arXiv subjects

Dongsheng Yuan

Publications and source records attributed to Dongsheng Yuan.

7 recordsLinked to original sources

arXiv2026

Twinning of domains and spin anisotropy in K$_5$Fe$_4$Ag$_6$Te$_{10}$

The Fe-based superconductors are derived from metallic parent compounds with nematic and stripe magnetic orders, which lead to two types of magnetic domains. Recently it was found that K$_5$Fe$_4$Ag$_6$Te$_{10}$ (KFAT), an Fe-based semiconductor, exhibits similar nematic and stripe magnetic orders, and is thus an analogue to the Fe-based superconductors in the limit of localized electrons. In this work, the superstructure and magnetic domains of KFAT are elucidated by fully mapping the reciprocal space using time-of-flight single crystal neutron diffraction. In KFAT, Fe and Ag atoms order to form a $\sqrt{5}\times\sqrt{5}$ superstructure containing $2\times2$ Fe blocks, which leads to two superstructure domains with identical main Bragg peaks but distinct superstructure peaks. Below $T_{\rm N}\approx35$~K, magnetic and nematic orders break in-plane rotational symmetry of the tetragonal $\sqrt{5}\times\sqrt{5}$ superstructure, and further give rise to two magnetic domains. These four equally populated domains account for the complex scattering pattern observed in our time-of-flight elastic neutron scattering measurements. Using polarized neutron scattering, we demonstrate a prominent spin anisotropy with an easy-plane spanned by the $c$-axis and the intra-block antiferromagnetic Fe-Fe bond direction. Such an anisotropy at ${\bf q}\neq0$ persists well above $T_{\rm N}$, accounts for the in-plane ${\bf q}=0$ magnetic anisotropy observed in uniaxial-strained KFAT, and offers an indicator for discovering similar piezomagnetic effects in other materials.

cond-mat.str-el↗

arXiv2026

OpAgent: Operator Agent for Web Navigation

To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinforcement Learning (RL) using static datasets. However, these methods suffer from severe distributional shifts, as offline trajectories fail to capture the stochastic state transitions and real-time feedback of unconstrained wide web environments. In this paper, we propose a robust Online Reinforcement Learning WebAgent, designed to optimize its policy through direct, iterative interactions with unconstrained wide websites. Our approach comprises three core innovations: 1) Hierarchical Multi-Task Fine-tuning: We curate a comprehensive mixture of datasets categorized by functional primitives -- Planning, Acting, and Grounding -- establishing a Vision-Language Model (VLM) with strong instruction-following capabilities for Web GUI tasks. 2) Online Agentic RL in the Wild: We develop an online interaction environment and fine-tune the VLM using a specialized RL pipeline. We introduce a Hybrid Reward Mechanism that combines a ground-truth-agnostic WebJudge for holistic outcome assessment with a Rule-based Decision Tree (RDT) for progress reward. This system effectively mitigates the credit assignment challenge in long-horizon navigation. Notably, our RL-enhanced model achieves a 38.1\% success rate (pass@5) on WebArena, outperforming all existing monolithic baselines. 3) Operator Agent: We introduce a modular agentic framework, namely \textbf{OpAgent}, orchestrating a Planner, Grounder, Reflector, and Summarizer. This synergy enables robust error recovery and self-correction, elevating the agent's performance to a new State-of-the-Art (SOTA) success rate of \textbf{71.6\%}.

cs.AI↗

arXiv2025

Anisotropic Fourier Features for Positional Encoding in Medical Imaging

The adoption of Transformer-based architectures in the medical domain is growing rapidly. In medical imaging, the analysis of complex shapes - such as organs, tissues, or other anatomical structures - combined with the often anisotropic nature of high-dimensional images complicates these adaptations. In this study, we critically examine the role of Positional Encodings (PEs), arguing that commonly used approaches may be suboptimal for the specific challenges of medical imaging. Sinusoidal Positional Encodings (SPEs) have proven effective in vision tasks, but they struggle to preserve Euclidean distances in higher-dimensional spaces. Isotropic Fourier Feature Positional Encodings (IFPEs) have been proposed to better preserve Euclidean distances, but they lack the ability to account for anisotropy in images. To address these limitations, we propose Anisotropic Fourier Feature Positional Encoding (AFPE), a generalization of IFPE that incorporates anisotropic, class-specific, and domain-specific spatial dependencies. We systematically benchmark AFPE against commonly used PEs on multi-label classification in chest X-rays, organ classification in CT images, and ejection fraction regression in echocardiography. Our results demonstrate that choosing the correct PE can significantly improve model performance. We show that the optimal PE depends on the shape of the structure of interest and the anisotropy of the data. Finally, our proposed AFPE significantly outperforms state-of-the-art PEs in all tested anisotropic settings. We conclude that, in anisotropic medical images and videos, it is of paramount importance to choose an anisotropic PE that fits the data and the shape of interest.

