Attitudes of U.S. Wind Turbine Neighbors: Analysis of a Nationwide Survey

Experts predict continuing deployment of wind turbines in the United States, which will create more interactions between turbines and surrounding communities. Policymakers can benefit from analyses of existing wind projects that enable them to better understand likely effects on residents around proposed projects. Our analysis of a randomly drawn, representative national survey of 1705 existing U.S. wind project neighbors provides previously unavailable detail about factors influencing the attitudes of these neighbors toward their local wind projects. Overall, we find positive-leaning attitudes, which improve over time as individuals self-select into communities near existing wind projects. Hearing wind turbines leads to less-positive attitudes, although living very near to turbines does not, nor does seeing wind turbines. In fact, our findings suggest complex relationships among nearby residents’ attitudes, their perceptions about the particular fit of turbines within their landscape and community, and their perceptions of wind project impacts on property values. These findings—along with the positive correlation between perceived planning-process fairness and attitude—suggest areas of focus for wind project development that may influence social outcomes and acceptance of wind energy. The concluding discussion provides a number of policy and future research recommendations based on the research

Generalized Matsui Algorithm 1 with application for the full DES

In this paper we introduce the strictly zero-correlation attack. We extend the work of Ashur and Posteuca in BalkanCryptSec 2018 and build a 0-correlation key-dependent linear trails covering the full DES. We show how this approximation can be used for a key recovery attack and empirically verify our claims through a series of experiments. To the best of our knowledge, this paper is the first to use this kind of property to leverage a meaningful attack against a symmetric-key algorithm

Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models

Koinonia: verifiable e-voting with long-term privacy

Despite years of research, many existing e-voting systems do not adequately protect voting privacy. In most cases, such systems only achieve "immediate privacy", that is, they only protect voting privacy against today's adversaries, but not against a future adversary, who may possess better attack technologies like new cryptanalysis algorithms and/or quantum computers. Previous attempts at providing long-term voting privacy (dubbed "everlasting privacy" in the literature) often require additional trusts in parties that do not need to be trusted for immediate privacy. In this paper, we present a framework of adversary models regarding e-voting systems, and analyze possible threats to voting privacy under each model. Based on our analysis, we argue that secret-sharing based voting protocols offer a more natural and elegant privacy-preserving solution than their encryption-based counterparts. We thus design and implement Koinonia, a voting system that provides long-term privacy against powerful adversaries and enables anyone to verify that each ballot is well-formed and the tallying is done correctly. Our experiments show that Koinonia protects voting privacy with a reasonable performance