arXiv subjects

Xiaopu Zhang

Publications and source records attributed to Xiaopu Zhang.

13 recordsLinked to original sources

arXiv2026

Effect of Rashba spin-orbit coupling on Faraday rotation in an extended Haldane model

Utilization of Faraday rotation (FR) properties of topological materials offers a promising route toward novel magneto-optical devices. We systematically investigated the effect of Rashba spin-orbit coupling (SOC) on FR spectra in an extended Haldane model, which incorporates Rashba SOC and exchange splitting into the original spinless Haldane framework. Using the Kubo formalism, we calculated the FR spectra across the model's rich topological phase diagram. We found that in the Chern number C=2 region, in the absence of exchange splitting, the FR angle can exceed 4$^\circ$ and its peak position is tunable by the Rashba SOC. In contrast, with the inclusion of exchange splitting, a nearly flat FR profile emerges over a broad frequency range, and the FR peak values increase monotonically with the Rashba SOC strength. The Rashba SOC opens additional transition channels, whose net contribution constructively enhances the FR peak. Furthermore, we derived a low-energy effective Hamiltonian expanded up to quadratic terms, the results of which are in good agreement with tight-binding model calculations, thereby validating our numerical results. Our findings suggest that magneto-optical device characteristics can be designed and optimized through Rashba SOC engineering.

cond-mat.mes-hall↗

arXiv2026

AEF-Econ: Toward Plug-and-Play Socioeconomic Foundation Embeddings from AlphaEarth for Urban Remote Sensing

AlphaEarth Foundations (AEF) unify global remote sensing foundation embeddings through multimodal self-supervised learning, but their pretraining focuses on physical land-surface signals, limiting plug-and-play use in socioeconomic tasks. We integrate seven heterogeneous data streams across 36 Chinese cities over eight years - AEF embeddings, population, nighttime lights, remote sensing indices, points of interest (POIs), urban morphology, and cross-lingual text - and construct CHN-Econ, a socioeconomic benchmark with 16 labels in three categories. We conduct 31 controlled experiments along five axes: fusion architecture, self-supervised objective, text integration, embedding dimensionality, and normalization. Used alone as a linear probe, AEF achieves R2 values of only 0.301 for cross-region and 0.160 for cross-tier evaluation. The five-axis ablated backbone improves these scores to 0.832 and 0.671, respectively, but reveals that low-dimensional semantic streams are consistently suppressed by high-dimensional streams under shared reconstruction. To address this bottleneck, we propose Capacity-Adaptive Reconstruction (CAR), replacing shared reconstruction with per-stream decoders and stream-level losses to mitigate inter-stream capacity competition. CAR further raises cross-region and cross-tier R2 to 0.848 and 0.693, and restores collapsed labels from negative R2 to a stable range. Using CAR, we infer 14.4 million pixels across 36 cities and eight years and release AEF-Econ, including 128d and 64d compressed versions. Self-diagnostics and case studies show that AEF-Econ captures cross-city hierarchies and intra-urban spatial organization under unsupervised settings, providing a socioeconomic remote sensing foundation embedding complementary to AEF physical embeddings.

cs.CV↗

arXiv2026

Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities

Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global comparability. AlphaEarth Foundations (AEF), a globally consistent 64-dimensional annual surface embedding at 10 m, offers a new analysis-ready basis for lightweight slum monitoring, but its applicability to slum detection - an indirectly coupled task shaped by both built form and socio-economic processes - remains untested. We evaluate AEF on slum classification and sub-pixel density estimation across 12 cities and 69 city-year pairs (2017-2024), using GRAM pseudo-masks as supervisory labels. The evaluation spans four training strategies, two protocols (random split and 3x3 spatial block cross-validation), six auxiliary feature configurations, and five baseline models, complemented by representation-level analyses (PCA, SHAP) and full-AOI mapping. Five findings emerge. (1) Same-city cross-year training is optimal under both protocols (median spatial F1 = 0.616, R^2 = 0.466); temporal expansion outperforms cross-city transfer, indicating city-scale representational drift. (2) Regression R^2 is driven primarily by zero/non-zero boundary discrimination: positive-pixel R^2 is consistently negative across all cities, revealing limited capacity to model intra-pixel density gradients at 10 m. (3) PC36 is consistently top-ranked across tasks; classification saturates at k = 32 while regression remains unsaturated at k = 64. (4) POI features yield the largest density gain (Delta R^2 = +0.064). (5) For six cities meeting dual-task usability thresholds, full-AOI inference across 2017-2024 preserves slum cluster structure (mean SSIM = 0.926). The study delineates the capabilities and complementarity needs of foundation-model embeddings for slum monitoring.

cs.CV↗

arXiv2026

Pathway to lowest-energy structures and stress relaxation for the surface triple junction verified by machine learning

