LLNL-led team uses machine learning to derive black hole motion from gravitational wave data

A team including a Lawrence Livermore National Laboratory (LLNL) mathematician and collaborators at the University of Massachusetts, Dartmouth and the University of Mississippi, has developed a machine learning-based technique capable of automatically deriving the motion of binary black holes from raw gravitational wave data.

Novel Tag Provides First Detailed Look into Goliath Grouper Behavior

A study is the first to reveal detailed behavior of massive goliath groupers. Until now, no studies have documented their fine-scale behavior. What is known about them has been learned from divers, underwater video footage, and observing them in captivity. Using a multi-sensor tag with a three axis accelerometer, gyroscope and magnetometer as well as a temperature, pressure and light sensor, a video camera and a hydrophone, researchers show how this species navigates through complex artificial reef environments, maintain themselves in high current areas, and how much time they spend in different cracks and crevices – none of which would be possible without the tag.

Researchers create a breakthrough tool for superfast molecular movies

Certain biological events, such as proteins changing their shapes to perform some functions, occur so quickly that current methods of molecular imaging cannot capture them. Now, a research team has created a machine-learning technique that can “fill in” missing data needed to document proteins in action in time scales of a few quadrillionths of a second.

DOE grants will help advance AI techniques to address data challenges

Argonne scientists have received two high-profile grants from the U.S. Department of Energy that will help scientists at the U.S. National Laboratories take advantage of the latest developments in machine learning technology.

Computational discovery of complex alloys could speed the way to green aviation

Experts at the U.S. Department of Energy’s Ames Laboratory and their collaborators have identified the way to tune the strength and ductility of a class of materials called high-entropy alloys. The discovery may help power-generation and aviation industry develop more efficient engines.

UA Little Rock Postdoctoral Researcher Receives $40K Grant to Create Predictive Modeling of Refugee Numbers

The Arkansas Economic Development Commission, using flow-through funding from the National Science Foundation, has awarded a postdoctoral research fellow at UA Little Rock a grant worth more than $40,000 to create a machine learning model to predict refugee counts in the United States.

All About Eve

New AI model called EVE, developed by scientists at Harvard Medical School and Oxford University, outperforms other AI methods in determining whether a gene variant is benign or disease-causing.
When applied to more than 36 million variants across 3,219 disease-associated proteins and genes, EVE indicated more than 256,000 human gene variants of unknown significance that should be reclassified as benign or pathogenic.

AI-driven dynamic face mask adapts to exercise, pollution levels

Researchers reporting in ACS Nano have developed a dynamic respirator that modulates its pore size in response to changing conditions, such as exercise or air pollution levels, allowing the wearer to breathe easier when the highest levels of filtration are not required.

FAU Receives NSF Grant to Explore Trait Evolution Across Species

The NSF grant will enable scientists to elucidate trait evolution across species using statistical and supervised machine learning approaches to vigorously and accurately predict general and specific evolutionary mechanisms that also will be applicable to various genomic and transcriptomic data for evolutionary discovery.

New machine learning method to analyze complex scientific data of proteins

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.

Argonne and Parallel Works Inc. win FLC recognition for commercializing lab’s machine learning-based design optimization software technology

Argonne and Parallel Works, Inc., won the Federal Laboratory Consortium’s Midwest Regional Award for Excellence in Technology Transfer for bringing Argonne’s Machine Learning-Genetic Algorithm (ML-GA) design optimization software to commercialization.

Department of Energy Invests $1 Million in Artificial Intelligence Research for Privacy-Sensitive Datasets

The U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets.

Peachy Robot: A Glimpse into the Peach Orchard of the Future

Researchers are developing a robot that utilizes deep learning to automate certain aspects of the peach cultivation process, which could be a boon for many Georgia peach farms grappling with a shortage of workers. The self-navigating robot uses an embedded 3D camera to determine which trees need to be pruned or thinned, and removes the branches or peaches using a claw-like device attached to its arm.

Department of Energy Invests $16 Million in Data-Intensive Scientific Machine Learning Research and Analysis

The U.S. Department of Energy (DOE) announced $16 million for five collaborative research projects to develop artificial intelligence (AI) and machine learning (ML) algorithms for enabling scientific insights and discoveries from data generated by computational simulations, experiments, and observations.

Science Snapshots from Berkeley Lab

An experiment to study gravity at the quantum scale, insights into an antibiotic-building enzyme, and the backstory of an incredible new protein prediction algorithm are featured in this month’s roundup of science highlights.

FAU Researcher Receives $1.8 Million NIH ‘Maximizing Investigators’ Research Award’

Raquel Assis, Ph.D., associate professor, College of Engineering and Computer Science, and a fellow of FAU’s Institute for Human Health and Disease Intervention, has received a five-year, $1.8 million “Maximizing Investigators’ Research Award” from the NIH. The goal of this early career award is to enhance the ability of investigators to take on ambitious scientific projects and approach problems more creatively.

UW to lead new NSF institute for using artificial intelligence to understand dynamic systems

The University of Washington will lead a new artificial intelligence research institute that will focus on fundamental AI and machine learning theory, algorithms and applications for real-time learning and control of complex dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict.

NSF makes $20 Million investment in Optimization-focused AI Research Institute led by UC San Diego

The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.

Machine Learning for Cardiovascular Disease Improves When Social, Environmental Factors Are Included

Machine learning can accurately predict cardiovascular disease and guide treatment — but models that incorporate social determinants of health better capture risk and outcomes for diverse groups, finds a new study by researchers at the New York University Tandon School of Engineering and the NYU School of Global Public Health.

AI learns physics to optimize particle accelerator performance

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have demonstrated that they can use machine learning to optimize the performance of particle accelerators by teaching the algorithms the basic physics principles behind accelerator operations – no prior data needed.

Automatically Steering Experiments Toward Scientific Discovery

Scientists at Brookhaven and Lawrence Berkeley National Laboratories have been developing an automated experimental setup of data collection, analysis, and decision making.

New Tool Predicts Sudden Death in Inflammatory Heart Disease

Johns Hopkins University scientists have developed a new tool for predicting which patients suffering from a complex inflammatory heart disease are at risk of sudden cardiac arrest. Published in Science Advances, their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning.

Now in 3D: Deep learning techniques help visualize X-ray data in three dimensions

A team of Argonne scientists has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.

Department of Energy awards $4.15 million to Argonne to support collaborations with industry

The U.S. Department of Energy has awarded $4.15 million to Argonne National Laboratory to support collaborations with industry aimed at commercializing promising energy technologies.

Novel Method Predicts if COVID-19 Clinical Trials Will Fail or Succeed

Researchers are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. They collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed with 693 dimensional features created to represent each clinical trial. These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.

Liquid Metal Sensors and AI Could Help Prosthetic Hands to ‘Feel’

Prosthetics currently lack the sensation of “touch.” To enable a more natural feeling prosthetic hand interface, researchers are the first to incorporate stretchable tactile sensors using liquid metal and machine learning. This hierarchical multi-finger tactile sensation integration could provide a higher level of intelligence for artificial hands by improving control, providing haptic feedback and reconnecting amputees to a previously severed sense of touch.

SLAC hosts Secretary of Energy Jennifer Granholm for a virtual visit

Highlights of the two-hour visit included behind-the-scenes looks at one of the most powerful X-ray sources on the planet and at the construction of the world’s largest digital camera for astronomy. She also joined presentations of the lab’s research in machine learning, quantum technology and climate science and engaged in discussions about diversity, equity and inclusion at SLAC.

Using Computation to Improve Words: Model Offers Novel Tool for Improving Serious Illness Conversations

Conversations between seriously ill people, their families and palliative care specialists lead to better quality-of-life. Understanding what happens during these conversations – and how they vary by cultural, clinical, and situational contexts – is essential to guide healthcare communication improvement efforts. To gain true understanding, new methods to study conversations in large, inclusive, and multi-site epidemiological studies are required. A new computer model offers an automated and valid tool for such large-scale scientific analyses.