arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 145 records · Page 8Linked to original sources

arXiv2025

Hierarchical Coarse Basis by Randomised SVD: the Helmholtz Problem

The oscillatory waves require sufficient degrees of freedom to resolve. That restriction usually applies also to coarse problems for Schwarz methods. The resulting coarse problem is then too large. To address the issue, a new form of Schwarz methods with coarse correction is proposed for the Helmholtz problem. There are two components in the proposed form: randomised SVD of interface iteration, and hierarchical domain decomposition. The resulting coarse problem is hierarchical and can be solved in a decoupled way at each level of hierarchy.

math.NA↗

arXiv2025

On degenerate preconditioned proximal point methods under restricted monotonicity

This work investigates the fundamental properties of the degenerate preconditioned resolvent under restricted monotonicity. We extend key notions of non-expansiveness and demiclosedness to the degenerate case. By deriving an explicit characterization of the solution set of the resolvent, we establish several necessary and sufficient conditions for both the well-posedness of the degenerate resolvent and the weak convergence of the associated degenerate proximal point method -- considered within either the range space of the preconditioner or the entire Hilbert space. These results provide new insights into the behavior of various operator splitting algorithms, particularly within the range space of the preconditioner. Numerical examples extend the augmented Lagrangian method and Douglas--Rachford splitting algorithm to non-convex settings under restricted maximal monotonicity.

math.OC↗

arXiv2025

Anomalous Hall effect and rich magnetic phase diagram of Mn$_{100-x}$Rh$_{x}$ epitaxial films

A series of Mn$_{100-x}$Rh$_x$ ($20 \le x \le 50$) thin films were epitaxially grown on the MgO substrate using magnetron sputtering technique, and were systematically investigated by magnetization, longitudinal electrical resistivity, and transverse Hall resistivity. After optimizing the growth conditions, phase-pure Mn$_{100-x}$Rh$_x$ films with a cubic CsCl-type structure were obtained, and their magnetic phase diagram was built. The manipulation of Rh content leads to a rich magnetic phase diagram, where three different regimes can be identified: for $x < 40$, Mn$_{100-x}$Rh$_x$ films undergo a ferromagnetic (FM) transition below $T_\mathrm{C} \approx$ 330-350 K; for $40 \le x \le 45$, in addition to the FM transition at $T_\mathrm{C} \approx$ 200 K, Mn$_{100-x}$Rh$_x$ films undergo a FM-to-antiferromagnetic (AFM) transition at $T_\mathrm{N} \approx$ 120 K; finally for $x > 45$, only one AFM transition at $T_\mathrm{N} \approx$ 150 K can be tracked. All the Mn$_{100-x}$Rh$_x$ films exhibit distinct anomalous Hall effect in their magnetically ordered state, which is most likely due to the intrinsic Berry-curvature mechanism. In addition, all the anomalous Hall transport properties, including the resistivity, conductivity, and angle exhibit a strong correlation with the magnetic properties of Mn$_{100-x}$Rh$_x$ films, which become most evident for $x$ = 35. Our systematic investigations suggest a strong correlation between magnetic properties and electronic band topology in Mn$_{100-x}$Rh$_x$ films, highlighting their great potential for AFM spintronics.

cond-mat.str-el↗

arXiv2025

Abelianized Descent Obstruction for 0-Cycles

Classical descent theory of Colliot-Thélène and Sansuc for rational points tells that, over a smooth variety $X$, the algebraic Brauer--Manin subset equals the descent obstruction subset defined by a universal torsor. Moreover, Harari shows that the Brauer--Manin subset equals the descent obstruction subset defined by torsors under connected linear groups. By using the abelian cohomology theory by Borovoi, we define abelianized descent obstructions for 0-cycles by torsors under connected linear groups. As an analogy, we show the equality between the Brauer--Manin obstruction and the abelianized descent obstruction for 0-cycles. We also show that the abelianized descent obstruction is the closure of the descent obstruction defined by Balestrieri and Berg when $X$ is a projective rationally connected variety or a projective K3 surface.

math.AG↗

arXiv2025

A Survey of Quantum Transformers: Architectures, Challenges and Outlooks

Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing. Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications. To address the growing need for consolidation, this paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models. First, we delineate the research scope, focusing on improving Transformer parts with quantum methods, and introduce foundational concepts in classical Transformers and quantum machine learning. Then we organize existing studies into two main paradigms: PQC-based and QLA-based, with PQC-based paradigm further divided into QKV-only Quantum Mapping, Quantum Pairwise Attention, Quantum Holistic Attention. and Quantum-Assisted Optimization, analyzing their core mechanisms and architectural traits. We also summarize empirical results that demonstrate preliminary quantum advantages, especially on small-scale tasks or resource-constrained settings. Following this, we examine key technical challenges, such as complexity-resource trade-offs, scalability and generalization limitations, and trainability issues including barren plateaus, and provide potential solutions, including quantumizing classical transformer variants with lower complexity, hybrid designs, and improved optimization strategies. Finally, we propose several promising future directions, e.g., scaling quantum modules into large architectures, applying quantum Transformers to domains with inherently quantum data (e.g., physics, chemistry), and developing theory-driven designs grounded in quantum information science. This survey will help researchers and practitioners quickly grasp the overall landscape of current quantum Transformer research and promote future developments in this emerging field.

