arXiv subjects

Jun Lu

Publications and source records attributed to Jun Lu.

At least 19 recordsLinked to original sources

arXiv2026

Hardware-aware quantum attention for fast radio burst identification

Fast radio bursts (FRBs) are millisecond-duration extragalactic pulses whose discovery requires searches over large signal-parameter spaces and the rejection of candidate sets dominated by radio-frequency interference and noise. Integrating quantum processors into FRB searches requires a workflow connecting telescop data to hardware-compatible models. Here we develop a pipeline that converts raw search-mode PSRFITS data into dynamic-spectrum segments and identifies FRB-like signals using a hybrid Quantum Vision Transformer (QViT). On labelled FAST data, the selected QViT achieves a mean accuracy of 94.00% and recall of 98.30%, with similar performance to a compact classical Vision Transformer. Recall reaches 97.11% on an FRB source excluded from training and model selection. Real-device execution on 82 segments yields a mean simulator-hardware Hellinger fidelity of 0.9161 +/- 0.0074. The workflow connects raw telescope data to quantum-assisted identification and provides an experimental basis for assessing the measurement requirements that must be addressed before survey-scale deployment.

astro-ph.HE↗

arXiv2026

KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image

Due to the complexity of hand kinematics and self-occlusion, existing 3D hand pose estimation methods based on single depth images struggle to comprehensively model the topological dependencies among hand joints. Furthermore, traditional hierarchical multitask architectures enforce a shared feature space for both 2D joint localization and depth estimation, which can induce mutual interference. To address these challenges, we propose a Kinematics-Aware Decoupled Learning Network (KAD-Net) for robust 3D hand pose estimation. Specifically, we first design a Finger Topology Constraint (FTC) module to enhance the representation of distal joints. This module utilizes three consecutive finger joints to construct a local kinematic representation to impose topological constraints, which supplements the kinematic features of the distal joints. The FTC module leverages the structural context from visible joints to assist in locating occluded distal joints, thereby improving robustness to occlusion. Additionally, we propose a task-decoupled hierarchical multitask framework. This framework separates 2D joint localization from depth estimation and incorporates a dedicated multitask learning strategy for depth regression, effectively isolating the UV and depth features to mitigate mutual interference and negative transfer. Extensive experiments demonstrate that KAD-Net outperforms existing methods on several benchmark datasets (ICVL, NYU, and MSRA), achieving state-of-the-art accuracy in 3D hand pose estimation. Potential applications of KAD-Net include human-computer interaction, virtual reality and gesture-based control systems.

cs.CV↗

arXiv2026

Generative Models: Principles, Architectures, and Applications

Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content. From photorealistic images to coherent text, from immersive videos to novel molecular structures, generative models now power applications that were once confined to science fiction. This book is designed to guide readers through the foundational principles, mathematical underpinnings, and practical architectures that underpin this revolution.

cs.AI↗

arXiv2026

VFM-Loc: Training-Free Cross-View Geo-Localization via Aligning Discriminative Visual Hierarchies

Cross-View Geo-Localization (CVGL) in remote sensing aims to locate a drone-view query by matching it to geo-tagged satellite images. Although supervised methods have achieved strong results on close-set benchmarks, they often fail to generalize to unconstrained, real-world scenarios due to severe viewpoint differences and dataset bias. To overcome these limitations, we present VFM-Loc, a training-free CVGL framework that leverages the generalizable visual representations from vision foundational models (VFMs). VFM-Loc identifies and matches discriminative visual clues across different viewpoints through a progressive alignment strategy. First, we design a hierarchical clue extraction mechanism using Generalized Mean pooling and Scale-Weighted R-MAC to preserve distinctive visual clues across scales while maintaining hierarchical confidence. Second, we introduce a statistical manifold alignment pipeline based on domain-wise PCA and Orthogonal Procrustes analysis, linearly aligning heterogeneous feature distributions in a shared metric space. Experiments demonstrate that VFM-Loc exhibits high accuracy on standard benchmarks and surpasses supervised methods by over 20\% in Recall@1 on the challenging LO-UCV dataset with large oblique angles. This work highlights that principled alignment of pre-trained features can effectively bridge the cross-view gap, establishing a robust and training-free paradigm for real-world CVGL. The relevant code is made available at: github.com/DingLei14/VFM-Loc.

