26,892 research outputs found
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 (). For photon energies in the range of 9 - 46 MeV, we obtain a new
limit of . 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
, and .
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
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