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Scalable Graph Self-Supervised Learning
Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance.
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
Furthermore, we illustrate the underlying aspects of the proposed model in effectively capturing extensive temporal dependencies in dynamic graphs.
DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis
To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, referred to as the Dual Frequency Aggregated steady-state motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length.
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