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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantum Associative Memory in Hep Track Pattern Recognition

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A general model for retroviral capsid pattern recognition by TRIM5 proteins

Restriction factors are intrinsic cellular defense proteins that have evolved to block microbial infections. Retroviruses such as HIV-1 are restricted by TRIM5 proteins, which recognize the viral capsid shell that surrounds, organizes, and protects the viral genome. TRIM5α uses a SPRY domain to bind capsids with low intrinsic affinity ( K D >1 mM), and therefore requires higher-order assembly into a hexagonal lattice to generate sufficient avidity for productive capsid recognition. TRIMCyp, on the other hand, binds HIV-1 capsids through a cyclophilin A domain, which has a well-defined binding site and higher ( K D ~10 μM) affinity for isolated capsid subunits. It has therefore been argued that TRIMCyp proteins may have dispensed with the need for higher-order assembly to function as antiviral factors. Here, we show that, consistent with its high degree of sequence similarity with TRIM5α, the TRIMCyp B-box 2 domain shares the same ability to self-associate and facilitate assembly of a TRIMCyp hexagonal lattice that can wrap about the HIV-1 capsid. We also show that under stringent experimental conditions, TRIMCyp-mediated restriction of HIV-1 is indeed dependent on higher-order assembly. Both forms of TRIM5 therefore use the same mechanism of avidity-driven capsid pattern recognition. IMPORTANCE Rhesus macaques and owl monkeys are highly resistant to HIV-1 infection due to the activity of TRIM5 restriction factors. The rhesus macaque TRIM5α protein blocks HIV-1 through a mechanism that requires self-assembly of a hexagonal TRIM5α lattice around the invading viral core. Lattice assembly amplifies very weak interactions between the TRIM5α SPRY domain and the HIV-1 capsid. Assembly also promotes dimerization of the TRIM5α RING E3 ligase domain, resulting in synthesis of polyubiquitin chains that mediate downstream steps of restriction. In contrast to rhesus TRIM5α, the owl monkey TRIM5 homolog, TRIMCyp, binds isolated HIV-1 CA subunits more tightly through its cyclophilin A domain, and was therefore thought to act independent of higher-order assembly. Here, we show that TRIMCyp shares the assembly properties of TRIM5α and that both forms of TRIM5 use the same mechanism of hexagonal lattice formation to promote viral recognition and restriction.

Wagner, Jonathan M.↗
DOE OSTI2022

Machine Learning Pattern Recognition Algorithm With Applications to Coherent Laser Combination

Herein we analyze a new kind of machine learning algorithm designed to feedback stabilize coherently combined lasers. This algorithm learns differential, rather than absolute, values of action in phase space, in order to facilitate learning on initially unstable systems. Experiments have shown that this approach can control small-scale spatial beam combination with high stability. In this paper we analyze the algorithm's performance and limitations in depth, showing that it can continuously learn during operation in order to track changes. Using simulation, we extend the application to temporal combination, and show that it scales to more complex instances by combining 81 beams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Modeling of the L-shell copper X-pinch plasma produced by the compact generator of Ecole polytechnique using pattern recognition

Principal component analysis is applied and compared with the line ratios of special Ne-like transitions for investigating the electron beam effects on the L-shell Cu synthetic spectra. The database for the principal component extraction is created over a non Local Thermodynamic Equilibrium (non-LTE) collisional radiative L-shell Copper model. The extracted principal components are used as a database for Artificial Neural Network in order to estimate the plasma electron temperature, density, and beam fractions from a representative time-integrated spatially resolved L-shell Cu X-pinch plasma spectrum. The spectrum is produced by the explosion of 25-μm Cu wires on a compact LC (40 kV, 200 kA, and 200 ns) generator. The modeled plasma electron temperatures are about T{sub e} ∼ 150 eV and N{sub e} = 5 × 10{sup 19} cm{sup −3} in the presence of the fraction of the beams with f ∼ 0.05 and a centered energy of ∼10 keV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Pattern recognition techniques to reduce backgrounds in the search for the {sup 136}Xe double beta decay with gaseous TPCs

