arXiv subjects

Jingwei Wang

Publications and source records attributed to Jingwei Wang.

At least 19 recordsLinked to original sources

arXiv2026

Learning light scattering from operator parameter spaces to Galerkin-consistent solution spaces

Efficient and generalizable full-wave simulation is essential for nanophotonic analysis and inverse design, yet existing methods face a tradeoff between the high computational cost of numerical solvers and the limited generalizability of neural operator models for complex optical scattering. Here, we introduce FEMONet, a finite-element-constrained operator-learning framework that learns light scattering from an operator parameter space to a Galerkin-consistent solution space. The operator parameter space encodes the physical entities defining a wave-equation problem, while the variational weak form links this space to the coordinate and physical solution spaces. Integrated with operator-learning networks, FEMONet extends classical solvers from isolated problem instances to parameterized scattering operators. To our knowledge, FEMONet represents the first Galerkin-consistent operator-learning framework for complex-valued optical scattering, grounded in the variational weak form of the governing vector wave equations. Finite-element discretization absorbs spatial derivatives into assembled stiffness matrices and load vectors, removing coordinate-based derivatives of the neural-network output from the physics loss and improving training efficiency. By predicting finite-element expansion coefficients rather than unconstrained field values, the Galerkin-consistent formulation preserves compatible trial and test spaces, achieving high accuracy, stable training, and generalization across dielectric, metallic, arrayed, plasmonic, and three-dimensional nanophotonic structures.

physics.optics↗

arXiv2026

Vacuum Decay Rate in D-dimensional Electroweak theories

We present a systematic framework for computing the functional determinant contribution to vacuum decay rates in D-dimensional electroweak theories. It consistently incorporates quantum fluctuations from scalar, fermion, and gauge fields while ensuring rapid convergence even at high angular momenta. Demonstrated through applications to the $D=4$ SMEFT and its $D=3$ thermal counterpart, our method provides a general and efficient tool for analyzing vacuum stability and decay across dimensions in the presence of new physics beyond the Standard Model.

hep-ph↗

arXiv2025

R-Tuning: Wavelet-Decomposed Replay and Semantic Alignment for Continual Adaptation of Pretrained Time-Series Models

Pre-trained models have demonstrated exceptional generalization capabilities in time-series forecasting; however, adapting them to evolving data distributions remains a significant challenge. A key hurdle lies in accessing the original training data, as fine-tuning solely on new data often leads to catastrophic forgetting. To address this issue, we propose Replay Tuning (R-Tuning), a novel framework designed for the continual adaptation of pre-trained time-series models. R-Tuning constructs a unified latent space that captures both prior and current task knowledge through a frequency-aware replay strategy. Specifically, it augments model-generated samples via wavelet-based decomposition across multiple frequency bands, generating trend-preserving and fusion-enhanced variants to improve representation diversity and replay efficiency. To further reduce reliance on synthetic samples, R-Tuning introduces a latent consistency constraint that aligns new representations with the prior task space. This constraint guides joint optimization within a compact and semantically coherent latent space, ensuring robust knowledge retention and adaptation. Extensive experimental results demonstrate the superiority of R-Tuning, which reduces MAE and MSE by up to 46.9% and 46.8%, respectively, on new tasks, while preserving prior knowledge with gains of up to 5.7% and 6.0% on old tasks. Notably, under few-shot settings, R-Tuning outperforms all state-of-the-art baselines even when synthetic proxy samples account for only 5% of the new task dataset.

cs.LG↗

arXiv2025

Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph

Teaching large language models (LLMs) to use tools is crucial for improving their problem-solving abilities and expanding their applications. However, effectively using tools is challenging because it requires a deep understanding of tool functionalities and user intentions. Previous methods relied mainly on LLMs to generate instruction data, but the quality of these data was often insufficient. In this paper, we propose a new method that uses knowledge graphs to generate high-quality instruction data for LLMs. Knowledge graphs are manually curated datasets rich in semantic information. We begin by extracting various query pathways from a given knowledge graph, which are transformed into a broad spectrum of user queries. We then translate the relationships between entities into actionable tools and parse the pathways of each query into detailed solution steps, thereby creating high-quality instruction data. Our experiments show that fine-tuning on just a small sample of this synthetic data can significantly improve the tool utilization and overall capabilities of LLMs.

cs.LG↗

arXiv2025

Nonsymmorphic symmetry adapted finite element modeling of glide-symmetric photonic structures

