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UrduScientists have devised a way to engineer yeast to produce sustainable, eco-friendly commodity chemicals using computing power as a guide.
Tag: Machine Learning
Machine learning refines earthquake detection capabilities
Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection.
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.
Early warning system model predicts deterioration of hospitalized cancer patients
Researchers at Washington University in St. Louis have recently developed a successful predictive model for hospitalized cancer patients that integrates heterogeneous data available in electronic health records.
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.
The Brain Changes Its Rhythm Within Minutes of Therapeutic Stimulation During Deep Brain Stimulation Surgery for Treatment-Resistant Depression
Deep brain stimulation (DBS) has been demonstrated to be an effective treatment for many patients suffering with treatment-resistant depression, but exactly how it works is not known.
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.
Multi-Algorithm Approach Helps Deliver Personalized Medicine for Cancer Patients
John F. McDonald and his research team have created a ‘multi-algorithm’ machine learning approach to boost accuracy in predicting drug responses for ovarian cancer patients.
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.
Expert in Ecological Applications of AI Joins Newly Announced Imageomics Institute
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.
Artificial Intelligence Tool Improves Accuracy of Breast Cancer Imaging
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.
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.
Argonne teams up with GEVO to apply lab’s GREET Model to company’s net-zero project
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.
World first for AI and machine learning to treat COVID-19 patients worldwide
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.
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.
‘Whoop’ – New Autonomous Method Precisely Detects Endangered Whale Vocalizations
One of the frequently used methods to monitor endangered whales is called passive acoustics technology, which doesn’t always perform well.
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.
Argonne collaborations bring computational tools to the forefront of COVID-19 research
Argonne, industry and academia collaborate to bring innovative AI and simulation tools to the COVID-19 battlefront.
How extreme cold can crack lithium-ion battery materials, degrading performance
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 develop ‘nanopore-tal’ that enables cells to talk to computers
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.
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.
Connective issue: AI learns by doing more with less
Research from the lab of Shantanu Chakrabartty reveals constraints can lead to learning in AI systems.
Georgia Tech Joins the U.S. National Science Foundation to Advance AI Research and Education
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.
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.
Platform teaches nonexperts to use machine learning
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.
New Research Infuses Equity Principles Into the Algorithm Development Process
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
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.
Artificial intelligence models to analyze cancer images can take shortcuts that introduce bias for minority patients
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
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.
Artificial Intelligence Could Be New Blueprint for Precision Drug Discovery
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.
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.
Machine learning tool sorts the nuances of quantum data
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.
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.
World-first artificial intelligence study to map risks of ovarian cancer in women
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.
Machine learning algorithm predicts how genes are regulated in individual cells
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.