arXiv subjects

Xiaobing Chen

Publications and source records attributed to Xiaobing Chen.

At least 19 recordsLinked to original sources

arXiv2026

Spin-orbit magnetism in altermagnets

While the anomalous Hall effect was proposed as a transport fingerprint of altermagnets, it originates from the spin-orbit coupling-induced net magnetization, corresponding to a distinct magnetic phase termed spin-orbit magnetism. However, the microscopic mechanism enabling spin-orbit magnets to yield a prominent anomalous Hall response despite a vanishingly small net magnetization remains elusive. Here, by employing oriented spin group theory and spin-orbit-coupling tensor expansion, we systematically disentangle the perturbative behaviors of orbital and spin magnetizations with respect to spin-orbit coupling in altermagnets. Remarkably, we find that only if the opposite-spin sublattices are connected through a fourfold rotation, the orbital and spin magnetizations exhibit distinct perturbative orders. In these altermagnets, we further discover a coaxial Hall effect characterized by the induced spin and orbital magnetizations aligning parallel to the N\'eel vector, which we further demonstrate by first-principles calculations in the altermagnet KV$_{2}$Se$_{2}$O. This effect holds great promise for achieving deterministic switching of the N\'eel order under weak external fields. Our work provides a systematic symmetry approach to identify potential altermagnetic candidates combining a large anomalous Hall effect with minimal net magnetization, paving the way for high-performance, stray-field-free spintronic applications.

cond-mat.mtrl-sci↗

arXiv2026

ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL

Text-to-SQL maps natural language questions to executable SQL queries. Modern databases often contain large and complex schemas, making schema linking a critical step for accurate SQL generation. Existing methods either rely on full-schema generation, which leaves schema linking implicit within a large search space, or use a separate retriever trained with static gold-column supervision, whose targets may be suboptimal for the current generator policy. To address this issue, we propose Adaptive Co-optimization via Empirical Credit Assignment for Text-to-SQL (ACE-SQL), a reinforcement learning (RL) framework that jointly optimizes schema retrieval and SQL generation under execution feedback. ACE-SQL constructs an online column-set pool from generator rollouts and derives adaptive on-policy retrieval targets from the column set most frequently associated with execution-correct rollouts. This induces bidirectional adaptation, where the retriever adapts toward column sets that the generator can execute correctly, while the generator adapts to the retriever's evolving schema selections under execution feedback. With approximately 3k synthetic Text-to-SQL question-database pairs for RL training, ACE-SQL achieves 65.3% greedy execution accuracy on BIRD Dev while using 0.93k output tokens per query. The repository is available at https://github.com/xbchen1/ACE-SQL.

cs.CL↗

arXiv2026

FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing

Mixture-of-Experts (MoE) models enable scalable neural networks through conditional computation, offering enhanced effectiveness and efficiency for next-generation wireless communications. However, deploying MoE with federated learning (FL) over wireless and IoT edge networks faces two critical challenges: 1) resource-constrained clients cannot store large AI models with full expert sets, and 2) non-IID data distributions cause severe expert load imbalance that degrades model performance. To this end, we propose FLEX-MoE, a federated MoE framework that jointly optimizes expert assignment and load balancing under limited client capacity. Specifically, our approach introduces client-expert fitness scores that quantify expert suitability for local datasets through training feedback, and employs an optimization-based algorithm to maximize client-expert specialization while enforcing balanced expert utilization system-wide. Unlike greedy methods that focus solely on personalization while ignoring load imbalance, FLEX-MoE addresses expert utilization skew, which is particularly severe in heterogeneous edge FL. Our experimental results demonstrate superior accuracy and consistently balanced expert utilization across diverse resource-constrained scenarios for edge computing.

cs.LG↗

arXiv2026

Identifying Oriented Spin Space Groups and Related Physical Properties Using an Online Platform FINDSPINGROUP

