AI reveals new physics in dusty plasma

Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.

The journal PNAS published the findings by experimental and theoretical physicists at Emory University, based on a neural network model and data from laboratory experiments on dusty plasma — ionized gas containing suspended dust particles. 

The work is one of the relatively few instances of using AI not as a data processing or predictive tool, but to discover new physical laws governing the natural world.

"We showed that we can us AI to discover new physics," says Justin Burton, an Emory professor of experimental physics and senior co-author of the paper. "Our AI method is not a black box: we understand how and why it works. The framework it provides is also universal. It could potentially be applied to other many-body systems to open to new routes to discovery."

Evolving views: A new look at the Scopes Trial

Emory professor of psychology Harold Gouzoules, left, and his son Alexander Gouzoules, an Emory alum who is a legal scholar.

A combination of inherited genes and life experiences led Alexander Gouzoules (a legal scholar) and his father, Harold Gouzoules (an evolutionary biologist), to co-author a book about the 1925 Scopes trial. 

“The Hundred Years’ Trial: Law, Evolution, and the Long Shadow of Scopes v. Tennessee” blends their expertise. Johns Hopkins University Press published the book, marking the centenary of the fierce, public legal battle over the right to teach evolution in a Tennessee high school. 

“We had the ideal meshing of interests to take on the topic in a new way,” says Harold Gouzoules, an Emory professor of psychology who studies the evolution of primate social behavior. “I took on the science and Alex covered the legal ramifications of the trial.”

Read the full story here.

Exploring the frontiers of data science

Satellite technology is transforming the field of geography, says Xiao Huang. "It's kind of like being an astronaut in that satellites give you a view of Earth from space."

As a high-tech geographer, Xiao Huang uses remove sensing and AI for insights into how to design more equitable cities, improve management of natural resources, lessen the impact of natural and human-caused disasters, and improve public health policies.

"I love geography and computer technology," says Huang, assistant professor in Emory's Department of Environmental Sciences. "I want to use my knowledge of these fields to help humanity, especially socially disadvantaged communities."

Read the full story here.

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"At Emory, I look forward to continuing this line of work and exploring innovative ways AI can help improve patient care," says Shengpu Tang, who recently joined the university as assistant professor of computer science.

Decision-making forms the core of hospital patient care, involving an array of clinicians whose duties span diagnosis, treatment and resource allocation. The complexity of these interrelated decisions makes it challenging for physicians, nurses and other caretakers to connect all the dots in real time. 

Shengpu Tang, assistant professor of computer science at Emory University, is developing AI tools to identify, validate and transmit key data needed to most effectively support healthcare workers in decision-making processes. 

 

“The end goal is to improve patient care and patient outcomes,” Tang says. 

JAMA Open Network published the results of Tang’s latest collaborative project: the first AI guidance deployed in a hospital setting aimed at guiding best practices to prevent the spread of dangerous infections of Clostridioides difficile. 

Analysis by the researchers found that the new AI-guided protocol significantly reduced antibiotic prescriptions at Michigan Medicine — a factor that increases infection risk for vulnerable patients — with 10% to 15% fewer days on antimicrobials. Importantly, reducing days on antimicrobials did not increase the length of stay, readmission rate or mortality among patients. The already low incidence of Clostridioides difficle trended downwards during the study, but that reduction did not reach statistical significance.

Read the full story here.

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Children as young as five can navigate a 'Tiny Town'

A playground scene in Tiny Town. (Dilks lab)

Many behavioral studies suggest that using landmarks to navigate through large-scale spaces — known as map-based navigation — is not established until around age 12. 

A neuroscience study at Emory University counters that assumption. Through experiments combining brain scans and a virtual environment the researchers dubbed Tiny Town, they showed that five-year-olds have the brain system that supports map-based navigation. 

The journal Proceedings of the National Academy of Sciences published the finding, the first neural evidence that this cognitive ability is in place in such young children. 

“While large-scale navigation abilities certainly continue to develop throughout childhood, our findings show that the underlying neural system is established remarkably early,” says Yaelan Jung, first author of the study and a postdoctoral fellow in Emory’s Department of Psychology. 

“Rather than taking a decade or more, map-based navigation is underway in half that time,” adds Daniel Dilks, associate professor of psychology and senior author of the study. “Five-year-olds have the brain system enabling them to find their way around a tiny, virtual town. They not only know that the ice cream store in the mountain region is different than the ice cream store in the lake region, they know how to navigate the streets to get to each of them.”

Read the full story here.

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