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UrduA 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.
Tag: Machine Learning
Physicist Dan Boyer wins Early Career Award for research in Artificial Intelligence methods to advance fusion energy
Dan Boyer of PPPL receives DOE Early Career Award to accelerate predictive models of spherical tokamak plasmas with machine learning methods.
AI outperforms humans in creating cancer treatments, but do doctors trust it? It depends!
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.
Medical AI models rely on ‘shortcuts’ that could lead to misdiagnosis of COVID-19 and other diseases, UW researchers find
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.
DOE names six Argonne scientists to receive Early Career Research Program awards
Six Argonne scientists receive Department of Energy’s Early Career Research Program Awards.
Argonne researchers using artificial intelligence to shape the future of science
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.
Lasers, levitation and machine learning make better heat-resistant materials
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.
ORNL’s Sergei Kalinin elected Fellow of the Microscopy Society of America
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.
Catastrophic Sea-Level Rise from Antarctic Melting is Possible with Severe Global Warming
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.
Helping companies use high-performance computing to improve U.S. manufacturing
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.
ORNL’s superb materials expertise, data and AI tools propel progress
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.
CHOP Researchers Demonstrate How Dynamic Changes in Early Childhood Development May Lead to Changes in Autism Diagnosis
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.
New rapid COVID-19 test the result of university-industry partnership
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.
New Argonne partnership to predict fuel injector dynamics
Collaborators use experiments, high-fidelity simulations and machine learning to deliver predictive tools to engine manufacturers.
ACR DSI Links Use Cases to NCI Archive Datasets to Streamline Artificial Intelligence Development
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.
Machine learning model generates realistic seismic waveforms
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.
‘Best White Paper’ Shows Potential Way to Harness AI for a More Equitable Workplace
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.
8 Things Argonne is Doing to Save the Earth
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.
Pierson Uses Data Science to Highlight Societal Inequities
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.
Software Package Enables Deeper Understanding of Cancer Immune Responses
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.
April Snapshots
Science Snapshots from Berkeley Lab: X-rays accelerate battery R&D; infrared microscopy goes off grid; substrates support 2D tech
New system that uses smartphone or computer cameras to measure pulse, respiration rate could help future personalized telehealth appointments
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 Integrates Machine Learning Into Digital Platform
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.
DOE Announces $29 Million for Ultramodern Data Analysis Tools
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.
Game on: Science Edition
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.
Argonne’s 2021 Maria Goeppert Mayer Fellows bring new energy, promise to their fields
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.
New machine learning tool diagnoses electron beams in an efficient, non-invasive way
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.
U.S. Department of Energy Announces $34.5 Million for Data Science and Computation Tools to Advance Climate Solutions
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.
Virtual Argonne workshop provides guidance on using AI and supercomputing tools for science
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.
NUS researchers harness AI to identify cancer cells by their acidity
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.
NYU Tandon professor creating efficient deep learning models wins NSF award for promising young researchers
The National Science Foundation (NSF) selected NYU Tandon assistant professor Anna Choromanska, who is developing new approaches to training deep learning systems, to receive its most prestigious award for promising young academics.
Machine Learning Can Identify Cancerous Cells by Their Acidity
Researchers have developed a method, described in APL Bioengineering, that uses machine learning to determine whether a single cell is cancerous by detecting its pH. Their approach can discriminate cells originating from normal tissues from cells originating from cancerous tissues, as well as among different types of cancer, while keeping the cells alive. The method relies on treating the cells with bromothymol blue, a pH-sensitive dye that changes color depending on acidity.
Sneak preview: New platform allows scientists to explore research environments virtually
The Department of Energy pledged $1.68 million to Argonne National Laboratory over three years so it can create a virtual platform or digital twin that will allow experimentalists to explore their proposed studies prior to visiting the labs.
Argonne innovations and technology to help drive circular economy
In a collaborative effort to “recover, recycle and reuse,” Argonne strengthens research that addresses pollution, greenhouse gases and climate change and aligns with new policies for carbon emission reduction.
More Intelligent Medicine
Leaders in biomedical informatics and medicine discuss ways to optimize the integration of AI in clinical medicine
Automated chemistry sets new pace for materials discovery
Researchers at the Department of Energy’s Oak Ridge National Laboratory and the University of Tennessee developed an automated workflow to study metal halide perovskites, materials with outstanding properties for harnessing light that can be used to make solar cells, energy-efficient lighting and sensors.
Alexa, do I have an irregular heart rhythm? First AI system for contactless monitoring of heart rhythm using smart speakers
University of Washington researchers have developed a new skill for a smart speaker that for the first time monitors both regular and irregular heartbeats without physical contact.
In a leap for battery research, machine learning gets scientific smarts
Researchers combined machine learning with knowledge gained from experiments and equations guided by physics to discover and explain a process that shortens the lifetimes of fast-charging lithium-ion batteries.
Machine learning aids in simulating dynamics of interacting atoms
A revolutionary machine-learning (ML) approach to simulate the motions of atoms in materials such as aluminum is described in this week’s Nature Communications journal.
Researchers Hunt for New Particles in Particle Collider Data
Berkeley Lab researchers participated in a study that used machine learning to scan for new particles in three years of particle-collision data from CERN’s ATLAS detector.
Scientists Use Supercomputers to Study Reliable Fusion Reactor Design, Operation
A team used two DOE supercomputers to complete simulations of the full-power ITER fusion device and found that the component that removes exhaust heat from ITER may be more likely to maintain its integrity than was predicted by the current trend of fusion devices.
Machine learning blazes path to reliable near-term quantum computers
Using machine learning to develop algorithms that compensate for the crippling noise endemic on today’s quantum computers offers a way to maximize their power for reliably performing actual tasks, according to a new paper.
Deep learning may help doctors choose better lung cancer treatments
Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers.
New machine learning theory that can be applied to fusion energy raises questions about the very nature of science
A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
The AI-driven initiative that’s hastening the discovery of drugs to treat COVID-19
Ten organizations have created a pipeline of artificial intelligence and simulation tools to narrow the search for drug candidates that can inhibit SARS-CoV-2.
Federal COVID-19 response taps UCI Health as a model for delivering monoclonal antibody therapy
Irvine, Calif., Feb. 9, 2021 — Monoclonal antibodies are showing promise for improving outcomes for COVID-19 patients, but when a hospital is already beyond capacity, administering them can be a challenge. As hospitalizations soared across California, clinicians with UCI Health created a system for delivering monoclonal antibodies that is keeping hospital beds available for patients with the greatest need.
Argonne Training Program on Extreme-Scale Computing seeks applications for 2021
ATPESC provides in-depth training on using supercomputers, including next-generation exascale systems, to facilitate breakthrough science and engineering.
‘Audeo’ teaches artificial intelligence to play the piano
A University of Washington team created Audeo, a system that can generate music using only visual cues of someone playing the piano.
Machine learning algorithm may be the key to timely, inexpensive cyber-defense
Attacks on vulnerable computer networks and cyber-infrastructure — often called zero-day attacks — can quickly overwhelm traditional defenses, resulting in billions of dollars of damage and requiring weeks of manual patching work to shore up the systems after the intrusion. Now, a Penn State-led team of researchers used a machine learning approach, based on a technique known as reinforcement learning, to create an adaptive cyber defense against these attacks.
Computer model makes strides in search for COVID-19 treatments
A new deep-learning model that can predict how human genes and medicines will interact has identified at least 10 compounds that may hold promise as treatments for COVID-19.