Unconventional magnets that combine antiferromagnetic structures with ferromagnetic-like responses are essential for the development of next-generation spintronics. Their emergent properties are fundamentally dictated by the interplay between exchange-driven magnetic geometry and spin-orbit coupling, which are described by spin space group (SSG) and magnetic space group (MSG) frameworks, respectively. However, the lack of direct correspondence between these frameworks, developed in different eras, hinders the systematic tracking of symmetry evolution of these intertwined physical contributions. In this work, we introduce FINDSPINGROUP, a computational architecture that implements the recently emerged, oriented spin space group framework to unify SSG and MSG descriptions. By automating the tracking of symmetry-breaking pathways from the non-relativistic to the relativistic limit, this online platform enables the classification of magnetic phases and the derivation of symmetry-constrained tensors for phenomena such as momentum-dependent spin splitting and the anomalous Hall effect. By standardizing data exchange through the spin crystallographic information file, this architecture establishes a computational infrastructure for the high-throughput discovery and design of unconventional magnets.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry Classification of Magnetic Orders using Oriented Spin Space Groups

Magnetism has witnessed remarkable progress in recent decades, largely driven by its potential for next-generation storage devices. However, the classification of magnetic orders, even for fundamental concepts such as ferromagnetism and antiferromagnetism, remains a topic of active evolution, particularly with the discovery of unconventional magnetic materials and advances in antiferromagnetic spintronics. Here, we present a unified classification of magnetic order utilizing the state-of-the-art spin space group (SSG) theory. Based on whether the net spin magnetization is constrained to zero by SSG, we systematically categorize magnetic orders into ferromagnetism (including ferrimagnetism) and antiferromagnetism. We further introduce an oriented SSG description, i.e., an SSG with a fixed magnetic orientation, thereby unifying the SSG and magnetic space group frameworks. This approach clearly reveals the symmetry-breaking pathway induced by spin-orbit coupling. The proposed group framework completes the intrinsic logic of magnetic symmetry and identifies a distinct magnetic phase, termed spin-orbit magnetism, in which the net spin magnetization is induced by spin-orbit coupling. Our work provides a comprehensive symmetry-based perspective for classifying magnetic order, offering fresh insights into unconventional magnets and broad applicability in spintronics and quantum material design.

cond-mat.mtrl-sci↗

arXiv2026

The rise of unconventional magnetism

Unconventional magnetism represents a paradigm shift in condensed matter physics, effectively bridging the fast, high-density advantages of antiferromagnets with the facile read-write capability of ferromagnets. Recent developments in spin space group theory have established a systematic methodology to decouple magnetic geometry from relativistic spin-orbit coupling, driving the exploration of unconventional magnets that exhibit compensated magnetization with time-reversal-odd responses. Here, we review unconventional magnetism across three pivotal facets in momentum space: spin textures, quantum geometry, and emergent quasiparticles. From the perspective of symmetry analysis, we elucidate the mechanisms underlying time-reversal-odd physical responses, including non-relativistic spin splitting, anomalous and nonlinear Hall effects, and exotic electronic and magnonic topological phases. Finally, we provide a forward-looking perspective on coupling unconventional magnetism with ferroelectricity, superconductivity, and moiré engineering. By exploiting symmetry-driven insights, this review highlights the functional potential of unconventional magnets in developing next-generation, high-speed, and energy-efficient spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Electric field switching of altermagnetic spin-splitting in multiferroic skyrmions

Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase featuring lifted Kramers degeneracy-have remained unreported until now. In this study, we demonstrate that BiFeO3, a multiferroic renowned for its strong coupling between ferroelectricity and magnetism, can transit from a spin cycloid to a Neel-type skyrmion under antidamping spin-orbit torque at room temperature. Strikingly, the altermagnetic spin splitting within BiFeO3 skyrmion can be reversed through the application of an electric field, revealed via the Circular photogalvanic effect. This quasiparticle, which possesses a neutral topological charge, holds substantial promise for diverse applications-most notably, enabling the development of unconventional computing systems with low power consumption and magnetoelectric controllability.

