arXiv subjects

Yuan Chen

Publications and source records attributed to Yuan Chen.

At least 55 records · Page 3Linked to original sources

arXiv2025

Dual-comb correlation spectroscopy reveals laser dynamics

Laser dynamics underpin a broad range of modern photonic technologies and continue to reveal rich nonlinear behaviors. However, existing spectroscopic tools, most notably time-stretched dispersive Fourier transform spectroscopy (TS-DFT), remain limited in spectral resolution, accuracy, and their ability to capture continuous waveforms and complex field dynamics. Here, we introduce dual-comb correlation spectroscopy (DCCS) as a powerful approach for resolving fast and intricate laser behaviors that are inaccessible to TS-DFT and conventional spectrometers. By correlating two sequences of heterodyne spectra produced by mixing a test laser with a pair of optical combs, DCCS enables rapid (e.g., 1 us) and high-resolution (0.08 pm) spectral retrieval over broad optical bandwidths. Leveraging these capabilities, we reveal mode-hopping and mode-competition dynamics in continuous-wave lasers, as well as the buildup process of a mode-locked laser. These results establish DCCS as a versatile and complementary tool to TS-DFT for exploring transient, broadband, and previously unresolvable behaviors in lasers and other time-evolving optical systems.

physics.optics↗

arXiv2025

Volume Preserving Willmore Flow in a Generalized Cahn-Hilliard Flow

We investigate the mass-preserving $L^2$-gradient flow associated with a generalized Cahn--Hilliard equation. Our focus is on the sharp interface regime, where the interface width parameter $\varepsilon > 0$ is small. For well-prepared initial data, we rigorously prove that, as $\varepsilon \to 0$, solutions of the diffuse-interface model converge to the \emph{volume-preserving Willmore flow} in arbitrary spatial dimensions $n \geq 2$. The proof incorporates matched asymptotic expansions and energy estimates to establish convergence of the order parameter away from the interface, alongside precise motion law derivation for the limiting interface. This result extends the analysis of Fei and Liu~\cite{fei2021phase} from two-dimensional settings to general $n$-dimensional domains, and it applies to a broad class of symmetric double-well potentials beyond the classical quartic form. Our work thus provides a general PDE framework linking higher-order phase-field models to volume-preserving curvature flows in the sharp interface limit.

math.AP↗

arXiv2025

FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling

Federated Learning (FL) enables collaborative model training across distributed clients without data sharing, but its high computational and communication demands strain resource-constrained devices. While existing methods use dynamic pruning to improve efficiency by periodically adjusting sparse model topologies while maintaining sparsity, these approaches suffer from issues such as greedy adjustments, unstable topologies, and communication inefficiency, resulting in less robust models and suboptimal performance under data heterogeneity and partial client availability. To address these challenges, we propose Federated Robust pruning via combinatorial Thompson Sampling (FedRTS), a novel framework designed to develop robust sparse models. FedRTS enhances robustness and performance through its Thompson Sampling-based Adjustment (TSAdj) mechanism, which uses probabilistic decisions informed by stable, farsighted information instead of deterministic decisions reliant on unstable and myopic information in previous methods. Extensive experiments demonstrate that FedRTS achieves state-of-the-art performance in computer vision and natural language processing tasks while reducing communication costs, particularly excelling in scenarios with heterogeneous data distributions and partial client participation. Our codes are available at: https://github.com/Little0o0/FedRTS

cs.LG↗

arXiv2025

Ultracompact Wide-FOV Near-infrared Camera with Wafer-level Manufactured Meta-Aspheric Lens

