arXiv subjects

Kai Jiang

Publications and source records attributed to Kai Jiang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Terahertz-Driven Nano-tip Field-Emission Electron Gun and Cascaded Acceleration

This paper reports two versions of terahertz (THz)-driven nanotip field-emission electron guns: single-layer reflective guns (SLRGs) and double-layer reflective guns (DLRGs). Both guns use nanotip emitters and accelerate electrons through the electric field of the THz wave. SLRGs employ a reflective structure to superimpose the initial and subsequent half-cycles of the THz electric field, enhancing the field amplitude and acceleration efficiency. Experiments have demonstrated that SLRGs achieve higher acceleration efficiency than single-layer nonreflective guns (SLNRGs) for identical THz input energies. This constitutes direct experimental verification of the efficacy of the reflective structure. Theoretically, SLRGs operating in single-feed mode can match the acceleration efficiency of dual-feed SLNRGs while reducing operational complexity. DLRGs demonstrate THz-driven cascaded electron acceleration through precise scanning of the delay between two incident THz beams. This represents a direct experimental demonstration of cascaded acceleration in THz-driven electron sources. The experimental results of DLRGs align closely with the results of electron dynamics predicted by simulations. This establishes the foundation for developing multilayer high-acceleration-efficiency THz-driven high-energy electron guns. The ability to manipulate the THz for each layer individually holds promising potential for improving the beam quality of THz electron guns.

physics.acc-ph↗

arXiv2025

Two efficient block preconditioners for the mass-conserved Ohta-Kawasaki equation

In this paper, we propose two efficient block preconditioners to solve the mass-conserved Ohta-Kawasaki equation with finite element discretization. We also study the spectral distribution of these two preconditioners, \textit{i.e.,} Schur complement preconditioner and the modified Hermitian and skew-Hermitian splitting (MHSS in short) preconditioner. Besides, Newton method and Picard method are used to address the implicitly nonlinear term. We rigorously analyze the convergence of Newton method. Finally, we offer numerical examples to support the theoretical analysis and indicate the efficiency of the proposed preconditioners for the mass-conserved Ohta-Kawasaki equation.

math.NA↗

arXiv2025

Observation and Interpretation of Field Emission Saturation Induced by an Ultra-fast Intense Terahertz Field

Field emission under ultra-fast intense terahertz fields provides a promising approach for generating electron bunches with ultrashort pulse duration and high charge densities. It is generally believed that the field emission current described by traditional field emission theory increases dramatically with the applied electric field. However, we conducted extensive field emission experiments using quasi-single-cycle strong-field terahertz radiation at various energy levels and different temperatures and observed an intriguing phenomenon where the emitted charge reached saturation. A novel model is proposed to interpret this phenomenon, which considers the contribution of surface valence electrons and the dynamic replenishment of free electrons from the bulk to the surface. The experimentally observed convex relationship between the emitted charge and terahertz energy is consistent with the model prediction, unlike the concave relationship derived from the traditional field emission formula. In addition, another observed counter-intuitive phenomenon, the inverse correlation between the cathode temperature and saturated emission charge, is also well interpreted by the model. This work offers comprehensive insights into field emission dynamics under ultra-fast intense fields, paving the way for generating electron bunches with unprecedented temporal resolution.

physics.acc-ph↗

arXiv2025

Comprehensive characterization and validation of a fast-resolving (1000 Hz) plastic scintillator for ultra-high dose rate electron dosimetry

