arXiv subjects

Xia Hong

Publications and source records attributed to Xia Hong.

At least 19 recordsLinked to original sources

arXiv2026

Probing GHz Spin Dynamics Across Magnetic Phase Transitions in CrCl3 Nanoflakes Using Nitrogen-Vacancy Microscopy

CrCl3, a layered van der Waals (vdW) magnet, exhibits in-plane magnetic anisotropy and enhanced interlayer coupling upon stacking, making it an ideal platform to host exotic nanoscale magnetic phenomena such as magnon hydrodynamics and meron-like topological spin defects. When interfaced with other vdW materials, its antiferromagnetic-to-ferromagnetic and ferromagnetic-to-paramagnetic phase transitions and magnetic anisotropy can be tuned by voltage, strain, and layer stacking. Understanding the spin dynamics of CrCl3 at its magnetic phase transitions is crucial to its applications in magnonics. Here, we investigate the spin dynamics of CrCl3 nanoflakes using cryogenic diamond quantum sensing microscopy, based on measuring optically detected magnetic resonance, Rabi oscillations, and spin-lattice relaxation time (T1) of shallow nitrogen vacancy (NV) centers in diamond. In the ferromagnetic regime, we observe a pronounced reduction in the NV spin resonance contrast, a collapse of the Rabi oscillations, and a strong enhancement by two orders of magnitude of the relaxation rate G1 = 1/T1. These observations indicate intensified spin fluctuations in the gigahertz range. Broadband ferromagnetic resonance spectroscopy on CrCl3 crystals reveals resonance frequencies in the 4-15 GHz range together with a linewidth of ~24 mT, further supporting the NV measurements. A phenomenological model of magnetic-noise-induced NV relaxation reproduces the temperature dependence of G1 by combining antiferromagnetic, ferromagnetic, and paramagnetic fluctuation channels, indicating that magnetic noise is strongest in the ferromagnetic regime and evolves markedly across the phase transition. These results are crucial for using CrCl3 in 2D magnonics and hybrid quantum-magnon systems.

cond-mat.mes-hall↗

arXiv2025

Structured Basis Function Networks: Loss-Centric Multi-Hypothesis Ensembles with Controllable Diversity

Existing approaches to predictive uncertainty rely either on multi-hypothesis prediction, which promotes diversity but lacks principled aggregation, or on ensemble learning, which improves accuracy but rarely captures the structured ambiguity. This implicitly means that a unified framework consistent with the loss geometry remains absent. The Structured Basis Function Network addresses this gap by linking multi-hypothesis prediction and ensembling through centroidal aggregation induced by Bregman divergences. The formulation applies across regression and classification by aligning predictions with the geometry of the loss, and supports both a closed-form least-squares estimator and a gradient-based procedure for general objectives. A tunable diversity mechanism provides parametric control of the bias-variance-diversity trade-off, connecting multi-hypothesis generalisation with loss-aware ensemble aggregation. Experiments validate this relation and use the mechanism to study the complexity-capacity-diversity trade-off across datasets of increasing difficulty with deep-learning predictors.

cs.LG↗

arXiv2025

Nitrogen-Vacancy Magnetometry of Edge Magnetism in WS2 Flakes

Two-dimensional (2D) magnets are of significant interest both as a platform for exploring novel fundamental physics and for their potential in spintronic and optoelectronic devices. Recent bulk magnetometry studies have indicated a weak ferromagnetic response in WS2, and theoretical predictions suggest edge-localized magnetization in flakes with partial hydrogenation. Here, we use room-temperature wide-field quantum diamond magnetometry to image pristine and Fe-implanted WS2 flakes of varying thicknesses (45-160 nm), exfoliated from bulk crystals and transferred to NV-doped diamond substrates. We observe direct evidence of edge-localized stray magnetic fields, which scale linearly with applied external magnetic field (4.4-220 mT), reaching up to 4.7 uT. The edge signal shows a limited dependence on the flake thickness, consistent with dipolar field decay and sensing geometry. Magnetic simulations using five alternative models favor the presence of edge magnetization aligned along an axis slightly tilted from the normal to the WS2 flake plane, consistent with spin canting in antiferromagnetically coupled edge states. Our findings establish WS2 as a promising platform for edge-controlled 2D spintronics.

