news, journals and articles from all over the world.
Roboticists at the University of California San Diego have developed an affordable, easy to use system to track the location of flexible surgical robots inside the human body. The system performs as well as current state of the art methods, but is much less expensive.
Johns Hopkins researchers recently received a $195,000 Rapid Response Research grant from the National Science Foundation to, using machine learning, identify which COVID-19 patients are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death.
Researchers at Columbia Engineering and the University of South Carolina have developed a method that combines big data and machine learning to selectively design gas-filtering polymer membranes to reduce greenhouse gas emissions. Their study, published today in Science Advances, is the first to apply an experimentally validated machine learning method to rapidly design and develop advanced gas separation membranes.
Berkeley Lab’s Kristin Persson shares her thoughts on what inspired her to launch the Materials Project online database, the future of materials research and machine learning, and how she found her own way into a STEM career.
New machine learning methods bring insights into how lithium ion batteries degrade, and show it’s more complicated than many thought.
A group of Harrisburg University of Science and Technology students and professors have developed an automated computer classifier capable of predicting with 80% accuracy and no racial bias whether someone is likely to be a criminal based solely on a picture of their face.
Jonathan W. Korn, a PhD student in Harrisburg University of Science and Technology’s Data Science program and a NYPD veteran; Prof. Nathaniel J.S. Ashby, and Prof. Roozbeh Sadeghian’s research titled “A Deep Neural Network Model to Predict Criminality Using Image Processing” will appear in the forthcoming Springer Nature – Research Book Series: Transactions on Computational Science & Computational Intelligence
Identifying sources of light plays an important role in the development of many photonic technologies, such as lidar, remote sensing, and microscopy. Traditionally, identifying light sources as diverse as sunlight, laser radiation, or molecule fluorescence has required millions of measurements, particularly in low-light environments, which limits the realistic implementation of quantum photonic technologies. In Applied Physics Reviews, researchers demonstrated a smart quantum technology that enables a dramatic reduction in the number of measurements required to identify light sources
University of Alabama in Huntsville (UAH) professor of biological science Dr. Jerome Baudry is collaborating with Hewlett Packard Enterprise (HPE) to use HPE’s Cray Sentinel supercomputer to search for natural products that are effective against the COVID-19 virus.
Lawrence Livermore National Laboratory researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2, the virus that causes COVID-19. The research team is performing experimental testing on the chosen antibody designs.
Irvine, Calif., April 29, 2020 — Researchers at the University of California, Irvine have developed a new mathematical machine-intelligence-based technique that spatially delineates highly complicated cell-to-cell and gene-gene interactions. The powerful method could help with the diagnosis and treatment of diseases ranging from cancer to COVID-19 through quantifing crosstalks between “good” cells and “bad” cells.
SLAC researchers have developed a new tool, using machine learning, that may make part of the accelerator tuning process five times faster compared to previous methods.
A team of materials scientists at Lawrence Berkeley National Laboratory – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day.
Medical physicists at the Mayo Clinic have just made a unique library of computed tomography (CT) data publicly available so that imaging researchers can study, develop, validate, and optimize algorithms and enhance imaging hardware to produce peak-quality CT images using low radiation doses.
“There are no simple, robust, general tools that, for example, officials in Albany could use to make projections,” said Magdon-Ismail, a professor of computer science, and expert in machine learning, data mining, and pattern recognition. “These models show that the projections vary enormously from one city to another. This knowledge could relieve some of the uncertainty that is around in developing policy.”
The lab is responding to the coronavirus crisis by imaging disease-related biomolecules, developing standards for reliable coronavirus testing and enabling other essential research.
The INCITE program is now seeking proposals for high-impact, computationally intensive research projects that require the power and scale of DOE’s leadership-class supercomputers.
Bioengineers have combined standard microscopy, infrared light, and artificial intelligence to assemble digital biopsies that identify important molecular characteristics of cancer biopsy samples.
Argonne researchers have invented a machine-learning based algorithm for quantitatively characterizing material microstructure in three dimensions and in real time. This algorithm applies to most structural materials of interest to industry.
Cornell researchers have developed an automated system that uses machine learning, data analysis and human feedback to automatically verify statistical claims about the new coronavirus.
Activity from phony Twitter accounts established by the Russian Internet Research Agency between 2015 and 2017 may have contributed to politicizing Americans’ position on the nature and efficacy of vaccines, a health care topic which has not historically fallen along party lines, according to new research published in the American Journal of Public Health.
Argonne scientists are working around the clock to analyze the virus to find new treatments and cures, predict how it will propagate through the population, and make sure that our supply chains remain intact.
Argonne scientists and engineers are looking toward AI — specifically, machine learning — to help us better understand the mechanics that govern nuclear reactors.
Researchers have used artificial intelligence to detect Vietnam War-era bomb craters in Cambodia from satellite images – with the hope that it can help find unexploded bombs.
In a machine learning challenge dubbed the 2020 Large Hadron Collider Olympics, a team of cosmologists from Berkeley Lab developed a code that best identified a mock signal hidden in simulated particle-collision data.
