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Integrating the results of elemental methods is a filtering problem, where observations are input features extracted with some preprocessing methods and latent states are the results of integration.
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One of the most striking observations is that INPUT-chip and INPUT-seq profiles appear to be substantially different, even though the same input DNA material was used for microarray hybridization and sequencing.
In batch-based training protocol, all input observations are presented to the model before the parameter update takes place.
The basic idea here is to gradually present input observations to the HDLA model so that at the early stages of training the coarse-scale properties of input observations are captured while the fine-scale characteristics are learned in later stages.
Experimental observations are required to constitute the input and calibration datasets.
These observations are not explicitly provided in input (as it happens with abduction) but, rather, they are hidden in the given knowledge base.
Likewise as with the input, all transformed observations are represented graphically in offset histograms, yet parallel to the new dimensions.
Although transport theories need the spatial and time variations of the magnetic field as input, in situ observations are very limited.
Some small-scale structures are not reproduced by the model, which might be due to the fact that the model is a 1-day result with 131-day averaged solar wind and IMF parameters as input, while the observations are an accumulation of results over 131 days, as well as due to the fact that the tidal effects are not included in the model.
Taken together, these observations are consistent with pain resulting from input from neurons that normally subserve innocuous sensation (Table 1).
These observations are surprising, given the fact that input resistance of L5 thick-tufted pyramidal neurons is considerably lower than that of spiny stellates and because of the strong attenuation of post-synaptic potentials (PSPs) along their basal dendrites (Nevian et al. 2007).
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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