arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 109 records · Page 6Linked to original sources

arXiv2025

Transitions from Composite Fermi Liquid to Moore-Read States in Weyl Semimetals

Weyl semimetals represent a significant class of topological gapless materials in three dimensions and have been shown to exhibit three-dimensional quantum Hall effect. However, existing research mainly focuses on scenarios without interactions. Recent studies suggest that the fractional quantum Hall effect can arise in a Weyl semimetal with a one-third filled Landau level under a magnetic field. However, it remains unclear whether more exotic states, such as composite Fermi liquid and MooreRead states, can appear at half filling. Here we surprisingly find the existence of composite Fermi liquid, Moore-Read states and charge density waves in the same Weyl-orbit-based Landau level of a Weyl semimetal and their transitions induced by varying the distance between Weyl points. We attribute these transitions to a significant change in the single-particle wave functions of the Landau level as the distance between Weyl points is varied. Finally, we demonstrate that a transition from composite Fermi liquid to Moore-Read states can be induced by tuning the direction of a magnetic field, a process that is more experimentally accessible.

cond-mat.mes-hall↗

arXiv2025

Three-Dimensional Quantum Hall Effect in Topological Amorphous Metals

Weyl semimetals have been theoretically predicted to become topological metals with anomalous Hall conductivity in amorphous systems. However, measuring the anomalous Hall conductivity in realistic materials, particularly those with multiple pairs of Weyl points, is a significant challenge. If a system respects time-reversal symmetry, then the anomalous Hall conductivity even vanishes. As such, it remains an open question how to probe the Weyl band like topology in amorphous materials. Here, we theoretically demonstrate that, under magnetic fields, a topological metal slab in amorphous systems exhibits three-dimensional quantum Hall effect, even in time-reversal invariant systems, thereby providing a feasible approach to exploring Weyl band like topology in amorphous materials. We unveil the topological origin of the quantized Hall conductance by calculating the Bott index. The index is carried by broadened Landau levels with bulk states spatially localized except at critical transition energies.The topological property also results in edge states localized at distinct hinges on two opposite surfaces.

cond-mat.mes-hall↗

arXiv2025

Urban Computing in the Era of Large Language Models

Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and contextual understanding. The advent of Large Language Models (LLMs) offers transformative potential in this domain. This survey explores the intersection of LLMs and urban computing, emphasizing the impact of LLMs in processing and analyzing urban data, enhancing decision-making, and fostering citizen engagement. We provide a concise overview of the evolution and core technologies of LLMs. Additionally, we survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring, summarizing essential tasks and prior works in various urban contexts, while highlighting LLMs' functional roles and implementation patterns. Building on this, we propose potential LLM-based solutions to address unresolved challenges. To facilitate in-depth research, we compile a list of available datasets and tools applicable to diverse urban scenarios. Finally, we discuss the limitations of current approaches and outline future directions for advancing LLMs in urban computing.

cs.CY↗

arXiv2025

Probing Gravitational Dark Matter with Ultra-high Frequency Gravitational Waves

The evidence for the existence of dark matter (DM) is compelling, yet its nature remains elusive. A minimal scenario involves DM interacting solely through gravity. However, the detection would be extremely challenging. In the early Universe, such DM can be unavoidably generated via annihilation of particles in the standard model (SM) thermal plasma. It is known that the SM thermal plasma also produces gravitational waves (GWs). In this study, we establish a simple connection between the amplitude of thermal GWs and the properties of pure gravitational DM. Notably, future GW experiments in the ultra-high frequency regime have the potential to shed light on the mass and spin of pure gravitational DM.

hep-ph↗

arXiv2025

Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation

Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code repetition, which refers to the model's tendency to generate structurally redundant code, resulting in inefficiencies and reduced readability. To address this, we conduct the first empirical study to investigate the prevalence and nature of repetition across 19 state-of-the-art code LLMs using three widely-used benchmarks. Our study includes both quantitative and qualitative analyses, revealing that repetition is pervasive and manifests at various granularities and extents, including character, statement, and block levels. We further summarize a taxonomy of 20 repetition patterns. Building on our findings, we propose DeRep, a rule-based technique designed to detect and mitigate repetition in generated code. We evaluate DeRep using both open-source benchmarks and in an industrial setting. Our results demonstrate that DeRep significantly outperforms baselines in reducing repetition (with an average improvements of 91.3%, 93.5%, and 79.9% in rep-3, rep-line, and sim-line metrics) and enhancing code quality (with a Pass@1 increase of 208.3% over greedy search). Furthermore, integrating DeRep improves the performance of existing repetition mitigation methods, with Pass@1 improvements ranging from 53.7% to 215.7%.