Space group theory is pivotal in the design of nanophotonics devices, enabling the characterization of periodic optical structures such as photonic crystals. The aim of this study is to extend the application of nonsymmorphic space groups in the field of numerical analysis for research and design of nanophotonics devices. In this work, we introduce the nonsymmorphic symmetry adapted finite element method, and provide a systematic approach for efficient band structure analysis of photonic structures with nonsymmorphic groups. We offer a formal and rigorous treatment by specifically deriving the boundary constraint conditions associated with the symmetry operations and their irreducible representations and decomposing the original problem into different subtasks. our method fully accounting for non-primitive translations and nonstructural symmetries like time-reversal symmetry and hidden symmetries. We demonstrate the effectiveness of our method via computing the band structure of photonic structures with a layer group, a plane group, and a space group. The results exhibit excellent agreement with those obtained using the standard finite element method, showcasing improved computational efficiency. Furthermore, the decomposition of the original problem facilitates band structure classification and analysis, enabling the identification of the different bands among the band structure in various subtasks. This advancement paves the way for innovative designs in nanophotonics.

physics.optics↗

arXiv2025

Categorize coalescing quasi-normal modes through far-field scattering patterns

Resonances in the form of quasi-normal modes (QNMs) for open scattering systems can be generally identified in the far field through peaks of scattering spectra (\textit{e.g.} cross sections of scattering, extinction and absorption). Nevertheless, when the resonant frequencies of different QNMs are spectrally overlapped or sufficiently close, the scattering peaks merge and it then becomes extremely challenging to reveal the mode constituents underlying the scattering spectra solely in the far field. Here in this work, we study open systems scattering electromagnetic plane waves, and reveal that spectrally close or even overlapped QNMs can be selectively excited through tuning incident directions and polarizations. Such a far-field technique can be further applied to categorize the nature of degenerate QNMs sharing identical complex eigenfrequencies (coalescent eigenvalues): at effectively Hermitian degeneracies (conical or Dirac points), the eigenvectors are not coalescent and thus the QNMs can still be selectively excited, producing distinct scattering patterns; while for non-Hermitian degeneracies (exceptional points), eigenvectors also coalesce and thus selective QNM excitation does not exist, leading to invariant scattering patterns. Our technique sheds new light on the borderlands of Mie theory, QNMs, non-Hermitian photonics and singular optics, which can empower new explorations and applications and cross-fertilize all those disciplines.

physics.optics↗

arXiv2025

Quasi-normal modes empowered coherent control of electromagnetic interactions

Quasi-normal modes (QNMs) and coherent control of light-matter interactions (through synchronized multiple coherent incident waves) are profound and pervasive concepts in and beyond photonics, making accessible photonic manipulations with extreme precision and efficiency. Though each has been playing essential roles in its own, these two sweeping concepts remain largely segregated with little interactions, blocking vast opportunities of cross-fertilization to explore. Here we unify both concepts into a novel framework of coherent control for light interacting with open photonic systems. From the QNM perspective, scattered waves are superimposed radiations from all QNMs excited, and thus coherent controls can be mapped into another problem of QNM excitation manipulations. Within our framework, all incident properties (amplitudes, phases and polarizations) of waves from different directions can be exploited simultaneously in a synchronous manner, facilitating independent manipulations of each QNM and thus unlocking enormous flexibilities for coherent controls of both scattering intensity and polarization: (i) A visible structure under a single incident wave can be made invisible through shining extra waves; (ii) Along a direction where QNMs' radiation polarizations are identical, scattering along this direction can be fully eliminated, thus generalizing Kerker effects from a distinct QNM perspective; (iii) Along a direction of distinct QNM radiation polarizations, arbitrary scattering polarizations can be obtained. Given the ubiquity and profundity of QNMs and coherent control in almost all branches of wave physics, our framework and its underlying principles will inspire further fundamental explorations and practical applications beyond photonics, opening new opportunities for various forms of wave-matter interactions.

physics.optics↗

arXiv2025

Exclusive Generation of Single-Atom Sulfur for Ultrahigh Quality Monolayer MoS$_2$ Growth