Background: The normal tissue sparing effect of ultra-high dose rate irradiation (>40 Gy/s, UHDR), as compared to conventional dose rate (CONV), has attracted significant research interest for FLASH radiotherapy (RT). Accurate, dose rate independent, fast-responding dosimeters capable of resolving the spatiotemporal characteristics of UHDR beams are urgently needed to facilitate FLASH research and support its clinical translation. Tissue-equivalent scintillators, with millimeter-level spatial resolution and millisecond-level temporal resolution, possess these required characteristics and show strong potential for use in UHDR dosimetry. Purpose: We investigated the performance of the HYPERSCINT RP-FLASH scintillator system at up to 1000 Hz sampling frequency (fs) for UHDR electron beam dosimetry. Methods: The scintillator was exposed to CONV and UHDR electron irradiation using a LINAC-based FLASH platform. Its spectral characteristics were delineated with a four-component calibration, followed by a signal-to-dose calibration using 18 MeV CONV electron beam. The dose linearity and dosimetric accuracy in response to CONV and UHDR irradiation at 1 and 1000 Hz fs were quantified against ion chamber and EBT-XD film measurements. The response of the scintillator system was investigated as a function of beam energy (6 and 18 MeV), field size (2x2 to 25x25 cm2), dose per pulse (DPP, 0.8 to 2.3 Gy/pulse), and pulse repetition frequency (PRF, 30 to 180 Hz). Relative signal sensitivity was quantified against accumulated dose to account for the scintillator's radiation degradation. Pulse-resolved dose measurements at 18 MeV UHDR, obtained using the scintillator with 1000 Hz fs for a train of 10 pulses at 180 Hz PRF, were validated with a PMT-fiber optic scattered radiation detector.

physics.med-ph↗

arXiv2025

Prediction of A15 Tilt Grain Boundary Structures

In this work, we present a theoretical method to predict all coincidence site lattice (CSL) tilt grain boundaries (GBs) in A15, especially high-$Σ$ CSL GBs. This method includes a modified Farey diagram (MFD) and a computational framework based on the 3D phase field crystal model. Applied to [001] CSL symmetric tilt grain boundaries (STGBs) in A15, this method identifies building blocks of A15 GBs, known as structural units (SUs). The MFD predicts the quantity and proportion of SUs within GBs. The developed computational approach further determines the arrangement of these SUs. The predictive rule reveals the SU arrangement of A15 [001] CSL STGBs.

cond-mat.mtrl-sci↗

arXiv2025

A Hybrid Iterative Neural Solver Based on Spectral Analysis for Parametric PDEs

Deep learning-based hybrid iterative methods (DL-HIM) have emerged as a promising approach for designing fast neural solvers to tackle large-scale sparse linear systems. DL-HIM combine the smoothing effect of simple iterative methods with the spectral bias of neural networks, which allows them to effectively eliminate both high-frequency and low-frequency error components. However, their efficiency may decrease if simple iterative methods can not provide effective smoothing, making it difficult for the neural network to learn mid-frequency and high-frequency components. This paper first conducts a convergence analysis for general DL-HIM from a spectral viewpoint, concluding that under reasonable assumptions, DL-HIM exhibit a convergence rate independent of grid size $h$ and physical parameters $\boldsymbolμ$. To meet these assumptions, we design a neural network from an eigen perspective, focusing on learning the eigenvalues and eigenvectors corresponding to error components that simple iterative methods struggle to eliminate. Specifically, the eigenvalues are learned by a meta subnet, while the eigenvectors are approximated using Fourier modes with a transition matrix provided by another meta subnet. The resulting DL-HIM, termed the Fourier Neural Solver (FNS), can be trained to achieve a convergence rate independent of PDE parameters and grid size within a local neighborhood of the training scale by designing a loss function that ensures the neural network complements the smoothing effect of the damped Jacobi iterative methods. We verify the performance of FNS on five types of linear parametric PDEs.

math.NA↗

arXiv2025

Projection method for quasiperiodic elliptic equations and application to quasiperiodic homogenization

In this study, we address the challenge of solving elliptic equations with quasiperiodic coefficients. To achieve accurate and efficient computation, we introduce the projection method, which enables the embedding of quasiperiodic systems into higher-dimensional periodic systems. To enhance the computational efficiency, we propose a compressed storage strategy for the stiffness matrix by its multi-level block circulant structure, significantly reducing memory requirements. Furthermore, we design a diagonal preconditioner to efficiently solve the resulting high-dimensional linear system by reducing the condition number of the stiffness matrix. These techniques collectively contribute to the computational effectiveness of our proposed approach. Convergence analysis shows the polynomial accuracy of the proposed method. We demonstrate the effectiveness and accuracy of our approach through a series of numerical examples. Moreover, we apply our method to achieve a highly accurate computation of the homogenized coefficients for a quasiperiodic multiscale elliptic equation.

