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IMAGE: (a) Scale-out approach: improve computing performance by increasing the numbers of computing chips; (b) All-to-all connection type combinatorial optimization problems: all variables interact with each other. view more
Credit: Toshiba Corporation
TOKYO - Toshiba Corporation (TOKYO: 6502), the industry leader in solutions for large-scale optimization problems, today announced a scale-out technology that minimizes hardware limitations, an evolution of its optimization computer, the Simulation Bifurcation Machine (SBM), that supports continued increases in computing speed and scale. Toshiba expects the new SBM to be a game changer for real-world problems that require large-scale, high-speed and low-latency, such as simultaneous financial transactions involving large numbers of stock, and complex control of multiple robots. The research results were published in
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What do you do after solving the answer to life, the universe, and everything? If you re mathematicians Drew Sutherland and Andy Booker, you go for the harder problem.
In 2019, Booker, at the University of Bristol, and Sutherland, principal research scientist at MIT, were the first to find the answer to 42. The number has pop culture significance as the fictional answer to the ultimate question of life, the universe, and everything, as Douglas Adams famously penned in his novel The Hitchhiker s Guide to the Galaxy. The question that begets 42, at least in the novel, is frustratingly, hilariously unknown.
In mathematics, entirely by coincidence, there exists a polynomial equation for which the answer, 42, had similarly eluded mathematicians for decades. The equation x3+y3+z3=k is known as the sum of cubes problem. While seemingly straightforward, the equation becomes exponentially difficult to solve when framed as a Diophantine equation a problem that stipulate
Credit: Flockine, Pixabay
A comprehensive modeling study sheds new light on socioeconomic-based mechanisms that drive disparities in influenza burden across the U.S. Casey Zipfel of Georgetown University in Washington D.C. and colleagues present this analysis in the open-access journal
PLOS Computational Biology.
People of lower socioeconomic status experience increased burden of influenza. Past studies have identified various factors that underlie this health inequity, including decreased flu vaccination, lack of access to paid sick leave, lack of healthcare access, increased susceptibility to infection, and different exposure patterns. However, no previous study has considered all of these factors at once.
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Robots solving computer games, recognizing human voices, or helping in finding optimal medical treatments: those are only a few astonishing examples of what the field of artificial intelligence has produced in the past years. The ongoing race for better machines has led to the question of how and with what means improvements can be achieved. In parallel, huge recent progress in quantum technologies have confirmed the power of quantum physics, not only for its often peculiar and puzzling theories, but also for real-life applications. Hence, the idea of merging the two fields: on one hand, artificial intelligence with its autonomous machines; on the other hand, quantum physics with its powerful algorithms.
A new study demonstrates that machine-learning strategies can be applied to routinely collected physiological data, such as heart rate and blood pressure, to provide clues about pain levels in people with sickle cell disease. Mark Panaggio of Johns Hopkins University Applied Physics Laboratory and colleagues present these findings in the open-access journal PLOS Computational Biology.