Preparation of high-quality two-dimensional (2D) transition metal dichalcogenides (TMDCs) is the precondition for realizing their applications. However, the synthesized 2D TMDCs (e.g., MoS$_2$) crystals suffer from low quality due to the massive defects formed during the growth. Here, we report the single-atom sulfur (S1) as a highly reactive sulfur species to grow ultrahigh-quality monolayer MoS$_2$. Derived from battery waste, the sulfurized polyacrylonitrile (SPAN) is found to be exclusive and efficient in releasing S1. The monolayer MoS$_2$ prepared by SPAN exhibits an ultralow defect density of $~7\times 10^{12}$ cm$^{-2}$ and the narrowest photoluminescence (PL) emission peak with full-width at half-maximum of ~47.11 meV at room temperature. Moreover, the statistical resonance Raman and low-temperature PL results further verify the significantly lower defect density and higher optical quality of SPAN-grown MoS$_2$ than the conventional S-powder-grown samples. This work provides an effective approach for preparing ultrahigh-quality 2D single crystals, facilitating their industrial applications.

cond-mat.mtrl-sci↗

arXiv2025

Cu Intercalation-stabilized 1T'-MoS2 with Electrical Insulating Behavior

The intercalated two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted much attention for their designable structure and novel properties. Among this family, host materials with low symmetry such as 1T' phase TMDCs are particularly interesting because of their potentials in inducing unconventional phenomena. However, such systems typically have low quality and poor stability, hindering further study in the structure-property relationship and applications. In this work, we intercalated Cu into 1T' MoS2 with high crystallinity and high thermal stability up to ~300 oC. We identified the distribution and arrangement of Cu intercalators for the first time, and the results show that Cu occupy partial of the tetrahedral interstices aligned with Mo sites. The obtained Cu-1T' MoS2 exhibits an insulating hopping transport behavior with a large temperature coefficient of resistance reaching -4 ~ -2 % K-1. This work broadens the artificial intercalated structure library and promotes structure design and property modulation of layered materials.

cond-mat.mtrl-sci↗

arXiv2025

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of "thought" -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs' reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to "think" with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI's o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions.

cs.AI↗

arXiv2024

Galaxy-Point Spread Function correlations as a probe of weak-lensing systematics with UNIONS data

Weak gravitational lensing requires precise measurements of galaxy shapes and therefore an accurate knowledge of the PSF model. The latter can be a source of systematics that affect the shear two-point correlation function. A key stake of weak lensing analysis is to forecast the systematics due to the PSF. Correlation functions of galaxies and the PSF, the so-called $\rho$- and $\tau$-statistics, are used to evaluate the level of systematics coming from the PSF model and PSF corrections, and contributing to the two-point correlation function used to perform cosmological inference. Our goal is to introduce a fast and simple method to estimate this level of systematics and assess its agreement with state-of-the-art approaches. We introduce a new way to estimate the covariance matrix of the $\tau$-statistics using analytical expressions. The covariance allows us to estimate parameters directly related to the level of systematics associated with the PSF and provides us with a tool to validate the PSF model used in a weak-lensing analysis. We apply those methods to data from the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We show that the semi-analytical covariance yields comparable results than using covariances obtained from simulations or jackknife resampling. It requires less computation time and is therefore well suited for rapid comparison of the systematic level obtained from different catalogs. We also show how one can break degeneracies between parameters with a redefinition of the $\tau$-statistics. The methods developed in this work will be useful tools in the analysis of current weak-lensing data but also of Stage IV surveys such as Euclid, LSST or Roman. They provide fast and accurate diagnostics on PSF systematics that are crucial to understand in the context of cosmic shear studies.

astro-ph.CO↗

arXiv2024

Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting

Encoding time series into tokens and using language models for processing has been shown to substantially augment the models' ability to generalize to unseen tasks. However, existing language models for time series forecasting encounter several obstacles, including aliasing distortion and prolonged inference times, primarily due to the limitations of quantization processes and the computational demands of large models. This paper introduces Apollo-Forecast, a novel framework that tackles these challenges with two key innovations: the Anti-Aliasing Quantization Module (AAQM) and the Race Decoding (RD) technique. AAQM adeptly encodes sequences into tokens while mitigating high-frequency noise in the original signals, thus enhancing both signal fidelity and overall quantization efficiency. RD employs a draft model to enable parallel processing and results integration, which markedly accelerates the inference speed for long-term predictions, particularly in large-scale models. Extensive experiments on various real-world datasets show that Apollo-Forecast outperforms state-of-the-art methods by 35.41\% and 18.99\% in WQL and MASE metrics, respectively, in zero-shot scenarios. Furthermore, our method achieves a 1.9X-2.7X acceleration in inference speed over baseline methods.

