![]() ![]() Only absentee ballot votes registered electronically for the candidate. Northampton County, on Pennsylvania’s eastern edge, became ground zero last November in the debate over ballot-marking devices when its newly purchased ES&S ExpressVote XLs failed in two different ways.įirst, a ballot programming error prevented votes cast for one of three candidates in a judge’s race from registering in the bar codes used to count the vote. “When we give them a paper ballot, the very first thing they say to us is, ‘We’re going back in time,’” he said. Michael Anderson, elections director for Pennsylvania’s Lebanon County, said “voters want it.” The county offers all voters both machine- and hand-marked ballots. They like them because the touch screens are familiar to voters, looking and feeling like what they’ve been using for nearly two decades, and they can use one voting method for everyone. The state is banning bar codes from ballot-marking voting machines beginning in 2021.īut some election officials see ballot-marking devices as improvements over paperless touch screens, which were used by 27 percent of voters in 2018. It is a stance also shared by Colorado, a national leader in election security. voters used in 20 and will again rely on in November. It’s an idea supported by a 2018 National Academies of Sciences report that favors hand-marked ballots tallied by optical scanners, which 70 percent of U.S. ![]() We further validate our algorithm by demonstrating that the learnedpolicies can successfully transfer to a real quadruped robot, for example,achieving a 100% success rate on the real-world stepping stone environment,dramatically improving prior results achieving 40% success.“There are a huge number of reasons to reject today’s ballot-marking devices - except for limited use as assistive devices for those unable to mark a paper ballot themselves,” says Doug Jones, a University of Iowa election security expert.Ĭritics say currently available ballot-marking devices undermine the very idea of retaining a paper record. ![]() Across all tasks, PI-ARS demonstratessignificantly better learning efficiency and performance compared to the ARSbaseline. We evaluate PI-ARS on a set of challengingvisual-locomotion tasks where a quadruped robot needs to walk on unevenstepping stones, quincuncial piles, and moving platforms, as well as tocomplete an indoor navigation task. Namely, PI-ARS combines agradient-based representation learning technique, Predictive Information (PI),with a gradient-free ES algorithm, Augmented Random Search (ARS), to trainpolicies that can process complex robot sensory inputs and handle highlynonlinear robot dynamics. In this work, we developPredictive Information Augmented Random Search (PI-ARS) to mitigate thislimitation by leveraging recent advancements in representation learning toreduce the parameter search space for ES. However, a key limitation of ES is its scalability to large capacitymodels, including modern neural network architectures. Evolution Strategy (ES) algorithms have shown promising results in trainingcomplex robotic control policies due to their massive parallelism capability,simple implementation, effective parameter-space exploration, and fast trainingtime. ![]()
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