arXiv subjects

Xinru Li

Publications and source records attributed to Xinru Li.

9 recordsLinked to original sources

arXiv2026

Symmetry-Enforced Ferroelectric Switching of Two-Dimensional Altermagnetism

Altermagnetism features strong momentum-dependent spin splitting despite zero net magnetization, offering a transformative platform for next-generation spintronics. However, the nonvolatile and deterministic switching between its two equivalent spin-splitting states remains a fundamental bottleneck. Here, we propose a universal layer-engineering paradigm to achieve symmetry-enforced ferroelectric switching of two-dimensional altermagnetism. By sandwiching a conventional antiferromagnetic monolayer between two identical ferroelectric layers, the out-of-plane polarization cleanly breaks the spatial symmetry to induce robust altermagnetic splitting. Crucially, the global combined parity-time symmetry dictates that reversing the ferroelectric polarization exactly inverts the altermagnetic spin-splitting pattern. We rigorously validate this mechanism in the In2Se3/MnPTe3/In2Se3 trilayer using first-principles calculations. As a direct consequence, the ferroelectrically driven spin-splitting reversal deterministically flips the anomalous Hall effect signal, providing an unambiguous transport fingerprint to electrically distinguish the two altermagnetic states. Unconstrained by the stringent symmetry requirements of intrinsic single-phase materials, our findings establish a versatile physical framework for electrically addressable altermagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry-dictated switching of antiferromagnetic magnon transport in 2D multiferroics

While antiferromagnetic magnons in two-dimensional (2D) materials hold immense promise for high-frequency spintronics, achieving their efficient active control remains a critical challenge. Here, we propose a universal mechanism for the nonvolatile ferroelectric (FE) switching of antiferromagnetic magnon transport in 2D multiferroic lattices. Our mechanism relies on coupling the magnon geometric phase to the FE-induced sublattice asymmetry in exchange and Dzyaloshinskii-Moriya interactions. This explicitly breaks the exact compensation of opposite-chirality magnons inherent to collinear antiferromagnets, lifting their spin degeneracy and inducing a highly tunable net Berry curvature. Crucially, reversing the FE polarization deterministically swaps these magnetic asymmetries, which completely inverts the net magnon Berry curvature and the resulting anomalous thermal Hall conductivity. Using first-principles and linear spin-wave theory, we rigorously validate this geometric-phase-driven mechanism in single-layer CuCr2Se4. Our findings establish a robust paradigm for coupling multiferroicity with the magnon geometric phase, paving the way for nonvolatile and electrically switchable antiferromagnetic magnonics.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Informed Glass-Structure Descriptors for Assessing the Intrinsic Reactivity of Mixed Amorphous-Crystalline Precursors in Alkali-Activated Materials

Rapid and reliable assessment of the intrinsic reactivity of amorphous aluminosilicates is critical for their application in alkali-activated materials (AAMs) and blended cements. Although physics-informed glass-structure descriptors have demonstrated strong structure-reactivity relationships for predominantly amorphous systems, their extension to heterogeneous precursors with mixed crystalline-amorphous phases has been limited. Here, quantitative X-ray diffraction combined with bulk compositional analysis was used to reconstruct the effective amorphous compositions of five fly ashes (FAs) and three ground granulated blast-furnace slags (GGBSs). These compositions served as inputs for molecular dynamics simulations employing a melt-and-quench approach to generate atomic-scale structural models of the glassy phases. Based on these structures, the previously introduced descriptors, i.e., average metal oxygen dissociation energy and average metal oxygen bond strength, were refined to cover a broader compositional space spanning SiO2-Al2O3-TiO2-Fe2O3-CaO-MgO-MnO-Na2O-K2O. The refined descriptors exhibit strong inverse correlations with multiple independent reactivity indicators, including cumulative heat release from isothermal calorimetry, bound water content from thermogravimetric analysis, and compressive strength, for both single precursors and binary FA-GGBS blends activated with NaOH. These results demonstrate that physics-informed glass-structure descriptors can be extended from ideal amorphous systems to heterogeneous mixed-phase precursors and capture relative intrinsic reactivity trends in alkaline solutions. The proposed framework provides a transferable, structure-informed basis for comparative assessment of precursor reactivity that complements experimental testing and may inform precursor screening and mix designs for AAM and blended cement systems.

