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Scientific data sets are becoming more dynamic, requiring new mathematical techniques on par with the invention of calculus.

As physics prepares for ambitious projects like the Large Synoptic Survey Telescope, the field is seeking new methods of data-driven discovery.

As science dives headlong into an ocean of data, the demands of large-scale interdisciplinary collaborations are growing increasingly acute.

How do you know if a quantum computer is doing what it claims? A new protocol offers a possible solution and a boost to quantum cryptography.

Studies show that computer models called “neural networks” behave strikingly similar to actual brains when performing certain tasks, suggesting the two may learn in the same way.

As the role of computers in pure mathematics grows, researchers debate their reliability.

An infinitesimal advance in the traveling salesman problem breathes new life into the search for improved approximate solutions.

Computer scientists are finding that “thinking quantumly” can lead to new insights into long-standing problems in classical computer science, mathematics and cryptography, regardless of whether quantum computers ever materialize.

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