cs.SE↗

arXiv2025

Audio-Visual Speaker Tracking: Progress, Challenges, and Future Directions

Audio-visual speaker tracking has drawn increasing attention over the past few years due to its academic values and wide applications. Audio and visual modalities can provide complementary information for localization and tracking. With audio and visual information, the Bayesian-based filter and deep learning-based methods can solve the problem of data association, audio-visual fusion and track management. In this paper, we conduct a comprehensive overview of audio-visual speaker tracking. To our knowledge, this is the first extensive survey over the past five years. We introduce the family of Bayesian filters and summarize the methods for obtaining audio-visual measurements. In addition, the existing trackers and their performance on the AV16.3 dataset are summarized. In the past few years, deep learning techniques have thrived, which also boost the development of audio-visual speaker tracking. The influence of deep learning techniques in terms of measurement extraction and state estimation is also discussed. Finally, we discuss the connections between audio-visual speaker tracking and other areas such as speech separation and distributed speaker tracking.

cs.MM↗

arXiv2025

Unsupervised 3D Point Cloud Completion via Multi-view Adversarial Learning

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The tasks of self-supervised and weakly-supervised point cloud completion involve reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current methods either rely on multiple views of partial observations for supervision or overlook the intrinsic geometric similarity that can be identified and utilized from the given partial point clouds. In this paper, we propose MAL-UPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures. Our MAL-UPC does not require any 3D complete supervision and only necessitates single-view partial observations in the training set. Specifically, we first introduce a Pattern Retrieval Network to retrieve similar position and curvature patterns between the partial input and the predicted shape, then leverage these similarities to densify and refine the reconstructed results. Additionally, we render the reconstructed complete shape into multi-view depth maps and design an adversarial learning module to learn the geometry of the target shape from category-specific single-view depth images of the partial point clouds in the training set. To achieve anisotropic rendering, we design a density-aware radius estimation algorithm to improve the quality of the rendered images. Our MAL-UPC outperforms current state-of-the-art self-supervised methods and even some unpaired approaches. We will make the source code publicly available at https://github.com/ltwu6/malspc

cs.CV↗

arXiv2025

Spin-chirality-driven second-harmonic generation in two-dimensional magnet CrSBr

The interplay between magnetism and light can create abundant optical phenomena. Here, we demonstrate the emergence of an unconventional magnetization-induced second-harmonic generation (MSHG) stemming from vector spin chirality, denoted as chiral second-harmonic generation (SHG). Taking the antiferromagnetic (AFM) CrSBr bilayer as a prototype, we theoretically show that, via spin canting, the chiral SHG can be continuously tuned from zero to a value one order of magnitude larger than its intrinsic MSHG. Chiral SHG is found to be proportional to spin chirality and spin-canting-induced electric polarization, while intrinsic MSHG is proportional to the Néel vector, demonstrating their different physical mechanisms. Additionally, we reveal a unique interference effect between these two types of MSHG under the reversal of spin-canting direction, generating a giant modulation of SHG signals. Our work not only uncovers a unique SHG with exceptional tunability but also promotes the applications of AFM optical devices and magnetoelectric detection techniques.

cond-mat.mtrl-sci↗

arXiv2025

AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction

The integration of pathologic images and genomic data for survival analysis has gained increasing attention with advances in multimodal learning. However, current methods often ignore biological characteristics, such as heterogeneity and sparsity, both within and across modalities, ultimately limiting their adaptability to clinical practice. To address these challenges, we propose AdaMHF: Adaptive Multimodal Hierarchical Fusion, a framework designed for efficient, comprehensive, and tailored feature extraction and fusion. AdaMHF is specifically adapted to the uniqueness of medical data, enabling accurate predictions with minimal resource consumption, even under challenging scenarios with missing modalities. Initially, AdaMHF employs an experts expansion and residual structure to activate specialized experts for extracting heterogeneous and sparse features. Extracted tokens undergo refinement via selection and aggregation, reducing the weight of non-dominant features while preserving comprehensive information. Subsequently, the encoded features are hierarchically fused, allowing multi-grained interactions across modalities to be captured. Furthermore, we introduce a survival prediction benchmark designed to resolve scenarios with missing modalities, mirroring real-world clinical conditions. Extensive experiments on TCGA datasets demonstrate that AdaMHF surpasses current state-of-the-art (SOTA) methods, showcasing exceptional performance in both complete and incomplete modality settings.

