Exact(9)
But let's allow ourselves to imagine a worst case scenario: if a military member instigates an anonymous report qua "incident," and his (or her) commander happens to be one of, say, three officers in the entire unit, and they are all social intimates, then the likelihood is very high that the commander would give biased input to substantiate the "abuse" for his friend's sake.
In particular, these can be biased input data and insufficient accounting for time lag.
The post-categorization bias model is better than the next best model (biased input gain) by 50 log-units.
Over time this biased input causes the automatic processing of affective information to be shifted more negatively, creating stable self-reinforcing negative schemata.
Then the biased input measurements are calibrated through Bayesian estimation and the first-stage SVR model is thus built for output measurement reconciliation.
The next best model (which was worse by ≈50 log-units than the post-decision model, Figure 6A) was (b), the biased input gain model.
Similar(51)
First, biased inputs from the V1 and V2b INs would facilitate the graded activation of synergist and antagonist motor neurons.
A neuron in the network receives weighted inputs from the neuron and a bias input from outside.
Here maximal means that we maximize over all possible signal input phases relative to the bias input.
As shown in Figure 5, the β neuron has α inputs from the previous layer neurons with their specified weights, i.e., v βα and a bias input b β.
However, for different bias input amplitudes and consequently lower gain values the output will generally feature a linear imaginary quadrature component (x' = [g_{rr} epsilon_{0}) + i g_{ir} epsilon_{0}) ]x) as well.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com