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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nucleic acids encoding HIV-1 GP140 immunogens comprising modified NHR1 regions that stabilize pre-fusion conformations

The present invention provides HIV-1 vaccine immunogens. Some of the immunogens contain a soluble gp140-derived protein that harbors a modified N-terminus of the HR1 region in gp41. Some of the immunogens contain an HIV-1 Env-derived trimer protein that is presented on a nanoparticle platform. The invention also provides methods of using the HIV-1 vaccine immunogens for eliciting an immune response or treating HIV infections.

Kong, Leopold↗
DOE OSTI2024

Self-assembled elastomers with molecularly encoded tissue-like softness, strain-adaptive stiffening and coloration

The invention generally relates to linear-bottlebrush-linear copolymer blocks and methods of making and using same. The disclosed copolymer blocks can be useful in, for example, the formation of polymer networks that replicate biological stress-strain behavior. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.

Sheiko, Sergei↗
DOE OSTI2022

Thousands of small, novel genes predicted in global phage genomes

Small genes (<150nucleotides) have been systematically overlooked in phage genomes. We employ a large scale comparative genomics approach to predict >40,000 small-gene families in 2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000families encode predicted anti-CRISPR proteins, and more than500families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.

Fremin, Brayon↗
DOE OSTI2023

Quantum error correction from complexity in Brownian SYK

We study the robustness of quantum error correction in a one-parameter ensemble of codes generated by the Brownian SYK model, where the parameter quantifies the encoding complexity. The robustness of error correction by a quantum code is upper bounded by the “mutual purity” of a certain entangled state between the code subspace and environment in the isometric extension of the error channel, where the mutual purity of a density matrix ρAB is the difference $\mathcal{F}$ p ($A : B$) ≡ $\mathrm{T}$r $p^{2}_{AB}$ - $\mathrm{T}$r $p^{2}_{A}$ $\mathrm{T}$r $p^{2}_{B}$. We show that when the encoding complexity is small, the mutual purity is O(1) for the erasure of a small number of qubits (i.e., the encoding is fragile). However, this quantity decays exponentially, becoming O(1/N) for O(log N) encoding complexity. Further, at polynomial encoding complexity, the mutual purity saturates to a plateau of O(e -N ). We also find a hierarchy of complexity scales associated to a tower of subleading contributions to the mutual purity that quantitatively, but not qualitatively, adjust our error correction bound as encoding complexity increases. In the AdS/CFT context, our results suggest that any portion of the entanglement wedge of a general boundary subregion A with sufficiently high encoding complexity is robustly protected against low-rank errors acting on A with no prior access to the encoding map. From the bulk point of view, we expect such bulk degrees of freedom to be causally inaccessible from the region A despite being encoded in it.

1/N expansion↗
DOE OSTI2023

Arithmetic Primitives for Efficient Neuromorphic Computing

Neuromorphic computing is steadily gaining popularity in many scientific and engineering disciplines. However, one of the biggest problems that has prevented widespread usage of neuromorphic computing is the lack of efficient encoding methods. Traditional encoding methods such as binning, rate encoding, and temporal encoding are based on unary encoding and generate a large number of spikes for certain applications, making them less energy efficient. Lack of better encoding methods has also prevented preprocessing operations from being carried out on neuromorphic computers. As a result, over 99% of the time can be spent on data preprocessing and data transfer operations in some cases, leading to an inefficient workflow. In this paper, we present preliminary results that would enable us to efficiently encode data and perform basic arithmetic operations on neuromorphic computers. First, we present a neuromorphic approach for the two’s complement encoding of numbers and leverage it to devise addition and multiplication circuits, which could be used in preprocessing operations on neuromorphic computers. We test our approach on the SuperNeuroMAT simulator. Our results indicate that two’s complement is a highly efficient encoding method in terms of time, space, and energy complexity and that the addition and multiplication circuits produce accurate results on two numbers having arbitrary precision.

Wurm, Ahna↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Lightweight LSTM for CAN Signal Decoding

This paper describes an approach to identify undecoded Controller Area Network (CAN) data from one vehicle, based on the data similarity to previously decoded CAN data from another vehicle. Modern vehicles communicate data and signals from on-board sensors and controllers through the CAN bus. Networked sensors contain information such as wheel speeds, fuel gauges, turn signals, and radar signals. In the effort to use this information and make cars safer through human-in-the-loop CPS, signals on the CAN bus such as wheel speed and radar can be used to support the driver. However, data from the CAN bus are encoded and in some cases compressed, and different car manufacturers use different encoding schemes to represent data on the CAN bus. With hundreds of messages and thousands of possible encoding schemes to consider, it is laborious to identify the unique bits and encoding schemes that represent signals on each vehicle. In this study, we propose a method for training a Long Short-Term Memory (LSTM) neural network on known radar signals from one vehicle manufacturer, a Toyota, and successfully apply the network to identify the encoding for radar signals on a different vehicle, a Honda. By augmenting the training dataset with varied encoding bit boundaries, a small and lightweight LSTM network can learn to recognize radar data across different encoding schemes. The results are an improvement on exhaustive-search algorithms and other methods previously used in the search for such signals.

