Inequality and Network Formation Games

This paper addresses the matter of inequality in network formation games. We employ a quantity that we are calling the Nash Inequality Ratio (NIR), defined as the maximal ratio between the highest and lowest costs incurred to individual agents in a Nash equilibrium strategy, to characterize the extent to which inequality is possible in equilibrium. We give tight upper bounds on the NIR for the network formation games of Fabrikant et al. (PODC '03) and Ehsani et al. (SPAA '11). With respect to the relationship between equality and social efficiency, we show that, contrary to common expectations, efficiency does not necessarily come at the expense of increased inequality.Comment: 27 pages. 4 figures. Accepted to Internet Mathematics (2014

Manufacturing flexible light-emitting polymer displays with conductive lithographic film technology

We report on a new low-cost manufacturing process for flexible displays that has the potential to rapidly expand the market into areas that have traditionally been outside the scope of such technology. In this paper we consider the feasibility of using offset-lithography to deposit contacts for polymer light-emitting displays. We compare and contrast manufacturing criteria and present a case study detailing our initial results. It is expected that these developments will stimulate further progress in multilayer device fabrication. Cheap, flexible conductive interconnects have the potential to find applications in a wide variety of device structures. For the more challenging exploitation areas in multilayer devices, such as displays, it was found that the properties of conductive lithographic films were not optimal in their current form. Three parameters (conductivity, surface roughness and surface work function) were identified as critical to device fabrication. Calendering and electroless plating were investigated as methods to improve these properties. Both methods aimed to modify the surface roughness and conductivity, with the plating study also modifying the work function

Constraints on Scalar Asymmetric Dark Matter from Black Hole Formation in Neutron Stars

We consider possibly observable effects of asymmetric dark matter (ADM) in neutron stars. Since dark matter does not self-annihilate in the ADM scenario, dark matter accumulates in neutron stars, eventually reaching the Chandrasekhar limit and forming a black hole. We focus on the case of scalar ADM, where the constraints from Bose-Einstein condensation and subsequent black hole formation are most severe due to the absence of Fermi degeneracy pressure. We also note that in some portions of this constrained parameter space, non-trivial effects from Hawking radiation can modify our limits. We find that for scalar ADM with mass between 100 keV and 10^5 GeV, the constraint from pulsars in globular clusters on the scattering cross-section with neutrons ranges from \sigma_n < 10^{-45} cm^2 to 10^{-52} cm}^2. In particular, for scalar ADM with mass between 1 GeV and 1 TeV (in the case where black hole evaporation due to Hawking radiation is unimportant), the constraint on the scattering cross-section is below what is reachable with ton scale direct detection experiments.Comment: 24 pages, 5 figures; updated to match published versio

Extrasolar Planets in the Classroom

The field of extrasolar planets is still, in comparison with other astrophysical topics, in its infancy. There have been about 300 or so extrasolar planets detected and their detection has been accomplished by various different techniques. Here we present a simple laboratory experiment to show how planets are detected using the transit technique. Following the simple analysis procedure describe we are able to determine the planetary radius to be 1.27 +/- 0.20 R_{J} which, within errors agrees with the establish value of 1.32 +/- 0.25 R_{J}.Comment: 7 pages, 2 figures, published in Physics Educatio

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Tillage Research in Ohio A Guide to the Selection of Profitable Tillage Systems

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