Overcoming the trade-off between wide field of view (FOV) and compactness remains a central challenge for integrating near-infrared (NIR) imaging into smartphones and AR glasses. Existing refractive NIR optics cannot simultaneously achieve ultra-wide angles above 100° and ultrathin total track length (TTL) below 5 mm, limiting their use in portable devices. Here, we present a wafer-level-manufactured meta-aspheric lens (MAL) that achieves a 101.5° FOV, 3.39 mm TTL, and F/1.64 aperture within a compact volume of 0.02 cubic centimeters. Unlike previous hybrid lenses with separate refractive and diffractive components, our MAL features a fully integrated structure, which enables a compact form factor. This integration also simplifies fabrication, allowing high-throughput production via micrometer-level precision alignment and bonding on a single wafer, with only one dicing step and no need for additional mechanical fixtures. Furthermore, the design process explicitly considers manufacturability and accurately models metalens dispersion, ensuring that experimental performance matches simulated results. We validate our MAL through both direct and computational imaging experiments. Despite its small form factor, our scalable MAL demonstrates strong NIR imaging performance in blood vessel imaging, eye tracking, and computational pixel super-resolution tasks. This scalable MAL technology establishes a new benchmark for high-performance, miniaturized NIR imaging and opens the door to next-generation smartphone and AR optical systems.

physics.optics↗

arXiv2025

$Υ(5S)$ in the unquenched quark model

The observation of the $Υ(10753)$ state by Belle II Collaboration has sparked significant interest in the theoretical understanding of such states within the context of hadron physics. Considering the similar mass and the decay with, as well as the same quantum numbers $J^{PC}=1^{--}$ with the $Υ(10860)$ state, which is referred to be the $Υ(5S)$ in PDG, in this work, we try to calculate the mass of the $Υ(5S)$ state. The model used to predict the high-energy spectrum of these states generally involves a constituent quark model, which can describe a variety of properties of hadrons containing heavy quarks. In the framework of the unquenched quark model, a coupled-channel calculation is employed to explore the effect of open-bottom meson-meson thresholds on the $Υ(10860)$ state. The hypothesis is that coupled-channel effects could be large enough to create new dynamically generated states, thus potentially explaining the nature of the $Υ(10860)$ state, as well as whether the $Υ(10860)$ and $Υ(10753)$ is the same state. The results indicate that unquenched effects play a crucial role in explaining the $Υ(10860)$ state, providing a plausible mechanism for its formation. Besides in our calculations, the $Υ(10860)$ and $Υ(10753)$ may be two different states.

hep-ph↗

arXiv2025

Rapid and precise distance measurement using balanced cross-correlation of a single frequency-modulated electro-optic comb

Ultra-rapid, high-precision distance metrology is critical for both advanced scientific research and practical applications. However, current light detection and ranging technologies struggle to simultaneously achieve high measurement speed, accuracy, and a large non-ambiguity range. Here, we present a time-of-flight optical ranging technique based on a repetition-frequency-modulated femtosecond electro-optic comb and balanced nonlinear cross-correlation detection. In this approach, a target distance is determined as an integer multiple of the comb repetition period. By rapidly sweeping the comb repetition frequency, we achieve absolute distance measurements within 500 ns and real-time displacement tracking at single-pulse resolution (corresponding to a refresh rate of 172 MHz). Furthermore, our system attains an ultimate ranging precision of 5 nm (with 0.3 s integration time). Our method uniquely integrates nanometer-scale precision, megahertz-level refresh rates, and a theoretically unlimited ambiguity range within a single platform, while also supporting multi-target detection. These advances pave the way for high-speed, high-precision ranging systems in emerging applications such as structural health monitoring, industrial manufacturing, and satellite formation flying.

physics.optics↗

arXiv2025

Self-Organizing State-Space Models with Artificial Dynamics

We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter $θ$. A popular idea to address this problem consists of incorporating $θ$ in the state of the system and allowing its time evolution, modelled as a Markov chain $(θ_t)_{t\geq 1}$. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a theoretically justified manner has remained an open problem until now. In this paper we fill this gap and in particular show that theoretically consistent SO-SSMs can be defined such that $\|\mathrm{Var}(θ_{t+1}|θ_{t})\|\rightarrow 0$ slowly as $t\rightarrow\infty$. This, in turn, leads to particle filter algorithms for online inference in SSMs which we find to be robust in simulation. We also develop constructions of $(θ_t)_{t\geq 1}$ and associated theoretical guarantees tailored to the application of SO-SSMs to maximum likelihood estimation in SSMs, leading to novel iterated filtering algorithms. The algorithms developed in this work have the advantage of being simple to implement and to require minimal tuning to perform well.

