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A typical objective function consists of an error term and a regularization term.
The algorithm consists of an error model for the expression of probe pair signals in which the detected signal is assumed to be proportional to the probe pair signal for highly expressed genes, while approaching a background noise level for genes with low levels of expression.
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The assumptions for this model include: (1) the positional error consists of a systematic error and a random error, (2) the random error is normally distributed in all dimensions, (3) random errors in X, Y, and Z directions are independent, and (4) horizontal errors are not different in X and Y dimensions.
Considering all static/quasi-static error sources deteriorating relative positioning accuracy, an effective error compensation method which consists of a new error prediction model and an error compensation strategy is proposed for coordinated five-axis machines.
The NIST metric consists of a single error score which takes into account substitution errors (mislabeling an active voice, E subs ), miss errors (when a voice is truly active but results in no transcript, E miss ), and false alarm errors (when an active voice is reported without any underlying source, E fa ).
We propose a model validation procedure that consists of a prediction error identification experiment with a full order model.
The resulting objective function consists of a weighted Euclidean error function and the regularization terms due to bases Ar and A h ( l ) which are expressed by ∑ l = 1 L ω ( l ) ∥ X l l ) − A r S r ( l ) − A h ( l ) S h ( l ) ∥ 2 + ηL ∥ A r ∥ 2 + η ∑ l = 1 L ∥ A h ( l ) ∥ 2 (3).
Data is collected at the individual ICU and uploaded to the central repository (ANZICS APD) for processing and quality assurance; which consists of a cycle of error and exception checks, site feed-back, resubmission and incorporation into a final reporting data-set.
It consists of a pulse generator, an error detector and a multiplexer along with a conventional flip-flop.
The mathematical formulation consists of an ANN with an unsupervised error, which is minimized by tuning weights of the network.
Generally, the prediction error consists of a reducible and an irreducible error term.
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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