cs.CV↗

arXiv2026

High-Fidelity Hole Spin Qubits Reveal Quadrupolar Nuclear-Bath Dynamics in Isotopically Purified Planar Germanium

Planar Germanium has emerged as a promising platform to build spin-based large scale quantum computers. By exploiting the anisotropic hyperfine interaction of holes in Ge, qubits with long T2* have been recently realized. While the performance of single qubits is still more or less limited by 73Ge nuclear spin fluctuations, the site-to-site variation of qubit sweet spot becomes obstacles to maintaining high fidelity of each qubit across the whole wafer. To achieve high performance Ge-based quantum circuit, it is therefore essential to eliminate the origin source of hyperfine noise. In its Silicon counterparts, reduction of 29Si abundance enables exceptional high-fidelity operation. In contrast, hole qubits based on isotopically purified Ge have not been demonstrated. Here, we report the synthesis of high quality 2-dimensional hole gas (2DHG) with enriched 70GeH4 precursor. Due to the suppression of nonzero spin nucleus, the qubits' T2* on the sweet spot is moderately extended beyond 20 us, surpassing the previous best reported Ge hole qubits. More importantly, the qubits' T2* off the sweet spot is enhanced to above 3 us, enabling single qubit gate fidelity exceeding 99.9% in both operating regimes. Hahn-echo spectroscopy further resolves a finite-frequency nuclear-noise channel that is distinct from the conventional Larmor-linked hyperfine response. We associate this channel with quadrupole-modified dynamics of residual 73Ge nuclei sampling local electric-field gradients near the Ge/SiGe interface. Its field scaling and angle-dependent visibility are consistent with a qubit-visible quadrupolar nuclear-noise component transduced through the anisotropic hyperfine interaction of Ge holes. These results establish isotopically purified planar Ge as a high-coherence scalable platform for hole spin qubits and provide a spectroscopic probe of interfacial quadrupolar nuclear dynamics.

quant-ph↗

arXiv2026

Canonical Models of Adjoint Foliated Structures on Surfaces

In this paper, we study adjoint foliated structures of the form $K_{\mathcal{F}}+D$ on algebraic surfaces and their minimal and canonical models. We investigate the effective behavior of the linear systems $|m(K_{\mathcal{F}}+D)|$ for sufficiently divisible $m>0$. As an application, we obtain an effective answer to a boundedness problem for foliated surfaces of general type posed by Hacon and Langer.

math.AG↗

arXiv2026

Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models

Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport, defect chemistry, mechanical integrity, and interfacial stability. Here, we outline a framework for autonomous SE discovery enabled by large artificial intelligence (AI) models, including machine learning interatomic potentials (MLIPs) and large language models (LLMs). We discuss the evolution from static materials databases to dynamic, self-updating knowledge systems, the role of MLIPs in bridging density functional theory (DFT) and long-timescale ion migration, and the emergence of LLMs as engines for literature mining, hypothesis generation, and scientific reasoning. We further describe a closed-loop architecture integrating AI-driven candidate design, multiscale simulation, uncertainty-aware selection, and experimental validation. Such systems shift SE research from intuition-guided exploration to data-informed, self-improving cycles. We conclude by highlighting challenges in data standardization, interfacial complexity, and reproducibility, and we propose design principles for building autonomous laboratories for solid-state battery materials.

cond-mat.mtrl-sci↗

arXiv2026

ac strain based thermodynamic criterion for vortex lattice in type-II superconductors

