Search alternatives:
significant attention » significant potential (Expand Search), significant reduction (Expand Search)
fold decrease » fold increase (Expand Search), fold increased (Expand Search), fold increases (Expand Search)
_ decrease » _ decreased (Expand Search), _ decreasing (Expand Search)
significant attention » significant potential (Expand Search), significant reduction (Expand Search)
fold decrease » fold increase (Expand Search), fold increased (Expand Search), fold increases (Expand Search)
_ decrease » _ decreased (Expand Search), _ decreasing (Expand Search)
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ECoG timescales decrease during spatial attention.
Published 2025“…Bottom: timescales significantly decrease during covert attention relative to the attend-out condition (two locations: <i>p</i> = 0.0244; four locations: <i>p</i> < 0.0001; mean ± SEM; whiskers indicate maximum and minimum; dots correspond to individual electrodes). …”
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Data Sheet 1_Elevated relative humidity significantly decreases cannabinoid concentrations while delaying flowering development in Cannabis sativa L..docx
Published 2025“…Furthermore, high RH significantly suppressed cannabinoid accumulation: cannabidiolic acid (CBD-A), cannabidiol (CBD), and cannabichromenic acid (CBC-A) levels decreased by approximately 4.9-fold, 3.2-fold, and 13-fold, respectively. …”
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PCA-CGAN K-fold experiment table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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PCAECG-GAN K-fold experiment table.
Published 2025“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
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