arXiv subjects

Fei Xue

Publications and source records attributed to Fei Xue.

At least 37 records · Page 2Linked to original sources

arXiv2026

Nucleation and Antiphase Twin Control in Bi$_2$Se$_3$ via Step-Terminated Al$_2$O$_3$ Substrates

The epitaxial synthesis of high-quality 2D layered materials is an essential driver of both fundamental physics studies and technological applications. Bi$_2$Se$_3$, a prototypical 2D layered topological insulator, is sensitive to defects imparted during the growth, either thermodynamically or due to the film-substrate interaction. In this study, it is shown that step-terminated Al$_2$O$_3$ substrates with a high miscut angle (3°) can effectively suppress a particular hard-to-mitigate defect, the antiphase twin. Systematic investigations across a range of growth temperatures and substrate miscut angles confirm that atomic step edges act as preferential nucleation sites, stabilizing a single twin domain. First-principles calculations suggest that there is a significant energy barrier for twin boundary formation at step edges, supporting the experimental observations. Detailed structural characterization indicates that this twin-selectivity is lost through the mechanism of the 2D layers overgrowing the step edges, leading to higher twin density as the thickness increases. These findings highlight the complex energy landscape unique to 2D materials that is driven by the interplay between substrate properties, nucleation dynamics, and defect formation, and overcoming and controlling these are critical to improve material quality for quantum and electronic applications.

cond-mat.mtrl-sci↗

arXiv2026

Field-Tunable Meissner-Levitated Ferromagnetic Microsphere Sensor for Cryogenic Casimir and Short-Range Gravity Tests

Near-field force measurements at submicron separations can probe Casimir effects and hypothetical short-range interactions, but require cryogenic operation and stable, \textit{in situ} control of separation-dependent backgrounds. We propose a self-calibrating quantum force-gradient sensor in which a ferromagnetic microsphere is Meissner-levitated above a type-I superconducting plane, while a bias magnetic field reproducibly tunes the equilibrium gap for in situ separation scans without mechanical approach. The force gradient is encoded as a resonance-frequency shift tracked by a phase-locked loop, and the motion is read out with a SQUID-coupled, flux-tunable microwave resonator that provides adjustable measurement strength without optical heating. Using the input--output formalism, we derive the conditions for reaching the standard quantum limit (SQL) and identify a counterintuitive scaling law: because displacement-to-flux transduction increases with microsphere size, larger microspheres require fewer photons to reach the SQL, enabling a pathway to macroscopic quantum metrology. We quantify the trade-off between suppression of electrostatic patch potentials (via Au coating) and eddy-current dissipation, project force sensitivities of $\sim 10^{-19}\,\rm{N\,Hz^{-1/2}}$ at millikelvin temperatures, and outline protocols to extract Casimir pressure and constrain Yukawa-type deviations from Newtonian gravity over $0.1$--$10\,μ\mathrm{m}$.

quant-ph↗

arXiv2026

Empirical Bayes data integreation for multi-response regression

Motivated by applications in tissue-wide association studies (TWAS), we develop a flexible and theoretically grounded empirical Bayes approach for integrating %vector-valued outcomes data obtained from different sources. We propose a linear shrinkage estimator that effectively shrinks singular values of a data matrix. This problem is closely connected to estimating covariance matrices under a specific loss, for which we develop asymptotically optimal estimators. The basic linear shrinkage estimator is then extended to a local linear shrinkage estimator, offering greater flexibility. Crucially, the proposed method works under sparse/dense or low-rank/non low-rank parameter settings unlike well-known sparse or reduced rank estimators in the literature. Furthermore, the empirical Bayes approach offers greater scalability in computation compared to intensive full Bayes procedures. The method is evaluated through an extensive set of numerical experiments, and applied to a real TWAS data obtained from the Genotype-Tissue Expression (GTEx) project.

stat.ME↗

arXiv2026

2D ferroelectric narrow-bandgap semiconductor Wurtzite' type alpha-In2Se3 and its silicon-compatible growth

