Voters punish politicians for misinformation that portrays them in a favourable light, but not for inaccurate information that attacks their opponents.

What impact does inaccurate information have on political campaigning? Outlining the results of three studies on the role of misinformation in politics, Michael D. Cobb finds that voters react to positive and negative information in very different ways. While negative misinformation, such as using false figures to attack a political opponent, tends to linger in the minds of voters even after it is retracted; there is no such effect when positive information about a politician is debunked. Indeed, voters appear to actively punish politicians in the aftermath of positive misinformation

State-level corruption scandals do little to change voters’ minds about political parties.

Political corruption scandals seem to have become commonplace in American politics and the state-level is no exception. But do such scandals hurt the affected party at the ballot box? In new research which examines the effects of corruption scandals in North Carolina, Michael D. Cobb and Andrew J. Taylor find that voters are generally unable to identify the scandalous politician or their party. They argue that corruption scandals do little to affect a party’s vote, and that citizens tend to base their voting choices on other matters

Simplex Solutions for Optimal Control Flight Paths in Urban Environments

This paper identifies feasible fight paths for Small Unmanned Aircraft Systems in a highly constrained environment. Optimal control software has long been used for vehicle path planning and has proven most successful when an adequate initial guess is presented flight to an optimal control solver. Leveraging fast geometric planning techniques, a large search space is discretized into a set of simplexes where a Dubins path solution is generated and contained in a polygonal search corridor free of path constraints. Direct optimal control methods are then used to determine the optimal flight path through the newly defined search corridor. Two scenarios are evaluated. The first is limited to heading rate control only, requiring the air vehicle to maintain constant speed. The second allows for velocity control which permits slower speeds, reducing the vehicles minimum turn radius and increasing the search domain. Results illustrate the benefits gained when including speed control to path planning algorithms by comparing trajectory and convergence times, resulting in a reliable, hybrid solution method to the SUAS constrained optimal control problem

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply \texttt{plan-net} to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters

2013-14 Guest Artist Series: American Brass Quintet

Kennesaw State University School of Music presents the American Brass Quintet.https://digitalcommons.kennesaw.edu/musicprograms/1385/thumbnail.jp

2011-2012 Collaborative Spotlight: The American Brass Quintet

Past Collaborative Spotlight Concerts 2011 - Duo Pianists Leonard and Shenhttps://spiral.lynn.edu/conservatory_otherseasonalconcerts/1017/thumbnail.jp

Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843--0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90--180x faster per chain than BART on that CPU.Comment: 16 pages, 4 figures, submitted to PSJ 3/4/2020, revised 1/22/2021. Text restructured and updated for clarity, model updated and expanded to work for range of hot Jupiters, results/plots updated, two new appendices to further justify model selection and methodolog

Far Ultraviolet Absolute Flux of alpha Virginis

We present the far ultraviolet spectrum of alpha Virginis taken with EURD spectrograph on-board MINISAT-01. The spectral range covered is from ~900 to 1080 A with 5 A spectral resolution. We have fitted Kurucz models to IUE spectra of alpha Vir and compared the extension of the model to our wavelengths with EURD data. This comparison shows that EURD fluxes are consistent with the prediction of the model within 20-30%, depending on the reddening assumed. EURD fluxes are consistent with Voyager observations but are ~60% higher than most previous rocket observations of alpha Vir.Comment: 13 pages, 4 figures. Submitted to The Astrophysical Journa