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Ames Lab Scientists' Surprising Discovery: Making Ferromagnets Stronger by Adding Non-Magnetic Element

Researchers at the U.S. Department of Energy's Ames Laboratory discovered that they could functionalize magnetic materials through a thoroughly unlikely method, by adding amounts of the virtually non-magnetic element scandium to a gadolinium-germanium alloy. It was so unlikely they called it a "counterintuitive experimental finding" in their published work on the research.

Cut U.S. Commercial Building Energy Use 29% with Widespread Controls

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How a Single Chemical Bond Balances Cells Between Life and Death

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New Efficient, Low-Temperature Catalyst for Converting Water and CO to Hydrogen Gas and CO2

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Study Sheds Light on How Bacterial Organelles Assemble

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A Single Electron's Tiny Leap Sets Off 'Molecular Sunscreen' Response

In experiments at the Department of Energy's SLAC National Accelerator Laboratory, scientists were able to see the first step of a process that protects a DNA building block called thymine from sun damage: When it's hit with ultraviolet light, a single electron jumps into a slightly higher orbit around the nucleus of a single oxygen atom.

Researchers Find New Mechanism for Genome Regulation

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The Rise of Giant Viruses

Research reveals that giant viruses acquire genes piecemeal from others, with implications for bioenergy production and environmental cleanup.

Grasses: The Secrets Behind Their Success

Researchers find a grass gene affecting how plants manage water and carbon dioxide that could be useful to growing biofuel crops on marginal land.

SLAC Experiment is First to Decipher Atomic Structure of an Intact Virus with an X-ray Laser

An international team of scientists has for the first time used an X-ray free-electron laser to unravel the structure of an intact virus particle on the atomic level. The method dramatically reduces the amount of virus material required, while also allowing the investigations to be carried out several times faster than before. This opens up entirely new research opportunities.


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Chicago Quantum Exchange to Create Technologically Transformative Ecosystem

The University of Chicago is collaborating with the U.S. Department of Energy's Argonne National Laboratory and Fermi National Accelerator Laboratory to launch an intellectual hub for advancing academic, industrial and governmental efforts in the science and engineering of quantum information.

Department of Energy Awards Six Research Contracts Totaling $258 Million to Accelerate U.S. Supercomputing Technology

Today U.S. Secretary of Energy Rick Perry announced that six leading U.S. technology companies will receive funding from the Department of Energy's Exascale Computing Project (ECP) as part of its new PathForward program, accelerating the research necessary to deploy the nation's first exascale supercomputers.

Cynthia Jenks Named Director of Argonne's Chemical Sciences and Engineering Division

Argonne has named Cynthia Jenks the next director of the laboratory's Chemical Sciences and Engineering Division. Jenks currently serves as the assistant director for scientific planning and the director of the Chemical and Biological Sciences Division at Ames Laboratory.

Argonne-Developed Technology for Producing Graphene Wins TechConnect National Innovation Award

A method that significantly cuts the time and cost needed to grow graphene has won a 2017 TechConnect National Innovation Award. This is the second year in a row that a team at Argonne's Center for Nanoscale Materials has received this award.

Honeywell UOP and Argonne Seek Research Collaborations in Catalysis Under Technologist in Residence Program

Researchers at Argonne are collaborating with Honeywell UOP scientists to explore innovative energy and chemicals production.

Follow the Fantastic Voyage of the ICARUS Neutrino Detector

The ICARUS neutrino detector, born at Gran Sasso National Lab in Italy and refurbished at CERN, will make its way across the sea to Fermilab this summer. Follow along using an interactive map online.

JSA Awards Graduate Fellowships for Research at Jefferson Lab

Jefferson Sciences Associates announced today the award of eight JSA/Jefferson Lab graduate fellowships. The doctoral students will use the fellowships to support their advanced studies at their universities and conduct research at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) - a U.S. Department of Energy nuclear physics laboratory managed and operated by JSA, a joint venture between SURA and PAE Applied Technologies.

