Sigma-Pi Interaction Accompanied by Stereoselection

Black-box Identity Testing for Low Degree Unmixed $\Sigma\Pi\Sigma\Pi(k)$ Circuits

A ΣΠΣΠ(k) circuit C = ∑k i=1 Fi = ∑k i=1 ∏di j=1 fij is unmixed if for each i ∈ [k], Fi = fi1(x1) · · · fin(xn), where each fij is a univariate polynomial given in the sparse representation. In this paper, we give a polynomial time black-box algorithm of identity testing for the low degree unmixed ΣΠΣΠ(k) circuits. In order to obtain the black-box algorithm, we first show that a special class o...

Existence of Sigma Meson in Pi-pi Scattering *

In this talk I summarize a recently proposed mechanism to understand ππ scattering to 1 GeV. The model is motivated by the 1/NC expansion to QCD, and includes a current algebra contact term and resonant pole exchanges. Chiral symmetry plays an important role in restricting the form of the interactions. The existence of a broad low energy scalar (σ) is indicated.

Adaptive critic for sigma-pi networks

-This article presents an investigation which studied how training o f sigma-pi networks with the associative reward-penalty ( A R-p ) regime may be enhanced by using two networks in parallel. The technique uses what has been termed an unsupervised "'adaptive critic element" (ACE) to give critical advice to the supervised sigma-pi network. We utilise the conventions that the sigma-pi neuron mod...

Randomly Connected Sigma-Pi Neurons Can Form . . .

Lp approximation of Sigma-Pi neural networks

A feedforward Sigma-Pi neural network with a single hidden layer of m neurons is given by mSigma(j=1) cjg (nPi(k=1) xk-thetak(j)/lambdak(j)) where cj, thetak(j), lambdak are elements of R. In this paper, we investigate the approximation of arbitrary functions f: Rn-->R by a Sigma-Pi neural network in the Lp norm. An Lp locally integrable function g(t) can approximate any given function, if and ...