cond-mat.mtrl-sci↗

arXiv2025

Symmetry-guided prediction of magnetic-ordered ground states

Given the scarcity of experimentally confirmed magnetic structures, the prediction of magnetic ground states is crucial yet remains a long-sought challenge due to the complex potential energy landscape. Here, we propose a symmetry-guided framework that systematically generates magnetic configurations without requiring any experimental input or prior assumptions. Within a symmetry-breaking scenario, we incorporate the recently developed oriented spin space group formalism, which captures symmetry-breaking induced by both magnetic ordering and spin-orbit coupling. By performing nonrelativistic and relativistic first-principles calculations, we establish the energy ladder of the generated magnetic configurations. Exemplified by three prominent unconventional magnets, we demonstrate that only a few dozen calculations are sufficient to identify the ground-state magnetic structure. To demonstrate the universality and robustness of our approach, we conduct large-scale benchmark tests on the MAGNDATA database. Our framework successfully reproduces experimentally reported magnetic geometries for 78% of the surveyed materials, among which 93% have their spin orientations successfully generated when considering SOC. Furthermore, in a large-scale first-principles benchmark involving 305 compounds, 82% of experimentally reported magnetic structures are accurately captured within an energy tolerance of 5 meV per magnetic atom. Beyond reproducing known magnetic configurations, our framework further predicts a variety of low-energy metastable phases, including altermagnets, spin-orbit magnets, and noncollinear antiferromagnets with spin splitting or geometric Hall effect. Our work establishes a general and efficient route toward large-scale prediction of magnetic structures and unconventional magnets, and offers insight into the origins of magnetic interactions across diverse material systems.

cond-mat.mtrl-sci↗

arXiv2025

Competition between Weak Localization and Antilocalization of Dirac-like Fermions in a Spin-Polarized Two-Dimensional Electron Gas at KTaO3 (111) Interface

Quantum transport phenomena in two-dimensional electron gases (2DEGs) at oxide interfaces have garnered significant interest owing to their potential in spintronic and quantum information technologies. Here, we systematically investigate the quantum conductance corrections of spin-polarized 2DEGs formed at the interfaces between two insulating oxides, ferromagnetic EuTiO3 (ETO) films and (111)-oriented KTaO3 (KTO) substrates. The anomalous Hall effect and hysteretic magnetoresistance provide clear evidence for long-range ferromagnetic order in the 2DEGs, which could be attributed to interfacial Eu doping in combination with the magnetic proximity effect of the ETO layer. The breaking of time-reversal symmetry by ferromagnetism in the 2DEGs, and with the assistance of spin-orbit coupling effect, gives rise to a nontrivial Berry phase. This results in a competition between weak localization (WL) and weak antilocalization (WAL) in the quantum transport of Dirac-like fermions at the KTO (111) interfaces. Notably, this competitive behavior can be effectively tuned by optical gating via a photoexcitation-induced shift of the Fermi level. Our findings demonstrate a controllable platform based on spin-polarized oxide 2DEGs for quantum transport, opening new avenues for spin-orbitronic and topological electronic applications.

cond-mat.str-el↗

arXiv2025

Pruning and Malicious Injection: A Retraining-Free Backdoor Attack on Transformer Models

Transformer models have demonstrated exceptional performance and have become indispensable in computer vision (CV) and natural language processing (NLP) tasks. However, recent studies reveal that transformers are susceptible to backdoor attacks. Prior backdoor attack methods typically rely on retraining with clean data or altering the model architecture, both of which can be resource-intensive and intrusive. In this paper, we propose Head-wise Pruning and Malicious Injection (HPMI), a novel retraining-free backdoor attack on transformers that does not alter the model's architecture. Our approach requires only a small subset of the original data and basic knowledge of the model architecture, eliminating the need for retraining the target transformer. Technically, HPMI works by pruning the least important head and injecting a pre-trained malicious head to establish the backdoor. We provide a rigorous theoretical justification demonstrating that the implanted backdoor resists detection and removal by state-of-the-art defense techniques, under reasonable assumptions. Experimental evaluations across multiple datasets further validate the effectiveness of HPMI, showing that it 1) incurs negligible clean accuracy loss, 2) achieves at least 99.55% attack success rate, and 3) bypasses four advanced defense mechanisms. Additionally, relative to state-of-the-art retraining-dependent attacks, HPMI achieves greater concealment and robustness against diverse defense strategies, while maintaining minimal impact on clean accuracy.