cs.CV↗

arXiv2024

ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical Images

This paper demonstrates that spatial information can be used to learn interpretable representations in medical images using Self-Supervised Learning (SSL). Our proposed method, ISImed, is based on the observation that medical images exhibit a much lower variability among different images compared to classic data vision benchmarks. By leveraging this resemblance of human body structures across multiple images, we establish a self-supervised objective that creates a latent representation capable of capturing its location in the physical realm. More specifically, our method involves sampling image crops and creating a distance matrix that compares the learned representation vectors of all possible combinations of these crops to the true distance between them. The intuition is, that the learned latent space is a positional encoding for a given image crop. We hypothesize, that by learning these positional encodings, comprehensive image representations have to be generated. To test this hypothesis and evaluate our method, we compare our learned representation with two state-of-the-art SSL benchmarking methods on two publicly available medical imaging datasets. We show that our method can efficiently learn representations that capture the underlying structure of the data and can be used to transfer to a downstream classification task.

cs.CV↗

arXiv2023

Two-Step Electronic Response to Magnetic Ordering in a van der Waals Ferromagnet

The two-dimensional (2D) material Cr$_2$Ge$_2$Te$_6$ is a member of the class of insulating van der Waals magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te $p$-orbital-dominated bands to undergo changes at the Curie transition temperature T$_C$ while the Cr $d$-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

cond-mat.str-el↗

arXiv2021

Structural and Magnetic Transitions in the Planar Antiferromagnet Ba$_4$Ir$_3$O$_{10}$

We report the structural and magnetic ground state properties of the monoclinic compound barium iridium oxide Ba$_4$Ir$_3$O$_{10}$ using a combination of resonant x-ray scattering, magnetometry, and thermodynamic techniques. Magnetic susceptibility exhibits a pronounced antiferromagnetic transition at $T_{\text{N}}$ $\approx$ 25K, a weaker anomaly at $T_{\text{S}}$ $\approx$ 142K, and strong magnetic anisotropy at all temperatures. Resonant elastic x-ray scattering experiments reveal a second order structural phase transition at $T_{\text{S}}$ and a magnetic transition at $T_{\text{N}}$. Both structural and magnetic superlattice peaks are observed at $L$ = half integer values. The magnetization anomaly at $T_{\text{S}}$ implies the presence of magneto-elastic coupling, which conceivably facilitates the symmetry lowering. Mean field critical scattering is observed above $T_{\text{S}}$. The magnetic structure of the antiferromagnetic ground state is discussed based on the measured magnetic superlattice peak intensity. Our study not only presents essential information for understanding the intertwined structural and magnetic properties in Ba$_4$Ir$_3$O$_{10}$, but also highlights the necessary ingredients for exploring novel ground states with octahedra trimers.

cond-mat.str-el↗

arXiv2019

Strain-induced spin-nematic state and nematic susceptibility arising from $2\times2$ Fe clusters in KFe$_{0.8}$Ag$_{1.2}$Te$_2$

Spin nematics break spin-rotational symmetry while maintaining time-reversal symmetry, analogous to liquid crystal nematics that break spatial rotational symmetry while maintaining translational symmetry. Although several candidate spin nematics have been proposed, the identification and characterization of such a state remain challenging because the spin-nematic order parameter does not couple directly to experimental probes. KFe$_{0.8}$Ag$_{1.2}$Te$_2$ (K$_5$Fe$_4$Ag$_6$Te$_{10}$, KFAT) is a local-moment magnet consisting of well-separated 2$\times$2 Fe clusters, and in its ground state the clusters order magnetically, breaking both spin-rotational and time-reversal symmetries. Using uniform magnetic susceptibility and neutron scattering measurements we find a small strain induces sizable spin anisotropy in the paramagnetic state of KFAT, manifestly breaking spin-rotational symmetry while retaining time-reversal symmetry, resulting in a strain-induced spin-nematic state in which the $2\times2$ clusters act as the spin analogue of molecules in a liquid crystal nematic. The strain-induced spin anisotropy in KFAT allows us to probe its nematic susceptibility, revealing a divergent-like increase upon cooling, indicating the ordered ground state is driven by a spin-orbital entangled nematic order parameter.

cond-mat.str-el↗