AI-based approach could make it easier to incorporate metabolic tumor volume into clinical trials and possibly patient care
Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters.
After 11 years spent in medical school, residency and fellowships, Elizabeth Chou, MD, a vascular surgeon who recently joined the Smidt Heart Institute at Cedars-Sinai, has earned her dream career. And she has no plans of stopping there. She’s on a path toward ensuring women in vascular surgery are represented—as incoming physicians and as patients.
Deep learning models represent “an entirely new paradigm for studying dementia”
University of Minnesota Twin Cities researchers have found a way to improve the performance of traditional Magnetic Resonance Imaging (MRI) reconstruction techniques, allowing for faster MRIs without relying on the use of newer deep learning methods.
Dr. Yangyang Xu, assistant professor of mathematical sciences at Rensselaer Polytechnic Institute, has received a $250,000 grant from the National Science Foundation (NSF) to research challenges associated with distributed big data in machine learning.Machine learning algorithms allow computers to make decisions, predictions, and recommendations on the basis of input training data without being explicitly told what information to look for in the data.
University of Washington researchers created ClearBuds, earbuds that enhance the speaker’s voice and reduce background noise.
A novel AI-based approach to detect earthquakes early uses prompt elasto-gravity signals, or PEGS, gravitational changes generated by large-mass motion in megaquakes. PEGS carry information about an ongoing earthquake at the speed of light, arriving much faster than even the fastest seismic waves.
As the size and number of acoustic datasets increase, accurately and quickly matching the bioacoustics signals to their corresponding sources becomes more challenging and important. This is especially difficult in noisy, natural acoustic environments. At the 182nd ASA Meeting, Elizabeth Ferguson, from Ocean Science Analytics, will describe how DeepSqueak, a deep learning tool, can classify underwater acoustic signals. It uses deep neural network image recognition and classification methods to determine the important features within spectrograms, then match those features to specific sources.
Proteins are the molecular machinery that makes life possible, and researchers have long been interested in a key trait of protein function: their three-dimensional structure. A new study by Georgia Tech and Oak Ridge National Laboratory details a computational tool able to predict the structure protein complexes – and lends new insights into the biomolecular mechanisms of their function.
A study is the first to reveal detailed behavior of massive goliath groupers. Until now, no studies have documented their fine-scale behavior. What is known about them has been learned from divers, underwater video footage, and observing them in captivity. Using a multi-sensor tag with a three axis accelerometer, gyroscope and magnetometer as well as a temperature, pressure and light sensor, a video camera and a hydrophone, researchers show how this species navigates through complex artificial reef environments, maintain themselves in high current areas, and how much time they spend in different cracks and crevices – none of which would be possible without the tag.
Argonne, industry and academia collaborate to bring innovative AI and simulation tools to the COVID-19 battlefront.
People’s social behavior, reflected in their mobility data, is providing scientists with a way to forecast the spread of COVID-19 nationwide at the county level. Researchers have developed the first data-driven deep learning model with the potential to predict an outbreak in COVID-19 cases two weeks in advance. Feeding the mobility data to epidemiological forecasting models helps to estimate COVID-19 growth as well as evaluating the effects of government policies such as mandating masks on the spread of COVID-19.
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.
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.
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
UW researchers have developed a deep learning method that can produce a seamlessly looping, realistic looking video from a single photo.
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.
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.
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.
Dr. Corey Arnold and his graduate student, Karthik Sarma, explain how federated learning can enable more powerful AI models while enhancing the protection of patient data
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.
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.
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.
Researchers from multiple institutions in North America have developed a fully automated, deep-learning (DL), artificial-intelligence clinical tool that can measure the volume of cerebral ventricles on magnetic resonance images (MRIs) in children within about 25 minutes.
A research team, including scientists from UC San Diego, Argonne National Laboratory and Oak Ridge National Laboratory, wins the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research, presented during the SC20 virtual conference.
The new projects will use DOE’s leadership-class supercomputers to pursue transformational advances in science and engineering.
