Showing 1 - 20 results of 4,236 for search '(( scale ((from decrease) OR (fold decrease)) ) OR ( a ((larger decrease) OR (marked decrease)) ))', query time: 0.65s Refine Results
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    5-fold cross-validation comparison results. by Chunming Wen (17616274)

    Published 2025
    “…The computational cost decreased from 11.6 GFlops to 6.6 GFlops.</p></div>…”
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    Biases in larger populations. by Sander W. Keemink (21253563)

    Published 2025
    “…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”
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    Parameters used in the simulations. by Yongfeng Zhu (7361045)

    Published 2025
    “…The results show strong agreement between numerical simulations and theoretical predictions at the particle scale, validating the modified contact model. At the sample scale, the peak deviatoric stress increased by approximately 15–40% as aspect ratio decreased from 1.00 to 0.33 and sphericity decreased from 1.00 to 0.11. …”
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    Two ellipsoidal particles in contact. by Yongfeng Zhu (7361045)

    Published 2025
    “…The results show strong agreement between numerical simulations and theoretical predictions at the particle scale, validating the modified contact model. At the sample scale, the peak deviatoric stress increased by approximately 15–40% as aspect ratio decreased from 1.00 to 0.33 and sphericity decreased from 1.00 to 0.11. …”
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    Different contact patterns for two ellipsoids. by Yongfeng Zhu (7361045)

    Published 2025
    “…The results show strong agreement between numerical simulations and theoretical predictions at the particle scale, validating the modified contact model. At the sample scale, the peak deviatoric stress increased by approximately 15–40% as aspect ratio decreased from 1.00 to 0.33 and sphericity decreased from 1.00 to 0.11. …”
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    PCA-CGAN K-fold experiment table. by Chao Tang (10925)

    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. …”