arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2025

Production of Iodine Isotopes via Ultra-intense Laser Driven Photonuclear Reactions

The investigation and production of proton-rich iodine isotopes predominantly rely on conventional accelerator-based methods, typically requiring prolonged irradiation periods to measure or achieve quantifiable yields for isotopic isolation. Bremsstrahlung radiation sources generated by high-power laser-plasma-accelerated electron beams with ultrahigh charge (tens of nanocoulombs) bombarding high-Z targets demonstrate extraordinary photon flux characteristics. An electron beam with a total charge of approximately 47.7 nC (E$_e$ $\gt$ 10.4 MeV) was generated in our experiment by focusing a ultra-intense laser pulse onto a deuterium gas jet. Laser-driven bremsstrahlung was employed to induce $^{127}I$$(γ,xn)$ ($x$ = 1,3,4,6-8), and the product yields and the corresponding flux-weighted average cross sections are reported. Our results demonstrate production of medical isotopes, with average yields of $^{124}$I and $^{123}$I at approximately $9.83\pm0.45\times10^{5}$/shot and $2.81\pm0.11\times10^{5}$/shot, respectively. This method, utilizing high-power lasers to generate bremsstrahlung radiation, shows significant potential for medical applications and opens new avenues for studying photonuclear processes in astrophysical contexts.

nucl-ex↗

arXiv2025

Light-Induced Giant Enhancement of the Nonlinear Hall Effect in Two-Dimensional Electron Gases at KTaO3 (111) Interfaces

The nonlinear Hall effect (NLHE), an emergent phenomenon in noncentrosymmetric systems, enables the generation of a transverse voltage without an external magnetic field through a second-order electrical response. However, achieving a sizable NLHE signal remains a critical challenge for its application in frequency-doubling and rectifying devices. Here, we report a light-induced giant enhancement of the NLHE in the two-dimensional electron gas (2DEG) at the CaZrO3/KTaO3 (111) interface. Under light illumination, the second harmonic Hall voltage (V2ω y) increases substantially and undergoes a sign reversal. Correspondingly,the second-order transverse conductivity increases by nearly five orders of magnitude, reaching 2.4 um V-1 omega-1, while also reversing its sign. Scaling analysis indicates that skew scattering is the dominant mechanism underlying the NLHE and is highly tunable via optical gating. Photoexcitation pumps electrons from in-gap states into the higher-lying Ta 5d conduction band, generating high-mobility photocarriers that significantly increase the cubic transport scattering time, thereby driving a dramatic enhancement of σ(2) yxx. First-principles calculations further reveal that the Berry curvature distribution on the Fermi surface strongly depends on band filling. As the Fermi level approaches a band crossing in the Ta 5d subband near the M point, the Berry curvature triple undergoes a sign change, accounting for the experimentally observed sign reversal of the nonlinear Hall response. Our work offers a new strategy to optically boost and tune the nonlinear Hall effect in oxide 2DEG systems, paving the way for applications in light-controlled rectification and nonlinear electronic devices.

cond-mat.mes-hall↗

arXiv2025

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.

cs.CV↗

arXiv2025

Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects

In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1\% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.

cs.CV↗

arXiv2025

DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design

We present DreamPoster, a Text-to-Image generation framework that intelligently synthesizes high-quality posters from user-provided images and text prompts while maintaining content fidelity and supporting flexible resolution and layout outputs. Specifically, DreamPoster is built upon our T2I model, Seedream3.0 to uniformly process different poster generating types. For dataset construction, we propose a systematic data annotation pipeline that precisely annotates textual content and typographic hierarchy information within poster images, while employing comprehensive methodologies to construct paired datasets comprising source materials (e.g., raw graphics/text) and their corresponding final poster outputs. Additionally, we implement a progressive training strategy that enables the model to hierarchically acquire multi-task generation capabilities while maintaining high-quality generation. Evaluations on our testing benchmarks demonstrate DreamPoster's superiority over existing methods, achieving a high usability rate of 88.55\%, compared to GPT-4o (47.56\%) and SeedEdit3.0 (25.96\%). DreamPoster will be online in Jimeng and other Bytedance Apps.

