Steven R. Skinner

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D[x(t)] exp(i 5 m T 0 dJ[ 1 2 m x 2 V(x)]) |R(x 0 ,0)> lim N$" m (x N1 x f ,T) (x 0 ,0) dx 1 (((dx N (m 2Bi5)t) (N 1)/2 exp(i)t 5 j N j 0 [ m 2 (x j 1 x j)t) 2 V(x j)])|R(x 0 ,0)> Abstract We present a mathematical implementation of a quantum mechanical artificial neural network, in the quasi-continuum regime, using the nonlinearity inherent in the(More)
BACKGROUND Measurement scales seeking to quantify latent traits like attitudes, are often developed using traditional psychometric approaches. Application of the Rasch unidimensional measurement model may complement or replace these techniques, as the model can be used to construct scales and check their psychometric properties. If data fit the model, then(More)
Artificial neural networks are learning systems modeled loosely after the architecture of the brain. These are usually envisioned as a collection of discrete nonlinear processors called artificial neurons with a massive number of artificial synapses connecting them together. A major hurdle, when attempting to build neural networks in hardware is(More)
The optical bench training of an optical feedforward neural network, developed by the authors, is presented. The network uses an optical nonlinear material for neuron processing and a trainable applied optical pattern as the network weights. The nonlinear material, with the applied weight pattern, modulates the phase front of a forward propagating(More)