cond-mat.mes-hall↗

arXiv2025

Ultrafast dynamics of ferroelectric polarization of NbOI$_{2}$ captured with femtosecond electron diffraction

Two-dimensional (2D) ferroelectric materials like NbOI$_{2}$ have garnered significant interest, yet their temporal response and synergetic interaction with light remain underexplored. Previous studies on the polarization of oxide ferroelectrics have relied on time-resolved optical second harmonic generation or ultrafast X-ray scattering. Here, we probe the laser-induced polarization dynamics of 2D NbOI$_{2}$ nanocrystals using ultrafast transmission electron diffraction and deflectometry. The deflection of the electron pulses is directly sensitive to the changes in the polarization, while the diffraction signal captures the structural evolution. Excited with a UV laser pulse, the polarization of NbOI$_{2}$ is initially suppressed for two picoseconds, then it recovers and overshoots, leading to a transiently enhanced polarization persisting for over 200 ps. This recovery coincides with coherent acoustic phonon generation, triggering a piezoresponse in the NbOI$_{2}$ nanocrystals. Our results offer a new method for sensing the ferroelectric order parameter in femtosecond time scales.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Hypothesis Prediction for Portfolio Optimization: A Structured Ensemble Learning Approach to Risk Diversification

This work proposes a unified framework for portfolio allocation, covering both asset selection and optimization, based on a multiple-hypothesis predict-then-optimize approach. The portfolio is modeled as a structured ensemble, where each predictor corresponds to a specific asset or hypothesis. Structured ensembles formally link predictors' diversity, captured via ensemble loss decomposition, to out-of-sample risk diversification. A structured data set of predictor output is constructed with a parametric diversity control, which influences both the training process and the diversification outcomes. This data set is used as input for a supervised ensemble model, the target portfolio of which must align with the ensemble combiner rule implied by the loss. For squared loss, the arithmetic mean applies, yielding the equal-weighted portfolio as the optimal target. For asset selection, a novel method is introduced which prioritizes assets from more diverse predictor sets, even at the expense of lower average predicted returns, through a diversity-quality trade-off. This form of diversity is applied before the portfolio optimization stage and is compatible with a wide range of allocation techniques. Experiments conducted on the full S&P 500 universe and a data set of 1.300 global bonds of various types over more than two decades validate the theoretical framework. Results show that both sources of diversity effectively extend the boundaries of achievable portfolio diversification, delivering strong performance across both one-step and multi-step allocation tasks.

q-fin.PM↗

arXiv2024

Multi-State-Action Tokenisation in Decision Transformers for Multi-Discrete Action Spaces

Decision Transformers, in their vanilla form, struggle to perform on image-based environments with multi-discrete action spaces. Although enhanced Decision Transformer architectures have been developed to improve performance, these methods have not specifically addressed this problem of multi-discrete action spaces which hampers existing Decision Transformer architectures from learning good representations. To mitigate this, we propose Multi-State Action Tokenisation (M-SAT), an approach for tokenising actions in multi-discrete action spaces that enhances the model's performance in such environments. Our approach involves two key changes: disentangling actions to the individual action level and tokenising the actions with auxiliary state information. These two key changes also improve individual action level interpretability and visibility within the attention layers. We demonstrate the performance gains of M-SAT on challenging ViZDoom environments with multi-discrete action spaces and image-based state spaces, including the Deadly Corridor and My Way Home scenarios, where M-SAT outperforms the baseline Decision Transformer without any additional data or heavy computational overheads. Additionally, we find that removing positional encoding does not adversely affect M-SAT's performance and, in some cases, even improves it.

cs.LG↗

arXiv2024

Semi-supervised segmentation of land cover images using nonlinear canonical correlation analysis with multiple features and t-SNE

