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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2019

Complexity and entropy representation for machine component diagnostics

The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has only two dimensions: normalized permutation entropy and Jensen-Shannon complexity of a time series. This two-dimensional representation allows for detection of slow or rapid drifts in the condition of mechanical components monitored through sensor measurements. The CECP representation can be used for both predictive analytics and visual monitoring of changes in component condition. This method requires minimal pre-processing of raw signals. Furthermore, it is insensitive to noise, stationarity, and trends. These desirable properties make CECP a good candidate for machine condition monitoring and fault diagnostics. In this work we study the effectiveness of CECP on three rotary component condition assessment applications. We use CECP representation of vibration signals to differentiate various machine component health conditions for rotary machine components, namely roller bearing and gears. The results confirm that the CECP representation is able to detect, with high accuracy, changes in underlying dynamics of machine component degradation states. From class separability perspective, the CECP representation is able to generate linearly separable classes for the classification of different fault states. This classification performance improves with increasing signal length. For signal length of 16,384 data points, the fault classification accuracy varies from 90% to 100% for bearing applications, and from 85% to 100% for gear applications. We observed that the optimum parameter for CECP representating depends on the application. For bearing applications we found that embedding dimension D = 4, 5, 6, and embedding delay τ = 1, 2, 3 are suitable for good fault classification. For gear applications we find that embedding dimension D = 4, 5, and embedding delay τ = 1, 5 are suitable for fault classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Co-variability of S 6+ , S 4+ , and S 2- in apatite as a function of oxidation state: Implications for a new oxybarometer

In this study, we use micro-X-ray absorption near-edge structures (μ-XANES) spectroscopy at the S K-edge to investigate the oxidation state of S in natural magmatic-hydrothermal apatite (Durango, Mexico, and Mina Carmen, Chile) and experimental apatites crystallized from volatile-saturated lamproitic melts at 1000 °C and 300 MPa over a broad range of oxygen fugacities [( Embedded Image , FMQ+1.2, FMQ+3; FMQ = fayalite-magnetite-quartz solid buffer]. The data are used to test the hypothesis that S oxidation states other than S6+ may substitute into the apatite structure. Peak energies corresponding to sulfate S6+ (~2482 eV), sulfite S4+ (~2478 eV), and sulfide S2- (~2470 eV) were observed in apatite, and the integrated areas of the different sulfur peaks correspond to changes in Embedded Image and bulk S content. Here, multiple tests confirmed that the S oxidation state in apatite remains constant when exposed to the synchrotron beam, at least for up to 1 h exposure (i.e., no irradiation damages). To our knowledge, this observation makes apatite the first mineral to incorporate reduced (S2-), intermediate (S4+), and oxidized (S6+) S in variable proportions as a function of the prevailing Embedded Image of the system. Apatites crystallized under oxidizing conditions (FMQ+1.2 and FMQ+3), where the S6+/STotal peak area ratio in the coexisting glass (i.e., quenched melt) is ~1, are dominated by S6+ with a small contribution of S4+, whereas apatites crystallizing at reduced conditions (FMQ) contain predominantly S2-, lesser amounts of S6+, and possibly traces of S4+. A sulfur oxidation state vs. S concentration analytical line transect across hydrothermally altered apatite from the Mina Carmen iron oxide-apatite (IOA) deposit (Chile) demonstrates that apatite can become enriched in S4+ relative to S6+, indicating metasomatic overprinting via a SO2-bearing fluid or vapor phase. This XANES study demonstrates that as the Embedded Image increases from FQM to FMQ+1.2 to FMQ+3 the oxidation state of S in igneous apatite changes from S2- dominant to S6+ > S4+ to S6+ >> S4+. Furthermore, these results suggest that spectroscopic studies of igneous apatite have potential to trace the oxidation state of S in magmas. The presence of three S oxidations states in apatite may in part explain the non-Henrian partitioning of S between apatite and melt. Our study reveals the potential to use the S signature of apatite to elucidate both oxygen and sulfur fugacity in magmatic and hydrothermal systems.