cs.CV↗

arXiv2025

A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention

Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but lack accuracy for transition states and fine conformational details critical in many chemical and biological processes. Quantum Mechanics (QM) methods are highly accurate but computationally infeasible for large-scale or long-time simulations. AI-based force fields (AIFFs) aim to achieve QM-level accuracy with efficiency but struggle to balance many-body modeling complexity, accuracy, and speed, often constrained by limited training data and insufficient validation for generalizability. To overcome these challenges, we introduce LiTEN, a novel equivariant neural network with Tensorized Quadrangle Attention (TQA). TQA efficiently models three- and four-body interactions with linear complexity by reparameterizing high-order tensor features via vector operations, avoiding costly spherical harmonics. Building on LiTEN, LiTEN-FF is a robust AIFF foundation model, pre-trained on the extensive nablaDFT dataset for broad chemical generalization and fine-tuned on SPICE for accurate solvated system simulations. LiTEN achieves state-of-the-art (SOTA) performance across most evaluation subsets of rMD17, MD22, and Chignolin, outperforming leading models such as MACE, NequIP, and EquiFormer. LiTEN-FF enables the most comprehensive suite of downstream biomolecular modeling tasks to date, including QM-level conformer searches, geometry optimization, and free energy surface construction, while offering 10x faster inference than MACE-OFF for large biomolecules (~1000 atoms). In summary, we present a physically grounded, highly efficient framework that advances complex biomolecular modeling, providing a versatile foundation for drug discovery and related applications.

physics.chem-ph↗

arXiv2025

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies

The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision positioning, intelligent aircraft-to-everything communication, dynamic airspace management, and UAV operational services. UTICN introduces key technologies such as integrated sensing and communication (ISAC), passive and active positioning, intelligent machine communication, swarm coordination, and control-data decoupled management frameworks. We demonstrate UTICN's feasibility through two use cases, i.e., a city-level LAE management platform and a multi-frequency collaborative ISAC system. This work provides a fundamental reference for building a unified operational foundation and airspace management architecture for the LAE.

eess.SY↗

arXiv2025

Evidence of Ferroelectricity in an Antiferromagnetic Vanadium Trichloride Monolayer

A reduced dimensionality of multiferroic materials is highly desired for device miniaturization, but the coexistence of ferroelectricity and magnetism at the two-dimensional limit is yet to be conclusively demonstrated. Here, we used a NbSe2 substrate to break both the C3 rotational and inversion symmetries in monolayer VCl3 and thus introduced exceptional in-plane ferroelectricity into a two-dimensional magnet. Scanning tunneling spectroscopy directly visualized ferroelectric domains and manipulated their domain boundaries in monolayer VCl3, where coexisting antiferromagnetic order with canted magnetic moments was verified by vibrating sample magnetometer measurements. Our density functional theory calculations highlight the crucial role that highly directional interfacial Cl-Se interactions play in breaking the symmetries and thus in introducing in-plane ferroelectricity, which was further verified by examining an ML-VCl3/graphene sample. Our work demonstrates an approach to manipulate the ferroelectric states in monolayered magnets through van der Waals interfacial interactions.

cond-mat.mtrl-sci↗

arXiv2025

Functional renormalization group study of the quark-meson model with omega and rho vector mesons

We employ the functional renormalization group flow equations to investigate the phase structure of the two-flavor quark-meson model in the presence of a finite isospin chemical potential, incorporating interactions with omega and rho vector mesons. For comparison, we also compute the phase diagram in the chiral limit using the mean-field approximation. Our findings demonstrate that omega and rho mesons affect the phase structure in markedly distinct ways, and the introduction of an isospin chemical potential leads to significant modifications in the phase boundaries and critical region. Increasing the isospin chemical potential lowers the tricritical points temperature, and tends to suppress the unphysical ``back-bending" of the FRG phase boundary at low temperature.

hep-ph↗

arXiv2025

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation

Synthesizing high-quality dynamic medical videos remains a significant challenge due to the need for modeling both spatial consistency and temporal dynamics. Existing Transformer-based approaches face critical limitations, including insufficient channel interactions, high computational complexity from self-attention, and coarse denoising guidance from timestep embeddings when handling varying noise levels. In this work, we propose FEAT, a full-dimensional efficient attention Transformer, which addresses these issues through three key innovations: (1) a unified paradigm with sequential spatial-temporal-channel attention mechanisms to capture global dependencies across all dimensions, (2) a linear-complexity design for attention mechanisms in each dimension, utilizing weighted key-value attention and global channel attention, and (3) a residual value guidance module that provides fine-grained pixel-level guidance to adapt to different noise levels. We evaluate FEAT on standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only 23\% of the parameters of the state-of-the-art model Endora, achieves comparable or even superior performance. Furthermore, FEAT-L surpasses all comparison methods across multiple datasets, showcasing both superior effectiveness and scalability. Code is available at https://github.com/Yaziwel/FEAT.

