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Supercomputer's Model of Human Contact Simulates Swine Flu

A group at Virginia Tech is working with the U.S. Department of Defense to tackle the H1N1 outbreak

2 min read

6 May 2009—An extravagantly detailed computer model of the U.S. population is taking a crack at understanding the H1N1 ”swine flu” outbreak. The model, built by researchers at Virginia Polytechnic Institute and State University, in Blacksburg, Va., is composed of realistic representations of the major ways that people in the United States come into contact with one another—in other words, real-world social networks. Last Thursday, the U.S. Department of Defense began using the model to provide recommendations to the Department of Health and Human Services, according to the Virginia Tech engineers.

In the model, called EpiSimdemics, real cities are represented as groups of artificial people whose demographic attributes match data from the last census and land-use databases. By seeding the model with a handful of infected individuals in a manner that mirrors the real cases—say, 45 teenagers in one part of New York City—the model can run hundreds of simulations to illustrate possible future infection patterns across a population of between 50 million and 60 million in nine regions, according to Madhav Marathe, a deputy director of Virginia Tech’s Network Dynamics and Simulation Science Laboratory (NDSSL). In one experiment, for example, the model was asked to determine the impact of school closures on flu transmissions.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
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Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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