In type-I superconductors, zero electrical resistivity and perfect diamagnetism define two fundamental criteria for superconducting behavior. In contrast, type-II superconductors exhibit more complex mixed state physics, where magnetic flux penetrates the material above the lower critical field Hc1 in the form of quantized vortices, each carrying a single flux quantum. These vortices form a two dimensional lattice which persists up to another irreversible field (Hirr) and then melts into a dissipative liquid phase. The vortex lattice is fundamental to the magnetic and electrical properties of type II superconductors, ac strain susceptibility-a thermodynamic criterion-for identifying this phase has remained elusive. Here, we report the discovery of a dynamic magnetostrictive effect, wherein the geometry of the superconductor oscillates only under an applied alternating magnetic field due to the disturbance of the vortex lattice. This effect is detected by a thin piezoelectric transducer, which converts the excited geometric deformation into an in-phase ac voltage. Notably, we find a direct and nearly linear relationship between the signal amplitude and the vortex density in lattice across several representative type-II superconductors. In the vortex liquid phase above Hirr, the signal amplitude rapidly decays to zero near the upper critical field (Hc2), accompanied by a pronounced out-of-phase component due to enhanced dissipation. This dynamic magnetostrictive effect not only reveals an unexplored magnetoelastic property of the vortex lattice but also establishes a fundamental criterion for identifying the type-II superconductors.

cond-mat.supr-con↗

arXiv2026

Control of turn-to-turn contact resistivity in resistively insulated REBCO coils

Resistively insulated (RI) REBCO magnets feature short ramp times and low ramp losses while maintaining the advantages of no-insulation coils with high engineering current density and tolerance for defects in the REBCO conductor. Control of the turn-to-turn contact resistivity Rc is key to RI technology. Rc must be sufficiently high to prevent a large transient current, which could result in high mechanical stress during magnet quenches. Meanwhile it must be lower than the quench propagation limit to avoid conductor burn-out during a quench. Therefore, it is critical to control Rc within a suitable range of values which is usually coil specific. Previously, we discovered that Rc between two REBCO tapes with a stainless steel interlayer decreases dramatically with contact pressure cycling by up to three orders of magnitude. This drastic change made it impossible to design a suitable Rc value for a stainless steel co-wound RI magnet. In this work, we first present methods for mitigating Rc pressure cycling sensitivity. We found that by adding conductive fillers, such as conductive paste or epoxy, the Rc load cycling sensitivity is largely mitigated. For dry-wound coils, Rc load cycling sensitivity is mitigated by coating REBCO tape with a layer of 2- 3 um of PbSn solder. In addition, Rc can be controlled by oxidizing the stainless steel co-wind tape by heating stainless steel tapes at different temperatures in air. Using above methods, short sample tests showed that Rc was controlled to prescribed values of 1000 and 5000 uOhm-cm2 and was not sensitive to contact pressure cycling up to 30,000 cycles at 4.2 K. The new Rc control method was applied to a 6 double-pancake test coil which was tested at 4.2 K. The Rc in this test coil was comparable with the short sample results. This demonstrated the ability of this new method to control Rc in large coils.

cond-mat.supr-con↗

arXiv2026

Bayesian Matrix Decomposition and Applications

The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of variational inference for conducting the optimization. We refer the reader to literature in the field of Bayesian analysis for a more detailed introduction to the related fields. This book is primarily a summary of purpose, significance of important Bayesian matrix decomposition methods, e.g., real-valued decomposition, nonnegative matrix factorization, Bayesian interpolative decomposition, and the origin and complexity of the methods which shed light on their applications. The mathematical prerequisite is a first course in statistics and linear algebra. Other than this modest background, the development is self-contained, with rigorous proof provided throughout.

math.NA↗

arXiv2026

A First Course in Sparse Optimization

This article aims to provide a comprehensive overview of sparse optimization, with a focus on both sparse signal recovery and sparse regularization techniques. We will begin by exploring the foundations of sparse optimization, delving into the mathematical tools and models that underpin sparse signal recovery and LASSO. We will then discuss key algorithms for both sparse recovery (e.g., basis pursuit, matching pursuit) and sparse regularization (e.g., LASSO, elastic net), along with their applications in real-world problems. Throughout the text, we balance intuitive explanations with rigorous mathematical formulations to provide a comprehensive resource for both newcomers and experts in the field. Our aim is twofold: to provide a self-contained entry point for students and researchers new to the field, and to offer a rigorous reference for practitioners seeking to apply sparse optimization in science and engineering.