math.NA↗

arXiv2025

A Neural Multigrid Solver for Helmholtz Equations with High Wavenumber and Heterogeneous Media

In this paper, we propose a deep learning-enhanced multigrid solver for high-frequency and heterogeneous Helmholtz equations. By applying spectral analysis, we categorize the iteration error into characteristic and non-characteristic components. We eliminate the non-characteristic components by a multigrid wave cycle, which employs carefully selected smoothers on each grid. We diminish the characteristic components by a learned phase function and the approximate solution of an advection-diffusion-reaction (ADR) equation, which is solved using another multigrid V-cycle on a coarser scale, referred to as the ADR cycle. The resulting solver, termed Wave-ADR-NS, enables the handling of error components with varying frequencies and overcomes constraints on the number of grid points per wavelength on coarse grids. Furthermore, we provide an efficient implementation using differentiable programming, making Wave-ADR-NS an end-to-end Helmholtz solver that incorporates parameters learned through a semi-supervised training. Wave-ADR-NS demonstrates robust generalization capabilities for both in-distribution and out-of-distribution velocity fields of varying difficulty. Comparative experiments with other multigrid methods validate its superior performance in solving heterogeneous 2D Helmholtz equations with wavenumbers exceeding 2000.

math.NA↗

arXiv2025

M$^3$amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification

Multi-modal fusion holds great promise for integrating information from different modalities. However, due to a lack of consideration for modal consistency, existing multi-modal fusion methods in the field of remote sensing still face challenges of incomplete semantic information and low computational efficiency in their fusion designs. Inspired by the observation that the visual language pre-training model CLIP can effectively extract strong semantic information from visual features, we propose M$^3$amba, a novel end-to-end CLIP-driven Mamba model for multi-modal fusion to address these challenges. Specifically, we introduce CLIP-driven modality-specific adapters in the fusion architecture to avoid the bias of understanding specific domains caused by direct inference, making the original CLIP encoder modality-specific perception. This unified framework enables minimal training to achieve a comprehensive semantic understanding of different modalities, thereby guiding cross-modal feature fusion. To further enhance the consistent association between modality mappings, a multi-modal Mamba fusion architecture with linear complexity and a cross-attention module Cross-SS2D are designed, which fully considers effective and efficient information interaction to achieve complete fusion. Extensive experiments have shown that M$^3$amba has an average performance improvement of at least 5.98\% compared with the state-of-the-art methods in multi-modal hyperspectral image classification tasks in the remote sensing field, while also demonstrating excellent training efficiency, achieving a double improvement in accuracy and efficiency. The code is released at https://github.com/kaka-Cao/M3amba.

cs.CV↗

arXiv2025

RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering

Remote Sensing Image Captioning (RSIC) presents unique challenges and plays a critical role in applications. Traditional RSIC methods often struggle to produce rich and diverse descriptions. Recently, with advancements in VLMs, efforts have emerged to integrate these models into the remote sensing domain and to introduce descriptive datasets specifically designed to enhance VLM training. This paper proposes RS-MoE, a first Mixture of Expert based VLM specifically customized for remote sensing domain. Unlike traditional MoE models, the core of RS-MoE is the MoE Block, which incorporates a novel Instruction Router and multiple lightweight Large Language Models (LLMs) as expert models. The Instruction Router is designed to generate specific prompts tailored for each corresponding LLM, guiding them to focus on distinct aspects of the RSIC task. This design not only allows each expert LLM to concentrate on a specific subset of the task, thereby enhancing the specificity and accuracy of the generated captions, but also improves the scalability of the model by facilitating parallel processing of sub-tasks. Additionally, we present a two-stage training strategy for tuning our RS-MoE model to prevent performance degradation due to sparsity. We fine-tuned our model on the RSICap dataset using our proposed training strategy. Experimental results on the RSICap dataset, along with evaluations on other traditional datasets where no additional fine-tuning was applied, demonstrate that our model achieves state-of-the-art performance in generating precise and contextually relevant captions. Notably, our RS-MoE-1B variant achieves performance comparable to 13B VLMs, demonstrating the efficiency of our model design. Moreover, our model demonstrates promising generalization capabilities by consistently achieving state-of-the-art performance on the Remote Sensing Visual Question Answering (RSVQA) task.