math.ST↗

arXiv2025

HDO ice detected toward an isolated low-mass protostar with JWST

Water is detected in environments representing every stage of star and solar system formation, but its chemical evolution throughout these stages remains poorly constrained. Deuterium ratios offer a means of probing chemical links between water in different cosmic regions because of their sensitivity to physicochemical conditions. Here, we present the first detection of the 4.1 $μ$m HDO ice feature with JWST toward a low-mass protostar, L1527 IRS, which may eventually grow to a sun-like mass. We measure an ice HDO/H$_{2}$O ratio of 4.4$^{+3.7}_{-1.7}$$\times$10$^{-3}$, where the reported error is dominated by uncertainties in continuum definition and ice band strengths. This fraction is similar to the gas HDO/H$_{2}$O ratios measured in the warm ($>$100 K) inner cores of other low-mass protostellar envelopes and protoplanetary disks found in comparably isolated star-forming regions. Such a similarity tentatively supports the assumption that water vapor detected in these regions is not significantly altered by gas-phase reactions following ice sublimation. It also supports the hypothesis that pre- and protostellar water ice is largely inherited in a chemically unaltered state by outer protoplanetary disks. However, the fraction is a factor of $\sim$4-10 times higher than the gas HDO/H$_{2}$O ratios measured toward comets and low-mass protostars in clustered star-forming regions. This difference may be due to either gas-phase water reprocessing in protostellar envelopes and protoplanetary disks, or differences between prestellar conditions of isolated dense cores and the clustered star-forming regions that are more analogous to the environment in which our Sun formed.

astro-ph.SR↗

arXiv2025

Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition

Pattern recognition based on RGB-Event data is a newly arising research topic and previous works usually learn their features using CNN or Transformer. As we know, CNN captures the local features well and the cascaded self-attention mechanisms are good at extracting the long-range global relations. It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}. In this work, we propose a novel RGB-Event based recognition framework termed TSCFormer, which is a relatively lightweight CNN-Transformer model. Specifically, we mainly adopt the CNN as the backbone network to first encode both RGB and Event data. Meanwhile, we initialize global tokens as the input and fuse them with RGB and Event features using the BridgeFormer module. It captures the global long-range relations well between both modalities and maintains the simplicity of the whole model architecture at the same time. The enhanced features will be projected and fused into the RGB and Event CNN blocks, respectively, in an interactive manner using F2E and F2V modules. Similar operations are conducted for other CNN blocks to achieve adaptive fusion and local-global feature enhancement under different resolutions. Finally, we concatenate these three features and feed them into the classification head for pattern recognition. Extensive experiments on two large-scale RGB-Event benchmark datasets (PokerEvent and HARDVS) fully validated the effectiveness of our proposed TSCFormer. The source code and pre-trained models will be released at https://github.com/Event-AHU/TSCFormer.

