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In a watershed moment for cryptography, computer scientists have proposed a solution to a fundamental problem called “program obfuscation.”
Improvements in how densely spheres and other shapes can be packed together could lead to advances in materials science, deep space communication and theoretical physics.
The nature of computing has changed dramatically over the last decade, and more innovation is needed to weather the gathering data storm.
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.
Machine learning techniques are helping scientists pinpoint the mutations that allow bird and pig viruses to infect humans.
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.
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