math.HO↗

arXiv2025

Observation of REBCO delamination in the resistive insulation nested coils

The REBCO coated conductor has the potential to be widely used in ultrahigh field magnets. It is well known, however, that it is not mechanically strong against delamination in the direction normal to its surface due to its intrinsic layered structure. Therefore, conductor delamination is one of the major design challenges for REBCO magnet coils. As a part of the development of the 40 T all-superconducting magnet at the National High Magnetic Field Laboratory, USA (NHMFL), a dry-wound resistive-insulation-nested-coils (RINC) was designed to reach 25.8 T. It used surface-treated stainless-steel tape as a co-wind to control the turn-to-turn contact resistance, and was fabricated and tested in a liquid helium bath. During the test, two of the double pancake modules exhibited resistive transitions at a current significantly lower than the designed value. The postmortem inspection of the REBCO conductor of these modules by reel-to-reel magnetization at 77 K found sections of very low critical current. Further investigations of one section by chemical etching, visual inspection, and electron microscopy revealed that conductor of this section was delaminated. We present the detailed findings of these postmortem characterizations. The implication of this type of delamination for future magnet designs will be discussed.

cond-mat.supr-con↗

arXiv2025

ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Images

Change detection (CD) in multitemporal remote sensing imagery presents significant challenges for fine-grained recognition, owing to heterogeneity and spatiotemporal misalignment. However, existing methodologies based on vision transformers or state-space models typically disrupt local structural consistency during temporal serialization, obscuring discriminative cues under misalignment and hindering reliable change localization. To address this, we introduce ChessMamba, a structure-aware framework leveraging interleaved state-space modeling for robust CD with multi-temporal inputs. ChessMamba integrates a SpatialMamba encoder with a lightweight cross-source interaction module, featuring two key innovations: (i) Chessboard interleaving with snake scanning order, which serializes multi-temporal features into a unified sequence within a single forward pass, thereby shortening interaction paths and enabling direct comparison for accurate change localization; and (ii) Structure-aware fusion via multi-dilated convolutions, selectively capturing center-and-corner neighborhood contexts within each mono-temporal. Comprehensive evaluations on three CD tasks, including binary CD, semantic CD and multimodal building damage assessment, demonstrate that ChessMamba effectively fuses heterogeneous features and achieves substantial accuracy improvements over state-of-the-art methods.The relevant code will be available at: github.com/DingLei14/ChessMamba.

cs.CV↗

arXiv2025

Fine-Grained AI Model Caching and Downloading With Coordinated Multipoint Broadcasting in Multi-Cell Edge Networks

6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for on-device AI inference. However, the substantial size of contemporary AI models poses significant challenges for edge caching under limited storage capacity, as well as for the concurrent delivery of heterogeneous models over wireless channels. To address these challenges, we propose a fine-grained AI model caching and downloading system that exploits parameter reusability, stemming from the common practice of fine-tuning task-specific models from a shared pre-trained model with frozen parameters. This system selectively caches model parameter blocks (PBs) at edge nodes, eliminating redundant storage of reusable parameters across different cached models. Additionally, it incorporates coordinated multipoint (CoMP) broadcasting to simultaneously deliver reusable PBs to multiple users, thereby enhancing downlink spectrum utilization. Under this arrangement, we formulate a model downloading delay minimization problem to jointly optimize PB caching, migration (among edge nodes), and broadcasting beamforming. To tackle this intractable problem, we develop a distributed multi-agent learning framework that enables edge nodes to explicitly learn mutual influence among their actions, thereby facilitating cooperation. Furthermore, a data augmentation approach is proposed to adaptively generate synthetic training samples through a predictive model, boosting sample efficiency and accelerating policy learning. Both theoretical analysis and simulation experiments validate the superior convergence performance of the proposed learning framework.

