arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2025

Etching-free dual-lift-off for direct patterning of epitaxial oxide thin films

Although monocrystalline oxide films offer broad functional capabilities, their practical use is hampered by challenges in patterning. Traditional patterning relies on etching, which can be costly and prone to issues like film or substrate damage, under-etching, over-etching, and lateral etching. In this study, we introduce a dual-lift-off method for direct patterning of oxide films, circumventing the etching process and associated issues. Our method involves an initial lift-off of amorphous Sr$_3$Al$_2$O$_6$ or Sr$_4$Al$_2$O$_7$ ($a$SAO) through stripping the photoresist, followed by a subsequent lift-off of the functional oxide thin films by dissolving the $a$SAO layer. $a$SAO functions as a ``high-temperature photoresist", making it compatible with the high-temperature growth of monocrystalline oxides. Using this method, patterned ferromagnetic La$_{0.67}$Sr$_{0.33}$MnO$_{3}$ and ferroelectric BiFeO$_3$ were fabricated, accurately mirroring the shape of the photoresist. Our study presents a straightforward, flexible, precise, environmentally friendly, and cost-effective method for patterning high-quality oxide thin films.

cond-mat.mtrl-sci↗

arXiv2025

Sky Background Building of Multi-objective Fiber spectra Based on Mutual Information Network

Sky background subtraction is a critical step in Multi-objective Fiber spectra process. However, current subtraction relies mainly on sky fiber spectra to build Super Sky. These average spectra are lacking in the modeling of the environment surrounding the objects. To address this issue, a sky background estimation model: Sky background building based on Mutual Information (SMI) is proposed. SMI based on mutual information and incremental training approach. It utilizes spectra from all fibers in the plate to estimate the sky background. SMI contains two main networks, the first network applies a wavelength calibration module to extract sky features from spectra, and can effectively solve the feature shift problem according to the corresponding emission position. The second network employs an incremental training approach to maximize mutual information between representations of different spectra to capturing the common component. Then, it minimizes the mutual information between adjoining spectra representations to obtain individual components. This network yields an individual sky background at each location of the object. To verify the effectiveness of the method in this paper, we conducted experiments on the spectra of LAMOST. Results show that SMI can obtain a better object sky background during the observation, especially in the blue end.

cs.CV↗

arXiv2025

Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals

The structure of grey matter has long been a key focus in neuroscience, as cell morphology varies by type and can be affected by neurological conditions. Understanding these variations is essential for studying brain function and disease. Diffusion-weighted MRI (dMRI) is a powerful non-invasive tool for examining cellular microstructure in vivo. However, for dMRI to accurately reflect cellular features, it is crucial to determine which aspects of morphology influence its measurements. Proper interpretation of dMRI data depends on understanding its sensitivity to different cellular characteristics. Despite growing interest in cellular morphology, there has been no systematic report on the key features defining different neural cell types. To address this, we analyzed over 11,500 three-dimensional cellular reconstructions across three species and nine cell types, establishing reference values for critical morphological traits. These traits fall into three categories: structural features that define the cell's skeletal framework, shape features that describe spatial organization, and topological features that break down cellular structure to distinguish cell types. Beyond reporting these reference values, we examine their relevance for dMRI, identifying which neural features dMRI can detect and which cell types may be distinguishable. To complement the statistical analysis, we also provide high resolution 3D surface meshes representative of each cell type and species. This work provides essential benchmarks for grey matter research, offering new guidelines on linking neuroimaging measurements to neurobiology. These reference values will be a valuable resource for neuroscientists and neuroimaging researchers, aiding in the interpretation of imaging data and the refinement of brain tissue models.

physics.bio-ph↗

arXiv2025

Impact of Target and Tool Visualization on Depth Perception and Usability in Optical See-Through AR