58 GEOSCIENCES↗
DOE OSTI2016

(Active) Learning on Groups of Data with Information-Theoretic Estimators

A wide range of machine learning problems, including astronomical inference about galaxy clusters, scene classification, parametric statistical inference, and predictions of public opinion, can be well-modeled as learning a function on (samples from) distributions. This project explores problems in learning such functions via kernel methods, particularly for large-scale problems. When learning from large numbers of distributions, the computation of typical methods scales between quadratically and cubically, and so they are not amenable to large datasets. We investigate the approach of approximate embeddings into Euclidean spaces such that inner products in the embedding space approximate kernel values between the source distributions. We first improve the understanding of the workhorse methods of random Fourier features: we show that of the two approaches in common usage, one is strictly superior. We then present a new embedding for a class of information-theoretic distribution distances, and evaluate it and existing embeddings on several real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development of self-sensing materials for extreme environments based on metamaterial concept and additive manufacturing

Structural health monitoring of an engineered component in a harsh environment is critical for multiple DOE missions including nuclear fuel cycle, subsurface energy production/storage, and energy conversion. Supported by a seeding Laboratory Directed Research & Development (LDRD) project, we have explored a new concept for structural health monitoring by introducing a self-sensing capability into structural components. The concept is based on two recent technological advances: metamaterials and additive manufacturing. A self-sensing capability can be engineered by embedding a metastructure, for example, a sheet of electromagnetic resonators, either metallic or dielectric, into a material component. This embedment can now be realized using 3-D printing. The precise geometry of the embedded metastructure determines how the material interacts with an incident electromagnetic wave. Any change in the structure of the material (e.g., straining, degradation, etc.) would inevitably perturbate the embedded metastructures or metasurface array and therefore alter the electromagnetic response of the material, thus resulting in a frequency shift of a reflection spectrum that can be detected passively and remotely. This new sensing approach eliminates complicated environmental shielding, in-situ power supply, and wire routing that are generally required by the existing active-circuit-based sensors. The work documented in this report has preliminarily demonstrated the feasibility of the proposed concept. The work has established the needed simulation tools and experimental capabilities for future studies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

DYNAMICAL ACCRETION OF PRIMORDIAL ATMOSPHERES AROUND PLANETS WITH MASSES BETWEEN 0.1 AND 5 M {sub ⊕} IN THE HABITABLE ZONE

In the early, disk-embedded phase of evolution of terrestrial planets, a protoplanetary core can accumulate gas from the circumstellar disk into a planetary envelope. In order to relate the accumulation and structure of this primordial atmosphere to the thermal evolution of the planetary core, we calculated atmosphere models characterized by the surface temperature of the core. We considered cores with masses between 0.1 and 5 M {sub ⊕} situated in the habitable zone around a solar-like star. The time-dependent simulations in 1D-spherical symmetry include the hydrodynamics equations, gray radiative transport, and convective energy transport. Using an implicit time integration scheme, we can use large time steps and and thus efficiently cover evolutionary timescales. Our results show that planetary atmospheres, when considered with reference to a fixed core temperature, are not necessarily stable, and multiple solutions may exist for one core temperature. As the structure and properties of nebula-embedded planetary atmospheres are an inherently time-dependent problem, we calculated estimates for the amount of primordial atmosphere by simulating the accretion process of disk gas onto planetary cores and the subsequent evolution of the embedded atmospheres. The temperature of the planetary core is thereby determined from the computation of the internal energy budget of the core. For cores more massive than about one Earth mass, we obtain that a comparatively short duration of the disk-embedded phase (∼10{sup 5} years) is sufficient for the accumulation of significant amounts of hydrogen atmosphere that are unlikely to be removed by later atmospheric escape processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Influence of Disk Composition on the Evolution of Stars in the Disks of Active Galactic Nuclei