2D van der Waals ferroelectrics, particularly alpha-In2Se3, have emerged as an attractive building block for next-generation information storage technologies due to their moderate band gap and robust ferroelectricity stabilized by dipole locking. alpha-In2Se3 can adopt either the distorted zincblende or wurtzite structures; however, the wurtzite phase has yet to be experimental-ly validated, and its large-scale synthesis poses significant challenges. Here, we report an in-situ transport growth of centimeter-scale wurtzite type alpha-In2Se3 films directly on SiO2 substrates using a process combining pulsed laser deposition and chemical vapor deposition. We demonstrate that it is a narrow bandgap ferroelectric semiconductor, featuring a Curie tem-perature exceeding 620 K, a tunable bandgap (0.8-1.6 eV) modulated by charged domain walls, and a large optical absorption coefficient of 1.3 times 10 powers 6 per centemeter. Moreover, light absorption promotes the dynamic conductance range, linearity, and symmetry of the synapse devices, leading to a high recognition accuracy of 92.3 percent in a supervised pattern classification task for neuromorphic computing. Our findings demonstrate a ferroelectric polymorphism of In2Se3, highlighting its potential in ferroelectric synapses for neuromorphic computing.

cond-mat.mtrl-sci↗

arXiv2026

Packing minima of convex bodies

In 2021, Henk, Schymura and Xue introduced packing minima, associated with a convex body and a lattice, as packing counterparts to the covering minima of Kannan and Lovász. Motivated by conjectures on the volume inequalities for the successive minima, we generalized the definition of the packing minima to the class of all convex bodies that contain the origin in their interior. For these packing minima, we presented several novel volume inequalities and calculated the specific values of the packing minima for several special convex bodies.

math.MG↗

arXiv2026

Generalized Heterogeneous Functional Model with Applications to Large-scale Mobile Health Data

Physical activity is crucial for human health. With the increasing availability of large-scale mobile health data, strong associations have been found between physical activity and various diseases. However, accurately capturing this complex relationship is challenging, possibly because it varies across different subgroups of subjects, especially in large-scale datasets. To fill this gap, we propose a generalized heterogeneous functional method which simultaneously estimates functional effects and identifies subgroups within the generalized functional regression framework. The proposed method captures subgroup-specific functional relationships between physical activity and diseases, providing a more nuanced understanding of these associations. Additionally, we develop a pre-clustering method that enhances computational efficiency for large-scale data through a finer partition of subjects compared to true subgroups. We further introduce a testing procedure to assess whether the different subgroups exhibit distinct functional effects. In the real data application, we examine the impact of physical activity on the risk of dementia using the UK Biobank dataset, which includes over 96,433 participants. Our proposed method outperforms existing methods in future-day prediction accuracy, identifying three distinct subgroups, with detailed scientific interpretations for each subgroup. We also demonstrate the theoretical consistency of our methods. Codes implementing the proposed method are available at: https://github.com/xiaojing777/GHFM.

stat.ME↗

arXiv2026

Search for Ultralight Axion Dark Matter with a Levitated Ferromagnetic Torsional Oscillator

We present a search for ultralight axion dark matter coupled to electron spins using a levitated ferromagnetic torsional oscillator (FMTO). This platform directly measures axion-induced torques on a macroscopic spin-polarized body, combining large spin density with strong mechanical isolation to probe magnetic fluctuations below 10 Hz while suppressing gradient-field noise. In a first implementation, the experiment yielded 18000 s of analyzable data at room temperature under high vacuum with optical readout and triple-layer magnetic shielding. A likelihood-based statistical framework, incorporating stochastic fluctuations in the axion-field amplitude, was used to evaluate the data. No excess consistent with an axion-induced pseudo-magnetic field was observed near 2e-14 eV. To account for possible shielding-induced signal attenuation, we quantify its effect and report both the uncorrected (g_aee < 1e-7) and attenuation-corrected (g_aee < 6e-5) 90% CL limits on the axion-electron coupling. Looking ahead, improvements guided by both noise-budget analysis and shielding-attenuation considerations, including optimized levitation geometry, cryogenic operation, and superconducting shielding, are expected to boost sensitivity by multiple orders of magnitude.

hep-ph↗

arXiv2025

On Higher-Power Moments of $ Δ_a(x) $ for $-1/2<a<0$

Let $-1/2<a<0$ be a fixed real number and \begin{equation*} Δ_{a}(x)=\sideset{}{'}\sum_{n\leq x} σ_a(n)-ζ(1-a)x-\frac{ζ(1+a)}{1+a}x^{1+a}+\frac{1}{2}ζ(-a). \end{equation*} In this paper, we investigate the higher--power moments of $Δ_a(x)$ and give the corresponding asymptotic formula for the integral $\int_{1}^{T}Δ_a^k(x)\mathrm{d}x$, which constitutes an improvement upon the previous result of Zhai [9] for $k=3,4,5$ and an enlargement of the upper bound of $k$ to $7$.

