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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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.
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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.
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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.
Read moreDOE 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.
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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.
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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.
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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.
Read moreU.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.
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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.
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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.
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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.
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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.
Read moreSneak 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.
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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.
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More Intelligent Medicine
Leaders in biomedical informatics and medicine discuss ways to optimize the integration of AI in clinical medicine
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
Read more‘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.
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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.
Read moreComputer 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.
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Threads That Sense How and When You Move? New Technology Makes It Possible
Engineers 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 moreDetecting ADHD with near perfect accuracy
A 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 Build Models Using Machine Learning Technique to Enhance Predictions of COVID-19 Outcomes
Mount 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.
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Story Tips from Johns Hopkins Experts on COVID-19
Vaccines 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 Build Models Using Machine Learning Technique to Enhance Predictions of COVID-19 Outcomes
Mount 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.
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Using neural networks for faster X-ray imaging
A team of scientists from Argonne is using artificial intelligence to decode X-ray images faster, which could aid innovations in medicine, materials and energy.
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Jefferson Lab Launches Virtual AI Winter School for Physicists
Artificial 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.”
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Advanced tools reveal critical infrastructure connections and help mitigate disasters
A 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 moreAccelerating AI computing to the speed of light
A 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.
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Fermilab receives DOE award to develop machine learning for particle accelerators
Fermilab 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.
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10 ways Argonne science is combatting COVID-19
Argonne 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.
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UCI researchers use deep learning to identify gene regulation at single-cell level
Irvine, 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.
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Advanced materials in a snap
A 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.
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Machine Learning Improves Particle Accelerator Diagnostics
Operators 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 moreArtificial intelligence predicts gestational diabetes in Chinese women
Machine 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.
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Developing Smarter, Faster Machine Intelligence with Light
SUMMARYResearchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture
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UCI researchers create model to calculate COVID-19 health outcomes
Irvine, 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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