cond-mat.mtrl-sci↗

arXiv2025

ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation

Reactive dance generation (RDG), the task of generating a dance conditioned on a lead dancer's motion, holds significant promise for enhancing human-robot interaction and immersive digital entertainment. Despite progress in duet synchronization and motion-music alignment, two key challenges remain: generating fine-grained spatial interactions and ensuring long-term temporal coherence. In this work, we introduce \textbf{ReactDance}, a diffusion framework that operates on a novel hierarchical latent space to address these spatiotemporal challenges in RDG. First, for high-fidelity spatial expression and fine-grained control, we propose Hierarchical Finite Scalar Quantization (\textbf{HFSQ}). This multi-scale motion representation effectively disentangles coarse body posture from subtle limb dynamics, enabling independent and detailed control over both aspects through a layered guidance mechanism. Second, to efficiently generate long sequences with high temporal coherence, we propose Blockwise Local Context (\textbf{BLC}), a non-autoregressive sampling strategy. Departing from slow, frame-by-frame generation, BLC partitions the sequence into blocks and synthesizes them in parallel via periodic causal masking and positional encodings. Coherence across these blocks is ensured by a dense sliding-window training approach that enriches the representation with local temporal context. Extensive experiments show that ReactDance substantially outperforms state-of-the-art methods in motion quality, long-term coherence, and sampling efficiency. Project page: https://ripemangobox.github.io/ReactDance.

cs.CV↗

arXiv2025

Light-Induced Spin Slanting in 2D Multiferroic Magnet

Controlling spin orientation of two-dimensional (2D) materials has emerged as a frontier of condensed-matter physics, resulting in the discovery of various phases of matter. However, in most cases, spin orientation can be stablished only at specific directions of out-of-plane and in-plane, which is a drawback compared with three-dimensional systems, limiting exploration of novel physics. Here, we introduce a methodology for manipulating spin slanting in 2D multiferroic materials through ultrafast pulses of light. Based on model analysis, we find that simultaneous triggering spin-orbit coupling induced interactions from in-plane and out-of-plane orbitals can generate spin slanting. By choosing 2D multiferroic materials with specific low-energy composition endowed by symmetry, such triggering can be readily achieved through ultrafast light illumination, leading to light-induced spin slanting. Using real-time time-dependent density-functional theory, we demonstrate this approach in multiferroic single-layer CuCr2Se4. This study provides an efficient way to manipulate spin orientation in 2D materials and establishes a general platform to explore physics and applications associated with spin slanting.

cond-mat.mes-hall↗

arXiv2025

Quantum Geometric Engineering of Dual Hall Effects in 2D Antiferromagnetic Bilayers via Interlayer Magnetic Coupling

The interplay between quantum geometry and magnetic order offers a novel strategy for designing next-generation nanodevices. Here, we demonstrate that interlayer magnetic coupling in two-dimensional (2D) CoPSe3 bilayers enables precise control over quantum geometric mechanisms, unlocking dual intrinsic Hall effects. Our first-principles calculations reveal that the altermagnetic (AM) phase exhibits a giant anisotropic anomalous Hall effect (AHE) ($\sigma_{xy}$ is approximately 46 S/cm) driven by Berry curvature localized at generic k-points, while the PT-symmetric antiferromagnetic (AFM) phase hosts an intrinsic second-order nonlinear anomalous Hall effect (NAHE) ($\chi_{xyy}$ is approximately 160 ${\mu}$S/V) originating from quantum metric accumulation at high-symmetry k-points. By tuning interlayer magnetic couplings, we achieve reversible switching between these phases, leveraging their distinct band structures and symmetry constraints. The Neel-vector-dependent AHE in the AM phase and the symmetry-protected NAHE in the AFM phase highlight quantum geometry as a versatile tool for manipulating transport properties. Our work establishes 2D antiferromagnets as a promising platform for multifunctional device architectures, bridging linear and nonlinear magnetoelectric responses through tailored quantum geometric engineering.