math.NT↗

arXiv2025

Domain Wall Sliding-induced Polarization Switching in Multilayer Graphene

Electric polarization and metallicity are long believed not to coexist until the emergence of exceptionally rare material examples including the bulk polar metals and more recently two-dimensional (2D) van der Waals (vdW) materials such as 1T' WTe2. The electric polarization for the latter represents a new and distinguishable paradigm in materials science and physics because its electric polarization states embedded in the conduction electron sea are able to couple with (and controlled by) the external electric field. However, the microscopic polarization switching process and mechanism in these 2D vdW metallic materials have not been experimentally observed and remain elusive. Here, we report the first direct experimental imaging of the microscopic mechanism behind electric-field-coupled polarization switching in a metallic system. Our gate-tunable nanoscale optical imaging identifies the robust coexistence of electric polarization and appreciable carrier densities in adjacent polar domains hosting opposite electric polarizations in a 2D elemental metallic material, tetralayer graphene. We directly visualize and verify that the sliding domain wall (DW) solitons confined at the middlemost interface are responsible for the polarization switching in tetralayer graphene upon the application of electric fields and mechanical forces. Our work provides the first direct visualization of domain wall sliding-induced polarization switching in 2D elemental carbon at room temperature, significantly expanding and advancing the research of "ferroelectric metal" initially dubbed by P. W. Anderson and coauthors in 1965.

cond-mat.mes-hall↗

arXiv2025

Symmetry-designed BiFeO3 single domain spin cycloid for efficient spintronics

Deterministic control of coupled ferroelectric and antiferromagnetic orders remains a central challenge in multiferroics, limiting their integration into functional magnetoelectrics and magnonic-devices. (111)pc BiFeO3 with a robust single spin cycloid, offers direct magnetoelectric-coupling and a platform for efficient spin transport, yet multi-magnetic domains and ferroelectric-fatigue have prevented reproducible control. Here, we show that anisotropic-compressive in-plane strain stabilizes a single antiferromagnetic domain with unique spin-cycloid vector, by breaking the symmetry of the (111)pc plane. Epitaxial BiFeO3 films grown on orthorhombic NdGaO3 (011)o [(111)pc] substrates impose the required anisotropic in-plane strain and stabilizes single antiferromagnetic domain, as confirmed through direct imaging with scanning NV microscopy and non-resonant-x-ray-magnetic-scattering. Remarkably, these engineered films exhibit deterministic and non-volatile 180° switching of ferroelectric and single antiferromagnetic domains over 1,000 cycles. The monodomain state also enables anisotropic and threefold enhanced magnon transport with reduced scattering. Thus, symmetry-designed (111)pc monodomain BiFeO3 offers a robust platform for advanced magnetoelectric and magnonic applications.

cond-mat.mtrl-sci↗

arXiv2025

Accelerating Resonant Spectroscopy Simulations Using Multi-Shifted Bi-Conjugate Gradient

Resonant spectroscopies, which involve intermediate states with finite lifetimes, provide essential insights into collective excitations in quantum materials that are otherwise inaccessible. However, theoretical understanding in this area is often limited by the numerical challenges of solving Kramers-Heisenberg-type response functions for large-scale systems. To address this, we introduce a multi-shifted biconjugate gradient algorithm that exploits the shared structure of Krylov subspaces across spectra with varying incident energies, effectively reducing the computational complexity to that of linear spectroscopies. Both mathematical proofs and numerical benchmarks confirm that this algorithm substantially accelerates spectral simulations, achieving constant complexity independent of the number of incident energies, while ensuring accuracy and stability. This development provides a scalable, versatile framework for simulating advanced spectroscopies in quantum materials.

cond-mat.str-el↗

arXiv2025

On lattice coverings by locally anti-blocking bodies and polytopes with few vertices