The observation of the neutrinoless double beta decay may provide essential information on the nature of neutrinos. Among the current experimental approaches, a high pressure gaseous TPC is an attractive option for the search of double beta decay due to its good energy resolution and the detailed topological information of each event. We present in this talk a detailed study of the ionization topology of the {sup 136}Xe double beta decay events in a High Pressure Xenon TPC, as well as that of the typical competing backgrounds. We define some observables based on graph theory concepts to develop automated discrimination algorithms. Our criteria are able to reduce the background level by about three orders of magnitude in the region of interest of the {sup 136}Xe Q{sub ββ} for a signal acceptance of 40%. This result provides a quantitative assessment of the benefit of topological information offered by gaseous TPCs for double beta decay search, and proves that it is a promising feature in view of future experiments in the field. Possible ideas for further improvement in the discrimination algorithms and the dependency of these results with the gas diffusion and readout granularity will be also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Trace-Driven Memory Access Pattern Recognition in Computational Kernels. In: WOSC '14 Proceedings of the Second Workshop on Optimizing Stencil Computations

Classifying memory access patterns is paramount to the selection of the right set of optimizations and determination of the parallelization strategy. Static analyses suffer from ambiguities present in source code, which modern compilation techniques, such as profile-guided optimization, alleviate by observing runtime behavior and feeding back into the compilation flow. This paper discusses a dynamic analysis technique for recognizing memory access patterns, with application to the stencils domain, and presents our design and C++ implementation using the memory-tracing tool Gleipnir. Finally, we evaluate and discuss the performance and matching capability of our classifiers in the context of the Polybench scientific benchmark suite, which includes both stencil and matrix computations.

Park, Eunjung↗
DOE OSTI2019

Deterministic stabilization of eight-way 2D diffractive beam combining using pattern recognition

We demonstrate a new method for controlling diffractive, high-power beam combination, sensing phase errors by analyzing the intensity pattern of uncombined side beams at the output. A square array of eight beams is combined with <0.3% stability and 84.6% efficiency. As the channel count is increased, so does the usable information, enabling scaling to large channel counts without significant slowing of control loop response time, an advantage over single-input algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the 20 Ne ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the 20-42 Mg isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of 24 Si and 40 Mg of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as 166,168 Er and 236 U, that build on first-principles considerations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗
DOE OSTI2015

TECA: Petascale pattern recognition for climate science

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Isotopomer-selective spectra of a single intact H 2 O molecule in the Cs + (D 2 O) 5 H 2 O isotopologue: Going beyond pattern recognition to harvest the structural information encoded in vibrational spectra

We report the vibrational signatures of a single H 2 O water molecule occupying distinct sites of the hydration network in the Cs + (H 2 O) 6 cluster. This is accomplished using isotopomer selective IR-IR hole-burning on the Cs + (D 2 O) 5 (H 2 O) clusters formed by gas-phase exchange of a single, intact H 2 O molecule for D 2 O in the Cs + (D 2 O) 6 ion. The OH stretching pattern of the Cs + (H 2 O) 6 isotopologue is accurately recovered by superposition of the isotopomer spectra, thus establishing that the H 2 O incorporation is random and that the OH stretching manifold is largely due to contributions from decoupled water molecules. This behavior enables a powerful new way to extract structural information from vibrational spectra of size-selected clusters by explicitly identifying the local environments responsible for specific infrared features. The Cs + (H 2 O) 6 structure was unambiguously assigned to the 4.1.1 isomer (a homodromic water tetramer with two additional flanking water molecules) from the fact that its computed IR spectrum matches the observed overall pattern and recovers the embedded correlations in the two OH stretching bands of the water molecule in the Cs + (D 2 O) 5 (H 2 O) isotopomers. The 4.1.1 isomer is the lowest in energy among other candidate networks at advanced (e.g., CCSD(T)) levels of theoretical treatment after corrections for (anharmonic) zero-point energy (ZPE). With the structure in hand, we then explore the mechanical origin of the various band locations using a local electric field formalism. Furthermore, this approach promises to provide a transferrable scheme for the prediction of the OH stretching fundamentals displayed by water networks in close proximity to solute ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Supervised enhancer prediction with epigenetic pattern recognition and targeted validation

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. Here, we demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.

59 BASIC BIOLOGICAL SCIENCES↗