cs.LG↗

arXiv2024

Numerical approximation of slowlingly varying envelope in finite element electromagnetism: a ray-wave method of modeling multi-scale devices

In this work we propose an efficient and accurate multi-scale optical simulation algorithm by applying a numerical version of slowly varying envelope approximation in FEM. Specifically, we employ the fast iterative method to quickly compute the phase distribution of the electric field within the computational domain and construct a novel multi-scale basis function that combines the conventional polynomial basis function together with numerically resolved phase information of optical waves. Utilizing this multi-scale basis function, the finite element method can significantly reduce the degrees of freedom required for the solution while maintaining computational accuracy, thereby improving computational efficiency. Without loss of generality, we illustrate our approach via simulating the examples of lens groups and gradient-index lenses, accompanied with performance benchmark against the standard finite element method. The results demonstrate that the proposed method achieves consistent results with the standard finite element method but with a computational speed improved by an order of magnitude.

physics.optics↗

arXiv2024

Efficient optimization of plasma surface high harmonic generation by an improved Bayesian strategy

Plasma surface high-order harmonics generation (SHHG) driven by intense laser pulses on plasma targets enables a high-quality extreme ultraviolet source with high pulse energy and outstanding spatiotemporal coherence. Optimizing the performance of SHHG is important for its applications in single-shot imaging and absorption spectroscopy. In this work, we demonstrate the optimization of laser-driven SHHG by an improved Bayesian strategy in conjunction with particle-in-cell simulations. A traditional Bayesian algorithm is first employed to optimize the SHHG intensity in a two-dimensional space of parameter. Then an improved Bayesian strategy, using the Latin hypercube sampling technique and a dynamic acquisition strategy, is developed to overcome the curse of dimensionality and the risk of local optima in a high-dimensional space optimization. The improved Bayesian optimization approach is efficient and robust in three-dimensionally optimizing the harmonic ellipticity, paving the way for the upcoming SHHG experiments with a considerable repetition rate.

physics.plasm-ph↗

arXiv2024

Efficient finite element modeling of photonic modal analysis augmented by combined symmetry

In this work, we present an efficient numerical implementation of the finite element method for modal analysis that leverages various symmetry operations, including spatial symmetry in point groups and space-time symmetry in pseudo-Hermiticity systems. We provide a formal and rigorous treatment, specifically deriving the boundary constraint conditions corresponding to symmetry constraints. Without loss of generality, we illustrate our approach via computing the modes of optical waveguides with complex cross-sections, accompanied with performance benchmark against the standard finite element method. The obtained results demonstrate excellent agreement between our method and standard FEM with significantly improved computational efficiency. Specifically, the calculation speed increased by a factor of $23$ in the hollow-core fiber. Furthermore, our method directly classifies and computes the modes based on symmetry, facilitating the modal analysis of complex waveguides.

physics.optics↗

arXiv2024

Analyzing Multi-Head Attention on Trojan BERT Models

This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

cs.CL↗

arXiv2024

Hierarchical Large Language Models in Cloud Edge End Architecture for Heterogeneous Robot Cluster Control

Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi agent strategy generation and motion control, we propose an innovative architecture that employs the concept of a cloud edge end hierarchical structure. By leveraging multiple large language models with distinct areas of expertise, we can efficiently generate strategies and perform task decomposition. Introducing the cosine similarity approach,aligning task decomposition instructions with robot task sequences at the vector level, we can identify subtasks with incomplete task decomposition and iterate on them multiple times to ultimately generate executable machine task sequences.The robot is guided through these task sequences to complete tasks of higher complexity. With this architecture, we implement the process of natural language control of robots to perform complex tasks, and successfully address the challenge of multi agent execution of open tasks in open scenarios and the problem of task decomposition.

cs.RO↗

arXiv2024

MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment. Traditional sequential methods and graph-based models have been applied in stock movement prediction, but they have limitations in capturing the multifaceted and temporal influences in stock price movements. To address these challenges, the Multi-relational Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a discrete dynamic graph to comprehensively capture multifaceted relations among stocks and their evolution over time. The representation generated from the graph offers a complete perspective on the interrelationships among stocks and associated entities. Additionally, the power of the Transformer structure is leveraged to encode the temporal evolution of multiplex relations, providing a dynamic and effective approach to predicting stock investment. Further, our proposed MDGNN framework achieves the best performance in public datasets compared with state-of-the-art (SOTA) stock investment methods.

q-fin.ST↗