arXiv subjects

Xiaoyin Li

Publications and source records attributed to Xiaoyin Li.

15 recordsLinked to original sources

arXiv2026

Spin-chirality-driven nonrelativistic Edelstein effects in two-dimensional antiferromagnets

Charge current-induced magnetic moment accumulation-Edelstein effect has been extensively attracting attention for its promising applications in spintronics. While most prior works focus on the spin-orbit coupling (SOC) induced Edelstein responses that rely on the presence of heavy elements, the nonrelativistic Edelstein effect (in the absence of SOC) that could be applied in a broader material family has been largely unexplored. Here, we perform a combined group-theoretical and ab initio numerical simulation study to show that vector spin chirality could serve as an effective control parameter of nonrelativistic Edelstein responses in antiferromagnetic system. In addition to spin degree of freedom, we also explore the orbital angular momentum contributions to current-induced magnetic moments (dubbed orbital Edelstein effect), which obey distinct symmetry constraints from the spin counterpart. Microscopically, vector spin chirality k gives rise to electronic and Zeeman-like band-geometric quantities, such as the anomalous spin/orbital polarizability and the Berry connection polarizability, which govern the nonrelativistic Edelstein responses. Our work identifies vector spin chirality as a key magnetic order parameter to enable and tune nonrelativistic Edelstein effects, and uncovers a new route toward electrically controlling magnetization without relying on SOC effect.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry-Protected Weyl Nodal Loops in a Triangular Altermagnet

Weyl semimetals and altermagnets represent two distinct classes of quantum materials exhibiting nontrivial topological and magnetic order, respectively. Here we report the realization of a Weyl nodal-loop altermagnet in Cr$_7$Se$_8$, combining neutron diffraction and first-principles calculations. The hexagonal system hosts a coplanar $120^\circ$ compensated magnetic order on a triangular lattice, which breaks inversion-time-reversal and translation-time-reversal symmetries simultaneously while preserving a crystalline mirror plane. The resulting electronic structure features linearly dispersing nodal loops close to the Fermi level ($E_F$) confined to the mirror-invariant $k_z=0$ plane. Along high-symmetry directions, the crossings near $E_F$ form Dirac-like fourfold degeneracies in the absence of spin-orbit coupling; at generic momenta, these crossings split into twofold and form continuous Weyl-like nodal loops protected by mirror symmetry. The momentum-dependent spin polarization exhibits an $f$-wave-like pattern characteristic of odd-parity altermagnets.

cond-mat.mtrl-sci↗

arXiv2026

Interplay of Quantum Size Effect and Tensile Strain on Surface Morphology of Sn(100) Islands

The quantum size effect (QSE) and strain effect are two key factors influencing the surface morphology of thin films, which can increase film surface roughness through QSE-induced thickness oscillation and strain-induced island formation, respectively. Surface roughness usually manifests in the early stages of film growth and diminishes beyond a critical thickness. In this work, we employ molecular beam epitaxy (MBE) to grow Sn(100) islands with varying thickness N on bilayer graphene-terminated 6H-SiC(0001) substrates. Scanning tunneling microscopy and spectroscopy measurements reveal an inverse surface roughness effect that highlights the interplay of QSE and misfit strain in shaping the surface morphology of Sn(100) islands. For N =< 10, the islands exhibit flat surfaces, while for N >= 26, the island surfaces become corrugated and patterned. For the intermediate range, i.e., 12 =< N =<24, both flat and patterned surfaces coexist, with the percentage coverage of the patterned surface oscillating as a function of N. By performing density functional theory calculations, we demonstrate that the unusual surface pattern evolution in our MBE-grown Sn(100) islands is a result of the interplay between QSE-induced surface roughing and tensile strain-induced smoothening effect.

cond-mat.mtrl-sci↗

arXiv2026

FedRD: Reducing Divergences for Generalized Federated Learning via Heterogeneity-aware Parameter Guidance

Heterogeneous federated learning (HFL) aims to ensure effective and privacy-preserving collaboration among different entities. As newly joined clients require significant adjustments and additional training to align with the existing system, the problem of generalizing federated learning models to unseen clients under heterogeneous data has become progressively crucial. Consequently, we highlight two unsolved challenging issues in federated domain generalization: Optimization Divergence and Performance Divergence. To tackle the above challenges, we propose FedRD, a novel heterogeneity-aware federated learning algorithm that collaboratively utilizes parameter-guided global generalization aggregation and local debiased classification to reduce divergences, aiming to obtain an optimal global model for participating and unseen clients. Extensive experiments on public multi-domain datasets demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.

cs.LG↗

arXiv2026

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices

With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional client-specific encoder. To enhance multi-source knowledge transfer, we adopt an attention-based global aggregation strategy. We conducted extensive experiments on diverse datasets to assess the efficacy of FedCCA. The experimental results demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.