Abstract Disks of gas accreting onto supermassive black holes, powering active galactic nuclei (AGN), can capture stars from nuclear star clusters or form stars in situ via gravitational instability. The density and thermal conditions of these disks can result in rapid accretion onto embedded stars, dramatically altering their evolution in comparison to stars in the interstellar medium. Theoretical models predict that, when subjected to sufficiently rapid accretion, fresh gas replenishes hydrogen in the cores of these stars as quickly as it is burned into helium, reaching a quasi-steady state. Such massive, long-lived (“immortal”) stars may be capable of dramatically enriching AGN disks with helium, and would increase the helium abundance in AGN broad-line regions relative to that in the corresponding narrow-line regions and hosts. We investigate how the helium abundance of AGN disks alters the evolution of stars embedded therein. We find, in agreement with analytical arguments, that stars at a given mass are more luminous at higher helium mass fractions, and so undergo more radiation-driven mass loss. We further find that embedded stars tend to be less massive in disks with higher helium mass fractions, and that immortal stars are less common in such disks. Thus, disk composition can alter the rates of electromagnetic and gravitational wave transients as well as further chemical enrichment by embedded stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Hydrodynamic Effects on Modeling and Control of a High Temperature Active Magnetic Bearing Pump with a Canned Rotor

Embedding instrumentation and control Embedding instrumentation and control (I\&C) at the component level in nuclear power plants can improve component performance, lifetime, and resilience by optimizing operation, reducing the constraints on physical design, and providing on-board prognostics and diagnostics. However, the extreme environments that many nuclear power plant components operate in makes embedding instrumentation and control at the component level difficult. Successfully utilizing embedded I\&C requires developing a deep understanding of the system's dynamics and using that knowledge to overcome material and physical limitations imposed by the environment. In this paper, we will develop a coupled dynamic model of a high temperature (700 $^\circ$C) canned rotor pump that incorporates rotordynamics, hydrodynamics, and active magnetic bearing dynamics. Then we will compare two control design methods, one that uses a simplified decoupled model of the system and another that utilizes the full coupled system model. It will be seen that utilizing all the available knowledge of the system dynamics in the controller design yield an order of magnitude improvement in the magnitude of the magnetic bearing response to disturbances at the same level of control effort, a large reduction in the settling time of the system, and a smoother control action.

Melin, Alexander M↗
DOE OSTI2020

Selecting the independent coordinates of manifolds with large aspect ratios

Many manifold embedding algorithms fail apparently when the data manifold has a large aspect ratio (such as a long, thin strip). Here, we formulate success and failure in terms of finding a smooth embedding, showing also that the problem is pervasive and more complex than previously recognized. Mathematically, success is possible under very broad conditions, provided that embedding is done by carefully selected eigenfunctions of the Laplace-Beltrami operator Δ. Hence, we propose a bicriterial Independent Eigencoordinate Selection (IES) algorithm that selects smooth embeddings with few eigenvectors. The algorithm is grounded in theory, has low computational overhead, and is successful on synthetic and large real data.

Meila, Marina↗
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2022

Sub-electrode microlens array for organic light emitting devices

Substrates are disclosed that include an embedded or partially-embedded microlens array. Devices are disclosed that include an OLED disposed over a substrate having an embedded or partially embedded micro lens array. Devices as disclosed herein redirect up to 100% of the light that otherwise would be confined in organic and electrode layers toward the substrate and thus provide improved light extraction and device efficiency.

Forrest, Stephen R.↗
DOE OSTI2025

DNA-PAINT Imaging with Hydrogel Imprinting and Clearing

Hydrogel-embedding is a versatile technique in fluorescence microscopy, offering stabilization, optical clearing, and the physical expansion of biological specimens. DNA-PAINT is a super-resolution microscopy approach based on the diffusion and transient binding of fluorescently labeled oligos, but its feasibility in hydrogels has not yet been explored. In this study, we demonstrate that polyacrylamide hydrogels support sufficient diffusion for effective DNA-PAINT imaging. Using acrydite-anchored oligonucleotides imprinted from patterned DNA origami nanostructures and microtubule filaments in fixed cells, we find that hydrogel embedding preserves docking strand positioning at the nanoscale. Sample clearing via protease treatment had minor structural effects on the microtubule structure and enhanced diffusion and accessibility to hydrogel-imprinted docking strands. Our work demonstrates promising potential for diffusion and binding-based fluorescence imaging applications in hydrogel-embedded samples.

