Snapshots of an evolved DNA polymerase pre- and post-incorporation of an unnatural nucleotide
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Isha Singh↗
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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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American Samoa Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for American Samoa.
ETI, Energy Transitions Initiative, Islands, Ameri↗
Barbados Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Barbados.
ETI, Energy Transitions Initiative, Islands, Barba↗
Belize Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Belize.
ETI, Energy Transitions Initiative, Islands, Beliz↗
British Virgin Islands Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for the British Virgin Islands.
ETI, Energy Transitions Initiative, Islands, BVI, ↗
Aruba Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Aruba.
ETI, Energy Transition Initiative, Islands, Aruba↗
Antigua & Barbuda Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Antigua and Barbuda.
ETI, Energy Transitions Initiative, Islands, Antig↗
Bonaire Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Bonaire.
ETI, Energy Transitions Initiative, Islands, Bonai↗
Commonwealth of the Northern Mariana Islands Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for the Commonwealth of the Northern Mariana Islands.
ETI, Energy Transitions Initiative, Islands, North↗
Curacao Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for Curacao.
ETI, Energy Transitions Initiative, Islands, Curac↗
Cayman Islands Energy Snapshot 2020
This fact sheet is an update of Energy Transitions Initiative Islands series energy profile for the Cayman Islands.
ETI, Energy Transitions Initiative, Islands, Cayma↗
Partitioning Communication Streams into Graph Snapshots.
Abstract not provided.
Field, Jr., Richard V.↗
Decarbonization Technology Snapshot: Heat Pump Water Heaters
Heat pump water heaters use electricity to move heat from one place to another instead of generating heat directly. In many cases they replace gas-fired water heaters and are significantly more energy efficient, reducing on-site Scope 1 emissions and often resulting in reduced total emissions.
building decarbonization↗
Approximating Ground States of Quantum Hamiltonians with Snapshot-QAOA
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97 MATHEMATICS AND COMPUTING↗
Optimizing Management of Persistent Data Structures in High-Performance Analytics
Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.
Computer science↗
A dictionary learning algorithm for compression and reconstruction of streaming data in preset order
There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.
97 MATHEMATICS AND COMPUTING↗
REFAME: Rain Estimation Using Forward Adjusted-Advection of Microwave Estimates
Sensors flying on satellites provide the only practical means of estimating the precipitation that falls over the entire globe, particularly across the vast unpopulated expanses of Earth s oceans. The sensors that observe the Earth using microwave frequencies provide the best data, but currently these are mounted only on satellites flying in "low Earth orbit". Such satellites constantly move across the Earth s surface, providing snapshots of any given location every 12-36 hours. The entire constellation of low-orbit satellites numbers less than a dozen, and their orbits are not coordinated, so a location will frequently go two or more hours between snapshots. "Geosynchronous Earth orbit" (GEO) satellites continuously observe the same region of the globe, allowing them to provide very frequent pictures. For example, the "satellite movies" shown on television come from GEO satellites. However, the sensors available on GEO satellites cannot match the skill of the low-orbit microwave sensors in estimating precipitation. It is perhaps obvious that scientists should try to combine these very different kinds of data, taking advantage of the strengths of each, but this simple concept has proved to be a huge challenge. The scheme in this paper is "Lagrangian", meaning we follow the storm systems, rather than being tied to a fixed grid of boxes on the Earth s surface. Whenever a microwave snapshot occurs, we gladly use the resulting precipitation estimate. Then at all the times between the microwave snapshots we force the storm system to make a smooth transition from one snapshot s values to the next. We know that a lot more changes occur between the snapshots, but this smooth transition the best we can do with the microwave data alone. The key new contribution in this paper is that we also look at the relative variations in the GEO estimates during these in-between times and force the estimated changes in the precipitation to have similar variations. Preliminary testing shows that this approach has enough promise that it should be developed and studied.
Behrangi, Ali↗
SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction
Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.
97 MATHEMATICS AND COMPUTING↗