cs.CV↗

arXiv2025

Efficient numerical methods for computing stationary states of spherical Landau-Brazovskii model

In this paper, we develop a set of efficient methods to compute stationary states of the spherical Landau-Brazovskii (LB) model in a discretization-then-optimization way. First, we discretize the spherical LB energy functional into a finite-dimensional energy function by the spherical harmonic expansion. Then five optimization methods are developed to compute stationary states of the discretized energy function, including the accelerated adaptive Bregman proximal gradient, Nesterov, adaptive Nesterov, adaptive nonlinear conjugate gradient and adaptive gradient descent methods. To speed up the convergence, we propose a principal mode analysis (PMA) method to estimate good initial configurations and sphere radius. The PMA method also reveals the relationship between the optimal sphere radius and the dominant degree of spherical harmonics. Numerical experiments show that our approaches significantly reduce the number of iterations and the computational time

math.NA↗

arXiv2024

Convergence analysis of PM-BDF2 method for quasiperiodic parabolic equations

Numerically solving parabolic equations with quasiperiodic coefficients is a significant challenge due to the potential formation of space-filling quasiperiodic structures that lack translational symmetry or decay. In this paper, we introduce a highly accurate numerical method for solving time-dependent quasiperiodic parabolic equations. We discretize the spatial variables using the projection method (PM) and the time variable with the second-order backward differentiation formula (BDF2). We provide a complexity analysis for the resulting PM-BDF2 method. Furthermore, we conduct a detailed convergence analysis, demonstrating that the proposed method exhibits spectral accuracy in space and second-order accuracy in time. Numerical results in both one and two dimensions validate these convergence results, highlighting the PM-BDF2 method as a highly efficient algorithm for addressing quasiperiodic parabolic equations.

math.NA↗

arXiv2024

DiffCLIP: Few-shot Language-driven Multimodal Classifier

Visual language models like Contrastive Language-Image Pretraining (CLIP) have shown impressive performance in analyzing natural images with language information. However, these models often encounter challenges when applied to specialized domains such as remote sensing due to the limited availability of image-text pairs for training. To tackle this issue, we introduce DiffCLIP, a novel framework that extends CLIP to effectively convey comprehensive language-driven semantic information for accurate classification of high-dimensional multimodal remote sensing images. DiffCLIP is a few-shot learning method that leverages unlabeled images for pretraining. It employs unsupervised mask diffusion learning to capture the distribution of diverse modalities without requiring labels. The modality-shared image encoder maps multimodal data into a unified subspace, extracting shared features with consistent parameters across modalities. A well-trained image encoder further enhances learning by aligning visual representations with class-label text information from CLIP. By integrating these approaches, DiffCLIP significantly boosts CLIP performance using a minimal number of image-text pairs. We evaluate DiffCLIP on widely used high-dimensional multimodal datasets, demonstrating its effectiveness in addressing few-shot annotated classification tasks. DiffCLIP achieves an overall accuracy improvement of 10.65% across three remote sensing datasets compared with CLIP, while utilizing only 2-shot image-text pairs. The code has been released at https://github.com/icey-zhang/DiffCLIP.

cs.CV↗

arXiv2024

Irrational-window-filter projection method and application to quasiperiodic Schrödinger eigenproblems

In this paper, we propose a new algorithm, the irrational-window-filter projection method (IWFPM), for quasiperiodic systems with concentrated spectral point distribution. Based on the projection method (PM), IWFPM filters out dominant spectral points by defining an irrational window and uses a corresponding index-shift transform to make the FFT available. The error analysis on the function approximation level is also given. We apply IWFPM to 1D, 2D, and 3D quasiperiodic Schrödinger eigenproblems (QSEs) to demonstrate its accuracy and efficiency. IWFPM exhibits a significant computational advantage over PM for both extended and localized quantum states. More importantly, by using IWFPM, the existence of Anderson localization in 2D and 3D QSEs is numerically verified.