quant-ph↗

arXiv2025

TEFormer: Texture-Aware and Edge-Guided Transformer for Semantic Segmentation of Urban Remote Sensing Images

Accurate semantic segmentation of urban remote sensing images (URSIs) is essential for urban planning and environmental monitoring. However, it remains challenging due to the subtle texture differences and similar spatial structures among geospatial objects, which cause semantic ambiguity and misclassification. Additional complexities arise from irregular object shapes, blurred boundaries, and overlapping spatial distributions of objects, resulting in diverse and intricate edge morphologies. To address these issues, we propose TEFormer, a texture-aware and edge-guided Transformer. Our model features a texture-aware module (TaM) in the encoder to capture fine-grained texture distinctions between visually similar categories, thereby enhancing semantic discrimination. The decoder incorporates an edge-guided tri-branch decoder (Eg3Head) to preserve local edges and details while maintaining multiscale context-awareness. Finally, an edge-guided feature fusion module (EgFFM) effectively integrates contextual, detail, and edge information to achieve refined semantic segmentation. Extensive evaluation demonstrates that TEFormer yields mIoU scores of 88.57% on Potsdam and 81.46% on Vaihingen, exceeding the next best methods by 0.73% and 0.22%. On the LoveDA dataset, it secures the second position with an overall mIoU of 53.55%, trailing the optimal performance by a narrow margin of 0.19%.

cs.CV↗

arXiv2025

HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image Restoration

Recently, the degenerate preconditioned proximal point (PPP) method provides a unified and flexible framework for designing and analyzing operator-splitting algorithms such as Douglas-Rachford (DR). However, the degenerate PPP method exhibits weak convergence in the infinite-dimensional Hilbert space and lacks accelerated variants. To address these issues, we propose a Halpern-type PPP (HPPP) algorithm, which leverages the strong convergence and acceleration properties of Halpern's iteration method. Moreover, we propose a novel algorithm for image restoration by combining HPPP with denoiser priors such as Plug-and-Play (PnP) prior, which can be viewed as an accelerated PnP method. Finally, numerical experiments including several toy examples and image restoration validate the effectiveness of our proposed algorithms.

cs.CV↗

arXiv2025

First-principles prediction of altermagnetism in transition metal graphite intercalation compounds

We report the emergence of altermagnetism, a magnetic phase characterized by the coexistence of compensated spin ordering and momentum-dependent spin splitting, in graphite intercalation compounds (GICs), a prototypical material system long investigated for its tunable electronic and structural properties. Through first-principles calculations, we demonstrate that vanadium-intercalated stage-1 graphite compounds, exhibit inherent altermagnetic properties. The hexagonal crystal system and antiferromagnetic ordering of V atoms generate a magnetic space group that enforces alternating spin polarization in momentum space while maintaining zero net magnetization. The calculated band structure reveals robust altermagnetic signatures: along the high-symmetry direction, we observe a pronounced spin splitting of ~270 meV with alternating spin polarization. Crucially, the spin splitting exhibits minimal sensitivity to spin-orbit coupling (SOC) effect, highlighting the dominance of exchange interactions over relativistic effects. From Monte Carlo simulations, we predict a magnetic transition temperature ($T_m$ ) of ~228 K, indicating stable magnetic ordering above liquid nitrogen temperatures. The combination of symmetry-protected spin textures, SOC-independent splitting, and elevated $T_m$ temperature makes V-GICs as a promising candidate for spintronic applications, particularly for zero-field spin-polarized current generation and topologically robust spin transport. As the first demonstration of carbon-based alternating magnetic systems, this work offers a design paradigm for engineering spin-polarized quantum states governed by crystalline symmetry constraints.

cond-mat.mtrl-sci↗

arXiv2025

Remember Me: Bridging the Long-Range Gap in LVLMs with Three-Step Inference-Only Decay Resilience Strategies

