight into the phi-nucleus interaction, supporting the notion of weak phi-nucleus coupling. We further explore the dependence of |alpha_VA| on the vector meson mass, the target atom mass, and the threshold energy. An approximate exponential suppression of |alpha_VA| is observed with increasing vector meson mass or target atom mass, indicating that heavier vector mesons or heavier target nuclei exhibit weaker couplings in vector meson-nucleus interactions.

hep-ph↗

arXiv2025

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Developing efficient CUDA kernels is increasingly critical for AI applications such as large-scale LLM training. However, manual kernel design is both costly and time-consuming, motivating automatic approaches that leverage LLMs for code generation. Existing methods for automatic kernel generation, however, often produce low-efficiency kernels, incur high computational overhead, and fail to generalize across settings. In this work, we propose CudaForge, a training-free multi-agent workflow for CUDA kernel generation and optimization. Our workflow is inspired by the iterative workflow of human experts, which contains steps such as developing initial kernels, testing correctness, analyzing hardware feedback, and iterative improvement. More specifically, CudaForge employs two LLM agents: a Coder and a Judge, that iteratively generate, correct, and optimize CUDA kernels, while integrating hardware feedback such as Nsight Compute (NCU) metrics. In extensive evaluations, we show that CudaForge, by leveraging base models like OpenAI-o3, achieves 97.6\% correctness of generated kernels and an average 1.68$\times$ speedup over PyTorch baselines, substantially surpassing state-of-the-art models including OpenAI-o3 and Kevin on KernelBench.Beyond accuracy and speed, CudaForge demonstrates strong generalization across GPUs (A100, RTX 6000, 4090, 3090) and base models (OpenAI-o3, GPT-5, gpt-oss-120B, Claude-Sonnet-4, QwQ-32B), while maintaining high efficiency. In particular, generating an optimized kernel takes about 26.5 minutes on one RTX6000 and incurs about \$ 0.3 API cost, which is significantly cheaper than existing agentic work that costs 6 H100 hours and \$ 5 API cost per kernel. Our results highlight that multi-agent, training-free workflows can enable cost-effective, generalizable, and high-performance CUDA kernel optimization. Code available at https://github.com/OptimAI-Lab/CudaForge

cs.LG↗

arXiv2025

High-Precision Physics Experiments at Huizhou Large-Scale Scientific Facilities

In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility (HIAF) and the Accelerator-Driven Subcritical System (CiADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility (CNUF), we are assembling a consortium of experts in relevant discipline--both domestically and internationally--to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF. Our focus encompasses six primary domains of inquiry: hadron physics--including endeavors such as the super eta factory and investigations into light hadron structures; muon physics; neutrino physics; neutron physics; the testing of fundamental symmetries; and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators. We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.

hep-ph↗

arXiv2025

A Framework for Quantifying How Pre-Training and Context Benefit In-Context Learning

Pre-trained large language models have demonstrated a strong ability to learn from context, known as in-context learning (ICL). Despite a surge of recent applications that leverage such capabilities, it is by no means clear, at least theoretically, how the ICL capabilities arise, and in particular, what is the precise role played by key factors such as pre-training procedure as well as context construction. In this work, we propose a new framework to analyze the ICL performance, for a class of realistic settings, which includes network architectures, data encoding, data generation, and prompt construction process. As a first step, we construct a simple example with a one-layer transformer, and show an interesting result, namely when the pre-train data distribution is different from the query task distribution, a properly constructed context can shift the output distribution towards the query task distribution, in a quantifiable manner, leading to accurate prediction on the query topic. We then extend the findings in the previous step to a more general case, and derive the precise relationship between ICL performance, context length and the KL divergence between pre-train and query task distribution. Finally, we provide experiments to validate our theoretical results.

cs.AI↗

arXiv2025

The fragility of "cultural tendencies" in LLMs

In a recent study, Lu, Song, and Zhang (2025) (LSZ) propose that large language models (LLMs), when prompted in different languages, display culturally specific tendencies. They report that the two models (i.e., GPT and ERNIE) respond in more interdependent and holistic ways when prompted in Chinese, and more independent and analytic ways when prompted in English. LSZ attribute these differences to deep-seated cultural patterns in the models, claiming that prompt language alone can induce substantial cultural shifts. While we acknowledge the empirical patterns they observed, we find their experiments, methods, and interpretations problematic. In this paper, we critically re-evaluate the methodology, theoretical framing, and conclusions of LSZ. We argue that the reported "cultural tendencies" are not stable traits but fragile artifacts of specific models and task design. To test this, we conducted targeted replications using a broader set of LLMs and a larger number of test items. Our results show that prompt language has minimal effect on outputs, challenging LSZ's claim that these models encode grounded cultural beliefs.

cs.CL↗