arXiv subjects

Qinghua Zhang

Publications and source records attributed to Qinghua Zhang.

At least 19 recordsLinked to original sources

arXiv2026

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity. We present AutoMat, a failure-aware agentic controller that performs inference-time hypothesis search with closed-loop verification to convert Scanning Transmission Electron Microscopy (STEM) images into simulation-ready crystal structures and downstream properties. AutoMat composes perception and physics modules---pattern-adaptive denoising, physics-guided template retrieval as a state-dependent auxiliary branch, symmetry-constrained atomic reconstruction, and MLIP-based relaxation/validation---and triggers rollback-and-retry when verification fails. For systematic evaluation, we introduce STEM2Mat-Bench, a benchmark dataset containing 450+ annotated samples. Performance is assessed using lattice root-mean-square deviation (RMSD), formation energy mean absolute error (MAE), and structure matching accuracy. Results demonstrate that AutoMat outperforms existing approaches including SOTA models, specialized domain tools, and closed-source multimodal large models. This work establishes a direct pathway from microscopic characterization to atomic-scale modeling, addressing a fundamental challenge in materials science.

cs.CV↗

arXiv2026

Are All Tokens Necessary for Visual Place Recognition? An Empirical Study of Token Reduction for Efficient Inference

Recent visual place recognition (VPR) methods based on vision transformers, particularly foundation models, have achieved remarkable recognition performance. However, these models process all visual tokens throughout the entire network, resulting in substantial computational overhead, which hinders their deployment in real-time and resource-constrained scenarios. A natural question thus arises: are all visual tokens necessary for VPR? To answer this question, we present the first systematic benchmark of token reduction for efficient visual place recognition. Our benchmark comprehensively evaluates representative token pruning, token merging, and hybrid pruning-merging methods across multiple state-of-the-art VPR models and diverse benchmark datasets covering urban, suburban, and natural environments. We further investigate token reduction from multiple perspectives, including recognition performance under different reduction configurations, computational complexity, inference speed, qualitative visualization, and deployment efficiency on edge devices. Through extensive experiments and in-depth analysis, our benchmark reveals multiple important characteristics of token reduction in VPR and provides several practical insights into the trade-offs between accuracy and inference efficiency. For example, token reduction can reduce computational cost by up to 29\% and improve throughput by up to 44\%, while incurring less than 1\% degradation in recognition accuracy. Overall, this work establishes a comprehensive foundation for future research on token-efficient VPR and efficient visual retrieval systems. Our codes and models will be available at https://github.com/Tong-Jin01/TokenReduction4VPR

cs.CV↗

arXiv2026

Deep Neural Networks Inspired by Differential Equations

Deep learning has become a pivotal technology in fields such as computer vision, scientific computing, and dynamical systems, significantly advancing these disciplines. However, neural Networks persistently face challenges related to theoretical understanding, interpretability, and generalization. To address these issues, researchers are increasingly adopting a differential equations perspective to propose a unified theoretical framework and systematic design methodologies for neural networks. In this paper, we provide an extensive review of deep neural network architectures and dynamic modeling methods inspired by differential equations. We specifically examine deep neural network models and deterministic dynamical network constructs based on ordinary differential equations (ODEs), as well as regularization techniques and stochastic dynamical network models informed by stochastic differential equations (SDEs). We present numerical comparisons of these models to illustrate their characteristics and performance. Finally, we explore promising research directions in integrating differential equations with deep learning to offer new insights for developing intelligent computational methods that boast enhanced interpretability and generalization capabilities.

cs.LG↗

arXiv2026

Saturated and Anisotropic Magnetostriction in an Altermagnet

Magnetostriction, a fundamental phenomenon bridging magnetism and mechanics, has enabled a broad spectrum of applications. For almost two centuries, it has been mainly investigated for ferromagnets. Regarding the magnetostriction of antiferromagnets (AFMs), limitedly known examples for both conventional collinear AFMs and noncollinear AFMs predominantly exhibit non-saturating magnetic-field dependence. Herein, we report an easily saturated magnetostriction effect in a prototypical altermagnet - MnTe, which is an emerging class of collinear AFMs with special crystal symmetries. For high-quality MnTe single crystals, the magnetostriction saturates under a moderate field of ~0.7 T with an intriguing two-fold-symmetry anisotropy. First-principles calculations reveal that the saturated and anisotropic magnetostriction originates from symmetry-allowed coupling between elastic strain and its Néel order parameter. These findings break the traditional wisdom on antiferromagnetic magnetostriction.