cs.NI↗

arXiv2025

Tunable Wigner Molecules in a Germanium Quantum Dot

The interplay between Coulomb interactions and kinetic energy underlies many exotic phases in condensed matter physics. In a two-dimensional electronic system, If Coulomb interaction dominates over kinetic energy, electrons condense into a crystalline phase which is referred as Wigner crystal. This ordered state manifests as Wigner molecule for few electrons at the microscopic scale. Observation of Wigner molecules has been reported in quantum dot and moire superlattice systems. Here we demonstrate hole Wigner molecules can be formed in a gate-defined germanium quantum dot with high tunability. By varying voltages applied to the quantum dot device, we can precisely tune the hole density by either changing the hole occupancy or the quantum dot size. For densities smaller than a certain critical value, Coulomb interaction localizes individual holes into ordered lattice sites, forming a Wigner molecule. By increasing the densities, melting process from a Wigner molecule to Fermi liquid-like particles is observed. An intermediate configuration which indicates the coexistence of ordered structure and disordered structure can be formed within a narrow effective density range. Our results provide a new platform for further exploration of the microscopic feature of strong correlated physics and open an avenue to exploit the application of Wigner molecules for quantum information in a very promising spin qubit platform.

cond-mat.mes-hall↗

arXiv2025

Chiral quantum magnets with optically and catalytically active spin ladders

Chiral quantum magnets with spin-states separated by a large energy gap are technologically attractive but difficult to realize. Geometrically frustrated topological states with nanoscale chirality may offer a chemical pathway to such materials. However, room temperature spin misalignment, weakness of Dzyaloshinskii-Moriya interactions, and high energy requirements for lattice distortions set high physicochemical barriers for their realization. Here, we show that layered iron oxyhydroxides (LIOX) address these challenges due to chirality transfer from surface ligands into spin-states of dimerized FeO6 octahedra with zig-zag stacking. The intercalation of chiral amino acids induces angular displacements in the antiferromagnetic spin pairs with a helical coupling of magnetic moments along the screw axis of the zig-zag chains, or helical spin-ladders. Unlike other chiral magnets, the spin states in LIOX are chemically and optically accessible, they display strong optical resonances with helicity-matching photons and enable spin-selective charge transport. The static rather than dynamic polarization of spin ladders in LIOX makes them particularly suitable for catalysis. Room-temperature spin pairing, field-tunability, environmental robustness, and synthetic simplicity make LIOX and its intercalates a uniquely practical family of quantum magnets.

cond-mat.mtrl-sci↗

arXiv2025

Matrix Decomposition and Applications

In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices. And now, matrix decomposition has become a core technology in machine learning, largely due to the development of the backpropagation algorithm in fitting a neural network. The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in numerical linear algebra and matrix analysis in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning matrix decomposition, given the paucity of scope to present this discussion, e.g., the separated analysis of the Euclidean space, Hermitian space, Hilbert space, and things in the complex domain. We refer the reader to literature in the field of linear algebra for a more detailed introduction to the related fields.

math.NA↗

arXiv2025

DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection

The goal of video anomaly detection is tantamount to performing spatio-temporal localization of abnormal events in the video. The multiscale temporal dependencies, visual-semantic heterogeneity, and the scarcity of labeled data exhibited by video anomalies collectively present a challenging research problem in computer vision. This study offers a dual-path architecture called the Dual-Branch Adaptive Multiscale Spatiotemporal Framework (DAMS), which is based on multilevel feature decoupling and fusion, enabling efficient anomaly detection modeling by integrating hierarchical feature learning and complementary information. The main processing path of this framework integrates the Adaptive Multiscale Time Pyramid Network (AMTPN) with the Convolutional Block Attention Mechanism (CBAM). AMTPN enables multigrained representation and dynamically weighted reconstruction of temporal features through a three-level cascade structure (time pyramid pooling, adaptive feature fusion, and temporal context enhancement). CBAM maximizes the entropy distribution of feature channels and spatial dimensions through dual attention mapping. Simultaneously, the parallel path driven by CLIP introduces a contrastive language-visual pre-training paradigm. Cross-modal semantic alignment and a multiscale instance selection mechanism provide high-order semantic guidance for spatio-temporal features. This creates a complete inference chain from the underlying spatio-temporal features to high-level semantic concepts. The orthogonal complementarity of the two paths and the information fusion mechanism jointly construct a comprehensive representation and identification capability for anomalous events. Extensive experimental results on the UCF-Crime and XD-Violence benchmarks establish the effectiveness of the DAMS framework.

cs.CV↗