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The results show that the proposed model performs on an average 25% better than the other models.
The implication of our finding is straightforward: If you are thinking about changing your business model or entering an industry with a new model, you can rate yourself on how well your model performs on the six features.
Also, the AR log-transformed model performs on average more than 13% better than the AR non-linear model.
Beyond accuracy on the entire dataset, it is important to consider how well the model performs on the users it is the most or least confident about.
The non-linear AR model performs on average 39% better than the nonlinear model (i.e. the average reduction in the RMSPE of the models for the 16 countries that have been considered is 39%), while the autoregressive log-transformed model performs on average 49% better than the log-transformed model.
The pairwise model performs on average slightly yet significantly better (p < 10−4, two-sided paired sign test).
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We also report on the first ever benchmark of a 8 trillion particles simulation of the same model, performed on Titan at Oak Ridge using 18,000 GPU-accelerated nodes.
In Figs. 1 and 2, we show the denoising results from our new model and the CEP- L 2 model performed on the Lena image with additive Gaussian white noise of σ=10 and σ=15 respectively.
Simulations for our signalling model, performed on hexagonal cells with circular nuclei, are demonstrated in figures 7, 8, 9, 10, 11, and 12, and also in two Supporting Information files (see "Animation S3" and "Animation S4").
The second sensitivity analysis was an intent-to-treat linear mixed-effects model performed on absolute values of the outcome, as opposed to changes in the outcome.
The multivariate logistic regression model performed on iELISA results (Table 3) showed year of sampling and age were significantly associated with boar seropositivity.
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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