DNA origami↗
DOE OSTI2015

Analysis of Joist Masonry Moisture Content Monitoring

There are many existing buildings with load-bearing mass masonry walls, whose energy performance could be improved with the retrofit of insulation. However, adding insulation to the interior side of walls of such masonry buildings in cold (and wet) climates may cause performance and durability problems. Some concerns, such as condensation and freeze-thaw have known solutions. But wood members embedded in the masonry structure will be colder (and potentially wetter) after an interior insulation retrofit. Moisture content & relative humidity were monitored at joist ends in historic mass brick masonry walls retrofitted with interior insulation in a cold climate (Zone 5A); data were collected from 2012-2015. Eleven joist ends were monitored in all four orientations. One limitation of these results is that the renovation is still ongoing, with limited wintertime construction heating and no permanent occupancy to date. Measurements show that many joists ends remain at high moisture contents, especially at north- and east-facing orientations, with constant 100% RH conditions at the worst cases. These high moisture levels are not conducive for wood durability, but no evidence for actual structural damage has been observed. Insulated vs. non-insulated joist pockets do not show large differences. South facing joists have safe (10-15%) moisture contents. Given the uncertainty pointed out by research, definitive guidance on the vulnerability of embedded wood members is difficult to formulate. In high-risk situations, or when a very conservative approach is warranted, the embedded wood member condition can be eliminated entirely, supporting the joist ends outside of the masonry pocket.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Enhancing the Carbon Monoxide Oxidation Performance through Surface Defect Enrichment of Ceria-Based Supports for Platinum Catalyst

Effective synthesis and application of single-atom catalysts on supports lacking enough defects remain a significant challenge in environmental catalysis. Herein, we present a universal defect-enrichment strategy to increase the surface defects of CeO 2 -based supports through H 2 reduction pretreatment. The Pt catalysts supported by defective CeO 2 -based supports, including CeO 2 , CeZrO x , and CeO 2 /Al 2 O 3 (CA), exhibit much higher Pt dispersion and CO oxidation activity upon reduction activation compared to their counterpart catalysts without defect enrichment. Specifically, Pt is present as embedded single atoms on the CA support with enriched surface defects (CA-HD) based on which the highly active catalyst showing embedded Pt clusters (Pt C ) with the bottom layer of Pt atoms substituting the Ce cations in the CeO 2 surface lattice can be obtained through reduction activation. Embedded PtC can better facilitate CO adsorption and promote O 2 activation at Pt C –CeO 2 interfaces, thereby contributing to the superior low-temperature CO oxidation activity of the Pt/CA-HD catalyst after activation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Faster Johnson–Lindenstrauss transforms via Kronecker products

The Kronecker product is an important matrix operation with a wide range of applications in signal processing, graph theory, quantum computing and deep learning. In this work, we introduce a generalization of the fast Johnson–Lindenstrauss projection for embedding vectors with Kronecker product structure, the Kronecker fast Johnson–Lindenstrauss transform (KFJLT). The KFJLT reduces the embedding cost by an exponential factor of the standard fast Johnson–Lindenstrauss transform’s cost when applied to vectors with Kronecker structure, by avoiding explicitly forming the full Kronecker products. Here, we prove that this computational gain comes with only a small price in embedding power: consider a finite set of $p$ points in a tensor product of $d$ constituent Euclidean spaces $\bigotimes _{k=d}^{1}{\mathbb{R}}^{n_k}$, and let $N = \prod _{k=1}^{d}n_k$. With high probability, a random KFJLT matrix of dimension $m \times N$ embeds the set of points up to multiplicative distortion $(1\pm \varepsilon )$ provided $m \gtrsim \varepsilon ^{-2} \, \log ^{2d - 1} (p) \, \log N$. We conclude by describing a direct application of the KFJLT to the efficient solution of large-scale Kronecker-structured least squares problems for fitting the CP tensor decomposition.

Kronecker structure↗
DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