cs.CV↗

arXiv2025

TC-GS: Tri-plane based compression for 3D Gaussian Splatting

Recently, 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for novel view synthesis, providing high fidelity and rapid rendering speed. However, the substantial data volume of 3DGS and its attributes impede its practical utility, requiring compression techniques for reducing memory cost. Nevertheless, the unorganized shape of 3DGS leads to difficulties in compression. To formulate unstructured attributes into normative distribution, we propose a well-structured tri-plane to encode Gaussian attributes, leveraging the distribution of attributes for compression. To exploit the correlations among adjacent Gaussians, K-Nearest Neighbors (KNN) is used when decoding Gaussian distribution from the Tri-plane. We also introduce Gaussian position information as a prior of the position-sensitive decoder. Additionally, we incorporate an adaptive wavelet loss, aiming to focus on the high-frequency details as iterations increase. Our approach has achieved results that are comparable to or surpass that of SOTA 3D Gaussians Splatting compression work in extensive experiments across multiple datasets. The codes are released at https://github.com/timwang2001/TC-GS.

cs.CV↗

arXiv2025

Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://github.com/HuYongting/WGLIN.

eess.IV↗

arXiv2025

The pseudo-analytical density solution to parameterized Fokker-Planck equations via deep learning

Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density functions are only attainable in specific cases. To speed up the solving process of parameterized FPEs with several system parameters, we introduce a deep learning-based method to obtain the pseudo-analytical density (PAD). Unlike previous numerical methodologies that necessitate solving the FPE separately for each set of system parameters, the PAD simultaneously addresses all the FPEs within a predefined continuous range of system parameters during a single training phase. The approach utilizes a Gaussian mixture distribution (GMD) to represent the stationary probability density, the solution to the FPE. By leveraging a deep residual network, each system parameter configuration is mapped to the parameters of the GMD, ensuring that the weights, means, and variances of the Gaussian components adaptively align with the corresponding true density functions. A grid-free algorithm is further developed to effectively train the residual network, resulting in a feasible PAD obeying necessary normalization and boundary conditions. Extensive numerical studies validate the accuracy and efficiency of our method, promising significant acceleration in the response analysis of multi-parameter, multi-dimensional stochastic nonlinear systems.

physics.comp-ph↗

arXiv2025

Non-Markovian dynamics: the memory-dependent probability density evolution equations

This paper aims to investigate the non-Markovian dynamics. The governing equations are derived for the probability density functions (PDFs) of non-Markovian stochastic responses to Langevin equation excited by combined fractional Gaussian noise (FGN) and Gaussian white noise (GWN). The main difficulty here is that the Langevin equation excited by FGN cannot be augmented by a filter excited by GWN, leading to the inapplicability of Itô stochastic calculus theory. Thus, in the present work, based on the fractional Wick Itô Skorohod integral and rough path theory, a new non-Markovian probability density evolution method is established to derive theoretically the memory-dependent probability density evolution equation (PDEEs) for the PDFs of non-Markovian stochastic responses to Langevin equation excited by combined FGN and GWN, which is a breakthrough to stochastic dynamics. Then, we extend an efficient algorithm, the local discontinuous Galerkin method, to numerically solve the memory-dependent PDEEs. Remarkably, this proposed method attains a higher accuracy compared to the prevalent methods such as finite difference, path integral (PI) and Monte Carlo methods, and boasts a broader applicability than the PI method, which fails to solve the memory-dependent PDEEs. Finally, several numerical examples are illustrated to verify the proposed scheme.

math.PR↗

arXiv2025

Direct detonation initiation and propagation in methane/air mixtures containing coal particles

The mechanisms of direct detonation initiation (DDI) in methane/air mixtures containing coal particles are investigated through simulations conducted using the Eulerian-Lagrangian method in a two-dimensional configuration. Methane-air combustion is modelled with a detailed chemical mechanism involving 36 species and 219 reactions, while coal particle surface reactions are computed using a kinetic/diffusion-limited rate model. The findings indicate that shock waves generated from the hotspot can initiate detonation through heterogeneous and homogeneous reactions, with contributions from both methane and particle combustion. Coal particle surface reactions provide the dominant energy for detonation initiation, whereas gas-phase reactions enhance detonation stability during propagation. The difficulty of achieving detonation initiation exhibits a non-linear dependence on particle concentrations and gas equivalence ratios. An optimal particle concentration and gas equivalence ratio for successful DDI is identified. Smaller particles are found to facilitate detonation initiation more effectively. Key processes in DDI of two-phase mixtures are identified, including particle heating, methane combustion, and particle burning. Three DDI modes, critical, stable, and cell-free, are observed based on particle concentration. As particle concentration increases, the temperatures of both particles and gas become close, initially rising and then decreasing with further increases in particle concentration. Additionally, the introduction of coal particles gives rise to two distinct stages in gas-phase reactions.

