Local network externalities and market segmentation

This paper models interaction between groups of agents by means of a graph where each node represents a group of agents and an arc represents bilateral interaction. It departs from the standard Katz-Shapiro framework by assuming that network benefits are restricted only amongst groups of linked agents. It shows that even if rival firms engage in Bertrand competition, this form of network externalities permits strong market segmentation in which firms divide up the market and earn positive profits. The analysis also shows that some graphs or network structures do not permit such segmentation, while for others, there are easy to interpret conditions under which market segmentation obtains in equilibrium.network structure, network externalities, price competition, market segmentation

Networks, Network Externalities and Market Segmentation

This paper models interaction between groups of agents by means of a graph where each node represents a group of agents and an arc represents bilateral interaction. It departs from the standard Katz-Shapiro framework by assuming that network benefits are restricted only amongst groups of linked agents. It shows that even if rival firms engage in Bertrand competition, this form of network externalities permits strong market segmentation in which firms divide up the market and earn positive profits.network structure, network externalities, price competition, market segmentation.

Advancements in Image Classification using Convolutional Neural Network

Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet model. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.Comment: 9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks(ICRCICN 2018

A test of local Lorentz invariance with Compton scattering asymmetry

We report on a measurement of the constancy and anisotropy of the speed of light relative to the electrons in photon-electron scattering. We used the Compton scattering asymmetry measured by the new Compton polarimeter in Hall~C at Jefferson Lab to test for deviations from unity of the vacuum refractive index ($n$). For photon energies in the range of 9 - 46 MeV, we obtain a new limit of $1-n < 1.4 \times 10^{-8}$. In addition, the absence of sidereal variation over the six month period of the measurement constrains any anisotropies in the speed of light. These constitute the first study of Lorentz invariance using Compton asymmetry. Within the minimal standard model extension framework, our result yield limits on the photon and electron coefficients $\tilde{\kappa}_{0^+}^{YZ}, c_{TX}, \tilde{\kappa}_{0^+}^{ZX}$, and $c_{TY}$. Although, these limits are several orders of magnitude larger than the current best limits, they demonstrate the feasibility of using Compton asymmetry for tests of Lorentz invariance. Future parity violating electron scattering experiments at Jefferson Lab will use higher energy electrons enabling better constraints.Comment: 7 pages, 5 figure