A Particle Swarm Optimization algorithm is used to search for input parameters in order to minimize and maximize system outputs by an anti-optimization and thus build their envelopes.
So, if we set a direction in this plane, given by a vector w, we can search for input variables whose representation on the kernel PCA plane are correlated with this direction.
If one were to search for inputs that are most influential in this process, they might want to look at those features that can influence the success of one phenotype in relation to its alternatives (ultimate mechanism).
Global outsourcing – when a company searches for inputs, production sites and outputs where costs are lower and regulatory, political and social regimes favourable – enables TFCs to cut costs and helps safeguard against the uncertainty of commodity production and product sales [ 39].
Then, if we know that a certain input variable is useful for interpreting the kernel PCA representation, we can search for other input variables whose representation on the kernel PCA plane are correlated with this feature.
The STD subsystem integrates the Kaldi term detector [81, 101, 102] which searches for the input terms within the word lattices obtained in the previous step.
To do so, the system integrates the Kaldi term detector [60, 64, 65], which searches for the input queries within those word lattices.
The STD subsystem consists of the Kaldi term detector [68 70] to search for the input terms within the word lattices and the Kaldi decision maker [42] to output reliable detections.
To assess the probability of an input spike given an output spike, the 18 ms preceding each output spike was searched for a 1 in the binarized input trace (indicating an input spike).
We used SNPnexus with BTG2 SNPs as input and searched for overlaps with miRNA.