cond-mat.mes-hall↗

arXiv2025

EICopilot: Search and Explore Enterprise Information over Large-scale Knowledge Graphs with LLM-driven Agents

The paper introduces EICopilot, an novel agent-based solution enhancing search and exploration of enterprise registration data within extensive online knowledge graphs like those detailing legal entities, registered capital, and major shareholders. Traditional methods necessitate text-based queries and manual subgraph explorations, often resulting in time-consuming processes. EICopilot, deployed as a chatbot via Baidu Enterprise Search, improves this landscape by utilizing Large Language Models (LLMs) to interpret natural language queries. This solution automatically generates and executes Gremlin scripts, providing efficient summaries of complex enterprise relationships. Distinct feature a data pre-processing pipeline that compiles and annotates representative queries into a vector database of examples for In-context learning (ICL), a comprehensive reasoning pipeline combining Chain-of-Thought with ICL to enhance Gremlin script generation for knowledge graph search and exploration, and a novel query masking strategy that improves intent recognition for heightened script accuracy. Empirical evaluations demonstrate the superior performance of EICopilot, including speed and accuracy, over baseline methods, with the \emph{Full Mask} variant achieving a syntax error rate reduction to as low as 10.00% and an execution correctness of up to 82.14%. These components collectively contribute to superior querying capabilities and summarization of intricate datasets, positioning EICopilot as a groundbreaking tool in the exploration and exploitation of large-scale knowledge graphs for enterprise information search.

cs.IR↗

arXiv2024

Solve paint color effect prediction problem in trajectory optimization of spray painting robot using artificial neural network inspired by the Kubelka Munk model

Currently, the spray-painting robot trajectory planning technology aiming at spray painting quality mainly applies to single-color spraying. Conventional methods of optimizing the spray gun trajectory based on simulated thickness can only qualitatively reflect the color distribution, and can not simulate the color effect of spray painting at the pixel level. Therefore, it is not possible to accurately control the area covered by the color and the gradation of the edges of the area, and it is also difficult to deal with the situation where multiple colors of paint are sprayed in combination. To solve the above problems, this paper is inspired by the Kubelka-Munk model and combines the 3D machine vision method and artificial neural network to propose a spray painting color effect prediction method. The method is enabled to predict the execution effect of the spray gun trajectory with pixel-level accuracy from the dimension of the surface color of the workpiece after spray painting. On this basis, the method can be used to replace the traditional thickness simulation method to establish the objective function of the spray gun trajectory optimization problem, and thus solve the difficult problem of spray gun trajectory optimization for multi-color paint combination spraying. In this paper, the mathematical model of the spray painting color effect prediction problem is first determined through the analysis of the Kubelka-Munk paint film color rendering model, and at the same time, the spray painting color effect dataset is established with the help of the depth camera and point cloud processing algorithm. After that, the multilayer perceptron model was improved with the help of gating and residual structure and was used for the color prediction task. To verify ...

cs.RO↗

arXiv2022

NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences

Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also in assistive navigation for the visually impaired population. To enable a long-term VPR system on a large scale, several challenges need to be addressed. First, different applications could require different image view directions, such as front views for self-driving cars while side views for the low vision people. Second, VPR in metropolitan scenes can often cause privacy concerns due to the imaging of pedestrian and vehicle identity information, calling for the need for data anonymization before VPR queries and database construction. Both factors could lead to VPR performance variations that are not well understood yet. To study their influences, we present the NYU-VPR dataset that contains more than 200,000 images over a 2km by 2km area near the New York University campus, taken within the whole year of 2016. We present benchmark results on several popular VPR algorithms showing that side views are significantly more challenging for current VPR methods while the influence of data anonymization is almost negligible, together with our hypothetical explanations and in-depth analysis.

cs.CV↗