In 2021, Ordentlich, Regev and Weiss made a breakthrough that the lattice covering density of any $n$-dimensional convex body is upper bounded by $cn^{2}$, improving on the best previous bound established by Rogers in 1959. However, for the Euclidean ball, Rogers obtained the better upper bound $n(\log_{e}n)^{c}$, and this result was extended to certain symmetric convex bodies by Gritzmann. The constant $c$ above is independent on $n$. In this paper, we show that such a bound can be achieved for more general classes of convex bodies without symmetry, including anti-blocking bodies, locally anti-blocking bodies and $n$-dimensional polytopes with $n+2$ vertices.

math.MG↗

arXiv2025

High-dimensional statistical inference for linkage disequilibrium score regression and its cross-ancestry extensions

Linkage disequilibrium score regression (LDSC) has emerged as an essential tool for genetic and genomic analyses of complex traits, utilizing high-dimensional data derived from genome-wide association studies (GWAS). LDSC computes the linkage disequilibrium (LD) scores using an external reference panel, and integrates the LD scores with only summary data from the original GWAS. In this paper, we investigate LDSC within a fixed-effect data integration framework, underscoring its ability to merge multi-source GWAS data and reference panels. In particular, we take account of the genome-wide dependence among the high-dimensional GWAS summary statistics, along with the block-diagonal dependence pattern in estimated LD scores. Our analysis uncovers several key factors of both the original GWAS and reference panel datasets that determine the performance of LDSC. We show that it is relatively feasible for LDSC-based estimators to achieve asymptotic normality when applied to genome-wide genetic variants (e.g., in genetic variance and covariance estimation), whereas it becomes considerably challenging when we focus on a much smaller subset of genetic variants (e.g., in partitioned heritability analysis). Moreover, by modeling the disparities in LD patterns across different populations, we unveil that LDSC can be expanded to conduct cross-ancestry analyses using data from distinct global populations (such as European and Asian). We validate our theoretical findings through extensive numerical evaluations using real genetic data from the UK Biobank study.

stat.ME↗

arXiv2025

Symmetry breaking of large-amplitude parametric oscillations in few-layer graphene nanomechanical resonators

Graphene nanomechanical resonators are well suited for the study of parametric oscillations. Their large frequency tunability and their pronounced nonlinearities enable an efficient modulation of their resonant frequencies. Here, we present measurements of the response of few-layer graphene nanomechanical resonators, each driven by a large parametric pump at frequency $2ω$ and a weak external drive at $ω$, where $ω$ is set near the mechanical resonant frequency $ω_0$. The pump actuates the resonator beyond the threshold for large-amplitude parametric oscillations, while the drive breaks the symmetry between the parametric phase states. By increasing and decreasing a gate voltage to detune $ω_0$ in the presence of the pump and the drive, we observe a double hysteresis in the response. The double hysteresis reveals the existence of two possible large-amplitude vibrational states whose phase difference is nearly $π$ radians. We deterministically prepare the resonator in either one of these states by cycling the gate voltage. We measure the stationary occupation probabilities of the two states in the presence of a white Gaussian force noise, and find that they strongly depend on the amplitude and on the phase of the external drive. The phase states of parametric oscillations with broken amplitude symmetry can be mapped to biased bi-modal degrees of freedom, such as Ising spins in an external magnetic field. Therefore, they hold promise as units of binary information.

cond-mat.mes-hall↗

arXiv2025

Ultralow-pressure mechanical-motion switching of ferroelectric polarization

Ferroelectric polarization switching, achieved by mechanical forces, enables the storage of stress information in ferroelectrics, and holds promise for human-interfacing applications. The prevailing mechanical approach is locally induced flexoelectricity with large strain gradients. However, this approach usually requires huge mechanical pressures, which greatly impedes device applications. Here, we report an approach of using triboelectric effect to mechanically, reversibly switch ferroelectric polarization across α-In2Se3 ferroelectric memristors. Through contact electrification and electrostatic induction effects, triboelectric units are used to sensitively detect mechanical forces and generate electrical voltage pulses to trigger α-In2Se3 resistance switching. We realize multilevel resistance states under different mechanical forces, by which a neuromorphic stress system is demonstrated. Strikingly, we achieve the reversal of α-In2Se3 ferroelectric polarization with a record-low mechanical pressure of ~ 10 kPa, and even with tactile touches. Our work provides a fundamental but pragmatic strategy for creating mechanical-tactile ferroelectric memory devices.