The behavior of surface triple junctions (STJ) at emergent grain boundaries on free surfaces is critical to the microstructure evolution, and therefore to the stability of the next generation interconnect. Yet,despite this significant importance, its lowest-energy structure and local stress have remained persistently unknown. Here, we fill this critical gap through high-resolution experimental mapping of the local surface deformation at STJ, the analysis of the local structure and stress relaxation, and ergodic searching metastable structures. We establish the zipped Y-shaped notch as the universal lowest-energy structures. This energetic preference was well explained by the distinctive local stress mechanism and was excellently verified with machine learning methods for a wide range of boundaries. By revealing the elusive thermodynamics of STJs, our findings advance the research field by redefining the energetic framework for capillary driven structure evolution and providing foundation for understanding kinetically diffusive deformation and for engineering thin-film interconnects and related materials.

cond-mat.mtrl-sci↗

arXiv2025

AutoGEEval++: A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine

Geospatial code generation is becoming a key frontier in integrating artificial intelligence with geo-scientific analysis, yet standardised automated evaluation tools for this task remain absent. This study presents AutoGEEval++, an enhanced framework building on AutoGEEval, and the first automated assessment system for large language models (LLMs) generating geospatial code on Google Earth Engine (GEE). It supports diverse data modalities and varying task complexities. Built on the GEE Python API, AutoGEEval++ features a benchmark dataset-AutoGEEval++-Bench-with 6,365 test cases across 26 data types and three task categories: unit, combo, and theme tests. It includes a submission programme and a judge module to realise an end-to-end automated evaluation pipeline from code generation to execution-based validation. The framework adopts multi-dimensional metrics-accuracy, resource usage, run-time efficiency, and error types-balancing hallucination control and efficiency, and enabling boundary testing and error pattern analysis. Using AutoGEEval++, we evaluate 24 state-of-the-art LLMs (as of June 2025), including general-purpose, reasoning-enhanced, code-centric, and geoscience-specific models. Results reveal clear performance, stability, and error differences across task types, model designs, and deployment settings, confirming AutoGEEval++'s practical value and scalability in vertical-domain code generation. This work establishes the first standardised evaluation protocol and foundational benchmark for GEE-based LLM code generation, providing a unified basis for performance comparison and a methodological framework for systematic, domain-specific code evaluation.

cs.SE↗

arXiv2025

AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models

Geospatial code generation is emerging as a key direction in the integration of artificial intelligence and geoscientific analysis. However, there remains a lack of standardized tools for automatic evaluation in this domain. To address this gap, we propose AutoGEEval, the first multimodal, unit-level automated evaluation framework for geospatial code generation tasks on the Google Earth Engine (GEE) platform powered by large language models (LLMs). Built upon the GEE Python API, AutoGEEval establishes a benchmark suite (AutoGEEval-Bench) comprising 1325 test cases that span 26 GEE data types. The framework integrates both question generation and answer verification components to enable an end-to-end automated evaluation pipeline-from function invocation to execution validation. AutoGEEval supports multidimensional quantitative analysis of model outputs in terms of accuracy, resource consumption, execution efficiency, and error types. We evaluate 18 state-of-the-art LLMs-including general-purpose, reasoning-augmented, code-centric, and geoscience-specialized models-revealing their performance characteristics and potential optimization pathways in GEE code generation. This work provides a unified protocol and foundational resource for the development and assessment of geospatial code generation models, advancing the frontier of automated natural language to domain-specific code translation.

cs.SE↗

arXiv2025

Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective

Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled while most target samples are unlabeled, with only a small fraction labeled. Moreover, there is no one-to-one correspondence between source and target samples. Although various SHDA methods have been developed to tackle this problem, the nature of the knowledge transferred across heterogeneous domains remains unclear. This paper delves into this question from an empirical perspective. We conduct extensive experiments on about 330 SHDA tasks, employing two supervised learning methods and seven representative SHDA methods. Surprisingly, our observations indicate that both the category and feature information of source samples do not significantly impact the performance of the target domain. Additionally, noise drawn from simple distributions, when used as source samples, may contain transferable knowledge. Based on this insight, we perform a series of experiments to uncover the underlying principles of transferable knowledge in SHDA. Specifically, we design a unified Knowledge Transfer Framework (KTF) for SHDA. Based on the KTF, we find that the transferable knowledge in SHDA primarily stems from the transferability and discriminability of the source domain. Consequently, ensuring those properties in source samples, regardless of their origin (e.g., image, text, noise), can enhance the effectiveness of knowledge transfer in SHDA tasks. The codes and datasets are available at https://github.com/yyyaoyuan/SHDA.

cs.LG↗

arXiv2024

Chain-of-Programming (CoP) : Empowering Large Language Models for Geospatial Code Generation