Muon Magnet's Moment Has Arrived

On May 31, the 50-foot-wide superconducting electromagnet at the center of the Muon g-2 experiment saw its first beam of muon particles from Fermilab's accelerators, kicking off a three-year effort to measure just what happens to those particles when placed in a stunningly precise magnetic field. The answer could rewrite scientists' picture of the universe and how it works.

Seven Small Businesses to Collaborate with Argonne to Solve Technical Challenges

Seven small businesses have been selected to collaborate with researchers at Argonne to address technical challenges as part of DOE's Small Business Vouchers Program.

JSA Names Charles Perdrisat and Charles Sinclair as Co-Recipients of its 2017 Outstanding Nuclear Physicist Prize

Jefferson Science Associates, LLC, announced today that Charles Perdrisat and Charles Sinclair are the recipients of the 2017 Outstanding Nuclear Physicist Prize. The 2017 JSA Outstanding Nuclear Physicist Award is jointly awarded to Charles Perdrisat for his pioneering implementation of the polarization transfer technique to determine proton elastic form factors, and to Charles Sinclair for his crucial development of polarized electron beam technology, which made such measurements, and many others, possible.


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Oxygen: The Jekyll and Hyde of Biofuels

Scientists are devising ways to protect plants, biofuels and, ultimately, the atmosphere itself from damage caused by an element that sustains life on earth.

The Rise of Giant Viruses

Research reveals that giant viruses acquire genes piecemeal from others, with implications for bioenergy production and environmental cleanup.

Grasses: The Secrets Behind Their Success

Researchers find a grass gene affecting how plants manage water and carbon dioxide that could be useful to growing biofuel crops on marginal land.

New Perspectives Into Arctic Cloud Phases

Teamwork provides insight into complicated cloud processes that are important to potential environmental changes in the Arctic.

Mountaintop Plants and Soils to Become Out of Sync

Plants and soil microbes may be altered by climate warming at different rates and in different ways, meaning vital nutrient patterns could be misaligned.

If a Tree Falls in the Amazon

For the first time, scientists pinpointed how often storms topple trees, helping to predict how changes in Amazonia affect the world.

Turning Waste into Fuels, Microbial Style

A newly discovered metabolic process linking different bacteria in a community could enhance bioenergy production.

Department of Energy Awards Six Research Contracts Totaling $258 Million to Accelerate U.S. Supercomputing Technology

Today U.S. Secretary of Energy Rick Perry announced that six leading U.S. technology companies will receive funding from the Department of Energy's Exascale Computing Project (ECP) as part of its new PathForward program, accelerating the research necessary to deploy the nation's first exascale supercomputers.

Electrifying Magnetism

Researchers create materials with controllable electrical and magnetic properties, even at room temperature.

One Step Closer to Practical Fast Charging Batteries

Novel electrode materials have designed pathways for electrons and ions during the charge/discharge cycle.


Visualizing Scientific Big Data in Informative and Interactive Ways

Article ID: 672198

Released: 2017-03-31 08:05:56

Source Newsroom: Brookhaven National Laboratory

  • Credit: Brookhaven National Laboratory

    Wei Xu, a computer scientist who is part of Brookhaven Lab¹s Computational Science Initiative, helps scientists analyze large and varied datasets by developing visualization tools, such as the color-mapping tool seen projected from her laptop onto the large screen.

  • Credit: Brookhaven National Laboratory

    The color-mapping tool was used to visualize a multivariable fluorescence dataset from the Hard X-ray Nanoprobe (HXN) beamline at Brookhaven's National Synchrotron Light Source II. The color map (a) shows how the different variables—the chemical elements cerium (Ce), cobalt (Co), iron (Fe), and gadolinium (Gd)—are distributed in a sample of an electrolyte material used in solid oxide fuel cells. The fluorescence spectrum of the selected data point (the circle indicated by the overlaid white arrows) is shown by the colored bars, with their height representing the relative elemental ratios. The fluorescence image (b), pseudo-colored based on the color map in (a), represents a joint colorization of the individual images in (d), whose colors are based on the four points at the circle boundary (a) for each of the four elements. The arrow indicates where new chemical phases can exist—something hard to detect when observing the individual plots (d). Enhancing the color contrast—for example, of the rectangular region in (b)—enables a more detailed view, in this case providing better contrast between Fe (red) and Co (green) in image (c).