Optical see-through augmented reality (OST-AR) systems like Microsoft HoloLens 2 hold promise for arm's distance guidance (e.g., surgery), but depth perception of the hologram and occlusion of real instruments remain challenging. We present an evaluation of how visualizing the target object with different transparencies and visualizing a tracked tool (virtual proxy vs. real tool vs. no tool tracking) affects depth perception and system usability. Ten participants performed two experiments on HoloLens 2. In Experiment 1, we compared high-transparency vs. low-transparency target rendering in a depth matching task at arm's length. In Experiment 2, participants performed a simulated surgical pinpoint task on a frontal bone target under six visualization conditions ($2 \times 3$: two target transparencies and three tool visualization modes: virtual tool hologram, real tool, or no tool tracking). We collected data on depth matching error, target localization error, system usability, task workload, and qualitative feedback. Results show that a more opaque target yields significantly lower depth estimation error than a highly transparent target at arm's distance. Moreover, showing the real tool (occluding the virtual target) led to the highest accuracy and usability with the lowest workload, while not tracking the tool yielded the worst performance and user ratings. However, making the target highly transparent, while allowing the real tool to remain visible, slightly impaired depth cues and did not improve usability. Our findings underscore that correct occlusion cues, rendering virtual content opaque and occluding it with real tools in real time, are critical for depth perception and precision in OST-AR. Designers of arm-distance AR systems should prioritize robust tool tracking and occlusion handling; if unavailable, cautiously use transparency to balance depth perception and tool visibility.

cs.HC↗

arXiv2025

RobustDexGrasp: Robust Dexterous Grasping of General Objects

The ability to robustly grasp a variety of objects is essential for dexterous robots. In this paper, we present a framework for zero-shot dynamic dexterous grasping using single-view visual inputs, designed to be resilient to various disturbances. Our approach utilizes a hand-centric object shape representation based on dynamic distance vectors between finger joints and object surfaces. This representation captures the local shape around potential contact regions rather than focusing on detailed global object geometry, thereby enhancing generalization to shape variations and uncertainties. To address perception limitations, we integrate a privileged teacher policy with a mixed curriculum learning approach, allowing the student policy to effectively distill grasping capabilities and explore for adaptation to disturbances. Trained in simulation, our method achieves success rates of 97.0% across 247,786 simulated objects and 94.6% across 512 real objects, demonstrating remarkable generalization. Quantitative and qualitative results validate the robustness of our policy against various disturbances.

cs.RO↗

arXiv2025

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance

Heat transfer in composites is critical in engineering, where imperfect layer contact causes thermal contact resistance (TCR), leading to interfacial temperature discontinuity. We propose solving this numerically using the optimized Schwarz method (OSM), which decouples the heterogeneous problem into homogeneous subproblems. This avoids ill-conditioned systems from monolithic solving due to high contrast and interface jumps. Both energy estimate and Fourier analysis are used to prove the convergence of this algorithm when the standard Robin condition is applied to transmit information between subdomains. To achieve fast convergence, instead of the standard Robin, the scaled Robin transmission condition is proposed, and the involved free parameter is rigorously optimized. The results reveal several new findings due to the presence of TCR: first, the larger the TCR, the faster the OSM converges; second, mesh-independent convergence is achieved in the asymptotic sense, in contrast to the mesh-dependent results without TCR; and last, the heterogeneity contrast benefits the convergence, with a larger contrast leading to faster convergence. Interestingly, different from the case without TCR, the thermal conductivity also benefits the convergence, similar to the effect of heterogeneity. Numerical experiments confirm the theoretical findings and demonstrate the method's potential for nonlinear problems on irregular domains.

math.NA↗

arXiv2025

CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation

Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I) generation has been proposed to leverage region-specific positions and descriptions to enable more precise and controllable generation. However, previous methods primarily focus on UNet-based models (\eg SD1.5 and SDXL), and limited effort has explored Multimodal Diffusion Transformers (MM-DiTs), which have demonstrated powerful image generation capabilities. Enabling MM-DiT for layout-to-image generation seems straightforward but is challenging due to the complexity of how layout is introduced, integrated, and balanced among multiple modalities. To this end, we explore various network variants to efficiently incorporate layout guidance into MM-DiT, and ultimately present SiamLayout. To inherit the advantages of MM-DiT, we use a separate set of network weights to process the layout, treating it as equally important as the image and text modalities. Meanwhile, to alleviate the competition among modalities, we decouple the image-layout interaction into a siamese branch alongside the image-text one and fuse them in the later stage. Moreover, we contribute a large-scale layout dataset, named LayoutSAM, which includes 2.7 million image-text pairs and 10.7 million entities. Each entity is annotated with a bounding box and a detailed description. We further construct the LayoutSAM-Eval benchmark as a comprehensive tool for evaluating the L2I generation quality. Finally, we introduce the Layout Designer, which taps into the potential of large language models in layout planning, transforming them into experts in layout generation and optimization. These components form CreatiLayout -- a systematic solution that integrates the layout model, dataset, and planner for creative layout-to-image generation.