Researchers from Argonne have developed a new way to accurately forecast traffic and proved that it could work using as their model the California highway system, the busiest in the United States.
Researchers nationwide are building the software and applications that will run on some the world’s fastest supercomputers. Among them are members of DOE’s Exascale Computing Project who recently published a paper highlighting their progress so far.
Penn State researchers have used artificial intelligence (AI) to clear up that noise, drastically facilitating and improving near real-time observation of volcanic movements and the detection of volcanic activity and unrest.
Researchers at Mass Eye and Ear have developed a unique diagnostic tool called DystoniaNet that uses artificial intelligence to detect dystonia from MRI scans in 0.36 seconds. DystoniaNet is the first technology of its kind to provide an objective diagnosis of the disorder. In a new study of 612 brain MRI scans, the platform diagnosed dystonia with 98.8 percent accuracy.
UC San Diego researchers published a study that used the ‘Comet’ supercomputer at the San Diego Supercomputer Center on campus showing how machine learning produced a model for plasma turbulence.
The role of AI in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning. In this Q&A, NERSC’s Karthik Kashinath discusses what is driving the scientific community to embrace these new methodologies.
A team of researchers have developed an algorithm through machine learning that helps predict sites of DNA methylation – a process that can change the activity of DNA without changing its overall structure – and could identify disease-causing mechanisms that would otherwise be missed by conventional screening methods.
Researchers at the University of Chicago Medicine Comprehensive Cancer Center, working with colleagues in Europe, created a deep learning algorithm that can infer molecular alterations directly from routine histology images across multiple common tumor types. The findings were published July 27 in Nature Cancer.
The NSF has awarded the San Diego Supercomputer Center (SDSC) at UC San Diego a $5 million grant to develop a high-performance resource for conducting artificial intelligence (AI) research across a wide swath of science and engineering domains.
This new agreement will dramatically improve and reduce the computational expense of fluid dynamics models. Both partners aim to improve the design and durability of engine components.
In a recent preprint (available through Cornell University’s open access website arXiv), a team led by a Lawrence Livermore National Laboratory computer scientist proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.
Using advanced machine learning, drones could be used to detect dangerous “butterfly” landmines in remote regions of post-conflict countries, according to research from Binghamton University, State University at New York.
Scientists have deployed artificial intelligence to identify more of the billions of metabolites that are currently unknown. The small molecules underlie and inform every aspect of our lives, including energy production, the fate of the planet, and our health. “Beast Mode” helps explain how they did it.
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.
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.
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…
An artificial intelligence (AI) device that has been fast-tracked for approval by the Food and Drug Administration may help identify newborns at risk for aggressive posterior retinopathy of prematurity (AP-ROP). AP-ROP is the most severe form of ROP and can be difficult to diagnose in time to save vision.
Dr. Kang Zhang uses artificial intelligence (AI) to teach computers to create illustrations in the style of the famous masters: Jackson Pollock and his paint splatters or Joan Miró and his curved shapes and sharp lines. The process involves feeding computers examples of colors, abstract shapes and layouts so they can learn to produce their own versions of masterpieces.
To better leverage cancer data for research, scientists at ORNL are developing an artificial intelligence (AI)-based natural language processing tool to improve information extraction from textual pathology reports. In a first for cancer pathology reports, the team developed a multitask convolutional neural network (CNN)—a deep learning model that learns to perform tasks, such as identifying key words in a body of text, by processing language as a two-dimensional numerical dataset.
Using aerosols as ground truth, researchers at the McKelvey School of Engineering at Washington University in St. Louis have developed a deep learning method that accurately simulates chaotic trajectories — from the spread of poisonous gas to the path of foraging animals.
Computer scientists at Lawrence Livermore National Laboratory are preparing the future of commuter traffic by applying Deep Reinforcement Learning — the same kind of goal-driven algorithms that have defeated video game experts and world champions in the strategy game Go — to determine the most efficient strategy for charging and driving electric vehicles used for ride-sharing services.
A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches.