arXiv subjects

Runhan Li

Publications and source records attributed to Runhan Li.

12 recordsLinked to original sources

arXiv2026

Altermagnetism from a Cu-Fe Lieb Lattice in FeSe/Cuprate Heterostructures

Realizing altermagnetism in high-$T_c$ cuprate-based systems would provide a direct route for studying spin-split electronic bands in the absence of net magnetization and investigate their interplay with unconventional superconductivity. Here, we propose that FeSe/cuprate heterostructures offer such a platform, where a 45$^\circ$ twist of Cu and Fe layers creates an effective CuFe$_2$ Lieb lattice in which Fe magnetic order and Cu-Fe hybridization through the ligands induces altermagnetic $d$-wave spin splitting. A minimal tight-binding model shows that this mechanism is generic. Furthermore, a substrate-induced inequivalence of the two Se sites in FeSe provides a second route in which altermagnetism originates in the Fe layer and is transferred to the cuprate layer by proximity. Density functional theory calculations for FeSe/Bi$_2$Sr$_2$CuO$_6$ heterostructures confirm the viability of both mechanisms and reveal ways to enhance the spin splitting. These results establish superconducting cuprate/transition metal chalcogenide heterostructures as a promising setting for engineering altermagnetism and studying its coupling to unconventional superconductivity.

cond-mat.str-el↗

arXiv2026

Chiral Weyl-Kondo semimetals and hexagonal heavy fermion systems

Strong correlation, in concert with symmetry and topology, engenders novel gapless phases of matter, though only a tip of the iceberg has been seen. An exemplary framework is provided by Weyl-Kondo semimetals, in which Weyl fermions develop through crystalline symmetry constraints on the emergent low-energy heavy-fermion excitations. This paradigm has opened up new opportunities to explore correlated topologies without a noninteracting counterpart, but fully realizing this potential requires a large base of candidate materials. Here we confront the challenge on both fronts by studying heavy fermion systems with hexagonal space groups. This family contains a large number of chiral nonsymmorphic crystal structures that promote Weyl degeneracies and, in addition, feature geometric frustration in the $f$-electron magnetism. Our calculations for the heavy fermion states identify Weyl-Kondo semimetals with chiral or achiral Weyl nodes in the respective structural classes. We also develop the first search strategy of any kind for the difficult case of strongly correlated materials, which is also suitable for automation, using a combination of materials database, symmetry classification and search for desired experimental properties, and propose as candidate topological heavy fermion systems the chiral CePt$_2$B and achiral Ce$_2$NiGe$_3$ and Ce$_6$Co$_{2-\delta}$Si$_3$. Our findings raise the prospect for strongly correlated metallic topology in the unusual setting of exotic quantum magnetism and, moreover, point a way to go beyond serendipity in the search for novel strongly correlated quantum materials.

cond-mat.str-el↗

arXiv2026

SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity

Industrial recommender systems typically rely on multi-task learning to estimate diverse user feedback signals and aggregate them for ranking. Recent advances in model scaling have shown promising gains in recommendation. However, naively increasing model capacity imposes prohibitive online inference costs and often yields diminishing returns for sparse tasks with skewed label distributions. This mismatch between uniform parameter scaling and heterogeneous task capacity demands poses a fundamental challenge for scalable multi-task recommendation. In this work, we investigate parameter sparsification as a principled scaling paradigm and identify two critical obstacles when applying sparse Mixture-of-Experts (MoE) to multi-task recommendation: exploded expert activation that undermines instance-level sparsity and expert load skew caused by independent task-wise routing. To address these challenges, we propose SMES, a scalable sparse MoE framework with progressive expert routing. SMES decomposes expert activation into a task-shared expert subset jointly selected across tasks and task-adaptive private experts, explicitly bounding per-instance expert execution while preserving task-specific capacity. In addition, SMES introduces a global multi-gate load-balancing regularizer that stabilizes training by regulating aggregated expert utilization across all tasks. SMES has been deployed in Kuaishou large-scale short-video services, supporting over 400 million daily active users. Extensive online experiments demonstrate stable improvements, with GAUC gain of 0.29% and a 0.31% uplift in user watch time.

cs.IR↗

arXiv2025

Tight-binding and density-functional study of the Raman tensor in two-dimensional massive Dirac fermion systems

Recently, two unusual features were theoretically predicted for the Raman response of out-of-plane phonons in magnetic two-dimensional materials hosting massive Dirac fermions. First, the phase difference between certain Raman tensor elements was found to be quantized to $\pm \pi/2$, sensitive only to the sign of the Dirac fermion mass. Second, a selection rule was identified in the Raman intensity under circularly polarized light, which generalizes the well-known optical valley selection rule. These predictions were based on a low-energy effective model in the continuum approximation. Here, we test the robustness of those results for more realistic theoretical approaches. First, we calculate the Raman tensor for an electronic tight-binding model on a honeycomb lattice with broken time-reversal and inversion symmetries. Second, we compute the Raman tensor from density-functional theory for a monolayer of ferromagnetic 2H-RuCl$_2$. Both calculations corroborate the analytical results found in the continuum model, thereby theoretically confirming the peculiar behavior of the Raman tensor for two dimensional massive Dirac fermion systems.

