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Researchers develop speedier network analysis for a range of computer hardware

 E-Mail Graphs data structures that show the relationship among objects are highly versatile. It s easy to imagine a graph depicting a social media network s web of connections. But graphs are also used in programs as diverse as content recommendation (what to watch next on Netflix?) and navigation (what s the quickest route to the beach?). As Ajay Brahmakshatriya summarizes: graphs are basically everywhere. Brahmakshatriya has developed software to more efficiently run graph applications on a wider range of computer hardware. The software extends GraphIt, a state-of-the-art graph programming language, to run on graphics processing units (GPUs), hardware that processes many data streams in parallel. The advance could accelerate graph analysis, especially for applications that benefit from a GPU s parallelism, such as recommendation algorithms.

AI-defined COVID-19 testing strategy could lead to fewer infections

AI-defined COVID-19 testing strategy could lead to fewer infections When the novel coronavirus pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus and when with limited testing supplies. Now, a new algorithm developed by researchers at Penn State’s College of Information Sciences and Technology could help leaders make better informed decisions on how many symptomatic and asymptomatic individuals to test with rationed daily tests, and at what stage of the pandemic. The model’s simulated testing strategies resulted in approximately 40% fewer infections. “Our goal was to find out how do you distribute an allocation of tests that you have every day,” said Amulya Yadav, PNC Technologies Career Development Assistant Professor at the College of IST. “How do you distribute them among symptomatic and asymptomatic people? And how should this allocation change over time?”

AI-defined COVID-19 testing strategy could lead to fewer infections

AI-defined COVID-19 testing strategy could lead to fewer infections A new algorithm developed by researchers at the College of Information Sciences and Technology could help leaders of governments and organizations make better informed decisions on how many symptomatic and asymptomatic individuals to test for COVID-19 with a limited supply of daily tests, and at what stage of the pandemic. Image: Adobe Stock: Giovanni Cancemi AI-defined COVID-19 testing strategy could lead to fewer infections January 21, 2021 UNIVERSITY PARK, Pa. When the novel coronavirus pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus and when with limited testing supplies.

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