cond-mat.mtrl-sci↗

arXiv2026

Proximal observers for secure state estimation

This paper discusses a general framework for designing robust state estimators for a class of discrete-time nonlinear systems. We consider systems that may be impacted by impulsive (sparse but otherwise arbitrary) measurement noise sequences. We show that a family of state estimators, robust to this type of undesired signal, can be obtained by minimizing a class of nonsmooth convex functions at each time step. The resulting state observers are defined through proximal operators. We obtain a nonlinear implicit dynamical system in term of estimation error and prove, in the noise-free setting, that it vanishes asymptotically when the minimized loss function and the to-be-observed system enjoy appropriate properties. From a computational perspective, even though the proposed observers can be implemented via efficient numerical procedures, they do not admit closed-form expressions. The paper argues that by adopting appropriate relaxations, simple and fast analytic expressions can be derived.

math.OC↗

arXiv2026

SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on $k$-nearest neighbors ($k$NN) or other node-level full search algorithms (FSA) to mine semantic relationships via exhaustive pairwise similarity computation, which suffer from high computational complexity and rigid neighbor selection, limiting scalability and introducing noisy connections. In this paper, we propose the Semantic Consistency enhanced Graph Neural Network (SCGNN), a novel plug-and-play framework that leverages granular-ball computing (GBC) to efficiently capture semantic consistency in a scalable manner. Unlike node-level FSA methods, SCGNN models group-level semantic structure by adaptively partitioning nodes into granular balls, significantly reducing computational cost while improving robustness to noise. To effectively utilize the discovered group-level semantic consistency, we design a dual enhancement strategy. Specifically, (1) a structure enhancement module constructs an anchor-based graph structure, where each anchor is a virtual node representing the group-level semantic carried by a granular ball, then injecting group-level semantic information into the graph structure; and (2) a supervision enhancement module performs label consistency checking (LCC) by combining GBC predictions with model-generated pseudo-labels, thereby producing more reliable supervision signals. SCGNN is compatible with various GNN backbones. During the forward propagation of SCGNN, the vanilla graph and the augment graph are jointly encoded, and their predictions are fused; during the backpropagation, the supervision enhancement module provides enhanced supervision signals to guide parameter updates.

cs.AI↗

arXiv2026

Bragg-Williams order competes with superconductivity

Orderings in charge and spin have been extensively studied to unravel their correlation to emergent superconductivity over the past decades. Bragg-Williams order (BWO), a classical structural order parameter describing site occupancy in alloys, has long been speculated to influence superconducting behavior. Yet, its role still remains ambiguous, largely due to the difficulty of isolating BWO from concomitant charge doping or competing electronic instabilities. Here, we establish In2/3PSe3 as a platform wherein indium vacancies are reversibly configurable between ordered and disordered states via thermal treatment. We show that the disordered phase undergoes a pressure-induced superconducting transition with a Tc of 11 K, significantly higher than the 7 K observed in its ordered counterpart. This constitutes a rare instance in which pure BWO variation drives a substantial shift in Tc. By combining a Ginzburg-Landau phenomenological analysis with a BCS-McMillan microscopic description, we demonstrate that BWO naturally suppresses superconductivity through electron-phonon interactions, a mechanism supported by ultra-low-wavenumber Raman measurements. Our findings support BWO as an independent order parameter that competes directly with superconductivity, extending the concept of competing orders beyond conventional electronic and magnetic degrees of freedom.

cond-mat.supr-con↗

arXiv2026

Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion

The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. However, most existing GCN-based multi-view methods suffer from several limitations. First, current approaches predominantly rely on KNN for topology construction, where the artificial selection of the k value significantly constrains the effective exploitation of inter-node consistency. Second, the inter-feature consistency within individual views is often overlooked, which adversely affects the quality of the final embedding representations. Moreover, these methods fail to fully utilize inter-view consistency as the fusion of embedded representations from multiple views is often implemented after the intra-view graph convolutional operation. Collectively, these issues limit the model's capacity to fully capture inter-node, inter-feature and inter-view consistency. To address these issues, this paper proposes the multi-view graph convolutional network with fully leveraging consistency via GB-based topology construction, feature enhancement and interactive fusion (MGCN-FLC). MGCN-FLC can fully utilize three types of consistency via the following three modules to enhance learning ability:The topology construction module based on the granular ball algorithm, which clusters nodes into granular balls with high internal similarity to capture inter-node consistency;The feature enhancement module that improves feature representations by capturing inter-feature consistency;The interactive fusion module that enables each view to deeply interact with all other views, thereby obtaining more comprehensive inter-view consistency. Experimental results on nine datasets show that the proposed MGCN-FLC outperforms state-of-the-art semi-supervised node classification methods.

