A5043192992- Machine Learning in Materials Science
- Nuclear Physics and Applications
- Meteorological Phenomena and Simulations
- Medical Imaging Techniques and Applications
- Scientific Computing and Data Management
- Probabilistic and Robust Engineering Design
- Sparse and Compressive Sensing Techniques
- Image and Signal Denoising Methods
- Electron and X-Ray Spectroscopy Techniques
- Distributed and Parallel Computing Systems
- Force Microscopy Techniques and Applications
- Gaussian Processes and Bayesian Inference
- Numerical methods in inverse problems
- Target Tracking and Data Fusion in Sensor Networks
- Advanced Numerical Methods in Computational Mathematics
- Nuclear reactor physics and engineering
- Advanced MRI Techniques and Applications
- Stochastic Gradient Optimization Techniques
- Advanced Data Storage Technologies
- Photoacoustic and Ultrasonic Imaging
- Advanced Electron Microscopy Techniques and Applications
- Parallel Computing and Optimization Techniques
- X-ray Diffraction in Crystallography
- Advanced X-ray and CT Imaging
- Electronic and Structural Properties of Oxides
Oak Ridge National Laboratory
2016-2025
Government of the United States of America
2023
Dartmouth College
2019
Simon Fraser University
2017
University of Tennessee at Knoxville
2007
Arizona State University
2002-2004
Halstead Hospital
1994
Junta de Andalucía
1963
John Brown University
1920-1952
Harvard University
1947
Hyperspectral images consist of large number bands which require sophisticated analysis to extract. One approach reduce computational cost, information representation, and accelerate knowledge discovery is eliminate that do not add value the classification method being applied. In particular, algorithms perform band elimination should be designed take advantage structure used. This letter introduces an embedded-feature-selection (EFS) algorithm tailored operate with support vector machines...
Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging manipulation of structure functionality matter at nanometer atomic scales. Here, we analyze scientific discovery process in SPM following information flow from tip-surface junction, knowledge adoption wider community. We further discuss challenges opportunities offered merging with advanced data mining, visual analytics, technologies.
Data-driven science and technology offer transformative tools methods to science. This review article highlights the latest development progress in interdisciplinary field of data-driven plasma (DDPS), i.e., whose is driven strongly by data analyses. Plasma considered be most ubiquitous form observable matter universe. Data associated with plasmas can, therefore, cover extremely large spatial temporal scales, often provide essential information for other scientific disciplines. Thanks...
We propose a new edge detection method that is effective on multivariate irregular data in any domain. The based local polynomial annihilation technique and can be characterized by its convergence to zero for value away from discontinuities. numerically cost efficient entirely independent of specific shape or complexity boundaries. Application the minmod function various orders ensures high rate discontinuities while reducing inherent oscillationsnear It further enables distinction jump...
Using neutron tomographic imaging, we report for the first time three-dimensional spatial distribution of lithium products in electrochemically discharged lithium-air cathodes. Neutron imaging finds a nonuniform product across electrode thickness, with species concentration being higher near edges Li-air and relatively uniform center electrode. The experimental images were analyzed context results obtained from 3D modeling that maps spatiotemporal variation using kinetically coupled...
Abstract Scanning transmission electron microscopy (STEM) has emerged as one of the foremost techniques to analyze materials at atomic resolution. However, two practical difficulties inherent STEM imaging are: radiation damage imparted by beam, which can potentially or otherwise modify specimen and slow-scan image acquisition, limits ability capture dynamic changes high temporal Furthermore, due in part scan flyback corrections, typical raster methods result an uneven distribution dose...
Gibbs ringing is a well known artifact that effects reconstruction of images having discontinuities. This problem in the magnetic resonance imaging (MRI) data due to many different tissues normally present each scan. The manifests itself at boundaries tissues, making it difficult determine structure brain tissue. Gegenbauer method has been shown effectively eliminate other applications. paper presents application neuro-imaging.
Neutron crystallography offers enormous potential to complement structures from X-ray by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing challenge integrating peak shapes pulsed-source experiments. To advance existing software, this article demonstrates use machine learning refine locations, predict and yield more accurate integrated intensities when applied whole data sets a protein crystal. The...
Neutron crystallography is a powerful technique for directly visualizing the locations of H atoms in biological macromolecules. This information has provided key new insights into enzyme mechanisms, ligand binding and hydration. However, despite importance this information, application neutron biology been limited by relatively low flux available beams large incoherent scattering from hydrogen, both which contribute to weak diffraction data with signal-to-background ratios. A method...
A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given limited information content said data large number models that can be employed. Machine learning (ML) methods are powerful tools classifying have found diverse applications many fields science. Here, ML applied problem SAS most appropriate model use analysis. The approach employed is built around method weighted k nearest neighbors (wKNN), utilizes a...
The suitability of a spectral element based dynamical core (HOMME) within the Community Atmospheric Model (CAM) for GPU-based architectures is examined and initial performance results are reported. This work was done project to enable CAM run at high resolution on next-generation, multi-petaflop systems. present focus because it dominates profile our target problem. HOMME enjoys good scalability due its underlying cubed-sphere mesh with full two-dimensional decomposition localization all...
.The aim of this paper is to carry out convergence analysis and algorithm implementation a novel sample-wise backpropagation method for training class stochastic neural networks (SNNs). The preliminary discussion on such an SNN framework was first introduced in [Archibald et al., Discrete Contin. Dyn. Syst. Ser. S, 15 (2022), pp. 2807–2835]. structure the formulated as discretization differential equation (SDE). A optimal control model procedure, approximation scheme adjoint backward SDE...
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional compression capability precise error control, MGARD addresses wide range of requirements, including storage reduction, high-performance I/O, in-situ analysis. It features unified application programming interface (API) that seamlessly operates across diverse computing architectures. has been optimized with highly-tuned GPU kernels...
A microcantilever array sensor with cantilevers differentially functionalized self-assembled monolayers (SAMs) of thiolated ligands is prepared by simultaneous capillary coating. This described for the detection metal ions including Li+, Cs+, Cu2+, Co2+, Fe3+, and Al3+. Binding charged cations to surface sensors produces stress that causes bending detected as tip deflection using an vertical cavity emitting lasers a position-sensitive detector. Optimization studies nanostructured dealloyed...
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths SVM are exploited provide a fast and accurate calculated representation high-dimensional data sets that may consist multiple distributions. Once computed, number distributions can be determined without prior knowledge. For each...
Training Convolutional Neural Network (CNN) is a computationally intensive task, requiring efficient parallelization to shorten the execution time. Considering ever-increasing size of available training data, CNN becomes more important. Data-parallelism, popular strategy that distributes input data among compute processes, requires mini-batch be sufficiently large achieve high degree parallelism. However, with batch known produce low convergence accuracy. In image restoration problems, for...