cs.CV↗

arXiv2025

Discovery and dynamics of a Sedna-like object with a perihelion of 66 au

Trans-Neptunian objects (TNOs) with large perihelion distances ($q > 60$ au) and semi-major axes ($a > 200$ au) provide insights into the early evolution of the solar system and the existence of a hypothetical distant planet. These objects are still rare and their detection is challenging, yet they play a crucial role in constraining models of solar system formation. Here we report the discovery of a Sedna-like TNO, 2023\,KQ$_{14}$, nicknamed `Ammonite', with $q = 66$ au, $a = 252$ au, and inclination $i=11^\circ$. Ammonite's orbit does not align with those of the other Sedna-like objects and fills the previously unexplained `$q$-gap' in the observed distribution of distant solar system objects. Simulations demonstrate that Ammonite is dynamically stable over 4.5 billion years. % with less than 1\% variation in its semi-major axis. Our analysis suggests that Ammonite and the other Sedna-like objects may have shared a primordial orbital clustering around 4.2 billion years ago. Furthermore, Ammonite's stable orbit favors larger orbits ($\sim$ 500 au) rather than closer ones for a large hypothetical planet in present-day trans-Neptunian space.

astro-ph.EP↗

arXiv2025

Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation

Panoramic image processing is essential for omni-context perception, yet faces constraints like distortions, perspective occlusions, and limited annotations. Previous unsupervised domain adaptation methods transfer knowledge from labeled pinhole data to unlabeled panoramic images, but they require access to source pinhole data. To address these, we introduce a more practical task, i.e., Source-Free Occlusion-Aware Seamless Segmentation (SFOASS), and propose its first solution, called UNconstrained Learning Omni-Context Knowledge (UNLOCK). Specifically, UNLOCK includes two key modules: Omni Pseudo-Labeling Learning and Amodal-Driven Context Learning. While adapting without relying on source data or target labels, this framework enhances models to achieve segmentation with 360° viewpoint coverage and occlusion-aware reasoning. Furthermore, we benchmark the proposed SFOASS task through both real-to-real and synthetic-to-real adaptation settings. Experimental results show that our source-free method achieves performance comparable to source-dependent methods, yielding state-of-the-art scores of 10.9 in mAAP and 11.6 in mAP, along with an absolute improvement of +4.3 in mAPQ over the source-only method. All data and code will be made publicly available at https://github.com/yihong-97/UNLOCK.

cs.CV↗

arXiv2025

All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior

All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI, CT, and PET) from low-quality (LQ) counterparts. However, all-in-one MedIR presents significant challenges due to the heterogeneity across different tasks. Each task involves distinct degradations, leading to diverse information losses in LQ images. Existing methods struggle to handle these diverse information losses associated with different tasks. To address these challenges, we propose a latent diffusion-enhanced vector-quantized codebook prior and develop \textbf{DiffCode}, a novel framework leveraging this prior for all-in-one MedIR. Specifically, to compensate for diverse information losses associated with different tasks, DiffCode constructs a task-adaptive codebook bank to integrate task-specific HQ prior features across tasks, capturing a comprehensive prior. Furthermore, to enhance prior retrieval from the codebook bank, DiffCode introduces a latent diffusion strategy that utilizes the diffusion model's powerful mapping capabilities to iteratively refine the latent feature distribution, estimating more accurate HQ prior features during restoration. With the help of the task-adaptive codebook bank and latent diffusion strategy, DiffCode achieves superior performance in both quantitative metrics and visual quality across three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis.

cs.CV↗

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↗