Large Vision-Language Models (LVLMs) have achieved impressive performance across a wide range of multimodal tasks. However, they still face critical challenges in modeling long-range dependencies under the usage of Rotary Positional Encoding (ROPE). Although it can facilitate precise modeling of token positions, it induces progressive attention decay as token distance increases, especially with progressive attention decay over distant token pairs, which severely impairs the model's ability to remember global context. To alleviate this issue, we propose inference-only Three-step Decay Resilience Strategies (T-DRS), comprising (1) Semantic-Driven DRS (SD-DRS), amplifying semantically meaningful but distant signals via content-aware residuals, (2) Distance-aware Control DRS (DC-DRS), which can purify attention by smoothly modulating weights based on positional distances, suppressing noise while preserving locality, and (3) re-Reinforce Distant DRS (reRD-DRS), consolidating the remaining informative remote dependencies to maintain global coherence. Together, the T-DRS recover suppressed long-range token pairs without harming local inductive biases. Extensive experiments on Vision Question Answering (VQA) benchmarks demonstrate that T-DRS can consistently improve performance in a training-free manner. The code can be accessed in https://github.com/labixiaoq-qq/Remember-me

cs.CV↗

arXiv2025

Competition between Weak Localization and Antilocalization of Dirac-like Fermions in a Spin-Polarized Two-Dimensional Electron Gas at KTaO3 (111) Interface

Quantum transport phenomena in two-dimensional electron gases (2DEGs) at oxide interfaces have garnered significant interest owing to their potential in spintronic and quantum information technologies. Here, we systematically investigate the quantum conductance corrections of spin-polarized 2DEGs formed at the interfaces between two insulating oxides, ferromagnetic EuTiO3 (ETO) films and (111)-oriented KTaO3 (KTO) substrates. The anomalous Hall effect and hysteretic magnetoresistance provide clear evidence for long-range ferromagnetic order in the 2DEGs, which could be attributed to interfacial Eu doping in combination with the magnetic proximity effect of the ETO layer. The breaking of time-reversal symmetry by ferromagnetism in the 2DEGs, and with the assistance of spin-orbit coupling effect, gives rise to a nontrivial Berry phase. This results in a competition between weak localization (WL) and weak antilocalization (WAL) in the quantum transport of Dirac-like fermions at the KTO (111) interfaces. Notably, this competitive behavior can be effectively tuned by optical gating via a photoexcitation-induced shift of the Fermi level. Our findings demonstrate a controllable platform based on spin-polarized oxide 2DEGs for quantum transport, opening new avenues for spin-orbitronic and topological electronic applications.

cond-mat.str-el↗

arXiv2025

Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation

We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting-based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly-mask-caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template-based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal-category image using user-defined prompts, establishing a versatile foundation model for anomaly generation.

cs.CV↗

arXiv2025

Seg2Any: Open-set Segmentation-Mask-to-Image Generation with Precise Shape and Semantic Control

Despite recent advances in diffusion models, top-tier text-to-image (T2I) models still struggle to achieve precise spatial layout control, i.e. accurately generating entities with specified attributes and locations. Segmentation-mask-to-image (S2I) generation has emerged as a promising solution by incorporating pixel-level spatial guidance and regional text prompts. However, existing S2I methods fail to simultaneously ensure semantic consistency and shape consistency. To address these challenges, we propose Seg2Any, a novel S2I framework built upon advanced multimodal diffusion transformers (e.g. FLUX). First, to achieve both semantic and shape consistency, we decouple segmentation mask conditions into regional semantic and high-frequency shape components. The regional semantic condition is introduced by a Semantic Alignment Attention Mask, ensuring that generated entities adhere to their assigned text prompts. The high-frequency shape condition, representing entity boundaries, is encoded as an Entity Contour Map and then introduced as an additional modality via multi-modal attention to guide image spatial structure. Second, to prevent attribute leakage across entities in multi-entity scenarios, we introduce an Attribute Isolation Attention Mask mechanism, which constrains each entity's image tokens to attend exclusively to themselves during image self-attention. To support open-set S2I generation, we construct SACap-1M, a large-scale dataset containing 1 million images with 5.9 million segmented entities and detailed regional captions, along with a SACap-Eval benchmark for comprehensive S2I evaluation. Extensive experiments demonstrate that Seg2Any achieves state-of-the-art performance on both open-set and closed-set S2I benchmarks, particularly in fine-grained spatial and attribute control of entities.

cs.CV↗

arXiv2025

MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance

Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore, we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics. Our project page are publicly available at https://quanhaol.github.io/magicmotion-site.

cs.CV↗

arXiv2025

Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need

Existing deep learning-based models for remote sensing pansharpening exhibit exceptional performance on training datasets. However, due to sensor-specific characteristics and varying imaging conditions, these models suffer from substantial performance degradation when applied to unseen satellite data, lacking generalizability and thus limiting their applicability. We argue that the performance drops stem primarily from distributional discrepancies from different sources and the key to addressing this challenge lies in bridging the gap between training and testing distributions. To validate the idea and further achieve a "train once, deploy forever" capability, this paper introduces a novel and intuitive approach to enpower any pansharpening models with generalizability by employing a unified distribution strategy (UniPAN). Specifically, we construct a distribution transformation function that normalizes the pixels sampled from different sources to conform to an identical distribution. The deep models are trained on the transformed domain, and during testing on new datasets, the new data are also transformed to match the training distribution. UniPAN aims to train and test the model on a unified and consistent distribution, thereby enhancing its generalizability. Extensive experiments validate the efficacy of UniPAN, demonstrating its potential to significantly enhance the performance of deep pansharpening models across diverse satellite sensors. Codes: https://github.com/yc-cui/UniPAN.