cs.LG↗

arXiv2025

Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs

The adaptation of large language models (LLMs) to time series forecasting poses unique challenges, as time series data is continuous in nature, while LLMs operate on discrete tokens. Despite the success of LLMs in natural language processing (NLP) and other structured domains, aligning time series data with language-based representations while maintaining both predictive accuracy and interpretability remains a significant hurdle. Existing methods have attempted to reprogram time series data into text-based forms, but these often fall short in delivering meaningful, interpretable results. In this paper, we propose a multi-level text alignment framework for time series forecasting using LLMs that not only improves prediction accuracy but also enhances the interpretability of time series representations. Our method decomposes time series into trend, seasonal, and residual components, which are then reprogrammed into component-specific text representations. We introduce a multi-level alignment mechanism, where component-specific embeddings are aligned with pre-trained word tokens, enabling more interpretable forecasts. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art models in accuracy while providing good interpretability.

cs.CL↗

arXiv2025

Light-Induced Giant Enhancement of the Nonlinear Hall Effect in Two-Dimensional Electron Gases at KTaO3 (111) Interfaces

The nonlinear Hall effect (NLHE), an emergent phenomenon in noncentrosymmetric systems, enables the generation of a transverse voltage without an external magnetic field through a second-order electrical response. However, achieving a sizable NLHE signal remains a critical challenge for its application in frequency-doubling and rectifying devices. Here, we report a light-induced giant enhancement of the NLHE in the two-dimensional electron gas (2DEG) at the CaZrO3/KTaO3 (111) interface. Under light illumination, the second harmonic Hall voltage (V2ω y) increases substantially and undergoes a sign reversal. Correspondingly,the second-order transverse conductivity increases by nearly five orders of magnitude, reaching 2.4 um V-1 omega-1, while also reversing its sign. Scaling analysis indicates that skew scattering is the dominant mechanism underlying the NLHE and is highly tunable via optical gating. Photoexcitation pumps electrons from in-gap states into the higher-lying Ta 5d conduction band, generating high-mobility photocarriers that significantly increase the cubic transport scattering time, thereby driving a dramatic enhancement of σ(2) yxx. First-principles calculations further reveal that the Berry curvature distribution on the Fermi surface strongly depends on band filling. As the Fermi level approaches a band crossing in the Ta 5d subband near the M point, the Berry curvature triple undergoes a sign change, accounting for the experimentally observed sign reversal of the nonlinear Hall response. Our work offers a new strategy to optically boost and tune the nonlinear Hall effect in oxide 2DEG systems, paving the way for applications in light-controlled rectification and nonlinear electronic devices.

cond-mat.mes-hall↗

arXiv2025

Sliding ferroelectric control of unconventional magnetism in stacked bilayers

The control of unconventional magnetism, which displays ferromagnetism-like properties with compensated magnetization, has drawn intense attention for advancing antiferromagnetic spintronics. Here, through symmetry analysis, we propose a general stacking rule, characterized by a connection operator linking two stacked bilayers, for controlling unconventional magnetism via sliding ferroelectricity. Such rule enables the simultaneous switching of both electric polarization and nonrelativistic spin splitting or anomalous Hall effect in altermagnets, a class of collinear unconventional magnets. By comprehensively surveying the 80 layer groups, we identify all the stacking orders that allow for such two types of simultaneous switching. Furthermore, we extend the stacking rule to collinear compensated ferrimagnets, where the opposite-spin sublattices are not connected by any symmetry operator, yet the net magnetization remains zero. Combined with first-principles calculations, we demonstrate the sliding ferroelectric control of spin polarization and anomalous Hall effect in the altermagnetic AgF2 and Fe2MoSe4 bilayers. Our work provides a symmetry strategy for achieving ferroelectric control of unconventional magnetism in bilayer systems and opens avenues for exploring new types of magnetoelectric coupling.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach

The integration of Federated Learning (FL) and Mixture-of-Experts (MoE) presents a compelling pathway for training more powerful, large-scale artificial intelligence models (LAMs) on decentralized data while preserving privacy. However, efficient federated training of these complex MoE-structured LAMs is hindered by significant system-level challenges, particularly in managing the interplay between heterogeneous client resources and the sophisticated coordination required for numerous specialized experts. This article highlights a critical, yet underexplored concept: the absence of robust quantitative strategies for dynamic client-expert alignment that holistically considers varying client capacities and the imperative for system-wise load balancing. Specifically, we propose a conceptual system design for intelligent client-expert alignment that incorporates dynamic fitness scoring, global expert load monitoring, and client capacity profiling. By tackling these systemic issues, we can unlock more scalable, efficient, and robust training mechanisms {with fewer communication rounds for convergence}, paving the way for the widespread deployment of large-scale federated MoE-structured LAMs in edge computing with ultra-high communication efficiency.

cs.LG↗

arXiv2025

Magnetic geometry induced quantum geometry and nonlinear transports

The combination of quantum geometry and magnetic geometry in magnets excites diverse phenomena, some critical for antiferromagnetic spintronics. However, very few material platforms have been predicted and experimentally verified to date, with the material pool restricted by the assumed need for strong spin-orbit coupling (SOC). Here, we bypass the need for SOC by considering magnetic order induced quantum geometry and corresponding nonlinear transports (NLTs) in antiferromagnets (AFMs). By integrating spin space group theory into the symmetry analysis, we find that collinear and coplanar magnetic geometries can only induce NLT driven by Berry curvature dipole, and noncoplanar ones may trigger NLT driven by dipoles of Berry curvature, inverse mass, and quantum metric. Using this approach, we establish a materials database of 260 AFMs with SOC-free NLT effects, and complement this with first-principles calculations on several prototypical material candidates. Our work not only provides a universal theoretical framework for studying various magnetism-driven transport effects, but also predicts broad, experimentally accessible material platforms for antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Topological Charge Quadrupole Protected by Spin-Orbit U(1) Quasi-Symmetry in Antiferromagnet NdBiPt

The interplay of symmetry and topology in crystal solids has given rise to various elementary excitations as quasiparticles. Among these, those with significant Berry-phase-related transport responses are of particular interest. Here, we predict a new type of quasiparticle called topological charge quadruple (TCQ), which is analogous to a charge quadrupole but consists of two closely-packed pairs of Weyl points in momentum space, specifically in a half-Heusler antiferromagnet NdBiPt. Interestingly, the TCQ is protected by the spin-orbit $U(1)$ quasi-symmetry, rather than any exact crystallographic symmetries. This quasi-symmetry restricts the energy splitting induced by symmetry-lowering perturbations to a second-order effect. Furthermore, the closely located Berry curvature sources and sinks in the TCQ lead to a large Berry curvature dipole, resulting in a significant nonlinear Hall effect. Our work opens an avenue for designing novel quasiparticles using quasi-symmetries and developing materials with enhanced nonlinear responses.

cond-mat.mtrl-sci↗

arXiv2025

Ferroelectric switchable altermagnetism

We propose a novel ferroelectric switchable altermagnetism effect, the reversal of ferroelectric polarization is coupled to the switching of altermagnetic spin splitting. We demonstrate the design principles for the ferroelectric altermagnets and the additional symmetry constraints necessary for switching the altermagnetic spin splitting through flipping the electric polarization based on the state-of-the-art spin-group symmetry techniques. 22 ferroelectric altermagnets are found by screening through the 2001 experimental reported magnetic structures in the MAGNDATA database and 2 of them are identified as ferroelectric switchable altermagnets. Using the hybrid improper ferroelectric material [C(NH2)3]Cr(HCOO)3 as an example, we show how the altermagnetic spin splitting is tightly coupled to the ferroelectric polarization, providing an ideal platform for designing electric-field-controllable multiferroic devices. Finally, we find that such manipulation of altermagnetism can be detected by monitoring the physical quantities that are related to the non-vanishing Berry curvature dipole, such as the linearly polarized photogalvanic spin current.

cond-mat.mtrl-sci↗

arXiv2024

DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility.

cs.GT↗