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She Studies Growing Arteries to Aid Heart Attack Recovery

February 13, 2023

Regenerative medicine researcher Kristy Red Horse’s discoveries may someday help damaged hearts heal better. Her stewardship of her Native American heritage may advance science in other ways too.

Mathematicians Complete Quest to Build ‘Spherical Cubes’

February 10, 2023

Is it possible to fill space “cubically” with shapes that act like spheres? A proof at the intersection of geometry and theoretical computer science says yes.

The Joy of Asking About Infinity, Jellyfish and the End of the Universe

February 9, 2023

As The Joy of Why podcast returns for a second season, producer Polly Stryker and host Steven Strogatz invite listeners to join them and their brilliant new guests on another voyage of discovery.

What Lights the Universe’s Standard Candles?

February 8, 2023

Type Ia supernovas are astronomers’ best tools for measuring cosmic distances. In a first, researchers have managed to re-create one on a supercomputer, giving a boost to a leading hypothesis for how they form.

Researchers Discover a More Flexible Approach to Machine Learning

February 7, 2023

“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.

How Our Reality May Be a Sum of All Possible Realities

February 6, 2023

Richard Feynman’s path integral is both a powerful prediction machine and a philosophy about how the world is. But physicists are still struggling to figure out how to use it, and what it means.

Mathematicians Eliminate Long-Standing Threat to Knot Conjecture

February 2, 2023

A new proof shows that a knot some thought would contradict the famed slice-ribbon conjecture doesn’t.

Machines Learn Better if We Teach Them the Basics

February 1, 2023

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

When Does the Brain Operate at Peak Performance?

January 31, 2023

The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong.

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