Tang Jin | Searcharxiv

arXiv2025

Thermal Stability of Skyrmion Tubes in Nanostructured Cuboids

Magnetic skyrmions in bulk materials are typically regarded as two-dimensional structures. However, they also exhibit three-dimensional configurations, known as skyrmion tubes, which elongate and extend in-depth. Understanding the configurations and stabilization mechanism of skyrmion tubes is crucial for the development of advanced spintronic devices. However, the generation and annihilation of skyrmion tubes in confined geometries are still rarely reported. Here, we present direct imaging of skyrmion tubes in nanostructured cuboids of a chiral magnet FeGe using Lorentz transmission electronic microscopy (TEM), while applying an in-plane magnetic field. It is observed that skyrmion tubes stabilize in a narrow field-temperature region near the Curie temperature (Tc). Through a field cooling process, metastable skyrmion tubes can exist in a larger region of the field-temperature diagram. Combining these experimental findings with micromagnetic simulations, we attribute these phenomena to energy differences and thermal fluctuations. Our results could promote topological spintronic devices based on skyrmion tubes.

cond-mat.mes-hall↗

arXiv2022

S-Rocket: Selective Random Convolution Kernels for Time Series Classification

Random convolution kernel transform (Rocket) is a fast, efficient, and novel approach for time series feature extraction using a large number of independent randomly initialized 1-D convolution kernels of different configurations. The output of the convolution operation on each time series is represented by a partial positive value (PPV). A concatenation of PPVs from all kernels is the input feature vector to a Ridge regression classifier. Unlike typical deep learning models, the kernels are not trained and there is no weighted/trainable connection between kernels or concatenated features and the classifier. Since these kernels are generated randomly, a portion of these kernels may not positively contribute in performance of the model. Hence, selection of the most important kernels and pruning the redundant and less important ones is necessary to reduce computational complexity and accelerate inference of Rocket for applications on the edge devices. Selection of these kernels is a combinatorial optimization problem. In this paper, we propose a scheme for selecting these kernels while maintaining the classification performance. First, the original model is pre-trained at full capacity. Then, a population of binary candidate state vectors is initialized where each element of a vector represents the active/inactive status of a kernel. A population-based optimization algorithm evolves the population in order to find a best state vector which minimizes the number of active kernels while maximizing the accuracy of the classifier. This activation function is a linear combination of the total number of active kernels and the classification accuracy of the pre-trained classifier with the active kernels. Finally, the selected kernels in the best state vector are utilized to train the Ridge regression classifier with the selected kernels.

cs.LG↗