Image segmentation is a clustering task whereby each pixel is assigned a cluster label. Remote sensing data usually consists of multiple bands of spectral images in which there exist semantically meaningful land cover subregions, co-registered with other source data such as LIDAR (LIght Detection And Ranging) data, where available. This suggests that, in order to account for spatial correlation between pixels, a feature vector associated with each pixel may be a vectorized tensor representing the multiple bands and a local patch as appropriate. Similarly, multiple types of texture features based on a pixel's local patch would also be beneficial for encoding locally statistical information and spatial variations, without necessarily labelling pixel-wise a large amount of ground truth, then training a supervised model, which is sometimes impractical. In this work, by resorting to label only a small quantity of pixels, a new semi-supervised segmentation approach is proposed. Initially, over all pixels, an image data matrix is created in high dimensional feature space. Then, t-SNE projects the high dimensional data onto 3D embedding. By using radial basis functions as input features, which use the labelled data samples as centres, to pair with the output class labels, a modified canonical correlation analysis algorithm, referred to as RBF-CCA, is introduced which learns the associated projection matrix via the small labelled data set. The associated canonical variables, obtained for the full image, are applied by k-means clustering algorithm. The proposed semi-supervised RBF-CCA algorithm has been implemented on several remotely sensed multispectral images, demonstrating excellent segmentation results.

cs.CV↗

arXiv2023

Transport Anisotropy in One-dimensional Graphene Superlattice in the High Kronig-Penney Potential Limit

One-dimensional graphene superlattice subjected to strong Kronig-Penney (KP) potential is promising for achieving electron lensing effect, while previous studies utilizing the modulated dielectric gates can only yield a moderate, spatially dispersed potential profile. Here, we realize high KP potential modulation of graphene via nanoscale ferroelectric domain gating. Graphene transistors are fabricated on PbZr$_{0.2}$Ti$_{0.8}$O$_{3}$ back-gates patterned with periodic, 100-200 nm wide stripe domains. Due to band reconstruction, the h-BN top-gating induces satellite Dirac points in samples with current along the superlattice vector $\hat{s}$, a feature absent in samples with current perpendicular to $\hat{s}$. The satellite Dirac point position scales with the superlattice period ($L$) as $\propto L^{\beta}$, with $\beta = -1.18 \pm 0.06$. These results can be well explained by the high KP potential scenario, with the Fermi velocity perpendicular to $\hat{s}$ quenched to about 1% of that for pristine graphene. Our study presents a promising material platform for realizing electron supercollimation and investigating flat band phenomena.

cond-mat.mes-hall↗

arXiv2023

Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction

Multi-modal problems can be effectively addressed using multiple hypothesis frameworks, but integrating these frameworks into learning models poses significant challenges. This paper introduces a Structured Radial Basis Function Network (s-RBFN) as an ensemble of multiple hypothesis predictors for regression. During the training of the predictors, first the centroidal Voronoi tessellations are formed based on their losses and the true labels, representing geometrically the set of multiple hypotheses. Then, the trained predictors are used to compute a structured dataset with their predictions, including centers and scales for the basis functions. A radial basis function network, with each basis function focused on a particular hypothesis, is subsequently trained using this structured dataset for multiple hypotheses prediction. The s-RBFN is designed to train efficiently while controlling diversity in ensemble learning parametrically. The least-squares approach for training the structured ensemble model provides a closed-form solution for multiple hypotheses and structured predictions. During the formation of the structured dataset, a parameter is employed to avoid mode collapse by controlling tessellation shapes. This parameter provides a mechanism to balance diversity and generalization performance for the s-RBFN. The empirical validation on two multivariate prediction datasets-air quality and energy appliance predictions-demonstrates the superior generalization performance and computational efficiency of the structured ensemble model compared to other models and their single-hypothesis counterparts.

cs.LG↗

arXiv2023

Interface-tuning of ferroelectricity and quadruple-well state in CuInP$_2$S$_6$ via ferroelectric oxide