cs.CV↗

arXiv2026

Layer Embedding Deep Fusion Graph Neural Network

Graph Neural Networks (GNNs) have demonstrated impressive performance in learning representations from graph-structured data. However, their message-passing mechanism inherently relies on the assumption of label consistency among connected nodes, limiting their applicability to low-homophily settings. Moreover, since message passing operates as a hierarchical diffusion process, GNNs face challenges in capturing long-range dependencies. As network depth increases, the structural noise along heterophilic edges tends to be amplified, resulting in over-smoothing. This issue becomes especially prominent in highly heterophilic graphs, where the propagation of inconsistent semantics across the topology continually exacerbates misaggregation. To address this issue, we propose a novel framework named Layer Embedding Deep Fusion Graph Neural Network (LEDF-GNN). Specifically, we design a Layer Embedding Deep Fusion (LEDF) operator that nonlinearly fuses multi-layer embeddings to capture inter-layer dependencies and effectively alleviate deep propagation degradation. Meanwhile, to mitigate structural heterophily, LEDF-GNN employs a Dual-Topology Parallel Strategy (DTPS) that simultaneously leverages the original and reconstructed topologies, allowing for adaptive structure-semantics co-optimization under diverse homophily conditions. Extensive semi-supervised classification experiments on the citation and image benchmarks demonstrate that, under both homophilic and heterophilic settings, LEDF-GNN consistently outperforms state-of-the-art baselines, validating its effectiveness and generalization capability across diverse graph types.

cs.LG↗

arXiv2026

Intrinsically ultralow thermal conductivity in all-inorganic superatomic bulk crystals

Superatomic compounds, composed of atomic clusters interwoven by weak chemical bonds exhibit large anharmonicity vibrations, are excellent candidates for ultralow thermal conductivity (\k{appa}) materials. However, growing bulk superatomic single crystals is challenging due to complex chemical composition and chemical bonds, and studies on their intrinsic thermal property are scarce. Here, we grew high-quality superatomic single crystals of Re6Se8Te7 and Re6Te15, both of which are narrow band gap semiconductors that change into metals under external physical pressure. At room-temperature, the \k{appa} are 0.32 W m-1 K-1 and 0.53 W m-1 K-1 in Re6Se8Te7 and Re6Te15, respectively, ranking among the lowest value reported in all-inorganic bulk crystals. It is mainly attributed to the large Grüneisen parameter (1.93) and low average sound speed (< 1482 m/s), which are due to soft Te7 nets weakly embedded among the rigid Re6Se8 (Re6Te8) quasi-cubic clusters. The appearance of boson peak, i. e., hump of C(T)/T3, verifies the existence of disordered phonon transports. Besides, the temperature dependence of \k{appa} can be described by classic Debye-Callaway model. Notably, above 350 K, the \k{appa} values of Re6Se8Te7 and Re6Te15 are remarkably close to the upper limit derived from glassy-like diffusion model. This finding sets the superatomic compounds as a promising family for searching ultralow-\k{appa} and energy management materials.

cond-mat.mtrl-sci↗

arXiv2026

Long-range magnetic order with disordered spin orientations in a high-entropy antiferromagnet

Disorder in magnetic systems typically suppresses long-range order, promoting short-range states such as spin glasses and magnetic clusters. This is particularly prominent in high-entropy materials, characterized by the random distributions of local magnetic entities and exchange interactions. However, in rare exceptions, long-range magnetic order can persist in high-entropy systems, while the microscopic characters and underlying mechanisms remain elusive, especially the magnetic behaviors of individual elements. Here, combining neutron diffraction and resonant soft x-ray scattering, we have conducted an element-specific investigation into the magnetic order of a high-entropy honeycomb-lattice van der Waals material (Mn1/4Fe1/4Co1/4Ni1/4)PS3. Despite significant atomic disorder, long-range zigzag antiferromagnetic order is observed below 72 K, with all four transition-metal elements participating in a unified phase transition. However, the spin orientations of various elements are distinct, attributed to the competition between single-ion anisotropies and exchange interactions. Our findings showcase a novel form of long-range magnetic order with disordered spin orientations, which is synergically stabilized by distinct magnetic elements in a high entropy magnet, offering a new paradigm for understanding complex magnetic systems.