cs.CV↗

arXiv2025

Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning

Modern diffusion MRI sequences commonly acquire a large number of volumes with diffusion sensitization gradients of differing strengths or directions. Such sequences rely on echo-planar imaging (EPI) to achieve reasonable scan duration. However, EPI is vulnerable to off-resonance effects, leading to tissue susceptibility and eddy-current induced distortions. The latter is particularly problematic because it causes misalignment between volumes, disrupting downstream modelling and analysis. The essential correction of eddy distortions is typically done post-acquisition, with image registration. However, this is non-trivial because correspondence between volumes can be severely disrupted due to volume-specific signal attenuations induced by varying directions and strengths of the applied gradients. This challenge has been successfully addressed by the popular FSL~Eddy tool but at considerable computational cost. We propose an alternative approach, leveraging recent advances in image processing enabled by deep learning (DL). It consists of two convolutional neural networks: 1) An image translator to restore correspondence between images; 2) A registration model to align the translated images. Results demonstrate comparable distortion estimates to FSL~Eddy, while requiring only modest training sample sizes. This work, to the best of our knowledge, is the first to tackle this problem with deep learning. Together with recently developed DL-based susceptibility correction techniques, they pave the way for real-time preprocessing of diffusion MRI, facilitating its wider uptake in the clinic.

eess.IV↗

arXiv2025

The nucleon properties in finite temperature and density with Gaussian fluctuations

We investigate the properties of nucleons at finite temperature and density using a two-flavor quark meson model with Gaussian fluctuations that extend beyond the mean-field approximation. Our findings suggest that Gaussian fluctuations lead to a non-monotonic behavior of the nucleon mass as a function of temperature and density, which may play an important role in the study of the hadronization process of relativistic heavy-ion collisions. Moreover, we observe an increase in the nucleon radius due to Gaussian fluctuations, suggesting an effective repulsive force akin to the Casimir effect, as observed in the gold-bromobenzene-silica system. This study offers new insights into how temperature, density, and quantum fluctuations affect the structure and properties of nucleons under extreme conditions.

hep-th↗

arXiv2025

DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD

cs.CV↗

arXiv2025

Lattice Chiral Fermion without Hermiticity

Our review of the lattice chiral fermion delves into some critical areas of lattice field theory. By abandoning Hermiticity, the non-Hermitian formulation circumvents the Nielsen-Ninomiya theorem while maintaining chiral symmetry, a novel approach. Comparing the Wilson and overlap fermions gives insight into how lattice formulations handle chiral symmetry. The Wilson fermion explicitly breaks chiral symmetry to eliminate doublers. In contrast, the overlap fermion restores a modified form of chiral symmetry using the Ginsparg-Wilson relation. We investigate how the (1+1)D Wilson fermion relates to the (1+1)D overlap fermion in the Hamiltonian formulation. This connection could provide a clearer physical understanding of how chiral symmetry manifests at the lattice level. Depending on Hermiticity for efficiency, Monte Carlo methods face unique challenges in a non-Hermitian setting. We investigate how to correctly apply this method to non-Hermitian lattice fermions, which is essential for practical simulations. Finally, the review of topological charge is crucial, as topological features in lattice formulations are strongly connected to chiral symmetry, anomalies, and the index theorem.

hep-lat↗

arXiv2025

SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation

Graphs are able to model interconnected entities in many online services, supporting a wide range of applications on the Web. This raises an important question: How can we train a graph foundational model on multiple source domains and adapt to an unseen target domain? A major obstacle is that graphs from different domains often exhibit divergent characteristics. Some studies leverage large language models to align multiple domains based on textual descriptions associated with the graphs, limiting their applicability to text-attributed graphs. For text-free graphs, a few recent works attempt to align different feature distributions across domains, while generally neglecting structural differences. In this work, we propose a novel Structure Alignment framework for text-free Multi-domain Graph Pre-Training and cross-domain adaptation (SAMGPT). It is designed to learn multi-domain knowledge from graphs originating in multiple source domains, which can then be adapted to address applications in an unseen target domain. Specifically, we introduce a set of structure tokens to harmonize structure-based aggregation across source domains during the pre-training phase. Next, for cross-domain adaptation, we design dual prompts, namely, holistic prompts and specific prompts, which adapt unified multi-domain structural knowledge and fine-grained, domain-specific information, respectively, to a target domain. Finally, we conduct comprehensive experiments on seven public datasets to evaluate and analyze the effectiveness of SAMGPT.