cs.LG↗

arXiv2024

DMP_AI: An AI-Aided K-12 System for Teaching and Learning in Diverse Schools

The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and development is needed to realize its potential. Moreover, the creation of a comprehensive and cohesive system that effectively harnesses AI to support teaching and learning across a diverse range of primary and secondary schools presents substantial challenges that need to be addressed. To fill these gaps, especially in countries like China, we designed and implemented the DMP_AI (Data Management Platform_Artificial Intelligence) system, an innovative AI-aided educational system specifically designed for K-12 education. The system utilizes data mining, natural language processing, and machine learning, along with learning analytics, to offer a wide range of features, including student academic performance and behavior prediction, early warning system, analytics of Individualized Education Plan, talented students prediction and identification, and cross-school personalized electives recommendation. The development of this system has been meticulously carried out while prioritizing user privacy and addressing the challenges posed by data heterogeneity. We successfully implemented the DMP_AI system in real-world primary and secondary schools, allowing us to gain valuable insights into the potential and challenges of integrating AI into K-12 education in the real world. This system will serve as a valuable resource for supporting educators in providing effective and inclusive K-12 education.

cs.CY↗

arXiv2024

200mm Optical synthetic aperture imaging over 120 meters distance via Macroscopic Fourier ptychography

Fourier ptychography (FP) imaging, drawing on the idea of synthetic aperture, has been demonstrated as a potential approach for remote sub-diffraction-limited imaging. Nevertheless, the farthest imaging distance is still limited around 10 m even though there has been a significant improvement in macroscopic FP. The most severely issue in increasing the imaging distance is field of view (FoV) limitation caused by far-field condition for diffraction. Here, we propose to modify the Fourier far-field condition for rough reflective objects, aiming to overcome the small FoV limitation by using a divergent beam to illuminate objects. A joint optimization of pupil function and target image is utilized to attain the aberration-free image while estimating the pupil function simultaneously. Benefiting from the optimized reconstruction algorithm which effectively expands the camera's effective aperture, we experimentally implement several FP systems suited for imaging distance of 12 m, 65 m and 120m with the maximum synthetic aperture of 200 mm. The maximum synthetic aperture is thus improved by more than one order of magnitude of the state-of-the-art works from the furthest distance, with an over fourfold improvement in the resolution compare to single aperture. Our findings demonstrate significant potential for advancing the field of macroscopic FP, propelling it into a new stage of development.

physics.optics↗

arXiv2024

La$_2$O$_3$Mn$_2$Se$_2$: a correlated insulating layered d-wave altermagnet

Altermagnets represent a new class of magnetic phases without net magnetization that are invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated states and important material properties deriving from their non-relativistic spin-split band structure. Indeed, they are the magnetic analogue of unconventional superconductors and can yield spin polarized electrical currents in the absence of external magnetic fields, making them promising candidates for next-generation spintronics. Here, we report altermagnetism in the correlated insulator, magnetically-ordered tetragonal oxychalcogenide, La$_2$O$_3$Mn$_2$Se$_2$. Symmetry analysis reveals a $\mathit{d}_{x^2 - y^2}$-wave type spin momentum locking, which is supported by density functional theory (DFT) calculations. Magnetic measurements confirm the AFM transition below $\sim$166 K while neutron pair distribution function analysis reveals a 2D short-range magnetic order that persists above the Néel temperature. Single crystals are grown and characterized using X-ray diffraction, optical and electron microscopy, and microRaman spectroscopy to confirm the crystal structure, stoichiometry, and uniformity.

cond-mat.mtrl-sci↗

arXiv2023

Graph Theorem for Chiral Exact Flat Bands at Charge Neutrality

Chiral exact flat bands (FBs) at charge neutrality have attracted much recent interest, presenting an intriguing condensed-matter system to realize exact many-body phenomena, as specifically shown in "magic angle" twisted bilayer graphene for superconductivity and triangulene-based superatomic graphene for excitonic condensation. Yet, no generic physical model to realize such FBs has been developed. Here we present a new mathematical theorem, called bipartite double cover (BDC) theorem, and prove that the BDC of line-graph (LG) lattices hosts at least two chiral exact FBs of opposite chirality, i.e., yin-yang FBs, centered-around/at charge neutrality (E = 0) akin to the "chiral limit" of twisted bilayer graphene. We illustrate this theorem by mapping it exactly onto tight-binding lattice models of the BDC of LGs of hexagonal lattice for strong topological and of triangular lattice for fragile topological FBs, respectively. Moreover, we use orbital design principle to realize such exotic yin-yang FBs in non-BDC lattices to instigate their real material discovery. This work not only enables the search for exact chiral FBs at zero energy beyond moiré heterostructures, but also opens the door to discovering quantum semiconductor features with FB-enabled strongly correlated carriers.

cond-mat.mtrl-sci↗

arXiv2023

Recovery of phase constant from two-photon interference pattern by phase retrieval algorithm