cond-mat.str-el↗

arXiv2026

Bulk OsO2 Single Crystals: Superior Catalysts for Water Oxidation

Although rutile RuO2 has been a well-known and almost the best oxygen evolution reaction (OER) catalyst, the OER properties for the similar rutile oxide OsO2 with the same group element with Ru have been unknown, mainly due to long-standing synthesis difficulties. In this work, we report the successful synthesis of high-quality OsO2 single crystals, and the ground micrometer-size single crystals are chemically stable in alkaline solutions and exhibit robust OER performance. In sharp contrast, OsO2 nanopowder reacts quickly with KOH solutions and cannot work for OER. Compared with commercial RuO2 nanopowder, the OsO2 single crystals show comparable catalytic current densities, remarkably lower overpotentials at high current densities and better stability. These findings question the universal applicability of nanoscaling and highlight crystal integrity as a key descriptor for achieving stable and efficient OER electrocatalysis.

cond-mat.mtrl-sci↗

arXiv2026

Pressure-Induced Metal-Insulator and Paramagnet-Altermagnet Transitions in Rutile OsO2 Single Crystals

Altermagnets with compensated spin structures and nonrelativistic spin splitting have emerged as a new class of magnetic materials. Rutile OsO2 has been theoretically predicted to be altermagnetic, but experimental studies have been limited by synthesis challenges. We have succeeded in synthesizing high-quality single crystals of rutile OsO2. Electrical transport studies reveal that OsO2 is highly conductive and exhibits clear Fermi liquid behavior, indicating strong electron-electron scattering. Magnetic measurements show that the crystals are isotropically paramagnetic. Density-functional theory calculations indicate that bulk OsO2 is semimetallic with coexisting electron and hole pockets, with its magnetic ground state strongly dependent on the on-site Coulomb correlation U. Angle-resolved photoemission spectroscopy studies unveil that the bulk bands do not yet show altermagnetic spin splitting. Interestingly, resistivity is rather pressure sensitive: at 44 GPa, a clear metal-insulator transition occurs. Hybrid functional calculations reveal that applying pressure significantly increases the Hubbard U value, driving a phase transition from a paramagnetic metal to an altermagnetic metal, and eventually to an altermagnetic insulator. These findings suggest that tuning external pressure effectively modulates the magnetic ground state of OsO2, providing a pathway to realize altermagnetism in this material.

cond-mat.mes-hall↗

arXiv2026

HyperParallel: A Supernode-Affinity AI Framework

The emergence of large-scale, sparse, multimodal, and agentic AI models has coincided with a shift in hardware toward supernode architectures that integrate hundreds to thousands of accelerators with ultra-low-latency interconnects and unified memory pools. However, existing AI frameworks are not designed to exploit these architectures efficiently, leading to high programming complexity, load imbalance, and poor memory utilization. In this paper, we propose a supernode-affinity AI framework that treats the supernode as a single logical computer and embeds hardware-aware orchestration into the framework. Implemented in MindSpore, our HyperParallel architecture comprises HyperOffload for automated hierarchical memory management, HyperMPMD for fine-grained MPMD parallelism across heterogeneous workloads, and HyperShard for declarative parallel strategy specification. Together, these techniques significantly improve training and inference efficiency while reducing parallel programming and system tuning overhead, demonstrating the necessity of supernode affinity for next-generation AI frameworks.

