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 These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended.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.
University of Washington researchers discovered that AI models ignored clinically significant indicators on X-rays and relied instead on characteristics such as text markers or patient positioning that were specific to each dataset to predict whether someone had COVID-19.
Six Argonne scientists receive Department of Energy’s Early Career Research Program Awards.
Artificial intelligence is being called “the next generation of the way we do science.” At Argonne, researchers are leveraging the lab’s state-of-the-art-facilities and unparalleled expertise to shape the very future of science.
Argonne scientists across several disciplines have combined forces to create a new process for testing and predicting the effects of high temperatures on refractory oxides.
Sergei Kalinin, a scientist and inventor at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the Microscopy Society of America professional society.
The Antarctic ice sheet is much less likely to become unstable and cause dramatic sea-level rise in upcoming centuries if the world follows policies that keep global warming below a key 2015 Paris climate agreement target, according to a Rutgers coauthored study. But if global warming exceeds the target – 2 degrees Celsius (3.6 degrees Fahrenheit) – the risk of ice shelves around the ice sheet’s perimeter melting would increase significantly, and their collapse would trigger rapid Antarctic melting. That would result in at least 0.07 inches of global average sea-level rise a year in 2060 and beyond, according to the study in the journal Nature.
Argonne is helping U.S. companies solve pressing manufacturing challenges through an innovative program that provides access to Argonne’s world-class computing resources and technical expertise.
At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.
Researchers found that difficulties in diagnosing toddlers with autism spectrum disorder (ASD) might be due to the dynamic nature of the disorder during child development. Children with clinical characteristics that put them on the diagnostic border of autism have an increased susceptibility to gaining or losing that diagnosis at later ages.
A partnership between UC Davis and Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company, has led to a 20-minute COVID-19 test. The method pairs a mass spectrometer with a powerful machine-learning platform to detect SARS-CoV-2 in nasal swabs. A recent study published in Nature Scientific Reports shows the test to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests.
Collaborators use experiments, high-fidelity simulations and machine learning to deliver predictive tools to engine manufacturers.
The American College of Radiology® (ACR®) Data Science Institute® (DSI) and the Cancer Imaging Archive (TCIA), funded by the National Cancer Institute (NCI), have teamed up to connect use cases and datasets to speed medical imaging artificial intelligence (AI) development.
A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth.
New research that garnered a Best White Paper award at the 2021 Wharton Analytics Conference shows a way to harness artificial intelligence and machine learning tools to build a more equitable workforce.
Stepping into their superhero gear, Argonne scientists are using science and the world’s best technology to combat some of Earth’s toughest foes, from pollution to climate change.
Hertz Fellow Emma Pierson wields machine learning like a Swiss Army knife to investigate a range of problems, including disparities in COVID-19 testing, the treatment of osteoarthritis, and police discrimination.
Researchers at the Bloomberg Kimmel Institute for Caner Immunotherapy at the Johns Hopkins Kimmel Cancer Center have developed DeepTCR, a software package that employs deep-learning algorithms to analyze T-cell receptor (TCR) sequencing data. T-cell receptors are found on the surface of immune T cells. These receptors bind to certain antigens, or proteins, found on abnormal cells, such as cancer cells and cells infected with a virus or bacteria, to guide the T cells to attack and destroy the affected cells.
Science Snapshots from Berkeley Lab: X-rays accelerate battery R&D; infrared microscopy goes off grid; substrates support 2D tech
A University of Washington-led team has developed a method that uses the camera on a person’s smartphone or computer to take their pulse and breathing rate from a real-time video of their face.
Unbound Medicine® today announced a major upgrade to their digital publishing platform. Unbound developed Unbound Intelligence™‒ exclusive artificial intelligence and machine learning tools to help clinicians keep up to date with current research, as well as discover and fill knowledge gaps.
The U.S. Department of Energy (DOE) today announced $29 million to develop new tools to analyze massive amounts of scientific information, including artificial intelligence, machine learning, and advanced algorithms.
UPTON, NY — Inspired by the mastery of artificial intelligence (AI) over games like Go and Super Mario, scientists at the National Synchrotron Light Source II (NSLS-II) trained an AI agent — an autonomous computational program that observes and acts — how to conduct research experiments at superhuman levels by using the same approach. The Brookhaven team published their findings in the journal Machine Learning: Science and Technology and implemented the AI agent as part of the research capabilities at NSLS-II.
The Department of Energy’s Argonne National Laboratory is proud to welcome five new FY21 Maria Goeppert Mayer Fellows to campus, each chosen for their incredible promise in their respective fields.
For the past few years, researchers at the Department of Energy’s SLAC National Accelerator Laboratory have been developing “virtual diagnostics” that use machine learning to obtain crucial information about electron beam quality in an efficient, non-invasive way. Now, a new virtual diagnostic approach incorporates additional information about the beam that allows the method to work in situations where conventional diagnostics have failed.
The U.S. Department of Energy (DOE) today announced up to $34.5 million to harness cutting-edge research tools for new scientific discoveries, including clean energy and climate solutions. Two new funding opportunities will support researchers using data science and computation-based methods—including artificial intelligence and machine learning—to tackle basic science challenges, advance clean energy technologies, improve energy efficiency, and predict extreme weather and climate patterns.
The Argonne Leadership Computing Facility continues its efforts to build a community of scientists who can employ AI and data-intensive analysis at a scale that requires DOE supercomputers.
Healthy and cancer cells can look similar under a microscope. One way of differentiating them is by examining the level of acidity, or pH level, inside the cells. Tapping on this distinguishing characteristic, a research team from the National University of Singapore (NUS) has developed a technique that uses artificial intelligence (AI) to determine whether a single cell is healthy or cancerous by analysing its pH. Each cancer test can be completed in under 35 minutes, and single cells can be classified with an accuracy rate of more than 95 per cent.