arXiv subjects

Kaiyan Cao

Publications and source records attributed to Kaiyan Cao.

3 recordsLinked to original sources

arXiv2025

Observation of Stable Bimeron Transport Driven by Spoof Surface Acoustic Waves on Chiral Metastructures

Topological quasiparticles, such as merons and bimerons, are characterized by non-trivial textures that exhibit remarkably robust transport against deformation, offering significant potential for information processing. While these phenomena have been explored in various systems, acoustic realizations remain challenging. Here, we report that acoustic meron topological textures were successfully realized using designed Archimedeanlike square spiral metastructures via the excitation of spoof surface acoustic waves (SSAWs). By applying mirror-symmetric combinatorial operations to the unit structures, we further construct composite chiral metastructures that enable both one-dimensional and two-dimensional stable transport of acoustic bimerons. It is further revealed that bimeron transport originates from the locked opposite phase differences of SSAWs, induced by the handedness of the cavity resonant modes. The intrinsic robustness of the meron textures against structural defects is confirmed through the calculation of their topological charge. Our findings establish stable acoustic bimeron transport as a topologically resilient foundation for future acoustic information processing and storage technologies.

cond-mat.mtrl-sci↗

arXiv2022

Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension

The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor coverage of concepts, especially fine-grained concepts. In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities. Specifically, MRC-CE is built with a machine reading comprehension model based on BERT, which can extract more fine-grained concepts with a pointer network. Furthermore, a random forest and rule-based pruning are also adopted to enhance MRC-CE's precision and recall simultaneously. Our experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction models in KG completion. Particularly, after running MRC-CE for each entity in CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are supplied into the KG. The code and datasets have been released at https://github.com/fcihraeipnusnacwh/MRC-CE

cs.IR↗

arXiv2019

Machine learning magnetic parameters from spin configurations

Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained, which can serve as an input to machine learning (ML) algorithms to extract Hamiltonian parameters. However, the number of labeled images is rather limited. Here, we provide a protocol for Hamiltonian parameter estimation based on a machine learning architecture, which is trained on a small amount of simulated images and applied to experimental spin configuration images. Sliding windows on the input images enlarges the number of training images; therefore we can train well a neural network on a small dataset of simulated images which are generated adaptively using the same external conditions such as temperature and magnetic field as the experiment. The neural network is applied to the experimental image and estimates magnetic parameters efficiently. We demonstrate the success of the estimation by reproducing the same configuration from simulation and predict a hysteresis loop accurately. Our approach paves a way to a stable and general parameter estimation.

cond-mat.dis-nn↗