cs.DC↗

arXiv2026

Interface Spin-orbit Coupling Induced Room-temperature Ferromagnetic Insulator

Fabricating room-temperature ferromagnetic insulators, which are crucial candidates for next-generation dissipation-free quantum and spintronic devices, remains a significant challenge. In this Letter, we report on the epitaxial synthesis of novel room-temperature ferromagnetic insulating thin films created through the precise construction of (111)-oriented 3d/5d interfaces. Our analysis indicates that, unlike conventional doping methods, the (111)-oriented SrIrO3/La2/3Sr1/3MnO3 (SIO/LSMO) interfaces exhibit markedly enhanced spin-orbit coupling. This enhanced interfacial spin-orbit coupling strengthens the electron-phonon coupling in LSMO, thereby shortening the electronic mean free path. As a result, the intrinsic metallicity of LSMO is suppressed, giving rise to a new ferromagnetic insulating phase that emerges between the ferromagnetic metal and paramagnetic insulator regimes of the LSMO phase diagram. Furthermore, the temperature window of the ferromagnetic insulating phase can be tuned by precisely controlling the thickness of the LSMO layers. Our Letter reveals a new strategy for developing ferromagnetic insulators by engineering 3d/5d interfaces and orientations, paving a way for the development of novel dissipation-free quantum and spintronic devices.

cond-mat.str-el↗

arXiv2026

HyperOffload: Graph-Driven Hierarchical Memory Management for Large Language Models on SuperNode Architectures

The rapid evolution of Large Language Models (LLMs) towards long-context reasoning and sparse architectures has pushed memory requirements far beyond the capacity of individual device HBM. While emerging supernode architectures offer terabyte-scale shared memory pools via high-bandwidth interconnects, existing software stacks fail to exploit this hardware effectively. Current runtime-based offloading and swapping techniques operate with a local view, leading to reactive scheduling and exposed communication latency that stall the computation pipeline. In this paper, we propose the SuperNode Memory Management Framework (\textbf{HyperOffload}). It employs a compiler-assisted approach that leverages graph-driven memory management to treat remote memory access as explicit operations in the computation graph, specifically designed for hierarchical SuperNode architectures. Unlike reactive runtime systems, SuperNode represents data movement using cache operators within the compiler's Intermediate Representation (IR). This design enables a global, compile-time analysis of tensor lifetimes and execution dependencies. Leveraging this visibility, we develop a global execution-order refinement algorithm that statically schedules data transfers to hide remote memory latency behind compute-intensive regions. We implement SuperNode within the production deep learning framework MindSpore, adding a remote memory backend and specialized compiler passes. Evaluation on representative LLM workloads shows that SuperNode reduces peak device memory usage by up to 26\% for inference while maintaining end-to-end performance. Our work demonstrates that integrating memory-augmented hardware into the compiler's optimization framework is essential for scaling next-generation AI workloads.

cs.DC↗

arXiv2025

Matrix Completion Via Reweighted Logarithmic Norm Minimization

Low-rank matrix completion (LRMC) has demonstrated remarkable success in a wide range of applications. To address the NP-hard nature of the rank minimization problem, the nuclear norm is commonly used as a convex and computationally tractable surrogate for the rank function. However, this approach often yields suboptimal solutions due to the excessive shrinkage of singular values. In this letter, we propose a novel reweighted logarithmic norm as a more effective nonconvex surrogate, which provides a closer approximation than many existing alternatives. We efficiently solve the resulting optimization problem by employing the alternating direction method of multipliers (ADMM). Experimental results on image inpainting demonstrate that the proposed method achieves superior performance compared to state-of-the-art LRMC approaches, both in terms of visual quality and quantitative metrics.

cs.CV↗

arXiv2025

Observation of square-like moire lattice and quasicrystalline order in twisted rock-salt nitrides

Twistronics, which exploits moire modulation of lattice and electronic structures in twisted bilayers, has emerged as a powerful approach to engineer novel quantum states. Recent efforts have expanded beyond two dimensional van der Waals (vdWs) crystals to more complex, strongly correlated materials, where interfacial moire effects can dominate physical properties. Here we demonstrate a generalizable route to fabricate twisted bilayers of transition metal nitrides with vdWs like interfaces, using freestanding CrN membranes as a model system. Twisted bilayer CrN (tCrN) is realized by employing cubic alkaline earth metal monoxides as sacrificial layers, enabling the assembly of clean, controllable interfaces. Electron ptychography reveals well defined, periodic square moire superlattices in tCrN. For a twist angle of 16.3 degree, we identify a nearly commensurate moire lattice with coincident Cr columns, whereas at 45 degree we uncover localized octagonal quasicrystalline order with clear self-similarity. These results establish a practical platform for twisted TMNs and open avenues to explore moire-induced atomic configurations and emergent correlated phenomena in nitride based heterostructures.

cond-mat.mtrl-sci↗