cs.CV↗

arXiv2025

CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation

Collaborative perception has been proven to improve individual perception in autonomous driving through multi-agent interaction. Nevertheless, most methods often assume identical encoders for all agents, which does not hold true when these models are deployed in real-world applications. To realize collaborative perception in actual heterogeneous scenarios, existing methods usually align neighbor features to those of the ego vehicle, which is vulnerable to noise from domain gaps and thus fails to address feature discrepancies effectively. Moreover, they adopt transformer-based modules for domain adaptation, which causes the model inference inefficiency on mobile devices. To tackle these issues, we propose CoDS, a Collaborative perception method that leverages Domain Separation to address feature discrepancies in heterogeneous scenarios. The CoDS employs two feature alignment modules, i.e., Lightweight Spatial-Channel Resizer (LSCR) and Distribution Alignment via Domain Separation (DADS). Besides, it utilizes the Domain Alignment Mutual Information (DAMI) loss to ensure effective feature alignment. Specifically, the LSCR aligns the neighbor feature across spatial and channel dimensions using a lightweight convolutional layer. Subsequently, the DADS mitigates feature distribution discrepancy with encoder-specific and encoder-agnostic domain separation modules. The former removes domain-dependent information and the latter captures task-related information. During training, the DAMI loss maximizes the mutual information between aligned heterogeneous features to enhance the domain separation process. The CoDS employs a fully convolutional architecture, which ensures high inference efficiency. Extensive experiments demonstrate that the CoDS effectively mitigates feature discrepancies in heterogeneous scenarios and achieves a trade-off between detection accuracy and inference efficiency.

cs.CV↗

arXiv2025

ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation

Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and structural richness poses challenges for large vision-language models (LVLMs), which are typically trained on plain charts. To bridge this gap, we introduce ChartGalaxy, a million-scale dataset designed to advance the understanding and generation of infographic charts. The dataset is constructed through an inductive process that identifies 75 chart types, 440 chart variations, and 68 layout templates from real infographic charts and uses them to create synthetic ones programmatically. We showcase the utility of this dataset through: 1) improving infographic chart understanding via fine-tuning, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation. By capturing the visual and structural complexity of real design, ChartGalaxy provides a useful resource for enhancing multimodal reasoning and generation in LVLMs.

cs.CV↗

arXiv2025

CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling

Time series anomaly detection plays a crucial role in a wide range of real-world applications. Given that time series data can exhibit different patterns at different sampling granularities, multi-scale modeling has proven beneficial for uncovering latent anomaly patterns that may not be apparent at a single scale. However, existing methods often model multi-scale information independently or rely on simple feature fusion strategies, neglecting the dynamic changes in cross-scale associations that occur during anomalies. Moreover, most approaches perform multi-scale modeling based on fixed sliding windows, which limits their ability to capture comprehensive contextual information. In this work, we propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window modeling into account. We propose a cross-scale reconstruction that reconstructs fine-grained series from coarser series, explicitly capturing cross-scale associations. Furthermore, we design a query library and incorporate global multi-scale context to overcome the limitations imposed by fixed window sizes. Extensive experiments conducted on multiple real-world datasets using nine evaluation metrics validate the effectiveness of CrossAD, demonstrating state-of-the-art performance in anomaly detection.

cs.LG↗

arXiv2025

Probing the Neutron Skin with Extreme Collision Geometries in Heavy-Ion Collisions

Understanding how protons and neutrons are located differently in an atomic nucleus can provide fundamental information on nuclear structure and have far-reaching implications for astrophysics. A precise determination of this important difference, often quantified by the so-called neutron skin thickness, is challenging both theoretically and experimentally. Here we show how one can use a new category of observables in heavy ion collisions to probe the neutron skin thickness of nuclei like $^{208}$Pb and $^{48}$Ca, by utilizing the asymmetry between neutrons and protons of spectator nucleons in super-central collisions as well as that of participant nucleons in peripheral collisions. Using quantitative simulations, we demonstrate their sensitivity and great potential in constraining neutron skin thickness for both $^{208}$Pb and $^{48}$Ca nuclei in these extreme event geometries. Furthermore, we propose the asymmetric collisions between $^{48}$Ca and $^{40}$Ca nuclei as a unique and powerful way to nail down the neutron skin thickness.

nucl-th↗