arXiv subjects

Weidong Yan

Publications and source records attributed to Weidong Yan.

6 recordsLinked to original sources

arXiv2025

THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations

Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

eess.SP↗

arXiv2024

Frictional Dissipation and Scaling Laws at van der Waals Interface: The Key Role of Elastic Pinning of Moir\'e at Edges and Corners

Van der Waals heterogeneous interfaces are promising candidates for the scaling up of structural superlubricity to meet practical applications. Several factors, however, have been identified that may eliminate superlubricity. Elasticity is one such intrinsic factor, where shear induced lattice reconstruction leads to local interfacial pinning, even at clean pristine contacts. Here, through detailed atomistic simulations, we reveal that incomplete moir\'e tile pinning at the corners and edges of finite sliders dominates friction from the nano- to the microscales. We further demonstrate that slider shape tailoring and twisting allow to control energy dissipation and its scaling with contact size, thus opening the way to achieve large-scale superlubricity.

cond-mat.mes-hall↗

arXiv2023

Unsupervised convolutional neural network fusion approach for change detection in remote sensing images

With the rapid development of deep learning, a variety of change detection methods based on deep learning have emerged in recent years. However, these methods usually require a large number of training samples to train the network model, so it is very expensive. In this paper, we introduce a completely unsupervised shallow convolutional neural network (USCNN) fusion approach for change detection. Firstly, the bi-temporal images are transformed into different feature spaces by using convolution kernels of different sizes to extract multi-scale information of the images. Secondly, the output features of bi-temporal images at the same convolution kernels are subtracted to obtain the corresponding difference images, and the difference feature images at the same scale are fused into one feature image by using 1 * 1 convolution layer. Finally, the output features of different scales are concatenated and a 1 * 1 convolution layer is used to fuse the multi-scale information of the image. The model parameters are obtained by a redesigned sparse function. Our model has three features: the entire training process is conducted in an unsupervised manner, the network architecture is shallow, and the objective function is sparse. Thus, it can be seen as a kind of lightweight network model. Experimental results on four real remote sensing datasets indicate the feasibility and effectiveness of the proposed approach.

cs.CV↗

arXiv2023

Shape-dependent friction scaling laws in twisted layered material interfaces

Static friction induced by moiré superstructure in twisted incommensurate finite layered material interfaces reveals unique double periodicity and lack of scaling with contact size. The underlying mechanism involves compensation of incomplete moiré tiles at the rim of rigid polygonal graphene flakes sliding atop fixed graphene or h-BN substrates. The scaling of friction (or lack thereof) with contact size is found to strongly depend on the shape of the slider and the relative orientation between its edges and the emerging superstructure, partially rationalizing scattered experimental data. With careful consideration of the flake edge orientation, twist angle, and sliding direction along the substrate, one should therefore be able to achieve large-scale superlubricity via shape tailoring.

physics.app-ph↗

arXiv2022

Origin of frictional scaling law in circular twist layered interfaces: simulations and theory

Structural superlubricity based on twisted layered materials has stimulated great research interests. Recent MD simulations show that the circular twisted bilayer graphene (tBLG) presenting a size scaling of friction with strong Moiré-level oscillations. To reveal the physical origin of observed abnormal scaling, we proposed a theoretical formula and derived the analytic expression of frictional size scaling law of tBLG. The predicted twist angle dependent scaling law agrees well with MD simulations and provides a rationalizing explanation for the scattered power scaling law measured in various experiments. Finally, we show clear evidence that the origin of the scaling law comes from the Moiré boundary, that is, the remaining part of the twisted layered interfaces after deleting the internal complete Moiré supercells. Our work provides new physical insights into the friction origin of layered materials and highlights the importance of accounting for Moiré boundary in the thermodynamic models of layered materials.

physics.app-ph↗

arXiv2021

Thermodynamic model of twisted bilayer graphene: Configuration entropy matters

Twisted bilayer materials have attracted tremendous attention due to their unique and novel properties. Here, we derive a thermodynamic model for twisted bilayer graphene (tBLG) within the framework of the classical statistical mechanics, based on which, the configuration entropy reflecting the number of micro-status in moire unit-cells, is directly derived from the Helmholtz free energy with a clear physical interpretation. More importantly, we show the configuration entropy of tBLG relative to the AB-stacked bilayer graphene is proportional to the logarithmic function of the ratio of moire period and the atomic lattice constant, which we found dominates the Helmholtz free energy of tBLG and can well explain experimental observations in superlubric contacts. Our work provides a theoretical foundation for studying moire effect of incommensurate contact interfaces and could facilitate twisting based applications such as superlubricity.

cond-mat.mes-hall↗