arXiv subjects

Xuecheng Zhang

Publications and source records attributed to Xuecheng Zhang.

3 recordsLinked to original sources

arXiv2026

AI+CAD Data Representation Architecture: From DeepCAD Solid Modeling to WHUCAD Industrial-Level Parametric Feature Modeling

In July 2025, Study Times, sponsored by the Party School of the Central Committee of the CPC, pointed out that 95% of industrial software for R&D and design in China relies on imports, and that 90% of the high-end CAD/CAE/CAM software market is monopolized by European and American giants. This is a typical strategic bottleneck problem. Unlike the visually oriented goal of "visual plausibility" pursued by related sister disciplines such as CV and CG, CAD places greater emphasis on "industrial usability". In CAD, data representation architecture is more foundational than the optimization of network algorithms. This paper first starts from data representation in AI+CAD and reports a classification paradigm and research progress in AI+CAD. Then, using the open-source DeepCAD data representation as an example, it analyzes the pain points of representative AI+CAD work and the gap between such work and real industrial-level parametric feature modeling. Next, by comparison with the open-source WHUCAD data representation, it discusses how its three-level architecture provides fundamental support for industrial-grade parametric feature modeling. Finally, in view of the rapid iteration of the AI wave, large models, and agents, this paper offers an outlook on AI+industrial-grade CAD.

cs.GR↗

arXiv2025

Observation of robust one-dimensional edge channels in a three-dimensional quantum spin Hall insulator

Topologically protected edge channels show prospects for quantum devices. They have been found experimentally in two-dimensional (2D) quantum spin Hall insulators (QSHIs), weak topological insulators and higher-order topological insulators (HOTIs), but the number of materials realizing these topologies is still quite limited. Here, we provide evidence for topological edge states within a novel topology named three-dimensional (3D) QSHIs. Its topology originates solely from a nonzero $S_z$ spin Chern number for each $k_z$ plane of the crystal and is realized in bulk $α$-Bi$_4$I$_4$ with trivial symmetry indicators, as we show by density functional theory calculations. We experimentally observe the related edge states at each type of monolayer and bilayer step of this material by scanning tunneling microscopy. Consistently, the edge states are neither interrupted, nor backscattered by defects at the step edges corroborating their helical character as expected from the nontrivial topology. Furthermore, two individual edge channels are directly observed at bilayer steps without visible interaction gap opening, demonstrating the robustness of these edge modes against vertical stacking. Our results establish $α$-Bi$_4$I$_4$ as the first material realization of a 3D QSHI whose definition goes beyond the scope of topological symmetry indicators, and provide a pathway for realizing nearly-quantized spin Hall conductivity per unit cell in a bulk crystal.

cond-mat.mes-hall↗

arXiv2021

TNNT: The Named Entity Recognition Toolkit

Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art Natural Language Processing (NLP) tools and NER models. TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enhanced data analysis to support the KGCP, and also, to aid further NLP tasks.

cs.CL↗