  • Credit: Brookhaven National Laboratory

    The multilevel display tool enables scientists conducting scattering experiments to explore the resulting image sets at the scatterplot level (0), attribute pseudo-color level (1), zoom-in attribute level (2), raw image level (3), zoom-in raw image level (4), and pixel level (5), all in a single display.

  • Credit: Brookhaven National Laboratory

    The scatter plots above are based on a dataset containing 46 universities with 14 attributes of interest for prospective students: academics, athletics, housing, location, nightlife, safety, transportation, weather, score, tuition, dining, PhD/faculty, population, and income. The large red nodes represent the attributes and the small blue points represent the universities; the contour lines (middle plot) show how the numerical values of the attributes change. This prospective student wants to attend a university with good academics (>9/10). Universities that meet this criterion are within the contours lines whose value exceeds 9. To determine which universities meet multiple criteria, students would see where the universities and attributes overlap (right plot).

Humans are visual creatures: our brain processes images 60,000 times faster than text, and 90 percent of information sent to the brain is visual. Visualization is becoming increasingly useful in the era of big data, in which we are generating so much data at such high rates that we cannot keep up with making sense of it all. In particular, visual analytics—a research discipline that combines automated data analysis with interactive visualizations—has emerged as a promising approach to dealing with this information overload.

“Visual analytics provides a bridge between advanced computational capabilities and human knowledge and judgment,” said Wei Xu, a computer scientist in the Computational Science Initiative (CSI) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and a research assistant professor in the Department of Computer Science at Stony Brook University. “The interactive visual representations and interfaces enable users to efficiently explore and gain insights from massive datasets.”

At Brookhaven, Xu has been leading the development of several visual analytics tools to facilitate the scientific decision-making and discovery process. She works closely with Brookhaven scientists, particularly those at the National Synchrotron Light Source II (NSLS-II) and the Center for Functional Nanomaterials (CFN)—both DOE Office of Science User Facilities. By talking to researchers early on, Xu learns about their data analysis challenges and requirements. She continues the conversation throughout the development process, demoing initial prototypes and making refinements based on their feedback. She also does her own research and proposes innovative visual analytics methods to the scientists.

Recently, Xu has been collaborating with the Visual Analytics and Imaging (VAI) Lab at Stony Brook University—her alma mater, where she completed doctoral work in computed tomography with graphics processing unit (GPU)-accelerated computing.

Though Xu continued work in these and related fields when she first joined Brookhaven Lab in 2013, she switched her focus to visualization by the end of 2015.

“I realized how important visualization is to the big data era,” Xu said. “The visualization domain, especially information visualization, is flourishing, and I knew there would be lots of research directions to pursue because we are dealing with an unsolved problem: how can we most efficiently and effectively understand the data? That is a quite interesting problem not only in the scientific world but also in general.”

It was at this time that Xu was awarded a grant for a visualization project proposal she submitted to DOE’s Laboratory Directed Research and Development program, which funds innovative and creative research in areas of importance to the nation’s energy security. At the same time, Klaus Mueller—Xu’s PhD advisor at Stony Brook and director of the VAI Lab—was seeking to extend his research to a broader domain. Xu thought it would be a great opportunity to collaborate: she would present the visualization problem that originated from scientific experiments and potential approaches to solve it, and, in turn, doctoral students in Mueller’s lab would work with her and their professor to come up with cutting-edge solutions.

This Brookhaven-Stony Brook collaboration first led to the development of an automated method for mapping data involving multiple variables to color. Variables with a similar distribution of data points have similar colors. Users can manipulate the color maps, for example, enhancing the contrast to view the data in more detail. According to Xu, these maps would be helpful for any image dataset involving multiple variables.

