Republican candidates often receive between 30% and 40% of the two-way vote share in statewide elections in Massachusetts. For the last three Census cycles, MA has held 9-10 seats in the House of Representatives, which means that a district can be won with as little as 6% of the statewide vote. Putting these two facts together, one may be surprised to learn that a Massachusetts Republican has not won a seat in the U.S. House of Representatives since 1994. We argue that the underperformance of Republicans in Massachusetts is not attributable to gerrymandering, nor to the failure of Republicans to field House candidates, but is a structural mathematical feature of the distribution of votes. For several of the elections studied here, there are more ways of building a valid districting plan than there are particles in the galaxy, and every one of them will produce a 9-0 Democratic delegation. arXiv:1810.09051v1 [physics.soc-ph]

Satellite image analysis is widely used in many real-time applications, from agriculture to the military. Due to the wide range of Generative Adversarial Network (GAN) applications in multiple areas of satellite imaging, a comprehensive review is required in this area. This paper takes the first step in this direction by categorizing the GAN-based satellite imaging research using seven considerations. We discuss not only the challenges but also future research trends and directions. Among the major findings, we have observed increasing componentization and modularization of GANs to be used as elements of larger systems. In addition to the GAN types used exclusively in each application, we demonstrate the deep neural network architectures used as the generator structure. Eventually, we summarize the results and evaluate the significant impact of GANs on improving performance compared to traditional approaches.

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