physics.chem-ph↗

arXiv2025

Magnetorotons in Moiré Fractional Chern Insulators

The discovery of fractional Chern insulators (FCIs) unlocks exciting opportunities to explore emergent physical excitations arising from topological and geometric effects in novel phases of quantum matter. Here we investigate the intraband neutral excitations, namely magnetorotons, in moiré FCIs within twisted $\rm{MoTe}_2$ by applying the Girvin, MacDonald, and Platzman (GMP) ansatz together with the method of dynamical geometric response. We reveal the universal existence of the finite-momentum magnetorotons in moiré FCIs and predict their characteristic scales. Furthermore, we explore the geometric nature of magnetorotons in the long-wavelength limit, identifying their gapped chiral nature with angular momentum-2, which originates from the momentum-space incompressibility of FCIs. Utilizing the excellent tunability of moiré systems, we extend our analysis to other incompressible phases and uncover the dynamical properties of geometric excitations influenced by quantum phase transitions. Finally, we provide experimental proposals for detecting and advancing the study of intraband neutral excitations in moiré FCIs.

cond-mat.str-el↗

arXiv2025

Revisiting Classification Taxonomy for Grammatical Errors

Grammatical error classification plays a crucial role in language learning systems, but existing classification taxonomies often lack rigorous validation, leading to inconsistencies and unreliable feedback. In this paper, we revisit previous classification taxonomies for grammatical errors by introducing a systematic and qualitative evaluation framework. Our approach examines four aspects of a taxonomy, i.e., exclusivity, coverage, balance, and usability. Then, we construct a high-quality grammatical error classification dataset annotated with multiple classification taxonomies and evaluate them grounding on our proposed evaluation framework. Our experiments reveal the drawbacks of existing taxonomies. Our contributions aim to improve the precision and effectiveness of error analysis, providing more understandable and actionable feedback for language learners.

cs.CL↗

arXiv2025

On the Effectiveness of Large Language Models in Domain-Specific Code Generation

Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum of open-domain knowledge. Besides, their evaluation revolves around open-domain benchmarks like HumanEval, which primarily consist of programming contests. Therefore, it is hard to fully characterize the intricacies and challenges associated with particular domains (e.g., web, game, and math). In this paper, we conduct an in-depth study of the LLMs in domain-specific code generation. Our results demonstrate that LLMs exhibit sub-optimal performance in generating domain-specific code, due to their limited proficiency in utilizing domain-specific libraries. We further observe that incorporating API knowledge as prompts can empower LLMs to generate more professional code. Based on these findings, we further investigate how to effectively incorporate API knowledge into the code generation process. We experiment with three strategies for incorporating domain knowledge, namely, external knowledge inquirer, chain-of-thought prompting, and chain-of-thought fine-tuning. We refer to these strategies as a new code generation approach called DomCoder. Experimental results show that all strategies of DomCoder lead to improvement in the effectiveness of domain-specific code generation under certain settings.

cs.SE↗

arXiv2025

Nonlinearity-induced Fractional Thouless Pumping of Solitons

Recent studies have shown that a soliton can be {\it fractionally} transported by slowly varying a system parameter over one period in a nonlinear system. This phenomenon is attributed to the nontrivial topology of the corresponding energy bands of a linear Hamiltonian. Here we find the occurrence of fractional Thouless pumping of solitons in a nonlinear off-diagonal Aubry-André-Harper model. Surprisingly, this happens despite the fact that all the energy bands of the linear Hamiltonian are topologically trivial, indicating that nonlinearity can induce fractional Thouless pumping of solitons. Specifically, our results show that a soliton can be pumped across one unit cell over one, two, three or four pump periods, implying an average displacement of $1$, $1/2$, $1/3$ or $1/4$ unit cells per cycle, respectively. We attribute these behaviors to changes in on-site potentials induced by a soliton solution, leading to the nontrivial topology for the modified linear Hamiltonian. Given that our model relies solely on varying nearest-neighbor hoppings, it is readily implementable on existing state-of-the-art photonic platforms.

nlin.PS↗