cond-mat.mtrl-sci↗

arXiv2025

PRAM: Place Recognition Anywhere Model for Efficient Visual Localization

Visual localization is a key technique to a variety of applications, e.g., autonomous driving, AR/VR, and robotics. For these real applications, both efficiency and accuracy are important especially on edge devices with limited computing resources. However, previous frameworks, e.g., absolute pose regression (APR), scene coordinate regression (SCR), and the hierarchical method (HM), have limited either accuracy or efficiency in both indoor and outdoor environments. In this paper, we propose the place recognition anywhere model (PRAM), a new framework, to perform visual localization efficiently and accurately by recognizing 3D landmarks. Specifically, PRAM first generates landmarks directly in 3D space in a self-supervised manner. Without relying on commonly used classic semantic labels, these 3D landmarks can be defined in any place in indoor and outdoor scenes with higher generalization ability. Representing the map with 3D landmarks, PRAM discards global descriptors, repetitive local descriptors, and redundant 3D points, increasing the memory efficiency significantly. Then, sparse keypoints, rather than dense pixels, are utilized as the input tokens to a transformer-based recognition module for landmark recognition, which enables PRAM to recognize hundreds of landmarks with high time and memory efficiency. At test time, sparse keypoints and predicted landmark labels are utilized for outlier removal and landmark-wise 2D-3D matching as opposed to exhaustive 2D-2D matching, which further increases the time efficiency. A comprehensive evaluation of APRs, SCRs, HMs, and PRAM on both indoor and outdoor datasets demonstrates that PRAM outperforms ARPs and SCRs in large-scale scenes with a large margin and gives competitive accuracy to HMs but reduces over 90\% memory cost and runs 2.4 times faster, leading to a better balance between efficiency and accuracy.

cs.CV↗

arXiv2025

Superconductivity and a van Hove singularity confined to the surface of a topological semimetal

The interplay between electronic topology and superconductivity is the subject of great current interest in condensed matter physics. For example, superconductivity induced on the surface of topological insulators is predicted to be triplet in nature, while the interplay between electronic correlations and topology may lead to unconventional superconductivity as in twisted bilayer graphene. Here, we unveil an unconventional two-dimensional superconducting state in the recently discovered Dirac nodal line semimetal ZrAs2 which is exclusively confined to the top and bottom surfaces within the crystal's ab plane. As a remarkable consequence of this emergent state, we observe a Berezinskii-Kosterlitz-Thouless (BKT) transition, the hallmark of two-dimensional superconductivity. Notably, this is the first observation of a BKT transition on the surface of a three-dimensional system. Furthermore, employing angle-resolved photoemission spectroscopy and first-principles calculations, we find that these same surfaces also host a two-dimensional van Hove singularity near the Fermi energy. The proximity of van Hove singularity to the Fermi level leads to enhanced electronic correlations contributing to the stabilization of superconductivity at the surface of ZrAs2, a unique phenomenon among topological semimetals. The surface-confined nature of the van Hove singularity, and associated superconductivity, realized for the first time, opens new avenues to explore the interplay between low-dimensional quantum topology, correlations, and superconductivity in a bulk material without resorting to the superconducting proximity effect.

cond-mat.supr-con↗

arXiv2025

MATCHA:Towards Matching Anything

Establishing correspondences across images is a fundamental challenge in computer vision, underpinning tasks like Structure-from-Motion, image editing, and point tracking. Traditional methods are often specialized for specific correspondence types, geometric, semantic, or temporal, whereas humans naturally identify alignments across these domains. Inspired by this flexibility, we propose MATCHA, a unified feature model designed to ``rule them all'', establishing robust correspondences across diverse matching tasks. Building on insights that diffusion model features can encode multiple correspondence types, MATCHA augments this capacity by dynamically fusing high-level semantic and low-level geometric features through an attention-based module, creating expressive, versatile, and robust features. Additionally, MATCHA integrates object-level features from DINOv2 to further boost generalization, enabling a single feature capable of matching anything. Extensive experiments validate that MATCHA consistently surpasses state-of-the-art methods across geometric, semantic, and temporal matching tasks, setting a new foundation for a unified approach for the fundamental correspondence problem in computer vision. To the best of our knowledge, MATCHA is the first approach that is able to effectively tackle diverse matching tasks with a single unified feature.

cs.CV↗