“Different imaging modalities—such as fluorescence, differential phase contrasts, x-ray scattering, and tomography—would benefit from this technique, especially when integrating the results of these modalities,” she said. “Even subtle differences that are hard to identify in separate image displays, such as differences in elemental ratios, can be picked up with our tool—a capability essential for new scientific discovery.” Currently, Xu is trying to install the color mapping at NSLS-II beamlines, and advanced features will be added gradually.

In conjunction with CFN scientists, the team is also developing a multilevel display for exploring large image sets. When scientists scan a sample, they generate one scattering image at each point within the sample, known as the raw image level. They can zoom in on this image to check the individual pixel values (the pixel level). For each raw image, scientific analysis tools are used to generate a series of attributes that represent the analyzed properties of the sample (the attribute level), with a scatterplot showing a pseudo-color map of any user-chosen attribute from the series—for example, the sample’s temperature or density. In the past, scientists had to hop between multiple plots to view these different levels. The interactive display under development will enable scientists to see all of these levels in a single view, making it easier to identify how the raw data are related and to analyze data across the entire scanned sample. Users will be able to zoom in and out on different levels of interest, similar to how Google Maps works.

The ability to visually reconstruct a complete joint dataset from several partial marginal datasets is at the core of another visual analytics tool that Xu’s Stony Brook collaborators developed. This web-based tool enables users to reconstruct all possible solutions to a given problem and locate the subset of preferred solutions through interactive filtering.

“Scientists commonly describe a single object with datasets from different sources—each covering only a portion of the complete properties of that object—for example, the same sample scanned in different beamlines,” explained Xu. “With this tool, scientists can recover a property with missing fields by refining its potential ranges and interactively acquiring feedback about whether the result makes sense.”

Their research led to a paper that was published in the Institute of Electrical and Electronics Engineers (IEEE) journal Transactions on Visualization and Computer Graphics and awarded the Visual Analytics Science and Technology (VAST) Best Paper Honorable Mention at the 2016 IEEE VIS conference.

At this same conference, another group of VAI Lab students whom Xu worked with were awarded the Scientific Visualization (SciVis) Best Poster Honorable Mention for their poster, “Extending Scatterplots to Scalar Fields.” Their plotting technique helps users link correlations between attributes and data points in a single view, with contour lines that show how the numerical values of the attributes change. For their case study, the students demonstrated how the technique could help college applications select the right university by plotting the desired attributes (e.g., low tuition, high safety, small campus size) with different universities (e.g., University of Virginia, Stanford University, MIT). The closer a particular college is to some attribute, the higher that attribute value.

According to Xu, this kind of technique also could be applied to visualize artificial neural networks—the deep learning (a type of machine learning) frameworks that are used to address problems such as image classification and speech recognition.

“Because neural network models have a complex structure, it is hard to understand how their intrinsic learning process works and how they arrive at intermediate results, and thus quite challenging to debug them,” explained Xu. “Neural networks are still largely regarded as black boxes. Visualization tools like this one could help researchers get a better idea of their model’s performance.”

Besides her Stony Brook collaborations, Xu is currently involved in the Co-Design Center for Online Data Analysis and Reduction at the Exascale (CODAR), which Brookhaven is partnering on with other national laboratories and universities through DOE’s Exascale Computing Project. Her role is to visualize data evaluating the performance of computing clusters, applications, and workflows that the CODAR team is developing to analyze and reduce data online before the data are written to disk for possible further offline analysis. Exascale computer systems are projected to provide unprecedented increases in computational speed but the input/output (I/O) rates of transferring the computed results to storage disks are not expected to keep pace, so it will be infeasible for scientists to save all of their scientific results for offline analysis. Xu’s visualization will help the team “diagnose” any performance issues with the computation processes, including individual application execution, computation job management in the clusters, I/O performance in the runtime system, and data reduction and reconstruction efficiency.

Xu is also part of a CSI effort to build a virtual reality (VR) lab for an interactive data visualization experience. “It would be a more natural way to observe and interact with data. VR techniques replicate a realistic and immersive 3D environment,” she said.

For Xu, her passion for visualization most likely stemmed from an early interest in drawing.

“As a child, I liked to draw,” she said. “In growing up, I took my drawings from paper to the computer.”