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.
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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.
Read moreHertz 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.
Read moreScience 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.
Read moreUnbound 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.
Read moreThe 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.
Read moreUPTON, 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.
Read moreThe 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.
Read moreFor 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.
Read moreThe 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.
Read moreThe 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.
Read moreHealthy 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.
Read moreThe 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.
Read moreResearchers 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.
Read moreThe 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.
Read moreIn 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.
Read moreLeaders in biomedical informatics and medicine discuss ways to optimize the integration of AI in clinical medicine
Read moreResearchers 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.
Read moreUniversity 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.
Read moreResearchers 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.
Read moreA 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.
Read moreBerkeley 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.
Read moreA 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.
Read moreUsing 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.
Read moreDoctors 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.
Read moreA 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.
Read moreTen organizations have created a pipeline of artificial intelligence and simulation tools to narrow the search for drug candidates that can inhibit SARS-CoV-2.
Read moreIrvine, 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.
Read moreATPESC provides in-depth training on using supercomputers, including next-generation exascale systems, to facilitate breakthrough science and engineering.
Read moreA University of Washington team created Audeo, a system that can generate music using only visual cues of someone playing the piano.
Read moreAttacks 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.
Read moreA 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.
Read moreEngineers have developed a thread-based sensor capable of monitoring the direction, angle of rotation and degree of displacement of the head. The design is a proof of principle that could be extended to measuring movements of other limbs by sensors attached like tatoos to the skin.
Read moreA new study led by a University at Buffalo researcher has identified how specific communication among different brain regions, known as brain connectivity, can serve as a biomarker for attention deficit hyperactivity disorder (ADHD).
Read moreMount Sinai researchers have published one of the first studies using a machine learning technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress.
Read moreVaccines take time to work. After getting a COVID-19 vaccine, it takes a while for the immune system to fully respond and provide protection from the virus. For the Moderna and Pfizer COVID-19 vaccines, it takes up to two weeks after the second shot to become appropriately protected.
Read moreMount Sinai researchers have published one of the first studies using federated learning to examine electronic health records to better predict how COVID-19 patients will progress.
Read moreA team of scientists from Argonne is using artificial intelligence to decode X-ray images faster, which could aid innovations in medicine, materials and energy.
Read moreArtificial intelligence is a game-changer in nuclear physics, able to enhance and accelerate fundamental research and analysis by orders of magnitude. DOE’s Jefferson Lab is exploring the expanding synergy between nuclear physics and computer science as it co-hosts together with The Catholic University of America and the University of Maryland a virtual weeklong series of lectures and hands-on exercises Jan. 11-15 for graduate students, postdoctoral researchers and even “absolute beginners.”
Read moreA cross-platform Argonne collaboration is optimizing a tool developed after Hurricane Maria to find essential connections between critical infrastructure that will help owners and operators plan for and mitigate a variety of potential hazards.
Read moreA University of Washington-led team has come up with a system that could help speed up AI performance and find ways to reduce its energy consumption: an optical computing core prototype that uses phase-change material.
Read moreFermilab scientists and engineers are developing a machine learning platform to help run Fermilab’s accelerator complex alongside a fast-response machine learning application for accelerating particle beams. The programs will work in tandem to boost efficiency and energy conservation in Fermilab accelerators.
Read moreArgonne scientists and research facilities have made a difference in the fight against COVID-19 in the year since the first gene sequence for the virus was published.
Read moreIrvine, Calif., Jan. 5, 2021 — Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving.
Read moreA research team at Sandia National Laboratories has successfully used machine learning — computer algorithms that improve themselves by learning patterns in data — to complete cumbersome materials science calculations more than 40,000 times faster than normal.
Read moreOperators of Jefferson Lab’s primary particle accelerator are getting a new tool to help them quickly address issues that can prevent it from running smoothly. The machine learning system has passed its first two-week test, correctly identifying glitchy accelerator components and the type of glitches they’re experiencing in near-real-time. An analysis of the results of the first field test of the custom-built machine learning system was recently published in the journal Physical Review Accelerators and Beams.
Read moreMachine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in the Endocrine Society’s Journal of Clinical Endocrinology & Metabolism.
Read moreSUMMARYResearchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture
Read moreIrvine, Calif., Dec. 17, 2020 —University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. “The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset,” said Daniel S.
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