Proteins are the building blocks of life and scientists have long studied how to improve them or design new ones. Traditionally, new proteins are created by mimicking existing proteins or manually editing their amino acids. This process is time-consuming, and it is difficult to predict the impact of changing an amino acid. In APL Bioengineering, researchers explore how to create new proteins by using machine learning to translate protein structures into musical scores, presenting an unusual way to translate physics concepts across domains.
Recent advances in unmanned‐aerial‐vehicle‐ (UAV‐) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide‐area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of…
In a study, the researchers used a machine learning algorithm to classify more than 110 types of plastics, including commercial and lab-made varieties, to better understand how they might degrade in the ocean.
Astrophysicists supported by the Department of Energy’s Office of Science are developing these guides in the form of computer models that rely on machine learning to examine the LSST data.
Applying deep learning to seismic data has revealed tremor and slip occur at all times—before and after known large-scale slow-slip earthquakes—rather than intermittently in discrete bursts, as previously believed.
Scientists may be a step closer to solving some of anthropology’s biggest mysteries thanks to a machine learning algorithm that can scour through remote sensing data, such as satellite imagery, looking for signs of human settlements, according to an international team of researchers.
Machine learning is helping Penn Medicine researchers identify the size and shape of brain networks in individual children, which may be useful for understanding psychiatric disorders. In a new study published in Neuron, a multidisciplinary team showed how brain networks unique to each child can predict cognition. The study is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development.
Argonne scientists are combining one-of-a-kind x-ray experiments with novel computer simulations to help engineers at aerospace and defense companies save time and money.
In the future, robots could take blood samples, benefiting patients and healthcare workers alike. A Rutgers-led team has created a blood-sampling robot that performed as well or better than people, according to the first human clinical trial of an automated blood drawing and testing device.
Bias in artificial intelligence is well established. Researchers are now proposing that developers incorporate the concept of “feminist design thinking” into their process as a way of improving equity – particularly in the development of software used in hiring.
As a staff member in the Theory and Computation Group at Brookhaven Lab’s Center for Functional Nanomaterials, Qu applies various approaches in artificial intelligence to analyze experimental and computational nanoscience data.
Rhinoplasty may make a woman appear to be three years younger, machine learning shows
The American Physical Society (APS) has recognized a former PPPL summer intern for producing an outstanding research poster at the world-wide APS Division of Plasma Physics (DPP) gathering last October. The student used machine learning to accelerate a leading PPPL computer code known as XGC.
A new study finds that an interactive voice application using artificial intelligence is as accurate at tracking the wellbeing of patients being treated for serious mental illness as their physicians.
Iowa State engineers are working with collaborators to develop machine learning theories and software tools that can quickly and cheaply design better solar cells. Those theories and tools could also be applied to the rapid design of all kinds of new technologies.
In two new papers, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate battery discovery.
Several Argonne researchers will attend the Supercomputing 2019 (SC19) conference to share scientific computing advances and insights with an eye toward the upcoming exascale era.
A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches.
By blasting a frustrated mixture of materials with quick pulses of laser light, researchers transformed a superlattice into a supercrystal, a rare, repeating, three-dimensional structural much larger than an ordinary crystal. Using machine learning techniques, they studied the underlying structure of this sample at the nanoscale level before and after applying the laser pulse treatment.
⎯ A team of researchers from the National University of Singapore (NUS) has put Singapore on the global map of Artificial Intelligence (AI) and big data analytics. Their open-source project, called Apache SINGA, “graduated” from the Apache Incubator on 16 October 2019 and is now Southeast Asia’s first Top-Level Project (TLP) under the Apache Software Foundation, the world’s largest open-source software community.
Physical exams only provide a snapshot of a Parkinson’s patient’s daily tremor experience. Scientists have developed algorithms that, combined with wearable sensors, can continuously monitor patients and estimate total Parkinsonian tremor as they perform a variety of free body movements in their natural settings. This new method holds great potential for providing a full spectrum of patients’ tremors and medication response, providing clinicians with key information to effectively manage and treat their patients with this disorder.
What can reading 26,000 books tell researchers about how language environment affects language behavior? Brendan T. Johns, an assistant professor of communicative disorders and sciences at UB has published a computational modeling study that suggests our experience and interaction with specific learning environments, like the characteristics of what we read, leads to differences in language behavior that were once attributed to differences in cognition.
New research has predicted that driverless cars could worsen traffic congestion in the coming decades, partly because of drivers’ attitudes to the emerging technology and a lack of willingness to share their rides.
Scientists seeking to design new catalysts to convert carbon dioxide (CO2) to methane have used a novel artificial intelligence (AI) approach to identify key catalytic properties. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts.
Scientists at Sanford Burnham Prebys Medical Discovery Institute have developed a machine-learning algorithm that gleans information from microscope images—allowing for high-throughput epigenetic drug screens that could unlock new treatments for cancer, heart disease, mental illness and more. The study was published in eLife.
ISPOR—the professional society for health economics and outcomes research—will begin its ISPOR Europe 2019 2-6 November 2019 in Copenhagen, Denmark.
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