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.
Chuck Stewart, an expert in the ecological applications of computer vision, is part of the newly created Imageomics Institute, founded with a $15 million grant from the National Science Foundation to use images of living organisms to understand biological processes.
A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows.
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.
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., 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.
Argonne recently teamed up with a Colorado-based biofuel company to perform a critical lifecycle analysis of its Next Gen technology to produce renewable jet fuel from corn grain in what could be a game-changer in biofuel industry.
Addenbrooke’s Hospital in Cambridge along with 20 other hospitals from across the world and healthcare technology leader, NVIDIA, have used artificial intelligence (AI) to predict Covid patients’ oxygen needs on a global scale.
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.
One of the frequently used methods to monitor endangered whales is called passive acoustics technology, which doesn’t always perform well.
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.
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.
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.
Argonne, industry and academia collaborate to bring innovative AI and simulation tools to the COVID-19 battlefront.
Storing the rechargeable batteries at sub-freezing temperatures can crack the battery cathode and separate it from other parts of the battery, a new study shows.
University of Washington and Microsoft researchers have introduced a new class of reporter proteins that can be directly read by a commercially available nanopore sensing device.
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.
Research from the lab of Shantanu Chakrabartty reveals constraints can lead to learning in AI systems.
Today, Georgia Tech received two National Science Foundation (NSF) Artificial Intelligence Research Institutes awards, totaling $40 million. A third award for $20 million was granted to the Georgia Research Alliance (GRA), with Georgia Tech serving as one of the leading academic institutions.
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.
New award-winning research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help nonexperts effectively, efficiently and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of AI.
In the U.S., the place where one is born, one’s social and economic background, the neighborhoods in which one spends one’s formative years, and where one grows old are factors that account for a quarter to 60% of deaths in any given year
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 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.
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.
Scientists at Brookhaven and Lawrence Berkeley National Laboratories have been developing an automated experimental setup of data collection, analysis, and decision making.
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.
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.
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.
New study of artificial intelligence tools that analyze tumor images shows how they can make inaccurate predictions based on the institution that submitted the image
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.
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.
Researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval.
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.
An interdisciplinary team of Cornell and Harvard University researchers developed a machine learning tool to parse quantum matter and make crucial distinctions in the data, an approach that will help scientists unravel the most confounding phenomena in the subatomic realm.
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.
The University of South Australia will lead a world-first study, using artificial intelligence, to map the risks of the most fatal reproductive cancer in women worldwide so it can be detected and treated earlier.
Researchers have developed a software tool that identify the regulators of genes. The system leverages a machine learning algorithm to predict which transcription factors are most likely to be active in individual cells.
New Columbia Engineering study unveils a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.“Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours,” said Computer Science Professor Carl Vondrick. “Our results open a number of possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.”
Machine learning can improve the ability of scientists to optimize the components of experiments on spherical tokamaks that heat and shape the magnetically confined plasma that fuels fusion reactions.
New research led by investigators at Harvard School of Dental Medicine suggests that machine learning tools can help identify those at greatest risk for tooth loss and refer them for further dental assessment in an effort to ensure early interventions to avert or delay the condition.
It is possible to re-create a bird’s song by reading only its brain activity, shows a first proof-of-concept study from UC San Diego. Reproducing the songbird’s complex vocalizations – down to the pitch, volume and timbre of the original – lays the foundation for building vocal prostheses for humans who have lost their ability to speak.
PPPL forges ahead with development of streaming media to provide rapid analysis of key findings of remote fusion experiments.
UW researchers have developed a deep learning method that can produce a seamlessly looping, realistic looking video from a single photo.
UC San Diego School of Medicine researchers discovered gene expression patterns associated with pandemic viral infections, providing a map to help define patients’ immune responses, measure disease severity, predict outcomes and test therapies — for current and future pandemics.
Researchers at University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition and lifestyle factors, to generate individualized predictions of depression.
Researchers at the Finnish Center for Artificial Intelligence have developed a machine learning-based method that produces synthetic data, making it possible for researchers to share even sensitive data with one other without privacy concerns.
A machine learning system is helping operators resolve routine faults at the Continuous Electron Beam Accelerator Facility (CEBAF). The system monitors the accelerator cavities, where faults can trip off the CEBAF. The system identified which cavities were tripping off about 85% of the time and identified the type of fault about 78% of the time.
Dan Boyer of PPPL receives DOE Early Career Award to accelerate predictive models of spherical tokamak plasmas with machine learning methods.
The impact of deploying Artificial Intelligence (AI) for radiation cancer therapy in a real-world clinical setting has been tested by Princess Margaret researchers in a unique study involving physicians and their patients.