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Using synthetic and real-world mobility traces, the proposed predictor does not only outperform existing mobility prediction algorithms but achieves accuracy scores higher by an order of magnitude.
The closed-loop predictor does not invoke any additional delay for inner calculation.
On the other hand, when b̂ is not a constant, the predictor does not detect closed-form solutions and then the predictor of Case 2 below must be applied.
This showed that our predictor does not rely on the experiment information too heavily.
We conclude that the Euclidean distance-to-center predictor does not substantially underestimate the contribution of the central bias in our model.
Thus, this example shows the properties of useful predictor for genetic redundancy, accurately delineating redundancy within a gene family in a non-trivial way; that is, the predictor does not simply label the most closely related genes as redundant.
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History of depression was noted to be a potential predictor of oral DMD initiation; however, the estimate for this predictor did not reach statistical significance (OR = 1.35; 95% CI = 0.99-1.84).
The predictor did not perform well for TFs with a binding mode of β-hairpin/ribbon.
If a predictor did not satisfy the proportional hazards assumption this was defined as time-dependent whenever relevant.
Fitting density as a linear predictor did not (BIC = 1476.15), and removal of the interaction also gave a poorer fit (BIC = 1456.41).
Unlike in the one-dimensional case, however, including functions of the migration rate m as a predictor did not improve the model in two dimensions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com