With the rapid growth of interdisciplinary demands for geospatial modeling and the rise of large language models (LLMs), geospatial code generation technology has seen significant advancements. However, existing LLMs often face challenges in the geospatial code generation process due to incomplete or unclear user requirements and insufficient knowledge of specific platform syntax rules, leading to the generation of non-executable code, a phenomenon known as "code hallucination." To address this issue, this paper proposes a Chain of Programming (CoP) framework, which decomposes the code generation process into five steps: requirement analysis, algorithm design, code implementation, code debugging, and code annotation. The framework incorporates a shared information pool, knowledge base retrieval, and user feedback mechanisms, forming an end-to-end code generation flow from requirements to code without the need for model fine-tuning. Based on a geospatial problem classification framework and evaluation benchmarks, the CoP strategy significantly improves the logical clarity, syntactical correctness, and executability of the generated code, with improvements ranging from 3.0% to 48.8%. Comparative and ablation experiments further validate the superiority of the CoP strategy over other optimization approaches and confirm the rationality and necessity of its key components. Through case studies on building data visualization and fire data analysis, this paper demonstrates the application and effectiveness of CoP in various geospatial scenarios. The CoP framework offers a systematic, step-by-step approach to LLM-based geospatial code generation tasks, significantly enhancing code generation performance in geospatial tasks and providing valuable insights for code generation in other vertical domains.

cs.SE↗

arXiv2024

Egret: Reinforcement Mechanism for Sequential Computation Offloading in Edge Computing

As an emerging computing paradigm, edge computing offers computing resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical in reality. However, previous works assume user privacy information to be known or consider the number of users in edge scenarios to be static. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computing resources with different configurations to clients in turn. Clients independently choose which computing resources to purchase and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without considering clients' preferences. Experimental results show that the revenue of ECSP in Egret is only 1.29\% lower than Oracle and 23.43\% better than the state-of-the-art when the client arrives dynamically.

cs.DC↗

arXiv2023

Universal preference for low energy core-shifted grain boundaries at the surfaces of fcc metals

Grain boundaries with [111] tilt axes are common in polycrystalline face centered cubic metals. For copper (111) films, emergent grain boundaries close to surface have tilt axes that are shifted away from [111] that are lower in energy than the corresponding truncated bulk boundaries. Geometrical analysis and atomic calculations were used to study the driving force for this same relaxation phenomenon in representative fcc elemental metals. We show that the reduction in boundary energy scales with the elimination of energetically costly boundary core facets. We find that for a wide range of misorientation angles low energy core-shifted boundaries are also favored in Al, Ni, Au and Pt and discuss the significance for electromigration and other metal properties.

cond-mat.mtrl-sci↗

arXiv2023

Core shift controls grain boundary energy scaling in Cu and Al

Grain boundary energies in different elements are correlated. The proportional scaling constants relating the energies of crystallography-equivalent boundaries in any two f.c.c. elements are nearly constant, with the notable exception of aluminum where these constants are known to vary significantly. However, the origins of the exceptional behavior of aluminum are not understood. Previously, we reported that for fcc metals there is a preference for boundaries to shift their tilt axis across the (1-10) plane towards [112] and to ultimately form low energy [112] core shifted boundaries (CSBs). By comparing grain boundary energies in copper and aluminum with different tilt axis in (1-10) plane, we now report the existence of a well-defined scaling behavior for the case of low angle boundaries. In contrast, the scaling constant for high angle boundaries is essentially fixed regardless of their tilt axis shift. This results in a gradual change in the scaling constants from low angle to high angle boundaries, which is responsible for the apparent exceptional scaling behavior found in aluminum. An analysis of structure evolution during core shifting points to the significance of boundary-core dissociation, a form of correlated relaxation of individual atoms at boundaries, in controlling the scaling of the boundary energies.

cond-mat.mtrl-sci↗

arXiv2023

Wedge disclination description of emergent core-shifted grain boundaries at free surfaces

Emergent grain boundaries at free surface control material properties such as nanomaterial strength, catalysis, and corrosion. Recently the restructuring of emergent boundaries on copper (111) surfaces was discovered experimentally and atomic calculations point to its universality in fcc metal systems. Restructuring is due to a preference for boundaries to shift their tilt axis across the (1-10) plane towards [112] and ultimately to form low energy [112] core shifted boundaries (CSBs). However, the observed geometry of these emergent boundaries is not reproduced by atomic calculations and the driving force is still controversial due to inconsistencies between the computational continuum analysis and atomic calculations. Here, using atomic calculations that involve a methodical shift of the dislocation core, we confirmed the core shift of emergent boundaries observed in experiment and reconciled the atomic calculations with the elastic analysis through the inclusion of a straight wedge disclination at the free surface.

cond-mat.mtrl-sci↗

arXiv2011

Asymmetric magnetization splitting in diamond domain structure: Dependence on exchange interaction and anisotropy

The distributions of magnetization orientation for both Landau and diamond domain structures in nano-rectangles have been investigated by micromagnetic simulation with various exchange coefficient and anisotropy constant. Both symmetric and asymmetric magnetization splitting are found in diamond domain structure, as well as only symmetric magnetization splitting in Landau structure. In the Landau structure, the splitting angle increases with the exchange coefficient but decreases slightly with the anisotropy constant, suggesting that the exchange interaction mainly contributes to the magnetization splitting in Landau structure. However in the diamond structure, the splitting angle increases with the anisotropy constant but derceases with the exchange coefficient, indicating that the magnetization splitting in diamond structure is resulted from magnetic anisotropy.

cond-mat.mtrl-sci↗