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chromatography, hydrophobic precipitation and tangential flow filtration, etc. are also utilized [3]. Current approaches for characterizing the particle size distribution and/or particle number concentration
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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is to measure to high accuracy the SI-traceable spectral energy distribution over the visible and near infrared wavelength range for a set of stars for use as flux standards for astronomy. In
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Sonmez Turan meltem.turan@nist.gov 301.975.4391 Description NIST standardized cryptographic algorithms are intended to be "bulletproof". That is, the computational complexity needed to break them is
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., database composition, algorithm biases). Proposals should address these challenges with strategies to evaluate the metagenomic identification process and enable end users to select the appropriate tools
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continues to push patterning to new limits. There are significant needs to understand how the components in these resists are distributed, and critically whether there is aggregation that could contribute
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
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resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
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Machine Learning for High Throughput Materials Discovery and Optimization Applications NIST only participates in the February and August reviews. We are developing machine learning algorithms
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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different