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Painful exercises in basic arithmetic are a vivid part of our elementary school memories. A multiplication like 3,752 ? 6,901 carried out with just pencil and paper for assistance may well take up to a minute. Of course, today, with a cellphone always at hand, we can quickly check that the result of our little exercise is 25,892,552. Indeed, the processors in modern cellphones can together carry out more than 100 billion such operations per second. What's more, the chips consume just a few watts of power, making them vastly more efficient than our slow brains, which consume about 20 watts and need significantly more time to achieve the same result. Of course, the brain didn't evolve to perform arithmetic. So it does that rather badly. But it excels at processing a continuous stream of information from our surroundings. And it acts on that information-sometimes far more rapidly than we're aware of. No matter how much energy a conventional computer consumes, it will struggle with feats the brain finds easy, such as understanding language and running up a flight of stairs. [ABSTRACT FROM AUTHOR]

Wearing face coverings, practicing social distancing, washing hands, and shuttering schools and stores. These are some of the measures that have been implemented to protect people from catching the virus responsible for COVID-19. But just how effective have these interventions been? • Engineers at Carnegie Mellon and Princeton believe they have developed a mathematical modeling approach that could tell us. Their project, called “Modeling and Control of COVID-19 Propagation for Assessing and Optimizing Intervention Policies,” looks at various factors that can impact the spread of the SARS-CoV-2 virus, including virus mutations as well as preventive measures such as wearing masks. • H. Vincent Poor, an IEEE Life Fellow, is the principal investigator of the team. Also on the team are IEEE Senior Member Osman Yağan, an associate research professor of electrical and computer engineering at Carnegie Mellon, and mathematical biologists from Princeton and the University of Pennsylvania who study viral spread. • The model recently received a grant from the C3.ai Digital Transformation Institute, a new research consortium funding projects that could reduce the effects of pandemics. Related projects on viral spread by Osman and Poor are also being funded by the U.S. Army Research Office and the United States' National Science Foundation. • The template for the new model was adapted from a mathematical theory the researchers developed in 2019 that investigated the process by which viruses and their mutations spread. In their paper, published in March in the Proceedings of the National Academy of Sciences, they compared how a virus spreads and mutates to how information changes as it's circulated by networks of people. [ABSTRACT FROM AUTHOR]