Ngo, Paul↗
DOE OSTI2025

Author Correction: Genome-guided isolation of the hyperthermophilic aerobe Fervidibacter sacchari reveals conserved polysaccharide metabolism in the Armatimonadota

Correction to: Nature Communicationshttps://doi.org/10.1038/s41467-024-53784-3, published online 4 November 2024 In the version of this article initially published, Table 1 did not include the properties of the taxa being proposed or refer directly to another location in the main manuscript describing the properties. As such, the original manuscript did not comply with Rule 27 (2)(c) of the ICNP. Also, Table 1 listed the order Fervidibacterales as the nomenclatural type for the class Fervidibacteria, which violates latest emended version of Rule 15 stating that the nomenclatural type for a class must be a genus. Below we provide a modification of Table 1 containing protologues with these errors corrected. We have also changed the order of the taxa in the table to meet the most common ordering. (Table presented.) Taxon names proposed under the ICNP Proposed taxon Etymology Description Genus Fervidibacter Fer.vi.di.bac’ter. L. masc. adj. fervidus, hot, steaming; N.L. masc. n. bacter, a rod; N.L. masc. n. Fervidibacter, a hot rod Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic, with high-affinity and/or low-affinity terminal oxidases present in the genomes. The oxidative pentose phosphate pathway and the tricarboxylic acid cycle are complete in genomes belonging to the genus. Gram-stain-negative and diderm cell envelope structure. Ovoid- to rod-shaped morphology. Spores are not formed. The genus is a distinct phylogenetic lineage in the family Fervidibacteraceae, the order Fervidibacterales, and the class Fervidibacteria in the phylum Armatimonadota. The type species is Fervidibacter sacchariT. Species Fervidibacter sacchari sac’cha.ri. N.L. gen. n. sacchari, of sugar Hyperthermophilic, microaerophilic, facultatively anaerobic, and grows chemoheterotrophically on monosaccharides and polysaccharides. Cells are ovoid- to rod-shaped, Gram-stain negative, and are 0.9–1.3 µm in width and 1.6–3.6 µm in length. Grows between 65 and 87.5 °C and an optimum temperature of 80 °C, and a pH range of 6.5–8.6 with an optimum pH of 7.5. Grows at an optimum O2 concentration of 5–10%. Grows on D-arabinose, D-galactose, D-glucose, D-rhamnose, D-ribose, D-xylose, chondroitin sulfate, colloidal chitin, galactan, gellan gum, guar gum, karaya gum, locust bean gum, xantham gum, xyloglucan, β-glucan, glycogen, starch, AFEX-pretreated corn stover, miscanthus, sugarcane bagasse, acetate and casamino acids. Grows weakly on xyloglucan under fermentation conditions. The major fatty acids (>10%) are C16:0, C18:0 and/or cyclo-C17:0, and iso-C16:0. The major respiratory quinones (>10%) are MK-8 and MK-9. The isolate and genomes of the species have been recovered from geothermal springs in the Great Basin, Nevada, USA. GC content of genomes range between 51–52%. Subunits for both the high-affinity and low-affinity terminal oxidases are encoded in the genomes. Genomes also encode a Group 3d [NiFe] hydrogenase, which produces hydrogen as an electron sink for NAD+ regeneration. The type strain PD1T (= JCM 39283T = DSM 113467T) was isolated from Great Boiling Spring in Nevada, USA. Family Fervidibacteraceae Fer.vi.di.bac.te.ra’ce.ae. N.L. masc. n. Fervidibacter type genus of the family; L. suff. -aceae ending to denote a family; N.L. fem. pl. n. Fervidibacteraceae the family of the genus Fervidibacter Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic, with high-affinity and/or low-affinity terminal oxidases present in the genomes. The oxidative pentose phosphate pathway and the tricarboxylic acid cycle are complete in genomes belonging to the family. The family is a distinct phylogenetic lineage in the order Fervidibacterales and the class Fervidibacteria in the phylum Armatimonadota. The type genus is Fervidibacter. Order Fervidibacterales Fer.vi.di.bac.te.ra’les. N.L. masc. n. Fervidibacter type genus of the order; L. suff. -ales ending to denote an order; N.L. fem. pl. n. Fervidibacterales the order of the genus Fervidibacter Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic or strictly anaerobic. Phylogenomic placement of this lineage within the Fervidibacteria and relative evolutionary divergence supports delineation of this lineage as an order within the class Fervidibacteria and phylum Armatimonadota. The type genus is Fervidibacter. Class Fervidibacteria Fer.vi.di.bac.te’ri.a. N.L. masc. n. Fervidibacter type genus of the type order of the class; L. suff. -ia ending to denote a class; N.L. neut. pl. n. Fervidibacteria the class of the order Fervidibacterales Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic or strictly anaerobic. Phylogenomic placement of this lineage within the Armatimonadota and relative evolutionary divergence supports delineation of this lineage as a class within the Armatimonadota. The type genus is Fervidibacter. The error has not been corrected in the PDF or HTML versions of the Article.

Nou, Nancy O↗
DOE OSTI2025

Noisy quantum trees: infinite protection without correction

We study quantum networks with tree structures, in which information propagates from a root to leaves. At each node in the network, the received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by the delocalization of information. In the classical setting, where each node simply copies the input bit into multiple output bits, this model has been studied as the broadcasting or reconstruction problem on trees, which has broad applications. In this work, we study the quantum version of this problem. We consider a Clifford encoder at each node that encodes the input qubit in a stabilizer code, along with a single qubit Pauli noise channel at each edge. Such noisy quantum trees describe a scenario in which one has access to a stream of fresh (low-entropy) ancilla qubits, but cannot perform error correction. Therefore, they provide a different perspective on quantum fault tolerance. Furthermore, they provide a useful model for describing the effect of noise within the encoders of concatenated codes. We prove that above certain noise thresholds, which depend on the properties of the code such as its distance, as well as the properties of the encoder, information decays exponentially with the depth of the tree. On the other hand, by studying certain efficient decoders, we prove that for codes with distance d ≥ 2 and for sufficiently small (but non-zero) noise, classical information and entanglement propagate over a noisy tree with infinite depth. Indeed, we find that this remains true even for binary trees with certain 2-qubit encoders at each node, which encodes the received qubit in the binary repetition code with distance d = 1.

Quantum information↗