For a HOM interferometer with two independent incident pulses, the interference pattern can be affected by adding a dispersion medium on one of the incident directions, but there hasn't been a method to reconstruct the phase constant of the medium from the interference pattern. To solve it, we adapted two phase retrieval algorithms and used them to recover the phase difference function between the two incident fields, from which the phase constant can be derived. Through simulations, we verified the convergence, accuracy, and robustness of the algorithms, indicating that this phase recovery process can be completed well with negligible error. Our research finds a new application direction for the phase recovery algorithm, provides an algorithmic tool for high-order dispersion measurement using two-photon interference, and paves the way for a higher resolution and phase-sensitive quantum tomography.

quant-ph↗

arXiv2023

Higher-order Topological Point State

Higher-order topological insulators (HOTIs) have attracted increasing interest as a unique class of topological quantum materials. One distinct property of HOTIs is the crystalline symmetry-imposed topological state at the lower-dimensional outer boundary, e.g. the zero-dimensional (0D) corner state of a 2D HOTI, used exclusively as a universal signature to identify higher-order topology but yet with uncertainty. Strikingly, we discover the existence of inner topological point states (TPS) in a 2D HOTI, as the embedded "end" states of 1D first-order TI, as exemplified by those located at the vacancies in a Kekule lattice. Significantly, we demonstrate that such inner TPS can be unambiguously distinguished from the trivial point-defect states, by their unique topology-endowed inter-TPS interaction and correlated magnetic response in spectroscopy measurements, overcoming an outstanding experimental challenge. Furthermore, based on first-principles calculations, we propose γ-graphyne as a promising material to observe the higher-order TPS. Our findings shed new light on our fundamental understanding of HOTIs, and also open an avenue to experimentally distinguishing and tuning TPS in the interior of a 2D sample for potential applications.

cond-mat.mtrl-sci↗

arXiv2022

Topological field-effect transistor with quantized ON/OFF conductance of helical/chiral dislocation states

Topology is a key ingredient driving the emergence of quantum devices. Topological field-effect transistor (TFET) has been proposed to outperform the conventional FET by replacing the ON state with topology-protected quantized conductance, while the OFF state remains the same normal insulating characteristics and hence bears similar drawbacks. Here, we demonstrate a proof-of-concept TFET having both ON and OFF quantized conductance, by switching between helical and chiral topological screw dislocation (SD) states in three-dimensional topological insulators. A pair of SDs are configured with one acting as channel and the other as gate controlled by local magnetic field. A reversible field-switching is achieved with the ON and OFF conductance of $2e^2/h$ and $e^2/h$, respectively, as shown by tight-binding quantum transport calculations. Furthermore, BaBiO$_3$ is shown as a candidate material having the desired topological SD states, based on first-principles calculations. Our findings open a new route to high-fidelity topological quantum devices.

cond-mat.mtrl-sci↗

arXiv2021

Giant Dzyaloshinskii-Moriya interaction and ratchet motion of bimeronic excitations in two-dimensional magnets

Topological magnetic excitations rooted in Dzyaloshinskii-Moriya interaction (DMI) are promising information carriers for next-generation memory and spintronic devices. The recently discovered two-dimensional (2D) magnets provide fertile new platforms for revealing rich physical phenomena with atomic-scale precision, as exemplified by the enhanced DMI associated with an effective electric field that breaks the inversion symmetry. Here we use first-principles calculations to establish a conceptually difierent compositional engineering approach to induce giant DMI in a representative CrMnI$_6$ 2D magnet, and the underlying mechanism is rooted in the spontaneous inversion symmetry breaking in the bipartite system. Using atomistic magnetics simulations, we further reveal that bimeronic excitations can emerge upon cooling a spin random state or perturbing a ferromagnetic state. Strikingly, the bimeron can exhibit unidirectional soliton-like propagation, and such a ratchet phenomenon is highly desirable for racetrack memories. These findings may prove instrumental in developing novel devices for quantum information based on 2D magnets.

cond-mat.mtrl-sci↗

arXiv2012

Prediction of time series by statistical learning: general losses and fast rates

We establish rates of convergences in time series forecasting using the statistical learning approach based on oracle inequalities. A series of papers extends the oracle inequalities obtained for iid observations to time series under weak dependence conditions. Given a family of predictors and $n$ observations, oracle inequalities state that a predictor forecasts the series as well as the best predictor in the family up to a remainder term $Δ_n$. Using the PAC-Bayesian approach, we establish under weak dependence conditions oracle inequalities with optimal rates of convergence. We extend previous results for the absolute loss function to any Lipschitz loss function with rates $Δ_n\sim\sqrt{c(Θ)/ n}$ where $c(Θ)$ measures the complexity of the model. We apply the method for quantile loss functions to forecast the french GDP. Under additional conditions on the loss functions (satisfied by the quadratic loss function) and on the time series, we refine the rates of convergence to $Δ_n \sim c(Θ)/n$. We achieve for the first time these fast rates for uniformly mixing processes. These rates are known to be optimal in the iid case and for individual sequences. In particular, we generalize the results of Dalalyan and Tsybakov on sparse regression estimation to the case of autoregression.

math.ST↗

arXiv2012

Prediction of quantiles by statistical learning and application to GDP forecasting

In this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator (also known as Exponentially Weighted aggregate) is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of Koenker and Bassett (1978), this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results.

math.ST↗