Voting rules allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions. Our contribution is twofold. First, we show that, whenever there are at least five alternatives and seven agents, every Pareto-optimal majoritarian voting rule suffers from the no-show paradox with respect to Fishburn's extension. This is achieved by reducing the statement to a finite - yet very large - problem, which is encoded as a formula in propositional logic and then shown to be unsatisfiable by a SAT solver. We also provide a human-readable proof which we extracted from a minimal unsatisfiable core of the formula. Secondly, we prove that every voting rule that satisfies two natural conditions cannot be manipulated by strategic abstention with respect to Kelly's extension and give examples of well-known Pareto-optimal majoritarian voting rules that meet these requirements.

In 2012, Moritz Simon Geist left a promising career as a research engineer in Germany to build robots and travel the world playing music full-time. He'd studied classical music in school, starting with the clarinet and piano, and toured with punk bands since he was a teenager in the '90s. That was when he started tinkering with equipment, building cheap solutions for complex audio problems and creating brand-new tools. Eventually, Geist's aptitude with electronics and music transformed into a new beast. Today, he's a solo artist who plays robots as music, relying on physical motion and contact microphones rather than electronics to create his beats.

When he plays a techno show, Moritz Simon Geist doesn't reach for a laptop. Instead, he calls on his army of sonic robots--a collection of small, motorized creations that click, clank, and whirr in an intricate mechanical symphony. Geist composes robotic electronic music, a burgeoning genre of electro jams that relies on hardware, not software, to engineer electronic sounds and beats. His forthcoming EP, The Material Turn, debuts in October with four tracks made entirely from self-fashioned instruments--futuristic robo-kalimbas, a droning guitar, and salvaged hard drives turned into percussive beat machines. Watching Geist play music is a little like watching a mad scientist in a lab.

Exascale computing promises to bring significant changes to both the high-performance computing space and eventually enterprise datacenter infrastructures. The systems, which are being developed in multiple countries around the globe, promise 50 times the performance of current 20 petaflop-capable systems that are now among the fastest in the world, and that bring corresponding improvements in such areas as energy efficiency and physical footprint. The systems need to be powerful run the increasingly complex applications being used by engineers and scientists, but they can't be so expensive to acquire or run that only a handful of organizations can use them. At the same time, the emergence of high-level data analytics and machine learning is forcing some changes in the exascale efforts in the United States, changes that play a role in everything from the software stacks that are being developed for the systems to the competition with Chinese companies that also are aggressively pursuing exascale computing. During a talk last week at the OpenFabrics Workshop in Austin, Texas, Al Geist, from the Oak Ridge National Laboratory and CTO of the Exascale Computing Project (ECP), outlined the work the ECP is doing to develop exascale-capable systems within the next few years.

Exascale computing promises to bring significant changes to both the high-performance computing space and eventually enterprise datacenter infrastructures. The systems, which are being developed in multiple countries around the globe, promise 50 times the performance of current 20 petaflop-capable systems that are now among the fastest in the world, and that bring corresponding improvements in such areas as energy efficiency and physical footprint. The systems need to be powerful run the increasingly complex applications being used by engineers and scientists, but they can't be so expensive to acquire or run that only a handful of organizations can use them. At the same time, the emergence of high-level data analytics and machine learning is forcing some changes in the exascale efforts in the United States, changes that play a role in everything from the software stacks that are being developed for the systems to the competition with Chinese companies that also are aggressively pursuing exascale computing. During a talk last week at the OpenFabrics Workshop in Austin, Texas, Al Geist, from the Oak Ridge National Laboratory and CTO of the Exascale Computing Project (ECP), outlined the work the ECP is doing to develop exascale-capable systems within the next few years.