cs.CV↗

arXiv2025

FB$^+$-tree: A Memory-Optimized B$^+$-tree with Latch-Free Update

B$^+$-trees are prevalent in traditional database systems due to their versatility and balanced structure. While binary search is typically utilized for branch operations, it may lead to inefficient cache utilization in main-memory scenarios. In contrast, trie-based index structures drive branch operations through prefix matching. While these structures generally produce fewer cache misses and are thus increasingly popular, they may underperform in range scans because of frequent pointer chasing. This paper proposes a new high-performance B$^+$-tree variant called \textbf{Feature B$^+$-tree (FB$^+$-tree)}. Similar to employing bit or byte for branch operation in tries, FB$^+$-tree progressively considers several bytes following the common prefix on each level of its inner nodes\textemdash referred to as features, which allows FB$^+$-tree to benefit from prefix skewness. FB$^+$-tree blurs the lines between B$^+$-trees and tries, while still retaining balance. In the best case, FB$^+$-tree almost becomes a trie, whereas in the worst case, it continues to function as a B$^+$-tree. Meanwhile, a crafted synchronization protocol that combines the link technique and optimistic lock is designed to support efficient concurrent index access. Distinctively, FB$^+$-tree leverages subtle atomic operations seamlessly coordinated with optimistic lock to facilitate latch-free updates, which can be easily extended to other structures. Intensive experiments on multiple workload-dataset combinations demonstrate that FB$^+$-tree shows comparable lookup performance to state-of-the-art trie-based indexes and outperforms popular B$^+$-trees by 2.3x$\ \sim\ $3.7x under 96 threads. FB$^+$-tree also exhibits significant potential on other workloads, especially update workloads under contention and scan workloads.

cs.DB↗

arXiv2025

Space compatibility of emerging, wide-bandgap, ultralow-loss integrated photonics

Integrated photonics has revolutionized optical communication, sensing, and computation, offering miniaturized and lightweight solutions for spacecraft with limited size and payload. Novel chip-scale instruments based on ultralow-loss integrated photonic platforms, including lasers, frequency combs and atomic traps, have been developed for space applications. Therefore, quantifying the space compatibility of ultralow-loss photonic integrated circuits (PICs), particularly their radiation resistance, is critical. This study experimentally evaluates the radiation resistance of ultralow-loss Si$_3$N$_4$, 4H-SiC, and LiNbO$_3$ PICs under intense $γ$-ray and high-energy proton irradiation. Results show that proton irradiation with $1.1 \times 10^{10}$ $\mathrm{p/cm^2}$ total flux does not significantly increase optical loss or alter the refractive index of these PICs, while $γ$-ray irradiation with 1.2 Mrad accumulated dose only marginally increases their optical loss. These findings provide preliminary evidence of the excellent space compatibility of ultralow-loss Si$_3$N$_4$, 4H-SiC, and LiNbO$_3$ PICs, highlighting their potential for compact and lightweight space systems.

physics.optics↗

arXiv2025

Modeling Unknown Stochastic Dynamical System Subject to External Excitation

We present a numerical method for learning unknown nonautonomous stochastic dynamical system, i.e., stochastic system subject to time dependent excitation or control signals. Our basic assumption is that the governing equations for the stochastic system are unavailable. However, short bursts of input/output (I/O) data consisting of certain known excitation signals and their corresponding system responses are available. When a sufficient amount of such I/O data are available, our method is capable of learning the unknown dynamics and producing an accurate predictive model for the stochastic responses of the system subject to arbitrary excitation signals not in the training data. Our method has two key components: (1) a local approximation of the training I/O data to transfer the learning into a parameterized form; and (2) a generative model to approximate the underlying unknown stochastic flow map in distribution. After presenting the method in detail, we present a comprehensive set of numerical examples to demonstrate the performance of the proposed method, especially for long-term system predictions.

cs.LG↗

arXiv2025

Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations

Nonlinear time-dependent partial differential equations are essential in modeling complex phenomena across diverse fields, yet they pose significant challenges due to their computational complexity, especially in higher dimensions. This study explores Quantum Recurrent Neural Networks within an encoder-decoder framework, integrating Variational Quantum Circuits into Gated Recurrent Units and Long Short-Term Memory networks. Using this architecture, the model efficiently compresses high-dimensional spatiotemporal data into a compact latent space, facilitating more efficient temporal evolution. We evaluate the algorithms on the Hamilton-Jacobi-Bellman equation, Burgers' equation, the Gray-Scott reaction-diffusion system, and the three dimensional Michaelis-Menten reaction-diffusion equation. The results demonstrate the superior performance of the quantum-based algorithms in capturing nonlinear dynamics, handling high-dimensional spaces, and providing stable solutions, highlighting their potential as an innovative tool in solving challenging and complex systems.

