Using neural networks for faster X-ray imaging
A team of scientists from Argonne is using artificial intelligence to decode X-ray images faster, which could aid innovations in medicine, materials and energy.
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Machine Learning
Jefferson Lab Launches Virtual AI Winter School for Physicists
Artificial intelligence is a game-changer in nuclear physics, able to enhance and accelerate fundamental research and analysis by orders of magnitude. DOE’s Jefferson Lab is exploring the expanding synergy between nuclear physics and computer science as it co-hosts together with The Catholic University of America and the University of Maryland a virtual weeklong series of lectures and hands-on exercises Jan. 11-15 for graduate students, postdoctoral researchers and even “absolute beginners.”
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Advanced tools reveal critical infrastructure connections and help mitigate disasters
A cross-platform Argonne collaboration is optimizing a tool developed after Hurricane Maria to find essential connections between critical infrastructure that will help owners and operators plan for and mitigate a variety of potential hazards.
Read moreAccelerating AI computing to the speed of light
A University of Washington-led team has come up with a system that could help speed up AI performance and find ways to reduce its energy consumption: an optical computing core prototype that uses phase-change material.
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Fermilab receives DOE award to develop machine learning for particle accelerators
Fermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.
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10 ways Argonne science is combatting COVID-19
Argonne scientists and research facilities have made a difference in the fight against COVID-19 in the year since the first gene sequence for the virus was published.
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UCI researchers use deep learning to identify gene regulation at single-cell level
Irvine, Calif., Jan. 5, 2021 — Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving.
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Advanced materials in a snap
A research team at Sandia National Laboratories has successfully used machine learning — computer algorithms that improve themselves by learning patterns in data — to complete cumbersome materials science calculations more than 40,000 times faster than normal.
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Machine Learning Improves Particle Accelerator Diagnostics
Operators of Jefferson Lab’s primary particle accelerator are getting a new tool to help them quickly address issues that can prevent it from running smoothly. The machine learning system has passed its first two-week test, correctly identifying glitchy accelerator components and the type of glitches they’re experiencing in near-real-time. An analysis of the results of the first field test of the custom-built machine learning system was recently published in the journal Physical Review Accelerators and Beams.
Read moreArtificial intelligence predicts gestational diabetes in Chinese women
Machine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in the Endocrine Society’s Journal of Clinical Endocrinology & Metabolism.
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Developing Smarter, Faster Machine Intelligence with Light
SUMMARYResearchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture
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UCI researchers create model to calculate COVID-19 health outcomes
Irvine, Calif., Dec. 17, 2020 —University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. “The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset,” said Daniel S.
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