Ferroelectric van der Waals CuInP$_2$S$_6$ possesses intriguing quadruple-well states and negative piezoelectricity. Its technological implementation has been impeded by the relatively low Curie temperature (bulk $T_C$ ~42 {\deg}C) and the lack of precise domain control. Here we show that CuInP$_2$S$_6$ can be immune to the finite size effect and exhibits enhanced ferroelectricity, piezoelectricity, and polar alignment in the ultrathin limit when interfaced with ferroelectric oxide PbZr$_{0.2}$Ti$_{0.8}$O$_3$ films. Piezoresponse force microscopy studies reveal that the polar domains in thin CuInP$_2$S$_6$ fully conform to those of underlying PbZr$_{0.2}$Ti$_{0.8}$O$_3$, where the piezoelectric coefficient changes sign and increases sharply with reducing thickness. High temperature $in$ $situ$ domain imaging points to a significantly enhanced $T_C$ exceeding 200 {\deg}C for 13 nm CuInP$_2$S$_6$ on PbZr$_{0.2}$Ti$_{0.8}$O$_3$. Density functional theory modeling and Monte Carlo simulations show that the enhanced polar alignment and $T_C$ can be attributed to interface-mediated structure distortion in CuInP$_2$S$_6$. Our study provides an effective material strategy to engineer the polar properties of CuInP$_2$S$_6$ for flexible nanoelectronic, optoelectronic, and mechanical applications.

cond-mat.mtrl-sci↗

arXiv2022

Remote Surface Optical Phonon Scattering in Ferroelectric Ba$_{0.6}$Sr$_{0.4}$TiO$_{3}$ Gated Graphene

We report the effect of remote surface optical (RSO) phonon scattering on carrier mobility in monolayer graphene gated by ferroelectric oxide. We fabricate monolayer graphene transistors back-gated by epitaxial (001) Ba$_{0.6}$Sr$_{0.4}$TiO$_{3}$ films, with field effect mobility up to 23,000 cm$^{2}$V$^{-1}$s$^{-1}$ achieved. Switching the ferroelectric polarization induces nonvolatile modulation of resistance and quantum Hall effect in graphene at low temperatures. Ellipsometry spectroscopy studies reveal four pairs of optical phonon modes in Ba$_{0.6}$Sr$_{0.4}$TiO$_{3}$, from which we extract the RSO phonon frequencies. The temperature dependence of resistivity in graphene can be well accounted for by considering the scattering from the intrinsic longitudinal acoustic phonon and the RSO phonon, with the latter dominated by the mode at 35.8 meV. Our study reveals the room temperature mobility limit of ferroelectric-gated graphene transistors imposed by RSO phonon scattering.

cond-mat.mes-hall↗

arXiv2022

Physical Vapor Transport Growth of Antiferromagnetic CrCl$_3$ Flakes Down to Monolayer Thickness

The van der Waals magnets CrX$_3$ (X = I, Br, and Cl) exhibit highly tunable magnetic properties and are promising candidates for developing novel two-dimensional (2D) magnetic devices such as magnetic tunnel junctions and spin tunneling transistors. Previous studies of CrCl$_3$ have mainly focused on mechanically exfoliated samples. Controlled synthesis of high quality atomically thin flakes is critical for their technological implementation but has not been achieved to date. Here, we report the growth of large CrCl$_3$ flakes with well-defined facets down to monolayer thickness (~0.6 nm) via the physical vapor transport technique. Both isolated flakes with well-defined facets and long stripe samples with the trilayer portion exceeding 60 $\mu$m have been obtained. High-resolution transmission electron microscopy studies show that the CrCl$_3$ flakes are single crystalline in the monoclinic structure, consistent with the Raman results. The room temperature stability of the CrCl$_3$ flakes decreases with decreasing thickness. The tunneling magnetoresistance of graphite/CrCl$_3$/graphite tunnel junctions confirms that few-layer CrCl$_3$ possesses in-plane magnetic anisotropy and N\'eel temperature of 17 K. Our study paves the path for developing CrCl$_3$-based scalable 2D spintronic applications.

cond-mat.mes-hall↗

arXiv2022

Perspective on Epitaxial NiCo2O4 Film as an Emergent Spintronic Material: Magnetism and Transport Properties

