arXiv subjects

Kailin Xu

Publications and source records attributed to Kailin Xu.

4 recordsLinked to original sources

arXiv2026

Decoupled domain-texture switching from magnetic easy axis in kagome ferromagnet EuTi3Bi4

Magnetic anisotropy defines the easy axis of a magnetic material and governs the spatial arrangement of its domains. To date, anisotropy engineering has focused on reorienting the easy axis or tuning the anisotropy energy, both of which demand substantial energy input. Here, we demonstrate that magnetic domain textures can be switched without reorienting the easy axis, as observed in a kagome ferromagnet EuTi3Bi4 crystal. Using low-temperature magnetic force microscopy, we observe that the preferred orientation of magnetic domains switches from the a-axis to the b-axis upon temperature variation, and that this switching can also be triggered by an out-of-plane magnetic-field reset. Magnetization measurements and density functional theory calculations confirm a robust c-axis easy magnetization, ruling out a conventional spin-reorientation transition. Instead, the texture switching is governed by the temperature dependence of the in-plane variation of the Magnetic anisotropy energy landscape, which arises from two competing interactions with different decay rates: single-ion anisotropy favors a-oriented spin components, while nearest-neighbor anisotropic exchange favors b-oriented ones. Furthermore, the critical switching temperature is substantially elevated in a mechanically exfoliated EuTi3Bi4 flake. Our findings establish that macroscopic magnetic textures can be effectively manipulated by tuning the competition between in-plane anisotropic interactions, without the energy cost of reorienting the easy axis.

cond-mat.mtrl-sci↗

arXiv2026

Spin-mediated hysteretic switching of unidirectional charge density waves by rotating magnetic fields

Charge density waves (CDWs) are a widespread collective electronic order in quantum materials, furnishing key insights into symmetry breaking and competing phases. However, their dynamic control with external fields remains a pivotal challenge. Here, we report deterministic and hysteretic switching of unidirectional CDW orientation via in-plane magnetic field rotation in magnetic kagome metal GdTi3Bi4. Atomically resolved spectroscopy shows two types of 3a0*1a0 CDW domains, Q1 and Q2 oriented 60 degree apart along two distinct crystallographic directions and separated by atomically sharp domain walls. Rotating the magnetic field drives reversible transitions between these CDW configurations, exhibiting a robust C2-symmetric phase diagram with pronounced hysteresis. This hysteretic switching is mediated by a field-dependent reorientation of underlying antiferromagnetic spins, revealing a tunable energy landscape with stable and metastable states and modulates the electronic charge order via spin-lattice coupling. Our findings not only demonstrate the switching of CDW configurations by in-plane magnetic field but also reveal the mechanism of coupling between CDW and magnetic fields, offering new insights into CDW manipulation and versatile platform for developing a spin-mediated multistate spin-charge coupling memory and programmable quantum devices.

cond-mat.str-el↗

arXiv2025

Alleviating Seasickness through Brain-Computer Interface-based Attention Shift

Seasickness poses a widespread problem that adversely impacts both passenger comfort and the operational efficiency of maritime crews. Although attention shift has been proposed as a potential method to alleviate symptoms of motion sickness, its efficacy remains to be rigorously validated, especially in maritime environments. In this study, we develop an AI-driven brain-computer interface (BCI) to realize sustained and practical attention shift by incorporating tasks such as breath counting. Forty-three participants completed a real-world nautical experiment consisting of a real-feedback session, a resting session, and a pseudo-feedback session. Notably, 81.39\% of the participants reported that the BCI intervention was effective. EEG analysis revealed that the proposed system can effectively regulate motion sickness EEG signatures, such as an decrease in total band power, along with an increase in theta relative power and a decrease in beta relative power. Furthermore, an indicator of attentional focus, the theta/beta ratio, exhibited a significant reduction during the real-feedback session, providing further evidence to support the effectiveness of the BCI in shifting attention. Collectively, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, which has the potential to open up a brand-new application domain for BCIs.

cs.HC↗

arXiv2021

Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition

In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received less attention. One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. By applying the module to the coordinate level and the joint level subsequently, the topology feature is effectively enhanced. Notably, we theoretically prove that graph convolution is a special case of normal convolution when the joint dimension is treated as channels. This confirms that the topology modeling power of GCNs can also be implemented by using a CNN. Moreover, we creatively design a SkeletonMix strategy which mixes two persons in a unique manner and further boosts the performance. Extensive experiments are conducted on four widely used datasets, i.e. N-UCLA, SBU, NTU RGB+D and NTU RGB+D 120 to verify the effectiveness of Ta-CNN. We surpass existing CNN-based methods significantly. Compared with leading GCN-based methods, we achieve comparable performance with much less complexity in terms of the required GFLOPs and parameters.

cs.CV↗