math.NA↗

arXiv2024

A Survey on Vision Autoregressive Model

Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision, which perform next-token predictions by representing visual data as visual tokens and enables autoregressive modelling for a wide range of vision tasks, ranging from visual generation and visual understanding to the very recent multimodal generation that unifies visual generation and understanding with a single autoregressive model. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods and highlighting their major contributions, strengths, and limitations, covering various vision tasks such as image generation, video generation, image editing, motion generation, medical image analysis, 3D generation, robotic manipulation, unified multimodal generation, etc. Besides, we investigate and analyze the latest advancements in autoregressive models, including thorough benchmarking and discussion of existing methods across various evaluation datasets. Finally, we outline key challenges and promising directions for future research, offering a roadmap to guide further advancements in vision autoregressive models.

cs.CV↗

arXiv2024

Convergence analysis of time-splitting projection method for nonlinear quasiperiodic Schrödinger equation

This work proposes and analyzes an efficient numerical method for solving the nonlinear Schrödinger equation with quasiperiodic potential, where the projection method is applied in space to account for the quasiperiodic structure and the Strang splitting method is used in time.While the transfer between spaces of low-dimensional quasiperiodic and high-dimensional periodic functions and its coupling with the nonlinearity of the operator splitting scheme make the analysis more challenging. Meanwhile, compared to conventional numerical analysis of periodic Schrödinger systems, many of the tools and theories are not applicable to the quasiperiodic case. We address these issues to prove the spectral accuracy in space and the second-order accuracy in time. Numerical experiments are performed to substantiate the theoretical findings.

math.NA↗

arXiv2024

On the approximation of quasiperiodic functions with Diophantine frequencies by periodic functions

We present an analysis of the approximation error for a $d$-dimensional quasiperiodic function $f$ with Diophantine frequencies, approximated by a periodic function with the fundamental domain $[0,L_1)\times [0,L_2)\times \cdots \times[0,L_d)$. When $f$ has a certain regularity, its global behavior can be described by a finite number of Fourier components and has a polynomial decay at infinity. The dominant part of periodic approximation error is bounded by $O(\max_{1\leq j \leq d} L_j^{-s_j})$, where $L_j$ belongs to the best simultaneous approximation sequence and $s_j$ is the number of different irrational elements in $j$-th dimension component of Fourier frequencies, respectively. Meanwhile, we discuss the approximation rate. Finally, these analytical results are verified by some examples.

math.NT↗

arXiv2024

FEED: Fairness-Enhanced Meta-Learning for Domain Generalization

Generalizing to out-of-distribution data while being aware of model fairness is a significant and challenging problem in meta-learning. The goal of this problem is to find a set of fairness-aware invariant parameters of classifier that is trained using data drawn from a family of related training domains with distribution shift on non-sensitive features as well as different levels of dependence between model predictions and sensitive features so that the classifier can achieve good generalization performance on unknown but distinct test domains. To tackle this challenge, existing state-of-the-art methods either address the domain generalization problem but completely ignore learning with fairness or solely specify shifted domains with various fairness levels. This paper introduces an approach to fairness-aware meta-learning that significantly enhances domain generalization capabilities. Our framework, Fairness-Enhanced Meta-Learning for Domain Generalization (FEED), disentangles latent data representations into content, style, and sensitive vectors. This disentanglement facilitates the robust generalization of machine learning models across diverse domains while adhering to fairness constraints. Unlike traditional methods that focus primarily on domain invariance or sensitivity to shifts, our model integrates a fairness-aware invariance criterion directly into the meta-learning process. This integration ensures that the learned parameters uphold fairness consistently, even when domain characteristics vary widely. We validate our approach through extensive experiments across multiple benchmarks, demonstrating not only superior performance in maintaining high accuracy and fairness but also significant improvements over existing state-of-the-art methods in domain generalization tasks.

cs.LG↗