Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels

We investigate connections between information-theoretic and estimation-theoretic quantities in vector Poisson channel models. In particular, we generalize the gradient of mutual information with respect to key system parameters from the scalar to the vector Poisson channel model. We also propose, as another contribution, a generalization of the classical Bregman divergence that offers a means to encapsulate under a unifying framework the gradient of mutual information results for scalar and vector Poisson and Gaussian channel models. The so-called generalized Bregman divergence is also shown to exhibit various properties akin to the properties of the classical version. The vector Poisson channel model is drawing considerable attention in view of its application in various domains: as an example, the availability of the gradient of mutual information can be used in conjunction with gradient descent methods to effect compressive-sensing projection designs in emerging X-ray and document classification applications

Who gets caught for corruption when corruption is pervasive? Evidence from China’s anti-bribery blacklist

© 2016 Informa UK Limited, trading as Taylor & Francis Group. This article empirically investigates why in a corruption-pervasive country only a minority of the firms get caught for bribery while the majority get away with it. By matching manufacturing firms to a blacklist of bribers in the healthcare sector of a province in China, we show that the government-led blacklisting is selective: while economically more visible firms are slightly more likely to be blacklisted, state-controlled firms are the most protected compared to their private and foreign competitors. Our finding points to the fact that a government can use regulations to impose its preferences when the rule of law is weak and the rule of government is strong

Large Deviations for Stochastic Generalized Porous Media Equations

The large deviation principle is established for the distributions of a class of generalized stochastic porous media equations for both small noise and short time.Comment: 15 pages; BiBoS-Preprint No. 05-11-196; publication in preparatio