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_ larger » _ large (Expand Search), _ largest (Expand Search), a large (Expand Search)
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larger decrease » marked decrease (Expand Search)
values decrease » values increased (Expand Search), largest decrease (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
_ larger » _ large (Expand Search), _ largest (Expand Search), a large (Expand Search)
i values » _ values (Expand Search)
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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness.
Published 2025“…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. …”
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Scheme of g-λ model with larger values λ.
Published 2024“…And if the value of λ assumes larger values, the distortion in the shape of the transmitted wave is associated with the plastic deformation in the uncoupled rock mass. …”
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Biases in larger populations.
Published 2025“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. Bias decreases with increasing neurons in the population. …”
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The MAE value of the model under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Three error values under raw data.
Published 2025“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”
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Average stability values (mean ± standard deviation) and statistical results.
Published 2024Subjects: -
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Individual stability values for different gait events under various conditions.
Published 2024Subjects: -
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Comparison of environmental perception time results at different learning rates.
Published 2025Subjects: -
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