cs.LG↗

arXiv2025

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction

This study proposes a deep learning model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) for discriminant analysis of financial systemic risk. The model first uses CNN to extract local patterns of multidimensional features of financial markets, and then models the bidirectional dependency of time series through BiLSTM, to comprehensively characterize the changing laws of systemic risk in spatial features and temporal dynamics. The experiment is based on real financial data sets. The results show that the model is significantly superior to traditional single models (such as BiLSTM, CNN, Transformer, and TCN) in terms of accuracy, recall, and F1 score. The F1-score reaches 0.88, showing extremely high discriminant ability. This shows that the joint strategy of combining CNN and BiLSTM can not only fully capture the complex patterns of market data but also effectively deal with the long-term dependency problem in time series data. In addition, this study also explores the robustness of the model in dealing with data noise and processing high-dimensional data, providing strong support for intelligent financial risk management. In the future, the research will further optimize the model structure, introduce methods such as reinforcement learning and multimodal data analysis, and improve the efficiency and generalization ability of the model to cope with a more complex financial environment.

cs.LG↗

arXiv2024

Chebyshev Feature Neural Network for Accurate Function Approximation

We present a new Deep Neural Network (DNN) architecture capable of approximating functions up to machine accuracy. Termed Chebyshev Feature Neural Network (CFNN), the new structure employs Chebyshev functions with learnable frequencies as the first hidden layer, followed by the standard fully connected hidden layers. The learnable frequencies of the Chebyshev layer are initialized with exponential distributions to cover a wide range of frequencies. Combined with a multi-stage training strategy, we demonstrate that this CFNN structure can achieve machine accuracy during training. A comprehensive set of numerical examples for dimensions up to $20$ are provided to demonstrate the effectiveness and scalability of the method.

cs.LG↗

arXiv2024

Modeling Unknown Stochastic Dynamical System via Autoencoder

We present a numerical method to learn an accurate predictive model for an unknown stochastic dynamical system from its trajectory data. The method seeks to approximate the unknown flow map of the underlying system. It employs the idea of autoencoder to identify the unobserved latent random variables. In our approach, we design an encoding function to discover the latent variables, which are modeled as unit Gaussian, and a decoding function to reconstruct the future states of the system. Both the encoder and decoder are expressed as deep neural networks (DNNs). Once the DNNs are trained by the trajectory data, the decoder serves as a predictive model for the unknown stochastic system. Through an extensive set of numerical examples, we demonstrate that the method is able to produce long-term system predictions by using short bursts of trajectory data. It is also applicable to systems driven by non-Gaussian noises.

cs.LG↗

arXiv2024

Lightweight Multiplane Images Network for Real-Time Stereoscopic Conversion from Planar Video

With the rapid development of stereoscopic display technologies, especially glasses-free 3D screens, and virtual reality devices, stereoscopic conversion has become an important task to address the lack of high-quality stereoscopic image and video resources. Current stereoscopic conversion algorithms typically struggle to balance reconstruction performance and inference efficiency. This paper proposes a planar video real-time stereoscopic conversion network based on multi-plane images (MPI), which consists of a detail branch for generating MPI and a depth-semantic branch for perceiving depth information. Unlike models that depend on explicit depth map inputs, the proposed method employs a lightweight depth-semantic branch to extract depth-aware features implicitly. To optimize the lightweight branch, a heavy training but light inference strategy is adopted, which involves designing a coarse-to-fine auxiliary branch that is only used during the training stage. In addition, the proposed method simplifies the MPI rendering process for stereoscopic conversion scenarios to further accelerate the inference. Experimental results demonstrate that the proposed method can achieve comparable performance to some state-of-the-art (SOTA) models and support real-time inference at 2K resolution. Compared to the SOTA TMPI algorithm, the proposed method obtains similar subjective quality while achieving over $40\times$ inference acceleration.

cs.CV↗

arXiv2024

Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.

q-fin.CP↗