Character measure is a probability on irreducible representations of semisimple Lie algebra. It appears from the decomposition into irreducibles tensor power fundamental representation. In this paper we calculate asymptotics character so2n+1 in regime near boundary weight diagram. We find out that it converges to Poisson-type distribution.

We use the transfer equation in relativistic form to develop an expansion of the onephoton distribution for a medium with constant photon mean free path, ǫ. On carrying out appropriate integrations and manipulations, we convert this expansion into one for the frequency-integrated intensity. We regroup the terms of the intensity expansion according to both the power of ǫ and the angular structur...

We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction neural-network layers that efficiently perform contractions use commonly adopted nonlinear activation functions. resulting feature number edges closely match contraction complexity to be approximated. In context many-body quantum states, ...

We study slow-roll inflation and the generation of primordial fluctuations in $F(T)$ gravity's rainbow. obtain second order action for scalar tensor perturbations then calculate power spectrum them. Thus, after calculating inflationary observables up to first approximation, namely spectral index ${n}_{s}$ tensor-to ratio $r$, we confront predictions model with current PLANCK BICEP/Keck data.

This paper is a follow-up to [5], in which the first author studied primitive association schemes lying between tensor power

Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture c...