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A Turing machine is an abstract device that receives information as input and produces other information as output, the particular output depending on the input, the internal state of the machine, and a finite set of rules that associate input and machine-state with output.
Neural networks are quantitative models which learn to associate input and output patterns adaptively with the use of learning algorithms.
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To each InterfaceType, there are associated input and output ports.
There are of course other synthesis techniques, together with methods for obtaining the associated input parameters, such as [14].
The scripts and associated input files (e.g., genome and annotations) that they rely on are available at https://github.com/blobbybirdman/MrpC_MXanthus.
All of the datasets, as well as the associated input files for PartitionFinder, are available from figShare (http://dx.doi.org/10.6084/m9.figshare.938920), and references for the datasets and the studies that they are associated with are provided in Table 1.
Feed-forward ANN tends to be a straight forward network that associates inputs with outputs.
Methods: computational method description (e.g. model building, equilibration procedure, production runs, enhanced sampling methodology) and associated inputs / parameters.
A review of the governing equations, boundary conditions and the associated inputs by way of physico chemical models and their partially successful application is given.
All the resulting transfers of associated inputs and outputs are made at the same time as the use and make matrices are compiled.
The parameters mainly associate the input files to variable names in the command line and specify if streaming is enabled or not.
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CEO of Professional Science Editing for Scientists @ prosciediting.com