cs.CL↗

arXiv2025

A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method

Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, iris images captured by different devices exhibit certain and device-related consistent differences, which has a greater impact on the classification algorithm for anti-spoofing. The iris of various races would also affect the classification, causing the risk of identity theft. So it is necessary to improve the cross-domain capabilities of the iris anti-spoofing (IAS) methods to enable it more robust in facing different races and devices. However, there is no existing protocol that is comprehensively available. To address this gap, we propose an Iris Anti-Spoofing Cross-Domain-Testing (IAS-CDT) Protocol, which involves 10 datasets, belonging to 7 databases, published by 4 institutions, and collected with 6 different devices. It contains three sub-protocols hierarchically, aimed at evaluating average performance, cross-racial generalization, and cross-device generalization of IAS models. Moreover, to address the cross-device generalization challenge brought by the IAS-CDT Protocol, we employ multiple model parameter sets to learn from the multiple sub-datasets. Specifically, we utilize the Mixture of Experts (MoE) to fit complex data distributions using multiple sub-neural networks. To further enhance the generalization capabilities, we propose a novel method Masked-MoE (MMoE), which randomly masks a portion of tokens for some experts and requires their outputs to be similar to the unmasked experts, which can effectively mitigate the overfitting issue of MoE. For the evaluation, we selected ResNet50, VIT-B/16, CLIP, and FLIP as representative models and benchmarked them under the proposed IAS-CDT Protocol.

cs.CV↗

arXiv2025

ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian Splatting

Creating 3D content from single-view images is a challenging problem that has attracted considerable attention in recent years. Current approaches typically utilize score distillation sampling (SDS) from pre-trained 2D diffusion models to generate multi-view 3D representations. Although some methods have made notable progress by balancing generation speed and model quality, their performance is often limited by the visual inconsistencies of the diffusion model outputs. In this work, we propose ContrastiveGaussian, which integrates contrastive learning into the generative process. By using a perceptual loss, we effectively differentiate between positive and negative samples, leveraging the visual inconsistencies to improve 3D generation quality. To further enhance sample differentiation and improve contrastive learning, we incorporate a super-resolution model and introduce another Quantity-Aware Triplet Loss to address varying sample distributions during training. Our experiments demonstrate that our approach achieves superior texture fidelity and improved geometric consistency.

cs.CV↗

arXiv2025

HQViT: Hybrid Quantum Vision Transformer for Image Classification

Transformer-based architectures have revolutionized the landscape of deep learning. In computer vision domain, Vision Transformer demonstrates remarkable performance on par with or even surpassing that of convolutional neural networks. However, the quadratic computational complexity of its self-attention mechanism poses challenges for classical computing, making model training with high-dimensional input data, e.g., images, particularly expensive. To address such limitations, we propose a Hybrid Quantum Vision Transformer (HQViT), that leverages the principles of quantum computing to accelerate model training while enhancing model performance. HQViT introduces whole-image processing with amplitude encoding to better preserve global image information without additional positional encoding. By leveraging quantum computation on the most critical steps and selectively handling other components in a classical way, we lower the cost of quantum resources for HQViT. The qubit requirement is minimized to $O(log_2N)$ and the number of parameterized quantum gates is only $O(log_2d)$, making it well-suited for Noisy Intermediate-Scale Quantum devices. By offloading the computationally intensive attention coefficient matrix calculation to the quantum framework, HQViT reduces the classical computational load by $O(T^2d)$. Extensive experiments across various computer vision datasets demonstrate that HQViT outperforms existing models, achieving a maximum improvement of up to $10.9\%$ (on the MNIST 10-classification task) over the state of the art. This work highlights the great potential to combine quantum and classical computing to cope with complex image classification tasks.

cs.CV↗