The ferrimagnetic inverse spinel NiCo2O4 has attracted extensive research interests for its versatile electrochemical properties, robust magnetic order, high conductivity, and fast spin dynamics, as well as its highly tunable nature due to the closely coupled charge, spin, orbital, lattice, and defect effects. Single-crystalline epitaxial thin films of NiCo2O4 present a model system for elucidating the intrinsic physical properties and strong tunability, which are not viable in bulk single crystals. In this perspective, we discuss the recent advances in epitaxial NiCo2O4 thin films, focusing on understanding its unusual magnetic and transport properties in light of crystal structure and electronic structure. The perpendicular magnetic anisotropy in compressively strained NiCo2O4 films is explained by considering the strong spin-lattice coupling, particularly on Co ions. The prominent effect of growth conditions reveals the complex interplay between the crystal structure, cation stoichiometry, valence state, and site occupancy. NiCo2O4 thin films also exhibit various magnetotransport anomalies, including linear magnetoresistance and sign change in anomalous Hall effect, which illustrate the competing effects of band intrinsic Berry phase and impurity scattering. The fundamental understanding of these phenomena will facilitate the functional design of NiCo2O4 thin films for nanoscale spintronic applications.

cond-mat.mtrl-sci↗

arXiv2022

Anomalous Hall Effect and Perpendicular Magnetic Anisotropy in Ultrathin Ferrimagnetic NiCo$_2$O$_4$ Films

The inverse spinel ferrimagnetic NiCo$_2$O$_4$ possesses high magnetic Curie temperature $T_C$, high spin polarization, and strain-tunable magnetic anisotropy. Understanding the thickness scaling limit of these intriguing magnetic properties in NiCo$_2$O$_4$ thin films is critical for their implementation in nanoscale spintronic applications. In this work, we report the unconventional magnetotransport properties of epitaxial (001) NiCo$_2$O$_4$ films on MgAl$_2$O$_4$ substrates in the ultrathin limit. Anomalous Hall effect measurements reveal strong perpendicular magnetic anisotropy for films down to 1.5 unit cell (1.2 nm), while $T_C$ for 3 unit cell and thicker films remains above 300 K. The sign change in the anomalous Hall conductivity ($\sigma_{xy}$) and its scaling relation with the longitudinal conductivity ($\sigma_{xx}$) can be attributed to the competing effects between impurity scattering and band intrinsic Berry curvature, with the latter vanishing upon the thickness driven metal-insulator transition. Our study reveals the critical role of film thickness in tuning the relative strength of charge correlation, Berry phase effect, spin orbit interaction, and impurity scattering, providing important material information for designing scalable epitaxial magnetic tunnel junctions and sensing devices using NiCo$_2$O$_4$.

cond-mat.mtrl-sci↗

arXiv2022

Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring

In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.

cs.LG↗

arXiv2021

Giant Transport Anisotropy in ReS$_2$ Revealed via Nanoscale Conducting Path Control

The low in-plane symmetry in layered 1T'-ReS$_2$ results in strong band anisotropy, while its manifestation in the electronic properties is challenging to resolve due to the lack of effective approaches for controlling the local current path. In this work, we reveal the giant transport anisotropy in monolayer to four-layer ReS$_2$ by creating directional conducting paths via nanoscale ferroelectric control. By reversing the polarization of a ferroelectric polymer top layer, we induce conductivity switching ratio of >1.5x10$^8$ in the ReS$_2$ channel at 300 K. Characterizing the domain-defined conducting nanowires in an insulating background shows that the conductivity ratio between the directions along and perpendicular to the Re-chain can exceed 5.5x10$^4$. Theoretical modeling points to the band origin of the transport anomaly, and further reveals the emergence of a flat band in few-layer ReS$_2$. Our work paves the path for implementing the highly anisotropic 2D materials for designing novel collective phenomena and electron lensing applications.

cond-mat.mes-hall↗

arXiv2020

Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rm SPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.

stat.ME↗

arXiv2020

An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilised as a metrics to detect potential abnormalities. The virtues of the proposed algorithm have been discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further.

stat.ME↗