cond-mat.mtrl-sci↗

arXiv2024

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corresponding annotations of different clients often contain complex label noise of varying degrees, which inevitably causes the performance degradation. Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients. In this work, we propose a two-stage framework FedELC to tackle this complicated label noise issue. The first stage aims to guide the detection of noisy clients with higher label noise, while the second stage aims to correct the labels of noisy clients' data via an end-to-end label correction framework which is achieved by learning possible ground-truth labels of noisy clients' datasets via back propagation. We implement sixteen related methods and evaluate five datasets with three types of complicated label noise scenarios for a comprehensive comparison. Extensive experimental results demonstrate our proposed framework achieves superior performance than its counterparts for different scenarios. Additionally, we effectively improve the data quality of detected noisy clients' local datasets with our label correction framework. The code is available at https://github.com/Sprinter1999/FedELC.

cs.LG↗

arXiv2024

Topology-engineered orbital Hall effect in two-dimensional ferromagnets

Recent advances in manipulation of orbital angular momentum (OAM) within the paradigm of orbitronics present a promising avenue for the design of future electronic devices. In this context, the recently observed orbital Hall effect (OHE) occupies a special place. Here, focusing on both the second-order topological and quantum anomalous Hall insulators in two-dimensional ferromagnets, we demonstrate that topological phase transitions present an efficient and straightforward way to engineer the OHE, where the OAM distribution can be controlled by the nature of the band inversion. Using first-principles calculations, we identify Janus RuBrCl and three septuple layers of MnBi$_2$Te$_4$ as experimentally feasible examples of the proposed mechanism of OHE engineering by topology. With our work we open up new possibilities for innovative applications in topological spintronics and orbitronics.

cond-mat.mtrl-sci↗

arXiv2023

Federated Skewed Label Learning with Logits Fusion

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods.

cs.LG↗

arXiv2023

Managed Geo-Distributed Feature Store: Architecture and System Design

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose, this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.

cs.LG↗

arXiv2023

Knowledge Distillation in Federated Edge Learning: A Survey

The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data. Limited by device hardware, diverse user behaviors and network infrastructure, the algorithm design of FEL faces challenges related to resources, personalization and network environments. Fortunately, Knowledge Distillation (KD) has been leveraged as an important technique to tackle the above challenges in FEL. In this paper, we investigate the works that KD applies to FEL, discuss the limitations and open problems of existing KD-based FEL approaches, and provide guidance for their real deployment.

cs.LG↗

arXiv2022

Ferroelectric higher-order topological insulator in two dimensions

The interplay between ferroelectricity and band topology can give rise to a wide range of both fundamental and applied research. Here, we map out the emergence of nontrivial corner states in two-dimensional ferroelectrics, and remarkably demonstrate that ferroelectricity and corner states are coupled together by crystallographic symmetry to realize the electric control of higher-order topology. Implemented by density functional theory, we identify a series of experimentally synthesized two-dimensional ferroelectrics, such as In$_2$Se$_3$, BN bilayers, and SnS, as realistic material candidates for the proposed ferroelectric higher-order topological insulators. Our work not only sheds new light on traditional ferroelectric materials but also opens an avenue to bridge the higher-order topology and ferroelectricity that provides a nonvolatile handle to manipulate the topology in next-generation electronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Orbital shift-induced boundary obstructed topological materials with a large energy gap

We propose boundary obstructed topological phases caused by Wannier orbital shift between ordinary atomic sites, which, however, cannot be indicated by symmetry eigenvalues at high symmetry momenta (symmetry indicators) in bulk. On the open boundary, Wannier charge centers can shift to different atoms from those in bulk, leading to in-gap surface states, higher-order hinge states or corner states. To demonstrate such orbital-shift-induced boundary obstructed topological insulators, we predict eight material candidates, all of which were overlooked in present topological databases. Metallic surface states, hinge states, or corner states cover the large bulk energy gap (for example, more than 1 eV in TlGaTe$_2$) at related boundary, which are ready for experimental detection. Additionally, we find these materials are also fragile topological insulators with hourglass like surface states.

cond-mat.mes-hall↗

arXiv2022

Doubled Quantum Spin Hall Effect with High-Spin Chern Number in $\alpha$-Antimonene and $\alpha$-Bismuthene

The discovery of quantum spin Hall effect has ignited the field of topological physics with vast variety of exotic properties. Here, we present the emergence of doubled quantum spin Hall effect in two dimensions characterized with a high spin Chern number of ${\mathcal C_S}=2$ and two pairs of helical edge states. Although is overlooked and invisible in topological quantum chemistry and symmetry indicator theory, the already experimentally synthesized $\alpha$-antimonene and $\alpha$-bismuthene are revealed as realistic material candidates of predicted topological states with band inversions emerging at generic $k$-points, rather than the high-symmetry momenta. Remarkably, the nontrivial energy gap can be as large as 464 meV for $\alpha$-bismuthene, indicating the high possibility of room-temperature observation of the doubled quantum spin Hall effect. Moreover, a four-band effective model is constructed to demonstrate further the feasibility of attaining this type of nontrivial topology. Our results not only uncover a novel topological character of antimony and bismuth, but will also facilitate the experimental characterization of the previously overlooked hidden topology.

cond-mat.mtrl-sci↗