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Staffing in postnatal wards is a challenging issue, and varies with hospital locality and model of care.
There was also no difference in the ability of models fit to modern vs. historical training data to predict the four North American oryx localities; both model groups successfully predicted the localities at the generous MTP threshold but failed to predict them with the more stringent MTSS threshold.
For Maxent (version 2.3) [63], we used the default convergence threshold (10−5) and maximum number of iterations (500) values, using 25% of localities for model training.
Observed versus predicted response in morphological variation between sampling localities, indicating model performance.
Observed versus predicted response in genetic variation (AFLP markers) between sampling localities, indicating model performance.
We interviewed a large number of women from diverse localities and models of maternity care.
Sample sizes of unique localities for modelling distributions of individual species were highest for birds, followed by mammals, plants, and amphibians.
We do this by exploring and comparing two approaches in parallel: first, augmenting the input with multi-resolution features and therefore dealing with the locality outside the model, and second, exploiting the locality with a model that integrates that concept, all within the context of deep learning.
Multi-resolution analysis approaches the issue of characterizing spectro-temporal locality outside the model, while the CNN approach itself exploits local features.
In this paper, we propose a novel set-based matching model, "Locality Based Discriminative Measure", to re-identify the human body when a set of test samples for each person are available.
The second approach is intended to integrate the spectro-temporal locality in the model itself.
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