22,233 research outputs found
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
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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
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